{ "cells": [ { "cell_type": "code", "execution_count": 1, "id": "4a8f7bdb", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Requirement already satisfied: pvlib in c:\\users\\magda\\anaconda3\\lib\\site-packages (0.10.5)\n", "Requirement already satisfied: numpy>=1.17.3 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (1.26.4)\n", "Requirement already satisfied: pandas>=1.3.0 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (2.1.4)\n", "Requirement already satisfied: pytz in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (2023.3.post1)\n", "Requirement already satisfied: requests in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (2.31.0)\n", "Requirement already satisfied: scipy>=1.6.0 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (1.11.4)\n", "Requirement already satisfied: h5py in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pvlib) (3.9.0)\n", "Requirement already satisfied: python-dateutil>=2.8.2 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pandas>=1.3.0->pvlib) (2.8.2)\n", "Requirement already satisfied: tzdata>=2022.1 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from pandas>=1.3.0->pvlib) (2023.3)\n", "Requirement already satisfied: charset-normalizer<4,>=2 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from requests->pvlib) (2.0.4)\n", "Requirement already satisfied: idna<4,>=2.5 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from requests->pvlib) (3.4)\n", "Requirement already satisfied: urllib3<3,>=1.21.1 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from requests->pvlib) (2.0.7)\n", "Requirement already satisfied: certifi>=2017.4.17 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from requests->pvlib) (2024.2.2)\n", "Requirement already satisfied: six>=1.5 in c:\\users\\magda\\anaconda3\\lib\\site-packages (from python-dateutil>=2.8.2->pandas>=1.3.0->pvlib) (1.16.0)\n" ] } ], "source": [ "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import os\n", "import datetime\n", "\n", "!pip install pvlib\n", "\n", "from matplotlib.dates import DateFormatter\n", "from datetime import time\n", "import matplotlib.colors as mcolors\n" ] }, { "cell_type": "code", "execution_count": 2, "id": "522299ed", "metadata": {}, "outputs": [], "source": [ "import pvlib\n" ] }, { "cell_type": "code", "execution_count": 3, "id": "84205a2f", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Loaded Corrected LWP Data:\n", " TIMESTAMP LWP_Corrected\n", "0 2024-03-01 18:00:00 28.360662\n", "1 2024-03-01 18:00:01 25.115103\n", "2 2024-03-01 18:00:02 24.036722\n", "3 2024-03-01 18:00:03 24.512901\n", "4 2024-03-01 18:00:04 26.155781\n" ] } ], "source": [ "\n", "# Define the base directory where the Parquet files are saved\n", "base_dir_mwr = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\Microwave_radiometer'\n", "\n", "# Define the filenames for the Parquet files\n", "filename_lwp_corrected = 'Corrected_LWP_Data.parquet'\n", "\n", "\n", "# Create the full paths for loading the Parquet files\n", "parquet_path_lwp_corrected = os.path.join(base_dir_mwr, filename_lwp_corrected)\n", "\n", "# Load the Parquet files into DataFrames\n", "df_lwp_corrected = pd.read_parquet(parquet_path_lwp_corrected)\n", "\n", "# Display the first few rows of each DataFrame to verify\n", "print(\"Loaded Corrected LWP Data:\")\n", "print(df_lwp_corrected.head())\n", "\n" ] }, { "cell_type": "code", "execution_count": 4, "id": "d4552709", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TIMESTAMP LWP_Corrected\n", "0 2024-03-01 18:00:00 28.360662\n", "1 2024-03-01 18:00:01 25.115103\n", "2 2024-03-01 18:00:02 24.036722\n", "3 2024-03-01 18:00:03 24.512901\n", "4 2024-03-01 18:00:04 26.155781\n", "... ... ...\n", "6488789 2024-06-12 08:00:36 8.273018\n", "6488790 2024-06-12 08:00:37 2.470345\n", "6488791 2024-06-12 08:00:38 6.229584\n", "6488792 2024-06-12 08:00:39 4.804169\n", "6488793 2024-06-12 08:00:40 7.745605\n", "\n", "[6488794 rows x 2 columns]\n" ] } ], "source": [ "print(df_lwp_corrected)\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "eb94a7fe", "metadata": { "scrolled": true }, "outputs": [ { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] }, { "name": "stderr", "output_type": "stream", "text": [ "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", "C:\\Users\\magda\\AppData\\Local\\Temp\\ipykernel_20560\\1111397893.py:38: UserWarning: Could not infer format, so each element will be parsed individually, falling back to `dateutil`. To ensure parsing is consistent and as-expected, please specify a format.\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n" ] } ], "source": [ "# Base folder path containing monthly folders\n", "base_folder = 'C:\\\\Users\\\\magda\\\\Master_Thesis\\\\radiometer'\n", "\n", "# Initialize lists to store data\n", "all_data_swr_dn = []\n", "all_data_lwr_dn = []\n", "all_solar_angles = []\n", "\n", "# Location information for Alkmaar, Netherlands\n", "latitude = 52.6324 # degrees North\n", "longitude = 4.7534 # degrees East\n", "altitude = 0 # altitude above sea level in meters (assuming ground level)\n", "\n", "# Loop through each month's folder\n", "for month_folder in ['2024-03', '2024-04', '2024-05', '2024-06']:# '2024-07']:\n", " month_folder_path = os.path.join(base_folder, month_folder)\n", " \n", " # Check if the folder exists\n", " if not os.path.exists(month_folder_path):\n", " continue\n", " \n", " # Loop through each day's folder in the month\n", " for day_folder in os.listdir(month_folder_path):\n", " day_folder_path = os.path.join(month_folder_path, day_folder)\n", " \n", " # Get a list of all .dat files in the folder\n", " data_files_solar = [file for file in os.listdir(day_folder_path) if file.endswith('.dat')]\n", " \n", " # Loop through each file in the folder\n", " for file in data_files_solar:\n", " # Construct the full file path\n", " file_path = os.path.join(day_folder_path, file)\n", " \n", " # Read the data from the file\n", " data = pd.read_csv(file_path, skiprows=1, delimiter=',', encoding='latin1')\n", " \n", " # Convert TIMESTAMP column to datetime format with error handling\n", " data['TIMESTAMP'] = pd.to_datetime(data['TIMESTAMP'], errors='coerce')\n", " \n", " # Drop rows with NaT values in the TIMESTAMP column\n", " data.dropna(subset=['TIMESTAMP'], inplace=True)\n", " \n", " # Select relevant columns containing short wave down radiation and long wave down radiation data\n", " swr_column = 'SR15D1Dn_Irr' # Adjust column name if needed\n", " lwr_column = 'IR20Dn' # Adjust column name if needed\n", " lwr_up_column = 'IR20Up' # Upward long-wave radiation\n", "\n", " # Calculate solar zenith angle\n", " times = data['TIMESTAMP']\n", " solar_position = pvlib.solarposition.get_solarposition(times, latitude, longitude, altitude)\n", " \n", " # Convert the columns to numeric type\n", " data[swr_column] = pd.to_numeric(data[swr_column], errors='coerce')\n", " data[lwr_column] = pd.to_numeric(data[lwr_column], errors='coerce')\n", " data[lwr_up_column] = pd.to_numeric(data[lwr_up_column], errors='coerce')\n", "\n", " # Append the data to the lists\n", " all_data_swr_dn.append(data[['TIMESTAMP', swr_column]])\n", " all_data_lwr_dn.append(data[['TIMESTAMP', lwr_column, lwr_up_column]])\n", "\n", " all_solar_angles.append(solar_position)\n", "\n", "# Combine data from all files\n", "all_data_combined_swr = pd.concat(all_data_swr_dn, ignore_index=True)\n", "all_data_combined_lwr = pd.concat(all_data_lwr_dn, ignore_index=True)\n", "\n", "# Concatenate solar angles into a single DataFrame\n", "all_solar_angles_df = pd.concat(all_solar_angles, ignore_index=True)\n", "\n", "# Merge the dataframes on the TIMESTAMP column\n", "#final_combined_df = all_data_combined_swr.merge(all_data_combined_lwr, on='TIMESTAMP').merge(all_solar_angles_df, left_on='TIMESTAMP', right_on='apparent_zenith')\n", "\n", "# Print or process final_combined_df as needed\n", "#print(final_combined_df.head())\n" ] }, { "cell_type": "code", "execution_count": 6, "id": "76d2ec8b", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Combined SWR DataFrame saved to C:\\Users\\magda\\Master_Thesis\\radiometer\\Combined_SWR_Data.parquet\n", "Combined LWR DataFrame saved to C:\\Users\\magda\\Master_Thesis\\radiometer\\Combined_LWR_Data.parquet\n" ] } ], "source": [ "# Save the combined SWR data to Parquet format\n", "parquet_file_swr = os.path.join(base_folder, 'Combined_SWR_Data.parquet')\n", "all_data_combined_swr.to_parquet(parquet_file_swr, compression='gzip')\n", "print(f\"Combined SWR DataFrame saved to {parquet_file_swr}\")\n", "\n", "# Save the combined LWR data to Parquet format\n", "parquet_file_lwr = os.path.join(base_folder, 'Combined_LWR_Data.parquet')\n", "all_data_combined_lwr.to_parquet(parquet_file_lwr, compression='gzip')\n", "print(f\"Combined LWR DataFrame saved to {parquet_file_lwr}\")" ] }, { "cell_type": "code", "execution_count": 8, "id": "5e412126", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " apparent_zenith zenith apparent_elevation elevation \\\n", "0 59.927867 59.956806 30.072133 30.043194 \n", "1 59.918398 59.947326 30.081602 30.052674 \n", "2 59.908938 59.937855 30.091062 30.062145 \n", "3 59.899485 59.928391 30.100515 30.071609 \n", "4 59.890041 59.918936 30.109959 30.081064 \n", "... ... ... ... ... \n", "9401637 104.212059 104.212059 -14.212059 -14.212059 \n", "9401638 104.211902 104.211902 -14.211902 -14.211902 \n", "9401639 104.211745 104.211745 -14.211745 -14.211745 \n", "9401640 104.211588 104.211588 -14.211588 -14.211588 \n", "9401641 104.211430 104.211430 -14.211430 -14.211430 \n", "\n", " azimuth equation_of_time \n", "0 147.728023 -9.336757 \n", "1 147.759858 -9.336735 \n", "2 147.791700 -9.336712 \n", "3 147.823547 -9.336690 \n", "4 147.855401 -9.336668 \n", "... ... ... \n", "9401637 3.572163 -3.885184 \n", "9401638 3.576113 -3.885186 \n", "9401639 3.580063 -3.885188 \n", "9401640 3.584013 -3.885191 \n", "9401641 3.587963 -3.885193 \n", "\n", "[9401642 rows x 6 columns]\n" ] } ], "source": [ "#print(all_solar_angles_df)\n", "print(all_solar_angles_df)" ] }, { "cell_type": "code", "execution_count": 9, "id": "15a49e94", "metadata": { "scrolled": false }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Filter out the zero values in shortwave down radiation\n", "filtered_data = all_data_combined_swr[all_data_combined_swr['SR15D1Dn_Irr'] > 10]\n", "filtered_angles = all_solar_angles_df.loc[filtered_data.index]\n", "\n", "# Plot the scatter plot excluding zero values in radiation\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Adjust marker size (s), color (c), and transparency (alpha)\n", "plt.plot(filtered_angles['apparent_zenith'], filtered_data['SR15D1Dn_Irr'], 'ko', markersize=0.1, alpha=0.1)\n", "\n", "plt.xlabel('Apparent Solar Zenith Angle (degrees)')\n", "plt.ylabel('Shortwave Down Radiation (W/m^2)')\n", "plt.title('Shortwave Down Radiation vs Solar Zenith Angle in Alkmaar, Netherlands (March-June 2024)')\n", "# Save the plot as a PDF file\n", "#output_file = 'shortwave_radiation_vs_zenith_angle.png'\n", "#plt.savefig(output_file, format='png', dpi=300) # Save as PDF with 300 dpi for high quality\n", "\n", "# Show the plot (optional)\n", "plt.show()\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 10, "id": "6c7b45d6", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Merged DataFrame:\n", " TIMESTAMP SR15D1Dn_Irr apparent_zenith\n", "120 2024-03-13 10:14:04 199.70 58.853356\n", "121 2024-03-13 10:14:08 201.00 58.848531\n", "122 2024-03-13 10:14:12 200.50 58.843709\n", "123 2024-03-13 10:14:16 199.60 58.838890\n", "124 2024-03-13 10:14:20 195.40 58.834074\n", "... ... ... ...\n", "9388090 2024-06-30 20:14:09 10.09 91.534230\n", "9388091 2024-06-30 20:14:10 10.08 91.536081\n", "9388092 2024-06-30 20:14:11 10.06 91.537931\n", "9388093 2024-06-30 20:14:12 10.04 91.539782\n", "9388095 2024-06-30 20:14:14 10.02 91.543483\n", "\n", "[5511327 rows x 3 columns]\n" ] } ], "source": [ "#print(filtered_data)\n", "#print(filtered_angles)\n", "\n", "# Concatenate DataFrames along the columns\n", "merged_df = pd.concat([filtered_data, filtered_angles['apparent_zenith']], axis=1)\n", "\n", "# Print the merged DataFrame\n", "print(\"\\nMerged DataFrame:\")\n", "print(merged_df)" ] }, { "cell_type": "code", "execution_count": 11, "id": "bc43aba8", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "\n", "Final Merged DataFrame:\n", " TIMESTAMP SR15D1Dn_Irr apparent_zenith LWP_Corrected\n", "0 2024-03-13 10:14:04 199.7 58.853356 68.871277\n", "1 2024-03-13 10:14:08 201.0 58.848531 63.201721\n", "2 2024-03-13 10:14:12 200.5 58.843709 68.508179\n", "3 2024-03-13 10:14:16 199.6 58.838890 68.810669\n", "4 2024-03-13 10:14:20 195.4 58.834074 66.352661\n" ] } ], "source": [ "# Ensure TIMESTAMP columns are in datetime format for proper merging\n", "df_lwp_corrected['TIMESTAMP'] = pd.to_datetime(df_lwp_corrected['TIMESTAMP'])\n", "merged_df['TIMESTAMP'] = pd.to_datetime(merged_df['TIMESTAMP'])\n", "\n", "# Merge DataFrames on the 'TIMESTAMP' column\n", "merged_final_df = pd.merge(merged_df, df_lwp_corrected, on='TIMESTAMP', how='inner')\n", "\n", "# Print the final merged DataFrame\n", "print(\"\\nFinal Merged DataFrame:\")\n", "print(merged_final_df.head())" ] }, { "cell_type": "code", "execution_count": 18, "id": "bd69f122", "metadata": {}, "outputs": [ { "data": { "image/png": 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ll19OOByOvtLS0ujdu3eTR1fS0tI45ZRTGpX16tXrsPfiSP36179m5cqVvPjii/Tt27fJ9tbS3j59+pCdnR19b7PZ6NSp02HrHTZsGDabjblz5wINj6uNGjWKM888k8WLF+P1esnPz2fbtm3NPu5yNCZMmIDRaIy+79WrF8Axn3uvXr1ITEyMXuv58+eTlpZG586dARgxYkS0jx5sPhpoGA1kNpuxWCx07dqVxYsX8+CDD3LjjTceU/v2d7TfNQfu261btyb956qrrkIpxddff92ofNKkSZjN5qNu49VXX83MmTOpqKjg5ZdfZvTo0bRt27bZWI/Hw913302HDh0wmUyYTCZiYmKor69v9vN+JN8h0DCJ8aRJkxqVTZkyJTpK6nAO3PfAvvbNN98AcNFFFzWKmzx5cpO/a0IIIU5ukqQRQrRKb7zxBitWrODrr7/m+uuvZ9OmTVx66aWNYj788EMuuugiMjMzeeutt1iyZAkrVqzg6quvxu/3R+MqKiowmUwkJiY22j8tLe2w7dB1nXHjxvHhhx9y11138dVXX7F8+fLovBZHk8zYX0VFBWlpaU0eV0hJScFkMjV5zOjAtkPDZK+HO77JZGLq1Kl89NFH0SH1r732Gunp6ZxxxhnRuGeeeYa7776bjz/+mNGjR5OQkMC555572GXP6+rqeP/99znllFNITk6murqa6upqzjvvPDRNa3YC4eau+76yA8/7YLH74n7M/Tlw5a6SkhKUUqSmpmI2mxu9li5d2uQRih97L47Es88+y+uvv87NN9/M1KlTm41pLe39sfXabDaGDRsWTdJ89dVXnH766YwaNYpIJMLChQujjz0da5LmwDbum3T4WM9d0zRGjhzJokWLCIVCzJs3j5EjR0a3jxw5km+++QalFPPmzSMtLa3ZR38uuugiVqxYwXfffceWLVuoqKg46Dwz++xLjO17BPRwjva75sB9m1vpLiMjI7p9fz9mVTxoSFTYbDb+9re/8cknn3DNNdccNHbKlCk899xzXHvttXz55ZcsX76cFStWkJyc3Ox9Pdx3yD77J44P3PdIHvs8XF/bV8eBx2nub5MQQoiTm6TuhRCtUteuXaOTBY8ePZpIJMK///1vPvjgg+h/EX7rrbdo164d7733XqMfIAdOhpmYmEg4HKaioqLR/xkuLi4+bDvWr1/P2rVree2117jyyiuj5du3bz+m80tMTGTZsmUopRq1vbS0lHA4TFJS0jHVv79p06bx5JNP8u6773LxxRczc+ZMbr311kYjDJxOJw888AAPPPAAJSUl0VE1Z599Nps3bz5o3e+88w5er5fly5cTHx/fZPtHH31EVVVVo23NXfd9ZQf+WDlYbIcOHYAfd38O/LGalJSEpmksXLiw2ZWDfq7VhBYuXMgdd9zB8OHD+etf/3rQuNbS3mMxZswY/vjHP7J8+XIKCgo4/fTTiY2NZeDAgcyZM4fCwkI6depEVlZWSzf1oEaPHs2HH37IsmXLWLhwIY899lh028iRIykvL2flypUsXbqU8847r9k6kpOTo99zR2rAgAEkJCQwY8YMHnvsscPOS3Ms3zWJiYkUFRU1Kd83Ie6B+x7JHDnNcTgcXHLJJTz22GO4XK6DzpVTU1PDp59+yp/+9CfuueeeaPm++cmac7jvkH0OnMtp/32PRxJlXx0lJSVkZmZGy/f9bRJCCCH2kZE0QohfhCeeeIL4+Hj++Mc/RifB1TQNi8XS6IdBcXFxk9Wd9j1m8Pbbbzcqnz59+mGPu6/uA3/4vvjii01ij+a/0o8ZMwaPx9NkSfF9Ex6PGTPmsHUcqa5duzJo0CBeffVVpk+fTiAQYNq0aQeNT01N5aqrruLSSy9ly5Yth1ym+uWXXyY2NpavvvqKefPmNXo9+eSTBAKBJtf9q6++avSDKBKJ8N5775Gbm9tkadsD9128eDG7d++OrpR1NPfnYCZOnIhSir179zJgwIAmr4NNXHsoRztiY+/evVx44YUkJSXx/vvvH/KRkZ+ivcfjHI7G2LFjCYfD3HfffbRp0yY6ymTs2LHMnTuXr7/++ohG0RyvEUw/xr7vlb/97W/U1NQ0Wr2te/fuJCYm8thjj0Un7j1ezGYzd999N5s3b+ahhx5qNqa0tJRFixYBx/ZdM2bMGDZu3MiqVaua7Ktp2nE9rxtuuIGzzz6bP/7xj9hstmZjNE1DKdXk8/7vf/+7ySpM+xzuO2Sfuro6Zs6c2ahs+vTp0dW8jtW+Ot57771G5R988EGjVcKEEEIIGUkjhPhFiI+P59577+Wuu+5i+vTpXH755UycOJEPP/yQG2+8kcmTJ5Ofn89DDz1Eenp6o8d0xo0bx4gRI7jrrruor69nwIABLFq0iDfffPOwx+3SpQu5ubncc889KKVISEjgk08+abQKzT77fhz//e9/58orr8RsNtO5c+dGc8nsc8UVV/CPf/yDK6+8kry8PHr27Mm3337Lo48+yllnnXXMj3kc6Oqrr+b666+nsLCQoUOHRufO2GfQoEFMnDiRXr16ER8fz6ZNm3jzzTcZMmQIDoej2TrXr1/P8uXLueGGGzjttNOabB82bBhPPfUUL7/8cqOVYJKSkjjttNO47777cDqdPP/882zevLnJMtzQsJLNtddey4UXXkh+fj6///3vyczMjM7ZcTT352CGDRvGr371K6ZNm8Z3333HiBEjcDqdFBUV8e2339KzZ09uuOGGI64PGpZSttvtvP3223Tt2pWYmBgyMjKij4nsLxgMcv7551NSUsJTTz1FXl5eoyXg93G5XHTr1u0naW9zjqY/H63+/fsTHx/P7NmzGyUMx44dG008HMlnoGfPnnz44Yf885//pH///hgMhqMemXIoB1uue+TIkXTv3p2UlBQ++ugjkpOT6dq1a3S7pmmMGDGCjz76CGh+Pppj8bvf/Y5Nmzbxpz/9ieXLlzNlyhSysrKoqalhwYIF/Otf/+KBBx5g2LBhx/Rdc9ttt/HGG28wYcIEHnzwQXJycvjss894/vnnueGGGxqttnes+vTp0ySRdCCXy8WIESN48sknSUpKom3btnzzzTe8/PLLxMXFNbvP4b5D9klMTOSGG25gz549dOrUic8//5yXXnqJG264odHcSz9W9+7dufTSS3nqqacwGo2cdtppbNiwgaeeegq3293sSlZCCCFOUi00YbEQQjRr30oZK1asaLLN5/Op7Oxs1bFjx+iKLX/+859V27ZtldVqVV27dlUvvfRSdBWf/VVXV6urr75axcXFKYfDoU4//XS1efPmI1rdaePGjer0009XsbGxKj4+Xl144YVqz549TfZVSql7771XZWRkKIPB0GhVkgNXd1JKqYqKCvXrX/9apaenK5PJpHJyctS9996r/H5/ozhA3XTTTU2ux4ErsRxKTU2NstvtClAvvfRSk+333HOPGjBggIqPj1dWq1W1b99e3Xbbbaq8vPygdd56660KUGvWrDlozL7VYVauXNnoXJ5//nmVm5urzGaz6tKli3r77bcb7bfvPsyePVtNnTpVxcXFKbvdrs466yy1bdu2RrFHen/29YuysrJm2/rKK6+oQYMGKafTqex2u8rNzVVXXHGF+u6776IxI0eOVN27d2+y75VXXtlklaF33nlHdenSRZnN5mb7yj77VsY53OvA/nM829vc6k5KHbw/778yz/6a6+cHc9555ymg0b0PBoPK6XQqg8GgqqqqGsU399msrKxUkydPVnFxcUrTtOjnft/5PPnkk02Oe6h7sc++1Z0O9tp3HS666CIFRFcm2t/TTz+tAJWZmdnsMQ72uT4aM2bMUBMmTFDJycnKZDKp+Ph4NXr0aPXCCy+oQCAQjTvS75rmvlN2796tpkyZohITE5XZbFadO3dWTz75pIpEItGYQ13vgzlYH9pfc6s7FRQUqAsuuEDFx8er2NhYdeaZZ6r169c3afvRfIfs+5zMnz9fDRgwQFmtVpWenq7+7//+r8mKggf7m3Hg36x9fWj/tvv9fnX77berlJQUZbPZ1ODBg9WSJUuU2+1Wt91225FdOCGEECc8TSmlfoLcjxBCCNGEpmncdNNNPPfcc4eMe+2115g2bRorVqw4riMjhBAnh6P5Dhk1ahTl5eWsX7/+Z2rdDxYvXsywYcN4++23j3jFQSGEECc2edxJCCGEEEKIn9icOXNYsmQJ/fv3x263s3btWv785z/TsWPHg06WLIQQ4uQjSRohhBBCCCF+Yi6Xi9mzZ/P0009TV1dHUlIS48eP57HHHjvoZMlCCCFOPvK4kxBCCCGEEEIIIUQrIFPJCyGEEEIIIYQQQrQCkqQRQgghhBBCCCGEaAUkSSOEEEIIIYQQQgjRCsjEwUdI13UKCwuJjY1F07SWbo4QQgghhBBCiCOklKKuro6MjAwMhhNvrILf7ycYDLZ0M6IsFotMiv4jSZLmCBUWFpKVldXSzRBCCCGEEEII8SPl5+fTpk2blm7GceX3+2nXNoHiEl9LNyXK5XKRnp6OwWDgpptu4qabbmrpJv1iSJLmCMXGxgINH2qXy9XCrTk6oVCI2bNnM27cOMxmc0s3R7QC0ifE/qQ/iANJnxAHkj4hDiR9Quzvl9AfamtrycrKiv6uO5EEg0GKS3zsXjcFV6ylpZtDbV2QnJ7Tf5G/nVsDSdIcoX2POLlcrl9cRwuFQjgcDlwuV6v90hQ/L+kTYn/SH8SBpE+IA0mfEAeSPiH290vqDyfy1BWuWAsuV8snacSxOfEexhNCCCGEEEIIIYT4BZIkjRBCCCGEEEIIIUQrIEkaIYQQQgghhBBCiFZAkjRCCCGEEEIIIYQQrYAkaYQQQgghhBBCCCFaAUnSCCGEEEIIIYQQQrQCkqQRQgghhBBCCCGEaAUkSSOEEEIIIYQQQgjRCkiSRgghhBBCCCGEEKIVkCSNEEIIIYQQQgghRCsgSRohhBBCCCGEEEKIVkCSNEIIIYQQQgghhBCtgCRphBBCCCGEEEIIIVoBSdIIIYQQQgghhBBCtAKSpBFCCCGEEEIIIYRoBVo0SbNgwQLOPvtsMjIy0DSNjz/++KCx119/PZqm8fTTTzcqDwQC/OY3vyEpKQmn08mkSZMoKChoFFNVVcXUqVNxu9243W6mTp1KdXX18T8hIYQQQgghhBBCiB+pRZM09fX19O7dm+eee+6QcR9//DHLli0jIyOjybZbb72Vjz76iHfffZdvv/0Wj8fDxIkTiUQi0ZgpU6awZs0aZs2axaxZs1izZg1Tp0497ucjhBBCCCGEEEII8WOZWvLg48ePZ/z48YeM2bt3LzfffDNffvklEyZMaLStpqaGl19+mTfffJOxY8cC8NZbb5GVlcXcuXM544wz2LRpE7NmzWLp0qUMGjQIgJdeeokhQ4awZcsWOnfu/NOcnBBCCCGEEEIIIcRRaNVz0ui6ztSpU/nd735H9+7dm2xfuXIloVCIcePGRcsyMjLo0aMHixcvBmDJkiW43e5oggZg8ODBuN3uaIwQQgghhBBCCCFES2vRkTSH8/jjj2Mymfjtb3/b7Pbi4mIsFgvx8fGNylNTUykuLo7GpKSkNNk3JSUlGtOcQCBAIBCIvq+trQUgFAoRCoWO+lxa0r72/tLaLX460ifE/qQ/iANJnxAHkj4hDiR9Quzvl9AfWnPbhNhfq03SrFy5kr///e+sWrUKTdOOal+lVKN9mtv/wJgDPfbYYzzwwANNymfPno3D4Tiq9rQWc+bMaekmiFZG+oTYn/QHcSDpE+JA0ifEgaRPiP215v7g9XpbuglCHJFWm6RZuHAhpaWlZGdnR8sikQh33HEHTz/9NHl5eaSlpREMBqmqqmo0mqa0tJShQ4cCkJaWRklJSZP6y8rKSE1NPejx7733Xm6//fbo+9raWrKyshg3bhwul+t4nOLPJhQKMWfOHE4//XTMZnNLN0e0AtInxP6kP4gDSZ8QB5I+IQ4kfULs75fQH/Y9GSFEa9dqkzRTp06NTga8zxlnnMHUqVOZNm0aAP3798dsNjNnzhwuuugiAIqKili/fj1PPPEEAEOGDKGmpobly5dzyimnALBs2TJqamqiiZzmWK1WrFZrk3Kz2dxqv3gO55fcdvHTkD4h9if9QRxI+oQ4kPQJcSDpE2J/rbk/tNZ2CXGgFk3SeDwetm/fHn2/a9cu1qxZQ0JCAtnZ2SQmJjaKN5vNpKWlRVdkcrvdXHPNNdxxxx0kJiaSkJDAnXfeSc+ePaMJnq5du3LmmWdy3XXX8eKLLwLwq1/9iokTJ8rKTkIIIYQQQgghhGg1WjRJ89133zF69Ojo+32PF1155ZW89tprR1TH3/72N0wmExdddBE+n48xY8bw2muvYTQaozFvv/02v/3tb6OrQE2aNInnnnvu+J2IEEIIIYQQQgghxDFq0STNqFGjUEodcXxeXl6TMpvNxrPPPsuzzz570P0SEhJ46623fkwThRBCCCGEEEIIIX4WhpZugBBCCCGEEEIIIYSQJI0QQgghhBBCCCFEqyBJGnHclXgXsq3qJYIRWeZOCCGEEEIIIcSReeyxx9A0jVtvvTVadtVVV6FpWqPX4MGDD1tX27Ztm+x3zz33NIrZs2cPZ599Nk6nk6SkJH77298SDAaP92kdlVa7BLf45dpT+xG+8F7irD1Idgxp6eYIIYQQQgghhGjlVqxYwb/+9S969erVZNuZZ57Jq6++Gn1vsViOqM4HH3yQ6667Lvo+JiYm+u9IJMKECRNITk7m22+/paKigiuvvBKl1CHnvP2pSZJGHHeZlhSq9N24TWkt3RQhhBBCCCGEEK2cx+Phsssu46WXXuLhhx9ust1qtZKWdvS/L2NjYw+63+zZs9m4cSP5+flkZGQA8NRTT3HVVVfxyCOP4HK5jvp4x4M87iSOu0LvXKpCBdT5F7R0U4QQQgghhBBCtHI33XQTEyZMYOzYsc1unz9/PikpKXTq1InrrruO0tLSI6r38ccfJzExkT59+vDII480epRpyZIl9OjRI5qgATjjjDMIBAKsXLny2E7oGMhIGnHcxTnOotq/GpttVEs3RQghhBBCCCFEC6itbTxHqdVqxWq1Nol79913WbVqFStWrGi2nvHjx3PhhReSk5PDrl27uO+++zjttNNYuXJls/Xtc8stt9CvXz/i4+NZvnw59957L7t27eLf//43AMXFxaSmpjbaJz4+HovFQnFx8dGe7nEjSRpx3HVIuL2lmyCEEEIIIYQQogVlZWU1ev+nP/2J+++/v1FZfn4+t9xyC7Nnz8ZmszVbz8UXXxz9d48ePRgwYAA5OTl89tlnnH/++fz617/mrbfeisZ4PB4AbrvttmhZr169iI+PZ/LkydHRNQCapjU5nlKq2fKfiyRphBBCCCGEEEIIcVzl5+c3mteluVEvK1eupLS0lP79+0fLIpEICxYs4LnnniMQCGA0Ghvtk56eTk5ODtu2bQMaJge+8847D9uefStCbd++ncTERNLS0li2bFmjmKqqKkKhUJMRNj8nSdIIIYQQQgghhBDiuHK5XIedfHfMmDGsW7euUdm0adPo0qULd999d5MEDUBFRQX5+fmkp6cDkJKSQkpKymHbs3r1aoDofkOGDOGRRx6hqKgoWjZ79mysVmujpNHPTZI0QgghhBBCCCGE+NnFxsbSo0ePRmVOp5PExER69OiBx+Ph/vvv54ILLiA9PZ28vDz+7//+j6SkJM4777yD1rtkyRKWLl3K6NGjcbvdrFixgttuu41JkyaRnZ0NwLhx4+jWrRtTp07lySefpLKykjvvvJPrrruuxVZ2AknSCCGEEEIIIYQQohUyGo2sW7eON954g+rqatLT0xk9ejTvvfcesbGxB93ParXy3nvv8cADDxAIBMjJyeG6667jrrvualT3Z599xo033siwYcOw2+1MmTKFv/zlLz/HqR2UJGlEq7Om7N/sqH4OzdiG89t/2tLNEUIIIYQQQgjxM5k/f37033a7nS+//PKo6+jXrx9Lly49bFx2djafftq6fnMaWroB4sSzrfQWvssfi9e/6sftX/0aZoMOev5xbpkQQgghhBBCCNF6SZLmJBQKR/CGAiilfpL6qwOb8Ot1+ILrDh/cjBT7BELKgF9vuecAhRBCCCGEEEKIn5s87nSSKa2u45p/v0+1quOmCwdwSdthx/0Y3VP+gjf4PfHOS37U/okxo9ji3YTVlHucWyaEEEIIIYQQQrRekqQ5yWwqLqPA7yGoImyo2Attj/8xHNZeOKy9fvT+NgpJNe8l1hJ/HFslhBBCCCGEEEK0bpKkOcn0zckgOy6GEr0Kh8Xc0s1pVmbMMMJ6Ecn2wS3dFCGEEEIIIYQQ4mcjSZqTTFF5LbH1VvSIi/Hp/Vq6Oc2ym9LoknBzSzdDCCGEEEIIIYT4WUmS5iSTm5HExSP6kOBy0icpp6Wb0yylFHn120mwJuE2yyNPQgghhBBCCCFODpKkOcmYjAbOHtK9pZtxSDs8m/mk8H0SLUlc0e7Glm6OEEIIIYQQQgjxs5AluE8ySineW7aSa2e9zGcFq1q6Oc2yGYIkGEvJsKe3dFOEEEIIIYQQQoifjSRpTjKrNhfw7+cWsvXdKjZV7m3p5jTr26J7MOrLiAS/bOmmCCGEEEIIIYQQPxt53Okk47bbcFlsaGa4ov3Ilm5OswyqEqchiDec39JNEUIIIYQQQgghfjaSpDnJdGibzN8euBC7zUJyTGxLN6dZbWwpVIUK6OZqnUkkIYQQQgghhBDipyBJmpNQdpvElm7CQa2p3IzD2IkY6kiz92jp5gghhBBCCCGEED8bSdKI46rU6+H+Fa/TyV3PjT2vx2J0H/G+i8vW8OeN/0VTYS5I19Fte+jj/AkbK4QQQgghhBBCtCIycfBJRinFnI2buHvhu3xTsvG41/9Z/ves8hQxs6iKuuC2o9o3zhKLEbAZA5g0PzvqZqOr8HFvoxBCCCGEEEII0RpJkuYks7OoghdmLmXxvGKWl20/7vWfmp5LTkwcp6ZlEW/r3WzM9BVr6fv4c9wzc1aj8i6udtzcqQNjEgtZVd2BWFMfNIzHvY1CCCGEEEIIIURrJEmak4zDaMK6rIqcbTAle9hxr7+DK5U3ht3K/X1uwKCZm4154dtleAMhZq7d1Kh8V/02vi79ivyQhttaw1ZvDJqmHfc2CiGEEEIIIYQQrZEkaU4yhrAiJ85FjjWWRPPxX91pS1UZ0+b8l2fXLjloTK3HB0oRCapG5cnWNCwGNyYVIaIbiDFuOe7tE0IIIYQQQgghWiuZOPgkk5oZz6//72wsNjM2uwWAPd5d7PRsYWDCMJymY0vcVAf8hFWE4nrPQWMMdg3qAEvjJM3Our0U+r0YlJshcUVkWAPoKoxBk24qhBBCCCGEEOLEJ79+T0I5HVMbvV9SPo+d9VtxmePoFz/4mOo+JbUNDw8ZR5rz4Mme64YM4rmFS+mXld6ovC5ci1GLEFEGgrpOsjVXEjRCCCGEEEIIIU4a8gv4JFPv8fPw1OdwJcZy97+uw2AwMDBhOAmWZDrGdD0ux9CDCs1+8O3XDh5A56Qk+udkNipPtNqJMdmI6JUkmqrx6yXHpT1CCCGEEEIIIcQvgcxJc5LZuGwbG77dxIIZS5i9aTUAbeyp9I/LwmlyHnP9S/PyeWjWPP42b9FBY/717QrunjmLR7+c36jcYrCiEyaImT2B9uS6rzzm9gghhBBCCCGEEL8UkqQ5yfQb1Y2eV/THPjWLddpeALZV/YuNFU9R6l14zPUnOByYjQbSXAd/3Gn+1p3U+oIs3bGnUXm7mI44jA4AqiIWDJr1mNsjhBBCCCGEEEL8UkiS5iRjNBq59A8TibkglXRbPACxlg6YDbHYTRnHXH/n1CReuvQ8rhs64KAxWlhHC0PIH27cNs3I2ORhtLVW0N1eDqr57qmUQleq2W1CCCGEEEIIIcQvlSRpTjI1tT6e++t8KmeFKPFVA5DtOo9B6c/jtnY+LscwG41omtbstrCuU6MFCMeGidHMTbZb9K/pbC8k0VTM92XPoJTeJOahWfM4519vsqWk7Li0VwghhBBCCCGEaA0kSXOSqa8PYA6ZsNXZGOzuBkChL59vy+ZSHz74stnHQ0TXufa9D9lZW03IpYhv13QOnHhbP5SC+oiBgL4HRaRJzMfrN7K9vJKZGzf9pO0VQgghhBBCCCF+TpKkOclkpMfR/+wOVA/Q+HT3NgAWlX3FwvK5bKr9/rgco9BTSyASblLuC4fYUFaKAQ1TGLqYE5vEuG0X887eYXxY3I+UmKsxNDPaJjc7AXeclcG5WcelvUIIIYQQQgghRGsgS3CfhDLa2rH66zA56gHolzAEpymWDjFdjrnuFcUFPLlyAX1SMvi/gaMabYuxWLllzBDmbtmBXhFhUu/uTfZXBDFGNOwYiDX6mj3Gy+ddQIWvnnbuhGNurxBCCCGEEEII0VrISJqTkM+rk2yKpb2rIcmRG9OZszIuIM5y7EkPk8EAmobFYARAVzq763fgi3gBOD2rI6G6COURPwFD00eZqnxb6OIoIui34LJ2bfYYLotVEjRCCCGEEEIIIU44MpLmJLOjvJKPlm7DaEzmsuGnHvf6s9yx9GxjoVO8HYANNWuYW/IJ7ZwdmZB+MX/4bA7bKiuwuUwoS9P9feHvSI8ppzpoZkfFQ6Q4PznubRRCCCGEEEKIE82qoJmYQNPpIn5unmDDSrwDBw7EaDRy0003cdNNN7Vwq345JElzkomz23BYLGS4YzH/b7TL8ZTnKWVXfSn1kQCTsgbgtsRjNlhIsqZgNBjIinNT4KkhLtVGRmxsk/0t5lNZ61nO3ogbPbwRXYUxaNJNhRBCCCGEEOKXZMWKFbhcrpZuxi+O/Po9ySQ6HfzjorMBDrpM9v7CegSjZjiiWIA+8W2ZljuKLEfDpMDZjnbc2OFuDFrDk3V/OGMUO2p38U7em6ytstPOfU6j/XPMe/lVcgFL6sJ0T7lfEjRCCCGEEEIIIU4a8gv4BPPdnr2U1Ho4o1vHhvlhmqFpGkr3oHCiaRobqlezpmYF49PPJ8GSFI3bUlvIM5s/YUhSNy5vf2SPRpkMRvrGt8dpaniWKazXU1Q/lzhrdzzhJP5v3ky83jJcKWXUeNZybtsfkjSl/hoe27iaBK0NExMKUQbJugohhBBCCCGEOHlIkuYEEtF1Hpg3F08kRHpcDP3aZDaJydtaxKLPP2fMGXNIyZ6I5rycDwrepDJYjobGlJzrorHbareAYS1bPFXAkSVp1pQV8diK+QzPyOE3fYZS6l3Ezpq3iLP2YG3daHaQh0PF0aZ+EGNz+zTa1xP2Ux1xsc2XyrK6DJ5OjSP+WC6IEEIIIYQQQgjxCyJJmhOIpmn47GF8oRBBg95szD/+8D6b1u2mcGeAC2/5kra5lxNjiqEmWEX8fqNoANrG2Ei2Kto5j7yb1IeC6EpRFwoCEG/rTYqtD4n2oThsHVlalEdCXBJXtT+FtimNV2iyGQO0j9lMb2cxZT43qRZns8dYmpfP+sISJvftQZzddsRtE0IIIYQQQgghWjNJ0pxADJrGiNS27KqpolNcUrMxFrOGXu/Bb69l4ZJk2ubCuW0uY0fdZgYnjWoUa9IL6GjdQrL5yOajARiank2meTUp5u9Ren9sqoIOrEYLVhHnGkF5jZmNNXvZvOxzLuzXi4tH9YnuqxQYNR2zQSekK3ZUv0uf1NuaHOOD1evZXlZB+6R4TuuUe8RtE0IIIYQQQgghWjNJ0pxA6oJ1zN24HKVM7K2qJsFhbxLTYUAHtuXtgZQYEno2jFTJjelMbkznJrHzKjZSFzSzw1/FuKwja4OmaWQbP4VwDYSHUheuIBLaQyDsJc1o5PoeA1m4NY+8veU4rI2Xh8t0ZJNoHczGmq/ZHY6h0re82WNMGdCbTcWlDMxuc2SNEkIIIYQQQgghfgEkSXMCmV8yl6CznnDAhNVRB6Q3iUlrk4g7y8Hu0jbULoSzeqmDrtxk1GLIDybhMDT/2NHBaDE3QmQPmHth1ErZTC9iTF1JA8Zkd6DC76PG6Kd/z6aZnyEpfdlYt5AOjgoy7T2brb9fVgb9sjKOqk1CCCGEEEIIIURrJ0maE0hhrZdwjRVd19hQXE6n+E5NYhxOG2abkdLKGAIBC/5ACLvN0mx9XV292FG3lU4x3Y6qHZqlH9APAKe5Df1Tn0fbr6stLP4er3kl764to+JrN2ef2ZvBAxseW2pnT2aAYzegYzAf2WTFQgghhBBCCCHEiaD5NZrFL5LD4MQdU4/VFCLT5Wg2ZvvGvdRXVtO5Ux4jzll20AQNgMlgwW624zTH/Og2Kb0KreYOqHsIpXRqAj7O65hFpstMUC+jtKyOnXll0fgNlW+gawE21yeyvS7wo48rhBBCCCGEEEL80shImhOIOdKW0upNKF0jGIhrNmbw8Fx2bPqAnqfsIDbGesj6UmxpuMzxtHG0/dFtUpEqwqHtaFoBL2yczTt5y+lpa8+1PS8mp10W+bFeOndMi8anO4exrHw5laFECsMHTyAJIYQQQgghhBAnGknSnED6ZSdhTw6iB42kxpubjZn/7iI2faLYvHwQrvh4zvgqgslsbDa2zF9MfdhDqb/4qNpR4q8hweLEbDDhDxdQHCgkjIUyXyWBcIRVxXt5YGuE34yK5Yw+HRvt6wkGsEUMVAXN9I475aiOK4QQQgghhBBC/JLJ404nkGSrE7tBYdEULkvTlZ0ARk4eTEivx+DUqa6sRikFgPLNQK9/DaWC0djKgJn8eo1Cb/NJnOZ8V7GDP659j5e3zwNA02tIMuokm3Tu6XUuFyQPJ60mjaLyWlZszW+yf33d53R0FHBG0g6S2HE0py+EEEIIIYQQQvyiyUiaE8juuiKK9zpB11iYt4PJ3ROaxHQ+pSPtT6vCkKThLQGT2Yg/XInyvI7VYAHrcDB1AKAiWM7eejMZtprDHlspxda6IkJ6BACToSH/Z7OPwajfgGbKRRmtTOzUg/bhJGYu3Ui/tHQ27i4mNyMJq9nEzppK7l+biFEbwqCsnQQ9X6ASpqBpkksUQgghhBBCCHHikyTNCSTOlIChXkMLa6TY45qN+fTtxWxb2h5rkgU9YiQYCDOnYg4hfxL947qQbWwXje2TkM3WuqUMT8457LEXlW3h6XWf0cmZzuP9LiPW3DCSRzM4sMTeyOLy+czb9jQr8+M4K7sHj16Ty+otZTz+3hrG9e/EZWP6YzeZsRkzKfZ7mbGzB20dO+kkCRohhBBCCCGEECcJ+QV8AllTXIa5yoC9IMzTd33I0nmbmsTM+PtMQsXV1AWsmLMSsVhNxJuTqNfrKAgWEla+aOz3ZfWsLIrl+/LIYY+9Pn83Bd5Klu3YihMrxv8lV5RSqMBS8mu/IkgZDouPsprv+LbwUTT3exTW1rGqqAilFOnOWO4YeCohuwmDzUCS7fDJISGEEEIIIYQQ4kQhSZoTiNUAQRdYaoL4ttUy96OVTWJ8FbVgtxJJiKVONxIMhRmSNJxcpxNNVRII/7Ac9qvffE9RpY/PVu087LFtDgtoCqvd1Ggi4nBwBXVVt3O6Yz4XtLmAV067nsJiK+V1JpSWQ3Kum3LdRyjSkAgymkLYzDo2i6JT3MXH4aoIIYQQQgghhBC/DJKkOYEYDWF0CwRcBgwxJtKyms5Jk5Gbiq4UJm8IKurRFBgNVnok3U3nhJtxmttGY2tNdRhdYeoc1Yc9ttlkwoKJxJR4NE2LloeUnbAK4VdGusV1x2I0EwonMWvN2dj0c7BXa8R5zWg07OPWdnNd1nr6xxeS73n6WC+JEEIIIYQQQgjxiyFz0pxAeqRnYnF4sVcFiLMZSErcG129aZ/MDqmsLfURdtnApKH/rzzGlAy6s1GCxWxTBACjRedQdF3nq9/NxL6rBP+vkmD4D9vsRgMmYwqxmg2jZmHhjjzsZjPxDgfGiMaOveUYDAY8/gDxMQ5soY8YHbsHi8GHOaKI6AGMButxukJCCCGEEEIIIUTrJUmaE4g/soN2e8sJb7NSqWmU7V0FehkQH41Zt7qA2m5xOGsMoBQmowFd90P5OaD86PH/xmDpCYBvRxits4FIofmQxw3rEUprvCinG31dNUW7KwiGwuR0SEWpECa8YEgmEFH8ffEC9lbWEw4rvti+lQp7EE1p+INhAAzWsezwbKMw5OTUxO4YNMtPdr2EEEIIIYQQQojWRJI0J5AMR0d6tfGwChso2PB9KhiSQQ9HY/LwoVljsJR6UEYNo9FAw2AbHVD/+98GKlZhMCm0mEMf14ABPSOHsDdIXZWFX036GzhCnPpsGuOy4uilaaBpfF64mtq0tfTvWMj6rV0o88ehhxWaUlRV1ZOe4CJiGsHc+sWEVCmfVpu4PvmHkT1fzFlHZXU9k88ZgNUiXVcIIYQQQgghxIlF5qQ5gYQiMSyp6koEDYVGTY250eNLACaXHQtQ1yGW2k4uNE3DYLBBwoeQ+C4GS+9obGK2Bc2scKceuptoQELAilap0yYrjnAognG8l0/Linht+xIwuEGFqPZ48fsNEIAze2/m3A65xG8MEbchRF2Vlz315Ty16Qs2Vpsw48fKnugxAsEwX8xZx+KlOyjYW3U8L5sQQgghhBBCCNEqSJLmBFIXrKcsPUjEBroRisu9TWKGdsyG2AjeNmb86UY+WPo9AAZTMgZT+0axXdxt0KqM2Bc4mf/tloMe12A0kJRtx5AcoPrUeuzD0vBpyRRXullZksJblZmsi4zE4nETznMQ8FgoCdj4vvprjCGFMaxYvXw7DqMFq9FEus1EJ3s52daq6Jw6VouJqZcM5fxJ/WibnXgcr5oQQgghhBBCCNE6SJLmBOIwe7HrOvgCBJ1G9ECwSUxGUgLa/CLCDoVuBnusGaUUet3T6DX3oPSaaGw4qDCVWwiVRVi5evdBj6tpGuNuHk7wCjeV1nr8XbzYAkasRhiYUkpxoIKtdas5q0cXRnRri+aw449E2KvvAl8IAmFWzdpIpCLCPd3PIslezVZ/CmipjY4zsF9bxozsitEo3VYIIYQQQgghxIlHJvY4geyq1TDURDCYzBj31pDSKaNJTJ03RKLfQJ1Tw+qy0iUlBQhBaC2oUMNEwwY3ABd164vXVMeQTp04r1//Qx7bpJupKTGSkGLEuC1MLD6GZvvpFu+mm20PLquZGKvGxC7zKawr4+tdWRg9OZgiIQy+CCnds3HFOdjkXU19xIdJM6BUxU9xmYQQQgghhBBCiFZJhiScQDrEJdHRaQOHFZUSR1K39CYxZ10yCK8lRJ07QrnZy8qSPWiaBc11H1rsnWDMjca2N6VxnmUYU0YMJS3VfchjL1qwCcuqEJH8eBxt3dQ741BbTahwMpo1ix2Bal7a+Wd2ecrAUMmF3Ubx6Jm/xdomDnP7RM67ZgQmu8am2u/RNAsZtlhyXBMbzakTCkfwBULH74IJIYQQQgghhBCtiIykOYFUhyooyvNgqAuB1UBuetNlmeoqPHjtDjRNR6Fht1kB0A6Yjwbgv9+uY8nqnWycv5WLzhtMh24ZfLFiM0bNwLgBnUAvBf8X6MZB7J21C3elD9cQN952fjylAapnJRBfns4lv53MGzsfpjK0A3+4C9X+cgp93/DZpl0Yc5NR9RHat09mp2crW+s2ENRD1EXSyYq9NNoWXVc8+NYcquq8/HHqOFLiDrPklBBCCCGEEEII8QsjI2lOIHrEirdeQwOMgQirvl6C0j2NYnZUejAHFTGrdRJ3aIzMapqc2ee0Ph3obIshb+luZn2wgtJqDx8s+J73vllDRa0XAt+gvP9BC31G77PaY+xrIDXZjmm3GdNeM3WnhtiQXE2MuR1rHkxj5U2ZnOo8j5LyYYSCUK/7mHBGby477xQivhBtHG3pEz+IZGsqcZYE9P2WA9eVIq+4kr0Vtfj8TefaEUIIIYQQQgghfukkSXMCqfQEMA4wocyg0CgpMjaJ+XL6fLQ9ZQRTzYQS7CzZmX/Q+vp2yOR3N45n3Ln9GTupL8nuGIb3bc+IfrkkuhxgPRXNfi6a7SwSRrrxjtPxujxc234wp/e2Ew6ZKav1U1lbz87tZupCdh68eT7K6yPH0Z7r+l3I1QP7su611fz5jnfxVUQ4N/NSzkg7j3Gpk7AbHdG2hMIRjAYDMTYLkf+t+HQwBVsLuWX4H3j53rd//MUUQgghhBBCCCF+ZvK40wkk2erAvSWWUKhh6W1XUiyaIQYiP8zj4kLDbDaRZrTiTHDTLS0ZgPpQPhHdh8vaqVGdcYkxXHjtSACqvD6+Ld4DaJzr74HDmsyG4Ol0tKVT6ssjFNHxRRTjB00ns7KSD0q74jCbsFrNRGIsoGkkpJXTv9tCClUsS6q30dn+d/K2FRMJ69TV+Nhj2cQ7e15Cw8DvujxImj0TALvVzLQzB1LnDZCdEn/I67Dgg6VsW7WT4l0lXPPYZUd07ZRSPL3lX1QGy7i1800kWg99DCGEEEIIIYQQ4niTkTQnkH9/tIi6rVUoAygNxkzJbRIzckwvYs0m0ov3kNnBSGKMg4ju5/vyh1lf8QT1oYJo7NdrtvP7V75g+95yAGxmE7UeP999n8ftL3/EnKLveWbLF7ybtwhj0EbEZ6A6PwTGTJSKw2t2UGu0oRk0bv71GE4f3pmrH0jEbDKj6WF0vxefx0/A40OFwiSmxKIrnZAKEVZBSv3FjdveK5eJg7thOswS3NldM4lxO2jTqenEyQcT0sPs8KyjIriXTTXbjng/IYQQQgghhBDHx2OPPYamadx6663RMqUU999/PxkZGdjtdkaNGsWGDRuOuM5AIECfPn3QNI01a9Y02qZpWpPXCy+8cJzO5seRJM0J5JTsbOKwo+mgKVjxdVmTmIIthXjqPKzJtrFs73aWFudj0MzEGmKwamAx/rCK09odhewuqWRHYcNS2HazmZpdNdh2R9iysIAsRyIus4N2MSmcYetE4qd2kmbohK13ELLeScQPyq8wahrG4grCG3Yxa3MN39W2oXJtNr5vJ7BnaxEhX4igN4DSdYwYsWgWLJoVi9H6o66DJdZB2OnEnJRwxPuYDSYq/NnsqE0m1Zb9o44rhBBCCCGEEOLHWbFiBf/617/o1atXo/InnniCv/71rzz33HOsWLGCtLQ0Tj/9dOrq6o6o3rvuuouMjIyDbn/11VcpKiqKvq688spjOo9jJUmaE0iPbpmMGtYt+r5wS9MkzcAJ/TFna5BsJBCykuaIBVVHN1MxfS0+TKo2Gjt5ZE8uHtOHUX0aRuSU1Hqoi1F4swyYnGbaWFMYbT6FLrYcvs/bRW3PWgqTK6mr8fJ10UIitRG8dX4Atq3aRVVxNflbzeyuimW7L4dup/RmzrLtBNNicDkMhM0BllcuxG2JIdHsJxJe96Ouw57dlYQdsewu9KDr+uF3oGEkTUUgSEg3ku8t/1HHFUIIIYQQQghx9DweD5dddhkvvfQS8fE/TD2hlOLpp5/m97//Peeffz49evTg9ddfx+v1Mn369MPW+8UXXzB79mz+8pe/HDQmLi6OtLS06Mtutx+Xc/qxJElzAvnvjJUs+X5P9H04FGkSs6O4HF+1E/OCerKqYugQlwhaLFiHgKUfGBrmqInoOhdOf5U/fjSLmd83JEvinXb6tsvEgYkeA7OZvWkb/1q0gvdXryMSA8qsCCUo5u3ZhcEQIOKMgK1hwt9rHruMSx+6iPp2namubk/PrCwGdsqkrMZHSNMIxlgJGPwE9ACoCB0cYKLykOcbCkeoqfc3Ka/YVkCoqpZIdS0Gw5F1cYvRzGVtT+X0tF6MSOl6RPsIIYQQQgghhGhebW1to1cgEDho7E033cSECRMYO3Zso/Jdu3ZRXFzMuHHjomVWq5WRI0eyePHiQx6/pKSE6667jjfffBOHw3HQuJtvvpmkpCQGDhzICy+8cMT/of+nIkmaE8jwIZ1o1zkNbV9BM4sgbfy+BC0jjVBbN4XBKuqCATTNiCHmZgyxd6BpFgDqgwGqqyOEwzB3x3aK80oxGwycEZdDSr6Gs0QnUh/GU+knXBdhTIeeGL41wFojn+3ZRpBEwlaNgMVAIBImMT2eU0b15JzcblzV3c6k3h9Tp/+HzEQnmsmA12ImyZRKl5guEKlmsy+B3Lhphzzfv/73G+566VN2FDYe+VJb6yNSXIJW7znInk35wkG+LdvMhrIidtfLSBohhBBCCCGEOBZZWVm43e7o67HHHms27t1332XVqlXNbi8ubpinNDU1tVF5ampqdFtzlFJcddVV/PrXv2bAgAEHjXvooYd4//33mTt3Lpdccgl33HEHjz766JGc3k9GVnc6gfTslknexgLWHiLGaDWiVISI1YCWbEBHkV9Wjcthw+20ReNsJjP+DEUwECFcUMnjzz3LJfech9cfwuMLsGFXEdpWO75iP9tjSkmeFEft0HjqCfPrzh2wGNZhQGE0gL5fsshYbyQS3oWK3YWnKpHVWzSMdgtnj+yCxWomKTKTS+I2sdCbi8lw8GwnQCisoxRE9MbZqK2Lt6CAYP3BM7UHshpMhMptbM+rZbmriNz+qYffSQghhBBCCCFEs/Lz83G5XNH3VmvTOUfz8/O55ZZbmD17Njabrcn2fTRNa/ReKRUt+/Wvf81bb70V3ebxeHj22Wepra3l3nvvPWQb//CHP0T/3adPHwAefPDBRuU/NxlJcwJ5870lfPrlOkCBakhc7PHuQqkfkhgd2yZiLCwj5/NSHh53BtWVPh5+ey6PvvNVo7qUUhgNGhEbaPY6fN4gmBRrLfOJH1SKRwuys7yCSEQnv6YagJAOCgNl9fVc2Hki758Wz6yJp+L634fREwjy3IJveX2pHZcvjZ4pg6jz+IkojdicFDbuLeGzVe2J6EZizUmHPd87LxzFI1efSac2ydGydQs3kvd9HkR0gvW+I7523kiQAn85vkiQqnD9Ee8nfnkKCqsoLKpu6WYIIYQQQghxQnO5XI1ezSVpVq5cSWlpKf3798dkMmEymfjmm2945plnMJlM0RE0B46aKS0tjW578MEHWbNmTfQF8PXXX7N06VKsVismk4kOHToAMGDAgENODDx48GBqa2spKSk5HpfgR5EkzQkkzu0gJs1FRNNAA1dv+DD/TXbUb4nGGPwhVK2P0M4qnlv4HRF0dgSqKDkgMWE1m4kpViSuVPiNtViyM6k2eAm5iojrUkNSnBVXqoOQC1xpTlbvWU+f2F3k7qlh/Xeb+eeMefRucy257iRUaDMAQT2IxV2BMvrRNTAaNAafkos71oYpGGHKM9N5/2sXf5xxFpk2jXLfd4c8X5vFRLI7plHZmq/XR/9tsVsaJagOxW6yYE4IEt/JS7/2yYffQTRLD1dQXjqNyqonWropzaqu8fLwU5/x0FOfUlt35Ek8IYQQQgghxPE3ZswY1q1b1yjJMmDAAC677DLWrFlD+/btSUtLY86cOdF9gsEg33zzDUOHDgUgJSWFDh06RF8AzzzzDGvXro3W+fnnnwPw3nvv8cgjjxy0PatXr8ZmsxEXF/fTnfRhyONOJ5DJ5/Rn7JjujK/Nh0qdwddVEtANOI0/JDJSu2SiEpzo6SbKvs9jz8AaPKYIRZH6RkPG/OEwliINUzWUL7ewY1gZ382aQ3zYzpCBfRl2/inM3bELs66RZYpl5+73uG3wKt4PdaG0QmNlzXbiipZjGlzGK3vSuKzrEAyWeMwZEZzeCF6zk7JwDMFdFXi/L2T61jIY4MaIorTISpy2lSr/GpLsB39+sDlnXjOGL/+zhPKyevqN791kWNzBeMMBnEYbZnuYTMeRL90tGttd+wGO4HICwVXo7juPeOLmn4vHH2B3SRVoEAw3nVhbCCGEEEII8fOJjY2lR48ejcqcTieJiYnR8ltvvZVHH32Ujh070rFjRx599FEcDgdTpkw5aL3Z2dmN3sfENPwmzs3NpU2bNgB88sknFBcXM2TIEOx2O/PmzeP3v/89v/rVr5od9fNzkSTNCUTTNEIBL3ZVQzjVSJXNQ4zmpDZcE43p0T+X4NRcKhINGPGiDIpEh532KRWUeheQ4hiBpmk8/tZX1KSBxWbAEwSnSwfNQM0cFx/nF/JF+7l0N7sxV0XwFdSSkm6jYFscqXqAUYM3Em+pZf12B5vK21Dut7CqtIBLunahX1pHRqRksKx+IWuLt7FkXhUqrNBsJiIWAA3NEmR1fXcuTDvvqK+BOymWurogSjNQUX7kEwfXBcLklSjQDOgR41EfVzTwaF3YGnBRE3Fz4REmyH5ORs2AxWTAoGlEIi07a7sQQgghhBDi8O666y58Ph833ngjVVVVDBo0iNmzZxMbG3tM9ZrNZp5//nluv/12dF2nffv2PPjgg9x0003HqeU/Tov+Z+4FCxZw9tlnk5GRgaZpfPzxx9FtoVCIu+++m549e+J0OsnIyOCKK66gsLCwUR2BQIDf/OY3JCUl4XQ6mTRpEgUFBY1iqqqqmDp1anRW6alTp1JdXf0znOHPSynF35/5GOu3Cuc3EfZ8aiLdnkGKNS0ak+CyQURHhSCorPRMScNpMXBKh4Vsq36FuuA2APaUVKLpBgJuDUu1EfvnCsO3JnxpRpRFQzdotHHEEFcYpK3DxWf/SeSN//TkzeLOvPqfrhSbemPqWcf4Nnv4Tec8LnaWY9prwV6UzuJ16yGyE0e4nmCig2Cyg+R0F9ZaHWuVjqYUXr0GqzHxqK/BN198T6CyFr2yCn9xxRHvF4wEqfGFqfXKD/dj0TamNwWMw+48/4hHMf2ckhNiGD+iO+OGdyXB7Wzp5gghhBBCCCEOMH/+fJ5++unoe03TuP/++ykqKsLv9/PNN980GX1zOG3btkUpFZ0cGODMM89k9erV1NXVUV9fz7p167jlllswmVp2LEuLJmnq6+vp3bs3zz33XJNtXq+XVatWcd9997Fq1So+/PBDtm7dyqRJkxrF3XrrrXz00Ue8++67fPvtt3g8HiZOnEgk8sOjDFOmTGHNmjXMmjWLWbNmsWbNGqZOnfqTn9/Prcxby1ZVhjJp6AaoWRzD+LTLiLf8kOyY8fY3mN7YRtzHRSR9Ucm2onL2lNSydmcbEqx9cJgbhn5dOLI3Cg1NabSJWHHs0ND9RuxlOrExFh6/ajwZ2UmYYxy428bSb3QX9ibbqUsxsybbzSubMpm/cCzvzjqdqoJ2zJxr4bXpiyiqrsOmldMvppTOxhDOoI65NkBZaR2aAmUEZ0RhNrhR4bxG5+f1Banz+A95DSrLPJjMJowGjc4Dc4/42i3ak4fVUY/V4cEfDh35RReNRCIFdLLtIFFbgVKtL+FlMhm57NIhXHHZMMxmGTElhBBCCCGEaF1aNEU0fvx4xo8f3+w2t9vdaHIggGeffZZTTjmFPXv2kJ2dTU1NDS+//DJvvvkmY8eOBeCtt94iKyuLuXPncsYZZ7Bp0yZmzZrF0qVLGTRoEAAvvfQSQ4YMYcuWLXTu3PmnPcmf0ecvLcFkNhCJA0O1Rrib4v/WvMONuadHY9w5yWhxMZg1G0aDQjdE6GhNQKsYR7eks6NxA7plo30DRDTK6muIDYSw+CxgNaEKAnzw9nJ2rNtLnc3PW/XzGdGrC3X5dpymIL5U2OYz0j/cFo8/zOc7Y0m32Dl7WEc+rtxJAgF6WMKYTCZys5PYsL2IwlQjBrsRhWLQQCOD7DvB/wXE3ABAMBTmsac+JxAMce/tZxEf1/woiImXDuKtO18mEoqw9bsdR3ztFq/cg7/OgaZDnOngS7+JQ4uxtMdhHojbko2mta75aACqPD7+9NoszCYjj1x9FjaLPPEphBBCCCGEaD1+Ub9Qampq0DQtOtPyypUrCYVCjBs3LhqTkZFBjx49WLx4MWeccQZLlizB7XZHEzTQsKyW2+1m8eLFB03SBAIBAoFA9H1tbS3Q8BhWKNQ6R1poGrTNi8dbX4sygNFhxqRr6P+bIDUUCmHx69gykvBkOAknhLE4I5x/RjJeSyHl9aW4LfEA6ErHqYyElSKcY8VrNuFYV4GWEAORIIW7K9CrvNjdYQIBCHiCpNT50dtFcJnC5NgSGNQxm/yiKjYvzcdnCPFR4Wo2uOspqY6js7k3uqEXBW0r8TviMTgMWM0m0BShBMW2QE86GEah/e9ah0JhDAYwGiAcDh/0HmgGRVJWPKW7ywn4Avi8Pkzmw3fzkm0VxOoWtAi4LdZWe4+Pl1D0uh7f8/yubAXv5q3Fbd7B73ufhaGVJWrC4TBGDYyaIhwKEdKObPWvE91P1R/EL5f0CXEg6RPiQNInxP5+Cf2hNbdNiP1p6kjXKP6JaZrGRx99xLnnntvsdr/fz/Dhw+nSpQtvvfUWANOnT2fatGmNkikA48aNo127drz44os8+uijvPbaa2zdurVRTKdOnZg2bRr33ntvs8e7//77eeCBB5qUT58+HYfD8SPOUAghhBBCCCFES/B6vUyZMoWamhpcLldLN+e4qq2txe1289XW64iJtbR0c/DUBRnT6aUT8lr/HH4RI2lCoRCXXHIJuq7z/PPPHzZ+/6WkgWYnMD0w5kD33nsvt99+e/R9bW0tWVlZjBs3rlV3tM0rt/D7W/+N0WUmPEgjO+kUTBqclmOmV59TePe+/7Dgw2UEOyZRcUEbXr7yUv697CsSYr0Ya+eT0qWKDrGXMzj5Ak59+UVUALrrZezcm0Li10Wc9fwu+iRXsHDnaRg292bxim34+pgwh80EV3mpba8T6RKib0oWI+K68snMlfi+LcRmN5PbzsnCDEjqVMSF3bfw1dwh5K21gA6BBI2wTcNWoTP+6iqyUncyJHkCbV0XHfRciyvrKKyopXduOsb9lnresnIHiz5axrhrR1KfWE2Ooz02o/2Q1+2qx95m97Zy7DFmPn/mxuN2P1qrUCjEnDlzOP300zGbzcet3k0lc5hd/Cox2Lmq50uN7ktrsWV7CVaLkbbZSS3dlFbjp+oP4pdL+oQ4kPQJcSDpE2J/v4T+sO/JCCFau1afpAmFQlx00UXs2rWLr7/+ulGCJC0tjWAwSFVVFfHx8dHy0tJShg4dGo0pKSlpUm9ZWRmpqakHPa7Vam12bXSz2dxqv3gAtq3cQl3IAhEneD0kumPweRtGGv37tUX06pvL19MXY9hSgbEqmQSHA0+dgy1zK3GtymLEb6vZ2G0mSb7ReIMKexEUFLkxRsIEDCbaempISarHvquOM87qz6x5qykOm3FbbDjKvZiDinCWmwHpHVn0bR4VxiDGRDOdhnk5a8pWdnzdlt0lCVS3H8FVE8Zy/7yZYICwwUYw3ohSEazxfTktcyjJjiGYjQe/1v/8dCmFlbXcNGkYAztnRcs/f2Eu+VsKsQ6MUNJ9GwMTTuW01LMOed1O7duJ4op6xg3p2qrv7/F2vPuz0zGITzbvItHm4lf9zBhaWZKmqKSGl15biMGg8ch95+FwNP2Mn8xa+/eb+PlJnxAHkj4hDiR9QuyvNfeH1touIQ7Uun5BHWBfgmbbtm3MnTuXxMTGSzL3798fs9ncaILhoqIi1q9fH03SDBkyhJqaGpYvXx6NWbZsGTU1NdGYE8X7H6/gvfwC9AQnYbuJYLmN3iOd3HzuYAC6d8ukotqLFmNDZSbQJ7UND018kp3/WUmZSxGoN+GpsFK+uT2F1bXYKhT2Sh2jI0RMqgddwRP5A3ni+1P477oETEYDluIQSd966ZxnRxkVxpCBK7P6MqlrZ4rMZZjtOrZMOxVVVgoLjAxM7YOhNobpc218PW8PtvIwlpIQ1lqdiAVCTgNr8kpIdY7Daow/5Pn2zs0gM9FNVnJco3Kj2UjxrhLiyxbw27gZdDGuPGQ9kYjOx/PX4QmF6JiZfEz34GQXCSuy9RTig05a39pO4HbZMJuMuF12rFb5Qy2EEEIIIYRoXVp0JI3H42H79u3R97t27WLNmjUkJCSQkZHB5MmTWbVqFZ9++imRSITi4mIAEhISsFgsuN1urrnmGu644w4SExNJSEjgzjvvpGfPntHVnrp27cqZZ57Jddddx4svvgjAr371KyZOnHhCrewEMHvpBooDZZgDOj5HiISLqnn23a9It2/iknapXHjuANalxfHlfxdRbzewd+V6cjUr8dUh2BUEBTurU6ioSqWkcAMxhWCqhUAvsJ1ag2Wug4oVFpalZhBfaWDr3jIMZXU494aI1Jmp6+HEZNFJcDvYUVdOaYqPB4bMJzts5J/vn87qL7uxuLqQiC9I2KCxcOcOVIIdjBCJMRBxgrFOcUF6X0xHMOHsxaP6cPGoPo3KgsEw27eUEtGMBOuKMRLBobY2X8H/+AIhygJeQnYorpFhkMeiY3IiNw0fRHKME1MrG0UDsDOvnM3bijGZDHh9QWJjZCUvIYQQQgghROvRokma7777jtGjR0ff75sD5sorr+T+++9n5syZAPTp06fRfvPmzWPUqFEA/O1vf8NkMnHRRRfh8/kYM2YMr732GkajMRr/9ttv89vf/ja6CtSkSZN47rnnfsIzaxldsjLYW1xCOFJFXH0QQ7mGHjZQXFcDNDza5XbZsDsq6HFWFWtLsul/zllUb86ncksJoKH7DXTrkERuRg4bfEUELHX4EhRhj5MEE7g3hqEWwj2MLN+1Fz0QRNMVFYXlOKb66ZBcxo6Qkz3z2xHjhWSnTpeURH73m9EYrRl8+9DraDpYdHDEWinXdJxmE1VpETQFhpAO7keYk5/CkLQ/EGtpHz0/rz+IPxgmwXXwiZuD/iC6yYgzyU23CRMoMc4kLvb2g8YDWMxGdKeBkFUnz193PG7FSWtbaTkPfDmXLKuLf151fqubk8YVayMmxorVasIqy28LIYQQQgghWpkW/ZUyatQoDrW41JEsPGWz2Xj22Wd59tlnDxqTkJAQXRHqRHbD1aNwvhRmRv18NAUVZXbo4UXTggAEI0H+85dPGXRhPl/l9sLvsTB2fD+qrCYqNpShOwzomQbOG5vJoOxBbDbvZUHdWky6wu+zoJtC+DJjUel+VE49nXMSWeGwodttaFoQzaBAU2wvKqFypU7M1nI2qcvoN20kllAMNpuZjt1TKC7MJ7FXDaWfxGOp1dG1CCaXGd2iCLtMbPVHcIQLyPF8SeeEGwDQdcVDb82hut7P76eMoc0BjzjtM/2f88hfsxO3206oogPhnMewWRIOed1CkQhtYmKpiPiY0KPTcb0nJ5uVZXvJN9Xi1UO0jnXjGstuk8jfHrsEm9WERZI0QgghhBBCiFZGfqWcQBx2C3sqitDDRiJ+I8E40MwabWx+qAeTwUDfqcNYuGIp5oiGN2wmv6aWtfWl2LuHSIitx2uHdQWV9MoIsSCvAK/VjKHYgDXXi0LDGNQIVtjwleis0IowZyXiqfeRlJRC/U4Hu3ckkFhqpX2yi5IeZhZUKRJWvcrGjeWU7B5KXe8IKZ39pLhqqFIuwn6dsBnMPjORiIYpJkyCJUhA60VGzOnRc9M0MJmMaJqGwXDwVbn27irDEB9HWqcUtqdVMf27D8i05vL0oCkHHdXx7IKllAa9pFucpDpjjvdtOamM79iZFbsL6JeaicnYukbR7BMMhltt24QQQgghhBAnN0nSnECe+PxL5q/Lx2nS0CJgWmLA39GJxR0LGWDQTGwtrmTR9DiMSUGKJ7VBR8dsVPzu9HnYfPX8pyaH9qY2GA0G9Pwwmt+OtTCAFtExBHU8bQwE4zRCSqdLZjIRt5Vtu8uITY/Bm+UiLpJAWmEdo/p3ZnZFEaFQIWV1C3Alh/mmKIvdlUZSaxK4rEt/bCOCzPl2O7qmMOgKFYIRnSvJdvakb/JdxFrSo+emaRp/uGwswXCEWHvTFXkC4TDPfrMU58QcpvbP4bSz+/F21TvE23eyu76KovqJtImNa/a6Ld+Sj78qxO5INZ8s28gdWSk/1S064W3eWcqqr/ew1lDABT27s7m2jOe+X8xlnfpyWlaHlm4eBYVV/P2fczGbjTz8h/Mwm42H30kIIYQQQgghfiaSpDlB6LrOZ19soj7XChFIrK+nyhpHpMxKdcgOGQ1xxo1FmLaWYShQZHVLoZM7iZSAk6ICJ6m2EAVF8VS09WM1mhhgSmXd3kJMBSFMS7wYNAvWCj9Gt5PJvXpy/Zgh/P617zHaHTidMSS5nJzdowsTr+2ERw/xnw/z2VNhpGpGB1wVYYztY1ClNWhbg5iz26FVbsFRC3pQRzeHCWYYuXzErwhbQ/j9m1HGGjRzl+g5Ws0mrObmu2xJrYfVBYVomsaNV5xDjNWCudaLxRjBbgniV/6DXrvCmhoiFiAI/Tq3OZ635aSTEheDxWTC7bRhMZl4b8dKlhbvJkSgVSRpYpxW7HYLSQlOjDKaRgghhBBCCNHKSJLmBBIsDWEPaLgH1PP7Cxdy45fnEKyHdrlJ0RiH1YLRobB20LB1LGP2zFXk7yhjft5QHHt9uM8uY+22PC4aCJeP6s/TGyqosOqozGQMBdUkbPLR057CQ2c1TMIccEPtVVBi8DKiSyZflG9hYG4mSTYH8bF2VhR7qMrJ5FTrZn436TQeeuI9IirMW7MWsmt9DUqHfSvBhzVYs7eQZabvSDDk8ecOlZDwLzSt8Qo8SikiSjVaPSgr3s3NIwbjsJixahq6rjMwcShrqrahkYLLYj/odTPbTYRtQYzxYXwuD2E9gsnQMMIiFAzzz0c+IRKOcMMfJmGzW47PzTpBtUtL4J5rxpJgt2E0GsiNd2G2hMh2H3yy559TnNvBQ78/F01rGJ0lhBBCCCGEEK2JJGlOEJ46L8E4DVN2Hb6lYWanZZJmt5Gna8Q6rRAJ8OVr8+g9rjf//WwO7olB8vc6WLRgDXUhH0ZjGHulB70QNnoalqyu3FVJKL8WzW7EENHRTEZMCiqLavGHwtjMJnaWl6J3gfztEV5/cTExeoh1HTrzVcVKvIlhtK0aobCZ+Cwvg/u2J3mAgYCuYVeB/yVoQDeCJ8tIIE6jfWIyReE0ulgqwJILNH206ZWlK1m4PY/R2e3omprMKV2y0TSNIe2y+Wz6Eh697zFyOqRw3/u30TvuHIJ6GLf54EmCwe2y+DJ/M5aO9bywaS5psS76JTSsKuX3BincXY5S4PUEjluSJhyOMP/bLaSnxdG9S8ZxqbM12F5WwcuLv8No0Hhpynl8X1aK16+xsbyspZsWdag5jYQQQgghhBCiJUmS5gRRUepBi+jYF3uxbjOw+q00Ui92sae0mpXaXga2S+Kff/+EcycNJiGrnkFd9mCoUySoXlT7LKQk7GT0lNXMWNcLVeoCYNfWIvTaADYf6L4wYasBd5yDmjZmLnx5OneOPxXfRh+Wah1/rIZKUKgYjQ6uOD4q9rKjrhyDw4rJGyBvWW+2nlHM3s/9KBSWa7cT26YNniKIWEALaZi8GvFmB3/sdskhz3VvdS11vgAzlm5ggclGz3bp2K1mAIoLKvHXedmzqYDi7SXc3O/Mw1473QzmugjaHjNpKfG0i/lhThqr3YzZYiIUDGM+YDUgpRRflaxHKcXYtJ5HNTJj89Zi/jtjFU6nhb88fNER7/dT8gfD7C2vITcj8UfXkRnnpm9WOglOBxajkcpwHgZzkAq94Di2VAghhBBCCCFOTJKkOUFYbWZMtRpeuxujrZbTzipn1oI8rDYHXk8NtEvC0z2VFcVFlKxwMy8/l/S/6Fx+xhk8Mn0OTrMPpz2IP2zAWN6Q8DhtUj+2by7k+2AF+AwYI1b8yTEUm3XC1bW8sXEVgRioah+Hwa9jceoQ1HB5l/PbdI0/vO7GZoZ2bZIYfVkauh1i+vkorndSbbdTgYFQmoa5WsccUJjijaQluw97rreNHkZeeSXLvt9NfKwD237Jk8tuPp3Kbfmsn7+edQs20bFf+8PW1ybBRTjTQEKdm0cGXUS85YcVnvSIQjNomExGIuFIo/3KA7W8sHU2OtAzLps0e9yR3SygbU4SA/u1JTvr0MuD/5xufvZDthaUcdM5w7h4VJ8fVYfDYubOMadG3yc7a0iMD5FgCxynVgohhBBCCCHEiUuSNCcKE0QsGrrFiT/Xji85SCQQxmA1EQo1hKRHbAQ8Otic1Pns2EK5+IJhiuILuWvgRjylVuLqIpSrhmREzwFtSb4wi8LyegzVipyP/JS6DegeE0MS2nJ5n/78wb4GNI1wrBlLGMwGP3b1Lm6fhVB9fzxG2JW3B6f+Lbr/S+7/1Vq+KsnksxkdoUrHZNQgFMbvtqCbdeyGcvS6l9Gsw9EsA5s91ViblZ5t0unZJr3JNpvDwuRbziI9J4l+Z/bi7V3fYjOaOT/rlCYjXarK63jyrvcoSPViDIIeo0hMiGlS3yXXj8YZYyUusfG2jdUFVAXrcRithHX9qG5XjNPK1VOHH9U+P7WwrhNRikjk6M7lUM7PsOIyrqdTTOtYRUkpxUefriYS0Tnv7H6yFLcQQgghhBCiVZEkzQnCZjaD0rFWhjDVw541MUTqTehu0P+Xm8iPhe6lBowWMzXDHMQG4ijze6k3Kj4pa8e88rZYOwexVSoA/vHPT5hfuZLkmiDB9lYiHcMYA3aUxci0006hX3omplqNlMW11LVzojpruFPrUY5ryE6s5KJLc3h/4Urq68zoSqPWXc0cTyfsFiOOmDoCYQdmTwTNH6LOaMLiNWIMrQH1NUp5D5qkOZx2PbJp1yObvd5K5n+/AU3TGJvWE7el8bw03y3cyua1+RR1thFJsRLRNQ58YmnbhgKevOs9bA4Lz/33NxhNPyQbllVsRwEJVicpttijamOdx8+zL35F+7bJXHLBKT/qPI+37n3SqXGESG1z+NFMR2prbS5LttURzGkdSZrqGi/zFmxGKcXIYZ1ISXa1dJOEEEIIIYQQIkqSNCeIuIQY4lFUJISwFwcp+D5EvdsBGmBsyDx404zs+XIbutdHj6oIVr+XT981Yii3sDA2k4AyQqqBhKSGbrHOX0rQZcC8OxZDWQj/QBMpH+tMu3QY/TpkAmCM6NQnWjCEw3hzzOyIJFFY15OEpAymnlFPRehtaqpL8e8yoqcOYnVZAadQiVaSijEAptogBl3h2uqH4TFYY0eihcNg6XdU5+8NhvjbvEXEO2wMCTmZ88Y39L2qCx1TK+kce0qTBA3A8HHdWTR3A5a2YXZuqYA9fmbP38gZo7tHY/SwTm2VF583SE1lPQkpP/yoH5/Rly8L11Ho8bCnvoIOrrQjbu83i7Ywf9EWlq7YycXnD0TTNOqCfqZ9PR2jZuCNsZdjNf68H8+MeDcxsTbiHQdfCetozVhbyZ49KXi8fhh83Kr90dwuO0MH5WK1mklOOrrEmhBCCCGEEEL81GSs/wkiElHoJSHMPiN6N8hK8WD0BTH4I2iBhseXDF4dWyQIPh/dUgvp4NzBIncx3pgwqbG12KwBkmPqCOd7ALjnonMYbdSI84YwbTRiWWDAX+Fh3cKG1Z+qfX6G3jsS5yid9FNChGusBD0a1b6G+UeMBiuDusaQGFPDXls8BNpySZKXjJ0hLCUOInbQIjoRuwlbeRizxYgnZEdzXIRm6nBU519S52FTcRlL8wpYu3QLO7/fzZrla9GpJc4Sbnafrev3UlflJb3OiK0KVFBnwdJtjWKyO6SSmhVPfFIswWDjeto7U/F5DNR4QviDoaNqr+Y0okwa9iR79DGsPZ5KtldXsbWqkgJP1VHVdzxM6d+bl6ecR7+s47faVJtEF2G3jjO2dYykydtTwdIVO1m8bDsRXbV0c4QQQgghhBCiERlJc4IwmQyktE0hXFKNqUuIKedsZPVdo9HQMPwvt+AoU5x5/VhmTJ9DsT/CtqpMbClWaqyK9t4KemTtJVxgZIO/YbLd7HQjN541g7Vtk/msoA2bnoglbDZR6avm28LdbN9Twae7tuKPOLCFIqSuCRFyWFBnNfz4Desag9o9zN3fP46vY4D2RSuY0PlaHp2xkBSrgb3WABGHGcwGlNVEJy2OWJuZOUXf0y4mhQ6xRz4ypW1CHL8ZORinxUKqMtGpew4dhmdTbMynQ0zXJvFKKZIyXCSlubG2iWNRWSVGn2LcGT0axVlsZvoN7Uh9rR9XvLPRtu+qFtA9YzNVPgera9fQIynriNvbpX06aX1TGNazXbSse0IGdw8YyZaZ23nlzk/41d0TyOmQesR1HitN07CYju9XQrI7GXtMEW5X3HGt98dKSowhNcVFaooboyzFLYQQQgghhGhlJElzgtA0jZH92jNr1QbCDh9leUl4c20Y6iBibUiaGH0RlMFAuCzEilmJ9M7sg1/3o2pgVV5H+pXnkVeSiLa3IV7DDpYwvXrn8/nKDHDHEEl1sabSR8HqRVyS1QutPExIGdH8GrhD6NlhdIOiwFPD/y2aTVywio6WPZRtcGGzhqhIKaA2EsHktJLoMuPfUow5ECYQZ2HL7lKWFG3j3fwvSbal8+e+lx/V+Q9qm8Un05fw5pfruOKWcaQkppJC80mOGXvfYa9/D9c+No3n564ntBYiTgPVfn+jOKPRwLW/O6vZOmpCFf97nExRHz66JaZXb9hD0boyFlUE+fXEodHySzsM4IzlCyiNVcx/Yjpzn70Zo0HDYv5lflSv6jKQ0mAF47K6tHRTAHDF2vm/Oya0dDOEEEIIIYQQolnyuNMJoqa6nk/+M59KfxXe2Yq3Z/bE6VFgNmHQGx41sVXUc/Zlw4lrn4IxI55Kr4+SDdvx2ALY0kJ0n1yAQWm4UpwEg2E+mbOOpZ4kvvPEU7PNicntwKxBTKyNQWltqK73o8WbsRbrGCoUnjgrdQ4jb723kA2bCwjoITawl72mWLR1sHqmm7sumkvx99upCUXIdbjBZCCQ6ACDhjPZjse/kmHxn5Nl+wZdNf+YUnOUUnz8/Ua+LSwgHI5QW+U99PUKVRFRYfwRLwO7mIjrEmTEqQlMHNytUVwwEOLz95bx3cItTeo4PX0SJkOEVIeHPfWlR9xWAF1XeGOhWv/hMSld6Syt+Ia9rgi17SIUtw1xy/P/5f43ZhPeb8WlYCRCSZ3nqI7XUpJsLgYldCPL1nQlLiGEEEIIIYQQjf0y//O8aMJut6CZDZj2hjGVRfBEbGgWDYvZjzPY8ANfq9dxup2c/eJlfLRhA6X/WoW9oIKwxUifXmV8Fe5BxthajO/BzNfm89XmIsqH9cBu9VJcFEMkEMKwYRehjFjWlKXxq86nMDQ3m6Vxy4iUWQiXmLCthx2VJaw2b6bHGSms91dQXKKhUh24y8PUKxMmg5GKijoCSRBMtGIKaxhjraTluPGEazFoGnbDHnbWvEWHuKuO6PyLaz28t2odeoLOvXePp3fPnEPGT866Ek+4llRbBm8ULsXr0dliqeKpFQu5oEsPct2JAOzcXMTsD1did1joP7xTo2W87UYHedVtMBk9tDO3O9ihmtWjSyYd2qbQNj0xWlYeKGVx+TxCsXZ6D9xJN1cZ3y61ERt2oNQP86c8M38JawoKuXX0MAZkZx7VcX9uK0oKeHvzGjrFJTIwtU1LN0cIIYQQQgghWjVJ0pwgaqvr8ddpaCEdLQKRugh6ohF7SQDT/+beCHsquW34H7j6099SZKkjPMCFStQJuUyoEh0tB1QAgr4QGRlxjElz8ZJvA2UBKxZ/kJzUZPa4EjHaTA1LVZsVXrsP95Z6gnsjGCMWrNUR2qYYSR23hfeLYjDlmXF/Y8WWEqDH76ws/WMV3l1mAnFGbFVhMBhQJpg4phdjTu/GS3tmE4qMZXLqJvbs1GnTLYTNZj7s+ae6YrigT3eMmkbvnjmNkinNcZpicJpiANi4QcdfbGO3HuajHRtJcDqiSZrk9DicsVbad8lotk7l6cjm2nIGdDy6ZEm3nFSeuP5s3E5btCzRmkyueTD9DOv4c+cFJJgiDHYbGJlzPeb9lv5umEtFw3CYc2wNwpqXWiqoVPrhg4UQQgghhBDiJCdJmhOE0WwiGGvDUh5GQ2FWYPCEqGtrx0bDj/mI20HJ1goem/YvAslhAoPSMKTZsO4OsHZhe3K/rcRblsmFtw1jyIT+rNn5Bv0qd1FbaaLQkUBdTyclXgOaMcL0UZP47/b1bN5bjnljDKpWodI1rIl2Mtt6SE/aRD9fNhuru1ISa6Z9u1rqYkqI7R/BZfVAXJCYQAzGZCNVPj9z1mzjrHN7oVSIgA67N5zHqq+97B30HVMvHoKuK97+z1L8gRDZA9NYkpdPB2c8BUXVXHPmKSTHxTC5T4/DXKXGNle9wbrKj+jbuT8b1jux1Ri5omtfxrftHI3Zm1dOVbmHzavyUEo1StR4wnVEjHvRlYnNtYVHfc/SEhovAV0fCDFjiZdQvRm7BhbAFTHx4fcbuOKUvtFj/2bkEGp9fhKcTZcVbw02FZfitFjITogjv7oW3VhPUb08WSmEEEIIIYQQhyNJmhOE3WHFWhtAC+ooA3itOpFsJ/4UC5re8OO+vm8S1hoDVGq4Srw4+7sxrKnHU+Qhkm6mZk8cRoPCYLXw6XvLmL2plD2Dk4nXjVx11YU8vGoxwXgDhoABq4LRWe3Z0qWMbV11iss8BN1memaksje/Ek/hMH7VZwjPbivC7w+xY5cRG22IK/+G3rcUUecAV+hO3vhkGRGLgdrKejJd8VzWrgvflH2J11uN2WwnNdkFgNcXZMWqXShgm9PD9qoqdgbKUfURthWWkxwX0+h6VPi8rKsoZnBaFjZT8yNx3sqbz/c1GQyN24o50hd7rYEruvTFabdGYzr1zESvrMZbHiFvQz7temRHt31U8C6ZybuJWNzUhZOP+R5aTSYSHXY8O8t5/pbhnHGTm9cLexJmB5cO6I3F2DCapqyunp0VlQxqm4XJ0LqSH3kVVfx5zgKMmsY/LzkHVWfBmxeHyWA9/M5CCCGEEEIIcZKTJM0JIhQMoTQIuWwYLToBXx09nWWs87dD1xt+yBvqghgAo9WMo106gQVlhCu9BNNjcW0swNHPTGiLg1fueQNfYjy1F9hBuSjSwOUuIpCm0FJ9BOvN7F5XwDvhPBbs3okK1KHpBvSIzrq8EoKJsPtbJ2MH9uDP1/bjukfeobSsjt17FPEbzfirNPIKHVx4Tiptvray0eLHEtGJtVtpH+jJY0+vxRT28dKTF5KU2DDaJMZp5borRxAIhknJiWNjcSk5rjgKS6v598XP8EyNlxdXP4E7sSGpc8v8T1lRWsB13Qdy54BTm71mRd40yvwRfDFuYkMG/JrOZws2cNEZ/aIxVpuFAUNzKdldRnyqu9H+2fZ2mLU1+EM2xqYfeg6cI2E1mXj83DMp6tWXHat30XtQL64oLiXBYY8maACenr+YXRWV/GbkEE7NbXvMxz2e4h12kpwOkmKcmI1Gemem4tAttE1KaOmmCSGEEEIIIUSrJ0maE4TNbsERVviIYLeEiNV12uf6WbvLgOF/i3jZt9RgNpnQHGZqwxG0bSWYaryEbWm4MjTadCxhzeIEImYrlFRi2tSGcHcDYeCrzXmkmovJ7FBLcbGTjWX5vL1tFQQhya8w+hUms0JTOn1ys2jTJhGbOYJB09i0u4SYnV68qWG694TzT61ia9FOYjQLSW4rlnZ1KF/DSIvv84up2etH02DDlkJGDv3h0aOe3X+YeLZ4VxXVnjoS/WHyd5ahGY28/ecZ3PDE5Xy3cCt7VxQSygizp7LmoNfMYsjCQD4lFelE0oNoVSZ8RBrFaJrG4AuGUlpUTWx849E6I1PHMn1rEQG1jY3VR7cEd30wyJzN2+mZnkpu8g+TBxs0jczcNKyp37Or+mI6J08hzT2l0b4DszPRlU7bhPhDHiPoDxIMholx/XyPRbntNp46/4clyytCPkJxESqMh15tSwghhBBCCHFsvvdkY9dshw/8ifk8fgAGDhyI0Wjkpptu4qabbmrhVv1ySJLmBKEUROIsaDU+LCqMv17D5IhBM4MeaIix5lUQiY8nLmQi2DsZFWvGt7mEcKgea1YNOV0qWWNrhwkIGwxklNWjPskmLyNE7A4vrqQgppoQ8esr+adtNrolgYhLEWlvxLoabHUa507ozc2/Gku1fz2rSn9Lsn0IcT6dUJUfoxU6nrYXizVCdno1i/KLiXXUc22PNZR7Hcz6bhMb8lcQjAFDBOxplc2ea1V1Pa+9sxiF4qYrTsUQ6wSzGWd6ElVldfznpfkklPuoHxHD2EG5zdYRDkWoLw4SCkPhoirSU1IpCPiZXvU9cTucnJPbsBR3KBji/657kUgwRGysjYGjGy/R/d22KmrMNpbsrYAxR36/vt2xm9eWraJnehoPTRzbZPuums+ZXWGlX2AW4w9I0kzu24PJfRvPv1Nb58Nus2A2N4y40XWdG875O+XbC7nqd2dx3q9PP/LGHUcZNhdtDfFkmGIPHyyEEEIIIYQ4YaxYsQKXy9XSzfjFaV0TWogfzWIx0eGsLiSNbEfbCxKJ7+NjR0lv9EoD5upwQ5A/SKS4jLo9xfhm7aYiI4a60zsQ7BBP3fwA9fM1wjFmQm4rrpQgf/n7bC45/WvuGt4LR6IHzwaNsjv8VD0eoH5vDfbkekz2MP4cDaVBJBQhJanhQxhSHhQRApEqjBqEkmyEXUZmfNOHeUuTeHFuf7w1AeI61jDaVcy5ibt57quFpMRZCScaiKQrNns+bvZcN1dWsNFZR4HZj8moYQA0u52FX67DnRjDoFFdyeyTQYe0FOLs9mbrePnJz9mql4ATtA4mavLCeEMh6v1BaoKBaFxJaS3BZBehtHhsrqZZaYvPTMhnwuA7upWWememMbRdNqd36dD8Ofr6MH9vLgurux62rh27SvnjIzP458vzomWhYJiibUX4isr45LnPCQVDR9W+Y+GprqeqtGEEU4ekRIa3acuELoc/DyGEEEIIIYQ42clImhPItHMG8fxXcykvctDpzCz27nBg9NVgsP5vPhOrgb5Du1HoC7L1FCOG2CBnd+vF3Be/wlcNKxa68XZ2ols0Mv2QmejFcOpW7t75AdbhFuocFrSnFQCOWjsk+rCGQjiVC3NVAC2sYfI3JISSbIOwJ6Xy2mfbqStfCSYzmgqQNclLl9gwFlsVed4gH222Y9I6U1oTS4cOmSS2LcIZ78NsVOQ4mx/98d/V6ynHjz9k4MNVW9DsVjAYqKn2YTQamHzNCE6rH8i6wiKGt89utO/qqmWUBUoIEsTl8GGxhTDYnJSbQuAzcH5qdy7t3Csan54ez5A+OXhr6+nUreky27efMow/fzqPrhmpR3Wv0lyx3DV2xEG3F25LI/j0LtZ3s0P/Q9cV0RvuSTjywzLXIX8QU101YbuZgRMHYLYcfhnz4yHoD/LktH8Q9Ae585Wb2BmsZ1lePltLypnYo/PhK/iJ+f0h/jtzJWmpbsaMlMSREEIIIYQQonWRJM0JIhSKcMfrH+Iv9mCs19Dn+kmd6ieyS2EKN4zyCMeayVufT487T2eVdz2RcJg9n+wg7DSjlEaFMQZPtgndpOFQERQ0PHdkDdPe5WXj9tSGMiAjJ8RuTWE2azg3B6AqCLqisryOjZsLaZuTxPoyxbzS3aBpoEH3ITtp37eMcIWBcJ2BSFUtg1L2UlhjpsIcw9/H9WJO3haUruGw2xiUNbLZc72oX0/yNpRSF/Hx5eptOHSFUgqlGpIUvkCIKf9+lwq/lz+dNYaJPbsAoJTi/byP8YTqOaXfGIwGCOpGUkxQaNBQGOiZkobZ8MMkvZFwhNoV2wiHI+zdVkz7Xj9MELyyYifbbbuYekYfRmR1PG73srrCw6Y3tmD9vgz/qkJ8D/uwx9ipCdYzv2QTp6V1J9b8wwihTrmp3HfXRGJibPjCIT7ZtJkhaZmk9GhPgdHAVmUgEAhhtR57okZFykAvA1PXRsuR76MZNEwWE+FQBKPJQI+kVM7r1Y22iYeeP+fnkrennEVLt2OxmBg1vDNGowwmFEIIIYQQQrQekqQ5QWgGjUidkWC8gewkNxeOHsjqzotxfGPEGPxf0sFkpjSvHFfXEG39JXgfclC6fRcxmTEQ0okJVlEQCxigvNRJSIFHGYi3emmXUcmWtbEo+/+zd99hdpzl4fe/U09v2/uudtWb1WW5995tOsYG4mA6ISRAAoQUIKGD6d3BNhhi3HuTJVvFkqzepdX2eno/0573j2NkhCVbBuUX0Duf69Ll1Zln2pmZY5177+e+PWCaZCydXN6Lf1KQ3wdynRel4vDgQ5vZcHCMmac28cDBvTiWjWQLJNMml4XvrTudn4xUmDMaY8V1E8z2vcS3NlyKOiHzzsodnBabxCi0UrAC/Mc3H+a6ixey6JQjOyctm9LOnZ98B5/86SPsG57ENiwkn4Qwq0V/N+8dYnAshe2RGM1mX3mPJIn1B8OgqGS2jdN5QRy/x2B63RmM92dRZZkz5k05Yl+6R+Pt/3wdTz2+lXuf28N7O+uJRqqFeNfG97E928/bOttY1NZywq7lj/7rYdL7E4hcDoBdGw6w+Nx5fHLLXWxN9nNX32oWxLr4m6nnU+8NMxnP8Yu7XmD+nDbWyxP87qWdzGqso7O7ifGJNLKunLBghMj+GzhppPAnQZv7quWarvHJ2z+Ebdn4gtVA0juWLjgh+z4RpnY3cPXlC6mvC7oBGpfL5XK5XC6Xy/UXxw3SnCRUReaD15zBl59+jvR4Gf3KFkafasYsJJCk6pdRXQ1gSCme2TROfKgZpT2JvttA7Z8EoJJXaP7FXswpdTizFTQgKgm6vZNknlORVB2pVEEA2S/3E/nNNPzDDvlhFWQHO6iTUST8MZPNsYPkarNIgwECRQOhKjxf38MnT3+ekGoxmpnF/QUf0/QOal4w0fIO2TGVFf9pkOlXqIyFGO5LsmPX8KuCNKl8iW0HR3jPpcsYjWf55q93I4TAsqt5PmtW7SO8z8IKSFwx/cgpNnW5Tg5mknzs3GXUNf4Ar2ry3I6DiKYmPLoHy3GOGC+EQA4GGDElrP44w6Ppw0Ga69qXMzXUxOn1J3YaT9uUOqIRP6WaGNg2/YcSLD4XBlMFMjmVEbL41TEO5Seo94bpG4hz8NAk5bKJf3EYxmwU1WJ4ay9mrsLk7Bi5UoVY6AR0eVK7wdoDcu0xh+he/fDPk/kCX3lqFdMb6vib05b++fv/M6mqwsXnz/m/PgyXy+VyuVwul8vlOir3V8knkWvPmkd4bx42pfjaB++i2FtEyAqOU80wcXQTHND7VNQhlc6z5mC/eyqpixoAiZIZQBtJ4d82itdvIYDMhMbuDyus+ZcGrGi1cK4EyBboup9Si0ShWSU7RUVIoIVV3vWuZdhBG7neQmqRkC0bPVlkajDOuxoOcm1NP5OZOKYj2FdqQMmaSMk88mCFM9q+ibw1hNSX47KzZnHlpae86jzf+73f8r7fPMBPnnqQ1rrvYttGdUoVcO93HmPDT57BM1rAn4WmuiOriXfbEeYadTTWBzAVSNsS9SGLctCiuSVIyOs5PFYIwb7dQ9z1vaeRJ/O844blzJzWdHh5iz/GJS0Ljph6dCK8/f3n856PXQS2A4qCz1N9TMtFBTPuw8j5eU/PuSyqqWb9LJzfwY1vXcG733E66WwZtWyRL1SQfD50r4fmaJjAHwRO/iySB4QEwj7mkB0v7GH9Iy8hhGA4nWH3yCRP7z54YvbvcrlcLpfL5XK5XCcxN5PmJLJl7QECukmpYiHZgkN7x6EhisTLtUOyJeSOVkb2pEGXsfeazL5qPru/vxZFdwg4ZUwBlMr0NJaoIOFEBPsK89FzfSg7eg/vy+PX0DUHs8tALhjI21WMS4vIusLzj/dzwSkLuHi2Ss0pQb56xz2YUS+FB3T2nxGk3wwROGjT3ZWCtMJwScYxS4iCQ6li4asLEnLgkksXEIq8OgDSW0whPJA0h9h7sAKOB4RAOA7rnttDoSJQJQNPwPuquikfvvZMhuMZtty/lUpsFv6pJSTpTE5py9G5rcw6cyOnXrEEgO/te4KdmUHa5kSYXt/CiuU9R63D8qewbIfnnt9LbU2ABfM6XrX8rCsX8eKz21m1fjePJEa50HHooInB9ABeOcCZDTMPj1VVhdOXV7tEJdaNUrMyQUGKM7enDi2osmR6J7J8guKx1n4QuWpdGtpetbhUKPPzz/wKxxG09DQSi/kolsuIik2mUCYSeHWHLJfL5XK5XC6Xy+VyVbmZNCcJo2Ly6+8/iz9vEZlnoWhg1fuxvRJW4OXLrAJeD2rRANNB7xZsvXM7VkZFbm4gHKp2ZhKSYHJXDfuHgzw81E18iYJQwdFkrBoveFX+9d5/xHKCWJKE1VbGW3DAA0XN4ent+3jkwQO8a/YKZnkbEGEdx6+Q7/Lzsd3n8OnN5/HixjZUy8FJyaiTebSJAvpwmo9e8G+sfWAtNAeOGqAB8Pur2S6D6UZsbRqyqoAkYZoO7/yHK2nsacCOVOvs/LGW2jBTWmL89tEXuf/7ndzxzaUUEjKz96k8/snf8aUbv006Xq1jM1HJ4CiCa95/Bje896wTFqABONg7we8e2MQv7lxzRFemQtkgVywDMGXpNMycTO+dO7Fsh85wDU5EoHiPHVuNrx9GypWQ8wb9+0c5YMN3f/wsa9ft/5OPdbTwDIO5+xHCRgp9+uV6NAuOOtbr97DiyiXMP3MWjZ31TNpFJqIlRiN5fCegcLHL5XK5XC6Xy+VynczcTJqThO7RmLG8G7vXyxXXzWbPlgS379qDKQQUq8EXshWcOlCzFeSCwWBRYnKWgl7nZeH0NNl0ABIyoqmWvZONfPArndTd0kc9WRTFwUHFM6cJddckO9ftY2xunnIyRGithlRW0Z714+0IYAhBfSzIlnUHufO7TyEpEk3NKUoLNAZ3NaGNCEy9xIAaRW4AUc6iOA4gGBjOkFvYyKp9vXzgGJkXVy+YxcPb9xIo2/x2TQGfv0LJ0FAUWDc0hqPJVMIyhRqbZKFETeCVYM/63f18/tknEKnqeyLlbTqiEX71xGYs00aWJCyzuuzjM68gaeTpCtS/5ntvGhaP3L2eSE2Qcy5/9fSso+lor2H50h4a60OoLxewLRkmn/3FY1iWzb/efAkhj44qyQR1D15NRZEk1AmZordyzO2+7aYV/PBLjyFJMnMvnsvw9iEEEIv8afVoLKdAb/q/EdhEPfPxqY0Y1OA/RsBKkiRu+PiVh/++YWiIrF1BtiFTKFIfCf1Jx+FyuVwul8vlcrlc/3/gZtKcRFK2Tapk84svr+bZ+zYj5Qwk0wFPtbtTfukUbF0CRUYo4A/X0DXpIba9DJJE88Is9pwuCAfw5irIioxY68X3vI6kRxE4WBuGKScK/PY/72dqJIeCg6MoGH6JgEfQPsVH2FEJ+iVAYAubQKxMruThS4sexT+o4CnIVA6pGEMaxpCGrAGyDI4NhRJYFpnJLMfKW/m7807n5++6nkumz6CBGG///Bk0tMZQu2r47RMbGVq7C/XQKIUawbeffRzTMQ6ve+8LO+gfzmH5VSyfSrbOQ9kwueTCxWj1MbTaKPrLGR8R3c+UYMPhDJqSZbIzMf6q4sJDfXGevHcT993+PEbFPK5r5fPqvOutK7j4/Fc6JElIIEqAgQS0dtURivhoaa8W6Y2P5YnusJFeKjI8mjq8Xi5T4pfffpLVj2/H1x1GVhQkWaK2KcZtX3873/ry25g7t/24juuPqXKAzsibaAtdQUBrZ1fia2yZ/Byp8vbjWv+l8WGQwZFhJJF9/RVcLpfL5XK5XC6X6//H3Eyak8i5Z81E0SusHxui7Eh4ShKGT0W8HIrz5mxMSWCGdcphQUtQ553LVvD1e37D7t21UDJRkjuhrZHQzChnnvsSwqxhs8dPHhtNE5T9PvSySSDi5+KWPQQq9Rxq6CCLn/oL+jGCA5hb2uk3E2y3Q3jrvHiDCZJP13Lnd5ZSbLKo21hAG7ZRf1YtKuzkLahUQFGQFIXQhhF6Fk8hfIz6JbIk8/SL+9m/b4K+Aw4bNGidUk9LUCfWGWK9voeKpuEftxkzn+bZUYWLWqvZHW87dyG7RsYYmiMAAUJhKJ1loTeA8PioAPlUgXDNKxkfe146xG+/9TClU8Lsma5w8+xFXNU9CyEEO8cmsMtFKuMJ0BQQ4k++fh55jH+95nGE0AkFrmHTeJpUpkg6VyafK2GNlrD9EsiwYXsfrc0xAA7tHWXj8/s4uGeE7g91AdWix5sf3ojfLPPWT137Jx8TQFvw8sM/q3IQCRlFPvq1EUJwzzceIhPP8Y7PXE97LAJjgAyx2IktsOxyuVwul8vlcrlcJxs3SHMSeeTJbew8sI0z3jmGUj6T3/62H7nsoIhqJogvX8YqCfKzYiTnS2RTk1wldWCPTQICXk44cYYnUKblqe+JM+o3uFJJ8/B/1VFRGyFv4QCpVI7tSR+XdG9mt5Xgqa1zMYWKrMg0neFltD7FL55dj3+rRCkZYsH5/Vzw7m28+KMuwr0mll9FMm2SFwbRXjIJvDABjkOwvZFS2SB0SvcxzzORKXDfCzso6QUii6E8x+DLH3gXmq4wdChO/6YRkokUkuRglizU8isBhVN6Wpgxr5HB4Thy2aFuTxbvjCKn3LiAqFdCkSW0P6id4jgOX/3UbxkfmKR2RIdpHdS+3AFq59gE//XkKvwmtDYFCUYDKKryp19AOUDA6wU5AiiYPg0r7EVoCmXDIlCRUco2ji4zNpA4vNrMBR1c/c7TaOmspXZKhBfP2U5ubwYnl+X+u1Yz0Bzg4++8AF378x/3WTUfwRYVVPno06fKxQprH9yIcAQTA3Hm1zch7QEZiVpvACEE23pHCfk9dDcfu423y+VyuVwul8vlcv3/kRukOUk8/thWnl65h3LMR35TO1fNH8MuGARyDvrLmTRBa4RSL5z1pvnc55/ANhx++c37q9kfMuBRoWJBTYhBtYn1a2R6Z9bSUjNGLmRhlSSw5Gq7a4+HPekW9pRrqfhkmjSb3lU9vPsty9ib3cGAnMHTGkBvkclOFGlaksEfsGgjiwPIUxXkKwuIpA8rpSPviSEkiVIyj5Akop5j35p+r07FtLArKm9600KWdUxB96r86OerWLtyFxNPbkZqCZC6sJkDz3aw8OJTj1i/oT5IpK8AJQc5Z7Nr+wBbxmbwlo9cTCVXorYldnjsjo19ZNJFbGQaa1PcdMohpoSnAN3UBwOEvR42jA3yUrTIl9507p8VpJHkGES/DchIksyFZ85i8MPnUhcNUlcb4qzlU9n0P9uQHMGcv205vN5YOscjo0OcGlW4wdfBt7/7AWzLZu3jW/jx4xs4mMqRLpRpiAb/5GP7vaI1TM44SIP/DGTp1YWAfQEv7/3i28mni7TPaKE8ptGY8xIyVSpli/F0ntvuW40iK9z2oWtOSODojRIvZzudyELQLpfL5XK5XC6Xy3UiuEGak8Qzq/aAEJh+mWRB56A5jO6PYZZl0KpfRitZEykYZfS5Yd50fgfbVm3H6MuAqkCphHpKAEOfgRH14Gn1sl6P4hkQWCUVr2bjyBKOnUePduDxKSRLrSz2JGgb0rh3byMysPOBPvbsHEeSU1iqRb7XgYDKaqsDX8FCS1QwHQ9KrYHPW0FtjWMkVSrNMWTJQk8YSBL07x0+5rnqmkJrXYSKZVApmSTieX7y1Rfo7ZskORBHMk2k8Sxqrhl53GRiPEus9pXpS3W2H6tGQx23kIIqvsVN/PiBtch3vYiGxJKLFtCzoAuAQNQHQQ8KNjMXdxP2jBPSq1k+dUE/lyxvY9XWLUSf6ec/V/2IFRtnEYgE/uTrKEmvPJKFbIldD+4kEPZx1fnz2RvNI11Qx/y6Js48a/bhcUPxDPFMgV394wgh+NlXH2O4L86HPn8Nn5rXju2IExKgAdib+gEFow9Z0mjwn3HUMbNXzDj887Smej4683Q0RaYmFmDsYJbejSN4/Try/0GQJJ7I8fXvPElba4z3v/ccN1DjcrlcLpfL5XK5/qK4QZqTxKLF3azf0o8n79DdrrOoschAXTujw9WiwACyHxxvkPhEhsn/XsvSt53KtoEt2IZDxclRUWN4xhKowQaU8Txmt4qwFKI/ziISWeSAH6tWx4lnUbUgTd4Y2+OLeTKRIVKwkBx4iTECz+6Cio3cJhCGhaenhsENTdylBvH2y+iaQ3Gfj3iNB+VQjsmFMS790jamiARPfGIpjgCxKIoQ4vCXaCEEmWyJaMSPR1P51DvO4z/+7X+441/W4F3moSVfi2PY0BdHKhmI9jYa1jjURyN0dB/ZnWnP5lGMQBCrzkJTLBbP6EDd1cemA+MIR7B/cy89C7p4cWyQteN9ZGM6lgbx/QEWN37l8HYO5Ma4b2Q96nQDURclUBPAFzp23ZVtyU08P/EUF0avZ1rT1Ne9pjv3DrNn7whIEsMjSZY3tTN+ZZ53z1l8xLjlMzvwaCoxXcdxHPr7xjCKNtlUgTkzmo/7HjoeDb4ziAsPOStKneMgy69de3xwMs2vt25HkSQuPm0WxaKBR1GRLYFhWqh/zvSwP0EmW6ZUNhgaSb3+4L9AQgiy6SLhqN8NMLlcLpfL5XK5XCchN0hzkrjwgtn84qfP4EvZBJ5L8+CdKpnZFbCcw0GabMqD3aCAA6XmKE8k+qmxqh2VnLCEPJbCyViUnTBSv0WD8NE2pZnJTBEcBwol9FQGORbFTBX48qkX8s0nn2S0fpjKIhVtsx/DLuKv2EiAKJSqxYAn40SfLlBINSKcEkZ9gFKDINGmMmWpoKMY58apO6hTbWq/IXNg5HouX7AGylHwXQPA1370JOte7OVD7zkHpdHD59Y9Ra6zQnhYolB20FaEiP46Sa5sIDU14tSGcXQF06Nimja+P6hz2xOL8mIBpJxDY2OIa86dz4rORm75x7ur71M8B8B3t65jslAgPC2IdKDI9Is6eXr8YZbETiOix2jwRAhqPppyYUwdAlqZdGk/NYEZHM2Pen/Cvo/HuC//C97zsYspTfezuKOV+S1NRx3f0BpDjnkpFQ0+/Y17ufPrt3BGa9erximyjDNa4Hs/epjpb/YSvHGQ2cpS/EEv8bEMgZAXX8DzJ91Xf6wtdBm/GRxhT/4bzA6dysdm3fKa4wuOyXAgj4YMMiyZ18lH33c+dTUB/L4Tc0xvRM+Uej78vvOJhH1/lUGOX92+it/84nnOvfwUPvoPV/xfH47L5XK5XC6Xy+U6wdwgzUlix6ExpHgBDIe6ZZ1Mlk08mkZRcUCufhmVKhL5Ghs9UoNR48EnVPQuQXnDATyWjKXYCACvh7K/gr8ocf7yWfz6J89BNAwVA2wDJ54g3FVH8aVRev/jWTrnOgxf0Y7vOQsRgt/3N5IjYVAUKl5B6PJufvSWS4nWR/jMf/2O4WKSipElVQogBExRTKIqXDHbIHZaA6KYpVCq8JvnXqSrqYanNh2gmCqwatshnHqF4UQGr+SwrCWE94wuFnZ38Wj5EJIQIIEkSzi6RLlOQdGOzNbQ631Qo2JNZEmuG2PrhoMsP2Mmmq5ilE2S4xkA3jbjFA6kEyxrr+Wl+zayX95KYqwfCYnzGi9jpJQkZ5YwfUWkZBrLrGBWKnCM2U4T42GYFKAK7vjuU0jvmUpfMs38liaEEAwNp6ipCRDwV4MXueEsxEvoJZOx5wcZiWdoqYscddumYQFg2AaTmw0eXrWdxyr7KRUNWqc38G/fftcJC0ocKm5kim+I0dLzwGsHaSzFQatR0GUFXVOYSOd5cMsevLrKKTPa0I4zk0Y4SUTh10j6KaD2ADKS0vAnHX93V/3rD/oLdd9T20lpEo8/v8cN0rhcLpfL5XK5XCchN0hzklg4o41Zy7pJDyQZlWVESy1dIQ/ZnAHFcnWQISh3elFLNp6cydyWGHtXmJQqUUL9JaSJIgKwRBlNQEdDDX27BpBqo8iBAE6xSN2F88mv3UOkIUailCOolZjfWmDGjFG+p5xLpKThABLgTEyCqqHMb0PzaFh+nZG+CTyXyKi39bJkJIX0sQCWpaAYKpJmEZZMdk+cSlvNAg6MqTy7dTXh/V5ap9dyQHc4dekU2uqjbN03gvXYTvZOHEB1ChyKjjGxYwCnVIZcHm1hC/mQTCZfplwyCHj1w++VIkmUsdAkgUDQ0FgNfPw+0LFrzR4ALumaDsBjP3uGTQ9sxBm3ydzkZ2G0+n5ODTVxacsCvnfvY1h2Gb8SIeI9ehYNQED0kFb7Mev82MDb581iaWcbADv3jPDjX6yivbWGT3zkYoQQPHjnWiRZwvRrmDEv967dxgevPPOIbdqOw66xCeae3kPPrBZqGoLcsOhzVIoTKOUSckstEyMOW4ZGWNje+mfeZbAjs5lGPU3e8RHTS6873q9plMoVTFvBNB0kCWRJQlOU6k1ynERlM3tTq2jy7SbCXiq2wwv5L3PB1EV/xtn89fHWBCBZxFN79O5aLpfL5XK5XC6X66/baxeUcP3VCPk8fPmbN9K6bAqDo2lK2RKJA2PV7AmlepklIPTCBP7xcaS5GeItOUZjDqoexLGq06BwwDOQI5AUlPcMsv6XLyBsp9oRx7IIjqeoyB4ODmT5xcBqYv/YzIHaGfzu6QWUamTKRnWqE6oCpoUELGyt41NXn89Xvnof//L1H1MaW4mQQ+xaMJ0WUSG8t8BgPIwQ8PwLnXzmI3fwzV/uYm5XMzeceQrvuWQZUa+XWr8Pr65jD6aI3rGFqY1hlKvrKdWpZDaOYPk9FM6aipjajmHYaDkHkTIpm9YR71VM1gmlBYqloLbVQkhn0/4hNG+1W5H6Rx2Hll+xmKs+cDE186dQ6Q9iizoAPIrGmzpXUGyqY+LtMwh8/HS8/mNP4TGHZQQChMDn03jPisXMb61OdQr4PSiKTCTySk2bETNOBQejzk+xRaVWfXW9m+cOHOKLjz/HD154kcbWGJqmoekqolRGKZeohCuMLwkyksm9sRvqGLakXkTYEpKQqdNePyPl0GSCbMUgVSmxeWsffk2jWw4yP1qH+hr1bByrD6fwCxynAMCmQiPfGJrH94angpOgYozzlQ2P8Mi+vSfkvP5aNNaGkGSJ5qbo//WhuFwul8vlcrlcrv8FbibNScKyHdbvHSSdSyDLDjMWt9MdCjK0eh/yy0GHSrsPq1bFbAerEzxtCq2/leFgFkwTqAZyfJMlKKaZLJURiozZUYueTGI7Jg1NMQZGCgghmDCLlLQ6rKKHgmGheSSQFIQEks+H5NHB40GVFVrqo2heA19LDq3ZpKIKTE1hf8VHfLfCF397Cte+xebxR3soDSYpTEuiaypXnzYHgPvu2YQ37SAVLDY8upnJgUmE43DLt97LwOpJfvvos5gBBcsvITX4QFYQOhgRGUU6MhjQURehqaSQLjvYQZXP/vQxZFVGjwYwigZ6+JX5SkIIBsazhLqb6HvPb9EUOHfbXADGSsNsSq5FK2ZQLBl9pPKa12hyWxHbo6KmSnz47y47YvrRlM46vvQv16Hr1WslSRKZs02K8QCSJdNWK3HJqV2v2qaRNRgeSjHL/0rb8J6FnewTNuGiSs3cTjLtTYeDQX+u8xsv5xvDKiVzFyHfpa87fmptPb60hJoTRII+RsfSvLCvD+2QwpuuXoKmHX26U2riJoKMkytupK7+O9R5G/BoLbQF20BuYbwSx8Ai4NGPuv7Jqq45jOJRCNQeu0C1y+VyuVwul8vl+uvlBmlOEhv3DfLDh16gsXE318aGeOKeFobnT0HIAilVnZai5k3KFzZSu8XA3gxzuwN0ST7WjW6k1OXDa3qQhAwVCwwDAKFA7sJO0j0Wmilz8GAeYVpICBY0zqVfsRhvyGDuE8gVB8mwkQMBkCWEokAiSXJimJf2PU7xEiiNRUhPxsgEHJYmt3BabIihaQG2r1/K3LOf5Zf/WcIu6Qw/JRgdTfGdn6xkxtRGls7vxDZsOtpqWfiPVyMELL34FOa3zOGq33wDx3bg4DC+uJeox0u8KQpqEC3tUF8TxDKtwxkyidE0PsOhCOSyJSYPJpixsB29u4WCIaG93EL7l72rWDe6n8pjMtk9GUplk2DQR0iuZsvc2f9jBouHmNEdpf/7EZI1o6TfmSdae/R21xHHS1IC2XJ4+p6NXHTVwiMCNR6Pxu59o/QfGGPNj5+kmJ6EiwJcF+7lihV72H3oECti3+NgboyBYoIz62cynsxSCJYZyr/SrUhri6H0einnUxx4eAvzlEW0vePotWzeqGZfG++fu5idiVW0+yd4fONeFk1rpT5y9HNuCgSpXV3NHto+f5B5p07hULSMJEuMZnN01EaPut6IITFFg8GySh0wJdjAt5bcBMKG5HeI6A7TauoJ/wnFhy3bQZGlv8rCwY3BAHXRAPM7TmzXLpfL5XK5XC6Xy/WXwQ3SnCQ6G2O0NAXwNObQdhnYZShN5Cn6JDxxBwC7LYxT5yVxagCPXuS5n2zCWZkGWaZ2Liz9YI6XfhggFw+A4qBQwjw9RPC5fuoe0RCyQzwIUiqD5Dj8/Yqz+fH67QwPJJFsCTQJr6aCz4ugmg0iNJXBvhIh6+cMj5+KPBZgTI+giwliPgNv0MJb6yC35fHXFmnqTjO2w0trV4WxsQfJ5TQOHpqk4YwGDtSXeW5/H28/eyFv/vz1BLVqFkUw6iHTHMJpmYbjWGTWHSAQz6HM6aSrs571D2/inm88xPUfu4LTrl7K6RfOQfLI3PPv/0Nu1wiGprBw6Rw2BvxIpkmhbANwID9GwakwkS2ST5coXzSTup4WAi9n2kgiiuFA4/QW2s7ooCUaIhg+dobDdefN52fPHUIWIBzxqiCBadr88OfPkU3mkSayFLaNo+U7eGFuB05Zou70QVYAP9z/FPFKjrDqI9gKb/O/hCVmHd7O6adPR9Fkdq5NUzmo8tKTA2SzRSKRY1Q0fh2OMBHCQXk5OOVVkgRUkwNjL/Gbx2yGJqfz3kuXHx6fKBQJenQ8qorjWBhBAarE/h3DXHHFQmrDflRVoSF87OO5L/suJko7uKLt7YdfkyUZgUlFqHg9Md4xYxYL6t9YsGJiMstXb3uCzo4aPvg3573Bd+L/3txZrfT2TbJ4Xsf/9aG4XC6Xy+VyuVyu/wVukOYk0VwTZsk59dzRG6Y036CuyWSyO4Q+FMdRq9N9RLyE4ciEszai7KekmHiDZaTGADd/bAenTx3nwZ4u7v5YI1HvBLIi8EXijFrTwCcjiiXs3gQYBkKWsQybi06ZzvhkljX7B3DCCurGOE4siiiVcSYT4NGxKw7Pb+hidqGJaT2dPLWxFzHoY7Q0E99TFfZvqGesL4CSVTj3bSbbdtdy1vUT1DY/xfvf+1/U1Qb5+po1jPaleSCxk4rX4onUQd4+8xSunjIbyalOsXL8HoygBz3sAUcmVhNGHcmw8r6NmBWLxEiSXXtGeGTLHjYUxtHPaMNKV5AswYvrDzDzgjkMZiu0LZ8KwIenX8LusRHuEOvY6RvFDursHI2ze2s/s07pZGq4m8HyPoJlicVLpnH+VQtRjzF9B2DdC3sQPg1RNDH+qE4OQL5QZsXSHpLJPPmgzoGYieVVyWZkUpogXVIxTYuzG2ezJztMd6iB8YF7uLZ1J3lrEPg0AJdeOI/FC7v4dG4V2WEdByjmjT8pSPPCwEcpmevxeeaxtPkrKHKI3/UWWT/YQGxoGvOnNLNoWtvh8at7+/j4g4/QEAtw3zvfiWU6SJIAW9DQFMEQDvW1ISzTIpEt0HqMTJozGpaxeiLMvFjP4dccx+G7P1nNSKabBRdv5Od7VuHTprGstf24zyebKzFSyBE/VEaIVwfK/tKNT2RJpYpkc69ftNnlcrlcLpfL5XL99XELB59E2rQm5kdaWNI1l5v+6S20tKWZOSdLKDEBgB324uk3UPbkkXIOclxCqQ2DqrBbijJgetitxxDJFJl+jYZLVUxvA06ugJPN49g2IuCFapdrVE1hwbRWUrpBMQJlv8AuvBx8UBWwLTBNREDwnLqUv//b62iImzRPmgRUleQehee+VE98vYW/Eufmv72B++9ezLaX2vj+/UsZs1qoC/tJjWX45IVnc+MlLZy7qJaSx8YYyzE6meTvP34bjapJs67jmcigD2fwBKN45vdQ2DPEob4Uq57azcyLFjPjzNn87sGXeH7DAQYOxekbSiFiQZSKQ2EyR/3sRuw5tegdIQDqvGHO7JrJgtY6goM5PONl/IdyfPPf7wdgec1SFkWWEL8fHvufF+nbP/6a12fZ+dMwozq2X+XKd552xLKtOwb5wN/fyYZtfSyd08zelTvwDJr4Rk0U26IUEozvrKGQLXF56yL+ftaVxPQgWmQxWwuNbC2dcnhbFcPCqJhMXRwluNCi/SI/sZoAjz+2jVLJOO77yXIsctbTSHKeTGU7jjB5fPQ+Xsyupb8YZiIv+Ke3n8/Cqa90jcpVKjiOoGSYOEIQDge46YIlrOhq56Z3n0NtwM+iSC3m6j5+89m7j7nvvdlRhktJJivZw68NDCV44JFtvPh8PU+/NIfJUpiccfznA1DbEMYMKxgBiZFE9vVXOE57syPc3vsc46X0Cdvm0RSKFRwhKBTf2Hm7XC6Xy+VyuVyuvw5uJs1JYvPQCLc9tZF+M8CWvWlmpzfygZt7iffu53vJaqaBHfGgFh2UZAZjqomzv0Jl3IMsBL96ZBkPzZhFdnuYpuIWbFti19cVRHcQ0RFEGktC0EdFKPgSGVAtdo0OYYsc28bGkYVAzkmIXB4pGEBkc4ieLqTJBCyJcnGrwe4dv2DtfWmCHo3L3reA737yUUofbaCkQNf+NNkJLw3RDKmhIM4ehVbf9Xz8hu9TLhlc9dkoWs+vGJ+o58GV56HvTPDkt3eRPzCOEvTTuXwekZpaxoaTWLqX5voIh/aPo+gaQpJ5alcfwWdCXHbhPLr21rDx+T1kMxZDpSJSxSY3mWfPRJxJzWDTwMiRb24mj162qBkoYtgOveYIQgh2Z5L8z0CcmnMCXDUxn46e1+529PYrT2dk9yT71hyiUjjyS/aB3gmSqQJDapnEMwboCpY/h+L34OR1xhJ+zOEQhiOOWK8xNpW7Rq9iRqDl8Gs/+e9VbNs6SNG28Dfl6Wxo4dobvkXRcfjej5/h/ns+dlz3VL5SYZZmUas47DdjeNV6BIKeEOT6wmix2KsyUc6f2s3K9k6a/QF0WcFxBBv7xshWygwl0nS31mHZNk7RxJTNY+5bII74L0BdTRCfV8N2FKbHrqfRY3F6W+dxncvvJSdzSBkLPSDTVBN6Q+u+lidHt7F6cg9RPcDVbUtO2Hb/2LS5zezPp5k288QUgna5XC6Xy+VyuVx/WdwgzUnCq6rEKRKXCyirxnjRH2TLf7RiZsPoxTgAIuojtH4MJZtH7bSxp3pRJyScZBJ9k4d0MYJn1wRSwIuTLSAJQJFxol6EE0IZGEfxqICE7BNkkjkSeYNIv0Pe5+BJgGNUEGMFUBQUAY4kIR2y2fqLtYhTLc6+8TL8zSkeeWkD1uw2EBkwbeIbfcidMstPKzP0Mw8eWyd+MIgZVLAtmUJoNWHZZNVzp1BJOFSEjG8wiR4LUNdWR9ojUVEEhVYP3kN5dFlCCvvBcMCrE5kR45zLT6GjpwEzXeSJF/ZTqpjItoIkyZSx2DA4jOUIRrJHtqsOt9TijwbIZstIAgoRL0IIMlaRrFmibJUZ2XmAX76oUB+JcNX82Ue9RiOjaba9cJDUwTiJkcQRyy4+fy6FQoUXhofx+jSmv+1MttrriTyggSSTeKAFb0cN39q9lvfoS+iJ1AIwuCbBoZ8nGYqUueUbBWpCMj49QWJrH4XREoo/RHw2FEoGTlgjVSy/7r1kOxZ3HvgYJcfkYo8Xj1qky1MN9F3SfA3LYufwtDVCe+zVxYgPjMV5fv0eEIIbZs8iEAmRGM5SNiyKeYOhVIa1IyOMtfkox+1jHsMtU88nbRSo84YPv+bx6DQ2hKmU49TJd7N9YgWHEklmNTW87jkd3oau0uIN0N5cg/IaLcDfqEtbFlDnCXF63fQTts2jeX5nH1v7RplzYJiOxtjrr+ByuVwul8vlcrn+qrhBmpPErKYGPnHOGfxw54vIy/soDNsU/AFKDWFioy93aioaODhIWRvlgAGDJrJRgxMNEzlUQs8X8BzKYga8yLmXgzSHhpA6fWhZA4GMyJcQgF+L8KN3/RRjaReeYBRp0kHxKbQu72FkXW+1pbdtI/k8+AyZGrmBWn8jW54dp+tcH5W8oLG5wMLGHez/nUZ/qh25pkIl72X23By2qOPr33iE8SadYIuPRn0qZQYw0hpqQWBLCnZdDDPoo6mnmfURhWI8j+SV0BXIFipo9QGskRyVqI9rT5tFR0/1y7zfp5MdSVC2bKitAwnUWi/+Tj8DxSxmU7XQsiMEGweGWXjpHKL1YX7+tUfJ5St4EyYHd49yxezFSMAjX3iUJ4d72aeMUGpSaFS9zJvazlt+dhcIwd3veTt+j47Pp6FUyiipJB77yCySYMDDu952Gm81LWwhuO/252FjGMNfQhIOIi9RTpZY9+JBFja3Hg7SPPLwZshIVEolxrI5apTHedclD7L+9h7ysoJtCGRJRYppFGtVPNbr12AZKGznwVETB2iN1BBSLCxlCgCKpKLZAXr3TmI3WpzefWQmy7TGOuYXdUTeIBoJEoz6+fj7LsQwLRbOaiOXLVG/NY/VlyY6Y8oxj0GVlSMCNACyLNHcGCY52cu5U5N008C0hrrXPZ8/5Pd7qK8N0tVe84bWez09oSZ6Qv/72S0d/hA1OZmu6Inp1uVyuVwul8vlcrn+srg1aU4iF3ZN465L3srMjlksPUfizGVzCPcVkSNRAPSChO4NIQW9WGfEcK6rR2p2kGUFzZaZ0t0IMzuhoxGpNowESKaNOpJALpqIfAlJSAhFplBMY2cqSOv6yPstHA1syyZYG0RSZNA0nESKltYUZ92axDIr3POFrWxYuZPf/rKPCaOBRs8gvqkmTedb2JKD7Zg88GuNyQm46i0ZQh6FcqFCfiTHU+vrOU0uoU7IqFkHWQ9gNEawe8fY/shmZuke/OsH8G4bJaDJfPqLb+KS06fiN9J4MqOs/PIjxIcTlAoVfvKDp6mf20lDSxhJchClEp6RAvUph1KbRU6u1tXZ0DfEZ+5/nA/+4ndMDExiY+NYJr6coJAtoUgyV7Yu4RPvuxFprELHg5NEDub518/fx/qBfrYr42xXJtgxMgZALBrgiksWQKnMxMDkUa/hPV97gG+8+3vIhkmsLUW5w0upxYftkUEI5ldizIs28dTeg6QyBQ71ToAj0DwKLeEwKB3IapCP/vsSpLDAkSX27x7BX+OhVA/l44hp1Ott1PmKhLQS3xpewFf7F7I1fvrh5duGxnhi9z7uWruZpzfvJ5UrHl6m6xrf/fYH+d5P/45QLIgkSZy1YhoXnDULSZLYfWiQgdFhKj6H0ULiaLs/JgEUCgaS2sLT20/nqfUOpfIbq83SP5hg975RVq898IbW+0sxOpTCKlmMj524ejoul8vlcrlcLpfrL4cbpDnJCCF4xzXXcvkZt/BPt1xGzJaRy9WsDV3xoup+5LY2JI8faUSmUqsgeT2UO0PsCILpEWhZE6ligkeHSBCp5AGfB8UfQEQDSIqEZVW7GMmFCjgCtQyYsGVsBJHLQ7kMxRLDBwye3evlwIsTWCUT4dEoL2/D0xKjd1eA/ZkGJhtrUK0STqVCJV6kf02WHasO8JabZGpKGhHVS7c+k7/91LsRRR9CkZBtqPREkDw6/powN164GN2rgSpDyM+d/3QXkmGSj5Qpn6ogpnoJ1QR54dmdbOub5GA8R94ycITAnkyS2d1Hed0QWl5FkavZJpOFAmO5PClhsvfgJOWaAE5rjBnz2+me2cxQPsN7n/odXx/fRskoolgGHb4x7EyGlmiYlmiOhlCRlj+YFjS4ZwjLsDi0bQDTtLFt54jrt2vtPgb2DPHYbQ8RWjOCUjCRyhZasgSmjTNa5NcbtvL91ev50U+exBnIoGbLWGWYHM8iec/lmdUf4Qdf6kcbjGMVclQCOmqtjt2coTIzSdGovOY9ZFNCUx3Kls5YJcjD8Sn8cl0fmUJ1qlROL1PoMsi3lPnxI+u494UdR6yvKAqKevQuV3XdAUqX2JSvtem4WDv+GxuQJRibyLBrX44fPGjy0Pp9bNg39Ia24TgOQvBX19Xp90xF0J9IszM98X99KC6Xy+VyuVwu15/t+9//PvPnzyccDhMOh1mxYgWPPvro4eU333wzkiQd8efUU0897u1XKhUWLFiAJEls2bLliGUDAwNceeWVBAIB6urq+MhHPoLxBhuT/G9wpzudRMYKOT7ylTtwXsrwgfddzA927aboZHHiL2cslE0cTUY2BJ5VglBIY3Kajce2sfwyQpUwFQspW4CSAZEwkiOQHEBYCEVGzpZxAEWRAQc8OlpextbA8YJXaEh1taAqiHwBsjbOZgPZp+EYDuEVLQwt1rmwZgZP9+ZJrVmDkxeIeAXhTSLZMsJy2LtBR0yLUTAGiIZ97E0k8Z2apPKoF68iYRYkIqkswhegiEy0vRbfVfPJp4uo++Ksf2AbQlNRm3RCRY2PfvlmPD4PXllGLpSRVZnLPnohD/9iHfn91XbGqe3DaD1NGI3VxyJVLGEj8AZ13vfuy/joR3+JkTMYyhXwBTwcymQpmAYjhSzyta0ER3dywbI0V75zK4KpTI9O4jgQz2Roe3l6yi3/9U5aeproOnM67/n0f9PREOPfP3E18suBIQeJseEkdqGCFYdQsBcpXkbqjoKt0N/cy2VTzqJi2ZR2Vb+oS6aDLBy8vmrQY7gvTnJgknI8j0weaU4rLdPrGKofxLIULnv6Szx5wT8f8z7yKmFUwHFAlqtBDSsryBXLRAJenhzaQcVXYnq9zVRlKqd0txxzW39MODLJej+K5JDKHVkfZzKeY+2GgyxbNIWmxldP55EkiUy2hG0L5Jc/O/vHE8DU495/V0cdp8xrZ+a05uM7XiEYKz6DhEyj/5z/8+DOvuE4JY/gwPAby0JyuVwul8vlcrn+ErW1tfGf//mfTJ1a/Tf97bffztVXX83mzZuZM2cOAJdccgk///nPD6+j6/pxb/8f//EfaWlpYevWrUe8bts2l19+OfX19Tz//PMkEgluuukmhBDcdtttJ+DM/nRukOYkkStXuPOlreTjRYx8kX+7+z7KzSGMBVEad1a7FYliDscbBQ2ksoU5WcE3LrATGXzJJMpugTZRQlgCJAlsG9nvR5gmkqpDoQjCQtZtECp4ZPB7wHYQHhWPItPVGmFo5SAIG2QFQgEUu0zzDS1EMzUcuMhLbalEx6wYH3nfxXz+jOcRlkAC9NEsjq4iKga7N5iY3f0IwyE3nKLnfBXaJ8je04ykyshFgb25HyyBkGU2P7wJY8sQKBLxTX1IAiTTQorLRNabFIZz0AlT5rTRqKiULOjp+Qahchv5l9/D/GiOuucj5GqCAHTWRPHEVGJeP6m+CTwKpP0yQ0GBJMH8uiY+u/w8gorOh97zQZIFg7PfMkA0YiKkXcQngphllf2xNAs6OwAIRAK8+R+u5gs/eIDRvaPkjWpLZRkJ07JZbRcxl7axqCCQFpTY1RtAJMFzdoqxgAIzC1w8axoXz5pG/pwib3p6N3LFRo0XGBlI0NFWyzU3nkZLW5Sff/xnCFniunecxko9QTHvwSrpiOBrR4cdKjR6FEYdDaOigoB/fvMFtNVHSVd2sbQhjuPYXNi9iCsvvPiN3admFk2ykSQwx4tHLHt29R7uf3gzuVyJN9+wlJRRoMH7SrBGkiRqagKUKxa6R0LRVS5ZMvMN7d/n0ZgWC9Hx8jV+PWV7nAPp6v8Qop55eNWjzxezhUO8nKXBG/lfDeTUNocIJP2cv+h/t0Cxy+VyuVwul8v1/8KVV155xN+/8IUv8P3vf59169YdDtJ4PB6amt54/cdHH32UJ554gnvuueeI7ByAJ554gl27djE4OEhLS/WXzl/72te4+eab+cIXvkA4HD7aJv+fcKc7nSReGhxh9e4+mpd3QKeBk41Tg8Pls2Yg/NVIo1oE0TsMvUMQz1BJZhG6ghQKIvn8eMbLyF6F0txmnIgfR5FwRscRqlJNq/B7yZ83neK8KVj+AI6iILXW4HgUHA+okQKTYhKKRShVIKrjXWQRu8ZgTLXYWymQrxmmfsYeUqWfMWxsIX11F0azHwBZgKhUO/44luDQI9vQ9gwhbztEQ6GJ8qZa1JKJWjCpxBRsXQXTAtPEqpho+ybxbhpClE0EoPl13vW5N3PK+fNY+fh2RvrjqKpCy9RGHMmipymO9Pt4RcCHLGxKpyjIHUWEEHj8Ch5VwdEFd37xHgort6HsG8JbEVTKJpIkMbe2kc5IFKNYnVJ29/cbeXh9D3rob+nb2crQvkYmyoUjrpVl2kysHiZmSbz5sgUUMwXG+6s1ajrnd+CvCWIpCpX5efSgQPZrdOcVmsM5VjQcPLydXZsHQYBQJGxFJluuTmO6/4413H/nOgJzuqk9r4XZF9XTFI1ij3pRB3SmVbpe817yqY1c13o5Z0Rr8CkOYVliTkf1g2swdx+zgvfyuemPstT39Bu+Txv9Mmd27eesKXu54uaFRyyz0yXsRBEnU+GH+5/is1vvZmuq/4gxTV4vcrJIIG4ypbmWanjv+O3e0s9T927igTvXHNd4GR3LzmM5RRTJe8xxv+1fy2e23s3K8Z1v6HjeKCVjE0oJamTP/+p+XC6Xy+VyuVyu/9ds2+bXv/41hUKBFStWHH595cqVNDQ0MH36dG655RYmJl5/6v/4+Di33HILv/zlL/H7/a9avnbtWubOnXs4QANw8cUXU6lU2LRp04k5oT+RG6Q5SSxsb+bq+bNYEleQ9mkoEzWc5zTQ4PeT/1gPAE5LHaK5BrsphrAtJEVQ0gwkCWRJwm6Pkb9mDubMBuyQhpzIgBAwOIYzMgaZHCLoQz+URM6XkCoWUq6ELAmEBo4skRsxQQJkCae+hqK/mbpFQViXQNo+idgOsiXYt3qc7/9oD8WWKKVpL3fasR0kf/XLp1IXoFJSkYYnqIt5ue6K01gROw9PWMeISJR6NMS50wAQjuDeX64iJykosRokT3XaT6VosG3bfl5atYPHv/8Yqx7aQmNrjHd//irsgIdcRSI6Q6e4rBvrv1rJvrsL36gFssAWDk4FKpM26UN5vAEvdQ1hAmWHxoTF4KE4Jctgd2YYWzjMXqGiex0GRBO3r5uDI7XixMCKCArqkZkrqqbw5pvP4qYbz+G6cxbx5Zu+w7/c8FW2rd3P9z77Tmo3DXNo5S6K31KYfrGXGVNi7OutpfJ4Hem1r2SOKKqEkGUcn4pQJVRDVF9XZCRZoiIbTPqS/Gj9//DB01YQSnqIPTdJ5WubmRiKH3FMjuPwYO9uBrJpAFLFx9HkXmxHQbfD2E512+3Bq/CocynaNgOFAfbsG6ViWMd9n9bo9SyIjDEvNE537dwjljXWhWj1e2lpCKNIMhIS8h9lpYztHkVKl/DkC1wwfypNsdBx7xugqasOX1OYzvntxzVeklT8ejtBrQNJOvbHpSopIIH8GmNOhLb6CC21YWojgf/V/bhcLpfL5XK5XH+ubDZ7xJ9K5ei1Mbdv304wGMTj8XDrrbdy7733Mnv2bAAuvfRS7rzzTp555hm+9rWvsWHDBs4777xjbguqJQtuvvlmbr31VpYsWXLUMWNjYzQ2Nh7xWiwWQ9d1xsbG/sQzPjHc6U4nibDXy7tPXcwdzw1jGxZSxMfMC+Zyz+CvectlO+DQfOgbR0xpqgZDHAUnl8dbrhbOlQCpvQHPSBE7WUIZSh/etvB4kGwbdA3fviROLISEhDKRQS9XkEsCrSKwMh58SgwnlAFNQXZknAEZaVMP0hWjtI82E5k5ld6nNrFnQsJMaASSRfyHXp724lGpb64hJTlItUEkj4bQPUg1MaIRP7JhUTowiurTmaNK1M1sYZcsIdfEKOZsFMNAqvFgaxpKyUASsPGZLfhaFRojDSw/rwdhbKCmtp1KjYblSKQTfkLvdOhaOoy9SGH/T1rw+EqYtkWhWEJOmtiGzehYguBFc0jvTlDOVFjz5E7W+g/wfHwvN7QvZ8Zpp5JIb6b+nAwLZr3IQOoJwl2QyJvUxF6d7XH6hdXUPdu22b56N8V8me9982G++tNbufqmc7nvGw/jra1l4zM6MhUUVQNL0FTTTLq8A12Jsei0aciajFIw8MkeZsysRoF7ZrUwOpik5dz5/PjeZzDu8eA/T2GFVMeBg704Xi9f+tB/c+HfvhLw+dBz9/No335qvT6eu+FamtnL3+0/i3TeS4YKiWyBxliIqHcuju9f+OI9t2MU/WQOPsL558ziXW897bju06Rp8dODy5AlG9/ujbz92leO4dwrFrBwxVSitUHOFg55q0xUPzIY0dQWZiSepjI+zK7Na8mePpdo5NWR8WMZGk2TVqF3MnNc43UlzOKG/wQkVPnY+7m+YznnNc0lpr86eJIplDkwEmd+dzOacvSCyscr0OBHa/JR1/R/l37pcrlcLpfL5XIdj/b2I38x+i//8i98/vOff9W4GTNmsGXLFtLpNPfccw833XQTzz33HLNnz+Ytb3nL4XFz585lyZIldHZ28vDDD3Pddddx6623cscddxwek8/nue2228hms3z6059+zeM7WpkCIcT/eR1KN0hzkqkoKgFUJFVh1txO2hIl+rfV0RECy29jqBV8kxXsqB8UgWaAo2mAQIunURNpKh0NEAliJ7LIgCPKyE0NOAjUA2MgHIrzWlBaQjhlsLGRJRnJI3P6zJmsfnR3tZ5NnYJsBdi9Jk12S4byvj78922m+bMXsHPPKLIQBAcslEQJAWA7fOBrN3LHAy8QneVj039uQg6HiY+k+a9vP8yL63eglspIFQMp42N8Mo/UWY+mBbAcgWTa2H4NW4qiZKtTjELLoenGAB8/92/p8qxHZH/Lmv3noxYrJAoe8oYHthqIpRJmQSF3uYRcERRsk64mH76ohWY5zLpqASuf78dMlVArDru3DDDtSi9edR+ZQz2sW58nSxPZEUFrR5b8hE5LQwUpmEPWSkdcIyEEuzb34w96aeuqxTItECBXTB749iMkhhJ8d/0Xef/37qYSLyObFjOCSd5/03bmTdfYkdxKKe1H7L8Z1RIIAQFdRYhqtsuWtQfZvXYfqx9fhRQIUQhAciLLwQd2Ift8IEmMjuWOOKYN6R0INHJ2DlUOUBYa6Uo1q0kADdFXarg8u6uPhywTyUnTMOzw/IsHjjtI89jAVko7IwhbYktmP2+/9pVlkiQRq6tmxqiS8qoADcCcOR3sXLObaFOeHXvSOC9n+ByvBfPaedM1S5jSWX/c66jy62etrJrYzZNj27ip+2ymhY4sSvz9h9awbs8At1y6nMuXzXpDx/uHTMvmRz9dRcUw+V1bDR9689l/8rZcLpfL5XK5XK7/bYODg0fUdvF4jj5lX9f1w4WDlyxZwoYNG/jWt77FD3/4w1eNbW5uprOzk/379wPwb//2b3ziE584YswzzzzDunXrXrW/JUuW8I53vIPbb7+dpqYm1q9ff8TyVCqFaZqvyrD5f82d7nSSWXr+bDSh4Ms4GJNFDt4Vo/9/qkWW1JKN2jeGNDKJUyjgIHCSKahUoFiidn4dsldDyDJS2Th8cyihGhTVi5QrVac/mTbePWN4R0rIA3H0vaM4GoiSw7bndlXr1wiBIjkgoHiwiHYghQSUcmV6X9yKkjIRlgNjk4hiNYihhb1kQzovNcKapIHI5BCTcUQmy4YD/aRavTiawPHrDBYMhoo2ZmsdcxZ34pgGwragUEbRfVjLZmF3NFB60ca71U9DsAGULpBr6W5oJ9AawzY9FMMhnOcMet8mcDbNJ5sNkE75sGyBLoWpb6mjrquZ93z0GhbO7wABlk/lnMvn49dLdPiDdDYFqeRL6MJh56Mt3Psfp/GJj21kVijH1EiSRt+RqXh7tg7wbx+6g8/feju2LVhx1RLapzfzma/eyO41exnaN0J6IsO/vutKWm0N76RF/ICP3QdWUNJuZrp8iEe/pPDNz9yL9HJgJh0v8NOvVothXXvzGXS2hTEOJBGlFIFldTS11yDpKsKqTk3y+I6MzwaDeeoa09TWZvAoMUadJtAEqmbh8Zh88In7yVSq3Zha22NoAQUlqqBFNOraoq95T/7ssRf5xA8fZDSZZZqvDTIaZFXOWn7OG76/Vz25C1v2MVaYSi7ho1h+5b11HIc77l7HA49sOeb6Ho/GhefOYWp3wxve92vZnh5goBDnYG78Vcv68xkmSwWG87mjrHn8JKDsWDgI1m/p+7O25XK5XC6Xy+Vy/W/7fVvt3/85VpDmjwkhjjmdKZFIMDg4SHNz9RejDQ0NTJ069fAfgG9/+9ts3bqVLVu2sGXLFh555BEA7r77br7whS8AsGLFCnbs2MHo6OjhbT/xxBN4PB4WL178J5/zieBm0pxknnvxAJYqEQp68AQ8eOpk8lZ1ikXYU2Lx1RPsfLyReF0ApT+BpRhIwkJxoNijYeidSJkSVAwEL5eX8fvAspEVHZrqIZNDMk0krxc75EU016AVbII2SLliNUgDSCKM7NHQ6nVEvR/Gi8y4Ksu8W15k632z2b2uBjn/SpaJWTKwUgUiGYv6kIeJSAhRLkNtlIJl4d+TQik5yEYFw6chJFC3D7Bz+wB4fFATxvarqJbAQgZZxpqwGRpU+dm/PsVV7zyNKTN+yMTKdfhbE3x21UWYDUFEQ5gr6jezuw6srAZIjGZyzKirZ0FLJyGvh1jQz2nXLeKx9ASKJTj1/Fk8NiizYavOOee28vdfuJ7f/eApMjsnQJeoeCwmc0X8/jIDhewR1yifL5MzTIqWBQgsw8YX8pEaTfH+b9zMrjV7+cmn7mT5ZYtwgjqGZeCXVR7eFOQRdZyPL57KxJCObTlEa4NMJLJYRpm+vknGh1M0tsY457pl9G3uJe/1Eo42Mpkp8IXb3smnb/4JEoLpi7sPH8/4RBZPVkWP5WjzVT/sAnoHXtlGDhiYFZmkVSJVKRHxeJEViIZ9TI/UcfNpCw+34B7MPIhlHqSz5lZkuVpkd3AyzbNbDyBLMhOpPJV0mcDuMiJos/bZ9Vxx7blHvY+LxSco5v+bYPjv8XqrBYZLFZPxLp20P4R3ywiiKUI0/MoUpNVr9vHTX65GApYtmUJTw6vbeKczRYIBL6p6/PHpcslA09WX284f3du7zmBRzRQW1XS/atm1p83FH/NwztxXL3sjVFXBH/SQK5XpaTt6lymXy+VyuVwul+uvyT/90z9x6aWX0t7eTi6X49e//jUrV67kscceI5/P8/nPf57rr7+e5uZm+vr6+Kd/+ifq6uq49tprj7nNjo6OI/4eDFZnBfT09NDW1gbARRddxOzZs7nxxhv5yle+QjKZ5BOf+AS33HLL/2lnJ3AzaU46yxZ3s+SKU7jhI+fztcfWcLA2yPg11YhlYUQwsV1n5tsszOYA+SU15OaEkKoNlfBsS6IMZZHiWbAd7NMCCB3MfA67XMRoDmJFvdjt9RgLu1GmNOCf24mkKmjJMrVRPx0z2pEa6sDnxSyVUAwDtTuE7yOno3k1IlMcWlsrXHjpPnQ7ga1V5/tJsgyWYMPda+h8dpDTnQCyz4tSX4esK4h8nnBfGSUUhmgYiiX8eyfxxkL4akNIoQAS0su9fiTUQomAYUGjTulAkafv28id33ua5FiaX/3wcXaNjlMsebH8ErZfYl9LPRc3T0WpyCiGhFdW8esan7v0PN4+fTb3/vIFHn10C560gUeRqZRNsgWBio9Dg5Pc9eX72L9hP6JcwvYoSJZgNNfAgUwtI/lXugKlKyUMSSBsB8d2GDoUp5QrM7x/lHQ8S+esNupaazArJmN9E+g+FU2W8Qd1HFFG/Go9//7hIPGyF49P49I3LSVYKqAm8kzu6ufx/9kAwGlXL+XUt53JRFOIPQNjjMSzLD51OqeePYMAFlOm1B4+pkQyR3ZvDcnBes70VYMmc+s+ybnBOoopLzWpEJ899Ty6wjEAbtu4lsFMhhcO9DF6ME4+W8YWNnLui0QqtzOZe6W93W+e20KqXGLZjHbmdzezZ9MwWkUhnDNpm35kjZfx4RQP3rmW8eEU+fS/I5kbyKb/5fDysmGSsGyskAdjagOK6ZBKvdI5S1WrU74ccfQpUI89uY1r3/Jtrn/7bcf9PO3dNcT1H/gOH/nk7a85rsYT5LT6GXiVl4tW2xapcpFEtsCFM6fytesuY3rDnx9YqegC268wpaX29Qe7XC6Xy+VyuVx/4cbHx7nxxhuZMWMG559/PuvXr+exxx7jwgsvRFEUtm/fztVXX8306dO56aabmD59OmvXriUUemMNRP6Yoig8/PDDeL1eTj/9dN785jdzzTXX8NWvfvUEndmfzs2kOYlsGxljY2KUgGGy/qFN5Dt9eLwKiawPakEgMbwpzFCiA62uRLlZQ/N6q0VHNJViTkOqlFFH40gSBNUCFQUs4ZCfVY/el0TRfCBAzVe49M3LSRsWzzy2DcuvMrSvn8myjeT1QqlMpVxAjCbh8TK5K6cwb0EX0eJy4ulhsvFe8u9oodLrxbdmHG9fBoolWppj7HluF/6gDzuTwWmuQaqtpUmXMLJDCEkGISguq8WeLJJd2sjVNV2semR7tSaLBI4iQdSPUTKQijLOrgKaVmHZGVOJNoRxpjdQ89wQTrhM4uw6JGHR70Q5SB61rCCAQ+kULaEwD67dyeSucdat3M1QBGRVoaW3zM+++hgf/dINnDm1i413r2ejYuAN6qD6sfwyqgC/3cRgysLUfAAczCT4zLOPo61KEwp4CIW8tHfXMzkUxzQsBnYNAbD4olOwbYfRJpnKHSuJxiuEgmFmXPICfV/OcMiIIqkQjPh44t5N2L4AUrqEMG0U00AIwQ+/9BAP3b0J0SCTmaVR/3LGSVtEZ+tEgsceWcdbP7gEIQSTkzm0vTJqPMLF11QLGnuUWi6ZnSWrxDmreSnNwWpdFsuxWBCKsHGXRGhC4YFtWzEqDu98y6mEVA+KKKOUFTJmlkhdmFWVAQ7Wpilp1ZblF1wwh41Dj9M6fYTUFv2I+3fVo9t47LcbKJcNZl3sZ9LUmOJ7pRZOLOSnWXiZTGURmgdLh0KhTC6VxzJt/D6dao0vCU199Ufbr3+2CjVeIJ8sHXdBsP9ZtZVDTYIBJ/WGioj98wtPsGVkhLpRH29aMo/rz5x/XOu9HiVnoxkOmUyRfLbIJ2/6CeGYny/97G9OyPZdLpfL5XK5XK7/l376058ec5nP5+Pxxx//s/fR1dV1uH7nH+ro6OChhx76s7d/orlBmpPIA9t2s+aJHUhPjYJhcvE/Zjh//iR3vVjtoCMBZlmHQ0m8wxIiX4s8UUCuiSIF/OSsAkrSQLJMNK9GZbMJloacKRDYNISQQIRMyObwTaulvbue3/34GeSKg1QsI+8ZxrId0DSQZLzeIFZnDEmxMXdM0rdnDKuuQq9WYNw3BcNRQBLV6VH5AugqxYBOwaPQn8uz6O1ns2HPMP6AB3GaBas8IASSoqDHDXIrWvBvHWdXOY0zmcbpaEJ4dBzLRMlUkBxwyhUUWRAJaNRGvSiKwtwlUxh/dhAl4aHp6TK1/xonmfKRLKexww7CFqStElsODvPw+t00hQJceM5c1g+M0Lt6P6miYMeuEXyaytzmRrZ012EHbPwNBoVCCNkSgEBP+KlMBClZNvf84GnkaRHMoSKlbQlUw6FnZgser870xd2kJ7I4juAXn/01DR11PHXHKvynNOI9r46GboXM3jz9hSjCziPbKlLJIpnPIskysizhZLI4FQ9NzRGK+QqrHt0KtkAtOHi9OrUvF/4tlkzsUAjbfOW+ObRnFGmyggbUBX2HX1/YcBm12lru2JvhxfJv+ey869mcXkk59iKLIhFso4bzF3Vz2vIeLMdhOO8lopa46zsrSe3ewSd/+WH8Pg0tp0J1BhxNLY2kFs3j0GgbnzvjzCPuXwGYpkW5aOBVW4jYo3iUI4vw/vSrN/Lv33+AxycPYTaqpNQKv3rP9zAqJived/HLM+0EiWSO2pojC/76dA0z4EHNVXjhyR2ccdG8132mLjlnPo/c3ktjOPiGqrw/v7ufVKFEaaTAUEPyuNYpFitMxPN0ttccc1+f+MBFbN47xLvfdBoP/fcaevdU59D27Rula3rzUddxuVwul8vlcrlcfz3c6U4nkWtPmc3y2d3ofkEoZtDdI9AUUCb+4AufRwfLwvJryJKKPpxHcgSF7gBmdyNSZxtOSz0Np88ANYDS3oKsaciyCjUxci0qIp2lsvoAv/7iw5AtYYV0bJ9aLSosAMNECvohGEB1FOqmthEZKiJLElKPl9QTJco/niB0f57G57fh2TlaLTSsySy9YTm+qxcSmN3KTe89m7ZwgOVzOjAOyhDyI3k9lKdESJ/fgRPQ8FQkzIqFk88hI6HlDBTDQbIddMeCfAEHiZys89LKXQA8sm4P8QVhyvU6pgNhf5ae6WMsatZRK9W4UdjxsmhaG1etmMPNly+nPawTiBcJej3I9QFmnjMdRa3W+pk1tZXu3kPU5yaYMW0CSUgIx0bkHeQi9H39eX7w4R/zg7f8gKvb53LN1cvxBzwM98cpFioc2j1CIVvi6bvXsPp369m78QDFXImZDY3cdsHVfPPWN3Pq1XPYcKCLxJJ2jNoItiohNAU77KEU9SE11uFpquW8t59BIOTlyreeSsAp09kU4qzdgvu++RCO46DVxpAiYWZMnwK83FHJr0NPEePsAg+PbD58q0REmtyOl0g+lcFKOSiSjCZp+D06y+c3ceMVs1m2uJvmpgiyJJGwGpk0arAMH6quoqgK373wKlaM1jGyZpx4Isf60QPsG0sxYdpsXbnziPu3paOWhpYYTe01dCkJ5ukVGqTRI8akBuIoT+2jZ1OcOTObiYYC1X0pMrlKhUpMxgxIeL2vjj+feuZ0ZMtBMWyee2TbcT1Ti2e288Cn38Od//DO4xoPLxc6syxsxcHMWmzZMcgXHn+W3vhrB2vefPMPuOnWn/KdHz19zDFLZ3QQsTS2bh1kzuJOACQJPF79mOu4XC6Xy+VyuVyuvx5uJs1JpDUWZnP/MKnOCDNmm6w5dAH9Kw9RyNiw8OVBlo0zJYp5nR9l7Tiy5WCJPNKIjN9Tj2QJiEQZNSoIzYMwLeTW5moaTjGPf+MAUtFAKAqZXJ5iZxRU8Cccqvkj1aFkc9WfvR4SIykC9SoiqTF60MbZbGPvtNF8ZRzNg0QRAF/My+D4VgrJMs8+sYtrTp/LD25/H16fxkuf/xopTJTGKKYfhCoj5U28K6aRtyzYN4qQqwEWYdpYYZXYjCmUHkxgajqa10v7/GpgQn1pEmV6DMnjwXYstBEHfZrB849lkIoyqgajoxlC8z287bzqG3fXh37OPgsapzfzsQ9fzOxZbQwUE3x7zyMsr5vKrZ+bTzGxiwce09ByBpZHoVX4OJCSIVMB28apGHQ31rLiymXs2tCHadoMHZqke0kP/QcmaF44lQULO3jyrhcY3tFPz8IptASrRatmeqNEtwpSozUIzcaRIORVseoCUDaxLQdRKDAxGKexo54Fi9p5sdZH8sAE+zJFkjuHuey95zOWzFOK+hhJ5YBqXZP4aAY1YWFaR7a9lo2nmdbWz9lTu7hw4XXUeIKc03AJnYEWflD6AQOVdeTvXMo5K2bz1uuXsWzqDzHsAn/3X0GEAK/fg27atAcjZHMl9uwb48knt9L4XAmjSaNhdvSI+/f0C+dwyrJughEfYvL3F/PIosv+sI+KZGPVKFzY1cWshkY+efuHsG2HjQeGkRQZKXj06U4dnfXIioTi1bjibace1zMVH89w53efZvbCTi689viqvAugrjYAGUGsTmNcr7Bhwzaw4Z8vO3qhZIBcvlrB/v6Ht/Dh911w1DH7Do6zbecg2VyJm65ZBPEEjhAUM3l+fz3/XCXTZMvQKHObGwl5j68Cv8vlcrlcLpfL5Tox3Eyak8jqkT4ORfJkm2ReFK1EIw0MCIXcjOoUFgE4PgV5low6z0C90oPaIyH9fSvBkRwYBuQKSI6guyOGKJSwagIICSRJRrIrqMkSkiMQNWFqr55Bsc2LUrDQ+kaQ7Vfm+QnTAtNCUlWkggET1fbDsh1HDVjYrTqFKUEqvFLwSVmcY+XkPfhrc4SCXqIRP4GQF0VVyA/JTL65h7F5HkpTg9Tef5Cap0YojxcYqxgIn4ptWJheGaErFLsjDCQTIEk46SxSpULPjOp0EKNOxvIIHE3G8alYbRIqNolUiWDOxBsHvyRzYDLBC739PP/wS0Tb62mI+lm0uJv58zrQNIU7tm7gyXWj/HDLaqyZb2Xa1V/nohvfjRT1UtdeQ9/2cTxxm2BTFMWr09TdyOmnTiM5mUPXVdqm1NEzq4VTbzyb1reeQf3yaew+kGQ0bSA0jefvWc/G1Xv53S9W86vvPIMoC1AUvIkS0kSSYiLHFaf2sLS9BhJJShNpVt69BoCZy6dx7ccuJ7SwG8vjJ9DaSLg2RKQxjFIwMGzn8Pvu83vx7ZCZ/UQd5zTORjhpnMynQPLQ0Pkx3vGOv6e1vlo0WJIkJoqjbEl52VHQ0XwKLc1RAHTVS9BTi8fnweuvfrnXNIVPffwyQOJzP32YTS8NgaTRUPTyjn+6/oj7V5IkQlE/kiRhoCMEGPaR2SdNXQ3In5vN/mtj/Pf+9dX9enV8AS+qIfCkbbSERTpbfNXzkSsbSH4dpT7AtLltx/VMDRyc4MX4EE+s33Fc4wEcIfDlNCKmny/dehXlANUaQ4nsa69YMZEmUjTWB485ZMmCTt5y3TLe/qZTyaRKOIqCUFVeem7PcR/f63loxx6+8vRqfrVp6wnbpsvlcrlcLpfL5To+bpDmJFG2LAqGQX2fSiytsqKhmZvPXYxf8yD/Qf2R8o1himdqqCv91GomlBy0RBZblhHD4zjDo2jZNNpYESviR+4fRzbtaoTHrk6pcEJ+7J5m7LSDnhLYQZ1KrPqlXFZkfj+5SuTyOIkUUqXAR7/0NxAKYGwsUllvk1jQQXphGN8ZDr6zghAJUzmoMP6LOqYURzi1RiYzniWdL/EPP3oIkoLgoI0W8OPtK+A9mGDJqTORUMAWOD4vmgN61kSWZLScoK6pBklTUGMRKorO9pf6AJBMQX6KIN1tUW7QkR0vY0aMG8/cR8yuoBkOlbzJvz72NLc+dR/vX/Ugz9+7DrNiYTnO4XohQTOEbnmJGTXc/tgmvnnPenYcitO1rJvlF82lkimjZixCzTX46yKYkSDrNhxkYiRNNldi3eZePvnv/8P0nkauuHg+41sHsR0HRZHANHEUhR/d9gQrN66krXuE9sYIIqCDBLKuI8kyhaLFnp0jgIStSPRFBL3xJMVsid/+5Cl61/chdC/JnIFp2rTVR1DKJtmR9OF7YmI0hWXZWCWr+oI9CdYQ2P046oWo2pG1Xeq0qXgkHY+iY19RQJlWDc4J4SCEyR9TFZlkMk/GY1BsUpA9Khddespr1njZbtisKWvsMV79EXX19Hk01Uqc39XOyMExPnPll/j1f91LR2stqiQjSxKPrt/NSCJzxHpzpjURLNsEyzZmxTrmvo849p4A8dP9HFquYDvO669A9UM10Z8j11dgMJmlPhpAEzI9ra+d6eLZehB180G0PUPHHuPROP/sWXR11DJldguKYyNZFlNmnZh6NDkzw87KUzhq+oR0o3K5XC6Xy+VyuVxvjDvd6SSxZrSfn63fiC/mZXF9np74A3zmHzeTLtfiL1Zrp6CB3CZh+mQSvgDxiSnUlAooP80gmaJaQDgWwywW2L1yN6qiYMxsR8tWkE0Dp1DNTqhMreNvbj2fR3cfQNtvgaoip0oIAFlGEiA5DgiByBdwOsNkdIGVzuJYNvps8EZtVJ9Bw6IMUsVheLNF+YAXOQ079ypsy65h5W938Y6vvpk1ew5BrYIiy+h1Kuo+GzXgZeejL8GcNnQMrKiOkk4j1cSwfRqSYeGTTMbeX0ftsyaSXGZSvgc4B7lQQDY8YFeITsiMfz5E+1lj9EfrkWSBZgtMy6EuFKBcsFBb/dT1NDLrqm62d/WyZnIvp9XP4MJ59QQi3VzQtpy7ntzI9KkHCZQ7kOU6rr1yEaXeCUq5lXS06TwXPgXbgpGxDCWfzsG+Ccq6hD2ZpFgy6aiP8HiqSLps4DdLWJJE7axm9toltGGFK2+I078nCKqKGfWihHS8qortUUAVIASlmVEeFrsYfTLOkrEWkv1JcBSEDJ0L2tB1lcR4GmELRMk+fO/s3zEMQpArVbOdUKcihf6esWGb2/7hLjrmyFz1ty00By5k0/P7+e8fPIVvSYzSAsGBRIkd0RFOq5uOyP472EMQ+XckpQkA07ZJ5AoEAh7UlIJkCySfTFYdx7QNNOXotVQU2U9eKETkV2eVnNs0h9WXV7tQbVu1i2K2xMCuYRobwmiqTLqc447VW3hg5TZW/vTvDq9XWxciapmoiozuUY7rueqM1rBkSgddoRiKfHwxbdsRNPuCZCjTHg4zp62RnGUws6nhNddzCmUkIUgMxI85plQ2WLl6L91d9UydUk/n4mkUsyVq205MQKVg54gXy4xmHQ6lkpxD9wnZrsvlcrlcLpfL5To+bpDmJDGnthF5UICqEnbGeehRhf4zguRqFaRUNWNBMgU99/cxevkUUl4VWTjYTbWoiTGQQWqLIpVknHYddlRA80IsBIOJaneeUgnbr1HpruHUFd38bs129ISNYpkomXL1QMwjMxTktlp8H51DMp1D0zSMpKC8RaE0VcXRVXKHNJRBC0fo4FhIqRSKDpYtUUllufM/H0OPqZTqvSglQWBfCQhiBL2QKeIcGsda0op38yAUTcxoACEHQFMYMG2EpNB1ZS+z5meYsbCOFx99iY5JG/Grfk4/ayHpgE1/n8745hhcr6JaRbwZjZm1MS5bPAd7lUlk3MEzL8rGX2xFPt9LclYBgPWZJ0h6xymo7cyzJ+lbs5oZ54V487vPpSWkYI4NccPbtiDLgkPFdtJmG7Zl85O71mD5dfwqvONdK+iZUs+AAgsvncPmHcMUhkfBtAguaEPuz2FaMr3r6ug1KlhhDRH2UdOXxUgarHlwK2rEgIAff7fJm5evQTzr5ZmH5+BICiLiwYgGMYPVR33zC/twcnnslzNU8tkyxUKZ0BkFohfF2ZrewCnRpaAvZGRsP6OjKbLSKHMzKwnpU0kl8qSDDiUlQL0cZO9EEaPFAwhwxgEDRP7w9b/lF79j19gky701BGt85IfzmGXBhr1b+PFtOT7wsfcd/X4OnUuH2EXU89qBjXlnzmLF285C83k4eGgCy3aQyzaSA+Xf35MvK+XLZEbjKLZDMVvGF/C+7nMV8Xj5wmkXve64P6QqMvOmNZMplmmMBPngWcs5vbuDs6ZOec31JL8flAqOqh1zzNbtQ/zuwZdob63hM/9wOb5oEDw6hmEfc503osnbRiU/hYIxzuqBft69bMkJ2a7L5XK5XC6Xy+U6Pm6Q5iTR6A/yicvO4uBoktMalhKoPMuhKS1UghLrnu0FQPFJ+MMheETFs6RCZLaB4gsjNVpIqobjk5HzFk7Yixz2Y7c1oY/kkIO/rxvj4MztwJdT+OD7f0xuqo43kUMZGEcyjj59xG/aXO+fyab7dlDMlaodoGwHNZWnHNE5tL+J2MP9SFhg2ch+D7GpUSaTPhAOEhKKEAjTxlQc9P4CkgNqRz1i1yBOKo9v40B1u4oCXh1ksJwKnskCgZ8OMlBfz9uvuoE5TRfwzoWfIp8uEGutY/7FWaYvqef+76bpWHGIydhM9qZ1tOECI0NJ4tNfoql1D9t+Wia/1cRRNRpf8nJx03wAltacTn+hl1ilkd99aTOplJeKd5zc8l+g99QwOGbz7FMd+NU8e+7YhdSeYbWqUlMbYFpPA7e+5xyamqNs2dDLJ3/0EL6Ql8/degkd/3w1P/zBk2zpn2TR/GZyQ7vYuU8Db4ByjYwz1aLSLqE9LZFRJSwNdJ8XvS9DwLLpWZJlaL1F2grhNEXIPLeb8WSKwievIehTq++TVn30v/ev9zB9XhvbrEH8YS+GXTl87SqyhNMQwtfqpdE/G7/WzrmXd/KVymb6KymKkxX8q8HwppEWyBD5D3CySGrX4W0k8kUqpsmURc2g+Hnph7vAkSgP+Dnax8+zdz/Poe2DXPHxNspyhLyopesPlqcm0uzaupvcnHGmh2fTLHWy+qndyLJMpK0GXVNxenPoiTKNf9BOHODgcIrC1Fb8jkD3H183JEdY9Gd/iyoHaQtecVxtuEeSWR57cS+WbdM/kqSztYZ+M4siy5wz7diZKYqqINQATW2xY45pa60uq68LIssyV75jBWNDSTp7Go/rfF7PUDFBOZKmuUvng6cfX3Fll8vlcrlcLpfLdeK4QZqTyAWLpvP7njA//MhdxO/cwd/f/kH2PP0IXDkdKxJhazpEcCLP3KYJ6hoMdo5GkT1eJEWllMigjRcpdDYTGs9CRxOSaYNwAAnba4BHRxKCol/FsnWKdRL+pEDLgPRHv8wXqoxy3gx2jCYREsiOjQ2gCIzZHioRCb0YBKsa4LGDHiqntGIWJRRJYOcKxGY3ciiQx1uUkIWM0ezFVmWiO8cJBDxkckXkiTxmaxQnVoMUCoBto2UMPLVZ7B02uQNpPrPsIT749TrMOhNFVahgkPE+wZ5RH6d/oIIsTfDk86eQMwSKV6GSyeMUDJITORQ9iKxZVCbjDA0JBvoT9HQ3MjeyiLmRRRzY1k/WlDHVCNsf9ZMrp2lqcvCHvTz2YBfOyBiYZVSzjzd/9z10dzSQz5bo752gsSlyuIaPLElM6awnFvTRXyqTzJRYumIq77i0yCc+1Q+SSWWKg3dqEaVDx9nvoFh+RB6EBOZolNU/nsX5/34F+dF7yWULFEsOsulQVxMESeJTt93Mzed+6fA12vTCQU69cTn5Z9upGe1kUWzF4WXzZrdx6ZULmT61kWmxarvnu3e9yGAxhZDANiuYeYUDg9UaKvevHWEyk+fGC9rw6tWPlhVNbZTHyoxsGmG8zUStCFAkjEmdv/n5jTjCojdzBzIKUyLv4Nsf+AmlfJnY9LOpPa+RmFYNPgghOJgf53NXfI30SIH2t8F5H06yYEKhNDxBsDbEmWfNpOzYfHvP3YiKw5QzjsxccUwLybaRTYtKvkQo4n/dZ2psci+b++/CF/DRPPM8VCnwuuv4PRqO7eA4AhmJ/ZMJBq0cO0bGjxmkcRwHs5BGKjnEzu885rYffWIbB3onGBhK8v73nku8d5TMcALHPjGZNBISqlAImBLS8ZXtcblcLpfL5XK5XCeQG6Q5CeXSOfauPwDA1771MFZz9YuuZCl4NwxjC0h+0aZyyUyQEjiTCSSfF9HqgXoPjq4CAuE4CL+GVHQQhTKKrFIMGHg1D1a7H1F0oDlIfOFUQs8MEdqUQOKVNtxOY4hhDUYODPCx957O5LrtTA4mwILgUwn0KQpqnRdJU8G2MXtqsabWYR9MEDiQROQKiGSZ2K4UxZl1pJb5UWyJhieTiJKC4fNCModsOegTZax2H6gyatmESAghq8g9Wey9Bkq9zYbkU0S/7EOUYPxDDpvu6cZbozB1LjQtKCJsFQZzyAOTpAemM/fAfMb/ux9jAiibkC/DSI5DO0bo6X4lc2HfiwcwGn04io+JXofxgocDDzyH3txJS08N5boSky8lMCUfo5P9LF0whQ/dcBtm0eDDn7+Wi65exDe815DPl3nqB0+SSWSpiwU55Evw7NZ9bF1ZQYkbRNODKDObcZIS/r0KMSfIKRfMITeY4PndI5Rm1rI57Of2bTne/ImrGBtK8vjq/XintfDPX30bgbAfVVexJ+MosWp2lG0aPPrthynMiHLPXp3T9vVx3sweAALKE7z5gq1IwVsPn+vqgZ349TJTlWZaU37WNw7TUFuPadk8vH4Xli04e34P09vqAXjzGaeQ3BcntWkEc0JBzhs4fh0lVUTVVSr2BOPFZ0FItIYuq95zjiCVLMOgh3BPtQ7S1nQ/P9j3JCMXg/aNPP7Rbk6vOw+7oBILavTMakLVFJYt7qZuQRu5iSzX3nTGEc/FKYu68BgGpiWwj9Ki+2gGd0vsfWEK4XAd6uzXD9AAqKpCOOSlWDHpUYL0OnkkpNfNwpGjAYRRwB8OH3PMrOnNaJpCbU2Awd4JfvOz5xGOw+lXLmbeGbOO6/heS4svxnQxlZWpPp470Mei9tY/e5sul8vlcrlcLpfr+LlBmpOIEIJfP7KOp3+5ASkcwLINhoIaoq7aVUZkssiKBsKhUt+I/eQIwjAQ+QIiX0CZOh3FcJCDXnBs8HuQJpJImo4UVkF1CPdWCC6L4aQqqHvzhM9Ok9hSj1L2ga5gyyB8HvyGjZ01kJMmsiZ4eus2EhOp6oGa4DS3olseApqJVBMFXUdqNYgstihbJcSzaQB8dSHyzZCb4cdRJWxVIKdKYDi0zGnh4EgCIQmEY5FrAa1iIwUk0AT+nRnMgw5KiwemRch3JLFUlbKsYtYI9o41E9ht07vBonXhUrRTywT9Olatn0LZYtqyJlJrPZgNZaSDJigymkdj2dkzjnjfF54/l/yjj+PdOoqv0Uu0Mszwej/RhSof+eZ7ePY3z7Jyx3NIPh/rtie49lKFSqaEYzsMHRgnXirwo9seJbU/TungII2tNfR89Bzqa5oY+c4axgYzyDU1FJUmzt5TYffzHkpDFlqTxMXnzGbbi70cmN9GvJDHjvhpCYeYs2gqD/zNd6mULGavmEM4Vg0w6B4NLeBHBKrduMxiCd1QUXdmCN2SZ5/1AudRDdJQeaZaCNjaB0o16HLr4gto2r6Jt8w/lV8+u4VKNI4WDaOpCvPOirIzN0gg9kpR3qmtdfzDey5iy+yDrB8f57mHHkYxbT7yvfciyzJeqZEm/wVIkoziRGiZ2ozjZFj/VAr7uTo+8PVqAeKo5keTFdQJo9r5agja/F1MBCbRNBVVq+7zuz9+liwO4Z462tqP7KaUTRUoqR5QYfjQJI2Nkdd9pk5ZNpV8+ma6pjcd51MIIa+HJV3t7Dw4TG9/nNaOGrRmL6dOaT/mOrIsc+0tl7PpyW285b2XHnPc3JktnD+9je4ZTQRCPjpmtqJrMjOXTT3u43stuxITPLl5D5Vxk1PPdgM0LpfL5XK5XC7X/2tukOYksi8d579//Tz27hTU1yL1D2EHQcgv/wZflUCoVKbV47SUYWsJpSAjKTJ2p0rDFycwKwrxuzVi09uIOwLJskFxqp2bJiykJihuHCfSEKBStLAGPbS9c4TsYy2k68Loe0eRyhVKQQ1yJlrGJFjrIZ96CadSbWFc7eYtQJUpZAMEJhKgKighlUhkjHZ/kT5eOWQnqOJJOJR0BbkiqEhllKE4E6UcoilMsS2CFVXJLNKQhETkgMCSbWpjQQwJDDWGpNeyVMvzky+mMSeLyOE6zAYdz3Aaj0dm6pQUIb/J80mL8fEwoy/tRZGuprDdQcpKSJqDbDtYY0l+8svn+fD7zmdl/GEGSocIPNmKtKeAk7RYfOsh6ruzbPpBDbWBGM+s2sNYASqtAcwWHz7Fy2jvGObAMMLjIeRV+PGOjeztG0P0JpDRaImGeHZylOFElukzm0iMZVDqHTB1xte0URtUqDSUSU7meOhXazk0FCc9uwxFhaJqcWgiwZZRm81b9iE8MSpiN2+ZPJvGthokSSIyp410rnT4vnESSfDp+CIa2/JbD78uhT4EVi/oSw+/Nrelnbkt1WDDxafOYlgUOXN+dQrPLnsXwyTZmT1Ia6Dm8DqNrTEuvn4JLQeGeXrlblRdoWtuNQBgOGnGiysBQWvgEj783VsoJn7Mf/5LCUnWaPdUs2G6gg18Y/FNXHnD32E31jOaNgDY/9IhdqzZw76Xeln4plPZnouTDNuE27ykKxVa/uD5aGyK0qqqlMsWHVPqX/d5GhpO8v2frWTJwi56ZrW87vjfM0yL0XgG0xG89ZKFqHU6Tw8cQlNeu6PU+z93Hc4/X4OiHnvc+FCKiaEk+XSRN73nLN7/6cuID8aRlePrPPV6xkt5ikNFPJNQSpRffwWXy+VyuVwul8t1Qp2Yf9m7/iI0+UM0LW+utjmWFCRFg0QFuWC+PEJGCgYQ4SzWuTLFd9eDYUAsinJ2kBp/kfpoAalQRpQqRFUVR1Nw0lmcgWGcRBqhytgIKnvGwIFkMsLwfd1cc9pStFIOz/k63mYDb4OC3tCAZjo0t9TQEezm98VXhApmrUS++f9j777j5bjKg4//ztTte+/eXiVd9S5Zlm2594axDaYaHCD0ktDSScAQYmoCiSEBgsEGAwbcC+7dlmRJVrF6ubpXt7ftdep5/1gjI4xtyAuB+DPfv6SZs2dndmdGn330nOeRyFyRUnMEHBdtr8XsO3zG7hYY8RDhxii+FMQPl4iMSUITElS/3nZaEcQao+jNDfhzWlAtiTFmET1sERquIQyfxct7cVbPAzOEOmlzcMMKbrnlK1z54ddSPqUZq1UgPLjk/Coff+sm5JRHrSSRxRLZsSztqQSNrk3YKdNgCPB9pOsxNJKmUrV5fPoZtmcPIJZo+PEm/Fgz44faKAzrZPpNWua2MZMuMbWln/BgnrY9Wd567kr++uIvInUdLJt7t/TTmNaQuTJ+sYwRC3PmG9YRzoDIusT1EKEuBbFCwUuqzJ7fxlf+692sPWUBjuWy/qE9xKIqPRfnCV9QIaNVuXv/Lg6N5Qj1taM06XjTRZ59eOfR6ySeCKO4sv5d5Itg2fiFEoOH2lkSPp/x6gibM0/jKD2I0PkIcWyRXSkljxzoRxGC6972es54vmvRskaVxQ3Q8xJdk5bP6+K6f17F4jaT/77+SRzHQ1diJIyFJIyF7N40xQ3/+ShDYwlKRZVyyaNc+vrR12uKinTqRa4r2Xq78LUXrWbN+atYc/4KivhEk2FmdTfR25aivfHY9t2+J4nEw5Aw2bNnGLtmv+z99Myzg2zeOsgdv9j+suN+XdlyMA2N3tZGLjprKeN2mUy5wmA6+7KvE0K8bIAGYMHybt7+kXN5/9+9BoDr/+kmfvKF29i78eDvdIwvZXlzO2vPnctplyzmlJN+P9k5gUAgEAgEAoFA4LcXZNK8iiTNEN98zxt507d243o+QkrCM1X8RP0HOc2NyOFpRKOKF41hhDS8lgTqWIbIuMnJ0UNUHY2dw82Y7SnShoKCApEwVKpI28aNG6j7R6BQRugGmhNFOj4b73wWY2iS5BpBftSFuIkS0Qm5PuecuZgTl/fygf84ABKUmQzhRBV3GGIHS8RXzaGwpR89HqO/P4WnFPCsAuGkx8LL5vLMIzU8XUXRwWx0qJzSgaEZzDttFc7+KSKNLpXBAvGfFhCpBLVmgTECA34RreoRalAwkjXc+cM0tBi89a3ncs87d1EqSnxdZWBfASS4CzzyahJEGTNqoiqChqkJRnbkUdojoChgGhiFGrqmIOQCHG+KeQuWs7B3mAkPCpsi3P9dG4Hg9AtXcmFvGz8ZGGPDtsPUihUSDRFcU0PWaghFJTdVZMd/PIXcOYKiqbz1g2ex7tLV/NunnkPzYfDmTfiOhz9hIOamUCQMH57iNW85gcGD42RmSky5MzTYIRpaoihVi1gpxPfu2opZMjBKOaqZPA9e9zAXvO1UVE3l1LOW8dMbnwLAWm3iegLtqRLLRhfy4RXn853+f2O0egQkrG065UXX2c+27uT7z2zFQGFeKMl7zjmRJbPaubL3SoaroyyML/2N16f0i7RHb2BBbytNnbPQNAUhVJY2/TVP3reTXc8eoFa1efCOGfyiiy8ljj776OsLtoXp2dSqYOr1rCzD1Pn0zz4BgOf7JOIRNu8f5r7N+7hz4x7eef4LWUCFYo0x26ZULPGFS79IqjnOd3d97SVbcZfLNaQvqVRePpjz6xpjYf76jWfW/xyP8M4Tj+Ok2T2s7u74neb5TYQQrF73QvBk3WvXMLBziJ6Fv32mz8tpCUc5Z9Zc/nv3k9y0ZyvvWHXC72XeQCAQCAQCgUAg8NsJMmleRWo1m0/d+SD+Sc24doXGhgptuTyxkXpbZRkNgevhNKfwn2uACRVnXgdS12jucliil1lsFBGmxAyrFDwXmc3VAzRO/Yeqnq0hag4IgRMS9Q5ODkxKgdOSwG6MEo74iFoVfJfOqMabXnc8pqnVa9sYRv21Ax5KzQchsA6mUVyJPy9F9/J2zGi9kLBdcHhm4yChDGglh6ZZOVYuHyQc9qjMb2FXqMhUdwZrQx41F0NJxpGAWlMJTUlK+Soht0w0UkJ6ktkLDnPX1z7MZ17/BWoHsiiKhpMM0dJWb2PTma4Sa42jGCpZT3LHAzsY3lVAtLViiwT0dcO8Hra5Bb7400d4fefZdItVJJQE5YKFly0ys3cYRShohkYhXaE7uZETzhlADSn4QuMfP/Ejple1UTqtD2loKJpCeiiNRICqsnXbEPfesRXLcSFv4/vPB9iEQB2a5OF7nuJz1/2Er159G0MDM2SmCxQmipQfrbBvvY7tROloaEIBhCeRkRDqcSEWX7bqaJbGvs2HMav1a8K9OI5zcRLZqVN4epjNT+zHky6WV6PmVfl1mVKZH2/YTs12KFbLPGrt5huPPQ7Aj45s4lsHn2HbaD+FdPHoa44cnOTm655gcswhnDybZSfnyC3fRtqeBmBqLMedN66nf884V/3lecSUBH46i5/JcmBiCIChQo4PPXIHlubgT06j+/6Ljk1VFJbP6eChrQeYypUYGM8cs3/bziOUPA9ZrOFaHtPjeWqll17Ss2phJwlVZVb8lbtA/bp5Xc3M62oGIBkOceLsHoxXKFZ8+Lkj/PyrdzI9kj5m+4FdI9zy/SfJzhRf9JrOpXPoWbsII2z+zsf4UrbmD5MLT/NMft/vbc5AIBAIBAKBQCDw2wmCNK8i3/zvR9m2cwj7YI5Qg8mbPlfhnVeP4xr1H/piPI3vOoiqhjotMe4toRg6QlMJj9p4nkLYUsAIUbElaq+L1xrFC+mQTEAygqjaiIYkIhLBb4wiLQs9ohPtbcKaPYfSvl4ufFeZ1vYM4ajDZ2/8MKqqomkqZDL4k9Og+igZj2qjoOoVqdYyCMslOlPmwg9tZfGJMygGqJpHbniMWN5B+B5aq4WmezSroxiH00x3zVA9XuAYCr6nIFWFcm+UwmyDjpYI3liGM/98K2f+2VbizhHWSB/X8RkbSKPVPMypMnquxr69s0hnOpjTEuPEK0dI9bkYcQXb80Co/HKdlvAkLQ1ROue04APrx0Z5dGiIx8YH6DuhCaVawPNtGvqqeJ7D7d9/gKn+a1mxZi/dy+sZSbmpPMU2SWm+hm/XuOzyNSy94jjUxjCe57P1if3c+u3HaPdMoofz9VpAApRMDt/K4hXzTFWyHNw6RDZdwqo6tHWYvOv8HXQaORrGVD59znkoxSpyOk2sM0T0Tadz3BtPPHqdlA6PUKuWAWjcp6GtL6FO+7Q2x/Fcn6gWx1RDxLT4i66xb//wFsbdAaSocOKaJqKdDl0L65koju/huT7Xf/anfOnPrj0aqHny/p08eNuzbHliP888cwLf/1I3h+5w8WW9bXRDU4xwWCfVHGX1unmc9NYLCCVV2lcU8Zt3IKXElT4SidoQBiAc+83ZL5qq8OcXnMAbTl3Bx684/Zh9yxd3IXNVRKaC0tuF6O6kZr90n+l41GR+Ikr3ry2b+l1VbYfD42mklC877tGbnuL+6x9l833bj9n+natv5YbP/JRf/OjpF73mjhvX8/Ad2ziwa+T/6xh/yfNt5mgjdDsR3jQ7yKIJBAKBQCAQCAT+twXLnV5FDENjYS3OdMseRMWieW6N6760CK1c/zGMqiIkqJNFQodzKA0KtVNNzKEk0bYKS6I1CqqC3l+k/bQuQmf1k9WT5Ha3oFguyr4juL6DMjYJkRBKyUIkNCr9hzFW9+Br4E563LZtAbasUD21kf+47ct85F0XU8g9/0NXUcAwieUNIv0eVp+JE/FQbslSfG6Epz9fIn1Ixa+GcGwFcbiGEdKwuiOMjDYj8IlsH6JtdBL1wPFM5FysfBlVKritjVS6DJwGldH7BjAPTuLVAF9ijarMWvcx5p4ueO7wZjY+cQCkRFgO2bTKT3+0nIaFESrlg2QHBKFohpGHt4Ou48/UMxtEVzv5Yo3Y/jx/9ZHLSftVNEUhesDmtetu5sCNERa+Lcu8C4vsubmZWtbknrsWct6lcxjZvhncHKrbjJXUcW0PfV4zh8YLbB2bxkqEQdGQiTCW7bJqThvb94zjGTr4PvgSr9XAWxulYVkDscwIuekosqCRnqwRS9aoSpWS7fDpG36B51mIcgmxB75ywyfoSL7Q1tltSOCE6pkX8/Nt7LxvmO4F3fzDv11Ja2cDufF95Jw0SaPxRddYuX0CuyzxnSpL1CilmXbOXVBv/fzhBRcwVchxXeZ6PM07Wsz2tAuXE46arD1jEQMHJgjbSdjqMfDwGM0XN3Pfg19j6+MTqJpGeirPfY/vRVs+j8WX9dMUaUVKyc679nHqRJTHa4I0kJwdo+pVqExZ3PK1u1h68iJOubweVLjwhEVceMKiFx17Y2MELVvDC4fBcTBCBo3NL93ues7CDv7my28mEv/NAaFf2jU+yZ0793L58iUs6Wh90f7r7n2GbYfGeNcFazl12ZyXnOest5xKoinB2gtXHbN9+vAYNcdm9MDoi15z2dtPZujwFAuWdb/kvJbt8sEPXw8SvvWf78QwXvqxX3YH2bpjlMpIjFqPD7NecmggEAgEAoFAIBD4AwiCNK8iH/jzM3jffbeRXzof+zPP8vlPL0RNGxh6PRPEajNRRsEYTiMcicwrmJtVFN/klOMyhDQwIz5CqJgtzQj7ILLi1oMErodimMhsCXV2D970DLqn4E+X0NMFGruqFHrCeAUXFB0lL5H7J9mra2w596ts+q9mpGEgNA2/VKoXN3YkWsVFK8tf1hRm/8YwsYQHSQHdGpETVIozEdwI+FGPsifQJ0BXExQHbNifg0oNO6yipBUinoNMhjCnXISExz7fiRYG0ZxiX/mbCAXe/nd/xWP7BzGzLlKqWJYknVvFFeddwrYPfR+vtpVyrcLG2zcjUxEUWwUB/ugEtttEbqfAszzmNjcxd3kTP9n2FJtmGrGrFtWsiu8LHDtKzoGdO+czRjtWcg/aTJnjj59H/qHtlCyb6Aw4rke4P01lLA+6irL7MH4kzI4djUhXohgGuiowO1NUVXAP+MTnTHLh57dg2wo3f+50Lnn3s9xrdDJ73ihDm1qY2TCG7iq43Q2EV84hf2AKN1GkZ2G9o9LA/gm0VH0Jj+dDuDXFmkvW0tZVD8pc0HE5p3vnE9VenEHylxddhfns3Uh1KxHxDPIXp5KJ52AxmKpOc6wB4z3HEzcNoskIjx8c4IF9B/nz1x5PW3Mj2/aOUoobZI5M8eStz7Ds/Db6n9uMPR5GAIapUUxPYmerTD0Xo+WKaezKEZ6+fxeO41LI1JcnHdo5wAMTd9C+YyE7Ht1Nejx3NEjzUsKREG6k/sirxWOc97rjMUP6y74m1frSQZxfemZwmPWHh+hIxI8J0uzoH+P29bvQVJVy1SL0MsERgP4dg4wdGkeIY7cve+M6shsP0bFuIcVije/d+BRdXY284dI1rD55HqtPfvkCv08+sZcju0aRwH337eDSS9e85NiY3sebzjiOw8MaJy/pe8VzDwQCgUAgEAgEAr9fQZDmVeQbTzzGofJBKq5K3JKIZ3L4egU/UV8iotfAB6QPmQvbUSyf2EAF6SnkbY3tNY2YA8TCdM3tY9O3D5OPVWjcehBUDV8I/JiOn02jGAbS9yEeJxEL0dgY5tCyAk5EEtqcQRTLxGckuYMhbprsZOq+DNIHhEAqAn98CqcnhbEzjRnXcVUFRRGE421Yjo8xR6H29hDFu6Ng1aBqUtM0psoJFC2CMVajOqtE8cQUUnoI3aTtsTLSkyiTVUQigZfO4odiuA0NqJrG+G6VVF+Bf/nBTWTXxAmNWmgVDy0R4ZSzVDqaJ1l32lJ23rkVgNMvPp4nHt1P0VGQvo+c8vFU8EIq/vNLVypVm4c3H+LQyEq0+DDFOwWH13fgmUmSy1R6Vvdw38Ag7kmziT12iEd/9DhqtUbClRQNle0b+2n2FXBcROn5GjClCvniGHqtSigcxjMNyqaJkq0iJguU7SQh00FRBIvOGQdDJ62GWTI3zfSecXAVJs0QIhwj25/m6x/8b5KpKJ+9/W8JRUxam6LMZEoA9G8/gqMauI539DpShHJMgCZv7cf1i6RCa2gNddAab+ThYY053VVIxWmbVw9M7Juc5sDkDLsmp1AVhel0gbvu2sIuN0dLTeOk2bNJNURo7koxb2kXl71uLbrnsX1XEip5pAL92waINJvUalVWX36AaWuCOZE7uOovLyM7U+SbOwfJ1qokF4dpNtpYdUYboX/pIdl53m93k1gu0lBRMmU+/pHzEb8eEfkfeO2yRTRFI5zS90LayUguz61bd7N3cIKWhhiqqjCReXFNmV+1+b5tjPdPcmTPCM1dTUe3n3TGEkbzVRYu7mJ0PMvB/kmGRtK8/pLjuOu/7mPPxoO8+5orae1p/o3ztrYlUasOSEhEXr52jZA1TmnbzikdzYhXGBsIBAKBQCAQCAR+/4IgzavE4XyGWw9ux22xCdsaejhKrDuKkyvjl+v1RzRbYAFOyqC0phElahC5uQTpGjuKzTw62EOvM4MmBBueOIAd1/HjJsKyQQcvFQMB0vNA0XErRZzGRir7JjlwaAw9niC6Pocs1RASJOBHDSYfKdSjQwBSongSv7kRLZKAxgp2Po3eESEuDda85jge+PkzCNfg9bUh7rIXongafqgeXJJZA3HQBscnsmWcyF4Tqen4Lc0QCeHrAoRfL26saVCpIfUyLT09rFv0fu7d+B4Wa0Pss1fh2wJrdpTZjsfxq+/ELTzEYNPCo59pqq+TeTXBtif2g+8jVIHfnEAmQojnqzkZhoadrdCcSJObzAMST4YRmsappyzkyg+czda/v4HJ0TSK5UAkih+JgqogCyWqmRJjEQM0FWwHqakIRUU/lEHVQyiGgVAUnKSB6dTw54FznMuTWy5Dr+3HejTMXU8v5riPjfDwE4s448zt+At17vrSWjxTIlImHe1J2mY1YzyfNdLZGmV63yAAQlGRnmB6vHD0vCvOKFlrJ22R00AqPDf1FRTVZ0XTPxJWetmTLlO1IkStEDKuMpUtYbsuX7j/MVwpuWLVUua3NvPIz7Yw8oOthJsjPJkosad7mHe8bph/+7s0SvKzKGqC/tENjI62okRsUBW+/883cygURdMStKkRFhsWirORZcd/lGKuSs/KOSRa4nzii29lccs8/OK/smz1k4hoF7CQwaEZjgylOfnEeej6i9tZ+01RqFj44RCq+vIluao1mw2bDrNofjudHQ0vOa41HuPyFUuO2Xbt4xs5mJvhzBPm0pds5OFtB5nV9uLlY7/qz65+M8P7Rll55rHdsfY8tovJuzchzl3CgtWzueot62huiqEogl1P7WNiaJrRg+MvGaRp6Wgg0pNAOj6LVvS87DG47gSF0h5MLUQs5gDGy44PBAKBQCAQCAQCv19BkOZVYqxcJJlqJqqk6HMVtulZbMdBGC906PFrVQRQO7MNJ2WgGz7Ou3W0b1Z5Nt1JbirKnkIrbdM5lp63gslDEygzPhgGIFBqDn5PK14qijJTo3hCB9VOg/hUGpGtEM54qI6DnwTpCHx0RHMcRgsvOl6haEhNg3AIWVLwWuLYR8rUZjeRu2gRsUqehct2ceeGEBVLIZKXeIbASEvUsAJFiEUjlIWBMMMIq94pSqgKVksEzXEQKii2h0xn6W5dyKbpCQaicbYPNmCZBo1DJU5umcVzm/r52Y2zeMfH2rCaykeP8Z4bn6Zs+fi5+vHPW9PH3kQCQwgaG6JAvVBtq1CJLt3D+rtaIBrFL5VItIR4/ycvwgwZXDh7Frd8t94BSfouE1f2IFVJ6w0VpG3jxEz0aAhpuSiuhwhraCJEbGEXyuECVmcI462dXLTyeO7IPEWtArWh2RzoyuDFQiT7HIZySzgsDBaV4th7GtFsDzemkLsixKIz1/HmWScfPS+p1LtyASRjBlMTRaTjHN0/kP8JM7Vn8KXLLT80OTS6hDe/0+LfP3kLO57Yz/FfOYPzVkVZbZzE5Kwu1izroZQuUVo/iEyanPem15IIh1k/9Ai10SnCborm2R109DbSmXqcgR2HeOqR7/DOz32SVEjFLHrInvpSrElfB1/iuj6GrZFUJLjTXPuZ25jM5EHUl94ZRj3gNDHSxaEdDuXCBGf8mc3nvn0fTtEmEjFZe9zsY665YrGGEjNwQhq4L+4O9es2bj7Mj3/+DEsXdfDxD5//smOPlKbZkTvCmW1LSehh1s2uB0Nef+IyXM8nJyy62hpedo7u+R10z39xm+6bf/oMTiLGd77zCJfHdWa1NTJ3dj17adYHTuTW+x+kZO/j+yz/jfN2NCe45ht/hqaqtLcmX/YYdmRD3H5wLbrWyOfbggBNIBAIBAKBQCDwvy0I0rxKnNzRy2fPOA9TqPzH396BHw+RWxWn5ofp/NkwADISAtNg+YIRdjwoaX6fg2FI7D5JLg+Oq6JWNHxDY/OuYdh1GFPVUFqaQFEoxx3cbgOzqEHZQ5oKQghCa/uI7p5m7MkpvBVNRL0stSMuaApqRzPseKHgqaIp+K6PHzWRhoqfiiJzKureGcxklMFDE1BzqFg6P71lCe6MW681PKDRPCJxoirmuQ0sDi3Hy5bZ8/AuZDKKG9eRqgKairB9/JYEysAotS4TYRg4zRY37djJ3snFGEoJX1HwwoIDA1M4ts+zW3p5f8MneOvx/TzO3wGQSkVoMlT27jkEwLBdJjl7FrppUMxXaUjVlwRd9raT+cb3p4FBRCSMa6osOVGwdcMhHt01wKZfbAIhQEowVPxUCInEDangVFFCSdadt5z1P9uAFBBPRmh+7TrSj2yneGQKywmRTyeRB0Ic37MPW2rEdckJqUP0fnoK19eYnV3I22qLufvpU4hvtgiZForvUxuxGJ2eodpUJRyrL3sTvW24J9aXa+WPjOPNlPBqLyzVaYmcjI9F0ljK1Mw2NL+DJvV0Ntzx19iWw+M/WM/sk6/ivptzbN64lzldTaw8rpe5/VWMkEtYrT9Wzn3LOib6J9i2e5LJvRNc869Xkhlu5trvXEc6e4BzssP84xsfRtagnnclKGVqaHEfRYNnpMoZEqSoMFxO89yacZTbZ+CAxbf/6Wd8+Y6/5rYNjdz82BmsiUNo3xDTfo1wTKNv9ouzSm6/ZxuaqeNYVZraXrlj08J57cyf28pxK1+5eu7NwxvZmhnAUDQu7FzFa5Yt5PQ5s0klInzgv29l+/5RRifz/O2lZ77iXL9O62jA8jyMzgZuemwbTfEoX//QZQBsHRhDlgW7Rsdedo4ls9p/q/ea29BEJHwCS5teXAA5EAgEAoFAIBAI/OEFQZpXCUUIxkpF/vXJJwnrNn5LiNSJeao5H6k8X3fDdkAXTF8naSzM0Orl6bnEYe+uEJMre6nkTLysRNGhvGsIUbGQWPitzSiKgj07QmlJHMWWtB8p0PjgEUqLm3Ab42RKJVTLp1KJctJbV7Pv6X2kpUdsTYjyoxqKVW91rIV17KIF+RJETZRMoV4zJ19AMzSGbniSqO1x2kcWEevOMrKpnrFSTUXqKy98yE0meVbPQK6C5jvIsUmUCQV3bhf6WAbF8fFjIQiHsDpNut7gc/5rd7LhYJTpI2W8cJpENYuZViim6p17cuky48MZ5vbMJdzZRK1Q5bgzFrFlw2FoSIIiqOUKRKo1Qk1xDEPj8EyGB/cd4pzj+jiwA5KjvRh5C8VzueQ9F3DDt59gj1FDHZhE0VRQFIQWpuO+Mr70kI6CMjCF0xhi+6a9KI4Lmka8t51D+XE8t4puO+jTCu2/KLF98W6WvbdA1o2w+16YbEtx8McRSgmTrpYm1CkFCSgugEAJ+bTbKkN/+yxfTO7jUz/+GEbIwClUEc9nkgh8EBALv1BAtzVyMq2ReubNxz7SxBee/Qnfz95OtDGKO10gPLeNPcNFonMMBq0wk83Q0dfGO//zOFxzGLQaoBOOhWib3YI9XsBNmlRtl20jEXZ6CwktkFiWjWN7KKpEEZKmtiiTkxaG79KYqmA3CSYchRZD4cq/PIf0wH2MVdIQjTA8XkJKSdOcVtqH2zj5jJUsm9POBSctZvW8LppSLw7CDI9l0DSFpo4GVi178bIf13H5xX8/TKwxypJ1C6h6PpPTBR58dA8nHj+HUOilM0vOaltKSNFZ0VAP6Hz7rg1s7x/jzaeupCee4HAoTU/y5bNYfhMpJTOzDDxfkmvVSIRMFnS/EICqPDlO02CNeM+L26X/qvFMgUQkRPRlzgGg0QzzmZPO+Z2PMxAIBAKBQCAQCPx+vHxRhj+wJ554gte+9rV0dnYihOD2228/Zr+UkquvvprOzk7C4TBnnnkmu3fvPmaMZVn8xV/8Bc3NzUSjUS699FJGRkaOGZPNZrnqqqtIJpMkk0muuuoqcrncH/js/vfdsn0XMwdylLM5ZHWcjtg4C/tKuIt7AfB0AVWX6oSESpXxBw02/UuKwrCO3RAnMmgivwIlpwABAABJREFU7BANZy1E+dUfuZNT+OkskcNVjKyHOeMhLBsxmUGZytJ/nMHw23qwm0KYz42x+cuP0iE0ej4+F8eMUDlnwdHlNbZXDwpoB0fRNu2DkWm08TJKY4xISwJLaMhYjPbQBs49+RD1DAuJb6oIp56Q4rXpWGGBj4MTMxE1G6ViYQxNoQodEYmg1FwolAkN1Lh01Qoev6GR9X89hrxH5eyFJT505l5CnToyrCMVAIl8vhiwPZNH1mxu/dJt6EKiJGKIkIkaCZE8fg4NK7oIR00eOzjA7c/t5SePPovRUGbiDIPsUh2RKfLjzzzNulMXEHdU/MYEIhKBUKheb6YoMYuAJhCuj2d5eKc21D9r12Vs/ygzCSis7cBPRfGbI1D0GCmaPP7DPnZ+ro1QwmTX/QvYfss8Dt7Qw9PpJSxenaSvMkF4pIxieSQuLaJ7KoODaXK5ytGvUy/XCB0aByCZSpDqaOSyD11wdP9dP97At665k1rVJpFQWdl7L0uiv2DWqg5iqRgTeZefrt/DvaMDmBGDeDKClJI93k0ctO5kqrINgE2/2Mbme7bQgMfcxZ0YhoaUElMzaY62MqdlDudfsZrenjJRpUhHezNazERrbYAFSXSzSE4KjlQrFPQcn17+BuY3zibWoLHgijgD5YO84fSVfPEDl3L56StpScb4yGWncMrS2b/x/rj84tVceO4ymhqjlCs1cvnKMfuP7Bnhp1+5ne/87Y186c+u5aYv3EapZLFzzwjfueGJl733jkv18eGFF9IZqdedydsWM8USP7xpA91+mO/+5Rt46+mrXnaO30QIga4oqDa4VZd8uUb/ePro/jWvS9F9eoZ1p9deco4DI9N85ob7+fJPH/2d3z8QCAQCgUAgEAj87/qjBmnK5TIrV67kG9/4xm/c/+Uvf5l/+7d/4xvf+AabN2+mvb2d8847j2LxhS4pH/vYx7jtttu46aabeOqppyiVSlxyySV43gvdaq688kq2b9/Offfdx3333cf27du56qqr/uDn97+tNRQlmnEwR2soB10Kt7rk7omiztRrqkjHrhf+fb5gqgRcGcKPGIh4DK0Gqm2yF5dCZxgpBAigVIVcASVuITyJVvGRvgemgdBVRKaGOlnFb1IQCsiaw+En+tH/ZQDlqSxh+1c66NSc+pIkKcFxEAKE5eIXqoweHEdGDKTvsmtnC/c8sYBfNudWhay3wVYEcStDyJ1gVjSKEo0hoyFIRKFUBk0BVYFCEWEamNM1tvzdCM/ekUB6YJQdLjt9H2eeMcHKi7JYisBqihCLh+noSXFg1wjS88F18aTHaz40i0gygqxUwbKZ3jVC7kiaPXvHOG/RPN6yZjmnz+rlxPtbmO1OEenPQbHIvs0HeezOrZzeuIH3f/QgkVAZXBc5PY2fL+BPTCHDZr3YsSY4551no8SjIAQRU6XhsEViTwk1VyWcqRHuSJBKKlg/c+k+roJnzbBIjSCFCqhcunwJHV06oQUeMqzhNIfpGHOZ2zlJ12uO521ffDvG81kUZ111Gk3z2wBINsfpWzGLtlktALiOxw3//iD3/mwTTz+4C12RLA7nWBAusHhNG3OW9jD7+LmEFZ3yWInKw5McuPsAg4dGuXN3jKcHUxTsTgBOuXwtJ752LYWpCqOP7ydsalx+4Sq++cUr+dZn3kY+U2bH5oPkMh6u73NwOEc5rGHVLNqSURaYHhlfxZFw7d77aVIiMJah42yHSWear//9bVRLNWa1NaIor9ylacmiTma1NTA9muW554Z52+Vf5R8u/jy+X88q0g0NTdcwDA2hCFpSMd73ztPp7UkR/R06HWUqVQ7ZOUSLTkTTiMVC9LY2/o86Sfm+pKGqEUl7RAsQCxvM72o5ut/q2Iu+uEbFPPSScxiailNxwX7lOjwAzx4c4Ys/eYRDozO/8/EGAoFAIBAIBAKB/z9/1OVOF110ERdddNFv3Cel5Otf/zqf+tSneP3rXw/ADTfcQFtbGz/+8Y95//vfTz6f57rrruOHP/wh5557LgA33ngjPT09PPTQQ1xwwQXs3buX++67j40bN3LiiScC8N///d+sW7eO/fv3s3Dhwt/4/v8XzWtq4okVCdrbYhS/M0LxAZ3w0gQiexgAtezgux52bwPmWAk/pmFfEMF4tIo2XsTpS+EhcR2LWrdO2NRQ3HpARigCfySPe0I7tUaFxN0lmN2NZho03jWItziJe2oU0QnKg1ncksfYnhyiXSESqWFrPtKlHhkyJXgKiqIRSkSxswUEYLencLtbEbkSftZh550xPM1C8QWVDo9Sl05kVOJsa0Df1c9Ek41Sqi+jctobUQYtRKWGEo3gKyrgQMhEEQI8iVTAU6BfV8nkojy7vxNUBa1kIyIh9j83zPf+9T5ELAL5EvFek6eH7mL5eQvY/EMNR1WJrZskr8IXv3k7X/rHt/GOE49jajzLZL6G/1CYlpV5wm/WSI70MfREjvf8+QjxWJXZs1rYvTkGmoYwDaT0MfQwSjxCeKLCti8/x6zVfRzZuBczFiJypIIzOg1C0NLZyF98/o3cu389j/54L36HBgMwMpLDSZgIX/LG5Uv5+cF+dk9FEcJFEwpZ32DO2j0MaMexcHn30etkumJjPx8wqJgRLrzydCbyVWpk6OxIUivX8DzJ+FCG9ISFfvBEVq7Iccv6SUpFl79dcyLuvCT/dNUNVDzJ+MAM0UQIFZW01UxDKAJAa28LFUNFe72Prk0xM5mha1Y7ZrPC/sooq5pn8Zb3nMemTTpP3jGJ9CWaKvBUnwM78oilnbx9xSS7rBT7hmtUFtmMDWeQBZeaDCO9HN9P3M3Wm59k+alL+OR1Hzx6jtu3DnL40CSXv2EtilIPSk6MZ/n2NXdh2x52XEOZybP10CilXJlEKk7Poi7efc2VRBIRlqxbgBkxUFWVxQs7CL/CMqGSU2NXfpgVDb2YmkoiZJKKhPmHt5/Brqkp/ub2+3jXScexuP0313rxpc93Dj5MxbP40ILzCan19xMC+ha0stMapXNWiiNWiSMTmaOv2/JNj5mBVhreWoaVv/nYoppOvADFcoFyxXrFgNODOw5w76GDdLQmmNf1mztGBQKBQCAQCAQCgT+MP9maNAMDA0xMTHD++S90VTFNkzPOOIP169fz/ve/n2effRbHcY4Z09nZybJly1i/fj0XXHABGzZsIJlMHg3QAJx00kkkk0nWr1//kkEay7KwLOvo3wuFejaK4zg4v9IJ50/JwdEp+sIp3nrxMu65f5SsqpOO6GgL6p1zjLCBlwyj5aqIqIGSUPHbVNSlEZZt30fncSmeLPYhVYXG/hIhAegKtLYgNA3fydH2nEW1S0OPmTg9CQxdo7w2htWiY07WSD0xDYYGKRWt0USuDONmXTRNh3rdWqQEGgRqPIUaixCKK4hYGKe3GU3oSCNESJnk3W/p51//cym+KgiFVTwJDTM+RqmKahpQqBBqbcQyGlESETS1EyVdQGamUTQdETORvs9Vn3sT1376Foo1CHUneCSziMqtcdz9BkZSYBoKjc0xEqkI8YYQrT2NzCiSQjzKs7enkNM2WkczanMDp5y2ifyowni+hqLD/Yf3MfHAYapTOQzHJ77ExuyEhY1NDN53iB9eO4umZR6HDpvoTSpCU1EaokjHwPNtVEVBrbikM0WqQmIkwkRiJn5nA0VKeG6Eye44X3znt6mtTmKgs1FZRDxaJbrfIhTyMTRBNG5iJn7EyngDu8RsRMkityvBen0RGaYZSmdY1FbPwDj3zEVM7jgIQCZf41uf+Tkd5y2noTXJP3zyYiJxE9tyaetp4AfXPsTk6Bze+J7TyHAXZbXKd/ofJ10O4QuPcMEilgqhKhpdTgotDBEiR++RiZhLbrodL1JEmvV75+u776bi2nxwwfksO6mPG+5/BhExOOPMpZz12lV84QvX41FFb4LvDi5HaBB14aabv05sUQvqQouZp1RQYbh/nEK2yM6n9xxzX/7d3/0I6cPYyBTv/4sLAcgWykhDQVOhc+k0+jZJ6wUrCMXMo69dd9nxR+fwfR/f9zF0Bc9z+ZXkvBe5dfAZHpvcxWu6juPS7uP54mvPQwCqorB9eITBmTR7xiaZ1/Sb23BXXJsdA/1U+ysMNaxiTnPb0X3nnb6IvGLTObeBM7rmM7s99cK5hgUoJjqVl3wumaZCe2uMkKmjCPkbx/1ym+M4PDM6RF5UWT88yJXOS0R+Aq96v3pNBAIQXBOBFwuuicCv+r9wPfwpH1sg8KuE/GUhjj8yIQS33XYbl19+OQDr16/nlFNOYXR0lM7OzqPj3ve+93HkyBHuv/9+fvzjH/Oud73rmGAKwPnnn8+cOXP49re/zTXXXMP111/PgQMHjhmzYMEC3vWud/H3f//3v/F4rr76aj772c++aPuPf/xjIpHI/+fZBgKBQCAQCAQCgUDgf0ulUuHKK68kn8+TSCT+2Ifze1UoFEgmk3xt6z8Tjof+2IdDtVjj48f906vys/7f8CebSfNLv17HQUr5irUdfn3Mbxr/SvP8/d//PZ/4xCeO/r1QKNDT08P555//J3mhbZwY5j93bKQv0cin1pzBNVf+O4N7RpjpakU1BO/78Eq+93ePYI+lEfE4ZPPA82V52xv50Mf3sfaUDD/8r27Wb13G/IVd7L59PRJgdheaqlENeVTnJ/B06NhjkW+P4EQE+YUKdrtCZARS21yMbQehUEUqULxoMaFoCP325xA+qPEIUhX4MwWkAtmLe6AjSuOdI6SSEXKpCsqBChR9PAfknE4iPRqRVRbTDzmIyRzmaR3kpkzs7iRKi4faXkTdZ+FkG4g/l0Y1wkirij81A0Kw7G2nc/D+7cxaNZsBXWe0oYYQCqGsJBkzmYpaxJotVp9UZm9+LsoHn8Yv2fXPxwT9iiXIjWVkrYY/PsF7r5ukY6nLePQTPDF+L9GfOpQKGoNpA3urh9rYgKapGAZkJSjlMspEBkXW8Nt1lCs7KG0PE9mUgXwOmhoQCxtYdUof6t4SB0emObwsQeyxAeI2aMkGamWHxpVR0vf3g+2h9HSCAsL3UcKSvjNWsCd9mEICGp4F3xBMntFA45Yc0Z0TLFwxm7++7v00NSd46I6tfOufbuLPv3gm3//o/Zz7urUMvi7B1uI+FjW2oT0+QqlvAs9dy4LxFmbSJd71tlNpCBvM5ApYXT5L4inece6/49UESszgC9++km88+U3GrTihvZIf/MenAfjurY9w3dbthCckt/7bBznobOP20euJKD5/PffPeGZ7O/tz25g3q4Hw1Gzu+OHTxObXiF/+OLaf5EONz+IBn957HEs6xjiUace+fy6VGQ93zOSK951HpdPk9HmzaYnVO4ENDU/zgY/+CJCEqw633Ps3VKs2Tz1ziH//zweRwuPky59l5rEejFof//zf78Iw9F+/pX5rUkrytkXSMP9HdWegvtzpytP/Ebus0jcvyVd/9lcAFCsWH/3P23Bcn6994DJy2TL/dd1j6JrK5//pdfzD537A/g2Hueh9p/CBN1zwsu9RKlmEwzqq+uJSZI7j8OCDD3Leeefxky07uWnLc7z71OO5YvWy/9H5BP7v+9VrQtf/5/dH4NUjuCYCvy64JgK/6v/C9fDLlRGBwJ+6P9kgTXt7OwATExN0dHQc3T41NUVbW9vRMbZtk81maWxsPGbMySeffHTM5OTki+afnp4+Os9vYpompvni2g26rv9JPnhO7Z5DQzhCdbzGz25/ln07j1AcyaJJDV+t/3C0pUHhjDa8hjChDRra8AzCdnFaU2TcGFNDGQpTQNVhRhE41edTAtNFqiENfcYnvHsUL2KgHzeL6bNd3IqJ54Ot1Ls2qQ0q+uxmolsG8IUg/MgROntSjFU9cFxQJbE2j6ztI5F02xZirspUTMceL5AcdrEqHnatXmuGg2Ok589hNB+jdHIMc7yZngO78WUrfs1BeySLSHqU5nZQ6wkT2uzh4yFrHp4noFxl8LE9KI6DU64g8w4tj09QXN3O2kvX0Dpiccf2vRiqxZmze4lNz2LBtXO44crv4PuSSk8Uf12NS0qbePynKQD++6fraO2L8o6/7MDydUoZj8KAT21EwwfcYgU/EaFalmQXNZBYn4aaR9MbfBoX+aSftFAMn9pUBmk7QBltMM6OPVvw0kVkMoJs0rEbE5ywoJfdi20mh6eJN3h4D4NUIiguSAH+xAyoMN02hjs1g7Ooh3yrR2iyRg0fR1exLI9dWwf4yFu/zRe+/S6y0yXcSv27dQtlvN4od/Ufxox4NIQbmbv0brymNJq1lNe95hym0yXm97Wy/qE9FLJlVrSliNX+gauvyfCPf7uED3zyYqSls6xxlJjbSK0zjq7rSCm58fBO0r0+KcUhHDJo0VvxhaTsezy4tcT9dz3DzMAAW6jyjg9cyXv+6jXs06/B1wfIuCn2eT41Kdj9YJI9K5uoCJOF2TSxdoPKkMWuYoYHHznCrmcO8rkPXVFvFe+AU3ORUhKveKiqyuuv+i8cyyUeMSkVbB7/5iIaUJgzVyEcDh2tWwNQq9g8eNuzdPe1sHrdvFe8927r382P9m3nbfNWsOdrmzHCOp/817cdM+dvw7RUyiUbCs7RZ4xh+AzPFPF8yWi2yJKeNpYv6aG9NUk4ZOIdOoQyPIpSGXrZ59KWHUf46jcfYPnSLj710Ytfcpyu62ydmGDSqrJxeIy3nLD6dzqHwKvPn+q/eYE/nuCaCPy64JoI/Ko/5evhT/W4fp8OFlow/fAf+zCwSlUA1q5di6qqfPjDH+bDH/7wH/mo/u/4o3Z3ejlz5syhvb2dBx988Og227Z5/PHHjwZg1qxZg67rx4wZHx9n165dR8esW7eOfD7Ppk2bjo555plnyOfzR8e8GgghWN7czlOPH+Dn920hIyWe72OPziCm6oVGpaxRm9eM05LAWd2M35QAQ6D6gh/dPI+rnzmXJ/d1URmcYGb93hcmn0ojDQ2mM1C1ETUbZAFhSlB96vk4oOgKvqFQW5CC9iSKokLVYnKiCKYBApySS74cpbQiTub8Nmp+lsb50zSeFiGCgmPLFwI0AKaGo+n4hy0QAqkLnGGX8N5J4s+NomermLkYyYMOWtmn1hZBui5SV+H5ZjYpYz8f/Kd9nHDVVoqVGppQ+Oxbz+ff3nc55ekyqqOSHUnSw2V89qTzOW/dKuJN9Rbkockya70hek54vsWxIiimosS0VhZHFmFu6GLFnx8g2Vesd8MyDPxKFXtsAlkoo1Zc/BXthE4xyd4T4/A1OtkZHVZYSOf586zUW0Fr0TB6KoE0I8QPlmiclaSxr4vJX0yhH/JJP2mjmAYilcCJ6PiWDa4Htk+7lKioaLpD4/wSQnFo2Oej5wRyVjtGTydIqNUcjJBOorden+ak16/DP7UTV4KTiXJJdCXK4CpqGztYEzqRxoYoC+a2YVsut//gKX72/Se49t9/ziNPV0mEJkFKDmwfpmd2E3vuMSjsCLHrqXoA6Imfb8C8fh+JDdPQ51FRbebFF7O0otJdLJPfsYGzT1vO3L44YSOFXRU88+hu+p+wEMInqVpURZxKTiF+V5HqEQGjFsN35kjua8Q/UqNtxia8L8PknTuZPDJdv1zHcii+RHF91i5uplyxcCwPNVulNpxDLdqoVZcKcPlVp7womHJg1wgP3vYst//g6WO2bz8yysd+eC0/27jxmO225yElbLxxK089up9HfrGLPdsG2Tc5zbWPb+CerXv5zj0bmc6XXvYedko1/GzumOtfVRQ6UgmaEhG6WxqIhA3e+47Tee1F9VoxxhmzKS1PYc/reKlpAdg1MM50vsTuwxMvOw7gg6edxOUrlvDBU054xbGBQCAQCAQCgcBL2bx5M3v27AkCNL+jP2omTalU4tChF1rHDgwMsH37dlKpFL29vXzsYx/jmmuuYf78+cyfP59rrrmGSCTClVdeCUAymeTd7343n/zkJ2lqaiKVSvFXf/VXLF++/Gi3p8WLF3PhhRfy3ve+l29/+9tAva7NJZdc8qrq7PRLF567jCcP7cTKllA0hUhnM7Ze/xEqChbGcBYZ0tC3l1DSBbROgapXmHVimdWnznBHaS7lWyTMFJDALxdvuO0R1AkdperjL4uTmywS93y0vTrVcBi3Q6XBSjLZ5qOPVzFDCpaigC/xPB836aE2JpFmHDFHJb9Uw43oZDdJjB1hom4nyR6FocMTv/K+knCXJBsOE3nsMNpoEfNwjqaTNcYeV9Gak9AYRVogQwbGjEdleQo5UMMppokP1CO4viOINbhk9kF5YRNdZ81np2Ox8+v3snztXB4ZHgNV4T3//ANku8p173wjhXwNYRioZZ991xmMxlbgx8p4IQj3uLz5ipNob06S/ckMP62sxthdQZ1O19vxREyouciEih9WCTVUUIZ1KBdBSsShLL4WrQeuala945Nf46w3DzM8GWf3EwmU6TzVB/PcsTmL0AXVTo2wr9O2ag6TI3m8eAil5oBVX5a1v5DBTSZpm5lk1uvSbKr1ISbAiWmEiw4nn72IaDJM1NR49O4dWM/HAZafvpi5RjvO8hNYEmll3ZK5HL/yHxgenOa2B3cxkNnG5ZesxgzpXPr2k9m+/VbOveQ+irUklbLAaTLZO55h4vAkuza1IDub0cbS9ckVQZMRxyoJmiYTtJgJKpbDj++cg9R6WFxRuPajJ/D6S9YwPZHnwM5hfvqtxwi3xOg6XaeqKCyPfZCP/93T5CIKka9NIg0VpaziVhSsqk1fRzOfbDkNx3Jo7a13Ijr1tIVcsLaPvRsOcuJrjsfzJS42QvUxCi4SsDrjCM9j+XG9R+8dx3a55ftPoiiCsy9dTc/cF1pd78t8i59v3cI71+7iSP4B4K6j+944fznrOnr5l299rx6okxLF1Hj84AD37TlAowzhZGxmtTVywfEv/cyxZ7LIQoX00AuZf2FT59sfewOelDTGXvy/ItWeFrKn1pisvfyjPNUUQ5gK0cZXXqO8rKONZa956SzDQCAQCAQCgUAg8IfzRw3SbNmyhbPOOuvo339ZA+Yd73gH119/PX/zN39DtVrlQx/6ENlslhNPPJEHHniAeDx+9DVf+9rX0DSNN73pTVSrVc455xyuv/56VFU9OuZHP/oRf/mXf3m0C9Sll17KN77xjf+ls/zf1daRpPT4OBRspCIodqTQwvWvWTQkiDwwgVKsHh2vzvjMvjjLKatHWd2VZayjgYcXzkHZsBt4vmYNoFZciJrg+6gZAWqEeckS42MmldeaaE0uykyVUDqEyHvYM5V6npblglWDZvAbJdqIj15Wad01RcU3IRdhcmML4RRUJnJUbAvBL4M0AhSBGrconNlG6bhGEk+G+ZtrHuX2D85nKBPlrKtO44FH9yBrLr6pIHUFu8kgsrf0yxkYONTApz/cieoVWX7FdraGTmDsB08RtRVOPm0h8cNlfLXMxNkRqik4aOdAUfhltEhuLVJsSeF3piBscoIyjPR+BHyBbFSgJ1XO/OgRBm5uYOTpOG5vE1a7ilyVRN9nY+9R8acNvEQItephvSVJar+OJXJIRUFYNr6is/mRZnpPmsJsmoObnkLEk0hVwctmMYouxQvaGD1NRdxZQ2ZqyJB6NIhWsDzcJWHilSJ73xkhrk/jLIkgIwYXvX0dhzcPUjs0xfmXr+G1bzmRxx9+DoAtt23giRtq+OfMI7VAhSVghAwOj+e49+k9hDbs5zUXrEDXVU67YDlDnT9hH1HQdHY+OJ9cY5ihYpG2Wc3QGqM6V8Fd2MoXH3yc9ikbGWkgmpa4T1o8t3WQmaEZKocBHRad14OUksPF7+GGyxSLaxFCICU88V/LaDjfYqG+nJG2EUhBbFJCNApxwYTtURUKY3IK5dQ0p7ecf/SeVzWV5avnMLh3gtHRPGt1lWpnGK3gUGuLImouUvrIgyP83YWf51tbv4IQgpnJAlue2I9QBJ/+xp8RT74QFBnM/wQv0c4NH00xMaxx8gNpWrqbAFCEoDfeQENM54jn41sW2zf1c/HrVhMzDXoicbZuHWJZz7GBjz394+w/Ms1lZy5DURQc3QClRq7iHzMuEX3pwMpFXUvo35/FH335WjjjY1ncskt24uWzeQAypQp3793LRYsX0hKLveL4QCAQCAQCgUAg8PvzRw3SnHnmmbxccykhBFdffTVXX331S44JhUJce+21XHvttS85JpVKceONN/7/HOr/Cddt3MIPD22jdLxJYgc43THUagVVrf/YlIkwyv4qflsjXlsjselJ/uwDE9i1aR64JsHMWxp4ZlcnSjqPsBx+9ZsxZ6rY7Sm8JguvOUHSddDMAnq4DGYDUoWSWyM2qGMVvXpGQbVWD3TYFZS+ZsQDGaRfwKom0QoVkqJCdU0zaslh1oIWCmvnkNEKRHfOEBl3QFHJT7nYfhinsYbwVfyoQKkJShnBhZetZOvucaQrER7oBQs1Y+FFQ+giBNSLg0VntVOtWMhime4FGXZO1PBiKmpVZfniTp64eQuiZBPbH8LIeZz23tl8FxfP8hC6htBNVDMMnoIdC3NobzNvurC+pEfNwvLUCB0ry5SNECNbWvAbY1Raqpg9BpEJl2pFw4/ECO+r0nO7Sf4Wj+KkBpEQwrIhHMIbmWTysM3kJp15JziUzl/OyPZRRNTAL/iIqktsoIa7RqXvgg4O1dL4kwqhfSpIMMZqtJzv4OyuoCQSWNLH2DOMjIaZ97cXMvjsEXzPRwBbHtrBrgd3cNJF57H1oZ0oIZ3J83Qmw8/SNTbDfOtkRnIFKnGBI32+eu39fPDdZ9KQjHDLnna6Onr48+b9KEs97hpcTE1IJo/MMO9dw+gxn/XPLuPuh3dyptGEbbkIRyJrPof3jOFUbVIHciRasqw5fQWerDBd3YBEMjM9CzNqYlcUDj8XpX12hNVn5ommgRoUVrfSqOuc2dGKvrSXqckCxmqH/ko/Ryr9dEVeyIo55fxl6KbGirV9mJqKnVJBV5EhHRnSEEMziJk8ZeOFYG57dyNveM8ZmKZ+TIAGIKTNY3gGph7zwYNN927jNe8995gxi0+ax7Yn9iHCIZ7JTXNlYwNvW7uKXzywky1PHEKr+nzkfeccHf/hr95M1fWoVm2ufM3x9K1dwMF9EygcG6R5Ocu62ukzm1jY0fKy40INIWSDRrQr+opzXn3fQzyS7uep8SN859LX/9bHEggEAoFAIBAIBP7//ckWDg787h7Ye4Axp0BkaRKvNUz+7F4MQ6flziPAakQ6Dyr4yShEQkQXdnPEj7D51hzZQw75n+j42hDqdAg/GsKNaxgzVYSigm6iDk4gyjW8YonQWSlsW0NXKqjDNbyGEH5B4Fo+qtSg4gCinspiRlHHGpBeGqTELxZRXYnUFBRdRUuXGXhsHye95Xj27t+L3R0nsn8ICXiLugiPS4yij1v1CO+s8Jl/vpjQzBCPPrSLUkcj+BI7oRI+kAVNodinY81OoY9PoQBatUz3JUuZsgbZk2kntNGirTnBpZ88nczBHLUmA9fU0TMOoQK87eyvoNr1gIZ0XGQkRChq4k2mUWplInNMNKMBKSWzVnYzNG2RGUswNNyKSQ4hFJRwlPxMCWXXBH5rE34qhBuS9H/Zw1Tj+GmBeL6gFr4PngeuCxKGdo9Qmy6hmmGE5UN7K2JoDG1Yxf+5SfLsLrymKqGcA6pa/5xdD+/UBuKhQayDYbQD/fUgWzrLF//jHk6KN1Mp1cilizz9xGFUrV44TQK+5TKnM0JHi+BQaS93/WeedKmCqDr4ZZfRsSzjk3kakhF4vMLoSQkKjkn/wQRSCGJSYd7qOeQfaSJ3OILpSPyZCsmOJJe/9wzuePg5kqbBua9djSU9vss91JoMfvLI0xw/53Xc9M0Tmdg2Q1t0FKvm4Pk+JBS6Vw2il37KP72um8//4CyszhjFsMnrPnQxffPaEULQX7wTmZlhdjhK1XF4/OAASzta6Wls4LzL1xy9N6KtJoxNw+wuKFfRUQg1xFh78eqjHZlKuTKPfPcBUh0NrDyp75haNduyFS5bMsL1WiueVFDUF2euXPymE7nhhkexG0wOtR84un1geIbJmSLDY7mj26SUmJ6C7XskwvUC5W699jZqtXZ0nOv5/NvNjzPoTXDlWas5tWPRMe9pOvDm+fM44YyXXka198A4v7h3B5YBmvbKZchWtLezMTvMitb2VxwbCAQCgUAgEAgEfr+CIM2rSclDqJLEQR0xt4+E4hBXpgip9QIkwnLrNVMsG2VgnImVce5/uhtPGoguh5FFHajTRUKmjpBNWLNi+CUHfcbBDwnUYRvhSdSSRbfWxqEvjWHMh8R6QXFUoPa7+H4GRREIX+J3tCETEbBqKLk8JONYpxo4Z8YI31rFHBa44RBeroK6c5jDmRINwsYYL9ePF1BbGhG2j7AEnlDxG2MUF8bxy02I4SJuCOyUgaiCZnl4hg6mTmVuhIYN9SBEdniG40/rZM/PKriWIDk5TqNmcM7SPv72a9chFIXSgiiJPRX0XA3fdpB+PY9IhEMo0ShC92AmiwoM72zm79Z3svTIA1zwjtP50rdyZI7ECM146GYVUbawGnQ8IfEikuiGQWrLG1BsG2dYZ/J1MRItNslDKlgW2A767C7m9jYweGQGLxRGEYBVD8K09FQwYxZTeQP2DrG7UsDUdLTnxkFTEPEEIhLG3uzRLk0mXQcB+LEwMhFBDhbwFqcIRQzSM0V8AUo4Ur9momFaVnbwvbd/lEOlvfzrlXczdGgvCEHHohRmKs7K5T3Mnd2CY7s4dw9R+16Oz3esxmo1YSG4qkBRFCYH4lgpHWouJ/lRzjhjGTtGc+RqLjUf4g0RKuUcRkqCEiKulyiUa0wesbBKPq0LG5gYyhBLhJh3dop3zt+MQNLUWsVOCjxToeQ6fOwD3+O4hd00NSe44KOTdJgVPH+SJw4N8p2nN7O8q43PXvxClouUkisXLuWO8nb8bBEB2DEdfcJj88O7cR0PTVfJzxTJTmQppIu4jodh1gMau2cm+Pn2uSxuixCOFaiVFBJNLyy5/KW7vv8ERsVFETaL5o4e3T40NoNfsrArtaPHUy3V+NQHLuS5/jHOPrkeYFm4qpcje0bQIiGklAghsB2XfTPj7O3dx4Hdh/h+4kP0RJuOzn3zdY9zYOcIqqpw6vm/uV322HiOcr6CWrLxwtYrPkbec+oJvOfUoGhwIBAIBAKBQCDwxxAEaV4lirZFR0eCifIkZsXBVhQuWr2V1t4c22cWACCzOURTEtnVgjgwgrJxFGGNozoubnuyvhykIYJ+uIaQEO8v4TRFUcp59KKCN6sLR3Hwm6L4OYFA4hQ1qqYJh8HYNYpUobq6E+l6kE7jdTeAalK5uEz4xirVXoNa2ERrddD2FtFMWc8gsF3S/VOEVQWRTCDdCqg26pExRDSG0xOm2q7hhptQLYnVncRPekgcrB6D+J4yTrGAFwZBI05UHi1ALMJhnvrq4+jNcfRyFCWdw466bB55gnkL2ziytYAyUwJb4sZDkHNQFYH0JUpTCoSgOpR9oaDxviyGFmG6VEbvjBN3CsgdKsLxcMIaU2cI9IkiqYxKpJhAPylK9RKF2o4UpWQDxDWUWhWaG+tZNIZOrTvJwVQCpTnJOWvn8syz+8nevQPheXQsEwzs9hFGBU2oOFMOopIF2wXpI3UNISWRhwsMJjtharCehTS7DRmPoKZL5EsVivkq1UoNJ2Yi1OeX1PguGZFnuJLm5k9v48jBNG7MRDge/hmtDOXLPLp5L26nTf7+aayJDNL18KpFkq4gvCdHx/wuHNshg00tptG9vci/3f0pAPZf/ziqBF2ClC4tzuf51NytFC2VB3au5atfu4+uvjbOeeup7Hz0ALbl0N7Tye5Gg688fA5XX7qEa36+GTukYsd9hIRyzWL7ziFWL+ulw3gDUX81XbHjob3Miq52Tps7+5h7I5Mt8/iWg4i2JkQkhNQVkmfl8R5uoqOj72iQpnt+B+//13cQTUQwzBdaNCqeQnq4iaf3JumZ2QTSJz2WObp/JJfnsYMDRDoT6EgaWy3e2nolP9qyg9csXcjCjiYOeweZk6oHdu797sM8dOMTHFrXTc5zmDu7hQvXLuJ17zqDgYPTxJKRo3NHQgafeN2Z/M2eITRNJaQc2zpy7ekLEUIwb0nnSz4bTlgzh8kniujP6ex1J19yXCAQCAQCgUAgEPjjC4I0rxLPTAyzSxuDAw6256IMjjN8v0ZtTTNWpV6rw+9qxm1qgFwJpVzF9yVCSqSUSKeCreZIFmoosv4jUbF9tHwV1ZEIBYSiQUzFyHuMF6cw5swnq4Pa7ZJqyVGyE9gVgZ6o1/mQtoOYyoKpgBSIoTLF9i4sPYqWLBFGQTmQRZ3KwPOZK0pzEyIRR8bj+EeGUYYyKGQwpyLYKQM9byOkAoUCUtfQczZqWCM2NIVfs/EjOlrawm0VSA2EC0o4hHMgjWKWEafM5cyu1Wwf2sn6qa2c+/aLefKmp0kdEtiLunFiKqpUiEZD1GoVZKEImga2/cKHbXk0F6qsXRxm4NE0a87J8sztCrW0xOnSkZ4LrktsexEjlKB3ficHHuhH22ITmTOFX6igjqh4hoFi22DbSOFQrTrgS7btHWFGB9XzkI7LoaeikGwAT0NWQZECPxbGN3SEYaDkKvilMuWsiT4vTlVaaIAykcW3HEptESZzFZx8hTtv34pRtNDU5zOFfHAnLG750qOsf2A3jq6gDE+DplJ6YpizrljDLuMAz3CAhnEPGY2iSIlwPZrPGWd6Uw+qkPRvHyS6PYcbV5FNIX64aRtvOm45vbNStEQUOpMJfvzzZzhrVY0lKY/DMx7Hr1jHli01DFXlnHOWU5sss33DIZKrZzjp+M38YqyPp8cWMJ7egxIBadS7hVWbNWxdpV9UedeHrydZ0/jMNbPpW9jB1RfXa75MTRd46LG9HH/cbObObmHOkma2rB9Fzzloiz1iRolyT5xP/MtbCEWMo1/tvFVzjrmvpvMlwppGLGzg5jzUFCQvVpjSxhgfztDSnuS+PQe5dcduzgi3sXjtXJq7nuHLtz/MZGU2SdNk4axWNiYitD6ffVPMlfB8nwFRxjNh18gEF65dxOwF7bznHy4h1Rw/ugQLoKe5gdmJFlQhiBvHFhE+6ewlnHT2kpd9NmzaPoDeVaFUS6K3vHImzZ7xKZ46fIRLly+iPfHijKFAIBAIBAKBQCDwhxMEaV4l1rR20TwQYTJbRClLKFbovztE//YOuhfVi4WK6RzqoXH8VIhaZwTFdVHLKko5j70qjnV8iKLmoRcEGh7JS4pY20xqRR3PdfCsItqRIjIWJh+N42+axo+YtJ3oklhkUQ13ksm1sGLhAazNDqUhDTE4ScsshYmH28FTONrUW9Tr1Yiag1KzQFFQGxLQGAdfgKbivzAaPV0hddNOlDYVGk2MCfAVFWflPC64cAvyuBLPfreVYtFDHOwnNaLhhQVKUSIti1nr5rJrtoUjVbbvGWGidxbpHxn48wYQsVi9i5NTxT5PATReN3k29/18C87AdP0AwubRY5HAxp0TbB7I8s13nU9k09tpvmSSLY33k71Nw/i5iiZdtKZGKINf8jG32nj5CkrNR2nVEa4PxQLCq2e0mP0ziL4QNVeSLs/QtLKFTNhAcRyqnkrHbI/soI6dLyLtKt7KeegFC6VQA1VFDZn4jkOXEaFcri9vU2bylOc1InoaKQxV8ZoMJjsckkdAdetno7S0YkYi3L7tIHpMRUvP1FuDS0l1ME3/rc+yckEjfe2LuX/jzWBZiKYmpAGhI+O0XxTj7af+ObOX9aJ4IB3BTHeUnz2zg77GOP9Z2sr0RVF6xxI88Mg+opG3cuLqp5ndXqV11ibOOfVvuPfAQW7cuoO5HQm6Z7dw9slP09A4RMmLcXKvjxvTEAiMIkgEetXHiev0l0qEJorkgS98+AY+95130T6nlduf3sVTj+9nYjBLsVhjwdw23nPRCWzZtg97xIRCkfwv4oiKQa3y0kGL0Zk8n//xQ6iagsRHbQ4RPy1M4mKH3Yf62faB73Hqxas4602rsV2Xs+bM5u6hncQSJZycSq1cZPuuYdzhMkdKVTbtGuYtwMXvPZfeRV0MDu5lqlTmrFXzABifyPHdmzZgmhpfuPoKNFU5er0ZioYqFF6mzvpLGiuWWNYxwvaSSU/b9CuOv2fPfh4/OEAqGuYNq37zEqpAIBAIBAKBQCDwhxEEaV4lGkNhkjurlAccFMuhaoSQogq6xkzm+RovlosT15m6ajF+RCd5/yDmaBqpqPQZk4zvk4itNXTZhnYqxE6ooVTB6jdhJotSLoMEUSuDboKQaBGP3KMqZZopHYghO4GIwnk/ynDr6W14czqYFBL5zATS9ok/kEVdpRHdpeFpoGUz9S5QjUlEqgH5K41tfr00q9A0Ql0NhPQSTjmOaG+krAocW6d1roUZqVGoeGBqSEVHrT3/i7ZcoawI3K4YMgdidivNrVHsvEtXTws7LBukREmG8BWBgsKFHzmPO0dyiIKL0DW8TPaF1uCKILRvBnd+J83zkpy0ZhWf/MznyD6oY+8vYdZi9KyOcMHFe/nxDYvo7msh79YYKWcRfRYN1QRurd5titoUAEqxSthy8CoevqHyN1eezcc3HMQ86GP1NZEedwlrMygRlXLIQLFclJqHQCBVFdHYAMVivdAwgKogNZ2IH0LbOIY0DAqnNGNOSTInp4g+MQOAl8shTEltXiuekEQyJmokDEi0dJFMepptjx7gjW9exQOA8Fx8u0rT6gp9l1bYtjuMpimEIiaKFiE+rCHyMxQbbT7VdAM5BVwtzslnrsNJO5x80iL2pucTEYcZGVrEBWvC3H+wH8+XLFq2EvI5fn7rXKbVFsqLLZzud/P2k+dz4/qV6BVAgl+sYuYqdC7vobpUo7RlhpFsjp984wE+cM2bufWRHUzunaElFuWs0xdx48CTTFXzKCmTcs0hNGbgT/skdI+xQ+PMWdhxzHWWHs/ys6/cQddxs1GFIGRoxFodpOuSvt6nNJwk+VSN6rP72BUSvPXD5/KhliYGDkxwaKeHRwtrr9zDw4eX89yhcYpH8jBdZmjnGAC3fu0etj2yi7XvWAPze1jwfGcmiWTvwXF8JEeGZpg7pxWABiPC51a+CQVBWDM4PDhNNGLS1pr4rZ4Nvakk0+u7OW3NXs7uueIVx1+6bDEtsSin/9qysUAgEAgEAoFAIPCH98qtPgL/ZyRaYtR0BbvmIRJxMAzs1ii11hfa7npRFXwfY3YFztSRmoBoDDkhads4hj5k4Rsq5UMmU882Ud4ikY6Lr9Q7D8HzgYrxDGJWI60fsCmuijDTHIYQREckh57o48EdZyFNo97uuzWF6gkUwGuIIlwV6Vl4vS348RAYBng+bq6AcB3wfWSxhNLdCnN7wNAQQtD09gR+1cTNRfDKGYp7htEyZZ74+XLu+dJqZmYa0TUTvbkFzQHpPn+8sSjpfXlaD6e5/LLHOPt0+OY/vJm/vepM3v1np6F0piCVJDc/Qq4Uxn/GIH2kTEn3EZ6HcDwUr95yG0VBSGjYN05vcoiNj/XzjhP/kV03TGONhvCK9dorVklh4EAD0hX0j2doD2uIBTH0tQZuxkFRFZREDGJR/ESIcz5xAXbNQ3F8RMnGt10qS1rIXr6U0soWfN+gmNWp2ZJoWwQ/l0dmcvWaNkLUa9DYLmODkwhAaW5GbW1CDE/B2BSMTZLcWya+P0/0iF3PVgIolaj0jxOuSMJDJaQePhodU10XoQuSPTaH8t+llCtihnSqx3eSXjwXpbORihvmxh+tByApFBTpowkd1Vd52/yNfHLBo/Q9mefOn2zlrNMW0docp4Wzqc300RSeQ8TQ+cCpJ/BnJ6zixs/cyXOH02zeYHOov5niZBRkjeNmjeIrEqXogi0xKj6hqRoXrVzAkmKCcL5+VVY9QSRk8JZzVtPYGGXx4k46ehM8ObWPfYVRzl28mDYtgWr5+DMZclMFvv6xH+D7x7a8PrxjkF1P7WXXvTv48nsv4aNvOhVh1rCHPXJ9LewzZzPcHqalo4FVpy44+rreua284apzKZ/vs7c1zklr5/HWs1ZzQmcbqi+Z1ZsCINXRgKIp7KgV2DU+yUA6C0AoZJCL+xQSksee3Hvsva2HiekhhkcyXPvth/nqtffjON5v9VxYd1wfjd0r2Tp2Nra/+Oh2KSWD5UNM1caPGb+wrZl3nngcrfHYbzV/IBAIBAKBQCAQ+P0JMmleRXYkKlQ6dRJpqK+LkFRTKl7zC8VGtYJDZOcw6qwoSisk3jaHmf1lPKtEZtlc/F4FpWKg5iym9jQSm/LQFYls0hGFY5eGeANFOlaWGVofRjZIxLSKMZhHH52m8K5m0q+dR6wUQi3UlzOBR6ScRbQoaLUcykgeL2rWd+UKoCr4hsSfLtWDDycsxA2ZNL/JJRSxyd0bQ21QCLlZpvd7oFg42OTnx7H3RFAXhVAyGkrNwZgpUP8E6m20Fd/HGCjRqRWphAa48/tPMTKzhcGZLqxUGFl1OWv2OBesO8DN04sZHp4invMRkcjzS7M0CCv4jTHEZBav4LOk7RDf//LD6DkfFB1XmqjxHHrEJp02eeyhHqx8lkppL4NtccJrJYnFRaSjUftODIkCszpxeg0ar1hI4207GO8fgmSC9c8N0LBlhtqsBoRr4XkuQphgF6n252DJLPzpUcKRBmxNq3dz0hSKlgOKqNe5Can4IQ3F9xDJBFqp3p1LODbSfqHN87xTlzA845Ip2xi6iazVwPWgU9QziDSDRFMDVnkc1/bwJ3IkVkS5oHGCgUgPC3rrrZrf9lcreGD3Q4xmFtPbPouKa/LQ59uwFIMRf4z+gXFamuPMa3mOvsYcRqJ+Pf2y0O/6uW0cqNrojsvqE5KcfsFKmlMn85+PPoMUkoatMxA2ECUPNRLlwXt2ktZBdsQ554S5vPMj5zM2nuPJzfsZSVXpbHWJG2HeP/9cap7NSSfMZ8+sIX4w8wAjCNKTBTzfw/f8Y9ptrzxzKW/6q8uYtbSHSMhg9/A4kzsN1ILAaG5BK2k4ZZNQXyu2ET76OlVVOO/S45nY9AHu37+HK885j6Wd7ayZ20ViTiNvvaTeMemS95/PBe88iwf29nMkk2Nxez2TJh4Lodk+PhztLvbr4vEQ8XiI5lTst2qnDVAs1mge0mhqb+O83nlHt0/Wxrhj9CY0ofLnvR/7reYKBAKBQCAQCAQCf1hBJs2ryMVnLWHW+V0sXt4GrguGgqYbaLUXfvBpZQ8t61MqheBpm5k5NbwzDKotvVjRMFOLklQWxrBmheGcKqLDRGg6akl90fojv1RitpwmfFeWyD+NoRysYu4fRx8vYz5exp4Vx0qpCB2E6yHa2xB9EfQ2F9mlI4VElGuIqg26irWwBb3XgvZWlI523L0jKIUq0rdQEzaereC0mIROM1D0+sHYbRGUYhX9wDjKvhkyy2LYdhnfUepLswAsC29snKoVYf2dy3n4niaeqh2h7zUbaVn1MLGGIlJVmBtK062WmR3P8r2P/xA2HMSfSSPTGfA8/M5m3Flt+B31jIjN18RBSqQCaBq+ClJpxZ2O4BWqgIJu+ehlGzGSoyOepuSZVJco4NZvPqkJ5nQ0cn7rXMb3DNeDI5Uat9z8NFrFITFYIj5SQU7MIJ168MRvSULExGuI4lpuvcisAEJmvdNTNIyIRrBkFb3iI5oaSbQ0UmnWKcwLUeuJIkrl579EmB7JYA1Nkz0xBb6HPzWNPz1DTfPxp2oURlWO7/4crl3P3Ag9N8bkZocffqmV/aKJzt56S+gFvYuY+XoN/Y5+Ll/Sx+I2QeU5n4Ur+mntnKKzux4s3G+rHHB0ZrxjY8QnHtdL0lQ4/aKljM5v4KbDedZPrmRA6cCNCxTHR53KQ6heM2cw4VHNl7CyBbywwp9/9Hr+/rO3sGvrCLLi0SpNAI5LzeGk5vn88/fu4ps/fZKPfvENdJ02l9KSFgqeS/+OI0ePwXM9VE3ljDedzOylPQDctX4X0lFwQ4LY7izRI2BOWrR1p5i7+NiuSrbtcu78BUz3W/zjD+7Hcl2+fP3D/HzDbr76g0eOjnN9n7sf382z24YYGK93itrw4G7iJUGiqjJrVj1wk82Xj5m/IRnhs39/GX/x/nOOKS78cqZmilQmqmgHHWK6eXR7Qm+gUW+iN9KHKtTfaq5AIBAIBAKBQCDwhxVk0ryKLHAaqORLKJEyu02fzs9rOMkRxu6b/cIgRcFpNnFrGl5ZIXJXFiWkkzabmZ4tUaoWlSzEwj6q5+HFPHSpoITj0BPDn07Xa8ioCoaicWjTLJw1c2B4Gv3QOMLxQIL7XJF4SwUhVMwj+fqSkskpjCfaUbM6MpOifFqUVMZGHpjClw4pf5qIX8ORDSBByxZgWCfzcDOlokt4RRVljsRcaaPGdPyqIDRmIWQYIRVqi1qJbxxCH84dPV0JhKMemiZxGgvsM3sxKoJIXzN2bAXdLa14WwYJCYf7butl31gbB56MIJwpsL16RpJpgudDqYoI64hCBQDfgnC1gtakEU/mmdqugxGBSAxp1TNVREuKqz5yDt/e/SB9a/axJDKB4bo8xHEgJVrVY3FjG9T8evYQQLWKmrGRNQvfdhCJGH4kghaLgRRo4SiyXEZM53EsF2NOL74Pigdki4h4DBEOES66+LUaVGs4roNsnYPVYqIVXPxfic8Wx9LYNRcnlUQK8XwWlk/snIWwZxvCe/7QdAXp+KiexNw4xPZ4mMoKhfZkCNdx+eGXf46neOj5Mqvnd7N5+3xqI6NM3VpFUQv0dtQ7J22ZambEKfH2eL2myrPbj5DLVxjpn8DxJLmpCiuaeohqsDjyFKuSkzw70oCv+GjRCNKxkIpGoVWh+ckRhJTcfd82rHmNZNJpLlqziOPnd3Dua1cjpeShoUM8cvAAG57djxyFv7n6ZobVGrUOA6PSRCJVXw6YSZf45tW3Y5gaH/+XK4624e6MxTGwETWP0O4jhLYOcsJFK/mna99+zP235cn9/PQ7j7PwgvlUaw41y6VSc4gpGqojiVAPhNz+jXt56rZNFM6YQ9qycNx68Gt0cJqEpmC2xVm4oINPfv5mNm8Z4LjjZvHejy7A9i1WNZzwWwdnfqng2YxrFpH2BFXLIfz8eTm+TcUrYXjmK8wQCAQCgUAgEAgE/rcEQZpXkW/f+ARj8SrtikQUZlB0BUPVCBVKAGjdSZSEScuRDOGBMrrnEe6MYG13cE6rIqoqoUMVGnfZOD1tiFFBrVnH6HdBFchKDWdBF/qhMXA86ExxJCvA8VFsD1G2QFfxEia+5xPafAQ9nEBmC0gJAokyVULZHcbSPBRX4Fsq9opeQnOTuA9tpDiu4ufrBVbRdeR0BqUjRWZ1CqKSxbP6KRyMQ8pE8SSyMUo466IlmrA0HVmxUVqbQUqcwjRKyWP2oiqjh03mhAvk0pCsarz35BNY3vlaNt20ETW3F0dalBZ28tzOBoTwCKkqamMSLzuFzOYAsNtb0SbTKIV6BdtQwmfO0iyLrxjmyEMaxR0JUCOUx30I1VslaxGTntWzUbMhKqqJ9F2y1RggkcUy0veJRU3a25P1QFA0ggiFMCvu0f1SU1GSMfxoGNVxUW0fOT6NLFWRvsTQJdWKi4hGENEwOE69rXrIhFIZNI2mnmayzi+LCguI1tusI6Clp5nhwTQND2eRoTDC0Am1hZjKTBCvebiVKpvu3caC0xYxsKkfLRbCilSIXqDhSJuRHSMcDJnQdoST/qNAZXgpPQs6ueFGDzyf9O4XHjNj/RPc+87DOIkaeyrfx8oIYit6ic9p47XnLaV1skikKcFxnZOE5XrCYogPLKvxwWfPwivlUXwDrBqivY3oEQsao4iqg5cKgSrwTZVlx9/BQH+a3Rs/T8/aPq7dtoGByQxm0iVyRGF0KIPdawKCZecsoqOvnVy+wr/86z0Mjs6wqCmB6/oYz8cuahGBrvm43R6Fy1qJZySfuukTL7r/8pkynufR4Kh8/IrT6U4laYyFOffUxaSnilx85lIA0mNZauUao0oFKyaZLJZYClTGZ5h2q4Q8nf7pDAf6J5GO5NDwCPeO7kFVBV3hWbSG2n+n50KuXGM6VGTGyfDEvrlcsLLesrvmV3GlS9HJIfkftI0KBAKBQCAQCAQCv3fBcqdXkVVnzcGep5JeWiTVWiTztSqT/64hh/IA6L0KIgX2yiRuMoIrfbRLe1CaU7TuzbPkwQN0PjKMenAaNTuJP1fiLQEnqeLncvj5ArJao7awA2fZLFiboOOsMUR2BK9aQOoqvqkhEXiNEUrLmynHPfxcAXi+7rCmIosFxPgMlTk6k+sSSFdg7C/Q3KcRjnoQDUMygfQ8hKqhZIq4cQ07qtG/t5vag600dDSh6DooCn7MRMZMokNViMfqdWgUQemCJbg9JkcOhKhWNAb3J/BHwuSaFL7z9CZu27KLzFQe//lsGXeOBBQIaxiNUXzXZvYZKa74RAfyvMVUV3VQPmlWfX4hqJg6e++oMaFG0C8yaVrhk5wjQdOQUYkXlngrbe6vbiB8t8fUjeDcUyFWAl+Cn84g0xkWLGpleP9o/UusWYiwiVA1WlZ2IKWPpwn8mQxuwsCRDng26txW1I42WDCLmieQCISuobQ0Q83Cn5xGmZ5GVCpQq9LY1Yw5UyW1OY0+46BG6kEkWpsZ3jkIhSLRdAXd8dA6W/E7kjh9YdIXt4P0yWfKfPnWv+XNP38fhfcvwCqq5B/QcH9aZnA0zexlPSw5cZqeWBaxfYap4RlmH/caZGsUv8OEcP39HNuhoVvDzqikRz3KtmRm2xFOPmku+x98jpmHdqJMjrP/yC1k7fVsrpbYuX0+tUZJZW4Mf2oG4UqkL2kcrhJSY7TO6iGs1Isnh+Mqoms7bScfYSa3n1QozOvnL+X4pi4qrSpqoUIkVyM8aROacVFy9Ywnz/MRimD2wg4+/JnLiERfyC4Zjebwu21odam8dg7ah1YTjr1Qi8b1fQ7PZFi5bh4LlnWzs5Lh5j17sEU9KPbAPVsZ2zTIU4/tAaCpoxHX8WgJm3SkEsztrC8Xe+ahnSh7J7HHyhzZP0nM1LHjClkhGNgVY46xnMkJnyOTmd/puXDucfNZdHIcc65DKV46ur0t1Mmbe97FW3rfgyJe/p+CQ3tG+eeP3kixVPmd3jsQCAQCgUAgEAj8boJMmleRv3/TuRh3a4zu20bnO6rsuQGqh2z055MmnIEssl3g7dSQvQnCs1sI3VqhKiW+FqbPmGCPlUR6HupQBWV9BKVqYGRtfNcDy0Y7OAqGhj+3Cz8dovivNvq+IhJwkyZuewLzcBoZVnB7QxhCQ0Yz9SwbAM9F2hLh+2A5oEjsBOQLFvGKR7mgQrkKVOvjYxGkFFS7fES7TWMqAzWXwpb6D1s1U4GQjshXUEwd1fORpXoURM03UFm3FO2+fQjfpqJHsXp0/IrAc33uv2UzfQWdaF8XTtHBOGzjNUq0CYE3lgHXp1yeS5pOXCNT76Jk6vimhhpTYNrG6WvisEiihXzS8QT2ZmCWIP2eKGpV8C+nbyQS2sSOhSuwHncZ1UxOO20Mt2UZyqgKjuQrn7mJJl0BXQXHQ2bzEI/SObeLaWcAdXgC35f4jovUgNYa8bkGhbH60iTflUjHAV/iux4eoCkK0WiIouOBL5kZmkHNWSAg7PjoyXqQQYmEjnbtYiYPTQ2AwJ9RaJIWM6qCHo2wYE0f0USElgWt5B+sktA0bDWMPa+FaGeKcDTEqr738MidP2die4Qbv/Uo6YUTVD/SiowpNN6rUilbHAltYMdFPcTDNcTHp5GlEn4yhj/LZNNnnqOWKbJ9TxrlmhWc8LoQZ1+SYujZbmIT0whPQ+vuwNUUvIiKq6sYVY/Fp8/HuftpypEWTjh+IQPZNhxXslB2IoTgnUvXcGJnF+96dA90eISrEQq+REjB+HQ9gNiUivEPn3wNmqaQiL8QgNly/3b23bsBtzuBHvE4f8lizutbcMySo1u27+K2HXs4kUYOPztINSrgnFaU58csnd3O+I4xFs2qt9Q2IybhaAhjRjIxUeRA/ySzmhpJxAzUg0WKfoVvP/4MkZLAjQp8CWKyj1RxFX/5gzvQVIXbPvNOYhGTw/vGSE8VWXv6wpd8Llg1h5bBBA2dYc7oWnzMvpbns3Icx3nZZ8tH3vANfF3l6XWf576d17zs2EAgEAgEAoFAIPA/FwRpXkVCmk50DAp74+zfuYxch45cFcJU6/9L7o6DfdhGDfuYI0XOeOMZZPqnyEyOcsW7Blm1dopbfmDw1OOd+LUa4ZtG8JoTOCULpVj/H3QBSNtFZArIiQy1bhNJPWNAqblUlrehV22iEirDNpFxUCJReD5I42sqblMYdbBM6uaDCNeHhhjl47so7LOxqhpKQxxME6VSpdpkEDJCGNOS0KRAP0HiJzQqERWjItCyFWS1gtA0/HIVX3frNXMUBa3gIqwSIhRBSTSiOi6EJcz4NGgGw6NljhSg4Q2LGNg2QPK5IurmKWRDEltRwFBRajMc2FkgUYliqRGUwUmE60G23t9bcVysoopT00iP6YSnxvHDBsVIM0ajS14xMFyf3MIEDfNMWpNljoS66gGVuV2IQomOOUXkVJ75bziegz/ZjixXELEIh/Y+3xrZlyhAOFNClj28/SXiDWVyExZ4PmLubGS1hp8vHP2ORHMjmfkG6r4QiiOYHplBGiayWEKkEojmeuROFOuFaSXUg1BevdqyKsCaMjBmbNq6G1Ger5dz4awF/GgsTiYJseOKTPaP8MxDGu997zmY3vHsv3k9EyMZsndvRbYbiF0lvBMS+GUHTVMYyk5TLYfwbZUGMYPS1ISLy/fu3ci5561k65P7cVxwJhzcg6cweVcX4wefJDqWZub4OKFd09gdjfidTVgpk4ivMunWmFIk5WbBhrER+mZ14/seyq69cNk6ADqiCZqUBLkzq9h7w+hb0+BLRjyN6ZE0Da1JbvjGg0QjJu/5+AVHuz09dOOT5NM1VL0RbIOT18zi3N55uI7Hvv/H3n+HW3rW9f74676fuvpae+0+e3qvySSZJJOENAiGFqoKKCjiAQT1oKB4xKPYQOGoWICIUgWlh56EJKT3TO9tz8zudfXy1Pv+/bGGGaIJih7PV/Nbr+va18ys9Tz3euq953mvz+f93j/O8tX9ZBwHKQSrt46wLpFnxfoBBtf1UzzXUvbG/3EDL3vlDgq9nUjrF735Rq586SW85gP/SC0I2H1kgpsu38CSnUt5xGsQDWQILc1SJ0mr3mA4ZfKGDav52BcexBtrYPY7GIbA9wJ++YbfJ/IjfuNTb+eml1/6jPPCw0+cYs+dExhBTLgtgKUX3ptoncExEuRlz4+cW2JLIqOY0Pjx/HC6dOnSpUuXLl26dOny49EVaZ5jvPgFW3jwMw9jVAxs3aZ0eZr4XCzTDwomZDvCaUccGyvhuw5BtsVczsLpD2kuxES9OcxDJUSsEGGELNfPjx/bApFKYoYgpM2iKuDdYGONldFxSPrJCcyyT6gFqQcmMPv7EK5LLAQIgbd9GdgW2pCIeoBRbUHdI7NvhvyLoBbnsRdjhNboVhvrbAO9rofcKbCzCjFpEdRimFpAaRNVKqEzSYSbgFYTK5TIZSMIITAW6jhjc2BaaNvHmG2Te0ISLhli//Qs6ZUpLl2+mm/Pn8TrtUgnJIbroqMQo68fXa4QqzL25UlyUUTlAYUx00AqDY4EX+FIj0xsoAwDyxBEaRsjNknviYiu0PzWoasZeVwR1E2mL1+Gc+UcUWMAs9xGWYJwdS/bX7/IzrVTvO91R1GZJCQdhBHTt36I5vRCJ/EplSAWBsK2EU2Dyuwg0h5HuYlOvHmsIIzAMDpC2uwsqiYwUwWEIUgOFqmdnEBYJrpUo210RJe40ehEdp+LfNaRhmYNt8/mtC6Q3JLC2BfQijR/9Y6/Z+zIBFarTVFUyI+H9F7hM/l1uPOrT3LX13cxP7FI9ZI+5NY81q4S5t11/HKSRn+WMIy5etklFLidfKJKyzJQzSYil+aWocd5/vNv4OieBIulBm6vZuNLd7Pvc0ex7BTq4iXIuAZ+jM6k8QaTaKGRuQSlqQrCdNAS5oMI7hmhOD7JyLUXkpcShkVjn8SrWjRHqyxelsSdC8meqZNIuxw6OME9T55CCsFr3ngNPX0ZAF7/3lfywAe/gLNyARmYLNOKY0+eZO/eCb72yQfZdsUq3vvhn+HaNStJOzY8g06y7+A437x9H694yXa2be5cmz2DBa69aDVPHhvnxc/bjNYxrfn9RKuHMNuSWMEbf3In37p9H6sCj0//9j9yCjAHipieAiGozlcJ5ysAfP3v73lWkWbLxmH6Mgnap0t85A+/ye9/9I0YpsG8P8ttE59HCsmblv3q+eVn6w1OzC1y+YoRbKNjdmwEneulGwLVpUuXLl26dOnSpct/Ll1PmucYq1b2YWuFNAwyc9B7d4Xik+daGWzr/HIaiBZreM0AXZNUCbjvmMtkNYWs1MA20ZaBrHpPG7952RDVK3sgDHF7PMylHnEuQTiUxVuew1poQcNHGgoZqY5/i+ejtUYlbIQJwhSQ0lgzFZRtgISc0kSZDPaGAs5mOilSWqNdC5XoaInJTTV6rljEHK4ixmfQUzOooSLxigHitAWFHPGyXpASDZihAAUEAbpWhyhCNQMsTPzJNsFEjTNHJpAPzJM/0iY2NO2NReLeNDqOoNGkeSqiuKxJfdHFG3Bo7hhBCdFJY9IQVyxm/qbIzF/0ECQzNG5cj0wnMZuaoGxhLygaGQOnrGgfd9lzeoRBeQkIgVAQm5KDJ3q5e+91xGWI1y0hXj6AjmLmd2ZBxZBOQRTT7A1pr0zAtpVU0ynIpcAw0X6Abp1rDwtDDNtACxOj3YnzJo6xbIno60EODSAKOfRcGQCRySBzHUECIRBCoJstzFjR1zJIJZK0hEWj3OTgQ0c4vusUi6cX6b/M5uo3BUzP9ePXA5as6KWelLQvXUOwtZ+BzYOMDKfBMtC9Dku2L2JYbfYc+ieMwzatR7MdQ+Zmi7SY47qfeIJoyYdoLJYJHIEaTDM330s2tQRhGlATmDKLTNoYjotTiQmzCvoMrviJ9ciz81jzbaSWNLwUMpHjge8fO3/dVoImZk5hJDTNzQ6RHWPvm6DQ0oSRYtWaAdauH+LSHavIF9Pn11u2YYTcijxDw2UGB+b41Hv+kb97z+dYnKkSRTH6nLiVduxnvSePnZhlbHyR4ydnn/a6tCSFfJJSo0WsYOMrYwo9PmFeYmuDl958ER/585/lyhu2kMgnwXJAgReGICBVSKEKaVTGZXTi2X1qhgby/PmHXs/2S5azav0g8lxlXcpIk7FyDLojGOKCXv+Rhx7l/Y/dzjePHDj/2vn6Gaf7K6NLly5dunTp0qVLl/9Muv/jfo7xyc89jLmkp+NR0myTnFQkJgIApG1jjAwjkwmEFLQOjZGZm6e9eYjvP7iDR/6qn5mZHoS00LaBvyZL2G+jzAstDoljC0hXQ6tFe6KJ21oksWuc5J5JUkfnaa5MERVd/LV5vK1L0K12R3ABSCcQIx5qWw2jXkZEMWa5hXJsDEtS/So4tZjQLyKyGURvD6adJM65xI6g4mWpLWRwr5DE25KdCpNao9NKVaqhLYlOuKjZOdTYBFHOIu5xOiKHY0MyiS7FJB45SfrJcXTG59DYJHY5xinFVLeniHMOcV8SSmVEPoev01y3WlPfZyErbczZGqgLWTiq0SKaUcTzCqQkPdZCuC6FAwG9e9v0jjcQ19cIrQCzHdHXqlP65BQ/sDTJnG6Q2Ct44fIRRKQRpTqi7SPaEY2HpzpqWqOJ9gOS957FufcEWVORXmmTf/VFHcELDbaFHB5EZNIoJLKQJ17V3xGs/JBqPcRIp0FKhOMgfrADbR+VTiOWjSCkRDWaaMMgMAT1Vpvo4Cz1coN7vvQYYRCRyCQYuWo9TzQv4i8OXsHCqIGSmsuet57gUvBeDPmFkF/7iWtp3D+BXW2z3TrO1esPcuDsNM35flYVymwoTEGsIZvBzQtWmDH9RkyrGaErLaplzcZ8g+tvuBuVjaisz6JME+oBcnQGqxoSLgmZcxsM7ihi1nx6vzaK046QdsxNN5/EL0+fv24HEnne9fzr2VkcJjOlcebbWPMeldEZnrz9KTIZl9e+ZDvLTcmhh4+cX6+y2OBossro4yOcnV/Cph0bWLl1Ga9/+4285/+8ll/5/Vd2DqMXoPUzJyRdd806tm9bxrbNIwCMHZ3kzs/ex/hsmYVai2ImSaBivuhdyaLOEFuwaqSI1powjJjNOlRe4JK4AoK8wOuVKKUQgErZqJSDK9WzzgmVRpsHjpxm3QvWs+n69ef9dJJmip9f+cu8eukbnmYc7PYEtPOLHApHz78WqYiQsBNL36VLly5dunTp0qVLl/80uu1OzyFue2A/t39rF+HZCpgSVa9Te95SrEwnqcbIJAgNC21Z0GrTbLRILDZJn5wlXJJnlJVUbhnBrihST4QYizW8dTnMyQvpTFF/EnfaQEcKIvAPxRiZFtF1CYw9LVInKsS9KeTBRZxwHsxO9Y4ARCpL4glNuBSMZg4SVTTgXTTM7GwNs6KxApMo5yA9H+E66ChEK42yIJx3mT1WIEhkkP40Km9ihiH2bBWtBcwsYoU5MAxIJpB2Eyvto8MBRI8JQmA2yxDHJBM2wU0pgq9EGEmN4cckZ0K0ZSFDiIYKGLMVRLXB1z6yBKsckjm+SNyqd/bl3DGPRvo6+zs5T5jqwylpMASGr3GdGvqRCmLOJl45Qu+Ix4aBCU7MuwhlYqBRbY9LNx/j1t9dRMUK88wsZNJIw6SvpJj5Z+dYALXxowz+cpr5j/UhUyl02++0OGmNNk207xEtltGRAYFERzGq2UJkMqhaA2FfuO1lfy9GOyI+OwaGgW574Hk0j7XI9DYwJkK8OZPd8wsMFFyiMKbiR4g0yA0hVG1M3REfJlfOEeuARlnymS88TBy6aE8xcwcMRFv5u8lDzM8NEZ9WiHwLkUwgTIOGV2RmscjpPWvRiyWMTJqiXmDaP8JFl+R59f+8kYe/vB+VsEBpzNkytAKcn12KeSbk8e8cQKuYxmVDkNZkbI94SpLtu9DupLXmS9/fw5mpMpmpmKWLmnYoQSn+4lc/y9brtnLrH3+Lhekyu+58nD+/831YtsWf/f5tqOU15jZbrEj18LZf/TkAaqU6s0fG6C0k2HVgjL/+8vfo35Dh6r71vOall5FMXkiH2n9wgl17z6K1Zt2aAW77y+9w6IkTNG/eyJJilmzKxTIkjVMVVEthAG3P58tff4r7Hz3OjD1Pfscow+s15bszZGwbyzRpNJqIegutNOneC9U//5xvP3yQv7v1flSsSCUdPv+xN1MsdJZvRU0s+fQqoJ/ZfDmcCbhuYNOF60QLDG0QBT/aYLhLly5dunTp0qVLly7/MboizXOIT33hYeoqwtUKlXBQ+TTRQAZpdb4lD9MuyjaJEkms/TXIpUmkE8iJEtaJObydq8AQKFtgKZvWUAqdzyKYBxRCgDvbhNYPfZtumAQv6yHamsQopEl+YRJrtgWxQmuICThvYzE7j5FdRjweI8IYZRnobJrk0TmiLUswdAs72UCuEUS7TVSzRZTpVP1YDYURSIL5FH4fNF5pY1QChj57Ft0MEaaJbnvn23UwTYz5CAKFEDEi4aKVQuZ7ibyQMHC5Tm3h27nDWJM+theTOaGIzZAw66CKGQgizFKN/uIKFhNz+I4kavyzagnbRBuS2DFBgJ+zMHyFbMWIxyTNNT1gg9UX0e/WWDM8zWwuwZzox3SSyFrMXY8PETQFyHMJWLU6Sgo2Xnklc8dnUS3vvCikASUGKH3WwowcogSEi+XOO/MLKNck2DSEcbaEK1NQtNHVZkfAabU76VrtC+dPK4VWcccnqO0jsxnQETKp8V+Yw5wMSH62AUKy4xVXMnZoHL1ukNn7d+H+UwWu7YfHQyZOz1NczDIZLSAnbfw1EeG6PvRTU3hzcO1Nr6C9tpeJuSp3DYxiKY/m5zwII3xP8Le/+0p23rCFRPrrBMkErVmDU0+OIK8YYWjDZoj3dlrnAGGZmH1ZLh0aZ/6rRU54p9Cre2hc2g+uSSuIOZFZzYtfd/X5/ZzzqhxfqBJKzRU3r2GwJvnex+eIpCCq1lE6ZnaqQqzg5E3qwj1TbdDzd2fxb+nhA7/x+vPj7b5rP9/62J2M7j/LdCLFQnGW8t11TocLuMIgXpWgJ5nkJzauZcvGJVx2yQquvGwVAFe/8goSGZfo4hXce+QMh8/Ocmx+nvKchxYgYk1PZDM7V2Nhvs7KbUuxjJDjRxYRVSiaNs2Gj4kgYVv4rYCh1IVEqn+ObGtoBsQpA58I+5xIt+DP8cWxT5Czevip4TedX355uo/f3vLKp49xroXQwqJLly5dunTp0qVLly7/eXTbnZ5DbNu2jNzyPJmt/fg9FirtILwYEXZaIUIzovFa8N7ooIYd8pv6qLVDjKEBrGWD9KxVZA63ye+uISOJ6Vukj7VRK/LQa8NSA1ENYfqHjIQXYuzHApzdisSuENGp6UCdU2a08UOiRrNFz+AsV732CJltLfxXp4lHfIzFBvn9U8hKk/aBJo2nmjRfvxyenyVeNoCstIldgeoPia73iIc18ZBDXHSJ050qAN320HGMOuc9Q7MFk210A1SlRjw+SSB8alvyxIUUZiliz+I8rT5Ba6mLFqBNiWz4WEcnkNNl5Dnflpve8Hx6hUc8V8H4Z90e8tQUxuEzyMU67mwbK4rwr1UE68HvT1O/YoDqtmEaaYfDx5fy5H0rODzTT32phe+GhAWH2VMuVbdAOJwDrTutWdKgOTbPxp/bcMEPhE4lTf5qTX5rSHp5P+2RNLEKO+sYEn8gjbcmjzXQD7k0eGHHGNiy0GEEiQS4zoU7Xyl0rMALOkKNUGit0JbAGAdjLIleMoBOOhw5OENFGxxtL2LUSjhBgLQVxJpH7j7M4h6JvaefVNVFPjmFOrt4frsXpkq8/KotvP3lV9ETLafWyiPSSeRAHzKbZWLfGLPTFexcCsuyIDbY881e/umLZ/mLr3wDAkV295nOsQgj4kqTtG2Rf14LU8esHFmGe7ZB+lSAeyBm9JMLfOgttxKeq/zoc7OsXpolIRSZxhTb3/xdLnp5Baqda/noU6eJdSfZauGpNJNzCwC89heuJVFuM3jnFOO7ZoiUYmK+wvLLVnHta67kxtddw9VXrKF4KknOkvQ4Nk7a5hv7j/DXDz7Knz31IG7B5u1vvoFLLloOwGUvvIi3fPCNTNTqTJdq7D05yQc++F3UibjTMiUFP3HDVtavHcAyTQaMFH/84nfzU5f/FE7WojTb5IMfvh03k2BoIA/tgMN37X3WeWFgMIe2TZQJK1b2kkp07hmlO55RsY6edd3z17llgBAYqzP/6rJdunTp0qVLly5dunT599OtpHkO0RYxZjPEmvEwmwpVyJF6/Az2YA+8DKxjM8j5XnRKQBDTyDexRgPQLmnH43+/6Qn+6N3bqdeSqGYL58wipJNE21YQrzApvG6M0vvbyIaC2Y7PDVpj7KpinVDoVovQEFjpBN6wi2j5yGQGeXQace45cNnlZ1j3kioLFztMTfSjshp5NMB3NJYXYWTymJ4gaCXwG5Koz8KabBPbAr0yRAyEWI4mGWj6v1LHyAygowq028RpC28oTXrPJLgOOgjBlshUD9r3UUkXDIlut1FT88gDQ5iWxmx2TIDbS0zqwwmKn57ALHdavDZe3WD3ma8yfy5FB861Ojk2+AFSaVQYQcrFrkXEayDapNFFaI0aGP0RetEk8ByqbYe7S5dQvM4ipIW+bZpIg1WqEyck0YDGGQX8zrE9cmiaYCwDm5YTH5tAxDEkTTLXltE5m/KZMk5soJwEJFy055ORbQqzZTwnjQgidBQjTAsdxZ19n5rp7EDynErjWHAuuUcVM5heZ/nk+n68lsY4o4myLrLuc83LL+K2D32d2q4aKimIPIt226YQhFz1gk08bJ5m72NjxPMeC22fqF05P8Hc/rkHeP3/ehWPfvMprK8+yZrlAaMDg502NdtkxUXLyGRcWpFAKY/MAOjY4K7Rdcy1A6RWZLe1aB/vjKdKNTKNIpmry5x+CGbPSNKLi4hhiU44NEoZLCfiUx/4Orf85k0s+nUSm2KSXx/D/tVjvOfwJeRek0N/chIBnNk3StCT7KSKJUy8igcDMDq+yDV/1uRkope/mH2c6Sck+x6bIJN0+ODvvAYhBFu05opr1pHJubSaAdliitauRaZqtzFTO83DJ3swR32ue/5mkqkLbVCXrlvK+HyVS9eN8NW796EzEq0BKRAGXL59FZVqm/nY4y0f/CKHD09DSpDJ29i2iZSCwY1LmZhpknaePRp7IJciPZRiPvY5Va0xvVBjSX+efneQVyx5EyW/dX7Zst/mE3sfZktxgJet2nj+dTNpEdd9lO5GcHfp0qVLly5dunTp8p9Jt5LmOcQbb7qM5126mnw6gVFqYJyZwZlq4M6da6NRkPyzGVIfmEYuRIhlSdqLDeKpaVb1nabXbuCGbXS5Cm0PYRgdwcAPQUH53iL1l21A/9yFqF+BQKSTnQqQIEQXsohsnkTFwG5bJCpg9BTOL3/stjRH9hUpfyHC+W4V+zGXuD+HOVMFP0BYFqZpkwsbGJHErUVEhQRKasyjNuKAg95j0v+RWcTpOdo5RZi3INZ4G4v4G4roQhZjyRBy2RJomQjLROaymJNl0gcryONjiGYb/4u7cWoaGQm0AfXVFs316Y4psOxEhm+9JqZUMUltX3luf89xTkjRQPnmEYJtwzSLEuuYwnlI496nyR/zKd4/z3J3nMxsgNmGqKBZutFk1TUJBp+/DMMTBMtT1F+dwD7duHAyhaAxVyUcn0G0FSLtIt1OZdT83wbMHkhSnQkRjQCRy3bOkR8QlaC+KEEoUKpjEm2ZCEN2qosM2fHsOWctEqcdoowNQmP15DqtYpksjdjGbLpYcYg5UQZP8/nPfo+FiQVkxcdoKpRjEaQFThyyZEUvzrYYY2we49gE4WyVeNGHXI7UZVkWBm1OnJolW0yDirH9RbTnE8/MoZotbvzJK9i0fTmW3yZrzPGXH7+Hv7z1+6Q8ha5GxK5BXJOQOdfyNWQT9i8gUx6bXjmOihTLNy3F2ncKe89JRi6rI1JJrEKa9x+6jb85fgeLC228cszj3grqrQTjtRxRTwqAb37kDurrXRrLXVpDkrNH5wj8kJ6hHhazScxkjJlw6EunMA2Dnkzyh06VoH84z0O3PcFvveQDfO7/fJdLlj3Ma9c/zi3LTvLw3+3m1vd/m4/+2e3n19Fa0zfX4meWL+XGi9eAK1G2wLAkPekEI30F8rkkr7rlUvaNz3BsYh7PVASRZmVvkXe+7QWYpkFsJ3B781z64meO3wbYsHaIn73uYgZbgiVtSX/+gn/NH+69nXc9+SXunj7cuT/LCzw6Pca3Ro8+bYzezYNEaZvMyt5n/ZwuXbp06dKlS5cuXbr8x+mKNM8hPnf3bu4cn8BwDQzDxF2eQZoecbvZWSDlICIN7U4LUuNAk8gxQGliH/727y9mdOkqWiNJaHeim0UQYu45iZ6uwcN1ZCkm74Tn1QotISg6RISddgjDQvfYXL5zgVTUBmlgpDJoo9MIVTtgcOgTGUpfETj3K5xZcCoh8lyjlGq1MeKA4romdo/CSvrIdojRUERDmnBtRGutSaPfQccRQcElXDuMf8Vq4qUFDNtFLx1Ei8545HKoIEQ1mkgVIUvV88crbAQkz3oYfoQemyb3nTF6nmh3PHSURmvNVz67jPz0GmoTT48iB84nPGUencVdUNin5hHlNs6DIWL3AsauM2S+O0HxE2MkDwSkxhpYJcW6ZB8DyQzRpId5ZpbkdJmhE3OIdOrC4EKA3yYhNW7YRjba4PkIpVnwspzp76N6nYmvFEIaiFQK4broUCBWDfLW//kT9GQdUq48V0VjdHx6RoYxRoZR7rkalyhGNgMMyyTpRxjLlyHTCWRV0WO6SFtiZNKAQbMFuCaqP4keHEAkhmj5GZbvWAfAVT1HWHfJHLYZgudjCoFjNxi+UeEMWxw4eoIlG0ZYe01AfzYmnpxBppJoy2DVlStw8lmyywYozxic3u+QKwT4qYjIivHzBrVTLqoJ2jYJrSSNPQ4rjZjZh/qxdYgo10Fp0JpEYJDLJ7j5NVcw7BbIWini2TwqcDmzP027ZeO1HDA6VUR+3ecf3vpyVmKy5XGPr33yYb7/zb184xMPcMzrp+wkedHyYa5dspzhwGYgvlARA+eMiT91FxPVmC99+hE+cm+Gr8+N8J2JDdhph2baIjQuVKGM7j/LB3/1s/zVe77M3vsPc9HwAEklydo2SdeikO54zJiG5Kp1y0iIzn2qUDw+McWv/v6XALjmxg3kHWjOl591XjAMyfKVfWxKZrh2zVJM67xLFK1wjP7UGeaa8wBc0jfMmzZeypVDS5ltXWhrrJ+aRk9MQ+1f3gc/DocfPc7/vOZ3uP2T9/yHxunSpUuXLl26dOnS5blKV6R5DrFndJJSo4nRm0Q4Ap1PoIezhCv7ANCpBNoQRDmb1to89oEKlgIEHNhd5MEHCkSOSTSQgSjqmM0CIlY4nsK0XZq5gLl9p89/ZrS0QOuiIdqbB0ApjFKN5//lWd7yzsNc+4JptABpCxgodMYyDOKGQAsgCNBRhA5ClAKFRnseQa3NeK2XVskn3DeJeWIKYQn0SIjMxhjJCKPiEdsmRt3HfvIkIT6RaYLqVIoooVBT0x0xScUgBHHSgMoFkQalQSmUDhG9eVzS2OMRyuyYpGrbJEznOXNshjiIeKZODwHY9Y6pssonOtVHjonIpjEsSdwQ+LMCp+Jjeorc3hbvvuHl/FrmZhIlC51JYg1JrJUCudD8oW1ToOAVv/EyElJeUISAXDFmyZIyadvrpPv4PmpuHt1oon0fGnUa9QbNyQXq43Noz0M6NhRyaN9HC4FMdQQh6YdEaQOjmMZrtFEzs6hGG+lr2oebRFNVtOehShV2vnwrP//hn6f/gzfgrrcZGGnjzgeUJhcJgpCHT7rMXZpHXdFL89rlhC/cSjsy8aKYrVdqzEKaibEFdl41Ra5Ip9XKdVDFLL/8V19l31MnabVCRCbL771lAx/4zcuopgbwEwLT08hWhFAggghzscFjj67k7OLVNLf1Yjk2tmFjr11Bdt1y+sJVvPkdNzA03MN7Nr+cD1z0Wtb09OHMhvhlG7lgIecsYrtzEoWUPPy1J3jBxRtYsXKA3oEcI6v6OG755HMezdjh3tIRfuUjX+GpQ2McPjJFrC6clBP1CcZqNbSUoDXjdwn+4rFrODO2ioWmD65JI7rg/ZLpTRPEirDtcfrYDOF0m2zLYFWxB9e2ODW1QKwUjbbPP9zxFKVGGzPQyFBjL3gsVjoCysr1Q6xYP8SL3nrTs84L33vsKH/00TuYdgVvfOcLz0dwA1zcVyBp5lhX6FwPtmFQb/t8at8u/nrPoxfu8+kKUcpkUvrcvfv4s37Wv8YXP/QNDj96jM+874v/7jG6dOnSpUuXLl26dHku0/WkeQ4xtDbP1EKNypI0E7KHdD2AjMTblgPAQBKYkqiYwF+aYQkOrekqfiKBLBQwT82SMiSi0vm2XADakMjBARAQ4RM5DtVlRZK6YwprVH2EMJGWi7YthFZMj6V516+voTZnIHoaKDuPzqeJWy1kOkNT59CbIPI9zNk6kohgRZ5ElMAwLISK8WNQrolhO5DLIuo+YcvAigPS2Sbh5mFUv4c9UccwLORcBTeTBSlppyJSUy0w7U7bViaNVopw5RBhtoF7dLIjGAHWwwdpbh+mvTKJAtwji8ioU10hbIsoYRG2AgxDEmfTUG0gMy5KeYgfFBXYDsKPkJkU/uqYaKPAecLFOFpk6zVTlMdMFmSCyBVYS33uemA3u76wj/KpeYx8Dm+wj3JPAss4e0E1NQwwDE7sGuXmN9/AP77vi+e3eeinAkyjwtyjAhVKvFIVwhAkiFwGayHi0QdPUhzuYeLgaYTsmL4KIVBBCPUGRsYFQM+VUFmLxFuvhC8ep7pYRdsWupAltA20nYCxSZCCFSPDDKWzuF8/yP94z5P00OYT71+K7Wzm+JMn2Xcopk4Of7kDdorhOEtt0wqOH3ao2MMcnNtDz+kGC3t3cNnFE5j5HMo0wBBUKm2iwRbKbyETDtoqgr2c3JICk/UGZs0izCexzplWi1SSoWSNU+Yk8nLo27+eTNnm7OE9MJxk4MYrqKVThGGMabQRwmVH3zD7o5j0gzVSa4uIGGQAyUwCpRTFkSKHji+Sybg0R8e49/P3UbPA+/IAS1+Yp6d/mDlHk1rp8o433ohpdM7WeKXCHcePo0oxhqHQQrBj/XK8UpmfuWwb39j/GJVBTUVcqEJZbASocgW0ZmF0mudfs4Fjx6aJI5/nXbKG6y5ew3u+cQftMGLD0n6e3HcGd9pDKIUZaITujHXrb/4DowfHiQ2TK2666BnnhUYQYA95tOt1vvTlR/j5n7vhvFBzupHkZC3BWCviB/lQJ47O0yoFuL0XKm7e+Ps/xce+/ABBT5J9o1O84JJ1P+bs1CGVTSCEOFc316VLly5dunTp0qVLl39OV6R5DnHF0qU85U8QeAFitsHCZhejP4djn3sgMgwMP8ZsRqT3zVMp+ziZBMLtGOqKdJp0bBBnc4SzFYgiZCrVia8OQ8TZGYY/Po/qudDqYTQDkmNN0Bodx4hYceRXBKrUubTiwCOqltBegIwiZLlBtKSXMGcSFPPE/RmScy1kuYpoa7RlIhybxPfOEC4dQuXSoCXeiEsUgl0LySVrlFIpRMshqSNIC1K+Jjg8TdCXRqckJBMwOw9Sok0LHYXEPcugL0sc+BgzTXSrjUinSE16tAcLNFYaOA9dqBLSfoBshWy6dDmZ/gxHXEFYqqG9ANEEtcqi2ZsheaSEilpobRD1RsR5TeyaaDvJY6c2o1IOphcTbAO13eAjT3wP66EaQgtUsw2PLiCms8hq+7wQQy4LAp68fS+PHB4l2jCAc3wOEWvqj5okbzbxa4I4UhjFfKcaxDSIhEC0Q8YOTWOnXYzVy6HhgdZIKVGmgcj1IUQnpko3m5DIsklneSISGEP9aMchEDGi3oRUonMsXZcvv+OTrNg0gr9G892JlWwolHhi8yq+9FNvYmBJDy+6cw3333WAegO8gkd+e4GwGmMEEVeuHOGeU2dpPngKgIe+l0G0q+C6oDQiUnyrr8SpV/QwdFsFMT/LxO4zVC7ZQf9ID4tHW9gTpfPXsZnIEh10yJ2qkNzcYO/BGV548QbMQoSzvMl37ttPJjnG1s0Bq3s/yplSP9nUW1Ebe4n6NYbsCDQJO0FrvoSUknU71vDknin8lg9eRHmiRL/v0Typ8I8H/NF3X8346hrZlMtQT7Zz/LTmd971WcYmFjBMC1WroxHUzBBzLmSh3CQu2PiGQdVR56+t4eE89ZvWEadspip1tgwmefShSWrtgEfn51kyUuTR0+PEWvO3r3kJh75zlNjWeL0udi1m4Fzly7brNnLm2AxR69nbkF521Wb2Td/NI58N+eLkU7z4RZcyMNARbo/XZ2jGAbsWT3MNeQBeunUD6X0WP73t4vNjfO0zj+BFIbEfPa2q68flDb/7k3hNn6tfsePfP0iXLl26dOnSpUuXLs9huu1OzxG01jxy4BSnj80w98VjZA6WkUGAtiISZ9sAqMXOQ64128Ra9NBKY7o2ut5g5dAYl71mAZXVBMdPQxzDudYNHYToehMZg9FSGPGFpzThh5iHzmAePIOIO5G+2jLQjoG2DJoXD9FelSLKWcgIdBQhz87iHJ7GWfBw6xCn01i1qPNZvo8OQiyVJjXv4c7WMUenEWFMOtUi3QypncmQuf8syYfPoMKQ2DYIBjOIlo9zeBx37yQ0GtBT6KQeVSrQ9gh6TcK0gbFpeUd0SieRPXmMVBplCbxBA7944ZZQYUw8mOOhuw9xYvcYnmMS9+eg0oC6T3VNH41L+mmtyuL3uzQ2FTF2CRJ/X8Ha46ENiRmAM1XHXmhiH1cwY6GPR9hLJVooiCKEH+AeXUBGneOqAUplWCxDWtLe1Iu/vp+oLw2WRf3+Aot/5mKOAEm3E6Edx50Vqw2IYlS1gZ22O9VQQqM9v7PPrtvxGrKszk7aFsqVHN59BIFGuC5CgpyeR8yWEaUaMpPptHFJSbE/w9qfMXlgfIQ/u+9KFvp6GFzRj5NO8OJfehnSzRD3ZrAbJtWFMqpUxWm0eNmOjbxq6xo6WecCFYfEAEJihBplCkbLNbweE98MWXZVDT8vGC/CbK4OKUXUfy7+OY4JZUizJ8X+42twCNGYLNm4lCXvTDP4TpNVlwiu2LGKsnOSBe8QTfUoX7pvH+UNDpXLkjSWQpCRtM4ViyilKM+U+dXffyWv/sXrGL58I6/5jVdQNT3CtKS62OCLH7+PdSN9DGYr6HOVLLOTZeb2zGHMRcRLkpBOkhjO8e2Fcc7KBrc/coh8LkMudtg4PHT+2kq4NqoniUo7+P1pmn6InXQQtoHp2ESRZmUxz9J8lo9/6gEwJcqSneQnW/COt9zAxOl5Si2NO9RLLm0969xgWyY3bb0aN1ZkpSSfu2B6rLTCNUIceWH9KzYu5z2vvZEVgz3nX5tvtZH1kKgdcsP2Nf/adPSsDK0a4He//C6e/zPX/rvH6NKlS5cuXbp06fLc4WMf+xjbtm0jm82SzWbZuXMnt9/+9MCN973vfQwPD5NIJLj++us5dOjQvzruLbfcwrJly3Bdl6GhId7whjcwNTX1tGXEuW6DH/659dZb/6/v449Lt5LmOcJTs5McHVvAmYlQKZe+l+YIH2rAYAq30nkA04UMtGP8pRlaK9Ok98/RMG3ISfY6Odorili2S6YhMA+f7Ygm9QbK88+10whUxiFMpDHpCD8aCNcNYp6YOa/4xSFIP0ZLSO2bRromKtbIYobIdZCLdUSkSI020KagnTEQ/QWsSKKlQLkmOmgRnV1E5BLonjx27JM9rmhPZ1EFSbwyi+sHqHoTVSmDOQjFLFHWwp5tItMpEKAqChwHlMZwDIyKT+RrZCGNmKt0Io8dm/hUlWjnCoLBFMmZSme/BlxqlyhamYjUnSXkGd1JR/J80JDcN4ds+CQrElMrvNUhQjrUtq7AWvRJzkTo2QVErYlOpTAnDBLfSbDkeVXcFzdY+CdJ4+EIAcRpG9kILjSBCAGWhRACd6xK7GnMhSbadhBSdw58bGFKSSxALZSR2Qwy4XYEGNsiXVqk1laoWqOzzUp3KmNabVS60+4k+/tINHyqd51A9vcibBsdx5jZPCBQi2VExum0suXT/Oz7XsukcZZjf38vi6UaqazkHnkf3qYBPnzwEfQVOcy9IRSSTKQEfZZNqxHw5Y/dRf/yHqRtgVKk1jaon3BQ5QoqbWM2cpQTCiUkrU1FTlQH4fll0oUWSknS2qR+9RqkMwVxSHNzkVUvmWN4ZIGlxiK7aibLNi5l6ZksDX+aY0dbzPUc47u75/mZdTkqQY6BVAJ7XpKpt2iuKhAtF7AmjzPWES8f/sbjtJXB5z5yN5WFBqZtMn+5g38RLNsV01BNCB5HN/6K+cbFfOPeK1m5pICTtPFVRBYTM6GZ2OrgFUEbMDVaZXWmwDqd5JrVIxduWKVJPXyasDfNmjWr+dnnX0LStbjtwQOsGSpy0/a1fO/MSY7PzHNwokRwiQJHYR9UyCb846cewmqGjJ2aQwmDbS+67BnnhdPj43zrjqe4bPMWLl4+gpuwkLJzlS14NZakmlhGmR19WZiMn3V+ed4br+Sr+w5gDSUYa9W45Mecn7p06dKlS5cuXbp0eSZGRkb4kz/5E9as6XwR+JnPfIaXv/zl7Nmzh82bN/PBD36QP//zP+fTn/4069at44/+6I+46aabOHbsGJlM5lnHveGGG/jt3/5thoaGmJyc5N3vfjevec1reOSRR5623Kc+9Sluvvnm8//O5XL/OTv6Y9AVaZ4jJCwLISFMSfKNJtujCdZdH3HvkW0EhXOP/lFMuHYJ5cvThL0uRi3AemoOlEIM54liiRH8y+IqEXbymsPVSwhTCln1SPSk8astWktSVC/JYi4zKX5rtLN8s7O8UB2xxvAVMRochcymEFqg9blqnCjC2X0GbUhUfz8qZdMackgeKiECBY0YmbEwFw08z0Gg0bmQ2oos9p4JCENEFCFLDeI1g2gnTxwvYtRaCNPsGPCiIVZcuXGEAyePYjUilNcxcdWtFjRb5EqQ+V/7OPvnl5LZvwuhwLtiAH+TJLRMnMuTuLeOd4SrcxqJPdXAmW5gbliJkFA8Xqe1JIs3YhHmLdyzM8hyHWF2qlAwLdKjZYydJsQKvRCgXAflpNCbV8GTxzCanWhvTANsE+0b2HtnIfzBA3SACgJG/ned9uGQ4MwINc/v+Pa4DnEcw/gUuDYTbYEe6MGMUohkgjjoJHBp6JgpA0ILRKON9nxUs4VotRDJJFLFxPUmiWKeqCdNYEjMRsAH/9dXSGUcakcaGApaZ2vcU3+I6973Kmhr4mZEcrGNEjFa2GitEcCl167jk//0CHHCRoYxVW8Iu18SlT2EglTBZj4MwYE446DrgkW/SP/pKnrGpGHnUMmAcFUO39bkIp+RNTMkMhEt4eAOCrZftZbGYJvJ2hTlVJJjJwXTa3L83eL1EKTIq3mCgkXuoKC9pJOyHgxnzreY6VjzD7f+Ax4GmXwvW3asxGyfpl0OiZfBfo6zEGQpIjl8wuHxp0ap1ob4s797M3fdvourLmnyD39ykMNDLkpqtAHtdsChx07BWJ29PUWuu2oDAIYpSc40iM6WWfJLeYIopi+VZGGqxujkAkO9We49dgpfKYoWqPUBSHBOW9hjbdzl/UhLc8lVayjNN9h50+ZnnBfueOwfePDxiFKzzK/+0avJphNYdmfaN6VB3s4iRItlqQHGmHrGMQB2DvVz5FSORtElZds/Yibq0qVLly5dunTp0uXfzste9rKn/fuP//iP+djHPsZjjz3Gpk2b+PCHP8x73/teXvWqVwEdEWdgYIB//Md/5K1vfeuzjvtrv/Zr5/++fPlyfuu3fotXvOIVhGGIZV2oIs/n8wwODv5f3qv/GN12p+cIW4oDGFKiDQhKghMHe2gc1J2KDLtzmqPhAqo3jU5YGF6MM14/J2KAe6qF9UCNwrcnsOoRItVpidAArgO2jc5IzMUGRsOnb+saXvTbP028ogdihVELz2+LjONOe4xpwLIBGOgDpdCtADGxCMNFyGc66U5zJfS5lCW9sAhnpzHPzNGzysDo7yGx0iHMWMj5JlG9gXeVD6s8er5+FhF0BA3RW8Ro+GBb6PkK5nwZHYYoz0N7HgQhsVDsPjWBb2viWrNTGSR4mr+GBCi1qb5uG1yyEXfQJpPySJd8xKkkKpuGc5anAlCOpLk5S+K6Gtmr61hLI6xSRPZoi9yBBhiCqD8P/X1Ew1lC3SBaWGDfRJI9u1bQPGGhgxBDCWSgUBtXoeUP0s01NFrQaHdMhH9AFEPDJ6jZtEeT7HzxVi69bAW6UiWemoG5hY4A02zB5Wtwgk4Ed7RYQs8vIOQ5I+hzYwqtOy00+RyYJsIwSWyMEKKBLQKWD7jY1yeRXoRUGmlISu0FjEYdUa4weOlaJgOTM/eO8gp7JYV9ITKIMSKBM99Gm4J0IcnOl1yCu7qHoDdBayRN7MWElSba88HzaXoR6dAnbfg4C51rKbGo8I6kCWcTNBxIPHwW98FT5O47TXiqiduKGbIr+JFNUFXc/pUn+NTbshz6x4sw6usRoSZZh/aJPMZEiglZo7ZW016RRFmaoKCI8hemwImzU1ivq5B+3Tyv+d3ref4tl/DHV9zMVe1eerQDlYDawipE4VYu3/k2Xn3Lpbz6lksYXtnHC26eZeOmr/BHn+8n3WOhHdAmOH0ml129iuKaPq58/gUhRUqJVjFaaWaOz/BP39/Dn37qDqqlKpHWnJpaQJgaI9T0ZVIYexNYB11oSchJXvqzV/C+j/0cU2Mljh+Y4E/f9aVnnBeuuKKPi6+ZZdKvcPuXfo+JA7+IisYByNsp3rzqJWzNryFr/ehvDG776uMsPDjJYFVy04Z/f7tTly5dunTp0qVLly7PRhzHfOELX6DZbLJz505Onz7NzMwML3zhC88v4zgO11133b+oiPlRlEolPv/5z3PVVVc9TaAB+OVf/mV6e3vZsWMHt956K0qpZxnl/x1dkeY5xNpVA5A08JelmJxQ7KqsRWuFOvfNuW41kfOnyY6Ok//OKPb0uchnyyJ7ucNFN0+T2ewhzpkAawDLRA4NIIf7kV4D/8phwo15pvaP8v3P3klmYpL83adI7lsAxwbbghiMpUP42/vBtdFegA7jjsDgODiNGbwXW9R/wSRORmAI/NV9YFsIx8Epx3hxgBYxTVejDROVdvGKNjIBuDHq0hyitwdMEytdI729hVVq4C62oVYH0wLDQJsGyhDofIo4imkPutSWGPg5k3MWMOd1Gg303TaFbAj01AziVB1ZE7gPB4gKNHcsoX79amLZqUapXVVk4ZUjTLYsgqhFcN8c9plFMqfaWAt1WCyTWNlk+J0VdFFjWjbtEZf2igSNfodIxhj1AEIf5qYwRycRiU4b0vnKmTiCgn1hO10HPZIlyBoI02bZYIFr3ngNViaB0DE6jjpVO4ZB/5I8qh2hPA9ihTAMVLOJOn0W6ufOvWmCZaHLFShXCUdyJDbHpF5gs+J56/n1z/0K+UtS6DAinprB8eqkXl1CZ21EPkNpsk6j0ubB2/dhnp5DeB5+X4qgx6VdNMC1aZTbfObWuxkyEzilNqYXUehJoWdL6GoNXanT24LQEpijMbHTxKx6GMIjuqpFtK5N+nQNZ6qO0J3UrziXYM/HB5k8UuTAJ1cgY4u/f/wpzgwnOP1AgslDZcyGRnoK15fYocCugjsjCHM2phWz/uIzpJZXz7eYxdIirlgETZezi512vvH756l9c47e0zH6To9P/9a3EDJNKuXw4hduZdWKPu669zBfuH2SAzMVvrtYZc2lJqiOPvp7P/ki/vevvYJbP/0WrrjygrgRBiFR0DnHY8cmWTaQp1JqEIvO1bj75BTb3UFGVJpX3LCdZaKf5JkEWklKa7N8ac9R7IRJoS8NQKPWfsY5YfvSX+SFV70bbyLFoLufoHEUv3Hk/Psz3lnmvVkmW2d/5NziJ20C2ySh/2NVNIf3nOX33/FZHr3n8H9onC5dunTp0qVLly7/9anVak/78X3/GZc7cOAA6XQax3F429vexm233camTZuYmZkBYGBg4GnLDwwMnH/vR/Ge97yHVCpFsVhkbGyMb3zjG097/w//8A/58pe/zN13381rX/ta3vWud/H+97//37m3//foijTPIT739p/m6+/9edYFCiar6Ezngcqodx7g7OkS/hvyxC9J03jxMN6qc9+ehyFObxNrQGIXG6ipGYJ1JmEOtGuj0jZxxkUVBdnHyySPNogqNdqTJdRohDXlo6RCZDKdigytiaTGcnpw5DmzU0Gnv6TZIpluEK8w0L0W8YocuA6GlhhDgxiD/YieDJVLkzStFu1hiVVqYQYxiaqBnjYQVY2OEqhYQbVG8Wc8Cm+QZJbVMEyXeGk/UasGCyVkHKNWDhNcuhQvBgyJ2dIYnqZ9+RK8lXl0JkHrslW0thTx1mSIMhqCCGtvE+e2JIlDJlZLEeccVNZF9XT8bpwJD+lLUlIS7lWIRoD2PMKUSWtlljjtkNoGZjYisRBjVxQyEuR2tei5t4bRjAn700TrRghXDqGVOq8Y6XM/wxuGoNQGIVC2ATri2l84wHUrj5FZF3L/gTFu+/Zeem/cjJFLIBwDOdiPHBpgcmwRHXrQbINjoaO4E0mey3a8dX5A1Gn9wvcJ85LFr6eo3mkxNhcTt2LWV5cRJTWx4XHq0Ci7WgOohQX0zDRqbJq4UqVdWcR74VlcIZBBAMTIut+pZPJ8zEabay9bjeWY9MqAazYdRWRTaEMiNFx70QqGv91m4Js+qTMxVqWNymmiPATLNKldcxgVH5VO0rh5I9Zig/ibC5z8kyIXLX0+lzx/Awwk8HskvtkRQJRQxGmDWCm8MOLta3eQPx6jhWbFmmmwYXDsQotP0pRsbt7MxMFNzCy2AJiPfKKEQc+aAeraYrocMDm2gFIXSrCkFDwuM7xj3xXc+pWAk7vnEVaTdMYkN5Tiw3c8yBX/51b+8Nv3AHDbX3+X/33Ln+KmXKQpWX/lOu654yAONmTdTkuahuNj8yxWW+w+NsHiXJ1cLkGxmMYUkmMTs7z9z3+dUv9JXvyzO3nXB17zjHOCEIJLVi9ng53k859Ywxc+sxY/uuBfM9VK8cR8hiAu/si5RSvQCYuCeHaD4n8L46PzLM7VOXNi9j80TpcuXbp06dKlS5f/+ixdupRcLnf+5wMf+MAzLrd+/Xr27t3LY489xi/90i/xcz/3cxw+fOFLPSHE05bXWp9/7W1vexvpdPr8zw/zG7/xG+zZs4fvfe97GIbBG9/4xgu2G8Dv/M7vsHPnTi6++GLe9a538Qd/8Ad86EMf+r+1+/9uup40zyH+6MPf5fjJWXqli6kMRpqnyTtVpnYX4ae3QEtj7m7ib8ihQoM4fyFKO3rCJ7U8oPyUjVYWoTSxWyC0RxQF0A6waiZ6rgJSokyJCDulYAKw6yFa1ECc69eZmkMUMiQKknDRRAuJMCTaD8iuBfuRLConMD0DjCZOZMK5578oCokPpUj6BurhKpRLmEuXoBMgd1v4/QL51Fl0O0aYBuVvFZAJm7Ci0SpG9ecwJ+ZACOK0jTANdE+AabhEkcKZb2M5KaxZqK3oxTYDxGCW5mCRYACyxzXRygGkFsS2gQCMIMauxNBqY3kxWoO7oBk6aLHpiibCX+DowRxagqFijLREb1lGabFK41MLqMUQsxhCq0F+Pk2VJMI0MT1F0JtHCEm8cRlxK0CcncRohRBrpo5Nw7mKOyOIkZYml2uhagl2DFzMyRmTVqNOTc2i3QSEMWpqFrTCmppGGRKRSXUe/Fs+Mp+DZBIpzgkzWnd+zvGC5ac4eNqiMhPgtZr83bs+zeQVIwRX2HBjL9a8RuQUthURtQVWb0y7YuDns+iionckS3vfBGEc0HfvItoRBCttzMjneTdt4d4vfp/po6c5ncgg+/sQaR8Mg8eeOEOfl6QWNzA9A2UbtF2TdtVE+AbZhteJ6g5iZFMjmwGmHaPiEpe9Kc0N/S/iXe/8e6LdDZTfRFUFzbUZ2lmBnARTQna9hVPyMJqS1vUGi3cOkH4iIoWHAHp6s0RLMtTHFffuPsHEVInSmRprd6zm5996PX/1ke/SEIoP/M2drF89yK+89florbn9noMoaZA6a2I87FNsSIrA8Xc0+eMn7kUcCvHDiN3HxuClMHVihqAdkN+xnmo7Zvmlq3nsroMkbZtE3mXb6iHecNNlvOVvvgLAwbk5alnN6g29jB0bh3qb9E2neMHWR9FtyXd/J8GlV65m5foL6VE/zEO7TnHf3fvpvb7NxBaYC2fJ00luunP6EIuBx6HKNCt+xNzSi2RvL3xtYpSFT3yTv3zzLT/O1HSeG156EYMjBdZsWvLvWr9Lly5dunTp0qXLfx/Gx8fJZrPn/+04zjMuZ9v2eePgyy67jCeffJK//Mu/5D3veQ8AMzMzDA1d+L/u3Nzc+eqaP/iDP+Dd7373M47b29tLb28v69atY+PGjSxdupTHHnuMnTt3PuPyV155JbVajdnZ2X9RvfP/kq5I8xxi77EJaotNqmcryHyOS2/Yz8D6Oo9Pdx7IBeB+uYJt1XD7k9gzzfPrrr6+zuYbqjjNAk/d2oM2XRprDPJHShhHxjotIcU8uC4UsuiwAbNlRNhJVW5uzZM50kI4DjqKEEGIX0xScwOiShmhFMoykI7N2MM57CU+YlwQK43sLyINqxMlHUYI2wDbQdoaqXvQskxoabRtIgxwzwTEG1eiK03ypo9OZmjOCtTcIiY+9jXLicZSqFgRbF5KZjYinND8zh8P81cfmsHbOIw6No+MBW7kEi1JktpYJV0QNB8sIJsB1skZQKO2WMiJmEA6hL0ulu/gbdQ4JxdgsIhua8aP5Ojb2CBelsPQSUSoseY0cU+MOr5AOOVjpAP6Xt9CRwatp2Ks4wpdyCAkWEGbYqLFTE8Wa7qBNEyUHWM0wqefYNtCWRb3/p91iK0FWuE87/6Vm/nO3keYbgvciR7iMyDCZqfVq1qDvh5kNgsaVBCgm62On47fOfe60SQOgvM+O5dfO4m/kOfJySy60WZ2bJ5rf2kHR743iyzAEjOPapbx2zY6VEQiS7gki85lCB5dxnF9GjubRggI623Cn0rC2gSbXnwx+WKa616UZp8xy5lmjni+hNHXizYktbRkIHTQ1TbalqAhyptYVoRWEbLcRmjQxB1RUPm88M+mMXOaycYRxMCLyfoWticIPQeZssieiIm2RQS9Br6ET558GF0IsOYlzS9nGHpyFt2mI2TFilQuzZPf3oXtWKQjgyd2nSGOOyLWPSdHeaqnjWjELC1F9Fc6x08IQXpFGu9omSu3befww7vwiNAI+uZ91FJ43aVbeOypU7zlhdcA8Mbf/yn2PHSMv/jsQ0TtEC/S/NY7X4TnBTRUxJolvRiGpGcwTWO+id5fIoHEG24RnChjDCucXETaiFCWpDZVZeroJFy/8RnnhVTWhbMLGKkQEDT8C/f9z6y4mkcXDvCa5Vfw1KmHz7/eiOoYwiBhdLypntp9mmhTEqU0i7XGv2d6AsBv+Zx69ChmHLH5qvX/7nG6dOnSpUuXLl26/NfnB7HaPy5aa3zfZ+XKlQwODnLXXXexfft2AIIg4P777+dP//RPAejv76e/v//fNCbwrC1XAHv27MF1XfL5/I+9zf836Yo0zyHe+gvXs/vwGA9+/AHiOODYqaU0JqeYP+w+bTkZKpzJpz9onX2qF2tAcjjYTGunTerRs8Qr+s6/rwWdFCCzE8MtcFGmgaw0WXz5MNpXZI6eM+q1LPBiMAReRWA12h0RwDKR+UJHhAk6ph1CKSLHxog0KI1aLCEGiigU2g+QpoXuySMnFjBXSkjaxH0W8XFBIi9493sPgyN4/9/soD2vyRQiFpWJ2LmMdUtGOP390wAkzyqWmxvYsSzmoW8+TmVLAcsXuCcriD4X96omFSuBij2iByUmGmkrrrnxAIUbm9z50cuwT5dI1AJaS/KEm5dgzwfI8Qr5V8yhewzETxSxm20Sa5qU700TExEVC+AtIk9VqdyXxL4qR20hjenFkMrRdD0yB09RHypAn41YrKJzaVRPBjE6ixE/3bhKp1PU7QLxYhJlBHz0I99jIKiiVpsEhQShMHCOhJ3WJstCt1po24YoRrfaoCPitIlxzjBLNZud9CkAKXhg3xDjbhFSIXHgUdtW4Ej8TdzpZfj1LK3+BKu+GzKb60eLBnFVYUxNoXrzqPYqYkeiBMS2QGYzJB9PsmX5Onas2cDnvvQYX76rRaFvDWezFmayiWq30CrC0D20lcLPOdDwMYTEijVO1iPx2RhZ7whWst4muWcKYYBhKey0ZvTQfu44vJtb3nodE5Uqp/ecgbkyquCiz1TRy/uJLcXSdh+nrwlJHkiwwbI44bTQtoUSMcxXOLR/HGMgRUJBMZsik3YY3jnArqlpLk+53LRhDQK4adkqLlox3DkfWrNjwzLONKtcs3U9S18p+cY/PIpA87+uvpatV1zKqmwPN29dd74kc2G+wXe+sgtjpkTGMhnI2AwN/Evj3qu3reTwAyepLszjG4Kik2AsFlhnYH66h8RAmnjKom7YPHrnbl75thecX/eh2x5n4vgUL3/HzVy2YSlSSuY/ojEHDbb/4qXnl+tLtOhNjHG2dfD8a9WwzOfPfhxL2Pz8yl/GkhayHVJ8vEx7Y5Zb/8+r/5WZ6AJfn/giJxtHed3S/8H7vvEY0d5p7O+d5uTe012RpkuXLl26dOnSpQu//du/zYte9CKWLl1KvV7nC1/4Avfddx933HEHQgje+c538v73v5+1a9eydu1a3v/+95NMJnn961//rGM+8cQTPPHEE1xzzTUUCgVGR0f53d/9XVavXn2+iuZb3/oWMzMz7Ny5k0Qiwb333st73/te3vKWtzxrxc//K7oizXOIF16+npsusTnx0c9zdqrJ2DeznO7fhLkh0VnAEMjeHnQQomv1p61bPqB4dHKY5lU5TNVCGgbKi9G5NHrZALrVRp6aQvgg9p/stAABQgp6v3AW/KjjlGqZYFoo20I2fcRc6bwxK36IardJbpS0jphoJMI0EFKDNhCGRKQSxI0G5pxA1xvofB6BwEgk6d02g7NNU7/DpF4uYpma0NHopKT5PJO4uYK4p00+G9CwUpw5M0VUK2OlcggpcaRPe2wOOVnCTQoMO4UsNVC1Js39CcL1BoFvYWiQvUXUCpNdx2z67BKiVMeZryE1JDyFage0lqSwPE3162CuSxAfExRf3cBeGuPbBs1lkt5Jj5nPprDbCzT32FRKeYy5CpRaIAWNqws4CyaZREgjlSQe7EEU82AKmKtB9ULVQ5xwEFKB52PYSXxbMuf71OZCMmEaX0MilUSEPnE7QIcRIpdFOA5D63t53htezj/ctRv70TlEu+NTJDJpRKneOXdC8PCXcuh+F6deRgjwVANpgDIAAWGfQUuqTqVOGKJDA6E1SihyqwfoOT6DFzUQkYJ0liib5ODdc8Tv0Dz0yHEavqBHOZihQObz6NEZEIolcwEzzRizHaBMQahCUkccotUS54lmx9dIaTAkYr5M2I648zeGSa+W6BtX0iztZv0N65iZrhIO2TgNl/awQXNDkp6TIYV0mrf/1HX8Vfg4DVpssfKceOgkQghEo6Omn959mg/c+3t4Qcg9H78HNTZHfm0ao2QgkiGFVT63jFzG6syF0scnd5/hE397PzVCvh0e5JUbexBCIyX0ksIpKfZMneVPf+6v6c+k+ZOv/Sbjh8eZ2HUcYRm0e9LMlJsc+OoTxEpx1fXr+PTDT3Fp/yCjCyW8AYfcRf2cabUZPTpNLAQqVjRnkhxoJJGhQPQ7lM5Fyv+Ab936PVq1Nluft4nNV63Hsg38dpJgKn5aD68hOie282cHiUQgkUKeyzKD/qVF5sdKvOzijaTdf/svre/PfQ+lAz555Ms8eEQhVcivvOoynnfjRf/mMbp06dKlS5cuXbo8d5mdneUNb3gD09PT5HI5tm3bxh133MFNN90EwG/+5m/Sbrd5+9vfTrlc5oorruB73/semUzmWcdMJBJ87Wtf4/d+7/doNpsMDQ1x880384UvfOG8AGNZFh/96Ef59V//dZRSrFq1ij/4gz/gHe94x/+T/f5RdEWa5xD3f+d+9j74VdZdppiaKKCqLVo3rj2f7oRlQSKBziTwegT2RAMZXajUMKs+uT0Lndhn14VsCiFN4kIKMi7i7CxE8XnRRUDHJ8SPOn63WkMQQhAiAXv/IVSo0T9YNpOBRgtnk0vVW9Jxxp2cQyRcjIUWqlZHZNP4gzakkuRXLmIXqswfTyFMk3jRwrBbGDkboTR+4PCB919Ka1ua+kiK1JYq01GKsOXSHooZ/OZxjFATtwLW3mDSqPm87l0v5rGjZ1FJm3a/QzrK4VQ16m9mGXmbia/aLMw5qLl5It+l3r+S5tmAxNgEUgpWbR3h5Ewb7bexxn1kK6A8J2ilemE4x8IDAcklIVHSIBiwMGWAFDbadZCVOpZWyFYIlQposM9I5m5eQ3gsxGyZsGwAvdhJWTJqrafHhAuNiDRU2+jZBI4VohwTNWxy2WXHefIbw4ShQGYymGkIZ+YRSRft+1RPnqHYcx1RuY2tOe9DI1wXjI5JLrHCXmhQX1fANiTkswxuHOLqmbXsPnE37Uv7qZbatC7XpI54iDDqXCOOi41kMqgwnwxJGwKUQFsGWgjiKMIwBc5QEnPSJC4p5KNNhJtB9xXweiWHRwRLvTyViQrKlux83l4eu2c77S/ZGEMGUWoZYnwBYcborItfzCDOzNM6ZfNr776ZlasGUJbgvh4Lsd3AOxCSPFAjSklSpRRWVlGeqjB55yjRk2c5vH01en4BmXCR+SxaaPRgkc985Umi0RnO7h7n8OOjfOH4X3DLlo387a7vc1CNsjRVfJpIc+r0HK1GgAR0v8nAwAJCaoSEv/69e1DqHq761Ssohz6yrKnM15g6NokVhUwM5cC0+M59Rwk1LM5VeWD/cfZQ5qn2cd7xiutobRb85cn78IRgyg/QS5IYZZPCTMCSZIMoNNHtmJGV+afNBa//X69i+vQcp5fU+eKuLyHX5lBHxzFXpmlWW2QKHVO1LdlLMIXNanc9M9wLQMbK8aaVv4wUBqbszB2//zc/y+NPjnLT87f8WHNSj7kcPxqlOT2IDKfAsnj5O25muJj/V9ft0qVLly5dunTp8tznE5/4xI98XwjB+973Pt73vvf9m8fcunUr3//+93/kMjfffDM333zzv3nM/5d0RZrnELsenuLkYZf52SFUDlRfntSZNu65p3yZTROVqlQv6aG9bjnpXbOkd8+dX1/YNsKwISVQrTbkM+Ar5OQC1JqIKEb/0Oed/7sA8cNvCAEpGyr+0+LDhG3hrzCYyKRxUy3CloEMfazpqBPR7fng2IRL+5CmwbWvOIVhwT1/sYnmQo7G4xkCHNp7z8UAa42eMXEaEVZtHP8nbYy5JDRiGjJAhDEEis2XlnnTL0xROn2S737/dSQbEj1TRfgBdqGPKK1wilUSW0KCb5dxTimINNZsHXHvSSrXDiHHXZyJOjOn5wlzGbz1g+iUQWLfJPM/PUywJE3mNISP2tT3m6icQeaowG+ZyEKB1gsGkF5E4vGT0GqfP3iJMzXqVw8jR2cRhkW8fIDWEgtjcvJphr4akPUWwooQ6TSiHoAh0VZEbnmL9PKzuNoiqrrEqSR4Adg20qswsrxB2Ag5vv8M5okZwoUWRk++c00YBiQTUKoAYBZM3MUmRiaLsBPMf3mazy0cxmg0ibYnkWmXVVeXqHyiQuy6mHEDGSaQaE58Yh8UYlAK6UXopCQCYkfwG396G4en58hszFG/axY9X0enI9ojKfyig5fQpKTBQsLCaVYY2FSlspDDzwuWVBaIf0HSejyD9WAF3dtHY2WOaFM/qUOT3PLTVwLw1S89Tm0mJHs2IuhJolyD3AsE8puCqO7x9QcOEhybxRwvcbLiIXJ5knkXY0UfpROzkE9i2yYykehEoacd2nHAex/+J44cn2NFZYChpX0cPjvLpuUdoeay7Suw/vEhPBFz5vGT/Ol3F9EaYiWoVTrVSnP3T7Bs+yraD57iL972d7zqN17B9peUOXZ8AjOGjesHcJOaL/7TaeLKOM4SSVAPODtXZupEk2YYgiFQQiNTGncsZN1cmYyXQBgW2XyOS16642lzwdHj80ydXWRywzgHR08h4xb+2wYx1mQ5oee5hDR+tMhtY7/PyVbIVYWXnl/3xNFx3vGXn2f5kn4+/t43IYSgdyBHLQkztQbFnqe75j8bkYpI8iRZK0DnJrGaojNXGOJfX7lLly5dunTp0qVLl/8/pSvSPId47Vtv4tCedXzmb+5CNyoMbmtjnG1RLaUA0JaFantY03X8JUnMhfbT1tftNiyW0GGI6Csi/BDDchCNJsxXnybQ/DBx1sWsemggzjuYtkssBbLhn6+60UBoR0TrXdSAQXOVQ0u42NmYwv3TiFQKcmmiXAZtSIxqwOkDBQZHWgQ1B4Qgqhm0nsgh2z5GqwlCogOfwHURvRmS3xUEaR/H9zBqJsroRH/PnxWUSy5HzxQ4sH8CHAspsqQqMYFs0hpJ0Z4q0DhjY95xGqMRohI24eYh2iMpvBUOyb0CR2salQYyigkuWg9FG50wEYaJjA3sMkR5F6PqEzmKdLxAMFpDSoEZZaEdgWl2xBMVomJBVEwilUY0Wsh2hLYN4r4cmaMllAXGOe9gYRkdY2XTRPs+QsZoISFWVPa4PNoYoX60ihY+5DKAhDBEJXqZqRdYeekUO19yKXf97d2odAZ5Lp5Oaw1zC+fP5cU7NJW0x6kx0NUanmPjhZ3tFgmbVUaWlXPD7HIXod4AJ4lI+2jLIppo4LZMQkdgBBLTi3FnGrSGbHYfGEcIKJoOSTdJM47RtQbW2QBzsoxotVmsV3EbGqE1D35oJWK7QiiDxpjD5jtLtIOI4wmTyPOI7QLaEFheRBzFGKbBxJFp6hs9qlMtXFzERoe4XxEXHHTkUJ9vYjgOZsombHnodkBu2WqCQopwWQG0Zr1rsSuKyPRnePnrd/Khw9/giB4jLgoy44JPffMpQPDnv3QL2ZRLTyHFutUDHOuZJHyyRhy3MByLMDDPX/sn955h0ZvEf4HL3B2jtD52L7e89jLkfXtQaYvX/9SVfO1b3yWd9Vm1ao7rf+ooi2GaJx68GO11ZE4tBHHCRPe3aLaTHDRNzGqNKDCoRy32P3KKW165g8NHp6jV2+x/YpTAD7mmupnxbx0g2XKQKkuobBJBR+RsRZNMeYvUIkHKzNCgDMB3vvwQs6HP4sQUQRzjmCa//cGvsWvvWf5pQ46v/M7PUUwln3Eu0N696NbnEalfZCLoYzxIoUWK4VSVKJEHAabR+bVztDTHaK3MC5etxZTyGcf778Av//XXOD6xwJ/+j5ewfU03tapLly5dunTp0qXLf4yuSPMc4u5Hj7N73xiDfSlmjp9FXp4ge5nP8JMzAOR6SsyejEgcL5M4XkZYFnJkCN320IudBzTVbCEsiV4sY5Qr6FwOI50idizww3/xmaonTSKRIGpFqDiCKMZ3fIRlPq0tSkuBWtZP4rFForE6YTaPsVRg1zQUMqjFaicyuz8DEsT4Arv39GCJYYycS1yrIaKY1ovz1FYlKXy2gj0eggXlSwfJn1FoLyZOWlgND3vRR3oheBFzi2n+/O/TZF+fIl7uElZN7LEaohViHi+TPRzTvHwZ0cMp7MXOPhpeiFmHZs5FpRSt9SmSxztClerNYrYEfhGC1TkKXpNlRxewCoqz8wXCnI2ztU3PDRHhmKTxT4lOXPliGWoNfuI1M9z4ygqf/tAgh/dbpCZ8VH8OMVlBzFWQFZMobYOTJHvtJmoHyygUnBhDxIrmtSupbUxS2FMnebqOMC3mT/ZiF0J0bw5ZaaJn5jvtbVFMENgctJfR++0n0IZEGC6q0TGO1vU6ub4MlcmOd9CxJzxuemOTUyoPfoBKupg9/eiCw8BxlwW/zOSeBey+EayTxzBbMUo1EKkk2gBl2SjLxmi00RJkDKIRE6yUgCCpLVoIyGYQYYiwDGSlQeaRGcKVfeDkUFrTrPbhzgTIyEFFAYd354lFgJyvIott3COzOCfnCQPFO6/539z4M9dwy2uv4I6v7cOPM/hzMcZxA6Ek1lgbaUVYxTS4TkfcsixkKsXseAmOjWMnkhArvnVynsg0SAwWOH1ognuHTxKJmM1qhPe89cX87i/8NVII5FtfBsB3vrOXI3vG8K/3SL7IYclsldFDEn8uRvb2IRwHD6i9awB6BfHKDIv3NNiVnaF1/TCGNhgtV9lzNGZBpDAKWXa4dQxfs/9sFauY6Jh2x5pUVaNmBGECAmEzc6KA0YpIVzUrlvfi+yF/+6n7UUrzs29+HknTJJM0uP2Ypl5pk/5KxOZXXcmanx0EIO9sxTVXMyQV/e4gs+dEmjQ2+QdnKBZzOKbJm9/2d5w6OIG/OoGfjLht32F+8arLnnEO0tFpUGWIz+DIZUy2clSDJLXEItrIgwFeGNBuGrzryX+iFjfQ4uW8ZPmP10b1X4nR6RLtIOTk5EJXpOnSpUuXLl26dOnyH6Yr0jyHuHP3MeYnqxRnWhiJBKcWCogCvGbNEwBk8g3mzjUgaUDYZudBXl3wjRHEjKyKmTgJ2jCgXCHSEdoPz4sukQRTdcbQgU9YaoAQmK5JGMbESYfm9gx94xcSpFTGwWiHKNNBHPVJFuokHppDKwXFPMI0UabAMGyST44hWhGyFUGms73CNEldAcMvnmW81kNlZRptaJKxSe5wC7vVqRTSnkdUKROLGNGfxphvYGqNril6hl0sx2KiGBAdLoEfY7QjQOOcKhNcnEX8wKJHa3SjhSaPe7pB6mRnX4SQqIEeXAQtRxFmJfHZGLPss/niBRJ2mX3hCEwbNMeTNJIp4pU15BmBXWmDZdA/HGDZikJfSDSQJii6ZFalidsRxlyV1KFFKi9cCYaFnTcI8w5CacxMEmm7xCkTKSA2NXGpDK6L7dnE/TninItRb2H4wbmTFaGaLfiaz+PFADYOw6Fy57wDqt6gvnE5cagw5yo0ZmCqYqH1uQMRhrRWZDCSSWTZo+UFuDMB5nwDHI0upAAbDInK2oSJmNhUqCy4gYmONSploByJBH7+DVcx8UQ/nz8xD3YW6g1+8FEigKjHRjsGLLTRZoxfBFVIIluca8WLsVox1oyPTBcILc3k5CS3//09rL9sNTu9NdxujxFukZhJH2OLh7MokYsRVtrCSjnInjRmwyfMpImqVVTLg1ob0OiUS7x5JRVL8MiDx0kaCjUsSJ4xObZyATtU9GZTqKBj1Bv4IRowH7GIV8LUoUGS1jiBJQh1jBAQSAM/CWnDJywmCLfA3uQYS7cVyLRyWBhUG2WUofDdBIFncfDONSw+tYBMSMTGJNqDKIiwfY072QAE494QI4uaFzx/Iztv3Ihtm1x1xRoq1Ray0ebwvjPoVIrll6zh+O5TJCzJ9TtWY1nGuWtZ8PoV/5NaWGHIWsoBjnT2yYtIti0ycefXw+kj0+C6uAsRN75mA5cuG37WOUgkfxrsrWBtQ7QXmPfSaCTtVg92E9CKP77p/VTmqqT+bhOxZdGbeOaqnP8uvOGG7Rw+McOLLl33//WmdOnSpUuXLl26dHkO0BVpnkMM7+gnWmKhvzVK3GohMw7pTMQjH06zdjuMP2gDEVHGwmyECClB63PiTIdNV9aYOuMiUylUK0JHPixc8JbRgO5JIMpBpx2nca66RmvidoQAEqebJBebHSFAd1p0ZN5B7hlFOw5i5Qigz3nc6E6STLEHvVgiajYxKxFSSPAjRAYQAi2g+Nombi5iWb2Mmh9GRJKkaWHO11C1GuFwCm2l8dYViUxNNJLFOl0i+9g0wRSI7/bTl8xzbHYexw9QBRdZ9RBSoCyBmJo9L1ZpOlUmmSN1zNk6lD1wbLRpQBQhWyF9T/m0Bk3MgzNUhcC7yGCsVEQ0SjR78pQ+5tLcbBHucCBjYh+tolTMlz7azyN35jh1LMHiL4zQF7Xp2dZgvjGErrSwahHJOYUdKKz986AEcdpGLe3HbMTkjzbJzLUYzJaZ6ynQWF0kdbqCCBXOQotYC0QhB7ECx0YEISKCOIi4qj/HI6OtjnExIHvymEmHsN46fx3dkxggkQoxLRslQVWrkEwitSZ71MP0FcQS50YXr5xHTofoRgscSTCYxtQGVgkMLwDTJLIFzbUB1qTgT794D2XLI9doAU1UHCMNCY6D6ssS9SQh7HgfKVOAhnaPhZU1cR49BsE58SmRQDs21rBi1cdybNi/jepCnelHjiC25bHmNAVfE/ckUbINkWL62Dy+JVl29Sb0aIn5+QY61iAEygYtDfq2LWdcdT7XSTuI++rYiRQ9V6f40uOHCW9Yy6tfeQ3ZYsdN/s1vvp57jo9yOCizxc0ztCTJT7/rOGfOSD7x8QQCQcY26bVrLM+VmSoXGfb6ySeLPHhmkrk5j30bpnjejg3o47uoLPbz5b+5nsZ0iihhoQ2B7Qk8oaBZJ7g+Jn2vhWoZJJbk2bI0zSN3HUYrza+875X89Ks63jTvfen7OXt0Ctnfy8glq4i2r2HDukGuevnTK2AKdpGCXSQML1TJHTsyg3ZdKu1Og+Omi5Zy4Pgs2rFY2pNnIPMjPGmEC7IfMLGMDP1um6zdZplcw1nDw620Gd13Fq00lz6epu8Fq9mU/e9bffLU2CQffewJ3AYcPDLFlTtW/3+9SV26dOnSpUuXLl3+m/Pf1wigy7/g52/YgTFkcmSdSdxocmP6ADdv2E1q8ELjUeviARbeuJXaDcvRzTa6VkdXqliOYuXGNkefzFOZcVEqZv3laxEpsxMH/YMRDIltZ8E0MLwYOCdonHu/09QC1IA4hlQShEQuBKA7BsM6BuXYYFsYqRS0PIRhIHNZLGkSF23q62yUJUAptOcjopioLAlLiuoXwDxaQcchDRGjVKfVyji1gDIF8UCWhEjS/2SD9DyEQykWr1zK3lHN7Hwdlc8SrC/irxhApWyIIlJuQGp0piPQZBJEa5eAaWKfXkQGMSJSqKEeou2rMX2BMhX1NQkwJDKOqC/4fP+pNfg6QUInKByN0W0P9/tTJD5fxT0ZoXpzaMvEaxucOpgkNkzcqoVxUFDeX4RWG9H2wQ+RvsI8MkZ8cgLh+RhhfF5JkzHYiwqtBMqxMIXZOcZeeO591TEXzmVBSERPntYLBglHLJSbh1hDuuNThNLkS1VkOzh/jkPTRqYS6DhG2DbmWAmn2cKueFhNEDEIS1I3ewiUS1yuoBZLMFOi+GSZ3BcPYh2fRrcDKiMaXqj4zeu+R/5wQP1EBUZ9KtsKBHkbhvtQvVkY6iG1uQ9ihdH0EVMTuIua7GiMcJPUlxjo4MI2ErTY+vIzbH3+OGUirvzJnUSxJrphkfXbprnmkkHW2VkusqGYshBa0SrXoRVwZq7MVKuN1ud8mLyA4PIs1Q8vpZV1kdU25mKT9rExphOKqgl7njzDSGxx/ZUb2H7JGuIo5vSxacIg4nd/+WV86E0vZSioc/ZMiYMHe6iJQTQSrWDL1avJjraxmwHFyKNRDDjTnMfXdah7uPWI+x8/zdlRqHid9DTX9gl6LZQj0XGMcgDbRU26DL51BawsUDCmGZseoxz6nDo50/EXOsdL3/pCrn/t1Vz00xezsMNizRWDXP28DZRnKv/qPLKwUOmIrnGnxOmyHasxy238PHzjwBEeOnX2WdfVra+iK7+Cbn2RhHTI2gotBAU3ZPPIINdctRU3aSNNyemMwcOHp9g3OfOvbtN/VbTW9PSkWL6syKYNz15h1KVLly5dunTp0qXLv5VuJc1ziL3zk+w9MomjBGrdCK1SxBJZJW79kJeM63bMax23I4CcS/V5xdvn2fnCGt/8dC/3fT0PjZATTxzDyLpErR8yGC7kz1XZ+J247R96MAQuxG0LQCmo1i+8lkhATx60xmqF6GSyU52iYpCSOJvAUAKdNWkvjTAXkiR8B7w22g+Z/EMLZhpozv30OOi0S9CXgoE0cmwW98gc5vEqccoiREOtgbepl/baHEz7jJc82lkHtWOE9Kk2st5JWtpQnODgwU7lg+rNofvzKK3R1SaiVAMUcS6BdizwYpieg1SBdsEhUQnAdjAijdkI0YYFsUankhCGCCGRkcSotKDtg+OA72MEmsSEj4oMAsPFCFud1jNDYrZU57iZoiPa1AOkDoibATLhgmsyPduDTGqcxRZYBsqWxL0pjHYTvVgFQDpJtAPWtiSea/H4/ccQ7RYynwVApNPMHzmN+KFzWDjgEUsbM5lAaY3jC4yyjwojdBSRHchTkR71FyVYNz3M3K7jneug2UYv2uhIQbWFchzcAxWaqsi3H1uJ0QiQhoMMYsxTFexWCEkLUhmE0ow1Fyg83PHdUXGEytiYbdBxSLTMeJpxdXKVxZqXtVBpiz3f7uW3PvkN3v3mm7DsNJlejxdvXUl8Sz9PePeilwuaD89h9CRpDmbweyzs2RYC3RESEfSYNdZvaFJauxmv6ZA8U+1c3z0OzqhHpOHEN/ahHnLRP7GD799+gNu/9ARDWwa5a2YKE83ypWWKvWV2PW7jDocoSyBDePLJE0SnDSZ7E4y8vIgWJmfHS9jjMXZbsW7TCOsnZ5j6zhRL1hTYuiHFiZk+IuFSdF0O1RY7ptIJG1UT7N1X4eqBCj//sgdR5HnXZ65lMms87T7c+bLL2Pmyy/jr++/j0V1HWXEqov3V/Rim5He++OskMwmgk8A01hql3xw6v+6iNAmzNsa5FKf3R/cSvtmhcKTKBmcd161Z+eyTUHQQ4nEI94L9auZaKUrtPFtW9PBn/+NlpBI2f/7gGY4dneUVF2+jmTG4eGTo2cf7L85gNsOWZYM8f/1qsueOaZcuXbp06dKlS5cu/xG6Is1ziBcuX8un99xBPO9jlSPO/GOWUyfXMrex8w2vtsCOFine42GMVp62bnnOIo4ElYULl4SKFJRaiB+ut0omIIyQSQdt9qAXFoELLULn0R1pRvT2dPSahRLC96DRQCQdItPEX5fFKcXEbgaFQNY6bSkikGSfKGNWQnDtTgVOOok3aGDPlC98Rq9LY9Ahc7CBTjr4y9Ikx30MIZGNiNpgRPJsg9STAXFKU78oDVOCMCtQJqxYVSa3pcWpA0mOPmZ39sKQyOkShBFioWMULOK4kzg1sYiOQS7UkcN5klUDI/CJkwbmXAM96HeOhdZUN1g0V6XIHklihDay1ETFIcK2EKYFKka5NlY7wmjHxIaBa1gw1E+YN0FFROsHWCslp/dPoqMY2SihbBd/aQ6dSiIbPioG7ZiIIEKHASiFcBJES21QGuvsPEImcb+tiXs9aJuQyqB8//x5Ckf6EScmzx/Wq1LLeDBVQ2AhhEbPzNLIGxgzs5htRVhMgakQZVi48yjnTWW0QrgOyjIRMiJOGZgzHrIdc2rDRrITBzHEagylsbSHrofg+QhrECWhPSRp/8p6lnx+BjGzSJgAp60wyi2IUk+7XptnFHs+0YM/UqDWSCKmyzx4+wGu3foi5vYvcttn9tLe7JDYUWBpIs3e8kkoN5DtEHPnGhJIIkPiDvVSSAiu+vVHyfdEfHXWJcytQLVa9PVnCFL6fLy8qQP8FUVO1svkelIYpoFlGrTqHmYtJPe8NawolDh6V8i4kUOUGqhqA5IDIFyiRZs1rc2s27KMj37pfupeGzFsc3JmgfZEm6gpaDxVY79dxos9XvozL+V5l63lbZ++jYqKUGjihEF6XFCzLZQyKTUzrF4/xPZ1SxBC8PCeU5SqLV563RaEEFycGGDp3har/QTVpSaNrIH8oQjs3eVHeWD+bjant/ODUi290ESkLBLtTqVc4CVAC8qbk4zeNcrAm35Eu5McoeMYXSAmoN5IsFhLsFh06cl2vGdGx6tMn5jGOnkrr/vpYYS1HOgkTnlRjc8c+wVC5fBLmz+DIf9r/4o6MDXDoZk5bMPkxnXdVqcuXbp06dKlS5cu/3G67U7PIaYPzqGEgbIMtCHwFmIWxoYIBzoPuNHONMGlLmJhkfa6BGHeOr9uZdGkXpOEwQ871JxDXWhp8nstmmtyYNuQS3XahZ4RAYaBSLiI/x977x0m2VWf+X/OubliV3XuyXk0yhpJKCMhQOTkQLCNjY29jqy9Nqx3vQbndcKwa/8csQ2YjJFkDAhEUE4jjUZhcu7pHCqnG8/5/XFbPRqyMCxeb32eZ57pnjp177mhqua89f2+r+chDAOUJrs7Zs3PzRFe0mHh5izzLyoCYHTDNBFKCoxEoocH2f7SLje94wRmHvAD5Ok0KlqnU2LM9BCujTg9i5qZonaFS2+di2q3CD0BpQLCcjA7AYX7ZzFjTe50j/xJn4nHTvFrN97Dz7xzlsGxiDgwwUgjrYUfIqeXkdLEKBRIdk1gbFmH4WYQsSTeMIx2LJKsgy7l6dy0HTVeRqqVRa4U9Laa9MZNGjs9lCGIxnKwdoToqp3oTpd44xjxtgl6Yw5xXtJJbXoQ+RzmTB3mlukUEo4tNNCei9YK1QnQ1TZyukJsa4xujHItZBac63tIaaQVREUba6qCNVNDeC5ECYarsAo+igQhJXKlAirpdRFf1QJjbh1AeRaYBvQCNl4ZEOccTCeLKBboVlrIxQB7b5N4oZZ630BaIaQ1wnEJhgsEORAx2I0EGQjsV+ewFzs48z1kM0aOjkK2gJqag5kFMidbBOM28UgGaVuYlRB/SBI7kGRMlJ2+XSnTwLNiXnnjMq/aPcnGV58mHofpU0uMrxvikkt3EKqEA3qJhystbp96evXY7Pk6hWqPZMDAUhFFW0CccOjQCNUwy9zmIhfHDiM7NvC8H7ye619XQq+8TOTzJMdenOd3H7uLXdds4vf//ie5/sUXkatBOZvh3voiT6sO17/rKQYvD9BJnKZpCYkwTbBsvvKZJ/nshx+ksNSjeGiBtfNdNngZ9s8uE2ZN5soGW39wEWdTyM6hHNRDrMUIswsiinHaimxPsFwr8DdfuIKvHN4Bnz4Adx2l2fb5zffdwbv/+R72PD1Jkij+v/d+ifgLs3RO1dlzrcuTFxp8bvL46vl4aGmKmW6DrBw4ewNUahjVLmuyaXXOK7c/xpt234tuJNzyA1d9g9d7ShwfZj4O8YOnyFt5XrdlO9dtWmT3cJF33PpRfv3TH2Jq/xlUr8f4+sPUO08SR2fbp+6ZfgBTzuGap/mHfV/5pvv698Dzt23ip6++grdcddn3eyp9+vTp06dPnz59/oPw7/tryj7Pif/1m7cSFCLsgoR5aJ83hj8kKe5ZgJt3IaYSjNOK7sYitReOkjnSZOST0wBsv7zDuvMCtl7S5cCer/2mXJAKI42dWdSQg30iJs7ZGIb4mrGrJAlqqQorbSUacNZFmCOSnNvBaiRkTyWY7TD1qgl8IkIqV0yQqwZc8eYFDEuRWWPTbEdYSwHdbTkqr5ygcO8S/mSVDa5HZ2QUMwqwqhGtnR7u/kVUz8I9Y0LHBzRmI2LsgwvYoyXcvIn64jwnn+dgu5pWfaVVJFEgZZo4BeAH6PXDqEGHxAdp2yRZSW9jBhAoBDpjIBKDaPMYQhrENR/tGpT2+6wrtTCVYEmUSWwIxjKYiU6To8p5hJTUz3cwWwalJ7tEtkQUXMyqS2LHFO46BVoS512kEePmFWFdYCw28Q4uIxwHc6aCDrqEyxZ+yUZIgYiT9FjiODU6Hsygr9FUdBnnhI/X0Qgvbc0Q0oC8C63O6mW757ZHMbMjiEQjhcGZapF4i4NyTcxOBKYFaNyxDMW8TVN2Efkc2jDQhoQgQA6vwV6sIQQYkUI7CvU7VdSEi2GZiJKNMg1ExiM0InANPCyMT81gH29ijwaoPcextq7FDASlx5sYWiAAw4TsBps122OaFjifb5LtZHjDL92E74fct7jAibJA6Yi810MunTVFBoHTDQmHLEStwsJymkyWfUxinA/bx5Z569vfyh2ffJRrX34Jmy9/Oce+8Bc0jzbZfl2ZemmYopXBNSxMKbn33sP4UUJUTTBmNSfuHKHYtrn+/BcyNb4X1YlxHIeuSL1megMOx1pdVHYeSwXYbYOn9k7SbvUQsSJeCLEmfwDzwcf44D0fxVo/Qc/yoZyafa9bVGTMgPGLcmx61ZNEQciJW3fw5VuXKQzmMP7uPpxda9j4rjJCQASIvMf45lHW54pM+U12Dg4D0I58vjA3idbDFNdvoMZTxHGMWlhGL1bIbi9RaS5AyaBFjjdf/zD/6Yp3ftP3oMlknAfaQ2xJ8lynNRsKdVpa0qov8NTnZtGnF1Pj7YEifzu7kxFX84KteW5cmz5/S343/3jsfMLERJ+c46d3f9Pdfd/J2jYvv2DH93saffr06dOnT58+ff4D0Rdp/gNx2bVb0Q8eJS8VB1VEe10GhcI9klagGIua7qUl7IUm9oKPO3N28XqyY7IuNpj9esVVQoDWSGD4IwdQpKKNux3QYrXK5tlyjQa0JSEOELFefazyFZd2XKb7pGIi6mAoUFIS5204NUk0kUP0Iux5n6AJTk5jZhQMlYiMkGjQQtsG8YgLxTZJaRKtDYS0KD0dYS43Eb0Qc7mF9hNEmPrxqIxDp2RQ25AlySuGE8Hf/vbXSZVZEWgEoJUiyCS01iVkJhPCjWW8M23yx1ogDaIdJuJ4FxUJ/M0lzIWEaNxDWYJ1wwu84ML9RLHFPz9+I8qSGF2FWauTWT9Kr9pFm5KB+xvUnjdIb8jCq6g09nzNMMmogVk/BkpghQGJZ9Nasxa7s5SmIK3LY3QVxt4TqVlzO0Ny/SjdDQajJ2JkPpdWwpgmcSFL+IRN40Zwrs8ijsU4h5vpdarVsPyIyHNSvxxANprYlxeJ9hporYiWBLR8jAiwbUQUIAYsmkGBXHsOkc0iSgMIaaBdC1XKo2oNzEqHZKxI68Iy9rEqdhRDrwtWnnguBKcKBY9gbRbhSHJfPoMbrVRsdQB6iCAiHi5idrtc9cqE/V9O6DSgNlfkfe/exsJkSKumGFgreOy+oxw/NMvUeofO6WVueskhdu86zieWt1AzrbRSKgjpPHKUy3/xRp64+/hKWlfC6cfHUNYQ0ajDu/7sb1ibN/jQ/mP8/mf+O7/yltdQWWiSxIrzxzayYevo6u2ydm0ZQwrGhnP84M2X8oWn7udSJ+RjM9N0ZYyVxLhlh061S1g0UJ6NsDTJK4uIB7tM3XGCj99jMjFW4PTjdaxEsW7DcXrnl3jwVItEBZidBDEgsNohnchAJJrp/QEbGpuBiIwxwO4f3sYdf/8VUODNVBkdLpDECWulhb97B8NjWeYOTDF83ijNJL3O3SRgwssixBJZM73va4sNtGMRn7+BhVKRTnUZrSDRknGvgxDfRJQF7p41eCoc56EzIRu8w5xpHOCJSo4hncE70MHDpJ5J0+NG7IsJTIftA8Orz99SHmbfveeBhAm632RPffr06dOnT58+ffr8x6Qv0vwHYtdbMzRfETByeIJDP7GPQr2CjFVaAgNooSnsXSLJmBQebSHDswuuzaWIrV7MnvirFmGmgRwdgSQhmV8EwGBFhDkKDJxrHPwMKmNSee1WZKgYvPVYGnUMJGaB7gEPKRR6foHEDxADBXDKhNtG8HcOEg1a1LTmjr+4guHCIq1TEpFEqHab/KMaa9HHnelRfo+ByPWYfUoSVwq4TRslHLTnEm8cxj4xj5ASDYSWxHCy5CdbmE+dRmSz0O2iVyLIU5FpxUcHQAiEFLhtB+eOZWJD4ZyoI7sRxtAQ2nMYHK3iP2qhYolyu9iBJFqo4R1bZGlTgYcGz8Pzm6hdPcSki3F6HlFpE1oGMp9HdXvYC4psRqITTdyKMWJJaLv4a4vEbCTz1CxZs8fS+RvQykYsxwTnjxEOubhT7dVroRpdMo9MEgxvB522rOluL61wkYIoJyiYPtnHNTLjMbi7nl6ojEfU6CJNE2VZEEVkbjFIOqQR7Z4LNQ0S4qEMVi3EbPp0uw3a0qGS6eL2fKgA+Szakigh6G4vkysPg2WR3zNHNJpFJwrRaJJIA6QE30fHIblF9YwbyjNnPz2orEOytoTQApYjzuxXOBlNpyURGY/jR22SKEAOGPQQPHDHk+y8dCM33XghH9j3OaJJi3CTRXMqA+6K+CgkIgy5bWOLKy/ewuz+04h2j7CuWJgfoPOQgQ4DTu2fxTQl9eUWH/ubu6lX2iDg2MEZ3vr2l5HNuwDMzDeIlUZdfJyj5Xle9KMON190hI99cRDV6qEqHdpPBtzwI9cQGwH3fPowwkq4YefFPHTfEZZetx5/RHDC7bJbrqOcmaVw0V5GBi+idvsIoh5hNTTWri7+RQJ1h0msXLJZh1de8l/oLTUZ/YH9PP/11/DFjz8EuSxGLq2SMkyDF9xyAXv+dS8P/sseWCeILY8dr0lFkRG3yKvXbWJfbYoDzUcBj3q1DfksmCZWKUOxOMLeRzfSjWx2bqp+y/egerXAdDIAFZNW06OrNtJLehxeWMY3JKKV4AiN7Zr8zLXXsHbT8DnPF0Iw5Fu0g4jzzxv5lvv790AUJ9Q7PYaL38Srp0+fPn369OnTp0+fb5PnJNI0Gg1uu+027rvvPk6fPk2322V4eJhLL72UW265hWuuueZ7Nc8+3waf+Ow0pxddLio0iQou2CbmchM94ABQe8kG8v90gMTJ0b5iDJFo3JN1hJ8Q1QwaUxav/MEWG3ZZ/OPbiwhDplU0K3++7nfo9XN/Xa2okYAQaFOultgIQGccKGWg7aO7XYQQxEJhBQlJq0N2Nk9+MqA9YRG0PGanR1FSgk6wW6nalDmZtubUbwNGTGoT6zHKkD20jGq2aT9/ExgW5mITo9ZCeC7SUMhKG1ub4PvphEwzbQlanf1ZuUaOjYBloZRC9HykbWAECpQmcE2iLUWSnoE93EAtCgw/ojFkk9uTVpboqTYHZ3cint9Gl8C4XSMrbXBMIqURi8toyyRZP0ziCor3L2C0Q1TGJi4XSTIjqE1l3Mk6WS+hsxCQJIpoJINV9dP0qPl00ZzkTOrXD+OdajP4j48SrxvBrLXANpCmiVXr4V7Uonz5MrP/vJYkI1gI0moQOTqIGUYMZiwWTqYiXOOQiS7lse2IxJZpZVItwB9xcE2DiCJmFJN7so7tD6DNpfRaAswvYShF8c7UULpz7XYcM4NTSdt9sCyYW0jbykirlZ4V8H5ORZYwzfR3BZTyLCyPs+Y8g8yEx2wlRGQ8aGtqL9hM9mSVtW3Na95wJZfeeB4Fz+Hd7/4sk3sniOIFZBRBkEZ4J57JDRsPsxCtS9O6ohhtWvgnTZRn0NuQI+e32LRxhNF1g1z34vM5/NQUC7mEPftOUnn7x/j1d7+RTNahUWuj0STETM9XqTw6TK41zu7RIe7uzCNyOcI45rpfvJDf+/NPEY3kMLsxVy9s4I7oNMqVCEOh2jEzrRYzszaVv7mQNdeMA11U3kB1EiwvRscRQlmEzQBvW547lk/g/9MBHv/8k2QHMsTtLvQiKDqrZzSz1ebpdbPwUMDgaYOL1Sgl92wK0dVDz0MIn53ZCznAET7/oQcRSmCeWabwExdiWNDsZYiUJC8Tjp9aYOums5VEX01gZCAQtLsO0rCYyF2NXXuUfDmPPdZh7IIJrhrLc95V275GoHmGP/3ZV/H5p4/w1udf8Q338++JX/vQZzk8v8TvvPaFPG/Hhu/3dPr06dOnT58+ffr8X863JdLMzc3xzne+kw9/+MOMjY1x5ZVXcskll+B5HtVqlbvuuos//dM/ZcOGDbzrXe/i9a9//fd63n2+Dmuz6wg8F6eq6F22FlnpkJRzmNn0MuefqGJODNO8eAgjlDj75xF+muDysc+v4YuZzTTvA8PPQdmHdg8si2huAan1OSLNVws24qv+Ntoxg7cdRyQKEetVIcScryJMO42ItizExCiGYxElGjdbRtVitBQUHllAZj10p4fM5wjMkPDK9TiHFjDbId7VNq27EsTaIbJD0BqRJLU6xAlCWGRmfeRyHTSoVge1dZTmVcOMfObU6pz1ikDzzJz1Vx+ZVqhmE+GHCMsj3rURo9FDjeZRnSbxvgV6V+XxPlDBaBgEmzaQN01oNFO5p9FFSRPR1bRGXaKXbKUcW8j7TgACYVr4u4ZJZAth22grQSRgdiPCjMad6SIXW8xpk/bNQ2hT4iz7JDoic/dBlJV66fS25OhcUSIac/FmE8xEIIfLqCBEByHCsdGPmBzfMU62GaEHfLorbW1yqUpYaeINTeBmHfxOgDwaEF8eow0BGsqXbGauUMCq+yitMUYytPw2wsoglxvoRCHLpfSUtdo823ra23cGxgcwtAmDJYTnohstkKk4ow0DTYJQrLYerYqCrS5yroJhuMQZSWwVWK5J3vuhn+XnX/sX6fxtC+dkjcvWDPOSl13Gez9xK9npIr/9Iz/Be8d/jF97+0dgvYRjU2RKGTq1LjKMYXKQgbUu3SfaqKxLMlFELwbE4x5x2cW8eAvPf+2V/OJL/phmoOh2QtT5BZIkohfHq8fX0SG9QcnUQ6Os6Qxx7fkXYOZs7v+p/03kOFh+l4tv2MUD//Q0/j5Bb8wie7DJSDFH9miF+o1DiAjKD0lkIjFdl/kwy2IscVyToBOiNyRsHK0glwNmF8cxTYvli+F9Tz/Gi563jhcO3cDuF12UnnUBjndWpNkXT1G9AIxpD/NoDm8pJokTDDO9d4acEV4x8UNEUcQBjrDv4BQkMaIdUtpYwDBdPCtAKpOCHfBnH7yLv3zXG77RWxCjXp2wK8m6PUqO5sbiBYDg8uxmLnzTHDu3jSP9mEfuPsTa8zcwvq78Ndu4est6rt6y/hvu49/Ko/PT3HnmGG/acTGbil+7/+fKwdoS1aTHZL3B874L8+vTp0+fPn369Onz/zbflkhz8cUX8+Y3v5k9e/ZwwQUXfN0xvV6P22+/nT/7sz9jamqKX/u1X/uuTrTPt+YdP/wCukGEX2mx9x8eJAg1zWvWgJUuKd26QudzSC1w5nxyTy+ni00DXvn2Fq98wRH+cWwzj7w3i3RddLND7HeI1mdQVhfvKOcswDWAECRbxkFpjJNz54g3RidCPTNu5W8pDGQnQieaxDAQpolINLEBKIGIEuJeG7GwjJISOTEGaMLhDNF4AaPWxVqOCKZykGuBZSBCTeZIHbVhlMSWmMpANv3UjHhlvuHaAnHGxo+7GIMZgl1j2MeWsBfaaP2sli3HQnt22tolBCiFlpCMDKCLGYxugtFNsA6eQfgBQimkMDAyA5RPJeAHq8fqPTZFZ9MFWO0IchIpTZbGHQbHBxH1Ltg2+XsmYVcBZZvItSNox0aeXqB4PMKsJFidGAyNjENiy8GqhritHgMXCNrNIsl8h/ySBV+uYjUh2bUBY6mLMEyEEKilCnKwTFj2qC/nyXXmoSLxlirAJZD3wLKoNwKiFf8e0VJkN8XENYFIIDNeIHaArMRqxmzeMMyT3YhMHSgPIBwn9b+RX+tnlKwp071okMHHl8B1UAvL4LmYmR6JLiPrjTTBW4hUqIp76baUTpO2ziwSjeTxN44RrzFZO1TDGztGZAaoRhez0sGbmmPnr+xgSmrOXGhgLXX5+V//MC++9CJcQ9Jt+xAn9Bq99P60JU/Wt/GD5w0wc8eB1LdnpIxqLuIs+cggIeO4fPgvv4RfbWMOFPByLtddtJX1127gpedvx8umQkg5k8WtaASSwvlFGhfFNG+vExcGEQ5ov0YYhzz2uWM4rQB/0CEp2Ow32oxtHWI+H2MEGm3aeH5EI+OzpjCC+7TJ5pEJHrz/OF1PQw/0fECkIjIbxri0sIFHW/McD9v891/9ARzTxFAJcRRz0fVnjWyfZ23k7gcfpHVKI6TJQ/ccpVZpMzSapqpNtpf4yOQD3FBOnzNvWOjBIixVSd5/EOtFL2JjtslSkKFxOMe2nePf9D0o6m5ksTKDXUlYWlJcODHEj29+PgDnv24dAL/zs//I/sdOUcg7/PDP3vxNt/e94K7pk9wzc4otA4PfFZHmt1/9Io4uLPPq3bu+C7Pr06dPnz59+vTp8/8635ZIc+DAAYaHv35p+jN4nscb3/hG3vjGN7K0tPRdmVyf54ZlGuw/fIa///Ij1LYU6Wy3kK0eG3pV4AK8gR5Rkqc83SA4toAM0yoaErhhfJY1Xpcfe9Fpjn16gvq0iVEeINE+0a4C0ZvXIf7babyTwTn71I6FLufTX6YWIUrSRXacbvvZy3YN6CBExxG62yPxLFTRQWuQjQDV6aLrDWRnxTA0SVDtDtTquMsuxnIba7KGtl2E1hBGNIc04fYccjEgV/HAEFh7jkH9bFpRAuBKHF9iaIhH88SjeYQwsYNFqNVXxZx4x1pwHYxj04hae8VAGPSWCdAaVa0hF0KSzeMgUjNgkQ3AMLDbMfEzHVOAyCbYm9sMHujgfbKNXjPE7PYM+ZKBE7kkSYCKA/KnfGR5iGgkD1EM08uQJIhEoSwD04Ch5hnCkzZ0Muj5JYKiYtcPzbH/M1sRiaLwdJ3EhGRLEV2tkQQR5HPIcglhmXSGLZRtITIOZk8jdHplOhuGyFbaWKYgiVbMiwzJuu1tdl64wOFqiYVHPQxfYjcVRixYOFMj2wwRfpQmYfV66F6QnqhnkQC164fQeYfe4z72TD0VSUoDBJkianwIueBhnFmARKF7fvp4FIMhEdkMVjNNPUp2ryXDMq9+4wE600dob7kSTZHsA6cxw5Atl26idOVmNt8xRe1MjcZiwO2dpxhYrMPMIqiz1VyJYRJXYh5aniLethYKGdDQvllT/JIgzirql3rEX5jHWq4S9Xr87b4/Zu2mYU42PsyJ3qfY6b0N1xyiWu8hgdJwQLz+aR5arqHu6iJdB5GzsIdzjLxkOwf+4m7CNWOYHY1Tdvij/Q9hbTQx90l642Cu7VDdr2kO5Oi0OpjdLq1TNTqxRrcsTn5iI5nP7YNdgtZii/0fOszL//OVXL1xA45pcmBpnvq4hXs84MRjJ1avwSN//RDFR2PinMDotciN5hgcKaw+frA5zcH6NFksNgCohGhtCbPRZGLLGLFOWLJK+IR86itX8fb/9HXMtp/FurENdPbvR4Qmi+UK/+PxO4lxecnYLpaXPkw5twWjm0CziSvU193GcrvDf/v0newYHeLXbr7+m+7vO+EN2y9ic7HMzeu2/Ju3FSUJSmtu2bUNx+xbvPXp06dPnz59+vT5t/Nt/a/yGYEmiiJ+5md+ht/8zd9k8+bN33J8n/+z/OWRO/n43z2GmFMMkhA3Bc01grxOPVhU2SNYjGm81oHWEJm/WEDEqSnsX//pel75qy0++dCFLAkTa3GJpOdjeB7FQ5L6p23ii7agZ44ighhYcXDxQ+TxWYTSiGhF9DFNiBO0gGTnejAk8tAZRKLQrTaq1QZAlgtwYppk5zpM30cohXJdZLGAWlhMjWZrdQCMpo/RXPGSCTsk7Q7KEERjGZKsSbAxS3dzjFWNGH5Kg21DmHqQGICx3EFONjE6EfJEBeHHdC6fwDxq4fCsdq1mDy1l2uIEPGNmK6cWETNLiESkIsKGUXBMeg54rgOdGB1qxNbN6GMnV+ascQ70CD48hyyVWL4yi4gixPQyqp2gNo7hr8+QjSVWtYE43UCILLpUQgU97L0nEGGCOC9L4QdNKn+dIYkTRDZLJCT1GYmWEl2vQ7uT2gA9dgSeqSDKZBDF1H+keDpG5WKSShWZaJLhlQhupZFZm8GhHMWyx/G9pxGDNlu8Jdbk28Sh5PDSMNagnXoMxQmbzhvnyS9Po2vt1fsg/SH9SQyW0Z6L0Wkz8JVJ4gvGsc+spEkBYnEJsX4I0WijXXOlxEqmFTndFYGuVEJXa+l9ogXuiWXMkwu8/8sFtl8WYHR9lGOiNo5iHJxi/Xlr2b5tLTfE49w5VMV6rMgWaTLdC88RDQGMTshaYVI4XqcxtYQayNK4aZxNuR7drEe4OUs7bjN0IhXL6PaoN7uc+vzTyF0Poo0W9e4kR/dXGR7wIEm44qbH2ZAEbNt4KadeUeKf/uEuOiMORaA910Qv1nGlRKkSvu6ikjLemQQ7NIjnOmRrIe58RDarWcoMEJkwaUTosgRDILvAYBlDmigBzV7AvSeO8erLzsePY/7bVz7H0mvXMPZPk+TK2dVjzQ1kEd0QQ0iIDTKuOCeh6frh87CEwXm5CR47/gA6DMGysG7YwVt+741ESUxUMYlCqOct/ufd9zI4kuOiibGv+x60Y3CEjXKI8zeN8L6T/4ghOrTDEmv8o9wyci+JepCF4zfSnV2iW29/3W18/uBR7j85ycOnp/iVm67FkJJEq/QYvgusLwywvjDwXdnWgyfP8L/veZCmH3Djts38+otuwPg6FWV9+vTp06dPnz59+ny7PKf/TVqWxW233fa9mkuffwNHaku874mn6Q0neG7IkNFjc2eZHz7vUapT6aKt0TDpbMwTr7dIduTR2XSxphTUNmb4tF5La8ygN5BH9bppspNrIZDYZ3qEIkEY5jl3jQBktYWst88a8EapiIOU6KyL9hywUj0wHnap//haepvzSD9EdkNk04dOFxwbOT5IPJxHrCzInon31p5NMjGItozV/RiJxju4jNGIcE91Ua5AWAbGYAljqHx2PlJg9CAz1UBaJoYSuLMd8k/XsJYrK4MUwk4wNy9hPXEC0QufdYQgD00hmz6604N8ZtVj2JhvoDyb3kSWXhakFOCmyT8qtpG31tDaYHBzj7+++lP8hvt5kjhAFbNgGuRP+iSzPZJmHSodhGEgXIfsnilkL05btvQQc383RDzfhmYL3WgS1RRnHh5JZzc2jNq9k+TSDQgznbK2JYnfQScJWmuULdPUp6yNMTCA1U2rGOzTS0SLDerVDj/wK6/AsAWy63P/+20+/le7uOtjm8BOr4KMNarV5tAX9xIOZ9Kz49hnbwYpwbEhipAqDWq3tgxiXuDS2zH8rLMJcr6OcXwG4+R8miaUy6LDAGwTnXGh0UhvTK2JbUk4koVGRHdJsu+ePN5siFsXmMpExQmGIbjrEw/y2de8j9LRBzCdBh3HYPmmElHBODtH00Bp8P/2QRYnaxj1LtaZJXaePsCuqQbRgI3CxD4eg7si0+Uz/PGv/zMf/euvMHvvjewo/zwnDhb40Mcf5qEnJtGGwZlTYywv57igdCWv+6krefWfvBQrjOjtOcjUbV/Cznn0TEE4nsG0c5RbIWI0RNsRRibCOKHIrRtnw00XIkXqBSRdhcpIlCNQFqjAJxx2MOo9Bi8qsmPHBGUngyHg1OQJ5JhAItn2wguJk/T6fumOrxBcUCXutUkc6PVclhcaACQq4vNzHyb2/4qMegKA5k6P5YstMtcNIYSg0ujS2ZMjfqKIJzJkMzZnqvWv8w6UMnmyQvOpBo9/5QRrrBlmlkvMN2yWRJepxGEqyeDlPBzPITuQ/fobMUChCHXM6dYUR5qz/OKef+Bjpx/4hvv9frFpsMRoPochJUcXlwmT5Fs/qU+fPn369OnTp0+fb8Jzrs9+7Wtfy+23385/+S//5Xsxnz7fIUu9Lq5Q6G0+V73cJ/lHQfllx8jIHpO1FgBmRxG1FOZdFnbV59KrL+LJr+xHjRTpHO/i7IsJHu9gNzOYno2dczAHO7R80POK4pNdCDUq60GUIFeqTfSz/ghIqw8AkSiMQ2dACKQfogFrySfzwCJqKURny1DOo3MOulpHew6JAVon6Dg+5/iStcPo4YF0WzPLq/8uLBOzE2NWFPbn5rAqEVpnCQoGpgChAa2xj88jg2dt0xDYUzXUqh+NAC3Q86NoMYvQ55ojy2f9HTgac7aKzrkY1SZ6zShCCqSRplBhmRCAlopo4wBiuUO5cQbHVYyv9XEqCZWXDDG8rw5+gGWY6MQiHikhSh50exgiTT+Kzl+DGPLoTRio7RvIPjaFmXdI1g4jFxpIrdG2SeyZCMPGiFaOJpvFKJdTXxqVABZJViEWW+ghi8RMj84mwR8roSzJ5OGnSUINEZgvNOh83kAnCUyAFQbIoIZRXSQsFDEi0BKE66atSZadVmFo0O0OyXARGRroKiQnNckl44iDC6vnUxWyiEYnPcn1tMpGOSAjidBxWvnieaATkp1r0eUc3QvHye6bRVsWqpRBWhbKCRn4kSJ//3ufYnaqiuomlBoNXPtJ7u7uoHzrEYx2nFZWodErpr86UsSV5kpqmeZS32DvqSGk1gyFDZRVITMiaXUsdDGHzpis3z7GeeddwbC3kWRTgx1bxyguuVQqHQ7u3cLUifMx5u6isnQvD39xlGSpS2yUmJ1uIl+1hh4WVkdhGBbyaQlhB7VJoXOgPXjD667BPW+IvU/OQqBQEyEsmKAFVjsBKemscch7DhdHRfb/ydP8zxf6vPPnXkI+bKMTkHHIvvkOJ08tsn3rGPZNIfmbJHzS54xdYK1VoFjKonTMk0vvRIYPs9lq0mtOAv8JI44xe4Ldg2mC07GZRYjSYPq4k/Cqm7bz4vO2fcP3IduroowY04nYmc9y11QGLUAldW6dXI/S8Gt/+WKWDpvkrhnh1jOP8OLxi8lZ7uo2nIwmyqWv1U+f+TxrChdxpDVDoELesPHab7jv7wfrywP82etezk9+5J9JVEKivn4LV58+ffr06dOnT58+3y7PWaTZunUrv/u7v8uDDz7I7t27yWbP/Tb0bW9723dtcn2+fa4dX89l3TU8flrx4Okixr5TDMVl3GzE8uEcALLepayhcxqEEDgv3Ubp8oBas0cQFNG3nSI808Edcbj8xgvYe/9TBLMxRq6FGdpIX6MNidoyBq0QcWLmW85LdHyeJYMAYB1fEXeCFgzkMKaWEUqRTM3Bso2MknNSlwDkUgMlBLLWQtmSzmWjmMtd7LkOxmKLcP0A7avX4x2tYd8/Q2JmMSzO+sOYJjpSaatHkoAfAuGzioIEOhFoN5+mToURFCQ0k9XHEQIhBc5sc/VZCkg0yCjGWkpbpbAttGVCxmHXuhaL43Ds0x5/+T/WUFu0CMcHqN2cY/iuyTQWWmtuek+Thz6ZpdsMMCNNNGEheiaub5C0E1TORk4HiGqH7O4hihM+5a0dDn4OdGEkjbKuhKvzMhwPDBPd60G7g9B5nEoGZQoCO6YwXwcgjEKi9Wtotbp8/h+fSJ+sobXeQUuJESr0qSbBTptszSc7atK0SkjTIHYb6FYbZ9t64lAhwgg1M5duwrORQYLdVJgnO1SuKD3rphCI5bSag+hZ/6xWIrmfWejGMaCxF9oEZRN7pon2bNS2tchejIw0SXMZ3hLx1CdPM797Lew+j00XPk39KzlKZ6oYvbTtrjcocOfOiorPkCnlWLNllJ5fJDp6HHPII7mzhqzW0gSsjAfrBqnYCfd3ZtnQWeQCNjI2WuRXf+nF/Pzvf4jWUEKuadBZbPEvv3+UjZea2GaMHyXQCwjGTBa3Wpixy+BTMbGl8QcE9pKFcdLGUCaMwYt/8Ao++bnH0SZEjiA7oDBPJehIYAYKw/YY39vAwmTP0QNsumYayz+BKV6K978X0bZALsdcdcUW1q0t89S9B+k8bmEMGPhP+OibBTdeexGWbZLokFh3yFqDLCUOPZma3hbPG6bX7HLpZalfy+TsIvULIxAQPQAH9s9gv+TKr3md759dYLyQJytHcUOwQotri7/F/5q6lQRFLVfgwVoTJLzv0w8Q7G+gfnM9y16PQSfP80fPmu7uGh/GGQ4QUrEmdxGbc6Os8cpsK0x8zX7/PXCyWuGJxXk0ms9OfYHXbX4plrS+39Pq06dPnz59+vTp838pz1mked/73sfAwAB79+5l79695zwmhOiLNN8nhBBsyk7wSH2BxrTC9j3i9xskg0NY8YqXS7sLcZx6VGQt9t7+FZJaBK7Dj/5Zm1uuOcknv7SVz37UZX+rhRISoyRIGhrDzUDZg3YXK5FoLVMhIjpbnSLhawSZZ8QWvTJHlfdQIwMY08vITAaZSLQ2UM88Z6XNqLtlgMyJ+upzZb2NXPGwCDcW6Fw0hGxHDH/0MOG4l6YqmQZapuPdudQ4OBqzMQMQ2QFkEZASValCzz/n/PXWuLSvGsNtanJhhLZN0BYi14M26IECergAp2eRtoeOo7QdLIzh+Cw4LqLVoby1xdbX13jq8Ba600WwGrRvnQMkpw97aEPgX5jFthVdLyLTTUWgWSNHLlggaDoIz0avycFsh7jXISkKnMPz2HsDjHZA93iLV7+0Qnsy5GA0jI40Vi8B207Po2Gg0eD76FYHGk1otMjVWoRSYFbPmiqr6TpqbIhktkp97qz4JP+sRlIeIsmYhOtzDI+GDORsuvtHEF0TLUBEMbKYJ4l1uk+ZoE1BVPYwT8+TJApZyLLu1W2mcmXKroOwrFTvan6tH0lUsqldM8TIZ+aQhpUKWIAxvYza7IBOkEEIhyeJztuA9eQJTMOi+XMxbM2TbBYoK8M9Zy7HHbV5U/lpBq4L+ci+DUSLIXp+IY1kNwTGig8OUnLyqUkmpSByXMwEdK6AVgIdBYihMnGUEDRaWJMJh4ZPwA2pSDFVrfDgiSnMbkzPSsiel8Cs5NJdlzJ2w6U8dnqWvW+/HWeqR/ZAA3fQwmiGaGLCtRa2r0gyoKLUl+c/v/MTnKFDEidoSxDVLfLNBB0ZYJiodcN0b/AZnLUZa1hcdMsJ3KxFvX6S3Rfv5ODDx/j1W3+V615zFY3lJv/8Z/9Kd3SIRsvC7iyz7it1Lv3xVOgwhM3Fw79NHJ7C6PwJWkecBuJKhIoVR08u8bILdvKBE0+vvpg7OxShpfkf/99neNubns9IKTUM//+++CB/fu8jWFLwyDt+jvPtEkJDxsuRHVF0/Yi5Rh4jl4BIg7sArhKb6I1JLiqdG7c9ahfJD/YATSs4yGWlF/NTG1/CheVvX6RpBwH/tOcJtg4P8qKdW895bN/iLF+ZOsEPbbvwu+JN49kWnmUR6h5Hu3uZ8y9kfWbTv3m7ffr06dOnT58+ff7f5DmJNFpr7rrrLkZGRshkMt+rOfX5Dtl98Xq+9KX9iDMtdBOiTJ72VRuwn5W6o8dMwpkAs9NFC9BCYCSK89YsMZAN2LC5hTcS0Y0MjB3biRbnUJ0GwbAi3FKieH+EPjKZKiG2g4hiVMZBrR9BLtaR1dY5cwrGBM68TsUWrVNfmXIB0fERQUKSsyE20EIgMh4oReL7+FdsIHOi/nWP055pk31iCWupR+fSErUXjpI52GTwY4cw2tFqdYwGaAmS0TIy58JSF7QGQ4JloqP4rEcKkuzTFfwtgygpUBdsAkNiPn0KCIlykt4lIxgjJtkHThNfsgVMA3upi5CSuNdDtVpsvLrGlucvU1szyiP71zEll3imWUoDUcnF31JAdgxkT62KWkf+yqB4xSD6IROlBOZJoA2qUUEEPpm6j14RxIJNNrf/0wbCSoQxmkWjEbUORBF4bio+mBa62U4FGgFIgXAc7FqDaNMYej714hGAPrWADhQyl0E1UwFHVx0MV6GlwJu2ufzSOYqqzb1PjxJ7qb2NGB+GMEGZJjJRCMukcc0G9HgJ+9gCubkYFQZMfzGP+dO9VEiLk5UKmWdO/NlVe+3la0nyBtoWED6rbUSAe7wBryhjf2aaqJtQW5thcL8JQZo2phpNNi9tQO1cZCYZIFKSa14zz8YxOFxPeOyw4JnONplopGXglIugFdqyKQ7nWESjsiZEoHMDyLka+tQ0Kh4gWxii5OUZ1anhchAH/PoT/5UJo0h0zEUbgrHXLSLUIA986ijWmhx7qguMDhlYJxTDX1zCua5EMF/D3+xhJFmSHCRFCxlorOWYgzML1NdJrETgzSiSrEFsgRmBWmkoVAVIKiEn7zyBVEWy23JcVgIn47B99yZEIcOJ2WU++Av/wOzJBfzXAaFDtH2Y1pElqktnX5+OUcKyOvQ0QOotdP3oOh44OsWOlWjq0ItxvQDQ+F2DI49O0WuFWAm8622vAOD9n3sEndGEiaZSazJ5ZgFlwtFGjZYdk5iaejXBdSJMqfnVP/opZDNhaM3g1319Zy0HsXLE052IL0+d4D1P3M/V4+v5zSvPRnZrrTk8tcjoQJ5y4dzPo0fPTPP+vY9TznhfI9LceeYYXz5zgolc4bsi0pQyHpu3lOglJlcMXsiEu+7fvM0+ffr06dOnT58+/+/ynEWa7du3c+DAAbZt+8a+BH2+P3zwgX00ioo1CrpaYgwMIBMQK1KAllDb7MFyD5W10JFAVkK0pfjk7RuxMglfOV5k6PUVqg8OMrwcs3iyQSdKMOfbBGtcEALbMgg7QWruaxqpZ0e5gNIaWW+x9UY4/pV0TlZNn01OAsRCle6uPPZpH0ta6IIDvQDLdZGDZYhjekGN8seeXD2u1Uoc0vYikSjye+YRQtA1switMZsRVjP1vdGWgc5n0qSmcQu1vYhcSmBpARKRGhp/lcGnVY3BMCndcXqlSiRCa/Osv043RNsGohulZzOI0radOCYeyNFZXyBfbXD4kw7+/ABHC+PY23qUTtdYfNZ+zEqP/J55nBfnaL+jjPNby+n5OZLQXO4gywWUViSVOlJr/MvWE60dwD4yj3NgIR1bdJneNEh5Tx2rp1BBi+AWkGdC3C8DSqEXlyCK0Voj10ykwpTroOcXMU/Ng5e2Y+iMhcAgiUM2nb+WEw8dAcAYjMjd0CKYsokWMjz48HqMdoju+Ui/B7Um/pVbEVpAtYGzfxqkQdYbxfAD/GIJfegkyZYRmluLJE6CAoyv8hpCaTAESJPRD5xCGyAjvXKlVwgj2DjIaLtF8QbJ1P4s9HpEAxmshfSaR2td4k7ClRfOcoU9y5ETa7j3jss4Ulrm6S8BZxZW7yWkwCgZDF2dYeHOJVQvIFlboHHZ2tRfpxeCUmTrVYxEYfkKazpieeYwTxMCr+F48xSFgk91qowMFUiYecBjuTDA6K556r0FVC9EzKUVQ51xm0ZJkRn0kG2D7KIm8gNM00EkGiNrEpgKbUtMWxLlBCAIihCbIEnInO6R2xviPx5AHHP8jjz29qv51et+C5UoXv8br+PD+4+jnjrK+tE8yw8dw/54j2RTES2yhBsEfvvcCrKDnUXurm5hwtmKAZTGixSWq5w5OsXcuiE2DueZnO2kZ04lJJ0YEWtcfdaMua0VOOmr/J+/8CjRqQ5awNxclU7XRANxFLG5UMHSEmEHDK0Z5RvhmjaDMkLLiFH7ZpbCOnO9Gvub5/q9fObhg/zBR7/MUCHLv/7eT53z2FzSoun4+EaE1vqcRKsf2nYh63JFXrT+XPHmO8WWBkPZLIIsN4zchCn7Udx9+vTp06dPnz59vnOeU7qTlJJt27ZRqVS+9eA+/8dxeoJSKc+rfvoGRvImdHpkTrfRKx4f0jIZvW4H7WuHiE0TnQiElIhcjsXJAU5MFmgdEeh6THIowQ3bbLk4RqzPYHYSinfPI1od5Op6R4OUyKU68tQc8swSuC7z3Y3YIyvJONtzqLyXLo5tC6OQo3dVDJZAtH3MZoDVjtG+j2qk7TYZ5X3DYxRAOOaiMgZIgR16TLznKMV7l1arUpKNYyTb16KGC6jtBua+k4SGRsWklTRJks7HNNM0IkCGMUMXeKiNHupCD7n/NOYTJ2AlVtzyNe6xFt6+GQRgHDqD+cQJkpl54hMnMZZbJKamNWPx5G0luhlJaXsd0RRn55V1EBqy+yuYbo/65jXEhdQgWMegFjqoTg+pQUiDoGwTbhsmHPFoXD1GtC4PpkEUZAnLNtXLB0iSGG22UJsc4iuzaN9PRY1wZXE6MYz0XKRtE1eXV8+hsNJFts57qM1jsHGE9nwtFZ5Ghshdl6F4S4vMJT1YrBHVFWE1QUUCadmIUpHQ1fiDEtHoIMfHMNaMYVY6KMvAWupAr0dzV5buJpvh2xdTj6Czd85Zb5hk5ZoojYxJBaVnxklBcPUO4lcbVE4OMHlyO2HPRYYRjVftxN8wQFK0MKs23dkme94/gn9YkFkeoNbZxl3/PEQbn2SljEYDYdFG2pKcYxPGCjTUZ+vkzAbFwTZyxV/HiAKStYNQzOF3YxIkx08tMj1T5fzyThaODpMMxQiRtlB1D2UIHYvWBYOURpsk4wZyMG0JUlkbZ9rHbCvsaoTViPFme2Qem8Q7UiXpJWQqsOFUglGN02olT6MNiHICoy2QmZjEtlGul15FQxL++UOolSQnQwrGSjlK+Qy/8Kc/wdDzt7H9lSZDa0IYlIhKA71SefSlhc/wO/t/m/cdux9bHcLuHgYg3nOKl29ey8F/eoA//8W/Z+vARBqtLsBrB+SKLjJvsXH90Oo1is2EzGTI4GM97n7qGPHUHMncMtWwu/JeoUnmBfKoINOSfO7dd/PQvz72jV/jQlCpDzG1NEitk3DtxCau3VzipZs2nzPuwQMnCYKYmaVneUR1b+Opu/4LT73nPvILMWsLxXMEGoDNxTJv2nkJQ943SJd6jmQsmz+/8ZX87xtfScayv+nYSEXsbzxOLex/hvbp06dPnz59+vT5+jwnkQbgj//4j3n729/O/v37vxfz6fNv4K0vP59stsqtp/bR3JQhXlgialVRbrogN0ZGaH54BqNcoL27iDYluZ+5CLGuxM0vOMVlW5Y5v5Bw6gObGBZDHD2Z4cknLLCKyKFBRKIh45IfLKTVD2aa9yziBGOuiuwF0PVpH6qQxC7NX5ig+hNr6bxwUxqrLCRCGJjDYIUmUgpkK4D55VQ8abXQUiK8ry/SaGNlsaUkwk8gUciZShp5PTGctvoAotmFIEJ0fNzPNTDmQpyHj59zs2tAOEb6k2UhBlyCEwZixyj+j5TR623EysJe5HPIkWG8Rmo8e9ZwJ33caivsY4sgJCpn03nBTvx2jnrbYWH3EElOEBfObg8g/JzGMBP8y4rnGNnSbqdpTF0fq+ZjPzUD3S5mL0G7gmR7DnfRx+wqrFqMjgKMwz7Ox6u4f7+SemVZyLXj6PEhsB10GKHjGCNWZ41zV8QnudCERBEYJmJwAC0EenGZ5u1dZt4zSuXuMqroEuUslCNAq7SCKuPiVkPMJR9zqQmmAVIiMlns+RZGtQ0ZDxGmbUbqlFo1CX6mKmo1GcwywbEwMi7G+LkLZzEArbcptu6eJhxwCe1UWHMXfATQvX4LenwcQ9gIA1oLHrcu7+C+KOKRTz7A2NZReteP0bp2eHXf9ZsGkXaGE5+ZTlvELBN5wRDVzVnY4jMxPo332DRJG4giOoOa2O/AgEt56zjlcjrHW9/0HgpsQsUJ9ELigoFWmmDZ4pYn97K1k0E2FFgm7kKItxhjhKRFQmGMcWYZc7aOsExINMZynfije8h+/FGCoqJbXoniVqCFpGN6BEsJ5c3llZY9mQpcQHShR2V9zH/9oRv5g598KcWcx9r/vJGli9YRhhlsJXDaAYWhAgAn20ephks0w1kGpSDfmAJg6ckzvPT6CxjfOMLOq7aRkTZGU2I0JbvWzFPcPoC9LsPwxoHVa7RjR5Xi0Qi3pqjvmU/vDz9kZt9RxoeajA42kYbEyCtqXwj52O/exh//xF+QxN84rtqvOKiazen5HhuyQ/z5FT/BT2y56ZwxnXa0eiMlK0IV0ZPc+8Uehx+dZ/DpmFzDRmv9tTv4LhKuxNzLrxKDvh4Hm09wx9xtfGH+X76nc+rTp0+fPn369Onzfy/PWaT50R/9Ufbs2cPFF1+M53mUy+Vz/vT5/vGBL+/j1AnNfKXFifEsnWvW403XyeydBkDFESiF2QrJLnUg53HFlbswugaH9w5y+kmLg931hBN5jrYq+FGIaChkl9TI1ZBQLNAqlbCGy6QtEIpkg034ogLaS6tCaLQYHWxibxmgq7N0t9hEEwXIeeish3jIxhwcRq6dQAJCK4RtQ7GIXq6QLC2tHlNczqKBYH2exR/bRfuyEZxlH7myJpOxQtgmhuthjA6DEBgLNax9x5HN7qoY8NU3ujEAa35PU3i5IkkiGheWiOoRKu4hZzSGPYpYMw6GgfQ8hNaolSoEdBr9/WyknYGxIZKhAmrQQ8QmP7jmcV65+yil6/N0Xn4RqlxAAP5aj84ZTTloIF+Zg4xEGwLlmqiMR2ejS29DFuUaONM1Ju57ivHPPIVzoomYaSMbTUa/vExpfwszk8NYO4H1RIBxJiQ6b30aAS4lMkpQzbTKQEjJs/Oyzlm2Bl3UUIFueWC1NU6FBmoBRFNjYKJtgzBvov1gpVonxloOyS6GSGlB109byKq11JR5rgI9H0WMtiTR6FnPkGcvZQWkAqDjkEQx2snByoJbA8abc9y09RjNjkfsWmhLwEIF99giaz9/kmsHc4iLFLQbjBZM0IqwJInshDgMOPXkGYae9LGzOdpv2M3yWy8mWlOgvW6QyLJWjIJiWluK+MphsVpksVgi3FVGdLrQC0ikoH7LRoJrJhiPQ4jPtt0ktkFc8ogykAxZxKUEY7nLF/7Y5cK1xbRdzjQxQ4FpylSo04BlgGuDY2N0I2QYwdMnV+5pDb0QLQVGTyMjTW/UIBjMkbQ9SuvyiFwekS0Qli2SnKT7jlE+VTrAZ+65f7Vy5MYtFzCWGcVoJ6iTNcL1E3z6gaNorXnd2h/ljWuu41UDU1xkn2bLmjRt661//GOMbRzl1z/4S7z5nT/EwZPzeIsSb0FS7xaphD5+HDNfO+tt8ycv2orVjTCbAZlcDjFQRA6Vybt1dhRn2JCr0jMjGmcKxPMGSaSIo4TaQv2c11DPD4lWxEOzYxO2bdr11DxaCEGvExBHZ4Wdxv0nGfzcJLkHplHPCKq5n2d88zXkSnm6GhaP14m+iRj0b+XxqVl+7IOf5LV/9yF+8zNf/JaC0Li7jiF7jG25875nc+rTp0+fPn369OnzfzfPuXn+ve997/dgGn2+Gzx5ukYkBKqgEE1FXEhjp62VyGi1sExp/Qjq1hm0hNzEIE++607UwACPPzXC45+OWXzzKMaQgVEXDDw2D46L7vmoThvhaKg38BeWQGu0CRgGwWvLJJst6CqsB9q4Jc0Vb8+xLpnhM/s2YrY0Zr5INCCwfEVuf0B7RFE4HoJtQyYLuQxCC1Stvno8wQsvSQWRhw6hig7aFCRFOzXGfbZnSddHOT6EaZz1V8d3ryJl6tcCGBkQIqF9t0AKib0coBeWMRc0ur4d4QAiIHxxEeNwF9EI0PUmxjPbjmJiz8bohai8jXIFZiLp3jCKvKaDeSjiyfeMMHpLi7kfHkbe4aOLLkxD5ZVrCddlWPPoDKO7Qqo9BYZEDo0gilmEtOhcv5X4yRm8Y8sEvkN2NCBsgVaC7piHypkMHo9WTIJNtBSgQDsGqtWGKEaYBkZp4KwHT7sHz8w/d1Y0iXfGJKGPtGwmXuIz+zkH/AClEmQkgRijGyO7Co1GL1Yg6yIcBwbyMDxAfHIa6XkQR8T5DGL9OCJMMNoBwo+Q6qzw8tXXRrfa4PuAQC02wfOg10MA0/U1HNubgZaBu01SOG7i5jx0lNBOYg7YcwQ3WeiahVkJyL7KxdSC/BfnMX1YOjmHck0qL9pKZjqguKeBsiRGtoCxboIomUG5Jk5L4y5rhLRwlYN4YgZMSSwk+emIoavK1I8uceyiMk0V88zZay91UHkX6QkGx338lsSarqbn2Vjxf5ECI5vFMSRdYpKMoLHJwLPXkJn3kU2fgg7pSrHaBhauAW0l2E3QUqMQCCTJfIXjn5wD2wKtsTsKFSuMnsbzLDbvOGtae+b+Uxz5xGmSRhk9MogKQp567BQAQ84Ig8Mv5mH/ADPhPGYyAsD6nWvQOgTVQhiDvP6iC9n78Ex6GIUe5omYwa7BlsGzkeqbx99IMPAezE5M4YJBWjMt0JoBYwfFo2c4PjUB0waT0Rq2e4J86QCFoQLd1ll/nKnpKm/9pfdjWya3feQXcKSJ0Ytoh6lIszBT43+/6zaGxwf4z7/zWv71Qw8ye+9RaLRx5hQ/fe3/4P2P/E+EMcIrfvQNqKF9vPvOB7GCZFXA+Xapdnt8Zv9hdq+b4Pzxb+yd88xYP4w4Va8x123zK/6137SNasmPeGTJRqkeu/vfafTp06dPnz59+vT5OjxnkebHf/zHvxfz6PNd4C0vv5R/2ncXsfRZrkrGDrahNABhNx2gNJs3DbO01EAN5GktVLFjxfqrNnN6roUdjmAEPmHepHisDkojBwrg2CQliVmpQSdCWBqjJGi9YADR1dh3N4gqHuahHjprE1w2yJcm8xSzPXYcWWZxUSBOLWCYBoltYp2O0SMeSS1E5nKoahWRzaDdlXalbAZsm9gzsBsByQWbEEGP8r+exKz66EidXehbJtKPUHOLZ7uQpEC7NnSDsxU0hrFqAgwQzcL8nyRoewiRD1FDeRJ7GSMUGDMVoqxDcl5E+IocVlbj/HNt9bmrSz7bRvshJAlytoEaMxBtjYwsDANmnxxl8vEs8a8IvFaMXY/AkNgtC92QJI9BN8mg7S7oGBU3kV2BjDK4MyHeoWVEL0JbBp3ABlsjIk32qSW6O8roxEJImcZsRzG988eINmTR9TLuTCutqNEKNTcPQqatPSuIldYwPJtO6DJw3ylCLRl/Jcyi0fkcyjYQQpCYJtgmwjCQrgfDFrrTQc8uQKMNSYywLUQhB0IiHRs9t4gOI+yFQaL1AtkkNeUtDyB6AbrdQY6PortddLuTVs+4TlqFI0hb1zQYXQMqFkjQCoQSxBduRoU9DGmROSkJkxbWcpbZk/OEf5WQ/MwFSMNOr7dSyE6Ic6ZLpmYjeyHWQoQqJoheSJyzkO0AMdnDHCuyplDnpWsPcPv4GMlsG9OwkU4W9cgSSRijqzUidfY+yoSKVi9ifEONxTs8RhaOYc+kYtgv3vxqfnZ0PyrSDK0rU1lsEluSRArinKS7OYMZalwEvSfPQC9NGxOGIFMMSOwEN+/i1zR2U5MISDYMYcz5JDNzaUqWLZFBQvm3l/jvn/5VLt141tD989HnGbklZO4DRYRMjcPdWmO10kbIMnN6OxF7GLNTw6DpY/NsWPMPEB+Hwn9jbbGE2U3v+NlTY2xsBFixRDyrOEVKk9oFGWQEO9eV6Bhn0EoxUhjk83+/hu46CyOIyQSCG6/ZyaOH5imNFVm/c83qNv71s0/g+zG+H3Ng/zR2RqI7mrZIq9eiMEYpRa+T/v7xv/0KWA5i0EJms8xNnn19Oq7FrkvXY9/zKJZpPOd2pwdPTvLJffs5Val9S5HmBds3U+91+f0v37MSL/7N9+UnERpNNwme05z69OnTp0+fPn36/PviyJEjfPSjH+W+++7j9OnTdLtdhoeHufTSS7nlllv4gR/4ARzH+Y62/W2LNM1m81sPAgqFwnc0kT7/djZMxIzua9CaM6kHAjFQQJcMEuts1cmBh44QrxtLKyF0zPoLJuj1JElsEmUcLp1b5OSnG5i1AAyJ6nUgidGih+pB68px7IzHjueNcDA/iXEswry1ivlEDwxJdNVGrJ4iqIUMXr7I9CMjqOMNZBhDGEMUISQMDRm0RCkVXOIEOl2wLNRgAXNwEKIEqxVi10IQgnihhbHQXT2OZ5ZCYiWW+tnVGWrTOHowjzw5D8tpGwfJ17Y8BFhoO8bwCsjTtdRXJUyQ89W0Dcu3UJkE8+EuuuDR3TGMudjGnUwrJcygk3qGdBOijYOYZ5bwpiDpbMJYMkiSLtR9sn9VRw+W0c20wqBw7xxqwMOuNGm9cQhTKAhA1EOIOziLHjJnIzrROVHgIohIci4ylyMbZJBxFy0lOnnWOdAac8hFdEQqftTriEIelEJFYXquAZVbMdIdLGDWE2QzpNuNOPyxHCpvkmyfQMzXiKWEjaMgBNpKRRtcB3wfnctAu4MG5NgIwnHQnQ70ugjHQeayRKM5opKNIgbLwshm0a5LIgRGPgeei+4FIEIufsMccWJz4J+yqc8Q4FQT9DxIH6SvsLoJwZBJe20Or6pZ17KIH4pS4+VmB+9ai2yS0Lt0LfnpeXRaBMLIgyHCgqQdIettRDdA6gSz46Ntk0RLPEOyvlDjM3t3EQwZiMhEzlTQvovvlUksiYFgXWlg9R5ac8EYhx46xXzLw27VYbq3+tgv/8XtmL9yAYVPLdCr9VCJwkBCTzP4SIv2+izBiI3VjaDVOxsHn7cojfVQGsLYxnUMOlJhAMZkhaQdIEyJFhKZsVHdiHCmyz++5YNc+OV3kSvn+OCefXgNi2RzE52VJBgYjYjsBROr81vyW3xw8kmuLJnMns6xGfiN1/4x7/10iaHh9I6qN7rISIEAc9mmWfMxFBw8PMuVl21KXwfxHfzF6w/y9OJuXnP5jfz8Pz6GVgnj+RyhSAiLFmYDkk6Cj8mLf/z5bLxg/TmvxfN3TRDdmd7D46MFvKIk9hN820drRSV3Gy//zS4XD70BIQQj20eZP3Aaa3gIhEB+1eeOdARxRoOliJ+jSPO8jeuYbbS4csPabzlWCoHQgotGx3jxeVsZyeS+6fjLypt454U/yJCTf05z6tOnT58+ffr06fPvg3379vGOd7yD++67j2uuuYYrr7yS17zmNXieR7VaZf/+/fzGb/wGv/RLv8Q73vEOfvmXf/k5izXftkgzMDDwNSkZz+aZmNPk6yyG+/yf4aOfOc3MAwNIAYNlj+bOEKtr4aizi5RdV23n0T0nSTIOYrmOvGwD09NNoqyLt95k1+gJztyZAwRGziUqAIsVTB+0CZYysWuaUwfaeI81YWuO4KpB7IeXERpGR8osz9XomCZHWqMo36B92QjeUwK7FpGM5PjRtxyjeVGOf314CP5FginBMIjzNkQQE5GMFxGGSZLVIATJmjLmQnX1OJ5pm9GAylgE24fxnphNF7pxknr6PnMvem5aocFZcSfeMUDw1gF0qLE/m8F5okIiDZJyBsOPIY4x5hKsO3rY+RJqxEGP5IlzLkxW0/37Z8+rVVlMO7CkIB5yiWIH10/QnkF3+wCG4WBMexgInPk2TNXTVqXba0hfrcZ6J+MuUgqwDXTeBaXSdJ2Va6g9m2BDEWna2EjohGiliXe42DNLOKcqiDEbHRmpeCNMjEI+FVmaLSDBX58nfl5qpCsrLfLVNtJPUIUc+QFBezlEdHxktYWQgmS4mPqnBAnaNBFKgTSQpRIqihFhBEEAlpXuI0jbzpRpkNh5tE6I8xIdBZSzkuWuQAhA69QrJwpBwrEvlbn59+c5escQYbWBMEzsE4tQzqdtb7UO8XId5RQwq1nsmmZhpkY2Y9OuNhFSkr8BFnwDCRRvcah/OEjP3VIVPT5CtL6E1epgGYKgvtJuEyuivEB8cT/dnwwYuC4hqZYJzyiSvEXiKFylQGssQzB3aoHxTWmFxYFTi5B1oCZojkq8o2lqjwCah+vIy7LEew4RhRFsW4dU6ftkNJwlN9NDJzF6uopwbUQ3AClJYgl/KTF/PMKSGgVoA7QAs9aCIEbmM0TaQG8ocsHmeU4eznGmEPN3j3+FGzZeyhcPHydc2kn9rx9DjQsEAr3OYuK8Dav3rGdaDLsuiXboydR02rQ0zuB/RgwIhCyzVHkMu5aKgD3LWK2gGR58lhghcly/o8v1F4/xlcOnkdkI7SdsuXCMH37L9fzF3sdSP6tNGn9Tj1e/6rUceXqaTssnm0/FuFvvexoZQexCfbmJNxBiJT6Bk1AL5nho4Z+ZaRco5F/IRfkLyI+V0FvWoGpdRBRRGD/bfgUgbEFrW4xpSJJnt0Z+GwznsvzMtVegtebg5DyDhSyjpa8vqmit+cyBI/hhxAVj37zqBlJvnTWZfp9Tnz59+vTp0+d7Q6OWwQ4z33rg95iwk+oGV1xxBYZh8Au/8Av8wi/8wvd5Vt8dXvOa1/D2t7+dj3/849/Uk/ehhx7iPe95D+9+97v57//9vz+nfXzbIs1dd931nDbc5/88E6UKc/k2xhnFyLo5Dm4o0q260Du7SDkxWeH6d76c+//8TrQUHPvUo8jhDK0Xb2N57QAd+3Jc+zBxYqAHB5FDBknXR/gxIgF7soZl5wjthN7548Rbc4hegDkZkI9NcjtHOV0IEFe1qGMRd3qE4yU6b9hJ6ekIbElhyxnuZphgjSLclFColBDlAn7ewj05TRKHdK4YJ7OsSQoCAkVS9VEjA7BYJ15nIjoKqyFWWlo00UQR99AiIoiRkwuI6SWMZxJf/CBNoorj1fMgMnlELBFdAZFGDpURS8vQ7KEl6EIG0ehiYKRChC+wjy1iVDur21it5gForchGSiOOh5hCICwTdZ6N98AZ7HpI4pqw0h5FIQ/tDuLh6GxEdymLKnrgpoa2IopRZ2awGiH+zhGS4RzOiRrm0Tl6548iXRu7o0l2Gvg/MIhxTGI9bKIbXcRaD9FwMObr6ChK57ZSdeRvLREPpr4ZulxEJqBMAyMWmAM2ZqMFR6fAcZG5Am5Hof0GCaTGtyKNf1bNVuofIw2E7YAUCDM1F8Y0QGnyj85jNxIyT9YggXBhFtUCYVrobg8VhOmJTKBbs7j7wUtoj9g41TR5yT5VRW7bDAJEB4xjSzhnaqh1Q+iJYZQUBPUuRhChDYPGEwalV84TfFAR+UCSevUQ+ejpOaLhYShKdCNEmEZaWeTa6KVl3DMNGg8Ihn5W4o9nCI47NC4ukJRcvCMBTjWgUzD49V/5G973if+GZVsQazAEyjKRRlrpJSwT6Vn80CuuYOdYid/upu+dQSHAaVu01rr01lu4LsRFCzsD3tMROogQloUc1HRvsjHf28PfqhE2dIZMRKzJjpaRnQhsA28DvOxNk/zAlWf48mdH+aNd2/iwv4c7Hj7K2y56OfuDI3xRlxDaQAuBtTamZs+tCuo50+W1azawKbkDlbM5vh/eeXuZ4tDg6j3+4J7TqZGx0HgLGmWZCCmw7Wd9dNhXAQn3PDLA/3rP/Sg/g22ZdNqafZ89RDZMMA3J8368gTFyiM98dpB7PnaYy67dypvf9mIANgyX2KtPIRKIggQRJwShhQ4FMl6gUSvxr2fOY6lzlL+++QJ0L0S0e+hGC5KY4o7hc94LE60JiImUIFTf2RcHh6cWec+n7iPnObzn5171dccIIfjF51/FXKPFrrGR72g/ffr06dOnT58+/1F59NFH/8N12hw7dgzbtr/luKuvvpqrr76acMVj8bnwbYs0z3/+85/Thv/wD/+Qn/3Zn2VgYOC5zqnPd8hPv3wnxuwhHjxY4uh8gRt2nuLup7cgs9bqmOr0EvN3HWXgV6+j9pufhR4YdoRBarC5tOQx7mZwHCtNWmkJdCxQcmVtXuugohZuxaa3ewyl8mQaErV+De7yFBMbJKe7Cnewg2uGzF5WIvYyDP/LaZzjy8Rbx/mjE5cRy5CBe7t4+yKi8SIqlyFca6Ey4wRbCiRFiSYkO6MwggRZ9GhePo6c6JL94BFEV6FJK3CSnI33+DQiiM8a0ybP+vZc61SgGShCvUHzmvUkm4ax7vaxazGi2+XKtxznkd9xQEvUxCBqwwhiqYk5X8PPgXNqDtuQyGIBvWJAvBpn/VWYyx2EB1f+wlGifI8nbs0AAiNMq14ARKsNUqBkmk4loxjKBQzTRfVCZCtGtgOkTo8nXDOAzliYYgmr1cM9UkEYJroTQhgh2y664pIMZzD8IBWqeml7k1pMI8611ihD4h1vI+0WXAsiTohUgL2StNOogPjrdfCzU+g4QhsaYUWIyEJIkS7wIRWaggAdReh1Y7BQAf9Zb1ZJkqabL3Wx2otpdQ0Q9DRCgRgqp8JZL20P0kB71zALaohS/TR65fxKQNY66KyTVvvkMxhRjEgE3lQTNCSVBrrdQRPRev4IiVHE1m0uuHmKR/YNPXNHIIII90gdIQXSdInHcxizy8hEI2NBnLOJtY1VV0xctkjtNoN8Q6Nci84lW7ClQXfC4sCQ4IFDkzxxfAYhNMZSl3DAwMn3cG9ySKbHQBqUfMEV15xHaaJAu92EtosRQ6YSEQ9plC3RQqDyHoYGhcbJdsm/ukB9IYO0Ato5AzMQxB6AwM8JMotdpJdn5C0tnn/5JIPliOddt8xQ2MNQmrik2L52kHfvn0c7Gi00yhQ0u1kOT0Ucm1lm+9pU1NhdfiHzS58k1mla02dmY27y5rh4zTgAb3zlFTx4b5o6ZUhFPGFRLuS55I/rtO8AAQAASURBVNJntSv5n4buJxjJXUaoM3j5DDu2jDI6McBUpUEyZCBCTe3JiIFLFRvWj5MvTrJ+yyjt5j+SRPvZOvFGXC0oJQYbto9jP5Jl9P37iYc95l7xCAeiAkjNQjcVwjqOROccRJBFmiZ28dxKF7GSU6Y1xN+hSDNUyFLIumwZH/ym40ZyWWqd3rdlUHyoOs+79n6Om8Z28HMXXvsdzatPnz59+vTp06fP949vR6D5t4yH7yCC+9vlD/7gD6hWq996YJ/vGtHcxewa/RFGXrKF9dcUmPyYSe7JJfJfOLQ6RseK04+ephPXab9xgsINWS5/x25GH5hjeE+bwUcb0OqRtP3U98KWqB0bUdvXQQK1G4aRE4NsuGAd66qK/BfPYB5pots+C7WYBz/0FNlmwitHTvCa4RPooQxIA2uhjUgUYr7CwEeOsXV+Cns/SMtKvU6kJDMfIIo57K7EbINyFK2dBq2NBloaJLZAnI7TtjvbINhUpnPhEOFEZrXC5ZmGPEFaQHHOsqneILEk4aZBmueZ1J6XQ0gDc7bC8dsEIxMRN/9AlYLRhG6I0iGti4bSthxAFgqIfD71eHnWfp79kwbkiVn07DyVA5p9P2Wwulp8RqAxJGJsJPVd8STSkKlJ7vFZjKeOYzVDZPSsmZsmRi/15rEWOshYY7R60Oqgmm2M6QDvDxZojEoaOzWx0UV2fEScpLvu+Wl1CxBtHsEcHibz2IpZS7WOdabGszsZmw2beK2FGB1Cxwm6q9BxDEvVVTNeHYRpmlaiiDKC2NDQaCFdB7lmPDUtHh6E0eFUiFEKMh7a9sA0EVoj0AjXXj173sFFineewqh00VYqwAnTQCUBVqNHt5TQe9462i89H5HLgE7bTXSwUp0UaniqS3jUo7dzBF997eLarHUxOgrhuQxvHkfnMyAEnu8yVh7k6h+fZzhpctklRxGVNkYjhGyGaEQQjDswkEAXfvv3Ps6//PJHUAtNjCjBnm3CnUskh2KQJiD44Efv59HDZ3AnJrDXbUCbqVgaZ0xklFA4VKewv0b+jA+9AKE0fuhgbTZIYoVCYSmJMAR2E+wmWMstUAkqTOidtIlCE62gFxtU6xnO1MqUzQmQguUnZkjiLiQr990SmJF5TuvOaGYTmfIHeLgzAMDH7s3xp1++n9bK/bK02EYGATLwiYWgNJBF+wnHjy8+66RuB2M9MdvxbIuRnaPc8vor0J6gd30GkQRYyyEnPj3M3AfWctnu8/jdv30Lz3/ZRcj2H+IG/8pS81+wpMQyJJWFJmN1C3eyR3Z/kw+e9DkTF5gYqvGiDWm7VmgKVM5LfZCEQItzP8oKjkXONcg4Eim/s4+54YEc7/5Pr+TnX3XNNx33tw88yl8/sIf7Tkx+y23eOXuAmWiaOxf3fUdz6tOnT58+ffr06fP95wMf+ABXX301e/bsAeBlL3vZd3X7zznd6dvluSZq9Pm388G/vYO9J0+z/vmj/OGPvI271L28/3c+SfdZVSVGqUximui/OIQbdunOBRw8eYJoowW2AfkMyjCItcQIAhJHguuiDQvtWmRP+SQzdU4XHcTJReyODxqCCQ+jFeEv15i4IMurshWqPWh3Hcyupnf5JuzJKnHQwjzp498DankZZVtIV6B0QuPqMrnTPu5MiD9o4lQiGueZ6LyJjDrkT/oUN7fotSO0NpFhQrAuTziRepbknk59NQRAxiExNLIVpr9vgTgsk2ycQDugzJAoa2A0u+huwPLxDG/+xRmueXET29Pc8aGI2s3r8XcOkosj3BkT7ftgW+ggREhJVLQId5Rxp3yMmdrZfVsGeusER/aswczNQ1BfPf+JIzBzAwjDQEuJUT1b/SMAwgixsIQYLaFXqmh0HKNdK23NybmIXkKwJotZsODxecJ1A/QuXYO92CFxfKzpFsI00GF8jqGyBhpXDVKY07iVlZjgUhHR9M9Rs4bPzNHOuhihA6ZNMreEsEwEIi2nWmnl0iJ9kjG1jAxTQQjLSse4GViqnN2oaSLLJZIkQlgOqtlCZFyU9s8RusxahBHEacR0IQOdLsbTp0kyNtkgJlpfpve8TbTXWMjTMeZyGyFIk8GiNu4nmjR+UcG4TXPuJuCec14jOnVXxig4bNwwRO3L+yBR6GqV9ddfw1MPK87MjWPsr2IzjRQ2duAycfkZKq1BNs8uM39sDf5n96ODGLnYQGzbDGGIKhfojQ5gLnURXR9n5wBP3H+IxYPHSQqS9rVbycwbRC5oP0IutzD8EF3Kg2Wik5hoTYmT+SK5y6vouyQyEDgFh+aKaCJHy9hrQ6KWy+IH6iS3BEQCanmH7rRDVHeZTkKGtEOpVUe9PI94FGSssRXk5gPynk2sYha6j5DoJcbcmzk4/3LWAvJol/MvGCDrpOLZ0swyzmxavaLzPpuv3kil26ZSaZ+9avblCPtyPvI3f0frS/vo5B3+5NAk29uXcnokwtrl4sQxzg543gu3UVuoc/+tj3DJCy6g69gMmhGPNqAnNNLS5AY8tg0N8kQiIIKwWiZO5qgpkz2Hp/n5i6A0nCUsm1inu1Cp4118rh+MFOBkumgg1jHfS67bshGAHSND33LsqzdexKnuLDeN7fqezqlPnz59+vTp06fP944//MM/5H3vex+/8Ru/wXvf+15qtdq3ftJz4Hsm0vT5P4+7PUOl7rH8dJs3/sb7KQxYsGUd4tCpdIBtYhdzqMAnWGxhNnxipWkt1NnxQ5ewz2hjfmwemSgwFEm7gchkEcIACcKU2CcbiDghOTiNyLvpgtxzkb5CZU2MdoxiCT+RLM9I7OPLuP4gpuERb11D3Jon8mN6ponMhUjHw5irIufB3prFH3bxhxNEqMifTCg/0UH0FEZJoRsBS3EJZyRCjwyh2g28wzWk4ZGsGyKcbmJqC3NoBDW/CEWId2SwD/Uw2g7RrkEIIoqP1Mg+1GHph9ex+OI85Y84CEMwff4EZ4Rk/8G0AsM7VkMVM9hnWiihoecjej4inwfToHNFiWzXRQy5sNRGh1EqqkQJYqGemqW20gofL5vw4h9tsj+zjYOfs3Aml9F+cFackamfjQaMsVGkEMSZDKy0d3gPnEJ7FqaVRRQ9nGoDudhOq4VcC4TEExF2p4mlYpTtpaVEcZyaBq9UAw195ADLb9xJ/kBqmqsC/xwhB2D3xTPc+4FhdNYF10YMFFJfn3JpVaTRgLJtRC9EmQLsLGazi6rW02P5agPxOE49bIIAjUAZAp34CDv19dAC2i/bhQgVxfvOQK0J7S5YJmYEsUpNk6PRImYvprXFIxqxWf/pBXrdLtr10CMDJNvWYvqC2FCMXrcZY/8Zjj92anUayrNoXzlB/kSdI596ALEiYO647jxK4wM8qAp0MyZhI8OoaSJMgTYkm5N5drgmM6ck2RN1gig9PhElKKWId2YgHgFhkfgJZqvDJVds5DWvuIpP/8aHETrGUJoka2NHCd7ek1BtoaWged1GMjkbuxlSOr+D9XTIYi9LnATQSGglPZKCQEtgpkbU6JAMDROsH+LwQAEnTri3NoKY8bAjifjnk/zR+99LfptNa95GC03spdfEXOxxaN8ke7zPM6se4rJ8wpWj6/mt636UB790Dy9bN8FP3ngVH3jkcS5bN8Heuw+wcGUeJBT2tTm4XGF8KM8VV2z+mvefJ+89kgqJPUUQm0x+do6RcZOwqbCvtBm53uJpd4rBT0f8y19+gaXpCiduuYhBq8vc1DBJ4tMMFMeenuaSHRv55EgRw7UR71sklF2ql4+QmEtA6jkjtAYpkEODJMI4Zy6RUvT8NH67GXYhW/qa+X4relHErU8cYF1pgBu2bvyG467dup7LNk4w7GW/5TY35oZ575U/9pzn0qdPnz59+vTp0+ffDyMjI1x77bV85CMf4U1vehOdTudbP+k50Bdp/oPwVG2SOxsLBJ6JPa2oVBrMBhWMkRxDUytJLLbLxMYhoueNcODRI+QfmkbUuhRGStQNm2qUMEqy0iv0TOyuSZIJUM1Oaoo7UED4ITqOMRFE0gDLwuoEoBW0WvT29firvVs48gmb/N4l2jcMoJoQWwlsGiFshURGjGcmkMsgA4UwTEpPd+hstAnzBspSJBkTGWrigo1zdYfKMY/2oMmwa6eLs0Bhn2piLwX0zi+z8JMbcaqKkduX0EmMMgxoB2gVMbKpw2U/c4bb/+cOjFN19KiNUKBNgZ0rk9ni84IrDjJs9mgPrUM7GitxKM4aiK5KvVAqqW8HcYjIZPGONvHPc/CqETKMgLMFKebkwuq10cD6nx7k5h+a4cKpg/zWhzavth89e8wzgo2Oo9SMN4pWH/e3FknyFsbBFoZtQauDjtIqHPv4EkILordYhGoIcTzE6g4jYoXudNIo7pX5GQlkGj3al6eLVhklRJaJjmJkxoU44YFfKRGMeVDrYmc9dMaCfI6oYCFaMSJO8Cdc5P4qJtB61Tj5BzvQ9iGK0nvHscFzERkPXW8A6b1RHC9RWWoiIgUXTeBM1YlkkvoddUIyToK3PkcPDVECjgOeS+uCHNGoi70Y4042ydQjrEZE88IJ9GQTmUjopv42hSM+cdbi4OQJfu9vfpa37f6v6XzCEBkkZA4sEhYc5FKYJmxZFr1uzM+842Xc+p8O077MBAYIx7IYrodhw0P/egGyFqPOVIgHYzIrghrAwkUu0Y2CzJOC/JM9ZK0DWrNXN3mrBKSD4UZkozphRWHMLKN7fhplLg0KB+vQbKC6AcYTbQbXWzS6HiQQ92Io2yReWgUUlkyMxZjYjlDFHO8/tJtNo8scemwTpk5IIoluRTTaTUZrAr8zi/LKGKMFjAjMvMOH/uLLVOwltv/yKIPeOor2DnSSVs7819/6ab507BS3PXmQk8s1Hoh6JINpAlN9V8ibdm9nx7oRRkpfGzUdZVzk6Ejajqg1WWGx4UTEVODTbRt0J0yuueY8Lr15LcuzVS570Sa+VM1xQJXYlD1FXY9BIthx8ToWKy1kroSSgsWTS+T3L6DnEoavSwXUUChiqdGmBGlxNNfmzumDvHhtWqFSsBwGizFKa1zzuX3Maa053qiyVGtz+1MHKbjuNxRplNb8+v1foB70eOelN+JUI9bvXPNNkxD79OnTp0+fPn36/N9NNpslSRKGh4f53d/9XW644Ybv6vb7Is1/EExpMLzdwC0oQunTOhgyMnsG79JR/E66eKXd5qVvvJy/PXAYNpWxC5LSwxV+6vfexKPFFgc/PoNRC1FZF9HxsdwyOtKIVkjidxHFDKqUw5hrQKdLlBhp1UQcp/4kaNDQmpM8/PkthOWA4VdXaHQNCnuPIyJF44oy9mwLz1Ao5aAkJBkLESmSkge2IMml/9bYaZE/GYGAZD71BFEZaK+3KBxZRjR6xJeWMM40cWbblPd76EoDGk2ikQzddRmyh2pggl/VHPqESxyGkDVRpQGyJyXlPVVYrFB9a5m/nL6UH3EP0TrsEhcVUiXIToA0zFSkeoY4bY+xzBKibqNKDtFWjXFqbrUyI70oJjqOiTYYnH54gC/rIU4/aYCfkKwfAdPAODWf+rM8KyY9mZpFeRYyWqnMMQTt3aNgCNwTDeTCUpr8JAV60wQyAWuxhvGFAUTRY3jAZQkDqysQhULqhxM2VkUFe7ND1y4CoIdL0FkAaaIAI4wgBO9ECzWUQ2idRnkridUEXamR9HpYvo9YOVS7mdDZ5GGfrqUCDZDoBGN0GKUSwtIA9skqQoPfDZGeB2icCZdwUqANkJbJyFMncEo+pnRx8xG1aYnIZBCOQ7QmRzDqIJIumbbJwL5lVM6hebOLfIGF99kA8+F5dOhTe/P5iAiiJ+sszNQh46ZG0vk8qttB+jFGNwZLgK8gCOjKhHf9zWcwazFRLkabEIx4FE52EGMe1Az05CLm1DLGggk5L00XAtS2NqJRJiyBM1VF+zGYJnFB8qe33YUYGUUliuiMwO1FaMcjyCYYnotIBLpSg2odgGoLLgmPMVNZQ7QoYX2e/PEq8eYMhrLxd29CBJreRaOoDCzvHeK0PcR103nuubqB7kpGZruc6rZQm0cRdY0IuxhDRTAEWsOxAzPkii4/veWX8BPB8VaDh+Y+T5lBPvD7f88r3vZGLt1VIvFqOOs19TABA9xygbe+/CpGil8r0ABgGYTlDG61R5SNODZUI7PYw0w0ccdj7t6IebvD+OtHueqnbuBXHvgsh6a2YPUMilsOYT0wgGgo9t19kJNz8xhBjJaC5aUm0jKwY4tLM2lbU7cXQhBiKAMhoNPscNvk3lWRRgqB1GkFmXrWa+vb4StTJ/irpx9h99AaXnPRLtaVBr7h2OV2h+OnKxiu4C/f/26alUXecNmbefEbXvANnzPTPcMj1XvZXbqaDdktz2luffr06dOnT58+fb7/fPKTn8Qw0kruq666ipmZme/q9vsizX8QdhXX8ivXXsEnD36UqX0G9pzkJW+fxc3PcM+Bs34Ndxw7yKSuYC926H3hJFFLc88nHuT1f/hGPj1zL/GuDQhhYJ5ZQllOGm9dcNDaQ87MpMlD9S4IGBofZGm6gipnECUHOV+HWGPIkOyek3gxyDcUcKYSRMdH9kKK1SzO5QXcHPj7TOJmkLbBKI2OYkQQktg2sScx2wrpRyjPxK8UcafrhJcU6J4/iLPQw3HLhD/kQuiQ/ZjCPRqg2gqEREYa92QbZRhoJanNwtKChSVDWi/cRG+dh12NkJaJKmRQocPxJ3L80Z3jxGMBvTUuRjvGsDN4c1Xks5qCdBSl35QvNxCWgf7/2XvvMMmO8t7/U3Vi5+6ZnrizOUnaoLDKQhKgQBYZY2GTzDUi2RjwvRiMwQFsuNdcbIzBRIExOWcFhCQUVmGl1a42x9ndyaFz94lVvz/OaFeLVjJg2eD768/z7LPT3TV16pxTU2fqnff9fpf0Q28OPVNF1FonrLmjCPuNMHyezbG/9Pj29QUIIvzlDnqkjFPTUOvAXPXkm6k1sp24IWmAWFP4+THijIU10+Jlb5ymUIr49DfPwx8eQHVCjKMTyP0dWJZlbr8GqmhpIywr0VxZ6Dq2JdGRFOl8EkwJm3WEFyQb+MEBGE0WGAHgh0Syjeq0kX4EuSzU6hDFJ5VIqf40qZ+MJVk0QiBcF+F5tIoBwWAWf+kghVKW1N4qXqOZBI0sC2XZhGkTqwG6E9BxcnjTAalegYglWCZBBhrnZ7EbGp3W2G2JbLeQpSISMHrbqEJM1BNilUuoNSF6KMQ4KPDnW3zwf3wquYj+wqY+l1lwmhILgcWEI5V5Dm7ZT/rgHIMP1jF/x6W20SK3MyCwNFJrzEoboQE/onTpeuZv2gpAzzdmsVMu0o+RmQxkMjAzQWpvjYoliXozWBWPxmoDZ1uYZJ71RHBsDu3FmK5FlHah7QGCn3yjjIwUMopJzYWEXotoUwntCdpl8EsrMUMDrRSpWQWBz2xtlOX5AvsBUy9YuA+FDL9aMP6jHJFpJCVlE3VsLWhUPUyV4gM7vkIj7LAoepgeruaWH+/j4mfPMGmPc7A2jV7kEFcMEOC12rzrkz/g03/68lOuQdMXuIh6yPDmkOZym84gGG4WbxbciZA+K2SidYQ/OOftbLkqT304TZzVWB3Bw4dXsOToHJ5nsHvLIZa9tID4hwARK4zAQ5kWVg32/Hw/n//wj9h/0y4M20JFIYa0Kbl5XrfmcnQ8B/EBIn8t87sttNSMLW2xqnBinJt/tovNt+ziJa+9lJHlfY85j4KTwhCSgWyWF608g7//+E1syx/kTa992mMyZHZNzpCKLEasHOHiGeRAyNHZJxYQ3tfcye76drJmrhuk6dKlS5cuXbp0+W9IJnNymXtfXx/NZhOl1Env/7r24/9p7k6XXnopqVTqP6v7Lqfg7J6zWNY8l9aONKlynqg2TKuqifSJHcrDt+5DKR/78DzakWAbnPPMjXz/2/9Efkgl2iOAUBo1M0c8PkEsNCqXBiETbRYADZWpCqpcID5tEf4zh5Ab0pCB4gskxSUB5b4GY2N9yCBAtnxEpDC2TKIPN6nWFKEp0aZE28aCXkYVe94nNRUhQo1ZC7BrPrIdEEmJ25CUtvv03DpJ6qFp5FQFea+J2hMRzdTRc1UwDCiX0Gt7WdbT4lnPmCNetww9vBgzU8RMZykcDMmORnT6LebPyuOXU7SmUjQmMgSWg8ikkjKrrKSxwiAYcNCGQGTSyKEBcJxE4yVWMD2HsWMUeXgKsZBtI5YNEmwYZPr3ljC7fgXhLoVodRInpAGHud9ZyfxFaVSzBrXmY2y8T1Uo4R6okdk2i+to7DNy3CzOI7UoEenVroVOp6DSgnv3w3QdMxLgB6jxSVS9kRwj42KesRoVSYxWEp+1akmWlRQGZq2ZXL9HMAx06CNcN7HLbraTjJQFXZpHiKY7iS5OOgWZdHKsQhqtFGYjQkYKU1kI2yLbk02+N47IlvuwVixKXK8ME5oddGzQrhu0U8OIvjJBycTPSdIPTpHb0SR9qImq1JLSMCHIfLVJ+qPjmHdMg+dhT6RxOx65gy10JyBqtMA/UVpWP6uMP5hGqZPFZJ0Dc2S2TWMbKYp3C36vfDcXPnsMs9yL4SSOVLh2cm4aKvfuP37f3AMeKuMQ5xyC/gzKNojdHIM72+Skg3JNMAT9HScJYknBsy4fg3aIjhWBhswZHkYJpKuRFY0x10SjkSUHNZTFqPrES1r8/Uu/w++8YDNKapz5kOIhKB4QzD1F06l5lA9G6J407tJBsosj3KU+ZkmhBWgEojdDelGOvtMUbvhJhlJF+pw85y9+EQD5vgFiadBqBQgtGMrnCIsxYTFGd2IC6+QHzyP4YYhQDmYnyV4pbfe5OrWUNz57NTKUxD026pltvuEfZudQiC8lZsPArpigoBh6OPEc2vM4uuMoT9t4OctWxMipYyxa3EcYBFgTDWq7jvLFj96E2DOHaDQRzRYiVjh9afxIo5v/jG78H9L6Lpz9DsY+Gzc4+TF398072X7vQfZsO3bKczl3YBHXX/0SXnPGJrY8fIRbbtvFt3/4IJVa+zFtL1i2mNdeeC5vvvQSeu5fhrohw5pFp5+y30fYVLqIKwaey/k9lz5um6OtWa6751N8dv/PnrCvLl26dOnSpUuXLr85Dh06xHOe8xwymQyFQoFSqUSpVKJYLFIq/eqaiI/wa2XSKKXYv38/09PTj4kWPVKP9aMf/ejXHlSXXw9TWNxxa4cmJvNZ2HxTikJxCa34xARJT0kWf69Obc4nqihQin/70PfIrZCsKlXZc0ChTBstjcQiOYwx940lTj4qxmyf2JzHfoRoeWhTITox6UuWkn1dTFxRXP6h+zl8qMyRTyikp4n6M5iTTYQQhBMxYdRh7GVDZCYcircfQ4aKOOtgRJrcaBshMqTGQ5QpiDImZkuhCmmMWBNmbaIeG5GxCKt1qmt78S5z6P/BNOHKxcggJn+55A1XzmJVW9z6dz5aCUi7+FaIcCVR2gAtsQ7MYQiT+mk2yhQYHcg/rMjce5T6OUWiVBqv18DaESJ6c2BbyGwGFYZopTBsE1lroWsnyqHE4UmMTcuJ+jPYP/U5UlkKZ2qcwyaiWkd0AMsgemEW5/+Mc5L5jG0d1485FV5H8q3vL6PaVyIen8MsBmAZhFcsSUqz7jiKaViITAr1iPU1CzJDHY+o5BDmDdzOgrBvnAgKE0WJdowApCTMuaSXDtJ2LKyWgiCCSg1kLmnv+eh6otFjjNWS7I1iHuYqIARKabKTGlnT5LYfgnYHpWLClYMw34RIET28kzWnn8ne+/aBl1iMhxeehrl7DEaPQsolVRMYVR9jvIE52yQu56CUQ8344HlktE10NCIIIjrnlhGmBUfztJZKxPQ4zoSmuTJLZl8y1jhnIrwQEZ28bulFkuCFJtZ3TJx8wNYP5lHNCkr1o1sewkiCXjKTRmuFrjVPiD6bBjKIiVImQiZBR8MyGVy7mGf97kX82Tc3E803QfdhIhFakR15xLoLtClR0zECQRwIpEoyfGQMfqjw1vSg7Ii/vupGnlma5urSNF/rPRddU4g4uYfOjM9Iu8nUnRAEPu1UwKaLJ7A6sPdQkXikB2XBQCnL4tOLrBm5n4naVu4Yv4il1mouWXkeP+dnzD90gF2HjzEWNdAZxSsuPp8tX/0paOip2yhLsO/YDKtHTs5AsQyD1HyM0THRQmDVNa176/RummLVwBzjlSJjR21EJFnyjKVE22rUsxrX8vC9FDLnMdcx0XPzqE4fbrybM1dMYcyWsNMOccqgdV4v6XkTY6yKarTRI6nkEmpNdb7DrvlpVrg2lrLJWcsYnN6LH2jSxsmiwp0woOH7eGHA45FasEsvF7O4KRvTMki51mPauZbJs9et4dZ9hxhZeTbyUIplZyx+3H4BclaBC3ofP0ATRjH/6/PfZqJ1jOr5+3ntqqc9YX9dunTp0qVLly5dfjO84hWvAOCzn/0sAwMDT5ou4a8cpNm8eTPXXnsto6Ojj7HZFkIQ/6KrS5f/Mj6y61+YmprArkWYTfD/xGY6yKBEIvyZycVc8NyNTKkOc1++B0wDHSgqtYBwIouatslnDWqZDLQ6yGIe5QApQbM/JnePSkqdHsGxMKSJdThAHlGIS00KjQZLnnUOY1Iy2c5h+C2Ehonr1mCNNSlvCZDtiOYGg/4vHSAa6gE/QDhppGER5iQiSra/rUVpnGoHd9ojLkua/Q7Si+n0Z6Cexh1tYe/pYC+1MGMDtXaA9uI0RsOnti/i7267AJlpE249Blmb8KyVTF+eJixozKpg0Y8TDY/5i4s4mQBf2jRXanK3zyBm60RujqBH0zmtgOkZpLccRRoSkc0glUJVqojFRYxaTDw+f1JGjF3TLP5hhzAlOPYsCxHHDN1Xx6qFDHx6J3FfFnp68C5Yh3X7DgwNWCaUe2B86pH9e4IAoRde9xZp3jIP/QFOKg+dCCVBrVYEnSy2tQFxYAbpg5HPo6SRaJ7EMUJBYMdk7xsnnU10RYRloQlPyt4x16zA0CBaHcxsCqH8ZBCZNLrVQlg2+lGixraKMWdisBzIZJD5HJbnoRtNkCba9yGKiPM2NRVgxyo5XqVO5FZQXoAEhKm48jUPcdv/6gFIxHWtNI5KE6Y7yDhETswizl+K3jsHjQ6hKlAaKjEWBcxd1kP/zfPYB+p4K4uIfBbZFxGe43Lk5UvpubNBdlcDo+odv74C0Lk0algSDxm0npli6dpxtv/TIA4+5B3sSCPG6yjTQhfSyTe2x5NzEIDrYrQDYlNjdRTCi9hw8SqWvmwddtZCehGxTLLTEILYtvj8fZtIqQMIwGh06NQTBzUjr1DV5G6IbBY6Psp36NlaYe8VfdyR9pkOXBaff4ypahmvZZM9FHLBohmaQ5KpnxURTR/lazoFm3CZSXptH7OmRFmC1sEqwWyF+PxlfGHpEurbPY6trPDDnzxA3oVWo4VZ81B2jkYjIPAEQiVaNkILxm6b4B0PfZ3P/s3v0dt/In1TSonRjBFaoKeS4H2r3k80o5k4nKFpx+htDraA+X6TgUaT8848SCt02LztdLJ2SGXxIkSqiTQExEfpW2uj9maYn+zQOqdI9SlZ/IdblKct6OtBoog9D8NxMWc0z16a50B9JwKbc4wR7JqP8iNUcPIz6d6DE9QMuHXHIZ7zkgtOuZa2Ip+0YTMwmCe7Kk8u7WJaxinbPjwxxafuuo96pcK6pkcUPv4zsNnxybj2Ez7Amx2fwweaeGEOe03ncdt16dKlS5cuXbp0+c2ybds2tmzZwtq1a5/Ufn/lIM11113Hueeeyw9/+EOGhoa6Lha/RYy3Z0mdVUU94EIoacg0nQfLyJwDwKan1pl46CDb0tN4Z5o4twoEgkzG4tKr5pnbWeHh8R4MnUP09VBanWdubxVZj8ntn0PU24kbDhq0xnUsAjtxhTEdg9mtHWpfnWbnp2/GefcaBoMR5jaNYVYUhBKRzjJ/sSS7r4PVaiIicPZXMGaaICW6P0tzeQllQmY8Bi1waomorjw8g+gbwGzF5A6F2AebyGaEUdEMfe4wT3+Vx7JXe3ziU2uRwiYbm3SkAdUc4ullRl50hE56jNmdq4hNkFZEa4WBnLToLFIgTRwjQkcG9mg1iUnsrhMuLeJOC2So8PvSuI1OYhndSURjxXwH5SRlfcc3/UCr38IKJeLBPaQXDSGkwGhGCMCe7aBmO3ivFETrygh5Jsa2UQgjlGser0GMUjbCtZJSqzBExoLA6OD4EfbRKvH6EkYco7wQ0YmxD6fQCKK0iTlXh3wWmcuiwxBdqaFtib1nAnuihRh+5CAnB2jkcB/KFlTXWmS2tXDGGygzyRCJpQH1FiqoJBk/kGz815RJj9aTYFLaRdgWwjSIJ6dRrTYCiAoulWcspXDb0ePHah5WVH50aGER0lgpRc6pIdpJWZFO2fhrBvHXpmkXi4S9UL69QWp/BCSi1Z4tmXvBKuIdbfq/N4V9aBY7VSUcySMbIf4ZESm/hfNv09hWCZAIJIk/+cI9C0LkfTFGYRGEFg/K0zEWKwJicuM+ypAYeqEUcEHgV9lJ9owQElpttCkxGgGy1kH7PltvOsbDqySbvTT09SDCEOF5xCKkuSiLCh4V0nvkSwVxKFFpE6MdoVWI0Qgx6zHOXMQD31rB11afiXFAM/ysWRrlPLpPUDkdVgiXxnez6CMR5ryXzMFJAzudwhA2qVmFlprI8wm0ZGbcpnVfmr59s7x03Tq+/u4v8sr3X421pswLX3slP7/d53Cjyth8E7NhgNDEYUwUx3h3HuX3Lv9bXvy6y3jd2591/DTy5RT+zllENothmVx67gqKI2m81FZES+HUFVpCfWKeFbNHmfeKHAl6CYsx7XETUWkhZ2vM2wLcZ9GhRrq/gWNUcfbvJzXSJL2rBiIF9SZyXT+yUERLQdMLeaBaZUVqE5aRx5ApolKKsOUTGSc/o+IgCZZFzVMHUx6qjPLxvTdySd9a1gTLmGy1mG63aXUCitnHlvAu6ylx1sgQh3fOEwcRtZn6Kfu95cF9XH/j/bz08o0878J1p2wD4NoWXqBQkeDokcLjtuvSpUuXLl26dOnym+W8887j6NGjv/kgzb59+/jGN77BqlWrntSBdPmP4+wooe9MrLIdy8AcEBirGrAt2ZDuvD9DxTxG5496iKTA2RkjJmMuf4bL6958LxOjkh9+3eHe+2zm9lcouopKpYmybKRtEzttDMNJynG0Ikg5pEbKNIUB+0ax6q3E8Wc0xt/eodM4iNs0iA2D3ns6GOOzNM7phVqDpYV5ZrN5LNtAt2NUygbHRhsx+bEO1myMPBeMi2MaN5hYPmQOttFaEbkCf2Uf1mQL2eggIs3qtVV+dO9ZiEwO48AU0aIyRCFybBbZO4B0JZbUKASZUUGU8omaVaI+k0wuwJnxqasCPbfXEa1Ew0S2NOnDEZmH5jCOzeKftRhGG6i5CrBQQmS7GE3/eIAGEqvk1rIsxa88iFTQ+8MJBJqBxQGVGYtAOUghMO+qE28sgS9QzVbi2JR1IJ9BqxjvnGHsyCZ2JFFaoIXGve/Q8fstD00S5zMox8D+fBNzICK3DKoPHUMbBirwEOk0esHdq7O6B0c5kNWwUM5BKgWtE1kxwrBoDlvUV1ukj2TQFZVoxkQRIo7RjgNxBOkUOgiJBzO4lQZ4HdAS5WrUYAnhL/QpRZJdM9JL8eZRxEL2nZIgpMaoJGPTCIKW4KY/LqH9heBJJ0CrNtUNRfAUMopQpiRqB9jlHmSpiCkUtT1tTMsiWjGEvW8SOmA4Aao+SXBtAfO+NqkjTZQdoheVIVIgJcIwIAwTK3PTIMjn0abE2h/jBBHWrE9QqWAXSknWmV7IALIsiEK0ayH8CCyT9goX96a9qDBO3M6kQW6/R3NphubqPOZEG3dOodI21nQHv3wiSGO6FpEXJrGaFuiiBe0IMd9AFbNgQhi2mToyQO+XdyQuaTen4LUCoSF2bLbemKW/x4Vm4/jczB2NMIc7NPo9qDgIJQjSLqvPGiFYX0Gedj9rsznOWR/xhXqyRrgkWR6mlMRKYWkDbQMaoqzBmvQ0s3tNlCn58dfuPSlI0/JDKLjY9QgpBOXBEg9OGNgtSRhqgrRGW2DEitH2KlJ7AzpZGxkYFOcN5o9MQaPFmddeQrulcNJLePo1Lg/8ZAvuvgyp782BNMAJMXpLFO0UXlxFOimMhsedk8d4zrl/lMwvpYgzBhEmkXhUQAx4+58+h5tu2M4b33jVKdfRduSj0DQjj7NWDvOCi9dTLmROGaABKKRc3nnV5bTOP5ep0VmWb1hyyna3PbCfQzsmudGwnzBIo5XGmYzRQDRmP267Ll26dOnSpUuXLr9ZPv3pT3PdddcxNjbG+vXrsayTy+M3btz4a/X7KwdpLrjgAvbv398N0vwWsu3n0K7ncM0WGoX2fLJr2+ifJhvh2QkbSgbONknYa9O7doRMrs6ac86hHeb512/u5oorjvCzL8WopsH+bR6itxdh28hyEbOnhTcNpheD0qjBIn7OJYo1Ku0gqi0UoAuSc849ykuzo7z/ny5kvK+Mc7iCGJ8nbcQ44y38sI1TyoFrw6LhxGZba9J7GmREG2yL0oY6xmJFe+cg0uilvG4KYWt2BosJCyXSRwJSo228AclHdyzFPiwTPZWhEnFvmvSefUjDg6NjTHwgTf30EnqdxPQgtaNO9v451KosI985TFSLOfLmNahnC+KdNsZoQPuiPjqn55Bek9yoRlTb+KvKOFvbxDpGFTJYK/IwEUGtBXFMbCS6uOaRKeSjZE/Wn9/iVf9rkn3bUnzi75YQFRzkoTaZ9x9CFUtgm2g/RNbaiIyLKmWwD04hnBTxsl68oSzaEGR+Gh0PBpWyTWb6h9F9eWoXeJi1iOJggJFLIfIldLtDVKkiLQOFgZAa2zchl0HpBT2ORzk/YQiINXZdkBmNsQKZ6K/MzKKFwF9t4ugc5DJo30cPpWhcXaanWQXXAWlCxgWtUbaRaLZoQEpUpYk57yHkQgBGgVb8gouVxK+eEHkVgHOoTmZ3L6IWkH54itRYAMv7kYaBME0MrcmOdfCGBFHeRvb1olQM4SzWWAC3NBEVARHIyEd1/CRQJAUMl4lVQKdHEaZtrIxNGMQQRogj0+hDE1jFHHFRoUOf+mXLKdx5FCwLo9lCe4mbFW0P3RLI5kJpim1CEDJ73z68vCTImRjTEKQFqsfBndJEfoSWyfUxVywiOjqNiiNEkAR4AHQUoXM2zsEpooEcpmEjwmRSWcc6iBBEDMqC0/64zeQHY8xFTcKZ5NpV4n7q93v4i6owPgQxeFMNttz0EPVdU4y83+T8ZxYxMkuQ6TQA3myb+nyD8VaTea/N2sEy6WqiFz3gVek/Y5bJBxajHJPBlYPH71UYhIijNYSdQpsSL2vwL7fcR1C2CWOFaUu0FghPozJ5vBUG8YM+qVYT68WK17z4PP7h1tuxszbrLzmNvduO8f1/vQNDa7xih/a1NjqTJfOjCF01odrCMkdoh6MI7RKWDWTnxENRCIHfZ9JJxYTy5CDNBeevZHh5L0ODp85SubC8mkXpHgbcAo5h8raXXP74i+6jyBQyrNiYedzP40qAUY2ojdVpeD451zl1Ox0TuQZCKUR46hKrLl26dOnSpUuXLr95ZmZmOHDgAK95zWuOvydEYqTxH5GC+ZWDNG95y1t4+9vfzuTkJBs2bHjSokVd/uP8/ksv4uaf72J6bIZo2xTrfrQWMZTm/u33HW/jFHvglhBRn2G+0mQ+Unzp70N2D1zJ5r4m8z8X+DJCpSVxPoXtOqA1+f42Rq1BJ0wnmRXAW99wD9umV3PjFzKIuXoSPDBAF01qaYd7c2XqVoxXFlhDLukJkO2QeFEJN2fSnPXBNDCsFCKCZhncLccI6z6qJ0vz3jzxERP/kMZJReQuaIPQ9H6/QX3Kwhqt019sM39+L5W5HCnDh1kDq9SDcWQa3eyQvlhgLdbMf13RfEoOlQLqAtXfg7cqwvI01YM5lAjwMUlnIkQ6h7Dq2JmAqM/H6IsRlolsB8hOjI5jOpetJM7bpBe1sAYitG8hJ2LMIAk4uDtn8IZtUuMBMpclTFs4mXHiGKpXLiHsT1O45SiDF7eYu6mNaHQQQLhyiLjg4hckqS1VdLuDdXSe1goH/WilGgFnXK259WgO5Ui8RRasAHvDPLJWRN+vQSuEYxOuGISJcfx+A9oR7jxYmWSDqj3/hD6LYaIaDYTs4RxRoTGQY74eoGuJO5QZLEKmEn2jKAqJlw3SOS/DitkDHP5pL7rdhiBAzNcToV0SUVelQ+T4QgmIOlESJi2JNiTCCxYyVEx0EJ4Q5AWMRkDPnRXmL+7FbGlEGMHBaSj3QspNsmDyFtqA2BVJGZbloFQWvUziPhwRpARaVBA6yc6hryc5grTolAKEF5ESRZiLEFITFy3Mg+PJcGt15LJhgkUu0XCWqJTGrLRP3AetIZVC+gr97E3ou3YjmonmDQdmiS5egV1XSCWQocCe8hCxotOvSJF0E0iDYOMIlZWKzF2TZPY3k1tsm5ixpJN38JZkyU1oYh5tyRejHcnGC3Zydc8on9+5kqH1EUcX5kfh4pj4IYP8P/u0To9RGZMg5fC8115OZck4Kj/PoVrAHXM3Y48kGRvRfJWH79hNRIyrLEzDYKhmUq206MvU2fXAEoTXxlAuQwO54yP5/F98Ffve/cTDZcjk8PtctCMp2BaTxRAZaLQpECGY9YAgJ8kVWyy76BAXrvLZOHg7T33dRiL/ci598YX87Gt3MbfnCNrzaV+dIjqrgHIk7YpD6nYT6m1ai21mXrwKq6HR6wVXjSw7Ph4hBColiCII5Mki0Z/+8T08uH+MV119LpdvXMmOiWk+ccc9vPTs9Vy2ajlCCJZkysfb7z02Qy7tMNRzagvFmVqTmWqL05f0P2H577qlgzyQOsxMUOfvf/IR3vvsqxD2OY9pl7JtoqwEJMX8qQM5Xbp06dKlS5cuXX7zvPa1r+Xss8/my1/+8m9WOPjFL37x8QE9wpMRLeryH2O8XWFzeQv6WeN0vixBC/x5eNNbrmbLR+9MGknBaesG2bF3kqim0Qt/lZ9pe/zswS2cVtvD7s9lQeaQRoxyrCS7QgriVkBrNIX1KEOUT3xmFdVVi1C9LZyxE+/HGzIcsrM0OxZhUaLy4K3MktptY3uS9ojNntVLKP10DGIPshnE9Czpg/Vkow50lqSoxmXsg5K4GBNWYybvKNPWDn7LoXjrbjJ2iyv/cRq/L8WPP3QuUcegXgyQXoA5M49qQOtejVHpwejP0LfVY7woaQ9b2L0GlPsx728RVwSNtWky85KLhg6xp68Xb3eE+5MpDGFh31gDFZFuVGGxSbu/gGx4qLSJ2F6lODfNWW9sMHr3Eg59Jyk3kYZNOJyCyVnMJRkWb/KQeQczK5D1AHpTyE5E7ZYYOds4cfG8AF1yCYYyRE9ZTs8P9yEqdbLbXXQYIDsLVlBpmzu+n8EwDmH25Mk0Q5z5DsfEMJ2SSW5sB7KQx+ztQU7WCf0YTIepF5QpbPNJ70h+Tk9aRpRCt9oU5g5z3XsO8OOvDLD5yAheyk1Mn4QDWqERhKUMOGl676gw/XMNXhtZHkBHISKM0XF0QqNHaU42QQZtGcS9JeR0FSwTEcUL9tTyuCMV5R4IQozZNoXNAnPegyhGFTMwOZ2MJY6ZvfgsXva2O1CmZO2LKnz5uiVwY4DIZRamvUm4KI8IYsimsY7NITJpmJkjeyhx5YoHTFLLhmh2fAwpUI+MXQgkGmm6ZPa1iNMG5rE2aI0WIIpFZF8vGBqnFeOdt4bo2AzWvgmkIUm1I+gIcO1EsyaI0WiCXhOVtZC+IGVZtNtNSj9vY40+SphbaWS9g7upSDsr0DMSddoIcvex5Nr6EbaC3wn3c9OfDGG064xuzyHNNk4hy+HNaTrbHQxXYjYD/KyBcnwWbVzOU9ZeTN74U3aLfXx2+kJCK16YAopd8QRjU/O42kV6gkY/tFKSxpiFmYuJHSDWNNonyuQqUzWIYkQYokxITXVYcdoQU3tnsVKK0JFgSEaqITOTFcRpfTACuY0x86aBFygqu3x6hmKEEAwu7cPstLEci/A+n+Z5Cn9JiihjkG11CDMp4qzCTmWIh3xKS9vk8+7JcywtCIA4PjlI01/M4h2ocPOn7+L0d/bypS1buX3/YSrtDpetWn5S273HZvjjf/4OlmHw9b/4fZxf+KPEnqlZ/vjj3yFr2vzxMy8iH0vWb1qGYT42A+aM5f0sFibzqRb9qSPo4J5TBmlM0+CSS1dyz6FR3v+yZz3m8yeLrz2wjS9v2ca7n/FUzl0y8p92nC5dunTp0qVLl/9XGR0d5Xvf+96TXmX0i3unf5dDhw495t/BgweP//9kE0URf/7nf87y5ctJpVKsWLGCv/qrvzrJ+ltrzfve9z6Gh4dJpVI89alPZceOHSf14/s+b3nLWyiXy2QyGa655hqOHTv2pI/3N8Wh1jQHmpNMB236RyIKaZPG4Qk+/Z6vQsdLGimN5doQg8ikEeV0kg8QxaRTErtXAwayWMAo9WL5JjpWBD0pKsYgQeTy6G1943BMWNM4ZYk5UkTIxDbYud+gjxrGQYVz1CTT6iBP06jTlkDaxX14ioHvjSIWjcDwMEFvKslSiQUCSXzmSprnLUaGgtyD8/TurqBbHeYfjGhOW0QZi7g3ix9YNKYsmns1ck8dY86j8NNDuFuPoFwLBKiKIDwUJQK27YDi9pDeewNSjQhnokN6TqFtE2uswelinL64gT/aAa0xJkMKP51maV8dM60wbIXlNzDaHs72CXIPzBDNu1TjHJtn1rJ91Oa5b6ry7s+Ns2RthtyhCGFJsosqLFtfBRSHx/Nk7hmn/4fj2NNt9BTHM0c0YOw9hr15L5nRgMx0hCgXQQpSR9uYNR+x8BObTvukrTaGYWIKm8L9dex9Hby7QuIwTXTmcuJCCi0FutnCrAa4B5uk9tTI33IUVameNH8EQDYLhoEXWrSqEjdtkk3VEPkcIpdFdzRea55WME1nVQ53WYRp+vhiEC2SEh8dxknQLZsDIZKMmaaHcE5oa2gA20IN96DKBWRvCYoFtB8g+sqwIEaNBlkqIrMZWqdlEF6I1hq/4BKXMpBJEy3pI+4VRIZBQ7nMLnE471lBom8URgjHgbSDWLEMtWENwfIizYuXolrNRFvGdcBxMIVFsGMCYo30Y3QmyWAQsYZYYzZ93AOzONPJ3NCPXLQ4QjmS2LboxAplGNj55OeGMMZsxag4wqi2kV5IPF+lnQOnk0H1F4hWjVDJWNj757EPzSMeZZlOJg0ND3uPIqNb6FQSlIo3rCQ+Zy3uQY/It/joG1Zy+OcxcU0jZ1rguqxYt4iWn0W1LZAGGlBSYJgu//qPP+XfPnYLkbGCQKeQopc4lZxvcXEve0d2od15immB41jUo4jYNmlkU6x85hEKqz3QmtrkCYHcl7zjeajhMvHyIQxbYLYjJu48Qn3QJk4ZpLWB7UgOn23gXeQSZWBK5rhj+3q+cHA9X/nytex+KMP+hxJh6clDU3htn06zg5zzGZivkI89XC9CSY2F5Nxli1mfL5OrS1qB5p6Dj4oUA2YkMVsQ6pODNC+7/EzOypVoTNU5cmCKjG0RxjFBM+Cv3vyvbLlz7/G2sdL4YUQQxfhB9Jh1986H9lJpt2gFPrd84z6u/8gNPHTPqZ+D08fmsULFMwZXc92lz0SkXnzKdgAf/d3ncec738hlq5c/bpv/KP90+2Z2Tc7wj7fd/Z92jC5dunTp0qVLl/+XefrTn85DDz30pPf7K2fSLF269EkfxBPxwQ9+kE984hN8/vOfZ926ddx///285jWvoVAo8Md//McAfOhDH+LDH/4w119/PWvWrOFv/uZvuOqqq9izZw+5XJKS/9a3vpXvf//7fOUrX6G3t5e3v/3tPPe5z2XLli0Yxn//uv/Tc/DsgQk60w637x3CK9eI751kdO8EInNCJ2Hvzx7i3Gedw3337aQ9GyIBPd9i/uP78c8sYC5Pg+8mQTAN2g/QpkQLjV69GDE2DdOJcG7eq7Foz0ME5yxFxxY1IdE5F9FnYBwL6NyeQgQSYwLMhyoYu2rEwyUySzSpSFKJQRkysSEuFiEbE9arGCvBKCtkRSPDxN2JTht7roM10UYt7sHM9+AN93PD3+VoXrgYa1VE+t6jiEChswY4DklJi6S+xqR+QZ7M9ja6P4XTgEqvhkIK90ADqxUT2QF3T6zg7rE1ZEemyY22EMCmp9SZ2R2htYtfF6hdMToHwdp+HJXCTAuqhTTBhEV+fozTzmzRO6wYWOwxMVNCBz6VB+Cn9hCfvrlILASGIRFz1eP35NGKGQIgijCbHmHBwhtMY1eqiJaP3VwQrjUNvBYsXuNzWPWAp9BCYCweJl0TNPtiSKeITYtYaczDHggw2hGD355CdwJ04eQ5rwHpB2ghCMc9/uq61bgjg9BpoqfG0AL0YAljDoKCRWtVmstGdrL1u4PEtgLVRo1Pgmmg6s2FE0kKtEQqBdk0YnYeLTWqnCdetwyafhJ4cVPQqYMh0M1mIsoLCK0RCy5Sqf1NtCWQoSZ1ZA5j2dIk8ydro2ODRuzgRybTXpG9+4sQhSAkcb2JbvnEG0cQkxniniy0OqiMg1IhpmVCGCVCwlGMPVFP7M6FCSQC0kFKY3ZCVKeNEcZQKiCqdVAaXW8SxkEituuFaEdAtYb39A1gGSipCAcs4lYNY8cYsreHaDCH0YgwD86ihjUMFNBpB9VsJ8deMJ8S9SZkNPpYA3dnDZFvEfoB9WcPIgcizPtNQiEJe2zcSXHiRno+wxcsw7Y194ujhAWBciRCCCLXpjyU4/SzlmDmnscX9wwTiJArX7we8LnuQ6/CH/wmxw5KnFjiSEksAAMCy2ZXXxF12MbwFKO7xum0fVJph59/fTNGrYmupHDLNn4DWp2Apg6RSIIgxhyPERmIzzKpBYq06dM66GIMepx+6en46SrLLiygtWbbbTsTseIgJnuuTc+6Nv3GFBM/7EFic/H/uowtX7kbdWCOaE0/RloQnNt+9JRGFySBEAQiCdL8wZs+y45DM0ipeeqz1rB6ZCVbfngfN0wdwlyVp9poc2DHHDvv72fTJWuSdXVJP3//+mtwLIN85rHCwbuuvx+v12NGRTx0XpPsYsHQkt5TrtGXXr0BN+NwxplLkOlTl04dXweEwP5Pfi5tWDzALUcO8ZSVpxY67tKlS5cuXbp06fLEPO95z+NP/uRP2L59+ymlYK655ppfq99fOUhzyy238K1vfYvDhw8jhGD58uW85CUv4bLLLvu1BvDvcffdd/P85z+f5zznOQAsW7aML3/5y9x///1AkkXzkY98hHe/+9286EUvAuDzn/88AwMDfOlLX+L1r389tVqNz3zmM/zrv/4rV155JQBf/OIXWbx4MTfffDPPeMYz/lPG/l9JrOZZl29jZst8e4Wgtszk6f3r6Ix2OLz5YQBETwmv1eKYivEMB7OUxjIC2laE0YkIdgtaaQfTDDEbEbrVhmYLIwrQQYBatRidMrAdi1gropLEer7F8g0dHt7toiXEhoJ2i9n3dNAtD7U8Syefp7DVR2uF12/jX72CjWM15h9Igj/rens4FE+ifZ/mOf2Y51kIpUg1PTAlOtR4SwrYQRljuoKsT1NbNUy4OI28ZBFixsGwXOTwILHfQS0vgyOIZ+egFRGWDOK0hVpcxq4m5svWtEGcMQkbFYz5No2nL0avUiw3Jqn/XCfOP/ksD96bIp6cJGwCUqANgYoVpnSJMzZypkZ2zxz+YIrKM5bxf7cWWHd7nR3byhhhhDZt4laHA9vySBEgbZtoIEcgIpz9M4g4CWQEBZva72yk99P3I2Kw7tpFsHGQ6iUj9GbBmtWo3UfRCoTQZAds/MDH6PhJRkhPDhwbMxaYTR+r5sGBY4nTkEpKc4z1OcS4wdLVU8zNloBkT68BIQU6TkqO8EO0lARRjKFNcByEFMj5JiKVwZmsUL6nzkN7l2HmY/xOhGmaYJpJcOYRe/KFvgkCRPRIZgrIqTo6NYVavYh4zTBq92GsMESYFkSJKLUAqNWhr4dYAiNN2q8cxv3iPObuCK0SG3hhxVgdwU/uPYuw5jKQm6LQmoLIgawDkzPgSHS5ifBCMvtTiE6H9hn9ZO45go4UwpDoRhMjOwB+omgcW8nSKAB77xTa85LrowWJ+vGJ0JqYq8BwAUNEyEqLeJFNbb1Ndhxs30RUQsyqj3BtcBwy++rUFgUIBcaxOXQ6hRrqxZirQpTYQ+usRGgH2d+LDkJkLkcsFNFpJq+49nYu7pnkY8MX8MBDa3DGWhzPcHMd8ANu/NrdmFcNQ6ODtmyohqh+m2wmIPOGQ/QOruFTP76HrQ9UWXa+y6Hv3cnKa8/lq3/9df7uZ3/EV6KbieI0E/VmojOlwbNt4m050pU2aBMdxERRBDisf8pp9HztTqYMk850BJUWdiHNU59yL3d+81xCRxDkJRmpyOfrtCo9pN0ANSUwzhTY6RYP/vwoe+cOcOXFT+HsKzbww0/fjEBgzNj4+xX+EY3bifn7b7yez937MPUHjyCrHm4swOnnOctPP2lNlFokDmyxQmvNQ4dmybQCcAzu/O4e+pf3wL37kXOzDK85iyucIttm9hJXaif1c/aqRY+77l7w1DO4Ye9Wojgx1jrtkgGGFvecsu2eA1N8/aZtXBlGXPPssx63z/8qxuMGKqc40Kn8pofSpUuXLl26dOny35LrrrsOgL/6q796zGf/ZcLB1113HZ/85CcplUqsWbMGrTV33XUXH/vYx3jjG9/IRz/60V9rEE/EU57yFD7xiU+wd+9e1qxZw0MPPcQdd9zBRz7yESApv5qcnOTqq68+/j2O43D55Zdz11138frXv54tW7YQhuFJbYaHh1m/fj133XXXKYM0vu/j+/7x1/V6ktofhiFhGD6m/W+agrmJrfe9kPt2VMiNNUiZDs//vUsZGSrxJxfvBMBKmwhfkGrH9C/p44zzlnH4zr0Yq7KYl/Vz4EfHiHvy6EDh3nowsVleshTTV8TNJpEOsSwXMewgZ2ex6hHyDsV9lRWIVRq7E6PCkJQMiIoWsSEw+k36DoS0Ny3F2VelEJpUjqR4kDxlVUcGiovPWsrotsOEpoVa34fXEmQzPjJvI1UDoWKKMw7x4jzZRfP0vEgwPesxbtqIgiQ+BsUvt7Bck9B0MTXQCBGmi+gJGdgdUfRCwnIGQ0CrFGPkJamhDm5WYQnF8HcP4+4SnH5pjWNGinnbgNAjblooLOycTOyIFGgUsWuCJTFdF7IZZC6DMScwe1Psq2UwinFSKpNxiYM2DJYQtonWMf7pvUlmQxRjjieCy+bAAO7PxpDOiehrcd8c5RU2b/6b+5i4PcfXPjQAcYzMwMbfmee+G5Yh8xlEy0eaaXTeQtkGrpZYvofIpxH5LGq+gm57mFuryNWLkOUCkZ9sJK1couMhBGAkuiE6UNCThoyJmTKR5hBaSoSKwTCQ6TJRo4HMZCifGTDWm8I6KhHFMtowYGI6CdKYEpVxCYdzmKNzyGIa/EdEjRShmVxTsxMiHAOjlMWQEcE8iZ0QwNwMIm1jD6WQhoVZdrCcGKpziS32YD+ZRkil7lLeHhCmCkTTVSxTo40IXAMjZeLnsnRWluh94BiyWkPoNHbGgMgAy0AYAkkEhkQZBjKfAq+dBJlCb2GllIkgchxAykocopTGankY8zX0uYMYs01Wyl4mlsfo2EIFGnfcxCiVcLGIZioYQE4bWFkLQyie96ZtfOXWC7GOOIntcsbErvgIQyFSFkpGGOksftlGL81ghxO87Ucv5vLl+9k/r0il3ESYFxBEYEtMP+CqF21hj+zl2OZFCGESHGnTv15DLGh4Ndoti6XpIi8cWM83Rh8AYOrYNA/ccTuvuOAoBfu5DLp9FNMKcTQgOxrit7OYBYExXUfVG3z009/lipddwqozlzL81DOp37kH0WpB5JHyQDUcXCS4gjgtCG2DRV6NRYurrLCnuH3yAlIPlXnYnUHVbFr7LDI6S9+SXvqWlNGRove8tTx4oE1q6zjp+Xk+9mdfYk8UwUiOgR6Pq689xP2VRSy2R05amwuepN4RiFbMg1v3Y6Yl6XEP6YdEJQfr3CKtcongkKRZVtyvKsSOpIrmvd+/iaetXs4lK5cyXW2SdiyyqceK+F710gv59ufGGR2vYh+VFMMpJkoTlIfLj2lbrTYRQjNfafy7zxCtFfP+g6SMIdLW8BO2/XV56vAS6rUOlwyOnHI8j7z32/i86/KboTsnuvwi3TnR5dH8d5gPv81j6/Lfk0dLsDyZCK21/vebwbe//W1e/vKX8y//8i+86lWvOq5crJTi+uuv5w1veANf//rXf+2UnsdDa8273vUuPvjBD2IYBnEc8/73v58/+7M/A+Cuu+7ikksuYWxsjOHhE7/M/uEf/iGjo6PccMMNfOlLX+I1r3nNSUEXgKuvvprly5fzL//yL4857vve9z7+8i//8jHvf+lLXyK9YFfbpUuXLl26dOnSpUuXLl1++2m321x77bXUajXy+ScuPf7vRr1ep1Ao8IpvfwE785vfqwatNv/2wlf+P3mtAa699lpe8IIX8KxnPeu4vMqTyS+dSfO5z32Ot73tbbz61a8+6X0pJa997WvZs2cPn/nMZ570IM1Xv/pVvvjFL/KlL32JdevWsXXrVt761rcyPDzMq171quPtftHu6hG3qSfiidr82Z/9GW9729uOv67X6yxevJirr776t3aifXH3F7nptt1UfmgTmoLTXjdGZz7N2HeKvOG15/LZP/gmQ+cvpf6GIvl7Iqo3ztM8OIm2wd+Ywx9ZTGbbDO7oPEHTAwl66TDxohLRYITdCpB3zUKrnZSlAJf+yVNwrvH58U/HEV9rkB0oMXLuNJlzTfZUMnjfSjN1RpbOsElpqyYz3kbrCp0Bm8IuByFAuho12yHoy2F4MXFvhiht0u6VFG48iHBMmpuGyD00idSSaFkKpVOEQZPWxjSl7QFypoXRCIn7i4T9WYxKiyhool6oER2T1PdtVM6kviKFbnl4Qw5iRJIeD/Ha4Ex1yO1qMv3cXqK8pP/fDmKPeejlw8T1CiLSRCMlwiVpgpJJ8SfHEJaDLPdQXekghQEzFdI7ZjCdDLqQS6pi2g0sQ2CkHfwjVQIrovK8FWjXpXD3PEYpD4aBOTMLh2aJszbG1AlBVgEIB9wlNu0DAcK2QWtkuZfYkkQ5G2OigvBC5l66lrBkkH+ojTtawY5MkJJ4vpo4HWVsKssMckfrWOV+/sfr1/PZt/yIYC5xl9KuhQhC9HAf/rpF2LuOYsy2kuwX20avWUKcMTHrPlpA0Kwgslms/ZPgBWAayfjCCMIQTJO4lMJfUsQ+UkFGgvb6frRtkjvUQrsW7ahKek8FgWblWR6juxwiP5FSPl4yVSpAqw6BRphADGavTZzqIzBDRLNNsKqf9LYJaHXQKQvRSXRtgg2LcT0DoTRRSlK7JEuzN6IwXyH/L5NJPZZtgmVC20NbJqLcg67VEbFKzmuBqDeLMdc8/lpmUpDPofI2lQsGyW+rsDI3yh+8fZz/sfWZRG1JegbsiiJzsIHtxYSzc4j5GnppD4zNQwQ6bdD3lIDZnyblYq2zB4iLOdKzElMnZV0j100SVUIO3no6OqNIjYVgCgI7xN1yDNFoP6q+DHrX9FN8d4vR/1NGe8bC25qenM3Vz13HgWUPkl2nKeyeZMs/CibrLq/+y6u55eNbePunXs5ff/1OBFmWXWDz1YM7GLw+RrY17ZEUft7ktEmfwXKJxW85k9W9ZcRDDT76t9+HPoPhdIVjOxy0NPAXOcSGRWexizAkL7zkNMb++RO86SOT9BQ7vPr65xLenUaGMW4YMXJOkb//v29m85av8s//cxuBZxPFirb2sEUaUwue/tyNBMunufUr28kcC9n0ujWYV53Oa1Y8jbx9QjfmD9/4OSZn6rziJefzza/eQ00r3L0T4IXEKw1m/8cQTd9gsNbg9y7dzLif4yfj67jyx4u55E8uY91QP7EX8ycf/S5p2+ST73w5tnWyTsw//dmX+cGh/fhXl8n3wAXpBn943msol/r+Q2u5H82yp/LP5Ow1LC+8/D/U1+Pxnhtu4pbdB3nV+Wfzhxed/5jPwzDkpptu4qqrrnpMfXWX/3/SnRNdfpHunOjyaP47zIdHKiO6dPmPsnbtWj74wQ/yyle+kssuu4znP//5XHPNNSxevPhJ6f+XDtI88MAD/Pmf//njfv7iF7/4uCbMk8mf/umf8s53vpOXvzz5RXXDhg2Mjo7yt3/7t7zqVa9icHAQgMnJSYaGho5/3/T0NAMDAwAMDg4SBAGVSoVSqXRSm4svvviUx3UcB8d5bHq7ZVm/tQvPQG+OPunS8Q3WLO5nhbiNe8c30OlLNi5hJ2LoohXcu3sC+2dHyY22odImzlp0cr1Ye5rog7O0Kq1ENBhgzxHiap0wbaLdAmbDg5a3oGMiueP6+yg+RdDuLSCGYsSOSe4fN2iLZSzvqbDjGTkKuyH0FE1boRtz2KPzGDtMmkuGwLJwDk6jGh6RYxH3ZtBC46chDkIi04ZOgHX3UQLbJioazK026fl5BePoLMIdIKpLZBtiX+M5AtoR1p5jCC/EHgsIjQJ+Txk6GnGsg711FLXcZfI1K6j3WvRtrRJIH3uNSWxYRM2AeLJDEMRo28ayc8TVCmrPDNNXryTOWLAyS3baJNYRHA2pbSgRjpTJpExKhxSxbeH1CNI/myWUAjFcRrsu8cQ01j2TBCv78EsZzBiIY3pe0GDq9iE67Q4iJ8lunzlxYztQXTVEfKFDevMosh2i62NIpdCAP1zCX9uLGPcpfWobQpr4SwcgCBGWRVxvIoWJ0Abubg+/E6LzybY9nGsQdkLCNWnMwyEyCIkn5uj7Q0gfOsaMbxK0JSiBDmKClInjK7QpkS2TUGp0Og2BQrYDUAYY5kJpmIDRecwj80S2CRLCZp64kEIYEYawER0bLwqREex9yCQKY+QvZKJqby4R0zUNMFOIZhNSKWIt8RwH//ShpOyo0oI4hk4IKYfINqit7aG8o4q2oDqk8Ne06TgucU8aU2gMP0aECl2y0UtGMJDQaIOVgrmZk7RngmoiHCyjBQHnTgidiNBziJp9NJst9h7O8A9/uYo1KzRzu+aJXRtlGQR+lFyD8VmEBrVniqg/jTXVJhjKMfVgGSUqiZ7PmId1sEk0NEhQTIPS+CjiMKaRh/zOOiyISFvn2/htH0PLpEQsSC7enmGL+Oal9Hp1iASRI7EbIZHU3PaluylvynHR5eew9rxVzK64iSMH9gEwbYW8/5rPgmPy0g//Pn9b+wyZzQ7htEJKSTOTwTFjpo5VmLjnILU+ydDvXcaLXnAef/eB7yJ8k8OjBmp0HG1I5n7nTLK3NuCYJs6aXLK4j3/QA/zDF5bz0mduR6/yOOZnGLgpIF2OmR89zMP37aIvfxGt6k7iKCKcnAEVE5cl2rXZctMOarXDzK3spZ33mXTWsCrupxqFGOYx5r0tLMo+F2u2jZxuIeshvbk0M9U6sZsiZaTQbdBKEQKaiFXZGgEWVd/kd96whvXrTwNgZrbBiE5jBAIVg5U+ee2/86ZtjL2ggDHf5EPPewUXDz++sP7o2Dxf/OZmnvHUdZy78YkF+C1riE2pv37CNv9R9tertOOY0Wb9CZ9pv83PvC6/Gbpzossv0p0TXR7Nb/N8+G0dV5f/frz3ve/lve99L8eOHeN73/se3/3ud3n729/OGWecwTXXXMPzn/98zj777F+7/1/agnt2dpZFix5fQHHRokXMzc392gN5PNrtNlKePEzDMI7Xfy1fvpzBwUFuuumm458HQcBtt912PACzadMmLMs6qc3ExAQPP/zw4wZp/jvyrKEXk55dT9tNcciOuHtqFTl/FmfHdNJAQL1o0Ck4NDaWYL4DGkQjREz7hGUboRNXJ5WV+NctIry6FzlVRewJaVxko5b3IC0j2aSmUggN/ZZHsRJjrs6iwgCr4oEbsH+iB0KBNg3sCjitNvahGipM9r56ep7WgEWwahFq6SBmAEGfgzI0qaNNCtvryCDGUAIjUsT1KqEIibIS1aogg4B0XRLmLFrDFiKM0K6JVfMJT1tEa32J6qYVWOU+sEy8oUxiR2wZhGUbTE1mwic7HWP7gv1X9BDlBEsfbGBlS8jhQaQJ0hAUl4ZYfouVD+xjeGIC2jFMzmLXIzJTIemHZkjNgUwVCDImQcmicXqW1ukllNaEThLQ0QLSe6qkxyPsaog100JEipmbypgHPVJ75knvOyHkqQHVV0QXcqhcCp1PIaRGaIWWElkqEJZThEN5zFBihBrph1j7x5OsFiEQmQyqVieu1mjTJizamFsPHD+GMAyCfBYpDZASIxKcYxzmd982w9qngbIMItckMmPm1xp4y2yEUAgFTqWNaaWQlpOI1nY6CK2RuRw6ilGWoP/MkDgtEV5E+s7D5G/cg4hilI4R1SYil0GnLGJPIEpFHlN/qTUy1kgtEUqjHQt/VhGnLcyUiaEgyvJod/hEbNcLsOZ82ssN2mdoRE8W9/aIs0b2Y+cjwqE0YcGCOEZUmthKQsdDj03AxPRJARoAK4igJ4swF2LbUkKrjTHXQKUNDGHCUJmpag/RaIDVVljNAKEUpgVx2kSZSSaG1GAql8orlhIVA7SIAY2INfbRCkalg+50kK0Q6ceM/Us/499dghFDkDFRs3PEE1NcXDqI6islY3Wd43MmdBWlW6bRo7PE7Rai0SK2DDrSYMbTPOXyp3H14DUsXXoGf/qZt2AWEtvz2b3THHxolMP3HuBjb/gMTSysYwECAW0PO9QUj0WJBZWAsXqDW/Yd5MFbtsPeowSTVWJfoRcEoM85YmHNh0R9DnHG4Od7v8nqC+sIITAE/P7KB/nSdd+g+RSLII6obq2x45Y9BG1BJpvCMA3oLaEGe/H7c1z7jmewYm2ZZtMmdfcM1kOzNHeO8u0DO7nl6EGO1L/Fkfp3mO1sJm1ZmJ0I0zaoBBGEMXFPosFk1GHgUBoZCpYbFTa5LS5xq0RHMnyh8Y/EykdrTV85x5tf93Te8voryC3oNz2aMzcuo/eeOheoEucOjDzh+vz572zmltt389mv3fWE7f6ruGhoMUUnxcrCqd2ounTp0qVLly5dujwxIyMjvPGNb+SGG25gZmaGd77znezbt48rrriCpUuX8uY3v5kdO3b8yv3+0pk0QRBg2/bjd2SaBEHwuJ//ujzvec/j/e9/P0uWLGHdunU8+OCDfPjDH+a1r30tkJQ5vfWtb+UDH/gAq1evZvXq1XzgAx8gnU5z7bXXAlAoFPiDP/gD3v72t9Pb20tPTw/veMc72LBhw3G3p/8XEEKw/rTF7NkxiTjWZj53Bq2bj2DuWgjSaJA/HaPnGSXa0zMQJ5tQKcCKTJw7DxJXOoBGZSXOThvZcBCLHCIREDVtnIkGYRiDBKF97F7N0R9czulXFrjT3Y45H2KiMed8zChF76ig1QthLxiLJfZsjkYuS88OD1Uq4HgGOg06TEPDw24onCkPGUbEtkSbEkPa6CCAuIOSBgO3BHjrlhOub5GpCbQGu8WC5bFG2wa66KCEQjbbGB0HbWrsioeINNMvXkU45GPUQqz5GI5OYU1rmM6i8il8ncKyI9Aa7ddY+aoa2ZlJds7necbv7ScuOvx482oCwwApEAjigoM7FyNMBSWbMG+g8+A/bylqeIDcnnn0fCNxc1IRdsXHMGyULQFBrA2kY2FkisQFhRidQUpJnLVRKwaxPLAOTWBM1MisU+hML+0xG2HapNoCuXMaWfePlwihFKrVQmiBancQrTa61Sb7SIJOauEvCVIQxjHt1VnSW+eQpgm+z+aP9TF5Vj97D2QQxhyGrxDVkPKdc8hylsJKqO2vghKIbAbaHQD8DUuwDs8gKxV0FCEkzBxOY3gxKm0hgwgRK5xWk8AyCUoSM9sHR2eR7RAxVye2LYyFjBAMgV5wwCIIEI+sMVGE7gT4Vzkoo4qx6xccl2KF1orUWIvYBPvwNCzx0UuGmN+nKaQCjr1oKcV7p7FvnTpevhdkDCwWHK8McfxnBEAEMaLhJ45Zjp0EaaIIaVgUtlag0sTwa2gv5LSrLufen22D2RaF0KV9ZJ7okpVYrglhciwZxpT+bRQEdM4sYPomZmChsw7CCxGtAEpJ5CmyUhhTNdxcjsi1oFoHy+Tmn/VgtjtJSdqCKLMA+g6EidOUHyBjjcq4oKBuGaiRMlt2T/GchfOKw4gz00lGIjOd4+fb2nEU9AqQDjrvgmtjTXi0laR/qEBopTh98RKuXrGSmz76U2au3YBKS9KHOhSOToPSTN83Do6FCBW4sKXT4KW/e4jLMjvpKXT4/LFlnOY0uXzlUbbsWsLyC5tc/Aqb5cuW85LXXc7dt+ziof0TRAUXLJjthPSduQqOVNC7D4MXsmR5Aasvw1NHllF2ryFtLaKcOh87dRQhBEIIKofnsHoMotNDlAJjBup9LWLbYHe2l+l2gftGh5FtwffG1/Oc8VdQdM7kzL73sHb14OOuuX/+hTfx1mqLVCHNPZNHWVXsZSCdPWXbyy9aw/6js1z99HWn/NwLIz540204psk7rrwUU/7Sf0P5tbh85QpmGm3OW/LEwaUuXbp06dKlS5cu/z65XI6XvexlvOxlLyOOY2699Va+973vcffdd7Nu3al//3s8fiV3p/e85z2PK5rbbrd/pQP/snz0ox/lPe95D2984xuZnp5meHiY17/+9fzFX/zF8Tb/83/+TzqdDm984xupVCpccMEF3HjjjSeJ+Pzf//t/MU2Tl73sZXQ6Ha644gquv/56DMM41WH/W6K1Zt0Vg+y4NcPDd06xxhjAGSixa//Cztyx2LvlGK2zU9jzj9rQp1OUhUN9rsUjohbmZASZBlJmIeUQL8qQ/cIRgsaCfgkgOxHzUy4DxRSHvSrBwTwp2wBbM9jTRrYsJpsZzJpE2+A0BGHbxp2uEaczkHWIygZGGCKmO+jD4xiORGNArDHbMTguGo3OZGgvLWJUPJAGKm2jegW1C0L8ikt5R4NwVmF6YNQ98DzSVR89X0X1lYiKWcxGiJaClI4ZWllj/8wAnSFFutfGnGpylXkE2XQYr0TMT7TQQiBaBlPTJtWdJXyZYnYsh65A4BcJVzqEKYfWoIHVjEjfuQ+dslGWJDvocPU1dVYUqnzttjXIokNcLOPvn0UqQVTOQgRR2gapsfbNI+od4mX96LRLmHIxD44jq2302AzSspFjNYRlEjytj/aBAXTcwan7yEjh7J9F+8GJZBIp0fUm2vMT1yZOJJro43cZtNLotQMEyzL4wzapiQiyadqzmt07eyFSyN4edBQSxh52y0bbPkPnT1GttZJ+Gs2kbwEp34Rckbg9A2mB8DW0YqQCf7iAe3gWFFjDIe1Mganz+3DGmpQPR8l4YpUEWB45j1iDJdGhOj5+AehIQauJsSeLkbbQUxVIudBsH5/XQoExWSVebBJbGgyBrDTofKEDjTb9so43cGKNCHpa+EvA2rnwRjYPtdqJnxNI9HYMkQREDJnMz9DHPDRNXK8RXLAO88gM915/E8rNIvyA1tgc4dkriTodnOajxMu95GcQx8KUoFREbAt0IYMRNtE9WbRrJtluTQ+ddojSAumZ4NrgB4hY4a0oYk1Vj7tNAegIVl61hL3f2QezFaQGkc8QozDaIdlikhUycWiKf3zjpxmrNdkE6EWDiGAW2h20BMNT6BZw6BgqnyPuKaANm+pUC6MTcd/WfTzwjc3EB+fx3rYeEWj8soXI59C2hZIKlUv+l2cEzCyy+fKRTfRuuJeBQHFHa5i1003ueGAZvbUGf/GxO0iZt9P28rzkD57Oi197GS9+1v9mdkmN4FzByLl5bv3b+7BqIZ0LV1Bcn+VWu85w3MHHo+RupORufGRBRCtN4EcIrYmWBoQXaLTokPr0JNUjq2BVlvlKjrtn/icfa9yHtbFGu2HSCA7R9Ccpt97AomWPdWp6hHbL54F7D9FYavDpfVs4o2eAD1xy9SnbXn7OKi4/Z9Xj9jVRrXPj9n0IIfiDi86lL5d53La/LhOdYwgEg6lFmK5AlRTa+aW8A7p06dKlS5cuXbr8khiGwRVXXMEVV1zxa33/Lx2kueyyy9izZ8+/2+bJJpfL8ZGPfOS45fapEELwvve9j/e9732P28Z1XT760Y/+p9iE/7Zw/YGf8rU7boMHYwg0aUty+rNPY/fWvQDIQh7lpsjU21QuLZN7eBIxF9HTl+N5L1vON3c+SLP6qKDVgVn08jTCNnDnwZMSv8fFmWwjHAOFAeuXcN01lxA6IX//wM9pXbQcK9vmjy7bwd4DvXz7wVVEYy6pcQd7tJOInEaaYCSguqmPRaZHOmwTOw1qfVnioktoSYRvkpr2kCpG+wFRGJN6oI3hhbB4CLvpUDy9QvbMJnrc5MwzD/GT52+A71v4aKzJOkJp4lwanbGJshaRXyNOW0RBnqMHhhA5CPpNmpcME9sWu1IdXti/nYlJAaGdbPQ1dG4Lqe41idcPkh8+hhEHpP15mu08cb9B/sEqUmniYgadz2DWPLSZ5uLcLpx0hLPUp2A06dsww/0fykM9QExViJb2Q9vD2DsGlQZIA2Omil4+iJAC7VpIL8SYayI1YBgQKtTtHeSigNgUKNdChirJ6ngEIUApZDaDtu0kIuOdCA78olS2U9MM/6iJmNMLG32F7vio6QpaKgQmtDtI7aI7LVJugHY6qIEexEwNI5VCKwUdD+WHCM+HMCRlhkRIdEYyf2Y/znx8POARHfQJL7FJT3g4+2pYcx7CMGCwDzo+zFdOBJVi9ZgxAzA5i5zuRTQ9zOkORBFhOoUeLGAdnAQg6HWwW1B/+mLicgb3ljGs2RoIsNM9OGMnbPPClI8RPGrDaprHxXiPB2q0Qg0VGDItKrN1vChMMmNm55FAbECcc7HjLGqgHyUFcssuZNsnUw1PRMeKBbAs0BFCGLQGszilNF7LI3OwDlGInK6ibQepNCqOqGyy0JYmNW2gW0nGi5hrYquF8SkNjoNMaapDFof2H0ruu5TowIfIxhprQq3BwymfB1+1hZ3+D1j5v0weeOXGhWvtJ/ePhSm1OYOoBjAyiPY9aqscemuSXKoXf7ZJI+yQDVyMkWFe9YybmDaK3PCZs9GtNgQW7tpeOo0A2SuxI43pKGzb513br2CwUGfH4UVMHluMO1AkU2vSTkUgNNt3f5HZm+s897qryQmYymok0DOcZv2lq9lTreH12RyZ9uj5aZvaljSlM08OanS8iChWzM83sWxJPAp9q1I0N88ihwYRDYFWID1JI9vDSnMlD0xPc81qC4OYqNPhQx/8Bk954SaGF5W45ILHBlj+93u+yX2372H1M1YydGWBs/uGHtPmlyVn29izCoGg2fFAQF/2yQvUVPwK/7j3IwgEf3bGn/Px++/l55OHyEibTf2PX8rcpUuXLl26dOnS5dScffbZpzQiEkLgui6rVq3i1a9+NU972tN+pX5/6SDNrbfe+it13OW/np+N3UFktjGVjQFkh22ufNFVfPevfgyAbrZYe2EvB5d3UMpC5QzkXERlao4VG6/noo/1ctObBFTj48LAuuMhpEQYJka2QHski1M7hu5EeBcuxV9a5HPfvZN/+KPLGGzNotfMccaGQwTFiHiwQn1Ljb7pQ9jPLDAzl0OLZNfrTHv07Gzg9zuEjQxGx0D0SlTaQUvw+x2wBM54G9H0sdohQclELTdAJ6Umph/Tm2+yNJojHXTQHcjlG8wbWdojA2QONLCjHGpQIJoh3rICWggiUxPGYM1rDB9SUxrPbjFPih98azFNv4FNsglOn6GRRZPWPk1nJI2fSeNUYlSoETNzOCEIrVGej17Sg7ZMlOkTRwYfet96dH+G9MYmQy+eZVgH7DzDwb8vwpitES3uQ9baCD9A2ybCdgiG8/i6SXbXKFJaYFpJNoztgCFQpqa9aISgnMYZG0cYLoQBZsdbCGRohAudQpaUIMlomJ45ORvkUQgBWscYYzW0kOjIR/gBWCbCsiAIiJs1RKyQhka3fDo7Fbv+twtlC8pFhJtGaPDr01hCo7IOoUgR9GdI72/SWJQDJTEmKgSLUjiTHVQg8OMGQSlNfNYA9rRHaiKAXAoxN4961Hjlo22LHv2VteDKVMoj5tqAQK1bgvPw4eNtZSTwBjPoKwWmbqNGCwRWROucfoZurmH5itgSyFBj39NGW48K0oQBasMI7byLYbu4Byvgtcit6Gdu80GiOCbOuZheeCLLZ6aOnJ5BD/ej0jay6YPSyGoLqiecochmYGYOaVpEroFOu/gli8wuUCtzMDaHzmUQUYyIkiyg1nILAdh1kUjCKDBn2xhBiDaSC2NbbfLnG8wUB4gmq0kgyLGgWIJ2C5pN8HxMGXDz5BY+df/zsOYFw+6x5NoWs6CS8kgVQdySmMrA8CMom5QKVaxdOWwL3JRFtK+VnDdQ2ZPl3IsOcbN3VlKOhubYxgh7u6awr0W2t017PdTjNNWtJaZLPcgK6LDNtVdu4rJ3PZ1d8z9CCpjxDvLA1+9m6brFTG7dh7U9hsuW87T/sY7L37SOT07toCM0xR0eZj2is6PJjUdv4DU9v0sUxhzaO0lUdIgyFnUJy/pKjB+e5ZXlS/jk9Di66GOcm8dAUejpsFJ+BL3kDD7zvLdhhA8xNvuvhKbiqO/xjR9twbYtLti0HNM8OfNydnyOyrIUD8/X+My6a1g6UOLX5fCBGaI9TZTUvONrP6KnJ8v/fuGzKKYeq4Xz69CKImoLQUg/1gyILItFkU2lboCmS5cuXbp06dLl1+GZz3wmH//4x9mwYQPnn38+Wmvuv/9+tm3bxqtf/Wp27tzJlVdeybe+9S2e//zn/9L9/krlTl1+uzk4Z1Any8hQDAHcEY2yf/OXSC2UqOm2x9yDRzH68kjfRjYT3Q8daX5+dJAfXp/CMutIy0h2wq6bCNd2fOS+CczhxeR3TyI6SWlKqjOPzGRZuvIAdxxQsHSc6sfneeCnLtGLity2YymFmQamFZPu8ciMOSANhFJoP8BXbfxCnrSIMWKF12MRpsBuhOjIQIxO0ey1yE+ECENi12Jyr1E0t8W0UgZzBxzcb5gcvm0VXsoh90ofY5+BWmogpIGwHVQUoCMTXDA8jbIkTk0TqzaqN4vWkuYKg8zWGXK7fCJlwUAvVJJNa+9gm8HTfTbf1odqKb77yY1kf36ASGrozSfaJEEMPQ5y3zEwTaSSyIaHrtVhv0DOWfSshqvWj9F7neSf66uwAwsMAzXUi045GDHEUjN7eRGCGHf3OFYzRpbLGK0OulIFH7QLIhbghYgoRo5PwtqlSdqDSkIbWrgYWAjDhDBMgjxALBLB2pPIu4jFBUQtgiBNI+WT3TOXbOyzKdBpjEwa5RrQ6kC9k4gRL5QkyUgRV2uIOEZIkFaiWxWIgNmXl9lgzBNNWMRfmEM5Bu3zR2BPB6PSprw3xDs6g1eQmJMtwvUrEZ0IM4hOBJQMI3FsehSPfKbCGFFvJ65SjSZ6cT+iHieaMQvI2TpiqICcA20qnK2T2EGHpt+Pnp5LOlsoEZKRAstBs1A21u7AhI2dLwAWuq+I8lPMxYpCLkUYRpCyoXpCx8XaM4qZt4j9KjpTIt+vCMprafomxtaDGP6C1s7EZJIhZLqISJPeNoVzIHGTivqLGOVeVNbBaHjICIQEs25i+hKzFUMmhbRT0GqhGiEiXkiY8myaB/uInwLSyqD7M2BZKL9Du8/Eu3QtqZkKr/5wma9Wmgx9sA5Fh6hnoaTHcRI5eQWYgsLBJkbLwC9YyGaEtUOghMY3NaetHWButAoIYuCBT2zk7rkVlI9pVF8RkcmQviMi6isQ5QKa2yVi2KPn0gqThQ7GeB4tYcXVh9l3ZI7Vmx2OLCnTa9U5YA1w4YsuYtv2CYy0i1Fro2YlDz50lE1nLWUwbzB1rI07WSc2bLQW9BfmONbYzEM/0Hzrn2+klUvh96R5YNcYQxkbN23jlNwkEPiSmKVD00y1SpyWnWFZdgrlN9k8ewOrs4PsC01ayub8S1dy7/5JSEnqbZ+e/Mnlvte99Rm8799uJjWc56uH7+JCsYLL+k9/3DX6iRgopZHtCCTYGlzLfFJ1aRaly5xZvBxHmpTdIn/6tEt5cHKcS5Y8sdNUly5dunTp0qVLl1MzOzvL29/+dt7znvec9P7f/M3fMDo6yo033sh73/te/vqv//o3E6Q5evQo733ve/nsZz/7ZHXZ5VfkZWtWc+vYTrIbe8HzqLYDjE6a8cUn/rqb7h0kvquGOzqDaiSCrIXBIsd2XYyx5QZkXUEujZEvgGkk2h+NOqoZEi8F0ZNHNj0MB4r1Gc5aM8b5l9TZPgHVKR9vHqLA5vb/I1DGOPHGVbg5g8pkD9oKUb0ZmGsgBMxfmGVwa4AMY8wwIDYUUcogGHATS+dam1QNIgPMVoCOY+of0PS9bIyan0fu1cx9tYMwA9oXjBB+O0eq6pFWDVQpA/GCzolnIIMIR0q8ARtjap7eO6apPXWYqD+NVmBVfGojBsK1yByIE7tnIZl6OE/QqiCbHrmb9oCUKCXQwy5Gby9KKWIayIyDDBW600b19UC5gM45cGiClruUnfflOD83wf7bBOmxNrTaRL6PWjmE7skReSEijFBp0DmT+jWLyd84i5tJEedctN9Btn2ktkgdauDoCD1ZxcxmETMNot4CXl7hLcmTbTmE9Rb2VJLlQByjRRLgWUgQOpGNksklTlsqRhgWWekSXpZGzAikNhFhjHAcUBH6ETM4KRGpFMZsIxHc1Ymbj2FINEntjdOC4v4OrbPz9GfnOJYxMEWa7FyacGkB3R9gTrVIjbax6zW0odEK7FiiB8qo6VlEOoUoFtDjU48J1ACIXBrSDsq2MPYfgwMdrFIx0YoJH2kvCMom7hebhCUbo+FjL1JkjRiVsiAMEI80DUL0gmBxIhwsMYo5tKdQhkJWWoh2hyBTIHXGYvxqG7nryEkZSiKbQ2XzMNWgMVzDH+gn78csW5pn+oEDRI9c+zgps1LlHuRsBfvA9PH3YlckJWyhQglJOJxJXM48gRGA3VIY2TzCdVC9adR8BWs20QfSJkTaxxsQRK6NYcYIw0Cb0DlrAG2bFNZmOK18Cy80+9nqFkjVYiwnyRCxj84izpFE9yuko7GNkBAD7Rh4uRSmaLNu/TRnnbYKLywiXYXyBEiBUjH1HX0UXYEe6MXwYox6SDwIjZVZtFDYeyyCDQI95KEsG2krjFnB5vv3sefQ/ZR+dyXuSo/TSheSGerljm/ez+DZqzlQbWFYBndu3sems5ayYpmk9YMx7IM14nwfT//UEVbyIz6z43buvC2LtVNhD5XRS/qZDztERYtgucU/trZRXWeTu7HOzGm9NKMUo3aApTPkUzXu/MmtTC/twVmiiU3NH//h5fz1J2/D9yPSzmNtO9dftJZ/3bSCO8f38o9bv8sdD97L6qvfyNCKgcddpztxG1emHpsaKyTpsUTz65XDa7niBZuwn0TNtHunv8p3jm5GCourh8/jvvEZ/nXXg4z5dX537ZlP2nG6dOnSpUuXLl3+/8LXvvY1tmzZ8pj3X/7yl7Np0yY+9alP8bu/+7t8+MMf/pX6fdL+TDc/P8/nP//5J6u7Lr8G5w1uJHIktUXjPPO541z/wVcyNNdPZzgJ0mjggOsz9nQXtcTG1BLh2Gx8xWXsDFqgFjYNWiRuSkonLk6lIpSLULCxN61k8KqzGFrdT/WoxeQ381hWmSP3HcH6YZWUEZNbqTGyFkJI5FyD2lFJfTpFlHFpnTdC57LlzD9vCU7bRiDRpoVoalIzISIOiGyBtgTe6YP460bQ/UXEQB9CKUSkqNyZovDgPE4lSDaHWuEvzVNfZtNcnkIV08jxWYzth8HzMWpNZN1D1tsoESIcG20biMgj7NXYo5NEUZ3KU3uYf0qBSHaS4EMY0mlYjN3fAyyI0fb3oHuzaNOgc1VE+6kBfk9AYyjEX12gtbFMfZUk7skiFhXoucAg9AP2t0q856Nn8rOv56DaQEmRBB4ihegECB0nQq2RRqDJjyrmnzpMkDMJ+3NEG5ajChni5YtgcgbZ6BCsGkA3muhmi9a5w1SfswbvvH689QZ2p4Oq15NsHhJtHZHNoEo5RMpJsoAAOgFiqgOHJ9GTMxiBwhkT6J4cQoFAJHbKbR8mZ8EykD0l5COaKuUSsqeESLmITDrRwokUMpMmt0Nj3hqye34Rk69aRbCiDzk2h7NlP+w/TOi1iAxondVHXHSRR2cQfpiUWTkWoq8XYRiITOqU8114ASgwtu5PXiuNrlYIc3aiJyMlRj5L7sE5aq80aT3HIl4ygr89wpy1iRb3IlrByUEWKU5k6uQyaNfCqvvYe8YRHQ8pJanJFm/+42cido0iI4V69KCCMLmvQUjplgMEKsJreMxt3k8UJkFDIUA4ScZRFLVRxEnmkhAoQ6AsiXLBbIdox8CseYgworgvInc4hk5AlBKoZgO/L4W2DDTg91kUrksTvbbM+Zfswx5vo5stdOATNZpkbz2IO9EmvqHDsf1nc9nI5cgoTrKwFjI2bKtG9HDytftKm+CwhiBAzjWJHEH2WW0YLHPrl6vc9Nl7KTyjRu6SCjrWGEgsErez6vIMBAGplI8VBWhTJIHPVphYqhsmI9OzlL9ns/2bZzES2EyVbCbuKnGumOXqfotLn7mR5//+JTz9RecQDxnYEnK2xa4HR5n6QZs4Z2FmEu2m/qKHKRSL0k2efuFRmkvSLD53ObENHRT7ixFe1sCwJXHeJBW55EUHy4xx7ZBI1SjrgKljsOv+DIbQWCKm7bdoHKnjT7aZnqmfch4ebMyTsjMUd0mym2MmDk0/7hp9oLmHTx74e346/cPHfDY80sPGC1ey/PQhLrt6w5MaoAHQeicFq03R8ihZeQyR2KjLU9RRd+nSpUuXLl26/Ffxt3/7t5x33nnkcjn6+/t5wQte8Bgt3Fe/+tXHXTsf+XfhhRf+u31fc801LFmyBNd1GRoa4vd///cZHx8/qc2RI0d43vOeRyaToVwu80d/9Ee/tGu167rcddddj3n/rrvuwnWTknWlFI7j/FL9PcIvnUnzve997wk/P3jw4K904C5PPhlZw2zMEH0rxZdradrBXeSqglRrIVXAMuiUWkSn57FmXHiwAcTc+Y27qFy5ip52MhlV6BH12pjT00glIZtG5WOiszyKc1WKiw2aURa1Y5rJfVlWFd/OQfk5Sm6FmbbB/A6JuS5FbfUQq66cYHtlCZmtAfLIFGa6l/wBH8IYnWkQ9GVBgs7ZBFaMci3sbUdIHWmgn9pPmLZQdRt9bAZraS/psqIxYSO1wu8DlXcw93pYRypEy3oIeh2chkBEESLw0eUWBhmMmqAmAyLt4s7WEakMwYoCub0BmXGF8jT5hz1wLbR4lNW8FORW5lm0YpbeAY/vbTyd2BG4P5/DWAbGngD7aAtjNsCsxhgS4r4CmFWueN0RzjjvKJ/7QYnqbSmULKPNNsgYVcigygVExwckRruOmmthzC5B2gahkcZbncHduZtgrpc4ZYBlYUzOE6NRvWmC1b0YCEinMZox+VuPwIAgPzdPNCeQI0OIKEaNT4HWqHQG+otwaAo5UASgeU4/eqKNO20mrkVotADMhfitTsSEdZRYkhODDgK0BO15CNNAZLPoRhOURrdaRGGMjDRMtwmDIQpTPuFGFzkxjTEbQLWe6BM7HXAs0qMgAokReehAQTaDLJZQlSoSiXpEJBdACLTWIJIgjbXt4HH7bIConIHlw/DQUbBMdL2J+xcul1+4l1k/x66tKyjO9mPftg9DLwQnVHwis2jhtQBiU2B0AoQCVW0cr7MSlsUH3vxFdLTwXaaAha91u4Nunxhv5uEphJuj2eggsk4SwFo4D0yJbHTQpsRPW1hRCIbEqLQRTp44Y2LUA/DbTF9dJi6Y5EY19qyP8EJ0s4U5YSJdF2G0cDe4+HMOX/z9HzDY5/OHxQuZjxL3KrPhEQcG8uA86tAkH393L+/9/jN5/gcCfvKuw8evn1coEvZ4WON1wgdilCExlCZOSUzTo769wP4HQ0TOJVQWmX6B9A0ioZFaY3aSi6RcgRgpMrIuYNcelbh0GSA2WJxfOsQhL8vknT1oNyDOuhw1A/y8ywyDVMan6d24lr7eAs9+2fm88+s/YGaDxtvp82//dDN7L1xK46cHsFOK1a+02f09m+s/eQZbXtzPfYcHecNZ99D/gjzrz6iy85sOzQWXp6YyeduyC/jw7DHs/gp96YChdBPL1NxWHcIyPE5fM82e0Yt5uLkSJUwuMQzkSJpGx6dYeqyzYd33eNetN6IMhb+mgF6RZvnFyx53jfZjD601Xtx5zGeGaWAu78FqZTg2WWNt7tTByV+XfvcMXrv4ToYzV5EyHZ6zfC3nD47Ql3ryXaS6dOnSpUuXLl1+WW677Tbe9KY3cd555xFFEe9+97u5+uqr2blzJ5nMid9TnvnMZ/K5z33u+Gvbtk/V3Uk87WlP413vehdDQ0OMjY3xjne8g5e85CXHAytxHPOc5zyHvr4+7rjjDubm5njVq16F1vqXMhx6y1vewnXXXceWLVs477zzEEJw77338ulPf5p3vetdANxwww2cffbZv9I1+aWDNC94wQsQj2yQHodTKRt3+a9j+/QEDx8dYkmthdVQfOVr9yMGUwwFNeB0RBiTvbfN0k2rmNjdoPWIc810E6sWHhf1bZ85SLSkD/vQHOnDLXSlAtik7hXE0RwHZkPE8l5EymR41RAzW1JY11eprcjgPxyhNbQNhbfOxRqIkTpGlQWNJTb5gxXQDloKhBeg52YR+SI0Aii5RCmLTBSCVNRKgoF2k5bXwG51uPjPJiivbnP3F9YwvtnAXd2k/LSQsX9zKMza6EaH1kgKIkHY6yBNF/+lWcwJQeYbCqOQgTjEOjJPVLRZlE1hPxwQyDRxKqa0WyF0QByo5FpojRFognlJReZIl0L61jTwTAuOGjjfiem5MsR+tcH89hyViosbdCidpbFDi2sv24VMab4gLDaOTHBks0ezrVCGQM5WwTLQg73gmgiVxsra9D7QorkixdxTXGStQVy3MaMAOd2B+TqYBt45w5iZEulxD5VLI0wL93AFeXgavddALSqBbiKEhIwLw4NJNkqvC45N59zFBL3JBrAzYBEVc6TqIpkLShM1a1BOEzkgpYEMgIFeVCmLPDiWZMyg0VoTExPaGjMIEg2c2Ro6jpIyOaGwjAAXRd+P5pDCJs5qRGChS1lENsf/x95/B0qW1nX++Ot5Tq5cN4e+nfN0T2ByYGYYlCiSVFYQxLiIYVeXIJgQRXddRRdRki4KAiISRAaZASbnme6ZnpnO+eZcuU5+nt8f504PI+CERdnffuv11+2qU+ecqjpVp8+73p/3O01izMllEBolJcLI8m5IUxCg0xTxraNOWqMq+czJU+9AEj/lM2C3FNTirKUqSQl/scLs0hgzB/v5yLVf4teO9pMOFHHmlhEDA9n73O486aZZE3yUaWQV13aCfiJ8VwPNVtZi1e6AFJl7R0jg28exAIzZOlafRZKkUC4hmiE6TdFJTFK20FpgrwbIZoDQoF0Ts1DBSAV0YnQUEVYgHHPRhiQsa6QNaU4i8yXCLQMUHl0EV1DwOrTv9vmMGuLN75uj7uQRnoeWEsZGsGKF1VklVRHLxzt8Zd+XWBx/DHOwgK5l+xsM9hH0Q2mxTR9dutfEtO7MQZ+LVY+ITAejm9IaykKb5+8awzxSx9QQm5DY0B4G56GzqNMtTrU2ERbIhFgp0AVNN3AIl1ysg3O0rlpHMJJDd1JK0wplw9adFqkOzr2GV+zayBdOHcQIQNmSuw/N4EhFkioOPd6PESbk7jOovrHNz1x2H3/z+F7+5w33k3MVd66/jMdrI9htQRonNCs19GBK91aXmfvH8fs9htYvU907x7o4x2ce3Mb0mTOE548iTYPuep/jU0vEccr8covSv3J13X78DLNnm8RmSv/6Nq6rWIqnqXpZLo1OF7n9xkeZnYLXveUF7Cqdz4AzTNXu+47Hy+ZNg5w6s0xf9XsvnEy27+bxtqSpHmPPIDSCkPl6mwEv/53b03r06NGjR48ePf4D+NrXvvaUf3/84x9naGiIffv2PaU92nEcRkZGntW6f/VXf/Xc3xs2bODXf/3XedWrXkUcx1iWxc0338yhQ4eYmppibGwMgD/5kz/hzW9+M+973/solUr/5vp/8zd/k02bNvHBD36QT37ykwDs2LGDj33sY7z+9a8H4C1veQu/8Au/8Kz2+xmLNKOjo/zFX/wFr3rVq77j/Y888ggXX3zxs9p4j+8thxsNVA46O03Ob1ZY2OMx0olY+crpc8s4Hfizt/0XfmbuT+g8vAqpRoyXURss2pcOUzq0guuA3wyxVnyE62Q1wIaL9k3i4ihyU5ehDYpkMkJsjjlwbAb/rMQ/mV0cR5flCH5pM92zFofnx3BzCbJsQaUEx6ZIky5x1cWcbyIKBrZZyBpwjp5FbthE98XjpDIgqOao39ql3IkwBvqIgi5ad4lDCe0O9mxAOFSk+9+G8T5sYHbAnffpjnvQX0SstDGPKawjKeliC3s6IHbGCDaUyO81+O0fvIcz5Tyf+OddGI9a4Iksn6XWQK6JkUmzjrdOko9bHLnX4fk/eBCKkgfu3ITO2RTWhZx/5RT2Vaf5wnsvRwyWKe5aIF7Q3Pz5MVQK6r6EN//mIW4tF/mXT/YTb+gnLTkYysIKFcL3oV5DGA5m4IABSR767g/QMy1ESUO9no2L5HOYcw2MqkU05BEXDZwzDZAWyQVboBMgDBtjxCM2JeQMxEydxPJp7lxP5aCPKSzkYja60ffl4yT1EG1aUCmQbBxBVT0ouSR5B2O5g0w1RqxBGMiRIdJyIRNTogi1cRDtWOiZJdCZ9ZBIZaNQcUwwH6GiNnK1hc67+D+5Hb2zj/xnFcaSRnc7aJ0ikxTtOqS7N2A2IvTsPMo0MOrNtVrwNacKYNQ7mUCzfgwpTVQwu9YmBKpigAgw/AAsm/wX8+RpsHpBCXmxwD48h9q6HmwLVArtzrnPxhPys2AtYLnloyvVbJl2BwoFsC10FCO0zpxFgIjTp6xDfMvfcrWNUykhLZe01oScRyISkqpNZ2eV/KPLaLE2jkbmDlL1OuF4FUMJjDDCPrJE8Q6Ix6sYqoBOwa51MVc7mAstjNSAWNI+5ZGGmlu8Ldz72G4CmccxdGYJtUAiEW4ZN2qQRhHmaZ+te5bY8BML/NMn9gKQf3Aye68TRW3aobwnom0XUYakz4jwzyZoYaMN0BLisQDZMNkk+5lcquMsBCA0+dPNLKfINQnzmSsJFNv1Is7phDP7q/TLBrnjTbyRPmqGQpkic2DplHZ8BoC3/9Y/8MC+M4wAOpWgYwZig9bAABQtWpFDn9FFGIJXjP0+7/rJz3L1eWcIXy6p1QaZnqkgigKhQEbwubP3UnieQ/ipVdQjBfRleTYlNQa8QYryVwnSRxhxlzhTX0J6gm98+kGIUkwhcM1vP10O5HPkDBtLSN64dQOjA4KKMciF/+N/YKuEj73iPv7H23dlOU22wWt/7gUMuSMcfOQs//A3d/KGn30B2/c82a70M298/rP92n/GnOosMhn1s5ikvFJFfOC2eziysMRbrrmca7du/Hfbbo8ePXr06NHj/7s0m08dF3cc52lHfxqNBgB9fU/9Ueu2225jaGiISqXCddddx/ve9z6Ghoae8b6srq7yqU99iquuugrLyrIG7733Xvbs2XNOoAF48YtfTBiG7Nu37xlVZ7/hDW/gDW94w3e93/OevTv6GYs0F198Mfv37/+uIs3TuWx6/PvzM7uv4Ej7fzP8I4q9J67iqufvYNH3+YN7Z84t0651+a1Pf5mZR6Yw9doYwtllXvY8n+7GVY78gYc6sYIZtxHVKto10EtLxFWXcMDD1TmErtK3+TSqBUeKNqfPzlC+dCetQ8cgSan8vEnsdVnVNjXhUU1DtC/wApPgRwYRwkYteAQLNmY5hzUnQBgoKYgrKQk2tvLIH5LkFgS6mCeO6tz70TGc/i2kASSb8lRfPs1SwSV80CLcLuk/GWKk4DRiUt/HON3E/JswC05VCm1I7LYi2NRH7XLwbcnARSHJfSXEsI+MJVqKLFdlaBAsk3RlmSjxsY2EsOlw17v7AdAVjaraLNxXouO7iGIHGl2UbZDMatonc3zMuh7RDSlshZm0xJUvbzF9xOTe4jZUNY+7ECLuPwVRiFYpqe2gN41CalA6AvlTPtr3Ub4Pto2KIkpbA3J5RWMKdHEwG4FabkGpiirnoVpA1wK0kIiiiUoSZNfHOuNjHbFpb5pAplCazBwoMokzkUFr6AQolYItIRWYNR/TT9dGjEB0AlTeIx7MZ4rC6QjZCZGBQuQ8EtfEWGkiDRPQkPfQXg7h5BCxJhhxkVMWiRWhp1fR9RjZboNpIFWCThW0fJhcQKQKoxNk40dag6EhVU8RQMTkLGrrRhgso+dWAEiUJjnfIn9cgFCoIyfAlOTL2/jUO4aR3Qhx4ASpAMMPz30uvvWbK56QWNMKaVuIVGdjU6nKgqh1FraNbaHDLM9GWFYWVhyE5/YvXRMcRKxQU5OkiZMFA5cLmLFE+QnuYkLzZbtxT61SOFxHLDVQOiXYUEWWXEg0smVDrKjcNofqX6F7xXaESiCVoEG2Q0SpBJUy8XKDeEOFleoQySMu4+kc6WobYRrELuArVH+JsJ6DKOHv3juNzG/nF/7wNlIns2Da/VVQkMZ1hN1F5xSqCI2tJkubSmz6UIvUSog8UDmBcZ7AFJL5W2pU+gtEsUY5JunGYcRiHRE0SMeHEUojteDsX8Dhq7eRDw3S8T5MXYBjPnKvSepIVAJXb/gZKt75/NWv/x37PnUXxvohShd2UTOaYMpGDbvIroWRt9nxkrOYiybh/Zu5eGQTxolpXvbTBxksKs4svoZdSlPcMMz0cpOXXLeB7oTmzjsfY8E08R5dov2CAWbirQy6FTaNDvHf3/Nabv6HO7ixHqObsPiXX8VZt46hSzZTLH/7CfbKTev51Bt/lMVOl10jg/z63TfxZyufoesLusOCz/69heoGCMvk7JFpWn7IoakZ/vQdn6U20+L0yUU+8c+/dm59jaZPqx2wbuy5V3l/N66tnMfp4FH6rT5AsLG/ytnVOiOlwvd8Wz169OjRo0ePHgATExNP+ffv/M7v8J73vOe7Lq+15td+7de45ppr2LNnz7nbX/rSl/KjP/qjbNiwgdOnT/Nbv/Vb3HDDDezbt+9pRZ93vvOdfPCDH6Tb7XLFFVfwla985dx98/PzDA8/tfChWq1i2zbz8/PP4pl+d7TWz3ri6BmLNG9/+9vpdDrf9f6tW7dy6623PquN9/jeUjK7vHooz6ffqzj+9X/g6xsHyb/5Ivzz1w68fA6iJg/efRDOPJmxYWkIv15j/atrnLAlyoJwHmQuQoUhOkowFluY28ZYN7bAxPYVpg55qMc0jt2kaMG7/+hN/NLP/yHxchcWJUY+xG4K0q6FOGliHZzDnFwh2V5C7TRZnywRtyStWYFGkeRN4vPXIfI5vKZADiWYxQirFWNEmtiVNPYMYy60yYcaVfY4eGwvxU9OM1DogpaofhMZadxaiCLG9BLiIEXkCshSEWmCv62KP5KQu2me33n8POR4CYmJutxDf3UJ0V/CdL21dicBliQ52OV46qIR6CjJxnD8DtFonvr2Il//81HyvoJmgjw7yeqpCsF5JRgySAcLrAg4+ng/P/mCaV786oR9H10m3mpi1LroNEbZAoGDLJUwuimFMyEq70D4ZJU0UYQUIP0WzVwJbYTIKKZ0OkW4JbQC0exy6TUL+F2XQ/sqGHGCTNOsv1kr3GXo7DBIJSy8IrPuiWofSqwg83lEbBPZXdKRCm4DlG0S9ZkYQYISYCz7hHaC9k2MOEVXPSh4mF2FKFdgfgFZqaBVik5TiBOEFmCZyGoFVVbIZhfrCzOwGKDDGG0YYBrk1jkksUPQiiFR2bE50JcJG74PnS7aMBBplh8TvryMXmfhfnoKsWkC1kQaEcboZStr4QpiJKATRZ4GB28uo02BkehMQ9qS0DlpQqWEqGcqvwb0uIOeixC2RdpqIcIISkV0FGbNVmt157paQhtrTUZzKyhTY8Qpe99Q49GvldGz2dNY24tsdKvWRAxUkN0OWpK5lNwSyYSFudogLudI7TUhSGuapqb2zuex7s8PINoJhvAxFlvZqJwhwQBVbyLqTaztJsbjS4zuDpjasBFrbhE62YnBsAWpFCizgOWHoNVablPMR95xFejs9VNGDq0j3EtdNo0MwQ0tZsogFlKG/ylC2gLpJKS2wGxq5D4TDieoVNA+u4jwwL90nGTDCJWvdtCrEZwnMmeSSrnh7Wc4smAydXQYKST+kEmw0QYnRXkSopgHb7uHq68f49i+U4hWgDPq03/NMrqdMHWHQzfqYiLZ+gMhI1ctIdE8el+eh48+ysZXwBe/NM7FLx/jx9/4Jq64QVBrdqnNNFieqfOCTRdyoDjD0ugwYtTEzZl45TqTnTuYan+erz+0k0e/OoZ+SSY0JhUXpxbSOt7Ijst/xam5Ff7os7dy3sYRGpe0uWXuEHE3IRwzQUke/HAfwlFoP2DDtkHe/4+38zjfoNMRCC2J2k9m02iteeevfYpGrcPv/vfXsX3bs7PzPh2us5khfYQB+wIsafGmyy7ijZde2BtT7tGjR48ePXr8uzE1NfWUkaGnE1R+6Zd+iUcffZS77rrrKbe/7nWvO/f3nj17uOSSS9iwYQM33ngjr3nNa3jLW97C3/3d351bpt1un/v77W9/Oz/zMz/D2bNn+d3f/V3e9KY38ZWvfOXc/4G+0/+F/i1hZdeuXfzWb/0WP/IjP/Jv5uIcP36c97///efGrJ4Nz1ikef7z/20bdj6f57rrrntWG+/xvWX/4vux3SMUK3tYThRpskydU2gvs3MJ10FKgVGpEu4eRHYWkSIiQnIk6mc+HKd/j2R6so3o2qhaA2Fmj3U2hPSfP09psk73gRbNAwr/pEFZtxi+IuEjf/gh3G0JsQ+loQ7N1VFKR33cpiYqO1jTdWQzwL6pgDghqUc5xGyM9Jt0tuVxOhIr8ag8GuOvtwgHDYy5BKGzRGyqJYRtIcsF5GoXIYGiRuU1lp8SFyVhKYdVDzFyKcrzKF1QoRC3mNuXy1qKLEkwaGMstfFuqWOEimiPIniTSao01mdqOJFGJSmqvpiNq6SKdGwAXXCRZxfYsatBUrQ5csbBr5bxVzzERD/GHYvofhPZMUkKNsZim/yKRicJcUESb8syV1TJzbJuAg35HGp8CBaWUJUCaqgPDE2cl9jNDqSQ5h3MTub4UFKgLIE+FWE1FXbSRlTKCK1RSrN93Sw//vrjpG2DX1n6QZyFEOvoIlplDhRVyiH8BJXPGoQAjFwJI2chOwZJu06waZBwXGI+lqBKJkYiUAUbo+6jwxCCBHvRxJAWqhmSmg204UCaIkwTWSqiTcnqFoviPVNoy8gEB8NAtkKM1WXM6RZIiR7uR5kSuVSjMaVpXztO6UgdY6gfDIMkZyFqTbQlUP15dBxhuwUuv2aBb54/Bo4kHTEx06yRSQNmLUIcWEDHybnbNNDuutiGgdo6jnFkCgBfrMPcYqCwUUkKhkRIjVqyUVaCiCJ0EmH4a7k3rk06PoA5twpBCF0fNTEIaYzudJGGiRjrQ3gRgxs6LM5mQbMjFxeYu7N9TnjRSQxa4JxcJnE17gML4LmQpNiLbaxVn/jyKpiSzmV99B+MENUyYV7jlypUuiuQJAilQMETT1S3ymgrYOBwk6svv4P9xhBRIQeGARWPYMhElwqYh6ezMUetIQjRtoVhPnkSUsogmK0y9NL1HDuygDhqM3Cig9lVuP0u7fUSf9RAxFB6xMJeaKENk8RzEbPTiFYZ8iWMkQHcAZWNqwkBhokzobnwgmNM9ReoSwEFk8TTOMsCbBAORGM3MxNJfvEDP81PvfnD+IFL+1CTsF5C5RNkLIgFxA832fDmGjYRx/wx/uwvv4B1seCeBy/j7lvhpm98nOKlYxw7tUB5IWYdFqPr++i2ItKSTf38ItUgxxt2DjLVNbFoUalM0RHD5M6apJbGWUoojXmUq3mEEMzO1SmVPAr57D8YnSAiVZopf5kbj91OsRoyPjDIA5M+/bfFaM+GpVUQkpe88Tomv/zXdGqK3IUBnbvyTOx+0jETRwlTB2fwPfgfX7+dt5duYOfw4Pfs/HCq8QhfWx3Aa8zwqo0RYay4/fhpLtuwjtFy8Xu2nR49evTo0aNHjycolUpPm+vyBL/8y7/Ml7/8Ze644w7WrVv3by47OjrKhg0bOH78OADvfe97edvb3vYdlx0YGGBgYIDt27eza9cuJiYmuO+++7jyyisZGRnh/vvvf8rytVqNOI6/zWHzBH/xF3/BO9/5Tn7xF3+RF73oRVxyySWMjY3hui61Wo1Dhw5x1113cejQIX7pl36Jt771rc/o+X8rz1ik+Va01qysrCCEoL+//7msose/A920ysxCP9GFedpnC/z2r5wiGp3lS4vZPJ/udlBIOvNdTOUiQ5U113gWC3IC/x+WkF+dIo1EFpSaajAThAGFV7p077Y5c8RBhh7haoxOoeMrugVNcyGmeKROekYR/fcYs3iEF19jcMfh85BpJhAJM6RyRUjllfPM3NRPcraDsVijc5FF44J+qkcVRpTiTcXUd1q4rLXEaAOjG+NEdZwVTexCUEixzWXi1xWw/1kghEnYb2HNNGhstUlHHNblNJ2HqiQTg4hGh/YGC7vmo3Ax/BRSTZiXpAcE7oE6bq6aja10/czB8QRRjB4cpcwyP/ebs2AY/M77RwnqEssFUxYJzjNwji0QbqhitWLsUIPSpCurRBePcMvKVgbuTnjg1Dgib2NoA5HEmKFCKYFu+KRWDf+CEdK8xJhfwZ5eRtmS+mVjFB9bAs8h6qQYgQIHEpFgWjILu211mJ3u4/4jY5w9VAFpoe0spwUBiWPh1BXWqQ6iFaBncrCDbLQrMBCmgayWqTwW0wkt7JZGSw1WlgejLIN0fRXdaiNmlkj6S2jbRM6uEg0NZF8kUsNqjahiU7ovpLujD6MmkRqSnMQ73kCZEl3Ko0s50m3jEASwvIKMFIX7zyK8AkoCpolsdBCmTSojEk/j1jJHgl0usf4bs8x3chgnQlJxBoO1TBcg/VeOB3+swrbBGtOeRTAg6FzZR+5wC2e1RSJtqM2jijlkEIKQ6FwJkTSycOvoyXUJ18U4Ow85Lxt7CmPk5CKiXEJ4HqQpqtHlsZuGUSsrPDFENXfnk0o+hgGWRGAiwpjiffPIOIWwjZZr79VggahskP+XR3Erl1A60UHmKqRFiYgURi4PnQDtpVkgbxAhlCLxJCI1aBxISFYTcptc0rkyaE17ZwkhLMx2kmXgCLHmpNMIy8J0s7pnZRnIWCNMxal9MyQXtRHJEJ0JD3cxRLc1wYzGGMmERpEkSNNeO45C4ryJSAzMliYxJSAzgXTt6d/ztV1wrU+nDJ3zy7hzGqsNKAGpRpsCz7qQIe9q7vjm4xh1n9ixWLppGIEAuwGnZ0DAbDjAgx89D+YjlmtVRhpLzP/VAKLaJndghlknx47Lxmj5EZGKufyC9WzdPY73wQgSRVw0WQg6fODeFX784i5NtZ6zD26hz/DQUymxBxfs3sQ7//jNVEf7mJuv85cfu5WBgQK/8bYfAmDIdrk8V2Vkd5VDq0cAhel1QAjy0ynd7UPkCwXWbRqksdLlFVdbfO4/51he14e5SXPasc4dGrZjcdmrzufu5VmmVZf7Tk99T0WaIElwzQgpa6Q64abDJ/i7Bw9wZrXGf7n+qu/Zdnr06NGjR48ePZ4NWmt++Zd/mS9+8YvcdtttbNq06Wkfs7KywtTUFKOjowAMDQ09o3yaJ+JZwjD7EfzKK6/kfe97H3Nzc+fWdfPNN+M4znfN273hhht48MEHueeee/jsZz/Lpz/9ac6cOYPv+wwMDHDRRRfxpje9iZ/4iZ+gUqk8k5fg23hWIs38/DzveMc7+PKXv0yr1QIydezVr341f/iHf/hd1aYe/zFIUeGeT66n9i9dvLzJUvMNLKSSYwf384OXA6YNnqTYHxI6Gu43s4uJSzZQ1wbtIxG5eO0X9TgBaWSBrYbFwqdMulfl6SuWaUQtrGPLyLk2zWs8HnIbyKEKAwtLGLSI2pIEhV1RbNm8zLGzIwgMkBJDxEgMiqzQmu6AUuRnE6KcgkYX0QkwlKZ6t4fEIKoUMOMWYgtE18aoU4rwqAudDoWv1bBeWCClSmdU0FkXYZ1KyZ+KCRci5qYcRJIinEWUJSk8NIMqGCTFCiKF1DOoXTtA9SxUF1qc/6YGy2fLHL8zjzndWBtSAWHZWDOrdFpw/GgFLJvoaEBxehlvuARRRLcQ4dWyFqpk1wRqOUDorKVIFW0aZyw+NHsJiZcyJDqkscKqhYicSzq0HlZbmEs1RKeCGVsYcbZ1YSj8SwbQI2WslQDj/vaTRUJGQJSTyFaMoRTRquYL75sgCUO8gRaWr2B4AOW6GNU8qQFWPUSbJkY3++jrJMkye9IUtbgCuSGEcFC5CBFqzGYAqcAIYlRXwFIIjguNFumGAVS1H7tRRzWaWXBvs4vREIgU5B6LcNjDqSm0aZCu64flWjYqNFhAWAKx2kGGKVJIiIGCCQboxUWE4yJyOcRqE7dhZG6TYp6HD4CaXsGe62Zup3+VhZXkBEYD5FpD1wUXCd7wY/v5/fu24h5ZJOq3iIcd7NMhiBhch87F67EX6rQ3eeSXBKpaQLSDbEznCcHOD7PmKQ0IkTlRogQpDKgWSGuroBVRuR/l2Ni12ew1BuQTO5em6MUm0jSINw/QzWuKj68gE4V2LdKiQzxcJc0JpGXj+TFEKToMMU0XoxlArMAwEHEMcYKWAoVm8bo83glYrxTbXtNh/khKax5AsLrXxZtLya9GyDgBBCLnQ9c75zgCEGmKsFOKV3fpHyty4rTAWgwIxnKsPs9h4BEf2Y7IzSiMMOU9P387q9M5PvHR88hJRWSYuIfm6F6xgaRq0z7TwNYW4fo8pIrmPht9RGKVY5wdmvxMivAFumhiLaeEg5Cmw5x5eIlPvueLkIBpmUiViX3pcj0bRdRAx6f1AofZ/z6OI1KQNobp4s3UMfwEmSa8+SWXsf/AKUQjIBp2yRdd7Csq8JV9ONMW0c4yadPBpo8+0eDk1FmSx0dhwkK6BqeOrPCV249Sn1pi34e/RuK5bHnXa84da/d84yAPfOVRXmhcxG+98sd4z76v0mpJ/uz6Pbz/M3diKUApmrMNHr33ODfHNsUjLcLV7Jhf9hpPsdP+zjtfzUKzzZ2nznDtlo3/B2eDb8f1djHo3Y0pTbppEzRMrtZ4ZHrue7qdHj169OjRo0ePZ8Mv/uIv8ulPf5p/+qd/olgsnsuCKZfLeJ5Hu93mPe95D6997WsZHR3lzJkzvPvd72ZgYIBXv/rV33W9DzzwAA888ADXXHMN1WqVU6dO8du//dts2bKFK6+8EoAXvehF7N69mze+8Y38z//5P1ldXeVtb3sbP/dzP/e0DqCrrrqKq6769/mh6xmLNM1mk6uuuop2u81P/dRPsXPnTrTWHDp0iM985jPcdddd7N+/n0KhF0L4/cLkTtZXCnSdIvVyhQcnY6bNGL+49mttEGBVYORFc8RJyvwjfeiDC3QfOoWR34FyCySVNqaW6E6YZW9YFrK/D1tKkhWNiDu4+2YxY0UaKOQJTdzXRQYJiSmxSx7acTDqdfZ/zsS5oIMWKfFwEdv3Wf2nLhzW7L5IcZ/Ich+M5YCSbKNPz6Btk+i8EeJ+h8KchTQA18aYDzAWJdakgbkiiRXYrqLY59OomgROkXjQoru3inlaYNZW0X4TEcTIuTgb9xmyScdt0opLxximeXWetGrjfnaK4T0ttl07z0CtxSPh87EHV8g/PJO5MwwTmh0SX/GXf7IeXS5iqZBw2MVcbZJaKVYrQEiJLuRon9dPJ4WBuw4z8laFu98nmIXmVlA5A9lNEHOrqEYLtX4IncsjbBMxuUDxG0fxL1yPgYnwHIK9LnF/Dm+uS9onSQ0LqROEA8IxwbPQSMRS1qZDPcKMYnSiEKUySmVjLUnBQdkm6VIToQRGJxvhMYc16SLoRGeiRphiL0XYzYBo3EHWEoyuIjUlwnURg/0I20a0W+iVLkJG+OMuwrUIdw6R37eCsdwEIVBdResSk6GHfIgk/i8UsW4NsL9Ww1JTxCc6iMUWwjCyZqhKGeHY6DjOipOkQFjmWgaMhjiGXI5WLBFRALKZjfxIAa4LXT8TigyZiSJag5RUwhn+7s83kXYVph9mo1mxRhkG2g+RQH41IekvE23wKN17FGO1k23TdYAnsoHW/v1EI5SUiEIeXfAQSqE3jqJcE6TEr1rodhF3spUJPd+CAHSS0t6ZuVz00RoiTjFihVzuIlKBmc+BSsk9MoP2fbQfIvwirqxk2/f9TEgFpNJooLS/gXe4QS1n8XhznJZnAwItYf1dJ1BnoLt5MBNfAdFnoRwPEYN6oua83SHpLJEGVV543Q8y+cGbEd2EpJAQFzXyyBmi9X2kuSrS02za3uaS8xf5xF/tYXTrKPWpRURfEaUighEXe85G5WxEpDCjFq0dOeqXGGhLMvzNmPyZiNhNUbGL6Sdoz2I6vJ/QmUTmTVj1iftzOMtB9n4Iea4mnTTlMucYnykPYdgOC50BYstAGi5SWIhyjo9+9T5KjzYx/JQ7//IeWq+7nh/YtZ39ux/H3+5SKkh+Onclr9nyI3z27z/G9MPZO+QemEUNllALTb5686MEtx2ApSbQ5JrdT9pvL7tuJ3GUcMUNuxmoVthZ8jCMgJesv4wPlO9HNVPSbof+qsNgf44NSwmP51ysjgITxKkGX77nIK+8+slgvEYQ8PlHDlLv+vzsVZc+x7PBt7Oz+kp2teaZyG1h0Bnms/u/SZCk3H9m6nu2jR49evTo0aNHj2fLhz70IQCuv/76p9z+8Y9/nDe/+c0YhsFjjz3GJz7xCer1OqOjo7zgBS/gs5/9LMXidx/Z9jyPL3zhC/zO7/wOnU6H0dFRXvKSl/D3f//357JxDMPgxhtv5K1vfStXX301nufx+te/nj/+4z/+d3u+z4RnLNL8r//1vzAMg4MHDzI4+FQL9m/+5m9y9dVX84EPfIB3v/vd3/Od7PHM6HcvY+eP3c/Wq23+8dAOxM4yb79oDx9YWPulNE5QsoAObBKlkOfnsU4aJHGKd7IG7S5eziLXZ9KYFAgvnz1OCkQ7oHg0JNURYjVAaVAlh3jdEOGGArEKMA9aFBc6FMYE7oBLx0iIJwRzF0uc2SrVr4fYJ33aJxRTW8uIdQX0wgpGItCOJtxQwY1MnI6mbRnUzxcMn/JRtRi5JHC/YmIGgFY42sG8apClYxHpwTbWNknxWD/Okk3QD+5iB9NLkIOS5DBoQ9K6eDNxv8bfWEBcUyYe0BjtGGOlw3Tb5eAD64mFhSyZiGoZOa5QtQa63SZ/UUQaavwHfXSQkFRzEAeY5TxOKyU9uZA12Cw1yD0yg5AmpT0pxWsMrMEGMx/VFI6lOEd9xHwXoRRRTuBXEgrHZ2G4H4p5zCTAXq+JVyukwwWWL7Ww5n28R2eR3RhDpMQvLWLc1soMHTrLUUlKDuZcDUwTMTIIWmf77piEY0VEq40e9UgGi8h2lLUVAbmty0SLOYSVx6iU0QjsToIMNKItEPETR5cidSQoA9uIIYowcxJVKJOOuqy8ahhvUZCbAmqZQ8o50kQXSsiaxghCwklBusFFG4L0WIrhhOhYZxfdGvT8Inp4AIoFpDVAGocwM7923EpksYAUmiAvcUolVN5GLDfBNM+JNAJwp79lvAjNw1Pnk0Q+ghmE1lj1GColdKe1FiycYrYihLKoHFcI00aoNlqILHsGwFkLBTNNYO02pRDFAqpWR8cxqjqOdi2M/SfQl1dJSva5eu1zGDJ77QXEg3mcxRgZpec+n1oKoh0jWEqAaaJbnTVVR0OzBevK0Hgy5PiJJBkBbDHnWNA5UuFx0+d34rZDBqI6IlWIYzGyEVLYkEduWQeJJhyOCF8o8L6hcA9mAbaq1YWlmPrXNSs/3mDohwIWH5b4OyysJYUc6MMyPKJIIyPFvOXwwMFBQDDfDjOHUaowO5AO23QmCmAL8BXe3DzdfB9JoQxCkOZNlJdCwcada2GkgJUQptv46BmD7iuHMT91Au06IENSQxKv64emgzmzghCwQ7cY31BlIYjAlIxdssRW+wx9zip3fWgD5ZzzZOCvUnzz1sd5eGkS3Y4QoaRbbPONjcf5kbTIgc97+A2FEOBfsQmVs9i8ZwN1y4CxvjWRBj763i/wlze/C4DxjQP82M9dn70fWrN5aJ4kbbMQTvPXf/ZTvP/DX+e6n7wG24+56IY9NG8O+ZJ1GlKNqzQYBp0wynZPa7508hC3Pn6KI/NLNP3gKSKN1prVpRaVvjyGaTzd6eDbeLxxmFvmQ8a8WV69DuqBv3ZI9oKDe/To0aNHjx7fP56uIdrzPG666aZnvd69e/dyyy23PO1y69evf0rj0/8NPGOR5sYbb+Td7373twk0kM2Avetd7+JjH/tYT6T5PnLHsWv47EN99FcDxuMib7jofMaLBa6a2A5kv7bHuSJn/thj9SV9vO6SDZy8r8XsXA3DdZBBhAod2k0HIUL0ah2kQLc7gCYta/qjiLprkfoxJhGV8YRN5jChiMjvnGJo3Oe8H6rz4MnLmFztsDrYj0jBiBVqoAynF1GeojVnIrwccp1Dfpti4yUr7Lt3gqC+gjO9glgPqlIlWk2ydqcBj6ho0NpokJuLKJ7wmR4cJI0DvPE68UiOgQdbCA0z1xZpbdxC8dgyla9MZTkzQO6xeVTBxGqmNK6qYJgxVCTBtgEiLOa+footP7jKnh0ep44MITwX6bnoKMIaWaF8tWRu0iGODWSji+3EWBcYcEagct5aA5HAbMVY8ysEizGji5qTZwV6dgEZV3BXm5kjYHgAM4ko7VtCrh9F+DF6cICr//NBqpumue0LFzPd7KNy6wzFlTrmYkDSlcSbHfTxGNnR0O0gzyxlF/NIlGOi+ooYSiG6IXp5FW0ZSJki4pTWDSVyZwx0INDHJ4HLaHxFE7dbUMmadaRTIcxZpLZBWDKw59e+NFPQKkLOLqNKGlkso6wu6UCO4nEfqwnas9FlC9VXwQC0q3GPNsH0SH0f++seuuiCYUOcwmgZUHB87Zd8paDRQgQROoqQSQJRnImERuaqoRviLEm0ayMaAbobAGSOmjWeGFMDEIZBOjOLHiqfEzQoF0hGypi17KJbpCnqxGni7esw4jyY8slxpmIBwggsE/IedMOniiOei/QDlI4xV7vo4zPItk/hkZTuBQPIw9lTPPeYVKFtg7RkU3pgBnchRhfy6HprzRFkERZj4qJH+XELkjh7rZIEIQTGbA0KuSwTp/NkMxAGDF8cE2jFCsOMfuwYIueiVmrZfk5UwfMw44R0pga2SZrPgSVRpo/KrV30R5kApU3FP3/kFANXbqEedfAeklmluLAQhsRdSdFS8N9OPp/KQwaerek0O5jHZ9Ceg9R9bLh0I1NzCayVdSlzFEMprKZGKIU3HUOqkO0wE2gAdzkgUDkaYUStalG+fAOiIBGxh1IabXmk64qYyw2cfIyTNzEHE8yxFnFeINyYdZUGpp9S2mrx4J3HiHaVKB9qotHMtts8JFbR3Tqi209oGOyfW+HWMx/gx9+0xPt+8yIwJAVtEiL5oTdczZ9/7HbIezyRHnP80RMopTh5aJaH7zvBq954NbZjIYTgUutWtDlH1A7Zvu79/Pnv/Sd+6+c/TuDH7Lx8Kw+cnseONHHRILpylL5ygVdedR4AU606f3fkEVqdkJLn8MLtm5/y/f7wPSf4zIdv4eJrtvOf/vMLnsEZ4amMe1spWfsxRAmFZiCXY6beYqiQf9br6tGjR48ePXr06PHvxzMWaY4dO/ZvzlxdddVV3zVRucd/DDc/NEerZZOolM11Tf3kH/O3D3Z47Ox2fmHDFgxToIyY7kQ/g/MGz9sySG2sj8lTCzC9jK61SFON8GwSS2OkCTg5ZKUMgJ5bZLWbIks5tB+jU4tXrp8hvvglPB4s8drnL3PdyFEOrnrc8pETGAt5Cu0KRlvhLids2jJPuBKxeEjSeKSO3FQCJbAmFEcGS0QlQW7fIqIdM7IU0k005oEZpLJILtpCWhCogsa5MCJ08jgzksA0aV80TP5oCyNIsmrlqYjuZg/dNbLa5nweWWtgLrdp79lKOlAklqBDM2tQGiwjal1WTxvoBwvkiosYj7RR1SqU8hDHNL6saM2XqL14HYHtU7p9kcpVCerGadKWDWOjKFPRHjHJdxyUVPT/Xki0sc0FRZ/Zewe46Koup+4t0liSJBv6UWmC3QlIpYCchTIkqWeBaRAXTFRdUVhIkZHAqaTEXYnommgPyOcxiwWS5VW0bWKUSqTj/ahqEZUmWI/Xs0adOEXWO2jLoFsEx1/BPtk8JzLIfB6RxuhaA9knMYYhNGTWutQMEalGS9CWgdlJwHHQpouulEhrCp0mCATOUkC4zoJ6FxknyIE+7FYLOgFisICslEj6iuggRksBsUbPLqCGq0/9EjLNbLwnziw8QggYG4FaPRM4DNBCIPwA7bnnHqbb3ae4Ss7p8Z4L0iBJYp4oyFP1NqL+pNtGA+RdrNPzpN0AY6Ge3WEYyGoFDImKI8KNVczDZ5/Ml4HMTdNfRdRqsFRDdNeEk34TdUkBvvHUz6gGRJQiOzHO8VWUlBheHuXaaKFBgndkFf8CJxOH0Gthwxoch6Qvh7lYRw4PouIQngg2TuGxL6/H3SVQHYnZamcCU6rQaLojRdROm8I325CmEGnc/Q5qTmMuelBYqyB0XWj4GGdXWPFT7A1XkOoQU2uEAmlmDVqpa5CM+pgGqFaKkAZ6toHwstEvf3OJyaNLKEsiU1ASdqxf5NiBEgM3tnHIYdc1Ub8HUpKsjTINbJqn6N3PtRv7+No9V6K9HKkrae6RqCoM3drBm0/AEuQ8+KPbL2PSjcg3LbxcQO3+ErfePEqh2mV+tkB0mQIJqS0I+jzump2FDTbRVRsY8k0avk+uPyTiFI/1D5BWU5gP+JPf+GlmHp/Eti1QGrP65BivDhI+8qV7+dof3EwYRNw/Pct5L9nBlv4+jkcBLTXMxeqbfPDLn+a//tCLufia7UydWuKW+EFO+qcQGlLXJFzpMljMkVtzaY0Xyrx8ww4eODrFpNWg4LpPOXZWFptMn1lmcLTCc2FHeYLfPf8nKJoehpBcsHGUxxYXKBbcp39wjx49evTo0aNHj/8wnlUmzb+VTlypVGg2m9+LferxHAljBUpg1yV+4NPoHOL8nQ6PzU4AoBLNEIKFisNokvDp93yO5nITogQdhqj+AuZSHVMH6DrgeYBG+z7CNdFGDiE6bDh/IycePUu6eZR9BySdiUnmRJM7jRwD+SKHljbiTwwQqoTEM7GbBnGfzaU7ZljVEbXTfWgliA0fbTpMS004bIIH+rxtEPsM/tg8dbdB7dJ+nA93oBPinlimWg0ZvjbFL+U5eWgdiZZozySt2PhuPmurciROQ5NbSBm+XhMt+tQPSHQxh2m4JGjcqo9edIkLgC2INpVp7xog3CJYf+8ChmXRrUrinWXcQxFWwyCe9nCdBKNVx5nusvpIlfxiB6ED6utSSnWHyrwmMkLiPXDaqxJ2TURtgRe9s8XLN57kwfEB/vbv92ROkECj0ciVJnq5ga643P7hjfT9cEq6RSEnBWpdP/G0xggTcCSymyJbCqSVVUZPjKI8k0gqMEyiPhvhgzFYxopLaK1JLQg3V3FOpLj3LYICtTkL+S7vMLGtJjO3ROR2CtzXNah/vR+jBXZbkZSzCzitNXaQoAsFgq0DCA1ROWs4cpVCCgNnvkuUy6GcLiQROu+xlg6DjhJkkBD12/gv3klu/wxCGshaO3OGtLusbQhRKSHyHursNFpDMCqpvXY9A/tNcB1kJ0IuhMjVxlM/AEJkmTGGgeh0MkWk60OSEu3eiD25DKyF+JomJFmmi4bMJWLZ6CBBVkropVVwbHQQgOeSCpCGQzoxglw59eQ2tUZoEEqj1satAGyjSt+ffctyPOmm0QBKkwwVCbcOkH9sCVyLpOoAkmSoQOmhVbAtUArl2hD46L48SgrE2DBJyYXUz0SxVINpIK0cQoIZa7BMRBKjbQt/NEfthSVytSATfoIQLAtDWHC6C9JAu5lPRHc6T+5vJ2L+qycYsgQn31TGrAsqJ21SP6W9MYeVK7LLO4a1JccG5yLu+McGSkiEFFAuEqcSo6OwG5rEghPlPgwVUzzagZEcSclCRDHasYjzNkjB5PA6jk83ORwMU9+u6Z9R6H4I11mYfkhsBLitAJEr08Ah/LsA9QZBv+0zMrjKss4TbpeodXk4uII93Yc1tYxhOjDkEhma5uE5qo8EGHGLPS+uIcZCDoX9fO3MbnIVjWukfHbpL1h5xGfxrwfQ1TJJCpWxMu3ZBtFwEc+AKExorXe5TS1y370ttvT10V2/i0hbFKvHePjEHO/66xt5+4+9gJ0vGuKX3vvnyH9oIfMljNihGCSMG0+egk0pObNvmcZSh4mxMldsfGr1ZKcVkMYp7VbwNGeC74yODrBR344wXw2UqBY9LMvA8Z5TyWOPHj169OjRo0ePfyee8f/OtNZIKb/r/UKIp50n6/Hvy4ZNK3RPCIqHAk4tL/Plb+zi9a87i/l4E25YWyjQjA/laT4yS/f0AiBACJINedTzhnHvrpB2I0hXIOdmTTxSoP0OOm9D12Rhpk11WNEupQzunsU6uI3ksjY3ndzCQDdk8+oyfS/Yy5FpD6uu8UcNnFaLf/7TEjoCUR3EdhSOhFaqcL5ZQ8yG2Ks2WDapadJYbDC0pYY9WmRx7zgqSPBUB/m4QXqFQb1tI4IYq9XBnvFxllJY7yDnV1FjVTrrJfbBiOX7JHLMRTs+DFRwlwKSvIW7KHAPQZzXmKshYd5BqiL68YSFRwfRZYkuuSip0SUPc2Kc1BIYPjgrHtJL8ZI8eqhC6rfRliYtmBhxjBmDOK6JbktZOqZonPEY/8k8e70yRw4VMVaapMMl8CNEy4c+G6E0xpkFdMFGpQUGJrrUjDKtLS7l0yFh20BqjU5S1Po8RtsF28YwLFLPor2jiDFdx+i0MBbSbPRIJaAVor+E2wKzqYnHB7BWW8ilTOBozxnEsoDy6hRfbHBkfozuepPCZIpVB7Mbo2wDoxOhO110q0nSMLC0hbVax8FD2DZaK/TUMoZQKM/BmFvORI9yHjW/CKaJszGH77lgGYjBKkYEiRAws5yNM2kBUoJpoWst9OggKozpXl9CiCLhcIxT1ygbRCmHaHZhoA8dhuhWZ61tKQaRPGmlsW2wNIn5r3I3BvtgbhEAOdCH8FyEaWKZJmhNEkXo8RFkHKMabRgoZa4Z13vKalTXz5wu9SaikM+yY7o+Qgn0v6oC/1anj3DzRM/bRJwT8PACot7BrmcCibXSQZcKiHIxex5+F+nH0Ilwak10JyIVRbq7qhQe1RClWQZRu4057hBHeZwkOTcK5tUiVoMUM1Cg00zMkoIkbyGaCbrbAi8HgJYyy9ExzEzs0hojBruZvaBJGBH2OyhbEvspc/+tzMALA1YX2gxuHGFhsYE/ZGJGYIQaGWkEWTt7e9mhfHKSnRd2uPYnpvmr9wyRhCD6B4hHCyAMohWH2790IcGYoG8hRvdbQMqOr8/yqv90ivj8hC8d35o1WyUGxAnFhTalfMS64grL6yV9r7GQhiasWUSTgrhioJQgGHIYSGyWhmKiQYORdSUWO5o+tchdSztZFkUqhYRyvkNuzIWrbOYWq9BWDA4WuOhFF/KNz9xFMOZRsly8oovvJyjLpONHnDwznbmqTMH+0y/FlS6GlJw8OMPN/3w7ySMKuxsj3YihzWBNGJy2Y7544BCvuTAbeaoWPBoLEbVazIGZec4fHz13/Lg5m1zBZWDo324a+G7o8OsQ3gbGCJgTnNc3zFi+RFX2nDQ9evTo0aNHjx7PlGdjTHm6hqjvxrMSabZv336uKvQ73d/j+8tVF25mxbuP6J4cwg8JF8fYvvM9uEefHEPrhqC+MkPo+wgtEHkPnRPELxslnhQUDRC2lV14NpqQzyMrRdRShK41oFihYxtsyS+xe9ske18cM5S+g3cc+Ef68j7qTJfbP+3gOI9Scc8nnLBIN6fopQa+Y5LmbYbGNCQmzaM1jChG6BRjKab2wgrVMylGqGjsq9AZcyic1AQVB7MI3Zf2Ee3r0vpDQdQfMrjzNMW9Mat3JEQD/cjlBnJuFWMO5BjYp1aIopTu7o3IwQ4iVqgRj6RgoQ4FaDcldybErmnSfoV1Xw26Ce1dg9iH5vEeWqDfN9n9s132//4Q8XCZzlAeO7UpdmzsjiYZ6AerQjqYoxNDSkxlXUorGKTdLhDpFhsvXub5exb4m3+8gKWvLiCDJuZ8AxodhGkgqmVQIC2T1Fb4sy6tMx7m0RXkK0z8H61g3RPg7F9BRJCWTIzVDhoFfTmioo1INcmwgeHnUUET5rqgsotxmaSkHqxealP+pkSsH8aez1wlSWiR+BAPFZn5tAVbBLbZQscWqSOw6ykySlGAME102yd/pol0PZKlBhgd0rFh6HSh0URYEl1xM5dKmlB023SFjW10+M133sG9j67jC/97AGO2iR7uQ9ZbJCUDjcRsKYQhkAuL6FSh3SrG8ACF6ZT6SAppTHMc/AmD4a+kmIis+SmKn/wQPNFSdO6A98G2sZSEYh5aa06R5pOOEcIITBM5aGHtiQhaFuJeN2t+0iCFJF1pITsBIv5X62+0II5AgDY00bCLfawLZ+bRGyuIEyvAkw6ac2NmhRy5qTa51Ray3oK1+7UUkGr0SD9hwYWFGvZqLRN/4hQzV0TjYymb/FSITDVicCDL5FEp6SmH+vkulZl+nHoW8CzClPLDXUQ5RJkCUS2TrB+AAQezbiLJkQ5kyfiyUkYoCUG0tk8atVKjsK9DusnGHKxhzFTxN3gUj/kQQuOuAjteWGWy2UZ3JO0tOYQBlq+w+zqk3TxGAoZrE/2Ai72ty87NSwwNFJidyWWZQ9IAIbDbGmVq9JKJ0dWYfQFRSzHyjhrfUKO8+7K7+eJflLLxML9Gsm2AJIXR0QW2FuY5dKPJwsZhLl93mt0v9/nmF4ZYHRkCQDkmj6lZoq4gvrZAIe2jfijFLXT40YnD/O3SRSzvLdL5bJGtzWsZuXADh++9GyNssnH9ACdu2Y+MUvLHm/zzw4cZGa8QnF1mb3GYO5tzdJMYU0okks78AB4JP/r88zn4uUdpHA4wBgvIZRNnXYnp52m82xpYImTlmiePxf/6mmu5dmorXz96gss2TDzlUNt7xRYeOnCW86/f/vQng++A8F7DZFjE1dsZBk6v1DAQPP8ZVn3PdCe5belrXFy9kp2lvc9pH3r06NGjR48ePf7/nUql8l01kX9N+q+vTZ4hz1ik+fjHP/6cNtDjP47rywYXbD/Mn79oNyf3FfjJ113HaF+Jylj13DJpt8vyD4wR4TGwWsfIl0i32KR7EvJfXcBstIgpZxeuWmVOmnYX3e4ghUZbEbYDpx/yOHu2xNn1uxmoHOOahSqN+CgPfdGmQIje6JHEBpVHAsJ5B3GqgzIMGpcPkE61kJUSXriUhaLmLLQQFO6dJpwYxMZmeUeeoF4iF5mIksZuhGiVNQGJTozb1BCGOD/hQa1CsDiGsxShEYhSmYGvLmL6mnDIIzUlluWhHEU44GZNQomJWt/GWE0QywZWGjN2dcSKzDE30UeuCH1fPY5Y7GCkHTBs5PEW2148jXkRLL5fEBQH6Fw0hDcTEVcc+r50msLFAX2vs3BW8yw/VkKIMte98DiXjc5x6vo+Tk2OUHm0jhloEsci3jyKiELM6WXI54kvMlB3LSPJY042yX9SQEEiak8KEWZDIyxBPFCEsoMZaUKpIVJZrkkUIeothFdAhzGJkdC4sEqaF4iCB5EmOW8jANqUmVOqnENHmvJtU6goQjgW/uUbUasRItEoWyIbmRgh17mIUJDGDrodYywtQ6pIByoIv4sRpGjbwhAx1pYc4awLwuUfPijYcrmPfWIBdOZCkX5MOuYRD7s4B1tgOxBnLh8xu4LwY3LBAPGOgNx8jGcLvPmU1iaTxvZ++m6axFhrxwGgVEQW8qjF5SwwOozobu3DnG/CtzbiPDHWI4AoRCtF+UcV1WvbRA2TmcdsVJig6i1QKfRXUZaNoSKUYyLDJHP9JMlaLXRComPkcoKWoKqS7puGEPvWY06u4D48mQk0QkBfBVnIkSqdBUav7YfqL9LdMYC7mmJFGrnYIQrj7HOoNWiFLBWhVETkItK5RhZWnSi0SsgNS4qVHJXHczg4EPlQLqHzNt6RVfB9ZK4A7Q7GjIY+D2mAKOZJi9lro20ja0PyXLBM1NQMyjAo79OIf5kjcg2M9TmGvLPo5RLKNok6KXccmiIKY+TiKoXbFoj2bADbpVz2WZnNgmn1aEp8XoX7u0XOfzBg4w8WmLl7HDFfQyR5tGmQuAI9liJ3NdFHbAqtGktmP3fcfyEI+NjlTcZf3GH2GyYYEpnL0fcoTLaH2LxlAXe3YO6sRWnEJ1dOKFcDVhbz5Je6dE1BekwRXxKRvzXizMUSQ0pEXbHXW6aaRjRnQSSKv/zYY9gjZ7HDFGlIpjYsEv/XCslvF1BOiUenFunvJAR9OQ4cncV1oLq+hV1oorQg9BvIJYuq4fKCV1zE6IZ+jp+c4cDkSeKuojyQw87FbO0b5Q2XXXjusJRScMmGcS7ZMP5t3+/HzyxxarWFfWCS619w3rM+P5z2Pb659DVceSOv3vQXHJpboODYbOyr/puP278wy+XrNjDZPcV09wxlq9oTaXr06NGjR48e/5/l1ltvPff3mTNn+PVf/3Xe/OY3c+WVVwJw77338rd/+7f84R/+4XPexjMWaX7yJ3/yOW+kx38MQ8YchhWze4vP3HyOZOx+VroeOfvJt7mUt1jY6BIvdVE6wdCKjS8r8IJtB7hvO5xehLhVgzQBwwTbQgcBAo055CDyUCw41IA0kqQdkweSaVo6ohr3YyvN5a9ocuEbTvDu38tapRSK9u4yxaDLJn+BxkmFGstGqQD0hn7CkoF1Ygl5epHWzhKdjVXsOth1QeIK7P0+3LFM67wR9DUe7kpKdyJl+XZJOhNid0PMRkjaX0JiYWoHabnosocjmujnQ3rCJSpotAA7hFYxR+1qQX+6zGhfSHrWZXg84JQEW9mIShl2tnnkwSKqESOlIt3hEVoGYjDBH3cI+i1SU5DmTFhuEJ02iVsuwRmHwpkQbRjc+MUtzF5S5putDXQucKhMgzIkutUGbRD1ORhnYmg0oFZGhBotAoTWiJZGRQJ/2wBqr4E936Lww3kcFZDenukHpIrmNgUyj7kgyMshTKuIfPxsNsLTgvKdbcp5k/ZYSn7fEiLqByAuOaQpyCBFHz6JQiANK3vfdXrO/pF6FmJhEXIuxVdHmLsEJ2/bjDwYUHxoCpEkMNoPI/0wNYe/a4CCpZHjMfbRCIqCxx8Z5OF5iVSza0ejBgHOiRruibWbck+OCAlAdbqovjr5M1l7lb2SYNc04vU2XilEnR6Fg3PoOCTa2I+x0sVstjLxJIucQW3ug3oEQQdq/8qemC9kGwpD2rc75Pf4JLMRaq4NvsiauDwXWh1M6aMbLWTOBRUiKiV0vZG9RjkPkYaQCtKSS/SKAYyGQVSy0VUH2zYRUYIeqCBKeVLPJK5YJP0DuH4H6SeoSgEqeVJS7HqCSHUWGBxk4hjx2qiS1lz+skkOPzbA8t1FRC1z4vgr0DlbxcgLMG2EVgjbJhYprR0l3FkbM3TQlRRRyCHmfIyciQ4E5poTUzebWctUpwNmEfyQ2hufRzhgUf36DO7j0wgjhzjoExcctCWxsVB+gpxZxlhsoAsGJAnxoMHiuEvcjkljC7RN4b6U9dYql//8PBf4dQ4cGydYsjBWArSA1oRFeayF70lUwaR9b0hwlYk50SVumcgkZvxdFrN3exCFGM0Iw7NYbRa5b/8O4r0a+6DDre09VE6kLJwu07rYpO+hOsozsTsKY1GRP6uIhn1qOyQPnRji9x/Yy+MX91H0IkQcYxiCYLWLvRBy3jXbqFygeWymCwUD+0yNdjCIARh+jOMVEWguHd/MtD2FRuA0c8w0OnRbAS978fnMmBGz9x5B+wGJsrn8sfW8++M/imFILPupp+F/fugQ/+vLd/GSy3byth+69tztF1+4gXqjy+4dozwXHKkxRIAtNRKb/3L91ZxZrXH5xonvuPzMmpX3Lf/4RT79htdzUd/l5Mw8G/Nbn9P2e/To0aNHjx49/l/guuuuO/f3e9/7Xt7//vfz4z/+4+du++Ef/mH27t3LRz/60eesofQSA/8fQcdHMNPbOBI4nLBdrixIdP4Wfu83Z3l4W4EXAtK1KfeXuHZLyoP7FjCWO0QrHc5zlzh5U4EDx4eQC6cRaZwFq1om5HNghuiCCxeuJ4cgzQdsvt5FGDZvuGGGn77TBgkjow4bd/ts2VtnwIvhSsXKepd2xQRzmF+55F42p0t8YssEh10Puz6EIQwWry/jzjlo08E8u4iBR/40WC0wu1noaFrxUG6VYGc/2paoQSBWBFGKS0hhso2MUszFBug+TMtDDxs4k4uEV9ikfRbpiF4bAQKjm+Ddskx3V4n2aJ5GOSQ/HRKsJBT8gOIZH5HP0b6nS3L+GOl5MeaJRaZbZbAELk3ysy3S63IUmwF9sUnSl0OcadL9oEvcMCgbXYIhh8WhIl852YdGIUVKaCn8HQG5+zuImiDZO4w6W4cwJBgchSGBaIYUarOkOUF64SDB1hKp4dO8ug+jFiIfDhCTdcTWHMoy8OZAm4LSSYFIFHgO6UVbsefbKAmrVzpoT5C4GnO5iyUdAEaGF6m1HOK6SWq5pHEAI4Mox0K2AtACFFjNEPoq6CCk9uGAZNyF0Qiddwg29eGcXgHPRlg2YnQEy80RmgaT8x3y0SysrKKThM75W8lXmsh6G2O5Sa6c0OnyZIaMzJwm2vdJhyowpAl/zMVcAO+rSSbcaMGGPQuYnmJla5nIHydM24S7hzCX2hTuPptpS0LA4ADWTBNL51BJ+GQzkyFBGllosczGwsJTNmfe3Y+x0ED7CVKpTBQJQ0S1jG40s3EqP0AnKdKxoVhEt1rQ9THJmqeS3QMEooSFIiqA3lEhVWMYocZVeQhjwnGHYNjFXA3wLxonf3/mtHEfmUGvHyHqz2F2IiIhMNeEGVwnc7bFMY89kKe4QxFP9WEFPlIlONebeJsbHIrGGDwgMc4mGEJgYJKO9mGcnYG15i0QpFpDkDlozMKaotUOIVkTp/ysjruzR1Aca9DoFPFOu6SmxjRDjDBGdEGMu7jtmCjSqIJHvKlIrqYhn3LRjuM8enwLq5YHCVjfXKT/JUvUcgkPnxhicp3F4CMRwjTBtdisFrjuwkeZW65y68FLCEdt7As7mBUNfQEqZ3Pf5ARGdyoLS16sY04IGn0jnD47glPuYN0l6TQ0rYJHuNXGO9VFeS6djQ7FxKYZdXFmmrhJQPWOJrIRs7TZoZLrYtYVuC5DXh6zmXDl1dtYf/1mdC6h025QP9xGa4PU0NjzHcKiprm5y/hInh3nbWa5fS9CCFYP1uguhxy66xgvuH4Xf/7p25HHJrGXl9H9FS7acSdx6zSff+yFXLpjK9vXDZ77Ln/45CydTshjZ+ae8h1fyDtceeFGBoaf22yz3/kYfeYyJcMDETNeGWa88t3XVV+reE/bmm4Q4hpDXFC59Dlt+/vB9MwqR08scNXlW/Bc++kf0KNHjx49evTo8Sy59957+fCHP/xtt19yySX87M/+7HNe7zMWaW6++WZuuOEGTDN7yKc//Wn+6I/+iOPHjzM6Osqv/Mqv8Cu/8ivPeUd6/B+iVglSH1uGLGDweF3zAysv5cG4RWd9NjNnFPPUF5vIL+bJ31XnCc/CTX+Ww9g5jJiay369B+ivYpRL6ChCGybpaB5RcrENk9Zdh7j6l08xs2Tw8GGQegIlDKL72szeL7nXLRPsLDE2scTSfeOUHtcQhEyuFJhphsw/IJCXxmv1z5ri8QRjuYMZCdqXbMOMJdWDGtBYdR/H0nQ2uYjExIggLiRo00BbgmidgNRGHWwjEkgNAXGCNiUpEqnBvD3AuXEV0QFetAssiXOijjm1ins2IO53ab+xyNlJiYj6CUdslNDIro+MEozlLsFrqlgthfG1FvSbyLkYXYgp3DKNO5Sy45Uph80tXPO8h6jkT3H3X9ksnSiTuAnK87B8cGqgkxScOuV9LUQuTzhYwekoZCfMmphKDkYCSIvUNUhzBrnTAdYjy4h2QLC5yPpfEMitKTN/WUYnCTgGg/d0SPMWcdUlcSR2GCM7a2NAEqIJC2VA+c4G3mkfS2ShsjsuOU1rW45HPjUKSmGEKWmzjto8RjBskV/JLtREnKBLRYgSjNhCHPVxjQ5oB0OZmdum3WT+lUPkpiT9j3UYmQioXtPi0Mki2vch72EJF3XeBMb+ExClxD5gSwhVJqyEEUiJUBpLC9TJBqMfW+QlP7XK4RtGaNZtzh4bY/FABac/INjXRpUGMCmTTjewZ5soxwAFIlRQrxOPDxBudLDrEteyMleKIcH1YGUVlEZXihi+z+i6JZYWXBJDZlkp5RJ6pZa1T6UacjmIY4ROUEsrkPPAcSBJwTJJBkpE6ypweUocG0SLChWahJcNUT7SxToRkfpdgk0mQdVl9JZFpJ8iHA85lYV1B36Alcrss5ipTWS9zSmqVocopn40R22+hHF2Dq0FlG3S2X7qxwS5sSXMpgOujQ4jpJ0jf6yOdXYVHAe51tqWALZMCf2EonjyWFFxlGXoBCEy5/J719/Mi8dm+UL/BJ++73Jiv46xLodalZiNCHVkOcssKuYRE2PIqoVsCdKGIr5jkEJd0IkS0AJ9QYU6Tf7o/qtp3luidDYkTROEY6I8E+EISDSiYWD5gtxsjrnHSoiOpDsRcmplKwP4LFRcrJqPzNmUR0wa57fQFeiENm4F3BN15DIYWlIwTIL1HiLVpFMd4ssU0VKD9IoSLTGM9hV9O3y2FBZRhkNIDpHCxA9s5errL+TPPn8HixtnKY1Jouv6ECdAxppAK7qDDkFbsnw85G9qB9l8XondA2N8/dEpLG3yzb+9nV99z2vYai6ihpeYPS2RCCZX2+jgX3josM1qO32KSPPLL7+KasHjFZftfspX/H23HObzH7+TK27YxY/89LU8W7qpywV2RM7QuHSJ0pRWENKfz33H5U8enscE+s4q3HxWDLASLVG1+zGE8R0f838Tn/3iQxw+OothSK6/Zsf3e3d69OjRo0ePHv8PMjExwYc//GH+5E/+5Cm3f+QjH2Fi4ju7lZ8Jz1ikeelLX8rc3BxDQ0N8/vOf501vehNvfetbecc73sH+/ft5xzveweDg4FOsPj3+A7GvpGNfTGR8ndeER/hYd4ijX8jz2it2c9PxMwA8/zWXQwqPHpzBLJWJwoTKRIVGxaE5UaF0y2k0kLgS0aplIw+Gieiv0t5t84KXbefgPxwhDhwO3DbAmUchaFtc9CPLNGo5wi/X8IXD0Zlh7vinPcQixUwsLJWQFgy+MrWd9bWjxKcS7OV5Wns3YsWCREd4R+ZISg7phf3Yq9m1qdIpRqSQsUZbiuZ5NkaoyPmanXqRY9Ego2ED96E5WNFEWybQOZvVvTbKkpQeWiLXCehuGMA56WNqgbY12hK4sUXaX8VZaONMdmhePEIaFtm5eYl97So6DtGmBDS4HvnpFso0cG/vQgpiZJhUhjiHBfqoprO7DV88S7BeoCxNfLqOqBtEdj+JTBG2xF0FOgGiKgiNIkLlCYcKODrBcGzigkNaEJQf7YLStC4fJaqayNumcRfaKCFwY4PksTyXveRxZjeP4/gWSTPC8BMMP6G1wUUkIEILsxWiLUFqGlQeV5Rcm5ZVRLB87rB58F+2Iepd0vmVc+NnaqhMZ3sZ99QKhDFCCuKFRegrY5SLSMtCFz3EZAs9YkCq0K6NxGDwn2ZJbIUWA8ggIloV6HYLWS4TGjGJGWMZKf17IlYeNkkik3yfpruQ7Y9Ok6zpSQBCIGoBrZrmX/64TKQ19Odw9QzBP4wSrIYYSAwHtNDIhQayE9C9YBSx0CR3qgZRjEIQDruUrTxaNtBhnOXDBFHmUAHo+qh2l0VKWC/3iO6SmH4KYYw21o4DYy3+1zIz8TLnIgYqWZiwUig/QI7247RgYCqh1nRJuwbGahfn8Wm80AFtI1PoO6tJltuIWGHk86iVejaiFcWYJMgN60AJwo1Fco8vAEaWTSME2DbxtjFAINotzNkaNASpA9oA71gdGkk2rgcYgynFho+qVlBxcu69d2VIsNxGtbr4k5mjQo6OwOQ8JCk6VdAN2JHUcIXiPKeF4cckmwdpJSZyMCE/14QoBETmJMubSA1aKbQhePTkKHEKUdVAhoqGs47wUpPc7ytkt4a3vci7/vgQ99w2wDce28vC4RwP/fUwy94Wtu/ucHZaYLcyQcCbdug+ntJVFpve1mb617tEG6sEnTxVq8G2TXXO3lah0c0hbRtME5FKlCkRhsRpCJSfkiuFWKuQziqoCLxqytbCAqkpSUqSxDN542uu5PoX7uHU6SXchuKaynaCvM+y1SQeU1htQTRcxA411xY2cfrxBWZOtTnyLwnzd+0nXA4RnoeOI9IkxbpunjMPTGCMaxCCi/c8hmfDyy4eYMPEUwWE/cdmeOTwNMOFPBuvezIvxjAlSZJiWU8vkGgdQPwYmLsQsgDAeR6ESGxpg/D4o6/fwbHFZd71ouvYtRau/K1ErRjTgkHpsqMyyv7afdyxdDPPq17JdUMvetp9+H5z9eVbsCyDHdtGvt+70qNHjx49evT4f5Q//dM/5bWvfS033XQTV1xxBQD33XcfJ0+e5POf//xzXu+zanf61p35jd/4DX73d38XgNe//vWMjIzwp3/6pz2R5vuEEALP2oCasdn/oTH67j7C6dc0eM0NI/zA+T/P6VPT3HL7MV7zqot44Suex0y5zT33HKbVdWns8tCnfVIhkQJsU5GmFtLLoZpNokpEay/8zcJj9PcJBi/ZjLVjN/n5kySEzMyV0EGKnersWnJa8+Kdh7jj5BZmt7u48xbYBqITkmwtow9lzUP+OgOv4MIDEcJ1ETkX+cQP+qdnsGoNWpesIxrOk5sJCfpivCNtjNIg5g7B9dsOoDfH1IZtpn83T4rMglgtD2WDXGghlCItaJZevwF72cRMTFJToy1Jd8cg1koOcXYZmg7tjaMcXcwx8tUjyMIQysuhjSZG08e5Y5E0VqRCoi0HYUjSjQbGrINCcvpPQ0QQcddfj6Oe7yA7K4hOE++hNp6G1VdsQzVa5O45hTIl4bYK1moDd6oBF40h8yZGrUP5gVVkN0U7NjrSqFwO5dnZ9TkaS0Y4noXr+KhA058KFkyZXcB5BjIRWB2NUAo9t4QOU8TECNXHu1TGLOZ3eDS37aLYzi7Ww7SftG2BGUCkSPMm0ZYq0k8xFtvoRoSWAoFA2A60OijfR0tN55Iqtl/EPn0yG4lqxdDo4JgGWIvMzMTI+QEEq+jVGuZFOdLLQqJvtukuWqhqAV0p0J1dBbrZeFK5DEmMjmJUrZmNJxkGXe2gUxuRWCjtocwUO2cjlCQs2pAkyNUWpIrETWi9bIjcB2vZwTRQIn+yg0SSlkuwmDUunRNoRFZDD5rYtKirISx7EXliHq01wrGQA/1ZBXmjBUGAEIJ4uA9ndgXhuZlAIwTp1BKje13MsyZtncPTEXKqhT3ro8oGxuwy0nbAtEjdAjqJUWErG6Na2wekJM7bmEFKPOSAY0McgxaIQiGrCHclUlgwUMZcaWGaEdteNcPRzwhUqQLNFGFbCCCdSZBVC1G0kGsijeVEvPJVJ/jHz+7E8HJgZONvAmDdMKklMY5MAvBHr1vHS366wY1f3IqqNbDvnSXdsw57uQu1VbBthDSQTZ9koITKCdTSIkk+hxUl6HGNjm1SSyACQbdVIrd4CoIA1WpQKnbZubfDrQ/GhP0G7SWX173vJM/bfANvfWAKZyVBJpBOhEgFxMAjTRRgW3m6jYRX9J3ltRtO8cCVA/zZ7buQjRbaNjEqJTBBWy6JZxDmDPqrLYLzPPSJKiMnp3nZH52gUI25fXo7y4cEu3cOsWVikE+8/yZu3neKmh8yYDsMbOjDfWwFsdqieMMu9u4eY0YErORTQBM4EfmbZuj6a4eX74NjYzsW++/eRF/UzYRHS3D81CBfuWOcn/mpdWz7FhfNE3ynrsSTh2aZPr3E7OTKt92XpIrFepvRvmL2OfK/hO5+DtyXIQo/k7231ibcQILsR2j/3Ha+WzHjS39gL7ff/k1e9JrzkULip11Ww2Xq0SqHGyeRQrKjtOk7P/j/Aq64dAuXX7L5Gbcv9OjRo0ePHj16PFte9rKXcezYMT70oQ9x5MgRtNa88pWv5C1vect/jJPmWzl+/Dgf+MAHnnLbD//wD/P7v//7z3lHevyfM9kuccv9O5k8IEi7IadusWmGM3zgq/fwup3r0abgq6ce4IIfvJp77j4FXzyN4blUL3weC4MhnSvWU3rgLMIVSKMEnoeQAvNIjcJ+oKwwZ02u/aHLuKRyF//wWMqOLYLHrRKNuIu/e4jc9Crje1d4+SWLLJ4uMS9zgEbUAryjSzRSk/iCLVT2Nii0moTzAtHUyP4+LDRmJ0SnAoiRjsLopmjLwghjxv+pibYt/E2CY4/3c975B2krm6YukoyNYh9dQLfaDPgjiFozq6leV6G00CbdXUEoB4HASKG+wyUctVFbbKxRMGNFMBATVVyM7WPk5lJot0FrVKNJqJzsQjpNiddXEEGXeJ0HSRG0wFypowt59ESecI+Foccwb0+wjs9SusEk5ywTtG20IeletZG06sHZFdxZH3MmZeBqmD08jNVKoNFCmwLHq6AGTeKNA3hHlwFB1Vjih356H5/9iedhLddpVkrYSpMUbLTQGKHOpnnqbeRiHeWaKKFI84KNLx7k8OkaZgDJ9uzX+O6IhbMoshagpRVWf2qCpF+TX+oiLYlwbNAac1cffS9s0/ymIDwcIyolvI6BzluogQpkJdoIdFYhHYYkTuZgMPuqoDX9L+yQ2z7L2a/20c6tQ00U0WhYaSA7gOsgq2XU6UnQGtH2s2zhYpFkvI9wwCb1HFTO4EW77mJ4a43b/mAdwXSM3jgMhQIkEUnZJP9YnbS/jAhD/EFIRy3kags3sNBGNk6FacCaaCHiBHIe0SaJcf8p3CNZaKoAsG20IAvTlmsXfI6FudxElApZy5OTZV4YzS5hKNkU1dGnO7QcTX3RJdgxjC0dREcji3nSro/uGhiphHojq7zPuSQFB5HP4TQiRKrIz5hP9na7NjoMQaUkliapSiyngkw1V/zoQSbOb7D86CgtPQgyRccJaq3FKukqrGpfdjwHIZE2OPF4jgEXalskg69dc1elCtmJkbVaNhKWKhZnPP72E1uIowDyErOtcVc1SA8lDGi1ibeuQ3guIlHIrsDf0o8qm9AQRL5EpJkjThmgD+RQQYC0TAgT3vnqTZTP76e9waE9buE+UKF16lLm021ocxa7oxFohvJ1ugsSkSimTpWo//gWckeWsIt5io2YqhOxdV2HeJOD8ZBAW5kzJi0XsFdi3IUm0k/p/G+bsALxLhPsMtP/6JC+YJiHpzZSGEw4VW/zlS8/xENfP0wzTdBFh2OPnOTeLx/F6AaIAKrdhCuft5EznS9wVgseS0foi33cCyC4D7BNcD2ckpe9rKUC3TFFwbOgLbjjoY0keHzpAZ8fGaixqf9Jx8yuzcNsnu9nJv8w9y7XuXLgegAWZ2vEQcz81Oq3ffd/7MZ7+fr+4/zcyy7npZftQssNIPJgPCmiyPyPoa1NoOpgbuUdP7CZuh8wWMh/x/PJQwfOAPCFbx7gmkt3YEkb1/RYCZu86+TfAIKPXv5fGXL7/o2z0vePG296lG/efpife/O17Nr+3MKWe/To0aNHjx49no6JiQn+4A/+4Hu6zmcl0hw6dIj5+Xk8z0Mp9ZT7lFLPuQe8x/eGvzryEEdWhylt6mInisve1CEI+zgRZfXN/qYOrd1wx99/g/SBBtZKF0WTYc+hk/oERYe4WMDoBGgvwZQSjOwCq/LlGpoVxOb1PHTPCV7yK5u54EU3sm2vhT64gTv2HcU60ECvBkw/nMeXJhdvW+S+2zbi3ncGa7mLiBVysITZ6dK3u0nrM0XMpS6xZaCTNAtj7UbYKkL0lYlHy+QaJlEjIaoIvAUXLcFuJqTK4c7PXUyp1GTmTJWcaJAGIWhwGh3SQCGkxM57DIz7LAc2Kg9uI3P70OmQ27+KxCDa1I9R6zJ8oEOy3iIu59ArXbAsNCAdDwI/y7pRCtnoIIII5xYB60poS0J/BaPjYx4RYEBqFnn+z+5Hy4C56hCYdY6srEO1q9l6cy7xtlFsmiQYTE0O0d1q4egA94SJEAbW3CrmXI14az+i5KIjTe4GzdnDRVpTBjrponJ5BGDWfITWmO2YuOqhLQs10ocMU4Q0KL26w/LuBxnyB2h1BHIgO2YCO8ZcWkZ0Q+xiyo+/ZB+JY3PTP12AyOWyEGIhsC7qkL+4S+deIExQIsVYjFEbId0yivAj4jQh2tWHfd8xLMNAeQaGH6JX2iAFnSMm6kSIPDgHpoXuXxM4Olk+DirNMnZMAxEnCNuGIEAbJrLp4283sGKwmor8UIQ0FPawgVYjyHaECEOwJKUjCf62QcLtPmJmkdZlebQjcReK5B4Mob8Ci6vnBBqt9Zr7IMA5EKObyVM+V3rdEJFQWCprS6LrZ2NDcYju+OhKgXSkgrHUQoqEF7/1IOp4jofOPg8118I6s4RhjCLnVqBUQAchGJK0aKBdc83KoNFhgEwixHKTdLgPNVQmMSwSQ2OmClZqKNPAGBnCXY1p97sIKVGDJfZ9aYgH/mwUWj5Ofw2UiSgVwTZIcxbMr6DmFtCmRE0MIxtdHjk8jmfOsGNklZ3lbvZkbRvaAURxJjx5NjoMmHrDEKmlGPlaF2N+lcZ5ZYSC/OQMKI2cXCC9fHdWNxaluI8tEm0aIik6CMNGxYq0z4ANIcaqBinQSUr9Vzdhjmv4RILZAaFNxMUS1v0tR5uDCLkLghTlwrLtkY+7oDW628FMBbR9RN6lPOZjGZot1Qb5kyadjf24fRVIDbSZCTZmPXtfzZpJnOuAkyAKCYf/d5WR/kGuuGya5domqozwAz/8PDSCf7n3WBYHVG/htDskq1mQ9sLdR9j/6gO8dssksWdy4Bsvpp16OCuAAMuUCJXwip+4GoDBowlxBwoxkChK3g4Wj03yzdsPI8b6eNsLn3/uePvqkaN8/sBBSgMBTmXxnEgzUDBQ8wuo+rc7bz537HFm3RZ3nTjDSy/bhdBLaGIE9acsdzKcoGrvpF9IFlpN3vsvt7JnbJj/cv1V37ZOM29CHRJbMOTkWe9chNIpnujjc5OZk8YW1tOclb5/TM/WCKOEpeVWT6Tp0aNHjx49evy7ceedd/KRj3yEU6dO8bnPfY7x8XE++clPsmnTJq655prntE759Is8yQtf+EIuvPBCJicnufvuu59y38MPP8z69euf0070+N5wRb9BNGaTXpjD2TPE9a+p0Fl8KXrt+ss51sC5sYl51yrGtwhqtd+7nQ2Lx7GWfPTEEOn4AKITZrkSq3UII3QQoZIUv6RZGIxpj1YYfMUqentK5CiSNEAGGqFh5w+3+OriFh4aLmNd2EJ5Fsoz6Vw+QLoth9GKWb49RzJqkKQSI1IoQxD0m4ABjibZLIg2eai8xa4ty7h2SmjGLF5TojUhULamuepxvD2EPjNP+uAcRBGMDZC7JAfjg/gXjBN2UlYeyhN5gqSk0VIjogSx1cVqJFjaIz+fklRstly3xM6r53H2ZL/4C8NAei743aySes1FoSxJMlImGvJIkxCz7iMk4DoIIZEzNoNGnQuvP8OFz58nnHFZOl2k5SaYtS7evmnsJMGpzWOcmEaemUVPzeHddpz0Ap9khwFWgjVdx5lrMNyqMXSVRArN7N8aLB0vYQ4XYLBKtwTKFOjVGnQDZJBgdGOwTdRgmXS4gpxeIFgSNOqZSOJ9eorCBxYByC1r0rIDRQdpabxFn3I3YEOphvZc4qJNWHWI7i8w/+eDxKvFrJI650LJRbYCRDtARAn2UoARgjKAVht7voVcaUAphyp5rJzwqH8hQRchXWcga11U0sW/ZiJz/KiUSARZOK/nwugA2BZuuIS1Mkf/rbOUTvqYKwG3/8EIt71nlPmz/WBKtGWgbBO6IWK5ibnYQZVdtC0QCkSisdsJiafRQ1W0Z2UihOtmRpUohihG1KJsJMh4MvdDBjFmlCAsK3PSmCbScZFaosKAeLBAOjZAmrepbO1gOjGFdQZuERIjD7aN0Yyg3UavrKK7PiryifcUSItZaKuWgmgsh+ovoQo2rfP7aW3NIYJOFiqtFO0LhhD9VYTrkhYEUUGjui3sB48TH9GgJVopjJUWulFHqxSBwAw00nbQ7U5WJR4DcUrlB5v0/SpMzpe57XeGAUjKLvFwFUYGIcz22d9Yypxarks3XQYhsu8GrbKcHEAVcug0JShIkryJdXKZwjcOUfrqoxQfOovjJ9moTz5F7IiREvxtZaJ0kM7MEOavxngLIWlRMDM6yqPz4/SXR7J5HFNCN6SzYqIaTdRKjahqEsn/H3v/HWfZdZf5wt+11o4n16lc1d3VWa1WK1qSJTnJNtgmmRmiCTYDQxwb+MzAHe4AMyTDAJfhwh1gzDgBhsFgbJwjli0nZbVaUudY3ZXr1Mln573W+8fukRDmnWtkM/Lrt77/dNfZu9ZZ55wdaj3n93seQGckVcmUGgJw/nGPfhQQ3DxNb9oncRUGSeY8fatLrYxw5xym7cBjcPAlAc+7bZ2tXplkKibsBAzI+T9+8V/wLa++hX17p9hxyz6c15bBdxElHzTc/ytlRg585Ilr2JhyGU3bxHtLhYl2muLUygTDIiHL9DKQkpLvcvh5u5i2EroX+thfaPHya/Y94zq+1R6i2uCOqrx67rufenzxcoe8Wme9Nfqia39c1+QVEJXiGlV8NgnGPH2dP9Nf4U2n38ofnvxDANpBSJRlXOn0/tH7yc3XFiW633zX9TRLJcpWhTsn7mbG38m0O8u8N49nuf/o73418P3ffQdv+NGX8YLnb0eGb7PNNttss802/zy8+93v5pWvfCW+7/Poo48Sx8XffoPB4MuqrvmSK2kuXrz4jJ8rlcozfk7TlJ/7uZ971hPZ5sunko+RtCB5LMVsCX7935SY/26Bt1QcLNYVTXVygmRiwODlZcpPjLAeD0hHCaNLAl1RiKUQsTVAmEKkMLJYrIqmz+DmCeobiomjPd7z7x5m7vtmueHm5/FL3/8qvvl9n8ZqBxgFy3fvIRi55EqiminRTTvI2hGVBy9hxg15CMnHFP5dM+Q1Ad2sSNOp+nitEFsIkoOafu5gNRV3XXucTz6xh/WaWxgb+wLrckw0rRnOQWWjh8k0wrHJfJvOsI7yEvyzK5h2H6EsrHQeZ00WqTW2IA88hs+fRxqFOxihqhYdq05JBuQtqxAg+iFifAwGI8xwiLEkwmhkkpF5EpOBLrtEdZvqlcvEaR28OjLRBE8oFh+eQHhw+TNz9PY5KGnId8wgMo1tAuxV0C7ITGJtDBG2BR/oEt3aoHLCJTxUR6MZm+qiqgkmt2AgGD4EehhgUoXXz8HxwVLk3S6iPA1hir3ZxXS6RVJSmnNltAt11MHZ6FEKDWqtMKIodQxpdQLhpcRbio//QY36rWWCloMdDMmsoorIpBbJho3pRUjfoeaNYI+m9Zkh0oBMdFENtaeOUKpIOjcG3ayS756GTh9nEDG6ZQHL6mF1JNaFFVo/sJtkxqE5bFJ+YAMZZsjJcZACTZH4lOaKiamY9eUIWRpiZx7hzA6yKEBlNrnRmF6PfKaEey7ATKSYb0qwHwqRpkbjSY3Yl6BKbTjRIp1rIByBHQI8syLQaF0cI1dFTAGYpTXk2BhSSrTOoVKGLIX+gHzvHKIfIIIYudml3XJ4/79bIBrYpLKF8F2yiWoRDe95CEshGjWkKxEbGToNkI6FsSTBjRPUP7eJiHNIc6xeihrERWsWYHUjcCuYPEckhZGxHCWIKEEmGfWDKf2tafIkRrb7GDRYNjLJEKMQIyVYiqxkYedl8nYMmSbbMgT9wjg2L1kYVaSoUa+hB0Py+QZZTbP7PxzF7mfkdQ+52cO53EWESRGF7btYSx08y8JYFlpJtG8jgwR7LQN7RDzhYPoKZ9Pih391jT/8ryWqiwajILjBwpEgMQhpENpCOy8E+yQgEAbsZn41Ll1iHI27uEFedxgdiDkaTnA4H/LWty8wunmavOZRuhRid0M0FiLKSXyB1RqQ7CvTP6ionMnIpmt830+3uOlAi9qZiD/8+I2oe0/SuetGuGEv1x6a5QP3PEH2/BHRoRn8F/awP95G5Bk6AG8qYHHFJmlIhBF0J8YZ0wFaGyK/wmNPrBFHKbQD1EabcKFJ+Y5PsfFIDas5j+tUKHc17Hj6GLxr5wIPNi5z2JTwOlW4Gr50893XsbjY4RXf/sUx2HfM7eRzS4u86tBVE2JRK47e/Gn/ml6yiGc9QiIkG+Epbpi7hl945d1MVYt7uTEGkz4JcgJpzXLubCHkjk72EN9YiD+pTtCM2Fvv4QhZeGl/lbJyscVDnz7FRM1ncqbxXE9nm2222Wabbbb5GuSNb3wjb3rTm3jd617HO9/5zqcev+uuu/jVX/3VZz3ulyzSLCws/C+3v+51r3vWk9jmK8P5UZeoVkNmBhBEXZfs+CovGCs8A4xfInc97NkaWaKR3zDJQtRhRRg2F6bI0gR9ahNnrQ22jW5tge9CEEAkaJzJGJupk5++wvHPdbl0n8H8ymnufp3k1oM7ePJjK0VrwFpKPlVluV8hP+bjt8DaiFCdELOVE99RRaTTxCtXKxPSALsXYYdpkchyGNYbTTLbgkjz/g8cIQokSsLEg31ELtBpjNk5jn96EzXMMLaFyDXWKEV3IjJPYFdKpCWFUZLK/SvISGHGy2QzNVIk8YzGDjQ6z3EvB4xPdjk8v0gSuQxHFYTrFoatV2O9hTFEu+uk10zhjmykrdC+xVhjxKv+7TKf+FCd5VaN5pkhRks+8l9vZ7SnTKYDmn+9SDZRw/Yq5D6IloOuuTi5Sz5KEVojJ5pYgeFgfZPLzXm2XrYHrRNqHz2NPqkRjsC4Ta58RlGa0IysCsYUVSTp1AQaUEmOWu9ihkFxHJRd5GSNxqmQkq8JegYzMUZaL74Bbz3fof7ZDCEU5JL4dE4rrGH2KIJyjjpzGaEs4vlZhOdgbbSQaYrjljiwa4NPXnsQt5tiFpdJGh5Z1ULP+tjrI/BLyCiDboTYHDK8aYpssoLzOFhnliHLKB9tw+EK3maOsBQq1kXKlLILf5OZKbQUrFdDkANMmmE8hxe86iFmrxvw+T8+zPqwBK0Bo9umkKMYdit0XZLvTrFWK5S8kOTanHS6jPNAH3skyB0H0xty1ar3qX/5e/8+9VieI4zGhCnEaVE9EhcO16LdQ8QpGDCOjbEUw9UMgUFWBFmzAlt9TJIgpCoiu8MIIvAuS7Qy6DxjeG2D+oUc1Q0xWUblgSXMWAXR6iMMYNsIz4eNEdr3EIlPPc4pezGZErg7DHf82yt87DfmMWQYAWIYkhwcw37wdPFiPBcmxooKMScnPJbSeU/GxFTE977xOPD1uFsJxlyNQ0cgqhWMb2g8kuL0s0I0m6uTzdSQYYpzuYOYncFybIwQT5m0JofnGd08jb3YoXa8g9ONMXbCK+qXuX9zjtLtLqrWwLnSQ3sKN4/RgcNUL0QowY21LT781/chrqSYuSZiUqBqOWKygYhzhi+QVD/bIds9TaXm8r4P7efcfS7nVRN3JUSsRFQfWEeNjWNHQ7QFvSMNvCuK8nqK/VebRHfU0VKylNe4WW5w6+wG5fMBZhRy/OwS3Hect7/zcyRhSi5z7NyC/XXMhzcQwNgNcG0t5Fdu+zzftTxJb7VKPOM9dT02AqZ3NlGWgOVNZLdLSo/JfZt0TpTIpEWgBO/+s8/xhl/5l3i2hW0pDixMMu9ZHP/rB3jn4pA3/N4PAvCKf/k8rrtlN/ML41907X9wY5leHvGFxUVeeHAP5KcgvwR65al9fMsB4SCFQooqJ9Y2uLjVYe9VP5wgupdjG79AXdU4PP9hpieqLC3C/r1FldWF4Rk+sPLXXFu9getqe6moGiaX/8R63P99/M5//gBnV9ucubjOG3/ne5/r6WyzzTbbbLPNNl+DnD59mhe/+MVf9HitVqPb7T7rcZ+VcfA/RpZlrKysbLc8PYd8tpVjuYLx13XYfMcEKhb4I8HJlTXufPkMDAbsufNazq91qJ7JKY80/UFI4lro1QBRl6TNEs6ldhEH3OlBow6eVyxKjaEnNft2jDPY7DNoaT6xOc1NS5f55pffxpN//BAmNWQfzDn3ynHCzMF1BE5JE19TRW6UIQ6Jr63hrNSQvajwl8gjpO2DgdyWBHGZcpDRqyj8TU2QeYg8Qy1tYBsFrk3n+gYH9q/ittssOhZGSILr5rASgzOMMVEGUULvRbO4nYTyQ5dRUUbe8BnevZ9s3kJqg4oVjY+vIlLDjhevM2n1WfDPcHx0ABPkmNYWCFkk2MQxg7tmycdL6JbAigWZJ5BTOcZVxI/EVK+cI5+aQilB6hrymYgZ2gQnM0ySYkoClWiMbwozVS8nKrvIToZ0LUzFZfmsw/jkGtEfL5HWLBoLKeGKwkSK0mTEv/+tK1gO/F9vu43+ig2hxGgDvo3sx9DuoaVB7ZlDGYnJNf5Gxs69Y5yxE9pfP4EtilM/bhZpV2z2IE2gWiaqWJQcB2dMEu2dxN/MSITG6o0QQYQRgla/Rvv9Eo8eynYxU1PgC0aNIV6jSPuhWkYYUJfXIcvxj62QT1awL3XR9TIyzqge7VB9vI8YHyuqtvpD8lGI3LOr8ACKEkyjQuT7+DM+OC5ylFCqJyihsfIOVn+M4JoJ4jkXI8apPrCGxGAfHWHXK+iuJO2DOp8ie2BMhkh1IWSYp0UaKBbWcn8T1iLMMCg2SkGyYwxndQC+h0hTTJwUm7ojuDpOBph9c9jrvSJWW0lw7CKi2xhMmmL6A2SpBFNjuFsZOpEk8xPULgQIs1Wcd4AchoWpcpoW88sy1NIWZpQhtCGtj5NVBVRKeLZD6Xmw0t2J1BJTqSATyKsuanEdHYYI20I26uhmBWtHzC0/c5ETp2YY/Dx0VzQXP+5y6DuKKjNxaatoN/NcEAIvD/E/VlRkCMC5uFX4PfVyKHnknkSst8j2zSBsB5kbEjfHKIlulEn32NjdmHf9m3exqxnQDhx+5mN3IXsBcqmDsCR6rEbcWyNqHcTzBY89Nsfa75/HMpC3eug7F7DujzD9GO0LrFvHyE8kiDxHb1jwwU3OGsWhf5Uy2PCxjy1i4oR8uvj8jARhDDLJMULAHhdxU4gK4S2XD+BPxZy4MIM1P4bbNpy3ch762EO0swhjwfjiJMHRRez7MvTh3VQO70APr7CZnCQQMDxlUVmIsM92n7oeH9rX5PW/+C0MuwGqWmXm+i57XrbGuh6j9KoR4UUPtaxozVn83Js/yMxYlf/4/V/P5y9c5mjcQ+8s0w2ebldSlmLXvi+OygZYGFQZbYXU5wsDayEqGLUDrINP7XNd/Rb+1d5fpmrXmfBm+aXPvp/1wZDxcok79+xiJe7zqf40047kOiGYni4i3JcPrDNIQ4J8hDaaKA/YaR/i197+WX63/3b+6he+j0al9GXdu/45CKPiHB30o+d6Kttss80222yzzdcos7OznDt3jt27dz/j8c997nPs3bv3WY/7FRNpjh8/zi233LJtHvwcsjAREvktnte8yGfWbiV/cJ2LPrSaVQC0LVhZ3EK4ktG8Q7cpqM/WGavU2KpEWFcG6JpPcMdevDMt0BrpuVD2EGlM7OW4szav++Fv5ewXHuRCdZ0HnRr/7U33kk4MGLy4SfnBLqEtqa9LZB/EnohOxSYrC6SaQdUzgqrAPj9EjTQiS2EzQCcDxMQYwbSkciBgzy1rlLOUJx48fLWawWAm6yRhTDxXIp4s4exIKN1u6HsLqM9HpNdNgQZ3cYRa20Js9Rn7bEb/6w6QHpwm3xphSja65FA9q0mmDNqHeK6KlcQ88dgOxEiy+qhP2Vpj2LaKtJuZSbAVerNN5ZF14p1VeneOIXOL+qmc7uMu7zt7I+ZiByvJyeIYIS1U5rB7bIvarQPafcPaKYdgb5nKk23cqZDpPUP2Trf5+OcOY583pFsthJjAnIfYq2J1N0jnmpyZ2kvl3CpWM0PqFsLNMK5Ff0GQrSrcNKeUD0gaZUxHIuo1sqoh311lbmBonVsDy2bm2hkeDGOGByTV5aLNZ+rCCBOkRbS0EFApc+T6Hrffvkqn6/HBt8+gcolc7kGjWgh2lkInCXlskMMOZqIJro28ssXYRor2HdA5eThC+D6y5JOVLdTiOirSmLQwB2aiCaOwiLDu9BDJ1fz1LCMrWzDoIEYhedPHye1iP0uQlz2+8I7rKQWLdBcVYi7B6ghkZGOtB4Q3TdC9uYJXHzHzUI59VCIvgdyw0E4FudZBavNUxYyEIskszzC+JPXLWCpBKAttNMlCAxVFYHKIEqhVoD8oqquUKlK/KPyozYlFqJQxJTCOQlc8mKphTI6UCjkIMVNNkIJYhIhBH6Up2tLM070jWoEYRUVVESA8g5XmxZyDkMzKGXg20UIDbvcIN9r0Lg0RBmRnhGl3kD2JyfLifYtjzChAJGPky5orj9XZuDKJlfbJM4EUxbuhLy1DotFBCNMTKMeh4cClWx2mOj7ECY5KsPuA5ZLbCpMmoHNEkqEnaugsxzu5hhWDNApT8onmJNV6gDFguQlX3jOHY9YLIUgKOhslatkAOUgxOCRbEjM9Dq6LHvWxq4bGngGdT8ToXDAaTlFa6WPGQc9WcWxN2c75hudf5OEn90PZR7uK3vMmEEJSPjeifryHSHQRnx5IRJiTDyTJvSV+Ze1F5MKm+nUe3/niV7LUgMYooBEILi2vMa/KnG8nkBkwGd1RRGmtxA9+66vJqhbymhLeMMD5dIC2FCiLc/ef4j9902/wPb/6PWQVl8CeJp2KePT4Lo6NdjB+JOWaPGHPoRk+/ZFT5JOFwfv+ySbXzk3jtn1e8vW3fknX/j2ixvL6FnJUHC+h3EMrE0waD//qPkII9lcPPfU733zkECdWNzg0XRgRx+wkNNOMmCmOhat+Q2f6qywFbcbsJlPuDFPeHB9bei+NI1uc+ug1bHZHX5UizcteeA0feteDvPTFh/7fd95mm2222WabbbZ5FvzYj/0YP/3TP83b3vY2hBCsrKxw33338bM/+7P8p//0n571uF8xkWab556qO0f+mYTPr92KLK9z55En+Vz7WrS56h+UZLRFROj4qL5AnVlFnmkxmB6DO2eQUUyydw6NwI8dTKeHGaWYiRqmnSDXe4iVLm89tcW/eD0c//RxGk9WWJ67AVyJvKUCIiR6RYnxTg8n6DJzc8rp1VlGSyXkTo0Zz5n47xvIDYkcq2HFAVkQYFDoYZ/RlIXQI/ILAw7ftsLKrlk2L48hkhRTcsFziGYtQHBxa5LIHUefGFK+1EeUfPRYFa+XEpcUoNG+gxWBmZlkdO00uAq0wRnmJJM2GIOaaFD5uiFjC21Of24X/YFDurZBPi7Ib57APZ4hbA+TZ/inO7iLA6IbxtFexmi/A4+HyPvblKs52huHtS7ac9DT82w9OYmZShiei7ECg3umD67D7A+usbw8w+Oruzl0eI2VBzxMvYYajsDzsMYUwd37iacrCAP64Czm6EWCLcXbPrVA7wEJG31sJCZPkYnCGiWEY1WULVHA7HUzXPnkMZx2G5kbVK+PKSuMMDRPxvACKC1nRMJC16rkrkK6DmsrFo2pEa0rEt3tIzRkYxWYqGDqHtbFdeTSBvgehqvVJ0iMEFiJIs8zyDViFKLyjMGhCYa3TtF87xBrlCOmJ5CZQQgJlTJCSoy62jPhuwhtEOeXoF7BeDnCtRnudKmfCYvKF0uR6grdlQr4FgiBs5Uw9dF10tk6iVdBCIG9lRDXPILDDSw/pNoDEXsgi2ND6KKtBwlC54VxcALW41eK6hXbBkcRzzvULiQwCCApkplo1BHCFGOtbT51DgpAD0eYJIW982iZEx0cozQcInox1MqF8GLbDG+ZxD4HpeUIWa0X7VX9wgBXZBqhnx5UlCTGAVYNOBYSF2sEMrEId5VoLpzjtlddYOmT4xDbhfGxZyOMKIxup3xkK0a3OrAS0/1jg9kTIFIDWnDqWJ2D3w4iSTDSQU2OFd4vQHxeMfd8xejlu3E+eYk8EYhRhFAKZ9bCHKoQP2ww1RIECdgKc2gXSilMf4C1tEX1RT79DEZScHR5lrXbK+x45CKiF2DqHlF9msqNE2TfAPGqpjUQGMfBlH2yqotXjXHmBdm+KUa7LHQkSJoeXrXG2LrBHZOMlTIsW5AsuKhek7RmYxyFyEFXHXScY6WFEOguZVifkphehrWW032ZRCzk7A9m+dzHjrM0LpncN8EN4036ssVMZDj9inF0eQt90SGPYrIwRVhgbIkQgmDDwbcDNBJsqxD1gPs/eRwryhmuuTz+/r0EL/Zwexb2UkJwIUM/3KauLSqh5EMPnOStDz3MehLyLXft45YXHPiSrv1HnreDJ9rruLsKSWZp8D5Wostk4oPsrXwHAKnOeHDrPHvKU8yVxviGwwf5hsMHnzGOr6qU7aKC5uxyEcvur9S55s5Z7t38GJeCc4w540zKOR5ZzPG0ZGbsmf50Xy3UGiXKEyXOjjpPJ7hts80222yzzTbbfAX59//+39Pr9XjpS19KFEW8+MUvxnVdfvZnf5Y3vOENz3rcL1mkueWWW/6X28MwfNaT2OYrQ4WDmN4pZKpZ6klO9xpM7Ynor139LtWxYbWFnJpAOjbWlSEiSJGbPZS9G/vcBkLaiFKpSG8ZDMFxyRslTMNFb65jLQ/Y6G7xgbfYbCz5ODXJZAD9JCF/f588g7zvkP3hMnKUMGiUqN82wiprdAwMJaaiMEMX3euTJBnSVhjLxuyawN1I8D6xyWpouPeuaXr7S2gbsikXZ5AjowRvI6O/X9K/UCPuQDlN0FKhSza1c8Ui18ojVD2nZPdRZ7p0j/hkJZvKaoR9ZYSdK5yuIWzKIpb7jCac9OgNKyRjLrT76BsnMWNV4ptbqGNbCDKEhO5Ld5E2wVQtrBGkUy6+0XhZyGCpDeNNjCuZfFmb8v4h5x6aJdppU1qHsGnoXVui88Ah5JMRpZMrnLulQTloIbQkGlckdcN4PWfiuiHx2TbBeUlWLqOkIbNdlj46Sd4OEZbGuBozCNHGIinXwNbkvo3MNCunNonGfWxbgtY88OHHGfv6BcJBXrTiANHtFtkxQzo1g3O5jegN6Z0v89tvuhPVjlDZOrrsEx6oYm/2sY2NtGxyz0Z6NvmOcXS9hOpEKMfGciuQhkXy1V6X2V5AiRx9cYTxbEglqlRC5AaTJCAlOs8xQQBSICpl2Ooicw16ALkmHg8JZx38sxF2ZmMN4iLq2fOKdqDlVQCM76EWNyht9PAvjEE7IdldRluC1FIgQFVLaDVbmEH3ekQLJba+eZbZ3z+D+nvnkgB0mqI9B39doHKBzjMwIEolsFRRfTQaYv6eoY2gKEjSnkU25iMzgeqNkL0hshMDA9jtossO2lWYsSqhlDjdCBvnaV8cKRFXq2gwoDuFvxAihiRF9WPcfhmMwV9OWOm5nLhlhsTzcJwIuWOWXImijSuHXIJSktxVKJ1xzQ0t+qeKzxKZcmXz6kLb88Cyi+ogY0BIjLBIzpWQSiBnp2AUYvoDTBBi5z4qaZO0A0zDIx0v441MEUsvNOFMGXtjCOciKkMX40l+7fMvQ8hC6AEwYUqeQrrbgqkcKQRrj9iUbYEWBkdL0icMqw9WsBOLyqZN1HHo3ziFyauMtTR9dzcyvsLffmQe97E+2UQJU5GUL43ILI3dNoheSN7uQ8nDHPQIvglEJKm8WTLxtpTv+s+vorSp+dDwYSJHY22VuWd5g9iB8+sdVuYUux6X2HGO2WwjXZ+4aZNUFamrsXWOdwTSCzkMR1z70uv4np//Nt74s3919eAwlPanlEsOncc19pYhDwVCa77tW25hx+wY7/jCY2z0RoxMwsfe8xC1J/q8/pe+jaWtX6MV3sehid/C867/4mt/zcdfqOCXi3Yn5VyHDo/ieK94ap8HWud4y/l72FeZ5j9e/+1fNEbTncC3SuzwC/+5+y5cZgpYuhghheSWsTvwpM+h2vW4K1v8yX0fQNma3Bj+w3s/hqMkv/iNL8NW6ovGfi54yTfeyG+Hx3hAnmHH8R18z5Ebn+spbbPNNttss802X4P8+q//Or/wC7/AiRMn0Fpz+PDhLwpZ+qfyJYs0J06c4DWveQ179uz5R7evrq5y5syZL2sy23x5/Ktrn4f7oo9Sb414x/03se+VgpcffBVvfeM9AAhPgoypzgTctvM6rvSHjHwbsXMKPblFasB75BJmsk6+ewZZryC6ATI2mM02zmqfye+tUbk5Z/ONPZKR4j/86R1o91X86h+8lTSyUEPD4OIUY6aLUJJut0oy8DE1kIMUBgpz2wzJ2RJZGUSWU35wCZMk9HZ4CGMjPQMjaK+XSWyJledgcpIxF5UEWCsj7GmFu2nIKjbxgWnMWBPjW6RLLWScITcC8rZB54a8NsBZFYjIkNgSXcmxB5KsDO5mgMgUnSsNlh5woG7RmE0pbdWxFg1JmiBWAtTKEFJAguwEjP/FFsHNTapDF+dMSCYhGlgYDdlEBd0ooeIryCyn8cQSw3wfBk3mKowUZIFFY7WHvdzHjFnoF+5FfmaRwSv2gaMYZDbp+54kH1WwjUGttECA8TSybEFapPxkvT7GkZhGFZNr6BcVRfYwRQ8F0Z3j+KGNfTkgTyLskwE7ToFVLU79TlCjPBjhhlmR7rLVRosclVaxlI01WbSLuP2MLB5ivBqiXIJmiaTdQY+XEb0RDAO0qxCWwaQZQgvK6wGjvoeIFLWLLeK5KvH+McTqALHZhiBCNOroqoe2QGQCwrgQJ6SAWhVjcsyUpP7YCmyE9K4bp7YlydMIWfaJ5sdhEOCdWseEIVgWxhjUpCStOZAlDPdojFCUyw7hREQwZdH8jMCJXEbXNzC+TbqngbzYfcb5JABhuTgBRby672OyrKguqleRvocOQyj5GKPRJRer1QfPQ46PF+loQuD0wCARppBgcsfCeDblCxG61yd3DXKljU5yjCsRiSZv+MjW6GlTY23QSiGNAcvGDQSxNqCLc6j6aIfyzj70QxDFItkIgbEERmakgx6qFaGkgANjvOJ1i9zzew3GTm9Co0Y8XhhJ59fuQKwFmFYXYymoesi8SJbSlgQEc/syWo8FxFIRdyLEKAdLon2LrCLJ4hxpNElZIC63SE3CjgM59WpCJhTCEYWBz1URKqvaxEMPZ3FAvCUQKxK5OsAID2dxHcIQXa+QzNjkjiR5nos6mODe62JKMIwSTNUnu71J/wUB6sGQ+G6FaRrK79aULmlSKTAbLUScQJpgLgTI1hhsGsadTZTJubkyTW8mojLl0mmtc+XjZ8iVIHnVYQYTMbOP9lHSwsQjTJaB6+OHBn2NS3Uph15Ad9FGiQQQnDm5wi/+i98mr9QQtSrOXEbl9oA4NtjZBEZJHFvx6H1nefLYZf7Db7+GH//mO7nu7CXe9paPY50acnTtNACPd/6WXGbYvbdxnfd/f9G1f6zpkY5lePXivM5NglLj5OLpiOy9lWl2lxvcMvaPhwBsxZukOqEdF5VhgyRhCmilEZnW1O0x7py4G4CTq6dIaoXQe/TSMh+/9xRCwA/feRs7Jxr/r/eq/x00J6sEQhPrjPXe8LmezjbbbLPNNtts8zXID/3QD/H7v//7VKtVbr316Tb10WjET/7kT/K2t73tWY37JYs0R44c4fnPfz4/8RM/8Y9uf+yxx3jzm9/8rCaxzVeGXfV9XFP+Ef74w5/iyHUtXvGNN3K4cQNl614ARGYjZMqrfuJuvvl5CW/5rSHH36UZPHwWRg2M72BGKaLVQ8UpAolwHKy1DmajA0nG4GGXyWss+hs9DPAnfz2gqz/N8GSK146wEkN9s8fwdXPsGG5y4XKdpKmwZ0d4f7gBZUn8wzPkSylSuqAExrZQnk+lrbBCDbNzaL0KlFF5UQEhRE7pkUuYrT5ZReKIIc5qTFCZJl1o0DwaklYtWGuhgbxZwmoHZNUK+XSZ0rE1jILeKw+SztSIdEBSNlSf3MKZhx3Xdjgb7UJPgK5tIKWCRGM/uIjjDAhqPmorAQO1R9aRqcHbyFGzs8hMYnLF2M2KzfYElDxUP6bz3jq9piZug5mVGM+itJXibw6YPdJF7Y9ZHzSxu6AWtxCDmOr9Kzheje4RC8plaBY+O4lKyBsu/uNLBN+ZY31YIM9niCgFrRCpQQtQF9cxcxNozyOcsfA2IqzQIBwbr1aiU3dw1wOkXRwz/oUR1qhYVGYix5Qt4p1lzLiDFWQYR2GMwR4Y/KiGiQwIgc5zRMkhTyLcs0vIRJPvnaP14gmctQATJ6jH2og8JA9CGI4wlUmElOjBCDEKijajfp+4IYhun6V8bAOlrULc8FxEvYroDbDfv4nAkNdswt0lSudb2KlLrhNE52oikmMV7VNxAnlOTo6u+piKRVoTyMjCCodE44J43iNq9nAuJ9RP5NQudnHWQtKyQo0K35enAmuEQY9GmHoNlEDYHpR8hGUjlER6LrrVxmTJ0ybEto3UhQCY+zZpyYJaFbqFUbZ0HOQoxWQB1mofbUtknGGEZOP7D1E52qL8aBF//PcbNDQZQskiqn1riBqWyX2L6sEKO+cEUsUozyZPKVKyMlPsf2mdktbIqQn02gY8scG7/2QOmVowHAEgJwtD2rjp4ywPCrPdJEaFKcZWzB/cYrDp014J8SvL+A2XOFQgBKbkIP2MubKrAAEAAElEQVQKXjvDXt1EGui+cAe66iDrDpmAqZtOoGyDMDnqRMiOd19C6uL9MjpHh5JsHmQlw1IpztEReVkV8xMCaUQRE78rIb2rhDIp3tktzO6diPMriFwzUoLmnTG27aKCnLxqEBmI3KDWNyHLIdeQJKi+ovSOjM4LS3z7/3mZnbLHX/z2n7D71juQI4PnOAS5QWhDQsr6XEjtXhBakDddjKzhWTZhmiDPRQjbAeWR3l5CbQlwfIJ+iKzWwHEweU4+Usi6RlYSrv3R47RPVol/12Y9yzEIPvL+R/mJf/sq9s2N8843fpjEs3FrHt/7R39GeurruO7rz+De1OS6f+TavzTskYqc3/rEvfz34AH++79+IQP7Ap6afmofKQLG/Uv08wD4Yq8bW9jkJse3ygCcv7TJvjmbsBPxDxuFmo0SxgJLSQSy8Or5B8frc83fffokMtR4FYudE/XnejrbbLPNNttss83XIH/6p3/Kb/7mb1KtVp/xeBiG/Nmf/dk/v0jzwhe+kNOnT/9/3V6tVv/R+Klt/vfRjY8zvvscM3tcNt7d4q2tZSpzHR67qc7XAbrmMT/v8MB9F3n3f9vE/bsOeQQoQZLaJIfKVD8fgDaFVwUZlEuQ5aS7J7E7AZnnM18/zgnpEx2Z4wFVxhUxTiYRUqMsheNZNBfglbs3+G8f30UibFQWQQJqI0Z9dIt87y7kpQh/tYUaDEA42M0aMtVkkx6Dw3Nk5RKVcznSsjGDCGu9i0lz7BDcrZS8ZGOPa9z6ENG08edCtPGI1xVqY1gsALOM0ZTAPpejpcJIsDdDSm1BWlcw3UDYkvhDbSZXTxL82DTlBzbRHR+RpzhqwJHXDPnw8BpEJmn83RXsdoy2IZq0SQ6XqF4IyGZ2ER/pIr/gYrdHaAxkBt22EP0BpuFinApWCs5mQtRThONlMgN2N4blLUSu8S+NsPY3yC9pYgnBDhurG5PVKuR1F9kbw/+Yh+6sI0sJxq5Bd4RMNDIyRVR0nBDtqjPa61I+N0BmGmEL5m4usyag3NTM37oOgKM1Jtbk0YBMZGy97gBu4GENi/SpXArChSrlywGQIRDFytpVZK5HeT3GWD5ZGdJxF4yh9PAypdBCljx0WSC2uuCXkUnOaALM7gls10JXfSyjKPUjxLkOahBjqlbRyhSE6CRCtHtYtQomjJCiQmWjRP9l+2l8fBEVa1QrQUdJ4WMjCjPgcJfPxvePQQ473jeicjpDhQYVG5pHc6JTLUpPDjC5xl7chDSHehU5WSbZUScONik/3kEGOaI9JK/7iLIDM/NYl7eKqqE8Q9gOolFDNBuYXg/RHhQnYpYVlUDaFPHdK12olmEyRwhJVnEwGvJAYXY20EJibY3IKjaNizF+t/Ayear1SUC04CMjg9UrIsptz8fbzNEVSfSFS5w4AzCJ4Azy4F5ErtFjoEIgjBATzcJjByDJuXTKQZaDp54n2XHVXDyNCHt9vE4f49noLGXmpiE3fc8FOltVPvUH+yjvvsTSah10YXSrxoZkKymiUSsqdba6VB8Q9F+0A2kstGM4E1Y5Fdt0Ewfvs12U5WHoAxDNe1j9BPVEiiMF9X5O98A81qVCcJX1CqZaxtVV0rUeyeUSIoXSoEUp1NDtgzboU5KV/1LGRALvbzNMFqBMqagQG4wQnnP1s7ERdY+FV24RXa948+hmfoKHOHdug/zFEWqmTrSSY+8aY5TGBLtg8lQFZW2BATtLyIyFERbumE/gaESSQ5Cgd0mo+AhjoafGMb0EYTRCKcKeYs2UGUQu836Pu19wkQ/9YoPyjoMoo9BhTGu9R+5I+jMue6baDM53OXMsZTZwYcuimwVfdN2/stnl/3rzp9AejPKcLRnyOx/8IC+89bOkWcZ0+YVXP2dTpIyhv2gMACklJVXBlUWM+K2zc8AmzY5EyUK2zHSGFJLNaJOKq9lBnflmHXfCoWQ7TDW+evxpbCUptQzGgVn3q2de22yzzTbbbLPN/+/T7/eLvzGNYTAY4HneU9vyPOfDH/4wU1P/eCrnl8KXLNL83u/93v9y+759+/jUpz71rCeyzZfPhd5f8Kcfjbl0b4U8niZ5z0X6d+WMpotYX9HvUvutCgNzhrlhTudzGqMU6ZEFwp0NxGYXXBekLIw1TFE1YUYhebOCFUZYm5s48wmVl9ZxvZy2ZyglArO0STLtEv3IHAevu5nN7iJX0hpWLQMcjGcTv2gS5951rKWE0sQ63amdDC0LpSWVyEeP22RDQVzJYdWQHXBJxhTeSECpSj43gdjoFt9Oey4mD3HqKbps0G6CvxcGMzMEm2Uaf/IQANZqDxXU0dKQjXsI2yKfqmPpNqP9FXTNproaw5qH7Efcduk8Mz/Y497fmianRLLnAFd66ygpSZs+6d4mvg8bN1cJD9SY+3gHe5RC2EF+U4oYL5GOfIyryAcBwT6HYLxK+WSAk2aF74nWpJ4gGSaoxS7CdjG+C6MIXfZIbRu6Kfa5AZXOBsmeMUzZBUuS7WgS90KGt89TeWyNMT8j9qYQSiEyQ3jNJMr20CqlfFqjXZe8rBj/lk0qN49Y+M8ZaqyF+2gKB0CsdjDrQ+QwwMagmSKasJi4EGONUtAGbyshD4tjKFdFdYJKBZS8wtum4mEHCfZWykAmOP1C3NO5wXR7ECYgLQZH6sRjCmupj+U6UC0hljuY7hDXCLwpTXTDBHy6SE2StgfNRuGPohQmT3E7GdqR6H4Py6tAyUd5XrE9STCdNtm4i7YEdijpHygR7LEQ2tA8Ck4ETlzBTDmYMMTECSIeIMpllFNiVBVEC7OUjw8Q5GBZWL0QeheePtEaVQiT4nerZYRlYey0aEUChFv4guSeIk8iVKtXtICNj2HiFHV+lXysjNrqQpZidQtvFrsXI8730Gu9pyt5AO0p2t+2h/on13BaKcJWZCUb7VvoLEWdWb+659U6hlGIdhRZoLEig5ES0+tjHBtcB60zBmENaYqIYgPku8cBMJ6NqdjEfQkLE/iJw3BdcO9jN9PtVcinBZd+aAF7sUe0UTylMwfZsoFOD5K0qBZa2aL0gCa/Zieqp6l+Z5/PRxMMMwe5McTggesUT+5JqqdDhOWSj0LK+0KSKZd0SSAbQBijDy2gfZtKKye7P0IMY4J9dcIdirHJCVSu0avrpN2UtZ/aweRfLCJbfXS9hplsYCo+uuKhggiUxc5bAm599QV2qU3e+c7r+K9/MU06DueDLt5SiF5soxc7WELjXMkYH3gMU4nRGt2JsWyNmKyhm2UGkynlMwG216N6rE8+BONVGBxoUn0iQfUDTNmnvJBgRRlK2byqtshLG8vcN7afTmbQyvD+//EAn//Ik/zsH3w/r752hW+46yGWL87waw/eiUYQYJNol3/I773nXvJOBommMRsRxCW+cD7gfudF/MjBTe6YL/ab8eb5kSlNMnyAYw/ezA233foMM92GPc6kO83u8j4A5qbqRJubNBpFZc2n1z/K/e3PcGP9VXw2fxJzaEDzzDRGG8ojRdlXyK8mc95hzIiUOBE8dnmZu/5BNOY222yzzTbbbLPNs6XRaCCEQAjBwYMHv2i7EIJf+ZVfedbjb6c7fQ2xs/JqfPM+1OUB6kqLOFcEL/BwesUfzkJ62O0KC4fK9K5/lLYuytT9Vshw2md4+ySNKxoQhanr2gZCjSHG6niLbcwoJMocPve7swyOQnpDE19YzM3VWaxZxJMW/T0WJ8UFfvzFN/LJS6eYnu8QPeagr9XkL3BI7Brsc+CBEV57SH+vIrphBnE0w9nMCXaW0UrjH9/C6qXIxhQIA5GGmUkst4JRAudlCfVrofWOlLwRY04ZNpx5dN2H3ND/xsN4p9ZI9k2gXYtkNkZPNRA5qNgQ98DpprRvcdi0PbxWmen3N2hlbZyszNhdFptP+FBy6YRjVK+sk2mH0koOXoWJx4boCzlWkCKExLZcTpyZZkKlqCBFBCmpC53DDiqxMBWfzBjK7YA8Tqjt00TnawjdJa56eJPjkGQYnWAswHJQjRrpTIV40sU/3yXdrVCHYsynM4ypknk2ydBCaUF/r0NWdzHOGKXPXaB6RZMOw8LUdrzG4GQZMQZuFrH2uEfUcOC7QV7ukkpFfmQ3aqXN7NsvYG7eidOCfFyQTQvEUoxrIFcgg6Q4Nrwq9EOyso1uVHDSDO0atDb0bpmiemyDWn2IGo/oDx2MEshU4F0J8R9bRkYZ2fMPweommSvovGo3bmWcsdWIfKyOsWyEEBglMFtdpGvIOwL7rMHeqoNdQo9GiDQDpRCOjapWSPaOMXxeDW9FULtsyFyL0rLECE0ejKA9RJbLSN9HD0eFL4oQmNGI+O4S2XVQ+USEZXlQVsjmGGY4Qne6wNV2pu4ApiYQ5VIRwQ2YfeOYjSUkoHsDxFgNYyuMX8b0A/TMGFlmoNVHDUNUGJPMVbCudJ5xDtthjs71U1U0ALpsM37PAEwJZB+UQFuGeNJCDJMvug5ky6sEN0+hJxuooYsV9mCQYvK8MAUWEqlt/KWgeB4BchQDUPr8ObJOzPBgk+SQg95S6MsWmxsz2BE0NrZY/39mYZjgEiAMhEdBVMvQevq1yDRHVz3SqsQaZpQrMRtJDYuc/kvmmfzQ5avXJIlTycDrkFZtsm+osWnB3MWLtJIck0EeJ8itPtRLEGc4FyPy0Yj2Dx0AA3k0zcyDnUJYzjXu4xsYoYuqkThGDIb84ruf5JH7pvnY782B67FxQXN5bYaltXHqn+ux+eqdJIebvP7265m/zuZ//Ld7GE6OQ55TWTRsuhFWCirP0LbAjJUZWpBuDYkO+UhrwL7dVwgji2HXIrEqGGUxu2uKzukV4uVV7Jt2shoqImNxsjfGkeoyQWaTaVP010lBpz3iT/7i8/zgN7+Apv0k47c8nzfsejEffOhvGc77jHuF+a0xht95699BnnPfvcfJJlySWbjWXWGiHPO55d2EcYmLo7FnHBs/+T3rXFycgrU3kdfKLLyoyetf+y+47RU3sTg6x5XwIp5V4nD9JrCL+8aBGwoB77Hu/bTiNVrxeWbdcbrtnOp8ldX+gG4Q0o8izq9scc3OZ/+t0VeSXGvk5ohs3KNu2c/1dLbZZpttttlmm68hPvWpT2GM4WUvexnvfve7aTabT21zHIeFhQXm5uae9fhfMZHmPe95D7/8y7/M448//pUacpt/IpOlO1i5/35IF9GWYPfuAW3595Z7nQELj93Ozd90F7++3if5jh7qfwyJ1jt4SpAerEOWEc2X6F7fpPKgptpVCNtC1muFD0mzQe/YCONKjFIkvYxzakD+gt107wApBZ4u8bt/eppa4xpaV3o4JiZXGqoCUaRek0cuwthUtvpM3b+OuJRgsDDT12CEAmOotAPs4RqpV6c0n9OPLDJfkk8ImjeOCJcclC6jU5voYE7SsKlcDvBaOXHTI7h1BiMFpBbBLbNYuAgDMjdkJRcVSyqXBHFd47aBksdKa4GNT8XklztYGxtImWLvzHCORbiJBiXRpYRcAjWfLI2xhEKVHPa1hwz7NirUCIpWk/mPBMRlSXkjIy8pWs2c+uKI9tIkJivm4iy3yG6q43YFWTBCnlsB28ZqSPzeOqGsYJ9dx12MSabHqLVaqMUO3Rc18bdsnPOGaIeL3dKoKEd5ZcqOpNcdQZ4hVzpkuU3AbswhTTA2QTroFsdEqjElB3V5A+NYyIoifLVAflagVkF0IhB+sZjXFAvfLEMmFOlGywH6wCyxq1DaonksZeNF43ReOo5/qs/sX50DUmSSUjs9ZFjXqEEI2iDXOujxOno0wNoMiGSZ5MkIudXBKEm+bwdGaOrzCQe+qccT75wgHYXglWCsiog8hO8gpAVphtEaewTVVkgp65FvejBm8X3fcJLhyOGTf+mj8wxpO+g0w3S6VwOMBCYOoZThpQb/XAuCEBpXfSzM35dMrgo1GMizIjFNG8JdY3hLQ+wrXYxrIyo+JDlGKPL9c2BbiIfPIgZDxOw0UlmINMV4NiLO0EaDbZG7BpU9sx1FaImul3FWB8X73xti2n1UWMXq8UVzA0hu3E3qRZQ+0cIEGmtqgjzPYbON0DnStiAI+Z/BVNZmkc4nohSpDf5qH3s4YvjyBdyehdPK8QKDCBL8Vcg3xdPvRSgRExVMmkMUQbWKtBXJkTmsJAcE5XaGPxmTBIp4p1uIY+nV6qzbJlFDsO5dI3+FB4spnUcdsmFKXi6j9zSxuzG2TEEoouubxHVJ1ixMbbQj0aMQXSshuwPGTvTQWVGlI4KI33/wIeoVw+79fb7w9jr9kWIQlPj4R25Gu4YXvOILbBye5ZHuFA+euMAPvfQIEz/WJf/LNsP7LHRawUo0Zm0THSXIWolkoo6dRtz6zWdYmx3jkWQP639+if2/mBM+rBCX1pkIEoYVn9yvQJgxOiuINkuwM2E1dzk+qiCmx7G2RtRmG2hHMX9wBn+mxGfSSV598B0sTM1wV3edL4QjrHqKkoXo8va/uY/3vfcoWoH9hTOk37efvORzyR7nOvcYRzhA69wFHnxDn196yW/xK+/9Of78rf+RlW6OOH8JJieQWcZL/u39/NztJ3n3ypu4fvx5SKFYuFpJ4zsOETDc9QVWwiNMW0v0VMatzevZ2W7w25v3UL7W44Gzl0mlwVGSWvnpUt/nmk9+8klIcpwtOHXiCtx1+3M9pW222WabbbbZ5muEl7zkJQBcvHiRXbt2PaM6+SvBP0mkefOb38zHP/5xbNvmp3/6p3n+85/PPffcw8/8zM9w+vRpXvva135FJ7fNP40gW+W7fvIU935Y88T9s7z6l57kyXdlkD39MS+fXWV6tYVOwTglzJQk3VMjqHrU/uYMZn0ITg05dMibZcxKD1GpoIcBuDZqssGufXu4+L4HEE9eYudL5zh/dhIz4VLf16fSlSyGCZYPXupSPlkkvWQrhryT0rlmHK8lcOYqjAcRe8a6eMMOZ6+UCTMw/QFuO8ZeHoBjEHZAaT/sfWXGA6aJvmSTH4hZfLKGfdnGH+RY/QQxaRMJ8LYSZAJSa7yuIWOE6YHVbJDbOdZGD6sVEd89hmy7lC9DydJY6wnqyiZGCrQUyPU2IjOw3EesgbhasGByTU7C1qv3oese85/QSAmTkyOs9S5jTc1abx49TNH9AZZuQE8V7UyWhRmzUGMGs66xNrdgFGGA7kGXqQdG6N1TyEsbmDxnorRK1JdE6yOCW+bwT6wxvjygPF8iOedg6hb9nTZj61B/uI/TCqHkYkYx1d1TdMwUYSWnsp5hbIveKCGpOLz4liVedNslEgoPKWXZmNEQOYoxkw4GQbxH4azksCnQdYc06yKHfTJsrPEmRgqEBrNjBlmugGNjdSPyQUxWFlgR+JsJmTQoAQiJ1RridJMikQiwghykRPUTqg9tYvwu0ipBlmGUTVKR2JdH6LqmddrFGYNovok375CezoqFfg7CkmQli2DOxlrqUv7sCPPaMcxaj8OHEw7ffAEQ3D99HeGai3EUMtMYxwEEerKOinO8zxrycdBeDVkBVfLROitatnjaFNUApCnpZA0nL2QOPTJkN+3HzPawlgagDSo3yERj4sIwWGgDlnW1Vc9gXAsz1YAgw8w2ULYLefRUp+H/xMqAQYYd5hhtMJaAJEJFOVbri1NrTK2oOGlc1JjZCSSFkCSiuEglEqDz4r1DSqRSZOM1AJLDs6hLIWqrRVaWhNM2owVFXhLI5RHCgDYGY1sgIatYuI2pwqC4UQeriR4FaNvGP9tHeC4oBT2b/c4ij60uUD41ekqgASj9WRcRKYTrcM3uFt3VCm12Iqa7iEwjwgSTJjAyoDVWy8MIaIxpdCbwWi56awtcG40gjwzJ/iruVg9pSTCG73joW+mFJV70racYvtvD8gTyeUPK9pBrd7U4kLQ5/55x4iRi7bYK9YWE4Z2K0VEXpEAjsV0PpEWpUiGLM5oTHfbftMau8hYnj+8iXROcfoNCNKqIhkW14lKerLGx1icr+aQGxGkfObSZuT3g/NoMgfbJJyqMdvj8yKvu5iVfdx1/dP9DvO+xE9zzPx7kzvk57tfLJEvjHHrBkHi2qHg6dXaFpCFRcWFzXTrfpz/hc+34EtGGovOJdaTVJ+lp7v/gI3zPn/4aJx6rMv2GAP+0T/i7mzjfN1Z4zUh4y3/4H/zsW/4Ns94hGnZxLPiuTQewlUAbw/5yhQU/Z3/1EIvdS1Q6Bquf84lzpxEpZGn+VdXutHhhA1+7KKHIyvlzPZ1tttlmm2222eZrkHvuuYdKpcJ3fud3PuPxd73rXQRBwA/8wA88q3G/ZJHmd37nd/j5n/95brjhBk6ePMn73vc+fuEXfoHf/d3f5Sd/8id5/etfz8TExLOaxDZfGXrxSZozGxy520ekL2Lj0jHGqwMG/avfbgo4+Lz9/MWPv5thPMAbV7zsG+d5dBUuSwviHG007qUOY50QGWeIyWmQEuG7NKqK62+a5zt//OX81HvuR7RG7Bs/x4nVSWzh8wrxOB9r30Bayrj2OsXilZDwZhc7y/A+t0bp4QC1dwZVm2Smvk78hMUFxskbDlsv8vAbHv79HaxQo3INiUBbmuf9y2UerOzCPmHIUk0a+SRxmdqOPjJPiZYtrAubNDd90qkG2aRCjRL8zQQGIfktJfYfWuLiuRnSS23yqSqiY6Fig5GCzjWaki+YvGKjLUlkJdi6MKEVBoIHeKrioDQp6N81Q17NKT22jKGM0ZLNFRvdsjFhiLOvRzaCennId73+MqeXpvn449eReYLKlSH+VEoeG6Kqi2lLjITGB8+Q5wrR2AmjEJnmNO9Q5IlmfVFiGi5Zo0RrPSNdUYjFDWb+vEN2xw7cgQSlSTxFWlc4l9rEOkZt9qn6Ptl4Fbm+TtLwGJwPaNHnM3tmuWOuEDjEMEAkheghNkF+RJHVfPSkwb0QYW32yaZHoBNEnBfijMkxgxHm2h2I/ggQ5GmOzgLKF30aZ1N0OyzalYRAGE15PCBXFfKDO1CX1sHkCKeEGGtAo4TIs2KhpzVkKcYyCA0xLmvnbXIs9DXjhKnF+OE1el9I0YOMLLOJD0/QP1zGKRu8vqE128S6IaDyqT5rt1cZRD5bpXHMjVX85RhrMIJGDVMqIWo+JsuRYYJJPQh6iEGAzkHPNRi+ah/+45vYsYKtTiHWZDkIQVK1UZtDSkfXoFFGtLvIdgiWRJRKiEQ/ffDMz2CEQPf6ZDJBbiTIMIU4Rax00K4NFZ+sUUZGaZFYFafosoNpd9GWjako8tkyojGJlRn0Qh1z2UV2Y+ymIFQN9Hid2l89irl+H9LzEEagh8MipcqxEXGCfWa5iOq2LLTROBNFypPPgMzyEFMTuBUHldkkTasQ1oxB28X/1TACZRMdnsTtuJAa9OZm8Tp10WrkWZLUTyCJ2HzIo7e7wtqnJ6nd3wLXQVTKmGFA+44J7PWI+v1baNcn2W0Tn7PxW6bwoEozhCvRqQYN9ShC7yzTfVkdkRmyGRuSrKhy2zfHYK9DOlMin7sNJHz/f7mF7vUCGSq+0LyGOTvAlDXJnCC2q9z/6AL9vk/585cZ+T5/+/MZ1u0e/QckmASWNpALOwtz6dxA2UEFCa3NJk+8dzcbB3y0lGAJyAxmOECUfGQM3/+Gb+cP/s+/JO52Kd2S4dyc0PFdPvjwreiWQ3V4DlnJyVsBf/fuR/j0ux+hN2PTn4gJ8pCHPnaS3m63MEQfznBNsoMgiPGnS2QXBVpBeWyMsfs6TNy4D/sLJR57tEI6m5N5LhwN0drQ+sHHmQJGR/ez8wfa/N9Lv8dP/Pkv8kevnqSaBWQkvP3CO7lv6wvcUH8BP3nNa1iYGmPlNIyvv5SKnifUr+bRwWeplE4ROi5pVaIbFgvlcbY6AVIKesOQ6bHqP7w1PSe85LY9LH38GNqxeOXdR57r6WyzzTbbbLPNNl+D/OZv/iZvetObvujxqakpfvRHf/SfX6R561vfypve9CZ+6Id+iE9/+tO87GUv45577uHcuXM0Go1n9eTbfGXZCh8miEIeObaLlcGQyoPfQrs3RDnF1/JerUxLw4bjYywH8+lL3PPJDuyaInnFbtKX76P5oXMQxlh2BbbWMc288N3IMzqrQz7/0ScolyTm6lf9+nLEofk2W0zzse7zSdycSdVjvHmJ3l+PI3WN7qsUru3AyRAlLVhIWFuuUzcjdD+BYxs0DaQ3lklum0GeVkRlSJsOJSo8/KihNj4ifwhGk4bBjhL1tYg4D9m4tQlHMmZ+fwlrGCOqFaz1AflkHZRGBimHX7zEjbdcxnsi5PjiDGm9hLMYoWsu4bQkmZYILLRnE834hNMeIs5AQrJ3nPLnL+LfnlGxMn7qxy+T5xf5+XfcjLgQYaRCGzDDEcJSGCmJegrZqLDz1pjDNw9oXKd5d/8G6hczpLRw7Ixh2wLXx+ydh40WVr/4hlyutTFTTUyQ8ORj+2DYx1lcRM7VsS+1inm9ZAbO5hALxMUU4drYcyndm+qMhi61eILd8zNsfeJxdK2EyDLIDHNjPp2L6zzg74B+jTvmAG0wYVIkEQEmzYjvsNm5f53RfT7B+RTlSPTeCbIbAuxFiXU2A52QzbsQRKhzKyDAOBbohOqJMkJYUC2B5yCGKUQJ133DRZ48fzPdiw6mN4LhEJHlMD6GHi8V8ciLawhtyKUhGIuwJ+r4Nxuc2YT2ByTOfeepvKxJ/uQAM0oQGvTcPHY7xr8CVmgTLOwgw0KUDemxK7zj+3YTHJkiOTSOyBVqPceaUvgLHUbnNSYtzFiNZaPCDFbbmFwjsj5MVHCvxKheDI7/VDtRHoxgvYuVaRiOcHQOrg/aBqfwfhFXd9amiJkWCFBgGmUST+JcLipssG1IUkScYIYjlKGo6Br3EZZG5hrplEhETvj23Qgb7Pfk2MsGtGB08y5eOnGahz5QgX0TqMtrUPLJPQdtS0wUFsbI5RJ5qwUxCA1FGZLAZBnTYg2Aut2hZabRnoNxLexeIZTJWGMPClNgkxb/kmX4yxG5b2EJCYhCkGrUwAhEkmNnEXkc851fv8wNu7fAnOZSbwYqVz9vpRhd30DtS6k+0Oby2xsMnSpkATf/6xdy9A8/DZ0h2fMrWPcPULZiz2sizs9MU/5sQlSvQ/tq29T+nVDzqd1/jvSW/Ui3EGG1LZAtTd4URL0ywoqRWxLvY7B1xGFtfopezcee65B0BNlWSvJoCetMG9Mf4BmD9/XXo4MYM8xojFcYrfXIXYcnTi+wPldClF22vv0A4399Buk6GCHYcH1+/5feQxTmVF4MUz8RcW5zDO0YVDlDb7rke6ewtIeKNKvxkIPzTTItmK6VqR/sck1tB09EIasqQx+b49+/4538i9cc5Xu//TzvFN9N4/0KLq3gLExTUi5Obzfp0mkwCdZ3S7IPPPMe4Z9Z47te9ZPUGuP84nf8Cr/6R79GUDOY17Y42juKFIZzwxMAXFnvAvDYQyv8p/WPoqfPsuPaiEHa5/DuI1Qsh9FawNe/+ABHj15BCsHiRpeDXyWeNM3ZBo6GsRG85M5rn+vpbLPNNttss802X4MsLi6yZ8+eL3p8YWGBy5cvP+txv2SRZnFxka/7uq8D4O6778a2bX791399W6D5KsIgePRT+xicWKBWgffWtphekWhVfMzXffNtDHRONlMFY9BVDzlKMVtdVJiQ1jyU6yIqVfIoAs/DDIcY38cMhuA45FLwqUcXMa5NvmeGk12bVBvKYxcRfxJS6cQs/KThydEMyXdYxJ9zqTsjzEtLJC/xSI+5yMkRWdlh2C7j9RJkuVosJDsxYm1A58YdpCWbUksjT2qG6xZbnkMpDXDORcyHmrzbRSkPcY1BxoBj4c5PIcZ81uYdpFaUUwsx1mBp0aU5H3JpfRrZ0JRWI6SCOAdnPcVbzSitlRiVctTiOsqZwN4YoD2riFbzbNJTGfk8hc+FMUhZIbihjttSWFlKFmck9TLWxDTCdjCbXY7/neFdM/s47k5ghEaNUqxORE9UyaoOIk6Rjo3eNUu+uoGlbYSwUJYLDQ8zTBGpg3Y93KUBRBkGSK+KbiQKS3okVUnt5j6NBUG03MSkKY88sUq+aweWyclrHo7vs+u2gzxxZoPSuRSdDOAQ6JqPsBwYBBitSQ9X0O0yrTOSyfFVkq24EBG2ZiEcgycugnAQQtD93hm8ByNK4zWwFFprkorL4EgZ4Vpkdkp+5052/v4ZhNZk7Zzds+d54vF9yEoN6hLCEJ2kZDLE3hghkgwhBBJF+ZEWsq0ZfEhjco1xNNg2w3sMwptElLtkNRfV6iO2utSOaYQBccMc0Y5dyMAim1FgJK2XlzFVxdgToF2L2u1Dai/RqI+N2Do+jcgNwtaQG6hVMf0BRkp0FCNmpqGVEOwcw3u4W/gN5ZCqFBIDaRFFzaUlcF1kvVa0A1EYvCYmRW1uFa1Ou2bI45is5tH/zkOUTnWpnxqhB21kkBOP+ziDonomafp450ak82WcqRx5YpOxyiRVJ2L9pgbWkzEkGdbJS5zc7SCmJ1CPnwUK+yDR6mGMIR8OsSdTVLNEntdgGD7VukWWITybaKvIkzIlDzMaYdaGCAXqRTZ2q4Y1zIoI52GANAadZmAM9pU2xu5jpiZhvAFVH2kkIs3JOz3o9oqKKZNiGYNvFabIqlwGz4X+kObne9jdHGt+lvwzm9TMRnGOiTJyawBSIDariLrk+pvX+a4fWGFdr/E3FxYwWZfFo0V8kWMk9GJ0ptE5qG6Idi206zB2JkK/JiUcqSJpy7UIfYfJXsQ3Pu8YMQ4f+e1JerfvpnlFUJ4oEbU7heeNhLpTIt8h6J6ETn+AtiRqmBDttItWLccQHhorhDnHRU1NUHSZ5ciST55qZJRQHdiMLvlYiwo71uCUsDZDRAr9Zonv+LGXct0Nu/jC0rvBO82u+iR/968HJBWLC0c3EDl8+D37ee1rz/D9N3yee/74NoTOyV2Ltc0+pboHwYhKohB/PaDvO+Th1euFgL947LeZnp8E4ODUBN/x5tcilkZcnP40MoQo9zhQKUzu7Kz4vXHpIqVg1m5zbWmJXV7MeLnOmHQYtAPuOXkOI8FzbW69ZudX+rb2rLnjlTfwlpNPsrZX8MDaFe6Y3fVcT2mbbbbZZptt/tlx2gonUs/1NCAo5nDbbbehlOL1r389r3/965/jSX3lmZqa4vHHH2f3P0iRPHbsGOPj48963C9ZpImi6Bn5347jMDk5+ayfeJuvPKf7F7j4pEt4skPrgEdynU28mOPKYkl28cQVxjA0+wN0PcNEGjk9RTYlyW/JEZfBqKt/0McpYBC1GsKyEeUYbUtUo0Y4yjC1EmasSqctyE9oND5ea4gip5zmZNgY1yD3JjhWxsQTGwzeHmNuG8N5TwhbErs6hbJsmGgighEM+riPdMn7ksmjG2ApwruvJanbVE/18ZoJopkSX1H0X9pkqrWJ+zGLpB2SuYLwljql8ynVdspop4/Vj9BCs/JwmYubt2BqHtX1dbxBgKn5kGvszT6lJCTdXya+rUI6Vqe8qEinqqjNAaXHllGDGG2ge97it96wgPZcRjdK8qpFsF8wfj4lPRMjhzGDV+9DOxb2SQtrf5n6HSc5stxm894qGAukIPNssqoDJRsVZ4heBGGKaNaRVf+qQS8kVYWxLKhNonoB6kKItgUD26KuDbLkIuOcfGQxvM+hnm2gHl7HutYm+EIVxi06B8cpPbrGjTfuYWzMw2xu4Qcu3mIA3w1iRpF3XVSv8DaJXjIFWhC1HIInS7CzhgwMKgUGGaoXQEWBY+NcFnhxDd0sKkJST5POlklnSpgoQ7qSyU8OyWsusp/wyB83MCUPaxawLLKSjSUk4ayk9QIXq6WZfdsmolFDJhlOT8PUGCy3INXoioXaMY4MBEYbZK2KmKyQByFWGEMSQ5jin24xMT1NWpKYg3OEzSqSDJMZbrzuPIOaYvhwjl03BCcssBT27pixwxt0P9wgK5fQcYjMDLI3RKx7yP4IZzEmmrDxW4Uoo8MIeunTJ6CUhe+QkqR1h2TMxjuzhbXWwgBSC8QoA0tRCi3c+9p4l9Zxdgp024ASWHGOjDJUlGF1CjNfrXMmv7XPxnEJKbR0BXuYYWwfkRiEgNZ8Cff08KnobgmIS2uYw3sR1Qqme4X0co60SjxlS3w1Br37/AaHdhS+O8nqONRzRKeHyaH215cIr9vF+AFI1odklwfkWYYu28hhXIhiucasb0Ceo4UAz4OpcchSyHME8BvfN83BG2qcOVYqXlMQIrIMbWLk81zEkgWtwuvGHJjjiCc59/CFwhBaCoylMP0+iQpYtxO2eikmUaSBDbaFsRQYURiFZxoRZeRlFzFKsWILW0QMPl6lvpIz1Bq7Iene4NJTDlnVpqQS9PUV3JNbWIFh755xjksBeRFhfvvhHdx/6jwmy0h7KdJyUZnGCAuVQlrOyGo5xhKIbhfte5Rn6vz39/0M/+67/ojKbIdbq1eY/rojvOUzis5jIZkRyFGKjHNkYkiznGP3nWX24BSffHCNQ9cOsFNJLiT2wKCNQQrBMJYYA0NszDVN4nAGVzmYKYtvu+U2fu+RCwzLLvVz5xACHN+mVCvxjgt/iOc/HeH96OYKf945w1jT57f3vJ7Ptz7FcniJl07fBcDBAzNcvAA//NoX8lvv/xyV2QGeiFDCwvcdvvs7bsOWinddPkU2Ibhu/yxjFf8rdTv7sumsDpCuxhjB8rD/XE9nm2222Wabbf7/koceeoharfZcT+Ofjde85jX81E/9FNVqlRe/uPD7vPfee/npn/5pXvOa1zzrcf9JxsFvectbqFQqAGRZxp/8yZ98kQ/NT/3UTz3ryWzz5XFqWRPVMoQV4l0wvOLmRdw7Ba37bgZg2IlwlWHOSG74noD3L5eQOahQMP6xAOH2mL09ZeWjw+Jbf60LUxYBRmvM1dQZ0ZBwYoBs9RkcmkSOMuQopDIbkV7UzJT6vHhHwH9/uMTmuqC2NKL7qEZeyPA32+iKhRxYiOEWRlnFIiwIUVogyx52J0OkGjJNOqMhFsiSZPKH+qBz1t7sYDo+t33XFdaP9njiXR6ik5F/9DRRYxrLUrhntzCXNhH1Kv5GgLWiWH/tQWphhK6UsbsRetyQeiHx4ToayMdcjAQRx8goxYrzQjyxKIyWjSQYQFKvUTq+BRaI22cIhz4YC1232XqZx8ybL9K+aw7mbZbZZHayz217LnDs8i6CnRV0HGINR6goR2YgUOhaGTCE4xaUfIwxdK+xERiqD2yg6jnG0ghHMt1ZJzYGwgAdh2QzVcLhGPpDIftvX6Z/WjLqpSTzZYwlkLUyZ06toL0+Jolpfo9h7M5CcNXfNkl4LKS0qtCui3tcUrp8Fmn75CWfeF7hnc8wrRFWGCM9H722AfMzlE7m4DtYZ5chy5EVn/JqzGi3i3epw9jJBGVsyG1MFgICE6bowRDhe6hBURkke4LaZ7rkdRedpchO4dIsSx55rQztQbHYFxIRF9UpeqMFaUYuc9gzA65LUMnwji5Bo4Y1SGnsGhJoCy0V9aMRzfEU/fWGzYfHEJdyot8z6J1jiLEcR/QZvGuAGWSYxEZogy47ZAvj4Niolocoe2S3TGA+eAaRpLi99GrS01UEmIMT0JfE0x5pzUIMSnjrAqplaDSutjxJ8FzUSgudpmCD9EEPBdboaUNdtMEIyEo2/XsMwi3Tb/nkDYHbjrG0RngWUhi8T/Ux9j8wbc01chQhyiWyuIJCYFpbRTIVAuKEnBRr3eLysoEXQKIVDEY89cK0RXU2YS4KSGYkl85DunucaP847tlN3JPrCK0xloW2ncLXJwjJ4wDhyqdEozyTnHq0/D/fJhgMMUC+x8bM2GRWRv7uLYKX78UdSS7XawQVH9MLENog19uQZ2Se4VJaI44c7LMZSasKuUH7Hkrrwi8py8GzkalGl23GLnZJS+Af20IkKclYCdXNIAUtJVlg4w1zrMuasFxmdrICdQ/hKkyQoVzDoYZPfGgfn3zsPpzpJjorKoLq5yLSiiSdcqhUHMROB7OcYjzFcBTxSz/y89R2NhC7NU80xjnefYxd19RQQ4fNT86S1G2iisbfyigNAtQwoF726HYWOHv8h3nRq15Ibv6IycYQ299i2Kmgooj3daf5u/fehpNCeMskjc+vMTF3LedbHUSlCp4i9crsvGacxQeWMIMAnT3TPHdfvcnk59skX1jhI7/X5px9HAFsRGtcUz3M/Ve+AMBDm6us9rawnZxjg73cOnMjv/bnn+Dvjp7je156EzXjoPpw/Mk1VrZ6zI3Xn/U97CtN5fMj5m8c51t+fLvdaZttttlmm222+crzxje+kcXFRV7+8pdjWYW0orXmda97Hb/xG7/xrMf9kkWaXbt28eY3v/mpn2dmZnjHO97xjH2EENsizXPIxsij8vI+js7R9zp0Plvnph9aYuPRAIC026eV54TCcPrRQ2R3ZJQ+cwG1miDkBHK9x9Z0CTU/Tp6lsHgFvbpBtmMSFDAM6N9lcejgDlae3EAqm2Q1ZbDgMfF3ywxqCuNIHlyeo9q4hDl9E42PbiKCHukLfOQ+jXUuRoWa8i0NRmdiiAJklCIshbAcOnfvgmGIe76FqQisMMO9NCS1LUZbZSyTkGoflUgefHQ/8ZND4l0OxgaZSZKZClgS+2IP8hy6fZhwyMY9pBbk8xNoJRGtLolyCV64i9STWCOoLOaY/SMqJzexOzFisobXdNl721nuO3uI8Jox6p+8jDNKiF5eRYQWpSVQiSYTApXAxCe6uFsOzccSwsshZz44yXXfOeKbfuAEf/OXAR8IbseZd5nebNP7ZAtju4jJMcTMFAwiEJqsrFC9mPLRLZKFMYxrwXKEdm1UmlO+Nid+CKSfk+oBZlglrfnQ8Tj7lz5aS3JHkEyVIE5RQYZoZqwn6+gJB3sXSFUIbsEjBu8y6MO7CXZVqTywBP0R0smQcx5pUxFtxKhMYaHA9zG1KsL3Uf0QrYEkQ2Ya2RtBP8A9V6H68AZqYLCmy2SjoFiYuxZJ08HqbKGGDvlUg3xyDHpbeKsDol0abQtEbDCujayUUeeWIctgdoJs5xjDHSXkMKJ6YQT1Gs4gJE5TMt8h3ltCWHvwHr2COHMZ96BH3K2QTYLBImgLlt4EadhBuQrVzdG2whmlxEdtTE8VyUVRgsiL1ic3cclshWjUsZKU0pUE4fuQpM849wxg0hzxyCVyS+DXr0GFNsJ2SZ93AJUaVCcidUAOY+QwQK+0EdqQ9BVZ3SXfUcfqxqjlNkjxlHGwjDOC+xKUJRi1PZINj7LqMbkxAm3QeSHOiNQ8QzQSjo25sEQ+1UBNTUGSk/spajF5KqvbWDbe5YhgrKiwGNRy7DNDjCXJ6g7hwQbJ7DgOLtNLfRhvgu+ApQpPmavosoPcMYd44hxGCuRWD5E8LQqUqjkHrg85dbREEsqnHlcXU7w/20Kupeg8Y+0bq4jckH5ogNcPryY2Xa2i6iuWp3bw2KUJJvwRX//KE8jWIn/7a3eS+Q7aAiMg9x2ElEWbVJIWLYq9CBwXU/GxU02UD3CqdXQuOPrWnSQXKli2R75Q4v/8rz/KX7/lXk54NibVCODPPvIwTeVA2SfShdeNEUBmwNH4ZYvpUJL+5hzmL1PUxRJ20ObQL58BKbnvL69lXGcgNLIfk5QswkqMt7FCMF1GVl2mYptbXnItG50haZ6z0TEcf2wJL8mIpzxMmMPlNVIl+M2/uZuRU8IbaNzAoHoJr9y/j6W3PoL1+CXUzDjR/gnWL14BDWmQIuQzRbymV+KmqMrp3KLX7+JPOsx5C1xTO8LS4AP0878CvodIZ1w7N4dt+sxXJnFViY8+eJoozfjcExfYszBeCGkGLPVVUF59Fcez2L1rkpop4XwVzWubbbbZZptttvnawXEc/uqv/opf+7Vf49ixY/i+z/XXX8/CwsKXNe6XLNJcunTpy3qibf75ec3h5/ORK+/FPjTiwaPjLIspLr13ASWKRVFzV4/V8j7a0zZC5NiOpLlzmlZtQNyskh4ZY/KxDiZJYDAEIdEVn3yyjii5WE/0qDzWY+N4D44sYKUap+pTXwZ17R6Cr4tIQwPYbC575FMZ2Z4yXn+EezyAjazwmilrpg5Izrp7EO0R8tQVQECa4W6mmGFMNu6R7Szhjhm2GiX8dcnyx+ax+zHGFqjMkBib3u451GMtorkmslYDx0KFEO2epnRpE7TBniizfvssMtQ4A000Y9G/cwEnBncL0nmQOTieIGs4ZN86Te2RLaLzJcILm5y8VEO+VIAE7VuYLELfVcJY0DgxQJUT+n2D0CWsY4a0UqeyrCkvCdq7mxxfi9nT6HHq3By+jLF0RnkM+vUyJswxFCktRidsXe9ihSMoZYzdH2M1bPTEGO1DdYyaZm5thYXv2iQ+rdAxJGmN0YEyKhHYp2KyVIItSPZNoZslwgkQVh0vG7H/FVNc7FisvmOTYErBv4PKxy+Shin5dQtYuea2n9jgzNuhH1VI65DvyFGLgswqgQyQnQ4YDTrHxCGm7mOqPjpMEFGCsW1wIZkt4zb6JC9VyD+4Wh1SLhFeW6F2/zqKmGR+AiEVOTWGBz3S2TLVh1sAmGoJJsYQWY7pRGjPQbgOmQd+T5BdsxPZGyG6Q9RqB1WrQTrA2VBPtR314hqJK4kmFTRcqlsj0jM5dpIgRkUrEUurxId24Q5covl5hvtLVC4EuCfXEJrC76NsoxOwO4J8lBJNQbn3zHPv7y9/ZWZQ/RTZCYj2jCOEhKyoDtKOhVX4Lj8llOgliakJ2t88i3dlSH29g66XEEmOEQbtKIylyJMIec7BUzaxqBHuGeGsRIi8iNU2rkJkRSWJsKzCjBhQG11kvYkJArJrpsi7AXY3QQCjF+7B2gyQ5aKyarS/xvgShDtc8kEPZ2lItjTGYGiRbFSQIsQ5s4G9OijixV2HPE6wak1kbMhLPsJzEWGE4WmR5lte1+KF39jjQ38xzgc/tQNrtXj/DWA9HoLvMrxzF2AQBrwZTb5zEtmP0HlKNu1jtxOWhuP0V5psOQ32hF3yz6oibSrRZCYvos6TDG1JEAJGA/QgRpQtBLKYMyB3OJTqIUZJFs9VqLVdwJCWBM2JKslMieCFuxDn1nHWYi5f2KArbfJOn7zmES9UsYcZMkjw1w3x5RR9PqY579E/M0QODZmb0aFClkta7UnGRRdhQR6H+H/jMT2xRHhqxNiJEfrwXlbrDgdvP4CwJDfsmcORkg98+CjaeNANqByw2FopgWcjPh8RfVOVxM4pP7GBTDWNik/bsUBA5uTYJ7okg0K2U7bCL/ucfOwyH/6rB3jlt9/KkVv38P3/8Tu4cmoFvf8eLgZP8PzJb2Lam2Mta3BDuc0xgH6PcyttGqbMqZmLvPforyCHc0hhODI1yee2VskyQ6Iycv20cPdcc/DIDv71//ENeL6DMQbxVRQPvs0222yzzTbbfG2xe/dujDHs27fvqYqaL4cvf4RtvmqYqRmm1rqcuG8HsSM4fMsKJy7tRV9dK2nlkk74ZDttdl+ziAozjowd4YELDu1xm/rHF4kGOXYqAQOWhQxiaPURYVosNje2qO1scuMPlLFnL/LkE4e58NkRue/w7QdO8F2HLzA78cv8lw+dIyovkRyaxHueofanS8SnNHq2QlZusNKqITyB6gwhzzC+h5ysU1lJEJkmLkH0L2cZq46InvRw+gKhDXYnxjiCoFEixcb4AXJlRHlzgB6rkNy8B4OFDIrEIgPkqzmT7zpHeHgSsz7CG/n0bptC5ClOR1IxhsOizNKxDtmgStqA8vf3iH91iIlSNFD5zBKl5iZmM0HvqKAfdHA3UtQtmixzUPN15GZAnil0mhA3qwghyMrwsdFB7nvXblytkXGKfS5gs1Em29cgqijSikX5VAstEtzlgOr9mwTXjtG7fQrpCDLbUGmmJFWJd43Dlf+nzuhYABnofRa64hUtZzsr1O8e4ey3WH+kQr40JHNcnHNt4g4sDRvEMzXk6pD+wAF4KoHIjde5/fkX2HlLh6UP1RheTtEIKk8YYkuiLElWdrDzFIKQbGOVvGohjETvnsGEMfQG9F44hWnU6e+qYUQffTamLq+mCVUqlNycnS8MsZXm7LkWescEyXyVxuIQ8dgW0kiM0GBb5J6FDEOEMajLa6hEM75RQqY5yXi1iKwuu1jCQg1jGidyhLKR87NgDFulCo3FAZP1Lp4foz8hyaeaWFEOdDBhSF5S9A/ajJ/MiXd4JDtLhEbi9CfJPEWys0znWsnUOzswAttu0LthApUanCsDZE4hBhjzVNK2AHSWcfkbGsw9HEOUoVa2yLc6iLkm2Y4Z0C4sFZUywvfJRc74u08TzfiAQPXCp47f0qUhIs3QNY//D3v/HS9Zdpf3wt+11k61K56c+nTu6Z7p6clZoyyhkRAiGxu4CAwGbF54X/vi15K5WHCFjTHc68C14RpJryUZIyFAAwihgEaaoMmhZ3qmczw5VE47rrXuH9UaIUs2IyFe/MHn+9+pXbVq1967ap317N/veeTmkOiwh+oLsokiJggIWn3EZn0kQkyPk69vovI/1zYFWD0SMKrOJYbRl8WT3RNlROsCN7xBAHciFeC5FJqW/p5p8IeoXGK3cmw2Sp1yJsZGiW+exmiNTVLs5jZmYhw8F1rtkVD259hYL5KkfZY3Kwz3T1JtbgFQ3dvlVe8e8JkP34H/0iZTv9Pj2vsS7viuS3zmpf0MZRlZq1Ddu0pOAdlQuKsKDXzuzC3s+tQmjsgwxRDx4qXRmwUuZhhDMQBHIYsh4USH7/2tx/m979hPd8tBbYPd3IcWisBabNbGDIEo5d3/7Ld5KuoyfwuYPCQbBngq4ObbDvDURsRgNiCddCFJKCYpbBr0mENjxhJ8dA2aA7rXFHGWBnzsE/dilWDx/BAzBFW2TB/q8fp/cZEP//O3ow52qE+nVOsJXtfy2J+9xNYw4bO/8SC33nuIdKNHrZuT/EmThvRwpkJsrrGhiw0h9xWdGytMPpJSdQN++Be/hz87u0YmDc7ZDQyCyV1j3Pd33wjA2RPLXDy9zunnl7n+tn1Uxsscvecw7z//QVYjzWK5zlTBcHaYMdfbBcCfPX6F3iAnTDXtQUSeu6SBxQCXOh2ayZCwrpHWYNKvbKn660QIQbPR533/4dO8/k038IM//oa/7l3aYYcddthhhx3+hjEcDvnpn/5pPvjBDwJw9uxZ9u/fz8/8zM8wPz/Pu971rm9o3Fcs0jzxxBM0m03e+ta3vvzYhz70Id7znvcwGAz4ju/4Dn79138d3/f/O6Ps8FfJ1jDgEw/ciDwtEBa+9egl6o1JNvpjAHTacwS9IbuTlDfd9xLCGD7/fzYZ2grzsSK/3EVYkIsLICUmyRC1Mm4mAIlxHYSwREmTZHiKJbGPC6KJXiigHMvUM2s88nnB9J2/znjnR3DTTQYVSz/zELfWuKa/ytKjbYzpEseLmNkqjlRYITCBSzgvSdoCm7lE++eJKZKuxaS9jCAyyFQjgcGUQ/e1EtNw8bYMaIV1wCiLdgzudg91ehUAUyuhJ8vI7RZqqJFboxIIOTFEDyxKu7hDy4vTHbi5TDLm4Hct22cmEIOVUbKRtRDnyEaKzQRiNcEbLpHuLtP9XIG84pMFBRw0wgpEEGAcgVASZ6WDP14irfi4UYZIJbavyTc1+ZRDtKsAFpKFCv6qJnypg78e4w666GtqeK2TyCgnvmaeQq/JYDtl0HahpzG+xGum1E60cfs5wvjE2/P0/BA9dPGzFLGu8a40EUGZPMvJ9zrIN0+gnht99d0FSXZFoldTkuWIpVvGyecVZsWilSAaUwR5MFIfQp/82kVEc4jJYnTNxX/x8qilpBxi8xx/LSap1pC5Q7s6hjszpFjxcBOJKLoUbrEcuM5y/JcKqNUG2e4aVrmIbh+11gBjEFIit9sgFWJuBtY3sVmOHfZR5SIoidOMyD2BObiAaUXIQcrgtT6yk+OdLdC+roCMLXoMdl+3hZKG7SOTREsVMmtwtxqja32YoXo5IPEaFicb4jY12UwF0RmgXlph8pRF9jNEN0YEBaafg9ybJt9bxZgehZU+ZKPF6cv36pdW0LUpTEkiz69C4+p1t1LHzkxBnCIzPWrPCRTW6lH7kOcipcOo3kMjcoMVFr1YpXP7BHmlAI7AZBm2n+GsJ1BvjsS2OMMKiViYJKu3kFe9YpxEQ5JgMSRLLlKPvHQskMkB3/U9W9x5e58zbXA2M2yaYZUinvVRkz7+tiafLeJmA0QuIDVY18XWG5gvtbJZi1AKOxiCZZTGJSBdLJNNFthzaAMlDZXpiMLSADkzBVozf88Ws4c7XHv4FC885BMmCcXXWjafFMSnuiATzNRuim9QbP9qHUeUCeoGZ6mD55TQ/T7C2P/KG0iiLm2gd01gpMW0u/SygCvH55jb16Z7sYgSwIMZclfA4o8PEJll89cnyPbVuPyFy1RCh+KhHp3LGSSwYHO+73+5l3NPXCKOU2wvo+L0eMs/fZ6NYo2PP30XU59tk3klxJhLeqhMvneM2d9vgu+QDxMGF0Nqx/rMem3C0pD+iSsgBM47Z8jWDYUXFLOL43zgFz5OHGU8+bmTlAIPkhyBQHoe0WwwSoi7/aq/j2sQkSU5ssD7vvgUP3btjTjKIfeuHgsLe952jB96z98C4A3vuIWpuRrX37r3K+aOwNlDbs5ztvU+Eh3wR2sfJ36mypsnob+ZkASat977h3iBYHf3p3j+jV+k4g5wV1wWZ6osnRxQLQS434Q7R99MHnj8z7jS7fOFp47viDQ77LDDDjvssMM3nXe/+908//zzfOELX+C+++57+fE3velNvOc97/mrF2l+4Rd+gde97nUvizQnTpzgR3/0R/nhH/5hrr32Wn71V3+V+fl5fuEXfuEb2pEd/vLk9lY2rrtIWB9QOpPxkY9eS/ugD/FouzSaoODRjSVrj4wRNNvYQQGnO8S7yUPXyohhitmqI8bHQGfYTo+8VkRIF1EMWVissL11hWgo2VddZ21viRtnMi5+bsgnPj3BpB8h/tSwtfWHFH52gb7jkW6UkA+sMNgUL7d4IAXu0KA8D1OrgLB0ymWico43cOjdUMWkgjWvQNjT5GUXFUlUL6V3qIDWCg5KZDNCpYas5JMd3UXl4hBhLfnMBAxTKBVI9k8yuGccJBSXErq3TmCqIMsK2iMfi+GUxetIPD+nYDLipzxCt4yYd7Cd3iiCPBYIQDsKV7t4DRdn1iBNilrMiBoFVGqIFkKKpxoI38fpZyRW4Az1yDNEgNYjDx45THHaGSDxNyPcrQQxGPmFWCVIZhSlkiR60UUbhcXDVnJEHCP7YMYUYraEM8ixQpCXFQNVgA2HQqOPTTMyE2PDADyPvOQSlgZ0rY84etUEupuBMVgrWb88h/s0tB7oIQMfv26QiQWpMTInq3lkEwIXj+G+Au5KiyDPkUqha2UQ4KUuepDSn3dwSSlsJjj9HIzljfeeZvw2ye+/dw+6bRB5k8ITlynf7jP1IzGt/yxJTl6twBACoS1OyaX6cyGNDxm0OwZFh9xXyNRifQenMSSpBUSVgNKDA7Jdkva1BfKywiVHNFxWT0ziFxOGwyqUBHKYYJMEYS1OL6O8JshLPnKtQaEbjmKzgxznwjpOlGB9h2S+jDPIQCkkYLa3EEf3oRgjm8yQF9aw8QA1HIk1edHB6wpyX1K4KtC8fOm3BjCIRuKfBbnZQkrIpkMkErRGSoGWo+oc7VnSHw3wAH1ZYo1BWfByH+EbCAPojnynWN9EFgvI3XvYft0EVkpKL7QIWxqZW0wHcCNEbrES+ucGPBnPEsgOwQGonk3QAownEO0e6XQZVZDYLMWMhajLq+hhSnpkBjsT4J3tjSqIamVykWOUGCWByVHlW+91e9Ell+X+FY7GA7avr+E1LXq8iIxzxvZr8kQRDV0QEonH+Y+Ncb7nYJIIq1LylTUuv79I8WwXUTMIK1CZQeQZWlmcJBt5BHkuohBg8xy53UY1uujrDyInxjEKPvOZG1BfOAW+C46Dd7FDPF8hyRz8VJMKidd2sBocLKEc0BYVEClry01+7f/9/2OQgjfM8bdaFKfayJKhPDZEl0bCFS7gKGpXJEJDMuXjbw3pH52gaTWbWyEHRZ3exYBgMgUP+IDGOA71YyFzB6epzRa5vLkE4UhgTroRYpAitcHdjpCpobA2ElHHkgzxdJP8hr1kueHKS2u4nZR4UmJDHyMMj2w2X271KVUCjtywSLkWvjxvaKvZiNaJDGjTYiO+SFm2OHJoFVqvY/HwCqXYZWqsh7YO/oJmaTvmR3e/iD5wgX94/g64z8HrzjA9Wf6mzml/Wcp2iDPsMenIv/jJO+ywww477LDDDl8n999/Px/96Ee56667vqK1+rrrruPChQvf8LivWKQ5fvw4733ve1/++yMf+Qh33nnny2bCi4uLvOc979kRaf4a+eLai+hKRnaXJVsSNI8LZhdqVPaPYs/cZpPWW/cwvD7loWev57aTFxm2UmRmGDzs4E5MYfItTBqRiR5yLECUq9ipCqo5QNRjVusFZEHQ2l2hZ13WTIF+VEN88gzZvpBrDsWc+aRLMm/YfXiN6kqFi4yTf0sFdS7n3jdu89LKDI1xl+yiQAmBzVJErBGXhxTqdUzJg6M+NlRkUiELAbkG7VjEqW0KZ8D3DaUHtkiHe8jmi+RFh+ygg1hNsVKQhx5qvITFUrgu4q1HV3hwexdOWKSwmtOrFqCV4Zy6gjWGmecFpYWMiXem+NfmXHx4FlHIIVEjA2IhsFPjELjYQOE0h8jYQpDgLrh0nsgYHigiigWMtujZKm4Sc8v1TU4s+QypIeMc2+sjhhHpdAlKPjJnZLq71sT2IoSwyJkpRDmgdzBg6B+m5MVIp4C4TuHc4nHw0fOc/1gJAh8RS4SxaFcw2F1EFyRec1QNwVYTfyPFOhI9E2JKgvyyiyla7JqFcQhuDOi8IEinq2xuF7GflChzBakzpE74nh++lz9+3xeJqkV0QRDNFzBlB7c5INlbQp+LyXbNIqXCXWqg1vpk4w5B0+d2fZ71Rw06N6Bg774+Fx4YR59ug7waL55riuMDijdLkAEbvxJCNERUq6R7JnAWUgZ/1IflHDHhki5OAhbranAkcpjhdRPykgsCnHWJpzPCtQS3nZDXSvCAC00QVYsROW59CGYkmVjAjpUQmcZxA7JAIfMc58IGIhu1DAkrKKwPQEgYRujBAEeCubCCnR3HGoO+cR/EOe3qgOoLPYZ3zmKlQT380svv8yXybhsZ5wjfQwgwjFqRVCvB2VrFasDwstGrFJaDN3ZoX8xZ0WWyKYPbtTj1/ugYOh4wfHl86yjiBXcUSy0EZrKIandHLU+t7tW2LIuwAvf0FieWfC5+foqffB9kBY2MDep8ndr5nOj2fWR7xhARsDVEdAeQG5wTS/S/82bcrSGiPSDvdjDlcVToIrKrVUFCUG6kvO5bniM7P+R/e+de7KwhnZ0h21dGDQ17XhXR3Zrg9LP7UO46KEU27oOtoJoJgpEhd+wHUCvgb0cUsja6NyQbt5haAfSoPVOOVbFJSlQxBMsWgyapgRcBQtCe86ge2oVzehmSlNKJhPhV+1j7091460OMbFE4rdFXNsFall6wiJILhZC82WRdbZDMz4MGr96js5zw0C8tsvLG3VAEr55hOj3wXZytkbH04KZx+ocrCAlyOqNWSvj9J48xPZ1y7/99AXFZ8NDPz2KsId7MKPguSTGn8YYyfix4y95r+OLnniYOUmxdo1yFkAr/ikak8Jo7rkffeIwX6xuYSp+11tUIbM8hveMApX0NIvHl2MsH/ug5Pvm7T/K2v3UHb/z2W4BRAsGzG+s4QYm6mOfWyi3IfJk7dj3IpRbc/roTfPriYdzmFKfd/VxOPofqHKTbWGFYzthsl/GDjHsP7/tmTWXfNL7lTd/N6lO/x31vet1f967ssMMOO+ywww5/A9ne3mZ6evqrHh8MBn8pP7xXfHup1WoxMzPz8t8PPvjgV5T03H777SwvL3/DO7LDX57n66sUVMahvWv0j4S89dvOcvSmJcxSA4Bd1+1izAZ4v7MNlyKWmoADtuBgJ6ujhWsxRIcORmr6R0JEbnD6OVIL8DyEEIS7q8jdkgnVRWvJ3OQ+KrfuQR+bZeV1h5j957swvzRNqxAwPdZB5pqqCBnmC5z6XIXqRof//TufY2wQIbVFCIUUEild5PgUamaBqadiZj83oHrKoguK3DWok5dQvZjwYg//iTpmKYE0xyxMkOyroYVHFkqMBLFRR261EK0ubzl8mXceO8F3j53Dpikq0ky8lFC9mBBmEd5SC2epQ/ZUn+a/t6xfGiefqqCHKWZlbVRFk+fooiKfqaI06G4H3Wph35wTuQlqu4e7VMcoCOsZVgj27Ovxd//+ad76tiXicUFWVtiCB4MY58IGNLoYZTGORaKQU5PIcgVZKiGlS7AW4axphApQiabwrOT2cy/S+KJCBmM40cjsVFiQjMxq/QtN3OUOIkogTTChIjk0zmBacfOuacJn61xz4jLTj46+q40rZTDgn6+jziwjVrfp37sfrVN03ODz6TMM3mHxmkPCpSHhWkpxKcEWPLJxH1sr4C43cTY60Gih221EZph8ImL90+PogQEJtlLmqef38fzW3CiC2nPRx/aR33MdaXGa4QVovH8A3R5WQnadwsy69Lsl4vMKEoHqj1qE1CAnnnGJapK05OAMMxgMyZrb9MspjTe5NG5zYHUD2e5BEmPQyDTHayfQbI+SeRgJIbI9RA5GYpYNPfLAgzTHBh62Eo72N0ogy2AwQBiDiDPkRhNR7+Bc3kSdXMLrpUyezvGtS+FiD2+th9tLvup7qupdZJQgfQ+UQz5WxBR9VKJBCawS2MAl2z9JXvVIv22c+CGf/D8U8eoRTkcgEo31HPAd5OQYqJFhspicIJ+vUr87QPVzVCfBX1vCu72PcBKoFrGBw8hp2JJ3YqQB615Nv/FdMmlBKVQOTgwy1qhWMorvzkcVWFJDsDZERCMxwolyRJJjpqovC2AGcOcUM/NdXvXGNv/ydy5y7XwfpKRwromxhqwtyAYa6zrke0PS7w3J9mn6dy2S759BeC6OW6D6+Dp0Ypz6EKcZ4dWHqFwg+ym2PwClRm1alQL175h7+VjnFZes4pGNhxT3t7GN7svbRKapnUpR7ZGg5EYezpUmJBlCOqAZmahHQ8hzFt9SwclT3FhjxkuIapXO8Zzyb25QfinC6gSJRfSHLH93md4+D7QhCwVaCe47cIL9pW3YK+mWHQb4FPcM0e6owlAkI5FmolJDCkWlV2B2r0/17gzvRo+xAw2E64CQFDcyyhcT2mdbbK21KU40mJp9kU74HAII1hOCZk7xGktxNX75M8dRSr8TEcdfTieTSLY6ARvDEotOlwtffIaL/2Uv23ok7pw7v8Dy6Vkef/ZGNvMOfqGPWz7Dn5x4I0/1Svzw0Yfxcknpf8BW50Y7IXHLnDq99de9KzvssMMOO+yww99Abr/9dv7kT/7k5b+/JMz81m/9Fnffffc3PO4rrqSZmZnh0qVLLC4ukqYpzz77LL/4i7/48vZer4frut/wjuzwl+cHj7ya99z/J5xe348zpbnp7jb/5wWfYWl0sZw7fgXvlEtR55Rf7ZMcqjL39mmaWyGd832S5RZuvUW2r0y2dxynF4ESIyFFOVjPxSQxw5UE0TF88fh+uqUAb79D4Rf2ErWbNI2goz1ur77IYGB5+OQhorJDPjFAT/mI60oEeZOtK2YkfFjo3zOPN1B49RisJBMCp54ghSWaLaJdQXi5j78RI4RAlEIGby0RJ4LixTIyFziNCF3y8LqjdpMsdNElh3QhpFJuMVYbcPDQGqhd2DDACs3MZMKPvOsyf/TIbp57sER4pUfLmyC5PEOhAnpmBredYIcxxpNYCdZobKdH/8Zx9ERAEKdUJlLUqxUiKHJreplL2xWG/ZigsMn6domTySzdQz4iM4w/PMSVAhFlOJc2KfYTqFYQxoKSiDDF5hnkmrEnIvL5GjpQOK0UKXJOP7SfuJ/ARBWJIHcgLyqsEjgX1lC9BNUaYMOAbKZGPlOkNJCYzPD09DrF2xy4McC/Ks+6y01snCGuLtrRGlfsgckyxhnS/I1zyF0BVk2D5zL2fBejM9w1CF/KcK50kOXy6NwOQqK9RWyxgO0P0cMEWSyiM41o9Xj+kVmsNYhhAgWB9V2EscSnC2w2y+RpB4nFKoO7oZAqRusi2aF9uNs9bLWETTJkDn5bo6Mhegh5twNDTf9oDX9jiNBjuJsxTj1BiR52PiA7KlDnI2Q68lfiaj2JqRQxleBqe1UPtx2PYrGnx3CUxLbqWJkhv1SoUipBmiKyFJFpdHc4ih/WIBON2RqJD4E3yfRz2csVNAYQjnxZ5MBR4AcIJVHdNnKQYEsFtDAIV6HaEUnVoflDt1DutWifj1FaoAbgDAQydbD5AF1yUVYjJ8ZGaU5hgKc9nMjgbQwgTqGTkVysIJQAkRLeWmD4hQyrxdWobxfRG6UtuesDEA6iXMT4DqLZgoUQf6DJv9SJBiAFQSMlcxXqqg7lrTTB80bXEYxEoEbC2cEMu6sJUvWZmNZ4D65CnFAYJmydKbN6agzhueTHKmQ3K8TlAvKkJjk4iRIOKgXb7n5FhJa1IDODlQJcBzOMUGnCrt2C7fhajABpIHj0Avk1e1BjLfyfXcLsm4PtDpSKyFwznI6YeDpFCoNVPk4OswtjbDQixOQYNssxW3X0bo/+sXn8R9fozBRIJzwKJyK8rRQdKNx6Ql52cbcjcByMJ8llgl/PRpHluWY7K3P2yi7STkCtGfFA92Z0KqjpCCGAq34uP/Ujbyb7YJvj9R4feN9T1GYFxWLGbT9V58/+2TxCSoSUmJLLU8kGk13B6+5Y5GT9LIO1IrlwsErguZbeJyp43pePWxylI0Pzq1ViT3/meR7+4yewb5QIDLFK2CicopstkkYj35vHnrwB5Ugezx1+4PWPsJLVuGnPWWbHLjI3v8UTkc+tk5epD75czfU/CoPaBnW7wk175v7iJ++www477LDDDjt8nfzyL/8y9913HydPniTPc/7tv/23vPTSSzz22GM8+OCD3/C4r1ikue+++3jXu97Fr/zKr3D//fcThiGvfvWrX97+wgsvcODAgW94R3b4JpCs452xuM2R6e8HizfSbxWI8qutHTonH2i+/cfeyBnWeOk/LbH+6S4Tt+xh6WiZyqllyHOcgUGeb4JSiNiSV0Jksw/NNiIMUEVDGCfwiQ7Fgx7Hdr3Amw+8inPxTXziwjle2m7wrLuA0x+gVg3q5oTt3R52TmD/YIrWp3JOPVHDdlfJQ4U9OoZxFcmUx6Ck8ett5Moq8VxIXpzBGRhsEI4WWFIgigXkWIY3Bf7xDKEFNpfY6SHGFQgDri4iZgKs0yMdQD9zSCONsBY70SetpNxz6xbTCxneviLphTLFYUDv1jLZmMHfMhT9GHnbFLbfpDVbRjV9vOU2aSBp37cHISTJ/duYzCfrGNRYh+T7HcQpg7PZ5tR2kZ/LjmICRbHYxyiFm0vs7ARZHuKsNEaL9YKHqJYxBYfCDKQXQSiJ6aeQG7CCxq0Vgr0xC9MD8t8Myb0CWIu/2SH3PNjcQut0lI4kBUIIhjfPoXKDODcEHy5WWojdVfSVCWa2r0YgK7AS0CP/E6Et/laMnZ1C6jbyXB3VtWTXFBDakBYE0f4xiispIo3I5st4xkNZBzs9ga4p8gmPfi1n9nIOpRCZZaOKhCzHCjsyB04S3MYQaQQCS14qYSZBrncR/QyGObIJekxgywXy/hDrgnAcVHvAsd3r7Lu5wR9/YhYaHezuCtHRCdhXYr/XQn9uFBFNmiGzIt45i7YQzQUU8iKi3R2JLVGC6EfYSogNPGTjqldNpUxe9REVjYn6eK0eSAH9/shMGoGolvErFYw1o8hrrRHjVUw0BM9DSYEeq6CHQ9TCPGZjfXQ+uVqh0WiMjGPzkYGwCTzEeBnnzAoCqDy9TuHpddxCQFoGd3IaHQSoZKRXDA5VkFrgH+9AowvWjky4c5+FT1jM8hoUAuT4GGJLgoXymyXXvrXJha5i67SP2TWD18uQU6MFOZ0OsqjRiYJ2gtuCbKqEsD7h+e0vt23Nzoz8cw7OwvFLV82DGXnD/DnUY1dovNXl4y8u4P1On0unQigLMBapDbPX9clRnF72YAXEcQ/TdSluDHAa/S9dnLB3F9ZqxOU1jLWIwEfWe3TeeICZ+jYFaek/02J7E8ZOKezCBKw3seNjqJeWUJ3hyPt6KyHxPBgMACidivBmwaSGrOtAqcihO/aw+tBZ7K4x6PRxtqDxPfMsOylTR4qwrbGOuhrzLUmOzJIu1gjOXwbHRRjD1P0NvHaKcdZRdpFC0OPyYIJmr4zXg2vnVuj9yWFkR6OMwRgweUaj06c4FtIw6+ihD47LeKnIG/7WZ6iMD3lg77WYSz5WGKIZh6wkGLopW5/psH56jthKoprEPbmMrYUcuKPO+snxl8/H3mvmmJytsufQLABPfeo5Tj92DnHQhyMRF5Ia+645zPXhGQ6Vm7wEFKqQeYbX3vUUpzu7uZxWedVMm5t2r3NiMMZSVGQjmeB7j+z6uqesz5+9yIeefI4ff9Xt3L1v99f9+r+I04+dJD6/yvalyW/62DvssMMOO+ywww733HMPjz76KL/6q7/KgQMH+MxnPsMtt9zCY489xrFjx77hcV+xSPNLv/RLfNd3fRevfe1rKZVKfPCDH8T7c7foPvCBD/At3/It3/CO7PCX5zNnHiaXHp4R5DLjcbuf2stVA2B8heN4fH6tQXdcIwsWX0va4zmVT1/G3RqAFXjNBJsmo4XUwjxsdjH1JmhNXisw81Nljl17jva3uFyTxLx+eoPf+UXF8IZ96OoSB2RGsTTFhcQwmLWkiUDGLirTZHcFtDtjhOua4PkWwgpkalC9UWWNGi+RTwiyyYBkscjeW5e5przNY793A2amhpVgHIGMNN5Wjr+dgIW04KEez7DdGOs7CAQiNQTK4QPP3sXD2/tZOxlQoU/yJoXwfD726QM8cvIAl7sV8vEIGeRc42wzsS/hyu9OonFQuxPcco7/6W3k1DjSD3Glg9fJsYsaAovpSaQJsV3F2bUi9s05jgpQ52OE7yE9Relci+arawy1j808gtNDQCCSDGMsWc1hsNeDEyAHfbTrgtao1Qatb9tLOuGS9yStQYC8vAUVjUgy8nzUTkSag7Ck8yFMzzJRLhB3ErQv6OwJSPZIkpZHIW4TpDHDN4yMQ+P5ELMV4XQzRBCg5saRjRbkAuF5yMlZcg3uUhOKBfKC87LprL05QF1MscdbWKWw1RBnZZtcpsQ3jSOEwHR72O7IXBY9qvjQBQeFh1UCXXAQ603k2TrSVaNjog0sbZMFPlSLuOs9ZC8l32rB/kVix/K2t1/gwGKT1WctT3Vn0JNVQKBrDq24RHBIEkRgpwJsYDHpkHh2nqzi4L20ipOmAPTumCW6qULlssbJHHSQYT0Hi0VeWsdmKcyOYcMM6bijypM4HXmuKDX6arke5NnIH6ZSQs9WkZHGxBl6/zwm9FHrHYi/HIttLaDNl4tD7Ogcys32qEzyajWKG/gQxXgR2O1lGNuN2y0hEklhLUJkFtEZjGLApcDmGSISWDOqFBKOuvr9F1hrqFPlid+qIZ67gMxTxIU18AJsebSIjQ7W2DfTpn5WMRgKSAxikJLMFPjzdZIqLGBzjVrrvbz74uUPNkIA9e9ZpF1UuP2E4sV1hCMQOkMKWLxxnckDfYpTCY8/lHL7206x1a5ySu9j9tAq9RMJKEX7J3ex941bnD8xx/g/LyBanVHbIiCsZvoARG2P8GCN4cUYs7aNag9AW7yza4jSyKdFOIpj376Hpz92Adqj8x+aNm/8pUu0u0Ue+L9uwT3V49FPPovUoLsl6I/EzMy60LWQufgDQ1jvgPFAQLZQIasI5JiCixtYIVC1efI9Y3gnLuM+Z5CLOc2kRHGhy6LuYi5J0sCh0NRYIUFneOsxK+ttnvvY42z8X2s4ymLuvpbXvuZu5tNP4UYCs+1hAb+joZfiq4AbjM+D9EmPTVB+oYVJR0JjKehyyw+scP6hL5+3S6fXWb28zUtPX2bPsXlu/YF7OHLHIVYaA55vP0A2e4DxYJaJqT/mhThHAUNrSIY51rmJrcEFHCmpCI0QsKprnM+muGV2D/fs2/P1TVjAbz99nKeXVin73l+JSHPbobt4sXCFmw/d9k0fe4cddthhhx12+J+bLMv48R//cX7+53/+5QjubxavWKSZmpri4YcfptPpUCqVUEp9xfaPfexjlEqlb+rO7fD1sXt+hi+aDmSC8rTLQEkqu8qknVE/ghxmxK6lq1Mc31D6BzX2FtqcvpDj3d+86m5qoVxG9IA4xgz62HIIaYoEnMggT0xReYfmDX97mdWtBT72/sOcXYGeXmZ4j2F/kjG38AItz+fywh7wwH0iY+F4wtzEOt29lrV7diEGZVwhcS5HkGtMGFA530cYQ1Qpkk2XOFA4w+RMHzuRwxVw2xkizhi2Usw1RdKSM6pwGKQ4V1qw2YNiSDZTRbWaZKlCJpYzm7ME3T623sR7YIL8miLtXUXyJUvYSnCLktW3VVlckIiHJYGXkfQgO73OcN1gx2pkk2WMk5HUFOPHNWl9gGhuYvs+NqggHYVpaZI3CjynSqEd4kQGYyXdo0V6Bx2SKcnCvz+L7McYz0W0esjc0Hr7PP39DnY4Te1cBhjM2jIUPPzVCONYghNb8NwWolaDZhebaagWSfdOQdlHSQevH5Eh6a/1cEOJ2xfE00XwBI6TIdb0yH9lIQAgumMfzoPncGILBYdd72kyeCZn41M1KIXoiQrOmWVUvY8IApzegGLRx+6eRBzSOA/WsZElt5rBZEr7ngOoxEEZA7mFdnckugA4DjLNyD0fu7qNTQqYmXGcdhcjQZdcnFY0WuyXSzjCwQwzlAUT+piqQ9CJcRzFJx8+xNHwLBfPVHByQR4EOF2LtA5eO8NrSORYjbRsye5O0FMewScS3EsN1Gb76v4o9OLYqAJmeQ3Rg3zXNEiJ7EbIXoQolyDzYHIMocHmOQwi0qkKXmtk3KuqPlnBQY+FiDRDXdxEVqsYR5GMubhX6rDZQdpRVDS+T757ArXaQA2v9gopwfDYNO5GD9Ue+bagFMRf9rQR1lK41CI7MI1KLSoZecaYyTFUtwdRetU7J8eWSzA9CUk6EnAY+e94zwmymoPje8g8gVSDGXCg1gVeTbyrhAiG5BMV8rEJcEJk2UfX21/+oVmcH/nfGIvQFspFbG/wNX+Tis9GeCct2XWGbKqCWx8gxOg4rj9b4fRnF7CbcO3YaXbv3WDatkh8Dz6fQJxiCz7/r+99gjdNr/LHE7v5vXeNYWbHEQOJyAzGk3Slx+HdDS480cZsWZQxSCURQH4sxBYEzpMgq9B4+yp8tgz9AXg+2VJMLYzwrUae3cTM1uhMGMoXM5xUYp0CJiww/XtbdO9M8Y53UQNGbWTFAgiBzA3jV3q89u8t8ejJykiMjBN0aRK0wToutlPGe2aA3SV44Y+uQw0N8YKHTAR2PMN7rEV8cILf//3H2fjkcxjXw/MdJu+a53Nnl2mvvI7JjSvorkHK0dwbNDLCsQp7I8XxSkSxmBDuTXCeTnGsZLCS8IVfnMaWKxhjkFJy6cw6rXqfjeUGv/inD9AaRrzrza/h9bPTjK91Od8/je84bOZjlF3DOODMGzqJ5dylRUS+yLe/7l+zpnK8SHDE3+bB4Qy3zc8Tel9/u/O3Hj3MeqfH244e/rpf+0o4JzUb01M8tdXhTX8l77DDDjvssMMOO/zPiuu6fPzjH+fnf/7nv+ljv2KR5ktUq9Wv+fj4+PjXfHyH///RkwtkB9v4W4bCacP+xDB3e5XVrAmAsKOEWLeZjHwcHtB0D1e48+6QLwqBsJasEuCmA4hG3gp2vIzyA/SeOUQvBt+jc75JdVMQzg9xx5b50+Y8WktuffURLqcNomibF76gyNAU6kPc7gDphJRsRu95gZVDqt8XU79hHqed4K1G2G6L6rRHnFfQbYu/0oZoyCPVo9SUpi8CgmKOumpiWqxHDK4LSWd8ZDdGbHUpbHQhy4EhrhlDlMrYdgthBKXnGzibI48OeTLDGXjY6yTYBLeTIHyFXXRYrwZUJzS7X7vN6idTehcMwrEM7pjHJaRxk0F7ltLJIe6FIWYgcMuGeNJB9gXBiwZKoB5tY5oprudiCi7sGqN0WSBbEaI7BG0x8zVkJ8aWAoJNkPUWfsdDSgejc4SU2DRHZAaR5gRrfVQG1hjMrinsRBktBIM9PtxZ49Z0lQsv1EjTDD/OyMYLozhkAaprCR8b0jgwjRf3cU57sBtc7eA4Jci3IRsSnYDenxr0nKX56gmm1xTtSpHiswluR6PSUdyxHMR4H5xFrxuktuRJk2hhHtXqYcMaIIinfNyhB3mOlQInTsjjIc7MFBhQvRi7N0DOTBHLCB26CAROP0NMjqOQqF6CVoJobxU/SxHLKVjDC09WOHVxF6JWRhQ9dMXFzRVuK8WpZzhtTV7yYKWBWDV4QYC3nmD7McZ3R7441lJ9tosOwb3Yx86UaRyxlC/m+IMcMTU9ElVSTd7pQj8Ga9GTYyQ37yLVlsrlkThhHTmqZNEGBhG2VMK6Lv7WEFtvQZLB9BRg0ZMlZLODSFOMFBhHog9MoydHIndwvo41BvFfCeEANh+Z3NrcgLFkNR+5bcGVkHzJV8hAr4+aGMdko9jwlytcPEH9lhJjyz6FwVUBKLdcOO7xJqD6+CaXZI6MGui7rxu1NAGFl9ZGSVjOKILcWgtGYz2JkD7ia4g0Aii/OABjcF5wiG6aR41lOFtDiDKyzSFr95f5sZ+9QPv6Nh/5wAQtd5G6KTJvGoiiS1YpEmyBmAZ/ycLMFGayiMBg1+o47ZyNrTJbaUBwqYG42rZnp2owGBD/0CRYg1xLERF0roAqeGgLDIcwhIc/cJTWakA8rYhuKOJcjrCbChw1akPbM4s3SJn4Qg9Rb2GmamAN1mgYq9H3Eyq5xwubB8Dvguchowy7vAV5Do0m6sg8lbkBpiyoT7cYuhPIQBMdy5mf2KT7wIDw2QGPPbIyOldGI6+ZwVjL2voGU3NNbrrnPMtPXI8tltEOeI2YsbzHqX7EDbee4aY3bjPcG/Dg6WNkoU+h26a7VGRisszDv/8Er/3eu/meH30N0/M13vjtt/DhcyfpJynlwKcTnWJ3/iHmi4J91XdysfcoNTGqrtrfnyLoDdlVncT6DQI58v1qWklZFviu8ZybwitYa7/uFIMHzl5kqdXh4QuXue+6a76u174Seut9TCdl+2z9mz72DjvssMMOO+yww3d+53dy//3384/+0T/6po77ikSan/zJn+Tnfu7nWFxc/Auf+9GPfpQ8z/mBH/iBv/TO7fD1safqM3nU4kY+8kFDtxfTeXGVuKRffk6xViJfmGK4vER+pcvmFiw/2Mf4Cj1VIluYQD19ERyFSDNkZhGORfkFpONjHEFxso+oZBSERTs56WbKsN7jyd94geW3TiL9gKoZtRtN//EKJk/ovG6Shj+k0I+JtUOaOlhhMa5COhBfUyO3BhOAUx6QKg2BwOlqOr0QhwiROyQVBzHQ1GSC1DHZeo53chNdUJiihylWsOUCAQpRCGHYp/Lps8hBOlr8VEq4RiHWB0xtDGjdXgNtqC0OOVTooKZS1hplNp8uoi5nWKEwjkPh8cvow4sUnxiQzLmETzbREwGdu/dTOzfEKVt6YwUqFyL8B8HWFfgu6b0OWSDROQgNKvfIpss4iUUZCb6PrFUpHm9Q2qyjqyFmamrUPhT6WEegyy7JVED/VXtwXujjnF+HWvhy1LHTEnzX687wtsXT/Gk6y++fvgVvb4n2dAGnl6GGCYU0JXsb2G5OdLBMZe2qJ8fJJrlR5I5AxILt3xYYvwRZldKyZThMyOfG6b1pHG8jofxnZ5HDHLQF6yD8AkbmuInHxB+uYkKP/mtDTOgQyBAqOXZ1cxQFLTIIA0wpwJmawCgF9Tb5lER1hzjrObJvEZUKUikMYFyFdSROKvG9DOEYZGpRy9toYdBjDnasiIhzguPb9K4NqH9bmdpn+1QfX0f1I+iHqFscbBAibAkdutiVLYQQdG8uES0qZhOQ0sFrJph6C5sUIM8xFY/kVoX7RA3RjLHNNrISoqxCWgvGYvIU2xvi9koI5SDmZhBSorTFeBIdBljHR3ou2WQJUfCQ/RhVHQMpsTrG3xyg4pw0SxnuqpDcc4DgMydwVIjbGBmyjmQWgdezIDxkqwVIZJJjZyoQGsRm42oVjkS32gwOT1Cqa0gzTKeLnZmkWh/g1vt8ZWPSaHHdPVjCSTV+I0Om+ch/R4qRsTUgPY1tNLDlMiYaopXE8X30sf2oC6sj8evPE48MdLL5EHetjxivQpiD64Dr0owDGt1NzqxOc+alebQrEQWP5dUKws8Z3raH//irUzw0vcGFpwvYdgtTHbXKpffdiJcqwmLGnXctc3zXYepbPt7TV3BLRdju4D7cwZYVcjPHCkXvjyqY3ID+8m/i0scVciwkmHXRF3tUP7+OvWYvVkrysouodzDdPqLdw1bLCCERY+OQpZBlTN1/jvjtx1i+OMvYYB3aHYSUOP0hw+kixCmlCxHRaRd9nyD/Not7OcZ81iV8WLM9PYc/1UbEEWyPBHU8l6EnGReSvYUi1+0/R+tiCRmM2hTdHHTZYXx3jUO1XTxxokn32SZXvuihAwdbLRD8HdgVufC4S6k2et2+w3PsOzwy0X3X3teQGUNm+vyzZ3+bJJ/gh+efI00/x81jb2ez82/JgCyJGA+LRJXfhamEpy8c5ra9l7iiyxz0Y+4qnhmJUeTAV1bTLLfaFFyXyVLxa85Zzy6vkmrNlWYbay0GgxJfLU5+CWtThPD+m9v/a37ku1+Fs51w12v+aip1dthhhx122GGH/7k5ePAg733ve3n00Ue59dZbKRa/8n+en/mZn/mGxn1FIs3U1BTXX38999xzD+94xzu47bbbmJ+fJwgCWq0WJ0+e5JFHHuEjH/kICwsL/Mf/+B+/oZ3Z4S9H0d2m6rbQtwbIYEi9M48rLFlxtADT8zUyv8pEI0bOSnq1KllzCP0hInCQwqFwvg7eKNrZlEPcJMMWfAwGkedYa1HVnLZn2YgnMO0FhrGDRRAHBlGJ2ec0mKzlLPkO/bdXUJcHhM0++viQ/lChhprwIysUjxnScy622cGLCqROgTjIuObACqd37aa/f4KwrpDDUYVFXFHEcw725j1EaU6t3ka0MmRmSHaViY6WKb6widpoYYoVRCkkL3vIYUo2W0ZGOW7HjOKpXQ/XCvKSQ3PeRc3G7Nrboaccds93aJ2MSbRGX7cHsdlEDmPsaoPqagvrK9LXVxkeGEcLj6CZU+n0yecl/X1F/EaGp8fIxyC6R2AUJGct5CBiRf+NhwjqKcHDZ5HDZJRSE/qQa6RmFGEsJXJ+hvotFQSCyuNb+Jfa6JlJ7IFpxKWNUWy5Bb85TmNG0H+DZOMFi39ujcHuOexMiJUObitCoek2fMqrMb29Bl0ZLXSyfou8H8PsBKI+IM0Mw9ftxxkIxHaCKblMf6aFmSwRTzoMbp6l/NgKuApTdkn3HkD2UmypgDq7TDbpgufibceUizFpt0sCI4+XsSrWaESrD66DHAxxtuvY3UVqd+W0HwJ70mK7PexYBaEluadwL20w/S0dyq+TbP1RjezBCBElmFKA3OwwmAyRg5jwpU1Et8DgpjJW5ohhglSWfMYnnylRHPTJlvq4jsLsnkNFGTLJycouyUxAcS2n8kwPsdbBukNskpKPTWLyKWxVIyKBsU0ymxM8dhY7N4WOR1HnstvHSgdRLpNVC7jtGDMYYo2HM9QYNKaXIk2G64dYqyAYJSEpx4HmECdKsfum6F07hhCS+FX7SSccxv70CrI9wMlApQavc9XbJs1w19uYOEPGRayjRmlHaYodDkmO7sLUCtjtqy1n3T5qrYabK5AuluRqxhXYqyKNmysGe6qULp+Fh19geMMiQjgIbRAK/MOQLAnYaiCspX/vAuG2RvnuqOpLSSiGyLExzNo6Whps4BLNelQeWwW/DlaM2rDCkGarxK+8907yokt2bZ/siMaeGaL8Kv5SHxVp1m4q0T8/T7m/CcMYm0X0b19AFjxEz/CmO8/xt9/yAr//R4e5/+K1SNfHxjGELv4fdUbHSknAkJ/tgxOgJkN0fThKVNpsISfLBJGGrWRUQRMlGKUwtQqOXyDfa9D7PeTaBM7AoGIDvg9Fi9Nso/rgJBaDfTlpq7e/QutbF/B6gvjENvaDgsW3dyj7CeZ5iVlysYFBTlvSliQOQsYHdlQN2O9jnIz3/sh9iHSbfPNP2FAdHlO8LK45Wc5KeIn66YTmJcNDj04hCgU46FDc6zLzqjqViYh3/sMfZt/MjV81Xwgh6CUv8szWP2dleZHqhIu0c2h1F5+99G/Y6/coAJ0rCb2xiJnZmGIx4Xx9hjfaNRaDjEf7c9xVTsG7k//634nlVpt3/eFnCD2Xf/9978D7GpVhfZNisWxFPf6P079KO23xM4f/IdPB9Fc91wzvh+j3oPQPEP49r2hOfOnZK5x88hK9ep+b7jz4il6zww477LDDDjvs8Ep53/veR61W45lnnuGZZ575im1CiL9akea9730vP/3TP8373/9+fvM3f5MXX3zxK7aXy2Xe9KY38b73vW/HPPivkZXmJMdXJvCjjPEXBCrMcCsBOrx6FzyF2DFs17uIAohSDeIMOxTocjCqjgBsUZEuTqGFRp5ukO+bBM9FbfSRw4j6wy7L3zbF9qEybljg9p/eYGm9zGONfZSjHoX9Q8zjPkr4xLe49G6aZLzZwgQOjhI4f9rAIrBnXJQR6ChGdlIGfwv4bI+VB13SWyTpWMB40ifuuMiaIZg1WAFxptBC0slCCnsc2FvDXerjnG0gOkPEMMNIBzsRkhyYxWqDu9nHaXVIPEt8wy4qz7dRxRJpFXBAZhA/6aH3S4K9MePfr1h/t0G2+phaCZnkeBs9TCHAzrq4Z/sUpYMwUFhLMKmmsrJG+w178brxqO2iA+lmiLEQnGljCy6KAjJNR21OduTVYrIUmymEvdoqYw3G99DdAardR8+OocYmMCn4Ly0hwwrxvhm8zS5OuYzN4fiDc3zxuRnkdh+V5zjtFL+lITfIC6tYV1BgBtWV+OsZYjjypMEPyCclXlhFOEXyWoK8vI67MsBiab1tF713lJFCEq65lE/kCCQ4kugOD+/FBKklWiryw4s07vSYeC7Df+YynQlDd0+V2pVotLKUEiYDzFYToQJsFIMQHJnZ4qZ3NDk/N8UT79uLns7J3gHeoxr3eBu6PVQ4OlbOVIq4EeIXAqTrQneI04sQ5QLO5BRhL2f2Ex0yv4ApF3HDPmEtpnxdm+GmgiRBhmNIx4GKonYyxetYvO6orUdmYATQ64+OTyfHu6Jx+w44ZpTGZUcJVaofI4MCuQJT8hGVEnKosUajjSafqYDv4KxuvlxhIoYZtnb1e9ZqgzFYVyD8ArgOIskIL/ZhskJS9EkXQ5JXH8aZbuH85iWEsQQnV0FAGkiS8RDVacOVVSgXoVgELDZwSfZVMY4krXn4W63Rew4GOBsx6XwZb1VijUVGMZRH5rruWI3C6pCsDWiLs9mCqSlEGCJcSe37Bmx/ZALTWEUA1UdWsb6PcruQ65EYUiyOfKLGxxH1BqaqCE/UUZmBPB61XxkDSYputBDlEHzYdW+Hxj/pYSz079pDMltiUJPYw0MKL6Wj5w0ipHCQSmAlaA+o9ui7OedPFXG7GdlUSDpVwo1mEY0IsdWA3gArNHbSQW2n6MoUtFdGFTVKUp7dYEMfQM0E0Oqj8xjbijHjHnmakfxEGT0L/u+76LMR0mrEIIY4GV3aCxY70DBRg83mKH68Osn8AwPcdkp/b5HwVUNEO2fiqRb1p0pIY7EaytWIj33kE2gBP3jnMeK8AtbivdDg7Nl1fveZ51jJb+amQ2cgy8AdiR3Wt7RvgnCXT5p7qDTCUw4qTsmXHMyTFcrTHeT+L1c3RYOYh//wKbJSyL2vu47//NAHmF04T+/dHkd/ss7qeIWHXnya5y4eofraBQpA3spRAzjgtgncISuNOUpOjic09X6Jp4fLlNNPcrj09xEifPm92r2IS+tNSq6L5Gu3Qe0bH+PUWp25apEvbJwiNfDc1AXeMv9lkcbaBHDBbAEZmFfeurT/yBzX3byHo7fufcWv2WGHHXbYYYcddnglWGv5/Oc/z/T0NGEY/sUv+Dp4xZ4009PTvPvd7+bd73437XabK1euEEURk5OTHDhw4OvuRd/hm8/Tm5vkRjBxIqPyQoJbNOhdLoXlGK6DXOboUGLyFFM00O/hlCT5wMWUXNLdVaJdAcFRQzsqU3lkHVP2yQvgDnNslmGzjMIexdhCF99LGK6WeNtr1/nl52cI3ruGm+YMfqSESQPyAczdXyedKxO/roCd9pgTDcLvCGj9aZHsSQXSYl2HvOaiTveRGwnp1Cy6UiSpWMwly2AOJsYHaFVgaqxF+7mQtBJg+2pkiuoqtE4Z3r1IXnUIn1ul3AmRuaDQMsRFi3elOUoTKipsoKDsQz9i6vN1koWQ8WxIq1VGXKMJfyih0Sph3QS5uo2UEhu6ULCIXoxcSrHVAPe8QdUEytfojQi3VCRciYj3O/gNjcxBXPARjqU2XGfuO3May+NsnyrhtGKEFRjfxfgCXVa4TYXIMqKKxWv1UOt1in2X9n0lEB5ib4i9ZDBBinuqDVJg/AJCOJjc4l9okguDcgsjcaYTYz2JFaMFnV4AMyZRVlP+s9H3NT46Q+IoFubWCCcSlj7kwXILsgwTSOQgofB8HbvHhxsrmHMGp1WmtCsgeGGTaDCG1BrjDlFZRmmphsrAKI2JoXYiRsxMw1YdohS5ZbCuS1hsMyjvgk6XxmWX9sk+9YfAjFfQB1OcL7SQTw3BKZPOT7D9e03cB8GbHDD9Ey7NT4zRP+6BdBjcMEm4mY3Sp6qC7lt9bENSuSLJy2Wy3SWGn5WQC+yRAn7XkNyrEVIgzySE51PMWBErIJutoBcryFOXcVsxcrOJnRmHTGO1xWpLfmgBAg/V7ENuEI0WolbE9YrY7SZuA2Sthujn5GYUoU7gI8dqCMfBaoPtdEZijwA15SCnipi2D76ke/sUbneU3uX2QVmBroWjBKU4w6aj6hAxP471XSj40B2O4q9FDhZEu4v/UoCuBqjnLmNTDWEBkgzZb1KbcYikh8gSxNwsMhgt/FORIXMBExOYbgcxNo7TSSBJsRmkGz40vxyzLUshdnYSVjYBsMYi2h2oVqDTRRiD00owvoTxGggJSQLDiLLf5Z/8+hKf/8I8xy/VeM3e03zcLCAQ+MtdnF6Gk7v0/rZl8HpJZcMiq2WEG6A6BjU0yMzw+Bf3c+Kl3SzctUp6ehrd7zGYLjF2LkcaOxLQcg27Cug9AbIRgSPIJiq4nQGkObXyFt3mAnnkIJod7MQcxBm4Htb0kC8MMeNlxGAbdaKDCVxUlrL39X166y6ruYO1gty3eFf9f8Yeuoy54Rqy8ZDCZkpvdoIZ2SaPPJKyh0DiiYgffctz/JOVu7i7tM67/vgMv/jmO7HFEBzF73zkCbbHY9qBx5PM4VoQxoCUqIZm4kzIO7/t9fzO5z5Nd5AhPUs+J3D2Vlj/3Qba30vnmoPwmtH5+tCv/BEf/8Rx5PwY7ThjOdbUDnoc/L6YA9cXKdYyOieH1NjF68ZfxdmlJ9EOZLnh+ScPc/DgClurC/za58pcc90SztIs0dwQSwkhCl8xH02WitwQTjJVKaLk1/7/4M17D7J1ucfewjgNEWLIGffLL2836QXo/H/AuxFR+Xnw7wXnlXvXTM5U+cl/+m0ARMmzCDSBf/srfv0OO+ywww477LDDfwtrLddccw0vvfQShw4d+qaO/XUbBwPUajVqtdo3dUd2+Mvzw0dv4ELzEhU7unOa1gR6AirPdQFQnQFptQBrHcy2hDxFxDnGdVDNIeFWn6x8iOD+NabbBnfo4UxMQt/g9HOkkOhhn9oenwtMMT3s8eL7x9iefBure0N07SLutmaub6HfZ3jRgxNtpmp9TLFI4RNtGnMe7ZsKZEkFNxSY7hDV6aFiF6anMQtlVDuh9ullsoVd9AcVSluQbRaYuKtLuuFSOBsRhDlZCZAh1oHh7RMj3w8tyfZMMbO9zPa2TzY/TuYXUHsnIMnw1rr4z3dw5lP0Uh8vj3CZJ/Y8KEiGgxL1Z0LEOQjSdQQgpyawNZ/8zTnqA1sYLTB7d2GLLtRAN50vWXpgyKBfABJ0wQFhkVriHitDu004FZFfqWImythBhFprInqG5NpJ3DUHkaeIOCUtSoJEY8sObiu7asIrceeKOE2Dvn4vNsnQmUYFASiwmyneIIVCDN0h/sY2/r45Ui/AtgY4nxAM7nZwywLrjxblUmnGTYdj371GLiE+NUO9W8F2ekhjmfyDFeQgI9k9QTodYBZz7FMdtDtkdk/GOVtCDRLEdp/enQvQ6NGbL5IeXmTujy8gw/LI6NZViEKAzTJoR7zu/9jmE//6AKJtqZ/y+fQ/O4iV4LbP4h1XWGEQGsS4oDTukKpZ8pUtbMvSfchjcNnFhj7Cc3EiS+faAtZVZLMCUcrw8z46WUMsQypd8gNFZOph50PUZ88iTluyN5bwHu2j6wlOMFo0C+XQPVaAPfupfv4yTjvBDjsIG0AaY0s+1pHIjQaiE2GlQghQvQgzPorUtnpU8WWFQNaHiFoVen2E5yEchWk0IU1HlUUOuLtKOHtcouc84jlJWoO86lJ4fJsgCJFxDkqj75xGPrGFqZWgVEA2OtDsjSK4AQJnFPOdpAghCE6sAl9uj2EYwTDCCujGIV6SXDUUtghv1P5WON0isZI87iOKAWSaaFeJYGUT6YF7QCCCELk4j1leg0GEQCHnZrD1JrbXhyjGxAnaU6iZCcx0hUzHOAMFxmLzDAQEJSjVYO+eAWcvQHkuobgrp9cO8KUFz0NdqVNfWUQ+A2lrA7evyRcr5OMecgg2h35cIE80rdOHEL6HeyWhcLqB0ysgEdjFCmw0UWkFNgxMFNDDjMH+MrXHWuC5XPp4hfi+APnFU3iAcAP04clRhHl/SOE/9bB/2EH2Ld3bZ4iOTnLt6klu/dEmWST5rc/lVJIBUnWgEEAUA5CXXKSCvOjDqmLrzBj+6RJHrpniXLfD23/2CX7t8VczvFzlsf2H+M07PzIyDFcKSDFRn/xSjDhkMWseZkIjW2Z03rIcPpXyocYztC5v47djnLKPKIfkscWdq6D6OY/98bP4hYDDtx9kcvKP+d6/PeATD93KtYfnWH+hSDcNueP7u+yr7UbbIT/61tt4ZkOwLLYA0Ptyhi2XsxuHmS58C8eutbz16MMcrL1IvPsKM9O/iacmv+pGzUylx7/6ro/iB3sR4q1fc84KckWYKrxY8a0LMzTSFnuKE19+wvD9oM9AdAVR/SVwr/u65sTt9TYf/Lef4didRe64638f/eZN/hc898DXNc4OO+ywww477LDDf42UkkOHDtFoNP7HEGl2+B+TNy/ezEO9R3i42cNZiBkWI8q5wNs/A4A9sIDwHYa3hcihRaYDUhQ2tQRn66hBRumpZVwZQ1uT7SqiTIY2Bk2G2+pCnNC/KFnsd/CKOeiUjSWBl2gO/qCL7HsMcsVbf/wsw36fK79Z4anlOeT769CxyIbBXMpwAhfp1zCzBbRTwvoVvFZI46YyxSeXkUAohuRuFZVZTOKw/GwNEacENmaiEtF2i+QJOC+uEUYjDw49U8ZJFM7KNmpQJDcT2NAlvnGB8MoQbEC8WCTtNigYg9YJw2pEfKNPf84nyTz8Zog7bXF2j48qcHKNSAyl63P0bod4O4R+hChDMeqiStsMEh8dRQzu3U/YAqxAJTluO2Ww1yN72KWZjzGcCyiaFJXkDPdXCLc7mEARnKkj7cjwdde+Lpvni2QTIWa6PIrcFRpVtZRnMlTVoZX55FMh7jMXkaaHnZtEmqtmqLkB14CVmEyD1lhpSMdjerUK0lr8agTA7ksv4lVjDvQzjIDnlwqkx8YgixFxH9trIF/McD7fJV0YRzwzBANpL6O9BXTWYZBDwaXw0hYiTcgXahQ7fcRyilbbCNeBUhlbKxHNF7HDAc/8YRmd5DhxAkKids2iowQ6g1HbjzDkN5bYuzvlzh88yblnd/HMn+7FqJjNF11EaST+OL0EJ5ZULhvSsiKtpZiiRRUF+V0h/u+3Kby0gmkG5EcXSG0B61jcxxOcsx65tTTfNE1xQ1O41MXJS4QnurgXG0ingB73oVDEZnZU+GIESkO+awo330QIhXA9cF10yceWZ3F6IwHG+orhhI+/0sBNU/JOE6skjh9g+8NRy89kiXxQJn06xyYD/Esab87BVEuElzs0bp2mcGKbsNxDdmLyOR9TqsJUDbHdxpZD1PI2oliCpdWRnxG8nOZkgXw8JJ+v4J/dRqQaYcGr9xCTs1hjIQiIpkaGr3p+DOeJcwhjMCpBGM3wtinSyn7Gz6zR+M/j5KUCIvCxGy5kGeLCZTiwD8ICJk9QRlyNbw8h1dhAoXFho/uy+TFSsb3m8Fv/4RhLl0OMk7N2sYMcl8iSYN+rt7j0e1MjY+ynHSY+fhyMwRRcZJqjMhjMQrgh8A7GlI92aH20gJUeTgYyc+hdP46IcvaaHj/z/jOcO7PFh3/7WqzjIxyFqgSj9jUEKEVzl2IuuxoXP1ZCGTDaQLlIfsSSvrpE8JEmphZgA4/tdJr145u0LngUT24jKw5YdyTQiFHSlsw0zkCjHYkxGqcteO0PP8bqygwnP3EzL370AHa7RLmviRKftaiEaXdH6VHTRaJjDsMXNWnfxagiftRDSIEdDsHz8H1FvWKJ95UpbEXc+PobeMkPiKKcdKvP6naHzlobhGBprcORI6sYPaDi3s0tN+7ht//dLOP1Mr25W6jds8i+YouwsMgft3+PzQcSfmxhH+W5HnngM+wrkqLmUtilrTtMSEPuVLiwNMaljW2+9c4pCv6XjYNt/HnavISXnGZa/wuk+uqI7m+74zpCx+Wu6/bQ9w9wZbjBmDf15SfISUACpVc8D77QvJ8L/eO8cfbvs3qlz9qVBsgBt9xdQmCQ4msnVO6www477LDDDjt8vfyrf/Wv+Mf/+B/zG7/xG1x//fXftHF3RJq/YTQ9g/BBuBKnFNKvCbKrnjS64mEt5HuKuG0orguiSoHO7hLuShvrOcS3LRIbTXD8Mrag4cwKwfQ0NkmxrS52/wJpHhNUEsqTKXf+yFnmnTewXD/NG256lMvdGv/fL76eHy+sMzmVMPZ9AQ99bC/FKz0oZmRvLuI8EpGqhPjaAnIuxxmbI/iUJMshfHaL6bdpmqdcCg8J4qkYSwBK4a8tUXq74vv3XsadHvCfH7idzqcbOGc2cQCpGqReERkJtot7kF6P3HUht1gpEM0ucq2OLzV5GqELHjZQiATm796mlZXYjsvUyinRoIgp+UR7ygRXmozP5hyL2jzenEHECc75NeySwLx5ErEwg3NhnX40jnLK5GWNUhpyKJ8fUlxLkENN5noQ+5jJDNNRUPKJ7thL4cQasjOKnQ5nB9R+aEj0npQBJUQWEp6NaDuCsVcN+K7vv4R8OuRDvyZG0cKJHi3oun0Y6lFOjyPR4zXS/RP0JjyMLjOxb4V9b+kRrdZI1yXFB1bg229lcBlaict/+uVrkIlDkvmYisIWi1CxIBLclyLUVobzRIxsjBaxcSEkkgVk0sUUXPS+GcgSstBQbjrYjQzre4g4HSW/JCmMVcimCxgnZPlyETe9WmXiuhghMRNFlJxF+B7WBddY3PwSVhq8QR2xVSW9dwp1aTDydvEVOjd4bY0OBOHFFv7JnFZlitQXkAl4i4N7sotIJO6qJtkH8YFxSoUcPVMBNDgJojPA9HtI3yG8mGN7GqoSU/Cw/Yg0tzhbdWytiBqkpAuTiOlxvKUGNjXoqRLGU9jAwcYpTquLTHNKhQJ2rY7NNTa2xDcvUtrSiCzDdHvE5QDlKpzNLZACFWeMfWoAjoMa5GjXISqB8ydDsgkPUy6ittrI3JAXPAhcbCmE7gA8F5NmyKtCjVECceQA6e6QbMxFpBr/7PaobSoTmNUNrABRLTG4dz8AWdTG1VeFCgu2UsQ9uUFZh0h3DDsGWVZAGIPCjgK6r1bjEBaw9QbMTZFHA5K5kPCJy6QFjdfLEaUSwl7VkfoDRLHE6ZMTaN8hq/h86rcnYX4Dehm1hfaofctROLkdmfQ6CqII0U9IVUQyVSL3LHp7nOZDFYIrlxFbMSIMyQ9OocsK6QpuHL/C0dsGbLTGsdUSqpsCIxNmY0E6CnLLzAdexM5NQr2N009H0fKBgz44j96zjJ3z0Xt8Sk9uE0YhcrvII/9y5J3i7u9h5g9jF0qIS6dRV0UaukOS3VOgLUZpslhirUZcPRCbz44TlAwWyd68xdkLFZxaQBQIhrfM4XVdvuvVx/hs4ySdpQFEZuTp47oIKUlSTbbfwTuyh9uOHODw4QM88ckXyAGpc+Ikp7RrnDUUn/qVP+Hg7tsYGxtQrizQbw9geozHPn0z7k8UuLKe808Pv5OSU6boLlGdSoB9+H4KgwJI8FLB/EQJxzuMK08S69v50J89Tb0zZPf0GHcc2f3yXNTQDs9nDgJ4k+kQqMmvmq+qpYBX3bSPxbEq/+uzn6CdDjjkr3OoWEEW3gilvwd6C9yjr3gOfGDzEzSyAePep7j3th/kf/npNzO/Z4KJ6R8EQMqvFot22GGHHXbYYYcdvhF+8Ad/kOFwyI033ojneRQKX9n+3Ww2v6Fxd0Sav0G8/8Wn6NdmKakVumsJP/F3nuEDzbuxatTj753cxJkr42+PoxlQKkc4UtDTJWwYIBqD0U1uR9B5yxRzFyXaH4ArEbmDNaNkpKid87vvfQPf/2ufoTimOPvoedJgmk8fP8jz7RnskuQLH5hiz00d7JE+/f0W7l0YORevZ3gf7+E1h5jrJOpMgIwVVgDdFk69jXvcoFZcpFCoJCKd98GC++aQV71mhTsqW9S3PPprksKLa6N44FqFgvZwHr8EaUo8PYGrZqmeH9A74oE10BtihUVu9/GbPazr0n77NZiiz4HaBY5OHudEbxdPd/YxP95m+9PgJpp8OiT825pn/6BI2hcQBlgRQZIy6NQQXZDbHbxuk3xtitq4xBEaLSSeY9E9Q+4rdNXDBoZ8xhCnBZwI4jmPfDIkuNgmHDhElYBzW1WYyVFdCblGhw6yl3H98rVce/AJNouWoFgkHWqyxQDruTidCOM56EDh7p1DVyrECz5ZNkAmMTNvSmgFRUrFBNNycEapzsRDlygoIOoCr5+SzSr6U5ZsXKLaLuOfsy/3y4TnI8wgG6UBjRVxjEJ4AbJUJPEKDI9MMn/rBP2PXEGoGWy7BXE66gQzBqKE4pUBeclF5RlSW4zRDGc8nKOSdENS7iqEBYY5drPHpY1Ztpo1Bs8NsKUIjIc55OA+00RcjLBTFeJiQF6SmEKRQgOC8w7plEe6p0j5SoUbvvN5ijR47NwCKIcgGrWVoS0i1YRbDlTGMXMu+ewY6uI65ro9CMdBnbiEyTOYrGLnZhAlB+srCttDRKYRVsBWHdntYK/fB3GGXK2PPnephHAU2leQa0Sao7oZNgwRucGmKcn+KsIrUNwukc9WcC5tIpIEEWUILwAB/bt3YcY8rO8SdhQiiMmKPk4iRm0xWQbN1khUmaqS94ZIJbFTYygDQT3GvymhcADiX3VQ5BAbQCAs2Cii+MwmfCcULrfJYRSR7bsIz6W8FuF4CioKd3dG9lyMVRKpnFHLDWA2tzFZQnZ4GouDG3sUv3gRkRvkICMveDiX10ex8Y4CR7G4v893/8QlHnp8D4+fXMC2EuSVGDGIeW55EoZDrCMRu4cj351hPDKgXdrCXVBEB0uYosLWNdo4ZEEJMyUJogAyg9PVGGF48vQC01eanCxPYssONjFobXCfuIQMPBjGMDGOsgbGxhCVGlnBGQkrJR+sxf+DFvrZAc7pFBOGqAgUYlQp1UgQaUK0W+EOygxfNUP1i5ujKiZPMNjtggVvI6cpqjz/mTmWHpjD91okZUtSBq9raaxJPvyjdzL5Z00aP7oPGTpcOzvLp//Fg+jEUpIjDy5hR0KNzXKs5zDT83AsnP7iEhvP15GBj+oNyXeXKMyUWTy8nxNPXSbup2hnP5dO1RksP0q312b5W2tU7ryG1x+dZmn1LP/h33+QW257DduuojY7hCZ0HprEmy4wcWOL7YXPkLQW8Rc/xVpax0s+ReVWy650D0f3zn7FfJSrKfqxj5KGWLcJ3K8WaX79wcd5bmWNn3r1nXz3XMhi/iHm823ouhj1WwjhY81FSFYw4TsQehWcw4j/Tkz3nWPfyr/8ieM81rzM2771k/yjd33bNz6p7rDDDjvssMMOO/x3+Df/5t/8lYy7I9L8DeIjZ19grd6hNtDIIMC1in9+70Nce82/5olHzkFvgM4Mw8VZXOXCqzW2rBBPZpiSh+nHOM2I8TsabEyPI+MaeU9gtMRut0aL7e4QlKSVBzx4+Sj1p+bYeFYSJOt0X3+QrOJQWu9w4oUi504VePUv19HtDna1S/qtVap/2kK6kE14uOsNxMqQ/i3T1C5osmKApEqz46HdCJFbQGCFQAuD9zsxTzxcJPxfFznxX2pwLgMl0RNlzFxpJPYMMkSuMd0eOjGoagmnmyNygxiksGsW2RogPNBSYQPNcMHwwvEZAtPlRTtB+aYEZznHbWlkalCRpvVZyfCpDFJAWBpvmaX6TAsSi6y3Ed0+ecmje12RmdwlOjCkPelTiCBr+1jA7Whkqik+muBHKXmng9vrE18ziXthG2pjmE6R6HiB4WTCxCNXwFGYwKWy6nGl4/Cbq7ej0hzhF3HKDn7NR6+3sdUi/SNH8E6vo4cZLgnhY+vQH9B62z62To6T9QqII+AdS5EzI98HXfHJqj7hmRWM42DHpkgmS8hSSu1zTdRW7+XrS1uNuirYuK0MOVXGlgXWc9GhJKk63Hv4IKvf5vH8h5/EpgmKkcYjwgKyECA7OdLNSfcJxFpM5uUc+94+77jmGd73czP0Nnx0UIDhcBSFXAjongYrFb2bpwh8sAUD2z0YxpjIR613EDMlkrGAZK/EmU4wQlB8oslR5xw/9O5letLh9P/d47JboPeWAlO/s0Xw7BZybgZ3MyPfN4OYnsBJcrA5YpiiPBDzM5AMMbNjmPYAtd2FPdPIzCC0IXdA6RzRTWG9AROVUbuLNtgoIp4oIIoS4Xs4vdFd/LwS4PSHxLtLxLtDJB7h/nm8bkS0WMbqkHzMR/gBFoM/0IT9AsFyhkCTjFfw11sYIbDeKM3pS0Ka3O6gyz7t+w7hDgR+LKCW0CmGdHfB2BELx+t8yUQpLyjczGK+tOY1V8O4sxyR56iLG5BrTClG6wLxS2N4vRSsxXguxPHo/Pb6CEC1I/J9c3jtDJkbLOA0IgQxZF9qx9NXRSvNvr0NOg3FM58ro6VFxBnCWMxSPPJ8cixOP0NWy1AsYvoD7GQZKXxUAlYJvOfXCZsZ3UMujp+RlAXp5BhZTVH6wgrtM3U+9s/GKN1qUEIDEkdCet0+7MkriDCEemN0jU9XkcLBxgmm1QVdg/ES5tBhbD9CT/YxtSLSkaNzXBgdOLWWE2wanNPruCaj+fY5an+2QZ4MQY2OtXUFUcPn6Bu2qJXh+P17ufKWCv1DkuJZmHggo786wfTSWa6V17NxNmZrtU7zcA0VaUpPr6J0hl8MiBKDtZby4hT/7u99P//gp3+Lvu9w2z0HmZOKh0+cp7NgmJNlfu6X/g4/8i2/irSW177lGOPVgI//u0+w+5oFzjsZu6tVfmzfNbyw/cvEd2v+1f0OG6+e46JVvMUHMxNgD3ioWoawKXGWsGlyusMSy5cO4M1bds8pioH3FfNRZ+Dx+4+9Gldq7pw+Qy346gjsku8hhKDgudzof5zz+QqRsQSqhJKzxIQo5/U4TgCDD2PTRxDFd0LwtT1uAD76Kx3quU+22+EPnniJmX8xul7n9s/yuu+7Bynlf/O1O+ywww477LDDDl8P73znO/9Kxt0Raf4G8frFA3wuP4u1bTIr+NRTR/mRa17A9q7GLVvQvT7xuMK4ltlqh8fPzuJu9HDXu2STIcxMMvztLUqVbZIww2kaCHwol9G9AaJSwngC3cloblepxxXSQk7gDbC1AsIzJNNVvG8pMn+ky2P/2wS1S1s4OiPZE6IuZMiixX+Nhz5RJ9MaL/VRsYdAsPWqKeZ0C/FEBtYDCWmJkbfFZsSwA//5kaNwzKfQdpFHQkypQPsGS+UMGLcGwwyvnqDrHcz2NtldM6BB7JpAVKvYcoBXChjscwle2Kb6qQHd2zwuTk3giZSxOzTr5RKq4mKrIM6tMjhfQk8IRKtP/1iFfN80iRinfH6ILRTJFqawJZexEwO8W6dovNWjJQKypkYvK8JLGl0N8J4+h4hSCENUsw3G4p/cQPQS9CCGvIS9vIjZW8T4CuM75LsncBuG6ekKGy+tE3gZeewhLCRLA3LHYjxB6akVbKsHxQLM+qhuhJECtGXr8jR+C1Qpwh/LMGUfgPjwLN7z66PYaUeQzRSxJYUtGLxMYl0FEoQV2EqBfNBDDXKIYnSgiKYr2KJLMlNEpfDJX/wsr/07tyCaA1SUk40XiI/MEKx08ba2kfOTZLtcRD0gn4Zgu8N1XEG0M2hpiFOIk1F7S5pj8xQ7XsHGDpRCzHKEkA753mlkN8JOj+NGCc56AlJglYYjKcVPb+F9oU1wa0a1oCgKaA58RDXHuBKRZKP46yRBeC7GU6goYfi9GqGm8X59G4aSfNcU0nhYIWCqQl4KSAKJmQqRWY5tJqgwhChG9iNMP0IiEKUSlEs4g5TudVXwXKqPt3E3OqhcgeuTHCmjKx7emkb2Ega7C5jcge6AdE8Jt57grnSpLhuCukYYMB5E4wIXF2d1CBSxUoHjQJ4jgOE1FWwaEXR93G7OIPQwDZ+h51A0Pb60lBaAq0cJa617K6Pr4WAZ95kvizi6oFADA/0hoj9EzE0grMFikWNVTPfLIp4QIIc5xGDi6Muhy1ojcvtVIcxLZxx++/2HuHBuDFpdVJJgrlbmCMBIkAODeM6QTBXwWikinMAVAkd6+A0LaY6/3Mb2YgIT4PQ0dmoce8hgXEHp+CZCCNInDO1TkvQWH1Ex+M0Ya3LUYoAJWvD81c+wskW+fxdqrY7oDbCui52sYH0HIQoYnSKTDEWO8RyG107grw4xvkSkMarZRQSayQciwMNda5MNNSqz6ACMlbhlwd67tnjhs/vp7xv9xuki4Hs4LYXeW6OpBOkwp8fI38Zag211yK2FSgURSnAd4ijh3JNnuVhLEMUCg5mA559cgdPrOJHLhe+u4AYOg06EtYYkzjD7JsluPsju245w6+kVzp7b5Nz0PlZaRRoDj0EGQSGif9WfJxOCQt3yd489RSQVH9u6g6eXj+IVGzy7chM/e/DVvGrmOnrDmHI4mmts+jxFe4UciyMNQ30W+NavmrN+5K5buG52ioOTE3Si+0h5iefzGoer/wJX+/zyiz/Fu2aepeI4iOC7AeeqT81/m81OE+M6WCXRUcqvXbqM6Gfc+0cfZn7Pj3Hknp3Kmh122GGHHXbY4ZuH1pr777+fU6dOIYTguuuu4x3veAdK/bcrf/8ivm6RZnNzk5/92Z/lc5/7HFtbW1hrv2K71vob3pkd/nK8Yfcm0bnP8vjGYeLJMgbBVGGA740ie60P2dtqCN8gUsPlP5ijUMyI5iokByeJKg6BtFipkEmK7A0RYWkUvT0cLbpMntI/MknxSsQNu5bZ7CvWP90DmdLe3od2XETu0dT7yNZatNebtN44y7S7zeJrBrTPBGQHJvA/32X2bbDeLyEjH3HKILUmrcHgUohfcTBDTVqFrKYgFwzfNom5RsMhC1cgLwuUrmAcw8QzPdyeJJ4rkoy5aMdHBgFmzeCdbxBfN0U2V8C92EQUfIznQegSbMeUvIjizYbw2MjnAQtZLrClAqM+KwcGGfntB2jc4pMXJeVnW/gnWjDQOItFxKADdhwrU5JLKXKuDG5OP4PJJU3qWPKyQFZ93DglGy+gGm3wFdm9+5APXcDO+UR/t4IRCbLuku2aJJ8vg+9hkj7f/YN38+Gff5DutktQDYiykHTXOEgx8u/oRwghsKUQ31MkrzpEnCXUHlql8bcWyCddpjdy8qcLyOFVX452n/49e3C3elgDxbWMjmNwNnL0VEDyljGcRyTqTM5wWlJcuvr9jmKGBy0yK6IteH2LxpDFMZ/7gxdwF6fw+x3iahHGSuhBjt3qYdOM0lbOcOghEo0sBHz2wwf4TL1G3AeUweoMc52L2nRJJ8t4mYOZCCi0BCpxyUPJ8EAVr1PCjSEve4hBhttM8dsZvZ4lb4V4ts2ZF0t8+A+LrCxME3xyFc8PSavgJwEEYEMf4znItW3Edht5ZRx70EMOhyOj62IBVQjJ1+swOwHFAGEgK0pU4mFmy/gDg526WoHhS4T0EGFhdC0FAcmxkGA1xZl0Ro+ZkQAhtpsUuy288SpWa2wmAYu/NKD86BJCW8T8LLIUkucxlEOi+SK65mOLknS6SriRo6QCzwVpyYoe0vEZe2AVWR1DBj7B2T69QyFWGmQpJN4/QeH/Ye+9w2W76vP+z1pr9+lnTi+3N92rXkBCgEQRiCbb2ARCsCGxKQaDCU6wHVwItgk4mACJISZxAIOJwQ7VogiQkJCE2tVVv72e3qaXXdf6/TFXF2SakUvy/J7zeZ7znHNm71mzzp45e81+5/t937UI0hRjW4heSOGeOlwH7ukuGjFIntKatJpDlzTO3OAcIk+tYEaGEVqC4xAPF3HWWgOfFMdGCgnrTUSre+68lE6UEKnBma2fu00AJjHcfdsoJucj9ArEyROEHBFnoDX5kz0Sq0WW2Vj5AtkQkK8hdQ7j20Sbh3CPr9DbPYS90sXLHLyVDNnoD453miI8hzTIgeNgyDAihMAjDgtUXgTtBwdzE+0exhIYx8JMVNGTg4ozgx5UxAwV0ZbCIJBRhlcbtOepviZ7YJZs1xhagjMbgjHYcwuUHqyh2iHtrUUSEXD31/eRnk5JjY13GtIh8NZgz+XLPGf4Ue7cupv0/8wjJnJMb6vQ+duHwPZQ5QKZZYNjD9p9jKFtMg7uP44YTihUQ27/+oO4Z9qodkx+xWfiMKRJRtbpgLKYP3wGf6aC1tBs95l9aJ7T3z3OX563yjrX8uh9FtnDy2QPtJj4fQUR2DZsKxcougm2ilgnZDS8hnuOHGVvp8F28xh/cWOdR8/0+Xcvu5Ydk0VM+08YJeGG8R5jpSbjzlN+6Jr19YNH+eQ9D/CM7Zs5Spnl3tv4txefx3BwObcs/B92+ScoqBRIQU0iSr+NEM4PHetxrr14D5+46RGslqY/4xGPCMSQwy/9whzV0kcx+pkIuWEevMEGG2ywwQYb/MM5duwYL3zhC5mfn2f37t0YYzhy5AgzMzPceOONbN/+5BIlf2qR5jWveQ1nzpzhd3/3d5mYmPiB2M0N/u8R6SbFfJ9USpKi4pRV5DNHnsn4J+5h63XjpE8JSK4rIOo9uE+SdBWZrzC2hVXrMXSgjp7ukm0pkz43JfelED3bJEMjmx0AZK2FrPtEymW6usaF186z/5vbuf1IiWxLBnYIJ3zaDZdGfoTseRLZ1rgv9lhLPKxr+mhssk0OshKxPD0Bdob3zQZSC+zExpgUjEb31jBtBdurWD3Dplc0cUcyDq/ZVB4LqSVDdEclspehbIllDFqA1dGDOOUsw/OLmJYkrUXIDJzQkOYtzPoatvTwhkuUr0tJLrI5PedhraeY8z02lWrMnWkTj+Qo70yJTmjEXUewduxFW5LgUA379DqiXMRqR4w/K+HoEYGe9Ig7fdRf9alMSXpjHmq9gxjP052SdKe2MHRrjaCh0aUAbWeIuQZYFqJaAs9BzwjkVIS8zccSPlm/x9plZf74r+9kquSxabTDUjuk2bIw6uwFr0gIqxbufIRpteluGqHsBniRoTFSYvizK1z73llKpYQH7tjB8uwQAOvPHyJSNhP/s4HqJlAdIv9gl/y8RrZT3C/WMEaDZw3MVJNz+Ty4960j1k7hIMnO30Y8UaB11TDBQkLz8iLP2jbL8b/wCY910OsdiFL0wipqYpSR4TXqR0IyISAviDsC0R9Ut6TPzBH9TBn7rh723y4BkE4ViXbOYPcEdmgQtQyrlSDTbBDOk2hMJSO6BLqlMqQFnIUucq3J7e/zWX3lJFPdw1hZiloLBxHZm6fRZQ9Ra0OcQgbutw3cB/QUxhZI3yNzJNapJqy2Bm1RgYfQBv9kDZUadGoQBR8Z60FVi9bohSVMtUQyM8bYd8KB/07goOJBfLLRGf6pLsLSiKyAtdIi99g6KAn2wLNFZAn4DkYKTDfCoIjLRXKP1YiCFNspY691UNLGlEsYW+BIG3cxQ2cCuj2k7iImixROGZwe2A2gXMTkJPULcqjUwrvrJP7hsyJMN0MHkAmFCM+mE9X7584x2lWDKPIwRgU58G2MUogsg0wi/TIkgO9jegPBI9xXIXfXIplvIfop55pNpIC1Gnp8G2rxe1Hhj68oMhpUcjQudJGhi3MsATQ4kF3po05kqJoBxyItO+SPtBB+gGx3cE6D1WgNjvVYlWz3NJ7p00syVHPgJ+Qtdcm6fdr/pccTPmqwFK09ZWSvRzIsKdUzTKqhFUKvjVQ2erSM1QgJdxqCs3ezV1qYC3ag44xoyIAyWHMgTixgen3yazX6V+3i1OwIbi1EkOCfCUmzAqljeN5LHuQv33Mtpm4jNXiNlDP/62bEaoOlV8xQ6FbJ37SElbOIY4FJU9J8kci12PbcOXpfyXDLMVvlbu51LEylQDKZcbg1j0wj0l6XC5+2i2ueez779kyyaXqIOz9zLyIfc3zoCLYlUEvDGMvD6QlgcM5/6hVl3njti/nO/DdpJfNctLXHq/b8DH92W5e3vvxT7Ch+A2fPOIdmX0maZYAN3nM4vLjCtw6s4zsxV+5+APjFH1izNg+VqQQ+O0eHsVNFnKXMFHchhOCioafQ6Xz/87LrJwo0AK60UUkKqaDS0iyO2QhjOL04wul5zUv35n7iGBtssMEGG2ywwQZ/H97ylrewfft27rrrLoaGzl5fra/zqle9ire85S3ceOONT2rcn1qkuf322/nOd77DxRdf/KQecIN/OnaWns/tXg0ramOvh0RG8PU7LWb+9+388nW/gHVU4tzSJSpVicqKcMZCrEPiGUhSZJJBvUe2uUopH2K5NskQCJ2gkRBFoA2ZJxAFn8DpYRzDv/6TE3zjKy/G+/YK6kxMdzhAXKBpBILR/9HAz6B9UYlo2sZNQqw7OxijaC5OUlm2Wdvp0N5r8BoKey3F6YUYE5Pu9EjSCihBlodcPsFzEkaCLsHWjP7tLoVvLNLfksdSBWRqcOshWeAMTHQDC5m6CGOQqUE6IdFlFvLhDqbZxA57aGua1fkqnd0K+ZEu0i0Q12xGLm4QVwetOvWux4heorXgYYUaicL4DsZzEJUymRI0HhbY9Q7ayeNdUCZdDZGhhUARF228R5dxW1WM75GOFclqKxCnpFc7OPfVkYmAFYFuK2QEYhlURyPaK1grNUR7hF6hxOn+dtLVDtiCZLJANGpjNxPqV3nk7DzOYg8jQHViWrN1Vi4vYHaNUrhrngf/dJjkdQXW3BLF3qDSobgMtKJB1YXp0t4e0NtaIi0l5M708PbbyH4MORfPcTESRAaiXMKRBbTuo6MIgybOGfyFBDeWuLMh9x3ehtfqILs9ZK0FpSIkCWa5AetN9NAE4uQqpmMjS3lMr4FAIBdTZCPDvqeHjJNBS9jpOnZQIK3kQUikEpjARicKyDCZxgQW1gKUaimZ1sh8CRpdNm3vM12eZWGkiO3lUd0+xnUwtQbiaB2BQG+fgn0O8dUO/i0GHSXIXA5OLSBLRZJyDhkliEKe/Jk6mauQmQFjEO0uwhiE54EjEI6NGB7COBZ2MyQuO1i9DNNqIzsDIUrb0D9vnNxyimhbJFNDyCRCNDqk28ZJp4cJzjTJen2y6QqUHEQaUjjaJRz1MSaDXkzqSESrB5lGBvmBYKcUanKczGjc5yVER3OYUooajxCVHjtnVjhzZAT/QJOsWoQwQqbfO4+kiSKZdHCWUqz5FkaAZhCELCONsgUmA3P8FNkzzsOeO+taHyeY5RVAYM5WVBob8g+sYzsFdKdOf2uJ4ORAEML3EcNDqEYXXBcRRgNBWH9vLkZCZ6ePKRcIDh9HnG4gG0VyuohYbxLOFAdmzCjs5TaYDoyNknqSXGQP4ueTFHo9ItfDPbiA6fbQWoMUiEIBIQelqI8LNXajhx11cRa7xOMBIvFIyj726XV02ETkijirfczKGtUjLdJJm+ziAOu7HYzUqDQj9jT2sdWBgNU969Ld6WGeE2O+qsgchXjwMNWDEG4rEV60mbwj6C4okBoRZ6jZZfq1QYVP9W9mWfv1CyjcUyRr96BYQXgushmx+YUT1G6U8P4eodY0r7MxSoIU6HiBI+3PIp6xlbiR8d8++V32XbWHqakK337oOMOXTGGpFZJil0YoaRZ9rDBFtuESfxYi2FHpYPmK88efwbHanTz7aS9iS2kcu6557LGtbN90mpnRGd75S9czWhm8ORG5V7NjR8yOLb9CUG7Q1tNUf8iaddHUBB/9lz/7Q9ezijvNTl+efW4cRHaKaP1G7OJrkfbOH7kO1lZaeKs9pFK87e0/y9u+/beYMOO/v2sTQile+uYnX3q8wQYbbLDBBhts8P3ceuutTxBoAKrVKu95z3u4+uqrn/S4P7VIMzMz8wMtThv8v8Ety5+hO/owQ1eMwm0QnEro7qjiXLZlsIN2cb7aIXxpSvnKdfTDGfIxn2jIQ8QaDAidIYwivleCkRiRgrTQeYXsD6JwvbUUvclh0k0pKk0zc9hU7LJwfw/R1di6j7NH4ZY17UvKWI2YjlPCjQ1W4GDXOjg7LDhjKJ5uY53xEJkgHvGx4xj2zyHW+zgVm/SSKhiDqsHi4RLiWAsqIe2lgCQTNF5QRSTgP5KR5hySooOMDMQxaEF7wkLkPNxYo6cDOleGeC1NEAui0ijxtM/oVIOLC3N8d8uFRF4e3TU0aDHsr7FUK6GNT78wgSqv4Zxu0S1bqOUO2lagFNoImr0hpI7RCOzFPqaX4DUE5kLJ6PAqzpllVlc99ISL3QoRtRZ0+tg3h6QXFLH9MiiF3TLoNYHZbxENZ9BXeGcMwXqLTf+mTfshm5W7S5jAIS1ZCAwGQ3BG41hD6OEYE0eDxKtMD46FVAijqFVd6u0xxHJCZWENgNH5HqnWRDM2mAJZwQUtkAkkviDvF9BhHd3vI/sF3FKesNFF5HyMA2JiCJRg/coCwX2n8R/p8OxXZTx8zxiN5R5Rr49VD5GWhRgqQ7dHtNpG+hK5tjZo0xFykOhUKoLvY+ccgiMSMZQjWpWkJR87AdWDpCrQzmD/TIK30qO/KU9nxkfqjFwrxupLvHZCOlUmWJ3nrf/2BJRO88HPb2HOG8HybGSYgeOedTUGhaL3AkWyHcgy/MdsZHkgYGidIpMEYcBpxgMRIczob8rjHFpCdLtI10H4AhXpQVUUYBwbAbS3OdhLPXIPL5MMlxFZiu72iSZHMcMWuZN9nFM1THWIbMcU8UQOmYCxbay0i0kz4orC6igMBu0oyAyJCBFEyHoNJSwIArJGE2wLVShghKH73QLpkEvsK2TVkFUyOg93KSY9smMuRtVpXVwl98j3WpEsJZEmRxakqDQmnSySdXsEtQTR6oDrDQyDtcE91SQdq2At1Qbx75ke/O1nxxIhyLUOxtMIIwhmv680otvD+O6gtSqMkGWBc6kivMNgUgOWIvMEOu+SX+1gLzQGrxclsZcBY3BON9G5ADNShM4ctAdGvaodn5uEqDVRtSZmyxQiyzBhNKhY0hLCELFpEnP0OKSDO2S+Tf+8EaRwSMdycKqHKdswmkesN1CNHrLqYaTEAMmzC6RPyw9SoPopqRS4ZzqMl/P0haKz1oKzx8QajpBNiTgbcy6B4FSL3NpDvPvlz8A7szhoQ5scQzRb5w6VyAzVLy2hE80FV+7h4QfnMAhEzuV/f+YB+sYnNxaA0Zx8dJF4TwUryrjqvCP89WcCVoISpuiQW9Lcd/sRxETAH91+M91OyMUXRthBk4IPba9AVrCZeM4STxlaJW7AXx7t8idzf86/v/gZvPrCfwPA3d8+yIl7T3Hm4RFGnrqbHTOXMJH/3puTOGshrR7PfNpBCqrHclpiy09Yv4xuY9r/GdQYIvdGhBBUbHm2/W6KI4vHeM9tHk/ffjuvu/ZHizQXXLyZ+287zO4LZrjgKdsZPVhi5cQ6ptsDAzrTKGtDqNlggw022GCDDf7huK5Lu93+gds7nQ6O85MrgH8UP3XMwQc+8AF+67d+i1OnTj3pB93gn4aqXeDIfJVavURacolVDEpgnroFACNSsqLL+NQZ4k1gNktUOyYuSLLAGrSA2DaqGRKNKbp7BdiStGCQzbMXPa6DLVzsdoyfwtx6nlaqmcx1sbYqhAvZ9RmBmyPXTHFrEfm5DsFqj4AQGWrsNKV6ng/zPcxqiGqukexUiJE2wXwPcfZTfdHMyD2yhruWYsfQOGWRfb1P6curXPai0yQjDtHmgHBbQBoYWucV6E25g5aneojTiFALPbQjMEowVV1DHQyx5hKiy8bJrvXJrAjzLYeT35pi4mdXUdMh/lQXUUxxK2Xyc32cWhvlrSGSLoXjqxQeW4M0gpwHCIwUROdPsvaK7bgyQ62vYPoh489e4Wd+/nZu+LUHufZ3llAjkLoGq5chHHfQ0iIUbt1BKAsjoRG5RI/5dCsuOnDJJofoPncPcaVM+4TG2Z0i51aQC+t4p5rYazFpyWY4jimJDHdLQLxzmKRiI7TBbSWILENogVowWL0YVQvRZxNnunGf8OQKevlsC8v8Kt7hGuWDPUonMvS4T3p1BYoBtrSJS0VEsTgwqqj3IBNQDJCdPt6ZLsYxWEmC1kC9i13vI6ZGkVumkUoNWmCaIZ1+DlPOo4aHkJNj6F4PHfUQaYpIbNJFG+eywRytxGAaTXS9QTIhSQpyEKWuQfUTkkCBJSAxjIga/+pFB3jRCw9h5wyh5fPYwSLHqTK7b/PAXFhKtC0wxtB55hRrL99KOJFDzEpEDayDNul4GQBdDIi3TZJsGyfbPErmWRhAJzHi2DyxN2g7MUKQJdHghdvrY6KYrNWmPWkTjQqMUJhtE2TbJ8h2T6MqIwTzMcaySFSCWa/D/Cq61iDJeqT9Fmm/Qzyax55v4nz3KO1RTX9IUDjYpHioiRNERFsBywwimft96HShH2LCEF30BmKWJzDCQt3bJYlDsghGdnUHQl6a4S18TwwA6FwyAmmGzOUIL9lE91m7iK85byCQbJpCzy+S9fuYSn4QbZ/zn3D/s7rXoG1JQjxaQPd6mCjCpE/0LLv+7ad52s+fJB0r4j/NpfA8G1FVCA3YFrroYrcF8rx04A2VZph6C6MHIocQgv7WAuFMnnQoAGMISxbdp85AY1Cx8/hc5Kl5RCGPKBUGkfBZBsU+se6B4w58hPIB68MhliVxvBzeajao0FuPwJKDdj99tuXPGkRiO/sjrDs7WA/0SU8so9MI0erhexZv+Z+/hvKdQWw9sBgWUEaSjOa+l3o2NYmRRVb2DyGCAFEsQbs7mN/Z++G5qFNNhO8xtW+GbCiPruRAChbvnKe8I0N4LsLzUBN50tGAzIErttRpLDgIDSrUmFafL37idr71xXtYlz16hZTmYx2STNBLLXZdtEDea1PeHTGlBo8fHYF4PeLPb76H3//E16m3e+y+oMyb3naYf/WGk/Tce3l07TODuRrDrQ8d47P3vpsHVn6PEUuRaqh8f6nWj8BkS5j0OMT3AAlCCEr2Phb1GHP6udy/fh0PrOT41CPH+dQDv8uJ9jKfP3Mv/TR+wjhjM0NUhotUtg5z+2cfYPddkumbE1SphDczviHQbLDBBhtssMEG/2i8+MUv5nWvex133303xhiMMdx111284Q1v4IYbbnjS4/7UlTQvf/nL6fV6bN++nSAIsG37CdtrtdqTnswGT57PfvRmvna0x9J4EdH3MHnQ+RIiZ5C1wZtY0UuwCmWSXAcDqMmEOE7RaQ57rUdW8Rn6WZdwOqYdjqCSPqp3FJMKpOWDVODZsLiGKfn80a9fxOrEdq586hJLCw7ZoTbCCGQYE24L2Zuc5sRhgc4pRh5o4d4B3qYOpYtTur0tpNnD6NUMMhf/aIbVSMkCMBdPoh+eRYUWJoqIhixyCyn+Vs3IizO2TvewpIPfsFH3tJGNCLGeEQdVVJTiNPvoWguzVEc9ZRtq3WBsTXdW4t6Z0N83xnmvPYGbW+box0axvQR7U8LasMuWZ59CegIRGmrNIQrn1Sm/sEfnViifH7H2NcEmtYUjuXWkdNBCk5ZyCDXwMHnGrz5K1W3wzU+dx+JQgeG4TKnTonbEpj1agjAhLgrcpIQeCnBKhqznEuUs4n6DaCjAtmz8eyOKp/ogoLWjQDRW4MzxHBeePMVTL1/lvjM7iMt5rOU28egwkyOrdJZcZD6inpVx5uoILOyDdfx2htk0TLpHImJFcnlApzcKgE5DkhRMpLHjCO9kD3e+R7R7Gi8RSAvMuI9s+6RdhVmtY+IU49iYbhfdaJDJMqW7LWQE8bZxFlaalEdCmgfCwcX24ipmahKkRPgeJoyQYYIoltGBx8S2iMt+L+TmE9M0PytwIo0406Gzfw0TSoQZVAVpk+LKPkk/h9QC2eyhV2vIdAhn3aCaEc1uypfLm9k+2qJ8uIezM+HLJ3fQma7QdQPyEmSUDgQK26Z1RZkkB97xFYLPtLHuGCOdLKHPL9HxoHisgzO7iinn0YWA1FaDqOlmgttISdMuMhfAeg0qRUw+QFgShER1QwqPNhDZKG7LQLEAmUF1EyxlUzyTUhvKMCbGJAkyTrCO93BJsJohst5HTU0iHBsrVQzfF6K9FJkBUuH0JWamg4wN5CySYQ/RNiglwffQliAtK8hrjAHrgQ6qE8JEkUY0SPdCCpIhnyQetOT0N/lkyiahgYwhK4+erToxg+cv08hd2yCKSG2QsUHNrz3R8Pf7fk4v2EJ/WwHuOY3qJqhGn+/n5687TVKQyOIEX39gglBYuBN1/DBCdfrIjkX1Oy264yV0PgBhBu1uYQi+j8zAWm6j0wS5WKd7/hjhFVOD59dzB4baZ0kun8ZqS2SlxNbzWpRLXQ7ckiNzW9SunmLoG8cB8E9Jas8YIXUEaWwT0EegobOEvrKKdVsfnSSwVgMM6kSMf7A7qKoZj8hyAZbOWD2zyJ+9829QgU/ajxHAdLZCODRCPGxRlGcNmoH+9jK5ukv7qVOUDrUwi6uDyrJ2B6E1Vs7FhIJkosxthxZw4oQs0ph+SDSV4WQK6RlMCq/819fyV489xryfsa3UYjpocXzBJbMg6vURvs2RLx9k0slYf/ow4t4GzisVvo6ZPTLD+l7B1jJ8ZnUrTwHG3SbllUlsz2M+a7JYa3PexAr7LmvR7sJf3ncBsTvO+UNt4jDjEzftp5cO8fqXt3HsEUq0iIX3E9ewj585jQk9rhq5jPMYvK9IZcDpuE03+mseOFRGOBltHE4kd/G5ez/DUrTOWr/La3dfe26c2kqLttHccu9xxqTCiQxOLyZ0bGIMnWaPfCk4t3+qNe/++rdphxG//8Jnk3fdnzjXDTbYYIMNNtjgH4//9J/+E5/73Oc4dOgQvu/ztKc9jfe+973s3r373D7GGP7jf/yPfPSjH6Ver/PUpz6VP/3TP2Xfvn0/ctxTp07xB3/wB9x8880sLS0xOTnJq171Kt7xjnc8ocrlh3nsfuQjH+ENb3jDT5z7hz70IV796ldz1VVXndNF0jTlhhtu4IMf/OBPcxiewE8t0nzgAx940g+2wT8dn/jAN8gwWFdIMltjEkWac8k1I3ZNlQFQw1XSVp/wgTLTV6wjI1geGcVbjVGdgZBTOzVKsEtAbNDdGJMJsokRVL2F7IaDT6C7XbSnqJmdRP0c9xxWOH96COlD9rIJ2jMlemis08OYkSb1l29i8pNtklBQ0H32ba6zVF4lPs9QMw5ZDtzFOsgcYcHBO1pDdgUm7BGP50kKgtQV2ImL9bM5Hu1VCA/aSCnwxDDq5GHCCZ80D8YRZ81ZMzIycBykNtBNWHm2y/C35unXcxjHgDTE0y7lixo4wyGrfY/1doHaoxXEokuuvUig+kwP9Zjb5GJvEsSfMyzedxobiYiaZDQR6zaCEdhUIana6FShpEP8HZ9D/igH36cQZzTepjOoXkKybQjtOSQFG+ErUtciKtjIeYnVksS2IL9Uh1hhbIl94CTJRJHh3YJfeO5hZrZFqC9McPt9VazD87h5h/leFa8ZkoYpbruGaickOYGMDKpnkGshetki3ioxY5rOcwcOEepkSGxZZGMlOpfn8B+aI8lBYamHNJL+WECsFG6jj+i1MCYjSxLSdp10wsE+kWKdXMPsnEImAvdUnUU3YL2Yg0INq5lBkmF6PUS5jCyV0MpCkGGkpD2jKF7pccX5yywel9wz62Acb1B50DaggKES1FtQrcBX51EXbcNIOTBKrliYLAXpgJD0SgVWay6yoLlq+wle+K+OkN8K82mJd81dQz3LU3w0RYWDKoHh7/bpD2v8w01EP8HpJohGiG5qdGBQ6x3MWh1lO0ihMDkbkWi0MBglsJwcOkmQaUriZPgXhgyth6wdChClPJ3z8iR5yJ/oYccGncTg+WgBSU5hDYFT10hXQKoQOY9ozxh6pYNnGhB4mEyTjZWQnodZXadXskk3VXHKBtHXYEuEEKhOSuJJVCcaxGsvtilMGLp4tCcCWldOo3ULfdAjbvYIxCy64JFMFLBPD9qQoq0FcrcvYixJ+ynjpBM2hf1z2JFElMvovIVIJdLzUPU6rNQQ1cogNv2HYB9dwg/7pNWAeFtA7o6TAwHFdUAb3vmKrbznxpP87HMO88X2FvhWjTSn0JMV5PFBrLrfU3SOeehKgJkeRS7W0H2DDEOEM2glM3FK5iiS7SMQGUhT+DtVO/ZoAdOPIYW52jTzE5p0LMHssBEX5jDfGAhMuRMdGjsiehOS6o0C0WrD0Bhm6zTWIwsDXyZlYKg8qFrq92k9rYpIDN7BDtZiHZlzKBVzuEUPs32KOG1hFQ3jEzWOZWX8Iy1EqYDp9MgadXpX7RwIZhUHBAjfHfj6aE2Ws+nPBHirKcQJ0mjS1ECvjwh8Vs+zaa8rxoMQ0ROcbt/Dzn3ncWz5OO87ejm7tqxysD6GfaaNeuQEw695LsfjFNHrE/QcqpdsIp+uMR3U+JnrDvDHR/YihKCWDYSVoafW+KVLr2fa2ctaq8t5m0aBKqudl/HQ4QyrsBXbUhQ9D8sXXHneZhxnissmX0Zv5d9j9wV33gLlZ64yvXXkh75ObnzkMJ++4yH+5SWnWcsdIExvwLen8aXPmBL0pGFnuc2XumAKis8fuJThzX1Sk7EWdp4wVqdiI7YX2eIGiPkm/X5CW7TBsrC7CZ/44Fd40+/9AkvdNv/jkXu5ZHiSU7U6mTY0+9GGSLPBBhtssMEG/8zceuutvOlNb+KKK64gTVPe8Y538LznPY/HHnuMXG5g+P/Hf/zHvP/97+fjH/84u3bt4g//8A+57rrrOHz4MIVC4YeOe+jQIbTW/Nmf/Rk7duzgkUce4bWvfS3dbpf3ve99T9j3Yx/7GNdff/2530ulv18SZLlc5otf/CLHjh3j4MGDGGPYu3cvO3bseJJHY8BPLdK8+tWv/gc94Ab/NOQLLtbQMs6MYfawR5QpnK7Eb2SknQgIIIoxi8tk7c34ToqYADMk6A2XcO4TyCTDb6zSGh+DXS2susEUhjC5YbLNPvL+o4M2FwPxyzXlezMiUrRtkaUaE0rq7ihxAnkTUetOIq4YxalZdPcmOJ0e9YWMm+/JkTt6it7xKi4WenEdEzaov+h8ivMxohsNnFYsQZa3yBREQ4r+8SHiMizjsbOzOEhpcSx2PS/hkf0e+QOrGMsmydnUrhvBecDHceQgJSbRlB/sM11s4vkJ9/z5RejNKWLeJt1eQyiNUYKo5jLqd2kIC1lr07sVdl3RYudYyu0zU+i9ZdRSDk4sgbBQpRxCG3S9TXDC48abnkKh00MtpMQXWXTro4xeaejsrKBm65heguprUjtFzvfoXFLEXe2TFkPykcBtgMmBWmyQzXUwSmIbQ1awqQ1V+dhfbOEXf+EU9YN9rFofsWszqpWSZh5RJ8V0BE4rREyMQsEmzUF9SFBagmhMoIYzWg2Xzd/qw6sHLWXC1WS+wlppYs83ic8rIhODUALZMgjXIhzL4bYEIukhMoGSCjPbx4yWB55Bp5eJNxVR7ZjRK9c4ubOEdf4Em758isZcCQp5tCuR3QiBwRsOCE+vEQ5v4kS7yF9/Ywun7zPIMMFYLrgW2reRXg4CH1MtEpVtRNjGXe7Rmw4GKU9DFRCSxANRywiWILlTsDKrWTnPJbtCUDstqVku/RUHjCGqSIxUBMf6yIUWciiPmBgja66QzoSoRxuoZgdv8xjCz2EqJbQciDJojdAaYTnEO8dwmhpqdXASWhcXWLu4RHKXheUItCUQKEqnulj9FJ1phDGYNCPL2WjPJlhNyD9fYoI84f4CxrOwhIsoK+yqRTQSQJIiZlt0hzQ2PslkET0UEMkMeUkO2dRYJ3JokyJjSLbYRE9LcO8JiOMUrknoBilZzsWkIxRWDfb+hUELUDcmd2AFzuoZuYNNdJoR7siTjLrk7ltCZgrTamPqIf1Lq9h1g9WMBlHbvgfrdXAcGC6TmQy5uH6umkb0QtzDIY4AIwX1F++gemAVZhsYYPZwntPfDeg4Pk+vPsbxrwrCZZt0zwzGVmQmprXLIaqA7MSIZojoxIAAx8OEIaLoYcYCcC2sriZzwT6yDL3vq9op5Ega4E8mhEsOcaxIj1moSYsXvvZ+0qri6/95YLwsNOwIVpg+ndCftli8LaG3LSbbo5HHE1QjhH4X+hkMD5FurtB8RhEMTB5soQ6eBqUYvfwSovUGZ47XyD3VJT4cktwRoloR6tD8oDIx04heSH7/Iv2Lpskt99FSktbqSNdBDUs6uyZpXzpEsq4ZPtBhLMrooBHFAgYoP5rAeT5muI/TSVm9b4Wl6iiVGswNVdg+ugCFjP6wIC8E2y+c4Ug3ZCl2Eb7FxedfyQMPHeCyyw+iLQvv+AgXXr2ZG2/rwh5YnR9n7wv2sN77PLrwKN848POIqMzH3llHa8N1v13GL7l4toUUgte+8KnsX3k7txzp8ZWvnY+/NErzwRzHj3+Ht77rpT+wdn3kdz7D5973RcRQkcbHi1TGllBmUGEkC/+WnaZPHEj+3Rczqj2LzjNCXnhpneOd7fQzw6789BPGO7K4RlZ1uebqvfyPf/856jtD+u+rQgpDb08pFAOMMexfmePupVmaccjvv+A5hEnKVLn4j7cob7DBBhtssMEGfy++9rWvPeH3j33sY4yOjrJ//36e+cxnYozhAx/4AO94xzt46UsH7yU+8YlPMDY2xqc//Wle//rX/9Bxr7/++icIL9u2bePw4cN85CMf+QGRplwuMz4+/lPNu9Vqkc/nkVKyY8eOc8KM1ppWq0Wx+OTfV/zUIg1AlmV84Qtf4ODBgwgh2Lt3LzfccANKbfR6/9/iiosnOJYdZGF+B1msMBK6UwI8j5WzJf96eQWRy5HUUua7RdI1izRTOGvJ4NNtx8IPCqQflzhlQ+gLVGzQrS5qvTPwpHBsdGChN6fsLp3m+F1bsLujrPzLMS7e18CamOem9hTDUy26vsVE0OZUNET4dJ+LLz7B3toc3/39MkvfLWG2BqhYD3wqDOSWY4wAWakQzRSwhEIVPLQyIAy5kR5bZlYYb1qcfngYO22Rc4o8//oFjt93Kf4pQyxaaN9H9CTF+1bQQyGqXAajGblvhef+ixo3fXcIVc/QvQwhBLP/ZwrrvC7zewJmVINquY03F9KSCh0LlmIHv58y/9uGMF+mVMoG7ToiQ46PgjHYay20qoMzSjrfxaz3yNolEj1EuqUA3Rx6ew4zHRIsRJi1DvFEaWCYbHXIjIPViRm9tY4uugi3hCkYRN4nzin6l02TZYpwKI8RkrgrEWdbJSzHhtUGCBuDGVQ7IRBKoaQgm3CoTxrCcUlgEoK1BG/57FV5IEmeWsBfTEnSBNIUnbNZfHpAcU4OzJxbApRApIZ0vYuMUlAKadukWwYns6S+xPoLhvHXM55zzYNcbfZz203TyPPLNGoOZnkVMVTCSIWQkjQGYwnyj9bRNXj4oQBWalCtEE4HOPUIugo6HUyawFgV23Mwm6bQ3Rh3qYelBSIVmKbG9AYmyamJMF6ZdPs4+w/leeQDw6zlXEzRp/TNo6Q/s4vaCwMKh1JyZywaV1RIKy5us0P2tCLphQ5y1Mb7RoKKMvAEjFQI3B79bnZWpBkcusb5HpWHU5QcGrTgpAZ1SiDmBM0Zi2jUw+qagY+JpTC1BiaKwHWx7FFiW0LTpvZglVQMU1DLpJNF7J7BWYkQGaAk2nXpPnMCe7aBigVqKSH2gZyGvEVyuYs8HGOKPgQuekaQahAVRd+zGBmuM3pymcZkFbEosRe7MDFK1kswSoHSmLPdKM2rqrh31tESvGN1GK3QnywhOiG5757CX4jpbM3RO98l7wyTe3R9UBVzlnjvGM7iOj+wEhgAQTKWQ1c0crZxzivmb/9qlENiO7v2HuOCfyW47e69WElAOjOClppwOofMBMIIrF6KUQotJcKyEL0ehsF5iSgDy8Jra3IH1wbtZZ3OoFXLdrCPLJGODWMEYAlUaJCpJnEUINAOSOkiDcR/YXHyLoMcs2GqQnZVgh4xxC/MY/0PjeieFYBabayupPRgjKhrVCcdtIdJwcPffgQrH2DabUysSGY1zc8ZdCHCimKM66KLHmiDKBfw2gaBjdAJvfMG7Yk7S33iv14itTOSaZeh96zRFHXk6xLS8ghGa3KzwKkuqivQWAQzLk5soyJ4+daDdHRAlgm0DcFwntO3HyRb6SECTdZM+dZHbyN81jR/NPdswlSRjfQ53DzN8GYX2It7f4mKU+V4/TFq3QW+dO9trJ4Zpd3qoY3hS7cdILe1wrN3bWe8OPgky5Z5kixGk6crOvQsl1r6w31p/vrTd5BcvRvn4TmSpRzT51sYfQqYBL0M8R0oA7L7AkTosGWtwDuu+C3e/8DXsNur7Ks8sTrnZU/ZR2m2xYzrkTb7xPsExkiMAmvG4qKrdvOnR77OY805XrprH8+Y2MVoPiJKVzGm+kNLnjfYYIMNNthgg38+ms2Br+DjiUknT55kaWmJ5z3veef2cV2Xa665hjvvvPNHijQ/auzvT2J6nF/7tV/jV37lV9i6dSu//Mu/zOte9zqk/NH2vZ///Of5zd/8TR544AGCIHjCtjAMueKKK3jf+97HS17ykr/33L6fn1qkOXbsGC984QuZn59n9+7dGGM4cuQIMzMz3HjjjWzfvv1JTWSDfxgHv3uYw8UxnEtt4r6iNSpo74bSssuMc/aFmBlSEkQ9pX57kcLJFNWDeK9PgB6kOtUksmAIgoidW1c53rCR5TzxsIe3MGgjiH5xCNPNiCZS9uxs0TdbsWcEF082aRAxtuAyNtSi8OxVtn5xleVTm3Av89ni17EdjVfRaM+hfX4VEcfkTy+gtES1U3SQYXIuZsgni7OBD4aARGmq/TaOn9Kq5UhLPnpS4C/Cg/tHEbaNRiOWu6DbZNnEIOXG8VBhhrZg6bTLR39vkmTCw/VPYXcjGKoSv6JAd4vEb3Zplyzij2/FOTDLzKU9Nl3bZvipLU7dUSQ85aC8Gt0LCujtRdRyRlJ0sVttcBzctoHZFu7JJvRC7L9tsri1THOtQD40YCDK2WR5jVBFRGKwjrTp7S5i9VLSdhs77aPsUZKRPCZKEVmGNV/He0Sh8w7OzBDfeXA3xx4BfXEwSNIxBqvexJBBlqEFpGUXPeyRyIjxW9oYWxGOWITnS9zNHaQ9+Ne3xwIK05pwGWS5TLzLoMeruK2I5oV5BII0SLBPG3QvQWjIXEW6eQQvdpDrPWJiJIKxz85Sf84I//1/X4LVMJjjKSruYXSC8D2EskmzLioVxItdQGJsSe54l7RgYzoK1enhNDySiSKWARbqiDCG+VVkPiAp5cgCe5A61kihH0FqMK4gHHfwjtVxZn2k6yKVRWtkgnCrwjnZJJ0JMA7IFEQnxGQZ3uEa0eYi1vEa6rEWWgyRyAKO08CEEXpEoMs+IukjOpAFNrKTIDJDmrPQBQstDPZyQuUBCbMhsh3jNxfxjCEbydO+fDPuSoSfpJjlVXAcDIbWNovCbELSdnDaCdLzSYsOSEVrp0/xnhVUzSLc7pOUXZzTArVUg1xAtm8IY7nIpIPKNGpxHWNNoCOBc3MLy+vQu2ILhVWJ9TmJfaWkfHAJ9VBImptGtgciV1bx6e4I6OwZnCN6e6q499TwTnax+gYqGbUxD1c4mJ1b4OQSYsRCjwVoW0ChCLUa5HJkwwXinMIqe8hG+ESfmmIBqgWCuQixWBuIKmfF41f+2jy/91tjHLinQvqiIutXD1Pdb3B6HrLeQYUgEpDeoBIK20Y7EhknA4FXSohTZJzi1GPM3DxE8eDLUqAziCIoFUmTaNB+1+pBsUDWaHHTf7kE/d2TyDgFBm1b2TcbqDgjqwvMniLq/gx9KWQrw7SfPUruu6dQ/RQsiTAZ1WJC+rUmWBZGD0Rnk/N4zuuu4aY/u5XeYw3Qkv4s6F0hAEanpGMVRLWEsRzigsAIgbMOeqxA8yKf1uEYq3mY0q1zmEnIvdlGCci/bjenPtMHaxAzfmlYZ36TpNYuU960g/sPtOiPwZdb25iIxxDrCcHhOp2FOscenMXK5xjL51k5VaMWdWn3+yB8EJDUNbXv9Pn5nzmIOf0sxhn4zO2qvIFPnngPHd2iONlh4hcWeOj2LYzeMc8rX/5sxgr5wXMtBGfiPaznT/OSp66RCx/iFn8fT7vmvB+6dtVeMYEe1XjVTXyr3eZl2QEkHoOmowKYkCwVFFY6mJbD0gUey70O8/H9BMES99fvZc/Q1LnxHvzKAe7++K2IWmfQEntQs7LZRUh42s/sZu8lm3nr+75Gpx1z8b9cZTJ/PvevvItu1uKKkbdR8S74B63FG2ywwQYbbLDB92i1nhhQ4bou7o9pLTbG8La3vY2nP/3pnH/++QAsLS0BMDY29oR9x8bGOH369N97LsePH+e//tf/yp/8yZ884fY/+IM/4DnPeQ6+7/Otb32L3/iN32BtbY3f+Z3f+ZFjfeQjH+Htb3/7Dwg0AEEQ8Ju/+Zv8t//23/75RJq3vOUtbN++/Ql54Ovr67zqVa/iLW95CzfeeOOTmsgG/zCWZmuYF+VobBeYSorT8VGxwe7YHJo9zWUXbMcUC+iZEWwB9scWSSctom1bKD1YG5iRZgnJySXENSVKBdhx9TLHHjwfEfkoDN1LpplJVtBfqiMzw6HnjlLIqjiRy0OPjfHA0hhuOcLOEuTtilwqufNvywiVoJ/u8Vh5CuvLXU5+V6AKGb5I0O0eIkzQArKsR7Q5wGkFZHmb1V0Z0tg4TU1wJoFDFu7PpkxM1UlPSvqrRUQC7mSHuNciGfHQU3mcSo/4Co9VuYuR/V1MYuiOu7irFkL5hDvLyDQmdSRMFnFW23gP9fGPtUn2BUxtTdhxyVEspRm7KGSpUeDITXmE7aCbLfS9HeTmUeIdHno4h2k1sdZrGKVIdwVUdkqiW2OymsDrJ6hRgzxqMEqiq4DJ0FVDutLDOb1OkKQwOYI9PI7MDEke5l4SUHzEovSNeUQ/wX9ggazi8ZJXNNm7dY1Hjl5FN0wHF4O+hS7lSOwMZzVEpClqdhlLj+JqgEGVh5U5BA8lmNkWzJQBUMNl7CSm0+9CJUBPDmE7PaycxtR79CtQOBUjV2LSkTyitI2+l1A41UfkbITRWAsNrDRDlstUbm9gbx/CLCmyxXW64x7RUycJHl1D2JLYSHKxwSQxQir8uiDyU/r5mMKJFEyGPL2EOysGST6AUBK8HDJMyPICVY/xajGp0ujAQduATimc7EO1Ch2NkGIgnjw6z/B9Ed2LxggnRxi6vUXly3WshQZGSoJWgeCxdWQuQMcSc9THqXdQnQSyEHkkwto1Q1S0wICqdRDSGXi+zMcQC2Stg+n1EUKC1kQVC9kC1UugGaIlIAfx9XrzOKoTYVodVJSnN+VjxaBqKakCe6lDd3uOzLOJxlzcTGCFFm4DKOYRhYCsXBxEq0caWikCGzk9hVSKzHPIEosssPEaBpVoEiXxlhTmwT5yJUWOJlin1pGtPnZmMOflSUYGCU3pqI37ygDTyKO/lCBTyLKYJO/ithTJZJFgTeP0+mTFMTI1O6ia0QnW7CrFBQmNgQiBpb7nC9NqQ6tNbmEZUa0OTHFrdSw3o1FzGN2TsTAK1af06cx1sNZt1NE5NJo4N4JxBOl6DcvxBtVIvgOOB1KSJSFGOshWmzg3jrdSP5cwxeMiztk0JxMEsLCKEALTD6HbxdzSeMJCaGBgsGwGKU7aaLxHl/E+2aNz2Ray4Rw6y5D9CDk8hFCK5G9WBoJplmGMAZ2y+ylb2VTOY9IMg4dgYM5MN8S4FjrwcLsQjzjokkvmC4wlCObADxXhosKdGiK0FSbwKE96jDsLJFhs/gXJyc8KpDZkQnLk5i7e5iKlQotjQ7ezpM7D2tzjWFLkmh1P5b4HvoQ8uYZVDHjRbzyPz8+dYffOSZr/+VaybgaJBksQ+Zpc3ucF0+P45tRgxnlDpjU3P3Q3PZYob+2zr9hF9BusHS6w/kDKC/buAuA786c41linY81y8nTM/7pnBE8+g3c+7SAXX77th65dY89cIudFHJcT5Mc77G/leXqlRgkQ1hTGew7t1SY6zCF1zJjIs6VYYSq/ldl+zKXDTxRV9l61i+MPnuLSZ1/It757msXRhGzdhkjy7W/cT7HhojsCmcCB+f1sH1M82hZ0EsOuiqby0y29G2ywwQYbbLDBj2FmZuYJv//+7/8+73znO3/k/r/2a7/GQw89xO233/4D2/5utasx5txtb3jDG/jUpz51blun80TPuoWFBa6//npe9rKX8Su/8itP2Pb9YszFF18MwLve9a4fK9I88sgjfPjDH/6R25/5zGf+2Pv/JH5qkebWW299gkADUK1Wec973sPVV1/9pCeywT+MF/7Kc/lifA/eeB3vZotuPaadzxOu9Wmtfa80X+gqSIEwim7RxkoMqpdiLIVIM5RviO/qs7qzwoF7d7F8wQQj90YYS5FNVbnwvFN868E83l5JtdynZ4Wk3S7CBbsaEcYKvt1Bf6OFuijkma9I+VR/ksIWQSYkZ+73Ie3hlS0qiyuErYSeFBgB2rNo7qxSPA06jCk9tkJv6xjadlHGEF3ok/RsvHxCyW6j133A0OlqaHWwW1161+yg+IuzpG6Xxj0uWkrwJd5KSLJnBoEgnDFkXp542sdup5RvbENqk0YQKcnOi06zeaLFI5+psmCKmAs8+hc7pAcymteN0NtTYPTWEHc9JBwuEe8YR3ZjJAmj57WpPDumf9EQS5/M4TQSrId62E2X1LVwag79F0iwQpwPD1o+5LCP1DbpiIW72h08P4nBOdMlGcsjSi6JSug8fZpbGyuoYxltUwUfqPdRYYLKBMooRLWMbjSwhIPspCAk3Wkfb6GPEQaMYfTamPKWGhjIkKQHLIK+oBf1yWwbezTBu1fgLTcpH1kcXOA6NmayQv1Zo0w+0scdh+5SC92NB1UplsRUfKIpB29KoNYEtLvoLUUQAl3OEY0HFE4JoAMihWIeKWzsMMHY7iCWPD4b9Z5pEGJwoV0poyrlQdyzihFpRntrgFuPQQuiEQd3LUIlGqRNnPexmiEiyjDLDTQZzlIBcgFJv43djQctJmh0zsMMFciGioML7OECMm1jSl1Eo4v2HLBsksjCkRn4PqI/uBCvfPsMqS8xXg5jSdKxHMIVROM5nGATST8iHfXR3sBXSfsSqysGcdnrDeyVCjLnoF1Aa+LJweu1s3Xw6ULuRICOQMUabzHFW44Q1SrpUA63YwbiiTQDI1vrbDT4WJnFXxonrjpMfrVHtpbSXc8R4dPZl0dvDsm1fLJgAueghG6XtPi9TzNkaNhqFghXYa4VoLUktSq0dgFCkE8DRCtGItGOTe+CcQrfaYLjYlKDqDcAMNUy6dZR1H1HgO9LfHIFSU5j1wfPcdoXfOCD++gXAibe1kUUIAhTup5EFgLSANKCfbbty8LUB+WvMstgsoQ0Br1lGKEU2bYJrHZC57IxSnctDh7Ps2ByHNGNBsfdGES1jEg0aI0RArW4fO7vN66LSBLwPegORJX090K817UxXfAfWaTzrJ3guehWiMrn0VmCsGyIY5KJIvZCEwEcufEAL/+la5GlPGarwdwyGM+bH7xp0AUz8BsSAhlqHCGRYUpckrT3WQy3Ja//19fz7q8sgu+R+A2WoxKt1CPorpOlEu27mDBibXiIorHYMr7M3gvPsP+mfWQ9m4CUoi3QF06TGcOu8TF6eyocWznM4uIsVz3vQk589QFanQSds3CN5rdf8wzWirexPDfBFmC9LzmxsM7//Pgq0bP2EOKwtbCZ7NH9PG/XMYb29jGmjxA+H3/sfhY7LS6ZtrG8DlIovDr85Zd3cXTpPrZtHaHbDvFTzaVP24lXsnjdtnuwheYLVpMtThOLn2HYeSYf/Mg3cORRXvdyH6v06yjnLxBofubKs8bGKWg2k5nvtdsBbLtwM7/+4deSZBnW1LdJ4y7+IQuZQjjq8ZW/uQ9/5zC20ZxXHmFLsIOV7gGU7pJXT0yr3GCDDTbYYIMN/mHMzs4+wZvlx1XRvPnNb+ZLX/oSt912G9PT3/Oce9wrZmlpiYmJiXO3r6ysnKuuede73sW/+3f/7oeOu7CwwLOe9SyuuuoqPvrRj/7EOV955ZW0Wi2Wl5d/oHrncer1OumPaOUGSJKEer3+Ex/rR/FTizSu69Jut3/g9k6n84Qoqw3+eXntH76c4YPL3Nq4nyV7hH6iKS30cQ8m2I9fITmSWPZw5vukU1XaVxdxVx0yp0Jwch4hwbpO0e0H9M8L0PYQmYA0byO0IQsU97Ed/aaQbuoyUTPU8i6lgy0Kp9aoP6NAOuwwviVj5rwm1a19zntujP8Y2A936S6EyMMxZKCmBZ2aCwiyzaOIbkTOtmi0JWFZEtx6Ev/wGt5Mi+ZzzyMpWohQ0a37GF9TMwUyV6ISzaP3bMMqGIyysBLFypER/Eu7lO5bww1tQJDEfaTyEZmhdPMK2ILFf7OHuCAQvoPoaxrPnyJxyjx4e5n1JOHEFyyiS8t4m0osTVUZv+AM62UbY0miMRclwFnvIlKDqlZQSZvuXzXR6w66UILhANGWOP0A7UpUkmJFEEcSqyEwI2IQYXyqBjtyiAjCnABpM/MXq3B6EWMMxrFIp4tIy+eRZJSJzhppIDFCYmkbvRaSKYnxwFrogGbg2+FIMldh9VLCLRL1c00KQz3ksIVJgTb0Qgu5HELeRwiF3c0QB/KE+QTTDvGNHlzclvP0duZJrD5Jt4XVTpCtHllPnVOw16/1qF+Sx7u7jcpLlNZ4Dy/iLLaw/BLtfR5idhBhjSUHlQpao7SkbAx6pkC42B1USCQppCnZdJXu+QVysynWqXW8m1foXLkVHEVS8lFnTaHDERenniCEQIwYjDE47VXsf2mY3TlFNysz9tUImS/Q21QmeHAJ2ehClKJsl9SxiSsuUTXCaloD7xljBl5N/YSsbJGtr2PSFFMZCJ3GDHxwLM9GjI1hdTOM1tiP1BCOg3EtsnWNFXfwl8OBEGkyRL9PVrDp7M3hLyQU7l9FKZtke4loyEZLgcwMMk5R3XTgB6QgLVjIfkoaSLSrSAoBITa5QgMAE8ewtE7p1oDVX5hG6xRLa3QERhiS4QCrG+LtP042XCSeKmKfCgkWE+KzH3KINGL2L2yyZY1QKaZYwI8CvG8tIDPIgiGc9YyonEMHkqSSg8kJWFj6XtWMUmBb4HloMSgiMmdPQdmkQo5bcHRQtmoMLNTz+POSufd79C/yMf/lJDkD6c5p1FgZ1U2RCeid45h7jg8qVuIEnXNguY5crkMxjzVfI3zqTno7i5Rm9aDyLecishSiFJNpVD/FZBljWyOWVzzMSgtj2wNhxvMQrjPw2GkN1jgDZK7Eutoh+XZM77wq0YiNunAcb6mCPDQPaYoYKoOUhDM2armJOnsoPv3ez5O025hFm8e7mh8/HVtrPXTeoObXsUoFbCkATTKikUkX+enH+EwbMidDZRlKNaiFPvUkx2N/A9b8MrpQoPWUKumUQ262jeNr5nplhIHyIwtcvXeRe/YndIojiIsnmbvlFH94/l5Orte5ZHqSl11yPu93BJ9aWqRSCNnkVrl231Y+ffqbePkGNCDb1ObuR8/QWMuIHxtix4VlXnnpK2DTC4iWX00w3Gep9VkmSq/meZM7+Oht9/JwQ3DtUx3++l8nNE9/nf/9t5cwNVHmCzc+wPpyk6Ae0ljrsrxzhV/Y0URgWCzPsRTmGeKlHDqyzFduehhMxAuvnsUJThCFMUrCrq1jGGOQYYVmt0bV/uHpC6uNDi0FI3PQsVKsEMY2Z/gzPebDBGNgtHYl08F2pnLfJvWWcZOvgr/3Sa7CG2ywwQYbbLDB36VYLP5EA11jDG9+85v5/Oc/z7e//W22bt36hO1bt25lfHycb3zjG1xyySUAxHHMrbfeynvf+14ARkdHGR0d/YGx5+fnedaznsVll13Gxz72sR/rM/M4Bw4cwPM8yuXyj9xny5Yt3HfffezZs+eHbr/vvvvYvHnzT3ysH8VPLdK8+MUv5nWvex1//ud/zlOe8hQA7r77bt7whjdwww03POmJbPAP403X/keO7Kqhr5ni/OfUOXm0SOJKigLGrh7062fnb6VzcQ6rrsnPJTh9g9XLSF2JzDRkhmCfS7zDZalhEzjLWKJEc0LgH7bJEkW53GdqaJXI2NSbO9BHMtonE7wTGaPJMq0XT+HnBJZlOPztPLec2EPsuLhHV+mHKaSDq7bKtORoYQgRauyTB3F0zJvecJTfe+RFhB6kz5rCWu+i1mK01NhtjRYZZ0SOoO2QfkGhssGn4dGKj12B0PHJXEEaWWQPWbj6++xLCwFyfhVbeghSOtMBRknAEEpNYbiCu+4gA4uVboXuV8qIbp/wRMbc3AwC6K25jOyfJx7zaN6wE2cqJjiQkElFamvSxR5SZ7S/mZKVe/QuLxNXLJx1g7AtRA9IEtS9An9RINoO8YjBhAoTJniNgZ+McTJM3kFINYjc7Se4Sx2ibkxxa5uVboVoyMLqG0RbgoR03CeqSOy0j7saoW1B8+ICufkIr5ERF2z6Pmwar9PsevS/XoBngLN2Bi1iUm+KcKSI3UqwOwbZlMSFgM7TtuHPtpGtPrnExbplgfRMl8hkZLHEDJUQjfbgonybYGK8TvF5Ca2giD7loZIUd9yQzraJfZ9+FXJtD4aGMKkPicEkbXQ5j17WiDRG2xb6wq3IKKM7k1G/3COpJFSPDV6rUVGQq6dIowYX/5YEJWhekceL25hNYHoC9d9acBNEuz2MNEQXaIrfMJgjc2gtUJaFWm+hU4OrbOK8TWuXj2x2sSoFZKOLafcw2aCtSfRCDGYQv13w6W2aIhp1GTqcgNbIRg9xNopaIDBxCr7Cbg7a0qSGtBhgyh6ZZVCdDOfkOmJxHV0todUQWii8xQSFwuQ90l6PZMjG2JLMcvFO9+hND4TCONDonkNTFSjRRWQDLxR3MSF/NMaZ62CXNNZWqAkJiYW/mCIMqJUWVi8knikRbR/GOltsp6Wk37exdYyUkmy8ireSIE82QRr0Zg8kqDAhrtgYCQvPyDF1+zhmYXkgrjk29ELUIyfJHB63eRm0iB2KSBsr5/4tBSCUjel0WOzbVN6/eE7MsI/OIZSDdyqkOjtoT9GlAhiDSRJWnlbCn3NxH17CfeA0CEEw10HOdhBaIy2L2MTQ60Kvj5QWMrTR7RZT+xp081O0Ov3vVflYauCdk6aYx0UaIWjcWyD+ZUX2VEW/XsI4CuG7aN8g9MAHik53cOzlZqQRPC5LHb//FNJ1oJ8Qb3VwTsbn/nbj2ZhNY6hWH9XoYXp9EKCzgPLXTiOAZl5jltewZ/skM3BxocZqVKR7ZIx4ukRiK3pTAU4nxkoN8+sVzrcayFhjf2GFAycsXvihU9z33TzpiTrFySr1Zpd+nLDa6QJwbGWF/JU1fnbrES41EbN3PZ1XPPWX+aM7TjIOyF0RX3noAfSQjVi0eOYlFzPql1i3NLfdsZ2Za4+R9x5logTfePA47U7EiDfCv5j5F4yYz3NfAxpHI+YLs6hJD9/TDAnJpu0jLAuLx+pTZHaX02GFRxqT/OZF2+jU+wRGILHxi69Ey/OZmXmIxGgu2zSFAeq9BJG6tOKY8dwProkTQ0VmxqucCjLaayFaQdyRbKmUWOwqtDF8cvYBjtzX5LUzITkSTvZPs2sj4GmDDTbYYIMN/ll505vexKc//Wm++MUvUigUznnQlEolfN9HCMFb3/pW3v3ud7Nz50527tzJu9/9boIg4JWvfOWPHHdhYYFrr72WTZs28b73vY/V1dVz2x6vzvnyl7/M0tISV111Fb7vc8stt/COd7yD173udT+26uelL30p73jHO7juuut+oNpmaWmJ3/md3+FVr3rVkz4mP1lK+jt86EMfYvv27Vx11VV4nofneVx99dXs2LGDD37wg096Ij+O+fl5XvWqV1GtVgmCgIsvvpj9+/ef226M4Z3vfCeTk5P4vs+1117Lo48++oQxoijizW9+M8PDw+RyOW644Qbm5ub+Seb7f4NT9xyhqJvMHI5Y2D+Kdn2spqK9TfFIfuARoU6tkT/YJXcmRKSG4c8vU9q/ji9itGNjbIvLxtbo31HB/5qkXc9hNyUqkiSWQlgZc8cLCAmelTIhj8GCJMlbWDMlfvEVJ7l68xyzhTJc6qE3+4SFPMZ1SC7II4cFWCAE6PUUEYMwAlP0sFxBvycIvQx3OcRupAMvCSVxZteJqhZx1YW2QyAj1ALoqE+SJeQu7ONclaBnLFQiEFMJ+usuTqjPJh2BtiWW7SGGckz8sUXrhklkBHZTQDFPu+Ci+gYZD75EpsGBoacIHDcZXHIVJCQGazVm/MurBHd0CYcD4pEcst1GtkPMJh9rxEYMlXEzibumEf0Y2YpQ3RglYkTOkAYOabFM1tTo4WGc4+voM3OIehN8iam1SMs2/T1VjKPQOQ//7jmqj/bQ6zYiybBrIaoZgmWhUoMREoQk8wQ6X6C4AE5HYNDYS5rcX1qcWK2yvFqmfqYMQNbQxDWN7WY46zH+coTb7OG7DQgz7Exh1XuIKMGNDMmecaJrh4kreUxmoNHCaIPRKf66jWM0bjVk7LI1zPMrjO/s40cdRC9k/Aur5E7FCGmhnRzJSB6dtzE5n0bFoS8HPitSWhgl0I7Enc/IHY/wj3QId43SfdZOpBegu11IEuj0QZuBiW1LE8WKfpSjTZ7E8UjmFKOfbFBwurA3pftzEP/qMHrbOHJ8DDU1gYxTdKNJ8J0zFO7vYKc5dDWHyfmISomsEGB8BzE6jJkeJhvOk5Zc0opHYT5Fu4q47CLcQTx9d8qjNakI84bmvjLxeI6k5JDkJZmlMQLsnkF1EtLRAqaSRxlB7kgDd76NSgUIQZITZKpNr5AS5lLCCqh+SmaDtsBdTXDWMqqfPIlZWiHJQnTBw7ZzFA8lOE6eKC5i7QKrLSicBFGukO7djKnkEJlGdiKMJdHWQFQwOZvWlaP0dg+hx4cwlTxSKcILRlClCn5HomKBvxIRLMXYXUPnogJdG9LRIbQtBrHY3T7CcwZeV3yvh1gA1tLgf9IAxfPgeb9ylHy1x9Cd34vufny7OXQCjq2gHziKPngCLAuZyyE9D68usY07SOE6ew/TbmPNNTGdHtmZeQhD7MxGCpv+njGyfh+iiPtutOkcjcH+PiH3CoNZXcOcbdli22bMeJWRz/bw/zig8EezVL+1TPlbs+RvOYa1fHY/paAfDrxyUkO8ewICH7yzkVmFPM7uPDs+qNj8TQ95pUdWLmBGK9gJiCBHZBlMvYlptrHWonNTetYF5+HNDhS0eBbO9IY53Ruiv6tJ7/xJKOZRMkPnJSpLCZKITsvBqEH5UjIrOexs59JRl7HVmADNlz9/F/PrTR6YX6AZhQTTFpmjOZSUSOfbfOQ//CVv/8uvIPyBEV5poos/0mB6qsz0TIWXXLWPZlznwOy93PflUW7/yz00Vl5MmmnGCjnGCnmuviTmi0sf43iUEAtIgFindHXKetinVu+xcGadNz3zaWyqPJuPz53HXfXtBF6GY1l0dUp+T4kL9k1RHn8OQiiSNGO+3eZdn7+ZU6vrOFKhjab0I95ACSH47be8gKc9fRNec7CutOY9Fser7Nw9yuTWCv64hyUkdefnuSeZxPef8xNW2g022GCDDTbY4B+bj3zkIzSbTa699lomJibOfX3mM585t8/b3/523vrWt/LGN76Ryy+/nPn5eW666SYKhcKPHPemm27i2LFj3HzzzUxPTz9h7MexbZsPf/jDXHXVVVx44YV88IMf5F3vetcPmAv/XX7rt36LQqHAzp07eeMb38gHP/hBPvShD/Grv/qr7Nq1i3w+z2/91m896WPyU1fSlMtlvvjFL3L06FEOHTqEMYa9e/eeywX/x6Zer3P11VfzrGc9i69+9auMjo5y/PjxJ5Qf/fEf/zHvf//7+fjHP86uXbv4wz/8Q6677joOHz587ol761vfype//GX+6q/+imq1ym/8xm/w4he/mP379///Izrc9xhd7zJyyTyLeoIRoamFFdz1NsnE2WYDSxLcdZrkwi2DZ36tyVXPX+BnX7/E16Ict3yuTNjKCI4m2MuCtvLoXehCAoGbIWyJKIfkVJ/EWDxwU5kmEjltmJyM+OaWnTQ6Ht2mz5HqPtxKB2u1RXRlBes7CdmSgb7BZBDV+4xYNbrCZv3SMt7NTT7xe0MMTy/grEfQ6pIJDZ6NznnovMTpZuyoLrHZWuI7xUuwluroNCX/zD6L392KsRXdIYFzMqBb6ZE/ac521KTIuRo6TXAqDq0HC6jMI+gJVCtFpwmW5TJc9jHbZ4nXFH0JwU4Bp/qMX7nCrJmi43uU8gG4Z18vGqRv0BhMuwW2g9El0j0BpulBBxwFSUVSOB2hLUGqHaxlgZEKUwhIVZ7cShfqLcgyZBaTXJlhHhGYRwVZwcHYFlnJQ6JYvqXEitYUo9pAmEFgCUlYtskdb2AdXEFkmuSCIYwClMBkFlJrjNCk33Ho2kX8ibP6rBCIBNKDLdwZF9WJmbqkgV1fRa8X6RW3kc4MIwxYW32wIrLteUTDQvQEWbWAOr2MLuZwDtlUpjOmLm9S2NMm+SOLtVMKXbYHsdN+AfH4/1oKCFAJCOHiHE/I1vogJGQadXgOGUYoW7LZGHqHIpLRYZyJMmlq8PoSu9YnCXvEow6qFhEsRqgsJbwyIWjHyBHN0M9JFjOJOKJRwkeeEJCtkNkWCDBSQuChTQqpZuirS7BjCnFoFhGliJE89nyNrF4nHa8SVgrkGn3UwTOU4j4Ml1HFMqY08ByS9SbJ6BD+SoTqJ2A0Oi9Iwwh1uo0qlbBCQ5r0KX/lNLqSw1Sr6BOLyF6Ekx9GFARxUdO6QDP8cITSArtjSCVE2xX+ckISCFI3I82lSCAjoz+kyT3aAM8hvHwQ5U3JUF8rkYwVsSKJ3dKE1QDXmwSpSKbygOFc0ZkUtK+eQNh1vFuXkOsdetM2wYMt2FKlPWpReKAB9QgtQ9LxMfzTGXP/ZoKpDz+GNeSjA5tgXWMsB/HSAuILHQaRY+Zc29Pj352gz73vdynv6dI64fJ4Dcrj8dwA1YOtgWCjDSJNIZdDZBnBmRaqEZKM5LDWWoNkpZUGxlGweQLR6iNLBfTpBVQ3xGp1SXdOIQ+eQvVidJyAEQMxWBqmcwlzYpDOJCbGUJYFWqAWV2FRQi+EA7N0J1yMFohWd1A5pM7OdM9WnATiShERWYhUo5eWwLJwN/VQcYKwJX4Z0plRxJkldJAhPR8r52EmhxFJijbJuePwxXf+9ffO8Y7idGuYjrQRl7oEH1jFFi5dY9COoLvmEfZcDn9jlBwZQgqG945zSftF/OXa3aBjkpMtwk/vZ+9LL+TEVMqv3/plnl6a5sFDCzy9ssztd5SpyQ7+zEPE2eAcUQ5HOPWnx1l4jkQHim8fPYkevZsD9YcQT89RuqLOd478LdXcDtpRjFKSWq9JJcjQmWS5McRlL5vll158FY/OJawvNlnbtsQVz9xNZjrcXruZq4ZTjuoxJBqM4Q//+ps83GryqGzCzd/ipmMnSDcn9DOXM48e4csHD3PB0yaQShJn+glL4dKpFW7/3N1c+eLLmN41ydv/xQ1856+O0XEN8ct79DYvsHCiygWHd/MbV17P9GiZA7UZzszv5e7fOch1rzzAU15wyZNahjfYYIMNNthgg58eY8xP3EcIwTvf+c4fazr8d3nNa17Da17zmh+7z/XXX8/111//9x7zcQqFAnfccQe//du/zWc+85lz/jOVSoVXvepVvPvd7/6xAtJP4qcWaR7n8VKjf2re+973MjMzw8c+9rFzt23ZsuXcz8YYPvCBD/COd7yDl770pQB84hOfYGxsjE9/+tO8/vWvp9ls8ud//ud88pOf5LnPfS4An/rUp5iZmeGb3/wmz3/+8//J/45/atTUBAsnNWv3aUJZJOm5qLxA5Aw7jnTgWlBCkcQJpYtCnnL1EQ5+s4iedrn36z7euEYOW9xW30NqgbTAIMgCKD9ssDuKcAzCpTKF6yMc0UNYVfzZPm4syW/qc6YbkGWSYi+juVKmcjKmUMvwdvewX+DinoyJDkm4M2T9uMPQFeAXEvz962TRwCvWXjhrQmpLMk+RTpRINhfxuwmTm1epjHVZ65SRzRAduMgzDVbfn8IzE/pWQFqS2IlE5yxM1gcEeArRjzDSIJpLLDyyC2t7RoaF++Ac1nwb6+Jp0htshrevoRckc387Qjw7j7Q1zZMOjEM2pJBRCqTEbkJSKKMDh8wIVDfD8vMQWeg5hQwgsQALgsYgalhkBhFlWP0MoSGuONiLLbRdQBZyyJzFJS+9iLt3noHdJdRKgNuMBtUDmUBEIdLOYSUGnQyqB4SUYIPqa6KyS7aritVIMY6hdgE4DRerniEFJFeFFL+R4U4kuOHgwlJmFiKLiYuGxuUuwze32bdvljv+ewFbJiRTYmC0KhSt2YhcJ8I8JsnqeuDjkaYYJZBRShwJppM66bzi5s555LqnB8JTBIwNE5wf0dvfx1gOJOAsdQaJSMaAslCWj7YyqDqYZgsiw+SFMef/Yp0ztwY8cnArVi9DmnTgRZNpZJTiL8cwv4wTWcRTBax1h2jWULjCpXBFnziKqN0+in9aIlebqJMNNC2y0CALeWQhT2JnyE6XdMLHrnUQloVQNgiBabbBc8nGKyjPRoQNdKeL0BlitYXwCzgLDdLVNUwxwKvFeM0Mk0n84yuYkS7Ogz30xBiZMIQTDs6RJrKXIdMe2cjowMQ5bxFPBFjtiPyjq1hzkuYzikhstNEYJciGBbm723R35onKimhE0nzGGMEZhbfYAiUhHxCOWbR3ecRVxeaHmwSLHZxpi7jtIjLOlrRI0IbiVw6SXDwGVwMpGBu0FZEFmriQEU9WqTzaJhICu6FRRmIyjbXSRrbHsEqS3EN14ukccbWIkDa5wzHSQH+vQo4WmDoe0TqwTn/pieettf2DVKnegkVpV8xFv9vnOwsXoX/zFJLvCTXnGoiMwQiDaXdQpzKkBsZzpJtHUct1FIJ0ZgQjQPg+SDFI1UpTklKAdCxMwYdGZ+Chk6ZgKaQbUfNKMJqH03OQxGRRhPBccJzB67wxMC32FyP0vk3IR89AsQBJSm8aXEtia4M3u4KycxjHxmydQMaG9p0R2YqhdwZ030WOGKJdI3SHU6qHDEnJQuXKiEyju13U/DoAppcSXzKFt9rinR99Pe+65dN4StE/0sGqgek3GL51HG1DkhdoS5IGkq1Pn2ab7WC/pMUjBw7j+BZJ0cMJNVGUMqptTpqQe/cfZ+6WHr/77+/h6n1naO+0+dU/3MGu0iKznYHXy+GjMWcunIDQkImMsVwOoTZzz/oD/OsX3cZEtcNXlwxTw0U6UcRKq8uYuZSf27SZE0f2c9tte5DKcP1zH+PC7ZcTTgxzv7TID+WohUu0yDBCkEaCVpbjljNHuO+xecCQZPDZRx4kSRTZlMA2Bv8MJKMpD9RPM+qXcP7Ohyx3fvFevvo/v0USpbzkV5/Hh3/9Y5hQI4BSuUc16JJWPV764kvZMjZEZjT/6/gtrK7WcFeXGbnlkQ2RZoMNNthggw02+ImUSiU+/OEP86d/+qesra1hjGFkZOQHUqieDH8vkeZtb3sbf/AHf0Aul+Ntb3vbj933/e9//z94Ut/Pl770JZ7//Ofzspe9jFtvvZWpqSne+MY38trXvhaAkydPsrS0xPOe97xz93Fdl2uuuYY777yT17/+9ezfv58kSZ6wz+TkJOeffz533nnn/y9EGktJUg3LIoeVOhCDkdDaMkbtbNl64gnSy3Yytn2R0U01Gs+Q3PSl7QztNwiTkmUSsZjQ3RzgJBBVNcqXCK0RKdjrGhMLlnoVlAPDzxak+/vYd9vM5wvETUHWysjddQpvXKK2VtBSYhUiwr0eQxcmtA9laAlTW7q86BcOccf+GbSKcfZkTF0a8+hjE/SGpkj7DYKHF7BXO9gixD2eIasCh5Sy3+FopzK4css06ZJBrc3Tn6kiUlAXN2nOFxi7x0GmgAQxMYoUCd3aKkJn6KZAF8D4Lqq1ir79OOplBbChdSYHnosjM9x8yrJRiF6Ke6ZG5grMuAthjExjtAQVgii5BDRZn6mQCs3uXQskOcWRhSmiEYOqKUScoRKDEZBUbKIhF/cxg/bayPExjLK5784FgvtsSluLLPltxEiFrBRCu4/dHcQJa5kNjG0t++wxMFin1qi/eAazrUBxvod3uMfU19p0hsA5ukw87lO7ZDPxRQkqy8jWBm7k5pIJosUaa9eOYHVi0qGAOz+/nTRo0a6U0Z6NkQIry7BXQ4TR9MYcdCKQro2eGkaXAtT8KrLX41sLu9iSLhOP2+QAtEAnGSKKKVzdJjqSp18qYpTAmqshhIXuh6QTOewTfdCaZG8F44+RLS/SOryMSQxxfnhwoa4EzmqEijQ6TSDnY4UZ/fMmkHN9wgmftvFwLlP0j05S+JvDFJI6F3ZDjrnb8M8soHCxigVS3UD0+5jRCjQ7NK4ZpXNFhcIpKBwuYtd6pI5ABQJVWyeRBrKBWJLsnESdWUE5g5YWE8cI14MgIFMxUdUhSw324TXEPT2EMZjNFiYfYM+to6erhGMW2Whp0EI0OU5mCwoneugkwpxcJlfLYYlx+nti0msSaEDx4wndIRutwFvJSHMx8ajF6JEetvBIN00jLJuxW0IWri/iRyk1r8Lua48R7ElZvGuc9GgJdWgJdWIJZ8kmyztYnUFLZK6vaXY1/b0VhJvDjn38uQgcG9kMiXbkBklfhYDwwhGkhLRoU76zib3cpb6nSJp3qB6KMZ0u9rtXES686HdX2frrEb/7kgl08sP7bNtHbO75txLvP3doXTCJc2SFLEpRfK/yRo8NDYSofojZOo5KJJaSyKH8QFmutfCaCdIfJDmZTgRSoM76ValWOGixkxIRnW0rSlOueccaD+Wm6T5gITbPQKMJ3UElDkqhk+ScIbI0Blk7GzPe6YIAYQ/TuHyIi60F1r/aIlxPMFJhTQ+RNZrQT6mtT9B6wRQ0epRXJVplBA+foJ+CzPIkQwEiyojGHQqHZhEIMAbveAPPaG7+6i14RyRWNyM+aEBKTBwTeyl6OI/TjOlvldQ2Bax99Bi1pTbmfomyjjK1bYyGsRi9aBun2iGfueU+8vt9sn0ZC5c67BdDTIV1JrwubREwGc4yURTQhvahcdIieO2MrcUqI0GOqvUUho8dYMeV30Eow+zRS2huDYnTjDhL+eahk1w6NMEf/faDdIxL6YVtDrU+zJdu2sPS4Us4dnyZ5z/3fC59zha+cuIikkzRxMFIwVv+z41YCELANoauA9LVg7bKriASGaoakQlJPe7yd98Gnf+MvZw6vMQVL7yExmqL5TOrg9dcJ2P17iHS3S4/t/lZ7Lxsgnfc/bcMyRLPHrmAR5eP8tDDh3mkfQit9d/LWHCDDTbYYIMNNthACMHIyMg/6ph/L5HmwIEDJEly7ud/Tk6cOMFHPvIR3va2t/Ef/sN/4J577uEtb3kLruvyS7/0S+eMhf6uYc/Y2BinT58GBuY9juNQqVR+YJ/H7/93iaKIKPqeN0Cr1QIGcVqPH4v/l9hzwThHji+gttiYnoVzPKIzYVESNrYavNlUJR8nsDhzywjWAx2WjubIZxG93WOM7mjTXiyxe884x79ZQ0eKcj9iznboXyzJfyOmNNvHBAobC50ISlZC066iRz200uStiN4pQ+5kTK4XM/6ukPu/sIn+1hzjWQNPGYITEcKHf/s7c1SmYP+hrfhXT5Ec0Zw5JDGVPEy5VG5ug2vR2zeGKbjYTkKynqNxsowcbmOPl6DRhpKLXU2wry2ilgZGsuXRkPj+Ao4tERKkIweVGwWDLkhkL0NIibuUYrZOYJ9YR8QpfC0j1R4XuBlHyjGdThHv/IjhC6F5LKK0nJCNOKz93AxDJyMCX5HUUxLPwbt6hqDSp7kqKJ+e59Knn8EtS1qfrNCoFGBSQj/BihNSxyGb8ZFSIGeGsNIIy28gRHVQJRBBc7mHZRmkZ2MUqE4Xq+CjfYWQGqHVoP3MiEHbUE8TdDUmsBiRLlExRiQG1wFZUIiqRalpYfUt0pEEUx5EzaYvsLDnc4zf0cFabGE8h20/16af2jywPEVvu0Mwn0Fk4SxJRCaw6xl6uQ+uhc4ytC0QymCt1KjeJQleLJiKY5JYYXyFcS2UMnS/WcTbnJKtxYNkKgVSCuIiZNt6BA9nCEcNLoZ9BykUcSvg5j/bhR0bAiLipgBXoEKNWquBFOjqJGHVIy35jMgO1lCIHklxFyPS79q89n/NYzzJBz5UpZtlg+qdJEZNDaNzDp3dRayojFOIcZWF1+pgW4JkWFAoOGRxwJYr4dhyQOaAOtHFUwKZczCuQOVshFdGN5vQbCEMdJ6+HZX4yDTBmgWRCRxLkZoMVWtBlNB9ySaygoXuavK3pjidPsZSJGUbWfEgcHFXM9wrIqKKAZWhPEj2BRhLUjnYJz/Xwl1uU7k0gS0Veg/5GEuS5CV2AFO3dsnaIT0zhl1oYYSLazR23kUVXRgr0L1mCs8dVLRUv7WInMhTOQwi1SirT2csIN1SQO4OkeUuUctHlIaRQxYJCbIo6D17Emuhh5iponSC7Ui00EhPQgZf/twE+3BR42DV+wgkw+dpbNFl/YxF3BwkA5quQ/LrTQLPR1qC7r5hWOzgNs76zswtImamSC7egigEWMs9pFDINAPXRUyNkXkK2QoxcUiyw5A9dwj3f7bxcj7+Qge91kH4FhSLUG8gbM3ExZrmeoNWXyOUhMCBfADtNqKQI1tfgcD+/9j77zjJrvLOH3+fc/OtXN3VaTpM0oxmlHNCEhIIDMImeBcHnMDG6Wt7Mbtfx9013rVZe72212ZtY3YNGNvYJmMySgihNBJKo9Hk6enu6dyVw83n/P4oWcCCfwsGW/i7/X69+vWquVV16rl3bt1T57nP8/kg0gwjp6heWqeZmIyPRbQaJpkrsI6son9wwMxBwZlfi0FJWFkfxoai8+IZ8n2FsFxEWeOfbWO0ErRt0JmyybczqDegWsPaNzOs2uv20INwWEW1JchNBARnbLRrIUouqtdFj5XAtMhqoBebTD0wgtNpk0mJsCSmabAc9wn2ldlte6hOC6/vkIQDyrM2Zk7xifZ5NJd9XpSsEi8pCisO1105zyPLUNwyGY36TBkOfRnwtk9+hn/zkttoR7M8cPg2CqMFJqovYKKU4xdfdD0fP3KCq2d38O7f+DvEckgB0F/Ic3wXPPGwSW9zlaQXEdV7/MT//ACBOYPWILrDzjM3FcN8GKCAsdCik8swOxIlNa4p0drA1pIsznjpW96BPRD8xg+/lFq5yJP3HWepFbKwUGffNTu58GUXs/q3h9h0LUbviyi3DF7/ikf5s5ML3LO+TNC2kWmO0laGrTPOnV4jTdNvyV2w/6/x9799vh1/A23z/LB9Tmzz5fxLOB++nWPbZpsvR+ivpwnsecS2ba688koeeOCB57b93M/9HI888ggPPvggDzzwADfccAMrKytfIQL0xje+kaWlJT796U/z3ve+l9e//vVfkXQBuO2229izZw9vf/vbv+pz3/KWt/Drv/7rX7X9ve99L77vfwv3cJttttlmm2222WabbbbZZpt/SgaDAd///d9Pu93+P9pC/0uj0+lQKpV4/Z/9Ffa3wVo1Hgx414++7v+Tx/qfg29Yk+YNb3gDf/AHf/BVQjj9fp+f/dmf5Z3vfOe3LDiAyclJDh48+BXbDhw4wAc/+EHgS/ZZa2trX5Gk2djYeK66ZmJigjiOaTabX1FNs7GxwfXXX/81P/eXf/mXv6K1q9PpMDMzw0te8pJvyxPtX8/9NJFK6L+ygn82h7GSoF6UcesbbmRnNoO9uMQ7//whfvAlT3L3QxMce2YEWc6R+jbhqMuP/sxd+KsJd390lsfmdpBMO6iuQPQchIbSEyHWYhO3Ivnh3/g8QsBfPXk9mycnIZ+w302o37OAfWxAJFxkeYzCSETzsSYqVQT/agp9tY39J22MlT7Gj4wSxDHUXJq9Eab+9hloxPRu2I250sc90xjaHeccurfupXZlnd6xApUdLcR5Ie33TOGcatEv9Mgud9g9EtHvFlinSnR1h+CQx46/OoOQBulkBfPcFtkVY+jlAbUXGNSP57jqVWeYmlzlsY/NMXue4FQoecmLTnFRKeQd//4gL3jZAu51NnduzPDEPr5q1QABAABJREFUsVnMOyO+41WP4FVi7vq1STq1GbzMGfZidHsYykAf8DCvSQgOS6qHz3DTr2zw8KH9rByeBgTGRg/tmVi9lMyEpLOBGUC2dwciTXFW6zhzE/TWWojVLbID4xi4ZJ5J5lrEZQtvuY9Ucihsm6ih9seEQ1AW+E2N0BJddunuEKgUZAZJUeOvdtGdgHB/jfy5mDd/9wX8j/c+g7ncQQYGG1cXKDy+zsRkhJGHzYVRUs+kN+fgrAU4x1eRQlJ/9QTWQkD5sRAQ6HPDarT2lRWkVyLXVyQ3mrjv2oAgAkOifJuVH5sDW1LK9ci/u41oA+USSdHAfOIMIsrQQHj+GOFVs1iNHtWVkH65ABkQRKi8i2z2CCc9UmPYrkZ+qEmRO9pmprrFJT90huaiz6P/3kEZkrUf3kk8mWP8dEDp6DrxYx3IeaT7ZuntkvTnPEYOdRlUJMF5BcxOhn+yg4gyCq3h2KklkFFGe7+NvxBhbwYQJpBE6MEAfB9VziH6IRggNjtDPZR9c4Q7y5j1AKuXomxB7IKzFZOVHVqXldBCIDsdqg/3SMaKtK7w6c9onIamdOcmaqJAMOsigpDaZ9ZhswlCoM6fZevqIt7xOvkzAdL2odsD30FJQbprFOvM2aF48QUz6KUQsbyBSFKYmkAXXFoHC2AKRu9b5I2/eSt/9it3EaAw19oQZWjPJDy4g3hkhML8OrqcRzR66IJHVvGxD50gdTTKEXSuqdG5oQZCs+e3z0K7h44TcCzYMU5WcTn38gLuQyuMPxpwzet6vPJHj7GYWtzxR/tIRJeldzpDV6Rnrcy//A7Cl9c1ZLUiyVwVv6HI4gC9vInUoHM2/T1l/JNbiAzCF+5GrLbIbdYxb8wTfag7PJcAbZuINAMPJv6nS2MjR/QrXYS0EDkXUoXM+8N2pzBA5vKkpmbnj61TuHiA87jm9ollToU5/rRzCcYhuObCJSbO2+LON0ygFUOL+pEyGk1QMfG/cHxobycgu+YgUdVBnl7G3Ohi9hK0Keletxv/i2cxmsFz+51dM83lN5/PEfsIfrPJwfp+iuUxPvv2O2i8chZTVrEigXmuhbWwjvXjs7QyG3k6xF/PSMdzjLt5/uA/nuDU4Q6/8cfThAWPKO/y3d9zD94On5mgzfyTI3zkt0uIosH0b/m8YnAD9519iODxGU4cbNGcytCZ4HuKj1HOdTnyRy7LTxawf+bF/LcffQ3f9VfvIQkVt17m0PvoMuufH/4fhqM2e691+dVf+A7GvQMA/NSr/pDVtRadfTmimoPRjAhmHLzljKuu3M35A5PTx9a46Sev4j985m6stqBf0fibAl3QDHYqyMBeMxCpQDYzakWXT7zlxxFC0BlE/Pbf3MXRhXnSz58ANYZ3ZcroC7a4xuySHjnI/LWX8tGFY+zLVxlvfgGIaPyRwXse/hMs2/oWz9L/8kmShDvuuIPbbrsNy9o+PttsnxPbfCX/Es6Hv++M2Gabb3e+4STNn//5n/Nbv/VbX5WkCYKA97znPd/yJM0NN9zA8ePHv2LbiRMnmJubA2DXrl1MTExwxx13cNllQ7G/OI659957+e3f/m0ArrjiCizL4o477uC1r30tAKurqzz99NP81//6X7/m5zqO8zW90S3L+va88ChY/6EasTaxFzskGbRnqiw81uGuzYf5qX1TlKoBH1y+mEtvWuXxVR9VkGSeIMzDC0rLPPThApv3RxjdBr3ZMWJpYTgZhTMaHZl099ao2HVaModWmq4Dq7MpSI142uWC7woojNaZv6dE94RAFSKSRJMlAi8d0D3s0dcarz6g+9k2uUabTFsUjBZhP8V8gU9ompg5B+3YxLNloukCcQHCcwXyz2jWTpcwyxmyGUM/wjrXwFyStGZHicYFejakKyUiCkl78VDDBRM1iEn6GvP4Gus3zuKPZDzz8BQnT6Y0TqecPH+Mg7dtIXuCdmCyVS/wxJ1lbp9a4kf2dPlsL+AT5jgjl3WRQuEV+7RPbdC8bh+2maCfDHF6mvL+hMq+Bp0zKfWTknvftYvGahERd4kmchhKozoRqhuiWx2SooFq9mFhC2UKbGXQOrGGMg1EPyFZ72AMGugUrN2jjOQk/QbEnj10QpIgTIO0mWD2NN2aR6meodsJadVAKYPU0Zj1DN30UL7LwFPYz1qTsxERbLaIRnz6fgHPy3PxvzqJ6ad87L0u8oktLDy6MzXS4zF2PSH38RCzMEJcyJO22xjhsHTUON4m2eXSLZVQT2sMYQwX+iJDjVax04yc28X8ow4681ACAgfinEGunyCfFUNO17tEOkGnIeH6gMT0kMcXkZttdMlncMUexNE1vHZCNFMhuHjYA+osbrGxGLDy/TkuVi2e9Mu0Iwtxqk3iGWRnLNq2gVSaTGWY9QFuZBBLhTq2Rq4xIL1pGpErkpk+qQgw+wOE65DlPTAyuqMOoqExVkLUVgudJqA0ggQ9mSOplogqBlqN4S8PMLSBqkeEpkFcAqOboBLJ6vVVqg+1iJRGSTDbMaHSiEGCczQgiDOs+QSx0EXFGZlTRvT7RJttpGHD+AgqMzGerpOYJikOMkzBdhGYZHFC3I2IUwGX5zB6CtEOIEhJx8sIW6KbHWJVQITZcw454ViRrJtC3EEFCVpDtt4lKZXI1rokWYYOQozVOtHFO1DjE5jz54h8TbbYx3Il4XkVokIeI1DosAOWJmt2UK0WUd+lf/MkI/ccoy9CTilFJ4DNjsXMzxok7wkhcSBIvvzSBnyljo1arCMX60Q5b+gIMBg6IhEk2K2ITEA6XUN3HIwTHVQ1gV6XJE3gWTkZ/ByUc8SFjGdOFek3TUbGbdxIYMgBZBFRz0Q6NpkyMKKMLILj8zWySoJ1SPDw8V10ShKt2zgbDs2qT6WSklg5EIIsEZi9ZOie1QsYXHkA4+QKcqOJXmmTrfSJ8jZyxyj+yS1SUxA6BsJPsfspoj1MU63tdNk6fILpm5rkr0p4+n8+TunkfpIgIT3bontjFfdcQinQyHyVslrAutxjQVWQacrOaUn0xR7PPLZBWJeMnG2S359j7poL6Y2eYSVpkCiTjqFZu6nGFZVVrqitwMINXHnVBnc+uIN+yyV2A2wr5djaOHurEd//Q+cYH0tYyapMlH6IGy/czWKnzS9dezvvf+QuPhE9igbiDFYXWnxy805+bPeFSCGpzVaZ32phzg/QzRh3qY9RL2JvBvzhn7+G3//VD2Bk8IEzR+hWE6RnoosJ0jDI57u4VbBFSnuzCkKiRzRxPSHTAs+2GClZvOFfX8uPPPgo6roqv+Tcyphbo77+ca7a8162igu8ryvQvua8sae4cM8znFqqEJXn8HPP/x3Ib2e+bX8HbfO8sX1ObPPlfDufD9+ucW3zLxOtNadOnSJJEvbt24dp/qM9mb6Kr3ukTqczFGLUmm63i+u6zz2XZRmf/OQnGRsb+5YF9vf8/M//PNdffz1vfetbee1rX8uhQ4d4xzvewTve8Q5gKNTzpje9ibe+9a3POU699a1vxfd9vv/7vx8YKi//6I/+KP/23/5bRkZGqFar/Lt/9++46KKLnnN7+peOMTVJZguUiklki2zMRpaLZL1V+ick7IOFehWdCc51dmH2NWYnBhKCis3HD4/TMB2WbjyPYNrGMGJENLTNdRpgKIXRU+yfXecav0kYGfyvyEMoiYwFbVvDhMkVP9ckGynw5N9axEFMNFEg2TWKrkOITXztFDLKsCsZxpkU0U2RWYjyLYLDBoVwBaNcIZsap39hnqRoIs0UY8VCGxnmIKN/V5HqZoB2LOw9PsnpAbK5xXm3x6xbE7gFRX3NQScJQj7rRDM1QZo5GLUaracg0xZmXxIzi/Q6jO0ccPl1pwklnD48Qr9o8eijRU4dP4/v+eVVksQk8or4PZvRQsRg2USYFqahKFZD1m+eIugbmMcaWN2U/n0ZWrqstacwPAM8MFd7GEIQ+zZqq4UIQ8SuKsbNVZIzDqLZJ9pokN5ewfpcH51lyEECUQq+R1ItUJcm0uiAbYJWoMBAYZ1ZQ5sGbrPMzM4x2vUW6WNdEsdAboQMLh1HJhZmXVN5JCK3PgBAyQyzDca5daxOjKFtjv21QyeeRfQtSEEMUoz8gOCGEs57lzHqIWLSIStDtmuc1l4DZ62HtRXhnq6TzRq42kAIEywLpECGCeeNr2P0u6yvpGhfoS0T3QrBLgyrC57FijJkJ8K/ZxEVJFiZgPUWAKIXYcRgDDSW7SP7kLZTtCmgMyBNE0aWIp54eBZus+GhHoVnmmRlj+Z5ZWi46O+dofrxFTi7gjEzTm5eYgQgY0XlSJ/MDKDeJpsbQ2uNPL6IMT0GeQ+hbBqX+PhnQ0S5RJY30baNIS1UmiJW2yT751CGQNgu9gCIEwY1gVHNsBYgsy2UpcnIyLKILOdQWldYvQh9bgvfd3DvC9CGRFdK5M5F+Cfmh8eoUiQr5Mh2VNBJQv7+JUSWQSEPuTzathCGRNguadjGWW+j7unRv6WCzId4dYEuuJDPIYIIY6ONuTV4zk1o6TUFCvc2KC04KEuQzdYwXQ8DTTSSG5olSR9lK6wjy2QX7kWuOXjtHvahDfSTdRZ/rUzkglNwke0GYhAjpwyCfJ60agzt1ztd8k4Tx0wZKaSUkja9dwy4/X0dPv8Ji8YHitjSA8eBZoPkglnsk2uIVhfSDPH3ScZ+8FXCscIyybIEY6WOLhYQxRLmjEXvs210OHTdFoDIUmTOwd7sIf5XF1dlGJlFpiTZcgtSBaJPlqSwawYNZBYEtoNqC1KtMKTggts2uej8Uzzyp3s5e8cYp//WRG5sgRSImSmCMRe3HhMfLJGdB3ZaRfa6JKMK80wfqx9idjXxBbNDkePUZOqVA+SjAVv3umgE+fuXsVLB4ME+vdcZRE9IzjVXASgcbmG7a/htDzPSCCnofsRC7XKILhFMf4fHbYf28xl9lPf9zfXc9pL9dPsfQZ1YZyU3j7szR9vv8LlDs5weq1K6SfGq6zd48MQok0ArvYafe/OL+Y+PfIFmexMzB/X+law+XeKlr1ylUM64vLpGP95iqd9BS+ilGT/y0y/nzg8cph9GyFTgzSaM2DX6/ZiTp9f5fLxMXAwpn05wmg5pp43dH2DEKd1Onzf+4u38t198H6t/cxr7YhNhC1RV4eQ0+y9a40hnDM+K2axmGG0wEs1XWIIBJdfBMCSGqTl4zR522jUevucRCqUI6QqCswEVy+L66VnqjcdZD4vsufrCb+HM/M2xFTyM1opR79ptjZxtttlmm222+Tbi7NmzvPKVr+Tpp58GYGZmhg996ENcfvnl35Lxv+4kTblcRgiBEIJ9+/Z91fNCiK+p4fLNctVVV/HhD3+YX/7lX+Y//af/xK5du/jv//2/87rXve651/zCL/wCQRDw0z/90zSbTa655ho++9nPfkW1z+///u9jmiavfe1rCYKAF73oRbz73e/G+N/sO/+lEnf71P4mofsqj95PTMKixqvG6HyA+djwrqDz4DxxouhdPYHr5hCtFOVLspzgmXc5tJZdzBemKNslHtjDCpnEpLNLMbaVkiYuJ5IKG8RgAz0DI5DICIrrMTqV2FIzcmVGcHKOtNNAlmOiS3JYj0W4azFCh9hLHdgU6EmbVIPSIPw80nCxHBORaMR4AmmCs5qSjZooIQirQ1FZs6nJWm2kgiS2UYM+3VMxR/6ugntNie992T3caY6xpSzQYPQiyDRGPQTHZnBZnuIXkmEdviFhbATX38ArxHQbNltTGZ2qxvFKtIXk9x7eRWO0xKTb4ObpdQxDs+sVO3ji6RqimKEiidQWsatRSyabnxKIVCLKDqkncdaHrRupLdHSQAtNNj2KYcC+W+uUdzU4NL8P8h5aCuLzQB7WGJsSkYFyXcLzR0lrDiiNV/aGVSrShFYXK9XoYhGSlGCyQDNv0jsXY3XA6DZQWYLo+HRny5AlFM8qqA7tdQezBbyzbaQsYGBDo8X6ionyEuQen2x2DvvYMubpddREARkP7+wHVoSz3EfYAjFnYrVjsjEH5xwMdlrEGRTm+8hCHuHa0AuI7o7xrrPAsVBRBAic41vYyx1EwUeHEVpKWi/aSTzm4O6tYZ7tg8qgUkQPQrKdNYRnY1SK0ArIrOGi210P0TOj7K2tsWcyIbt0g+z3XIyOoPFDc4SXFjDPJMjQIJ5wScZd7I02enENs1NE1crEe6q0LytTuWsJA4VIMgzPQzgOotFFSBO7lRKOm2AJZKlIOmYjDJvIM3AfOIbs9JHNGoZl43Q0iSdRvsDrB3iLoFNICWCzh3FqmdGzK0T7R3H7NsLz0I02dHqINBsuymSf7qU1rJN9zF6K5Y6QSQOlMsIdLsnVM+QeOIsehJDPo7s90koNocHWHtqzkEJidgXda6cQlRBdLmEECnOtSbLfxj+xTrR7BAC7nNC63cdaSrDqCtv0kAMFGwP6O/PYPYVYDWG0Cq06CFCVPLLVRaaaqGRQfGSA4fmkhkPupQXWLh/HPREQ358hEoG90gPLIItzXGqvcDw2yWUDWvcL4v2K2duh+addVNJBezadF5+Htd5BuGCXy7Cx+Q9eB8V4Db1VR2YKyJBBSDzmI87ZYGZo2SPLuxi9cJjoESB9FydUqGYXkpRMK4wwHg7o2IiRClmmyAxFMF0gXXawTmSYJzRmq8t0+wR50cc6mBJ2LawJA1W3SG70Sccl5pIgyxQjr92kMBqyaJTpz15I/kQDUalgJRZx1iIruGhDoEyFut9nY97BYHjt8E52SK7dS3pG035PB90xcXY6JHVQOQNzuY+u2ogwQ6eStOeT3elSLlvodZtPbj5Kt5FSqhX4o3//IZI4JQF0s0ej6xAZI+y5qoHfyBgVlzGaq9MTVQA+uhCz5n4R319H9ATueMILr7F5+IsNfu0vb+ICvcDtv/U9XDM2wg+efyn9JGauUMGUEv+8UdorTZQJG7trvGb6B/iLv32I+x86ibjvHLlWQNYJ0cJC6RRzbAzDsjBNA893eeKhU1hBgl/KEU8L/FxAqVjlSLNGo5WnIz2sQkCsfOy2hbJhcb2B242Z3jfFpF/m3Tf8FIlKOb+0g7/78KP84ZMXMrc6xZuuWGXcqTBIHYRxEx98+BQt02b1C4cJ+iFezv3aJ9k/E1HW4HjzTwDI27vwzInnNZ5tttlmm2222eZL/OIv/iJhGPIXf/EXuK7L7/zO7/CTP/mTHDp06Fsy/tedpLnnnnvQWnPrrbfywQ9+kGq1+txztm0zNzfH1NTUtySo/51XvOIVvOIVr/gHnxdC8Ja3vIW3vOUt/+BrXNflbW97G29729v+CSJ8/hECjFRir7iYDYespTD2xJgXjeCeWQHAkA5Ws44t80hhkBUylCkxEli7oIbZD6jNrrFp74JA4x3t4qwnaFPQGx8FJanOdoiFQAPjs5t0jhRwthTOMui781x7QUTDE0MNjSc9dF/gfmaFwcEJjAz8Q61hcqFWpbu3RHKRi9noo0ZsnOUUsRYhJDQrPuXPzoOAxu27EZHAOdPE9BWXfccqbRIWH8iRu8lAmAmUBdFlPqOFJvs2u9zzZAVKI6A02jaJoz6DC8fwzjSIcxb9XQ7u8TpOPUQHIXqPYH5Qo+14LNw5B1t5dFonq5ZQzTxql2ZQsznWtfAdxaIYR+Q9yr06Uofk7jmDMVmmddEEprmbwtk+KkuJC5A/2gHHQU3myZTGXGuhPAvaPc7dY9PcmcfqxshUo2plvLdvIurRsHLN84aLSWPY2uTUE0wl0LYErRGZRluCzHNIU4H19FmSm88ny2nk4WVwTfpX7yDcVUGnfSr3LKFGCkh3FACrG6PNFFktk1QtxEaduZfGLC/niTwTYQl02UAuKOydIyRmAwSkEzZOINB5H1VNEFIgywXiYp7cQ8sM5izCCYm30ELXKuhwQOcjA1qHRjDCAUQR2DYojewEJNfuBMdGK409yIhMRTTjk68LlGMjLJv+ZQ7yPBexFaHORujNLVTiYY26OFsxYr3H6TOKxcsv4oUjR1GtDBEZWLsFxmiIXHKxTqSop9Zwzw71baxSBWWbsLSGLJik15UZHBzDzjnYjRCpU3SWQgYijDHmtzD2TCIMnl24g98IMGwDIQRqpEDpiS0SMwUszOkK5iAjKQoiT+BsKIw4QPshpClmkOGsr6JnR2GjQLJ3kszQ2Gc2kJYDUtK4vkRBhPjHm2BrsA1EFKG1CaYxtIDOO6gogCgmCLYQXg7vzBZ7r884fiKH3OoiZ0aIZzyUKfA2W0ipKD29gtmOiK8fanWpukOaaeKJBH9zQKpSUIrUsBAakqpDVk9Iqzn6u3P4mwmiGw4t4QF3M8L58EnEjglUOUd8UYnEtpiY6XHLBaf47K9tgBLgusw6LZSyWQ3HOP8VIbkrA/SNER86tBfPOIPM54mLFlnVw3tyGZkloM3h/n4NuXsFyGZrmPV9lmDMJS2YWOeaiFYXARhBhFAaUoUIFXtuWmPqwgaPvmuc7opNNlFBHlsYFmQUCwjfx0AjU0FoCQgUck0Os4PrdUp+zFQ+IfZdhCGxlE+yLyN8QxGkIP1oRlIoYqkQtEZaBkascbqCxHPJZIbRi0miEG2bFO5fpueVMOMB+tkkjQTUZIVgoszkzOO0/5fBzmt38MzSJrJoE8mErYtg+tMx0vfQUlIs5DDbgl5Hke2tsW+kxMxYjScfOY7eM0lrq87SQYnqSIo5AzsXc1PxHPL093P5+PfyQO2LsNKnaXY4unwO4UCW1ej2BCv2CUp7Y777vEXkMzG/9FdL/OYPL1FwHZ6ur7M56OFkAqcVIsOI/kskP3fwMU4dfR/F/D6qlRzhgTGsVoj9xBKyGyEEeN9zgP/2U6/E813e/bsfZ1BvMjEXsZGrYHYMsrbDptPH9zI8O0EKxex4nX6xyWBxB24n5dd+4n8y3g546etv5eU/9iLGswKP3X+SsSsLeCMedeUyqFf4/OllbjswzZ2nu3zw4aOsBTuwR5rsvnYO1//qVud/bpaPtrnn77pM7xvnuu8eeb7D2WabbbbZZpttvoz77ruPv/7rv+bmm28G4Oqrr2Zubo4gCPA875se/+tO0vx9APPz88zMzCCl/D+8Y5t/bqJRm76fw9vKMFMT9ZhGXOywZ8dQQNmpCC5+dZOTxS0276sgrATRByvMses1fca/q8P8WTjSNCg9tE7prg2MSJEVbHo3VxEuDFKHWEm0Fnh2hqrGJALEYylmETaaZRobZYwIspwk/+Q6GFC8Maa3OEW4bxQ3STGEiXVsC9MyYWoMc3oN/VST0B/BPj+HsNKhja7vUlkIkCspxtl1XC9m10+uks06LKzsZmvOYP/3phRHetz5TIXqsdM880iei17ZY+vDw0RiEoT0XrgLtCCqedzkHOXUIQ9LekjfRzsOra7mzEKCnhB0AgfRiMn6XdK1PtnsDGKQZ6Qc8kg0xfGNCcyShdENSVdMEHmcXANzNYOgTVQwhgkULcgdaYDW6EEfrXLYi1to30U0+5Bk9Bhj9UiVsa01sK3hX2ZD9qxoqIBsbgz3mVVUYiIbEbriD1udBjG2VijDIqnlMJ88jdzqMggDopzAsSDzBP1LaihXYq0a2ANFtNMnM4ffX9voc8F/7JE7POCeZ3aT9x2+58fa3PWFHvc9MkZ0VYJ3OkRKxaDdxTp/FpkoRl8Ys/vqAU8cMRnszFNa0vh1QVQykM0Bnu9jNIcaM9o0SC0N501AuThsyRitkkbhcCEsBWIQDW14TRMzhNKTfXS/g1IKHSpEqnDuXyY0d+F1JEKaKNMAlTGYkJAJvGNtMkuxXDJ539yF1G46B3eE+J9vENtVWMsRHKhQ/NgmMkzQtonq9SDnIQYRUsWkbghxiJFJ5EodnddU5gJai0VUowUTeZRpkgYDZKgwZYG0ZCEHMdJyMAAhLAgysAVshohWD6NlE+zMkXmCLA5RnkGwr0K+GFC6MiEchfgvQdoWRhgPKyrCgKzkY7T6aJEhwwzBALWziIwjjE6I9+gSQkr8y2Oq3ytZeWuNXKRQmJQmYc90i8XHTGhE5Mt5BrvsoVaKK0FJtC+GyZXDw9YZ3TPwFzrkHzhH6vkw0LBcx6qVELlJxECRjeSwejGObRCOGBQe734pMWKbCMtCmhZykBLcmWfiBV1+9crHKF2V8ODSLrqfWgel+cg7x+m/0uB0WmT6gjpT13TpxTbOk01yV5dJl30sLUhzgt4rp5h1l0k+0SOWNegNhn7NvR5oPdStkXLYXhd/Sc/G+cIJshfsRaw3ntsmki+p3Og0ZdfV65TnQkYuGaefjkLVRxsSkSl0s42OE3Ru2HqVP9Uj0TGG4yNjTRrHXH/hgLmJkCf8NptbZRQmVlUhWxlYBubJNsFuj8n6gAunFpgYH/BI5yDKlvTGLfzHVpC2jZUYw/1otEgVdC8eI/fF9nPdO0YIUmvaD2UU98xyyc6dHC8eoX+wRGvGp/J48KW2QdNkq1knvSHP1MYOzgwSPnduk2x9k9zsKIYW6KKHm3doPhZRv6KAKQTLX6xw+O6HueL68/jBA1fz4Mo9zDRKrD8lGNk5wLQ0426eot1CpymzokNaS5HvP4PzBoMPn32GI/V16tkWW4+cI+6HaFdz3sEtFg6lvPsDhylUlwjPrFAo5kE4aNdFdHsIDTMHJjhv9xRri5v89e99GtO1+I6XneSCXI+PHNpNJ28hTdCZhWknlJ2ATuqyp9JksH+B+rvG2NjsUCnbFKp5+p0Bb3zz73NsBl50Zg+vueEKoooiKQkWs3Hc9BA5PcJqHbRpIfM257/0wLdFa9Gvv/6PeOLgBXBS8KrbM4rcCdkSIvcjCGE/3+Fts80222yzzf/VrK2tcf755z/37+npaTzPY319nZ07d37T43/D6jZ/L9g7GAxYXFwkjuOveP7iiy/+poPa5hvn+378Zt7+2U8iSwXcwyHKtvDddUZdi5t/51Laj0SMjK6w/KBLvLWCFQ2QWkGUUDyQcNPISWyZkdxhIUYy9ryyh3stnPpYlbiSxzZsMiEQSnAuqiLRmGcyigsxbitjcFuRwVTMMSdh00546S2PsHXW59GxXch+jJy0sdYk5tkW9tktEAIzy9CWCZUc5pOadEOR7ojRPY8pc4tuoiBIiLAwxj1IqgzMhKfmXQ4zS3SzgXlhyuPdWYxPQjxtsPoJeKJS5qU/0+KejwAIZJTir8QQpbhPz1O7dIOFzRI6p8D3EVISrggWP7mD7KUx/ZaH8FcpDkK0YSL3ddGzLhtPeTw+v4tzWZm+kadiBmiRINyU4AxYRoyMUqxBgJIG2hBY/RQ8D6HBihPksy14wnbBj+kfrFB6pA2miWp1SM+fRk3twjx5DiUgPW8HyjfRqszY8lmiyUnCVKOVJjUNpAJSjdGNUaU8oj0g3ehRun4H9WtSNr0izlKb0gNtgptG4V/XSFsl+mL41U9zLt87O4+3Q3N0for6dIFzmUtPgdkfELYdCrkEfzxmYZeHXo2QpsA60aXe0BhxjN20kX2BRjEY19iXVPG7HoYVEs8YdC4tkekcMzlFciJC5Tx03kOP5BC9PpTLJCVJlvZw0hzWaohcq0NngFYach7KshGZptjdoHSroPF+B5lkmB2F1YW44mLvnaK7E9SES2IktC+Ywr3nDNYTA9I9PmlF4T2WYDczyBSyHwERSgwrlbQlcc908J5qY3QShNJomafnTKPVBqiMcN8IYhDBZpPUFKSqgqVMIlfhtTooyyCueYhyAdMwod5FbDWhbWF6Enox9APcyCS8fBoz7BIxILzDR2iNPLsOpoFGDJMNpoE/H9C/rErxiy1ko4vI+bQuG0F5BvHuEbyTdaI1l62na2QFA5kqhNIEozuYb/gkoxXwLcz1HnLSQxsGWa1EfKVLuCtFNM6RE8PKAV3JkPUYkSmQAi0Ewh224cgM7GYImSKzhsdcZkCWPufC1LtgBGlZFFvDRa7eBHXEo/riGKEg7DF0e9Kw72U9bsptsbGVw3MjQkza/Rz5S3L4+SatpzZR2iAqTRBbBaLVEunxNnj9YWWZaUClhA4CpCHRwVAo/MsRgPfAaZRlIuOhuHAGCM9BFPIgJY+8bx/V2R7nnhoDNHY3QrgS3VdDTat2B7QizTnoKMM0beiF6DhFKrj3Lytc9eIuTyxMIHrDxGOyKcj95gYIjZGbxFvoUMn1SXsWqydG0KaktTNPOiKfbauzhpU9cUY64iFWuojGgMwEMx3uh4yGc218xMO4vMQjn3yCpJnCRszUx5cRWqBnnq1m1RpjPkCmmq1whdJsmVZxeGyCnKT0+ArK1OzJj3D8E08iDtSYMTIKU5L58YgP3vcZ9uddBDlGj0vIbHQ4RqUx4PuvvoTRyigf+YOAP723Tb9jELxojBF/nR+94CKONmd5ZnCC9VQRdgOsOGX1TyusHo1REzGt+gq9MQuzaGMGGaKSg2YArsuJ1jE2ww69ehfd7qISG5kFXMw8H/UOEiQmWSQZkKATSRg6XDR7jvX6xej2AsqzYWaUi19xKdd/15Usrq1x8rqUnnR4tLXJD2ORjaSE2uR0VuEmo8Z84HHl3A6WTj5BZAS0HjwCt7/qWzo//2Nonq0jd9oIDZ6RQuMXgBQtKojcv36+w9tmm2222Wab/6sRQnxV0YqUcmho8S3gG07SbG5u8vrXv55PfepTX/P5LMu+6aC2+ca58ZVX8b70s4xVlmg+OIYRJuw+b5F8pLjrzBmu5HaCnM+pwjSlQGGamqzXBQFNnWO5X2bC7TJYKeLUIryJDD3i0g+mKDxuYDYywhHQQlA0A1JlsLhQo/RoRFo06E1F7Nm9iudEnLfvHFlmMFY0eVDvJYuLuFmH/L0LyGY0tL21bEQ5R5ozyGUhetlDjo8gZA5zXZOZLoguuuhjDRTRmCQtTZK48PRjAe0LLWQlxglThAHeKaAMO24VXHdek9pkhGJogJQWvWEvhCFJM8Fjp2bJrB7ZZTbpSwXOvWCe1fRSl8GTOUzTILy2gpXPqNwa85LbnuGB1b1sBhZrvxxg6AHugTLOD5r4SxHtEzWCkTw81WD6QJmVB08jPB9dzCEzEM8qyYvLNOqQwIgZtqsEMYVDa7iyAJ4BpoTeAGEVyMZK6LxPljMZjEmk41Ofm2Ui69BfHmq8xJMFEqkw0FjdiMzPkVwwR7ZvhBfcPM0H+xtkbYfcJ9dwzvbIvidPMGMhHtXIhWEbRbaa8N63TnL+TZLOsoX0TT7dnaL+iZB0Y5lo1yzX/3jGbLfLn9yZYp5aBzTRUc26FgSXdRl93zJmMySbqRBOumDmyR/W4FgkUZs0P47QHm/4wbtZuc/lI39zkGTERxsCKnlEGtHfPUZarOAe6mKFGhwf7QHd7lA4GQF7Jqh+3wB3LKRzh0kWVBBCkDvaRBc8dNknnfMIugLWBaW/XAAFB16XsnKTYqXVR56RSAR4HmgNrkWw28U7nmHEgvE72qSuICtaSNPEKJVIQxsxWkNLyHLpsCJrwiea9Ohe5VF4OsZdH4BpkHkmtNsYiURMjRGNOLibFpQLSC9Htyaxey7CtPHO9Il6ivgLA9SYg3RcCGOicY/oBbvxHl9BGiYjxyXFpzaQ3RjtOaRln9zpgMyM6e/NoyyNM5AEJ0pQ04ggxmwOiJsmp+oVMCRmkDIwU7wNjZAZSmiicQcdugTX7cVfGApJO2MDEkA5JrrkYxoWoiTQBZvMhMySCK2HLtKDFP9zx8EwUTvKZDkHZ7WNYWVc+rqAQcvh8GeqRKbNmWYZWygKe1t0zQrxvMPV04+TdSDccimfFxJh8dGTl7LZLjN9YRfhhKheOkwWIWku58kjSX2D+PxJ7KdXMBu9566Bf1+V9eUIwFD6K6prJCCCCKwAch7tDYeooIetfYMMXd8cXi8sE5HPgcyj4xiDoRC2jmL02aXnxrvj/SN85gMj9H7Kp9TbQG/WwTKQrgdZhioKyuUBB3YvcvrUBKfma3jHzmIVLLRVReUdVBRhrrXpz7hEtTzllS65+RbRZAFztQuAfvgo4qoDiLEa7Y0Wp2uC7EX7yB1de9YCS0O9ia5WEI6NWA3IpEVPpsSjGXjDYyPTmGxuFGNpkwOzIyzvq9K3oLma597NSfovFZyqrbN5NuFGLmHhvQ+S9DOqP3Y9pYrHeGWUm6pX88HTp1g5tYI2JPv3rfPoU7+D17yQ73z1r/BSuZeT3hK//q4/QyiB0RKkoxWsw/MAGO4E7WvHsfrg6S5uZwStNemDKzz+ffO88MABJnaUWF/Y4t6PXcdrfuk1KP9zlEa6hIFFGgm0AD8f0ozzeEaCMx4SWgZWK+RzH32C1f6AH/+JFyEDyBkJ/SMB727eibM7JcZlXNQxzDw/8YIbuKx2Mf/r3H+hUuzx2NHmNz0ff7OcfmqBfpJS/eBJduyfxLZ8tPBBByC3W5+22WabbbbZ5vlGa82+ffu+ovq21+tx2WWXfUXyptFofK23/x/5hpM0b3rTm2g2mzz00EPccsstfPjDH2Z9fZ3f+I3f4Hd/93f/UUFs883zzl99L67Vpb+rQlrw0bbmilds4QmLj3/mGq68ElozNfr5UXZaG9x64TwPfCHPMyP7ScseOpP0Y4vU6KKl5PH5GcrFPvn9XexjHlk0FK0t+D1ERxHFPuKBECWL6NTBOSv5Qnc/rVs8jjyyh9JEj07XG+o2IFBNA8iQnRA0iEIOMT5CMpvDL9RpdiyCsRKWa5KzBwyqAnEowxAOshERTLpoE6yBwqlHVB/RmKfWoJqSjRlEs0X0FQaHN/ZyrbHMf/p3+0hECraFVCZmI0BrjbJcuis+jhOS7XJQZU02psi6LuEFFoVawAG5zNOFGumOInPXL1K2AibjHqv2+Tiqh9nso892UCN57HKGDhXGSZ/ghjIn8hm5kTJquob0FFJERC0bEaX0Ip/0GofykQ5mXQEaq5eRjZtoMfz9bTa6yMQgdQQDM2Yw4RFWwR2AZWqeqU3SvwWcJZ/y4ZR0NI9GYjYDEidBez7WPoeVzQ0mVZdoyyKtVehW83Auh7WmyOYCvMXhxcN/7BzPDByOnBpFlBTKlBx/eDeeWkTqBH814+6tPdTWUmLTx9SgDUlSMpEKlA8iCFFkdGck3pEmuSdaUCyjlcK0C5SfCRFRzAM3TXDtS5YRfxkgmhrt20hhoFttnHMBTt7AsEOiCYfM9ZENC+fxLlpnIEzEhMN622KkaxCuCYx6cyjs2k5g00CULNw9M4gtiTkToOckpZs8jMstfqR6jN88dy3W6S5kGSLnI6oVdFUibxAMzqvgHI7Qpklk9LCXegR7RhD5Ik47wcps0rIkGzMAA71vGqcT4r/3JKbrkEzVQG8g+zHadMhsgXZMlG8RT1cwnRxKAI4k8DPUhEPpU3V0PoeRKIxBRqpCBjfM0rmwiDIFNEuUHt9Ejdp0L8zjygjbryIci/6owj3SQ82VsOcbyEGGKubQcugcJUyBlWqqB/u0jvpoJVC1MqYhQCm0SrHXNakjEIMAlocL06Dn4n94A3O9i3JcsqlRRDfErPdQ1RxJxcVqR5i9mNzZHuYgQUuJWG+hCjbhzgIT5Sa7rjyHFhbH35MgVyT9DUVlV0Ruf4GtZ2pQg0SY5Fopi1GB0XioiWPmE8z3L7KKQM+UsY+2sZop2rVwzwWITBHPVYgnc7j3fSlBo5/9k+3u175AWiYk6Ve8XnS6qDgGI6Z/SpN7ZUR8KAeDwfAaZZqIUgFhmqggQHd7iJxPdKHAOjsc57lp2bWwTxrw7OeLJAMVQG2EzLeJRcr8Uzs4+1gNa62LVQ8xOiGy1yczIbh8FuWYiDBBnBsgno1xcM0szkeODI9NPyaLM/S5FcKcjygU8R9cIxkxkYYYtlj2B6j+ACyTaLdPnDfwn1omLoKYKKCFwD3bhmoRHJsT6hm6r5IoV/PxU3PQcNDegPanHKb/TR+AwvWCxj0mtfMKzDtd/vtnvsA7PvoQwYhES4FAYGUBH/nphGDjGcT6Z3n1z93OI3/5RYI0RUsTogxrEA4rrkoFpBSY7RQjjEkuz6PuXhh+1vwk14ycx3rjbTz8+hreyWk2E8GfvOYa/u6+EzzUPYrnx3RCl3wxxLIUQgh+9NJdfOS+U7TaEUY3oh2l3HXiLE+87cNwNkeuo8g8QXXHMzi+Rz+0aA4MTm+sUsoWuXzsYi7fmSc31eZc6e/b4Z4/3vlfPkw2OYJTVDSES5IozJEPglpHWN8a14htttlmm2222eYfz7ve9a5/0vG/4STN3XffzUc/+lGuuuoqpJTMzc1x2223USwW+S//5b9w++23/1PEuc3/gctffDGn7CfpfrQ01HKxYbU9QjGnEUkRgOBQysjIItfessFLbl1ARyMsvtsgu3qSnEzxTMXCC0ZJNh3EIgRrAeYNKZ0rU7zPOXh1ReOjEfe+f4Te9TtRuTyx76JdEyxNZX/AZGuDM38jObNvksGLS3h2RDIQCNek9+IZyn92dBiweFYTpRnTijW98zXNi112PKNw8wK9N2CQZuitOsHENMqUaM+EICX1cuSXQ6LdY6S767gfaJDJAG1NoXI53vfR85BxH6O1PryzPjsNQmGkCqNUJOsKVK2CdUhjLPQwnu6Rf1mBS66uUyt22Vnc4PLY5c5je1mPiyw3yswfn0UGAuVZGKsZulZCPeggbwmQ0yn+4ZRsOaJ10EdUy2jDwN89IPyijQQwDOKiRPYVyspQ1RIy7xFNOqANEAIZpkjfJy7ZyIVVnNUEZYMij9nOCPuC3hXDhWE0DvbfbUG1jJaS9oUWzcs97FXBSD3g1B1bJIZLYfUccpCQHhzHOgbCsTH6AvfYOQCy8RLi5BZyvUEyViIpGSTKInrRDKMfX8c/1mJ99ySdxQH+Ukg2VyMZFfiLLYyshLFpkbgGyha0rytjBgbFkxndYogME9zIIssUYrXBZ+7dxWcP7WUkF2D1U0Sni8ZAIXECk8wSjN20xVONnSQ1n+IXOjgSxGiZaG6UarGHY0fskV0aKxE4HoQxwjbQ4xXCHXnMFYWYzFAKjPMMeLnFaeFjfmEHxkMF0hKY1haGlCRJgHA8dE6TlfMYmxvUbx5DRnmslT6iG+L0JZY2h25JTcXE3Qmd/QZmX2IcXkZ2AqyRDHSG9h3Skk1w2TSZayIMA+UI7KqPOd/HjBTls9Apx4jLQqILJ7FSi94uE/9sD5lapHlnaCcuNf29HuXjPjpnkh606eZL1B6KkXGDbMTDFCble1cweilCg4wbaHeUwY4CqS/oewZiPIKn6iizSDRVZHpsi0Ktx8l7JxGZhZYGadlA+8OEhwoMBntKVO/eQKw1EaaBCDXKNgjHHexmhIxTtACjH6ENA/FslYrRDkgmxgmXuxhWRhSaZLYkywQbHY8dIqb1VA6jM2zb8Y2AsKPIPxNh7slwrBSvE+GdDhBKE034qJ0FzEgiUo0xGFZ/ucc30MIgOm8M78TGc9fAL6+h+fskB5Y1bI161rJb/W+vI4yee9j/QBdRyNDVMmw00UmI7g8QhTw6SYYi5IOAdNYhOWCQO/qlqlFZG8FsJaC/rJI0yxDhUKS41yhw36cuQYQZ5126SVwqsdwpojubmJvBUFso0yAF/YtnYSVChCFZyR8KIj8bdzzoYmcZdDr4X+wAYDZAmYApENqAOIUkRemhKLxhOlj1AGGYCCWwGgFs9RBRwpknmxg3CEAhHmwgduaxvygJttpcZpxAcxWOEChH8bicp4NFv+tQ6FkUJ0vovTOIVFObXeFsqklCxer8BmmS8rG3fQo9MoYUoKRCxyH5F9YYrHoo38B/bAl3vY96efk5MWgRmdBJ+YuVz2AtvAQZKKxzffb//O+Qv2UDwzIRAtLMIIlNhIrZVYm5tHIRHyt+GrsxtGSXlsab9DFNibhmQGYopk+M8Z3XXMADZ47Sxqea9nji49Mczq1i3P4M+/e22JOLKLxOEEcJtmN9HTPvPw0nPvc0luUCkixKyZIMOzcNTD9vMW2zzTbbbLPNNl/ih3/4h/9Jx/+G1X/7/T5jY2MAVKtVNjeHdqgXXXQRjz322Lc2um2+bl7x47cxedDBnQswopSo5PDJo5fxaHCAX37ZDQCMDhrMbC4w3yvzyUfO5wt3TmJ1Y/wjm1yZC7jGH8AKFO/awD43wDoeUPijlMoXYpSlCKqSAIN+6tDaKmE6BXBNklFBd1Zxwe4l3DDGs0OMdowwFMZ0jByLySKL7q4i8YQ3dCMqeSjLQG51SR9vYC9tIcdTdKTRDc0t551DFA0o+NhCYHdS5CBFZhqJJBrPo8om2bRNcomPsRXhHLNxlySiXMbICUSUIJKMrOiQVL1nWyE0/R0uhlPANIvYx1K8Wsz+25a5Yu4MY6MNjh3bgWhBIR9SsCNibdDNQ1IKsc82wDQwdYHsmEtwNk8072O1M+xehlOPSWsuRi8mlgapZ5IWHTLHJJ4CayvAGgikY6BKPmZTY292Ec0BGk28t4KqSmQw3FeEgb8QQilHUnWZ/nRI7XCItxFi5/yhq04Qo3do9IhEjCYEK4okn5BVFNZ6H2e1T+FwgNmKkf0Yx+7DYLiw7l3oInaMUdhb5ju+c55gr0l/XJGmCWKzhW53sNdT3PvnMY4tgDRxRR65lQ5tohONMcgweimFYwor8odtC2Wb6t6IPRcsY3U3IYiY/PxZLq+fRiqNSBU6y9CbdZJRB7m6jtxsca43R+2uBrWPLjPX6WPs3IFRKmIMEvqPd4j+IuLa3DlGZjWiXEKMVKCYo3/BCK3Li7QP5klXfOTnLIrtPmVnQG69zV2PThAPtkiTDnHVILJTepfW6Fsm1l81yR+KENIE3yGZLdO/fAd2CHakIdPoLAOtiQuS3qxBfzQlqZiInIG9q4B8coHN22usv3aMcMJCIInKkDiaxAOUQpkGIkzJyj7qpEuWN0krBu5SC1HvoB2D/L2nGfuzw5TuPkfto/OktRzhlSnmtTGlUCPTDN3sUHqig2sYuF4Z6dhEl03RvXQPWd5GRinmShu7nRL/bR211CEtdBFCc/3LnuKKa49Tm2uQ2ILMASwD8nkArKbEPx2ghUDGKeaZNUhSdByjTUHqGShTElcdegdrpNOjaM8d6ujYkt7BHJ0XjhB2XdLEIbi6iPAl6/dlGH1FpTFAWwJtSt7136b5g/93hqXfGrBxR4X2nZLNf9sm2pEjnvBpvnwnnZdXUVaP/NN1dKpYedU+OjvKuMfWSHeOomsFtIR/qPs3qfrD2OMEBdR/+3z+vvFJA2nRILW/NA2K7gDDdhHlArJSRjdbZO0OGMaz4sga91N93D0zYHwp3RN7gsBP0Un2XCzJHoekHJCIBHtKcc0155jds8XPvvph3vxvHsMdNYku3o3yXIzGAKMXg2mh8jZxwR/qAYkvJZUE4D2xQHdnaVjpw5eekynISEPJfW7fVM6nslRHdPoUkw7FR9YoHGuhHcHmq3fRuXYc45M9nIYJgUGyd5xE5kkPKLKGQp4KAegeg5m9fV438xT73BbKyhASrLyNtdWC+XM8+RaXS39QUhy3cVwb0zKZPrgDHUcIR2JOC7z9AZXv7GOVPdKpEsoyUFJjWRoxNgLlEkGs+Olb3sLsZoukHOO0FIM9HkoKxnMDyARJbCLR+DHMFtr4lsMDq6fJYnOouY5CxIof3LOfX//BF4OZoC1FeGINc3yTla0SWWry8DPTnN5dYL0Ws2ukyqHGOGejAqd7o9/o9Pstp9noo9a3yNY2SBdXUOr5r+7ZZpttttlmm22+xA/8wA/wzne+kzNnzvyTjP8NV9Ls37+f48ePs3PnTi699FL+9E//lJ07d/L2t7+dycnJf4oYt/k6ub7UJf7OgNNbk7SEhR1Y7BgLuefXP801bzhA6Iwxd02Tp1Z28MSjJubaArpiEp5f4vP/I893viEgfm9A5VSAKOdRIkNMj0ALsp2aKA/1ao2RxxWukRAAhp/g6Q7uA102dhd4+QsWOJcYPNqbZndhnfWoRCQEZqLwgxDP1wQTPmrMww40quAhoxyMWRTNEHNL0u9oTnxxhLH/Ktn8RAHVMjCDDDsQEGekeRNDAW2baNXAXFakIx4aiIvgbbQRmYe2UlAZcdnBiNXQnUeCqNZIshi53MWcKVJ+ecayafPY4iw73D5vmDjBnR+fIr3QZLdqcZFaxb7Tpp7lCK/ejaZFNtLHPGhyw+VP8fjD++kpl76GTIYEpkIXLMI1ia8FSIE1EbNzvENHjxKNGJj1CLMbI7UYWpJHKXHZRgkDrW2k7yEKedzQJvMNUtOAnMBYT8g9LTG+q4FYCWB6HG2ZuPMxo8UW5r0drK08yWieOJdHjDXRToJtCZQhMfoKsyFh//C7Kvwi6cgW03MhN12zwCMPjvOYO0KhY5JOVRAYSC2GNuCZwjmxBhMV5K4yqh0QjtcQFEmqHqpSQiiB2GxSbBsMMljwR5BjHjLo8K+/c4XdL5H82WdGWLvPJxMOsr5FOl7EXVojLvmYWw7p7nEkA+wsQ/QdtJKYmUa1UwaB5pN/VkIloFotlGPQuv0AypEYnRhpOsgQMsunOe+j/ypgo1bAuc7GvU/Ccky4r4R/JkEYBiJTKLuEiGMo5Kg+OUBvLGGud4btUEU1bAlaaSFsi9h0cdYNVLuHdDzkaIlMmQgvATWsopC9lHDMwVvPMCNAZWRSYzUHSMPAURZBLgdaE+UVVqFI8Z4U5dkYhglSUvjiGiJVQJvwwiLCBXfBRkc94rkKqlJgenqN5kMaVRZ0p8eHx74dUnh4BYKAbCJCOj5gQVbC3kg58vk9lGpdNjYqhJMWpBlmqGEwdBNTjqbwRANjxyRqdX1YgdJqo/ZOIvopsp+iLDCCDFEywRQYoyMgJf0xAbkc9dTh6F0zDNo+o2MJyV7N6KUas6joWgbKtdFaMZAFlhdB7RjhsU+XkXGA7K6gai6bPzAL5RRrVBPUXSpHe4hKgWRPnt7lJbrHKxQ6BgITYdmgsucSJMO6EJD757CURq23QcDguy7AKMbo6RycG7bymJ2M55a/YtgypM4tY4yNIYouynOHSReVgeeAaSIzBQ+2IZcn2l0jmyoNW4wcCa4NYYyqGoRvHgcJud/r88pbz3D7i05x1/0zrB+VDCiiN1JMRzC4epb8/fPITBBcOQeuRbhvBKveQpsQThXwVoZtVBKwzrae28/nQgdwoXR7Rvvdw21SQbyaQKqIjyt0miGShHS2SGkhoTtXRsyv01m0YJ9ElDLEoo1xRhJ6Dn/4jiv4f34eoiTPRlvy0SPTLMYlUkvjeCYaQdYYVvOIwGT+0DU4+VWOnTnHYBBy9vAS1MZIM8hSl3RJsXbIIo0ynLaid8kM0SU7yEY0E8wjKyVUo8NqzubPv/sAP/n/7GL9ZpO77jxKXMmx2XTA1CitcXIx0WYeZ26dLJX8zdFV3L6HjkLoJ6Suy4tvu4T8ZB7zMRvRzLigWOOej7WIEguNoOPZCGCimOfanTP8+3dew9t3X8h0NPO8VtForYcuf2kK0TClKA3xf3jXNttss80222zzz8nq6io/+7M/SxiGTE9Pc8stt3Drrbdyyy23MDMz802P/w1X0rzpTW9idXVo1/prv/ZrfPrTn2Z2dpY//MM/5K1vfes3HdA2/3hG/CrXjp1g7pIVrKDP7ivPYeuYw7Whc0t/osz8Vg1vbwuz1ye1Nd2rJ7BX+hy+1+OZRx36KxKZaMj5DF45yfp3+7Rut9C+xFqPKRxvYa+EJFsDBjWDgW+SruXJLfrseCTmQj/ktqvXMUqKSaeNESviwGLkoR6XH13numsbjLkRdlfj9FLcgSbbP4u1o0g+i9FZCqmmY3pkYx6dm3NsXp0n8yzsfEpl5wCVAyVAqYR0n0f31jH0nkkMw0IECaZpITwXKiVU3sJUYC03YKsJ9faw8qM+wEg0ekvT+ECJ1XvH6dYdal7IzFyX2hSogc3mY1XigYnWAsfMePNPH+YHfmGd+LU+fbvI4vunaZ3KI5wMVVDsu26DsV1bVLeO4MzWsfoxVj3ArKbIjkYOMkQEItNIBdoYOuhoU5BVXJK8ICmZpAas3+hSv1qiowS/E+Gd2MR8an7oDmRJ1GYb4dhoS2IuCPz3phiPDxj3cxibAU49ZXDDLlo3TpDJDFybLO/SGhSJ3eEixOxlmHGXOjnuOryHUwtFnC0ong6xnBwq59Lb6aClGFYSxAlsNFFLA+gI/I0AP/awWwpUSmyEiFwemQwdrHoTJaJmDxGlzB9x+OjC+awmLkaqsBKNVRsnX4f0+ypkP5TDWk6xNgc49y+weaiHVmoo/uw76F1TZMUKp5q7aQxKICXKNcE2MGLwzg2I8xCOS7rXZIg3l1mLZtBxCbMNJA5MjOMtpBhbPXKfO4270GFwaZnuRRViK0F0A0QvBNchzQb4B2Li3R4qjlD1BtGsTVyzKPQsnDaITBFpm8wwGP1Uk9qHNzEji9TSWN0Eu5HgLHYhSUmmy2SuiTGihgm6SFE60qP48ABjJURutmH3NNl5O+hcO0k0mSMZ8TEfE4gNRVKJSSs5WleUaF/gsFkYxXyhQ+L5uAtd/DMdrJNrEEVgmmSeSbyripiqkdRKpAUbtxbglkMspUFrUhf02sZQSBdwNodWQjoYHgNsG2anSOdqWKHAShRpwUY5BkJJTCuH6vVQrRb+akRuIWFft8GFr1nhyteeJD7i4V2WZ9eVKWlqUNgJRi9Cezbq/Bl0MY/R7vCy1z3Ad/70IYoH8uS2Ina+7Ti1/3UW1TVBCJoX+fSnPOKyBAvifXnyKwEi76NzHlp8aSrTQFow6c559M+r0v6+K+i/5gooOGhpEu0aOlwJwwC+bBLUepj8SDJUs4Xu9SEIoN8Hw4SN+jCPU85BKY8xXiMbLxLVbLKKiz/fRo6ODBMmqUYVJapgougz8BJCKyPOhTyZeHz+5AiDsouMMvIPLyCQdG+YI6k5IEEVDDSK0oefof9dF9CecZ6rnHH/t7IhJZ7V45kQxPPDRKEAnLPPWqMrBXFCunecbG4Et6NxNmIKjzfJxh30PgNtSOSZgJEdTawCeJeGOEsnhh+wXufFPxLg6gwRC2Ss+PlX3cRPf9f1WM9eR2SpyMZ6k+igz7GrPD6/scjBWy5B5y10ySXMO8TXVYjCMUgFhbkeVk9hJBpTqaGQ+GYdVfAwnRxyosYXP/IQuceWePfv/ABXn9L84e6fwHVtPC/DNBWWHVGzB+RNizSbJz/VxjQVpueDhh07R7l76TStK3Nk5xXYe/Venji8idUTOG3B2IMJkwsu//bAC0hUhqMKJCdKtNcSnk+EENgFl+TSPWQjBX78d38Qz3ef15i22WabbbbZZpuv5K677qLVavG5z32OH/uxH2NpaYmf+qmfYufOnezdu5c3vvGN/PVf//U/evxvuJLmda973XOPL7vsMs6ePcuxY8eYnZ1ldPT5LxP+v5VzJ1b43fdpmvuvZ/TpDRIroR07rDcizPzwB54wI0qvAvwO0SM2Wwf2kFYcmBSMTc5zx8l9GG6JOJ/Svb4Ml6fgKKzHW4gTGdbSgHS8wvXfs8Cn9X7iAEwlsPspQhjY7nCB4LoxE9MtNnQR1jXWVoZoBvTW+zR9QbQJsthFWx5aZcRFSa/sEzo28vtg5n09Ni7zGMt38WVGy7MQVsTNr3qMwmifO+64gtb9Cmd+g9y0w4GgxdbWCLEJJDbCMod3v10HSXdoy2sYiEIOTBMRKdKiidVP0LmhtXh2KML/6AYnb7N58Ecm+FRzmvKgx/KpAidvrrD2nQXm7BaX7DxHPzP4y3deiNGMOLqZBzMhLgnspsJ5KOaKV6/w9Cd8/McjktxQcNlYTAhPF0nKkrhiUHx6gDbksC3BN9GBwN4MyEoeIstQW3Wq72tQ/+45ovEC+9QKa6saI1OIKGNzocJEaUA6SMAUaKkxDAcRK7JWD2OtjlBdvKvKtDyHWG5iGBIhBFazj9Uctila55qwLtkMLM50d+KtrsB1M6Te0ELO6mXkVhKMTA5ddpSGJCa9uIBT94cCtzGYi1v4/SYFKVHCREiNDCPsxRgRpwjLYLU5wvIDFWrXNek+midrDzVydJqQKhtshdaazARDDXVEkrxBNOOh+4pcXwyrF0Y8zHIBox9hOiZmCLQHWGcbGLM5ejsNZE6T3BUxU9wgd6XN2fkxEsvH6mXYZh6mwd1nsbCnSjBiUPvgEtaZJnJ0lGTHGIMpi0lrnV5axNiK0f7QhtoiITIEIlLQaKEAXXJRu8Yxv3gKey2hZyq89QTv0FlwHbIoxAgzlGUS1TyyFMqHO6S+jdVL0KGD8l2EbWMMEoTIyCYd2jfOUGz5GId7FJ4W0LfBVEONId9kgINomqiFFnJMoucmMMpFtGFAoUBqJNiBGi7A4wwzyti/+xzSVFRLPZJTisG4g/DziHCoPK8ti2BfBfnUBgYm2f4Zgt0V0CATTeq7pJZG2TZWkA61XwYBIknBMCg93iGc9lk9V2TwVIw6tUkmBWs3mpTGLfqnjeE51BvgWBn58yz6Z0DGIeQNgvOrxKM+1kPHEXYF9+EBHnkwTeISQ72eaFj0snqtz847QVdtQmKcRvLsPhiooouQFlJaCCmJS89eKD8f4p4eILV6NrMhh4NlX0puADAIUM9WF1EpDZM1OY/UsbGCBIRBKtWwjUo6YEuMcw3wnr3WdhTeu+uosoOxEHDonhqF0ZDF5SpzF5yD4hbmxzy0aWBEAsvLo+oZCRmpL7E2eiAEVr2Pt3KW1ssupviOR5B8dWuXzkmySBGbHuqB+LntRhA9q/01rDCKah5qrIh3tIFhmVj1HiofIawaUmomLuphuZLcdB3bSxm/clhtNHFRwAtuarB5IsfRpSrKlczurHDZ+A4av/oa/uKtHx06pWlNpxWzNj/g9995D7VA4QiLybkRnu63qc/OMn1nG90NqNQ06We6WDLj4vPP8slbLya/OEpcMijet4AdC1JTsbqwxZFDC/zPv/057m0cI1pLURrCgY1wUyyZUbaeInnyAqxrFN5BCA9LpGHwPz50H3suGMM7lSLPwoPnzuLlUy7bt8CgY9LbzJPVBb//8Bf4H0sPk3t0hcLuIq+8cfc/chb+1pBlGQhF/2AJLihz4FVXP6/xbLPNNttss802XxvLsrjxxhu58cYb+Q//4T8QxzEPPfQQn/jEJ3j729/OO9/5Tr7v+77vHzX2N5yk+d/xfZ/LL992G3i+eee//2vCPTFp02F9MU9wwGfxMYvpW5b5nV94DUceeQj70ToblzqIQokg9jAjhULTq+bY86qAh/64gNozRTwiGOwwKVWa+I928N+2DtkG7ZdfgMorti6pYt1l4IYaRIbTTEFnPPhxi9mbizQncmSJxLAyCu9dJ7+uUIbAos3EgYTljkefHNHWJsnuHeh+H3HWJ3Ec9I4UHURYn2hjvURRGe2y1igSIVhYGcPRAYOyRIxlpB1BsmGzcL/ECtoYcyYpgG0N7zonERSHd+LT8RJmOhRymJht0TrpkY4WUXkHLRS59iaxUPTO2ux2ehy4foMTfzKBbgpQMaFjkhoGD8tRFsw8OT8gOa1Q83WwBNGbRqjd3WXixXVSKWifXyAtFXGeHJC6JrmDbXZfscQTnz5I6ksGuxyMkxlKgJhfQRkRZh/yqw3CuTxGkkEM/tN94stqnOzP4F8eYj1dIPVt7JMhIp9DS42Wctg21eqixkcYJAOSC8YYzJQRSymDazwGB/cy/TGFmQryzQ1Ma6g3QT9BdQZI08HfMCB0KDzYJfUlShqYqab8eJO47CNcC7nVRWiwLi/iLAk2hIv9wCJWGGPggC/R+SrdaUnlmQFWp0cWBlx4Q8gP/Mo6pzbb/MWTB1FNQINWGco0kU/YsGjT2mEQjViUHYHbSkhrDlHBIxnVuE8sITDQE7vIPInMhlVEdkuRDkLaN04SjxtkniJNTJpjVXaW25jjGTqK6WUhRUeiT3aovbqLf41JtGizVC/gHW8gggRabfTcTpzMorUYIjt1ZBShizn6l03Sv9Agbdu0Lx6hvNrGakfgm0itEJNjiIWl4UK/EQ6tmtHEc2Wc5Q5Cp2ROhh4Mz08zSEk9A1M7GBNjYIAyBXqzQ/F0C2wTZTqwe8fQftsxCGsWhfmMAIEiJfnUMhJBWi2iLUlmSox8DqQkTx4VwWDGJS1aWEeX+dy79uHvMKg/mGF0VjCK0+A7xDsnANBRn9Z1FYx6Hy90MFIIRwwKp7sILJRnYHdS3EZIUrSHCbNWCaMfIeIEeWqZtF/lns5FGGc2qE00GM1HLCmPP/jYLeyYb6CbLay1hN23Nbj4exs88HsT3PmrM+gpn/plIzAJ9vQkrdtqxGWBUCm1QwOclRD/s+dY/ZlLUK4impLDY62GyUttSpRnk5VcjH4IWwOouchYkeQkCMnLrpinP9bn8B/bmFE6TC7AcwnI55I0Ug6ruHwPOVpFrW2iwwjDbJJKA6NcRGQKe7GOfXQJVXBBCLQhoeAjugPsu7rgRpBpLtvV5IU7mty/luNzd13A2noOmXUxMYeVOo4PWYYRakAR7a0h0gznXA/7iEbslJz7oX3MvOfEV1z3NWD0hg1bxnoGjv0lF6v42YSNaaAdE0Pa2Of6WIMMIVJUmGIsNJn6+IDiqy22NkaptypU000uunyJ6dywxSo/m/DHf3keieGhpzSGLcmlwwqa+dUesjaCDmMyV5EdalA826H1igtJewFmlLL02BJ5x6B7Xp50fR2xGdF3JtAClKHZc+Ei2eAAjWkbchmyW6B6qE8yWmP+XJ2zf/JZJvaOsfOKGmli0h8YpLFBpBym7Yu51f9bPrtyCWFk0x/3MQ4Pd/vd9z3Cd8UXUWna9M2Y2ZkKu4tAbYEHBnOsT3kYx9okSx2iNxjkrp9k7oxgNvf8WlxLKZmaHWH92eS2Y33DBc/bbLPNNttss80/I2EYcv/99/O5z32Oe+65h0ceeYS5uTle+9rX/qPH/LqSNG9+85v5z//5P5PL5Xjzm9/8//e1v/d7v/ePDmabfzxX3HYxD5pn2aDKuKXJnxpQlpt0Goq5WpUjQLq7zLzlkz/r4hopTkvgRBnNPXBDocUt/+Z+3vjntxPXfabuHhCkEjUydKjR0kZVPa69+XGuuvA0z/zdPvIbGd0Zg/6kj2XEFNsJXSWIUWxtFZkqBfSFRmmI5kaZnm5xwfUZhxZKkFjIch4GEe7pDeIdJezCNIMpTbIa4690yXsx8SV5VA6KUwGPPbOHXNZj1zXnMK9VnP24QT1y8aY95oINlrwit9y6yKEPziLQICRaudALkJEicx2QghtufJgvhPtona4hezHG/Ao3v/Qs8auL6EnNpx7bz8PLM+T3RFgqozljg4IzYYmzzSr1okXvapcgiJlqaQbaRt6lKF5gI31JstOlcoVF4/MO8YiD0U/ony5ivrFJ/o97mF0DaytB5XzkcgPR7iDRYNnQ7eEfC8gUaMfELIwQacgciRlkdC4oIes9nMBCRSFisYeeGQM0BAGqVCM1TPQkCCR2W4EDIpUkRYG1HpMt9LF2Dr/6hpOnf9DEv2iS4jlFT4GyJWHZIq76ZJsx9ok2xkYHbT5baaAgnBcEVoW0JPGCDKEhvSFH/ZZJyndqtLQwSxkqjdEdaAmfuOqzec4hfH+E6eaGrSNZhpQSU9lkA5vBHhMsQTjlkd9MEUsxWbeJNFJUmiHRBCUwRUba6pGNj2CoDGHaYFk4DYHdFfRnBaqSY+34JP55A7bcPLmZAM6VCPcKeqaJOWYiv5BQWrbQjoPMQBQL9HZ7KJFQeqaNDBm2/IQZ+c2UdttCeaBdi+jSaazFkJe98DBPfzxheWMErTRmN6F7foWsJFA5F38rINEZfiAxFyOCUYVwIQ5jYt8mH4BQYCogSckcF+W4iDDBiGISlZH5Jq2Dkv6cyeidAd65AGOjjdYmsjZMQKpGDxlnCEOimg305BgyZzCmWjR6JZLzNZsLAcbHeljFMmnBw+gmsNnHPFMHoHBok9xaH+9MBylMmHXxFkPioo251cdsKUwt0UKgDA1RgtxsQZahpUR7Bqmr8dZjstoY17y6y8tfcobf+8295DcWSZ08suAjfOh0NEq2SGIDe4eN4Xex5+sY/QQjEeSPdGgfMAj2eBi9BLOTYEQGaUkNzx3TREcRKE2yo0jzohJGT+Od7WM3++iNJslEGTOUZCG45ZCfesnj8GLNL9SvZOs96ZdcoNSzyRpr+L3QSUoGWDkPUoVwbej1EZ6PsA0kAgwDvdobulvVByANZC6HGKmSLp5DBxEijMF3eWrBZ8fpjAeOTnNsfRp7tU9wnU/uVB9zOUStr1N/6Qh2PwPfBsNlcM0sptsGCdoSMDdCMFfCWWijLLASvszGCkQQQ87/6skhzUjHSvhPrSBHqwjbRicKHYRoS9C7cYSuMPHMCPlMStI3+O6XLqCQrALzXxhh0I5o31LDrAjKPZgZLQOQ9CPIFKrbozo3YGsJkh2j/Ocffglvf+YddBsmUg+PaW41GR6rMOLs2gT6GlC24sNPX0+llbF+YFglEzsWcsSjv7BCVm+h1zf51Q/dzYcu/hF2WTv5YriOS8b37Kth2EtYpf/Ira94H4slC7/SIzDyoCDdGnDPyln6gy7GSsCOXRlXXjXLm06UWVB5vEskE59ts/uSDV56cYOj/Wk+e2SWTzxxjNd+11Xfyun5G0IIwdvveysfedddTJw3yXnTY89bLNtss80222yzzdfmnnvuee7vkUceYffu3dx88838zM/8DDfffPM3rdX7dSVpHn/8cZIkee7xP4QQ2+J2zxe3v/E25h+9g7c/qDFlDm16qN0VAq/z3GuuuH4X4fsfR03WsAIDVbCREUPBSwF+LsM6kCE+p0FDsuJhZjbrP10ld8bCCDTezj4d7RD2LdCawslhS0DieUjfprg7ZaFVwBAaS0DnujHUQobhlrlXX8XaOw/Re2wNUS6i9+9AWgLd6RBMexitlMl3LUCQ4VyRIxnkUInAQpMvhQTnfNJFi/iAy/pqgerNDZYO59l4VYn47hKlh1Z5vFNDygyNRBgmDBTdfR6VU3USvwimwWf+/ArilkbmLaz6ABQ8eV+Ra2+MOOnlOXligmxNklcd+i8d6gOUGbDfavJif4UzSZ47H5mm+vQqxe+1CT5sYC9lnNvK0VspUv5+SUIJDdjNEGVJhFSARnkSfzlFDUJkJ0Ul6dAm2BSIyghCgGFIVLiKNs3hoj0PRj0krGuMgiSdsVAFC/VgE4FEFHpoywbDQEpNXPOIdmSU24vUC7WhiqoCbRoMZly8dp7yruFddnN+C7GrhG27DE6epbc3TzyVR7kmWgp6BRfHruEbEOVTvKfrYII72WVwXJA7myEcHx120SWJxsA7sonONNq0UP0eRs5kvj3Nmz9zNfpTi/hln9h3MTe76CRBWAZmLyGqupSPDrB7GXHBAAVGqvBOtHCCACpFpO9jBhnWyU3MrR6RbzC4fgznjEvlRIbWiiwv0JnE6kri202W4glix0QmGrOfkKWCxkqOzWN57FjinWyRegKkA7UCccUYaorYAr3ZRbgOuA5GqDDXUrIRB9mL8O87DcLg/r/J01vOSD2NruUx1rs4BYtgrozVC1CXhLiPK4SthuLNyiQxMtoHfez1AVobgEAAmVR09xWhZZJ/cBFtm9grLfozFu0LRnE2NGKrhy89NA7JRBVzMsLstkmXU0SpMqwc2zOONi127NzE2QEbCzniHy4SU6TwmwGi5WJK0KNFZKxQ2bAywmpFpP0UkQGkMAjwzxm0LxvBOdzEigQynyec8OnP+uSe2HzOgjur+NRfux8pNdeXD9MdlHhga4a1ZkRvOWOivUnkuGSOg9awuTbOx/5TkaQRcvv/OM3Rv7TpfH4B1ZRo36d4Z5dU1wjOz7F+s8/I3QluUzL+V02at1jIB9ZJ2wkyg8EFI5QXBHKzT9ZqIWOFEQgiQ2MASmgGgcMdd83wyDsN6ps22bSPud6ENEUbksSS2M+2cAmGk2O62cDMFCqJEbaFtC1UFIBvARpZKiIQqCCErTqq30c6DqI2il5ehUyB53FmMM1//9Re7K0QQ0RkIiUzXVoXQvWkCVKgXBvj7CbZ7AQiU2ghGHiQm68jdBm7pTBnp8hmJslW1jA3BghloJ9ty9KpRrT7X3N+MFYaMFIFIZGmQEUxKIV2BIFto5VBktr4rQSNzfHDBXJTMWIELnp9yqN/7GEOFElOs2lmrHaaLNy/xMOffQpWN5DjNRpbVayxJnLc54qp9zIbHef4Zg0KI0jfJZoyKUwV0OsBRiRw2xmZI+hM7WRUt+jdF5OVHKqfmGfiDyw46bD0W6AcgzhI+JU/+QDr569gW4q0ZxGHR6kZxzGtl/PKF/xH/vip32D8khaL7x86lRU2UtZ7PfS0x9THT7A1XeQz7z9Cd+oAwjWw12O8nQFsdWge36R3xiacnKI4l33NY/jPiWka/Ks3vuT5DmObbbbZZptttvkHeNGLXsTs7Cy/9Eu/xIc+9CFqtdq3dPyvK0lzzz33fM3H23z78PjdT/H4ZzcZPx+Y8mhXPLptB7TgL+54lBHg7D3zuFsRudGQ3S/Y4OTyHM12mYnpZR4LXHwUa+c8ehc6RBM2UUmyx25gf34MGQsEmtF2g6ObNdxOn2QgkXGCSASqCN54iikyCkbIDrdOamrcuzcxWgnBpRb+iEHjrEIkCaLdQatJsByCq3ZjCRjYCaKpEeU8vYFL7wQ0kxzVnT26pz3INJ3U4cjn9gIw6iqUb6GFIJ4twOeXyHABQRaEw5YY6aCSiGjEQLcUKmdibLiEYxInMzDRiFKRhijxiT+UXPWis/zA936Ge//wABtPV9g7u8XWTR5Ln7eYWTvHBb+whXlkQOWz57DCBLefoe0yWbdPuMfnOtlAPOBxquSR5UxIDVCKS/7fp5jN2jzlHaBaDWg2cqhORlYpo32N2e0jSSiM5un2JfgeYhCgux3oeVxZWeH48iQihTSxcJY7oDVivIppOqhmG90fYDQsOpfuhDXFZilH6tnkjoXYZ1sUCnmKUcrEd0rWPj7UzkgP7ICgyyvGPs/ID2/wjg/tJZzai0hNMhdQAiM1IMqwO4NnV68S7ozwkw7JVAW1bwfizAqRcsi6GvvE1lBnJp+DmQlCC7KijXnPOsaWJpvyhwtbnUG9CXaNfs0nHJVYG2DMr9O/skYhC6GfQhiClEjLIZzySXWA0w1Id1SGCQYEGOBuRCSljMZ3WiSpjflFi/7ZPEwIRCoRCz72M6fQaYw8qrEXDYyoRDJmI3p1lG8jU4X/ZB0pJNZKH2EYaMdG5WxMpSk/o6hfLUjsoSU3WUbPHkeXugyun0FnGVZPofKC0qkA5/Dq/4+99w6zLDvLe39rrR1PPpVz5+7JQTOSRmk0IzFIIkggZBEMyAIbMNgXBEYYg+FeR0w2YMAyFggw2QgkQEJhNAojjTSa2NPTOVZXrpPP2Xmtdf84rRnJwEUBEPat3/P0U9VVp3atOnufvc561/e9LyqOkHICrLjW5qQZzfmk+8oU02Wqf7aJ7fawk3XypRpp2VB5dBtrCpieQioPMxMiM1CpwDqWotdDCkW2WCKeaFDqbyFLLo6U5HWP0aEKTj+nfbpM3q9gPDBDgxppZHGtNcda/J2xYTBq3FLhdlOiCRcZB3iZgihGxDGlisKpNSHJsUJQlBTuQONt9Z65B6ncEpzvcuOxNe55/dMIK3nfpSMUsz6v+oEtek9U+dB7Nb3YRVjQViIjHxPA7k6NPM/RKFS9QpHnONZSenqA/HLQgUuyVMNNXbxayP6feBwAsTiHDXwmP7Izrp44OI0ahlilIc+oPLpG/6CPbU7hjlx+85duQV5ax8oMaTOQEn/SUplLGB1rkvzP4tmbqlIorbHt7riCrBKSlV3sRIhoDSE3CCkR8ppTjOdi2l2KmTq27OAUGlEpI6tVgs2YYsXBvbiDJ0PE5Q28UCGsHT8XjQrND6zjrw/oV6rY6er46ytV5PGrND7cYXRrFTnS2MBhdNdB0s0OjU9sIBxAXyuoKZdgGH3G3GAB5XvP/M/kBrvbQjSb2I11kjUXMSVQPYlRFieC//4rN3DsllVe+a2gJjXRvI/MQXsW4cA/f/0vkH3oFNPzGaOqIk5SGgsGtd9y5caQ7390m+3KMahbrB5Hx098tI+3U5BOTeA6BRiJLOXcd2OPeDkj+pan4JrGlFywNG5R6BsneMW39JhdeJz37tyJ4xm8zFL0PW6cGbES1nh8s8XvnH2KelDj6NJlzjUPo8se01Mhg7ZFoygvT7H4micx3ibyTw4h51ymbuoxvb/L9u/4vPM756l+2Q14k4bN9BLaapRQX+i0vMcee+yxxx57/B/K93//9/PBD36Q7/7u7+YXf/EXeelLX8o999zD3Xff/Tci2HzBnjR7/P3gx9/wX9gwmuRLyzjSGVcGlKDiaP7k0Yd4w3UHaV4veN4dLdanY5bu7jM4VWbroSY3zLY4pjV/9Pt3UtnxiFcE5C7OCFajWeqhRWUWkeY89K+bBEGBSROShRKtF1SonTXUF7cRLyuhreT0fw4pHmsz+J4K0U01vEsp1g3wkxFzN8Vc6k+jhEBtDhETBWqoUJkhenHI6Ctm8R40hCYh7oUU05LtrM5UN8XVFj+B0cx4fXzb/rOM/uR2xHqbcOgiqmWkzDB4SEB3RxjXJV4MKEYgl0tYAaqtcToZyT4P94kd5DBFzk0jMo8Ln5zh7ntXmcp3WI9cyBN+eN8pPticIu0VxCg++lgTmQtwfLama+Q3VpBPbeFd2EX/wwbva99BcCVH5hbrS6yVXD+zxR//x7uZqg6wx3yiqxX8KxmFGeJeaWEKg54rWFpc5On7T0GhEcZQOt/HLTW4csc8ap+L2Ry3nuU6QNYDZDlEVANkkiLKBZRDsBZZWOpPJFgvRQz62FabcMbglhQ9GRJNjX0XkuUSg1nDHc/Z5OF3B7hrEWJxl2LfAtpXOJEhflFCeH8HBOQrNfJ9NUq7CicWKC3IK4rBCxu0b52kfFmPzVP7OXY4QssJRgdqIAXKlqHfQbZ7uMJHKQ87PYUZxvirYHsDZFRAP6L+5BYk/lg80AYhBbiKrOmSNT28I/OoWp2xOwuQaGRSwPUQ1HNcYzBViVwNCbYMcrZACEk6FyAyiVWSSsfHlhVF1UUcW6G0naFdgTOIyFcmMJNl0ECzjvJ9MGPPGIylvKVh/wIi0hC4WD9B7Y7QjZCsUULlhvKVFOnX6N84T3FlgCM8VKpRcUoyXccEAmckMDpDJgk29rA0CC/1EdaQLpdwixyrPOpPp7gDcAaG/nKIs9VFBwFuyxIOwBUV8qNlso4hWnZBa8pXI7R2EVdzKg4E/3EbuZtjy3VsrGB3F9mvQugjkmtijYHwaowNXWSlDkqBtbjdDOk44/8PY4KTI/TKDEbIZ9OR4pTa/ZcZbNUQr/XQI5dRB3b8JssrfV565wUu5mUG79wHQCnMmDjY5/L5CU6+6yCu3EG1UmiCuW6FYr0Fsw1KlwUqlmPT5NYAyiHZDfugWUMUBd72aJxGpTVFEhJEOTbJscZSXL+Mk0ekrsXpG+rfmuKMAi4/1aTxPy5gAg/HNSjXsPgyzal3KtxMj68p/b9UVAxjbNnDeg4kIfbqFsL10EaPI8y1RhiLurKFXRy3qFhrsVoTlhMOXb/NhVZIZF3CVTkWcYVGKp/8wBTOpW0QhuY7T9J6w52ARZcEg3uPULscU/tQh7iiGK2U8LSkmJtCzUfU7rN032Fgy45TqK7x6THdNhtHxFtjsEWB7o8QUQpSkNRCjJXU3DVaty7SfGzI7F1dZu4e//2nvWlUd8DgrlmcWGICw+DyGocPJfzTf7vGoKP4T29yeNMPXqB2k+BffvQg1W3YuOKBA8q1DAMPdXGXbLeP3j9DdTZCTwte84qHOFzz+fB7b+SWf3szT37vkwB0/0DiPlamPupw322XcaoBUytfi1xa4L+95wmKmqRvdrkQbeExJNYRE16VtY0pdM0HIRhqi0JgLXQPz/K+d0nufvUu0URANgm7oyrzcQfpNWnuL3PbVy6yffocEzMb/PFHP0ZwMuXOL72FmZW/2Z2xPfbYY4899tjjf3/+03/6TwAMh0M+/OEP88ADD/DjP/7jfP3Xfz1Hjx7lpS99Kffeey+ve93rPq/jf1YizWtf+9rP+oB/+Id/+HkNZI8vjML3GR2uMFgImDiVYuctwliiX2ux8ooCOEhYxFw8rtg4usTxB2r0BmWQkEQh+SDg/IVJ/E0I1dhgV5ct4RNtYt9g603KH12jH7mIQ5DsaLLD4+jovOowOhByIZ/hxGAW3QObSNhUJEfniA8pyhcSbq9vElZSrpyZQAiBGUWY6RKiALTFuTIguLgLfkDcCVHCMnU8YmPZhZJBpIbS6V2Wro+4qhe4bm6HbdVmrW3x0gJdK4PjQTFObFFZQbE4DUrgYCgUkGY4p6/gtmJ0bx8yzsd+GoXGBnD4+ja33NCm+S19Vt9f4uArI6SwTBxzuOzU+aWHazz0ex7KAs0JxNk+vZv2ESaa4KkWHz8/weCAQG4l1PMM23LRoSTIJWrUY2An6Y0a2DLIpo/3sYsIbSD0wHN56viZcRtCGKCnHfRUDbnTpbM5h5MLwlGMnAzJZyqo5Qm8booOytiJCmKmjnEE1Z0+tgvCSkwM6WyFdEWhkwZZYRl5HkE4Xlrn3pDKR9b52XM3sBhIhrOCdH8DEUi8CGwA/oUOIh8vXP12Qf/5DqNbqsx8OGE4aQkePkMzM7itlNpu9ZmVoahUcHFwE4k7LBgcKeGuKoQjISrGO/9SQLePKyRibcjoxQeRvkc64RA81h7HB4ceuuFg50u4QuBmFtmoI5ICkhT/iQQdlNh8SXNsBtsbopqG4rocu2FQMxmVUGP2C4ZiH+Fjq5SPt4iDGH+rhZ1cQkh/vKrVBkyOGCSINAckwveRBSChe6sL5FSuZjjZuIWMtIfotfF3A+zQ0r85GKf/lB1E4GLLIcWyR+JZEJbGey9ROmnJa9OgLOIlZfRxj+jABGQZRlqGL1/Bu9RGbXkUpQA8l9LVFAFMbGdkhxZR57dQW33knMI6lryoke7zGa44ODEgJMJopDYUU2BHEhmBzROSqRIqckimHLztESoIx+esIbGxRfUSTKWEbJYAS64E0a0NjCtpvOMkIs2QgY+w8lkhoDDYss9uXuFn3/gisrrP+utC3NMC786UQ9NXmdzfJexL4tThOW9oM39jn+I3jnHL3Rc596c+nSLEtHtweAEz20AJSfV0QjYbEJxsI7damMVJWJxCRtnYR0ipcaKVNniXuuP0KMB6CusK8maASiCZlPh2iHtUIOMy+C7ad4kTQWotj+4eYWZhGy5t/ZX3WfXRp7CzdYgKhJRYHcMoHlewGDsWE5t1hONiAJEk2MEQ/dCA5/3wBqdrL6Q1LFNvHKJ7owdZwuQDLcqPr8NghHYEFAZ5pU3jvWexQPeewxQW5M4uYiDhWJ3CNcjtIakf0PkzQ54KpCdQvWfbWz+j+dhadBYz2DdD9UNXx63J1mJm6igkRJKk5aB6McNJxZ2HWkxPp5BC78IE6ZEAt2OR1uJEGndrRDYpsQZGQwcrHVpXfcySy0wyw+JdFznrawpTwSgHRyqKxUnU+i4yKXDKMcZ6tLq3Ub9Y4dFoiJnvoz7lsbOZsHjzAVo3TvGLH59koiJQgeVH7ryR9x1YR1poubvspAGvXXD4L/Ov5mpynJ/4L09h5HicaZSTlVz2za0zOz+g98AiJ37rVbjViEoq8c4ILm3OM1crEcxW2R749JbhrJ7k9B99gkPvvkpvp8/X/+BXf15z8t83THEZ9BbCe+5ea/oee+yxxx57/A1RqVR41atexate9SoA2u02P/3TP83P//zP88u//Mvj1MbPg89KpKnX6898bq3l7W9/O/V6nTvvvBOARx55hG63+zmJOXv8zZIaiZcpqpczSmsZaVURz6bED+b00hZ8E5x7TEE0RWtmlty42DzBvdxnN68xPTekkg3p7Agm+gW9GycRmabyyDZWWba/oUbwhIM3I6h8d0DrhwOqH1jHEUdQEYjzAdHXwiANWPqejJNPzpIuS3hCI4c5RQmm5hM+9BMNrIrH7Ra9Pv379oMSeBf7TL5nFbIcoSJMtYINPBw3oHIhojEdoc9YaittVl7e51DUZvWRSephympi0LsxedNHmdrYaFOOI6RxBbiCeiul6Eg4u4HTisaJsdvtceVJmmOKnHx2hlPRIk8WjxMcAt5V58Qv1hh9q+T+7g14ruEVtfOIYx6pW8WfCknnJY2P7WI2IoYvm6O04uOdH+A/dBn2TaDny0wc67BYjjg4PeThq3PoSCAykAWoUYb1POTKHNYROGlMcSjESkXnReNql8bvH6f2zh2Km49iSg6VrE868PFGCj1ZRfQiyIGGi0oL8kFBsTiBSjR+K0JpRbHYxJ4XDBddikmLulbJ78VlxBa0norJ56dJjzrER0L8bYvXM4Tnujir4xYQC4hexOQfXWH46mP0DtYROy1UYpCZxt1JyO4uof547ANC6INwCHoGEeUEV4cwUcMOU8ylDcBBNmroik/uDHDKAusphrfMUl4dYUKJaI2w+yeY/ReanQ9IzEiQz+bIgQcR+Kc2wWri66ZJ76hhIolnBVnsoDuKZA48V1Apa5QVeEOoPtKCQuPlmuTWGsk/spgdjfMbChsovCfX8M+1IM4xCxPYRojYGVEkI5pv36L9ygVGSz6V47vY7Q5C55AZyHNolBD9GCcI8PoFMjGYso+MC8qtEUYXiHIJXXNRnQiqFjPoUb7LYzhwQWdE100gk4LwUkJ2pIGvQ0BgohyVjyshnMgQ3TSJd8bg9TuEz4M0aKKGgsq5AieT5BUXv62xEpIXQHb7FO4fpwTv2CG69TA2n0KlAsSAwo6TfIqvmMP85irO0MBwiJ5uopsBGI0JFFYI7FwTnafkkx6lOB+b7ToOpuQRH52kmCmjHloH36H5aE6wY+k/6fHAjYs4SYOlxR5J5jDcDhjNp0SXFQ//QgOdFFAPMI0m5BqUxAqLO7CY0OB1M+QoRSaawte4oxyVFJjd9rjiSgik50KSYX2X9OZl8qai9aISbl9S/0SH7T/LKW4sM9gnqaU5bpqTzdfZ/pIVjPSwZR8aVegOnr3mP+2jAMSnWryUerbaZqIB2y2QoJtVhOcgXTU2Gq7XIHL46IcNXqpxcoMQinBTUHqyQ3Ahpjg8j0hSnPUeejik8d6zz/y+iQfOMbx5Aa/TQzkK4oTqB8+hhinJsRnY0Tgj85fOC5+K67ZKUCxNUH1yd5wAJQWmUkLfWCGYjbFGUHukj3y6RFH1eOr+RaKHtll+I7S8EKeiCG1ONB9jU4sJHc6szPOv/2UJNgqszvm5H9rPzf8WsnbI2cRg+xlFCDL00IGLao29c0Q0InabtHSF9/cstd0VysVT6MSQBgJiS9EDayzxkSYPjirsyyp80wvnefNv/CmPl7dRXsFLJw5yU91wQ/MrGOSnGKQf4uhiwCcfC0ELgpLLS48scfTwu/AdzWu+49v5jfetkz7+NKov0SXQdYGXx2RBlcXyEiLZZXszopw73Pllt3HnK24lLQoGScpUpfwFztJfPIwuoPXVYDNs/acQ4Su+2EPaY4899thjj/8jMMbw8MMP88ADD/DAAw/w4IMPMhwOWVlZ+YK0kc9KpPnVX/3VZz7/gR/4AV7/+tfzy7/8y6hrKz2tNd/5nd9JrVb7vAeyxxfGV3/jXfzZx9/D9vMkbluw4m3z+Nw82aubhK/MIIdiOMLUG1ipCHsZtx/8BOcf8xlF8zyeuhx6+QWuHj+G6g5w50OypSrxDbNkdUO64FLcssSL/q+zDEOfczMBjuvgXdu4tanB/8OMfW/scWI0TS7KuDam/u5LuJ2M/NgSj8bLuKXz2PU2VkiYmcQ/vYUpOyAlpuxCqHAyia0FsNHG+gG6Y4kJsSWPXhHQakN1tcWjPzGDduo4RR/iBNGsXBsMGE9hiwI3yMEKvOM5InTRM5PYTobsDNBLkxgE3jCmqJewriRKPVIkayOf/gMBUkPtRAn7jh32NXu85Hu2kd+u+INfOozoJ7ifcJA7hqKXkUtNV1dQso8cZORDD6UK7qjv8MJ6xAfqEv+TCSK1oBTC9TDlECM1acPFaUXs/5KbWXtwlSzPKSoF1hqstKRHZ7CzIcrAaMHgnsshyZBRDIMRxoLwFhHG4HRGKC+gqDjECw7JhEAUFl1S5K5DrdvDWb32VFmLEApZKuFVAhzh0jht6R02TDzQxvFCKNVBtLGOwlqDdCW1k5psWpNMNciXBnhXOtgKDJ7vUHsqQA1yjNUIV4wTnIYpldOXSZemEJPVcZqO1QjHAc8hfkMTc9TH+1ON183xewWqWkMLRd5sMPrQCK/fR2+7OCZE9IEopZgqYaoe+UwNd2CQUYGNXPzC4pyC/GZFYQV2oJGRwOlbOl95kNLxHv7lDs5qhjoVUUzXSKY9SrmhcnudwcM75CtV8psX8Poad30TmeW4dYfakzHFZAm720WkOdITlJcDBrnGrraoP94hu2kJTEAy7TGaE9SfylC9FBk62GaJZFJQffAKwjUwSEhvCyntRnBuFdFvIAaamhrxnd/2ML2Oz6/+0k3I/No5q/qMFlyGx6q4SzWmj48gjQg3urgDCVJhuz0o+eRTVXQ1JFvMKHzD3FfHVJ8nGT5qyatlzMigpwXFkXFl1UhXSO5cZPp4Pr5msgijQnBdKmeHaF+iQ4fB82YRhcEbgRPVwRi0zHBXt9GqSrF/Fq8dUX0qI59W5GcTzlyuM/GaJt/wXds8dWKC+//kME+9y2IGQ2xUhSymWKght7uQZpgDC5BrTFViHUmxbwZvfYDTjxDSYqWFQoM1IMdtWaQZeA7WVST7KgjlMHkctDseqxIWvxBEQ4cikDiJQeEw/bGMeBacnf7Y0yUMYGoS0R97P9Ef/sWb7qfvjEQx5BnWWlScIzCMvm8FsT2i/IAAx2H7N2Dl+3ZJH6gR10tok5FP1wif2EaeSTDzU2DMeF4t9DMCC4B3aQcDWGEZu4tLQGDrZSjGAo2Qclx59mkIwCgYPm8eVW/gd3ZA5lAUFGVJ1mgSum1QIGsK0+0inQZpS3BxtwZvhGRSUC4bTNfCuo+uWuRrDtHZJ8mOh0y+fQ013eAb/tkaU/U+/+3XH6O9VoJaiGqnyFqIiXPY6oy9pTyfhu3T0z7Lg3WS/AT77i5z4YElTLmHiPsIIPUDRJZTGhZUD3ust/psb/fRyxrCAR9rbZHpVV4wexajT9FOTrJ8aIpP2KNIIOrnfHhzkyi4gddeP8OZx+FDb30M8RwXhEAYiS4putsx8qUNPmE3WWgohLEcSCZoPbLDyY+f5ft/789wV+r8u695Fcdmpz7fKfqLi22DjRnLdtkXezR77LHHHnvs8b89P/ETP8EHPvABHnzwQQaDAYuLi9xzzz387M/+LPfeey8HDhz4go7/OXvSvPWtb+UjH/nIMwINgFKK7/3e7+WFL3whP/ETP/EFDWiPz483/D9fyx/91hDzwVX0VkFP1HCXBGmmyK8ltxidE3e7CDGPdh3MYp3GXQm9Xx1wZrdG5ZWG+FtyzPsd/HPdcWVKWMZ1csCgOpZKPWKf6jH1c9v8xk++BicbG1962wnmfJvHNmapL6WYNR9VytDKRZgC2+pzz8uusPjCdf7zm5bJtSRtSMqPrGKlINnXoPOqw6gkp/lwF7HTRYfuWIAQE/T8MuVc056pcPHBG1j60EnKfkq200Vai2g2kKOY7PASYBGrLaTWOC/P8DYt9sIm+oZlTK2OmJ2CfoQSDsVMSPQqgRo4iFMG3y24/xN3kLQt09NXCSKHXtMnMDleKJDSMDMzwpncYWlji2FzklY+TbwY0jtYYsIfMj+1SfG4YVSqYHPNUxfmcQTsxj5qmBPEBWnVQaR9MJp8vky6UEKFsHX+Mja1KCuYfV+X3CTYQytExxoIIyhvF7iFxutpivkmzjAD5aECFzkcv/kuwioISRFIBtcFDA4arIC591vCNMdJJcYf7wrnNY/oyw6hz8f4GwWuCLBtgd+2FL7GyQpkawgWbCXEHJxFtiPEIEbWPLLQI4gShLYE24J4Q+Otg5yeAimIqxJd8hBlh6pTQLNGfovGbY1Tp2xRkC5U0Yc8hBSoWOGMNIXV40VwrYxwPXbXoPzAORwlcOdm0TM1dOiTL69Q1F38rYjGx3pEsy6dLR+vVVDZtPhOzutmn+Ziv8GTp/eNzXu9ANcmqFoD2YtxfqFDfTFAZDHPv+siX/Etm7zlw7dw7i0JpUdWr7VBaYTv0n/hEvH1TbyRYPScJUqrEXe9fIvR+YhTJ8vknQFWCpztPsPnT5LXx6lSuuKRzlYgcRG9FBUbZKyhasnvqJIPBTgFbiWkenJA4UAwI5mdSpiqjAh1RoY3HspkFVcrShsZYadHIaE+FxO5AcPMhSjBRhGMRoheF6dexTs9jZnTtLIGrUaNCbdNezBD3BRo16H6CQv3gTwbkc1X4M+eHotrvofINMXKFCoHtdFCbLYIHY2ZrqOrAcp3xybXjkAkBflkQOWpPl/1pcc59CWanxvcSvfhBp6G4lSJ9WaTC8kURSgQWmC7Gc6SQPQ0fnmHZGiR2qBNgRQSCjtu3emN0FkG0pI1XVjfxUmAeg1W18diilKI6WmU1jQe60ItoHe0ijewiKkq/+S72izPXeHf/v40J//dzRx48xOYimK05GNGEU6Uj31bsgxhDbZcwhqNWJhBD/uoq7t/6f3X9vpYRyEcB6MzrK/IDwewr0L4oQQpBHbH0JzsUf3ESShNkb5kltJajMLBDmPU2aufccxPr+DxBmOFTuSG4OQWvXuP4eQaiUAW1x5pDPg+5NmzkeKA1FC+ksJ0BoMBxMn4GwMXoyQLwz7uVMHV5hx2JccZGWRPoybHmy7VczmlywVx1SHYcckLizc3zeSpHdSZBFNyCauSW29vIWzBpNtm86FpzEpM0B0hTR2RaLQFpurYhTINr4X88x3CVkb4+j7T1YOcPlmA64zPtatYUxr/kTbT/RTV97j7vru5/eA8740vcjnaolk+ScNP+cTqVf7nJ2KmllYISxHCgEgL8qogzXJO7U7yzo9KbkvXCPqw73FY3Q/ZhMBaB55/mNLVCP90zvmJBB14rKV9wvObnHxyjdWby4giJcs+zVT6fzfiT7WhNxDBV3xRh7LHHnvsscce/yfwMz/zM9xzzz385E/+JPfeey+HDx/+jO+vrq7yoz/6o7z1rW/9vI7/OYs0RVFw8uRJjh079hlfP3nyJMb85SXXe/ztI4BBB3oVH1G39K4v4bRzeot1vkLfA6JPMVuDoSatgQhgo1LnSrJI6eIVnviFGtctCORNHvxOjHtlgKgElLrjWOPhvGHheV1mnD5CWg6G0HllQe3jHlgoHe7RP1lmYr7Pma06npNxYGaTT37pdcjcED6+wx+c3U/tCYEGdD3AlDyMp0ApTC3EChjtDwhPC/xti9Rjbxyvk5MvgBlaBhWFsIqDr0/w/rzNqZNVhBDIgweQSmNHCaKwKAMmrJA86TN7fAcRZ3iPXcBM1ykqJZyiwIlzWO3BxQH5KyfwfcXsjbs89sElgrM5E6US+ZTkfK+C/VrFRzshqw/tp32dxP+anB/55qf50d95BYUo40qwIZSqCY3lnPhVNZI/S5G5YbTjkBu4MD1FriJkuYKzvgtRgpmqIo94qJ0+SiTM33QT6/1t0t0Bo30V8loTGTNuNQE4dQXniQR7ZA4ZF4jCYnZ2MVojl5aevRjSjKIR4rcFedWSzErcFLxCU9s3onf1Wum+vOZNYAXCdTCuZOy6LNA1H9EBcS35RyY5IsqRRgEWtTuibFKIM6wriO+cQJ0eYbeH6HKOkDAKFLgFtuzg7ZSYb47YetwhW2qAIxGjDJIW7u9YXCfE7WsYRKizV5EGKJeQeUH+nDKiVoK8IL6zBMNxlZPKLM52Sng5BldihSCZttTOFghtucPf4ctecoZtr8xH3rtI5ZOa2pPnMC2JbDZhMsTEA1ACMYw5/oDPoZvKRB+LcFoZlAJ0s0ThOjiVGqXIJzHjhbGslHBCGHUrzE2e58LQkJRr49aSZkDpE1eIbp1HFRLmhuQzEe7VAOfCEHv9CsMXOXg+eO1tvAf66FkHNAhtERi28wl+4Q+m0Sd7ZJmD/ZRDb66xZUnpsS3Udg91oEk4D6MXZHQebFA7IRFZhukPkIWFUhnnKZ9k1sGsZJS3M+LHXYLOZfT1DdKlKioaXwdmDoLz2+NodEC4LlJ6mMKiZYaTa0SmKW/lGJ2T+2A7XWzJxx1odCipHB+iNgYcfXUPd1pz4GqPR6vzyLdvcuP+Vb7uq57mS9oh/6V3C0uzA/78d+o4z62Sv2dAetqia2WkBW9YYD2FNgJhDKLQGCVJJxRpXVG6WGCsGgtFYThOAfN8hJSYbhelJLguWRXU0x2YqmNGhv/xQzWK/g6NNUV6sEbr3jqutYRdULPT6PVN0BppBYQhptvHKonTaEK1gT157i/egK8ZL4t6FbpDTE8z6OyHTBGYFv7WELTkqa8ByAnZIJt28GL1TJLbZyDH7VufqtYRMPa4AdzNIeHsCLoR4cl1hL7mwQOQjaPCST+zWsLmGd1bFBOX3LGw06zj9HuUz/fp/4cGetEynGug9mXUTg9pLIRMzo6rh5qPZBQZ4BbgGLSxxA93qa/lGBqYvMuoFfOWH1uiNumw1Z1BKoV7PqFvytiSh2MzbLeLmarDTp/LmwdQ+Q4Nk3Lk2HU8+kSJrG6oPTciegdQaPrLu9hPpggL/XMtfu23P0L9dpdLzYw0t9xe9dhfrfCu4+s8vL6DM5zjH7zwOBMLA/pXSuQ1hactN12/Td1X3LnwJcw2XspvPvgoWaXACsimDZevbrLymCX1ErJ76uBZdpKIf/7PX8nbfuw9NM5pCmOIdmJY/uvn4r+XZI8ydkFP9vxo9thjjz322ONvgPX19f/P77fbbd72trf93Yk0b3zjG/mWb/kWzp07x1133QXAQw89xI/92I/xxje+8fMaxB5fOMZaTMkgdIETaURk8XaGWFfyynvu4EMf/ADuTEhyeIKSzthpehw/PYuc96gd3qA6KhAtS4TAteBKyehYDeeRbUScIvMSZ9rLLDkZuVB02wI/SBnuC1AWbn1BG/drcmb7CR/73QUO/eMNjOOAqzFK4fcisncO6QwUpBZqEuanKLwyfivD29rBfV+P6AVHSa6bQ/kV1OUtxCjBiQrsKCXcTph9KOHqV9ZZWcrZ3je+fI3nYqqKweEmMjVUT3WwQiCnmpg22JKD8BxElkOUID7VHjCIQWpUZFEfguKAGqcqVwRWwWQz5tzWLDZTpB2fA40B33Hbx/mT3zvEbjNk+Z+m7LQr4+oGBTaBVmeCYL1APCiQuUEI+KY3fZwiV8SPlRALZZxeii2V0EpgpyewUqLiGNWYIezAvfcc40//8GGiRR+rxj412vfGi/Qsh8xiI8BqtCfGC7NckzkgLVhX4qaGoFVQhBKWQcWM26YCSbA0JFKlZ66dyvmcohLSzzOKihx3IglLZd3gWIEJxmkpNs9Z/4oKM+/t42/nFEsNVODCrdczaKZsvaKMt+pTK7aRuwkWKD1VIHNLsa/ChAd1MWTT3XdtAWqxviI5OIMXOVAYtBuTTTlUzoCoVhG1Cm5uMWlC0fCwh6tkXy5QHzWwpkiakqBrsArSSY9o2UMYy+7zfZwIjj9V4XefWGH14AzD/YJaoXnBK9o8+NPzICW64jK6bo6wZfAQJGqW//HHyxRnt1BJQT6lSA5UCZ/awmzuYPaHCAMyM4BEFpZyOaKsNPWJgrjIGc3MEG4mSBTBuQ6UA8TxDVxPoCoB0oRUH7iCPraM1x4hdwIoGxzpQac1NnTdN4nn+Zw/4eJ3S4iGJXmewHvEYtEM512CnqK8aWA7ZfPRGpvdKuWrGTZQiFoZaS1eYDE1l6RkccuaqOdRfbvB3RygewPKRYHbSrHXqgT0zZJ8OL7WhAWmJxGOg5aWaMJQ3dQ4zQYyLGFyjTQGIQTWUZj986g4Q5U9XvnNl/Fqlqu5T3liwJ0TfbZ9w3bXpdMOuHCuxuu+8imq1YLzH1mk90LYfUTArsBO18mn6njtGCczqGGO30rJtYOamMDfjpHDBPdyByYmUAXj1/puG3x33Oo1GGGyjNEtRylCi61LCunwc2+/A//iKiqLaLQNFBmONchC4YwM1oJoNrCjCOtIiCKIIsRajpmdRJDC/iW49L9UvbgOCIms1QCL6bcpv30DW3Vxr/RBW/5X6u9fRVTK2GwsiNGoAxaiBLIMJhtoneN0R+S+g0Qg0hwrwT2zibPdQ1zruHomyclatKuQ6WcaB/dvn0CqMmJxGoY5ZjBA1huonQThuIgPrdOMLo4f+5XH+J5Xn2a7NBZyTcWlyAyi3yd4ZIdSxScoItKpGiQ5Miug6rI2FByfmya702f6FAjXR23GmKRAWchuncKUpsBYpJ+DMuzEhs0TJzDZLOxfQQXymT+osXKZnfICKgWDZTQ3Yr3Rx6GE7daYdZeRcpPHuz3yqiEXDs0kYjQh4HiCKxVFrcyXhTvcdTCmOTnJ0a+b48ef/CiVVYNILZ26ouw6vOSeYzz/ufv5Vx/8Lfq1KsUuHPnyfaRJjjNMkNaSOX/xHH6Kb/zJ3+TU4xvccP0crT86R1oU/KPveBnf+O0v//+ct//O+NSFIhpf1GHssccee+yxxx6fHZ+zSPOTP/mTzM3N8TM/8zNsbGwAMD8/z5vf/Ga+7/u+7298gHt8lggI50es70iEMCye2mVnOKBz3wH+w/f8Ci/+6kPE5RoN3Wd9uYxstWi+d5vRzZO4XzfBNx48wQdrh+ifriC+LGTx19q4kSRtBJgDZUrtKvm+lJYQSCzdYZmwWmDdAptJXF9jlGC37KAPVnC8DtIvkBWNzcF6EgSIhsIWGplBaSNG5hYhJVIqZGJwexpbVtCoIi5uIvR4B70+O0BfdJEFVC5LEjdjsFgmvmOJvCFxO0OwFWzoki018c5sjkv/70hw3hWgYRyd6ygoeePnzFrS/U2CixYRlrGZx+XuFPmLCpIXWP71HR/n//qpL2d4qoxbMcwVEZ2OYrDhk2+5JIWkG/qkdQcRa8CS4LBxaYKZnbHxqPHhlqNtfu1tN1AexUQmGK+eamWKuVmMNfjvfxo5yqh9jct2r+D8aptioUnt6SFiFCMvt+m9/hhyKFCL8+PkoQREYbCeotg/j2sKrAbhO+MoasDfjIhfVkEdSQkvKoRxKZBsuhNM3jq69hwAeYbRlqnre3SUx27cINgGN7b0bg0wo4LGaQsWxCAjamicEy1oBDDTBGsJL6c0P2gZXedfqwQZM3z+PPVPtgl2BPFEibNmDl33sFhUPwMhqJ9OMYFLNhFg8ozgYovWlx9h+kSEMBKGQyYfMpj+EF3NMVENYyROZ0iwHZPNV8iOlTCeR2lTUNqC1m0SdwSDl7q8i0PUbM6+oE20XeXURw+g5mvEwtK7sURedSntjBC+h3UFvaUydnKZ0mPrDG+ZxNMh7tBggf6BYByNvpajruxgtMu+lSEL9YTjn2yQtyvkyw3CLEJEBRJBXguQURPZTsAFOxjgFAlxLSZcy5F+QLJQxW/n2JlJjLKk++tkVkMQ4gwj0tcbisMWMw/OnytKLUU2V6dwJLLtMXAqhGsCmebY7gDbGSCnJylCl+br+0wudDnz0RnkbQXpP5Co36miJ0OMpwieXoNwLNq5VzVRszIW0YyFwQAzM0EeWppPZQhZRjTGra7SWHToIWamENUACoNZmmJiJublr+/hacmD6zMMpwPcI5obv73N6U8e5Dt/4CgdJXjJzHkWF4ac25qh9HSJ2tcodn/XQ/Yz1NOXEbmByQlkGCBSi1hdH6dvLR/Cf/oCIskhTpClEnY0NiPHWqw39hwpSpLd2xXSGmRYRSaWdLqGuWWZ8kaKurCKVRrfLSj8ANQI094dVw9Vy+MKv964JVF4FZQBr+ySaoWWEiEYV7y47jgCnAI7HGG1RvQTGh9NxubHEzVsd4gQCnSB1ubZ2PLh6JnXinAdhO9jrrUjCcfDkYr+625AGkHwnsexBSTXz6BaMc6wgMH4dayv+esIQA3ja2lXz3rmVI934GyCLdcQW2vXKn+6ZC+5BX87GovX1x7rPnoV5x8J/uz/bvDN/wbUKAdd4J5eo/3qZdJDVRpZn+ScAyZBWFD9hGQI7nYL24ipHzzMotfivG7SLyzCd7HnE9iXk02UiBoZjSsRebvgI++Z4VX/YJ2Hh4tsXl2hzCkAOk+GOIUg9xVOUvDv/snr+N3Vj3Lq7IDUuFzonGRBbbAyW+ZiMo1jDZ31A7RHDerNjHRBkjQFvu0i4gBPDLgQl8mMQPouXmr4Bu8I/+xf3MvUdIOnuquISx7leET59zcov1Ghd9tgLUoX3HF06a+cfk9/fJ0wgovvuUQwylAC3vaz7/v7I9J4L4D8LASv+mKPZI899thjj79lvA74yRd7FMC1IuHnPve5KKX4ru/6Lr7ru77rizum/434nEUaKSVvfvObefOb30y/P3aN3TMM/uIjEUx6dS7ZAcaHwjiYySmcruLswPBiIJ0IaDUDTGZwuwKpBSJXTJb6RMcEs4Mdwgsj1OkUFYZULgwxl7cxdx6FtsUuZKRFQEkVqElDetUlDQV+Zlk2bXZVwHtWrycqTXHpAyH1m7rkhQIE0cunmHxhyI3Xn+PMbpXu762MjUmlxckNTnMKUYAf5QynfMDiaAvWIvKCwUHJ8HqF+/Eabifn9K8rBl1w0l3s0KN93wwLf3CJ7JaD4zaaZY/SHV2mr4tYv3+O5l2GrRMlzL45KIcI1yNtOKR1jSLDO7MKzz/AjQc3eNiZYUZEKFfzQ//4/VzWE/zZ5n7MeY+j+yIuv3aHd60f5OpmmUI6uD2NLgmEsZBp3IHBKIHSliKBb3zzNzCx00K/QJNmmvykj9fOEBqENsg4gwKG78nJbh8w9EpgLcG2RfQs+XSTRp4xykKKaoCbanQtQEU5Ks6R5RAtJWlJEO7mFDUPYUHGGc6yS5rm+DsgDbhDaDb7VKY0aHDTmODsOcDhrq9f58LqCtGDDbJQoI94mFfmiCeya+1WCpEJwtMDhNao06vkLsg4J53xcUxIeEUjw3BcDeA51EYu6W3LhJf6ZLGCrsZRCXnZhf4IUQmRqRkPDnC2+1jHEh9xaReS5vEU5boIR2KqkmS2hv+HEndtGzFKMPMTiM2Y+MgEUijc2ICE6mUwocU9O8Q5IJkWQ2QVNh8y9PMSNjTImYjmcUPvuhLxrI+6tE1hE3TuIep1zLElSqlEaIGYmcbWAqprht71gqwC1cTAcMA73zbN4RXDlXMl7KQEJcnyCH93iLl+EffEKmq3D4uzKCsp6mU6z51kcJNPUfOonc7B9Rgd8LFUKEoKXZYktQjhCfxdn+BPcopbc8QAtMc181hBeadFljRg20AjRHsSU1V4OxrT6eMedCkdiDGFYjTr41ViRGpQxpI3K6QVi9jwsfFY2HMeynEOV8f+LgBpjswtlYsDTLs99oiZncVKyCoOFoMNfFjdQlWrSN+jGyne/gf7sLUSD/ebyJfEMJDEUx6jyTqH6wMuPe3hllzwfVSpQfcxj85r66Rv9Jj4wxHO6QQKi4kiVBggsBjG84/XzzEF41Qp14Usx/Z6oA2mSGl/+SGEadJ4cIfJyzuY6Slse4iuNjGOQjaqpIkkKAwyM9grBnMQWi+uUT2+g6yUxzHeSYpNUliaR/k+QlmygUSrnOgFS5QevILQBvLi2RjyThfLuGPQAkYKxE4PXQlwhuN3TArIFhu4a91nzIFFKUS3OwhjEdaOE+rSFFkOKYYR1ANK8fie4V7sEGz0xuLQ0QNw5iIqMc8mUDXqY+Pjze1nzqOz2ccGCXIxHJstO874wUkCbgIOiGuWK+XtmF/74TLp9tgHR+px+6jIDfn8HDaES6MFmtMR7sfHpb5ixmXqloT+GhhbZtL3efKjEltWmAWFUw+QgwL5yCUGX7pMMpome+Uisoipv2iHh90q7YdLSPmsYXJ0KsfH4A412oG3fdtv8A//zT/g52uPI6pw43xAqi23T9zIh1e3qYcJVyOJMILcE2RViXXg33/sRRy+OuD2m7f5hq/aj7ulkQW4FY9DToWP33+aUqD41R9/B5WFgqgkKTWnyNN8LOpLidaGUuD9pXNvludYC6Y3xE0MhQLhjF+jo1FEuVz6S3/u7xJR/mbwngPOdV/soeyxxx577PH/Mx5++OE9reDz4HMWaT6dvSf87w/GWi63MuRA4PQhnfFJbwhxjaXRGL9JDK52EaKMsRp/y6XzZYcpyh6Bs0UoMx5+a5PJ378CysHqCuQplEsYDCiH2lKXtbyMn2uubwwI1zKSko8fZlzIKsxXY3YenkU0BLvDMqVohK8tYHEnUlZuuYz0NKOZOulsSJFYrLCU04JiJke4LtGNFnXBIoprSSbGYrVhcrrHzplDhCkgBFM3Zowu+YgzBn9tROP+bcSoINgcYlyJUwYu+2xfDLnvvgsUV1psTDR5uLWfcDcGv8RwypAcqKK6KeG5DlMv32G4BOWzEpNUCG+3+G7GjbUWuJbfjw5QFIowyXEeULztyVtRWQFWIYaW+qrBDgSVdYNQAmssRSAIdmNW72rg3BYjz7qI2QBpxq1J0nEY3byId6GFDKCSN6jPlGhv9dAB2FKITgqklOgAilCz77ldrlxaQA2vVbcUFmk0tuJSNEsIrenPSWrnMrYLjdooU+QFWIvEsHt/k+iwhVsh+OQqqgq65DGMSgg5IpqBwgffgawfEOgEoVyKmTKjW2uINGf2D68ZmdZqDK43yJ0epkjJ9vnEN03jXxkhJgNUbBgdCtHDLrXLGtvTUPUI1lLEIMVc3kHXPEx9FieJqU17rO+fx9Eu6YyEnTZmcgJ8gSMqqOsEes7g/8IQ0UswE2XyfVXc9T6iVgUrsPUcPSFxhgLdEqhH+qx9TZM8gd69IfXLII6CORySbpSICMkWBTKax82g3AL35DZuLokWypjAwTRK6IqHFAocS94UCASyWiHVBVy/g7hURXkh4aUhzloX0gK1PRgn/1iLiBNsUKJoVkAFlNY9sIa0KXDbKflMiHO1S9ASFFNlmp/oEzdAqCZO2+C8b1wpkoYpwwMlbOGwO9XANqsEPYFqDZFxRrGviVYB8sI69ukh0fkKa08voI9akvMelQdjbCqgbBB+CXtgEXtq3L5jfY9k0SW6cYrK6R40KlhdoEOFUBJTDnEAYcBJLblnsa0WwoL2HZwoh16fD16t8EM/c55vv+6TvOljdxOnAn+/ZsnrMBXF3PqyHrfc3ML3NadOTLJ2c8D8dX1OrC5iAxd7cAG2h0ip0IFidLCCnXOpnOuhS2NxSFaroAus60KtCnmBrXjoug/GkB+06Puq2DSj+acaUxkhbAm13kclBWKhikr61MKYblCgjOLidxxk4Y+3CUYCHIUIQ4TnAYIizlFS0npJmWjFw1YOUfnz83/hXvwpw18AmY+rWdTwM7e0RG4pblzBXLiKU6niCBdpDTYee9vQbI5btrBUPj5i+OrrxlU7xuBvD8biS5Yh0nwsVOX5OLcnEHh2LPRY+6xXjbUQHW1QTgSiXILhEJD4yQaL3wfZOZetf38tPiwoEe/kFCIYzy1KIDLBLd82ZHU+x1oHt1CMVIXg5hXK9z9N7/r9ROsjvKtduMHlyVmNlyTEUw562mPG84jTAgy4a0MWdMjSdTHbJqA1qBOfCylfGSB2n32e5KND1M3T5P7Y9+xDj1/m7B+9h84RRdXzyIsZRq0ddo5vcKhRAacg78xgHNDC4vU12QRsDwNy61Bb22HraodgPUMZiXVy7m8/hcoNq4+fx/SHiAtluq9ZJr9L8ol3PTYWm42Geg2t9WcEJnyK42ubRAuW+okUXXExArzVHRiM+JoD3827t//bZzmL/+0hhDcWafbYY4899thjj78R/rp47W63+wUd//MSaf7gD/6A3/u93+PKlStk2WcaFD766KNf0ID2+PyQQoxjmGNNISGeDHGForKbslbpAYsEcYHbyqiqmF3j4F7qYQ7UmPJ77HcK9GOa+pyhOxNiPzpE5h7MzrL+SoWILTcd2Wb36gQLS9vMOjn6A1B5+YA4EXxg6xg/NPcQ+bIEpVHaspuXkV2BysDb8ujKee574XHOOwlbywKvLfE7CdKC0C6DfwYmLxDbYAqBza4JAVHMl9+8wfmH9uEl4zfJd/3zNv13LHD0+R1aJwzrH8gxroubFKhEYEcKpnNwxv4Ha+d92iOLKKWIvAAL5SsFqr1DuD5i4sUe7qRBu9CVPv0NyQ99792MtOKGb2zx5Cc98kd6lH4w5jmNbf5cH2FyJsZsjs121VCTxwq3lyHjAivEeBM7UBzythGXGnQXPMqnEtgocDsZxXRtHKV7+yyDu2aoHG4xPzfP49tXkO+2kDAWVtodhtEEuiJIph1WDmxw9bE6Khm3FcnUwFQJ1xQIbbCOovSxS5hGDTUAf6OPW5TGHiMC0k0H9dgu3ArF0RLijoDB2Vnu/88TpLZN9nV9mHRJz0nMqiLtVKnqdUQnwt8yWD9AfmoxubZL8/SA3ZfPUSyU8So5Vil0GuGe7ZDcNQcjd9weZF1s4FFUQtzegKJZRi/WUeeuoqIMkUuybglvUmE3NcHxPiLOYHMHPBfmZ2DeIKdz7HOamNMJo+uauGsdvNM7Yy+R/XOYe/oMR1NULmvKLwio1zWrJ6pMvDfBWS4oej3soIEzcsiVggCEm+BFFic1iDhHpON0KW+zR3JgApFr1ChDT7oI5eAdX8ekBcVUGZFIWptNrnt1h+NvdxEld7wDnxtktcz2S2pMf6CDCMvkaGhHSFnGZhZ/O8U/uYOz0cFZrUJvBFLibISUqzm3v2SDx955A1aocXqPkhQTPnnVQSSS5PpZdMXFuWSx5ZDymTZitxgLFQi077H68BLxRInSxQJ3kGKEBzYHNfbUkVJBOK4SEG5CcGobhMYuzKKkQm9uIQ7OI1YWkImGsVaEcRXOpQ3kTgfrOOMKlFAikojQT7jh0Bb1iubu/hrvf/cKS80Wp07VONNqst0NODFqUp3NuHCxyn2ve5KSnyAed9m8XKVIIjDQe9E0woyFGhtWGQUVVFrgYRFSkE+WUMMRol6Hfh/VGjD5zovIYULy1XVUS5KPXAovBVciogLv7Oa46u3mRZp35Ky5VcKDPeROQDsIuPKaGeY/HlE+3UUszYGQYzE0y7Gui7ddkEw5eEMBpWCcpnXtPiz+l4/20z7n075GXpDNV+nd9xyqj7Spfnx1fH6FwCo5FlJKPpRC/FHCQAo6X3kTEye6iEjD1XXwXIzvIx1n3HqFRVRL2J0+tj94ZhxCQDZbxi5OoM6kmEYN4XsQBihviFUapssYd4AMQshyvv3fXOXn/vssAMZ3sLmh9VXTXB9t4G7BhfP7wLOo9S4AlY9cGP8ixyG3lptu30d0suCSGOGOLJuDCDE3gWcdxHSd/oUqFx4rc8ttl7n6Xpcs8kiWXYKTa5iSQEUWbwnSqwOk52LqVawbciYe8SXhMUpNl185cYzZjQkqq/v4wTfcyinzAKdO7aIKD+NZbCiwrsCvJ9RvgqXDZfYdmMbJLSpKsVJwaX+J1yzv58pDT4MjsWlOUXHpOw7vO34JOT8L1mCyjAc/eJK7X3bTX5h7H7+6iQ4EFouTW0xux21ovo/ONP3+gFqt+lfO3U9e2ODf/Y/38qIb9/Pdr70bgN21FmmcsXh4/q+e9PfYY4899thjjy8a9Xr9r/3+N3/zN3/ex/+cRZqf+7mf44d+6Id4wxvewB//8R/zxje+kfPnz/Pwww/v9Zl9kTnsFpw7F6HObeG6Ofn1DRhaRDI+zYUQZCrDizqIswkNWTAazhC/RqGAr/u/r/LAhRke2Z7G7DrIqMDBMveBiNGrCrafWuJq7FFyM05Vh6jNElYnlF1Lt1dG9QtsYK+tSgr8yxmjhYAkAHu5xOYpF7kS8o2z5/m3Zh+1SykiMxhhyfdJ5G46NiHFostybPQLyCijR5naRgq2BBaOTvV5dGnATTduMrwrYP3sUYTrYhyBKgArKN0Uk6cOM0sZh9+0zbn1eS79tkFfHlcNiAMLVC/HiOGIwRNljKmT3ugyyMpMnR9webuGLkseOT9PMoz42kPH2X8gxj0a8Xx3lQmbEfx8DBbyig9WklcVeSMYu662Ym64rsULjl3ltuVznDk5yamn9qM3r0JaQD2EksvyjS3aviSu+Dxx/hJmwuLoAXqrwJQ8HCsonR2SHKjgdQVPrB7CG40NW7EWgeDulx3jvb/3cYQfjNNtyiE6kDQ+tkVwsoU+vAJhGbCowQhnNwLA6ZcYnBDj9f+FTUpJgn9CEO2fpf6ApmYLhgsOVlz7fdKl+siV8eKzFOIYgRGK+nuvsv2NIYnxcVbKlK7WEcSEH9skeGSX4vAculLCOOMlq1XAKELmCjtdwkxLPCtIS2WcYUHw4EVUN4IgBKvHPycFs8cHrHz7Du49EZ/82CT+GQhUHbN/mdQvsIGmnVWRyuBMal6y7wyLjQHFlM+vrz2X6gc3cTaHOFcVxe4E0WFJPldgI4XbHY27rgQUOkNs9cAall9m2Y1c7GZBcajJxIdHhOtQVEKKioccDOmctrRPTWIqPmklw617+GGZZKFMeeiSH/UINyN0bYD7aId4aoai4qGGljDOQGtUBvgBWUkhhkOGXU27MYn1FTLSYAy64tE94lLUBFQklcsCb1ujUolViqLsYIdDhDLjePckpHQugSVBMiOJ5nzEtMXtG7Kqg9ICHSj85tgl5fDBbZ56qIq3maCnahQlH2etQA5y1GFBIR10IskDF//SADGKQUiE66Ln66TTPkfu3OXg9dv87rvn8SoO7/j9JiLPKUaC8mTC9MyQnQ1Ba5+hE/qIRsy5kzNU0x47D9pxnPVUlTywDJbB1Bzm3jcimggRxlAELoG2pKMBji3GHjDDGAZDUA5OpLGZYWVnF7+uObm7n+ENBlmT0PUoliapmAGzi1vMuAPWdueQRwxyaJGuYd8DI/yWhmrlMwx/Z24asfOooPZBTe0hF1mvw9wsZn0TY1NE+uy9+K/Lz5GdEU5kCHY0fk9jjUEYM67eEQI8F+GGCAumHDL5kR101UVZh3TKwwmWscMYsdOCcgkGA0RWoOIcM1VD7o5bkS3gH53hyjcsUr6QwDmJ8n10pwvbu+SdKhs/U8VmiuFdk1QfvAjG8LafncEb9QAIZgu+9BVP8/sXr6OXlNmXrGKlxkqFCcbPj0g1Ulqmvjyk/bTLd91+J/efN2z853egbz4AjoOdm8B2EmzJQ+Zjoe/RtQXsrRneJcloyad4/iLNSxk8FeM4GnNpc/xchg5bP3sA7/4hRuwQftkKnaLLvps13/6a53FVnOLSxiMw4eH4B8jKCmsFKgHPsXRHObZjCEIX13fJEYgpn1IBH37nJxGTTaQFUxSoVKCN5Vx3QNUdz51iFPHOX3/wLxVp/vyps1BxWX/1FDc/kjEcaQoloSjAdXnjc36Q/3nuF/7Ka+HtH3mSy+d26OyO+O7X3k2WZPz0t/1XdF7wfb/yT5lZmf5rrqY99thjjz322OPvml/91V/9Wz3+5yzS/OIv/iJvectb+Pqv/3re9ra38eY3v5mDBw/yIz/yI7Tb7b+NMe7xWaCt5cLJAZMvzIhWJ7huuscDXp1aq+COqRkAxLRHUnKwaopy8wrV10o6aY1z/XkMZzl6bMiwWuHhX65h5yRs9vEmNG7PMBMnLCxvkrVqrEx06OYB7kjRuVwnb1gqJ3b5qbffRHBTRjrjsvDWi0ihsd9WwkoHMdRMzw+ZKI34rT89Qq0XQ2ERnRFZs4R6eIPgqYL8+TMEp4ckUz5FyUHFBbawCKVRqUL1Y2770rN0S4Lz8yHF1WU656sI5Yz9I8JxiT4lTf89VXRL8s7lGQ69KGTg+CDGrQtWwuCGMo0nE/xZjXun4oqYomgJiqalt+xTikcoaRGpQDzP5/3qOu6JBqyvVvnIfztALYgJtseR326YUfgVnFaOyMEWhmIm4F/86P1o4E/PL+PMFMQPBQRX7dgUdb0Fz2ty5aEAkQww90rKD4ixWe5Zg+3H0JiAmRq2gOaTPXSg2PyqGtV3aNxBitCCPICPf+xp1OoWdqKOPryIcATFlI/XKOB0DycHIQusFOQrjXHyFuC2RpS0S++2AOM7qJoHapryJxRyoCGw4HpoX+FmhvmPbuB2OuPrKfCRtTLFdA253aJx/xrb33QA67vYSmnsy+EIbCVE18sUKsDYAmeQIq1ARDl6bQvv3pCp5/iU2zknOlVS4eA+fs18uBySTrpQLeHnilRVefJ8mVLRwngFagTFYkDWdMhLFmUk9dNdVr5kA1dqjHXYLGrsDyKKY5bs4gxeGmCbZbCCoJsS7YbUP9LFrK6hp+psv2yeqY/GqCJHNELuOXyF977DoZ/UoGdwkxKyUsEGBfLsVVRuScsNzKhHfEONwa11vJkQ9zxIO65Ck4XFOkBQhn0BzkBgfHAvdhE7XZAC7bukN8zTX3YIT2ziDyztlqVwNHJrd9zSIqbwhyUcCUnd4mUR3qrA1CuITGPQEJhxO0e9glMOUamhfK6PuVoQ3ztH5UQHI3zc1KGQBUVgKObGFWrDLYfo1lnCsz2s746rZlwH2+1QxEsYzydrgjO0yFYP8rG/iSl5pDNlkmnFlpzh+snLFE8IDt7SY0KEdC46XPqfgtv/4Sr3HWjx5CebvOejc4RzKfGVEp3DJTY+YimuDiEPUNZgL7SZ3m6T3b6Et50xmi2hhUQUkD7vOkzgQD9CJDny9NrY6NiTsDyHbXfo1DQrq5rS7oisUkXELnkN4ptmcKM623bIxoeg8rqMqfkhZ3cXEVphtca02thmBSUcUBLtSMSRAPnkCOMH4DkIpcZVMY6DLfmI7bEwUjD2nfnLhZpxbY0EjL0mrs1OUJRKFKMeKIfw1Bb24iroa/4skw0MFtV1IAzwYotwXfB8rAYbRePHXWuByu84iPvBE3yqMSdb7dD4I0N6sIyxoKyC/jheWx6MEf+0ivqYhrVxm5gAOldCgjsmxo95/Yive8EZ3vbt+xi9cILeL40QLxhgZmsIXFASVYYgzKk8d8DpcIaf+vm3Y091yaY92FpHzs4jIoMueVBYtAv5HPiHRxwYTtL6pU/ga8Pgy26Ac4CG4kmLdB3IC1Rc0Hi7Jq1b3l0+Qf4/z/D8mxMWSgGra9uwZGl6k5xfDygShVKa4KpGFZJb53vsb3Z577vq/LPnafxBhig0b3jtvbz9tz9MbzRAKh/p+NjEIjOLtILi0zqbZLPB7mb/Lz2jhdaIDKzn8vvvezMASZLwleVvgiSlPxgy6EZIJSlXg7/w86VC4PcNgRr7cilHMTHXYNgdEVT+4uP32GOPPfbYY4//8/mcRZorV67wwhe+EIAwDBkMxmXV3/RN38Rdd93FL/zCX71jtMffHo6U+EzS+YBBNSybtszCL13AzaH9mhrQoNZLUWsZpqZQ+wI6yyX6ecjo1DwPHlzk3omrCBWisUhrGL6gwtRKh8u7ZQ6lHV5829MMbECMQZ6eQWkoXxJE85rJ9xWo3Yz5s7uIRgORgxECb9tCJAh3LOfDBj/ywF2oD0OQZXR9RdBqI4dDkAU4Fm9NolKDcSHojremJTDh96AhMcrhwPO3SY0gERU+LJYJrhZU1i8gLNhaFVMYxCDHDhTWQPtMiawrSfsF1DRWgRWSbLJEEW5R7FgcEePVcnRJkE0prOfgXTVEOyVqjyj8UsHgUJk3nXsx9V6GawUrzYjHgzrOUKNSgxoVEErkMAdjMQPNiY9W2Dgb8kcbL2L2pRv4nZT8unmcfoEuC9yzBeWnLmJ0AR8QqNkFpPQpNHTvmceUPIKeQ21rhEWgooKwlCB2UlAeRilsFNMbWNREgJIuqhUxmvFxrKRQPr3XHGX6YwNEnmI9h1I1xyxd85O62kJ1Q8KlEuaVy5Sv67O72kBVNHYaBjd4ZAcszqM+znaMrhj0Qgl/e0T5SMLWbIjXKVBJhiMM1uTkJYHYbFHEMeaOo1jXQYcGv5VTmoqJRBmdGxhFGN+yecc8vStVgm6BFgLpGdTUNLKcYwZ9KlfaFFphFmfYUfNwsiC92SJuCBDCx+YF1kgIJRZDaVsTbfjoyKPajnnOV+zwdDpFMJUhXJ9iZQrrKYKTu9CuM9URiL7BOgJhDY50YasNjsLUqnz8k01MZYT0q/iZIp5W47affoRTMPYEURIpBUIqdOAgMo2OY9JGg+DxNWQvhnoDd0thXEnzrKFYK5BbEXgOphRAvUxecgj6kvjmBZwHr7LzvohgdQSj0Tji2YBTKOTQUDndxX/0ClJL8iPzOIlF9VLyIkFkCcKMq0G0gLTbQpTKhK2cMJbjWHALRmhIc9Lz46q1nTNl3MUeMisQZ68iAg+rC3AU8uw2dmkKL3EQSIwCNTuOVKZRGlchWWj1G7z7v9/F9/7L9zN5c8bCSondN2nyoWDthM/92xM8/fAS3/qN55HS8LO/neCMGpRfbLBnFSsv6nB1RdH+DwonVojTfdLQw3gKhikBBlNx8XcS0pkyEoW+uInKcwh98qkyTFUotgpOfywYVwFNWayQaGPxzm6TegFFJ0Oup/hPDrA3SRyv4NDPrdJezCk7MbkqcMIpgkSjQ58rGxPofTXKLYPWepygBIiVebRbIIYRIi4QVR/ZT9GhQsbPJiwt/Jii+4sO0ZXxfc372BnciSrm9sNYKXGPr2ObISTpMwKPLoXQrKB3d/FGCWYYQa08FqWNxWbjKixr9NgLx3NxI016ZI7wwjZog9WS6mO71E70oFnnpn98juM/U8ZELtxXZjjvUnpeSvU/tjGMBSasxdfjTZeqU2CspfLnayQbI+x2hhsLVLvA6ydMLU2y2R/iu4arb1VMnDrHujhHfsdh/NWxGJSHNUyzgbWa+EA4vq8nin913YMcf1vAB00D4TjU//wkcr6OSXPwvbGfE0CjRjrpULqs6d1QUOSS7ltyHjhzmfe1zvCv7v8hvurGf8jvPnKax+wqFknhjK/pb3zRE8xORex06kgJ+SjG9Ef89k//CaJZwcwG7BudYRAcJJzN2UkKjFTPJBJ+qpett/2XizTztRon13aZKj8rqARB8KwxEfCPv+KncRzFW97xPZRr4Wf8/L7JJmXf4+jSuGJGOYrv+5V/ijHmL/XA2WOPPfbYY489/s9H/vUP+Uzm5uZotVoA7Nu3j4ceegiAixcvPmNUuMffPYUxRA2NSjVuJ2FQKNKmR7CkWf6KNQBGO310BvlIMDoB0ds0s//1PKWHd/jxX7+brVaVG5otqvEQtxVjTcHaYQ8jJWwrNpIpHm/vw8GwfGyHvFQQLUB4aoC7MUSETWyjjCwMdmWO+EWHsUGA1IJ0QmG1oF0tEV0v6dYD0uUK+WwFshwhDdnLxh4ShSfQrsQsTXLNbAGdaYITm4j1Lh/6o1tZa9fYPVuh8bZNKqeycaKQNhS1gDyU2MSAVAhj0LlmeGFAthkjBiNEppFZjtNJMaZEMrmM7fkcefFFjt64Om5B2RGUv2bIwdduMnGkz6gJo6GPQdCRIbUvH/LGb3+K/pES6VSZdKpE4Um0J8GROGlBKc/54185wAd+d5n8SsLaA3O4kcGJBNF1EyRHG9giwwqBtBKVSEwcAZai7JJPhRSTASoumJmPMJ5ClGD/B68g13fRcQRG42YS0+nRevURsv1TZLNldNUhCyU6NZROtNBpiq746LJH2gqJymMPEjkp8RuWxrnLSK1Zfu4ut86uUQ9S0kWf+IhC5jFuO0VmFv/9LdxPDiGx9IqF8Q70dh/ZGuLEFsczWCEwaYJ0XLxOijdK8De6WA+ILeVyTD6jKAJBfLjK0Cuhc4MOLK6NKRcRlB0oBazcLPCf12D0kv3ImuHYkStUH4kI36awViGHGd7FbarnewQXIvbdsM5XvvERopJLt+bRurNOJMu8ZvoKlXqKF0E875FVBXqrjfPEefyn1kGDnqoyuG0GJ85BgMw0anfElRMhg12DKXJs4GOVwvoSL5aIiebYWNZajBJYASrKEblFT5XRvkANBog0vWZyC0YCVqBGBsolrOdilmewoQMWvFjgGMXwRYtYD4SSUKnAZAPH8SlfGOFvxDiXusg8I97XZHSgQjQLhczRkzXoDaEzRK6tE85m9O9dwi42aZ6ICNoGOUjJKlBM+JSOb6GaY73eXN8gOVLD+OPXjm1UoFZBdofIzgDRGyEQqEzD9AS21cG2OxglcHZGlC9HVFdHvP5VTzFzMOWidbkYVigWPWbuKmgs9nBuGFK/vcOmlnQCw9AG3Li8xgvvuMSd39Xh5lfscOeNq9gb5jEHZmm9ao7VN8yiyxAgEUJipUPW9BAZFEIiirGQZKMY0R6grmyTTFiykiKpObBRYHKJuzXCu7iLWt0hm6+ST5bYLZY4/dA+oo9o9Chh4uMtktmQ9ovmcc+swflVnM0u+VZOthiO70m6gDhBWIswUNT8cSKUBdVPGd02xdr3HGV48Nlkn/V/qZ8RaOCaGCIFjBKck5dRgNOJP+O+Lq/bjxOWcBcWxmJYHMNuG7O7QzIbomYmoVahmCpjZyYwzTLDxYD01rnxeACKDONIyAv0ZJ0/9e7GTrnjmp4HhszpHuVLQ2RhUYE/NhpGMLrsjsf5eM7vnWwAMPl0l8IFK8f3uaJZw/FdiufM8Kb/5wo//ANnqTUKhAWnk5BVAgTgnV5juOyNW/JKgqIkyB2LWxcEpbGpOYVGLs7jl/XYJDlOnk0Zm56iclYhspzpH73I3K9dRp7qUSRVTLXMb517lF89/WcUhx+lPneVO7/hkxQND6Tgjx69nvefPMCF9Uk6O0NsoRHVCkmkuXK4hBdn3PKjCaPCYZS43LiyiwB0zSOvOFg9bjVMe8O/dO7diPtIY/DDzxRUjj3/0DOftzc7tHcH7Lb+4jGee9t+7rx1H1/95bc/8zUhxJ5As8cee+yxxx7/P+ZzFmle9rKX8c53vhOAb/3Wb+VNb3oT9913H1/7tV/LV3/1V/+ND3CPz47CGDq9hOGCImtI2kfKDF62jzSaYPf8+M2eXyQIt0CRoW/3cMseM8mQ5naPfMLjidYs5+I6+vkFySEo3RChz2om//Asuw8Y3nP5Jj5x9TD9wTQPMom3nTBtIur3r0NvhK766OrYM0aXA+L943WFNoLhEcNzD1/km29/EhVZnJFFFoL8wBTJcgUcifeJEZ05Sfv2EuFugtsoIwIfgBPvm0XGLm43Yet0lZ954NWYUznORYuKgE/tTlqLrYRkSxNYV4JS2DTFn9WIJrjKQ+5fxs5MkE94OJMhtlomuerjhAbPWsKexTYsF7oLZE2H0j3b1OZGHJzbwqYw+Sua7PdCtjfLyCoMlz2iRY/yVk7hOwymHfKSw+yNCX65jqo38aTPhIzRgaKYKiMTg3EM3dsbtL90geQFs+ij0xQ3To0NWStV5s73ONJtszI9pBuHY+8bz2P3ygRCKYzvAgIbOGQHJxDFtTaq3FBeSzCOxhYad32AFBanlyJHKbrIkMV48TN8eQOv3ibsdhDbMbedG/Hi5avccmQTb1sjdyScFsikwBqD8SR4CkolLAonseN2D1dBlFJZc/AqYvy4PEdEMc3pFq4Icbs5SRYyylx6cwlymIBbo3pGIlWGvmuAIGXgVOkfamB8xSBtUL6jjlosYV5V5vzuAjqWyB0oRkPU2avE07D9ooDedSHrwxpOluLZHHLAM3SMYN7r4qqCYkVgHYGMDbI3JFp0aD/Pwz91FWejh1YF6ZRDcfMStuRD4GMcC0mGNjnJvEPnOSW2n1tDVwOy6TK65GKTBNwK8b4yuuzgX+ziXe7g72TM3FPHvaWJrSkwBcqA3WlTZL1xJUtRoHb6yFFO2Cow0lIEEOxkJM+dA8dFi4xURfRWFIWnGa34dO9eoDg6jbfewT+7A5Uq+fwEAf74/EgBLuy/d4v4+grDw9e8hSyAoagqcBSDLzmMUI3x6ycbEB3yab9iHtMoId0AZicR+xagFCA32zhPXES2RxAlmHqI1QViYwehFE4C1QnDC168RSMXPNVd4sqgSTEbsFpf5ondg5wuLbMj62zW4JJwcQ5Nk5UkOLDTDzj3p1OcfNsCg7mQ/tGAwUEHE4IOBaknKJQiKwmsFEgsIo5BKVAK4bqoKzuItW3s9g7DRUFeFeTVlGK5IF2skB1rEt8wSzZfIts/ga5XybcCSDSMelhrqT3VZeVt55FxPk6N6vXxi4yiDMmcy/aLys8WSmiDzHJs6Zr5MmACRTYfsvNtR3m2luYvbmLIrIBco2oN5PTkZxgOj/9dS2hSCj5l0q8NDGPiqiW5chm7s4uKCkgSrDYUTR8TiHElyLUBRYcbWEdCrqm/u09nfg4hJXPfoJidHzF7X8rg9lmSGxfGPyIEdnFc2bGq5nnkZ5comiHRCw+R3DiNHCZYY4kbgjSKKZs+vqdxPINyAddFbrbgyOJ4DCV/HE2fjtAeaB+q813e9uEVLp67HjlfhkYV4QXE/RI2y7BmHCuOAOFI1JUtvE+cRHgubiuhu9NHZCnZosulMOOTaw42LZM9PM3xn7oFmWgKx/Ln8T7e8uAtZLs+8yuTlJrVsdDmOeAKBrtt3nvhKIWV5A3D6mBsBJhMW4phH9PaRbc66N7gL5w/Ywynt3ZIpg1985khCl/6hnvA98FxUEVKbbLC5FTlLxzjQqvLFSLOXmsj/dvGpg9i2m/EJh/4O/l9e+yxxx577LHH587n3O70lre8BWPGvdPf8R3fwcTEBB/5yEf4yq/8Sr7jO77jb3yAe3x2SAFuXVI0PIo+qMTCUkHxEof0mIYIbniD5YOPl3HO72IvJ9z2bZr7mutcCpv8l09aPrC6j4P5LtnjkvBSSqMesf10GWMscpRze2ODE7155spbPPATN+HvFLzxhmV+s3VuvEjPIZl2wXUpfCgWM9yWJhMOpmz5+pufxq9pPrS1j1HHRSoL+GMTx8kAf7dMoDSbd7ggLQtnd8iPZ1gfDh3c5WR4jDxURAfLWKkZ3FYfm6c2FAIP/7+60BlhKz4i8NDKwbEZaMHyLUOGYciVpwJMGcTI4Ztvf4qXvu4y//1nDjBwSrx8Yp2HxCzOLRFaK8xvjljdzZn5TsP+m1u0+z5fK8/z0FqT1Lr81oduhragemmAyi15xYNAIPsOo2XBl3z/g/BhxcPvmWP+xdvUnxfxoeN3gBI4kab5gU3ymqJ93xxbdypMSXPruzqMsgKx1oV4RNwsoQ42iGwJU3JQMuPrvv0M+nTBhTO77LsF3vHgrZhtRW21h/ZdRFxgsYSPrWGMRu0MMDOCYqFOPgGjZcnsE+Md3eD9XdxlzfxrC86fD/jDt68QNquIJUMejANnvF6OdRVybgrqJUw2RMUOcr1H5ntQLWHnJjDxANO14GjkKINCc/jVZxDlGt1LdUBQmJSiPaLRLaMmJiltJTjJDtPXbXG4sDy2dhCOCIqSQx4qOucG6DMJNHt0X3IA63l0X+RRPxGhRorBSw+x/bJxG5C/KWhFdf70bUfp9y3TN+W88gVPYxzLQFiO1Ha5UCnh13fZ3pxEKElyrEq6v0S2WCK8GCNTCHc0yi2jbzmEag+xAkStiuN4+Cc6EENRD5DSQfUzijyBQRtz0xFUJig/uYsaZlBIFJKtXh0VDDFndhGJRS9MEh+tY4XA3XEw8xDfMDv+G3oWWRj8XY03yincAhONML7B6edkdoAuTSCVwCuBbgrcMzkyM4hcIj2XtJYjbz2I7fVJJ0s8+aRCHBW4PYv1fHSoUbHBHRgwFu2DGY2jj/37txH3zDJ4yRT1nRAxUJjQHVfIPdlDRDHCCthuoRZmyJZnUP0Rsh8hdvrkS5N0I4ff/vWjyG6bP3jNLRjPofYlOX6liy27bKsKpgTzlQSEJXFKDGshZ5ISckXy2L/Zj+kNqB0YMLghJLzqYn0HVQga5/tEc4rAcRHKxxYF4cYAkWtYmKOo+ci0ILdDhs+dwtmXYPOAweQU9oAh2Grhv7uNnLGkgUJ2EkShkQay/Q1GN09R++Q2VhtQAtGsge+jyj61b4zpP22xjqBYLpHuE4RbZlxNM4iRw3S8+LeW8Iqh9PSQouKQ37mC+uSVZ+7Vn5EENYiQo2QsGOTu+Fr7NC3HbvfRlXBcifXpuA5OLxubDItxShdao8M6XjsjDcCUfVAxuhzgb479g+TZS6jyCvn+STCbDD+SU3qOQ/yYRocOZr6MB4gwwGEsOt21cIlLFxPy6w6S759EnclwVIDqaWKdMr1vms6Hz/BTTyyjpKU7KIGEZKaOPLvO4PmHyA7XiQ4bKg8nOEOLcWFULnNh5wAvesFR1nY/yLAdjStnfP+Z5+dT8eG022TLdYLuODXJTNQI0yGO61FJHZ53YI5cCj70w9sYcvyOodQyZD0XfYdlRuekF0t8+BNn6boaV4hxxZ+w1L/JQ5x0IBdkfZctT5ItF/gXI5yrbVAusl6CIGA0iChVQraiDxKoGZ4442F6An/gMDtd/oxTdN8338PP/7NfHcemRxFv/vevo1Idbyb0BzEnTq5z601LdDsRW0/t8uhI8sZXPu8LeyPw2aCvgBmMP+6xxx577LHHHn8v+ZxFGiklUj5bgPP617+e17/+9QCsra2xuLj4Nze6PT5rXKl4/uwiJ95/msqlEeFly3BuhH1hiVrQhwievjqB70o0Ankl5ondKZ5zbILHLy2RNi13Na7w7p8/irtWIArN6IQlGCiSpVn6B6r82R8c4ZbnPM0nyge587qI938wxZt5AcPrziDShPTmJjpooafqxCsO0lpMySBTjVLwbz5wL0dv3WDjSzwqv2vHySnW4F3pgdLkUyXyyQAKQX/Z5YDqMzruEr4E6gc1w4ONcfSwBf9Cn+b9GwzvmiE9pgguS2S1BtUSJs9RFzaRtWvRaMOY8yeauGj0pAtSItKUWT/iz/9bmZ2Hh+x7QcHpt84wecggZsEWArk+IGBIpV2wk0/TGVVYbp/lybwgzQMeu3gD5d0UFBSBj/ElFAbtwf6vPs1jaZOkLbl4vEK4C0dfapBxn6JewunF0OqihgLnYohoKZbvzvi+f/Igv/SDc2x2FVhLkTt0egFKZ7gmozybcMvhEWKhYOn2gg13ggOXN3li8ygi17itAus5iCjBTE0idzvgOahOTFGJ8LZ8VEfjROP9/crNBc1bBcONBZy1iHxrQGZKRDTo3VYQ74PwkQG4Dtb3IM0QgyHYEkJKvH6GLnvER2fRVOGWCP+PIoS2CAUX3+rRuCEicCOyuIS3NsTt9FELIbJUQlmL28txa4LJxQRVVvjd8Q64uzWE/gCpNV4/IXrhIawBlYPa7OGdb+PNT9KYmiSvCpxYYjzJ1j0L1OcTRB4T2RLd3KPtZggJw8Ow2p1mwubkty5QP5Hj9vqEZ8YL/fBMi9ELq8QzEK5KPBRO36AHQ+xQUvbrMCNwekNIU0RucKyChTnipTLBpS7q4g7WVeS3rFAEisz6BGe7qEIhdQFrO3DLLGDxT11CZgXmuilM6OH1MtIqJLMuxbSLa4bYW6fR7QjbThFuSFELmHYjFluXWX26oLCa/5e9/46WLbvre9HPnHPlyrVzOrlPp9NZ3VKrWxKSQESJbBvDIBhsY2xjw7U9/Hz9fDG+2Pe962d4NgZf7AsYkMkgkRSQUKvV6qBude7TJ6edQ+Wqleec7486agmc5Die5f0ZY499zt5VtedatWquNb/r9/t+y5MzmEAhx5LRMZ/m2Qnq+h52EJL7y1Q2cuqXNLriY12DuzfBzBXYicTdTBC9aZKPjWpUXxgSnh8ADcqKj3UEapBTrkiUUFMxKMuxSYLjCFRrBrO3D7tdnMBDVSwHo5L1T0XU/3SMiRTehw1J12f1u3f5g2t3s+J3eXN7ggV+sUzpfhQmn5Jwnw9hQNnv4az3aK13yb9qlfyYC2mO9QT6azW6LlCP+7gv9WFjF2MNajzBkwrrKbb+9BrpnCTsKhzjoKxBnBU8PNzhapww7vlEL6bIXozruHj4TCoF7v5UrBIAgQ9pAdrgvy0mrQR0zoR0TghsZGl/NpnGM3sKqzOcqIqoVTEHPZzAwzmu0JGAuRD7vpOIZy/DNmimrU4WEL6LrYaYWCOK4o8JNABqPEYYQ9Gq/LGTtajXKFfqmMsHCG8afS1Si2iE+DeGOGoane3KJo7jogc9QE9FpEkHqJHMR7SqCcIB96gk/I0JgejQf+cS9ecn05hy4MvkOf7VZA43HWDrFbyz+9hbI4g10e4A5xtOYp65yKg7HaGVBcOvuRMlLDZrYBoRzc/uMfOhPumbbsEdG6wr0UNNMTB85PWn8GQCeY4tSqTRGCnATA2SbVFAZ4CnJCDQdcje5VPcU0P9Qgd1uc/Oa58mn9ll+5Y7AJ9iJ6Z9NkG0LX/r/he47ZEe/9fBm/jRf/y7lK7AtQasoRQg7/Q4/vZNrrHA6HcSim9tUlYt8n6f7NkmlY6LcKbC0fmX1lm4Z5sP3vg/aHqSn/nJr8AsWZAC+SfancLopsoNxAcjnn/957nvzX8R6R7l3/za03zsD1/lG7/pQR564Di3rMxydKH1X3IJ8MUTfhPCOQ3O7f99/t4hhxxyyCGHHPKfzH+ySPPvYmdnhx/7sR/jX/2rf0WSJP/xJxzyXx0hBI/OXyWub9O1NU7e2WM4Spk0MuaiHnQhfdZBtxTerEOy57K/4/K/PfVWKkNL40iC54zRk6lnhhl0MHMNUJKy6mADF4ylNS5oOIbrzwaU7oif/uCv4KZ9/AsxbiaQjJicHOJ+7QxpagheC5EW1Niyl1fZevU0ZUUTOQP86/vo/gDZS5Atwea9dWS1RrAvMFlJ9E0+r9fn+bKHL/Prjz1CNu/i9Sxic4IoOtPY105G+nyF5m9sIVKDquaotIBJBlGJNWDnZ9g5GrDSWSdKrpCM24itfX7+9+6h+qlXmXTgwqtVKt0a9rpDuVYhnVeoR5axt5acPdWkeXHM3KcG/Nr2Mr6fMKrXkWKCv6uxVrP5zhrNdYg6EM8IHr71EjdYYUwFFXmEOuJuNnihfSsyLhCZhkaVeNWl8uI2fuFR1xB8B8yvlexcczCug11qIW7GAJeBot/1+NlP3oV7zfJa1GLUjjC5xt/cw13vwGwLJ4gwhmk7iIGyHqBXlhHxCJG7lDWH/slp2f0kOwaXDiifKZFrEd231pBOnWAzw+1AumyJNhOCLIH+NrHj4dkUszNCzM5ijSV3NVqB7OZEV3KiCyPQBiMkw9uOMDjeoL6ZQgLS9SgrDukSOLEHQmB8j41Jk3zzgNptCen1gGgrRsQJVgiKmQjddrDKgLBocXNRikDudmn/Vh+xNEu2UKH7pggRCoRnmWQ+v3f+Nu6YO+CpkSYeuUykS2klZeRQVRod5wSjCOl7CJnSmh+xe2JaOeTEkuAAhJwmWVnfY3xLiNSa6qfXYVxghEDOz4ATkswYdN0jmgQ4gxI5KinbFmcvx401QjqwPIMcTghe7yGMRjgOjBNwJLI3gE5KJGrInQHuXo/F9/rMhz1emDtC/lAbdTBB5CWjA5etl+eQXo6aG+EJj7wE2R0RHYwoqy1cYZG5xsksOnfQSQd5dYtybQ6qCtux4AukdbCNqZG01Ibo1SHB+gSz5KKrFYQ22GqAiGeQeYaYnUGXJcJ1UKWdKhpSgTXIzT1W37zPIz+4zwt3LvJMTaCUobzWxU1T3nLsMr/72lvoZSVkEqzAWS65+lPzCDfEvpSDkyBrNbKmYvRAC5SPSsEdlRhjIbEQMU1Ra1cxo2Ta5rjdm5ZdVEJmfuWAyb1tzNwcWKjsGlRpWF4bMZidJm2114aoNOYgb5EoHzwXqwQiDKFZww7HEMdYLNKtEV3SOMmY4kQNSgHDGJ1P/WnGDy7gP9PDwQFXkjRGxIuzOK6mf0fI6ql9Wn+lTu1JwYufOUblE2eRSYFNC+QowTEO1Gvo/anf2xvJUP0htj/EmXxBm0wlRMy2qI4tlVCT7ZUQeBijKQOJU6mTR5ZgfQ+hHITjoDwPG4XIapVqKQguxOx/w63Mn74BcoiqCrxXLjP/v1uqaZXhpwfo4VSY+O312+h1BkhnBBu7WE+hLqxjW3XSls9n94e4noMop0a9GEvl9S5OGJJXHbQuMY0qZeBP5z3tYH2Lf2PMw/dd4NpzDqNLwPE1hDP1wbGLbcRggilK5JyLcAJktYas1Th4KELca2kEY77yzx1w8BG4+PMzhKd8mi93kBL6d7co6iWdrzIIYRECxHDMaJIRDXJMb3Czammer/L2+HNnXuWDczP8+NbXoo0CYwmDkuTWOb711jv4g597Bqzl7DMXubKQ8C+ffCutSsIgN8hsmtRWE+6/dV6ePzrL3vUDAH7nlzRf+e6PcOTEX+CVT19k8AfP8VufOc+pP/ybDJslJ+6e/692PfAfQggfvAf+u/ytQw455JBDDjnkP48v2pOm3+/z7d/+7czNzbG8vMw//af/FGMMf+/v/T1OnDjB008/zc/+7M/+txzrIf8BrLWc6w546Dtf58TX7vI9f/U1/tq3X+bofI+hnaZOCKsQuSWdrzL+07NEUhDtlLhLKf/ygd/k2X8Rsf9mh/4tEjUu0Xua8XLE/v0VkNPy8EsfafPZn1/h3LU1OFGnnDOYB6sgJaYSkDd91H0OGENpFMVcSd40xE3QvkF7Fh3AeMnBNCtQlNOxCYt5U4La6mCLjNpXDZCRoKxCnit2ZkPcO0ccv/sGzuYuwbWYyT0NTn2HYSEcIqIacrZFsRBRLFQ4+PIFdDyhTMbY0KP0PIZdD3OlwMFBtlskE0lvZhnrKiZ+nV3VYn9cpfnaBH9Lw5pheGeTQjqMP10hf6qK8FzCv+bR+5rjjM8E5KseoxNVshlLXodkTuLElhd/7E54Do5/xR6z7x3w1T98gWLoYUMf98Im6so2OlAIx1AeqeN4VTpPV/mHH3iE8+PbsI0KJnAokxFCT9sxVKpJF1w+Xp7kcX2KznWHybogD2tYXdw0ALp5K15KqtvXcDb2sK4kW45w5iL8N42xX3ADVe5KinMg+n2KMEc1I6wjSduC8GrG0Z++woxyUHOzCByoVzA9DVIha1WUBm9QEn36Mt7uPtwSIpoNkALhB3ilj9KWeCQoPDCRh2rPIg80uhJQRh7JUo3CBPQ/3qacKLAg43La1rDQwtESKStET17Df2UD40B+vIE4tYgwgDEkSyHZUkTQkfBqjfELVfrXA3Lf4Wx/nvnzhu3/dQb1ZICRiv59DsktJbZMcUqJe3SWYNZh7XSf2aubBLsF/m6O9QXLb9nHO17HzNRRI0Flu0S0a+C502SmUYyOR/gXxljg4FtXSe9p440s4U6Jl0J5cgEz30QAqlrB3euiNg8wa3OUS2289R7Rs5vIAnB8KluaYCNn8isu1/fnOPbgOq3d61Qev0r05HW8q0OSmYCiGSByi8oK/E6GnwJBSHaiSfyOW8hum0OVAjcryd7tYf2SYGuItAJv4uKOJGUgcIrpcSPzktHbFul9+RLpkQbBfo4zKqef/0qIUB5YiazWMbM1tC3RnS7ahfLkEloKMuXzlDpB9jUVFrod3CdHhBeGLJ7eJ4o0cgz2swm/9M+P8LM/cYStrSV0rQoIZJnw0D2XOdLYof/wHLoeki9I/CGUdY/CtQQ/vo33k10YG0Rusb6LadewzSo28MnbPhhF9VyGCQHsNPlNSC6VJ2g90GC0sEK1naBTS/PkiMnXK8anfbLjDQgDhOtNvzcbqLVVpHSprg058dtdbv3fr0FSMjlZhdEYDvq0Xo6hGYICG0i0EZgBFAeS2l6Jrkk2e20u3FjBKx3KR8+gfQfbqmJrIcaR2PRPtDR9IeXnS2yM1uimj3ZdKHMwZmpuG3i44xLnxcsEL15DpiW608N0+1PPpFKDkiAkIi0Idw3xSzWy64rur/sQl+z+Awubgrm7MvJgWgXyVO80M8c0+bEW2QNrYAzFcovsziV8IvZXoVhtYL+gkMR5eEz6kGVyPCJedjG9CZMzM0yORdN2uQKCh1Kq7pjsosXccgQdfv6ekaxFiHs0+p4GR75hDvdNa9NEMhRzn8kwFwLeuTPiG+/Z4IFvmpDvGLrPN1BRFS9zCNZj1r8qYjwI+f+89gB//TNfxivDI7jWYicxdjACoxHGkkwiilTx0k/egioygk7GsZ/cxv+VFO27PPbsNcxBF7Pf4cmPvca1G7OYl2r0PzVPmJhpIlYG3/7o541/P8d3/si3ThU3R3HryT2Wl6aPmTUljGOG13f56K8/xcEnrvN7/9en/sMn+UMOOeSQQw455H8avuhKmr/zd/4Ojz/+ON/1Xd/Fhz/8YX7oh36ID3/4w6Rpyoc+9CHe8Y53/Lcc5yH/EUpreGXvBI89E9IcDbi8eQCzhrCWMhpMe+V1NcMELn5vROSOKf+gQ9SIaBVVnj3jc2Nunt5aBbkQ0vzEJsGaw0NvucavO7dNK2wcmHl4H78fY44dIXnNxfkDQ3l9ANpQhA75bbeiux3qf28Tdec8wwcalJGhVcv4u+94gitlk5+6fje9u0Mmt4S0RI3aiwkyttz68Q0mLwj0a03KtTbqjOHEP77M9TtDxLdb7lbXuf2+LQbP3cZ20uDM+wr+2tHnueo1+EdXH6QynFb85AsRw9ss7c2bMajre4g8YvINbaLPRhRlDW9fUNlJKYQEz6EUmmzVJ9yIcXKDUiW9qod+XaGaLuFrXZjA0ZO7HLslZfMXhtOoaddSGRZMOg6l7/Ge1assr27zmd9Zwl/PWH14hw+tv40Tj+5ydWeF3BXIeghpjrPZo3KjJDnVYHS8DcYS9xv4KqdcquNc3sXZGmBairJZQWpBw8kYlTWsEgT7Oa0XeozevMrk/hV8FQFyKnIgKLfGKAlIgUoss/ck9Ec1KqIkdqb36k1TcfzNI9ZGHX7euYugKElaLuGgJLy2gUwzBtbDK0JMrvC6BaQWpH1j9tCOIXn7KfQxg7fbR90WUr5qEbUqbreET3QxnmT4wDxeKfG3U5ydMSKXICVp6OKPLFYLUu1MjVElmDJHVatgFbKTMzlWIZvzEUoyvNWn8vQNVJKx9xdOka1FVK5bnFFBFBuOvX0Hx2i2vRqRKJFXMspUoUqJ9QRFFbKFKkVUI8+qqH7B0euWzu4ip8M9Np9vgFUY5bDx6gKmEEgL5DE6qEIYYMQQWeipF8g4RcgKjlOFwMOTFXRopxUeN6vQjKOQnQHWUVjPwzRqkOU4GweoHYXwPfJIoiOFqkcoPY/e7RCc8gjf7DD35oQb/RbOgSY+2cCELs7BhOZ+jN2c4AgXoRyi2CHdT8EYZCmxJidrRJhaBX3PPOaqwGQZVlQxjsAkOdZMBYLsrmXyeZdspY373ASZlRijkUmOHKbIRgMTJ5RzVWwjwiYpTpph6wHx7W1qKPQ9DpvGo2pSHrp3k1/Zvp+7vu+Ab/yGXbqlJOhYnBH8wYdPIlxB/fYRo5mI8pXr3P+mHt/2l7fZ2wm4+Iu34+Y5g1s88oaDmyjylYjo6jRdqKw6hJsTdGeIjBaQs7OgBNmaz/DELJXNAutZ/H1NHoAy8HJ/Fn+8QDFvuPrUDEonOG93uO3RG6w/3YSf28XiIJp1ZLMxbcERgvisS1+eRNkRIlTc+tP7FO0QUYsQQmGUQK33sY4DiUZ1Le1fvIFst2imlsorBd15n+I1kFU7FbUfug35mXOYvKAYjnC2Dv79E3yRv1FeI7OCtF0FC7EToYgxShLfv0pZ9Wlc30dqSTZXww+nxUU2TmA8YbwYUKzUsIGLozyiozlCWoR706B4JBh+MGbmO2ZwfmHaElm7VrK71cKtJGSv7+AMMrKH5hHKQc9UMbLAPrpC8y09hr+gsUC2X8JfK4k/GWCFpbazj45Wbnr2SPKawP2pDs/tTFtSy8jB3x1joxAhBGYywRkWqKxP8ysjrsWz6KhExdPd0Pj4gNbX3c8ntgI+2hUIXOL5BsZXWCXJ6iUnH7zB9n4D/VSDoRC0JwnCWky7ghnE2Pk6IPjFp+7h4dUbXL7exMs1q7+yAfvgbUj6X9FkdG4LGQXgumyd3+SOiYcVkNUAo5ibuHzn+x7ka+657d962848ctvUYycvyC932U9fZCl8kItX1qFaQUjJn/n6R+lcHHDPPcf/c0//hxxyyCGHHHLIlxhftEjz+7//+/zcz/0cX/7lX84P/MAPcOrUKU6fPs1P/MRP/Dcc3iFfLK5U/JNv+Wr+/kd/nLDYx3Vy3nn6Ckczl3/267fDozCZDzmaXyZdz6ivBOwcbyNGE+Lf3+WD9lZuqV5n99UmzlBT3r3E3X+2A7HA2YOiYqldSoh/N8c6JacfucIL3MadK0d47fF1ANRkwOkv73L+9+YRhcLpFzilxBYG1XXYadS5u7nN2j9ywLhw5AheF6K6xguhf1eNYl2jKzXiLKTUAnqgnjPItk9v3ODqmZLxHW06R320vspnX1N88idDnOqY3gMrOBmE26AclzjIqFwZwe4BlY0KwV80ZH0Pe01ifAeZaaTyEK6LnPUxnmR8IsIZlSSzlrWfuY4pc7rvW8btDaCv8WTB1lPzuEONM4Hx1073j0NB9akrfPPPnMOtw+XfE1z4sOLV199G4+qYZ//R7Yhvy3F8gz4+jw1cxIV1KCzO3oThbU0md86ycCFGXljH6w2BmwnkDYtOE6hUOT7XZdJ3KfcioqFC1hsYZUmOeoyLJpXzIygBBKV1kZnG2YvJ75OM9zwiL+Mgq2FvLvh2H6wwPxvywvaj+Fdjoku78MACXlZSrFaxJ0Ls+RL1qX3k7Az2ZuUTLtj5gjIOYDxB7ZWIgcK7ejCNgHbUtJUtCilDgT25SG2nRGUGoRW2UUciwED11S5ikqJnA4a1OaTU2M09hNFQryOC6QLJCWpUEshijbNbIAYZSIl1HSwWUWjSWYel9pB6I6HYVaQ3BGM/4v2ffRA5H1D4BbbwcfclYhWykxHljkfeajM+n2I6dZw3N8ivaVQukZmeinE3ttDNgL1vXQFfUf9MSGunP135hwEuFueaJlvzqF1ycMYOpirRriWfcahdHKHSEp3FiJFGHV9FKUGaD5CuwlQ9zIklwjHEvYRkISSvKsKtHfo3fNJzbXKlKO91EM9kCKPQ0qKGE8qxh21b8sUq8nofIS2bXx2hJpKZF8aoxJLMOHibJf7ZcuorQ8nwqEBqaD6+DqOpSONv7jDzgT7psQqqCEFYDAaqAWY4RiYZsl5DaI1Nc2RvPBWgIg+vlyN6Iw7+Ddz66AH3LOxwftzEPSX4iiNdlhsl/dTD1SVerpBhhFCC0TDCeBoR+FyfrHLxRs6FV9tkaw7sGvLjgrIUOBmgFPrEMma1jt8rcZyCYnUOUQ8xwwxtSpJFHyeX08qiZwtkbFHphMk9s6TzFv/TfbzYoWzNk0torY6wZYodKGzooobTth0hJVY52I1NxjNr2P06zozG3RlArYqXTC1HbGefotVGL/uE2zk2y3AGioNvWsZGDv6GJlxMiG94+I5DaUpUYRFZgTlzFDvbJFuuMzgTMvORLzBzVXIqWALkxRfM9pZcZQjfJ3n0FpoffBXrQ3NtTNnWmGuLU0+kloCr2TRK3XWgKHA3+gjPQc5VSauwfa6KvJIyedmDk210p0PjTIpaUojeBIDKjZRytg07O1Qn03ZmOc4wVYG63qXRT4jOjxim5o0ROp9KGP+giw4BK8jXGmQtQIA7FFhjEem0jZA0QwxGjO9dJHr2BkIK8D30Ogg0z/2sgrkhdqYGgBHgxDEf/CcfR9Sh8i6P7M3L9OZctCeoXbSoasB8P8VebVJkY/SOSz7jYwQEGwLjCspGgFCCwpH86AceIRQJS+0eu4GC62OMIwg6mrJVw+1MIMuJHYXNNdoHmQNKgBLctbz47zwvL59cgpsVUt1Ny0c2Pst3t/48Vydj6q1p4lY8zKhKj0k/xlqLEII0zrDGEFbD/4SrgEMOOeSQQw455EuFL1qk2dra4o477gDgxIkTBEHA933f9/03G9gh/2lYa/m1yy+TNi2zmeXkXI+tV0L+6KN11lb2AVAJzLxHcerUPi//4b3MnvGRsxP2fqOkd7ZJJx+y2OkSrzQR0iPecokeHeJ+ANwMatcyvJkaD9zb5d6vHvLcy/dRO3MEfXQWNcjJVttc3HdIbq1RzkQU7QCMxZ0IhoS8/8U7cdMlvO0dnPkWZm+M2R8x0QFxZCnOlbhv9hhNIlLjUjg++BlmdZ686hEEOdGRnLlne+ze3mbX91l/NSQdSSrbY8xaitvVKBMgrEP3kSa1qwkcWcHRAp6JcJ8skLIkOaIwI6juCPStixTNOu7IUHqQzXrUr0xwJ5LSWERbU/9OyfxnB5x6EH7np44B08qiTFYY3WXxHouRA8sHP75I9R0ur29H6ImA9Qr+KCUfQvmrNbTngZLI2QoJbZy+xjkY0nxyH9NqELzahzzHuBJZmKl5qe8gtMAUBdvXGwSJQZcJQkmsAOsrhAaMxnxBA6OdaiAIbRCFobvTwJuAbln8halBamknbPxojBNfxj/ehtzi64z4zjmibkz+iMXZPcBKsP0+JvJBgTQC3fFRHlgrCJ/bAGnJTwWIjQR7z0msNSAE1pfkcyEMJqhdiylLVKExjqCYraGDEGkVWZChnACMwAQKtT+BwQjrOJhWBb9XII1FB4qs7aObFUyaM/cLVzBLNZJ7V/FGMHN6jDGS4UGVY7cPGcch3Zkqs/ke1UckvXKZYqwoRpLwQg+uxrhnDxjXXPITDeSWIKgX2HGO7QyJa4rKOEHmGe5GC70YgPUwwiDiGOG6iHqVsqLQoQQBmVMQjCTZQnjT18nFGZfYwEfJabCycSTZsXl0M6JYaxFtp8gio5zxyGd9ROkhy6OYwMX7tRTHh6gmmdy6MPXMGWY4wxx9bB4738Y4ktw1lDIj3LRIbemdjqjsGeTIEH3sOmzHUKlhmyFOBn7PIm45hu1O44WlrhCd7+FtJfTee4Tm0xNEO4A0R9QjxDgHbXA2u5SRi+pPQIDXSbCvbRPPKmRQ453ZVe6J+vTiNsq3fOS35xi1C/Zua5O2HIgswvfwhwaMQL2+joxzBqNVfvon7kdYy+h7Avp3ODiNFLFTIoTA62UQBngTi9NNKLICd3+CkS7agm5Xqb+eILQmXgJ7piD67S7+pSFFRVMOBcHze1jfJb/vFFJIth+voEXBQEW431gn/OwOjYtdVLuB7Q+gKBHdCXZ5Fr3QwDoSW4nwegliZ4RNM/wrHcTaMrIVYn0Xhl2yuRAv0QxvdUkWwdkt8A9y0uMh7vl9zGSAQGFbDUTo4qgG+UwFtzOZFs3oz4sef5Lq771MESgqqaZo+wghMVcV7aUBPdMgq/p4qcF0d6EooFqFLMOZxDhndxEnXXToMo48Bh9IwEygUWNy3wrqlSuMCwdHTAXZoqGgWzI+3aBo+NSe2kK8dhlvVCJLTQNImwG+nr5HWIuKLW0ZM7O3w043ojZTZZJM5yQrwO+WeM3ZqXfVcIyz1SO+dQabZ8hS4KzVKLcsQguc/RFMCmShMVqBBLnfnaZkDS2EgslXCto/9CLOOOfgW+7ERBEbm3PkRcDc/V18nXP1qSP4vot4PcEWObpM0e5UqEzdOXx3m7Io2X3HGk40YKbwCTZijKsgy8BYbCZ4eX0LL7FkkcDNbhY4iX/3+ySEoD5bY3gwYnfD518+d5zvvmvaemxGQ0zk05UZl7sH5M60milPc/5f3/nPKLKCv/Wv/wr1du0/9XLgkEMOOeSQQw75H5wvWqQxxuC6nzfGU0pRqVT+A8845L8nhTG8uP0StXoP3884uxMxuK6Q+5Iv/95zxFe+nkhITp/YIogM3/reFxns1bmy4LLdWKM/XGHhfIf+sIZMNd23ezROZzg3IOwUGNchEyWLd/Z4z/+yxS//5B2UnuS5q+sMT3nUPtWl+lzGsHUaeUKSHg2wUuAMNGo2xRm45Nc9JqLJwmwGnouJHPTqDNZz8PUW4Y2EcuzSfnkd+1JE9qrF+h5ZrULWcknvd+i2IzqfaJKmHruNFp++4w7KEzHBc9D68HWoRcSn2lSe2sZUQvKFCu64JLmrRfMzfUQBCov/Sg+TDDBOhfLIDNaCGuUI3yGZleRHfLS/xPBWh4Xb+3x7ZcQ/mbyV8x+3xA2Jq0rGSz6mGyB2ITnqY13JZ++aod9xkN/gYauGyk8OCMoDZo4mXP6y2yieBWeSkTZCDr58lfYVjf3MZQhCwoHEIlDtNmlLEt+/zMzjO2Asju9TWkvPNEGM8WLLsbdsMneyyzOffQB5XUEOk7cL/KcKlNaUq03KQUz7+CyDBYXsa4QGpTXuwVTNcUcWcgtWYByDTKFIQiqLY+KGh5gpKI4GhEpisDCJp4KQtgg7becpl6o4mwodSHa/8Qhr//wGWgC+h62FWGVRnZh0zcX4Hu5mH5lZKCxWCoRysVIiPIW1gqytKO9apPrJMXY4xPiKoglOpYnKNWpS0uyWxPcsY+OU4PkbxEdcrOfgZJbdfo2+EfRXAu7y+qSZR+uhCbe/eZv7q3v8zHnB62aZ8P1jgrNDzEwF7Ruk9Igeu0B+2xJCe6hMT42LG5L8SBt3b8zSL91AV0DfdgqnUkeoEFtmmIpL2gZne4i72ceVVazjEF0dki1UkJOSouahxi4ikmQNDxt4RK/vIjcPCF7coDy5iHY8tDKgLSopGJ908fYFVgpwLLplSeYkUluiawnBjSGmXZ/GRVuLaVdRmcfsqyWFY3HPriM8l+EjR9A1n3JSIA2YZgVvIPAmFiIfu9cDwDYj4mOSya0hRQQUOapTIvpjrASTa0gybJ4i82kbB0JijIFOD8f4TB5ucfGxGVarI159uUWZGi7+omUQ1HnfL1zjp77/df7yP/pGEuHi5oJo38ETApEX02MKgc0yXJtQzgTYWklZEahM4QhDPB/iZAJR8XDO7gICZ6uDSTPU5QIW5pg0NfHKAsGlIaKToSsetU9tUqkEiEJjKgE6kqiRYfmeXYK7Uq6tz9JfruBv15H9ELN3AOMJSAFtcPMJsgelq/B2RzhGoNstbODjWIMeJ5TNOmXDY3zEEFwd035qQPHIEWThELyqMXlM7aldKErSWYmteEgsziCjspuQzbYQgwTcafw1SfrvnO8l4KfTdiS3Oz3+bAKT1z1kCW6sKcghjqflPpMYezOV0QLpsTrjNQfv/BDREJiOwUyGBFctOvOID0Jqrenjxydr+OtdJvfPo2Yk0vSxB00UI2xvgAD8/s1xhi4k+c25JeWGnaF5YULeqiBTQIB2pwVotsinc8lojH64SfZlClHOUL9qaZ6ImWyXJFsuFHrqqaMHZEODaM1A6OKkGUiLGldx/2+FvzetWJn/hVfZ+sv3s92d49G3v0TQzBi8UkM7gvL6iOT+edxeDT1fwSkEZQjy1QnJhsNE1dAPO0SXeugLfdSRFe44c5RzGwc347gE12/sTGO1OxaU5Nhai1tX//2mvw+/90185Oc/QToTctCbvq/psRrBa1vYIuOZ0TYHX13nzbefmrZ6GYsuNMZYzH9AqDvkkEMOOeSQQ750+aJFGmst3/3d343vTxMf0jTl+7//+/8toea3fuu3/uuO8JAvCk8p7qrewauddSrjCcNbLGLW4y2PDvlUeZx7APXaBoOrLllLsXs0oLkMLz2zxrV8BekpvvIbt7jemSdZrVB9MOHGIIDfr1HfiRkei3Cv7HBtA35y/Qj7Z0Hpa3S/WsNQIzMNvgNliQ6n5sDGs/i700oLf88gC0UkID9SRxrDd3zvSzz96TUuv7qAXprDpEyrLEZjVFQlPxsiiImPVrESZLMgxkHWJsihQypdhhcV1JYo7i2oPrUBvotQEF0aYpwxe19zmiCWRO/YRP9iyBvelkZjXQt5iRgAixZ8F1UawFJ9fYjcHzLzQMiRdpdnLi8xS8mb71znwtEZnu6tUAK1bkFl32B8B5yIwvcQrxjUywFp22IbKcW64pH39EjuGLLx/j5yXKDvX6O272KkR3q8QqhbhLs5TiyQjuLhWzYJHxrz1FOzSG5WxFhB1lKElwvsMOf0m69TX825vLdLfL2GCXzKexKC5wt0LmnP9tmozODNN6AE53PrvVyhj00XUlJW2f/qU7iFS3CjDyrBKzR/48xL/Pj1e+loF399akps3anfjWC6XqGzSXzvKbi+h2MtuulSzAbkK3Xk9W1su4IyM7h5SekI3G0X0Jh2jXKwD6Mx6uIEfXIJWw1wk5K84mB8h3ylRn6sheyMGbxjBd30qV3JKI5Wp+a7HXAmhjIMyRcjivkablKQNl2OP7RHtZKBbXI83Gb/oEbz+JiKrznuJKxNxlzdL8lX5jFRg+D5a3iJxjQdbOATvL6FvWWN0mSI8Yj6k2PMTHNqEuwAZYFzfh3C6lRkqlSx3TGVayPcdoTsj2EtxDouItX4gxJZlGAtOk8xC20IvKmf6CDDJjnCWMxkxNbXr9J+LiV4ZYAUBnPSQ18rKH1F2RawZWHNogNJemuNfO4Y9Ze7kBaINEcKB1OLABBpjjNIcasxcayxS/N4ZR1ZQrYQoiuSUhu8gUXKmwJ8Q6HXmsSnIpx+BkIgrQBtsJUKIpTYokRYi65XUSKFskTGMRhBOVOjcjXhM3KBC9seW4MKZj8gn6tSm+sRzZUYJI0jMcVvj1GBj2jNkt++gjq7hd7cJnhTk+J8l+gjCh4NmRzzEK4la0uGi1VaL/bJZgPcfoZtNhAIrNHYPKdcqCAiSX0gcT65i8hKVCFQ7RmkE2F0hm7XKI62KX1J5eoIMba4Y0PlfEG5MMAbpli8qbjhezDXIv+zAcIp8X47wBYlqjAgJdLzsGkyTcjaH2KswclK5G0OetlHvMXjtD5g8yMVnAs7GF1iFpuoqzv4e4ri7jVgmsKmA4E4mGCNwRQ50nEQX9DyNJWv/t0UrZBJ5BA+N8J1BTigBoPp+PNyahqsp6KOCRQHD9ZpXtIs3JMy+xckvQ/WGP7OGHWQYJoVlM1Qcnp5IDNBMRuwsLlO+KaAei1n59c9zEwDMRy/8brBnQLnDAwekzgNQeK5zP5uh9K1HLxtAesKbAFKQ950yNUEdzvGug7ZeyOcWwt6rTb62RJZ7mO33De2Wa/MUJ5aROwmuEPN7ntXmPnNS0QerAfHEKFC3LKGv9uD4ZiZX3qJzg88xKIYcaq+yeRYhdGlFnu7NZRycISHnVgmbfALpvvIAlbg3RA0rx7w6F/usvHSiFve+S7OffJFRL0J1rK2b+lGgsIXRLuaUdGnyMp/77n5r/709/HhW3cZzmmKbDrv2hkfHAXW8uLVl1k+2ebr7zoz3Y+Rz9/6138FXWoas/V/7+secsghhxxyyCFfunzRIs13fdd3/bH/f8d3fMd/9cEc8p9PYUr21WsUryk+c2OFk2uCUktW7GVeHy1xDxDqmKd+pE54SxPvb2t8TzO64aOPGe7UXY6fieF0HVW6TDqGXafGsbUDsmQGd3+CKA3Ggd5Vj8KAc59h5sEe+SBFWCgXfR583y6fOHcUf+yiqwJ3oInaGWXLwSYewUzJ8Ostv3LP7zMflLzvKy/zzd/33VRNAqHFFCW61SCZgYpKoDC4O31UGbH3wQw155APxrjLPhyAeV1RHgnRNY/qlTlwJaYsGLx7EV1xMacK4kBzezlht1PBuALjCBQVJieh+oc9nLMZyZEzoMG6ElVA9HoXN85wn7F4d2vOHF/nruQa9z86oK8jnv3AIrYjaF4aICaKbL6CbCk6G4JCahpFQfRqh0bSZxQLHn99lYPZCtZLsbJEFZLqaxNSx1JgKPs76GOLKKWpdLdRnQF/6aFX+bT7PmQB1hWYExIZWYpWgNwd8qlfWYEHAm6kawQ3jYAnnYC4Oy2bj1nG7RxwbclFew5VYVC+oAwseWX6mNZOyZLT5cL8AkltAb0yg3QU516aJXjSUtznYyoVKsFw6kPyBcdcfGuV/knJwif70KghpMIIB9eJEDoG4aN9F6MkRpSYUCGSgqCboXKDyUryE3PgucjSUHgCr5sBltIF0px8tUnZ9NFND+kVBIOctDZdeLpaTxeQzTq1dUE+KxCx5a76DdrhhBud+xCeRPklxUiitebx/jwX9iNknE/TqsqS8X0twgsDRGWa5iMvbmDiGLWxj0gLrLbItMAutunf61F5bh9/PYZ+Om3nmmnBeIKNU7z5Wag5MMyhFBQNH/Xatem2zFXwjcWMJzBfB22RrSZWOpiDA9KjFcpahPUsiBwyg1qPkalDPO8Tz9UJ84RgIyVfrJI3JEGvnJrV5iVq8wChXMyJOQrXEh8PqdVb3HbbJhcuDhn3LepzlkK9gnQxIA/Af7UzrVQAyiOC8bzB1Cx56EI1gp0DMBaJRFQryFKjB1tIqoh69WYbSI5oNfBGBnXjGuIWH2/Zcqq5R/+FGnNLY1aWchaCkpYtcf6/E/ylWWQuKMsJTjfGFAXB8YxH/voV7pzv8P73t3ltrAjyElvROOs345k7HRxdBSdAtZpoa2BrF2e2TXK8QWU7AynwMwN+hFkIsUJNBadhQrrQID3ewO1mgKDzsSrx4xFyrGjcsoO4FmOGEaZeQVgwgz3EeA2US1lzMb5P0M0RNz8MQjpYJRFS4sQaW2qCXUlxEPPu23boPtVitwim3iSOwHoettXAKAOBiygtMispGh5GVnH2hthCU9YligoqqGB1iUhTGMWAnSa5wRufR39jiLsxRLoOog4y8mGvB/lUQMHaNwQPWRia56bR7NKzICwyENOqqGoFIaC8JYKP96fnlmqGGvbh/JBssc7+VoiOJ1CNaH5zC+exbXp7LnN/UaEWLc4MDP5vjfhhDxm4JHMKpQVoyD0QGWhfkLcF7g2LyHL8D/bIv6+JvCFxX9wlq+bIcNqiC0BWIiYWtdUF6RH1Hcq5EOmkWAE6lPiuC8PxdBsnGmcIv/mxR/mah5/lpLdLOMzx9jNwBdFWSjzn47YdHAH+mZw09TBS4HUKHvpTfR74vi73DgV/7+cuIt92jNYTGyAFTjfh6Ol5rmwNsK6gk6VsXN3h2pO/zexDz7LU+l6i4PNJT6W0yDMRJBPElsUYwwO3n+Tc0nWEkJQLllaloOJ9Ph6r2jysUj7kkEMOOeSQ/5n5okWan/u5n/tvOY5D/gtxhKIYjujoGmUF6k4FIyVPXjvG3meX4d2w+G01rlwO6LzTwzggtSW7A051tvnBt7/IOA/YPhIyyDxia4lzTfj8BNHZxS7XsUqw+kjC0pmclz50DI3LYuWAbkMwkpZve89FXt08zuIvXMS068RvWsPbNxSrHmLXByvR/QC8klm/oLSCG1fqaF0wuiNFznn4v11DNgT6Gz1qnT0mH9Oo0iIzjUkE9gY4oWLu19fRdYdgI6fYKHFLFxX4GClQfUN2XxN6LvU4xly37Dw3jzPWlBUfGRdT8edCgSg0lAZ3L4ZqhWLO4C53YFHhlTWyzYjdlxRPf9Ih35BoP+KiOo0dq2l1gS8gE2hXYAc57V9LGZ9U+AcSLwt56KFzXFhaJmxK/JddBnfNYpXE7xqsB+EL5wmEZusv3EoyW+H48+usnRizt+nxf/7gKrY3plxuY6QhXqhitKC2s0P7eEj/exqs06J6tmRQAVVAPUqnvRAA2kHEOcHbPOqfGlJaHyd0CWVBulGHO0BFmsGogfE0jeUOxRUf4Xt89ONHEQdQlZqk1SY7bZCJxju39cYxp++oE9/uUR5toWbmUFmO2y+QgwQpPYxVWE9hHYFGMzkiCLcEk4ok6oPwFBx0oVXDCoEOLNlMgDQCkyS43RRzYhnt5xQNcG4fQVmj8USJzDRWgVuCLxXpjCZZEQhRcmdjGwTcO3ODJ7onmUtTVtI9bqnv0J3UecviDT6xEiJ1DtsJg3csEt9WZelfb2Cq9eni/Pwm0q9AxcM6AjlKoBxRP18nmavh9jWqcGAwhO29aUuMlNiKP40K3u8jrAfu56dYxwmQkZhWBgC4CitAhQGi0cSVAfXPHCBiFx06iMEYo0K0axieDFFCUFQkKreoAhACp9C4/T5mMEH2xoj5WdydEc5BF+e8onfPMq8/EVKkkrKlYdrVRO5Z0AIkFPMhWWvahlFe6SArDRBVcAWXv6vByX9ZQFlCFGL7I6znQrOOzTI0JTJOIQqRtSo4JSJwSWPLjqnh1krmHhqweEpz8ceqPPnu9yCVi5yzmJqPHBeQF0ghSFabqHSft55apwxK3vZVF3jxmTXcRoGRAje1RJsZ8e1zFItVwvUYv6/RwqC0xnR6uCIjX13A7xfopo+pu8SLVfQwprY3wXNdvPPb5Le0scJilUBpgRlPF8h2vgIUqHqVyt2K5BLkqoJ+wsF3A4YnnGnFS1Gjenk8rVDJcmyRI+dmEEph+gNkUeehyxu89cF9fnZrGScuMDNtwMBuFyU90jsWsRKi3RhbapCC0ZpHcMnHrXnYcUwyD2GnNzVMnm+gRjEoBebzlRufq2yTgClKbKeH6PaxjpxGO93Eimkom40iwr0UI+DgMZf8cpvyRY1wpylkQlvoR+RHphU86YyHOlpFuhGxXIYliEyKdBxmgoJv/jcpf+s3H0W9dJa1asHoAwVWK4QbYAGvp4kLsBK8CaRz0/FEL47f2IKg7SP3hqjnPYJ9jXUNkx9aof4PNwFQ2z3s3Bzj21uEZ/epPVNCN2O4XMEfAc9dxtscYlwXURR4JajSgoYPvXI3p57vMkhnoKmZLCgwAXnLhUCQOYIbZpYj84b43DrVLUv6VoERkGVTDxyzWAOtEUZy5clLdI4VWFElbztoAX/n0f8npjSc/gtjKsd/ivrmO/hL/+t7mVloUHF8Hhrexq+vn8MZOZw7v8Xo3AHhQYHFEp11eNdddzLnH1bNHHLIIYcccsghU+R//CGH/I+AtpaNcoboWEy+oHi9u8D53jxP/9oC0p9eqK9/vI5+3oPf8hkOQ3qTCuWWpvylDh/9xzX+6OVjDHsS2U1x8gLXK4mHLjIuyE7MEr9pjTN/ZUTtFkGa+5QbLju9KsOPVqHeYPeqwyxjkJZ7ouv8ubnHaO0cYGsCo8HvGsoJzNKho+HKKzP8/D+/h+hqn+AjHdTlFJFYSDOcXsbMn4LFH3VIz9QhDMnuPoo5voxo1On9P44hGy4yMYCgWKiClOiahyxyonqCNBbT8Wh/ukAfM0iR4ewMETsdbJYhnRDhKorZKsv3pvzIX/04b7n3Ot6VCd7uAcViRnqP4EazRnDUpadbPLl5is/uLWGxuMspnRNVkkUPUxHYeolyDEFa4iYG4bmcvldjdZXrV2a5q3VAFOdTs1bENC62EqB9hcxy/NEIt5/x7T+yydptMaOhQlgNL11g9t4umeOQ+RJ6I87cdcC+qhEbl9KXtF7PCa6NKXvhtDfKAEIhfMvKfIfQy9G+JMsdRsMIe/Oj33NKRo+twyf34XcTvJeu4rx8ldFezLDp4O1o2o/v4q53kdr8sXYL99yQ2sVphYXNMvLAgLRYVyFCH5XmNz1LwNmbULta4IwLxnMB5WITIx1EXkzbqJTAHQMISl+RrdVJb5tB1wNq51LC8zGT3GdQ1LBKYlyFuXnjWRpIF0NUNq1oyI2DtoJ+GTHjJFRrY7J1Q1vlZD+vOff/9hEvZ6T3RcRvX8XJKwSXc8pbVshXa+gTs9hmdWrCPFNHzrQR7SaiEiJRBJmLPrqAnJ+ZCjPWousR5fH5adz71j52r0OmNDZ0EfccJVidw7ce5DmkOWQamZUgBaUjEO060WZK/clN1OVNVFoiwhC0QfkhwU5CeGVA5cKQ2oUhapJRRCXFEY/sVA1jNQQBpaPJqwI5zvD2U2xu6C/XGd9Sw9supttkDWYmRFrwYotUzhtiklzPaf7R1GgcC7riQSVCVKvoig+Og4wiZKOBajSxw9E03rkoKNc3KaoetbUCNSroJiH7SZ29YR1HSQpPYHcOICuQzjTG2GJxMjCjMYWbExz1mI8yNFCp5bh9TbwZEO/6GHGz185xQAiEmcoTWjE1x00zvIMEb2eEyAr8gxH3f8dFnNsLkpM1hm9bhixDCoieuoYyJSI0OOUbm8uOt0ou69TeXTLzZ/u0vmLC+MEFJrc3SVsSZwwytwgjyRs+IisgTUEqrDFT4TbNcXs91l8O+dTLRyikQ+5IzEEHuh3u/EsD7vyKi4juECEccBSmFRHs57TOFgjlIKoVRL1KMTutkiLNyWU+FV2KPy7QfCHy5pewFlXoP/Y7JaaPlq6Lt9XH2RvS/qqSxW/pUXubhZkWaq6NU2vw1od2WLttKtKoYY53kGPcqTeSCjU29MCCHRZc3m9SfXmX4dMB1/+6g66uYW45Pq3eSVOMK9BTjX5aIScgtOM3xm5dmP/hkiNv7tJeK1BRE5svwOsznx+8tZhAYWYqFG0XazW2LHE3RqjdIf61Du63z/FnPtnj3r8vaM7kOBd3AYtzacjoqR4MJmBAlpLR0QBbCtxMI0uQscU0PfJTs5jZGtdfqnHtxSqf+eDszR0rplVzN8cy83s95LQ/CpOVmHK6r4ZnJWd/vcVTHz/LB37xyTeG/3Vrt9J8xtJ8KeOJZy+xZOW01bY0XDab/OHOKxxko3/rvH7IIYcccsghh/zPyRddSXPI//8T+bOc3UipXLV0ZwOEhrVBj8Vs6iNkhxlMNO66ZrI+i/ZdasPpnf1PXT1B8sxRZn//ZWRcEN9XJxgk2BgMFjU06IrP5cdWeei9F3nwG6/xEU5R9hvUco2suXS7AXfdZSm+fhX7dIeXf08w/9YdNm2bML25oHIU47zGJ4dzzEU5YyOw0uIckXjvLCleGGOvDBCf8sm+xUUsQrSao7ZDcAKICmyqiaIcuV5gZ+oUa01wXFLjoCYFbPeYJLM4RqOGgnghxL29z5Fv2+HKN0egFFZMxyNqdVS7xuxsTGfd457GLh++6x7uaW+S3z/iObmAUZr43jZZssL73vocleplfvypB7m0vsDMuSHCWtYfdpB5RLRi8HZK/Aslt612GA0rDPseE+vw+/YYrWGJCAEszjCGSYY3N8vK70/IJx2MEjRmC0ZdF1xBfLpFdTDiulpBaIEnLH/qf+ngC0k40CjPIvZc/Eu7BHtdJrKF4GapvAOep1D/Zp9KfMBw5h6MFVAvqfrThV7rqsFtBUwKTdGo4zlD8rZLuhQR7A3xiyq2sBhtYTDGugLphpDESNGgecXBri6RhSWTkwFuppC+B2mGTTM0GhwHs1BDGUERBdR2BV5sEXMz5MkQcdPQVGpB7fyYfDbAvdRDbvRwrw1whEN41jB62xru7SnJuzTBRwymIjC+xCiBsBKVWSpXJ3QfW2X2nhEBlu9aeZo/uHyCc5WjPLYfkMucbh4xkiF65CM8j2BHooyijCBrSdQ7Ldabp/KroA4MWIuoVKaLSk/QfbhC7arFnrtpKgvgOozvrmP9Bu1n45utMNPffdc3v8AHfuY48URMF9jxBJPWCW6uybJGQO4LjBMRHkxAKWQ5jYAmGaMqPrc8tEt6wWV81cdkGW43JKy5hLnBlgH2WANzbYcyGZM3a/hrC5RVj3w+wiklalIiXAdyQFuiy0PSow1ULhBCom8eMu5BQnl3ayrylUBPYDd3p1nwjRDbqkEx9W0y1iKbDdjrTOOikxJvq0/lkYD6QyNm53ps7DVINiP2oxB9xGL3q5jxGMZjvHxmGnMNmCSlcjbGvzNHSvAseLFm9oUxRcWQ3SmZtOuMjri0nh4SnN/HkS7aGLw0AccFTyBbDWxhsKFDJgWXXz/CrcE2L12do/L83s2WH4GHQnzNmEnpEf0WOLvT7RelZrJaIXqyT3qhhQhKKhf3ENbBMzVUUuAkBfGSj/85n5dy+mV9Fx0n2DRF5IqyWefDO6ephDllTVLpxvjHDLXjOyyfGfO8DkgmoOYiRJKTLIeooSZ1C4KtHQDCmRpEATguYeEhF+YwO3ufn/SVREQRxAlW/3FR5gs9bMpAYFsV3KGBSgUbBeh2SOvUVYITOcktGZODVZxeSnCtz5c9vMHM2yWd9CtpvjjEXugjVisod0jt9pjddB6ZS17w1vjsR45Sv/Y6qptAUiIs6OVFhCkQnoPRMXndToW1MbReTPim9z7BH1IHBIXv0duoEUQp2UWFygr8jQPC7ZjSUchSI5SDPHuDSjaPutCFxUWEFFhtSMsOdWP4lu8/yzcsXqPyvZbJNyh+6PsrXDjnE316AzlKkdbCLUcIhiX+JYt3RHP7my7yqScfwEhBXxlMO0BOcva6q/zuD82gfR++iml7WTWEOINaA1vktD5wkd433IotDUKB1Zb23Zrh8wqbSxaWmwCMhwnPfewcC9diNu5r8C+eeoFvfPOtFE9cmVaTTQIemb2VlnfY4nTIIYcccsghh0w5FGm+RLAYRnRYO7rF+MkqM481Wb17m3f9ny9z4YO3AyAfrVP5zJAkljSeh7wtCF9IKBoRutLgjto2G2+eIb2RUroSdjQyU7AmGN4hWfhUxv5Ts8zfv8HD33GW33rxFDVfQ22E6U0YhXBxP6SzVmHz4gq1+Q72K33sFYkIDWIsyY8a+pMmv79xF48uX+JqOUO56uL+kMRTJdlRgXdBoOs+ncwDBEtBhz2xAlZQBC75CQ+cDjguspsQPXsD6fuUJ1bQkaI42kBtBsiNlEqakQQV/sbRV/nYhRo068iiRDgeWU2BDpGew8XfDdmStzNcdWmeLBhdWiL0EkatCvOfTPC/b8Lpd13hRlnl7I8WjNb38e5tYS1YU6KiHKtdnGWNHUp0BNfXIr7r4S1evdygfsKnr5eROFBq1ChBTqZxxtJMU5KcsUG2azx2dYaRFGQVwaIJiVeXUdczitWAdnXCm941RJSGub+0i1ObRbjhNFElLxDjYtrKVoJ2BeLUKja/gM0FKilJWwI5Y2kNpiJNqXzcyMcTktEdTbLVNkVTkToGs1THfy3G+g1EnuJs9aeLPqXBgtrKyO8QOLHBP7eFNk1sFEJvgokzhJhG0FoBtsymSTDKoNcWpy0ErkRe66OPLIHnIOICU/WnLVKLM4j1Pl4hELU6sigIUku5VFBKKC+GnMh2GeQR6wuziGJaURXWHN73yA0ct6Tl7NGxipnahJXbDpCh5s4/f8DT99+GU/UITQb9Ej1yqDy9gfC9aZyxTSG3mDihCGogFO5Ig4DxcY9kFqJLMXY0gSBAVCJUewY3MXTvrRB2BEFniMoNOjfsX/ep1gvikUI0atMvV2GlBiEwgUsWFNAMCFfmUON8amxtLPrIPN7dGv/OAjVTMnq2QuFoxkd9dN3D9gK8xQnuvkTW6lg3R231mLRrJMd98pakcjbHSS0KB2GmbTVqnFFUBBiDrrno2anfi55t0OjU6G0WFDWX+k6JjVyIc4pmiMQhq0qYJJRrbWReEkiFyQtM6FEen+XGrsei2SaICxSWfA6uXJ2j1ioZftks0fPb+Jf2MLUqynWwZYlot2Anx5MZi0JTcyyfubSAlYLaTMrcbMo5GlCWiCRDaNBlSnasiepJvKSCcBQoRdkI0M2QybJh2/Opv9Kg8dIO7lZCUXNwrItZaWA/W4OTKWI8rYIQQONCinNtTDmx6EyCE2BqKfnxElnNqH7C4AwL3Kokazo4PR8adeh0YfcAPA8RhpShpfO1bUxb4MceulmSnKww3KvzqU8vsKBnSPoSEQhEXCA29pHKUK7Nk6sqXjLGzjdRBwkiiqaeP8JBhO4fm/dFrYZo1MF1sd2bCV382ybDMrWYQYwOI1S3R1kPwBqUZ6Zm4EpTVgS11YKjs9t85ONtHvCGzL4VKvtbJJOMvKyTZRHpixHeCKwQ5A1FqUBXA9QgQzsK2ayjXBCFxY7GxD9QweuIaSWNA2XT5bGfvg3L9nRwyzMMf3GekY3p3V5ldn0H1W5hiwK9PIMcJBhHIocTvJ0JtjDQ7b1hqOx+8yxcTnny01Ue+rpdTngplYbhe77/Bv/gJ46jWwFykiEVRHbMwtKYne02fj1jM1kEV03bwC5vE7kee28NqTwzxHl8hKhpvJHGWtCDASopplVblQgpJPU/XGf08AKu75KnOadXUg4e2yUb+LRr0/dq60aHCy9dZ3GxycZN77CLvQGyVccbZqSPWf70330rjlQccsghhxxyyCGHwKFI8yWDQnJH6zWcy5ucvxAQ71UZn4UP/cvjnPiWqfvioGzQuMVhpt+jIxXuWOCMC9KaQA76PHjbPi/JB7DHoHa+z/DLqsxdMahwGtEcL0Xced+rnLy9w0hAHPt4pqR6VKM/NuFgy2exk2OsZPP4SW57j0W1C8p+Sep5KAfcTUV4zxgZGp4dHUVfzCGcMBmGTBxF7fkDxPwc/kSw91SBvjek9Tq0V3rc/+hFzr54jPXLi1SDlPGdy1TcMe/9tmscPbrOT/3OCkMb0HnXKgD1GynVYUq+FPDRX13hdZbI73RoXIixwxHu3BzJsRC3l2ElxLHL4ISPGxV4YUlrccSpa2Oid05goli454B/8pvvYen5VxBJRriQ4I00EsO9Ypv+rMOVGwvUX7TIJGNrvcHufM7MnT3etJRw6d+sgLb4YUEZaEzpIBfnSFahMB6cOsa8iXnyV85w/Cuuc/bXPSbdbZhp4V0eY8cpk/0x/9reghkWjK7EOJUDxEwTM5lgPYUo3DcWZ05SkPs+u/lJHASFDshrkluWOrztK89Dcj+QUFzbwzZCbLBGqW56czgSGZcIFIQOSvmo5cXpon5nd5qwtDNi/G7N7MUR4voAtTvGuAL6+bQ9qN1CDRL6RxVyTxOu99DzEd5eTDrjUQSWYK86XU0WGtvwsEZQ+oIyFOhGBbNzgKhGiGoFfz/H+2WJrUiiKKfazkn3fNRYIySY3OBXCnbDgsAYhhOH0pO8/6VHeHPzAkf9fTwBzWZKLFzqbsJgJiBZFTQCiXMQU/34JcxnBebONYRqw3jMZEFSubALrkLLI8z/2nXcToEdK6SUUKkgtEZMBE7fwwqJTmKMqlG0HH73iVsJ9tKpf4tz8/2RimIuwAqB2tgl6g0ol9uUXgihQHYmqGodW8Jwq4p5dhY+e7Naot+n8llDducyQVeiAlAXN1AoVMPFthsEz14hfF4SP3IcpVzyVsj+Q1XmnspxUoPqA6WmqDvIYYJIp0eNozxy4Ngv9ygWQmQmkAtz0zaRmouOC3AVSipsnFNEEiMEQgryW2bJjjbw+gUbjzXIQ6CtuG11iyQJ6I5nMLHL5Ewd9/IeSuup4S5gIh99dJ6DXp9+oegIwWgs6DxQQ4oAdjOstIRXJogoQAeCMnJIT9SR/WxeBzIAAQAASURBVBjvMzvTJCPHwbgVVK6RWuJ2c6wQpKtVHBUQ6XUoM8atRcSBInoFKN7IK0OWhngpoHF+mlpl45zR+xaJbw+QIkfPgcimVUOVnfE0TSsKsAdm+gqmRFSaZEuG+JSPiSyVG5aiLhmcCWl9NCffEGx3ZxDzIe7ITlva4gzru4h+TrLSoqjOI3wftT/Cvnxp2jokJdr3Pj/pOw42SRCei43jz6eu8XmB5nNijQBUbDA6RS4swDBB7PfZ/fkW1fsn9H5VM3/HNd7+TetUnJxnP7zAb/xym+9/K8wt9rhx0Uftj9FaYnyFshqZg5NYdCgoFiLkOCG5f45m30VkBnFtE4zBnltCtaEU4BQgChi5EZ4jEcbirLqYm4I1hqnwKQHPxfY7aAkiSSnW5lB+gC4amMUQ79UEA3zbV3+Gn+l8Bf2Ngh/4oaM8EJ5l5a9mZNpiQh/rBlhnjGhXiIuQ7ElJ710O0fEJi0d6XHn1yHR37k5wrlzHv+suRBsefM+AC+su2V6Bd20XLcvpHtXTdDOrFG5S4g1LBBIlBatOHZU6pN0h3a0uAJuvXie+fIM733GG9u0tro5G/KM/99V897/4DPZggKdq/Oirv8GP3PetePLwkuyQQw455JBDDjkUab5kyI2hP3IJV2uw1kKMLPZqnzLwaftVAKJ+gqxkqGMa6Rp0Jpjc1WBw9wz+jYzVldeQr1hUYlGlxD+hkZft9Lq0akkXXObfucslo7jwxAK1bk5xTBJ8mUfziZzasuX4GYPzuMIYMV1wF9OUKbFkCMcCZjWTyw2ulga7Lmj3drC7OfI3A9SLKXLXwEyE2S2o/vKI+rGAA6/FqSNDlo8fsJvUeH28TJF4CKVBSR47d5I6JWJRY3oghARjCRKXxVt3yM5onry+THXTwVs2mAsG1WoispLoxgjtCYr5Knlp0FUfURWsft8NjjQHbLYWCGZSdGaJVA65Ip+NUOOMtAbBECwSv1ZSDQTy9YCiYshbEscqaherbDy+TG8mI6gp1mY7/MO/+QmuHTT5Wz/xdqIkws0E9bbhB77/eT76+CxXnl7k3JPHUPkmpdWY2YjSi9B10LWAT98wNEXK8t/fZfxYwviJAnN0AWeUUL81Ijtf4m/l6CTFqS9iVqrc8+BVnvpwFZkbiszBiJstRkZgEotUGlFMl3alsrTXNVYpjDUIY5FLVexugsg/t+yz2GaFsCtJTjSxKkaHEm+9B30QQYBQktI1WAzlfEhaP4LaHeEdZIzvqSEnBXaxNV2cFiVSQe45lJFAFCXEKWQ543pO8sACtae3cEY58vQMDTnkSqtBT1ZQwyFyxqEoA+5/+3k+mSxSUxkf+qd38XrzFELD8LYmG3nCo+E2D9Q6XN2aoTI7IlESPanTe/sy7U93GL/jBN6FPdyhQrgKZ29I9doYmZRYR2DjCcr4OJlGzbaw2mA7XYq9CcOvehOyABNJjBToukveUGjj4e+lCM+jVBYVFwgbYpQARyJHCWKUYpTBVCOQljKS6LpLeH6ADksGTwiiaxbiAXahgYNL6ikmq4JgP0Dft0r1wgCv0Ij1HqI0UBrC833i++YZrRmMNFg02p+mWjm5oAxviiTV6V3/shkhNvoQBgTbGdplWkFjoQwlwQvXKM8cQ0mB6k4QL23DKMPWq0gtUZ0YPy6pLA442AhYPRkzU03ITw7IPzCkc/sKwfUDEGAdhTUFuhGiuiOENLTe7dORkq4VLC/uYGLIjEOWKbyqJehkWMdBzzWRhcbvlsiz26R+RnpHRG3TQSUpaiypDg2aJkjL8P6IZJDxlq0B402FvzZiI27QmhiM55BXDPmsSx4J2M+RC/NTD5KioPl71xArSwQ+yH4NU5PINMcOxljXQwQ+dn4ekcToWoDKDeH5EWo4g/UcZJ4RjiDPLa3rPZL9HH1kkcJTaGuReBR3HUUrQ+VGTP2yxdbBBBA3DNWb5r92PEROFEg5bS/zfUhTzN7B9PdA4UzNtHnjU/p54QZAZCXmxtSMVwK3zPc4cXvCE41Vuk9V2LmtxWjdo7+uEXL6zOGGP321JMeORzhBgIfEoJDa4AwM1lHTuaTlk98yIv8mj+hrDRKw1QBhpsbmWhooSqyxCNdBWM09d1zk3AdnKKmz8AmDMAaEhFJjFpq4r15/Y/w6SVBhSOctDebP7lDessqRyg0+8sP/il89e4Kf7b+TyxdbHFwqoG+wwuB0J1Bo9P4YeawFiWQSuYiqxV9I+ZyUZWbrmFFJc6nFPacvcWdPc/aXZ3G2euTzTVxZwLAke08V/6MJwlhQFvfGGLdVRxY5p77mW2l+4HGE9Dl215E3xl2thbz22Wv4m2O+93vfTny9ixhOsFmGyV2ev36D/O7yUKQ55JBDDjnkkEOAQ5HmSwYhLFf6Myx0DHlZR1RKBn8zQngelJ8Fvo7G3lXe+sPXkQ70H4/Y3qwhDjrY+5fQ7ZBz+3P4A4uREH+dof2Eg80h9wRFwyAyyTALOP+ay4d+/F5aHnT/tqGrIppnlhgVcGl7CMJO79zuSYbdKtUgJzMR6TLgS6qXDJU/qpHdl5O+q0r4Bx0qLwwRCAolMKMh49sqvOXWLmfuHPIpTvPy757m7P4aXT+grJb4PzMkXlPU4pTOlscwVQxmXO6+9ToXKjN4iaaM6jz6VRts+ifInRZuMWF5a8D20Vncrp6a+wIqt5RJifUlzkggreQ3z72Z+8Qeo4nPfKtL2oetokXa1Ox86xrBZkF902CVRImCt/t7NFZyfvpqm9rdYwavN9AVxSeDBR559zoviha1vQFvX1tnvh0z24pxVYqwCiycWuhw/903uBwJrl2dQ3cl+YGHrbuo0tCsu+zNSNKWD2mJe6TAOyGoHFjGL1fJV+rovAYHgqA+pnjxADk3C6XBXiv5zGIVtz3AiR2u3ZjnN7fn+KGvgLhRIaw5uMMMnUwoF6pYYXGNwWQW9/Ub1MKUd//Zff7gl5bJB3p6V19AuTqLMBaxs4O3OaZYa5It1Am396dVDdZS2dcEvT4Gi6hHFEmJ7Gc0XnYoQ0UWCaSyiBIkBVnDwwYKlCA/s4wcJQzetoqNHHj0CP5+RjlTEvzhiPyphNB2GP9Qm/KkS7Lp8NEbd/HNDz5Bbjw63RouJWUoecfp15ivDMmF5UK/htKGSc+hf1Al0JLs6CxpVsXUA7I3rxA8vocqcpgkiNJgXQXWYB1Jcv8q5cSizg8QewNMEpOcmad2A6SZVgPIeg15EJP1YyqfuoIpQZ9axfgWYQXe7gC7sYsNXPKFKsqEGN/BHaYUdXcq0NyYII2lcj3F5Am6ESHHY5ygQroaTf14pKCogT/0KIoE6brkdRd3eY7Ssag4p35hgjsROM9dRE0KxNIsuB7BTsLEAXdsYDKdR1R3RHnQA9fBKIWwluzErRSzAe6wBMdncGuIm1m8Tk50aeqDImpVnFGJd3GXypc3cP4MOKVimEWsyj6+MfiXLIt/dA6pLWXNQ04m2N4Q0fWR9QbFch3rDXl51GbWT3j8F47hf8WY5RMjtrotCu0iCoNyJGKYkvsKlTs4WwP2vvMYye0NxFMJwbUcFWvypiWf93DHluYVjdCKWM0w+56cF9eX8PsWKywqLRmeiYjXfKqvD6hcizHSRYYBIgxwDzpEUY51XFBghYRqgPFdRFYgqiHp14A1Hv4v7aOjCuq45IFTG4Q1RWdSZ+/WANmSfN3ffYlXPrnEK2eP4GaS3Df4/RxrNcYrMa5EpRm6EFBYgr30861Lucaip/82BnNqDXllc+qHc/M84HyBQPO5739MpPkT542Zkzle1VKbSeheDnn5/RXEJAed4zIV7iY9DygYvfMokzuauGlJJc/RsUD4hnBDTythrMU5SEgqIUqXlA+FeM9l+Jd65LfOIZTBKy2yNyA4t40FqkuCS48foxQKU5a4kz20rEBQAWumgtRN3L0BKAdT5jSedyjnIkSlwo/85NdhV0vCX9yicneP7obmyrVl5MCwMBjCYg3RHcEog+s72FaLY9f66DmBkobSF9MY+dkGOqxxoBPOTloc/PbUIN8pSsQzZzGzTZwoIj8VMIwrzD1xANLiTHKMNhSTCU8+98+YO/UWqs0Kx+5YAyCsBVgBtz1wnOcvd/mdj73MN7/9VpAS0WqAG+E8V6X6TcF/0TXAIYcccsghhxzypcNhutOXCMZAs9ZHpEAskKFkvz3PfrXF1z78GgDthZzeJZ/+vk90LSbaiEGAGmts4HBNBZShRNcl1AXCNegiRzZj6nMTvMLwwhOneS1dJCkccKYH0MhzGN3u0l906Q1C3tS4xPxsj7eceZ17j19BA4UFnSsy7dB8qoNzZY/wKYu6LNCRgw0l4qhCH6siji2R37rI7PcoEgJG11vEoc8gqyImCpnk+FdiZn7jClzfwv30DZKLfYqK4uK5FdKzNYadKjYwjMoIiWW+MmH5oTFnGleR+WQab5xlWPG5VoCC7kkX3TBE7YRyv8Er529hIiKCKKfi53zqmVMsMObYRwYsPTVCKzM1lDSw+XKdoXH4ka/9FP/ggac4FnSJbqT8/u8c4/3/7Dj6hk+zonnsygmSXDGJHWqLGdkDMLlb8PBfOs9rQvGx+BSb766SHk3Ix+BsHOBc3EL5BxQ3GxdOH+nRe9Vj9/0unV+wyFoNIVzS+YBx6DEqQ+T8ArRqFHMVTJaiPzgm3zc4uUuwBan4nD6rWPgmj9N/ZkKxEiJLgXTgzuU93rx0DlFo4gG89mwLqq1p64sjsHMtqAY4E8irClFoVDfGHRVTI9itHUyRY41GpiXad8nbFUSsEUohC4ObGGy9ihVATZI1fZQucc/tIscZancEros0FmEtCEUxE1KIgGwoEXkJYUBejTBCTmN/C43JoFwvmQwUyZzBnVjmK4a6sMwIQ9NLkLJk8kdVovMFWd3iHsSEL2/jlTFrd28h2op82YW5Ftldy5i5BrZWoX0BaldSSptPfXDSDIFAZDmiLNEhDI+Bs9tFbewTbqaowoAxqDjH7xY4hYW0QGiDTHJsqwpri7heDSNA3ehQeeUAlUO87GKKDCYxDCcUR9qUDsQrLjqyYAze5T3c165jy5LSpKR3z2PLHNUZY4XAOmra4pQZBHaaFiTktKXNKtQox9ucqjTTBfrU7BnXBSlwd2LCnQzvIEMEHvVXevi9kmi3hNWlafx2GOLGBSLLGZYhe7pOaybhjuV1Hqhc44TboX9sBXNqBb3cxlZ8ypaPVQKrBNSq6EbE/vkqJ9OSk4nFdyzjmsdeXGfZjqjccNArTYSQBEEGjkJiEUKQz3mUkSQ3ExzjTNvKqpVpElNhcGKLk8D2lVVe/sMT6K6heazLyt/Yw1vL8fsGd2TxrwyQnQG2158mKWmNHBuC93dxHyshMeSRYXDCIb19Ed2qo0VOeYePvjNCr1XR85rJn5/hYL7OqSMdvuO7z9I77iC2LShBvV5ipQAD/gTEMEa+dh1nd0jRuOk5I6B7TJEeqWDVGz8CPl8dY2oBeH/Co+ZPnBf+pEAj6zXk3Mwb4seLP9/ild+c4/oLM1MRcncIQQrJFzwxm37TbokONCo2rL1lg2CuDzvTOTC9c5m8HRFe6mD6ivzxOs5egFxZwsssWsSUiwX19z9P7Y+uYMcJphYw7vgkHYtRlmwuwLMDbH8EQqBDSXKy8fltGMfTqqV2DXdgcLo5VD1qrwpqH3YQ72jg3dDI2RkK4aCNgFfXSW1J59tOkZyowH4XxhOc1wXyZUvTKdCRJdyKibYniLSgeUFT+9AG/Z7GFiU2mbaSqYM+5XDAjBvTujNCL7agUUc3I+Jhgg6qXHgcXnh5m/MbY5775DkAXvjkOfZ3dmitbvDmr7+TcKHK3F2rzN+6DJ5LOh8iTld4rbP7Hz/RH3LIIYcccsgh/1NwWEnzJUS/CJjbK29eYEucHRdhBZvzLRxgL5vlws+uMF5RrKQxfirJG22CAx+vC+W9OdgSoy0zfsbCfTvsPldgNhUtIxgFLrOyC9sx6Z8qsdWSyJU0mCArgvEjisnYp7zus7jWIZeSX11/kKTwUBGIDvhXxqgLG0RVw9qZPV5/UlDOtbC5RVy2CLdKNutQDcfceP8slwanGPs+7tBQBgKZGfxUocYSVd5csEgwoYMzAV1I8opAOTCe9fj4udOUu5bGdkFqPQZLPhzEsDvGzrXRnryZ9mQRFYnU0/YKFBRVmDnd4yCucWl7nvfcssmX332BH/v5t5AlDvWrOVQ8Sin5zFKLJycN/krtVWqRphNUyRZD/A9sU4wKtp6rsPSNGU/fWGAUuzSilLvCLo+tRaiLlkpQcq9XMJONKDouR791RG9o2P8jBzp99gYBegmq1Zgfe99H+PMfuov8uQThSar1DvXlCaMyY8tZISoFQZlibIi3M6KoBNgkADcgnVFgLPHaTQ+OzLL/hMPkbx9BvuygcnClRmBZqGdQn8H0+lx81kUuSKwpsYEEkyAmKbYZEN9SpfLcDqo7Ij/SQLaaiMBHxwkMhuiji9jlNiiJt9JEXO9gOhm06zhlhFElUpbIfShWBbUXd9CVLtlyG6RABwJ/qJEjcEqLnwnssWVsUkCzQuXFjMmZKnakaB8Z8ezkOHU1IWsbdM3B9AQikRx1SvpW8K7jr/PJ88epXywROseUgtZvnkfmhuB6C3m/ZfedFUqpmGkKsigk7OxiywnxAzl2WKf2xDUwLqQZuurg9lOKLEY36zgxU6EDiVCK4uQS7uUdZFYiXHealFStINTU6NZxArxJgR4XaF3AkTnsMMHtpeQViV+PMK0Q3awgJhO8/QQ5ijBS4AyZxtCnOcVanWKlQbrmUcyGOFmBPu0TPQSdqy7yWJ1wI0YE4dRbqB7g5pCu1Zn58CvwXXdD5EPgIebaCKUo6wH+Xg82uhD4mOEQP8kQnWkMc9Gu4O50phOQMcgowsYTMJLCKsrEZ24mpblgcOsSTRNlZrBpSv9MDREvI5DMPTsgemUHMRrTfSakckIgXQ+tJEnpYLoKgUD7ApskBFlKbAJ06GBaNUQxrd6TtSqyKLFA1lJYx+AflFhjkKMC4RicwRjVG2F0iHhIIBoW/0qJMAVe5oLrIlxnOrekU4XCeTnB+BVsOyBZlpQR6EhgpMG5dkDwcy7lXWvotRriYBc7soyuRxxcmeVy06GZZfi/2uND4b2kXR/lT3CkRPsOZq6ONIb8aBV/oCHVkKbM/PZ14tvbmMBFaYlOMz5nLWsALlwjcUvCmz/7wqoZzee9aPjC77XqNDbe9xBJyv1/vkvrVMFk12X9E9PWNw7++JMffGefJz5aJb4jQtoE72KfS5eXyBciXJGQN1yko9D1ENtLsIGivG5gY4Aue+QnT1C0PIy5mTJWrUxbmtISK0CnCdl8neSOGdiNCV8X2DwnOT2LqSps6CCS6Xtq4gl29Qjyxi5Ca3TkoZKSMnQoroa4w5JyUUGmMd7UrDh5qMbodBWrLOHrPUyeUwssnVfaDDsGozQq1iAs7WKf4dHbmO2OuBLPI6sOhOHnzZizguxcyerbA84dmRDoKqI7hCzFxjHXPxPRuH0GuxfgVwNefPoSH/vEBXKnQj5znTi6i+uXD/jBd/9v07bcUYpJLNfXt/ijV1/nzncs/Fe5FjjkkEMOOeSQQ/7H5lCk+RLCWIds3WItoC21FzromseHam/jvcegl7eRbo67nzIgRGqL100JNj1W797h0u+cpn7JYBXkRxXn3Vmq6Q7MSYxfcMfxTY7fvo8nNPWLl/locAv/4vjH+OEPvoPsDwNWTne4ujBDK9O8/e5XccIMKaaeNqKE8OVtgtf3cBqSr//OXe55d8w/u7LCZuGQNleJbgyRysPZ05S/vE+x1OXO95xlN1pk/8YC/mTaDlVGCnvLGpxbx7qK7EQDb1CgdgtECTYHlMA4sDVqcmr1Gp0LdYoSnrntdpwqyLkA06pQ1AOkEBgs7125xMd3TkPi4g8M9UsTnJ7lyHs77PoN/uKZl6gGBV3X4u72Gd++jKs8Kgcp6prAvU/zExt3YzZ9yrI1TQxZW0Bc2kOVivSK4c/d/xnmGilSwg9+z2d54v9YQc8Jbquk1JVFPOPgv2zJRYVOEhDfVUUNM/wVn/6MJVGKT3+kjt6w+EsBi/URb/q6dZ5Om0x+pcR+2zGSikt4dAapHVQvBQX5qRXEUkY8L/9/7P15lGXZWd4J//beZ7zzvTFHZETOmTWrBtWguUoCqSQhkMBCNoMlFhamsY3784ftxqsxGJo2bcBtG7tlBF+LZhZIDJYEUkmoSqWSSjXPVVmVY2Rkxnzjzmfee39/nFSJMmAbGxtaK35rxcq4N849ee+555x99nPe93nAgjMs+yK8rCD2ptj7TAPZhtyHf/aOzzF4tMpnPrGMmauRznuofoKYrYK1uN0+jAzyUg8bzDD1exdQkUE3feo9iQg9TBQhJhOKxQ6m5VF4FjcpcCcZRawxkwgTKgqnSrwY0DkdIRKFLCrkR2ZIZ3xM1SNc3cWJBK3nY0CQVkC5IbodwO4e5Amu38I/mSP2PHbna8TbdYIiQ0kHtCQLNdd0BkgBy1Lzu4M5ihkY3q7QkYsThRSLTeQkIw8rbH6xhjMNpmWptftorRCZLtuYtjKcKEVWQswwxjRC0sUA44BoVHEyGB4pU2dso4LMTJlalaSwuY1cnAPXxYQKR4RgQW4NyV2B1RbZj7D1KkqDCDyqOxMmV83jZgo1SnG7BdQCvJElnS99n0JHUSzPYGcClOOhEkv/TbM0XmrRbA/YztoUbWhe6EFusYMhNGqQFxRVH9GLwLniUeT6COlg0wQ7HENeh+4QA8TXz8LxCvXHLmK2d6DVQNRd0AV6a6es0EBwvLlJNcw5PZnhwuMdkvfWOZO0CeYMo0BSXc9xRhkiM+RTPu5ehpaUqWRS8O9/9yoWvyPinOOgkRSpYm8xJZkY3G0HN0+4vHQIFVlUXsoSs7+zTnRtC/uWCsXZCd62S3jRolQIrsNoRSF1QHMtQl0cYoVlZ7GF+FiLYtVD2QJHgTAaOT+LNaY0Dh6Ov3aCzYuyhe9ihonGBBcn2MADIVBrFtOUZatnbCikQ+T6/OHFo9QmE5QcIwcZyaokORDixxpFmZKm87Ktp/HlVahUUbUqdneITCXNZ/qIsI7QZZsX47LERbTqCAqoeti9vNzf/vhY8J4D5DIm/Hj3ZfFGAGa3W1ZJxWUb1eCiS2MpZ7KagfW/tgJLWUkEVGYcpJVMrfepfotLoz9g84UZ3AjypoMcaExvQvPUHsaTZJ0as7/8KLIoj4PqA+cZv3EKaVKk48C4rNqKX7MCgUv4pVUqowx5uMJoEuBvXirHjEMtnInCGIu88v4JQ9xEY6daxLZgdMDByRzShiG9JqT6sTHeQOPvuHhnu0glcKoJrQdzwie65Xbo9hhtHkQ4MV/4yZu46+8/w8UH50kLjxu/bYffXlshWz2G54BNEoy8olYpiYgz7Kd3uXD8arh9HvUvHsZVqjQ9DgIGp3NmazlGGEyS8WsfeQAsuPU6y/M38OiXBvQev4i62McCxVyV6cMjko/2WXvYol//BpT686c8Pf/EKn7gcvTqxT/3a/fZZ5999tlnn7967Is0XydYYXAKidNS5AKka6g/tIl1JHxDHbgaNzbML47QLcPG02VKiNdLGLltDi5fZOfcDOQGco33r1dxCgdZWGS/oBb2iHpTfGLvehxp+JEbHmZ0qcVPnLmNH37dQzwULXIuW6S3NcOdNz7NC48fYuq2HRbdARcvLmAtGFlgQsV4dpqkozn1bEQ3mcMoB18FiKqGJCsjnl2PvXGTg+0CKwu2DYg4RcUToqtq1KIMJhG2U4X+GNwM95LEDHMa6SxmpkLcKLC+QRywVN0yDtivQq/TQOUO2le4owxZaApX8tL/PcY/NiC9qYpXaMJugZYOXmyYvhQxEQ5RrtA2R0xbildn7BU1TvSGVKcytiYhSSqIv2KoUfq2JDMKdzNEzxYsv2+Ne//tUf7G3c/wYqZYqGr8L+eY2xWZ0KzmEn9hQn5vk50/rMNjm1REl8G7b2LB9lg3gmHh82/OfCOt/AKTwx2W3+IzmtZcMHW8xR62MFjXgutcMfkFhMDdy8nrUISAhJkzKdwKmSfRUxVMblGFxRsV/NqXjvHtr3sCfe8s5pJGeAKbJ+Shxrs8LicroYMNPVRUQFR6+4iwSha6eBt9iBN0q4ZdnkcIcOICrMITGXnoILwmjlclXvIo6pLCdfAig7+jyQ9Mo02fyjPraFWgaw6VqYLloMvqRkH3vQdQf5ATxgkyShHtJvkLHrYiSfY8UqEwnqSiPJzYEAwMAys4qQzbGtbSDvVWTnS1R3HWpxbB5LYVdMvHKItxNF5kmf3KCN5j6O+EhMttVFIna3k4expVqzM8WKP+yAZ+z2X0ukNESxbhWpLp0qfFHJzHNisI18Vu7kKUYTa2KY4u4WSWYjhADsZlVLkjKZany7auOCNacaluRahBadwMIJTCupJ8to5QHlZKVKFx8LEzIQhN5eKEScPSeipCpYJB3kE5hrhhgNKDRfge1Cp4vZRoStF4bpPxaw6V/4fnQq2CDX1skmLDEKo5tuYhgyrWgfBgl8npGEKfeNqnUa9hB0NMUSCAJPZI/vmYzvv6aKP4dP8QQoIznzNpOTAc0X74Mu3zG4gkIT+yQD7doghCsuvmOPaNL3HTdau0XwyJPyLZ/vYVRjZE1Azeboaeq5O1XVyl8c+OELmmcByohhQzDvkJH3coMbMOaItwrqSUAXmUYBcVhQrYu7FG/f/ReKWWgEoKcCVWiHJbaw36ismLlPDiGlQr+KdWEaMIoRzsdAOxMIepuHhnzyCjhPx4BdFp4NULci2QX3awMsTWHUShse0Q08+QkUbmmmKS4q5vw2gM44iiU4dmE6du0cNheX73PZhMkIdWwFqK8YDxdbP0bqiy+OvnkFYSveEY7jOXcZ0KOpmjqBos3a+NEa6iOHkAkWbIF8sWnud+tcXlT9forX1NoPmP47u/8FsheC5TlyYstjMGQmOGI4pmE/dsD5lLqmc3YZQgfR/v9B4iMy+/3iiLM5RM/dtnoNAvPy/djCw0ZIs1rv4/IvzWOV7a6mAcp0zp8iUmAGW/9r6K7h7i2CLGhXRGoQOJCcHtRghlyGYkzqYmW6xzMHJRN4ZEpkky7eLulIbJOA7CUcgsRzd9kpGPO9HkqeHeJ47gtqB7+wzTT2uEEdhT53GvnYHndsr30TNkl1K8locJgZ1xWR3UbmGjmBcfOcvf+Cfv5uirVpC/9QgrR+eYjBPizWPMel3C44d5KN5lcnOb6S/u0ngzqJdy5g90yrS4Pydbl3v8ws/+IUIIfuJD76da2/e22WefffbZZ5//t7Mv0nydYI1lqtlHqPIS2/EE0asaZM2AvTMO3AhqLuf7v+9JTFXwb/7h9ex162TTVWzVYdORaE+gfFXeQN2zqDxDBoqKyXjqueOcCHKygaElI3RmWbtPcPnmNuagxz/4/mf5p783g/d8wYt5m8ufX6R1oEIeeWW1QC4ZvHkBs9DBSwN++/PTOMOY+GiKmzbAWCQGOxwi4gR5cI6FO3OWr11DbEkYNZGXe4j+EF81UBeGUAnKKpNRRnS8gns0h2dyTOhgtSDczMlmPU7fd5B2OsBXBQtfOU/UPY40EjEqS+ilBjdK6fUCiiJGX6cwk4zsTMDiNdu86tiQP3r0Gj7cO062BSLNmJ2LuOvIizx+YQV9PmRyNiSsasxujvelAekRj+LmCsFjLsVyB2/pAk8/eYTB5SqnMsmcp0mgTC/K2qyOK8xLTTQosEmONqacLBpL85GLPPfmWYwRGGkpPEVxcp7e7VU+fWkK70VD9s6Ivb87hT3tEV5K8bcmWKsoQoUsSk+XOPNAWnAsyU0JAFlHYHoJwcPbcHABf2SxFyKc98akuylqfYTKS6EkG+witscwLpDvXiKdNDA4cGgJO5yAq3DX+5CVJqLiqxqRr7HzCtuDG27d5cuPnMDEAhln1M5MyFsuwUaONKAPxIxmO2RBG7ebEKYClOC7/t4L3Fy/xK984iifFpZGXyCbbSgMudGkbUE+A3nNwdtUZFWJF0I6Z/D3FD/6O+/k//vtnyGVhoAMAXjSYjMBGoJuTlz30RWLc0uEzgT6vh7Jjw7x3lOKUNJKTCGJFwTissbTEuF7qF5EZTVi7wYfPSVhdEVU2ehiG1XscIwTZSAF+B5eDlZZZJSCNthWFRu6iMtdxDCGOCU/dITc5shxQu3sGEIXrQSMRsA0AoFKQWUC58wm1ELsbB2nO8EZu/h9IIpRDcVB1edLV3Xov+MArccj7Nws48MVrBJ4/Rwz20S3rzTNGANKIcOQol4hbznI02vINERmGpFbUhpwdBrjOwihyratWg27swe1Cltf0rgNn7mKxakJxk/4uK2MwYUqdh70VBW7NIc4cxFhLao7wlqBv7aFGsZ0hcuOctl6sEL1XET9TE56tcDdlBjXorYH+L1pZGaRSY7Vht13LBCfrNJ4JMN/3kEogVM3HLr7LBSw+ZsdXD0i+/IuSFDTIe0vj8vKuyvnUGFABAqzvYes18u2p1qtFEi2dnCTjHwwRB+aK0VQY/F2IqRS2LiAUYpJwL2YEP7agGBkSRYV6aJHtKKYHMxZ/NQYXfOwYw2OASUJkWjfhRFgwXoKZIAtLEVN4AxTxNYe+H5Z4ZMkyFFM9ZxD2pbISQ6hT9EJsa8+hHspQ2aGyiDH+gqRXhFGXAd8tzTBFgKs5ft+dIPlowm/8BOLPHuuij/+6tb443U5EuKE+CGf899t0NsG+zoDIscbxLiZj4mzcp1hgDfOsa4sPZsAc6COzvsoqcAVkBdkM1XcZwb44wR8mA4037Gyyuk3T/GhL9+ClQLrgBxnmJVFnDNrWEBpSyEF/Wt9nNWCoGdKXzFTUHlO452BoqHAUZw5XKFzKcb9zA7etE+62CBY7aGrHviSydEOw+MuM/dfYu2lOZieJe83SY7CwQNdsq0pTCYQSQatKazfR6Q5xckD5B1BLnLqy7Mw3sLmBcKa0msoCPiND93Hs185Q1dW8H3F3e+9lZtfe5yzzzyLFFWy3rN4Q/CTBvbTM/z1H7mN973zTQjxH7sK/efRWE51h0gpiJJsX6TZZ5999tlnn68D9o2Dv07Q9kqp/lfrwh2L7viIOUUtXQIgNZILQY2dzKM3UuSOxRYZxhOMqg1ab94mryqKuovzxhb6yCLU6gzlIpUlQR4Ipr7c5bXPnqVeZFx/6RKvbZ7imuYW2sIPvv1+8jznuedaaGOouQmbgxbWLcUGgaJo+VCA8RyK/g7h51eRpy6hKy7ZYhPTDLBao8d7fPPffYLLBxrkJ2OKaR+zMos+uUh0uMrGdy6x876D0GogfQ+3l5LWXba+5zjdV1cYLEvS+QCLwOtqcitJhpbzZ5ZwJpR3eqV8+Zax8Vzi65dIbpyGhsEJCvKq4D3vOsOxygR1FTzSX+TJCzN0iMjOOpz/9WXqX7HYTYUQUD2SkB0PoT6D71TwntOkHUWjnjN3Q8JoXCM74FLNYcGBZQUiHuEJw0/9/Dv4+U/dwV4yj2m7mDsMyc3LmBtnUXmBW4hyAgBkDUX/pjrxkiRasCAE2aWcxm9vUVuNaIyjsnLAkZiqSzITMFypMDoUYFywRuB8tmzj8C4P8HOJdAu8Z9aQ5zdYfu2Y1ZemyaIWMqyAveKPrCgdoIHsRY1WpQDnuj7piSmiY03IU9Bl2oxJJqiNi5hvHiIrgsJ3+NT6tUwqLs4oLePAxwPChy8hr8wjE99HGPBiRWXX4DlVamspf3DuKPesHePRwVU4n/dRWwbpuKigjDwvQokRIDOBN4RwA7LpctLk9Qt2H6/x8U9ezVbh0wkmSGVQtQLqBuMa6PXxL4xwE42TG/y8wNkYoCYFwdoEd6wR2pLMu/RPVOhdUyVaCrEYrC2PI+sqZFRQf3oEvofIcoqKhDTDCBCVCky1yuPVVdj5GezhJfThBaLr59EtB1Pzka5L/XJONt1CNWZJ50LyThVTC1CNBmKckLYk3lhgHEl0tI7cG+E/cobMF+BXiJoFejykM7jMe9/2DLPrBZ7TwK21EcbiphJ/InCsQqAIn7hSZSAltiigsHhxWZmDLavYvDO7yHObZGc2ySqStOMj8xwzGmGHQ5hMsN09ZCNDj2B4n8YcU5zxp3n+o1Xkc2PcgcEfK3Q9QE61kQeXsVNN3Ayk45LNh/Q+E/GFvz/N2sU5ihPz6NAhCwVFxSK2e6gTLdzIIDMLEuwkxsssbh+qZ3NU/4rykpT+Sq6jcbqGZDuk8a0h1VsgWNBMP50hLRhfkUxL4jmFSW0Za73Xw1gLnoeshtBsQLWKGqdQD6EeYis+RcUrha2igIkFDVrVkRcUzoUcNc4IHtnAPz+k8uIeWT4pJ/1ag7bYrED7ovQiAgylcIPnkE1VSNshZjQsW63iBMYTRLWCaDRwpE/4Uh+VaFQvInjiMs4oI24JgksD/J0Jqlrj5ZqWXOOs93BeWic91mL7e28gSh2MEWTWwzt0+BXSDFypIvrqSTJJMT1DMVOjmKoiswJ/Z4LMDVJI8DxErUpwbgeZm7LyxRVYp0rldIzNMiwCrcDJLCgX40qYWEb/PmGvD5VKDFhEXlYZJbMWozT6j70T1RsRXI6p6C7NRy7TefAcxVtgcptD9xsCNu+uggF3bAnP93Ev7VJ56hLeXlK+fhRjkpj3f8+neNv4fh77xTr0ImyeYjyJtycIrGBqukdlXHrfiLFGH/IpVmqM31rFJDlJkUGuy++lKDAbW9AfIMIAWavyFXJeWBSsDyMe+N2H+aV/9Wm+/IsPc2Z1i/YfbjD7YI4yDv3NEZ+vnGZUJH9iXN9OBvzLFz7JvVvP/Zlj/856nyIryNOCdJL+mcvts88+++yzz19l7r//ft71rnexuLiIEILf+73fe8XfP/CBDyCEeMXPHXfc8Z9c54ULF/je7/1eDh8+TBiGHD16lB/90R8ly7JXLHfx4kXe9a53Ua1WmZ6e5gd/8Af/xDL/o9mvpPk64Qsf/TKNmRHBtxmkNLgnU4b/NEEliu23tOF2cNcVH/4nt+G+akLcboBUkMa05JgXHz/I9WfOM8oWEQr+xlvO8lsfPkKR5MjthFrVJZOW/ptaPCJ83r28zvhwQLuV8Im1Gd69vM1Wr4LtJzS/dBn8EOkXkCgwoB1D5zMXoRA4slGmrxRltJIJJChJ3vHxVyVIQbsz5rF8gUS6nOou0H+VR+t5i1wE/6UdxgdaaKEQnQbBwSHuzRlxEMLEIrWhcn6MrQTYioeuOczemHFyscsjnz2APVVgXYHKDbrikHQU/ZsV2voozyL2JGozxtmT7HRD2rUxl0Y18l2LbcH/+oHnWN+c4sDSRfYmDp/5ylGW3rmDOlBQaaRcri6T5xJ3orHDAfmzW7xopnHek2HDKpvn5jhx0wZCwOveM+G+U4KsLjFDh6oq6MdV1PMCfEnadkgOtaiuFiSHQViBLCQys4TrUL1YpqsETwypPquR7Qmv+vaLPNU/gc7AH0XYyzsEtqBea7H6XUtwpCDdrQBQdCpoz5LcPkfro12MkmwG8/iBQXs+VpZijhMX1B/rv9wGIbcjxGyOFxeISY5TpOiF2iv2SZVZbCegPp2TOAkm8lBKkDYUxhMYIWB5htyLMDLA0eB0FVmr9DHKHQto4jmPRzaWeOzZGSp7knArRsRltU7acJi8rgpSITXIsSDYKhBWkE5JTGCpXiqTiQ5+xy4NP2HRDkk3JFnkYTJFVgNxuEblQozNa5iHqvgbCaPbD2FdQ80NsTICCVpKhFU4q7tUTu2RzDXIVupEx6qoWDLzm6uEp4cgHZI3HAQjEUWObVYwvRHFwSm8xODEhnS2Xk7I0eiGR3TjAk4MrfM5jAvCuECHgmwmxO8bRFaQV3zU7gC556OnKmgHsoMNxGqEHU9IpzziIyHjE7PM/3rOOINf/cUOohcTasC65aQ+KsqYbbcUP9JXr5T7gzGQZdjNHWy7iXUUVEqjYSUlJtHYIwfwJ5pc5hidY8fR19pjfA+9miGrkiIW+F7OoUN9Lr6U4/ZznMLB3RhhpMHWKgjPxTYdxEiT3nwc15fYyjbYDLndY/u7jlM4AisKikpOELTxg4ji8ggKsFlB7XWK6Vsus7E5i7qQkzkWV/kUQ4ftj3SwBSRCoaoOd7xqlydfcNnbjaHRAU9iPNj5hgCrBfODDGdHYqyGbhelLQQe2TfWyE9C+JEJIs4hB+sIdEVRrG0iOs2yUsVYitkOm9/SwtkrODgc4X9+nQvHjqKvCRk3ZiHwCXcyRKkxES01cDd3SoHEgjtIMdYiBjEqThBJGXuPLdsY8VxsvUI0ZUmnmlRP9RAGwicvYbsFo9sXKdCorMCMJux94zKde9dwalWEF2ADTbZSwxnl/MavLzPV7LP2jALOveL4rRz4U7xRUoMaRHhbfZQfI+sexvUgCJCNBmY4xulHwJV7BYWFzT5iabZ8fZ5jb6jh1w16IrAXq0DOZJxxbuwzjD0msy5ymJDM+qAK5M4QUfER0RUBYr1LzVHIs3sQNmFnl4lYRFnD265b5YmtebaGkuYLOXp5BilArO3gNSK0kKAcZD8iECnTC30E8+X7jTL0zh7xqxY4dX6Zxa9cwF7ol75BgzHi+DTdGxtkXoXpB3rs3VrHjiKoBzBOcDyHojNVVswJgXtxTHK0A1HG+d0xJ6ohR6Y7vPX2m/jnJ5+CtRgbBhRXCbrRmHGR0PQqr9jcLw7XeXjnAv005q65a//Usf/I8XkOzzbxQ5/5pc6fusw+++yzzz77/FVnMpnwqle9iu/5nu/h277t2/7UZe6++24+8pGPvPzY87z/5DpPnTqFMYaf//mf59ixYzz77LN88IMfZDKZ8DM/8zMAaK155zvfyczMDA888ADdbpf3v//9WGv5uZ/7ub+4D/jnZF+k+Trh/t/4Erw9o/oGQ+07N3lmsIAzGgOCvFaaTZrQYen6XSrTE55/tgZpga35/PXXvJGP9e7FPqixNYmV8LvdqxmLjKA/RPgKq13cs3tUrlXU0hEPmYDJis8XH7iOk1ft8O9+5d1YJMghTi/FejmjUxUcbcmNxOlqvPUJGI0zHCCRjL55mfREA3F+Gn8MRSCRboXx8RZFW/Pc83UOXjMikBloWDy+ww23neHeC9NUPucgUUghqB6HzrekTPW2WbsM2ccUtbMpmdyj95ZD+BsZi6/d44LrczDpcvaZCqI3oX7A48htEV8ZHcZMGUadshuoci4kODUApehMpaSRx9JCjxcG86jM8MUHTuD7lls+8Di3t2M+e9sy/kKCRRL6OZ6IEduGwhV445Q8BzWV071wANVz+PBv3MYbbvz98ouTHmoU857m49gw4ixLDGmgI4VKurhrKfYAxPN1VGyxjihbQ7YSGs9k4PsUocv4xByVtRG4DsMiwBkkyExirUHkORU/JZ/E6HmNUZLxrU0AbDNEJgV+y2N85zFIC7ae6pGl82jhIpwrp4ivWUxggeq3hSSPT5ATMMbgrvURm3uIwiAaFewwKmN0D/hcOtfBPVFQebGg1hkzWKtilWJ4zCWZ96hZiYxdtAGLxtke4UUCW3WR6ylZTWCFxdQVzqZFexIblZ4owescfvqbvsi//+SrebE3i8ygqLu4I02wY0nmLTp0EaQ4C2V7m6dy+js1nEjh+AbvoiV4vsvwlgXyKYnQLnpG4k0GTL2toH2xz+CCh80tnWcLujd71B7fQMYWWlNkfogQDkJTpgwZC1WfSmDJxxmM49J4WErkehdbb5bbqT/CtuvIUUZzkBLNOuiai1UFqjBlpZHQaAXy/AZyd0hxaAY736T1+fMM7lyhWPKpX8rxzJBcGsInV4mv0ZjbasRvWsZ/cMzWqT7uYB1jDcbzMM0atqPIp6qIHNygSiGv1FAM+hjHQQmBTmOKmSmE6uBsDEqD3LCCUQoZa7xeQqoKxFd9NIyBOKFyuEpYG3P9X+9RcS2f2LyJ3ht8pr4S457eKj1xjIFOBXLw1nrYqRY4EiEk2XuXSFdS9LbAaoEzAdOG2nMj1FDTubpg96KPAexoQlTxuOAvEc162L+mmf+ywelZogMu+aRBsKWRPrS9PW5eHHK6cZB+XKEocuiOqR9y6O8qciFR/QySFFlorO9iC4MxGdmNM8ixRQU1VD8lm/PK1pRJgjm8gLM9wBqLdSTxoiGZFcgZh3981cPIdyf8wMZ1JLh09mJoqDJ2/MpxpRKD7LTKVjcpMK7ERgliYxsTemQ3LBI8tQFJiq37ZVWi6xGOCpwLmyAEQpYtr+MbZnBThW4F8OIlpDb4fc3kpnlcGRJu5chaldbZAnFuzBv/4SUqzZTsX0g2nwrLiHYABF9zj3klIjP4F8t0N90fgzPEeBKZeKXZsuDlVkcsOIMEkZRVOcaTZK9fpsgN7m+uliJls8bmxZDTj3nsnmrgxhYRa0RW4O0muFesj7+6StUbIpoNbKUNeQ7LS9Qu58THFTNzI96Sxfz288dwLpUtmGIUgQBR2LJ6slZBIIkSyeE3TfgCV2qFGjWE42B9ARkUfh1ViUuPntVN6tc4DJ+2pLeExHMVjK/41n91gXyc8ZV/3MKrTDH3vQEv/IIELM7FdaxZYt4WpJ067/rAG7jxjmN8/ukzjK6axn/sSWS7xfHLK/zAbX+NpUoHYwxZUhBUyotO31RZW7U0/xOhT1YJvAMtrIXtnRGLC60/e+F99tlnn332+SvK29/+dt7+9rf/J5fxfZ/5+fn/4nXefffd3H333S8/PnLkCC+++CIf+tCHXhZp7rnnHp5//nnW1tZYXCwN+H/2Z3+WD3zgA/zkT/4kjUbjv+LT/Lez3+70dcJ7fvhbOKMOMEl9XtybY3PUIrwKzB0dstvKGbbvTbjj7me5/o1nacym2HaN5KTid9ZOUZzNyb6lgQ4Uha845S0RTBS0W8hWB/tHE+Svj1l/vMGoaHBZT6ETD+cewdk/XKLy5C4mmZCuNIjvmMMecNlrVHBsgd8HKTwGd60wet88LPnIVg3HzUFA8PgqRTohmQKbZlTOjVCPxYx+PGbrXIhILDNPQHS+zsaLs+g7W1SiACdzyZo+lZZh2eyyOWky9hySGQejIJ3y0D5Y6XH+kZDetsP5P8qJagY7GHJo6jzf+X3Pcu0719DHCsJaxNFej+Z5QVGZxrbb/PQnXs+/uuc1fNOB0yzO7nHd3BrDdcHlMw6P77T48qRBHEnmnB5Tzojp2gjp5+RTFUwjYHLrHHJKcPruJXp1j2gWip7l859b4p7fmWP9QoUiG3DhAcvDv9YiWpGo5ZwsMBQLVXTbx273mFu+RHWrizuYINBUz0/w+zkyypBAuK6wVZf8iMNLDx/GFmXfm1UOqtFCtaqIRh2bOBgtMKa8Sx7sGpx+RvNJDZ5HcLqL/WKXrTVJ0nHIGg7GkaWPhhKlQXAArdvHuJ0C7Uq0zjC2YPzqOXSgsK4HroNtN/BWfaY/E9OYmqBJGfYyVCLRVY/wckb4wh6inyEzDblGdocEL+zhbaW4hQfNGrXVBJEXCCHIQ1PG5YY+0vPhfsP6kzWyPCDcM6hCgCx9higMRU0yuFphrGXNtHkxXuBcMosauIiJgzUSDKQn5siWK1gFKrWo7T7VJ9ZI/uUm/V+8hN7awwgwnsFKGF8/h3NgAbdQ1C/FqEzgDgp6bz9Ieu0C4sA0unCw1sUEHiJJEZ6LObxAPlejSCLU6cuIM5eRukBu9/H2EuQowyhJEbhgLXrSQ7saq1RZdRa64DrIZp3mCzHVy5qsGZANoKh65O0Aca2P0ymIbwY5jsiumSK+drE0rY1i0ip4KYSrEXKYIocZ9QfLdidR5ChA1mpYzwVryn+zFIzGDsbYyQTjlJPDbMoHpSAsfTCssRx6/S53/ESXpavGeFJjPh3R+dw6YjSBuSn08SVkvQJRVhoO+y5mq4seD0m8DGfQx6wEDK9qo6sa3RBoAaObWsSH4PhCj3w6IO+EMInQFzPk6Ri5p2mdBSEcdOBSvVwgcdHNEJkZhtshH//Y1ewWc8TtaXR3F7p7mEAz80cxi5+JUMqHqQ50WkxuXADPRYQ12g9FrCz08Toa40isElglsXNt9FSNou6X7U/XHybYVoQvjahvjDhbkVyUIeZiTvDlAdVHLlM7NSjFAkobF6cbAQq5MIdcmifrBJhqGdUulMJNvbLdyvdgb4JZu4wYjhCjhMHtHdLlEGo1aFQRQqCLFKt12Va1tIB0K6SHW0gryOcqmIpHtlAnm64wPu+SDwXjbedKNVSpsMy/wTD3PeXYYt/aIr/pGGb6irDbqmJWZjAnl8tI70KjKCPLyfMyxv0/Inhyq/zFsVRaCWJTEr16hcmtyyRXzTB/3OHdJ9Z5353n8Xspbi4gz0mO1LELbWT1lfeTzNZuKfD4PkwivBcKilzRW6vwwNkVdLuCOH8Z96XLyP4E3fZgSZXioLHY4ZDNM1NUpzW6XcHUQnTdJ29KRAHtZswbX7PO7DU1xHQHaS3ZI4baU12CixGV7RyrJGvNJk/NrDDzZk3zZIveIyNEniOKAun5ZBVYffwlZpc6ZGlBtR7wb//156jtKAgCRLXC2gs7LLenAPjZf/Sb/MC7/iXPPHwWgOfWH2O0nfDc6Yuv+Pzd5DK/dPYf8fD2R6nXAt5617W88XUnmZ39y7mQ3GefffbZZ58/i+Fw+IqfNP2vb8297777mJ2d5cSJE3zwgx9ke3v7z72OwWBAp/O1ytMHH3yQ66677mWBBuBtb3sbaZry2GOP/Ve/1/9W9itpvk5YvmGFyy9MQ1cRRS7NeoQ8KBAjjdPz4QBkfYdzX5glPiYZDn2EAu3A7vY2zpkUMfFIZsrJuygMFBrpudgsJ/drjKp16CgGTpXQzfGVpuLmpKcLvJd2aPQ8Bt95jPi1s9gNh+of7pFd28HWfArf4CQNMDXkskCsK5wc6l/eRa0lFA2FGpYeJy8bl+4M2fzVBayWLBzaYuXkDnMndnh68zq0J8nTEXJdc/lXEi7/RofutYbuOxvwRkH3xhrzD0NjNae+MKb3uy7V4Qa666Gum2P37fOIoyk//au3cHGvDm/SBBcFHzz+LB9+8Dq8BzcwocNO7SrWpiv49x7jp77pfpzE8DtTM1wYVrgvvpqpbozXt9wY9nlotMzFtIEOMpgYrLBEhz2u3kk4O/ZxrKDwHPYWa/z0z12HfKlL4Wicox2O3rRKMe2QDSRmVyEmLvlNHpUzGkHKO25+iY//vmQy30TvNiiaHlkYsji3haP3OLc3jbul0V1NcrCFW83QVmFcibO1Q7ybIWoT3K2cykKBn5eTRBUV6CTH2SswTVOmgeWGZBDQ3khgJ8YWBjn6Y32ZrktWhCRTVfLCw1nbQEiHysBFNFowiUtBoCiwhcCpGIrPeyjt4T0yIq8MkK6Lc2kLbzwhe+1VZZVGPyFaaFB4VaqPriEnOcWhObKWhygUFsgOaYKWwF6S6K1tRKXCR3/tEFs3VimagqRtqOxI1Kgga4PIoH9MMve7Ead/1GP4zhrbX3LhlgRnZKk+28NWm9iKh0BgBdQbAya3OvTdaVr3bGKGBnSCaMPgaEj9kXVqT2xCcxqUwYYSGRXMvFD6t4yXq6ieh5k4yMygRjFog4njsuJAeohOi+LoAjgKe34D0xuRHqlQWZ2glIdRgrwCzkZK5fwIc2gOvTyN3BvjDiNkEKCPO7gjjTvSFFUPE0gGbz+JXTeYoKDYlrz/B0/zW5dfzeb6FIwneJsRydULqL5GS5DCQY4GuHtliwqTFItGV0KkBXFuC7IC22qC72HRmNkWaeBhpcDd7YPRQCkqWEdwMTvIbp4SbKVICaPCp0KE9SRynGJrGTLKsEWMjRKKuovvNcsEqLUutXe4JIEBK6g+vo5NWoiGj7CC2Av4o48ozI0KmSQo30ULcJ8OWFwdI5seemRw1vZw0gx59Qpmuo6uCDCCF7vTFA2PXFkqpww2yUk2HKwVKCnLliLfY/SqGfyhQbQaUGh8m1OppJhbU0bPz4FXtjbZrCBvOqjpkEo/xE0MOZb2H23R+65FfvHR21n+gzHuAZe8JrGhj6l7aJNhxwPMXBNvotGuAEoh1O0lyFyglxYQgcKq0tdItluYNINqtfTKwRLdtMDk9mWO/MIl9HhM7csXsIMxxXQNeXgFpKDSszDpw+6IwXVTTEc+MrdMvz7j7/7NTaKB4A92/lhrk5T0zzusTeZ4O5A0WqiawtYC2B0gxmPoDpGOj2g1sOMJRHH5A4gURq9ZpvrSANEdlm1PkS79g9oO2U0a//dSxrdOgyexOzGx9tFSMSo88mEfWQBjga7WGLcV1VWN8VRpXg1kQhOMx1htwHVJC4P3ossfXTpG1hLYUGAlIB2WfsPwwueOcbD9HN2RIV3rgZVsfh72bnIQWYbMBUwKrOvj78Hbb3iJO2+5xPTBnE9+pA2NOlp6OErROVXgDBMkdS5uTnPxnhr1zxVkwR624hDGEXgeFDlifYgXevzgj7+HA4dn2N0bkU5yJJTnx96ANM34iWc+zj+/6Ts49fBZujsjnnvwJa6/7ShXT89Tky9xpPlKM+DPb/wbnhxc5ML4LLfNvo9vfseNf5GXE/vss88+++zzF8by8vIrHv/oj/4oP/ZjP/bnXs/b3/523vve93Lw4EHOnz/Pj/zIj/DmN7+Zxx57DN//kzeI/jTOnj3Lz/3cz/GzP/uzLz+3ubnJ3NwrS1bb7Tae57G5ufnnfp9/UeyLNF8nbO0MCZ2MRCrwLbdMr/Ll4hb8Lz1H5dWHARBaMJdLWDM8I1wwhsZ9XaaX+kSPThAHfbbvvFJU7qek0yH+xghmA/qHpuFoh7sOnMKpwtlsltWlDrXv7MFGjd7lOtVXScLGmIXamLMfz1CTBH9hwODaEBHmiNMhzk6B2elDFsKjMf5LA0zF56AdMfz4CNGZgmYD4XvYzME+l0AzANvjpg+eItEu6oxCew5pVVB9Yg+tDFQETlej/AypIC8UecVneWGb19/2BHuvDeieU0RRwQUp8S9VefELh4gnLr4CVzpkKuSnajdyorPNTpQisoI33nIOcSzj85+5mic/fICjR86RrXUZGJeLp+ehI1k+q5m6xeXNtR2eGc+SH61jnpegBCrLaN6Y0X5gh2z2AFIL8qYC18fXCitzMt/jM587zDd86AWe+uQMZlwKKM4qpDc00JFk4fYXec/WKfqXXT6l38zgZEg+Jbj+mi7XtdbY/Njt2Mf6TK6fwgaaIgwgN6gsoRiOUdpAQ7D8wIjNoz4qKL9nowCdUWhbtgYUZVuO1zdYRyF89xX7mfVcKt86RWUxQvoglSyjppMU17ioMMRmOcZ3sZOYYrhF9v4q6tk2Qlt0PYBmBaeXIOoNdF6AEYhLW9i9If6wDksVRJJhfIE70tTOxcQLQTkxnReYmzLcZ8bkl1Nsobl8dI7RVeDtWlQuwAqEKUg7gmzaQWDA84mfqXLhyQDTyahOj6luDvBfmDA+6ZPMB/g9KCoGeVNOpQI77Q7uToT3PEjpQJzSeH6MWh0hjMFslyKRbAXgQNYQyEKQVVPk033cJMd2WmhHEd/Yov+OAzSeMTTODJFSIVwHowTFQhvhCXS7jk5TkG4pAAxK41dvaNFVkFJB4JKHAQdmuzx7YpHMurReSPEuuaTXzuOPwEQSfTmnemaTX+ocR14lUWlBcWIJ0+jj+AHjIwJrDfUzMaYZMrq2rJJAiLI6QRvY6SLy0tBW1wOkhPBQxGQSIkcWXXURW/1yWaMxCzOYuk++oln3GoR5Rv+iT64t0dVV2vdsI7wA0axTLE4hzl0GT1B0QjxRITjTRacJ5kyIXSi3qRhYGltDnLtrWCHQbZfiVQcJxgXWSvRoTH7NPNHBCqPjHo6yzP3+sDQ7DnwKckReoNc3yCoWWZ0ha7q4l0bY18whXhpgdxNEW6ENsLEJJqKaRMhmHdupI1PD4MkB2SlNYqZw2xlGW7TngHIwQhOkBlGpIMYp2CF7719CziqqYsxwJ6A1l5A8NUJEMXFL4Z3eRgwipOtAWMW6kklble2WO6XBrpEGG4aonQEMh5i+hUYden2s1phOg8Vf32TvtjpJFbwigJ0uaIOzPaJINU6zg1LgijHSFuStsnKGDjQWRzihpYpBOpZUClRmERqUXyWSdQAmgWbq/CZ7B2u4bzxA/YKhkFOY3RFmr18KW38MAfgX+thmA9kdfu3cYUCHiv5uSG3WIJMC43rYlmEUBnzo01dzqn8I35xGOYK842N86N/UInyui2q3YK2syHHSHLE0h9AaM4nAh2zWoNYtqhvhCkF6uIF8YY+FxZzgr58lvt8S71YQtryDd/7+OrVDBZ7J0YVCxAle5mOM5fHHV3juy8sMRgGp3sZZmaPz+m1G9wjs9giTxLhqCfG0j79dY3RkjuRADTtdofqRx9FRAELRfmqPt/+9d/LMI+c5cGiaSvC1i0jd8ZEbIwA2/nANeZOg0qoh9iI6S2VljbLLrLRWaAf1V2zjFU/yjEw56I6x1v65UqHSLMf33P/8gvvss88+++zzF8Da2torWob+SwWV/5j3ve99L/9+3XXX8epXv5qDBw/yqU99im/91m/l+7//+/nVX/3Vl5cZj8eveP36+jp33303733ve/lbf+tvveJvf9o4+ucdX/+i2Rdpvk747GeeY2puQiVIsQ68p3OGr5g7ECtLiK1yotXwx7z1bRewjuVLq4fZO+Mgz+8w2JJ4i8sgE5IpjdCG5WKEytIy/SkPcSIoQsnTuyvMJhE2y0kLh3WngvtchJ6uceDVKSeXz/Hg2hLp8TYkAeINipo/RqBJnZDa6THqwhCbTnB8gSgsxrd0XzS44xRTSVFVBystth+VMdm6wvYlxa/+5DcSzym2/ApL/U3CLEV0apgpReNv5GzkC2it0FrgRxm4AavrU9gf7TDeE5hUULlJUH9tSlJx0bkgazg4mUXkPl4GcQXWkyauGuNPO3zH7c8hXMup+VkmXUF4MCOuC4oth9qLLkXLpa80u0ZxNm4R9C29fhVchZGWrOJw+j/MkR9vUNkwaA+SGUvyLU3aCJw4J5dD0smYwQOKalawYyVKW8LPnSU/UWX0jUfY0xUe/5Ri40KAODbAOiFGCM53p+j+YkzWH+H7HoPX1nG2YpqPDsFabCVAGl2aX6oJTFwKfIL6leQWxyFfbqNe3EZkOcnVs9g8YXj1PEXfUolGqL4kXaziqgpyLyK9p0vtrQWNtEc86FCkmnx1A6nc0jRzOIGKh7AStsbYtWnMQozzFQGeKlukKg54PrY+XyYhJSlykiKSDDHrM75lkXTWo/lsRFZ3sYEia4C37dE+uMPs30zZ7Tuk6yHZoSrCghNbKqdG6JkGMtXUzxr61/tYV9D9gEvrKzOIh3fwZwwLV/fQd1XY/GIHHTaQSEQOnsyptWKUY9hxapiWS36whvvCJlKAuzFg765l/I2I5gPrOHFGWlfEFcvojgo6hPrzltwboYXEGYwQSpAcrmEDF1PJsVmMtQ7ulqaouWQLDYqlOvXLCXndRxYCOclxC5dkUZEt+djA4u8UZQyzI9kaNtCJQiogN8wu7jG+rsHWTo3w/Bj/S6dKUWy5yvDAIsbLCdIN8B3K7K1SzGJ7F/KM4rqjANjAgf4I5vyyQkYpqFURgwjchNYHHNwv9JhcmMI6gvzQFO56l/hIm/SaeRqfOU2jLdhO5hBRzuyjW3j3AWUTVVkp5zrYeoByPaRycfoCOyuxzSbGq1G7LmL22DYbZ2OKJwfEs0uIXYmVCut7OE6KijLAYppNluxF9A0BWcNhlIREX3So2TZYi1eA7Y0phEYklmAtxn/oIuaaGu57Q9KLAe5P7XBoeRX/Fsnpe0L0GlAMKYqUya1LtC6A1Zb0jEXMgXJzRKYplhpoJRDjFHl5iKjXMEVB0QrBcWiEMZ3mmPxvOczXYrprAXG1htqcwFQLlSqsX6HwFdoT5G2P1iDiuqvXOXO2TeE2EHGG6I/K7wsLcVy2llVDhOviasvcPZvguZCmZIdn0TZDej7SCxAx2PEQJ87Ij4UsPDgmPdZk8s0wXpRoBJmRNI8WqKEhXisn71ffpkmrpVjcqXjk2ie9dQozD1OLOySrYHKBSaI/tWfa3RhRHApRvJwNVXZSGYnzrEPlQpf4YJ20IWgIQRoqzu91aJwZYebmUIOY2otD4qN1cMBpNhCOW5qNW4sUDsYTiFwhGnVaz0fYb3FJ9qpY6SAKSdYKcdbHuGHG2WgK53CGtKUo8lVZyQs8jBFlNHmeM1xy0XXB6mqF2vBKddG5TTh5qEyDcz1MlCE8j2okye6FYGuXbNoijUFTIKYbSOrYJMHfmvDFzz/H+QtDbrjtMGmmKf11BNmbj+H+2hMADO5b56W/t0FSGFRYCpsAt6ws8T/f9XoOtF7ZxrTg17mrcYYFR6NNhKOq/0XXCW/+u/+WcT8lbLp84d/94H/Ra/bZZ5999tnnv4VGo/HfxddlYWGBgwcPcvr0aQB+/Md/nB/6oR/6U5ddX1/nrrvu4jWveQ0f/vCHX/G3+fl5HnrooVc81+v1yPP8T1TY/I9kX6T5OuHGq87z6PmCQcXFM4ZXeRkYizMnMFcuo4c9h9MvtpBkbF9SSFcyOdlg8I0zhDuWMDaoBBbuj6j5mqxeg0Ri6zWcroS2ZeCFFH2JOa9xXkwpGnWCL+ziTmIePnuSBzeXoCJoCosJZ+lHmkBkxMOQ9k4p9FhrodAIWabL5IshwYtDuGLsmCtFftjDr3h414JtgVtt0n+hTr6mqBVjhDbYpCC9fpH8pIPTivCeCRBNQ7idUclyrLaY2NK/GEKaggPDbZ/4TJv4m4bUngyIDtXRVYnQYBzDVUfX6T9aQ4cQW4s2Aq8w9J/08ddzDq0kvP5/mvDL/66N3Jtgm23GCwUvyZBnJm06nSGbyfyVWYDAXzeYuMXUOc34JOhQYroVirkcEwYsOgl3/MDzfOz/t4K/KBmcr2P1FXeI3OKdSXDfmLO9W2HTdeldV6fdH6JfamCaHt1n60zO1sFGmEhTe9gnPdRCKokVgqLqICwICa96XZc7P7DGz9pbaToGErASRAF504W1LeqPbpEt1wl7mmBPIwYxorCIIECFTVSmsElB74sdkkGNLNM4VsJ8CyNd2B0AoA9M4+mUcB5651poCX68hU0EVMpKKFdmjE/UKFyFWzmA2BogRjH5gRam5uOsbyE2e+Q3NTGuwNnoo3oa7QfE19SIQ4tME+qPp8QnazhjMJ5HXoPxQRdpBE4KhRJEywHKT6nfn5A/YdG7sPXXFkgPOYgko3YJTFBupyj3Ibd4qzn1+3Y4+M922PnnkPZz7KGDmLZHMS49dLJ2SLxYQdcFcjrHSSXGdxDWIpOc7KplHOPQPj+h/tx55NCgixwmBcoq5NIs41c3yesSWWjC9ZhoKUBWXKpjB6qK7u0B1dWc5lNDxFGfiVsjqQU0Xkgo2h4qhUoro3HNNrufrqP2vnbnoFXJqOo9tpIQ56E+apyTyRC/0yarSya3LuFeHoB/ZUJqTekzIiR2drrMXh8MS0+O+jRbjzZw8hHxtEs6HyD6Fq9dJbp1BWEM1uRc+l2f4Z0Oya7HwmKBvrnC7mQKzvVQtQbGk4hhjGq1EBZskkKSs3tXg+iAYi4+zfzUiFqYcvZtbeJuBVFPEL5FBg6Hr77E+vYsRihsNWDxxpzq/Dqn9TyezXEzhTBF2TYZFxQzFcbXH0MrTfixs4hJinw8g4dP0jmyy6jRYBDW8R8foN0q2DEiL7C4CKXQIciuRgiQflCKDlJgRHk8F+0GRWaRz11G24KgK2n+PsjbLBePNZgdJWgcdFxF1ARyOgRcyByKmouWGh0oTM3j5Mx5kif3qE4mTPwTyMJgpCj9V6yhCHycKMHu7MFwBIEP/UEpuzkKpqrErzmETDTh2QG20FgvxFY1qm+QVhGupgwvV9Eth7Rw0InGCEvYtiRr5W7w9Gdz4kPlnS6zVkU0M9pf2aD6Vklre4xXazBqphy4zdJbdRiuvyz9lecuwAQVrKsgL1uUsGAaITIOKKo+antA+0tdittnadw6ofb6IcVxl61f6iACH783pPHgZcLTI0JfEJs2xlqkEIiigFQjAg+badR6BrnBnUDh6TJGezVCu4L7v3gAbydi8LwgOLYCz5zmq81dj324CtUKstPGpAnNp/fYe0OFeMqhdn5QxpYbMFHM7kddIEemYEc5dmNAslFAVhCcn+AMc8L31InDAD8R4DgIWeHFm6a4eX6Wj/3WQ6w+uYaMckzVw/UrtOaa9LcGZKf6PNdf43/64Xfy2L3Pc+c7bqDczQR3HHplmTjArqkTIzibuUxnEdPhf16k2djokZ0b4UlJLMxf+h3CffbZZ5999vlvodvtsra2xsLCAgCzs7PMzs7+ieUuX77MXXfdxS233MJHPvIRpHzl7aXXvOY1/ORP/iQbGxsvr+uee+7B931uueWW//4f5M9gX6T5OuH+555j67Elbr3rJa46sMXQCKSVuM0JlW8sl2k0I05c1cPqgkaiyTNJfKCFtYa4oplMBYhJhnEsxkis8pB1CRUPVYAcgDPRXLWyysWLTeQpQ3Foiv7tM7S/NMBb6zO8tYM05YTTHO1D3CRKQ0I/QeBTfeISEouZqoF0yFsC78YWnB+DBuE6ZQztFy5g0oLRd83jV0PcS6VprXGg/XgXsbuHOebCUsTSXQmtqYje0x1mHxJULqSoakHsZehQsffNh3CbA7w0o1KVZKcLrq1sspvM4+0mqA2LcTX/4v+8Fy83/Mz2MfrkLB4a86ojZS/i9ImIUa54y50Xefa3mlT7YJ7fJrqhzfxkxHumN3ir3eHx1GNwvMOFx1YoQug8GSGRkGq8nsViqZgx2XmfvBkwPmMpdi3eixFrv+qR+AorwEqBvGEO43oo4OmXKly+a4FkJqB5eYR7f4ztx7DXw2rYu3sOrxvT+uIW5kCICa5MJs2VpBVriSYCDgj0JUlx5do8r0vy1BJogTo6TXE5ITjXw4vXsdNNxDgGLBQZtigwWIIDVaJRk0yF5B2J3slJVxaQKPxqFbu+g2sc2kuS6+/c4bPPgK1YTBZj/AARBGWicF9QSBcLGOGTH5tF5mArAmdicQcO0fVNsoWQQuQs/M4lsJZxd558q4POcmRH4u7miHYpOEWzDkVLEM/6+F2BSED4BulpGtMTTOCB66MqLRovJuy8tkZ+0BL80TY2TRm8Zgn5VA3TN4TPR1jf5cDVYyrvDTn3mSVU39A1OUInCKnKOGQjqK3B3oyLUJqj90XoQQYY1CTHSwsowFmNmVw/h5cJdD+CwqBnQqwQpRFtExrNXUb6CBrIw4TRVS4qLWh/fh2zOWRh1pC8YZnehYDNo1V0TVHPLVv5dfhfmNB4McF6AZObZ6m+MGDU8ykeLvDbObKfYQ2ET1wi+4Z2aUTsKNLlJp68IgwWAtGqgeeSLbcR53ZxdnJMPcBOdzAJZIs1JkWADR3UIAfpEKxHqK0BIjNce3QT59MZ1XbOdd815vx1AZsf7yBdHzGirKLpjsvakDSjcCxKCvKKQIeK5eM9ZA7Pby4wWRdopcgLFyMU7hjOJIcQS+XQJfOUM19ZYcEZU5UFqqGI/QDjGPAVIrfoMCAPDF4cQShhCHgOdmjIPzUG7bLXnaGYakMMjpNijaZoVmisGUScYwsLnl+en7Boq5HdAcppInKD2hpi+yPKzSjwLlrY9ZAdn344y3gvQaYpxhTkTR8nkSQdH5kZ3ExQ1MrjPqKCJxT9okG+GKJ2hhC6iG5Z+Sa0uWJ+a64kQblIaymrMxTOIMHfStBS467uYmtNEAY5DBDdHma6Cq5L5/dynh/O848feyPmvojBRobuxS+PJ5kJSbfKhCEdG+j1cU7tkT5dIX+HQiOZrkr+zt+5xNYFyc/8nSU0r7ygCJwx0R3HkRc38Vb74AtMSxE8cxnnwgBVb+LpmCyOEblFVgypLUVHgUSEDYyToQYpWTOAZIIMA4iTUpza2cW029g4RkpF9qCm/ckX2P3u40gEKAfRqLL7dJPm6S2cMxn2iIe8egVeuGLEKwTiq+1axiALSX3HIjNBtlTFe2kbfcDn6M3rXPt9ezx3YZGLP1yBaoizFqPjCF0JyaebuNsj4t8VVOMCvdlFtFuQ5zind3hs01AoFy700B1F1gT/fI+s1UFEGrs+5i2z1/HRf/1xnv7C8xw9Psub/8br/8zx3sbfzCfOPUw3rjInYqaPlmXZ5Ucqj2WbPQFYhHczxhi+9Qc+jPAl7rBA+y5JlhP6/+no0n322Wefffb5H8V4PObMmTMvPz5//jxPPvkknU6HTqfDj/3Yj/Ft3/ZtLCwscOHCBf7JP/knTE9P8573vOfPXOf6+jp33nknKysr/MzP/Aw7Ozsv/+2rKVFvfetbueaaa/ju7/5ufvqnf5q9vT1+6Id+iA9+8IN/aclOsC/SfN3wBxeXqaSCz1w4yWtntvAzhzzQxPcOef8PrUPvDSRBiAgFqhBEUxWM9hDRmOYDXdRAI8YJKMXONx3mjlsu8Ny/PYDRhqwi8XvgjSBe8LGTFqGWFJ7h1hvOMhrlrH6ioPWlLSbXN7Gew+CwoNKAo8fXGU0ClChIkwoyysFCflMHc7mHmFiYivDnfYpBgHRd7HCAyQwCyEewszfF7JrG5Ao3K8oJita464blN27AjsJUHNw9sDnkUyFJGJH4BkcqhPZxWhWc3Mc9XVBZjZk/NeYN33mGj3/xKmzfMllU/MpTN9C2Kd1I4KkRc0dj1JVo4vGUQ3qVJkok9913AhG4xFc7FKHGjBV+BmGQkeiAyoEEHjakdYf6pEzWEgL89QF5vU6W+uRTkmAwIuvHnP6dJu6g4MhrU1Yf0xQtD7SmcVeH0UtgG5ata1qc9LfYazeZ2rMMGyHGJDTFoDQ4ngqIlypkx1t4V4N91GIciRh8tapCcO4LFf7ZF69BLgkmT2m4BmRmcScW2+3htAMckSPRGDdBiBbMdjCDIdHxKeT5DJVGnLx7zC23rPLAw4d49v5pdDNAVhyKmkSkEudcjO5pxkfaLB/foTUekBiwzQaukci9iGyqgsgLgo0E40Je90C4IC1yaHAiMFM10kWBtyeQoUN0VQeRF0ztrtE4WyPWVfZOzCDrpXhEATZ0kYXE7xZoRyC0QKaCukw5PrPHqUNzFIMEbEbuCfJpD2cE3oUeGENtK+bIwQHP61nEQR+1Yfnuxfv41zvXgbFIYO6BCO0rxMI0KvCR1qAmiqlnJUpYRJzi1ppoR2ICHxMnyN4QkaZ4bgDtOsxC7Gbopo/MLEJblpa7vO7mF/n4/30Q4wm0KoimFabiIjKNcRXdmRbt6T7y4Qb6UAPjCig0xZmCN/zki3z+3C3knk8ypwkujBBRzs5rPYqWYuZiDW+vgOVp8iJCBzXcXo5AQfXKRNV1sMMxOBIWG6jtsjKKShUxSQgOGsZBgNouKByLt7oHoYtu+pjaDF7uoKd8vuknzuF0fK6ZmfC6sMuLl1cweY7ajTF7Q1RmKIoMJRTMNMkEtJ7OiXYhn3d4cOMYW1GT6oEcZ5jR90MAvCMZzpM+KpQIbZFKkO2N2Ru5mK7PpNJEBYaiLsCRGM9H+pbqVoR4lUEvB6hegrCCrDcmXfCxUZN8pUleVVR620glsGGA8qqI3gSLIT+2iPvUGfReHzvVRHsBuqJI2w7O9hjVT0oT6EKXMdaHpkjfGlB9NsF5vIuZqmEu7ZYx3VkF+UIPbzdCzE5BLSwr1FzJxfU21x+PycIlUAoRBhS5xpUSjEFIUT5fr2GlQEiBbdYxtRDpuIgoofLUJmYywSyW1X6MJ4hOG9tuoQ+UaQZGWE5cHPC9H3ye1ariV/7lEtbxIMtLcXcS4/XLEG6xM0Ts9K6cR3IuD68CfAZjy3jXsnn+imAmRVl5BQgs6uwWJ/9+yks7FayvAEuRufgbI6zRNH4gZ/eTxxBzIcG1Z7HrkviTQRnhDeSzIcXhBvaZLnmksaJAuBJhAyhy0Ppl7yHh+xy8Z8I1b+/xzOdP0z15FbIXoRJDw9Xob28h7x2i7r2AnJnBOg44qow1j2JMunElma6FMOA0DWFgqDgB409oDv7DlOlWzIw7Zn1uBYsgfG4Pdocohtgb5rGyPP8U9ZCoHVDbKsBRtJ7apa+2uPZNryI/NM2uHSOjAn8nId3qviyu3Pvh+7n5G24gizNOvPronzrOb63uUG1WOFDv8OVP3gQWurNjvrxzht/7hS8wM13j7/3Ye/DcARs7/4htbbl+/pe599E+uSOQBqQr8DyI432RZp999tlnn786PProo9x1110vP/4H/+AfAPD+97+fD33oQzzzzDP88i//Mv1+n4WFBe666y4++tGPUq/X/6xVcs8993DmzBnOnDnDgQMHXvG3r46/Sik+9alP8QM/8AO87nWvIwxDvuM7vuPliO6/LPZFmq8T3G6Ct6Vx5l1+/TeO80uNV1OEFuvAY5+e4jW3Q1hz6LQShDGozCKjjPyqKsnRKt6ljOYn1xCzc1R6DsNGQbG9h91JEO4UYilE+6XR7OWkQRRIqjN90mv6vCnY4Zd++SpMw2emMmJTeETTFl00CQcpTTTpb07jxBo918ImKdZU0Ud94lZEo5pDN4VYY5pNaDZQbUOReIyvqWGeVoyVxAss3m7M5LoZ3K0Q2R+z+imL//sx+mSIqAmMA5N2SueRNURYZfjWowTrgsZnCqQPSapIljweHR/h+vmzVKRm5Dm87k2XudM/x2+euZV+IHnz3zpH52pFasAAr55+nvueuo5nRxUaB0ZMzrZw3RpKW7q2wb/7vdt563ue5en4IGm9AGMJe5DXXPyoQM0Iiq0cISy+yDEbGZUn1jG2IIqrnHhDj9u/d5c/ui9D9jawxrJ14QAyVOSVnInrsXREYbdDZl53nhdemiM91qK+2mHxxcuk3RTzpR7db+jQnny1pcwgCo0Ig3JCIxXTv7dOcIeL/UQB/6xsqdIK1CRFvdCn+r4a4WbKhj9DftnD6cyi6wfQs5LYi6ne3yV5WnP9377A4KmEUy8sY1t1lJil2dYMa5IiEKg0ZTz0+K3/4zqitkM+E1Ic1VTWU3AdyDWTRZfRNQH1iwV+P6PQEmEhmZLoGjDJsX71ii+pYHzLIowM7UfGqJUMZ93BBha3GpOmDaxrcRIoXEN1NSU66DBuKmQmGA9CRrmDc2qrTMixliCqEVw3h0whum0ZbXOCGcFbXvsE2z98A9o28QaWmTBi78kUMxojGzWUoxB7Q2wm0LWAvKFwdwq8kYMowDmuKCJNkfrouo9JY4JntzFKlC0h2oIAZ2uMNJJoxqNoGIaXXfI8ROQF+XRA4dSwpKTTiq13HqJ+PkHoiNG9LnRlWXkmofAVXNzmD/8/S3CyBlIirWB88xytx9YRVybOZr6FCQx4HrruIhNwNYhUo5OvNarYK74nVggMBlUJkVu7iPGEeLcKB2Zp7U6w3R7ZyjTZwfIug8VStAPOX1jC6+TUwpQ0Drk63oPeECNziizG2Rxig4Di+AIqEzha4OYanabIjT6juxwGroeVFkcXiBldRmMD+bLHKNI0zwhEnuOu96h/Y43w3Rm7fyBIc0V1mCMwqHFOcViyc22D+ktN5DMFhbuHtBY7O4W9qo19vYdzv8T/4oSiotBhgKzWoBIg/RAxGWNdF4XAui70BxQNH7w6puZhPEE+XUE3A1QUI2yKaYRMrq+iZi3preDcO0bfVMOuTCMeinBHLtJQCgxWUDiWeEaR1Q03razzgbc8yx98IeGTHz+EAJQFWg0oCmy9AqHGBgFiMsFGKca1FMLA9Axe15Ym1+0a6UyIs5eU32oUY4scs1KKNHJrxNTyhNBPWVnOMckUZEVZrQjg21JZBnytSOenEf0xstPG6WmSRbC6yv/2I3dQDPoIopcFmq+iY8mlD41I33wQOU5wuhN0INl720Ha924w3czZbdZxdzR7Fxs0Lk0wmxIRpyQVQTJVw5nTFO/o4PyHCTI35Q6v0/L/cj3M5hbOygEQEiECTv2ygskYX2widzPkRh/3GUt2tIbczhGjCOv0oSh42ZnG91EzU4jRGHVmF9mo87q7LjHXGbJ6JuPRxVke2JmlZ6t85cxhGmUOXJlgd+Wzepd6pHM18qvnmX5xgpppYzcvl+9La7IspuUqvIPTPNrLyAs4cESytbqBnpTf0a//5Md41/e/jb2tAdVG+CfG+NUXLvEvvuf/ojXT4J9+7B8QdC1WWE4/u8lzT61x8ewm88MGSZThtSz/aqPDRlLj+5zf55/9QoxSgsK3mJFAjeE9P/jv+fQv/CChv28ivM8+++yzz18+d95558vCyZ/GZz7zmT/3Oj/wgQ/wgQ984D+73MrKCp/85Cf/3Ov/78m+SPN1glNY3LHGyV1CYxB7hqCvEUPNU+dP8prbwU9zqn6KteD1Eszkih2l8MnmPFieR/ghwcgyDANsNYC9DG9cijomVDTODnnPtz3Il547zsayz1PjIwTrOSaP8cSYPaeNUYbWEzu8OXuJ+49fz1C4XHPrLhcensOpeAivQCqFg4+eqeFuWFbeeZFz9y1jAxd0gahb1PqY7KV5rBREs4J4Cua+0kVMHWBySx3n9DqVJ7YolEMa16FetrxUugWe6JAJgzO0OGnOyrdusdurYwPFeraA0w15IesSbTm4acGp324yiGsEV29xaHqaJ4I7eMvcU1wsHLQVPPfzh6m7mtXtFp1/3GXrZ+vlHW0NRWB5Vra4+Mx1+NWcydMBVpXbNl5yaOxFXHPnkId/e5prbtqk2Y558PMHKWouMoXzYho1exj30Tle/faLPPwHB0hnQvyHVsmW2lRud7iqtcNLT62wM2iRjhyk76FRyEKgXY9x4tDSVaa/GJG32lTuSpBPFEzcCl5/hGjWsdmYyuWE4XQLv1W2Nmiv3GbJ0Q602myvKupOgHzYUJE5xnMIt4dov4G54lty4Qua46Lg3qSCcj2072KUZHQO1OUucnoGoS0HT44YPK6g12dvKQTpk895SA3EKem0hw0EeV2hfUk+Vba6ISzGQv3hy6TfdJiso0qPmDHIQhB+Y4upu9fZ/ZUU50VDezGk2DRYIYgWIWtZsqYlni0raQDynSqXak3c8SUkUNRcTMXF2ypwMkXRrpA2BH4y4blzK/RP1mn94Q75KOJ/+7Fr6DVnoJ9TVCSjOyXOZ3OCCymmFSIMuGMDicbvjFn5Rxts/C8ZnA8wwxnsOH55MpfWHBobE6ywFNogI4sylrhq2dlR/IefOoGvUoqag+ynBH5OekBQ70pcWSG7lDI6WEMsga5KEJC3BCaOkUkVohw8h9ZXdrCjPkxVmXreQ1ccsk4LJxrhnr5MfdTBnFgEqUhaFkeWVRO2yMp2NN8Hz2F80wLNL18sTah9B9odRJJgQoldnkbWqoi9Mc6pPqo7ZnJyikMnxtzYOs/ENFh0UmZnIqJjNbKWS7JoCM4OmP39LWRusOMMFQToSYSNxriTBL1ZIK4RiBz0GzTXFJf5yvAYpuJgC0l1LaWykaMDgWq3mawXnN2ZxrR96qc03l6CKMotHtiYMPAwToBai/GejqFeQ6/M4Oxa/J7BDhQ2NahUY5RATnfIqx5pKAmoIScpJnSR1iCsRe0NEZUqNpCk2sN6kmyhRkUrBIJ0xiVp1FBrUH3eUP/WKvZb+mxvNAj3XOrTY5JFAZ92SqNtKygqgtoDq5z1NbvXB6yfrSKzvJzk57r07lEOapKU7T6DIdaCqFYhG5f7/lVV1JkQOcpRhcXv5dBpIOs1bJ4j+oMyicl1MIt1notm+MhPbtF7wSIKg73iTWxdSXF9DT8qqwC339jG//wIz1SRgHEFaVMRZBoKg/FDZNVw7Xu3WTwR8eD/OU20U15amAF4z29AoVFpgZpkmJkqMtHE/3uD8FiMGrhMfr1FsSnLNiUEzvMXCXt9qm9qkH+xC2MDSQ6BB7nGCgFZVkZZhwESyK1BTMo+c/9iDzu9hLI7iIcGqIcHSEtphK0cqFVhPAGutNg6CuG5FPEY99w25xsVTo9DRo90kdWMC6sHOLc7S7CZUPMKVFb6BGkFyYlpUBBfM42rJPWVFpMoxpgcZSVkBY5f4bHPPgXXHiQ94qIbHvnFK2KTEGANw90xH/139+C7Dg/84ZO86wN3vmKMv3xhl421LsNxSmLP8c5v+hLPP3yYnfaE1732BFvdEaJVxSoBTHFp0mY9qrGXKNRajBIS7VqsI8kaErYjfvkPH+Jvv/vPbqvaZ5999tlnn33+ctgXab5OkJEtUy9i+PZ3nuHmq7f54A++nUJJrumUF9uVeo4fFuWF+F6EHSSkMyuQlr4go5kuoXKI5jWNj2+zJ+YQCxXyikPQ0+gY0prPvWvXsBY2MUZRneQ0Mo27NcGPNdnExVGK8Nkhp7oVmrf3CVYc4pcqCG0Je9sooYlGU7iZh2hJppcNVeshv6Qw5y9hKx756gSRFDjnctJDHiiJweL4ATrOGXY8sqs7XD29jSc0z+chaUNitSFMBXYxoJiuEewVnNAXueXW8ySF4BMPvA5vYFGbGQ890IaawCksufHY3KkzDGeYuVExPl9j466Az42WiI0knQpJXY/RjM8gCZFLe+zNzlEEAqk1C8Eeld2CZ++bIn7SB6XJKi7u89tEO0OeWpgi73isGZfHe/OcuK5P6/oa//CdX+IjP3+QL/Su48lHFrljKWbhhyXP3Nek9egIbytGU+NCv43TSUBk+CqmUHXmkgGD6AD9uWWMyRCBR/w3Xcy05hvSLdyDhnt+aRbyAru7x43vn3D9iT6/ePIQ9h/XIYO8Jcm0wFanCLoxdCPMC2s4joeotpCeh3I9qqsTikvrqH5MiuL+P2iyN2oi5maQriJzJO76FmLYQ8sAaSyLd8SskPLI6nG8EZjC4PYTrNHI02s0TzuoH5hi8kIbUYe8lcCWJpwrGCRVkvmA+iNbjG+Zo/LYNvm1iwgUl9ZnCe8d0b1fol9TpSdd4imJOwJ/oEAazGiELaZxJhIjTBlp/Wjy8vGSLlTpv2UZfIWbaGpfOEtFWUZvWeHszjTxSgf9rpDp3z7LC/cEsGQRBmRsqX1asHnrEjNZgumEWFdQBApvUmC2xnS/MoW3sovcMMSZIZ+bwqQZVhuo+qBHqMJghyOKZg01AbcnsFMtYr9G8OyI2qk9jJIUh13E9AhvSsLYx0zVUMojmTcvfxZrQR9dwGv55LnFeqAVOL0EM8mgOoUylmLWEB3yaZ422FaIDgQys2hXorxSgCuuWkTu6HLCjiUMOmQLA9z1PvrYErIdENUnpFMesnDxuwr/bI7aHmCkoXJxyGLjEqGTcyEKiI3H8JJDOFaYEPCqZIc8jNhC7Y2RlSqFryhmp1CP7iG0ZmQ8TCGxucR0FdXHFbXbM4bWQWiD9hVpG1QusWmG2AE9LIW+YGfyskADUIzAeSIoJ+jSIoKw9KoZjDHDnGwcYtckrs3BGHJHIus+6VRAFqpSNKw7ZB2XWqGR7Sa4LkWngrCKYG0MnsTdSyFJIAzx9jLcQYF0PHTq03jdOtKXRLsBduBy9z99ggf/9ym2Er9sLav6yEmOsxvTjSw//iN3YJUEJcrPNxxToJGej55q4KymEIYI38PqAjGxOBNL5WxGvFDDeAKvn2FMKWa4FR8rfUgS5FYfAp9ipcN8Z8i139Vl7xnB/T+zgpQFjAuUNthzElW9DFyN9iTFTIVwZ0DtnTkbe25Zibk7wWxsoSoBpDmHXj2iOlPQOpQRJQ3MOCrFpWYD/9waGKi80Cc5OkCEVeg6eAFYH1SzwBcD8ssNbFSKmioXOJ/dIL9Xl/Hmix3sJMHG6dd8ZAA7GVM0a4ju6OXnsiUP/9wW4sphYj0JqSm9bAKv/K6a9dLMeDjCKsjm6ySvugb/8oSzJqAaG1Rbky9I6vUE05P4zRhRCDASNVNh920r2GqAMRoTCMQoZ/fmkP4LKY3vuxn+r8fKNjgleO1fu4PPXtzBmwhiX1M7Ok+lENSCTZ757Ha5vzabHD6xwNSBmT8xxl91yxGu/cabOXLNEv3sWRav2Wa35RNMP8LeV95AfqGHndGEgYuQlqm0weY4pMZximofLDiJLmPgF+qI2YDntrf+q6859tlnn3322Wef/37sizRfJ9jUIjOwuSXRisWFMfXGhHy6wZz/GHANnZketrQGwMRD7DjFxpp8ToMB17goW1B9ssfqIyFiMUd4HjIp/SeKmiCZc5lsNWj5BcnTLtWRQFZjhPbJBpJDjV1enBxg8OYDhJ/ukf96ik0Fo8WAm77lNJVvGPLsp2ZROwZnGDH12yPiNzRovH6CubQNaQbjCekbDuA922VygynbdjDISXm31+9l1M4r5EaPG27Y5vHPVllcWuXCoeM4a4LozQVp7pJfEDSfiFg70uHefz6L6BXUrh0S9ULy/gRZCIrLl8kLQ3F8CfGmY1SkZpwrnH7CHhVMKolyl6O3bfPI3gqJKzlZ2ebj2ZsQE0l1S1AEkt5UyLbjMPeGIcOdOpMnJLIwKF0FX7E7qBIKwcZeDSlz3v+Ox3n9yjpVL+cd3+oy+H8qrJ2eZrflM3esx0NbJwjrGveCS3/H5W0zZ9h2q6Q4LDLg+dlZjAxoL8cUviU7a9EX1lDnZyjmGtxx9SXmqhM+/w8n5R13D1b+vuW4lzG3OUYaF7LSoBgLuupiBjlM1RBji22AEQVOCjrwSFsS/1QZ5W4F/NT/chJ9bBnHV+QNH1sz+CehmJ3CbgmcwuX0F8Y0o3VS42KMIGsI/Ms5GI32FFIL0i9VcHNDUYHgC1u4qyOGb1wmjKIy/SiGcEtTLDfJ6wrrClSiyD+RwbmMMIvYfcMhio6mqHkIQBYW3ZCYQFM0Bc4I7qw/xWpcKw2TLXhrI2qPbTO4c7m8m20sQlpyV7G1O4X1QYc++bEZqs0cZ3OTUU9ihcV2ZilmfPbuCKg/1sXfAVPxSUIPb+Kz+7kqNTxu+uZdHnq8gq6G4C5CofF7uvTC0KD8Ck43YWQDZOFgHVtWVlzcxOYa0arjBnXqjYTWnTkbD8yit3ycSYFb5NQTSKc9vAikkNh+jl1SpRnq5IoJrOMiz16GQFK/kJMvN6BRxcyV3htJS5TVC1e6ndytCO1VsY7AZgVCCdKlJm4vwVqLX02Za+1yfusA8uwW3voQG4ZYDCqoItodXvicQ/vdmtS4tIqEz//qq5DWQRoLWiBHBhlWSKZ9lO+Rz9VwMoEIfchyxmsu8ydHrA983CccNs+0kW+2BH5BISTJQoAyFn8rJVvpMPXaPa5evsj5l5YxjsI6Fqs1Mk5xXMA1kEmcmoLxGBMLqPmINIO9XRxnGjwPhEBNMiaH2hiZoysKE8fEhxTZgo9zcoZGHGCVoohzrAInKVAThbM1YHKwjqiG+I9foP64RTTqtHtb9D9dw2lJ7EMefi2jVZ8wffU0O6daWN8DUxBEFSa3LNBYMxR+iAkchLGIATiXC6QS5AdmmDu+S3i0YPPFFmiD3ekiC0MRuMQHQxrPD4iWKuS1ENFPCAYGG6ewuYPwPTzrQCHQoxixcqUyqKIoTiwQXDhfxrMrgdAg+qWfVevJAeLJHUxVUL3V0PziiOhSA09mfPAnLvHY+Rke+e0WX/7pGRrLOeuPhZgwpv93rkHsBVjXpfrsJhiLGqZM//oL4Cr04gLO5hDtwtz37rI802f3oz1WPwn62AJ4AZPPDMpqGUdBkkG9AkUG4+hrY1+vjxhGkKXYK61I1TMKmjVEKrD9AU7oEKz4JGcnsLsHvoftD19O6tKtCrJemirbMKTx6GVkqwGJRh0s8GZSbBvEPRY7SrDKIxtP0M0lRHdE64sXyVbaZMstwgcu03x6k2sqN3Gq0AilkInm0pPnOXrHSZ47u0FlwxCsCG5+80luuxP+3hWRxqQx/+uHv5eF5c6fGOOj/pj08hbn+33m/ue/TcX7WVbmd7ihMeDejevQg5i9OGdnY8Dy4Rk61dei+5d44pQlnnPAWioXMvyoAEfi9DPuW7u4n/K0zz777LPPPn8F2Rdpvk4Ity2igPoFgzVQaLAVj6jicPVVaxhAzkeczcuvPHl3nezRKrJw0G55VzLMHdyuZjzXRFybYQdXUiIchdLA2OC6cPOr1/jSqcPkqWCSejx18QjIPZRnqQwKqnlC9WyMV3fJmjXsBHAdzj1ygHS0gH1uAzG8hOm0EDZm+LhibWEBWg5sd9GhoveaBTphi6mVEclZTTBXIJ8pY2gBgpHGOd3jTOYTDSXD4wHi5gipFSvXDXjmiUOEgWJy3GOyUJCuTjOd90j2fILdFHxLsH6JbKQR1mIXEuZfr6iHCc93HaaeuUA9dIiNjydy1v/DELe+xex7RnTvb9I4nxCNXFpnIqJ5j87rFPH/n73/jLJtvcs70d8bZlp51apcu3beZ++Tz5GOdKQj6SgnhMgYbGyDceOL+2IbLti0bzuAQ5tuc23aNL4Y22AbsI0EaqGAApIOCifnvHOqXblq5bVmfN+3P8ytA1x8r8HuO4Zbo34f9h41Rq25Qs015zuf+fyfJ7ZY41CVMqtgsuDR7mXIqEJ7x2A6Pmpq6ayMeHVXstKp8NKlZX7p+dezes8+jfGEldftcf6hZap7hmw+xLtiyQqf19eu8G9uvIVeWuOqhTzxyREE4y2OX7nGC4MlXJqif2ubPJrjy9vHmV8dkK5U8G4M0Z7lRh5y0i+zQ75+UZ5VHXkBrgAbaGQSIxDY5Q6iUsH4ESI3YHPUKAUBTkpIDDb06R+rkHQURdUi9xaQO33k5h6u0SCYjKjOF1S7IyZ9SGtVUFC0IkS0iExBjTKckvjXU2JXjl45B5Xn1hH9KcUti9TWM9xJwWAKzoOAjPv/+hqP/c+rjJ6bIu8tEKHBSchrjmTJUFURKlWIgeLQ7i5Hjvd5mVOkH16h9tAFVJJhqn4pivia0duOkTQEftVy/+mLvDpZ4mq1wda3NBhsg49P65U1GI3RtSqVyx6tr22hb+yTLzXI33yU1W++TmNmyOXfXGHySoWvvHICqqp8QwLE2i7m9DLgcFogfA8nZVlxXgCxBe1wDYXYL7CeZK+jseuzmKcMbEfgg0TgDVOqOwnZnsV5Avb6FMuz5ThHXpAebeJLi19vYNsNbNOgv3Qe3dvDrCwhk4Lc00gn8PYt3KwjdKZsDZocrzA+HlA7PyHwqhS3HUZd3ebYmxK2zreo9Cx2Aro7RQQWnMANRjjfw90eMcp83tG8SNsULL1d8i9+816oasIbU4Kz+2Ww7dwyu29sIJxj5rkYudgCD/rzHQa9Jm7qMV0NuLoY4Ppj5JIhlBY98pGxRaVAf0z61S7+TIAcCKyz5J0ItbGPSxLyiw7d3kd6AWanh7YFsnCwtltGrszNYY1BOYeQlj/zN17ixccu88LH63iHW6RehXScU7QUvffOUfvtPk4IssUazjqk8CEpMLNV8sU6LvJRCw1UBrdUdvnLP/kSNzaqvGI07n7Jw//6NMZIUlHBzjaRSYGrRXixwBuAPwUjzWujhSLysYfmMEmKzA1v/pZLdC/W2b3gMJMU4gQnBEXdx0SSeNEnXB+hdVgGhzuLSFKcteW+EfggIWjEDLKQtWcabDztk9d9PFdWTFsf7Fsa2Ktl1lC0nRMXhv23rLL3mKK1qREWAuO4/Q1Tmsu7PPaRKv1rPv1rZRBt9tY68g0FXDaIsxKbJWUWE+WhRzgDWQa+Ql3ZoXppzJvevE/j+1N+7qFldu8+hCwgKDYRl/Lyu1IYWNso99NKVLar4RBZRjEdguNmtbYoxzu1LAOW+wNcPyPpZ+X3DSBOXqsLd7NN5GwHpKB2cUJQlaSHZimCAJXmhL0hS/U9TCLQ7xBs/KbDZQkyTch1jm9zrHCIcUp4tU9yvYsYxIzyApoNXJpCGHDstkN8///z2/iLf+fXyOQuwye/wG++Iqm3vpPm8kUGGzFemrK0OvOfFE0qjQg/8ukstQm8JsfDkJa3zZxe5Hu+7T5e+tSL5MaS52UT2IVhzNY05bM7F1Bjg8Oh9yaoQlC9FKNSQ3LcxziHPhBpDjjggAMOOOC/KQ5Emm8Q1MQgrEWOMh7bWODcZ5e4MjNPc/9VHvpUk7f/RXiqWGFmNCFziu2lGcyHPaoX+iBmy42MJritGL+9QnbiKO5qjD8y2JqPCctdJZ9RrK+3yCYS0R2StCI8LaDl4TdyjrR3mb7UYPP2gOIDOdmrNdTIYGoFs0nM1ITIaUGkE06/e8iT4xr6xQR5NkPUWmAs6aGI4MoY/8qItb0mh57awjxQIZc++fEFomCXSrhG6gqmusokV+T7HiQWsVMw/JmUxavnSU4vIDozpG1J/0+vYiotgmcKkmZA9ekRNgUpFK7I8QcTQr/CtApBMoIkxzkfX1qELXDvXWWczhMObnD2N9qE6YgiaCILh55a1q82kFHG62cuczlbZGd5CT/1sFogckdW0dhxQTB0RC2HF8BP/OMPsdOpkDY0w0Lx1/7OJ7n41RV6jzWoBjF5kqLO7sCbTrGIYbHVp+dqsG/LjBPrqKztM0o0ccOjJgRSg1T7dA6NeOrqKtP3NAm/cpXv+IELvK+zizASTxqqIgVAj6AQoG3ZHkOgEVLRf0ODaC0jeu4qbr6Nnfdfa1NBKVw1QGYF9QsjnA1oPjlEZgqu7yOnGfgJm08qeqJKuuCwDY2TDuFAT3PSZgC5RMYpajzB1KD3zcuooSXaU6RHZwifT3D1CDEBm0jqMibt+yjt+NSVBzh1y2XGF2t4XQd1H1u1pLcnIARpLlF7CpXBkdaAuZM91lyF6vWM6lMVrHSocQaJQ3Zjal+7TDjvk33TAlv1iJ0iwMUFc//+KlGUIe+Zxy62cdJQX0qwD20hN3YBwfR4hXhZMZ2RjG40qAw2SXcWsPUIEyhcHKN2eoj+iNhOCXQAWqDjDBeCN4K8ZvH3Y0w7ZPPPHaf1yatEL++Qzc7jb3vkWx4yB+OXToG8FaCGlO1IcU5UT5lKh5smZF7M/vs6zD4SUeklmG4CA1e6l1JHtlLH25sQjgT5UhVhPURSip9RvE838snaTZwSmMhHjAqQBU5Kzn1yBpkW6FaKcxpbr0CjBhs74Bx2MsbdLnl69yiHbY80jXnipXlECtFVS2NqMd4MYj6nspOjPnqe6dGQ4ZkFWpMKzjekWw2yuYBoXDo6DB61IqeIwCoBDYN/vsxp0YMpY+WxNz6COSJwIqCyk2F9HzFfQUwT9NVtJm8/BQvz1Fe65C8ZtFdgRwoZRTgFaaS5/47rfPDd62w+soRyTWTqMbsSk9g5XOgwWhKLCWpmDlMV5HWP6vUcuTMin61QuT6lWFDQbuE9f4mJHyGcQ3qWfiMi8nLMrE8tgo1HFXZrG7c4R1HzIM+YLChkDso4/F6CiwuKxRoyTtB7A5yFV7+wQiCnuNzg0gw8jbCWcDel/tU1/Nf7jJ4ew+EliqoHzhL0bCnOFAXD4wGmImnNTTm+sMvJ+7vMrnpc/6UUG5eNenIC9Qd6TNKyYchUPWylrB53Cmxo0QlkO1N+/Z/Ms3E9RKbmD52P5FaGGIDtKZASFwZIl0AOZEABbO4gfI+s6jPelDxWLNCqxthMUP2tJ8k+8AZcZxa7sYsajkCq17YvpmW4NUKAlKXIcHMEygFKKkx3gGj84cYHYW0ZGhwEpWtTCmRuEeMM8hw3mTA63sLv54i6g7VNXLuK7Stc7FBxihhm2KQ8Bvs9C502g7cHSDyquaPdDLlhU0bzIeQZQnu4OOWz/+YhrmdTLq0IVqOEfOJIJ4aXHl1j5fRxBhsvYyYZ//Z//Tzv/843/BE3jQp8Dj9wB6fuWAGgqevU2aKwCaduW6F+dAbjHMIvz9UX+7uIIOZ6N6dtJUYKPBSm4aELyFoBuupxIM8ccMABBxxwwH97HIg03yDYNEYKDVnB154+jg4DpA/CFrztw10A9kSHj6/fTm4UbtdHLPgkJ2apqQnSF+hKHUiRo5RURGhP4ZShCMuA0qwhcBIGVMl1QKhzqlcMt7+3z8NPWfKeZboRklzLqf7uOuaugPhYG9P2kOkEdyOlsRIT+wWRyLn/9utsffEw29dy1qfgdreQi3NUU5/Kp68ickPjYR/xyAT/pZhi6RD+3IRD376Nix1Ly0P8Wo3epEV8qIZ+zFF5yWGvpwRFgUwN9q4WMpdUdg12SaNOGibrNeQp6Pg5/SvT0h5/WPPX3/dVLmnHZ9YPc/H5aplhC8QmZK414lJ/iZcvtHHbZUV4fP88LtQUFcXoecPCm/ssrA5I79ScXz9RLv7zciNGafxhilQpagMe+td3kxQRriKQviXsx1x/sYo6k1PcKxCPpUTXbyCMxlUFl8dNXAzJ1Kd9flJWsBi4PjnM8I4KYquP8P1yfGFkuH/pBoWwrBcR6/NLfPIrKQ8lxzgcxMy8L0GJm8GeZCjrYzQgBZ4Ashw/jQi3YpQVuNEIoZrImxoNSiKFQk8twkL70V3s1h7m5DJipoFN+2UT0K0zFCMfszIDIsJ6Zd1dOhNhqz7sJORVicwkebvMHTI1D72Zk52cpwjALFaJfU04TagnGYx8jNbs9hvIh6uopQaBM+TKMZorMJkHueBOtc3uK8vgHNVTls9evhMvyFl8OEZXmxSeJjs2QzCw6EmO6scE04Rx2uHseBEZOtrtCTjwrCVyE7ZvncGsKoL1hNwNCKzFCUm2WENPBKOnZpFf2UO/kOGKAdrTqKSgqHjkyw3SVRA2QfYLZGGxk5iiM8vwFq8cUfKqKBzZjGPrLxzl2KevIJqQRo58xuLtlhfxADK1+FtTsuU6whQwTAiSNaaHj1OsVIle2cd/bpfcWOzJYzglMXNNbKMcXbJS0n2gggsE9Uug/NJJEwuFurZNvRoQzdaItmOy3W2kVLgjc5hphhhMsL7ANRrYwx3Ebh9GATIHKTWj81XsbRmf3rgV+fMjducPE45SjHJknQoyd7hOC3ojvI0Btf6E7rtXmP/UALezT9SSDG9fLr84pUkHqgqpLHEeYEJFI47J2yG+9rG3C/RignY+JtCk8yE6FLgc3EyAGA/LcRljmRZHcCcgGG5QNFt4OEzks/fOkM8Fx3jb89dJdRU0eGGGvEXirvrUzwpE6uHV5hCpI9jPiWc0JBn62jbyusPechiVOcKki2pZdi5a/sGPnaS/vMzOByoIC529nNpuFbMxRQxARFV87ZFvr1O5BtmxZeqbGQhB4YHqxoikgLxAFnDt8WVckpaNXdaWQoNSyP4Y5abcfcuQr1y+B5Qk7fjgGfwXB4g0h5kW0dUYU1fkm5Ir93dYeKrN5pc13qDA+BqRlWJL5XyfpChHiownUKOUmY9dwL6pTnBhjP+OOvGTAx5fb4IF5X4/IwmAdUXxRAMbaYRvSiHASuRhib1YvHZsdVmOl+XIuz1GUrJ53iPPMhQQffZJGj/UZnjNQf9mPpC+KdQUBkwBtTpMxn8ggNchgKLXR6Qppj/4oydLY14LDcY6iBNkWpRV3P0hcXOWyrPrYC2usNi9jGISYqaK+DGFzVPkzU0JFNZY/P0U05IQ+fzAD383j1xe4313nOLvbvVwn7wI1kC1xiu//jidO1aZftMRPvSDp/nsLz7F/HcfJ31qGx56GVmr8bmPPM78Uoul77n/D73sq+e3ePbLr9JLniJ83T/n6qTGsg55ftzm9hVNEEgKKciL8m8YRjlBMGCYVRBxFV87GncscfLYPI8kO/RdSmMKxjkUBxxwwAEHHHDAf0sciDTfKDQqFEmGqFQgc3i5Q0uHbNY5+3TKbR8Ese1x48o8NhdUNy0ylwht8O7IETh0q0Z83xJcu050vYmXFAgjkEPQvo+OLRiYjSZsDVo4pfHyjFeem0U0NcVoTJSPGVlFZZJQ20y475te4ukXzrA361ObH0G7IP6djHGsefblRc4tHqG+uo9zI6xyOF+hnMAqhcsy5p415IttcqERM1Xw95C+I9sSXP49jfdAk9yrEZgCznnkCyEiWIadPvEbF0hWINwz1J8tEM5gvknhfMvg1ohofwmvtYvILG4Scmqly7wVnB3PsPmuI2xdkcwdHbIxaBJ/bQZ7aMzS/QnhvT3O1yoUFcFopqwvTS9LjJMI49h5qU24NsCFHoKyTcP3LGlVUKk59l0VrzEheGyMWK5TvzgmntN86d/fQvPDiv1jdbjkqG4q7HwTVnPW4zqehKrKqJBhlUAohzSOShdMv8DZAnQEaZtzVnDL61+lcmaTX/n5e5lfH3BmJ+GBb8n4zWwZEZa7jXd9wOSeOZwEUo02Nx0VNxI86yHmOjjlqPd+P5STYyuYZgRbA/A1RRQgVmaZ3jWPMI762OD5AfFxyZn39zn3WUXx9cxeJfF6GQaJVRbpHDbUyJGFXICEvCJw0uEqPpPDGjyJveQoBgGeD0UITkmsM6i9Htn7j5J5gqpU8ELKshnwd7/ncX76sffR39Tcvtjjy43bqTUSJncL6l8skPsj6k87ZC9n58/eAuttRJEjKpKF+gA/LNhJq/S/fZXa5/YYfXREJNaRGwvIBpj5CkV/TLZQx6eG7DnsrELeVie0A8ZndZl9MTuDnhq08DDOIGsaAh+7s1UGOg8qVNebZFUIUsAHLQ361THpRUP1lQnFUZ9oR2BshLSW3Bf4I1AWGE7B1+iGx9jUsLMN/IkjuJqWwcEVH+vp8vp1po2+toPIStdd5cgRpCfI2wputvDmLR/RnaA3+ni5LMWBaYpMcoqlDtXZCXnXMIkMWlqkJzELDeJTVSo7BeFOQjTOafl9qpUpGwOLmBHYQDNd9DChJLjax+/GJEGOp2B43wyVXcF0tUa0tYvMFSIGJ23ZImUFtmzgRgiLk4LJIZ/aXoYYjdBfiPHuqJDNeuR7Am/fYbTA2xmBliTfewy2JcGTm9iVBYSGwjZxfgO3OURIR+H7FELzj3/qLuQUONFk2FCs7UZ4Akwd8nmobCu8SYGVEGzFkOTkc1VwFhF4SONYvbWHvV+w9uUO3WYVqyoEVy3GWZwV/OyvPIjVN8AOYTxFNKqI1Q6i2scvUr4+tIMB1/QQSuJ8DynA5hkMR9BsliM9gyEYg/Mly2/L6a43USODCgTVSyMSacsRH1k6TtSVDZSxcGiRatuyfGaIn3tcfyVjct9hql+7gK7C5KUW/s1wauH7BEdmmVvsU7xuzMQYTucbXFoOWWrFLB9P+OJHO68dHpySJKcXQCmyOoDAOoFteDglUA1HtSGJb5SKowDsrIcTllQpSqtNSWwGeJ0I489AlmMHfWxVInamZWV4t1c+p6fKscybCGsgyRCNBs5Y4o6mcnEXgOSOQ4QvrP3+70pVjoAqRXJqDhlqrK9Q1RZsbuP5Bl+mBEcdtqaY/LqE2CCEoKgKwvO7NB/ZwNWrjN5xlF/4qU8yzjMunTzP3W+7i+c/dRG0B2kKzuLvxgR3Cr54JOXoT7yZ35y+wGRpShWwacZ4q8sbHjwNwN72gP7eiJO3H6LdDDHbu5DukpuMqxtVPnL1fvaabb5Z3CB57jJxVtColgf3u1uaodelOyjYE1VCT/Fbv/p/B+CtP/bzBInA1Rzyv3DUaVwMqOnmf9FjDzjggAMOOOCA/98ciDTfIDjfw0UBWEMUTcn9ECMFdT/k4S92uO2D0Hx6SixbeAOL53uYnoGjKc4HHLzxfS/yqfh2RGuJ2o/vYIZDcALpWjDTpIgkzpNc73YoagJReOjKlPHFAOErUIrozJTm7gj5aIY6bznTvMpgPqI3XiX5sGX6kzGMHbnTPPL8KpV8jaJexcqAggoi1GTtAKkb+F2JOBwQzyoGrRrhHhRXI3b/bYPxsznsFnhFgs1CVAgFPrYGcgT2zsN42ie9edGvXYK3N2DyBUH6oRa1Z/uYQqG7g/JuauJhLdSc45as4LGX2iRnJYM/FTN6qsnyrfuMX6hyabzIX/qZi9w2mvD/+vRtSFHeU81XDNW8T38jpHutgbQON82Ij1SpNh1hlDEZRnQnIW7eILaruJagPc2RgSOzEOcB8tf7ZN+7gH/SQeMIdhBx54kr7LuQpfY+m7Swd3rkFw16IOm8N6b3ZY2NFRhD9k0h6etzPnv2dt7bfo6tvIH6oObdrxtw2z0xiwsZdnuZQRKVO44KXrsAdsuC1DnEkqIyGWBthPU1SEdebeLPZGX+RjXAaYXLM7IjLYJhgRQaPSnImh5MY6zn8+c/8BLZ8YSLxWlsATqBtFNBX9+BNMFXftmq0o4QOGQhsAqySOPvDKl99gJ7778XlKW2kWBFgzjS5Z37vqOYAf9ihr5osHfkzDVbdM6t8b33vsKp4z0q1YRe0OZrj6zgvc9i64LeG32qX44RQuDdGCDjHL2/h0oytMyYJrDcGCICS8+ExEs+Ix0Q6SnClGNrJw/v8967B3z8y8cYnGpR2bZUr8a0DvU4/adu8PypZcRP96GbY7hZHez71BoNrBXk8xVk1kRu90mbZY160HVEiQXP0nxuj+zTPZCWcOoz++u7xLNzFEqi+wneFNKWJtoEjEGd22A0EsgwZRpakkXNzHYNVxth5mr43TE28CiWaujBFJcXFJFCiyoyBhM5SgsV5O+qo4ZgTiwgUoO6toVMMmzkM//GLR787it89aHb6D+j0Wd3cNUmem0b7+QM0qthNAzepDg0n/C66hq3/z3BJ/7dabIkwQQz4Bx6fwphgNdusPXeFXTo07xU5hHZZoTKDDNXFdMwJ8zLjJP0VoVnS1tN/XJBdaNAX97D1SrIfkG6U8PMCooOFFVF46v7yFc2QAry++7GVhRB6JVV4g7yQOGdXcNNEtiApc8cw/kCvTEpHSpximvXkY0AtTNi8qY6zpcUFxT+qMAbW6bLVZh6FEtNxGhK+MxVXKDZ288Jf6zD3JGYrU/VsW2HOp1S+VSPYD8gFRXyowuE+1NEmmI9hWuHFPd7qK+mmDiDZhVROGRSYBsRDMfYqo+4tFa+hyRFzLQBKCJFMVfl0tk2Zr6Gm6UMfb68SZjkIAW2EiIbdVy3h3BAVuC94nAnLMm+wGYZlVd2EELh5iWTqx7eTbuI2t7HG3Wp3SXJ3yhQNUm4ZbnvQ5YPvGebtYshn/68R377MvgaNc7Ij7cRzuFPBCIuSnENBU4h5yxvfteUL/1L/dqozfgTOWImJH5KlflpAPUK+S9PoV5AvgOeDzMzSBzTP9vA+7Wt1xYwIjevPc7NakStDvt97N4+DpB33grdHIRgeqZD+MJa6eaJojJja2cPg8WcOIQqNNILEUGAq0QMI5/u9RbVf7ZPw8QQ36yrd47mJy6gOq3y3NuoYJxhlGbkFcm13R7HPc30gycIP3MRYQpEuwmRz146QHZ9rnzuKvpUxh2qyyVPoVsNcif4+R/9FUJP8NWny/alv/LT38oD77yVMNJMzp1kyT3IIz/6CfJUk96i6PzKDLIeIY3j8vVdFpdafMfx+/l3r54l7oUkS5Lvedvtvx8SXADGYaaGwli0/Lo36I/HP3jpT5OLgqmV/NzrPvYneuwBBxxwwAEHHPCf50Ck+Qahe0tA87phuOITHB2R7yuwZVuN0lUAlID531lHSsH4dXNkrQDnJFqAVoa5W7aZ/akGcsswum+V+me7YCxykmKcA1eOTyUjDz8XqInETT3ShkHuxDDscjlbYHy8RdvfwOSS3330NNPzNeqHYrbTFuJejd6eYEwdNc1hWCArE+xmji8yaBaMFhzyrTM0rmuSFLIbE4pFnyKdoi5MGQ+nWFcg8Zn0wNvfBNvA3tfAPzEgf34M7QaBKDi6uMmldhvVmeD/7wPM8hzzv9MnfHkHvxkyPVJB7yWYWsH/8D0n+Pb/2y7zZ2LMYs6DR29w9/I1fipZYfXoHuf7K1zfU/zDX/ggJz/9EupPC2zLILSjyBxbH8+o73ks3r1P72KHwmQI1SI/t01SC2FeELclagLVQQa5YoKPFjB/dJvp+THVuwyDxRRz2JA8ZLA5HGnsU+AIC0G3X+GBpU3S7j57+7Ok2kfFlqSi8FtlrfYo93npQpuN/TfTefuUY0s9vNsn2Njw3JeqjE+GZLr86jfWE/beUAMBsg/tizlyYQVTSISxsLMH8zPIXYdotxE4bDJFXR/iRlNmY431NNOpRG31CW4UiMEY1x9x/x09/tXF4xRZmWfhjwVCyjLHJPRhanBKkFcVfm4J928GB+PI6xq7GNFcHCOApbv32XyxgRxbEA7TFCTLNcIRhD3L/BMF+68fMvvMDl+96HPvgx755QH4bba2qyz+cspwXnLirj32VmYRNiWZC1G9IYfv3iX9xARpLXYiWdvsoFo5w1GESCUuCojvPUxtI8aPHXec3CUb+VR6jmgzwx9LZGYZvVrnmedOUcxnJN85Q/DREW62Cdd3ypBUrXDVEBFn2KVZNB4VE7A3I7CBQl9O0VLQHdSo5zvocY58aIhJKshejLdcVkMH3RykwmhRNkKlGaIwiIml+sIWqljARR7x/ceobCSoDERikYMYuTcEJYnfdAt4Em9UYDyHKrNGUU85qNSRRenSMpFm+qZDuGYNrhhe+ZIiHWjq2w5ba6DXe9CfondDsjNtitVZ9GqfRPjkuSIdOewwQ/YmhK9kuGqEuraLDX2oLKGpodLyUt0utMnRJEdrYCyNFwaM3lxWEdupIlMeIje0XpwgHeQ6R3oaORMgjMRaBcohhz2Cc7u4M8dwQhCtDShsiJjrQHeIqTj85zehVac4vYoaxPhjh7MWm6ZQFLg0wcw10A68czfofHGB9PgsIteYFhShh55Y4gUPYQX++hQmMYoKaVajOivx/RydZUw+WFDMCJL3NQj+ZcLRW7ewx3zW9haRmS3dTlaingR9QyKKAplbQCDinOLaNqQZenuNQpRtbBiL3e9iPUG+UKWYiRBhhWQlwiiBSgzV6wqhHaZZQQUhQmqy43OkDYmuNnHbcPYzHbYe83DTDGUNojBwySJmLXpw05my2yPtJ2w8XLB6NGavu8qrqkX3PyRsXznJxlMpESFmb4I3zZG5JT3aADykDkB65CeW4BaJOR2Bm/L8eYdrbMGwDPCtHrHMHkpY+KDg2VduwX/mEkwT8DUGg3ACMZ2UDhlPs7CXsX20gb46fE3okfUqbq6D2NnDpfvkCw3MbAW91qWoam587yk0kkJJagp8Q1nFLSViroMnC1wYUdQ1cU2jmwrZF9R2xnQqGaNhSj5VmNkIuVcel6L1PuxOKN5wK3KcIgw3s7cc+dEZ4jgjq1VIPniGym4Km33CYUbrrOLH/tQ6r5y4QbuZcte9u/zzh1e5+PQIpzWvfvVlZo8tvHZ+f+hTz3PXvUdQSjHpZlx7LqJIy2N4/bJhbA10Zsj6U3pbpevxzctH2d3p8O9eytGTmBeOXOZru2d52/ytFNMMf2wpWorQ+5MvAzdHDeq1ER7pQTvUAQcccMABB/z/gQOR5huER3/px7j3F36eWS/AvdgA6RCWchylUd6NrrVjRpMIIQx1N0Fdc+TVjAIorOJTn3odled62CwHf4ILNUwzpNKYQGAleM5ACjKV6LFFdjR7SyGV6/usnnG888hlrr5ymJ0PHOWO6BLbozZuQ1Id5uRjx/TbZpl9d0j8v82iuinFUgOxMboZPAHCD1iemTJ8VpEkAVnDo3Eho3L+OkvRiHTq6NXqyEQiE4M5NIOXCvJms7xT2u/i2hFBbUDnwYQ01ai+JftkRuNuxbhooaSParVBB/jxBDEZo66MmDQlO5s+X9pcYXOx4OLtmpd2zxC96vH0pVtJj3igC+R4itAOCoNaKhuPssOS7KUW6uiQ+Tv3efF3VyhmOjBO0Ds9ZE+yf3cHE4CIDfpaRv07E5oV2E199hZbzP8lwVG/y4WLDRq7O3hf3Ea0fU4EO2hhOL9+mI1+h0m0w6KasKc6hPWC3GjMQoPJiRnUbT0qG0Pqn9mlsZBx6P2OXlFDqoIvfKzOs/9+geJnPOxc+dUvqobKpsCGAjWlrHFPLXaaIQpwcYzY76EiDzfMsGmOrxSkGcVsk5/6h0+yOD/mh/7qG8hftqhRBoEHccq/fOIu+icMJnSQSlR3Co0IjEI6Sd7QZS31Xko+E6ISh9NglMN0QrIjNfJUIYRjbnfImpEUSxIcFA1DsC3Jbp0hDAvsQKD3E/qqweJszA2pmOoIi6OzPGGhNcTNFRT3SrYemRLsDhENyeTuBSpHuwQPJiS7Hq4RsX+uijkVkwmNURKjBaLhIzZSRAFf+Opx6kcMaaGorWVMjtXIwypmQcFFOORfJ2hVWfmuPS497iHmZyE32LUN5IkjMBohUQgpITPkLYlTMK0WSC8gXWiSt29h7rcuImMDmQHlI5IMYRQ6s2RC4HyNHxvEwiLZcB+9P8HWQ3TiGJ70QftUbyQUqsBUJP6VfYSDbL6B1y/TW8PKBEYLeH452pL/mSbev9lFv3INubwIK0t4YYPCOgor6G1Wuev4BR4dnilFvN6wzAFRGpvGuNSSC8G5G3Nc/bUQczXALbSQfhXZH6JfXEMUBlPk7N1dxx9ZJqugewIdC1wtoqgohHGo/rS846/A9Dy8Vk4xFeSdEFnA5OQStXVBcLgPRy22ZSjGPmY+JFlbpbGtcFLirI9r+RSBo1pz2L0Cpz3EOEZe28Y2qjgnEVIi52axaYqNFPlcHekEphkSnu1RHFogqxacCNZZ2+7gLvWZPngcgKgRE76+Rr7fptCS81/oEJoJ7sM5/iMwfoNPXPG59Tv6vOl9F7FIbjz2DvJcl81Lc4ZojTK4m1KkQpQOF9IMcJjEUTnTZnpuXAoLMy3E1hZ6kKIyh56p4XY0zvcI9hOKo/OIUYKTEiMUMrWM71+laGgqFyccu3ODu79pwMKbGnz5r9/MuBGyzHaxFlcpj8n3v3WXr34swn9zHXdEY2606McRtrPOM7st/O4+TmaEWxlymJbhw5sDgv2M/ntWMZFHVUHzvpgN1SB7NWD95Qk8cCsyNviDlOqb1yDIaRxOmc+36PkeIgjK2u7BEAtQCRGtJiLus33HYUarFaJ/+RxQ6lbCWszWNmpahqJnd58iX67hVTyKZoAfD0ln2zjPMrlvGf/xjTJs2PfKCnbtUb/Qx513pEdnmaxWkSsRrUdfwSsMsmIRCtSJJu53x6+de12WI6QoR0BFwZ1HrjNJU970oSGf/JUHKVqa5kaC38+ZHp9hcmWdlcUBd7WusD874vMfm2NhIcCEjXJ8DRh6gh/++R/iZ//WxwG48/XHmF/tcPc778Br+2zNjrFVEBNI5gM29oZYBK6wFDczaa4Pv0xUeYLKL9RoX1Vc/7hg9PLbAPBWQrrThFDJP5HIEmflKNpXLp3mdSfO0alNSIuM0Av+WI8/4IADDjjggAP+ePzJPK4H/DdLFEWc/Ym/wSd+4PvJL+VlVbICs7/PZLWcUV9sJwS1lB/8a88zmvdxvkelnjMTjJnxpmjArlRxzRDpBxT3nCC//Qhyvon1weUZ1ik6x/rMJgP8SUZuPBhpZJYziSW/9/JJassDdE/yZ757i2/98EVq7ZjKJEX+5gixkZH/XA92+phQITbGuP0E5jvIpQVc1eee9lXUdoKNM4K9FFmvU9uZsDIXk6UK3U8xTcX4RA01yJF+iMtzZL9LGixij6+S0WHtxWWuXF+heBq8zYTs2QJ5OmV0JqL3xg6tkwl2PkQohZsKpHbMvsnSNSHpFwO+8olbee7XT1I5PCpfY5HjcHj9lGAegonEunKdb5Y15p0znH3ubp594hi5yMiWqkSvc9QfrDHzvgZ+4FCpQBUCVYX2LUNMGwoisqEmezVly4WoLOeth17lSK3LoTv3+PTOnXxm7w6OdjaY1T2eubTIuldnPCNguXQAUBTotT5MC/wVi7/kmL1dUn1ccO+N86yKmMN3xWy0ZthN6nTTGgD67BYyd3hTEAh6t1dw6RY2nVJEEgIf6XnYdp1s1aeoKKjXEQvzeNUGZ59r4UUW7ltm9M6TZPPl+IA4tMw9s9vM1ickVY9k5qYoYyzUNN44xe8neOMcleQ4UTbL6JFFGIceWQavXyJQGb4u2H5lmSCVpA1L1nY45Rjf18Ft9Rg0a6SHfILrCeLEKvuztyKUR/NOjZoU3PPgJd7xI89TlRN2PhJiOk2y4x2E9Kk9vcX6Vw4x7B1muHgY8GCisSOPWj0B7bDvTJF3JWSzFbJ2yKRR5/LeAkWzAkKiknLckMiiw4JDSY9v/96HecePXeTWt7yI6w1w3R4uCOnd1aL37sNkKsVNJojr20QXxkSv9vE29qlfHJZOok6IUQJZUxAESCkpAklRUVgHVoLF4QKNadfQfkTjvT5mpYNQHs7zS7dJINh+T5XND9TImg4Rp3hoGud71M4NcZsSFd8UdAFpU4y22KBsfRFCveaqIU2pmCFPf2qJ0bLG7XUR1iEQFJGi8sgVal+7Sjp1VJ8Y4D0zQEwLCpUjzl9HrfewoaS+Kql0HPUrQ9rP9pn/chedgU4dNp5ifTAVgR6kFHVLXrfIgSDZrmCnESJJ8LoT/K5F5gbdNoiRwox9rBJUXh3jp3uYUGGVKCvKDSSzAoTGNCrYQ3OQ5sj9IWI0YXimwdY7WsSLIYwnuO4A/fJlxPUtsiNtikaAvzag9pULjJ7uIje38DYG+Fd6BP0ph/5izPxfNnjzN4XGXJJWPIoj4JRCnvM4Yvu8/b2v0mlNCdc9dD+F3OD1Uw7du07USNF+mXWSN0KSpTrWFK8d410usKNBGXybJDhPI4IQT1dJjrWYRjG6nxDsTFFxjr82QF/ZRV/aRHbXyWcHFLUyBN7mOZsXW6ydn+fi2aMUi62y8QhAgliMqN5WLg/e8617uLkq20dXeX7/BK//8Fnm3rJL/AMdbFXipIAku5nBVeLtp4zecpi8rnE2wz+3RfxPh4SbKdXPrqN29tGv3ECvdzGBwtbLHJ5R6tPddhh9MxQ5Tb/+khDTBIdDzbUxN3Q5StUu25scQGHQNwUagOBql2BjAoWg+tBFWp/cLHPPHFTHf0BUiBPcfpdif1jmSI0nyEmGyCx6d8yk0+KVpw8zeVWRvujga90/0oikuhOcKzCLlqizx/WvST76D48z3E6oX5uSnpJkLZ/pScnkvctc3lvgyrnv5F/8ZIcrj1h+5q+d5OrVeb6+YZs75g7Pvvbz4qE2fujzAz/9Pahvm+HT6cNk92XYOIZQl+egUYLILcONMiz5uW7Okp7Qv1puRI4dc345Ivfp/+EHaePhZfBbj7/4x1tkANOb+6P2DeM0YG+6zKTY+mM//oADDjjggAMO+ONxINJ8g/D0187z27/6MMIqZJaUzpQCpLK0ivLunLOKZrNAHx7xwBsu0D2jiJbHHG/sc6Taxc4r3Afq5D85C56GehW3Osvy6Rw/sHhTAwVERcJ9330e63u4xBLtxAQ7MdMrcOWpFv0XFN4LO/zeL8/wXXdc5vSJbSpJjv9ygbiY01tpU4i0zEfYi7GeBJ2TJyPcdMzzVwLs9jYyKRCFRToogoCdtAa1AFP3UIOc4OoQFVTL0Md2g3RhBbPUxCiJiy1h2CeLwNQaqHe3af+QRxgOUNc3EJu7FOe3ceQUZw6RH19mY3aez37iFtJpFS9RLH8pofNswl5axbu2i5xk+Fdiqk+OGF9VpMuWoudTbFTIc4W75lNcVQyemMF2qmQzip6pMz25QOsWBV2LNwV/6JC7lt1PNth/uYaNFe2dEbeO1tj8fMDsJ/YIrsFbfzREv2uW3bTB+nSG4VbIvc01Tg2usFH1SRYs8QK48YTq8ztUn7mB99QY52lW/pYkq8/z6q/VefanO1z5TAt3lyC7/3gprN10LukVRV5xGAVZVaAmBn6gjvuJOaSSSOVh45Rhp8Luu49SfOAUZqEOvoe08JFfvxVfFfi6IFsM6L13pRxTMBmD85r9n23TenFAZSsvR52EQe2NsIHGBJq8FTJdDvGVwQSWvKkIujmzj3aZ+8Im7mEPoWD2zgH+wFJ/0SKMIVgXTE55WAPBdoJ2Y4LzXcyFEYNLkv0YTAQo8JYtwgNfFiQvVAivjFBhneBqD4tj9rEhUVcQpZA3HdmMYYpmsB2CB/aER6MyROQWNckRwwInHEXVY7ocoTNBMLIENmOuPWTnYcNX/sYMO+sV1IqAvBRN5fI89ctjZO6Ib+nATAu5skD18pjG713DLc7hdCksWE+QLUbY2QI3HiNGU/xBhh87TM1DGTCtCFMPsKFE2BTvaBm+WlQcyfGyUUcvBIjElk64UOMEuDTDmILxmTaT43OUISUl1X+doswccmEOEMhxRhY58opEDVKu/XrCTmsWUYvITy+AEDhrCS/tkh2dQYuA2nzO/OtTZu63vOlDYyqNMTiDFRn+Ty7wS196mV/+xIu0z15HdifoK/soL+et73mCB06/QGVU5u4o6WG1wxsIjCfRlRxRM6hUYsIQnRd4aQ9dFGROlllFI4nVCr2ZIa/vowYx+uIYlUDocqRJUaO0DOtdWUQoBUFEshSQNz2yjoebTJFxDLjy+fczdCzKWubMsm/rDF9/hOnbT+Jd6eI9fYNkP6Doa/JEY4oCMTHUnp8S/B4kE8f0kMSkglE3JFKOJz61ipeleDd6yI093NWMxlsHoApcnmM8cBWJm8blsbuUzODY10/ZpTgmPI2rVfA3xwRX+qj+BIEjmQvJjsxiZ2q4ToTzAJVTuzDC35xSefIKxbVtLroF9lyd4ugCTglcy0PNW2bfv4t54QYAv/QvbsXMzXHoyhZ3JWvccfsVzhy5TuWLCn8/RDiH8D1kVKdolzlW+Yk5hNZIKxBG4JSg8HySnSr5chscqK0ecm0bxlM6FywMBDd+p8X+B46x9y3LFCbGJb8vugCwvkWQbtJaNqheihuWLU1SiFLUEeLrnxReLqi8uovphNjZGqEXsfqxLVY/uY+/2YdmA2pli5+dTHGDUekqAbwXr9P4zFkqT6+VGTvCJ19sla6bRuVm9ffvSzVmMGBwVNH8+DZXnj8EsYc5NwVP4zyPpbffoPm3N3nX9z2J1hnBLnz0X+3C17OOCwNKIu5dQDQboBQf//nP4GmF9hQnb1957bl6xQvMVjaYKca4bhcnBF/94svU6iH1RsT7v+s+AI7U7uefXnojX1/meaHH62aXcc4R+B6TCoxDx2CS8Od+5tf5H3/5d/6z6wyhymNFvR7TCGLeN/sKxq7/Zx93wAEHHHDAAQf8yTgYd/oG4eP/7mEGvSmzqw1cUd4tRQuscczesQnAvmowEY5QKQ4vb2NeGBPudymLPyxzPcPGxgxuJ2N6m4cXSw7LAcPvrKN/IydPLfX9Ie/5C8/ywhcPIfpTXCNCZxKcRDjLoWNb9OpthlfqnJ2t8L/87SrPfk5x6K4m+2JMMtJM39kmn5e0n0nJg4LiaEj1Roa6uIsTgo2/ukj1uQJTJChbYESBmBbsbkuC25fxnlhneN8cwllm/IK0vUSyEOKUxbeGyot7mP0u2YWQ8B1zCOMoKh6TnqK751F9sYecGKYWtM6wqy0KBFu3nmCajNGVfYTyboa6Qu2OAd/2PWf53z53jLyuMFVN1pV4ixOK/TpCOIzQjG9NWEgdEkP1hmA6SSmqPqM44NX9OZqvTokXHdpPKOYc3o7AScX0UMDJmuOf/uULvPNv3YHwNHtuhevnO2xrj86bunjS4iYR3gyM9ivc965rXOzN8UDrKg+1bsPEY4Qx2BMa6TuuJHPM3BFzcr9H/nLKkfmMn73+eoK+I5/+vjbbehDWJjnpvEJloLbH5E+Mif9ck8a1XVxeQODTenmK18uJPAcXN3HjGLe0wOvesUPgWd579zme2XgDaigRziFNzv6oxmizgkoK1DRH9xPSKIOdPfLDszhVvo60kuLP9bD+It6OJa9JxGCCt98jfELSvWOGnbV58qrAQ5BVLdmdDtmD3oMNOls5xSglGMTobobrBuQ5jLd9sJB1BD1Xoy9a2GoINYU2ApbnqfdixF4BWlPXOUNpmViNvw9WKZizRI+liE8UyPEGotlAxwIbeWRzpUip+mNMK+LM0bOcedc+L3wyIp1Itq5VMS/nkGY4LUtxMS4Q0wzbqJDOZ0gURVMh0hTVSxgfrZXjIlbijq0wrjia9RH4GlsL8UYGb3NE4ClM3UcNUkQxpBbskz0c4VYzBvcoxJyDsaOtY2bWUy632sjDVeyNjLwRUCw3kVLjjxwiL/DXyvENYX287ph0vkIaSeTaPoXfwFUUZraKOHOY2qsDvCpY38fUQ2RvjMws6YkOcibnZL4JfY83f3+fd89f5fBnt/kPr5zCRIq7miOaUVa2WJGSbu1g6z7N7h7nft4Qd31u/9BLPKfuQ93Yo7J+mGjfkFUkY1/iIok0CcIZrFLkPUP3kxO8H85xVSg88JymWD2E3JfknUoZXGsKuDQmr2fokcR26tiZGnJzB0YDwmttRBrhnd0GBcJTiGqNcH1CttIgnY9QfhPRCEkWQkxdY0YpxduOgrXc+Ld76LAGIZgsKxuIxhr/uQJRswyPC3YnTT7/6D3Uf/xRRhv7pFsZRW0WVRi6n2gglEb4HpgUPU7Jqx5CCF6T0KKQwtRA7JU/5xlkKQqJL6oQaKTTJL4knQ3xpg70Iv5eTFF00c+M8eq71F43xoxTKvM54YrCTkdMn/CR8/OYZAMUTLbBXisFki11DFUxvPHes7zhQ1exWC6lc0hhUJgyeDfPsdMRapyCkEgnUKOCKBWI3CFmO2RHKlSf30ZvDRHTAtFogjXMNVL+7BsuEgvFTw/egTCCYCstc2gEpVXxDzBe1/CL51DHl0pnHn/gd77+vxJlILznYZsKfTYBL0ReXodqGcSsjh3GWkchLO7UIYgTjCzQxkMPRxAb8lARdKfcuXAO9x2K3c+swvWdUswJQ4jL2jq1vsfsr+0i/IC+nkNEEpIJrjCAx9rnb0G9eZ8bao7qb3bR400O//1v5ZlPP1PmVQUaL/S4751v4rHuM6ia5aXzjyIaZ4jqIUdvWQRgZ22PBTtLWl9ETWNeaPXBwYu/8RhBo859770V72Y52KnWIi64FVHdwk0KOodn+a4////mttNL/D9+/ANYr3Tk/cbXnmVvbcLLF7f4kQ8/gF/x+Cu//UneeGSVH33LW/7QZ9/yysD5UMJqdciqP6Ym8j/hauWAAw444IADDvjPcSDSfIPw7d//Vtav7hEcqWAWvt5XC8bTDJ724Q7oixrjUGLjkJPEVO6ZMupFzBPjpODuhTWun2uB9dBS4iLB3loD1kNuWbrMtBJyeVLnY//+TQRfW0P2bpC+4RRBP0HMzSIpSE5m5IuKcdji/MWc/pMeLkpYObbIzqtXqW+OUa8L0HGttKevT2AYI1UTNz+H29ml/ek9sr5EKUV+uE0yXyfstMgrDjE7IW/6xKdmCJqGd33gEdJhhU987C3444RMhsjlOjZIMZ0aelqGOEaTEdOHUvQJj8ktbbzEx60ltFuK46evsr7UYi1oUG3G9J+U2KwgPtwA6/imM09w/dIMQW9MfGiGwQcXmfnKCKZeWVFeCJQzzA0SZg/D1sUQWSTUXtllctss1Thmmvo4T+OlUNyqcInB7OfsvyNAGMGaqbGz4XHYXGf3uuLFI23eesseagVarQnguFq0yK1kcrLN3wm/hLvD8PJDR/n8TEjeT/FaPqvvSIilYcc2eec9L3P6zT0e8GKqgePQq1Nuee8lhquG66a0vQfHfLyP7YA+jMwtwdYEtWvQjw1wWYawDrIMMZxSjzXOl7hJDEWBHQyZXZywuxPgrs1QuT4huDJASMncoZxjZ/borjq2f2cGrkLclCS3L5IdbVO5NMQttDAS8H0mV0Pi10umDYkaOcS9HcReQHGkhT4fMtjx8AtL2lJ4F0B1BOmMY+7j64w+fIh4pUkuJLO/t4fnJwQtg7xu8Mcpt19JuPzbS/SebyJzi5cpZFbgnAAtsViEhFEektpS4CwCg8okjA3FBR+MQAkQcc6wHaFSQ+PsALe5j9rokh9q0Xn7kBWvx40fr9ExE15/1waf/A+LEIUMHpgnua1JuJWSzSkqmxbbH8F8h3wmgM4h/L2cYMsQjCxpW2NqPn4/RgTezdBuC5lB7JUNPWJ+hmTGw4WzhDdiYudjaiGt5wT5msXfSbky3yI7XBDqHNnPIEnR13eYnp4h2k2RykM4cTOoFmyaYHd7KGHwtI/txeWFsAOVWagEVHcHJFbj0gJXq8BwiogCapcnNFSf5icEO/0qV96csrayz6XHfbz9BHfrMnUTgxNYYOn9FV59dgHnKZI0h4HEpoK8J/Azh1SKaMORzktMBVxS4OqSZC5E5xKGY0RmIYdRBVqzUxACf+owXg0TZqhhglOSYLdAGcUP/MA5dp/N+N8fugvXKHAVDyl86ps51W7Bu/7mNXa+5nj1KychLijynOlhwfieBuGewu9VqFyfILdg3FEIrRAoVFRH5AV4HiYAmVgmqyH182OCgaM9jGl4I9yTQx77whJD7SgqAjtTJT/exMx4NPYGcM1ik5TCK5BjDTPtcuQnyRD1kOLRfZyjzBAJfFwQ4cYjpm85CkD97BBRGByWrCbxM1GOD/YcZA5hEuzGEuYeyfjyGsErlvHDBjWMy2anoSBXmulOhaopXTzeJIPE8ulX7+TyrQ1u37vA0cUe15br+FdSRJYjrC0zhKzDzFTxMo3qFmQzHiJ3yP0BtdEUk5U11HauhafKzJndieCXNu7gWlCl21HUP7JOcK2PExoCiazXsLv7f+Bs52AK1Zc2+cPyzU2EwzmBmMSlcHUio1FNSD4/LbNnBiMIgtLxlOVIBEYJzGydZCUi3CuQgwlyksJ0DE3wziiEkHhfsOT1Bm4whjh5zbWDc4h6rRyd2t7Fphmu04CbrUlOQ+VnR6idEVQC0AEf++9/FXtEoq6BnUxgpYau17CjMa/7s2t4MyFPfNbg9sZcPb9Ne6bCP/mhX8QFcNePvJFHX/gUYprg76dcqWtEMiT7+MPsvXyFv/ORH2cj2STTm/inKgRrluDkAs+3E65vXuMfVCKa1ZBBmrKzMblpChKs7wz4x48/wnM3tnj60uYfEWl6WelcEgoqaN4aWXzxn/wrHHDAAQcccMAB/xUciDTfILzuLac4ff9hfuKpX8XO1MshfQfx6hzjjdJTXcRlIOfj+22+794e9SCnq1scMhnCCkZTgxpN8EQNcgNBgcTwvpmE73vPl3AWfvhvvw+5PyZfqJBW6qQNR/DcLowMxV1tzgVL1NOMI49dZvysJXvjIpgF9JlDeG+vU6lITLNAVeNyUZ8Z1MhAWyB8jROCKT46i8FleJcN+cpR4u/Q6AuG5HM9hA1oPtPFb+Z039LixsY84XqKCQSuKaHlM73lGNZXCC8nXhYESURjf4jqBbR3BEXDojsBDW/CkWjEd73nLL+ydobJ2Ke3aRFal5+hha8+chf3PfAijQuOaU9RvWJJpy28qUUvDkn2qiw8McAvHMfu7DNvM565UCM/HtIcZJgtRyOY0puvklc91C6YwZj8hS3khw6BFEyHgn/y1w8R3pWjhELdiHj/LU8xXbF8dbrKnf4O65V5XoxnKFoW33kc9SfszYzQ20Omyz45IRuuzjQO6E7q3Gi2OcaIqOqwwAdm1lg8NOHh7mGe/fICnIFjy1tcUwvsA41rU7ywgQqGVM/2cFqVobXWYUdjmkset5zp8lK4SNbXWN9jOhqS5B7ZpM/SfxSlqNNsoJcMrcNj7lqckL39Bp/6y0u4nQZqWgUEehzTu69zM6TUobopWdXh7+fM/tYFvJFj+J5TZHMhVhTUX9wjX66TVqt4DUtaLZj75B5BUCO/UeAaAhnW8eYnzCw6lrVBk+O3Up54osHelRnysULmBa6f4na6EARkVUF62xJpx0dtxuQaEBY9Vlhl8MeKSVtTH+0iM6BVRw9j4o5PsJtghQAB2inO/atbOdRWrC0tsq1y7nKbdHcbuCTBNELIcpxnyas+pgm+AwqL100wFU1WU/ipwJtYRJ6j1vYYnQioUE4ked2Eou7j0gy0pBj2Se48jPMUw9ESXmEoQoEeOqLPXkb3U8T9x6g2Ybyv0E/vIicF+XKEN8lIZjXh7hDRqGHdTUdCXmBDj2SlQmWiMdU5hAPZi1GjCXQi+m85zajuaH55nTDxyWcqmMUm/u4IJXoMqDA5pHiu3+CFv3eY6S2zpO9YQK/t85Urh3n44iH0XsLWsx5uoYU7oZhsFURviIlGCdenx/CFRawsU6iCrBPiNASxZhJZbCCQwxzR0AjPkYcaQkdNZeAEyeUIGyiKpZTaywWeE+BphAfT1Kd9WwKXcuJvitCvLBB8pihrsENJtFhw4h1jzj3sY2cjshmJawXIVOBNQCQF/v4EWdOs5EP6lyvQaCCdoFodIXVO4MFGzZDOamQe0N4eE92TM/Z9mg97bP7mMvG9Aa7qEe5Y3F5GelIgByAiQdbLcXOzeKMc9nuQ5lQXcsLZLt0diQCsllD1cYmH2O4it/oUqzMUkSYYFXhfukS22sTPdTkCFEbY/T4MgUoKiy3Ct1WZv7sLfZ/R1Yh8sYKOw1LYHhpkvQWAzB2ysMS+4OEXjtGwPc4c2eThJ47BlX4p4oUBFDnCWPTugHxzh+KOZUhTWg9dg3EpoOXHZ6ASkXYi1PObkOf0bl/kyWvzeDMFwV6GshpRqwEOqTSiXi/zcm5svLaPli+sPD47+EMZMSrSFEYhKiF2ts6hMzGtB2GwmzD4gkKuruDyHIZjXJoi0gz16hpoQTSaQ0aVMucryXBNn/FiyCtrM9QeBSYSXYnA15D+AQeJFKBk6dKZxLhQ45Y6cLP2PbgxRc7MoFSEixPszm5ZF7558/FBQBznPP7lV9CTMd2tNo2agG7ZcHXjyi4LSycIqwFrd+X8Ni8Tf3ge+fE+alwwONXAJimvzARM1nZvbnTMaTtl/5Yq3/zDH+LXt9eRG30Mjmu7PdKiIEsLFrqOYQVkYvi5n/0sNxoJgQAb8UdChRs3nTT9ieZ3bxzlf1x5Gdzgv3TZcsABBxxwwAEH/H/hQKT5BiKQmhldA72OmmgQDv/aDsGJMiSWxCIKxyOXlmksDxm/IrCZxJ0SCBxfmz+Ce19GtlXgPTTFP7sBxnHj/RBrh3UC18hxO4ajP5jz+BNHEJnB5aXbYlLLSQjwnUENLdnSHCgPrWCwNSR/nWIiQjpuzHS9gWkWxFGVYqHGzJrDXd0tR2W+Ft9cdJf/1k6OuOeda1w9NcvmZwV6lBB0GxRJwBdevIcs8ak2Ia848oZESkFtHeIFh5cJOJrzg++4RP4dhv/wk4vITWAck2/0GLdS1uSE5TnH5uMR7UMF95zc4+mH5/FNTNbUGC144UvH2HPzCCOItiwiq1F/ekD7hxM4vEfgW258ZZaJnXDuM5YskKTf3ab2hEH3wWqFHSRo7WELQeFDRVtEDISgU4Oec4yrq8RnZkFYfumf38UD7x7wU9/6DJevLNAVOYuNISe8XZ6/Mk+nEXN+fY7emxaQuwMmpzvMTwdkRYA3hoZKGVufa4lk13rkyrIOrOWS2Sd34AysPVdD3TOHm+bkdYm/kSIokAEUYYTQFtOq4I4EJNd3acxPWBwXXE9r4Bynb+3yc7/1IJfXWiTLuwRZgI4t7TeOmc5nvDg5QeEkyfGI6tP7zPsTenYO1vfwLlfJT8xA4QijKTIsSJconR0O9KSgej1hVItBabyJI9jNGJ8SKCnJjy8wVUOiXUHlEUsSCoyqMS08Vn3DyuGEy5/c5aXn4I3fuc35bp2xmYF+HzeewjTGG2uE1ATTKVZKktNncCEkDYeOHTOfvorTjo3vm+XQv9zE9Yfo7ZRwMMHkOZM7OyTvvpW5lzPEKENeF7z19HU2C5+N/Rbj6w2EiGk9uouYbSGR6KlCJwKqFYQxROd65MmIybvPIAY58tImoRXIUUK+EiHK3hZcmoIoMMcXmBytUXniGv7Ilu4m45gc9cmaIJtDKp/sgYHo+Q3coVmCjkBmZU6N343JB1NkWMfJAv+5K6ib4RiiEiGbbfwEundV0Xs5rf4OcVpjulqlHhV4DmbOO5Ahdjxk9NbDmEMtogtdVuubrHz7Lo3llBs/4nHHO/Z5atAkiUK8ROKeTvh7X7wP+eoaVmYQ9DBqBj/3mfjLiNE2zAjU9T1c6EExARuUoy+FRRSgRyl6VFBElvzQApk/RmwF3PDbeL2CzjnH5JShugGeU6XwRMb+iZCPPXwGlyjE6hSzAGxk6MEYe3IB4wk+9akHaHzm+TIbKFB4GwPaL/SpnF5BVCOCpy5AUiDf0Ea0FImsI63EnwqcE6QvG1yRUNu/QXz3LB9+1yXOHNvhMzNLfP78bYimojYymEEVFyqMN0Vmhhm3gbkyId/RBB643R2cjXDWIQrDZEsx7YTYZoocWGjWcRUf3fdwUhKe3cHIKkJKhJKomTaBVeQVD+9yqQSIWg2hBaZZQSYZb/kzlzEVzeFvyvitJ05hpSWUAbJVQa/1cJNyBK5o+AiTkRsBexWee+lOTPIsM38pZvBj5rUbArJSfa2dSFVTGu/dY/xF/2ZluMEB6ZEWbqaB2hnihmNkbmg+coN8fw5/JUK/aiC2kKY4KcnGE7yigOkU6jWIQugPQEhcUfD7oS4lDshTCxUf127gqj69ixVkz5JshIiWw21s3lR2BBiDUArVaCKlw7y0hmjVcdWIYjVCrW9RpJbkukdzLUcUlixSKF/j8hyhfXAWtIaZOtzYKd+/gMIPAIEsDHJqyjwoKcv3gYON7fJv0h0i24awl3C4IrG3HeLiiwr3HChnMdaydHiGarPK3/qPP8aT3cv85Ed+Ff1qgfJDnHNEE0cW+uSeYNQsP5PVsEbv713k8u9J/tlH/xW1v3EbgiYCgTOW/jQBCaMsoeZVCMeOtbiPvw2NO2rcsjL7R1qfSrkfDkUDXt1uAwEI779m2XLAAQcccMABB/wnOBBpvoHQUvFXj36Arz36JKM7fYQwBJWArFEOqathgpdYpq7Ck8Mj9GSL+q5lkEQ4K3EaJis1gr0Ben+MHJWZBC9/dIG/f+8bcQj2am3qnQrFJza5NbiIuN+y+UUPOU2p9GLCICfyc4rlCrnpYENF4kmuXt4mR9L0EtrnI4yrMg5zqi+OSEcZrrGCa9SQ/REq8CGNoRIhZmdYDYfUdyzq6SrZbbPor1zEDoYERyyn3jIhnvpsnj2MrRQY3y/v/jkQhaNeH3NMxry+uYtR8It3NqgMDGInxaWOfs/npWcVL5xrko1GLH0oplttEYxA9WNUIXn9O1/h8f/1dsxpAcaBcNg8Q58fc29zD+MkF1pN/Mcynr5aR0wTvBTUBcHcsR5D3aG7HaF2p+QVgalITKdNcDhGOYkRYKoVTMsjfqVSChLr23Q3Yr64XafYOMOnvnIHldomp//0iL3NOX7tqUV2n7F87qtzRLUcsRdzZv08h39nSu0v1gjmNT0HYySPZh2EyBnvNlkz87y6tsLuLS0Aro2PU0jNwq88h2oX2Fzhn9RU/1SN3s83y4uvNCFJFJESPPtIg9naOpY6hdb0t+DqVotJHtB72zy4lOVf22AaSi5c6tDz6ohqQbrYpubHxHEFRIbzJNGVLrLeQE8N9WMx1cqUcb/B1p87RfVKgVdA/dKU+mYP40mEivAmObqq8FoWv2WRVwPS1oTp26voyxK77bGfBbxkND2hydPSwfLcC0cZWw8ZFNBsIqYp2XITs9RE7U3Q/RjhKfIZQTpnsBrktTIoGOvI2k0obkBRQF4gJhm2oqAjyRdrkG+jXyq49USP+eaYj/90m1fPV3FCQhSigwiROtJZjQ00xBbdn+CMJW5JipNLBF1LcLWH2OwjEMi5DnqUlY1EAtjuIpXAHJqnsp0QfX8N97SA3ELokx+S1K86XK1GcqZatl3VaojUobcmqMUlhLHYLCV8ZRsTC7yrfeiPoFMFQPoBsnAkRyKmh0PC2TbZBYtUGt1N2Tkd0d6YosYGrMK0azRe3IMrGcnJBvW/GTLUNRQ57317n/d+8zad5zp85GFD2vFpvzrCTCc4JchWG+idIfrFAGYaBO8eMe35qF+6iCcjxHybhUNjBjs53iMZvQeXEQaKSJPNB+XFscgpZi1aCdj2WPyli8hRgbqsKE7PISyY7R2sErQnVeKVFiI0zN49JL5WQ20XiDBAxjkik+TnNROOwXDE8PY6rfUMgY/bHmJXo7IFyzmY5PQflYgjKTJzoAKSLnjVKmQB0dltahe6vP17rrF4+4hrQ8vv2tuwTtE/E2CqCqcV45U61ne0vjIhvD5GuII735tx162X+chH7yXLIphMMBXFtLXIm+94hvOfbJL3h3BVIypVaDURkYee5OQKXCDReXn8U8kUO42RWYGYraNmmojUwhlHpzElUjmTVoAIAiqPnkNMEggi5HSKa5Rjs0UjILce2bIka4F4Ycq1Cw322wrZ8JCDHGcK9t98mHCjQrAR0/pej9nDPbbuaJF/dATNBtYXJKdaeNsTWs9dQTZbuDzD+YbirQpzAkKbI590yDwnOzaHMAXuWheUQjbrICQ2y0uxw9rXzn1/0E0jjINpCmmKsB7Da1WGZzWzyQTqBkZlhTk3Q5lRqqw0F470SItobYw5cxisw5kplct9Zk9eJNmvYGOJXmjDOC6fNMvA86Ddgt3+zYwch7Uaub6Lq5XnIzwP3Z3gPIUJNa4+A5FmfPcc99x2gROvG/HkL2Q8+J3v5d0fvps/89Z/gA0ChJCYwvH4l89y6vZDaE/z5oVbqPxChrMOFDgtcIml3s3Js5xa5ANgi1dQkwSIyhHDz18jPHk7Skj607T8wBzYiiB2Fs9acA5rHaNOwXY7obAWLSWjvPyskqIU7hqVBM9qqH4/IvjDI1EHHHDAAQcccMB/PQcizTcYYajR+wOk6JRZFtUaDMvlazFTIcst43xKUT1D8+oUOdasD2YQwnLL3CZPPj5H+LDBLoVwuQ+A7UpevrwK0pHMhLTWemyfq1NfzVGHUooHZtEP1/GM48Hj53CpZvJ+D3l1l7NyBRTMzM6zfn6E2BAM4oJKb4rY6iIyQ3gjhkNj5HgKSmHbVeTXc0+kZf3RWXq9Dt3dFqGzsDqPu75DElWRFUuBw/kGiQaZk1cd0brD6YiBq9MdB3zi3CpnLy0ylhV63yyZ/7d7aCWxns/wWIfqoX14OeOl7eNMjmpagYd1IFLHF3/7HsK+Q05zvJEhygRZq4q4JaCdrfHg3D4v+Mv8iljBTAzKWWRiaX9kxFanyugeja1agqFXuhtkjsohUS10YjGRwnhwtrOK20oReQaVCt5diuFWxBd/4xaoxfSHPp976TbCYcTffO8j9C8Mae7eoDcK+Zs/+RTPf8Lj9W8d89WazxfjE5zMttjaq3Ho3AZzd1p6ezW+8K9PsXevT3hbKdyNgwj2FPkbj7J67Dz9J1cohh7F5qgcqykMtOoEqeBN37lF9xXIjeJ1d1/hmYdv4bc+dQ9v/fBVPnv9KMJp9MYEN55wdu849rl5bBqS3i5BZ7hWi+03z+K0ZObLlPXJX3kJGRVYPc/JxwesuSqxB9MTFYKdnIqf4Y+mFLONcvrMh8PzXVrLUybnKkwuC/Z+oM64ExB4jnitit/KGDpJVU84/L0RiydGBM07+Oo/exlG4zKzQwoUivjQDNH1HiItyCtlbT0CRA54ivGDxxG5A+ex/4ZFOi/1QKSI+Q5ytkHluKFopBxd2qd/sUFcCfjF9ROsfWWEHpnyRrMKEIXFVRSiEPhDh54UKOHhtCE92UF3p6h4iqsFxHctULRDWq9M8a9NsM0WQkvMYhvnDGapwvi4pBhVUZkBqWCcsPr8Hlu1w+RVSe8HjzD/21Mqex7WF6SH2oQv9XFSEc9UGL9pBVEYmsZhOhF2rlIeQLIc5wTxoo/1BIWGrOohY4M3yamsO5JRQegsYjzFHl8AIK4IqnsZ4yLEFpKqEpx7wXHiribn1hdx2kGasHN/Fd3XNB7q4+1OEEJjru+RHw0ZXclQvzPAVHy8YEr4OsfMOxPqWcz+c21c4LBaYAMPUgdSg3Ic/bYRz2c5oZ/BosYZRXasQ3R8ClcriHoNmSSwP8SzEqEyhh9PiSYFTjeR2qJ6CU4K8oYHSuJaVZo3MkS7hSsK4tN1PBPiTh1BJSnSc4THLXGjSi4l4YURJjH4AhqLfXpdjZMeP/O338TsPWMq3z8E58jmPepXC6h4IAzjWUnWFmTLTQh9om3DS1/ucvGheeQbE7gyxQQK1ZQcGl0jG0ryRIP2QCoEDtFqIrTEFRYV+hgFLolRoUKNJcb3cEkGRQoyhjjCbimmGwGd1Qkvfe0YMi3QXogjgckE6jVcs7zY18MUYywyjahbwxF3np0vh+iH9mBkcFpRzNQoTrSIj7YJH9lh/3oNoyd0r1aoax9GY/LjTdKOJHq+hyg01CNE4GNXW4goxjYMx99yjWPv6PPYP5rHqgAxzEBIqNex4wkYS/DAPOmrPVzPvibMOCFwlOI8SuHmZsoRLCFwNYE4BOaaQo0F9v+zMSrLsHtd8DX+6WVMN0Fe2oRaiOnMo0YDKmckyWMTKDRuX5U3Ab7+3NYiBgPcJEa2mtBqoAGbZ9jM4NUz6qdHjD4pkFEVYRzpgmDnW5bJ53Oencxy1+PbqDjluScu810/9A7q9ZB+7l7L3Pk3//yL/Nn//j0AXBn8B7757z7Gw794gu71FlQCDFApHOmooNaoYYylFT7A6i3/kXNPW1CKVt5mO5DY2LB5aY/qlYyiIqjtKlAFOvAospwikBgDkfNQQrCbDPmeL/88Sgr+4wM/AsC1zQ73d04h69/1f87C5YADDjjggAMO+EMciDTfYIxGCRhwnsJJgZOColkuJ1U/QRUWXxSk+w2+e2mPLz0KvjdB64Lxv0uYfeICXtdQzDYQSpaBoX5AfL0BlJkM2dE6s93r1N5judGrYNczRF5grqT4n+qz/ZUATlf50z9wjs9eEjx1eZEZYdFfEohYk7VBL0j6KOpXKO/e5Rkqzcv8hNhhfYUUgtwXFCqgv63xhyn+yEBYJb/7ONO7FWf75RhWpZvROxng7wvEZIIaKmTmkzuIRyGPP9Ni52UPrzZherSB7DTQRRNnLXoAH/zByzz8Lbfyyitt1MYAOUoRgUYkU4IXO/i7U+YfNwxPBJhUIIUgKWYZX57lzKE1Tr7xCh99661MXpbI5EYpbvTG6DQm2tsDXxLfeYx8ThE2DMfvu8HeQw1aL2r6d1SJTMb2HbNkBIhth7/psOcFxYzFXtpncmaB2h0ed75zh+3nmywf2+XEQkJvy+eVfp2gqfmJn1sD4Ee//C5GexXGixGnnnyV9U/N0n6d5S1/fY2/8WN9nv3XPh89/VYAkppCr4GqhngrHjzlg5PEj1QRKsNu70KeI6KAJ/9Nm2/+oQ2+4y/t8ei5Fs/93klc6njLHdf45NbteBOonC3zIuJ9j+rj+zDcgm4VPajhen0K3UY5hQxChDUkKz6tBxPiz0WsNQOS5QK9KzAZ5C2P/dsqLL5gYLZZ5jyEsH1xlrw/JbnmE+xdQb+q0PUqYs9w6gPrrNx+g2cmx8hmNJ1im/Raxq56FenACYlQ5UWdyiFvFph7Q5pdhT+1IAReV2Gdxb82ILyWM3p9h2jDMf7BGnP/k8RubKGERo4zOoMxcwsT3nj4LL+zeB+XkllGgY8+pBj2Q4YnanS+uIebThnf2SbsGZzNsdKU2Rq9PrWvDpBJjmq3Gd29wPj4DE45qtfGqAtdiHNckuHmm9gTK8jplGiS02pmTFIPdDkilt+AujdhnAiqn95CZpAfmieaE3j9IcW4j5pYonFANypoJDHyrZbopQCeXAPejPA9TJFRSye4AhaSdQbJLIPlCvGcT7ygUediqusZstGiSFKmUUIe+PhK8/LeCkI4jm2vs/dVn5dePsP0zXPIvEDHmqTuY6s+KIEUGt1o4loe0yjCjwMqwkEth35G/pTH8B0hO66FqwiyqijzcRILhQNPgYDbj2+RxwHz4YiXv/U4+fkqQkByywjvt/dgv4e1FqkUKlAoLyJ5cYToBNjVKsbEFHVJ1vZQV/bRRqD8CGMhb/iMD1eYHKkQ9ByV6xaBxhiwA4mnJSaU5DUfqSvML+xw54eu8PJvLrCz2yFTERsvhxTPt/FzhfRinFBYXyIcqBSaVyy4CsViFTPsIq4nTA3IswLbqcFunyiZEN2nuPDLzdKt0aohAh+EwIzH5MfnQYAbjbEmJz49g4xz6pOijG7REoGhuN5HBAWyU6e/0aSqcnYudFC5wC3Own63PJFMp4igFDPUKMOLM3wcbzt6g7yzQOLXsfQQicIlGao7xN8vEL7CCUk8rhK/VI5EUq9BnqGLsm0tOdxiYX2buD8AkyNuXSKpGoRT3PXmnGmiqC6mJN0EwgDnKWQUQCZx/SH5RlE61P4gAszKHHpqwBQIASK32O6IQKfYk4p46hM8ESC1KrOzoHTjaI2cnQFj0PsxbnYOHOSkuO6QIosYfi7BTsApibSurOLObmbSKIFLM1wtwiYJytURvo/Ic8LuOkv/XYJqCaafaZDVNHk7wEwz2nfto7Vhb6PKU78S4C9a/uKPfxClJK975+383u88D1ojrEWOpvzo9/08Ypyx8G1fo3YU/GMZ7ooBIVBpwfJ8i0kY89/d3AbMc+xNfwH9+Y8RVEO+5+9/H//Tv/g8yin+0T/7LPXdBCJRjpAVcHh1yPVND6EUJ44u0QsSntpeZy/vsb5vAcGL/YsA3Hc05Udufdv/yauXAw444IADDjjg68j//K8c8H8lTCQwoaOoQ14Foy2VelkTKgqDzAxH1A5nTmyydOsz0LWs1ros1QZQOCI7RTYL8pZfWrhnWhStACcdTjisB7JnmQTzbD08T/qYQ1+dYIdDcJYZOUYPC+JrcE97gm8dAYanPvcc/qPXycKE3l0+00MJ3/LeNZRwCANOCuzKHCwtkJ+Yg+UliGr4a/vorTHBRIDv49IEt7aBOn+Dok4p6gjIWgb/9JR8XuDPg5db1KTAm5YX5OKoz7wcIEKDKBzUQvB0mT0xmvDYr85h4ohGZw/dT3Ebm5heH7e+g5kTFA1FPO+TzPskfo7d3se0DBdnKlgrkcpx9P4eaFlmHSgJvqaYbSCtBKEwDVnmVhhJ+kxG9m9vwMYe4bZBbwjEIyHViwHty5LqeoJIHTJz5MfmYalJ7WhBvZbQOhKz0e1wz31TfuTvbWCrPv/oX7yPr16b4zd6HbLY497br/JXbvs8737HGvXTOZU3KG6rTHjg9BZvf/cu2FK4y5yh+ruvUnv1OtW3GKTOAYf0S+s7UsBkCoUlHin2J5INCe1bStv7u7/pGvfevclbT99AZQV6f1JeMPUFx++d4qUZoq/KTBGtCfdAZQIhJaohWXxgChsFaQWGq9D4+GXCp24gc5A56F4BDvTeBH93gvfkJeRX99m60iGxkK20GJ2aw8aKrKJ53F/g0b0TTGzAsFbh5Y82OffJOhefzst8hSiAegW5sgyNGiZIyRaqmFZEdtshSEFYgd+X1J/ewr+yjTVD0kMFUSVHCImcnaFow/LpCZO8gdf1eeRLJ+hXZnhs4xDbRY3uX1lh8y8fYfTBJkQh2XKd0YmQeM4nnfMZr0a4NMWmOUp7SCTFTAU9lTSuGhqXCri6UwZY90cQp+j9MQiBnBQsbwx5//ufpVYfocYJZhqTFtVSMJgNKDoBaEnerDAxVex8g8mDh6HdgGqF2eemyJMFxXscfO8Ebo5Ejo9qtPSpXcwJ9hK83+vhEPiJYLpUxUQBxakm0vMRzjLppEQ3BtAQbHxbhe6gRndQZ/8jAQJQ05S8rrBaoWLHvdfP8+crj9KoG2ThwFjiYzXSZU3tPoX7O7PgK9yWJT+bcvFKi31VhcKhE4caSvLA/H71MoLxlZB3dc6ypLu87egNKhRg4MjxnXKUJfQRwkGRM1/fIb3RReQFcneIfvEqtsgRTqATh9dPEQ5cUVCEHqYRIhOHtxujEovT4CTYSGATV37VDRD5+JtjNr6Y87X/eZHtpyTuqGH6VjChoKXHRC7n8GyPO+9/kbd/99OoxQRpyrEkaQUiB4EkXAE1F9I8BNJpZFqQdCVJNyz3X62hP8Dt91FBgWw2cL6HjXxkVEGNCkRcYEQCcYozOW5BwQ9WqB76P9j70zhbsrrMF/+utWLaseecpzNPdU4Np+YqKCiKGQRREXHAlkacWq6KYtvtVa/Yf9QW26mhRfTaQgvIoCgzFAU1UUXN06lzTp35ZObJOXPPe8e41vq/2EV103bf25/b2qKd3zf5ycgduSNix45Y8VvP73lSdNlHSY1bz0AK0obEa8Xk3n+Rdq0N9lnBiTCAq9B9n6+e38/iMyVQivzofvIXXIG+fA8Iide3qK0U0dzCGoMVDGOwZ4efVVocnmP5iE/cc5GBj5kYQXiCzBQIckPBGhpUaRUmoFomr5VgbmqoFisUwPeQro/eNT20uhGQBw7CWJy1FjSaw/SmrTZ2bR3Z6OJ9fQt1KkKdTobtQVoPizPGADlmpApSYH2PNOti19bJQ8HgigmSQzXsIKK37mNzQA6//0xNDM2m1dAPCsCO1TC7pjGN1lD1Vypg5CgbfzmOLQj0ZBGZWdRAYyoFtk5XWL9Ug1MhZ6/ZxXKlxNp6m0E3olgpoJ71zLEChO9z/OsXOHlyjc8+cBl/feZajq/UQArysoe2MZue5tbvPMq1z9v33Hhg/+EZPGEplOAzp+9HZgZna0DzgKK7M4D+8PpqFOya7ZBmkjwTPDq/wunTW9w5f56qVyAspZRKKaNBFYBbZ4+ypzb+dzRq2WabbbbZZptt/mu2lTT/hDDWcN/GZ5h4U8SlUQNSUHmwy8RVQzNHkVtEZnjJC0+yp9+hHGyigwDpWIRjufW7Onyudgj1lCVInOEDIgIbBMMBtwJySKRAiABc6KG48dUNzj6wAzVqUa9bwbmg6B9z2bjg8n1HnuLs2WvZVyty5twmzoklRquT/PBrznDl6CLPzE6zuuCjujFyZhqpLda16PEQudXDSMHovog3vPE0jzwywSMLZdA57ZfsQJ8AfVBhe5IgVfQcTb2X4nQsWLBqWIiwrqDxkA9rY1QbDtUzXdzNHDKNtTl53IUbi5xfqfHC3ce4T00gOgNEZ0B6wz4K1zToTZdoyyI4lkTFOFFEHiky10UIgxZQ6Wb0D9QppAm1pRVufmOTz63OEgfjWJeh6WkO7qdWWT+X4aqc9vMmEQUP03NwtiK8KMG6DrpeJHcTzHiFwgqotQGxHho0xtrhq6cP8MIjS2yteJTvPsuOqwIeVBWkTjm0a4u5sM1OP2bysGbfv73IpbzIVlqjVGhSmswZ//MFePMhqBnkIMG2IhqnXTy1TqJLjL28w6X7no1OcRV5qBCJy1qrxOkkJhAZP/Ou+zh2foKBELz+RV/n3vkXIsmRVUn1oU16L6lh31Kgk4wQrmSES5vDJKfEYLDIKcnKMyX0wCGtaHBBpga3l2CWIoQjKDy1hpwYw2qNzoYOpWqQI5OM2pkEm0oKy5riChjfsPZSh24/GEbwepYD/7yP241Z1hHt28tI5ZBUFH4rww/ajD6Z07j6IN2XVSida+B2LcIIjBL0r5wg6PURk4bawQ7iPRsYXUOFAbP7LEudcWQ3Y2e4yWPeAdyGYrbU4PHNPQhp0CXQ2mPldSOM3b5J7bEOVim0yQg6GaJSQrou0a4qwnNJqx5uH2yWQxxj9k1BI4V+BK6LrZVBgCl6zLmrTAU9Jg52OJvsQFUgvbhJNj2G2xOwawdmEtxBguxm5IkAJdC9LsZ3Ef4U7p0GMb5Fb1lQVMMHzcaNBUY3+iQ7XfpjRUauGUFuQmEtoX3QBwWV05K86jEYt8Q1SekJTfhkg+6NRYobOVhN0hjHr26hQ4Fq9PEfWURkhjf88jrX7l8l7c/xxflrkGcv0d0xrA4UW31qqsvKk31sJhAlD1GUBEFKOYfi7U3Wb3YYXCkpfdnFRhnCWBqmShoHqE3FrvkNxO6ddJXHxUGdkaLBBAXMxha206Ex8CEexghbZ6iKcGSB3u4SCJBLBexSg8BPef2PJtw3f5BTzTH8zRQ9oejtLOH2M8ZvadA7a+ioOgJBMMixViOspb9VhDwj3ueTX2GRTejcUeXG55/ise4Bmruq7DjZwY8T2LTD9pzAxevmqNQwuGonbEh6rQRW10AbRAlan4yRk+PYEQ9W1oZeNYMy+BZ3a4AJFU43A+niPHiJ5PrdmBEPoTLil7vsfWWXTjjJxsYMo5ttnLmEaMOFBGRucAYJ1AS0hkoROzUGgHYFsSvJQhc9gDzzkNM5FFxEy2K9odIlXBrQmywh6z55eTj/I6WgdqtPdxAiR0vIhGFhXBaQ5SJq1MeEOXO6SyHucbJboiEK6GIJ2xsWicXyOjbXiLFR1PgIB19xjuPJFfTHr6D44PywiCQyxGgdu7wGvgfFIiaPsfUSuXaQdwj8lQ4kjWGR6xsJUThgNWZ9E6Qg2xEyODRFYXWYauWdaxAdGsHdLxG3x2DMMFnLCgQSIQymVECGRZygSBYOh1RCCHAVNrOk6y7LaYnUVQRxjkpyMscy+VlBOhXgtXL8O5bRgc+H1j7C56tFKod2UayG9Ft9YNiylgcK3Y3xHpN0Vj1Kj6zhViconF+ie/003r4KD3z8a1y6+2l+6S/ejnIUv/LD7yNdbzJodNhNikgn0UUPjCCfdmmNFCmuWbKqy33nxzFaYgzYXg6BpN2OKTkF8kGZoifYWZjkPE9z0+iLkP+1mmmbbbbZZptttvk74x/VXfY3f/M3EULw9re//bll1lre+c53MjMzQ6FQ4LbbbuP48ePftF6SJPzUT/0UY2NjFItFXve613Hp0qX/xVv/909iYtbyBQr7BEUnpiBSVK9F+vWhkkbGEb7s8sqDW0zpArf/xylsknO+M8axzRnGR1PMx1vo4w3IDLJaQVbLONqgYovKLCqVeHVBGlZIZYV8ZJwNVaY/4dGcqHH6A7O01nxMW7Mx7zNe67P7hg7l7+ix+NOXIxKLf9c6xy+O8Mmv7GZtceh7YI1BB4rcl1irkVtdqJaR4yNcfqjH1ftXueXIPHKzhQkd0ukiuuQjP+Pj3OXiLQzItcQ5K/EfshglSOsOWgKOpTVTwZY1TjVF5QZaXUy7g4linKlpkscC9HmHZaeKs2mwCvLJKtENVTYbI5RmI1SUIaxBZBZ9uITIBOWwy5YWDKzgQjyGLbgkByeo1WNufsklriqt4w3A6w2LRkYx9DhxLJNzKYXmAGlB9lO6+yUqHyDiGNtpE8y3cTb7ONkwira+r0PBz5iZbvHwyg5+9QO38lu/fj3jkzG/9bv38FMz5zgSrPCO2WO8ws4zJjUNq6h5hn/7sTdy9+Isp1OXp58qEj7VHZ4TFUl88xiM+ISzGhsPEIMGanf+nCJI10uIbh+M4ezCXq4MuhxQEaEWPL6xi491ZvhkZ454qwa1IplQ2O6A2lSDajTASSx5xSWvFyg+vkqwOEC4Dh2vSG/nGNl0BYplxJwLBZC+Q6ENYVMjR55tHVAKHJf08Czp/nGMB1KDVA7BqiZY71NcGDArWty07wwHCyuMmya6GLLru2LipMQ3DB5UajDrm2TnW/itDsGGxonBjQ0UMkwB8qIlu6ZGteqz72/6VN+0QOXezrPfNEFjq4AUhma9wAP5DoobKY5JUDLHdTWu1JTcDFJB9/o6dm6GsGEpNDNKDyzinVwhLUoGB6pEO0tEU8NCRVIFcf4S3tOLyMUGIs0hSYcJRd8oqbsOJ5IppjxN2Vmlli0y2j7N7AtiyHK0ikjcHmq1jbMZ43YznNTgn1geeuNUn017ywT8dgu7ZBk/ODw4I91o+HCqNe5mQpAZvETidnKcjRQVgZMp0rEi0WyZ3guqpFM+MtW4mxHimYTqf5iH9VV0EpPVApy1HrKfEu2rML0npljVqP3DeGKza5JCe0D19Ab9P2yz+kc5IzsTcCT12QHmKZfwREbebSO0pbBiiCs+tuDiNSO8rQFSZghh6bYED/xFwGBNI/sprV6BPIuwaTY0i7WQ2TGo1SAsDD1LlERagb8eEzx2CWe1BYBf0Nxw7TJHr18lDwR5zcVKQTLhk4yF9J4p4m/2cBe38C61eN5tT3LTOy/hjj2b9uO6OHd08e43OKcFcRxy6mNz5L2czoUCS4+MkJ1RuN0U1UvIpYZLm4h2F7WVDZOfOg7YoVqndhVgLbbZhmYLtAZHDR+orYVcIxKDiRNynTO4YedQvVcPiQ7N0Rqd5cm/3sfF+QmyokAfNZyJp3gqm2VdVtES7NomNlKIyVHEaH0YMw0IKXBjSbgFTixBeSjhEqwOhsd/q4sdxLQur5DOuYhBgHEEeTi8hGws1cniFByP0rLBaWdonaLR1A62WDvkY45mdKY9Hu9McqFVxwwctCvBUc+d/zaOMOtbnL5nlM1CmWzGAc9FlIvgesPPWQDx0DRYT42gZ8YwU3XynWPQHUAcQ736zTfORgc5UkdWKrirXZz1AZ4XUDrbxVvp4NqEkfE2w95fgy775I6CLMPmmqwUIBBgDKLVg3oNKwQyzsnGQ6LxEhe2JvD3JjiuRhgQWY57doVweYDbipFS4Q4Szt53iofvOEYSpUg19M+xWgMaJ8pQvovqWqoPN3FTh9J4haxURpxpka9HXPRD5uNhutPffO4x+mFhWJQqFXnJZReY3KnwAa8LwljykiL3LDIzJEVvqFDLzfAcBnaXaggjqfoevgjYSFaH//vSh8lNzjbbbLPNNtts8/fDPxolzcMPP8wf//Efc9VVV33T8ne/+9387u/+Lh/4wAc4ePAg73rXu3j5y1/OqVOnKJfLALz97W/nM5/5DB/96EcZHR3lHe94B6997Wt59NFHUUr9Q+zO3zkf/+1Pce7Ji7zw517GVz5whsov9LCOwB2VmDPDffTLAs+FmoSta76f4l+dRPsDLj5Tgkxwcd8IohuBlMRjAc7WcNZQ2KHhqdACkRjiUUG41keXPcLHmnSWNnGSLr3v2c/a5zzid0wzs7BO6zLDaSOojDSZ//2YqhpGCRtt+OzTU1QedynaTQBkL0b2c3JSxPEN5GYEroOoVnj0a3Wqo3Mc//oEtubgLG5Q+/I8uhqSXrMTq+RwhvRSCfrDmFTZGRA82SffP01a09iSS/ATMb4T0b1YRj1dxbsnQmcWv92lUazgHsjR5wt4a/3hQDrP6U9LXAvJaZ/qEqS1jJ1jLmJcs5BLzIbHYFrQyQU3jpxm/vFR3KU2vUwy74C9rEvd3WT1+ARZQYAHnRfu4l9dcYKHPhEyvb7JpbER+nt99GhCelbhbebYWkhUkdiRCtmqGKaUdHwmR6Cc5FgheHR1hrFmD26qUK09AgKu83MeOyP54h9fz9yRNV77z09hU4UopRwUPUJhuHd9ZJiAArjFjMbrZrn8u/q4UznxhoPVkLUVplpE75wA18F56jykKdHjHe5//yhbm3W+/vh+ggnFY70ddPo+cUGgRj0KW4L46DRRv0l0zCeoRYhBTmAKUA7IrQNogvmMdNCg0DQkewNyXSe5rorz9ZSwsYwz65KNu+ieg0gMOo1RskZxMyVtJ9ho+DDvRMMChqs033fd/ewfb7OejFKrxFy4OMqTF3dyeHQnT8hFjLbQ7uOVBHmsUYkgPNvBVIoIGggzyY1XXODHrnyC9y1czuKjVXTZZXBlFf+SRlmL1Rpvc0CUevh1TeI4eHmMqmhm93dw1zShH6O7lrAOSezTORqQ1SylUwalqqhORvuGKtYRyBRkJjBFOfQPqfjYToIoOAzlRRLhOYgxH/IcsbBJvFtwMvWoPC8jvc/Bxh4MLG6rQ/SDPqX/ew2xkGL9Mu2r6/TnHOoXNUJIXC8kTRKyqktRjOPdkfGdvzgsWl/bvsixlRDRLzK+fJFEasSoRaVQqFdQE2PISOP0UmSswMLGS3fiqIh4T8jEDU3WrtgNPUXxZIfaOYtYaSNTjcwNZT/BUTCU5YFNE9SNAnExxT4O7hWKV7xhla+8+whL3jhXvXCDG4+c4/T5Eqc/meM1DHznAbaOwOyiQVgLdYkVGl2HDT2Cf98GxWZE6TsFuXGxa72hGTQME3GKBRAG0egMk3msxlvvYze62DRFjI7S14J/8/OX07t2NyIbqvG+0f7nxJYkC8mPu/jhAFX32XHlJkLlnNw1Ru6OgpQ4icA8roh+QmOmevAhn9pXl8gPjCMbAqKEeK5MVnXRBYkzFiJTQb5hsRPZ0A+qEEC3R+u4RI8JnJ4ZphJZC0FAWIsR1Tat9Tp6tIYZqyNXG+ShxOYWIR1yJ6L0xDK9y2u4bRfVTuitp7SOeFRygwgsuiCQBQ/te4i5AC72UJeGhVwdOGS+JfcFMhteN3wR44Wabq+E6AyI94yjHQtpyqAq8JoGvKGnWZxJSv0E60pQFuNahBUoK+mcKePO5rSaAZ5J0ShsbOm8waHyRYs61xoWDlwHHA/KRXIcglWDUAyLjjaFgjc0GHbdYQJbwUNtdjAW0j2TYAV2ZgwxSMAPgK3n7p1SyGHakxC4nRz3VBN90xT4EnkuJzcSfTJFuGCUj0xzbJQOp7gsBK0BJkoQbX/YojQ1hvHVMB1cCsgF8oTPjqOXKF7vcPw/7iMb9xD9MsHj5+HZAs83EDPT3PH0SZz9ZdxmMDQpHiTk1RCFoPvtLvnhcYIPNugpD7OqiadCllc7CG3YamUoR7HSaNO/fhp/zwhOrJnZd5Kbvg3OfqrMSjmjuGyYniwxmiR895tu5WF1kXsfeRIhFVYM/YMeWVjk2kNjqGIbR0hiM5zXmw13osQ/jbHTNttss80223wr8o9CSdPr9XjTm97En/zJn1Cv159bbq3l93//9/mlX/olXv/613PFFVfwwQ9+kMFgwEc+8hEA2u02f/qnf8rv/M7v8LKXvYxrrrmGD33oQxw7dow77rjjH2qX/s45/vVTrC9s4m86ZMshe+ot5sodpvZLjDs0/c0TRZ4oIuOwmo/TWT0DWlA8rqg8LYiaQx8JpCCZcukeqtI9WEYISWVeE2xYios5nUQRXOoQznfxlvtUZwdwtWT01nWcEUl5NOOW165QmYroWcnEVhOx3ic800Mal8CrM31fF2cjfm77he9DHKOiHHdrGPdp8hzb6TJ36BLhNZtMXHMJLcBKiYxSxLORrMIKhOey6+4uNb9JVBKkcQfVTVA5WGWRWEwi6Q4CnLYlf6HFBgHe4ham3SGe7lOYTajuivC0i3BdpO/jdS3FZEBhwwwl7hrKgWFtfRSZCvZ1B7S1JBWQGge/kSIdj35S5rOrR9AHLON7WwQqw40s7gBMRXHzKxvMXQ1pbQJ/DSqqizMweJtDY9JsOqR3tI63u4It+xhXYhbLvF4s8aaRi4RBQjqiyCoem3mN06nDSib4UneGWpSwslAmavrscXLKxmFu5xa6NjSs9YsJsjL86s9UW4QbEV4twStJvJeM4Fw7xWVzjWFCzNIWotkdqlmqNWRu+ci7p7jr4z66lzJXXOT85wssbIxilSLdWQFHYmbqTB5t4l2RMRiVuKdWAEt/T4n+ngJWWmSsqax6iIFDcTrCeILBi0cRsyH+pRXE19eJYkGn0icyLVInQcwvk7Y3UAsr9MZzzNoGdmBoHynTOFTjBdU2e1XGgWCdQ/UOTs1gM8XTX700VFUZjVhrknci8v17kbVRjDAYYeBshEqgv1bkV5av5oHBFB3ZY/D0CuHpPp4fYtMMqw1p0yDaA5xGRL/mog9apvdscOaJMoO+TzZwODC5AUpRrCV0Lxv6GqVTBeJrdxDdsBNvI0P1DCLJ0HkMUqAdQXT1DN0X7oIdM5jxKuyaofY8B/uajFK2RpBb9k116S15PP6x3URqjI6YoPHZBOfxS/hPxZgdLsaRmE6b9kFBtNcjOVBHaA29HtGURx5Kcs9gBzF//rGjAJz8fAnd6ZG4huRQjT0vzOCyENHu4YWVoSfOIEEOEsrnuoz/xTLCQjRdQfR9lDVM7O4OZ+LrNfKJUeKjc2SzVZzlLlhLrqEfD1Ui5BrjGfJrQ6KfnMQdsZhYoCoK+fQWrT8fPOs9UkR7kq3vuQyjXQa71XPxOh3fZz0q0dsIUD2J0gqZWgpTltKL0qEaQxuQcljQXd9EdLqIb8TzJPEwGnnn+FBhExZIIuhcsiSLAndgh3fKZ1cQecbYeJPw1irZ4Tn6oyF3vWcHD3zqcraKuyl8LwSHMqSQ5Ds1ek5gHUF4dczr3rPI83/mHJgt9OYmeR6RFSSObsOMJSsohB+QzOR0X7KfbP8kuB7aEcQ/N0V+U21YuLHDgkiufSInQCytI+ZXScaHiixnvQvLKzjPLBE+uUDxqRaTHzjP+EePU7/rPO5ewytmN/iRmbPU0xxTKZJdvhPxHeMQb8CgjXi2XXQwEzDY5bNr5yrfe+09uJWUduizGVfQLqT76pDkVL50itrfnMA7t074xBoitTgtw/iVHQ5d3kPOr9IfB7dvUYnFtrt45ZxyOULdP8D9kyZ0zDB5rQbxoZC84oCjEOUSouSjTYIe9JCRRmyl2H4fmm3YbA4VRkGAqZawYYAohMgkxbuxQf2yNcxMBTNagYUlrFLIwrOfv9aQZ5BmCGsRSYo8tYBY2oA8J58IGTwosQMJ34hrDwMYG0HUa4igMDTZT5Lh9mhNVi+QjRaRUYZabnPF/MMUDluSOTEs4BhLaAQ2zYeeX+Y/JzkJR9G5rEB7UiCDYSHEJENPLZVZTFVgXUk+5dE9fpG8sUlwfp18cRX3wjr9Ewt86QNf5bYXHCa42EasdyhVHO47dZSvPTPC+siw4GiB/Tsn+cNP/BQ3v/QIb7jh+Rx5zX52v3QPVklUBGPFEnPhCPuKU7xg/DIOlucA+LbpNwxbuv4H+YNf+SRvetFv8Oh9p/+H19lmm2222Wab/535R6Gkedvb3sZrXvMaXvayl/Gud73rueUXLlxgdXWVV7ziFc8t832fF73oRdx///38+I//OI8++ihZln3Ta2ZmZrjiiiu4//77eeUrX/m/dF/+vvgXv/vPWV/Y5PzME4x+Z58XtVdJC4qHHwtoz40AYI0kjRWLZ27l9F89RD+aItjoI6VAZoaFR3xkkGKKHtG1hqih0AWD/3VJYSGmUHCh36N9mcK02pjMJx+Fyhs9Dt+8gis0d/3EQcbcAWuDEolxOKPHWVn0KbxiQGd1jtr9a3QvCygsRribwwcn8Q2TyXYHk6bD2UdtIfCwtQr9vsLKnP76UDGh9k6z/t11jBKMnNBIIxE2Z7I0QB1OGHy2hJ2aon+whHCHRp/aCpprIWPnYoIHLW4IspcjvAD6PYKzHWZnBoyN9rhQGsMVAltyqKiUXDn4OmF1Jwjp8uo3nuOX7pultACLscdj69Nc8ivU5xJEmiIyi6nUWN5ySY4nZKNVssQFdxgR6wwEv/zbL2Lj9g7JQR/pG8S6Qs1qhBbokoNzYYuJT68SXjNN8chB5nd36VPAiQRtFdAYdTCJpFxPuGbHIh/cuJLAiwi14ub6IhM3J9z4g2dYyhXzGxU6SlINYixw4OVtLuzbBcCe6ibNTw1oflET/Kxk0BrB4nDP7dcT3WwJ776A4/mo6cmhmeggQDehNC15/VvOcPcXFM59kuKRkGRvGX89Id8/h9s1nFqYpnuqiJoSUKuQln3SmoffSIdKHgsSD1EM6fsWEtBWksyN0HlqGZXkqPuXCEKJDh1UN8FJDW4kUI4hv82B8x6DuYBwNUamhl8+cTM/esVT3Bg2uFROCfZowukB/X0xvbNlnCQFYRGuwLgFlJcxqBUQeY4qSaSGS0sV8qf7hO0mhUc7qHYGrhyaQTsK6YGJNViIvBhTdDEHNAdGmnz82OVoX2I9QakYE8bJMGmmnxE+XkD1Dc75ZUzgDb12mprm5S6khsJajtAWtzsgrSgGUx4qc+hNukSNNtHXcuwzrWdbmFLWHq4yeEhixiNEszs0abUW2Tdkr6ijkwnCryxQu79Duj/BP7GBtgbbalE65mCKLs7Ti4hqhXzrWfPTYojt9jFJQravzu5bl1j9wCotx0OgUJ2E2eQUW/05tE4Jl3uk40XSmTGEgAsLE1xWmGf2TzvYcpl0f518ukxUcPAePYsRkFpFt+sMtzcw2DEXgcH7QpPk8ZjTJ0fY6I8jk1VamyX+/JO3Mn7fFlZ3yAtgHdDWYoyBOMLzcjxp6GqB3Iwx4+Ns/Ngeor0x17/0It2PDq8zcm4Ga+zQHNeCqRSRngehj8pAdROs74HvYGaHaUnq0hYUHLQdJw8sbm7w12NM39Ap15E5eI8s0GoM2DxYg/GE5kWFebmL3ZNR+KpEfkmhX2j4vuc9xI7xNofjBk/7LsYY3MxFdRXpK12iiiXsC+R8RnptGfGEg+32sDpHCgWZIT1gce4eeuoQJ2A17eooBdFBdHv4x9cQ0iHdP4pcL6CeuQDCkj6vhPNAG+FCsJAiF1Km3C7WaopTGr2lsFIhnmrinEyHx6gaAFBcGiBDyWqpxunuHN3KCLmjcAZ9hISNV40SLFcY/chJLGBLBYSQOInF+pKrw1WmLltncwCteI7SI8swGGBURrgLLmU1fAzOkzFXvHqRvilxZnkW2xFku0bxuhYlFLbfQyc9kmtqSDVMestHi7gDDVE8TNRrt5GOA+NjsLYBacJMGFM5bGg2Je0HywgpwHW57U0RX/0ThubSnjdsh5RDVY1cb2Kfjdn26zmHrk+ZP+GStjrYVh+9ewapDWqkjpASLST0ewgpwUhEq4cJXESicVND8SdCZDVj5UIVkWS86BWn2Tm+zt2/OUd7sYz9RqpWtYxNM/atuAykh+tK4tUmFALyQYrruARfNfRvcPDu7OD3DY0X7iI41US2I6QJoVTivV+7kyA/RvD4PBhLwfN46U2/RipO84kvPMxglwWhOB93WW31+N2/upvlJ5ZYP9UEa/GOFsBKQlwaUUqnFdK3zv9wYebRe0/xW7/wca66cS+//Adv4isfu5+k3ePjf/RVrrvl4P/8gGebbbbZZptt/onzLV+k+ehHP8pjjz3Gww8//Lf+tro67I+enJz8puWTk5PMz88/9xrP875JgfON13xj/f8WSZKQJMlzv3c6Qz+KLMvIsuz/2878PTI6W0eOGz5y8g4mXg/3vquOu6RZvRBgXzpU0qhAohPJ+99jCFrn2H3tAc5uXcJrRUO/hbUSTklh45gdsx3WZhVukBPOF1C3ryAqFXR/i9r4JE4SY5wc/9odnP9cQvVqSWoUWdnnUlLGHShWhcGZ1tjIp9mo4EtFcn2FbG+IGvepaQ/b6WFabeTsODoZoAaGfCIcpgRN1BGVKlGtwD3P1GnndYLxBNHoUUgtBk0YKaQFVXW59l+f597HDxMIiQUSV6ELkkACmYubWVLhIwp9gp4FJ0e4FmpFctfntePnmUcQZJOI6TouULyQsjVSY+/ui3Qvd8m1Yr5YQVVdiknG5qcmue9MnfxVUC112blziY17zDARJ5pgWmScvlTD8eVwFrQoELmk4Y0ixi0yMATtAXrEkI96qKqH30tRaYbrKnSe0KtuMEFObTrHNxI3NIz6OU3H482vP8Hzrr/E5y8d4KR7Nd5jHX7pC4cRBUvJc9hMA843Kzg9jywNsL7PucE0s3sHMIDnFxd4bPoa7CAh9jy85jrkIYoavtV4FRfhCnpXlFCpwb+o4dAOjEj4m88HEEY4QRWnGzKyImjdMINaTqie7cPtIWEqcG2OmKwhVhr4Z1KE56OURMRtbLmOEcPijGcU3paLLQrUkb04Cxs4rR7aU2y+/jJGqys4j+Xs+v6Eopuw+lhONlqimBiK/aFp68Goy5eeOYqZOktzo8ruyRbVWg/vxoDm5jhZmFOIPEbHNZ1+ipsryv0m+UGFO+tRWWpy5NYuq1/bojUI6T9vipQcL/UoLyv2XNmFrmbxmfIw+j10MB2BczLga+oI5rIyYeyiRIDCQ/cLWCsoP7NB0HbQvouX58iNGDNeh15OZU1ALxn6TRiLh8BNLb2Kxj+7incB/M0+5aQzjAueHEUPNuhHJZzDo8TjIXJXGe/RFfRkgLrGQxmFp0PkDQcJBzHVL53BJgzVEb6PsBIGBrO7Qj7roJpD9Vpxh4a2h60XMGGRe+/cgQhivPGAascg04z9L7ZsfdHgJwYROjgll9B99laSSPpP1qg6HexWk8JYjpwoY/0Ae/1BlD1Nnlm0DfA8RV4r4guLaOeECzmu53DFTXDhQoGNYDelgcY5B8H1Ej6bEKw3MZeNgwOOjZFH+2RZgQyQ7RhnXKF3hHhBmQmvTckRlG/ziJ8uga/QO0cQO8o4py6RH90DUYpzfB7mxhCDLlaACQUFMyxQ6K0eNh2AN46nJDoQiBGPfttDjwt0zcFbqeDlGrcbIbba6J7P5lX7cOYSarUB8pIlLA2YHckI2xXazQJOz2JLClH2sa7Eu7eInsopbEW4cZ8XrC/zwKU9iM0u1lOICA59cYWN5Rq2MDQQt4C7tklpQmB31vE7OWJjAKNVRM9gkhTHlwjHwVmrEhwokDkZdIvsvtDBswPOZUXasaLUGqCVxTEOxR0C7NAwGMCp+hTaKWnmY/MQTxucXoIIPGRuKUUe2Q6X8Wsdagc15y/tRnYi8upQBZKu+DzxtRrRlkvpsMaTDk7RwYwXWX2mTHIoIDs4gf2ZKoePPErB22T9rio2dRFWInePoDa6pMLQf8VeXCXxtgSy1ad7ywgUAka/uo6I4mHhqhgiHYneMYK2Cf0koLzeh2WXIDeYvZOotQYP3lPFLQ6G6jDfASymMCweWtd97t5a/FqH4gtDxHKFUgDxpTZqbRUZ58PoKyGR1iBmxiDNsO0N3PUcAg85Pg7CcuHiFMmIj+kJSqMulSkNBY/sO+ZQf5FAOEzswgEZD9hfH2Xh+AadRhcni7CewBsrI3OLfqWDN2vgOyfxPt1m4lIGzcFQCVQbqsZKL07xSw36vgAjSDzJjrEaP/ZtN/JXH3+I0YtymGLnCprdHpWCx4UkxfOHPVyiJpCZYOHiBskNCc14Dcsyy71hW+T/2xjoQ394B3GU8PA9J8myDDcZkGcRIh58S46ftvn/zjc+z+3PdRv4x3E+fCtv2zbb/JcIa58L3fyWY3Fxkeuvv57bb7+do0eHcvzbbruNq6++mt///d/n/vvv55ZbbmF5eZnp6enn1vvRH/1RFhcX+eIXv8hHPvIR3vKWt3xTwQXg5S9/Ofv27eOP/uiP/pvv/c53vpNf+7Vf+1vLP/KRjxCG4d/hXm6zzTbbbLPNNttss80222zz98lgMOAHfuAHaLfbVCqVf+jN+Tul0+lQrVb58X/3YfzCP/yzahINeP/Pv+mf5LH+X8G3tJLm0UcfZX19neuuu+65ZVpr7rnnHt773vdy6tQpYKiW+S+LNOvr68+pa6ampkjTlGaz+U1qmvX1dZ7//Of/d9/7F3/xF/m5n/u5537vdDrs2LGDV7ziFd+SJ1qapLTaTT6y+etsLDeQRvGA2cnz5AJP/OoIP/ErL+eD73oEoy2//LnXcqB2Ge/6V+/la12feEahPcHEYhP/88vYKOXyL6YEjsZamDhrueNnPfIO+CXLT3x6nj/4jsuxgUJ8zyjeqUtkD2UoqYleViP+/jFeLU7yqZUr0BUJZ1JG/6KBWs2x06Nkk0VUlKNbbbJd4+gsonKyBVtNAJIjU4hOhLsVIap1RKlAVvWRlzYQm22sC5vfcxBb8Bm7awNPe9gkxSYRJnQRQZG05qArRfrTAr3L8oYjx2kUFXd/ci/VMwndORdn4FK+fx4Rpdj9I3Brkblrl5h//65hT/6FZbCWl/1on5kXX+TgSJcIyYhNecvvvRkp4Jd+9K/4d59/Nel8iIgzKpsam1uCdoeRH26y/nCFjcIMMoM0FERzhmumLhAGCYsf3UvcGRA+cAFTFFz8lSspHU+Z/FpMe79A65SRKzXj9xvUqKb96BbTI30a5ZC1ZZ/+DVWer1Je+fPH6FjNg52dnPjnE5hqGVMv8iO/9Wm6jo+ncz55z4v42Zd9hVE/4UtfnOWhj47w/T/33XyweA/RH23S/KGdCAnOV1ocvXCBcFJz4iuHQIphG0QhoLvLpfD4ImZMI91JxNYAaiWyy0vYjqA/rcj8hL3LfV7zfU9w59/sZ/1UnbTmUlhLod3FFHz0ZBWnOyDI+0zcHPO4PERWj4hqktFPr1G8MEBOlanIDu2TPQDsZXPs/vYBC92Am140j97QPPG7B8jXW6BzMIYX/cwm7SsnuOP0ZVw/sczPXX0/f/LQDJvLBWaOGh7446uQcYrbzoftdYOYuOYSXzZK0I5wTyyShxD8i1G+a+9pfm/1MK++c4XV0xU651vofsT3/8qAT396D/01H7O+ATbDXrYD3wjCiQFPXzOJN/BJ6jnZaMaeuTXOb8xSaOQUzgqK5xL6/0wAlvKHM5yFjMb1IyAccARuDzZuAaTAW8vZ8X+fh0oZ0+1iawWE7yN6A+rfBSMi5sTxvXjthKTiwNIqbjtF3TpOtKNIuuKhOhp3AO7qKuLk2vBYFhzEZXuQkcYsLoGjcUervPVdL+PPfuNRUinQgSRvbiIyQ37lLIUnN5HaYvcWyUSA2WjgrHWR/YR8JGTt+8ZIp6r4l7rs+OwwjUdvbTC4ZR95uYiuStCaT/78R3CE4V3vu5UzD40hmgMGhwakl4+QnR/gtFLsjmmKSwqwZMqQjQhu/r6nOP47e9i8rMRglwIJY1/skPyQZf/MFpmRVD/eZP0zls5khY3vP8iRXRf4jsOP8dXfmmX1/inseB0rDHR6yAtrpDMh6UxAaVWi8wTjSJz1DiK1iDDA7piGwKF9sIQpKERk8ZoGhSYLBAhNXvZQ613C5QTZ6CErZVjZgjjCjlXJr6mRvEBAAMU/a8F8xOU/G+N0ejz14C6au3cgWzGVLz+DKFcgjpG1KrKgsNLDxCk6jTDjUJ60ZAt1dLeHbbagFKKCjJlD6yzeVQIhMK+YJZEl4rECwePr+EtDU+zo1jH8288jjIXZGVSnz/e+++v4NclX/+Aoqyfq+Dv7zL1lnTO/46K7gqDp8MPv+Xa++KUHOb1cJ64KCss9vEY+TBwq+hhfsX5dQOFSwrXycaYu6/LYn9WJNhXs2kG0t8yv/eS9zJaa/MyZW8gmXdSWwr1TYh7McJyY/tsLpA1JOYipHx5wuLvAPb8wiXbG8JwAkWbYrS2s4yLnRtETLs1xl8KTywSLEWlN4EUuDPqQ5AjLsDWxWkEUfJJXhyRXSwr3W/yHQecZzF8CLIQutuijjA+ug8ky9Gwd4zuINEedmMeKnIkXTbKxHKLSFLPSxiYRJBqUwIzVUb0YggBZLGCNBsfBBh4srYOUtP7NCFuqhsAycq/CNFqM3L9mAhP/AAEAAElEQVQKOsOWi9hiAXFxheTwDOmRKfzTG/jHFjE7J9C7JrnpZRf5pe+7H4C3vOGldBZjyLJhupXjYDONzTLQGcQZm+/bh1hMGf39ZUChr9vLZz71r7n/y8f5Nx+8g960Ih0zjO2r8K+f/yK+/PhZFpa3aH34HLhDz5z2S+r88Mtu4ND+Cn927i52lMb4oV1Xc/zeZ7i07xRvPfjTf8s8+At/cT9/8lufB60R5QLjO0b5o4+9jTff9m/pdQfM7R3nDz7xU3/fw6Ft/heSZRlf/vKXefnLX477XyjQtvnfk38M58M3OiO22eZbnW/pIs1LX/pSjh079k3L3vKWt3DZZZfxr/7Vv2Lv3r1MTU3x5S9/mWuuuQaANE25++67+a3f+i0ArrvuOlzX5ctf/jJvfOMbAVhZWeHpp5/m3e9+93/3vX3fx/f9v7Xcdd1vyQvPe/+PP2Xl/Dqv/MXn84n3/wUH/8+M9008yc8eu4nGtcPiVJZo8iRHPVOk+uIa4irFv3j+x/j3d70WVbC0H+hS2+gSlFMWsxEqIqabudyTTOCLLUj7aFNgX7WFKVWxvuK2nRe589Nq2EqRKszdKcnrMs44LslDA9qX1fEKFq6eYOShGJtBr+Si6pZrpk9y4qst7JV7STe3EFEOCPqBxG0aNBqnoAGBWI/QnQTbS7DW4t6/TLRvhLYL5dUmDAb40zXIY3TPJfckWkVkUcDuyiav2HEWHRru3zdGetwlFuBpS55YaEfoE2ukU4eJCh79oofMIb5hJyqHZ648ydcvHOFf6qe4Zsc67/zYDRSfaJEVFWeWqvirCUnDQwwMSWohzmEl4ty9DibS9OogfDDCUjAdvvP6rwOW/3hrieiOEYSUmAK45yz+iZjszAJq08XJMtzY4TWvmsefUnzw6Ql+8lfPk1dcfuGhm9C7PW6t3ctVpT4n4pAbKyd5fF5AeYBJxliMC5xhmnG3zQ9d9zBd2ccXhmSjR3djeE48szxKYU9G+HtL9N4yRm/3OD//Qw/xuQ9cS98ReJEFX5L5DmLDkrkVYiLCjkE5RZRK2HVkk9Mnd5JJhbnUpXexQXUVVs7UoG+IawIcheN4aN8nQ+B0U0zcZrFRIws10RjocopY6JBGFqN8clEmq4CTS8gEppNzarLOuQdnCC51mWo2MN0YlEDkCec/BeW9GVmYENR7VIt9nvidIjW/y/Jima6rad5cYvKzazhBBSe3GKOwzQy5mGCykIFv6XSKfHV+ByNTXTotic4MYSFCqAHFcIDjaPI0xiqJ6Wt0JrE9Q9INMPsVzmJG5aEexakea68qkAzAO28JT/aIvICoaxHCoBo5+bigPesP46WfSchDB90c4M8PUJcikmYPoSW0e9hujJ2oofoJYbfFYrSDgW9pHiwhjab4TIxJNWI9YdBIUcspwqmSVgvkqcSNMsByy3et0fQcjj81TX54N7rk4YRDQWWeGhIlIEoR59oIBdUDDfqxxnFd0p6DFRonDdDjIck0cHGJ0fcu4Md9UicgcX2IYqwx5EtNoqtCyCxCCx7LchwHtvpgNiIMgmyzSLTsET68ANqS2xqxCcnLcGjnRZx+ROzlqOWY+iYEZyWbz1Ns7pPk50KOaUXecPmRyae51BiHuEeaWp68NEt1tMniX6bIckZSTPF6KVxcwlhL68o63cuLZLdvYMYnsZ6DvNGj8EgDP61BZIgLlgSBzS1OM8NEOd5GjPAFeShRF7q485uU7RaFMGeptRPHCOhnxK/R5C80eE9Yct8jWkmoXplQ3NllfKLPhZUBW2t9xHKbpFjCCUsIxydr9JD1MsIx6PUWNo2xQZ1yfZO1tEzejaEdYVF4GtZPlEgHGUjByHVbnD81itoYMBgrIZ9agfUOcm0DowRiyiEXiu6LijizOY7R5HlGHmvyswGDFYOKImxXkDKMat87ssTxMwWMUeQDgzAGEeWktRCkwvYy0sGAk5+VPF2fxmkMAEO/kJJMFug5GncsZubumBNRke5aicLONuU7eohOj7FH1llJRpn84R7KMZzrj7D6vGmKxxOIcmSUYloD8F3o+zhLESNjMKiOk10cYJYGZEIhazVEUWE2NsFY8nYHrRXqgkTPOuglSZaA3mxCP4XpEnZiGoPFbUYI4WI9h7SdYWoKrMLppUS3VNkxY9EdF6/bIulE2HoZm3WwYQGrFE5mQcdIv4CQLmiBySzWgJSSwXlLeiBHGBj0U8ZORGRjY9hLq4ilxrPm1hmlt2hWz2TEe0cYObkO7RT7tRPMj6T89qWjHL5sjVbTw8Q9bJZjjcCGDqIzNCw21SKmVmUjLrDrD86Tb6VQCsmPr7CxuMVtr7maf/feLxEbw2jD5Y/f/kZ2lKtsNRLiTspq+mzKlFKMFAN2jI1y+egMtcUSS2tt1oaBmbx05gUEXvC3xiHnTqyQ5YCV1CcqzF25A9d1uf62wzx450m++y0v+pYcP23zP8+36th4m38YvpXPh2/V7dpmm/+ab+kiTblc5oorrvimZcVikdHR0eeWv/3tb+c3fuM3OHDgAAcOHOA3fuM3CMOQH/iBHwCgWq3y1re+lXe84x2Mjo4yMjLCz//8z3PllVfyspe97H/5Pv19YbTF2JyW+QJXf/8Wb55r4gp4/9H7efP/dRu8Bmy7g+7FrF5cZzlaxH3KcvGaMd568yNcW97iPy3v5JmvWHQGV5cvcSqe5MpwmSfOHGE6WsMplzDdPrathgaRFr7w4EHcjbNIUUBMlIepQUqy8EGf+t3LZN9bws4Vyb2IdMLHZqBLLgwktR0p1aBD+9IWolAAMrxCTPjECnnRRfVT9u1Y4dxyBRO62FII7Q4itxSPbZJP1Umvnqa1o8/E3WfQvYSd397jwt01pBDYgUUIyaVj4/yVOMLiTkmvrnB2u6QjCmcAuhqiohhKiv6k4Akxw6Ert1g5XkYriKYcHrl7FzYu8PP37ODam49z1/ohRlSC35N85hPPQ+qMcGmAdiXKdDDzPcxSg/j8BOWXFnjh1AW+trQLkUqanmIzCfGkZrNVwe9ZsusmsB5ocvylPsYYdEkRz5WJa4JPfKBG4XVFBr5hakeCKOaUj1oaqWS1PMaE02Gk2OU9q9NQCoYpI1bz9Y09HFvcRxDGPL2S8dOv/yxHvYyDP7DCHXdeD0AhTIn8kGCpg/u+DuNxh+7ekEgXUBMe5uQmtlDHeHJosJllFJcFomSxDoz9YIOlx3diG96wWGJDNqodPqZmh6kpVmAcSEd8VDdGbHXwknzorZA4+GMJo6MtlmY9Ms+h9dpJxr6SY5VHUnQQdNF5RjLr8+BgL9WnNVnJkIwXyKrgUAXHYW5qhRtvbHPocItTF8d46c6LBK6mNTlJy47jXapjRl2sgmS2iogdrCMQjT7xSBmReXheyGUvaRBMzXPL3vN8vTHC5uUjHBprUBi0uf+Rg3z4zwSRK1FFRbAnp9kYwxQVNjJo1yEdt+Rdg96CTiek3Q2onXTJfEta94l2egS7W6Ah32XJgjGEBZlCcTMhG1PUbl8lWBhglUAWCtiCD7aEDAsI10eOunSdFHa1YLmA23JwU8PYywM2T4xhjEN45wlEptG7NMpUYGUYdX/ly7vc8i83sKLBmbeOoftgN1bw+zn84OWo2ZSjVyyx8Pgo7f4YGEP3QoYZDbF9TVYNkKmGToqKMhj0yaoFvERilYZUoIOY/EdqOE/kkJYgy1Gpg3ZhNS8RiIyu6xCP+4hUk5dy8gL0Lh9+z31VQK6D24WpmRYjYYv082UW4hhdLeMejRndn7Faq+CdzkhMkXe/4jMcrHZ4+OwcFzozqEFO6RnL2U8dxjPHsTrHkdC6LqTWKEIcUb13g2Apwpnvk05MIdY7qFqG2DOGvSARAtKKA1Lgq4zx568RfSWAXJDPuGR0yIslCsc6vP0/LBAUDe/5PwUrcW0YqRwLhCsIpSCodsi/LWDnd/TZbJW5dGeFfJ/B9ByYqqArI4hOgmob4h2KipdgBg6MV9CihLmswsIRxfjSCo3NoX+QaHUxukArlZiqA0rSXg8ht8h2inuhg8gyhB+g0pz0mjqlcUXekihAPtvpnEbDYYB2JMeeOkx5+iI61nidoUri7s/twDcx6f4irpdDptBFi+olIBWFRVBLMdJCXgjIZj1kK0J1EpyFFnctjbEVRAy0YutSDWEFQVIkKw9wupb5U7PcdMs5XEfiOBa9ntLffxmqY6hsaGxv6PRsewNUr4aRAfX6Fr4f0joboLoRIvQQUg0TuColDAazfxrtGLZ2llEncsoXOlghGVQFhZ6HmBlFphKkJd/YwhzehSk65IFAXGrQed4IQXWa4NgmG5dXSCsedC02iknrHoPnz1F5dAN5aQOSoceBkQpZr2AzTVYr4ViLyTRIgZCAAVlwETxrjl8rD5PQPJeXfXGdK+tnePD5M3zmD26DYjiMjk9Slu+qs1Gtc/+XdgAGUS5RKbv0Ch5xKUSuh8gLK0PT41IJ2daQaSwgtEGtNHjLwZ9GBJIX/Mvv5svri0wVSiw/usLTFx7hT9/2Z1AuYneNDxOstKX3tU0uPL9JecxwvHsaKROONReZ5no2+38OI39biXzrt13N7Z98DKs1VxyY4si+oaL5Ra++iuWLm+SZ+VvrbLPNNtts83dLYVPj+/ofejOQyXAbbrjhBpRSvO1tb+Ntb3vbP/BW/ePhW7pI8z/CL/zCLxBFET/5kz9Js9nkpptu4vbbb6dcLj/3mt/7vd/DcRze+MY3EkURL33pS/nABz6AUur/4T//4+Kn3vtW+p2I1fwv+PDvb9K9pUlNwVLskk57AJh2GxsbPv7uT3Ns1wi96iZP3jbCT/7FApNXRvzI65/hF379IFnfwW8b3jx7jD98//MQAYixGnazByMjvPsn92BdB+tKTJRBkkMaY6xBTzm4hRr+ckI2MKgYcgPl4x1s5iL6CYWNIlKnXDi9k+5aTPkKTcQIMtOIrIFbGsHp9rCxoTufYX2wq+vYNEPmw0GeccDpRNjY4G710YMYoxUbp2rDmN3coEd83M2MbNzjC3ft4x0/81VeJ8/z/jPX4bQt4RMbOPiIyXGsjGlebvi2nYu8ZHmBT3z1EM3zBXr7PYK1nMFUQjQacv4DB9h95QbZlS6dS+OYvkR2DKV2jHIE3sYFUjkN1QrFPOQXv/0r7BnvMPvg9Xzo6asg9nnfH7wOU5D4DY1TaDP4rhCEJXg0xnF9am8rYw56PJbOUL7QYfO+EbwLZarFi2SpRDqCskoIRML5Z2e7G1rwJ/e+kOmDFr9hyEcLnH2mhI0DooZPr7mJZfiFLxYs7o3DXtXn7zjHU3KE9dYM43+9gNvQHPuY4GWvfoLP3DNFtzgOcU7fTclHfIrH2rj1qWE7AdD+fJVIFBAWRGSh6pPt93lQTbFvMsY5ZalN99mI6/irFtnKEG6OUAq7s8zYqxoYmbC8sBOEhRmFU4uwVjA9uYXeWqPxhEfaLCKnA2rPREQTLq0jIfbGEcwd64j1Jlf9wIA3v20Va+F7zDFeMTFPHEkqC3poyOl16b9kD1ZagrUYxyiMp4hmAvAU0UyR2so5futfPIASkKSSK+SAHzl9A3P7L7Db3cI85uGoDBkJslwSNYoo5aO1IJ55Np56d0RvL3Suc3AcQ+pKnCyC0Cea8QizlO+ZfoTcCL64di2xitGBwt2KkWttPM/FGAeRWpRbgJIPm1uIWgVRCBCAzSTtv0nIawLz5pTg7ozATRnYEnmlgPYMcjrEW48gCEjGHfJajfJdEevzLspa+gOPDA85SCicaSILw2th+0cdxoubhOUO961fien2oFgFpcjJ0J4kLUu09giXUqSVmIkytgPprlFoDwimmsjneZQvtwzeY3DmB6w9r4JvIy6kY+hM0RuUGOwu4p9rE+9QBBc2CZ5qEu8pkR0WuC7IzHLxxCSy5HLxToe41SE6PML+F25gC9BLPCa+GiM1fHjsMPVQs7k4TqkC2Z6ItFcka+XInVMIT0KcEN6zjKpE6L5A9BL8VUH5+jEGJ9vYS8sILDgOYmwUCopCM8PUNJU9XUb2tVj745S05bL6nfvYHcNyL8Q5MELunWfkUMrokZiVjo8pF3A2iozeaVFdw/6blll3JzhQ7zIxGvExdx/tp0dxMxfhuBiVoaVFbLVQkUUcEqABGWCmy7jVLtWdHbpJBgqsBusofJkSFT3iuSJ62iPenMJLM8p0iYsheB6iVkVUIBkvEh5LcQoJwaIhSzyMNMSJD9aSVh2iWFM4lSG3cigOZxuFBaEtlbUNRpWlkRTJCx6dfZrK2QxTLuGOulgWh+2RYQhRjrveBVcxVowYL6UkjsTpC4SFvOkRXT9DNa1AwWfq6php06YUJlw8X6J+/wb+Qows1zCBhwgL2E4Pm2ukJ1B7JL0vBYiSg65rnDjFdLsQBpg8Bg02cJG+g9scEO0ss3pTCTeG7oERZCdj5EKM1/aG6Vn1ImYkBATOVotsuooph2T7BNUvLtLyCwzGHORAogo+biumcu9F7PgIIs2eK9LQ6WBrFUwlxBqD7fQQ2lAcKdGlBAYKT28NU6S6faxOyS6fxTu7yY/PzFOUMKPm+YyjMOUiTq5JD+5E+D5EGqsEwgqQko0dJbyWAQS2GGDG68hqCZtrvLUYXfVxOwN0niGKPnQjiA2nTlygedDjyaTHE/ef5a/e+SG6L7wMf6GJsAK3GSMTTZ4H7KhXwTi0OyWmii77xgYMlmGQLfw3xyFJphGeg9WSx+87Ta+T8J3/7BYevuc0a8sttja22wy22Wabbf534+GHH/6WtAr5VucfXZHmrrvu+qbfhRC8853v5J3vfOd/d50gCHjPe97De97znr/fjfsHJIlS+q0We8pP8urrI37iqsvJPEmcjWBeMyxYiUoVVEKxXEA8UGL35SHzYZs/e+soN3+bw7Vv2cLGAJbP3Vbjky++jazkYK4BGwbIkgXP44m5GcprPlZJ9MIWydEpwuNLmHYP/5ThukqDixVBJxAEK23soRz3qTXU+Ci20cG0erhXzrAcHYKregy0gaVltOOgezFqjwOewkiIQh/RjKATIfL8uf3tHyljwgw1v4rn9Mmlhyj4NEoh/rJBaoMupNhCiNfIeOtN93D9zDz9acllt85yz9ZeinE+jLgNC1hHE6xCd8ZlYi6iNpnSPOWjhSEeUaQ1F7eTMlbOsac17EqYeHOXxukazS+50OkjHQW5hZkqMikjpE+6VmbHXJOb9i/y56evxoSWQssimhqroF8vI1oDpLKIDhhf8uIXrvJ1PYNYtmQSSm5MaUcB70iJP3r/lXh7XQovibiyFHOxX+G9H72WT39hL6UjimI8QLiCOLW4DwaIqyxkFrvmIaUexkEDhdkYgJfUnmHGq/LH19yG8x9TRGJ54q9HWZOTVPYndB+zoDXxrEtW93CPzOAv5wiGRRqbKKwvEAyVD7HsM/LXm7TePopoGDAwZTt0jEfhqgbxX8UI38MahVAuzS9NIKyhsidGTLjIPMGkOZQVvedJBp0duCub2NEi+aglK0v0ZZpKc4ngvjbaDZAzY1Tn1gEwwCPnJ3jj+DzWgFA5Jo4RsQCbM/oo+F1DOuoP49+jAVnB4rYNh69ZJrbgA8ZqqoUMoS33H9tFnG8Q6gYvvGmFxx6ps3hWImemcEgRIicpBFhjqBQGOI6hk3vENqRYSBgcdakds9ROdRjZl/Nd9VXyTHJ/OKAxViMZAztIKOUZ1nUI1hQqFeSTBWhoyDW2FyH8ABMnEPgYWYXRBFtU6KKg1w1wH46xIz3ySpEgF9iiR1bzWL+tiDABbuJQr11kn6dJvRhRlORxQvy8UcL+sPipjeDsmWm6p2uINEM1uqQHp5GZQWQWlYFMcvxeBp4DCPyeQOTDSPXBkRFi5ZG3BWUSsopLVnIJV3KKoxnGCKQyDC4ZnIIhn1Y4zzOw6pBuhqQzZarzoAVkrmDtwhi1PW1ai32IJCLWbDxQwB51idd9dN3CmVU2f3XAqe/dxWTXkIw4JKmLrlnyooKDI8TTHt75VYrzMdboYbqVAR0qkiwFXEQxxLY7CMfFtlood5RibGgFJZqrZbxmFb3ZQALlC5bGyDherMgP7uR0d565wgL7XmlZHBEs9UfpTVdQ7RbTpoPcsDS+mjJ3bZsLbo2Nr8yhlCELBVrHlI+tQ7WM2T+Dv9Elv+SBlChr8C912PldSxSuS7n4/QU68wHuQzG6Ok110ELriNKjbWw1oHyLz8yRdRa/WMHEBu05FIua3Vc4nHiwj+0OcKoWkXkkiYvEQGLRW03EAIq1CbJbypiSxL2vMDwndE6exszuMdw4O8/nbz+C146pPNBl7dvHyacVtafALwRk+6cpfO0kQhviPSPEe8d57e57eeDDJWaiNU4WdmMt5EWJ6vUwjy4TbKXcsesyionglw/fz2c/V6Py5AqDQ3US6+CgMY5ClEvDzyiA/pJAzA+Q3RzKBYZ56Yp8q4FKcwSgWgNMwaO02KC/p8jEE9FQhbmY0t7r0Dc53lof1WhDs4uYHUMkGWphE7trnMq5DNU1pDcdxM4azLohLKcktyfgKmSmsRstzOQYsh+BBVEoYIQhrwUIbRBKgbUoq0GDOzAQAj2JdRRMjSOlwkhB6RvX5shHJnaoChICJSWpK4nrIU6ji5dDXguwrRyz1sTOTECa4RQCVGrRQqKijHTGI1gYoDKN3lFHhWWEgBe95jouHj9FyXVwD5YxErxLDaLr5giXEnQnAl8hopRzmw12FaoQV9naAGZGAKj7wyCHXppS8rzn7suHj+6EgktuDJ2lLfZ8900IIThz/BLxIEEK+Xc57Nlmm2222Wabf7L8oyvSbPPf5j3/x5/SXN3ih39xgBOALPrEq4AzgEH07KueDfKSgnt+8wmOvvhydKLp91xOLfpM2G/0aQ6jV5Oqw54bz5HOH0JbgaqXMFIQaagvN7Geg1IpHNqNVusIUhgYfiR4hi+9bYyL9xW5bzRg5HCTpF6Ago8ohcj1DexmGdcvMhgpYHJwo/i5d7a5BpuTv2qO+hUtxN2WF7y+z9Mnx7n4+S1MoGi9chbrKSY/NI8cPNt+pQ2lXp9MlslDBZnF6WmO7F/n2183D8bh04/vZ/6LGaN5G5lkw2OTZajdc4w8o3k6HOc9azexmpbxvQHGVWzcWmDmcz2mdJ9X/7NHWRoEFHYaHh/Mofb20V4FKwTkmrSr6O0s4HcEcR0ypeinDl+6fRdu21J6cAtTqQ+LUMYS9AX26yXaVyYcOrhM+yuGz394hs4NdSoPtfALKRjLmB9x+lNVls8OyPePM/a8dbpZyO5wgw89fh3iiiYjzhYv+aVTnFvcwcNPjCMiSzCvGUzBzMEtlLTkFrJM4gTDwfKEyvFKq/jFnPjKMoXHOpiCT5oXqMwqzJ0NSKA4XyKOwI0EWdRDSUWys05fF4fSfaC8Y4Hi5zeJah5BJ6PY7JCoMnJRcNDZ4uDBDb6SF9Frm0jXwVqf9qUdFMKYqO7T7yhm/nweMx/jHg7IzjmoLQ/vcoc3vXKem69a5g/UzZw56FH69y30eoqtldGlkHs/X+Lsp3fxhl/bYLYQIRUoV6BeWCX9Qh+ZGGQMWd3HGU0YGe+x1qwgrEIXc1RXkx4tczFTCCDJHDw0QcdiFTykjvCG6+7jtd+zxgN3FSEcw6LQoYsNoTy3xfndJfpbY2BgZHUDeXiATR1QDD0qYoeyF1HWgswI0rRP4eQWOg8pf30JY10yzxBfN4F/XqHWBtjNLjbXiDzDrAwLUbISMvGzOc3TFfz/0CY+XMKWBe5jm/iLIK7eg2McVD9l/foAtysQqcbbMjR6o3SWPQa2hNPLSI2l8apZ9EoKgPs3ARc+DcLtIbQFP0B1E4S1yEEKSUDuKqIqBN0UN7fkjsJPLV43Ixm4mMzQOxew8LiPHHfJqwFZqHFGXQ4Ha2zh42cBWIsWgqmRBv2iy8XX7EOmEB43ONGwC0S7ktVOBW1TZJxSuv0UeqPGWjaDMg52fQUbJ0gJWkjSuqAzGcApUDYmdCCtC5yeJttbJt2agkYfeamFURBXFDEB6nAF7Y1Q+vIzyFodVXQQUuG6OVIJ8syhcaxCOO5gqgFu7BKlDlIJ3F7G9FRC3dPsPTrgzi92cBoSNVnA2W3QM5Zzny8wopfYsgk9YVDaghboUFB4eAs1G+KOOsTdALXeGj6fx/HQoFdKRJ6SnjCEx3tEJycQfQ/Z2mIQFkn3B/jnY0ScUz7a5OSjY3iNHJGnWCz9jYinngmxeTT0m1EC2wyw+VCRk2ylaDSqk+E+uop94yilbsxoeXhNLngRzgtKLNiQxa875NMezokNnE6CEytMBCqC0qs9klWLLQbQi9FXCmQh5OzJgL/+5A4S10O8ApQGdyOlcrKNWIzAsQxiSU0ZhJbMzUQsPuljHReBxDgCVa2i19YxnqC4N6P6vD7RR2JYN2D0sHWoXkU4Q8NfANXoY2pd+leVEVJhBUgD1hiqX51H9JKh35XvQbGE2xgmQObjI8hSAa+R4jx5HjFSZ1xuEN1YIfv1obm9/EYrUaaHZr1SgZKIeg0lFSYZthsR+NBKaJwLya9RGMfByVykzrBhSOpLvHaGmKzT7zkUQs1HPnUtwXoGK2tobcgPzGKURqgYP9KkJRdbKyISTT42AmmCSHLoJ1AsYqUmO1jG+8ras7dzAeUQSQZS8tTdpyhPeQhX8NcrFxhcPgapoPzYGqIYoH2XtB5CrvmOKw/zSOMSSZ6jRc7p5CvM8Vp+766I5Xf9AtKVrN1SZ/ehcX7sJTczuSWQUY6ba2ypyDcyNa+5eR9ba2163W+MRf5+yLXmLx94ipsP7mb3eP3/fYVtttlmm222+RZlu0jzT4SRqSrdrS6i+tP82x9+N71BCUpDFQRqaIBsswyyjMpICakEx+49SdxNwRNc/i8SHvv4NEINe+UtUPmJZawD+RLopIdIXaTWjC71wDrQyXGnq8S+xBiLA5RGwR81zPl9pg736W/NMz6/xb21K2gfrlH+3BJSaG542THiuMgTHyyjd01gFchn2ydNr4/NYvJOkbyiuO6GdV773YvsOJHyvrvryF5E4WQb4Ujc9QSMRRZCZBCStQVmcws7UibbPYbfy0m6MOhLksTnC5+6EtPdxHMFhAVo98F1odNBpCFzecq3fecJPv7AFYx0GlhdJ6pbXHK6ax73fXKEK354g0cev4KFsxM45YikLCj1hilEaIWnMrKioHJ4wPvOXcG//8Eaojlg0nuQdGaE9KoK1nWGDxZWgIVSKcNdyFFbA3pfK9DJDNX7V9C7AgaH93LirI9ZO48TxYhWxOXnGqhrVqhvpTzkZ7x87GlW/tpyvuHQ3u9Q+PoFmJygsE/x1pseYK0ChzwNFqQSKDWc/bwuyFEyxQtyOj+2A/H+JkfMPDM7NolSMEIglcAUBL09OYVHVnBX+uiih91VR1oH+2zxb70wjXpHSD/1qX1ijXS1S76zxFYWklxpOXX8evxrNfLUAqY3QOHiDJpUd2kujPtoI+leW2d2a4G8e5HQzNKfkIjlhMlqG1dq6sUEHq3RumGOycImqarjJpbzZ0psGsXp++b43mvOsapdHvjUbgYLAebwTnp+RP+Aj9dVvOCqeV78kkX+04cu4/FSieAKi0gVS3qMD3z6haweH+c1b7ifqR0NhDDYXJCNuZR3FxAFxepCGdtrwcQIeTXA4DP1/CViU8fd0tSeThArivRCGVFMGGn6+FsJzR2C4lLAM6fqNHsB3WAcmRcpne1iCh7tl+7GawviOUU0M8nYx05h0hShJBRDpONipUQUAzpJgrjQx1nSeDMahB2m32CQKKJrdxNPGIq1Pt59fYJ7F5G5Zc13+b9+6BBmZgc6hfb1ZfwlgzJD1YRsuYjcQK+LHakMfXCePI8JPMxlO4eChUGCOrEM/Zh8tE4sU3SlCJ7EfWoZP1dUTvTIl9cwuzP6B0aIr67Tix2CzZCX7V7ib0INA8E7fvoOXnnNOpmFl37texjoIvGIS2EpxTm9jKmVGPQc5FWTmPsuDevMvQyUGJpiNTvUX2Px9gsGNcFmUKZwYhO34OPOt+AVdVTfwTklEBdzqNTIC2XcpRZSKJTxcbsFolkFgxiR5pi1DWxYQE2OkThg1NDrJK95yMUe2pPknqaq+8SNAkIqLl6qc+1Vayz0S8hJn/CRAYWkSzJaYdUU0RaKHZfWao/4kKRyWYvu6RCyADM3ihYOTjEhqEWkrRCnr4ctgY0WRDHnf6PE7Gua9B73cT3Q3T6y00W7PdStE7RfM0OhlLC2ETIISziXTiC0wTHDb6dq97DjNcxEFTsziggGCGGxRmPafczsFLbXZ+zqmHB2k+b7Jc3xCQBSr0x23FC7ccBy4CDnKujWgHznBLoUovrgr/VIsjpSJJjxGooWtlwiGkn4S3UNq1dXSGsBWW3Y/jq3HhNVq8jRnHTCJ/hPa+yfb/D4mMv9d9cw9RJefYI08JCpRp1pkXma675riZtfvchpW+fJWw8w+tEFxLP7aJtthP+fVR3GtgGBvbaObAmyqoNJLHZ5FdWOkJnBaosIAoTz7BSGBUco8jgnmV/GNZbM9jj1pVGKTzbxVr8xhTH8aYxBLW0OV9Ya2+1BF1Srg6lXIdcgBP6ZNt2jFay2JJMF/JZFAl47RxrASH7u7bcyWhrw4PIcrhehvIzceCglyYoO6uQWtl4ZKoccOSy+aI2MMjA5QmtsniMHKWN3akS1gCgJTJai0xQTZwglefpLj1N56/MRUrBzapQf/+1/zkfvepj1X78TqxTmhkPD75dyOL22wVy9Cm6OX2khsw0AfuP59/E9u26hdtca5lVjnO20+MW/+RIfuPVVeL4iSjM8H840OuS55pZXXsGZ40vsPzzzdzns+Vt8/3s/wjPz66iC5Ilf/9m/1/faZpttttlmm79Ptos0/0T4sd/+IfJM09nq0LtkgPawCBEE+J1nFTKeh3AD3vCOb6c2WeXOj3wNLNgc7v/zPXTv7A0LB4CYDRg1fY5/5QBZ3bD6ljn2/N4FyDWq2UXu3zOUYvfbRCJBpMMKS2vLY3OjwD3HDlI+a7jw5zXOOxN4gx7CcdD7pilsLbD7tg661+XsXwo6zSLfcFW0ADojH69BWMDrtjn29AR8yPLEgyOIrAFAcCkiPNVDGEsyEVLYHCpOzPIA4fmIboQUCl1WLHTGePqJCu3YI1xoEm/1MSLGOi5KCMhzkIJk2vD8I4u8dGKeyo6Ip4ICZ56YolSOUQowcOEJj7u/9HK+c9dFFhua/BnFTbvXOdvuDAfqjiDcGGCbDkkzRGCRkYsoOqgkofOCHZRWcoR1wUBWE6jAcNvcIkfHl/iAHsFKixkpks2USUfLeE1F4ko6L51m7PNr9K4a43P3zhJ8oss73nwvH/nFD/LAYzXW1BgTIwlPDjz85TbGCdlx1LAqXK6fPM+GhkRKhDAcndga9gYBUkB7vcTkYkr3ebPcenSRrzwwwcJyQHZE46YSv5vjuwKb6uFDiraohQ3s7AS25IEVZI6iGYwgS5p0b4W4IcB36WY9LkxPMn3MYkMPG7iQuuRrffJezmq4C3YPz7veS6bxnlhhsjxgpRHSmY/IjjX4o09cwcxBwfxTBeyEJdlZJX9zF/5CY4RDtmOW7nrCypNlPncw5Q8fvIH0MxWko5DdlMKFTdy1Lv3rd7BrdpmZqS30+VWCdh19UFB4sMdt37HEF5+6nt5mgUe+eoRXfPfX8J0unWKdyeVL3P7IHjYWQ8iGqhPR6CCLHjIzLP/ZON4tknDDUD6TY7REVCzFR1KUGs6qO6tr9DyHf/2b15PVCtjdJeyWQ3uXg1qXqCjHbzt0r9FoR9B43QwjdzRx1ns4nofVBiEAobi0OsGhF1/ECSRtA34pQVcS7JpEZDnW8ZEa5KyBWT00+zUGMelz3dubGBLu+9AhhPQoPdPCHR8DIH1lD7FZxV0CO4ixSAgCTMHBSoF1JNYMTWql6yHKZQrNJv0rQ6yQlM7myGdT8cSh3djZOv75ZaLDw1nt//jIUf7trmV0JlG5QTz7rLm+5DJ+xzm6/SLpdQewSYTa7KO6OdHVO3CFQu5JadwygUgtuZZYJTC76yRPr5E8klO6pYXtu7g1F7XsYKxPfyOkuDnAxpJkogjJAH+tgbCCrBLgbKW4a2exa2V0PQBHYVyJDSUmiclOdcivncIGimC1CXmOu9Kh3PNR+wuk0sE5fYnSNS366wHpw2Xic2PocRetXEQ3RyUaW1CUbi5wdmQH7fkQe6KA21TYQowzMNhAkiUuWTEnWO8CYL1gaEDsOuhdPhsH64ydabD29BY2irDWIpIUMStoTUxgCgPCD65QHe0P1VvZf1ZOCixEMVL5WM+hVtQIx2I1eNWQLNE4pk1hqYP/MCQXA5QaFu5EAhjLaNZiz5Eep6ygf3Edu38v2hdYYTHSkD+zjqyFw6KAAnfexRyNWUhqxFePYPKhz0745Cbp/fMwPgkTVWxZIh6PefgvQh4mxLgCW5LDfdcGOgNYWUdUHM7eNU73dMCV332B0fUM6XnYOH6ucMLYCLrdRSkBscFmAu/uFnpHlc7+Ml4jonpWYnNBmms8IaHfh5E65tIKYBG75rAetH7wEIX3PU7/QIX280dxVyL81W9oQ549tPab78O22YKROqpSQVmBnRzHrm2gAovfTRCpj5M7GFcgM4t1BKTDf3Juoc5i28PbkVIJI+KxKnnXElUU0WyAUQEjSwab5XitmKzoYZMc3wOL5JrL5njokUuoconglEFe6mNzy9pbdpPMhsx9oonXE8iJaXoLbX7w+2/hh191IwAjeyZ452/dOzxPljdRdgKwPPzMAvuvmMARDlmS88CDe3n9Hnj4wjjuuQGkGTI1GCOxieVXfubPEb1k6M+VGF73bVejteEHfvXDGGMR95/kttde/T80pvl/Yv6ZJXSWs/fKXd+0/OzpTRwjMIlBa/1Pyndwm2222Wab/73YLtL8E+EzH/46F06t8h0/ePNwgecOZ9lGathwGJWpqhV0P2XXkR285f/3fcT9hLs/+QD5mEN6k8/40w2Wuh5MOEz/GJTWM0rHBZ039AkrKes/WGfsfQsMhz3DfnnteJSPtZEj4+jlNWxq+IkHX8WP1M8yvxiSj1SJpjzC+7aw3hhIj3hrlNt/o0S9tEx/RaHGDDY3w5Yha7GdHulVkzgnVllbUWxNVlh7sIDbTJC+j05Sgtd7bJ7eTXguA0eR0MBfXIbLqvivsSRfhdSk+NJjbKTLzFzOtI7YtX+ZU09UsI4cGj5KgZwYByUprMCjzRHGvzDLfccu46Iu4vdjqo9dpFmZxZkr0OxNIDOPOz8/hxoYiqHmVa86yx99epQ8EZCDGDBU9xiLUtlQwZSkCMfBr+fUbUZ33YHc4g4E1oO7GzOsLCrI27jrfazJMRJULnD6GWZDM3XhPKZliOYCVGYptuHgjctMAIeujdn5J5fIpM8HH5xkX5AhI8vNP/AQc7UWY57ggna52cnwgKn1DozBfK/Ig+f2UTztUHm8S8HkrB0t8t0veYKS6PPHf3kTK5t17OUZ4f4UtwSiGOJWypjuANHukx4VpBslZD5sq5GBxoxP0XrxDOW7z6GabUYvSmQ7xEkkebWEDAMgIq8UKE8mFFxBVgAnU8R2N8tNjX5IU97cIPcUaUdy4VFBePwi8pYC9vIxVkWZsZJBNDJUmkIvwe9UeOLTVzOoCsZmY+LERQYGd0Oiqz7hgxf52MdrnLlPMbEj5mRTYR6wOMe7dA9m7N27xRONOTwn5su/MI06v4S6soyKLMZYkoHFzlZhoYWQEq8ZkSrBoA5mvEB/FkTmIfua4KlFjCwgqmVsoCCvoNoRg8MTZI6DDgRiSmAC8KVH7c4VxOT4sF2iIFEe6HqBbKZKuBjjiWcNwHONroBbNiRjJRLjkHuKmqOJrECkOU7qI5tQep9D5Ev0aAG5FRFWckYu04i0jSc1oxeaBDdassVh8VMkKXa6hG4MiI/swLuwSXLVGNZRqECifYF3oYdNU6wFkWuUnxGcXEOM1nGDIkaC0AxbQAB2TCKyofJg6eIcD82PDwsMUvKHf3oV3uX3MPAkY7N9Nrt1woYGFWJnx8nrJdKSh1jr45SLmNDDOjkqytC+Rzo1yuChHiLN6C0WKT15EVuQRNfOYIIAG0nS2MXTGgqKwVhK8Ejv2VaVDF0IEVFKcmAMU/JRzRgzVsVZbmFqARScYVHOd9l9uEWjOTrcEW3JmznW7cJgwKf+fIwnzQGeOjeFe6mLMBHELSZGHfpZgKl6bG6McXGzigo0Vx1YpbVZYnNjmqQc4UuDHcnxcLETNfyNVUyvRz5ahUGCeqZF+l7Y7LrYeDBsHbUW0gzv3g61YoZ8RZWi6bPn6CZPL+zCnI6HBt9aY7IUGyeowYDxvQMG+QSOMGAMaTMhn6xhBh7GlSw9NQmijWz0AZBZTJZrFqrjTLUEhadyEq9EqtPhNjzb6mn6MaoXQaWAHKS457qMPBaw74XrxI0KWeyQS0vl3gVsO0X2V2HPDElt6AMlgFyBGSuQTfi4ZxahXMRZa2Ndh2ysRNOpUM5yFo+N4ZyVUC6i4/gbjbyYQELqYwcxedlh7dt2Ex7rIXoDnIstKid6yHoNWy5yxfXPcPoviwjHwQ4GWKOH9x+ryWtFqgvDAll4vIv1JMGF/n/75uu5iCDA5hnCdYcpeFoP23aHtk28+AULXFCwuVoijys4mUVLWL+2yuTD7WGb70gBmVtybUmWFTor4NiYwmaCEorGwXGYb2JLljQssHd0meZayOtfc4oHHhxjY2wG/+cV8WUZpUdz2u8PcDf69K4oY11B95DP6FMWFefwxCY/8EdHSfKck6vrXL9zdniu7JoZttqZ4RG9sTZJoVhilJDqmQXOXbwW9sB777yFcOEYyWgR75JFaYuKoTcTUD6bgRCM7p3k8O5x/vorT2BzAwruWFjhnf+TY53zxxf48SvfAcBv3/lrXP2iI8/9zQXswJCU4ZnWPJeP7v2ffLdtttlmm222+Ydhu0jzT4THv36ObmvAymIDHAUIbCkcyq/10DhYR/2hUEYplk+vsHx2ddj+0rMk0wHf/ZMb/MEv7YKWYfDBDks/eBXJqOD5s+e4fHSJrz6zl+64g2lnfGN+yjgK4ann7G4EwElN69Ue3ATtLCTaFWBaFfy1dYRVqLBMr1kh9RWIFjLLYGoc4bjojS3IM6JJTXFZMOgI0qk2jZfVqR13KHx1leSaGsv1HTh7E/zlDEbKJKGHf6mNvExQ3t8lOzyG+bLEPdmidVbzsfwwMwebXPkSw9PHZpFInHOrw1YnLEIpXAyphA/fMUu9ZPCe0XhS8/9n7z+jJLvu8270t/c+sXJ1dZ7uyTMYYJBBkCCYxRwkU6KpbCrZsmnL9pWt15Kv/b6WrGtf27IsW/JVsGVTlkRRpMQgBjGABAEiEDkOMIOJ3T2dQ+WqE/fe98MZgqL1WvYnvV5c/ayFhZruqjrVp07Y+7+f/+8ZbLjoEwni4CTUSsyWt5h4xQhe4+LmGdXr2rhBE6MVJixjhcAKgXEF4ohLftbH7UXYLOO6+WX82zzs51qM2iFOLwUHRCNnfO6aQyM3BMs9/Ks9MhyyWgvGQ5w1Q5Zbwi1LuBUhc5gWRQjLUROj6/BXH3kzQUkjSiFCaz7/T47x0//5EYRRvNrXDDVkvQoXrpR51ST8nS++leF/3cO9YUCCRJQMeUVz03XbDLcE89N9Np83sNyldF8Vei6EXgGvnargzhuc9T5xWEaXBMo1NL++i8kPgjbIlkN8uMzoeJXqIxq5uoeKRtjJBv3XHcOUXXQ4IhlY3Nm8KGrJwl5Rs6vEyRhZdekuNvGuFg4D6iAqGje2sDTAXh0gpQUEUlTwljN+8b338ra/u8tDFxb4hx95N9HBU1hrmPqdp4kTRSoFTlUjjMG6GoPk6eUWvTeE9NuG6vU9Og+BV8pwX9pg3Bsj64pH7ylhj4O5cwZ/xWK1Jpr3oeIiF1KkMIyPaKZ+dxNvtUcWOOTHZovzr6o4yhY/8SP3cGZtmn//wh3MVXqMB2VEDvHxCRzhMv2JiKwp6CwYSmd2sTYvYgwPLgBgh0NmpwSj+12isy7OcYNbsQxmjmKrQCVEC8PooEN52cKrYfyBBYbnHaL5EPPwJOVzpihezI7pMYszUXgRwj8YkZ0bkN00TTZTwgRzkCSYkoOTuzgJuJc2EL24sGAtrSLma/hbfcgc8laV4bEyw8khlRfHRdRxs1wwbXJBUrf8+i+/ijCUJGWHaikgNRK3CrO/qPja1w9y9MMjbBjARANTd/FHBSA+lRo1Fji9DEoesYbyZoI9coAsEOiGxOgcnUuG8z5+O+f4zAbdsMx4pYLXg+qjW+hB4YZQgwRUH+/EAqXtjDwSOE6A3YuQe0OscrCtOuUNg6rFeKJMFjoIITBVH7HTI1zZQqY5Ayn4+vkDYC3V7S2IM8wNZdbePkHtrKGy2+HS6QXkFYeF7Q6dJZ+9ywn+0S7JoSbTJ7dp+mOu3jcDvuLwG/uIqM+Ln02QicbUA0Tsko+L41xUa4gKmM0tnAf6NMMIETnIv9Gg/UyCGNdQixMYAWk2RO70sGhk37B7XpK+tU4vKhO3Iesn2BMl9NxxxA8uU3pKM/jUAWy9cETlGcg4J+1Ids7WoSvIj9SIWx7edl5AdIMK1nMgybCTDdhoY1xJ8KUuUzfsobKCh5VWIT5ap/LcDqJSQWUw7YxJrt1ARrc1SE40iE41qT2T4Y4FnnaQWhPu9oiDHuGr9jC2j+2UsddYZt+49zS6VxkPFDoFJ1VUn2yTHmrhXe0Srg5Jp2u4aY4cjuleFhAGiMkJyDWiWiTlWengpJqs5mIOTeENMtyv7sE11o39xn3ummSjDmEInQ54HlbrItlpNEZXXaQQXO0usGNmGZUssmwI4gSkIFzu0JtxcdwyaU3iXm0j+z4pDmbYwYYewnFxegnhnofAEs14BG3F0lKJI/Uhq8suj5RP0Xxwj+F3BVgU9g6QbgnhJDixJXNASVPwwwx0rwv46//8Q2zVBbtkvO/W01ROHWR4YRVpLPbm4yAE/5+f/V3Gi4LOm2o0Fheony1SrKpxBy3BHJyjPDQkOxorwesbhJQIRxKNUn79Fz/Nq7//doQVyASyIOe/18Wzq3z4177C3//F76HRqv653//3OvPw+eLaIxWf+LU//ZYizZEwYKkzIqiN2Ys+Cvzj/+n77Wtf+9rXvvb1v6P2izTfJnrfj72WpfNbHD4xg1qYRy9dJY8E3sFJdFCswFtraAYev/SPPko5cBj1xggLaqwZXfZ55MoCeC66BE455p/90Kd4YvMw23GAow2tl3YZxQFMuS9vV2SW/lualP7bMt/4aeWM4et3zrDxbA2nM0bVKNqqXAmjFOmPkbWQ8LWW5CUPJcvkmQYB6VQJMdWkoh3Gb12kfM+Y2qZggMU9s4EIfNIPTFH2E9yXelSebhPf6OKt7BAvhLgPReyOFf27y8goJ2/4uInlqSfn2NoKubm3jHtuFSolrJLYPMNu7cBUg0ZN8/rZc5z/+SrO54e8dO4IqbIIY5E1Q+NUj2ygKd0PycmA9d0a7sAw+cqc9/3CBg9+bJal8z65VwxIraNQlZjBbQuor14oEAI4XGzPYAdFC0LWCrEK3GDI0TeMeOa3AsKjPqt3tsgaHvlMhakli/WqRJ1JXF8ULAkpcIY5P/j33s3v/sfPMenAMAHnPsnMkx1sXLB64hcDrnbrPH31Vt40v8wrp1YZimnah66d+lM5fnsIWz7JrYrg0pgLD5T5mcfewmTc4dyL85BsIiOBkg62XMOs75AdaaGna0SOwM37eOMMTwrcFSg/PGR0s8VZ7aAudshvLKGnPMSwC5UAshhTCxBhMdXJhMfpyQ0eHU4hBJjZGHG+w7CdYrVC5UUKC3FKNlshe1sJjGXU96naPmIcY1slkuNThEccsgcTXve2Xa4YxXXH15HDDKUhNANk2UN5ENwYcu5JiQ0hn3LQ0y4r0TTdl6YJcsvy8AAf/A/38Nh9OZ//lwGyHxHXa7hSkH63g1Ua+UttVOaSvPIYrek2adXH/0of59N91MBiPRdxcIa4WsQYZ77PzHTC8ek9fC/DPnMnv/pXPs6P/PwHGC04OImH0wehBcYo1BDiAwFZw6PyYhc6vYLDUi1TuxIxHoSk9XLh8ok1eArKLlaB42rCwyPMVzV5w8GSk9RDgh2HQ9elNEYJj58vk50H5fcJ1nx4G3RPz+Cvx8jNEd60RqUg+wZ7YRNxeA7rKPRMAxXHiNQUzKthhFloEV/fwFJGVzX5bInIrxCcVUgD5Sua/sGU2v0j5KxPWpJIA73zLp86fzMlN+HFf1FiOllj0CjjGx/d8Gj4MbfOrvPUIyFDRyFcB92qIXsaX4JF0Lp7D38xZ2V9gs6bj4AV6NBhYmLAa9/+HGmu+PhvvonhnKXOUdzJIfbJJbCWzb9xjJl7hwRbKf7QkqUa0yxja2Pk5h6sbBNeDlCz86yrMnq6DKJo8bGtGmaugTy3guqOCZ7dQHVHiGGCbAmyd9ZJb4DIVSw83cO6C1gN+mFJL/aRW9sESUK+0GT3qWm2HWhc6CByTcdx6fgHUXaMFYb+qyahXqJ27ypyrGHQgcwgLcWkuFxB+Q42BZP5cK2MntZcGDjgOpAbrABtJM3agHHg4noRIk6RGx3MrS2kD/5pRfcRD05c47sYgxiOqTyxRzQsQ8VD9GPClXWG33cClCTeFAQW7N03FI6MzT2sgszmPPHfFsleKUBnOCYnmyphA69gqCBwWhmZZ9BC4W/EhCvbRVukF1C5bwVG4+JcGo8pnRvhfGeG+64Q+297kIqXweUAgz31cgVF33KMkirjXx6hlruofgYHXWzJw+SazbM1RJZhkxQ7GBWu0ywlaTmIdkz3NhdGVepn+oXT5RoGCV5ekygeD0dFsXccQ3+IODCP8DyEVOhhHypllh/wSW8OyXOoJBm4DjJOKD+xgw1ckruuQ6YG6TiIXBeF/ijGBi5aZ3i9jPpzCWKzjdoNsCokd3xWX8q41D1NqZkjLVgrQYDEsHBCc/bWQ2jfwcsKGL/2fbKqIp73eCrpko8UeJKPffhefvhNR7nn2YtIATYq0tvUcpfyxSG61KL+5R3CO8bAKewfD/F8l5vnazz96BL+9AT4Dt4wxoY+Zd/l2OkDHDwyhVcLSJsSa+Gw47+83+I45jsXfgrZaiGE4KmH/hV/8uy/+J+OdaYOTYKxYHK+/olHv+V3jvBx8z6nF9Y4VnvX//S99rWvfe1rX/v631X7eYjfJvqT33uYB774POsrbcQ1joGpKuLFOiYsyieyUSEZpSRRSnO+SWO2jqCYQPJleHj1RvTBaVSWMVPSGBvwI4cv8vXzN/C5r9/O5gM1VO4ir2ZYVbgdrKeoP7iH0Oblz+I8t8ni+jI/86b7qb8UMffFXaQM0CcXUPUGyWaXbKTZe6CJrE4RD/rovTZmYxuVaSh5lC4ZJp7IwHEQWtJ8zODupaANYtNgdgN0o4U4MEV1F9zpCQZvXiQ+WGW06eP++yVUZNAlDwE4kwnDpuBrzx4uQI2OQ7pQxyxOk56aJT02zfZ5xZf/042sjCfJbobBUZ/REQ9neo7KIYfmm3cZlF2GaxEdV+LMJXhHYrpWMv+eIcfv6EC7C70icEQIS+DnLB7t4bgGoWBvs4HXGuMS4W2NEZlBWk2cOhyeG/PZC49z4y8lyIrL+JYp6joj7I0YT1h23nMAm2agBcJAXlFszU/xjn/6/XzH3/ou/uN/u5Xy42PqLRd9sIUpFUDMf/PF9/L550/y/73/tdz/oTpn4+uZuL4LwM0H1uFwSI2c8L49vC+2ufinhpdUiycGMxgryE7P039Fi3jRZVS3WFdihyOMzfGW97BLI4wVuJddnG2H9A2lIkEm9MlKPuUrKdWHDMQ52WSN9PgMulVHa4vxLKVWzOqwhjESYwTxrIsNPXRQwoQhWe4i2kP8yzs4mwNK6wmOhcmPR6TzDXTFx5Q8kutnEFKSHZviYq/KplasZIraS2NMqJG7At5dofQdIdt3hnTmWiSvrWDfUCJ68zT58RlK2xKVSE6V9th4oEIzHRfJKRZ0I2D0luvIbw2xTQcmQQcufiPnju+5iJSAJwq+0clZBncdRzQaJE1F0lDF/62iMwrYisocmNjlmc050mrRNuT0DTKH/tEK40NlsskK0alJ9GKTxX+rUDNlRLOOUIrhfRWSfpXxYonhQZ/0gMSPtxHGohKDv5JiA9CzFdwHfbw/SPB3DAt2xI+dPsP7/up5qoclpp3hfGUXlRQz29xPGL7mAPbIAZxBTlZSxLNlshvmi5QhbYlvniO+8yimWcFYi+lG2HYfO9SI3FB7fszUgxGNZ4sep9QD4UN56GNONMkqFfyhpLKWUBlr9McqNKMhdsdQvjLCxSP3BN7Q8t67zvGj73+Wt58+S/D0Ku5yG3W1Sx5kZGWLURanrgmmElqbIzzrkB4PSGuwfipk3dZYWplhNKuQux1EPye3spjHX+vUQRtM4GJcBb6HLQeYQ/Ngium/7MWENwre/P96hutuOEfugWgX564NPeK7j1P+azO8+R+vsHjHGHNTi/mfdzhyfIfGuZjS/X1WnmnSeEHTeEGQTxZFCpp1ohvnUKMcJwYVFy2SwlrWtlrkj+aIqSnEZJPkjVWqrRROLGKOTBVA2ijBYtAVDxH4yJcifmTqGRZPbGOzHIzFDkboksIqGN0wQTZTYXznUawvuCqaLNdmwAhknGHXEi79ySIX7ztEEgY0+4VrwmxsYuMx3k1VsqmQrO5jSbGeRY4yRGbwtxOsp6AcQOAV1+1On+y2o+i9MWY8QoeG8lWNp6r0XzcD9QrCUQzONjEobDXE0T4YQzzvQhaDciDLCcQAJQ1YWPuvDuJcAsmf9bNcK5woBUEAi7OYagCOxNRD2u86Su8Ni+Aq1DBGBCVar3aKNq2tnYJLJgQ2y7FpClWf+UcSwocuw9IqNs0wE1V01UO739xuDthxhNltF++hVMG4gQLi6xbFqHi6xKihMI7AOb+JlBLh+wW/qdUABXlDovMM4whs4BQx4mmOWtnGrqwjNtqIMMR5ahWTprDVRjfLxbaMJqv5pIkijhzKfsIv/NaXqN44xniW1JUYW0JmBhUZSuspbteAY8maGqou9/z7LxXvNd0kny6TV1z0dIPo+joTf7iN2AVz3y4A5cAyf2yGf/Xpn8NkKWq3i9rYIx+OSSZCehiefeQsu1lGlhnCnsUdGxpB8PK+e/8t/2fxpV2rfKVx9r801qmU/G/5d55/053jJ5IgsxwsKXz3L4YUp0nG//WP/5B/9k8+Rp7r/6Vt72tf+9rXvvb1l6X9Is23iY6cmqNaL1GthVjXBddDzEhGc5JvJGubzT2ijW1OH9W85uZfZ+PipZdf751bZmIuQlUljjRsLSu+/p9q3P/cIYQRtNs1rKghyjVy65BOBOQVl7zmw/IWTvmbgD5lU0bn4P6tWZSw2DgjuLBH+aUOXgxSOTAy2LEm3dohmwoQYQnCACe2qMEQc24JcWUXYQxBOqB2dYycmaL/2kN0603mRY/33naBRhgVNusE6pctFV3G2U5xpMSbzbDCYKKI6163SsPm+DVJevNh9JEZVHeM2u6QV12ihTLxpM9OHpB/TPHiLx7A8/q06iNMFtHZrHPmQwcp/ecOpuqS9nyyhxvIVcVOJunlLsmkQ1oLSRebpAtVdBzTvg6q70zQquDVXK0FjHWAmJCoUOD0E9Q4wS1pHhkssmcFgaOJm5Z0UlPOY3q+Q/+4TzLpsPyBRUoX9/CevAy9IZMLY/KJgPbJFo9eapFfGDLOfUy9jJ2oYmfK5CiMFAxGHvf8YYv+A09QMUVr1d4A0tQnujTELChUoMkaDm9+zbP80A8/wsxEh8CFctaEchNZLgbUjVRQWuvjXNnCvdpG7cW4AzChIHtdFaEt2WyF9NXHMFOzuBse+mAdtdvHvbKN8+ISOoRoEjZsie2NEtcfWuPukxdJ7szJmiXMgSmMhHSuSnZoguT6eeJj0xxKPI4tD6muCvLAx063GL/yIKSGA6ZPHgp+8uPv40Bimdrx+cCPPsoHfuAZ9u72aTy3SXJxSHdHYv4kIvzsgImfXaf+G0tUPn6GxmM7uJ2UVrDK539nioc+1kAgsdagdvr0D2rKMqFyOGfwTw9gF2cIn9YcsgU4Ws86CO3iaEXvVIjsx4wnIa0Jkgl4yCxy/kqDxz46zei+Or/2r99KOJHhtgTS8zASvLEg3LWoRDPxUsrECzHjCyHVYznZrEU3fIyw5MIwWlToQGECl/x8H3XmCu72ADXOsD0Luz2c7R6ls6s4nZzdfol7XjzGZ1ZPsDcKcG9wCU5azLXTd/KPlqjetwJWIIwmLwmymkKYoqVRKJCRIRwJvPoEtuyRT1exjoPERQhIKg60dFGwsoCE4QLkHhgPMs/gtWPkOAcPmLJEzRqy52OtQQ4T5LV58EtLU1xZbXIuOYbMDbX7r1C+PCAPXdKqAFew8bVp1n+vyfZOk7wCuZegq5Zx6vHHz97JA0+eLtwF7Ri7s4dY3iqA3hbm/uN5vNQjOVAjmauQNXyQAlv2MM1vtL9IJhqbNGa67B6s0zsGuW5jNzZJvZycnO+55Wned8tVjrzCkN9dITI+dgDijyA7r9Blwc3vWSe+NUPlBhNIBnfNk89WGS0K8gDIMowUGClgmBSg6CQmqznMLg6pyBS3neEOQVaqSM/HtKqM7ljA7LRJN7s8/OEWz/5mC72+gd7ZQ0dDwgsd3H5KfUPhzc6iGz5RVXG2N8OFwTTWQjJfIZ4vo2WA6Gn8nZjetQQ4551z2DccoJs6NGYzpBKMj7fYed9RTMUFKcjKEsfzCvB8rhFJhuwMcZd28KYlpadXCZ9cRgqJUi7y9SWyHwnRnkCudam+WXHg/+1QOlEG30UNwYkDbKOEqFXxSobv+dtblGcUWeqjLtlr1N5vLdQk81WyVolcGtKmSxpK0rpEeD56qoo7zDBIjAtzx9vFIgUUqVBSIMIA/9wWIk6wApxeSj5dY3TnHFaCFA7mYAHZFoDLfyetsd0eZmePfHMT0hxRKkGaYh2DmBghGRbcF0cweTzESSGpgvUVIkrIpirkEyVMKcCmWcGHcRzwHPB9gl6CGiQo7RE3PHTDJ5WmuCZohTGCfhTwh92TfOdNl1FDhYgFxiuKUuQW43sox79muLLFwg5glUQfmgEpwXegUYFhhjy0gDy0ULikgLDmUGtVEULw/R98C3avA6MI2m3QhtjkrJ1s8slnzrN1eQ8ncFA5lO03xwnxMIK0YLbpksvpVx/7Xxrr5Ln5lpHrV37/gZcfhzUfb7LEqYW7aPo3/4Xvc/7CBvesr/OltVVeOLf2v7Ttfe1rX/va177+srRfpPk20fV3H+eWd91IuR4UK3rjMc5zQ479tXPUywXLQyoFlQrj7QeYXVhDiejl17/qNXv86F95HjPVoPJ6h5F1ePBLs3zk6gl6SUjch3xlD73dwdicvOySTpUxZRdRh2NvL4arFnAOVJg5HnLuD6YxwsWWA6zWmL0Opt3FmJjxHRGjt+aQGfyhwvaHMByhx2NMGmOswcYpQghyW6Q/CSEJV0ZMf77H+09c4Adf9QJ3vnqJwVGXLIzxHl/GDgdUyHCE5vXHrpAFEtvp8pYTS3z3D13ijd+7guuawvp/LTxaS0M0rdAzFZyqxDzmIcplgihAj8aYXg8jR0w+mYKAvDPEfXAL55ER8Rd9vvrkaT65fRvdShUOzRb98kpBf0iWGcZCYQOFBRa2t3lVZQ3T9ohLHtFCmeRgyNHmHs3SiPOpQ+omiGqGW00Zrg2pvrBC46WUwGbYiosep1hrKTFmwezQWEqprmfMlAaYaglZsji9mLAU4b3fQiYw0uBUMl71PW2G3gTy6WICeu6Pb6L95kPsvPsGDmzv0qwn+NOa6vSI2PXw3ZRsKBlPqaK9pTtCDGOU4+JpiyxXyeab6IpHWhXEdYGSGvycoK1JPAWbO8S1jO1XeTjTYI8toiZbmDDHOKBLFncsmQ0HDLKQIzdskLY8bBYhtMbdHSGGEcmxBumJKUqfTfA+MwQJtZ0ISiGlrqK0q7n6TEjYsaR9n/s+dpLf+He3c3x+zKvmNqnNamanLbLnclu2i8ldvE6KtxsjjMGLMrLpCso6PLhyE95JKN9pGM37CCEojTxmP7XFbq9EJynTtwFOrFFDTefFGXQkcV9IcLdjnK0BIpDgCdyNAdGMIatqosCjdXKLO969BlWFmXTRkYO0BgRkFYfxjGA0ZXF2R9jldeTGOm9//9McedMmg0MVrv5QheUPFq03wVaO19OIHqjEona65HlCMl/GqRpkZ8T0d+yy8G88XJXjdCx//NAN/OGLpxlNufh3hDS/C1DXVpKlwIYuOGCFRe4NUbsD7O4OdIdFGlBusBiMKzCBS3qgTv/t1xGrHDlMGL9ZEr1PMXp7sTI+Ogijw5asmuFvxrQe6eH2U5xxTqJchgd9Ln1lAVBII8h9QzLjY4XlyRcP8C//yxt5rnsIe3Se4ZuvIzncACURAsYLFYZHKuwda5K7Cc1PnufAf1vH7VjCFUFNRTjlmPKGJciqRDfOkNy1gJhsIaZa9G+dIZorkdYU4zkXY3PkpU2cQYqYnUTXy9jQY+cBy+5KlV6/DkphHUhOTqEbBcC3PNTc85ETnP3CPO5XY148e5izX14grYd4r9T87X/+LL/8xnv5rte8QPsWn6TloXWKSHJUPCaZkKRNj1waSk7EjSd7OFNNbK5Rl7eJPx8htnJsnkEUIxwflEKphOrVuICTpxlPPDPP2DuAKJdBX4sqjyOMzslnQyzwgbc8wy+85yEmMs3WoAK1EsoJ8Dsap51Qm8mh5SF3C4eCqNYx4xKVyx2ScZHk5o7A20gQiKKFKhlDnEKWF0Uap4gtT07USN7ZQgVASeFf6RYJT9py/bE9Jg/uYnc79O+eYKU1AackZrJK65E9/FiST1TAUWh8jv9kj8btddzFGZy6i3vgzyf3OHsj1GYXsd1BjWPwLGoxx4stKhHoUgqeRQrBxXsmX36dTVKEUgjHLVpp+hEMYmytwvi2WfqvnGF4QwNRr+KKACv+3Ka/qdAnPzKFCENwHWyScPP0FSZqXWSQwTAiv7IECOYW+4jRGJOliG6EzhO8ToLTjZGNJrguyfEZSBLY62J39yBO0GWJdRTOhW3kMEF4HrKSEoQ5QZiDhC1dxi9n6EqRmmiCgCyOkaMI0R0V6VkpWCFIfVGwdrSBnTbGoQgGaPpk8zWGp6cZXT9JcLRok73jrpt56wfeCMBf/5c/RGOygh0MEblGjjJGN9SpbGeEOymffvgM4WwJt+wwPVHn/s8/x2gYYRTYckjuQl73aV4393+7O5f7HR5fP8exD/0TbvvYP2JZ9zhw900v//53f/6jLz8eVCxp3cGrTyPFnyuhfYsOHp6mohXlRNCarP2Fz93Xvva1r33t6y9b+0yabxN95OOP0R9E1Hz1cr+8BHbf1WXirxe2X+EHsDfi4I2vxeir9LuFbdgpwY//3A5PPNzEubDO/PeNeUbMcPANGd58QnLWxQqNtRohwPFhXHaKhUytqN/kcffby5x70kW0U6JXzvHMdTHjsoN9oEw051H90gBv5BbpF7mHM65gYoOoTxRrodUy5Bq9UCetSOTROqXVCHQOgUUiEeMIf7cH4yme6rZoJT3OdadRjiBJwU9SWMnIHAm55ZEPV6g0Y7KTB7h6eYsvLtxAN/bIRmPcgaV/3Ke+1Ucg8Ubg7CTkXopdUKiVlIN3DlmdkfRXSmSnfMyqRFUrmHYCSqAElMcJO883GM8r9roKRhFRs4Uz0jjNBuN1BzXVh5FFAN/3zkukkyHqBodHXzhCXlYoC4HIqOsBAyuxborWRf1UZOAHMFWL+OV3PMRHnj3Nygd9vE80yCYFG7/TQclNyqrMSuyxfTIkunMCIy0z3SFzVTAZ2ACktCz88Ig/+M1X4VqP6w/D6JDACItsCr7z3Vs8+pkG83fuEQSKjaTKXr3KsOISXuwRrCakQQXUkAOHmlzq97F4yP4Q+cISsnQYFYU0xJi1+RZx3cEKqMzWqW4l3Pl9S1z6ZAXXL4FyCPspw+slDjnh+THnP1cmvblE6f4c92qX4d+rUPulFAYJMtL4z61hA5+ZqQs8+0KT9FCAccG7PMDIMUJA/2idaLEAlBrP0rOCc57giauLDFbLTL5aklsfTIP+648QPrmCuzVAuA6UQ9KSJZ0RbOkK8qxk7Ep6d01SWh/jVMq40kF2YG9YI/BicgU2UHx+fBQ7B4O7J8g3p8FxceOcrCSZfzRhrVVFZg7ag1OVmGda4+IEFZBZhd7zSCsCYwzOpW1K59uMj5SxSYYb5ExXIlaOp0TLAdazCAXWU5S30qJdQEnsRIiRHmZ+AncuoxUMaAc+tmJA5uAI8Czf/a6HsSXDPc/dznjbJb8/Q29tAaBvbZBcX0NeBXdjjD+MiU7MYgcpUAIrkO0Rai9CjGNUtQblEgDuKEJ1QcZVjBDYSgHQDnYhLxePh0eL4kvQNrijHCMsy4/NUz7XwxvFZBMhueMhhzkis+BKrABlwTgWXS4m0dYtXDm5B0IKcs/FDVIwFtWJKV9OSGfL6PNVVFTso2yqDCVDdTjEdrtgwDSb5KMxlirupR3UWgfbrCOsxV7dKtpRSpKZu2Jmrx/xhtoL3PMHdyESH9XNkSVNUnP4vf96hMlZ6LcFuVdMfkkM2XSVuOJQKklyo3iyt8DwuCLoQHAwojcM8bdHZLOCrGVRl1Y5/d0Z7W4D4TvYNEYql/CjXfL5KjZKYa8Ptmj9mbjNobtqMFKCNlilSF1NKBW0JhgfcQguFY6R2vYSN/01j1NHtlmoDTgy6nJxq46sVXH7KaqXcuyvrhK+PWbpP80QP10HwIsSMr/MuNzAjgQyzVGeZeLzS4xumiFfbBBe6CLUtdhsISDJimLFbI2BV0a9YRqxl+Mtb5DEY/p7khd/V1MKDcaVbAyb2G2XiW1LJR6T9vbQ033UlQ622ye4zvDJf3qIzlKOIMabHhf3M8eC8iDNENZiPAVlH5HmuE9dwX1/jdlTQ7qdSVbPTNM7NUOwl+N3c5JOgGBcXGcD/1q6oMHOtWCmUbxfOSC40EHrmNJz29AtCvW6WX45/erPSt94BN0MEb0Ep1HHdnpYaVnqHmDQryLjDPaK9sl8bYO1iTo2EDjDoqA5PtakGhkQqkjOKpcIYoGYmcJ2+xAX0Gt3YODoNADdeQi3NaoDzp5BTWlOTWwzq0ZsZx5gIRCMZ6FlikjziZ7D9mwNmUu8joBSCX33jdjVbcbXt7B1gd8Hx49xRg4ikFgH1NEmAM8/fobe5pC3/PDrAbj5tdfztY8/UlyKUk3uUEB8pKB9sYNX85FJzrPtKzz96WepnZxCH5sFa3jfq4/xwCcepTYYAjAax6xu9zh5aJrbPv5zdFYqNI8Ned2xVaZLI551/wX+K+fhwWKfd7Z6L+//YXtM1hlydfAZ9uIWk+Gd/8Mxk+tIbliYRhuLEn9R1W1f+9rXvva1r7987Rdpvk303e+5jatrHY4emoI0hVIA45jJO2DQKlYcTW8AjsOXP7rJwdt+E9f/ZfI0JksV/+inTjB4wQLb0HPYPLlI5YWE/KyDOGjxl3pILGhQdfA3hphGCENN+6khH/taDG9fIBZVfJvTqWqcPCd1i5U63QwxViGkRDoS74qL2FOYug8lF0eDdR28bkyORVUqCCfDrK2QlSsEU1NoHBwBqadYr/p8NlmgM1ehcmaMLpcwgYdIEop4cAlBgNSWuOnxiU/fQHxdk1QK/MvnkGPLxKCE0Zbydo6spTidHD2d4R306adVdi0kz+3iryXIx2LcIdgkB99j/OoFFBLb6HDDm1ewdcFZ2yDKFVZa8kCiGmXiOjy/s8jMQgybGZ+xp+isVpgvDVHDlJBiBro6qrPWb3CgHBH1QkTbLVo0vDr1E3DyjZcJnZyT07v86MIq9rjkF37+dhAGohQT52SzNcZvPADKIjPBnj9DVIpIDRBYIg21MOGON5/n3OYiAFYY6k8lRIddfmP3zfzaz32OC6slfOUREbA2P0mWObS+PADl4o0l2ndoHKpjlyLMZheZC8gsXleTDwTNNGE3TUi0ixPDaHoCpyo5knbp3Vyl+4LGaUB1PUc+0sa5MMR5wcLlnHwFxCMj5E7MxENlbLlGOmtJD9WpfmkTNUq5/h/26U/W2d71sWlK2rDkYUqy4COVRAearGV57lCVW/IXuPKhkJ/+B8/yeP84peo2nfWM5x8JMfWAfLaG046KRLQsR+6N0Id9ymGf4ZZL7irkLRojDYkd0bnhIH/zxvu4JzrOnRNLfGHqLowfMu5nOCrCxD75TIXqhQHldoq5uovpjjHjCnoqBAuhhUkt8DsGrQxWCYYt8Dua3DfoJCvAtLUyshUSX1fmgcd8Lj3jUon7hB8V5FOaXLVI6i5BnCGkRcxUMY5GkGFcQeKD1x6TftYwsdgBJ0fNa8qVGKENze0+PQLMczlus2h3cLMqpZ5m55iL26gy8XifsdHYehkxKFwTvjTorZ2CezLyCNMEUalg61WsgtJ5SSpi3MdjjPEJui7eEOK6YrSgGB0JcUcjSguCD37XMzx25hAPX53FlAPSg3XMbIP6vReRiSY/fRhb8zEKVCej9Mw6tlmhf0OAFQI5jLBlB5cId2lM1ghQeUz1oWW6b7sOrMJIB6KYUBsyETIUDcr5EhaovNDFTx1GN1fwlzoQpYhoG12uYE/MI5SLs7LL7rjJlc40Z37/KF4nIm81McIiEKhBynBb0O9Kxq+eRDc8Kmd3MDMVescd4p7lYrvKMb3NTXaDzbOT+G1BVglwlAQZoCKQuYKDczz9NPihxYwjGMVFX021inUqOOWUvF2Aw0VuMdkYi4BqpXC1RGPU1gA7yKGraOT1l+8RNT3krcf2qPqa0MsJc40dAKMYUSljq5LgQAIezL5zh4uXi9cO4jJWSbwaeJ0B6bYkqYE3SKl/bQV1UCOCChkCudsvQMNSIMIStbNDhqMyuqpQeYxttxE7lsrXM2TPYrIaZj5k4o/WSA5WSKfnGB0F7RjcF4fInS4oxd5Ghb3VErJWRbiKh/7FQeLlIUKDyNOX/8b09sOEV4fYLMfHhY+N6NwXkf6EQqQZcpDh9jRqkHPb923w1K8GkNmXk53McIxxLLnTRLVHuLlFRBnel3cKl9k3yMGOJDncLI6ZPyN15gqmGeK6peKcaDag0yVrK/x2junYbzZoRQndq0CaIscpIjNoYVDdccGU29wp7uOBV5xrngNYRJIW3J1rql+IGB0N8Hdzqp9Imf3pPSyC43LEuaSGu+OQ1wx53WK397CVAHHHYYhiZGKwnkCNQfUSrFDIOMdUwO0nWEcW7Ycli9AawQ4AB6+rc/ufcbPc9KYbePBTj6ICn7jsoKQACsaSkRJSMFZifIV0JFvxAG8vQvsKI1Na1QAvzXlpaZMf+BcfBgM618z8UJ9OaQKpNLOlLsLxaOsqre/fRX71BBefvkKW5IwGY7zAo32hizaQ70xQcg78hWOmMPB4xytP0GkPmW5V/sLn7mtf+9rXvvb1l639Is23iQLfJQxdEBYRhqhSiVP/fJlPbd9FrVt8zaIcwqhHZ7PDny59imhYxJd6Jc3p1/Z55PkqGMvSH8BcuMTwhqO4vsY5pNFlBwzXEAACr58helnhZKiBrwLi8wOcdof0hilOmB22LtcJH1gnbwRIx2f7vVUQMPlHG4ggxDoS3XDwNnqY0biwyYc+XjvGM3Wo1TF9gyyXcYIUuzckjzSOtWyZMkki6JfLlFwXE/jIGzzkygYyTbFGYqs1+gsVRJoQnxsRmyY0NSZQCOviyZykVELhUFofMZrzcK5GDIYlwNB91EFW6qjZbZTRBWAUizNKYDginWmwfbiKchUngj7ZnMNuXCHopKAlVjr4W4LslECPMqQxrN47j4wVa0MPJQRON0aHgpcuLrKodnjigYOs9ur4c0Wxh7UhOzLljTcs8bOPv4HWlSE/9sFnSWuCtBbSueMQeeIzfX8XogF5yxacj6ElvCxRwoBfrG5bV/P7P3MQ9WqFf/MYYmg8OWawUCZoG+wzXX7rzAzSWNbKx9nJSrjrY/BqpC2foGMwwyHS8Xn8SxewCzW8OY+kUiPPfGzgQG55Y3ON57JDlPYKEK5xHawwvLh6hEQ7WEcSXj/ADyzO52ByKuf87QcInW2m3pqRVDzyR0vouAo+xHMG4+eYso9vNSdembN3IOGxT4Oz0qHcXeP6k33Wn6iyNnkD/pUc297kK9+xiLexwc6lgN/8k6P87Hu/xhP/vwbRqkI5inJpjBv7UK2iRwOkNniqhFyBTWeKUiuCXCFii8wt5JbK0h7JSsi/u/t+qsJy9QdqPP8fD+FKDyssop6hehmqnyKkQUuBrFWpP91jeFeASuD80gSff/AUFoHKQWYglIPKLNZY+q9YIDmVkU54LOy2GT+2x9VE0DszJnjNmDyYQF1NyI5RTMwdhaM0djXB22hDlJMtTCGMxLgO3/v957n1ph12PjzH89F1fObzr6a6nhANBN5wiDWWfLIAkJqaw0K9zXRm2VgtI2QDYzLiCZ9yt0fuN8gCgUyyIma45mAOzeLkgiiUeJtDMjNB6b/2kb0xyeky+WwL7YEwFusK0qZD98YqP/e2e3jbbVe46+Y1Hn76+zG1EiqV4FlQUC+PqS5usxJNImIfM1XFTIbFdSIvChWm5OCMFO5mCTUxQ3Jjjvf8KlYJ3IHBNnMwHqYkoa0RGqJJReApZKqRmUVqEIMU0Y9AgJ0BOxiCmi7cR8KSb2mWNmfIMoWMMvxM0ztcRVcldizJFqYwVRd7UjMOU5JyHb/mQGZx0pw/+cytfPax07SXmwTDDJlayvmIWFXwr45gOkB7FiEkRsO4ZzCpKQpR5RL9O6ZwYomX+ey9bgrynNYnL9A762LZRU4W0GxZStFBAv0M8hwlPaiV0cMRvT2XT/5Wkx/+0Daem3OwsYfjHMO6DoNZSfMVhtodI/omIF82cG4NuBN2+kivjLk4IBqVsK7A2xtBpsGATbKiz3UUI5P02vejIYowQmIdSRZasDE2SQle2inq6PMzEJRw4wjn8i7uXkZ8HKQbEf3gHN4LffA8RKWMaLiYK9tYoSjPuDAckubmW2OWAPfyLmJ3hHQc7NQEdrLOcC+l92KL+lfPIwcJ5tQh8mbAUNcQni4KIEIghED4Lrbmk044iCDE242RvofpDzENH6SHKAeMb5qmfqYHFEWab9BxLCA7ETkRjgX6AwgCvA1LPJlih/2iHVZb0BqztlnEtu/ugeez+aM30ejtIXKDnJ0m63WQ3T5iFIHrFildALsRzEiwII1E7eb4hyN02yU3Ep1DkgXYSCK1wOkJZA6UA7LZFtnaACcEGQpMKhBakAnwHJfKg0tkxxYwgYPc6pCfDnGtRWhD0i42f9PbXsHNt38z+jodpxhtIU54zRua3JPGhasq1lhP4c1USXsxW1nK5JTL3G11ltdHjBYlX4s6TBmL0ZpPPfwCUkMRUmV56cIMJJq97SYf2TrFD9x2kX7s0BuWWbh+kYtPXwHg3/3Er/NTv/23SUOLNXB08v2U/hfAwfd+8kmiJMWfKPO+7/kfu272ta997Wtf+/rL1j6T5ttEH/n4Y/zpF59j6WqnsJsLwZO7B3E7krxUjGRtnILj0Jht8PA/OPfyAPeWV3f4ez+7xHf+7A6EDtYBd62H+/gFqnqEI3KaX1kFDRqBqfnFIJtigkmgqM1XQPuoahPRSfmR/gVmnu6hXAdvLMimHYanXYanHcaHA/TaNqlrEJ0hZtAvPki5VDAYtFtE+1qLqjWRYZmRLZGPM0ySQm9MJB324jKjmofIDCI3OLFGTLeQM1OI2WnS0CerCyQuyalJunc6DCsB4tgkTqvM/NEEEXhoRzA4WioGpWmOGmYEe2NkahCpwUYW+aoYaw12eweiCGdpj/4JSWtmwI31bW4Id7jjxC42K4CTaEvahDyU6B2PXDpYo2HgYo3CW9foQJG0AuKJELZd3J7hVKWHJ52CCWAFzsoeyfqIL60fY3WnQdiMGClLHBgmDnYYXeeRzijy6Qr5TIiMQfUFKHC1ovawodaMmJjuoWzOznKJ9ueg+2DRg19Kwe1rKstjgvUh3aWApOeyc0Fi2ynhk6uQpyjPBSz4PnguIDAGnMNVbv3xl0gahnhaoj2JWhKIQzHuboYcaZhKqZ0fsvTCNO28hu0P6d7TpfeoQZSrjOqzRNdNM/H3PQ6cijj6oynmOwJMyUWu71K5f53SSwOMJzGx4UP/6TjLWwpyi62XeeN3blMtG1yTIuIUk0WoUY67leDdmDPzfhgMj/P1L91M6EKU+KwN6ri9DJkW/CaZ5IgsR+YGfyfBXXHpvOU61r7nCNENdQavmEamhuClXT76eyd5Ianx1dEE8SEXUXZwpgzdJyeIcwiXBoildfTaNv3TDWQ9JPBKNC5q6ssZ+RgG65K4UTgSsorABCC0xekn1M4nZFUHkpSg3UFmmko0xC5UqU2OYbeDXN3GSoEOJCaATCnMZA0xPYluVtG7OWudBmZugpf6B9jtuYyWi1h2389w6xl52SU/XaPzS0cZ3dUAoPOD4FxvaG+WGB8sMT7qUXtghcaXl8jcCtpVpM0Sxi1uHaYaIqQsnD86w72yg5YG0RkXfAtXMV6Azi0wPCj5BqnVIDg6s4eU4LkasbqL+9IqweOXCc7skp48wOL7m7zp3ZswrUjrgmTKY3SoRN7wEI7BKtBlhbGSzJPg5ARXBfH3LhC9/iColNwbYZShfyxkcLyME+S4Q4F0PESjhk1SUqNxLm1iAgcRZ+TXTaP8Cp4doZbWyPs9xv0cMW0ov3vMeL6EreziPb8KvSGV+8/jv7CCv7wOxxJmP/QShyfWmDq0R7XcozXoE3kOV8M63etd+idc5Cgm2lTIWGBqLmkJxChFWIGJYux4DNYUBYpWEy9RRCfqjFoK40tM2SkS9jKLCFyElAWnx7iocqu43gPWGozOyOdL9A7WOOctcKXncjF3GJGjAkMagK74xJ9qc/7f+KTWIfdUkUAFpCVLNOeze2CSrOxhPIes6pIeaZGePkB2qEEiMqRS6OMH0KcOFq1DuYYsoz7dx1aLm40QvMwTMklCHo0Kxwgg4xx3N8J0DKORQlpZsG3yHLPaAVsUz37i7z3Pja/XRdECCsjtNXlLu4hxgjGGdLpKNlUhufM40ohrRT2XZMJFO5Jzn21hRwV3x2xtY0djMpGTH6gjE4HbN8gwgFKImayDUOiJEtGUTxpaMGnBHrumP9tmrISA3TYohW6WGN42TTZfJb5lknSx8vLnljNTiNZEwcHBQqYRxiKEKFp956aKtCmALINxRNqowHQFZHENlOOU8kbC1Zsr+HfGOBjKbk7LiwpHk7Y4HU3rwT4iK6DO46oiDx2coUGaokA1PB6ifYUuOeiKg/P0BZz1XWr3Xi32cQXEVNFi9cn/cC+//4t//PLfXqoXLY9GW5596CL+sSG6olGpQdcduiVNUpcIDbs4XHy8TfvtPtHNLltnIiqNMs2ZBm+98Rh+R+O3NVLDsYUyJ64Luf97f5Lln/hlbm3+VR588Tq+tnyMl97wTabe8w++hDQwv6OZ2c648XjBtzHGcO9HHmTpxat/bszkeg4T18/y3DDiVz70Vf7WT//un3vOvva1r33ta1//T2nfSfNtou981y0sr7aZnSi9XKRxrSWvW1pzBTiYPEdGEQvHZ9ld3aPd64KF9eWAF54NEa0yHJ0hO9WHTw4Q/TF56mGbCe5uDAjURIPyayyjR7657TQp096KYbKFiA2OCPjVf7zAeCZgdNgjma9gvWuHmhHIShM5V8LbiRDnV4lONQhfaMNgiJACefggQqkChikUJo1JmiW8xUnUlXXM5g6j3QWSikZtCZIpiRY5UuQ4qcDGOUYVq6wmkOhFxSvu2OOv3f41fufFmzj7qCHbilivzWBVjjKCynJM7gjIM2SSIyOLQVO5eZd4L8V8qYJxQExNUmJA92CLyoamspYxOOTxfFCnMZsQvilg4AlkKslcjXQ1zlDg7MVYLfD6hQU8jy0yMMS1ggtkyhazHfCev/kCK1+rcunF2WJ1dqQRmWDr3qP8u7ffy6e2DvBo5GMQVG5O0VWBzRzGhx3CYA/ZykBYnG0HZUCfN3jfaRACSpOGG/7ugIOzIz79y8fgFVBqaVrRVbZHDfBc9kZl2nsJ/q/uIJVF+x7m9ggTJZjt3WIlV7mY0RD7UpeOmeIrD92B+q4B0fkKMrfc85kW05cGuHM53QNlbr51idXnZyiFMemOA7vdoiWv28cs1NlTxT6wfckz+TwCMIdzVK9H2ZMQQy4N/jhBDFPOfb2CPzeLSGK8BE79dI/P/+RRdrddpJcT3zhNfn2VdKbEJ0dv5q/edT+vnrrKodkB3QXLk8tzRMcVeriL+6hBJinGU6iwhDNMUYklDQOyVwBlUL5g732zCC/E6YzZu6PFp9ongKJLylGS9UpA4mTo5RrDo5ryTkBWEYjU0L6lCklAGAFIltM67337S3z5D06S1Ry8LCOc7ZGFE0jh4+8lNJ9O6d6i6CUOqmzZVNOo+gT+sQHOSyXEkRKtjXVG5UmieRcrBGHHxe8VbUDuRo55qQwmoXsxZ7sesnGshVtKecdbH0MayxeiuxgNfOKmorZUcB3cP+jywvQE5WUNpwRO2QGpEL6H9gVpU+GoHFkPcHeGuOttGGSYUzPUY42amcTvW2yzhB0myNSSVhVqXLRLkEkwgsqm5kCr4IFcbdfQvsC9xoUIL7Wxco/H1WGevTrHyHVwtEGkKXETtOuQ1ARIw3hRUl0RJAc18qkY/0JEMGyRVyztV0iyVpXgcgSUiGsxWcPH3c2xWYpIM1Rs2f6+OUqbTgG2Xenj37tGeIvDxmsWqEYO6QMe+kCDqN1FKoV0Jeaoh9wLKK+MUYMEYTSNUkSU5Hglw/SRAWYq5ZJo0HsphoGHzXwEGpkaZKIxsYMQCtsdkC/WycOA0vKoOB5NjnZBpikiTpgMEg4e63J+PFsk7NiiSu5XLck4wjRBCIGNNY7jof2CRZL54OaGfCLgJ//VFt0ln8ejebbHHsumwWivhL1zCncvQWyM6D0iqacKNa2RCyEAwULIIJMoHKSNkZklPtXEVmeY3O2ik5D6K5ocGm/xdPcACouxBoVApAmVA5bhUgVhysijh4iOCJztDmmtxMG5Hrsfvxb17Ss6b2iRHgpJJkLsKEa0mggEJo2KIoixnH+uzJXnPUCgyy4qNt96M/QU1hjwncJlNkzIaw7JTQvoQOLujHG3U7K5FO9i8RIRhuB7iFKIcDz8WIAXkEcdzFwTM3UAZ2kLd3kPR1j859Yw2iD1N6ObbSksHDyDAWp2GqTEbG6hljapHoLy7ASjCwne8uCbceGOg3AUdmYKWa1Qe3SbfBijhAN5jvDlt7iFLGAPzxfOVlHwX4SSiCQjuWLZOFmmIYfEuUuaCjxlcHuCYHmMOzL0X38YFUuMsYULytgCOD42tF/nIyc9xEaIzXLCa9sUGsaTEuF4LM4WBaNDN89y69038vHf/DLv/RtvYmKu+fJnjL6+Rfs759E3G0oNj9GcpNTV+KlB5haDQFhJ9UGNdSy5zpHTLapH5ji0MMkrrz/EE88tU0LgmDIuEtcWBcOba3dxx6F7efuBF7hv/Xr6SmC0JYlSSiWP604vkGU5s1NFq96//vFf597fvR+/5PHZ4Ye/5TDRxrLaHzA8aAlf3eWJ53P2ta997Wtf+/rfRftOmm8TPby8ysPra/Sy7NpyJdxeWaO0onhF9doq0jX7+fV3n+T/+qOfIawUw7BBt8SgV+ZLv9RAbEWM4jrxsQrpyXmyCZ87T1xGOgasRTguaiZHjMYFsDTPkLUaVGuMF0ukjYDRQsigPk3/xDyj01NkDRchFAwU9BRuO8PJLJ7xEFLidPXLdnGMxeY51lrMYIjJRggcpqYFszdJ3JqGWpXZ317DOZcz8Scb5J7AjQwqB7DYPMW22xgJQguoKRaJ8ITmgB7RulUiplokq33McIxIMtxBind1DxELZAbJZIgpuWyebxG1QwZ+jVzkEI2Imk3yuRr+Ss7g8RKf//z1/Oerd/HR7dMFH0a50B/iDLbxt8BJXbLZSpFOtTEm3BojY0s0XbS46Jrlxtsu0ffqPJQ2uNSbINg1uCODKrotqGQpc16Pw6OI9dUyT/1hiwPeJioFbybCvruHt94m1w557mIkxA3oTTh09srs7VTwhzBbiWmNY/LzhXupnzqcKF9AXllHbHexkcamAnNHSPqOGnnDofbgOk4vKb5v10GfmIeJAHKN044prWUcL60RjDRODKv5JHiacinh+vE6vY/WUSfBEyk28DDzkxhfYRdmwVGYUBGujtj5iM+MGrBQ7qEux5Q/N0B0YyQSd5gwetVhzOEW2ZtaXD1dIfL75MNdHKXZHlbQfr1YkXYl8eEK0x8+x4HfeZGHf/kW/uhTE1x4rMJvv7hAr5SinmjjPdBHDIYIa1FpMdmKQ8to3uO617W54dAKrqtJxw5+R+M6Af3XzaKrHqPMRwhJz4SMflpTn4yYXeiS1wzm0jZ77zpKfP0hxocruEkZYSRZCFkI//nM7Tz73DTOSOP2NbIrmJ/qgiNIGy6pm+Nc2kD2x+y+6hDm9CSxnEJPKaZO9xFCoCrg3Z1RqoywqcFYidY5ZmcXtTuA3OK2ASm565YtbrtzQGVRkeUOG5sT7O3WiNsucjOn+bAm2CluBeXnu5S+uoraHlJ+7Crely8jk5x0wiNc6uP0E/y9MWp87TqT5djRCDnKMcKQVV10WZHccoT4tgN0bm5Qv6hpnLeULuR4neK4xoDWAq3h4lZI9Tjoegkze22yF2c4nQhzOcIBgmyIWl5H9aICKpwIRF5MMoW1EAnUUhvR7pDVu+i0w+THlvF2Czpz7aouUre6BpvnCCGLdqBY86r3XsUd5mBADBNIc7KNHOsq3AlFtjCBF7ukD/jsfa2JSARy02Anq+QzDbKTs9zyQ/Dzv3KBH/OfZfx3Z9m+0KR9fwV7LsNbjSmfGjHzaB+vb8hLiqykyOdriNwhPTJDWhPkZUibDqJZw5maxAmqiCTFbO1QNZs45YTxEQelLP5qHxnl1G74BitEIizYso8+ZNC3HEXffgKvEyNSTXBlhDvUfLl/jCtRi7PRAfbSCkYZwm1D2M6uQXfBCOg+pZBZUR0YpBW8vkFYgfeqMf1TASaUoAw/+L0vUQozJici3vm3dnEXHUzVRy4eQMxNY3d36f6HNsFXVgmWukVKX9MhfF+DcCCgTcGDqZRRWjBxX5vaQ7uojsX6DnY0xsQxyvlmISZpG0rlDLIUOcoK1+U12ckmZBo5jKAzLPgyvkFGBiszogVw13ZhfRMhLd/IerfjCDscozbbyG+AaKVA9IYYaxGDEaRZwf5xXGScIXKNkUUqkq2VUEcOIGYnCwixUtfiqov3zy9GpA/0qH5huYiyxyKyBLO6jl7dwGztkI97hMsdFBJZKiHKJQj8b7nPC0CYHJWDysD6LroZQiWg9pKP91RIYhy0lYysT9NJcCOLN1a4uYRmhWy+VLRvKkHe8sgqAl2RSCtJDihUP8Z/ZvXl2lAeKnSpcD7FcQH3dW/3+Xc/84f85s9/nHcc/Pt49fDlz1iaVlgpoeXRPyZwY/AGBjJD3FBkleIyrZtjtm/TJN4e/faIi2fWaU1U+Ne/8D5O3LXI7Okp8twliV0qXlGkuaF1grtnLvHg4DqmGj3+yk+9E7/k8Vf+9tvpD2P6niQue/zWRx7kB/7Ob/Olzz1ZHDPjlP9ejpIMD2Xc/L4X8MYJ4fOa17/zF8my7P9mhLWvfe1rX/va11+u9p003yYaRgnGWuL0mwPW+GGDXcyKpBEJcmICRjFPfvE53vXjb0F6DpRLHD19jC+/0CQNFUKGeGsC41q8coLTghOdhKVcYiVYIdlKq4RbO5h6DTMaYT1FfaHFuRs9nCggqxqmdj1O3LXN1d8VZLM+5U2FiTuIxKBeaMNEE6sEcqKJDBwE1wbGrlMMRKVATrUQrsBaRTer0s0srYU9us/2CM73adqMYGVMFgSozhibCXQSY0WKCALcio/X1zgdw/0XDnL+8SlWLtY5dGib/nQNlcYIBXo8xvYHyDhB3HSIzJOopJjAhYnCIJC5wTMu6XiMuxeSh4LRootfFWSn4HBtj712ibaYxSqwgU+4rUiNZXiLRnbGWEdiSj5agFIpxhF0jkrCTHOsvstrbtjm6tkponsEno3xAg8chfAEW1suH/rlAzw6e5R3rGxw4VNlDr0lpVmLmHcHnDy1x8dPH4VIXJtsCkQK2XSI7SushOQlycNfn2DhcJXktoJBInLFE5+oYpNhsd+1BmFI31WHQDA8FqJejKm+ACzMg9bkwxFBxWV8Ypa8VcF4DksbU3ibFiEMpjzN3s92GZ8VjD4dIDXs3BRgdhWNzCCDAHvoAKMjDYJ2hhhbShfXUf0RwSsspde7TLYssbYwHBex7rsh7kRM6gv+5vde4BNfOUx3WXL7+6/yQPco0ajM4rsiKnNX+frmJCdvvkTcH+PqnPnJXdpqko8+2MLf2cDtReSHAvTBgOD+DnCtZUEbSmtjolOC8OvbeGcs+rWLNB9eo3bG4qkyVlrGcy4rnQlmakO24ippIlCq+O9wskqmPRpPDBG5we9vEr3rKFlTkKcOKpfonZA/fOYmRMniji0ih40vTyMs5CWoLLVxtobUH3bwm02srKEOZNiRwj/jY+YEnaNVoppH5T1bBB8to0sKVxfnM9USTg4qBRMqPvf5k6xcrtPfBTEF5VJMSaY4jibPgasKa4uJiUwMIsrQ9aRIsuknKGtxNgcIp0zt2V3GkxalTVFQBQyGtGQZL3qESyPMXBlxfED4+Tb1MxlmoWiREC5YD/KSJG1afuL/eBevedMS5et2eOWdZ7j//hk8ETHz/RFnL52k/vg6+dEDeLsaLzaE8YD03AA930SYHO0orGsoPXgVkoTouiZy0Caar5KXAyox+Os5blvj5lDyKjhdgcgUpuyj0uJv/r4j5/jF0SyVZ1awniSv+iTfMU+4rIlfHFO9uMPi3/J5/Q+fY2u3zhd/4S6io2Wc59eJZo9S6Vbx6lVs4BPTZHjWQz9j8UyIfL3Ca6TM7G2wMTmL9uv4XbCNCkhBXpaYVKJDgXUFUlvCekIyCBHaFIYZC8vLZfLPh/AKS3zaEu5kRVHmpjLq+bxwRgBymFE52qfz7AHyZoA+MY1asjhpxu9+9ihLx1rIZ6qsnugxaIeQW5QWjE9O4DQdbvz+i2gBC3dkdNVecV5IW7jCjGCj2yJbdJl4ZAk9H/DsE3NkEXjZkCe2WgXEGIvAYnOD2h4XLKJyjg08tBbkm1XMcoYzCuiOW4jyGNvrkx2dRvgu4Uqb0loOyoE8AyHRiSgg6VbQ7Ul2l4uJ/ssQ3nIJxmO0MjjGghCYegjGEh/0kbooRuQNjZHAOEM91i6cgUmG8D1kGGAGObLdJ59rYDONMx4juwPk6g7S9ZFTLYznIgZDdDVkfNs8/tktnNSQ+woZxwjfQ4Q+wgK+C2NdxLVPlHDaY7zlDn/mkxe7GGD9GvDFD7DjCDMaIkdBAcHn2n3d8xC+j4gzrCPJHYhbDqHMqTUSRrHPStSADOLSVRxnTLilcYY55AZrLMHmmPhAcf13RgZpFSoFGYOVgsqDlwvLTuBj04xssYzXz/GvRsRnDLwWnvroBrJcxnQ6YAX/6id+m+//ubfx9c9/irf8ZJsPlQ6z26swbBdFJmEgLynyisTr2sKdF1UJuwpRalIead73o69he6fPcBRTGQFWMRIFb8/8GTfRc8NTPLhyACEsL/zbD/C3f+XHAMi14boTswjgj7/0DFmcY+48hvOFZ68VzL5V1lqiq0PSSpnhVyepjIt9/KV7nuPd77rjzz1/X/va1772ta+/TO0Xab5NdMvEFBe6hobjYOMEpOCZe8u85XdfoC417IDVxQC1dn3Ai4+dZ9wdgbE8+/B5yssJ2ClskpGELpVLKTO3dZiZXeHef3UdtjJAlCFr+BjPQLWM7fUQpRD6AzZeHFG9vkk66VNZ0eyeqjLQIfa7QWx4CBsT3TyB7I9xzkikcjCOxMRx0fqiihVJUw1whMAMBsi6h7DXIneTope+txmCGGFLHkgX40K0WIFDNRqfO4s0prCRqwjTjth+hwJH4vyx4Ep7ApHkrD5Tx4lSSAbYvQSBQQQOnDyMni0hpIL1GGFB+h4cmMUJXOgVP9OjIbXzMclNLo239vDrOWUnJ9WatbkAUBhXYWoeKtHYaga+BOEgRykCUGUobVvc1FApj/GUZnfesvjYAB7ZwasOiV9xFNFoYAMHPVUmEiUSr8rc3TnJIOHgd4x48LOW7kqFr3+xgnu7JA4oBtgbxYSY3EVGgIH6gSGj0Ce8A/SRgkmjMoPjCvJKiN0ZF3BoBP5H29g5H6fp0H39JM2rA/xRjpbgXNnAn85JJg6TKnD7GeZ8RA7koaDSyRkNJGwDqSYNJfkbM1yZI/6vCJUqrHIIOhnO7oh8kKJf48KjFbYv1xGeoP7UGNUYobeL6YzqjgmejXG7EdufVhzOBjznzXDTjRG6tMdTwRFu+jvncB3NyoeuUPu3a0x9IObqvSXMmqZ8IKStAhInx3MMat6DLyVFfG+ui0F8pVwUqvZy1rsZvbaDOpbjvxQhEpBumcpyRrAuWK826MQVeDph4o83SN5ew76lxO17S9w/dYryM5tgLNJNGBx1EKlCVHLoKBpBjNPXaJuS1RuIJIaHRzitED8tElCsI1G5wAQKbSWO6aMuxDwxvUh+k8JqgRpK/K/XGGmJO7AYXyKkRLQHiHoNZ1i4H3b2anzl+YT++z2ckuXQgW2aTsqD4ZhBFhSulNVdAKwrMLMl8FuYwCG/uotc38UqSfcVMzTvu0I4HkBWuKqMBByJu7SDXDxCdqyECjPwLfGtdcwZn9FiEcVbOa/xd3LsjCLcTBlcdfnyJ07y2h9Pae8GCC8gmSjTvcMg9hyM6+AOIrTrkCtFp9QgOdZAaoWzq0knJHJskYMEpMHDx1VlyuciBq+somdaeD2DKQWYETiZJa1a3PUxVhvAIl1Dc2jQIsNLU6xwyBsupqFQawIhBdZ3MbpI1rFa8NpXr/KKdy/z8d3DnPuMAiF4+IVDnBEL7A5L2HSXI3dvIk6UuLi2wLBaovzwLmnWJ1AZjnYLp1M3Jav6CDkgWC0jUDhjKNcNNs1JKJEvzqCUJDuU4p3r0EgU6wdmye6YoPHZDfqXAtxTlnhDgxDITHP82B5P31NlPCWJb6lS8jLCM9vkT46obm0wuHGSTncKa0HInGFD445TOFnh+Cv3mCyN0VcrPL5wFICsKrA9Sx6Ad7GPmzSYXHAYfHXAc3FKfl2NM+NFzj1agdoQRgK7uoXQ+mWrbnS0QX6gRu1chLs2Im9H+DMSG/gwtNg4IXqHQ1DR9FZmiecDtCewwxGUS8wdTWhvQDJwWLrcYtgDwTcXJIginCqI4ajg4Uw2CNb6xcLCTkpy8gDl9ZRqlOCnIaYxRO1lYK65JkRx/0FAfmyWaFIhxjk+orj/CIFwHISUqCBAex5moorTiVCDBLTFbuxghxGi0Sz4OYICrgwEZ7fIZmu469+Mi/7vCzVcu3SjNcJzEaKC3dkFJSCjYIE5CmEsRgiIc0TTJy9JxGTK27/3CT7zGzchXIsUhpERvObwNlZa1OaA+EQTIUAOIiwlyAxeL8FLFFhL/4TDqXCWqB6SDFKIE4RS9N4+SXSTZeJJRePeoqAtnBSb5uB5YC3ROKFy54c4vedyyx0D3v7C83zmoVchd3ZBaPrXTxLuSLyuRg2LVDQrBE4iyFsVXnr4Ih/9N5/k3nbMeJxx650LzNWq7DiDAo78ZyKyk71bSDpDRCr48Lmn+MDpAvjb70dcvrIDQtOY7LC1UQU8OHyQd//Ya/7cvhZC8Pfe+UZ+7u+eR0wnOOc3IY7ZurL7Px5o7Wtf+9rXvvb1l6T9dqdvEz3x1BJrqx1W1zuYtQ3M6ga1Xp+/PnuO908uAWCGQwhyNl97jieuPla0r1gLcYoZGmxeHA7jmQA50eD4T2as3Ouh1zuYfh/b7pAEpjhqBiPE1CQYi6hVwffxm+vYRkb37hypAlgu4e04GC0QkaDx6Ij6syPITQEitKC0Ru4NsBYwFtWPYTTGtns4ww5uGGFzjXulDRdWsSbBegrtergjjQp9ZJRhQw+OLpC3KkWqhJDM3bEMYxfXWHZvULidIXgRZAWkFUdCJUD5DhOLGeEPpTR+KMOUfXQjJK+4pHWJEAJnmCOsRcYZ1pcIJGolYfdjGYOzksP+NkebWwUoN7fIQUy4fhU7A+EVF+s7GN8jmQ1JpgKytSIm2R1BmjqMdMDXP3IL98Wn0JWAbKqKkWCGI2ynj5dZ3v+da7zzjkssLGre9n+sodwW+ZWINEqx3T5ZLccvJTheSmkoUSm4icB4Fu0ZTr9hlWP/EibViNqvrgAQBkMO3Azdv3kIHRQDcIvAOZMQvFBi9omUIysrjK+vMD7RRFQTyA2DVQNXtqgujynvjil9qkfpvueZ767x43//07hfkfBEhzQf0rnRUP3tKvWHDCoYY0yGdGNkouHqBsG5DW6LNsjfcZDh4QqjOcl4zqBXhy8f3yZ0SA/U8SuwcPN7ePUPr5E1Q5qe4lZ/jEHy9asneH53kd2LNeKBg1ez+BPQj11qfkp+TNM9UaPfKJGuTpHdeBhTvwbxNBayFGMypl6nOPmBEVGrROszq/ReN8Pe61pYt4CP1l4ccLO8ymivhNg0yEhgrggW3T2kY4jumCRVCXS6uJlCpIW7ydu1BD3LYLXMda9dwXVc3DjHf2kVb2UXtdZGCIOYn0EfncPJHOTAkAvB8GyG2RlQjnZRFwXVbMSbuEDSVgwPKKIpyfBwgHBdRBhiXYmwEjnKsFGMHY1wLwxIJuCt021eN9XndXddwcl0kfriFJOgrOaT31jG1koI18VM1kAX7ic/UeT1EnpkCmirlMgD86jJFmKc4mwPCG2PyrkR5c8b7EqdfKaOiov961iXcCXC6xp0SWEcQUTIF3//Lp770kFEb4Ra2yOpaRrPXkB2Boj1PWSqSWsByXyVtApybQsLuEOJk7hEtx+i96ZDvObmZeReF//FHVoPdvFii5cqrAE5ThCpIfcdxid9CAumickUv/nTR1BzRXuNboQkt83i3Rchr45wLuyQLFZ4niM8eOk6zu0c4PZbNzjltTl0KUf7Dr0bq0Qlw0sVl52bJGnLwT8iCKo5Bw/uMnVjxuabp8gmC7dN7gvcq22Cp5dR3SFBu4vbTnGGOdJAr+OTJsW5qEol8oUy+WKF8mk4+v49/Awc7SCsAJWRtMHu7GL39hAlh02/jOyOKD+yQvNsH383JznaIhGK8tPbuGtdhBYoLaj5EU5qMSUXvaUZdQOyTHDPJ09y+dkFAMqkaK/wTFWev0r5/svsPJNhBkOIMxgneEHObLCN2sjwhhnCFi4rJODA7junaL+hip4J8ZouUucIk3Jgbg+7tYswFudPdzh+fJ20KbEiJm9IpDRUnS6HbxiiMwGeT19PQrOIBecb7UZSkiQO1nXAVZBmxDMB0UKIaoE7Lhxi46NV4kM1WKi/3BZMKcQMR+j1TXJyhHTwRuDv5WAtcc2w89euZ3yohB2P0YMBJCnu0jb++V1klIHvIscJaANZSloSJCRQCrHzU6Snp8gWGsTN0l94HxeAVNcA20lStFh9w0aSZhBFWM9BJTk4EovBSkPJ6dLfKeGGDsdruxyttTkWjPGqEdoVCEciEGi/AAZbV2F9RTITYKXB6aZ4bcnuM1vs3LGI+UYLmdaE50ZoKUhqAr2WFD/uZ1hrEPUawvVIo4wHPtvkiftq3H9vnRcv1XGcnMqzW9TuX2Hq00tU7nmR8p8+g0wzLBYvjxjNg3FyNl/dwK34jLb65K/t8ZUbzjF8dYeJsma6AqHjcvncOn/nu38V8ScR3qbC23L4tS98E44XBC6NeogqLfP273mCheNbBdcp8NhiBMDyuVXeNvWTvG3iJ1hf3iRJnuWDH9ik9uQadq8LgzF+/5tA4n3ta1/72te+/p/SvpPm20RHD09x8fI2oRRF4cUY8oHipU84NI7J4pvONNLTrH2qzOb5i9hvDP6sZeYtGUt/tAfKhfIUolGhl1VxbqoSixZ+2kNYi9fNOHq3z7k/LlO6uAmAOnEU6StecxTOp2PWX/RgL8HNDCpNMVMWtTNi+IoJsA71S2MwpujLHw+wroMYX/ssjVphXU9TLDn16T47gxAlXURrknRC42z4iGYJVVOU+hGjSoC/GRXgzIUpzDBBCviB73mR51bmueXwKr0jPutvapGcg9nfswVU2CpEnmInpxmkhr72KY8McpgDAhM4RH6EWW/DgSkqY4moVpE7Q8TyBuKoh3N2SFL3WHlHC6k0wfNbjG6aK9qjUknu5MRjB6klthnitVOMI7Hbe5iTLfJAYo3HxUfmyXshVxamGf11gbsTko1SGlmGFS5qPePTv1FjryF54E/fzisnrjDuu/h3GJw/6EBicWZrCD9DK4EXB9iMAiI7UFgsra/ucPGTFZ7LjxLkBbR1O/BoVBWNZgF0BJh+VUz9JsPFezKa0wlvfvNzfPz3HbqNSfxzgNHXsmYFNlQszvfoN2J2zyjii5t8+Mfvph5mDI4KsnhMfksF9dmU5EyD+BVlvEe2Cc93yG8o4zgSmUFZxvzR3/1D/tlXXscZUWZuboN1883V03SxjEg0ynO4P3qMZe8Y+vqMf/8rd/Pa9zyLTRJWf32elUCSmIzv+OCYg2/YofaeXa6cmeR73/ksYy2475lZfvET74GRZSEs09nag86gcF8lKdLE/KMfSPj5K4chyHEGxUr74LYqQSej8vUlwnWoXS8JfYd8bpr+a0pEbwXTT1iZPYjZcxm8doHWF1ZRXoi3JTCBRY0V4aYmaTpc6dXIcgccgZgPYRuk4+LvxSTTZfKpFnT3aO5mZI5B3+4hdlzidUleHxF8cYsHhYt81RBTrzFYdPD2iuKJ9QSjI1WEFTjRCFPxURNNotNVrFWUlSGQcP3sLl9wriOZctF3HQZg94MHCZwY98M5uuwhqiXwXGwlQMUGW/UZzjZoPDUuijTaYoMSZlZgG1W8L51F3jDP+A0uajUjzzyCvsUdZggk6Uy1aH0IFOP5CgpFGoCjGkXx1vfoveRRmu5i+y4ePuODJXSg8J8bUh9pvD707nBQiSxa+4SHYwQ3vbnPU+cWGVuJN5DkVcni4V06UUgyLGGtLlw/WxKkfJm5sfw4yDd4tN9/E6bkUn5yB+tXkJtdxDjBu7zH4C1zZDWHsJHyh79zI7c0yjx2poVzJCN3HSblLne/YYcXhotccKY5/2SMqpfoVmq86vQ5mnfnPGcOoGOBFZLMRDijMWpjiNoSpHeGWFdh1Ig8klhs4RRRYJ+6TOWlOu/9vRXW4xYvZkNsBCQ5VggGjQN4TR+5N0KaHF0D4fu4OofVIXatjWwF7L5rEW9lSL7QIFocMfclQ+kVmnzYw7ncg37M6C6ovjdGJDnmxQjeBe8+dgFnsM1z5Sozd7d5/Gc1YuAg4hwpFW4zoFLTvPZ1Iz7z201yLYt7EKCPu3inyjgVFzoGNTLYlQEMMtKkCycriMkW1mrCkcvlf2xo+VexjsTTJfKJOuNOh3ZbkacKHItNEpwJj3wcQq+YfNvAIzo5TTgClVuMtMTHqpgAqpcspeUB7dsaZBVIZnyCi98E8hqdF1c+q9EmK1IFI4OMNbZRJrp9Hl11yH1LHkeYkofimsOvWoWqQPd62JkmJo0hilC2AUaiplooN2PxOzpsjwXjSkj0ySFB8k0fjQCs7xcwa2MxaYYcFS24QJHqxMu3arx2/I2QNDLhoEZlRpdqfGHnDsrjZZQ0IKBnNN3UI68o0mZA7li8XEOcIqMU4wY4SIyjyCck/p5gzzEEnf7LceIWSCc8hAGxB5SKIpNOKqgqoCS2prBb2zwib6d58iofPXcr0+fP4Dvnsb0ipl2ut4vUKkCdWSJ+w2Giw2GRzlaHbLLF48+08XcjRjfkDLtNnty+wPtOBQgh0CT8gx/4DUZ7PaRQiA8sYkNLr5/wmz/3O5ydrvHGO47zkz/6BpaHa7ywphjHPo6G0i2Kn/7gW/j0b32BX/vgfymu9VMtfuT6f8hr/8938Yrr6sweq7L1ROFyevGhs8B7AUjSnL29IXOz9SJxa1/72te+9rWvvyTtF2m+TXR5aYfRKObKpY1ilVtJbBTzn35unht/JeONh4tBnb9tME+UmToyzTYXXn59uqGLlTqbUz47gE6Xo4d2OXSozfnxSfxHN7Ha4mY5u4mPKbsvDzLNXpuwVSM+M0P1+hbBs+eIjk8U3JYFw2jCgcwg+gm2qcicHF/LYvBpBCJKvmn8FgIcB9KMdCtnx53CyoJRo4zA3cpR1gM/oTzqMLwiKO1EyLCOlhSTm2YDXId77EFuWVzi+vIepiT59HqV8pkUK2voQCEyCbkuXCh5Tu1Bj9mZiMapLs9/ZQKhLbXtBNOOSSYzjLVQDhFJAkrSPz1F1HKp3mS5Lfw6Q8fj8bxFMmFxNnOslYTPjbEiQ0YaW1GoTgRYTHeI2EkwoQGpecvrnqF3a8jHPnU39fMCtbuH/9I2lEqIcojcHXBlw2dscipHRvzUv34KRxp+5clTPP/ZFtb4+HMWFWboXKBN0cJj5DXWANDdC8i1YudVs9RaxWphWNNsPSvYXvGoXD9PubfD0t85yJ6p4x2Iec/Bh1j+WBX7p1uU/C7aC1GOg1AWfWwejGR9pcLP/POn+JmH38ANnbNs/tctZDkk/tkp8qWQ0uN9MBUkgtwXhHtjSHPUpXXMgUnKw02+5wfWmaqM+d7T5zjaK/Hed17kn/2XE3Q2q1glSO6YxD0bo0djVNihZusMt3PUlua+D9WQvSGUS+RVF79U4lMfvpOfmniC5b5Dsj3gs/+txbt+eI9f//BrsBUJJiV6ZhXVT7CtBoxjRJ7DlMfHrjSIvtDB7RhGN0ySN2s0FtqUn9xFDVLysks2rZA7Od7YIb7J4203PUl/z2ftiw2qG9tUr+bIZgMRWF5/20W24zJLOxXGtk7Wytn5bAOz6FHxI+qvF3BAsfORGmlZ0L5RYV2FGpc4GQToxip6N6XzZQ+RV7DVKoRb+HlC3J/F3zVIAWIExhWM52vIXOBsjxDaIgTYUkA4gP6C5cXtaU7UO3z6sRuJDtYQwuCsFGkoeeiRkyLjhKzuo32FKLlYJRFaYzODMy1Ib5zEbYOVHiZNUN0e7mSZ5MZjmEMD0us8dBZCVxFXNaODEpkWK/h+1+L2NDIXxWdzBHFNkk/O8p73PkRldsiHg1OMn51k+qUMJ5a4kSU7dRDrWczYEowFcRWwEM8ojPv/Z++/wyXL7vJe/LPW2rFyOPl0zjPdk/OMZkY5BySCAANGBmzAEmCSwQghki9G2ORrESUMiCAkFFBEMyONRhN7Qvd0z3TuPn1yqly181q/P3ZrhHx9ZV/g+nl+uud9nvNH7aqza1fV3nt917ve7/vC7//lQdwHF7H2ephmgTu2neXmbz7L2kKRDy6/jM4hG6EFfijzVpQvwxGMDy+h52sgEqR0CXY6uGUH0RmgZ+tUgwC8jGFmsdIt4h5P6I8JjJZYrZSdpXkOjnURtuD83BRyT4Vmqc9at8nZzRle13+I0x91Gb1lHKMkw+umsTsF0h013MUBSdlGCEF/f5HqsQ4i1Zgsw6QRriXJbJsbKwOiQo3KcESkbHTJhlMGazr3F0lnqqinz7N5WoHvIG2Be21At+sQ7WggVYHBHRUwGmcUUuwlkKTc8dpVHv+LbQhiEsvFtjUzU4tsPJiTAxcKVaYqfR5yr8ZWKcUX9bBPBdjrwEiTZpLhSLG+4TDyFKYfYru5Kbw6G5IMAuwDAv90gswkyYSP6ozAwPzpOqIiWXt1gaguKT1hYa8EWJEFjTpZpUwh3EDaBm8sI9oImW304OpZLi5+5UfUvoPjlYisEO/iBnKzRyEN0dMNrOdXEEriT3r4wuQmyPUKrLVAp/l1D6BB9WNGZYGlBToKsdoDyp+7yOjmGQqnOmQHd+atkRt9SNJcRWoMsuCQTFUxng0rHdLVVVSpDEaThpL+cwbjhsiPtHETiRAmNwXOsrzd0lLIagPWNsG28thtfcUsWcj8RDcmNxMWeSuV1KAcHzsQIC2UsZCueEEgtJYU2QzKGFsQNC2kA+6xZdxAEbsOMjZkZNjDDG0LMjePay89lS++fJmoGd5YR2jyhMcwHzcyR2NGMvddvjJml+/roi+PsOxzXPVtIa044cTRMipMEUrm5D6gDLjTAfQlpmrnb+Rp2skmwsCY12dV1DBZyl77eqaqDTzpM5xbhk4Xts+SFFKyJpDBH31mnqxT5KHnLvHiyVksNc6hwz9O79zjSAV//l9W8Rt+TtBA/p2vb4CyuPDFRd7w2p/lTx/bweuK/4IkTFg4vQjAX/35l3jfXz9KYgw/8D338k1vuuV/uR7bwha2sIUtbOGfii2S5usExaJLqzVk98FtedGjNTteXGXmp5bwyjasXVk4FDZjZY+brt9G+9g4q3PrYCDazL0VMODOd0jW24TLNVqjIiaQeUHqeZjegDiaoLiYguvkq1KtDsGww+nOKtHKDFQjvKfnSQ5Os98s8mx7O9Z6SrSzADFYWr5QgGpbQfAPVgrXNyGOcsk6BnByL52Ki0g1VtAjXephOYJ4aoZsj49c7ZDWC8gowYkNItM4zjLtwGe22uEHm+dBwNygxOrVYwRPGqpXD3jdr63y6feUaX16AFVJOOaya8cax07NkNrgBAmO0oTakLmCbHUF2axDo0YyUYQww2pFjG8MEUJTtUfossBIQ+bByPGxRxJXpyQadKMEtouW4DhO7lkyiIlLikudOgO7gP/wOvGRCWZnV4jbKdmlFCFAOXV6zSpyJSKKY55/rsG+/W3CCZf93xdy7nKF3ZObjJVHLPRqLLcd9LjCjiFpSaQPm+2d7PupSzw+VyRt5iui6skAs2QoPLSBaoWo3oiXyws8XNjPQuRw7E+bHGh28LyM2FEoIxHNBkiBPciALPcDEYIf3P0QH/ubMYw0CG1IUpvSIx3c54YkezbR/gz15wN0qQZOjBh3kNsL3Pn6Nk+OZgmPbfInD17HvqlVVnSN2NuOrGuCm2votIiedOklhkuPRGw87+FdliQNC9nTUDWE++ooY2GtjYgixS/+zi2o2S7FExvsdPts7ndomypeK8NdCTFhipASk2X821+7QLphuP2tbf7dI6+kutqn17PRno1KJVYColpGS0XrxbNQ9BkWYobF3KR0oIuM1Tusr/iMP9tDF6eh5FCaCdhxcI1ZA/KnbeZevJ3iShE5WUMn0LxrmY2ihztNru6wRa66Eho5JVg745A9OYF96jTSgGq1MVc1ce8Zp7beZ0X5OSEz0IhMktmC1AVrlOKEKdrLFUKYhHC6RKPSY7lXphJm7Bjvc/x8bqDJlflg5dGY4X5F5glEBiozKC1JsgThSoSl6e+uUYsKCFfgro9QysbYDsaxkNrGjBfwKjHBZIxuW1SfbiFW1ulf1yC8egrdNVgjjUgzhFDYm4L+YcnMaMB1N1xAAC+70+OBc1OYup3PTwHtQTxVRkUZhdUM7Vv5cWuNRuE+P0T0MuznQ4avKnH28+NM3NOiv+5hL7RQ22ukFQdt50pD4eYtT2ndYWPPFHI1Q8UpsaeBDGtjgCm7JLNVdAvOtcfQRmJnhg01hnZsQGNd2OBiUkU92WBheQzbzdBlKJZDJpwWTpLw1Iea4LrYgczbOp+aI3rFQRAi99YxAmEg8QUoC2HSXKlkW+hUI45UOPb8BO3tFloISn7I2st3UjrZRg0ihK1yYqfTx7SLDMZalI4OSPfZbH7zDaQlmUejj3LVyO6/jcmKEvOhNY5FDsMjDbJbd9CvniTTkiN3Rjz593mizpnFcS4HGfo6SWIsgsMNPJZxw5hUKIw2hIHi/s/txFScXG1xxeNIAFkoSFD4tsBYAnOwTpwmFEY6V0nojMROQbrobQ3iGXA/s4AYbCBHIWFPce6LJfzJlExZCAFRANqWX1G0tPpoW2EKDnKji5Z5ZLRWGbpRQkUadylAz68h982SKQulNcJSiFIRkaQw3sB0eqgwA9vKfxNtcC+38RwLihXkc3MY34UgBCBdWkZaNsZkqHYHaiVsr4wq10ktg4kNlmwzrDQRZRu1x4V2CaFkbq/c6SIna5CmaJNfhEIIZK2KvvI8402k72OCAJMkmCRFWRZxGmGSAiZJUGcW0LPjpBciun+f4m4XLO8q0Q1KxH5G8+QK2WQFq1KHLI/jNsDwYEZWElhDSeYIrKX4yzbqL/x+Y59aoPVdO1DbBVhXDKrbGZk2oBQmiaHdxQqivDUrTvnRd7ZpRwnf+7kpyEZQr+SpatqQVXzGG0OiDynie1y8sxHRNpcd37nBuXdV8ExMuTgk8Wz++OJpPvKyn+TT778/J2ggN/lPY7LQAQWBJ3EAIw2OY1EsuDx3aZNgTCIyw+vevp+7+X3wPMhSSFI0IKfGKBdsrjuyHSEEs/umuHRinvpUjSefucTv/Z/3kVVcEIIHHz67RdJsYQtb2MIW/rdii6T5OkGSZCgl2H5wlm/5kdexemmFa37F49neMlaYe80IQPg+9tQEF09cotwsoWzF2vwKt7y2y+fbewnbBlGtYDbb3H14DRMJ2kWHs38WY6I8xlK3vDzCM9OQ5cWebWms68okEyV4ahM1lxdm/TtqiH4R4QY5KRGl0BmhkwSTZDAMv+pzCEBbCr13BjvoI1JgmGGqFkhInhoglEZVFWHXwdaGrFwEJZEpaAVyMMTZlfDL+5/i1KiCe2VlsR9W2eFtcvrCgEPfl3L79hUeUnXaNQ+raBGnRb705B7ERoxYaaE7A5xqAtrOP6cACi5sdvBP9FGbPtbagDCzmIvG8oTqbRlqMoNlCyouohthpMwnhI6N9izEIGLfWwzPnweJIS2DrWJOre/GuVZROtDmzrHLHHu4Smu8Tub4bN5QwT3forQcIUOP9/72zbh3B0y/YpXLy/ewsXeZfaVllIRJ1WFzpchwW4VUggpBp4beNodPtK7C6xrsbfnqsdxIEBnEEy7+hQ5SGl5UXOH1+5b4D/92N51MU/3dgHf96CY/+u43YT10EewEdIYp+ygEwsT84W/soyOr7Ln3HF+6tJvaqkfleU12d4lkyiK+xqVwH4iWwFpsIdKMu75xjf1XXWBx0+JD727wV+kYwe01Lj1T53h/LF+01RGlW0eM5mu4qyGv/TcnecOLzvOen7yXJVFDLvdJJyQogb2vT3qxTuCmsD7Eme+glzIGd9c5MTbOo2s1giMpxZMjdtTLXHX9Lh795KOkKz1sOyW2i6SR4l23fJ4f+6NDZAUbU5RUntlAHh/QuaeKKVSIjINzv0C+zMq5DS3odhyamYe6NU8W0icFxrPonBix9GsZlb2G+mZC99MRQWOC1AMvChh8voRlS5L9mqTWR4sK7qYhLmliT5Kcj5B9Q3m7RzRRICuXIU2ZLg7YMdOmdb9HVCqTlSrI2GASTVJTaFtimTJIgWoNkCubEJUIhjarSy7N8SInF8ZxWgFqmBJP5hdJ9VRCYqvc0BWN6kXIMAHXIqyCnxhq5wr4lwJ00f3KfSVK8BZ7hLsm8EYWNbFJqlJWAwe7FSE2Iwqn+4ixCfwVnScTGYPbHeFoQ3QmpVevkGqBEgbfTnnl93wRIT0e/+lriUYuVikktFzk0GCMjfVl64jAYBcjuGGSUWgTbvMxZUGQwt984m5qeogTpvgrGf0SCKkwEw1oDfO0nFFKOHSQvkUW9qEzojRwUedWQUq65YxopkzatlCppubaBKVJCpdaxO0Rar5F+qZtnPkMqKQLhw2FnV1EZoikZLwQsrFcIquXkGmuT6je5bIh8/tyWqoidB4H7V7oglC5mlCANKCLBeSzl/jLJ+uM3j3N2JGAXugR7S4gVztoXyNSB5NEqIKH+aRm+PIGpUf7iAsJ0fUGhCEwUNoQDCuaqOnjJBFJ0ScLMgQ2UsPmYpVeWGaahGI/94R64+QpNtuK7pqNFDD6koV6boC2BQQCZ2OIrhjEahcjNATRV27oCpxByPifPg+TZWRagFSgxwtY3QBjUkwKU3/XIRnz8RcjtNCkUYLa6LygsKyOZ1i7JCrO6KwprEKEmZlCrPfyc1AqyAy+udKKWfAwYxWyaZ9eYUj9gQ3SqoOaKJFWPUSaQbGAGozyNlnPId1WR0+WsSIBRoPtYixFeKCOrBbwHr2MMAYRfWVRQQQRcEUJ6jpktsTovKXIlG2EZVEQfTZPNpE7LczLaoi/GKC0g9lo5QsdGIySmCg3lTdJig5CzCg/wfPEvS83OAnY7GDGG8jLKwxvKpJUbaxxBz1TQrvbWPoPZ6n9cI3rdy4zZgKeeq6LtdxDjY9jlCIzGRkZaVGRVBRRM6N62iATgywWMHxFRZPZkqTmYTKBvDx6gcy1pxzSpZzs8Dyb4XgTYQxEIbLqEmlD1Q6xLEHqe+iyjeyGMIyxW5KhUyLrJExd0qSLKWJcsMuEnFnqk50sYR3Ky4uFqMOnFp/mv73zL1/4zk23R+m+ZdrfshMyGB42FJ5PwcC//rbb2L53hl//vc/itjVCQ2zBkw+dQhQ8zHAEroN/aIKXv+4uivvG+el3f4gf+oGXc8cbb2bt8gbTeyb5m488SeYptILMFYxNlL9m/bWFLWxhC1vYwj83toyDv06gM41UkizTfN//8e288y9+lB3+Jj4Jd1aW8hcJkFJg6YznT50kmO1gMLi+YMdYimn3YTRCpBkTd9doJw43zK7w2/c+iFUGLAttKVTbAVvmyQ62jfA9kmKNf/lrj3NoqofVk+htY8hiif6KD4lGDVKEELnZZbmQrxaXCl8+LABktYJo1JFSMHKGFGQPe8wg/AKHrOfYtXoUOUwxsSDJFGnJJZgqkEoQwxCW1omHHUx/SHDW8HvrB+hIh5/9w+t5x1Mv5vGFnayNOSDgmf+c8df372N1uY6aGEfVKrhxRtKXMATl5FL0rJ/lqh8DxmgCNwalUIlBVVzcl7q0b27SSXy6iU/jdQnTh9Yp7Iowgtz3Rxu0C2k5Nw3VZYdSaQ0vHKFGKXHF8KHn7mDjcomb75nnh29/mGDNIksUbMSUahaM9ylfLhDeNEM6ViC2BdWZAcdbs1hpH/WFAA2EmUIFUFnJECnIGKQANUq5tNpENxyySJIGOT+rghA90jhrAUJaVL9BIPcnfHp1G92rGvSGDn/7B3v403AfzvoAbUnIUmStgsHkxJlxudSZoRPVObe0D78bkqURhfslYx+3aJ4pUDsL2jGky6uIUQRxyrljs7TPl1ilhDsdUz8w4Jp/dRoRJfQ3bMyVyWryn5avtMrBPdeussdNeenLL2KpgLikCN4xif53TULtUzjfxV7popY2SSdc0qk6m7c22byxQXbapfKlZSpfXCC5tMnUjjF0RZFWHX79Hdv5w59t8I5fu53J0oj163Yxum4CUyxRvxDSOKtoPJ1QOKMpbMK//qZjvHl6EiIQPVh9cIzltSat41XKUqC7XegNIU5Y21XnaPUQL/2ZiHTbOHFZIADHtKkcDuD2GCEF1Vf0iG6IyXywRxn+ySHq+WXUiTmWWkUW9szQq1bIijbxpODi5RrZUh+10qWwkeH2MnSa0t8GqQ/GlhglyHwHU/DIqjahUPzhpw7z4x96FWeDCdRIoz07bxcDgkpCe6/LRGMZ++I69rGL6ChCbPaxPcl3/8BZbp2cR8Yaq5sbpcbhAJ0loBRZSdA5DHM0WCqWcNcMwb4G3bu3079zB1oJ4qrECEFcyc2DtRLU5lJKFwM+vHIjn+tcxYneNjwlKEp40zeepTk5wpOS5tEhzacHmDQjsw2Zk/tc3DK5QmcgsdYGyDCj9MXL6PVNJp0Vth9u0bvLIq5baMdgkoxsrJin5QwGqEGEUXnSmXt8DvfZZbJRgJluIscblC+GOKsj3FKEXUlI6mA1M9RkEYPC7gQkKyOyUx3ioz38lTabrQrHnt3D1OYI80QBo2qocg0Zaeyexi1bV+RBBv/sGjockRAjLEm1FiCkwci8LWvmzi7ZRobuaoq9mCizsEwGBzOW/20Fq5XhrAxzRY1UpCNNgsf8vzxAq9fAn8/IVIq7HpLGQ0rnM3BsssyidcsurJsnkbaNCg2ttSqnVqb4yOf3EGobgL95+Co+/5ntiL8uEnymjEwMdGPStkSPNyCIsfoRtHsQDTEmyr9b237B2ESaDNnOvVRYH2GfbRN0JGGQoH0XUZTIYg+9sYbY6KDao68a35yCZH25SZoISlWNnC7nqUcFH3wPih7jlT6jfTZ6tolOEzAaEaZ4XRtRqxLtKjK8dpLYN6jnLqN6A4TWmE4X0+1jRiGM4rydSQjwLXSjgDEGsbCJVuTK0S+3If3D8ddSJNfuhnKJLIkxaYocRegkJZ6soMY8lFH0nTraUehuPx+PG3WQkmB7jXTvVJ4w5zoIz0N4OQlqNlpk6xu5YkUAcYQZDDFJghzESGWjpupku23CaY+kYdOLa6xmdebDOt5KhIxTzNwKJs2wTl/GPT4PxuAvQ3FFYm8akrIkqH0l6zqzoPNt19N62QyJthhMVF4Yra3xhKThI4CRpWCijnQcsGz2Xj9DrdCmvSyQnRam18fe34Ugl8QJJeknBaKCQfQNIjO4S5q5x0eIWCMrINKMbrtAmko+dPkxsitFggGwLV45sRe8DFlO0NMSr2PwNlLe/eMf4DOfO8GP/ptXMl73iTF4rqEw3URWKohCrjxydkzwI7/0Taxt9AnjlE53xNhME2VbDDpDbrtlF4wS4ooirkoWu/1/RFW2hS1sYQtb2MI/HltKmq8TDEcxq+s9/mGs54Hi9bS9jzKpNS2AQoFU2aw+fZrO/IDOWcBYqBvGeH4YEvXyBA8WVnjHb12kUM69LgDe9mPL/N6v7iTa1cBZi/NlLn2FwIgTIOZiOkH3VR3qbUXrsxlilKAjg7Az0poCAdqVpBUXx/UwWQqr6/kRWwpZKeeTh26P8kOLjBQ07vIZ+S4yjTFK4E4WCUc2ulXE82KyfkZYdZArIxgGOIO87z1uK+b6De5/+iCvs+Z57f7jqGLEqWiM+CcsxMc3eej7e2CvMXZblSOvWubEk7vZbHsACNtBeg5CJIgwy6N4hcG+sA4RpBMVbj1ymcHxBGtfH20EtsjwkoRbtp3ldK3OnKqBAJEYGF2x5TW5vP/4+yuY/Qqh83Qge81GhIbbJze4frbHvjcN+N6P3UQxhfqYJP7LEJAERVCdiO71AraVcT/SZ3FuDiYcnu9MAIKpjQ3oD0lkglQ2qTRMfnIFp2Bzyw+d5OPDGzGtOjTAhCEGgT9UGJ2x8TmL+/fX+fsdh+jdqXAvDVCnU07+oIs5t4ColsHxwHFR+svGCBZGCjIV0dvwcE8tguyTHZqiImMsZThcG/AJf5yimcW9/xLSUQyOtbn/EUF9Z0jtVQVqbwahNOzSNIMR7ZVcwURgUCsDkskKUc9irq645oYFnnzG55FjO9lIJxDaMPZciL3QItlbR9Zh/GZony+jznkMdgrGOg7+ksFtxwysiE996CidG2sMlM/U+y9jBBzaNs+7Lh+m9vwm6emUtO5h7TEok2DWILsQ41cFr7j3Ke6xFZ/9iVlUV7HtbR2kl5EdlCxvTpAdSSg+fBGnkCK2V0lihTsFM7Uhlxc8jATu8DCTFhftKuFliyPlRfxP2qg4YTRtUzo5wu70QAm6N0+STHrYg5TS2SGdpRIH9rRYHi8jL29iZsfzS3+9RdIcxyzk8dsAiS2g6qBVHidcaI/QcQmdarKSA7Em3lUHoPdSD+FLauUeHTFFcHCcZKZE2iji9ODDD13Hy990kmcfbpLZCq1yNZEaQFjNI5Pj1CFou7hphudKghmPuOqDFjgDyEqg1zIouETXwbZbFwkWJK37bBaGNc4OJ5ny2iRZxosqa+y8NaEzKPCZj02hGx5Ch4gkxO0oMIKkJDn63DZIYtQoRtuC1ut3UXi+S79eZb5vY6qCqGYQtiFpqpykTNJ80m007tIQJ3GgXsOsrsEoxdQqmEFEXJdoYdBJnjYkBiOyC+tYdQcfH4BR1aFYVWTjhmSPj72WUv/AKfq2j3v9FN5okyQxFFczyAwbmzXYaSA1qLUB6ZiLdhWi4OOLHp1SicyykUHC6YPT6O/o4T2Y0WgYpkubrIdFrEaKKBqIMxIMygiM7xBcO8Xo6ir2QLJ4sMLu987j3FGn8lyM7nYwlRI0G9jdmNT1Wb2miHU6bxcZDAqceWKS9rY66Z1Xzh/HQ/kSmY7w5gdEYx7JrgYmSilYLsKyMJlGlAuk1+xAjAI4ei6fUSvg5jLB1Cz2BrhrCfLSCDHIMLMO2ViFFEFyj4XebSOMxnksztP3rqAynvBtP7XKp85mnPxog0alg+OAvLQKSiGbDTQwoETp2ojOoELpswHCcnDboBYjTMlDjlLSkoOcW0P1r8iwSkVEuQgra1jPnENbCqy9ZI0CohfgOOU83n61DakBR+ZjrC3A8xFhAL4PJsvJFWVQVgqexGy2SL2Y/uw00bSHjhVxK8HySlAwmFaY+8/YirTuIHsZQuXngJA5kflliMHwy9FPV1RW+XPuZpYnCO5O2f7iS1z8wyZOK6H51EXUt6Q0igNSE2AB1noXs95FCNj7DTdx5uwmxZbBGMOoKUHC9K89CeQ/XXjtLMWnlsjSIv3bGiitkbXc/2j44CbsyMdKlRi0II8FzzLijWV6oaC9ZpFG+dc1qo1hZSuoNMUMRxR+cxU9VoQr7Xe2Slk85mOI6J6pEHoF0kThas2dYzup/fAGz3z+Eo8+vZPJ6w/RDyUCjTVUaCvLyZs0YXmQ8Zn7TvCSuw8hHXCMIBxB7bX7Ob/QQXXKVO/rMzMzAcAPfu9LWN8YsH/vBM/9/XGiqQkW2jGvn6wjfBt7PYC6w79841ar0xa2sIUtbOF/L7aUNF8n8DyL6YkqaZq9sK1QeB2fefJNHFudBEDUK6RRRGu5DUagRF5cp09ucv8fjmG+TPBkGc8sN2l6AecDxSgD76oRsljCER72ygAKXj7BcRxwXbyS4XLYoKc9ZM2gwhR1eYPJA+vEdU0mE9y2wBnkiSxJzSWp5J4HeYGVoTtddBiBUggpsDy45VsWiKuKhZvG4E6bpKqQpeKVVWiQqUZlinSyAjOTuV8A4PiaiXTE1CdS1CsGHI9qXNtYZHOhSnqqhBo1YbyJva/CPf/1JNEM9BYthBSknoLBgGwQkAU6nzBok4dmlXyQgni6SG0iwGhwwog3N07xLY3T3NK4zKHyMgera7mEJYowcd6rbw0TrEGM6o6YeUlA5+oSw50ewz0OqZ9hNjTHN0tcjEs8nTQ4cuMckhRb918o2P1lgxCGbGAxd6FB8LkM92wX9gjC2CWKbVZP+5hel21/9Cyzf3KCqS9uYjXLRBhsB14xexrnysKgliJP11rpEO0qk3UMD79vko2FEvZigL0UUrnZ0AxHOE4GCEynh253MDpDZCukmxuoi8vUS+v4G22saglxsEnmlXjNz57gHb/+JcS1ASoyeJt5xHRxzwTNmSHSF2jXkGzaSAuEMFx77QI37X8ePb+IXl6FVGOv9BBJRsc1LIaKP/mZWRY+C3Yf2ACzDJXLGbJYRKYae4fLrbdfRO5IKBzuUT/pkRSupAG5FhhDMAipfHaZyb9YQHguolZj8jaX12ZdhmEhb8kRir03naWxZxmxvIl9cYPoUszTHZ8/emBAY63ImHaYv28bcxcmSU8ExK0MtdID2yEWNdRnN3jDtuO8Zs8c7/nhT2E7MYmO6JyEM/d5OM9E2Gs2Z87tYGK9hb84xG6lUHfJpibQtSZCZxgbwqYkeJlg9Ztd7tu3k3S2gpVqrIur2Bsj3FodvzBiOG7ytKRUg6vIKj7CA6tgcPYq3ECDNMgwQxpD98Z8wlX7b0tUzra5tDxDomLibeW8PSTOcs+TQcYnsl2kOkVcWECttZHKQaTgn2lRPDqPejzE66fIiw5hQ+K2NKXnetjz61dSwSBuWLjrKRUZUKjHWNfGsLhBKQ3ZX9kgDmweffE073v3DXzii4d46olZwrpP2LAJx12cCIgEmSMRKRTOdLHXRwgtyJoFhLKhWSFt2fhzQ+r3LVGci1B9sNcCUgfa11RyE1FtsI5eAqMxKk9MslZ7mPllsqUVIh+ygo38gov8nEftySFO18ddXaUwZRi8/CrSYhHcIqMDU+gZixffdJa733oZOw1JbEHQH5CND/NWoChCpCFyGKE2B5jDuzB27q6ilWJto4oIMqxBDMIQTxjcZ/qoVsDhqUXuqZ3mYGEVaUH4lwVEkGKloOMY6ReRvSC/z8ZQfWwdud5GrA0pRX1K6QBLR7grA4Q2qGGGPJHf22QiSIVg++E2O29Zxr8pJ0q8tQQ5ijH9PoPtkrgQ0TviYqcWWmtMkr6QlmWArOS+kBpNZkjmIalXwFKYJMNEeTuUthSU3Ty++kSA6FhYgwqiWnmhMJE2GC1ZPWsz3x5j9a7dnJ7excKcjxgEEIZgKZRtET2zztLpKpFVIN5Rwd4YYK310WFINhrhrUZUnu3gn29jPCe/Pxd8hO+DyP1npKVIKzbGupL+JUD7Ntn2SShY+flS8vMEwmCEqJSRjRpSWYjVNurCMmYwRG+2EVIig4i0H5Foi0QoUkuSipQsS6BRz+OahMK9MITNIaboY9IUozVI+RUjfcjfs1jIW7mSFAkUH5tDBSm1w+voQCCudGLF3Yxxt4/jZOixPK7ceE7exmRg8Y8fwQ4NZIbUFYTjeSuV9RVuDJNprNU+xRNdrL7CJF9uhILBa65BBl95sTYZjEYYrbl8NuWdz9zAac9GX1ElJct+bh6MyEmtVCPWuuzdOY57RDP1tg7ewSqmWqJ3qo5KBNVCwL7GOk88+dfc/erH+Ne/oNnxhhqbIuPGF+2n3C9gz1s4l1wyARLBzfsneMMbbuCn3/9JltMrqV0Cjj/Xwe2B9gv0b5lgz22zAPzlH36B97zrQ5w7tYQ9VSebbRBO19mxrYHRBjs0FNYSmpXS/0IVtoUtbGELW9jCPx+2lDRfJ3j7972U1fUeVx2cfmGbEJJzJyXzZpq3FoAowZOazFKUii7YDv3Vdv7a1le8YbTvsmZJRsbmUlrhB566k8J/LUJwEWtZE8iMihvSS76sQdZE+Hyrd4nf+EyTzQ+7YEJo91i/PAFTFhTKGONhFSyM20UXHRjksvEXSr3+AIYjGGtCqUiqU+7/r9txbE1wf8rChk3WzmCwSNoooKYKpL4iCQNKkYJhgAwTRKVEUvR5/Cd9BtdN8aO1R7m+2OXZYRV1vEhxuY9B5v33DoCmONPHyVZIC+NkxRpZX2JZFrgKjQTfRwgQqUZMT4Ht8JlHDlJaXCV+rMkv/OhppDDMuwm7spTnu5LMt0iiAEYBeFYeaWxAXFxh4p6Yp/ZrhrHDbKnF228/yq87N1POWhwNC1SsEaoTYjY2GTQzovoEwhisTogoG4oXFNYbR1ivLuGMMszBjGo5wBhYexwc34PuABOHhGPbsO0Sg5skT63voBHH2JtXfjvXwkQxugq1/QP0hZQUAVKQTHlkDZfScp+f/6PzPPNshd/4w20UT/UQlRIYCcUxMmVwww4vnj7PU8crOIdrrEZ1vI2Qv/iVG6m/pM95NY2/bLBGBuFY1K7uEAxmiQaGPdde5ISps4M2BsHkth4P/UItJ8YAUYJsokgh6HDiYx43vWxEydeUCymdzRE7f+cSIkzIds+SzRRo3Zxx3w9/CAl8+yvP0JSG901cxR9/7EV4RxzsUoEsrnDV7jlO/F2ENAoxVgHX5e8fKPLFk4bRddsIrsqQQcy53/SQGKbu7TP3pRpytc/vfegOuhdryFQzMdNndn+H0LV5bOTSeO4i8bLMPZeunsH9Xo/1adhIXbqRR3ZmE7fVQiVgNcZImg4LryYnPWdDWLDxJwOCgUNBaISjSBs+cpBQONOGWxzERIYqpsRVG3f7OEq6ua+JZaGijKyaEbsCpMpNhV1BsRljlWFmOGDpQhXfj7EGGoPBu6hhH8h+hl6AtW1lasc28J4PIUpzQnfPdsKex0BqSoNBfi1EGst1SFODlAKtJNULBooh4ShDjFyKFwfo1VUKKqb7LSUIXVAGb2NAGDgst1wWrx+nUljDjhMWhlXGvT59E1OdXObZzUnW20VkLsZBC0044eXx5VfuH97SkNFtTZLJMk4rw2qP8DYy+odt/AeWcVZ6rL+yQjht4ywIKo9uMJwugtjIDXqFZDCWke2cxs9S7PUhJslbW0SiEY5D9XSKTCErOtRKG+y9ZoPHnt6G3lsgnA0JxxwS18JWAcOqxzWvCjjRKLB5LoUJl9GbJWIUY38gwV7oo3fUkQLSqRJuJ1fqCR/CqzXus1d8QSxBb+TjGYVyDNfVNrjDGxLHLZrFPva3xrTnxqluJIiNPnp1k9KmT2t/hawsKT/RAjT+Uotd1TWyMqzPTLC+4mAkZAUHezNGmYRYOsgIbp9Z49kvTrH28QyOwGAioXRpiBwkxJFD/+oy9lCQVldRZ5YwYw1E0ac0m7FS1GglUNNV5NoAsixPqRKC1NFk/S5KOKjJOrI1IJmqYxwbtVzE+sCQwwdG1PcPefjPxZW8aRi0FV/8XAX/pgB7GNGMhngzhtaFct6iFEboNMXqD6h/5gIyikm3jYPIiRixNEJvt1FzAbLZQG6fJV1aRBig18cYzQusklQEUw5OINETHvax88S3HyKZLODGMfbCJrRyA1uUyj1OEJhhABUP3e6SKYNTbSCkRA9HxKe6pNumAEE65jE80qT2jERvtMhiC1MrQpQgHAtTdKA7yq9lnUG5dCUBKsXUyzCKMNUSsveVdrAgGbL4yW0I30enfQQQDi2e7cyQphbOmVX0jQcxlkSduIDoB5jNLqQamWZEMzYqUZjlr7T0GCA5MIZpFOkf8dGOAcdFFPIUuKhkcP9B/aFSg74Ssa0ti6d/dydfuP0apuQJlE7xL3VJtjVwL27kCxdhBAWPahrw3e95GfcffYyLf3cBEcY09vfIKpI1RxJnkrXPx/ztw5qpWyY4lTaIxyQvfuvNPPnQiEd2PoMxgmTDxb5s88QffI673nADYZTilh2SjST3qRMgMpApuJc69J6ZA+DBpy6y2Rvx6GMXeMkrDvP3j5zlyOFZ5hfbuI4iigW4ijMX1jh4YJqvhYceeJ6xiTKHDm/7Hz7/U5/5BB/beIb33PJGXnfgmq+5ry1sYQtb2MIWtkiarxN86nMnuHBpne3bmkyMfcXkTpWGPP/sLNwGZjgAx8EuSYq+Ta8zeOF1mU7y1iYDeuc4959P2DxkcXJjnHhlO6I6QA5GGEaYHnjXOfRWr/gqpAlZa8T7j+9Dn1ulMCsZPWMDhl63iGwqjCeIioKsInFPC6xuhBzmK6pfJefyXESpgOn1MMaQBQI7jNE3FUmCFNW78mpp0K0ORhWJtmsKT/YQykJ7FipKwCmDtLBHmsGFAuJIl/7ZAtXnY9SlTbQWCMshKJaYcfuI8ynZxohEDslmJsm8SWy7hEhj1OUlvJUQtIQoAdPHXxmRVT2igUPSs+l0fEqFmKzksbPQZl8pJaorKCmcdka2rZqrVhTY65usP2LztjccpUuJJxemOFJsc3NtlbLR7PE3MQZ6wSyiUAB3AHFuLGqUgfYQsxjCiQLWuTJJKmEYs6gqeE6Kb3dyWXyzgVux0K4iKUmyvSnnl6c5G0vKuYgJM9WAMYXnZ1QPrzNcl8y+POCsyr9n1Uk4tK/Lzv0R3dYId24dUa7kXhBpShp5WAXFS75nldtqfU59ocht113gr0/uRF/YRI48lh5oIidsjAVpzaJZKvCSHznB+3/qBsTSHGc+W2Ly5T3uLV5mYBR7t8Gnsv0ocuJwcMcU4Z5JnE+e5tkzVS5+osBAzzK+XWMCDzkMEZkhcS30pMX+o0/lthLANtuAgLGZEbKlkcdXEa2Qq27tsrQyg9k5QiyPiIMAkwzxP2Gz7zrDBSNzjycrxiQQG8VQAsMIWxjGiwmJV8QkGT/4XQ+xb1uH+VaDh298LfN7Gox99HlkANl1CmtC0FcuoVCkqzENdZnl2SnSaplCIBnsskn9DGzBhb02e9SIUbuOGQ3I+kNkvYp2FaWjK5QfW0E8U2b7WzocuKfF51s3kCUR0mhEoUgwLukXPWpPGIqnzuLWqyRxicQzhJmHGED7Yok0VcTrkoInMFpTONGBV0A8XkDLDC1AFErIXp/w0DhyoYeIEtRowMy5lM3tDUxJ4gdWbghu2XkizlSD3o0FhrstnE0Ld10yJGN0116cJEJlAjVM8ESCihLMyga91ZjeXR7pvU1MNGTPVIeNsED1eoerXruGMT3OndxGmEq0bZHZkNZdrG4++Tc6w/T7pI1tDO/dS6YgjTVxU+MvdXB7PYw23HD1PAd3rnLfuauQRqOmm2RiDuW6ICXiYpv0ZZPEe8dQoSYrulhrXaxuSGgZRDVFRoLMlLCF4OwjByABuxWhKxprQzF2dsRgRnN+NE5hXrO8vQpaMvZISPw5yCwLbz0hOrANIS30WIlscYB9qY3wHMR0QDYj4GSucqA7YvyPVgl3+RgZsrOW0FSGbbZmttBlcVAm3F+iutLOr3nPBb/A9pOaznVVbNtDpQHOhmbv3SHDnsWK9slKeby6Sg2DHT7XZaucbs0gUqgGIcc/MI0w+T0gmfCBLqx3KG60yeQ00cFxRDfI212TKybkk1VKYx2CtQoKBzwHHQdIwA4zhrsdaHn4PQsZGYQRqCfOkE02kIMR1kSTlUWbtlfAHhsQ9SN0kiud+n1JebFH85PnGFWrRFWDKJfy+5DjYJbaEMV4iylxw0bYitTz87jq2Qrxa5r4Hw3IgohwqoZ3KctJoDSD3lfGQQYjdBwTNEuIjsSp2gz3F4kmbLKoSWVhE+AFhYZJEsxmK1cRxSHGFigkslJCJymsreO4LrQzjBKoEqQliUlTiBMyR6FrHkQ21iBEBBlCg+72crP5UQBhPk6KFORYEwxkYvSCu+/Eh86T+hb6zqtwT6/l9/UUgtAl9SyGtzYpnY9B5omC6YHtpEUPf6mHFBBs98lih8LR5SsJj4bei3YjtCDZXsOUFPYA7KHMfYYA1TMIW5DZFmIUIZVCjjXQSQIT4xSfXcVfqmBsG61zBadlXMTkOGY4ytVX9Srasbg6uIov/d1Fsu5phE5xDgfMjm+QpRMU3BhzteEjP1XhTfu+C9aO4xpNZ7nP9774Jk6f/FLemnWHxlwuo22L/dMNfuKGPURxyvf9+z8lEwYZgkgNgd1B7K5yz+tvBmB29zjD86s0Z6poY3A9m1GQcMO1O9i1vcmZSxtUyi43Xbfza9Zff/knX+QP3nsfEsFHP/fvKRS9r3reGMNHWo8hbcOPP/3BLZJmC1vYwha28D/FVrvT1wm+8PxFjrfWmVtqfdX2rBlRevhKC1SSMtIpCMnVL9qTF3lXpNRWkCeJCEDMrdGd8/j7swe4PDfOO16xnRtveeqFfRoNg40QLAthWUTjZYxl0ToeM3zMkPV0XjwXFdEeTTSWElVTdBZhBhF2N8XuRcgoQ9nqq46XNMWMRpgkxmQJt91+gX1TczhLbTw7hboAx8bKbIRtYZKA6rE2uAZp2zDdRKcpZrOD7A6pbyS853dfws/82L38xk/dgnephVztYDY6ICRR3eEeL+CWnYbi7CSqWkY8v4B3qYPQBi0UILGGEUob7KkZRLGELBUR9RpyegJRqTC32GBprYqppHw2qtIqWKRFiRVk1LYF3Pv2s5SKA6zEYMUZ6hfrDEvwTLvE3GKNv7h/Fyd/LEVV8kty/bLD0mMO6co6OjTIIEEFCUHDBZOiRgnp+ZQw06RdzfBLmolfnMN7us/4bIg31kPojMwp5QlYBU3ad3AqEa4T4p3LV4PVMEGkBgtBrZxRfuMYS92rqNV6iIJBFxVzx4osXnRZvuwg0oyr715h752b7Nt7hqmbVrFuG/KgXeH8uQqWbdBDCU1FUDboYIS9keKtp9hD8tjmMYe9pWHuZ7J9HHG1T6tQ4yV+l2+utgiWNLpi037DPoKDTYZT4xghCfePs/MWg5qdIMsUwx6Qgdk5jd42CQWHLIWf/c1nWUkg0vC5hQo/+tuH+e23H0QIg1nURG1FP7DoJAW0tslGAeG+Mu3X7+fimw/yDd9zKieUHLAvbpIlElLon1PgKcxEgdHjffbMPk/sO/zqH97N6XaRP3v2CKF2UL7H8KZZTLWI7vpcnm/SWahSNikrbcVo2cdMTeAJDyuRqK4CLVC9mNpfbpJ9aRXfCnnNNy4g60Wk62LijHBniWSiiKkVOPPcDKd/1yedHyAXWujFFeZfW2Wwv4rVsin2bIqTTfxyiooyrN0xYeoQhjadtQJogdVNkZfXUPPr+Es5IaaClPoDq9gLHcz2cZKdddKpMp07ZwhMF07O0/sLB4YB9mKHbHMTvbCMCQLCcYdgyoW6xp2JsEye4pR5Dpnv4MxY2BEU5gP8S0PufcU5TKeH005RnYDGZxYIH0l5vj3Jcq/MxKstnv3CXh557Go6VZ+kZhFVIStaiDTL04WUoPjEAiaKcI4vUvnwMZzzLVAS4ygKWR/LztDbHV558DmOjC1yx+QlrFhgrbbRDT9P2Iljiksjqic6mILP8Pa9EEXILM1bxmyBN55iVTJUt0XnjEd/5JDWLAqff55bDlxiz8IG9eURhaWU9WGRJ588hHzGpd8tMPeNN7H7pk327t9AlItILIQxZDYUHz5PVhGMrnJQE4oduzeRV9pKxDDGaie4F0eojZQvHZ1mISrx0Qu7Ob/RZP6+SWQiWL++hhACyiVEwYMw5Jv3PcWbfmgNa6aJqU9xtHeYc8VDrHcrECaIOIJMUD6TMP94GX89xWtlPPzQFMlGgF7NY7Sddn7PT7dXSQuS0rFNap9dyH1dlCIjI6m4uHeP2P26TSbNeURvCGmGcRUoSfH+8zS/1KaUVIEAMwoQSiHDBDW3SuemJhs3l6nv6uGXEmSjgNk5Q7me9++EbcXLX7cMtoMoFdBxhh4Mc6WdEIhymaRkIzKNSgXZjI3qhYheiFgf4f7lCknZJTowTjDtk9kiH+wKPsYYNC94HGMqPtgCU3JRgcZqhYhEo9YG+fgoRN6KlF7xZBOQTZYZ3rGL4b37yZp14ixEh2FuKDxZwh5onK4msQzdPQ7pqA/GoISFNUxQQYyIDXR7mF4396QJIjK+0r7M6jpEMaY/yFWnV8ZrBbhBinXfs4z25Ox7bGwudxpc6jYR7QB14gLq6GkIY8KCZv3FDYwj0UKgx0OCWkDjeDtPr7Is9FgdhKRw9DLlo5u4LYkYRGTLy/l5aUuSeoGk5pF5NmnZIRO5IsmEI8TEGOr0ZdIxn3D/OOnlFnp+CWNZUC5hXItkoszsy67nwJFt7N9VoVj2cHZMcrgxz3ipS8UKcGVKtmZ4+Vtr1CpPIdIUOUr4g//4cfY446wfnaD7t9uI5guIzR4iSTEG9s2Osb1aJCoJjCtIagL71AJqtkZ45y5uu/sIAC+99xBHrp7l0IFpsis+SGmacf7iGrFr4ZQ9lGszv/jVddV/jwe/eIq05BIXHaJ/kP71ZXz8wRNYVsJPX/tZfv2mj3Gpffpr7m8LW9jCFrawhS2S5usE6Ywi3q5Qha8mPS6lFbzFKyuFStE6WKDVsDhXeI5RL8zl6JZ6oUBFSVQQ4ywJ9MUC9RXNIeWzkSho1vPefdsis8swGGGiGGupjXZ82hcmMZUy4VmJaXdI6gVE0UUYifQlZgoSMQRLoC1B5tu4JRdV+gfCaa0hjBCWjeVYvOqbl/ihX5jjxj0tpDAgfOT0JGK8iUlSnMxm91/H2NeMMDKEVh+ZaSQSSh6VyPDTP/EWeqNDpKnEKAtqFUStirEtBtstXCN4vD9N/zqBigPsS+uItW6+UKkk3TceQhiJKBZhNAAlyRolTMFBqbwo/sMP3cwH/u46jg4anAynOZtUIcuQzTqv/+sFbnrlPG/8iQfRGKSBt2y7xPdOneHbDpxhlLr8/Q8VyNYjPCtBAJe/VEG1NMLzWLS2YYUZMkyxfI3eOwFXN+h7FTizjlleRcwlVLIRU90Wsm2TbOTKBm0E9W9YQrxxE73iE86ViC6V8U/lK8JZMEL2A5xSSrkQMJorEV8S9PoeOhGQaprXSNR0jF3OUJbmznctseeeZd7+zlV+9R2P8IY9pxCfqHHfMwd5/ds2GY55tEsOo1u2IcbrCNcGIYga0Npd5FKY8hfvuQE5TJF+gfW7t9GjwPLQ5vOfL/E372nAKEKnQ7Ixj6xRIC4Zohmf583VtCtTFPQGm8+2QW6izBBbS+xWQHFhxKCYT1yefK7Ou/7TGznx3ireqQHGhCBssCwm9gfsuaGLUJpouoQ738Fa7kGmePtfvQErMKjQ4CwMwVWIsRrWEBQxPgNGxwXeSoLoxQQXJW8/+mo+cW474URKJjMc7ecEXgnsvsOFi1M8dHkHYzOGVPgUNlLsboKKMsprMcY1ZL5Cuw6xW6Az67L/yIAjt7TolRSNCwbh2/Sva9B+ZZPe/jpzpVmYqGDc3Ei0sCRxOuAtC+xYE48Uw4Eg7XZZqTdz4kIqGBcgBFmjgKqUUdUK1pXWQ7sVkTRcou1lxPIGMpZYlzYwBRt3M0Vrg7qwgsgkMtF5qloU5bN4Y9COQjUjnEKCayKKF4YwbJNWQ6zH+xxunuaWO89g3RNzdm4Ms2uC7NAUxbmUrFnMY7WNwHYMzRuXiJdTzj6zLfcHUhJsQBlK95/FuHnHnUoNKEnhuXXs9RH+qWXiskaMNPGhMdr37Kd7x27OPT5DZ7XMxad3YFyHl9x4juZ1DQgC4psPMnjpNaT9kOLjl/EvdxHVKtVDHsW7ymipSDsW/ijC7qQYrVh58wzBrIfTjLk5W6XmaSrXBQx3CYLAId0IqZ5YJ65C5CpWRI3S/gCZJMQVQWYLjIDOPbOk9QJpQZFMCUbPJ8CViV61Qu9lU3TfOk7/3hK/1bmdH3j+bh4c7ST8q0nqFx2s0BDXLEwYo8oliGPGGz3e/G3HefMbn2fH1Xny1jONCU6MpsEYhGshhERq0LbJE9sGMTJICMMCatCFbh5vrVKRe8c0q3RevxNjK6y1Th7NXithDuwga5bpPFmk+4Bg9HSSt60mMcQaMdSIYYRsDfL3nuvCegsdBDmpVPJJdpRJHcP6qsU933USa9oinakxsydv6ylOp1T3xPl5hiCR0N1XxvhunpbUKJE2ndzPJdWIy31i00JemkOGMWqpj3X6MvbZRWSUEu5vom2FDgLMZBPsK6Ji10bGeQqRWh+gbJfiBjSOxXgrV6LFRe5lo9UVIs338mtBG1QvRPWHqMvrsNlGTzVIZ5oYciNgIUVOSub2Xrl/Vi9CRRmJY/Lkw2IpbwfSV+LEq5W87ckY9NIKppPHuwsMOw8G+MWcyFFAfCj3n5OdBC8YUR4O8J5v5Wo320HWa4RXj9G4mBDMFugfKOOcLtF4NMxN2gGMprAaYbVGWGtD7PkWcRkSXyDiK23RIm/70wrMKCRpFBBhBFGIaHUxvo2uFnEutfDOrOex5UGMSSJSk5DsGCeeqvD40bNEQcyH/9Pf0l3tEl1cI1r1me812V9Z57biOW7Zs8ibvvs5rpr4W3ZUlrGGCYvzLZ548BT2ZZfUk+hVH+vUHJYl2X3NTj7+e3/Pt+/4fkghcyVqBMiM2llN86mQdJSrv177ymv5j+96C3t2jbN75xjv/InX8yM/8AqiKMVSEikFYZj8T0maesm9EqN4hcD77/CxTx1jYiNCYnE6anKy/eNfc39b2MIWtvD/RTz44IO84Q1vYGZmBiEEH/nIR77qeWMM7373u5mZmcH3fV784hdz8uTJ/+l+f/mXf5k777yTQqFArVb7H75GCPF/+Xvve9/7z/Cp/vHYanf6OkG4KyFJItKi+art/3LXy/gkH8wfeB4Ghcaw8FSSy+kB0iwX1WxXZL0C9AY4qaR6Fjxb0HYvcW6thtvpIDDIRpW0WYbzc5CmKNvCOizQy330qR4igWSizOi6WUzbxnEVTk1i+RJMEeOEpJ6FjFJ0nJEF8VcOONNgBHJqAgOstTY4d9Lj9f92jfhChcd/aIj0q5h2GxlrCm6Lo0t78eMhlTBCtYd55LXJSFRCZ6XFr//673PHT11ib2D4wg8eQvcHmEyjmyWsQPC3x67m0WaV5lsGrMxbZJslpOXmhBSG3uEi1RN9RBRj2m2MtLAaNTD55SNiw8XnSlw6XuCGm5e5ZvoyDy7vxlvJsGqrrCcl6u6IxaxCZ7/E/zRsLFlEexXPLk+SVAxunGHG6qRn+7gH+hy5M6Pyb5dJRiU+8Xi+Wg5QTwZkoU0qFJNZiKnVUP2M4TWTHPiu0zT2tPjsH9+IfYckOhrgbzzH5fFraK8ZpJJYbQedpYyuGQcgqTiYXkjl7ozwasHB6hkeeneT6qsmYbtGacO+1w/5m9+aZvFYmVJJcuYL23jmw3s4JjL83jyDVBEVBesDj8YNLj+w/yK9SwM++dzN2KdtTM/i6voKn71mL1miSMswfD4g9TyGxYzkyYQdrxnx5CdrPPSBat494VvUH1hBpGAvDtE7pyhc7pPYNvte2sLeMSIIPTq3FrEuWbnpZ5QhawP62uFEmvH+X9hP5eIc0d4xZKbwHp1HNOoIBM/1N+idrcLgPJv/4jDu5QHDwxXUMshIIjoZsgLe0gjcIsIrEa12UTLDTJa598XrFA45fK4H9G3itQKv0Y/zudbt+KspbjvBJCn79/T5lmvO8KX5HRyeWOGHf/JltHelqG0WvDhFpRr16XyybsqK0R07sZ9uE58y/Pz6vdTWDMm4RKUGt60Iyw7FbIgTaw79SIv5n3FJdk3TFwFRU2J6uWFwuqOHLKQkz4aYtS5ZafcLUgE3DDE9iXEMWbeX+19sy70msmqB4Q2TOCsR1ukFMAZbKWS9CbMTmDSjMNshaWdo18ZSDrLgo7MeyWwNy0jAwokT/P4QsxLTvbdKMmYx2FagPD+kd0QwDGIuPDBE7SpRnNDY0ykrkztJKwlV1QcE99+3C/8MqNkEayNPWJJJTFqVGF/nMePCYFVrCFkm9mLEmRUG108gU/B6GSNRIJko4y6PeOx9V/HFyrVIbeEVhtx7S5+5FY9uxac/5UNdEc1Mwl8tIMMQMTOJ2ePTKRYRicacCKAQgeNCZihellhRkeI3NBitSlLjYu8cMEjz71L0FtioSpKZfPKriNk+0aVbKtMqVPMJHZrgYB1vNUWh6WUO3gcSVGsVNTWLlBK7aFH6m3kIM0Z7Nul1FfGTQ6xDhnhljMwGa5QhBkOy/gAwVL9F810nXk/NHzKd5cSv29eENYPbVbmRrACEpD9lkfZCqk8vYcoFLj5hI2cK6JWcIHE3E1JXEIxDZhn6d+2i+tnT4DhoSyJafWTsYG8LWD+7E5ojrLXLaKWQcQY6gG0TcFWRpGAjhy6cSyCMkNNTYAz1zy9iLEFzx4CbjvT4b9c2SC9ZfNPPLfLQR+rseuWAT31w+kp7k42dCEQQY5RHXFBYzy8i/QxTLtC/rkHpVAszYRF8aw25ZPAeGCKiEBEk2MMMx/JhewXZy0DnYyCOjXYtvJUIIwLcB05Bo44cXRmfOoN8IcOyEKMA49sEu8o4AxDFIoUvzSHTDF3ykVoidIbqDPHaZcxU3u5EokgrTm6ea9mkBRvlKTJbMtrrYn2xh/SvxG8nKbLVQvgCUa3krURRBEJCknDjPQO+/UdWOXPM570/P0vn7hlKp4f5uVaAifKIrOZy9kWTOB+6jKxXwXGoffwcyR17KC6HJAVFb7tDoWMT22ClkO6aBksSz1YhTYhLBq0yxLhL9ZorJeN1Q8LxFPGAQ/WSQLSD3DAKgUAgBzF6bCw3d06vkD9CQHcIUiOiEclVY1w8dp75U4uYK6WIVoJV6cNQMrQidFmSBUWe+qJLsVaiPSqRFW1iS7Lvmm3oJzXJ4RGkguD8bm7bu4cvPHkfp9Z/H9lw8Y6tku0eR8YwOODQWOxhpYKP//kjfMfbX87xxy/w5ENneO1bb2Nytk4gMyyTce2Rbfy7H3wFv/P797O63iPTX11X/feYO71CpgwSgbLyhTJjDN901U/QloKs7KLHJ4hebCiqANe6Ib/fphGgUNZWKb6FLWxhC8PhkOuuu463ve1tfOM3fuP/5flf/dVf5b/8l//C+9//fg4cOMAv/dIv8YpXvILTp09TLpf/B3vMEccx3/zN38wdd9zBH/3RH/3fvu5973sfr371q194XK1W/2kf6J+IrZHh6wSlegfSPthfLbX9oVe/iON3XonVtBWldcn+UpVDr7yazx59AMhrJ+0rhpmDMxyAZ2MqDtoWxMpiPRiju1ZkIstXk+SBItlcBkqCFGRA7Z2S3X9ziWePlcCAHMSIUURakGSuxFgpv/XaT/KuN91OVmogTJ7uEYdRXiR/GZYCDKbbQym4+cgSG7uK/OYDV3Hp/QlWEKE3lxBFjxtf1GPmYMLvrBcorK+hN/ovEE7RpEdwaIzSo21mDrWw/JhCKUPt7ZFdjHODw8EQf7PGb/3pXbzhHZ/kw4/ey657L6IXZjGtXI5u0nzJU9daqNN9sGSeApNm+eo+kNowU+zyipfN8dDiOP7hixTSmOotberNAe87dhfFSogJDWPX5Z4Bv/srh/nPb52gkxQpFgOwbbI9U3zs0xPs621y6I7zrL6qzA6xiXqig5Y1hAbPiQg7DmkokZsJInFRRlJY0Mx+d8DKJRc1FTDqeMjhiNL+AjrT2NLGWBqNwHt6kcGRHQD072xS64xY+4xi42gdM9fCjWJ2zSyjM1i1NR8+voMDT62wdtFisKp58GcL1G8M0L4kTQyTVsh//q2P8f7f2E4WaT7ye2O0rBrmOpuZq1osfmmSpfUqUQ1UG4QxvPV7nuQ9n7uXkVXFme8wsDRHRYNQZCRliXPQxnkkJFoHazMgnI7IohAVp9x153M8HRxgvGazdG+dxAjs04q06nLvLx0ly1w2Ugg3AkxgCG5McS/XcZ+5gBb9PInmakN2QTBZDxl9bp7Wm/ZifEmlnce5agdIwJ2egEEIWjP1HQaroTn19BR/++Qk8aMGc2cAzxTwNySL9X2Un4vy1JteD9Pvc3BHl5dsv8yLt13mdASvv2uOP7pwE26oaezZQGaaSPuoqIQaWKjlNur8Gm53QPvbdmFWBGFNoLQk9SDxC3T2xjTXN9h8R0i8MURMjmE1ClgBuAODM0gprA5hKPDKNoEUVP9+hcyfxm3F2KsJphdhyjbScXKfCZ1P7jbfsI1IuFiLnXzl/qrd4LskjkChsG4pEUqJiHzUxBgi1JCmiDhDjWLSSgm5nmGPZfhTFt1rmhQuhQyKBnsz4yHnGkp/sIq0QtKDu7CsIdUdQ8wtkpVjVSwJYSCREuKxKnu/d4XEXeTc3+0mFRIRGowvaL9yL866RlcsggmfgswQtQrDMR+VgH82xB4JooaFyDIGuxyMq1AjcCLByC3zJ0dvYCPxSa7ZAwrszQxlYPTK65BxRGkjZWhr6BusK8RhNBBYxSG7D8wxdXjAI8d2oTyXa+88Q7E44FJUhHQ7KNh4zUSe8BMKjGWwHMPtxTUWnXG805toVzC4qoZ2JWY4wLSruL0YMkCnCB3CpIu9J8b5sxEiNBzeWCb8QEptUZMUXUKxQjxZpdgL8qSiYgEsxYOuRdIvsdKqEG8OsIWhu0vgtjUiMhhLkhYU2jKUj67ind5AhgailDAbY1gsUp6pAODN9xh5NsVNh9ITS5i6j0xz5RSdIVmjgvFdhmdK4FsvpBK5BUi6BoPM/XY6NqSK4a4JKnMhSIWO4lx90bDh+goyzfALGTv8Fmf6TcpNzd1vX6MoJZ8/q6DdRacp2Dalyx1krYYs+hBEqMyw+tYdyFGEeixkuHeCsuuQbPpQlsgohlBjLfdRK6uIKINqAYIIMd5ElksMdvnE23yIE3wDsQgxRRCDPtYowkiQcQy2xfCGKYbX1HE7UL4QoRZbKNsFu4CcrGCuJBZKq0vml8A2iMxCDTV4PqrmsuvqPsubLqPMRgYCEaUw6GKUwkQR2paIK2lY+B5y2zQ6SzEXF+i1FWkCrVUbIQSNLy6hr4g40h4sXCySjnnYz+aqUN3pInwPexhgHb2MGGsiXEFWt4kjTfDd1yOlRf3pIeK5OazCDO7pdUobI/yFBD1TYjSaymuGbSnCzZDTmqTqYiUaYTTGsjC1EtgST0oiIXPH3ivBUDoMMdUi6WQR40vMxpA91+7EH68yXOuSTVVZV4poucTFQZNus4DjOtxy1dv47HOX6NjzWK7BlFxmd4zR2J5yIXIAgWmEnG8PWfngU7z0m7tMPFOkeyxCxQYjDPFtVeRf5WP//sN5utPDf3+S409cYNeBKQaO5hc/8wBVz+M/f8NrePixc6xcWKc2WeH2m/d8zfprsTMkmyxigE/d/yxvfeMt/Pgb/hO93oj44BhON4XIcOrMHvzdGVc37uAPTvwrvr3xEKER1KdOItVWOb6FLWzh/9t4zWtew2te85r/4XPGGH7jN36Dn/mZn+Etb3kLAH/yJ3/C5OQkH/jAB/g3/+bf/N/u9+d//ucBeP/73/81379WqzE1NfWPO/j/F7DV7vR1gr3VZQ7WFihYwVdtN+llgka+EigN+Gt9srdsMnfr/XmajJQkEqI9ZUSic+PeMEFsRlijDDmA4XA2X4QXIC3J/p/bQPgucjyDOEXVKwxOF6hdG1NqpHm07yCi+OApRJSQVjX90KWwFmAut2E4QkQp7rCHzv67DyIleHmst3FchCfwp0ec+VMJCymiWEOOjyFKJb7zh+d54zdd5jW7znCovoYsFTDlArpawHFLTJsNBrOC9orNd4zN8U2Nee6+dx4hJSiFpVy0NAit+au/ehXjRGxs7kKFGuG7aNVl/QYLU01QG13SMQ9tAULkRXQcIzAYy+Y/vOsR3vYdx3nnS57OpeCpT+Ba3Okt4RYiotDmJ/c+wJsbx2m9fw/73zVgaqpDrTDimh0LMApI6x5BR/Dcb4Z89N+N89jbKvz1HxymYBla15dJ6gVqU8GVti8I7rbRnRZZb5N4T8DZfpXFh+qIz4P9tIMsV1jfnOKVf3OerOdi7xmgdvWZ+mSH7Eqktxok7GcBM5TEZyOi54Y4VkTRTSh7MZbR/OxtxxgclISJySdm0qWzUGD9pObbf6jNO96zwMR0wre+fQFbd/ncJ2qc+5CPupyyfLpJ5ir6lRK14gCnEPD66ilecmuLwuWI4mIXfyrgHVc/xaXZbfDNHhsbBSZX2/z0ny3g7vNAgIohs4D2BpcWBBePxWw8E+F8EYY3qlwRJAReKeVcVGU5qdCal5g7LArLPdzV0wifnHjIMhaO7CarerQ3KrjtGHspovnrx8gsg1ZAaihuLOG4GZmTYvubLH/JYn65irXYpZ84bLqbVN8XUlwe4SQxd918kXInI3MFqZVfL0/9n7DU8Ti6MslkoPjObzzJnmIXO054UXyOO+IL+ANNYU4TWynDfQWS6RKja6uI/THda/II+LgiEKnB6RucYsKgUGdpchYTJQgN/kqIzlJSaTCpQV9ok8yvExsbMkPp+Dw7fu0Zpt77NHFfkFQ9tGth4hgzGCDb+YXo9jukvibY7xJePUU22yBrFhFKIBJNfNklfMzAKAKlEGGIXl3DtFPc44ukdkQ/UywM61wwdcKyjzcsU7qsGO4tQ82DyRrab+aRzCOX0RMFLvZqyKtD6rs6DLo+/W4Be8qiWMzQPYNJwQo13tkBtfsXUUOF8RXCQDjpXYlpH1A8Oo9/fImoKOjtcdGOxN6IQUJ/t0N2pRtU25LTSxO0Qhu5uIpaG2BloCLwR5LEzZBza4QPLOIeb2ONRxTfFOAcibjjbRd4+TsXef3rj3PX9XP01jyklzH3aJ2FD04y2RrQGBsgmlBbM4hAovoSOXRwTyiW5kp4z63gH1vGaoVY3RgZg385wCzGtO+aJtrbRO4f4tza59DNq4jUIDQEzwqsYYIwIFsJxY0RjVNruIFCR2Eeg9zqMPMXbX7ptg/zlkNPguWCNtz1dye46cGnqHQ2cFYHpF6GMQnxdAkLB7VtDDU7hRoZbJ1iwry1Jav5mKKTx1JvG6d3TR1TLqILLqJcyr3BLImJbdSJBdTFFRhrcOdr2xTqGXLMxzosSQ+kOP0RXn+QGzVbuSKEwZD4iCJ9taD0ygijFK4J0K7i4587wq2u5jonZdw1ueIsDNDtTp7O1O3B0ipkGdG0R3SggjVMMVJAalBnJXRiWN/MFwCMRqxvIroRchTCYhs22gjPA6UY7auQFRVZxcE4FpQ8BlfX6L5ohtENM7l/kZOb53qn13GWBnjHFlEXV5FhjAkjRKZzUkJJqBZR8ylipGEk8dua+tkMPVbGCMO+mTXEWoDd6uKORN662GwgPBdhW6hqFQx5itQoQLe7CMfBOriPk94RfuY79/DB980ijAHHJvVVHtM9XmFQKjC0PUzhSpkXhJh2J08sa3UZxV2CCQdvNcaKBUq7gIKLS9ibPexWgtUKEJnG6STYz7fRca4oyYLcp0vNCUzZxzgKUy0jmnWE60EY42Ur+QqQuPL+2iCTFBFEiEECmUAXPPrdgHBmLG+NnZlg5XQFq5AibENsO4SywvX3KqLkSVRwhe0xhuefusRtbQ+rZWOv2QQCbr12B7fefZBT3Rt4tnwVL6wAGUNlvcPgUBF3qsRtL7kKgNd922286Tvv4pZ7DuLZNkpIar7HZ+47wQOfOo5ZHTArLXZub37N+kuVXKw4H6fOnVsF4NypJaiU6VxTQkUaNYwZbkyQUMdSExTkORTgCwNEX3P/W9jCFrbw/8/o9Xpf9RdF/8/veRcvXmRlZYVXvvKVL2xzXZd7772Xhx9++J/lON/+9rczNjbGLbfcwnvf+1601v/zf/p/EVvU/dcJbixdJDGb1OyvkDQ6OUe8+S/45X8lOXbhpxDFItIrsPfWc/zA3lM890GPX/zB6zDVCsGROt6l8+jxGmq0ikpABAaRGfqZIdpbIq3YbN8+wZHx5zlZ2YV1VgMSBgHbH18mOCSxxjTNRsbGcz5iCMrLQBhk1fDB/3gIv+Ixbp3hB35mjk/88RgPnai9kFKRH7QG10O6DkLAp0dNhExJHQvLVeh+H1WroeMA05TEjuDllYu03+DxoY0qYWyjgWysiNzVZfnGJt6HFSKGqquZdjTESf6e/R7uZomsrNh7sMfPv+YLPPmlKT74katRQnH1G27h04fWkDJg7qdvoPiUxdhfn4dOL59cDDUmcVB2jCrlxEmlFHC8vYvEc1nOanzkz/fzvTc9ynKtwgfL1/PSxy5TedGAlzZO8Yy1D7uQcld1kY/ZFbRtUHGKjFKiNYWUguisQ6faJG2UyToxh/cUGZ0NGaSScMKnNKPwBhGv2nOW+U6F9u9kODMaMwwwSYLQGU89ME61OGLkO3gf7iI8F33Fx8AUNVeZVcw2izhLmT8L6RqMzoNoa7I+nPBcNr5QYTgSiHGFY1sYKRF+md/+lRvZu7/Pf/iFx2nWU374VftZu2o3pmFT+nifZM1GF4EIdo7N8yPXXeDusVU2RxajQYGJx+eY2d6h85Ii2abL+nUT8K0Rm60SFXORbKqE2QxQwkYlBsvS7E40Z5Mel+aKECxTfraGooT0QxZ1k6oYEcQ2VqOBdcFFtTeweglZsYjJEqj59BfKuL7F5l270BY46zHsnaX5ydPIsTpR3EatB0SpZvANY3iNEPWgxn2kRxbE6IqH60VkmzG6adh5d5eLj9UQvQxHxbhOmWS6yFP3HOLNv3k9UbPAj937EHrFYXngYD1zmYeOj+GMNRkEPlY/RWUWwi7QuWc7di2iYMe49Rg3kAw3y/lxnlyne0MdgUPn5mnsxXXSgmCwp0o4aUM0RFQtCpbCdCPS05sv3A9EGFOY1XgvCVh6uEJctnBqBcQgRvi5t5K3kWDf28f6UoSq1vPVbwzD7QrviR5ibh212iGdqiBGKaIz5Mu9CmIUU78QMtjn5FMjbZCZwRpqvE2BdT7EGcuo3D5iodWg/HgP0Rxiv0kwUck4d6LMmmdjVQ0SQz/1eeZPDxJZNk4KaFD1Mv4Q0ssjwtkSmS1QiSGue8jYIpmqkIw7hNs8iMFOIa3Y1B/rYV1aw0w3oVFFBSn+0xuo5Q6NWooYlom8DJmYvA0pU2S+TWpsjHSxhhYrm1MUdwzorfoMI8WFVHJxo0SDLlWhoRcSXjYIFQM29kASjBR6X07mnlsu8yu/cxsYSLbVMBiqT25iL3eJ9o2h2h2Guyz6L5qg8ZwgeToiORMxfKNHvFPgLMbEzysybwpmLah0SLMqzppBFgqYJEELMJ6NwOHxczt5yY7zHB8dxAAyjui3BONXdbjULSAHMdmER7EjsCYnUXaKThRmpJmpnmH9bC4dNpYk8QVxQ5DaCu25pBUHuRGRzVSh6INtMdhZpPrUuXxi7rpMvzxlz0bEcxcM3XsEpmCY3t6j/HiXnufm/9ftg23hPDggOlLiwvk6v75xK58Pd5Hd6PLkpd2002ewFdT2OTgbiqhdQ68OUMMo95JRkrQJ3npI5YmAcLLG4GaF1Y/oLw/BLmK5FqboIYa52bHeWcXdaBN18mvDjEZgKTJXI82V9kPfwV7oXjGOzokbmep8cUNrnJ6m+el5KBVyRnbb1BWjbBsrIfcocYtkgUaux0ilqK0k6IKDmo4p7W5zNnIQpk86shE0wfeRqcnVbb4HJR/jurC6kY9bnR6m6BGFI9Jajfo9DfqPdYhwIYpwpAtphAhseDIluaoIExWKhQg8B9PpXfH1AWd+k9GLdlO+EGB8h8JaRlAXuWfSvm0YhxeiyaUAWSxfadEDWdK5H810iv3kECsTkGmMlJilNSi4dMeraLOK/O9aheQowhrY2IMU2/H5lhf9MjqOyPbPElcUajqjWg2ouJrLgxpl7eKmv8G//84B/3bOZXm9QtIe8dNv/U2+7WdvRS1vIDJQkzXe8eOvpxdF/PoDFcRzH4UUhAGhIdyE9k0unSQnjbrhIufsT3D3G95AwfEp4fO73/JGsizju//1H9Puhxw+NMVrvvGWrzr+X7nvC5xdWef3v/XNKJWzvpOTNRZafaSUvPjOgwAUq2UWd7pEh1LkJ1NUKyBppZTtCpnO+PkvvQRxx+c5uT7JL88U/5drva+F+x85he1Y3H3Tvn+W/W1hC1vYwj8Htm/f/lWPf+7nfo53v/vd/4/2sbKyAsDk5ORXbZ+cnGRubu6fdHwAv/iLv8jLXvYyfN/nvvvu48d+7MfY2Njgne985z953/9YbJE0XydwZECWaewrscUAi50/ZVy3adRyY169tk6WwNiFhN+s70ft0tTsNp15TbMfIwYR2hthbAuKIFIB0tCOhlx3V5fOtgl+5NX/jo8u/SQMQw68vk/Ul5x/yObSl4r0Nhx0mOTEDKCLDk0vwGlqCmHIUw9BbCcsZbP8xcerLD3VyQ/0H9ZwWkOrhanVMDpjp7UBXQdnI085ERbopRX0dI0n0gJKeBysrXGJHSSxjdAC0e8jCiNiMqb++DRmXWBbGUMh2Ld7BSjnq5OjkFffc5b7HtyH80gX7sk4+rFpwKClpN/uk0QKUVbUOh38kzaOkxJneYuHkQLT7uBKh8d1nYutAvOJywGvx/FgD95VfUKvwif+9gjqloiJFwcUSwO+cfIkFSvmjuo8TW/AdtVhz3fVef7mAb2TZcaYQd6p8Q4mZJ8qwsUenlcgVoZi6mHWFjHdKpmcxLYEwnIZZ8jwYUPHVLn2ji5PfraMtccQPdEmq/nUzk9xbZIy5Z7nybSBCnIyb+balOWP+SweN/jbfbAC0kRx/nd9/Ge7iAzu3dPnE2mKCCHdXaFRzehv5LeOMJasrvtIldEb2mRaEF89hbXUg0eGiLqLGgq0Nqyc2slPJrMUn8qwvujkJrZTDV7xpsu8/tY1fu8/387YthhtJAMPfvStN9C7eZLS8AziucsY38HKUrobNqRAZrBWA+RGhppQfMfvPcRRdpBkFvG5BFkvgJEIz8aRMaPNEOF46DjBeg7cpT6Z7uIu9fNV+WEGtTpCWVh9g0gNCEWqPC5Li6niGmtJAaukufqnTvPA3AFUOmLsmzQzYx38oYUaxHhWDP0QiECVCQ5beKOE33/0erwToDKJFWf0IxcXH2U0aiTQicHtGhrHR6gwY3hecT1rqG0RD/tXI6SmkCTYc0OGBxIKn0novmiSxM1VGF7LIZ20ueauS3TdEuq+BNNKCK/wtpmCa7+hR3tbmTP7Svm2dQ9beehSPtkoHVPEvVWyDIhq4LioMCa1CrSvUtTbAaIjEFGGyHQ+kTURaQDSUpgwIXV9bMsw7nWQp1PEypDxhstQe+iGZvlgmf6KQ/0Zj9Ar03nax4p6TH12RG9fmfbtBqMlqIzijgHCl+jT+cQq86y8cyLOIDW5FlQIMk+SKUFwy3Z0QSETmCgN+eF7H+P0pSYfPjWFHUSI8ytk9QpYAf09NmqsyJHmKp1FsBKNdizikqAwdNHjVfrXzYCSCA0yEwQdl8c+WqK0s87skQF6PCI8rxkMFMlqn+F5BeEMUtpgLJIbDI3GgOHAI6x7pCUXexCRHJwgKVnUHlzEqtURayGrr5mBLMJfzVDGwqqUKezrUXBC7Hs95IMaNbKxbUVpbwL/yuKAM8dDP30IEWaIQoHM91DCYITk6K/cyA3vWkIXHVQa0x2VCJOE5VGD4Q1lhts1lSf6qKEBYdC+hUkgNA7DXpUXVAhBiJIeTl+ia4riRY3RGu1KkpLAzTKIQdg26XU+6mSA0IbP792B9wsJ+u0x3uc3Ce6YIHrCJ1ixsBsqNxFu2mgBrVfX0MqjuX+d848UiG600K7G7Ip4MJhg+UNNzp2ZwqqmhBsK06zCcC0nLgo+lG0YpLjH5khfcQ1yxuDe3yLzILphAswM4aSNOzcgaxQRIiVpBVA0kBjMZjv/k5OIRGCEQUUZwnGpPDcknHLwH1/CJGl+r7AtRL2K2ezkiUtphvRdhJRkQR9VaZBWPNKijbw8QIucxLBGmizKsNsLBBciVl86gzstyVKPoKZQO0pULvZztWa3RzTh4eFjxpuwtgEyN92P9zS4/kCb7/uO5zh72ucPfrgBjpMTZGmGiXqIa0u45RDv0jqiWgbHxoQxjHKvIZmB/8VzyPo4o3GHtCQIq4Zodx21EpJVquBYEKXIBLRnM9juA6AeKqDrEfanu5jVHtnMJDLS+ffi2Jgkg26EKufiWDlrkxxPMFcivrWjSIqSQm8ImUC4LuLSItFLZxGxYO6xWcZuXiVRkpGOSbmeEyeOsdnyEamGgkfmu4zvbuKd2EAAd1w9g2NbjNkWv/yGV/D4v/5zUuWhtcm97vZZ1C4aurvy8/rnHv5Zrpk8zecvPMM79v8Sjakanm0RaYMxBr/scuM3XMsDa0tULzU5smuKpV6PPz75IJMHOtz+saM88eZfAeDwzgk6rQGzuyfYvq0BQK1eIJ4aMTbbB1vmfmCtOhXL59SzKd6TRX7xwuv59v2H4SX/mKrvq/H5R0/zc7/4MQTw2+/5Nq47vP1/+j9b2MIWtvC/A/Pz81QqlRceu677NV79tSGudAJ8GcaYF7Z9//d/P3/2Z3/2wnODweB/eb//kIy5/vrrAfiFX/iFLZJmC/90nBjNMEhHXKc9vswxfmHzIod9RZBe0fjbNiYKeeRoiUfEYYwRHJo9CRt5exJCYCkXU1KkjSKZIzBCsruyzMuqR3n2bIG//I2/49jxMcav7+P+QIkCGvc/jKELQzalA+t9TC8iHS8xvG0n99Sf43R5nKvqS5yxLeLOiDjSHDUTOMt5sYilYKwJK2s5SZPG6IUlAKbdjMc+X0WN8sJK1OsYozE1n/82fzPSMnyP/yCP/nkTHWukpcl6XbJik+DhEteOX2THnRlKCsrSUGt+hRESvsf0/tO453cwPGn4i8FOOjcLGsM+K6t1dty9iwInMO2AwvtWseYlcZLlyoFeH3FgN0I0sLwRNTfio53tXFuZZ6fToq477BgzRGZAthKzYzQg1nVOztT4jsZ5Tqc+Ly2tc1dxgz956BbMtoifPvgg1j2z/Je5W2m+8gxeLWGlbTF8PsVJJBqN7Y6YPZSycaqCOOoTJxqSjPPHmyz+nYfa52FlAf5LArb9sOFVS6usH1f8zWcKfP93az74f+RF8uTfLMA1N9A/Lfj/sfff8ZIlB3k3/q2qEzt33zw3TJ7dndnZ2RwVVkgICQVARCFAZGzAGGS/2ICN+YEtDC/Y4CCQQCBhAUISsnJE2pW0Oc7O7uzkeHPqHE6qqvePM1ohGxtj87OxPvP8MXOnb/eZ7tMnVD31hIVrIs70Sqy6ZbzCFhiDeyFCjSle8bINxkmJNwEhSANJ0oxAkq+qj/v0dwj++R/cxlYrIHGHhI8t47QHWAkmS8F3SSaLcDkjVh4MDFPdLsmGC06JV3xbC1dZ2uMuwR9EuJ/s4ewaolOXwmMX8mYQBPFsgWBJIIBOy8lXeYsC40q8asB1U13ieBGD4r1vnkHtiCEM8aRhZH0oF7D9EXK8wdgzGc5ml8LWNghIZooUuilsN7G+h2mUUXEGRlPsZEiRkawKvDgmnNTMu5sUg/0k31NkqrjCVH3Ikdd3eOpLcwQ6ZpBEqHaP8vFVWgdm+K75p/n4Q9chY4PxfPTuPXkLzXqEAIKexR0IZGZJai5O1+A+ucbFiRq3jq3wkX/6+3zqs/P86YlbcI5aVLeJv2wYzReQRhMeXyOxPuWphGIyQHczduyzHDm0zoffNcagrVAanvrjCmHXhwJYBNp1sGMuvsjPLyklxXOGSCYYtgh6GY5naVwOEGkRXR9H9XuISh0pFNaTiI116JrcSlJ1SGsBagDds0WCsYTKU5fQfYfR3hmSoM7glMQfS4jrDsK6uE1Lf7NM1Q5IKpJCMEArhXIzpl68iXW2OX9iD0ILhNFYJYn2VDGuQGjQBUFnWlA9HRGcapPsG8NvZ+zfvclctUtxIeVP9x9C1z3cC13Uyjr92+oMdtUQZGyeM5T2demv+WzNS7KCRGSSsRaMP9ujdV2InAXvAYscZjAxRuZuY4I+W7M+0c0lhsLllh/t8sWxQ0Q766iBIXGhUotQDoSFmHgrQNd9nEGMFRIdSEyjhGinCOXlrVZDiO4RuO0E+4xHvNxgxjnGxHeMcbIv4WiPr//msxR9n5fsXebv//63MNwVUDy5CQ7omoPoZdiiCwL+9J/uwgYKqwSt0hzGi/BHHnZNM5iXJBWoHe8h6w7RjRX0GUU07mG3IvxSbpN1OglOZDFOSLiY4RhJenAHqjCi+Ph5UjEOM2M49YTRm8cJ/ryFezRldauODiTu9ibOWszOQ6s0W9Pgl7BxClEEqcai0Tt8rCPo9jWNx1bR90xhPYWpZjwwWGD5sTEKI0v0bBsxHKDUFQuNzCf4/WvHkCVFpTVgECfUnrhAYhx6L5rGX+ljhcKELr1bprBK4nUigokyCIFpWWh3McbgjMQV3k+A5+VV4SJXmAnPRSQZpuBjr92VB/tuNhFGYSaqZOMlRGeAWm5jUjCiAmGVdNwnWShiRIZ/fIjMNNl2Fd1p4n9O4VgXEwrcPqiIXKUp8rBgtx2jvRCVZoj5PEcldTOE4xMNJEZDz6nQuWeWypeW+PLQ1cYWsQoID12uYi63wPewo68obQUQXmoTz0zQvt4HAYWTPfyTGwg/INIWWwggHWBcSW/Sw60GAOg5jS5YnKVVmNtNOl7C2eojm+28mapRh0JAYWeH4otcWh+XECpkqYjZ3EYOEuQwwkRD5FBjhYReF2etQPZij7TmsL1UZVBXpG3BdudN/OZ7NElscpJGCXQ8ot/SOBEg4cbJnV81JiqXS3TOredjiiTGfPMs4THDvXdcm4+Z1sfwgmme/sdDHnrq72ELAb/0qX/Czbcd4FvfcCuff/w0f/bpp4lD2DnV4Ppd04wXCuw5sMpcpQdVSNMU13U5udXBBA6vefURFuZya9T8DbuZ/fwxxLcNif++y/yxce794T2cjp/lg3/+LEFLELTh9lct/E2He38lnju5/ML3f/zk8lWS5iqu4ir+zqBSqXwVSfM/gy9nxaytrTEzM/PC4xsbGy+oa375l3+Zf/yP/3ba8+6880663S7r6+v/lXrnfxeukjRfI1iPi3RTh2H2lZihg2M/zeNrb6GfFtkLyKlJVE1zci1i5/Q2vpuyWR2jYtsQuNAXCCeXj8eORNclWsG903fz6Of/nEffE7B28kFSndFZ2MHRp24FI5h1lpg802EwO0m0p0rQiOifdRBFn+tLTV6183mKGfyL1rVIT5JN1VCVPGsEgGIRWS5htpqQZRBl6KrHrtkeH/9XM1z3UotMwfoOdnsbEQSIXodL7cOA5YNvu4bOWh0pM7J2D5VE6GFEVqox/n2CN922SmQdVrsun3jkGuLDO/G3R9AosZKW4OKQ4VrMh56+BZkoZqN11Chj7cQK4y/RjNwIP9QMJ3yCi0meSaANWTVEWMj6MZvDkB+fep7YKJ6MxvmuVx7nl5KvoyBakCSMDyPetOMZ/s2xA7g43BnE/OrFmwkvGz738GGqf3KU3/7Nfdz75peh9TLtP1WIs336uxS2VkQCCsnMwogHPj8BwmXsIYPcijBWcvGjCrvcQ5aHXJ6+ntHacxxur/P1t69hbhN8/BP7OXNiQHNNwXCEXM1Z7GFU4P5PV4j6LcSRGtaTyOYIJaD8sgPM3LrG239tF9pIkGAOVrCPj0jKAXKUQGTpn/c49VkXO1dHzxYJTq6iKz7xfB1vqYWIR2QHxyi1hlQOZNgauNURifaIteHCeZc9hxO+bnSU0487uUqmqRF9gxkrEx2cI55ycdZbGCQXLhTpDr18od+RSBc8u8Yez+LLDhUsf5bNkwWapOowNt5gvl3izNoQ6fvY6RqetYhhjEoM2he0XrGLwp+cBSEx61ugcnWBqpeYnk2ovLrD0ckG9XBAgqKjRtz42W2GPY/Ldoo7/1GTc8+O8x3f8gQf/dgN6B3jCE8R3AGltuVDp26D09vQDpCBAQ2iOQRkLiQzBheJ6mYMZ0NEw1Jc1azf6bK7eJF3fvgmHrF7iOYzSvEI9e41jBVUlI/ptJGRwcFBXTRsfyRg9Kih18tYOzZGGknShQnc9QFJb4hasbDzSv1vr4vqBUh7JXB8NMQrDEheXEA+N052qUmChnQSmQLGQfmSOI2QqcVKRVrwIc4rbdPTW3jdCrYokCWLPDnEaoi2E/xsleimBv6qQS0aVGRwhkMQguFkgbUX541HdqjwGhkWy9FsEi8TeCYjq3j4m4akkOYhtwOLaShEBgiBWtrG2ewhm118p8TZtSrvLNzMcq+MCR2ifQ2cxTbO5RZ+WRGXfNyRYfsJFzcbISdjROYy+Ugf4yrAIBODGllGLQdRMYSpxBEOg6JixdYojfeJ4iI3FyI4EvMnawXOakU6UpTUCG8rJhv3sEZiJWgpsI5AGIHMIK0o/J7GBJKJhzqooqQ1JrAHUzipcMop8bmQXbMr7L27wFFV5rFHxth62uc9772e+J4Q3NzeqS5t428r2rvLBJlD30uw2/P4m5sQhFipMaGLdBVJaIiLiolHVzFbCeFsQDLu02YCtGX05BCvd4VE9xyy8RCkJK5Ken7KVHebsb0tnNsDln4vv2fYrkb1NHI5gUHCS7bWENcrHpk4QDwUnJ0MELNlwtURzihGmwzqJWScMR9t0Nvt04hT4oqDsBKsoN8L2RqFRKcDKGd0bytR+ovOCw3lGIMdDCg8GJGVFKlwkO2IUephSwL/bJPSky2kcgnMDgbXTWAVeE8tkV2+YgV0HUQhxF6xMGUViWsz5MQYZtAljVroskehN8xtntqQFT3AA1ciXUW8ZxxH51lndEfY7TYqyVCDhOSGKVACIV0yV+L0Y3C9nKxyPYjBiQ3DImQ6D9wVljzzrD3ATIs8CyfKc6Cc7S5qo8uwn9BJPJrGx/WKGE8gE4sApAF7f0J8o4c7NOC7bN9SZfubdhMsDdnxtrOYUki8UCZ85gLhvCDeWad0rIl0fHSgMGU/t0YpB1MJ0TWHykaeIxA2RlRKQ5JdBZzmCGkldPrY7RbkpyP28jL9RRhuV7CXh6BTyDRqbgqRZPgnVlHLTfBchIV0R4n+vXVUVaMwZD2I1ovYYcwv/ejvka1sYOcbxFMVZGooF0r0VzvIYYYygk++5wv88BtezChb5x3HPsKxfzGJSSbY80PP5cHJWw7u5TbDdz7GJ1SFjZMTfHBlnN0nnsFai8Hyk3/yUV68dIiVB1ZZ3+7jDA2TCzVec/t1V04Fh7Ek5IHP7UJowWPzJ7h79jCt7oBemvFn//kxrjswzYF905QrBarFEjevH2LyNVW+7ydfSyvdpt6u8fzEJhcuroOAA3v/dkIqL621Xvh5367Jv5VtXsVVXMVV/F3B7t27mZ6e5rOf/Sw33ZQ35CVJwhe+8AV+7dd+DYDJyUkmJ/92rn9PP/00QRD8Nyu7/3fgKknzNYKz3Qb9LKKTfuUrvXniZg43PsPrv+mt7P0RSCYCsoHGn8h463WfwBeWX91xiO0I9lwzzfnNFlZrbKeLXPUZ7irkG8rqvPpVf0L32Y/w4fOfJB2ldM8pvDtAOpp+VmJSr1PIOqRLBjvS+EmDnekKXiqYczQyD7dAz9Qxe2ZwM82b3gIf+B1DrDOstfmA+8p7H+wtUAu22VrxCCvdXAWRpLDZxfYHpBMB7rJCpJqN5wRer4sNAmx/iM4Eqp/iJJoHvXF2d1NmggG/8eDdyAdqqEKGkB5WCp5fmib2PLTvIC+4TD3Sx/YChMnYygYI22Zups9L/+Mib/vIq9jxm6dAawhD3F6MdRVplPLU89N878vPAILfevalXHy4zw9/+8N8ajTBZuqyseSxEgne+vpnWE49/sXzL+JM3KByrEfc1YhuzD/7wzP4wXE+e/I1JGcVcsMjLQucdIRqDjDWIqJlOucm0JFDuruet131R8gZF+ukqLEi7JzAnPYRYkTbKAZG0inPcPTEFsubCZh2PpkAhdyz/QABAABJREFUypMKkwik0YzVerzm99bonYX3/aedjJsi73rHHHoVVBrn4dFrI+xmH9FbhzjDjeqAxQxiTJww2lsnvLxNtmcGP5M4To/BfJVoQjIKBbumu1xXWOLon9YxbRDG8ra3LvD3/+gCG0cFZGA9UFogMoP4Bwr75IDixjhV6SMLUKkZUtdFyCuBnJ2EXYeGdA00lOWDvz+NrRSw83XUE6t0j6f0FiR6soq2kIz5FC/289DTNMMmQyY+dApSC8aS7pxAtQbYbp+4EuLFfW5orFF4KXy6vYcfdj/PZ35jD4EMyC4W8D3D5HbE3A0bTNdHfOjDNyD7feRWj+bUAnHLxReC4U1jTP3ZFrHTh8U1pBHYXfNYCdaxWF9gM0tWsOhGEXnPHqwd8vhnqmzsLqLv1bzojed56h9NYI2LcQ3WdXFiQEgym1Ea61J6qUbZlIP0GawlPHu0lB+zSmE1NNyYta4BAWngIpd6yEpuY2DYImpnbCzsoX5uQKHZQ6YhzlCDkDhS417jo09JaLawsU90/QLB5ZM56VpykViMEqiFhLhfIlifQi5tEi+MoR2Jt92GIMbbDl5IrvfWU5yTKwgN7e+YIC07gMUZpgziIpN6i15ngqzkkxUbmOIVdWCUgeegrCS6dgzPNTjbI4yNSQ7v4bELs2RlgfXASkv/yBjZZMBoT53CaoZwXLKghB6BtZLqqSFZOQAlkOeb2NQg+z4kAZCiLm+hRgLpGUInYV9tne4Xx0heYvEdEMEQHVUQrsWsu3R2OsiTLsWnJc6tEoPGjju4EZiBgUySmBgjfPZPXOC02kf4xAZZ1Gf2J+CVh5/n7f/odSTTj7D0VIXhtiLzSog4wvR6iG0Ht13A6aVIz0dIhzAxYGLGz3QwWYLtDaBYRA2GCAfkxASVRBNXSpgwJB2XlMI24osduhNFslqIykRuTQGy8SJqkKCkIIsiaqc3SZOY5hdjxLU+rvKhOcJ4AfIZhR2EIBL8KGXOafGUcx1uUbAYh8yNx2RbgqzbhnIBUw2R/Qw9SJic6NLfDGG2lFvZXLi5dpkf2PEMv+S/GttQOIfnSM+5uBe3c0Wjk7dJud2EqjQEJqU9WWXr9R5GWupfWkQYwGQ4l5oU6lVY7+B+maABSDMsAqdcpnCmj64qwpU1bNeSjPk0pKFaH7Dw3RmPf3I8z4i5YrOz2qAP7gAsar2D6/iIsTF0ZhAzk9iNLbztCNmMQYOuB8hmB5WBLBSQrQEiLOT2n7qDdEKGBYO/3MGMVxGpRPUiRLmKubwEjsJMN2A4Yra+zUQw4MbSGp/sL3D5tw8z/r4lyvc1MY6g+bpJsrqLe0kRnOjhnRohnCni3VWiWypQmMMWfUblMrWn+pgPnmHjhw8z9hcjgks9vMU2apSCkkjXI2xl+Gu5nXpHr43XGNA8UqX/qQzlZVjlvlDUaDfz/WsbFZLSGLIKaruD3CGQiSSpl+ncVKCxPcBtxdCoEd0wSeb4RD2NNxKozGIVaKtY3BiAUya4HDHaWUcrEErR25YUVxJM4LI1NGw3+2zx5/ybR7rIJMAUMzZ/YIbJP1zFnYqQm030ZJ23ffRpahFUEfTvOkjxc8+ABO06uK4k0ALPU0wVS3zj3YdoVAovHC4/e8vP8N1ffB9Sw8PHmtw+bVgqxlBWHJddsiuV41ZY/NDlkNjPa/beCcBznac52n6MfTdey9lFl5mJKrvnxv/G472/Cg89e54rV0Umxv7bVbRXcRVXcRV/V9Hv9zl79uwL/75w4QJHjx6l0WiwsLDAT//0T/PWt76V/fv3s3//ft761rdSKBT47u/+7v/udi9fvkyz2eTy5ctorTl69CgA+/bto1Qq8dGPfpS1tTXuuusuwjDkvvvu4xd+4Rf40R/90f8la9b/Kq6SNF8jiLUi1opIf7VXz1Uee/fmsrDBroBsaGjs3eRGP0UK+LZ7LvP2P1ygOlHmPZd/h+970a9gXYdgPSHpGlRi0YmhOFPkB37lu7hw7CL3HzvB4FqXn37Jg1RUwtvvPwiXYNf1I55PqgxPDjHTHluzNZ4bTvISscKlLJdJZ4nBbrZIZ6psb/XAFiiOSQazZThncnFNrUzgS17xc+tMVzRPHCsjmj2EMTAXsGNXh8duuZmxo4ARqEghdJ7JAOSTkiTB3zLE5/bxq8uHkK6BTcuR69rcccslzj3e4MLZOpcWJ9n8hjHm36WpP5/h9DUYEFIRJ6tMFXtMhh0myz0q/Yis6uNENrdoRQkikWQFh+PRBCPjYq2g1a0wubrB7kKHiUKRTaosbxaZETCmoCYTntpcYN/0IoUNQXsu5kWv63Hw3g4pklecOM0nHrsWM11BlIvozRH+KEVZy/ETDnqQYPSAaMolHK8hyxVUI4JugpIxP/h9L+YXP3+M0Cb8xaCCY1MGO3xObTSw66dASfR8PjD0u5LDP2o4/XyF0W1Ffv+Jl1Cb6ENhQOeh81RvrNBKLJmvsEbTu6ZM5QsK2W4ixsYQMlcIyHodmXkIGyNcgVprwtQkhQN12rsmQAtGfoFTWx7DCxnpsQ4iM9jJAtFLHd6+fpjx74vYSCr4z/VIax5eIhg6Nex3CsJ/Z5gYS9gejDMQIq+GHmU4mxcJY8v3/b3zzEj4i8/O88Hf3IFTLuBc2sT0RhA4DBfqJLUADGRlRbgiYasPgwEKg1jPiF9VRV8TEDkh4UdiwrZBbfZZPjnBBz4wycr+MkYr3vvx29l/9hLT1444xT6sBeFl3HDtNmdP1Ri14yth2JbwiZRszuBtaKIZwfnvqDH+dIZZCCk/30FoC1KSBQ5yaCi3Uvoly2jKQyqwvsvrvmcLp7HBZ8Z38tPXHkd/2vDSd34/spcw/rRFVxeutP8YkEW6pyTWjHj1957ji38SMtd0OJlaVK+HBLKggDvIg67TskdQEmByJUzBTRm2oPLwkMqFEfF8nfjWMqoNzkoLLq6RzM6hPAGT49iKj97h5JNlrXFqdRB5nfCo4CNcix2vkDSKtO9uoAOBWy5jvQI2a6NDH1yHaEpTfmYADtjNCYI9CdYKxmo90mcFO69Z5vipKWQGMnSQWwlpzaHw+DICw+i2BUypyGifJRQt1CDFrK0yuHkvXk/grfYJjl5msL9B5yU7IDKUT7dRoYJygdqONt2VFJNkOChklJH4Dpy/jBtq1Pg8waUucrWFFZLJtEMh1bQesJSutaykVR5+ahdHul1OZzOYiiCaF+jMwV3I4PEQZ8mQlRWVYo/eoIyVgoXqFp3tEDUTs1GdYXCPovCBFBlritmI//yma/BaF0mGDnqYYQLIrEFKi9roYB/JGL78GqxOCNabUArxNjNEr49tthBKXmlhKlxpN9JI5WKNQA0sZqZM7NfJuts4zQzH9rFBgO9bGOT3EmsyRGZwRoa04WCKfk6EDSw82sEenkC4Lq6vsbvHMbNj2DPbPP0el/UnNlE6JpYhRUczc2MXrYssj3ZApYjQlnTcpV1V9D4dEdVcxp/qwcsNUgkOlDTJUQ9jBFuzVVp+SHi3y1hHY5otqFaQlTJ77l7kjn1bjJVG/OLRlNpDq6hWn9Z37USUfIpPdqBSJCtKhnsdxp6HO1/ZoTqW8dn3NzDWguugHInYHGHPdUFr3Lag+Po61AQHv7/Fxto0ly8VYJSAMcg4RYw0bquLag4RE+N5+VC5RDoW4q5oBjsL+D2NLjj4bYvY6mAadYS1CM/DCEt/Vwmnb9C+JLptgeHuiPJSh8H1C9QfWsVsbOb16q6DkRanMcZGvYjjrDJqQ3evS6pcOi+eofx4hCgWcLYTdMnPrw1AaTljz088wcpbroPxOiJycXoZIvMwYw7O0iY73n4MDdhaQPe2BsFWhMokScVFCxDDnKTptwL8esboooeoCkDm6cJ/GUIgogTRGyG3uxglyBYaBEcX0bGPMFfyZap501UmDTK1zP7ic3grKcmdNZJXX0Oha8grCiQkI9zVLknNg8zyqQ8cg7ka0oLyHCrlELI7kPHnkVpghw7hywT8IaQthehl/OA73sjP//59L7xN60qSa6Zx1rrUzmr+5a99Pb/1/CdIP3+C7/zh2/mm77zzqz7WoekZ/ChfdKrIgCzTpGWB8cBrwnXX5OOtIPAx2lKufYXgKagCAolXDXH2hRw8OPc3G+j9d5CEiqCdgYWzFzbYvWvib23bV3EVV3EV/zvwxBNP8LKXfSWk6y1veQsAb37zm3nXu97Fz/7szzIajfjxH/9xWq0Wd9xxB5/5zGcol//7xPQv/uIv8u53v/uFf39ZiXPfffdx77334roub3vb23jLW96CMYY9e/bwy7/8y/zET/zE/x8+5f84rpI0XwOw1tLreXRjgf4vSBqAwbOX4OumkXd10COFPjtkTUNbSxqeBiG49rb9FIshttfDoKFUpHw+AgHdzgCYQAjBZnOI17bUz/e4019DSpgfNelrQbZH0bplAvmpFuoZS2wk9z+7l9UDLpv9EuX5RTobHYhiBoeK3PvadZ59YJblroLV9lcChD0ff9uhsxIQjkc886ldqHodEAhnm6//iSYn3ycJlvJAKCUV1hMIz8ubi7TGRglubOmfc7A3WNLYoTLd46bxRR4LGhx50Qrn/0GdtBmg9xpskuJvRcjU5DYaIVBSsBaVyITlkc5eHKVwkgSbGkxsEcZiM4PrWtZ7NX7+xN0UZcrZbpXDymGPqxkfB6Qgjn3+/QdexK997+d4dtSgUIjYWeqxerbEm7/ncR56/xxaXMLHcvimdT5sr8HMKfz7EsAhaxSQG00++rvjOFmMFTHuSgsbZ9AfERUNNjD4WcL1B2Zx3CL3hYf5+tYFotDnjh86ysO/di1lbRFYPJOvubmLKZ87fQSnbxk85lO61KU/H+AsXUA3M5LTBdwso39wjs17q0ip8OYMaTKN8DysvrK/wgALFGaGCCEQ3QhGq0y9psd6MonsGwIkyYTLkp1nii6qYHjtv1zhvcEe5LZDS/jUJltkOPQP1pjQIbXjGYU7hmxPlhDuCtnRGmvnU9y+xRpF9tIp/ukrH+TQbQOUgj//8H5k0sJubeNGIMOMVLgMxxxMKQ+drSMZ7iyjNpbxrEGHDm4/Jbu1hK0r9m5foHU5gxREq4tolxmdn8IteGChe9MYiz5s9Hw8BPvvXsW/ZpttK7FZTFrxkRqEFKA8SuckXitDrvVo3VmjO6Eo9CzxXJmwC0YJpIXCWozTiWlsDUmLLtHdKf605dBrOjz4/HVcfGIf92Wae669xHwpZkWXcO0QhMIEFv+xcxReM8VUYZPL8RTZlOTl3ztg7RPztNiB201wpmMuvzLGfhIQIFoudtjBhvmtYJgEKD8mGKYIY8l2T6DdIhTAW9pCtBOysSGiUMaOVTGBYqfXZmPfDDguDBNIc6tKNnAQdYkZNRFCkhVzQi+ruLgjRTpeIikJsh0u3JPRjxZIhQETgk1QytBpFylfzrj4RB3RsEgt8AagpcS/1EVs9nDCAvJsj/5CETtVZjhRyZV0l7dBW0QqYDBCdiOqD6+gxwPSmSrhpkZ6A9J9ik1/nOBU3hjkZVfCiYWLObwPr9WhcimCahk9N84d336K8b0jzvQnOX1snOJrU379Cy9i8ESV3fvX8QYKHVn23rrJWNjn2Ooc0sLEl9Zp3T3F+vPjZHMC1ewzHDfEShEnAcNClXgrwRtK7LbLqX+3gLIZKuixedlj1BUkezycC0PQNidbxyoYKcmkJWuUsNNlVC9Bpi5ZwcHJQEQpRg4QjTLGJJhWG70wQbgYo6XBkYprvjtG64j0kQjTarJn7wontuoACOmQlSS66mBtgrcxwNRKyEFufTFRhG2UCL0OuijIWoLyuGFC9Xj+0zX0DkG4UzLz/rPsvW7A4tQ8tm0w2QjpuNjJBpyN8R7qIOsj6GrkKEN7kvNPBHzinYdwK5Zyc8Bw0iHYSl+owabTwyQJl5+QjB4pMrXfp/TIM/itFAuU72vibA6wwxHWaOQZqJ9fw/EN3/pjmyjHcmZpnovniljyZiY8gS0FiChFxAkTb96kNJ1yfqXG2kqY25ASDyssolpCRRnJVBlzYRVRreR2I8dBDhPE5DhqZBjNFSDTlP/zE9CoI6XMyZzRCIIq0hjiskQMNE7fkrkuMjUEI0nig9uPkFOTgMDZaMH+GkE1ouv7yAUH+YkMfzGi8vFVGMUI18NfS9F1kwegA1RKuI06s2+/BIMhzdcdINtZguGQ+n1bEKcIrgwK2xGlj5+idesC4XbGaCFAbPQYFfPrRLPqkWyWCFONi5vvu2iUX1MQeetWkiIGEWK7h5CKrOHDjhj3QoJ3OmH8vafytqyxIomX4Q1APpvireTvw32yg/dyS6Gt0aGAkUYUAoqXErzMR6cxuhi+0C5XKTm4rmK1V8Ed5NcgJ4woVnXebjamsQXBrV9/A2+OLvI77zlLqsBrQ3RontL6cfzM4imHfmuITjSby63/KqTSUYqXH9nPxbUmrziyn14cowu5KjQtW6IoJQw9jNZUaoWvKkY4UL6ezGYc23JRhU0Kgfc/Osz7azFbKrIlO0gNw2H617/gKq7iKq7i7xjuvffe3Fnx34AQgl/6pV/6GzdDvetd7+Jd73rXf/P3r3rVq3jVq171N9rm/w5cJWm+BmCtxcgMx9Fo/V8f3K2p3MpwePssP/ktz3K0UMETsM8zHGtJsJYP/vtP8saffwNiYgzZ6uH1m2SVEsJCs/+VxqiNlTZSC7J1ye//zCzlSsblzxs83yWLU0ppQKLruI6kPOzSiiZ58MlrkUPJdXtXECsGtCa6RnL4rgE/+wcX+anX1wmnPaKLbj6gGY4QXsK733YDyeEp1H0XESWN1QZbVjx1dgpnpFFbeQuGiTV2dgJ1hVyJpop45/oYqylcEDRev8rKoM6Nk5f49PHdbF1ocHrHLLtnt4naAbW5TWSziSwUIctJK5TEhhHTQUxzGPL5y5PU33ueb/0Pp3jvz+wlLviUexJR8IjLGrvh8bnzu6k+KSmXNa9+3TrTjmG4GUJYQCnF4+d28R3vvIft26cI/Iy1YYXBpkA9rOkuxpw6WmVhX8z7//wIHMkIGglxsYDjOQhrMNZgkFgrsZ4kqSrCYRex0UF0JYPbS9QXBwz7EWQZzc0CHzp6Pbe94jS7Sls8GiqEEJhigIzzCZa72kO6NURqcJe6RDomfHADRoCxZKIKVdDKgONisXhFjwwPk2WIXg9rLCQJuC47xwQXiyFCpLiFMq3nCmQHLLrkEC4OmLqnTb/n4JdT0oLPx36xwJhY5eY/SJif7fDHhw/Tbo7hrKZka13Sa8dpDSuUT0ScXywj4xEbLQe7sQlCUvhkkw8tjrOwP2JiNuX09iR+lkvt7/nmDqvPK04+rkiqKrfWCPjGFx3iQ7//MHbfLFm6iNMaYRyB/6dd7DU1ZHNIaUzQW/MxYxW07xD0YuTZIcMDI4IqXPMDbY790R4a20NWT9XJhg5+1TI1H6On67C8iRP4dA56FI+leO0RRqTIRBEvaArPgX+xhajWEGlG8fkB2WSBrOzjtSKc2OB9SRO/SfLzf3QTEwZa1sN1NZfbFdo9H3+xjzizSnx4N06mcYslgnqHXRNnWVmG088XeO9v3goCZrYWUQMDzxq8/6QRMsIqCf0UGyfYK8HB1Cegv463NUBbiYol/kbe1kYYYGWPTGTEP2ywjwQ4WIgt7mCAGYJVEjWSWAWjGYENLHrHGGqYYVWu3rEABYN1wHiS/j4Pb83int1kd2ub5v4FFg+Uka4hGypGBYe6a3GNRUhBYaGPM5ayebSEu2saJwJvZHH6mvLZEWnFY9BQ9A9PgCMJtzXb99Qg6lI4NcRramRoURaCwwOWbpiCgmbq823czT56rAIILBZTK+K7hmjeEPsO7Jkk2HmJTChausigYbl2eZXV6QlSqTnzRAm12+ANJd+/60E8YfC7mvODQ4jUwSQpVlrEICJ86jJx1cXUJb1dHkIY1NQIr51hWwIRAEJiY00aCdDgdiTGU7naz3Ux42WqJzu4fYvBIbEakaXYVhfrCnSaT9ZUqwtJhmMMpBk37hvwyjeu84fvv4azbo3xgyNmpiJufPkjvEh1+bW3XJfX8gAi0wilcvJBS3QjhOkGWZzgbPVQ3QgRjkiEwnvVJukfjPiWN6xx5gsZWSfARB060w38JcGzwyrbYx7epWWsp0h3TuDPJ+hLBeJ9U4T9AWndpXx9giHlYw/tJfixmIUPpIxtDrnp7st8kf2k2QgpIFmooufLxCdXiDcCli4JgjQnaARQeGrtKzfCNMM7vZrniCH5yLsnqTZSlpaq4LlEs0XiCReRCmrTRcKCpnnZsDN5noaf8ODHdxAvtfJjfLJC5kssEowhrbm4SmJX1rBaQ6mEna5gU0u4mUGQEAcDRLmE7XSxQiBKJVD5EExmFrDYUJAEEKxkeNrBXU/JCkVsJ8IMRojAR0xOIgcJp08E/PMHb6c1CqgsJZQ/cxxpye9dSUT/xgq67GDKuQ3JqVVBa1S9ihkMaXz8NKv/5Cay2TKjtsS73P6qcUPxYp/S9vNk9Sqj3Q1kpYLbHQAQvHsNEVrkIwOYn0PbjKzqo6IyIsoQY3WEBZOMEI4im6zirLfh3T060yHxwQLZXIXKFxZRxiD9EjZJ8c72YbyO9AJGhS6T//ZJoiPjCFUHMkR/hCiXIIoxFpzUggaUpWBzMmVSf4iKk9FLPYqNmF4/pAp4WxnCEWxvfQvX7dyG+JsIMkvsODhGEAceE9bQ7Y04vrzNqOjS2PdXW5He+oOvJk4ySqHP0XPLlM9ngEAGLs+fWuWWG3eiRgOy9U1qJfeF1z3RepBHt7/IyBwmdjNKlb89kua2W/fwuZNP4PRTeq3B39p2r+IqruIqruL/DK6SNF8jyFKJsZbsr2Ag0yQfbP/4K04xEybMvXKLUQxnhU97qZATHdqiM4v1XAh9MAOSRj6AGPwlkubn3vMP+Ie/9qckynL08TRfbVQZyTDl2DtLyOgC+lAB6TbQ0iFcjbHPlpCZodcJwKSgNYl1SAE5r9FxzNpYj3q9Cghsu4MRGrHZw65Mke7dgfvceay1pE6Jo3+ymzDaxC6vAmBcB7nVhokJwBKsDUisYTQlCTpD7q6fpjCX0Y89nn/8eioti970edW/fY6P/+IR/v2L7kN/RvJPvn8Ws7qBrFcRYYBNJMuDGsGxlPG3rlGaTumtSlQ/xTMGs9HLXS3XjuP0HNxnBcGWwY4sJ87VuffIGr43gqQEwxEIwx0HLvN8oJgMuzy+touGNNz/gWugMOBf/sCN1ALJys4K4ws9aoc6XJyX6POKzPeId87izxSpPXmeiTcI1m/yqJ7wYNtFulA61kKOW2Z3jSMHHd5x68N4txn+5b+7jQv9CegKrBLo6xbQxfyYGFzcxp0pYi20XjeGCSyNP7pA2MzyzAXfQZc9ZEmS1DMEligrAhbabfB9hBTY/gCkYGKpz+VlHxt66OlxJqd7LEYWlRqMb+k0y7gTmv0/m3Di/jLteCflM8vc/3NzNOp9hpcGlC93sb7EIvA+DeFxjU5lno9gLU5T49Sr6DgjWbKcezbkp153gI3CGEqv5JY5KXlgfR/feOMip56SpJMKGYFRFl/E+L28QaV99wL1j59CIJC2BhtlWvIAxYOL2C2JGMWI7hDfSIK2YsfZTQK/w/nv201aFwgL/e0Q72wFMdXl0ycCpFW4YRXV8PF0TvoZzyEZD/I2miij8Bfn8hrpcgVWNpBZhggnsJMF0qqPSDRWumSbQ9b/GBYP1xm9WPJn7Tl6P9snFMs4vVzV5Y1iskCR7pumvvMUjQOGsBzy3p+awe4BpQHrwrCX19+eSvAm+ljXgX6fryR4gx1F4HuIbp/evh1Ik+E3TT5h3zWZr5jXQ/x6iu0YnFFGKloUZgydC35uuepHUPFxhpBWbK4qihLUMMD6Eu0LUqtwggx3fYTaCCgeHVFaaeFOaK6/vc2G7zMYhlTqfebECLma0NusAZapr1tHeIbRmiXpuozGQoSxxOOSsOfgrHaprFjMWInOAUhci7uS0nrZHNRiRrsqeJsp3WuKbNdCXn3PExyqrfChB/cRvQ+0kphKAHGG6seIusC5IaIjAoJuxuc+dj3F8WmalEhugq7bIqlIvGaMWINCmCCrkpeFAwTw8LGIS1dCoqUUZKEkaKY4VqIDH6stRkFZtJh8uE/kaDR5PpIcttl1fY87X7rFn/zxQYrTBdZliaygCFf6OOc3EEZiSxW0JxjO+niuobCq8lwl12F4xwKF5zdwVIh1FWQpt73yItNzQ2YPpTzbcenXFIuZz+rJOv/xHa/BPnkBP8qv+zbOEIHKg7QzyMarWJsSxCmiWiKdrEIjRE9ZEmlxbiywd98pnvlYBTRYAVEgkW8eZ25Hl+RPU5J6A93tEh+YJLESPeNgnBKJHhCqDtdPrZIaxXPTc6gJSSYVcWBYCSboXFdCzeyl/Ng5rDF0vtlFLs4w9gdLOCKDLnn4+JUj2zqg6y6kDrKdtxtZ4OTJcXzbJ0sAkZGSIIzBbSf81P/zDNVqzH/4rRtJtAc6xtZD0oUx1OVNTJogUosYjTBxSLSjCrfsoPTUOhiLlRLrKpLxIlYnuO0Uf6XLYG8DMz+GXdrGTxWqJUBZ/O2EYDOhvb+ADT2sKzHDIYNd48iVLp41kKZYJejcOE71dB/ZE3S/EOCl4ES5uCoPIbeI1OBtZcSei9sDs2cC3ewg4gxTyrOgop0V6k9E2AL4i5AUHdxBlhM6V/YRPYvodRCDCDWUqHZO7qtTMe4wJ8PINEo5aM/DzE+hhgapNTbNQChkapHNFsQxWPDWI7K5MsM9DbLFGLHcQ242sbun6d45SbHfwqaWYClDRJriw+uYOYPc7MJYDZumuBsZOApdcJCjBKetUdMlPvW55yDJmAs7nLF1RGAgLymjf75ARWak+hi/fuJuWrcq5IahugbWWMyN+ykFFhPAkevnePzpSwyvBKL/l3jXo0/x9OIqP/fKl9Jd6VB7tpMH6+8eQ6n8emriBM9qsvgrqpZ9pWtpxpucixq4kz1MoP/K7f/PYNdMA1cI0tClf5WkuYqruIqr+L8eV0marxGkGQwTF2P/65t+28lXyaORQ1nmg7nvePQb2VFusz4TAE1KjQLaGJLxItQLiEIxr4GVAm+QvLCtaw7P09tfonrfIrI9wE7XsXGKbXdRXQsG5EaC6fXJWg1UElE5k2fGyEWbh8/6LuFlybN9xSArIGJL6dmEYNRiFLsQp5iujw0cjPGgzAvyN2+9D8bPJc5ZLmNu7Juh7RcwiUGYfAV8/f85TFYKmfizC7zv6btY2LfF5eUxwvM93EIZZ1tzMh7j4OuXqHsRdk4gZYItFRBXQqIG51ySG13scxZ/rEF1vknSDZGxRJWLiEr+nvRkFVUboTtFRuMSt9Tkj750kMWz41y6mIALuC7B+T7Th4fsvOU4zsDywKkjpAc6xCcD1JiPSFOsiXA2OsT/psX2MzUK1w/ojVziRoXBjCAthlSeKeI+tslLXnqUJa9OlKUwFMhMY8YcDIbq6yIcaZDC4u+O6Hy8xCvveoQHH5pAdAbgX/FvKheT9lBBwC992ycpqIzf/MJBOJd/NnNoklFmsHGESCVCQ7KVodQQajVst59nXZQK6HREZ80gMg2Jxp66RHfYR+1dQBcU2V5JZB0WKpvUD2WUL0myRcuBV0lWVMr3fvNDvO01c9g+JA0fsaeGnxrsaQfjKmzow2hEr+Mgqg64Aoyg23ZzxXuY0bl7gfonR+AqtnaM0wluoXztGZz5AfFakeJEjwsPfILB3A6EtpSfvIxKDVZKiBLk5S1ExSFdl0htIM6Q3QhTryE19KpFNkOH8JOGSS+GIjQWBhy4vsV6VKCw14IzwIq8oWX2mS0GWQW0T3Fds32LRvQha3h4LY2VeXOLVtDbU6J2po0YRiQzVUzWIp6ukvUdsi2N/9l1tN/Czyz+1gBTL5FNODiOIuzGpNUip+8/QGnLofl0G/wQNJhmB0bxC+eQlBqaXUSm833qOuDltwIR+IheDyMU2WwNtdFHtQ1ZxcXzMmIHsmqA+0yHzAUtFIk3TndZ4tEFawlalrgMnqeRWy5xIaZ4dJXasE3nFXvIgvxzFysZzrqifLRH2LEUvqnBEjXObVTYe+dZRirAlQmDi2PsO9Kl9zkBCLafaOA1IrJnEuK5MoM9PlYIKo+uIs5tIQYZQilstUDx3Ajd2abx0JDeXTsoL0J1qcWoodh6cQUrLLvCbRrugLu+e4P7Pz2HLrsQOBAoaEc4FWj1PJKawGY+UkFzcwzrCab9Lr3dVfbKLbburrDyZINRw+ElU4s8EpUpyozs2SvZG0mMF1e4YcdZLj69AzE/Rd/vEBxfIpieZGF3D33OY7h8hTwr9Ljl+3rcdH2fuds1P3PPWW6a3eQ3334bDz63G6MznO4I4zmkEyHGk6AFGIn2FLbgYpUk2VGheKEPiUFIB8oB77n/MFNPtnh+cYLyqzr0liVHf6tGt9MgKypkMcwJO8BWAhhqRGywnT7ptXX8Zy7mCptyCSUUmRQ43QznkQAVJTTqI5RTxriSdGcD79Qm8Yzi7JPj6GWNCB3kKEKbGDUMUFGKqUpMJKh1BtxbPsdIuzy/NkfqOViRstUocq5ZRGSCYOAiEoMaJox/eohyfOw1ASqNSU9rRC/PN7NAfLiO8GK8o8kLdKQbGP7hr5xBBpLf/tezbK4GlFqWxNMI49AdBlTqGVq4EMj8uPuCRGx1sYGLOruMKIRATtSOPdTDtK9MjK0FV+WE8iBBS4tIUpxWQv/l8+BJZDiJFgLVT/Av9fGi/NwM1hOyusAYjWm1kOdiXLeAHKtjNppoR9OfCyif7QFQPJ+iEoMJXMSOaeh2oT/EpgnRDg9dFAznApRy8Y2PbncRo5xo6XzDbiae1TCUWE/hDDMEvBA+K678KYHanx2l9bprSCfyfBXhGqo7EzqXPUxzFeFVUMt97I5xqFTRBlhdx4xVkLGGJAUL07fFdNd8wnYL50wDzxTI5gJEs0ux7WC2DcKKPK/nL603ya1u/m1eCW22QuBIRSYhKwriqZBulPChjzyJsQkT1/VZjAoIZfH9fJxQeWCddKzCr//KvZz3C4yOZLBgqf25QlowCk5UBD/7wKew612SJOPU2XW++NBpbjg0R636lWyZU+tbdKKI9V6frZUObLdBKV73o/dy5Pq8+lpnFmstfuErahlH1En0PrSzgSy2ccKvVKL/r+LM8jY6dBC9lOivf/pVXMVVXMVV/B3HVZLmawRmxcUbWuJb5Fc9bq3lmpsWgRtYP+4idoMScHjsMs+d3sOcu8JFaxn1Y4yE4ZRPYbGPumhwO2ugFBeevsDolUf4zAef4Jmza5QWNZTLyGoK42UOXrfGMx8SJIdLmEMebPbwHxc4yqIDRWkpV5xkA5tPpqsVyudjfuUddzE65oPd4g3fegbx+hD/oT7v+6XdSOFAQeK11hiV69jAQUZXVrV6/bySlHwgWfcdulUwHYWKNdZRpPUQYSSDm4pkF0NO9xcggTJdtAOOMtRNzEcfu5HGTV0A4rSPk4K0BoHi9a84w8mxMic/FmAtjGoz3P87IXJCYksBquwCFlMLCI/5JC/bwOsNKf+Ry2gW7nMXmCmeyZeShYVmj6P/aYI3VJb4i6f3EQ4VP/izn+Y93/VizGCIqlWZPbKPzS8eg9EIc8HBrvnceEvCF/b3seshxcfWsJ0ehdmMl6oVPuoXicIQdAZxStSFzEScv2mGd24vECrLfH3AhZkRz350GuEKnPUu8sur5KMUWRwyOZby6vF1hLB8Mtzk+MwYv/+ZX+SHPvkx4mMjvPUEqUWuxBASx/MQjoOYGgdrSbbWsJnh2ZVdFOwKIkmwrsPmhk90s0QEuZrEJIrhKKRzvkz2pCDYGOCMtnj5L5zh327fjeMvIvsWtx1jeuvEjiV8fZ10aZw0q1FcG5IaFzEYYt08C8FKQTRXoPmaXcyVelQbfcoTKffftYPTjSdRz47zxhsf4tnePPujFZ55+0GKKqE/5oHJsz2wNt+mcjHtjMJLx4i6EYwicBxkohlNlbhzcpKnP/QITq2PWchwC0VuvHuFwMuwjkF5Cnv9BvrjIWZpi4m/55J+vEZnZz7A91YNOAG9F+2Dix1KmwY7UUO7mnSugnh4jf7dO0lrHiKWlN6xinOkiHppSPLuDqkriXYI7KZFbnbxhETXK6Shj8GSZQ5b62NY1YRigJGgev1cyXXlfHEO+jgbkjSr5PabbhNEbok0rQ6VypCwnhJ3E0TmktUVSIHxhnzbjzzPpz6zE/GpIc1ra2jPQ1qBrRTJpIcV4PdcTEcwqki8nsRasI5AJhlOTyNdgXINvdkCyvepnB/iBprVwjRJxwEhOMIyl4sN5vwmTyxXueGGs5wR+zAObJ6tI2KLmLP0pz2crkYJkdtuhinGFUS7S3RudNl3U5PenynEJUtxMcWNFMKCrwzCSSEVPPHR/aQ3nefJfzWfE1ZRiory9qus6BHIEcGZjORgGW/LMnPbGuGc4fnTc9Qn+xScDOnAzJ3bnF7eiR1L6OxLua+3FwfNhs5JTjIDniCYi9DPCtKigzrbR3YiaksdZt7YxhmPOXq/D1IiHMlfPHMt1+17htg6vOi6JRxl+Ya7zvHkF6uYzSZGweCeXYhAwsjF+oqsFuLMjpHJMqboUzs1wHECkAZj8gyptcjSeTDBHy3h/4iieTkkXgeTDlE2hvE6jF1pNJAKZIZ66izSGJJCbo3EWKzvIkYx3pZASEM245NmhpOrNR7a3gPTMUkNwjM9xAmfNCliCg660yLaUyCd8ZGL20wdXyV67QK0LONTEQfDPl3jIFpD+uUixEALnDaIviYtCDrfNY+3JZh4OkFUwSQSfT7NrZpXIABnK8NdHILrgqOwmSZTDltbBUrT0BvJ3OY6Anezj8007/wPR3A9TXfG5faxiO2nfYZPDJF6lIcxey66UUL1h+A7yJHGZgI8D9IMFYbIrQHCCpTWGGnJMMhRhAkKmMKVGnbtQpSRzpWRax2Kpzt0DtbBZIhU4630EHKIkLlVlVEGwxSx0UJ5LnKsSlZ26e0J8fsexXM+ZriIMAaZ+ogh6IKHN4rB93CmJjCDAXGjSOWZLmm1jsoERhp6N45Rf/ovtV79pX0ogLGPniK+ZRaAm/9+j9kj2zzxzgkuPxygO92c1NnukFYKyGYHZypXtgpXXQnzH7LwkgEXTlURUw760YjY8TBFD095CGtQDdBKQz8hDQxu/MJABrI0tyAWPfRUFefUGkIJkt0lBIJ2f8DsZocbXpagd6xw8axLsRRhhpYREJxpI4MRz59zaRQj4kLA2Ge6DCZDRCFEaqAPyQdO03t4C7kwhVKCd//xg7zsJdfxPX8pQPhnX/FiVrs9btgxzQO/9wC22QHP5cyFDS5c2mLPrglcT+GHHlKpF173pY0TfHjpcaqqj1ADIv2X7Hj/i3jjy27k/KdOs745Qvf+9sifq7iKq7iKq/g/g6skzdcIimcl2UjjtL865O7Btd/h3tefgDOv4Xd/ZZbKTs3JT/p83rmF0q4RRz8/S42LxP0IbQ3DHRIjfYomw1/uYy1IazlzfJnPffhptIC0DKKsEI0a1nGQThHrZ2SOQ+9ASLAUE7R63L1ri48sz2P7AyzgT9WI13t4FzZhqU0WSdTDPTLgc4cO8dyDO6mWezSCNWSlDN0Vbr6lzZcuFvHqDumWQKQpRPGV2tTchfGqf62Jq5/hj3/oRehEQZYxWVljIEtMn+0y+GSL3rdci4g0yXWGsc+uMtrV4NP//FaCC03+5MW3Il1DsTzEubCMqNVACGZFwiuvfY6fcO6Cfp/OxTWQClEt4ZIBDtZC6dgmTqUGn/AJH1uBQhGv5dC+ISBeq8FoHQA1TLiYzPEbvzxLtn+Kkhpww1SE6XTyFW7g8lKHtOajBhETB2JG+xq02cu9d3+BJ35qnPKxDdCWb/zeLV7zsg6f/wODGh/Pc4kuL+KXEyI9wpmxvPv07RgLv7r5ORZuX+Po53xwBBiNM8xXYkkz0A7NcIy1ix5KwtoxUBt9PvuH96OuGdA6Ypl7X0awSR7G2PCwmzlJIooFrAv1bx1j9b4BzmYbAOt6hNdNUijHtEJBEkgmGXC5Wycqe8R1STCRkvZ97FiZpztFVrYbFL/JUHvfKk6cN33YDEZLAU7b4AcFvudfrfH+372OYRbgJCNGJYdovsD2t86RzBZ45aWH+Px6magnmR6PeOwzM4w324TNaa6trPHAbx1A6AClLSq1uC/ZBx96FhHn9i41Vgdr2Tm+TDNz88lbmqI9ASvrHD9+HtKMQkNTvbXN0mqVbkVwzCj2qpRSIinthWgwAm2xaQJuH6OqCGMpbEQIt0AaKobTBUqrHVjbwMES7CojqiV0wcG6EnfL4K/GFA9DMuOz9ep5husZ/oUVVB24s0h2PCPsZ4hoSDpXxysl7DqwxKL2oNVFDSMolyDL8nNHCC7dvI/9R1ex5xKE62KTGK5YIu1gyPwrhrz5Tav8+iev49JSDTnMCQspA3Yflnz7jkXe/ju7IRaEWFTZIIqWVAW5ekGCGhpiT+AIi66HjG6ep3OkiOpZ/EiikTirDrbu4PUTpGdIYhfZMzhaMzxd4Ntf+yQAvT0VasJgXTAOOAnEoSAtOzgCpJAIKYiumSI41yKtKfrfMMWhhS4/t/Nhlr/T4zeadxHtruGdHeIOM5KXQLXRxX3U5/KZWZZ/10F2+5D0wQNVbyAs6MAhagpKQhJugTBQumlEsabZd2iZziWPby6fYM4f8OHhfuyExiunDMcNe/1NttMS+//piFOf3Ec/LhMuaVZPTiAjix53KW4PwEC10OGx+6/Hudxi9zdcZvnEPNXphLHWRfTqJke8LVYzhW8NJy5UkIGHnq4h6xLdCCirLn0/QGWWrCIwmw5qkILSyARss40dDjEzYwhX8sZ7nuFCpDmq5thRy4jGC5R/3NJdLeI+atC9DN29ci+xNm/qMQYTpzhb/Zy4yVJ0u4WdncaPc7VC/64I78kRv/Klb0AWNglHI7ytHunXFdAvK5FdlGQXynR2T4ADamSwfp3RxSHFx5qIqT71ySI7raGXWTKvgDvIFXPOwKD6CfLCAPfcBoPvnSPa5ZDYFHVLivr9EYMju0hNSvmRVdAGEUrcjQEoiZgcx5R97KVlxCDh1KOSibslaeohAgetY8RohA59ktaIrFhArcFTH7mBzrKPqK8itpI8G62a14erYgHtS/R4lWDDwaRNUBLjKyQir4pOMqQRUCtSff8ztH7kTpASp6dxOgki9DGuxF1vgbFUjm0y2llFhCFOMST1gJVtpHSQgBIpthSShQIzGzCYUIjM0Nnj4q1KVLkA2uJ18qBuykXEygoUKuC6jPbVMK5EKwc3schTl3DWm7hZ3loohMBok1sX+ctmSCg8vwHAxrkyMze2yGwJW6zBYCu/Hw9j5PGLABha2FoJ4YeIhVlGQcqj79hCJBHiW6sMri8QPtLGy0KUEWR1i6pozGgImy3iaypYJ8VtjRBxrua1noeerEKUoJXE0VBcGZEVfUodTTIZceiWz/Efn3gJvbiAH2UIDGb3DsSltXzsEMVo12Xs3ReJ7thLOq1wNw1agdTgPbpFurSNt3uSA/umCQOPwwdnv2pcNVkuMVkuAXDyC8fzRSNjOHdxmwsXN9mza4LC/BRqsUd5svbC6148eR2p1Yj0zyg6bY5UG/8Do7v/MdTDkFIxYCPRDIbJX/+Cq7iKq7iKq/g7DfnXP+Uq/m9AuJoRbmbIrvmqx7ujzzEyV7JlymV+7RcO8b63zXDrzYv8x1d9mtfeehIAq/Ng2uBIi+DlW/TnHXZfN8PUZIkjL7qW/Ydm+fpvuYVXfvvtJGMGdUWera1iUxwg2llFti1jv7qEWMvAc3AKKU4BTKeH6faJtyPwAihLeM0E2WNDAJxvKnD6zCyyLxgslgGRh2LGHkefniXYiMjaIK5YMvA8sAZRq2ILIWO7OyjfQK+JvrQE3QHXihVuX7iI2k4JE0PjwS0qWwnlEwF2cQ3/zDrBF07gdNfRD1RJv9jA6eSZJygJQhAnghODCqWpEaRZbiWLE/R2G5IsVwUZgxkrIzKLDRzS6SKm3UWstagdHTL995q5HQgQ20OENkgD3vFlgjPLLLgaUgPelYYMJTHjAqeuuG5vl96Ey3P9HkkvpbhfY3wH4ys+8t5xnniwRDCRK4dwFDgCU3aJdcI3zB6nUh5w09RlPvT0bh78z3tJynXoDyBJcd2vrO7pRDEcuPzwA6/lx774EhZ3LpDMlLjxdTdz+8yQ8UcjVGIZezpj7JmMxp0ejZ/OaH3HGHP/dpvuoUm2ny7kVdzdXKEjiwV05DFoVRBtA4mht+whCxky1vR/pc9oWzAqVbi0Ms4XT+yj4EekX1dl63f3s/nD85iKwLpAT2EzEL7Cygl65WniiRLUQwoioXC+T/XRTaSnaeGjPIPjGRr3tantbUPgceHnpvn8P7ye+KkezWsKdBYK4Ppw8wirU6wxmMDL7UcClr6orwSnWhASt58RdDXxKEGPVRhN7SW+1IATfZ77N4LH31ujqRV+xTAuYmQlRO6Yxq05OLHAPdTBO9Jhp7xMXLAYV2KVg/Ec8D0kguJignR8Kqd7lC70sUWH4Q2ztB93GDwkyQpldK2K2bOAevkM4ZEqd/5GE3luEbG4grPeQ9mUxe0Q+kMYjCAI8iDsqcncKuC4OB2H225awcYxptWGWhGuWCBEucixz+7gN997C+daRTozGUT5eeGGCclUn9Gkw8Yb9+JSwGQe2+tFlNYkPsQl8oYXbcGHzMkoHN8gfPgiwYUO2sureoUVKOkRNA2ULLJoUbFGGo2/GXPu6A5cDB6GV+y+yPvedR1xVaKVICkIrCMgyK0mzlYf//k1rCcxroBZhTcfs6192oWAi2tFzPY2o5JBFz3iio+J8uYZMoGVYNIU6/mYyTo4FhUbZGJQ3RGDgaI3Ucgnramm+fmQYeJQ8mO6ZxVHik1m3IjXhudIKxbngs98qcVj7Z1kGewMtkl6IUKD9gXnwymaN1YZjnt56CngliyjxCHrKdqTBXSryYED5xhd7vP4R6q881O7aVlYySSDzKN9u0904w4yr4AjLcXtFLdrcIYWp2+wUYxzbhX3/AZZNcDa/L5gAgftwo13rfHd/2aJ+AcaxEYxsiGj64pUDip+47c/xK++7eN4rSuqiiRDJAbRGINSgPEV2XgBfB9nfBLHXgkYBrz7Q2zaIFOS4pkNvMUhTjNClxWp7xIJyAoeTj9vHSrXBkzeOKR4Wx07jIgfTTn5CcsnPjnHHz13mEovw1keoj2JG2d42TruyTW8bsbUp1tU4i7qNRGynKCXDINdJayvSMfC3C6TQFbx0TN1RjtLRLuqiCilXNe8/Jtb3DDbZKw+ygnachm9MImdqqGjCGstcgjZSOIEivqbrvhvpEDvmgYpGe30Ed0e2S1R3h4VBPk5py1agEwNWTkgnSghusOc8MjyLz11LGkxJx6HUypXc4zVEX6Am8g8xL9SwTNObpfzciWQQwG9MI5dmGEw5eIONLXjA8ITeT28mZlAzkzibkeIzFJ8aoPRbICORxgMzmof73wLqwQyzlDL24jMIslDokmzrxA0/lcH2wr3ymLCxyT/+btmWVuOuO5nl9Ev3k32ksOYQoicnUHUqvnzhzFmNMT4kmysgOgNITYkz0b0doaMShlieOXakzrw3DbqYhOZWrKiJLlnF8mte8n2zTF8xWGkuWLkcRSm5KNDhYg1Qlt8z+Xkw6d5+Jkp7q6cQDzZpJn5dEY+qlzKyaqxvLEsmx9DVIq0r1EgQPvgWHA09BoB1hEMvYy+b/inP/ONHDk8/1eOuwCaKx0wBoTg4HU7uPvO/QCsr3SwUtLa6n/V81OdMVfweNXkEn7L49te+lbe+duf+m9u/38Uv//uL7JybjNXZm1fzaS5iqu4iqv4vx1XSZqvEWROHkhp0q/OpAlJ2evlqgl3KyN6sguppvjERu4/H10hdYRAoPnuGx9k75+dpnHsDO0wxHo+/+mX38/pJ87yujfdxXPPXaJ23wayFyNGMSZN0auLhGda+Jd6qJFBjSxGSfyq4afe8mQuM1cKWynlBEgn45/94A/w7vY/4scevodEX4sxlunDGwQHelcqNS1qYhyr/LytZBTBYJQTGfMuVCowGIIHN9T2EC3eQNrKQ4mFtCjPgJHIsSC3Zg1HqL7IK7YdF9PuYT3J3je0GPvkRapPtVCRyX39Irf1CAOvqG2w918L8F32vuTgFduIIBulmG4P3R9gJmokdY/NlzpIHUOWgdZ4W32cMRdRyL8T/+sF3m9IzKAP3T5ZucDHPjKLaFRz4gmI6wF69xTMzXH2zE5u7p5i8sAG37ljkW+oHUcWJaNddUQgeSyokMkYuvn2cBXuWIi1W+w6XuL/2fkXHHr/WVY+bXEeOEfzUBEdOCQlyfe8Jc+kUYFFdEeoE8tsLiqGvz/EWR7QesNB/uEn7+dVtSK3Tp9EDWO81givGdF82uOwWuXtP/1Rfv7w46RZh86+MjoAU3LziUh/ANpgMkvY1FTPDHF6AiOh33Pp9n2iywKnGZFc1piST/XkkKmfPEWxkFD7BkheXEHPB9jFLaanN7n+5m0ev28Bu9JBXt5AigQTC6yA4pkOjVMbfGzpIKPYJe4EhA8MuM2/wKgO0VyRwaRLPF8nqrkYBakZkToCDNiJKnrXFGndRaQpbT2TT7Yc54WJCaUCGFBbXeKnN1mhhFYClQl2kFLJHNqZohwNMY2AZLKILEL9e1rccM9FbrjnEqbnMHv/FsXzEeVLUU5mJDkJYq8QXO5A420lxPWQ0a4atqdJToDXMnhdTXU1oTyb8KYbn+a2yVWE5+Z16IFLlAScue8rq7NZGuekCDZfRU4Sgos9Xvwdi4jAR5YKyDBETI7lL+gPkOUy60+OU1gcMXGqC74LCGQ35fifF3n6j4uMf04gUrBWoKWkKIYobRCavGFmmE9olciJDWUsqpNRe6z9QiWtVWCVwCtr+nFAMRvihyPc5pDBkz7XuynXuxnV2Q5dMUGwZRBSoEsSfFCXtgiOrxAeXyM4sY631CGdKjNc2IV+rsKlpMzPnXgR7+q8iO6r9qMnPNznL+GcuIh+xsV5Vx97rpcTrKUiplbIJ+lzUxhHkIUOoh9jO32UI4nmYbhbsVYr8/xzM6w8U2J8t+EnP/st1I2ikShmS12ykuGZ5jyPX9zNgxt7WU4akEqcIVgP4lKALihMKDEmr2I+N6hTGvQpZB5bW5NoD8ymwJueJBUB+6rbPBM3eC5ucKEd0i+VGVQlOkgxgwRzNkb7lrQssQJ0vYCtFrDVIghJtmMMJhs4a21Y3WbW0fy/5+9EerAyqHJ2s8y5k1PcM7HOnmLMtY2YyencMuG2RzjDJA9Tr5RwV7u4yx1kahBKYZwrZCYWBhITB+g0RQ0SrGMR2lD48yalX1jE+/Q2WIu/ZZj50ha33LzI7vktrt+xRv12iSpKhkOXd584wH1mB2osRcWWQUPSHwtoje1i+KprELUKxaFPpWcQv9eGX9gi7KTU7l+m8sga3naEtCBSgyh7iOkxTMlnMB8Sz1bptRSffaDM556ssLTiYpIkJyqtgc4A6XqY1XXM+jrh+RGNdptr55vISk5uy/UWcTBk89UNWjcGiP6AbG0FK0DNTCHjK0SHo7Chh3ElYpjkrVMRuTrNAe9yC9Fq4a/F2DTFfDlgN06h3cVeIXRkvZrf79KUymKGDVyEUpRXJX4TsqKD8MKcVDEZQkom3vMsO/7jU1QevkhwdB18FyFyq+5wV5l4LshJ/780XvjLPwvAJkl+z/4y0uyF3wHokw52t8P83BI28BC1EkjJ5IJGSIOoVBBhEXH6EoWHT76wmcL2iPFeBo0a2oXMkwwnfJznvpIbpDoJwfPb+OebSNejcK6NubCWH2dKYj0Pi0CWBDiCpufhbEd86Dcn+dg/DCmd32JH0GXa70KicSbGkfUaZqyKnihgG2VKlxOmvtSkcKFLMq6IZxTnZitoazC+w8x4ld/+5+/njff8Cs8fvchfhdF2Ox8XJDH33L6X+EpQ8N2vOMT1t+5m36EdLzz34a3TfHrtGOtRH1f6vO93n6C/0eN9v/uFv3LbfxMcPjhHMFYkmykTB+5f/4KruIqruIqr+DuNq3anrwFYa8mKghSw3lfbnaZcxRNRQBXQ1QK0E3Adnv23I37jMztZbRUh0KAUrnW4GE/Sb1iCrRSp8ujF1lobdUUN0nrqHO6Zdcy0h1lJcf0GtdeXuLDdQE+U0X5K4mUUPtVBFgRj4wnMToA2SCsgM1jgnq8/hOMqNo6NIHucfVOrvP66J+gnPp9hB0LUwRP4Qcqg+xXiSQjD9TvXOFmdh4tFLCm18Z/DnPsUdvTuLz+LA+EaqR+y1a4TOzWEU0I9fwmy3CP/pp9/Lb+3fT/nvm4C/RfrhMcGUAqxWYbpdBGex+rJjA+e2cUnHtnDbHqCznOLIAUMhlhZQLgCazSJlxIOOsyOWdy1fj5fGUVce2CNlbiKToZIYEGvcduRHh/YfwB7VqCdkPe9+xaEyjDNJlQqiNaVwF8tGK47RH3JK153gj9tXovUHaxWqEzSJeBIcYsH3THMWi5BpxhQy/pUZMrWxe/kD37rQaqX18i6GdlcFdyAzkt3UvUizp9fZt++/OOIQYKQKeFKQjznY0oKv6kRoxFvfceI6850kMkUGQXsYIC8sMpjq3t4pdniWFSFRhFNSrEX85J/P+LEP5uhm6SAwBrQjpNP9E2G6guyLGDzh+bgkqD0oT5hpUOtVSQspGjrIpdS0o0E+7oK/pfWsNbyYz9+lmIV/uC396LkOCaKUIOIkRHggnM5ofThLQavnkSmFl22NKf34zRWcOMY7+QWvW+ZJJmczMuVM0soFOn9Gsd3EFGC1RmGBKdsSaWLrVSwO+o4a21slmErAU45wMQpqlJFr1kGh8c5/Np1js7sI+6usbe2SrScoRFE8wV0WVGaHrK3sE0gU/TOBRafL1F++CS2VMKWKuA4kKYQuhghEHGGQBIuZsR+hKhq9F0+JBLhpxSrQ245uMpzH5mlHMdYa7COiy77kAjk5ldiI0dzIeVNjY3jFx5TzRFflDOIwAcE2oywgyu/D3xotdHlIuEtZTqtCioTCGMZxCXO3VfBjhJ2HHySkbOX4bCBUA6dgou7mUDiQWYQjkU4LsJLUYUSRrZp3L+CnazT2lXFSoFINVvXCLZkg6knMvbGm9hdLqufzcnWM4lLSRnaZRAr29jrSmAtaqRRA437zDoCQ1YrQqNAslAjmSxC4BGc7XFde5E7f2yJtUaDjzxyOyKDuCJRXYEzitn8lj2QZcy8+zyMTSDXmqj1FsxOkFVLWEciZAHXt6SxRGpLsi9FlAOGvkvmecTNgMSHf/bpr+PChXHivRr3jj6XN8axOFzaGEfNncBxNbGRkEGw1GO0uwRYKFWhoggWB3gJyC5ktRL6xiJNZTDS50XfnrLrjoQn+9M4ImN1sU4jMXne0Esziu9YQm/0CB0POzVOUgDjewzu2o1xoHyyj6sFdjDCtrtIR3HmpMNgzEeYfA6uPxWSHlZsDXxOJA4VYXGmrtR3JwYnNnlu1voWQlh0JUQ6xZyk8YEUhLAEOwcM/BL+qRihDbookEOLsBZT8LEzAd7pDcLja8jYYD/oMLnT8uyX6pTHhtT2D1i/CHtKm4ixgPTGkJVRHeeUob/PIfMkopBfAyM9JFyX+C1F72KKkVBst0gSh+jgHN7yJmqU4bQ0Vvfx4wAjBP5yh8KEpnRbTFYLCR7x0GcF1pEwihECvv+HnmF6PuG9b5/g1ps9gkCjZzo8PDGPLUjUhTWUV0Z1PJRfQzQlBPm136YZRkI2UUYMYiwW6zlsffM8wYl1rLJYBzASb32E2GqjMsBozPwUojfEWdpCuB622cLa3NJqgiBX+rVGCMdix8pYJYgmQpBFrAQdxTi6iO3FeU5ZlofsSm3yZidXowOXaF+VsGmwrkLfsR/56Jl8LEFO1HyZlhEW8hTg/2LM8Zd+Pvl7IcUE5EPHSG85wLff8wVe/EPrrD8Z8NafqEOnm1/fXAmBB1IyNj3EDNssLY3Q4w3UqUUKlwzZePGF7RbODFAqhWoJeW4xfz+ZhmEEhRAVpeiST1J0cKxAOhDtrDNvLrP0hmsZaJ9hb4DJJG4gKIwUWIMuORhpMeWQyskednkdT0hGh3NyWxjB1m37+XsvPcLr7jrIq3/gj0AI/sWPvov3P/ZL/9W++DKHJVzF+3/vLyiVAl75dYf41AceZ3u9y5E79nLjnXsBeMnkdXR6I+4qXqDXjWlkX7GUWWvz3KH/Sbzu1UdYXN7mQx97GspXSZqruIqruIr/23FVSfM1gsG0ZTgjiL9anUxZ9XhRoQOAcVVu4aiUEa5i2KxjO0OEUqAN6SjlmeYOVr53geFPznPkpfs5dNNOCuWQ7nauxqkvTMFEGTXWQDbqCK/M6vFJdDlEbPXInBRTdLDG0BOHaMmfQWU2J2jSDMK8hviZR88BsKNWxIk14aMC27eYSxluJcKMYsygz8AWoP/lEDwLVqD7AtWz2HYXRilaW1797XcgxmpQLkO5QljVTIZ9TDjGtTfswzoSOzOJLRf4zk+dZuK2faSL0L4P2i+fInCG3P3m52A0wvaHmK1tPv6BBr/zE3txVnywkCQZBAFirJEPoIcjhFQ4FzeR57e48dxZ+HLbplJsbzWIHvNeGNCONhTaSOzYNNlN+9BjZWxyZVB2RWJu+zHd21yi21wq9w6YnW+yMVnhiyev4al7rmN4yx52TlVYXS/wU595CUd3TkI1zxpgGCE2SgT+YW66fR8qKJCUJzFz46TXzOGkAlsu8a9/9DkeeHAKgEyG6KkqdnqM0sUMPRVSf6OkeCkhWItw2h7KURRLESIz+eq4zuj/UcSP/N4b+JU//EZEW/Ljex7gNV+/gdn06HeHGOFir0ROys4ot0w0E9yhxN8SZDGop7uY4RrDxZjKu4eYByTeeB3/P2xS+K1Ngn+3iVQaKRVPPzXB0nKZlaVSrnTKMoZrca7c0AY75RB/ax05ne9t64CSitMP7eTWX1lk1zsiBrZOiuVf3HoLE2sRhZURpXMRSgtkovPmFjvEz5apzmxhFIhIYzwH2x+iu22I+7hFgXUcxFafdDDkpn19fmDHGbpxibLMkB+OsZHFX41ZiSqsD0vM6CYzTptiFGONRcUZUujcYjAxhtgxjY0i9FYLu7gCG02cSCNTj/RwkWzOxyjLKLBcnq/z6ZMHeTidZfWSz/47JKpWhShGjDSmGOTHlKNIfIezPz4JjsRKcSXHSfHc2g7MYJgr1IyA+AqxYwy20yNThjgugnLzXAtjMUpwzbcnzN0yhCcyRisbvDBdG+a1wQxTiDKG4wIbZOgxk6ueRjHalyQlL1eQhJL+JIibItRNCcv3upz/fET3natkuyfQC5OcH5VY0S4bpoh/cQP1pWcpP75K/Ylt3K0evVunGO1t4F/azJV9FmwQQiel/Fyb5MYAEWg6q4Lw8VX8jSHNl+1Eh4rg8iZyaBDCQSagPBcxTPLJZH+EDSQyza2Eqq0R9YTSzABZ0vQoopEoX+OXIvbtusiXlubZ6BVJt1zCx1OksoTF3DIzqzqURYLIwDiG4PmN/LhNRP7/dXtM3Glzq6qFwlKK3Ip5vjJBeEfG7J4eDx9voITBEZbOlkdaBDsaYB8zBJQRlTGwAuOCM8xVLV4ro3RpiNeNkZnhjlf0uOaOlEJNcv3BPvVgQNXrU3N6BFWfrzt4gerYFivG4WTmsnjzNADJWEhWcLFpitUa6ymSW/ZiC3mwsDvgivoRZvavMnPTMiQpdryG40lMw8G+osDgpYeIJhsEz6/lVdhxwlPP1fnSh2r0IpA7JMn5/Dg8VGpya22D2+Y22XtLG+XYK0rNDK+ZYDGsvX6cywcmsAtFrC8xWiBciRBAGpPNVpCNsbzlbKOJGMTE0wG66DLqK+KRyyj20cvqSiNdltdXZ5pd12fUpiwLexN+8Mee4xtfd5oP/fQcenGA1AIBFE712PHvzlBoeshtBWmG1jFmcxOiJG8f7A/xYotxDdGCS/dF48hEIy1IbdHVAqZSRBc9TK2MEGDKIVZrjJM3RAml0M1Wfr/rdhEuyM0eNklo71ekDUthqYfINMH2ECvIjyuZW3mE60DBJ7YJdnUTd7RO7eZNkJD5kNQLtF97HUk9ZOs7D+Rn9H9BFtj/4m9B7tTLStD6zhm6FzRulOI+eJwH7/O4JBXZ7lxRihBYR6HLPlqnkGWcHY7xTHkCs6MO/RGiN0L2I7q3Vckafn6d0kCSUCj1KUx0IMlVNvKZ8zDMFYJuJ84/pxVkJR+umWewfyejnotVgpH1yFyFLCiygoPxFFHdRVcDhJLYRIPn4TgKmjGim2EzcF2PyHP40Hu+iM1y9ZAjv0JNZdrQGeTH6thMAyEFnqOQZ1fZM51bvV75hlu542XXsv/6r+TZHH9iiT/91fu57+NLyM0+zdUgv75KweCFcc7/HFa2O4x0hlKKMPD/l7Z1FVdxFVdxFf/ncVVJ8zUAIQTi8ADXy+jxDvobmxQnPoAQgr4YJzNbAOhswJ7vSdg4O2T4JUuSaEysIMuzYfrtPqPURy4lDB/VRAdHHLznIK1MMnfdHAC1GxZImiuoeIjT9TC1gOTZVSpfvAjaEJzzaX7jDDJJuHb8tRT8OmQPIaTCjiLsKGb82gWSOB/4BLUCJBmdZ4t88vuuIdqISb0QubGMncxbnazRX8UmNpc9sqEGY5BpjONIpmYbYAVyvE7Wb5JNethWhl+VJK7Lt33/XXzwd7+EmQhxfMP5x55h+qF1+s8EdG+qIaRP0vdAZjC6otzpWgrJkMLyadg1xfW37OTBDz6eD4JHMaJUxPZ6ZLfux40VmSmBzvc1SUK7XcR+0LyQO7H5cMBHX1LEeJvYQwvIjRZ6vYlpx7nlRV/JKggF64dD7HiF4ZcqPJfsooGD3Ui5a+hQaQxY3LCIKYFT1qjJcWwUY9fXeNm3xKSDi3RW2qitAWxsIybLqFhjjIPqZvzG/+824s28VcKOErLD81i3xGzvJJUvjkgu+5BUkCNNOkh47rmAaFaT3FyCnUVUVeCfWKH+60+TLkxy04vXecUbN+hELr/0CzdjBucRRoIbAhannyH7WZ66akoII6id7WCeG0HboI3AGWwj1iwmFcQLDooYbSQ/8o8W+fg7JvngO6cQB+bh8kVQGuIY6+dNPTYDbQXDTQczE1ABnKKiW9VU/uAcj528BvfEOtywwq5zDu87+iVElCEsFMcL9E/kdiPnkIs+rhn2XP7BvzrKr//qLKYSYF1wltZz9YfQ7N3fZ2VHhdaXYkpP9Dl2doLZa/usLhfIZiTjswlnN2KGB2tsDOuouATjgqqf8D3fc4J/8shOzMGdyMsbmP4GXLEoymqI6A6wSYr2JNZ3CTJJc+8s2nMpXdiieGyDbEeVZqPB2IllVp5pgudg+gnh3ARmK/jyYYRxHMyOIkG3xOVv85j/RDFvNsPl/PMl6sWcUZTC5i1XkOfWMIROjyzN8JUhjV1QkgN3nOfVb7iI+3Xw/zaKjM8OOP1ZDVZS7rUZ7QxJ+l307gLNa6q4Toa3kaGGMTJ06d2ygB4vkYUCYUAaSCJFpZSQzKaoRNNJFGmrQ3mXx+PtvZSSlIF2ScfyoGghEnQpxJR9Gp85gYwy4pvHUV0Hf9v8f+z9d7xlWV3nD7/X2vnkcHOoWzl0zt1AJzJNRkAFERSQwYhpBMcww/PgqCgqIhIURUSCILlJDd10bjpXd+VcdXM6+Zyd1/r9sW9XNYMzTng9j/PjVZ9/6tbJe+21117ruz4BazBgMOoQ1gwWSnk+s3g11kdWqBycY3B5irmphutWSGQfezmhfDLEHBklMqF7yTDlQw1SkSLiBCNMM1mFAmnG1G9ZZuXJTaQChC8YCJsh0eMZI2f4eqGI369Qvm0eb9Xk9X9zmJ2lNf722DUc7Y7S8y2SInhzCcLckIuYkBBjRRGLT46SN4xsbOn2sJe6+KVJzlxR44m7TnFo0mBma0Ar9jB8A6vRxDiyhKUSVG4SUTBIxiuYJ5aRqy3YMwm2ieEnmSQvTHjOixcYGx3w6JNDLLoW1WN9qo5P0HSJLHjT2FGU0WWnEzMbSxbCbHGpLANlG5imibJMlGuTFk2EtSHRLIO7loISrHxtGOtZAjtySS/agl5dJi+WsK53Se6JSU6uIIIY7Zik1Rzta4cZ/uJx+pdv4lSvSjlcAxT7PunyrEEHcZXDsbvG0cMG5vcPMzS/Qu8Z0+ilFZwzNt6oyZlNHtVUZQt1kbHSnONryEShHDsbV1MFS2t4T0D7xy5h/MQxnMICeijGysUkHTD8GA5n5r0f/otLyNcVx580OfMrdZy6QdgNM/Nt34dKGWnbqNW1zCumO0CVC5k/j7AQtoW92CYJfQxhkz8VU9Upcm4daynFf+GFpGioFRlMF3D6GnlwEaFARCFx1UK2OtjlWiY5imN0GKLTrNiKl0PGKTJI0CjM9T6iZoEG5fqodhNqZTAMhNIMthaJJyuYJ3uEmxIauQL1C7o0WkW07eG0cjTfcClRTZxjdfxrc42n/R1eU2DubbsgVRSSNgYtBLD4UIkPP18wmM+BjBDlIqDRZozohBBHhBePgJSkrsTtRCAlUcVBWDlkmNWInorgHpyMuPDNAfv/LpcVieplJJK4nkcqjdEMcGoOfiIwQ0lXjFL70pMMdhfpP3+MSAmsfIq9nhJrheOUsA40SaRAliyY9cFxqH91H62XXgoxpCZ8/GuPMnmmh94oNL3uV1569tj/6sv3cOD0Mr/6Yzdy1Y9dy/Lf3UF5qs4rXnMdW3aNA/D97x3kxP55brzlEvKFrHD++Sfvp39TzNxYn6TrEBVilAJDp3z5nx7gp97+nH+l1f/n8IEv3sOxY0vk8jaVsve//TnncR7ncR7n8X8HzhdpfgSgteZ1e+4mb0R4iY2XnqLR/BPqtd9ie/2Puf3xHwfgxe9a5nXPO8FyZPKu3ReSRgnUq7DaACH467u/DJMC55+byGMxsRMwX2sTJLDv+yf45t98h5PNAXJNo1W2oEiFYttlCxy9XSGAaNghnCwikgVc1yISirTZQpgmOk5wSi5/9o3fZmwqoxYvzzeRvRC93KDfixGmi2j0oVTE8PLQj0j+G8JX5EOS05hNjTQV3UFIKe/iRgP8My16LyozYwp2jvg8Uog4sbCGt93hjV+/m7rweUEx5MHaX/Dttc0UBj02rRmsDDQLDw5l0pOCA1qTJiHJpZtRJQ9nENBITBiuIwGlNLrTQY/WsdYSLDvPDbtfxRE+cPZ3qiQllck5vpqGpKsxnD7OiWNsuTnk+OeNrEADqEYLlcTU/k6DVsz/5DbiZgnPMnBVQtrQ6KKmudpnyNUMgghrq0HaaIPWXPB8yU+8/WF095Xc+umfwUg1KmcTjHjInI3sppRmF+icbmM7G8bBSuMebzBzxRpv/L0TDOZTPvwXUwykRnsZI8oQBvFwEeUIRCpJZ4aQpxuIboBcWufwoMxtnUnODIr0UxvGhzBaPmppGa1UFuerI7TKqgciUdR2RjQMjfJBS0007uD2IvTAx3byhMMeItZsvSrghc01vnTrBEHT540fWeFvf24ImSqktWFYmoC1FFO5d43GMzKDx9mXXIwquEz4Ryl+9zAa2LTcwdq1FdMyETlNNIgQx2YhzVYD6oE+7edtpasn+PLtNjLQaE9jRBqSBKMrSC1J6HkExTJqKCQechBynhM6pVDrsiORTF+Vcvchi84Wg6jrgHAwBppcAuNTAbrgZRIAU6FavSz5xTCwJm10Q2cLEdtEpBA7KU5L0OuamV+G1kgNYU7SmCljPdyDSIDSpJZAexZplF2Lcnoca3FAsLWCIUx0yUFWU/yrNOruCC0kMnPJyMyDAawsRjvZPoYZuCRC4A9BLkjwlKQfG+DAk80beOEN3+fIhmls1ymgWzbBJoe+rbF8xZbyAvyDg45TtCXp7yySeAYY2QIs8UwmR1ZJpEGhkJLsmQYjpbezhHEiZGnZxR4tMIgt/Le4TD1P86KRR/n0n18HK7nMMJsEcZlBJ5ikvHcNY34dd9sI0ivifkHR+QkTu6PRjkEwVcIOU9Kig9QW4VaDlW0FRu9K6I/a5M9oZD6PbrYobR/Q1sXM5DVNiUObuU+M0tvkYlkmlq0YRDarhSIV2eXnn3c3jx6bZu/dRYK84mfHj5E3Uk4Nn6CZWNAxMByQ/RT/sjFAoIUmLRuYHU2htYZdSol7FmE9TzTlYvYMjCNrfO3+aa79w+PE2mTCbGO1BTkZYE316eVcOq5F7kyCMiSi6yOiFHtpQLipSG9znvITTYRl8smP7mDsasnP/NSj3Ltk0X5fiJBwZucMReWz9JjNtVeleBI2mRpvNduNN1sBoh8j8jmQVdLREgKBtFJsu8/p62eY+tTprK3mKnQfzSGKLgw02ssjL/NQn+8x8ezDrDYsUJAMFxDtAd7xdfqX1MgNAqbHIkavjdk3Z7BwwuHTfz1B6SKBrtlAVpQnUBixgbBh+Itz1LfZnLqzgthgMFphQKgExDrT7Qw2GGKWifAj3CcXMdoJ0YLPwx8dZu1FW+mFDk7UzZL7ohhcm8UDIWrHFHLtKE8EuxDrgh97xxMcud3h9HGbZMc4wo8RwQBafcwjZxCDEGFIRL6ADgJEuZRJ/dIUIQTFJ3qw2MnGrCeW8C8YIdY+znyEdh3QCj2/hE4T0l2jCGmiziw+dZNHjI8i/IyVmBYt3NWQkfWQWAWEIkTnRmhtBu/RNUgURq2IFgK1tIJOc5m21TTwFgReT6HQWbEt0aAU8YYC72m3q3P3MiEw9LlHyi8VjDwvIDm9ypo5jlz/QXPcwanCxmdEJCNFdKOB0eghk+wzyredJr5gEnO2Bes9EGA5OXJPNBB+/DTKTuYPN/fkFO6lVYJAoCaGswKVH6NyFkYrIjfqoMOISNhMlhq8/Dtt/vpAnV7HRUrwKj6cASNNMAIDMwEDjTqzhIoiVN5BCkHljiM0nrsz6/chzNcLiGGP4RMdJOfM9uNEoTWkSvHYt/YiFLzmbc/jyhdcilIaw4Bv/v0dYNm8/Zrf5ta1jwGwya7zaPs0J+bHUWWTtaP5LEa8mOOTH7n9/6hIc/mOSRoLHbpNRfzfeBOex3mcx3mcx//7cF7u9COA2d4idbdPzoo4HeQJNXTDwwBY9gz3/N0QALWcIic1dSOL1wwHQaZb30iNeOTu00w4Dba8oIva5ZKbqPLat97Ea95yEzKJeOTbezly2xPYp5vIfha5iq858W377A6be2qAtaohZ+MVXPx+tKHTj0FAGKacfPx4FvMZ3s+w8TG8uAvNNqQK3esjpIROF93to9daiKcm2hvf0mnaWF4WMWm6mr0nFgD4cvMfeN+pP6X1ms2U3AhR0OSf7dMvKVb0Q5TNgEu8jDLdaxmoUJC2Bc+6/PlI12JkegCR2mAVCMw0Yqx5HDOO0YFiJYrQEkQhjxgdRs5sIi24GBjo1XV0/C8kW8ZIJ+pgSszUx94lMRw3S19yXUTORRgmuckcQTuPVkC5hBiqARrhuLgLfZx5H3MxQCcm7lIfs+NTKlV41z/+CqNX38jEtouwqhIlDJisg21y/D7N3vvyaK2pV11kwUOOjWJ5BaxWyMjWJq959TzbLuminaftibY7xLLLd0qTPHHBCC/4k3lkXpMGPsKWPOPNz8MqVvHHBd1piRoFWRSIfA45PES8N8/ffPEqvv3pnSRNhR6u0bmijnZdKBUw8y7CsRGlfCabcSTO6YDcSEpuJiHZ4RFcvxW5axRsF2G4eCLCyaXs69ZZW7TodwTp6jpffSCPHi0jqhUYtmHD1FdbEE3YpFKQegaG7eG04rOHKAFjZorKaJlyNUccpciVDmq1m0kABSjLJCxI2tsFdwV70K6BSDTKFIhCHnNoGFyHXi2Pur+LjmPSosPE+BrD1TaVYpe///CVTM/02XzZKvmTKd68gbtksEul7JIJViQwllrIMyuIpR4i2bBctQxuuXkVkhiRKszlNjJJERLyT7ao3Nsgt3+dwfYK5kKTiY8fJmdpNr8MnvW9Rf7DHUeIKBJXvczAEyi/wse5wsKKwYw0ydURanPMNcMHGDl2kjTtn/V7JdloK50gTRNjwyBUC1CeIHAsDu6foDVbw+4ahAWD4/dOo4QkMSSDsRypkqgFQSIlCZItfpfGH0mCi8fpXzNDd4tmMJmQepqkABQkoTJp7Btibd8IbsHH3uZip4p0WLIQDXOiM8SSX6axrcBrJx8nbwW85be+R8GKEQi0a2HvVZi+JNEJOk3QG7II3TVIv2Mhl0K0ZVK+/Rjevcfp1BWpDUY+JfUkSdEmLQhkmGZMopzLyKbMU0WbEgarVL0OI69Zh8kQLTWJMvGqAZEhObZQ58OffSlHP78beeEEg9ePc2xhiE7ocv+xHcx2SiAFbgtwLPqb8iRFSAsCNVbDqFWYubbJ9R88jvJDBpsc/ItHqWzvMznV44LfX8UdgV3eEmkiMnXaMwT+O0eZv3ET6890GUwVkd0uwQXjpFsm0GMVrJbC7huZHASYX61zz76tfP/QFBdWfIr1iNpUgJ/W6U6ZvO+3L+NbX91MGEse2LcJcyMgxoiy9BwhJTqMsI7OYx5fQfUTghWB00hhqIxRr2LEEudkwCAXQGdALu0h7oT4hEathOTGYigXMQ0XI0hxTnUoPrKGuRwyWA+54Rd99CYPBCQjedIXzmQMvFQTVx3WX7OD3kUWw+93qPy2S+voOF6SBwSGqanWQ2xPIdS5rq0hM+Y2JUk9T7hrCAyDXGBidu2sWLG+ju51MwPtYp4rf22J69/wJIQKqxEhu4rtlwxoUcr6mGWicy6yWMrMgQ2J0BqRyyPKRUQ+j7BthBBoQ6AtiS55GxQRkPkiRqBw9y1inlrFODIHhpX5OWmBqpUILt10TqLsOplsqVqCnIMIwqyfa0jHKiR7prFiiem4NF69K7vn+iF6MMgkQ4/MUvjeccx2SDAMpp+yrkvYLbAbGiU1hZMhxUeb2TXgZcwP4XmZYfSOKQYjuXOFm7rkgNyE0YiYevf3keeGWp5e3knGS0QlifBjChdD4eVeZv5ftOhcViWeqUIUIisVhGHh7V9EKoWwNzxVNgpDrdUKA7OElg7m6VXEagsZxNjLffAjeit91AAMYhaOSv7srsuRSR6lDFRi0C+CSGNixzz7+wSgul26r9zO+ntHafzmNmh3sE+tY/fBiMkKq9KgPexy3XMvOHtcv/Kq6/mDN9/C7qlhTh1fIwgVn/ijL/GW572Hd/3+50nTFBVEqCgkis95+uypT5P7Ro6Fr0xx50cuoN+rkpoC049Jjf+z6fiP33QZv/PmFzC+o8auiyb+7Tecx3mcx3mcx//VOM+k+RFAxa5y5+JOSk7AhO5yLATLPRcZ+cgd27nqFvj8n+2huQ8Of9tCaLAci6tfeTn3fO0AADM1nx+ffpgjt3us7YdHTtzJm9/wbG568SX0232kEPzlu78ICyvo4hTKNhB7j6GDp5mS+in5wx0IQizPIer6oFKEZWW0bQ3LpzJJUND5c26+6Qk6vS38zTuHkUIg8zlUN5Nf6U4no2tzztBQIDJGyprAlAZJaHDZ9oyWL4Rg7+oChoj528XdFK2YFgZ2oCm2r8OSh7in1eTZKL79uRpCahzPJOkO8MwEd7oDXSfztYljapebXP2f2+z9lwbHVkbhojpqsUnajqDRBNtGdjrIto8Q0J8/BOPPQCUphjAwSi6qoFFopDQgjkk2TcF4FcdaZqjWZq5YhFYbrTUUi2hD0r52kjQP0UyV8ndWiV2FCFK0qSgOlQhDzaAneObIabojFmfaddIoIexovvrJOp/48A6Of/8hwkunMXwDGSrsTsSuYJm4t0576CJE6VwCEMLgdFrj0f11xitNnlvQWGEX0hyi7ZOfGoKDS4hKTBrZJO0+apDtcErXw4wiau87gk4VyZ4ZcGzWX1Vm5y2rrH20TCokuAZIEKHCWBqw8E0Xb0fCxIc8ys2Uw+9ZxdxioZrj2EGMLqTgaO59tEjrXoXu9iFOWP2qQZy3UEgKVoPwKe+EBMSiJmjkEWEKSUrhntPZdz41RzYtWgvrWLaFGWlwCqTlGOl5KA1mnKJyHcbv0nS2QDCSx+qn+EZKdb2EcF1EEJHsW8HRIfGCgQhDKiNtpNDkCyHfe3Q7y84CBT+hcHAN8pmvx757yhSemXDbV8dgvYmIomyVICWkCmFoprf5kGwsToKQMGeAbWAdWUGsOsg4pfDEGkYvzPxfOhH5d/b4rbFZHAkfzUnCUEDBhqKH86IQdbBKvCwwFgOso4vc/BOLvP7yRf76tinu7G2iejq7PpnIPIp0zkEU85SskMFqj96FRZAGMkoIQ5srpxqsLmpm+geoixyLZGlOIgVUFomt7WwRdPTUCN49TYLdFm5zM7qgEQmkUiOzvCl2d9Y4cqREEjis2wb5MU3Yk3gLMfRN1gcFhKlJuyahkvz9mRvw0xzbXzOL89MGr9p8giuHHuN9n7ye28tbyNfyqFoRsT4gfW6MN+Ej7y9hpnmSeIDqdolrJuVXtJm4YI0j82NEXh67K2BxnTTUJK4kKMUY/RgDuOmdyzx6skAuHyGcBCMEz0iYHF4lQvDQp6/F6kv0mQDXUOjU4j899iIGl0AUm0xHisQWGAp03kae7KK2Zelq0WQZ1TJheo3bDu2mdzGooRxGLPAKPnN7h9nPJL9z3a0UhI0r2+i1BsHnAlrhNPGoSflAQuFoD7WyRrx1CFGtZsUJF4K6wFoEOwDT0kwU1rn+olmmhxP+7C9P8aJffD1uJeE5lx7kgX8Y52Mfu5TPfnc3Kw2PwsIReN0OoppHutbHHCSIOAU/wjq9DtUauhdQvncBKYtnx2lScKUBOZNcCXr3ZGlD5gh4ExbBgWpmJJz3MIIkK0yutFj1DX7z4Rsp3dCn+GCD/mVVen6ZkqkQaPzdFeLRPO665szR7QwmJckWi2qrwdhEyDv+aA7DVHzmI5M8cV8eHZ1bHGtLIhJFeOUMyobRayx+9tfmCGdW+L1PXgBCZkwakbHSpi7uYiZtvOFNGAsBsphw//fGaFs10m0S1toZw05oaIWoyVFEMItAIbRCWllhTBkCqUEFAWI9YzQxUkcmGqet0PUKqY4x/RQRq8w3KueiawXivEYonRUqlELHCaITYDg28uQCcU7SfMl2DCWoHBzQ3VHKDlYKcF3UcibRRGTG36a0EUGMdazD4MpxrJZEC4UgwV3XpLZARxE6DDfcxEBWS4hcjt60Q3LJEPrWfQCcOlNh+bIqYneZ7XoJsVHUze4nCRvOyMh+ZiCPZWBfbNNtjKGH1winCygbUltnmzP9PvR6iDSLssY8xzDNDslA9mJSoVBxiGgF6E0uSip0GhO7NTAldALkUof0LoeeUIjnDqFN0NUSOu5BwUYHKWHeQnkm1nqZ4NmaQJqwB8pXlHELPXq5embL08ukT1Q9hkbLZ3+PY5mMVAo0lproVsZkbScBwjR5+PvH2Hv/UWSpAFaWvHf2fSM5+jsESSfEVprEUsg4k35eftVm/k9xstFiWQU8Or/EK7j0//jzzuM8zuM8zuPfD+eZND8CcA2Ll4slftxdYEj6NNI8obHl7PPj21oAGA3FN/96nNMHa5nXgtYc3TdLvGOEeOswc70ZxkPNPR+vES5C9/Q693/lIfqdAW7e5QVvupnKplpmpCc3Imr9MKNib3yXck3i0SrKsjAdSTpIGLsuyTbEigWEitj9rGxH6nHfpSskg+0p/es2k/cs1HoLDJh4+RCGoX/QwFCKs98j3DyimGfq4u0UvYyW/9jy9/m7+77CZrPB8yvHmBQNvM83sPatYNyR8JyJr/MXH3kr+5eyVJuRHQHP/tklXvbzL+Sim8a44iVdRi8OoduFMGC+ZfOJpSvRVoOxSpNW3EFplVHoewP0WiMzRLYkolwmV1dsuirBCjVmoYRWklgbhNOlLL65VkZtrqFcA6oml910OovuTNJMZpUkKK0ZXDjG4MopZCKJVno42iD1LKZH1/jkh97N+uwiaW+NStzjJ4f3o7pd8AOoVXnkzhr9TpnItAhdSbK4iGq3QMHpRoF77qiw3CgRR09Lf3BdBvkqva7HUrPKV+66jsjKEQ7nCKsF+t02QmsKXkSahziwoO3DwEd3e1nseRgjkxSrGyGCmFK1y4uGD6FWG6SdDmnBJsltSGlqHkoXEKMe7f0upUpC7nSb9FAfAh8aEUbPwJYOy9OjONfZYJhgGqRuDVnII9IE14ggyhhV0nbwOgba1BgK6v0j2HaX8q4Ke264AAwDtd7A6HeJnvJfEVnamGq0Mg+SQo76wwnScCkfEbS3WbR3ucTTEmdi4/VKEDUEcROEZdG5Zox93Sm6qcP+7jiNdo/TXyqz8j2J1QzJLcXklmL+8W8v4td+9UYenBtGj5Uh74BjI0wz69P9hI/8+aXn9qDjBCsG2Q0xt83Qv2or0Z5phGkRDefo7y7Rnq5x6J93887PvojVbg7LzdhqlaGYVKWcWa7TWc+Dgvy+Nbgn5NE/LvLJr42yNChTurJMMloinqzzFEstvMZmy42w+dIQhxRvbkAu7GWeGVJxYG+NP3/bZszPNTAePYLdWkGnAc5SSGxrtC0hNTH7Bms9B/NUhPmIRlzWonZYUN5nEJshqauIKynPvfBJfvddn0FqgWGXiY+4JCl4jTa5Dyyj2wZq3URo+NSHrmNLaZ03b72b/GafRx7dyu6hBo6hePYtT6JMC2t1gPPkPHYvpW4M2HnhOsYVJsozibYPM7hxD4ZRwIoShNBYVsK128/wyk0PINsBNNqYK22OPlEkqjjERYvhyZg91VW6nyqTbwU4HaBpMN+oYBoJdk+wa9syTEn62yT9mokRKlhxsJsG2jeyRB8JyoL6fQtnGQLhSJ7e7iGOfqHC0rEK0VQBI5LYrZTTbon1Sz1aO1z6rqSPz1q/gBm3sNZCrDM+tm1SuWsV0ewgTAsZC8xuiNkNEUlMf0ry1r+6gz+69au847/cz+/+2l3UywO0hiePjmIGije96jFeXTrNVRcch4V1Ggsu3skGRje7tjAkiQW62UaMD5NsHyfYNZKlyvX7WEtdpMwKIhqIhkykq0BKwgCMnA2VYeyiQznfRxVcUp2gu32sXkByTR2zVkNMVCjOGnh+ieiSzXiPr8DCKqGT4o/aaNPGbcbkl0AfyaEftzH8AeLReUojKblNmqlrI979j0eYvKX4A/4p6YwLORczAOE4LIwOE0tB47ANvpFJuQC1so5udTn48DSn7yrQ75pEo0XcnQ7rD/XwTp8iqeZguIyIYnTew253IAhIL9icbSIsrqDT7N5lIBBCII1svNVRjG62kFGCRqPGKvhXzNC/ZhM6jtBSkGwZxWrFlA/2EaaVFV3yOaRtZya8/R4qZ2fSRk+SeIKobKEcgemnyNUIwijrY1KSDBXRUpIaGo0mLDt0HRe7q3HWUsxVH78uGUxahFvOpSvFVZfEM0gt0HkPAgW1TMbkXblBAUw1T1NBZTNKbfG8/88CxakUo+OTe2wOudaj9cWY5NAA/Bgv8HEW+thznaygOPAzzzoy+bY6y57dYEMVc0RVj8QyUFNDqB1TpEaKMbuKruTRQtBvrUKSkuZNms8epX31CN7RFG/OwD3SRRgmbj8h7TQJRxy0baB1ilpLyB8wyR00iMsa+USD2pce4fJWH50H6YL+78yUe10fo1qBcploagRRrWAv97n/64+h4gSdptSn62df/0hrgfWt0LrUQXSy1DXPtcltqVGeqP7rX/K/gF1TI+zeNMr1F2/5t198HudxHudxHv9X43yR5kcAc7NLVGsBlqO49ws7iY0tXFx/3dnnJ6+cB0AkGsOPswleqjAsk5tvvpCw6hLWPTZd4fKBD97AzEs05phg+5Wj7L52B+9+9Z/y4V/P4q1bh2YRSYK12sRsBT/0W7TUpB4MLhhHxymhTnj2e1f54wcfZ9NHffKbZ/D9bPL/rXaNP7j/Mr7ywQtxA4O+yiaj2DZr22tUtyYYnjgnack5YBjg2uggQLe7rC018DdSFv7LR+/D+EiC/pjDxYbPy8xlVlpDdC+uE+6aRXZ/gTde+wjv+tQbecsfzPGGX1ylOpQyNhWw9YU52hMOz//MKrYbg9LoFsQHi8wdzaOPatLvryGaXeh0s6jivIserUKlgk4jhDJpDWmmJmtggDIg3axReRO6Pej2kL0IudrDENA8OAymgSjmIUnQroOMEobuaFJ+YI2RTxxEpgrfFfiTOQJt8Mh3G6zt30eyeIxrozZ+rLOiUZoiHItNFwve+lsPsvlKD3t5gAwTjHaEFrC8WmJ5MY8+tI5c3tAyCMCyMGJJ8YTAa2hyXo9+yckKK2Wbhz91H6PlMxSCGLMvsPsZi0YDutGE1XUYHoLREVQUko4UKS75DBcjVLMN621SR6IcA9lPSWoW6cwYql1n8M082hDkXYk6PSA5swILa6jZFsmxBva0onHPKNgWwrJQUQ/RHaD7fex6AhuMDNIES+TwrC6YMPWzPmOvTTEMycn9s+B56E4Xt1bGqRZQFsSDDrqXnUuRc9GGgapaiNYAq98j2R7Q35Ew6a/jGmnGONEpYSAxayWMyTFykcWh/VN8bf5SykFAkqbYCnqrNkajQ27fErl9pzHKOWKvnCXkOBZaabSU2Y4xZH4bsUK7bsYaK+ZJHQOx3kcECcXZAbJUIrpqG/Nv283S27eTlE2ax0rsfSDHbQ9UEdrAtWPe/UvfQiNQ/1RANzImmr+rSlJ0WR8U6T1k8I7fP87fv+PTvOBFx5GpRuWzRaR6vMNFVx5maFoRDeVQOYfLLzkFQqNtQZBLEJ6mLXMc65UQ+2azdrONbAFpGyhHogzwax7rLxilff0Uq9sNnGWNt5SQa0HqKLSAf/neNaAE733bJ3CbMWiFciVBV1OY7yLcEBzQJYV6YIiry2cYs9rcWD1GrhDw4KDEE77Np5+8kDSvEWst5FoL2QvQ+01EAkbsk/oDtOXipBb5w03WPmVx8PEZOrNlfv31D/ALbz1IvR6AAH/Swd2hMv8M0+T+R7dw1Y8dQTkDvH9YxfAVJBKkIIhcqtYA3TdIEg/XMGFPhDs6QCQakUK05tIvK5TUyEgT7BjKaIEK0lzGANTKxl1JoevjrimsniYOXTCAELa7Ay51IujHXHxLh3SzS3B1kbHcOmm3RxIGDPZMYocGMlbIIEXYBo5s8ZLNDS7yYlwUf/2HF/O1r2/n4Klh/vjjN9CZ8ej6OVIM+n0Df9ph8VVV4ukaejyTyYr1PlYvAccmqeaJdo4hFRDEkKQEl06iNjw6QBOWLYYvWMWcX2TlcpdoPSA5s8bIUo9N6z2U30fZBnbdIJgsELl5TCnJJQJnWWepPhLcoy2G/nkOJQTKluRCh+KT/Yx5EIG3EDN06xJmP2ExX+XT3W18ZG079/k2ufEFnuLRaEAe7ULPxzq2jNWM2DxYwbVTxsw2stXMXuRYmZS1mGfPjStMvjjGnvFQrkW767B02sSfT3Hu3of5xCkIIkTXJw0DZHeAKObORShH2Zj7VMKdkAZqZIO9GCbZxobWSC03tJiSRKZoxwLLRMZpZmScbDBRev1srE4SWGxgjI5iOxWqj/Uwmwq/KrHn+uQePE1xrktsasTIMKJYwqjVs2t0qo7IeTiyhNXJiiHWeoDw8tiBgeFriHT2uwXEF0xnBfaFZWSrT241QlazY6g+Q2J5KYZMEcbGMQuR9WvT4Dvv3sTmmYwRSxCSzowRbBon6foYMsTZKjFHbNLxKvH0cOaMlc8hyyWUAWwwkc7Wf1IfqxMggwix1EQeX0DMLqBbbYSUCAWlM5lUOtxWQVVM4gkH59g6VjtBbsw5hAZHuDhn2kg/Rhdcil/rUPuXJvXPrGPMCYSvUKlirRtnh5SXCBuufPV7uX/fiR+Y73TWe6heHwYDRM7DtFwMBU8+ehq9vIo6dYZiNDj7+ktLYxTPaArzElHMc9NLLuHn/r+vhIpHWrD4mef9MW+55U9R6odjz/8tPPDQcf75qw9zaqnBgdPL/8vvP4/zOI/zOI//u3Be7vQjgNXlPu/76FXkxhLat1o8/08+9APPtxc3ihxBSOpqtJ9FPaZRyrUvvpIdP7eJh88c5lev/1lufP2fM2KXqV/f5GWv+kkcz0GlimBjZyvpDUCDqBqErklG5D23Z2n0U8qPN1CmR9SPee4VO3jp966hXuyzfkeRTd0OswfnuOJZO1kPXHqJQ6ozKrpMYlSQxdYur5RZveAyhsfbpA+tZXTvvAfaQNgmOh2ggWa7y3N//v387uuei3V3H3F8AbcX8UA7hxkIkkum6Y3nqc60iJOIuplg9RR3ORPc8acu/cjkiuse48Gmx+agSKIlStuAxl4LKR5PmBqO0CKi3XcxpZHp9f0AEIj5VUQpIBktc6qZYzYpc/WN0zTaqxhJQkn2OX7DCKLhoHIW6vQ8VsNnMaoTbzEyb5U4IR2toep5zPUBVi/Ava+F7g+QliQOYsxWTOr5tA42SEIJKFJp8+2v34CuBtCM0Fqz6+I1dl6zwFVP7GH+PpewPkzaswnLeaROYCVBVgXiKSlALgdC4zQ1xjGFdVJlhRKK9K7M6N3eSZdLbzzFk0eqtNsKtyHBFIhEI8dGsoX1eitjA6UxcimH/pjJF968OesrYYzZjUEKnKZCOSby8BkiK6T2XMWk0aD3UQPvw6u0D9usLXiwBjiC4cqAU56NaGVx0VHewTt+Cm3A4f44Ndaz3pem9PMhV1zUY3kISjlNOANdnRAMQsjloZCjMbeGcVkVhMQoeOiWjaxUQCt2vPwE02bAdx+eAkNw4Ucexr3Ao3VXjkGpgOr0snh2JRm4w0gFhhGz1BnitdFhes0cY9vGuObqdb763hBhOxhGzAXvX+XBj07SGbHY2hggzyxAolFawiA6e+1oxyTZOomdKqK6S1SS2KdC9HIHzCEi6YMDZkFi5UOEk1C87RQqjvi7WydQNzjYjuKxey2e+8YG08+9ky8dvYAjrU1MXBDTODlM/sllDs9VCDfPc2dY4PJXH+Jbt+04W633TnX5yu+PYb10mERaSCfhiaVJUkOiBTSXHObmqyQzI5y5uMaeK5/EX7LQUmD1NWEVkpLOCmUHOugvrdF+ZooiR+5IA2OxjxHZrJmTaGC+OUXzBRY7J3t84KOfZPbxIX7n1hchZ1cIApjKr7JoDlMu+LS3Fnj4w5sY/fk2tATjos/7//ZGcretonomzvUCbAvZ9kmiALuSIzpjkD4Blukh5ltZutWROZRrES+OIHOKRtPFtBXNjURx94zP1Mg6Zwo1tBAsWjmm3ICrXnmGbzwwSeIIhKEpOgHXB3M88PAk/rU5hAY50Fx5bB018Gg7oCSIyETMQNhMsYWB2lwHK0tDiksCnSSIMCS+ANRSEXUCtAHFIwnGQGHuhJEfT9BaU+k77PklcPs+ex8Z0PpKDTFlooUiHHMpL/n4I/mnjFioPtjD+cVshL7r81VmH+jzlfYEf3vftfiJjapIPvnAJXze2kkyOcB7VkrB9PGHLWyVyWeUZyJaAfHWEYQA2YtwDi1nUhwpCYYccmcGCHdDohJoxDGJEduI/SVUkCBTzWBvn9l2HrMXUZ8UvOLtXb7+fYe5z56gf+U4uWkTHYEIE4xIoa/ZQ1QENazRG0nxzkpEXNyQxKQp7csmqOgcrdaAR06PIU6nHPoHWH/knE9aNj4AKORqB0tKVtotHn9VhdXHBImfIAct4os9xOgQIDnsjSPEBlFPKQxTMzwes9Q1SOMUZJilspUL6LIN82uIlTa6XIBSMSsQCf0Du2CGH2145ChEnKIRaJXithSy1UcqiVEoIJbahNtGsEyJqNcAgWq3Uc0WOoowrCyxSbs2SghwHGSssdda0O4i10zSqoftekTKRNkC05Toto+yXNBQPBzhHG8itCDXTElyEj1dIBUWopRH2i759RTVDVEStNCZv81GseOnJw/TPl0m9ECnGlkpoXv9rNicZKa1T95bOXvs1qklDKB30zacgSC8okgwkOTRyFShhisYroPKe8S1Mex9c+h4cG5msf8MKpfDqFWQc1ky4VNtKw+cIvUchGkjOj6J6wOlLOmq5hHVTXRURh9czBiVAgzLhiBBLzVxmzZm0cg2gzqCXLGEH7ZhvYWTd1CxBzEEBXjnn32Bu/7uN88el9CaNElAgOsnZ+dCk9M1jhsShKAwXOYXX/BfWTuzxn/+1C9x3XKJY40Ip5Ln2pv3ENomlm1yz8fvJzLAQPD2136Aj/7LO/hfwRe/9hizjTZ6xCaMkn/7DedxHudxHufxfzXOM2l+BCAKBvH3TTpfdjGGjB96Pm5v7OAlMUndQ/YyD5koivn8+77CM8av4zdu+jkMwyQYNjhjFzneLCPjmG2Xbuadn/hlfuH9bwbAKlkgwd9cZjDpgRTnghgAoTT2mR65WBIGMTMTQ3z40jfxM/kfZ9c/LxEvrvD4fUcBWA9KrNQ8+lt7iKNzqJwHCMxKicqTbar7GtSnk2wXMVVnTUF1u4PR3UhE8ix82+B3v3oXO6t5iBN684I7v7mTu759CUHPpbQERqMOYpp7DmxCGRrnsd+kv+yh1iXF/IfwDc3H77+Rf7z/BvKVzC/CiFOK951m7d6IuOhh5CTlQoAyBRTyUCpm1Ow4JNpU4Zt7L2WLGMJfGCC6IbEPuTCi8qw2688ZovG8IiNTASJVdGqCX/vDz1O3FbrZRosULAscC20AlTKYJkmckhop4YggjFKarkSkMWEoqBT7fGdlG0Y+RKmENGrQbikWlSDcvcKWdx8j/642zs4AbUlkNYdUkJiQbPiGUCmQbBvD6KW4p9vIMzFiPkKHUVaEiBTjPzHD6ERMfDrGWwww1/2zMjTd7aHDCBWE2XmyTeTpFYwzK9ROn+uDZi/E7EXoMEBtKK1So0x33uXQYBTThst+dZ3B68qIdmbebESaghcgBhHEmcF1eqGFMEBIQe/FGj20QYe3LcxKAduI4boSvVnJ0vtSgoWIqW1j0GrDeoMLLp5C9QaIRCG0zJKdtEYnKTqCzqkQej3U0hrdAzZrt+Vxq3UiTIRjQ5xg2BJlSbAEqbLYUuhyWbXHNZvWeN7PX8GJBxNEP0THMY4Rs3VOIGID7UmMfAU1MY4yrR/wXNBAWvag6iEw6O8ogwHxRB7aHcTpeUrfOEThe8cp+j2KlQAnH6P9rHgqhIAoJWql/NPvTjDy1g4vueYMH/mpbxIOBeS8Pt6xJtqQzI5v4vb2DN9p7eFr0Q70mCKsZuXWG356FQ10Ozk0glRKjIEmrEhSA/7ys88g1haxm6ALNvMHxpDxRgIWENTZMKlReCsd3MUBI19aZOy9axjtASQp1qmE3LwmP68hNNljDhhzoIXgiivXSN0UogTpKPJrPZ659TgXjiyQ3tLixOOb+dbXruLYE5P0/madyvvPYB4KsJa7eI+ehm4f5Uh0zmLs2iY7tq4gN2SSsuMjelnftYoWly+tM/S+g7zzuTO8+e0vRKVywybI4Mw/gNo0oHDjIj/749/n46sXseRU6fzEMOGlIf7OiJ9eP0D4BZc01SwfAToRufWAxW96pLMRwksRKbTzLpUnFE6qsRSYWcUAJQVIQWqBWa4gFxOsfp84L0htSLVJ4poEFRgzUqZMxdL+HP/yXy4ifCxH/qiJ6KeQz0O1gtxgaDwddlWzGDtoYNeedYaHB1ywfYHVsk1rexaBLBD0lUXlqpCfecHd/OxVd+J1k7PR8NFwjqTooOMEneospcePEFJAKU95fwsVBqhOF9Xp4a5GbNnURPs97NPtjfQ0zcKsjSpJQidhcqLJzPgqQ4sNtGfQ3FNmeaiK1wjJLQ7ILUckNY94OAeJhdACqTOmllDZfUaYAn/UJq54pJHJwuFhVh8q03jM2ijK/DB0ySXdPETpAo8LLuyx50UJFFywbZKLCvivMhDP6fCGqQNcUl9AlAS4NqlweM7PhExeXUBtGSe6ZAa9ZQKSNCuw+CFipYFaWyf2DFJXYsgNPzUJqWuQ6nPpdkQJAjBjhb2W9VlZLqMbLZhfQa51CIZcYiK076PDEDo9RJJmzD+tifIG3cvKBCOS4qk+VmqRlj3CqSLmSpdUaeKKgZhdhkIBU9qkBRslNf5QijHfIpjIEdZsFILEFYg0RVU8lK2zMTJKkEGCd2AZ68g8ejHzk6v1E7wlA32/RVp00Y4D1jkj938NEijceRz9WJ/+PSZxZKFCH7nURLZ62RgswVhsk160BVEq/OAHDAbkd/Yz356njZsiTrHXOqh6AV3yKB2K2OousWn/HNg2aEFadrJXmyZ6ZRWlU9I4QsQpZqePquRJq3mMcolSwcEwJMWcohBHhElK6IHZgaal+M6Bw2d/UuQ/JSsTKNdCWZmx94FHTiGqVeT4CKpQYn8/ZGl6iN95zz9y8FmrqCvg+tdcyXNedjl5KVGnmqQajBRkojl9YOm/247/PfzEj13Njz3/Mm6+fDs3XLz17OOd6AS3nb6Fh5fe+b/8medxHudxHufx74fzRZofAYxMSQbTHkHNYdMvOT/0/CU3Z3p74hir9bRIaKU5/NBxovDcYtFMMnq24SdIJ1tND0/VWTi2xH96yX/F1D5SaESYoOou3Ru2MNj8g5MpGUekpiDZiFx+1oVb+ZnnXIOslpClAmGSsTgmDy7j/3oH43NNjI6PiFMueOWzmH7OpeQ8E2t1lfa9fkYZN43M/ybekCJtHIMUJt5CgmzEzM21IE0hTjn+RJ0jy5MEeZh0C/zczaew9bd51fX7AEGxmkdpA1Aszjb53Vd+il2ji1xRW0Q8jRkkFtfwZIBeCqkMOpiGQCSgZFacicdK+JfPoEwJoSSYH7D/TANj0OQd73iIn33BIaaHmrzlbd/j19/wHWSYSQTyh9doLDWRuRwi52IstDAaA4hS5GoTvd7I0qAsi6Hr+lzyc6fY+dpdvOy108QpqARiqUkKCjVSobY1oJAMKF0QMJdKgt0trq2f5MWbngTRxGwGWeQ6oLaMkW6YIPqXTNKbyWO2+pi9EGO1j4wE3gVTyEG2258vHeGBuzZRGA+xWgGqaGNNFzNNf5JkhYJRBzHloKdGYagCtRIXXZYxtgRsLOC6yFARlUHt2pSZdXoejqGJUxDHDXpfApFZBmEXUp6RPwmxyOKnDQNxQ4HBz49w451tXvbCWV77V7NsflOH1q8PE/5ODCpm52802cIqw1MBcajIV/J8+LH38o6//FnmDy8Q+zEoEEqCYaK6PYgTVraXuX3iItKqB0UvM3NWmue/8nJU2QHTRORdzKEydphkkoS25sThIR7aN8Hnj+/iG58/yOpaDKVsR910JCePDrNlaJVfuPledtQbyCRBbCTmPD2BRtkSTEny1P8tiXQLMFzHEEbGShKCKG/RbTlYZoQu5giu3o4q5TKJgClJUsWMGVA1NHVD86qJY8znBbIWEucM5JEVjg2GGQQWi36JP/mPX2B6/6MA/MKvLXPTNweEEw7KJKPx70/w1lKcrsZ9cI6BEWOu9Ni67zCeVUU/JfGQkNQk+dyAUnXAhOtj5DxEIY9c9hFxF4IQsdYjqmpSTxCWYdxVnIpg+b4893yuSuqamNMGk+81Kc4oHCMlZyZcJs4Qjlj8/ise5OdvOcCFt7iZZEyAsgzM9T7aEIQTZZLJKtX5Pi8YPsXYZX10HBPbit5VIzAzytt/b4G3v/pBzG6AjBKc+TnERmGUUpaQFpgxK99QfGt5B7P9Ku+YeIx0NOWGCw9x6QWneL97CU+8qkI6ZCJ6fax2RLBuEGmD9SUHVUkJRiB2BOX9PSqHfcxGn5eN30ViZrISZaosTM71iJUgwUBbWUJZWrDB9tgmfFyZ0V47cx7xosVjd45wug1B3SWquBDFTC6soHwfsbCG+dAhxHKbernBHYMid/WL7HpGyPBYQPNIwo+N7sUcgIw1SkKcT0mbBmtHiyRSkgsTBlNZws9gQkKSYD55HDG3TJwnM8AF6PsIJRGOi94opIpQsdisM3RFDrvVOTuaetUEf05hJxb7H6vxgd/dwWPz25CxwFmMsI72sR+fJY0iRG+AkoqwDLKvMUINiUTl7Cx9T2nsRkrp7nmsRoCpLHLL4M4rtBKkZZu07BJNlH8gStqfyNOfdrAKAuZjcirJiqVRxOaDTX678jBfft33uDrf5w3lBeqbDfJeSL3SZ2K4g+GYmE4eKiWSoQJEMXK1jUwU5L3Md60s0KZJ+pQKSGmMMEU63tm2ML0EaYJINUascXo6K4r5AcQJqmAxGDJpP28rwa4hZK264eQroFjK/rQM8qvgtSCs2Og4QfYj7KOrmKaNVhZmX5MMF9A5OytGpymdGUU0pGC8QjpSxJ/0GMzk0TaoYg5/2CW4MgZDZzLRnEM8msV5i43G/PBnt7BSlsT1iP7VU5nxr+/D0+YS/xokgEpxj7dx1jUyFVmak+OgKnn0qQWMM6vIo/PIcjkbRzeggd5dbcTzdpNctQu18ZjYuK+J9Tb4ESNvHDD2wDzu367jzDbQBgSjGulIpAnWs6aIeutwZgHxlKyoHyDmVtFJxPAVs2hgcSlm2zO6bBddyiWL3qUWqmjyi5/8+tnftHAsY5QJpUkLDlE9R29HlcbiWpZoCMw+cgS52MI72aBFl6SSsum6UwzSx9BaY6Sagm1kbbPRvpb1w5tt/xauuHSG6Z3D3PXkCT53196zjx9qfIAgnWdx8M3snnYe53Ee53Ee/6/A+SLNjwAKTpWrfu8AF//BUS6+cOcPPX/9yzP/ke3PdHGfW0KLc6c9Dn+QFmutxnhrKYYvMN1zqQTLp1bptwcINEJq0o1YV1EuYbjWuahTIK46GWvnaVIOgOG6h+cY3PKaawDYs7RMPe0xNvRUmlOfG6/fQmXbEIFWpL4mWg8zzbnWbLtw09nUCWNjpzQacghGDKrapDpcyTw+LAtroY3hp1jdhNnSgJXBPoSOqFV9orLB1O4p3vye13PFm1pcerXPFjfg76+9jV8a/2mku1HokhIMg67l0e0bdJo2QT9BFnIYGBi5PMb4GF4HvLk+NAdgCsq2BZ6NNe1j1n0uKCzxrMo6V5bb1H7KAKVQQPPM1+jPz2e7p3HCO/7wNfz2n/4kcqNM5NUKmEMVbh5Z549330fvjnlu/+tVwkgQpZK7/RKpKeg7OTpHJX5T0lyyUUgOzQ7xh19+OQ+e3kz07YwhoDeYGxrOMmG6MyaDsY0HDQlpir2ljjVTJfEEcVEw1yqDb3FmbpRwskha8BBODhGlkCqQ8NIPznHjn6+ia0UM28WwPZbX81DMGEdZ/LiH0U/QQiAPnkIvrRLsX+cZuWO8augJDuyvYK5G6FwB4dlcfFWPnf2N5DCl0WFE6c51pl7eJJI20ZzF1/5wkuOPDZFe5rGrtMK43SRnw6PVrfCTRRiusxxIjh5a4ZoXX8ENr7kOtx9kk/M0RUQhlHLEdZvOewZYd/SIhm2kaWW+Ef0WZmU/pUIfEOhYEQQGWmf+C3Yn5sp0kVdec4hfeMYjlOoiu6aSFLpd+qHg8btLbHn+ST6XbGP5+hx6fiWTuJjG2f6cnQ0BSmX9209xGgmJsxG/OzVOcO0W/GfvxP1ym/p/nSXcp0l3TmFaDs62bEGNAOPirfzZ266gm0jakcmXj+xEf6yHeVRT1CWqRongmGY632J3cYk795eJwmxMMCyNKGrCqmQwahDmTJJmgHFmFdo9kIJCM8FZjxmvhyAkiQtxbiPQJYW4a6MCSV35GOUcuVzENb/S4OJrAsRwDTNXwDBToppGIHnDLa/iV9/wU/zNW3bx0d/Zgne4jTRSkALblTiGwpQa00zxTvW4W5X4Rn+Cy19xGGGaIAz8yyaIpisYfoK92CZyQjp7S+zJhVx78yJRdx2VBIQTDrqe49jxCmb0tFQ6kZI4ICfHwLQRpkX9+CriQptH/6ZG3gj5DwdfwNXTC7ywvo/nD+/HLSa8cmgv44UBr/2pWYRSWTqNhNSHQhgBCqMniet5VN6jWbL45/VnYfgp2lDIRGL0A0R/QFwShFMuMtCY3RShsmSgraOrtLXA14JuI0Z5KWHXYH2HxeL1gn5NohebhNpC5mxENzNgVSpl/OIWQ3aPHjFf+vgo2y4OKFZSrrxkmbiqSYoaI9CYy13kXy/wrf+0g0997pmIbkK64TVi9ARRECL8CGNuFWeul0k+cx64LlHdIS16GEM1jFKRKGey2C3SXTDZdLWFdkyEBSMXh3iVOGM6YbA2b2IJF71tktKTLUrfOY5odZAnFyABq6vxjgUo0yC1RMY2STTOah+rGWQSqkuHUa6FdBy8psJKLIJLisQXbSJ85naSsQLJeOnsedauhXtolbk7Ev76PVN84A/2IJ0sGnvTpM8Fkx2cjfVxv2vRPGIh9s7yvJ2P8v2vuKwd6KCDMEtNanVItzioQuaVJnuZB5g6tIqz1kU+dVUrDVGMWFx96hJl982nscYigrpNWHaJNorAcnoSuXmaYMTDnm1S+u4xTF+jKzkYG4Wx0SwSG4HZTzACjRHAYHOOOGcTT5YJ94yhbHODddplcNEInYtH6M0USG0oPLRG5WuLiE6AvdjPDGgMgYgFWicYjT7WnT5qYQ1RKmEUS1AqEF04mRWJAD/vMtAOfiGPo61MCvU/C9tGWRKjH2Ou9tFmJvvVZQ/QiFRlRTqAMELvmjrbbiSg7ziGLrikOybpvKR+tvBlrPcomW06n3ZY+9YmRK6Iserj3n+cytdPkq6tMbXbYyhRiLqNMTSC2pYlQ7KylnmrLa4yfGkHYUlUkJDU87zyLyt87S9/gdTQJHkgVnz8ew8AYBVdZK2CqFZw2gFmO4QwJAoCCEOE1nTX2hiRIu10Uad8qqUmv3HDI7zitV+k12shBMytZQbKEsjlbK5/wUX/8+35NFw4M8oLr9rFq2+45OxjRWsHIDDJ/W995nmcx3mcx3n8++B8keZHAOv9iEf6UxyIR3nk9A/zvLc6WbrAH//jNpQ3yBbWG5jaOY75NIqysSEzwXPQrk2/3eeuz9/P9iu28Lb3/jSlnTFI0NJARhqpTWS9hnCfRnOOYlTRRdlPz9cAu17DHaqeZeg8501X8gv/+Qz/8R9O4o2UsWyT73ziLponl1GuBWgSL5NUISS287REImPDWHAQ0B8xePkrL+cP/+nt4LqQpnjzAcUTPXLLPmmYIopt7g9MIjfCDBS2Z/DqX3sp1/xqk1PKItXwvVaRg6WvYHl2JmdyXUgSJvMtQKKFJDSqEAQI14FUMTZcRBsCDElzV45TQyF2N0J5Hie1w+nYxLUj3vfEFXxsbhePP7IJHBv1uhqbt36NUJjIqQmCi8b401vv5fhaC2OohhiqErR66P6A7mqftla0TvXp+zE6BpUKIiFRhRTRWEUpAQKC0GYtynHzVIt/fPXXWFmayOj1iSKWEE4WkCstRDeTyYQ1iGspWmVpLMVKHn9PjeWwiz8sGIxrmmmOMwfypD48VU5Q3Y0FbhBC1+fh9+UZyTdx3S5qeQ291uDMkpkVK3p9SFJ0f4AZabycD70Nmc4KfPxjV/IXb305hx+7Ft2qoUcMdJTy2L1l3vXM3dBoZef6Ikn6Mo+CA1Wziz+ICc5I5FyM9VCX1b7DTdUzPDy/haOLo5wIh4hnl+nNLqJVyhf/8usIKZDrDdK5BdKlFXSccPFV23nDa65HBuDORuSbVhaLLSXTr0iR19/FVW84jA4CSBMSqbIYcFOya8ca3vF1jFyKa8Usf2E/7RPlLLUrjLj0wnlmZla57diFNL4zwre/fwXhTC0z2C2cK4IKwO5HyE5EbwsEVUlUMojrBv5vFgjeIIjtlCCnsZcDjJ7CPpAglhqIVpe836PSW+A5e/YhpSCd6/IL+6/iWX/9VtRAYC4EZwtzwhUknwp568iTvLl+lFbHo+tlEr/fP34xX9h7Kc50SDACeotFMqEwl5ewls9gBzFCCtJygfawh/QTzHaADCEogzY1YtklPlkgGsrhFAKCbsrhv7J41etOM1mchygiXxjgNCTWQBBM5khLLjpnox2TcJNLv2PT+HqOzgeNs9VfMW6T1z67nYAb8w3CMZvtr4yZfq1F+5pR/K2ljI3kSJRlMfbCLqEymW2UEaHCacTUHhowGI158MECn/izenadVyv4bUm/olALS7C2htnoU9geUHtOBMUp9q+N04o9LjWbvDA34IZ8k37bo7FSxJCSKy5aYWTGRzsSESlUEOEfM7HaEiPKyq7t57exn9vFCAWlvVCJewx1VijfdRA1twQioVQbsPPiM5ghmGl2TVfdPkuJ5GQkObZYY32PRq2sMPp3x6l+f564Bm4Kot0Fw8S0HKI9kySTda668fDZDnbPd6c5qTYz/MJhPnT/M0g9jdlWiDCl9vUziG6AOLFGelgTNHuIRlbcF0ITXDAMORfhusjVDvhhNj7WKgjbRs8voFAkORCjeXK1gN03LRBbfaKLNrHnNwPWjng0j1qYIaAhHamiuh3k8XlUEqI8Cyol1O7N4FoIrQmHBNrVCAFaK4IhAXGK8CP0IESVPLRUG9QzDanGPBxgPD6L973DeI/PYy53QIA2JOlICb2RqvbY8BbmRsezxD4B+x4v88lbL+f33nszex+t8Pq7b6FfCxh0ItoLimI5wbIUgzEPpRMY9vFfbxP81BTJSAUdBpCk2F4uM7ZXGVMu9SyCsouS51gMc09W6BcdZJIio4goD+RdhOsgHBu7FWKt+aA0abuFanfAs0hHSsSjeeKqi2j2sc90EN0Q51ALc72NNdskt38F2e6illfQqw0wHOweCCEz75pWSjpUoXfZCMGYhzJSQGMmisK+Js58FzkAggG62UR3+yThAIIQXcwW+sEnuyRNTRworNMdODel+LehNbpeySTMRQeBQFgmVisintooqCkNC4tZStXhOeLt45BzYXwEYVlw7z40ULp1/dwY6jp4JYkyy0jDQlfKqCgieOYWWi+aojtT59Cji6wdbeM+2oCT84jj8xnjaRCgHRs9M8W9n9kJscKIYwpbJcceqDO70GRqvIBOUsJqyvu+fB/f+peH+exf3YZqtUkDn93XbAY0zrE1TG2g1puIRoNSwUZrhRCCdKSIYWsmzYhtuYicEyENg8Q4N1eSpmTT1uH/hQY9Bz9N2D9Y53D3XLvU3IuwRJGqe9k5Y+uzp0Jzz/xfcWD18/9b33ce53Ee53Ee/7/D+SLNjwAefOQYi0frzJ4Y4okHZn/oebP0LgCMwlsRw/FZyjJAGEQY5rluYKLYtWUVOw+JH3Pn5x7gU3/wL3zjb7/Lhc/cxfyjDmki0aHCiDX5vAVthU70WSZNUs4Wn07e+4HfES6uEC6v4a81AZiZ+gVGL30p//Qnr4YwJl90ecbLr2LosilEv08SxNDoZolP5RLbLp+Aeg3qNZTnICplZC1FbPL5sWtvx+09BxUnqO1TiF6MjBRlaWIWLGwzZruVECjQlsTe0EtV3WlWUdwXWGy3uxwI99Gw5zLqtu+DZ+NICYlGiGwRoKPMC0TFITu3jXPBzDDVoTzpsIspJdNbNtIqDEWkJUNmhzO/n+e+9+wg9702mCbe5xcR3sshX868TgoeaZSy98ETJGfmUbOL6IGP6vWwBpoJM6XT0BAn6Dgza5wq9jCHmzhRlO2Gasnj8QwPdLYynevhypTRXA9lGSRDOaKiZPHtu9EVFyPKinnxWAwTMWnBIRwtEFgSdfcJcl87nBnACsmyYVPXy8h4YyauNOlaL/PQ2WAbGX149NgUz/+jhxFBAL0+h9dLUCkjxkYAhfCyAhozAeM3BAgBWy7Zwm9e8TrSusVgyMom4ke7WVFpEdIo8+645Y0N3v5bc4xeFLEa5TgeDhGveOiORHQ1ha83GP7gMlEiWeqXCUKbsGRnhtPdPrWKy4H7DvONv/0ufnuArFeRo8OQy3H5FZu45OY96O3DiEo1i8U2DECzODbEkpXjeG4oY20gkKUiRqzQhsAQF6IukTyeOjzYdTckYClprUCyfYIX/9QSddtHf99k6M41qrcPCLfk8F9bY/DSMsPXmlCvInZu4WNffAe3P/oe5M95pLmUsCZIHcGmi5cYbc1RvPs05W8dpX/VJnrXziCXA7oX5Fl6XpXl2ibauVHuOrCHxLJxiXnL5AFkCpawiS0Lwoh0cZkw32b9SY+80NRMxS3XrSEWs6LZLw4d4f6XfoHNFy1S3NVi89Y1/OcWYHuRYKIKQQRRQjRe5PT6KGq9i/vkHGpkmf6kidUyEJHA6AmebE7RLw+j7RLtlssTDxVZORyiF9d52ZX7kSpLW0Ga9MZNus/bxeprdqIxsbyYkTf0GXlTQC+SaKBheESiwImgRDN1OBNVKG8JWa+O8qyxWSqbDEwd4/QG5I/2KQ403U4Of7GAYbuIXA7rwBzusUVe9rx9vPF3FjGTTuZXFMQkE/XM+FRmbDwhQdUEXjslucOiva9GFYEtoCogjWJeMb7CejvHB99/BV3fywrCcZyZfocaq595zL75927l3S/8Bq/esxd9Uxs7VHhXLlK4pIOOMv8P70Cfso65+uaDqNpG/JPW2LmQJ4MKJ0OPuXScgSXRfoIMFPl9HWqfOZKNFcpBpwodJ3j3H6F04ARWDtIE9n6qjA4VD9wxwiceuYzFtTy1b62T27tIXBHIUG9c2or8QoBdrlI+0gVAC4G34mOMjYKUGVvI86BcQuRdjPYAsxOgD58gvcFCKvADD31hysr+OtHmCoee3MHykx5qIFGuTVJ0od2F7gBFSuMF0zReu5twzwT+phLtCyskMiHeBlZTY7VTwmGLqG6jbIFMwTRMSocGiGYfc6WFud7HnF1h/fUXoi6aQVUKmZfNhi5GpAr71DrhTIH+5jzBjhzti3NZbLyQ+L7NPXdN8dD9o/zWmy9noV3CWAsh1VxxXZu3/u4CL3nbgGgih9MMsZp5jBWB6IFMFWAgKyWk64LjIuMEbUninAm2ILlkG8owQAoaJ3MEPRPTV8hUU5jtY6QaJSD1JNZA4xp5xPQU8aY6RrmCNC2sQYJIFWneIq3lWL2+jBq0sE8soOMYEUQ4zQ7SkhjlPHJilNKggdkakOoIfyiT+PauLkCiSEsmiYiwjiyhTEnjWWWCyRxODUpTGStE9fsYgwhrtoGOM3Zsv+Yx8swm089u0H9BmXBrNesr/zMTljDCPLOK3U2RjUwSq5otdLuH1UzRlpERdp4qKgPGscXsuuwOYDDAjlNYXf6BmHXd6+ONNdEqRYUB9Dr0X7QDd12SW7UZXLOJwc276L9YZ6xRns5iBLVlE1KYJEmNwswQW5+1hdnOMN//bpfPfvEhvvuLb4MA7AaoQPP+3/gEZx48lBWUoojJ4Rxq0EdND20YWyeoKKZYyWOEA6iUiYYkgTAxhcBEI9Rh0BrpJ6Q5E2UIUg2tjQIpZIWU33v0l3nTPb9I01/nf4T5VofZZouHT8+ffayfzKFI6MRHSVX4A6//0okPcpn4ADPx7xAEx/9nzt55nMd5nMd5/P8J54s0PwKYX/sO5pyNecJhZHrth54Xxmj2ryzTfmgjj2ljArR4cpkoOKcjf9XLH+LVv3wbP/HTD5DGKetLDeaPLzF/PDOy8y8oIfOQ1jKpjUwFYhBCkmYJoSWL/tUzKGBivPoDvyMOIqJOH3+QSaykkcdffz3zJy2UUmy+aBOv/08/xmteejUv+fnnZDO+IIRWB9YbbLlwAHGMUCnppA1a8dwrVvit629nIfweWjdovXQXyy8YJ/jJrdz8K3vZ9sLDuN88yuffM8Ih36IXuiDAdDNWzqQ7QgeDFBhzNR1tcNVPtDZkPAIcj74xDImBiSRZXEN7mUGgUU75xXe/CtO1qHsev33jM3nns29kLvIxDMF1VZ+riyF7+5uxGgH5fWsZBT6MYA60+5OZH4UQ5E522dGC5qOns0KG3igGKeh1LITwGCyGbLwBlOChYBrVcyjpc1TzlYs28fDaNO8+cBUf/eIzWfi9PHq4QpiTiLxCDBISF6SfnQOvEGDlEpJ6AZWz8U2Bsexj9hNkJECkaG0gWu2zk2ZMgz/75/2ZP5BtgRAcnN7Mgclpvr52CVTLiHKRsg4Qto3IecRTFTSaZKxMvGazUt1D+caL+KUP/zxxd0BiWahjLfTiKvjn+oysSOILxji+toUHvriNik6o2j595bL4uVoWhY6JOJiy8K084kmLpG2h+wZ5sgWFTlPmF7q8+G3PZ+bCaaQhQGSxuMI0ufPj32FktEx/SwFlJ5CmaNNAK81st84/HXwG9x/fhuq0s3PT6qINgY5iyipCNGOOxkVOpiX8motKYsQgRBc8nIoNOiX32DycnsfYfwr3mhj5Co/o5UXuf/PFrL1yAiPnMFovkKYpZhqRGl0SSxNWFfvPDCFtA21I0CC1gZ16WAtd4qqDKlp0Ziy0aZHYLr1qmwO/czm/ft8rSDwNBkjLRFkGWmjc4wHmyoARA4oSttdihJ0x4T7yn2b4xj9W+fHkCd607QmGbw8pfMOi/+xx/JnKOVljGuHdtwwr60QiRDzSQNvZd6V5jdawfccSejQraGpMvnf7GKFRhHyO51dO4i4muIsJOFa2iB1yiKbzVO+ZJVdPMUoCM5dydGWUvcvjLPTL+HWXrzam+ZV9N/H42jgP/V2JXc4Cv3bF3dy49BAi0KhYYK/3+N7HN/Gpz1zM3r3FrBjkB5m3yv6QR75W5bLLfIa3JJkPjeNkxrvD9SxRp1QgdEskpomz7jOy12Bzd51iYZWjXY9f+sbz0KtVxmfaNMZzLHXKNI0S8XgRrTQIgbHaI7UBC070c1xlDdjmrqG7guf8xn0UPAfhnVsojtw6R7vp8cjjO+mXLVQUIfsxtYkeI1YfD4m/3aV+ZwtzoOldMooTl3BrNfBcdD4hmV+E2QWQkl3/cR1XpszfW+bBPx9Ct9ooFZG6EkWKc3QF+1ST1FJnC+vJUA4rNUl1ishnss9rW4/idWK0YWT9P4xZenOF+bc69MdADQaIoSqMjRCt2uhUkh53OfTZCVRqkXoGN/7cEYqjYWaULFJUHCCCGBlEYAgg8+ixV3qkLgzGTcK6SaQdUiFITIHRU7jrKUklRzSUIyk7hKN5kqE8dtTGOHwG2Vyl+rWj6G6P7nO24V85lUU6b7SxdWqdqCKIxwuISJM/NoB6FUZqyEIec7mLsf8kdLoIlWAf9jF7EcV8jO1ockUNGsIhC2KB8/EuzrfWM2bPlgm0ZaE7PbTvI5QmKTsIQ2bjZD/K7ikqE7aYgcAfztGd9EjjGJ2kSL0hPwtUxjAxJKbloA2R+Z6lKWIQQ5wS5SGumfg7ymDIjN2jIZYGKlToKCIZLSPOBJiHz6BJ0BISU+Ie7WEsNIhy2RVtdBPcx2fJ721ghClJLLjunV3kJSXCPaOEW+to18qkWwAzDlcNn+S5owcZurhBOjGOcsysEP8/wNmQgUoNK7EQlWIWK95so1bXEX0fISXE51KiECKbqPb60Oud/SyzlZz9TA3onMlqx0atNWC9BWttZAzKAm0InAC0lLSd7Vz53jav+bWF7B6jIdk6gdAKudFPWn1NcbhMW4XohTXu/qvbeNnu/4hRMrESk3BC0ZtfOcdKloIHv/AAw1GEs9hBDtUQuRxO3sUp5ajv3oZKI+RSyvxqhSU/Rycu4qe7CMIYa+NQU89kaLTEBVfMnD3OXniQUM9zRfUgf37oPf/dtl1cbmN1Fb9y0zN41/NvPPt4wZhgh5mw2ykhhf0D77l3eR2lBbGSmNL7bz/yPM7jPM7jPP4dcT6C+0cAV09O8g/+Mkli8cJtT/wPXytOS9SmPHIpBtNgZs8Ubv6c2XC6q8N3/e2M7m4zsWBwx6fvJeiHPH77PgCGdhSQvXU6scDQmrQdgH2uGwklyS+lGBWbPTvHf+C7w1wBXanQDs5JsrTv0zxymqgz4MBDh/nyJ+7h/tsP8fKffia3smEMCCAlnc4kwZ5ZzNk+6i1V1McUl+88wnOHVviLQy+ktHYAI9aMf+ogW97YZeb6FdJkjfnP1Vk/MUXQT/nyHbsxfZWxY4CZ+rsZNt6Dk3sRf3fovbzUO8q7rFcxVZ49O0n02wVUr4dSCm3ZiH4IWjPyvD6mIUmH86Q5k9pQn0/dtZ9VZ4BbdGgtlLBGQk4HQxC2QRYhzBaKDEMvmkcNAlhehUKOsakh7LzN6oOHsrbc8MsdhNtpx79BufYFmoMs+lsDj3a3IGYdkuVz7e/e2mXVrRIcU4z54ywVTzDQKVEOKp/aRylRWcLHRsElV4gwVUxim6RVg1ri4edMOs+YIi4o0irga/ZcPWDhcILwY0SimNgRY1QhbcfZwn9/h/V7R9j6YI6gfRKtwCNA9y2EaZK+oMSgOYbY7GMcsQnbCYN+wj/94ZeI/ZC0LNE5B0/Auam8Zttfhez/U4e50wnWqEkvtjC0QZqm1AyHtckKnDyN0BodwyO3ldCbTQygWSxSB+ycy8RMnbjVJQkTpJSk/aywZQhFaWaMic1DLF9bYuTuk3jL53YbrY5NcDqHM5cg8gVUp49K+7jtHmnfZ+/dqzgXRlgywbQS6vUB654NgxC5sM7IVIvIn8nabagOSrNndoGHatsITANlGzSvdBl+sMvf/+fPcsE1O+BgQuHoCmsvywOaXuJROpywZhvEwwWMOPOB6fzkZnS5hNMGChIlBTqf0H55BTnvEs/2qN51mHD3KPlBL/NMmRhB5U3MdIFWW7KQkxTRMMiO+WCSZ+9/3cGNr2/xyPHdDBYNXBHTxUDnzvUz5/g6hrJIURidBJEYJIUUGRokZQVaMPgXza//zv28/+HLEVLw1l/dyyf+4hJWVvLc/tg42iZLakk1kUvmxJSm7Lyly8onNWFDMJiTFC8OaYkcUSypNpY4OT9E68khmp5msneA07dG/MNDwywvyqzvGJJkuEhUKfGdx6sk9hKlzuq5MUfCzBUJrb5NV7hZsREQ3Zik28aINcK0sAYe5XaPpcoYumyiO5LfPfJijCMGoelgxvCc976eXCLRIjNk10KikxhhWUzvWeRYOoIGRno5bu0Ns+/YOD998hhL1gRL5TKQMlyJs4QYx6IbOzz58FYMO1t0Cj9hZKhFWcY00cTdFq7rIvIGqlzCeGIWYQ4QhQLq5CpywxSbnMOmK5d5oj9COqbwQwdtQJK3QEBcs+hdPoTshFiNmGSshGr6pGUbq9NF+Sl0WwB02gWMZOO6zOWIbU1/TyWTV06s4t7fRPtRVjuOZhAGpMLAXMuGUOmnjA63eMH7Yr70lmn0yUW0kFnSFmD6UPvaSSiVEY0eOhxBkdKLI+r3gyFTUtNEBxHeUhdhO6SOgZYCe6mHeXqNKABSHwOJ5cekUQc5GMKftHAeze4hGpCDGKsRkz/Uwll1cGd95GCjeDI0hDAkIudBmlD/q3nExhr8+7cVmNiquPOOafLrAavXFdEqodA0sg93XNR6F3K5zPTdtkhbLWQ1hxGlpJYkcW3Ehpm+6vbQAtAxBBHBiIu1FKOaHYRS+Fsr6PEiSAPrkaOw3EHlHeTwCEanh5EmqDimOJwjHvLoPm8n3kNnEL2AaFOd3PEGyk/g1CKWrUikgbuagiMxOgGFwyH9HUW0ayBiD11ycQ4uYuZNjACi6jBfunU79vwslhwQXDiBf8kU1mLGrsofgwmng0JwyY457qaGNKxM5illxkg7O4qfg5Ayk9YGPkoWMMLoac9rUkci+3FmJvxUQetfM7s1NhhdT73TEAwunaTllfFG5xEmmEsxuYMNeleOYQ7A6GuEkZLkDO7fuZXffvW3OHPA5aFv1TBPLMD4CBQz2adE8OinHwLjMqLHmhjFAjGCqtL84S98lqIZ807rBvjdpYytWXAY3jNJ79AyIhXQ6WAM1zEswaA9QNY8pDTwZ0w0BqcHZf75V+ocve+XCa7fhVGT5BoJ45vrKENwx7ef5OYXXwpAwdnNTncdMBkpdH+4LcjYNn/5oe/QGQTUd9W4bOcUm26oZG2ulxk2NaZogu6DOGfGfCas8Mq9L8Y04NtTE//qZ5/HeZzHeZzHvw/OM2l+BJDGe/iL6/6Fv3jOZ7jvvT9sHPx02PMtwucXEfUKOBZKadL4XNGkIQq8qnKKwUGLh7/5CM96+VV4eYeJraMsnVph6NI5knWF3YiwGwl2qMCzoZRp1WUQYjT7+NtyP6x/TlK04xA0O2cfU4aFEmA5KWMz6xSnPweA3z6XDJS9UPHkiYOYr1KU/t4kWgfV7nLTLasoJbm+u52Pfui1eA/OE+3ZRMuqsRoXWU2LNEou/puuJ29+gL37tmaJmU+lQ5mTFIY+RJC2+YnJGrfs/hC/MWQQXjQNcYxudyi6BmpxGbW8iq7XEIU8olCgtanOiX2neMmPe9zwc3fw0OnP0FifQ/cadNsO7/vgddxxYJj9K2Os//k4aWMN3emBbSPNMkHyJDSb2U5+q8PBA/PUKk8z99NkxsXRMO/7Dw/huhaGJcFx0KZBcs8Qw3sNgvhcEoR3xymmf+8ol98/x75vPUp/bgkW1jDCCBkphAJTmwiZLbhLTsBkoUtyscJ6Vpem5SOClNwTyyipyZ0xsHqSam2AUhpMifJMbvvOFBc+p4ixaxsUCkzoDm9z9rH9+pOZoahlYeuEq35rAWXbTDTW2PTyk1xUOYaKob2jgOkK1ubXWZlbB2Fgz3c2YqmfOuuCU5/MYfghQa/LoTeVWInqLAcVGo+VWe/ns0VpfC4jqXCFRiqB0IKa2cN+pSSOE5bmmtw7vcLyfxzjbX/xRoTjIA2DyWsuQFYrhEFCmktZf9NO0l2TWTKHlBjaIj8rsYSF/qMY9cIZmBgiXlxFtbrkh4tMvU4xZHYpxQNETyFGhpCGSRp3+fLHa1ibu1iii55fRC8u0TmU0EryBL6D93gLo5GZtN56+zHu/N4hknxE5atdvLiPZSRUTnWZe9LD6IY4Z1qYYYoZa6bukxlLRmgi18CvQnptH9sOUK7CXulhLwywZzs4gwiRakR7gD6zhkLw7dNF/mntAj4dbD67kgrnyrRvHKVcEgxWDJQhCAsmqZTIp/UzoxOibTPb5Y5TzH4WFR+PpWgDZBcWVgv8yc/uYf2SPL/4D4fJ7ZT0ghzStth7YhxtZOk2WoLbVHhrCitIeeNbTmLsNOhP5dHXuuwuLhEGBpaRctkH56mcjHAXDbwTJrElkZYijTXpsIPWoGJQzTWQArMbUxp5mpeVAGVKvnLPZfzkzc/CH5xbsHirA8woQmyk/bRu1zSiAsKPMfsao2OQs0PssQ6FY5LcrMAvFkgqCbufM48opaQOGMUiZsXj1W97nMTVKBs62uPGtMfMo5LVBY/9YRV1b4nkgWom8fPc7MclEalUGKFGa41INdNFn4pUlA3NyD0r5E+2sLwS9kofwgjV7aPbDXBkZrAtBF5d8syhVQpmwsSmkHj3VoQpSb2MK6A8aN5UxV7uUv/UExQeXkCkGrujka6dnduNRXC7U0BIsqpxzkUOFShVBpQqAxw3zmKhC3nIF6iU14mKII2U9bESOk0pfPNJvvEzo+z9xBBJbJLu3oTaNX22uJ/mLRov3kLn2irpeB3H1+TWwKrmSIRBp2ZhpCn9zRbdLQ4iTjA7IVY/wugriMKNmG+IlAGGRKYpqtjBbfXOFloAkrECadlGBinmWpb6pcMI5YdZQpEEOVzF3FrMivEbI0tS0NjFELfmY/Sa1O5ZwalopOsh8jko5BBxim53MnlusYCsVTCbfUSi0GGC2fMRjgu2hZFoUhe8h05Ruvc0VjsmqrioThvVaKGMFJ2zEabEaPSRlTJSmKj5JaRpgh+iCy5OX+C2MlmqMYiRcYpMNFTLEMUYjR6Bl0OP15G5HFIYCNcmvcGkcCZEGynaDJGWSzRZwF6PMFsBottB+AZpOUdazmFEmtyhNcwwmytUr2gzF1RZjKosHqojehGplBtMIZVFcbsuWgiSokWSN7NRfcMYXQc+QVGRWOqc3EjC4JIRtG2S5m1wfjip8iyeVqBBSrBNRKwyM/uLSqy8dozVV01QuPMUw586gNHRREWJkpBcPGCpWSVRsL7wtLFhcYU0DIjyFmnBy/yEvnEymwdsFIouOuGzzesxagVcc9M8bCQ47Zwexj+6wsrsGjqKUWGI0Aoj51LbPc34nnHyKiG/sE4vMXjnnVdx/K4sbtu55zD9LUXioRwTV2ziVBjx/ZPLdJ/ybRMSxxzisVNjHFtd+VebQwjBrp1jWHmLkytNbnv0yNnfLI1d7IsKHFVbQPwgW+amsQvo6yJbixf+99v6PM7jPM7jPP5dcJ5J8yOARx9b4rbffzlCa1Lvf3xKf/0f3sYX9n2Q2cVswdWYNzHMc4uv53krXOF1+eaXahze9wRX/PoruPTmi6hNVPmTn/kgJ0KXshXjLnTANAhM0CUXLBsYIBJYubmAHfywk6AaBKi1Br3WOT2LlhJzqIJa7OMYmvL4HO/80z9gaKzMn77uB98/G2k2f8Zh9l0LFFlFj9QplTKvmOuv/SD16S9zuNXh1FZYZQZ1CrqBQ2Ro5qMjfDG4lQvflGPfJy7FMH4w4nLv0Y+xo77Cqfjv+Ow/TWOGGibHoD/gmldfxxf3z2ZmxYZAVQqgFcEH1vmjv/lLXnDrM/nE3l2MrK2S3J4QDjtQslhWLpGtMd2UxHIZfHQ7+Xf3kH4mY+rPbwYnSz1SlknXEsydWP7Bgy4XiOMUw/aIy1XKO1xaLR/srH2VQyZB2ICwEkSzC7t7XH7p87n9zqMYSpObDc61eTkHhWyytrO8SiISrMsSvAnF2iMSBRRjiA6kqCRbqAfDGjNU0AnRwIe+ejXbxVq2kIwSEq158LZpZNGFMIu0XpstM/KrDfiIoHFvAfeJgFk9SXpDinN4DbUG1/7UjTQW1zm2uI6MyCb3ccJThRr3kpTkpEE0spnS44LO1REIza5DEwinR89Os1FsY6f/M/uuJZdkFbirrz5D8ssx+77j0EoHfHL2fswC/FHxIW55dZGDj8BFV8/QGf8K/zJ3D2JwCUnFI5wpYS60EeUSlm9AV+GZmumpLnNE6AUg5yFMk2f+8i20Kn8FIpsoa1MheiFpOEDkLNYiwRt/+SinN7m8/9e2AbBwuogMJaSakU+epnSpSdedIkwERw4uYex2EAqmfv5wpvi7dIS0oUBp5CDCWGwiRitMbh8QPlJg4RkRccEitk2U5TFZmmU036TxXQepJSLWWFKRINGd7tl+c/fqDMeMaYTQ2E42+TdPrzM2qvjS/DS20OgEtC0J123Knegss00ojYgiwpEclrARtoVzJCCZKaClzhLMrBTZ97GTAZW6DybkaindroNYkzhhRGQ42ANN4GiUELidmIWupGM6rA0qWZqcPWBrbR0pNKtRiau3n+JIewvagB23SJ5TXmbvfQWKMmBFOQjPIZmpgi0RtiDx8uRfCOE9kHg5jNiEuXVEHCMmcpkBrgCrm2TFhm4AxQK6H+GHNfKtcENG4sApByPVdEcBLdCWoOL6uOMh/aaNzgs4qfl/2HvPcEuu+sr7t3fFk+PNnXNQamWBEkkIkASYjMkYbAzYhmE82NjGGA82DoANDBmMTTDIZJBAoIByK7TUrVbncEPfHE4+p+Le74e66lYbzzzjD+943td3PY+e1j3n1DlVu6p27b32+q+VyXtcX+rxtU1TzDRLnLi/QjSQ5Zmvn2BPK03Kb/K9epbKYIeFZ1So3DYOUQhxTGxB7EjMWhItHtuCA36B4dhHahuRthFRhNXwCPozxNUcWyoHOTW/Ht2MKfZ5/O0d99MzDV6TmuJ424WMQ9hXwp5uIAzwhssYdoyhQQoJ1QJ0OsRpg3TWwBsuQy0hKfL5gF47KWuk3QXHw7KLIATWhQbiaxb4PihFX2cM4dikcj7HogHchyeRnZDGEYNWsIrYayTms0oT2xIjSKLmEZrIEojJOSwpEH4GsxsiA0EwnCUwBN1NBhILPZEY/cZpC2/AwHhMYRjytNm30BoMC7Pr0lnnkHpgAYKkvzTmu7izPmqwSHuDiwwDCj+dQAJ6qY52NNqPCVMxzUv6Kd89DUpzdF+GA9Yiz3jZQfbetRVrxiSzpYo3XwfHQtsGorVcilMsoHKJAbCONFID3Ta61kZFYZIcFwTYLY2pLYh87L0n0aUc5tAQRDEqm0K5YHoCnU8n5T+nO3mBrFaIdUiUtYhSAtkOEjJKSJxT9YS0Uoo4ZSLSOeLpebTbD1qjKlm6IzlKcRvlRIQmGLHCboCBiU4JDCOFNdElzudpbSmQOryEMdeA5TCAXl+BffMG2jdQd9pYWoBjgJlDOA46CIl1RDicpn71IM5Yi+KdE2eSmFp+QpTphCQRAOtHsGd7SC9CC71Mvj9NTft0JE7SiV+0ZYBt4hxZwEzlsdIWMlKYCx4S8Bs9hJGoclReEeQk+azH63c/G/9wE8mZcm89NokeA33JDrAMzKUGCJmYAJcKrC7u4POntmMZMeek57nj4gsx18cs3d5E2iYqjMBKxglmFBK12hy4ewY13IdudHH2+BjPUYhvnClL96oumcfnUf3lpH+fbSVlgcvR4kpH3L97mGN/neWwDX/4UPwr4xeAN732mcRKcc8TJ+kvZk8vktWDJwh0Fx2MEao2tlE4vc1FfUWebEtuGFn3K9+3ghWsYAUr+I/FipLm/wd49zuvRxVSxMU0F+9a+7/87DOffxnnuQFPLZ0vzdUJgjMx3LfctZNmz6F4UYr11+3k6pdfzod/+H5e9LbnIqTA1xminkCNT6HHpzAW6pCyzzLgE0rSG/HpdM42qYs7XbSG1lz99GsaiLVBrASxIdk2/I8cPTDJSy/6IJhnD8168yk+v/sjiP4qrBpGyOWxGuBr+P7HPsmuK0ZY/6JRLrr0GAfu3sTEQ6tpX5TlWVc8zlv7D/Dbmx8lTGlMeebSf+yOJ/jrtw7wib9fy7em1xD1W8xdmUWNn4K5RW7/2n2QTicGuQrMMMbwFSzWqC912Lvfxnsky+iT1UTyPhcgvAgRKg7duo6s0cMRPrmvTyb+G5YJxRy5wjAy4yILebxdq/CGbBYm/9VK2VKDcKHG2/7qdYR+SHOuDvUlpN3D36RYOkegTieda675yCJDL/SZNlLc+K4XEts2IuUSuWfasr7OYvbZiV+Q0sl/4QMe8VGD3EFBvpzj2hddgrsIvUs8gl0ev3x8LbbfIzYTxUr9GSZ7rNU01uaon78K29R02xaNhRSiXEL29dFdShO3wDQtSKfw4hJxEOKOdcg8Nos/VeOnv7yDWi4hmfxdqxFDZ9fML/RVoecj8gK7ISjZPVwjZDSKmKlYRJUMl7zteoQQCAm9VIH26hivqLhm9TGuXz0OlkVrrEYh3WMg26SYmWPP4wGtluKeH93OzE/ncOSTiFKASkWYdS9ZPZUCe6FLaj5kbW+GFy0eRz8cQKOXqJ8ENDYKRvI9pNA4mYhMuoaeX0CGGnMhYOTiLkdbEvPcp652jTJMUtMmmSkTqxUg97XprS7QV04zP98kzII6k6gNBVBnTjJCQZyNyA+GSEOw6gGbDTf3WP+dGlu+2CP9B32EH81BqkyctwiHUjz7XfOoaiFRSJCY2R6/PctSK8PsUoHoGQlpl3surP4ryfb31jlxo0QZmtjWRMCn3vdjyGfZ+lbB6mtDyDp0t5TBMtFKUb2vhllXWHWwFzW6amD0VSm6AadEzESkWCwX6KzOMDE/gOj4vPfXb+f1v/Yg4WCMcVkLP2vx5ZddQK+aY260THsuTWdfkVBJQi0Z61RZv3GGsBATVCLy1wm2XNCjbzDk5PFl89IwwkgLopzEq1pEbYeFd6xj+nVrOfHfdzD+rjUY2SyyUia0iqfLKaQC4WaRq0cQtkXU6xKdMhN1SqxQApYcl1qYhkijXYHR0niPK478soLsCIyeBjtGrg44qAR/8LwfkbotZma+gqcMTnYL3Ncc4jr/GJf98CGu/vkjFPuCpBRNSKyOgbZABBplWCAFx2KLptY8PFMlLOWTCdz0LLLZQ0QRYd5l8aSL8BRyaICX/NkCj0ZZHuzmedhL8ZMDa8A0ESkbudDFOjpHzulQGeoS/U4lIZ+7PYRlE6UFxqkZnnfFMWSc9Bnbtp3pr4kizJxiV/UUO0rTlNNNyjsavOrxk7z7wEG2b6hjao0VaaJyTLvoJmU9QCBSkEphLXax6h7CdVGuhVX3Kd05Q+XHxxBhgNHpYtcDdCyJHJNWOiKsWriTYDVFktKUMolzDnHOIdo+AoU85HKotEW0eQ3q2kHkeofUdovOzsrpfTC8iPxtxxFLHbzVGeyUTjxQnupBhYlu9jCne6SPtRC5HDg28zND7LQj1vcFMJCleLXAf9BLfI5qTfTsAiKfS1SE84tQbxGrGKESrzYjFihToxyB7vZACGJTIPrLic+aYyObncS0POVgYGD2NEFeEGzqJw48lI6hXEp6kTgmdMGvGMQpiVYCy0whK2V0IQNSoIXAjATWbAtdLSTePxIQMfbBEGPJx+gY2HOC1uoUItDEfVliCyIzJig5CCXJH+0QDhWJRyqQTtQt3UehtejSDlziioGqZBOyqt5ELywl5T6NNvZYjdJPR8k9PMOZHhAGr3Up9kXgeYlfDyDmajjjDUSUhBL8mybEA32Qy4DQCJLD0Tkb4YUI20JEGhU4rP/xPJVbkt+MLhhGuwKdlpDqMNRXp+j0GF/sQxrGGa+1ZRiAXlwi2jBA4h6uQAhUOc/GdSV+8oFtPHlsmE/88BqMmuTKFy/S7vg0p2qJyssVYJlElo1dLlLcuRbHNiGTxshV0FqSmlRE521AFTIU2zHpho873SZvWcheiOiGvOX6v+ElF32Q8elHYQ9JGtUTozz8ywP/Vssk+y4lg4N5isUzipmCdRHn2QYXOBJLnN2qmg4bcjG2/HdEqK9gBStYwQr+j2BFSfP/A+RyOX552x/8b312odllsrUaK7tI1IXqqgr20zxl7vvFJu69ayPx3CLPvbiLufzelos28qHv/z5f+NB3uHX/T5NIUz8Au5QMYpYaQOKWkj6/ybXrjhCrs9U0kZCoOKJWOzMgEEAcxSgPTjyc5v3Xf4R54dDt+MlEKJOCMCKOIqJuj699ezcj2waYPbbAZ79/HwC+Nnhs4lxG9zo859IML9t2P52FFL9533YAPCNLwVcMGAplQCZ9dqJVHClqXopfTpfZsLAJ+awnWVdcxPtEMhDszdYQQiaDcBRCi6SuvpiHThf/zicoPSIJsgbRuoFkwhXECC/i1JNFSs4CJa3pLIREaQs1VEalTBzHBDeRgvvVJKJ1cu+vpnP5fsjcVJ3O2BRRK2m7cBaqa2vUZouIp6Wup0oBlXelyT/SRiuNUhohQTqpZESrwFkKiDoJMTfXzeGLiNZhRe+uAmanSTjfZO89BwivqnDOBRMIIVC7Nf6hmKX3RGBKnH9IY/oKY6FDuC3HqLeVbdk6ET698XaiPGgKDrxvCyDwh7NgG3RWR0TNpKxARDFlJ+A1L/0sD7z/xYhmj42D8xwbzwDJYL7Vl0VeHzG4axo5pJk2s/jHXfItjZcSuGmTg3c8kXhxaolcWiJ9eZZuzcF2FPMnHSLPZ/f391C+ssPO4ixNlWLsOXka91fQj/lMzJeoe10uH/bZ+6TEWUqMm1W9gekFiEKOyq6TfO8TG6EeJFdtFCMyKdrNGImDpZNklvMuqDH+oxLSsiDt8MT3NnLLXw5xxcefOH2OVNomdg10mEj9e3Ga9FKM6VrEUiV+C2eET4SRieO6CDyUIehsr2DP15g90iZcJREmaEOTskx0ECNCRWq7YGGkQFTNQ0YxkhXEF27C2HMUFptJBPItXTrnFkAL6pv6AbDW6MQLAs3mcxY4UV+HKprook9ffxNzV5nqO+aZ9fP0f3CO6CEPQgNyWVzPZ+1fPYIyJI037GK122YhEAylWtiOR6BMwrSEjiCwXK5/yQFuvPwEWkN7jcfzNk3y7RNbuPerOZY2rIK6pNNJsbQ9hde0MCwIA8G2QpOooCGGvZ0+/uJFDc65rsHuy6rQBKKYVY1xpiplgrpFZBnYB7p0t6wCKfDXpAgH8ohMijj0cSol6HYQ+QKq00GEScld+9wcrQGJM9Cm/7wGzVIR5eRReZO+u7r4IxnspiJttNl26Ryt3cOgNaFpUNnaxNKaP7jxpbBK0L+xyeq+OlWnzjZjjMnpFBkZ0+kJfvOVP+fLt1ydqHlmOoR9FoaniLKCrmny3dmL8JXFOaemcEwX0hLtGERZA7PWJrX/FOTAMafR+X52XbPAtDKZjtIc923u+cxO6PTQtsTs+BiewJFdBss1AiRL6dTpcrfMEwtc9po2Rk0SOolHxbmXLXDnnauSa7fbZe2VHd5YnudkkGL39gHedPMoG2yP3d0sG5/X5Z6vmfRsTV9uAX84jzg+lVgFTS0g0MRZB7QmWlPFnZ0hakh65w4ggxDr4AzBxcPkTrRolFr0nlshW20T3WMRlCWpegp/yEb6EToMUYaNd34F5+AUWkrCtQOIrMPFVx7k+MwQ5CVzBU1nR5XMwQW0AOH5yDjEbAWk9rUT469l6FwaEYPudmGoAn4T6eapbl9i35zDP79nBPV4g/qxFEbWhGyauNVEOyYSDc02Oo4xMJBBkoyFhChjMPOqVQipGfrYUUQUIrTCq6ZwYpWQO0t1tOejU4lvkIhBRBDn02gJRseDSKFXj0DWQRUthEr8SKxehLQcRNrAkz3c2SBRFLU7aK+HSA9DECM7Ac0tDt4zTLK/jDA7UN7XAyHRvS5LV64isz9ClfKE63Pkj7UxfI3EIN40TC+bPDfNozHZf2jhDWcQExmwJDylyI3jJPkPgYhj7JkuWgpUPo1sdlm3tcfb3r6ftnL5s9vXooNEySJrbbRk2avnjI/QWRTKYo3IkJiGCSp5holY0T1vCOfQHMwsQtsjaHmnUyzNmSbBOcm9WfjUISaHd6Jcycgf7oPiAMbqHPGRs5ONjBMzCXdz2h4tMQPPDVTYaebZ/0OD1EyM/0aX2/Zt4Pm/+wDz39/FxPEZeoaBVAp6HkoWaTejRNEUx+i0gbIsGjdtozdikjleZOd9E8xHAgF0mx3U1Ay4DvXyKrQpuPsn81z5Apdjn1kArfjy+7/O5Q/95a+MEwCOzy/ykdt+iWMYfObVL8aUkuPtA+ykhak76KiOsIunP39x+ZkMuMOMpNb8m9+3ghWsYAUr+I/DipLmPxn8YJ63vPI+tlzQAKEJOsHypCyBEAJ8QWZ8mlO3j3LLF2/nvzzrgzzwo0fI5NOcODBJJM2k5r2vTJx2SBibp6TJghdsOkQqE+I6Z6/axH4AsaaxeMb8TrkmRt4FUyKlYHFmmvMvPoJtJZ+JOj1iHVF/+XbstuC+B47SdGyEZeBWQ0ZDie9nWDryOxjDg8z4glaYoprtUjnuUzmpsNqKL/zg+dwzvoOv3XEu3oKN65whpnY95xwWnruWqQvX0lfP89JLHuDqxhHE8qphfv0gODZKK0CiEWjTJB50qL19DQtxB9mLktVfUyBChVX3sNohYc5kolamHTn4KolBVq4FSjM3Po/qeoh9R5EtHxlqxL9SD+HYKDfNP336DuLMGf8M2Q5pt202nzvKmSmGYF9zNfffspND/ZuxHQttaCJXE2mNN5gjqLi4kx0qP03IoInZEpOzFXqymhArfoCOFV7bI9OOaAcutW6aTsFECegP6vA/ppChRAnN3A0FOkM2S88dZGopj1t1Eo+NXAZSLl7DSi4NDShNptxBXymIz3chm+GmG45zzuo2r9h8B4NTB5k5csYjQAPmt9qIn3WYLGcZlwUW2mmcR+wk3thTaAR+t3FayVX9eR3HCnCMkC+/bC3/+JK1oGKsnEPuawvcu7iZRxbXE19ps+6aecKRQVi9llf0tXnXltvZ/KMlTMNCGRLtmImiZmaOVBxjNKLElFUl5rTS63FFO8UvP+zS+EmeC+0QLk4RrKkiB/qQxRJTo1VQkumFMqdzfBwXpwZWHVACMDCaARPjDQJL0PId4qdZJWA6yI4PA30EF24kf6BO+UgXO2sQqwivJNEpkyhjEBRtwrLLQuQS2xBUBHY24EefqyzL1tTpyYuzqBFSYxV9gmxCjDW/G2MSYBLy4vUHiauJ/5HbhaIToSopfnZgK4cn+5neMoiWGoWiF/lcetkU2CYy7SI6MH3QQrfbpKwGzShFEBlEAQRZQZQ2eaA5QhhJvMAknnFZa/V41sApcEze9LJ7+L0bfkyhr4kX24hA0Oua+J7JuZbGcgJSfV2WdIoXvONV/NUdm9j2gjPmwIsTEqPhE5sBzv5JVolF8k+0yd29QP7eBjplolImxnQTci5Gfz+GbTHz0mEiYrx0TOsVm8gea+I8OI/93TqrDvo87/AYxdEezLVxpto4tYCU0Gw4b4pnv+Zu+m86ivksn+wzanx6+jLitMCpx1TrdUrpHtXI50OvfyGX72gj359lzxvO4aEn+tDz8+iZOdzFCDMbLnvSQLga5js5mj2Xxw+sTUoBNch0GnNqEWO+hRaK2s4ya34z5FPfv4tp5bLD8rjIXWLfX2xAdxPfFKkFYtnnJj6pqBodLs9Okh1JI8tFSDlkSylW3RQQ2hERCVP4qQPnEAxl8QezxCMVfuO1c1yeqfHy4hQ35Y/yL/NryUhYZweInqJbkvgFweC2OdLjHZRro6VAiCTJyWx5yF6IrKZRs8u+GyT3QipKU6y3sPoU6BgVSDoiQ68So0yRpKoZkrDg4A1nwIIom4K/L6LyDmbTR5mK+s0tsneewBuNsQ93ccfraFMu34ECGWoy0w10uZyo4pZhm01SO7IYzxomzNmEjiQczDNwtUcaTewv90yGAbFCewFy8xrCwTzMLSam8JCYUWuFlsl5FK0uSFAiGXJJBcRg9sJkFBZGCTE8v4AencQ8uUiU1ohYIcM4IS6ESMrxDIFOUsMRGoxAE6UtdNeja3m0zytCJo1cNZREpefzmJOLWGOLmI0erUsLyFEfPx0nJI8h0VKAbWNPtrEWeon6xpU0tuVobcgQuBHmY8cwD50CwHpRgH63g3WJRDx6AoIIDAPhOmeOXwj0skpGm4JgdYFofT9zwWomxvLMz+aIK/nE4822iUoZGteuJiq6SQlcKnWWr50GonwKddk2oqc99bQUSfy6F2IttEEYy6XJSVs77Q6i6WGeqiOB1R84wJo/PIgVWwjXTRaZllOpnnqOSMB82vBFbVuLtCyKq4q0TA1mhk5/CrsX4RwLMK51ufDt12FesR3dX0xIJsdGxYp8MUW1mAHXRQRgtgX6+S1Su+oEl0bJQtjyb23eOUJff56BgULiaRPErL6ixOy6TLIwkEozN/6rCZ5PoZROUUw5bOwrYyy33eb8DiwhMIUE3T7r85a02JDdgmP8r1O5VrCCFaxgBf/nsaKk+U+GvTPH0CdSHHkkg47B6/hnDYT8bBLvXLGytJc8fvz5nzF7Yp5b82muuPFibnzDlRzYPwbFpK45ThmIpxkPA7Q9m/PlFLadPut1bTuIskWndUYmoExBsKaEiDwuXlPGcR7j5a8Zp51xefTAst2IbZCZBkvFvOSGXXzvIyeZ6na528/RCGzWeZrOQoPyYImBVSX+6DuvJ9vxeM55a9mwscpf7X0U2RNsSbW5+vIZJo4PkLLPzIJ3/+xx+m4eJ1iVZ+B3L8IJFXd/YydG1SKamktImZ4HnS6qUgTLQHsBc3/ST6QkI3KCw98UhDuHmH6GSd+9Pk4tqYGPXJvmnEXLtKnqNk69h674SD/m5BMTMF8DwGlrFBHP+aNX8Ys/+/aZdAzXJZdZ9oN4miyfGPKdiH2PbKLvosNkH02UTId/uYv00RrRgRTelQHCBsPVtHeEFI8IlGGjLQfM5Phbh0v4aIqFLmqqDVIgpMByLETdZqGdxbIj3ME+pIzhwzH+DdsxahpzwUL4gt5QDGmY2pYlNR5BykbPNyGXIe520QKsRSPxB0jD6p11AsNBfabA4aMlivcFnDAzeJ4gfFqFnAQKP1tCbBymsa+KMEDubNInc9RzAWZdobRm3do2B6M8Zs0nuKFKbtYj89FRZo8vX38Zk4tecyX3fnSW4NVJl+dFDud0JlmoZ4itAF/5fGL/FRy7usLAj32sAZte3zDG48dBCKqXdjnnRTN85bfWJaN4z6ewaYS9tz9JZxH+4fBOHh7rcde+TfiXOKT2JiRj7Zw0xcd6HPvkCDCBBqxAYrVCzGaU8JtRjDYEUilCJNGSxdKvr6H/H8bRAupXFck/PodwXdJLEdIwKAwKLr28wdg+hWwoNBodRhAphOPgV4oQL69G7wuYPmrjbfTJPK1sCsAZbCMdcJZv4ahl8trS43ga4jCdfIeGdsrGNkBUOqz7u5MUCCj/scttzUvI3H6U7lWr2VR9kj1GEd0L8AohqWoGudhgfsJhKcoQx5Je2iBMC0hJJg8UuOlPX4dWcMkVx/hhp0JRR6x6zQLn5xc47uV57rYnuH3mXC4uTPBwexWpXMBnp9ewYXiO9ZVF5jyH2qOauUmB8FxAk8krtl/R4d7NJt2uiftIm9WHFrj46glu+6+r8UMTde4maPtoKaHZTW43rRALBosvWYtGY6RDLNvCMNL0lM0Hfut27JLH0nieD1z3TLpxF3+xS3GoQXYwRsUtmv+ymd5uhye3ryUeFqTyPnpa8M43PIhtgWUqisU684HNbUcugLkUt6SGWFM6BYZBai6m6HrMp10QGq+iCNtpMBT6oXmcRR/pRYmnlNIgBfYrNZ3r8hz4W83Si49SpMuJKMNdX61y4jYLo+IjnRB6mqDoEpdcek6OF5cOUl0XMfiBeb78hs14W4e4aPMkN1w4xZ5wkPvuSvqgraNtHrOraCHQm1cx9vgxLtleRwGmlBzp9XNi+iR/s+cyLo0W0bEk9EwOtwdJ5wLc8RCdkWgDiCW4FiKMMO4+lESVd3yydxwlXr8WUcrB0R7B1AyFrEXUn6GVr2BWIqzZGDo9pG+isTC1JkgJhBC4GyW17auwjkxh759gysgjsyYNL03Km8foRiCNxOi1UECrmGC4iN2NkX0VmFr2AjMVle1NJu4zSR9rICON9kIasyZ7ewU8w0JlTdTqKsZ0A21KCCPsU42kjFVpUDGq3kRZBrJcQqCxrCyrRtt0YgPDMHCHBGG+g54VyE0bUSfHz5TdSElcTpE93k66moKNTlnEtoPeMoxZ9xOT3GaI2U7K8GIDfMPHnOviFJbJEikR+Qy666GKaeLVRaxmQN8DEYvXZJC5gDBt0Niaxegosodj8vdPEZfSxK5J+v6TxLkUuphB2Uai5uklHUVUMTFdRWVrjcaVJYJZD5nLwUIz8SfSgGODjiEEoQTOXBfpuPh2hv/xxQsJKlkMsYTI5RCWie15GL7G7IRJUlTgJ35RvTMhAmKplRBAG0ZQh5e9hJTGPtVAhArCNoaQSRnVcjpjbLnIIDpNvmBbBOUMBBEiDIk7naRs7X8ClU9hGjaxJZk6OUvxnAFqB+doFWzCB3NUR6ZYPLaWfimZeX2TpTiHeM0G1nxoHt3zqU/PoNMuKpaIZhu6A3SOlEBoiqu75EpZpv3kGO2Uw0t//6U0ltr86FsPo4WgdUzRd94wxtolVCzx7V/1o3kK5Uyav3/5jWe9VowfSTpxYawsy65gBf9J4Mx1cOxf9Qb9P44g6dsuueQSDMPgne98J+985zv/g3fq/ztYIWn+k2F02uJksIbQS5ZuXv+nrziLpImtZJXOm23SCyPSeRelNIaVDAwcS6IVqLSFBlzTZ8eGGfbfk2wvgJ/97FJ+FknefPnZv22i0LNzqPSZaG7lSDw35PX//RGkpXn4fYPMT6SRwkHlNMILUBdtw5zvkK3meMFzz+EnH/8polDgloXtTPlFLqod4fGP/pDiplVk9+co9hnkqPCu37qeQ+MziMf2IDRk7SamqahWu2cZ741PTGIu9TA6Ef19NueVawTPmuIBXWKKEkJIeMoUcmwakc2QLVjcuP0JxnoFXi0O8AF/K6HRpjhVRecd9HSA0JCa76EX8+gYREMh623M6SY6jnHSbjKwL5UQloURwUN3HTy7Rr7RZPFkyIve9UIOPXjo9MsCePmqQ+wr5em7us2ePZuJ/2CS3E+fBAQyHmGxM0XW9uhpi3hQ4q0vEktJsHOAwaMdADJTydjNrseoegOZyyDryXvGdI+064MUqL4AhMXJt/Vjjkm6/RqjqMnsrhG9KYVpx3g3CfwPB2DaiEIuiSZdqoNhYNoOSEGnZuPdZ+N+bQmrFnLrbRv5yV2bYGoU1/fAeNpDpVJCLtZQPR/VTEilTsOlanbotqwkS5mIOIh44ycOcm91A28YuY2/2XsFra4CU4Jpo2yLo3fspxZI3M81WfuWFucGNRZPlNCtLipl8KhnJ+oTaRA5knCwH6cRozeuZevlYxRf0iE4TLI6a5iIXBbL8zl6fJZ0TnFyei0//YFN7mCAkanRkyHR6iqVyxbQxxzimoFaPm8ykyZzKllpj1MWptRYNQ8twTQsxJJPVM4htq2nvaOIMxIi+vsSU8s4Rg0N0Ld2lgcfGMEbNFFpA51yiZ0eqZqBV0ohNEnU/KkmpQfnOe/P6rTiCU7cFj2tfEBh2DFe4GDnkwmN0JqxXpkB2+dQWCAqBhAYyL6AA7Nlrr10L498p8C8crhxepJbrUtpn7cGaRqUVpOQmQJkM0TO1RFRTKRMToVlpFJEDcjUk98y7zvK9G/uwOwJBs6dZrvTpCktnvuWaR5qDXNZZpyT9+T5yKp78XTEVCmHRPEvc+eya2AKJU2GN/dYtDM0Zl0uvmqJySMpXvqWOa64qc72JYu/+9EVSG2CyDDxvRR+KEFKzANjCYGlNapawggSo9Xryoe45AXTfOnD57Bw4wBi0sAoVfARlMoeaUMRr2rjnvBISY/FSpo/XLeHo9jYUURjbwpbG4zuWUW7brK1NIOcBlkKORkIWkpy9bVH+b3LL0L/tk1YUggtMbMWcU8jHZfYANlViVrCl7TmswgNxckZ1HAVsdhG9HyMTkBccOk9bpL6yizRqjLf+uYWzr1+kVv+Yivz96YQtoEKPLDS0GhiNny0pVFuzOpMRNmMmSwqSKdprbe5b3wVLzk4wYO3CszZhECOfEEsBBKIUyal/piHH+7ni7k1LBlZXl/dz/1fLzAeZvivr3yCr98r6CoXaor0UgcUBGkHdXEK45CJztoQGZh+nJTkANo2IGUQTcyjRqqIXhY5WKb/oIFwW8Q7HLxKTNg2SNVjYsskTksSV15F70md3JeBQktB9/ztBGWTZtFAbiriHmsiOgFaKkzXJc5YGC2DVMMnss/4YPkTMPEDm7heR1sSpWOMpSbuAZ8tr+ryYKeI0QphbIb0zgI7rx7l8Hcb1H0JqTQEQUJslgrISoU4ZRAjMOKY870ZYhlyoDqA70dETZNwtYtVD4HlqOnl9lCmRC52iWVMUMli+xGyHaAPjkK5ghAOYd4kKBkJIdLWdC9ahTvWJPvoaGKjYtsQBohyGV3OJ88xKcic9AiviDHnA6TShDmwmjFGI+mDhFKYTQ+j5WFNNogrOdrXbsTftQ6DpB/uOQ55JyBjBwTnOXTulpjzdQy53A62Bd0uOmUnIpH+5bLobkAcBwSbI3TGghMRut1GZDPonodzqAuRBh0kbRGc7WtnSIHaewzDO9OPGW2fqOwiIoXyY5xGMyGIojhR81gGvX5J6uhymbUf4NoDhN0ljAGFCCTRUmI8rZ8yoH76jzZ7xGZSh/XF3/gMP+h8jaNHZvjt3/8q9qMn8Ns+Rx5dR33LIfyXmsQtAxD0NgsqEyGNdQOE5Qyi3aa4v4u1pCjvhciBAZFHGguJ/Z4UmKbBfb84gNf1yRfTTDc7fPpL32fkNXUsO4W/5BG1/+2J1y237WNqpkHl3DJ9+SxXblz2J7S2oMyNSOsihLn139x2BStYwQr+38TDDz9MPp//j96N/89hhaT5T4bff+nzeO6F38eihgA2X7z+rPdlpEFBHEYQa1qNHgpYWkhIigO7jyEXGijHQcYKZ8jiT/7gfl769Z1YkcbvAzZ6hDO/Gp+ZHqzQ1iZO5cyNapiK9eePctMFs2SlQv6Rpr46JL6/hRGbFIbL1MYWkL2IplL80du/wtx8G63g/qObCX2LXiPCadTxF+v0anMEl/VYmIi5/6f7+ML9jyUJRAp+5+YXstGZ4+4Da/jA04858JGxgsjn+c8YJt+OOO+tB9h5WZU/e+/l1A+dRLsOeqCEnK2hW20a2SLPK4yyqhpz63QRgKwbIFIdPMtGmYlwWkYKZxYiAuzZHoSaaGIKlGJhvgGZFGpuHuQaELB6yyBP3rP/rHYL/ZAffv4X6MXluG4hUGHIPfdu47Pv/AEiTvGR45dy0cev4kvv/S4AO951iC/v/SxRWEVYMfKoJHWshhbQO6dMry8ZbOdOeDixRiMTw0pDYrsWAoGXtuhz59iZn+bJYICljoUuFLF9hQ5BT8xTmha0rQBTCpQN+F1amwsUJ1NJ+oadkHlaCASC2LPp1krYYRPDtokXG2ApaCmiXhIPDMm8Q1ZKiclmKkV6KpnYt1dbTAY+YKFMiTJtRg9lOPpbq9j5VfjK8V08eXCYDXMLScJJ5EExw8knxtHdHkO9gF4+zZ3xOspqHm1kiHsBjzWGkfsMsocEKpNGV3roR0+Aijn3WxN4wsLcaCZeC6aJbrdZ+8pL2bdvFD2a4xntB9ndvZDgeoPBmyfpZNeTz3X5g5EH+fKGyxg9vGz42V/FXOhAVoEXorrhciWYQjsWvhUgZBnlgrJ6SEdhWDFCgw6XU0hqdaw1Afmwg2hplCWQgYFtZPHLMUtbDeIi5PfUKNx+AtlV9MU++78aIaIzA/zzr27xowUXYzRN6AK74NKbavxifjXDxQYTrSLDm2eoexkq+RbvfuBioo/7lIMF3LTi5MMuxe/tp/3MzShp8sVHL4N0UnKUGTuFrLeTY27F9N0+T+oyjcrapB+eh0gRpiWpbpPmNhvbEKw2IzpxxAdPPgs/spjsK7B41OA5L0nIyT95qIiUEHQFki4jdkxkSvaMDOFPe8iUxWveM8kr3ruIjcZtHubzHz8P0fF54udZPMNA6xDDC8EClU7inJVlYsYKTJN9D2Q5+eIL+cg3HyHA5qOPnocfZiDSHNxf4bxzF/nB4gZaoUPe7rDanmdxIY1ZiUiLGKPkU4hC0ufXONBai7HNQx21+GZzFVtTczTjFPkX1lCf2QzdEFJWojB5SUh4T5HapizipCIVgB1D15fkDxsEWc31Ny5y17dS4NhEnSaq6CKkIPVQG6EEpq95+HsX8uDtDrIdYJUCBDrxDoviJCa61aZ3QQZnKOZbhzews1bj639/PsKKiPtSqMN1/lvjGmpCsMpKSJqTeyRiS4BXtnn9c3bT2RVwYX6e/t8ZoNCqc7hqc+e/9NH3/DkMRyHzAhlD/oBCmGbimTLbZXi0zvFVOzClwHI0N71pljt/UKamc3SfsR5sl5wfowtpdFjGDgATVNWma5kUHu2QCgTOXISyJHHawepCp0/gOBE8NpakT1kmylLE/RBVNfMvX0tlT4fsD49CN0SNnUKnbaoZG9cp0JrrnjGp7XhJFrSUhMMu1vEmaqDC0fEcr5ydxpYSIQXCdekZOSprPdZdErD3aBUyKfD8hIjN55OyQgVxyUYem+DxeYm9yU36cSHRXajsqKEPKqL+aqLmCUPQkD60gKi3kIbANhRC6aSEptHBq6ToXDkCc90z9TmuRM5HWMfmIeUi/DBRk7DsryPyyb0YROioy9rtAZPbB6ESI+IYY18PIQ2a1wySOlIjzjmE1Qypw/PIlgeRRhkatRx1nhceXmTT6jrMj/dj1BuImRogEbaFFhJMi7gvjzW5hG55qDggDpMSK9EWxFsizPk6tNqoTgdlmRjDA0grMWPGD86O2l5O/rOW+8ynoE2Jt7mId84I6ZmYaKmJt8XBPdaicrLB7HOG8FeliPKS4h3jAKjRCYxyifjAXKJQ7a8mpciOiZheOIukkQAnp2BoAK01Ly6/jlXvuYEoZ+F4ASKMEcdnmG8E2NeD69qApnzKRduSoJhh/nINRpbZF6QY+YXGCCOkMnh2p8yYWeNFr7kc7bpc97KLmZ2u06p3sR2TH/zoUQxtQQyq5KGPJIpZ3wtw3DPkYhQrbrvjALXYp9EZJZN2uGjNMCnLYjpcYjQosc69hBFhMe/NkLeKOIZLL+5ysn2UDdktuMbZ8dwrWMEKVrCC/1iskDT/yWBZFu9956/xqd/+EgCrNg2ffi+OY8KsAWjMzS7FjkurpxC5HLVGIlk7ObYEYYg8NQNCINauI50O0Zdu5YJ3b2B3/kk2WJNMV89mTGfnmrRabeL5RUQ+uex237KHb/zt9znS53L85Snu7fRRyXV58niBbslA2SaB1BgND9Ipep2AKIiJvQDdbXPeqiYlt8fub/czGC/iel3Wrx1gylnAW+pAFbatrjI6doqX3HAPG9ZNc8ePz0d2BVprwu7XQcc8cftMwgoEMJDawGQ9TT2KeeTRNcSGQocRergP1fWQlTJCxahcmr84ejkXDo5x24mdwAz5Qg/r6nk6D7q0J4qJ9N2L6D/gs1Q1EmPFMFo2JoVOY9mwMZ85rZ5ZvWWQ/VIk5pUZFxFEjGweou1HYJiJfNyQmCpEP5zmW19/P4VKCTk3w0VvuoDpV0+x7/L7Of+SOUZHY051hiEXEecc/JEcccrAzTm0+p6KUrXRnRgzrdne1+HQbpfIj/G6PqLV5fryfobSHTY703wuexV63qZ7RQflSTbdXINSESNUdKdzGA5MX5UiSmXInZhHBDG61YZcNpHAI1i9ps24N0BuYxk1btK4SWA9Mk9fO6DbSVYgYbk5Gi3k6hGYnUE5A6CSiPFw1k0qv1QMIiaoa4xQc/jXevRf2eTCpSXq0oanqvCimP61/YwdPUV7QeEHFYQUzD6njLw9Atfmjvt3Im93SAmfOONCTjP3/gHe1rqALl8FrTnx41SSIhOEqHKW8y9cx8M/fBi75/OMZyxy5U33Y7o++7MVbvm6pN/vUV8waKsUUdFFDBYwtUA7JqLeJrSf1gGHIaKrkFLh1hK/iyBs082XaHUlA2aINozElLPnMfuQjbnKwumFSMNEeolfkjCMxKcihqhsolwJgeCYUUJ04rMmH2PHU6RmFL4QyCh55/f+YpI37t/IQ4sb6IUGxt6IkWc3iLRmx4Uz7PUGaEUu3mLAD77ch4mPc3iW7jXrmfX7qXSTSVBmFqShUBFYvuLBL/VzJYuowEMeHAXAHRkg/S9dyrkuM/+9jDc8yscfv4B1w3VMYsZnCgTTXVoqSf6tmB1mG0U2Dszyhr6jHOpl2Rr1uP3qncyPbqQyeJyNb9Ps6RUJVcwn/+5ZBGtyiGYZPbWEXOpgACJlEqxP0/5Aluxn80RdD/PYiYRUzBrMPTrEeH+JD/34Gux6k0p9CR0o/ua/Xsma90xQvrDDtuIcL8vvpXmwxA9/sI2L3nyEqWmHvjim2Uhh131wYtx1Md0tEVN+mZLh8Wv5RRYck+iCjRQebBP+rqCQ7XHs8QHyG11MlfTDCA9tuWQmQAiF3dRMNH30qRm0Iai/8jxkNybzyCiGI4gyabQrsLo9TKUxGh10uw2ZHKZlEU/NQc9DIKh6TTAtok7Ip96+AUQbSgWKvxgnKrk0ftaHt97ADJPaCJFKkZrqYPqaT01fBt82qA7NU773BF4jB3EGkRWcnCgws2DRrBpoIDRtBg4lZUQCSMsQ6UiIodQf8o4PTaOAH9ycQSiNnKuhpxbQtkDEEb2CRZyLibsdtChQmPaIs6nkGg5DZOQQZCHKauZ1kZxYREqJVgp7soW5ReJXU+hA0qlYZKVYFuppRNun0AxYLA2inQhtGYh0GqNcAsOgN6BYvMRi8PNNdKfHYrbMd27eTGGzT9QsoEpZ7GbAybtznLovmyh6chmYX1omQ3yE65AxPN785t3cHmTYO1mht8Yic6gHYYg522XT5Uc59P2tyGyWYFsK89Q8Oo7wNldwjgsUCsN0UbaB7IQI0yTqW3625m2MTowyIH28nRz7+kHi0RlMw4KomRDK+cyyJ5AA2yTe4uK7Cm9TDnPWwOj3ydZidDZFb1sZc75LuKaA6i9gxSbCtEiNtXD2jmObAthB/E9t+lyNcW6G3NYunVNZVK2BmK0nZr8pF/JZxLIpsCqmiJ0MxuQCMgCcKtZ9nCZhtNLEaweg1UI3PVTRxZg9uzTzKZXRU9fT6f8PItoXDpKbATMQRKU03bWSMJMn++BkUgaa1EWd/X3pFKKQQ50cR8wtgtYY2UyillwmbU97DOXSSAHRyABxt8PJe0exsjbRqirW2BzeSA7n+BQb8wVOWDbWjyCVdmjOLKBX58HRCK3QPYPM6GJyAKbE/4jJn77nv5Iv5wDodnz2P3KSTsujWetieBpnKWDN4gJBKs1yUR5HHjnGuVfuOH0opiF56+uvZGaxyUnZpprNkLKShZhIeSgdESufX87eyccPfY9BN8NnL/sLHli4i4eX7uXyyrVc038dK1jBClawgv97sELS/CfEi3/reog1leEy1eHK6dfjWGN0FKDJv3mQj7/9nby+9D6wTOYPdXj03iOYy2a6T9V7e8U0AghTLsN9bS4rjnNdfoJIne1TU8in6DU8RDbD9Ezi1zF2YIL2QovYseipkBcVxxmoKH7RKPJobTOx1aKz0EK6GXTGRbW6/NYHbuSWbz7Ao4/s4wNbv4stFX9e2sYYBr4fsXi8Rp9qMj3lYF2Z4/FH5ggKsGHjNJaIed6N+9i3fyNRNEan8TeARtovS3ZSCHpRl1c++lzCLpz3yElodjEtk3ihjlg1CB0PLSWXrMpzzzfyPH7eRlKPCEpDmtBt07hzgGhKIPwQ4UdE5QyVXBqO1BCVKqpWx/CTgd/E8RlkEKCESIwjDUnt2OyZKHM/xLJM6vMtQss8vZKIUrz8PS/iohuvYM3GAXLFFFc8p8a6LQPMnVri5B8FxB/byeRjfVgV0HGa/skANdtBm4Lrzt3O9/YdBJJFY8NUNMomxy/NoR9ThFGEEAkHsgqPXW7AvIwwehGVRzW9aprylx5NmJR0itLfLrL6k9N42kZtgumvl/C2j+DctQ8pJHp+AdFXBQSvGNrLqKhy5L5VzAsLowzBq/tJHZ6jy9Nq7YUEy0J1OujIZ+F5fhIzO6ORkQJTICNNnDbJjVTpTc6jgxDX9ZMVbSFJ6g4E0nVx0iZu3sUfdfBiE6kEekSQtiXS1WxaU+d4dgTftQntgFe+7T4e7a3lt69+HU+OfxRHKH7/a+dBo5lI+QsuD375s7zu109x5CGHcy/rcpw0qSWQj/vY9ZDamOKf5/sINgAa2ltyZI52sKfm0VIw/+wKQ6eWvZFihUSQNkzUTIySCmOqhXvKo7HTRZw2K40BTWNCkAk7yD6B3VKJaij0MIoRdi9HhESXXIx3DsJ3bGa/eAh/wzDm0sTpiRPEPH/DKD984lzE8vylreDq4in+MSjhmhGZfXDey4/xktIpekrSvSnP4R8aZNZJ7P9mce7QLL94h4kI1+H882PL36tJ1SPCdBptO3Q3dnCqggObh8nXF886xxqwWjHqc5rCpzRHqDD6xBB9/XWyX59GzPfxsdmNmBJevepRHq6uY609jykURcPj5t8ZIT82StRIM/ncDK9PJ8am35oYZLJZBQnBpir2xDxyeZIXWYLqixWfu+wWehfbvOP3XoWKYqQUiE7IunSd+a7Li1/4GFfMzvOpn10A2kRoxc7aDP3WIo+qdXzs155Pxo7oP7dJbMDmLS3m7DpHTBiPU1x/yZNcEdT4zJ27mPcKXFk9RNHQFI2QVLSIcbLLqlqNgQ0BB/p85o6sT9qjAeboMbztW0nPKaxGiN+f4sTe5XS5WFN+YI7GKpPFF22k/7uHkA0POgHt7f3kOwa61UHXmlBvozf0ocMwuYbCiGifRf9LQiY+ZibkqWnSWZ/BmOhgNnpY69qkTxSI7qvBq0jinMMQFS3yvpue4ECzjx/fdSHppeOAJhpMY7Q02WNtOnWDHiZKC264+nH2/rgf2l2UHzDXyCDbDYwhk3A+RhrQdKrIbkDuZ4cSD69VFUQug86ncRc6cGAG84Bm/mWCrhPgRkmkt3t4HvnEOM13bEUYoOYsjA0CddhAxJpImIiOy6W5UZb+R8DSwUzi5fPUpWdIXvqGWX58fz+TzRxxIYXZ9pOIeqUR823SR9zEIDxSyFDxe+/eyy9uH+HQbpv2bIinNHuXhkgFFqroEVVN7AkTEYSEfhfDKTKwKuThm21aC2CbaQarATXPW452jjm4by2hCME20IZA9FXwBlP0thcJB4o4gSS0ItJHJhKD/0oJWcklhIUHRqSxQoW74BGWHLQUBBvLyMMLiQIkkyZybaRhEaPBDGFynrCWZW2pwVi7iHtvhGiE6HYPZ7yFM9vF6GrEyQZhNYeRymIudMH3EctkbjgNS2N5yjs1Kq1pFSD/VJQ2JGlVCIyZGsJ2UGsHEj+hcA7hxVgPtxC2m1yfy+fEPHKKzkUjxHmHkR0xi7cY4J0ZR+jobJL5qR+TMRR2LyC7MVJLUpNL6FkXZ8oHX2E1NFELjNa/Gu5Oz6C2bkBuWo8+djJ5TcWJd5FhJAqbKErIt8U6qpxHD5RREx72kpco8JoeKm0Trs6jMw4j6UkmO3ncUwLfDzGyOSon6yxcnoG0AV4LJqbAstDVEl85VOOO0S/y05e+J2kDU+L3IoIgJntNgz/9jft57GQRz/IpVDS33JWMv3757QfOImkAzt25ih1K8cN9Bzm8Z5K7WgbXXrUNgYHWClNmuXvmVqY6KRa8CK01I+m1jHaOMZJa/a9bdgUrWMEKVvAfjBWS5j8pXvzOF/zKa7FS2D2NknDlyDUMFpZrmsOI2JR8/dO3M3Vi9sznSzlkN8YPobPJoVzs52SUJS1iMvbZwynXtVDNzmlyJ4oibvjN6xAbcuw++jCGFqREMu8fSXWgHSJ7MaLjo6oVzrtwiQ9/+D6UfJznv+FmNj1rLfjfQ5ia6LaY57+twd7dOeY7C5xz5b1Upwss1q5ksR2i0oqNhsdY7LLJaoGCUJuMhRECzVs/dgVx4HLuVds5vLhIY08F+0SdiR9pTFVHph3wImTLRxWz6ChgzZo+KpNLuN+P0EohbIvMNkmBRWani6iZOsI0kdIg15fjNf/lBXz36/dT290gTuVBSsaeGENnMsh6E2e8hpCSS950Fbv/+V6otwFBHIS0l6LEN6HTA9Pko7d+gKEdq/nGt3dziSE4ct8xDu87xevf/VzI5cjoEpfsvZa/fc91vPHAl/D8kPXaYrERMrSuj6Nf20dftweXgtvWtK0IqwmHx4bY0D4OGnptDwYtDv5ggAveMErjpIvaPkzhaIv8/gjjKRl6z2MNNsfqRYSAIJTYqKRcqb+alOm02qggBClYNeyx3ZjisB5JBucuSDsk9P+VGaJjJ/4OUpC+pg9hQKwlhcUOlmUTNw2ULfAKkrjXASRoWBxL0Zu0IFge3GtNWMlSn1zA7/hoMyKiSMn26GGhGk0o52kKQYiJRrJ4tcWz+2do703zFx/5Jq9+maCYU3Sm7KSEwPORxRzP/9sxvvOpPnrHY47sTXPDm07x2vddTe0XXcx8gOiF1E8Icht6EOYItqbJHuuc9jeKVm1AB6PJxKPbg1IBEUrcmS7KFBhDVVTZoNjfBQ0qm0J2BTgOXhjgT4RIt4syJLIdEKckRjviwT/8Er8zdjlH2xWs3QoF+CdNvGdWIITcwycAeOtfj5LZOM33JrcjKwlx8yePX0ZXBygl0UIQXB7zrHCBLXZIVwle+fZHmS/n+epfD7D+7iXe+q5TvP2bEzyweIzP/HgDLAEIen4KQgWqS3d1Ee/lNqVUjcz3MiQ0E8hsCn0o2ZfjHuiu4PDcGrQtODXVz5a72wSb8ow/NsCLrn6cKLD446GHyQlNqOHRXgkvLQk7CqPts/vWEsc/nJQqjI6b5I410QhKN4yT7msw8/lkglNOdagONthsR0DE884/zu47DOjGoGGpZ/P16a04mZgIwLaRsUS369zxhTLPaIeM76gw9rIq/fd6rF0zi2lFdGNJbdEh7Jqwx+Ke2fP4/d/8Op9MXcyDxzfwxwsG/3jJXdQWTYI9sxidgO6nAq797nHWXDjHN29dgxZgd0CcV8ZsWjjjCwgV43ohk69Zw+DHRhG2CSomfaSJO9XErPkoBOFgnmAoR7w0i5zTYFuIXIa//Ju7+fT+rYx9MFHG6HrI4v4clfKyp0sQEmY1rReuw1ARlbUeZqsOIjx9DwXz87z970Z52cA0nf5T/OITg6dvVXOuu2xsZfHJD29BXpsoVvbMrAXVwYgVEmhMCsSuPESahm8Tx6CiCGm6RCmB7IXYykLPt4h7PWj1oOth+hrRMwnWlUgfjQEBUYxRbyNaERgW6QdrrOmbZr6ZozVro/MOaU/xMnWcH8yXaM/pRNkoSEzTCzkef6zLwt4I01GIxTq63kD1F5FWCnu+iTnVIh6pYoUWngqxUjE/vms7sdFCVXKoSGMfm0JFGgHYroPatppoqYbYvh5d9zk5mqVTWk234xNWLcaOuOTzcdIvZUymzQrVHRp8jf3zJ9HZHN6l1aTZUxLfEUkA3FP0hOviF0yIY4ShMHsyKfs0JHqhBkKjh3KIRuIrRrfHha/QPPFgFq1A9MBWPpeoGjc+7zH+6Nbn4S9qZBAj80Wi4RKdXQJn1sc+sUDU8YnWSYyMQ7BtBLFscKsqOZYmhml8PYY5ge1I6HbP9N9aw1IdEUV0V7uo9hxOkEJiggrBC4jWD2JOuIiOd5qocSZa6JxDudeikU6qVf/1c0H5wRn/2+UN7Yk6suUnpUdeRO7B5eOXkt7GHMoRqKp11ldprTFiUO1OEpWuNcKx0e3uGTLIcRJTYctGdTxE2kEO9aG7PtouEK3uJ5I9oqEcpqd4YGoN7kCPqJ4m7HZRfgRKs+3TLZRQRPMLiedOzyPakiKXhyWmuObb7yf9QXjuTecRRTHNZptXvvQom/INitt7/MNPr2Xd4CVI48eoWHH3Iwd5xsQkF64eOeuYJmoNvnrrbqb317jlrif5zs4RlOkhhUWsPVYZM7RqG8lYIVprtuZ2sjW3kxWsYAUrWMH/fVghaVZwGh0vIHJBhJreciTjq//45fzof/yM3/ybN3D46CInHjiANCWX//qzeOjhMZjp8cH7nkl7UFEzXV6SO44lXHYO/vOvfH+6lKXrK/AD6gtNqoNlznnWFlLcyS++WOLct7YpWwFfH99E1FMYXgRSIrs+522dwbZjEHPcftsd6IF5vu+eS59s8PF/OUDOBM04b7k+R75Q5eRUH2Fe4jsxKi34wK038raL7+Qfls5Ft8ERJkWpiLVmuNjiz7//+wB8+7F7+NCv3cG+29I8+G0DoTVxFEHKQUQRotXFtk3e8fE3cctvfAzdkCAV4fgkG6029akKjTkbr5BHSRBK01WKF73sEp7z3J28ccu7iWpLAMzrAL3YAqVRJ8ZRaZfRiSWsWBECaMXI5iF6bY+FeNkPpdPj+J6TtB2bw8emUVozlEpq4G3HxO8FyGwGUya+Qk07RDngH5nFb3mM7T+F4RsY5WTCGhoxhWOJOWNzi0w8LLQm8EPsfIoffmEDj37JZr6Rh0sMouEiov50zYvmnCs2cyScZ6mWh0iSFw1EysUpZTC7ITRiqCe19ItOwJGJKoGSICGX6tBqWKQG19Jcmjz9nRhGshrb8+htNSlXkglA375T9OzNqIwFWtM6V9H/nSVYJnnCOYEhY2LLWVYnGWAbXHz9+dw/sUCj0WHAaDCc66I1nBgcwMj0mLhtkPx4FxBwU8gFbswf/vl5qN6T3P2TF/Ff3nMbaANsO4nSbXq87/7rcUbn6etO42Ri5vzXUUgP0rjoIfSoQsQG1TUhnZRAKk2m7mD6y2URGoKtIikNAAgjdBBipG2smpeoFwwXU8YgkhQos1JBjUi0lMhal0h2UK6JNd8hTlmojM3IxjZxLPACxRVzR9j7tQw0Crz6k6P83fgliNwA4csdfu8FP2QoHTLiR+y87DhDaQ9mz2H69T267QD3J4nqIBiU3P4vW7jwdx+lpwRXVX2i1y/w8B058BQZqXn/1Pnc/aPLEL8hGPn0Y8hUBjpdSFmIdIpd5UMct4aZbbrEGRMFdG8ahDBNytxINJRn4egUtzGFlsstIhVqyELGmlNfWMdfqz5UUfCqK78PQDuC7yycx453jHHV4Slu/ehWfFxu/psikw9lKF8eYs4n18zmi+dYHM2cvmLdKwRp2+SeWZfzih7Pu+YwY/dsZ+bRAIo5Zqb6GFw7TrbYISWTCWEMKGnTpczehsWRu3diC0VcPEDhBQ2ECabUTHXy1HUO7TmYbZPbZ4p4mTROHZ6c38ib/2yJmd4I0mmjuwEZM+Ror5+q28U+1YGsg1jsoKcsxLoY0euBIRGtkOJEhDAkGAa1bXmyj3VwxptJFYeE3gXDyIE09sIssZtC2zYy5TKwOmBXpctoph8RJPdh8LMWc58bgPs6EBjEUhCUswg3YnDVKb7w7Ht52eGk/CHcYmDMC+bucFHnCVq+zcCtcwCng5ClKaCYp12LKa5botHIMbxjigkhMJZLRiQC7cdI20CGiZLmvX9ykDvvfA61Kwcwmj6V7x9HxUVU6GMIB5RCKkgfqaMH+tEZk9DSiIZECIHwYmQN5FEYmy2x+LItEGnSDZtgSaFii8XZLKRcdDchR0UUQbPF3fvX4jY7yLyflBsqBfUulO2kLDUCEYaoXI7OUJbJ0KR9yCNa6GI1I9rP2IhzJOmzhBCIqSUsYSOqBaIgQjsCkc+w2HOgz2PqlTliG1KPnMLsRhh6ANom01dprImANT+OiEwPo95BF/IYxYjAs5HdZUNhpWFhCfuUi1xoYiz16F2yFkyHoN/Gq/RjLvpkHxpPnhVhCEoR+ALpx9gzbVCKtX0h733mcTqBQbNs077IonpHF6PlY0+3cY8uorN5otVVtFZIbaB6PZxjc5jlJDFPVwqkGxG5VI1aoYKWduJn91QPPlCCegPlSITSOMcXMOpRknglEo8yOsv93FPb2CZGrQ2NDkd1ChVFYOjEL+4p+AFqTR9ifP70SwKIqllEOY1zPImnFoZExwqUwplo0dvpYHT8ZBVI6+RfpVGLS2jbROSSiGt6/mmCRgsS9ZBtgtDITg+VdjEME2XFRK5AVrPI0CZzsotQgrlaBmE6rJ5vok6cOr2PTx3B0wfc4oNgGjHvWX0X+UcyfEI/g5//YB9ZV+DPLfGtW7Yz8Jo2+8aK9A3vY/TkKMrI0C1bjL1gmFf/w7fQTsTNb345F/RtAKA/l8EQEunHxMSEcczqVIaKHZCx+9hZeBPphYfIpIosdxsrWMEKVrCC/0ux0kev4DSCOKbbZ9Idsk6nH731Q6/i+/Nf5gVvvJbf+/OXkZUxhiEJXUmcsvDzLt1qSP+GJQZSD3BZZpad7hxHmz/6le9/x5/+GrrdgThCLcd229LBMCMe/MIAXz53I5988TZcL0Vs2IiRQaiUMAt5vv3N7Rw/VgRMrt7yae7wnuRUr8gT3VXI5VGVABZOLvJP79rGw99fQ9yNEgPJtGJM5/mLz97I7q9dgEiBECnWWBHr7RipG6f38bxVsCHT4voXzSFWF4gH0wgk+CE6m5TRfPvxj3BybIGeEaItSZyx0JZB2evQqzsEsYW5SnLZi+cpVT3m2gFf/dIvkZZBr3umzj5XSD1Nvq0xUil2//RJwtbyimSsSOdTZArpZRNIgTAkG89fy3A5S3bJY2M5SyptJ6aSUuDPzBFPz2J4HqZrIhCgYONlG8gW09j5DK5rU9ue+BqoSOJVHbqDLogIM5UMI+Mw5iuffwvbz13DzESK3qohzJMLyJk64sQM0hAIQ/DCV1zC7p89TvTzFqJmIGJBd02G9lowQp1Ix2VyLV3ywjpXbvC56sppzHMCFJqloRRuPWT20NTTrhSBMg10r4cq5hHjCltEuCJAlPpQSiCCGEKN6lis2+5BNrkAjJzGHjEQ5RKiWkaUi2jX4aH5Gs6FG7D7q2TnPO4fX8t94+th5xKDa3MM7lpMEkrTMTee+yh3TmRo51xq6zO87dP3sFBY9iowZUIeGQbdRQf3YJ1wHsZPWbj597Jz1zqe9axTBCNFgh0Fuu+uMLCqRmyAUYfVr4nANkldZLM+GE8iuAF6XbQhiZZaqLEJ1NxiknwTaLpdh946gZASGYDsRuhOB7PmI0/MoiemUCpCxJAyYg5GeW4YHmOhnSaVD9FC86Zfa7Dz2qP4F3S58tIjyEAx9oTNHTMZqukOk+0iAEOXK4LzK3TaJn7PIDQM5GzIb1+3nb96z1qe7OY51Ctw8gmX/V/IcU8tx2iripASnRZ0SgIWlpLV52YL3Whx4YaAF1SOMPW5AYK+LKoMF/3+FN3nmnjrymjDxN+xhi/tOY/qnRG5AzEDX5slsjOIGOKMTfaLab5e+ClLEfQU1IGj+wbY++lBjn41j7nQwZxtMXlnlrTQ9NkKNTmNmpxm4FSNVLNzxshif8ThT9t8+Sdb+cUJm+aGBSovFqjta/C29eGLiGK2yU1r97K5cgq1PEeOi1nUujUsLq7HT3dYc99BfvLp3bx9/QmOdCpk44iFqEycMoltAyOI+drv70r+btiolkWuP0f3olW0n70Zf+caBn4bdqWa9JuKVjHAPb6A0+jheDFxt4m/sR9/VRmQGPc3kyRdpemck6F9QZHYNTCdmHQq5PKRh9nx3ANEhzrQaCTEqGXymbnz+NLkNsau64NCHlHIY7QjFl+nQFiJV5ZhYXVBuD7z7X5eePULiKLkmjCWTAzD4ac/3sW7fuO5/PYrn5d4djyVQqdA+xEsLGEstLj6/D2c+5xDDJbrvPFTR0/f1XJQE+oI0WfyhjceeMrvFmot0BrpRWg0wWCGOOMiHAcMSSob01foEuYFZsPHanjo5U7fH9AEIxBZPngh1rEasSORU0sEE00++eVNNKYU1HunPXaf8jZxTzYQUcyWq4+djmAWcQyNBiLW6LSJ7vooy0AP5/jE1Dl4tR74EagYKQy0kZTsJSSKQrc7iIU2zM9hPnESOTGPEImyyPBAhiIxAQailEQoiV0zMWMrOd5MConG6Gjirkl6LsJZ0giVqBN1p4ucT64DBGFlQ4gAAQAASURBVOhIYXRDzJPzGK0Qc6GNKBdhzQiirwLpNIdus0AphE4UP0eezPL1b6/noT39NAMHZYmk1MoPoOsjuyGx4SFtH+vUEvbBSeKclZSOJrIejLFZchfP0vfqFrkr5lG2gXfJpqRczDAQs3WE6yJjgZEtIKWTeObYBsq2EF6AeXAc0UrOizZADvbBmiGCkRyBTLbVGfesMYQGxPRSsr/L51MNVJG9AGOujbd1AFXNnGU4XLx1lPyd4/R/9TCk0wmB9ZS/zcISzC1CykXZFrrbO2NKrIGUm5Q9SSMpUdt/FOH5GNrA6IaEGbAbEXZLYbRiREeQadWRRyf4n2HZTxp54xwSxQsLLX7w3W345RRRyqJxchraPXrfivmrV1/OgX9cg1LgKRdlWQTD2dPfFQfwunu+QrvbZnayxr79Yyx2u7gzHvaMx9xUHRGfIiNihJoiY+7EjorQSxPGMU/U9/CZY3/FsdbB/+n+rmAFK1jBCv5jsELSrOA0Or0AMwAjAFP/25fG2z7+Zp71ludx+UsupbU5R29dhtePPM6rdj2ElgM81ivxpFdgJH/TWdtprZmcbkGvB90eYaTodTwm9ozTaaWSQbfShD3B0pxDOc6gWy1oNJFRQBwovn/LBjq+ybHpIrVehk1qkeN7qjwcwHwAu5cgKpQIFrvoEzVUMyI3KbDnNP0NBzsSYAhImyCzQBVIg7X59H5urV5Jf/WP+PxdV6AH+9DFFDqXTQaEXgAC/vzdX+O7X7obRzWRHR9rroNU8ITayHQ0QKtc4JqXH+faNzzCDW98Eq0V9//8SSzbTCZEy4gVZFYPIsykrQeGurz3v/70TKMJwYkjx5g8OYHMOBjFPJdcdx5u1mX82Bwziy1++E/38+h9R6kttpifbqBTyUqnXcqTTrsEax06wwaP3HcEFSlSrgnNNiJKShmiVSbz22wUIRu+5hEt9ZZPGDz7y18hHkojBDSemUfaFmYIcqEGQmA7FosqoNv1yH1tAVWNEJmYcI2gdKSHIXTSwbRb4NoMPC9ISqK0wcThCuqtAQUnpGWkUerpeR0QryoTbuhHpw3E/SFbflZm++Qu1pSvpBoJooxNkLfIuj3E1hLp1TYITTAn6RwDuj1EJo3ueQgVc7jd5kDZxopC5r9g4wU2nY7L9PUjrN64hoFz6vR/7BT2h2dp6yz7GyXEUIo3/7dfckX/IrmBkHikDwb7ybxkHRc9ez0i7mHP9tAR3HxyI6VShre/8Woe/YcsYrFHqFweG9/IyDPHCbMmccbh5b+zh1+/dz+v/Yt9yEGRTLYAFStUOYtaqidpPPUGqufRMRw6XZvJ5xVpVDWRBF2vQ62BbnQRXR+0xmz7GFNN6rMRfz16IWPdAtkdiqGLg4RkBD69/Zd8+lnfQX9hidtevZEvveMCHvj0hRya66MdJcqqTb8P3Vf1M9D2iELJ8/vGeP8f7+W8V/lMPWs17zx4Db994lp6HRsps3z07c9h6vZyYmitofn67WdOomWCJbnkoiUWug7GVMDsZSZ+f5HWvMuzr9qD1fWwZpq86qKH+UjpEVYVW6THFJFjQypFUDBwplpY3YB3/9lzeahjcyKUHA9NbrhgN72xAqeeSFHo69G/pcfQZo/f+8QpZmtroVxGDPSz99NV/CNgXFJEp13qLZtcKWDqqwa7Pz1APzFGqcv4DXmmnp3CPqfNMT3AzyfOIZVKyvS0EFBvIayQWluy+a7d/Leb7+dkAOmeoP4Tm9/9oxcRhh3idhPRi0n/9ACc6iXJZl3A1yz057HaGsuX+Nv6sFN5Lk/VuTqzROp8F6UUGAZ6qUZQzbO4I4VfNAkG0jiTPjqdQpXz5MYV0lNYQjG83mPDuV1e95YTHHML6EweHYRQzBHXm3xzdCe9e02ytYDYMSCOoFxMDNEbPXS7i1kLcdbUyK7rUTjYh85Y+MvL/jqAwIgJox7jT0qCJ2cTMmN50i8BYRpgWUTDRR57k+C1fXu4qXScU+nE88zaJCi+3UFfnKc3bNNuqGSerKDfqlO+ZZTCnRMgBMHaMsGOfuqX5AnWFfnD207xtx/fy8YdS0g/RoYalbZAaeIBk7hq4D0/hwZyu6co3nIS08lgmA7nrJpPop/jsz3SiBUEAdoLOHJHZdlDhcRc1vNBgD+YYu61azDrPUQIvzx4DspTyXH3AmQMIlSnB1Fax4kyY2YBw2siIkVQdulWJYFr03efZvirc0gEwjKJXInTEggf7KZADvZjahCkcUd7FB6tEeuI2FZI20EYBirjEG4coHXFWlrP2YyQDunJHpRyLF5oEPXnELvyCNtEO06SNlVrEwUeyhCobhfRDfnnz43wmT/ZyZZgnPXWNGKqhu508bZUaV6zBpXJYE/GGF6MsgyMqTqsG0KUy8nBZtK0gwodP4N1F/DEHMQBQiZeLiKTRpkSUchj4cJQCUp54p3riVcPEG8aQqwdSvy9AGE5ScJTf47m8zYQr66gL6kgtSa7OQL3jJ+QzqcTP5un/lYKe7SOTlkEGyr0zh9BFPMIK0kW7Fw2SG97BX97FTFQhXz2jHoHEgVNvYmoN88QeYaRmLCLRCGGEOjZueS9gycQsSI11iJ9cJG4UUcfPoGYmMMZFVR/b5L/FZ66ZySQeuE0x7oG5Y0KTIOo4ECrk5gdLy6iOm1ueOF1xPsq1O6w0aYg1YxOl3nFGuLQ5IW3f4BP/un3+canb6fd7WG2A6yWz+iRWabiCvsDWNIjZB0bxzSxpEQpzaw3STtqMefP/C/3+enQqoXu/QQdT/9vb7OCFaxgBSv492OFpFnBafhdH+ErRKRI/09ImrtuO8j0kk99polVj7HqiiEhqXtZtJnhZ92L8LJ/TsEdOmu72ckau+86hBwcwBgapNP0+P4nb+Fb778Z6+4mWiWSArXaoj2cIV3MoOtNtB8Q1NpI0+S2uzfzwj95NX/91ct5w2UPcM26Q/zmlQ9w1K9wc3uQJ54YSCTeALEgjmLMrqL/x1NUfzDJw1/9L+z52u+z50vvRUoDUf0GlD6PcJ55ej+FEKwtX8/Jw1mEp1lVEciUiRKgOi3aW4sopWktdekTdawnxjGPTCKiiFc+f5Q4L9Fakh5uUHRCzKEusuNx0a412I6F1DG4DghBvRfhp9I4uQzYFjuf6bFh4xKFweUV6lIevwFRF3ZdO85vv+/5zJ2YZc8v9uGZgq4X0m57LM038Xohva5PbvUAqU1rKa+qYpsmRgiGL8mtKbLh/LUUKll6oaZ4IFHr9PoFcZ+JPRGgtUb3ktInDcRFwQP1qaTU5rkdJl5SZPKtaYTWKKUIg4hDdx1AROAGCiMS0DKQnonViJBPSRcU4AX8+OQW/mVpkC9NbseZVfj7HBa6acyTGu2YZ8WqmsdnMI5MYs63qOQt3v+O1zLfv8j06gnSrYggC9qB1OMux++r0l2dxsxqLFejQ40OAtTEJEQBYsglvRCSWoqIbUH2kMQ0QwardejTXHnlTnrKgpTBZt3hnlt2cd7QGn7+xXdx8NgANSXZWKiz7g8alN4b8+U/Pcb48RmMZohYnvsFExrfD9l3/xEWT7lYR6ZwploM/VOHB/VWqudOYgctDrWrHOkOcsIpMP3ECGp5bihjhdnykUon0nspktX9OEIJSaQ17sFZ9Owsuuclq/ZA1F9Crx8mLmVRxRRLiy6Hav3cN7OOFxw9yexPDfA83nLT5dz61TX85ZaNdB+1+d3/Psqv/cYsJ485FIIuLxx4AoBXrD7E515xK+ozFud/t82v5/Yh04LfeMsJeptcVGzx6/4jXPXaObBNuuszmJ0KTsPghcce4dbX3JwckG0l5Wax5p/2ruU77zyPxoCFMkG2fKZfGvDk8wQFbdIecnjrm55g14UN/uxld5C5/wSZJ2fBjxIT2JxFbJtc/f5j7J0tsz9IcypMs666gNnwUbEgbhtsvLDN8HPa/OX7Rqi3PRgqY27KEDQlR+7cRK+8Hp1N0w1dlq4ski/7pHIRza7BXT/dAcM+si8gSmmkoRkwQy7NxIRZmzhjIboe+uBh5MwcI5/M8g/zu/hubRMP3pHjvk9UEZ7F5K8XmXxDH+l9M6QXPORMA4ynJn6ag0MZeqmAwI4R0wvc9aPNzHRcpnsWsYKJG0rES0uoSpGwaNAbUrTWOOhqEd8xoN1BziySv+cU5RPTyG5Efc6i5RvMjjo0WhkiW6KHqqh2B3wf47hD+ZZ5ck8s4KVDkAZCyqSkRwqEYUCoCUYkXW1zYmQJu9PGGUvKg+RSm87lI4R5DY0G+inCQyRqBuVY6FV9qKyD9nyi8RSfvOtZ/OPeS/jn+6/gqs/PEzc04r42qUCQDgOGrp5Myt4EvOkzTbqXr6N72Tqi/hy9dWl6m4o0dpaYfvNGzH6NYSp2XjiGNiWxaxCVMkgNhUqPTNbD3XEmjdpu+AR9GYLBLJPPLqMdeLonuYaEtIkVaJWUFLoSnUmj8tnE82ltnrnXr6G3PUVtR5HUksIctxMVDYmaKUhrolLqdCmLtq3kvhWC7JaQuJjH21ohLDnEBUl9p8n8i4eQG6vIahHTAbOlSC+AMxMibAvhuMQixJypYXQDzFoHEUtEoYAoFTCKJQxstCsJ+i0QEGQNOhtyFA5q/HUZ2tUMkatBxYhCDpHPEkdJX6LnF5PSSlMgcm2sQY/sti7WSAlRzGP5ErsNUkiE4yL6+/AuGEEPVDDbEfKp54PUNKfSTHy2RHe/Q7EZYh+YR0cRRGFCaLQ6eJZHfYfFwjMyhNU0ZiQgn8HQiTpFuC5YVmJm3u4StuvI2Sa9VQ5RfQHR9mifyoJM4w/n8K7YjCnspAzwqefUSBlVcCGIMedbuJPtpL3yyeKKM9HCmu/hdCXSMJCl4ulr5XRpk/+vkqTiOCl3ymUAgfZ6PP0BFU9MEhRcjPkG5uEpRMdHLDaxD88kXlycvehw9l8wdMV2GB4A4H3bzqF2uIc5XcOaaSTqHT9EdkOMsVkmDvyE658zzo03TWKUS7gZB9tXZFwTd0OPtN1EScFi7i7yxQzdoQihFKaC9Wv7aMdz1JTAi5dQCnw/ZGTN49w9+22u7HsuLx15LZeUn8n/NryfoTufQ3e/8b+/zQpWsIIVrODfjRVPmhWcRjuKMdsxUoORNv7Nz1x81RZOHJqmf1UxWaEXilhn+eBFP+C7p74OCDJW9le2K1QyBLZEdTqgobXYYmj9AKmMA/0a2Vse+O8JsU70cIM0wrIgCLGKLm1bghKYhzvYgWKqVWBDcZHYy/GSoVN0leBzX9mRGP+hiVD4QYRR8zAnGvgCanMNqsPl0/skzHUIc92v7KvWGnnfPHLCo++qiGuunOcXNztMixGiQoqdF60lkHDoviy0xk9vFxsOzWYK04+pNTP8uLGVgcBDpx10ysQwDRzHpoeFKBaIfB9DStxChh5gVDW2HfN3PzW44SPbEW6K0jceRkeaTjtmZPMgTsqiuqpCY6aJVpqhdVWKGZeluQnazR4sLGK025RyFqYhMbWBIRVXvPYZPPucjXzzfV9lamYSnUrKnYJyTGjGBH3gjAaJh8sy0RUbEqsJSmneuO4hdg+uZWj3Eo/hgCIhalpnSB18gVXtAQLVDlEtL5kMSQlK0Zo0+fTha9FaM1jwsU1FOd0jPmijTfP0BAhAlkvJBGQkTwOfLz92P3Y2iX3fetlmDk0uoE2dEHtdj1R/TFwQyFghLYFKpxBmDhX0KKzyWDqUKIx65T7yYUg106HlpUk7Ees2rOeZHEMAGwdafPSCvcSAnU7zqTd9ifrif+dU5+f80TWPk2GJggG/+/opPvSt5xCbEiJFptWHNCQXXLkVXSlAJ8bsxUSOYOrWPoJ1kqxjsuin6cs0qXsp0hd30GULMblMLIaJ9wztLmTSyEIebImUGmyBLmeRYwtJnC7LK7LNLvGavuQEGAK9qEkfaHJic4m/PXQ5aTGPTrmMP7bIl56oYKCIFWw4p8vGi8Y592U1blmqUl9mi77THmJnVGfd+UucOlylUErOSTd2WJws8ea5x3j7m07BVfDY+cNM1QR73vFZTAkzIXzhyHq23gSHbzMgbaLQfGPPc3B3xrhdcPfGeLu2kV46BLHgDa+9mA/fu4d6YHLKiEmtb2PMt5BCoItdtJ3F6IWgFfWiy123XssbXnsPsRIUZRs5mXijxJHg4G1FnvilIqgbbBqZ5i//5REe+2Ge7/zxGoTZItQSs9NB6pjgF4LwkGDPyX4emtnFm1/zOH83fRHYGlXL8dILfs43n7yMo4aFaIUIKQkKOXSpnysuPcANfUc41styz7Ft7PmrTdjCJ9hhs3r1Iq22S+2CEn2LDWTGIqjEyEgSZSNoZxj+9AG0JTA6IXExxeE/UxQNzZuedyef/NlNTLxuB9IAZQqkVhhhjEbQeO56Bn42Cq6DYbvIevL6wKqI69+6xPu9F5IVMPesEiPfnkD0AsKCoPiVJ/FWO1gdMGNNtyxI15uIdDoh01pt4ryL7YYgwGp5EHQwl5I+WccKZj2M6R70guTcANoykFYyYRan5hFaY8lkOrpx95PIN1X4k5d/lxNBGTUPumNAWiBNRYSmoyArYc06D21LhBci/RCzEaIyNkZLoyPJz+tlKk6X4xTQpgFKE1RTpIBdw6foSpviXJ3jT/XDA1WMro8WsP/YBvIXN3FqXcxD8xCrpEprudzFKkuMVxV47er9/ONfbkNn00jbZfF5aaKiSelHU1inrCSdztOJkWw6nfR4ppmULkkStYntJOqklMnk2h0UW/M4SxEIiQhjlJmcU7wIQ2rSBNhHuyhXIqc76CBAN1uk7l/E3zQAsz2iTA4Z6sQbxU2SraKcIHu8B4ZBUDCIhINVD2ChBntDZNZGHpgG20kINSmwTBNhZdBxkpBkNQJa6Sxz+1ejHUVJ1jClgX18ntSTs4SlFGqhA2jC/CB2L+kHtJUMGcViE3e/hY0JS3XUcBXDdZBBBF0PcllUrYayBbrWoXjXIvHQMFY7RDkCvz+FDDVuLY9wHXSzjTQk1nyXVBgghgSBbWEKH+KEdHCmAvRMO/HmeToyLuH6ClEpRfr+k6hSmtlnr8I9EJFuCFJTAakTJ5Crh1GdVvKsqpRgsXbaE+xf/4vrIGwnIfIMA9Hfj5qcPpO86PlgKHTaRZrG6UCE4n3TT30DYo1AuhuIu13EqenTRI0AlppdVKeTEPJ+wJHbYpDTyR6Ey8/BMFHxPXR/Dqu6kcOP5iGTphc3sGyTVbki6VWjTPUU/U6TzW8Y5+3bXs6/fPTvCfMOypCkMjaxTqzapbBRsWKkup+LNz3JsdYT3P07e/iLT3+MfxfsCyDcj7D/HcTOClawghWs4N+NFSXNCk6j2ekSlU2Csonj/Cp/53UDXvDKS3n3n76UciVHkBMEOQMj983EOFHPsMqeJYzqv7JtuxPQkDKRknsetfkGu55zLq94341Em9NJChCguppjh4dIqQzGmhGMdWu46TeeDQWXTsZOViVtg30ffQGv2vIov3P+ZYzYmo224vipIVA6WRXrhfh+BI6JHu7D2bWOylDpf6sd6rVDZI/PohotxqfSrF0zS1iPMeabpBYi7v/5k+y7+wjBhAHWMpnlunz+wLMQlgYpOSlyLEQue1sVrJbPxnUDSRtJAa0WemaOeKlBuS/HDe+4HlYNsnFzC8eO+PmPBN6QS3MTiIE+RD7HhDHI/KkllNLMjc3jC4iLLoUNfaSzDkIIojDm4AOHqR8/xdiTSU28EBpCxW23Ps5H3/cNwlghs2ncTjJk1C5oQ4BpEZdSxDvWQDbFJS+5hL4HNe6MQpsWP7hqmJk3V3j8jwtnGkpC1AvQAuKsycZCjjgy8LsWzecb6CCAOGbrdRfCYB+Fik0m5ZHP9hjeUCNWBnU/jRpfYu4VO4itpD3lutXoYgbKOeyrepx4c5abvZNU3FX87VXX8PP6ApErUFpgCtCLS1QHawhTEAUmyjcSv4xYgRSMBiR+DEFI1GjQnV4ET/DObXfxlo3348Ww1W6xw2nwZCtRgD1lWSCETan6Ic5dez/r+r9Bdfl0+36R3KROfAqEIHQdQj/CjHbz/g88BkKhum3Mw2Okf+FTvNnAWNRILRgLqgTSxD0E0Xo3mfTZNioIUKUc8XmbEx8MDWuzmr6BOhdtOYnRbiCjKFnhXYbpJ/4c2jYg0mhTsrV+ivNXTfC+cx6Gnkb7HnrQwAgS9Y3fk6QymrSrqBQ88imT6/KJRP+64hxf+c0hxFibv/rwbaypekit+a33vpjct02m9kYonQSU6HNyFC5e5OqHb+Tvp9fRZ0Cn5LC3cQ468li4tsDJ952DQGMo0DbkxppkJ1r0Lt5E48pNfOqD/4w10eXJeoGKoSi6ii3v7KLzeQzHpr05y8KFRbppxZEnVuEO+PSpDkWrS9/RYNnfQ+P3BN2OJOpIQk/SOebwYKfEscfzhJ4BUYRWIUbkY3gRz8jO0cs4tPtyfOZDd/GO6x4jMyawZiwyo5ot+dn/h733jrOsqtN+v2vtdPKpOpWrujpHuoEm55wUFBEVFcygKOqIYBgzjlnHMOYsIoMRMaEkyRkaGuicQ+VwcthxrfvHLrpp0dG573vf69xbz+fTXVXn7L3PTmfttZ71/J4HNZ0kZ/toCVGkUT15gs40T69fyqmpgOWJGpPX2GjpILNZDuod4z2L7uf8wadpG4/wu7OEbQmsRhWd80AJpLZAhaBjH5Ao69BrRCgFB8+dIswC7YKWHRJmIzCaqLzB9CECb2UG+nuRfT2EbSmClImKBEN70vz2zsOxWybBnVkaq3PsunQO1dPzDL9uCc0XDNL2dJlUMaD4gnmUzh6EfAbqDfTEFGjFGa8cia03JNBU6GKwz6clkor2p0axyi1EGO0bkAo/RDVbcZsbRighECFEiQTr71rB4vQ0z7h9vELtBamY3JrHdw0C1+Sbpy8iKWPhiS8c3JzG7TaJLAPtGICmlY5N328dP4ibxlezs9WJWQ8wmiFGj8fpj9U4p2sbx+b3cFIm9rTSUkA+SZRwUAmHwh+apJ8Yx9xZRacS4ICxgpjQRBA0DNxbbW789AAWAlsbiFCRe2SC9j+Nk3m6ijU8jSqXQHu4+TiWOZSa5Egd6UUIBSKIoNmMJy6CCGohUUeIl5d4BYmYLiPqPkYjopAtYVanaORyCA2ypTDLNWbi8TCrLsJ2aB0+F7/XpnRMXOaCFMhUEqvs0+p1aPXZCDfEbGicp4ZxShF21SWxowQadBiTm6jYP8iPXGjPgW3RfZike2kTfI2uRqhiFe26mNM+OmEhrES8rgarHCAaPqpSRdVq+9oeZ5Wk818jnDPbQJiYSmN0dSK6OlC+h5QSswltdw5jj7ew1u8kCGogNWcs2cbqjp2EhQy6pwN/aR/uoYOE+SSJUkRqR4PS6QOEC/tiAj+M9UryOQSNBlQ2jRydxH5yiOQ9W/EGkwSLOokcSdCTjM2MRUjfeYCU6EYT7XmIfC72Z+JAggZA93VDZweEAZgGulqNS+MWztm3nDYMRCtAZ1KoRf1EnTmirDnzvkAhGDvhIOoZM/5c9it3tICmYSDnd6E682jHws4lY1ImCAFNZ5/Lh7+7E2txD77K8OvrF7F+fWfcY6+3cCstpjeP8fNTvsQb5mznxMJ2FiamoPQ2RFMQ5hL4GYdmpLB1DVNVsEREPpHAzQoKVo2CWWMqtZXLv3ApI62/7aHzlxDmYmT+GoRz7D+8zixmMYtZzOK/j1mSZhb7UI08tBBoKUhk7QPe27l5lGuuuI7rv3YHAJbSWOUQqxQSNOOR6zx7klWJCbqtxl/dfiodmw9impSGp7nhUzfy7St/jP2nOmHuWQMEyGzSnHLEkfGMmRRErYjpOQmMbgeZSeDOzfHTG98dL28sRmNQKZs099bRLRftugQpQRRFXP2BF7H4gkO49pdXxfXl/wDunLwObVkYjuDF529neMLArUl0GCGVhzs2hfY9XD/JR+9cz9KzQs69OqTZACoC5UDZTzLUyFEuGRjTDe654+l4dzPpAz5rYvsefnHNjzF37iAZuggB+XydFRtdFj5QxAhayFyW6cclx19wJGe+5mTOet0pdOSSOK2QeT152ruySEPiBRFuMoPOZfHN+PolphXOWIi7aYKpxzbzzD0bCIRB6Mdd0sK9HoX7ArJ7A8K0Q5RN8YovvJ7P/Pp9ZLbXMGZICGVaCNtCyecyBAa+G8RmpgLef1wPySccem8zCcJsHKNt2/zLJ1+OtG2CnM1bFzzC5fMe4dDuIQq6Qn+mQo82aBwSsudDq7jsxlGiUoWgK0PQkWDVimHMzgjTjpiWgoHsOUw5ErOl0Gj6p+OSjP6lDbL9Hs2KBDQykYhnQHtMWFBHaYVGoqKIqFTh6NFdvCI/xSXtE1jpx/nS7uP50ehhPF49gnr6M0Tpuw+4Tlpr/vDLMX79+0/i2h/n8z86gol6I+5Ua4VtaEzLYGznm/niVb3o8SJ6aByhAijW0CbU2xVjU3kGrCIJIyS6L4/2utDNZhw3LiKiud3otEM0twcqNSK5B7+ZYK7tkU004mASP4wHCoBquPHsfKRRSQOkyTxrkhe0r6cZtaE62okGOug9f78nx9RIXCYhgErN4K2DmxEzhqDC18jRiOJml6cfLcQz9xGMH29gODZT9Q4uWnsGb9h0Ck+N9jLy5AATT/fw69Hl3FeVMCzwuh1EqLC6+1j0b49jf+cRAhPwwcCAZGxUHXQ4TL54BfUlGT7/4ElUfYutu3Ns/Gkbhh/MqDUkCInfnWLrRBfT5Tauu/F0xMM2k2mL9oNbRBhoJL5r4DVjxd3kLgfT1PwuexJhW5Jwbhc65ZA7yMBJauoVG+vyPFOnLyFXiM9N554WhY0BibGI9dU+7CnNYdm96FKV0G8R5GwiSxIYOd79+VMY2ZlBDIGqNQjLZZI/rrFk2OWtvdtQUYRRqrH7Xf14/Vki08SqGNgtA+kqVMZm+qLVNA9byMZqG+9/4ly+vutYjAZIF/JaMTjRJJVU1BYrGgs0mXV1mJhEV6o0B2yCXAItBE0jwdDGPO0/EOQ3eLS1SiSDgJ7VHjLtQKmFDBSGG2BUFd991U856cWb49IUgMjnsJW7MYRCRgrnhykwzVg1ArGSwzTBib9Tf/HFiH/I2NgaASQtrvrYJs51p9n7Bsllp68mfbSDnGuBkoSuAS6Meg6ehrIncZImwjDBmfGPMUHN86ES0WjYTHtZwshETU6jfJ/Og4r0pqsstmu8PDXMxvHBeH+URjeaBGlBmBI443Xw3HhyoBn7R3lYEM3oHbwIVfHwKrHxr0iAHhkn+8gY+bvHCTqSNA/uQ6sQo1hlz0eWsuWLy9FdbViugmxs/i4VcfJbFCF9aNvT4KC+EtqRaAPEdAOVMNCmpBHaeKFFsx3qCxNUlyUwM2mEaSLy2VihdJiEaoloYUjkmEy/aw46lSTIxuo0zLhdjmwDsxniLe2hcdQgwcIe6O9BdHUgEiaipxsK7ehWC1lpoLs6MObPoXdhk1e8bjPtt++m8KtNmBM1dKOJoTSN05cTtcdlSFqCFhGRE0G9gShVAQiW9OIckyDT2yC9tE5pnkbLuDspEw7SMjE6O7AjJ1YSJROIQCEqdeavqPDyczfwxtdsQIxN0BpIEi7M0lqYwBqtICeqqHKV/h9PoZVCdeVjIvrZe27mOR51ZJECjL1ThH1Zqi9fTf3UpQhhUFjbIP9EOV7e82nvc5GWiRzoi42Vq/vJJvGcn8KQiNEJdKsJfgDFCqK9DUwDo97i2ZQCmUnDaBkxVURuHUaEEd7SNqIBC2UZVM9aSnocwt40emiE5/Y8tGEgA4ltOnErrjV+yL5tA5QmLU48r8pv7riVBcuyRGkbI9RQaoEhkM2A1lT87HvFkq9wRD7E9i2qxXUkmhLBfr+z+UaNoxKaggzozKeZbA1ySrLBS7M1Vr3IQw022d3YzixmMYtZzOKfC7MkzSz2oaRb+BmNn9YIE770wV/xjU/8liiMaDV9okhRLcdeJo5hYOwax9g1TqsZl7yscCIOSwgGrOffVpmUTdowodmCeoNKsc6uDUNMj5QwxwP89tgIUGpo29hgwUAPyvNRvs/eXZPUDhGESYFbMPBz+7c/1vgJrSiiZScx+noRHe3oMCLoShOEmvNOWcXhbsQvPvZLfNd/3n79NQx7BUIl6e+v84Jz9iCqJmFkILTi6EPnUR2exK82EUHET75wEnNWZrnvj4swx7w4wcMDpCBre7gtkyhps/G+bQBYSeeAzpgamsJvaaJ6RD2IiZUXXrCVl7ztKX73gxtJ6wpqaASj7nHTz+7mj9+/g19+8ff8/qbHKXs+T6zbi2WbpDIO2ayD8H1kKoUM4msSCU2QluhQozw/HsQ0W2gdz0xmpgyyUxatuVnMeoBZ99mybRyAWx6+BrPux51XFaFMCc+t3e9XiPzM4D9S7KjcSHYv2HXI7QKcBCKVJFvIgiEYaUtxuK5yAtNs/pOJmC4xN11m/pHL+O15v+SOM3/J7z9/CFHGRguBMkyCyQiBIpH06E0m0O5d5HIRbqeJ32Vy7Mo4etSZCxOPOHGiFAKsOFbVlh6nPZiJvV6MuMHTGia+ncLRkAAS4QQrU6OUWwlO7lrL/X8c4Jp3/oF1a3btO9Raucl9tzzDI/cUqbsvwC27SP3s7Kvm1a88FlOV+NUv24mq+9x40AnBhz/9SgqHTjL9ApN81eeU7Ahn53bS0elh1p5zPqeq6IyDyjqIhI3MZSic4EMyYlMrg+fHJUnPnU2WGrSliZIGSiiIFMlpWOo0+PaXD8czO1l40jjTK+cAMzO5SvLonW1Mjtok6xFSwDIVf6+/8OjB1AOTellyw49WceXXTua7u+Zx+oqNCGBb9zKGvxuycbIPw4gHp1KAnhS890OvZNkzLY4/cSsYEq/PAFPS40PvtY/ReetGJs71Z0oIQKUEUUYSdkDt1hSXXPoyPnbqQoxJLx4Q7plAuBEoaCwyUY5BmDQYGypgHuPSsdDj6M/U0JbYd2wEMYnQqhr8+q7DEIkco5etpLl6gDABOxcejNuTZbvfQ/RlH7speNnHXsl3HzyUIJKYtQAB3PCH05HbIhokMNNpEtrEDCKc6RayGfLMEwN85/Kj4sH/5CSyXGNsKMeVFx5C2DIwItDtOXDks5FCGL6EQFBb0oZRbJIZ9knvqfOjWw5ndGMfjz+2nMjRaBMCbSGnTIySxeBvmvTc4dO+JUR7PrpUwd5SobF8IeUTO6id1E0y6ZLzy8wz9pB9ss6iO7cT/bCKKLWQLb2PHNcmXPLIZfzKWr1vdCqykl+Pz6WDKolP2WAbRH2dqP7ueIEwBNdDp23CQ3rguUSzFRvuCKXBD9CZNNHCfk49eS9z5zY449VlrGpIY31Ea0E7z0ZlSeDKp0/g2slFfGHTUaQsDYHAO7oLy5U40wqmBB237MKbNIhCaDatmMwcHWPssTwPTCzCDBx6Ez6XnbF+3y6JiTJGqU5kg/biVDwtBVG/EUelDwf7S09yTmyy64XoiRLBtmF0My7JNbyQxO4KiW1TBIUU1pSacXwVNJdm0W5AozeN7m4nSifiMh9ARArTgbAnAUpAJKivHsRumVhNyVSiB51rI/vwNK12j8z2KiqK0GFsvm20O9iihTVZY64uka4oFhRKVOc5eF6d0Cb2+YriSYwwaqIjjyBn0Dykk+rqAkqHECqEiiCbRjkmOumg3Aa6WiHtNRipDSDek8f/1Dzqp81DOSbCdpAthUpbRG4L7Xok1+5F+iImoWZIOW95H1O7Cow83MvQaD+NXgs1NoH2A7QfxH426RTSsTG7OpHdnUSL+ggW9bJ3OM0j63u54655tJb0kNlVxymFpCZnfF9MI25Uag0sYYKKYqLHMmLiVmuipINZrCGaHiKdJuzJAgKrKcCwSE4FWM9W80ubDb/oQtUb6FI5JremivE9bR/ohbYvFWq6DOkkpFMxWRNFcehANofOZiCTwkgmMEaLsVKp1qR8wlyiyAbbwmmaMNkgceuTzyvPkmGEqSCqhxjSRGaykM/FXjRJBxAoBSMu7ArgLR/5FmYjfg5jGgjPJ+hS2E5MstoySzOwuOn6E/j3a4+n0J7ZR+JXai0EPqaA2659kCuOfD9v6ziGAHjStzihd5zp6yWr249mFrOYxSxm8c+FWZJmFvswt28MrzPC7w7pylaZGC4xtGMS1w1YsXouV37iQi59zwsBsBMWZjqNmcrQaMSEQMI5moQ1H9te9rxtu00fd6gcp2gkHBSCVgiqLYfdMkgMNWMPGssi6Eixed0Q0rYQpsXEyBhocB3QtqC9b3+yQ8nbRlVL6lYLZUqUbaBtGxFFBEFIo9pk+5M72fHMbqrTteft11/DuV1nQMtnetRidLfD7loH2rDQ2RQveeFRpObPwelrJ8gl2Frq54575uF5CnNiZjAkBaan6DaqmMRmkuHMLKOq1p5fUw+YCZORVpKahlENkw8dRiN0MDI2ZLPISLJp/XqGt47x4B+eYPeaPZjjDfypBubM7HbohqiWh5oqIoSMvXWaEU7lOekmCQeFIkrEHVhlx+tqywQN0gvZuW6IIIhwHIeDj5g3M1OsCW15QDoVuyJ0GbBthIBiTbJkfgehGZHcWUO3XJTWJNIO0jbxTYPfDC2jGaZJHd2GZVjYRLzri5fwiSffxBXvOpipyRSmsLCGprFGyqz9bhInEWHbigVyCF3/D+778Hxufs9rWfsfV7L54TjiN8xaMzsVz7iqySK6WMZXHqllFj29WUIpEek05DJMTudRQXy5OpN3YFsBF8x9kvX1LoqTNaJIUSnuV4Tl2tO84rJTeOkbTqK7vw3hhkgvRCdsnA6TC99yDpTPobnbes7MbEQ2rditigyfmqGto4nYm6DDUPQaEZmxceRocd/yquXuI/CUERNsz9gDmFbEmMrgl1SsBpsZ9wsRX09rsoVVaeFMuFB1UWWDuUrH0cgtn+0Le1FFjcpn4pQiKfm3y+bz+rOP4QNvOoy77szxhQ/ESoTd16YIywIdgt+RZrxW4Ni2Ehf07cDJNUksCGjfpBFfH0dvSxHZEGnF1PZ5tOakePzP3Xz2JffRWmXRysP2D62iviQPMzO7uZsnCM4zcdY+GXt8WNC2TeFEEr+QoPTClftVQoEfDwpDRZiU2EWNSmlkpcHmeg99UhOaKVAaYVv7kmI0Ao1kakMfwvJIbw/RVkyYmRNV9I4m3ro6KIVVUoQNm59cfxRNw0ClbLRlYgwJtBb4pQw9i6tIyyS1M/awaPQ71BdkaJwwLx6gQfxzahosi9t3d2FNNmhrryFRCCLsWhOjBTKE6pnzY/PWyRLOSInJxzswB+oUlhYxXDBbcaVJW76OsbxOanuVwlNVCENkPhsrxGTIx//lAX78+bXkuxR9p07wpW/fz5d/+CiLXgj1sTw6adL/cBNrfR2tNYQhrV4T04eWkSFc0I3KpqjP6aT8/k7qV2UhApoBslJDFGPVRGVphsaCHK3TPIzNU7Gp6rP3eBATC9oxQcC8I6do75/glkf62Dye4K6b43SnyNU4T/sk9laxJmIFQPmjDW4uzscQipa0EXkDI0pi18CqarpuqWDUPJzxOr5n4SQjwoE8mBbuiiTrvrIUW8beM+leLzaRnamh0t6MV5YtYKAP0dtNcEYX5kIBAUQWkAJDh1Bp7Vc6RIpoTmesThNglAOMsSrOzmmSG6fiWhUt8PMS3WyR3hwTE4YfxvffzPfLJGTn43mwBNgC2WcihESGGlVIovNp/EICfLCLIdKQoBUilUR19NB4uIldN1EbLAZ0FevJkNQD20muHyX1+G6yj47Qdu/uuDxq9xTO9imMYoMwimZqETX4EapUgUihTAP36IWQSqK15Om987lx/Uqm3BwlM0l9eZqw08GaaqINRTAnR9CfQydMwnwC6atYgZKOCTqzERF6BhMjBSodaTLDEXvfthK1Zwg1NIKeLqHLFVSpFBM32SSqvwO/P09ihcGDA508kurFrHroMEJMNvByKRonL0YvHETk2xDZLFppjGJj5p4T+66TDCNAxOe8q53UhEtm3RSJ6RBrqkE0XUJNFhHGTA2f68bJeZUqjE/NqKi8A8yD98Ey4tcqtTgJTUUIIUFJqFSQtTp6eAxRqc6082AgSK0vYRVdpOujd46Q2TSMpXSsKH0O9Exbr10X/CgmQVszaq+ZiSStJMPjBncUO3nnDcdz7GeeIMzZiJRFlDGRfkhTBdz7q4fQQpGgQTIRYFkRTSNEmQqj5TM1WaMUBVSjBnf85gm2PrGDe977a9avT9ItFE/dl+DY03cRRP91ItUsZjGLWczi/zxmSZpZ7MPJfYdy+Rn38M6z7uW4xYdx+QdfxNs/+hLSmQRCCAbmd1ItN3nozxvQrh/HBmtFbbgIgExdhGj/IcJa+Ve3L6SMZ6XCiNJElfJklWi6ghFKpK8gn0XkcwgFhx+3NI6R9TzGSiXe8aJnSM2zCOdIrrzod9yx4xiard1s9BKMBZItXoJA+0T1GiIIMH2B0ppcIculn72EN37iVXQOdPxD52Fhz2qkKWk1LT783pO47YE5kMshgaBWJ5qcRtaahGmbKG3jZ804QnpaIqJYzeA3DbY2u8lELag0mEnZRmZSB36YYSA6CzjpFMe/6nzubiV5zE1yxUevon3O49giB7UaRq1JItxBGISUJisoNFHSpOQHTJXqVEtNRraPg++Bjlj1ss/w9KajCKtx6oSxcxzCCNWRRQ12E7TFpQzC9YlMEF5A4IcgBI3hEubMDr/mijMhkYjJNaVpLevel2YCksgD4fsox2Eymsvy03p4wYcf4uSvriNyG+ixcTLZBG+48kzMQLCyfZp0LqT39CbSj/Cm4O13/oH3HX0B9XQCzUzZwfYhxOZdiLnxuEdIOKx3OZiLue/OkK986EbuvvkpUjNkU9VIMu+EOPFDALrZRJcrWCdqjn/jIPbWkdiANQoRjkPo+mxZlyXwBaFxPNtKc/jB9pNouhbLl3fTmYA5c/IHXKqjT1nOCWetJAhCZKhjv4OETfsciVc8FE2dA/y2TYtmPckDj60HW2OnI7Y+2k+3hh4hmNMlIJ9BGXLffstmgPDiWXDleyxKTxO0jLhE5FnTkH0ynfg/ZUhEKCBpwkSJhSfUaZoaYbfQu/aQ/vpu5j+zlSiTROj4/PorF6IjRd1MkSvYXPP12AA7ORITU9JT+AWHXbKDpG6x8eYF+C0Tf0pi7K3R/9A0R69Zy30vu578pKDv1mnatnts35Dl4nNPo3B+DlEzcNwkpfOXI1JJRFcn2fE0+bDOeT8Z5pZ/+z7fP+U63ATxBU4aZIZddC5WDCnLIjQipKe56c2/5IF3/YjX9j8KCYNC1WS14/OOZTuxdfydw7b3nR9tgj/Hwk7HJq1+VhA6EntrbDQsI43IZ0nurhAlIUzECS9uG7gFgdUAZdnc8pWjeWbVHFSzhdkssrp7BBwjVsikTFxz5sKZJnQUwDD4zydWoWsN1GiDg7qG6UrXOOgHz6CVRoTgzbGJTKgd3Y/IZsnsDPjJyX/iD8f/Dp0P8dsUUV4w0cxguBolINSxqS+9nTDQSePQDEd0jlDc45BfJ/DGkoiMBhtMFKK7QDjQxesvv50ubwKjrwfZ1UnPk0OECqJ20H0dkExhG22E0Uwam4xJMZnLYOTiAblUGqvYwvhTELffaETSIcqn9xtYOwlEb4qgFvGi167j+79bwbtfeSLDmzvR7TmipIHladAKw49JHlkO6bAbHDN3Mx2FCobTQJ7gE5ohET6ZtcMUXmghd0oSqQApNY2DciSWKE46cz0rr94Ehsdjnskm36C9v8n4Gw6mcvo8ZKoNp6KI5iRgfALKVexfTBFWJPhgBkBTYVkeg8s8ZG83oq8HZ44kPGg+7sL2OEJagzAEzSVdJGU7sgGyAarVwhgvI7XAMEyQBtpxED1d0N1JY1uTMJWJ2wilObl9B1YlwilrnAYYyiA97tM6SCIbPlHSQqcd/NAlmpiGXBppO9SHDMYLIWN/CpDSQEuJMV0nUddYyia9p463uJdwTifJTVN0/PIpEk8NEy3oRuVTMQEwOgaWSZiEIGyBHzCgR+l0K4gqdDxTI/3wGNZYI1b0zBCjMlSE7UmqJwzgzosNfmVP50zLH5dboRR2WSFyCVK7m2gVe+CEyscvl9CuTzRVRO0ext46Qf63T+43xhVg1Hz8Wp3AtBAtjTItpGEiZ77LsuEimjPt+j7lIoiEDX3dhCvn4fVlUJl4/3xHYGwfi8uZIgWOBZ4HfoDKJWOSqS2/v/n095vVzxzYfkMygGodihXU+CT76odm7nlRru5fLlLk794V+xFpSJU9ZFcnJGOT6edOywjbIkgmUFLC3hGYKqJLRTBM1LNR8AjW/snmV+cMEnykwZ3v7mLajJAJG6uocfZq0s80KY6VSQjBIakmH73sbj7+tjvJ+TbOaB17rEFl5zPsCCVr/S4a2kZIwcj2ccb+lMUZNUh1SXoPcZmqPc0sZjGLWczinwv/9CTNZz7zGY466iiy2Szd3d1ccMEFbN68+YBltNZcc8019Pf3k0wmOfXUU1m/fv0By3iexzvf+U46OztJp9Ocf/75DA0N/Z88lH96CO8B3tW/hbf2bkHoFguW9TFnQRcQn+PNz+zl2q/cyk+/fRdbNowid44itw3TaO4v2fhbvi+GY6JyCchlEdk0u3YVaUxXUJaFUk1oz8adlalpTD+i2fCQUoBjk8Tn8MxTnH3cCvyeiK78CKvsCmvGXoojNOv9DH0yjl61IgGpJGKiSOiF+F7Ar774B37xhd9RLf5jShqtNF0D3eDY1PpzpF2gVKEj4VD3NS4G2BbKiNXsfiGBn0/g1TRm1cWqeOwd7WTTzgH2FDtAC5YevQAAuXgQlswFy4qjuKMI3WwR5LIUwwEery3g4dpiNk1dwJ27jiKZ20+JtKlYhq9aPl5PhqAzxXQYsH7XBEoptq7dBu1tnHhZhXGrxYjjYdPCfHJb3NkNFaLSwKz7WMVYmu9mJfZ4A9kMaByaR1kSH1Azsu+UYyPCAJotrGKN1EgFOTN4Q0pKZ8TlRtr0GTPnEmXX8sa+7Vw6dxv9R7aQHQUs2+KVl5+DKDn86LvL+MZDq7nrez1MrQ+4a3wJm8UIl/zpP3njR59k+Rl7kUJAVwd0FMjOBUNEmAQs7zgekXo19WoftdEphNZEQoJhsKHaS+oMIx6oG8Y+g8clL/S5/jd30do2hp8y0Y4Vd8INg/ddsIArX30aD7tHcWrXZt658BGO79zMPb98mCdvXctDf3gCgNtvWsPn3vMzRnZPATA0k7CkkhZ66Rwuen+FchygweAid/+NFCkSi/roWZ4ha7boTtaYNFPs3NtBvdqHaqURlQbPMngCMDcP4Yw3kENTmMLlYKuBIzTzzBIMxoaWzzUOpuUh9kwRpUyiKMIuNxh9JknBiTBoIYQgGDMYMG1y9biMDcuEthQim0GaaR67b2Af72MHcfqL1IKLz3iID7z0Xu77fT+3/CoDRYU7bCDcCKFhao3ko984m457xjGf2ELm1nWgFM1GgvQik3ef8QCJiiJb9tCWAc0Guuly6eH3MK67aTfhoH6flbc8hFi7NS7hUALpxSlXUVqgEhaNLoPr/7gURyhOOngvKm2ya303Ck3gE6uPoigmEAwoXnAI7jHLsO7zMSpFJOCsGyexbjieGX/2ez4+hb1xiNTuGoliRMdwK9a9SEGyEpLZWSOxYRx7o4UamyTTBu/94ONg+PHFDjStFd1YfXEpEfU6Kp/C+0FsNFoL8siPBPRfsYn6RpNWX0Rzbog2NJYHflYSZi0SgaDfaJKUETnqxI9lgVeziO4ugGVghGCYNlFCUl+QIXvbKJ+/ahFT1QS1ss3I4x1cfekyrvjckaw18qjI58s/vJ1LXlTjFR+YAEOCbfG51z7Ob8+4jhtf/mPsZ/Yii2Xk3nG0UpiRwqiGSCUI+jKoVKwACHoyqEIWd3kB3W1BZyeiox0p4nZQA7pah2LI6KY8GxodfObT9zL36+NEzQbUmxjVEFUsofaOoGYGt2YiZH5ygp0TPTRbCboPUxx00hCJB7fhrNnJ4ssa7O0ZxFicof5klvraHIkRgftknW0nhFzyzBaiyGKeEbHUilj6pQS6PUVzZQGVNvAzMi5xDUJoNJCtAEYjCOFZu9hWxWDvMzIm0AyJSPZh1gP8+e2483JxiV1bEtpT6K4cVtXArBuEbYmYTM6kUbUG2hA0BjMQBIhqnVS/y7zDJkk0AnrcGmqHAaYgTMftlTKhuchE5mNPK+1YhAkj9ljxFUZgEaVM3N4k0XieaHEvRn8v5rxBpGEhLBNhGgjLwR9sQ83vxpysY7ghsulRX9JG4+SliJ5uRG8PlrDI37cnnihpNcimQhb01GgZFhP5LOmnJ5DSwsimSU8qkkVoHT2fxokLMHDQQYDW+/kLPy3J9DZYcd4O+vJFhGGinP1KF+2YSDWjGsnZKKFhbAIihfuDXez5ZjtTd+YwpisYzQb2yBS+4yPHJ9DbdqLHxhCNFlTr+z5TWwbuiu64JEiDnppG7B5DCpPycpPpgx2sRzfH5s0zZD9NLy7xVRGUa8hclmeLUcVzVaHPQhhxidNfvgwwNnnAa8/lcjQgIo3OpyAZP9tVsRg/472AZ0O5NeAXUngL2/Gqpf3bVxA5EmnK2PwauPETS9HPNleeoPi6NCVbEvakUFmHYF4OFUZobNqMBAVbYadO5/NvehF2oDjllB1c/IrPodx7icImUSCwbBMVKNY/mmbX+gT1psZKK8Z2jz//XPwdKFVGKfX3F5zFLGYxi1n838I/PUlzzz338Pa3v52HH36Y22+/nTAMOfvss2k09pcifP7zn+dLX/oSX//613nsscfo7e3lrLPOovacJIIrr7ySm266iZ/97Gfcf//91Ot1XvSiFxE9WwYyC+reWlwNdQVR5B3w3sa1e/j+5//IxEiZhcv7WLSiD1loR/Z00pgo/91tO45Fbk4bODYim6EyMo0bAFqT63LQrfq+ZYWr2LVlDJFJIgwDO2lzbNKlKjwCochKjSE0ebOd87INLs6VSY8dHnckXQ9drSOGi0STNaIwwmt6BF5AFPxj19owDV5y9Utgbi+tBXn0VB1SSZTn89Cdm3BNh727qwgH3HbIT8eyae1FBG1JwryDHkthbkhQ25tBGIJzzz8MAEuBSKf2+TxgGLFPT3GYlcG32NboZszNMVEvg5fAGmhANgO5HOXJ53SIZhRJhIqBjhwaGBmuQ7HEo/9pUxCKXAByvIUMo/2SbtcHP8ScijuIYZdDfUGK5twkKmmy91iNkuDNeM8kswl0swWux9UfvxCJiUzYhAcvxD/uIKrHd6AB99gsw80KY2u6qXkmQUtS3Z5Ei+c0MZaB+5/jrH9jE3HvOHgJMARzB8a57Lh7ua25gu335FFpKyYSpKR3YZ35mWkWZaZI1v6Arn6WrtTt+NNlijtHabUCUBFpO+C0kyehvwedsONOs4DdmQJysebct5yJEQQIN4h9PYIQ1VYgaOV46OmfkYo0T7a6YhPabIYwncaVFiO7p/j9DQ+xe9s4Q7tikmZk7zRGxcWoekQJC5mIeKTVgdZw6kurMySKovudEd/43ZG0teewjZBWaKE6PT72/dP54ehKHj81QBmSqCO37xQFXh3QmJHg8JXDRIYGoTE3uciRIlSrcUkKMzPBA30YuSxewmRqeRL1AnCrEisy+Op1L0SkHexERHGLR6HTQ0gFQYjx5Gb8iSmiSZ/JZIObH9ofTU8mBakEx/Tt5qXLtvLSE8eozk+z9JAJUo/u2l/O1QrwAdsdQy1diFq9jMiWOB2a1zUf5cz5u1m1cw9ta1wIPVQYolXE7z/dDzpgTQtCrWn6CZx6QMctW1EpF7wg9uaYbiK0pDmgual3Ma9Yfwx3Vtvof8l2bhlaxEu/+zIuee0RRGFc4rZs5RSr3m+RHgkJs1lyuz0yv4agLsmsmyC9dgJr2qN/gcvSo+IoXxEqZN4ld+kwQkN6zCU97mE0FaXz49I1c7yCMW8Ovpnhm/+5mpSoEoqI2gJN7dx+TntxGSyJzKf57s9uxjirG1ouotqgORQycdhcxi+Zi3A1zpSBMxkP+uZ+5WG0AD9pcM3bDuO7X1yOu1GDiNAoqos0C47fQ9/83YgZU1oxWUGGAtVms2b7XL7xtaMQoUZoQXMkQzDR4LRlm5DVSXQYD0dPuWiaN717LWdcvJHylM2/nreK960+GhotCENEuY7hWOiCpHEUjJzfjnJivyAA3ZbBTuXI1Rx834jVNhNTiErtQCNhx0ZkCxw5r4KjINwhEe0RknCmvGSGMGnG5UV6LEPJS9CcSlFzk/g1B6MpsUoe1kiVzcZyko8pzLttBn5RZ+A3TaxywIwxDJ+9Yj6fGz2cba08W70cN7eWkzHKoBStboPqIkFrThYK7ZBJoxzBAZIGBEQSqg30xCS61kDbJiIIST8xijMUP5NUxsYMBVFColEIJYg6nZj4qtcRYYQIIqQCp79KfWmB1315K8uWjKMdSHZGnHbhLhYNDBM6QZw0lpWI1dCecsE0MWoexsbdGFUX0dGGaAREKRuj5ROlNZEl0KaMCV3LitUugJ8Aw9d4bZLG8fNpLe2iduI85Ngk9rZxtOehR8ehVodiFTM0kF0FSDpM+ymsqsTwbZQA7fsoCX7OQFlxsp0zFpJ9bIzsUxNotwWlMgCmq0jmPaxESKqzQeaenfH7He1xWp1lIrRGpyymL1xG5cy5RJmY9BNConY0oBUiGi7C9ZHpDG07A9Lrp+J7RBHX/PkBGJKorwf38HmYxSbasdDpBIQRRrGGPVZHqpioNYOIoOAwdcEcmsvzM3FlsQhGaGJS8dlypvCv9AeiiLhjMgPbgNSMj9xfIXWeJVTEzO864RBaZlyqGYRxeRWgk9a+daJ0AhFFqM797T5SImsuqlKPi7oEkLAxMh1UD8oRdWh6vHHEB2tYe8tYYw2cjUW++75r+fmnP/bszkLiVA5d1E9OK864agumoWiXLjmjxIL5knQ+RfuCHg46OmL+ipBCv4udUmx9ZsPzz8V/gbB8DXriWKLpV/631pvFLGYxi1n843h+zvI/GW655ZYD/v7Rj35Ed3c3a9as4eSTT0ZrzVe+8hU+9KEPceGFFwLw4x//mJ6eHm644QYuv/xyKpUKP/jBD/jJT37CmWeeCcD111/P4OAgd9xxB+ecc87/8eP6Z0TOWc2EezMCQd5oP+C9ju4cmWySZYfM4VVvPZ0gCFFDw/EslTjx725bAumyC40myvUo9RaImi3wAg5JdfOUGuZZpwOtFUefupxMwqIWakToYRk9VD0XheZLG97Fv5/YwSqzGypXIIRi956zEfnH4g+r15H1AM+W2AmLrsEOasUGTsr5h8/F8iPmz0zsa1TTQwYRODmeeHAbWmuadZf7fhV3jkZHi7zv0h8ytnUM5negDUlmb0B2R0gk4xnbQ49aBIAlJUYrRLdliKrNfV4xbbkWffYWFqRWUanYvPWPL0couGigiLBLaNPkmZEJWoVOJt+yCjQUHnWZ35tjTleWjVLgt1qIni5CLViebMMQDxKNTh9wXCJS8eCiEEu+03tCSof6hNMpxByXTI9GP2py1Rt/wLd/8Q7S+dS+gc32tbvRDRcMg73nZgjbHdIzpezeqztpVDy27HQZKqcYSlqc9y+7+OVnlu77bJ2A+sFdtK2JZ+26XuhSv7PM1R98iLOyNXT3Tl4cnoEUDt4hSaw/buGUQ0bZmNa0yyaZtoXo5qMMj+Yo1UfYtnkbn/jJH/CbPm8rHk/7YJ1Gd5LkrplBoIZAOuT665zwkqO49t4tCNtGWBZaaKjXednbqqxYGXDjmgJnHT7EeNhOPZtC5nIoBI/du5lapcWqI+Zz+PFLABjbOhErMrwQjCzXTc7n31ZvYquf4tbquQxd3YaRCSiub/LbbwSc9eajuXfvEyTMgKgj4rsffS8v+Nevkrt5N7LWQtk5lCHihJh8BmdlmdZjSTY8ZNM9mqE1lWLH1i669Gh8XFYct41StA6ODYEjC2xhsOPYozh67f2sW5chmWxHN1x8DeUpk5NfUeSXXxsAK4GoNTEANT7Bg9ekeayti8t/BEd8qcHDF5mILCQKKcLQRWto7vXZNL8dkSnH9xHQvdpjus1h3lUBO74UodwQkUnTlx9hznyfq8+Zj1hgIlshoRDISKNTSYZH5zD63gmuuffQeEMpByTI9jwLDxln9GGF1gI5E0dLKg4aak8r2qTP9ukBhDQYn3LIbzUx8DE74a3fn2DdXnj89kXoGVNjnbAh00QGURwfXXA49qcTbGp2s/LrJZ58fDHVJUkmHu6j392DuTfCX5in1WvgZwzMUp2+kyeZvilJ3ZA89PRinGod91yFnwKJ4Bcjh1HINFBjE9idmoP27mGHEOBYaMvE0W2sfN1ObGOMY6LtbPhqlsNumOIHFy9GbtzOoSdPUdprcc+2Do64ZJQ7UznQGjOjeMsJ99NcAl+7KBufd1OifJ/aC1cQSs2ceyfRoQIVoYoh1XvgMd2LSBq84+yTqR8yQKKuiHIOZt3j9+UGiXA8Pu/5LKJaR6QSVF9jkHwMxCEatyqZODxN2osHpbLZRHoK4YbYZQ/lV2O1YsvbJycQEJM+jRZPXZtndx8Uvx8ilyfoPiFi7JfsNx1XNqK3G5FwWFPJ0eto/DaB3e4zdX0bRo9ETUzDAxUskghDEvotzF3TiFo8OaNnPnNodxdveGo5MqkJqxbL796De1ia+jwDiSacY4Lrgu/HSoeZtVPpiJZrxybIzea+AbV2bEIrhdEKEK0QqcCs1LF2NzCnW4goi0oqZGRhZDKxKsgE0Z7HcH0W9NYoTvdxRE+J5p4CVkNRTxrcvbCNgbYSa9Z1Y08b2Nsi/D1J6jpJuLhMezkmNQg85LiPmj8XoU0EJumn6pSW5RBSkN5SjomHIECbFgQeykpCEBF1pqEZYFRdkmvHMJpR3EbpmeMWoAIP4dVJ6hJFvQihACNWBlluC1FzsdbuxF3VG5fCjVfxu5NIN8DcOc78g2OlYJCA6vZ2vJqJdbuH7vLJPl2BYtw+uEu6MTtTiFwb+QfHiXIOMldA1ceQSoEbICcrqM48oljDsiyspiZIJqAxQ5KYZlwKmk8jlI9oBAhfoS0Tb3kPtm1gEJP52XV1ckNj6IEu3Plp6oc6RGmTxJYK2gYRge5sw5x5LmCZ8V307MRNLg3VmXvL2z9BJZSI1TiOFd902QS0vHiShQMqoNBtGXQ2hTlWQra3IfrbUC2XKPBwF/eQenInQoNhOrjSIJF0UJYR+zrNKFIkse2R0AJMC7Qgv7mKjkBNjVP8ynyC78/BvzsLQvCRy9Zy2oXPoLRgayDQ059mmXMetZrLFmXRHggGzIgFwuBhpwttQKXq88Dd7XT2aRKDJVKOz9O3jPDE3Kc4/LRD+Ufg+49ja0UYbf3nH0TMYhazmMX/UPyPa18rlQoAhUI887tz507GxsY4++yz9y3jOA6nnHIKDz74IJdffjlr1qwhCIIDlunv72fVqlU8+OCDf5Wk8TwP7zkP62p1JnoyCAiC4HnL/zPj2f39u/vtnEuX8zSYA0TKQj1n+UJ3hg/+x6v3bUcphT3YjQ4VoyPlv7vtRq3FrvV7sHsLcUypbSPqNUgnSJgmp7zyeO775SNoIDGnjVTGRtcqGF6EO6HQxgk0XBdHSOpBikzifALlg/liEClOfNEL+PqH7wchMBxjxkRY06y3GNo2QhhEFMeLWInuf+iczZnXgek1yU+HONkk+D5GJolhaIRhoJXad8zXfe73lPcWSSQsMABLIJsRRt0HLTBzSdI5hyAISKZMLMcgu3IB9Z17CYrxbO3hJ1cIlMOlyWnesu5kjj9qB8PFdlLVBTjdGh0pXvyaKb758FEMPFHHHq5hnjWf6z73Fn7y9TtoTk+jI4UZ+HTP6yLd8UWUUhjhmzGeM4sHIAihMyZpMtN1zHsjXvqFx5lyU/znnUfQrcqMjJbi/c0lsGbWnxqaxNIhRCGdT7TINELE0BT+0hRRM0dYM9G2YKOZZa5RYvJ4SSKfIggCHhvfjllQVF83n+yRCVbN28uC88o0XtVDrzCJVEygnXR6mUce6EA3JGbKojBuc8nBO5FVm2Z4Inb2NJ558BsEE1MkjK1kExEiYXCaV6QDA/fINtqeifdXCXBbGQ7v3kzfYB+JagMjl8ZU7Rx1/BhX/Ms6SmmBEjDYXM6WO0/n4te8kR+KTRgWNJsufYk2QNHV7xCGARrF5vV7sZMW2kggTIMrVh5HRj4MCN606hLuvPlBhuoN+taXuWfrBu7dsAvjSgOtBUsXTtFWyNJxxxDpeR7uBon2PcQHFtB6xME7Q3LiirXcvWYeQSvJ2lu7UUst0mWwEgbCB8wkSBMaDaLte7D6uwi7khiRwDYEH/vqTgTwzDPD2AmJigyKNZvf3diF6AblODhFEUuNdGz0YCXjR8EFy8dZ8kSePrPOx755CHMrU4zcatCuXUo6Q3t1FDNpIbIwNtFNa1eKqcEF6E6Hjg8XmZufoveeKnc9MIdVy3ze8LEHuPN38/ntT9ohkAQdbZAUaN2Jld6CQiOtWB2A22Ty9xnMZOzrgAG9W5oEZgo9xyB0TWp2mgX5IsNWnog0badFeHdamH2CD110BIe9VLJ6wW6CrMFmr5+TTltP99lNHns6RaNuUpNZbhk9khO6R3nipYtJ31vCMwpk75vAsMCKPEpna0LLJKMaHPSyMoWE5vH2TnQYYvohKIOuzgru1g7SU9Ca24W1RSGCBAkcPvT1Z3jrkYcR9rYjlSKyE7x7wXpyRsht5X4u+eIYVSLa+uDK725meijB1jVpTnr9OoKhLh6uLkEKTSAEe59YwoJFe7FSFiiFTjm0uQJZ1bgFidFogushwhAjaaDrFsZDPkKBQiMqLQzHxnp6F8Iw8eZ2YObTiJmKENozuMs7qfZmKb/YxDR9jvzGY0wtWETKDeHMZXTsqhN051DYINuwWrGHFXO7EaOT6KQVm1gLAUqz4748hS4T3UxgPR1HeNvJGRUFQHcOYZhoz6dZ6kCIJqaUOKYg2RvhJQ3CtKT/FVXGftsfl78NpFBT47QdZlDbIdC+RDgWbj1FumoTWGAHUF04l3RoIBMRSSSybGPlkuCbaK8JIVx501aSc0Lu/mQvj9yZRxuJWFGUSGLUk2inAikz9sKKwAhBTpQQ40USYRc6NNGWxpAa0dOOYUpIpQksj62P58klxqnsSZMQkqQUYAlSIqJRSWGkbNzDTdp3BYR9CSxbEK5qI3VHmaivgAgVBBGyHGBMl2is7iXM5mmf0GTHW0hCaM9gpOKS00TSwdeKXKVJmM3Qf1SL3Q/YJDs6MDpMVKkMrRZ6xqzeCiJohWzb2EEUeTjzBUpp0jUfwzbQfgsZaWS5hfY1drWKGbn4hwySKpU467NTUIeFS+psmWrHncoTLolQliRz/07MjB1f5ZPbmXN6ienfRhh/cpFlHx2FkE8i/BAraKFVSJRKIpbMRQQ+UmqqJw9gBxaZnQ2EHxH0dSInJyD0MQOFyMbXShkRcnEfYkbJZW2YQPa0oQY7MCOBfGCK5PY6VsomSgr8wRypRhLhtjBsE522IZEgWNIN0w2siUpcpjQDPVMvKwCyqZgYM2M1G/k0hCFhpYbx7D0NWJYZe9wkLTAiqFcxDAOzLYcZSPTqhWjHxMymsG2JyNuYhTT4LtLVB8Z0F9oQ+Qy62kQ4VkwsTcHS/CgH56a4f28PwX02K94XUMZBe5p7d/UgzZBC9nbkQI7vbziZyQXrCGoF3r36Qk64aBEP3v49zFqNBUtcFqyo0Wqa2KMW5d0RH37R5zj+JUfy/h+/8+/2j3zjZRRb3wJjKYV/tH85i/9f4B8eb/y/iH/mfZvFLJ4LofVzK2v/uaG15iUveQmlUon77rsPgAcffJATTjiB4eFh+vv79y37lre8hd27d3Prrbdyww038MY3vvEA0gXg7LPPZsGCBXznO9953mddc801fPzjH3/e6zfccAOp1PNrlmcxi1nMYhazmMUsZjGLWcxiFv+caDabXHzxxVQqFXK53N9f4X8QqtUq+Xye97/pWzh28u+v8P8wPL/F5374tv9Pnuv/E/gfpaR5xzvewdNPP83999//vPf+0rBWa/03TWz/kWU+8IEPcNVVV+37u1qtMjg4yNlnn/0/7kYLgoDbb7+ds846C8uy/v4KfwMTo2Wu//rtDO8q8uq3nspnrvoZGlh+zHw+/fXX/5frNhsurz/lM/jFMiQTUG+gwxAhJSdccATv+9qlXPCVf6dr3hi77urlo+eeww8+/itKU1WkUty0/T8459vXMlVtsHKgi+suueivfs5LB9+CagVopRh8xYl89Rtv4v3nfJJmpcHHfv1eeuZ2/cPHe/6qqwhtK04lcX3yC3uobBsFIXjrNS/lha87dd+yLznkA4BGOIm4jKTlxrOY3V1IKblpTUz4XfPmH7D2rnXIhCIaKu9b/6bij5Az9fNf/re3sPXxHCedv5szz/0Wlx36fkDzwtcO87L3THHZMcdAvQHpJL8d+jbf/bdfcfMP7iE4eB7YJma5yW/u+FeEEPzqG7dw/Sd/DWFAx0AnVTuN0gI7YfC6Dx7BD99/F6E2SLanqGowZtwNP/aVV3HE0XGp0kva4mvbPb+T6YkqUahRA90E8zqwy03Eq+cyNF0DqUnuVfz7y27kyJV1lIJLX3A6P75b897tFUaqeUCzsmOaBpDC5bWFEW6eOpS3rbycu7ZdzXho8vvXDdBsJKHuceSLfLZt6yGd7eJrN70bw5C89oj3Uyu5rDqjzmvetZ2pSbDm+jzqd6ERLDTfxod/twltSgwj5JWnPMCLOq/iKy+4ka1z87RvryMknHTcNq744ut5evtTfOS3Gd5w7l3MG0jyxLWv4t6b13Pw0QvoyjucdNFXKGYi0kpy2IIHefM5X2ByaAphWUT5JEd8toidGyLQJr1/ejW3Te7i9OMP4ooLTmeqWOOiy3/IqR9+iJztMVbN8c7CZ3nCv4zfXJRhathhYGkX33z0iwBs37yLq17wZcJCEmfvMEOvX4w7P0XbLaO0PVFDNPx9pR6YBhTyBIu7qXUmOMwy+eoP9s+CPvzUK/jOqyTTE0n0wgHEzhEIFRixLwQnLEBub4AXYNQrvOkHL+ObP9/GESt20VhhsvWy6dgfQsRlQyII0ZHaJ/H3FnfgnjyP3LwKI57m+otuZkjHj5S5gcW3JpfxxOgghw/sZufrBHbTJdEpKfYchHYMRNNHVgP02Diy0cCYNxcVBOih0ThGuZDHSCRQE5NEhyymfU4D69wWhw1M8vMnDkWsdSn8aS/5dI2XXjbJca8p89Rei/u+sIj7wh5qL+nHGoZXn3s329/lUJkyGMn3M+81ASesGOOp31o8cdAh9Fklrj3vdt544TG4W934HHUW0K0mUTaFkclgEs/Ye1mJHJlGaknx9EGMl1QwLYW+cpTMZMh/Pvk0Tz+WZPtUFxv+ZLL+z2no62D0Q12QAFqS2477OZl0LDxpuvCqT72YaKfF6z+0jhMWDvP6R86gLm2i0CClPcw/2WQfmcBsy6MbLbycRXl5Ozpnk3xygvR4A7PuQsJGex7N5QP4bkDHxE4m/2UBiftKpG+roi0TUglEucaKFxpsvjtCNTTunCzFfxtAa0FYhK4fF0ntGMdOJHjj11/CpuDX/PlLczCGy5iVacJQEh61hLbyOI1t1TjqnZlIeA2YAm+ggD1cjMvygMwCm6lDbMRISHBCnsLPRwhGJa0um4m3HIqMgBAGfj6CEQSoUpna8h7yRRX78SjFng/YBJOS+R/aggAWHtEgdXWKIZlj4vuLAUEooHiYQcfKcTp+WqBWDbGGyiAFfek9vOVftxGsTDDXavKTTxZYc08bmaMsJm6zEOk0Mp9DmE0afghKE80xcNuyuIvbSWyYQroG7qHdiABSW+qkduwF7eJ29aJSDukndiNaIcI28ecXMM7qxNk+hHigjCsdSmcO0n7LbgwPii/uob6qD93jsvA/Wkwe6VP4zRQkk6ieHExXoOkSzcuSs5O09pTR01XIpTEKBZCS6kF5nFGXMC9p9djg+dhVSD2+F5lMxeVOKYfQa2CWmtCeh0ghbJPR8/tRGQe7pOn68yi6WEEbmvKpizGFJPHIdmw83IULwfNI7tjF0ustTilfwA9+8ThtB1U55vgt7N7VwRP/XsDxDYSUtPUV6Vs9TfnPFoalGG4tQJs2tYUOqW0ux71pO4OrSjx050p2P9SB2QxQYYD0FVGzhpJgFDriEtC6F6dzRTpWsUQaTAH9XRhOJlZASUmt38TrSmHVAtJPjsbPUreFxoT2LDqfjlOa/BbGxIEBAsEJB2E9MOPJYhn7S6CIvWTCQxfHpWICrA1DyHItboAzGQg9aAX8VzOdz34tnkXz8EWIQh5z0y6s4eK+N0VHG0yX4z8liFwekc2gi0VotNBAbo7LVX/YzhQGAs19H+xl2cvK/OLSBfu2v/AXadZ8Moec24m1eZTU1thLLThkgIN7ethy79N09li87ztLsXgItz7GT7/SzdoHMnzk1+/lyNNX/RdHEyPynyKq/Qdj/ly6Cx/gzj//+X+5fzmL/2/gf9d44/9JPFsZMYtZ/LPjfwxJ8853vpPf/e533HvvvcyZM2ff6729vQCMjY3R19e37/WJiQl6enr2LeP7PqVSifb29gOWOf744//q5zmOg+M838PEsqx/2obn7+F/Zd+3rh/m+5//I3t3ThCFCtu2qadMRKCZtvXf3a5lhni+JvBVrB8PNfgRUdJi4bJBLMvisrMeYHlujOuay1HhWUT5HGHZRyfi/a43A/yWJgoFlmWhg42gG2AdsY9s84pu7Lmi4+M1DINGuYnX8GmVXaxF//jxz53Xw6bHtxMsG8TwFW9673n8++u/BZFiyepFBxzze/79lXzhPTdiyyJuPYtyA3QyG3c8A3/fspZtoRJptAwIWrHk0s6nDrjXquOrmZoYYu19R/Gay3sIqnEE6Zb1Bk3hEkyU42N069QrLkpbCMNErh8Cx+GYy07hp2tfwal9O7n99nMxOrtYMr+Nz/7ufVx45DV4I+NEM9HVka/way3MOR2E03VCwwCtOfaE/THqQaTBDxkfrrLgiIVs3TQJE1WM0TI4Fqe3ncx3Sk8iXIFdVXzp8iV86EvPMD6a5vgXH4SlvkiVoym0lxAoShJMEVLSDk+0lvLmQz+FFJql7ROcbAYM/qjMZ05ZCobBygt3su1LBbScxnFshBBUpppEfkS4PMJo8yiKHJ3NOv2ZaRQWJw2uwnC24ZUVoW1wcu+r2PazCm7VpZHtIjUcp3TcvifHk0f5vHy55MJjH6ejb4i6Erj+Znw3olHxeet7zuXOX4xw8Ituo1V9CQ/v3MTUznF8KdGlErJUJ50dZUQ5sQfGcpOnCgHPFJ/mLZzO6993LZEfkpANmsqgKzXJY7c8ybmXv49rx79O0ApQATSqHt/+9O+587q7CRsNXvaldbQd0uL++yT3PbYIdjWJnAy4dVS9EZM0NjBSZPqMBRBGLJ0//4B7cqqUYXxHC/BhrAbNAB0ptBSYA330u1sZVYPowEe1Yo+FpgF/3jmfN590LBtq/wGZFFGjSaI/SyHRyXglQE9OI1wPlRdgBhSrNnKPxZCKSFkBBoqm1Ny7ax7SUdy7ZR4L5k2RL1U577UjdB2yiWveejzm8DSRUtCTxxkIY7/QVoie+V6QAZ20iZSJb1qM782irm1j+q2CVFuFnD9Bs+EjjggZfFWV31XaefH8Ee7O1zCsLtJdddSUxc++dwa5zU+RCuuIySJj34v41bBF/T2LqO5JU1Np9hQTXP2BdVzzxmWITBZdrKGTCSJpovdOojs74vbFNJDpPIaUqHqFRLLOvEyFoc+l8C6uccFrX8S/fP5ezj9uO2eeK3j9UacQmgnk7z2CToEvTE566GISkYFrCRbnysiKie9qfn3DQra/MIN1uyY8NwAjRNkejhMxvrqL/CMT2GGE9BN03DPC2DkDNFZ2UDmojb6btkHNhVQC88ldWI0Wi06tURpVeB0p7KhElHR4wctHCfY0cP0e2tp9xqdg+sg0dc8EAVauQfdrJxj7hMSeGT3e+2gP2vHpmBNy9Zc389Ov9bCtdwPVZwyUq+OoYwDbAj8esNaTkOpoQ+6dAhTlXQKlQmQtQs8xUSogcCWmH5LYHuKUobgIImGgkzbKsLGGmrSyKUhbaAWVlkXb5gmCVsDcxS3edOUwxiKDb1WXsjfQCDSBCaqombgjywXz9/DHh9sJWgG61aIpBV+PjuTh/1xEdm6JwvUbOerl07z446N85JYjkFjIEIhMwnKVoD+Pf0gSvb6BF6YxhUn7tp00F3WCBtc2CU42sZ6S2A/vAjShAm1KRHcXOkoSbtDYT5RpTUcE2ZBab4qkArsZYD1ao3V8G/bGiLZThhh8QYNtvysgDAs1UgUvoLGii6A/hy65iFaEthwiw8QwDQw/whWgHANnxMPYPYFseLiLuzEVWNqEhI0amUA3mgSASqUxOwooAZ7S6FBDpAncCC0kqlzFuncLrSMHaR4/j75dmwhHyxD4REmb5jjggOtLvEkLpQK8PQaqpQmaLiLhUNqi6DnfIMhkSHT56DXjeE0Hc0JS6bSYn6yyerDKIxkft+FDLolRBWkF6IoLrocu+agoQpgm1L34eZdO4Sc0Vs0HV2HKCOGFYBiYd+6gdc5yWraB0ZHFKNWw61WCyMA6TBIOG3g97TSzSaJjByj8/Jk4RVAAdzyFDwhDQuvAMgjdCohKLlE+gbVmO1G9QSRE7GHT9EHpfetoIDIlMlT85dTfPmId0E/vJDh0Ic1lA+SmXUQxLuFnvAzPlmEIoM2ASgOsJMgAGi2KWw0mZMA82WKRDck3DfHp85bHbTwC2gRXrXiAl/ddQv6XT9A692C89zrY7ypib5ugku0k4TgcftwQGesZBpafjUi8jZcaBT5+82FxydY/gPWl37NmcpTvrhukzfoZV1hd/6P7xrP4349/5vvhn3W/ZjGLv8Q/fbqT1pp3vOMd/PrXv+bOO+9kwYIFB7y/YMECent7uf322/e95vs+99xzzz4C5ogjjsCyrAOWGR0dZd26dX+TpJnFgdBKMz1ZxW34hEHE6N5pGsckqC9KYp3Y+rvrixmViFloj80qPR9MAzOCl739LABWtB9MVJd4t7ioUCFDDWkHQ0EURahqiNkCJ5SocBo9fQm69FZ0uH3/B0nA9yGfY8uN9yKlxGt4uA0Xt+X9lT372/jGzf/K4nntmGu2curKTnoKGbTSaCniDt5zcPp5R/GnjZ9FKlCVWlxDPjSCrjeQxv5lM/kkqlxF1XycZfOwl8zn337z/gO29arLz2fRisWc96pz96lrAHbu7eIbYwdjpBxkXy9yoJf3Xfx1tm4eQ7TlWXTEEl75jrM5emWBee3jJGRIec8UQdOlVW1gWgZ6JlVqnzliLovIpTE8xS2PXsPJJy3juze87YD9MUwDclnChotfrEGxCK04lQTX49LzjqVzjaZjvcJqwEhPJz2HtVh9/iSXvus6ALaWuphspdhW7sXEI9SCNiF55crrSDudmFYnvWaAI2BxuoVIJNCmwVFzalz9vsfJpMr7iLjXXv0CtIbd4wafHl7NL0qLuPmag7kwJzjXSbDz3hKdKpYqCFfSsetwbvzKzUyPlEg9uiseTCYSRH7AxMRWXrLip7z1yKdYZUT0SQ1GBoB6pUHfgh5qk1386Ipj+fb7S/z4K7cRSAHVOrRnSQx0cmh2ijnWNEelxljHdghBBZqbfrOG8U4f01ckvJC81SJruSTa8nj1o/BasX/D9EiFDY9tozhVQyQcCAJWHlFkebLBFmsO7rwkppEEP4xnVg9bFg+f/QCvI4kMNCi44n3HMzZ8HDuGz+NN532OH72nn31Dg1IZhERIgcxmiAzJRFfbTO63uW+xxNMjPPLDq7n0lOP3RcSLhMO81x3DC99yDmYyGatwgGAypFU3CTYoOp6q8+N3H826Zp4v7jiJP1Xm8vqD1/LZw+9hZWKUxv2TDK8zuOPOPr76+kGs3eOIhINsyyOaEZHngBBxVK5hgGPHhqfNZjzCabZQjglNj1cOrsFqc4lWmBB4DD+S5AvfPJhikOfKbadx7gdHmP86F9fQWPM8tBAQRHhNE8v1UZsjdB3Ce2tkd4dkd4d859ajKU4n0QJ0pRIbeYbBPtNQVaujqjVUQhJmLExfUXhas7JtnLpn09vRQq1agG5JmmnBdCTZFTiogXYaqzrR+Txa5DBkhpRrgCU4fO4E113+O374kZuwNm2jfG/Ag58YwHgSujJVsimX/myJ1eIRuu/YgaM0or0N7Rj4mQTW/cOENkjDJOxrh3Sa1qIC81dUSSZ9Nt6bxLq9hb1RoB0HwzToT0yxY32StDNKy4uvY9stk0SBIAol58x9ipeduQFyNnLmOnt3B3QkJsmvamHamvaegMYdAkOpA3OI/XiAKYDERAUxMg2ORXNZJ7ghdiOgYLgclB6n59KQ/EqPnssijAYoU+BUobLIIMg5qFwCkUqDbSIbPmbTJ7lRUptfQBMbYdfaTbboNnY3C3g5CBzw2gQdD9XpXOvy9qtv46s/+TmZdA2Zz+GR4+EnFqCkprG5gA4lG+7MEnqCt9y4EXGwRHkuoauwJhoktk6h17lkH56i63dDFEoeOD04kxH2dIQUBsVT+ph+97w4KWyGq5IoSCXR9TpRRVErDBK1ZyifNocw68T3tYBEFRZ9pUr375tkzm/y4YOeQiuNmiqCIVEiRBbriKaPKjUhm0JIiaElIlRo18ccrxJZBq2BBGbVx6wGGJGgvqqbwAEdRSi/ue8SiUgRZmyUY5J7sopdAulF0Giip0qgNNZ4HXOihpCaD75rMyceNYHMtRH4aYY3xZNcaloysqHAz795Eo8+swTZCGJlX8vDLfrcd88htNrbeLRtOSPvOIigkEBUmyS3FUnODzATmvQ8D8OywJDI4UnE9lGkMBBeiG62EJ6PyKZjIkRD1JUn7Mmi5vaiewugQBkiXhZB4qlhUIr6vCRDFxWYOLYb3IjwoSnE2DDGrjGyDw+T2lImTFrEMd3PuX+f+zv71S9y7Rase55G1mcSRbWGSj12DQ7CmECaue/NUKEKGf4Sz+0t2DWPxKNbwTEQ6cxMe2cR2CZBRw5/0QAgUJaNMIzYTPg55Mm1H1vNIgcSEv79ZUv2b9iKKL17CRd89mKcjUMIIBqb5ohNG2ie1QXNgD13PkG11WDX5jyNxjwCeToyeQ5HnXPUP0zQaK2ZaN5Hxi7Rk66yt1b5h9abxSxmMYtZ/PfwT6+kefvb384NN9zAb3/7W7LZLGNjYwDk83mSySRCCK688ko+/elPs2TJEpYsWcKnP/1pUqkUF1988b5lL730Uq6++mo6OjooFAq85z3v4eCDD96X9jSL/xpDOycpTdVJZRMsXN7LYcct4c0r/8A9Iw3efeRJf3d9y56Jr9QaDBNsE9FsgiWw7dhUclHvv/HWN5qMjwWc9XJBNmtTmoh7vo8/sAldjrAVGC0B7m1Anb/UGWdXDFIbrkCjCYaJkALf9fFaPs3q3yeTnotr3nYte0uK5OJ5fOS6K/n2R36OJo5x9by/YTyW70drl8gQGMOjvPfrr+eEsw/ev389eWShDQBvajqWQhsHcqWrjljA5699GTS/i3Kr0NkeDzJrkrl2iSM//Fqu+9bdAOzxfNo8H6UU1UqLpx7azu6dEY9uOZnjDt+FN+ISlau00iClRCmN7CigknG30e9MESmNDANs2+aar7zmeYdU6MgwuTdOidr91C6wTbBMdL3B4NGLyWYTmH7cEQ0llM8exDYhaYAv27CjSUwD6r6DRYZt1S4iLTmycGDzc9P3DuKUF2/jF1/sRFer2HPzzOlxGexxmbN0f0fw4qsv4OKrL2CycQ/fXPdV2gIXNy8w8NGNCX766V/T/eqluHN2EAYG/Ut7ae9rZ2qoSHpvlZOva/DHT/RRu+QI8PS+Wc7Hx+ewaOFraU+1o9QkrVbIIa/6JPn7t2O0Qn517xNksgEfe/+xrH2ij6heJzmnwNGdr2NZ/VoKubezbehuttQG6E7VufvRDVgnTFPsbWNE5hnQZUo6y6abn+HP199DNFMKUjU0gwu6OPPCI3jk0U3QkWWpHeJITVVaBG0C4QXoSIMOQApUwkT4IbXVWVJmmeuvejNj1ffTJ6dpY5qpVwxQ/kSKffqsIFbKaIB6g9aiAvJ+E5lmJl42vhYXv/40vve5mzn5VYejW3GSV5TWDCydS49VQAF4LhrQUoKQpAcN5C6P8s4ufrixg1qQYt1EH9cddRtyImDxPXuY1DkQsOOeTJy2M7Mf2gnQfgAJQVQcBc+bKWuI0CpETVbBlNhrtqKyaShVecLvBy2ZcDPkVQkZavY8nGb7CQOg4N1TZ3NcZpxPLb2f+4f7eKCYwZg5fhX/iKsV7hxHn9yLWfHZLfI83d+P6O2FcgWEQBIyfuEisrdvJVOVCNskDGuotjacpsQIFeONDGcVdrEiWeaawQ7YneDJG5dhnLeZrX/sjT9JCp4N6tUaInMmwSU+mwilEE2PpLuXsKsfKSTy4jqv/N3j6KbFr385F6E9cE1kHjQWerCNNAo/UkhhsueSNqyqjVeIaOyaT+cjewCIvBpGRSDcEJRg7kERR9crrDrO576H0iDq2C3FwK9c5BVlrpi/nUERcYNbw2vEd48zHVIv55keMvmPBwbZs9khSAgK2ZCg/Nfneey6i/Y1hIqMKxF9PRx3ySZKdxoc2V3he0ccxaHHb2WrGsR5IiYZWx1QfZUk95UIHSqkkOhiDTFVBs8n1zmP1lxNWLBJ9jpkBiTd1jSHB3v4Td9cTNdECVCBj0zYHH3lpTz2Hz8glRVUN4wzlavxyYuf5PqJg1j/ZJy21yia/OGLfRz//kkYqeEls6iudrRbRiqNLHlo28DrTmCVBSIUBEmwXPALAkMESBOiJBit+L6KcjZEAWK8gmy6yLZ2SGdxioqmigkpoUGHilApyskqD5YX8uVJD6PQhhYCoTWBpRBaIJs+evcUWlnolB0T9yOT6EaT1O6Q1pmHEKVMGsfNx5huEqY0+Vu2I4MInUkjk88p4YkUViUm6BM+sLOKUwkR6SSi5SKSJloaGE6OY7113Pf7DrY9ncFyAxqLk/j5Zy+wQjcahIeYlC2PzP0CYVpoKZHCIHJ9qi+yKU9lyDzVwJuTJ0xKjLESN906wCMiYveDObQEEWkUEcIAIWbaY60RpgUzRIXSGlyPxHAZ8llUZxta+uh6Cz1dxh1IUz1xALsh0AmJJkIl9kdmG24L4QX4qRxkUlh/oZgRcADh+NxuxV+9wy0T75D52HuLiPESEMQrdbZjut7zyp/+suRJhBHJndOEe4fjTngUodqziN4uJAr/kMXYO0YRvd2gNVHCwijHZVqT9+WYdCUDSXUARyokfGzZrXz1Y3MJMfAWtZFdN87mpxyy0f6JLO1KnnocPvrGLOdcMsxln33+4d078hTf3fAHrjz4HI7sOfKA95SOG1FDKHBCOnLhXztDs5jFLGYxi/9F/NOTNN/61rcAOPXUUw94/Uc/+hFveMMbAHjf+95Hq9XiiiuuoFQqccwxx3DbbbeRzWb3Lf/lL38Z0zS56KKLaLVanHHGGVx77bUYhsEs/j62bhimWXfxXZ/OnjZsx+TSgz/KpQeNgzH4d9c3TZN9HtWmiQ58kILbp763bxnfj6jUTZAKv+Wz8vC57N4Wl6Zc+/k/Ei4wiSxwhAGJk6E5H2QnGAv3beOsK87h1x/4WTzQUwopJW7Dw3cDpkeK/61jfvyudSAk3owC56bv/TlOeUAzsLjnr64jhieIhImYifM868KjD3g/NbcdP2OgHAN21TG9gHTi+dJLv/Q97OhaaP4cgsMRholqBbx90TXY/3oMh525insf3Mqv/ryBCvEgJ4oi+g8bZOnSPt71gjNpBQ2ecr5DIHx8GX9GVKmCFhh2/Le5cQ9Rezvir+zDs3jhm07nuo//cv8Lz3b4kwm+efsH8YMQJUFbIDzAgl+sexFvOnIXUZTmh3ssJis5pBmxOj2PVGobCkHK2E/uCSEY3tFBsnsDl31uhO0T3eiMQsi4gytN9bz96kydSO4732LTGpedm5K899sfYM0toxx0fIGDjslySPZuFIKW8xYOO20lWx+LO6qrDk1w/WuWEBkGMpJ88ulXUbA38b1tR7Lu6NfxVPJWtIaR8RLpqofZCPGW5Wlrjzv2J542yhNr+yGbZOyZ7bz6NJcgfTZ33/9qFu+s8cFFv6VDncA1nRVqzRT55TWqTQdlF1Ba0DGQptnaXxOtIoM5gw/wtnftwMxmEG1JbpqYyyHmFKUJG1JgSxCWiTZMZDNERRqpoffJPRj3JZl8+TSdvRcx3XqYhpIU21KobptMQxFMGM+e5JkOvyQ42EKPuWghEdLYR+Js3ziO2wq56/F18cAliiBlUlgoyTVScXS5ejZ2OY4/kUh+85tf8dMblvD51mrm9E7Rm6rx2W1LyV5RZHy3jZGOwDI4/IyFPH7jjP+DIckkLaqmT9jQCNcD295XPqNbLYQfQGRAGCFLVY778Ak8PLUTr2lDG+QWd7FneQfpMUE2Pc2XV9zLeDPBHvIckinizAu5q/MwTCEwZoZJivieMhBEzZAwY1MfCuk7d5K+9Q6j07GySCN4Xe/DLHzzGN/+3EHoVovkHJveQ6YIx+O0Yff2JG98x24M4APvephPffBkHvpdG/ffezKZ0QBoktihceflkUlBkNTguARIDu8d5TM3Hcszd2cInWHyeQ+ze5TpnXmEiPjl6w9HRJqe/r1c9NYim7f1cdedXSDBtzWJgQrVo/J03CdIbpW4R3sYAkLRAcQkjVqqyTxUJTTSRH3tbPGy6MtD1qoE9YXtWDWFasuS2eURvXKa5GZNvWrQnJZYRnwdVHuOoNpC9jbYeU8ShMYddKhu2u/d8ZdQM0I93WZgVAN0pURXvsrji49g+mmfrj8Nsy4q0PXuKq6KB+ZGm0eoLVS1BNtHCJbOx6m14uhuxyaxboTpE5ZRsEs4bQ1WJMtYpubE7BS/WVgj3N1OZMDUcRky4wYYgtPPfRnt0RAy18Ydjz2EAE5te4DzNuYxUhrbVEysSaJvTRBtbyBFi/qiDKmqh+WGGAWL8dctIdEmyT01hl5vEqQ0fqdFaIF+Mk202kcnDZQCw4uIDAvb1+jBHlQ+BdUWjE+THg8xR8uEGQs95SJcn8Lh4xQuT7KjYnLrnkPoM3yEFmgVYu2awD1pIWFHCpVxkHUFPZ1x2VixEpP7IVjbRlGL21DzOohyNqpZQwYzCWmNFmgVqwcjRdCZflb4QXVpEpWR5Ha14nS1Qi5O6vICZBix7pFONk1DKFuohZ04dU1iYx3mg6oGdJ/hc9rpjzJ8V4o1xiDK8+LJkcAntafE8LrFWFZI7vatMQl3eC/jF/TTcdcwPWdvo+f0Ej8rnUBmWx1/fjeGirBcjbYNkAYSE+V5iChEKo1uBOiuNnQqiVFtobQiyjvIuknQmSCxaRJnpIXRCkl3ZkiZIa2jlpPZu52o5iHzgtbqQYKuFImHJPyNsiSVTYEEWWk+7/19CEKshzYhCnn0nB7E+GSc7FSpQxA8j5T5S2hTIjcNHdABt8eK6LEi4RHLwZS0Du0htaWKMA1MqfZtLzUxzmAy/m6e/vJJHri5g3rZxElojj+hxoK1W3jz687GqXkYfgQYGKlErHQLI0ibLL6gxeXnrmXzthbw2uft33se+DUTtSR76zdyzwUHkjRauSw2SqhEi6B3C9cNd8NsWM4sZjGLWfxvx/+Icqe/9u9ZggbiAd4111zD6Ogorutyzz33sGrVgeZniUSCr33ta0xPT9NsNvn973/P4ODfJxf+/4zydJ1aeUYqreOISBVptq4fZve2ccKwyt17Pss9e2/6h7anZ+TxRiLBHVPf547yjw4wbjYMiVYKrTWRUrzq8jPQrod2XbatGya0mDExFUizD9l9G7LzhgNKgubNK+DPayeybdw5bQBIKdBK4TX/e+VOpu2gyxV0ucJZ6degIuIOr4COrvxfXWflYfMxdo0iy3Uwn//1mtuZx+/NEuYSyHQakc+Sb4/Twn7xvbv5xDuv56c/e4irP23w5MZ+IMTqTaMMEJ0F6t5BABx05GL6F/fGxzaz7WbDZ+PWMR5cs51C+iAG2o5CZbKQy+HNTHYp20JkUggj7h6Krg7EVJWu7ra/eR7OesMpB/x9zhtP5fbiD7h96ruYpsltjzxKZBLLzx2wpzT1KZ+k8LHZwu+mB2m1LBrVJId0lzk0Ncr8xDTn9h1Yahhl4zL8rAGXvnsHc+fWGAthPIQ9m59/voUweOCPabY9kyIKQKZfwZlv+Bcu+8xreNWKo8iaFm2WQ08qy7HnHRGX+piSdOvfMKo2VlUiAljdeyHL5n6SB177DgC0odEahAKvN0fUlUMl8vzkRyt47JEevvsfqxAtD7vNwujvwfBCnEqsDpkKF3DXLy5kevJUplIt9KYspcc7aYyZzLOnWWRPcOiLM7gtH0FMFrQWdRJWf0w4VkF6IaKq2BR08eGhk4hmspJ7jWn0+CSUS6i0hbbiWNZw3MBv+kwOF1nYcR6XPPECXvboS3nFwAbsTcME1eeQ0Jk0sqMd1SZQGRft2LGKxpCxOgp49dtO5xWXncyJRyyJX7csUmcLFvYlOerkZWieJWjYF+vb8k1cIJg/Re6Hw1w08Dhnd27gxYMbqPsOGoEqmGRXSXYcPwXpJDgORj5L4AcYnkLUIwhDaDYhlYJkMh7lSIEgREhYdFgPna/sI2kqShN5arUOVC6LziVw6gHdlRYEig9+5xRGp+G+Rjfrqjl8GZtsPgsJhL3tRECUNFCOIMoZfLJ1Gk++ei7KFDNkUcidX4abvlbAbJQQfkBPfYSXv2ETn7vuAajUSN/VTT20UMDTN/eSPmiYxPQIKxc8w4vev4aD2zZz0fEPkm7C/GPHeeBjP+LPH/4p55+8he/tOJLfl1Yw2dFHx1UJFi5uMPWOLs6/Z4xjflbBKDaR1Rarj6hw9MsmOeu965i/bIqJ00C/eIzEeXXyWzyuOv9monab9rtMsmss2jbuc79gXtWEtrnI/m6ENNkRQqgVS5IlFh69Jx6olmoEGRvxiiRveNv5fOu6+eAq9LPlO56CUgO11gPLpHxePxOXLETIvz0MlcKOydViCNU62UILKxQMrppg55vakFdl0cOa4tYEWgi0AFc7BE2L2pUGhm3jbN7J3ksWo7IZ1KIBDDuDWalje4re3gDTiJ9JWeERtgu8LkVYUOguk/M/+ADdb9jB4PF7abh9BDOqRQBHgPOuYfzIopXpIcjM4cn78jH5HCpA4y/rRGtozMvjdic5uG2CTiFJWxHt95XQCswApGcgoiguzZ15lpnTDRgdJ9IhKmMTZR1E0ka6iuTWciyj0kCzyZwrfQbaaxzcMURlykYgQQiM7mEAwqTC7RSQSUE+C4ZAaAPZ243R3wfZDGa1hvP0KMZEDeFpsk8V0eYMu61iV2ftB9RPWkywtI8oYRI5BgKB8j1cw6XVY+HaEWrnEAyN4WyZINUh8QpZgjmdRPkEjl9izmGleL+6DIQTK15Mg7jUKWGhu7IsfrnLSZeN0DVeJbeuivAVRJrUjibJoomWNo4bQQDpp0egXCayNCQcwpyDv2IQPdgbBwtMl/aRwoaUWFONmFAKfNg7AoaJGugitW6KzNpJrLE6QYfJxLltTKzoJtoyRKuo0Y5Fefk8vN4UWoJ7xEL0sxMTCQdSiVgdaBmoFYP7riUJh3Dl3APu7RlvfaRS6KkS0dg4/rLBmATx/nofQwNB7/5JQ+FHyKb/vOUEYK7ZhPHYBswogdYKHYREwiSaaaMjz2Bkj4kCXvmOcY49r4wQGkzNZt/iyjOPwkmmoauAts24HTfM+BoBIumy86gBFh7UYsGyPVz1pm89bz/SroVVMuji+SoZGW5mntVgvqVYmGzQjP7phxGzmMUsZvE/Ev/0SppZ/L+D8nSdL37glximwb/++6u4+G1n4LYCxoaKVIoN8oU0v99yESflx1hTX8vtO/s5a8Gx/+U2LdsgbHocfNLSv/q+kOBHCiUEoYbugQJairjWXwoK94xTPqmHlPO31U+nnLCUT/UUCHo7kDMdJrfloyLNpse2/bfOwXkvO5xfX3s/ulwBKRHpFNQaYD3fUPpZfPwXV/GCzGtRQcS31zxfR7xgfifJ3VVQOiak/ID23rjOf8emUSqlOk8+tpNd2yWbdpzGYcccR8/xNzDy53Y0gq994Q987HOvBuDUk5Zz963P0Ki12C2n8IUm3F2MYyhm4EYgO9ppenGHcM7CbkZHqtiZBEDsUZNK8IZ3nPY3j6l33oGqoQvfdd4Bfx82ZzGGvg+hTEQAyaGAbbTDBfOxUstYVLiOHY0ChoxYnX+YtBgj0AbL8ssO2M6aW9Lwofj3uYMtliybwBFxoMfOnWm2Fq9nXu5CbDO1b53a3yhhyyT6WT54IyARRju9CxQHn7wCJ2nz9OR9fOKCO/jxUwezebyLK++4je+94DzyyXi7X9t6D53EJE1qe5HEcJXedI6fXX8QQmtUs4VWCrdSpbqgl7aJADJxU7p0XhfbN49x1+NPIg7x8ZMaIRU7h3s4dNUYQivWNp5h4wNbYyIAqB3XzZNPprGdEG+8DlLyxbmPcVu+l3duPZuB1ijl0RCw4tIgP0I23f1GlMDAothA/faz3kVr+gUkJLR9yOD692X2LSNqdXQmjTA0oZujtDSke1cEhuDZEfmvf3QfiaTDuz/1ctKG4N7R+5n7xjZO7VnN1HgV6YUoJ06RkX68Tqgkf5jKUo0sEjs0FT/Fd39zGnbTZY71DGAxd3WINeLRvr5Gcf6h4EXkO20qa7ahQ8kB886+D1KiFg0gd4yy8vBJ1j+QhmgPrpdkupnE90xsfNzt4ySnpyDMMPzeDNcctgrntt08fGuKC2+c4FfXno90dVyaxX7lh5g/gCw10PUWThMiW9DR02CwrcieI5O0bzLBNgirBsMDczCG1iFUC2/cZnVmAqklwrKolJr8yyWvorZhC62GZviiQfrUNCMPDrByfpNs+zbmLHU56JEKdw91c/feARJWwG+fPhjsCEKJtbvBFz72MFccdwrTrSx/mFiMkgLqw6Ag0xFx48ZuOvprHPveRzGab+OB+jDTd0PXvU0e29qOGIxI7WzERqtewM17nsZ14ZUvvnBGOaSxAs0Tnypw+YNPUW7kGFs2SH1VQKLqYLcC1M4OjGm4975usFtoZ+YOc11o1DGTCj9t0RzMknm8itYyLpNTKv73HCgRqxSEAPyA+Yc1ePXrpvnRTSchfphnr9WG2bOVKacfp98EDYmOClEGjBzkTvGpPlEgM1QnXDEIGQe6M8z9z51QrnHYyXuYiMBXgh+OLqe3q87YtjxEgsgSHJffzsOlfqz5EkNKxNw5fOqjIa9862b+9bwOEhrwYqLAcw28hsSSIZFloS0Da6yOAP4v9t47TK6jTPv+VZ3UuXt68oxGoxwt2bKRg5yNbYwDwSYYjElLXNIu0WTDksEkE21ewGQwwYBxwDjnoGDZylmaHDt3n1T1/XFGIwvLpF323ffbua+rL41On1DnnKrqqrvu536sEHrv62d3DyS3mMRtn3ixjg4DZCDRcYUalhEfIsPIB0tHmYj8lInXbpHcswfKlenGqq3ouSqgKVGlgU1TosT92+dMh9z4D4YRj7PYwZsN4b4Uzm4fL+VExFq5hjRNkKALLuQkft6iuqSKPZHE3ikRvppu+wCJA1UKa7IYQQqhNLLRwB4uUTg+j45bJN0yVr9A+4rUHJ+Fx+9jeHuSgeEW1HgFVfUJprpbgcneve38cuJkzNtLSCMSvMm4SSBhYruDud8jXnFRhqB6TBfpzZPkb+9j7OQ0P62uxrkrxOkbQmuQLc2YfkilxyG+z8PyVaTM8Z4i0QgCdL2BMTSOSsYwGi7mriHIpSEE7dgEy+egUxAmBfnZBcKUQ5hyUJUaSkjclkhBI9ubsJMjyIYPDZcQEKYByQRCRSqa6EX4kEhBWwsiEUfv64tIntk54nsiwspwQ9TQOIqpVc8/awsHVTWi4j9t21MNhZ+q2hEK9GgBkYhFyibTpHDSbHJ370ZJwXcr7TzwtW6+cf56znn/IOZsnwXHl/n9Z3IwWUMMTSJCFZGOSkX1b8pzRjWZDG3ppn52iJOt8cSD+zl96fuRU3XyhLPnIW4t0FNvMJa0ebh5AyecesyhwskmBCahDvhjqYO4GUa+xTOYwQxmMIP/UsxQ4DM4IgxDYpgGtm0gDUE6l+AdH7+Y7t4WUpk4nhuwqZpjIrR4stLCB27/w189541Pfppbdn+Rz//gTUf83vdCQj+AMKRSi5QJ8bYE2jDwHBvpC0wPls3qeMZrJJNJVi/pIqnhtt+/B4BgSkbywO8f+7ueweuvvCTypzAMhNKRMiaX4exXnPSMxwghuLX6I27zfsr8FXOf9n0qHUcUKohKHap1hGUSS0Skz+veez6ve8/59LSm0UMVDuxZhkycy+c/82XCpiQqHeOuDYeIpm995VaevHs7e9b34SUsQg3Vuke1fIi4UK6PKpYiLx3gmze9l7lLuokfXEUcKyAadY4/7VA2pyPhTV96FQDdizuYt6L3sO96F3SxoK0ZGQoEAqfkcfbJZyHT78ROXMB5Lbt55eKHePeyu2gTe7HR5PGxTfuw83jSxptaiEymfCb3WEgtMFyJqmm2TH6Om/ceTgSqUEcms7GnE2fCaEUYUVae1lnNfODH/8b7fvB2nrfwlzxv7k4+c+ad+JnIlPKt190EwONjd9IIpwxsdUB2tIRuNPDHSxy0VRIAQUjhxF6abtmIWLeF3//hvQDc+rt17NwxxIY5w1y27BEWdw/xvPwjVPdm2FptZ3O1G9feHMnOgwDleYQtBm97LMfsjqZppeBta9MsUJMIT9H+vQG8qgFohDQwC9XDpfQa5JRqS9i9+BUbPxDcObkE3z6kIdGArlRpdDdjluPImIOq1tANj8/f9TEAGg2PyYkqSile9Z4X8n++eBUfXfFhYmac//OFmxFOZHyKZSICjVHVmEXN4EAb7u5jKJ/VwboN83E2mth3eFRLNiJmYQ01KA6atBpVaj1ZagtaeM6Fx6GMg+sEUTl9E4w5bXgnLolMlDNpXAwQGpFQrGxxqEwmEQ2JEBbtZ/VQOKkT1ZQibEozeJuBdqGw0+HbN55BrWaj0JF58PSVwBqrIEOFWYxCE0xP8x8r7+Arix7iXZ/dAJPlKLOKbaJNENkspJPUwxiVQPDQvc2EXW1opRntn6Rei0K/2m/Yj2HEEMrgwCMuI3scfv6F+cRndbDyWJv33fdc/v2ui8C3iO8HUKQKZW76dZ7C+xT2YIMD4zkGJrI090Qznxuu66H2UDOtExatnQ3mmA8zOZamkI0jD5TZfEuKU3ZsoTonTpiWNHoTHP+ll1Eq2gRjE6higbB/kHB8gnDMxNKaNz9wHpt1B0Mv70GEISLUhNpClysQBPhNMWqLIvI4bE4hm+NcevU+5l95EtakILGphLJMZGc7orX5kPJgCrJ2KEOXcCTnnFNkOJQYN0PqT9vI3LoNe6BBqupRbRfU84Lehf20NFW4eNY6FnyySjwZ0vnIONoCe+0u7Cf7QIQkUgEnnVmmw4RWU/Oyjk28ofcBpOETxqL34NZNgsBl5GcZwlIZ3WjwwN1zeMel5/LWT43zhd9vId/dgPFJJrcUmZiIY5oas94gfet2nKGIVMk+OEC4UtEoteDFLRplFWUT2jtJrRuCrMRKh6hZJkYihWhvxl86i3pbE153BoQgRBDEn+KPUj2kTrDxaLWKxMyA2TdWpkxyD6nVxFKw2gNkW4AKPMxCA6vk4sclhCoy2lUaY6JG06Ob6fnsdpwDKgqzmm710MjZMFEhORJGr0oKhO3gzs8RGw2xyiGWb3MwvtTKmPTdbVPcKhHVBsIQBPk8u92uqbMKmjsmufDCh+h95zDVdhudjCGKDfoeiLPvLkm4b4CwWkE5BsqEQHtQKOGvUAR5Qf1oG9pakG0t6LhDELcIAg8RBtEzaM2j0olDfV2tgVYhGoHOpqKyosD1aPSkcDtShAkTQ0iaWutkiwXMYhmpAMMgSFmgFIYr0E1T6kHTgFgMMasdoRSy1sAseah0/Cn9hQY3yjJJZyveoi6MhfNo9LYgDAMRBNj944eRLHrKh0pMhdMLwKo0CKYWRw5uO7zRyMP7dctEV+tRmPjIOJZMUDumF29ZL9dfezqFO5p442mLuOELXfS+pMLOPofHru1AhApz3zBG32h0/SkFTce8lqhdy1bssmSttBno9VG2jyFBxUB6LuuvX49RKCMtE9NVfOgl32bowMihYtrzKcT+nYfqMeZlBqgHUVvfuan/z+9oBjOYwQxm8J/ADEkzgyMinUtwxVWX8u7PvgTbOeRXcv6lJ3DWRceweMUsTN3OO7aeTkFBoZz4C2f72yCkQAcKlKY+VADgtw99gkZrHBxBfW6WU1fM5dVnPOsvnufLX3wlt930PuLx+GHbG6XG31Ue0zTBthGWhbJN1ESJ23Zdxfs+d9nfdZ6nItecQvUNoPb3RSuDMB3ylcrEaW7L8MDDu6ilbB7fPABAS2sOv9khSDo0n5KcPtdEuY6yDELHwK74GPUANx9HtRzKLtE1rw2ZSTNrXgsQhaiUCzUajWhgZeRz3Nr39b9a7kvecSG3qev5/parj/j9Fe+58NDKoDTZeOuOaQ+iszPNvKSpn5e2HUOr6TLfDkkYT/eY8Ypldm7Nggbfs/jVLcfxza+fwAcufxbiGZbqnn3pGnA9mpriR/z+qUhk4nz97d9l3e4sbmiwdaIZUQenLFG5aOKUNiTC9lCj4+ihUWzbAdvBSsbxJkvohouq1yHwiY0HURhCZwvxZDT4NlMur3nxQzynqY/Hx2bTbJd54qOL0dJgQXyERclh9v1y6t6VwrdB2wHj+Dz/8pMBAfUGW/40h8ceTNLbVaCyw0QT1cVjThrl5DOefFrHPbhriAN7RnjJxV/iFS+8kAvPPp99Pw5otB2qL9PhHkMNEsM+Tn8dNTZBODLG5978PQBu9woMPGszhSMolPK9eULbQHk+OggwSlW0CaEjuOGbZ/Kn+1qIVxx2/HEOdilExOO4VppcW5W+rTEqZYNKwSA/MYK5ZxJLRVlwovXbKIVy9bOL+NA313PRMeuQ/WNQKLJ5XwaNZJPdztJswOmzN2PXNHiCke4S6YEQIQQyVEQxidE/g9ubkFogEdM+NwcpK73vAAQeQaqMn7YIUibdQUib6fGsVAFphxB3oOERy1cwWhywbMJEnveds4Zr3r0QY28/qn8QqaZqvtaYlkOQ1Zhxk/blo9SFgSdSNDxNqn0tTnsdq6uGDEKklrR/9QlUQ3Prl7uxJ+pY+3305jThttQUYanR1ZBXvmYrqxaUyOk4lcYI71p1Oyct24YeHoN6gz0PScSBIloY2JMh1k7JnRtaEL4PhTI0XKjUQGtet3ghtQEb4YNRFahckkZKE8bjBLZEhyHC18h69Cwv+fgTHP/8OiPrZ5E0B0htGMEZqSKb85HB85TX0VRpo2dha8QCwWRvD896nuLsl0wy11Yk1u2LlDmBwiwqfCeO6UUTxDk3+vifT3HjWXM4plTii3fez/xjBnB2DxC2ZAhbMrz7e5v55bZNbDZaCAMY2OnQQsDz0xMc27QfsygwC4KP3nQ+vMWiMTqMGh1FDQyjJgq86kN303SWT7gs5JS3afad0874RStZm1lJJXAIMZCBwmtOECRNUgmX49JDmKEkVD65dh8dBrQ/USW+uYzTWiPZ66HemkdWfYQfomxJbVUz2rbQIdSWdFE/6tDCwkElTRCXBKHJlk3z2Lp1IY0mH92oo8rVqWcJYdpE2BqV8NG2g/RCRKCwPRE99ylyVgPGFg9Z8DEGJjAKUfuVpkC2pInbGcxUArPoohCR4kdpnN3jZNeO0fpwCVlzOSh/KmyzaMTbqNViKN+j0RGn3q4J09F7DptjpHM1HDukt6dEgjFktYHOJKg9J0+9LYEWglyvJl6pkX1gP/WlLZROaMfdKjC2CZy7JH4uHhHsQYBR80hvniCoFlAodKGIMqPFEW2aUTYlpZHFKkbFhZgdqRGdGPWFTdRXdlLrcWh0eqRSLuMyRqglYVczwTHzadoVkH+silXWxDYORO3BNNGehyrXInWrnlKAzepANGWhsyPKLCUUXlOMYG47Cc/GlSH+0l782OGq3mmSxXFACrQOpxUsEBE1z4iYgzIlWkCQi1FenIWmDMKyEG0txAs+8QMF7L4JzFIdu68M2mTvIwnK/Ra3vG1O1MuLKSP45NSYzLIQtkk2k0RbEmukjG8qHtrZg9ICp7WOoaJxiLnnAIyMQrUKrodvRmTga571gcOK+mSjk77QjhZ/pkRHb73ih9Sqf98YawYzmMEMZvDMmAl3msEzIha3n7bte1fdTP/ecZpa07z/jG/x/Q98ll3ZbuyNz2ix9zdDBSEQ+RMM9hemt9e70ggTdAAnndeK/TemijyI5q4mxgcmyXc1/f2FmlqFktpk+WkL/8rOfx1P9c/BsQ9bgf7O527iwO5RSrUGSHCfIvX+483vZuPwZo7rWjW97Z3vuZDPhJqYIVn7u8dREnJNcZYcfchr6Xv3fgTX9XGmiDZpiMjzJ5waTuo/K9M/iKWLZjGrLU3/UAkj1JFiaOrerPabmYePEDbtg4umsj4/vb6EtQYfedUcXvPeAW7ffTSNljR/ejCBve0AqZYCRc/mlOaPHnbMFde9jSuue9vfVMatD+9g4z2b+EXvMXxjx0p8I81xa8bZWdNYps+B8m7mNZ3OVy8a59M/uhlhWeQWzaaxfwKSCVTVhUIB0gkMB1KbRhH5PHN6Dq2OOqUdLJk9QKc9yW9vOB9cwUUrk/w+O8Y+t5mYoRj74aGwG/2+Dt51zv2sHW/n9NPfwWfe+UOEKVh7X5rxvlOwXt1Ap7OR7D6VYN7R+9FnKbihJzIFtU2ou2AKXvG6azEbIbpcxNQGsl4nKQ+fRAjArIVQcA95YxiS0f7IVDu7ucDGbB7xIsGaaz9KxQ35wbEv43dfuplhXyDQEUGkQZQ9zKpCS4Hl+siGRMQSJHyodGrKcww69kKjbODWJDgm916fBw5gA3f+uoGeKHCQPmrucnn96vXonRUO3BtHTBTB9TCGBVpBri6phbPJBI0ojZgH1fuqxMI4slxHW2bkfzF1o8kimGFIGBfUl3eS3hCt9CpA+CEayYsv2s/vf5kHoD5qcJeR5pbCHF52/Vp+fOkpkE5iHh3QUCAfs9EqxKsGCM9Hex4CQVgoITNplNJggJmJkVQOWVycwKEwUcBxayhTkmyugxb0dnTx/U+8guff+u+EzRkqhTyxRBG3KkkOarQlaKipdqk1pYYkFQ+ZCGKcvHCIcSV5zopJvsuUqq1UJP1ICbl8IUJAaMb4xPYLmNMyjC6VEXUX7Xng+cw/1ic31MfWHfOJTYYwOUqwJIbaW0DKAMcLwbA5KB17sG8W1i6fDTtt3PHtuMfNxmhIrEKAmixE17dMtB9Eb9KICDT2aV57zT10t8dY93CWrfUmZq2o0nd3Kgqxw4LBOMlKgJeWPHJgIdl1u4h9NcaZC0dISHjHN7bxgecn2HzmXMRsn7seW8qzVz9Ib6LMhz8yjyd/mSZ/uuLEz07Qfew+HhtagNZQGW4mmQ6wtxWieEk0IpfluDUlem3FQAC37VqEXNxJOanp/MbuKBW1PkTktTVXidshfXfGCZMuolClqbnEl26/ly17Y3zgs+cQm+8jgWpvDLveQNcbWNUyetUqQk8gFZhll2DjMPve3E38l0MkkjGggjAlxqDm7cc9jkTz3svPJPy8Pz3RD7KCxd2DhNpg3M9gaI1yjMic2GuglEIYAkm02haFXElqR3UQX7sPw/UxMwbC9HAnXWaf67Bvd5LqrDhCQ2ibVOZlyd23nXBiEp00EKkkIqjidAYc+4J9PPqVDPURRTxUhE4Z/+wo9DW0DCYHcohAUR1OsPxlNdZfHaO8MsM5rxhk0xcdaoYk120xsl4hBdQWNKNaDbTpYbkuUodUjkrRyEnifS65PSFhNoEfB3vrKKJYxpAiyrbV3oTcvDciUDw/ClVseJCx0bYgtXaI8qnzCOIGKpekJz1AyQiZjBsIP4Cqixgvobo7kKHCHigi0OhGJN2UfpQ1TySTUbjTviGU0JBvQwYhXnsz0vUpL26gF4Y03TxAY0kbpecsIv2HLVjuU/p0op91MbcXtI/a3X8Yqf7UMNXDfgVtC+G60TYhsbYMonZEiRPIZTGCBFQaaFOSaCQIli7C8PfQtxl+cmHPdDvEjNKLi5gD8RihULzk3Rfw7HOO4W2nfpAggJZfPsHt/gm8+KN7mbt8lB2FPNb+YWjNR78rtTqlRUmyO0fBcwn8gIH9o3TNbgVgaXYNv9i3CFdJahNx6AJrpPF0ddAMZjCDGczgH8aMkmYGfxdG+gtMjpapTK20d9xWpOuXNVq2l/7KkX8dWhwS+zaeYqoXLhMYPgRp+O2mb/zd5/1Z3zVcP/pdft53zd997A17vog0DbKdOb78m/f+3ccfEc1NkM9FBqm12vRmJ2YhBMw2BMbwJMuanzL5t+KsnnXcYYRKW0uaL37h5ZywajZhykbFTMqbhujfPnTY5ZynKKGkFASVGroevT8d/telZfjp99/Mdz5/GQtjMQb7JgiDaOAqhEAIG1XdHHFSAmJG69NP0HCplByu/uh8Hr9JQs3HeHwvWgpKQY77B2dxzf5v/8Pl69s2QDwVx7ZjDBcyxAKHNyw5kUVNIyzLjXDHwK/R4TBzu0/AiMWQlknGEdjVMvN6MjQ3J2F2B0iDC36wkd4Ty8xa0+ALv//w9DXCYDHf2H0MX739BKyq5HWnrOKz170BO22zfaKNwVoarQ8NZZ91XJ3TmgZ4bvs+/vSbdUglkcKkMJmgPurTnakgHCtSLZQq1FaHNNpjYEpEcxOitQUMyZ6N+zG8EKPs4i3rJmhJ4h49m+oJ85+WZUQuSKIIUTEbcploxXXKH8eYKHHC8bu59p6v8v4zf8cVz76D11z9TUb2jzOxYyDyoTlIXJomdkXjlBXCU5iTdYK4wE8I/OManHbBRnLH19C+hlBC7WBepQgHNvQdVq7Xf/UAtcctkpkwWu23bWjKIrWBAI7rWYSut3LrjpUYDTA8COa1I8o1wgN96L6BaTNmbUcr7yomQIFhx6Ny555i3gmUZmvQIWjFzzYt4YPbns19Q/P5yuhzOOvn21nyhSKfeNld9CTHCQ2NqtWQoSLsaI6eXcyGIMAUGmGZ4IWYow2aMnFe9Pp9POeyMV750f1c8Y0f8taeeQSBiR8Y/MtLTyGVSkC5BpUoi1UtSFHZ14yy5FRWpwYHKYO3LV3Key5ZzpP1FLMMl1CbpJ7yZp/3zvNon9XAa43j52LUu23wDfad24wwTER7K2JWB3Y8YPvjSZaeu4fs5jKxwTqOEJibA5xyBduvoSVgClQmIsO33dzBYF8MvxJg1ALs0CVMW+jxCVAKLYAgjFKKCx2xYAjwNSN3NHHeyhHyq1xuTXfwlqt3RUamfgB+gDlB5GNVULzg5A1oAZ4y8TXUFfT1OSQzFZa9YDuXnPUQm1fGuX6ol/c8fDLrrkvhVWH4jhi3nNuD/TEPo6Qw6uAUfOSYi0wc6vt0qUyToZhQghYBpV+O0fy7rRiFOsoRh9kiWYU6pQGL0pjNcLyV2rwUjYV5hnZKiCkg4Lyjt1MPTaphDENoEhdE/Z02JcI0MOoaVdUsefUeJj61HN3TTuUtqxi8bE5U/9yQuy9OsrXQxP5aGu2CrUagFIVavfTLfVzetZb/mHMXqVhAmHaiMLJQo6XAjiUxEqnpuky+Ce+YHrx5OcRU3+sVNY2SQIchu8a6INT4dqR+Cx1JcmMZ0dqMbG9FN2XQcRuSCdqXBRx73hgdK0C2NiOEQbUjyWgyUmQqS2ClPFK5BssXH0DtzSAyaVYeP8KsPwxgPamwUlnGd0p8V6IbYFU9YqMhYa9goivO+Bkxas2C5IEG0rJpdKWpL2/D60pEoU1CRMQrHKYaIh5DJOOQSRPOaiGUYI43yP5xB6LhYZVDTDtAlEOCFgs5WsbZ3IfSAUFKIhT4ve14c7sO9YkNF6EBeSimVaPxcxZuawyRjGGM7uNd71/PO//9SYovT2MUqmTu24tsyR0yIQaEZUTl37UXlUlCZwuH6UalnGJxOBzlKtK28YHaym4S20and1GFItQbyK52VGsTXj6GqlaRfjBV1qmFKz/gYMywLpWjNlqq8MMf3stb33wNQd0Hz8f0BcZdo/z4w0fxkvP3ogZGp/xrNEiJu7ADpz2P15qMytnTxdtf+JXporbG83xx1XWsvWY51sORYseBaUXpDGYwgxn8vbjyyiun5guHPh0dz2xvAXDNNddwxhlnkMlkEEJQKBSets/k5CSXX3452WyWbDbL5ZdffsT9/idihqSZwd+Fwr4hvMkio3ujGOWU7USNSUlK5dpfOfovI5NNsfToWTS3JHn/lw+FFD1rXg8DF4ZMnBxS3Nz5D50715z+6zsdAclkklsHvsEvn/z8P3T8kSAsM0qJahpRFpsp9C5sp6UjC+UqVtGD+t9GoBx17GxkzUO6AZ4XMrpr7Bn3NS2TsFgiPLgCXj+y8e4/CmkIGjWPRs0jVH9GD9Svnp5Em/ZFTztWdeQhk0ClYjSJEHv9doypQaiZFazzlrKt0s2WXUv/obKdcenJ7FraTTEwQIMjkpzS+hy645O02GXuHXsSPfEamlsVBgpCRW2ijOuF7Hl0Ox/++mu4Zd0n+OPer5DO+xz31u2sfusW0rlDoX7v+MQL+WN5PutjTbhdAe9+6VlIKZFa0DeSY8eT6WggPKUy2vnKCR69M875zXtY1PNOwlwCd2kntc42/FkGMdunJdmHOtAProu1wmZRdgKRSqMr1SjcJVQsPn4BQb1BeJTCaIS4s5vQQiIb7tNXbLcXcYbHMfvHCBp1tClQ+SgsSgHVL7djjm5AyJCEqpIrx3jtp16O0ZRDaI3o7UF0tSNmdRFKgUYQJAyEgjDQiIZmYU8/K/ftRd3h4tWmJhDGMxl+R7Xiuz94Bb/9Vi/3PraQem42pBIY+SZEUxMayLZlyaRiVIppaChoKOSTRXStDqFC1xuRubdhELZl0WmFVKBiAu0YyDmziXenKZ4Q+RQFNnx78CTCsUnU8CgPbO4gbVf5xLI7+OyK27l5ZAFL85N0JlxWnVxE1jwMOwaGhZRW5M/TcKHRIGODHhwGzyc4MMzQo5spFX9E5uQ38L3GyTz//ZcyWryJyfE4hYk4u8tRGxVNaUjGKC/vYOjVi3B7Ulg1jQgEe+sLp3kDpSWFPZALDJ70TFY4k5TWBzR31vAz4JY1TkIzeKbF2LFxJo8h+nU3bGopUIND6Eadlg4fFUrufmkOY7yCHC+hVYLFPRUmTptFLZ1CJxyMmsIaifofZ18F5Wma2lxW/1uJ3N4RYhsHD729UBFLe9F7PKjOIkRITeaYkADBkB+jq6fKV7+6EDPZAD/AsxQnv2UdC/9lC4lMhees3IlBneHPB7zmmmfz2vefxidffS4t8yRntO7gqGQ/Zy/Yxlf+fRWVy8eByPfDXzWPTS/K8tBtOVp/sIHMowM03z6AGQQoz0ImJEgQ6RSTCApKsK0/jjFZJ763RNv1u7CmQmEP9k/dZ5SJp0POfdUI1XgWlbBwe7PUGwmGGyaFbRk6L6nQsnMEQwegNHM+MhXyJQWEkU31cZespXVZA+1JRNkgnNXA7HLZ/d3lSE8TBoKb3iY5OtPPd198Ay0fiMIdQdM4OsmtY0u5q9qJ7ItFJrgJizBhRSFCUiJNE5VJopIxtClRyRgIkPXIa4lQIfI5ZHcnOpRU5mdwiorQkZhVhd/ehHBsdMzGPa4HMVZEl8oMbnGYY2m6lpqR4jMZp7IiF5GtgJ8GbJ+EVUc1NJsfaEZaDtsfjXHPz7NURwKwHSrxQ+pVo+wj946S3D4ZRcSN1KEUkhzwkRWf0BR4MchuqUVRO5aJP6cFqnXMvYPR7+V0Njobo7UZjUBMlAhyDrX5Ocw6GKEBSiAtsMY8yo7CaraQrRZhzASpaKvvxBopTJdNKY0SECZkFHY9uwPd0UqYSaBtG2IG+SUZjk5OsCo1TrItJJjKoFVvNRFODJ2YIij8p6Smr/uoscJh6hlidkQ0icOH31orlOdhacjcvWNaHTXdf49NovbtpzE7S2zEJVkIYLLCdJK1eCzKAJZKRXXID6KEBwrs3cP4kwEik4FUAl1r4GCw7rYW3v/VMxBD41AqwfgkWoCdzhEbqSGamjB6Z2PE4xT3jRxWXtu2UI6Fn4/692v+8O/T333wZVdzYe/beezuTcxgBjOYwd+K5cuXMzg4OP154okn/uL+tVqN8847jw984APPuM/LX/5yNmzYwC233MItt9zChg0buPzyy/+ri/5PwQxJM4NnRF//BIPDxcO2zV/YRhKfxUdHaSlbV81CGAKdttm8ZeA/fc0v/vyt/Pi+D9PcnpvedtWlF5F9zCa73uY5a/5/8KNfKEGlDhMTh2WCWHf/DvbtGGZ4pIx2LPoH/jZ10twFXShDoqREGxA8c/IrpJSodAbZGq2IymTimXf+B6BCTaPhUa+600qagwi1ReWgZ2bmnU87VgxPoqsN5GSZnz75ZUinIBYDy6T3DWN0xyY5KbeDGOHTjv1bcPprv4FXD6dXzM1QEbMcdpba2FbqwBcKwp048RA1JQgYqwWEvR2QjPPQH9by8B/WIYTAsFagfIPtj1zK8jd/iaVv/hKu63LKjd8kVAplCPxkiO9FN+xNKmQVMlcciC4+5eHRKBkcv6bKqBdn7rwxgp405zx3O7//4S/40Ks3IoDv3bWF7977BNdtWc+i5CQ7/RxhLoGe00k45cFy+sUn0XieQfHlYBVcEvvLOKM14jsno6wlB5FvQgmL3OKQaruDDOskmwYjI1LASqUZr2vGaqfwxC/b+cHzFsHjFY599goWHTMn8rLwfUQ8hpIapCaMgZYmWkDqoX68tKC+Jc7q1goGRIRUMhFNIv4CZsdsqv1Vbv9ZJ3vHWiICc4rMEkDfjn5e1f0GdE+d1HiB5rVjmJUgynZjGVNhAtG17MkGQjt42al7HxyHWh05MUby5Tl2f/Z49l+5mnm/qmPaJsJxCEc1SxJjhEaDQHq8bOFmfvHWBNc8sYI/HFgaqRNqdQjDyJfIDxBTJNLRpy+PSlmfIoyCkL4dDWrN3+MHl/+Bt37wLm7ftADTVFi2YsfEOABxIzILLT7HIT438sIJYgJlwjGnVxFTXj0Ac05rsK3SwgpL0akU8ugEL7pjgsmXZLn57Ecp1Qyys8eorPBIdlWQvouQoHQd6g1SjWESc3U0sZVOpCAyJTQCPnXdgzyv+gi5vgKiKY0hLCwidVWtw0brYWY/r5/ahUnU5KE+66ATTTIx1SbFwSmlRCtBatDj7mIzH1+9EFsHDH7bIZg6/jXfO8C/zH2c9y56gKA4xLsuWYzvCjITVYp3ZvEfy4PbID2/TqtRJ1AGex5vxd49gZ7qW7RloCs1EuZsgqyDXW6QfmwQo+ZHniOGoNIUw006IBSmCTE0A33yUIiQCDAbTwlDyULpMYfKpMnvftQeZfwJQVsWITYvvP1FNJ3h4eosZ206wJr2HZzZsY1Nk93oZJ3Gko7I3yQ7Ts/ycRLxkFk9/fh2gGlA8qE4uTsTjJ8/Fw0ceCjJD0YW8uOR+TyW7UW2tQCC3z++nHt2LuLxgV6CMQuj4iErDeTwZEQ+GlGJw952wnmd6LhDOFSGRhDRV1PeJDqXAsfGWLsdt8lHhJAaDIiVNaIOYRBS7c2Q7KshM2mEY9NI2/xhZxsbH2tDV6pQr5Pc5WFNRu3JqgnaYyUWJ12O6y6y6Iw9aNPA2agZH7ZRdR9dLhNOhUwLIPXEGJkHB8k92KC7VKCt5GIpk0ZHimCOibA0RkhE9Mkp35xEAp1Po5uySGGCEOh6HTU+SVguI1QI+TSV1e3U1sxGpRy0lGjTQsUdMGO4Z/Yy/+JhZi/sQxsaUahT3CoxRg+NbTSgbYnWkdk++waQQ+PYo3Ws8QayHjLid/Ple1bzxQdPYGRvhsTWAUon9lA9dS6jF81FxRy0IadVM6FtEiQNjINhgAf7Jz8EP0Cbfx6KKqKMTBzBVPhgW1OQvH87sfuegC27Dx/A1xtQLEMYRkrdZBzR1BQZe+eSUX3PpJCZTNRCB8Zo9Gaox6bC2oUEKfE7OxAhEGqkIQn3HiA8MACFIo/+6fHpy42US7QmxzELU+35td+Z/u6x+3bgS4MPXPLVI9zJDGYwgxkcGaZp0tHRMf1pbT2C6v4p+Ld/+zeuuOIKTjzxyNmFt2zZwi233MJ3vvMdTjrpJE466SSuvfZabrzxRrZt2/bPuIX/UsyQNDM4IsbGy3zx67dx1dW3Uqu509s/+et38ZMdX2XV6csAWLG8m6ApgUo47Bl8ZgXHfwbpRJJ7P38hP/7QIP96/I//Kdf478Rz3/RsCHwwLY477+jp7cl0jMAPcTMpVNzGS/1tBIqUEmWLaJBpGowfGP2L+7t9g+jxKH1oLJf8i/v+I/C8ENf18b1DZIpf3IP2bkFrWOsuRprW04677MoXIUyDZFuGn3/2Bt782ZeBbbF89VyuefMv+T+9dzLfVtxcXUjDH3na8QDbHt3JA799lDA8nMhZc9FnQIHVCGBqbLyzMMqrH3gNnYky3akyMSukUAPTmU1YrqKKZXwhwDLwpOSBGx7h1u/fCcDF7VfTf93lPH79AHbdx9A+P9z2XJQ0sFprGB1VVv02IJwadAfpkKBhYpWjax9c/Jx8ZQsv2Xoe5659HhNeHLM74OVnP0EyHnD64j4wowF7V69PDYsbxlewq9FOpqNBmIqjVsznvPeei5QS3TOVpSdlInyF12QTdGQOdfKWiWxtRtfrHGhk0DELHUpKbhq/LRvtE4aEns+IM8pt1x5FcSSOaoqe5TFnLMaMW/SsnsP8K3xkpUYoQ5SE0NEwOok9MUnzL9cz3JslNkuxaHUV0wwj8qJyyBC1bX7b097dY795gHd8dAsXvHQXrUmH5vY0Z124Aj2l+nr89s3gC9p+YyP8FF7Gwu2OYRyc+EB0jVKZwKsilMKoKhACe38JNThMtQLhI2W0AcqCztNqkM1AaxO5fRa33b+CwHMIA5O1Y20EXo7bXpcm+5MypJKQjEMqgW1OqWha84i2JsSaeHRnpon2NbVKg8mhIm0xHykgb7h88Pi13H36j/n9iddjJbYD0LqkC2yL9sFBWr7YR3LzJFZV4VQVV33KZNnxJUAjHHjIPoNX5p7kJ32L2V13WJMscFKiyMoLFQOjFjVl0b5jiLndI7Rmy/SsHiBoUhjKwF84i7KZ5p7jo35b1+vIMMSo+wgp2VQTPPK7FlRZYioL0dUercoDyS3DuCNx1l7Tw6MfOoqWd0kmLuhFTbUjaWiK4xZ2LOS9V+/nXz54iKyvTUh+9doc1EMe3TNnKlQuqpGnry6y0Apps8D8daSMwQ+xRhskK36UlQfBzkcyhBX40q/O5o4/LEELRZizI9WLE0N5iuz2AMNVaC34lw/sBgEhmmBeF06+i/r5K1m8aA9VJQiUoLDvUP/jFA9X/Imyork9CrcNJgxSa8dI7A/JbKtR6QzRJnxg/Sncu7mTJ9aluXvzAm7uX8loNcNrHxwhnNeGwyi5l9SwCAlVyFkLdrLgY+sRg3WMkqaRh/qylik/GcEP9xzD93atJhAGuhGpevb35QnGYjx2Ty9aRYSUqLmIeAypo3AZJQTmnkGMgXFEzUP25jFKjUj94jhgGmgJengMEYbUu6uUlh+8X4ET2AjfJzACwqSFlpEhsRoVbBjpxRUmMhkjtAzskoE1HD0XLw3pTECXFdJpKpYdWyS0p7JSBRoRqigrox3iL0vgLetABAohJXbcYc6cAq3nlCAe+dWIJXXUHJf4jgF0GCDyOZRjIfcMojpbCDvzqEwMbZmRWW25AqPjGDv7kU4Mp9/FrAOGRBiCBclBkrJG0J5Bm5KhPxpMbhTYww1UOo7bk41Sak89CQloKVFSE/lga7TrIZSOTMl9jVEPuXvXAu7dOZ8wG0OFCpVx8JoEKiYwipXIoNwyCVcvRZ+wHOSf+edpHfmL+T4iiAh8nU2h53Sj7EP7PrVGHqamAQjDKRqUw/Y/+MFzI7LG9dCmJCUVRjKFLpbRY5OosXHIphG2ReVoSfH8GOPnH0PhRccyccFxlJek8dM2QVMMWQ/wWjPgeaAUH7vkC1HdHB3nuvtfxflr1pManSJM949z/71bor/rjUjpaUD5YPr5GcxgBjP4K9ixYwddXV3MnTuXSy+9lN27d/+nzvfggw+SzWY54YQTpredeOKJZLNZHnjggf9scf/pmCFpZnBExOM22UycluY01lOMeoUQmE/5/6IFbYQmhCbs7pv4p5Unm1rMiu4vYNv5f9o1/rvw7198DZ/43Xv48C/+jc/84pCiZHSwSHGyig4FQguk/nM3kWeG6YZoBbIRElSfnjnpqfDrLroaTZgXL/vHwseeCYZt4GUc6kkLzz0UrqXqL8CQkDJBhkdO1fmaD1zCl258L/MXdNDUkePi153FbYPf4Cu/jbyA0rPX84X9q/n+wAqu2fGbpx0fBAEff8lVXPW6b/L4XYcUV//+vu8QCjD9gKoJoa0JnBAn7xO3fQarcYquzZ5Sjl9XW6LwpPYWZFc7YdXDLHkI1yMIFJnmFJXaHRQmX0C6aZQV8zsjs1Fl0BmrcfVJv6EjVeR1HX382xXPnzbfzhkW0n2KwSeAabDiX0q8cfE9HNd5gBtvuZJKPuSbD65irBbjN08uZH85h5rSUmwabOGi/JNcmn8SJc1oMmGb/Pb7j0T3329SebiFkZMzjJ/YxOiJSZRtojvy4NjoTIrABukHhIRMHBtlNfKs3DRxpWo1TM/l9BVjHPe1AYIPNdH5pqge3vXHJ/AbPnt3jzJ/XjeUx4ndtx1jvEpsrIHOpSMiKJvihfOf4Bv3LqdSMTGtKa8DADSis5kf7/g6t6nrD3t/PfNrnPuiMZ730v20yEnyQYOk8qJJzfTREtTUU7QdRGBDOh1lYrKmGC0pKF/QhH3rNkJ8zE1DWIYBMYfCMWn8zQ2kD4Yv2DmviaA1RaMtTjKfJ7PT4crPXcqVN19I3xVNXHjRAPPbCwQ1H+HYyHQa4Xlo2yJszeK1Jmm0p3i0+nCkefF8yGYhFsM3Lfbf+16u+d5KfnvlOewQBu1WyIJEnaNT0eCgaWEnCIHrGoShJNQGjWYDNyYgcRlfumEvt/Q/wc82bMYZrPHly2ZR/FDIJ89eRKAEoRLs3t1OY6CT0BYEX6sxPzvOoqYxli8awE9obJHAKtZRLU3MWlw+1F4qJXQYoMsV6n/MoIUZEVHlCowVkPuj/twp+MjWPCLfRN+qGOfPqzB5YTMHBW1qSpz2rDMqrHlOiQteOUE6F72zP1zXwv5NbZQXtDJ8fxsEIbO/JAhtePj+KIxtYszEm9UMSk35goB0I5NqoUIGH87w3UtOQl1v0JibwO1KYlam6oQAY7yCM9ZABCHaEMiegBs33InqacPcPYyxbwiWDXP/m5Zy7f6lbPRtms4tY0z9lIhQPSVFeyShe8sXD5Bs8xEJiO0bJf/AEPGto8gioJM8v3OQF656hJ0f6+Kba27hQ/Nu5oLeDZQ9j9joZj7y7jtZne7nkckFbCp3cfezMgD0fnAXyoFgdYXkaQX6vx2Z0df74rgNh9pEkmCKQDcLccxxA2PCQMUstCnR6TjCkGjDiFQllkT4IbJQAaGxN/WRvXEzBEHUy7gusuJH5EAiTu6Bceo9MiI6AD9toZI2ctJFNitErY7QClkJKTxqEvgVQiGRjoUOG9OhNYYL9z6xmJGazUTd4pe3H4+bVowd50RGzVrDRAHtWNi5NI0zsqh8Gn9BJ+VMM0ONDH2VHM4Q+K7AL1q4WqJrAcZEGYSBKFcxCjXM3UMIXyFUpK6R2RQ4VtSnBAFi+34SO8ZJPXAA0QhJWC5dyQJt8QJiaJT0gYD9CxZRjHeiUjF0zKR8ziL6X7ucIG5Oh7jJuo8xUYO4g3fULGqr5qFMCVqhDY01ViE+EEAA8cEQEUaEHYBohNPkCVojCmVkzYtUdVPjJSWIwo9aWyDfBK2t0N1F2JrB786hWzLTbfPPlTRHGqg/NYTqqaFR7uoFeB056nPyVHSdSt8ooupBvY6u1cD1oFpDa8UFz99B60/rhF0a4YJ0gFRE1iEEUkpEZ2v0PqXALTf4wc/v4/IXfYMbvnI833rgrGn1jwp9fnfNHQDMP6YHMmmEZfOKJU9Xzc5gBjP434VSqXTYx3Xdp+1zwgkn8IMf/IBbb72Va6+9lqGhIdasWcP4+Pg/fN2hoSHa2p6+KNjW1sbQ0NARjvifhZnsTjM4IpIJh4+8L/INEeLPhwyHMGde2/ToYGflLys4ZnAIJ5x99NO2ReqTAFwfQ0Eu/dfTSh/EH5/4NGcved90tp6/BNHSgkxE5MHbP33p31XuvwZpCvSuQTSaYqFKS/uUQsN+Ebg/RGlYmr/xGY8/6pSlfPrmD2I5T1faWEaC8Yk0Xs3iT6LB25cd/r1AEE/FQRP9O4X1d+/FzsbxHYO4CpnIRpPBxqRF31gT1X6fzqPKdGcL9CZmRfdRrRP4AUprjKSmXnbpmJvHrfvsG/8A7bpAKB/H8k+mZf8ogyta+fyTq3jz0k28ZK7L21d957CyObf2M/e+wYhICAKC9jjGmM9pLXuxMTkjv4UnNndSd5IsPr6fEWHzm+psGo7Fj0YWsDJeYMuBF/LE45vwByqUh22k505llskB4Pcnyd8/RPHUNiqzDFJba9RaBbHNAVKHhA0PsW0fyVke4/M18dEAHReY9QBnOCLtJtd08LMPv5p46894WeNbBCsEJ932It7349/g+GE0AQkUK9sXsW3Rg+xZX8V8cDv+4tn4fh0nVKhijfPEXnbOSXKd7CF2YQA/i55DprtOfdYhYvA2dT1nyxcjgMv+bQApwYkpdq4bJKgrdjy687DnaPgBYcMHBAQhtgcUK+D6UehgLIYOQyw3QaI0ity6ndRgEtpbiAcNvNEqnpYoA6SCIKdQtodVFLhZwbHP3cqW8R7ed/JNjJzQxsvbD7D3uTne++L5qIlixEgYBitOnWDMj/OkcLBKPoNdaeJE4RMincKIOeS78jz5aIH7b5iPPE0zGgrKKmqiP97Rzcc3fIw1C7oItoF8YZLgVIfquubISiYpkLHTUNgI4YFW9O59jG/cvRPDhI+8fgFvO3ole94+n3pTGlN5NLwYwm7QZNRoSJvnJAb4taORE2XQGhkIVmUO8BiRQk/VGmhfgJSsX5vHMDQhkUGrLpYgE+0nUilUsUb7igavfdFaBt0YLaOVp0wcBaEvGNpvUatIGlVJpWhG7+g4C89rJ+kkMDa6jJ+6iNFHExiv9Llyy9F85voxZPds9HlQDkJarnsk8nMplwirHrpcjVIIY5H2StT3CuLbxpHBFFvQ8NBuHa99PomONqQl+Y+13VzynD9w9ddv450XHxuFHh3nIAXcO7mMllSNe+9YimqMIvGiyf40IR5N2b9jzSP/PoFRNCh+qY7oG+Bzd61jRa8GcScC+HD/IubHayx1PG4s9tAsa1zeWiP/5d38YmI+35q7llIoeNspPVhEhBRCYD8xiXmJQEpIzYrqg/1kBX9pHik0hdV58o9OkO5TSKVwnRDtCLQhCWISWVUEOsCQFsoEI5dF1+rofAZz217EwZTwDS9SYJgSMikEkNwPx67YwfYnF2OEkcm36m7FNkCuHYHJStS0UjHcn4wjXA+R8tEdeYzhMkxlHPTTQCi4/JOXI62Qpn1FPA/MlT7T/v9aYU66HPf8PawfWkBDGLjdrSgh2b2vHW1AOq5wqiHmphgqDqqnHSbG0KPjCH0wU5LELNbRhRLSixSoIhcntAxksQoqxI9Z1Fa2oW2JaWqOTQWMtPsYdY/AdXFn56nnmzANa9qzV2NQXtFE8/rJqX4thLqL6htCH9OJXZdYDQ8lBQECnbLIPjJJpiWFMVRE5TKEVuRLo31/2uBYJ2PgOGjHRFQ8xFRIs9QQxkxEoQCGCdTAcTBlCn+0iBiePIyc0ZaFCH2m46eEgFQ8Mhs/uMmxI2+sIIBanUpXllgoKJ01j+SDu4nVFaXV83G2D+OUoj4+IoqaMZIOs+tbCdqSGKtrBPc5MOhDQlLOgmlZiLyFqrtkUQgFGJLvvffnhEs6MScamI0UuhwpvwzDZtMfHgXgKze9jwuaXwcIarX/Wt+7GcxgBv/voaen57D/f/SjH+XKK688bNtzn/vc6b9XrFjBSSedxPz587nuuutoNBp86lOfmv5+8+bNzJ49+2+69pHmsFrrvzi3/Z+CGZJmBs+IbRsPYDsW85Y8s9piYU8rtVkgPcGiE/5xtnMGUO0bprZtD7ozet6tHdm/63gjZqIUtHZn/uJ+lltHudFAL5f/xwyVnwmOkOhY5D0yMTDJ/MVRBg0n/2HgwxjA0+mXQ5gYmuSOn9zHMWcexYJVc5/2fdiXwPQEg/7TiSjDNPiP372P0niFRcfNm97e+/I0+35YItXrcFI+zY8nQ1BgDwbI/9PLHde/+2nnksqP0jR7LmGpjEgluOSKF3Lsact4vPIDir7F/l1x4gS86dJnc+yZy5h71JF/MJRSOPf0gzUV+29aDL2pi/mf3sfLsqOYAm4vNfHlUy3yX6lw7lv6abMarGof5QG/gzvLnTSJBle87B24YcDu4gRv+cMXCZI2KEX6eS5KKXxbYr9wnNi2LM2bAkYuEjx35dFkZZz+HUOMDU9QNkKG1yyh0d2GvbuArCpsEWL1RwTDsa97ktmLu9D6rRT7f8JjFYdfn/p7Pte/EzlwLAKIacE5y17AumP2sW9iL8rzmDy5GV2x6PjFJEjB0I4UzzozR+G1+xne4XBf83xAcvKlVR5Ze3gNKFxyLNauERYetxWATRtbCDJ5MOpQOqT8yLSl+OXgd3n2SVcSWgbCDWFkijg5+FvbaOBbMGugxNmfH+b3H2nCI5qYnf+WbTz0YIrReIrRJg9tagqlJB2VcfwJha77LJg/zmnL97Dbb2ZepkGfTNGz0MA1bCiXI1+dSoVzTh/jy+tPQ1clfrNNQWXptvqQPqjRUYTjsO63D/HoHZtwcxncUz0+dtvzuPi4B/nl5mMY29tDZvUEG4p9JI/2idsBulNHXiLqKW2k5SHC8qcoln/H8Rc1wJpKD325INyU4qa3vpYzf/YLdFpg2jFkymHLVyT/cuYT9O1L0PXDnchcFjR4ZoX6jkMqO8PTYAvQmvU75iHUGBRL0NaKzDeBE7WxtFElFx/jS9/fSdbwub3YjvG7BjoOsg4ITXaei2dAxRCM1c0oqqMTKJhYCQsdKLx4iuoK6NjoI/9lnB+eeBsumjdf9RJc1QSmZPCyo+n62eNU55pYa6vQnEeXK1Cr0f/SucT31bEKEVGgiWw0aG9i7qsfZ2K9IOgL6BExBuoOy5bWSTe7DM9uJgwgDKOp+WiYoLy1mbQ3BFKilUJOpRCHqCrdecdp5I/dQuooA+s3PsH2Bt2z9EGLJLSGi9O7ePvobPYHkibpclJqmJiEk5Lj3Fdt5pU7T2b0kx7GiAYb8HyE0qT8kIlhA7tTwJQ5c/NN+xle2IzUgsqZ3eQHXHJ/3A6JBOW5KYSVQgQKqx4NOA0tCUsFEBJdrUVqs2IFZRoYoT6UPUhK1NgEEoFsyqHRHL93D9tbFlIPrUiFsX8YUjHEUAksC3wfZ6wCqSQqDFG5RJSyOx9DHcwuaIDucvFDB2fAIHZuQOUOiSFVlB1Mgag1SHRUeayymKE+g9aRcWRrCnyb+A4b6Ydk79gFCkqnzSe51yM24aEdY8orCJQpMW0LUa1HCh0h0fkUmBZhdx65fxSKJcrHtxBmHbCgJGPEhWb2/CiYrDQ7RaNL4AwL7IkQZUuU9GndXGNs1Sx4ZGpx6WAf4nlQDzAMGxU30EqjpKahNZl9BWQ9Ct9UbgCBC9omsbsQnSIeI1w6B9KxKe8ZHSlplEfQmcco1BDNOag3IiJUaWhuQhDylOSW0bl8PzKIdl2IxVGeF5H8hkSGCgwj8pYToAZHCKTEf9ZCzMkq9oEJzJ3jSCDx6C4A3Dg4daKMbJ5P6Ac8mGii5VVDdA1V2LlN4Ux4IA0qSYFoz+MKMGyH6tlHk7pjE0iJKSTFtCDZMAmkoDzfiQq8rw+3FtVn27YjxY7WYNvs39bH7MWzmMEMZvC/EwcOHCCTOTQ3cRznrx6TTCZZsWIFO3bs4JOf/CQveclLpr/r6ur6C0ceQkdHB8PDw0/bPjo6Snt7+990jv+bmCFpZnBEDPdP8t2rbkFIwUeuvpxk+simnzHb4WNvL7O7PMq/rvzwEfeZwd+GyliRcGrFE8Cy/77mefO6/2B8rExr218md/4wfA3lQpk77r7jHyzpMyOeiaPqNbSUjAwebjr9wG8fZd+WPp73r88hmTmy387a2zZy6/fuZPTA2BFJmljWp7lnjOpo8xGP75zbTufcQx3vLx+/kk++4Hr2ndfDyYtuYdOBQX71hZ+hgY+8/kJe9KyVRzyPk8/S2DsUha6MTaBtm1xrlnjKYZ7rMBI0MAbKNLx+Tn3BZbT1tDzjM/GUYuicNjr/OAKOg699AidP5/w9DEy5PC8xqzx80i/4/a5ZXHftYs58zhAPbJjNxMLIg2GoESmDHMNkab4NuytLvaFAwLnz1jIwNIrGJ9NqMznfY2XLIPvHHJZ2S/b7IbVynVd+6KVk29J8/oprSWwYoi4dzEkXbIXuiczZ3tL9JABC2DjNX2K296/cNdjBg3/sZnmlDohp0YHR1ArJYWQyjvA9smvLYJugND+9YR7dtTgbPxhHVTX4BQCWLSjy+F4Hz3sCz91EInYyTjKg61SfobRLvA69i8dRE5NILzjsOf5q6HsArD7KY93DAo0mbM0jay4qnSbsGyBoztB9vUf93jhzzxrF+GgqmrAaBhN7bJbOcckMpNkcCsKqhXYUouCiEkkIAl477zv864YrWRgf5edDCyk34rxJ7EVmsyjXhWoNDEnLsgKjGxIEVoiyIQgtipd14jxYJLnHR1VqTBZqBEohay5LusfZsHsev9+zmsktXWRweXHLdv7YP4/CYoORDR047XWsehSIEm9MoOp3IONn8UjhMVrbLOae0OCLfUfRkyjxo/pKjvrcMsxykjsuvpzzvn0NloyjtcZuH+MbVxyFCBUpz0UFVUgmMd+m2NOXQ+CDEJhvt+GmdhCCwv4K2tUECEwUOlSgov7nPd/axNo7m7n+Dwt4+YXbWFdupmmFRudA1wEDFjy3QlZq9sUFfpuKwtKaHcIFcewtIeW8DyRJbq0RGCbnzNuHIxU73CY+9PZb+dCXX4oCdCLB0KuPJ/3HrVH4kzSgpwNhCMKsgzE2fFiYR7E9wfBHetk7UWTeyG5MX2HuqvPdJ3s5pT7JKz85xBW1udTHErTnCyQTHuN7MjjDCt2aRwxOpTkWEfmhtSa0LdL3jdBY1sbYLo/ZO0fADxgehmxntO+abz+P/O80XUPb+JQzj+H9SVLrK6yKF+hyfL44axP/fsVJjN/PtPoEQBgGKpck9zMT808bpu8lOeKhnIjgiE3GkKGDbsuhRydIjmhUZ0TSHIT0A7SrUKYm6GzCLDQQhTJh0sSoRSSNDn3oakN5LnJ4EgUEnXF+/9kFiFMkMgfS1QT1GpY0EWEYqTBDCdU6Mp0mjNURWhBKgeEbiClRhJ8IkU6I4SkEUNidwNg7jLmjHikupt7PgWXNuO0GskmTHHNI37+L+hlHoRICpQXaFhh1IAChNY2EQWyoEYUxaTB9FZl1Z9JoyyRMOOhcEu1YiIZP2J1HN6VwJmo02nyCVpvJeIy7Brtp3jmKUdMs73uCk88Nub5/GXrSQHohoupjNQJs30AJgTSM6JqJKFzLKXj4bTGClI094eJ1OhSWJQgfl+S3VhGhQpVKaNNA1nxSj0/58QVhpGoZmYRsCiMVi0LSPBEpnLJpZCoFiQRhsRSZ/LoeOptAG3I6dfrB5ycabmS6rhQyk0aFflRPZ3dixJNQLqP9EB0GFJ/Vi+1pjKJLfMt+RDxGYGlUwsYeKkfPmciXmbEJNNB6oMrRz+rn8ZuaMdwOCBTaEaRqBpPaRyQswjqY0qK+cg7xoo+fsbFczWSHgWFJdHv8UIGB4niZbHOaMy89mTt/dA+ELm9c/UFuLl13hF/HGcxgBv8bkMlkDiNp/ha4rsuWLVs49dRTyefz5PN/v93FSSedRLFY5JFHHuH4448H4OGHH6ZYLLJmzZq/+3z/3ZghaWZwRGTzSeYsbCeWsInF/5L2AZ6/+D/+m0r1/29Um5sIfRNzapBrOX9f85RS/lWC5iBiyb+caecfRSoTQ+eziECzc8fh8Z43fedPFEZLLF+zmJWnLTvi8fOPmUPn/HYWrV4AwPjgJJnmFJYd1cEXrNrGi9vWsa7SCTxzyr2D2FV+nDV5jRSRyuvAaAGto4Hq/PyRiR6AuG1SampCuw3k7G5UucpHX3wVLU1J/vWWGh0yZNeuGLoywPuf+wkufscFXPD6c454Li/0KV/cQ/lFPaQfDvFbY1hFQb0D+gMDIeAox8OR8OznD3HVledyy0M+6pwaaatONYyzsOvwuP5LXn4iP/px5EVzYFMn7/7aF0ktaqM+EfKe4x/g5OZxbt0/hybZyb2b/kh5osIvPn8D44OTqEAhgaTtg9Iox8ZPRGTRxzeexk8jmwz2l1oQUuA8VKf1F0NMTlaQ+TzhFHfSNL+V8WObkCHkJkY59soyT77BBEsycnfIC5+/hcdaenEaVRItAaWxJMedNM6moSpb+t5FmzFMzHH50ocVrSb0hwYHgPJWGxOF0no6lNKKHWoLH/vC8Vx48iORqsESaBXtZz8rDds9tuzt4VSSjO1qJtZuUR+OFBADYxaDO7qpBwky59eYcDJIrWme3cFgXxmESYwcvzz9Wgar+7n+1p8Tm5B8vXkxs0a3RLKNTBqk4LPvWE5+fx/CDZk8pRPr5CrLLi5TXWBS/pwBUpJpyWLFY4hagPxSD9+/ponv/HIBheIYn3rVHyg31Vh1yi7e/NVLMVolam8S2QAELGovMVa+hrb4WfxmbDaTxWZGP5Ni/ZvnIxMKPW7y5Np9fOV3v+aEM5Zw7qzdbO1zEabGsjQ6E+f6u24mbsAfx3J85bj5ZFbHyLX6DOY1QSCwT5E0fjQZTeiDABDoXAK3M4M1UEDlo5UukYDbb5lLqSjZO9tiQfsYo3e30O4dICDKrH5r5Sj+4zX3MtcOqcip8I5uibVQMlnWmKEBQQ2dSeDvHeGm81Nc8EiMU1MFYkJz8dkP8LM7TgalqfWGnHJ2mZ2/sAGFsk2CrKDlTwMk9tSnTVU1oI9bhDGuCO08QkWKgbF9DrdfuYANLwt5ImxHBgKZCBgmw6I/jTCwdSGMVRFjhej+iJRzKpdD+Q1kvpnsUMDYb30a57SAMwkq5J0nHI2bT3Hg3YvJDBm86fs/pSftcX+1iVcmdvL+/+ik9wOKTrPBzvtirB/oxWDHVI4sEOk0VkIQNkLCeIxg1XzMLfsRhgHVBtktGj9rEu/30fEUQhH5l4z2Y8gUQgt8fKyqDzEb0ZRDmAKpQGRNtAjxOx1KR3cjCw1Ue4b0o8NYYwrR3Ym0LcyEoJaJITWk+gK0gvJZizAFJO+uYI7XIvWD56G0QvohOgwRmTjaMDGm5uNOdw23EiIkmLtGqRl5rJYM1oBGUz9kdluooruzhClw2yRprRBugLWrjr8gReXk+cRHNVZdE2QTEQG8ewyC8FDoj1II00SgMcYLqE0+avkcdKgJExbGrgPEC1XC1jRBe2SE/8Xdq/navF+DlJy5epTTl04weZvPPfvmRabhMRuhIL+5SpA0sStRuFIQ+pjCwnUUQcZExmy8ho9RDyIz6qyDtiKyWgiwigHGvnLk4QJoFSDKVXRrDspVVC6DmteFuW8Y7cQQlhmZEgugOR8ZqmtFIyuIZR3kyCFC72AdF/XGVBZIgbQssCx8QxI0x8EIMLYdQFkW6XEP+9G1aNtCWDakYoSOQmViMHRIkSg0VE5ZgNg8wNCgSXksR3UyRHsaJUEKiajWyW11Ka3ojAIAtSZoa6LaFtLwXQhDgt4YVl1Raj9cAlQuRCTNFd9/C3f+7H4QgqD+dP+JGcxgBjN4Kt797ndz0UUXMXv2bEZGRvjEJz5BqVTiVa961TMeMzQ0xNDQEDt3RqHxTzzxBOl0mtmzZ5PP51m6dCnnnXcer3/96/n2t78NwBve8AYuvPBCFi9e/N9yX/8ZzBgHz+CIEEIgDImUcjoN7v9WVAOXB0a2Ufb/ubHVXqAjKfLUJxn/f49DtWwLox4gqw32bhs87LvzX3c2x5x5FAufEor053j8zk1svGcz9//mYR69ZT3/dsqH+Oyrvjb9/cu7DRbbLuc3/XWTat/3+cUvnsUnN63mg9efDcC9G3fhpxQIzbz2Z2bls505dLkUTVgMAyMRRwYBjWqDx+oZ/lhvRdUF2vXYv3OEbY/sesZzuSokmfaJxTzsYgOrKjArsCXexe2fbWPrr5bTKJ5GvWbw7T8dB5ak0WURb2lgyJClWZtze8467JyvfumpGAIQIet+n2PTohRBDEYGlnLDtXPZeHsr6zZ1ckb7EopjZXzXZ6x/AhWo6UmumPJR8GMGtTlR2NvQOxuck3kV56Rfze7Crcy2GyybVyWX8yOzUkBNZSXJLWnBa5E0OiRrXjrO61Y9CVJOS9x/8PWFvPwn/ax5zwhtRzdAQr5VccwJY9w6kmXQd9BKs8CBFhPmmCE2mp99rgXlhViOyfxVc0HACc89dvreY7kXoiQo20B4mjAeA0NyzOlFjn9zF/e+/MN85M1vZ92Db6J43GJcy8ULxxl4JEuj5BNOVliiB8m0lEm2VNlfG8TPOHgtCe7/U2Q43ZmcTXa3JrfNI/145COE60ar+qUyhc1xRKUBtTrZJ4bojk9yVHKQ579wIzqbItaaISxXqA5NQBBQG5Qc1f5vXHTsJjSa7bbJg+U53FafiyMkQSzKmOO1CIIsSM/lyamseo/tncedQwvpm9eNvVdi9jnYRRM/DVIK0rkEuhjH8ANiymfiDwI7rJI2olWYo7JlsrObmR0vkF4aMu+NVeLPk1iTGituoWv1yLcFMLWBas9SP2EOqz6yHwAn9++kMmNQKbHhQsGjr03ztpfeC42IjIklFcY+Raxdsdk36QtlVK/OSBMYNhPPzlFcnkMnJIFZJbuuj2xdcu31y7CFJlRwdnonzmCJ6ryQDZd9n69duYEbnnyEbz94O9/57a/5yU9+RWZcYV+YxfhABp2B8sJmlARrN2BC8fQ23FYLexfoYpX1bh6hDmbGEYjxgPC7E+jBAqLhQe4goa1RgYBSKVJZeD5MFGm5owiBh5cWURgQAqFdGFSIpWN84ImLeNNDF3F6fJx4DDpmhdz6oTxPruvgm997NuVMiOk9xcRdNfCHS4gteyFQEI8hLQt/YS9hLkX++m203jNJ9o496L4B9O596CBACQfhBehKBQqVKENWEKBiBliSoFqC8Uk0CrcrB1qgOjIgwE1JVDZEjY/hpS3MHo0x4YMr8HMmQd6EpEV1loXXkZwiAyCouygnMhgWgcJ3DIySS2xeFCa7qG2EQER+Ruq5DsyDYHYr/qJZTF66EiEFOmHitcWYSmFFcLRP6FjE9o6RDMBv8gkdDyPUxEIRZaSzNbo5g04nDmU4siwCrwG1RpRtyfXRvo8xWpgy9LGpz8vixxVutw/5Bn4cfvXILIwFCe7/dZKbvpbl0btsgqRBmItDrQFBREoUTp8LlsGsBVXMRsjIcxcgqnWsoodZV8hQgpbQgNAWaEOgVUjoSAJ8nD2TBDET4nHCZByRy2D4GhGLEcQEouqiVYhVqCIKFdREAV2uIJuyyI42yKYJOlK481qi8KaD/fN0vZmqQ56L9lyUipIE2JN1rJKLzmUQbXmsKVWmCAIIfNRYAWuohDu3mdqfjfTth3eh1iyncndI40lFfb3GNDXKikLLVNzCEILM+gOYAjCjlPVKGliJBIbh4BYVlTkKZR9O0nz65VHabSklGCJSC6F5+PYnqNddDvQdOTPjDGYwg//d6Ovr42UvexmLFy/m4osvxrZtHnroIXp7e5/xmG9961usWrWK17/+9QCcdtpprFq1it/97nfT+/z4xz9mxYoVnHvuuZx77rmsXLmSH/7wh//0+/mvwP97s8AZ/LegMF5h+8YDSClp1LxnDHf634CPr/s9d408walty/nC8S/56wf8g1B+iJICUY4M/trnPHMIzf9khEOjkYfEusO333/Dw+xcv4cVpy7lhPOPPeKx9/zqIaqFGutvf4IlJyxkYrDArg17p79fkr+MsLybFnPJXy3HT399N5n7Gzz5xEIW5qLwqjvK+3BzmkZTSDb5zMbMi46dw/aNfQhDEtbrfPeO9/ORCz4FHWlatEsuUY8mWckEOlQM73vmgWfZqxOGBhpITjYorQqRSuD2ZLj/fc0cc8GxtLWt4eo3NRh/6QqISfA0Re1QH0+TkamnndOyTJIJk0FLIK1WUmeNUNiSAA17i61c9aNeKskQG0Gj1kAF+ukpQw6GLTV8vGQ0io8XPXwtoNHgt6+fxPhON+Eyn8lPtNDzY4ORe1yOWhNJbfIdKeSaKKTtuLkXsrM4hnQEStvQqBPL9HLHPR7dB/qQEgg9+gLoXVnhfZ8+mp/PWsHbW4tc9tzfA1BT8IPPNrFzfQZMg1DBKRefwKozl/Oqjx1ucL1gfit7H+9DxywEksbxJqUJyVs+sIK0HSfdAu95+3l88ap1PB7Lgg6ojo0Sxhx0pUL8j6AuiVbDUy/0qKcrrFi+l58/lOa5RJLY5LDCTRrIUHP90LUEQcDLVkbqLSGmJh/5HPKiMspzCCZiuLYNjQapdJoL33gu69b148diiGQCrRWnzP411VlvYN1YN3M7ilhCkUhVcb0Ehg+iIVAS1u7qInMfnDUH9g8Iwoxk5BybzGaBrAqkp1GiwTuvfh4d2Q72Xvk7doTjuHWD5bm5bNp2gP2uQc5S3DfYjPHtCX6/bSXzb+1H/7YOvkf1Rw1kaxLZ2R5NtssVKFeJr92P1ooXfH6Asa3wwLr99O1sBUuArPLpG7djSfhTfjEjVZuu3gbbl8Y54CdYbFXYUUijgfg8QeNOG7HYxavH8fMxvjDnYb72S4tErMHjv5/LJ9NxbtWzuHDZNuadcYAJdzG2MaXEEZAz4eGhNL94ZQdBucbgifPxCwni7y1irk2jLUhOSpJ3QrWnm7GLeon/Zj+zNwc4wxovH0JOoHyPhZ/rJ2jJUH1WBwiIrRsjVhhD+ALLCfBcE5EMopCVegOdTrLgfbvQXgOcJFY8JC192o/aSHaZxDYCtk620B/E6FQVXvC6Cn2PlLj6badCdZjmbBypn0LS+C4gEOU6wg/QtoXnOGBLCEHUq1hP7gXDRGfSMD4JY+NwkLCQUdY/YU95yewaiIiClIlZKEIqjtGoE79nEPfEBWQ3FdF+gNFSj8gDIaFhgi9IjHvULHtKjqSxx33s/im1hdaYQOh7GFaUyjtW9nHjJt2LJyEAW3rkYj5uIDC2G+iFAYn7DyDLDQypQWlCWxBmLUBjOx4x7SFdDyZKhGYWZYFydERAaTDHXdJuA+XEEIt6Eeu2RoRvoYQsBdPmzjKbxV67Ax0qxPAEYXczjVmCxsI8xjh4C3yasyV2/SROOF5gMDQZ7OukMT8PaRssm2qXQWxvCcuF3M6QXz2xnmRaoxScfW6aWm8zMTVlfu1Y2LWQ9j/2YzkppDJozG2i0J2i6Z5+vO4kjUXN6HWD2DVBYElE3UWbBmBQXZwjXSiB5USmv64XeVuZJsqQ6KSF2+qgVIbEw3uRUzKkw9JuGxJChQgj4la4PmHSRkwWomx9sRYYGIqOURrCEKkUKDBv2UT9ZccR//Ha6apo+xp946NMrMgi7yjjD2hkq0LGbIJCCcuyCQ0jUtZs2EN4zFyUBNP1CWORx1frgKCY0DgJCU9Zd9m7+cD032/66mv51r/9AEyLT731Oja9K0foGrTf4POn37yTVOrpv20zmMEM/nfiZz/72d99zJVXXvk0A+I/Rz6f50c/+tE/WKr/u5hR0szgiLBsk8BXKKUw/kq2oP+/497d/dQ9eGz/082n/isRKBWZoKZTkE6xeOX/o0Z7lQooRXHv4Uqakf4JhodKDO4ZJlQhDwx/jcfHfnX4PvsiE8dauc6sRd2kcgnaeg+RVSJ2JmbTV5DZT/z1cvSMIjwfa6jExa9Yzdeu/iVDjSqyqpDuX67Tl7/jPIQZha2IZJI96/Ywsn+MTbLOx69/Ae++4aXIuBOpSxybLQ/teMZzxc0JepoLzMoXaJF1VLeL1+UT7w3ANDC0YvWzlyBndRFkwM1qAltTKCWoVuJUy0cOYet3QlRcEmRMMq0NsieMkD1xGKEF5W6b9K5RfvOVmyKCBqZJmYMpZw/CjCWJD01NJv0g2jGXYe/G/ZzZews7fYPz23fw1g8/wq27v8RVP/pXAJpabErFOKVCgs6uo3j+cb/l1oFruOxfz+Q/fvyvXPWt1zP8WZ/11zWz7aYMp71qlEBLdjZGyN7v0vSTBhv7jsNTKxgfsXj76atY/93ZyFQMkc+hM2l+9Okb+N3Xb2Xzg9sOu/drbngnxG0kEg30H9eClvNZNu+y6X3a2zIsSS5GB6A9SVCLVqAh8gBZ1jHIwrZR5p80waLlfdy7cwmNFYdSt89a1MLY0TBykmD72CQtbU1gSISU0TNyXRgdp3hiC3sLzZz+rLfx8/efjVCRafbWtbuoNDlU52fx8klAEBg5fnbmdfQ/3sZF6SFekBlk0bGDWCWJWYDQDbBKAZaCG3YtZcO+fmKTHmEhAdIkP7sfuxgiG4p4ocA3tn6Zq977NYa3Nk29VclYVSJSSbrNkIzUPL9rlEDGMaVPY50JjRBckAjU6AS6WEKPjEImFa3aj0+wcsVevjUYzbwe71pP74oALJPlx1doAK6Gt13bz6uuGOCCr9SoHp3n6o3P5up9J3D1j47HbAvpnFWl4mfwJ+IEPYpEXPBIr88LXjfOx67bw0mde7l343y+85yb+eiyDfz4Rfdibx3lhF9cQtWPquJgAH232xzYmoBKDa+QQBmCWphG21MDGAE4kC6YpB+H+stmMxYPSRYNmvYImh7TfPTUGxASlCUjlagW1FbkqK9ZQhXNnKU1hCno6qrDl6u4K2czcdEiyhcsxwgA5WPnQToaY3OC89o2c1bzdmanSyQNQR2TPW6cU09swOgoVKoY/WOkW6fCVywLnmJ2XtcugSNgYQfCC1COSZAw0dk0NGUQ45MEMQfdcNFovFlNhG05sCVaR9moZCKBkUjh+AbCspBOHF2rYQ6WiG2fxHAVRgiZuEBmmzBdhT8AlVUZQlOCP5WxRwukjOHNb3tKliswyz5GcxPCMPDrFcJ6mSX5qbTsKERN4+YEbtqi7scQCqSviI1FiwwqYURMG5BMe4TtMXQ8hpICXatCqJFaYZYaGEGISFggJeHwCKVYAS0jdZeRbGA4elrRqyYLMJX2WdQbGP1jmKMVqgs11V5N18f2k7lkH+Eg4BFVknoD60CRenuCWpeNSkoCM0A3ZaArRzwxRQBJ+I+fPYJZcCOz5bofpXkvNxC5HKARQmNP+oSmSZB3qM/PIpMpaMlTbUsR4iP3DCB3DyC1xizVkTUPHQToeiMyeW40Io+rRgNlS0ShTOqBPdGDt+3pfnr6bYSHyD4BMDSCHC0hpIEIFaJaQofB4eqbqeeVDMB+YDdh+nCjThvIzCuSf5Uk9YomguYkQdrmO7e9F6U1WoI0TWJWjNiTfcRvepRAhnhhdHoNtGxVxKfyRUg7up5pHlr7veT150A8Dqakpg18aSJSika7yYue9TEA/viDu/juB36CUor/LEr1fhreP3ecNoMZzGAG/1343z37nsEzol51Ge6fYKhvgjAM//oB/z9G5QCU+1Lokb/uRv6fgudhYEaJOQQsWfHMEr//yTBzaeT8Oejmw5VAnrTwfMWBfRNsn7yODu/rmLUPU/XGpvdxEtEzlqbB7o17mRgusPmBQxN0IQTCWokwntlP5iAuO/F5XPLNJl733Tn86erb+LyzG5UDvwUC6y/X6abWLDIRR8dimL5Lc1cOEJDO4OPgluI85zUnItHoiQJ23IomT0dAe6yLzx1V4AsrSqghk1xTjXxrGTuhIZ1i7ooeOrtb0Y065XmK6twQs+4T2BIhNTZHrncqLam1S2pdBn1DOWanx2hxKmR/O8rzjIdY3DHOk/dt/avPiTDE3jsZ/R2zwTShUCLUmn2bB1hcK/K8TD83Fw/P8paSCeK7HGI7bSYLh0x+X/3hF3Hi+c+iOFaKzE5NCPM2xukGD9bTTPoehbNSFFdnOeq5DonuX/GKc05mopqHXJaVZyyP1AxA6Ad4DR9pPv2narJ5jGDbToafnSc+YDChxp+WUnHiQAM1NIIaHgEMmCyC5zOx12NRapgF6RGakxUe+dMyzBsS7L7m0JLwpS8/AXtOg1mLx/jK5rsBSOCjS2UYL4DjoGyJHgppbq1y6x0/Q2YEjE+iCmW++/6foB7bSaGzRmWOgxCCZMcjHLfmCW5652eIGwnCwGB0VyuGLzDPKiA6fTwzmiAD7J64ko7rxll01T5W791DYayV2nE+9eMC5lxS5qE1u7n1qnu4/w+PQy6DaMri1YbQ1SrvvnwZrpjPi37wQmzZIBerIOsldBBOT/4koB0H0mnMVIJZz17N67/2Bk5+3Uqe3xHVnV3FZhp1EyEF0jZoE5qM0Px64xJOe1OFyUwcYQYU9+d46IklBLscVpxUwg0FykiQ3wD5uyTvOv8ELGkSS4QIAcmwgVdxOBgtYQAn3L6F1nfuZMPtGWwtKK/N8/MvH3OoqgpFkAoJEvC819yMsoj0wD6EBsT3V+i5fhPe8e1UsybVHgMEXNRZ5KpH7qV3dgEdarTS1DstkiMB6pSjeKw0D7tF09zpce2qxwiyMZCCMG5AxkHXPUojFpNDDrUJA0NptBaM3+RyxdUnUfU0a+xxRgKijD0Hy1sFTB2FKMUP1WH7yQOYQxVUwkIrzZ4P9OD/sBXjMxPQF5Hbxqx2yGRwW1KYhRoiVASOga7VEVojUklkLIbIZhFdHQjLJL2xjBFLomImoecSqoBSxUBLge/X8ZVCepGCzKgFCC/EqgRoE9xVuUixAcRafFqv9shdVoBqHXv3ALF2g7d3bwHgjR1b8DcmSExoQsfBzYJub0Jk0vgdkZLKbYlhjxsYkxLfldQn46hFswiWdGPLOkFG4GdMZMXDmKyCNBANHy9nkVo7gpjqor2KRZhsni6bCAPUwUcpQNRd3OPSLOgZoqNtEiOZAieOilvYy6HpX0RU0RsNwEfbAqevCp2t1DstVEzymueswPNhuwvpdMi3r78PV9VRQiMn6hgVD2e0hlWOsv4ZNY/2G/uYuGAJXk8T6T11YjWJU2tgeAJsGy8XBw3V+UlwbIRjgxUpKrHtKOuaaSJ8haFMhBJR2nHXA8cGKSKhk3PQ1egQTECPjqPLFXQYol0PPaWwAtC2hUoniJxsIL1nErN8uC+MALwnQSYFOhFHWQbKFLxxzUcRoY9GITwfHYthJJOIVJLUbU+QuOVRLA1WGBIKOZXyHXLdbWBbePLPBPrtzejuNrRWUBWEgUnmrkG0p6hXG3zhtd/gp5/5DT/59OELNj/Z+g6ef9ubuGbzx552/0fC+7/2GYb6L2Dv/vOouoeImmptG2H98b/pHDOYwQxm8D8JMyTNDI6IcrlObaJMZbyE92dZVv7XYUxhTxhkJv65zcWr+ugpnXNkhWL8U6/3z4LRnEONjqOrh3v4NOXiiMDniT9t4Ib3bMXxoMUAWxwyplZThKBWmrt+fj9oaFT+MdNBQyZ5yynv48Wr38gxZx4FB3kZDU7zX/dZunnzp/njlk9z884vsu7OJ/FdHykDEn2aRJ/CL2sUArQinopTGCke8TzSSLNq1tdYNetqfvv45xHVNGEgqFcsgkSMZ192SrRj0qFnzgjz5wxiVms4mYB43CWTOHJZ7c462gEdgxe1Pcq+chZbFTnt/DHe+/FNfPybm9j68M5Dz+OZQhYtAx2LSIHRS4+Kwgg62kBKvvnO7/Plty7mg+9dyYbHDyfdslYc4QtEKKgX/aedNtWUwAgVIgb1pV1cfMIo3SacmejjU2+q8oa39PHyo6fc9W0HbAsRi3HOi09AxhyMmA1egDQEC1Yd7mMUhCFS5aJUvYZGWQKv+vTwwBe98yII/aml34OKIo3dbbM62cfixCCLY6MkDti88co/csWVN7JntA+AC5+1nEuWrudjy+5ibtddAPxo61XRynAuA6EmtBUazdi2JtZvb2LN6U8cuvhUhJlRU0zOO3yV2DAcFnbeyBu/+3z6C3m0JVD3tWAvKSEbAZZf5rQFT9KsD9BY0ErtqE5aK+OIksOpJz3JMat3sDg/PjUp09EnVGjfZ8W/DpA7UTD/dUli8RO55U17+emJb+ChF34FL555avEi+B7adQlDzeTEKBtu/yabH1jLtcdFYW3/0v4YtaqJnRQce1kJIWFTOUX/oiS37n4+937dIvvrAyQGDOIHTOL9Hjc2L6dQSyJc0AlJmJYcO3sOb5u7jV9+s5mr3tnLI7emQAte+bkXUatFoovv3b8dDdx4XTOPP97Eb7/fBgdNRwWkjRKff/ZNfOiM27n54RMwz53gqOX7pn1PrMky1mOa7I4KiVKALMPCzn30a8EiW3Ptjx+l2gnl2RLhKgKhcXM2PVc06FhZxWhSXPelboyGxhl2ST/cD8LEn92KSti4s0w2zV/Ie392GTd+YCGVqyw80+TS9S/kJduezwvvvPgpb1lTq1gQgLtMgeGgp7QOzmQNqaGRd/Dm5Ii3+3x+0R0sWhEw956QT27fyrLv7+cj993FO396E2ZuDO2FWLuH0I064dAIyjYIYxa67kYZfyaKCNdD+CFuewLSCUgm8CYTkbJncBJj9wAyEPgZgVnw0DEDP2VBLcRKhDCVFjX4RBtbrj+KbfuXMLowCQH4w8Nsr0chovcPd0E1gVmH2JjGrCqM4cgbh0yS4stOoPqcuaTml0kEAeqhJM2/byCVRBgm2jFIGi5GG4QpizAVQ4UB9ZYk1dU9CBUpOdwWh8E3LCd0FeSbwDIQaRuhp/pEFf1ezj62yPyWEZZ09iNHSojONnQqRlgzCXJxWBAjaJkKb5UCZ18BLaIGqqVguC/D8+YcxcO/bmaJFbIoUeGo/xggOKNEmJgiKKfJNxER2Z5H/jeb0XGDyeVxaqkQvzmO0T8KhqRxbDd9Jwq8jhRDL1tMiEIHIZgGIhaRtkJpMCUinqByfM+hqiMlorkZ74SFBEfNeZov4J+rIY26T9jZhk7GCNpyaMtEWQb1Fa343Wm8p4T26qe0fbUL+j7mI/cNIZ/YjSy7iGoDKnXk8ChBKoaQkc+N2d2GBGJEixxB2iBICihEZ9t56iyCpI32vMPKuujFR6GlpjovQ/ZhF2NcEI/HMJ0p0m3qRizrcHJnY6GfLy2+hznmH/hL0FoTuNvZNzgQkXZo3CD6PXp4/+WMDL+QNdf/hNN+8N6/eJ4ZzGAGM/ifhhmSZgZHxP5NBwgQ+IGmPF75v12c/6vIbHbJbgvIDxxZKfFfhY7ubBTL70dGg/+vIp2wEdkMIpfm19+9fXr77PltGFpTHC4ysT+JMD5Grvl7WNahcJ7jLzgOKQXzju5lzfNWA2DF/nJ2sb8GaUjmLO/BGnOhCqBZ//K3/l3ncGvRwLMt3I9lheQ7QurFWpQVx3a47EMXk/sLmbWEsBFTZNTK5h4KQ2mqFYfRsx3mLZsNgCnH+ciyu/nQ4nuR5yikAUFgkLKPTNatTFaID0N8BG7etJpzurbT/+RculYG2DFoyrqY9qEuPhQSWg4qkJ4yVDdtzLFoIjw7N4HozqE6ctCWpzBSwt2rKf1BUbz58HLELZMwBUECauHhJI1SivL4CKoOlMHZPMSejRkWOyWSBjxnwce5bP4LmRg5i9HB5yBMAxlz0KNjXPXab3HGmYt4+8dfiBUz6VnSTerPUrabhsFZL1oThQQYURpgjpCesXfpLMRTxv6Z9hyJphTzX3gcOak5JzHBSfES73rVfl7UOsD5+VFU+AsgMr58Rcs+1iQLvK4zCoNKp9O87xuvBB2ZT6dy7QRrWxD7YzgxzY69eeqrF6JMY5oUiteS5EaiZ1cZOhlvYBEjQ89HU8b2Q6wGWHUgVFQfsZnV2InxTk2x26Rez5N52SSxC6vMPqmKHfqEW2Icqw+w8edz0anI84OGG80dZUjh+Az7/rWXmxJzUcm3ItJXgH0K20d2Yk64029+OpzCCxCmiW40cGuSTevyTG626FgakaxHNVUBgZSQyQh+MjGLB71m3nL0G3nZSW9j97UOrbdWyG2p07S1wYnn7uO9b3+ULZtzxEY1yQGFPaDYMLmPf7/7HLRhMXggDloSmB5+zJgeiEgJpqVZf0+G93/wDNY+OR/Z3ASL5sKieawu9XNSdpRzWvtpWzjBivF+tu5u5ZJ3/A5scHtbqC/pwFvYinBB4fOWN9/PresWoYh42kX3bqP1kQodNw9AsUbHSweZs7eP/jvibPhdjsfvyBD3K9g+xM0yaIU4MEbhvKU4H+vkXRfeyYJj+hl08sjmJiwjTiwGk1ULlTSpJw/WNjH9r2qS1NcsnX7uAtDVKvE9FcyxOvYdNe5qdLPC7sMwDKQOeGPHZgwpWBN3uf5Hd4IuItBo10cnElE4StUFU6LK1WiyrxU980pc98Hf8e5338Ox12zkqO8O4QkPNasN0ZTD8sGZCImNujgTPrEJDyE0qa21yGfJtigeSBN0GrgpjXpFiqELuznx/2hur0QhuL/f1YNWmtAMSdywjs4vriPMGhCPo9JJQkeQP3WSBd1DNB0zwazv7yCxfRJKNYxyg6Z5cU5esZfFu7cTTlYprIxRW5CEzhROXRNkLMK4ycQLFqAyScYvXohfLqGCEJ8o5DBq+xKVdOhcVGNebISU9DDyTagOi2BVmlJTM3uH5xN/a5ziGXMImuLRQoiUxB/rw9lbxpisowMfMPjhuzt5oJrnzkoL68fb+eFrboQwxM/FCOImoRWZJQvLRIaa5I4i2es2Ih8eweybwDowiZysIkt1kvsrtKyrgdS4vs/kkgxhxkYlbLTnE46Noy1zyhboqdQJYFroTBLDcLBGy4eFoT0V4rC/oxgkWW2gLRPhNohtH6fRbmNns6gpU2IBKCmn+wHvMRgdsTEnyxjrtoHWqMkCurMdRwl0zEQFPmrnvun6m/7dOqQXYAbAQf7HE1Sf1UO++3Cy/HPvuZjygixh3Cb/0Dh2wyCMGZBLsva2jTR1/H/s/Xe4HVXZ/4+/1pq2ezm95CQnvZGEhEDovUlHiiiiiIqiiGKvYOcRRbGAXUFFBRWkSZPeewrpvZxe9tl9T12/P+aQEBPEx0cfn8/vu1/XdXLlzMxas/bM7Dmz3nPf7zvLxNmdHHneYbu1W5gOSMiAyVZtr5/9VWq5b6JGT+FHH7iXH784je/fehYN8fA6Lbr9fHXtAUSvlUS+6POm4774d/uqU6dOnf9L1EWaOnvlrt8/E+ZQOy7LX3j9yjX//04QBHhagFF00WP/M7Hgjbjpoc+jSYWmSW5/+Yv/1n39O7ngUyehvDAt5Gdfu2vn8nKhiuu4+L7PgiPn0tF9LlZkyW5tP/DtC7mrchPXP/cNLvra2/jtth9yW+6G/9F4+jcPcut3/8IRvx9h3ytf4fTf5zH1/965PO39J9A2pYVphUn87uuHc+sVF9DakQmNPGMRJi2YtEeqzetx5X5vwkrZROIuxUUtYRUMoDEY5eBoniWRAi2jNWq2iWMbRFR8r/1M2TgRWfEx8z722gZu//VROOsTvOu9HwRpoOsJblh/DVMXTub9174TNaEd3FffcoZTxQAIMlFUMjRw7Jmrk9gXim/SKe9nEE1YGMrCNyUHzth3t/1HLTN8u2z4lJy/iXbKvZfZU9/P1NMdyACWwbaxZlo0RVx6BL7DS8PvZ6sb8Ep1G6nODEG1Bn5A4AesfnY9T93+Aq7tIV/HE+uqy8OoBTcZYGcVeWvPaB4A/TXtJ05LkUxHmdE2jbuLU2nXFW06nDT9r7g04ROjO3Pgzu2bowfwp3wLa+1dUUiHnboYcgWEruOWqmSsQUgINnSk6HtlAhFX4k9pQ+oSf4IGJxVo1+ME3gARNYQmwPBXI/UOvnxOGaUUggApAjLbk4xN7qJ7ao50pMpa81jetOhFTjnkWSbtM8o3PzkJVhyI7D+Tmcd4RBZHOfB7NZLTPSiUSDRINtcaydWibMjF+eu2YdBnsH3o0/QV344xUkYA9ozmXQfID1C18LpQCjxh4Mw1iSTD6B9NU2RbKuhGgJ6ocUx6gNOyvTw69A2ufvFl0EFIQWxDjtSqEd591DYmxXJEqlHsDo1qg0DVCnzulqd5rKeD9vm50DhVCBpvehnrofVcfOQ0ygXByuejeO7492hTDwiJ77kIoQGSkecjbHMs1tSifH3C5az9SI0pb9vC/flFHHDaM2hRgTOlCR+NkUku6f2KPLqjjbctXks1gCcKUX74o8eIrBlA2g5axMQekixf3obvCfAVpT4dpyQg8Cm1xsF10ZWi8eaXeO+8pZzTvIWLpy7FeVCB55O8y6c4Br4n8B3Jjm/tu+srNo4WTCCQkmpndueyD33+Kb52/e3MMVfT+JQkucqh6On01mJ06j43D3XjAwkNninEaP/eGKW0jihXEEPD+Lk8XqkQlhIvFUJDWgWf//4GZnbkiB5YZnG6wGe7VnD5fas55P2jyIUZfBNkDfRihVqjTq3NROkauY4Ut6x4kQNPHiR4wMFHccAZS/naIbfjTG9ncWqEdzaFkXmRD/bTfMOzNN7wQigqaSZy8yiBJvCyFhiwX2YrhzWsZ36yBxGPgxCIkTx44D+Up23rAOrhEvrwKIUZksAQ+Loi/vwAKh2HZAxNmCgzgDEHzfd3Rs4xfs/0W5IUj5uBmfHYVGslYVZRlkbpaB37rQkqb24jCHRq96SIPrEFJxZgpwSe8rEqoW+LKNXGvX9AoHHjt5r53qZFfHTmkww6EUw/FGak7YPt7/J7EQIBpLdVyGzJYYzW0PMOgaXhzp2I0CPECzrWmmEm3ttLZP0QuhXHyDQggiBMVYpHkGUHa3kPiUc2hd/B8X9Vbz9iYBTVO8DfSjR7k2zU9n5UPELQnCZoTqPGjWM0T45XLnN2trWntxK8pq9gZPe+pCI0zY8ZCKVQto14jS+OBiRvf5nAUATJ8ELP/v4FUs9sZ3RL324p8vF4lPTTW0kv60ffNkjjc1V0F5yhEl968zcZ6RnFjJi0Ttxd3Dln5s34sQ8yofXvV2HxnT8gCbMJj180xA8++YWd6w6Z8BMOacphFVycpEXZFgwN5f5uf3Xq1Knzf4W6SFNnr8RiJsIwENEImczeJ4n/X2Dr2MB4LrwgMePfX4ng3tVXc9/abxGLxd544/+jnPS2w6Ecegy4o2EK0MDWYdqnt+I5PvnBAjdfffvrtjfMXQJK84QmrL3k5P93aJnYxGFnLeHItx5KNBUlN5DHqTlv3PA1tE5q5oY13+Pbj3yFqdkDSVsdzN6vG1WtoHI57vnN0/9wX90NjdgjUWpjUURl10yuoVOyzDFY7eikH3IwLRfTcml+nWthQnszSIEXl2g+mDmfYP8iWEcQaHNwjX1IZTP86MWrOeuyU6hOzODM68aesMvPR05sQy2UmCeHD+CfW3wPDaeO8bazH+GMy1/goLfOx4pFEWVFz4bibvvXpMTs0BGNgrbm5O6DC4YR+KS9CqISEEQNapq5c97qF3/MkijsF1HERMDnr307WsRE72xh0pwJvOur54FSaLrGlPmv780UT8cQ+ARRRfdBe9/OfY1+1NhmY5gGWb+ZEzvfFWbJCHB9CNK/5Mw/nsaZP3lw5yTjlZrFgtgoncYQJbsfACtigaFDEKCqNla3R9BdQ2ut4jfHKE9KYc/q4LTHxvjho2u5+9jbOfyYGlRu3fn5+/04QggO3f+bfOjEg0h6kugdq1E41OwoT26aypObp+CSpzteYVo0zyoaWDzlbIzhvzJ//o84fr+naOl12PpImlzMZMnTOu+4azu+q+O4Jp7SeN9Dt7O99ApfWz/G7SPTiS6y8LMxgomtBEIgxk09Cfyw2ozjIZMasxbmKdph9M9XvzOX3FCcSklny9JWPCWoBBr3bezkxjVLccV4TE61SlCp0t5VIyFARQXlCYriFEGiPJ7GqMHTJy+AfCE0Xgaya3MM9sQ4a/Y8xhLn7TxG0rYJcrnQHLVaRQYBUlXYuiPCzz6+gMvfcyPRWITRY8sk3zbIEj3PHVf9mquu+BPuW4rQrTht4gbObhuhw4SMBgviVTINCtJJRDJJEDVobchhBA6gCGISX0QQpgGBRkepD2qhIatQAYMbYFUhxtN3tiFrNvHIEKU2ReyJLEppKClROQPtZ2lobqRxseD0m/pgTMOoOHgLp+6MYGheUiXI+hxyVh9ySy+3XbkPT53ZiXNekXfMn8/TxzfyhWNn4PhwzZb9ef+k5dy29EW+8WyYUidqDkLXUChc08S3TALgiXtT1CqQyvsYwkegOCCe4+OnP8UlH3oB4YNTreHncuNarQi9U2IGqXjAB7/Ti2YHNDyykX0bBrim51BkTbBhoJFA/c0joxRhypFp4kVDM1474gOCRrdGq2YT32SjSmVUpYYqFPAyFp5v0JnK0TzHx56UxqhpRIcVek0gax76YBVlGWHkiSaothp4HS0UDmpn+C0zxss6A54iv4/GnS8u5v7H92H1UAsEPtoOC22LRPRLrI0OsVgVzZQ03bISa+UApX1S+MpG1gIqnQYDb+1CaQIE7PhNEu+kHdxxZhMfffsx4xWTwLckSnmomk1QKu2KbpECFY9BSwPSNBEtjdhdSQJT4lmSllUBsrGRhNEQmiFrEmKRMCUrpiMQJFaOYBac0Ppbk6DrKD8AL0AJsUdlvtfG3by6SvcV2tAY+pYBGMohlUQ1ZtAbOvBfPU/jRPtK+LMm4WZNytNilCY17PKzGf/xTIWbiWBnTbTe4V0RYK8ZR/y5XhpveHHnOESxjJvOcPW7r9t9wH3DsHkHwvOIDtXC4ze4y4tu4/It5EcKu19a0qC94UNEIwv4e9j6yVQUDHuwoXrwbuui1mQuWnw7wjCw22MYO0Y49+Av/93+6tSpU+f/CnWRps5e+dIv3ktLa5Jp05s59KSF/+nh/MdIW2NUDgzIL9Z5+8lt/+nh/L+DbqCUj9I1tm3o4YK5H+XHH9v1RqxW+vshzP9KNF1j6oJuWruaCHwfzw0nEf9MP6+Nltn/6DlQqYDjcc+1d+B5/3iKmumAPiaQGpz517CsczWtMeho7LANdiRTGDLA0j1aYq8jDgrQSwHWWIBeUwRvynFK5ilcZw2PFdfyaH4p1doLQBgR5hsCNxPBOnA6DwR/4M7edZxwQS/HXbQSf3qY2rK45VfIrgoJo0Y6WuXB3HOU+vIIT7HxlR17DOHak07h4wcfzsmzZu++ouGnBFo3G5+MoRyJuWWUx+/qQI2Xll069NudZyBt7M+vf/ooGCYqGueDN17Gkecewo61vei6Rvfcrt26XnT+1Sw6/2puefB5/py7kVnTY8SaHabO/BuhaJzJrxF5DnzPAKd/8EQWn7gvR2am8EQlwqgPz9Y0rnviCWp/Nhi9V+d7P7sXgCnpE2iSAS16gC5K3LPhDg6/62PMvc1FjeZQhQJfOOYMjpi6mc/MfoozLnuMS7/8B6QD7ckKnWaVhOZxfHeVbblw4iIEzOx8ZueY3nPyQZziGOw/tZ1AKfyCQCERmmDpwCamGVV0XZIWENE0knGNiOmTMFyUZjI11sbAh6dwVLaHubEc8xPD4I0fXQee3FriqSem8cgLc3jzJy5CzZ6KVQYRjYSGtpaJ1tyIls2gSkWCkmLt3THO+2VYSnfoV5JKVaCUZGhrCq2/jZcrV7G53IoQAV4E/Le2ERRLBJUyBc3n+g2L6eoagLRLtnWUObOGw9fvPthjFoW5u4RX9erFjODgQz/CIeeNMfvQCtEjBU7CQKWi+IkoyvXY3jSH6z59NP19bQRC4cydhEpnsbU0g3/QyZXjrBicgF20iGRsnhju4NHhTt7+zNEAjNoaj440MK97I7JcQy/UiN4yRvDyuMVqXGId0oyQOjJvMymbh3gULJPIlzJ8r/dYPvDCyWy+ycSP6PQ3NWK3JfA0iVpvorYkkHGP9DSfakeCc29aR8f+JY77wFJkwSHaV0J0hp/7me82MvJElPuuTtH6lihqYjP5WBdeLQVaCmouUbOdaz83i1mZEaabDs0GLJgQ8O4vbEJoGtIJELqBP2cSfmcL3vwp3Pjdbk6du4gLP3cyPzj7AL7+08U8/ucJeL5kvZ8Kj7QhQ++pvIesKrRqQKArbAX35duwggJ6VXLnEc0cltoMAn73uyWc/93XeO7oWjjx1ySjk1OMnTqT4qGT8LqzxF4eZb9sL5ZmM9schEIRqhWkH+DEAuhO4vkms88uMnxMFwECJylwMlpYocoHv61CfH4PTY+tJv1SLzITw26K46eiFKdlQAj0XAWnUUezBSoi2LiiGbVxO8bSMtE7QeWgtbIDc3s/IqpjDFZJP99P+YAMQ6d1UZgbYfD4BJX5LQyf3olKxlHpGGAw1mMRZBII10fWXIQXIJAE23tQfYO7rt9AIWs13AkNVA6eDgNjGEWFPlbDGqoQpCIoSyNIRug7uZPRqRJVqYHj4VWreLUywvPCCB1A6RpBSxq/vQHVkEBakVBMfRVNUk3rO787r/7IbCb8LkWjGLkyouog+0ZhxUZksbJbmqOwbcxNvbiLJjJywTS0sV1RiDtFGC8gMCSqd2S35eP1/wgAbTSH1tG6+w23VKI0Vtn5q11zOPHyU8NfDI1jFk0MzZH1Xemzmqmx8ondK/gBqNoDBKPvRTnP77b8lk3ncPvG+fzmyZO59G2C54oNLC0381Ru7xGyt710BbFNo2g1H5Uw+a/P3rTX7erUqVPn/xL6G29S5/+LWBGLXz32+f/0MP7jZGPTuedChascJjUf+MYN6gCQaIxR6i+AJnn/UV8LH/B0HfwwgmXG4sn/a2PZsa6Hr731OygF6aYkkXjkH05N+nvEk3HQtJ2ViHrW9zJpdtcbtAq5/pzj+eCTfwYVMOqFESpadR7rHspRLEUZnJzGc1xEIFnY2LHXPiLRsAax8kPlw17WTHVBlhoJcoGOqzQG7W10Rw/kxeWb0TyFtH0mzwgfqof8KB947woO/OVZnHvYWijC9x/6Ouu3TmNtuhuxzif2y20IbzxEvnnPKlNHdE3hiK4peyzHG0b6K5kyu5uXh0yEgh3FNJVgHyxWMy0ywpgD230o6h7tHVmWxXUCXePRe5azYOEkahUbIQX7HLZLAFp4/tWhOaQp+K+bHuXcY/YnNSFDpLSF1SNDez1OP3zhG1z3yaM56ugcf7m5gdLwUo678AjWDFzIM2PtvGj6bKtluH/TDtpKHpHeMvfe9CKXv+9kutInUdDLSEwi5jQ21t7DJ2fluXNwFj96bimfP/soDl18NAf2vR8BFDOS+0eb6dq3l5ZqjT+7bUyJl1Da9bRqPp6CAaeNidquYymE4LLr30u1WOWUK75PUBRI16fimrhJj29sX8zxjRu4fJ8/oWkaG59tZLrwePmxBKPbyqQyrcw/ZRs/3jqX+ekR1pcitD5fI98eweqFH11/N5H9WlDJKCecOZ/ff+F+lC7QZnTi7xgF18UfGgnVI13D0nT2P2OUjPB5NTEgUIDrsXlzkqXPT+DYM77Mllmd3LB1Ads/MJW410DTAVnclE7CeZ7vT32Biy85hMwcn+KTEQZOsZD9w0y9W4Jj03htlPV3zMd6fAvuxEbij2+gORVj6dJP8YlvbUUAd+QyXHfFiZSDBAEQXzNEqdNAtWVpfmQYHwVCMHJoNw3rCvz19yNM+fy7uLH/FfSYR1xzWVto4Ko1rXxxn0fY7kT4+B/exJa+FvwD8kx8agco2P5YhgmTc+zos4iUXM5d8BxHvm+UC99+Di+X20BJZEcz6502jB4NO2+QG1Dow3niYxq19hQxO4q5OqD1CyuJGD7DdpTy5yUeEo2ABYft4IGbD4HJY4weO5n0uZu5/8ctPPBjAAUPeaiZOmTisLkffAW6jj/i8swjkyk9GVD66ys0GXB1Xze1UzXEr3WklULhg++h9QwgLIsgm0R4HlpZ4QuLNbd08Iro5BsPHgS+TmJLH9Tc0Az35Y34h8/ATQE1xUkrTqI1yMEGF90bBS/g5scOwlnkY4wKqioU12rXTWLsDzXsmV0IT5HY4uCbFkFjWIobqbjXnUp51GJSZASVSSGqNWhIoBdcHNNgaHWE0jYJcR2tKKhlFEbeQ0smwbKIrB+FwSp6r4OjF1DbR2laUcN7ZhhRq4JSiIiFmRNIV1BtVCS+tikUI4IAL22RemYH1e1j2AJEl4eftvBTEfSiAt3EVx7C00HXqM7IIO4Z2lk2PLAF+kgRZSZDPyMJWtRAREzEq5GYuoaIRXEMyC0J03U8rYuOpjE2doRGx60P9yMcDz8dJzqiaNgUoJQiqJbRt5XCqCAnFEkUIDIZxMAYuDaB44ZG5UO7ok7Kk9IUD2ojdlNYaYvGNLV9JlCYJsjc52PkHDR2vYYwxj0FFa95K2s7oW+N46C0OF6TtXOb4vELiN2/jMCS2FlJcm3vbh5WqrsTNTAMmkRls+z2wiMeQ8+mefahNRzw5qsJfOiUUB6oMektR/Lz332Q3q39PPSbJ8HdVZBCAI0du1IBXyVw1jJQXUOce0k3hB51+dIWpL8BIeHGSxaAdPn6swchmiFrKpQ/wo7cd7Hs2xCxd9Kc/TjRaJQlx8/lvo07KE1J8JNoD7/68rdZdcVH9/o3o06dOnX+L1CPpKlT5+8ghE5n8+fobvk8QtQOKmSFAADauElEQVQ1zX+UH9z/mVDA0ARuoQKpBGTT3DLyc37X82Oue+7q/7Wx6KaBFbOIJqO857/ezmXXv5dYMvrGDf8B0h0NoT+Cpv3DAg3AcRP3pT07QnvjGAGSnyx7jB/+5Uo+8YEHaTnybOYdZeDkLOyKTtrc+1g9L0A4AX5EogSkBsq8c8kAEb2VMTeGF0hybhmA3z73SmiyGdU59KhZAOjagWwIJPEnDDatDoWgzUubOeewVxjzohQnCPxhtfMRXDT8N65/fQaKBtomjxvVagKnLUbcKKALn5QOowFIJB3xsznutAVQ9pAFmxef28iaZzcw0pvDrjoUh0MR6+d/fiysNBIANcWpB4efozCiURiLsK137ylsmqaxz6WdFFsFD/66iefuWcr9NzxCRpZ5V9sWfvzKody7cR8qfgR9qIRvSvzBXaldqfg5JOKnA3BqeoD9IxWOym5jQpvP7zZdD8D2WgwFvFBoZHtB411veZZPPX0MnYkyHXqBmabPRBO2e4Lna2egvA2oYJc3QjQeoaEty8lL9mfukn5uWnIfl85+iaaIy1PbpnFb30HoRuiLU85p/P47M3nyT504tqJ3Yz8j75FEvjlI7doDeGnzDMbmKdqecMhudJB9Y2R++xLJJ3u4dvu7w2PiKpqasoR5LhWwDIjFQAjKJZcXnmhinRsaYTd8KsAvlPFzeXrXKsbECiRjHN++gcCT0KhRa4ORxUlGF5j09MT51Xe76B9tIfdUA7YRJ3dzlal/cgmKRQig+w8lTlr0At7iKQRdGfxzFhM/cia//9YoOmHQzSvbJxFUE2g26DYUplhITyEDgT0lhWqIEiQNhNQZ7TaBUSLa1zm1dTUNRokrZj6IQYDwXVqzfSx3JFe/5QG+dPHL3HTUPeGM0/UhneGcr47QuaBG4MItP2jhqd9kUJ5PJRdFlcu4/YO03tZLy729WP0eshQKiNL2ifV5WEMVzNEqvR9uY/1oO6OlLLGoS5u0sQiYolWxowJ+uZ3qg830H9f9moI9AlmoIhwPmSuD74E5bggdKAIC4p7kkgOOYOMqg1E/yhc6N5JtUfieQ4BCW74ZWa4hRvPI0QKy4hD3dby2BENLGhlb0oTbGSW5vA+9bwx9qIBSiqgrkFUfqenEqxrGm3oYPa2GZkTG72uS+HafqOMT2yJw3DD64c5j7+K4GevIrvdIb/FREY2g6oDnImwfUiYLY70cl17HquIEBk6bCG3NqFScwIGm7Cba5xaZfEwVqwyaA7G+AMMB35QEhTzC9mASFN7cwMg5rRTnxBCBQh8rodcCEFCc3oywNVQgiWx3SIgIXnMac/02IkP96D05lKPwHEEgJLLioRUcun6wnqZb1pB5YAtGQaCXJbGVhTCFrFqDRBwiESwh8GMGQdwIqy6aGl7LayqkeT6qUMKd2ECs3yU67DL9mGEqpTiaEAgEIleEqo0cK2NUfYKIHpoPV2rIbBLZ2gSNjWGKk67hVysgQPogpQns8oJB0yiePB0nk2LHRfNwZneifB+9ZxinOUH+qImo6ROgMTt+Ze1it1QlI/SakZoFvo9WdMNUKyD94jbo6qSc1vAiYrc0J5FKYmkmCElg6fimRnFymA7v7T8DkUqG/j2mgbV5BCT0KYWWrzGyKRTROya18a6r30IQNXeOx616TFnQvce9e11thO8NNnHNthd2pqBuHf0cq8otvPRwC74WDqy4MU7Fi+DYLtWhs5G1P5CVNlR+ubOvr//g3TRMS+O0+QRAZG2F/d7ylT32WadOnTr/V6iLNHXq1PmX0zmpLXwjOZID3QgngqUK2WyKpvY9q/D8O2mZ2MSJ7zqKU953HAeesh8zF0/9l/X9xy3X84B3Mw+4v/9vt72w/TyqdoxyMc7Pttyzc/mlB1xIkz41LK+NoDOR2Wt7IxVHeN54uhM8c8MXWDD1SQyZoMUqkdar6CJMK1u5bBuFbkm+W3LmUWGOf8W9n5lmwOnnL2XVs2E1jONPXs3DPROprYnDmgxqvNStBD55/jN7HcfekJqO03gHS9elEZoGuo6TNhDsiiBp0iEe/IppjWezz4JulKWhIjrDA3mmLepmyvyJTJozgX2PnQfAdTc+g1YGbMAQfOk9pwCQLMUI+iLU+oO9jCTk6Mm/ZdE+f2Ly3Ek0tGc45PQDiGoJslLh90Swe2KIkoCmBF5HGndSI44TvukNggoDA+9gaORTGMZUlNA4LDZKUTt6Z/+TJy/l3urPadeGOG1iH1Q1pneVmWXl6YpGSGvhcfR9SRe3oobPQQ3vSh3JDeZZ/ex6PnvRiSRiLqWaSdTxWVPy8AJY2pchGE+HIBXj/udmMpr28b2A/GARkPQ9m2T6nG4OnbKB2dMGMJ5ehfHCOvSxIuga+rKt/NfEVUgVEJiSsYEiV/zqQjBNcByoVCEIkJNr7Di5i6wfTqG0xTGkcsA0UDVFXznGS7UYr9RiuCMB0z65DmU5lCd72E0Onzipmz9fl0ZzFEbJQXdcrvzrJP66+Ts8tOM6/rr5O7z8UCOPfzGDndIITElF89m+oocVz7dw5UcX8qUPzGRoa0B8c47IlhwEPvu8uYcjz3+Jqa196E0R3I44tUQEPQDNiLL5gydy6YPn8K4Jq7j3gDs4Ip1nxXF38fAJv+XAaJUu6dBTUzRJk1/8aAHEosi2FoQUfOuji9lhzsRsCpCGYlhK9Iwi1ekgTB3luVjr+jG3DBF/ZYzA9nZ6fEjbpX1aD34sgpmKEKuUmdbYT9yweW6gm33NGoaj46QEpoDOm1bS+czIbgV7zv7pRo6c9SwHvn0VqU43jDocT3GRiTgqN4pyHN5/xv4YfuiRc9ZZG/Bak/hRCzFhVyquMnW8zmaElET6SqRWjRHoipafvICMJ9CicUQ8SkO8jD5YxBp1sEZ8jPx4ZIOU4LphZJXUMDo89m3t4bLjn6RRhlEZt4xO4OQT+7BTYXU3NwrxfpfGZSWS64sk222OSQ1xaHyM1koBp8lkxwkduB0JgkYYGRIMro5z51fmoY/UsIb90IDalYjBUeRIHhwH60M+o60parE0w+d34Xc0oTqa8RImgaGTOzgLZoCKBEQ32ciWJjTfQQ8cpljbEON+QlKBH0siay6yVEMUSuh5h8jWIqJmk71zA9aOciiOKBCGgWzM4kiBRxmt6KCNlRD9I2gDY4hUEhEJRVMBxFbsIL18lPRTfWz/kUulZqJQoUGw76OqVTA0rMe3UI16+G4NXw9TznA8qJTDFxmaRCSSiHgMknH8xvDlBuPlqf2EFZYQl6AiFlogka6PVnLAENgTUxiOQEWjqLiJNy+BN20vnm6uC55PMJaHQOFmx7eJRVCRKLoIMCyToFLd2UQAqlDE92q4LSm8ORMJYjrWugEAxqbFGVvYhFsaY8LCIR7+y2389Qc/Q1RyDAZDVCIGf3zmBe7asJK3XXIS9vxulKHt8taRe0a3ri5r9NXSbCg3sSq/DIA5Te+hQ6/y+Hf3JwgUrimYMiGDJ3V8s8wtYwF9fjNDvqSg7V4x6oHvfwQZQLzXp+HhXrJPDnDa3Hpp7jp16vzfpC7S1KlT599D4O96wDRNZOI/Y4Y8uG2YF+5fxjN3vkB+uPDGDf6XeNeCQwhQYAY0xHYf19rCEFbKRuo+UfN1jJPb0ujlABGAbu+a8UkpkQRIAUU7NBlVy3PEBn2SWx2SsXBy8dRIho2u5IjD1/P4zz8GwLeePIZVj03EboEbvjBtt90dOuudewzBCWw2lzbgBd4e61ABQ+vTEI0gTQs3C6R/RaBCz83HnsrQ1DAHCKNdBAHS9bBNiWmZ/Oilb/HT5d8mGoswMjICKfBTofnsSzd+YuduUsJEK0ncwl7G8BoikWn8eOk1/G7bj2mZ2EQ0/i6QOjecvoJYR5nOfQY5/Ix54wdRUC6Hxrb5seuIes8QsW8jm7qC1o6ldE5aTVPrj3br/5Qph9Ecz9ImFRPbxvjZydfQ1vgNJC660Lht4BicIEFGr6AoQ9BHMJ7+99NP/prrPvwLlj2ykqPmvZNPPXM4tzodbMtPQSmJFwhWjPQCoOs6VtcYeoMb+qVICaUyFMvMnKfz7TnbuX7B0/zulSKiEqaFYO/ynPATOoEQ1AxYsKQLLaKBF4CpQSSCtz1J/tMN/ODlsPLasidmohISRnNQKGFPH+HhH6aobagw42OvIIGJsRGE5dFlD+zcjzZ+OvRKQGf0kN2O1f07ruO8T52KXgswxjzwBbXmDL5T44WHUjz/UCObvtoBEjQF+nCJzoU5pOUz9YQt9C0qY7og3ADcAL9Jw3I1GLN4qJBlsytZ40n8YIxW3SMuFUrTSHkGkxKL2Li2KSwXPTCIGhlB6lG8nEfgWUy4yOaFIydiX17COqUGgUInjCrBdok9uYHX1nX30nE++IWngADhBohrG9g41kTejvHD9Qdw3jWnc/FnziPjVtBaUmDp0N4E82bQckCadz+1kY4FDhd8Zj1XvmsjP737FWS8iGjIoLW24FerUK6hqjayIUn/5/bnB49Np/2EQbQRG31wDL1QQWmCWnuU8swkYqyMPlRCeAqj6HHKtHuQSiGFgIYwnchtbEVPJNEGqiTXFkitGUOZOh1zahx4SQW9wYKIidgQcM1+j3De4jV87qjHACgDDyQmEKzux7Uk1MDL6gS6ABXQPWWUe0cnsaKSZku2iYbcCJEnNiJ8Hy2wiDakOKi1hLupQOrRTURHXCK5AKuocGa0hREZhonULDqu20brbVtofLwfbAef0CMmsDS8Zh0lBJ6hSD6xA1Wq4E/OctAZVQ57cwWV3eXn5U1uxJ7SQWXhRIKJ4T0wAKxeG6uvinQCiEQgEiEoFnFHBnBTBtW2LKNzDaQ0IBYnSEWhsxnR3LjL5DoAVShA1IL2UDBzARwPFTEQ0SjK8zCVINZnEzQmyJ06A8+pEgwOQ60W/igVpiB5PjKZQJl6mE6biKOkQLoB2eVVkps9Ui+XkPky+AHCjCBLEHmyH7W9HzEyij8nSvX9zdgXT3zde6LIV9HzGlpl3B/GdhC+h7A93Kgk+ciG3bcHhGliCAMVN3EyFuM6PlZPmdS9q5GjZbYsjyOAuITE26qkos0MTTX4zhfv54rP38V+v7kSL23gN4TnRwG+t+f9e272HF7qmcjy3gksHQnvLXrsKH5/0eHggS4Emu1x3JGL0HRF3rf4+YUH8ZljlvDOI08hY3x9jz43XvlJVEYifIXjehz9g9Vce8/JfPdP33/d41SnTp06/wnq+Rt16tT5t3DD8qu4cOKl0NYCmuS+nuveuNG/gZaJTZx88XHohk66KfXGDf4XmZIZGY9JD/A8D10Pb8nDKoddMDET9uu29bsieAmTymSJ8MB2PazxN66eklR8k6gWViRyNEitL+EZu8waf/nSSfwx28eW7c28cEm4LP0yiD4b5kSY33oekxc8z+Zl20BCbPJ+lO2NxK1dkUj3bf8WEechNkRP5bgJH9ltfAEeU47sZ93dbXiGz7XH3wf5P/C175/EYw9nCAScctau0tZuKgIICAJ8zyc/UqShNQPAcRf9HKGB0oG/yf6KNBiItYpq7PUjaf6WwN2CVbsVjEM4dPpPWDY1nCDoJ+vMvO0FmpsSZLNhGH/EWoC0x98kiwpC7BLNgsofwX4YkbycgAZeqWnMjWhsd5PMNkwCeQxoM0EmOKr5RWpuhes2LuDLc5Zjah0I4QEmg9uH6d3Qz9hgnnPfcgh3bF/DJudZkvE8hXKYujC/KXScTfgOG2USx+wMX/urXZ97ygKNhJamx+2jamzll2Nf58LMjeGEaarEFyZWRFDzQsPT8579CmJaDFaVwbIQCJQA3fWplizIQGRQC68lpUDC4xdkoSx44dvNvJpAsfCZlRx2os6mH2oMESHoaEWUKyjLxGnTmdZwzB7n4JKPnsZPz78a3wiTKaRuoMo1pO+DpnH8Eavw01keemQ6iXKFRYlt9LgZLARWxqZSLGCSojDNxBcSzwztTI/IvIMN/nUoAiaJgCFPR0hBEzbnvnIq/bUcB8dMcmWFPbkDCXi6gLEA0lHsqR6rx1rIxHzsOQ2UzrZJPBYN05BGx9DHHVh3VsPJJKn5BvPnbWfF863IqmJgaRw6YkDA4JIo6SGLDx7+LMdctoWLjppH0NzMW9/6V45/Ww4dWLU+zsRZ4XlMZ+D2Z5dy5/AafvvRmfgPj3uBqADlOLRGIzy8eQbO7LAiFUIiLAvZ1obV24c1WEMlY6AiUCoj/IDSjka0iIOnbLSajjBNqn0VhC6xnBoiVwnFPKlz5o/KTG3IU+xOsuaaBtq7csSkQgeCIHyv92TfZKak+qgtyGJGJMQkDjr5hAkbc2zIZnjk1uOIZGoY0SpWT5WmvEmQz+POaaeUa+JrX1yCJvJoToDYOorX3QQK9FroKYNp4McNYpscaj6Y/VW0oofuBviV0HcluRWk51NukUTKLtR87EXtPOIewH0rXAy9B3v/6fiZCEEtjPBzs3H8QR1pgvLBnpwm7/hENuXA0BGahgoChA+UqjjnRAhyEvWKQaBrBF3tONkEslBF394zfiMIYGAYEgmCVHjf0HVJfFkJ1ZIl8BUM5iAZRxomsuYQ6akim7NIPQqjY1CphdX6vAqGlgpLfCugbzD0rFEKJUBIDWPExszVIFcEx0OOFRG5Ck1P79gpyIrVDtqTGkZ1d6+XcYtsALSyj+ZqaK/6+EsNajWUYSA0DT9iAZXdKjoFShAko+gvbEIkzJ2Gx7rto1SAAJpaa2yzdV6uZCn+OUlUBShNoLsKraYYcSStmkZtThuJoQJK17j1T8/y1vMP322sUxJTKZei+IGgpxD+bTtx8RdwBRgScBWp1gQNEYUpasQMm9gXC4xdHgpo5xz6DX7/6MdoaWnZrd9VV32K437+PsondpLWN/O9nx+CCMrccPvVCKnx0g0f2+N+VadOnTr/29RFmjp16vxb6JzQyr3u7+lZ38PEWa//Nu/fjZSS4y444j+2/7/Hwkwny4vbiUubO7b9kcXZDF2Z46lsklg1AVntdds2NlmMLoS3HLwCpWCoUGBC43gqmR9DKKi5YaSGmzDwEgZS7AqefO/Bd/HccDcnTX0F+BwA8dGAUsxk2oFh9ZKfvHwNG5dvZl38Ra5c8VXyToTrDriWmBnuZ6F8ED3ai8+dwEd2G58uTB6ddCTe+yJgCGY0/A7waJw9gno8i2/JnaIUgLB9pFL4UnL6lEuw+/O85cpzmHrcdIiNCzRleODb795tPxM7A+wuF930cH0HQ/v7JduV8qB6KyrYDMFmVPUJjNiusPhz37z/bttH48fj8EWUN4xu/o15uLsSgiHwtrEt/x1+NTCNRekBXsy3cfx0kFoKL/EpqvmfkhFjKBPeMWUpjnE+VuYtCBlGl02Y3k6ub4xMS+gD85tz345S5/Oblb9mduNkFrfvGl+2JUWtocqmjEmnGAzf/lfDWVbThCNZs+NGNgVhad/+oY3EH8iQd+KUlY8uI3RNGGXdurAM+08nPclVzUtYKzWCIECkYshA4nsuLZNHwnCAA4tM2ifH4Ge0MAXGk+xeiFfxvvddR/fcDk47+0IAtFgMISXYNsd88e989/QAPEmgQ6lJw2pthP5h5uwzwmVXbQe2s2lHEyNbTY7UytTMMl8fPIDO1gLVj0viHxwluQXuXnECVH4ByStwlEQfvBaNcCI3y/AQAgquQV8tjhl1GfRcdASfvvxhvva9o5GFKsSiVDeN0vt5EJe7bJ/Yip6yaXs4Fz4pVWo7TcJ3uwQ6IuRHDuLDn3uQi084C1IW0+5Q9FxeDsUVX6GX4aYPRrlJCyO15FiB9sU2HUY4YU5OqRD4YVAUhCHOI1qc1JhNyUogGxqgXMEzLda9vInhaS0sLXUhDREauJbKUK4iDB0cj6Bic8hl23j6hi6EkKy8q5VTPzfMHV/Yjj9/BjJXQBg6p5y1mUNPLfKps6aEp7Qhw8P9FiWjj1dKnWhUEVn41sYFdAY2v9+6D5dnYfOtU1kju3EPKyJfjKEFAs8CTQmiI1UG17SQHPGotFo03rgeoRuM7ddK7MUd6EUPc7ACCERnG8rzGJit07ZpDJTArlSwMmlUrYbyFcIPMHbUKBzVhHLLWCWFBJymOLFBED4Eno8buAxeOIfUoKRgFynMidNmG6RG+lEXm3RmK7z4h30xt+aQgzVwwY9roEn8TITI5gKUfEjFQ7ESgW6WIdeMoSuCIKxWKIQGuiRIWATj54po+B10Rocx1zl4nY0QRDCLFbzmNAIwffCHR1GjOXxLEVk7gNbYgmjIoFwfSmWCqCRIRggGxxAqQDO1UKDx/TB92PPR8lXspMSdnMVyCljbQ5FDegGZrE2hP3SRkaWA6G9HEZMiKF2G0XLs7lMjqx6R/gB93GsI14UxF9qbcWPgHthGbEvuNQ0kQaDQtvYj8kVEScOfHP4tkCV3Z9+DY61cdPoMAgeiSSi1x2n8+XMApIDMHQqFwAfcuZMwXY2fX/vgHiKNoesIW0faMFwMxSe76iEsiZs2QUl+95eP0etuZ0ZmEFO6VGIGOT303lamxjknfpeJExv49Z93F14eGPwxh8y9HONij+SmKqW5EZLrC0THfN50yBXc82S9VHedOnX+s9TTnerUqfNvQ9O0/6hA83+ds7suxJQentD4wcpnOP3KV7ht6c1Q0sATRMuvr6PPb4nTPWeQM7rXc9bkdVjWrofpfKBRVYoRv4LneRijDnrRJ7sr64UtdiOHt60iYZV3Lit2aBS7NM5Z3Ibn9FPq24fm5tNZVnqCheltHNa0gaFKfuf2j5VifGbHAfxmeM/y16ZQlGIGKiIITIWyvgWJT7FZX8zIvklK8yK7N7D0MKXEUtRGKxAoHnzkJT523X3stLLRoamxcbdmp3T7HDxtA6fMWo7n9/3d4/3Q5rcy0LMPT/TfvLN2bbkQVrF79u4X+cyJX+WlB1fs0W5DNckpT2/k3Ic+sdtykbgUkfw4WIfj61NIGxXuHphJTRnkq+H5eMsTN/CdTXk0AZqAdsOjUvkV1YGT2LptFkrV8H0fzdAojOwyLF6fu51hfsva0jW77TOWSRB7eoyaioZeMpYF6RQ0ZRnO/4yV9g6qrk5L9L1c/YTGuuFWBksp9jWPhWAthx68BeErpOtzz+3TmNo+CLqO9AN0XyCVotRksGJraIS9/+RRkgc6YDtQszEiklhDlfCdvM9bfrqV9EyPDxz6Vchk8Dta8GMGni6Qhs4Vx5zyuufj4jPzBBGBVCClTm5OBr9UYmAjODWBU5PkygbFfpcvvnse118ylbVeIxe3vMwts+/m2p8/TFtbAIVPg/cyvcs+yq3fXs7YuhiWVKRkOL/NjVr8fuPZSOnijL+ZBzjs6BxC1xGJWJiuUnNwCxLvaYNgSxx+44WRSq4H0QjTDwnTEndG0QAYGvc+fgRNAowEBAWHvgVxqtss9C0G2tIElSYRbux54U+hyLWnzsUb72jV0hRnzpzHlz7RxogLvx+L02CU8SMNyHQaEYlgTUxRmtnMiO1g3LOZMTuK/GYe4oXQ8wuFUCBQ6I6H3u+jpIbbksbLpNjROzFMVymWQdcR6RTZSTpSU9DViZjQDukkL36nle987xDseywC02Tzhhb+smImN66azcS2sBxzrNchscEhGANPE9gpMAZKJB5YS3TEofHu7aTvW0fDU6No7e1oLc3oJYU9qx3HthGuE6bB1mrg+9RmpREbdiA2bEOiCPoHUaUy+mAOggDdVqTuGcQaqVJMj4sNUyYQ35zDHC6SerEHIxbH7K2hbxkh+WQ/TY8Mc+Y56zj2iz1M1YrEVOhP43akUGkdCJCuCn2YSg5KgkJBsQxBQPO0GiQM/H4Tpz9KNSsRfhAqaa/mbMJ41cJQRNErNp7v4CY1mh/oxcDAylWxRqthKe9YDDctMeMZjGqAtD2E6yOiFmRSeK0p7M4G3KyJchyE7aISUVQsQpCMInUDvb+AmXMQ8W14CQvheUjbQfmCK3+6mV1FucfZ2ovw9owyFIBW9rBGHYyy2j1apm+IQLpU9klROHjCrnVBgFYoQL6IMiT9l8xh6OxJ4WFYuW3nbsXAKAMtCqmDVvJJbMvvNCCW43sXhNqnuXIrvqaQ/t4jIc2Sjl6GHb0Fvv2lWyGqoaoBWtHhyitPJxKJsL7wIHHdIaL5pLQKR32hhhAgAoWsBWzfOszpx3xtj76fXPkd3n7og0w4sxvlgTVkE2zYjLe6l5XPr9vreOrUqVPnf4u6SFOnTp06/yGmZyezsdDI2nwLMxr6iW0N+O29I9QmONQ6XJqi8ddtG9eivHffx+gJBMuG0zTFd5XCFkhG3QQ13+Pm+19CaKBXHNoXtO/c5uJp1/FUYR8G3IN3LsvPC6gurHH+tMvJFb+PoRxMalzQ0UbebSDvTWVSZlf59FXVGMdnXiGm5SjXqhSrlZ3rbH8Q1QBO1sPO+kybcgQyfh7fOu8SfvLJLh76+pm7fR7L1FAIhO0TuWQK7pHddJ83C2UIXi2F89SvLtvjOExJHcMVc0e4bIpBxAg/X1C5gyD3AZS7eveN/Y3oQiF1l5IPxQDMoA+vWuWlv65g04qtrHxyzR77uGXbSi6Z+jDvmPwoj+34xa7jrDUizAMQQmdqw0c5urGbjvgYUc1muHo7T/SspCFeZGsQ46ViO8uqGV6xkzQbCktCkwxQ9lISmQS1ik0stcu36aHe3xF4EZ4b2z0yaNmfniCxbICJV60C89WJboBmO4y6Fj4KW+kkjCYyiQ10jvk0VW2+dvibIHERJxyzFekGoEmefbqVg45XULWhXEG5Lsp1aRwY4Mbj/wjA+VMUFd8MKw5lUriuyarLjmLrJw/GuHEC5QP2odXqYseyjWAZaMkUmhug+XDBp3d/M/63XHzKJ8JyXS7ggYob5N66PyPDJucevIQ3feIshrwmvIjkld4unls6kalM4Kbn5tFkwJTpZb7yowdxCMvKP//obO775eO8fG8TGanIBwLXEXz28gu45KgvEQQ6uIJaq6TWJLn0+mNZ8o7N5OenUXYYjaQ00M0GGp+v0vbEaDhpl4KTzt7EN361eXzkCoEiNb1GtDTKQTMLvOJavP+6e/EbI3iNAlIaTs1COgK5ZWvYzNQhYkLNxpI1hgZM8mWdWLrG95etxPQl509fTNRx6DBqVEY9VOCDW+Osd63h8Rt+zZIPb8M+IEXOjpBTKVKLS6GQlIhBOh2OTMDTD8/CS0VQhkaQjfPsMzpHnX8ogRcgdAMqVW7+zQy+f+1C7KRGrT2Bk42g9VeIDGsoSwvNXBeCag+oZTQsPUy/1Eer6ENl0usg0MAogbUphzZcRtg1tOE8uC7G1iEQAgVYZUVgmejJGHYkAr6PEoL+N01Ey3uQSYdViSKR8DgF4+KYJkGF6VZ+RpLcVg0n+L6Gn4qiOQpr4yAUyzTetzkUHDqieF0pxooa9zy2mNKISTTv7BTVqk1hFSIva2HuyJN+sgc/HcFN6jvDmUbXasTvHyDz0DaMosJpiqIqDlrPEKJcC9OXGlLIjjZkexs+AqVJRDyGdMH1FbX2OPlpCUoTLIJ8HnJjiGwrviEJOlvD+5oQyFgMKhW04TLGWAl9zIZ8EapV6GpDdLXjTGpAxS1kxEIv2MitgvjGgZ3CSNvLZZYvbWHa/FB0F+NVoV4r2ewe/wbmSSmMiIFe2V0g0YC2I/LMja4msnV4p8ACIIZzCKDWFcfuiOCNV/177WRCuj6dD27EX7kBtWMb+qrNO9eFSYm7I2s1Mm17T0XWqiADQX64xF9ufxGEQI/pXPOLd3H0ifsCsCi9gFa9yHRzmJSsEpk+yK/v+QhKCpQFohZQ6C9x4qLP7HUfv/3C+3j5d58Eu0IwdSLO1A4+8P5f73XbOnXq1Pnfoi7S1KlTp85/kLd3PccJras5qmktzK+Ra/aIt5VJdhY5Yt/u120XNRpYMTCRHbU4P/75IsSumr6M+A3UAp0BN8Gflr5CtT1KuTvBuecs3rlNY7ybby76LR/d53s7lxkxl9mNDlJKktHz6fEFOxxJc+JkLtvnL1y+z+4PrnFV4YbNh/DQwDSqWxaw5Mvf5fivh2VNP/Xkt0g05QlSioa20Z1t7r5/Fdd9ZYzc4O4P5dlslCCioTSDj556Dg8/cBV3P9kfPtArhdAFkcjfRN8AUksyPf1WJllJ8LaEC71VEAyCv3W3bTcH7+RPuQn8YfAAIno7SQmGgO1D+2FEdMyISaoxscc+3j75dGZbLsfGaqT8vRtMCiE4bsKHOLnxFU5rWkGL2cTtfd9jsJrGFwFrgytRspWXX+MRUfRBWIv4681P0LdxgNu+f/fOdYsjWzk5vR3T2/Vn2nV3hUJp1SCMbolFIfAwklHQaky2SsyJFGiyZjLwQAule7PU7mjENE1k4mKa5qzETej4ls5wZTIL33QbIhkLjXTLZYJSGaek4Y1Hmwj7cXKlGGgalCrg+7zr5AdITR9jIJbg3OjzfPmT74BIjKBQAHyC8cCR8y86da/Haue50xv46EeeD2eAERBVEBHJyDuWYExoptLaiJCgNTWiSxNlO3y46Sw2v9hFvhrOpzONNobahCPnoMVPxKm59OQVQ77Oi5UsLz/TxMiWPmpVh03v/AwoRZARqIjgyXgTS6fH8F+jhWo+JLdUMfor0JhGTewkKFe57HObefqe8NqYtKTIyRf28duH19L58DJmJb5HwYtARmPB4rUIG8SwxGkPKE33+Nx3VvLeO3IMnz2fclcqjCIxNBpbHWIxD2OyTSIScNE3epl69AD7Nwom6CYVOwFCopTPKWdsRhfwzgt6MFdmOLxhPae3vUz6fAO7XcFYAcplhGUipCIIFLLkQioaHt9MnIcfWIO2vQ9VLOP7HvZAma2rTBxLoefLUK4wcnGK+OohYqsHcIWDHJBE8zWanRG0cXNurTeH7BkisqKKqgXYMUVi9Qi6o8LIrkgEolF8DXypwkgJqRFdsQ1trIywArz+fgZPmghC0f1oFWdGG25XFrqawuusZlOZoYUVkMbRk11o4xln1RYDJ6WHqTxKhX4oviIasylOaqSWTHLbyyfRP9bISy/OQ8NDaYRGt4FABSC9ICz5rQtqE2OoRAQMIxQSfEngCczNw1TaFX5CIDzQyi7K0iGTwGvLQrWKKpXxsxGErqHlqyhd4E/JMrREUJtm4MYkQa0CqRQqFUGOFPGjOkoT4LoErgstzdTmt+M1JlBRHS9qIWJx0MNwMGPLCBQqoZH0pu3Enh8m8HWUgMDUCVD87jez+a9btvLHlctp7nQRQqEbu45fYL7G8DppURrrRO+voY/V+FtOmbeMtqd6sYZ2rXttqpTZXyXS42Hk9qzK9OodywL0koN8TVs3phPMnIqj72qnbIehoSKrVm7boy/X9yGAkWAAN2IiAggkLNx/Bkoptgx/gXz51+yfFLQbo0yLDtAzKJkwoZVHX/p6qAjpEi2AqoIjFn2WYrG4x34A7t/2A7yojub7WFWPSy744V63q1OnTp3/DeqeNHXq1KnzH6QzUqMzEj6cnnrSo/xm62HEpEe+YvKRUw573XaapvHiH/ajsNHmmuvP2W2dIk7ZD5A4bC7lCZp1AglHLZr1d8fy4js/gWEYAERic5hkPgooNL19r9vfs3068yYM4tmKQ37yblSTYnNc48DfXMG5+2zh211L2VRL02nsetD/1nfvA+A9H7qBh+7cVf500ZKp3H33UrAkt9z6PE+s34oeCJSj8OKw/Mef+Nvd78JdDcEI+DvAmIFIXAzukWAu2m2zd8/4IPBBALxSgVp+Ma4CQ3kcftZBVIs1Fp+4cI/up2VnM1DyWVWxCP7Ou42YNYnTO78J7gtokRN56MYVkNfYPtHhhq8ezR1rbuHd2YdYZVvM7XqBNs1ioDTIaG/4dvqZe1/e2ddzlSYmWSU22VkCdxtO/jM8de8ukUpIGZbtLYVlcn/81BexosvpL+mApCE2HS+voZ2Ro6M5x7Kt21gwKUw9FAi0ikNVk1iWiTBNREwn6B8MxRjfZ750eRjIaAHuT+IEQQ3peeBBPm9x9oJn+fFTh/PdxAJe/FUU3Cp0toZpII5Dx9TdzTpfj+O6v0L+M1/nN9+cjhuPQDVAVKF/aitT2x2+9/nfYgnFh95yOIOVJCuWbaOxoPjRrfvymQuWhuaqgB6sYXDbFoq5EhvWxrmr2IxXTvPgtyZR1Q2ev/Mshia3kk0niK3W0cqKZpkkHanxo0/+jM9vWMDgg4CUqEoVLVDgKIR0UQ1J3rTwaNToGKSinPvLjRyULFEKBOURyYZNSX689WB+c8w9HPGJFzl2+yZ+cPyssOoWMPxZm2siZ6Dmg900Ge2P69lx7nQ2OMtRAnJulKws4yr4xA+24wRRggC8mkR4DiKaIG0pfAXPl5Lom/r4SPNmmiM2j68c5Yd6B6NafKc3EQiU56IbOo7r40uFF9Pw4xpHXFTm2ceiUKqiMglE7wCxgkb/VVNxxqII0yWVXEV+cxI7kWLSlCGiTVVKY1EumLiV4R5oPNnjwNNGeex2jVHVirUyB/E4yg8Qmg6VMRDgT0gjEiZCjRtSj1bwkyVw/bASVtUl+lIO15BgGaiYBUpRa0oTGSuhWmIEZg3NUZBM4kcNZEOGcpvG6L4W0ofm61dimiYC8EwYy1fJLB+iOiVN8ehZoShjwrKVEzDXDOC0pwii4aOvkpLyvnFqXdPJPNaPPlwNBZxsAmW7yIqNrHrEewW+qaG290M8Bq6CRBQ5UiIYClPAVGeWIKKhxxNEN4yGXjmHN6CsKon+AkZLG56moGYjXDc0yh4eRFWq4XcuFcda5eA1xwkMDbc9juFpqNEiFEpI2w0jDXtCM3gJUBuPDgoCgqSFvX6Itx21P2++PM+5l/Zz649aOPhNef74ozBqR29rxsvnEQGoORNRgUJ4Yfnr10a4SKFYkhhie6Ud/N0jcMRr/yc0tHFvez+dgPEUz51ePePM2b/IwA6T0piGqIC3diNyejf+8CjJuMlYSwOguOTs77J4chOXfvN8Js+diOf7eA0BCohZebS8BoZGRAt7d4MRNuXvRAiXgwyFNH2u2zidtS9NhhPCfT+y7CqOnvsZPFOOR5xFOOXYq/nuD9/Jvot2r2AohOCYE/fhyT+vYHhenGGZ383Qv06dOnX+N6lH0tSpU6fOf5CUPBgTAb7GpISHFnVwBGSzFZLWnpEjr+Xu73+Ox//yZQ7unrfbcuWnmR/r5dKWzdRcH88CXwtNlP87aHrb6wo0AGd1HkbZ1RGmRHkBXfMH2G+fTeQ9Dy3wGXIjNBlVjmz91q6xjVcDqf1N2e63vuVgEBogWb15iJtfWIVrjBsGq78/bpG8FOKXoszQ9FfIBoR14G6VmP4WPZHCSH4WKRO0pz/OCw8sY+VTa1n68JP0DH2cam33t7qD6kjev/ZkLl7xJv606a7X7Ver/BCt+ksofJDkXwfIPLYVesLxL3vmGC5bdxC/WL2A/q2hWHXNr/+KlxkvWZXcNQX6w8Acru1ZSEt0PuX8l9Dc51ly9ONA6K3RsnAKt677Ng+Uf80D5V/T3lEkG92POS3XsrD9V/i+g5pfpj0+xuaHp/Dph3ZFTEWFQFQcVLHGtu3DBMUytZFBfAg9NnSNj184H4CsAR/+9GN4TQmUoUMsxktfjrG92IDpatQCjyOO3gHRGCJioaQGhsmN93zq756zV2lPzOYdUy7is1fcwYTm7Wi1sDBROl7hPSfeSlZXxCUce+4GlC649857iW12uXXpvjzan8V2Q5NQ2w0YK/wez3XQ8lDxIrxYaMYeFqiRPOvXSVYywG8OeogZsXU4QY5qpMhP9v0uo2WNSrMB6STEotjDg2hjJSgWw6pOA6OochWtexKxFosFiRLbbIPbXk5jmJI/2LNp7K5SdiSxQPHELxqwzwintvlfTOGm/Q6ChIOMBnhNAfa0VvY9YT0PDU6m4EX4xab9KChY4xoIQzJQdTnhnnPZcU2G3M8acHSdIPVb/nTfN/jNwm4amoZotMLZ8dL74+S2aKHpqxCQzUJDFmwHqQT05ZFCoFcVTG6nbX4lzB4MApTtQKWK8lwWaJuIT8gzc8ZW2hfUQJfEMGnN5ZkSH2SiKCByocHsAUfleNOUrRx4dA7Ng2pHCm/2BLy5EwmKxVCo81y0IEYQkQRRHeXaYdRLJoHqbAQpSN++huhIjuHuFEG5ChUHOVpFzOhEeT7a+gBUmLjjRUyMkUoolDk+CpCFGsIMQuUgFkMJHXOogggA4aNXgvFqUdD3bJTYij6iy3vRR6r4hsRrkkRSJfwWgdvVgJ8OU/q8hVMJupoBkJ7C6qlijYLe0QZSYo1U0Icq6L4MI9liUYyBAtpIgcCxoW+IYHAI0w3IOh7FzhiViQncSY2hEXAijl6ww4gjGaY8qbECxlAZc3MOrVAjsmUUiiXUwDCqWEFpEvU3kYSBHhofB9kEfiqAhI2dV/zmum6WPZ/le/es57B3Vel9x74Es7tRTWnGjpuNe9Asgmw8NCyu1nam+gkgmNSJaDGYoVwa4kF4Sx73FX5tzIxQYIx4GMUwtEkau4SM196tFbDwpAKN7R7ZjvEqegC1MlrVpjJcQK7dgVizFXP9Dpbf+xIXL/403/jAz9A1jakRg2y0SH51ChXTAcW1N10MgKk1sW1HA1XbRBM+GuC92ER89DXma8BDK69ivwMmQsxEFKrIqsfl5/+cX//0/j3uR1dc9TZUg4VV8Gl4Ps+xh9cNhOvUqfOfoS7S1KlTp85/kMVtX6DoGTiBRjoSvnkP5zj//Nu77uR+JLXwoTUwwU0F+Gn1hu3+u3x4/zdz1T7f48uzr+epr1zEyZOWc3znKiqjGX6++gDuGZ7BhGqZRKwNgMc2P83wNEk5A0MLd/98XV1NuDEDL2JQ0QTaKGjVsOzz8p98/I0HU/k15D+LUq9ftvxvMVMXkup4CS1zMbOXTGfK/EnMOv6LxJw76R06nmWbv7JzWy16PsGfsyR/EuWbX3+ZwN8zRSAkPM7PPOLht2bwG9O0rA7D6xe3TqV2XZpv7fscrdGPE4x+il65jcnnFBEmoHRcJ0zR+stRX+HH+3+aHy65hFHPpBAI8r7giEvDt9W/ff4qkukw/SZwVqByH0aNXU7gb0YKHU+VOeyYVWx5oJsFs3pJFHaNMBGEEytVrPLrde/G2b8dJrTjz5sArg2Boq0jnOisHTT481fmIpoSeHO6yZ84h1plIp6V5tjDVnDx7DV88tub8Wvl0FA1AO2/eamNeS4xLD5yyYscuriJKA6//tSf2a+pyHpHssXW+O3XpyCGRtn6Yh67XCX+SpFLlp/AC3YzvY7FSACJWITAsvD9OGelBjFMH3QNqRvc/IspLEwMMjtW40vf2sibPjVEtSNGJpPhhup8trZ2hoaopTKRghtGwWTTULHDByU/IBgZ5SOfeolmDWZFXQYfnkFjx0RKXoSxWpS5cQdDhy9/dgMHXjiEnvb563F/5p4D7mTe1F5cWxIoAX6Jozs2ctMrC7jwrrfw6Prp/PjHs5gb8ZigB7QpAzNjc+CELfzXvHv4wr33oRXexg2fugeMCO+4dTtLbagFsH6FhfLDlB8sC5WIECgP+odoSqxm7px1+EDvgTrFToNbf3YQvmlANoVnaOORGD6b753FqaxlSU8va56ZiWzIIn2Y2d7D5NgQo77OD+4ORdCHls3gD0tnc+/LM0GTxMsKDA1lhj94HkiJjOjYNRtFgOwdASkRhQrYDsUjp6G7oentt99zDIEmUEIRWILA0kJj2+EywlcIIN9tsONYnZFDGjBHyjT9cQWtd71Cfr9Gzvx8D1SraKUKWjkgMCVD53WjEYoswlXYE9JUJjfgtpjoJRs/bVFbFCVuFsms78Pcmkc4PuXDoDDbwJ6cxc+a+NkYeqCh2+MiRrWKsF2MsgOI0D/Y95HRKCIaRQaEXigNOuZWg0EzRWlyCuWFBsVaIMNqUK4DI7lQYYzHwB3P49Il9pw23PY0+YQg6GojaE4j4nEwTdB3VfwLYhGCyR34XS34kQSlU9MEDVmEbfPYvU2cfcGJXPKuQzC2jiJiUQJTR3N9qDhEihU6O3L4w4P4Y7vM4EXgY3ZMQEPn6LPHaDvcxYp7f2tHjAgCrBEHc0f4N0tWqqEJtyZDkfA198P0TJuOhRUaFlTHl4AqlMNqaX6AVrMx7HGRMWKB7fLX3z/D+k1bmThtOftO3UYpqyNcH88UzJg+YWfvf/7wHH74zcP49hVTueOxOdz37FQifmmPe8w1P30fP7junShNI/DCyc/Pvvcwx+7Fp+Yvj34es9cmPydLeWKCTVsG3/gmVqdOnTr/YuoiTZ06der8B4lHWkiIgBY9IBco5jZuIxmtcGzrPv90n6d1vp31NYunKzFiPYpon6Bxx7/ndt+eTNOSSNGUbsWQk/CVRbwzT7nUyPrBVpa0R0EL30o3Wu24kwNG9hW89Vhjj748K/wJEiqcYNXgfacs3mO7PVAKVBWCIZT658SohUfP45M3XIqtAgLAVrCy9me8QhjxMqf5CGI9PkIY6H1W6HuzN7K/g8yP+MYvZoRVWIAL3n4kAAceNhmnaZc5MO4qDti/n2lzRol3eRxzZh87Cg8DENUtpiRDQ9yWzJdYY0dZY8cYfNli4pyO3ffprgR/E4H7HP0j17Om9yPErU4+OvN8Jk/P86FDnufjxz9DyQ5TM9o6G8D1MObVuHd4OtgSMmEaR3FBB15bA0/3zwXguksPZuvaTiL5gMI+DdhtBoX9Wln/QZu3bFjDJN2mWhaI9laE7YBX5fhzdk8zeyNao3OZablMt3yu+uiRZLNhBJQIdP7rv07loxecCjWFaMiiDZdwdY2IaTCjaZBaBS5+eRFOAF7JQVgWxaGAmPQwdImyQ8FA1RzW1kLzmeZOlx0Ts7iBZNn29Yw5Oge9dTOvFtgWEE4gh0Ygnw+X1Gqo3Bib14TXbaDgqLO2cMW99/DRg16kd1Ur/villxs0+NKSP7HfgSViUhGVMMUvcvD8FxG2oLqfwZJ4P+dPeAkvooEueHDrIvpHTQIBrekqX+l6hiMbNjDo6TxV7ORHw80ENRd0g7HNJoPK4gVbw3mNZxG1ciiO5MugawyuTLL+rggxK09sWEBEoLkACl8Ho2LjzunG6Wih1qxz1xNL+PNvZ6EVS1iyTDRaRLMUjjRw8hobBjMAbBpN86sH57N9OIMWBHgWmANFjMESomKHpr+Ox2gX5OemMHMOWjKFaM7iTmvD62pCxA18UyK8gK9d+isqnXGKkyPkp8SIPrkGhEAzTLxYKOZGektUyxbFGa2Igou5w8aWUWrdLVwvjkJk44BAKJBOQGSlj1ICVwMU+A1RRk/upLhfC5X5jWhFl8yfcuh3VUj/cYT4iiFk2UV/skBhjs/I2Rp2xkIbq2C+uBHNVXhJDTWpDd8yQAiEpkFDGhWNhAJDtQaOi5jQRmdskDknb8ZJBbhZhVASdImKGKhkWLEJTRs/Vm6YlgXgBVT37aR86BTsoyZhmBayqQFZKCOGR1G+D9k0CjDMCFosjqYkQpcUZjRDUxJ/chv+hFbK8RRea5bm54fRB/IYIxUcSzE4N8apZ6zjzNOWE4/YBJHXCOZSgaWzfUeK5x5KUV6nYWc78Pafw+Cijp1fEBUo8Hz0/nH199Xz7gfgeSgpUUBnt8Oi2VXec0U/bSfKnSlVyh+vdOZ6YeqmriFbmpDNjWGEUbXGJ878MEd2rOTIjtUcO2c9ftLi/Zcev9u947ATDqLyq34evKWBP94whdgrRVK5Mntj3sIp/PG+DyPkeHCmBNcyOOLAK3bbTtd19NZ46FUl4C1fuHGv/dWpU6fOv5O6SFOnTp06/2Gyeiu9vmSyXqXJrDE73c/VB73tn+7PNE1Om/ALBvLnER1WxAZ8JrXvvXrGv5IPzf0FJ3fewrtnvcBpC5dyXNdGQoeC8NF8bkc3f377O7j5bWfx5SMv36N9cYZH4Xib8hQ/fJMeg0vefPQb7ldojeD1grsOag/8jz7DxIZneLais7xqkNRcguKHdq6LxqywjKyCQm32XttLzURGjiT9zrUc/fOX4CyXt30wnFhkE1m+8ZUj6A8ORMi50Pg7LpjzEWacsC/HfXWMQz9cZtR9eY8+o5EWbjp3MTccN4HVT6a48o9/E1lkzAXRTIDObKtAtznMaPkpXOdlrr/ofWzNt9BbmkrMyAAwdX4XfmOS6Kk6Rzauw7+kGJoQCw0iFpVZsbBeOCB9F3yfwHZw4yAqLpERh1SmkVWT2+nx4vzi5WnhhNG0EOh87Evn8N8hYnbS3vwd2pqvxjS6ufMHn+DiK0/moq+exlLVxJoTm9AzEjU0AvEY+mgJNVZGfDnFI/lWvrPvC0yyYMKCApY3SrrZ4ebRySjpUp3dSmVuK85+Tbwt28OqKrxSi7FsfSf2UJz/euCndFtFBmoZJl2udkvpsCMSUGEFrVgUohE29acZDWCrJ+iYMEiDBkd05AgMxWG/PZN3nH0Idz57NdF4FJE8kIoLPY6F1TVKW4ODmF3lnadvIDoaMDtd4w8H/5EHj72JxtkOHzvvZN574ZHEJJwzfQvNAayptPJKpYuH8/MIjm/GndrBjWdOxqmCh0R9N0r4/VJc9NPtdE4dCvN7VBCKl64Pf9mMawRgKwodErFyI9rKTVDIQ9TEyCSxU+CkIGiM4CUsPvTL1XzyxldYnWtmeXUCIyTRxs1epacwbEhvq+FVFUbJR9oeetlB2g5YJp4u8CdmqeyvERgCPxNHxlNoNRfp+nhJC6cri9eQorSoBTI6RHRUTOJMboeGNN70DrRKGNEV6ykx5Za1dP1+I5ofGmdbvVUyj/WycNpmiCeRfrDz/KVfHoTxkuRuA+CDlrNpuuYlaq1ZZM1DVn1wFU46QWlBM26jhaG5WJsMxBaL+PYKQoE+XMTOSFQ0QhC38NLjooYEtymBiERQKJQUBBGDQNPY/8M+3oik5cEqQc1D3z6MPlZFH8zDyg1UEkboKZVKgGmGqWACnIkZAgleREPUFJ7vI3LlMFpHKYQVgVxY0lrZNoEhsbMaKMjcFcEPAuRQEW0kj9VTwhitESQtvNwoLi6LFveipT165/rcbUwkQKGMXVMBP1DYfTW+cWELFV3SPrHGjEuGaG4ZYrqWD9MhVehrJWseQSYUncNIGwWxCNgOIgjPxVCvwYqNKTZVI/RXszurZ+kVb2c7GjKQThGUyqhSOUzdi5jIfEAHDh26zUkdWxGjNd76jt0rxl38+TPRpk9B65qANh7d2IBP4O89orK5uZlHXrkKFQBRE8ouwnU5et5ncBxn53Z/feBzmDmIDXpEe5y99lWnTp06/07qIk2dOnXq/Ic5oPsecm6MFU4WOzBJ6dH/cZ/TsrOpDB9IYYZGqVtn8eIpb9zoX8Da0Z/x28378sLwRFoFoE9HaMmd6+e2dXJA19S9tn3soxcTf9Ak8aJONC15/lef3Ot2e0XLgDBA/s/EqEQyyUnTV3Js3OaoeC2c/o5H5/z+R+8lHjeZ2t1EOvX3/YKOnLOa2Zl+Fh+yabfKW9Oa30Rn56+Qrbch9QTZSDcHqU+y8vsdPHVTK/MaP7TX/tYvK9K3NQK6ZOKsrt3WSXMeoukWtNR/EWjtJOJvY83ge9leupXhymWcveQ2ztz/RqQMUyUOPGE+CsXaZRNY3z+ByGYNc9MgFOywHK9jkJyRBuCnf70Uv1hCeR7G+iGab11D6vbl+GMFRFyAAH2mhT+WJyiVmbto8h5j/0cwIofgBhHcgVk4A/O55TfvZngwRqlbECgodbUim5uQ6RSBYSBjFp1zunn7tCuZbrlIASedVuCk63o55vqtRK0cR2TXEK2WMIaqOJ7OgX+8iHZTY3akwpcPewyjJHjzQU8xJVrkna0reGjKYsRrqs5onmKnbOMHICUHHJDDDqABhVeGuISUBqeIl/CeS7F65lx++MpqHMfhSzd9krgB3x2eTjGIY0mXsyeu5SOzVjKlM2B2Qz8LmsaYmKhy+dHP0DHPZpuYiKPCLJgeUthKxwoEFzWt523vWYUSoSBz/byZPJdrR1hRPrR6M/fsWIF2oMcB3+5n8ZNV3LgA3w9TZIDOnyyl6dl+4gNh+pYQQNVBX7oBfdVWzLwf+q8UBG5LDGWGXik9v7R45v65lA2D+NowjSSiSQIdnLROdNjD3DJGMJLHt8MUIKTGwmP7qDWEv+oVH6PoID0Pc6CGOVCCqEXpoImodApl7R5VZ0+JYe8/HdWUQhsv5yQI0+hk1dn5u/QCEuuGGXivCqNSsqmdaTnlgzrwEoJKi40as0k+M0DXz9YRVdDy6+U7xRy1JcLg2VPQx2roJY9CvJHmF2u0Pm3jCxEev4iFPziIp3yCWgUnpqF0iR8z0A2TQFNQqSGqNkKEYsWNuQN42uhmdEmErnuqaCMFRM8QjJVQho7XkQTXDY2WPQ8xHoYlpEBIiV4LUBEQQ8MwMhpefwBNr0klKpbw+waRDuC4WIN5jLXb0PMlZKmKrNVAGghNRyIQGZ++vgSp9R5P/yjFyrsbqPkaypA7U5nEwDBs3cHADpPLPrODa29ez3vevIXv//Ahvv+nZ5jySxc0iZICITX0weKu89PYiGxpRkR33RtdR/KXp1q5edlUAleh4lHQtJ2TDwFgmDAwBI5DaDLlQ6FIOhkg8IgJn8fGWmib0rjHfSOZjOHFDPyYQa1iIQRIae1uoLMXHl59FdlsDEwNfcxBKThxwRfYsX1XalPcDdBHSpgjZW69+em/32GdOnXq/IupizR16tSp8x9GCEFH9F28Uuhkiin4zgG//Jf0+0j/5rBaCXDGIf98+tQ/ShD4ON6tvGvq05zR+TJnTHkPouHnf9fA97U0Nzfxwh8/xQt3fpIn/xEfmtcgGm9CNN2OjBzxzwx9DxraV2NE34fReNNOkaWpKcU9f/gIv7z+ot2El70RN09lR7mNN0/78Bvua8Xjq+ldqbP2jimY5rQ91j9970s7J2kTpnfssR5AaG1o0sIypmNFD8NAYAmFpvw9tl184DSCikNkU4VVT0+h+KAJCCxfER2o8OxfvsDN3/8AAGZiFmosh1KKhke2QKEEns/Ua0e4vX8iV2xczNLntXHDjoBrb7n0DT/vawnczQTOcgCGRy9FB3RlY0rJY3d/GmRoUlpq1lDxKMoM/UqcqOD7P3kvizoXEOinEgAbbZ3cnEb0liiTrBwtepXp8/IEqQiiGhDpFTxbzjLoGTxrN5BdC01mgageoGkBCMmO7x+AlgrNYKnZBKaOH4+AqYOm4cz1adMhZcA175gWZtopWDKwA7/Bwo0LhAvfePEWcB8AAZ+OruHjqZUcn+ynOzEQRkEoaI2GZdirAfxhzQy0U6octG8vPxiaytcG59BmlIhW8xyc3s6c6CjTMmW8tgTRpjC6ZHOunemxMo/kZlMNII/JOZntpIwa265cBBHClBJA83yarh5EBWq8rD0IpcLok0qNxudyZF+xiW8qoxzJNW/r4GsfnEL5KUXyL1XGcikmvmMoPGmuQmqSICowRmsklm2D4RHo7QuPU+ByyvkOqz/0M6jA6CRQo2MEvf34lQpK17Fe6id1+zrMfInssjHEYAVRrDLW5DIyL4mngxsR7Ptpb3fD2tdePIaOUgJPecgAtOZmtJnT0KdPoen0Pg44awX7HLqViWtHSKzPhdEuho6XMQhMgZKgYhH0rRXMgSqy7GLVHCodBtUWjcBiXDiwyTy5Hd+wqbZk0F2NansCuymGZwj8VAx8H6UUqmajDI2iFwMFgSFBC82Pw0pOgtqkLMFYWKVJeS5IDWdiI9VZrZQXTcDYMIys1JC+CMu1S7mzrL3Q9d0OgjGYx1rZQ3RzDrlhe5hy2JRBtTeFESqaj59M489sZeBDWXoXR2kurES/t0jqgRFcaSCRu7qMRncKXctLaaqB4P6h8J4TAMZkQd/idkQ8ilUWGGrXvT3IjREMDqO83U3h7S0+Q1c7DP7ORrW2IJqy4ed6FccOP1siDpYJmRQo6DiyRFGTDAWSDZ+rMnz3sxwn94zSyzTFUQLyJQvDsNCs/UKh5g348z2f4uBDphFYJgEgNMm7jv8Ojz+4AoAvfvoU9HwNrVDl19+4+w37q1OnTp1/JXWRpk6dOnX+D3DalIv4/pJb+NSim/5lfQ5vKlLt8Km2Bkxtbv6X9fu62I8wQxaZFC0xOzbKxKaTETL5xu3+BQgRRWht//D2gbuWwH7mdddLTcNq+BhaZP9/ajxvmf5ZLltwLwd1Hv6G2848YBoN7RmmLOjeuWw4l+OAG7/JD196iCtOumrn8q/c8fpVk5S3Cfwcwt/GzOxH6LIOZXbHbXtsJ6VEIomN+DQ/OECmKhBZCxEoNCE5/6hdFU2+9q4fhOWSXQ+RHj+XSrGh3MYJnSs5vHUl267IjpcD3rsfULnwKN956Rh+8vxRvP/hXaJVEBRg9HzIvYfAWYo0TiYXwJBvgMohnHv51RlnIlXAcZ9Yia8UgQ8yCGjUzV3l4luuwWhfxwBnIHUfnTz7mQ4LTYdIVwFZcXGzJvEB+N3AkXxw0+HcOziVwnRFSnhMMobYONAIkQp2vETPZRPANJHRKP4Bs1D7TMXpbgfXZVGrS6Cg4AhyxQx33tTC9o1Rnr4nRa1d4jQJ7BSc3Z2j4nsMeknufmR/bKmwtDILsn08W9X4zh3zcBRkNcCDJ7QO3j9/JR8+/0nma2N8rHkVC6Jj3L3mUNpSU1mWy2KnbeJH57nksc2g+1wwYSNvzm5lemyE2wopDrUKeIDQFN9eeDuHPpYne0IREdcYvbydSlGHvv49T5AQaBt2EF3Zh1aqES0MEpTTuOujiEwSY7SGIWskx9NBXFMhnAB9bY5E3t4V5eEHMKERJrVz1cfm4Qbw5YY7GT0oE3qpKAVODR9FYsV2IoOjsH0QlS/QsCxHsnEHqdl5jLlj1Jo07IzGs7k57GiJ7fQyAVCaCNNm3FAMEJaL8n1UEOCNpwKx3mTYjBKN+Xirh9D7x9DKNipuobIR7AlJ/JiBqAZ03rKJwqIO/ISJtAOKGUWFEtIej9CREr3mY8xySQ0UEcKmYozBuq1oq7ei9Qzjt6UJGlP4XS2omk3j/UWSq6HxeQ/fd3BbUmElp0qNSN4ntcUlcBxkOo3WkMYs+3hxjej6AtEVvcTv30gggjDyJBrZKQqrSjVMOWI8VcjzQlGkdxBRdXAtnarnQc1GuS66DSoiyM3UCV6IU9ocxzxU4jRb5A9ooLJPM7V5E3ZF0lR3GaJ/bvYkTlu0gD/86GCu2TSbb/fMZsfqJjhE4vhuKFCOnwMvnQzHUalCubLb5ZX3Y5SIUIua6Nt7QkHvtWrbaC78VWhgGqEwJgX5IQMFeAEML951bz9OnsPvv3krrzyzBoCb7/o4hx09lwMPngkIbOcfN92/6rsXctlnTgizO33wTMEVH/ktAIcdsQ+BbRPs6KW0dS/fmzp16tT5N1IXaerUqVPn/3EC+ymC2uN7LHd3uCS2ayQ3v0Hs978KYyG6rnFsrEST0fS/s89/giCowMhbIHcRQfWe//RwcG2PSrHKhpc34bkea3f0ctAfv8c5C+8niHwJ4zXeyROmvr4Q9dmVL/GhdVE2252kMu+hq/3HSG1Pg2YAM6ZjFFwEik9++yQ+dM1LIAXe8Cj9GwY5bcplADx3xwthA10PfxJRMA2GrsnyyyeP4IbPHQjlGlgGH//22/e6r19vuZLjU8PEY4pvz7yHlzYtfHUUICKABiLOhNYv0tixipaOlyB3CRSu5LCubSya0EaTVUC1O2E6iWVSGN2zutaRU75OizWL719zKo9UYzxWi8BMnyBpITWd6Bj8/MDLOa8tyU/n78Mnz/wLky2PWZbHn3ZMQjcVviOoTTMZMx0oVTCGa8iKja4A12NiQ2hK+rStsf8JW7n+ykl86MSpnPed8YmngIPmbKHFv57zHn+MmlPBTA4R9wRd0mckkFSQnH7CKkwB5QBOefw0lK0wrCp2psa6bVnOe/oE3vzA2ZwxdxmnT/o+CxtyzI6O8q6jn2C6VePoP2k8umMSRU9npjWMcFxs6fNgMUrl3iR/jbQxQDMtX84y6yEXdXCU/lojZtFBAvOOGNp14JSCUhXlOKh8gUJ3nPJsDZlOIuNxhNBYQC+rfxdGVFSbofmhPtI5QAn8TJqB07JU72zhzX98kmvv+wtXP/00p81Ywg8v6+TqM24Lrx3LRLMsqk3RsGS7lOOVs3zUjh7ET0Zpu3UjyhIYJR9rzKdQbeCbP34ZZ1LDLiHBV6FJrRAIATFqBBs3E2zvQY6NEeQLFO+H7aUs6/MtVDvGfVOUQstV0Acr+HEdFdMRhH15EUnh0EnkD56AShm0PZYLqxTFImHKUzKObZTJT7HAF2Qf6yeyeRhRc/AtBbEabpOFXnLQewaJru6lYVmNyNI+zP4SRsmBtEEQjyKtKHokQdDcGHpBEfr8xNaOoBdqeNkoRipO820b0Db3wUgOEYzLVOVyKBxBWLobCCSIWhhd5XWkcZo8HL+CH9QIiiW0/mFSjw0RHfTIrpa8+Zh19F88m/zRnRiOhjW8y3NFOrsi7ySgDSn0LaM8dqTOXY/uzyqvnczdI+i+QpZrOz1myIam3Gr8OO9WCcqExg9qJE/QIZ2CQKFqzu6SrmmgRkdRQ8NhyE4ySevs6qs9EDRor92an3/qd1x+8Bc4OXk+hqHzpavPY8F+k6h5Ls88uZx3XHb1HveH1+OMC47kp3++lMAATA2RinDBWdeGK/N5yGR5Hf25Tp06df5t1EWaOnXq1Pl/mMDZArmLYOy9BPaLu61Tnoc57GOUg703/hcj9QZIfIvl/ikc0P3X/5V9/nOYKBwUHtXa/9xrwPdrLN1xBusGPvZPtR/tzTHSk6N3Qz89G/s47ce/QZMBKaOKJTxibeEM4Yo/vX4KWNkZ48DsMk5sXscd23//hvu896WvcuV3z+d3D3+WY/ZbQtu8k1C2DdkU0gsQ0XFfJF0PDUxGRkMzWs0AKUg9vommD6whff9g+Pbc92g4vG+P/aigzJHpBIZymBopognoMEJTTykj0HQXNN2LNKYDIIQOGKB1goiCPpHfX3gu+0gX7cVRvIQOusBPG6zYfOvO/Tjle3ll0z4cGnmBWjXJ8lIXz5Wm0BItgy7RKz5axcEym3nrzKuZ0f4eVo91UQgkw77GebNXEPgCoSm8QDL09WnIQBEIIACJQJvRGXpvCGjUffY9pgQ1B7uqceC0MY7r3si05kG+fvxDbLQjvPJMGwUE1bKDUzJpM6FbunRqHhs8yYrBFrbbEbYVEjR3OVy6+XjuG5zGxKl5vrjoIX52zB84qVkihECINI1GwPEtw0yOwDmzNpFKD9OoFzk8VWReWrFg8lq2PfV1nr51Nh468lVtVovwtinL+OJ+j4ZVeYCljzbj7PMa/yBdg1wOHAcnFaU4rZNCuoyv6zhCsuOmNoKGcCLe9cwgKhlFKug53GHwe1mCtzeRsjz2iVaYagS4gc7Jdwzx3ie3ckbLKAE+1GxcLUCMV9Yx2hX6TA3luUhNw/CAW6HjvHVU2jRKk3QCQ+O7585FK1Rf1cB2IhJxZEc7lXwCYvFQvNBAjYwSe2aAkS0ZRrZmCIzxCb4UIBRGRkFSEWgKLT+GLDt0/mkD5XmN1GZmwYTBBUlk1Q4jVywTNJPoLwoEukFV2qjxaBE/EcE9OYrstTEKOXKJIJzQaxqxeJFgn2Yo5DFKo3ROLSLtEozlEUKgSxlGauQLBIGHihp4cYsglsDtzKJi0VBAgzA9TUpIpyGZgHQS2d6CyKQQxQpKhfd4ryFGeb8Wyvu2ohlRiBn4cyZBWzN+TMdPCBIpiV4Gq88junwAc10fMrNnxGMwfthSj28kBXR85mUiz1o4nZ3YXRmCTAK0cBqhsnGUsbuQMnJkJz7gDsDQtT7Fv/hQLO4heCjPB8fdGbWE70O+wJrb05Rsk00jzWgvVPaqkzhlh5uvuR2AuFS4vYNoW0bo/cHz1Kp7Nw/eG1OndnLcSQsQuoYcrdGzdoCzj/46rTMngC7BNBnsGf2H+6tTp06d/yl1kaZOnTp1/p/G59XaGgTDu6+JavhpDZn6x8O//6d0NbyJJV3f/l/b3z+DlDpjNFINoGKv/B/3t2rg/bSzhrR3F5Xa5v92e93a9af4W9/5HW5jgFoV5YbnD+TWb3WQv0sgdcFhZy553T5Gi4+xTyTHvOgYCzJ7GmzujcOOn09zewMA+0/6DJ//6YVhlIKmodzxt+uNDRCPcXftJuYcPAMioWeETEWRyQiMV4ZZ9/lpnP2njVQqu1IdgiBADR7LDGsd01ovIC++xAP5qejpn+7cRsoYUkvvNi4pJaLxj4jmx8bFm4AT28cQMR270cDNxLj4/cuZpn+GYOT9BEGZdYMfp8l0mKgP8YvP3czJ8W0cEd1GIRHDbvLRXJ9qi4n7Gr+M6uipfHzNEVzy4vFMsmzafh6+uQ9cC6V01n1nPqzfjB6AcBW+Mij6GoMeDAcm9/ygZWdfj6zJ0j29h3kzV7Ex0Bj2NEQTzIl6bN2aYsfWBAArKime+P+xd9bhclTnH/+ckXW77rlxdwVCCJYAwYJbKS4FKji0FC9eihR3bylQ3EICQZJAiLvn3tzkuq7vzsz5/TE3NwkJWgq/tvN5nvtk98w577wzO7vZ+e4rsVws6UIPpalJ5OHTs2QsO/rgw9YK+rij9HKb1Bl5DMyfSLa2L7nqFtImhBT7vr3EE2dPVxzVcmNYUKKkqN7Yj9MPuJD2vRU+fnEcHTGBkILWWCn7+FvoHjDZKnOogGvpBjIl2zruYErIGkQ+queUUz4iU6Cw7hQPon0jCIEl7NvktAiR8GmsvtTDfmfVEM0GSEcVGuMBVnV4WJj08UJ7f6pywzRn7UiuY49fgZSg1LdSuPQLRl27hZXXDqDhpgoyzwVpz5NdaVMKoLca6AnQkpBxaZjF2xXMpbM+i9uN0FRUt2ZHmMQTjL90E1esWMwjy+ejtWi4mnXc9Z0RGVICgqLdTSxdQU2YpCu9CEtScHgabX49UkhIKGQGFiAsi6I9A9DWAe3tuDIWWlsC75LN2/wIB8gO9dlOtxsY3TWU0mK0SC4iJQh1xFEzWYRhUu5rxS0MSCQwO2JIt9sWQC2JYknS3QtxJQ18a+tRqxuxjLSd/qPbLb/tbl1ZO/omkUTWNUImi+UPgGUhAdPIYGhuWscV2K21/T7QVGTEh+VX8XtSjA21kfVmyRSA4vFAwIf0bCv2awzyEL+4EOHeJokJQJegujSyvfOI7l9JstRNNmDXpJGm2VVke6uYZgiNbK4XaUrbfxeQNezXrvNPKl+5DdFUyGaRqkLDijC3PXsw/9g4kuzkEFJ0/U+3w3Xw6KXPYhgGnrwwwqUjciOI3Ai1dc18H35/y/G4PRqWAoph0LKxgc0taYjGoa2NJ2565duNODg4OPxIOCKNg4ODw38wiqsXhG6EwCUIz+QdtkUSAjVmsXdF5c/k3f9fPMFLSKhDCYQv+pdt5fomkwHSloJL+/5pXiP2G9r1+L2IhKTAjCg0zdHJdtaQfnjR7d9sJDsTl7B/xR+a88Pq6Ow1eQRvrrwZNLpqYLy2/DamdTyJy+Xi7tcuZVrtA9z9Rn/eWvwZry/8nH7DgsSeGYSnTse/UWPMLQ9hWRZWajo0jAA6b5RkhgN6HsIh/d8hPzT+W32xo0c6Wz6rAch/i3erLmSf/VdyzR1vMXJkDZoqqa6dC02TCOplXV9oCsJ55GuSqxYexGt1/Qju10TTYC/JEo17PprZtY9LJu7PsOxITqtcQI7vl4RjCoEZGVAzqHoGNdeivbeGCVimSSakc+EFZ1JSsZrxgfOoyvbtsrUm6KZnwQZKchL88q0TuOC9Iym2EiRMnS/+2YdkwhYrjsoxODTUQi/FJKKm6RNoZr9+K/FrGYQpOa0iTlirZIPRl93KniAVewQBuBRoMOC9VTm8+34llzw4mW7eGH5psCapkAUiLijQ4cPf/YNsGObMHMYXy0r54tUCLlgwASEl7kN3PM9qToBfXL8ajO3SXJqivDc+QGB2OznTUzSM6Y6lmKhxOzJBT9utuhVht/nuUdhIxvTSUJvDCydN4vW3+3fZemnWCE6ZP5o3G0sJ9YijZVKkahSsRSbuQJaTK+YzY/i7zJj9JWDZogTgrorhrjdwNZuk+heTGlpm16VRFISmIoRg7IRelBd4yC/e1tHtsxtKWHybjyIXBNIplJjE9NhdfyyfB6mq1L8G3tVptJiJp1NTrX9ZpSDuQxgSIUH6JNUXdaNhY8ZuUa1rICWWmcH0uJCqgnRrCI8X6xkP0gKRBM2IUn1QDkIRmKqLRJtASoG0YEu1G6nbXbNwa3aR30QS2dEBqQzeL9ajLa9C5gSReRHM7sWY+SFETqeIKSVEo3bEW9awU76SSTKFbnBpCAGBjR10u+tzet04F33eGrugiyUR8TSmBnk5MQwFpFdHaApKJIzi92NFowiv19aaDu9BgkqSu+XueLGUFZPOtTA1SFaaJCrcxAbZvqWkCUqnTCMAjxsr4iM1vJi4y4t+SZDUAWFQ1a8Uf1YRHhfS67EFqFTGfo39PkS3Mqw2F9YChexyBUXTEDkhpKraf527ksD+kTN475MFKEIg43FIpnltyaJdfbTswFsrz+CNpXswbc1CAN758CoUAYpQbRFQFQi/F6W8jGl/m8NzD77/rTYdHBwcfgwckcbBwcHhPxzFdwxK4KydOg49d8dp3HTGFP50+dSfx7H/x/j9h1NY9BJe77cLBt9GWeREykqXUl62DE37/oWStxbATSoSIRTUhECYku4PrrW3ezUqB3yz0BZhBWUaFGmQ45v8jXO/CbfbzbSN9/Laqq+PhsrLexgh7M6590y/iA+mXgpJiJcaxHKzkHgM2i4CktsWBX//g30CULRyFNcgzjxuJSNLW9AjHcxZW8qFj0xmzG8Po6LgZvzBV0i6L6a4ZBpPbD6BsWXrKAu2EPNmsVQ7feO1NvsmqzUVY9Lrj/BkQyuVxU+yW8Vv6TOwhaIlKpgmumoiswrNl/TlzseOpmVwhLqJYWYNlFiWRW7xr7CsYoxR/TEG96AkHEUVkm7eNkp71zFm+Eqa6yMc/OhhyGSaxV/mYJg+FNcAJv/1dM584ChUzcCtGlSE25lYtIqDypewX/cbKSt+gNGVr+OT6zEldJjQkIWMAbdfuj/+3a4lOLGOu5YO5tbnxnNz3R4Ic1t0gmkJlIwgoKcAhbw9aokvj5DvC3HZA0fT42j7nIqwm0OeyTJj4kAY/pUTXpiP7FtJsF3DE87prG1kf76kfRruDe2oX/h4Y2kP8vQOit5ro/sLUdLS4KO3h7Lk3j58/lRfUkvKGVKyGf8ojUCZgVQF0lJYMD2CWJ1kt2AzCuBV4MilTRz+e/v9qK3YZIf76CCL89ETYOUHEQE/orgIUZjPnJoOTr7iYPZ6tLzzgAR4dN5+eQRH9BnNiQe8RmBYM36lA5fHRM0YKIaJlRWorQmEaeFparbPmwXaunq05U1YPgspACtM3eg+pEty7OgVRSW0oBk8CiARhgWmxO91Y7jcSJ9KPCcEpo46oB2jNYmZGwRVxcwqtLSqCCULoaAdnbO5DrIZ8LhBVVAVgagoIRUJYkV8WLqC9HuQ6W01YzAtDG27mi8S/EtqsEw7SkprT9rnTQXhcWPFYohkGqW2DdfqzZQWtOEW4NYtJAJpWiCwCxQn7ferZ7mb8OIsZkk3sr27sfGRQVQdWgES3GXtiMRmcl+vxme0IztLXmV6hzADHjsVKzfHbtMd1Al2eMi1YriCkm7BVggF7E5XnX/GqDysXiVkd6vYdoyWhWEZZMoiZAM6qVYvaS1kFyYXCpmBpciAD9m9rOs8aIkEs6etonZSD6pPqAC/l4WLq77xM2W/u//CRU8P4pK/H4FpnMUzH3wOwAdLb0ZF2EWZVRWZNbBqtiAyWR778zvs2+tCOjo6vtG2g4ODw7/KTxcD7+Dg4ODwk1JSHKGkOPJzu/E/gaL8a/+dhopCZI8K4B3ZQqImjFuxbwKEAm/Fnv/W9R4RAwm6oqCo6rfO/6GcfMNTjN+3F6f3WElNLEDPkp74dTAjJqEerXjdFrcuXMnlfTQQvUHtDoFrUJTv1ob921Ddu9Oemo5HwBa/mzrVjcwo7H7adD5//lLAbjU/In8WvX3riJYqfNRcwgy/SiztpVthMzcsPIjqljAWQ5EI1kVr2JOeuEJ+skGd7i/pbDougxQSRQouqJ5JukgjGzJRswoj77ibhZddyMCxPfj8nWVIVeMfvx/KaTcvZmk8n9HhdWxM2BFVDUoO5780h3eOG85Ff74G1AoG73cRPm+Gxxv6ENHTbMkGKfO0kjY1NLctxq1ctDc9C7ZgAD4BPhVeWdeHmV/+iVP+djFj+zVSreWxqmEMgewirqkZzaw3+3HZce/iERaDRm1EYLL2vW7o41u5Z98PyM/5C3t5R7HXi8exfksNU2beT4bZKJTiuT1I2eZa1p3qA+ycKqFgV6VVsVORTPuW2GyJEZtQQG6thfV6PluSEQwh2ef0D1mqnET/0kd4969Tae0JFpIH3zqYG495hd5DkvzhQDv6SG6C0qtruPKaMp5ct44mU0VgcuavLc47exGmCTCL4688ihqRg5UB0ZFAWgKhCKx4Ahnt4E9n30vziByK/CbZuArJDKQyWH26Me3cSn559nKaRydYUuujKC/Gpg0eW9ASVqdQILsiMuiIUfqFpHp4PkIqmLqFGvERn1COJ62CNFFjcdwdKRR/AIkkU+mi9MJqdA0WPTcU4bftVxf2JH9jK0pTDGnLIaSjCtm4hogYYHWmAPl8EE92pjMBhoknkyXd2ETLXpUUtHSguV12PR1TgqphV5zOdr0nhAQtax9NwXmFbJyn4fu8FmGmcTXFUEwVszmGmgwxZdB68jQY6q5nQW0pQtMRUqDkRpANdpcl3/oEihCIVlB8btxLJfGD86kqTPPL1CzmrYrgSWTQyltZtXIQAHptB1prwj6ugB/Tq1L6fi3S58KnJXH/NUFOzzQbE35siaszhapXipZgMZRkyfkQtEsDeIZqVM8oRTFddr2iWBatIwNZE5JJUj0q0FoyaLobwkFojyIBbelGxNhCCIXZ+Ksga9Qa/nLBw1z417N3+VnSNjdL0IJ4ocqGeJh7//kpJ+9vp5S+s2JbR739cs7AHNYbEUuiZkwMFY6YdCeKlEz74rof/Fnm4PDvwtpSh6V7vn3iv9uPrF3gf8yYMaiqyvnnn8/555//M3v1n4MTSePg4ODg4PAz83LtY4w1VdJLIigJQc5FawD4R8PjO0VI7Qo153GEaxxK6IHvtL+thUa/L8vq63nkrvGMufVU6qMvdI2/ed4paBLqq/J4ZEUvyH8X8t9EybsfxV2EZUWxrB+2z+3pVnAfN8/fnw9aenDvnNG0FfsI5kZxNWWYOOWWrnlDg3EqVJMi3WDP/NH8YdRx9CtWGR3IUOSOsbamjIJQBwXhNo7sPhaA7t0mgtcFioLv7TqkFJiGTnUsyT+vPAE9quJqFWQ328dx8ukTERYoLovExCALVpzBIXmbCTRkWDxzEFJIug9dT9ar0u2ReoQ+lE3Rary+LH49zQszduPeeXuiJjSWx8op8YwlFosxYeJ19CzYgirsFt0qdumf80baX3hPHnoaHy4fwqfzhvD8rWfx+GAXd3ZfwF0nvMzDs/fkwxWDSMdgw7RyBu2zkZcnvMKY7lt4cOXZzF3fl7/M34uTF/4ZVYe4dFHpraNXqIWhA2q5bGkVVyxfgxFrhvUNyHQG98YU0f5BRh5VDYA5QiOjWBh+UCy47oLXycvPMP2ZiZyy+xLO6dvKSb94B4nAFVWxgJtuOZTfXrQnsC3iRwBlfU0+S+RQT4YSvQMhKgEFRbFL5ew+phpTgBnP2p2HBMi2DkTWQEmksVosAvPakei2QQXQXYjqWsyODj6aNZEPZ5TTHnNT1n88OVtF687sLtFqYungy81wxG3LkIpC8OlNiA4LV72CkIDbTcZjQiKFEXR11ldxIYJ+gmM72FfZyC19vuTm376OHjfJm76O4KyNjDp6JVKVSKEgEbZAA8iOKHg9EPRDfg5mJAD5eVCYb4dLrd6Eb2UtZQ/PQegaitcDii28WqrACG/rpATQOjKnM1JMsH9gMQNLmsgW2yKS0DSEUBC5AZoOL2K/7nZ0yYkVC9CbsyAl0jCQqW1FdtXGDkRjO3o0gyIU8j8zqHzGoNtjy5n5xwh7XtxMr9NS5OxpdHWD0lvjiM6i1DIWx/B5bGGvoZWm2m60rvPQvsZl15vZ7v0sn22lxFxL7oYmRB9Ivemh8f0Agw9bi9XcAEIj2T9CprAzsiiZRkmmMaRpC0LBQNc1pQLBF+eDIkETqNkQT0zYwPtLVu70OXLjA2+hJ0DJgKfV5K6npyCFYNIl9+00N7hHPzoGhWgfW4ghLZSwHzVtIDImsWhyp/kODg47MnfuXJYvX+4INN8TR6RxcHBwcHD4mbnxnZsZevpaHjtrECv+cBkfW/9gmvUPwrnfLX1KcfVGyXsGxbfPt879YMPxvLBmLH9fM/F7+3nUMflkShRyCj3sPqpf13j/wiI6GgIE1qh4VnpY3SBROouCWh23IhtGQ9OU772/XbHpw8Hc8tQkknMiWKaCljUJHbeF3r9ZzHkvnoOVjTGjPcJzrX1oYip797iZ/Ur34Jk9/8RRxY0cFGhkRGU17etzserDBN32L46nnDcJTAslnia/uQykCloWXzDJoR8/hmIohNYY5K5Ic8hZ99JvYAXZiJuWcwWrCgp4Nl1P3/J/cs1eAzh102wunPoGwysaKHZFGVFSA8Dchlf4S48vOKtwAyHhIviZh+mfDOa+4Rfz4uV+Djr6bqRbsj5lixuNKZX5NT6khIb0Jj5e35dh/iN5cb/3+OTkt/FqK9HMWQRcJvuXlzNj6mvcs8+nPLffuxx+3GouHbGekAZVWYh4TFzAZpmDFBbxDo3XNozg7U1juWvMq9z65TGcOeMozn76RGoeHoorFkPoFt4t7WAazJzbG4DcJ1ZT9tBcnvrFc8x+6FHGDejA3ZJFmLDgjVq8iuSA0Q24Xa1YbgkuCC5rwrehddsNuqJgAZ5r8hnsbWOwS6GqPYwrfChXnHsCy+cHWLIkzEszhqAnwD9na4FvCR5PVxtqAbjaDAzLZUelWEAmg0hl8YwzaVi6GTWtICyY9eFyWhtitpVM5419FpQsxNrcPPPWQVw7czoj/7aJbp/MxtUYt+NsNjbi2tBI/S97Y2lJRNqEpmbY0kDqiQ5W3ujCnYAxOe1onhq0Tc141jcwYcRGSvLaEdZ2nakEZHJ0aG23Oxq1diBSGeIVPhIFPqzmFlQpKR0f49erV/Gbv71DwojawpAAw6OgJjoFFb8XqSgkB+WACcKE198uoiwvhuaV3DptDXe8+jm+iAERD+7cLKgWDQ0aH/7ZT3j6RqRpC1+mmbUddLuQ0Q5kRxTLNDC0TuWrs+CvTEFb9xDRPSP01lohbnS+DmLrqwOAIdMU7h9H5IRJ+yMEy0IccmyM7NYJqgoBPwiV2PQI1rvNHHpThiEnxPG9UItWGyfy1kaMPIG7TRJc2o7IWvZefNA+MUL74Hy7+1p4W00iT8zA/8FalC1g+CwQkl8/8U8mKcfwxiPvds17/61lKBnQEyZnHzoS1bJQTYumlm2Fz7fS/4B+mG6JpcPB1x2AGU8iV29ArqniiPzT2bCq+rt+dDk4ODh8ZxyRxsHBweG/AMuMYphN3z7R4f8dj7z9LiX5s5hYVMWC6qe+U+TMv0J9dhM+JYsqvn9dhT9MPJ0l91zKR3/YueDyCaXDyLpBuuHS59/qGjcSf7OLrlob/xW3u/BlBHkrTbytkmN6lJFpCBLPehgYbmHZ5wXc+s8TOTKyjEnBjaxP73gudWsjQUVyXs+NvHTaWKafdnrXtlDIxxGTh2IlDWQyy5NDpxIMJQn4UxTlt3H1sWPQY2lQoK3aPndqNE2TGSTV7iHW7uXkg//BAeP9zHyvmJbZYaJpD6sTRayOF2FFH+Sw4Ev4FejvjTJ5rxQde6bxD2jnoH1eJN0YR2nLoLoEX6aDvNwR5Oa3x7BbtwRCQKEGLqmQ6VI62pBWHPQ9gBLwn4+nfDVJqeNT4LK+nzGs8s+4FahwQWPWTRTY17eB44tXcdnQuXy63xW8s+cl9LjhNrQmHYSFkSPxB5NY3QIkamvouFpHn9iBpzrWda4EcNnpfYkacPEDw4nGvIiUwScPl/N2WyWP1fbjjqNeR+itiAxom1pQknaKjsTu/NVw7EC+mDWYXM20i14LH7+88SHetQwuPHU0lx7YHRHPIiQkSyL22qyJmU7Bhd5tL6pbR4SDCO+28P7Ebr1JLfSS7l2IqrsxK4pQEim74C6gbPceEwCWxHSFufL8X7D6rH6kY72IfNFIwXubyPtgHUZBgFSOTrIiv+sYpJQESjNsyYbJqFC1Ikz4jSx63ECLW9w3pQe/PnUOBx7WDELiDduFvV3NGcjYRYORkmyOGzWWRG9o7vpSPujidrvukyLJfxz2uGsz7pIgmX7FSCEQBfkIrw+luJDAZ7VdxXg7+voJZKLk3pakqG+WnNIMef1jpIs0BpRvoskES5MYbpAhEEJFUVUCOSpIMJB2RyifFyuZRGmP0eLW2LSbwfobR1F13mhmXVZOfKGXVy4agqfRjiqz/G6ksu2cGoU6s0eVwZYGWLGBTes9/P3h7iiWifC4USJhRG4OSiiIumEze16c5ZgxaznvhCVkcvxUn6fbQVFxQWCzRHMHEZradQ1o2RzCr82FTbXISGiHrk+585opeehzvHntRFdFCL1h1/a655zHmBI4gdbWNkzLjgIDlV8dvT/ulMSVsY28/P7CHT4zzjlmT9QEKCZMn7eBmctvBylAURE5Ec6Z8CcOGnIlew+5kuMOvAUHBweHHwNHpHFwcHD4D8cwU+w77QbGv/Nnvqib/XO74/A9efS1z/nnlwN4aVMfFi4d9W/f357FN5CWQQq8B/6odq/eL8i88x/l5eP/zsbWtq7xuqxBVkKT+ePkyO89qifJPEEmBJteauSTt68kmXDz8p/3Qq4I8c6T4+3GN9LFIeW/2WGt9BxKqwySG7mCwbl7E3YX77A9nc5AQEWaWa495znIApaktjHM9avfwfIItPo4imHyp6tfRJNQWt5Kr551uNQstZ3iDZrGgJH1XFP2JadE1nJRxSEQfxKVzogFUcTZA8bwuzEfcWrFZ0ivhnS7wKthZVQqMrexZv5J3DF12dZ6vXaLZSz0rnthAdo4lLyHUYpnovgOBsAVughDqhhqD4QWxOPdgxUN/Xno1WM448lzuebe4zgtdz2nFq/jpRm/4NRrbyEQasdQQcR0PP5WAq4kyhSTnGwQ3yWtuG9NoOg71hVqOD2X3W8+gwt/M4+zr36H8x6fztDfruHE3A38qnAtDalCXjgplyMnf0puor0zG0mS1aHpnLEoBWGEVPnrxkG81VbMC5+PpFldQZ99W4gfX0JTSZDAZ2vwf7aCUEOCfk8maJk8gLaTxrC5bzfKDjcJDsmC349s7UCm0tQe2YvUfkNRNTvaxh1NIKUEq7OFdSeBbknIjUBkWxSGXFkPUqB4dDTLQnq9EIuBruE6WsE9MIp3XePWM2+nLm2E4y9rxGvoDOrZwT6TGrfbCl9MD5BfkkVRJOGSThUAO9tKSrubVXJMD0yfjshauIoBMrykjaQx42djIsKKxkJqhpex78vrqD/Mjd4YhVQKWtuQsTiB9dtSbtR/Jll2ehFGro9nmnvy9MaeVH9hErcMlr3Xh1/efQivkUvyyhD1v6xAVSTSkvSvbCE+IRf8GjQ2Q0sraksUM9kG/QVaIBfLVMj2EnS0q1Sdn0VfEUXvsKN6TMO0hVhdkBjgJz3OS58n1oFpj8vmGFvqIyimBR430sgia+uR8QTClCx53c+SllzeWdMDtS1m18bJyfKHfWYyomITan0bGCYSSI7w4UmAryKDAoiqmi6Rauu/OlBxxlp6XzOPSLV9fsxhfUmOGsTRe9+Bin0DpHUWPv7VvmNRkhZqQnLr0zt2cOpRkod3S5L8L1rJzK0HYMyhw8GlIRNJZDqDUVWL1KBuczv7DLqSWZ8txcHBweFfwSkc7ODg4PAfTtYyOKfbl+wRqOepTVsYW7z7z+2Sw3cknalnyJErWfjoEF74bChfPHvFv32flaF9qQzt+6PbNRJ34dVM+hS00V00do379F5syKyh+DukYn0XzjllH/755GxMw+JXt9rCxOcX3cTEOTd2FSSNa1MZUvpHNDWww9qS/Nu+0fZxJ43n7RnLESigSPLu1an7lYplCFxhiWeCi7aFbuIDJH9vXM9vz9+LF91vIYA80QG4sDDIOydBY46X5UaWiHsgI4tPwaz9CxnApfRFKXiNqi9X88azY0h0uMh4vOjSxCUFM9+1r4GJw/bGas9A8nEQKmrBEsZr3/61zRc8A4JnbBsI3chTi97HlG0gIWmoRIE8BY4Zt5pTdl+NBEb+/QQOGjCC6/c+iHPnnYH7WMnwkzbQVB/iwH6baIz7yKwfw/ULl/P7lw6k+9BaTix/jY8buzHHyudq10KyE2H5Eh+9B2c4pewWFPdYXMojTPtdiHdu7AaAyzDJDTfSlCxCAB1tGjd/PAUjP8t9x7zFKXcfSb+DNiGn6Kx7yU3B8210exv6h2NkfruSOa39YWEAV0UKudEDgQDC7yNSapBqCoBbIetRET4drTbBhS9/RNPiEM+da3cQsgD3bgrxv7cCAol9wx5KGrC+EUN3oagKylZRx7Qoao9Tr5jISg3qt3VbyraY3Hf5UMruWkBlaSubG3KA1s7IDsFuJ5gs/MBFopsXbZwOK+0aLgrYNVyaW/HPMhDpLCJtYOV6OeqFTbyhFzB/UxnTGgdREGlHFbBR5qM3hKj59RjK7v4CVQjoiO4gUJz20lo+dfdic7vGW8v7YyU1fKUQmrOJxKAS6lP5/HnmwQhdItp0rHQUhMLSxV48oShaVILXC/EESnkpWljDDLlRLAG6nVLlakuQCaqgC7w1DQAEF9fbqUyaQvc/SVou3gJbLLuWjlBIRwKI3BBaPIFo69ihLg1Ax2cZbh9bDopAyWap/L3K8COamPZFT0SLhUu6MFUFy6fTM9xI0+cNWHsrsGmbDeFxQSrT1Za7S8vMdhZZ9rqwPlmECrjX6hjFhfzhibMAOPvEiTz0xucIDYxdvKdCLRmkYeFusUWpm168hM+nzeeqY+7tzLnzolTVQ1ERlk/h9+c+hypg+uKbd2HNwcHB4dtxRBoHBweH/3C8eoBYWuH0mv0ZGWr4ud1x+B7oWj4TutXS/apmTul21c/tzr+E6p5KPLqMrITDhm4r1plb8AS52QUI954/yn40TeODT67eaXzmi1dxxz3vMnZ0d3qW9P/a9ZbZhjRrUfT+O6WWde9RRGHEQ31b3E6vMMCz2iAwvoPWaICaPnFUXefUAz8kX0syNm8f9kx9ggVcOH13jAx4fRZFk+L49DSLEnlc2MvuztVqGfiANquKtV+u5Yo/vsYRkzcxdGA9tz6+J6cetRe/OH7vHfxRwlcgg+eDCPzgNDihlXPfaacz4bJ7aW5PgtAQpobmMtA6TQpg2vFHUFQ8HIDB3lrKXO0UeTs4MreJhxu7s1ekkQx2Y/WD+qzgVyNXkLLAtJpZVF/AsnSEjBRc9YeJvPTJDSjuEC2J5WyUOr1OTsKfJEiFoQfHuPSCNwBb0opZKoeXrqN/XowaS0crVqmLhaksaMOcWEKkOkG3cB1HFtZySmEtF78Q5svW3mxoyiHZ5CK+Zy6xCg0trpGzNouettBUsNxgtbWR49cZdmArz1EGKAigZU4YiGGXm8Vui+11IVEQqTiW6qJ1lJvoefn0vjxN7fMZRqxfQpunlIa+fryrG20RQIKybgu/uX081m8MPBX1+NwuRDhEtl8Jl98xkpZwGznxGpbPC+CjfesesbDFGrVxm9CSXWcw+6k8bvjrHGpiFv2DVczJ9GVls5+6Of3R4ya6KVAVBSwLfD5EIoEExp7dRrdBKVZtKKCXv4mPN5eDWyA9abJlAaS0cKFjrjdQdTcoAtNMIFKg9s6ifZm1I8eyBug67kiGqMuDMCAZMVAaDQILoyA9pAfno26KItriO1xrMmlykr6YVVP9fPJGPvWNIfyqhijKRZEgg0FkOovwepCt7dsWGgampqBk7PQpd28VRbXwegyaBihYHyTAtFB7eAnVNpNoNkm9vN0tjCLAsNduL9BsfSwBdc5S+9y73ZDNom6q40/nPM3+K261Tfh1rKQd6XbZg69x27mHd5n/3dVTuf+G19hj30FdY+MmjWTUpMHM+2A5UlqoXg9mNAa+EFKxxZ6JI65k5gJHqHFwcPj+OCKNg4ODw38B77X35ozy5WzKBL59ssP/GxRF5ZQBH/3cbvwouMOnsGLd0+SEGpklcjg5k8Xj0lHUHFB/vMidi168DyVvKddOuImAK2eHbZf85ttTuJoaDkaxWhH+X5MX+dVO2//xyiVdj606u2X0HvMOQWahV1EDGVTOz6/Bo8CG7NV4VA1TSl659mp8N5diRp/n3MVvMq+9kqypd9kKKAqtpsBLmt9e+xpCVTjv2LnomsWQO3tSXLH3Lv0VyncrHv1tfHLbrwH4fNlafvXXBl685FXqszot6Sy5bgi676Y9cS3tDQfxi3yDV9sq6EWcBik5IH8zzYad7lTpO5LB+3Qg0ivQBTQZLrYk/dx7+AiQXqTLhRa/iJa2c5mfOAlwoajwmxXLiaaf5JCeGeqazkMg2ZJ2YaIwqijGGe9OIplxka5WCA2AT5d3J6dWEP71eFzy7/gBTYE7TpzJ2CdLsR7L4CZDzIriShZiqAqpIkkmKRk/ZhF1n4U49JwGHn1qb9yH1mAnW9k37ZPPqeH99SPpW7+CppE+Wh50oUiBIQ1kQQ5SVYhsNAm/t4n08Epcc9awaY4b4YmhXxBhSqiFd//cHZnOoERCdOwWYuhDi9jSu4COvm2423TMiBsla+Fuy6A323+xUSEC8zq2S4iiqyX1VhqW+Pg4kU+b4eGzPUPg1xAhUHdvIbRBIPwBW6DZOr9nELlPT6695mmyJlzfewYbvvDwkWcolqXQMtlF0bXtaJ83okZyCUqFpmExjFwFeZ8gdVUO3t5bYLtMWXfPLNH6KFoyRr4B+S4N6trRN26hdb8BmL3D+Fc3gN55G6FrkMyiAk8v7Ytekybu9RKfXIb/ixja+i1YuUEsVUeGA2jBkN2lS9ot0BW/D9UwwUgjgRU3+Nh8+DBi70sSA0K4SkFraCe/RxtSBLHVpO0EGUt23dFIQHi9kEzulAYFQDoNQiCw0NZVMXHAxUxbdDMPX3oEZ9z2Ekh4f+E6to+5O+DwURxw+M6pqLf842ImhU9F6B5oaUdTIKNbKOEQMiNREOwz6g888dIFdO9R8o3vTwcHB4ftcWrSODg4OPwXMC4nxW7+JqaEa35uVxz+h6l2Xcmr7QUM71FFwmz70e0f8ucrKOn/KsPK53PL3N92jbfE5/Pyqn15Y/UB39rqO27GMLGIZr5D3Yjcd4B8nu/1IZX5LayuK6TN5cGt2Dd+pS6NspLX6Vb6Fj53KQDCdzDXd9/C2cWbuXv0k12mqrIFPN9WzlPtZUjVxFSg2VLJSvgo1rjL3f87GDeoN6/eeRtaaDbFWpa+fijRIZr5jGjzZP5YPZR8DSYGazEsE0WR9M+5nbHFjwBQUnglkdzbmRUfxN01A7i8eg/O6jcHM5KLDHrIhlxM/sjFkfOfZOHiARRg8FltT2776GCeW72MkH8C7WYOzckMAS1NL08clzB5/MB3ufLA+Sx/+A8MZjw5mSzkZZi2qo1u5q1kgX8sLmPEo2cSeLmuS+jwLGoFQ4Ib4oUahaM3sfyKLHvkrmK3/ms55fg5rNvH/rorAEOBBz2D+cNFn3DZvRvZfUo7ez9Rzx5/WIpUFTRVxfLpGHleMuMHkzUkqCrC6yUbztBzUjvrPi5H5ITtYsXRGO53V7J62ACOOHMFBzzdyuSX1qFkBZamgjuC7Oy+FJzX0fXF+6txUdE9NeJDArQfnc+btUN58+4Ku85LMoUM+ih/uQbf/E145qzuKpTb1j9M/MQBKBLSElwqVH9ZyFMn9cVMKShCotUIXDET76p6pBBoDa1U3LOSsU/PxbwpH7VCwVdgIFQLNKjcO8bYu+pRO1L4ZDvHXjLLTlvKD9k+h1W05gRm2I3oTAnbPsqr8UqLTXODZDdItGZJIgiWAsKy0DbXotY3Y9TVdaWTCZduR+8ookusMjdHiT7aiqxPoG6yEGkDEbHwDbDofmwLme3mdpExkKLzvKoK6LodYbMrpLRPoGGipizGHH4Nvz7gVrIuFdOtknHvel1jYgXxzI7RqtPan4R4EtmZbuXa3ELfQfkICaZfQUo4/ej7yG5Nu3JwcHD4DjgijYODg8N/Ab8Z+Bc2Mxbdf9bP7YrD/zCHDtqHgfknMrbkHHK9BbucY1lpLGPzD7KvkORgfzN5wkQmK7vG5zXcy1BPG/3dDdTHF36jjdzITST0gyjP3Tllaqf9uXpB6Aq6heMolkV+XgdFkRS3bhqM5doHb95TKFoZilaCaZpUV4+itW5vigpfYlC3z9D1bZE0q61xrIkWsqC9gmCfJhQLLq/eneu2DOHp2rIfdD6+L9UdtRz+0Xlc/MUxbG6cgKKAS4GktBN/VAGGtCNmNsc9+FRBmynxp24i6BkGQE3bdF5buR+ProjwXntv4qabjOalcFCLnVaSNAh9rjIubzN/WzSC5rjGjHU9qFYKSRkreXflBIRVT/8gdNMtXq3viU+BsAqedAsbq/fnvqOOwi9KaHUHQIHffzaD384YzdWrDkAoEFjV0nVMraX5hJbGuHj/V/nw148yedBafJVpPtunJ5sVhb9td50AaBbs+cByylyt+FWDEck6PpqiMetUP/qqjVimhfSoWEInG1BIDMqh5fiRxNMpjKzG0pifdYtdkMnYxZx1jaIFMbTNjUhFkJE6wi3tFt8GJMq8OwkzX5UAwhcZyMvLqb2wO5uHlLFmUwE5z7QCktx+CZpHlnalRylZc9tCXwDFlKTcOhPvOJF9HpjK4yfYxbArrlhPwcMJCh9eh6UIsiEveiKDWtOESBs0L3dz6T3vc86N04gMMhClCqKnyudnD+bVaUNQ4hapqMbj6dF4rqiDliiEgvgWbiH8/hoIuBAhO8pLaKotWOkaiq6h1BooSQutI4smPMiCXIQ3gDDsNtpCUTFd9nUmJHaBYdPqOjeujInlcWFUFOJts9DaM7jDKkOmNlNmtsLwHhj/zNupfozoVG5kLI5hZO0Im12dcOx0M3QVWVVD4QaJ6g3jrspixNK4NzQzSTmGSdqxzPlgHgBjL/8L+1z5NuOufBLDzOxg66Wqv9ht0j1uCAVY/95KDvvFcBCgKApSmhww8Ar2H/zvrznm4ODw34Ej0jg4ODj8F+DWc5hQ+RD9C8/+uV1x+B9GCJVJlWewe+kxXz+p5SRoOhwr+fb3tv/SSfPo54uxtzfBn/a/oWt8XMnVrEjnsC5bTJF/+DfaCAcOpkfhX9C0oq6xaP25tNYMoGXzdby3ai9WVR27LSLHcwhRC14d9iFeIRkQ3kRRoIaXWxJE0ytojk0H4J01+1CqRwkrcera/rLTfh+61WRZWzFV0VysIQZ/vGh/mtOlfNLSk7aMf5e+zqy6kz98cSLtzfd8zzO1M1JKpm88j6Nzq5jf1o1a0+wcB6FN5Z+tw7irZiDHFJiIgmnsN2g+A90647zQVbsFaG6/hr1Cmzl94BKiy9wMCVUzwtXGGRfNJlkcJFUeZGOiFI+a5fg95vF6dBDNRh6YBpcP/5ze3jgr4pUEFcjVoMTTRpsJjaYgoQp0WU39hr78+bAnQFiUeJoYmFPLwgU9IWXX+tA7GxpJ4IwD1rP7gDUcM6CJIpfBr0eu58kZa8iGNRYH8tAshcbxIcACATc+v5rHP6qmpxeShsRVntrhPGnzVqAsW4dEIsN2DRvLDbEzRhMtzSFZr2HGkliJFKTSyMIwADnP1vL++BARGWfBb3Tc0+YT27COskfmArvUCQCIPZbLgONTWIZGz5JGBlZspu+Zi7u2D+sbRw5IYQ3o1WlHsjWORKtvpdvNc8n5qI7IPctw37gBC8h2L0Bt6sCzthUtnkA1JVp1EwagqrZwaGkaz18/lFFhg17dG/EWSPoWt3F7r9l4qrKdaUTwu56fcVbvBegbqjCzKVxpFVSNMk3y8GudOVJmFjIZzLww0jBQ0wbClHirW2nv60JtjyOjCRC25zLog16lGB7NjnpRFbst+favgwGWJpAuDaEIEk0eZt8xhLeuHIvSmGBzWz7Gy+V0utnF1ggrbftQm6+E3UhACfgha6KFg1BVjauhkcjsVYRmbiG4MgUuHTSNP06+hcvOuJN4MotIS7KWupPBcDjMja/92o7esSToOm/fNYM+vXOQqkCNmZgFQYQU7D/gCkzTxMHBweGbcGrSODg4ODg4OPx0yBQgQSa+91LNOwmSz+PxDtxhPOTpwWF9p/9gl8zsDIIqPNU4g1ML6oA6zMapKEWvoygK4ZKVVG/sz/VFM1nh0hju6SCeXcDCunPppWf5ss5PpZ5hVdpHQDHIDZ+3g/19DruNpJnDUSWfMDmymQ8KKjh45Ej6tOvcvvzvHFI2ZCefpJQ8W78QVVG4ZNV8HtnjBx8elrEJlEIm5SZpSLXT19dCRMtHLfkMgABwftHO69TCDyD+IHimdI2tjOcRcUVZEsvHiHq5ffSrPLnsLPYNf0FqlEW2TWfIHuup7fBg5aYZp29k6m4raeyIMDeaiyYs5m4u4cTCjSDg6B6/4dR3P2b33mt45tNxTJv6Mp9nFIZ623l41Es8VL87gUiSZKObDc0KREDxSGRKkKnw8ExJBQt/+RLm1vtmAUG3yeiyzeToCXrlNjL14WVkXRZWQtLD2naDbAA9NDjsT5uYeWcx7c0aEoFrUzuxAW0QzUH4wdAFSkaiDqlEvWcdecNjNM9Kg1tHrNu8dbcQhemjgtg38WnyOiN+dhZoJBaCwn/6aMjmUZNuo3+4htpUiOQBTeidq3R3lpqNHtxtHbiSVVjbWRNAYF2z/fqtqMHyuCGVBp8b3EFkDzdiu2PVAf3jRVhlRV11XLasz+WCoyZSOzBI8aKVrNP8dGveQln/VloBISyEnkTTLUZclGTh0wVYkRAyN8j+B60mr9CATZDfLcOWxRZqQ8v2nc7RGtrIn+tBxhKoJEBTEaqK9LiQ9a3ooRxoaQFVQ3YWQ966XEoTd2sa6XUhpMQIe2iZJUHNoNU1sdsb1ex5TTMfPzOQ2lvyYNnaHbs6fRvSAlW1U6LcHjAlIi+HwMZNoGuYWFgZEx2YNX0lxU0pGo/qhdcbRFPdO5nbbcJwLnvsDG475ym77o1hUfX2cvy9ykkqCnrGhEQKze3mkIG/551Vt35XTx0cHP4HcUQaBwcHBwcHh5+O3KfB3IjQR3zvpUr4Gghf8+P7pPiABGbnHebGDGyx1qFvHMZu3RehKArdShcT0YZSm/By38Yx1McDPDz8IzoMN6YiqDb8jNTb2Cz2oNK3LX2poTVKe6n9dWuIt5UKPcUeOVEA+oaH8MjuOws0W9l6w5m/XdrU98GKPQexGwALXAdRVnQ7ZVYrD/aa8J26RSlaMYSvBcDsrKlx3MDX2P1Pt5FKw34H2jerpw6y69X8/qTfU6G/S4vp4s5VYzms/CP66SYh3USE65jZnOE3fz+Y9351NiK4DoxNCN+RPH30iby55p/cceCnxBAUq+BRYI/yKDf9PUB0iA/zfRN/f9jvqLkctv8a0ssVVvfrxkOf+gA7IuixumKGeQ02WyqvvjeC3SeuYVTkUBZkngAD/KQ4sLCBtAlxE8p1qMnA/idE6XuUSXMql2PyNlAf17hsY4TNsSybZ+XgjbsQGUjkqMipfVl5fzsFmJjpLF9/GrdGXNgT9j2mjo9ezcPKaoDA19Pk15WrubwmxKqmEvK9cZQpDV2veTSsoZRXkF6ymXyqsMztozc6Y2ks2TXfSKXR8iLIWALZ2IBSkE+2sQ3XV1/TzVuQqAjTREsaWC1J8hozYFpkTYWrT+tO47k5uL2t4FZIqm6sOj96SsMM+8h2NOBpyLBumdYljGVGemFxHFNKO+ZKCGTneymhZMkNp+lzXJT6Kj+Nr6mgaHhyc+3rCqBh55pMCgqoKqaVRbEsLBeYtc2o0o6VqXvdTfYaDT2cJZ5tJxQMkM5kUI0MmvkdxJp4EimArIGQFuguO+3K6wHLRNU9qGEPmWwGb007uHTyX13Pm7UP7NJcbU0DfUf2pP9p/VjXcyFNd3gJuPOId8RRQj6yhoFb05CqHQvV0thObkH427x0cHD4H8URaRwcHBwcHBx+MhQ1F9Tcn3y/VmoaCB+Ke/xO2yKlC8mmV3BmQXfWNb1CVN6IBWSkgZQSIQSKx0OV3Jfurhms7CjB0OHTlkKGBttJWCrlWoao+1xGF120g+2G1ljX4wfumUivq1MMyzvnW/0VQnDzkDvZnKpjaM7gH3bQ8fvoSgaRTQj96wWh78OX1/5hl+NHD7iJsS9JYhmNh0d8QIeZIaMKthg6LmGhCQtvKEVZfgFQAO7dutYe0ucI4AjgzwRaW0nHxhHW4I3zX+GAM48jWerBVASD3Y30i6RYFfIyf3kJxw2aTXNG4aqq4aTSbgb3PotnNz5LYFiUt94dyeCj64loacb5GpkTL8SywK2ClYH1pkZAM8kaAo8KGctg9uoA8wN57N1tJQVqgt+3HcQNw9/iuvumoigm/hXApJHw8lxbkJBAwA8xux31jtEckmB+nPvnrScmYcXCIFvWdKYbbRHUbMnnAm0tj382gnUNxXh/qUHKREpB8B9zYYAfYVrslK/zlX1slWoiei3taS9KOoNoiXUJNNsFGdkCDRKpqJBIkhpWhraqkcyoPoi4pNEFjX5J4BIvhBTueHt3AHR3ltzoKtR4BplOs77GT6yze1nt5gga27XjVlUw7Ioxsls+rGokWafg+0UE80OJXt8Iefl2FItlklbB9ZUMIKsjhlJYgLq2FonEXVlI45heFH22yp6g6/zzmbF01Odg9YwhVzfiAYzcMC0+k7yaGF9HV22grS+WP4iUFjKVRjFMW/EL+yGVxNUeBVVFptJkvX7am6L4utnCYDabZd/D7kRduQW9I0G6IET773UiN+XgLc3HlBbC0DHas3gAqalYRgYhVE7Y8yaEafL3edcQDv84HdwcHBz+e3Bq0jg4ODg4ODj8V2NlFkP7ldD2OyyjlqrW1TvN0d0DUDQvfYpPolCo+IVFnqLtEHEyrPJBQOeqynfJJFz8tXYkt20ZQCqtcN3sMcyqz9/J7uCeJZx14DiOGlvMq3+9hj5Ff8Hn6f+d/M7z5v9wgQbAdzbgAXUgRB754Xa+B9cPX8nMCX+nTlgM81p8mfZTEwe/mWWfYCuHlW3Yac3d0+/mqKv+RCxmRxi5g5Ia6WFpRsPyhrn6z+/hGZvksCPmMM7fwrWfj0dRLDJVKn9bNIH9H/kF+/rXseHRMvbosSd1K/uy+bMytMokn7TM4936cq7fOI72d3RmLi0magjOeHd3YgrMbIqwSXppMuBAXz3j+8cYFmljQ6qA9ek8ElIBaZEa0I6vFhLFgvbegqbfDOOejUu56K+rbYHG7aZtpI909yJAsPtBdfxm6RZeWLiO2liQo/90BnMn7UVqUIh4j0JieYVctXQY01J5PHT6S+gHdHDMOR/QdlCU7JQ2pvxzA1bY/5WIkF0n9AjABXTUebuef5WtHaG2JksJy8KSFoaiQ65dLwUd9JomCh7J4n3Ni75oW60ky9KpP6w3WiwNQPucOG+ss8VWzxw7EiabE8AMB0jk+ImN6UHsoKG45lWTrFfY1JTLwpm9qTuiJ80Te9MwMo+mvkHqD+tNemDxTv42HjeEuom5mG4FSxEINHKazW0dm6SFeDFN0q8SmlnddcxaSzsBLQKaupPNnc6J2wXRBAgFobtQ/X4yPbqREQbUN0B71G4vbpp2J7HWOL/sfgGTlGO4/rg7uOiK59E3tqLoul11W3ORtlQIBEgHXaQKPMTDGsqqtVjxOD0HFCEyFggVmc5gIjh83I2M3/dGfv2bJ77VXwcHh/8dnEgaBwcHBwcHh/9u1BIQYRAufjvzDwTc7ZxcuoSALlAoorzi4x2ml3VbQKkVQ1FzdjI1pucyVqw/gCnhxTQF/fy5fAEm4B+4gDvrJA+vWsDmNj+oGn8ctZwzh73IOZM7oONPyIZHEUUzfqKDBiV4KgRP/bfvJ5ttpLFuHzJS0DfnHja1X8QYbzutKAzwncma7Hzy9Y/xahaX7jaXeXPXMmpMbwDSRgevnLwUV10bR9x0Ond9dgO9RqrELIlEAe/R7FvyFKMOfwmpSFBUGpp9NGaDHLDnAn7hm02ubvBmfXf6n76W597+glm/uojjp/2KuGUypWwZo/U056zejzkvD+DCX7wPwPMnVFIT+5weBW2cfWx/MtUw5bOVAAz1RJldlyD2eZrr951GRqbp/84SqkcNxijy8fuD3sKjSKqTgtEHJVl7WjdE9yLM/Cxaa5TxH2S4+pFFtFsqmoCB4Q6mHPoepw7dTO6VkrY09PBKMiYYKHwZzSEd9/FOW18O67OEXxauZmXWzYEnzuD9OcW42jsrJe8kv2yLovm6zlEAoqwE6dHI6G7UlWvtLlGAaGhG/yQOBbmQ50ORIFNp9DXVCF3HVRdCHWFA30IwQFFddtFd7Jbi+3ZrZslyEIYdq+Nuidkdr0ZUYoSDRN5Z3OVPfGYGb30r6bER8Gjote14V9ViBl1o61t28NfUgIAPS7HsIzQM0nlelA4D06Mjg34I+FAtheIH5247ZkWAriE21nQ+V7Asa9e/SOflIFQVGpqgsRFcOtLlhp7FqJntIpeyO/aQktgpWp8snI2/OB9EDkpbB4bLjXttFcUvhUgGApheCK5uR6idkUw1tayrqYXyYkhnUFw6Ekj1DOGuTbJ4UQ3HTL2Df7x6ya68dXBw+B/DiaRxcHBwcHBw+K9GUQtQCqejFLxD31A9J5SuQVcFpZqkWKvDytbuMF8IfZcCzVYG9HyPWya/SVjoSAVCGuxW3MHRpYvJWio989oBFy9sKrEXdPwFyIKs+fcd5M9IfXQ6OWqGIi3NB3V/ZWTPBRQFL6W/pz+jyw7jpD4PU9/UDynte96bbn2oa62uBHC1bkuV+eK9RXjcfRiYfx0D8y4lknM5euEs8ssX8acZl3LEX49ECvg0WobfZ1Dohl9OP4Y31w6lID/O02/b3ZTu2e0QzukzmxzdIIngyNIJ/GP2PShqCYriRfiOJhC8mN+v2YMhN0HgbxEuO6UHcz708/r9ES6sWM/wQ1PsFm7mb80jEZcXkHvHGoZ030Bz0s8NCyZxy/L9uemjYVhD8tESKp4aD55X2zji4IUYFrzdVMSdVcU81JRHToWBT9g1W2KKnSbkUsGtWIwPthIqbGK4exMRd4p1hpfDwgnO37eK5EF9+aZeQFu7GX0dErA212K6dDQFrIE9MLqXdNn0xFNoG7eQ1hWsNVWIZKcglM0iYwkKagV5HzUgLIluQrYo0mX7gnP236U/gQVVSK/7K12XBPFBIVwfVIEGSjaD2p5EtETZPuYl3i+PltPHIFWJwEKLplEMC/eaOqIDclCSWZRYEqUtgahrstuAhwL2YssW8bamebXmQevQUkygPaKDu7O2U2EBuF0Qi9k+WhLSWbBMXOu27ODP1gikrY8Fnb9wr1GIPRSD9iiebgUccPwYyGTRp8cIfVyFuzmNpatYfhfW6D7bDNbUQXOT3T0uk7GtRtxo9VFal2xmyYL13/BqOjg4/K/giDQODg4ODg4O/zOc0P8JnqzujzQlhoSUBJSd05S+C3/e7VmkZd8iGxIeXL075/X6gnjMx3sTX+bRYcs6Z24Vgb5z75n/KMpyjuPjWAnvtJdwUNnZALgyt6PI5dB6CgC9RrzG7IVFjJl5EvMOzuPR5XZnKUVRuPTl34Ku4Q77Oe3a4wCIBI4kJ2ivVdQIiubmnlPP5JLT9+TI3RawV3gjk3xtfNRRwG/Hf8hevVbTnA2SEXZURmHwYPbL+xCFYeRGXuGCocehKApK0UyU4kUo7qHkRc5iQV05k3Ma6K838vmZo7i/23De6jcQKaGIBE82DGJeVQXL3upG/elDsNotnlg5FverQRLPlTPt42GEyzvI9Ewhyxopnd/K5KPb2GvRFG5YNp6U24NfM8hVY3wSj7Ak5YFNkDHgpSU9+Tyu0SrhuUFvM6B4C6/ctht+VaIAbiHxBbJkDxyDyfaVaQy093PZ68ltaXtdlWu+8s1edA6pm+uhph5tVRVKNIU5pu8OtWp8c1ajdC8nVGZ3XZOANA0UrwelMEz+kjYkFrHjcsgO60Vqz0Fk17Z3rf+qWOR/fx5t+w1GBnxYIT+KKcl9cTFeCzAFcm0DImPiast2rZFAYq+eWBaoDR2UPPwl+H2gqgivFyS0HDUKJZ5CaWmDdGf79O2rNye3tVTPabRIjsyl7eABeNNZ6ib1tTc0NCJTKaQlO09X55kwTOjYVsvGYEeB5quInBCuUIh0a4LpT39qD6btdDDt46UoponeEMPVntlBsCKZgZotKIpKaGUrpLMoCGTQw29P+WnSEh0cHP5/46Q7OTg4ODg4OPzPkB8s5M69X/zR7OVUrMIymvEoET490v4NfveKP1NhxrFkgnl1XzLCtRtkPgZ93I+23/9PCCE4oO/Mr4zmAw3g2nbM99w6gtSpdpzDPcve4cyBdhHnyVPGMTn9Qte8rcWad8WRfQ8EDiQa3UJt477kuuOowiKJl6vz5nKmv1/XXG84n8nh577R96l9LX63ZBKTehZz1JBXGRqsJVOk4On2EWuWXkFHog2P2yJvkcKfH32BMX6Lo2bvS0M2QjIIWZ+b1AoX5Jlksjqn37sBUwUTL1JCL1cjy1N59HU3kUjCqf88Bn+tFyUL8e4ZKtds4KFDZ+JSJNHmCHkzqrjqjAM543cLWbuxEF0Kjj10AWuGF/LZ0kr0eJrhV7VwuPsULvuwL9r4JkIxBZmysDygmKBZkitvf4OHf92N5vWdUSYd8S79RolGkdE8jEE90JZt6BJy3IfWEn0kQFexaSTSo4EqEIqLjn5ANEhOZgPiyyS+Ywt2PJkuHZHJIgFX2oJsjKYTh+JetIXg53a0lHtzK54X5257rdmmKwmg8JG522rnKAr4vIiiAoxUHMUAyy1oPm4MgRkrcDfF7CK/KFh5OSjNrXYtGmNb7FHusgSxCi+x/CDpYX6M6Rpq0kCqBophIkvz2ee+Oj46QusSWNA1sCw00wKXTjaT7UoR26HHVn0TZn0TVp8KRCSCaO+ArAk6aKXlWJkspDLQ0IRwabYypypdRZWtjdUgBK4qsPpWQsJEuHSuOe9xrrv/9G+8bh0cHP67cUQaBwcHBwcHB4d/AUXL2+F5VE5mRscbNBpuHlrzAjGjgFuH3s3BfQ/4mTz86VGKP0VKAyG2fdU87MRDWZXzEdJSiXh37r5jWRbzq0eSlQYjC27AHTjia+0Hg6W8uuEGFnS8SkZR6KE3cdFH+9PR/9sLxm5lde1KspuKeH/qVMLBbvx9xVPMau+OWzF44eGHuHj/VZQVupldGycVjFDpshDA38fOYOJzPVFUASEFcjIoniSWy8WI/drQBZCVhEIpblh0INo7jSSG74NQBbIsRVa3yKo63pYsW/QyfIak0AvHRmp436yg+MuVHFW5kdDIdTz9ToJTDlmMJQVX/HU/PGRo+9TDFbPfRwmB2T2HTe4WSudgd4YSGoYCN150GOgAdg2a7SUv6XKDDiWTamk5EuQN9vj+7s3MCgRpa7drz6TyXBhmCm9CpXp/F5DG1z/BxpN7YfgEns7biKqz+uC9Zzk5mSzbk/PxRpr6FWGGtzUCNwVonUrH18WVdQk00oKmFjaeUk6gpnCHcJ3YgX1xvb4GIQXpIeVkNHB/2o5qmF3pSlJT2TJBo/DV9dSd2Zeej1ajJG2BRGmMMf5dwfiiRSyL5YLbQ9KUeC1zxzo0mSz6dr4Z2DdPO6RBrdlEzbG9UScOR4ln2LNgPmf+ajpP/7UfKx7R7DbfaqdX1ldicjpblSurNmL0KEWYOp/MXINhGGiac5vm4PC/ivPud3BwcHBwcHD4ERlTPIRhH08gY6iML1/HFw3lXDB7Pud/tpBH957KpJ7frbvTfzrbCzQAJ58ziYU3PUZqL0G3NTGqN9TRrce2zj5po5FGy0QCLfE3KPkGkQbg5KHHkFn6CG9t7EafsJdT9y1hZN4IpJTIjuvAaoXwLSiKd6e1C2ru5PmFC7hp3OcY0fuwfAvxuacyVPsHAokxeCWD/O1kfQpXj53JXx6byslnHc6rD7+BW7M48JJZvPT47tAmoVSgugTWJjd5QJsBH4x+hdP2HUO8zY9MGfDcQo5ZVEuF2s7EcJoJrx5HNl/FaPKxsCPCZE8b1Uv8PDDvYyrcoHWKEcMjqzCQWFKhT1kzZx+1AIng6M1HsaU9jEiBKMuhY3iG3HkJLAHCFF16hlVZClVbtr0mgCwJMvKR1TRmPPgzIaz8JKkaN0NOruPtm9Wuee4tCRqvc1FauIkNq3tSGW6iw+UiIXRIglDtGBijl4/Tv+yg3Bvjvj0Ksdq1rmiYgkc+Z/OvgyhGT7zlBka7ifbaZuDrelUBLt2ONpH2rIpX2hA+Fy2Dw+wxejUXHPYl05eW82hiAi6hYJkG2YY2opMG4c73QHuMnDdWoBgmve9cDYrAd80i8IquqB2J4JPHy6k9Msjip8rxpVew9SoxsWOJtK/4Z33luWBbJFDFi2vtOT6FdUkXlz88iIdWLOLc14ciGl22OCNAGnanKHRtp6LE2oYtZDRg39HsN+U2ZrxzOar63UVHBweH/x6cmjQODg4ODg4ODj8yD+yZy3H9FtCa8CAESEsFBFd/Oe1727LMOJbZ9qP7+HMgqvZj1a+6887tgzjj4Lt22OZ1FdHbtwfdXOUU5Nz/neydNvBtXtrvOC7b41EO73UlFZEDkWYDMvka2eQHVDU8u8t1dal15PhSIEDp7NkzpfIyspRiks+mReN5u6mCd2v6sWpmCDQVw8rl4mt/h6tsNX8ceTf/uHox827fnw0nLiXX7ADd4vQNu/N6Wz73NPQgXpeBVAqRSoHPw2OfjaKPW+BR4LPD/s7wAes4dNLH7FnQRkJCYFw7iqYQNSFqQjoJj95SycnjRvHr48axckkbphQYpiBu2NEp6TC4WhVOP+l9AvfWEu/MQJp84AZOOXMJ3jwdq3hbWpISMOnTYx0l6VZK9Ti5rijRPn6MiXDNWcWd16mNqsIBQxahhEzcDSkShiDbqqCpKVAMZKdi4QvHOLKwij1Czex9ewKE2KFGTfETGUKlBof8agmHXLaKZK/IDpEou75QFKTHBZEQyvoqxPJ15L28gOScLeS6Mwzv10hgeQO+95YSmrGaUEoj0pDCtTmJ3NRB6yEDuoojS8su2Gx8JYpFvFjD+uOjBN5ZgQJk+3fHUAQqdhBS8oLeZAZui5JTAJVt4tKOEkvnOUtYICVK2qA566LfhRqkM+DRwecjleNh09ljqHftYrWq4pIqfLwQmqPsO+5q/nL7q990lhwcHP5LcSJpHBwcHBwcHBw6+WDpS9z9xBr+eNYUxvYf9oPt7FF6IXuUXkhLSwv7fXQ7qmpgWQqP7n0UAMfOuJSmbIITyvblrMFHfa2dbHI+ou14AKzIC2jeUT/Yp/8P3PHQWUwaeAUuC3CpTDzgRma+d1XX9j7FD3394l2gKBp4J+0wJsiwpKUU1b2Zf6x7k+uKz9pp3QE97+Xl2ZdzVbuHI4cdyjjVrt1yYp83ATiuD8BvADjkhstRq5oRLkn+hQrza5czsmQgAyues6N24muZc8A8Lv10b4ojNVSbYepTIbq9C9UTUhAKItJJrp44lxVpLx3SQCpwd+/PMSS4O38yjaPSbghKVEGONDmmz/DOdBiDaIeOWJDDodNLSY2pINliUnLZKjbUd6N7cQ2ztUqe7TsH7XnJay1F9F6ZwO0x+bS8iFWxHBSZT68Ha1l/nM6G931UJYLscVeUMitG+Yo4M64M4MvseI5+94/F/HHtUZgpFSsnRfvsJB6jiOJlDegrqvGoGjzfh0SNj5nthZS44sy6zouUO4ohVk4A2aaRMRTOKFnHuR+s47hP9iZheFDPqkV4PeT9U5K5rpHmeSFcySy6riEyGbumi8BOfVJdVL+Zz0mvRsAw8chNXftQm6NkexYisaB7HvjdtB03En3uegLr21CQeA4U8NrWa8Rm+2gebeXGHSNl7luL61rgGju65qtdn776XLCjvembitj8oR9LZFDSGcgYeAFPUxJ54m5sUSxKctzcdugELt/zZrueTjqDy4TMyk3Qt4JXn/6cVx//nECRn969i7n70TN3upYdHBz++3BEGgcHBwcHBweHTi57ZBVHnPwpb7V/xkjj1X+5LkRubi4Ljrx5h7HGZDut2SiqgFdq3+WNhg94fd8Hdrk+k/kMT+djw1iBxn+2SANw9YMnURw6iYK8NNc9uMePYjMV/xs1yRIuW/QKRkrj5tIEr9VVsnRVJR90/5z9++5YtFlRFB49+fbvZLvDCNJ8SJKCye28/Sy8v+4p7jpgGiXuGMXhx/BG7oXsMnr3S7O8PY6lJ1GkScwK0O+LLKaVYu3uBh9X53DxgLUgFNYkNG5qHUzKynJuySqkkPQUJvWKShCLhxf14JxFG5gcaeeLVC57lX+Ey+NhU/VgFtet5dWnQyw+LkTu1FWcddVSFn2Yw6l37E/5uZs4ffBGbl41ngU13UkN6WDUeetZHy0jGMhDoRULFetTk3UXG+x3dwv9umWZldk+Bc9O7LnryMFkImtIXjwawyeZeGI9c2rbKd6rlvZzJaIzP6jnn5bw6FVFXat3qH8DaJuaURIdFK+2CHcHlwLv7PcRqzvg/g+6sXdJnC/f8/Dlwm74hQWk7FQgv9cuApzO2DVqUhm7k5NLt41YVlfKkBFwE9cTkB/BNWsj1u49yHtxAUiJVFWkacJrO6dXCSSyc/Sr27wSuGbbMWUqCqGpBS1p7NTN6qtrTWDG8ZXIsA9FtpD1eVAyMVL9CjGX1sHevZEo1GQMTnx6OkUl+bia2rrWu1JpMtVboFsp+F2YDXFWNq7jrVe/JL8kwhW/egIdeH/+n772unVwcPjPxRFpHBwcHBwcHBw6KR7ZSP9wHSCoTa6gIjjkR99HgTeMrklMC3rkdOBWLH43/1juGrlz1ylv8HzimXkI4SEQ/MWP7svPwZ4TBpOuS6FI+PU5s8lkYrhcgR9sL9ZyOZ70P7lu0T6EwhlSLp3TF4wnNMtFq6LDYeuBH9ZZK5FI0j4yDX0EFgJvi0VJv1Z6+GIUq2Alz6CqQ+WJ2t4UBToYmmcyt7kCv57E6ky4URVAV1g01Uf1Ui9zG4upjDQw0N9MY0blpuoxHJ6/gsvePxo9CnqPFEd0W8LrLTk0W+vY3dXC5sbh9KhYSYuyO/uXz2TAmfUcFPgF/rVu/vzLXvhaDYSElZcPpPvba5gf74UVNukxIcoxFau5YXEFUwqW8OKJfeDJDBJB7cwwzw4PwY4NortoQyF1zhi0dg01LVlU1w0Vg01qOWHWdQkT39RYvitipTnL+48pnDR527YCDxiLAlz7+CQCm1L4Aw2IWBw8brsmjSVtgQboe2M9q68tsCsPm6adi9Up0Pj61ROUKurCMDKSZuNvekAqTt7zssuHr0ut+jqB5qsogLapAWNUXwzDgpREW7Xua+tGqEC2uRW9qRUCHoS02317VzXQcMUIRnz4Ls2fR1A7nct6dazKcpQ1G7Y6hqs1gdW6FsOrY/XsjpDQp18xv73gaUhaZPI87D3+ej767Opv8d7BweE/DacmjYODg4ODg4NDJy8edSPLGntSEx1OeWDwv20/b+/3kJ3BgUU/dw2/iCzks/X9qN/y/g7zFEUhWPAkgfwH/22+/NQIIagxgjSaAsOStCSn/2BbWzbvSTr+TwCm5q2huimX9bX51BleTj3Dwx8vE+xbcfQPtr/n5feT1IPI+QECMk16ZAc19V5Mad/4WxLyVBNFClKmjikFR1eejlsdSFvKImsJYo0CdA+WqXDLaeM5u9t6xvtizKgvpFBEMVIGUekCTwokKJpE+jKcWLyEeCaXQ2cfyznLx3POF8dy/Kx8ht5+Fge+eAZ6uw/VBCVlddWHAVBUw06TkqCToUSoHJi3llsXTmJjaTeShV7EDlVhtjaX3vpnG9vziwyHHvUZ6V4JRBbGFS0nU6VxgP4liCzXvW2nG8mv/MGOosjWqJOWWUEOrejDlibIWmAqEB3iRkkJOvr5MPLDoAhIpblyxnzeXfs5xyyqIfhBgI4JZdyzfAXF0zRe3TC/M7rGVjjKjvcy6YW1TJ//Ia+8/yYLj3iU4W2LiJUHsFQV0zTJAKYudvJ3q39s5/eu6uVIwCyKgClBV1AR4PN8Y10dXYLh92IAarvdglxqCu7GLKc80WSnS3Ua0JJZ2n0JGk7e7jMnGEDx+3Els5jL1lCiJOk7oJzjTtody68iM1mIJpk46g/f4IWDg8N/Io5I4+Dg4ODg4ODQSSAQ4No9XubCkY8jxLf9vv6v8f4BD+FXcujnbScKxCSsSV7EFxuO+7fu9/8D3QpnscGspFb0ozB46A+yIaXETwNBDZqsAIcPf5b16/Op7wii1avcd32Ww3tfgaLo327sK1ipRRhb+vLpZQ+hxCGbo7Hu477Mv/Fmvjj5brpVrEbJfQs950UaLA+nl63CY/Wg3HMxkysmccvom3hp4nM8NOZZouf4wTDIDOtDwdEGQkBQh6cGzeMXRc28MHQup+U18MlBf2P2aY8yxKimxEqiC8ln0XLcHugWThJNe3GpgFBAgaxbYLogWh4kLSDuFSi/7+DleC5TD36HgorlPFA+hwnhVm4duISeX8a4vtcCdr9aZ+qTzQRzMuxaToFLV6/mqLy1lLlaGNtrHfp9c1hzEIyt+wzXYDjl0ypmb6oEICO2EzxUBbB4YN0qHt+4nLGfZLhq8VpQO2foQUryQVcgR4WLKpciA53rWzvIezDGQ2sXMbYSsia8vH4ovT3NtKd0qgwPtfEAwoKSg1vsdCcEa64L8f4p5YCt2+gqnHbqavSrXOh5cQ6Y1owxrAiZlbsUX7Z/bJ8BucM2A8DrRkbyISshatkCU0UYtV9v1H69MYb0JtOvx47GNQ0tnkSLpRCKQjsWHQf1pmJYA9JQ2efJ6A7TI4vq8Xy4hvjWgWgM4nGsTl82L61hknIMz519P7uN647ImGCBmrHYd8AV33JFOzg4/CfhpDs5ODg4ODg4OPxM3DLyYVo7VlLVdBgd6AxyZdCUBTTXTiSvZObP7d6/DZfbzZ7d3//2id+AEIJ2ZSBxcz2h3LvR9FKWn/VbDjzgDoSQxIpcrNgwgE0JD7OiPWlpO4p7DjzxuxmPPYIQcEdNb4489DOmTRvFBft+gWVegKLaVYIUdx8AelcsBuD8il2bernqISb5fwGWxYyqXlTHP6LcK1EU6OjQaZcj0Hzz2ZxW6O2Fy/aYRUg3Kc6dj5nan3OqiqmNeigNJTBMMMsSiM1urGCWjnCS/fZcw8BIDfe8NoVT+i0iosWZXjeRJleYDnUVEEciePzyuRSKJg63NvJ2Sx7XzN1Cs6nxZTrCfgE3Q90rsCQsjWlUeEyEgLivlbS6hb8sq+aqBbuRjvjo5tlAs1rAqLK1NHdAeko/EsKLe84SRhU10H1gkh6eFABDc1oYnxNn/KPtfHZaBFSNtlaFnByLtAXPb+5OWmS57rdvsu9VLbSbCmd3twt223JRCwtwg4hzh6xA0MgvRpTR/dIMHXPTxBvs1yK2OsR5B1Vy5xtVaALSQiDeSZFt8PDK+t7Imz0UetJkayRrP1EpfaSFjacMxOX2o7ZEKX1p5U6vWwaI7VlK3qdbIJnGtcpus71VvHEpAtEnCIAqVFQpyQzvA0kTEnFcm+rB5QLLImsYRFAIL1rI0Ms1BnrjtA72YRJEZVtKVqgmvaMTmkpmUE+kW6C2pRD1bWjtHcx/4D1cuiDbvxcyY3co23fA5cxYcet3u74dHBz+X+OINA4ODg4ODg4OPyM5of74XIv5YvNodAW2mAoRWYeRXo7mHvhzu/eT8enGjZz85osIKVj/60u/05puJa/u8Nzn83HuPaO5/Y35lA6vIU8z+VLxcHbZcq5NhYHvKNKE/4TZ8D4x4Wa3sg0MPLmWE0Jt0NXY+fsxLf4sp028BvOtFg6qOJ4ePVq4YcSRjOpTSZ7W0z4WYEvdieQwnw7pwhcbxZ4FeXy631r2fmw0K1RJ/sAGXj/yWbp5JGPfOZohJY38ov843nznCDzLlvNmxWDcow365G6hvc3NyYv34g89vqRP7sUUKL8jiU4ShT1zmzGkSh8ty8RAyw6+ZjUXjWYat4B5Nfmc1KsGBXCnTAKuFHNbihmXs5n3mioY7YLfXzaHx+/uxp/fnkdZrl0nZlPWjUtYLGvLYXK4nkFjG6gflWHtgiJOGDMGkinQdVAgx7uczIXbeiVtjV9T3CZWWrFHJICGBDoW5LPkJGGndLldoKgQT7JucYTDKyOI3aHjd0UEX6u3r4lLNxP/Wy8SpoepQ7/k3sVTSb3txlpkkmmD8eu/pJ7ADsle/X8VJXxYgKXHVuMtb6e5JgxooGkIywALpEsno4EiFIy0iSJsnwQC4Q9g9Qtua9NdsxkSSdqrfax51c3rh3Xj1csrUX1JSKR2OO4dMEw8i9aASyPZp5Lk6Eq8TUnieYCiE/pkNWraRPTrjeWCfYZcyYdLbt6VJQcHh/8gHJHGwcHBwcHBweFnxu3xMKHXUlqq+xJUJF4FhFUF/O+INFd+9B5SA2lC5QM3UfWr3/8gOyeOmMIRfdy0t52HgSCgpGk2PKxvKf3ONhQ9jFI8n5mzrkNKwbq2Ao6feCaq6v9BPgE8MfO6b51TWvw8lhWDhpFIwC2bKNaaeOHMnjxRs5bB3mryNcn6jE5bnZ+/7PsSST7mdwcJbjlrBQuqh5ORBqcXbeDdyEqOLGrHo0Ba/o6MhKQ0OPzZo/jDoZ/QkPDxy9INXfv+MBohV09RIpLMS7sY58vw675bqE64eK6pBycN/JKEcNHffyL5rpW8lxODOAzMaaFHQS4FoS5JgtUJDwO0KGfkriErYXdfkvEvJamKNXP8m8eT9/gi8tZkAQgaCs8MKeGL/X1UzfUB4LrRxxlHrOLyZ6aSiWS4Ycp0JoSauGpyNxpWB5Cys+RvOoOFXb+hS+SYDaHZW7DjSwRahyR881qG3RHlhXmjsYpTmNJFQd80qRvmUr/QLlq9rS23YNNuFWRXqLyycgYACauGTxIq09YMYekHwJoMYq2FumwNzQDj+xDsAFS7X5QUoEjALdBSFkavMjIJA0XVqH4Dqv8eQmmPoYbCXSLNN5IxcC9bh7oMlJ6VRJIK2tq1SAuy/Upt6XDpWrSCPA4aeiXvLHaEGgeH/2QckcbBwcHBwcHB4f8Jud1WY2Q2IMwNKJ49f253flLumnQgR7/0IlK1YxqeXfErfjFg163Jvw1vYD+8gVVYlsWhZhRND/NBj29ftz2q4qe30Plw/jCeOupINN+P3+lrVyhKAEsfh8wuRpDElNAn5xDuKx8PQDqd5oCH7kAWmGQU8CkAkkTyM+KWiYUgZQneTfTnSD7fZhcwpOR3U96jNR1gQF4VH8Tc7BtI8/SmXjzbUslf+n3EIR8exQf7vkSrAaiC59f2Y99emzFxM8qXQPI4mv95DhxbybQPP+XJ1/pzy6WzAfh7YxFl3hg3LJuAeV+Ao49fxO8OXQEqxFJw0bpJ4HEx8DE3DbEQh5Ut5sicRgoVSVsS7r+wkE/eLaI5Ukh/z1wMr0Ll4CaOya1FF3Dbu1Wc2rM3pb1j7Pt4HYPyTKQiefr8MlbNyGVruc2nvljBWXv1JZNy2dExn8DCcUEU6iijDpXtSyTviAASp7SxniwHXjQYUKGPlxvemcNuPauZu99Ycg6NMyS8hauK1vH6G34ePU/gfTsP6VZouDiIL6lgCCBtIYUAw7arKKBaYPnDmOEwreVh/HOTuJo7YDufzM5/la/45QJYX7VtTFdwbW5HetK0jOtBuFUlnkjwp4uf5g9//uW/cBU6ODj8nDiFgx0cHBwcHBwc/h+huXqgevdFCNfP7cpPyqiySnbv48EdjuEKJAhqDf+yTUVR0PTwD1orhOC5o2/iy/OvYVDxTyPQbEXJewateBGyYDlK4ULC/vFd29xuNxt/8weqzroaj9aTpIS0BLc+hP6RCyhUFBoMhWMjQa6v6cu8uBdTuQ5X7vOUV6ymV8Ep9Ig002oG8GByZ9UgfjnsFS7oWc6W9C/4/KCXSKoapy08mLUxN8+uHc0TG7qjChMLu6OVSFyD7g0BcOHxdjEeS8KzTwzj/PuPJ/tYHi5Dpby9tcvvgAd6BOpBQnPSQ8QVJUASQ9pNwCNe+P2DDdyzagkzprzGncvH4rJSNGzy02oqWECHqXDL+rU8MbOG3csM1pr5xHWNG57azLPVS/jjMyu4deZSispM/rl2JWB2FQIGcAm7PfZWvqk7E+h0zV6T5Mybz+DpmyvIc3UgpNlVR2a3gxIc8n4H5n3NuDNN5N/WTub2KKe+8D6uazbYKU9mFqlqXQKM1ZlbpSGwBlWSGdmHTGVel021qzk4ZPm6JumA0MDnRcuJkF+dIOrLolkK05+eycjCMzlz6EVdU1PfJWLHweFn5P7776dHjx54PB5GjRrFJ5988o3z9957b4QQO/wdf/zxP5G3/16cSBoHBwcHBwcHB4f/Fzw/5desa3qKxsQnjCl7/kex2dryR6SxnEDOnbj0yh/F5k+Fpml809f1nNJ3ydnueUHOuRTknAvAKODwXawZW/Ib4DcAtHW8TTff1VQ1nMTRfV9CURQumOnny5Yasmn45axjKapopsHKIapMpSP9D3JUE01PkG2YBFxIU+w1ml3wTH0vOiYr5N2UskNGJDxy71AOP6EOvVPr+EV+Dc8c9yUAqzIaWBZmTDI9rVOuqPTLTxG3BG6f5KGRs2DkLAAyFrxbm8/uBU2Ehf28hwd8tBJXTEL27qicYFCmW2xIQ4lL8tjGpVx+YA+aVtqC0vaqzFYRJEWSEx7t4KMrvbQ2hrqm7RRlIwTpZySZZ6KAydQ5VSzeEuSKcT3RO+dn35JYNOMBNtzg4aTDNvDIzcU8NPQdfjHrYLJ/U8lfJ+zX1Cswkbgyhl1Xx+MnMaGYJn+Mbu9W2YKNz4tIJL/5V/VoDCJhpKqQs6ENaZmga+R1xKlqamd/5RhaBuXD+ibyktuWHXjeflz813O/ybKDw0/G3//+d373u99x//33M378eB566CEOOuggli9fTrdu3b523VlnncX111/f9dzr9f4U7v7bcUQaBwcHBwcHBweH/zf0yj+FXpzyo9mTmRkoVoJE6pP/OJHm340n9ju6a1BjLGNFzQQGdfuMv068sGv7xbOnEjPd5FoJ8jxT6F16PbUNZzAvMYdKxVZevG7I1eGcknXMbO9N9qwmPNe5IJ0lmx9g2EOn8ZdjXuStpYN5L1POq+Om0zvYRtow6OWCv7b15dEPJuDZItBbJJkclb37L+TKSV9QFAZVgUeqy1hS34sRBbNZEg0xMNBOrjAodBt8ltQo1Q2yFvgVW4Up1MEjoFiHLy8azSeT/sHsKg/3793vK2dA4sHLy2d6iZb5iT3WDa8/hbamBfeD7ex2+GY+eGcg1ohy8p6ch+hqUy648ubRWDPS6J1xLl1tyLGr4cz+Ywmz/yjp3XsF6r2wT3YxjZcXs3B2X6aWLuCy8Ss49JgjARXiJggdLWOhDS6l9rRCPG8sJkd40RJJvpZMBgkYq9ftICoJAJ8XMlmEqpC3rAmADgHZQ8fAwjW8e/903n3sU0inGTh5MHe/e80PuoYcHH4M7rzzTs444wzOPPNMAO666y7ee+89HnjgAW6++etrLPl8PoqLi38qN38ynHQnBwcHBwcHBweH/1q00FVkvScS9B/1c7vy/45Mp7KQsgQK7p22/3n3V7lzzGNcOuZJxpUeCkBbZgVJKYhJWxbwCDvdypAQi6XJKWohcwgkBpUS7ZeLlBp/WjqB5VqYQn+SqS8dgSVhYSrClTVDOK5oNcHSJrJuSVZmoTrK+2uGoIdU4ijct6kfg9QOCkUN768q4vezh3LSZxN4YWMJ984fwZzG3rwT9RFLw+K0ZFlK4/O4l7SETSmNYDCLV4WmvADNz/bC+yqUDoihqAaF3WLUTC5g0+/H0XhJX7x5Bu2ZMPQp4KXPFnHT75v44pOPGS1n4jK2JR1JBOK1JFZ025jARGB1piwJe44ieOyjjbT1gSl71FIdDxErTPF82xBG3nsa6X1T7D/hA6wAEHHRMsiPsEC6FDJHDcXMraZ19+50KLvs/dS5352jfjJAZqu4Y27zMSQh78Pl5Fa30ZHjpnloGc1lAZa/v5RJyjFMUo7hoauf+Y5Xj4PDt9PR0bHDXzqd3mlOJpNh3rx5TJ48eYfxyZMnM2vWrG+0/9xzz5Gfn8+gQYO45JJLiEajP6r/PxdOJI2Dg4ODg4ODg8N/LSHfQeA76BvnzKl7BhL3sywV4ZS+r6Np+k/k3c9LoGg5W5oeJuSJU1q067bnfncAP4Gu5z3y7iA3+iBBQ2MJkBXlZEQ+xYUP88WRdqpBfI+XGPPYIrLtQZR+7TTHIqw883qqa/alYI8aPkm4yNFStGfcPNteRr/KLcy3wsR7WNDux6XEeHjREAYWN/NFawGnDmqnML+Bnp4Ek/o28vemPG5csA+s85EpNfn0oA24dOgjoFQzwGNgSUgYBrG4xpY07OdqYXX3+ayKFjH8+VZOzd/Iloybqe+MgzqJTAkUYdeCydNT5GggBNRmFG65azOuWzdzVN9hAF11brr3b2HqjQ3MyO/BoHCWdzeVsHBBb45Lz2TeTYHOyBu7ss38RDFJ04/PKzCXhhEZQbqthNfqj0DLzeCOG2Q8BkgFd1KiuVKk++6Dz4DsEQXUDk7w4OHPc93EgVh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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "filtered_df = merged_final_df[(merged_final_df['LWP_Corrected'] >= 0) & (merged_final_df['LWP_Corrected'] <= 50)]\n", "\n", "# Define LWP ranges and colors\n", "\n", "bin_size = 5\n", "lwp_bins = np.arange(0, 51, bin_size) \n", "#lwp_bins = np.arange(0, merged_final_df['LWP_Corrected'].max() + bin_size, bin_size) # Define ranges with step size of 200\n", "lwp_labels = [f'{b}-{b + bin_size}' for b in lwp_bins[:-1]]\n", "\n", "# Create a column for LWP range labels\n", "#merged_final_df['LWP_Range'] = pd.cut(merged_final_df['LWP_Corrected'], bins=lwp_bins, labels=lwp_labels, include_lowest=True)\n", "# Create a column for LWP range labels within the filtered DataFrame using .loc\n", "filtered_df.loc[:, 'LWP_Range'] = pd.cut(filtered_df['LWP_Corrected'], bins=lwp_bins, labels=lwp_labels, include_lowest=True)\n", "\n", "# Set up the color map\n", "cmap = plt.get_cmap('viridis') # Use a colormap from matplotlib\n", "norm = mcolors.BoundaryNorm(boundaries=lwp_bins, ncolors=cmap.N)\n", "\n", "# Plotting\n", "plt.figure(figsize=(12, 8))\n", "\n", "scatter = plt.scatter(\n", " filtered_df['apparent_zenith'],\n", " filtered_df['SR15D1Dn_Irr'],\n", " c=filtered_df['LWP_Corrected'],\n", " cmap=cmap,\n", " norm=norm,\n", " s=0.5,\n", " alpha=0.7\n", ")\n", "\n", "#scatter = plt.scatter(merged_final_df['apparent_zenith'],merged_final_df['SR15D1Dn_Irr'],c=merged_final_df['LWP_Corrected'],\n", " # cmap=cmap, norm=norm,s=0.5, # Size of the points alpha=0.7)\n", "\n", "# Add colorbar with labels\n", "cbar = plt.colorbar(scatter, boundaries=lwp_bins, ticks=lwp_bins, format='%1.1f')\n", "cbar.set_label('LWP Corrected (g/m²)')\n", "\n", "# Set colorbar ticks and labels\n", "cbar.set_ticks(lwp_bins[:-1] + bin_size / 2) # Set ticks to the middle of each bin\n", "cbar.set_ticklabels([f'{lwp_bins[i]}-{lwp_bins[i + 1]}' for i in range(len(lwp_bins) - 1)]) # Set labels\n", "\n", "# Labels and title\n", "plt.xlabel('Apparent Zenith (degrees)')\n", "plt.ylabel('Radiation (SR15D1Dn_Irr)')\n", "plt.title('Radiation vs Apparent Zenith with LWP Color Mapping')\n", "\n", "# Show grid\n", "plt.grid(True)\n", "\n", "# Show plot\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 30, "id": "c7b9a406", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TIMESTAMP SR15D1Dn_Irr apparent_zenith LWP_Corrected \\\n", "95 2024-03-13 10:17:13 125.6 58.628550 49.804138 \n", "96 2024-03-13 10:17:14 124.9 58.627378 47.703613 \n", "97 2024-03-13 10:17:15 124.9 58.626206 49.981384 \n", "103 2024-03-13 10:17:26 130.3 58.613327 48.218933 \n", "107 2024-03-13 10:17:30 144.4 58.608648 49.688721 \n", "... ... ... ... ... \n", "3194501 2024-06-12 08:00:36 562.4 50.764471 8.273018 \n", "3194502 2024-06-12 08:00:37 549.0 50.762012 2.470345 \n", "3194503 2024-06-12 08:00:38 536.5 50.759554 6.229584 \n", "3194504 2024-06-12 08:00:39 524.3 50.757095 4.804169 \n", "3194505 2024-06-12 08:00:40 511.9 50.754637 7.745605 \n", "\n", " LWP_Range \n", "95 45-50 \n", "96 45-50 \n", "97 45-50 \n", "103 45-50 \n", "107 45-50 \n", "... ... \n", "3194501 5-10 \n", "3194502 0-5 \n", "3194503 5-10 \n", "3194504 0-5 \n", "3194505 5-10 \n", "\n", "[1978816 rows x 5 columns]\n" ] } ], "source": [ "print(filtered_df)" ] }, { "cell_type": "code", "execution_count": 24, "id": "f8ebb066", "metadata": {}, "outputs": [ { "data": { "image/png": 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j9j1PH32NGjVKbdmy5YDbJpNJFY/H1ciRI9X3v//91PKzzz5bTZ48+YDbnn766aqkpER1d3enLb/22muV0+lUHR0dSqm990TTNLVu3bq0uNNOO63PNerPvnu4atWqftc/++yzClCPP/64Ukqpv//978pqtaqenh4VDAaVxWJRL7zwglJKqYcfflgB6p///Gdq+33P5a9//WuVSCRUNBpVa9asUTNmzFCAevHFFw+Y37x589T48eOVUkpdfvnlavr06UoppTZt2qQAtXTp0gF99uzvXux7lqZOnZr2LFRVVSmbzaa+9rWvpZZdfvnlB/y9+s4776SWlZWVqcsvv7zPcc4888y0bf/v//5PAWr58uVKKaU6OjqUw+FQX/jCF9Li9n0Gfvh3qhBCiH+TFilCHMNmzZqFzWbD5/NxxhlnkJmZyXPPPYfVmj580lNPPcXcuXPxer1YrVZsNhv3338/W7ZsScXsa269r/XFPpdeeumAcmlpaeGb3/wmw4YNSx2jrKwMIO04h+L111/H4/Gk/nK8z77mzx/9i/GCBQvw+Xypn/Pz88nLyztoU/BFixZRUFCQaiEB8Morr9DQ0MCVV16ZWnb88cfz0ksv8ZOf/ISlS5cSiUQGfC77Bpndt799M8xUV1f3OQ+AU045hfz8/NTPFouFL3zhC+zcuZO6urq02I/eszlz5lBWVpbWhP5Q78/nP//5tJ9feOEFNE3jy1/+cuov7clkkoKCAo477rg+TfkLCgo4/vjj05ZNmjTpsM1a8s1vfpM1a9Zw77339ju2wFDJd/LkyZSWlqZ+djqdjBo16qD7nTt3Lk6nkyVLlgCkmumfccYZLFu2jHA4TG1tLTt27ODUU0/9RDmeddZZWCyW1M+TJk0CBt6F4kDUv7rHfLQ70eF6ZsPhMG+++SYXX3zxYRsbRtd1rr32Wl544QVqamqAva1jXn75Za655prUuXz4uUomk2ldgSorK1m1ahWrVq1i+fLlPP7447hcLk455ZS0mdWSySS33XYb48aNw263Y7Vasdvt7NixI+19efzxx7N+/XquueYaXnnllVSLjn2i0SivvfYa559/Pm63Oy2vM888k2g0muqu9cYbbzB+/HiOO+64tH0M9LP+YObNm4eu66n32NKlS5k+fTperxefz8fUqVNT93np0qVYrVZOOOGEPvv58Y9/jM1mw+l0Mm3aNGpqarj33ns588wzB5zLlVdeyerVq9mwYQP3338/lZWVfcZi2Weg92KfSy+9NO25LisrY86cOX26LsH+f6/2F/tR5557btrPH31/rlixglgsxsUXX5wWN2vWLMrLyw+6fyGEOFZJIUWIY9hf/vIXVq1axeuvv87VV1/Nli1buOSSS9Jinn76aS6++GKKi4t59NFHWb58OatWreLKK68kGo2m4trb27FarWRnZ6dtX1BQcNA8TNNk4cKFPP300/zoRz/itddeY+XKlal/uB9KweHD2tvbKSgo6PMlLC8vD6vV2qdLzUdzh70DhB7s+Farlcsuu4xnnnkm1VT+oYceorCwkNNPPz0V94c//IEf//jHPPvssyxYsICsrCzOO++8g045vW+Aw+OPP57c3Fy6urro6uri/PPPR9O0VJHlw/q77vuWffS89xe7L+7j3J+PzsjU3NyMUor8/HxsNlvaa8WKFX3Grfi492Ig7rrrLh5++GGuvfZaLrvssn5jhkq+H3e/TqeTuXPnpgopr732GqeddlpqUOm333471cXnkxZSPpqjw+EADv6+LS0tTXUn3J99Y7UMGzYsbfnhemY7OzsxDIOSkpID5nGorrzySlwuV6pb2x//+EdcLldaYfWjz9XDDz+cWrdvnIrp06cza9YsLrnkEl566SUaGxv5xS9+kYq7/vrr+fnPf855553HP/7xD9577z1WrVrFcccdl3b9b7zxRv77v/+bFStWsGjRIrKzsznllFNSUxC3t7eTTCa56667+uS1r/Cw75nf97n6UQP5rB+IjIwMJk+enCoSvPHGG8ybNy+1ft68eakiyxtvvMH06dPTCuD7fO9732PVqlWsWbOGXbt20djYyDe+8Y1DyuWkk05i5MiR3HvvvTzyyCNceeWV/Y4RBAO/F/sc7Bne50C/VwfSLfRg7899+/hw4X2f/pYJIYTYS2btEeIYNnbs2NQAswsWLMAwDP785z/zt7/9LdWK49FHH6WiooK//vWvaf+A/OhgrdnZ2SSTSdrb29P+4fbRsQ36s3HjRtavX89DDz3E5Zdfnlq+r0/7x5Wdnc17772HUiot95aWFpLJJDk5OZ9o/x92xRVX8Nvf/pYnn3ySL3zhCzz//PNcd911aX+p93g83HLLLdxyyy00NzenWqecc845bN26db/7fuKJJ1LjJGRmZvZZ/8wzz9DZ2Zm2rr/rvm/ZR/9hvb/YESNGAB/v/nz0y0ZOTg6apvH222+n/iH/Yf0tOxLefvttfvCDH3DCCSdw55137jduqOT7SZxyyin84he/YOXKldTV1XHaaafh8/mYMWMGixcvpqGhgVGjRvUpUnxaTjvtNP74xz+yYsWKfsdJCYfDLF68mAkTJvT50nm4ntmsrCwsFkufVlqfVCAQ4PLLL+fPf/4zN9xwAw8++CCXXnopGRkZqZgPD/oLewccPpDCwkJycnJYv359atmjjz7KV77yFW677ba02La2trRjWa1Wrr/+eq6//nq6urpYsmQJP/3pTzn99NOpra0lMzMTi8XCZZddtt8xXPbll52dfcDPl8NhwYIF3HHHHXzwwQds2rSJ3/zmN6l18+bN48477+SDDz6gqqqqT/F/n5KSktTvt0/iiiuu4D/+4z/QNC3tWfqogd6LffZ3DT/6+Xyg36v9FVoP1b59fHTcp33HkVYpQgjRP2mRIoRI+c1vfkNmZia/+MUvUgOnapqG3W5P+2Lc1NTUZ9aefYP9PfbYY2nLH3/88YMed9++P/rl9N577+0TO9C/dsPeL5KhUIhnn302bfm+QXI/PK3lJzV27FhmzpzJgw8+yOOPP04sFjvgFNL5+fl89atf5ZJLLmHbtm19ZmD4sPvvvx+fz8drr73GG2+8kfb67W9/SywW63PdX3vttbR/GBuGwV//+lcqKyv7/PX9o9suW7aM6urq1GwNh3J/9ufss89GKUV9fX3qL+0ffu1vsNMDOZRnAaC+vp6LLrqInJwcnnrqqbTZTz6NfA/HORyKU089lWQyyc9//nNKSkoYM2ZMavmSJUt4/fXXB9Qa5XC1BPqo73//+7hcLr7zne/Q29vbZ/0NN9xAZ2cn//Ef/9Fn3eF6ZvfNlvLUU0/1O5vPgRzs3n33u9+lra2NCy+8kK6urj6DsX70mTrYl+K6ujra2trIy8tLLdM0rc85vvjii9TX1+93PxkZGVx44YV8+9vfpqOjg6qqKtxuNwsWLOD9999n0qRJ/T7z+/JbsGABmzZtSivowMA+6wdq3++TW265BV3X07ru7PvvfQPgDmSg2U/i8ssv55xzzuGHP/whxcXF+4071HvxxBNPpHXnqq6uZtmyZf3OkrO/36uHY0admTNn4nA4+Otf/5q2fMWKFYetK6UQQhyNpEWKECIlMzOTG2+8kR/96Ec8/vjjfPnLX+bss8/m6aef5pprruHCCy+ktraW//zP/6SwsDCtS8rChQs56aST+NGPfkRvby/Tp0/n3Xff5ZFHHjnocceMGUNlZSU/+clPUEqRlZXFP/7xj7TZRfbZ9wX297//PZdffjk2m43Ro0f327T7K1/5Cn/84x+5/PLLqaqqYuLEibzzzjvcdtttnHnmmZ+4S8NHXXnllVx99dU0NDQwZ84cRo8enbZ+5syZnH322UyaNInMzEy2bNnCI488wuzZs3G73f3uc+PGjaxcuZJvfetbnHzyyX3Wz507lzvuuIP7778/7YtaTk4OJ598Mj//+c/xeDzcc889bN26tc8UyACrV6/ma1/7GhdddBG1tbX87Gc/o7i4mGuuuQY4tPuzP3PnzuUb3/gGV1xxBatXr+akk07C4/HQ2NjIO++8w8SJE/nWt7414P3B3nEkXC4Xjz32GGPHjsXr9VJUVERRUVGf2Hg8zgUXXEBzczN33HEHVVVVadNv7+P3+xk3btwRybc/h/I8H6pp06aRmZnJq6++mlbUO/XUU/nP//zP1H8PJMenn36aP/3pT0ybNg1d1w/LX/orKyt55JFH+NKXvsSMGTO4/vrrGT16NM3NzTzwwAO89NJL3HDDDf3OTnI4n9k777yTE044gZkzZ/KTn/yEESNG0NzczPPPP8+9996733ux7979+te/ZtGiRVgsFiZNmoTdbgf2zn52xhln8NJLL3HCCSf0GVPkQCKRSKobkmEY7NmzJ9Uq47rrrkvFnX322Tz00EOMGTOGSZMmsWbNGn7729/2KZaec845TJgwgenTp5Obm0t1dTW/+93vKCsrY+TIkcDeZ/CEE07gxBNP5Fvf+hbl5eX09PSwc+dO/vGPf/D666+njv/AAw9w1llnceutt5Kfn89jjz12wFZ1/Xn99df7fQ+eeeaZnHTSSVgsFp555pk+XXcyMjI47rjjeOaZZ7DZbMydO/eQjnuoioqK+hTj+zPQe7FPS0sL559/Pl//+tfp7u7mpptuwul0cuONN6bF2e127rjjDkKhEDNmzGDZsmXceuutLFq0qN+xYQ5VVlYW119/PbfffjuZmZmcf/751NXVccstt1BYWNhnNjMhhBD/MkiD3AohBtGBZk2IRCKqtLRUjRw5MjUTx69+9StVXl6uHA6HGjt2rLrvvvtSs7N8WFdXl7ryyitVRkaGcrvd6rTTTlNbt24d0Kw9mzdvVqeddpry+XwqMzNTXXTRRaqmpqbPtkopdeONN6qioiKl63raLBEfnbVHKaXa29vVN7/5TVVYWKisVqsqKytTN954o4pGo2lxgPr2t7/d53p8dCaEA+nu7lYul0sB6r777uuz/ic/+YmaPn26yszMVA6HQw0fPlx9//vfV21tbfvd53XXXaeAPjNkfHS/gFqzZk3audxzzz2qsrJS2Ww2NWbMGPXYY4+lbbfvPrz66qvqsssuUxkZGcrlcqkzzzxT7dixIy12oPdn33PR2trab64PPPCAmjlzpvJ4PMrlcqnKykr1la98Ra1evToV8+FZMz7s8ssv7zN7zBNPPKHGjBmjbDZbv8/KPvtm8jjY66PPz+HMt79Ze5Ta//NcVlamzjrrrD777e8535/zzz9fAWn3Ph6PK4/Ho3RdV52dnWnx/b03Ozo61IUXXqgyMjKUpmmp9/2+8/ntb3/b57gHuhcftWnTJnX55ZerkpISZbPZVFZWljrjjDP6nV3lSDyz+2IvuugilZ2drex2uyotLVVf/epXU58T/c3aE4vF1Ne+9jWVm5ubui4fnX3poYceUoB68sknB3QtlOo7a4+u66qoqEgtWrRILV26NC22s7NTXXXVVSovL0+53W51wgknqLfffrvPM3LHHXeoOXPmqJycnNT5XXXVVaqqqiptf3v27FFXXnmlKi4uVjabTeXm5qo5c+b0mQ1p37V1Op0qKytLXXXVVeq55547pFl79vfadw2PP/54Bagbbrihzz72fS7OnTu3z7oDPZcDsb/384f1N2vPQO/FvmfpkUceUd/97ndVbm6ucjgc6sQTT0z7XFFq72eIx+NRH3zwgZo/f75yuVwqKytLfetb31KhUCgtdn+z9jz11FNpcf19DpmmqW699VZVUlKi7Ha7mjRpknrhhRfUcccd12eGLCGEEHtpSn2oXaEQQojPPE3T+Pa3v83dd999wLiHHnqIK664glWrVh2WFgZCHGmftWf285//PCtWrKCqquqAXcnEsWPp0qUsWLCAp556qs+Mch/11a9+lb/97W+EQqFPKbt/27NnD2PGjOGmm27ipz/96ad+fCGEGOqka48QQgghxGESi8VYu3YtK1eu5JlnnuHOO++UIooY0tavX88TTzzBnDlz8Pv9bNu2jd/85jf4/X6uuuqqwU5PCCGGJCmkCCGEEEIcJo2NjakvpFdffTXf+c53BjslIQ7I4/GwevVq7r//frq6uggEAsyfP5//+q//kimQhRBiP6RrjxBCCCGEEEIIIcQAyVDcQgghhBBCCCGE+NTdfPPNaJqW9iooKEitV0px8803U1RUhMvlYv78+WzatGkQM95LCilCCCGEEEIIIYQYFOPHj6exsTH12rBhQ2rdb37zG+68807uvvtuVq1aRUFBAaeddho9PT2DmLEUUoQQQgghhBBCCDFIrFYrBQUFqVdubi6wtzXK7373O372s59xwQUXMGHCBB5++GHC4TCPP/744OY8qEf/DDFNk4aGBnw+H5qmDXY6QgghhBBCCCEGSClFT08PRUVF6PrR154gGo0Sj8cHO40UpVSf780OhwOHw9EndseOHRQVFeFwOJg5cya33XYbw4cPZ8+ePTQ1NbFw4cK0fcybN49ly5Zx9dVXH/Hz2B8ppAxQQ0MDw4YNG+w0hBBCCCGEEEJ8TLW1tZSUlAx2GodVNBqlosxLU4sx2KmkeL1eQqFQ2rKbbrqJm2++OW3ZzJkz+ctf/sKoUaNobm7m1ltvZc6cOWzatImmpiaAPjOI5efnU11dfUTzPxgppAyQz+cD9r7x/H7/IGcjhBBCCCGEEGKggsEgw4YNS32vO5rE43GaWgyq15Tj9w1+a5tgj0nZtKo+3537a42yaNGi1H9PnDiR2bNnU1lZycMPP8ysWbMA+rRs6a+1y6dNCikDtO9G+f1+KaQIIYQQQgghxGfQYH8BP5L8Ph2/zzLYaaR8nO/OHo+HiRMnsmPHDs477zwAmpqaKCwsTMW0tLT0aaXyaRv8cpUQQgghhBBCCCE+EROFOST+pz72OcRiMbZs2UJhYSEVFRUUFBSwePHi1Pp4PM6bb77JnDlzDscl+9ikRYoQQgghhBBCCCE+dTfccAPnnHMOpaWltLS0cOuttxIMBrn88svRNI3rrruO2267jZEjRzJy5Ehuu+023G43l1566aDmLYUUIYQQQgghhBBCfOrq6uq45JJLaGtrIzc3l1mzZrFixQrKysoA+NGPfkQkEuGaa66hs7OTmTNn8uqrrw76WDeaUurjt7s5hgSDQQKBAN3d3TJGihBCCCGEEEJ8hhzN3+f2nVvLtrIhM9hs3ujqo/Ja7zP4V1kIIYQQQgghhBDiM0IKKUIIIYQQQgghhBADJGOkCCGEEEIIIYQQn3F7Z+0Z/JE7hkIOR5q0SBFCCCGEEEIIIYQYIGmRIoQQQgghhBBCfMaZmJiDnQQMkSyOLGmRIoQQQgghhBBCCDFAUkgRQgghhBBCCCGEGCDp2iOEEEIIIYQQQnzGGUphqMEf6HUo5HCkSYsUIYQQQgghhBBCiAGSQooQQgghhBBCCCHEAEnXHiGEEEIIIYQQ4jPORGEy+N1qhkIOR9qgtkh56623OOeccygqKkLTNJ599tn9xl599dVomsbvfve7tOWxWIzvfOc75OTk4PF4OPfcc6mrq0uL6ezs5LLLLiMQCBAIBLjsssvo6uo6/CckhBBCCCGEEEKIo9qgFlJ6e3s57rjjuPvuuw8Y9+yzz/Lee+9RVFTUZ911113HM888w5NPPsk777xDKBTi7LPPxjCMVMyll17KunXrePnll3n55ZdZt24dl1122WE/HyGEEEIIIYQQQhzdBrVrz6JFi1i0aNEBY+rr67n22mt55ZVXOOuss9LWdXd3c//99/PII49w6qmnAvDoo48ybNgwlixZwumnn86WLVt4+eWXWbFiBTNnzgTgvvvuY/bs2Wzbto3Ro0cfmZMTQgghhBBCCCE+JSYKYwh0q5GuPYPMNE0uu+wyfvjDHzJ+/Pg+69esWUMikWDhwoWpZUVFRUyYMIFly5YBsHz5cgKBQKqIAjBr1iwCgUAqRgghhBBCCCGEEGIghvRgs7/+9a+xWq1897vf7Xd9U1MTdrudzMzMtOX5+fk0NTWlYvLy8vpsm5eXl4rpTywWIxaLpX4OBoMf5xSEEEIIIYQQQogjTgab/fQM2RYpa9as4fe//z0PPfQQmqYd0rZKqbRt+tv+ozEfdfvtt6cGpw0EAgwbNuyQchBCCCGEEEIIIcTRZ8gWUt5++21aWlooLS3FarVitVqprq7mBz/4AeXl5QAUFBQQj8fp7OxM27alpYX8/PxUTHNzc5/9t7a2pmL6c+ONN9Ld3Z161dbWHr6TE0IIIYQQQgghxGfSkC2kXHbZZXzwwQesW7cu9SoqKuKHP/whr7zyCgDTpk3DZrOxePHi1HaNjY1s3LiROXPmADB79my6u7tZuXJlKua9996ju7s7FdMfh8OB3+9PewkhhBBCCCGEEEORodSQeR3tBnWMlFAoxM6dO1M/79mzh3Xr1pGVlUVpaSnZ2dlp8TabjYKCgtRMO4FAgKuuuoof/OAHZGdnk5WVxQ033MDEiRNTs/iMHTuWM844g69//evce++9AHzjG9/g7LPPlhl7hBBCCCGEEEIIcUgGtZCyevVqFixYkPr5+uuvB+Dyyy/noYceGtA+/ud//ger1crFF19MJBLhlFNO4aGHHsJisaRiHnvsMb773e+mZvc599xzufvuuw/fiQghhBBCCCGEEOKYoCl1DLS7OQyCwSCBQIDu7m7p5iOEEEIIIYQQnyFH8/e5fee2dUs+Pt/gj97R02MyZmzzUXmt9xn8qyyEEEIIIYQQQgjxGSGFFCGEEEIIIYQQQogBGtQxUsTRqaHnWbqiaxiRdR12S/bBNxBCCCGEEEII8YkYKAwGf+SOoZDDkSYtUsRhV9X9Z1rCi2mPrBjsVIQQQgghhBBCiMNKWqSIw67YPoy2aCvZtrLBTkUIIYQQQgghjgmG2vsabEMhhyNNWqSIw2577yoakwlae/8x2KkIIYQQQgghhBCHlRRSxGGX6z4VuyUXn+fCwU5FCCGEEEIIIYQ4rKRrjzjsJuf/arBTEEIIIYQQQohjivmv12AbCjkcadIiRQghhBBCCCGEEGKApJAihBBCCCGEEEIIMUDStUcIIYQQQgghhPiMM9Ew0AY7DcwhkMORJi1ShBBCCCGEEEIIIQZICiliyHmn8Q88vWsyj+44fbBTEUIIIYQQQggh0kghRRx2GxvO4d2q8fREXvtY21eF/gqAS2s5nGkJIYQQQgghxFHLVEPndbSTQsoxyDBNookESh2ZJ7wrvoeEMghFl3+s7bMdJ5M0dYKG5zBnJoQQQgghhBBCfDIy2OwxJhSN8auXX2dDqIbLTpzChWXHH/ZjTMq/h97oMvID13+s7Qu8C6mNbMLUxxzmzIQQQgghhBDi6GQMkcFmh0IOR5oUUo4xjd09bG5qoTESZnNnI5Qd/mP4XSfhd530sbd3qo0Ms1dhs0iDKSGEEEIIIYQQQ4sUUo4xJZkBRuZnE+zpxW3xDnY6/Sr1n0nC2EO+Z+FgpyKEEEIIIYQQQqSRQsoxJhyNM8qbh47GGcVjBzudfnlsZUzK+81gpyGEEEIIIYQQnxnStefTI4WUY0ym181J44Zzun0UJVkZg51Ov5RSbAnuId+ZTbYjMNjpCCGEEEIIIYQQKTIIxTFG1zVGFudQkpsx2Kns14bunTyw5znu3/3sYKcihBBCCCGEEEKkkRYpxxilFO/u2cXf6lZzRuV4ziieONgp9WHXovgtzVR4hmbXIyGEEEIIIYQYakylYarB71YzFHI40qRFyjGmpbOHvy1Zz/a17Wzrah7sdPr1TN0t2LT19MSfHuxUhBBCCCGEEEKINNIi5RjjtNkYk51PcVaAr1TOGex0+lVsayBgCdGSPPormUIIIYQQQgghPlukkHKMCfhcfHnRDKwWHafDNtjp9CvPphFXihkZYwY7FSGEEEIIIYT4TJBZez490rXnGOR1O4ZsEWVtxw40rRwrVvKc4wc7HSGEEEIIIYQQIo0UUsRh1RkJ85Nld3PX+p8TTbYf0rbvtG7gFx88yu+rcgglLVSF245QlkIIIYQQQgghxMcjhZRj0LqGWm5Z/TzvtOw47Pte0rCNpW1NPFcXpT226ZC2zbD50HUNtyWGW49SHXoVUyUPe45CCCGEEEIIcbQx0IfM62h39J+hSNPRE+aFtZtZu7meNW17Dvv+p+cNY4S/gBOLKihwz+435q1de7jg/kd56L21acvHBUr5ctlwJvg7eaVjEhbLJDR5RIUQQgghhBBCDCEy2OwxxmG1UqFl4MkcyRcrZhz2/Vf4cnjghG9is1jQtP4HGbp/xWo2NbTS0rOKr86cmlq+NbiTV5qXo+EkwxamKlqEpkkhRQghhBBCCCEORikNUw3+QK9qCORwpEkh5Rhj03WmDx+GrmvkOvyHff+1wS7+3werOS6ngAvHTOg3pqazC5Im7b3htOVFrnzcFi9xM0gkYcXiPPxdj4QQQgghhBBCiE9CCinHGLvDSllFLrqmoet7K4XVvVvZFfqA47MX4rVmfKL99yTiJAyD9mh4vzEOpw6aQv/IxEFVvdUYtGCik+WIMNzejqkS6NrQnGFICCGEEEIIIcSxRwopxyCnM70w8W7bP6gL78Bvy2Z61imfaN9js3L59pSZZLvc+435zty5/Pcbb3LyyJFpy3sSQSyagdIUhqnIck2SIooQQgghhBBCDICBhsHgd6sZCjkcaVJIOcYopWhp6MJq08nOCwAwM/sMsu2FjPJN+cT71zQNXeloB3jzLBozmoqMLMqzM9OWZ9gdeC0WElovpc5WQgnvJ85HCCGEEEIIIYQ4nGQkz2NMNJpga1U9d739Ou827gagwFnKCN9xeKyBT7z/7a1tPLzmfZ7btHW/MUu27uBXLy/l6ffTp0fWCNCVcNIa89MUL+C47K994nyEEEIIIYQQQojDSQopxxiXy04oS7HLFWRNex0ALzU8zPN197Kx691PvH+vw47TaiXL7dpvzKrd9WyubWb5jqq05WMDlbgtdvy2COgmmiYtUoQQQgghhBBiIAylD5nX0e7oP0PRR3lBFla/ItftAKDQVYHD4iLLUfiJ913k9/O9E2azcNSI/cbEzATJpEFnIpK23KJZOL1gOtm2XkptrdhJ7ncfSqlPnKsQQgghhBBCCHGopJByjIknkqzaUEWiOU5rrBuAubnncM3I3zLMPfIgWw+MRd//Y2UqRUc8guGHHLenz/rG8CpsukFUWXi3+TcoZfSJeej9tXzv5X9S2911WPIVQgghhBBCCCEGSgopx5hk0qTQmkm5tYATcsYAUBuu4rXmFwkle47osU2l+K+3lvJaUxURh0FGZt/uP+Wecdi0JG4thmE2YdK3kPLkhg28U1XF2zXVRzRfIYQQQgghhPisMNEw0YfA6+iftUcKKccYt8uOytPZaetmacMeAN5o/idvty5mQ9eaw3KMtkgvCaNvASSeTLKmoQGbppNhczAxO69PzBj/pbzfXM6bzaMo8HwLi2bvE3NcYQEVWZlMzMs/LPkKIYQQQgghhBADJdMfH4Psbo2wPUREhQGYmX0SPluA0f4Jn3jfW9pbeXjjWsbn5HHZ+PTplJ02G9fOnMWymhpchoXjy0r7bK9I4FNJPBgELJ39HuOm+ScTjEUp8Po+cb5CCCGEEEIIcTQw0DCGQGuQoZDDkSYtUo5BelKj0OGn1JcJwGj/BM4v+RJZ9pxPvG+rrqFrGjbdAoCpDKp6txBJhgCYVlhE0jDZE+mi10z02b49+j7H5dRg00y8run9HsNts0kRRQghhBBCCCHEoJBCyjGmraeXnXu6mcFILimfcdj3n+124s9K4PPtnVVnY/dynq27h382PoRhmjy0di3vNzfQngyT6Gf8k67oG2S6urE7eljS8N+HPT8hhBBCCCGEEOKTkK49xxiX3YbHYSPT4zrg7DofV3VvO7tDLfQmo5xVchwBWw66ZiXXUYyuaRR4vRT7/WT6neT2M2uPz3kea+t3sj2cTZl/B6Yy0DXLYc9TCCGEEEIIIY4mhtIx1OC3lTCUGuwUjjgppBxjPA47X5x5HJo2sH5rSdPAoukDjp+YUcJXK0+g2J0FQJlnDNeOvANd2/uG/uKkSRxf7ubJ2vtZ3dnDud4L07bPYyXfyl/Jm45RnFJ4gxRRhBBCCCGEEEIMKYNfrhKH1c72dlbX1WOY5n5jNE0jYXSh/lUpXNPxPndt/zMt0da0uB09Dfzw/Qd4qvqdAR/fqluYklFO6b8KKXEjyM6uB+mMbqA5EuQ/lr3APWuX0BbpYl37prRtW6Mhbt9cx2MNk5nurcNjdQ74uEIIIYQQQgghxKdBCilHEVMp/nfVSv539Ur2dPY/4008nmR7zdO8W3MGuzrvAOC+XU+xuPkDnqr9R1rspu5d1Ed3sLxj4NMi7+xs57blb/LM9s0A1IdeYmf3X9jc8QfeaNrMez3baIt7KFULOT330rRte5MxelQFK7rKuHHHIlqNkkM5fSGEEEIIIYQ4ZploQ+Z1tJOuPUeZsJkgbCRI7KdFSlNdO28sf5fcUXGwvseILAjY/XQlesix56XFFjideG2KAtfAH5NoMomhTMLJvTPy5Lnn0hJ+i0LvqRT4hvN+az3DbPl8YcRksj3pY6Toeogc70pG+JuIxmwUOhz9HmNXazt1XUFmlJXgttsGnJsQQgghhBBCCPFJSYuUo4iuaRxfVMLY3FxK/P1PDxxLKNq7C2gy8zGYA8Bl5RdxfvHZnFZ4UnqsUUOJswqfZeeAcxifk8clo2uYlvMgSaMTw6jBlnyHYOjPlHlycEQy+KCuncfeXcfGmqY+21s0E5tuYJiwuePP/R5jRVUtb+7cQ3VH/61uhBBCCCGEEEKII0VapBxFuuM9PLnrBZRp40u9x+Fz9B1jJCfHQ0UlJB1hEmoHABMClUwIVPaJfa11N21xO/WR6IBz0DQNw7iPhIrRE1tFONlKUvWQSNRi1XXOGz2WbU2tJDoT2KzpA8mWussY5p7D9uASapMZTOhd869ST7oTKsupyM6kIjtrwHkJIYQQQgghxNHMRMcYAm0lTI7+WXsG/yqLw2Zx46tE9SBhuknqbf3G2GxWtAyDzmYHW6uCqQFn++PUXTRGAySV+5DyKM+6nQLf1wm4TsLvPJ4Ipdjs8wCYWlBEcaafkNcgP9vbZ9uZOZOJaw7KXB0UuPoWdwDKsjKYM7wMp03qgEIIIYQQQgghPl3yTfQo0hEOQ8SCltTZ3dXFqIy+MZqmYTUV7d0+HDEbhqmwWvofDGhK5li29+xivG/kIeWR4TqZDNfJAPjsIzmh5GU0/j2WybKm7XwQXQk7mpiQHM/I0jwKc/wADHdnM9FRja6ZeGwTD+m4QgghhBBCCHGsMpSOoQa/rYRxgD/WHy0G/yqLw8Zvy8LniOCyJsh199+KJBZL0BZZT35RB3llO7Fa9v8IWHBht3hwWf0fOyfDaKK15QTa289DKYNQPMbUYZkE3DpdZjvt3WG6Q5FU/MrWe7FaTfb05rArJI+nEEIIIYQQQoihRVqkHEWy9JE0ta0GU8dt5vYb4/U5Kcz20Wnbg8t64Blvit25BKx+Kr0ffxpiw2ginuxEN7p4eOu73LftfU7MqeTy0Z+j0luCGbGQ5f930afMO48t3ZtoS2TTmPAcYM9CCCGEEEIIIcSnTwopR5GyHA+WTNAMhdPVf0wykWSS66es3PE3huefhFIKTeu/a09ztIGk6qA5VntIebREesh0uLHpFrrjVSSURtjUaY120hOPs6KhDr3LziVTc5hYVJC2bTjZi5seIqqAOTkzD+m4QgghhBBCCHGsMtExh0CnExlsVnym5Njd2CyABj573xl7AOwOOy0d1XR019DcVs2+7mvhnjsJdf0Ipf7dzSZmWOmKK3qTA6+3vd9exy3rXuYvO1cC4DJbyLcYVFiTXDt+IdeOOYkZrlKqmzvZ2dzeZ/u24OMUOTqYE9iFllg98JMXQgghhBBCCCE+BdIi5ShS3dNCW4sdZWisbqrljIpAnxiLVafXsQxL4SYiNtD1i+mNtxLpuRtNs+FwX4zNPh2AxmgXrTGdhkjPQY+tlGJnqJG4kUADrPreGp3H+0WixkY023FYrQ5OLh3BFF8Ry3bUMConh/buXgJeF1aLTl2oiwd3D8duyWBERjP13X9HFVyLplkOfHAhhBBCCCGEEOJTIoWUo0iuMxMzrENSJ9/Rt4gCEOzsZdeucra3l+PNruDzpuL/6ldgjZ3E8Rl5ZNkmp2LHB8pZ1raeaZkjDnrs99p38KftLzPOP4xbp52N37a3RYym+3Fl/oG3W19kdc3tVDeP4pRh4zn3eINwTxtL329ifEUhY8rycFis+O3jqQm9z/auXOLDm7lAiihCCCGEEEIIcVCG0jBU/8M2fNp5HO2ka89RZGdbN6rHihbUeOKtNYR6In1igt1h4nV2qqqHk+mrRNc18u2ZBFWUnbEOEmY4FVsf6iYUhdZwuM9+Pqo62EJNbxub2msI2Jzo/xp3RSmTSPgZaoPPEzIbcDl6aO19l/da/otd8V/zflMT79XvHYMl1+XlijELqInkElRuCl3Fh+nKCCGEEEIIIYQQh4cUUo4iNl2hFGgxRevuDpqbuvvEWKw6HS1BYo0GwZYEpqlYWDidUlcvpmqhN9mYin1h/W5q2kO8u6P5oMfWdROUidVipg1e2xt5hfaO73GS42XOLryYX876CqFQFuG4FSujcOXaaUmESRgGAEpLYrNpOK06p+YuOgxXRQghhBBCCCGEOHyka89RxLSYoINpA6fPjtNl7xPj9tnpiLYQj3Wxq20jmvZ5rJqTOQW3EzO6ybCPSsU2JTuweeM0xRv77KcvGw4rWG2OtKVxskiiEVFuxmfOwKZbSap81uy6miunTSNPq8NjtadasHi1ddw0+i2WdBRQG3yNUVlvfaJrIoQQQgghhBDHAgMdYwi0lTCOgVl7pJByFBmZmYmzNITWBrNKazGTT6DUNWktRKwWHbvViVIe6HSmpj/2W/MwLc60WJfDpFcDmz150GM3tdTgaEqg5fvTlrv0GEEsuLQoNs3KpuYWbLqFPI8HOzrVdR1YLRbi0w1cDp2cxL1U+KtQKklbwiRhRrHp/c9AJIQQQgghhBBCfNqkkHIUaYlVke/pxNxoZ1vPDqaUbKCo5Gws1rJUTGNdByviBsmklzxrGZqmYZhhGptnoCuDzOwn8bhOAKB5D/Tmu7BFMg54XEMZbG9YjTcWJrs7QTyeRJkmDqcdpeJAAoslj4Sp85eNq6luDWLEFYUuHzvbOrDqGknTAGxE7RfQHrqfDdEsTs4rxKo5DnhsIYQQQgghhBBgKh1TDX6LFFNJixTxGVLhG8nknF52Ba10dmXR0XUOumVYWsyKnfV0uXpxGjaW76jb2wJFKfa1Q1H8u/WJckLMtKLZDjxzjqZ0PD2jqK6roTWq8/en3qM72UPH1FrmZ4UpBnQ0XmnYxHrjNSZWVFFXN4vGcA6R3hi6phGNJfG5QDk/xz1Vu+lNduEOejmx+N8tZOqauojFEpQPy8aiD/4HhBBCCCGEEEKIY498Gz2KWPFS1TmHLquN7h4N05iApqXf4q7eXsrd7SRdFnoteyuFFt1Dfv67ZOe+hNc1PxWbW+Ahqes4Ageut2kaTM0/DqsqYlR5HjurWlhtX82rDdv4R+0qvLoTt9lBbzRCIq6wGkmmjFrNyZUlBEI6ni4wkyZ14Vb+tPMFmqMJNJXErVWnjmGYJrtqWtlT30FvOH7YrpkQQgghhBBCCHEopEXKUaQ7EaI62gQ46eiys3NPJid/JGbh9HG8W/8U4RILWGDV7lpmDB+G3VoC1pK02Ik5hezc00ZpJIPWth5yc3z9HlfTNCK+JJ1FEdbZmpgxphyvnkd7i8naiI0nAxpTA2PI03NxhBQRn4NQzEs8+jTxnlyUaRLs6iXL58GuWyl0KMa4qyi0WVHKRNN0LLrOuBGFJJIGPo909xFCCCGEEEKID5PBZj89g3+VxWFj0XpxJmI4kgqb1YnNausTU5SbwRnjLgKXwuLUsdgtKKXY0vI91jV8nrjRnop1my6ykx7cYQcdXb0HPHblsEIcRQ6ajXb2uJpw27Kw22xMy6+nOd7Lhu73mVFSwqyy6cSthZgqQVOsloieQBkmbS09+HDznZHnoVB8EBqGoRUC/+7ak5vlpSgvkDYgrhBCCCGEEEII8WmSFilHkc6Ik6RyY9ijOH1OhpVk94mJRRPMHDaKSd3NxJRBoc+DIk4wthIFxJL12C17t1tQUUl9spNT80ZR1s++PsyuWYl167gz7JSYASxahOFFEbK9uYxxbSZg8+GwmpTmvUQg1ktddzEZjCKc78dv2vH5XFisOnu6tqBZunDqSZLmgYs3QgghhBBCCCHEp00KKUeREq+fmcMy2bKxFSNgx3T1vb3+DBfLN25nXXsTSocN7U0UBkYzPu8+4kY7XvvEVOwoXx7fLD+ByoJsnPa+rVs+bEdTK4k2RcCSg71Ao7Urij2so2WXYLHl0Zzs4X93/QTd6MCqx1k4bCZzsi/if7YvxaHZyC/OwNASbA+uxq5byLJaKPQtSmt9YpgmpqmwWQ88+K0QQgghhBBCHGtMwFCD33rfHOwEPgVSSDmKtMVb2dTSQNJQlPktlBf0LTgYpuLd96vAaqDbLbite8cb8Tom9In9oLqJ9bvr6e4MM2VkMS6Xne0NbeiaxsiiHMxkFcnQn7C4LsCfdJJltZCVbWE124lbTczNufg9Y5lZuZAH99xKV7KRLH0kPfF2nmxfxbv1XdgC44gb4PM62RVaT1V4A1YtSpwCZuVcnMpFKcWS9TvpjcZYOGUUXqeMkyKEEEIIIYQQ4tMnY6QcRSw4CUYtxA0NT+/7dO76DsrsTItpaOvBUBZK2r0c7y5mWmHxfvc3sjCbMfm5JHsStLX20BuLs3J7Le9tryEci2OEH8OIPEMydBfOLCuJvBgup5VSo4C8WB7NeQne7Wkk0zme5M4KIu/lMj/ny8R6zyKWUHQnQsybWMmZM8aio1HqHsV4/ww8lmwCNj+m+nctUylobOumvi1IImkcsWsohBBCCCGEEEIciLRIOYpE4iY5+Q6i/jA9VQ6am518eLBWgG17mjDNJM4sF9GkYkdzG5NKCvvdX3FWgLwpXjo6Qng8DjwOO6NKckDTcNltKP0LKKMZi/ti2oMRuhxR2swQ/zHieJa2vME7G0Ls6owSjsfY8XwZwZ4Ij+s7sQxPMtk3ktOHncL4nDI2b65ny+YGxo4r4oKSqyl0vUOWLRu31ZPKxTBNfA4Hds2CxoGbqymliEXiWKw6toN0SRJCCCGEEEKIo4GJjjkE2koMhRyONCmkHEVyHF4qw8PZ3rqbLux4ii9B0zPSYvIzPIwdVoDfEydhURRlBgDoju0kqUJkOyenxdtsFvLz98b0xuKsa2wCYGJpAVZrOVvUjxih59IeWUHMMIgnIcN2GZV+Kx7XaQQcDnRdx5fhIWmC4XsPf3YNTYaV51rXMDbrAdqau0kkDEaOKmBtcAWP1zyIqaz8dOwtDHMX7c3DauH4MaXEE0n8bucBr0MykaSnK4TdaSeQNbBCilKK/9zwVxqj7dwy6cvkOQMD2k4IIYQQQgghxLFFCilHkTU762io6ybUFcK0JHEXd/aJGTeikFy/zvONz9FDBV6HjaQZYXnT9wDFnMK78NsrAdjT1MGOhjamjywhy+fGZrHQEQqxZONOIvEYY8Zm8beqNZyYP5JM3YvLcJAR92CxVuJRNVj0fEzlBTSu++4ZdAWjfODspDpci4aJQyVQSqGZJk67BYfDihZJ4NZDRA0H9eG2VCEFYFhexoCug2koEnEDi23gwxwlTIM1XVsxlcGGrmpOKZg04G2FEEIIIYQQYrAZSsdQg98aZCjkcKQd/Wd4DBmVm83c4jI8URPVrtFZ6+4To+s6m+qf4NW6Hbxet5JNbS1YNDteSwCbpuOw/Hua44bOIPXt3XT0hAGwWy00NXfTXd3D4ne3UOLOINPhptybzdnDxnG5fQZnO0bjy3wCd8ZTJMNWtOjejjgFeQFGlGazuaeT+mgueeFRLLT9kGTcwKbr2HULoLBpCoemE7AZ+G2Jj3UdlFJ0tIaIRga+vU23YDdzCEXdlLsLPtZxhRBCCCGEEEIc/aSQchTJzfRx7tTx2OJuCFmp39N3UFbNotMUzKEn4SWeyCTL6UKZ7Yy2rOQ423YsZnMqdnxZHtNHF1OenwVAdyRKEAPDbyEv00e5O5cv5M1mon8YVZ3tvNS5mVdqN2EYJq/Vv0VXbwf1wWY0wEiaGIZJWyibuh4vG1tHknTn0dDURSiaJKfAT0+yl382babXLMJGgs7Y+o91HWLRBD3BMJ2tPQPeJm4adCdiKCzURbo+1nGFEEIIIYQQQhz9pGvPUaSxsYutO5uIhJOQMGhv6+4TU9vZxfKdXkiUMn98JSW+AJqusDsWoFQEi6UU2Du46wVPP0CwI87/nHMeJ1YMx+OwM62ymO5wmBkTytnY2MxzH2xm3sgKfG4rVaEu9oQ6WFtfT1SFSWhgsSTRNQ2ry0ZcN0CVo+JZTCvKpyI7QO3Odlo6gvgzXNiHaUSNGBHDSqnDQdJoO+D5GqZJ0jBx2NIfY03TiYXjWPN8A752DouVy0fMpL63k7m5lQPeTgghhBBCCCGGAhMN8yATc3xaeRztpEXKUSQ720thQQaaMtF0nXAo3icm1NxLb02QVks3b/fuIpyMo2lWAln3kZH9KJruAiCWTFLb1UtPJMEbtTtQSmHVdc6tHMU8/zDGOLIxkyY9oShGwmRGYSmJEESSisVVO9G1XBJWnahuJW4a6LqO3+li0bCxfKESynJ+x57QH3G77DjsNkxlUuzKZ0qggky9iT3RYmbnffuA57tySw0vr9pKZyiStjwRj2PRwW6zDPjaRYw477VvYGdPDXXhAxdwhBBCCCGEEEIcu6SQchTxeJ1k5/pAB6VrhKOxvkGahsVlwbApgmYUE0VXb4RIPH08EZuuo3ksxAMaZVnZJOIJTNMkEkuwra6Z1TtqqG/upqWph+bWIHa7laziAFq+g+llJZQ6qrHpBk49gUodWiPL8GGLbCRptNPdu4rdNW2EEknKyvPQdZ2R/JHv573FdNcm7BZP3/w/JGmaoECZKm15OBQj1B3GMPp2bdofh2aFiI/qOth1CF2ChBBCCCGEEEIcW6Rrz1Fky54mNu9oRNl0MBSeLCe7QnsY7ilH0/Y2r8rIdDNleCleq5dzj5+EEVP88/2t+N0OzpsxPrUvBbiwYlqjhKJ1RCNjsOsmf297jqbCOnq7xxBv0ahvC7KzrQOAaDKBUiZdkTCfH30Ruc67KfCdiMdm37s+keCP7y7Fqvu5YrrB6OzJvNwapKmrh95EgobOIOurK8kr24nNknnQ8509rpx4IonbaU8ti8cStDZ2EOwKE48OvJASMePsDrXQETNojwUHvJ347AlH9rbUcrvsB4kUQgghhBDis0Nm7fn0HP1neAxx2m24fQ60SS4o0ghNqOKhqsfZGNySivE47fTGI2xsaOIfG7eRNE22tbTSEEwvHtitVhxGAltQUdu1lZamIA3dLbRq1XhKgowozsDlt2FmaLj9drZ37qI4sJusRJDG+q28t2U5J5X9J6P8mRixFQBEkjHijq3EVA9R5cIAjhtfwsjSHLwOG1f9v7/x3/8cy++WLcBri1Pf++YBz9dq0dOKKLB3oFndolNQkkVuQWDg185iR3ca+HPjDM8Z+HYiXTLRQGvjHFpbvzHYqfQrFk/y9qqdvLtmF4nEwAttQgghhBBCCLGPtEg5iowszaUoP8D/rP8bPbngLmkllPDjs3hTMbqu05oM0+EIsqx5Ny29s2iOhFAf6c0SM5LY4w7cMXB3lvH9Pc/TUheiIODl0gUnM3fCNFbV1FHq8TM6M4f2jof43ykvcY9lCs7IOjbvSVLi3s4H0cX8pX4sXxhRh2YpJpZpJSMRpRsXPWYhbl0j2txLzY4mmjq6MeIm1c0+ztHW0di7nGLPvEO6Bm6PA1+mh2gkgcU28Mc7kkzg0gJYbHEKXFmHdEzxb9Vd95Jp1kG8HtM00fWhVauNJ5Js2NOIrsPcGZXYGPg4OkIIIYQQQggBUkg5ChnEXTEsLo1dUQs5Vi9t8V7K/7XW6bTTm5OATg1liZDEJCfgoSS3mrqeZyn2noum6SxetY3GUAxdaazZ006do5u4ZtD4rpXHWnfy3ogwJ48sp9hwk6HZKdc7iYesjPQGGZ1dB3qU1zq91CYLaYm5WddeywXDpzAlbywzs/NYH/TQ2NJCy9+W09UQpKg4gAMrSZVAtxtsjs3kSyVXHvLZ6xadZMwgEooTjSYOvsG/RJIJWroMFDoW+XL9sfXox9OaeIr2pI9FQ3Cwbk3TcLvs6LqGpg4eL4QQQgghxGeFgY4xBDqdDIUcjrRBPcO33nqLc845h6KiIjRN49lnn02tSyQS/PjHP2bixIl4PB6Kior4yle+QkNDQ9o+YrEY3/nOd8jJycHj8XDuuedSV1eXFtPZ2clll11GIBAgEAhw2WWX0dXV9Smc4adLKcVLW97DuT2Jd3eccKOLUncBpe6CVIzLaSOoGRhxnUTEQllGBg43DMv7C9s6f09XbD0ADW2dWKI6KI0yRxYVtV4cOyHpAkOHmGFQkOFnTEYWxRk+WoPf5E/LF3LXhtks3/p9aq3nUmeFEf4mLhi2kbNyainT8pmkJlLTVEcsWY2HCCEjQVgz8WV50eMKaxwSmk7IiOK25h3yNQiHYvSGorg8NuyOgdcJo0aC9kiYzmgUlHzD/riG+2exLnEqMfvn0bWh9wHqdtqZN3UEJ06uxGG3DXY6QgghhBBCiM+gQf2m09vby3HHHcfdd9/dZ104HGbt2rX8/Oc/Z+3atTz99NNs376dc889Ny3uuuuu45lnnuHJJ5/knXfeIRQKcfbZZ6fN2HLppZeybt06Xn75ZV5++WXWrVvHZZdddsTP79PWGQ+xuGU9uHQsXTqObVlcVfE58pz/Hri1pa2HjHor+i4TV8hKW08vja1h6tsmk+mYgtc+AoAFk0ejWzU0q8ZxFSWcMXosNr8dLaCRXebnunNPINPjwu20YdogkJPPUx25tNitPFjfzas7x9NbN4Gm6rPIihTR3jiZTTsaaO3pRVdNjHa3UOYIMqG8iGG5mbQ0d5Fhc2BXGhaHm6jhIRH/IO38koZ50HEtDMPE4bTh9btxOAb+RXlLcwtxIsRVlHhSxs74uGLJHQy3b8SnXkWpoXcddV1jRFkuI8tz0fUh2GRGCCGEEEKIj8lU2pB5He0GtWvPokWLWLRoUb/rAoEAixcvTlt21113cfzxx1NTU0NpaSnd3d3cf//9PPLII5x66qkAPProowwbNowlS5Zw+umns2XLFl5++WVWrFjBzJkzAbjvvvuYPXs227ZtY/To0Uf2JD9FvS1xJvgraNF66alLEp5i8IsP/sY1o05lbKAYgKRp4FZWXEYS09ZDTCWYm19KjvXbTM0fm9pXUa4fT4aDuGFQ396BsbsXj+4g1hOluzrIzqpWPlhXw1ubtvN32wdMryhDs0HAGSFqM9nS5uLzBaeQiJpsbRrH6EAWE0Zk8u72BircjVTY6gnYM8ieUEp1QxvLdtdTVpBJIOBi0og6jnetojekyMj6E7C3QLJ2Yw3JpMm0iaU47P0/ul6/E4fTSkttOz35fnwZB55CeZ/dtR0kmu3oaATszk94J45dGY7x+OzHE7CPQNOGXhepcDzB86s3YbXofG76eGyWoZejEEIIIYQQYmgbem3vD6C7uxtN08jIyABgzZo1JBIJFi5cmIopKipiwoQJLFu2DIDly5cTCARSRRSAWbNmEQgEUjH9icViBIPBtNdQp2saCzxTmOEpJrfQgifg7hPjdzrwOa1oVgsonbg1zrCRYbqz36Ez3paKU2h4rTYsyuT1TdtZuquGDItOYYmPMTlZ1Nd2snlbPcH2MN3BCGbSxE8ctz9CXibMLiuktDibQJYLS4dBvDPGa1t28ty2LTz2QQF6YiZG9FJe27mbBi1GqxHD43SQk+Gky+qg1piC2/PVQ74GmqbhdjsxlU48GkcNsJtOa0MP9kYb3mYrAZfrkI8r9nq3ZR337O7gf/dswlTmYKcjhBBCCCGEEIfdZ2aw2Wg0yk9+8hMuvfRS/H4/AE1NTdjtdjIzM9Ni8/PzaWpqSsXk5fUdayMvLy8V05/bb7+dW2655TCewZFXVJxJIAvWu1fTPcFKqaFxfeU3yLL+e9YeTQOrVyfh0LEZNnI0P883LSZua6XQM4wS1zhGeIfhsdvwOkPEzCATZuxiafI4om2d5J7QQVtBL1MdlzKlrYTe9ihn+ydDh47W6iDqU2g+G516kA0tTWytaqZnQxfdGQEmTCgmFo1TYzh4qd5HfE8br68KgQJ/pos9nQqnI0lOpJ1gT4LPmbBvcmOLRWfqhFKUqbDZ9t+KQNM0vJke8oZl4s/woWkDa1a2tboFe4eJzaEPeBvR13utz9McdREzetEYetfRbbdx/owJaBrSGkUIIYQQQhxVzCEy2Kw5BHI40j4TZ5hIJPjiF7+IaZrcc889B41XSqV9Ge7vi/FHYz7qxhtvpLu7O/Wqra39eMl/ijRNw6pb6bTrGE6NHdEONmxt5q31u2ht7yEaTaBMxTu7t5BUSbo8BknNpNCYTbk2myW7/8RTe67m79WPEksmqQ+ZdIWddIXdKMMkbDG4cPQarhv2OlHXO5x04ljmnDyStfZGtidasUWsGHVutJCdAi2bk4aVM96XhQud4kIH48cmyfa5GTuslpK87fgym9GUhsNmIWYmaA9F0HvsDPc6mOKppy2yNO38rBY9rYjSE4nR0N7dp9WJ2+OguCwXe8DC2s5N9CYjB712k8cU43ToTBlVfFjuxbFqYe4cpgTqmRPoYqgO2dsbjhM5hBmdhBBCCCGEEOLDhnwhJZFIcPHFF7Nnzx4WL16cao0CUFBQQDwep7OzM22blpYW8vPzUzHNzc199tva2pqK6Y/D4cDv96e9PguiyRDhehexJgeYSfweJ06bjT172ti1uwXNqlNmZGGNKDwWDa/dQTDioXNnPhm741QYrWzvfom2nl56ohpGr4XduwtwtYPu0hhl6SCgxymPJqgYlkNrb5D3a2tpNILk4cPfaafcnceMvDJam3rYFm4gURGlY/gqPrA9SWleNy1tw7AbC/ncjIsYW5zNmMIcEpE4SWVS3x4k1z+b0wsvodR/xQHP9b0t1bzxwS4aO3rSlmu6jt1h5Y3WFTxa/XdebXr7oNdt1phy5k6r5PMnTflE1/9Yl+85habG82lou2IItkeB3kiclZuqWbWxhqQMKiyEEEIIIYT4GIZ0IWVfEWXHjh0sWbKE7OzstPXTpk3DZrOlDUrb2NjIxo0bmTNnDgCzZ8+mu7ublStXpmLee+89uru7UzFHi/qGTv66aj3JFjvRGgfJRgvdgVamjyshI+AiI8NNW0eIitwSxuSU8vXKBWzZ0cDaHXWsrG+kuc5PPGHB1lNORzCMIwHWKJh+A8ewHpSh+OmaM7lv/Wk8vqwSUASiDrK2WTjVOYKobqIpnavGzWBaSSH/7FiDYYmQl+3EG8vFkvBzyrCpZJn5vLgqj601UXJwEm+NEY0ZmDZIWGFzcytF/stwHGT648JsP/kBL363I215LBqjq62H0cbv+M+8Bxlj/ccB92OaJkvf30VrVy8FGd4DxooD05XGcd5yyu1ZmENwGmm7VUdTYLdZsFiG9MefEEIIIYQQh8RU+pB5He0GdYyUUCjEzp07Uz/v2bOHdevWkZWVRVFRERdeeCFr167lhRdewDCM1JgmWVlZ2O12AoEAV111FT/4wQ/Izs4mKyuLG264gYkTJ6Zm8Rk7dixnnHEGX//617n33nsB+MY3vsHZZ599VM3YA7ChqprXO1dhHxOmp8NF9tQY97z7NG8GdvObRV/EatEJBiO4cnVaYtW81tDJmWVfZmJmLtH2MLVdft7vKEPryWR1sh6/0ulNGIQDFjzjI7j+6KC+NYM3GksZEddp6uqhu6MXf4Mi07Ch+WzoRhKv00Ztbyf1Zg9/mvsA2XaDZTt+jztyLn9ZtZa6qk5Mu+Kf3VuIdoVxuGwkkzqmHayG4tKyuVgHME7J+LICxpcVpC1TStHR0kOkJ4rFVYOygdPcfMD9JA2TzTVNdCai9IQP3g1I7F+B38e548fgczqw6EPvAzTYG2N3TSsOu5XZk8qx2T4zw0QJIYQQQgghhohB/aazevVqpkyZwpQpe7tTXH/99UyZMoVf/OIX1NXV8fzzz1NXV8fkyZMpLCxMvT48287//M//cN5553HxxRczd+5c3G43//jHP7B8aCDJxx57jIkTJ7Jw4UIWLlzIpEmTeOSRRz718z3SynPyGOcvxxY1KSrswoJCmbCxfTd/2f0CAB6PnW5vI+UjamhPttJClMyAl3hcYcTt6BaYVFLJiIIcsot8JIcniLh1wnE7JMFZp1Hh8WErsVDV2kljayfJWJLnt26iq6wZ19Q63uxcSaglyVh3PgFbDKvFYO7UAqaOLaE7FsOCRqHPS36Gj4hVkVPix/BqoHT8LgvNxnd5es+lNEd3pZ1fImkQiR14bAvTNIknkug2C3rWf7LdPA5Pxm8PuI3FoqMcGj3WBDU9Q392pqGsoTvI/1u9kr+v34hpDr0WKQ67lUy/h0yfO+0zQgghhBBCCCEGalD/HDt//vwDTk87kKlrnU4nd911F3fdddd+Y7Kysnj00Uc/Vo6fJSMrC7jEcgrLXlyPBZ02nwdLFsTNEP9X+w5fGn468WCSnIx3WNXtx58ZpSIvi2Aojq0zSVXITovhZWxlPrPyKljWVU5NTTMmYMQtYOq4mnW2FjahXDpXBGYztrKAXZFmlBV0HTRNUdfeSXZbO2dnTySk/5XMbCdxswJd0zh+3DA25/YSKNlF8waNWCyOHt87tnTSCmZGkq1hHw69A2/nK5xeeA2w91l4+4M9hGMx5h1Xic/t7PcaNDV0smtbCyVlWVR4z0B3ziLLmd1v7D6GaXJcaSF5PV6mlclgs5/Elo5WPuhuojsZHexU+uV1Ozj35IlYLDq6PhRHcRFCCCGEEOLjMdAwhsBIhUMhhyNN2rUfRSwWnU3tdfTU++itViTPsRF3KwrdbUzI6MSmW+hWMQKJAFmWMFHDQ2c4yp6OTjR/I1kVnexRGVT3RJmSleT9qhaSQSt60sSalcCCBRIQrFMYccXW2maGlxdQV93FvJLR+HsySLZGmFVUQW62n7ZIhJW7TDbHXqG3BwrN86i1dpNZ2YzX3UDMno+m7GypaUWPgcWlsOZGiOFCUcJxGQvTz0/X0Djw9MSRcIJoNIHdbmVJ6xqeqHmZcudo/nPKJej72e6VTTvY3NLCmJwccjyew3pPjjXHF5dwzvCxTMjJG7KFCsM0h2xuQgghhBBCiKFPCilHkRc+2Mit/1wK2VZcjQaBsIeOZJyRBSO5esRYdM1Kc0eILZsX0BJ9n2RWEYZS2CwGc8bexBwV5d76+UzwlaNrGs6oBWejAy2ZROuy4dAVvXk2DK+BMnVK8jLJC1hpqe/EFrDjtLkoduUxJqeA/IIAb++sJpRopDuykrhKsqV+JFt7Oij2l3N66Vh6J5XxRP1m2pq6cOoaDovGjIlBilzDObf4+xS4SlLnpmkacydWYJom9n7GtYgbBi9t3Y4VjdnzRhHI9PDczmew6TVs7emiOXIWhe5Av9dt7Z561u1sZPuuVk4YU8G8iZVH6hYd9Tq6wlRtaaPR1s3sslJ2Bdt5aPsqLqiYyMz8ssFOj95wjNWbarBZLcyZPFwKKkIIIYQQQohDJoWUo8ibK3eRsIE1S2P4qGaC1hI628PYA5UUOOcBUJLjJ19L4lNJyv2ZDPP72e3y0xPKw2qvJ9jjojHSyXhLJWeNGENO2E5wVwc1b9ZhWMGBRkVhAWfPHM9p40dSs7OZYQWZ5GV4ybFGmFxaxIRhJUSTSToTEaraNRI7JuPTLPiHZeGKdZPbk81k6wls0Brx2mx0REH3QHF+Ft+c/kW6tC5aI2txas1kOKelzs9q0WE/M60Eo1G2tLSiaxqnjBqBzWYlZhpETDtxpRExYvu9btva2khaIaqSFGT7Du9NOcZ4XXY8TgdZXhdWXefp6vUsqdtObzI2JAopVqsFh82Kx2VnAOMZCyGEEEII8ZkxVGbMGQo5HGlSSDmKbNnRiL1DMerkXZw3bTX/u7wQ5ckiP9+H0773VjsdVjoyN5M9vIZ2h0FL23ya2kOs2PED3qt/F9vsKDVtXVAGJ42tgK44r23pxOZ0kuWzMqygmEtOPJ4pk8sB6DDCPO1+n4mJUsrzynitaRfluZlkOJw4nTbebtmBzdrLaa4gn58ygVX/WMfO7kZW78jj8Vc2U9ccJDffR3e8h4beEDs7m3mh+1U0cxNfLNzBrJJXsep9u9sYppk2K0yOx8MFE8Zht1hwWPcOIjo3dzZrOmtw6Dn4bK79XjeHw4LpAE+mlXraqTSzsep792GaitqadkxlUlqaI1PmHkSW183F8ybhczrQdZ1hngwA8l1DY1pph93K7MkVB+weJoQQQgghhBAHIoWUo0QikaAx3IsxIUZ1Tw672nMZkZ1NNGrD73RimiamYaKh09BhJ+61sKNNEcmOEA1GcWNiJwpmkvU7t8O0U7GaYI8ofA4buVluxo0uoLiskGhvjLhhYLdYeHPLDmq62uluS7KkqYaMfA87Skt5sWk5PUmTCEnChoOMYV0U52aQWRDEZSRI2Lpo6QwRjybptkWJBRRxLUaRJ4vSZBEedpHpmoNF61sAeXnHdtY2NnJCUSllGRmU5WQCMCo3h57uMDs21ZKV42d27hS25IWImwaBAxRS5lcOZ8OuJszsMP9v61tkOT1MzioF9s4C1BOKoNTeosrhmujFNBUNrd24nXayAu7Ds9MhoKWnl5e27MBusfDNE45nR0cH4bDJjvb2wU4tRYooQgghhBDiaGQwNAZ6NQY7gU+BFFKOEsmkiddqoSdhorp0GrJKGebPZWuwi521bazx1uC12agYlk3FsFbGVa5lScM4uvUELreV7uIXuXDWZh7aOBuHJReAeCSODcW4igI29yYw7DYCWW5ebazm+ZeruXjaBLZsbMK6E5IZBuHsJIVOF0VeD52JMNuCXWhuO1lJN47gpXSFImxZn0E42Yt//gvMmDWe3Ru9xKMxunrj2C12cpw+flz8JeBL+z3XtkiYrkiEpVt3U+IL8OW5U7D/qxVKMmEQDyeIhCJk5Hi5etSCg167GP8qMnXYqByRR7k3J7VO13Usuo5pmH0Gq1VK8WbLFkylWJA/7pC+oHf1hNmwvQGPy85J00cMeLsjKZk06QqHyfF//NYjmW4nY/Jz8Tsc2CwWdkQ3YVrD7I5vP4yZCiGEEEIIIcTgkULKUULTNCwhDX2bG19eki9W7uQv67didkFrs5sNjgZs6Ozp7aSzKkmrzUdhJgwvyWdtbQMJRxSXJUG824bT4QAgI8tL0ahcXm2vpjYSpLNekTUim40NTegBG8/t3kRVvIseZcep7GRlaWQk3PgSz/O1gj08tGMiWKzMKfMzvsRNbzKGM6+a5mCAjqQdoyZCZ9DArim8Dju5OQE8rv6nNf6wC8aOZ+6wMhpaunE7bKkiCkAgy4Om55GMGRgJE+sAnvC8gAdvrp3RgRz+Y9rZuOy21DqlFFaLjqlpmErx4QYp7bEQf9qxBKVMxgeKyXdlDPBugc/jpKI4G6/HMeBtjrT731zJ+upGrpg3nRmVwz7WPuxWK2eNH536OeDuIDMrSYZz/2PUCCGEEEIIIcRniRRSjhomvaZBUtcJhdx09niJU48ZKaCpo4288RMJ2O20BHupaxzFTsND0dQJxJNJ3g1v4/uTVtIVdeI3Y6zeXgeA2+vkA72Td5yNmMPj2Df3smTlVgy7xtlTxzFtVAmRjUHeq68iaTVw9LYTMWLEonfjTrjwRUfQEnLwgfEQSUstIxPT+MPCf7CitZIXN5zB7g0GBmGyPFYSASvBWAxTVbOl5Y/kec8h231av2fqttmoyMykIjOzzzpd1/EFPCRiSbDq/HXPGpwWK+cOm9SnxUgikWTXjiaq6negIr2YAWdqLJl9LBad4pIsdF3HZkvv17M1WEMw0Ynb4sJQ5iHdLZvVwpjh+Ye0zZGWSJrEEklMUx22fc7LdWG3bKTAcZj6RH1CSil217WhFFQOy5FuPkIIIYQQ4qghg81+eqSQcpTQNJ3MDBdN0R7QNLqiBUQ6yzFCCTpjBr/4vyVMKMjhmtNmUZmXz6qAwoqHYCxGOGHyQsM4XqyfgOlVTMjeO57Ihuo63q7ags+uEcs3IA45Pj+mXWfhxFEUZvkpjfl4vyqKtSSOZVQQf2EUj+cWfN7dnD33BJ5//126gh7ihkaVGeT15uMosnsxXA2YyQLQNMxoErfFSZ7NTWf0dTojb2GYof0WUg7GYtGxuO3Uh7tY2rQNHY0FBaPx29Nbu/QEI1TtaqG2LkQiAhh93/DRSJwtG2qx2i1MmlKe9sV7dcdONM0k2+Emx3Fos/0kkwabtjeQ6XdTWpL9sc7zcBtdlEs4kSDTd/jGbOmNlrOjthNroe3gwZ+CWCLJrtp2lFIU5QVwO+2DnZIQQgghhBDiM0YKKUcJu93GgimV/N/uD4irJCtbTWoiDnotOpgGcbfO6u42ara10F27nS+O+Se1yTlU7azA2+DieW0iwaQLuzfO8Iy9Y6Ssa6qnvrcLX1BDoWguiTLbnsPnFh5HYZYfgIDbhu6x4PK5Cebms93wE9POpTDg53hvD1tCSwjF2+gyPETUBDb27CIzqxFvpBJNaeiRBOEwOINx3BUOivyfx6LHyHaffEjnH08meWnzdjwOO/OHl5NMGHREa8nz1TA5MKtPEQUgI9PD8MoCxnhibOgMkWiyUNvQSWlxVirGVNDeEcJmtZJMmmmtUk4rmMKSpg20RnqpD3dQ4Rt4C5OGpm5eeWczuRk+rrh4DgDhRJzvvf08Nk3n9yd9DtvhGtl2gDK9LrL9btz2w1f0eGtPJ7UN2SSSEZh52Hb7sTntNoYXZaNbNCmiCCGEEEIIIT4WKaQcJZSC3vYoGVErlrJuji/czPI9IzB1K5rNgmYqLGiMnlDAuvadlBc2E42s4pnt02mJR8j2h4mHLGS6ezFqIgCcPX4CbvVPdm9t48X3ijATil0FLYS6I1ACvfE42TMKqDRCBFw9bOytIKEgHk8CYNVdVORnsK2jhaXVk7gkezQXFbzKSEs2GYHT2ai/jSVioqERbQ5jdVlJmgHKM6875PPvjsbY3d6JVdeZVVKCihvs7NlFzOzCaY30u00kHEehGJOXi9+0k+xNsKuuLa2Q4nRaKSvLAbS9F/lDhnsKCIWtxI0E8eShde3pMeO0JqO4bP+e2rku1M3W9jY0oDnSQ4k345D2+UlNryhhcmkR1sM4xXORP4c1jiDOIVK0CIdjdHWEsNosqBIlXXuEEEIIIcRRw1A6xhDoVjMUcjjSpJBylNA0mDiiiE2dTZj+OCofbEkNzdTB0HB2mVhdGiUVeWjvH8+ru7vZYR1FhteCJdPGCDNBYWE1ZrfGljW1AHgc3Zxa8gjdASeNairvPzGWsjFuWo1eNrQ20dEd4e/r3yeUCGHTg4wJDsO05qGZe7+cGkrjpMLf86v1d9GZ7KK65VG+OeNOqrZ6GTvMpHJsAdXL6kDT8NisTMzJw27Veb1xK2XebCp9uQM+/1yvh3MnjsVpteJy2lF2k1M8J1PsK2K8f3yfeKUUmhXcHgfumIlp1zE0jYqynLQ4XdfJzfNjGAqLNf0D4c22N8nP2ENb2M2yjvWMziwccL55GT7KK3OZWFqQWjYqM5cfzziJaFeMRFOU+LAEdsen2yXmcBZRAAq9BXg8teS4h0b3JZvNitfrxOW0SxFFCCGEEEII8bFIIeUooWkaU8eWsjPeSo3eiiPpx53hpKNbYXEZ2Mt78e3OJ5EwKTEq2L3rc3ztpEmsbWonGAvTs2U88fpNNNUFKEz+q+WF5qVTWbD74iS7nRR5nCTR+Mtba8ibmM25ZWMxYxY62n1odo1aezs2b4wwCVrCIX6//l1sdOF37cYIO3FaInT07qChYzjKojFjdClt29tJBhPofiebqppY21rDY7tfp8hdyH9OOf+QrkFlThatLUG2b21kWFk2fq+f47OO7zf2vt3/YHeoge+OupAdmztp6g3htzvojSf6xGbl+vvdR3usDU0zcVgTtMdrDynXto4emqu78Js2GDs8tfzs8nH88bE3uPjdpZw4upRff/s8NE077AWOT8v5lZOoi7Yxv2hoTPFss1kYM2rgBS8hhBBCCCGE+CgppBwlkkmDd97fxHutH2CLOtkSG0aG0Uh7uBBlAT0jSabVTm6un4wRGUR39LAnFGJpyy4iKsGkgk3MnbeG/7vjZM67aBamqWhsCfNGuBiNJBMrpuPNHEdbNE6F10ZlbgHRRBLcFrqC2XTGswjnJbE642za1cC4EYWEk3E2BGsJmT78iSS9TWfyX6uDdLev5JRJoyktzCQr00NPrBen24HTb6c5vIIRgZcwVAmmOhddG9g4IUopPqhror0tRK7FiXGQrjbtsSBJZdCbjODJjuIdEWducQnjhuWlxZmmSbA7gs1mweNNH2flvJLP8UrTq2Q5DRojbYd0v5QJwWiMztC/ux2ZyuTtttf587IP6C5J8GzvLia/+z4Bj4tzpo5D1/e2oEiaJqF4jAyn65COORhynD5Oyh9PhXdotEgRQgghhBDiaKXQMBn8VtdqCORwpH02/8wt+tB1HcM0SAST9DYkaWmYQHYwD0tQYW+JY9MMeltCABRU5OKu9NHa00NvTwQTk/yCOha3jGPOVSFmTh5OZ0eIPVXdrGqcysqWsbQ3ZdHRG6U7GKY7GmNPeyf5Pi/zKyvInxQlZ0yEvIST0uYMWrd2oAdNxgTyKPTkETAKqGQ6Re6J1G5spnZLA2+s28lLtduo7Q0Rs0FFaTYl+RmEElFMpaFrDSxvvX/A5x+Mxnh7RxWbOlopLM0kkHHgmWe+PfJ8vjfqIkb6hrG5o4lgNMK2rmae2ryRhp5gKi4eS9Lc2Elrc3effXisbpq6S2kK+bHGRw04V4D8HB+zJpRz3Mii1LLmaCNvtrwO2WEmzKjmwlNX8G7bThKGieLf47M8u2ULf1ixgh3t7Yd0zMGwqaOZv+5YxzO7Ngx2KkIIIYQQQogh7vbbb0fTNK677rrUMqUUN998M0VFRbhcLubPn8+mTZsGL0mkRcpRI5lMEmlKMrq7hJZwmGRvBk4d9Hg1xDR66u0U5rppbegkx+dia7QJZ1jDYbOhkmAxLeiArlwk40lcLjtlpRm0b/MR7HVQGDOZeFwRGzs7iagQmqYR15PU621YXd1YgjrlzmEM92dTMTzAa7FneKfeSqaZy/R4MVF3L+0l28kdbif4QZIwCVwxA0fARkl+gItOmkLWMA9/2lVFd2Iux2etI9oLhmFiGUC3Fr/TwdwRZWiaRk6G9+DxNg/+fw30GmmxE69xUhcyeEHbhs/poMi3tzuPxaKjaRp2R/9vlXxtNGtb6ynNK+h3/f5k+z2cM2c89g/NApTnLGCyZw6Beb2cO+pBsi0Gb2ZEWVjxPBb939dg39Aen4UhPkJGL3WJJpzR5GCnIoQQQgghxFHtsz7Y7KpVq/h//+//MWnSpLTlv/nNb7jzzjt56KGHGDVqFLfeeiunnXYa27Ztw+fzHY6UD9ngX2VxWGiaRnVbD10dSQp9PkYVZdHdFSHuhLhdJ5alU9XRTHdXDw8vX8WW1hb2RLvJ8/vJsDiJVn+J+b0nMU2/jKxcP26PgybbUrzeBgoCIXIKnGyMdfHQpvVsa2rjuuPnUNXbwQdtzXiDZThbCglFFDn5ASyZXYS1jWR59lBg+NjR1k2Ho5Fd8d2UT7UyZVacokKdqYESzps5EW/AxbpdDTh1K0kVJ6GSDFNfRms4iZr6DmBvFXJ7VQubdzWysaGZZ9dtZvWuOt7csIveaBxN05hSWsTkYQMf/+Kd5vu5beMlFORswWK14dYcXDRuAjMKS1Ix8XiSWCxJbzjeZ/tgoodWo4aEirKhq/6Q75nHaU+b4jiRVLTVFmCJFpGhgx2NMqvG+zUNadudN2Ys182ew4isodldpqm7h47eMABtvWFiZoTW3p5BzkoIIYQQQggxVIVCIb70pS9x3333kZmZmVqulOJ3v/sdP/vZz7jggguYMGECDz/8MOFwmMcff3zQ8pVCylFC13VcDisJM8nmaBsvdu3AWeqBgI1kthVbUifotbFjWxMZrTqBsJNpgRLm2YrJiTrA5sKrjcOh62gaNDd3s3OnTmPQi1XL5vx5J7GxrYUuI8a29lY8djsz84Yxo7CMafljsLkyCNsVmfk+HIliJrhO49sTv8CYgnyyszxEdmcyJjSVE4oCTD11NcfNfYPjJ5bS3htmR10Lu2tbyXP7+XxZEcP9LTQma3E67Dide2etSRomdc1dNLQE2VTfzJbmFt6vqmd3cyftPeE+16M7FmVdcyMxY/8tIR6tfo+32nLZpW0k4HKSbXVx+vAR5Lj/3S3I7XGQk+0lw+/EMNLHXXm46m+4vHXkZwfpSfY/xfKhsOgafpeDgNNJb+wuOjmf92q+x4b6Jgzz38fujsao7w6mLRsq2kNhnlu3mWff30zCMPCbXhztOXiDOQffWAghhBBCCHFM+va3v81ZZ53FqaeemrZ8z549NDU1sXDhwtQyh8PBvHnzWLZs2aedZop07TlqKDxOG3aniTUQIaK7WTCjCRwaK9riWL1x4poTq25jdFYOE3t7SOwJ8/qeZkLJGBZ2sT1fo6TkYt5fs5sVa6p5P6uVruEOukMxnqhdS6c1jLUoTCLuwDRNnt69gdfrttNT143WZUE3HKzeUoMzy4GrLZuTTh7P6OlWoh1R/rq2muLM4XT2agQzDRpbPZww10lWoZdQtUHEm8RhszLJezyvvNJKpzWbqReU4HTYAbBZLUweXYxhKiwOnYbuHvI8HkKRGIm2ENsbgoyYWIL+ry4wv1nxFktr9nDN1Jl8afzkfq9YxPASMxQaAUb5/OgRnT317Ywuz0/FaJpGZrYX0zBTg73uU+kp5x19E0bSweziik98B20WC5+fNgEjaZBMGNgcpzHf0o3bYU/r2vPsls1UdXbyhYmTGJ+fd4A9fvrcdhuZbjc+px2rrlOZlUWu7mNkxtBsPSOEEEIIIcTRwlQaphr8/v/7cggGg2nLHQ4HDoejT/yTTz7J2rVrWbVqVZ91TU1NAOTn56ctz8/Pp7q6+nClfMikkHKU0HWdYQUBNra/y7DCKGOzx6EZD9PTOxUtmkuw2UZuwkIgz8ummhp2trfj6IRcmxPNHWPc6HfwZcdYs7Gclp0Wqho6cRTZ0QpcJPQkSz7Yw/C8TuZXbKW9M5sPmqr5w3vLUBjkRzR0U+E13GgKRhTn4PO60PQ4mmbhjW27aO3qYV1DA1nWbXxhykZqMuvxWKwUZ/swKuPUebsAqOrsYtUHTXhsQS49OUxRvj11jlkZntR/x6NJknGDAr+HRx94h1jCICs/QE5BgGg4Tl1LJ6F4nKae/XcpybQNp45aTKOCOmcr+UYm0X5asGi6jmn0bf1xZtECXq8OsiWyha0dLYd0v+LJJFsb2yjM8JHr+/d5aZqG1WalLvIMW5v/mzL/lynO+E7atqNzcjCVIt/r+ehu05imiVIKi2VgMx8dDi67jYtnTEz93BWPYloV7fFP3mJHCCGEEEII8dkxbNiwtJ9vuukmbr755rRltbW1fO973+PVV1/F6UyfJfXDtI8MEKmU6rPs0ySFlKOEUtCNicuXT35WF3Y3OJ3D0SwuHEkv9t3dLBhTQTJpMDG/gFZ3DD1fo6k1SDDYS1fSTUCPsWNznFBXAh2NEmsPpxrzeTfYjKcpiZsm4jk6/niMm9Y9iZHUUHaNkoJsyvVsXJqDc0+ayNSxZdT0buThPT9ghG8GGf5MsCXRHCb5pVvRgDxfDzVdQezuHr487RW6Y3621p3Djpa3QO8gTDcdbKKIuX3ONRpPsGpzDaaCBVMrsXscxLvCKM3ESBp0tAaZbMslZjeZV9R/SxGlFI5kABVrI6CyqMxSRKwaf9r9Hl90TmZ+8fBU7Mb11cQTCSZPHY7LnV5B3VnTRU8Y3utohJMGfr92tXTw6qYdjMrP4dwpY/us39DxLCuDBTTHX2FsdnohZW5ZGXPLytKWJRIGFoue1mpm87oaQqEoE6aU4/Xt/0PpSMpzeRiTmUexf3AGgRJCCCGEEEIMjtraWvx+f+rn/lqjrFmzhpaWFqZNm5ZaZhgGb731FnfffTfbtm0D9rZMKSz893iYLS0tfVqpfJqkkHKU0HWNoooc7Jmz6cxZSq25AVv4IsJd7ZDsQLNa6O2OkpfloaklSGYVVDujdFoT6B4rORnd5AS6CFu60L0ZjB29knPPeZGO4HRmZPyKx95dhWouZlxtOU/v2EB0TAh3lpW4YaHNFcdrhqlQTrI8e8cXiRq9gCKc7MJiyUD3KFqNRjZt6sXiKmBV7WROHGnSmVjL6Mx6cNfz8HsrOXWcBavfxOUIs7HzASbk9y2k1HR182ZdNYUOLwu0EVSMLGDX1gZa6rvIzc/EG3BRWpCN8tvxOfovILQ2d7Oqs4YulaTXniSzPpvqzkbcmU56E/8eWDYaS9DSFcJIGChN9dmP33ChhSxY1aG1+ijO9HPcsEIqcjP7Xd+aPJFljTuI5h58BqJQb4xN2xrI8LsYPeLfswe1N3XT0x0mOjr/Uy2k7G0Js3fGowK/j8nFBZRn9X+eQgghhBBCiMPDQMcYAsOg7svB7/enFVL6c8opp7Bhw4a0ZVdccQVjxozhxz/+McOHD6egoIDFixczZcoUAOLxOG+++Sa//vWvj8wJDIAUUo4i00YV8/D6leg9lYwvyUevzycUaaDXliQ6wWR0YTFFZdm09kZ5PVmH1+Hm7IljeXjZarZtGkZrYw51CTtOq8HUop20KDv4NnBP3W+JZ2SDrZjjQ5MoMbqJKJ2ov4ZoworLbmDXdWxxhVXfW1AY6TueDHs+7+8Jsem919FikCjsIlLm5+TcXYyxfcCuuMnLHwSYOXwMkWgBo4qLCLt2ESgO4dAVw/2f7/c836mqZmtHG1FHnDU76jBQBHvjdHT2AuDP8HDGlDFUdXYzOj99kNNlbctoijYxnTm4nd249DAJomxpaiXek+DqU2dzSunIVLzDbmXUyEJMQ+Fy9q2gfm/2HGIvJ5hTWdZn3YH4XU5OGz9iv+u13smE1/TQMbwIpu03DNjbugal9v7/h5YVFvtxeW3YnPYDbH14KaXobO3BMEyy8wNUd3extrmBmp5uji8pOfgOjrCkYVJV14bLaac4P2Ow0xFCCCGEEOKY5vP5mDBhQtoyj8dDdnZ2avl1113HbbfdxsiRIxk5ciS33XYbbrebSy+9dDBSBqSQctQwTcWDb77HKzVbybK6WJSzkLWezSS9IVQkiWlJsqyxjrO7JxJ1J+jN7EEZSZwtimRXnM3NxWTlOwj6NJqNCEFlI46OYWq0JlyMsFTjbZ3Mkm07aLS1khmwYsQ00GC8o4Bc5SWcTBCNxenpieJ222kOOnil6gPCvUlc9jhTR+8kIzeBUjrJpJ1Mm05lWTM9cTedus41EzN5rSGIltDI9GczLf+Mfs/1pOHlhDujhDoiLH5/G5WGg1g0QTy+d3yTRNLg9pffZFdnBzecfCLTSouBvV/yn6n5J2EjQnFRBRY9SRLIckKrVcfjdjIqOxurnl7FDbgcmOb/Z++/4+W47/ve/zV9Zns/vRf0TgLspCiKkiiq2rLkbqfZcYp/j3tzE/uX5hvf+Dr5pSe/a8dNdpxYkq1iFUsiJfaOQvQOnN7P9jp97h+HAgWBIEGJMilonn8BZ2dmZ3dnF2ff+Hw/nwDP9ZGVVytPjlWmeaZzmp991052Fa5e//f98DwfYSEgftjGWWoQfHJj/V/dbvPM2nnu6dpCXHm1wiQe09m9YxBZErFclxcW5thZ6MYTJZZLbaTpdXbvMJCk7z+dttwFLHeBuHbgumsSBUG40vR3KJniwfEJuqJvXFnz16HZMpmaL6FrMr2F5Nu6rjIUCoVCoVAoFAq9sX/8j/8xnU6HX/mVX6FSqXDgwAEeffRR4vG3r31AGKTcJAQBxI5IEoNbB4cZHS7w1enPE0lXiDogNjxk12Zuao3l7jXUviLS0Sin56bZJiXZ+ZEvI8d8fvdbD2P5AnVTRwg2Oi5Lgk8y1YbpgJl2hVZSQjMVWg0DBY/ji0t0r6eI+yovHJ9ivDcPGYl/d+gpXNffGOdrdjDbHn9w6FY+176LXxi8ldTkJeTEGT794lbcqspi60+5r+c8enOMvsjdHD+9wOhQjlQyctVjHc9l+eUHbudrB89yfmEdoeLQbpvIr/QHWa80efHcDA0cip32dzxHAqWlAVp+jVVBZDi6hiJZ9EVT2EMxZFGiL5/6rudVIJGOsrRcoblQYqA/i/JKmHKweJHjtRk2DfUykLl6v+9HpdigP9BImxJ6yaHTtohEdf6/Rz/PC+vTjEUPciA3yS9M7CerR7Ftl/n5EslkhL9auMifHj/Ogd5efmlkN4EUYHveWxYYnF/7GTy/xVjuP5PQ77jmdkEQSOfjBEGAKIpIiNw/MvoaR3p7JGIGW8d70DU5DFFCoVAoFAqFQjeVd9rUnu/Vk08+edXfBUHgN37jN65pVPt2CoOUm4QgCHzwzm2ce6zM3HqVlVaL8dp+Xlw9iN1qI8fbZGxwPY+D04tYyxpCQSLSMWg0arTKIqrsEKw4KIgoQxqTqsmqqzAcKWFOxaHtktIE3KkGZmSI7PYmtBt01hyKlkmtY2FOzaDkZQ5XT7PMAmorxbBkUK95fH1lN//03m8QlWyK7VP8x4Ve+nUDbUYgsurTkhN073S4Z1+cQamXRqNDrd65JkhpWw7LlTr7Nw+ybbibE4+f5YLl4rgbk3UWlypEF3yiUZU7B6+uFNmtbePcapGt+7qhcxJVcGm0V+loOjlDw7+2DQqm7VKqtPADyOcSV4KUD/bfykism9tzk2/pa6moMplcEsMwNh5vyyQS1Vmst6m3BWbcBklllZlmmawepd22KZebuK6PjkSnbKF0ibRNi5VqHScusdt1MVTl+z63iLKFpn0MRbp+YydBEK6EFDXT5LOnTjKQTPL+ibf2efpeiKJAf3fq7T6NUCgUCoVCoVAo9EMsDFJuItuGemh0aiwerfE/S09y713b0OQ4tt6BQGStuY7VtpiUeji7usqBfWMs2z4rMw4nDk9S1Twil00CRUG6JUACNFdk9XNZSpcHuG9ThLsY4OljVbSOjBaLsOo3EA0ZzXOIOW0S0TwD40kem2uiJ11iooDnuAiOxGRqmftTUwB8qt5DEPSwYiXoWnPprMBAX44Pj3yG89YKpuWQy8fp6kpe8zj/6zee469ePsvP35Nn2+B/4UtT+xGbw9imQ6dlYTU7REWF7kSCmHF1k9WduW56lTia6mPYNgSgy0UWxCS5RARdefUtEQQBjVaH6ekiAjA2micWe7VPSo+Rpsd465uoJtNRxrf2MLmlB88HWd4IbgJLI6hoRDIR/sbkAXZlNpYsJZMG4+PdGIbCsyfn0X0fx3GRRIF0LEouEUOV35oRyLKos3Gka8dBf5vnegRBgKzIlNptLqwXWak33hFBSigUCoVCoVAodLPyEfHfAc1m3wnn8IMWBik3kVqtxUB2nvmYw9pcnLOnF1HQkD2ZQApYqzS5cGqWo60WcitC1szQ1a0yFESIx79OxW3zV8/lcGWJ4e42iiAQUQRO1YbQcy5aVKHsOtx+3zZu++g+/kPpa/gxFzHfRm1UmN+q4hSmaC1s5eP5ezHzDhk1zm8/83UcQaDyZBfnDySZczJsp8JprYNuGbhKBkesoroeng8d10HXFXq700jytW/CwzMLlByT+fJZugoiLbON2OkQBAH1aovSSp18Jspgf+6afW/ZPECzYyNaLmLndixxkZj8MLt7qxxI9WGZNtorzVl//9JjnKst8cnkHQzGcmTSb12fjyAIWC81URXpmoobgEQiwid/6T4+++IxvnD2HD9/+z52RHo536rQlUxze+HVsc6CIJBJRwEQiw48U2fm8jSF37yT7UGAHjcQ36JlLG3rBEFgYrtzGMrENbcHQUCnbUEQYMRE0pqO77hIgoZpu+hq+JETCoVCoVAoFAqFfriF32puEr4fMDdXJBULMO516FnPs2qZ1B0LNwjwOwGuYnLu3CprzTbNpMeKViW+IHHq4DQf2b5Mb7fNwXwRt7CJpbk9VCYv8lhxiPYWl6BqsVprUO+W2dxb4JbtYwTPKoiSTzvZphQksCSXhabLV184zb6xQX7+A/uptNtYEfB0n3ZPhN869z7W60m2NlZo5FUM14XAxggEYrLCieNznLi8wPatg68ZogBkU1HUapliZxOG3OCWTJaLjkpxpU4qGyOVT+I4AfJrLNOJGRqBCL/1pcdpLbybRDbJw+9K83Cqh8XLK8y31xjd0osoiqybdbzAJ1Ew6E6m3tLXq9myuDS1hqZK7N01dGUpjON6+EGApshIikRpsUanZuHdFjCezBHRVVK6cd3jKpUAuWJTb9eZmy/xx188iCCJ/O+//CC9hWure27EmdqjmF6d3emPMlb4Yyxnmrh+92tuKwgCqqbgez6iKLBiNjjrrGAECpL09q/XDIVCoVAoFAqFQqHvVxik3CREUSCRiPJgz0/QNWhgliP80eOHaHgOfs1HngNFtrBEH6dpo0YCVswaL7caNCMdlqw8aamGlJIxTYf1+l5++d91EXzsPJlcDUM10RcFNo32M1rI0u7YrDRaVDoG0qoEbobeOZ2J8W5kXSIR1TE7NmaxQ1ciijU0g7BFZeViF35ZYKWdw+nyaEo+WWUJI5JjIBPla597kWOVEg05YMemvquW2nzbB/duQZZF0lqNp86vMTh2Abn6k2h5g3NLRUzXoWS1OV1ap2XZRLVXx/+ulOr89nNP8tiRc0jAhyIxsvEIz52dobZYY6ArdSXU+Aeb30/ZajIUvbay5TsFQUCj1kGSRKJx/XW3/baIodLfm0JTlVdDFM/jsaMX8fyAd++ZIK5pHOjpI5OLoUoSQiBAM6BSbl/3uO9/z3ae/tpxNF1FVVUScR3fB+M1nscbYXlNXir+DwAGIrtJKt0E0us3r1W1V3uxXCqWmC7ViYgKpu2gGG/NEqNQKBQKhUKhUCh0NS8Q8N4BzWbfCefwgxYGKTcR1/UILJ2LxzuYToNCLIJgBci+hCAoBGIX5DXshoAlwZCWY3tvD6vFIn7QhWxUscQYvgU98QitkQIrjWmcio1fEnjBmGZopYflpUVW4lHGEnVqHQnNCZBEmd7eKHJBIFnVcFSTIAhoui3coSmCJZFf2/dX/Ms/+SR2Q8NZV2AVIokOFEApSSCKnD4xT0X3KK1Vrvs437t1kv3D/Zydm+Pc8gp7R0doprdxbGmV2ukZjKpDzTUpNVy+euYFPrrrdlRxI0w5P7fO4YtzKHWBAFgxW9iOxy0TAyyJKpqiEAQb44YTikFCebX6w/Jc5ptVRuIZpO8YkezYLqW1OqIkEIlpNzQNRpJEBvuzV/1MQMDylvA9FZjA0BUG+jKkX1lS1Glb+PMmK8Uy7Y5NxNh4TJ7rUyo10HWFNaeFHffRfIhEVf4/f+cBAh/SqeiNXkZX0aQYezMfx/QaZLRhnlz6R9TsWW7v+qf0RG55w/3PrK5hOz4eDs2ORdy4saApFAqFQqFQKBQKhd6pwiDlJpLLxanaS1xYv4zX7iIa0/GkAEEXUPFB8chHY8znTKxkhyAi8/f23slj5aNcuniAw06cmiXQlVEodHsEWx9ji+zT9qdIJpo8U9rPsbU1CpLC/TGD/cFJHL+LTidCuZnkTH4B34NMs4tjlTbGok/WimIlW9SeNHjmqw/ipHQs2SZ9WST1LYXRXUXOz3YR83zslkl3bxq11OR9uze/ZjUKgCgILK/WWS2ZHF/pRYzkuDOfZE9UIZk0KC/USJ/QqbkVjlqfJ76yykO9PwHA1uEuHuid5EvnTiIAngrldof9Y/0Eay3aTfOa+3McF9f1+OLCaZ5ZmeLjo7u5v28cgJlqhaisEIlqyNdZinSjLG+aVPe/AmQU+SuU6jaPvnCOaExnbHMPcgekakCnYbJUrDE+kAfAtByKaw2MiMoZ1liPWqiuhmO6xCM60vfZaHZX+sNX/qyKCQQEFPHavi7f5tguQRCgqDK5eIRACfAFD1kLP25CoVAoFAqFQqHQD7/wm81N5MLMMl8//0f0jVfZHv0VnnqxgWO5eB0HuVSnZ0KiJx0nG3WYSVi8sDJN09rPkyfOkoj+FfnARFjeSkNPMNc4gTqwyGqgsmfzGs2yyoPx91GqqRw5cY7//tIzrPeleH/vcVa0FAc7A1QcETkqURjVmBVW+V/nn+bW2l56To3SP/oyux4+x1c+/2ESMyIuAoZqcz6ax+mVSa7pSIbKQx/ax9JKlXjs+hUUpu3w1LFLLHrr1DNVLioBv7jpXjaLApbl8tJqk7ih01IlBCApvVr5kU/HGBrLIF+QSQQKQ0qMbiOCqin0jeQRReGaipLpS6u4touidnD8MslXGqbOVCv8ybGjxFWNX9657zX3fTNkMYEqqyhSBkGQaVpNyo5Ns+Xjuj45VUcyfRAFGs1Xl/cYhkpvfxpNU3iXFKX10Q79foJ0NsaLZ2YQZYn9mwevqqL5Xt3Z/S9x/Q6q9NqNd4MgwLYcBEBWJCayOQxZQRdlEspGBU2x2kSRJZKx6/d6CYVCoVAoFAqFQm+OHwj474BlNe+Ec/hBC4OUm8TicoVPf+EQS2YvDUtj84FTnJvWEFcdNNPHKzp89O8/idH+JBPDk1w6+yhRVWZxvUw6HUeWwPJEvCBKc83lwmwvKWkLZ2IKJ2JdeOsiufVZ4tUoq3NFvB6RTnyQ/17NYvoiD6UcFhe38uGd27lQOsViRyChxBEVkeoMDH28gST57EzPMdsaJDYg0n6wjWVpBNGAy6cqLDY61D0fDRlVuf6XflkSaVg2ihPhJw/cyaZsD5IkMjtbZHGhTHWxgpl0WVVFtk/dz617771qf8NQ0SMyWkuApkuj0eHSaom+RAxZEq8KQ1pNi+JKDd/z0Ye+wd19Z4koPcAwKV0nqek8eWGKR46c449+8qP0RTS+V5qc50DfXyEgIggSI705fu7Hbica0dB1hU0TXWSjBoYk05t7dexys2NxeGqRgXyKTYMFfv7OOzam51gOq9UWCAKm7RLV1de59xtTthdYMy8zGb8LWbz2eIIgoEc0CAJEUWQilePO5BBZxSAIoNbs8MLJGVRF5oH9k29JuPNmfXvpVigUCoVCoVAoFAp9L8Ig5SYxNbvOynKdppGhWm5zevFr9OTfzYwsYrs+UdelYUmIboBWdvjN7Q/gBz5i3aPHUjlTipC+RUAZimGoCUaG+zgtKuQcn3PT04iLDma5RqtZQ5Ik7hvZzDcsnfsyz7DLyPP8hc3ovotaEjhxbA07rlK2l3luYRE90uaF4ghOVaAZKGRsCSNh40gO2bRJq61iOlGCuMv0pVVEUabSaF33sYqiyFhvFtNx8HwH07Y5enyOpeUyC1Pr2DWTuKCgOAKKJmJbDup3jN3t0iLktCjRdkAsZbBgtpk9tsxtfd2M5DOk8zKStPEFPxBgzbSIGTIDiV24ZpGcvjH2N65pbOlP8jvPLiGUBX7tc1/jT/7OTyF+H+GAKLzaqNX3AryGQ8cKCAYCDjdXSD+Y5T2D43TlE1e2a3YsKo0OuiqziQLF1Rq25VLoSXH3rlEIeEtCFIDHV36HqrOEJMhsStzzmtt8+7kD6Ekk+Pu7b0MSBXRNodGxePbEFKm4wXv2T74l5/RmdCyHQ6dnScYMdk32/bXffygUCoVCoVAoFPrhFwYpN4m+nhQ9Q2lyImzfFLB1eA7WB3nWvYivKFQjBkV7kvn1NY4fP8XoeIF379vK4kqFJw+dpyQUcNJxJFVnoDtLQpJZrNTIx2IMPJGmvlKmO6dSLohk0zGGczk2uy4r1Q9w+FINuVhENAP+8vBpCqkcK0GN8pxFz3qNreOLnH1mP0/KW/EPKSQbDuYJETeZpGrL2FGDe/+PM4yll7j4tQ9gW1lmzBX2B+NXKgeCIMB2PDRVRpZE7t07xleeP8T/+Mox9N4YP1G4nVbL5JEvH0b2AsZvH2VTPcGWTV0bFRLfoVbqoHVkrI5FzbUZKqSRVQnN8SmvVUikI0iSytnSOpfXiqw3WtTbIncnP8zurh+/cpypxjqfnz+KlDfxixqD6fjrhijHys/xxNq3+PG+n2UgPvqGr+l6tcGXnjqBLEuMjRfYnu2itLXFvaMTV23Xk01wz65RIupGo9xSq4LmG/i+Tz752ktwvlcT8TuZahzC9dP4vv+GoVGt1eGx05dQJImJ/jyO4xGPaciyhOsFqH/NBSm27dKxHOCHtzLFcTwUJZx+FAqFQqFQKBS6WhCI+MFff8X3a53HzS4MUm4SfT1p+rrTyKLA/aN30qy+l2rjPIHtIng+Ys3ji6cz3KI1KfkmK5fnmPWa3Cv10kjKdGIRmkEEz3WplldIlG0251PcMTTO4sA0J5bbpBSdOwaHGBnI0J+I8yujw3zq5CGerpxHy0okTmuUkiYZF5K2iufNU6pFUTKwP+dz7rEYhVSMitEg6JVZyQoMD64SeDU+MnqYtGTS++G/JGh+EiP5z1isf4L+5K8A8PzJaY5dWOSDd22nIzr8/44+TdByiPlx2h2NOaXFZDpOa7WBLIgslhvUHJeG7RD4AXzH987RbIaJRAoUj01j3Wwf7GbHUDer80Vsy0Z8paLi8+dOsdZqsT/fxUgkxUxnnjOlC9ybO0BGS5PX4kRljR3pfqrpFUTjUZbbm+mJbHrN1+i3z/w5S08bPNP6Q/79L/8qi3aH4UyK/nTyNbfXNAUpIvPy1BK//+hL/G8fvZfd+d5rthMEAUOSWZwtMW+c5KXmk9ydezf94v04josoildViXw/9mQ+yNeWavzh7B9zZ2Y3/3T7z77u9m3X5nh7mYik4AcBvfkkn3j3XqKagvo2hAHJuMFtO4bRFPmHMkS5PLPGU89dYP/eYbZv6X+7TycUCoVCoVAoFPqRFAYpN4n1Wgu74+AHAh3L4eJimUQ0QiSuUV9tI9ccxCmN9e4aliGiItEvJkmn4uy+bZy5dZnpRo2KJ+B3PM7Uy7xrZAt3bh5jTjcIAh8jFiGSiRIYCrmuJLlYnOPFWSYTS8yLWeTTOoaoEvgOejvKpmQvF+IST1+eYMc9I3ztoQeQBJEvPP4yx2rT1GqnqVs6QSBQkBuowJZ0ia7hgKV6E8uxODm1TDKq88TxS5ybWWNiKI+nwuXlEnk1wq8/+CAXvTqjmQzBXId0Jooqy+QLcSrrNaquBd/1fdkWA4S4hNls4VYsGo0OqVQUQQiwTQfXcZFlmfeOTrDQqHNf7xC6KPO7c3/A6cYFJDw+1PcBljt1Go6NLbsk4w6i5OMH7nVfo0o9hft8QKXm8uW+g9SGI+zs674SpLTa1kbFzStTdmQE3JZD6XKFz1cP8nfee4BY5Drjg4OAwA9wAhunGTBfqzJbXaXaMlFiMju3DL4VlxkA52vHuDV5gYVWB3j9IKUVODSjNq4cIEkiLdPm3PwaqiLTlU3ccI8Uy13lcuU/kjXuJq9vBSRE+Xt7TD/MTW6fPnyJrzx1irLZCYOUUCgUCoVCodBVPAS87/7y8zadx80uDFJuEoVUjNtvGcMzXYrlNoogcNv2IWrrTZ76qyP4XRHGynka4yK27DCgJrl7fIiTZpGZxRLjw48Ra0Z4fmUnAio93Sn27xphrVHnMy8dR2xbvHvbINnBHBFFJBo1WO3U2Jo9yIHEc9QEnc8e+yX25fu4MD2P1eiQ6r+VnWMuZ+UGqipjmg6qJDOdWWW+dZC91VWCTQJeIJJBJKUINHwLOfgZJjLvxrZ6ODwzTTKqMdSdodK2GO3LkYrqfHxxB5Ln8sjCKTQvgrzms3J6GXSZQn+OzZM9nKvWWF6r4/k+yneUpMiCwMnqNK1SC1mT+PFX+qfUq23WFirkujPohs6tPf3c2gOe5+NYDm2rRN32aLtVAMbiOR7u387B9hwzksaBrvfSG9l63ddoLDLCsfYlCBTMpsu7JkcZyW40jS3X2hw/u0AmFWHX5o0vyJ2GiRwBRwmw5YATs0vcseXqJUG+H7Bcb5CNRhib6GZS+QDNl+OsXG4ys32VY5V1yjikuhIMZlLf93V2tHKQ0dgcgghD0ZU33D4qq3hWgGADwUamJYoikiggvIkP2GL7KU5WXqLQOct+5RQBcDH4Int6d37Pj+WHkacIuIZAoN/85ZKhUCgUCoVCodA7Vfjb+E1CVWTuu22SXDbK2fPLiF5AVJbJxQw2D3Zx69YxuvoybDJS7OnPs5gp8mRzioPLixxbXOD043kido1cwuf9I0PcPjGELAgcOXqJyycXmF4u0TQdJgey2DWTlw9f5DdPfImKrXNsaoyjFyaoeB1my1WUtsCokURu+eSiBv/Hw+/i795+Gy8ce5H/9q1fpRl8EbXtc9oeYTCoEK+4iG4MDQGxk+EvvnWMIxcEcqk4+zb1s2ein1TcYLiQRJElUhGdH9u1ibGJLGeFImcay7SqLeaWy1hpldxgiuWlGuVig+Pn53E976rnqjceZzAaQVdE9IiO5XkslmroUQPVUBC/a2KQKAooqkxMG0ckjSz2bDznksxHhnaxUDU57za4YHmvu1wk0jJAU/E0ka6eNHePD1+pRlFkEVkWUZVXs82vzjzPU7OncdMCFCBjXFtJcWGtyJ8fOcmT56dQNRlJlEhH0viuTzodY1VzmHIbFFvta/b9XhyqPI/rb6y9jMrZN9x+rdFicb3B1GqZYqmBKkuMpJOM5TKI4vWfq7Z9hoXKb+L6dQCW7S18q3wXz1VvB1wEXH7v4CMcXFx4Sx7XD4u+bJKudJyRrszbfSqhUCgUCoVCodCPrLAi5Sbh+wHL5Trr1RN4whLRwj3oEY1Wy2Z2rorYabPnQ1tY6hGYsdYpR9qsaQ0mxRQz0ybLSylmL8Vp97c5KMwzFEsyc3qeVrGGh0SzK+AYFT6gSywtlFAMhdVch5Y7xqX1CUpFB2yXldUqPYKCKok01ju0VQtdkogbOs3gMnZ0BRQJVckiGwbnWgWc8ghTsz+HOvwtFtY/SH2xijTUjSSKjPflAMiqOg01giZI2LaL2bLZGivwc9tvR67B7/6nJzB9m67hLMNbe3nyyGXcIKDjuOBfHYwYusLHRvZzUVqjajt88blTCLLA9lwazbh2fHG13kYUBB6M3MmQP8i7uvcAMNta5un1YxSiCucRyMmvv2TEnYdAlRGiAe974OpKinhU5469o4jfEcS81LpEW/NJJDzes01lqOvayTuBF1AsNfDTr4YafUM5fKC7P83d0RFmajWG3oJqFICHuj/Gp0ppGu0pxnrf94bb98bjDEpxNGdjak/HdDh0cR5Vkxnqz143TDm98jHEoM16+wh7+v6SvNFNShtlNN5HxY9guh5rroUq/2g1XU3FDXqyceLXW+IVCoVCoVAoFPqR5QfgB2//sho/eLvP4AcvDFJuEivlOs8cP4Pg/kdu2VHk+UstZtZuo+27VCstRNdloLfAerzBhJDFDSpMplTeV9jGU41v4mQlGuMxPFGk7rSIJjQW5ta53Kkg5GTMEYHLWpX/fPpRTKfDB/r38XDhVs6Xiswnymh1k6bcIiFrVKsVFubrDKtp0pkIlxb/gmV1nGcUjXZ7O61pm3Kli/t3zrJ56AzigIne/Dgt8UtcXn8a39tJ1B3HNG0uXl4lkYgw2JNBliTiUQ3DUMn3pVFVmV4lz7/9yiPMLVURTIuP3b2DfVsHeOnSPJogMljIEI0oVz1XrusR0VXyiTjV5XXchkW+P4UmyCwurlHoy5FMw2dmn+JYeYrbzB1EnQi9iQi35bYQlzYCk/984TNcas7TFe1ld3SAuCfhef51G7tOpro5rsySMSLUqh26e9JX3S6JIuV6G8t2yaei3C3uZnX0JX5p8iuMdM1wbvlF9ox+junmCgudIrdnt1A3TZb8Jgud+quPLwgQVZmz1dM8f/lb3D18D9lo5C25zvojg/z8pknOl/6EnmiW2ZXNFNIxDE15ze0zRoSJtSiVapNSpUU0pfPkyhySKPJgZ8t1z6vu6qSkNhUvDsBQNM+/2f3zEDicWlSwA5vtXX1ElNe+39fj+wGCwA9ls9lUTGd0IM/gd107oVAoFAqFQqFQ6K9PGKTcJBJRnVhS42InQkGRkG0V23KpuyaeCL4u48cVLNXjYqdIw3d5cf0svdEK0QRkR+rc/fEz/NnzOzHKEVzBZjp/iJlAoDynIk/ZWCWT+WYMNSpQ6I7xrp0jPHHqMv/zYgXNlMmk0+wd7OHrj6/jLXeYs12WIi5izzz3df85682fxjcHWVoPECyPbETAx6XaqVO++CT3DgTEuzqMiicpOj/DbPHn6JgfRhBNVsQOR1cW8aMi+0cHEFQJ6ZVqhP5CCkEUkGI6c9UGW8oN3rVzDFkQuWfPJI7t0mmaGDEdVVNIpqIMTRToKiRoVtvUVzvsv2MbpfUGK6sNSuUmPQMZpprLNN0Oy/Uy0nKTI1abrZv7+VD/RvVHVIrj+QFGxOATt93KWDr9ustVHtg/ybOPniYmK5gd+5rbfT/gyNk5bMdj72Qf088Vya9nOKtlUKmxFl1lD/Cp6W9StOrE5Qi20uLOic8iifuA/QB0dyWRRJFHpx5j8WCJzx95ngd33n+lie2b5foOAR6KuFEFIbCAJDqUW8c5eekudo71sG2058r2DctCl2UUSUIQBC4trNM2bYprNQb60hTSMSRJJKFfW/3zbYcav86KeZSH+3/mys9EQcQLfDqBhC9EeN/oIBPpN15e9J06psOxswsk4zpbx3veeId3mFw6zpaRLrqzibf7VEKhUCgUCoVCoR9ZYZByk4gZGk4+4JFzm1mScnz89t28MG3RSrYQAhd8CBomc8UK9WUTO9CIpgTOqy9AkOBX/+FLTGSr9Kfq/NFvfZjjw8+Q2H2JLbZO44t9yHWJTswkd2c/H3toF/GIjiiKbO/v5sPbt/EnTxwBQ8A1bYJVF8GRcRoWTs2nXM1SXBnip/MHSIzE+frRc2hlgVilwExriQsnBmi83OSOex0KMZF6K42QaFHjy2yZ/GVUTebMybNcWiwhWFD3LJ5ZnuV9YxPc0T9IOmYwOZRnudPimYvzCE5APp/kwOQwI5kE1XITRZII/IBqvc2J+RUOrywybCRYL9WYnSkxN7NOtpBESmjIr1RX/NL4Qyw0ilgEvDQ7xcnLqyyuN7h/3wSxmMGW5ACXmpfIqzpbCgXSmejrVjmcm11mudpAdrmmbwtsNLUd6srQsR0cy+GQPctKwcRe7Ufq7jBt5fhkEHBXbisXGosMRwucrPwnbsu/hBOcAH4NgEI+QTJh8Oj8LKLrg1DGtt3vKUj59OVfpWifRpfH+KmRf4MuxfnmSoZzxdvZLt3H5t4shXT8yvZnV9f5V48+Rk86wb996H34nke2EEdvWsRTESzfI542CIKAlm2Teo2+LwB3Fm7hudUs29JXT+Y5eW6F86vdFAYW+cK5F0lpO9iSK9zw43Fcj8VijWKtyZax7h+6qpSOZdPq2NjO9adDhUKhUCgUCoV+NPnBRi/Dt9s74Rx+0MIg5SYyFulhb6afTakc3Zleqpd/i94ekyktitXRqdVatJoOQT1AClyqtYCYOoy3XuNEM08u1eJkM0ezbjF9VCYzmGX9fIJIRcaVBIRAQhJU2u0yF80GA2M9pFMRTjZWWejUUFyF5KUmzlqLwLbB8REFF7dvPyfrXfz6gW2IfoBSFVjRa8Q1mUMndxHpukgrkubf/PNf4973PsH5dQUt38X990TQNBnX9Xh4x2YS0SJSoFP3LCzHpdRp85mzL5PKa3zw3Ts4NbvCarFKPhZBkiXWViucee4CvaMZPvKR2wmAy3NFzs6ssNKs4zgmnXqHdsOi2TTJ9KVoKj4dYSPkyGoJslqChXaRwUSMc4GE2HA5c3qJ/QfGuCe/m6JVZKwzwuJiBcNQMSLX9jH5tu6BNHKXjCoo9A9dXUlRqbV4/uBlMpkYO7b2srJUpRlv4rvgSRrrYgx3IY3rejzYs48He/Zt7KjeyUzrK9SD7ex55Vie5+P5AV0DvfTuP0QhvRVNk1lartJViCNJNxaouL5LyT5GQrSpONN4gcNn577O11eO0mhmyeoiP3dg6Kp9Oo6N6/u0HZsgCFAVhb/7k3ezUmyyaawbSRK5tbeXTstCsgO4TluZi411FttVSlaLnshG9UWzZfH1Zy5QaT9At3SU1XaOtnttZc/rUVWZju/iuD7Njk08cv2qmDfjQn2Fw6Up3t29jS4j+ZYc87XYjofn+7iu/wO7j1AoFAqFQqFQKPT6wiDlJjFXrvLEqRlmyj2Y1QjbgzZ3jc8giDYvNXcRWBLRfILm8iye7yCkTSptGf/CHnCf5f/51D18+pZ9VGbiDPSo6KrB8S/ICB509avUbBMrqtDwGzy79mfIEYHR6v9D2Ql4ZOYylhgQs2G93MTpmCCCmBJRVBs7u8KOAZOz1X/LpsivsGk8TaF3hT94fIXTXRW0eJxtd9SRpnJoyXWk5QGi9iTbsh/ixJFpzLaDNXyCF+w/o9XUmZ/6ILuzvazOFHm8conRRJaP9WxjpC9HabVGp9Uh0Zfmq189zEuPncV/EXq29HHbliGG+jIk4zq7Kw3oeLyw6rDgFVmu1LFWFZ6fXUBLqezZ1H/luTU0hXw+wYFtgxw6M8/0yir7GWO6WeTZtUUWNJd/PLwdTX/9fh37x0f4v/7Bx3BqNpp89ba1usnMYplDs4ssdBrc0teNmRBQGjaa00XdbNJjbMLzfL6zLUgmNsmfzf8KWxK9V352fnqNpdUKNkmkHoOedIE/+OOnefy5c7z/gR38wk/ddUPXVNuxuc2YJy+bXLAnSSh5/CCgJ6KSa3WjCLFr9tnZ28PHtm+jKx7bqAIKAi4ulag3LZpti3hMR0NCQsZ/jaqcb/ODgAAIglc7VamqRCymoai7GUo+yIdiFjvyb255juO46IKErijEjOuHXm/WE6tneHr1HEk1wgf6dr9lx/1u8ZhONKYTj701AVAoFAqFQqFQKBR688Ig5SahSBILZpVZu0J1aomvzB9kKPsPEToKlvktEEXqnoPbtJE0Ez8noIkGdtVFEDwiK1X8pSh9GPR2J6i3bJTFNma3SCUmYLUkHMXj8oVVUgMCWgEc36dVt+lxDDzXIocKeUAAQZBQElDY6tBYc6m2HmG+ZNIffZCq+zzPnXuamfU9tBIJNNElVp8gmoVM3mJgVWOk8DHiwjZa5kUI4EzrCUQx4PKFHlYWq6QsHUGIc+fYONtzeaamK1RMi4QkUYjoqKrMerNNoEj4okDZbxFPGKiagiyIrM1XqDXalCp1/MDjhbUFFKosduqcKxavem4lRSKViVI+PcdKpcGhS8t8Aqg7JjWnzbRXBCng6Zkp4orGvoH+13yNOqZNZ71Daa3OppHuq27r60nx7ns2c3x+FVWWMR0Xu6UhXpZoWzEGgvsxuvP86bkTPDQ2SV98o0oj2THwnpNZSLZpjzmoqkXLOUK7mkNc6SFv3MJY+gB/dPw4K+UGJ0/Nv+G15PkOv3/hl7E8jwdjHk4AY9pG6PDJwYd4oOtOplabZCPXNoottdosVxsUG23uGh3C9wLWSk0aHRPb8ai0OxyeW8KzXXYPXj8E+YWJA9SGO2S16KuvgyTS05Nirfp1JP8QS7VfYLU5wUDyxitAJFFkIJckEdPf0mU97+nZTlaLcSA39pYd87UsFmtcXi5SyMRJRF9/SlQoFAqFQqFQ6EeLj4DP2790/Z1wDj9oYZByk+hJxvn47h14l3ykxlM4lSJTpSGw87jdLpLv01xvIKkiclVBWDDRuwwmRwu8mIoRNwW642nWi+vM1SykloZYtgh6ZVq9IuqMg9hW6Agig/wNHuzfydePXebM8jo9noHoOAz0p3nPQxP8zoUinaJFLlZmICOS7B7njsIu0EvY1UFaznkC36M/adPTs0xscRK3ZeAZOpOqTu/EGcTIGU6dSXF2tUQmFmVQidP0XPyGiFNzsfMeDccmsWTQ1RPnhfIC82sl0orGXaP9SKbLrfvGmVoqI8VF7t46jvpK7xNJEnBtj4XFCuVai1hcx0hHEFIqjYhLSbGAjWqI6VKFpKoyMFpgeHqNC5eXGVB1bMvhfb17AJFIW+aF89P8kxe/BbLANz7xi3RnkvzmNx9DQOCfveddqLKMJIpEDQ05nyDyXUuAFFli82g340MFAgKWFioo0zKOBXJaY2nF4uzKNIaaZCKTvRKknLqwzPTZIlK+TtVs027//7EjXyKR+TALJ7dRkzOImxP0TOQ4adfwMm9chXGpfoxnSuAHMmNinrHoMpo4AoAsSqTlBGKrjs+1FSWFWJQHNo9jKDKqLIMMH7l3B67v05WN4zguWUlEimgY2vWrKmRRvCpEARAFgXxSo9x8gb7ELH056Eu8uaarsiSSjhkkEm9tCDEaKzAau/FeLd+rdEQnoxukwvHHoVAoFAqFQqHQ2yYMUm4i+3sG2NPVy2PHolSzZ8iKt/HMC2fxczJQZ+HCCnlNRspqtIdcBvsjrNo1AkNANwxu3zXOoy82seZU2kt1RAu0VQl9m0NQbCM1ZYKMzlML32Bh+g8p1H6SdsthpmViWxZKS0PUZLbvGuDQ46foSRT50D0LVKKzONbH8Zbu4rn5s5T9AkH8l6krT2PE26zELmKeTNOfTHD2FIz0dTAKTxHUdrK+3gDTpxzfyf0DX+S0O4bfDphbrVGzmiwvlBnLJ9k90YvUtlhbadDwfO7YPcjkpm5G9+WpdWqkZR3f3+grMTu9TiKhs2//MGtmnW7RYNfYCC96RayoT9U1AZgqVfi9Zw+SETX+xv49RGMqY105JvryeK6Hqik81Lsb1/H4iwufZ7t8ibN2D1/4yhHue2gbf7lwFlGAny3vYaKQQ1VlJie6WZxaw7Icoq/RIMSzXVzXxdBk0sMOxbrHum2SbvoErs97u4YZT2Y4vrzMpmyO5YUSCgLdXUnSEQPR2sS6KDMyvpknZuocOT6D+UJAOtNNJy2yJplveB31RAZJaiamK/LvL93LXZnLfGTwb165fb5S5Q9fPkRXLMbfNm7bGH+sboRUgiCws++7qm26Ulf+vFAr8+npbxFIPg/s3fSaz8HricsRxnO/SLs9S6c+jtPtoSk3/jHW6djML1ZIt2x6ulNvuP07jW15qIKIEzabDYVCoVAoFAp9Fy8Q8IK3vxrknXAOP2hhkHIT2j9+AMe9hWwiglPyOPb0OZy2gtfwoeWguCDnJISGwCONl7E0G6cBz75cok8bo6FWsTISqwIoRoTuBZmVBZPA8sFrU3NVztYypDqnuV29m6+Y0zi2x+VikT89eJS1U5fw6w6nLnZTuCjj5MF54TH65AQzkkInLjGgpWjN9VHPV/B0iXJtnVqpTVK4leXKywynXyYe+xKF9D2kYgZbM3fwyOP/lSHJY1GcJe2rCIaIWXORApF7d49hlVssrTawPZd2zSSQ4auNQ5iew2azn53CEKVSg0e+cRxRhPitaU50VllfktBcgdjOJEogIb8ywrjS6XB6bZW8FqFYbrJWa6MkVcY296HqKuvtFr9//DAKIhdWn2FrYR5dslm/OE42civZhEXgS2Sjry6B8T0P1/OxTQffDxAErlpi4jgutuWgSALDuwRqLzr4LYe4FKGt2KQljaemZziztsb9hSEe++ZprLqDURbomC7diR9Dc97H6nKZzenzHPcWaNY9+kY1Gn11TkZbmK6DLl+/n4vjt/GBiqWxZCf4i5XdxK0GW7scDFVh0akyp63Tlpo8efwye8b72Dl6Y71KBN2ik2uCCFOd5+lNfvCGr2tBEFheqXF5Ks4jy1Fs6VnGuwqM9+Zu+Bie72N7Hq5//f4s72QVs8Pj5y4jpRR6vyOgCoVCoVAoFAqFQn99bv65RD9C1jtN/sm3vsJ/fOJxJD/g7PIaPaNpPnT7Xh7Ys5tcKoHsiSiWyvjxAg9ldqC5GoIt4ClwuVjmglmipxBn8+4+AlXAbplUghXwPWQ3QJlt4HbAWotgN9JUl1p0d3Tiskba0EnqCm6tgyfIeFWZp16YpNNMkevtpbe3l/TmOKv5NnvH+rh7ZBfyeo5gKU7H9rBln+Gx9zM89gso0iiitZulpSq+D6vFKp3cRU5PX8RcatKutbljtJ8thSytegcvCGiKYGQjdBWSPP3ESf74s89QKCcY17oZzXchCAKaIpOMG6RTMXaOdDHaY5CUVcy2hWh5CIGPHWxUrlTMDjXPpik6DA7n8FxYXKtzaW4dURSoWSYNy2K2VsXIjOEm49xXmOHjP/XPadp/Qi5TJZsp0ei0r7xGyUyMkckepLjCnz1xmBdOTl3VUNVxXFp1E9t0yYhZYqpA1pNY7ZnnfG6Z3yt/jq2FPJO5LHFkXNtDcF0SERX1lfHGlunQrJsU7BR7EmP0d/XgGgFCl0VH93jPN/7D615HupQkInnERAeBAHxQbBnrlSqIFxbP0Qwq6IlZhrrS5FPR1z3ed/I9mY6jYjsSNfvqjx/TcphbKtMxnevuX6u3qNRaVOsdGqbNWr15w/cNEI3qDA1k6e9L39D2QRBwofZlLta+etXr9HZZKdeptEyWKrW3+1RCoVAoFAqFQqEfWWFFyk2i4zg8MT1Fqdlkdq3Fbz/+FTJaF4YkMzu9gO8EDE/kmG41sNaaKIqCWpcYvNjDiaCJrors6emFlEh/LYIgCggtB0eFascl6pgEtkjgOmR6LMRljWq2SZ+hs7x0EStwGcr3s2Oym6VMimazTiCANr5CNWOxNTlEob+XuYVniLg2RlLioTu2ceQLp1g5sYbui4h1i2hEY3j4NkqROuXlGua6SCVSId5nYevnqa/lEZsa9bUWPbKO3CXTnY7iux6qG1Attjh+fAapYeKJArdGJ7hr0zhxZWMJiaar7NkzxOpqjaX2L9KfVVk23kMs00XDtBBbHmZyo0dKTzxGKqlTSMRQRIlcKsJ6vcXBc/M89N5djKUy/J3dtxKRFU4dd1hf/DQTE+uIgoPoPYdV3IltChQnTIZeKZoQRZF4KsJzx87yrdMX2DHcy+07RgHw/YBnTkzTalm865YJjHQNxegQ6+3CnajQbqapyxX29Payp7cXz/MYnSzQODOH5dk4tgcRyBXiqLpEOa6T6UmS6c3wFxdPY1savidSbL7+shA3sEjIsBZoBI6IEIj8xIFdpKIG8+0zZOKL3NHvck//Xu4benPNVRtOAy8Q8X2B1HdVxays1zl2eoGdW/ro709Tszvk9asnA6VTMZKpBtuULiRDZsfg1cuI3ogsiRtLkW5wYk/TXeJY6Q8RgN7IrUSVrtfczgt8SladvJZ8S5vYfrdkLMJQIcPeodduaBwKhUKhUCgU+tHlByJ+8PbXSrwTzuEH7eZ/hD8iLpXLHJlbZlt+gExvwEV9jlgabpsYItWXwUhFWDu1jlbroBPQaZnMLRQpBRZmTsZOyyRzAovaDBelIk3bxWk7SG2HZhClkZIJCmC9K8fC2Qnmj45w9JCNHRcxjBr5aAshcoinGhfZNNxLUJBpj0nEJj2EvM+LFYk/P3WU5eA00dwLLNR/g4X2tzijV7EnLbSShTtb49Chy3RY5EL5a5ye/RIxzyYb05mMbSLfniBiqASSjxgVODq1QqtjgSDg2A6u5dJpt3n51Dxzq1XuPDDBfbdtQVZlqqUmju0iiJDJxik3GsSVdeJem5LwMn94+gk8ycLJd2jE6wRBgCALKLKAiUuj1qIrHWWoP0NvLoHn+QiCwEgqTVcshlM9RHlVZn42wdziCAPpf0F1LsfabIylduOq1yoIAvKCzi0Dfdy/awIA3/cJCFANFSSRWq3N5dYcaqyDrbe5xagyEVvhlszUleN0WjaKKmH6LhcqazTsjf4nxfUGi3NlKk6VU8K3MKNLFOIxgpqEWFK4J775da+lpNrD+7o/xK5ULzFZozeSoje1MRnnUOnLKPKXeaj3L8hIn37T12lcCxjpWmO0sEYgt6+6TULAkEQUQeRTF5/nN459lZOVxau2SSUNBGAim+bA+CC8ydCi1bKYny+xslK9oe1FNEzPx/RBEq6dUvRtX154nn99+s94tnj6TZ3Pm6WLEr3RKEkjbDYbCoVCoVAoFAq9XcKKlJvEWCbDe8bH6dIN0jWXmeY6P3HvPsqVDqcSy2SECJWjRWo+eIJL2WkzIrZwIzKiD0HT4eWFEp2ESFyViesRsFzkkoWmuTj9cXwchFQEr6qguB5WRUGURHTdRfAhEBScesB0pULbgMawxuLpAvFJE0X12CJ2M9WOIsdWsBvrXJo7jFwfgppCLGYTBBqbx7sp1gWmV8cR1/Jkc2l27x2hZ6hA2vhJ3NsO89ilS3R1Z5FiOsW6Sadh8vQLl3j0mXNYpk211mK10aL+DZHMSALVE5Fb0D2QIZGKEu+Occ6q0RUESKrP2moSd1vAl04eRur1CWQBL/CRfAG75mOaDq7rMzSc512tUWRZwbFdXMFjtlVkLNaFUjhG2nR54thuiq0+3r9/E17yWSwloOZbV71WgiAwMdzNaH+eVCZKu2nS6tjoUZX7901w9tQ8y5fXuT/3AY6MnuUu8QDPH5ewS0tE5O9Y0iGAJioYkspgJIUhXv12vtg4yWn/MKXpCn972//Op54/Qnm1Tiar4Hk+kvRqjur7Pk+vTDOZzNMbTTDb+Bo4bTx/PykhShBsZBa3ZB7maHER359m1Vyn1ugQi2hXHev1dOl57spMAQGjsaurWeJRjeHeDPGYhtQREQBRuPq4rUaHeqVG94TO7tFeYvqNVZZ8m6rKCKKIql+/R8x3EgUFRe5CREYUpNfZTkRAQPoBj3rLJaJ4fTmiN3j+oVAoFAqFQqFQ6K0XBik3iYii8MD4GPVKi/81W8NWVFqWz58vfJ6R25+l1o6gfm0QNAnfF/Btj6eXL7I1M05Jl5FtCbGhcFvvFvrVKFLHA4KNmiXZIZMIsOoG7YaGYDv4qoQiS/TmRf7ypQy+J7Il0827eoY4kj6P6raQGzJYCQb8JPNGg550jId4iItzTyOLM5yf24o/V6RdLhPpyvC+ezaz944Jnph6gmIL5LaHPV+l3TbRVBkxEFg5V2bQi/HJfbvQNY3ls4tYtsvp8wsszxaJagp+y8asmlx48SK/vbVJql/ln2/5MSIRj6DzVSTxFiwRHF+haep4ew323jKN40o0Z4ZRdBfX97Adm2a7jNUSWSvXWW22sS0HzfYol5o86Z7mufVzfHTgNsYK/4BO4xm61YvsGXiBqfpXiOYEajUfUbt2KU0i+eq0mk7H5MWXpzGSEe7eN8bwSBfJmIFZk1g9vsLz6jKSnyUuF9mSGWOxfZyInCEdH6Arl2AgleLA+AiJ+LeXLynoUY3RxG6+9Nw8seoY+k6Zf7hjP7/7pb9k4ex5Lu/bw+T2gSvn8M8OfZ3PXT5JfzzFF9/7XrbJB/ndmQ9QNX1OdFZoWzYxQ2Mwup2c8m/500N/RjTo4enpC2wZ72F85MZG/1Zthy/O7UISfN7VbdL7HUUeqXSUWFxHliV+IXk7TXcvKfXqqT75fIxV8QgNq05uPcKWwS409cY/xkzTwXJc2m37hrY35CQfG/xPCIio0vUrUj7Udzv3FHaQUmLX3GbaLqVmi+5UHEn8/ooAPSGg4Toob+Ixh0KhUCgUCoV+NPgI+O+AiTn+D/g/F98Jwt/GbzKu55OLR6gJPglDR9YqzJfTRKMmJbuN1ZMg6us0Kj6WaeMutkn5MTK9GrduHyMViVBZrZPNRElu6qJybJ5Ut4uQNBB9EbUDsivS0UVS8Tgvn1zDLwv4hkTC17l1aIhnW0eJ622GN4ts0XYyPb/My/NN1iqP839+9CPUk/v4nSdkcIpIbQvZFcH3iCR0BkYLjHZ2E+mKcOTRZQ6/fIYgcPigKvAH3/wLLp/xiUcMNvXmODu7jhwx6ADLy3UIIJ6N0Q4sqItIsoCQXaCtirRjLUTzdwnan2Wm/DCKfws6LmZHIePW8AMB19VxIgqeLdHxXBIxj0S6gSZ7uJrLmeOrtJYa9Kai9A7m6egX0ZUTCM4OnMZO5E4XQfWPkAbKqOQx0jOoagNTurYxaKtlIYoChqGiaAoRXUFTJCRBIJE0SCQNvvrEBRardTqBxSe3tvlHH/oy6YjJZ5enwdN4X+Y/oCkiuVyU3nzyyiqXWq3N6mqNr6yc5fKSiJBu4HkBLz52DnuxxYpscvn88lVBymMrJ/DwWO2UUMUENT9K2YwQuCI+AjFDu7LtpZUKX74kIlirbK9ZIIo3HKQ8vnSWxnKcwIPGjmsDJvmVhrmyKF4TogDk0gnSBY2K2uLLz1zkfXvvuaH7/bZ4XGdstEDkBnukAGjSteHId3tubYrHls/zM6O3MpbIX3XbM2enODq9xAdv2cqW/ht7nl6L7wf8r68fZn61Sjoe4bbtQ9/zsUKhUCgUCoVCodD3LgxSbjKyKjGsp9B0BUOQMNa30lwt0xRUrKUGVkrCEyAIJMxFl3KkRdFsUNYb7BuehIqAJIuMDWfpT0VoBiLWYRlvRwTXFUlEFVpWB8EVcas2QhuEjo8UeGieyMxikU6zgS87pHINGqsOc9MWwZJA/UKFf7H258j78jgrLnpGYmR7H+aFNuXVBkJCZa3W4pun54hoCUorFwgsG7Np8Y3DpzhR9JAMD9m2eOLwWQ6dWyWXiPLT79nLk8cv4yw7KFGZdCdBW/GIaCL5mkwir5DTUgjeDgLh8+SSm+kr5Ih4IiuLBVIdn+qzEoXN91Otz6KKMn4QEJGyjHRNoEkKE4MDuG2Jp5ZP4kuQK8SQmmUKukwuKiC1BPL5NJe/vodnvzhM9cE2Y7fWkNUyCfXqyTKtlsmLz17EiKjcduckuqZy+74xjKh+pVGpIAh88pa9tIsWK+crFEsDdNy/iail6DX/PWvzAzx15DSKJBFVZF5+8QIjI3nGt/TS05NC8ANGKglyKZVNvQVUVWaoJ8VBVQIRPOnqlFjRWyRlHwkw5CxrwSCWICOKPqLo83+/9CT/YO/txBQN2ZAQdAGUgIQUIZ58/X4dJ6eWWKs0ObB1iK3xQYS2hBTARHT0TV/fU9NrLFwa4tS0gRBt4rouaK8uc5meWUdRJfp7M6+5vySJ5HPxN32/b+RMdYWZRompRumaIGWp1mCmVKHUbH1f9yEIsFKss1qqc252JQxSQqFQKBQKhUKht0kYpNxkSqUWpuOTSCkEAhgRgY6nIgcewwdq3ParZzl4cIyXj03S220QGUtirF0k11tmtd7L3HSO0ViMSEyHzQbBtIZXBr/kExgCsYIOikdryWdgNE3LcRBlAcEOGOxKE1FlCmoMxwvYG7ufmZKHbASkxzSG2zGS+4+x+46vYOXTPPPIfgqLBY6W6+B6rDdaiBIM59IkojoXfB8xorP1lhEen1ulVQ9QRRc8gU8/cZDheA+KolFfqxOTBKSGR3GlzubRbvy6iapLLC15GAP9OOUAJ/0QateHMSpNssmL/PqTD1Oa6sYXA37r7v/Fl5t34dRUBEmi1GwzlEpza/dmDEUhqqmkcjEuBR3WGgE/nYwy3vgAS/PnSebH6RtNMj9bpLzcoFm1+dKjx9iyxyapdlgzr/4CbdkuU7NFoobCgTvG8Vwf34PADxBlEd/3cWyXdMQgp8RYpEKgSfz56c1MRxe5p68LqxrQLDeY3NJPtdRkfnadtbU6Q+MFNE2huyfFw8JuDDWBp4g0OhY/9dN3c+S5izgdl7GxVysjOqZNjxBjSSkzGt2YStOlqiRVizIykitStJrUbYuYohEAiYjBtnSBn53cTSG9EUwcK36Wln2W/d3/BEXcqCapt0xOTK2gSCIdy0EKQHNd0H3KToMeXnsKzlL90yzVf4/R9G+Qid4LgOt6TFeqmEKALwaIsoCmvhqirK7V+O9//hy6IvFrf/e96K/RP8WyXRRZRHwTS2x8P0AQeN1pPD8+vIddmT52Z66dpnNgYpB4RGNTb/419rxxgiAw3JemYdsMdr12UBQKhUKhUCgU+tEVILwjltUE74Bz+EELg5SbTDodZd/eYYyExteOnefkqsNSLIpWj7JvUxlkkcSwhbmm0rOph8tmGbcusVWYZ8vA7/BY+WdRI9tZtGpcGixSf7eK+hXwYjKeIdNxXFwU9FTAtp3DSBGZs3NFDESyuQQjfRkUWaZhq3TsGBMDCguygxHV+dnbNzGtzKAoJpu2LFJZPM/jz2sEsxWEjgNtGzEQ+Bv37cVxfR5VvoWsqyhRhU7FIftiEzyfTtLBXlhnOVbnI7/+MRRdwarb4AQElkO9YbPj9gkmRtMckheYEHJcPrXE8KZuCj1J5hZLfPnlM1TdGK4OgSDwrfJOPjKxgyOXjiF6IpqsoMkyP7NrN7btsrpa5+LMKqogkk7q+L4PjkFS7KLRcVhuVigV6yAJ+JKAgEip0c+iGzChpa68Pg3bwgd818O0RSzLxfN8zLaFEXv1i7/vB4CHqAp05xNkUjFOWBfx5TN848StRItZ7u9KMTHZQxD4dFomrXqbWqVFrpBEM1T0iMoL03OIssSDuybpzyb5tX/xMYrLVXJdKVzPR5Y2ziFXH6CtSPzE+AMA7O36L3x08At8+lyLXalefmXX7fRGEwB85txxTqwvUyq1+PmxXQRegCd7WM1/TlSwuVgeZmvuFwA4ObvMcrPO3ZtGyCWjzK/Z5N0mimZi+ZWrrl3bdmk3TaJxnbnyvwI6XCz+Iw5EDwHg+j6nplepVkwkW0JpgOO4qMrGx5gXQKNtYasSPsE1742FxQr//X8+xdhQll/45N039H5qtky+/OwJerNJ7rtl03W3y2gRDuSHr/zd8Twsz0MKBDZ159jc8/2FKN/WdjxURSaXvH6/llAoFAqFQqFQKPSDFQYpN5H5So1La0Umkyk8wHI7qJKFX1NRXZ/Wme0caXVxyE8jiD6VagvVFWEhQsvTaVUs1HiLTEbDdmsk06uQ9akmU3hRDdHywAmIpnRy/XHuu28rrY7FY8+eQwBeOjfNWrvIlLxGEA8oVhs0V22sjIXXJZPdnmYw9k95euXfUWwf46/kERqDDvELDnLLpwuJrkycWq1FJKKzY88Y52aWESMqt00O8Fets4iCiCcHtFMKfq3G15dn+fUd97LWamH6Hp7tAgGSJnBsagWt7LH7oW5yg0mS6QiSLHGpVGL+0hp5Q6Ld30CmwXQ1x2KzihRIOI7PaqNJIRLl5PwKmi+wMlfh2KUlBtMJDowOUF6rs390gLGuLL7l8T8efwm36WJkDXTP5sCuEZqyxKVaG92PArDYrPNfX36elKWwZzhHIhVB11UqjRpm28K1I8iyjCiKSLLIS+tzPGNNMxJPkEvGyEa/xi7tPI+8fBs1s5d40mB2tohtenRn4xiafCVUmJ1e439++TmOt6ZJ59LEX6nO6MonaFaaPHH0IoXuNPftGsN1PYYaOQqRDHf2TAKgyz30ZZa5dbDCu/r6yegby3dc32VLKs6LbowBP8rli6sI4wJ9vWkykg04pFVpY3y0IPCXi2c5Vltis9uFIAj0d+UY3FxDTZoo3/Xx06x3mJ1eZ3A4RzNIUPN1etVXl+HoqsL2wW6m59cpBS0cIcB1fYJgIzRRVRHVkFElCfE1UvCjJ6c5dmSGmYurNxyknJpd5osnz5KORF43SPlunzp2hDMra2zX8ty9aYRNA997b5TvJAWgCAIdy8XzPA6+dBldV9mzd/gtOX4oFAqFQqFQ6IeXH7xDms2+A87hBy0MUm4iL88vcfDCDJecBbpzp4n2O+zKTzN9bJJWUadRNmiWEghdAVLSI5jvoGZ0VDHGXPEuFosmVUflHl0kqaukLA1zziZwPNRiBwUgoxNP6dyya4hsKsL/+MLzlFebSIFPKa7S12eQ2hpHbcsMRxKsR+vEZZ+l+TpPH7pAY2ydxxoiaudBqp6FlrAweptoy3EG+3MsFGscOjPHlpEuHvrkfvzHTyJENObtObwMiE2BnGhTSau4mwosrtWYKVaRcgauLCO7Lh3HoVJsoUQkZEkiGtfJ5BPIr4QMekzFczysls67+kZZ2vM12k2Zlmviqz6e69KwLRbLNZ67MEt/KsEdm/oRNYnaap1z55doOx7v6c/Qm0pwemaZs+sz9KmrmHYWXHDbLr1enkgpTbzHoLhWp2y38CyX+pqNHUmSycYQRYFIVMfs2ATCxhhiz/XptCysjoOe1ehS0zgdH7NmEeu2ibcs5GKS+Uur6BGVZMLAw8dnY2KP5/mcP7vM8mwVLSnSlYwR1TeaxdqOR7XewbZ9/FcCiEbTRKy5pA2RhPZqVcxd3R+k23iBx+ZtXq5+lV/f8RBPFx/nhHeU24fG6Q+GGBvtIp2K4Pk+DTeOJtWpd9LEZBvNUNFUGV3fWGYGENHjyMaDBB2fgdjkVdev6/pYloPreChSHj2YQRZ7rtrmEw/fglZQ+K1Hn0I0ZIquiWEr+L5Py3RYLzbQNBnTdq9Z2pOI6SiCgOT61KotkqnoG76nhruzbOvtpjebfBPvRHjs0hTzlSqC5rKjp/uG9nFdD8t2iUa0627zcw/vZ2Gtyu5NfawsVfj6l48hKRKTm3qIRq+/XygUCoVCoVAoFHrrhEHKTeSWwT5kF84f/zSaPUda6UPzRMRWlLScZ3A0Qt02WXOadGyJgZ48yAIX3HVa2WXaNYXWUZGnO1O8J7WbLbMHmH3xBKLbxjU8BDvAi2WxXZ9LLy9w3Ihy9OLCRuNSS8BMBtRl+Af3vp/TB2d5+eUZJFni1vsnWCzW0H2JiuXj2B5WpUa6FOGWbcepL8k4aYVYIYWiyLRsh3rLYt9EH/e2LHL5OI88eQQhriBZEKgBuS1JorEehqMJfMvFMl1k2wdEvI6NhsB9e8aorTZp1C2mzq8wNFFA1eIcO7dAKwGRuk+x1CIWbRJPQEFvk8GgIdikFYP+bIq7Nw3TnYrTFY/iOA5LooQW1ch3J6/0zMglo/zS7r8kF1nkCftuPvPFXo6dmOGj9w8Ra8g0pup86tFvYaQMfuEj+zAmZaYvrFKrmnieT6NpMj1TxHR9Body+L6P73nc2TPMjt4+slqE5y/McPmxd1Fei3L56ARxscbW4W7KpsVqs41TNYm9sqQHYNvOQQQh4AHVozebwmpZyAmDZttmfb3F1i097NgxCEAyqtMcrDKdXOEbyzke7rsVgHhwgXTnCbzV+wlyBpIgoooqiiQxOpBgezxHMhlB0xQEQaDpFXDxiEu5K9fkr912L187eBajtdEj5XhxmiMLi8RlDbNlQ+zVMEM3FFKZOKqukmwtoUtN9GD2qmtcVSR+7MAuLjtVar6DJm98hAmCQNu0sfWNbZRXpv98p+HBPGODOXRJpFRq3lCQ0p1J8E9+7H5k+c2NLW45DqbnUap1ODm/xGWvyv2jo3THrt/o9vf+9GmOnprj7//ifey6TiPZ/nwKs+1QqbVJpCLku+JoqoyiXPt4Q6FQKBQKhUKh0A9GGKTcROKGyqOz55gp7mBPPMpk8ZPYVYe1YyfoNlTuengHM4sVLp44Q3XMZKVQZ3unl0o+TjR3nLm5XqSSx7lzK3RvXebCYpHAAyli4I85OALsK+Rp2zZLCxVeurxAMKRjz7eJVwWsmMCxmWWKp0tkPIn51TLVZoP5zDnGt+2ikOrh8bNtrJSCsNahqxrHnN9Nx52jN7kF5IApc4rVYoV6pcOOkR4O7B/fqNo4LtAa0/F9l2hBxHUSEAhkCwlenlslJkmIkrjxhVIQMDWBjiiyUm5iOR4Dg7krDUZ7TJVoTUAtBaz5dXotEXSfmQstOgs+miZhti10ReaW0Y3moY1ai6XlKoIkcc/tm4jHDRbaVf7bmafYnx/ivrFNeOYMhfwgCg6apjCSSDMZSZNXDabbq2hxjUI8Rj4bx2k7uK6HbbkEApSrbdK5OK7rUV6r0a636R3pohDbGL3bm4jTxzCnv+kRtFwkw2d4KEPZcqhX2zRaLoYi43n+xmSafIxtW/tZXatRXGmyoJaY2NxLrd5herFCujuBIm18+bYsh7ivISORUl/tvVFp/S9UdZqPjN3J1pGHSWtRPtDzPoYj/fzr0/+DJyoH+VtLP8PoQBd9vWkODH8F128SVV9dxhJRVAaTKTqmTaPRYXWmQeSyTVd3FEP9roqRZIRoVEeSRZbb4AoBGo1rrvOZepkL3gr390/Sn0hc+blqyMQSOgldRXqNZrKxiMb4cIGooZLPJ665/bU4jkdptU40ppPOvHHwAuAHAQOpBEIQsNnIc7pa5MTcOqIn8Mk9O6+73yNPnaZW7fCVR05eN0ipNTrML1ewHZedm/qYGMrgucBr9IT5Xtmex0y5wkAqiaEob7xDKBQKhUKhUOgdwQ9E/ODN/QfgD+o8bnZhkHITOba+xOHGMi1FRi7dwS0TXbxw/CImAfWWhdU2udAq0ui2CEYspoNp8l0vgDDADuMuOuurOL7Czk0DjI/lePTkYayoj+E4xKMdlITDhZcXaEVMSCoUJhKY5Xl0SWSiN814LM/BqXmWF6qIA2ls0ccJHNodC0WqU7dsVsUzBGoHL6cSN7M0lgosnRWI9QecEc5xePE00VgvO9Xt6KqMJG28CfPtXuzuFmbahYbODiFKLpFkaanM+eV5hGAZMR3HtQOW1us04xL5uRXsagsh8BmSCxjRjS/uJbGDkwrwbGgFHp4qEREdlleq9IoWjtlDTFZZbTSpd0x6IzEq1TZ116U3EyP5SqPPr148w6MnLnM0t8iO/f+UXOGfc+e7RJbLB+kfyLG+WiflyvTkkwzev53u/sxGiOJ4QEAkoqIbCu2OTLYriaorzM6WOP3yFE7dRJRk8v1ZWi2TC+dXcDoubQG8uk256TA3tcYHf+wAxVKTRz53ELdlYZs2RlRHUWUSmShHp5aYurBEJhO/0nvFFwOaTZN22yIS0RAlkc3eAO9O7GJvfgLbK3Jq9ZfRpW66kx9kS+/PocgbIYIgCCy117hUVpAEH7FXQtc3vmxrcgSNq5ugiqLAgR1DPH94ij/5xkEurK6Rr6S4f/tWkulrgwnplcoPHxURD9O/euKRIAh8avogL5bmuNQq8nObb71ymy5KxHwZ1RKwbffKeX2b7froMY1UKkbkBpfBWKbNocUFupNxbs/c2LjmIAjIixECDX76/lv4z998DrNh02hYr7vfcE+Gi60Vtky89iQjgFwmxq4t/cSjGq7j0q7brzQrtlHVtyb0ODK/yNfPX+TukSHeMzn+lhwzFAqFQqFQKBS6mYRByk3C9jxcF0ZJ4moB900Oce+mEU4em0P0QPDhxQtzPNUzh9dvk2g6JApNBKEDsSKnFnvYM7QPtTTPrdsG2NXfj9cWCEwRpy2gzNm4gky5Uke0NfonDNSOhLji4pkB080S9oVDSK0YOanNlp4iFy8JSIHKbaPb+Vv3fJTHnn6KMWOBWtLl5KUJznoWW3a4ROo29aUOK4drBJkit/Q9xT3Dv4EAdGyHx45fxCkL9FsxWrLPQFTj7p0TbN81yB997SD1pkVEkXE6AWLHwY0GaDWLkd4cvqojuFBcK9Ko9ZHNJ4kYKk6XgGLIxO0YYqCxZsn8+IGDPOMnWFjRcP2Azxw5ziNLFxnR43wkMkozCLB858pzPqxk6RITjKppnj8zSzJqMJJMsmnXMOmUwZGTs8iBgGGo6BGR+bU62XKTiKbQ7FgcPz/DplqTfVsHGRnOUyu3cD2PaMzA8QNWi3VWqm2awWH0ZINbt+zlwsUlGipIkkwncHFcl0uXV1lcKpEtRDizusZgPksuFuHi0hp/9fgx3KrDti2D+H5AJhUhF9eplBpcuLzG7h0DmG2bWq1DX7BRoWK6i1jeMpYnMJn778jS1aHDSHSMqPwUkgCfs47zSVclS4wg8AAXQbh6e0EQqDfaXFovsiybDPcn2L/n9UOJGSdNzQnIyg7fXcPx3r5NPLNymXu7R/Fcj0atjaopJGMRuhNxAs/j3NwaW8e6iRmvnks6GWWoL0Msql1pUPtGVr02TzTniDkqB4IRxNcZgfxtoiCwtFxjvdGi3GzRE4+SkQ36069fBbN/Uy9Ctc1AV+q620iiSG/h1X4tI5sKmB0bI3LtqOfvRdmu8YXVr1J2dHoT29+SY4ZCoVAoFAqFQjebMEi5SZwtrvPFE6fp1lM8MP4sXbnf4vD5f81q1UWp+Hgq6JEy2UKRoh+lvhbFdAyMdoGVIxauPUNxYIG4oiF7ARdPLeEcNQkQ8FSJ+moEXxdwIwJqUuFf/swnePrkZXBccMBsOawtidCusaP/Mhmjw9iWXSzPdLMgRyi6FkPdgxxZk4hJDnHDwnTXKfetIL9fJvdIH+KUzAFvDc11eHHqCKPbezG6onzx0Glcz2HrcA+m6pNzFGIxg7OXlvH8AOwAVwTPFRBkkUAU6UpGkdWAz0dPk67OodszrC3m+Fv5/0YhF2OgFieZ0NlHN/WVKpm0TcWOIaoqRiSJ53vENI2aZbIsifQOZKk4i/xx7Vv4xXs4kJukv0fmZ24b5j39Bzg5PUMk/ggRbScFtjA6kMMQJVqlPyWbepnD0w/iOxt9PHzP45EjZzm+tEI1sNm9aaPCYHp6jYVSjeHuFHo+TlkL+NOnDmKIz/Geuy+SSo6AKqHkI4yPFhBiBuWGiaJL6JrCXKfFUyefYG8uwY+N3odo+rg+1D0HNSEjigKe4zNzch4fgVtvHQOguF5nbn6dzJABjBJXdzKZ/S0CN8n5c+tYyhHSXS2GE5+g2bBZuWzSRx81yeRSucb5zBr784NUix/Cc2dI5b+MLG8c2w8CGqZFRNfQDRW7WaehWBxtnWPcLyCLr93bw8fARsLh2iqLe3omePrhXwXAthw8x8cVXeKpKLmYzkvHp/k//2CWd++b4H/72Qeu7KdqEoODWSRRuFLp9Ea6InG2Fgr0ROM3FKJ8+zFvKRTIGE1y0SgTPQVarkt/5vUb1k7NlVhcqrC0WAImXnMb1/NZWK2SjOmk4gapXALbcvHfopU9dadJuWMyVbFYaNTZ1n396phQKBQKhUKh0DtLOLXnr08YpNwkhpJJDEsiKqukM4vUOlGenFrmbEvB0QUkNSDT/Tw/WZjmc4v7OWen0XSZdDPPQvUyTtxlNWqxuh7wZ1PPsbOyFUESCSQBwfcQCwFSl0NnOYaaVkjnDUrNFr4NattHsHyqvoZoaiyW+hlzJe7a9V6mt4nUu3Walk02nmH8/Ce52JpDKFtIWgmzJhFclAgqDqLmsnf/3yMQTjI3tZPyapXGSgW7aeF6PrIYoHlQrbZ5qTKL7wdML67jOSJ2Wwbfx9MURBl8TeJrhy9QHTDpHamTSrRwIxuX+z0TE2iCzHhfN1bV5kunRJKKyXI8SzmWIufoDOfSjA1kCQSLkWSBTDzK6okijmpTsTeWm3xj7VusWOtscbrZPDjH2eIXqJqPkcn9GKL+UZKRDr3x3ycIJArJTTjONvACjpydQ/UkJoe6uW/fJNGoRrtjk8hHMWyLIAA9btBo1xBcAbuRpFPMMFf1WVMs7KxHOtJCPb1MTyzCWqZNDZOK3OaW4b9knz/PyVN/n670+xkeznFWLFJxNipplufXuXRmGUmRqFY7eJ6PZbqU++Z4NH6UTEngtux+spF7qVbbzCyeYZE/YVirkdZ2ErjDVE2buJAir8ssNEyiGICP5y0CAYFfvXJd/s4TL3J8fpn3DI2QzcZory8xVavxrdkVdg8PsiU99prX8+2pXUxVT9Krvn4Vh6opiLKIF0Ct2aHStqgXG7RwWV2qXrWt5/nUay1kScL3fSTpjRu0xlSVX9514A23+06SKDLYnSKTNkgZOg9sGWOiO8uWrtcfgWw2O3imQ7ttX3eb9UqTl8/N05WNc+euURRZBp/XGPb8vRmO9tEtTHLOWuSlpXneO/HagU4oFAqFQqFQKPSjLAxSbhIp3eDv3LWfcqPDtsF/j21fxFZy9BVW+TP3IHHVRxdvwW0XCdYM9EiHbFcbaTGP6IoobQVHDJAQmHVaZJer+KqMVDYRHZfAASchIa9reO2Av/3Hn8EVAzzXxxd8cD1cM0Bs1XDTm1DkW7HW4eH9Y2jdMYKqy/xMEbFeI9NdIhWJsGgmmTkdED1okW038GyPSv1pHPcsa+s/Q72zg0jGIB3XKK23ueRWqNsdkpbCXVvGMESRsyvrCAJIGniCAPgIvkSj3Saa1BiQXPapH2Nzv8NIag9m20IQYHMujx//KpFMgz1eiU60QYkuXM/GtZrYpstTrb9gPX6Z7uqDnJ/rEC/FeM/QPh7o3lhscl/hdi40phmLDdFai2At3sZKfIU5+8tEpAxiaSfnZm4hnXUQvBHwXVbXaqQSEe6/YwtbN/Wiawq1eocvPn4cVVd4960TZJIRLlxcobNq8vG7JqhXfo8z010cnb2AlQCp18FI2zRm4djFeY6kFnEuFuknSeo9AVHFRqNFLBGhvzuDWbXo0gyCICAWj5CI61iOx4WTc/R1JckU4iQrEWpKBct79Uu86bq0fYF05H56onXi6jiCqvLV2iUOlxYZEwXUYhTZshEEiXT+r/C9Eor66mKcSqNNvWmRSkeJay1az7SI1AVG6GfAuHYssOu4eJ4P8g66FYmOeHXTVd/3KbbLPFl+iR2pTUxGRymu1fGBVD5GNhkhLknIDY9377w6pGk2LWbny6QSEW40evAClxfXP4sux9ib/tCVSU2vp9pq88WXz9Dq2LgNFy2hcqFZ4ZO7d7C96/oVHuNbu6k322zb2n/dbeIRDd/10V8Z5Z3Ox/EcD/ktmtqz0CqzJpbp6Vb5xPYdb8kxQ6FQKBQKhUKhm00YpNxExnty0LPxZ8GNcqDX487xQS4vztOYalJpdPHkUhzfWebOA+fIJBzMwztRWi6S51LzZZBF7KaEXW1gpDXskokb8/HTAu2ChjwngSSwUmzhuQJOLMDJCWCKGOd1JKeF6EKl3kFORplvdoit+XRpBooqs2hOUeI0RWMYx8+iNmNons3qWhU7gHOLUSJSlJmzDbqHqgxt62H2W8+xut4m1h8n6NbQJR3FELl96xAnpxax6g3EniTtmovXCRBc8FseQa5ERV7i6WMuscX7GX9/khOzJ2mt2UxdLtJz96eQZZloj0mUDi9dVlloxREdBUkWCZwAz/PxbB+PgIVqk0jg0N7nkIhK3JrZxa2ZXdiWw9wyWEs/xvnqN4lsvcxKUqFLFDn68o/jYFHL/jciGYW/t+/fE43o+F6AY7no2sbSFQEQBUjGIyiyzKmFdQ5fmueDAzsZG3yYw2fnMM0Ae7ROoq+JFfEZ2jNOt5CjU7U52yjTOeOQaf8mXZtB6mxmbq3G/EqZeESjL7+xrGR8aw8H7t/CqaOzvHxohljCYHRXP9nmJvZq+7grt+/K9ZRJRti9ZYBUYhP59Mb0oCfnTnOuskAQBDieRa1kcnZlhg/v3MupBYGmlWD/mHdlItD9Y6PEbImspNGqWETOOIhAfC5DTI/iBx4vrv8+oiCxP/c3qKzXsDoOQi7PWbeLtNwHbDRwnW6WsVc6HJ45yovGYUp2lfHBYRRFRpIECrkEH7hvB0sXVimtN8h8d68RAQxdIxrVb/g9tdae5UT5mwiCwLbku9Gl2Bvuo8oyvu3h2z6aJHN2ZY2T1XUODPRfN0jxfZ/P1k5gxVuUDPO6x56ZL3Lw2AzzC2X2bR0k8HwCghsKeG6IICAJIlFFQuLm77YeCoVCoVAodDPxEfDfslrl7+88bnZhkHKTWp0v4jke026TM6dWsJfqRGMBjtJBbCVY+NoI0h2jJOM6QuCjptukDZmikkQvQ1c8gitLXFbreKKCrwZoFRnSIttHepjzG6zNNZH0ADsiISs6JAN8vcCKJjE02ocbk3nmzDRt1+GXPnwHQ+MFEvMJFh2IdWq4ZZFE1sN2wDM93JjEvHA/49H3EpGKRGXI6BravIlvWiyNKfSrEXqEKGtzFU5pOtNzRWprJoovEo/qdHwfwfaJyQo7lSEuL3msHGvxl/u+SmP2CC/VTuALMucOD7Pd3MWezVnS6SP40SXwfOoVidXaCq7t8WD6E3x69rMsdpbYo/firbfxHAHXcuE7Bs64jsdyrcmh8jT1RopLc30khSaW22D/QztYrj/Pf/mLHgJF4eG7LrA7uZennjpNs9bm3vdsJ5mO8tF378RxfTRFIgg2msIaUY2nT04xlL0boT7Fjvw6wVAR3xUYcyK8d/tOMpkU9zkT/OHxFnXN4/nZOq4xxHvGo4xoKrdtGyFp6HT1pxEEAUVRiMR0VE3B7DiIAvzfn/kmS+0G4+1ehgtDV/piyObvMBZ9EiH+H4GNAOFw6QyRRIM745Ps1Qb5SvIk2zMjuJ7P0dklTMdjojtHIbGx/dbhbrRARHMhaclIpoMgiBD4BEFAw1nhcuMpALanPkLgb/QB8W0byzFoyxuhwqnKMr977nkSLYm76nnuyN/Kbd07EUWBZMpA1RQEQSCTibLlliHq1Q7DQ7mr3hPxmI7peayW6ried0N9UqJeN0Puu4jpyRsKUWBjac9INkut3aE/GWferKEHMprw+lUjhqziqg66dP2P5VwmRm82wWBPGtt2mb64goDA5A4NVfv+p/b0GSlui2/h+co8J1ZXGM9mv+9jhkKhUCgUCoVCN5swSLmJBEHAy3MLrM1VSQki0ajGl186T6nskzF0EqqOvFShCchSgtxcmudOTuEC/qLM+kAE0Ydmt8TmyQFmjlwE00NNO6iWi6f5+K0kuwZ7aM65lOtVcnesYx6LkXbSzCsegqHT0CE1nGL21BLVuQpySuf5mXPQrmLKIp4sUGrFcV2ddMakLINrO8iTPpfz50gk+ukfzZPrTWOJPs6BLI1qFScmsdhuMFkU6S6kGehKk0nGaDXadHfHkZMRlufXcVSBWLNDl5jk/LEkba2GpNicqT6FFWi0OykakRaHpgbJCTspauvku21c32W3scT5lR6CIMBTLE6uL6LaDYbSQxQSMfqGciQSxlXPu6orfO7UEaa9cyRkn3Srw8qFMpMjMDnZR7R6H46/BLbE9JLA3jGBeqlBcb2OZTtUrQ5PnblA1tYZGc6S70qhZzTSQwmmzq1w+eQ8nbUOqUySn07mmJqtEStuQtwt0debol43ec8v3kbHcbjst0kbBrIisbayTi1oMJLOIIoboYEgCGTzcRbrDdp1m9mZVUqnVyDqYT6wxjNVgW3dn9h4YJ3PgF8H+xDIgwD8+Ojd9Oo53j2wh2OXVslEutDUKLIkIuQdLtcW8KRNV56bVMxg5+Y+zLbNhN3C79KRXJ9PfvJOBEEgofSyKfFuBESicg4la6MZc1yaPUSjqdMzsXG/CVVHl2S6ozG0usy7C7fTFcvieRuBzLdD7/OXVmi0bWKZyDWTbBzHo1Zp43kedsdFu4FxwbFYhDv7P46q3fhHpabI3DoxwPn5NebW62zOFEjEI2wq5K+7jyiK/JcPfYK19Tpb+6+/tCebivH+u7YQj+uIgkA8FUWSxLckRAGYqlZ46vwUVsdl3+09b8kxQ6FQKBQKhUJ/PcJms399wiDlJrLQqPFHz7zEwol19iRy3LF7BFmRsB1otGDBquPbAk6PQD3T4Jn6JcyGDb6Pk5UZfLiI44jMHMyzffsAq50WS4dn8csu6C5SKYLTcjl3fIE9vTkWW8sEJZ3Cu1rc4x/gT79wBt/yEDSNP3vuGMJSi5wRZWiwi+Puo5RfapCwB7nY3UcjMJAkFWNtBM89g+B6+OvrxJMN8tETjO/4dXRDIRk1yCfjTM3X8GIby19OLa2Q1BTiukLfQJoltcVqvE474VIfA8EOqCsBWkRBiqmYi3G6VhU+sP8CVTPgxdadXIxcQIy6yE0PNabQnWzwEf08vUNFvq7tI5HQCIjSnhapWyKrW54iKu/Hs10uTa8xOdbN5xce4WJzjp/v/wgL58oEOdh6xwzpfIdCfYL92waZXilTLLXpGu3CDjy64kk8z0fXRHq64+iqzOemTvHYydNsbiVRg81EIjrPX5jl0nKROzb1cSpxjPX+MqsnN7Fd+il6uk2UbECzYbK6XGWx3ODp4ou4rkRTTjNQS9JpFfjMicMcvjTDymSTv9mTQXvly7YUUVirt/HtgLbtIax00IYDRKPDy7WTwEaQIqT+MzjHwXj4yjU2nOxmOLnR22S8z2NHs8FQVwaAZ2rHmG2WOFEbpye658o+iiKjJGV2jw6w9fYx4rqKZmyEHB2vxEzjEQC2ph7G0HKUqv8XK3MXySrbmIh/EIChWIZ/c+uHePnZcxy9PEXvWDddvVlcx6NWaqLqKhY+h+YWma1XGU8WaNsO0e8Yf6xpMgMDaSzTRTPeOHhoti2OnJ2jr5BiPHX9EOS7uZ7PYrlGy7W5ZfdmTMGjumQhia//D8rAQJ7+/tzrLtOxbZdOxyHwA3p7M/QOZPDfqpE9QNFsMVeqkHQU9CD85yEUCoVCoVAoFHot4W/KN5G0HmG4N495qk59sUatu0pBMjBsH890MAyF8VSG0u4OF5Jlmk6d5JkYbUFAvaPNSLKE44vU1nMYusotm0d5Pn2GxqVFqHuIRCEhMbdcxm6YyIiY57LslkZ578f28fnHz2D1mqjDZUpyjd7aGMNDBQb6cwTRrbzkn2d6cQW/ICBrEhFFw7E9Irt7SeXSPHBPDxnp01y+cBuVtEe/oSN6ArflepizVyhWAugSicU0+vrzZP9f9v47TrL7LvC9PyfXqZyrc+4JPTlJoxydIwJkA0tYfMHA3eVhgeUul33uhRu8u6TdB7wsC3gxiwGDMQbZxpJsy5KVNUGTQ3dP5+7qyrnq5PP8MWZkoeRlwRi53q+X/ug6vzr9q37VOXqd73xDMsJIOsmJ5jIWBtQlJNEF2cPXA0xOZOHxi3gmWOd8Bj74OxzbmyWdXeHprVVc20Fuw9uObpNOlDizkaVjhIhJB3Asj0QswL5RKNW3SYTjXFgtoTU7DO0YxHU9vlx4kZrVZCGxwUxigMVLHsqkhJ7aZCpxADkg02wb2JbDh48dYWg4ye4dwyycW+PRvziJFtQIj6YZTkVIKjphRUVUZAIBlYSvIHZ8UrqClq6jaRa9oEc6GeXgwUm283VOPX+NK+c3iU4Ak+eoNn3OXJ2kYnbYH8kyoERJRcPEfI1e17oRSAmFguiahim6VKotHM/Hriu4+XHuOnozG90V1rrXOJK4DU09+qrftfntMook8sO3v3R8PCyBKJL9uuDF1xtIR/jIDwqcuVTg0nye44en0MQoqcAOAMyOyvLGFoo2wcJCgXqggLTr53lr+AsAyKKIYTo02l3abRMALaCQysUQJYFKz0JSJUaHUgylo4T/1j58H4KhAD2zQ7PVJZ16/YlAm6UGj51YZNd4hpnRbzyQYrkuEV0jomtMDqd4ZmmNSqdHudNlJPH6I5DfqNdJKKQxNZVF+Vpz2Va9i2M7iJno30tWyo5Emrcd3kFWCpFOfmOlTH19fX19fX19fX3fbvqBlDeRsKryU3fdzlNunJVzGwQUmblMnNOChtGxcVST2R05snaYq3aRUCAKsoCnCYSELjv0ApYrY3jH6bkei/kKAuCKIm7HRG/ZxKNRelt1Nqwirh7CUhVWKw2WNyr4yQ7BHS3QbMKOzqHJIQaHUoxNZrkn9yA89TCbFx8ljAkxCX8tgCT5vOWmOULHFYJhDbH9/9CurvNo93HCgQbviH43z5a2aYfA1X28oAHDUfSETsuwGUvH2RmUWet1iL4Q46rvYCQF9I7E8maFoOkSjVSJvqPOC+YXGOE7OTQ2wT2Z3TzzqQtspat028tYCY+LfoovX3k79yR343guoihw0w6Jq1dr9DoiW40uQXyiuoogQladxnXLJNQ0H3zvMZbXSuzfMYRve6RiYaLhAEf3jGF0DBbOrtIrtlF2i5TrLRZWSoiKxL6tCjuUAd4ZnaQXcJneNQiywMZaDcUApQ7JC4fZWCsynhkEz8cybVLpMFOzOTqdDi9WThBOxekGZcyYTSSo8v/595+i1zEYurlHMRPCs/fe+J5MT2a4+9ZdVFpdnlavYh9w8dZlfmDwPt638wC/Mf9/kzfWAZFb0/e84nv21PIKXzg/T0RUGM/EuWfnNAPxKD86/V2sdvIciO961e+n51awjf+dkJYmGt+FKE4DKvcN/b/Uax2qlQ6GYbO1vcDCmQxEPbTvGLvx/q5tMzicYOeuQQaGXgpIRL/2wB/yg7z14A42inVOL26SjoQ5ODN0Y53tuGzma+S36wiWxZ49IwxPZm+UPb2C66ECuP9jGR9BVeFdh67/DUKayu0z40xnkowl4/9D53ktX19apoc0bEtCkv9+pvYkAjr3DE3zRxdPMboS577p/vjjvr6+vr6+vr5/KvqlPd88/UDKm4jjevz+ydNsGzVGQxJdb4t0aIq2bWEKPqquMTU9zGdWL2CXgnijEi3PxQsohAWXMbWC5UtczKi0Ohanz6/Qapl4Ati5AGrQRambhAywfAUkEUnwwIPLiwXMsoJQVPiegyeIGyZe4Dj794+SzsaxTBtNFBFdFeomSkBEMCQs2cGxXbyIwlq7xaHhKPsnoxR6ZRrUmF/aQmvAsBKiueca4aEG+adGOHEtTzvmcTW/hda9ypjZpSmESHeSKJ7IrlyWQr6G0amRTbdQNZGa+ymurP4X0sE/4ysvnMCqiixQwZXfAvwJu7QKpVCQiGfx1Pll9jg2Xz6/m62TIkdGdxNLGwRjOs8urFOwDL5n/C6e2Fgio8ao2C1kScKzHAKaDL6A5/oo/Hd69jKPfXEXtbKJF5B5fGOLWk4h2PIJ6hp218YxXQKyxPJqmWAoQLXawfM8SuUqvfwjjKsybSHC//74Zzg2OcktwSnKxQZtr8a8cIaI0MJs3oQu2AwlElzoruObHp2ex4pQpOnZJP/me2I5HN03ihKUeTx/FjEnIDQ8Ir5Mp2Vg+WC6Dh3nldNjOqbFo1cWqJhdmj48cvUKNbfHT9x6K3+5dokLtS2icoqdsdyN7ArTsOm0DcLRKKHwA/ip8/xJ5zQ/0dvDkJ7GshzymzV8H2Zmsmysv5t46lFINBCccwAUO21+78WT6F2fW3MppFcJfgiCwEA8wpdPzrOSrzIz9PJGqZVah3y5gWGYbCyVsbs2QxOvnWkykIqyZzTHyGDif/hazERe6kasKwpT6eTrrL7OdVxsy0UNyC8L7vS6Ft2OSSwRRP5bARNRFBFFkb+voT0AJ8prnOluEC0E+oGUvr6+vr6+vr6+vlfRD6S8ibx4dYMra0UapWvI8ovsPXQRW8uRO/BdtBfqhEMBLq5vE+sGGewZHBka4XFpFc91UUsOpicj4dHuOLRaPapOHlezsdMqTsyjPimSuOYg2R6hkEpXFAloKtNjKeJJHToaPOdz+O48ritwptAjmb5ePiGKIqPTWbimYucimCsxHNNiM7pNtZ7nu9VbGI4GsBI/g694ZEvTtIwWlxcvc9f0UZ72V6hHbSTZIzpVwVoc58nuMttSg+HaFEK9jKBl8FwBJaZx7Ogkj3zxPLf+4OcJx03E6geYi50EByr5BnJdxHR8UskwjnMnLecvyQR63LL3U5g9H6f+b3HdYRrbXVpbEkW/zoFD4wzPDLKNjev7XCqXeGZzg8FgjHSki7e9ysWzNSKDdfZN3crm2gqh4L9HlhRSqe9lZT5KudRgS2xjz2hka0EO7hljqVnlQqmBuGHzuacuMTc3yvFd45y/tMZfPn2B8VSQW6dW+bTa5ZzQY3GtRm+jS6nU5ODBEfaOD3D76Gf47PkyuN/Hj+w/zspwnnqlw6075giHU0jqS5e6okjg+7SbJt+dPsbn1s9x080zDA3E8PFJKCotRSChhl7xHXt28RznKhcY1kcZn0hyfmMNL+wCYHkuvg+GYWFpFmpARRAE2q0e+c0aI2MpBPH/4LNX/wgj3MPzPQBkWURRRCRJIh4PYZjvZP87riEkTtP1rjeT9XwfzwcX0MMBlNdo/iqKAnfun2bHSIYDX5eNApCI6ZTW65TzDQZiQRzbvd6o9jWoqszMWIZw5Bsfl/xqbNel0TVIhYOvW7pjmw7dloEoBlEDLwVSlq8VWF4scuTmaQaG4i97T73axuhaKKpM4G811/077dUzCCkvsjcs8v7pPf/T5+vr6+vr6+vr6+t7M+oHUt5EZFHgWGqQjfBfYW0X8RWbpcshUjUBw/JZrZTQ7C6TIzkcx6HSbiEeaSCfdDh4cI1jwW1sV+IJS0LO1onft4Q3KJP/ZByp4yJu+jQFCzXoMzAWp1Lq0DMszlYKqLkA8WiIeh1+69M/STXjkfA3kaL/G/fvvZeYPIMqSAwlsjRkB9kW6fke9kQNMe7w3MXz3Dc9jTa4RiRqo20OUrFAbAUJDqss6nW2igl8wWc6mWdoqEQ0fohnqitIsoCYTeNY0PVbIHqcubhGoV7FMUV830ftDjKY+V3ApysMMthLseZWiAZ0KmWHtjh6vcwpIOK5Isd2jpNIJVAtAbtpsSnUSG5E6ckK8VyEO26bpGR2EQWBfZEsFfG70OIeeX8Md2Sdi/UNJkMfxLE/TDLdxff2EAiW0BSVnm4THw3xXfcdpd4x+PLVRc4vrRFc86itdVADCu89cIzVtW1cyyOauY/lpkkw6JAYbzIqxtkqP8e1LoSLPUJSGXnKoeEG2Ki0eejFSwgymLrLvuQEtx6fJaq/FAyo1Dt8/isXSA7EeM/9R9g3O8r4bI7xmRyKIpNt5qjbBeLKKzMxLpiXKYstPHeb98UniAiHeOvIbgA+vOM2GrZBlJc/0IcjOkMjAqFIgG7X4t2Zu0gO6AzrGVzP4rHVj7LyosfezDsYGU+xsLRBq5FDTWaIitcDcbot8s8m9+AYFkbDIC8UCDk6AUGj2zJQAzJa4PrvHR9IMD7wyr0HNIXTz1+j0+rh7xlm15EJJOm1S2J0XWXHzsE3HJO8UW9wam2Lo2PDDMdf2XflmflVFrcr3D03xXTutccJK5pMUNCR1ZfvqdM2qFSb9HrWK94TT4axww5q4LVv5Z7n8RePnAHggbcdfO1SJqBkXGN1+wJqVSfhvXqvm76+vr6+vr6+vm9N/dKeb55+IOVNZP/sMF8oL7Ft3UG2tcCVrzyA2w5x8dwS3YjAhKaxb3aQMh7z3SLOpokw2UNKSByZLiChoUoupicg+0EEwGkLCKIIpo9UBk8Fy4etegvRFwmJEnFFYTwZ5dRuFafQ4opi0GpYRGSH6GqRwYFfwmvH0P3/g/3RSb6ynces1AkqIggWgikxRJpUNIWEhue4vBjSMQMiu2YrLLabeBp4mk/Pk8m6BvtGp6nZAeqbBltuh4yicSiWw2nbYIusnVzDabf59L8/ii54vPd7NB7b/lUA7s38Kl0VPBEMRaDX1ZgI/1dGxtNsF1qUi0U6JZFYxCU1EKM+GCUpBVAlhdPnVognIrz7vv0Mh6N8x8we8vkaW4UoolCkWQkQ8Xx6RpC1TpV0/LtpWTKW+iITMzn2zo1Qu2Zy6qrFgB7GNG32hzMUq9usrZWwWwa9eodnLizRaNvcvnuWuelhxKCEWZ6nWCsykCww+fYTTLxV4ORXWrzrwEk+Xj9Eb8hB3RZobLUoWF0qmsVmr01QUfA878YD9PNPLzC/UCBe63D/HbuJp8IkUhEU5frt4F1D3889bpuw/MrGqA/ufiuW/0U2jJNcNC5zl/BTxLkepFElmRg6D1+aR1cV7t8xzUKhzKWtIrfNjqMoMq1ei2bPJuPF8H2fpr3NauNpVvIBhpWjiOIuLhU+z/L2IrO5EApVDOsKjVoKz3YpbzU4VTjLfH2eQ1NzfM/g++i2DVxXuRFIeS2iKNK2HGxR4FqtwY8fGHvD5q6q+sa3yGvlKqfWN0kE9ZcFUjaqDc6s5sHz6fQs1NcJ2sD18eWC8MqGs+FUiHA6gqhKOI7L8lqZkK4yNJggEFTfMBNlY7vGn/3FC/g+HJkbY3I8/Zprc/pO7tlxP85ohOFvoBypr6+vr6+vr6+v79tRP5DyJvK5q2d5oXkasR0kIO/mnsFp7K7JxfgGsgaHJsYYSMb44ulzbKVcMmocLrq4az5VV2epFyUmmEixCGE1y/ZDY6xYNlrQROz6eIj4qoRgezRbXYJikJgU5jt270ELGazH1jElCbGkkBA9Dso5uttBrl4RSbkOGdOjpbr0il3kfBM1Eia4uYOpnVnuvX0ns7uHEaWvUioUyTW+wDVxi5VLSaJZi5gcxPWbNFoxrq7ew8hQiBWtip2QkC0FpSuRP1+hYZq4HQuaJpKmIEo6bg8kAihOCl8y+PSTn2BTt+mOamxGLToxn1BiGVvIMzxwnMpqi17HQlMVvuPYfp5tK7iugxiQWLxYRmu18bleEuK4HucW8pw4+SFUr8fVUys880yYY8dC7JgwyeQSPPr8FS4sFog0TU4/N89QVGNhw+bi1Q26osSO8Sxi08NoW8iOx0AizOV6ibrfYTo8SqPeolCyoasSdBUEVUeWPFwkZuaKhAWBhqszEq7jphZJixJWyMEQXVYqZerlNqGIhh66HvCY3Zll+PkIqVyc/FqFiKoAL5W4iIL0siDKRneenttmJnyIIT1HRg/zXMkhHSrjOAKifD1AU2i22ao3mS9VkCWR2ybGuLK6zaWtIik1wEwujarIhMM64WAAURSRvB7r8y7byxLuYAdBEIhHMmjxy6ipMmXbotb6bbIDv4LjOLQaHRLBOJqmMBjIglTADz6OHnrPN3SNhOIajbJDz7CY2zn493LdHRoZJKyp7Mi8FKCod3u8sLTOtUKVgXCYsKzQNezXPY9ju7iOi6y8vHlsJhWlM2YRCQfoGTbVaoeOZjI4EMc0bGzTJhjRXzNzJqRrqAj4XM/KeT2C1+BA4HcQQyMo8lu+8T9CX19fX19fX19f37eRfiDlTSLfbvHHl0/Q07vIisFasIM+O0eop5CZv0hE0fCtEdaulZAHVMyEhB/WCF9LU3ManCyMcMkZYFRtkEwGubSwhSXFcCN1fNVCaPv44vWHMDcgIFoeXcmjXWpSLtTJd1ZQfAsp6JEKxvnxnfdzZilPVcngFPZSnK/jpmoMhaMIqwU8s0sz6tFtC5RLXWaiGSbcIUJBnScvFom2CnzP3FN8xRlmOBjmz0oVbBnoibiboGc8cuEA3zkxS7Pc4akTV9lsdLGDMrLjkU2HGBhLUVsoEVUVjty8g93jb+ETT7yF4dxz3HbnLh7/q0NMjKYYTJhsWz+PXxI43fs+1osd3jP8PjodAz0UIBEPI6siRcPAlAU8z+NvWmtIooDs+YyOPc3ymTUq1wZxQrC2d5N/tv+dDA4mWM1XWTu7QWmryPnTy6QiOturZS7Vm5Rcn7uPzNBzfHzPwwXi0RCuWyaiKziOh287mKJLNVqlkaxgRySSlQ9Qsx7jHlb59JU5Du4s8ORzs9x//EkU7yTuifvxIg5t1UbVZMSve8ienBrg2LEptIiOKkuUSy2M3ksP+VVzk9XOeXbH7kBA5C/XfwNB9ImO/mtygQm2WnXCrsE+aQXPMrEsF8f1eOjMJSzX486pcQZiEYyWSdKQmFEibK1UKRcbZAd/ndRQnmz6UwCU2lucvTyBsSayMmHQMyzW54cJug8S8/9P4kody3yOQFrFdSVS2RjHYwd5YPxeAgGN5dKHaBvPIUg1ctpP0utZdLsWiUQIUXxltsnwjhzV1hpKSHnDbBTH9ahU20TCAYL6a2d9RAMBjowOv+y1xy5f41q5yqGJQYajMa7ly8RDr99rRQ0o+J78igk8rXaPXqeHrskEdJXZ6RyaJiMIArZp0+6aqAEFSXr1PQYCCnfevwfPcYnF9Vdd8zdsexnbWUV0VsA3QXj99X19fX19fX19fd86+qU93zz9QMqbRLXXZSg8xJiaYedIkOX5TSKeRqX6Asaaj9GzOV+5gpGo0bo3g6jIlL066lEbuSPy1+f2YuxRKUmD/NTcKBMjWSobVZ58cYFGvYMtgCdZ+I6CYAmEFZlqVMCOCaQnEvQ8B23lGkrIRsqV+f3tRzA3s+zfMcqBXI5nn19HUz10JQCFFhQ9cF2aSQMr5dKoddlarUJK5YVSiYBqcjjikIt5nF8pIhsOiiUSECwyEy5DAykOjqV47LFLtLYbGI0ejuOD7YGmcHDfOD1ctnsGvqqQycW4tF2kqemUC8MUenHirsKP3XyUSxc3keTDJAd9Ss0m7V6Hre0ishKm1+zRqDaJp0PcffccV8p1conojX/ZFwSBkUyMrn6Fz/zaOF5bwBcF7jm8n6mJLIIgcGzXKIUza1wqtsiOxPlSa43GHhnzYgNLDzK/VWJmKMHa5U0EXyCVjbKzDbnhGHviKR4qLqCkdI5OTHNlcwnHUsg5+/hK+xpPVWcYiHZYqmRYtkJUjCBpZz8j4Rj1QJ0rUpUvN67xHYn9N74rruvhKyKpbJihVJRzpTaO5944/mTpk6x1zuPiohT3423MkZ7soBgRLuVXGY2Ositicjh5CMHKEosEkCWRmUyKtmlyYHgQTZGpl1tENIUhP4yjiCghD0m+iuVYlGrXGEwfJap5TOtFVswsXlfiysVNlq4V8CSPO1ybkGgRpM7KcomG0SOiKiiyeKMMKaTcQ6O1iuAcxHZcvnxqHsUVuPnAJPF48GXXiGU7RKI6BGWi4Zcfe9Vrqtbh4tU8QwMxds0MvO7atU6Ji401bs/MEVF0pjPXy2KOTo7g+j41u0c49Po9R0RRhFdJKvnTz7zA0lIR0/GY2jFANBQgFLx+rlP1bf6fpx/j1vEJ/q/bXz2DJKRrfOfbDyGKAsE3KH+abw/z+Ob3IssZfnyoH0Tp6+vr6+vr6+vrezX9QMqbxO50lp84ehxFFKmvt4gH4vzB1gWknkSxGKNHkFSkQXp8m+nhKsWTMwzdXEFKebQvJ6nVVQxTpNaxqQY3sZdFlq8UMa/U8UUBKynQGZHQt0WULsgBFUG8PiWlYZrMZUb4TCsFhkV0wqbRtui2u6iOwOhIkpXsl0nHSmR2/hwzs9NcLi4hWBK+6UPHx/e7xKIBVpotFEFgOHA75WvTtMtpqmaDrBzDWG9DVEC6e5PYSAJkkVBIYbXRwMIFWUBCJKbKnDy5RMfuYichoNusN/M8u93kE2dvYipTYrWVQnUNLl0rUt5uMTb+b9g3MEM6lufs9hV0I04sHkBTRYrbDbrdHvucWe7aNYGqKdi2e336DTA4nOTiC/+SlvxVxIBFKNMhOrROr2txfn2blcUi1UYTS3BpmHkK3Ty+qBPLxWl0HSaGkvzgdxxn6fImvu8zs3OQqJ0C02azVGOj0kATLO4fSzCibuN4IIi/z4emarRmisREg7eHUjy4eJBnTh2m1zlMyy3i4NIxLYq91tf6b1yPDHd7FtuVDmokgNe2KZeaL8tI2Rm9Bce3GNX3sNRz2BW8h/2pYTqVHo9cOMNz6iYPzv0UAX+atWKZaERHUWXumJ0AuDGiNxwLkhtNsnZimYCucNtNe+iaH+MPz/wpG+6X+BdHRlg7m2DQFfGGLaLxIKvXCqjqBlLwEl/uDfFjYgkEi812nd/fPMGAEuQ7g3OEojrJTJRi9T5OnZ9C2DlK065xqVQiGwwRfJW+IQtrJSLJIBMzGW47OvOG11QkrJFLh0nG3jjo8tDmC5yuXkMRJe4fOMjekQFmcmlCmspvPf4sT1xZ5vtuOch7D8y94bn+NjEg0xN9TN/hxJV1EhGdtx7bCcDlfJFqvcNZZet1z5GKvXIC06sZiyRQ9bcxFX3tprh9fX19fX19fX193+76gZQ3CVEQKPY6/N7Jk4z2dHYmY9TlZQJSGD8yhGzDgYNxApNdTj0mE1hrcvO9y4j4PNsOU8hG8Joijumy3GyRKJfIbxRohQ08RcbXRZS2jJ0N4BsuwY6Itt0FV2OtWaPSLjPlr1Hopnlw7D3MB7b4snqZWq4GkkgsbNK1NETZRNidhNOrCKpEPGLhSjbbnSsEQ3cw4rjcMzzE2NQleuYLnHvqbtSmjz8ADiBUNITlcR5ZnafW6xBUZZKazqYNnucjCS50HGrtLr04dHfKzB1xeEb8HfzQQYZcjwUlRiBroAJr+Sq9Wpd2s4NlOQzqg3jTEs1Wh+GRJCvzBSrFNvWqQK3QxHY9NElEFAUK7TYvbm5xcGiQX115js7OEGJPZ0iBvYN3cG29xOMXFilt1miv1+lWuxRXXiS2HCUQciG0i64q4AY8/nr9HHv2DmK2XBQRLm9ts5mvITRNphNRZsUs1VIFXTPoejKFdZWWnWUsUOWcOUJGlzkevoONUoXt5Sp22CSY6jGdzPG20AzNWodoIvS1chAPz/WQRJGgrpDLRF6WLbEreiu7orcCENxt8pEX/4ovLZziwwP3cXBkgtWGQ7FscSFQ4tn8Gn5K46ZEmPXuVymZF9mf/H4CUgxZkQjHQpR6BpJt4rge9doQL5xIIqdsrIMWriuSThfIpvNMz/ZYXgoQy7SJDZcpSDoVJ8aAapEbiaM3NNp1i616DS0cIJmJIkgCWkgjENYYiEe4ZfcEI8nYqzaJrbcNArrK/bfs4uCOkVcc932fbttEkgQUTcH3fQzTYStfJ5UMv+70ntszc2iizN7YOABPXFlmqVjl7p2TDEWijMRi5MKRv9O1fbFSZtPpku90iUZ1Il9XIhSxIea7jIdeP1DS6Broqowqv/4tP6YF+PDe43+nffb19fX19fX19f3j8gGPf/yyGv+Nl/yT948aSPnqV7/Kr/zKr3Dq1Cny+Tyf+cxneP/733/juO/7/NIv/RK/8zu/Q61W4+abb+Y//+f/zJ49e26sMU2Tn/3Zn+VP/uRP6PV63HffffzWb/0WIyMvPSjVajV+8id/koceegiA9773vfzmb/4m8Xj8m/VRvym+tLTE+ZU8FVwcbwF/QEUNR2mlNbymSzA8gJl/O7WVi0Qlg62vvoVrSwXaRR0vIaFsa4hhjcHEMCFJZTgTZa3TwhM81J6I1PaxHRu555MI6lSsJk5Q4wuVVcJ0CNsizZ7Ds6fnufu2LNfCcfyixKVyndCun6a0kecvlhwqqsPg/lGKAybtqEVUEMjKO6m3u9QKi2Q1m2Dgt4hEaxjtSapNhe2Ij2AHwYPF9XXqBEllE1y8VqC5UkHo2IiyiCd6tB0P3/NRt02khM493zfH5bUtik8VKZzW+eCDL3LT9CYfHbgbx3YRRfFGzxMAVZMxNw2alSYBXSU1mADPxXZ9tLiOFg4gigLntws8Mr+AZToEEzb5mQCSA+HFEaxSnHQsREYJcmFzhVK5wVAogEgUoSsiCAo1u0dPg8vreRZHPEbMEGFL5fL8Ji8sr9O0TTIo5IIpxJ7HelnisjuI3rK5O3eMpxclPrWaQiRM8Jb9yFkDT1pCXk2gSxZTY9vE3CFWrpURJwSiiesP26GQxuG5YXbMDRPUZAaHU2RyLzWXLRab9HomIyMpRNkhFP4sPg6udhPjuQFWr5znmfkzHEoPMqxdDzD4vs+Thd/F8esk1F3Mxu7H931kRWR0LAmigCBc/+9o6gDJXJDx8AhMylyydqH7FtnsfgrlEnOxd3NKMRDFJeq+gGN6lCnwvx+6n/JanVJtm5PCoyitW9k9upuRdJxoUEMQBO7YNfma18fO8SxBTbmegWPaWLaDqrx0C3Rsl+JmFYB4KowHdDompVKLSCTA9GT2Nc99MDHJwcRLv7ttWlSbHS7P5zkwMcCdOydJhP5upTKBgIIoivRMB0yLcqdz45iabTC+Y4ls9rVv5cVGmy+cuUo6EuRdh3f/nfbQ19fX19fX19fX1/eS1/4n1m+CTqfDgQMH+OhHP/qqx3/5l3+ZX//1X+ejH/0oJ06cYGBggLe85S20Wq0ba37qp36Kz3zmM3zyk5/kqaeeot1u8+53vxvXfannw/d+7/dy5swZHn74YR5++GHOnDnD93//9/+Df75vtnQwgB4U8b0ScXeRVMlnv3mYjKgjNnyeOHGNlc06M+M5vvOO4+htnWY9hCeLyK6IYIHTFHjxfImzm0UMRSLRVtArPn7Iwdrl4ak+gufg2A6qrOCaPr2OBWKdohSmI8gsr5zn/NX/zncnx5lyBzB6Nmc32izXZGQf0nqI47dGmDyUIxCW2JPKkRZTLC2vcyn/66y1f51O7UEWl25nfkvEiNUIBrsIjg++x8DOEra5wrsO7yLkSFh4OCL4to8nCDiA2LOQayYTBZVb1TtIXL6f/AsKStvhB247z81j27znrWcplBpYlklA5Oh67QABAABJREFUV1FVmW7XpNFoUS02qXdKOKlVbnvLLnbtH8VzHK5d2aJSalJr9Dg0OMh75nZzYGCAX575IR7YrXKgJLBuV/njF55heanIpPh7/Ov3f5x4ss3AQJjIiIQX8CCuYPlgex6yLfMdO46xa3yEZCbC6GCGHfEEKV9hOhjl+NQISkpHUnpkt+q8f+oUDe8T3DcWI1caYpc5wL075wiFJOywhysKhO0Cb5Ovsjf2NKFokFgqfKO0J5oIMjSWJh4PEdA10rkI0tcCCr7vc+bkMieeuUaz0UXBYUDdIq2UiCs1spEwBwYGSeshtC4Elmz0to/h2Jwtz7HcnAKul50IgoDnuFAzCPRcVEVidDjJDz1wGw/eexOO47FWXmOj47PUk9jYqtPuGkiyz4HsCEeim7RxqfgSv33lywwEowwl45i5AhdKl3jkxa/iez6xUOANG8cCpOMhMtEgha06569s8JWvXKS4WbtxXBQFFFVGkkXAR9dV9s4NMzaaRHqD0cVfr2Na5FtNZFUiqeuoikwyHPyG9vi3+b7PaCpOUg+QCAQQJYF4+KWAzIL9LK7eY9u9+prnkCURx3bB+8Z+53qlzhfPzVNudt54cV9fX19fX19f37eMv2k2+63w35vdP2pGyjve8Q7e8Y53vOox3/f5T//pP/ELv/ALPPDAAwD8wR/8Ablcjj/+4z/mwx/+MI1Gg4997GP84R/+Iffffz8An/jEJxgdHeVLX/oSb3vb27h8+TIPP/wwzz33HDfffDMAv/u7v8stt9zC1atX2blz5zfnw34TTEQTpDMR9k3PcTScpFc4QsBJsiaWqdQK1DcadCptBhNRQjMqJze3cY55SMse0ZZIPeyB4LHeqRKVAhyaHQQPrqytYcoOtupjz0pYFR9lW8NQDbSqj77pMXTA4pwURIh4NCyfC8UAAbnN6VYJPVAn7sUItWO84/guvlx8iGc/t0o6p/Br3/8z6J5MrW2xvl3ikTO7CMZ63HH8/Yj576LpfRJRVGhGNQwRAnnY/GqcelfihcU1cokwKxsKXsTF7zj4MoiOh67KgEgyEUFWZKaHc1wZKmJ0BJ6zogzbJqcbKXQN4mKAgKbQ6ZgsLxXptUxCiTAn2r9PpV7kQO4BMkzz539xgo2dJ0l1uxSeej8/cO87uHd6Csd2ef6Fa8jzIwzMniF93zajg4eobTS45/BzCILH3t0Fts7EMF0Vx/AQ1ACCKiB2bDa2a0yU4qgHs3SaBiODcaIXZVKCxtBQgomxNPszUZ7caPKF50O0BA0fj9NbZRQbBAdunhqlYn+ZlZUiA0MZGl6ZR9vT7E6WaG9vsnffSxlavZ5Nvd5DEWuEghrpgRjNtoGqSAR1leJmjVq1g9GzCIZ07pFFlEAZ2Uljeg6/cOddLLWqPPzZs+TzFRrNHoLg4UoOJSdN+OvKagqlOn+8+SUCQYlDxhQhXacptFluNdkXn+TA9F56yv1UVnw8VyIS03lm7S9QG9fQdozx/sFTLPUyLGy2cRyX1ZUSIWEE98woNSPC+kgJyfeJxELE0y+VztQbHVotk9GR5Euf27D4s788yckXV0jEg4QPjLEsb5EejF0fxSxLZIbiCAgoX5uKEwSiB8aRXmUC0Ndr2yaX6wX2JgZRJJFwQCOm6xzeP8ZqvcEnTp7h3tkphmLRV32/53v8/uJTdF2TH5m9m4D0UjPjneNpao0u8USQ9U6Lart7431SvsHAZpfEDus19xZUFCZCMRRPwnHcGz1sXsuF9W0eubhARNdIR7+x3ip9fX19fX19fX19306+ZXukLC8vs729zVvf+tYbr2maxl133cUzzzzDhz/8YU6dOoVt2y9bMzQ0xN69e3nmmWd429vexrPPPkssFrsRRAE4fvw4sViMZ5555jUDKaZpYprmjZ+bzeY/wKf8+7VZa7I3PMgH9uxlUNDZCtS5vJDHcVxEQcAVBGQHWptVtjYKCAkfL+khdOFQvsDcoWX+5NpRFCHOtBYhLSoUHQHVkJCqHkbCR+w4IPsYYaCjIHg+ZkziZGMYoWMTqlnYO2y25QzVjogtmFhmF1k0UGrrPL3e5nmngZuIUX1BYuWeDU56z5PuTbNecFkRkiiGSbH3X5DSZ0gM3o6dlBFE0BWHdEelWY1iZhQuLm1y+9gEpxc2sdsCflDC98F3HGRVRh1MMD6TI5YIMTM7zHvfJbHcbfJY/QR62WVhfpDDEY3B4SShsIYiSwQCCromY/dMjGKOq1ddNsrbaJUaVxe3eOePncEt6Wx0/wBffAfPbK+S9QOUtmv06h302Tpy2md3LszmhS6//Ct3kTxU4fMLKaLNGo2OgGwGabYEVMUjULag6/Pnjz7H8QO7iAQDIMKOHQM0Ih6fW7jGtV6b79m/m3pPwylH+d2n7iEgCtxSdhlK6LzzXYeRZYme+jFumbC5WK/SdDJcK4eo+TqjlQL1roGuXn84z2QiuLbN+sI2C6U2mcEYbVkgFApw84EJktkogiig6SqFfBuj+zFy2QRL82Uu5F/kbGqDmiNS3GgRqPv0PAfRl5jSkvSEDlHlpYDGtWqFjbZNUDMxBYsQOv95/iE6rsGPz76HqcgQS6UsjUaT47fkGBiMc/mxv8SzTLpKkP+8ehsuAjgGJ5Y/gdXdw5K8SqFdo9P1MLoWnumCz8sCKf/vr36eSrnFv/npd7JjdhAAy3ap1jr0rBZzO64xt2cf2dTA9Wk5X6O9ylQb+XV6o/yNz65d4JGNKzwweYD3ju3le44eAK5nuSxXaiyVq+zMpF8zkGK4Ni+WVnAtl/JIm5Fw4saxA7Oj9GyPbDLK7ESWVOSl5rdGu02pEiNn119zb7IsEY/oyLL4ss/6Wl5Y3eDFfJ7Z9TTHZ8ffcH1fX19fX19fX1/ft5tv2UDK9vY2ALlc7mWv53I5VldXb6xRVZVEIvGKNX/z/u3tbbLZV/Y2yGazN9a8mn/37/4dv/RLv/Q/9Rm+mTZaDZZ6NcaScXak07SrXeKxIM1mj0rHxvVFBFfA9sGpdjl7YZPBsSS9yw2Eks8P/4svMznaJJhyOHflA9w7Oc2zj1wgv1lA7plojoi1LWDuUhFbDjO7BlgsLSH6PqbqYkdc9J6I3lGZMHN0bAuh7JN0szj1OOLSaTaLRUa2HCRBx1kxaYwG+Mi554hmbQ7JJfThIWzJRvZMHPsslltmarpBK24h+zFGxQ4ze2/lyw+dROj2WF8zqf5QGuGuCvJVMC5r4F8P9lhNm0AmTrNpUqy0MCWf8dkBtGaE33l8D6lwg/p2hF7U5cpWma3gOrdmhzG1ceSCwWOffIFgbBQtOoZr2xTzZdyGyS6hxPiONkUnyLOla/zBtS/xwMCnGL/dJDvzEdZPHqW8kEc6nqFr5Hn66TidpSTGuE9UcbEsATejYIoOg5pGUe8hKgLzwRoj1NkrD1Nr9HhsfZV8r4PhQX6lyl8uniI6GCbU9hh9wWJqYooSa5hs8/vzl7ntrn/HycoUM94qC+UUodos3UgXrZjhXcemyHzduF9VlWlUO1y5skVQVRkaT6FrEgviNulOiInZDNtqj4rZw2gZFLabjI6lkUSRotOmaDTJRbIsX21Tqzk88dWr3HxglDvFL1A2HLr1DxBKhwFIxWLI3TiSJGGbIhteia3uBVS/TlrYTddKUxVMvJRMOKmT36hyfOr9fLLhcyQc5F/kfouOJ/IpvcoCX6aYmGGv8yHGDuiMWhMMjaYQfNACL93KKtU2Zy6uY5suzzw/fyOQUig2aDcNotk1xg8/x4YrkpN/7mUTjf6uZqIZroSLTIavZ8CIX5fBcsf0OOPJODPp5Gu9HV1SyZkNtlcq2LMd+FogxfU8FtZL1Fpd0tEgk4NJ8qUmFa9DKhYips1h2JskldeeQiRLIkf2jn3Dn2UoEiGj6Yy+RtCnr6+vr6+vr6/vW9O3SlnNt8Ie/qF9ywZS/sbffsD5Rh56/vaaV1v/Ruf5+Z//eX76p3/6xs/NZpPR0dFvdNvfdHXDwMNHEAUUWUZSRDRVQgn6rCvrOFEBqSMQiwbp2TYVTN4yt5PVz1cQPB/f9pBEF0n0iQVFsgMJxmdzrDhLVJsWejVIT1BwWx7BOkzty+J68/gBCdex0WSZsKISEiUmGSAVUnAkG1kJMJCL0jJabEoms6O7kIptnvdW8adE7IiC0VGRxRwfOLKbnemPMSK18Ko/xhMvXOLqpQjRuW1SMy2SzVE2Q+eI3+ZgfsXDGdaYr29DzoURsPMKmBZaTQHFpdMzsCyL9WKV5ZUy4yMpLlzbxiuH2doIIkUEMuMJLl1bw24vcuX8GWzjKAftJLIksGE2Oa112XM0jbEKdrfJ1noYV5SRVYm8v44tLVEuClhWmHNXPk9yJYrTHaY82iY3FEdNBLG6Hupll8PvPskLIzvxNBvn6QA7vRwDYhB5d5DLyRKZmSS7U0MsLm+zvl5B8AU+eGA3YUXm6pUthnd7DBsVPNtkNHyIWr3ESKiKr9d4/sUlNq++lc9VltEqCodjUeyAh2VJqB2Xwlad9EAMTZVp1bs889gllue3uOute9mxZ5TfWnyGR7fOctJc4bAb4gVOU100eE9uD4lUGEEUmd0zzNBMiiWryI5IlH8V+TNWSy3CmoLrGnx5K8hyK0a+/Bzf/44pAEwchIBM3A0R13Weqp2h7RlEJYGIsEWrY7MnWyERi6JLMhXbZd28Siq4yMV2hHcFISB6FLtREuEuW5bC7mALNVgiEBim1jPY6rXZM5jlb3JJApqMrojYpovgXO+V5DgeQV0jkw6TX4nQqMaIsoN6oE12IPY/FUjxfZ9dsRxHD4++6nnCmsbcwGs3qgXw8el2Wzh1m2q7ziTXS7Ec16PW6hIOaAwkIjTaBmfmN9AUhXuOzTIcOUo47DEev/sN92nbLrIsvuFnnR3OcL5UJJ34u00Z6uvr6+vr6+vr63uz+5YNpAwMDADXM0oGBwdvvF4sFm9kqQwMDGBZFrVa7WVZKcVikVtvvfXGmkKh8Irzl0qlV2S7fD1N09A07TWPf6vZk87y4YM3oToil5a2MI0ecTFALN1AUjrImo4YCmLMiejBJNFlGEvE+b9+7B38t99+jEolSG6kjdUZZWdknJ7t0JVcVi7Z9LQIgiqill3klosa1kiORjHf4uLaIk4XED0c16HuOjy1ts7NO4awOhapUIA7d01wsmYR1CYQlBeZnvoCbmsYLRKis+t+Sq0mlctdTpwv8B33/hKCb7K5kiLjBhhUN9nuxOnUY6xf9dEjdfa+b5Nu+QDBVBZ7DexLQXqWhJ2UUYsOsu8ghDUMXaKj+cRDOrmkiS8aBGSJH91/kOcK27z96E725bJUl6u4SpKDIzFcY4qbc6MY2VF+/fnHKQl1loQV3nPfNn9+PsujT+xh96EmieRu5vYNIbph1q0kdlOkcsqkUqmRSugYis/C6jb+ZBh7vYloC+QH4xwbXMYsqxQPZtjdHebi6WUqaz2iVY1GqsN8aQvTscjIQbLRMO+4ZY7/uvgUX8kuIrhZ9IEenbJOz/GQGyFQCwTsAJbpICs1xLKKl7CI+AphUSEbDLJdblF94jLxZIi77t6N7bgIAgSDARKpKJdbJf7qyjyu6rNraIiu+gg5fYupyBGmxjOYlkM4pNHrmCi+xLQKi8X38iM/HeD5L/wMH3jwViRRJxFt0RUdZLV643v5e2dPshFuEsVBFAWG9QgDegcZh7P1IVobF3lx7THCSYv9R48wMZnhua1nmPC2qVg9LllJup6CsHkTX+y1aFtBbhvvoYQ9BLfLpWKBJ5ZW6fUs7p27npVhOS5Dw0nkYpPhwev3hf/024+ysVnj4Nwoq2vTPP3JEd5xzwzOwCsDrZ7n0W4aKKqEHnzje8BTm6t8bvEq75neybQQRRIFskOJN3zf1xMFkfdl3sOFzSUycuZlxy5vlTBth3rHIBsPMTaQJBRQEEUBN7GCMrZGJ3ANOPqa5y9UWjzy7GVmRtPcemDqdfeyUKqwVm8wXyhx69Q3nsnS19fX19fX19fX9+3iWzaQMjk5ycDAAF/84hc5dOgQAJZl8cQTT/Af/sN/AODIkSMoisIXv/hFHnzwQQDy+TwXLlzgl3/5lwG45ZZbaDQavPDCC9x0000APP/88zQajRvBljcDQRCYjic5c2Wd/3biSWpuhQ9M38RoeBf74ydYqYvkw9CMdZE8EVX0eXJ1ndv3D1OWLf79J+5j+nSbdTXFkexlsqEo165sYl/bRkwlaM5ECHQdkFz8kIIbdvFCQM/D74oogkw6FqagtilaBpbvkw1rGFtdLi5s8Gxpi7AjoV4QCGRStOIKa0aCUvMCakRkx9QcuWwUQZzm0uUtKsU1PCVPzTLp2KCUfMyWh2Z6ZKw68dkxZvbu4C8/fxrWBaSwhxD2cGIiRlBE0MAN+5xyNnjo6u/wrt2fZKWR4omLP8IHbjnEh77zdjRd5cWzK5TKHTR3mLe8890MJqO4jsvVQofWyRoDNze5JbrE7Chk0hmqpRjrlQYHx+/hSGIHxxM3U3fPY7sudl6g3VBpKlt8des0iW6Ohmki6TB0X4GVC8Nc+lQY8eYeY3M6p7+8wNbyNkooQHYyTta+gqns5fnz29RaDW65dQItoHDqygptTE4v5jmc1TAkBUHyGQvtYeEMTJYn2PXOFLubbVYHGqTSTYIXBhiqSSR0CUO30XQNzwfXvT7neXz3MInBBOOzgzze3sDxfLJOigdHj7DpSax2L3H3wB0oioSiSPi+T73WoVSo0fK+gq11CIdbBBWZdsMknYqh1W5Hqi3gRK8HNIyuRbvdxpEsmlmPhttmZ+QAh9UGFcMiZp9kcOwHqQoH0KUgkh+g1TLoNB2kkIAqubR9BVnwyV5QWN8bx250eP6pNj/4v/4wQSuBq2vUGz2ycgDHdpEVCdN0yeXiJMIB9u4bw3ZcHnvyMkbLoF1ps7Fdw3BdljarvOddh18RSLEMh3KhgaYrDI+9FEjJV5t84fzn2D92iKOTL40Rtl0X1/co5Rs89bkTRGJBfvgn3kLVMjibzzMWimGbLjtHMgRfpQfL3wgKQaJ+FM9+aeKYKAqMDSTomRbhoIYsS+ydeSmwvGVFme/mOGrFX/f+sFltcHY5j+E7bxhIedfenaT0ILfO9IMofX19fX19fX3/lPRLe755/lEDKe12m8XFxRs/Ly8vc+bMGZLJJGNjY/zUT/0UH/nIR5idnWV2dpaPfOQjBINBvvd7vxeAWCzGhz70IX7mZ36GVCpFMpnkZ3/2Z9m3b9+NKT67d+/m7W9/Oz/yIz/Cf/2v/xWAH/3RH+Xd7373m2piz98YH0qhxUWslkMoqqMJQXZP3EersUzBKBHvamiaT9k3qETLPGRewT/mcCha4F03XeZjTx2nUIiTjzSoNxpYrourmPh6BFOTIOVhaTaV7RZi20Fp26glndCYwqHMII9Fl3Fcj8GhKOfmV4n0zrB66VkuhAbY5Q4x1Jtls9XkZFLC9AJEViXkqMCdO+a4ddcUL26s89jpeZTqX7J39iJDEwfY3NqJXzIJ13xCaovGiXF6V1vsnPHYu2+EM55PxetiOB5+SMZNCLgxkLsGou3T6Ti4vkO753O1Vacpu5xd2SIqa2QzETKZMD3B5yd+9U+IT4f5yIPv5kt//gK9yyUaYYGFxB5m2seYPtrmjFvGDIlMTuwkHND4roFb+NdPXKI2XyXsFuh0JVxDZP5CgffPzSAEJCLJDrHhDqtfjaJtm7QyIVK7dFa2qxiGhRyCybFn2T/5IoVeiF77w5iNHk88dJ5nP3EGI+hg5zTUcJadozfR6b5IrbpMMjxJREqDKPHQ4jwNY4LdY59DTdsslro0zgu0ezZv3TvNwYOjKIqCqkisr5SpVjuoQRUtoPD2xDSO6zIXz5BJRchwB3PWLVxe3CYe9RgfSSEIAvFEiM32rxHV/4iGFUTwbU5cWGRyxwgTkxm80h0svpBl7q3Xe4H4vs/7h/cgb9nMhTKk1CiO6/HsxUO0nSZ7hDneeTTLyOCP4jgu7bbJ2TMr+LZEdleDGi5jkX/Biyd0KlaPuQsyL5yssyIUiTgZuh2L8aEYw/Eovg+ycn0aTS4T4fu+4xidRo9IJIjn+khhha5jUKq28BwXQZIIBhRisZdGCfu+T73awfd9UtkoytdNH3o0/7s8felL3DV+lvVulqN89caxu8em2JvOsXF+m4cubBFLBnFdjwuFAl9evMZkMEHIkoiFAkwNpl7z2hV8j3q1Qadj3HhNkSR+8N6jeL5PUFNe8Z6OGaBnhWl1XjtAAxDUFAKKRDjwxhk2Q/EoDxzZ84br+vr6+vr6+vr6+r5d/aMGUk6ePMk999xz4+e/6Unygz/4g3z84x/n537u5+j1evzET/wEtVqNm2++mUcffZRI5KXa/f/4H/8jsizz4IMP0uv1uO+++/j4xz+OJL004vOP/uiP+Mmf/Mkb033e+9738tGPfvSb9Cm/ufSAQqoSx887hKc0Lq1tsbhVxXVhNBrlUHyM55cuoagWc2mX/eNf5PHqFO8/MM/MQJV7JvJcPHmMxcsbKIaPH5Ow0goC4CEhthyUqsjVhSKzY1XMkkQxomBHSzS9FPvGBtioNOgZDtGITcxdphyPY0ajRJFYuLBNIlUkN67RE1Wa50ZpCgHWIy3OKuv8++cexr7UZbwASjNCz9Mg6tLQBVpjEkMJl0PvuMyGncTq7uf2W3az3e7SWXZomyZ2GDxJItTxGFbizEQT3BqY42N/kqOXfJHjx1/k01cSRC5pHEsN8PZbd3PTYI7PfeU8S/EW9nadq/UyeAKWayN2JaqXkhSzKrWGic46twydYq1TYze/wfm1SzSddfbcmcfDZ+MzSTxVwVdllkZMbtemefIJE/U5m8iQTi3rE76lyUHtAO5okXXPJhbXqdQUNrbi+IMmtx+bI7gY4tzjCxR6Dp0BEeIK1USTz5iX8OPbBG1Qm1kCWoBKp8vm/Apq2GLZHcR6CiJKnog3RjQa5MjNU3TbFo2GyeBQguHRBI7rEk0EkQQBo2kRMWSEl57fKVSbfO6piySjOj/64O1Iooge0pinRaCbRCLA2gs7uFo0uLCc5567dtNybZaCXT67vIh0PsKBeBq9CcNbKYRekHqjiwBYVbAVgVg8gu97nCn/Oyy3yaz6c4R1lSgpmqUQZTVMOHEzF0rzFPQu96SzzA4bpHIRNktNei2LSqLFCeMi7xu+gymGgOvZWblMjLzp0uvZxHQVLyVj2iL1yvUxz4okIFsuxc0aA6PXgxuO49KsdxEEGB5PI33dtJ5T1UdoqQK9pkipLeB53o0JOKIgkA2FqUQD1Do9SnaPQrnOkeEhAorMsB6hVu2Qi4dfdq1WGh2qzS4zI2kEQaBQbDC/UERPLbPnyEtZIwH1tW/T7x3ZT7PgMuS+diNbgG7XotkwaLd6b3gPaZkmz2yscPPwGPGA/obr+/r6+vr6+vr6+r7d/KMGUu6++25833/N44Ig8Iu/+Iv84i/+4muuCQQC/OZv/ia/+Zu/+Zprkskkn/jEJ/5ntvpPwqMLi/zR4mlCYYGJUJjtdgsxKGFbNjYeR3YPU1PWKa/k0aMa9+wMEIw3OJxY48p2BFv1ePLSFDlZxKkbOLJKKpGhVLEwEz6+LCJ0dETJISB8BVEVUQcc9I6NHzDJ57fJ1Yfw2y5m0+Zdo4eY77boZfNYZobW020a+SYdkkxezVPyk1R6Dq5okEmE2eq0qTcttHKJ4pUgje2DbCXCiOMinuZCQEaISOh4HD58EzN753hxYYNmsYtRNpAEDzsi4gtAy2V8R5JdM2NkB5PEVpIEtA7J4R7XVpsoehJbhlQ8gqSpBAMaweUGfkfkWG6ExHf6fP7J0zhNn5FWBDklUC+1MMwgWlFmZMf1qEN3q8JIqE40alKcCxP7okWrFSYcj1JUTHZlYgyNZNhsyEwlYoTevoXVUHj61DJORSAbDJLSoiwUZ/i1x0P0Ihp/+L+MI0UV1i8WMattwoqP03ZJtIPILowM72A11aVt+sTXVJpLRRLTYcZ3pdhcN1HXZVzaNEs6NQXUoIJjeLiihyBAs97l5IkFTMNlz+wg7SR8Yv00UrzA++VDvDt1F+VWlw2jScMzWVwsMDWZRVEkXsjfREDL88+HT5Dd0+PPHt1P17ZxbI/FwS8wN1rk5Lk9BM+GGTikY/Rs7K5NR+jR69nIosDuZBE3vkUwVML22uQ7TyAgUOquUmuA0b6dz697TE6rTIUuE2iKyHWX2qDLO/75bdyyZ5RS3cAIm5wyz3O1ucaVyBpT4aEb10I4EmBoNIUeUhFFATMp4lZ8HHxER0AVwLQdLNO58R5FkUnnooii8LIgCkBMGeVMp8tjG7tomxP84LHr5VIvWxMLUtIcPBEev7bGhyZu4q7JSbYLDRbzLbYCAWanXurL9LHPPEup1uGH33czu6cGSA8kCMZDhGJBvlFjyThHs8OMJeKvu86TgbCEHHplVsvf9vEzJ/ir9YvcU5rmF47f/w3vpa+vr6+vr6+v7x9Xv7Tnm+dbtkdK3/+451dXuFIrMBVP8j037+ThlXWikSD7pwe44uQZGUzSlTbwJAF8iVbjbVimwF//eZfcnms0fZV2rYIg1KDhUXAthEQUwegg2SDYDlYcvADkBqtc8QZAdEgpAWqmTXWzS32tiBaQEdoeTzw5j1OP8ME73sOfPn2BF+cvY/YsBCHEJXc/o6MxBLEOpssTZxd54K0HmU0MU5UN3OYK3lAUUQ4gdAUU00Nv+jRW9/HnC7cyGXapGdcoNjtUqi16Evimh1T0UXSVkWSCpuCyuLrN7gMjjB4Y53PPTSE96SIUFN729mn2zY7R7hicXtlCSGgICz6S6fBvf/HP+f53HyFEAAomk0dSxHIxXHwCAYuQbhKVHXzfZ2hwjNZKiFOLMcqnogSbJuoI+GGJtXKNUNFku9mhGxaoKCbbKyKSo+Gv2Ch1l3hQRlZk3rX/AA+vBNhBnOJWjUcev8B2qYkvwMxwlp3DWUK+jLsuMX0sxye7LzCbyCEPttlYKjBf36Qr+EzkChT0DJ2Si7xRxw1r/M4nn+J77z9Kr2dj2za/+WsPc+XqBrFwAL9jcct79zKU1WnIHufqiwxvjbNda+M6LoZjUm/2MEwbRZGY7qR5Np/GTMosLA/j4REPB1A1ma5hUm8m0CWLsWiUscEk2VAYU/RIJSJkMxEs1+WkpiC5WQIXlzmQi9BY/V8pFZskRiKUK1ts1ZoE4xkU7RkWq3/AA3eM8H+ffIBTa9u0AyJ3HZlldur6aN5W6TSb1jJToTuxHJfFYoXBWIRESCcafykgkcpE2Fop44kuQ7EYviywY8cQ8VToxhrP8zB7FpIkvqLJ7Grb4rbhZfZE1xDdPKIs8bdlczEaUxIuIg/VP8eHuN6TqdLocHVpm1hUf1kgJaypNKXejYwTTVMZHU2S/rrMFc/zefLSEle7qxyaGuJYetfLfmdYUXn73CzR1wm+NFo9FuYLCK5PWH39EiCA2WSGRCHIrsTrTxrq6+vr6+vr6+vr+3bVD6S8iWSFECFHQysLnJ9fo+YuEpiVSA/exJSZQVMlbk7cxBm5wnqvza9dOsHd7jEMd57TDwd47lkXfaKDbxvUyxaW4CMbPn5IRTYlbN3CVwWsgMjO0Q9won4BQ2wzVZji9NlVrHUJMeYTzYUYH0vw3EIeo9njwkKeC4tr9FwX4bCLeNAkU9b4jgM3oT83z+LVPOJWhwEhwHEpzkPPbSLqHpH7CjhXU5Qc0KpdfN+nE1Wo6jYr25usFZr4qkzNM/EEkB0fbAE/BE5U4tpqicVCj1atx10/cYTCVga3DSOqx11Tk4yOpXn6uXkulEo4og+Kj+j6XDy3TvvOXQRkD9eBTFhncDhKOhliYQMe3dzNiTNvYX/hHLePTSA9FmTzbBa17mD3bHzJpqr2wPDZaDhIlo2WhZAm0mzKeLrA3qM5dKvHyvl1SoUmd751L7/5nu9l/uo2q+s1DM9GVEWykSA7jjxLLGGhr/4zqk2HlfkqN/lTaJsuUjZCeCZNXqphVRRmlBCRwTIXigEso4uXUHnu4Qt88L7DCJKAYTgsrhTpGS7RtEhVN1jplflPtz/IheY1Ns/U+fQfPcXMviFmRzLE9QDZdIRQUMP3fTK9MNu/leWn1B+ABPhhML7WwDZu3cbVxgK74tN84Ph+MvEw89UCFxe3GRmykGWJlt0mq7SJiCY5bQPLceg0Mni9MKGIxvhIksnJDAejQaKxj9P1gVCZtuLTtg2s1S3+8NNPc8+xXURCGj15lUGtRc/dYrFY4fPnr7Izl+a9B3e/7Nr40ZuP8IvntogmAqQGYtiWh2U7bG/ViMRDCIKA5/o4tovreC8bj75ar/H4xUEmsh4HcyYp9firjhCu1zpEB5p0NZXBodUbr5dqLcqNDj3Ledn6991/gGKlycjA9Qk/oXAAJaBiOd6NNa7nsVgr8FfWYzx8SeajR/8lQ8GX+qyUS01q1Q6iKLxmMKXXs9hqNbhWLnDAHHjD+8jbZ3fy9tk3X/+ovr6+vr6+vr43O98X8L8FskG+FfbwD60fSHmT6NoWgi6QjmrE2i1Wty8xfPcC4UEfVRnn5uH91ApNil2Dgi1geB5ls04irlCrNHECAugBnKCAWHEQ2h6yYyMqErIlIVjg+zJC10eqi4T0vcjWJUQjwEahSXdVRDBcUBwMy2G+WOBar0hA1Xhk7SLVm0ogSciDPl1dxrSbrM+XUeIOgl9n9XydF5+5yOVrn7qesaEpBKZAWG7gNsCXwIsreDGVpKxR1x1KlolomFghAcFw8T0fV/SRHIFVu0U0JCI4Do1yh7XlClLGR22LHJkZYaG3yGLpKmPZaXKBAA3DoGy6+KJMKKiSHUwwcesW1UqHC1eDvHB1g8JiHrFhUyzvZHRXmnqvR9fbYiZ3jY1CGNeQsMISzf0aA3aQfWSIhWVq9wqc8ddpdy0kEURJIOTKyGENy/PwLIfPXfoKS802iiJx77FZakGHZU1n71AMc/hpzAZoAdA8mTOX10hHg9wyM8KlpRKhRIhdSpB7Y3t5bFHnmflL+NsFdNFDM8BRbbqGTafdw3E8gskAXddiem6QK/I2K/k6x3ozxKtxHv7KGZYXtqk2W0x8/x5qZpsnFi4Qt4McC0wzGIkg4mKLFu+/5QJ/8cxxxlLXs0M8IYzkD3Dv9C7Gstd7dmiyRCIcJBMN4fsWwdZ7+Jc7a7iewOrG93Dy4hpjmQQ7ju5AcQTKazWGx9M8va4TaP4oD+wP8eknXQSlSVdzqHUa/MEXXqDZMHjwnUe4ZeCHmArdwljkIK2ey66BNDty6ZddG6ZpM79RxnZduq7IWr2EMFNmqRHguDJ9I2giKxKJdARBEF4WKBER6VQjnCvv4Nnq+7l3fBzbspG/lpVS7XS5XCgxqAV5cCzApn6V7x/7Hk4ubzA3nGMoE2fnRI6hzPW/k+t6eK7Lwycvcy1fJRoNMj2UIjMQ48BN0wT0l8pvFFniLft28cjZpxAFEVV8eWlOJHq9h0lAf+1Mk2QizKe7z9LzRX538at8iDfPtLK+vr6+vr6+vr6+fwz9QMqbxOVKiSfrKxRrdZq9Anv0JcyrUXLhNJnULKGwzrWlIlvbdeJBnWarSVhSGYqFsH0LPW1hD0YZ3uvjP+UiAggCoYhGxTYQPAnRFZEaPhFXplc3uN24k4tbec7KeYRDLcQLOolUmGxSwpM2aYlFepEw46MRZE3C0wQaIRkDhZDiU95usNW7CJ6FbUiUqi2WxBB+ViIeC5NZn+LCI88jV03stIoW1xgdTJBWAvQGQ3QaBpNqFLe4gZnoUpMk7JSM1nKRdQVpSGfY15jMZWnn24gjHtmxGD98/638x8u/jd/1+aGpQcblENumw7om4YQErKBHKpdg7CYfdaXH6jNF8msufq8FHZ+pZgJNKpBJ6+j2rRw6uIPSwgrnPj2ANyLj+SI1z+DsxXWiJZkP33YP8rZLO1/HGm3gBEQCp30ahokvAzKUpQ4nF9cRe5CNh9gsNCmX25RQGcv9AMVKi3LHZ3gozna1Rbtrs1RqYuIRlALcPJllZyzBs1mN9hgoNR/R9nFSMupsmIXlbbbX6ziCT6vcQ7A8wrLCLiVDQAthbJtcOLFKobFB1+zS3jARN0o8cPwAn88/gVwWGIrH2d6qYadE5HED+5CN+qJFQFbwfZ/d4RhLapmaZXByeYMDY4OEozr7944wlI6zla8RcXrEdYNlI87I+F3Uah66qjCSS9KstQlGApzpfRYp8Rc8mj/IHd1/yUr+aapODcMHUfCwE/CFhUVGF9K8cGGTmzOjjBwRSIR03nPgeiaKZTmUK21iUZ1gUCWZCkJEBcfDG6wS3FGnUN9geCJ1o2ks8LJJPQA90yaoKIzF4rQaBpVKhS8kllHbdzKn7ECWJc5ubfPo1QXuHpvg/n3votT9NH/23Ceo9Wx+4p7bGE6GmRxMEg29VC7keR6PXLtGudvl3lKV6aEUuq4yOZNF/Vt7iId0hsNZJEEgrLy8+Wskqt8IpryWrXIDAi38aBQv2HnDe8lmvcliqcKBkUHieuAN1/f19fX19fX19fV9u+kHUt4kdiTT7CGL75hkNImA1cU5PcLI8G0YsoKacBAQUUWR3GCUlYEOESVIq9Zl/HCe8KEeV70hKsUc+xIDNLKbSMcaRMoytasihizgJx2kIZCdAJ998QqW4ONUbJyxKmq6RweZvG+SmDuFIuWZk3RWNqN898zv4z29jyvnhmiNAh40TJ+y18H1PUTdIZwMkM7GESoZ7LhB19doliTEhoPg+ghFk2BlleBkm2hjF9975B6KpSbzyyX27L2EHGxz9gvjFNZEJE9kKBmkoLfpBCAQ1Jg7MMYjZxcxWjbzSyUCxVka1zp0AxKL17YpNJsERjQae7u0ItDTXJL2D1Brnqe6XsT3HBLZBNVSgyeWVuh9NkYo0+OTHz7IMfXfMvWhAh85/Cu0r8j4Z2Fgp4MT8rC3okiWyFFphI9vriKmbYaTESRBoFJoInk+siaxZ+AgYSPA+fObbG3UGR2Is2ws8+jpBQ7LPrsOreMV30UwJBMYC1C40qNQaqO0XQTLJhYNISLwA3sO8Olzp5AHFcRokOT0MARVTp1fZWO1yDmlTjCnEGirqL7IgJNmTMjx+UfPU9qs0h5cAD+M6/oUm23Wmw3eP3cThmgzJqVZVYqous7OoWskwy123r3GkYNjCIKAqEjURIMvLS1hOiKJsMLHLjyHZ6m8yw3QWjaYmfpjFox3I8kVTPUPOT76G5zfKHBiaYNdmRTpbJS28tcc8lfpujGm4nnOF4p4koBsAJ4Alk+71eOhZy4wH6qwVqpyc3KIsdkBJEnk3Fqe5dUSIUdidirH+HiaI7MjhCc1iPsEK3HMsw4juTE877WvqXbP5KmzS9i+h+qLJEJB0mMeS6EVvrQuE67GSaQj7M5lcVyXfUMDXFr/byA7GKbC1epVSqUDNIttTl9YxZcEhoaTiKKAIIjsnRxko95gevh6Bk2nZ3Hq0jqqLHL70ZkbWTG+76OJMpIgAq/dnPu1dLoGY+kqS5ZKJtF4w/VnNvM8v7JOSFM4OjbyP/z7+vr6+vr6+vr6/nF4CHj845fVfCvs4R9aP5DyJhFRNY7FBkm3FLKBAOfPh/H0Hj3H48pKnj2hUUYHYzTcNmcSPbqOjlo0KLpVvBIcG10h7bRZudBgdHoP9USZ2m4Dc9vFWYjhKR6uZyNvOde7QUddfN2gpVZp5GU0V8VaV3EHHFzPQxtw0QsS4bzG2RdHWXJ1GlMQetHHGxHhqkd71mV26ghnnruMGIliuRahJQGjptASHUJ6ACeqIHQ9nJSEeoeAHikQSW3Rqt+EpqicX9hGj3nEoj4OLqLr43o+zYKJjUV902LDyTNhjeB5Ar4BXdthV3aGhtIlEQ2TGo5Tx2D64BBP2wuYhoMkySy1LM6veRh4eAERdzCMKxuIhoxkeHSXNGxFZCAd57EXH0FWXKLTHqbR5gdufZZMts0fffyfk85E0XQVR9JwRIW0maK22qJR6iAgMTIywPH0BPVuj7PdZayeyQ/eeyuf/eJZKpLH48sW0eENxhMXaStZLnTLtIwueltga34bz7KZncqSHYgyMpxDKjh4hkBnOMSkI+NsGbRsi6tpG2k+jzat8X3BnfimiST5OBIs1+rYhsWCE8PHR1R8Yp5MdaXO+yZHCKcVJENl/8Ex3lrZR2v4eaSgR7MRBvH6jdKsOoQ2RQYyGsOBIP/m5G+z1egh21l+YO8hEnKAZCpMeW0PMqvE3bcgSiJn17ewXY+pdIJ0LsrCxvfwxNKnWJCjLGW+j5985x7+z0/djmh6SL6PbzhERY0H7tjPs70NkgWfk08vEo4HiaXDPH1lleXlEodGBzmeCvGnyy9Q6LVIhUL0OiYHh3fj9ByOzkzhOS7w8nIZ1/VoN7o4+EiiCAL4MQfHc1HaA5hWgkx2gm7TIBwJMJBOMhCdxTJt2u1BtnppcuMlFFrkSw2qxSZXFgtEozq3H5+lVe/SbZvcPTVO1TIYjF0f5y4AL55fxXE8Ds6NEgldzwaJqUF+Ye93IgoCAUml3TGRZZGA9sYTeACiYZ10dz+ByQu8c+Tdb7j+yOgQsUCAHdn0G67t6+vr6+vr6+vr+3bUD6S8magCq6Ualhqk67jogsjVjRLPbeTJVTZ4cHYHn9tcpCOYBMe6SJLESDDHo3+RYu32DOnhNmfXVdbi25jrQTpOHGlDJGxLNBMGWstBLNuYm1ucOVkl9UGJzF011I0MQsAnmHcJVQK4Z3ZT7aUwz40RbpnUWx9AWJ1HLTYJplN0Ox4CDpVemXYdHEOlhsVXVv6K7PE2zbUBLFPmnlv3cPLCGun7L+HFfWYUlYFOmdlAieWFq4gkyUWDzH9pP7E9MWrqErIjILkOZq1F2JIQmgZtsculixsMBDZ5y1vPMJ0YY2rwHZhdi3AkwIG9EwwPpnjeK9CrBYiseygfhPVmCze8TOJwFaMTpllUkaoe8Uidg3ed53JhkGrL4tr2FvN/0KKZC2PYPkJEwOiqdFo67YbH/EaR3RNZwhnYjtlcqG0xLaRBFHCCKt2dKvG5OMWnWnSqPa5d3LpexhFVsU0VVxSYXxtibdlg30CVm4ZH+Pwz51jtGGRiOk5PRtEkBFGkWKmjXGugdD2MjI1RbmN3bFRNQxY8tJaHFRJYKxYIJ3UGAnEy8TBjqTirq6vUXQ1dtggqIsmgDprL5ca/QvN85qK/hZ5M05Z9zm9M4Q94lBoRTp1d4133x5keyzBZvkYsoOC4HvcPfoXusEr+bJrlhbcwddtOVFXm9tBhMIt01BFUWeLeuRlMx8FqGKzlq2w00jyxtIfQaAsBj8NjlxGl2/AdE8kGv2uTjMW458Ash+sjnGku4pkuju2iyjK37xzH7FgMD8VBE3iyuIDn+3zfvkMUl1t4JYOa3ebquXUy8RD7jk697DKyLYd20yAQVLnn8AxVo0dn4wnMhs1ntxYppUViNLlrV4hw9KUGr6qmcNvsW/nhF76ED9w5fTPHhscoxBs0t9tMjmUAkGQJBJ+LhRI9x6Hc7jCSiCGJAksbZdpdi818nV0zLzWGjXytpKfbs7h4dRNZkji0bwxRfONo/1AmxlBmDxdbaTLa3I3Xfd/nWmeekBRmUB++8fpANMJANPJ3uAH19fX19fX19fX1fXvoB1LeRD6/sshyr4qOSDoRRfFdnuvW2XR69GqQb7dpS3XCfhVNc4gkU7gjPtZuhfsPbbKwPEB0TCCuRSg4FZyFOKInICQ6qAds/JqF/fkO8lYHAgaVUzlG723jF7P4GR+3K6H6Aoq9Qc/ssUibqKYhSyrxRQdnpUfmgwqNbAdKLhvbLhGhjeDZuAaUayqhMZ9wuIueHufSepFwJEpsRCUcNSmeTxJ1EqzUfPxqgFa9TM3uspVyCdQdpmKDrIWKBDC5c88KvvUulhuL+MBkJIEhV0jqdSrmcww276bYeganYbBSEqkUW9x29zl+9C0P82d/fiftjsnOaIxgusjq+QCRqIuATN0VmNi1zZS9ybE7N/nIX36B3UaGtSslyqfjhA53UOdMPvXY3Wj5OJmxNFcLJZbWi9SCZfSIBapFIj6O4JcgqdDKQlHqsW/fKI8/cp5IVOPyWolAxSFVcpEkk0Y6RndF4eSlKwzvH8TqeZiqxDvec4T1lTLxTJQnn7rIhcurKJstxFAQdIlwR8f2JY4enWL1yXNYusBYOMqIFqdt2RimSzCoMpVJcqV3FaUEYstBimisxcvU1DoHnCnCYgm7q1LaqFKqtZgb3uLu7DyFWI5c9noT1cRsi5h0idrqCK40SdMNsVIOUS8n+Hz9sxzcN8RwJkVEfBi0EonoAnATU19rTLu8VGB1u85MZogfuv04A0mFueQ4/7+HDWRVROx00QoClgmmbDF/Jc+Xzy+B6/ND7ztGZjCGYdhU6m2KXpdNq82dSoD/ZfZOTNfmpvQk7UmD1ZUyxVKD/GKJbtt8xXWkBRSSmQiyIiHLEt2eSXPRxjJd9C0LHIFyo019uEcgpBGKvNRHJJWM86+m/zdOb67zwV2HGYpFySUjiLrEvulBAEKRAMGwxl1MUml3GIpd//spikwsGMAyXfBevYRHkSX0gIqmKd9QEAXAtl0OagOMBWLcnB298fqWscGfrX8CEYmf3flvkcX+/w76+vr6+vr6+v4p83wB71tgYs63wh7+oYlvvKTvn4o790xy6NAo737LfjLDMaSEynKjge37GJsNoobI+2dniJYtrI0Yu5RxPlt8EfuYS68QIF+OUxMy2KEgZFSaR2zaoyZyR0LclpDaKooogCTgywLZiTIpuU1sxcT8dARrU6Rqtik0IuzAIBIIY8UVtrwe5UYH35UpKx3kuIc7IOMJAiFBYUc4S1TTIa3hxBVMM0i13uGRJ0/T9Wyq23Falk67oLMeblLIVpjencVqd1kplWl7NmvlKlpFwQmq2AE483yca+c2EUQJRYFkIIDcuZMzT9/K/LnjfOLsM6y6v0TJ/zVcVilu1cmoC0QUg8mhLT772dM0ix2c8jSNC0EKz4eoWzZOSGPjhQibj4dhWcRrlTm3mqfqOAgWOPMy/ooGFY2oqnNo1whDsQhmtUM83MT2JBxFwDZccDy0lsuDew5y58Akp84t0+126TS6/Le/eIq1q1uI6w0CbYG0tYuBeAo9KOM7Ho7lYroutuXQ65gszxc4f3KJzcU8WUfCj2uoqoBd7yKZPjt2DCIpMm5UIRkIE0+GCXrgGjaO6SJ6LtciFlgu+CBEZXpml1q1ge78OMeHP8PKosmpZ6+SlmS2NsJUqmGWjCyhyPUmqjl9GK0d5nB2kDt2TZINeXirHomRJmoujxywAPhKbw/PGeMsO0Mv+/5mszFGhpKE0zqXDZcntgyWjA9ysTNJIKGxRx0m3FOQBQHXcPjT58/y0NMX+fxzl6i2Tf7g08/x2NNXqG40CSMzHL0eoDiYHOVYeoI/O32Ch148y+RMjlBMZ6FQpVxpYJnWjT34/vUAhh7SbjSePbuWp9dzcTyf4WCYwS2ZY4kskYiOHnz5tBzf9zk8PEKtYPHfHz+F47o8eeYan3riDE+dW/q6dbCyUaFUaNPo9ADotA1mRtLsnRm88Tc1TPtl51cUif1zI+yczn3D94WeaSP0YEKIE1Re2m9cSZBQUkyFZ5AE6Rs+X19fX19fX19fX9+3u/4/Qb6JHIgNMGAHGU7FeQ6TpX1fYe8el7OPTBLRREzDYq86xUAsT8+z0M0Qw4E4xTNFPitNcDUwSEdRWK3VkDURz3MxZIe0oeC+oGJp4MZltGEZQQ6Q1BUaG206pSieLCA5HrohomoZZPluRsMFgpJAJhJkHhFfEYidVanXHShL+JrI4akRUjs0dvjQGjuJluliZ6MUOya9uosUkmg+NcbmZ5vEjzYJpR2qSZv6aotWx8Rf6xJydQK+hBXy8FseZlVmqxMgEC4SGdcYtFs4gd+j4EW4VsqSaKr0EgEsdz/ZiMR0cppaKM/m2j/j2Rcf4cLzE3zH/SJRR6Pa8vB7Hq5r42odcATMbRF/SmRQrHNr2GJzoEjZ61C8EMHZFuHpGOlcElmVsR144M79/EZwkYl0nrgfJCTamOY0uB5ix+W24TEUX+LME1eoXVynmUmwZziEIgi4nkXP73DplI2PxqGdAyicJZcwqW3EUDyX8fEUpuGQiwRxyhL6rinmQkn8mkxJbBFQZCK6yvRImkudOktn1xGokozqSIqMadksL5WopTy8EQ1P8Zg7OoaeiTMsKkyFsgiCQL7doWQajA8m+dLlJBvXbqcmBBlMXC8DqbYstqwQqbjKSCZGqjbO6rMWwaLKoWNT5B68npFxpTfMWg9+PHU9E6Va72DbLgFVAg8CyMwlsuiSQ0b8GDcNeFQ3B1ASEko8gOCZJAZjnCkUsAUHRxB47KsX+cLJefZMDfBj330796R2Ek+G8H2f54vrnM2v8ddnT+BsCGhthYv5Imd7dWb9geulNoBtO9cbAEsi6YHYjWavA+EIQdVDlD1iAxrH3Czf9/7jpHPxl11/nbZBMV/HUKDW6dE2DCzHJaapRESFqHY9OGKZNpZpU2n32Kw0uM2duvF6LhMl6rvEo0Eee/YKD33pHO++Zx+JOQfDM7g1dfRlo5m/ET3bYaVaZ0iKYjsuytc+r+mZdNwmmqPg4yN8GzQF6+vr6+vr6+vr6/v70A+kvIl8+fmrPFte4/Ydo2ixDkrQI63IvO2ITc5JsFHO81ztaSIliYQDzww/z8HRGW4OTNKJXUNzTDyjhqpJGOUkiuVgKxLN3TIseWAJSAkdsjHcusG2WiEjBfCGHJyqiFRzURSNlmHz8Jl5HMlnvKsylggzOJslv1hlIJEgv1XDkUSSIYVQV6DmG+ijYQq9AGO9Dt1dFulNi0YljODaoKj0BjSMqkbOM4gUxxAI4koSpqogVh16lkVoPI3qetczNkISnUkBcQfMjV4kGVninYksn1p6gF1zWUbmhhlP/gai5dMeXeDChTIn1hrka3uQJQE8H6dnceTIWaxyiMXaME42in+1hijL7DnaQh/UmPC7pG89j+P4bK1FqK9HufzQFJFYgFBEQw+pSLKEF/KQJI+M3EZwFa5sVq5PoJEEFEUhHNQYzkaZb5g4Rplmc5w737WPZx79KjYCli0gSz6X57dIh+YZS2hEvCwRXSGRjHDl3DqSJDG5YwzLhkrLYnwizdhomoAoMTQYx/PAM1x6HRch7DG3Z4hoPEIoGGB7q8ZsWWQhIZCKqIwc7LHiCbS2Q2yKZYbHM+w/ME4qF6VnGiglBzEooLgWruvheR7XKk+Qy1ymq2QRRZFS1cIvKLQvSrST1zMhHNvFrkQIOBmKawb12hU6voeiKcxOZJBUGdEXGI9dRHYfoWuc4n17Aly69v9FCTiM1qOYxTpRTeOWRJIvV1cZj0cp9nq0BY+6bVDWP0KhKXJT8FcwRY//fvkkG+U6piFAzee5E8vUEy5dxUUfjiJJErbtcvnyFs16l6nxDL7v3whYFM0ObqKNqficlW3KusG/zkRfcf25jofr+mTiIX743qOkw0GCmsrkSJrbuxazo9d7pHiuh2O7XG2WKdtdGr0euXgYURTZLjcwJWh0DK6tltnM17i4ts5W+DSCCJOhMYb0bzwbBcC0Ha6087zgzDNZTLB/6Ho/FNM1cH2Hhl3/n7jr9PX19fX19fX1favwfQH/W6Cs5lthD//Q+oGUN5HcZIyqa/H59le5Y/Yie+t3ESDM495XMMXLhIeOUGKbVi9M7apIy5IxRQej1eNQdI2d+jYnL01QW43QVuvYEx6e79Npqvi+hywLSIJIV3TwsipDQwbJoRaBwQ71no7XESi5PRRUJiNhCoUa6+UuxaBBJ9tDk6J0ohZ+x0a0RJSRCM92i+y0IqQyEXaECsTENmOjIu3yfuKxIKKi4xoegq6A71O5OM4DB+8im4lQ3zXMUqFO1XYRfRfB9AjHNHptCx8BTQwTEgNsrBxlWte4VN6PUozyYrLIpUtdjo4Osy+VwbZc5s9ssHGzjRINEpWDjE/mWF8skBg8xs//6jX+yyM388SVbSzPQ1+TebTzAL/3VJndQpjkUATDlwgEXPS4g2f7XFHXietBVsU8rcUtbqrt5q+9xxhONvH8JLIs4Qoe8bhONBHEdT2GB2KIkoArSdiWzf67d1HtPs7Zyw4dySdYF8gMBQkND+F4GsOpnXQ8KC4Wmb+8SWGlDEmQUwpxP8r+gyPoAYVexySWDFPZrqI1bfBcxg8PoAeD4EK71mEkE0byTWYGqxiywdXuFnmSbJeGOKRmsEyb2ZEsm0KLPz1/iXIhir7uIC3LbH1Xk1QqiqM9zphYYbcSw3U9Dgz8OE/s+TPMQIjj9+8FwPM8jkZGuFJNcumREudPn+Te9x7k8K07EX3QEFBiUK59nJheYdELMa38AKGEjte1yaZ8lubLNAMdxsfT3JcZ5bbbdlDyTc7nS2QHfVr24wBUepcYShzk7WM7uaIW+VL7Cma4x2A2hOQYtH2FtH69v4nv+wiiQCwRIjMYQxRfqnq8ZG7jxR1ExUOxEqSimZdlhXieT7XbJapraEGFZ9fWWWzWefeenQAsb1S4tJAnk4qQSUXwfR+jazGQiBDQFVLREAC1SpNmqUMLl0azSyCgYHke66U2me4444MR/J5G1e2SDL/U5PaNTOQShKc91tp11t0S+7keSBnUh/mhiR9Dl0KIwutXebbbJsvLBeb2jFyfZNTX19fX19fX19f3bawfSHkTeev+nTRFk8v5Irbrs32mQjo+TL01StkQORSIEhESlGoRvCGJW4bH+YC4i/+4/ecYX9zB0ZuWKbYn6S2Dp5noEZm4HcBYduh2wQy5oHaJ+DKao6G4HqLjI224hNb+/+z9d7Rl6XnX+37fmVcOe6+dc+Wq7uquzkmtbmVZwrZkCzBwTLC5GJtrOFy4cO4ZcDiHdK8vGK7hEOwDx4AxxkGSLVlZaqmjuruqK8ed894rp7lmft/7R4mWBWrjY7Att9enxh41xlxzrjX3H/NZVb/xvs8TkuQdkBrj+SwfOnuMf/j8S8TFmGOxwnUjQhWzE7aIuw5oGlEsyTs2MQqvH2BMKKqdFJoeUFP7JKkyRZEjSBRRKcKe88gsHPJ8/Tke6T+JrisKQhAoheY4mKZxtzluJNGUwHEc1JrgsF5m0/7TvB5t0TI6ZBpZ5uczrK/XyLoCqWv0XR/numL04RQPjcwg+yFpU8eynqblPozyI/QItJSJdbbA1H3zbH+xwVZHsXzkKBgS+asW8pYBWUnroYS2aPEPHv8Elp7wqdf/LOWJAQ4R0tpHmccgY+KGEf/s557j3kfmOPfUKZ7/zCW6/YD7l6Y5NzXBtZMW2WoVOZUlqJuYqZi1vMtC/gBj+zF2N+p0/RilC3YHLoNOj5SpcyKVpjCap7bbpHnQo9/3UdUQq+shSybHT05T2+kwPVGk1+kT9T1iWcczd4i6GsWUjzkeYRdOMXNkAjt1d0WJoWn4bkTKs6BuoxyBUDEA50b/FNvNy6Tdk6yuHvD17kusHNPQjib4oxpJInlt7//gH1xbppx2qa9NE+x6dC+bzD4yh1OP2V6vkemk2a9/AP3Ip8mPFRgt/Wken15l0PAYBC66scme7tPf3mc6k6EyXeTBuRKNxOeexQq1wQxRIjiTmkEIwXcvneH0yBifaZ5HESNLJtdf2cTvRlSrXQAsy+DE8Uk0TWAY3+wXEvgRWQFhK0UqJfnjZx7gsZmFbwlSLm7v8dLKJg9MTJDxNdyOB4I3z5mpFHAXxhkf+caYYyEwDINJlaJbd+l3fUYyafLFNKENL6xv0/1KQim06AcR1abLU+Ic96Ym+MlPvYht6PzNj70XxzIIvJA4SchkU29ZF5JE8rB1lImRAo+PHf2W18adqbe46lv91b/0b1i5uc+Dzx7n//13fuC3dM3Q0NDQ0NDQ0NDvrmGz2d89wyDlbcTWDU6Yoyj5NHe+WuTOcovRM3V0NceEr9Hd6OLfgrFZnXtOTfOnjj6BjYBH4F0fusPJ6UMi/TqfWL+f2IvhioU26lAe0XBbVZQZEhyDnJfi/lYJrXmC6uvbuLeyoGJEkDB5dIylxTFGSjnKto0MdT68dJpXvUvUeoe0/TKICD1UGKse9ahDTRg8dGaWTjJG6w2H3gsmcl0Q2B77D/sUpIXmKUxXYoiItttnrddg++I2BztNdNtmfC7L7eVdfL7ZA8IPY0pWGjXo04s9+vYAr6RQQmJZOu7hgK2wiT8CvTkT53qP3tYOlR86xuXX19jabvKRj32a61sJM8U/xEhoUJMCo5BGiyLEKMhOhNmNSCyD7nYaM5ZEKPpdG2PEpxc7lHWXK6ZHOiij7A5BUiRtW3Qdm8iGwfjPsOZ3kKmfYOIdS4xsuUxV8mys1dla1uhvGExNdJAjCRzkIYa5KY9OI013r8vADUk7FoYhUANJKFMUZkb4VzcvY7UiFjsWBzsNDBS6gslKAT+WWAWbVMYkBK56dVTPhNYIcZTmdCri0kBDjAhmF0Yw9Lul4smJBeojLf6P0auUnmiw8ulZbq/WOH1qjinrKXLOOV69vEK13+DiSINBT8dKgS4FQgh23Ta9fhpiQUvFaAgub1b5+Zcu8eNPPEJlLM9etUvRfYBJ+xHO5u8jGujM5wr8yt4OlaLN6HSeLTOim5YURnQ6UcCd9UN2vS7+fkS1cBwpJQ/mWsxydztNJZVhxirRTQckAvo9n8iLuXx7h4+86340XWN7p4ll6MzOjbz5TPmDALsmMDsWdivFO55Z4r6JKZRShEGMYerYho6paxQLaSbGMxx1JhCmIPuNnijzc6NMT5XeDGictI1pm9z+Qp3VnTpbhy3mJ8s0equ8vLtKbRBy/nCfH733QZbXq7z37DHec+9R/s2Xz3PpxjZHFyoYmoZSis/+2nm6TY/v/qOPUixlv21dOKj12L3SIWWmyZ5yvuW1dXeDlJ5iwvnNtwvdWTkkUYrXLqz/Xy9MQ0NDQ0NDQ0NDQ28zwyDlbebIzCj13TavtXXCis2eE+EonROZUQ6aHo2uS7pjMGo67O51SGRMsZCjbqQxrIS43UIrSKgqZNuj0/MIpwqoMEJrSPDSuAjmFitMz5/ml1+9QaLvIk2IRg0OxYDpgUen0eMHpo7R6gaMTxRIrZ8n6GYJVYEkpaPMGGELgrpE0yPmR8vsmDZxOaHhOJiaQoaSqB8gXEUq0ZiYdDE90HwHtz7AdQNIJImWkC2mCG41QRNYhoZKEsxBTGvlkHC3yyC/xdGnDGLvDKWRLOd3djhuFXny2Di/dOsqScFAmgLpSj77qTeYPzZJveuym5PsOBnKqR1yF4/Rdl1wE1QssWdN0j0dO+chDY1OJo+vm9hSYV9MkO+FH7/4UYpNhVMr0dF8jHsEjpomliFoitK0zbGJGmNjTT7+1S/x2qtt0j3wH0jzfflxcuUU6ayOHga09wvsxvCO1EfQmiMYVkC9fUi9encVyr3jFe6kIbs0RV+LeGl5Cy2SjJUWsWyH8ZEsux0fGcY0Gz0Wp8q0Gy49EdO2FdjwzvIzWEUHZybD9voljmQ0XC+iGCeAQiaSidEiP/DdXyOT8vhkVaKimG57QO2gg9/3OYg99pVPvV8h3miDabNXDFBK8dDkgxTznyWf6RCqAh4Jdl9yduwXyOZdwvKTdHoeF4IX6CRdCp0c49oRVuoN7nSaNBODcs5hfyApOA6FYhoVSjZ36xw02mw3O0zn7+GesTRT2bE3nwtHNxE1jaAmiVMB9kIKox3z8PEphCbodD3euLyJbuhMTBQxrbuhR7aQwhQpJsdbFFIu82NZpJT0OgO212qMTRa5Z3qC4+OjWMa3L6ettsvWXou56TKlwt0tObqu8eiZObJpm5MLEygVs1b/6xj2OQQT2ELnXU+eZHFmhLmpMs1ql1/50kX6QYSxoKGEQim4eXWXWrXLg08cecsgZaSYZnGqjNcP2dqoc+T4OEIIDvxD/s36z6MLjb926v+BpZkAdDyfg16fI6NljG9s4ylM56jv9tFL5n+nSjU0NDQ0NDQ0NDT0+9dws/vbTDGXolJIM+lkWOybnNoXjNzok+y36eTAtSNqQY1mtY2ME5JBQnxe0e7l+PL2cfa9HIWShkgStAMXudPFiwMIYzAEiWYwSAmKUwX2Z18g8+gbaKm7Ez/CnMBPInr7t1np/Ava9OkMQgI/YeP6DO32CG0nRM0lyHJIv9FGBBGWofHUg0fYjTRWtQlO/OEa4rgHRRCDiFhTaAK8nKBVMumNBhwcVMmkdeKcRZAx6QcJpgDDl4RujAgUeWES9wNIgdcK6a66jFom05kM3s6A3cMmO3s1kusdRvYTMBWDRZNmtcvNKxtoYYyWJDi5iEErSzMJ8HVFfzDgteoVWjs+QQi1T46w+5lRfM1CWjp6oqElCi8wSXydPiblA4P+Tpbrm7MczTyObppoQlE0ClT33k2t+cfYvJUmtZsgXMXGdpPP1TZwiy30XETsBniGTb9gst0PubHSoBuEZEcyKCSmrmGnTL7/3Q/xf3v8CfYPGhAlqDjh+uFtDB3mlipIS6fV6tHaaVFwTAI/Jmc6ZNsa/kqHmzd3WNuuk7NsTuXHWcqO0ut4SClJEkkYxkS9iO2DKaLIonGtQNj1MC2D/Xafj1+4wXK/Q3kix6nKCDlzQKbksel8mUi2ubH1d9G6Cq1hYRR7SOCD773A0eOvcqX3vxCGA4QuKKQmMKIcebMMSuB4cM4a5d1zR1CJQnZD2ppH2/YpTGaQ7QHuaoutZpsLm226B9vs7bbefC460YDICTHzcJDxqJsRZtFgNJNDSUUmbTM7W2ZpYRTD/GZZ1HWddb9JZqyNWWnScOsEXoRMFHF0d1Q08JYhCkDPDag1evT6/rccTzsWE+UcQRSRSEGQrTA52cFKa0xkclQqeR5+cIliKYNmQOLG4CnWWk0SpQCBtAwGStLtDN7y81Mpi/c+c5oHzs6RztpvbjnKGVmKVp7p9BTGbxh//Os3b/GPX3+Bl7Y33jy2OFNB0yXT06X/ehEaGhoaGhoaGhr6PfGfms1+J/y83Q1XpLzNrG7UaPd9pip5jIGH6iS4+wF+XnD25BEu3dlBJ6EWd3BGBKNmCT+jc/tTDzN37pCLbZuscNGVhjejoXcUrgxRUwKV1tEShciaOLbO5eYyKuNhanmSwMAaicG2OezEXKrlKXk15vVxUtYvoKIuiGnM0Zh4MUbcThB3FFYPDKExkBGypVG2u/SrRaK6QG8LzExAVEyBozMgBYMYNd0jd3yc8WaO23stiBOqhy2kVCS2hoojbEcnM10gM2dz+NI2ZSYwPJ2BNCnYDs2ky75scfU/bKIve+huTPeRFMrU0QwJq30iFdGpnuDY7Aa3t4+gyRZSU0gVYt7wCVoBcT6POMzhaQkGEqsdYWZN8usS+XCClzGRk6CuhmQCDceMmR44XNc0bNPg6FiBp6YrjMxt8mlP0h9ECEvn6alZDnsJd+oOcdMhWXfQfY9S2eDo+1LccDco2ydRns3IaBG/0UYh8dCoHrYpWDZK19EbMalWn+Vbe6i0hXQ0hJBoUcKg71OvtjE9h1QtRHRieqGPEfkkUYw2CGn26nTSkwzc4BvjhDWEofGrX34aTwyw92OqXY9M1uF8tMur1hbP5E7wwftP8NOvX6LUb3Pk8T4zCxbbrS4lcYqnJ9ewrAGfuf4UZsZldrLKiDYgQWNntcVKs8thf4QfOvPLpOTn6Qc/x5dubxIOQo6ZObp7LXTX57AE9UFE0xhghZLxKy3678uhOx3uv+8r9AfvBhYBGHMK/KXH3sP2TovGnkfTHVDpKrZu7vHwI0cwLZPTx6dI4gSZyDdHIidxwgudDZqNcSrjJjNP3O27UihnOHl2DtO+W0LjRGLo3z6XrpSzuIOAUv5uH5MkkcRRzHq9xU6rwzOmTqwkX2v8Ee50DrDshPuO3B0VnUjF7f0aX1x/kcKxEHdFp2uHKKWQMqGufDq2xMm/dfNZP4zZOGigdEXK/mZgkjEy/KXjf+G/OL9v+Kwmh7ze2uCd8/9pNHOIFkiiMPm/WJGGhoaGhoaGhoaG3n6GQcrbyJWtfb76yk3yoUYcxkRdn3VLsqx5SL/Fnzn1EN+1dZZXbt9hZe+Af33ha/yv7/gIY5kURGn6jR51q4CvBxgzFt15G72XoA0UYdnAGOhoDcVpvUgjrtNMTAZzEUlBEcwZ2NkBjiEpGTM4rYj16z2SsmCydMCpExEr6zNEzQrLfgfpJqRTAtWNsAppPvPCTeKNBZLFfZQmMTM6dBR+rIEvUXmFfyeLowv6pk4tcNnSfGInQe9JpNBQBmhoKBPsrENjZA9ndp9sUiS9UWCzvctetMK7zZPcc3yaqj5g4+U9QAOhsA8UUVGheQrCAHoDDhvgr8zQr3Y5Xioy0CNES1Jq6hhrIZn5CFkeodW9G2SIQYhWsJmeqxCaNVwnAaVRpU+h4DI3Ume5cZmM5TAwDJSmIUs/zdpeDV1/FmkqDBKOTYzxyMI4q69t0+8KjIMehjcgbeZ5UT1PMN6mt9Gn0p7Hd0P2Nuu4XsBVb4BeTDMRQW5eUjZi7GASrx+QMU3ShokhFEtLFW5f22Vj+ZB8KcVIIY0cizg6PUl5XMfpxmwEa9BMuBilmJ0bJZOxaXUGVNt9ZDohnk0wBhrxN5ZlfLp/jWZhwHJS5fWVHdLBHHGww9jaab7riae4uNyh2f6T7PTucFPcYXFknMPyBt1akUgekNK/m3Q+RXPngOLU57kaDXhIDcgWIupRgBf6dJoDHMsgH2g0Cz6+F7Cxfcjjjyzy5au3MPEZK3dJy1nGSpNvPhtKKV6+tcPGboNHzAn+WOUEK5tbNLabHO41GZ8e5fqVbVq9Lg8+sMT4eBmA1ZVDRJzQLxkcLYxjO3eb7t5doZOgJxrVRo9PvnaFKB/zwYXTLE1X0H9DqNLt+RwedjE0jVwuReiFDFwfXdcYH8lhmya6JqhXGzS2BggABWtbNdZ2mny9eovVwmtk74+w9+ZZKpTe7JEiLXAyJkqot6wLd3aq/MzHX6LV8rjvxBR/4Y88Tcq++3sM4h6mZmNq1pvnf2DpBF0GvGPiyJvHmntdRHR3qtLQ0NDQ0NDQ0NDQH3TDIOVt5OOvXGPzsMForNPZa6Lt1bld0hlokjs3d7l0c4Nb7Ta+jNEjCSJBaArDTZBINOMIhqph5my4HaKXdIy6gBhMG/ROArMgScjl0kw0j3KnvkJvziEeE2g7eRaL40wVR2ndOMAbNDlMmxzuvRcZ7HDv/adpZSS3r/dQhiSpWMyPlDj3yDEGBqjDiJEHXKShU35W0ryZJ+jnUCGoSCF6kL41Rt30ac5HdKKE9LpOthaRLVgIAwI/RlMa9EKKoUOsx2gqYHN/E3UgkVe6/LuTz1N5dJo/+vQ5/lG+hexECCNFyk3IbsdEKJRpgGNy+zMV/uLffzfeoxlWuh2++somRT2how+QUUJT1SmrEsViik7TQ6R0jKyN60Y8WJ3lS9ltVKSISjGFbJ/RfAe8PUaPPszkiQqObrC+8i6S3AYDCshMTChhda/Jhx88yn2VcV4NN0jqd+MKw1FkViaJRzTudY5TPFFiVSasrApcXeHbCs2I0VcHlDyNiRPzTFsWqaxNJghJmwa5rEmpmGV9pcrA9dBkgp3SCE0dPX+RtlpjPzxKv2SR1i2MnkGvM2BkLI+pCQrFFKmCD6Me+phFtzHA80LOOTNc8/YYj3OEUUQrUah8mcr4ceYLZ5BzbTqjPl+48nV8LWSj2cYIyxwevAvR/XtMzB1Dn2lxT9vlaitFv51HjR5DM2YwrEuoviKKYh5/6gyREfOpqc/ibZpcv3OLBx6cYsdRNA1J5Ge5nExwZvyb21DqvsuX1pcZDCI+8NgJzk5MkEtbVLfr+K4PKL7w0hW+uHaL1EDjcx/9a+hCR0rFs8EMO1MeP/rEM2++X+BFNGs9svkUaxs1Pr97DfeOh7/m8cff9xj74YC0ZXF6aox83mFmqsToyN0eJqZtkgaOT47yxvIuza7LTiei3/WRGphCMJnJ0B347Nc7nKrMYpmn2Gv06VsOp3IVkkRiGTrPnjnK3kGLiXL+LeuCLgW1wy59N8BUAv0bW3tq/i7/cesnyZsj/A8L/9ObW37msiX+ytl3fct7JH6MJsBR+n/x/kNDQ0NDQ0NDQ98Z1HfI1J7h1p6h31fumZsAJSlYFoe7VYKWSxCBUBrh5j5ux2Xt2AB1PMtozeHEkUV2D7os97qkyiEPFmNmD/oU+ga3lI0wLDRDoI/5FB7q4fsGPcNhM66SG7uf3M4UrYshRnqANhAU2hapGYOKabG+oFErG8h+wMVOk9GkTEmB0m7y/d/7Gi/eXqSlTaDcFFNjOVKOzZeKIc31EpapQcmm9LSO8UKKft1DGgJZSGge69ALBTIN5ThFMQpB+owXMxzKCFdGiH6A2QP3vKC45FBzY+LQwE/rqLRFslylno6oHB0hKevoMstIYJDOmYwog6tfvg5BiNBhacnl6NS9pOZ1XvnFLxFu9Wm6MWOPhriOgTGliHdjDE+hNFApg9a8T9IwqHoRcWihfI1wImT7xgjFiobVOcFO0uGd47PoseKTL+Zxxh5lNdkgFUmMTsD1i3f40T/3fiYfVcitHmpDQSgIQxgteyxOZpiOJljZbHFISMPS0AZgZT3Gpky8NZ1iZPFQrsLK+g6f/KVXyTg2JDF7/U1e9m8zf+/38OLVZSZCg5PzS/RSPntag8PeBBMlHU1aZLoO++029XaPYjNL1w949XAXshE5w8fXdHQE7faAiXoJ0dVZKBV5eHqS1/a2oDGgtdFGKcmp2buTYR7fOcPL+y3GspPUrDb9Xp76oUFlNELTNSYny/Tcp+mvLnOeFbTwcxQiHXvgcRBuM7q0xOlzi6ylBrRRzB3kSAsL4zDA0mNSI4pbvwz/0v4Mf/1vfwwhBCNOhgfnptnab5FNNfh69HeZXHo31v5Rtu4cMrkwzpdfW0Z6GpuvQPMDPSqZIkeOjfHIzghPZbukNJNYSnw/QtOgOJLFsk2WZkd498EJqkGX+yszhEheWd6iFric7FT48JFTHF36ZuNbw9QxTJ1Ga8DWbou9mQ6/9MJluhvg6BqFlMN9S7Nk0xaaqTFXHOGjp3+IV8ub/ET1q1zc2+X1q1s8eW6JR+5fYHM5hfbWC1LIpC3ypTSaBktzFUzzbtlXSBSKREX/1dry8DuP89zXbrJwbva/qUYNDQ0NDQ0NDQ0NvR0Mg5S3Ew1yps20mWYwUqE2E3Dw6nWU0hFmyIjjMJF32Bzs0cz6XBzscNaZIvY9vu8jn2PpnhY3V59mb3uCqAzO1Qb0fNJnilgHNsbJGLctKDsFpNSJvBiURL/SJP2CJJzKcWs6ggK0jZBUOgXtiLWRNjueoNOXHLv/dY6c2mWvnOWV9RJ3qjXOrxXQEci6QmsI4lEdUxc0exJpSnRbB6GQ4yH+SIzhGaSkyUPNInv9DknOYXymRI0eXjMhsxIgPZ/ufoyzWsC9FUIIiQjQhEDpglHTIYuJHSqkf7dnRZAy2ZyIkJd0rKbJ0aU6P/bnn2N7fwIpPko0iDGqXYyaT2ZCkRQhm81zpDzCpe1DpKkRjkm8KUlsh3hBB6wEMdCRnk030bi2XOHsqSKJ0WbgR6wu77HWryFtRTKvUF8bIHoR3fUmy7d26G8GRKUK4XtdMrsmhfEyvfnXGRgQH8xRSs+QRcc0NETGYfFMlYw0qOYKpFMOMlGsb9XxewFNTaDlQC8naPYu//rlS5hdn7U44rtOjCHqA0Ivz9hMSKpUwt2q0+7VeG37JtWNPf7eQz/K9maHN1a3OYgstG4B3dMYGU9RLKYZOWKztuLi99IQKXzfR96ocX4tZO8PP8mRE7OEfsTZ8YsUUjfx3Um+vixImRbl0SyOZbBT7dHrtlC5z+OohJWNI3S7NsgB+vtuUusnvHhNUb8B+UcniYo7RFddVjngo0/dx899/hrBlsVyzWdXu83edgNjTKcVuHRyfaTr83L0/+MXrp6kYF3nL2tL+J6HYQh8XaA5YAQaJndXXrRaLs3i3+ZamGfj9W0+OvckVsMg7Zg8c+4oQggmJ0v8ufc/jW4IlAShC865Llutn2K3e47r1R9mwcwzNpr/li0/C5MlOv1JZitFVveb9Pshui0wdZ2AhKPlPA+fmafhDvi5L17g+TeWkZZiejKHbepvPvQCULx1klLMpjh3ZJqLd3a5tV3lXX5INmUz5szy0Zm/QjMI3zy3FwZ86s5tloolHpv5ZmiSLmWwymmSeNgjZWhoaGhoaGhoaGgYpLyNPHF8nplcDtGPqWpbtFVMJmMS7EdwOk0Sw30DRXt9k6BgoolJdDeGvR6zx+oYuqSUN6gqB7MXoN9powOJ7hKmc8QHJs68xdRcgXTGZr/aJJXP4td8RD9B70fIMzk2gzZt1We+kWE2M0a9UOQg3SbVtXjtxjzxTIwb62irCXJDslZuMVrOU0z3GNV7lCb77JbHaB7kELJJz8uQ5DXYMUkpC31g8uTCAs3b+1S1BMOLyOWzmCVBgkcyk0ZJA0PXia/nkfUqBoJM4iIPI6x8jkUrxQknx+daErft40eKaD4i0TT0gkahI5m/RyeVknjNIkpGfPCB41z4p1+GALy6ASLiYF/SG+li+xmSWofUgcK4osh0NHK5iAVnHTknWHlukYGwkGlBegxSZUVRZshe04jtiGRBIvdNkDoiiuiu1PmZf/453vHYGY6GPvG4YqJicmezxuqXJ7COJtRv+9wzMSDuxjx4ZIaJ8QLL9TqJSvNdH57CBHr9EGEKhFIQS1SsEUQOd3aniHsK03FQscbxM0tEK/vsr9a5vLJOs7XPA6OztAsuuvA4bPv825fe4I07mxx4LRJLkkQ6KqdzdGmcVMpimR2qsspSnEMTiqTtox0EaKd3+MIrX+RPTP8xHMvAEAl5s0aMjiYhjBUTU2UyOYexsTzbg6ssPfASUkJw6RhVP2Hb75PzQJOSg506u45Oxd1DCkXx7EXS9QfJnJrF/PR5QPHkRxt0bjyAMuAnb32cUCb4A4d+3ee11BRdL0XXT9Fo90n6AbX9FuaxNL1GgBqBwI2QKYWmCVpRBtuIQBgUUykiIyFtW29uhQGwbAO377O9Xqc4ksbJ/CL3O5eYjgdQ+0E+d/sajz6wxOnjU29eM1cpMlHKkkk7pNMW1sBkrlJgabJMMZvCtgyOzoxy5eWrXF7bY2OvxWgpwzvPLnHv8Sk0TRBGCU4uRTqXesu6kM+m+NDjpxlNpygV0mScb/ZD+QfXnudO95C/fPp9vHvqFNudDhf299jvd78lSKlMFbALDiMj337E8tDQ0NDQ0NDQ0O89BajfZKXy75bvgFv4HTcMUt5GXr25xZ29Gu+Yn6HZ7rGbbmMWdYJuiGFWEBmLCXMeqit02gp9zORKq45KNG5cnMQeM7jZXCLOC4q1hH4siR1JMOVj3cmhoWEkkD0T8+XoOYpjeSxN59r8CN5yA6FrJF4XcoofPfk8N146RcF6ivsnlvj1C1fYWqnTVHmW761gmTHZnYDw0KYZtDh1LM9uPkZkAvqTOuXxDiN0MT3Jq9un0BNFVJC4UxHRQHBR1SnkBWHRQgqNV1a2qJ5SSCPGByxdQxvEdNabxIaGEIqlP/YIB+cPSL6yzoWDGu/58MOIdgRKINyA7C3IiCLttkCYJnf2Huc/fu77+NAHHudLz91gf7uGU8hBnFDOFdhp7BFYAscdMJGZQNg+g15Abscml7aZLWY4l/l1tJziH+4tomfA8hWnimMkXky5a6FFkpFKD6uQsHerhKnbaHpIgOL2jXV++M9+gPJUkYuvrnLz2iZ+a0DniEVH1xDFPcw7GhmlUczYlEtp9J0U5fEC7//QOWq7LXp9j4vPXWWr7ZEvZXBKGQ46LpOteWJRZ3ckwU6lUFJy6tgEphSs7m/T8zyOjI+zs3UNzYwJGybbu000XccqGZg1A7knULbG/NIEAHO5F1CzAbPuJEIIjjhlGhOKB3+sSjt6nsP2UyxMHMH0PscxY4/n/PMocQbd1NBtnShKCJRiMnUKz5/EzOzjyhY1r8GO7yK/chSrHaB7gkktxVw4ilZc52z0FJWJPGbWZHLGxCjt884nn8B55GFGRwtM9sq0Q5fJeIrq3hqHcgaZTtAQDMIAR9NBKr76t36UX/3qRdKRxsZaE10YDPoBX2g9SmC3+b7ZBR4Ym+Z2t0r2N4QRcLeZ7Qt3bvDqJ26wMDnBzv1nGc2t0gzexbOaYO+gQxDGb56fJJI7V7cJg4gzDy7y4YdO8sr1DaaKBcZLWYqOA4CmCRbGStyw9xBSsbvX5p/+wgu4bsif/J5HGZ8qUiylyeadt6wLmiZIZ2zOHp2iVMp8SwB04B0QiioHgyYAx8ojfO/Jk3RCn6Y/oOzcnQYUejEaCvnf+K2YJAluz8dJWVi2+d/2ZkNDQ0NDQ0NDQ0O/R4ZBytvIhfUdthsdzk6MsWO2qB2VSBRRO89oJs1ev087CZH2JMIIeDgzzVyqyNT8OF/6mfcxenyM7IhBkvfRWyFRysSbBVVMQ1chdYFraKwMDsg0e0y3EgbYeNMZ4iDC2u2RyfR5/Jkt/tD0bZ49vsbHP/5ulCVY8ztIJyTfcTDaCkyF2QkRkUTIBneu1Gldz9PLFrDMAUmShhaIEIQBUoGYiBDZmIwOohnS00DTQKZh34ro1BL0UKB9Y2qRVu+jkhjDMQlHbVb9JjLqk4kURIo33lgjkjFSSxhMQ9IPOSpTNNIW/iDi1soh2x2Pxx++h93tGpv7dWZPz3L61Awvh19FDTRymwPM9TSrR2uMHS1hHMZgGhx2fIyDGh8xfDJ6RNYOUW2To36W/+HUY8hIcvHCGndmilhzMWYqxnJCoqwBmbs9NEQlRWW2jG7ojJZyWJqGoQnK+YD8aED9sEAuZzOfzeL2PBq1LlJLcJMAhUJTgm6tT6mYodVwqYwXubO+Dwk8fe4IL+RgbSvEzQjqAx8rltiG4CSLPFrJUOik8K+byJyCOMO7Hz3GUqXCv1p/lZs3vsaE0+Fy7QSWraOUYre/wGpwgNpyqXuryLRD/ql70UyDiYkUiUrh+xHHRqv0OxpanCWQkhvtFv/00y/xo+95hH7Xo13tU7vy/+L+Z2v0VYjh2NjRgCgGegmapfCKMOg9Syp7igvofNg0yNopPvzuZymPpLln8QiZrI1jmfzF498LwC+4V0gOrxAEEmPaQAOKCyM8ee8xiuU8mqbx8PFFmo0e6bSNk7L4jy9epm+3Ubrkc4evsrvqM+IVObMwiZR3V6wALPd2+cev/wrJS4ru4gBjfJ6Pr3433zVzir4eomkavv/NXiRCwO5eExXDET8iL0yOj44yMV5ASkmn71HMpYiThM+/douXr27S6wcQK6RUHDY6AGgC4igmiSW6/u0bwS7v1vilL13i+NQo33vs7Le8dqSYJ+z0mMzeXdFi6jp+HPGLt66w63b5c2cf+cbnKKSA23s11g4aLE2M/LZqVLflsnpth4m5EWaWxn9b7zE0NDQ0NDQ0NPTtSQTiO2DKovwOuIffacMg5W1kZDyLq8dsxF1W3ZBBKNAOLQanCuRtg7JtE7RCzJTN0WKJP3LmHMKP+FrWwg1DKrksT9y3yO3LO9Rc6CWbxIGO1rPQEkVsSaKSjt0Z477E5PrVKxwctElmRzAjiWkKtIpDL8hxZy3P9s4sUpfUuj0SICnoxJ2Qzf9zEi0X06ulmDiTp6VL9kOLSHmIA0l/NY2aMIkGFvqehWErMrpBN9YwNImW6VLdjajoWfKewoslmhthhRpCgUxpJF6C3nfBsdF8SSwSwqpEVfLkToPUJCvbh6jDNnIyRThqk2TgzvoBWqNPRILhhXgdj3zG4cyRSfK5NOP5NOW5Ejudz9CraYhOTHc7hKZGccli/YhGazbGeDlmd0enI7McNgy6gYOmS2pGlZXaHnrL4OblbXTLoL4+ifAjun2NnOGjaza21CjJFALF9PwIy7fTpHI2OSmZfXIbjwG5w1Eef2ABr9oh6r5GV9jcbpikujbXbu0yO1okihLK02XabkSEhH4Muka/1We0lMO4pZCmzuWgxeOpCrdvLjPwPFZXD6h3PFp7Ej3Kkz1S4Fh+lLFMlqfFLO955ouYWoz7qs5IJYeUkjBaxPQKbHoKr99iPl3ALJSor7+fUW2K56pbjKdqcPgxTp9+jfGJpxhoG7Q6Lq9fW0f78BNkSw57+xoDDUq5Z3EKN0kHXXIbBt0kwURgejETWoZQ22Dbe51EaLzDuo+ikWa/vcu2VWOsnaMcjTDlWAg6IDKcmRgj31Z0tiMi7o6CnrtvlHKlQBQnWFFCFMYUCikqo3d/p61Wh3xnnsWHUmRSJiNhgVIqw7mTM2+GKLVBn/O7u8TbMaqrs7/f5s8ffxfOSoqPHbmX1fVDlgdN7g29N59VdxASaRqRDEmlTc4en2G6UkJPaVimwWSlwL97/SJBnHB0tsIXvnIN2Q7RE4VlGMT+3V4l1b02B1sNppcqTM2Nftu64EUxrt3lYvuQe7YqnFya+eZ9RDqdSNL4DX1S4m6MPoCKSL957MlHjrPa7tBTMbu1zm87SEEJhCaQUv72rh8aGhoaGhoaGhr6DjAMUt5G7p2cwFQCoQRhx6ebM9EqBsI0MXIppkdKHJsapbKZxklbZDWDvWaHRjvAj0BaGl4Rjt47ze6dbaQbgm+DbkCUEI0LQichNuFdC/fx/OELaM0IWx+gGRpiLM8Z7x4aG1/nr77yXYzXLZzZA9wArECHbgxSELYNkiCFGtEpTVaYKE+xcfuAQPcRscDZESSRj2EprMRioIOfgTg20f0QIx0Spyxmp3KEm5KD3TZWoggzkkjXUSWNxADNsRBSEJEgdYlKDJSAwvec5GBlm807WxitPkbWxKkZhDPghhFW00VLEvSshnIkqZzF/Q8v4l9Yp9ccENe6bH1hCUs/pLpbREUx5p7HQm6EFa+BloqJTYkXG/yNf/IhZFrHFhr+vQHVk33+91e/iPvJED2IGV8sEbqKSDqIKEDaEj8tkZFAr7v8rZ/9FcYWTKrPd6mtNhlkLVJrRfRJhW4WWd1ooXqrFLI3yTsSUb+fqBVy+Y0NtsYKqEFMupSlVHbp9zxSaZMgUty4sM3SiVFMoQDB41MzXHnuNlcvbqPmevSkRtg3kG6Ilkj612u8/IXLvPdDD7JeW2XXO8XR0iG3/ElSWQdN0/hLp9/FjZUtfvnVC8jEY/Keaaq2w3Q6w/GZUb52c50v/6uX8dom7/2BH0NUDDq1HiJtEXci/uG15zk87FC+JKke7HLvub/NYe8MWvZZcAySQUDsDgiVxL3eotrbYvH+Ln5JZ2e9zgPvPsrg9A325Ba/+qLBuclHyadvYfh/mlAdI2v/LEceW6C5vMW8UaCUSpOx06zdPqA0ksHJ2MRRglKKJE7QNI1j42kaGxrPDM7ygSfup+36WKZBLmW/+dz98iuX+MTNqzBIQybCTjtESjLtZAmCGF/GDAxJ3R28eY1l6VxuHtJxfR7rDQhIOL++w9pBg3Ihw0feeS8vrG4QS8Xf/cB7+KmDz4ItCIs6diSZrNwdd5wrpnF7Hqm0xVs5PTuOvd/n6kt7fNx9lf9xapyUc3dbzUp/i1D1eaN5h4/NPwbAuZlpykaaUwvfXDFy88YeO+tVnMk8aN/2Y35LCiMZjt07i5166/sdGhoaGhoaGhoa+k43DFLeJpRSvLa5yfJWlacnZxgv2DQXAuR2RO58i+y0xbVXb5LJZZiZH0MZgq7vIUwDZMwP/rFLFI6+wUo9x8X1R9n64jUiBHYzJlnpIxKF0U9h+gFl3cDzIoRtghkjlIYmYrRsDj8OyVllhCaohzq5lEuqOMZkXuJ3upgFg47soNo+esai2elS1LKM+Tr9vkQhQQlOqQ7pItwMLXxPQ4907KyHk0SEhxkWsjC2L9gOEyIDNNvA0SU5R0egE2c1oq6DGHhoumDCyRJm0viDiMVsAT/Twj88QPgJUoGyIBxRGHmF+HyECCTRhIZKGbz+yhpp26BR7VHdbzA5KDDYS6g5ZbwpC0sOcCfTfPzOCql1n2wavD2B0EELFbomyWdtokYCdYGZGLS0Q3Iix4HrImoRlpug9TxUEuMetVFVRdYxadj7rO35yLpJNopIlMbaeYPMvWnGjyT01gcEvRw4eZLEIF2zUAcR4XGP9NIYm9UunjsglhLd0plbqrC369LzfKJIMpUr0MvCWq3Bjdv7DNwAx1Sogg9BCn0HRNoCP2F7o06lksddWuWlG6f45TsP4tgGuq6j1N0x21qk49UC8lKnOfCI9JjF8RJnF6ewbJ0X/9lLRK7H85vPo6JRCIC0wvVDbu4e0uy6TOwlPPvhX2cljHk+mAB2mC0V0NoaccogtBT7KmDQyRJtzLBU2mFnvUnoJwxyIRKDiWM5joyNs+5+kkUtYpCscW1jn1thm8NJSax8vECysX3IzusbjE+V+Niffz9LR8YJghB/EFIsp7jirrPZr/PKtducPb3AzEyZQXSLRM6haxnCMOb25T26Oy6TUxXmHZ35EzP8la98lrKZImNYOCmD6ZEiM5Xim8+rruv045jDvsdurYNtWQQyoREGpKQNCk6NV/DDmBfP3yHRITFB5AXl8TwfePoMQRDR6XrEQmA5b91vxNA13rfwIP2bCcdGp7DMb24BkiQIobC1bx6bGMkzMZL/lvd44doadzbqTKQERya+/cqX3wpN08iXhg1rh4aGhoaGhoZ+JyglUOr3flvNd8I9/E4bBilvE7eaNX710nVkI6azW2d8bo/d9SzJro9Ri9hp7HDwpdsITUcuCsY+NEnJPck7UlPkMj7x3IBf7S4QHOqs/4eb6GgIJQlSEdogQFkmZg0KL0lKoSB6TPLEe+/nxc+8QZgVCMfECaB22EJuuZSCmG5qhKJu8mBxktTZKcRZwfJanbX1bVruAWYnIugEdKVAtFwMX6IExEWDjO+ztVOiNNqhHYxijwocw8NfSSOFRXvWYqfqcljtIC1BrAny5RypjM3A94kHElU28cuCVD0mSeDYxChbGzUcT+d4z+BSI0AlCXG3gXdmBJVOMSbzJErDTQviERP/Ifg3na/xmFpk3CnRxMAfhHitPpopsAyF4UqSAUSWh+WGhHmLZERHa4NSEgKQdsR0kCN1wyLzgTqlxX3C1xS3bmnEWR0rrVFMHKKgT8sIUFKDRHJSO0pDb9My+xSncuQXx7gk1yARGH6K8dE8dSH51O1ZEkOSa4VonYjWTpf3f2+F5c09mt0+accklzaZPzWGFLtUVchyq4sXJwyqHr/wa88jD0Nyhsa0s8Bm1CZXsrjng0cwRx1e/OJtFBpCE3xg8n30dl7k+o7Je5bOoAm4sr3PP7nwMq4boDIaJoqPX7tBuNXFDAQnjk5RNhxGixkOtBbHz92k2z7LvkhDohjJptgMuniExP2AL37lQTL3d4nyAvA4uTiH1ha0NYGrSZZOjFOf28bJ97BFTFZlMEyDk84M17tN9lYk5+NNfqHT5ENHFulGI5y1NFRHkncF7VxITfO5U29g+DGdZhs/CAgDxfrKAYOOz9FTk6xZIdV7NJStU/X3SA1eZa35v+CIc+STv0c6bXFktMLGXotnJo/xxKMz/M0bX6aTBHQIeWVrnY/ccw9PLMxyYrLy5vOqa4J3TE2zY7WZGykwMpInFhI9pTFVKnB0YpSxrR3Wek2+vrmFfwKUJTmSlEiHDheubzFXKrC12QClmJouk/k2+USnX2dl9zZHRs/ypx8rYRj6m81mG0EPR1cooThRGPtN68u7nj7FStAlP5amOnCZ+s+ClqGhoaGhoaGhoaE/SIZBytuErRvkDQdX+JwYvcqfeOI1vnDjCL94foGkaGOutFCajjQNeqUCoS8IVuvspAw6HZPzrx2hccRh0E7hJW0c07g7OytO0AMPkc6gNA2hm9zcrvGxDz/Eh37wGW6oActhDyNKMK95hLUuaieDVW/jZEweHz/OkfIIr2/t4RRTyLIgXI3QFYxNdBgfP2CzNk/LA6kBUiILJs1+CpqSej+PMqFfS4hEFmVAkEpo9SRGrYcQCt2TFMZ1nFwKAdQ7fRJPogNmVqBsnaYV8SfOzmCGktFsir4p0EwNGcRkrnexV3sEH67Qf2aJzLvnSVZ7hItpkmMJ+5ZPzejwrtF7sC2d16+s0SdGhQKr26f0zgSpR7QuZZApE6+sI1ICpx8zkrNw3Ri3HyJtg0fvm2OghySRYtCJ6Fecu1OFAo1WBEbGxlEKsyuRekQ2yXJyZIZP1F+gqny2vj6Ap9oYx33a2ztMZx+kkDP5+pcVJAqjaBITcFBv86nXbmBnHWbKJR64bxaRKA42D8mVs6xu7OAGMfOjRRquz6A3QCSCXDrF7GSJMTNLPs5w8vQc5+/sINMma7stbl/fJW2Nsnnbpn3Y48b4IWGcYOk6JBD4EUXdoFLMcnvQI0kktqlRKGf4+a9coKUF+FaaN1ZOc25qhOyiRxRbnD43y8rgDjGKwNbZ35ukb41i3Bdih9CVGqmyQ6pkU/Vc7j8xzgu5AVKA1AXZaZ18MY3RvMm43GVfrbK3VqRKnp+tv4uF1CRBfpeBoZiWGW6bHkGsWO60ee/xCvl8GiXhyvYy7b7LbG6cdNaho0eEDqzJK3xi+wp/qnI/IAn8ItvVJuNjeX74+5/kXY8tUCxfQ/YNaq96KA0UipuNHZaWC6Qjk97UGIyXABBCcHRmlOl8lnw2hZSK0XSadmPAG7d3GMml+dUrN2j1PY4bJeJ5iWYK0j0LswNp08CyDY4eG8MPYvKFbz/++Kurf4fbK23unf8Qz9zzPVim8WZvF0NolK0CnThhITvxm9aXB49M893dU+wFA2zj2ze1HRoaGhoaGhoaGvqDYhikvE0sFcuMpbNsEbB+MMZ+NYe7rKEPEqRjIXMWwlcoXSCkItzTWSzaCCK8vs/VrxWRfozd6ZFLp4nsEBKF7UpEosAJiOYtwpMGYSKxUzaV8QJjp6ZYbu9gJhryag+6kqLtMrVYx9WnuC7aVESJbNlmd7vJrc1V+rkBSoBmQdxROHqVTmocvQBmJ8GacHn3/be4ZkzhpTzOv3GMgjCIA4velI+IE+ZXLdIDRVWYaFqEITR2DjrIJCZsD7A1gaULrLRB15GEoyleXr1Dw004aPWQ4xaVR+c4/NIyQoEZSfxbTWpPjJOupNEOQuyixLAjjH2Fuy+5dfyQdrPHjTu7yIxJrIO5IOGBAEuPEfspjPWE9JYCXyHQOXNqhuXNOq3IRxU18nmbFy8Z7B5MkWkYhCMhhpGgDwzQdKKyg30g0eIAgpCUbtJ3bxPKBlEvReJGiDsW2jskFW+KE49MoScJX/nl8yAE9VQISYyKJbppUBQGnbCPbeqMTxfxBgH5nS6F2CByI6Q2IO/BbLHM5H1T5HJpLs6dR3mC75l5lpNHJ/lcfAl9EpamxpFSsdPc4pH5FCUyPD2zwNdfvM3S0jh/7tTDfPpLl3l9b41BnPD4qVlaQZPHzx2jWMjgODZeMYWXliRehpWqIikoTN9gr9ZjpKTRVQrL1wCBUTeQVUXGyHHLaxKEETMiRUbXuXpln7mn+pipBqaSeI2QVqNLeee70JxVTh55lBu3axxNj1Dds6mMFHk12WY/16dAEakPCG3Jpt/H1YqYhOy3avx849cJk4S/OvOnyRfT/J0HP8jnbt3EcJcxrTbl9DMs5N4HKkMjPyCXtTEtHTP3Kxx6P0ch+yy58jyNtgYSUqU0p05MEnUSKqO5b3lmC/kUupQIDa5vHPDatQ2W1w/ZDQbsNTooA3RD4/jsCFu3R0llBX2tjTWmc/zEBJPlIreubrN2cx/H0jl5z+x/URcmK5O0gg02mgE31v4WlcJLTIz9SyzzJAUrww8feT9fPPw6JSv3X1z7G11e3uHm7T1Onprm1ORvvnplaGhoaGhoaGjo94b8Rr/M32vyO+AefqcNg5S3kQeOzjBwI46PLPH5L07w0oV1kizEZRulZXFWW6hRsAYd9C82uDrZZPy9D6ESRX5xwMQzHbpfDKmVxrEKo/SjJqm2jxgzUUmEPyKQxbsjUoOez2e6LzM4fgWxN0J/OyLVDsBPaD5nct8feRSruMB6q83XL93hys1tLMsikzIpzTaozuhcDUeZu9EhcMbQszqiFaJZGrrnczkZZy9doO2OYGoGkS2ISgbjI2kaYY+RMMNgO0YlAcZohDjeIr6eIvYlVtaGKQ2yFtJMkH0NIy8Z+C5dX2CaGr0gxC5Y9MdMksMYYWr0jzuoWoJfBxSojobW0TDPS+pRl1/0rxOXYVAxiE0LWdI4PGIjtwwK6Rjfs0nJmHJg0BwLCEzF7Yk7REdd+lcKaAXB66pGzQ5xCwbpVoIWgl5QxCMxHOiMd9J4tkeSAauQZn5ScKn3HMF4BXM0g7YZIhdj0raiMpajmM9gWgbf8yefZstr88uvfJ2Uqcg7NsemRzm4fkC10ade7ZNO2aSzFhvt5+j6EUpNUZgboz7wWKvWuVnscW+8QJACc1wyWi6THc3g5j3IKUIVkium+Lz4DHW3TWn0LAebHQ7ah0ipOHPvLMemylxwNuipkJYKYBBz+bV1jhyd5ERlhKlcgYye5smzS1y9sspeGDCCzrmZMV6Pt5CRZOF0lsMLLlo6JpmOCGKPSrNAre/jE+KkLDquj3PzNNn7LtC+cZLJTJ6fuXCBwX7IB+bfxd5+m6gdUszbRHaCo5uUlMNBv8t+7BFmQ86c2aBXHcOozjDoeESuxAgsojCh796dilPy0iy1Sjx68vuZny2jBwZG7u7qj/Gxu9tb1ncbXN+2KY+FrASSoycVO5ciDMPg73/XH+Le6UniOMH4z1ZyuK6P2/eIwoRCxqHr+2imwOtFXFzZ49m5RTaqbZ46s0TJcNjea/F6bRXX1nnpxiZ/+J0l7JSBEIq3GoLz8OT/k3GzznOX1rH4KaK4gR+cxzJP3r33wS57Xp2V/jYn8wtvWVv8QUSvMSCrv3Uvlt+KgRtweNBmZDT/lqtohoaGhoaGhoaGhr7TDYOUt5G//P538GeffoT9gzb/+NUN4kIKwgh9ECBcH11JOh/NIh0NXlWI8022t/eQA5/K43X0fIwq6iQlj2Q6TzRlwcd9nEEG/16LJFTkXvcIG3X+w8FXyXz3IZ7botRNgypAAhECc8djuzXCgm4zMzXN159/g+RqjaCQQj0xwg+8/zy/tHEOtiSHYY6pUh4GXXq+RCQ6aBoX1SQtUWDEhthWJFFC1JL4hzpO3kILNeysCaGk8JEuzkyPnF/Gu5VGL6TpmT5eHOFEAt1UaIWY9f2IJFScckqw61OzYPCuIzjNhHTaoj0akega7YLC6ZiIqoX4nI5V7cC0S1sJ4gE4GRM6CdIViEjDQmF6CicWmGmT9HSORjlA9cHL9dDTErMX4gc6V0WVopVGOwwQnQjN1pCtDHpGkZaCgjCJ0ll60wY1U7Jweok7LxexhU7aSRGWBB/+6Fcojbjc0KbY3m3i+RHF8SLLOw2s4gBtQscppTmodckV0sxNlShV0qxe3yYUHvvrIckmaGWXsfECsR+ye3sfv5XmlrtH7nyK6bOzbCU9zh7TeX/2If7J1hd4bfIOmddTvJwuMqP2KaSu0KtXWF1tcN26zcmJUe4vn+M9Dx+nYfj04whN6JimIJuxOZ2f5NkHj1LMfZaFpZ+icfhjrB1oaEHEM+eO8GvPXSc68DldrFDzO4hUArYiNmPMSBAdenTCEH8+z8mpSXJejnTnBO+47zQ6Brc371DXPF7cv8ngckjfSGjNS9qDgLTu8j++50l+euV5luMOR45v0dUsUuN7sH8fiRRMjpX5EfFH+dWXb9Dp+ADUewN6YUAA1Pd7KAUzizq2Zb7ZawTgpV6WC60Pk1YmvX4TsywoOSUSR/LZ67f52dfe4GP338v3n7sHfxDgDwJ0UyeVS6PrOuu7DVaaTeoqJGtbmELn8toeB+0+K9MNtg86TFeKjJbu49LOARc3tqkNPknRGucdT3w/4xOlb1sThBDMjY7y+EzIldd/hPGZTeamvvfN18PYpOYrMlrhN60ttmawWCgxmf3NV6781wRBzMANyWTDYZAyNDQ0NDQ0NDT0+9YwSHkb+erFFTYPWjx7ZoHZkSy99jrjR1tc+WQF2ZTM3D+Hd6tDWDGIfR2pCxrLu6j9BrWXY8aLCYNlg9Z4Fj8HVl0g9iWu5hGd0TAsG+OOi5CSqy/coXxmHr8jyFRH6cceXilB348ZmAaXl9cAweljaRpBHRUbqC60vrDJ9WPjZG8p0ps6WkMR5Hw6WwO0CAgiktct4pyAqoD5Lm4HHMfBagmCw4TC2YixskHHMJBeTO8Fi/ikAb082YUUUUqDgYcS4JugIkVpdMAoadxDGDdTNN06joqJbY2s43DPqWnqa5vEBhh9dbfHhQYiFmjSpLMBmh8zMmqTais6LiSJJH074t4f2CGdDVjfqnCgwb7wCRyFIQS9m1l0J0bekUw9LDEn+jxVOcPtxh6bm3tYkSJxQOtriBBCQyDn0siugW9ofGVzja+8Po6zpwhMj5bfp2C4mFLj3WMPoHsabhhwzd+n2e5jZExUzieXgUUzhZ0x8YXOja8vc/P1FeZOznBk8Tg3dzdZWpgmm01j6xpGZJFZT3joo5fojxTJZ44QBxH97oBmOyY6npCcCriV3qYV6XxgZAUCwe7BJO1OHrUeUHmyg5k1SBVtDtwWS4UcuXsNnCN5SpUMptB58r46Qf0rxL0UzqCPfijwk4CbNw754cmHuFrdZMopY6V0ugb4gUGSmMQkxEkCsUY8iDGkYjd3h2rQYLqU5SMz70Fqiv+49W/pORdZ2zuLdWyOtWRAJGGEDFW7i56OyN6M8eIcuysZdBWgNJ3SaBrbsXCjhK39FoNByBP3LJJRBg8cnWN+tswL19eQkWS91WVqtMCZY5MArG7VsNwcKTKcatV58ddKpG3B4fs9/unVl1j0Rui4HuvVJgBJLJGJotX1GbgBk4nEQqPspCkXsjyyNMsTpxb48X//a4RKcmljj51aizPHp3hu8wqrvTqZhTVOHDlPrODm8n2Uyw/gvMXknu1qi3//5TfYsPZJYzHrd1g073am/fLBDVqBz83uHs9OnH3L2uLYOuc7VT752U3+eOd+fujph39bNapQTGNZOo4zHH88NDQ0NDQ0NPTfm1J3f36vfSfcw++0YZDyNnJhfY+9wzZ5dKIo5gN/9gaZkRh/RXLny2UefOdJMtfXuf3Pr5PkTfROQDiehlgyPu4ztxCQPCjYvhaT2RVE1QxJto2IQsyVNIZ7d1SwZtwdL6w5OfTQwT0tqd/xKbQ0hG6gJYpwuceO1WJyNkNuIkPDCogGEXao8cr/Zwn9HhPLVahQ0dt30SwD3Y0QusVAk8g9m0wtgjWJ0/LRFk0STUfLRwS2y/nGFtFBQq4SI3Z06rfTqPGEyckax463WRYTwBSrtw/RA5CNiB/7IYtbry1x0OoRFkyyrobvBbh6zLpaIb8UElxOI9ohICBOsAs+qh4hlYPQBQ8cn6dZ7+HdOsDrRiRdjb2VAvZMQmSkiLMBoRVBTUNIkGdjZJCgawrnvjZ6RbF9sILupkAHXdPI5Ac8NrnGza0FMt4EWtGiQx87hoNGA5XEeEmInTcIfYN/8xPPcN+7JplIWXzkAyVWafDK2iaTWppcq0x3a49W1CF8IOSlzS20dkQ5EBiOiZfWOHX0QRI3R2fg88brq/hahAolTibm3HuvEw10bn75KZqdPr3OgLkph5xZpNuDh8aPE3R3eWN1ieLAw2+NIITkzOgRvjv/ILdvNNhtdInchMXZMl/I3SKJ2zzeq3NPeYpKpUtsBtzpTHC5n0M6TQJ0VtttzoxVyDoZNjar6JZGnFMoJLFKUImEOEGZJmmpkTUk9j3nGdFDal4FeA8TVoZJYfDK1xcZXPSQaxucOXaavUwAOvzb2y/TLLeojBdJbZ+m/cIqmtTJfE+Gex9exDANyvkUxZRN2bJptwegFKamcWX/kF9fXcEIBQ+PTlLOpd987toipNs2+ONnPgzBNhdbr+PrOpNaQJwonj29yAPjkzx6ZB6AVNZGCdiud/H9iJNxwoP3zHH/qRncMKKcTyMQHJ0a5bDVp1Xt02/7tAddrm/fpusaZJ0QXdz9hqpXu8gkecu6oOs6SW+Am+nBwKIfe2++9kfnnuTrjet8/+xj33JNJ+xhaAYZ4+6qkU987iK3tg+R4yYt1+O3SwgwTR34A/DtOjQ0NDQ0NDQ09LY1DFLeRj782Ck2q20OV+tcqXepfXKB+YUaW+fzKF0QDkKeef+j3Pn0LYxuBLqBN4hBKA6fz2IuWWzfmSAXFEk3NZo9gRIZNJ27jT9VjDQFMmthBnCqnsEbtblqt1CDBM2wEGGAbgrCbkwrDPnS86skKz6mLxHy7lYIex70+QjV0fBaFoECpxMhYkAotASiZkLSFfhNGzOWqLUehQ8o7ClJmOj4G5KsNuD//NnPgw4/+He/m9au4PTpmwgTzp6KOTX7XfzEG59C7yf4l7KcGH2S/FmHf/1rL7FveIzYGjYGUkvoHuvhaQZamKA9FyPTBk7J45kfuIYRJXzlfzuL7iccmazgDQKUoUMSQC/BHTUIbQN7JmYpoxgUmhx+Jk3gmMhDC6Puou9EdD9roj2m0V73OK2nmSjnCcYVDxy7wgQdqvkc27tpMkaKlIKC0ige2IypFAeyjmfp6LqD2ynQ23ZoiANM4woPnJthMSmTSQwGYUBY9Bk/OkFhLIPpGthC57HTR9l/eIbnOwe0wi56OUNvv4Nrx4jpFM5UjvGxPOtrNerNPBv7XYJqG20u5kr+c9Q7U4S9NHLU4S/OPMlPX4+5uNYiq8f0XJ9OLyRNikQHQ9c4VikzbmU5VR0jtZBhKT/K8nqVF16tIlMP8nP1RexswPxcBREJsikLTSiclEEusDB0Ez1SJCSkGjH+zSZO3Ucfc6g1+ry8usWTj0k0PUb2N2m0+uRGMxw79gEuXP4aHXmADCKuXd1hcXSSbKJz3Jlno5LhXHme03Oj/MSXdxFSoOkav/aJi3zoYwZWPs29x6bJmhamadAzE24d1HigNM37Th3DEjoPTU8yWf7mVpiT4xXqPZdHZ+YwR8b40qc/j8rE/PjTf4JK5T5mMgXE/De3AcWxpNceMFHKYaVMctnUN8IFyGacN897eHGGTadBGLiYrsRMG/h9A9HSiLtF5kQTEVm8tjGg03QplL45/ziOYmSiMG2D6dECjx2fpf+JPY7p09zzoSNvnudYHZS+wrXeJd6VegaAZtjhH93+d5iayf906s9gaiaRF5PfSzi2OMVfePfjv+Wa9Cs7P8ta/yZ/Yub/zi+/voWj6/yRe85gOSb6cPrP0NDQ0NDQ0NB/V0oJ1HdAo9fvhHv4nTYMUt5GTk6PsVA54PleE8PWufjcFFfbI6iiTX7KZnppEmGYiIkMqu6DLgj7EeRtuptw/ZeKDIwslgzASqO7EcrIoGmKRAfRDUBX0PIRHY/Xfv41fuB//giiCv7nL0IqhXIjYsNAtxTJQYvBnIHjRTBQCA2U0LCfDQk6FnFVEOkSvR8iAgUCpGEg8wZpNyLl+ATCRpMSMZBkigPSZ2K8V8eJrwSotETpGkpXyCdLyE/AxatHePCJFo3oFG/sHyCFxEgSdB900SGdztGu9eEgpJ0IZqaKiF5Cez+LKCcYkUmkB+iJDrHO/sYoadNDxBLDiyg7JrlAoHuSwYSFYcQMLlg4FZN8P0/m3DLKbGM/LojnAwpehHeriKbaeJc1XDdLoRQzOmtTbXfpOZLX6/M8PrrCbr1AWPdJhIK0RnrUonN7n4ML1burMUpprFBy+sQGmdw6L924lwsbOwS9gGfnlrjTqFG5d57Rx08wlnGIGiEnVQ6XmOJonsJ9Y3zqC/s0Vutoq20SN2S8WOLU4jRJroCTS9O6No+rIlIln5rb5HZYR2iC2BJEsWIj7qC3BNVwQCgT+lGAT8JmrcOhN6DW62MNFHml8flfusBWq8dTz57GfFTnzvoO19fvsHCsRiZcYtrMspQpMpAB52bHuX37gOs39jh1tMIz98/z/P4WNWB0C/zdFlYo0YQAR2O3NuD5Xz7Nu97ZIaXeSb/oUYt83lg/IJmNwNYJJg26xRDDkDw7u8iHzp3h4xev0e+HTE6W+eh77yMME178ynVuXtxhe7vO//qTP8iHnj5N2AvIZyxSjoVhagxEQCvd5YMzp5nLld985urNPp9/7jq3DqssZIs8fDzhyYc2MAzFQm6MnJWl1nN5fWOHszMTzJaLyEQSBhGmAYetHtOux6AfoBSkizYvrm1yslKh0R8QoqjMFtnodDnc75F0LFSiaNdzrHhjWLFBL+7S6/nfUgsCL0RKhW5o6IZOebTI+NgIRybmvqW3iyH0b/kbQHzjz91jd8999qnTOI7J9334ERzrt95w9pXGl1Aq5j9u/Ar//mUdQwjef/IY09awP8rQ0NDQ0NDQ0NDvX8Mg5W2kWr3GrfUfIeUk3Dv2QxxWXYLjY5DSiYo6WsZh0AuYeXCB1Vv7mHs9RhZK7Gw0SLcixJUeqVKAUhbhlEIkGuggCyaxnqA32mhdH/oxWqKQtTZ2J+B4OssbgUIZCSKVRpTBqEri9/fR5jX0gxj6GonQ0TQNGWrIvkkUmMR6hIFAxBKlgxHHRKaBCBWjc130kmDnUhE0QW83xeA05LNppPJwXY0f+xc/SPOUhm2M4B7fZyMpEV5apFWR0L5OYiiUKXnsmSpJZDFayREDGjpxRrG4NEF2IDjYbuCO3KI32mEnVYYEgobF9c/OYTYCVCxJGQaPv+MEL794h6alSNIC4WsEvolqlPGzgtHVRQ4OYsxKhI7FeKHDYWUBrzwglhpGM4R6SNVoIlwfOoI9RvnUbhmnmmCrCC8ncHOC/W4d72sHyGaANpVCSgNfDlBxxOvrc0RxhJ4IvnL1Ih98wKWzNUrGXUDPpjF0jZ6MqLsdVHKRRitmdvZx9FghYkkcJUjdQLcMJrJZtnd7aE5CvpjifNBENXz0tM7JqXm+754P8VfPf5aruzUOan3uJMvYk9ssMEZwmKLa6VPWTNa9BufdLSp1g7zUWF49INAFMkkQmqArwMyeY6nY5IPrJygXCryyssOK7HJw5SpPVmZJNAiFZPbJT5H9pQn8r44xa8Od0zqqrTM1bzGdmebLl2+zfJBm7upZ/rc/9SEcxyQbpjk1ladaadG9kJA6kKSrAWMjOjkESRSzsdqgdtjhkclJxsZLmJbO7nYDDEGYKO7c2UcHDrcazMyN8MS5Be6dGefnbpznxdYaU+kCC78hSGl0XN5Y3WV30GU77nIqvERDaGjK5ub1AFOsEo2ZXNjaxUIwVcijaYJcPsUvvnCZld0mGBpZ06LbHhBm4MXtTXrtAU+fWKQXhvyzT77Ila09av0eJhqaqcj3A3KWj2E7HFuapVz51iawTtpGSsVXqje5UN+k4hWQiznieRul1JthykOlhzBEivuKp9+8tmTl+eun/gy60DG1u18R73znCU7dO830+G/elPY/l9GO04s2Sbmn8dxraAIK2dRwNcrQ0NDQ0NDQ0NDva8Mg5W2k27XoNEt0mkVakYGoZDC8BEhI5gekMjopLU04k0M7kSK32WdROTRbHahGqFhAT0eMm+hSB0tDmBqJUMSxjx6FJEECJiSaiWEIvvDqdbLChFwK4QcwnUKb0eHeAfF4gu0OmD7rcTCTpdnMkzViBo6BrJlIqWMOAgglytBA15FxiFIKmdP40J+/Bgl84m+co7lXRNy0iSoW4YEDuCAF7rWYydQEqh+ymovRYxPH04jNCAPQFNz36A7f97GXWT+4SNj/RQqxQAsizJTFXC7P9qDFAQ1GKx1EQSK/XgIXNCmI2glR0cKSIcqLqLd6VHsuflpDZXSELmgfS5PKwIRr4N6B7oUc1gMekzWfdr9MqzUgOVrEbMZo1R5GL+RG8w4ji+MIYWKZYCmJkjGRAz1HEWUUVhdEysTTfTJHC4T9AF2D/a1RwrKG3YsRaERRk71wm2SkSsk6xfp+g37dZk3UyE98modmLyHtz9Lu/SolJ00za2JP5LBaCdOzFWzHRGgKL0yYOeIR3VrDu5OibKY58o4i1TsuFdshmVRYJcHkzBq+7RO4IdNj40w7OWYmCiwv75CICJEyKOopVJKQWAa+qfjEVy/z8vIm49MnmZ4+it7YoS8Ua6JHI3RJDut84NRxTiyMIfQWWm6djegkg6KiknQZe7zOwDUpZvJM9UroaQtNT8hPZSiWMgAc1LsEDcGTUw+z9J46ta3n8R61Obh8g93DKV65tkW11iUtNQ632ng9n2MnJjn15DG+vryDLDqYpkEwCDis9kjlHLwk4Cev/yK7e30esk5wMjXOYafPeOHuNpqJ0TxTUwUazYCXd6/QcG/hJQa+b/CFL79OUZR5z0cf4KHRCaY0h8OdJrlyBmFq+EGEIQTTY0XipMrXLv00TtniNc/k/F7C0tifp+clvL6yg0qgGYVosx5aU3KmkGAosLWI/MQIuvmfjVbu+YRhzEu1ZTa6NVK6yepCj7XeNeZbY5wtT9ONGvzk7f8vu17Cd02+j4/MvPfutX7IL796jVI+zR+6/zRCCCzLpDHwyLg247/FFSmRjOjGd9C1CN/cQNMEhqaRqLeY1Tw0NDQ0NDQ0NPTfZLi153fPMEh5G5mdXSSf/3lefWMVX7/C5Hv6lP09bn+hzD1PgWU6HKoeSUFSTmWYn0jhbTT47g++k0/d+QxRFKP3JMe++zTGQHBppYoWg+lJ9MQgtixkJJBJjB3HuHmDFa3LQ0eOoL2oiCo25Y8eIzrYoW0KgmCAvR6iTYbIqRRJwUaZCsYl8YSGr1noNpg7LiBQOkSaQBoaDHQ2b4wyMtLD7aXRTJNQOLg3TIKojyUkZsbgA+97gI1+h1ev3SZrCZJcQpKLmcEh46RYjnscHJZx+yb7O1OsrN+kHvjklI7d0XjjlXU2/Q6ulHjXpuh0JEko0ROBMnUomfgzCiEhqEk+9eXLdMIQbIFyBJFuoA1ClJKoZkir5RMZgo7IIE2IqqAGISptIqMYpASVoOvQGvQJrRKJDqGIsZVC2RpaAnpfYRWzvO9H7uNzX73O4rEZgs0e66tVpILjU9tcvnUUJRXtGykuvDjJpD2OrS3T72g0thPMcZO1VpkpRmBecLScw0ppjBgWh+t7SM0gYxnc//Ai1a06oaY4Nv8yH9SW+ewL95Mom62Vfb76+SuMnigxnSnw8OQUuvUBXo2+hpuEkNiMZUwauy302EYzBK3DPoKQjh+TBBFfOH8b++YmmtC4b3KCQsrGL2epNhpggAhj4jBCtkOipsvhgYuXPEXG0PGVwN+0eWzMYXvcZVdlCFa3QdPQtLthwn/iD0IuBpsc7rR5MprBK+eoYRLrZdAEfdcnn7I4MTGKIRMGrkc6k0IZOknOoR1ETIxmubrcI13JcuzUFP/45q/zau8WOTvNqew4r97cRiD42BNncUyDlG3y/nPH6e4MuNHdZCa9i8jBnZ1RtMMB05rGxmaDi7VNfq1Q5Ul5gjOLCywuVRgpZihXMpxaHOe12+dRqQ52vspDCwXcxOK1nS1Keg6R3G3NGhmSuJQQdWzW91NY7QXAZHF3l6MLUzBWoNfziZOEcBCSxJI/PvMYd2r73JuaorcSsxW2KOh3t9U0gn12B33aMeTNb65oqbW7LO9VSbdtPnj2JKau8+nnL/GrL91kcqnE//yx95Cz7W9bg6T775D9n0DL/202wzN0IwHY5K19LKuIbZsY2t3QZ7PTYs/t8cjEDLqm/Y7UxN8Nv/jCJVYPGvypdz3M5Ej+9/p2hoaGhoaGhoaGfhcMg5S3kZXdBluHLRZmRslO+aQf9sjoitzcLoOJHP/qf/8ptA88zqFR5Whuhhlnkpcub3PrK+dRQqBpCi0lOXPvAhe+chuiGKE0RCSRhoKcjTcn0NqKeFsSjDlkHJsHTy7yoZ+6h1/cvslB4rLf0xH5hGRgEFwyKI9HhHKAFU8SXAF1o4vWlWjFGH0gQSiUbYAu7gYSKR0/bfBrP/cgzmofkbLQDYkZRohzks6MxNqwOd6ZoulILh426JYFhgtiIPAIQUgGkSB2BIcHOf7V5XdQmkgxkYHCbBGhQS7UOGx0CHo9yNr0LxdIui7W/gBN1yFnE0xpRFMSwxXYDVhbrdLpeBiaIsoJVEExO9JioTZgZlHx8tU5EAn2LEw80iA6p3H7UzkSCaQ0LKX43h+/zZMfvMXP/ctn6HUKJHlBYIPIgtWOiVPm3a1MCHojDm7aZuuwQy74RgBTSbGfKqKKBqIdg1TUXsyTHY+4UFsjtejTeg6EJim8Y4qfcTMkMsPxiQ5BP+LwapW4H5JEPumMTqlSIJ022F+/yWH9AqdmQlYWptBjm8PagHq9z+TJSZ4KZ2hXPZ7b3MXuzzNh6Tx5bImvvnoR0xZI3SAlLNAVbd9DJQqBoNN3Gck6WJbGTC5H4MXcuLZDuz3gobExXok9bFsnUAHL61UO1hqs31aMP5DFaoZkAsHOyklWlrvkioKN9TrCkdhRhNgJaDd6pDI2i/MVUrM2cT2hbsTkDt/J7ldr3Dc2gxYpMpaJoXS6LR/blliWwaDnUnQMDF+SdAbcvLTNxn4NUg5ekPB8dZV+ZPBM6QxP33uUr1y4g64LzG/8x39zp84v/doFro/VyS8WmSqPkOlfYaClaBUdJlJ5tqodvlS+gmtHdKKA8X6JO/UtXosPSSmL3W4Pr7vIne69jIy4zDsv0fDSdAcJkeVSGk3RbHosZApc3+xCS+DbFrW1URzLZKoyTiZnkySSlbVDkkRy/Mg4tm0ybuqMJBniKOa7yqdw0jazuRIA85nTlOwF0qZkJjX5Zh0ZSWd4x/gM4xNFTF3n45+5wD/6188RFiGcMfj61g7vPXbk21QgUNFlUBIVXcPWHqXpp+lGaSKnRiwLqDgiUAlKKf7mpV+iHrb5a3yUp6eO/k6Xx98xW9U2za5HvecOg5ShoaGhoaGhoT8ghkHK28hnr99m/aDJe2bnmSlP8OnqHJYV8/D4VeJMjJlT1H7pGubjYxwdq3DiyDyvfPEKqtlHe2iCDAnf/yMvsnj87yNaf4GN6/ugaag0SEeDjiReLKFjopI2spQihcAOBKlxiw+cO8nPfPlr9LISlbOwLrWxkphrbgUxMc2oXWF3RsANF6UJzMO7TWs1dChqIARGKJn0TTpmQiwB2wKhQRiTPh4w9nifqpuhVivguQrd1ClpFrVgQFx0IIyQkYmbJEhLJyqbZOOI/KigNGHxQH6SKHtATTUZi/Mk21WMTkSk6yQFhQglSiYolSACDT2wsXcT7O27K1U0CYmm4QhFZMZI06CzleO973qR0kjAzhtweaWC20/TradxXQP/wQSxK9DroAnB3BEfpQTFWRe1LHBiC80OiHXQwgQiiczpqFix0e3gSxB+QCeIEKZBFFoQgZISlSRIJNmtCCuTIY4detsJctAj1hP2bh3SDVOIlMVuscpEKkW/tMKBkyNJDEzL5ObyIb1BSOI6DOoLiFIRmTKIqwFeGOCP2txuNmnt90kpB08OILvP4UjA9YMcmhD4AvYYkC+lOFaZoNXqs3utjlBgKUFoQy5jcub0NI5tsLlTx+0EOBmb+XaaYwsTDFoBO50BMm8SdSOEFxAUNHITBeaOTbB9LSTj6nhxgtmI0fyI7c0qjU6DclIiX87yh+cf5l9svMTtRp+5E2msoka11sNwTRaMCUayaUbyKabyOXa2GlQPOnSiADf0CFou6LDbcTm8vc9UMctHJh/i1e1VntBOstfpU86kOTo+wn/q1xpLRdV1CVYSRgt55s2PoJVf48HFfc6vSyIE3c4AvxKRtX08LWLd2uV8+ybp8TGOaNOkNYOt3jLdpIsauNwnXNxGmsuvbVEYzZDKOmRjSUpqTJo5lBkxaqdJlp/kybPHOHf/UQqlDLquMVbJE4YxpmkQhxGBH5IoRa3tMjVRZGQkh6bdvXlNaPzVkz9MM+hwPDf3Zh2RUpKRGsVvrDr5ysu3kVGM5gr+0P2nODpS/s9Lz5u0/N9A2c8i7GfQvSbtKIVCQDKJoQssQyfseOxVXdKWIINBzv79/TX0nvuPUW/1OTld+b2+laGhoaGhoaE/4KQSiO+AbTXyO+Aefqf9/v4X7NC3GBnP4lkSw9HZqNbRJhMs6fHa3yxjjmn0r4EqDFB6DU/3mXygwIkzi7zWa6JcF1mJuPe+KkLoTD8wTeoTVwkrPaInHZzGAPH1AO1WGj1QaJFNZjnBf2OdG/lxGs0KX9xc52CrylylSa7Sp/eiDi1IH85Tuv8Y7msNAneAnTXQu0CcQKIg9BGOiTI0hJswcDSMWojRSwiERGk6SZRgvsdF2YrRgcR83uKRRytM2xl88mzshcjJBHoavbxEMwyyuTRxN0KZHsW1HZ6a+j7Gi2O8cuFVmlEXrx/htj2EAt3WISMIBwKRtzEQaIHC2glJXwlI8hY4Go2DDoQxQggyXcVA19DihEuXFjh5dsDm5ihaCB4ay18bx/Fi9CMGoq5hDEKS8Sxf/cz3kBnb4LPNCv0JnUq5wfHZA/a/VMGLLVJNSYyO2VOMjVhYscI2TAZ6go5GPhgwWWyhTIM75yeQpoZMIvZWWiSGgZB3w6fYgUhKTFeRDBStZo/vf+wI/+5qlwMzC6aOVKBpgukj42Qdg2z6Cf7R2ldhe5fUoc3BXpfB0TT9riDxfYoHNpMTEZ4WoPJw4ZUDRgYGKWEycD3a2Zi5kTJ27KAbgiRUqBQ0y3cn0/zzz76CzMA1vcuxsSx6rLOvB2jdLqdSGZSpAQLDMRippFG6JJfL0FYRXqtHUGuBMNC8kKQzoH/PNf59f5cfGf1x4jhNybHIOhoDU2eklKGWc9jd79JrhEzuFdndrHPyHafJZx06zT5CSsIcdGcVQtcYmIrqYRffC7EtgyedY+RlmlwqzYu3NwjCmKOLY2jfWJFy8sgET77rJJ9fW+XByVnmpy2cTJvBdI8Lu5CybBbnxsiJgJFUHz1yOZ1fpKP1ae0Z7Fdb1I/1eeTIfdzpryDVFJdvtejVRhGeIq5HWEVBgqS6U0N/d0DatRjtFRidKHPunlmquy1sS8OuFJieurvapFXr0mm6JInEQ7G81+BMymLC+taSX7GLVOzitxw73G1x441NZKJYODLBh997ltsbh0xMFZnJ5Sk6Dm9JZAj0RRxh4xgFUobC0T2OZ6ap5SMyeYt6tQNuwg+MPc1O2ONkfvq/XxH8XbbaaPJv37jEiG5z3+IUlZHcf/2ioaGhoaGhoaGh3/eGQcrbyLuPH+FnXj/Pz75xmdatDmfv3aeQG3BjzKR9zQBNwy84uJNFvnj1EkcfHqU9vgPtNvaOx9Hs/5+9/46WLMsKO//vOdeGj3gmnjfpbaUtl2W62jsauptuTAMCZiSBRhp5wYzEQrBk5qfFT5gRI7UEMiDR2KaroYH2XV1dPquyMiu9ey/zeRs+4vp7zvyRTZVKtEVVNAPxWev98da9cd+OteLtG7HjnL17PPrvBth3ICYzpfn3v/P3+bXzT/DpracxSzHYEvdTCiUdMDVmx0d7Ceefm6P+nx+nO5VBugaZzTYHhhY5Hc+AYxIEKYufn4MLHbIakowLtoWMYtIBl8QAw0+RfkJacWh2OySTmsLVEEIFwkBZBklbYsWK6FaO8UyJTMZlbb1FpDQ6NrDnItJiAVUU3DU8QpoqlO6RunDu1n4Sv87bH+gwJssEKgVfkFoGdqq465BBK2yx0FCoKMWwLaTWGH4KjiQt3tlu04pTpKfRbkqak7gyJZ8LOf3CPaxsDOG3FhBRRGYpxW6l5BOTaClFhBGplGjHZKWhKQZ7KTkBcVuiYwNfuQhXkX656awILUxPkelqJjM5Op2ADB5hIrFkhB0qAlOSuhIpgKYgTTWYIA1BUshgoRCpJhkTJLMB527c4IGTe3Hit5F25kEarC7U+c7vf4ClF2/itXq0lY+KHAhTAjRevY27KrjrwE7auk01l0NKk+upTedGkUFRIPCbNFeb7MqNMLB7lHePzvB8ZwlcSW9EUrpvmx+85zS//Evv4vNzl7GHLcgIcjsrRNc8ZNYiW86TjNkMDWawIoPjp2aZT2KS7S6rGZ9t3aET9yisd/FMG52z2PsD25R3e2x1LBKlUYni8c5vUty7wqn972C2MIWMWsgdVeysy+pGkysLW7S4wIPTY7QbHiNDRXTcw72ngygZ3NhaYrHeYCCfY7jk8NSVec5cW6Fe73Hk+Cy5kstQKY/WGt+PcByLH3rTSd5+z24ub3+Ws+ttTkxWCdQ4pl8GrZjeNYB5QRAVTYaUZDHe4nZSxxBZqs4oWcvi6fkaNzZKaDdkrChxCiHlag4nNVhVTQIzobYdMridY+JIlcFbRaS6wcqGTW/LIVNwGBh+ZaJOtuBimAZrXoen1xfYNT5AdbCAUurlItBXs7zW4srcOoOTd4oyO6eGsZWmIyO+cH2ejG1xz8zkV3zsRutnqXf+E5X8D1Eu/CNKlkLplIwd8F1HjjNUzDFSKRI4MY9v3Ga12+HoUIND1eprlQb/TGk0hbzDaKFEuZj9VofT19fX19fX19f3Z6RfSPkLZK5Z5zNXb6D8iJHSNjkd4VoKlZUwa8GSQkgDbVmo7Q6/+s/+G+KQgfYF//vP3OL+tzb4o98vsbaaY9DqEO0MyKsS7RezZE3IraaIio3uCoQCTAsjI2ittiDVZG/1UIZJOiVxTc3YiTYrt4cxYwhv1bCwkQKMRKFlSmoIcG3UgIFpa/xY4NQUSSGle5eDDFKyzRgRJ6Ch+fMutdkQtxNT3gPLvQ4qhtAP0ZMuPdfBqWvMls9Sr0bLShgqZUjthG42y7WOQ/niApGnMVMTKUDbYEUe7/m236e5YfAb5w7T6o5A2UbZFtoE0QwgSRGOgyEkaZoQ2tATLqZKqJ8vMOAIxgaLzCkQloXbVUhMAltgdFKMQJMYKfgpvgjJC4NCCzpC4TczLHfG8JsJ+TjCDCRxUZNkYGyyQkjK9RtPcezYKteeGKZ5u8ItHBqFIiKjkZ0Uo+CSpAlpxkDHCpUxiS2QrsLel2KPhJw/u8Qv/9tPM7NnDIwFSGFxbpNOy2N9cZuN5W123buTH953Px9znqWrA0SaIpodwuZ1bs0LwqEy7/vgm/gX136PaKfioRO7uHbzLK1uwOLCJs1sxOM3fXJDBSb3jHDGaJKslUhvm2SDkLhrYudgxMly/8gEL964yahwKGZsPrZymZ61xmhcZ8nocKldIGmDDlJExSUsmzhdAyUtslOS6btbSFtw7clZHtte4DseKWJqh1wh4ejYCEtRm9urSxwYnuGdY0e4vtHkxVtrbPZ61No+dsYmUimlvMHB3AZGBeKNbTqGRkY+QgqeXbvO8uUamc2Edzx0iF2zIwgh2NrqsLRUQ9uSL12ap6PaRGMXOaAvsrnRpaMXWPRqjBSLPHn+FsHpLMJPOXHoMK5h02uGjIdldg0PkM87jAwVsC9rdk+Oc2Cgjt8+zKpdYbJYYOnSZeSWh28JltcS4myH7616jOz8z0ijyOrqP0Vs1Nl74JWVHY5r47g2TzVXeGZ7mfyUg6EUvbZPvpR9efxxrGJudheYzU2SMe6sNFlc2WYrivG508n33U98mPQtFtnLdb6/fB/7Roa+ag4Ko7NIuoTRc2gN216Whl/gZKnC9z5wDNs0CXoBii5vq+xivddjV6Xy+iXF19lQJsuR6iiHJ0awrP5I576+vr6+vr5vLa3v/Hyr/XmI4fXWL6T8BXJybJzDVKg3Fzk1eoHJqM3pM29j7UAHZ2fMYDYmzfYoP7tMuh3AhIJtjZhyWF/JYNp1urEiKTp87lee4NP/4XHSapZMlELXJkk11l4D/WIEOgEpMGwDogSilLSgwRWYd2vSao7sqQmCNIPUHXSi0UkChgQnRUgIZ0yEsBGuhiRBNiJkR+EA6oaFsyVBCjQgtCYqWYReQtr1aIRtpquzzC83sE2NPyYhTbGbBjKW1NfaNPeZGDdDdhlN3FGXrWKFBbPDFh6NbMKpygpTwQZXzw5QW3e5+CmbomqSiAxttwgpYGpsoVGBQhMRdEKMIMXQBq6XEEob8gJdj5BKYQh9J3HsTulqm/ymRjU1Uic4ORtcm8mZQbyGR6o0gU7QhiIa1NixRFom2jFJTUFakhw8PMn5a8uklsAsK4Z31VjOjKFLDqaXoH2NEUFhIEtju0WKRioDw7FRpkY3e9iBgBYYKDbWWnQtjZYgUmgnEf/l0We5a9coYRDjbXT44DveyJPWOXwCkIIf+KtP8p/OvJmWYxB2FFu+jx9CEmk2612srI10Dbp+h/rVLp3yOsdmjzB1oMrZy1v4nssvnnknJWMFR+fYNyIZ2NFizbjG6IEMy+dNtpOQhuGT7M2R/n4LP68xSppYC0SckhMW3UqecmGAjc0O7Z7g2pemGRmvkHaLoFJWN5q8o/p99KIu8Yag2Vhl0BzhgYljbFysc/H6Im7WwHEcJsfLCD+lUM6yc98Qx/UyQmryoeau2THK0mRorEIsrpBKl6aCbbGIbFg4pkXOMjFNiTAN6s0urTBi546TjAxuo5srnN6YoREn9OIAJ5SkKzaxP8K73v4Qjmvh31TcrreouxHbHZ9C6jLaGWCk2WI9PU8Y1Hnj4b/PjpEBXlxfZ2W+hg41Vlsy3S2hCoAQ6LjK2HiZseqdYsTyZhM/jNkzdadfx5Fyle74Hk6NT3GzUWet2+OdxX0vF1Ke2HqBP1r/IvcMHOF7pr4NADfVDNs2U19e4ZJ0MqAF3oEUb6tH8Wts7cmZs1jRE5jGEAkxQWTRCyzCOEPWse/EuFBn7uIyhx74CPty85jyPwGjAPhJk4/OfQ+JdvnBvR/FkN/YqOVvlZVGm/mtOq5psm+03yOlr6+vr6+vr+8vi34h5S8QEWnKwiF1BrGt+zDXxiguuwyVt5nNF9j3V8o83rtI47keybyinHfIjSxy6sT9tG87fP52jXNfqHL9ixVQyyAMQGEdypDYVXQqSNIsejZFzrcQBRsxmSG6sI60Ddp3SYQpGBmPWC0MEy8cJFdYQaxG2LdqpMMVzOM2zhsTWi9adMoWsgfFm4JUm8RDEjMOQULuSsih71/GLsRc+IVRdFcigxSymrCoaI/5nJwcY3WhSWWgQK8MZsPAMRJS2yKNDDK3NfFqxJ5jdRqOA7kdHHQrlJTNXPcmf+v9v0WaKn7pvz7E5x/fw9ZzCo0kGHTQFhhBDMIgGjAxtERqE51G6IKFZSsircGAeMhENEMyzp0VN0IIVFETG6BiG7GZkpqCN71hPxvtACvWrNa7hAWLMG+gBehcikxSDCXIxoJIa2Jb8OTz11lvdPBUlrppsbo1BCi01DhNIE4p7glITmwT/GGeNO+g4oTM7Qi7naA7KZYCaSUEboZdu6vM1VtgCASCKGNx4/IaO4/uIEwE0ra53l2HiiC3KZk+PM3Izk/TftYFI0VlDf7bS0+RqhiVc6l7HXqtAC0NLE8gGwlhnHKuucG4PYwRRhgtC2UZ5A4o1Mc8lq9uMfbgBf7o/DQFa5Ddpf1M7Zng9Noq7TQlKWbo1iPK24otU+AHCXHPx7FM7t45wxmxQrO9wV990+PEeZN/W3kXdXeGVstneLhILjfM7eYGz2xd54Zxi2Qz5eHOTmoXltizp0o86HJWb/KWyVmmhysMFPL01l0G8h2eWmnyt97+JrZqXTSSU8dLPLoRsOp3+OjNRxHmfZTsEv/w1EMcPjxFs+Ux4xbwShl+b/0ic2WX+8YKRIHDeLGMTsC2BRpFlCQ8/sxl9u0cZ6RYYmyowvSeEaYGS/zSUxd4dmOVW3mHd+4M6dWzzFTL5KSFFWkKpoOUCUdGxzkyPIFIJEtzf43h4Tz7BysMDhQIo4Rf/8IZojjlr777PkYHijzz9DyNrTZJrsrPvPAMnSikVMzy4MwMAOcbC2wFLcrWK9NmxsdLHN0/yu49d4obbzl4nkw24Q+e3c/hh2e+Zg5KkwusJkVGogtUrTzvnTnImcZLHKyU+f89+4dgaE5tjbO2tMkB8WmCWBOEl8ln7/yt0+tP4shtHDSPXn+aD+5/5LVPlK+hPSODvJt9jJX7vVH6+vr6+vr6vvXurEj51jd67a9I6fv/lHNnF8lZFoUdowyInTzZW4UhuHtzELOVsqswzmPzi/iGQfMNkMkXeD9vIbrVRI5cJzsUM3Oqx/UvDICUCKXRWiOveMj8Mml1mCBfYfzAGKYV4YyUmN/uIfMZDGViqQgaMd0/MulcK/HAnippq8n6fAPppyQyQe6wMSoKK59gbILVNNA9iTYhKUNv2ka7FvlawMjhDsJQZKoSL3HQUUzGNqnfP4Sbq1A2Ld5ybBdb6y3yN0z8iiQYTrGWPIxuBrsp0QlYBZ/mlQEO74rZzHQ5sncc+4ltlucHkdJnmjX2T9f5mDgEqSAObEwdQ6qIBk2iURenCU4tAaGJMwaGmSI9hWEIUKAzDlbOIQVSS1BSPfZUtpGJy7w5jLQkvgl21mHIMcituVQqOWoVnyA1yM3HZLYFkpRsQ9GaTikgeOazl/FsKO3Pg50jrJqY2xG6DiI2kKkmX+3QiEyCvEQULdIAHJVipwnkbFraQtiaNGezboY0MymgyeZcZmcHmd4/TjmfZeXWGn4a8+9e/H3qxYiBkQJdR/Do9aOIQCJCjWnCZhBg5CTjA3neOrmbP/ijJUBj2BlEEmCXCkxkhhnyDVJXgk6ROYX6mEIAQc2iVnfvNAQ2S1zteqzfXuM9kwd45vYzVPb5qHKd9s0A3RpBRRqx3GP/ZJWhssndb9lNba7N+GiH1EjJGV3MJGXXzipoeOHmEmeuLLPZqsN4i6Xnb6P272K0WGSmVOFGGZaiDpGluXl1DWEK/CFB3c6R4yY7d1bJZh1yOYe/cvLHWdr+GFuby0xWwdXDlJ0CtmEgpWBjvcXSSoNM2URWTeZe3MP9j+zgPbvu48XVlCFl0XNBOpJYwseevsjuq6sMHXOZjsuMlgs0hUHHi/CCkNVNxbHcvyVyYtYWa1xphSyuNsAQHJga4b0n95Mr5kicDledj7OtQM+VmOnuY88RlwFhkOZtilkXIQRpkpIkKaZlMl0ucbNZZ7J0p2jSiwOe2JxHk2c680qBxLFMxsZKZAs2m+01mk6eRiL49vsvcXLfV+6N8sduJsf4QluwPzvApNaUnBpDGYEftzh/dQ1Dwt3VEXbsHeVTG38XJRc5Ze3m5Jfbi+wuPsh/vnYXfmKzy+/ywf2vfZ58LdmmyaGJkW91GH19fX19fX19fX/G+oWUv0DGJ8q8IZ1k/44JLl5ZIN2wUFGK21OgBfFtxdD2II1ughWH+EbEst/gxifOMvoOk+LRHE2dBa0AgXYURAJLmKhWgtXdIB3XZMdAjCzS3mNTPJaj86wgugEDFyBODfxIY8+UePM7DnH16auQpPTyIPIewZkq0XaKuphSXO9iDJXQhkArTVwRZFcFytCYfsLO/CoaydXsBJ6ZIa4YdKYtlBbYqcnFzlWevXqF4GoBK4phQ5Gd8+6MS664yEShE0Wgi4Ta4aNXUmx9g4/dnqd6fonLz7yTxnKNHftrDE05xE0D2zSReYnR8tCOiUxMiBQq0WjbQIgUESq0MjBGNG6+R9hyCIZtYpUSjDnEjmRnZYlve+Q8Wgn+zQvvI80KLiysUzBd3v3Ou1EJ+LFmIFPmWtjCjzsIU5MS4kVg9mwoSjYLAQedTaYKgpfO72MrbJNJNIllIPMG2a5P/bMG7FZITxKMg+0piCFEoHOSZD5L7AniMZvFOCYumRS6efZkhrnr+Cy7909RdCWN1Rqpr+kkdfR+g0k9zUarh7n5boRYQkYppgn3jE7TzoVs1w2SjGJ4sAhIar5Pms2zuzzMg+OzNG+skRYVqdAM99bJn0hRWwaD5hCbCxUyfsx4PM2leJ1me47x5irKq2BbDbbbGYTbhsYwyhSM2ll+9IN/iG1e5PfP/yCdqzme+Oh3U949yCPhTu7etZNeN6BZ63Lt9iYXb6yw4+QzjO7eoBFVaKzey8XTN/jsJ05z9HuO813f9TD5Fnzk9AU2VuoMvrHI4F6PzlWDZ3IXOHZiJ2kYUS4U+ccPv584SjAMSTbr4LivbDfJ5GyyeYd9O6q88a4DdEcbHB34Q37x8hynFxIeLoyxb2KS4mwFL58w5OdoFTxu59ZJ1CTl+Twvbq4zMVxiwHUZUg5O5nfIxPfy+2cD1hot1qM6mZJN1oNWNyRbzODqHEXuQsuInVO7GB0bpNv0ODoyyNTOMXIZB601e6aHmRwuUSrnyGdspo0S3SQGwE9jhp0hYr2BLRVwZ/RxEESs1bosrTbIFj1MkaIwKNrB181BL7YmeKlT4/qWzYPVmyx1zjLfqvCmUpk3Z7KUB7OMDwyQxIqw9GaueTWmi4MvP36iWOH5x4+SKqjuSl7T/NjX19fX19fX19f3WukXUv4CueGe5emBZyjlvo2ZqWG+jYSwE7AxuMV8q8dSvcXx/CC5NY+1tQJ2tsDesRFe7HY4UukymAl4+N0wFll86ckx2iubqLUITInAhCQl86UNNtbrmG6KvQPcYU1zN6SpQVmVYL1DOh3DkSX+1cIfoY4W0Qs2tgxJx1LStR60MhhKkjoOMtGkQiGFgbOdohyTaNSiabm8+LuzDI92qN3OoVOFthRm10ekGpIeL5x8huCAR+tKASKTwlKAs+ITZiS6XCIlJm12eea3R/GqDnYxxUwUer1FcLVNOGNhVrJcu2lxdX6AqR3rOBWfpWYGL3IwtMb2JNZiSlS2iTMgm2D2YrQpcB8KMOyUdi2H7EgmyjmE0Igw5vqZcSaKbYJYog6EqMuQsW0O7qzQbPVYWW0QRDELkaKdUYRljQwU2VAhlSbISHQqcCzBe950g/naEAVlYG0WSZHookUsQQaSXi8kXssi/BaOLclHYClNIATCS9FGQpx1yBQ8Bq+XqA6c5+7vvUHa+JusN0KCG8s8cvcuTr7xIKtrGySFlKSpeOHMbdJIcbycxTZNEjehUMnx4Mhe/u+LT9GJQn7l5jrDsUkUxoRCoqVmceMmw7HLxZtt8rWE3pQkZ+WINkeZHh6gGEF3s8L2DYsX5TJT5YAd5TM8lJvngneK5oJJvGVh7CxAViM7morpkMR1TGFwtOJwabXHMytF3jN2nJmpQSzXZHOtSbmS52337KdX79FtWpipwuoOMHd5g9WlFlGY8PQXLvLUjk1+/tD3EkUJzVbA5q8MU5yV+LdzfPz2C2ScDDOzQxTKeZYWa3TaPhnHojpaZGS0gmHemXzTaPss1JqcH73KwIbkf5t0Kbu/TRo8SBQeZnl9k1P7Z/npH3w3kYw588kbqKJCTYxR3MzzH89c4KpoMVUq8MPvPkWh8Cxr4e/i9ea4vPkw240GYTtA3F3nPBL/PNwX7WZ4qMy7HvpnGEIQhQlu1mZxbp2NlRZjs3dWSAghmByv0Gp61BtdnKZmOJthunin98mQW+AtY+M8U5/jmfrT7CrMEscxq6tNNuo9wkgxUS6zfHYAP7a5Z+b6181BZlzFCxyCuouRDoLYiRYR612PUUqMGAXGJgYJwoSDo2XebR/4E9c4NTrBpZVNTsyMv3bJ8XWUKkUYJWRd+1sdSl9fX19fX99fclqLPydbe771MbzevqlCSqvV4tFHH+WJJ57g9u3beJ7H8PAwx48f5x3veAcPPPDA6xVn3zfgys0mK5sm87LG9pUudi9hcqzIZ+QibaWxrTIfPLqPWqfHtaiB2u4x192gbmpi7SCVzYGRRU799WXcgR/nD38NfBnQrHUwU1CAkyqYi0gGTfhYhsKog17extYmD77jEV5qb1D3zpKsSTYKLfLDBQquDT6k1xWhHaKyLmYak6oAU5mI1AAJ8bCN2RUYPU1SsnnhqV1Y6xHaNklNidwMyS33yM906S52iAExBN1FhR4QJF6AmTVRgy5pwSCVYHYMvF7K+s4Msi4ZaMa4N+vIEMJ6j3SgQlqQKCOlU8hRHvaRtTsTd7QwUaZGJRpxZ1cPSEFkahhwiDcNrGEf0TWIZcoLjc07hYNmB3+6wKc/e5TknRGc0GTmUtx1n+qpIr/27Euw3cN1bJoJRAbkehAnijQn7zSulRKtNRrF+WdHyI9HrN3KohyJ0QPRS1CGIpEJ5kCGXlXiXOshz/ew37CD9LbA6MUIy6TitynOmGTHIvKfr5DL91hdKrJr4hpB734q+SyVwQL5Qo6tpRoD8QAb6wm+gtQ0sAxJohV+1WBw5wBP3J4n3vaxmiGtgiRZ9xnIu1ga4k6I5wZ84r9dwmw5pLZBuWgxICpsLddYvLVCDoPSbhfLyiDyDgOxjbHl8vnHd7I+n8fagDCXJZ0ukTZNnG5KpBXPnf/fOHIIpnfei7Z+i6Ab8/SNZfytLb7t5H5O7JhgeLSE41h877ffy29+JKD2uTn82jDD4zbW8SzteYNgT4VHRq9wfWWTQCt0VlJUVZyegTVmUjcUa16PE2O7MC2DarWAbZssdJucP7/ByfYk+/eNYxgSz/e5eXuDeKgGI1lW7UNMWfdz78whbl/IQsentd3BOVHg3z/xNMlqwOHOMH9j8u38p5ee46ZqEhcSVrw2fpQQru8mbDyAXTxEisZ2HfSGgSYFQ9PxfM5dWmHqZEKyZHLfwDhey2ewWqLZ7NDrhkRh/HJOqJkdvtB7icPBDPsKQ4xXyxRs5+Xjb6jeQ0KPk5UTAGyvNXn6mWusxj3G3z3LHnOUwLcIlUlWKppt72uO+U20JEwMVAIKwf7yI1ztPIdpCsyshV10KQ7kKUmJYXzlMcw/+V1v4/ziGg/v3/FapMXX3UefvcD8Ro0feOgEU8Plb3U4fX19fX19fX19fwa+oULK2toa//Sf/lM+8pGPMDo6yr333suxY8fIZDLU63Uee+wx/vW//tfMzMzwUz/1U3zP93zP6x1331dwd+UehpnmUHGWp9157BQWO138riKrUu6ZGEW6NovKZ7vrYaMR1XG4d5JPrk+wfhV2BpvYdUkvLZErZ/Az0LvHxLm+iXtBoYVAxEBDowqC1qUWxY0InJgraytsP30eMgrzqKIc+BhKYBkWkRb0egJR72AKie9IsBVYGiEhIaIwb6EEKDcGS5AJIRYSLUE7kGqDYhJRfqvCbCVs/1KWNGuiJk1IIbET4oKLrmSh00UaBuiEYNTFEhbxLpP2+jYlYRJnEpxdBURbEmtNMGrhOWVqS3nkloNpCSJXolwD0UswIo3KmKBB52zinEk0HqOzDo4BqiK4FjbJVSz8W11MW5HmB0k7JsLW+GWLXjlHNy9pdDs4nZBqJocrE1ILjk5VudpYJQwDjFSjvrzVItuWXGkdYSEIiIcEiQlxLsW90cUwTUxbEk3miXdKhNSYNxoI24SciQ4lCEG4kcVdi1iTBcr1GE9NsL5hkPr7OTiWIytMfD8mm3XYjup0trvkZJ6emSKRvOGRg3zxD7cxCOkYIWNjFcxNE/NWF2vZQTRDei4USwWUH9HbCMhc7IHroPaP8G3HDrK+0mZrtYMOfYI0YOaeayxvTkIg2FrRBJemMb2I0EyxBYzubuIWtljYPIw0JPmiSxRk8YMpdu8ZZ2zvKGs3NpG+Yma6xKFd44yMlPn84iVuRMt8YMcpfuRH3sav/3aBJ6/cZOc9E/zMh76Pv/Gxj1AsNSh3DJwdWYqVLKutNmMTBfIe1FONl7fY6vlEseLW9TWatS7tXshzzTV6XsjusVems8QKlAZxusx3nHyQuyeOIMwHqawtUHTPsP/ecY7du4MbjSbrL3UgFezZP0oh63Dvzgl+KXoOITTOoqS22WVsqMKLmw9ipw7DhRidaERRYDoJbiKZLQzilPNclU1OX9gk2pXy1tkd2K6FJQxcx8CyX9l69HzrBk/1rjG3XaNwLcuh992P1vrlqT0j7ggfmn4lX3/xzDw3btWgbJDLuVhmBsNOMVNB1kz4/JnrfOBNx75qDpJmk0hJLCch68Q8XD2AAO4f2Is1YFPIu4RRSqfVZWAwj+38yVvQ7HCF2eHXbyTyldomp9eXedvMbsbzxa//gK/j2voWK4022z2vX0jp6+vr6+vr6/tL4hsqpBw9epQf/MEf5PTp0xw+fPgrnuP7Ph//+Mf5uZ/7OZaWlvhH/+gfvaaB9n19Dxzewb3pDLZpoLoRz336PBeDNrJtMe7ZFBNJHCV4hkbHCQMyxyOHdzEzXqVW+ln2j77IFy8e5rOXTjJqL7Lv2E62cgskBY35iIH3Gxs4z6V3xgJHinSpgXAFacFFSMnqZy5AJ4WiQLhwT2mDM1d2kwhNXMijWl2sFJxIwXRE8qyHmB0ALRA6QXQThANOkJBVGukpEnln/G2SA+W6xHaeKPXx50yUbaKyFqbtYG17qOEyccHFbCnMDR+RhugoJqpmMTBIupK46mB2Yjo788zsn6L29ApCgdExiHMOarmD1fYRygMcknwGS+YQpoRUgTCQCYgowWhrUgEyVOTXIg7vHWS5s4LsBmhbocZL5FYkalMQJxJfKC6vbNLLapCStVaHfCGDV4A50UY4EgMDLRXOaow0DCbyeXZPDbLhzOE1bKx2guyF7Nm7wcaNIfBsMptt0g2NuR4h3AxOU+OnBmHFQqYCZRps90qEizabmw2aV0ZJLLgdzmE+4nLP1Bi9ZoCbtRBunbi9TX4iYXBykFzPxM7YiEggQ42swfsfPsjpS9epDWTRhsJsGQhb0mh3sVKFYwyj7HVCqZC2wa3Y48CBKht+h9H8CLW1NdKygOezxFc9fDdBRCkZaSMLXaQJZSvE6YQMpSl+3qFdFLxobFLIt6n1brA9GdOtCx4aLnJy/yxT1TLXNmp8dO55akGT1o2E9x+4h3zWhSDG7/p4rXG+e2o/8/4VzrffygdHq/y7lRW8bspTpYS7ViO0Mihlh3FdkxdfnGft+gbFgRzVsQpvO7wXkZEcGx97eTVF1rEZyGcYyGdYz2zzyc4LvDN/gtPPzrO41KCyM0szTbh5s4axpumJlDXP44X1VdyCjbQVUmhiUzE9FNIVWxwf34FKNPtPVPnUs5dY8leRsUA1NJVSluJwkR3jeZ5eWWah3sQ+ZGMYknwpw+BoifJA7uWc8OaRIzSaAZ/4xHXU7Q2q2QI/umsM0zIAWOzV+e1bL/CGkT3cO7yDLz1/g44tKGLxyOAUjpljZ6FGPczhBHlGvzwS+aupmvuJvVtkAgOd5JkqZvnQ7MN3Dn65ZrG6XGdro4XjmthO/rVOg1/XmY1VnlxZYDJfek0KKX/l4RNstLscnuo3ne3r6+vr6+v71tJf/vlW+/MQw+vtGyqkXLp0ieHh4a95TiaT4UMf+hAf+tCH2Nraek2C6/vmGFKyuN3kcxdukG50+aK3ShL5DExcYuzYFqG9h1RXODA9ysZmk5MDY8hYsG94iM38VUyZ8NaD56kmDX7j9Dv5/kMnad2Cur6BMWih3lUl2mqT9UL0UkQ3ijHGLNxSAd0K0Z0UUdEgBMaFhGCgTT6I8AQkLshpQXo7YWB3md7ehPDJLsaXP9CpoEvoWJhYWCqFToxuRGDYaM/Hagj0uIOxV7P63DCVKzFh1SSYyBLNOIiNBKcp0BrMpRY0e8g0whnM0B3OkpgSC03GcMDysfMZWr2A2FcI2yIdB3tPTP4WJC0D3Wojmj2Mngs7sgglkSIFE0SqENLAOSeQqUZGAlH3OPaBYS5utkHaJOPg79QYZkJhvYc6KzD2jbIWtYlLBt1pRWBANqswWgqnGJO6gtQ2MK0UISVCQieKWcgvUphuY/cU8WMmOyobnHjXAhvLDZ74zSPIVFK86hHEMebBIoFXQ4V3Xg8WYPuCZs/G6UjSUNNtdIhzFgmSz1+9zQMHd1IZztOqdRlRO3jfZMTF5BpH//rvcv3WvczXGlheAp6gamUI2iG9no/oxUQlzWC5yGShwvloDXMNdrklxmeqLA1qulmbpaBLdsugXmuR6By+5bD01L1ktw0S1cVdCxFxjDRjZBCTRorFXgYCB8QWjFbYiPN49hYzg79Hu9VGxt9Fw66w4Hm8MWshpKCUczni7mGttUXtVsQXOjc4WK1QLWZZXd3msccCtroOW85hikWLzUabrgIpTUQsmN8fUH7WIE18XlI1Nus9elfXOXhilnc9sAfHsTi99ct8Zv0Kbxz9P8lbVTpeSC6fYXi3Zk08z63aBLu3JvC7AUpCoZJhm4hzrS3Gxsr4rYhuXvHPnvgcAy0H2TYICgbjY7C0/V+Ya1URfg8ZFcgdFjz1wjxxIGl9qkJ5rsPS+xq0XtjmQDzJG45Pc6Q6jmUY3OrUubC9SiWVxFH6ck6omkVO2ft5ZvgF0uEag7PHXi6iAFxtrnOhsULWtDkxOMONtS20beLmXBzbJNGKpWgPQRJxafVvcO+prz3md6IwRj4dZzxb4kL3Np+5eRaEw9vHd7PW/TBlZzcPFP930iQlm3W+4jV6YcRHnjvLVKXMu+7a9z+fGP8Hb5vZzUS+yMnRif/pa6VKkWrNgfEqlmF8/Qf09fX19fX19fX9hfANFVL+uIgSxzE/8iM/wk/+5E+yc+fOr3t+35+tX77+BT7y1GmKrS7H82skueNYI5LRcpuNWoWV4iJxb5jz6QLFu2x2WSVeODPHlk4Irfdw131fYmvFBT9llIi1tTpua4tx1WKlPoy1nCO7t8r9UwM88+HPIW1IxxSZapbgGQ8xnoUpAZ/1iepwabhCMuCDaePqFHHEJhoR3P9DR3hucYVmtYFQX24+0gmxIx+dKRDECcZyHakFuAV0HGNEirzpM/SIon4zRa8UUaYkGbBQGZN0yiWeVDidFMs2MHIWeApZHcAQJkagya36VP2UZVcT2ArV9kAYGKlieHcbMSWQR4HbKSQaFygdb7DrrW2e+oPj6FghghilFLRitJZoxyI1JKYT8+jHnyNKXfSwhCzItsDcjlFPhBjFHMGYJFQJOpUIYYKCWGmyAyGlqR6MQ3DJRAiJlglaQxxICm4e3+qRWc3TqQfUGkXq6wU2F0qkAANZKORx8Cm9v4FKNa21Ilpn7oxmDhS5xRiVsUmjhNiPSF0TM1WIWBGlCjfr4uZsgm7IJzYXGHBvUnZ7DBZu8x9/7xloxxgKpNIMD+apFspsem2MsoMRS6qGg/AEIjQYnRzg+NGdtI2E09tbWAUH01fo9TY6UuTGXMan52nWC/hrOWSiIErRiUJoE4Qm6WhkNwWjSzSYZRKDAVlkrbmD0dIFdDuLFQlaUUImn0VKwfRgmdK4w/N+l3v3znJydpLRbJbjd+/g+vwKcRAykcsxO1JkcHaAnaUyD+yp8sXldYKDEVPao3c6TzrW4frWRY5mbOSWS3GxThDEeF7EQvcZYtWjFtym23Ip50xq2x3GH3mGMTPi0OBdzIhhMjvyWI6DMWASpzEqVdxz/wxyLUY4knazi7MaslOkNApd3mDvJ+9IdhjbPHkuohlssOp18JUiMQEhCW3FC/VlWJOs5j0mR+vcOzNFmCb8mwtPsLywybujCY6Ye1/OCVprpodKjI0nNBpgDYevyhkPVndhSsmh8jhzjW22zRDHFex9eJp8KUuUJCSeII4kq6nHf3n2DH/zjfczWfnKK1Mm82X2Zkc5MjLKz177VRLdI02LTLmXmXaeRkXP0Gn/IEE3JIkLr5qA9MdeXFzh106/RN6xeefhvXfGOGuFIb5yT5Vv1kguz0jutVkJM7dV59Fzl1jzuzywc5oP3nUYKf7iN1fr6+vr6+vr6/vL7ptqNmtZFo8++ig/+ZM/+XrF0/endKtT579eO4tn+gwUOpwcncOqlrHHm9jzR7i5ItHBDi4l66y5XcrlDIOlEiuLNc4trRHvz3Fu/WGcLUG0mhK7LjJrY4wvYa9rjEQjTJMjO8a479Runv3YaWzXwxrIEm/GSG2gK4M4tkGYzoOWsA3K0BD4aB0TmjnSUyV+q3EOv54lN1VC+oI0jNBrHtK2kMMJKk4IlSQjNLadEiUCw8ygNqF9KSYJXAIvwYxjXMckdEyQBtG4hc7EmOMFdN3EGCwxenCK9SRG+TElx8TfbKAtQWY5xNuVIu9SlC4F7N29jVmFG6sTJLQwDYlR0rgjNqnXQbfrKP/ONhPXNzBaEdoRRCUDE4MgY7Kx1sayDTTWnWk/z5sY24qkkGPnsZCf+/v/gRdvDfE3PvntyEggmzGWH6J2OHR3QbQhsSIQSkGgUBY4rs0bhk7wsS9eJGkH7J0Z5OoLKY99+DB22cWQErPgoruKtCKwd9RIGgJlO6Q5k56jKWezSC1QhqB7yMUMBFaYYmKwZ2iQnG3SbXtUJ/L01McZk2ucb42xeaHEjZVROnbA3sE2Y4U6Sdvj2vUZpgpDFI6abLYbyJaiY0c4ocQJY5Io4YULC4xNDVGqahasS6xvViBRGNseD5yaIsx+gYtqBEILoRVaKcSxDLqtSW9HSD9E6AQskzAnWfN6fF+8h/iJHXx60KGiNml1t8gHUNtqMrVnBK8X8PHnPwvlDS4ozVgwwJneCo4bMWS4NLY6fGF5nmQqxw+P3M/a7RpTcY67H7lEr+pw4qbBFyvDtM2UtGuwWl1lT/EAnko588I8hUKGYxN/D7PYwg72cu7KMvMr27SjiKUbFapjAccH7iVvmuw7MsFzm4tEtf+Cqnjszf0oLz6xQLcZMDBtIYc9NhzNiGuSkxJ7yaR96EcQw5Lo+hy9JAK7Aa4BscBIBXE+Q0sYZPNw9OQUA8NFynYGCSy1Vln0a6zOGQRRQvHLfVCuLc/z/MpZ7GQHUbSJ64+TJimGaZCqiF9b+C3c9DH2Zt/DgPMOolGLeFggRu5MoAmimMzlHEQxO44PYTg2tZ73VQspfidCraywUG8ys+cWS+1BOqnPQi9kzLBRWDg5l8hWSPmVCw6RTqnFPbZVj7n2CloY/D/XP8nD1QN8cPrU65VC/1SG8lmGC3nWwy63m03iNMUx+8Pw+vr6+vr6+r41+lN7/ux80+/43v/+9/Pxj3+cf/AP/sHrEU/fn1Ij9LCNCFHx2TNuMFL8XuzeH5G1DELvGKXtPJXJDCKwOdkY49DAOIf2T9PYDmgpTa29RWMjZmhjiGpllDec2EugAjYag5h6BX8NTDMg6Pj84aUb6OEs1pZJeSVPx4/QGRO3VCCq97izn0RDBSxPo1ULuT+hvV0gr5qUD2hEu0O80UNQQvd8zChFmSaqkyDTECMvCJRESgORAxXEKKdI7bdjoqkEJ0zJRhaGYWEmJj0vxejGGJ6PMgy0AaSaG3qVUqXMiC/RLR9vMyRrg9EWZJZjaqeGiSOP1mWHbEPSOT+A3p0h7kZEloCbq2xfL6EWtjG1JLKKxHYW6YXIjsIoZ5CmROdsVJyAUsgU4kEDRkykl5AM5ikUliBV7Co3cC5uERUrDGwl2J2YKNHU3QJpQ2FkFCJKsVLoFRRhxuTpm7dYlC2kY7B/9wDcXMFJImTWwohTUtfEUgpRieldNwmWUkRk4uRMZEFQqmRp1ALivCC1NdkLHZxIIkcH2b9/FD9NiNOU7drj3Jz7A6asgHPFU1z1C4QlScH1OGgsU7S6jOV7fGlxnuvtbQqmYCRfxrQFugeOJxCBotby2G4GLNW6yMMJQU6RZBTYBrGfcv5Sk9XeXah5BXGKiiOMskC8xSE508G8kSCKESIBORRhF3PIRHNzq0VmzWNnYQe9bA6RbrKcLPJ7N7cZ31OlUfeYtVZwBraJm4v83jWX224N2Y05TpEymnbPR11o8qmNJ7jv7n0MDuR5b26a8Zk5NsLvZ2PHNsn4RW46CW8elkw/uI9OrEjROCWb0eI4hUIGz4oYr5YwDUl1sEBt/iC9yi6eNy9yoffbnDm3n2bT4tZFi3K9gzqi2MgGlCLJRGWAZ+dWMeqKxoEe0lZsa5+fuvdeGknIJ5+8QRIpGGvgeDlEPce4YzM+kscp2uzeM8T37TzGE9du8dngBu84sRfbC8m0fWq1FssrTfL5LLmcw2dWvsil6AohQ6ybg/gZjTQkSsecXvt+xsQtBuwmG82n2Df6VnLDJnFXc9+OO9team2PWjMEDbanefvxPRwa/+q9QFI9j+/Pk7W67MkLzm/mSbXAjNf56MoRFJr7x24wMXCcOW+ba4sbvHlsH3nrlW0+qZESZhK01nxk/gvcNbify61VwlT9uSukDOSy/MB9x/jnX3yMXhKh9F+GHcF9fX19fX19fX3fdCFl9+7d/PN//s95+umnOXnyJLlc7lXH/87f+TuvWXB937jjgxO8J3eIx26doxePcX2wTDMZRasBzp1VGEmX79xZxbYsJuazpL7G68VURktM9AJaV3uMXI8xIpd3vfEQu/eN8W++9Fs829omybsYGxpnI6E5HKANk17FJesLekLh7ZIkA3kcM4P2enB4GIYCWjLFnxQMjVXJ7Von/5sehhK0XIn87TrZTowuSZI4RgFREN2ZPFMwSPeOIW7VYLOJqwUaQaokZjtAXQvQ40X8IZeBXBaVCDylEKaJLV1ir4fwQ5JhiA8Z1HSLyZWhO692naBChdEJMAywbqcIlWHldwZwd5TRpol0XEglTjWhen9KWNP4t01UYmD5Gl0CehHSS5E1Dyo2shtCqtGRQsYxduJQiHt8/wfP8uS1SZ55rMw/uXCI1SUHO+7Q+bvDJJ8IsOoRqUx46D036Nwoc2FtDFEUyKMRdhv8lZBL0QZeNSGzpjg/t8TY/hWOPrhCbS7PE390iDibYJsWY2kVdcXAe2oFmY3J9RzK2QzhchuHFOmZxHGIdHOkLY/k5jqbnke4sI7pWlSTHST+JMuBoJbJ0wgdrDRB6QRnJCRX8Ol4PZ5/bg4PQduAYxMjPHRsL88/dR2pACmQhoWKQqIggQWNdTAmch30gM1gnGFtYZXezU2kD8K1kEpTGhunlORYMxJSI6G6q0EcGfjZIZIVn6gXk46NYY7mOb2wgR0petmYjdEOYnSLL159juc3U853xtiT+oxlxjg2uZMnO3At3ODmuM+bC2MUb26QWA4jg2XyWZdDd+9gbPJ+8kWX7u05oM7exoO8c28b1z9EMm4Rtjyer6+y6F3jB7IneKiwk6xrc2T/BL/z2FmaacBdk1U2l5s816tj7oZSNqBplMml+7i0cohbo4t4A4rd1hDNOCLUBk4gCM5lkQOC3P4Kg4N51m51yNoWW66mkAuRRUXYLDBWLHKwXObN+4cQWvLi6ZusOs/gRy6W3McPD9zP6XPnue/tR6gOl3BdiyROmI5nWKltUIlm2DVe4Z7ZWYQQKJWSqA6OdKnFI2TsPUiR5d7xWVa32+wZqgLQaHfp7ohBwuOXrnBozyQnd0/+ifyz3GhRzrhUsrvZO1ymkpvgOya/j197+iP00ph6fowX1tsIoXlhVfGOnZqP3j7L1dYGA06Oh0Z2vXytmUoRWQnRUrOrMMGO3CiT7hh78lN/Fqn0m7be7vDFG/MkKuTojhrfPvM+LGl/q8Pq6+vr6+vr6+t7HX3ThZT/+B//I+VymTNnznDmzJlXHRNC9Asp3yJCCO6rHuGZS21WGymGPc7IlfvQYZaZwYSCgrgbU57I4U7bNKOY31o4w0g7jxTwvd/5UQ5NXeLJ60f5xZuKw71xMq0CSAvVFeSXQIYgpyz2l0dojTaQtkep6BBP+uQKBQ4Xs5ytOSghYCImux3g+xkakzZhrYTo9FC2TXjeoBxJtOsiXRuhU2JDoDMSDHCnKgQ7B4jntyAUpIAhFHbTwxcQjlvIvUWSyMBvd0gsE0OZaExSKUgy6Z3tENspLBlI3yJJU0amSvRWGphND6VSHJUwlunSkROIaUn2kTFqZ2vYnRgzTgjXod0pE3ZitFsAIdG2gEihLI0pFLLtQ66AEWik51HeEzL91m0uX5phz1CL2dk2iX+F82vTnJkvIYQiOZRF5jRbb4ipfC5AmNCOMzgDCYYCJQGpSV2IZEq2DU7o4V4KiEdypFMWwyNNglUJSYoRpndGSrcD8k4ezZ0PcUaiscMUlYJT62K2HSwE4aCBbHgINM+dv8HM7CQHRgepr0a0N9/Hi/oprOEWoldFRRbGRMjAoIdrpqwv5vC9GKklhUyCqifU1hpUyjkyrkOcTdhsNAlshczmMat19h29wacvVnAq0LrVJnxxGREJNCDDFHPQZc/sMIvaRU9byIlV/G5Kr2ug1h3IR6hBWDN7bHhbzF0T5DI29e0A9zcVjUWTyl8Zo+fPkXZLnLm8l4NWlu9/8Eu8PXeFj559N/XA4cr8Jj1idlarvPODp7CUwMRgc6VOGhfZ2GyxXetgugYD1V30zDa/dv4irmPztnv2EDcCep3g5f+5eq/HP/7CZxBZxdqNDg/eP86OgUOc3Hc/6d4Brm41CJcaBPNtBqwIZyzDsDZxBmyeXlhA1XxU2UR1TBrrPT7x+EWub2zhNwKEhqhrkEnuTEvaqPcopfCSvYitLD6w9wSr2etgm9SjVe49coj9kzOMTg9hOxZKKbxuQOJmCTtVDmanOLp/D2ODd7bkGNLlnrH/Sie8xHz9/8SSK6Q6RnkafNhu9mACfvnM6Turu4D5bIwXRXz29DUePLKDrHvndfbYpTn+8e98itFint/9u3+Ft971/SihcZw8Zskh7qUEURW3UEMIjfzy1pd3ThxkJjfAofLYq3LZoJvHLsVordkKznG08iZ+dPeb2VeufsP50I9jPjc/x3ihyMnx8Vcdu9rY5LmNRd4+tZex3P/81B7bNBjMZNgMa1zuznHCP8ps7rVvktvX19fX19fX93X1x/b8mfmmCilaax577DGq1SrZbPb1iqnvT6kykGW6UMYQKcNOju1Ojkuxx8yeKkfsPI5tctp/kt9fexIYZblmclhP8N2zx9g9so4tYbK6xdqNW2yutvlh9xRvWLN49IsXCfcprLaEOKUkTfKxoN72CVY6FLZz7LvLo+ufZng0x96dPV4qCNI2bG1lkEkCocYes/BMB6NaJJiGeDsg5xhYqUVqCOSAgylsrKEikZuAJTCUIgYMaaDRJI4kmbRxl5rYXUmjKjGPzyA2YkbWIbmxRnO1jZFKTG1gvFhBxymdXI94fBMyCSow8YYkcS5lttcj9Vo0943SStowCGJbI2wLITTbTwwgmh1wLdJ8hjSjIQoxzBI69cDNgIQ0LxHaYvzBdUbu7dIuNnlhY4JOK2XjIx6yqemNglHOEc/kMUKw3BTh+8gu3F5zKegCOlbomsB8ykKUFLELcSwovqjxpyXhoYh6PM7Wr+XQ60WkjjHVnV40rbZHs+WjAKIEL/HopSFjxSxW1iaVBjJNsLyEuGhDlNJZbbPdmeeNU+MMFvKM7BjEO9dBNk2y6wrtS8Igw12n6hhmyMLpCqmdkkt7FHOa+YU63VbAjmoJypKWlkSOgWgkaO3jRibJigHlEKwe3TUfUlBSIBGYaEozA9iuSxDFCGFimRYd3wVfkUiNjO9sl7ImbjBycI6t3jRb2yNYjQCzF9I7Z+P+LZv3TO+l4z1FLbG41V7Ddf4zmoQj+SOs9+7hmeWrREmKsgQdo8twrkgQRfS6IcPjA8SWJM2abGiP85sXkJkK2+0OadHhyPQkB7LD7HWHAAiTkL/z3N/lyA6L9d+cQJYVy6NniYrDUJ/mc+0Ffv3qeYalyQcqU7yzNE0nI8gUNDdUm+JgHm9EECQKEUlkAp86c5Ub3SY6ATvSJA0XlUJkK8ySJAWUVggLrp/dopndyej+MiU5QkBEsZKnF0Z04xgZpnRaHp+tX2E+2qJ+sUeuWGRsdgjbvpP2XbNKqtporRHCIW87fPfhw5y9vsxMuQxAQ/YQ2QQERG3BtRub3FqpYUjBm+++09T2Vz/1LPU4pL4d0gtD/uul54mV4q8NPUiXhNQCkZrkhMS2JPdP7cUwDU4OTXNyaPpP5LGC4yLQIGAzSHl2c5F/f+Vp7qtO87cOPfyqc+ttj6xj4Tqvblp7dXuLX37xBSYKBU6Ov/dVx55eX+CxlZuMZAuvSSEl7zgcmhplLIK7Bw4wmfnqjdj7+vr6+vr6+vr+YvimCyl79+7l0qVL7Nmz5/WKqe9P6fOX59gMezw0PE7OslFoJALXssjkM0hL8tmNG9zu5jiVXeRWdyedyOMZ8zmuy3HeO+vxpeYh9h/eYKg7QzZ6gZP7fpcXFg5wxR0mrUi82wG7d45w8dItGpdCdC+hEwTUJjRJxWLPfo+j9y9y+dpuohY4rRhxS5CzqlCO6QwZRNMZugccjLkmuRcVGJp01MUYLiNSA+VY6FoPOycxtSJFEGRAK43/4DDJvhLGE3XsSKFCgSkFByZHmHQlTz57AdFLMRwbI1XErodRLJB0NDofg4zxJDSP5cguaJafH0DaMXIxRtgh1pqPaAaIXAZlalSjg2FYaNdEuRbKTECZiE6AQKN1isoaqIKBFILLl4fo5FyWbg2SPdYjW+qgBm2iIWCmiOvnMBcCCt/jEUmBvk9ifEki6pBV+Tsfak0IzTurjIzEIHAMjKzEUpKebRIJi4XhEfJ+SKYGkatIH/CQGxr7kyHa10gR4Q9JkiGTbgJjE0MsbjZJLINirFHVCp2sQtgOKgLPSHj44ARnz3nQcbGUz8zb1qm9VKC3VeDzz+xgcrDOlaUhBlZ8jr21x7M3p0hzBl1Xc/vmGv7WNmq8gKFMTJVgdWMSz+HyuVn0MYPsvjLD113W1prIvIGTKCjmSLIutdU2u3oZMsUMc5ZJ4ih0QeCZGbTtEpdNRtUWGRmTBg7SMAgmMgidkuxK+OS585ws7uDEzqu42Yjl5m7a3X9MPnuDmzf20um2GevBRqKYmc3wydrnGRke4u3pwyRJShCHnL65yLaOcdI6eNs8NJpg6ilsITAbijRICfw7U29utG8xWOqS+EWk0tAS9NYMPr22STQd8VSzSTsIyMUG7pBkHZ/P31qmiktWSaKNCG84hU0wDU11tEyPCCORTJcHuLoYoX2JHzmElsKsGrAQI5c9rFRy+/IGN8M81vA+zj87j2VJdh6e4tMXbqK05k17ZlhZqpN70cBel/TaiqV6E9Srvx640m3zmfZbmc7t4rgwKBcyVAcKJGlKmiomSyWeqa/fOVlpOm2fOIix5Sujfk+vraPzgIAzc4t0bnfxSen6EV6gSLVECii5ETnbxE99KvZXn5pjS5OKI4gJ2Zd7gI2wxq3eGnYjBF4ppFxb3OAXP/EUO0YG+IcffOOrrlELfLaDHhHplwtFrzQ8e/vUXkayee4f+ZNFnD8NS0qGczmq+V28aeReTNlvNtvX19fX19f3LfLnpNksfx5ieJ19U+/4pJTs2bOHWq3WL6T8OVSxHHYPDfHA0d0MWgZLVwZx05jUtfFtKA3k2adOEAQvUdG7yaUhI0aR260FNjYq3DWcZ2stjzMcMhtNUCn9Aa67zr59Q9w4PYq7FrHj8DATg0VKlgupQmgNjk3ehZonaG/tpbt0D7uGljm9FZDdmRBulMiVbYItDzmYwIEYfcPG1Fm00cEYFWBX0NpEKUWw2UbWmoh2jJkxSQ2BMVNAbQR3xh0POIiKA40ER9jctTZA2unyzOo69ZwFuQxauGhDEj1iYKQR6nEL7w8ge1tjkmIGEiM20Tog1iZSCWYGt2k54AHm7RbCUKBSRM5GeimpDFEFibQNtABR7yGjgHDchWKOMBUEBZfN7TI5S1MdbJO1Q1oPTRBXJfQKyNUIqxkhswmN5iDGAYfs2YjM53K44xWk3iRxDIJRE39QkukaaE/TG3NxFlPsz0uiuwxkBqKShV2IMYdBVWK0m6IEqKyBVmCbgtQ0MBNBEt5p6oqKiGJBfqpIdjzP7biDmZpoS1IoZnj80lXSqxn0Icgc9sgkFslLBT713BEMfGimZLTmxe0xgoLEMAzcnE1jcxPaGjcXoUsZdDvEiGKM/DCbWZep7BbvLT9D4+GjrDVKaE8hDmp6okIpNHEdi6nJMhgRG9UVMm4DFblsDleIDZvs8R6P/+EsZjBJrC2MfEQ4VsQruYyOSAZrWZabdb5w/hj3PHSdsckHULwFqd/JnPEUl1imkiZkEtgQMU4uw3hxmMtPLON3fcqDedzhOkP2BtF6FttM0WoIJ2tjGJJbixtklIHtWOzcPcrhgf34z+2nEXeQRoywIbo6zLXhFpZ1iewgDOV2cSQoc+vCBkGjw63tbVqxyXv27mJv6PJE2sTqgmlZLK7UuGtqjD17h3jxxgqRpbCyGkGC0cnApmZhbZ34YJdmM2I2k6cwUCBJYm5eWCSXc9lzdIZKLkOUpgwMFpgrrHHogZsc2p5jee1hDuwaw/ryapQ/XP0op2vnaIfj7Mq+RIkG3daHmBooUs66FAyTbstjT3UMcfs6AGMiZc90lWLGpVp5pRASuiGWZ2GG8OlLV1he2UYLQSvs4UgNiSafuOwyszgZhfAEoYi/4uhjuFNAxBukGXr4E5K3DM1w7/ggJyuvXulxeXGdM9eWubmy/XIhJWz//+nWHqXU+kmq0mW2PPSqIgrAZL7EZP4rTx3603BMk799z/0AGPJrj2iOVcy55iVmc1MMO4OvWQx9fX19fX19fX1/tr72u76v4Gd+5mf4sR/7MS5evPh6xNP3P+H4vgpL6iy/8tKnuFZrsu/wFHuPTNJTCTU/4KULy9x4cpVr2/DxzS3qgY8+YHEwe5D37lhnrLjF8coGlRffweHRA6w2PsC/vfJBPrN2ECORiAjK+QyZnM3d9+wC0wRxZ1yvm71OphSzvOXwiU9aTPbeRmuggpfNEI0YrHV8Or7CiDWWoSjMh+QXBWkpSzfNI5QNpiC1NLrWIBOmuI4Je8fQB6expquI0SyFqxHFsx7FzRQMgUbiZiwmp4dJmgHSkEgXjDAkdBWyHmNcbROMhQhPowAdK+xGit1M0BlJMmph71HsO7KIPiYIH7FJnQhzq4fpR5hIzFQgkhSZNdAZB/I5hCNQqUJ2ojtbcgxBdtHCqdlEPZfGdoFLG1N0rQz4Nk4g0ColzQr8p00sM0HmBAjJlpNwvVNDC4FCAClGGJOYCUQRZqjoHndovcdAEGC2Esx2gkgNBjYsrM+n2J8z0EWHJGeRllxsxyV/O6Kz0WVjeRs6McZaj3SzTbjcYHyoiEgjzJ7m4otLXL2+xsb1Ldjo0H7cYe7ZEZZfHEDZmqBsok0bUxhYpo2poTyQMIDE6CTEjiTVGqsJhTDE8gLoJQReSiIMhrw6W+ctOu1NktBCdAT6kiSfyYDjogZcjp7aT35yjtQK8Vs2WqRUH9oi86YuO3etEwsTHRuQKNzEYKjsMjE9wPt2H+XAjnHaUURrOc+jp+/hE+c8rt1YwXQtvHJKNGxi7S/TuivLhcEeRwYe5ERwjKDpYZsGW8LjXLpAOtnA3R8wOv1u0s4DCEOQupLVepfsYJbB4SKmeWc1xi+966f5wMh7EdoiMmw6CNJOTLHR4sf3/QHvGhzDnvN54dl5XnjhJqWWwlmPaHYDhsbLZBZTshsRzlaKGWhcy2TcSEmjW0SuoClcAj9HECpubHTo+SaVuSHeffJBDu+ZZu/sGA9OjjM+UyHeY/BSZ4G3Hd3De04ewDZNzmRu8JwRsegVqQ76TIyVMa07Kf9G9wqtuEYjuM2ISJgWV4iiGEdKZscHyGRtLNskJ21kw0Q2TN5w6CLDQ3lyZQcn80oR5NiBFYpzKYX5lNMvLJKuhMTLAV6vxmh1i8nRbYRS5Aur5NNtbs7duPN6/Bpkz0V1DVrNmKncEP/6+A/zfTseedU5caxAK+LwzoQfABV+kXotZOXaFXamJSbM/MvHXi+JUndiFl//m5cXGuf5naU/4KNLf/C6xtTX19fX19fX9/8VH/7whzly5AjFYpFiscipU6f45Cc/+fJxrTU//dM/zfj4OJlMhje+8Y1cunTpWxjxHd/0GuQf+IEfwPM8jh49im3bZDKZVx2v1+uvWXB935zHrtzkxlyI4Sxw5brmr957jAf3TdOo92je2mQtMZAkZESCJTS2WeK+iRmW5jbZWL6bqcEmT82P8OTmTerP9nA6BquhibERoX0DbUiePjvP6K4qu0ZKGK5F4qeoIc3VzAQzokZj2+Th+x/jyMy/o/jM+1ho5Ria6eKIFC90YFugXjQQgLQEluphjXcIkzxd30QsNUjTlPyBaQb3TbKw3ERtewSFmPi7Stgtjf2sgZA2BB1oNbl9qUTxxE7IZyl31yEWiFgQ1xKsSxnsukfpLSFiWNEMLYJhyHZ7HPmpkHhTcOEP8rScDNuLGeJ6iOxpaKeIjIUBpJFCOpJUgAgUViIQpklazBNXgHIGdzVEmAKtDWTHQFiaDx16AdB8+jP7WTUmUFmFMoCMgz8nKd/l3dnGo0G5Lk0ScpgkRUVSkQgtyHYi3vC2ywzYXT56+gQqY5KWwVpRWBsBMoLQFjhzgrGpAVbHBSxtYvoxwrAgayFSfeePmCbSsSEIaQrB+Wu3UFMBSdni6asr3HdXnawJouRjR9B6wgGRIsYEOgutguBApsLuQoWLN5ZJugF2wWYz7hBnBcKSmKmiUi4QzNcQpkRYGtkMuVar0tqKKa9MorsNZKyhZzCgbeqNgJutkK16D8feR6+9hbcdURrxMUzBPRML3NwcQkkNSAw0Qkj+jzc/QG4qx7mVBUxP8x1vPcwnn7lMXJZsJx5LK9sk4Q7eVtyNrGnkqElGp9DTrF1rkCqfnuezZ3qcuU6X9e0yUWJiBjl27XMZtW2sBUmsDZbNgB2OZmZmGClf+cCcKk1ctAjHIBoSGBrGshK7dx+lySIv9i6T6oSM63LPvhnCFY8oSumFKcVIEhlgWJqhiTw9Av7wM0/QlhukEweIlYPbESgU2oYNS5DpOORLJS594TJntzeYt9v8zWMn+Ffrj5Jc0PyC/QMcHrgz3ebE0G7aQZ1pP6Bzc5hPffQMb33fSfbuG+f7pv86L9V+n9v1j/Ge0m0sIdAFD2lWMQyDQvnONLaFzSZW585zvd0oUAlrRGFC58tbnAD+8YOT/K3fBqHgyOgo5zc8dJxgWTV2DawTxQbtmsfgapXilE0myaMTjVIK+d+t4EhShRQCKQVDSY6tpkfSu1OkuDNpSCGEeHmFiRVEuOc3sA+OoJTGMARO5cMUvKdxG7vZWr2OtkxSpTCNV7YivZbma3V+8+wFYi/m6OQY7z1+8E+sgPnvzWQnGXGHOVza/7rE09fX19fX1/eXm9Z3fr7VvpkYJicn+Vf/6l+xe/duAH71V3+V9773vZw9e5ZDhw7xMz/zM/zcz/0cv/Irv8LevXv5F//iX/C2t72Na9euUSgUXqdn8PV904WUX/iFX3gdwuh7Lbw4X6cTW4iMIgg9at0eDc/niy9exF9o8YFvO8WD+w9zbWuO2qpiaKLCuFfkaXGba/P7WVt/gM/XrhLZihcbi4xednEwsDoKY6dH/mhI8pzDZ88/wVOjijANwYTkYYP6njLy9k4GsyUGRlusRIM8Mn2FXzv9IA2yjJp1yBYQHR+dGnRnNUYnQcYZ4qyJypiELTDMFOmldKUijhK6aYq2NXrAhKxGpTFmWWLEBqrTQ4UpS5dvIifyyFIGf8lCCZCtCKvbIxN6UHKgHUHeQu10SJTAbHtIS4MhMD1JGmjSMgyfW6f+fBVhZ0gKGn2XwHipS+qZhIdGsBsJIojQhkk6VCQ6DFgGRpDi1hLap2zSQyHmElx+dpLCni6rD1fgCwrhg1SS7g6HeIdJPgywbU1QzZFb9EA5CMPCDEwszyRyIcn5LNcHyU6GZJdAbmm8GZtkTJG7rTFjQRz4iMCnIxSGYSGVQHQ9nEqBUj5HL46IvQC56aGQkM2DIbC6CssMUSMuomPji23e9Nd/j+KnXM4tTCCXQszARBuKpBhBEepVzaAJ0eIWKtYE0kAYEOfBCD1UMc/O/ZPUbm6gtMZqR8gkRE8LNhijMDKIaXeJ/QgswXe/834++amLBL0AF0U2M41uDBB16mw1HWovDHNpexbhC7JjYKcaHYC0NGevrdIQEdf9LbxOlYOFGd759/fye+fmoAaXFpeZ+pzDi8kWF3MNDpkjPKAm2fID3MBASFjxQlYvLnJwYBc6dNlaM7krHzA7UGJ25yDOM2dxW4LjJydYqrf4yHPn+IE3nKSUcQFY32xBoimGAUemllnujjA6+EGktQePa0ipGBrIsX9qiqM7JrjcWuRWp8H5tEl+vMhALMkPOjxwYhenry2y3nQYmnVQZUBpqqU865sdtNYoR7DQ2Obf//wnGKpZxGnMwL0OxUyOspUjJCVnOS/ngwIu5obLk0/B3NWbDPiKvUcn2LtvnKo7yiMj76XnP8OtqIGUQ+xzqghhkKqARNVxzHG+4+BBfv1TZ+88V8NFAhPlPIOFV8beH5/6eyTDv0gSaEamKqQbJmksGLQOsNt7keduFsltQL2xh0eGjjO9dwzLMl91g+32Av7Drz9BLuvwo9/3MFnTQsbgRREASZyysdrAsk2qY2W2Nhv84v/xKHgxwcoCzz1znQce2o80p6lOTTErNkkuX8cPwm/6zUQnDHlhZYVdAwNMf7np7lfTjSLaQcDphSUu17d488HdFF3nq57fjgNWvITbTouHh7+5uPr6+vr6+vr6/iL69m//9lf9/i//5b/kwx/+MM8++ywHDx7kF37hF/iJn/gJvvM7vxO4U2gZGRnh13/91/nRH/3Rb0XIwJ+ikPJDP/RDr0ccfa+BDzxwiPWXloiUxVh+kOxij6v+LZIUJILhoRI7Z0boNj3W9TrP3VjiIaZ5+MgeVlZq7MmUOUqDq51NJmoWgU6w3SJJGqGHQGVThspZ7D0d1IhF7TsLGAshxTMhIlKcGJshdE2+cOWNWP4tilmP8ajFRlQkK0vYOYeN2EcmEi0V3rRG3zJIURhLGitroitZtJGQCoNavYsWkAy6FDwTznXx1hSdckq4tYUpJDqnMSOD1doGWdfDywqinIOWmvLNBJmLMAiJP56hVUvQ0secNugph2s/b6MSF0NmUIZgLapQ2pPAuZA0iInfUkSeskBo0i/4KBET5SxkHAEJMtDIroG2UqQHquCibQVohIKXfn8n8eEUDqRIFE5LgSkxewK1JUjLBp3IJh2D1rEc9i1B5lqM6UmcVU06oolDycpWic2XCogNyciwTd03SCwo5DKoNIU0IXEsWlqhnQBDpZBoRJxSGcihayl1QxIFXcwwxRrIsetkl7Q7Qi/MEtdd9oyVGcu7RDkY3d9FX1XYKiU1TYxA40RgbUV40uf2oTO4s4LoZhZVbyPR5GOIfUVKwFNPvgQtD5GFqOOCY9Cu2dgipbRriObNDqy1GJypsrrR4d57ZtlYaRFogRnGOBoSVQIJ5pyNkTNBgUw0AtCOgTIFtzbrHBgbpdnrUPQdul0ftSoxPAeVpohEc+vqGgv+GsaJLJNDBZbnt7her9OLY9551358NKurdfZujjFm57ln6mm++66zfP5ixIXBdxBt+EhM8l3BubNrlMbzqIde+WQ+Vi5gpHDs7uvIFUnaWEH0Fgmnp/hf73qAiXe5uNspozuGadS6XLq+wlLapTsE0rUpVDJMzQ4TeCH78nnMXbuYOPkGzgVPEyeC+w8f5Iln5mg0fZQpUZ4iVpqR2XEyZsLoeAnlp/zU1LeTHc4yU3hlRPBvLfwKjcwSVzf3E1sCU0qyFffl47Y5QiDewef8GiWrxM5IIaTmSu2v40U3OFD9Nwxn9yFjBQasXx9he6ZLPm9hvGrVhYEcsogTzdBAgZ0jg/hhzHRxmPbND/D49ReJlcfDs9Mc2j9OaSCPEBLDeGU1yvzCNqfP3cYyDb73PSfQriaWmtWkDYDS+s43LF/eRnP94io6URAnIOHSC3M88NCdVR5SCsqFLJVchoxjob/J+XtXt7b4w2vXODE+zvd9nULKXaMjfPvh/Vza3CQWmjubB7+6IL0z1tlPwq95Xl9fX19fX1/fX0ZpmvI7v/M79Ho9Tp06xa1bt1hfX+ftb3/7y+c4jsMjjzzC008//Q0VUq5du8Zv/MZv8MQTT3D79m08z2N4eJjjx4/zjne8gw984AM4zlf/Iuyr+YYLKe12+xs6r1j8nx8n2fenY+V6ZNwmpm9QEQ7bHZ+2F3P/zp2Y4xH5cp5Oq8fiepPVRpuGkyCrLuUOXL3RprHD5oePF/j0J7Zw0zI7v2Oaa7UmN59rYy0kBCsOzSkYqc9yfPcUc5UzBEsR5nWNsajJ/i8Z2kZCo6EwtINT8MjGETrVdJSJ7cUoqRgca7NzeJ3za7MIlWDf1IhQQ9VCDVYxcylhbGJ1FSK9s/y/UrBYutzG6KVIqYj8BFMrRAymSvCvNeGARGgDOViB4WGS5irKB21CuA2mApkoRNumd1+Zes7A3RDISKJa0D5TIN4yiQsphkhhzUGcVagLIVoCSiINidmL0QIyJwKyMsC/XEbbLqlKyDyrUKsGtAyUJXAXQuILEp3GRJaJFBK7oXF1B3U8RpYE1hwMRz3ImASyhFYgEo3VUYhUEzcs5EKIKX0mD4/SWGtTdCx+4Lvv42O/9wLddQ8lJYkZoqoBZkUTfSFLbEJhMEOn0cYgIRrPkHRjdjy8xdCkyfK8jVqvIEop67dW+L9+7TK7Tj5EY6JHckmTpmCMpGjHQmsTI1BYjZRu12JkZ5clPYJbM5GpoKcj0BIdRIRXtjAMiZkaqOydiTsijiGBbz94mF8/32TVhO1ezKOfO8f733aUiekKa6OfwZU5Ro8MsXmxTtJL0bEmuwYyBFNrSrZDUyiCgqDrag4NjmCupxgarp5fYi46j7ZNMjpHaW9IQ/scujmIuZ4l11PMn10GYM++WXbODOJ3fLxGwO3FOuWKw0S+wacv7GPulqCiI7JuFhWn1DseZgg7ciUquVe2M05MVdCOyaVbdyYGdeZcLnabjM/W+bWNm7SdiG/btwMzkayvNdja6iLiiBHPYWDEIcjaPLu6wqdvX6Pim+zPlNg/ZJLzPFSqWb7W5MSBST7/7E1IUwxfU5oscOyhXVxar5GNDdaXa9iuTdnKofJ3tr98af42+TSPn9HIoRQrssm6GTrJKx/060GX/3r7EocrDl5U5fb1daQU6IoENCAIwhgjuPO7GTt8sXmDg5Uqb7x3N9XBO0sp273/h5/5nl+nGbyHt584Sr19BUvnGS+VeD6eY9P3EJFmq9mj140YHzH/xPaXSiWHWTEZLubJZTOkuYSokrAhWmidcqnxL0ntmGMj/xQAmbGwHEnsaUzTQqevLpakQtMkwFcJ6Te5JGXf0BBv272bPYNfvxmsEAJXmrxxdpZ7ZiYpuZmvef7R8i5+7MCHGLL798m+vr6+vr6+157+czK1549j+B9rCI7jfMWixYULFzh16hRBEJDP53n00Uc5ePAgTz/9NAAjIyOvOn9kZISFhYWvGcPZs2f58R//cZ544gkeeOAB7r33Xt73vveRyWSo1+tcvHiRn/iJn+Bv/+2/zY//+I/z9/7e3/umCirfcCGlXC5/zb3ffzxiMk3Tb/iP9722zl1t0LruYBhZqrNFmjKgu+VR3ggZDBXdZpex2UH27RllXQTYRoJlmly6vsjV2xvc8i7y1+76bU7sy7O9+HeZmhjkYxsX0TMxwo0I84K0G8NWj6XbXQ4sVljZ2CR9uya+bDFcyVEqOzy7nMUK82S8mJ6fRZUNvBhSlZIZzPELH/oPnPEn6ZUs5j41gbMliKoO2rWRCFJTIeyAkooJehnYDNla74FjI7sBZi1FDjmkdYXoRkSDNmmaklvI0ej1MFKBEpK06uDnEpK2A06EjlKUlEQTFtZYnvw9AUZPE39UI7sa1bPwYwvteKAjxLZGNBy0kUWXozvNbbWCdoAoWBjDHupGSr60jVXM0Lqcwz9gEO2zcC5IzEAQJXmSYQFKoh2BUQ9xGhEi8BCXU+KTJhXZJbMUkkY5AlegbHnnP1MIkqyBbMVk822SMMOt1RZxwSCJE5q9AIFERxANS9yog5V2UWmGMCMQnTZrt1eIOim6q5BI0uE83lDMkmmztTpELkyJ10ISw4aZMtH6Mu85do1fV3eh2iGZSUU2l6HeDCBMkUqw+EwZJ3Ho3m1z9+E9XN+s0VnZwsqBbkZorZFIRKmAtiMsJ8WITXYXh9kzWyLQHVQ3JQljIhsuljy+58AoX+zMI3MxNyZ2kEgbXkrQ3S523UQkgmxe8L985308fW2Jja7HhMxQ2+xw1+Ep6kt1kiCiOdpANoYxrIitahvZkNy9f4xMO8fCVpPGQIptCnaPD3HjyjIbW01iDb6tmW+tMnXQIika6O0mhgZr1KHe8WhEIXGSkM87r+rt8ZlnrxEZmmi+wCes48igQad5nd0re2hmAtSYJGtJ5mtXuB71MByBbduokkM+59Je7xBttEmyCfXU56qMWfq8R1wxkLOajGMiE4GRpBCmOA3N1GSJbpBya6nBaruO2LVCtfAg21mPIJ7j7uI+nl9YRsb3cjC4i2V3iU4pwKoC/11/F8e0GMk45KUiTVdYaPxNJir/K4eq/56UFo45yrK/itlOAU1SlpipREaKrP1Ks1kpK8xWG5TyBeYaS6xYt5BaIF3BkeMzZC+fJ23FxEWPheQ2e8UIoR9jOebLq1KeunqLpVaX7ThEpSmuG2IUArq6SSNY5Hb791n1SmSdSxwcPI6btdlzah83X7iGxmR6/6tvrolI2cz4WIb8pgspRdflHV+eSrfZ7JJ1LPKZr35TPbOwglCCnQMDX/faQgjGM/1pPX19fX19fX1/OUxNTb3q95/6qZ/ip3/6p//Eefv27ePcuXM0m01+93d/lx/6oR/i8ccff/n4/1iH+OPaw9fyvve9jx/7sR/jt37rtxj4Gu/TnnnmGX7+53+en/3Zn+Wf/JN/8g08qzu+4ULKY4899g1ftO9bw3BuMjLQJJcoXOdXWVVHWamP0Iw07U2P/LVV2q6JnxdsZxSZZsqzZ76An6lgHshztRvwGxfv4/0TN7CdCS7fXqURdaCoIdJIJbBDSVXaxFFCy9To+y2wBK4ZMDM7xI0oIJAav+Dgx8O0ZAY0eFKiLRA2bHaKnI0mCYYkwVhC5owGxyGxJMLUCFuSijyDzhJrniL1QUcRxlSeqGgSvlljbBpkf7uNsARJOYPKSLrtCMeRROsBaUWRz3mYbgzTCTgm2svRLNkk+4axEIgYVFdi9hRCK5TUSD/FTASEEpHEJEUHVSqgN7Yx1tuY7QTdDYgdjfeUIKynDI7EvOl7r/O55bjsY4AAAQAASURBVLtoFSroTYGIJcpQpEMGkdfDrWmSiotS6s6WhIam94JJ1M3Ta0oGV2OiUoFg0MTxgEiTFjSpoRnMbcH+O41W7SUXREScE3z69m2KZZtaPkPnaIq/riifzhIqwA7Rccz27Rq5fAFPBog0BWlxpTtLmloMOQk6TtCGgTI0MlTsfiBk5r6IY1cXePbz41SHK/QsDztJERHIFCIl0ZsK/aUam28e5r4je+ls9AizglhKZC9BC4GUisz1DqV3pox2B3j/XXfhq18mm+lgmiXIOKhRSboV8qvXL+MOzqKLJmGoKTQkvp+SawR45JFCYCWS8XyGuyerbLUCuq2QWqPLSCVPr9EhW7DJyinEdIud68Ps33bw4y4FWUJmwCnaeOPgiZQXLswTtyM2shFWaHIjqSETjwQDs5yQn6qju+BlY9omzG1vsbnVIL40z+FbMxzddeemIBINCozYwMhFpJM2hcImE1Nf5E3H/iFjwwUuXf4SL9Q+xRU9BrkiLaBdjml1msgEkkBhBxpnNgPCI9kzj9+aILqhaPo9tugRlCSkAmfLZLPps1+BkQ94w8Nf4NTBl9jaOs/fWTlBuCR4Q+4yf/PQO1lvt7j57ALhRkqHCD3Q5aZ/m3dwCICc6fBtY0O8y7kAwKPZQar538M034/JnZUVt5a2kF/eHON0IFO2GSoXMM1Xbh2F7PuwdEqj+0ZeeHyDofYO/l/2/jxO8qs87P0/53zX2qt637tn37VvI4FAgMBgLGzjeAEHbBzbxLETxyQmvibxFi/4/q7j3CQ38S+ATYzBNsQYYcQiARIIoW0kjWbfenrfq2uv73rOuX+MLVsgAcIiEN96v171mpmuU9861f2a51Q/dc7z7BkbhVSimgk70hz5UY/sTSd5VF9geqMfp1Gg3Jejb+DKrpapgTJZy2bA89HGIFWbpOvQTBOs5BJpu8xH5q9nLVrlN/qvRaealJQ47+F4Fnbh2TtBUqOp0cU2gvibTO5vNtrc//Qlcr7Dd9908HnHvebgXhpByGjp21fsrKenp6enp6fnO9Hi4uKzTqw8344P13WfKTZ7ww038Nhjj/Ef/+N/5J3vfCcAa2trjI6OPjN+Y2Pjq3apfKULFy7guu7XnePRo0c5evQo8V/X5vtGfcOJlJe97GVff9Df8Tu/8zu8/e1vp/x1zpj3vHhecWgvsTjNYw/n2FwY4PsOH2eo+SbahLChWFneRo/kGfH7yfkOa/ZTJNkz2LlBfHMTXtdjZeEoHe8nGBwusvBkg0o1S7cawN4Ue1xywBtgor/MjoERPrPVYSuwGKBDtG6TF/+K6f53sWdXhfXKKrZMEZM2W/Uydj3FaqWoguQXnvx+7DRBrll4szbpuI0WApOTGAOqT6AKmrVqjqjjInWMcm2iikc6KomKMdKX5BKQro0TGEwjxN4/RnZBkbRDRMUmSgX2kgUXgcQgPIUasSDrkfgWjRM23kKKbwL2v22dpBWz9ME+kFlU3kI6FtKGpJjBmU+Rp7chY2OkxEIgDyvklzR6RXHhuI/qOIhA4LUFMtHs/ZF5nNGIs7/kYy9puoM+eiwPQoAEJ06J7ARRs4gTG6UsrBhEqhC2BB1j6BJd0CgKpH0e8XAMJkV2JLEKyNgZrNRGS4UJNcGwjax3KXYcjI4oeA6ObzBxgrYtTMlHdhROK8VdT4mLKXrSwX2sg6i3+VLUx7GrbqPVF2GVJZRa5PZs0WoWiBt/vQvBsnCkg2wlzP/Faa7+t3sZGuxjcWUJabuIrIvoRqShxl6KcC2YHC0yMFLEkfvIFr+EVYqZ3NHmpv3fzSMnz7PVDcjmdjBSrnCTHXBWLNF1QuyMhTQKS0k6UrC4tkXUTslYLtLXxN2EM8cXaK5ss++6UXLeME7dIZtmKRz6DM6mi92eIK2NEaqE/OmYJNFU+2vsvmaCtLuCt5TiNw0T08NEjctkM23kqGbcDFC70EElkuVCE12SrDoBH3ziOKV8jrXNBo5vIZXBuHBDZR5/Vxd5GeYuLHH7bXkGCgUGKhOUh1wyZ1w6GxHhqAVaUBMJE5kMDamRjYjMVD/fd9OnqBuP5sYASw2P0+01VOqSFCyE1mjXMNiXZ/nSOumtVW48cJ4+NyU3cha5ejUFS3MuXWe4lOO/PvUIC/Mr+MogtCDYcrk022X7xi59hSwAtwzdSWP7/09qEhr4fG51kL5sncm+MgA3HtqJMBKjQfowNFViz/4R+ivZZ+JO2PoPxJ0P4XKMTvcHmcnt5YbdO3Eci7nlbVrdEGELppJ+RCaiki0ThwLXtVna/i260eOMlf8Tb7hqH7smBnBdhwF2IronMLHHhdaDfGh7H03tst5tA7De6rCpU8g64FhkPIdnEQYjNRpIzTeXSMl6LoWs96zCus+l4Lu04whtDF+vN9Bsc5PfO/kZbhvaw4/svumbmldPT09PT09Pz/My4srt2+2v5/A3LY1f8MONIYoiduzYwcjICPfeey/XXnstAHEc88ADD/Dud7/7a17jG0mi/H3Gy68/5JvzW7/1W71WyP+L7fNfzg/P/DuuO3QrN+7fzY3DP8urDuzipqE+Oht1apt1Pr95jmOLF5lNtzgtU8pTJW7fdxO3T/aR0S1kENNJUqQ2VP2U5WxANGCTbHkEbZuFcoyb97j+mml+8hU3Yh3PsPHRAmk74T+dGObBJ05QIuaOgYu8duAcjq0RqUAGGktDLDUDTovJwTreSY3VUgghsLRERBpHXaljggaGDWqXoTsgwZJYocZdiMlspWSOJ8hKifDgCPFkFl/YdE6tEW2GuEagtUunb4SwA9gGPSkJ+wVqsEjiCKIBSTDooLISYTR+f8ig1+T733ySsr+NTBRaK+JBF5HEyKbGDSUi40IhhynYuDMCd1hDKpn9UhndCMksJoi1BiLoki01ybQbZDdjRKiQ9fBKL7A0RRUNZlggZ0JyoYOVKrylJv5iE8IULQ3aB1MPUFWNWJCYFZuoE0GiEEZj+Q7L81skF9pkvqhIyoLwoMbtqxF3E4QvaMYNtlsN8olAZ2x0MYtVd/DOBUSNkDSFvn5Fxm6RyaaoZsTqQpF2tgjFAttVl3DNu9J+1jEkQpNL2uy+eZmhfRHas3j4xGXWCIldEKnAQkDGJc7YBK5Dt5FQyzSxpI3nvpbde+5g7/513GKb9bDNjbtnuH5wmFvzA+zr12wPPUT/bk1xsEKuv5+03yKqSOI8fOzsZf5qbpEzS6tsrrWorjTotkOkJVChQMaazrpiMQopmIPkvTztNR+TppQdj/7EIbdlyBez3Hx0P33axe9ornb7+IMfehM7RzawNQyUq+hWhCVBWylRvkMylDA84UJL88cfe5jf/C/3cOLiCkIYhDF0lrL4kWZztsylU33c/bknWNlq4MZl7sj+BLsyOxGpBq2wY3A6BlENEbHGIAmW2jx1/gCR5dAJHLKpQ6kU45WdZ3rZWXsE6YRm3Q1Jui6OvBLwhciAgk7sMpHtJ8VwbmWVlbRBECV4toe3UaTfLpH1/naRmMjtIih/ij+s7WK7neWvjhe5+8RZwiQFIIojrFQhVYpjBAenR/CkTbv7t8VSbfcWhCyDdQsD5SzjEyX6hvMEKuExuc6mFbK43qD74A7uiH+QocIgE9MD5Ao+zc5/IU0f5mL13zFUypHzXdJEcXVxjHzdoa/p8uHlYTZVjmwu4iUjM1fmpTXakTieS8Z2SKNnF3nNOR55zyHrf2Vh3G9cznd57Q37uWnf5Ncc97nzs9z99BkubFS/7jXvXz/JxeACn1l/7JuaU09PT09PT0/PPzT/x//xfzxTCPbEiRP88i//Mvfffz9vfvObEULw8z//8/zWb/0WH/3oRzl58iQ/9mM/Rjab5U1vetPXvfb73/9+jh49yqOPPgrA6173uhdt3i+4a883ynwnNLD+/5gnZs/woac/zs6xSf75bW+/0h1iMKK13iSXtdCOJGkkXNpeI530kQxwffIq9nUH+O/Nj9F0YhwdMHtuAwJNsZRiNSPktkRuO4iMIeoPeHB1lmZW88p9O5nxM8zHLZJRwVP1aWaTiDsPlnlJ8RJxCn8QvAIZG6xUkJRsopLBVdGVWiNDKbFxsbcUumijC5JEg4jAamqyOqYeFiAjoRrhdDSZ/Sletk2Q80gTRVKRxH0uNGOy8x1c3wFbYkYSWnaZoR8Ad0NTu7FAa3kQUfORriHJKtACNET9Dk/fP8M7vu9eDs2sYWv48/90iGg4R9QvsBsCU86hRQr5LEpr0qxkeasfOdplaKOFs2Uw3TZxJ8W7mGBVipz/d8PY3QDRbqONjZXz6fY7eKEGkZBOh9iLQCgwzQ7WVojGICb7sfIFnIbEXlF4qU0n54GyKa4qKPg0xiQHDozy9CPLV45CJC6VrQaTBzZ51StO8aGll9HVFtkthdApSINxfcJBidMwONs2kSWISz65Y1uI7QBKksFru5TGBPNLg6giOInN0qUC9rZCpgbiBBFY5HyFM5lSnXNZmd9CVTIY30K3FVJLwqyF5RocpckuD/LS626l0wqpnlpma06wUN9BuNdmo7TAG8UR4maO89U6dGuoQsTQUEB+coYnZ5fQcYjwczihYL0ZkMm6nPYbDFY87prZz0g2y4Un5zh3cpGifBgZjdPFZdr+UXKZlLvPPkgSVdlz3Q5efsdhZmc3eemdh5CeQ39kE7oOu3aOsr3V4dz8GCc2pvHGOmRXNhjURdJqnemXLVBvZ7nz4ChjwX4ee+w88XwNpy+DJSRexhBph3Nz46gkYsJvoW1NsxVw4vyv8blVxWb2auK8jbOVEPmKXKLIJhY1zwKpodPkYmuMs50M3u4I//N5BmdG2dtX4ovHL4MRyD5Fd0cHtZInnisTvuTKUbjl1CONJEnDY9OJGM7mGO+H2ER4zRzaSNCCAcfDd20SnbLQvp9UzTOW+wHW29/HU8uX8K0y+4cH8OwreyvCMMWpxljG4CjBvpFBiDRJ8re7PNzM63Azr+PJJy/zpS9+gYWxOqWzGW659hCf2rxEMqw41FdG77XoljRKaeIwwfVsOsolI2O+sN6HWVhjj1HcZO1hf2WQvZ0SZZFhQJbQ6RxJbLG20YH9kMl6WDkH8hLL8iiW/GfFQglEMgRhiHX6LY27+4ev9DEe+QaO9tw5dphLnUVeOnjoWzqnnp6enp6env9v+uvP3r7tXsgc1tfX+cf/+B+zurpKqVTiqquu4lOf+hR33nknAL/4i79IEAT8zM/8DLVajZtvvpnPfOYzFApf/73X7/zO7/Ce97yHX/7lX+b3f//3qdVq3+xL+irfskRKz/96y3GNtQ1YXl6luXIfM2MVxlSGgZECMwcmyJdyXDtRoBHGPNhd4uqkSPfpLeaHFS/Ze4SF9mMU5/Mszm7gG1gZPUnXMthaIgGnrRi2JHbOotFscWp5g1zBw/Nt/M4Ixkik9gnlIgqbZmgRG/ATgyUtEgeKrsXSwihyIiQYcyhWBSJMEF2JKFtIA6ktITFsnq/gx4rUUYh9BqUk7XaO7rxEbbnYxS7OXAflFVAll7iYYHsS49hYEhhWrB0YI7uosM9rQs+GNMVuWZh1Q1KG7g6HXJQSd1wutwYZbLd5/LEptGshtMBuKqwWpBN5tGfQ0oJOh9Ty0LGNGM3QiWyyF9ZIYoFdjxCBxrIDROxipEsYWmQOOvz0v7rII2sBn79vEqtm4XwZqMXYrkanilQpRJJiN7pXXoMBpxuSSIETgIlTVGxQnQjL2JSv8tkxM8TZyxsggKZkNB+wvVxifOcM60sbkIsol0ps6y7akshWQloW6D6BTjxSB7phjBUIvB1w1Uu2KO9b5b8/NUBnSFLOu3i1FN1pQiIwsSa0LM7WR4kKQ0RujXR+k3hgHM+TCGNAayxhYVdjvIxkQJcYGxhEYBPVu6xXW9RreYSd4rkp1WpIdrBI0Exw5TBTLZ8Bb4hFp4leaJHxFNFMBiUtBrIZ+os5njBVYpkwvmsAu5qQr+RoBAmdLRc/NNi2xjgWe/aO42e+zPpyjRNnFgiKLsmgTWJLiGN2D5SgWOaWl+wD4MLCBA2dRa16HF9cohZ2UVIw5Wxyw5TLcHgLo3mfG3eP80R4DN2IaUSGxpgikv0MrcVYSxkaxWF275lgZqyP93xCMZcrEbfapL6HFBbZ7ZT9dpZMLkNnxNDqpNy48zzB3gK2Tji9OUF3XTGv6tTqMUYBAtxuithusac1yImNLseifloy5Vh9D2Yzg9202Qy6dKpdDowN0Ki2KZR8VGJjx5q+jE+3HfJfFz7M+fYDvLxvhZK7l9+68Qf5YOkR9haHuXl4ii/OzjNTKVPfqFPtFxhLMNBKeGx+mYNjg5SK2a+KP5/93ElOPz1H1yi6mwXi/pCdpkwnjSjMeMyXm3yqepYbhidobLfJFzM8un6EPrfDenU/zYUqYZzwxjBhvL/IvsF+Cjmfu/JH+LPPnaDhWZzrbgIgJeQzDg1HktiG9CtaHEc6QYkYY6CZBoxSesHxNE4VTy2uUMll2DM08Lzjdg31MdFXpPx1OvYATOYG+c1r3vKC59LT09PT09PT8w/Ve9/73q95vxCCX/3VX33OIrVfz9DQELfddhsf/OAHedOb3kSn0/kmZ/nVeomUfyBO1Jf4yMULtNISwWzKvUtnsQdtrvMGeeX0BOW+Av3jFcZmRnlgbZHZrTpNbG4aGWdkqo+nlGEu8CjbHV4+OcrYeIWgsANn8QLCNZhxQ2I026sJb33ZNTRVSqmQ5VyQkMSKMZXl9fuvpS0V7dJ5Hlrayex8BW9bkLoQ//VpgiFRYK6jCKtgxyAwGEciQ4W/GpEWPKxIkfqKgf4GjaUimZEc5qUdamclsefgLoO3nSJjsBJDUo1RLtReXaLdhr6Hu3TWbPQBjckCIylHb7pMkp3n7j+/FpFaOE2DykmEMGSWYgqHWhw6OI/rRrRcH+NKhLbwNjTGAZPJIJIUJUOMEFj1kMx5STKTg2GBCUAuaQg1cc5CdiKIFamAdFLwyn+U8uqjl7ktWOILHx1HGoV0FAiNagJKI8VfN1UJUqxUkOk2cbZi0sgjyUm0LRG1FEKDsxLjrgfYlSzesksSGro32DzS2kn2oSEG/ATTNAh8Gt2QWMTITgcnDhn4Pgt/VFP7cgHdFbR35qmEPkmQ4cTnDfrLNqLjogoWN85Mshytcrm7RSRA+YJo3Ke1YxR9oYvYk0UXJGrEwoQWhUSBJXASCUKi7ZSFkQvcF3v88MT30j9YQNsGy1Hst3dy29AMIyWPejtmXHaoWZcI69ts1yxWllpg23jYBGWbbtFmdaXBqw7sZvN8mzG/yMefOI1ejzmcL1LqzzHVvoWgvYE2Wdbnt8ke3sHAcIUH7z+DWqrC/kHkeJan5xYp2zvYNz6EROA5ktGpAaY6EywXWhBKLl/YINzjoRF86hPX8LNH9/GxR9tk/Qe4/sA49WaAarbZuqGMOWRg22FwpcDl1cvE7TZPLK1w074Z2os3YkqXGSvlOVetoRNFPOWyWGtxKF/gdbv2oM29DFRGsAYfod0psB0PcV5rIhJyrsH4YCR43Sz7zRTnZzdZa3X48LkbmBjaxt08St7vUGsltOOYZrfLDw3cguwaUlezhcJIkNJie6tFazMgUxhjwJ+iz78G18rwM4dfDsDx5TXuO3uRI2MjPPrI0yT7smBANyWvvGo3kwMlsr7zVTFodaFOmgjcs5KXvnonOwr9DGXz/MnC4ywux+wvj/HymT04rk2hnAOxyclGkVD1czj/aR5lH8ir8DMOCkO32qVbDXEsSaXhEhQS9uavVFzvxgm1MKCjUiKj+A+n7+Ptwy/n9pG9AOQdh4F8jDIa7+sVLvkKxhhW2i26YcwXL8xRzj5/IkUbw39+8mGaUcjPXHMzw9n8Mx2denp6enp6enp6vr1yuRxKKQYHB/mN3/gNbr/99hft2r1Eyj8QjpDk+iXCTdkzmCVYy5AttbluKqHPz9NGMLFzkL7xMqvnT1MUGUZGsxzZO83E5CCzSwHZnEUlkFh1CIOUfUO7KDQv0xUpyjWILYt9eycoVwpcmJ3HuIKt1SY6VGQyDlprNtdrrHZnmOv00XZCJm/cYP5LY1iJwhKCLdlGqJSZcJutjSLKtzE7BKphg+MgLIH2JHiCrUIZqyKRyqLS7KPmhCgHon4JlgDlICsSZziCJxLSoQJWDMZIwhGbwPHwWxGZpE5iBFFHooUgLVnIVCDbAmslJPWgejjDnyzfwhsHnqBx0QUJMjIgDBJQUYpE4dS7qEaIsCQy8KFQIswMYC+nyHibtCCRnkBq0K6FvBOsa8s8/kmH+4amOfF4GbsRkvTbIF2MaGO1kyvzUQbjuZiMwWRtEuPheDZIi7hypU6GrKa4YYoJUj55z9Nce3QfqW8hIs2elRqHdzR46T+/zG/8h1FiT2CsFKelCVyD1gbj2ziORUtliSsWaaSRbh5rr0t3PmbtwTxGGdTLG2i3jz1jAyxeWsNywJYp4XAWlXOQjYTU61B4vEl4JEe0T2NnDOgUhSQe8ph2+6lNN0hmYGsjoJVt4eczlAuGLa9GlE/obI7xZLvG2HCZ/cMxJ6Kncce32Z0cY3b29eDatEopoW+RlgWtWNPYDhiYi2mvbvLkVSFWWdFX30E59ciVMswjccKINE5JohSlNGqzS4rB6fPp9z0acZXa0ChBq0uhkqNrDF86fplDgyM8qBpggzuQZdzxWd5sIbTD/NmUoNklaQucrI8/kKMTJaibEqQStPMJGSvEsRNmZmp00oj7n74ISRbzxBDH+zvkggiZGGLbRo/b7N4/xpEj4zQ3fpBz55awzDl2XLeO/LKNGydYSrK3v8JiUIdY0hIewwMlHs3VUI2UlftK1HaM8au33sBD9U+RVBysJvy7+z7FdWKUJ46tcs3UCFGY0unGJCplZX6Lu3J3MHN4mFQJ5tsBX1z/K4R6mDtH38augQOMT/pcMnO0M4ZUAxL6xnK85OAO8v5zF+Iy0iAShWwo6t2QT1cv0qkHNL0YrwuZZZuBXTks26JlOvzqQ4/w2ROHcWKBe/WT7N11jpnyFEEQM7s5x8VzF7G1y+riVbzu0CGe3Njg1rEpADpRQqwNdqRRKmW2tczHF596JpFiCRsXD4VC6xeWSXlqfY2PnDnF4YEhXrpnhkru+XeatMOI+bUa2HD/6meJVJsfmPl+8v7zF6i93Fnks+sPcfvgTewt7HhBc+vp6enp6enp+ZrMX9++3b4T5gB8+MMfxrKuvBe85ZZbWF5eftGu3Uuk/AOxvzTGTxy5ig/PfQysDj88+nqCHb8CIsFv/0uC7SK1jRbnTYvHW8ssR5tU17q8r6p5R+W7uXN8D9Unt6h2m0QiIkgVcw8uUGn6dHSAUhpprtQ3KBWzvOS6vUyMVNh8aIGHS/OMXj/EQjrH6vCXqWVtrKRCSXRRkYMVmysFUsOUAb/JSw5fxnEjtq08T5zbgVNwSCKJkaA9UJ5BZyRJIBEKlDb0q2HSXId508KtOARxE0tI5HUCkVO4pwzuZxuYUp6o30FnfEQI3SezLC31MV8ZROd8TCAxGUNSsCDSCM+hu9vBUimnnpxg4VNDpMIimbIRloMVaaxajBCa1FaIToIwMVYkSHKCJKexU4Nys+B3iKccbN+n04BMNqX/roic3WV5u8Lvf/oq0mlDtC+D1jZOV0MxRC5EV4q5ChAZC5HLIbeamJ0uzo4h9k2Mc/LpRVQUIWLBv3nXk/RXAv7tb91Bsx6iU4GdasyGzc2FZWqbPmmSoByDdrjyi6TvI3yDM54lOlthaCRPOilZml3GrcXsGu/jxOIqVpiCUriXDcmhMmcvrREFKWGfh531kdLCDhRW02DPdknCEF2uILVCdiJkotEZh2DIQd40Sd4ZYKHeQrZHWWKLQiWHEzpkHQ+Z5HhycZmo2WF5YYurJoYoO+P0FeZoNgfxbZtOVhP1+4hOitOWYBQjI0WeqnfRqaHvNTYqq9lMu2Q63pX2xl7KcMvFixUXTs6T7c8hPAeZaNRih83VLtueza037GRzcRtLw6W5Nc4tV7FSQ7G/SXEgxDDD9FaBrc0aQmseOPkUt9w6xvQ1AuU7dMYE7U8v4qwMkn2ijLcd8lQcM5j3OXzbDGmmSL3exRM2u4YGWChvkBZtSA0H9m2wM7qBwf4cg+NFoihhbavNieMvofFkhF7wGBQeY0PjbAchQkswFisj8AfnTiMrOUI7S2a7S2nQZtO0mCxLzkUgA83WWoNPuufpv2ub819M2Or4xGnCysI2Q55PDshZWd51/OPU44B+92H6Opd4svUFvuvaQ5yKz3OpvUKxMoweNCBgfrPFn3/pKd72yufuNnOmL2DjepvR45qT2+s4U2WKRR+1ITBdw9A1FheTRSa2CvzbBz7FfavzmJxAxJL1+R28om+WsL2DdjOg4a+QHa7hd4sMDRcZqBfZWprlwuU19o4PcPz0ImE3Iuc5ZN0s4319vGXXrcTpCkH8BFK8HLNSITUpSRf4O8Xim40u9VqXoZES/nPsrMm7Lp4l6c9luW5qnIefnqOx1eWGg1NfNXa12WbMKTBSznGqcw9pmrLSXWWvv/t5Y/XJxnmebpyl5BR6iZSenp6enp6enm+hXO7ZH24NDg7SbrfR+tmNCr6ZzkLfskTKS1/6UjKZr39mvOfFc1P/1axubXP8+DabIxH9epJOOkcaZrA9yeL8Nts1w0gmgxXkqTkRI/1lsnnB7PmfwsUnSu9gIezSaseUUsGIKLDUjBBoRMewaG2zsLTJxGCFznZE0C+pZVw+mSyyz2ki+wNynk3JTsnLgPs+cQgjNMQpoh6xv93CG4k4owbYTPqwXMjkmnSqRZK8RBiFpSVGC6xUI1PQYcLqShMlU8YTl4N7RvjsF5/CMgLrSYtkCNINg50asCDtzzDoan7vznvodmx+4f+8Fd2SWJFBhim2sEhiiAoCVbQRXY1q2mDZWHkXSycYNMqHYNTFHrAwzQh/PcaWNp2pHJaSyMBG+WB3FHEe/IEMTGaQTh415VEfSpHtNk6zhbEczN4UXZG0SnnQ4Ad1/E1FchaM1ghHIst5tO2hGjWcS23CfUMkiSBb7RA0AvJTWSYPBhxfGWH8UJu5zRp4NtoIVtpj/PZ/KpImApTG76RIDZHnkBQ9TMXDRB4qK2lva8qeRYUsM7kKL9k9w9LpLTqeDZHAJcNEkseRDtutAJ1xMcYiux3i+RZhLSR0JKQGqdokbgFjQYLGFCQ4ko1GmwOlMtpI+k0eP5+lPFiibUssNc5V/YfYjlo4tkft4ianlhcoDl3NYmMKYSyMSjAZC52xcLoGazEizSkceSXRp8OUN3ANTTtiYXmb0yuLxJEkMyqYKRZZmV1l+fQ8ew5Pkx/IU9/o0t6ZAUuSv9RiY7bKxuIWRimGZ8vsHClx/nyVmzPzTE9U2e5rcfjRN3CueZk0VZDCE09e4mzHpr7VpCY6lJVF5f0BzBSxAoNMHBqhxeL8AX70FVMsbreICnVkrso/uepG7pk7wc7SJX72tZ/nr/5S8pnPhSxVG6wd/DCNH0q4RjW47/+5FmEBSCpDRUQuhaU1xHjA+77/f3J8c4T/+ulXgxIUl3ymajafbj2C5Wmm7DxJxeYnr7mVP770P8gNhLSGmrDo4wiL/v482g2Ion9PUi8zmvlhPGlzy+AP0lw+zs7sy4njlCROQFtMFvI85NVAgIgk1SR4ztiTKsV8rYuUHu0+w7W6yB0Hr6VQWOH3Ts/jOTm+YD3B8tOSzYkO55c3kaHE2EBi0Wdt4ZfP4poNpIAbh27hx342oNAaJJ/P8L6HHqN5bps/fmSF+S/N0UxDhDIc2T1Bf8GlW/Aw2mJp+58TxqcZKPxr9oXDtKOYovXsZEm12mZ9tU6+6D9nImVPXz/vvO12XGmxtFHnf37+KVzH5tDOETJfsRtnx0CF7z68n+FinrltSbWzzbg3/jXj9EsHbiRnZbm6vP95xyx3t/g/z3yUG/p28qM7Xvk1r9fT09PT09PT0/O1Xb58mZ/92Z/l/vvvJwzDZ75ujEEIgVLqazz6uX1TiRStNRcvXmRjY+Orsjl/c+7onnvu+WYu3fP34AiHhTMW9z26wrHxJj+WeRnrWyN8buHjVOwyM+ow/ZkB3r7vRu49c5EzF5dwMi5L8xcRnXNcM2PRl7mLpz5fpdtNmZkeo20rTGeDqA8sy+LgQB8ZJTl9epkgUViRxNZQMg5DuoFmJ2FtGHf6ywS1MgYQxqDyAiNtWsl+Vi/lmfV92hmfbB/kA43QBm1pZCqxIhDSQMclKWhkpEnaEbGjEZHh1NY63rRHcDnCtgSd4SzOrTbu+RTHsYlHU3ZNttg/VsO2DJm8JlYSqQVJv43JCJAGqQXGGPxtReugRNsgOpLSsRSnGtIqWiT9HtoVkJNkL9Yx0sJ2fNRIFuNrPDRWaIhzgkwzJXumhR6TmLKNqEP3Hp8lkyHdI7HPStJCFybBSEmzVEFfALdUR7kCmSmi/SykCTJMSC1J6idk+10YymO0pu328d5PHCL0XJr9CrUQIbSGXRpCm2Azg/IShGWTegavLsCAvZ2S2DYUNEEjIrYTIssmbHSY26yxdWaJsVKBo//6tdz9wCkCoXnj7deyVe/iZzJ02gl2qBDa4GQyxCqGaoJjBNQjLBNBkqJyFnG/hcGh1NKMpDAYuQyMO3zyC2d42csO0GgHtLqX6IRbvProj3L27DL1xW3Wq002a4YRy6W9usWFEysUMjZpkgMDwpNYgeTpp5ewCjaZrMfegQmmpvv488cf4L6NSySkoLOcigVDcphMlHKeNrXrM+gzinTAg1QhCy44Gi/nEgYBD18+T21S4EmLwVILGppRe54wCNk3k+PU2Qag0ZFP2IQoSZFSEg3lkL6PUSmWJcAG2/YoFnK4jsM1Byb46BfWaG+EqOU6oy2XXC5FScFJaQibdVbWCogbmuRSQRC47J8aZv1si1bQ4eFTl+k/UIGc5P+662Psym6wd3qT/zp4O0JJvIykniRUIsFAdoVWkmE2gr9cP8/IRB0SgxuNEdspWd9h355R8vk5LDVPGD7FF1ev4XB5J68au5nlYAc61aw3OlSrklRmeeVVV/PJk2tEsWJmsI9EKGqtLpXCs4vN2paFHWtEIsAI9gwPUenYpMVTHDo8S1Tfx/ktiyDVNNwu44HLmtPBcxVaa7bzDp/Z2MXtXAZjSJOHGHZ+hcLgjyPkbkhSjFSkXcXsxXW8kSyZMZdi1mNlq0Ft0GK2sc10foJILzLlHuLWYUGzFZB1vWfNtatSZmtVduqR542lnnVlacxlPIb6CmRdB8f56iNCrmVxZHyEU2vr9LtT7PZ347nPffTpb5TdIq8YPvq89yut+b0HPs0T1Tkujy/0Eik9PT09PT093zBjBMaIb/c0viPm8He9+c1vBuB973sfw8PDCPH3n98LTqQ8/PDDvOlNb2J+fv6rWhx/s9mcnhfH/+/sf+bzW/Pk4gLNFcEHg1NIzzDSFzE6fYKrMi9jevcBHt9Y5Uy9SqcVcerEGoXIZrj4T5jamWHX9GH2ldq4lsWSanO8soDyUpKcTxImXNzY4sjUEIP9BUxGUvZtOo8F7Jnexthz9Nkpl3e/hbnuITLpAMbaBtvQ3emAcDi3FhA0i0TTAiEVCRbtTga7FeHbBjEcodsZnGyKGVTEkUNhCw7sHOVCp06z0aEtIpIpH50q3NWAdMSDQY/M+YRUWNgrmuOZIr/4H15KB5u664MHie+QDLgkJZAdQ2YlxWhFNHDlWI1xIR7QGJMgDOg8JGWD0BKnpTBCoDyJF9jo9YDkZptMXrFrpk5kp1xazeJ0Y0wqsEJN8WKCcQTbr5AYach+sYOZj8msVWGoD+M6iE4Ztc8mLWqcVY8r/yUlYqiEGBCUR9oMDv0pG7vH6KTTGM9jedZjbF+VmaKkuj0Co2CuS1ENxfhGh85ih61GCYoZNBYqiBFKIWIDKOzlCASoskcaKcJLa+hmQnN4m++t3M71R/Zw9swKvuewf8cQZ2eXqa810Ebg9eUxGHLZAs5gSCsjsfZA2hEUliVe6uHPazrtOh3b4csLLYo5m1NLdRaTNpd1G7Ndo6jrDDmP03Few3angTcCM694CGFbbP35rdSrLXQrwJDFiRTdcR+hNU7XcGzqODqyKZz36IYx+XKOtTRidaeLG0vchibWKfGAZGZ4mIWspjOTxdrSZC8ESK0Z7qtQ3Q6x+orEIiLNRLSlZLNPEQ4W+fJTVzNUbPCKdkRUyCBMDWMEjiUZn9hB48Iy2ZrCyeUJcg7YhjDj4nQUu28Y4c7XX8uKCdiNzx7rWhbNBtsXFdISnDwzw8+9b4pcMWZmwCerUy49lUEMWJQHY9ZNSlunWFh0OyGq1qYUG84t9bM47lOPs4xMr1JNhxG+i4/gppFzHE9DtvQC6HE2NjcZnb5SS2hyh8f8akgrVoRhws6pG7Csd/Fny3NsVRMW7Bqz61u8774vMFXp583fdRRf5thKFCKRpArQFgOez+bZJn/SfJyfesOtuO6zl48x6bPRaZMLNeWiRzdIGExWWF7NUd9MCGwfRxjCoqBCzA2HFugkFhe3R0l9yeWlSY4O7MRyLMLkLFqn1NtncPMh+eE8q36IY8cQGySGnOvzpWOXCDe6HB09zPVD8OXNRQzjXOscIExPESiFNs9O9n/y2CkefmIWvz/D9w9c85yxtJPGZC2HnO8wMlgi57tI8dxFZBdqdf7y9Bk8I/nxg1d/1br4d8Vpimt/7WU3SlIuztaoNXJQbH/NsT09PT09PT09PV/f008/zbFjx9i3b9+Lds0XnEh5+9vfzg033MAnPvEJRkdHX5RsTs+LYyXYxJnoUJwR+MkYy2kFvciV7i9jEabyCRzrMA9unmHF2+RQpkQ+sRgbbpIffQ+dTITTnuTQ5AhuxqE7onn6iUvYqcFuJihPsTlbY3Z4i8N7Jpkc72dDCfqGijjFMh89XSNdsWF6lpFcmZl4gtRvIFON1b1SZ6GaU1ihwUQCkRGINMFuaoQlIRYkoQt5DaHBhA52CDqjWI/qxG5KkipydYkSmmZToYVgx2qVf/mzn0O8Fv7tO+9CbkCaujyajIOlyGcMM/9ojdi1OP7YLgwG7RvSPoNpQDwlMR4IATIxyFqMykhsJbAbGqslMJahedMATi3BasSYWgdx2cXe7yIKBq0k4ZEKYSvGLrmIlsQuWMgowjnbhjDFeawGqcZY26TXtXB/ogQ1iflUDitNEVpfabusDGnWJ67YqI7DxbWdyGyMkRAXNetrJUbHm6yuVNBSYJoaqpJSM6ZQ6JLNVWmfNsQ7HCwsUtcmtSTJoI1UYHU0pIqoIDGkmEgBBpEKigMlWhfXWOmPeHD2Mi+dnmFto4ZONAjobDeJY0WY0wxNZxHTFfRoGXl+EydIQV1pf+zNx4h0k7Rks+262H4/3TGPmh9xYGSAdneFJy5cx2Mrs+wyPmN9LtVsgnQCFrYu4IQ+DBWIK3nSHR5p0UJlwTvdpbnq4KxKxHrCB/7yQU44dU6e2cCNU/rqHQZ2tWk4MxSlxUqhiefkGb9ss7lusKSDHQRIadhcrXPOrSHHirxl6gDnFzo8/vAcT8c7cM5n2cpk0FlNrR2jChYyiAnrIZuX15Gug/QUvuuSKE007OBuKGS1xfqcxeVGnUfn1jmY72dhJcLWRaZnKmSzDsmAzZLokjqKHQfGGHZtnnpEYiYMncJOanYNu5LCio+MDbnUIrvt8MWPHWL2mjxp02LHng2Wmj5z+wXr7RaHum2sjqS00aWcZmlQI1USmUqcjCavLBKtaXUCtDHY1h3M8Hle7VzidTtu4mNnHuBk6yTO6DX09xU4PDpOvpWjEcTQAmlgo9smkCGduSXes9DiDT90M+MTf9vN5o6r9nD3Z45TquQZLOe5+sAkLfEKuukTaOMRbWmQENQUpXgZbdVoJyXiDEQtFztS5LLjOLZNpfCTJOUZtHcIz82QNhPcaorXUNQbTYIwonJVP0vVDiLRfPncOV7eGmEkcw2+XcaRWVoqoZEkxF+R3HcSi6x08fVXH+sBOFFb5g/OPchtQ7t4WWkv8xvbuI5FnKT43lc/Ziif5+DQEDltoZRG6+dOpFxaq3L/iVmO7p/i4OTw88Zxx7ZY2A5RHZfNde95x/X09PT09PT09HxjbrzxRhYXF7+9iZQLFy7wkY98hN27n7+YXs+3x+5gnMbSCrZoc+c1+/lI4RTpiGG0UcSJs1w6NoVfrnIqXaRb6DA4OcWNg5Ps3fVfaLhraDzi5E8Y3FEhqn0v33XtVciNkP/2xAOIMYUeAW/BZXiwwnqjxdyJgIlykexgDr+wRdORdGOQ6wFZoQkyEpFqZCpwFwypo4knwBpSfN/hx3nkqX1s13M4UhLbYLTEKSbky12iTQ+9E6ysIjrjUO02CSMBsYK8QyZMaSMpD6TsntyklAuYXxjCixWmY9CORwL4jQQHgzQaW4FIwWlLkoomHBWQE5RLXRypqK0VyF6IsQWkxiA7muy8wanHJAVISi6WLzFxAB2JfVnTKme5vA6d2EakCjPukbgWrrJIczGiE5K/dxNSw+59XVZnHdpdibzQRXQ8TCaDcQSJK7ELAjcRCEBb0N2VQddzRJsOcdNFTdqEw4a05nH28UF0UMA4Ah2C+LSFP9Dmjp84zqWuZN5UrrRiFhJiDSVJUrDwNxTCunKkySQGK7Ug46JlijPWT7MesZy02RpKOBFsYD1lCOshUimkLYkSkFoQ9VmYq6oof5RyKSAsGVIPnEBgbIOMDboWUBoaQzguV109w7GlFa7ZNUl7o86lxhg1lWFnPQUV45UrnP7YLlrnGgRzId1OjDVYQI15tHfZyERjNzUYiLSFTsGKBWfXV7n0qQitwWnbiLpiO3Yo7Ippb21yfCrAtCwKqSbjCJJOilSGrc020hN0hm1ExTCyb5yTj86R+A7O8Rz2dowEwkMxnSAG10LZIOOE9oUNkqkSZtBnULg0ml3IWPiNbey1FsHyNn/a+Ete+Y7XoALFfKtFv23RMkXatmC4WGTDj0iNDyWHm268jrnzGzx6YotqziLZHWBVNCwnWJaLoy2iSFFbryCTmMxil4VWGZTAAjpFj888McToTEQoJSMRTPVPMezNUteSc7Yi2+eStBTVdhcv4/CxzU/yhPo0e0aaVIrXsnGhirsEV998JTEipCE1CQ4CoQED1Wabl73sc3zxAzu5/8IWO3YNPyuRMrtRJRWGaruLbVt4jmS2dpCybtJIW9SSDhioJQ0uPeYyMJyj5hXJ21kmfZu+ZDfNZoR0LDAuee8OREaiUoXjeDhbBrcLsdK4seaG6XEucZFEQ2xCnqiu8UtH3gVcOe/aVQnNNCL9iuOnP/Dq67hh/xTXHJ58zjgaqARtNJ00Yqic5zU37SfrOc+ZRAHIug5vPHIIrTUq1Tjucy+r5xY2eOzEHAXX+ZqJFGFAVQVWDKrmP++4np6enp6enp7n9B3SMec7yXve8x7e/va3s7y8zOHDh3GcZ7+vu+qqq17wNV9wIuXmm2/m4sWLvUTKd6BkZYbm/AYTXpl8OYsrNF5fyDXiFs791RgjI0XcyGa0WaHbsOgb7ufI1dMUSj9GJjA8cGaMqYk/Jztg+MClPOoBi/sfPo+9LajMKAoETB3Zx47dg6xtt1ncqmNZgpZKaG/mUVKQSIGXCbjt4FP86NAp7r/4I2x3iwhlkEpjdWA4X+P6ymW2/DKdeAeOSIlSF5MTWFXDyK5t1tISzqEU4YJaKrOnb4TN0ik6RCytjWOK7pUt+5shD3emeew/T5I/ExN2Bapkg2NTUCHFO2rEJ1yWf69Aa08eRgQyBacqSH3Ipyk3Dl/G9hQPObuhJEjXJWJLkQzbmJJELkjszYR2v0GMSJAGqyvI70pxr+4QXHYxLYu8n9IOQQuDaEeoNMJrp6RZjztescK/+fXzXDiZ5V+89Vp0MU90fwYrdrBTgfFtUhngtxQmSjF5G7QhrGickzapDyZj4y6HDOttXv3aUxSGFO//g9dgbAh2O4StPv7y8zfTnbXQjsDuXGklbXIOSVGiCwaxbMCA8R1EwpVAO1xAIhicGKLTChjxcixX6xzsG6RbD6iUG6zgkB4WyEcEhBIKFmLYR7uabKbJeD4iUtaV5/rr7tR+0SVE4bahWu2Q29Lsy5R43G6DcCnKIs3VNhvKQglQW+OE2wlJp4NwbUyjS26tAAM2KgVVVfirCV4aE6cSEVpkzyvCXHKljfagh+rmsYcMmWIBZ05xkxjncr3DxuYqvi1IhURrQ9JNWVhpEh6toAqwHHdJPcHQrj4mmylzC/NIpVnvXCS2LbSwSSeymI7E244QscaECZ12iIhSrGqOm15+hCdXHsKEUF/cZvHiOtUooR5HuJEgMilLq9v0ZX1Gp4fpRiElmefyyjZybJjhSKBUSD0RyDCCsiAe0uR9yeVMjIkEmdMhqtPFLLiI4b4rPz9Ps+v6beS6oHjYIno4oRMk9Nk3UV1vkDga4UpCo6jWuzRqbT638BC66CIH9iLtPQw2tmm2FAPdEsYYVtttNrtdJvvKTOcKbG63GcvXiMsKfzzGaTepDJeeFX82Wk1ErEmAha0Wlz53nPOdBheWNlGBRgrQArbqEbWBHN1PzOBcb7Nrr8W/u+0OPt12GBkfwHVs4jBldXEbx5Ec21xmvm+L5KWSwkMOYjbGzXt42qY0VWZjvUnUp/HS/DNzEUIQSU1gFMlX7BAZ7i+Qzbpkn6eN843904xlSgz6BWxLcvTQzDcUf6WUSPe5j/8AuEYgQ0OzGRAkCRnnuRMz2hhQICXYUa+xXk9PT09PT0/P39fm5iaXLl3ix3/8x5/5mhDif22x2Z/7uZ/jHe94B2traxw5cuRFyeb0vDhed9O1jFYGWe80sTI+75r4eWqtGp86N8elZp3JQ6M06wEHg1G6bYEi4NijF9l//T5Otn6Sv5i7j/3bV9N2FbNJzEdPPM3FcBU5Bod3reH0xZx/YIOzG5v4zkl++nv+G+dXbuR89OPcM3+BNM5SLKekxMwHeT6xvYMIQVwyWBGY1KCNYKNZ4sT2HpZEP85kFxUp0nmHpChII5fzTw2TNBxycYwpC4LLDttOA/faKq6AtbUOQeBjMobSZIvoQIFqo0Q3jRCLAun5+EqQGQ/JHlFY2Zjqn/cRZrIYH2RHYLUNIpGousXyff2oaUiHJdlMjMa+stOjqNEjijgyeHWJcDTEErwsZsjH3RWR5g2ZPSEDps01rHL/o+OsLORRJYdkwsdqxeRn8nRHhrGdcwSFHJ2XTIAnKexo48qA5OE8XjOApTpRpYAZKxBVHOyGQDsGghg7lyFG4TWvHMNxPY2WNkYKVMEmGjI4OzXWrgQ54MCajTSgfYGRkmRKkIxrglTg1TW2stACkqzBky6T/UMc3DOJ61tIIbljaJ09O8ZY9T5P3WrTzkyx1ZfD8gUmlfiBpLuap7PbZmJwnmQ1ZWFiiKhuMI0YmaQIKUk9Q9c1PPrIGah1eXKiyPTkAFkvQ1AUXFhcwG6BssDodaTTQmZz6IwHSiCqEROzLrVhSSs0CGlQ6wK1BWiFkRBlJFIDjkT4LkppOl2LwR3D3DRzmP/+8UepmQTbE0gLZNZBRCADwV5ZwoQQz3bwAyi7Pv6wxNkxQKPa4GxUJy6V8WPQWZek30EIsITA1RoSgbRslE7J7R7iB37tH3Hir+5laVXxpcfOUJn0SFsxzdTCiRRiI6A1ZljLhSRBxNzpddrKcGZ9k02riemPMY0OlA1JJ4MRhnW3TWNAkkuh1C2wnY9QZbD8GGlp9lx3mdtL86wuuJT7HKKRCQrjkkX5Bbo6YvcTN3Jmq0OzFfLok7O8/c0v47vFK5mPL9DuJPzfZx7lqsODnKtdZtmtYoxBKkneeFiWxSt37OBLK+fpH6tx9sI0DMD42DaDA4VnYk+aplRwWEs1OBaL3Rae5bGzv8KTzhKdJAYEriPYme1j3q4zc3Sd7OEuP7njDINWlZuv+ydY/g/h+g5hGBN1Qjqp4qMXTxIMKoQF3WlDbtFiaLTEqVaNzWGLoOyQGVHcNjzzrHjY1ilNExPp9Flff3p2lYW1Gjfsn2RiqMxCvc5HT5/mlTt3cXjkSvGxiVzlmfH1VoBjW+Qyz5146YQx7TBmuJx/zvv/xvRwH2MjJZ6oniZ4+P287YbvJZf5rq8a59gWuBKlDVePjH3Na/b09PT09PT0/F29YrPP7W1vexvXXnstH/rQh759xWbf+MY3PjOZv/H3zeb0/P2tBjX+oPpXbGeq5OvjsJByYPxqdh+Y5nP3zqHaMb7tMDpWwtt08YzHVrXD1maTM1GLRfvjXL3/Asc+fYT11Sx+IHH2gey3UG3NxtN5dMfCaqWsrdS5+arHeHKhzMnlhG6rSb7isNyMKePQloaz4SD1NIvOgbENKhFYGxIrhtB3uGf+NoJWl7LXwfcUHSmwmwqZGOLQR/iCZNkin4a0XVheaGM9PkQiBNGmg4wj/L6Q237oHKkj+dgnryOMfeICUBKoDLROWcjYJ1jPICwLqwtCG6I+sC2FZTSiLllbHiBWgt3D61w7NceXB3bR2LLJHg+JEwtnVTJyeI2bDtZ4enYH9dksbrWLmRXkMgHTO+oMjzQ4GK1zz5O7kQoyS4q0IOm/SVHaG3LnvouAJF/UpEUbz6S4wwFO0WA+2cUstbEdQWB8VE5icjbaNjg1haUEoqVIPImopIRhhj//q5vRQQnSFGdd43uQGVR0yjna7RzKjbDaKSKxkNJBVlNkX5a4BM19ktIlsHSKvKYFcymx08foRAXHsenzjvOW7/8Lws6nuD+qUFutkKsZNhdtuuMCldggHVSQIJWmoJo0ih5mQtIdk9iXHcT5DuF8AO2IxvgwYq2JaHfpbLVYty2eXr2MW8rT3Y5ZSCIWvSa2H6N8Hxwby73ySzy2RdZ1KQuHM/UOJlWoDYnd0SgD0YRFkje88h8fQ0vBkXiJT/6X62htaBYLLgcPtQm3uxgtiBHktWZkrExts0OiDIXHO9Q32xzfbbj+lVfx8PFLbCUJQyN5tKOhonAagpLnEzRCuijs7RSTSyntmiKhjUkUOiMYcnyarUvc/n2PceaRHF+Sw4zlL9CQw+DmaW6FWCaFOGa9E2GClLNzCW+5dS9PRTFbaYJ5apNcu012uIiVFvCdItaoj/S7UBeUZ/owZY9lq4OXyVKgyVtLT9N4wkG6Hk88PMXR/iwv3befDyzOc+LEOpxfQrs2FGA7qIIQfP/Ud3Fx49eo+y3+bPUHWA5vYD3T4tj2ZcZXz3N2Y4MBp4CrLbbSCFWURGoY6SziFTQi9UmSv431RhvWNxsYAcGQZpFtfvDQjTgxzA0OMt9uERjFj9x2hNZ2l1IxRY9pKgN1FqIcR1gnDvrpL1xZkmxbUh7IgRAcONXPU+0VVJ8gtiVZC+bXt5m2xhkfKbNoWuTGI1z/2QtiZCvaVkKqn70m5XyXqBuztlJjqJzn0xcvcPfZs2wHAYdHnn3kZrvZ5X98+nF8x+Env+dmLOvZO05WGk3+709/if5MljcfvZr+fA7Xs59zcR4o57lqZoRm9XHy9iXa3bufM5EihOCn77qFL16Y55e+5xUvZBl4Qb4we5m7z57jp2+6gT0DA1//AT09PT09PT09/5uan5/n7rvvflFP1Tz/PuTncfny5a+6zc7OPvPniy1NU971rnexY8cOMpkMO3fu5Nd//def1XbZGMOv/uqvMjY2RiaT4eUvfzmnTp161nWiKOLnfu7nGBgYIJfLcdddd7G0tPSiz/fbZa69ycXWOutRk1Ix5Ya902RzLtVqizsO7+SGPZMMFvJksh79mSwV3yMzlidJFFIZcn4JYSyKZpi96SjjSYWhhkt21eCuSbbODNFYKJF1PfYM9IOXo9qETqLRmW0Oj+cpZhXh5RB9wscXCZ3AJg0hm+/ijwZYRmIkCEuxU51lqN4mXPAoqJ34tkC2EpAaS4GwBRkvJLfVYVxuoJ2E+nqR+kYR49poaaGaDrVmhkYzi/krhXs+xJproh2DdgSpgM4XLLikMamC1OBuafzNFMtVCJkgQoN2wKnBtL9FyQ4IazYi1DjVhJ/d+wQf+Nm/YGZ3jb6BNgODDfBspDY4W4bqpSHOnp7kwScP8IsPvp6XvnqWd/7b+xktdshcDAiMS3E0wC+1EQJOLQxiGQtvrYu1HaDu7pKe7xIm0LFdnLbGXwnILCYIbVBZC5W1cWODrTSlvU2Gr2/zO//sAV7/6lMgLaxQk5s3qIsO649U6C5lsKQDvovEQaYpSLC2DVbAlVojlgFf4OVTLFtT3W5w/PgcF08vE0R9KGWR6J0UhlKMrVGxBfM23SmX7m6XaMJhcu86pUwXXYdLs/10Nw3WlsYKJHKyAELQWm2hNjYhurJDpTRdYSXcIq6HbM+3SQJFkE2JSmBKIxi7hPYzSEte+Tl3EupbIWv9ku4gJDmDznvQb4NjsNoGVEoiLFpphiWvzHU75zDHA7Y2W2xXu9A1eHVwmoJkwCezr48jV0/S15ej0Yqp1wIuPL5AY3aTonQZtjN4+SxWf5adA+MM50vMlAv02w7+QhvZipBBytV7h64kvPIOWsDxsytU2z7G9pHWIJPaYdwNKNgJ47kcfQWfyfEKo6ODjK/mGWh4eL7Pn3/2BGW/gD/bxVvWmDVJTgxR8rOMRnn2BwOMaRurpBFpSmbJonLWw6kqqlsZfuM/voqcZ7F4uUT1YobIGIbG+wiiMt3YoZuDUGsUgqHJHEvzW2yuNXHsQ2gcPLdMJxAMyiHunLiWu5fuZVsvUfANnrQ5ubLOpo5ZiwzurgB7OkarUWqNzjPxR1oSIWx8N+a6yWXynKez3ub8Zg3Lsbh+epzpsQp/tHKcB/Qi7aLNpeYAxy7t4rcvvILTW3dTrV5FpxleuaABL+OSK2aYrlQYjgNyKsYKQTs2V1+3i5t2TPLq/bsZsIs0GlkubDSeFRNDUkJSgq/YkbJvaohrdo5BCkEQk7EcukECKawtbRN0omfGGgydKKabhCTP8SHBfLXGRqtNIwiJOzFba3WiMHnOGK1SxWg+z0/d8P1876Hvoa/0zueN5//sZUf5wNt+kAPDQ18n8n/z/vtjj3HfxYv8yVPHv2XP0dPT09PT09PzneAVr3gFx4+/uO95XvCOlOnp6Rd1Al/Pu9/9bv7bf/tvvP/97+fQoUM8/vjj/PiP/zilUol/8S/+BQC/+7u/y+/93u/xR3/0R+zdu5d//+//PXfeeSfnzp2jULiy/fznf/7n+fjHP86f/umf0t/fzzve8Q5e//rXc+zYsSuffP9vbiaXcrTvAgUxgpmdYlMHDG23Wdlo0WjGZCoZhnb0kc153Hx4J3NPtbm4sM5kweXM2irzrQx+cA2H+/vwogzbK3XW5rbRcYq2bZAG7Xn4o0Vufek+jm1VGMk3yQzN0qn1sSezh3Qz5sF0DY9tslaMnyYoI1FtG1ET2JFBGMFN+y/x+tJZPrVxDU/HkwzmChTLguPRKjorcPpjRFni1lP0vKG76dOdvlIAVnQ1JiOQtkAELh/+4C2oYY9cpUl2IcBLNGxEiP4MVgJY0NnrEBzx0N0rRxbstqHbsVCWTU4kKAuEJ7nn9DWYM5Adiimttkk8wyuuv0TGUbTW81xa62Pj8hDCgrjfobkJst+wuVkmCQRKGvZOrpN3Iwb3dNm85NC93MfigsNnrkv5teNTtNoujk5xTID5jIZVUEITD3uYXQO4NYHdUMiMAsslLWriLYVTM7ibEmvYMODEzC3nuOmqWe7+zGGEbSOSFAKNVU3BE6S+g+1YGGWQXU0um9CyPPKLId6cwuv36dQNyUMubEaUBzMk7YQaLeLiKL/8Kz/Ly+84RLm/xmp0D1tBjoK0yCUllrIpJjUcHFplqFpg7eIArUUXWTfYzRg6Ev9ImYndYzz58ePIuTqUHPLjFeKyy86+CRzrMkuLDu2mgVhjLENCQNoRIDX4NsIItIZOu0t6XmO1Q3Q3xbRiiG3CYQvHWFihRTvxCFKbOJWcOzGMVhov77G6XKOdtkh2CzLrksxkgcxYiXijjV4RxM0ALYBUc/LJWfYcmeTyxW0KQ1mKroO2JE7G4ASCwdEy1curpMaAY7O1WCeMI5TrIBJJNtehU4j5cPda6kseRiSIyitQhRpySHHw8DhPdWPWJBT8LNmsQ7PapV1rQT6DarURjkTEgmYnxIsiWqtN1lar7BjIU1uoI+lweZ9ADBlyCwbjG1LX4cmzA8S1EKvVwFeajTjgldPX8eR9a0SBxkoN0rOYKk9iZQS5Qoah4vv470/+KbFI+a5r99GYnOL2q/aztPJx6tmYKc/GtSTNJKJjUtxUsObkiTZsgstZHnvkIjfdtBvLkiRhysEdg5yJ1rh5eImzx4b5yOOPMnjDELUg5fT2Nk0REg1rklKb7TFFztOsbxfJjmny5QpPj56jmlvhiJkmSVI2V+t4GQfd16Xfb1NKIdPYyXWvmebq7znIibk1sgoay13UuKamu8+KicYVRK4h/usdKR/52CO898NfotCf5Xv/0Y0cGRxGYGjUO5RdH5Sm2ejiZ10yuSvdcvqLOX7iu29GCoHvfnVNkwHj0YlDzjQ6/F+L8xwojfFT4699zhhdqeQQu4cp5H1c98DXjem2fMGfc7wgV4+OMteoc/1Y7/hQT09PT0/PPxiG74xis98Jc/g7vud7vod/+S//JSdOnHjO8iR33XXXC77mC06kfO5zn+Mv/uIvmJubQwjBjh07+IEf+AFuv/32F/zk34gvf/nLvOENb+C7v/u7AZiZmeFDH/oQjz/+OHBlN8rv//7v88u//Mt8//d/PwDvf//7GR4e5oMf/CA//dM/TaPR4L3vfS9//Md/zKte9SoAPvCBDzA5Ocl9993Ha17zmm/J3P9XCtUGO3Jt8nbMA7LLwnbArqEBRkZKnH3iIpfPrDM43M/eq2Z4cn6FS4tbDA+WODo2xRee+Ayx7OKrlPVqnXJd4fk2O6ZG6KfEI0/OYxCYBMbLRZJYsTR7Dfe4LbxBj5H8p/CjTRI5iDdZoKq6FNoWaWRhNm1M20HUDEhIs4ILrQm2SytUVYmh/jI/8urr+NOPP4Jp2JiKR/ZwG51KmqFD7GawnYS4KHHbAqujCbMxRjpE/RI1BGynUPQQGY2FwWtJ1KCh9TIP94KLKku0L7DbFk4DYlsgOgKRt4iGDZmlmGjcUBxvszezwZlLYxhtsGOHd7znlYxMN3ns8ijKsbCRCKGRjot0BJxLsPZapL6DzAj+6BO3sc/b4szsBMbv4FQbNDJ5vvDgEZRIseIIWwnSTRe72UZFAst18QaGSQpFiENUUCM1FnbDBmVR8UGrLolxqB4vMTgTsJqzacY+dqjBFsjUQsbgtTSpVshUIUKFwYBWZDY1uT0N1JbN0Z88SxT4PP35KarTDk5eUb8YUNJtkmaXtfmYjrA4fWGFo7kZzs5OkNqajFHcVBohai0TzkVs9FXoK7ZJXQc7NViBQLsWxJo9o8M4RZf8kEdjtYsVaqxSH1eVBjh3cY6oC4VcTLjlsTJh48QJJoYBQGgBxnDL0b0ce+gcUZAQ7m3j7Tekd4OcTXCAaMjFHVBYscWxkztQkUO7P4NjFKmA+e02M0FCuNsi7LcpXXI5kO2n0415oL4NJYNjW5S2NdqCxbUWxf4Wy3Nb+Nse190wzfnzc9Tnuviuw1X7x7GFjRYRAoMnLUSSgOOAY6g5FxB2wmB2gpOHDXYMtw6WWKrOU/cCas4Y9WaH7lpId49Fp97EdBQCh7WNDZzEwghF/gZDMhqQPuzjdUDJgK7rYdKE1bFtfuQfn+FgaY3/eewWHntoF06Y8NSxfowTkSxl+fBnHuFLXoup/gyjHY9WK6aYkWgvT3lS8572B/juwh3ocxVmz7UZnHJx8h3awQKtxig/PPMzhKuP4jPEdjf8611koBKH+mYe2VSkzYjN5QZ/s0pajsUrjx7g4uUtzl30OL8Axq9yww1f4uTHrqUbZLCLkmJWUuyrUlV53FxE2PRAWDRFjbufehjZpzg6cRXT/jjZvE8SKfYPzvDAuQrppsMb33gbr3v11XzkiVM8cOoCrVqT9nZCOW/xsomZZ8VEFwtHyysVboHffs/9eKmmvRbwX1fv44fffJRXmZ1cW+yntifirl37yGsP7yu684xUCjyfSjlLU8XUWx1kTTFUbmI7z52Yr7cCTl5cZXq8j5nx/m8y0r94NsIuwhGsdFvf7qn09PT09PT09HxLvf3tbwfg13/917/qvm+2PMkL+sjr7W9/O6961av40Ic+RLVaZXNzkz/5kz/hjjvu4Od+7ude8JN/I17ykpfw2c9+lvPnzwNw/PhxHnzwQV73utcBV44ara2t8epXv/qZx3iex8te9jIeeughAI4dO0aSJM8aMzY2xuHDh58Z85WiKKLZbD7r9p1sJn87A8HPsH32Lpy6odi16C/4TOwoMz0+gBSCxnaDJE6ZKBa5bec0b3vJTdx27R5+9KVHufPILbzEvg1JhRUdslFvUSWh7Jex2jYi1sjY0F6qc/LhWWR8gp+c/BxvzDzEE6cH+KtLDbqewS/5XNO/QklEJMGVrf7umgYjUJbC7qRsN8r8x0fuZCGsUOnLsHfHMJYQmIyFcCXbWwWUEkSRQ+IoXAX+nMKKDJmpgMlXbVO6ukm8Q5HMKJIJjbsYoLUhyrrEHohQYXVAlQXZy4rcYwZnU4A2pLbCoJHlBAYTEislO9fh6OA5XrvnSfa4q8gwQaSKldkcD1wav1LIVIDQCqulcRoKEVyZk91MsDsadyshablcNkPYEzH9R7sceN0qjquQwmDZEjvWICzSoQKByaK0hxksY8c2/mKEDFOIYkyni91VHNixzr/4jU/wy//PvSjRIdffxr+9zfZgjuPrkxCnGKVxiy54NsqykMpgNyNkO8bqKpSETttle8VHdy0sT7M9V8SJgTBFSU233aXWDWm0OtTW60RpynY7JDIaO+dCzsbtz9AMI4qpi8ppLq8MEjsO9f4M24N5gryDUhCNWNTbAafTS6hDZeSRCRgukJko4BVC5orH6PYpGtjIvRrhAqmmvy8g2Q1RVpAYxXxYp393Ba1TRAYQApkReF2NjFJktYsa1kgFUceFNcn8xUE6ixmMbxPrlHqzQa4lSTqwkiSsLGxAGqJlipwJ6AwJajtclJYkrZBGrYVf9BnsL5JF0zy3RKhSGmlEfy6DLNmkFrieRbkvi6MkRBFKGPzCMMOZXbxt/2sIxxVBxXBMraA2XYKLWXzTh5/1yTkuO4MsXqT5yX/2Ed75m/8D47aRlg1Zj9Z+D69iyGQzVyJ0ArU0pjNqaAwOk2rBbz78egYGq8hIIS9vMz3SQuVzRFKQdiNsGWHv+hhDd53G1hrb8tg9NUhmwEIbTSNpkaaGo/07ecvUS3l47Rj3X36MxxZPsNA+S2XsBLcfrLCzr8LQkE2fJ5koFWnP5gkDF9+1QCs+f/ES56pbSCnIOj67B/sZLB2hrUDIiDiCpHkluZb1HEJlmPE6XD91mVdPnuGg6uNouAu1qegsOLSXXQa8IkII8qUcmbxPGAnMxT2MbO2maDnMXljno59/krmtGu/47v/JB/7VH/KKAyX2FSaeFRPHnDyDIkNW2GzXWxgPRKuL3G6RVlusFta5p3GGx+qrPF5d4qOXTzC7sMbcxjZ//OSTnFhfB64Uk42T9CtDLgCVcp7X7NnLiFdmMhnl9soYqXrusXGiSFJFFH/9hVobzbnmMTbD5RewCrwwN46NM1koc9XwyLfsOXp6enp6enp6vhNorZ/39s3WeP2Gd6R89KMf5Q//8A953/vex1vf+tZniulprfmjP/oj/uk//afceeed39S2mK/lne98J41Gg/3792NZFkopfvM3f5Mf+ZEfAWBtbQ2A4eFnFwkcHh5mfn7+mTGu61KpVL5qzN88/iv99m//Nr/2a7/2or6WbyUhJCoc4oHZx5jb6DIYWbwz9xgzo4PcNX4LFoLWdsB21OWst8H1103QqAYszG8xOuTwwMVFgsUM+6aH0Haelc4aOSWpb7ZJioJ4SuBsJJSLOVrtkP7yIEJAKWPRX9mNTDzchsNkqc1N15zgMwuH0AXJwNU1Vh700TmBtSVxwxTL6ZAUM+TwOTw1xsNn5ylJFx+J3IpprgsabgmMwVmPsLrguJqub3B9AzFYTQFCoJUFnkH3gbegqO/1cC0PEWpyjRT7lhixRyI+bRMWQDsGf1MR9kmMr8EBy5HY6xbWU/B0dZqV+hC4AcoXpEWJEA7G1ggkWhiMD9Z2gIgM2DbKFkjBlS5AFy0wbThtsG+Evr4ub/lnn+P+e67h7MIwTisg3umBk0E2SpgghUrpSg2RRhdLGFQsEQWBThJ2TszSxEVKjbfZwh3VBKHLyukci/de+R6hFWlTEY1lMXkH2dUI18ENQowAKSRpwSYtQOOIy+OrO8m0NEFHI7ZTjL7yi9vcSpXp0RIJFsqTkLXRQYKXQpizMI6F7iTkKh4qslivVqh9ZojK7k1MFtKSRAYgAsNWp8vVOze4IBwueRPoTpGFzTrv/exjVA6Ck0nYNB4hBrGWMF5p8p//6adJjMWP//R3kTbgwiNzWBMOnrFJH05Jz2jc0wIEGGlhtVOShQzS0oi6Jn8mwfgaNSWwLmpELeLxxxZZHbbQ2kdlBWeTkG5fk1x/TNZtI5wMrUqZsBwweixgY6VGdiBPZbKM1ga5BY4fIR1JeTBDM0wQ0tCJFEYKpNBoy8U4kjFvL3tGawwM/gAm+CdoY7G02cXHYFYFTz22imVZDPcXoN8nd9W9/PniNQxbIW/4pcf5yL85SjzkEvhlyls5FCFBwcPSClUOsGKJaUnu/vJNxGdL3Fe4nlJbYiJJuT9AhkXEtM9k3zg/8fID/EHtIdYaHmkYESSKkXCQqzNX8z27bqKeVFmsPIVnPsVGOk0pcz2FSpeJ3CTnGh/n/MoqevsE6Q6f9f5ldNdQXSoQVjMEyqFUMdgFj3tmzzNcLfLjO6+iudWiUw8YnbkfKzNKo5Vn8cu3oYxCW4Y0MlRMkUx9kh89/MdYBh7b6HL8ocss379GMmbh+pJaGBCLizyy8l8Yz76ehfoki80GjUhS7YaMAcZNEJ5i78gmnq+wJhY531plX/Fvj6lMiBytOEvWWLz3Qw8iUoPuKmRk8PIR99YuUl+yOOwN4hfPcP+mxXkxwg/EL+FUsoVr2cwUSjxwfBbftXnldXu+qohsqxFw+/A4JxtrnE7PUdg4xmh2iL2Vg18Vo0cGCuQyM+Syz9395++63DnFJ1ffT9Yq8FO7f/Pvuzw8p6zjMJjJEj5Pkqinp6enp6fnf0fir2/fbt8Jc4A3velNfO/3fi+vfe1rnyn58WL5hhMpf/iHf8gv/MIv8GM/9mPP+rqUkre97W2cO3eO9773vS96IuXP/uzP+MAHPsAHP/hBDh06xFNPPcXP//zPMzY2xlvf+tZnxn3lG9y/6SL0tXytMb/0S7/EL/zCLzzz72azyeTk5N/jlXzrLRU+j7PzHJn2GEr2I+0ucytLPLw5z1a9gXAkT24tcXfjJBdrmxxpjdFphZysn2Ul2kIkJYbzO9h78wRfCmJEKtkUEaoEetqQ9mm6rZjh8RJBcj3LS7/JjgPTHNn3ME+u1ji33eXQwARztWEO9K3TaHisdYpEE6ArCscSOBmwCinEESxITl1YRfmStOKSLoO1neLZksSBJCswlkBLc2U3QpjQXnIJamVM18EeAZPTuLMJ9laCBiwsjCWws4LcaIg7HdM+lcGKDRJQriQdBLRGzNtYqSHxJNaQw8Nf2k/9pixyH+RaCVYoSLM2RoPThDQv0WXQloBIIYSFdCywbIwWaF8SB4b4Hpc0SVjKD5LJJIzm21RyHZyGInUl0YAFGQe7XgAp8byI8mST2pezpJ5DpASqJNFJlyc+20ew6rHwwDBpKlB2m1xHMdRokM1vcaI6jRQOSRCT7s1jCgKvC8K1MK5FkrdIXYG/mOBXBZ0xiyh28DMRJtXoGHAEWtiI4Rzrqx3wXVLP4GYE99zzFMlil0xWErqKU+ur9O0o42+DsgXEGnFeUQwFacciccGvJqhCxOHiSxH5p1hSmtgY4opkPXFp3ztAe1KSOJIwa/CMAAdsWxPEDs5yBzoKU7JoTZW5/tYdfKGxjE4V4zdvsHNvk/Of6aM1nyGsp5gxj8xFg7eVQpIisjG66LJdsHG6BlyBWwtJyg7pgYjIcjEeOF0bs2GRr6bIQGDykiBK6K42SMOE22/bieP7mEYIIyUaYYrxHaRrkbOhu9VFZjxUVmIsyReeOkm1vcKN+w0jIqAa5RGRQEYaRwpKhSzNap12q8vF0W3mz06TnrCZ332lG1JUcdCujbXskHQlkTZYQqBci8qrtzENQ+tzo5hzLk5NQcPi+qun6XYtzjwAlR0J/YUS1xyaRkUVSmaMtVoTQoWwbMaLefLCopgUeN/c+0itgBGhaNdjFhcVqHHcXJ6XD7+JpUsPIfUEDyyfZ70r8Tc16XqILLgkGcmrXrWb6ZHrmM9GTBSK+BmX42eXaHOCTP959k+2qG4dpJMvo+QWAP3ZLD99+214nfsZo03GS3AnF+k8PUIjDrjh8BSTO/uYzo9wYvVjLM5vs20eR3UqMNemrTQXLq9x9ObdvPrVZdqnZvmL+Texf2IQPzPDaObZifKS5dKnXRwh6ct52Aq0lWIQaOWSGkkqNMJtMDM8Tyl2OL7mc2hilOnCKNPlMtrA3Oo2Bd9Fa4NlPXu9SNOUJzeXiHKKAaefYb/IWP7ZO2P+hhCCYt7/hmJ5vztKxR1iIvPiVZb/SrO1bU5srnP7jplv2XP09PT09PT09Hw77du3j3e/+9285S1v4fbbb+cNb3gDd91114vye/03nEh54okneNe73vW897/xjW98pkbJi+lf/+t/zb/5N/+GH/7hHwbgyJEjzM/P89u//du89a1vZWTkyrbktbU1RkdHn3ncxsbGM7tURkZGiOOYWq32rF0pGxsb3Hrrrc/5vJ7n4Xnei/56vpXK2SKDuQzTo7u4ftcBHrM/z+LlAqc3lhjcNczBHePsGB2ldvlpvnBxEZMRjMssJy+EdFSGYtsHYxgaKDAzMcDlc8uUSz5WM8VUIqRjsbbSRjkW93zxDCpKubUwz5P5C6zLPGm/oSGK3HPhCIMyJu+0WbaKZBOJSAxKgvIkdgysC5okXGzWONIdIxdLpIY07xC7Bp0BtMBWAqE0sh4h8g74Np2yj21pdCuBNYO3qLC6YNAI30F0FI5I6KxkGd7aovGJfoznYLUNRqUIoUn6JUnWQoYGr2sQrZipA5vk3RKb7RxpZLBth4oVky0E1E5kSFxD+2ofYxlURaJDTVLQ2KHCGBdlCeKpHHpbYSUWkWdzenmMZFlTvTSKoyxUPou/rlElkK4DicHyYfT2LTpbE9Sli0kk1laE1TXUFvt4/MQQwnNRgwZ9WhMYw4qdww420fUGplwhGcpgR2CaHZyVNvgZUleQ5DTKg8yaRrQFJCmdWoYk8NGNFrKgUIMZ1IwLbYPSing33PK6eX5q/1/w59F1LC/1kTgC7QtUwWJ9s4XTVrjdENGOiERKXlbpmGGM7WBsgxUJ/vTUkwxk17nlSJl7liSqIHAbBhXYRAULuyPwmoY0azFn+viR9/wwXaXJdRawY42KPZKMYWC0SGVtlSTWpPkc58+VKe/bptEeRDY6qIk8jrFBpcggRUUO8VCOdMBDKcjMdBBlTQeJW0mxLQvtK9Yvl3GNwRcGabloX2NCBWlC2I1ZXakTV9uQxNx03QGuOTLDfSfn0RmLG/ZOslptggvakhhHEOqIJx4Z5APxT/HTB6/mo1+8TNCISDIOfeM5RibKOFGXWtBFAqHlY2dAORabjx4B04U4QdYdHK2JE42QAq0MwtKQGJAGeUngqhjjaioD57jq5dfxqY+eot+0ubxa5MM8zd2sctXwEa4JGjzlrWAwBLUA27IJ6jE3ZW4izce8pO9aTNvn2MK9nNNzPLjUz48Ub+DVU0fJlbK85YnfJRs45C7bOA2DrCiG+utc2thgfX2O/j19WD4US1ka9TZSKE6fGUdrw749Mzw8FKDPKdy2oZDGXD+R5+RTZZ4+M8Wu6TWypZilfYqpLZ9wcJUl1miGr2Cm+I+4nLHIyD3c9+giaTVGCtAWBO2Q+UtfIl7N8PhWk8nC9zDab9OMYprJJS63j3Ft3/dQzLr05Twcy2JqbBDft4jLeaRJyRkXLJ+O26Xgw3W5BZZFnge7exgeXmRv+WYAumHMzFAZS0qU1l/V/nh9rc6n1i/gljx+8bo3cPXA8xdubbZDnr60wt7JIYb68l87lrsDvHXH86+3L4alZoMoVqy229/S5+np6enp6enp+Xb5lV/5FX7lV36FpaUl7r77bj72sY/xjne8g4MHD3LXXXfxhje8gWuvvfabuvY3nEjZ2tpifHz8ee8fHx+nWq1+U5P4WrrdLvIruhdYlvVM++MdO3YwMjLCvffe+8w3IY5jHnjgAd797ncDcP311+M4Dvfeey8/+IM/CMDq6ionT57kd3/3d1/0OX+7fO/4mxlaP8+jnQXuXbhEp7iTvOqwszJNX8bi8M07mA+7dGNFK4oJTEQnSEk3Y4SdYWS4SGqH/PZjH2GQPs7vrRG3UiiHRJs5rAHFyO5BIgOhu8hVux/H0QWG5TAhBdoli4utJRIhaaUFFjcrkBNoW+DGKd5gRLvjEzUzuOGVbWe2J1lcqrJ1dgMTJURDLiiNbCmk4srfIwUW4GZRnoV2JcrRZN0QkbVI+x2SkiKNNDpjI4OE1Jd0RzOcWJhhaCqi1XKuHHOJDXFOohEYRyADjR8YVMVi9UiJXKjYv7jGYlTCytjsnp6nWAgYeGmD1qbDE3O7iQsO7aEsQgu0I5AthVEalEQkGtON0ZUsuuKibJfZS1NkY0BoRBRhh2DhQKKQXUOIw6nPTWEuauyhFBEn2OsJ2JI0m0G5ChuDVBpnZ8rQgRalKOaJMwMIbaGFxvT7WEpgLXcRjQBjxVilPJYSmEgTS40dBkR9ZbRnYbdirGaAXEqJSlkSAV6tjeyGiIsxR2cuMT7c4IYbFrn36VG6wyCERVQWDHS63L7vMksXs1x+2EIYgZ0DK5sQlzxE0aXrSGpdn+38EP/sZSf46Oevw66niEaC1dWYxMcICSbFMhJt2TTrV67V3JujOJ9C0cESFh86fYLB2S5C2jTDLCLnUG+6CM9FZhQiiIh8QeIK3PqVNstWO0aUHLyGxjoiSTIpubMam4Tpqze51BhCODbRuMHpStx2dOXnmGoMKZETsVZrYRINiYXjraMRTI8P4vWVKP2/7P13mGXZWdj7f1fY4eTKuTrnnpnumemZkWaUBw0iDIgkQATLYBsQ5l5sgoGLEL9rg8O1ZUDI4Qpjgi0JYYSQUE4jaXLsnp7p3F1dXTmefHZca/3+KIzRIwmPdIcrXXw+z1NPP0/tXWvtPl291z7vedf7WsnAnjHk4hbLURMCiWzn5FGJBx+xvHG6yG3lcZbyFtUDBY7uGsM6iS4XaVxYIV7uUp1WxIdzjsVFRm8aZ+nKVWSco2Jo9iK0dBhP4YSj8a4RAteFgiCrObzVlHIQc3LXQzzx/hnufe0FtrdLzJ0boReHJCLCrGa0b+TY3OCcY3Ozzepmg7WtNvfccQ9Do1UAXMGhQ4mqwzMX5ul+YoNVk3DnPYfpOEHalRQ7DplZ9oxMcGBkjAFvP51ewtxmnVTC0bFRVAZXnx9hX6wo670EzudH9h7kdx55kI7MWaqv8bHLb6WmevhCURAeP3bsMWZe0uJffejnUFJBU6CMh1YhBybvI+pEtLtXsErhh/BDb3gZ0sHFc0Wiukdxqk7LRjxyfYlAaZz/pyxHF6h4oxSCIxQCD6UEW40uFSFJxgsUTErR+NxR3M8Hk8sc9Of45soym4HHb9+4j7ee/TX+9d3vQcmQYuhz9y37kELge1+8XI4OV7lvbD+ViQrHhsa/6Phf9djFeT760DnuPrGP73rliRf9/v+VuntyF+tbXQ7VvvaFb/v6+vr6+vpeJP2uPV/SzMwMb37zm3nzm99Mu93mIx/5CH/2Z3/GvffeS6VS4f777+cnfuInOH78+Ase8wUHUtI0xfe//N5urTVpmr7giV+o+++/n1/7tV9j165dHD9+nGeeeYa3ve1t/MiP/Aiwky790z/90/z6r/86Bw8e5ODBg/z6r/86xWKRN77xjQDUajV+9Ed/lJ/5mZ9heHiYoaEhfvZnf5abb775L7v4/G0ghWRsaJDhkTr1OCW2t3P/4SOoRsKlJ+ZZu9HgzgNHmFI1nmuvcmbjGt20zPGBAUS1wEt37WKh/q843G3yOXsS7DCtVpekI3FlMD2fV+zdzY3F5xk9cJ3BPR22bIVT7TcxXKvz4bXThB2Dl1tEGlAWPVQnIKlakhFwSuKvZbTjAmFm8J3E+fB0fIMhX6N6oDsgUodILda3ZIPgtkBIhfXA+pLjySahneBKJUIFGTiNiiwUAnoalLBYKzBIdFtS3d0iveoRRxrnK9I9Oy2WXQo4h7UWZ6HdDVHERMvVnQBEbmj6HqNHGwyPtwjLmu+87QkQgj957qW0N4rg7WyjsaFAZiBnLFmxAIsaPWTxAkPUVISJwzkDzkcnAtt1ICRoh3WOyJXxKwnl6wkWiy34WC0gVIhUIToJejvBVhzRsQLDz+e0tkoItRM0cEt1slIBK3KEzZCNLjRjvG4VVfGxjQau3kMmHdyRMUx35zzdBZV5xLvKuG5GdVMQbsJ//s1jLJwY5aGze7CAboDUFhkbbrl9lRN7l7njpgb/+rE7iHJB1PRBgilozLBHJ3Z4BcGginnvU4M7XXuMRo93mbpvnYqrcH1hjExpVOYgsuieQWYCMz2C7DUwRY2VjnDFkIgcsdVFRD5usIxwHpTA3O0g6OKf0TjhEFZgtCULM/ylLp4RqCslokSRV4pw3ic+YNgtDBcDh6g4ZJYjkhwnJFlVYqUgzByps4iCAGl47qmEgYkbRJ2MUBgKxQKZVuSdnMKNHqKYoY6G0MzwtObQ0TEud+uEFZ/7X32CdLnLI9eWGBwKqQ1X6F1cJ90Eb9Vjq5LjVyyMBMhlibAOm6eIxODY6ZhjW4pu7qElGCdJCjmZVLzzj1/LAT/jgat7qc9JXJxSzRscH7iZe7xp/vjRG6Qux2WOM3Mr1M4MoJxmatfwXwZShBD849u/kUcuX+Xh9z7LAw+epjQQUgtC5CmHTCRYhVOSq/OrNDe77H9Zkd0D+/GmSxzeNU63E/OkbrN9a5FSo8JEMMD2VouRhecphhtE21VKA12e3Ii4efoa905vEfqGpzZGeIm3yakDz/LpZ49yZPc6Sf7nlErfzdTuYVqNHrWBMlJtoEsBvTijqBU2GoasSykcZHREM6V9bh2dRKhvZ7Azxf7yXcz7XTylkELy6IOXaPdiRk/UOHXHfpKllEd6z1PPcj7VCPhJp7m8MYEw8IHrt/HNs99ALTzBXRP/nqFq8cvec8emB/nRyVfggItbm0xVKgyEhS957v7pEY4emuToni9d3DU1hvecfZZAKb77+E2ov+H2x7dMTdJNMw6O9gMpfX19fX19ff/rqFQqvOENb+ANb3gDxhgeeOABPvCBD/DII4/8zQRSAN7ylrdQLH7ph8per/eVDPWCvf3tb+ctb3kLb37zm1lfX2dqaoof+7Ef41d+5Vf+8pyf//mfJ4oi3vzmN1Ov17nrrrv4+Mc//gUFZf7tv/23aK15wxveQBRF3Hvvvfze7/0eSn3pVpX/X+ScIx+K2Lenhnwu58DQBOUpya9cfye5tfxv9vuIGzHr9S5pnrNZ7+GbnPtvvpXZXWN4Sc71NqQtxcGwQrZW4dpGFxEKktgyerXIw8E15taa9EZHmV+ukXUO8KYTPpdbW2QLHsWWpDRkmJ26zkS3zWOL+9jslJGeRVlBd9VDhganNIkEVzZ4xQS/UmTkqs9mnpBai7AOkTqcVuRFyEsCW/FQDtY3KgyWNUQeZpSdQrZHHHXPw9u2yEaGH4Oop4hijJnRZEL+Re0lh1aW6liHjY0auRNkFYHfNvzIgUeZ9Fq869GTrM4FKOFwFVjMBwgWMhoLZQZ3dxGBo1MPcQJs0cPWwAmBasSoQoLTUAhSvv1VjzFc7vDupXsI9xuS2CddFjhfYj0BSuAEiGZCZclhGzlJTSORqG6G8R1WOEScIVoRInO0khKnLw5x5gGFoYv0Y6TZKfrM/DLVKiQx2LpBaBB5jugpRCcFB95WjrjeQBiHMxY7UiLchHjEwWhIdi1GG0d83uMjahe2lGMTRV6WiMhRaznmXZGNgZBnP3YTslzEWIfMwSlNYc1Q3DZEzZTiTExZpaxsVxGpQAjwpcNVIZAJ8rLGjguss0gXI7sOfIcQmqJO6OQpak3iiZC8ExHGoJwjTzu4aoiQAjVnYUqitnMSLcgKjngMXMWjOJ9gtWB7ymJXCwgNeJKlZ8fZJ0uI8hZEmszP0KGkfdghJz0GrjkGc8XU8ADN4UHi+XWal+p8VD3B2NG97Du6ixP7xlnaavPEtTXSIaCS4Eo+QehRsIr5tTpPffoM+ZVN3n1ui0KtQtN31H2PEV+Dc8hIoJ0AA8O1IoeCUS7V1/F6FtIc1M7vrPQUxI4cjRMCKRTJ8RzVsizIMkERggHoPbRT66Z2LGOj08HeloAwiE6G8xXtPOfGeoPxchnP37nvWWvJM8OV61s01zKuzG+RtrbJY8n1pxycFIgUhHNkQKqg0Srw2DNr+DZnuZ4zfCHkH+y+hWthGyRsC8fy8jZSSi635rBlHzfqiPdanonHef7iKAduf4BxG/HR7f0cDDc5feZWlFniV17+Loigox2Dw29gcLjMTTfP8lBvnt5R2PC7jLQDZgsjtGRGeXIf737uGYYmBV3X5VjpFnaVbgHA2A652emUs7zSIJkyLEy2CCJB8VrK6qEOzi8wvz7IM71/x/9+9SHY18NFgtR2WO89TpbmeP6XXyaNsbRbEVejBn907ixHRkb5kZO3f8lz900Ms+91Xz5oUe/1+PSFa0gp+KaDh6mGL/7W0hvdJYQQzBanyJVhU3WJ6Reb7evr6+vr+1ujn5HyFVFKce+993Lvvfd+xT/7ggMpr3jFK7h48eL/9JwXW6VS4Td+4zf4jd/4jS97jhCCX/3VX+VXf/VXv+w5YRjy9re/nbe//e0v+jV+vfiv1z/Ne658lunlQV4an6BWCmm6JiKA3MvodGI2VpqUbQdXsHBUcM/EEV5xx2HKgxvMP7fIqPkBKoUes+EgZ9waw6Mhhc4W3UVJz8/4/MoKGsfB0k1stlc4PDDF7YdnOWBGePfmGRpRi6DU5eVHPkFrW/LE2gxifeeT9zyQhHs60AanS6Qln1uGDOO1awx6wzzQ2EVctxhPYVOQicOLBcKTFIqSXKTYRBBpiP02U0e3GJjpMjgeMTOyxccu30z78z75gIdYN/hNQyZD2sshNvEwvsOEEtFU1BdrOKERwmELlqwccKY9y+y+s2Rlu7MNJ8oxxZwk0jzwx8cxsebef/QsSPCLGVEvRGYZYskih0H1HG5eo9oGhGY23ML3DZXRiLE7t/ELOQ/93mH0YoYVBokG61AAFzsEgwWMg7wW4pdDEpUicQgvx1mNEDn6iMMvp2S7QuRGiNOaLJQYnTIQN/j2ly3SNDX+9JPj+EmAtgqZG0QxJPF3OvCIbg65QRgPoQL8tiB4sofILclwgF5swXIPxjXq3grmusKmAVLmBE0YM3Va1jE3XyKoeBw6tITnWS5eHafb9oh7OaKX8cqJ84yM9/isd5gr0RRh05DHil5bk2sfJwU6AZFYSudSwm1J+6DCaxjiSDIqBZ1ejiTBhApdz3FZilMC1dXgYtTzGvV0hrBghzSineNNjuEiS1ZVyNggGxJT1kjpyIUjU4bMazN6wbA94tCxQUWW5KSGqiG6ljLtD3Bw3yjddo/n8hSswmuAX1KM7x7h1pccpnB9hfKzc6jtLVhrE+QJRpShFkLu0NsJeS/n2ukL3HP/qzg0XiHd6nLm8hKeCah2c+LrdRKvzfl9JSZHhhEljclzglxgPYVQCi0VudchGtYo3zIofKLMx6gMkcNIqch8s0rp5T1am4p7XnuNDy0d5g+vfZhsQSOcRFpHVtWcvbbAQpBw60aZ1eK/4ULrEQoU+YN3fxvdTkZYEvgnLa5raLkWZrWEXPUxlQw5nGGqmsPD45zafZDFzSZPtC7Q+HjGW0cu8IYffYrtvMxn33ET3balXCtweOguzj5/EeUCJsIeK6FPIUj52ae+iZHBDpcuT9B5ZoS9o1UGahfYNlCS8Fzzo7yk9D0IIRgeKlEYClAVDxEICjYk8UNavSKPne8SxobRqML4iS8sNpskGc12TJpmvPyeQzzw7CUOjE+y/KFFVhYa+OsaMSbwUsG6GeelteN8auEa331I0zYBAsnC/Aa6GFAIPUaHv7jS+7Wrazzz1BzjR0aYqlQ5MPjVZ3dU/IBxW0Dl4i866Tiq4QsrTvtCbCV1fv38v0Mg+Oe3/DwfOH+ej964REUGHBkafdHm6evr6+vr6+v7enPrrbd+ySYzQgjCMOTAgQO86U1v4tWvfvULHvMFB1IeeOCBFzxo39fGR+Yfp9uJSWzK9FQNV4GDtd38vbHv4vnHL9FTXaoDn+PVU49it3eTqJzlaIk/fH6T77r1X3Cttotk6o08eXYTsVInqQtGB28Q+ZZWyRE3R8grIbtLFRYX14l0l6c7V/jYsxP8yMunOVV8NysHA0b2zuOHliTULG9VOLR7GRkKri4PIQEZZOiOxWFYWmzjbdfYjALWOj3wdoILBB4oRR5kxFmKzHJ6gaB6uIO/LunENcqFhJFqh0lVp1hI8Qspe46ucvn0GNGoj+5aVCXDP94lmxOkWYDxIfMhQyBzh8gNQd1gCpKnHpjh2gcG2OqGhOUYjCbvCdJtj2RLY4c1JtMokWPznf94qmV25nGCdGCne4vbdKQB/Pa/fB2i7BEc7xAOBtwcNBjZk9DoFfCznf/ILrUQW1zgkzYSGPJwUY9WQSJjgx3w8coBVjV2snSGNdlSiWwjQPUaSGsxykeZHJoZQ4Mtju7PeN+lm8m3MuhZZG5R1Sr4DtYbYMzOdqZhD6HB5BYvAhfudBQK5nKEEJgLCiqGeMZHKIdwkPZSuucSPv2+CVxPYGwXNSY53x2hORmgI7BFTT6o+OgjJ3jdS0NOt0sIaTGjlm4eMGB8enFIFjqcluhYUegqpAWZSERmIKwgfIu40UUUBKYWEE1bgqYlr2pop0gVIAsKlETUuwRNSEpFvHoPvRqRDihMUMT1itg7LNgUfUEhIsmmsdy0FXL10jrpnpAiCvW0wZTB1h1RMSFNLCO3zeNHimS7gG8UKnGMDRXZ2mpx9toqK9sN8qRFsJUgI4ubLrD7gKO33aQ4C+WBLkO7JNXRMp6BpJdSGyiAr1i6sY7IHMneAq3NiDjaZEJ4bFmHdAqRg0xyiFPUOGS7ffJU0l3O8NsFRAR7ZqqcnBrisg4oDiX84zc8wTPRLGYJcs/DRhIpLN5AB7wCmdOYIMMEHT69fpH/dv61iBQKnSb0AkQgEaZGba/h1MhRrplFvK6lTE5pd5NSpczB9lFmyzVKgz6ffv9TsJQjRRvXcuwf3eBBJxifGSQYDPmEXCGb6SLWDJWwTTOskBqPzqUy294QrucICwHf95I7GJ4+wqdWP4USjq7b4i6381HG9Rub6CuOW4YneNn4PgRwydsglinZZoS8Ibhcb/D+U5/nR498E8450iQnzQz1TkyrF3PbrXsplwrcd+JmfuV976E3ZDC7QRvLRCnihP+juOmX8y9f81ZIP8XcVoeisFy+sMp6khGGHt/92pNfcgG+2Kizft3wd157isHyl97W80JE3RR7tUvsLP/p0ScoFHz+/p2nKP01W2q/Ej2TERsDQO4c+wqD3Fae4uTw5P/kJ/v6+vr6+vr6/r/tda97Hf/+3/97br75Zu68806cczz55JM8++yzvOlNb+LcuXN8wzd8A+973/v49m//9hc05le0tafv69v6tqPTK+CrCiu9Du89+xzl5RI/MXInc/YGYcFjdKjHpN9hXPdYSDQrlzu0OnB2ZpYPXTtG0T7I0ak5PvXMK7g5mCVNBxkZuIi1GpYcu5oVJkSJutSM37LG+roh8d/N5ZVbuVbMiJOYai+mGI7xyYcmqLYyKscjXFHgXzOkPUXeEPgYcq1YSytYL6SsKuTlBHKL7ADCYgpgKj6+NZSWLLJrmd7bolQyXLxYZePMKGLLcvpze+hOaKZe2iTfEHixwHoOsHh5juloskjhPBBOoHKBjcB5AIq8JpGpIbiUEeceXgFcNaBUa3P04ApKCB745FGMUXz4D29BGYvJNJ614MAKS1L1MBqMNhSiGJn6UPHIhOSe2asc8jd47cRV7vzWZX5h7ruweY5UDhlKhPFwzuICQfO2MqYkcb2UcE0w5neoHWzQxmK3JNGqwmYKOqCERKQGVQugkxKnBX7vT27BFcfwrMN1YowyJMOa8qgj2Bdj3pWS5yAqAaaiUacMMtTwgMApBSWPdMxHr+foUGOvKPyyJB2W6JUc0+mw9qESGsi8DGcsZx4Zobc3gCZ4ABaScY13l6A9e5HZ9VnmLoyihUWGitWrO0WrfT8nFRLjHBqFKUpKmwbRNeAF6ChHyp1sCj8BkftkLkLPx+BJhNDY2GGt2dkqYywqVhiboeMctZoRzfqoVCAiAdKhk4yajpHrBUrCw+0u4gYsyfUUb03hbe50Z2nHXT75589S2Q3D1ZSoV0FlGpGmNJoRzUpC0stpXl0juBHjtXq47ZjSwZy9N3+KjfoM1dlXMzHTYqB6M0sLXWq5Y2ygSKnic35jnWhMYnKPZFcJejnd5S26eY6tFZCZRSHBOJwQiG2BMqByh0pSignsHR6H1OPC6RX01ZDE+PyLp1+PCDTRNHzH2Mv4bPkJhI2p7W+xaiZwBR8xkzIz8TQfvzaD/0GJt2VRuxxB0WMkl6RZhd3+AAO6xqhaZDuxRE4TJiAjyYa5SietsWtmEl8qnOfYWq7x6bffjf6mdUbbimu6QSH22LreIGuWCLqO+QtjrE8ElHYn6FqKWQ1BQPGuORbqDQ6MznA1mmDQ73CtN0ia5DQbPYSS4BRZy7HV6DI6WGbPlCbJu2gl0TbENQUFb57Ljc8zzu1srNRptXostdqcubTM3okhhkZKGOXoTmrswYyJsW3KcYGjQ1sM+tvsL36WT65+hmPVKo9Hk2jgjtka5y8souOUKM0oBl8Y1Ni7b4ybNmappzHvnnuME+Mz3DP21bUsLhV8/BSMNRS1JtAe+kWskzJTGOWlQ/fgK4+RoMY3HStyeHKUw6P9bJS+vr6+vr6/NZzY+fpa+3q4hr9ic3OTn/mZn+Etb3nLF3z/n/2zf8b8/Dwf//jHeetb38o//af/9P/9QMrCwgJvfetb+d3f/d0Xa8i+r9D9B4/yueXnOaTHmfY8Sl0fmUqevrYKvodUmp77QUrVcbaubaBbjplSle96xUvYzl5CYftJpsqfIm0NsMsOMjhYwsmTLCzPsrbdpTCsWW9HlEKfV99+nZGJc6ztrjJc7PF0b4rzdgiZxRxoZlTyX+T6Ix9CRYK1R4ZwZR+17CGEw4SWqKRwgaC4YMljQTCbIzs5RCC1BhyJzhBGk5UlNgTdcfSe9Bk7tYF/0dK+WqL95CROC7ISLL5/Al0H6Qy643BYemGJ7gW706nGWZyVGGUJ6oZ4t8QJgZEG3bTE4xLlQK1YVA5do6ivlrBWIjo5MhG4nsMKhfB2CuCakiMJc8yIxN92xL4jD0DnYDQkRbh6Y5JYh7xkaIGnz0/j1WNUx+DCAAoKp3OEBekcdhIILeFlg9dyuN2OzXaV2swGkSignizvdDCSGRh2AgpSkE1WyXfXaE5Iwp7GtQw6SlGdHNHo4aYHEb6PnXQQFcgHA4QnkdbgpEMIi0gkXk+hXj2GWmzDDQ+Z5IRrGaqnKa4aSAw2lHRTi+rFqHJAlgQEz5qd4q+jArXqKNywjL6qQ0TAvpENrpZH8eckOtf0RgXOd+guyIZloOrR2WsJ1gVyO0c6SV4LiFyEqXjQyVGxQOaCXAiUEygjMXEKIxUwChl1EVGOkDm6J3a6MhUl+bgkG80JnnY4BcXJmPKRNr3nQ3qpY3rD0V7LaLSAVkYe5FjPkqoQkpwkgU4xxPoCsRmzvd7mxvI2+3dNsHuiRiHJcBmIVOKsw17qMvd4hYVtTWcoo9J9FccGJzl0W4kS0F5qsn5tE2szcmGR5QDVNQiTIZsJRjvwBSaUO1vELLiCTyIUIhOIXJBLSbdsKbkcpyDebmFW3E43q/0xqVfBDaVca20hJhVhZ4BmNIQq7BQEnp1pM8gHuW9ojDNbExTaFnfecfSbK2w/vUX8LXWefXKZ+vUGaqKB1x0nDSWLV8c5vD9l5NanKO6vov0Zwqoh0waMRPYsW2cnqfqCvJMgtlPuOLqLa4UtusUi6w1NugL5sCQbzpEqRijLSNdyZu4qa1f/FRt7hrjqjXFT9SS9bsrGWovZ6WFmpoZQUrKy0WJ0sMzYkMeFNUfF7+EHA9z25k8wJi7zh1cOcvryLn525qcwzpJZw5mVVTLPYvycX7vwSbZLMfmTHo39FTpZgetBxqoZYxFQ3cs8mwRsmyIOweFDw6x2EvLcEHyJrj1aK771Jcd5bH2O/3T1ORbiTV46su+LOs39Vd08pqiCL8puccDYQIk8Nbzh6M2MjlVe1EDKx5b/iPfeeAwlPF47cTuX603ed+0s3xQf5Zt3H3nR5unr6+vr6+vr+3rz3ve+l6eeeuqLvv993/d93H777bzzne/k+7//+3nb2972gsd80QIp29vb/P7v/34/kPI1dMfkET6xdZbPdj/P0dpZfvXU7/DwhYQHr1xmuKiJlOOPH3qGs4UYFfhMdzTDpQp+IeCRC1fotTwGi9/LcNEjmO5x9uISWyZlqFZCj/uMTg6yZ3aM245MMzY1yebq8+wpKRa0YLF9g1PldWZqWzTEANfYxPoKi6HdKmK7HiJzGOMQucYFDtVz6IagqH0IfXQekVYUlp2sES/RWCGwJYEpSLy2xV7ShMMGf86SdwxkEucs0VARLwCXWaQTWGMQLYMtWKwziI7Bln2scITbhrQKQlrA4coptpWTTvk4D8qNBF3PyPF5/LMHCFYsx3ddYzMps9YZQ2lHiiN/SYpTjqjrY4sS4xvyUNKarCAuQCAMxakWV5bGmTOjPHDtAMwJaolBOnDOYlOBFyQce80y168NsSlKIAXeYkZvtsje6W2a6wHDBY8rixWEFog0wylBOhbgr/cgzXCDRdL9HvpITsl2ST8TYkMfsd1F9RT2iRS7UcAVBlAliQsVOIN/PofMQCsj8HPoSNIFEEERGYJMLHKzi/YqyNVVRMvgyh5MliA14BeQKASOfFiTj2pcmuLfcMQf9di+tcSjhd2kAwo7JnBLBhVBXHQUcsnA1ZzaoRKtssHfzJBRCkmOK2rSokc6WsbEEaKTEdbBlUJcN8EhkA5sbqEVIdoRrlQET9E7WCJczZEAVYEXJ4g7wCQS+zlLHoNAUB6sMtCATqNNXSUQx6hGhF/wEFVFFnq00gDTg7RnUL2U7PwWK9OLHPuh1zBY0Ei/SDbh8KKYoGswlyVntkaIrCQ5MkfpDo9i03DL8Vni3BHFKaWZQU5ke7i6uEp9SOOUAyOxJZ+sYvF8RVqV+E2zk43BTvAoWM5R0qNbyDDDjuXtHqVCGScVtizp3FrhyF2LXLGwb2KdezZv548KK6y2Ivw0JHQ5zgn87RLSneQV0/v4ndEySaeDQLDmX8N7qcBt59jzjtVv3CDf8iF1EIAq5WwcjHHqMP76IT41/yD+iXXIfeQD1Z0uWNKgUk08HLBblpjat4Vcu84TnYO4RFO1m/RsgIfmZCHAvzBG1j7GiYMVPnVxGJkk3Hx3ixODHpVagRk5TFALGLh6lSm/TEFJut0EtV1COMn0YIQYqTBQ6gKCsbDDa3c9yzvOPMRPnno5XXLOza/zRGOV+0/sx+CIixaXC4peSoyP0obtvEFRKOZWPcLKBKpoEQjiPMazAmklvSilUvrimiXznTpFL+SVQ4fZVRjC/TXF1Z5rXOM/z32Yu4aP8YZdr/mCY2Ho8erXHKfbSxgbrbzoXXva6XlKOkUJx5BfRYr2zu/Wl9iu1NfX19fX19f3t0kYhjz88MMcOPCFmcMPP/ww4V/UpLPWEgQvvNj/Cw6kfOADH/hrj1+7du0FT9r3N0OxiU3XCVfhcwu72TvcYv/gGFeqUPJChPS40LnOUrDF5EgNvZ1SG/C4dHmFpeUG9Y0WcXGAqb3jLLTn6GQJWZqwPSpY2xXRVjmDg5tcGngS2vcy4P0mB8ZGGfXP8d5rf8pPTT1GoZ7y8aUhrhdXyGY0d9w5z8L6CI3nqqRSILUk3Mzx5g0WRa4VruAxMTjI1YUWwklM1VKqdBkfaDO/PUzeEgjrkAVLeTDGrSpEx2FLhmzGotcEiJ1sk2xYIh2QG4JWzs33LHEwXOdzDx1hLR/Ceg4ZGXQrR/oBqm2QwpKPSrxNB1IiUoUrCJzcGXPvvm1+7u8+QqFk+M4P/RBZScAZSzhmwDr0tiZTFpWCDcGrS6x2/NCbHqU60eP3PnMHK8+PIhyQpWSBQ2cO+d8z77RiPqiSKoW4AZQkrlghnVZUxyMmw21Wn6sirECkBqMF2WhAVvMgSvESh26klC7FDN3RYUD0WOhOIAslmNLIKEIEIVxLMGM+WUnglEAaQXSogF3LKG3kYCEIJablQHiQJ5BadCeCpQail+G0wjeWvBoic43LHTJ1YBxi3eAlDrnpcMKgtlPMlkDXLFnRkAnQLkc1DVQ0XgLF0QoDizlbbYNsZ+jeznYpthNEoMj2abLcI1gSEFlkGJLvi7HtFJUq1FYX4gwk4ByEChdoTJgiEwEyY+RkyrGTizSyMqfP7kN8sEftYJ0mjgP7JiiUfKaimM1uQpgqMGCNJLeCvKVQ2zl+kpAbg00zHv/oc/yXg5/gyLEp3HaddEyjBwrYXg+Z51DP0b6kmMBMpcLKWoflz5xlsBxQ8DTkECNoDkqisiLPLHZAI4RFuQzZzpC5QqgMEJBndI5qbFURLApqN8DOG3qHLfvGB5HNlDmzycBsD7Iyf3zve6gow2NPTdBeBKQkiRyqnTAw26VWuMFS67XURr+LH/4/HuF3//fTYFJmi1eoqxOUnylhzDbVLY9rAwbrWYyv8Kcz0jWfhZVpLtYyHntmFXeHwgnQ1oFzlJd3WlarkubUbQeZrf5XsqUU0h5oGLjFctfENZ7anEI8M81Gs004WuBss8eTS3tRa4YjJ64xPX0Uz1MMDJX44MJ5nigsU3+6xXs+/BS/8U++E7eeMRU1ePWxM5yfuoNPfvp1nDt5htM3pnnDzU9SSk5TCDYYGj5Ir5mRdDKWr7T4R9//Sn717EdZO7JBtRQx4PcoaMmlzgRbeZGBwvOMcDfn8hrOCVIgtjlRluN5X9zhrZulvO2pz5GJnVpHD4kbnJreS1V96SKxkUmwOHp5/EXHhBAYLTCepNtLqFa++norX8qByizfOvkoB8p3UNQBr5jcy81DEwwGL+48fX19fX19fV87bueR7Gvu6+Ea/qqf+qmf4sd//Md56qmnuOOOOxBC8Pjjj/M7v/M7/NIv/RIAH/vYx7j11ltf8JgvOJDy+te/HiEE7q95Vb5UIb6+//fM1Re4sjpGbTXnaOkufv+xGxycXKVV+SirHXjd+I/yQzMvYc5tsry1xWflcwxs+dT8ErPlMh3qmDjlXSvnuL68SdlZqrpAvdcjjwQucjT2XCNZj1jpPs1Jf5abjsww6Z1in3k7/3XuBAObGQ9vTaNHmoiywAsNNgRRsUQFHy136pRYAcbtdAHpioSFpW1kWyAHoFiNyD3Bui5TCXv00iJJ0fDK119jZKLHwmfHyQNJ6dV1ho+3uPr5cWqPaGwR8pJD5BYyi5qyNKZDLjdHmBXbbG9WSHaHZAMaN+g4PL6FSSwbz5cxRu4EN3AUhaAnHVm4EyCob4XEnRKrdcXMwW0SFN1rJdznPUq395BDMa3VAt28RHkjY3hPi2rB8L3HzyOADwdH2HVkm7W1EourI+S+JUgsWRmEBKkVk36X7ZKgeSElPlygd0QgewkLtkyl6WjXDSbLEBWPxq1FlNV4KwloHysUOrGwHdP7bUWvMoxrGISvoVQAfKwnsAWJyh25L3AVjUshEhY9rlHXNKVaCSZLxBtNRGbJ9gp0YpC5xBuq4WiTG4ssakDg0p3ClVI4XG5RLUsuPdKSoNA2fO8PP0muChS6MQ8vHEQ3BaYikLtTfCMY2CjiIUnbPbxmgs1znJYIAyI2CCFxRYFXV0hhQCpUBvHeIWQuEO2UwtU6yjlwFmdyZORTXNzp8oNz6MM589EoW3NF/vXt7+MpdxAXaLILCVtDbW45tY/RoZBZBBc8xeULy5jM4sr+TveoQKLbApdkuHQn2CQyy+c/dpZQQagDCgsdXAbSGqRzMCShUsCVArS9RlHVyIVHPc6I45x9U8M8EV9nY0xTMAq7ZkAZVMfgNWNUqhAUyAoaGeU728cmQqyn0OvgsLgEHJJGlJLeqKOrOeW9XbaNT2wVUsAjTR9FinOCzFh0z1JYaTA8UmWscIrPrj3Kg93PMfbKMu5Sk5dO3eD9j+9nZbPAeEcyXfZIjta5slJk18AGc2oA1xXUtMezV1doriv40yHUYsbhE7N0Q8uZbhuUYAhJfbGOS17CM1cXaFkPKpZUSyLjUdk0+M0cAtiwEU/MLaEig/QAXxGZ/9GSd+/wML3eTmcmowW/+d7PM3l4iMWlMT7/6MtYatbZSCX7C4JvO3mWPzpzGx/4jvchpabqfQ82G8Yz8OjzN3hFdBBTMuQLPtuX9rHh58yOrzI+FjPoCvQu3sa2zLhUmQEBUZ4zv1InSQ29KCP0vS+4515Z22JjoUeqctJyHaksc50lTgztByDKFmnU6/hiN8OjVU4NHWGyMMJYMPAl7+Hl8k4AJvhrWi5/tebaj7KWaJy7yGuAbpbRjJJ+IKWvr6+vr6/vb71f/uVfZu/evfz2b/82f/iHfwjA4cOHeec738kb3/hGAH78x3+cn/iJn3jBY77gp7XJyUne8Y538PrXv/5LHj99+jS33377C56478V3oZVihKQ7qLnrpps5G9UZr5XZSFNoFkkHYk5M7ueOwQP8i0c+Rak0yM21XSybjE4V9HSZvLKG9FbIXZmCVeSBYVumiHWN64K/dAtx1GTPzGWOT/wbnrv+bUyO/h/saY3znmWPelzCTWQMDTqiRcVT87sRJYepCPS2wAkQSpGMKqzLoZSTb1nWGgkosL5CSKAo6HZCTFNQvpZTWBDkLYmasGROEA87bju0SqY8KnsMnbMCbS06MmQFgZQaU1ckZwLa25L1i2VUyUGaYwuSyeEe7773I8SR5NvTb8J8QqNCHxM6jLTIzZhA5nRmPKanuuwaySltwZHOCrLgeOz8MfLEEIxkHNq1QkDOo39yGK9gGTu2RWeuxINP7EJXDKYJb/y2B0kizVt+6XvBQjZRxAmJTByCnHoSYBB4maDnC/Ii+NuC+vkStCOyBY0reZiSxvgC1bM7mTe1ENtKEFrhwgq2HSOkh/AlNs2g6OMEdPeDC3zC8wkyCTAWbGLRSymqk5LnlrwkyLEMTA7Q7LZwVUW2X8NiF51JRNknLYAbH8EWPVzUQZQDrK9Q9QRrDLagyJXFScnqRhkjKqSRRi3nCN9DTQvUYWAtJX20Q1pzZNZxx6susrJUZvHiNHRynM7BCbwNg6un+B2HygWYFDZzGKwihjy8Zg1jmogEMIa8BKbqcLHDGYt9rspQx9I5UoMTFpka8CThsEfVc+zfPYTJBTcu3GDuwhyZi8m9IUTPIW0XbIjLcpxxCE/jnEM4S6lWZve+CUoDBbbWmkix8wZbFATMSqRfgKhF1r7IoaPbDPi/xDNnFyh5Gi/QNMsOJyUZkA9K9FaMv2YpGocNJHEBwq7YSbTZygkutcmnyshoJ9CUjFp0w7JQqBOsNxkcaxLVfbr1Mn9PvJ5CNaU4VwVVxyiB9DSCjMJeSV3Vudo8w95axrb/HLu/wfKJ9BZGJ9uUwlVYHKFb79JdKLPrJZKVUkLbhtwxdJ1OK2TWO8l8GiO0I5vNGalW+KEffDnv/9AzXL68QTTukbVS1l2L4b0HmWs2MQMC14Ijeo2BpYRHnhjjQLHEzEgZN+hzJd5A5I5imOGEYT2ZB+AzD57n3/yHT7BnoMDFXoIVlm88tY8Hri5iuyFNPcnQeI4MPP7Ovn/FT/2D/8Lr7jzL6naJSmmCtY1pVBoDjnLR4z3XnmJwqkAmQ+xyAT0ecUt1jYpf4njttzi7ZghCiWwOYYUh6rmdFuVSovUXb7UpBz67i0MESnPzsf24IGEyHOaWf/d/4ZHxf973AZ7600lG5Rv43jd9C0PDVWaKozSbEc/PLXNg/xiVv5J5cnDv2N/Y+nC9V+fZ9gy+sPxdm/Fn589zZWuL7zp+nJvGx//G5u3r6+vr6+vr+3rwAz/wA/zAD/zAlz1eKHxlHy694EDK7bffztNPP/1lAyn/s2yVvr95bzxwB8/Un2Vmt+HorkleNXKYRhSzevZ7qSZNmvUuc0sbPH31Ao9eu8KA7zE2UKWX9dg9+26ygw02l3NKNsVM30SwNcv1bh1XcAih6aWGy3Ntdm2Mwth5ViKfhy5tMlq9QG34BzBLD2P9DoMjPUIVgQromALFPIGupNA0cGuCGlCoTU3qK3wMVlukJ3FO4jJLoxGiGwJv0yHTnOK2Q8fw1P89w9qRmNm9bYqzCuM8OklId7FAOiQIprooCZwLEalDrVnanw6ol4rYwk52g06AbkZ0QhJZRaoV6WAAkxZZd+ApUDkyy3E2R2rHchYgJMyvVvnM7x5COYnKFNJ3tJ8vkvo+/pjZKW67qYiXfBqXSvz/Hngt+aBPcbJHs1NmptDlpXdd5eHTBzAlb6e9cdfQank0Lo/gL1gUEM6n6MhHtyF1ms2FIjoziAHN1B3bTBdT5p8fQfg+ZA7dzXFVH6SAwSKilyOcJT4Ykk0KxFKEvwTRbIAZLyJ9gVyLUN0Y+0qBlCnuo4Zou0PXGIRxeNYhug7dUeQjA6Qyw7MW3y9ixc6WHtvJ8ZSPyC17bp7m2qVVdMvgZxZZ8fn0J2+iMpXSvFam2uuy/g1DBE2FWC+QbxoKaicoUR5WnHrNJU4/tJvVi6PkaYbxPbQSFOcNzpOIBJSxAAxvp+SugV+oEQ8H5AMl9DMxeVHRvjvEjnoUP5ciOj2KZy1OasSuMv/lnbcSLnawRcHs+Ch7pkexThH1EhqbLTKVEE+CVAZ1zkJmCCKHEw4XSiiFeFIBjsVGg/JwkV3HZli4sIYKQ0zNQCnF8yWJLZCEGU0tGZqto7c0LjGExZCJ4QqirhCJJReSMBXotkQaS6FYoFHxELkE7UBZwKCkw/kpNiiCUhR6glxJSlJgcZhxQbNRwHUEjWtVrPWpxuuEdQNDmqygkJEkXhnGPF/h2acF//6heYYH9nLLWz/LVnQ/Dy+dQHUyyiczRNcRzhTJvRJZTbLkqmw1K/xAPo3wrrHZLtMdyFBHHMEej9XlJqdu28UzV5fJFeQV6KUZ5ZJHyk6dF5kLFp4q82jjACIFOeHxxGMXacWWzss9ykrQtpLX73ojuyq3EvcS/uBdn6O+0aY3FlNxHmZd0YoMgyZgVHoce81pJvwx9svXc8vIFCdun+J77v8dSsUUof4Rf+d1r+I7bcL80hZ3ndrHE/EC6wtdmqziIkdtqMhieheJV8YvDvDyuwZpdSJufKREbDLesfQQqq25bc8s/pcoNrt/dJhf/uZX00kSxiplfuuZR/jfzv4JvTUQFcHFG5pH/myaavEi3/Y995FWc9ZaTT79uTOce3SNe++9ifvuu/kvx0vSnCw3lIsvfH/uC3VHdZjTrZRBrwAIJisVVtttBr/Ch4a+vr6+vr6+r2PuL76+1r4eruEr5Jz7inbYvOBAys/93M/R7Xa/7PEDBw7wmc985gVP3Pfikyrm26YnufD0FP/xs5/m1G17WdEpn928xj4lGafKWhTxwNVLJL06XiXEInjl/nGuuMvYUkp7+ziR3cDGVQTghYqq1eRrAjeoOHakw95D86xv10jKkuHyOjUv4PV33sQH184TNRK0E0hjkanAbSrsehGvZZAigTDHq2Tcs2uObtPnsdOHyDOBkBLhJEKD0mCrFps55BKQOqRS+KWAxWbA5nMTFEaKnP/94yTrbbRvUV6OGxFI5/C2ctSwYfKOTeYbUyTWEWw6jJLkZYUpQG40r/n06/GsIzcF7F0W9akYmRhMlJGMaDLPQ8Y+25cyvv2HvoV02McMeJjEEUpFfjIl3qt57Ok9qFWBiT1kI2VxfgIXKgQgY0dzq8T8jQkO3v4c93/DGR5+aj8uySFzCM9hQ43acHgJSGHxtgVSgJHgtMDrWVQmsEXLsfsXeXR7D3HRRz2rKV7rIa0j3+ohx0Nedd9z1JdLnH14Fw5DXhJ41lFYNXhtSXSoROY78lmP0uM9VOpwgxJKgnw7RRQsvf1FSguSTAi0cEjjoCNQnk9aU6SDEqElOi9hc4XILQOVEGkMJrfkZQm5paELNK4VCLugnUNqSy41aV1TXTLYHERLkVQCHnzoNkQWY5oGLdVfLAKOQsey+9A088UtmtpS2gZcSne3pTuU4C9IorGQYF3icg2xQhgPChk2zREbGQwHHC2scOGzs6g0Q2rJctrmphNltIbVlTpz17dIpgukuyXegkQah3DghMWEClvw8ALJ6HiJjettuktdPva+R5m/sQmhh/Uc7oCHNoqj/2CdhXnF2ierbLd8bnCcvYAnIdSKgaEyY2GZG6ZJ0SqSjQSRS7JA0KtCVACvJXDC0MoSet88jIgyZLlNoeLj4hzhKWRumDAV5IRmK2qya7DH6uc1e75vjYVklMnWFu1uhazkMCIgLwlGdu1lLKvxwXc/gi0EJBvD/MEf3sL8Ypv4T0+ydnELdciibnHce/+dfCL6MH7N7LRf3tLUjp6j6y5w7fxLyXoKdUaz1mjy+PAlbr9tL0f2DfKg1yCTHhNpjZpQmJrcqcnjpxy5b4Vg23HxuSmMhu3MkI1rhMxxvocKc6S5TJZPQjZJnlrUdIZ3exedOnrnqqxtNLlr/zSH75pnffQay1zlufdkTBz8fsrfOMeHl17Ga2dq3HT4e5iYLNBLcm4/spsstfzwzF28feOzdIuOoucxUPLoyiXO1J/iXOPj1NdO0L56lDjfyXxrpBGqKWmWO3ypJ4JGJ+LBs3NMDlVZTOu8//pp0iwjHwKk5MN/cAf5hibzegSB4OHnrvN7839G+8YSdrvMncn0F4z3sc+fY6vZ5fXfcILBavFFXR/Gi4c55n2Kycqr8aXmNfv28Zp9+17UOfr6+vr6+vr6vl4cPXqUt7zlLXz3d383vu9/2fMuX77M2972Nnbv3s0v/MIvvODxX3Ag5eUvf/lfe7xUKvHKV77yBU/c9+L72PJv07KrREPjzD9+OzPdP+eCq9HUu7jeSLlJjDEtPAZNmUWnuLbV4dPyCifOTnNl8FtpVjc5NXgTS89tE0QVmmaObGwbN1CiPNPBKY8DR7fRtRXy+q089szLOHnwDg5NjfOZJ38CF/rIYIJa0GMw383lVg8vZiegkBroOMxpSbmacXFkHLMmsCIhndX4W5JgHcJVS+IkSUUgtEAHFoTbybYAwtyQlFKyaoYsBMhB8K6kqJGc9qUKupcR7xF4EzB5rMfAc2tcfWSctOeg5IgCgR2DfE2iehYyj/yQxcWQBZKgnSG7ICqA7xi9FiNSRVrSCKW468QSaeg4/exeZMsj2dTICHTX4m2lCKkgkJBaVGTwm+CtWdp3KbpW0rQajEVgsVpA6tA56FTgpCT3JHngsIM5etuSlxXpUIFC3WETxZUnRtgaLaBVihQ7b1BRGuVyDu69yrd+92kEjn90+vvwl8G/liEaMViBUAG6Z7B6ZwsVgSD4lMVWHGJdQCBJJgLSSY1sZeSDGn8BUBohIcOQjWiEcYjc7fwJSOt49tGryCTHBh55SWM9R2/S2wlqrRtUx1G7mNLZIyivJwxGhlj5WCWo7BdcvHaIbtsgx7rorkEojZGSXmK5vL1B6+6QNMsYXYr5jz//CX76uZdxebmEPxfjrft4sY/oZciLMYkqojoOHcRkscYUfS5+ehpZUFhlkb2UzmLEE8tnmFpVrC0nbDUs2nh40mKXc5LZMsFaggsMLjW4KCdXChdlBGWfLIo5N7dGqkFIDzehMAc1LpE4U2dssktem6LmrzMcnmTh+hatepd9s6NIKTGxwuUaTzk6LkVUHBZLXWWoSCOdIw0F6aESfgtUT5LYMnJDoHKL0iAiwWSlTCcTbC1MU5gbx69fovjxhG/9ztNc/szkTm0crZChJqvAikjpXF0GJcBapHBsPzFMmrTZrhSQxYDskkGs1eB+n/Z6iaxhCTZ3/g2DW/ay0mvSRCICyC57xJnhRq+Oc3CptUo04+0UVh7RdPJPIkQJ5wmc55OEPuP76lxyw3zk8jylCmRFSVC3kAqGRjtca7+TZv4MJ0d+g/Fdw1x5ro6a8xBrYJGc/dRzPFs/zSt/JGBsdwtf5lxa2uZ9j/43NnXEU0/u59GPjvH3XnedpCQ4f32N2XKFIzOjBIEmyy3KV3RKsM8Oc/euDgvRFXrEOO8yc9tT7Per5B58z8mb2FyLqFVLCATdKMXX6i8Lz2a5Ic8tN3ob/M7yx5C1iFvCKT7RaaKvGxI0yhNQEYyMD5Ev/Tlx9xrBTIqIJIXx/xGcsdZx5uIii40WyZDk2249ylSt+qKtD+cbp/nU5gGK9W2+90CGMY7n19Y5MDzMULGfldLX19fX1/e3gvvv3Sy+Dq7ja+wd73gH/+Sf/BN+8id/kvvuu49Tp04xNTVFGIbU63XOnTvHgw8+yLlz5/iH//Af8uY3v/krGv+rqmjnnGNrawshBMPDw1/NEH1/AwalII0zBIah2QFuPnGd8bjGLbtP4p4bwm8rrl5eZWsrIo8KBE4wO1UjamYkyRGsXeHkwNsYKYf8X+uvptKNGLdt9ECH5eIIrg4r87dTmFigtbmbZH2Gwr5B0u1lllbrvGJwi8tyi5JM2DQ5/+Du6/zRJ+8hciEWUNYxOBExcqLF2nyNetMjXIyp7ynhhhReE3Qnw183pMM+zhlsQZPVDH43JR/O8RILOURFSfnw5k6XnQlDe6uC6Sh0AlbliBAKLuf6fJFeSSI9RV5S2IKDLnjbBhdKsupO7Qb/isNruZ2iobGjllrstkOVHR0EdiBgZLLHr3z/xwD40evfz/ZciDQK3bPYosKuGtyERsegEovuJDgLZtLj0/M3M3gu4rFnD+J3LLmEbFig4p22zqKdgCdIR4rkI5J00FJspwjnE+8tE4gcZRy1Ysxkt0UlSFkdHiFdUJBmyNxx/co4z17ZxfzcCPFkCRnnBEsGmRgw4HoZ/nIPveYIlzT+qkUgUZsCPIWTULyaIiOx07mmBOzPYMNiIwfC4RKLtxXhlEJkEhGnOAlWKnI/I9mlyZVl4HJGsqeACx3piEKFOaWFnCCSuCFFZTTGaY0/mLAVB6yXYnQXilJjXYarhLjMoBJFnivUlYxS1xKKjDyT/OODT/HTv38KdSaCWgmHgiwjzVPyrYRy1+CHOWkhoDgrue+OM7z/E3eSVh2m7KOu9qivVxjqWNI8ohkYSH304w3yYyM4JTG+IKyAXUh26tFkHdYWN9GTZUCxud4hyprQ6CJqFURTkzRCnvsP03ihx9hkjUPBfQyu72azOc9g1adUKhIpS6WrGdqAGeFoGkdSyhGet9Otp7tTl0N0YwIbIm2K17LYrsQKRx5ICkrhWhnnryyjU4vrpARVibenxlLTctwtM3Fyi/q5CspJesMKL8npNGKmhI/wJR4ZWoPyUoqyh/IrFLRHEnYoHY1obDWxItnpYqUlMoCVa+M8d/4YUnaRGUgLhQxqBR8v0MSDBt3wEEbQkRHOCbQzpEgEcO6j08QnBbHQmBFLPgmqBaa5E5hoJQVCcYSp8rfQ7cR0uzE2l0QPVvDbGYiMeDmCNOPj/0XyzZVXcOn8GluNAiWbUH7sONnCJivNbd7//kd53Q+8lOfmVrng1ji8d4xSKeBWf4xns4B55Tizuk5LXOZbj9/gRrSXwegVjO/aT6cdEUnD7Qf3MPHSGkJKoijl9PlFyqWQ247PAjBQCjm1b4pltY3X2mn3nngNhBSUr0PcrKF3Od7yc68nSTKmJ+aY/kidC/W9DBV2sbX2P7r7SCl4xUsO8tmr17nS2Oby5taLGkhppZIgMEjZwtic08vrfODcBV6+dw/fevTwizZPX19fX19fX9/Xg9e85jU88cQTPPzww/zRH/0R73rXu7h+/TpRFDEyMsKtt97KD//wD/ODP/iDDAwMfMXjf0WBlNXVVX7+53+eD3zgA7TbbQCq1Srf8R3fwT//5/+c8X7Buq+pjGmu34i5dGmSkzpkV/EdmGSdxW3DUJiTdywVpSkMd3H1NiwKtjpNTu05ypUrLeYvOZ6o7aF+3UFHE7drZJcULFdYPaIQxYDN4XGuLXkUCtAYXOdCe53i6graHuKW4mWOl5fZMiU2sg67Rut866mz/MnjpxDqL2pNlBxSwiQt7JMFjJCojZ03ZM5aMl/gQou/meNSgc4ERgnSfZL4Dkg3JXZBk8eagogJEkt0I6A7Am7IIlNHYV7gX1c8+qd7iIcVecXiZQ4zZJElhV4RyA1J77jADCmCXk7BJdxy/yLNSwXWzg5BZ+c6cwWik2MlrPd8zi+MITNHelEStg0qT0mqEltTO9t5coPRHkpZjBLYALJhj9WozDs+8Y24PGeYeCfBxjqckkgJeB7EKSK3eFsCF+WYSUs+nWDqZdq+xl9P6Cx6TAZ1VtJh8jLYAXAbAiEd2Ybkd3/9ZWDBK+eIzKIHA0TXw+Y7XYm0TQgODdNZ7iB8CZEBX+PyHKIcYUK8SBLPeqhyjhIWmTvwHG4LdKdHMqrJJkOKqwbT6KBL5Z1tRAMFsrLe6ZZjcsZ2bZPWfLo3QkQHrG/JZIpLNKODTYrVDEXK/FMeOrQU1xwqs+B7aF9Ax+Ayh+gkVE/nmGLIRrHKr/z2y+k2IDwTQeYg+YsaO54gKwnS3QGdzTZyXlOb7vDjP/QMh4+t8onP3UpUCchGBUniMyH3sHvfKEtXlon2DWJrCrsnoLpidv4O3QxXFkjfRyJxIkcqH9O2ZAWN2Gwj1jtI38fkErsRIIUi7RZwQrKW53SuXmBxc5tiLikGmmfOXKW8UMF2Upwn0KWAsK5h02I9EJ5ERQZb1ngrHYoDQBwgewZdVBhPYshI1yNyD1pXUnoTHiGOR9Uihds0Y0mbQ8fX2S52OV/bg9A7QSHZUXhCctPxEdqVixSOr1G5mrJ6ZZhsO+Tm2VGSbsbGWIPiXT3q1UX8RkbBWJraYSc8CCTrm138gqNIj9/8p++j2Rzl+tVf4eCRcVqPpHz2yjouFAwO19jo3oa/fgYzriiUI+J5Tf1KAHs0Xs0RblhElJNWNc6D1PgYdxjcMKsLm6SdZKdTknXIHJTKEVrgIgva8XRtmblHZqkNJyS9FWbH76G+HbOw1COY9rjryC7e98AZNrdaNNIYpSTLXkI07JgqF9nUlgKzZPYqQ0GX9uYczeertExCbizbtzZpdhOcg+nhCs4YwuB/LJu9TkKv0ePgxARvPf4GfuvZT6AzxW++9hj/8nOfw+kcmVoCoel1Ez5+5WbOfjYizT2CA4brC9tfcA9/5W0HOXF0lkvrmxwZH3lR14eh0gnGSmdQQtPO61hrOb2wzFBQ6AdS+vr6+vr6+v7Wuvvuu7n77rtf9HFfcCCl1Wpx99130+l0+Lt/9+9y5MgRnHOcO3eOd7/73Tz44IM8/fTTlMvlF/0i+16YxD7GxIjCRgLXuYvVdoVz600eWHqGai/iuwbuYmJfkaJ5ikNpyvn6NEtZlz2tHvX5FrYn+Gj4UrzyCsWbxqmdz8iiMolz+M2cseEBysWQXY0ytWCb5wrbJMkoKvDwmSVKl6iohIKXMzCRcyGfYGg0olhM6BhNFng01svcNbLIt99+jv/7Y7ezkAwQRA5pLdYZbEWSVzxs1TK0bpDaEGkBkUT1DN4G0MixoWKs1MWrONY8D91xpAOQVwUqVuTTDq9mkM8LVDvF24B01sdVLKYjoe2TjexsO/IbholDTaburDN0pMv1+jSmqQgbCXnuUE7iN3NiKfm5//QdVJczTGzJBnfeoNpA4sROkIYcbFWSOc34aIdD37fC0tMjXFjcRV6WCKXIShKLIQ8kDEjKidhpK4wguN4lGffxhIdxGlcQqIZEOIMUkrkHx0nuFNTGYmxDI4oeppgh2waZ5vjLGeSGvFJA+JosEOy9fS/zF1fBQN5LcStN8ENEZhh8RY90SxM9abBC4sKd9tTSOJJA4TcVYdcgHBgc2ilET+JWYlzbIUOfaFQQ7Q3xmlBYtxwYKbKct0i0hxmUDC82iDqa+Ds8nHNUojrNo5LisyWWz4aoJpTXLbKdgLSIkSrCODAgjUW0Y0xRkZUlWVVyvj2Ov5YiKwqZpwgkAkUeSBwW3XPI1JBrRbTseOITI3ziIwdIlnPCpsXblsjccok5Lr73DCLKCIPddIc93GQZd34Nv51gs5yoXEX3HMZptAqQaQeEI5AWnMYWyggE2UyICwqQS0QvoTWqyQcTDpTXKcgryM1XUaqU2VppERQiRksBazZhOUqpOYVrxOS+BixKKmxgQVi861s4T+IqITKqIFNLuJUhMoOMLMQ9/DXwagF6zaOUw1pziHP1Kba7RRIhUWOGXeNr1Dtl2pEmK3qIYpXOvOXJbo1yC3wlmRyosm2aNLeqDGwPcc/LXsblhQ3iJCEfsEhfcUN1iYoWV/QYGesyOBwzM32VRx5rclttL+2NBJVbUi1YyHpcutjApYKgbhg80iKpenRtGadh+LGMckeRCjC+g9ziq5TL7cfpuhvcXH4LhaK/E3BEYELBULWGFgktl6FHLLftvs61gb34FcmTj+zGy24QpSk61IzODPHYuXnceo/o+S3+4Hc+z2tuPcypmRmemBjmfKnO+HCVn77lhxmd+BZOz3+MzzxTJG22uX5uEeEJ4vtP8dTFRZIo5Y6ZUbSS7J4c/Mt7bqEUMDY5SLEccFROcXCgCKLDfTMn+PzJJR596DKTEwNMTw9QLPgMF3Iy56OMpZDAyuIm5xbWODb7Pz6E6KUpn7x8lVac8A2H979o68Otwz/I482YfeXdjIeTvO35jzHXavDBC+f5J/e+4kWbp6+vr6+vr+9rR+wkkX/NfT1cw9+0FxxI+c3f/E2UUjz//POMjo5+wbFf/uVf5p577uG3fuu3+KVf+qUX/SL7XphR/zjNyjmO3xxxba7AfNzmO26/hbjcoLDmqKQhiZV4toQwmvGjk3zj4Vvx2oq2zBkKAg4eeIhds+tcPOuxWTpAVsmJTJfaYMRtkwOo1JG1HaKxwvHx62xHjszfz2rW4XzzMMNhj1P7r0HpNh5vxlz0AtYPaWgq9JrC1DJkNebZ7SEaqopUHrtcl3rR0VQBOIe0DpkoCrMdDpTWePbSFGYlQKxZ/FWDSgOChqTRLJGteSz7QwgtKVwHfxNwDjvgEEMGXVCU5iXdAY889TF1n7wM7qSBigFrcYlh9dIAc8UEGwmED1YrjNCIxIKBwVvbVE+0uPHwBLnnkQWCfFoRZA7ZsOTdnNkw5Ea3i7MWMsve12+gJx1TL9/ixtumSAYENnOoKAdP4JxC4vCVxOQWlER7Djdt6RoP29GYZYnazgkXY4LGTqFUsxtacUiGJrQKW/R3CvPGyU6L3oKPNgYXBNjQo+0r0vECSnWRawGmlcG4pVCLGfvGFsmyx8L5ENFTyE6KGS6jWqC0wq5rHAbiHKkFLreUjnapzeZ0nqmQL/lIAa6oEB1HUE/ZijuYksfGVgUN6AsZXg5OOZwQJOcVjYmY7o0OSgpyiqgkhTzfee06XWRYBWFwSYJ0gm6tgAoUQgpcbEBJstkKOINoZTu1adcjCssJSXEdG2TYgkBS4vOPTWFthOtE+CbAGB98iVuWkKTkAsLLGxTdKI2JApRC3EobEwgyzyACjZ9YRDmArAhJCq0u1i8gPZ/cZOiGJRvJIVHQyejOlKAk6JY1I5Utmplm774xiiMVPCV5Zn6BSEPWjSnFMbaoyQAtIC35+JFBloOdjKHIYq1DZ12sKwE5TgisSdGpxY8tB16RcufLz/HZ/3qSzUKND//H2xGZpWBiStWYUhaThRJdjrjUWyOr19jMYyqvS8l9TbOjuHplkWizxUa9S2t9lLV7GySDGeZQikURRpDEGaqbkQWK1bjCmtMsLEyTJoZr81tcubSCCgUMFghLPtvKktd8XO5IA0fiJGbQ7Wzf9SXFWoCnwDQSnAeyafHFMf74UpkLxfOcXl8FLf6yAH0Q+PhjHrG3zvCeHvvVNm9440t55PI8m5ubVA5eZ3RijVmdklwep1zw2VhqY7sZc5dXWVqp8+z2MldX1tgcMGzqNm9b/AT/fHiM2uZJkuYCpZJHWg1AC1QpJMeR5IbLz92gvhlRGqxw8ra9APi+xh/aWUats+jC82Sux1J8nV/++6/jU7fs5fZDswxUQ4KCz2v3vobPH5vHT4tM7Z8m9nfqrOz8vONzi9d5an6Jx+YXaPSiLwqkpGmO56mvqKL8f/dU4zKPbiTc6Kzzw3ugFUc4AVqrr27B6evr6+vr6+v7X9gLDqR86EMf4pd+6Ze+KIgCMDY2xi/+4i/yzne+sx9I+RpqNF/HR5+pMD7q8d0js7xk/26Gi0X+zqFX0xtscGl5i//46Bmubs8wNjXAP3rVy7h7epp3ff4ZdFVzbGiE8XCTfEPiuZjhgYCzpsF61iUtpTxXfIS3TEUsF/8e15czOtXTeJ7gwPAeWiLF51HuGlqmbap0tl7O6ecv0RMaJAhjEUKhEsXVzhDVMMaOCgY7KfeeushaUOG9D5zCqAwnc6zxaLcLLNWHMSshXiaI6iHtGUWlDX6hwPLqFElmsKFGJhCu7HSY6c5oTM+HRUPh+QSnBU44hAHdtNgSZD4IAUIJso5P0PLRapmJ2+sYo1n5+CQogfMU1FMqhzqUJlIqkxHLsYdsOQaPNglkTu/xEtmGIilCVlb4nYygbVl9rsZrds3xzKVxVGIobApskqOaOdITDD7WYXT/BC95zSE+/IFnsBkc/bElSjMRZz5ygLWoisgsM5Pb3PVNF3nioUkWS+METYMnc/JShggMhIo0VfhJgAsUGIkWjjzOyEsey+0OblyTHvcpPmKQ5x1icYtI+qx/sEjaENieBCkQCIzJcMYn9yyegjzxsDh8/uI1mzJIX6DGHXldI+oZgw/3MEVNXpR0u+DnUD0do5DIWDC+b5Qbn+3ihIR1i70S0xkp4Ho+ohsjOhEydzhPY+MMEeeoJEMohXUOL7IIYfEaBjxJKh3Z4E5xXjusCVuK4prA20gwMsMMa5IpRZDUsJ4iL2lcZrElSTrq49UzWNkCIREIXJZRzXu0cg+lPaQAJTTySo6qeohBn+JIlcw26W51cFlK0FPYmgdovO0c1d4JoqkoRwQZphJQHpbMnX41zcUVmssNGkojCz6XBxq0PMlAVWG6Cr1pUYkhrjni3RrWc6pbIcGQJFrrIDJLGMOhY+e5vDiN7wcUjzQwDylMR3H83g7D0zE3vQRuPBfiLXfJ1iKcr4nXQuSAptcrMLtrm15kmZkcpt4UgMPWoKaqyDZE3YSYjC4RW89abhu/k8euPYMoSJra4hotvMxCutPt6k1n3sjtdoZvOTrJ1Y1N3EYbVRaYvR7H9o6zcG4LIRKEb2leKoNVeNoiHASbBm+ix3B1lGQrwqaOcuwjsaw2elx86gxxDmLQQykHdcd2AXzf5+Y7V6geS4iEZNO/Tn7sNJgiKjAMjLZxLcfV+Yt4jw5S3F2mU++SVjSrrRYPrs+zNZThJR51r8eTa3N8ev4/cWqqh6d/kt0Hxth1YpJeYhgerXDxExukcYprGU4/d4P1guXkbXvp9RLarYix8RpCCKSQHPYepig3mKtHHJj9D3zXa29l7soa66stwtDnmZU1LtcNRtUZGt3DntERjs6OAbDe6/BnV8/R7MQM1Yq8ZM/MF9zfm80eN+Y3GRmtMPlXsmJeqEOVPYyHw9S8KhbHbGmAmvC5eWjs//Ha09fX19fX19f3v5oXHEi5dOnSX7u36O677+Znf/ZnX5SL6vvqfO7CBmtrJbomZnK8gIl+nY89v8nFxe/i5Og04xMD6GsesimZ8cqcmp7G4Vi6toVsWJrNJqr2MvLBKzzf3MdRK8FBseIIcseIbNLYmie2DfZMPEqlNEc4W2GlUGG5scarBq9ysLrOmW6JevwU3aUxaoUuaVxgwHY5cXCFbzpxnbc9dAt/Fg5RGh0jKTRZCmp8Pt6FwCKlpFDuMTKzzcbZGiudKn43R3o+wgmiEcfIvi6VRoVgNSAfbeOMorCSo7Kdjji6leM8DzY1dgi0tBTrGZknMBWNMAozFJPlEpFD0NQYLbjRqiHjhDtOnuNzn05YN+NI5SFEzo0/q1G6c4C1xgDxAUewbgkqEX6Y0e0VKCiPTsEiEoG2krxqqb0mYrVa4FVHFrg8sotvuWOOzz88zbrxcVUfWoqVTsyffvws+BJfC5wUIAROSGTdEPQshVKOKhrwFCoTpEohlh2FdoZcDHY6sAiBHSiQDfmoTobaznBaoaIU40M2ronDAN2p7xSxTTNESaJ9sJ4Ez0fPGKyn0UZhUovqgEgdTgls6MN2D4DoMxV6w5I4DaEq0ZsG4RzJhI9DULzUJZGK4kIGSGRBMbV/jBun58HkZNrDrQncoEAkAiX/+2+wA+dwKLI03clOMgLUXxxLcxAaZ0Ag0V0HMiUTGQkBJe2BEITrArMV44UecgqstKiGhZ4lriQYz0N1WxClgEOERWQ5pLUcI4IA180RwU4A0EchnIYBn2CwwPh4iSfPd/FWLaKTo3sJQgvIBM73kO0YlTsCkyAHDeHiJn5zC1W4iSjLSTox3dSSD2b4TlGNiwxkAcsuxwwVkNKBAqdyRC8HTyOlAqGQa4bFsTF6wwFxCiduccQHOqw+OMz55wc468rsrU2xOywxH/YQBkTmyHNNZkZIR2Pqzw2S1x1eWaPnCvhbFpUUuOnWKajXaWeO9GiI6qYsLm1zrLYLlz2LCB1GOgrFcwRZFdtR2JsChBD0OgkmEKxdWkM3BeQCkcPZ0zfIyEE6pHTcPLnIhYv7UDdSVCKR1tAjYWtZYR3gDJN7ruKpc9y3t8a7HngdpOC0ozsl4IAH5yJUnDBxJ+wanufd51/J6tp1QukTjsUEl6dYf7BKe3WL+kVNYpcoVn2SYR9bkHz28nVESZCFknumZni+eINcLROpnKebg8y3m1TWKvzcL9yP52nW623iKGGkVka4hMV2m+tPX+GR565y/ewac9fWufO+I3gDAeOVMme7BVr2EHeVn+D3HvoYr7/l5VRrReI45Q9vfIxLm0u0gxTrOx66/AwTg3fj651leLRQ4t7Z/Ty7vMaSaBEWvrBNX54aNtfbFEIPJr/y9eFwdZr/85bvo6xDlNjZoqRvKPzSV1Vzvq+vr6+vr+/r0X9P4/1a+3q4hr9hX1GNlL+umu3AwACtVuvFuKa+r1YqwAgGjGJte439lY+wr1jk8c4enp5PuP3gFN95/Ch/5gS37J2is90h8BTHZscxec5qvcFgeI79Ayuszb2E7naXsTjn7pvOMzPb4crFI2zM3sfx24+wtXacq4tXGXKHOe2vcLG9hnFTGG+NG/EJltJ92LEmUcNDJYJ6Uubv3fEsuwY6/NjNGXNxyAe7dwNDvD+SZCWFLQsK25bxmQ6V2S7dimKrXsY9KNBRhl9MGJ5KOHR0HbXR5dITU8QUkBWLqoJJJM6Bdg4ZpYQyYf93rFKqJcy/b4KmKSEcOG2pjnRp9SpYIbCewZQVN+QwZsNxx+QcA8M5ay1JVvHQ9Qh5Oacd1wgGLbkP/oZj6dFJVDdHihw9LilbQbhqsMphDsC5aJyorRkqRvz9Nz3J/ceucNfhSX7xn70CmwFK4aTZaS8cW3LguT/Zw+hrNwl2xai5EBNoFq4Nka4epT0XEEwY8hEFawq7plC+xkmJKWhEmmM9iZGghQWpkUKgI0Pa1XiPOLwbOViHLfoUZiwDp2JUwRJtayrfn2IDQ+d9FWQu0SkIqf5iS47FIRBa0hkKEJ7EGoNa7eLaKbJSINy2WE9AqCB1pDg859DVgNXzi9CKsAMFREmSeSOoWIA1GK0QvkYKh1QKq9TOa6MdlpR0LKB9R5HqOYtMNMI5itMatZLQWYvJBizKcyAEwg+ReY6NLUhIOz2o+phaAXoRtFM8IwnWchASnAElkE6SKhDWgg+yEEKUITJLnpid4IjT+MUA7UvIHC5zYDOQAhtq8BVoiTOG4oWEouuyfVmz0bWktTZpx1BoGUSthKzlCL9ArjR53MXaHIOHTh16K8ZbzwCBNQ4R+phagDGOrVTih5K0CpeWDmOSJmLNEB+r02sVuD55jY3ufvLE4juBFZLykSFWajFGpKQXc5oxNHV95zVeFLhSTjuN6G12yDNH2MpgSfLJ9z/JY39+DuF1SV4XEmQSzAyFsQbJaIHRgSq7hp5nsGI4Mj7BxuLGTmHbgkLic3W5TixzVGIhcFypj+H5Pm4rQzmHqSq6LY1JLcIDrOJcvos9S9ucWZ+lOWUY62q6viUbEkhjQRpUYqkvfAdXz5/hRjBMt2I4MNlkz8gGvcUym16EPpbh5i3bC008Y5AVR1YOccCFtSuk6x2M2mb/vpzVJGYuGuXR8y8h67aQixd529WrnKjcwvLjFVbX23hS8Y333cIfn7sEFR+tFKsrDS5sbnD1bIYIFLfOTPL55ACJ1YyLHk889yS0B/jGO47SKzV5z+OfQlyBPeUh1jPDwYFNJmvpX96+lZTIJoRdya5SjaPDX1hsVgiBFiD56nTjzxAk76IS/AxQpVLwKfoeodcPpPT19fX19fX1faVe8BOUcw4pv/wjnBAC5/4XCD19HVNjFxhMcnKX8uHHLiDdSU4cWWN/4QRrm03Ww00OHzrC8X1TLNfr/MJ/+wD3HtjPaKFKYayI1IuYSHDhmWnKpafpefeSbAdsrta4MjDK4vYsMtnN4EidfOgpVsMSsfcct+hvZ84/z6PXpziiVrhz4HHWvVsQiSLNJQqFDA2/9pE7eeXxG9xSucJ4xXFV7OZCcpCsBaYr8VsOFcHm1WGyqqK2L0YEsD0+TFho4DDYi4JsVlPfDqFlUNWcUivBbzgy45H70NunkMpSbFvY1KS5xeYOagq0wxQdpqUJ2xbjSUSe45yHvyyILlX52MatbF4rYqsSqxxOKpQPXi6RLSiftvixQwc5Akcyq0kmPEwuMbmmsO4oril6DwdsD5Z59qZxRrYFd88u8dCFXejcYns5xBlZWWFdjsp2AjBKSvRITqnSYvtTFXpFgTCCrfkKupUR2Ay5N0cagRU+SIX1BflwGdNJkMqhUweBj6kI8rLGlDz8FtB2mOkBnG6AVyCLJQuPS8y0T6vmUfJzNpcGiMY1XsuiM4cwDmUczgJSkCmHMxlGaMLtHkHPQu5wuUVvpmQYUAqZG6QTpNJhtxLWWk2Ksx5dT+AcKOmhrMMKQGSkowHBWrqTeeEENgcbaGwxILqnjBkM6CUZ4YrABoKgK4hjiw4CKg1DPO1hZQflcpTJyJUiq3j4kcXoBDNaQgmJ5yRitYdGge8htA9K7Wzx8SWFrRyMQ2QGWy5gAg86XTIT0m7FmCSj1oC465DZTv0cawwSD5eH+LUAttqk4xrTgmNDCavdAkmqiJMOaiPCjw1VU6OxXxHbjFY3QsQxOra4wQLVRoQxAXlFgIyRKTjjyMo7QUmlwRUULTRuu0DFdjCrGn/CkvqWRi3H1cHmBukssp1jtAMpcAZwFqc89GhKrDwyk3HFbjHtScCh8xzrQtKVHls6peiBiIqYyDFSPMrlqetkSrK21GO06zF2Rw5GcuLEXj647wytGY0LBUnq8D1F5AQuknTmC4wPJBy9aZ7v/qGn+K/veRnzl4bImynOlyAkzcUyf/THr8CWHEobXEniMExG6/wf3/Q4jVmP33zPK9CDZVYbu9mtfa7EixR0TiFMWZnaZPjOCCUc3U6VxmMZLrNUvRK9os9MtYqKY7yaY2JW8dTmNrbseHDjKJu5oIJEDmf0XMpcdAObH6ZY8Dl5aJqbjkxRGi1ilaAchuzfM0bTpWwFsNrqcnZ1gVZpkBxDvXE/uwsDeErT68U8M3+V+mIJ/7rjG46PMzar2BbjXG4GnFlY4cTsTopJMfDYTiLaacZ8o8HeoaG/vL/7vmJ4qEy5Uviq1odG5z8TJQ/h6X0EtV/k0OAIJ4cnmSnW/p8tPH19fX19fX19X4e+kiSParX6FY//FQVSDh069GWL3PWDKF97t+4+xnUeo7McQDlEDfwcA2MHuP3gAs+uXmRy9whjpTLFxYzPnLmOjbosFAZ5prhK3e+RtwqsuyrVsMVSt8aewQLdNOH03CxbhRRvqMNqfZtz5xa5+Z7LjO2TWE9zcnSK333IY3e5SZGMmsy4Z/jP+c9L34o/FBNNBrhSyrOrEzz12Aw/fJtgKIuYvz5ALlNkw+1s+RjeaWGrYshWK5RqGrepECi6foDYn8BVydN/spd8d8jBuxco1zrMvXeCOCru1CmwjjwUeLkkbSmWz07SFYa8p7DjAicUOoL46QLeoRxvMYdAIZzEX5QU0wKLzWGUn+HpmJPfcwVj4fl/t5c0gHxAoRIgNfixw1lLcdUjKGsaIx6deox/ICFth5itAmtLPoN+xN5Tl/jpP7iPlYVB/Ky101I4zhDVIkHoY4XFZDn5Zs7WMzVc7JN1Hf6pHlrnmMzDW0oQI6DLDnNQ/P/Z++9oWbO7vBP/7L3fVLnq5HPuPTfneztHqVtqlBFBMgLb4CAwxmPjMcue+WHsn5lhDQzGZjw2xl4eAzbCkmyCsQABFkgtqSV1q3PuvjmfHCvXG3eYP46mQSNhS22BNPh81qq1Tr3vW7v2rbvW3lXP+/0+D2ktxHYAAVJAPrfj94EGdTNGK4WVAlNS2KqhmPcInk0JSzWIFMI4Bs+WKK6XsWXF6gcDTD1AtuyOCW9ucHKnqsFEIHyB6o/whEcxW4ZhijA+1jlskuME5PtCiA1Rx9KcGHDXm27y3KcPMrGnw1/8h8/x0nP7+c3/8EZk5nZ+PGMZTWnSoyVqLzuqKxqT5NhQIqIIOxnhdzVF2eJ8n+E+Ax5UXkyRscZqg/I88AWm4iFNiAhDfFOQ93OyhiCda+A7UM5HRw7XzSC1+EpCvUaeZQgJ3qxA3VGgz3nwskRXIoR0uHqFXDvizohypYTrGqSR4HvgLGQGF0tsnjAYd4SrOeXHuuj5iGPfUqFcn+LyaoXcJHhlS6lWJbc7sduyX6A7BSq36ClHOh0QDhUBElsVCN8itmOUsVjjCPMY5xxqqky9V2bUVdh6RBT4zEdr9JMWL84K1EgQ3ASrBJ3tEaLs45qC8IBBLBfYsqR1ZsiWqZOswiDM8PeEZNsB4WKBzXKIdhJzrHWQJ/jlgMePPIprWfTCOK0bAemVEpMTp2FGsJ2m7Nk3wXJjiHDQrETUx5e5dr2KkBKnSoxm1qhMJYy3Btxz+go3Vm9Hbiu0K4ECv2MwLYHogPIl4b6E/rrk+LvX+EV9jL/3lsfx/72hLQtGLY+xsITTDr+ZEfgFvp+xsNHi0OQ2h+5b5tXrh0jTkLwCKMd/uPk0nmuyt+mwZpZk4wr1A5Y/s/cJfrV7L71bZjnOPG+pHWQqmuGT4Q32zDQ5tG+SrfaQZCMDX/Hwyxe4a3qWPe0mtx0Y49defoVz29fIqzmekuwTZ/AaIfccn6eMojxq0Aw9ZAlcc5pfGlzFTxS3Rm1GafbaGv7QqUPsnW7y4uoqxya+uCJFBRIdSJz/+mpSxuv/Py6391CSDwKwORgRSMXpmen/yit3uD5a5LeXP8WbJ+/ljtap1zWHXXbZZZdddtnljxkndh5fb74B5tBsNr9ig35jzFc9/lcspPzSL/3SVz34Ln+y3N/sctz/JL8j3s7E2DQP3XGMShjQaFWpN2soqVBScHbQpi80t9TGmdJlXhI3GNRiSoXP4TNLuLbg5accVxaWyXyHMIL6hmBypkM2WzC97zTr63tplq5wPr2f5+N1vnff7Vzofob1bshmIHmxtx9TlpS7kGcOrQWiUOiax69cv5fJTsq4m6IpBGkak0pBOm7RUqKtYFgL6C2V8LcFlCFDEToBDjwsqmOJ6hlizOGdNpiXPKQFmQlqZy2uKRge80mlQi17hFZT6UB/fMe/wUqB1hLGHGHuCGzOmXsX2Oy2aG+OwXrMlNpm4mgXVYELby7gXMDMO9bxI83Gr7XI8UiOBgQdSzmAMHeEexNad/RJOxGLF6YQseLOozfZP73JybsXWB3OIrwRInPokkIYgTEOMo3Ujv6bfAatgOhVDxk4TK9AVR1qI0Ubi5d6ROT0J0KMBYlFdnxs4FC+QjuHDQS2FuBlUHgWMbSYlkUYR9kJ8BWu5CO0RViHzDSu7mEDRbCZEywZTMkxuqVKtAXkFhcI6CWoQtP4LglixFqrgXxmCPXSTlpQrlGxRtcEcSPgllt77Lm1z8UFTbYZ0t/yadRHqLiAAkCCczvtUYXAyyUGhbUC0gynJAiH/4qg1LEIteP5oYaG4QEfN6konzcoYUnLHn49QMkAN8oRvYxA+QxmPYrDVVzbEZQ8dFjgpjz8bYFfCnHW7SQBSQjfZgjvz7FvKEj/gd2JtDY7BrukGh0X9KwjUWqnvQeJ0UDoI6ylf2tAMedRSwr00TLFoRofv7qPeqvMtLHQS6FVplSNyLKU0A8pW4d1jsSXZHMldF3iJSDtF9qMxgMyEeP1R4i4oDIuEA3NsdNXkcmDLJYCDk+PU5hFpg8OEUtzVFYdsuNDoLAl8FqKUtdRrBuqkxn1+R7djRqlakGt3mM7aUA5J9tXYNcEQScglTsJS0Jo7Cij9e/bUPVZPdMkPG5p3bYJVydJBoKzN9soc5ZXLq0wKNKdpCsnyeyQ2vgiwjuGNR52vCCbj3ise4i7Xlxj0KuhjEDrFCEDHB4CgZ4yiL0Fbs2nmBpiBiGPPH0b4PhQ0OOWN2+yh4i2H+ESRyWtsLZVY2ZPDxt6bF6sc3RsCz80+BMFg6s+4UBjxiT5MKXjDxHdgM+JawyGdU7oBe6LFnmYM0zoBuVywOc+sUlY6lNgCCKfx/KLtDcG2HGHS+DjL13BThZcuLzKnE2YNBH5+BZj1RjtBFumzWExT6scUY4C3nDnCf63yRpPtM9R9Cyt+TITUYn3Hb2Few7Ov7aGCyE4MjHOkYnxL1nfu4OMhY0eVgqmJ776uyZr+RTnh5/HH32WtwUfZKkb0yqVmK1V/8jXXG230c5wfHySy4Ob3IgXGe81d4WUXXbZZZdddtnlG55HHnnktb9v3LjB3//7f5/v+77v4w1veAMATzzxBB/84Af5R//oH72u8b9iIeV7v/d7X9cb7PInR8AlAqk5Nd4nGxhe3Pw/OD7+bsLSAarjFcKyT6VRYssbMiin9APD8eN3sBS8wp3Tn4aaYzgo0Q9Thhs18vYIW/KoT9WYnqoycetNcCH1oEpoEkZZThPDw9eusBx38GotQl1jcmKTvdOXCIJTjLIAGwoKETIz3efu2RU+e+MwqjyL1WAyg4oUYhTgdQVOS/Skg4ZAbkMwFOjIorsBdtlhIsHk5Ihi0+fCszPI/YZ8EOJrDYVApobAKgpl8Y1AeA5VytHfZImWQmwgcNJRaQs6FR8nPFQ759TYGscPLnLAX+Ujn3wQ3/MZrjW4uDJDVvWwmz5CCdyYIRMSUYfCCUxVkluBVRbdH1FyUIsV+ZJPtGqwFj71y2dYe0OLR7ePk0qD70kEIBBYKfHHQ/QgAwtOSZx2O+0XQpBfCMkTUIHFPFCGccG+iYxqmFJs+3htD+s7ioM7EabTSYmsH2NDi8gsQSxQxmAXCiKT0z+6YxBaupEjAaMEXm7IVU4+5Wj0CrzUYho+WIcodirNvG6OSDJEMyTcm0NT4N2skVU9vFYJqQ0iCAkzhxplpHOKhbNj1IOMjZcibCfg3/5vD5CpGmQa4cTOD2cl8TsC7/NDVGKxUu1UL1iHwyClT9GEYKOA0MNLLUIb3AlFUIL51jzVzKNdbJDUDUGicZFDjCRCeHiBR96Q2NRilcKoAhF5jE54lDohpA5rwQUB6fMC76TG9RwGgWgPcIGHMCBHFl2tMdoakU+GpJOCyuUEGQiwCmck0YpGWI1fBMR7SrjQZ1satgcDjjQb3HdyLyubCesrbXoNn0IJoskSuasRFwm0fKKewMtBeyARDGc00QsalRuEtYiBZP5kh3vOXGd9NWbp2tuRY5IJ7z6WO/vYeqGG06DwqFRCqCs2WpJCKLyOYWt5jEE3xA4Vnu5x8F2a9aomrwoOjvcZPjWNKEuyIsEJhdBD0oN18tlJyo+tUTurSZMK/lzOwZlZrgw2uHVuD+koZxDnZHECdQdaIish13ULdaqPu1ZDJh6VTcl8a4U33HedN957necv7mX7KtjY4JTF7QlpzvUY1Ty8LGA9qYJ1qMmcYqComyHhtyYUjxi6yzFxpplSBdevjvNqXRAKUDclLyT7qG9Ad7FBMqOp3choTTQ4VYzzwvVFsqRg/0Sd857h8sW7+eHtMS5mc9w74Zgfa7LgCoo0p1ErcfL2aS42llntdXC+g6Flqd2nvNej5PlEVZ9aWuJbD97JozwMCE7qadJMU2hLpRqx0hnQ28rorI+wbcGPvOFtvOG2w0gpv+ROyXM3l/mlx5/lHaeO8t7b/kCwmGhVuOX4HM3X2drjC41zO+lXUkS8+9RetoYjDk9+qWgDsDEc8j/8p9/AOscHv/u7eHDiLqpemRP1Q6/r/XfZZZdddtlllz8Bds1mX+Ohhx567e+f+Imf4J/9s3/G93zP97x27D3veQ+33HILv/ALv/C6tI5dl7k/JfTS5+imn+XKsMXjnYD3jV1mO/sEj7wyZPnmuzhYKlPxfEzuOHkmoe02EbnDbyTMN89SrOR84tHbaHhHKOKIerGNrQlsIBkM+wwGhnu630ZUVVzvf4TTk9s0lOXNkwv8q9++irGSu2uG+w5cZbLVJxIlGgeGDOKQXHiQKX78zQ9zYmyDmbk2v/zqXYwWFcfnpug3K8gueFsWXXaUMokeSmQXwCG1wAXgrIdueMQtR2IlbjMirgkCZVDVgtJ1g3MCBNAXuI6j4vuMjhbYaSgKg+z5WATCOlwucIHAyIAFv8aerMxCu4EnUsI1jcws1//lHkQtIK849LRlaaVBueIhYk0lylDNAhU7An9Imkd4A4H5vE+8WiLQBdm4ZKtd5+EXJrGBRSUFxnPEb5PILYl3yXDi+CyvrAxw0uJfEZhGgDUOD40zjnCfJJ2OsM4h+pLe8xXK2wZ/UUDJ4ZcF/rLBSEe6HTNdCVkjg0CCdTgriO+MSM2OKS1OIJdzwtWc6btj9ADStIbnDE45RBARtg1czVCFRFiBTDVOKdxI0/6wT3KihDqfIIXEVDxMLvBGFuc5/OUEPy4xUBUevX4QkeYE2jFol8kPlvHimGAtwXYGtGYG1N4rWFuMSJ4JCI0ETyECH+oh+2+f4sahEem1mMbaCDqKoqeoHeogI4dsV6llTSaWh/RKjmxaogqHaYbIVYsNFAwtXqpAged51EcjXN2HJMTmAhEGCOVhbyo6PxsQjGKcsQiZwiBGSg8PAUEBuUYEAuGbL1SySLQUeL6gdLUgvGxxZR+5oLEHIA8EBBI5vpPMM1ooKHCUfB9Z9bFDR4rES3dSiGxsdyKov+ANQuhh6yFmZBFao0eCkdV8ZvMYMySs9mOSTcP08QF+ax371k2Spw6jumCs2PGbScAhIFWYLMeNAoSE0dUWbxn/Zp7J/zNOwmgUMFNqclms4gR4PngqZHRPRPlQRjKqULspEbmjvqap+SU83yMpOY43x9lY7nBtNMD3NJUe3HFkivaep+kO66yJCp5x3DvzMv14kmtZi7X1JudLM1TDTbyRRYQBp/Yu8+1veZyb6xN8+vJ72ZrtUD6TEU3m2HHBtmpy5cosb8UxPVUnTzUH9rQJBpuwHVLUwGwHDFZ8ktBHtyRqRZPXPNy4x+GDc7ycXyUdxgy9babDMrXU48lkjMlGH25USEo5U/MNaoHPG07vY2Qt0y5i2vd4dnsJgcNmjorvURkLeVIt0pyJuHvyGPHaswghsKlksDUkHiToyRoPf/48K4tbOzHiyqNZ+SmWtnI2hz/O/slZJut/UBVybavNenfIpY2tL1rjfU8xN1nH89Tr2iNWu/8LfesoOZ/CWVrlEq3yHy3KJFnBdi/GSkdRGMpeiTdO3Pm63vvrwSjJ6Q8SJsdreOr1WvTusssuu+yyyy5/GnjiiSf4uZ/7uS85fvfdd/MDP/ADr2vMr/jbxSc+8Qm01q89/+Vf/mVuv/12KpUKR44c4V/8i3/xuiawy9eG3KyhnSXwNMt5yMOvjjFfeh/nLt/B71+5xjNbWxSZIR6knC7PMl+N2VtpMts8wPLy23lq5TuIOE5gpklzj9b8JBOzLfpuyKCe02vGJLIgEzlxegl/apPFRpmX0m08laCEJvcCcqtITMhqvJ/S2IhoS9N8UlE753jq0kF6ccS59gQCiyogTKHSU7hip/pBSY9qGlC/Lim3odSzhFuaZByKcYEShiQU+I2c+mQMDUM25bBlwBc7IqwrwLeECJwnkTd8wic91HMFMnZ4CVhr8TcNXg9kUTBshjy8egp6Pnune0RTMTYxO14eicA9UFDam6MuGW4X86gBuDFLEGaUbknZ+9Amx06v8lf/yuf4vr/6GY7u3yJQigkVYUOFBMKeIOg6zB2Osfu7NO8bYkuOqbBKTfmITOM8uSMEKXA6R1po6oRoUxN1C1Q358xbFjn41nWEp5BCcrDZonJVU7lmMcaxmWQUkQIlEZ5ChAI97cjmQ/y2IxwozGwZ74zP0fevceL7NmiuDKmeT/E2d1KJrBSk0wpdEThrEEoiKhHaQmFr2LWdthvRT8FJpJRoP6d3u6R7ZwU/K3j7d7zCt/yDVzFzPqbqgydRvmL89pzm6RRPw+RMhqpbvCZYYXBC4nyFkzB3ZIrORp8Hpp7jH/yl3+LB73yVY29dJmxlxC9FxNd89tsJSpHPmPHxRw7lO/TxkPxMjWzSkWcGcoUpCZyxaCno5z7x1RyXW3ZcbQ1uOMIv5cwcXEWNMnwBnhREtQIvz5HCIrojSDJKCz0q12KEkuAF+FGEcHYngroWUEyXyN9YIj8EpulwvmPoaZZFzHCUYmONaimGNUcvHkGvwEsFlVVNZZDjZXpH6MND9UOYDV9LY/ItdK81efX5eR79xHHMwLB5bUS1VCffhrXrNXSUEbQ1JpeoRDLT95EjixwUiFyDJ3YSYKZSOm5EJcyZKKXYdkY7LCimFdaXCClQOP7p+36L33/HB/hf/tajSCvJ56BSz7hctBm4hKtP3uCpT50nMoKgUIQDi9jQXL/c5a7RBmf8TfLEoxiGtHUZLZr86/P38Esv3IXzBKK5U4WELwkteFISFZJTlQYnni6hFyJGn50gvlFidinjNr3MyvIGYSQ5dnyaqdNHaJ8K2d4LA1MhnZO4WcF8NaI6yvHHPFQtpOo87MDQG9fEBx296Q7rYz1uTIyojefMT3aYn+lzfaGNLjTVVgUZBTx7cYlfu/Zpnhq+TO3kjucQ2nFtqc1i3Gcw0LS3U37z5UuEtsmJ+hEameTmSwucf/YGSkoOzfeYOfabDDubXLmxSG/wLDp/lFcXP82F5c0vWsu/7ZYT/MCb7uGv3P/FosWwn7BwbZNue/i69ojMVjgSbDIfbKBoo61lmOV/5PXxMKM5MIw5i/Y01lk20lWM++p7iL8eXFvY5OULS2y1B1/vqeyyyy677LLLLl9n5ufnv6yQ8vM///PMz89/mVf81/mKK1Le/e53s7q6ytTUFB/5yEd4//vfz9/8m3+TH/mRH+H555/nR37kR5icnPyicpld/uSYKH8Lwv9dsuxlvnP6BW6O3sapqW8jvv0mXrDBW04d5HCrRRj5HOmd4Ii/yg1R8FR3gWG3yUIPvungLC1P8aFLLzMz3US/vEkqCvSUQowFTB9vkS3n1NNvp72ucJXrCO3z5w5fQcTTPLpo+eSlk2zeMsaLaydYa3vIbY8wFTgEH37hbm5SobcZUe5o4lbAlbBHkeeEfUHeUOhIko0sgYWar4gLg3SgsOi9IEdQzRXfd/rTPHXlGFG1RHuzSjQIMIGiqAnkCUNuPRpk2OWQtObQTYhShyccRoEcGUqbkiJxOCmQ50NEoTl27wYLw0lkWbAl6zgsIi9oVGLoeDSzGP2phMBJBksO2ZOYRoE3ofFTw3BQol5K0G2fcCQYVAtsCM5aVGoRowIhNdu9Bmpb4FUkV1cf5vRdA174zCwJdqeVxAl0U6Hriu2tiKlsi4GuU5k1TLiUvXdscv33D/HOB27jxadvIA24wmKajlQ7CBVWWtAQ+IL73TS337qP5+UK5xeWMYkkDsssvTwFRjNMIegbRN3DNkNMSWIijTcoEBlY3+H5ClkrE65kzO2dZTQ5ot3ZwtMW4Swyd3jbBVILKm+YZ//R5+l4AjELqfVxRlFqpsyc6DP/zjUe+bvHuf50g3lviFs2hAMPSgbinDAKKaTDtS0H0wHJhofoQa01YmwuJv+9OsL32Pvnp3jq4gLDrKDqhcRDj6InMWWN56mdZCBP4AKJrgVIZUhnPPyuoWjnEHgIbUFJlE5onEhwzYDVZ0Pc0NC81TH4tCB0Hg6zI8w5HxOnuGoFEcqdz933EUbjpiZRoeAd1UU24jovd2dQuaHtd2kYgdQGtGPYyfAoKK8kuNFOUhAmodyokFuD0B7Cs9iSh5UWM6WQHSgySbohMIsVshgQFmVhcmMv7ZcKhDR4pRQKCxaKviaa2GDPRMbwsQpF5KGkYCwacPt9HRbWVxAjg9QG76Khe7bNzO3rrG9OI4scX8HhUhuAk81tZFygBgHZdo22jomCEdsXc4bWZ+b0HKZe4Co7n2dbbfPvzz9IvmXRgUJaOPfsKUoPdOl8cD8qcRw6tcQ/+Sef4OO/e5Rf/dg9XH1pik988iFKtT777jjPq882UNsh0oJeKvHo04dImiG33f0U6aU30c4LTo0O0vSf4tbpRa4u7ycNyvg9ScnzEEOHnIDpyRoNL2Jlo42dcQQ9QTmtoGyFgdziYH2LQijq+2ocHdvLQ/eeZGqyTncQ4zvBG8ZPsyLWUE3Nc3aZyUYFL/DY5zX59tOHuL6yxe8tXiIoGry1VOPiszc598xlnDF823ffz28UH+Xll/cyq29gzDxjzQQl4K79jpnxyS9ay5e3e2xsDVitDZn4Q/4lQu4k4wnxX7//Ye2ILP0UYfRmpGwCcDjocM1ohPBQoszHXr3IQqfL+24/zVzjSz1XKsrnjdPzlKcCDtcnebr9WR7Z+Bh3tx7kHTPvfd171Z8UMxN1lJQ0Xmcr1C677LLLLrv8f5Ld1p4vy8/8zM/wnd/5nXz84x/n/vvvB+DJJ5/k6tWrfOQjH3ldY35VqT1/eCI/+qM/yo//+I8D8Bf+wl9gZmaGn/mZn9kVUr5OCCEYD/dxo/si0YbjoeoED1/+QWR1wA+84//k0f+8wpa3wUPvPM2h+XmOqHs49+qLvHRpjcIokn7BY+0F5irPcWr/JmcvnWay7UMI5gAkdcuPPfNp5vpV/uy+k+yt/3VmzcfpiDfwuY2EUm+Bw4M1/EbC3WOXubWxzKOXTvG5zhFIxU4aT+ZY2RjDTxN0v4SZ8xmfaDA4uwWxgbJDWIFAUIwb9uyPOLviiBUEXY0uO9S2pKLr3KjuZ89tm3iNChOtmOHzh+gUKaIJhfWwVpFsKVqzPcIJSKOQxEr8VGF98CsBwlicdICAkYeOBI88c4bTbpXllxtIKTFSIo1k+kqH1m0JK6+MM9AJhRAkd3gEGw5xVXD5X+7DKcX56QN4hzLCtEQ44ZGNO/LY4A8NMikQaU7xgiCZDRCpoN7N2PvmdSbLPW5ebpC0I2zoI4wFz6G0QhuP9kKVfZUSydw2AxWyputEKuTWk3t59PHLmECiPTCRRMYWZ3fEG+EcUktORRPsMTV+bzhk1AiwFY1NJZc/tweZGkRtiFM7P9x1KNGhwsgckRhk4TA5uECBAFdoFgZb+OUQe3gSMbLIROMNIXomJel0mf3uA/zyL96ObBj8KxJRl7gzhqLh0duMSF+ZxUU+GM3S4y2cLZDK4gqJEAqTpHSfWIHM8NFfOMr48Tn6okJYhe2lGgLH5J4GU3MNxjoNhnnOyBicLkinPJzyKekQlxpsvPNvoOwjrER6ZdRmho0TkBHSD8EaZE+yuVJjmNR4873XUBNtHr5xlPjOgOZZi0gFHgKnDbISYQOJi2NcYZHG4ioB6JRDh1LePXOTs5tTrFFjEAfYLUdSgOcMAodzENmAihvStxaHhbLEpMWOT05hAIkJJSIxiHlBOJJgBcp6eD2HY6cCa3wuJPJf4r73LhOsvZMPXVkE10VaQFtCazHConyHbVu8qODPfturPPSmNT70xJt3qkFyy/bFMfxuDrlCVQVipDEdxT/8gft53w9e51c+cCtgKS93WHpF4o+vU5tLiKmCDKlWAvyOTznLCKb7jHQVW2SoOUfRLeMsDPpl5NCgBvmO785KSrmsueOeVX7lEYlpKmbKJ5i+9wnOTB7io94W3sgiBeydXqH/2Qo6lDTftsYddxxju5fz2Yev8J1/8SJvnb3ExfsW+f8/8R1kscU4h5k1MKPRxjKUmq3OBupIH6Ul3vVJ3ja+l86ZT6C8nGdXjhAm88ztH6NZK7O83OY/f/4s1xa2ee9bb+FIZT+NqQXSmZxbTu/jm245zI1Oh+U8puJ5hLnGZdf41AuXmd8+Ta6hKDRCCJ55YT92WdCdFPz5u+5ExTUWlyLOnBmjVf+jzV7/MPEwY3mpQ7kS0hyrfNE5ax2jLKcaBQghGA7+Kcnow4TRt9Ec+xkAwug2VPokkhDrRsB/WWCYnW7ypntPEtUCpJCMiozlOONAecAr3et4QnGyse+r26j+BJkcrzE5Xvt6T2OXXXbZZZdddvkG4Fu+5Vu4dOkS//pf/2suXLiAc473vve9/I2/8Tf++CtS/jCXL1/+klae97znPfzkT/7k65rELl8b2mmLy0szTCwMGeYb1MsDKns6/MazF3jpWo8jYZV8TWISnycur3Kg3eSWqSkuy4TNZMBWu0/VGurGY6LmUwoiqt0S7qWC9EGNLRKGseLMyTlC/0e5cuU69fonOVL+31m+sYezmeXI/hfJfMH4eI/T7RWe3NpPHvso7VCxZvnaGIUSzBzpUAoLfN1EaIUoCrwB2LpARBI9lrI1topanUMaibOK0isa6QRJOeOxl07xlmNPE6BRI0FROLzYorYKqOTQ8ahqj7kHNtBZlSz3MfigHVIKdB3ycR9dcXixQW5pkgOWjg24+OI+hMpwwiKcAKPZ/O1xNn/TIjyYf0uLpQsJ3sAiU5C5gMLgnEEcl2T7QeQWddNSDApqBzOEFngrAUVokUoQXPPRFKi+5srTs9QfaLA9bCG0xJVBlD0qHQFth9MegfSZ3dNi5p1PI+ctg6sh+aDH8y8tYpzFhWKnUc+AcIKpWkB3NUULQS4Fj527yuGH6gzSbKdKY8xSiJ22Ci9xBCOF8QTJvWVMCH5uCEKLCrydWOmxnPK9I7JnPOyWRBQSs10gawEukLi4wPVHiFFBuZBce+QS2wcqiNRRSTNU6KjemSFCx9rP1xD9BiIQuFKDIuljwhB/UyPyFLIcWy9jA5ApJFs+l6qzFGMeYuDhhZb7/8oFZu4wyOEZ3vHGo/zWJ1K2V/qI1OL3LWJkiAaOxBPkDYGrCOS2IOh55KHFuwZilOI8h6mWEKmjN6noNSvUxZBkZYg/hNZoiJ46wKjkqLwQQ5qDc1jPh1GKzDVCW/QXigScgUFmmG2t0lBbvPBqxJAJNuQ4I6EJBineIKPSFsxNtBBBmdgfkoz55DNl7GqO1xFI32IFRCNIQ0F0vqCQAmm9nTYluSP0Sw2dhZTKd5wnrA0RZpPDCy2WgxiLxityhucsA+Njhz5RryCP4dy1CSoHJN/zjm/mcukidnqDfM+A5eUJzMBH9YeI9RxtFEv5FP/iZ48wDAzmsCJa3CToBpB7xJkECZkH6+0RiohWLDCiRmeQE6Q7rVoYgxMeLoTG4jjXJzaoXu3RXVP82N+5l85wgmyPhxsPKFXmuUWdYN4dJLP/nqZMqFYL5g4NuNAK8LqKj/3HN/Bc83nGY0l/I+Y7nEfH+tRrKZEIKdCMt2pcqm4jMp+0HZNu9cjbI5KyIhh3rMqYK96QU/k9XBqtsbF8kGdXtiBepNWqcv3KBk8+e43cWJaWO/zawsOY8pBvOvogJ/ZPMzVZZtH+B7JewdXFk5QWE5LjGZ0uvOvBQ4SuwoPv2jGLnXIzrAVtTh6/nT21MZaW3wlig5eW93ImGjFR/QNhZLJZpd70+XT3d9iMDvLW6bcDkGYFaZyRJMWXrP3PXFrgucvLvOPOoxzdM4nvnSHDIwhuf+2aVuPvc9S/k8JsUA1u41vOCOK8oB6FX3Y/WW33+dBTLxFUPQ7PTiJEHSGarCaaD1/7EAL4wP0/xHRp7Guwe33tWVnvsrTS4cSRGeq7VSm77LLLLrvs8t898/Pz/NRP/dTXbLyvSkg5d+4ca2trlEolrLVfdM5a+7ryl3f52vEfrr7KpY2j3F4ack/tZbxWTl40eHRhg7jmMb3f8SsLz+Fteixc6xKtjUAXHL79KJ3+ZVa6MQvDQ4QTAwp8Dk63iEuOxOtQ3dxptZlXVV65scQ77nyQjbE1iqrH7f48Ly8vMxhqlrdbvLq2nzvrq5TH+gRGo32FzS1C5Zj6jkHn/MEt9EpIcHPIVBKyKRIIBA6JKaU4CcMbFaJtR2YM6Ywi0A60IfVAeILnPnYrlcqQ9at1WnesY1ZDyB1RK0a6iKpfp7J1hCNTXbY2I0a5xJeCQoGeEGhfo3KxEzWrLfWbGUxbjPVREXi5xA1TROowgSCIQigpovEyKEn5ZYsoQDiF9iVF2eBfFigZkK863vGXPocRimeyQyBhc3sSt1VG9AyqkOD7uFbE6mLEk4/uY1hP8eo7niFu6FHWHkQj9h9YZ3C5xuR4g4FoMmnWKZZL6KFgfb3LWLmCKAYYY4iHFpNbRqMUo93Or21f4O5Y5JPFAtFYmX5RQkxZ/FhilUDXFXIUUo1i3vbnX8Ai+dRvnUZ8wWNGVwXRnY7gLo1DYH9DUV7VFJnGHqogrMAaSzHXpJiUhK9uIDZjSgqsLXB+gCl5pMvgpZp8r8BbKvCGATjH8EBIPg7VGwGlLYFAYaWHRIByCKUR85a0ISltSGSnoDRZYI1jcWmR7vUBneUO1hqUlVSvGIqKJZso4aQh36ewJUFkJIwcvvExVbnTJpEYjLEQCdxYQa2iSXs5Ny5GNGoGVIvyfIAZOXyVIjwPVwpgOEQqD2cMIHCRjxKSyr6Yt/6tVxhFKZevtdi6VIUVQ3Fbgugm5J0hXq4IE0ttqspGd4RbzUnnKri9UFUGvW7IfYGt+7ghUKqSJl3KsSBvOFSlgkkNuuQhLIgRbG4+wAv/bon45VUG0z6Rs0gERglG6w7d1/ijFBdFaE/y+eem2Rp7iJljbd57ssSGXeTZ8XmoRXQWFWI7RiY70dCmpth8IMJGjulPDwh8TXKyidWKYM3glwR55FjrxPiNAGl96olGJEPEXlBZGTYNelLBXMGSThg724fMEf/5OheO1BH/yUPEkrik+ezGEzT0C9zYmkGZw8Qjn7whGBZjZPh4RoMRrMYFOgebaq4FKc4qkIb73SEuui4T5SpyJcBJGCtXaS8vQmapPhYRvcFjNB7S0wlT197CYO4Fbjt+nkbtfsaqdeZmmljrmDrbwGQGrTVuepViwfG5C09yLf48r+hlbpk5y4my45FPVun3YCwAqobxuZAze+7jwPE5AG4PpnjFFNwzNUulEjAx/Te5stnmhUsbJN4y7z597LV1/PnFZX7hyafxmuukwdprQkqjWaFaDQmCLzWb/Y2zr/Lc6jJzM3WO7pkktIuEUoDb+KLruvY2mkEFIRTdeMSHX3iRQ2NjvPfUyS8ZM7cG+YVAoWZY4p7yrRhnaHotPrZ8CSkkkQy+FlvXHwujUbYjPmWa+m5hyi677LLLLv+9sNva80fy6KOP8vM///Ncu3aNX//1X2fPnj18+MMf5uDBgzz44INf9XhflZDytre97bUWn89//vPcfffdr5174YUX2LfvG7fM978HbmtaLi0KRkisfJSjNUc7/0vUXMR0Q/HG+UMEiUfiddmub+FpR5FbprMRo5mP8+r5W5CbAUHHQ4YQTHgcmZmks7WKTjy6aYm43CYb3uAgk2w36sSqyr5RzkW3hh2MmKym7J0a8uz2XoYiJNyXEm+UsJFAN3y8VoEaWhZenKPiGUrrHoEEyoq87iEzoAKuBNmwhB8avvXOyzyxspeuiShaCv0F082sVyYelnAuxfYcfs1QGs+peJqguQcR1LhyfUT8uCY7GaAbDm8EwgOqBtV3eIlEOUNR8Tjyli1U07Diz5A/ExCuZwjHTqWHFRgPGpMlPCGJSpKhtRAp/FRgIoMe81F9AZck03u3ufPETayDlz+5nw2vSn8GKm1Q2lLMWnTd4HUgTAsGrqCoO+Rddqf142yCigMOHl3l6LFV7OFxTod7OLt8H671u6Q2JJvy0DXBvmqLfUWDl88u4ecGm1tMIHdMbrVFpJq879EfakwX/I5Gaku45CM2NfmEwk06qDo8bZG+48gDOeuP18lbDlqO9GKIN4Lkuocpgws9RCPCaQ0a/AKcD4UvKabL+ElOsJninCOf9claAdlTAt+zZG/2MAdCvN/N0FWJ9AP8QuD8gqIqCBKBlALrdn7Ffdf3v4i31/HZT5/k5sIMaqh5+ZcOU9+rOD1VIy00whhUkWMTjQg8KCtsSaLzAqxA5jseMsURjdsKUYdb2Es5KjOIG+soX1HGIWcMbkWyvliibSpwf4Ajp1QOIFQ74lSpjG6E+L0YNTTYyEcEClNS+KWEzAYUTtHpVYgvKVSeEp6TmCzBTyy5dCRnJJv+FmWnwfcJlwvCccveWo8bc2PksyVsoPA0kBbYiTLtwzXCDU1pW+P7jmJekm87ojXD5356C/IIl2SE1iGEwAmFkxJZCkEnuNgiqgH4HqYXUdqX8YG13+bO/BRnf91w47md5CdrIwgsXgA0fYp6iXS/D55j+4jH7JWdPcDJnTY8UQKwgEX4Aj9UuG5Oy2sTHkrwSj62PcegZHFjBqscM3d0WbpYozM1jevDwf9hEX6hQjIfcXNY4tpGkwf2TWKGOUorhlUP78UmQqZkexSjeUG56ygs+E2fhuoBDmvgqeVl2qOC0bmUEI/CWq6tbVEtHGHFY/mAoRAB1Q7EXY049QhH6p/jubWTKNmH3JIWmjtO70MIweZmH+kseSfA2YJkpc3zL0Q8c/thzvzd81xZuJOrxsKEoDqMGK918AJBGIRYt3PDYVL77FNVZms16nsqhEFB92qHvfUSp2envmgdb48STC6ZEvv48/v+YFMf9GLanZhGN+H/XYDa9hLysiXz/p+bGRZwOyVSX+DKYJV/e/kXqXmSH731HzDIcxJdsDkafdn9ZH6yyXvvOU29HH2haiXkHTNvYj3pcKjaIpA+ofeNK6Qc3DfB9GSdWjX6ek9ll1122WWXXXb5OvORj3yEv/yX/zJ/8S/+RZ5//nmyLANgMBjwUz/1U3zsYx/7qsf8ioWU69evf9HzavWL+7qLouDv/b2/91VPYJevHVPKkicelzbHkKsnuZ5VOXjkFMeDbWaCEvWe5L1jt/LRyq9jxw3RVMB7Zu/kucvXEbN1ooqkLLc4Mb7BC2vHOLuUsWfK8e67X+WlcJZLGxPM0+RNzX3kvW08W2Fv9RbeffAMP3f+Yfo9w4qb4dV+mS00KJBlgypASPA2NK5qoS7ZutngwJ4bDMcKlrdq6MDfaQOwmsg4mHagI+q1Pt9x56scrWzxr16+j8KHoiSo3oDBlMbMetgliX+5TuDHWClZ3WhxIKywZ6zBysUt0n5BqgJkT2I1CF9QMyWGRmMjg6s6pIRRGlFSKbqQVEMfVbLkw2SnYiVQ5J5kM9Mc1pa4DvFYgMwgNIKSTBiNVZHG4fctW+crXL8wjQgcq78zwehkSKgFYmiR2hKMJ5S1wQmDnymCdoGqO8x5BQccwbZgdS8s6Xn2lLaQtR5PLX6G8e19iL7Homlg9+VcXFqikVURFjJhKdci8q0hHgLhC/IUpNWsLzaYnNzHcHkJb5RTvuyQQmOFgK5DHTQk4z6f+bWTlOoGeUIjWgX5rECHArnsMXhJYUPL3K1dvvmbXuHxK8c4+/I+rM4ReYFwPnLkKCaq5N0BYScH5aFyRdg2GLsjwKhzKWpUAWsZHYwoxhWVl4YEHYtUChd6oBTS7niCGCc4Pb3B86PDBD2NM5Z0xaG3cz6Vv8KxBw+ymeckkcTiQckSP6AovVhQugh6RcN+jT+TEvkFA+vjwhBbdsgURKERuqC/JJGPAJ5DVxVh7JGMLEG7wMYZQkh85yh8gfMdcq3AVjxMI8JlGqs1g4cdj1zcx+/lx9FZgIgznOfhrWUEBjw8ipIEAoLEkaGhKsnrAaOaj7nqQ66RiUFkFlHLcaZACIepKsRCjtBgh5owKJNJjS0J3FbBkXs3uPlSi9wEOAcCC7WIcKagFFkGzymsJ7G6AOVoRVUykVCTDdrXa2SjLWwjwnkgbUh9vsnqxnAnpaZiECsZLu6zVgvx/ILm9RjPFYRjBrNVoRJIZoxPs1TjwqiH3goorfXQhcP0CkzdIdseB1qC7/mfX+I3fvUUvc4BEBA7D6cEYVhQq6ZU7Yh68BBCPIU0jvKoQB12pDct+QzQEkQOih60b9csZi0Oii0Whw1WbYYtS0ZbObWOJc01xljSWshb3n6KjxSXyMsCL5Msrw05810fZLyaEIiM3/jUXWzaDcIHzwDQrJf4yKdfYnFmiU65DNOGhkjwUo1dK2ipPhf0CF0DVwjWt5rM5RlLC2tkJR8hBK3xGoEQVIVjO0tYT38M2d1iI/9Obik9xHT5i/1Obpud4aFDB7l3/x7mowOvHa/VIw4enmJub+tL1v57ZvaR54679uxUwCDGAQlm7bVr1uNFCvEyW0ayNHqFg61b+N477nyttcc5xyB9jEDtIQoOMRpmTJoSLfkHbTGZycnsiGqQ4sscKcR/8771x0WWabrdmCj0CYLX1cW8yy677LLLLrv8KeEnf/In+bmf+zne//7386u/+quvHX/jG9/IT/zET7yuMb/ibxf79+//L55///vf/7omsMvXjisDQe4UVTnk2sYcW89Ueee9Te6YmOLmq0ss3thir5CU9HFGXc2BgzMcOzHDpXaXs4vfyTBfojbTZd/0kIWiRLyV02nD6ktHSGyFo7Mt/uwb7uTg3DjdGKo2Z9mcR+4x/LXbb+UX25+imIopVEozLnOtHdI518TLHVhDYB22Z8imFeU0o3cxoiaHjM8ahqZEb6FCYBzhkZwtati6IN8q8Z+euI2VhQrhAIQuYFqCFtjCBwMUDucH4MXE24pBELJVHVIdhqSeQ3sSUVhEBk4JjJNYCUVDgxM4T1AZWo5mW9w5cYnfy+6k3Z/lwJFprm6OcN5OdZrIM4pyyHY1J/cE0gj8oeHUm65z9IElPv7Y7Syn43iDHCclv/LTb6J6fAwth0QXLCYUiNxiIkFt2yCHYHsWnQmG2wm+quDWPG5/wyW6t9S5mOzDBSCEYcyO+PzTXZ5/wVF/9ATeyYTA08ihJUeTWUOuBEWeIa3DZgV33LGfp568gQPCWLIvanAhWUDkjqyqcJEiH3Oo2BHdFLiBZLRVIV0SiKFh4IdkdYeyEDQM5YFDV2DPVJtmGHPn4Ru8enY/rlUiLQxKSEzDR8uddBsXBThrdqpfVkbEB+vYapnSqwlie4Btlgi3ChAQxgI1ynFRhCl7OO1QgUCMLL/xK/fx8acMw5sR0gNnHe/5O89x4NZtfv2n7+Hi2QCLpqj7qAKyAxIzJciPSEqLEk8b8mkHhaTSzhCjgjT2cHtmiPqrJMLDCoFtOuwYBE1F+VyAqgaUVyyUvxBtLCwq8ClJyfxYk3KjybWtDi61FGWL1B4u8IlvFOhSgfAEMvAhUJBrrPDRnsW3hsoNx1Zg6U9LRFVixsIdIWngCLuaIigQvocqG2QzJV8viFYT/NFOu1aocm6bv4w3lfPYwwc48n0rvOuhV3j8V4/w/OdvwWQFnvQoSorcpUQbEDZ9dKDwI8P9bzxPtH2I99Ufotp6nv/xH/1H/t0vneHVZ/bjNNiiYDhQCCGgahFLhtmPdjCDEaNjdexMiNu2qFFOUM7JhaCMz3izRm4gjxTDuQnMY02CmofwcsJGwjsOXOLq1gkGpsRmMk7d9bBVn3KRk1rByWaXRqPDXa0Fzi9cQ0YeGmDeYCcsqgQy9JG+5FB9jJtXNglO5nz07J0kus76tdsoWR+GBVM6YDtJEImGkkA3fTa3R5zJxljfSGkc8LiZDFmLa0zUEg6OLTMlSojcsrHdo5tlfOrxC7xyYRk5llCtReALZo7sQc2vce/bnsGGGd955GnOLuyjv1nBaIXeOESkaoTjAaVKiJSSuBMjTEqunsOvrKETxZXRIsW5JW65Yz8zcy2UkigpGW9UODY3TpA5Bt0RzS8YptYaZY6dnMP/MqLAS+urrA+HXNzY4sjUJJv5qyxkCfvlOv9PvYuSEdqFSCHwZJOb3S7rwyFT1VkAVocf5dOrP01dwbcfepwo9KlXQ5q1MgDn+1f54PWPclfrJKcbh2j4daT7rycIfb149rnrPPHUFd75jjPcefuBr/d0dtlll1122eVPBid2Hl9vvhHm8Ie4ePEib37zm7/keL1ep9vtvq4xv2a3abTWrKys7Lb3fB05v20oeZoTt6wScj9HpsZoeCE3VtqMCs3kVJ09+yeQz68zN5zigJljebtHkWk2b7TJck3fTfE7a4rtbERdOHp1jds7iVysYQLJEzeWuO2OeUrZXj786l425Bj3Tq9z/+Hb+cVzz+BEiUOexyeuz7PWt/gZWN9iQ4k1kmxMYqRg5Hy24gqtMYctObgOwcihcoe9EVI6WpAYKNo+n149gXUaz8+xniRMFPFeOH5ihT1jCReXDxI76KU1ZFFgfYd1hqsr23SbQOATXk+wFQXlEDxBGhhcIPBiiYslaMfBPWs0ooR3vvMFPnajhPHHsYFAZAYlQA5zuocVL4c9sqZA9L9gtCkDECAyize0ICS6rlAdzfi0ZXJfn2vP+hhZhkgiCoiiHOFbTMWRUAJt8EcWjCBZqlIfH7HnacW6l9Gdq7G11GT76QqNqT4//U9/F+VZfug330clLTGu61xZa2MQOG2RucHWPVzJ403fdIyXXrrOrXN7ePfRozwWnac3JdDCIcIdISXY0Nh1CSNwylCUFUdaXTbTKouygj8whE5gnIbC8eqTB1i6OM2oEKhBjKgEICTFuGC0XxOsWtRGhosChHGIXIOFcDnBNQ2qn+GkwFpHtKQJF3KkdTuils0RLkBVIiwWVw0BQXc5wDcGYQTKQn0swVlH1DSwAsZprO8hfUl4wWDKhsaLOVURkvUsRdshtwSF8JFdQ3BNUp+sI5MCm6SIXkHlmMR/q8F16mTrEXRH2JYPgY/JBQiNbyyB5zO+t8WpI7MEL9zk3ItXIUsw4xEqDzBJjswczpc4T4DdiaN2gcJZgTUFrhJRbGsiTyA3QW1YSiFMxz6dQGMDgZSQt0EOHf4AsnFDVhaooSSYdCzpGmWbYco+XV1mbXuctY1pcF9IonIGlQjMmEYEDul5eBOG0/ddJ3kg5PG1l/iPv7/BL3zPL7OnMeKv/bWn+dtP70PkFmEFRVogjcUHKgsZXtcibQj7R0zMJKhrEf6kI2j56GWInWW7yDC5JcfiAoEWAj8XeOWU/+P9H2UmGjDSj/O3H/4u/C1DlBZ4MwVFI0SZjM72Xvy6YWmzjuv+NjK7Fxv4sOLTcNBbSRDzIELHcC2BsqR2xRHdnnNH0SOZvEJx/VvpM6Az6mOlQHkSv+ZTrpfQhaFiFfsOlbl2aAE5qfmxF97E333jp7i6dZDJvWPMiTJX1rusXh6wsNEhCjze4N3Cx1deIb4woqiGfPNb38vljQMk5p+QCY/+hsep6TW2fEX3wjSdyZwHb5tndu/4TnKPhdqtr1DsucjCcJqh77NSalEeJLy4uo5Y2aBeiXjo1sOc29jk0zeus8eWmW+1XhNShBAEof9l1/47KtPkawXT3o7oEVPDiTqxd8tr19w3cQvV4H+l5deYKc3yL156gtXBgFoYcnJykrUs5eV4Dy1P8x4hKJcCbjQ2eHx0ie8r3sSgiLE4hjrlVPUWfvfRy1zRn+HvfMebiYIvP6+vJ3leYLQhS/Kv91R22WWXXXbZZZevM7Ozs1y5coUDBw580fHHHnuMQ4cOva4xv2ZCytmzZ7nzzjt3DWe/jjQaBWU1ZO/0Fr6doaUCnrp0nd979UkmSgF/4Za3MBjEhEj6NuPTi6+yutLmgcn9UBboWJObPkdby/Sv7SV3EiqScsPnwb1HuRwP8eo+zWYNWOaBwTiPrx/h2RcW+bnkVRaVxpOGDZ1xSNbY3upgpzVp4uNCRZgIwjFDbCVUDakrw8x+kvZ18jZYZUgOKCZv63J0f49SUfDc1ROYPNjxy/AVwoOiInGhZM/+LSiBOaEJFhqMehJRDnaSfZyjnOQoBXlJ4bsQa3d8HaSF0nVwYw7ng0GhGwUPP3aab1MpUZhx6JZ1utsn8AIFsQZjIcsob2SoPVUGFRDCEa1prn5smqUnx2hHAZP0GLgKhXVMzTUo7VukdGyLadFi+cUKTjhEXOAOFNw5s8KeWwb8h1+7iyKTFIkjHFkqSxljk0OoVhj0Cx7//C00FlNcTTD7/iUIdjwq8rpEOsVGvsTMvVsM1mYZ3FCkoUL6kvHZOvcc3MPKy0t0ljoEFQ8z5RFPOtSWprRtGJ8ZIpVh5AU4TyELwZ23LvCd3/40ozzg73/kz+CNLLcemOPy88tkSUHmKVZHdeKWpdozkFl04CF6Gr/nIfAp9pcJwzLpMEcNMkzJUXRzwj6ABWdRhcVpgyhJbOqQ2qHKFqPZEXpKPs7zdipnahabSoJth1SKj/zv99AY67FypY5oFnhFgS6BzR12ooSxAYMxh39zROCVEUOJ3/eI9zQJrhb4cY6JC2QgUJkPnsVelAyONXHLEYFnScdChrdJqCrCi1Ba3jGmLUxBIQS/+ZFnyQcpdqNPmBQUKzn5dAUlLJ4T4ElsNYBhhhMSPV1CWvC7CvEFDxecIyg8hIA8LeimQ5JqGSMdDoezHrYSEP6QI9h0ZE8IGFragzJrWxEuF8iWY/TJCkvlFqN+DVE4ZLHzelEY6nWDOViF5yqYVLMRNxkfdhmulkg7kkEf0oZh6VoF4hSR73z2xhMIJJHSrJ0WlK8FSGf4s+++ytbjLdTxB1lwy8h8nDBwWGNY3+ojnEQlFhONSIMMlkLcjIf1AASZkPBICdnO8HROPOPjDQ2BkcSZo5t7lG3K8loJnVlM1VA5liCmDeLZnZhyY2D96hbTcxOMT2Ucbq5QjtY5tH+ZX/j4g8S9AV5ekLY8FD4zeMxEFdYubdDtj6CIMLMC0/ZIPlvm747+HIX0uH9+nT931zu5tNlB+ZKJMMKLLcempvnM2jMU5RTpNM9fWGS+vJef+7G/yVKrQHiO6tQ6reGQ5OY0Hd1nfbmNJyXVVom1zS6bwwjvZMjZ5f08193LbfVNTu8VNGolzl9ZI23stMvuqVU5MjHB6fI4jf9XzPEfxYnWJFvBgEjtCBq+fzfb9tOMi/HXrhFCcKbxB6a2b5zfx81ul/lGY+e8Oox24yAnAbDO8uj6JTKrWY47zEQVDlcC9lfG+dWF3+dm2KNy5SSjNP+GFFKOHZ1hNMzZv3/i6z2VXXbZZZdddvkTQ7idx9ebb4Q5/GH++l//6/ztv/23+cAHPoAQgpWVFZ544gl++Id/mB/7sR97XWPuNg7/KSIK99JfKLjUPsDB8HEahz7C6qV30xlIKtUO/fhent3e4mMvXmJrmBCmI0bTHdbbE+TCEYagYri5NkeFkPpYizAKSHqaJ80lYhcx7kUM2iNi8dOI4CoPzkzzwqe+n824oLLfUS2PuJJ7HDkWE7S6jE0mbC42MWtlgtkMv1Xgt8HGJW6rb/G+g7/Lb3knebY9Ry4k8X7FViVitrTFgelNerfXeOX5g1gDyiqcgrwqkFby6o29uIZjs+8xbTS1aki6kSCFw0QQjWtm8hKbvYwwA2+uxDAEWziUM6ihxETgRYLmmYRgtuBjF+5k4tqQtZtTiMk2yR2S4HmByCTGD6g+0eW+oyf43ZVrmHFLcdTD3YB0KPmWt7+A3o549pmD+EGdESkrF6pMNlK6m3XEQKNig5SgxjUXBtN8evsEt9y9wrmPjFNMVfBlQas5ZM++Hl0hcImHHBXoMEBVAoZXx/nHrQcJUkvNj6kmiqzeI2omuCJhe6mGrfkgBFMHx/nt1ass5EPC9R6TzSq1qQprUQ8vkpTCnPmJLeJOQFYKSD0F2rKwOIb0DDdXxpBDhxWKIPDwKz6xgyIQ4BtsSVCUFcr3EUogNHgbYANHMqewxjLhRmyMlTBTEfQL/IGP3RwQjHLIdoxWhd3xxhGAGAgECU6C9RXClzgBRU0glcC1NRZJMvJIN6oInWG7DpUVVFYH6FaFdKYCCEQhyJoB8d0RYtIgXxTYkUSkBhkXZA5Mq0RYzcgzjdMF6bkMpQ1eYtFTEcWkgkBQ66Q71VLOUSSaeGtIrsEJiRMK4VJkAcVUhKs61FaBUwIHZEcq5H5BNLI46eEKRy9N0LNlghRKvk+qDUJ5xHrHIyhLDcYWO1UsswokyJYB5SEUqEJAXyF+mVUYAAEAAElEQVScQCrHoW/b4LY33yTtRLxw8xAovtCP5jCrJfoTkqDmCJVGDDXNjZQ90TF+w27y64/dw19+6w1++V8dQ1ggz7Blbyf6W4O+oTiwRzM4HsDzik/85L0EueHWd8XsuyvGy0J6yw0MjnLk0+0nGCEIVRnTCnGrmrnZEc4KLudN+r0qy/vrTCRdglwzaHloW2KqlhOfaNMZNvnM9fs4+1wZHVnIDbJkMM4Qj5cwKsA5SU5Kuau5/4HbGZhF+sqnKCSJG5EOM5QTaCmwuWWjH1P0cuLNIeBw3QGzF2ZYe6mNulmQax8xJzkzNcPVxS2euLrIgbkxTu+dwsUFc2MNxtIW5qQle97y4pWbuMkJplo1tuQA6QSL509Su3sZd7xN1R6iWo0Ax8Zyn+WFLbqtCu0Lp8HVKK8LqqWCetmxp1RlrVSiWQo5v7TBr734Clf6bY7e0qJe/8pie4NWQNqArtipvjjf/U9sFSmi/zEOt/46ANoanm1f5UBliplSk7v37OHuPXteG8M5QaiqlLwdYWW7FzO3NUVrIuJYfZqH13+LlfQik+EY+0tztAc+0/U6tT8iPvnrTRQF1MdKLA4GzPGNGdG8yy677LLLLrv8yfAjP/Ij9Ho93vKWt5CmKW9+85sJw5Af/uEf5m/9rb/1usb8ioWUO++88794PkmS1zWBXb527I1OUS6eZ8xL+eSNIbY+w0Sjw5l9xxkr5lhZj1kLh5Q9Sd0IJrwy75w/yHh1mpFL6cSblPM+DZuw3aoyNz9BkhkWB2vYaoHWik57m0evPcptR+5BuyWk8rnv6Cw3rl5nrauww3FoJFzr+/jSgHFEjRg1keAKhzESaT0iFTJVHXFzuUELjS4CaEn8HojQsuiqNGZTVrstXAjaFztJJL7b6VrAsLrdQvQl/lAwzDTV6TL+hsMI8A92KZ9cZ/zlo/hPCexsyMT8BPONBpdfWeJ6uUCODAjwR1BpB+RjHv12lY2lcYK4QFQTimmFKzz8rkE3JNl2hacHWxRNsPM7nidiW2I9yxvnl3jsxnGUtHRTQ+OBPsGhjN4LhyhWC7ysQFcVo30+9to48aaPG3i8ImewBy0yFQyFx/nNWTp5iY3jG4QTA9JnK+QDiR86+stTlERO0RXkSZlSGDJcqLJZBAy7VUxhAQFS8urZRZbNgELspPE8+/glzkxPcX21h4ohKwLWR2O4qiSNIoLNFK9bsOUq/M///C8jlMHXO61X10cdZE2QlyVex+6IHMaBD8YTOAFGWEwNhJaYmkft0JBvP/Mcrz62lycXj6MKiTMWVa+ABZnoHSHCOJyxOxUUDiQOb7TjpG1w2JMCDpdxVwpE1SBGEiclKIFwEpGkOK2RDpSAaM3gdxwuyXGewKHAOqSyqKr7QqSrROaa7lGFP9PE+5TBX02IznWQucFZhXR1wuvjBN0clVmsAlsYnBewvNrHDVMwDlmtokMPmWVUsyGleySDZ8exZX/nczno4SoewcMpBGArAWIAXq9gfOAYBIbCl0jn4fcdaVagrEFYD60cdl2SPxwgRAmlHQQGLPgxGOGoeB7B9CRXRj2u3zyIEA5nHVaC5wRG+8SFwkSWwknmxze488hVbj5mUCk8+cJB8uX7We6sQHmnkkVmOcKFGGGRhcB8vkTgCQg0FIbCWW68aPjudz6Ir8c5579IIiwUGbnOkAZkJvG7EUYmZFs5t4UJDnjXZ78LEUhSqXCZpNCSJPZI5g00CgIleCUPUNsFpQ2J9izJo2X8WOM5jdcpoVNBMRZRBD5lNcbapbexXonYfjGg2QtIZEpRkqhRjkh2WubycYu5meGcxY15rBzvIfZIgg9FPHRlnD/7HW/ncGWMzzx7iZujdaayEp9+8TK9XgqB4vKCQS8FVFczXL+gaGjqe6uk8QCnHLVMEVR9vJJiqlRnYq5JrVHh8d9+lkFqUSVHdUww5lUYvZKT16roqSnKvsc9p/dTK4d8/vJN1rp9RnHKwtVNtibmmJ5t8sraD7IUv8SDs/8ntdIbv2Tt931FpR7h+zvRyKXgjWwN25xovve1a55pX+HfXfs0h6oz/L1T3/ElY0yE03iyznzpCABXNra5eqPHiSJACsl9Yw9RUhXONO6k50mev/wx4kqGFY7fePYsnhJ8620nUPIbwzel1arwq5sXuLHQ5UeDb+Khg6+vbHeXXXbZZZdddvnTwT/8h/+QH/3RH+XcuXNYazl16tSXBOh8NXzFQsq5c+f47u/+bg4ePPhlz6+urnLp0qXXPZFd/tt538Fbaea/yIQe8OLqUWrjB/i2Q/8jnbUyzz56mfViyI3yOpP3l/jHx7+dipRYY7m62qEdPcZ23GCQ1rjl5CLLCwdY34hJkxwvDPG3yszumWLq9Hlerb3Mof51Dudt9sy9ncbsKX6n+wuo9hR5UibqHySxGbrkE8eWwnjIksYB+WaZWrtK2+R88tI8DTmO8coUocD4AllI9tR7eJ7m8tVZ+tereLHFVRxCKWzoEJ7BBtDUPs1GlUEvpfANg34GZYXvHHlXsn01pGYUo2xE0bU0cs3N5S1WzZBRRWMjhyk75KYkWB3j6kaECB1h3ULH7BiwDjX+NYt8s8T5AltTbMcJzllEx1LeKpCLkDR8NtplBsMyLvCwKZTKllLgc/rQFI8/v84otJiqhFAhB5JJv09vWMPECn1c4d3I0KrM1XyKh+7+FtYGn4MVj3AjR8cB4ZiidKjAqxmKrk9+uYE7aOn0fYZ+CJ4jGGRIJ5CR4sqFVTrTDk95EMD29oDR5ojWWYvxwZQEa9daeKnDLyzSCISn8KyltmYIZkqsE4N1rPkJTV8CZuf/QUic0ggEVjooQJQVuixQuUIUlhnbIerl1ExC5dyA5GAN3fCRA004IyiupFgZYKOAbF8D1csIr3URyofCYErejt9HlBOIFD0m6Ucl6hcMzhMoY3dSZrRFDXMcFsIQW1j0uAe+hL4ljUBkHlGmGc440tsCqi8bVMdhmhI/FIhKhAmG4CzCSqRyOC0or4IyHngGJwElcUJiYo0IFHLHigTlVRHbwE2NvEMjUo0IfJQvoWuhMEjrMM7uVHt4ktJCwSDLSPeVcMbhDyzpTBUdCvzMQ+Y77RgiT7B9D1FVCFfgPI8wdUhtyTyBxXBsdhkVCMo2oudSpBDkZfAS0D1DbhV+4ZCZwHOC2eqID11cRrbBGkEybdDRjjglBylCSKSDvOphM4dnLdJJTBjw9nc+yy23XuOjH34LU8U3s7A4xBQWExicb4km+6TtCKdCxj2PdaM5fHQTIdxOXLKUoMH1czSWLPSg7yOWNHYdZEeipoEUsJagb+jVDY1jY6Tn+rhpjfIlwSWHavqsLm2xFF9jSQxRe6HbbpLdbTBVS/XzgiCFcH8Z+jnUM5yBPPAxeUEplxw7lnDH7YZTYzPowhCXB7SjFR5/uU/ejWjONjifrzIcdvC3wZclolJIpV7m4PQkCzpl9UaXNIVyNs18WMMqwbVLG6RJxtWzq+hCU0VQblrSvE9Qn0ZWZ9g7M8GVq+ugJHffdZAHTx5golnhpbM34eaQ7QPdHSGl/xyRzHl+69/y0PyXCileSbKieuSeBmBkHVKOUYjGa9ccqEwxXx7ntuaXN45fzzbITcF6uglAL065st5GhQprHc1gnDdPvguAV7austId0ElSlrd6/PvPPYuUijcdO0ir8pVV0fxxEwQem+mQdj6iHe/e6Nlll1122eW/E9wXHl9vvhHm8If4/u//fn72Z3+WWq3G3Xff/drx0WjED/3QD/GBD3zgqx7zKxZSzpw5w3333ccP/uAPftnzL774Iv/m3/ybr3oCu3zt2Fs/zX1TP8fPfuJD3HH6Jkdm76ZRmyPrxdTGIryBIGvBycN7qNR/jUubL9Hv/1U++lsXcXt7+F6FctUjDXyGPUut3UcGknIlRFuYGPcY7x7D1K+zck2SdNdpx+P4xU2W1/YSDQuOhHM0T5S5PrjG7aVlHru5D+cUyX4BbYcRlmImI+1LZAbbWYQ/gppzZIXEWcMaE3j7cqoXxlFdBwqq4zHS12z1K+hI4XDo2LAtYhiHKAOTO7Sz4BeIVJMtNIiFJKkIrNZcX9qmCCQ63RFRigpIzzHxwCaNKMZ7KYJZUGGMe9lSqZQojZaRFZ98ZQrVBNNWeMBySWC2fPSiw8fhF4KNMEJPx+hNgSvAfmqSxqkaGzHIsqJeaXDb3hbXFjZpHt3Er8QsXJWkNwWp81DbIEmxk4rfWnkZr99isJxhyo7AFoR1n7nyNOcqCUXhGPMUFc8DI/FSgRMGmVmEBc8T5C3BKNTs3V9jIoQ9+yb5/NI6OIf1IK8qgtWUUtUn1w6XaeIxH703ItAhSaoxAVjP4Q8N5bYiizVJDfS0xFqFCSxO7nSSyEwiTYD1HEUdzp2bYfRsmc1BHSKLQCK1ReQQNhKy6Sq2k2ACSVGXOCRRGOKcwZUCpOfhrMX/vMa/oBnsq2KrDkYZXqwxUUBR8xHWIo1DCDDOIZxGdtnxoil5GA+EVIjEIjPIxyW6IfAGisZZR6Nh6PcMnXftIWs4/MUerVc0/dM1vEhSv1zg1XxyI5B9jYtzhFLgKYRQmFCBA1e32DRn9EQFoSQq0yB9vMRHZgIr7Y4psRRQ7FTy4IfIgUHmFqEUoh4Slj3E1QG0c2xdkTcF8YSlmmoC6bAxyMww60eYac0tR2eIQsOyNujJHrZcRliBmdC4ZYFLLdGNhNLSF8Q9o7myfITMlFGjHhh4sbdGUNupoJElqJYS4kxhywHW0xw+uUZ7rcJmr8m73/cixdBx/NZX+Jef+h3efuZ+ykIRJKCcYSgha4ZYL6CfW2wzYHx/h5FzWGfRsSDcTgnaCbYiMUqhUrCTFqqOysgxuBLifIMfD6A9IgwV3XcJTBxAwyGsxreAdDy1soAdDKgVUG4WKF8TWo/UGnwj8KVkqlxhc2lI6ENaFphUkJ/3yabhvf/T77O3FnPl8q1Mjh9jxm8xEdUouzLXixiXDDm/3UEMNcpzzByco9T3OHlsjvVRlyubq5gNx8gaCk8QNiVH+vDsy9dYWe6Qd2M8AaJQFH6GLQ2ZfNcyotukZ0+zeXWb/iBlbq7JkcMzPFirsPLqGlcGW8Ro/u4nPwr9w9TnBtwyM/Nl1/610YDMFfy7J5/jEXmV733bGxnmF5gK6q9dU9iUkVnl4rDgm/nSCtNABBhnqXg7viyLm12yJGezPfiSa8vlgKgeMFYtoa0l1xYlQHwDfWva2h7QKPkMw4Bm4xuz/WiXXXbZZZdddvmT4YMf/CD/+B//Y2q12hcdT5KED33oQ3+8QsqDDz7IxYsX/8jztVrty0YK7fInx3L8Eou8wL6DB8jcJp9fvsDa9se4erHBaDvmLZNzvOfIOI9euciPX7zIWw9c5dzmx7iSNShu7KM27nGsdpkZ3WPOyyj7NWSo2BAaPRGiygrbvsLRw5/iWnIf5/onWHtkk9Pj19mjD9AbX+H02CT9KGafW+Kb9p3j3Et7WSuHuMIiY4vwNKoqaLUqCJkgwoxisURlVEaYhKofs65KZDqiWgdvVSBzR1Ad7cTkpgH5piFa1chSiJm2hGXB/v0zZOWbGLbZeHoCtwFjjQjXKnGzNSILPdJQ4/keIneUl8GeKEgiiacdhphoC8SUJtwoMH4EjS5H/kwbvyL4/UfnEIHiUBTh5Y6bVY2peMT7BWWXoRLBC4sHEYUkHfnoEgR+SDSs0V3vIQT41YBeUjDlleh9ZoY1v8doLdhp41goQHlIsRNHnPZS6nmJLFJYaRFBxKYyHN6C49vHuTK6yGh+kxfP54CiHIO2YAOFlTB3Yho3WyIftDk83eLA4YhidoVoTDF1usPR2xd56vIxCivJBwZTcZipEvEphZSKwUZBtJki5yXeWIX5NQWiQOgd8ckJh5MOLy5AeGCgiMSOyanRlDZjjo+vMLM3pvNqBduzyDilmKwgDSCnmD66wfaNCkVfIq73Ub7BNgLEIMVZAzgwBgm4jk9tlBIfa5Dsq1B5cQsAbwg2M2AFTjqKBoxOljGtAGtDolUo3dSQGVTqiK4ZnDJ4fcFgQlIZFGTDYifmVypU5JHe4dOd1phCkDuLijSNuocxZWSnt1MlYx3SClAOr6ToU1DKdypXbFGA9CBUYBz+ao6r+ghP7ggcqUVZsDpHlH2C7RiUwJQ95m2b0yc2eO7FGeJth9QedqxCfigitj7yhQJfaJQTkDqaboJgu8zn/y9FJymRNmOoVdGFQ+4xmK7AOEP5ZkGQSXIjWEwqrK+2OHrvHp5aHoESWBkgtSOv+yjlaEylCCdJsoj9d27w5vedZdgv8eH/65vYoIbtWbazCkl/mRv+eUKl0HHOYCsFFSH2SEwDhgLMmMer7VkuZTX6OsJu+dSvx9A3eJllONRIDfJcSmQlM1XFgAA3GUJzC/0pS2krQH8owZ4RyKUSFEBWcOLIDE9fuIBLfJrL47Q/H+LpMuELBUILVCoIpEClOdVKicg4NjsC2ynYd3+fda/Gh2/cy/fve4Zf+exLvPMNNUQsOTQ6DPsd54KbEMItbg8vlDfxY029aSjrKs1qiDcxQT4w4Al0bMm3BPuaUyz3Y2pRFT8KKY177N2bsTle0NXbaOvT8nPuOvAijz/7BvY3mhydmMKTijzX5NZwcWsLf/Icz64pHj53hfubJcrjCev5lwoVvVHCB37nWYwyXF/Z5hVWCWrPUNvzEsrFHKm/DfjCjSHhcM5+2f1DSknFKxOpCIDb5+d4OKhxojGB3OmHQ1uNFJLlfJ3D++vcMb2PsVqZwzPj1KOAWhR9Tfe0/xYEME2FnsyYCl9/2e4uu+yyyy677PL/Xfr9Ps45nHMMBgOiP/RdxRjDxz72Maampl7X2F+xkPLP//k//y+eP3z4MI888sjrmsQuXxue3vogj5/tcfGVg0yFd3Hknm02ViLO3lhn3Cm2TJvzw19nuzai05J8NptlKghpHGiwvhrTKQQr22X8YoJRLyMwBTJ3jETBdj3nnF3nnY3LaJGyXB1RiwI2Y8NkK0I6WE3bPJw9ybur30JqDrGSbJNFIYEqYU2KzKAogyKhMp2Sdcuk22XysZA/c+QYZwcP01kP8NbL5DIkNhBWDeEQ+pfGsUAhHVG+U54/FlYpygUjPyfS4JdT2rGPrihE5lM3PiKBa1Ufo0Aoickh8jwmJjvcqJYpPMn62hiMFLpd8EB+mdvev8Rng9Msv1Kjs1mn7OUoPycPItrxkNvHZ7kwShESKhczvMRhyjCs+AQGiCRKK6L5EpcmU/pZTmnNcHSsxskDM1x9dYkkBhc4LHanBcX3ERacEPhtEI875iY9XC8nyxKyvVUMkBqH8g3d446oDh5DsrM1nNsREkwgKJUCyvvrrKx3GS8Cxlsl9G3nuVhb4cG33cvNhSvM7t1gMAp5YXsvprtTteAlBpl7eC0Pr52hRpZwy/KOB44Spx06NmahNkSXBNZ3qFzifB/PeVhnsHXQNYu/YpEp3HJ0ieZExsYg4ub2FPFchJ5WYGFzISeMq0hnwRecetcaU6f6PPfLBxmdA1svo6xAhBJlBcazOM8AYqfFyhfI1CJ7OcKAcxbtgRUhelxBWSCyENuyFA2QqSUdGZwVlLbBeR6iETLyDXZoEApq51I695bwEEgrcKMCl1uKmk++XiCiES7JoHAIX4AF4Tm0ViS3RuQ3MxpXM1ASG/oYXyJDD+eBUQJf+dg8RfYMrqTwMo3oZ9giQzcihLaMdJmbV8dwSmA9AU7gb2u8WKAKkEiKiocyklg5Gs5h1kcMXpwn7vWYODrFcFyRlB0T3iQmjMmGBjESWLcTMb29Nk4sImrzFtGIKITAVkPopxRl8JSHrViW5TRBp2C73+DSlb2sLYyBEvzcxW/iVH2R571ZqrOWrDSkcBWctZBkWOdwWxYRVnFo/MRRPxPz6dExCquIUrCDFC+1BLmgvmawicOWS5h2hjrQZ99+w+alJtOHRqw94uMqJQIvRN6EAo3ILVlVsdEd4iJB7Fk2Hq4w2sro324p9SRRXxBWPTzjuL7eQWvBgXqMa1eZO9Ln1K2rDPKIT//HM/zT5YOIeyXXtttUR4qNtR7SGmw3hkmPY/smcNv76do2e8wU9fkKwpeozDHpJlkJuvitGP/gkMdWXqS70WK2mOC9+w6S9nOsg3x2k4XYIygXPFS/yH2Nm/yet0iyXeLFdpfnn7rKGx44xt1vPMrb7nmF8ea/I9an+NDltzAyEUMTMjLNL1n3f/vZs5y7sIqSsH+2oD0s88jygCy7le/Y13vtun3lGf7c1DlyfY3t7v2MN49+0TjjwSQtfz+HK8d3ntfKHJsbZ9/0jlHrJ9Z+i8e3P8O9rffw29sX6IylvL1yAhw0VEjDC18TXL4REEKw1Y/JpGGp2+P05Jev5tlll1122WWXXf700mw2d1rlheDYsWNfcl4IwY//+I+/rrF3U3v+FHFr6zt41fs4awvQMRt816m384uvnmNlrUutVqVQlgm1B7/WpTd/HX+o6fTbfOvcm/nE8CqrlZR4M6TdL6G9mK32AFkp4aIEsaYYVkNW8ndyKT7Czd4cE6WY6dYEUzNjPLb5CDfKVYzweLp9g/edfCv/ebVgfH/B8GKOC6GYExgh8EJJUWgUBqIa1fmAzTRjq1dnWIQI4xNsFIi6w8sUFA4KgSuBX1hM6FN/aISYWeT49hmWOyOWVnsML41ReAWF9nAtw01SQmVwMQShIvAUSaYxqWWwVcU1oJiVdJSiSCVRnmG1R7dXozyeU+QhvfUqhUpphH0ykSO6mtQ0ODKUbCYxorA4A9YzXHluhnLTQiEJ2gWLwTbtaglRBq+k8Oo+ftWjVQ7o3X8D0w3AFoyuVZBS4exOG5OVEpE7VrdjYrlTzRBtZthmweq+FaKLEYnzGThJw8vxsRSzJVIcshAoobhzbpqly1tsdUa8vJlz/+EpJmbhQPkYv/bkUQ4drXL+xXls38MFApGA2sqYeCRl/m0TbCjLYH9BelDwOxfOc9tWFWktk0nAipfjJYAV2FCiHVjpEFKgegYRCJJxj7Nr8xzO2ixfn8ZVPITn4cWgejkCg7ZlnLS4luaV6kHCJKdatjAR4SFxhSGU4EqOsfkhWws1ohsDMCCth7ApwjmkkIAglAphJBEgh5pIF8TDEBCIocF5EmctBocqBF7mKJoKMbC40COdc9imI7wG5TVFgCaONGJriLueIF2Bq0QIUUI4s/Oj0Ze4QKBbAU7ncFUBPrbk4xS4QKGMQSaOHeVFItMcpyzpRAUvznA9Q95ySAlZV5F4gsSF6LLF1X3wBOOfyXaMj4XBBYpcKIKaT4FlfXmToXW4wjHa6FMeaxGXFZV6BVd3yD3bQE7+YoSLIWkHHJ6a4uB6jSs59LVFh5ZhBbCWUu5o0yDfa1Gpo9Ot8PFP3kWQgfd/s/fnUZZmZ33v+d17v9OZT5yYp5ynqqysuVRVKlVpFhLGWMIIMGbwBZt7u7lA3+VhAW4MvmCDG5t7aTwAAoyMG5AFSEhCQlOpSqWap6ysnOfIiIw54szvuIf+I3Rl62J3S7JwVbfjs1asXPFGrh175crMHec5z/497Zi1D01w/bZRgouaiVLMwekTtMObaNdHFAacw9/OKQ5qdFkirxmiSsbARPho4oqktDLEMyBajvIdA9iAQVgju7PEtZrg+ME2U5UbXP3TBna2gk5zyo0Qv6awuUFLS3a8xOfNCpN5QWt2E9uxRDXJVlijVJPcs3+e1cU2W4vb/KN/8SFeeWWGP/3Xd+EJy9ZShY31Bis3RwivCtYOKKqjkjsPz3Kk3uJkrcLmcoetzQGrizlPdhcwG5pWOMZiu0e22OaWRLPRHdIRGaLlcWAuw68kiEZKIacYIeDYsTk67T4OaExHXIxXMEhWhk3a1XW2nu9RqW0zKBz5dsKZszcJRyvccvA2hhnM1G7jF9/9V/ntc/+K7aDGG8q3fuX/+2dPXcdXkk89fwEdWrKK5f67nmNczvCbN8fJMsla+p8WNhznXlzk3/6ju8iy36S4xeOf/fzf5KHDt6CU4nzvOlcGS5S9EveO3oYWjtpUhfHZnXdunt9+jvW0x83kEnuicXwb4wJJO0k5u7RKxQ9o92Na9a9tZPNfNmssbqBJAk01CF7r7ezatWvXrl27XgNf+MIXcM7xtre9jT/+4z+m1fqPk/yCIGDv3r3MzMx8Q2t/0wopf/Inf8LP/dzPcerUqW/Wkru+TgdrD8PWEJO/gokcRfRhtuIRrCvhl0Nmp1q8afJvc8lu8cUb/5JSf0hweYJLI2t4heVotUFU32Z1o852a5RKAaEuiHTB1EZCozPCubU+R90dHBhd59xmBy+MeOHyMutZFd3QhAKqMuIjjy8T1Q9xYWsVKhqBxEUCme90FfRuNAiSEo8cv4io9hjp3YLoWIqyxCKJioLGyJA9pstoJaVf8Xju+hw689ANRXl/issFFze3mPJGcA1D0smx2xAUBVQkKw0fnwSjwDcW3wtI8xwyg1QKkRf42wKtJNEQSlby8uMHuHB2nnirRLneoz7Vw3Qk6tGQsC4Qq5INEZNbg/ENQehwfclctU3Jz7iSTaOdw4sNctHR8jyKnsZoy2PXr/HFjQUOXpGIAyOYfIDekKQtnyAB6SQ2kMjYIH0PNRJTLw/YHo7TJIDpFFvT1A9eRffqxEFI1IL5vaO4/VWuXl1H5oZqo8yt0xN8RpxmPdB0BzHbL43y7Xd8Ky9cW2KlP8biyyOEGwXeMEfsKWMqHm7MIfKClaNt3CyYrkO0HHJBM8gyJptVRkyZjX6Bc4qi4ih0ged5IC2ldQ/fWrYfjLClkJPX9rH6sREG4z6h5+F1DbaweH2DTTVSgNU7E4+S1ZB0K8BdzTESCk/gC0mmHI98/yWOPbTGE39wjPOPze5MC/IUrlreCYAVO8kMrtB4A0ulnxMFBWI7wW6H/OD7n2XQD/jQHz+CMAJyjdQKKTRGKmwlwElDccRDlB3CWLyhRShJCQ/jKUSvwDMabIGaaIBQaOPwQo/I85GJQHk19JhEaoWSCmcstp/gSQ8CBYVFphoKh8wsMskQxmFHSmT7Jb72qMqNnc4aJ1GeJBUKESpsKDAlsfPvKDHYVogoKTYHMbYXU/J9zFgdWzgG13sYNcLV0jr10BLlA9xWgPIVTgpsqlla6zA1JZkxkrbJECbaGS/ezYhUyEbqIaTGjEHpIuQ1DVogs4JoTeINAlQno5OnpHNDyp6knzmsEQjfQ1QFBDtZRqAIY0s0kmIKiSvpncku0hIdTEhUgBQG/1IPs6eKt+CISvu48VmIlzJEDeSIwptVeKmPqknyisCM7fxfkl53hErw4J3n8MsFHz/VIEhbLG1t0B0M+bnf+l0GeBx/ZJGn/vAAnRtVksWQJ75wOyLXvPXbT7Md1bnQr3Oz3WWm6fP8yFOUsiZeyUPEGj8zdDeHtFoe9eky564uc8O3HHnz53hQKz579m5am4eopteYNFMcPngnE/MtPAcqt2xvDahEdcqlCraa0hMRXV2iszXEiwe85a/dRUl41MaqxL7j1c1buGv+eVqVEdJgg/Jqjgs0jS+PJj55domf/rWPY7VBrG5j7i5haooXOxP8tfke75q9h1fEKQI7QVEU+L7PUxd+nT/4zTn0Zg5lH7clOZ3+T+iP/yJve+/DPDB6Aikkx2r7APCV4HLtORbygjsHNZryKlvS577WHbyzeoQ/XTxNLQo5v7LOWhIz5yvCwH9tDr//jOuLm1w6s0o6Kun2dsNmd+3atWvXrv8evfnNbwbg2rVr7NmzZ+c6/zfJ11VI+cAHPsBnPvMZfN/nJ37iJ7j//vt59NFH+bt/9+9y4cIFvv/7v/+btrFdX79OfpM9d32Gd5VqlJP78af+PiOj78IU89xzZI752RbKU8TDHGctLTXAm61jawFBENG/uUYp06Ac1kjK1YCqduR+myAoODA3Q9So8MhDR+hmZYLNpyhKHdKFSSaCMt1okylRcCNt4+Hh8gCbCdACEyuMEQjro5KAcVdl9sAmByZWeeueM1y/eRtPqBpaGaywlBPJgWiMudZFZvYN6U8pnpyYJd8AM2G4cX2MsGOQKylZvsHh6SlWc4coACvQRmI8cM4gc4e1jtzl+BspUgsGxy34imBb4SvH/HrAFgqRZPTXAlRh8KIUPygIxxRqy8AABlM+wzlL0QxRecFcfwNT+Hz3tz1Hsz7kxXSO54sjbC/VUErR7Eq21mKyiYgitaAcq90UcWGceM0jFxY9L7BNn9KKxpUUEslIxeed3/85rHF89I/fBI0K+9stpg7GJK2TlLcqZGWLG+xcB6p2HeXlDKsdM/sqjI7VqJZLeN0+fgHrm13WN/qYtODb7nyZ9zz0Mr/1Ow9z8elRotCRe5oiK3BCUBQG5fuIdogyhtlumcmJOgeP1um/muP3HLmziH6+ky8TQVD4CG12QldLApWCawUM5yOE70NmCIaWPIGa75G4DIYOUfKRXUv5uWSnQ0GDawY4T2AycMIR5yHtvEyW+niTjvpEQvtMCZE7nJAgFc5qiskITwomDrTRE9D9I59bv2WZmVu2EQKqLwyIL5RR+EgcfiHxEgehQhRQeb4gnQWGHvbLb2AXkwFmsgmXEpTeGSttthPcRAOrQCmFcSBSkIWPPlgnWi0wFpQGJ0MsDisdCrvTxRNISAxKOWRucaEk0lWEk9irhnQswvkCNe2hpI8rBJgClRrILcaTRFYgkpzMprgw4L4Hj7Pn9ik++ifP74Thphp5LcG+mlD0FK5ksV6B9X1cALHo8erTy6hlULMRlip61EMMHem1Lt6Kg/kQg4f1HNKAv5UipYfUDuKdXJqNSZ+zq8t0NyxJVoCS4CnCyRq1OKSzPAQp8bqO5t6UrXaNyrkcYSGNJJ4uUf9iH31K4u2LmLy9R/FSic2XJ4hKOVG1zVAnRCdiTC8jSMcYdx7rg4RoROO0QKUe8Qr0mmVG6n18v8nQQLHWxklBqiU/de59DLOIB956ha1/X8dIyKYTWn7MgdtXOCTWiD7/Rg66KovZEtveBiocIu0EQkBnM6HXi1lODe94521cObOK5irTe84zrhxPXTvK4jXJG9fvYnN7myk/4Y7pJl7g09nus7i8RbK9jZxpUs5CDo29wnanzrYIuCI3iLeu839/9ztojdb49PlLPHn+Kv2NKeZntvm1Vz5HsekzdzAnm9qZyrO02SbVGpsborUUteFD3WO6mbO4Wqc76GFnHFFSJ0tzfvSTP88XT5WY/74SI7fmbD4nmPm+bTwBF06tcO/bBtRqFY7VDjEW7oSweZ4iChVIjcFxsCzYE2bcWr+VzU3LdFBmIirxgefO0YtTtnwP81/IX3ktrK92Yb3AL3yC9PUxknnXrl27du36yyYA8TrIfn/9XPbd8eijj1KtVnn/+9//Vc8//OEPE8cxP/iDP/h1r/k1F1L++T//5/z0T/80t99+O+fOneNP//RP+Yf/8B/yK7/yK/zYj/0YP/qjP8rY2NjXvYFd3zzLyatodYN9+yc4FB9EuIMcnt5i0rubB4/vpxWGVGolTr56g6WFOpXqkJ/+1ik2Orfw7x99ibOb0NN7mBsukixVqfgCNxEyTGrEkeXdb7+Vw4fmmJpvcfJkzvVhSmnuOq9cuZ+ZsMFE4xRX1mboBz2O7FNsbVUxVQGZh9YOETvkhsIvQtTIIl6/zXOvTvOpxf0MTB19dBu1DMG2ISgEd9Zv45nri+x9y3M83r6TEYZsqTo2U7gNj2MTizRnelx94iimrxlNfdpJgalKgq0CL5PUlMCILofu3uTalXnixELJx/YUsmnRvsPh6JUtDBX4HsKXyMIyWKqy9CEH2x4UDhVBs5qTN0OWxgJcUWLzehPPOa5dHSeUdTZcldDPQRsmp3J+5n/5LE+9MMZvfPI+WiJk3AWId7+Kiz2WP1vDJuBKEmcMxhaoXFEKfNJhxqBbpj4+IHOKTjems9qjVa0wHo5RGibQMvgbku1kjXjWYpoeOpVUQp+x0SrVHKIrCVmgCGY9JDDsZOybXeXycJy9B7Y4+8oUxaDAmQSRC6wF/+mQkbkRXBHjJ4LjR+dY9pZ5bOQG1b0R3mnHYM5RWjQoQmSx8yIlq1vEUCOKYGcKS27IJ33ktsEqSXNiQFH4ZD2Pyl5Lr7MzqUdaAbkAJSkqgqwORV1TXpeI2HJucJjzl/aQBjD2lhTPF0SlNktPNrF5hpQBxZhPsreE3y7oxyEb3RquVXDt2Ql6D5YYpgFb/RpqBLwth7IWEAipkOlOeKzaNmg/AGORQ4OOBNQkVEvoA2OUFvtgLBZw/RhGymhroJPQeM4S76+iRiNKo4rhhsEhEULujP1NcxwSQgHaYKUhny4hE423nePfBJwla0cMKcNEiGo4/LIh60jMqkMWFhsIPFFgFzPSSZ9ixJHvqTE1e5Khv0Y/O4zXy6ktdnFCIJ3d6R4a5MiZEL8G4/MpqTOoOEM5Rb27zvD2Mnc8dJ3ly6PolzykFhhPYT2JbYidJOO8wJYChIEo0eQI4ttrLG3l+LHBSomUBqRDdXPGylVSA3l7QHreZ+62bbaujVBZjkk8iwx86Am2j0ygD2jqMsVJgW5JtgcFc3MN2jc7CCyi6RClFHTBPlPCdgd0EgcO8oogH2qe2d5P+ozH4PAAFwbY2Sr4gv/1d97Pxr4mIpO8JA9SHwM9mWNHBJtUeeG5vQw3S0SXN8jekFFcCBlPDtHqVjklNyg1JKWwQtofMtGoUWhI04LrFxqc+sKdbE2UmQnGqY5GLNgVhqrP4fIsQRgwMTvCwvV1Qk8y3LOJvy8l9xUf/NLbSFY9RDUmawg2zl3hb99+LxtrPUg0q4M+zw8KRG+CeF2T9uqQOe5rHEBrg1/ymJlpQL+gvhciH+7Yc4x7S3toX/J5rPEyvSJgOTtHr93nlf99yGhWsLoxxq0PX+P3f/b/wU9/7hd5+o/v4m+86QBOGT5w5Q95bONF7m+9gZ84+l2M1yq80/sWamXFbLiPlfD7ea7zSfb0TlItbqWfFwx0wdGZcZ45c50Ij7ww8PqYfsztx+c4NjOBrioO7xl/rbeza9euXbt27XoN/dIv/RK//uu//heeT0xM8CM/8iN/uYWU3/7t3+bXf/3X+aEf+iEee+wx3va2t/Hoo49y+fJlms3m1/2Nd33zrQ8fo0SPlcW7uZpu8uDcv6HhztDRXa4ubiLHWrDS5tHPX8N0QzZKdT5nDVl+hStrWxglkMOClYURiAvSsYys6ogjiUkVf/KJF3j4vj4PvfVWPv3UVVzJMX4w5vDRx1mJ7+aV9huwMqehrjL0EkQyCYWPqQpExE6eRSawDc3iehOhDMZAoxiw1W+jDsd4hY9cDFk9JvngxkXG/AoXLs9xornKqe4eeinE1idKHLN7NngxniN+k2bylObuA7OcXF5juDZEaY+jrSnWNjc5+K2XOX7vTZqXBrxw6QjaOehCoQRy1FAEPqIS4iWOWDp0VWC3DQSCnvEp9/uMf1eP2TcO+RsHnsdYxY99/Pvo9iT5pkchBX/66btxkaNUc7giojEhuPPuBeZnN/mr4x3+9XMPIGPLejFkf2tIGnv4okyOh4otKjN4ucNGFm0NOnV87AMPkjc1NrGopA+ZQXcK1g6UkAON7INYAo5n2D0ZarRMf6nGle02nr8zphnnENoy2SgzNzfKl56/yMcvPIKc19hLJVSYEZQk3cJHoMnLjt6hArf/CvmVEvFmlUU/YUNnaKlRoiCsp8xOdFkbqcHZAqM1SkM2IjCjAeGmw/lQu5wjCgjahnCyy3f+nRdZXRjjsQ/ehj+dwrCJcxKkQEQKJ8AGEt005PsUni/wLjripCAqQzAi8AY5k0e7XH2hhWv42HKZIlCYksBVC1g2bFwdYXDMJ6gr7OczfvNH30z3aAnX3Ok8MSWFLVvEwQK3IZBrBpTARQobSfyNDAc7V3R6FpXleLnBKInLc3TNw5UtnpC4JMcVDs8TVFYL/CDDGAtphBQC4SzWObR0uJbCBeCvOoqSRzofEq4Lwo2MQgmcNUzsm+BqqMlCSWO8SxZKPB1QlMD6Eu/vDFAB6D8B1/awQtKfEEwc+iKf+e1Z0tUYioLAA613Cg3OJHjTIXrUwx8dEHd8ovqAcD7GJA2wjtk9m+yfXaNVH3Dyg3uxVR/POWzhsFKg8p1rSTbwcT472UDWojJLLCx7JsdxSUHH9bEmo2h32UZSLgXkw5zv/NZzqFoXf3aMl3rjGAG+ESgs4pAgLVcIvyTQH/awccT4Yce3ffvt/O7zG/RWcrpDB7lhLoG5d51kc7VJ/8UyplzGTxKqIwWzJueMP06wkGAbQD3CFrDRHsOrGXQIyaDCSCXFmywhFgSmoliuNxmWAqK4x8vnl1ja7LMVwI18kysXrhEcHuHYbbczN9dgXCtGx8rsn2uRac3lq/dzycbotCAUmvKyxwgVHl+8wsnldf7Ht76F9RsdandsU9x5naXVOplUuNJOFpTf8LG5IuhYrt7Y5JbDM9TCkNmxOjV/myOzt/F+/35OLa3wYG2SFz59jfED76HWyrly+If4ts1bqVTq3HVkDFEtc2LyTtYOblLrPIdUCXtUnTPPXqZxMyYVBTPC8B1v+ntEUY1/8PBPsXZrl32tJr9x4zd5qXMR4QxnexcB6A1SlpY6VMshX4gvseBdR5c1/aLNnnqZUjlgGOfcd2CWj1fPUA0CtvtDxl4nGSmVepn3vfsuao0yeydHX+vt7Nq1a9euXbteQwsLC+zfv/8vPN+7dy83btz4htb8mgspCwsLvOMd7wDgLW95C77v80/+yT/ZLaK8jggkg80S4+4gRUvxKy8+yf6oTuIKzmxucvuBGTrDBNu3lIaOMd3nsRfPUZ2dJPUzfCORYQWnFUIKpve0OHdgyGDd4bTjwvYG2TMw9CXrnQEiPkB5sU0SdyB6nsX1o1RVlzdPLfHF9mEGJYnYFoRhgfYFwncwachaOVb5LF8e5S3Vy0zJLTpBnaEHxoNkVGLLiqKwrK7X6T93K8OWhA5kRhLM5Nh5x8lz8/RrEdp6yJGQ5myNh2ZqfOZTr+B8QZKlpN2U6xcnGD844PryBFHL4XUDbJiSa5+s6yPnCqo9n8GIwWv3MChcYRCR2rmKESnCMZA+IBwSmLwpKaxBTBvGj3SRlZwbr0zRC0qEX+5a+MJze5iYvJcXlkfxhKARRGwnQy58bC9qKNBDA2UPPB/jwPkOoySDPMdHQWEINgWucIjUIKVFbAW4RpXiqsOuSMghO6tp3TogONAnVQE3P5/zd3/8g4zMNnZmnlrLHXcfBE9wdnGDrL9zzWgmC2nsb7Cc9PFWdgoA7h4foTzyQYA/n5BsVDCpZiabpP9qk3pmGfod9KKPe4ujK0NKVx1eAio32KrEliTOWeI5Dz2iGHne4Q/BFYJSLQarUR0IPENaSLKqRHkOObAIA+EayEITbgpwkvyVMtl5QWU+ZbnX4MaXRnAZ2LLElAKUsIjUomsGN2ORRuD3BEpLeu/wQFiKMYmNLN4ZiXAg783x7rFUXvEw26Byh5Nip4PESfAVQljkpsVPAA2DQyNs3e3wFvqUVh311ODl4Ho7AbhqbgRWwDoBxuKUAusQxuCHCmMgL3m4yGJLDuc7hBJIJPHRna6crR6U/QzdD4lXFf3xErosqYwVBN4Ar6SR0pKcCAk+6ZNuCvRyRv/Id3HmhQ2MgrSkEdLHFBadGMoHqvgPg1VD0nMNMD3oaYp2gO7ncDto7XOH3ubpbgUnHUEppjGVk21HGFVHZRrlNCJJCROwJsFmjtIrIIxPNClpW4NqWOwwQw1TiqGPzkPIYwrrCAQk6x2KpQGlUkA4lrPvyBaDfsHSczVoF8jlKtW6zxumZ3nL4X18vlVjbbMNV6rIVHPv/Zf529/yHGke8H978SF8NyT/0BT7I0WeOzw08kaP+FgFkRuEkrjEEZqY8v6CfBiiTvrorkYqSb0e89CdlzBG8eRjt3A27XBsfITJcZ9wkHLj1DW8OGC2VuOsv8D5V7bIa5ZePyGMfHzPR/QllZLPpomJi5jJrMHmepfYaQZphrEWEUc0Pcke1WBtrYK7GEPHEdRD7LDATyQXlzZ4+9tu5Uh1mnTxHBfSx1io91lc3MvS+S1Ob1hWFz7CXXqUhx6+zl878Tj7LzyMLGWsZ4b+ap/V8SHNepm3eFXWoss07ZvwA4/3/8A7CZtVvu//+g6k3Okgm6hXWY77dNFs5SnGKrT1OFqbBUBJSateoVYOv9yfa5FoIi+iWg4ZrZcptOHZG0tknqHSiJgfH/lvf/D9F5TLAYthzEu9a4xsNbltdHdqz65du3bt+u+A25n6+Jp7PezhPzExMcGpU6fYt2/fVz1/5ZVXGB39xt5w+ZoLKWmaftXc5SAIGB/fbZd9PdlMLhN3C7a2Fjm5kvCyWqGTNhiRoxhnsVqzZ6rJD73ndpZW/gXlrSEfu1xjdUQgZwzV1RZ3jbQ5P2EZ9mrc+uABXumewd+QqFiQjDtubvV5/swNIjUgcT2uLEesbhwidh4zVZ/mRI+x0EO7MlYaZKugpHIq1YS8K+jVy9hYITZ9vJ7m0o1RVhslxB5HeNPRSGNWShVayzBRibhWy+mJkPCcI6olVGoDuheqZG+Q1CcTprcylrcbdBqO637CTBJQG6sy7CX0khSM4Mapcc6n+1AC5vo59dGQTRdTSIX1BIlU5BH0I8ug5BPeAJE4ZGwIkgLpHFufmUZeVfyLq3OM7ZtkfUKRlRKCQwleI0d0DGbU0N9rCAaC0jWJ2puwfLBNo5xQe3SCsWMlyq2IiwsaU3a4XGMiCZ7EGYfIwMwIrPFRawarBKaqoFD4zmIPK/SMj2n70M1QzmIqEhk72l+qU397l6lwEw57LHyp4PLqFnoiItpIOXzrFKVywHp/gIgdwdAgfUt3qoOteYiuwN9y2JLAJIL0RogbhkS+pJV5GC3RBOjBgKyjoAlmNSBcUygs0jmiDUfJU3TGBIUnMRWFSixFTaCu+vz+33+AbKRMJAp8IGpkiNTSC0J6d0rkQNB41OBnGgYStMBVFCoHlzh6yxFT93Xx3DzbV9poDc5anO8hTAFyJ58lbOcEJ3eyR/KjHmQK2Stwvs+Bu5dxewybJyNES+KuKYSSeLMp0bsGqHMOfV0g1M6fRbZHIhYNXlZmcNjDNkBIRTwvSCLJzJ93Ee0+RoCMy1gZYZ3ERhJdlfg9g7AOWWhEDj4ClMBvC4IzQ+o1g3hnimyUSUPJ5kLKpNnCXKgQ7w8xkU/RVIy8eQu3YekOy2ilkAOH8yXhIEM4x4cXYsxhDU8N8XVBOFHC5pLECzBZRKD6yA2PZt8nmfIQiz6Dnsfw/jJhYHnb5CKz+ZBb222ecXt3riMlBikhDzUH9m+QxJY4VrgCZJAj2uAtO5wC6jklB1PHbnLtPLhhgOnlmGQA2zH/+Hvv5NB9m1x6dBRySx4WhK2cLc/D7Bd4xx1uxeJHPg99ywm+768/QBbnbG73QFukb/CMYyAKXsoVxcCDQuIynw6GF7wKY2s+hA434nbyZAKLDRx77DYrDUn3dIuw6+j1c/IRSA4qsqBM6nsIT9AfFxhT8MYT+5iYavCbH/08VC2Bp7j/6B7Wz19nyW1DDklasLzcoVd16LmI4IClaBqqRPzVxgletNe5/d79PPDGYzytBd2VLiPVC7z5+BR/5iY589Iq+bAgtzvdUJ6ENC9IkgLhK1ZWDGlZY7Iy59c32ewOESMNhAsIEx9fCDIhWdIx7WGX5SRm7vAEe+ZG+Re/9VmuD3JOvNOwaK/z5gPfweE79zG796sLCZe3t/jDs6/SKpX4X+76O3x27TFWkmu8a+peAFqNMg/esZ9qKeA/PH2Kdm2TsGzxhI/vKW45MEmgPNavpJQqAfcenqP0Ogqb1doSK03uNGvxgNt2m1J27dq1a9eu/259z/d8Dz/+4z9OrVbjkUceAeDxxx/nJ37iJ/ie7/meb2jNryts9rd+67eoVqsAaK353d/93b+Qi/LjP/7j39BGdv3Xu9wJuOkaOHGRzfWQv/HQM9SUZSr+n5n0p+it9xlr1XjTHUd5YTTl/JmQB0q38fKgj29vMNPa5pbpV1G9Bq+cuxVlFAf9ca66VYyxZFmOLFXxJnxu8fdxpbfOWlYmMw1kpDkyd4budo1qMODv3rrC752e5tq2QI8VdLolZMeC2QkkFT2HN9CkRUSe+xRUaA6XmAu32e5V6dYsWyM98Kt4sUJEGQe/exmE48KflDFXQw7+9XXcnGT92RGW+rB2eZtht0mARFR9wlSiyx5eoNFNi0aSKcX2IGPgAtJZg60IhIIkzUkbGmsUQhUoY1FFTmvfJsmSJF8O6NoqOqkQFCFumCB0jhpC75lxRF5gDvkUYwqigqHy0BMBa3mV8WbMO95xibPXyuS1OnlD4doFng9eZnFbFtnLEVaSNSTKCGQMxajChBLVzVFNgTmWU2n0GR1bYzChGDw9gU7BeZJ4tQqftjzyP52GY47Pv3IHRRAgfIkRgmdPnaeqDXHDUL2rT2U6pnRqguGhPqLwyfoB3oYhfAxkM0c7iSgJJvdW2V+qc2lhizxOaA866FwQxwH2ZkA5BVILmUZqi9+JqeQSU0TULibIDMJljTVQ+ArnwPg++w5s0/Ml169OozYLHAGu7kDmkAl8Z/GkQluJ8wXaaIRTbF9tYjb6lEODiTWmIpDS35kUEwucsCDAK6B6tIcWBf3FCtGwoHq8S/mhlM28jusJij8CU1cIAWJOI0uOoJVS+HWQBpRC+AInM1gbok4r1PwoxWyEtxwju0PSuiPqCJyncL4kr0LQB1sR6KoEuXO1CQPSCehbTCQR5RA74jC39Rm9K8GcHbD5Uo1g3WFvWPxkiDkU4qxFpI7iqsLzFPF6QFGXhIkjiQvCISATtgcFZsohE4My4NYMBEBL4jJF/JEaqqLwjnU48NYFtr5UJtmMcBVgXfDoyf00i5gvXd+L8yTisiQjJG2FHDu0ynfe9SLpQz6/8TsPQWrI/Ra2PkCsd/CdhsOOA/ft5Uo8gPFVTDtAa4eMM4SzFH3Bq18cw8/NTkBv2cNe1XRcAPdHeLd7pE8YsvEArwSnTi+xstFhaCyBbxgd71CqFrSjMl/sHiLoa9SCw28HGG0oRhzbax5uaHBxBOMhOIFBs71dQd9wjF0q8AaWdARkX0ABSIEpPJS05APHjRttJt7bYHmrTzt5ElstMX9ghX3TLb6v9HbGt04xWq+xOdFnZXEbkUlkPUeVK8xWG4jyKh/b/gx7uQXVh2dPfZYsMGQTmo+196NYxRvTyLsN9vEmZtSA85BVj1sPT1GKPEJPEeb7Oeb9OA+MHeLxyp8THL1GfeYmRa8KRnA1GeeZR99Ea7DCStJDWMvRxl5ubm7zytlFtKjQuz7J+9/0HSgpUFLinPuqpPqpao0jzRFmohpf3PgUp3tP4wnLZn4F427hxeWTnLxUcNvsDNe2V1AjC/guYq50Cx9//gyfeeUS3/XQHTS8EGLH2Wur9B/IqJXC1+T8+z9zxnLctDgyNsojs3+xlXfXrl27du36/0vuyx+vtdfDHv4Tv/ALv8DCwgJvf/vbdyaOAtZafuAHfoB/+k//6Te05tdcSNmzZw8f+MAHvvL51NQUv/d7v/dVv0cIsVtIeQ2tpRVodZGiz93dFLsV0jw0wDOaXneRzlaFsBHx7LUlnr15Ozoz3D3ewmbbZKnC9wzXuoe50oOkI3j+sbO4skfkUoYyIPckyQl46LaD5Nd7pN1DbLZjosjnwGyFJ/t1BrGPXJ3iQS7DoAq9UeKGB57dma7SE7RKGd9zz6t8auswzdIIfs1js5eQbTY5tbdJHpYoqikucCinsc6j0D7D9QgZGhIbIDJ49bl9BJMJ257EWIsdwgCNaCa46jLpuSaDfs747X1ifKSEuJfjfA89qghXHfks+D3JMNOEmwX+gZiwmyOWS4zsi5l/cEA4kvLMb49RzAaMpD7NsRpZrY1sW2ziQVOgzysay4KiromWJZXUUi1qdK7cybe998+542+c4WOf7fPbX7qfqSNrHCmtoy8HnD45izd0OCTG8xC5gdDHhmC/PHUIY7BtD7FsqCU9/EMav+IYObRG51+NwnAnFDVklJVnauSZR69VxXoSNTQEnuRcfoF4bZ3yvip6LEdJsNqS3PQwRuByDzMa4a0WeA5kyaLrirRuGYqMi68uIj2BNxdhmiGeVqTWUEhBlDsC4SGERcQabwDNxHBbbYx1sUBX7byw0k2f/tGQ8g0DUcIxf8i2bNHWPnqoEH6AJUN4CkoBDApkYRBKIPwAU/foRw6vleMtgKl5SOUjjMVKiCcUpQUJqUMrqN3WI+8qOu0G/gGNEJatM3X67RLGFoQWrLEIJ0mfVeRthVsQCM+CE8gcSgvg9RV+FDDeFixdTnAVD9KCQhf05iVZuYYeCcj3Fvj9lJoM8XsKEWQYF2DqIS41yGGO8QNE5sAYXCAYXA5QUwGd1QoyE4hYE4d1PJvidQS25hCZIzlVRpcMxYiPjgOkzajlBikFpu2T3Swwnk84HeKv5Dgn0EkKg50ijwgDZODR0WWyay3ERon7bt/P2atrDDcTtpt1fv2FB6GiaZUSdCkiboTIULHebXK1O0m1SCiaAXLL4QKFcn2ckUgH108usP/eA7jFEYq1Psb6mCDGjRZ4C5ZSzXH4gS4XTjbpl6okswnukqFx1eI+mxFuRHQPVth+WPIH4jxHPrOC7WpQEh0qXE3geRmLpsXppRY1L+b2ty2j4nUWzh2nsD5S71ylIgyRSgE71+XIDDOLAd3U4mUWfwvSJkShxhnByEKP05/ZS6kt6bRypuZbpNoilY8e9/CqAR9/9iwj5RLb/ZzV9irLm31MYXC5IfNgvOWxr1Hn1WKJDWCftVy+do7B3f8eIuhd+E5cZQknUiIhaE2F3NyraUymLBU+g0yiapJyOaKbZiRWU8SSYljQoKAz1uf6YEi2dQfLG4oznYjtxGe2ALFZ4AeSB/bM0qJEmFtypVnOJM+s/C7N0t/fKcj9n8b91cOQ7z9+J1ZbPrzxRUIZMlea49b63Ty/9Wc81f0YNjqG1n+dO2f3su4doRl5hKrKnz77JS7c3ODg5Ch7JpvgwEMiv4kjBf9rSSWZm25RigJ8qV7r7ezatWvXrl27XkNBEPChD32In//5n+eVV16hVCpx4sQJ9u7d+w2v+TUXUq5fv/4Nf5Nd/238lT0P8OjqJ2hV+myK03zu4t3MXW+ycfM8EzOrHC+1WL8h+cTSZWw/4pbKNrfvneTyWpfN1YylxSmqjafxeiVICtbyPu3bJDYpIzV421AaCvy+YdvC/NwYNZOT9wqqxkOWAsq+wm2P0C8VmEjgVxPCMKMSphgjWV4b4Z17r3E0bHN6LmZpuUophlvmJjmXbhG7MsozhKmPqGtaU30WOyN4fcnFT84igHxEISU0Zwb0tUejWhC2R0lSR1sVpKsW1YyYIacGsALNqSF57CFTgfYFpr4z2lf1QfiCVlRCq5Q4MgT7HBOj23TPBXSvRbgbPrf+zUW24z6dhTlMzRGWFLGnGbmthxKO44cWqUY5f/bSG9CJRGxrVDtm/VCFk8sHODixxsmL08gkZ6rW5qG5mywWDVaWmqxtVnHWgWfRYx4EgqQhCHoOgUKPRlDWRHXFvW+4wnLDJxlWQSiKmsKWIsrSY2qkzsL2CHgCqR2mJCjGAhSCmmuxd7LC5Vf6LN8YYSw23KKniK+v0RcJql3gp4bjP7hAngrOfXEvTFuy0T7XFjWFBKkER6YmeXk1x9QNbtZhJQitmG00WLnWxjmPoQ2RGDhwjdn9bcbOGV59cgZXdtiSI9qbcvr0HPFYj/Z2DVUolFWIoUBIH3BQkuhCIQsNTmIDhcPhrEOanWwUW/Z2shuMRVqBw+D3JCoXmAjWXxlB+5DWQopCEbgBg4tVbCzxugkIDxcqQOClPrxY7IxQzjRCC/BBpR7phI/sGbxM0DxrWP92HxU0cVsxaskg/Ayd9glfyvGkQgBOVZBbA2x9BKcUTlp0OcA5iY8BCV5fI7cs21drJLcF2D0eRdmjerZDMtGAmo9zHsQZNnI7AbA3JaIuMYTE8zmldY1aN+Shhy0HcEsFOmuoXopnHYUawoQHgUKWfAKtad+YoBwLyreMUy1JkscvI7QFIckL0A2J8RWuIsE5uttlPv4Hx9HCInGoxOISjQ4E+ZEq8npMUDguPnmVaiOk46okDKDqYVHIGwXv/rFFbn37FpOfm+MPBwcQr26RN3yKd8WMp1MMLmnkpIcQEAiP8lhOryeIfIWWIes3x4j7LfSRiPVlywZ1xnWKPhuirCTe3rke40uBUBJbGFTgY6fAbIEsFHtnSqxe7ODnICvgTSY4BI99+jCcLlEOCzojEj/wWEkHeOMPo+wKF26G9G9e4uDeUdywoMhTNtMYoxTSWfxNx2xR5rZsjFuab6SuJJtLA7akYTuP0E7hsjmkOIVB4Nsh3xW+k71vOM3GjSorww5+ZvmnTzzOex+5h0Y54sBEi1AqbtzYIjAVuDnKxJ5R4ok9nD0rWMoKbEUwXq+gNwYMPUe1WqKuyszuG+V89yYytnRsTK0R0Rjd6SRNk5xuO6bRLBOVA4LQx/qOqVCzlbZ5ZOzbmSntYSu9xpHoDOXwDMSPcGNlCM1bedmd4bEL/wY/38OYH3FiZpKP37jIStxlPK3g3Ovn7adyJWTfvnGEfP0Ud3bt2rVr165dr619+/bhnOPgwYNf6Uz5Rslv0p52vQ5UImiIIYNulZu9/bz1jjPU5AaZhgLHsOsY5Cnas5QPtikODBidHTKnpiEJuZkWnFo4gFMChUfkK6xzGKchMwSJ5i3hXg7MNSnfdY3B7X+CnN+gM8wYbqV8//7n+PUHf49ffd+97J39G6RBEyMCPAV7Rzrsa2wTDjTPvbiPZ6/vZbs6ii17DCmYGx9F7cuYsh1mxtZpHdumPJFRnU0oSY0swDcKmdqdDgTfERcBWdlHlzNW0jaDImUQpxTakW2F9E1Is9Uk3q6zeaVCb7WM9QReWiAqkHsGYUAOHT/8yH20tqrkz1YQvZDWAx1qB2Kuf2qEtc9ETI9vcHD/Av09QxZMh9qrktbnLe6awt/0mZrsMd7sUpcDhDVYX9IPJMMRwccv3MN3//Mf5uTVPQgk18+M8sXnDrKw2WLFG2E4HWACgQolSkj8xOAnAj9xX36B6yiP5qjpgtPZGP66oPhUQP4vx5AixEaK1Hf4TUn1bkvlDkMpTolWC9TQIAaaC59tc2/yCFtDg9EKtVXlr7z9Du66/yZebpm7bYN7vvcSoyeGjN02hHGH9iXZAEpRDVH2iEYiZibr4FlMGZywBOsGv+2wQ0PSUKzdGWJKHvF0wHLF58LmFG2vjjdaIvAdrWDIiUducOj9GyyuNEhzH3zF2KMxY5/toFKDMgKROFzZx0Q7o6FFUaAGmmjNEq7nICQIgVUSaR0qM5RuOor6TiaJkI5er0wiPepTA1pTPfJXA9LIx0QamVm0MCQzHslcgFEWPVYmny6jmyHkBU5aioYjrzhsmlDEPUpDg7+uoFFBjNYpJiNsxSfatKh1hxuCV4TYsuLmO0dxwuEyi8tzrM0RgcOGEjwPYR3OCZT0cMJBYRDdDJFrUDvXoKQt8AcChKNoCciAQqCGDlv2yEc8vBRKXwJ5ERhA5/4GacUilMBJnySS5CVHZb/P0XctMTa1CaU+dhL+5nc8yBvfd40f+faXqN6SImoKkUJpKSdas3jJzqQjtynwrse4WICFUEtE5OOaHqYkcdWQ7e0+W+0ehXOo3CHzCFYdUkg2rpZxRnAjHUUuO1RSYnLa8Nffv8C3/dBLiClBNuoxvhrxbdNbPPhtn+Hg7Ys45QgjuO19l5l/7zUqNkJcleibAc88c4zVR6fwegWlDUtQsmAtrhwilQMlsZ6kpApOfOcLPPQP/pSJVh+vHxOtg9nyyLd8vIHEqxfc8ZaLTE5V+cRTp/nXn3uWTrRJudGmoWMOtEZ46Nh+JhoVcvflyU9CY5VDdR1swuPrNxkbNAkHEau1Icfm5/joJ97DH33i7cwWY6AVnjCYYJHN+V/lLvcIR0eOYcYFugqRL+l1ExZvbnP5uUXCxBBIwV2HZzmU3cVTp/ex6hVkLQ/ngy0Lzm1tMRz3qVQjAiVpjVdxzZ2rboMLAQ/Up5jeP0VrYicENksL+t2YNC2AnS5SpSRf2mzzYqfKYtzBWsvVoSAyAVevzfD0mWusbPZI8oI4y8lzQz/J0ZlmozNgrdfH5I6t7cFOQfh1xBjN6bNn2FzvvdZb2bVr165du/7bcK+jj9eROI754R/+YcrlMsePH//KpJ4f//Ef55d+6Ze+oTW/5jLMs88+y/b2Nu95z3u+8uzf/bt/x8/+7M8yHA5573vfy6/92q8Rhq+P+9H/PUqLkC9cvIVKW9Byhrfvf54vbOfEg0cYXTuGiD3CpuEQhtbBRQKlebbzAs90xzm25xSnl/Zh25LNXhmjMvbvnyWSGYudNqLrqI6WmZ8Z5fObn6DdeJTNtMK17bPsbTRIvCkOhjfopobOzd/haOXXabkrrNGjl/tcHY7w07Of5we+5Vl+5RPfyh+d30NRczRVivENC2mXu45eZ+nKGGt2hM5GHVcVRIWPXJKoxIG1KA0CizvkWPErlIcFLJTRQiLLoJQk6GvINcYLuZEOyEseOInUO2uYioerGhiziA2wPnzqxgVuNA1xHuC2FBvnRulf8iFLSXseX/qzgxQHysibkHUSNqqaYFRQeRnUQ44nL92J28zYbtdwkYeQlpKWqIFHZ5ijYggCSRSVWV80PHelgQlAtwKs7ygyj9BKZGKJepo3ju5hVffpDhYZ299maWmCckuQ3Jzl9PNTVOaBeUf5isAqjXTQW8zR7Wl6aQ5DD1EGPzF4qWF1qYN2BluxVKdiitiw3k8ImaYe9hmfalObjVkYjJMOJDoTlHoR07UR2jcGgMBD0Rtm4AdENzL89QK/IxF5QU/k5C0fhEJYgVCCi+kE0lkGmw2mRyPS2S3kvgSrFPEVRa9T2plcIwSeKBCDHBv6COVQEoz1wPOQHihfYBwI7VDFTtaOw+LVyzjfgoRsv8aWHSIXFHWPWR3QXi8Ye7iLdI7OuQA1lJgoxFZzRFogewUiUggkcmAIty0qFTgUXr+AURCE6KqPFxeIjW3mfm8T2ypT1EtsvrWG2LT4wpDOOYgkZjUh8wMI68R7FOWlFLfSxfgOYesQRAhjEUZgFLjIJ9gCU/nyDbjE4K/3yCdHKEKHbyVef6dwIqyHC0B1LV6nwFvLsVmCvyFQGwU0IvSdJczBJsXVFDsaoFQJEwi8hiS7MUEz2GZlX50vFVc5GMzzN//6deYbfTgl+JfP3U64XmCtAyEQ1uGl4BplfOWTjEKQOGTh0I2Q/j5LtJbBQFEUkC30yM0QE0qcZ1GZQM/XeedDZzjRjHnlDTHnAkd+UZC3AjxfIxsd7vu+s3zyI3fgDQ1RkZNnBUm6TLevMMpHho7CWbZfXiOcKFFai2GsifIlqiJp1nJajSFdr0oig51rLNZR7vmIrYBn/8PtzEx8gbE9y6y/MobNc9SZOWyzYM93tRFa0nu6wUBqrpy5ietmiP3buMEWoqZ5850HuO3oPLqTs9qPUbmjNhLzbX/vJbaKKn/6dJ2RbY8PnLrKQKWYqSppojhyrczSapcXNq/hajX0ZIfD0SqT/jZP3VgCBI0RRREWTFMjCD1OPnGDJ750kWEv4b4T+0gzzfJwyMp6m/4ey57pCqaegmdRgccwyRlmmj86c5q/c+e9hIFPlis6aw3+8OmEqZE1Hji6D4BqvYTnK6JS8FVnRzPYhxxeZ6H7r/icLfM71x6j2ruX41v3stjxuNFb5fve+kGOeYKo/L/yJ/5TzLgVclflwHiTxfI2x+cnUer19d7Ml67+Pk88/wL3xW/ifRPf/1pvZ9euXbt27dr1Gvmpn/opXnnlFR577DHe/e53f+X5O97xDn72Z3+Wn/zJn/y61/yaCyk/93M/x1ve8pavFFJeffVVfviHf5i/9bf+Frfccgu//Mu/zMzMDD/3cz/3dW9i1zdHw3uEobfOQDu8TckTL99Fj3nqrTEeOj7LVFimazJeOHeJyS3DnqlNxvU4xUaC3BcyJTT9gUKvV3YmpmhN2/TRFUNoBGNzoxy9ZZSnl5/CaY/RUp+NkQHv2HuapW6Dj5+/lbfMX+DcVov7Rzp4SZ9SPUMXPnniUSchk4KxA10Wz7SI2gLaGj8xXLaraHmYlXQEVwgoS0Ri6boGHil51eINBTaCfNJHSIvnQSbhFircVCHlsRL1tmQt6VL1x9BZQiIMuqywPlBY0JC1FGnFYo1DIkDAxe4WYcWjXB8QrVvWH5uE9QGlAzFuS+G+FDG2Oo5NNWo+YqE+wDQlI3N9tA7InM/1hVmogvQ8SlsZZBnpsiJshYSeIUss250YQsj8L+dw9AoIQMYGL7c0rkmyvuZk7yZ7Dk4yNb5B3K4QTWoyMUo+75iuRcQzmwxtQXlYI7kZI6Vg4CzD1RJFV6IiQRo6dEkhQ4UygtxZvFCTRRJ3NOfCygKjB05y9Lji8sVp2perdPeWsMLhtwV7oypvqc3zaZ4Gp5A+XBy0KQJHPrpzraiSZiAkhZ+B8XDGoiNDUZKUTIrfdpQu5UwdKfG97/k4w8Dn13/nYdJTPkJn+ImhfFTQ+B+2iJ8v0f50gA0lzZk6/U4KDYd6Z4K4EODWfUzXUdR3Jj45KXHaUoQ7E3LKz0F6VKOlxQWStTSj0Q4pzoxSiCF6w0dUJcIT6MAQbCZEV0GOKFyoEOs9RCPEBB7KBzE0yG2HDBSu4kPk44IA44Me5CgrmH4sRWQehA3at0u8VDMMC5JDPjKTuJIjFRkEGbbmke2FsRsOFFghULGFWONaAudJyDUEHtIpnC4wwsevZnhvyGklju7ZGtTVTuaFk7jAYaRDFA7pHKVA4Z/RGFun875RrAkJ11KkKOie69D3JhifG7JnuoctzzCqIz7+ymHeOnODpz4yxcRTPTwnyCKFkw7nC2xiETrFCQ8vNdjIkYcSWwEhDX4tQgwFWmuKikAPJNZpaMfoW8ZxVZ+rN5vcqdqs6zqeyLAlD0lIs5zjiZBsu0Z1SRNtDllu76F+Yp5i+zJJkTIYrXDmk3sZX1hDrKX4UYDo5VC3DEqCmVFF/Uifss6I94Vk10NcL8czlpG0zlamsUbw4gtHWHiygdBD3NAS9QpCqyhyD1nA6VPzyCMFbX9Ia6KMaq6xueJItvucuXATFXosrm7T7yd4vRzZzJGho16LScoFzQ3oX9K4UcFEFDEellgdS+j34Uo+ICg8imENW/ao2UmGxTKdIkFdL6AfcGVPFzxBsxmRVx1FKOjlBd0kJ9uKCTcy6hOSaCMhqAqcErzj1kNs3uhyftghLzRow2QQInOD1HDpfI1fK57ggX+wD9gpNEeR/1UFD+MMS8nazohqBrSLs9S9LrdMLSMReKURjjdWKEUJICjXthnp9fnJ/Z9G8Fl+99T7yI7W8CuHKUfBf/Zseq1U6z61sYyR0dfXvnbt2rVr165d/2199KMf5UMf+hAPPPDAV+XG3XrrrVy5cuUbWvNrLqScPHmSn//5n//K53/4h3/I/fff/5UA2vn5eX72Z392t5DyGnpy7QwxfUbGPaaHBzkc/CjVfQ2MhPsnJvGl4n9/6ZOsHFylf/U27t9zEfJDDLMXeOWZg9SXFYVrIxuOrKrYpkAMfDxnMcKyeX2D/9cffImNN1XQWlCRhs20wdPiLi6f1myOC26fX+TQ2CX+4PrncK1FDsSCS90RtOfxxPatvHvsIhPTiqnMMFyKKA3BZY6y87h5ZhTXcpiygsTiSo6lXoKLBF4AonD4K4a8JygdjimXe8SfGudS12FHC7bKA0o3JKIQWKkJYk0wyCgdznnfO87zmQu3UlwO8TcM5oLAVj10ZHFOMLPqUZ5PaB9bwctg8/wM0b4BI3d20V2PzmNN5vePs7SwxaHxCS52eojAkEtIVySbL5fIjkmElIx2BH7gIVXO8ROXkOZuTg4t2WCALhyiaaApsH1J0AVTAM6RoLE9EIGgU7EMvS6qt4daUZD1yxy+c5TNxjWO3/Ek3fU3cuO8Ik7AOcgl9CPInSTIPeZada4sb4ADHUFgFKmw6FUPW3XYFVhb7MBoi5vtEoP1EYYr4F5y5GVBPqGoH6nzhrcd4dnuC2wuwobUWC8lGwdrBSiLCUA2ApQNKbmcLHVkFYOwknccPYO4abh2eR7qmtl6hyz10cs+tiMI6oY88PHnM8rTBnE0Z/vpFiYU3H/rXl48s8j25ACUQdZT5CsepuLjlEQHAheKr/wqPZDCo3zGoWKD7RbYyDFoCpJHDWHbQwSGdNbh9y1CW6QB8+VpO06DKEUYXSCkw2UaZx2lmzneRoFrBAhf4TIwIyF2ronUoLdyvJKHixRTLxtWHqziKYdEIgEzHKCW25gSZHMlbD1AeyA9CU4hckMx6pPOy51ck+XhzgSgiofKFCo3qBGLv99gN0Aq0E2L13ao7RQ/EcioBDbDCyUy8CgKQzoisDrCVQVu3lGrDBA3BLWkz123XWFabVL07+Gu2Wl+42MzfPiPDxFqRzguMNs5cpASrEiM9SH0ENrilEOYFJdYAi/Cxh6V9ZyJ8RbbOqUQ3s4UpBsJojAUNxNKIwnv/B/Psdgv8z/84HvJbmsizhdQr2I8j8g3RLbO+U8cxwvTnWt7lYCXOjki30/a3YQpix9Zpo/HDNsT6I0MnYNTlsLziPsSc72OVZa4qJM3wAQWv+TYnM1gzWGBS/4EpdkC03VI6yjWYqxscukTe6BcoMqOmucTlRR+ohk8N4K9liIHJa7rFW42l3nx1HXMeUGx1sduV/n0v7qb7v4Ko40IOTCQOdQA3jW2l9l6iysjMUXfRzV8ys0eYZTwyfNv4LkRy7G3/RknTI+F/+dbSLseibP4SrElc1ZmNWeDNt9+8A56pkO/N8DrG0YSx9pCj7GBxds7wr3v3IN/UFC5fJVO0GF12EMo8AOJailGb+1TJP/xjIgHGZ3tAc1WlXJ1p3vUOcfF7jqaKtt5i7ub9zETDHhb6wm+GOaU0zpLyy3mtM+n4vs5P3yKI+44xtSIKVjvVBCh466Ds6/Bqff/2QNz38ve4GHGR15/e9u1a9euXbv+Mgi38/Faez3s4T+1sbHBxMTEX3g+HA7/QiD/1+prLqS0220mJye/8vnjjz/+VW0x9913H4uLi9/QJnZ9c5zuLBIGOeO1LTZuzjC9519SuBql6CdQQoJUtEoj0Bfkps6/XT7GEXmFqekxNrorZMOYvOER3NqlJ1r0Mp9gW8CqRnYLUpmysZGzTx7jtLvCPm+L63qCRuWt3HVLwsud67y49h5GZMpKtM3AhcyEPZSGhjaMdi0pVf7W3NO07sr59GM/zXNpzlo45OjMBBtnF6ncLHBSkE9ZlCjIi51uCt/PMM4jMAK/n5MnEA9DzCDYyZcoS4pCgXLU6hZV3WKQV4gjwfe+7VXeestZWsGA3/vQm/AyS7WSYZ1EBYIk85nwQuJBRr7p0e+XsA50u4zJt0lWJXjgl72dbA7tKG9BtsfRbZaRJYHJdsJQEUBiUWWfE286x4PveJ6rFzb4/MIDFFUJKXhK4/clJhUE0ic2BuvtTJtRvsQVGj+VTOYB3a0Sg6yMZwUPsA/Veo40S2ifGsCNaepNwVY/wfqSipTsHW9SH/cZn25ydXWLrKUwFcmkLXNgdpxW6qF62xTbPpsLbdJPHmMtHmJ7GSpz2EqIqfpECoZjMf92/Qsk+6roJ4aovmVkIGlnlv64xdUd3oaktS6oT1foFBrjOaQB0dNcfnqcWj4kyxULw3H+7R++iQ0qFL0ALzTYKAIZIkxCRaTYwKKlQ0zAxcYGOjKohQDdLuFWQGqByAt8DboekFYEColMwXoO60PWguRwiNdzqMHOVbBgKLEpCG0RRqOGGpWC8DyEJ3GewDlw0uGlAiMMeZqjogAXgbeVYXSOVB4i0ciuwhiLxeEmKritFC82yNwy+5RDaE3cUnTvkbCSoDoxxR4fKmXCbkFWV/ipw2YFdhL0uEJmIFHYhoK+xEmF9SxmLMLWLa7r4IxFDHJk4YMU+EPYmfUsoORRICl6Od07AorZAH9VU8ol4vaYaDaliELWPzLgxsooRmk++2uf4/Y77yPSbRRVbCApqhbVduAHBD1HFhW4qkD1NXK2oDyVYdcdgfUYTvlks7B1cpFQ1ykseB2NkT5qdRuVQTQtac0n3Fvd4O+94wz/4sMP8PlLx8CAC3dGQhsjiCIPfyKkdxyE8smGBVtpjBgJqPYGTGbrbG973HbfHp779CVsWeIKh6+gu1Ug16uY2MOVYXhLCZsPiXoa5QlUC3SgqE4myOOg1iRu1cJyhiyljFZ7dAqFWwkQIwn3PDTD5EqbU6+k2CLBdw5PSczoEH/ckT4To41BWsfwQplw0af2zhA35nAXt/GU4M/VMvd3NEJbtA9bScyJsTU28wpFM2NLQFtXmC510YEC5/C1JPAUrVKJehQyKkr05YAvyucZ//bLTF2KmGrcw0oMxVaPpq0gM4MRkrDVphu9zPP9HtPhDM3QkE5p/DFNY/k/HvNGG4aDlFq99JVnSiic3ulsmvQ6hIMFJrZOcLr6JL1+xPLGKJcX9rDSqnFZdTAqZ8u+wsXlf8CZ4MP8wPGn+OTFh2l6r79rvXlm0cMyQz+jVIle6+3s2rVr165du14j9913H3/2Z3/Gj/3YjwF8pXjygQ98gAcffPAbWvNrLqRMTk5y7do15ufnyfOcl156iX/8j//xV77e7/fxff8b2sSub46/eeCt/MK1T3F1Y4RZf8DdU6f5jRfvoZe9xIY6iMos77rzNsqDBkuHF3lx8xTdiQFvnDvIF2+8wLIcQ7UdzvfxywH1MGTVSxHGIK3ELynqlTKdxNIYyVlcmqDXqVKak1yf7aNqIQvFNJtCcHfj0+hc8tipowxNhVIj58+yg1zOm3z3xEmkgrFKnWveAquTOSrtYqIULcEJhbetaYwO2BiUsUoQugS3Vtr5S18uyKRkkDWIZhy1jmRmELJetvh5zqTs0gsyuvtrmCygCCxbLmJ8rI20YK3BBZZvve0cb7v/PH/4xJtwvb10zjt6m2MkLsCvOGSnwuBzNbI1Sf+djhfNMjXAaUM6HZG1fKweUmtmyBOaPdeH/MgDL/LvP3M3lzcm2Zs0iIclrixM7nQItDy8xQyR+bSCAG0hV4IocRBbUD7lwwnIGJZGeaA8xfnOBtd1n2CoWVteoyXfwsbSQW5eK9Efphw9MMFmd0BSGIrIMqw4bhkfJZD+TpBpBn5mySp9Prr8GNW9Q+yBnPJoRrjcpLc1xIkCmRpEt0DmlsCXyKpj23aJbxrSPIcySO0x3vdQuiBZtQijoAsDvyAjI6kLiDz8WOB8x/Jyk6BTI+xbRtdznnx8GicFoZ/uZJCEBc76bF2pk1109FcipC6QIx7Xs03UuMStK+xND2fAhgoyi/VAZCBDh/MEVhiMNlhPoPr2yxVwR7SoIZJ4Ryy8yWGf2HmhLzNACZwFvJ0Co1MWnEUZcDlQ9tHjPvkhh3/ZEJ7OwGTgSfLukNLGTrZLMp4jggqqn+10kggQSlG5mRL0NGzEZH1JfijEVxIvN7hkQGIMCgeuhDIScb2PswYvNpjxCtppwIeSjx2k5DcVbCgwFm/gUIXAKIEUgBPYMMR6DokiaFvsOERbOWpoSENFu9tEvqBRiWbtepXzWw1M3ubvve2f0n7THM73QWu81QSMw1Y80ikfW5IoZ3CRRJcDxLpFkSPrETIQlEJLYB1sDCl5llxKiCQ6iBC6YLhteWF5D3ft71AUkrlGl6oQZOtDghFBf3WavHsHgzRFT1g6JcmTa+vsTZpsaImoeNhYsNkL4IYl215EBAHOaoxwSLGTHVOp5Hzfe/6cjBK/9el3YkVEFOeUt32ytQ2i2QTvn2nu+Ntv41TtGZL1IWnD0rqnw961NaYHitPxPOaVDY7/1F5Q8MwzV9C5IQRu5m1GdYs3HTzB5xqn6DU9StsF3iAnHQvo9RJGaiETzQYDUZALw9lrK6ieIJACTxuGJqC/VUb2fOaDJk98sclj+k5atkTPy1HhzoH+4PH9dIotPv7ks/zuh7aI9qxxYPw6k3ds89zv38GdDx7m5gsXWLMpn7p+kTtnp7l7z15ubp2mYsqcvLzOmBtl374aF17scPRY6ytnRJJpuoOMkUJTA6y1JHmGEyHOFfStx+n4Bc53JviOfQbhSrx4/k5myyU+fWmKY/f8ATfSUe4Zucb9lc/zsGf4o/4Id01ep59m/20PvK/By/3TfOTa53nvsbfzMPe+1tvZtWvXrl27dr1GfvEXf5F3v/vdnD17Fq01v/qrv8qZM2d4+umnefzxx7+hNb/mQsq73/1ufvInf5J/9s/+GR/96Ecpl8s8/PDDX/n6qVOnOHjw4De0iV3fHCa7hGwPqPVKHJqe5KPXv5siKXP2iiYOFrkjrFMNfX74PQ/wb17eRJwOUDbgyIFxPh2F6AlJqeuwfZ9wUDAsxcjNHOFLREVTbZTpJTl7h32EF3O9GCfCcDD8Td6z731cy97I528scL6zzkuDW6m6BLMd4k1m5BXJkqlw9uIoT20e4k3hPNNpwGYpRitJv58hxhyDeghWIbSPKVcpJYJsy5G363ilnc4NlXgoP8UFFiUhSDT5MGXiPuh0cgaDEnLVUJceIwe7KAnWCZwWCGfhoMOOGcbGY6SESpBzsrNFtchJao7MA68Djf1bRLcbDuYbPH2+SYEhNo644ijCAO0MvZdqeDWB6fg09/VY9mscmdzg/GKLMy/McvnV70VpGC8bTKjoGsiEh3EeqdLYwkKgMAiELymf6CF6jiLLKJV8bCigJOiVBY/XLzDdLNi/fYzWuEXJmH0To2xd3WIt6bM6GrNtEw7EdQ5NTFDUPZCC8GZGfJfm0+uncPUqvRujeD3LiC5IdExaCwhSg1IWP7O4gWGyPMqIrnAL49yI2zwdLIEWtM02W4HEQ6ESg2t6FGsa1TX4ZUnmg3OCouoIS5IcgSKjs9ZDCA8dKXTmkIVBZRach9oWrD85grdlCFODu5ZRChLsaovUt1jhQaoRgUQJR+4L3nDHVe65c4V//4n76fZCpBDk4wFllTJT36B7sQpLjvhuQe6DGALKobqGoiJxZQEDQHrIQmMChS4H+HmByyX/x8RUowSekDsjVAXgLFG+082CtkRbOcVEiFAStMGyM4FJIvD7Ft2oEI7VmFqWbO/zcHmONA5XOMCC0lTbm2TLMa5WJpuMCK5tEHYKxLhHWsrwL/XRLUgONNFVhRo6ZG4pxnxU6iAu8LQBsZMFVL6ZM5KEmBs9rKfwuj6iolB9mHvfNnd/yzU6axGfC4/gPR4jKRF0CtJQ4nVS6n8tQe8JiZdGsAOL15E7nVgrCWQD5KaPjlOCoaBRHuPW/VOcW7qMyDNcOUT1NYzVKUYsJs5YWynxK9fu5OCpDsv9eVpexLrJ8cyQuBSz3VlgMNiPuABWVbCxZFP3uXtskvOdVfLQpx01qFRzUgDrkM6ii4KkVebwzCZvPnwFlEQnguqiQQc+ngwYrzZJ5iT9py4h52f41sO30NuzwtXF81gE5e2I5eEI8bbA+BKZWyqNiN6woJ/l5K5Pg5Crc4an17ucGLQwdqdQKCsRbpCQS0feKXCxpN3bRpQz7hveRmelC1qzxxOk0TqLgxF6/SrVPGdk4grh4h7GvTYTjZiTnTK584jTHCElF9IFltcHlAv4gfveQDD/KfxAUzmesHR1m16eMqj7PLW5SDJq+b+IN/DI5reycKPDleEFrLG8IWjibrnOaNV85YyISh7VakgY7bzpYY0F4zgUzHNVn2XZNNhXnaU+v8Kbq9cY+Bv82ciDFJ5l7NZn6CQ1ttMKjZomlgP6RZX1rMpKOs2xifGv+8w6s7LGn5+/xF89fowjE2P/dQfgf8Z6skXhFWzp9jd97V27du3atet16fUyMef1sIf/xBvf+EaeeuopfvmXf5mDBw/ymc98hrvvvpunn36aEydOfENrfs2FlF/4hV/gO77jO3jzm99MtVrlgx/8IEHwHwPcfud3fod3vetd39Amdn1zfOb6R9mwU1S9AWf0dZ47dZgD4RjrZoNYah6anOHi4hafuHCRF4otGhMFJ6YCPhVfpYimaEYFcyM3CXoxmxsHCEqKjdCifYmyIc5TtGYaML3GfGWb3myZ+ZkBR1on2V6sc05PIOInOOjHUD3MYlfSrTqsFdhCIYxDzDnWnMf51ZxqnHDA1llOY1q5Ym1FEuyFogQoSWF93vPwWWaqC/yH334zuQq+HGwKgZaU/AQ5kBSZJMkKimtl9FJBZyiR/YgoguZ9fT7y6IM8M3qU9WergCN/o0A1BH/08t08/uwJbnQnqbcs4WTG4TuWmK/0eenVW8jyAGxGX5eoPOsIWmCGlu2VAY2apncwwZMF9pRHuuRzenuCTlhiqxxRmbCEa4os9BDSUF3VLN0CGoktYCstKLUtMlBYZ7AVRTEq2X5mhKiW4lYi/Ns85mtVLmc90pqivRXSGLWEhaQZBAQ1x9pWj6Hcyfoo9QXedMSwZ4haETqw6JpHMqrIKh60QWq9k5sy5+h2HUNbxuUQbihM6AiFQNYMnvCQVxTz4+Os3IwJNsBYvVP8qfo7N0putXjDHLXiIzWkvqAoaYSvML7cGd8L2KYiWwNfSZQFqgqGAmMDCqlQuaV80mBNgTCOihtwRG1zLmkhlIHYIvMvd5w4S5Fbfui7n2ViZMCNpSYf+9wxEBIrQYxA4TxE02GqCqmhsALvvEQUEW7UQ2Q5LvRxgSWeUJhxj3DN4qo+/nIPv3DgS9i0uNGIeDTANRyloUa4nXwVJ3emR/lOURQOaxwY8+Ug253RwAwznPAhCPC0Ri4MoC6Q7RBvSkIvpzTeobyWInJH1pOYtS7+mkZakKuWkU9v4hvQWU647lDjDagpBA4bgZdYZGEQoxKvpJFJiMoV5GbnuhIgDET+TlFoud8kffZWkjVBOFFBHxT4K0Okk+ijJQYHy/yd976IF8NvcB/u3IC+38JWPIL1DJNIpARrDGopp73R5+x4AJGkaKfYskCPeigHxkuJ727RI8IfGobLAbfcpogHHkqk3PLwAvXRHqVGRqN1iOPfeY61zTrnL+/lrlsv07+xn1Q51FHL4TducuWFaaonS4iXN/BzA04Qz9b5lrsuMVXps7ba4PlPzmINuFxTDAZ0lyKa1RL9Zp1KPcDsTWntr3HtZIVy7uNpw5v/59O0hxU+9dN3oTuOp5+9yIuPn2ek1iGpr7L+kmZjdQ7dKGOG4GeOqKNxzu78PbECjUW4AQQJCku/EzNzeIaadGRn17ihHWkRIkcKxuMeYRFh646bq6MMSt2da1A9Qy9O8a2gsdBiXLUYG53g4J476UkFNsd0JS88dZmsKCjqFfbQ5J17DvPR0+c4d32d0kJMJfSZ2dskmziHGjvF5rYFvhWALCmIewnJMEeFHsMso1Uu89NH388fr/9rNhnS9Gdw0Qt8bDBOw9W5mefkWcIb3FE28mtUKGiQEwJPZi0uFdO8YWaeA+Ot/9LR9F/0uQtX+NzFK9TC8C+lkPKOqYeoJS3un7rlm772rl27du3atev/NxRFwY/8yI/wMz/zM3zwgx/8pq37NRdSxsfHeeKJJ+h2u1SrVZRSX/X1D3/4w1Sr1W/axnZ9/UYbx8ltlzSDqhMYqSk1I2ZknZFKgKckpxZusqITNmqbuEbKKe9ZbmZTOKmZzCP80oBqfUDDV6xZn2FDY4XCH0JSWAqtmfJOMBJ8FG/EUWRN8pUmJzszfDG5jBaO26s9GqUX6echrnQAIyW9JcUJucRbbznPq3qe5fwY7WGTY41J6qtthsMYz8tprRdkWUTaMijfMdO6SqWUIecy9GJAOMhxWpBjCFHoSCFK4G0XsJLiFg0qKzBVD+lLlp6bZDBS4uxwL6XBkEq3h/9kgLktBGe5sdEiyuHO6QleIudBb8iD4gYT7xzw9FP7GMu3YF0S1ss4q3BVx0qRoF4eUkoNzTf0qd2VYotRsp7HwmqD4I6Ehi+Jtn0C6bEwjMlmIZ7ViKGgekrj5wWmpCjlAm0svRlJNqPgakh+0Uc14Mb2Nms2RUjw+4b5tMq93hGG3QKvJMGThJWQsf0t2lc1aknjXY65UVrHDQqE3hkXbJo+MndIqyEGUyioakaiCmk3RTuLHitRag858JPLOAVnP+8RZnWaS6s8vXKNNMjxhgo5LDg0ucxVM00xoqGqkcrHpjl5TSFUhJeDHDpkLJC5o259+qrA4fAyEKFGKkuZDBUoktSnGLFoPNgyDJMyF2/MooYOkVukMejRAG9GM7bdp3Ojxu//0T3ce9sCrz46QwlB6knEUJDqED82mOsK8PA2HNkeQ3LEUXvC4qUFNk1RqcVzAheIL2fbaJwE24yQKYjCggM5tFD1cHsb6OUYNSwo8hhTK+H7Hkr7+FEIPhhpUcYhewkiMRjfw1YDbHeA6he0th3+uI+JFZ0oRE9WIYyhXuDGI/KwjH/VgtUYJQisQGQGUxiEB8HFGK8jyI9EqNSgjEVlgJSIgwZfOOrao39S4/oF0lM4rZFNiTFQeILkbMhGr0VpMGDmnpSx+Xk6jZe5782rfPrlO+hsCnxj6fUriG2fbtLCBgpXNqjQA7+KGVqUVMiST1UrzKLG6gIjLDoUMMiwGEQQ4PWBTYvKBVPHD/PQiSNcvt5hlQXOf3aG1nzO3sY4D75xnfKBbUb3dfE9y/49N7i26XD2MD/3/i/w7tEV/mh6nsevfwdn5RbCFYhCo1LHF88d4K/ccwa3FjMWbXAtmQClkFsDemMWRsZRVcWed1X5wPofM1AS7QxFnjMiO9TClFBpcs8nPhHyjz7254ydTFEh2PI0nmsz/lhKfWKGu3SDzy+fRYQhzllcKPBCj6nEcuJ9F7nxmYjuhRFu3CxoHnCMKQ9tDGqpSnWzjVcKSJ84wXpQJ26uQiFp7w3wbig0gnMXb3JgbIR9UZ2RO+5AjJd55sYmw+GP4a2t4S+PkqabOOvwUsfR2hh3j87yh+0XkY1L1Oaa3N3fw7e843a+0MvordUIkumvnBFpUrC1OWB8qsnnT56lE6e8965bmZ4YYa6YJhts4SvJ0IQgNEEQU6sL2nHIeH4fYXYHB/b9NLEyPBU3uSdc4cl4lv2tKoH3Nf848RVv3L+X1W6PB/b85YTBnl7b4okLW/hylbfceugv5Xvs2rVr165dryu7HSl/ge/7fOQjH+FnfuZnvqnrft0/+TQajf/s81br6383atc3l/GP4U+8RGADTsh5ksY4R0fGmTIlHjm6l0npIzc6hOsd0jhia5hRDgRv3l/mY5/oci6HyaLJI3duspRLyjcj3JfDXj2lITcsL25TWzvHkdmYa4xTeBmnN26D9Ha+49g8CxtVimyV9QWDjVep2pSh8CgiycOjV9iTb6OFZBjuYzEdstDrQF5wbK7FzIFleltlVl+YxPoCawUf+vybqKuM9qCOpwQmd3jrGeSW3EpUJLFjjmEuCXoG5SkQUB1xtOqKlb7amSTi3M61HqGwNwUUIcMZhXQOETs8z5KPxDyfzjPrdQj3pBwYrBKVBoiDHievQrnuI0fKdEy+86LfSYoLAXY0pz8aEeMTLluCaonqShmcQw9zyCxoD3/NIWKLWk8wLYHeLyldF7gE/K4FMvztnU4DKy1XNrYIVURpJKCpfN5x4DA3Xlmmn+WoWsBWJ8WUffKyxO0reHDyGs89P8Vw4DgwfoAzN1bpDA2m5qE2LfVXYaNUxnoSuWGYlFV63YTcOlAKrwmqZtGxwEwXCKOoTZfoW0E6IameFqhaxGanRC4L8hcjsnLEZNOSpAWmGWCVwxsIpBZgJJ4n2LdvmoViiH1qDVcYtAiQqsB5PsJzUFPkowIbegRxn1K1QVWXadsEIf2dXJzQ49j+BarjQ1whefLZQzz5pXl87bANRTYVIpVAdgXZSkCwCXkEGInqGLyGxg8FriNAiJ3uKA3BagZDkIkhmxMkkyVqL6SENy3WSaJVja6ycy2ocAjPw+0fw4yVscLib+WIHGwkIAghtzsdIbaAaoCrKqCES0FaRxJLfN+julaQxppCVul6FVAhouVTWIvpZxhP4ScOZ8FUPPy4wCpHMV1Fj5Vxq32CzQJ8D+d5mOUQGVoGA0uRaoTRCD/Em7HYObBLkmxEkB32yEuKKHWEq12uPh9z/JFFxqfXOHGzyslzR/nfTr+VyrBgc3kSzypEZhBpiit50E3II0k5tVhjQGtAQFhG2gJVWNRAY4sCL/UpdQrUqRx90MfbW+faSoyOHTJpkRY9ek83ePNPPEY8r/jffv9hkuo0W9cl8+UhOjoIZY9aatAIaj3HlQtb2GaE9QX5uKJhJWeXJrmc1fjBmadpyxrS83ZGYwtL706fbmnI7GpI4RSmkBROYvyMaimmvenxuT+7jeVunXTUw5bETiFWa1BlikwiG1M0o4ijK2W++PFnIPly4c2ClgJmfA7OTNLpdej0NKYPomVIbnY5vdbZuRbVNMiKRVQ0o3eUWbmcUwsT9AOa/VMFJ6+WIDT86m9+nImbmuZcnRPvuwesY3H9Knn1SQ7dscLamWlKFZ/UWELpc6Q1Sn9zyF+7a43cfxQVT/DMn3wnH/ncKe5/5C5mSrfyyJHDXzkjJqYalMoB9WaZ5mCLtCiIfI+1+GWi7APcE8Hx1k/wUud52u5OynqS7528h8V2wr6oRScaUlYFAD0X8EIxxh3lDvu8F3Due7/u1PuXFm/yytIqJ2amuX1u5ptyDn4VY7G5Qefm//vv3bVr165du3b9v9n772hbt7O8E/zN8KWV187h7JPPPeGem6+ykECIDDIIY4wRiC7XsNUug10e0A0ObVfh0Y3tripslwMUuN3tQDBBIISQULooXenme0+4J4ed08prfWmG/mOroFSCAglcYNi/MdY/59tnrjn2WmO/3/fM932eP7V827d9G+9973v5W3/rb/2RrfkHElLe85738Hf+zt9hZWXl9/3Zn/u5n8MYw3d/93f/oTd3yJfGXBIyPRNworJIMGhwr9NjNMgYFgXHZxvMz82xMttke5DiBiUeyygvufTsIlvFgd9FJ5vm1p0hL4shX3PsJCe0w/X6DEcHD8/KOSbFDnuuRlNmjHzIUnCX0d4zvPAKXEpTJsox1TQI2abWm2BmLLai+Wh+iqFRPD85znDiqU0KzKQga0v6SwKXNjAakiMZYd1STAukU2zfbaJTC4XCNDTOC6boYwNITUDRD0F6iliRHonRmaXMLNn+kEQl+OLgYc8JcCH4MECknuR2SbagKRqSbHGPY1M7uMTzi7cfpHK7oHgqJKSHsxHZkwXeCZKJR0iHNCVmUbGbN8hfjYkrgmGiKTuG9BlFq1ZjpAp6yyXZNLgU5FChhg6XaGQOlR1FkIHJId5Lie6ArAa4mgatWKrVuJ1N2DEWF3k+vrpK0s+oBhoQFGmBsR6hPe/52k/w5jNXed+pi3zg19/O8oMLrAx6hGtd9nfGoB3dswLnwFlIMsXOXu/gVN2BF56yo7n7wRmysEK+X+GR0zO8/MIa042ETs/hl0KyhqaZhcQ3++gBNGYrHFmuc7W/hXeWcBygxwKRG5JdS2gdeTzh9Sfm+VzYpfTmIGXGQkGAkHXCKUFwdoLdKtBjj28r6jpgqDNK53FWEaQQuYCg5sn64EyGbSiMDnCJOjCYtZ7sRIZraOJXPfGGwxuPviFIHhaItscbgdMRJlZYX1DOxpi5gOrtEmUcxns8B2MjDk/2uKJcgeQzEm8NVklsFBx0s3hJWjhiLw4Mar07EGmkACxiUiB7ApGViETh0TjcgdFtIIm6JdKW2GaA1Q6VOybHa4yjmECETOQI01ComxPqt1Nc4BBpgRDgphN8z6KNwHhHXj/QNMywQFkHJXhtiE46OmlEEFpEAqoi0BQk+ylzzXnu7K5x+VcW2L4ZsSWn6D7exo4yhqEjDi2kEkqP6ubIGUH1yYzRq3VkUaEvMjKRMn/PI4wCFRGUJT5UaBGCk5BbimaEjSXl7oDtEobbQ2xP4GXI9vYsk0nEKy/Ps/b0Md7yTY/wTLHO+nMnaRyvI8wt/vb/+PU8fmaN2y8uMt4dwHyDsh7gZ0N0LaCmB7zloVf54MsX2KFJUteYQiB0QHJ5jJqv04paLPTnuZie4hcuPcuxR29RGfZpNC2fu/dG8sAQSoiupVS3CnysMLHEKYFIAk4/sMhzn/4MvjAY6wkIMWVOUDrCjRFqZZHLN5aRWY84tJxs1ujd2+PGvS3imSbWWvJ9i2iVXFu6Rqs6jfn5Ovl1z/WZAGFzRCEZPrtG2rHcW92kc2GKr370HK9bPs5+bYTpLSGDkDgIqbYCoukmrVaF5lSF2s2H2PZPM7r/MJU4YE/k/Ozub/LYySWOzLzmt2tEFAe/7Y/y9Q8+gPOe1Pb47176aVJzkm+eeYnB5Gd4y+y38nLnn3N7MsBlF5itHeMTk39ON5hwYutRFqdvs5ZXWYmGnE2ukxZdPCWC8AtqUnecEihJLf7dE30+ef8e90c9bnX38d7jcCihftefBfA+R4g/eDrQ604eYz6osDDzux8AHXLIIYcccsghfzY4ffo0P/qjP8qnP/1pnnjiCarV6hdc/4Ef+IEvec0/kJAyOzvLxYsXeeMb38g73vEOnnzySZaWlojjmG63y5UrV/jkJz/Jz/7sz7K8vMxP/uRPfskbOeQPjxHrRME+/XqXRrDBfv8JxqbK7bTH8NWCZirQMuREq8HTdkJ/OyLdDNjf2EQaceAtMJHc3J0jj0Ou968wm65TCE1P1yEQPHBuCZfcZigUu2WFxNe5lI1YdSHXzA62vsVr6qsE9TqvDCoMliN8zdJQGd1ewq92HubY8j6nlu9QDEM62wInDBtRl+m9ZUZ5xuu/7RWe2z6OK2PCXc14qBG5gEpAVnW4KKDXUUxNdRiPBKpwTMuQ3VlPOdDoMUhp0GFEFh50IKAlNta4EBo6ZCJBji1pGJLXFXfKbc7O7bOfJzxyYkDnlQarWYTPKohI07wDugFVb6mfrLB1coCdUYhugJ8riOYnRC/HuCDEh5ZhnrMxHrG3BFaCuvf5k1qjEAsJ1gnCocFPSrAHnQdCOlpTVdLCwo6jeiRkoz448LrIDfOtKr2wYGc0oVXXzMxWefjUPGZc0Ns+ij31Kq9uzrAw1eDVl1fpDcaE1YDqFmR1R1kXxCOPKhWh8oyrjgKwpSfse3CarRvTZEdjaoXk/gtbdHsT4r2CuB4jjwjq7TqnJtP0miXjmmBQ9XSzHJ3HVNctNvGoAnTHsXi0w7HHtrj9/Byf+eyQLPHEoaDldynyCKVDrCuQU5bz57YYtxcZ3klYXG7TiiKGacZkf4goHVNvGzF4xHPjN06T9y3alehRiZ9S5FgwCj0s8SFkM1BMC5KbBfLREuohGs/0E/t0P9igGGmkFMg4xFckNhGYWBDfhWjdoPc81pVIEcCCJ9QClxhMorCRwquDqGshxEH3ZFFCqJAOQOKwSCfwwzEiTxFhhEAgXEEIOA1q5AGDeW2EndWIVzUYj1Qh5ZQg3PSYC9PkMwoZ1IiXuiQ3+tjtEdQGCO9w/QHOK3yU4MMSPwErPcI5EB4XavaHDWxhURnUh5JQS4bVA2+P+cWnkeIoE5lw76WA0ZtmcQGo/YDjF9aovWHCvU+vYJ/LkP2M5BsVPORIqin66TphHFIcaxG0HNzsU4wtcOCPoxQgBCb0pIshzUaNuYFAjFKKUYZIBTKP6GxV+Sf//Xu4u7aPfqTgo8c+y3iYcG89R/z6PdxUxP5SyG/uHWNqu0OgLYV2mFYI1RAfOr7lzc/xtU++xPn5Ff6nn/hzSFcSeIufnWEpFcS7Md3uiOcHtzklZjFpyc5wjuPTOddv1uFUn2o1wIcBymssKa6weFXBI6DwfPrV65jzIVFXQgiV2Sbp7gDKkmAPRvdH5DIjLKHZTAgDzc18zP3vPIIsYOr5IfLjFabOdvFWEY43Eeks+oTHz+cENzRZrNCtGEZDhtOS/SjnG19/AWvW2Oxdx7YEH7h/mtGo5Pj0HPONGi+Vt3BrGc98dszly1/ByWaLxeUq0YJmt7HPy5M7vNKb4eH2F/uXCSHoTD7FC9s/SJ4/gVCKWCbE0Tfya9f/PbXAMu2Ocm0nZCDuEp/tQyi4tjPH2xef502VTf7d7iNcqAyZid+A4AtT+7rjlJ/57EtUQs33vPFxlJRftIetdEDpPfeGXf7uy/+S/bzH33/or7KYfLFfih3+T/jxv0I2fwyZvPMPVBNHo4xhN0U6wYnaYfzxIYcccsghf/oRns8nWP7x7+NPEj/1Uz9Fq9Xiueee47nnnvuCa0KI/3xCyo/+6I/y/d///fz0T/80//pf/2suXbr0Bdfr9Tpvf/vb+amf+qlDw9k/Rmy2wM3NVSI/YX9vip39lPPzDWo6oC41O7f32Mozlo/NMnSWdL8K9ZxgYMBKRCSwEcSLBpsmDHoSNY7YzaYo/YFh5HD7aUxi6Z5M2JZNIlHh1Ikd2rOrtNePkwYDoiSn2i8R3TpOKgZZSBBaEILmVErYMEjlGd0fUx1FqI4nDAXm8YxxN2Jjp05vnGBDxXRzQFGdJ0kykotDRBox3knwUjAuEmRLYNuW8Jan6Gd4DXnrwJPBViQiFOAEeHCJoPeWmGSomC0rbJh9yioQQxmUZGlwYFraEjTeNEJ8sg5egnLMJxXSnkNLODHTZCvYRWpDZA1RUuIzR73vcHMBwbJheC/Fji1yJzgYORl7nPIIAWUVhHX0q5Zwp8B6wehIQG3PIgJFgUN4yfXVDuWRg4fgwAmmziTsdrYxd0cEMzWOHJlFeMVgWFCMv5bvvfkQZW54soTPfOImaVsTtytU6hFmPmNmPsNYgS0PRISZQYiXE7LA4mOFCxRBv6TINZFSrIsxbkqQzYT4UNB2ivmR5NbaHdK6xZyV6L2C7rrDGU88kYwVhOsGnRXwZsuL2VGmjg0Zr2fwKIznDPIjET4VuGSMCFMePLbDg6fWWNO7fCR+gu2lDlsnA2qVAPkbDmdLZGwBT+VYgYpLxp+u4QvwYw9VD84ddC3dNwQjSTCWSDTRyRE+MDRFRkSBig16H4g9YlpRvZsRdkLCgUBJiS8FLpDIicPnlvC6R0QKtixYDpJ7Qg/WIZxECw9K4JRA6gNDZTxgHdQTlALRLzBaElhQsQIjsMbhiwLKEJ8a1EQiawHB0GM8aANWS5CSsB6SXIyQuYP7FrU1xikFgxxXibBuRPiKQlYqyELihUcogZ1KiFSBMaCNwVpJGXhMJBhWm6zuPI5eAnSISQJUotAlVFFkt0OilZJAFAgH0jmM0ySJoCg0+V4PQhCyZLSe8cDsDPc6O4BEW4NIIrwSaO+JN0vUMKWnLekwozdM8VIgdUYUSpRsEMeWE2+5x0YvYo9ZCpfAYgTdnPyiJUot4+2IxhWHSi2iHmDxVJIEW7VkPmBIiCgdKlHYhkaMHa0j02x2BpSTHDfjuDo3IVooKe4lXL57lNJ62jMFUR263YDKQshwkGGkx0UalCSdlAy+HsxClVqZU/tMTtKuYJ2h6Ke0pqqIJYl3KXma07+TEeaezBuqNy1Rz5JPhQQzBpuFxOt1spdzdAFIy2y75Id/6OcIcPz9H3kHU8wDIx6PpugPJ3zo6h0+sP1WHjq2gRmWUDrqkWakx1wpO+xPhgRzbYqswtGFRfLU4scxj8o2QXWD3I1/u0Y45xgPM9K8pN2u8cnb7yNKhqysbTJzcpZdd5wbq3sEmxeJj87zxuY38dzO81AVHK8Osdqyns/T1BkST+Hq3MgbRMVnODk1Qqv6b7/XJC+4urFDqxLzew38PDg/x25nwtFGk6f2nqd0jhc791hc/h0hxbnJQReKvQeEeLP6B66JSRIyPV2n3jgUUQ455JBDDjnkzyreez72sY8xNzdHpVL5I1v3D+yRMjc3x4/8yI/wIz/yI/R6Pe7du0eapszMzHDq1KkveTb6kD96Xt7fIy89QQ4z4xHbI0NRLZhuFoTtffr3Eu5FBTudbbJKQKYcS0nO1RS8PhBRwhmHO+kp9jXjfUWWHGVQLYn3AGOYmD3aCzk+VMyIIQ0bc6K1xyuDk1zrdYjDFtOxx5kmFDFzdBgVU/hZgVKeZiWnF9QZrsakJsSrEqkDglCSRQaH4uXbx8nbElmBnW6dfBqalYyoVhK0c+zQE4QSmUscDhl5Ohs9ghNV0qkQMZoQFRKCg9hjAonKLF55XE0Tqwpx7qmMMvo2xhmYZsx4I8LWJD6GtVvTeCS+KInDiNlzCbf3dphcU9x9epOWEoyigtoseCnhkwGhKcjqgmwlI5kJYSCJ1xU+EtSSEfNv3WX/ZoP1nRZRD2QpQEpMAr4i0HVJo1VhaFNMp6DIUvRY4KUkspKb4S73j3Sp3nYEuac/noD03L+2BQr0FBTSc3O3SzpMsT7ggXNH6KQOWxnipMJNh2y3M/yrOcPNgKQRUlRKjr92j1or5fYvzhO+VJBOW+zcgd+MD0G1BXY6pdPJycoub39oh6t+iq29OYpJRuIEDxxb4dLNXZQTmKqkr2q4dU9/I0GklmQHTNdx6rU7hDiuvzhDfLJkb6vJpLPNxtVpXCZIC9ATw2hgwB2Mwmz/ZgtuRVSf3CJ5IMeNYsbPhQgJblrjI4VLJGYxxJ1y6GFA/SOQvy+gOBaxvZcQVVukpULHJcGTOeKERb6gUNclPlAIBDYJ8bFCSYfRApuB2rPIAoQSmHoA1QhRGmzpURa8kAfzUk7jrTtInJKgjAUrEM4SCInDI0qLkBJVGhyS2GeoM55xP8T1FSUaEUvytoXSonKJ9J7JKCSo19GqB50xGIOI9YFYsZ8STGt8RWEnB2NOajhC387woSeUAbasYEpLrUi5eP4+kfHc+/QjRJUh+bhECUvUdciJAyPYzlus7zRpXjOoUYpQimjTUTUlZS+hlBIxyAm3e4ikwXrZwQUCrwUiVeA9QlqccIjVFHtcsSE9aW+ALXJq1SonL27z7d//Ei9+8iIzA8nDF2+yc73CT318CR8apDHEGxl2OsKl4M87/GqAkwKGFh1K0nzEldtLjG0FEx+MZZWLMYVzVIcOLRWNVsLenS4lJTaR+DlPvquw1RqNjR7Ncp8n3r7H0ztfw3wyxZXLm1h18Hl7dZDIo7cstu5gJ0MKQWpKCgyzX9UjV7DSPMb1zg4mFrjuhO31PlEoqQQRulAEmSS/WFKVDuOqnJ1+mP3wPuHuiG//tqf5F6tv5Xx1k//2H3yQ3/yHf5NZ02ZhEnLl2gbX9/bZmrQZdiOmCIjrEiElzW7AG8PTvOXUBV5wewRVxRPHTnD11hbjfMKKfStvmP8mTrQe+O0asbPV4+lP38AIeN3rz3C3H5PoacySZ7qVY8SIrMh5c/tJ3nj8BNu9AZl1FANDtvYYtbmbTHon+PEX/isaC9c4Kc7gxS5Ghij5hS2ytTjiiWPLB0LK73F/8M0nzzHYznlwap5P9WOML2mHv3ODM85e5O7eX6ASXOT47L/DJ38BEb7md13rdyMMNStHpwHopR/Fe0O7cnjYc8ghhxxyyCF/lvDe88ADD3D58mXOnDnz+/+HPyBfus0+0Gq1aLVaf2SbOOSPhm8/fYFXOtdo9QdMsjp5A4aRoRF5pJM8cGaRzf0dBvtjur2AcrHAaw4eFgIPeYFAM8wU1ThlWrTZtAKFO5iOsRHhdIientC1CSeiPdROnSL/XtRknjxcpUrGMTZ4YObN3Nx8lqPRNjuTCk5AqzahMCDSENeZYlx1sCSo3fcMtw3iuQq6E6AAUkuuFeVeTDLwZHmFqDVgspHgnSMynup2gx3GeOlJV2K8CtCZwMqIr3rNJTIVcGlrhf20gdOCcAOSgeCJR+cZPbxJ97M5QWqwXjLankY0h3T2K5SbEnklIJQ50oCINbcW7mMet4QqoHMV/MAgT8TEDw6wE4/PAnp1R6YhuB1QLRP0tKZbLUnHJWo5RylLZSGl7DbRoQADqhVhmgJdkVR6jkdOL+HHXXbp4fazg2jVKngLneF96jM9FBF3O33SQQ9pQHYLajMJ0ZEavrmODyziJUHoPK8/tcxnOznpmmH4WwXlGUs4UxIpMF1DUJG0wwlPfsM9Mi8xd0PuPD1PtJYTbRbItEA1AiZviShnJZFq81C5xhuX9/mLSzf4S9e+BS0LCgSrgwFRSzIRlmxGMOtTZK4YD2NiHVDpJBRpBz3refid97lyaYFJoRjdaPDhn6oTtucIbUl0VRHcd4R7kjSEqOWZ6Ih0P6B6d47JTof0VQFKgJeIzOGmBaYeQrvAVzwuTkkTg91TcBeajwf0MXg0PnP4vkBJz5Su0as5MqdwuQGhmByJkPMSneekpwVqIqg/DQKJCELE/5rrbB2lsgTlQQSu0BLhPN5L5HwDKz35xoBKoFDGIj14rymLAikEwlv0RUHQdrhjhuySxUUSMx3gKwo1ziDUKOUoZzzidU2KUqN3AmTukHc66P0RWEt4OsGcMYhPGYKxQ0wFcH/IuBQUy1UqWY7C4rsl3f2QM8f3ODJd4cbNPUTpKLQn2RWQZ5ShxrU0wfAgctotROheATOQW43fV0gj8M5SUVWEDCkLh6ppUuUJRxO88KRTClepYE5UMXs5wcjgo5AidIhWzAMPRkgtWDy2x+DyPFFsaa3kVOcLbF9jtjLi1NLtBHBDUfgRZS3A1Sv4AMDhvWdQxlzpL+G6oFNPmIFUgjBQHFlqcWa+wgc/dx+/Lwj2awfjRm1HKAT+rqF7N+LOpxs4GRHXAljdQzcr2AWPDjwuT0l+sUcFS5RB+tgMvdOCIy5n+R37ON/lhd88yky1zf5DAf5OAdcLRCtk1kh66kBYGj3fonVS8bapB3n4gQcYuQkPfsuv8ZPPvIG9+7NcXlnh4htXecPXXGRnr8P+TMGiMpzTi1yVN1FlTPt1TdrdKigJKTw0XOLO9T6Xbq/zxulFFppV8sUZ7l27S7coUG4OkztEeGDEvTX6+1SOXGW8+X+jXotZrs1xtWxRmYI42MXhuHiyzc2+5eXJdWpFnbLVYa8T0tt/nMdn/zzNcxNOzP17pHoG61/iofl/QyWYR4gvHN2pBPf5iqN/iTg8gRC/8bvWrLqOWIprNFXMV8wvs5v1OFL9HeP68fDH8H5MVnwOIauI6I1fUk00paWzO0DE6+yP333QUKbeTzV6+Eta55BDDjnkkEP+i8GLg9cfN38S9vB5pJScOXOG/f39P34h5ZA/mbxt+SLDmQ/yW3fuw0zCq0bRimLOxDM8tjLPM1d2eHF/A2kktggIIs3e6ARokN4QpWMiFeFLjx6VHKm2yL1jYwQ2gdyWJNVt4jyjRkFbpnT8BkH652gOJnzD8mVE4dnJa7Ta7+fx1w94orjN2jNTDK1ChRkjm2D6AXavQAxDxLRg+FpPtZfDZhWhJEUCrmJQ2iELhR55KATbV6c+bxjrWFncppikuE4TX1q8tqixo6xKRCH46ovXuLK1xK3dZVTpCSeOaOyQY8F2NuH2DUNaaMazCteE/UqFvCYY5xFqBPGsILznIFQHJrPO4CsFWlsGWYRWgrBV4gaS4MGUclVgOnVcqBCDgNQ5Cjem33AIKRk/U2VnLSAtNXHL40OPDQJmdIIOYbTex2/ldAeXecPjAz58a4b9tECPAlxVoLyjWu2xsrBDZ3majTXLsCqIS0GYSkQlYnlKMji+iz4tyD7ZhlLirKXeqrB3/y575yAPNGERokcZ1f0e5/7aLlGr5K3BDkOpefH2acyswF6MiO6lTC5EyIkjvC0JZitspCWde4u87XWrvLA5g9MOmTuED9gcjDBzmmrfIIxn6/k6ToKWnkBDOpVhTcj69jTD/08FUQSwAcYIchFjc4u0niIqyZcd0+erPJz3ePO3PsOlZ4/ziRceZVCRHN9/mI5dwycSYRxBBm7g8QhsIcAKqJYUFwVmJBAthQsMEYZUKIoZhXw1QL5s6Z8QjB6M8LcKgtxDYZEpOOkhdfg8gPzgAdEbjygNIonxziPVwWcrncNribEG6hHSWoSXlDHY+SqTcY56ZZtYKwSWIAigKGBiGD8N8Zk6k1sRTjrCtTEEdeTQENwY0fuGKn7oqVKgZwyTJ+r4FyKi/RzZzPCpwXtDvleFOMd1DXqSQaRQbQ1zCWq+znBGE2yNKdZyxHXLS8/P03/1GiqOQStcPSCta8K9Ell4lpNN2uOc1dYCg3YD1Q0R3ZzyssXuRCjtKfsjGBnk7AzEEcI6kmqIqUnIBGa6hpoU+CSkTArsRk5chTiX+DTjpU+eZ2urjpmcwvicwWCDzUmFpceGPLJymU/8ozOUTqJe1LQujxGpxTVBWY+tKMppSSITTKKpzI9JdyKEFDR7jq4ydJoBz61t8ZYTLf7Rv/slVm+2eOrp93Dr+j4VM0YejZFhC7M+4dLPtMleX5DdWQNjoVlBlZYwUeTWUZ6JyB8M0M8a7HQVpwX5fovJap1xt4LqWfLMUtYMcSCgl+PyEne6DqFAGE85EQRlh/a5f82ru0fo9Z7A7QakN5okuwJPjO9ppJJc1yMudfYo5kKCUtLt13Cu4HglIt8y6Dhger5Oq13l5ckWHZmTTCVMzTb53CtrqMKTT0qe31ijJo9Tr1XIBhN89Cmm50rOrxhazQrz2RvYyG5RaV4kq0xYSlaJg+P8wvbP010r0PsDGkd2GbFMf9RmLEpupB1mih5zFYOUVcrJae6MUx48Ygn07xjF9se/yD3rCbKbLNgcpb7YJPbi0QVqYcjyTJMF8zbuT3ZZiH9nrEfKWaYkqP+d/8r/EZ/e+Rmu9F/k24/+DRI7QzYp0S7Gi/Aggl3N/iGq7CGHHHLIIYcc8l8i//gf/2N+6Id+iH/1r/4VFy9e/CNZ81BI+VNGt8gwSjHfVJwVMwwGOR/d2Od27T71jQYu9kTNAPo9jrl9irDKdiDwGeAPRgXSvSppXVHWHXJkCEcT5L5C554XXjrDo8dvci7eRAaSxuKYI42It0/f4GTzE/SLmHd9/Lt5Qv4GC80SBpLd4RRUcnwoKQOB9JY88fgVT1ATKCGQ+wEqFZTCcvbNm3SCgJ27LazUhB58oHAVS+PkkL/58EcZJhG/9MqjyDt1SEtExyPLg7QN7zz/8jfeyrEjA+Jyloa3WHHgawECZQVDM8FOApRTqLFk4fxtjNasT1qoCFw3xAUCU48ItOLNy7vEU+vcmDnBjq/jPJihovqABSHRr8lIfqaCqghmZyrMpK8iraMzmEfvOaSRTPYTTChpNgeMR1X8UJBGkqqHLHPYwBG87jk2FyRzS132r81Q7Zcs5A2K0zEziebB2R1q33qfX/ipKUzUIvESHUl0pPjUC+vMTSo8fOQIc49Msd4b89J+h009gW+cMH+yYHOnSdkL4K7BFRIGlkxrfvgfvg25bTEmwZ4FX1O4kwE+cHgNog/JVsyWGGLjOn/1U1+L1yBDceB3YRU2AhdJklVDuGkpqgJhLHpsoQDnJLau2e7O072fo6ICJhJXgB0klKUiNyVhBsoHLNWmqIgbeAdRlJNbGEWSwgxRgceXDldaxFAQyJKiJqk8b8kKjW0L/L7EHzGEVQ+XA8SuBy3wgUc68GHIxDpkoikbFj+0CCeIdywiLwBN477BNT1We3RFIcRBlDYChBf4KMQKd/C9Ex4fCaxR6HGByiROgjsmMIvTZNcyEqeIOzki0LiGohg3yFerWG2w2pH0Qb3URfcLxNCClRgdMLhRQU952I1wicPMSyKahF2DdSVGB4ykxJ8rie+UYC2ji/PIekJtHURNY5YN+saIwdUW0dDiWoLxcUWyBmY+pGwrGBWE+xOePHqP9b06DTmkn0ZEqUaag+41woO/FyoDicDlE2QS4ocpFGNspY70JdI4nNbo+xOCscWEAtkxqEBAmbO6NmZ9c5rZec/K6SO88Mvfy535exThgDAw6GMZvX4dkXv8qESlBqstoiywywl5W2GSiN5qkyEJtlFSaouINEVFYrVg15fM6N9kem4CWnDtl7awY4f2irpIaJwO2Nm6gxg5/HOrFM5CKEEejEiZ0iHqVcojDrMQMjoWUrs/IchAmICXfuMIajkkOeKo7ldx9wI8Y7wCoSQryy0u9VOc8dhQ4LtQGoexJeNRyqmO5GG7x6vZHF+58gKvjJd4qKG5u5MzMGD6gm94/Bxb1/u4bkZrSzIiRzgw1tIdTLiv+0QLIWfOLVIax/31LoN+yp7v8onNa2SPZzw5fYwb1zZZWv5RosouUfJ6rLW4sEZz41v5pL9LnM/w9y5+D7UgwfsNms1tOrsz1LRB5gEyhKoMWKzWSILz1ORnqOmHefrGfXaHY6ZqFY7OtH6nFtkm98sphIAnbI+Kmv+iehUEirnZOq1qwo+9+Al6xZhz4SscSwJU9Xtot/47xvtr6PCxP3AN/NX1D7NfehaSD/BNy/8Vc0stdDDNvHoV8CgZ/r5rHHLIIYcccsghf7p417vexWQy4ZFHHiEMQ5Ik+YLrnU7nS17zUEj5U8R7b13mpn6C43PHeHmz5Lvf9L/w/lcfR1XPcdq1qJeSt73mLHc2+rzkX+bU4gY39gLExvJBqoiOsZTUKzlmKuLx5WNcvncH23Qk2yVhEaCN5OZnViizChfecB/tNOvrv0kun+SZ/jme2lqiGMYMTYTVik6o2a+H1NrFQUpK6RDeUXEGY0Pk1ZCg57Eji/WW9IjiwrE1XlmfY9SvIUqJJ0Q4iBZLXn/xFg8ubpKVmv9v8RVIYxGBoaxKgl1P0C/BwWbRZmrqPNLneFsickuZCJRWdLYHNKb7DGSI0Ad+sg9M73Kmvc2rgwU+vneWxoU+k2ebiCAkKj1X1Bx2I8HcXmJhscFmf0DZhHQcE9/LiYxDJIqVesJrT1e4MPUM43GEfabNrX6MCAWlArmcUV+ckK9G6I4irUv6LqecCWiHAc/vnEaEhmISIxWIQBIiOarq/OULf51x8O2MG3DubEi3N42vSZJpSWMh4sqVfbY2F/nu06/lA1vPsZqlrG6tU1Rg6khOz0UIIWinOeGWx0rFMz97lGKlRrinCHoZ8bKmr8FogR4Lms+MMIXBtwKqO7CsQtYnKT4AW/c4n1G0NEoptA5416OPcGnnNrf2dpD9EisETAyB0oiOouw7SEpcALF3jJOItBWxsuLZ6YPb8ph9Q7NTMFFQyd/Mz/2/K+z065QrEm8cL833mVrVDNbSA2FD8XkRQyKEIFwX5DZCBo76C/Dmb1yl/pVjPvofj6FLjd4r0IMC2YgI9h1BR9ATCh9ocA6JQGqNExq9K0kbGdRCfO6RSiKMxeXlwXiLkvhIQukQcQDGI6zDj3OoakQgcHWP8Z6oXcUoTdqoIvbHRN0xPuAgVrkqcNWILAmxzhAMLdWbA9AetMTbmMx4QgGiogCJtYoytNggJOiXB8JOLSQ/MwWRg1YCqYfCUDlSEJSW4ESTh75ji+HLhmfvz5M/cJA2FK8XUAb4zgD6KZ+7uUxSCNKtmFa3j6zVEDjcuc93/JQWGSXYqEQriUBSzNYpggzdyxAqQAwMyhh8JcBIiRoP8FmGNQoqJYtvGvJ93/Ycn/3oI9y7+VpmFqdpvrzErXXPb3zuPP1uE/Nah5t3uJsKWTiCSYDzjqhfUjYlJnZkg4h0S5NbQw1JYQy61EjviQK4v/V6fmNrk3t7bXrLBaEW+C3BOCiZqodI6YnyErHlMSsLcKQGSYRTB9NjZCXxpyfoHUEwDqBi0dsl4UxIdiQhr6bYyoAgL0iwiNcb8qjKEVen3o4Z6xyERPU9m2Wb2y+0eaTy9axMvcpav8pOniC85GP7pxi1QlZ2+jRna6ROcmJ5hr1Xd1j4uCGuVWkfr6GTgHv39xgMU0bDnLNHZhiQE6RQhoZGHBCYhFpVobEs11vcvb3L9maP4ysPEnpNZ3vEdpbyscEq1RnNVyyeoTu4wosvfYCZpTcySRXNWkmrHtO/9DoWF+vMHVnnM/4povQxpPgFjC8w5Ud5Sj7KmfkTLLRqX1CPvDrOTtZACM/IdKmEXyyk/Nqrr3J5Z4c/f/Ei3zDjmbY/yWLZ5SAIaxFETGCvQfoKrv7XsOYaOnwdQvzety5fu/D1/NL7rvCJDyUcf+c6Fy8c+fyV3ztW+ZBDDjnkkEP+1OA///rj5k/CHv43/PiP//gf+ZqHQsqfIn751mVu7e9zYrPKzlqf6aDHf/OGj3B68S/zwZ/f59LwHs89c4f7g4xidpZ71TFZkCCuAg7K2NOsZsyf3sa4RephTBYEGOeI4pJaFhGYkuGu5vbOHGHPkm2F/OqrIUnzeV6xb6VUkqQw3Bovk6WSBxublFrgpEDg0aUlVgVZXkPFJaYiKOuS6l2PDSTFnODjn3mQYR2yiqTdLymiCG9Adzwv3l/hV8ox159eIRq3SLMCU5Nkc55w6HHS4yRQeLa3+2gpcbnDjy0yNxAofO5YWumDr9IzVdL5gEv3lqgFKc9vHSGJSiq9gnxkwVh8YRj0QfTb1G2T5vEG1ymRjZxyGBLmJYWNKRYjum1oH5lnX0xxSzXZrEPeOGjtF05SDjX9WwlsK8RQUFUOsRjT7U3QUUjr3gl2RhO26pakPiIpJWZQ4u5PKHdTbq69h63dOxxbeD21RkG9VUE7z15/xImVeaRWbK8P2N8ZUUxJiv0JcVxhslnBJ1CtZdTPFEw/tMTk/ghxsU4xKVDjjMp0lYWjM2xP9aCRI9Y8UgaoSYGLHY24SrJWMlot8NqTzkp8CJO5AK8CnBC85vhRVr6rwY//u4/ghcMbT7lcIQwamNIg3Agugus7gtuGeMrz+tdf5y++7Sl+/flH+bWbD6KCkjOLt7DqPJtrPXbXmuRtDUGAsAU4Qe4k3magooNIaS/Q1lIeC/ALBcKWNG7nfPVfeIW3vuYGIxvx7Odm6JUxxVxI+3OGymZBoAKW0pCysOT2ICo7DAXkntJahAORBmjr0YD1B+a/Qit0oCjyEsqD7gthNU4IyEtkYUBBZgKKukRmwUEXiwUCiVuoMZqJKCsQeFDG443DaoPTmsligMcgxhZChRo5glGOMgoZhvjCI0uLjcEJgdMgxyUuEmRLVUQ1gNKjvcAfNQxbCaqpmLmVsbMVIqUlXjfQtCSbHlsJcOTUxn3OPNQnGzS5vNkg3s6Qxh8IM33wL1p86lF5AcpTzIaEWQCmwKsQH4f42CGGjmB3CEpi2gHCloQbPVDgnQajebhxm7nFPhceusrHf3GOfpHSLQxuYClWFfYJRVk9SN3yUYCPJQKPCCRCKFQmSGWB71vSxFGmIbXzKf2GZrImyOsSmVmuZyHPX3qMPMioyoJ8O8EXHvYNjWadrZMW7kNgNHmgMPXKgTDmPFGgmJQWMTuF2suRMyG+e2A4O9dI2NWerAcyc6jMYVJLMGOx8xXkTcvavS3McgWUwKeGtKtpP/Yio/GIce8x/lHvtRQXFc1LgvJ2hbwaI63jsZNLfPjqdYpBxn/66BW6qz2+7slznDw7T7WakE9Kdrb7TE/XecfrzvNvP/U5/n+fepb/y1tex2OPH+epF2+wmg356ofO8pYLZ3lq4xXiIGDxSJukEjHojrGROBirqdb4ruMR4+1/At7yT576UfZbJ7i657GTgOMzIVnDMlAdEp/hyxFOFHRslSu75/HSIioZof7C24miqNG52yZQhr3lK8xVzn1RzYqDACUlkVKsyP+FlzPH8eCgS1GoE2RMYaKvR8g6rv+3KfPfIq79DeL6//X3rIPDa2d54YN3sH6VUfEJ/ocfficICMIDwfeQQw455JBDDvmzx7vf/e4/8jUPhZQ/Rbxl+QSlK1kYJqyXXZ698gBveXKNerxCNRygnaFvBBNriVWPY0mPT92Yw3sHQImnHCRsXm0h5mI2JnvENzPQHj+qMRnnlFaQDaqIe550UKU7SBioiKzhaNgRvaxOMqnRzZZZqK2ysVZjoRgwniicV9TiMY0oIzidkaWacQBJ6Ai7jqJfAyuYP98hyjSd/Qa1Rsl22QDjELsB+60G/2H9tWgTILsO7QKEsZi2I5sRSKfwxqIyx6g3ptWsQiSwVQG5JlCKZCYi3C7pUgcESVrSmTTYnTRJpCUKC3quAh7UxGIyw/jXZimEZyPOmMQOkwui/QjXMaT9ClVdoTGjaRhNISxP66Nc9dOIkwIzFOg9gQ9AdQRlP0ZaCUVJ2h3Snpsm85p6IHmwOc/e1jbej6AWYJyCQDPdrtJoV7EvSeJBh73eNsnMNFdfWmWS5+hE8+B8m63VDjtRh8XlFpNshJQahpbeXg0lDPHJMSos2Zx1nJ07TorD3NuFumJuqUUwHeIqHleFZkNBUzIuMhg7WrWY28sSty3wpUNNBN5IZBghrUD2HD/+t3+ZH/r77yDoGoJRRjoX4KMKjVqT0XgDHxdkQ4W4pSn6DQSWk0c3AUG9TJGlJ2zk6EmClI4ssggviEuY9zFnRjP0c8vNWg87XUcPDcp5XGmRHSD0lCdKwr2S9mTEdDKkrjMaOmU/i/GJO/g+z4Dd8AhjsCNLGEgmWmDfXuIiqP+mwuwe5Mq7XBBoDbHEhfIg6tgrwooknw3xtwsCC6YsgADhQEURIspZam5yiwVMUxD2JkBAIBVIxfB4hK8H+DspwW6BXwyI+haTlGRxhA9Arqeo+RjZM4Qjj5QCozxLsz2eOHKHSxsr3ErruFgepCWFYKY02kvCgUUPPUUUYnZj0jLEODAv1xC3E6KuJnzJ4YRnfFZjKg7TriJrmxw7pbl6v4mNNewMUEUBJxViZhbuWZySiFghawFSB7hxSbBv8GOJ8hKPR24PDrpyiirSSJwQiLREKIfXnqf+7QnyYcKV51fwqWDv8jZMVwjSEovDCovaDhAjh1kIKCSEA4lHIkuPmjgWqgn3JwVZFiB7EjmEYCLxgQfhEJU+G7d6PGiPcH3tLkUeYYU6iGjec0yaGbXvNoz+o8Dd0/i8QFbrlIzRXqDCEN9qILxH2AhbWmSh8CZnd39CvujxIiDsa5KGwBuB0LOcfHXEUljBhBJZeAQeHyp0HtJKOuh6j1JeoKxKvIIyscR3HeWRkIXXz3Gts8Won9NrpSh14NETR5rttQ7NmTrd7pCtnT6DYYazjkubu9zb73Bze4/V2x0+cf0ua3NDJl348+IJ7q3us78/oigd5CVruwNOHJvh7e1jbHVG9CZN0kmTrW7AKBPIeB/vDUUhiHLNXCH5zuVfZuAiXtr/Di7tvwmpVrm2/o1815Ov5bH5E+SlIQoObinK7MM03Isc6IaetHgReOcX1ayvOXmKM80pluoN9vy3Uo5/ll8Yn+Qr5v8uU7bF/3j1v+avzDxNICCO3wq+ROojX7TO/5btcZ8CjwgEt9a3efvf+Jecajf5O++ucOTEESqVN/8RVdxDDjnkkEMO+ZOH8AevP27+JOzhf4+1lve+971cvXoVIQQXLlzgHe94x5d90PIlCynb29v84A/+IB/5yEfY2dnB+y/8LVlrv6yNHPKH51j7Jqf77+X29llst0lTTWhEPYy9izclE2XYPWII9kOM0OzdqREMFQLwgcRGmnLPI/tV5JxmlI9oTgJExSAmGa7wDBqKspWgb+ccm95nTjW4OKeYqCG/tRqi+w47SSlvF6SzLW7kC3R1jWajS7VmGI0D0klEWDjmTkzY6ivKkSY0Fj88SE3Z227ijaO0AXnNIScGrUo4XRCdzHCFIrukoNQ4JfHOE45L9ERDJPAyQHgockeRWQoJqh5CDtKD9AEbq4sUzYSwYVg5vkdtyiArguVowFbWODAWNSC0AuVZqLW5PxkzOAJZ1ZPsGuJVRzXJaDw0YHI3x0/a2KjEZTVk/SKF6VBKQX3fkVc0piqRGYSlJjaColqQnZ+wFxXUkhpB6PjMseu4RU/0nEOOPGZG0Y8FrcUmR07McHH3p5Ay5bc+ZijGf46RLdm4u0dgPa4zohSCYycXeNtbL7A2GnF7vcOzH7rMUAFnLEfKHp0rLUQ/wNQdIs2ZJA5bj9nRlvmbI+zDBusFw9Bi32YwWUD0MclaMuZWksNZRTgwZI9XiPY0iZOUmcMbx2hryE/+5FOoKOAbvulzbO22eP7SWfb2h6jFMTLxRBsWP4HAhYStAZfvzbG/v8Anf+400mX4UPOMmifbvEMWKaKKY+5YnSh15NrRnG3QnIwZ3c+RYYD1AplaZGpRHnzi0eua4UbMr/+nh9gO2myXdeRVQS2wiKYiTiu4JMfEmsyWFGV58OVQB/HFKIEwJT4KSAYB4WzMcGLwQuElWCkwFooQwgjEGHQpKBMQVh5EbfuEBoZg6PG7nnrfYUOLSA4ir7NFiY8cMlOUYYyrSowQ6FtDmrf6BKOSJIiYvGGOrO6J72SUJ0N8INGR5xPZA0TeEPdK9FSOOQGDUQU18oT9nPjFMe2GYWe6hpceSg+BZvPYItVJn+oohyI7eNi9IzErEi0LKqcte6MQuzSNr2WoNMf3U8TnCsTMEFc0kELiGlVCJyEtwCucl6gDyyDwHjnfoojAa4mrRaSnW+RySLxlaYw8xVjy0f90Cl+L8bUA2RviTUmgFVY6dCEgK6jcMpQLESIFJyXyRIlopATDFjtFRkmMiwVeKfINR7sCdSkRbUk4Br9XIKYiyhc18qRHlAeR1S6QrK7nNC9UEbM55rpEmBK8Jywl1XqMVO5gbMz6gw5V73GhxDvobgyo6IRWeJBg1AhazCzX2NgcUJkNGG/06G4MqS428aUnPRLhtxM+9WsP0ao3qNYahPfGlA0Idh1vfuMVzj+5wwc/q9npJMRe8MAjsxw7L7lZamYWGnQ6GXqcM+hM6O4PWd/pYr0ly3pYtckrV9Y53W4RiAGtcc7b2yeQQtLrTcjGOdZZSiOx1lEax7CfsnZrl/cl9+hNfpjxjQrRpMu9yyOmzxfUmyPCfskTc0eItSW3hq3hiOX2d/DZezf5imSXE8GHeOXmV7HZVXz1w6eZqivSznuo2pCH5htUo5xm+LuLF9e2dvnYlds8fHSBT40fY2tyjO8+e5yT9Sf40Pq/5/WNKyT/axiQOkpz8d8gRPx/WAffdOYE/772OYwHEys61ZKu7RLE/5BeVxFFn0YdGs4ecsghhxxyyJ8pbt68yTd+4zeyvr7O2bNn8d5z/fp1VlZWeP/738+pU6e+5DW/ZCHl+77v+7h//z5/7+/9PRYXFxHiT0600Z91Utcn0Tl5XlBKzy9dfwKx9CaOT51je/vDrFczVpsF2kdM9qbo7GtUGYIUB94OuUGlHjmXYudiEhfzyJljvNDfIcv3oHCIUmCnItSwZLm+TdS6x/lM8hNX/jp7jXvYxCHWJWt7CdfKafx0ThlCVPWMywgU5EUAhSfQI/rFHC4UNI4M8NugnEekkomW2GOOwbUKQelQueD4hW10o+T2/hTRcEKWeApiysjD8MCbgtIfKKBSIHJHNigIHKgkQE4MjTDk2NIU9+83CfolLvSIimOC4tZeEy80LpacPLbHIImpPOyYP9XndbNv59VLu3zWdtg3Br0xJuha6uc6zJ8cIh/b5/lfbRF4xyurW3RvOUTFEycRSV2QC8imQTjN/A6MtzJUoLCtEts3VEaGPVlgjCNtlMjHgcsaqwRl6HnZdvmVD73E6P4Sb3zDNTo7dZpLhv4kJTOWYcWzW/RQpSDYqlAIzclTc9SOSj6dlTQ/mvEXvvFzzM0Nedo9wNrmSZrTFT6tdxlPeeLrBvKM4Qgq1w1FQ1Buemh5hBTIGUGHMTa1BJODU3lGlqQVIHseV3iKmmJyImE/T/GnYvScozUeEW9l0Cmw+xrRgMVTfYJTnt7lCkza2I2IvWIeLQxhmGMeqJE+kuHqKfLpgHDiyXJBT5WMh2PmIo3plKihw8UOESiEl5hlQ9EUZISE06D3C/ppk1/+9AK2JjlSm2ANqIkhnI/IJxJvHUVuiBPFpLDoj4bUmwlZNsFJBwqcgtGwBO/wWuCtQOJRI0Ot8Jj9EgoBMSA1Xgus8BRJwO3hHJW1DCklJpAHSXDZQaywGn/e5LbhCLYFZj9DFh633ICdMWF3QKhBv9Cn0Rnim1VGTyhUari/0UB5KPY8qigJNyRB3XDktyY4lYExiNQwfSpj5pEhV3dO4ioGPfGYJhQLIfZkhWjVw90eg4saJz3rK9McTQ13tuYYnwyJ7mQo6z4fSe7Rz2e4OMZXKoiKwhQeP5kg1IEJry8lSI+bcjgqjFcUshWitg1yoYatShpH+lTqGYPP1XE+xEchxArbrKA3O4h2lanZFpWhpJcaxuciwoFH5hZqGnmqxM9b7MuGfFwgtMSHEjlyeOcxeyllXeN7KWpjDFqiLnyGN3xVyqc+fhofG1xm0UWJqAryj9VgrwZ6gJeSSkOysxizFwQknYKK9Rg8uSoozoTMvpgdiEOTkmEjQ1QdUxsCk1mmTZXtbpcsUfRCQb7RJx5bnAURBKilOquvnGTl0VPUappgSyCExFUEX/fNl7n2/BF2b1/DlOdJGgGh8wwXNLdrEbVqwVxf0WjX2Nru0e+n7HSH7HWH7J35JHIMexR872v+Bj+/+jGijuSVyRWuD1d44okTDPsTWs0K1VpCox6TxCG9vSH3koIP9J4hUIJH7KN86qn7BA2BOAe1qZRHV6b4lsfexK+uvo5hucfJlQHvPPf1POKmOLLyTowpCCo/A91/gfMeiAgqf56d/S0ur2aEYcnphWd/15rVriZM1RLmGjVO6WlKazlRO4cQgsfbb+KFjZiAERZPEj7++4ooAM045sLMDJVaxPKpGf7tlZfRxrO1PsXUzDSLS80/spp7yCGHHHLIIYf8l8EP/MAPcOrUKZ5++mmmpqYA2N/f513vehc/8AM/wPvf//4vec0vWUj55Cc/ySc+8QkeffTRL/nNDvnPy0Otb+HyjmCmLrkrtlkfNvi5z9V4a32HjTs76G7O8mKD0UDSEw4bVEmIccojSovODbYEPYJQTahIyc7IEDe3KS9A+arCxxInPGpWknsYlFWalT3usIZ1AmksMQ69IiidIu+E+BJG44ASTSRLhHC4GMqupLLj6LuEUTGFrCnCDYs8W1JGETJ22CLEWYUuDUFkkdIjJUTLKaovYKiYtBTVNQ9CoFOHVwIrwSMpYok0gsB5hsctebtgo9+n1A6XQDmO6XQTxJRn6/YMNWOIFwyLi12aD2SUjTomDhm5PeIwpqUqtBci7qsRPvH0bk5Tm7IElIihJzCWs18xywc+dgdiwWTREskA2cmJooOxj82ipO4cPvPkTUssYa9b4NC4fY8IQ9xGjh45Qp0TRAl1obl9Y5OnP/Qkv/RLjzC/VKcT7jNyB2kvgwcDvPKEdyzPra0jR5ZL+7ts6ZzR8YjkZs5H/+cHsN+lWFudodnr8mCySFA6ajJDFAnT1YS9Vs54Kj5Iymk45j8hyExJMS2Zb9dQUcRePkSOIbnvOXN2ig1GDGOHFZ5kt8C1Na264Nf+zRtQY4PpO5z1iDQgDjNOPrJD3C55ejRLPvJE/bOcPrZIXfd4+pNj4t0I6QVmNIaBx+aSYnNEWI8htuSjnDDWoAWUBbQTnHAUxyVKeGp3DWGhCOOAyMTMz+6RtHI2kgbBBIRTBLqHcTF+nFObqjK90AS6dKYz0pcmmKxAFJagyHH1AKMVQkmEteiJgUgjrcQWBdoJpHW43FMPx0x2QyQBwcRQ1BV+PjkQ7SKFmBgCY/DaEWxI4g4EqcBGgjIUBEOBq4X4UGI2hgdRxs0EN1XFRp6gW6DRMPJQeIzkwDB51cK2wEuFFPJAzKnC7FvHdAd14tkUqgaVG970llUu31xgc6uJmxaYE3NQpFSuD7EVwfWPTVGcruACS76cIN68gtgeMH1nB+882ufYrRI3FUMS45IA6S2i9KA1Qjnskx571eOOe8gsUVoS3cuJWppKrWDuaMlk2lDsx5DliFyjrKFYiXGBRPTH6HqTfMaTHtfYgUSmgmBs4FkFbYHcyrFNSTyZMO4H1C6NkYVFWY2qa2xmsSMPsuTa5RaPPJQjshLhDwSlCCA0TC916cUJk22JLxyuYRm1Q4R3jCJFvO9AOkzFUi5Yqish45sGMRqTPC/ovF5SXIhobfdoNU+wsjCFbGpMI2EvLemt9bHpBNVKSJ5UVF5o8PSdVSZbQ2qBZzQfEY4coYONSy22bzXQyZAsyPmXf/c/UEY5u+88ipuxfC1L1OohWekwzhHiWfOb9Lsx/t8Inpvsc+0fbHLcnWRi70H5Mjf2r5GX72Rnb8yHn7rCN3zNI8RxwNpuj9Q5JlVBz2eYicSv7WM0NKqGb5l5hUaYsxxepJSOE83XcXf4NG+68GbmmjWuuojR6CuJ659ABS2+4Ynz1JMDw9mk9WMcrRmqd99JEBv2yy+OPgZYbjf57jceJPKcZ+4Lrk0lx7gYO0rh8Xiy8hKdzX/B9NT/QBg9+XvWQVNaFisVZqYb/Lmvf4LV2pjuvS4/8xPv5tHXPsDZC4epPYcccsghh/wp5tBs9nflqaee+gIRBWB6epof+7Ef401vetOXteaXLKSsrKx80TjPIX8y+IXVn+OV7BozJ2YQn61SKTzjcc7+W0rKRszWpRGjDxfUH6oz/UgXU+SY1YRgWMP5A9NKD5QbAX4X1tMBd4Z36WqJO+IJe4pwaHBrJbQ08+GQmszZzhssxTvc7E+hC8lyPM3R9kk+3r9NFkq88PQnMdWKxSNJVElrQRINLUuyi3w6RC168obGJYJeHqPGBmoGpys4Jyil5Ob1eeRchnce0fGM9mqkixBkBhFZrAxQA4/MHVJ48FC0FAmOEzNrXFusMW56JrcL4gdHDNZj8shzvDHm9PQOH711DvYrFN7SbyZceN0Gd24EdK/XKV5bZdLeh9USmhoXCvLpkDILufHMcbzyRMMcH2hMaTF5iQolzWZKs95DKM/23hQ+lAen08bjvEWNPDKRWA/SemwHpls19p43uERAVnIma/DI8To7Jz/NVFmw+dQMIyXJihJTGrxWRLserw98IwSC0XjCjp0wqDtMInFSsN2u0x828FrhqpZsp89DZ29h5i03N5fR4RRZb4KJJHBgGKoI8KMJlSXLEer89Tc/wg+++HOMGgViWXAl3qQ61WS4IMB5Kq8U/N//+vvpd0J+/vJbuVNpImuOYC3DGM9kLMn2ppmrtHl04RRbeyPKEWTGUNchcRAgx/DQZJZbCx12axW0UZSZQ4kJuQrojTWl8jAVoXZ7BK2AiXDINUeYWar7kql2jU4jR42G/NW3fxyr4T+++Hp2Lk0hhGDWdLnvFlChplKJCJ1m63xKOXcQbZx88uCPo0ciHZ+PPJYI6xHSU9YMuQ2ZJJJkD6RRiEiSjkICU1JKi5cH41zFrMIXJfG2hVhTakm+EGOWA9SugcwhY4GMY0zIQYyUV5ij04itLiBwNcXopMa0JaLj0BKEcBStgHArA+vBO4QWYC3CeaSHS0+dRLQcpqEQs5aqKTkxtUu8mPKh3zpDUQ2x9Qi14xG5w7bVQSdHNybYF4heibARUihsolDHCuQ1hxsBZYGYAZEE+NLDJEcbh6uFyE9b6HZIRAManuROihAOxoLRDclkJ8ZnBhDIwoL21N86of3OCXeencL8umD+YpvrURcRSBKVoVopfh+4I1G3BVVRMg4cSjkq9xzBfo6SFlSM9hY3cIhJhrSG4kbAZTtPY86Q5Y5yLDHOM64EJEmdiXbk05ow9Zi7FndMoHOHUx5TFPgKyNAT5RPS2womBoqSMHVEIsEf9XhXEoUB21t9zlfn+Lbv/WrC74WPvP8F3vfeD1EmEj2dU44sGTluNKHaz6gOayy+YY1/cPMbqd0uKSeCkgnjl3dR6wWT5oGA9Pr6DEEgmV9sE1cC2Bd0tses3x9irIDtAFla/tOvfI6twJEUVd68/AJPP32Mj33qaSo64l31RxmPMkam5J9/9pMUpWFxboRzY3TiuDu6w9JJhXxthwca25Qo/uMVy//rlf/ADz72Nv7Smb8GQL835srL61j17Rz9utdyrHWaetL47VqU2y6FG3Lu2BaLukvfPfr71i9nO4w770KqFSrtf40Ukh0XsqzAE7HeeYUPXZ7hwaOf4Gse+b2FlPZUja/7moeZW2iS1CJazSqDaEhuPKU5HD0+5JBDDjnkkD+LRFHEcDj8on8fjUaE4Zd3yPIlCyk//uM/zg//8A/zEz/xExw/fvzLetND/vOwEms+0q9gty3Se/JI0Io1r+52EIsVBspTBoL4yCb9JCSUkrynKWtACj4WtIMx6YIl04KrwRWy4ym2IQkuacS+J3QeocD2YWIjbg/nmY17HGt0WbEBhUrYOtnnLLPUGdIXFVzsqYUl1ehgBGLKTziazNEtBvSFpniiRGuLtCV+NST0FlPNSTolWT9DFVWcFowzzWKW8zVLr1K+Q/Hz/3aOsiEQiUcPPNYIXEUi9wpw/sCrYWJQrZIkGfO6pW0+9ep5jskW61MSnIGxR/Ud99QsR8902Ik9KnYUgSLNE1q1MWWkeb7xInk1I1k/QhKFXDnuiFc12nlE4cmXNdmMhO0hG/ev4XLDQ991j7Ov3SRyFjOO+IWffAO5iQnGHqEEQkpcKYk3QuzIYRJBkQbM51NsxWNs2yIIyRohG/1tSpdRPZWjPm6JlKKqA4bpmBzBrJ/Qnh2xW0R0xhWG2jE7VaeoFOS9CSiF6niiYUnmFG7Gs5aO2dmdohb3ybsBAz0mK3NEGaEnnjxzbJ/MsE3NwqZkIajzoc89RRSPqUUF3R2FLDVFy+MTh5947EJAqXNGaZWzK+uMtiSjLMYbga150tDz1EvTHNtcYKq9xsL0MkpX6XfG3FjfIbMOWS24uT+BBUHgJd6Cd5aoNqb5YMbOvRp56tBRSDuI8a2IsbMwEhxv3eed/+1nmGzO8IEPPsLGtYibazNU5nPy9hEkDkTJWByIh/aUZucRRXvd0lgN2dMFYl9SzmoUGleCi4OD8RPtcMXB90oWjtKVhLE9EMCUhyiACLAlKvVkFYcPaggk5CXlUh0lJSL3BBOH27N4KSnqAj3MEQMQpcLOBDilMI0ANzWPvLeP6BfIo21kAXJ4kEClFz3ZCSCB5B5QOHyZI9SBQbGrapSQuNhjowCvHcOHA1bCXeYXunz2mQXsVMDu+QQ7I8iXEmwtwCx6ipUYgSD0Gt+MIDfYfoh74SBu2jYkPggQmcPOB8S3RrA3QliDjCIEDpVUkF2JyQQORxwUqDqgI9w9C/OSd/33n2J/tcpv/PTDRPMGrTxxWDLcc9x7YYfg0QA5tMRPZIhujnMaMocINMiDhiTVitF9B1FEmUAWhowbEVqlOKlwk4wwlTSONVmcfpW1203khZKZdodX5TG2r0/RYo/lI3t07k6zt2TwDYcfOERHonOLDTyiBsG+wIzKg8/Ug/GW6ksGk0O4XfLx+5fZ76bYbJfv/YuPs3TkCI+9bsyvPfscg6ol3apzPkhIF1JcrhAjiRo5Xh4tMXm5RZYOmIoLXKxp7AjCxQYcjamO6+T3R6zplNcHigcfXqHfm6CUwO17aokhu6gJep5z546gewNEVlJLDNs7NTLjcSZHZZa9rR77FDw/WCPWAY9XFqmkOQjP3MUt2ulJsukJ88GQSb/G7rajq1J+46XrhEPFGy8cJ0lGfOvX/VN28xk+lEbcza/xaPtrALixtcPndn+IdqPLohrTswmzwd7vW7+suYU1d7DmJpAhREIqF3g2c8wlX09afgu/evfX+HjvHpvhj/LWI/8Nr3Q3+eqlB0h08NvrRHHA3HwdHWvcyPDnZk9zeSPkhWbB9GLj997AIYcccsghhxzyp5Zv/uZv5q/8lb/CT//0T/Pa174WgM9+9rO85z3v4R3veMeXteaXLKR853d+J5PJhFOnTlGpVAiC4AuudzqdL2sjh/zh2N8dMNl5lM5el+0NRRB6iiOwMFcjtILx1hidAwPDRCmcF0jlMF4dxIsKiw9Dzr7lKreYYtyZQZWSrSMe6yUzOxpXlAcPMpWD0/mXdpe5c2eR8zNb+LLNZKhBW0zd86rbYH5uj969acYqYU6NmfITKmHGYlzQHn0lO2mfQRjhS2ikJWkRoOKCqG6ZRAo/EJRKI2JB0LckUcHUzISobihzhRo7atclYuQAja0JkKCVx0QFXkMRBGSdgHutNst7BZVPRIy+Cvy5jKnIoj7SRCUWQslExjSPj3CBwJSKa59cRi6U5Bc1i/RpTe8wsyJ5/aNv5rO3bqKyg+6EIJCUiaRM4Lu+6ymm2yPW77yBtWqbYFJySu+Tb9TIhcYmAucFAkU57YiFYNAXuKbEhoKiIRDTAbWhw/YsPoYNPWLt446zw2PU+9f4qre9xPOXnkRxYEg51IKFi/vUGxl+KmH7ZovBesr5c0fo2l06sSBbDklbILuC4FxOVo15cbMLYQw7FYJcEkiBtyBLgZAOAjB1j68JWpUFJhlMJtCe61IEElVUULHG4NFDi88lzMQkpafSyqgfXWclMtzLlxnJKt4ZpIc8s9zt7bK977k7s8U3PzCLOPVBrry4wuRuheSmI2pKJl5gAoGUEownLaqcnWpguhWsL3CDFBUEBF7RNJ5s7FAzjt/oPsQjczt83de+yK/rc3zipQfJTkmED1BBgXEwyRV4T/e4YFSfkEyHfFV5mqf+48t0Fyu4WoA8oXAbGd5CiMcUDiEEaiLR2xPCiqbvPTKQUBh8BNG8RwtFds8SOI3oWOQQqMSQg7cOZUFaEDuGfFZjZzzsZyT7lqISYWdjvBR4a5Eigpk6ppqS3C4pnSMowChFtd4nnYoIbjm8t0xORKjcEG86cA4xNihvqTYM3byGG0oyqXn1+UX27lRAKcIkOTBSjTTueIBLQsSVFBNZEAEIAdahcoeoVgCPb1YRAVDmeONJk4I4KxHGgbE4m6Pi4MBjJojwVUWx0sAXhqQsEUsgsxGnH9/mjW+8R+Y1Mg/55U89iq07spsRjUoKqaD9bIlLDflCgh6WqAJwfL4rUqA8WGPwQLFcxdcijNbYWBLECkKBrErE8Qbx8jJ74y0mus6Dj93gwqk1/CfmeG7Psjc/xVS3IPIF6XGBi8A0JPq+wHmHiz0cd1Qyj4wl3iioxxQCknWHXp3gH2zSp8DHUG3vcH/tvYwH76QsHc05zyjJmZ5Nkck0Omwg2pZJ2yK1xPsKUUfh2xEPzG9x7TdjTFEyfXaGc295hNmlNuFqyv2tHpcurXHy5Bwvfe4WunTc7W1R1mPi0yWNMuXtbz1HfGONS7dXmQm2OD6/wKs3TxEXEdt7fUxpuXdji/nVmPp0wpKboSUlrswZbh8nnQtZqlp+Zf81jD96lodXDG+Iz9HSdXb6I0ZpTit6mUpyk6PxNYo77wE5Ty896Bb86JU73B8t8roHBxSyzbTaoBRHf98a9jNrGXH5EBfa52ly4IUSyIihEwzHHybfe4xBpOjbOi8PXuQDn3sfN4Y7dLIJ3/vAa357HWsdg3HO9r09Ws0K7XrChTOL7N7ZRSKw1n6BO7/znve+fIVJUfLnH71IHByGGR5yyCGHHPJfMH9CUnv+pI32/LN/9s9497vfzRve8Ibf1i+MMbzjHe/gn/7Tf/plrflldaQc8ieP5z91neuXb9FsD+kHVUwtQQqoVxMurMzxan4FnTqEyRhdj5k70SfJS0w+hbcw8Y5QF9zdnsbNSYT05GKKLN9F5pJxmFIrQHlwyuKPOl65coL1zVlMUeGN4dvw289RW6oztoq74w5TtkE1KMhFwnArZFRGvO30db5r7grPq4s09BipAlyrJL0tsCPNxIS4+wpXKRm1LLRCBJJgYMFJ9mSd9+09RLoaoioOtSdwI8gXImxFg/L4Pc3wvAcNwZ7EpZ6tXpvN+Qqvv7DGVHTmIJ1FeMJ5SNoVbDxG5Z5xGpDuVPA9TXTDkrSHHGt0UDpCSsue3aZ7u0f7qiR1HhsIYi+ZsxHrgxQfHcSZmqZi7eY0Tku2nn2I6ftVlBniE0PZgHENZCCxRmNRqOhgoiPoCvbXJxz/qgW6H95hkpeke2NELHjzhaf42kcv0ytA/HLIy5e+gqqT9LZHbHymxfz5lPVeHT2yVEeCgczp7k4QBgg8tqEo6gGRKhCxI9615IHHjyXCg59N0KsjfGxxuYHSIG4GBAG4SLE032Ch/hC/9dwOpj7GT3lqq5riRk6cSGwssIHi8v4xPrBzDudKTupNxncFonREI4E9EtGIujRbJWVnEZotblau84aZWzxyfsy6fJhiz7C86sn6M9hggpsSBBOBFjUeCh9DVrYxImY9HWO6Od3dAWOhCL3n5t155I2C2knH62p3+P6/+QFcCPu2zk/sfjXd6Wmq+2CFxxtovGywM2N2XcoxF6HHgrBvKBW4BOpRgDeWRGv2OwVWC1ASH2gC7XClOEiskZbsQcPyguXRb3qZ555a4e6dWbI5iTCecM8RTMxB/LAAjcB7iI+UhEdzvDbwKzlmQSMcB6M5UiKFwCYBZTsg04LAQVEBH2r2RANTWFRo8GFIuRSSRRHR6gBf0XhnOXV6h3EjpBxKyjzBacn7PvhGKEti0cF2MtS4jdUKW/GIQGIKj1rPKc5VsEITXB8Qbaf4xJK/sUJwXSL3UkQ/RQCNMsGHCX46hNEE8hIQkHvojhFBTJlIvFWk3vLWJ+4w6Sr6qkK/K2i0HN/5HZf52Y3HGaQVYkoINSZUICSx8fRuRbhJhVhnSAPOOWQoCC341GILgZtOEFrhHci0RHgBRYF0nuriIitH6/zW6iwulHz2lx/m1dMn2NybhiMWNy/Z260y1SvRrkDMaGTqaQ1jRGZRIkA7TyUZY6IAG0j0w4bkjqQEBm+tUW+3YN0TCMficsrM9JMESqMrAeKk4cSDfURdsr3RYaW5SHy8xeXufXwzYjGusJXmVJuO6zsLONcFrWnP1nhkdo5R4BkoTxJFJJWIF19eZW2tx9rOgJOvOUO/12CuuU3kCz4zeB/N1pt4xfT4+VuPc2y6Q3t2zEx/iqXFBjoJyIY5MyamTR0ZO1q+Sruyxld+8/P8zM13IYSgY6oMjqbMHH2Vbz39JGcqjzHJS2YaVeArMer7GYyWOFc5jZeCSqCRQvLg8hzn+R4eXI7Z2v5OApcx7HXIZkviOPhd69fL61t88qXP8rbTd9mevMLJ8nuohKeJJLRleiCAVO8zCDU+8vzm5XMsLI7IXclePvqCtTb7Q270uixNNamFGgRsZX1evnafO3d3ed1XnGV5ZYa9dMwv3r7EhdYsm4MhpXVMyvJQSDnkkEMOOeSQP4W0Wi1+5Vd+hZs3b3L16lW891y4cIHTp09/2Wt+yXcM7373u7/sNzvkPx8zC0121l/h3JFNOnmTm1sriKFgkQGx1sxPNyDL8CpAKEcUOKT2UHMYBHKgmK31YLqklzRwtZK46xHXAmSpEWWBSnNkAPatFn/akoxLwqSkFs9z6XqPbKeKIcG0clRYMNyoEkwMQSxJdULYznllc5EokwzcmN7GQRpHETiGfQmmgiwNIv28Ga3UyEgiCzB1TX67St6CromZFSPiVsbpxX2Otjf55c+8BhkIhJG4EIrlkGBXIaSHQOBTwevEfd71zqdZ3Rzy3hdPYloesRaRHgUtJc4I9CimFhbkTlI2JOfaXb5l6WV6usrnsuOsrbcxy5JRzaL6IHLIZiZkRcFUUOdnfvEbmEks3WuG8E2evBfyyGMxtjZD/WrKCIfYtUjl8QIGxyKWxrA35chbEKSW0WRCT6bEjRJ5IycWnuF0xFraZGPQoFLrsXO3Stkb013rIjODuRWwdS3BtSx1SoJKlbs7fdLEIboGvWOwSyGyphhtNqj3BefDkBc2d7HJQaTrjskxocDnFnKPHAtUqQmcYlJx9CYpO6og7UG5m6BfEviBRWuLq2rKeogae/xMDZ8phHCcqHW5zAWcFPgZT6VZcK69y8UHNzDxX+bFScxm5yQv33qEKy9UEGOPqSlu1SXWDKjsOYqGYnQkpls3fPTZK1RzTTJbI5UBpTRMuhkuifHGokpN8XTAs8+cQVzwrJzaYjxKyHyVbDcG48lEQOmnCBmihgovJGYLrpX7TB6Q+J5FTgRFDrJU1Jp1qu2QYVmQTcyB0BGGrD6mCO9YXALZXMhwTnC7VjAznkEONWEJZWmRuUAPyoMOEyUQfYOZidE5hOOCuJGS10KyoyFCh+h9i8CjjMRpj7cHup+tCdS+x2uFt4q8b7ClwCmJUwKVhcjCkj0SkS9pknsJGQN6p2LMRoEJNKqnQZZQHKTIuFDirYFAIJCI0pKdrFK8JkaPPNWewUQRJjH4YxYxH2B6MbozQU0yhBAIZ/CNOugAP91AbO4g3EF6lsxLyo5AzSWYWFAs1imW52jNrnHr1Wn+n//sO/iJH34faaZ5+2uusrdX5eoz83jUgfiiPUU9xGqHkKDGFunBVDRWSxgYdMXghSYoJR6wziIwiPVdRFEig4BJmrN1M+ex83OsZ0M2rxdsX5tCLMCT33MdH8PTqwtwtSAM4dhUn8fFJttPtLmyukx5vMQd8ZSXNGIi8S2HqPmDqOyjIdkjVXIM85cMqjtm/+4UleQMUzN1rl5ZJVlwiJZFKUuxM+R2UxFVILYSYTS1KCabGPJ7CkpJcL6CmR1R+4a7/PyzVfar8OjUPCenqsxN1dBRgA0UzsGxskJnqclkfxsVWJbEcXa7GX6/pOg3ODrbJWhK6rU6p1ZmaTQr1OYaDLY2CYXhWPMUR7ce4Ilj7yf0mgvxw7RaEZdellxZnVCvl5xqPsAznf/E6uQqj0++l6acY/Xa27DWMrdSRYWKQCmEELzpgWP80v3v55dupYzvnqPi2kybiGbS4fTp+S+qXRtru/yj/8fPkbcco6WII40Owh0cYz0w+z+T7P3X4AP++ftzmuuSyfmSrzu7Tt89TGYt55tfuObOYEzmPTMLdT7x4as8X9zl2eVX8d8hePMHTqCkxHvPle4Wz2yvMixyvu+xJyitY6qS/Ocu1YcccsghhxxyyP/JDAYDarUaUkpOnz792+KJc47BYECj8eWN/n5ZRy/WWt773vdy9epVhBBcuHCBd7zjHV/QLnvI/7k8+NhxjtgRazdq5HlCEQombcnzO/t8VW/EaFx8PplHURYwKTU2jbBegBMoXdIOeqzEXdbvzZBXFaM0Q3iPSyw2EuhIU23FbC87ykBz5NgOC3HKscbX8JnjN/n+tzzFnl3ifWsPU2t2yPKY441dPjM4g244vu7sVS5Ut3lldYFidA/fb9H2GYOrU9SBwnis8FijcGWIyjxq4nHaU7SgupLTmh5TKzJGdxLyUhI1S77pra/ygU8+STY2SKcoYxCRRfcFlB6Lx4+gfa+k8paCS58SKGcpxhpdluTPVMnOOdb1FCeUoRQGMcoZixhTKpzw+FyT30ww99vwNeAajqArkIBuFOQjRc9k6G3HvpS06xXGLmMyqnAv66KyJkWoiWNHvmcRBZgEXC5IK4Js1iMLR3wvZwmJqyZsrQ4oPz82VDQjfu251zE7s81pZxjuGvZ3JvhaBaeyg7QSCTLQeOOIKyH1ekLXFZiaZXJeYUNBYiVq4mDbctsX2CPgAH3PIzslJjeIMKYMLTrTSBkQ9j3eeyZZwfM3b2L7Dqwg3vLYROErivES+BrUZMHXL9zk0eYG19Yicg9hy5JPAkQiyGKFiwSDcp6b4hr3NyJiu8QDc99HZ+saO/Ea6bxCT3mqeR9ZwGToSAqHTgU3ahkLqeIcDSqlY+IPujcC42k0IzoD8/kOEc0zr57l5X98HFOb5uzsFA9c2eT5mmV0MqQ2FOi1lNFySNxpEVlH/8KEou0xlzRsBsjMko9z4qRHbSokGLShcBjjQAjyaY3cA59xkASzLxmPQl69v/T/Z++/o2XLzvJc/JlhpcpVO4eT8+nTOUlqBZQQoIAtDDZGBifCxYYBAiMw3PED4yETfDEYI90rLpgsMBZCgHKiJXWrW92tTqf7dJ8cdg6Vq1acc/7+KFnDMnCNJATYYz9j1Bi1a601a9beu2qu+tb3vi/Drma8FBH2UnQBTikwBXpgwRMUvsBaQXo5IokjUgfiUIGIFWJokOMCIoVTAgJJ3nLIzIFxqMxgQ4UfeCTGYasOGXl4fYeNU9LbNFlDYoTHM1vLnPauc7C2xfWLc3g3DP65bWQlolgoI3KHSgU2ErhQ4nyH9fTE7zawVFp9OsMpTOSjkgxxrqDIDf2TVUqiINyIccaBsRQtRdbSBNSR60OwFpIcV5NQWExLY2sejz91hJmaZj4Z8i2veYRPP/hqHrq+wKFXfZwl2jx+6RClJwzKOqxxJLMKF3iodDKmE4KiGmEbYJMcveXwAkdeHWNciBoUuN4ucns4MeGdD6g3ykyXKtS2p3n6/LM4MvTQIHbBIXACsumAjC7ZWon8quTq2iwyBYyjOG1xUwJ3IkN/WFHsarJzAWnJoGcLqkFM/IxCrQzQJiaQPd7/0cf4qntvwjMOM1aU2znWKZwpyO2AsdVMH6hytDpL33Nspl2EERQC3H2zFMsRwdwm4Bj3hmzvL3Ht2MN8qnc//8cd38IH3/cEo9wQpTBfVmyvO/K0RCozfBT1WPA9J59kaxhRooTSAhFqAq1w0iF9gTKwfa1N3D3Erz77z7EuItx/gX2jJ7FTmqmzt3Ivd9DyZ1gZP0cv7fDRK49TSQ6yfalN4SxiVKJUCTg9P0sjCgFBoCpYl+CJZch6kFeIk/zPXbv+0//zEa6e38VZi32tpSozxuY5Io6RF1fI8ycBuHT9DKJX4qt0lX9z7y/z9nMfIxdbHKs1v2C8Ww8tsFAt06iWuHR5m+f3rTBIfBxwz+uPUWuU+eVLH+C5wQqvOXQHL5g+QhD0yPMNnLsdIcRXapneY4899thjj688e6k9X8C73/1u3vKWt/DEE09QKpW+YFuSJNx99938+3//73n961//RY/9RRdSLl68yNd93dexurrKiRMncM5x/vx59u3bx3vf+16OHDnyRU9ijy+fQS/mQPIGnkweY2dYws1I8qal3pqh2Sgxv68KvoLAQ6wJes+XOSg6jMwkenVKj8kSSWECMJYXVa+yf7nN767ehY018YJHkXvcc9dJng/PYY1joEMOz6cEKmBeXWN6po8yjko2w3KrS2k243Yu85nnlyjXMubDAZ4wVAvDxQtN4t0q1kp8oSkLSWIM9UafwaiBShVFapBO4JzASnBDEApkIdFKkWWabq/ESruMTUCVJYUH2U0JVgXIwiFzh04cJrB89rnTXPg/b2NFGOr37KJ74NplspsCNiuCPIVtB/bd83jbCQeOb3PHV11DR45wq0bnD45jLTy/ukW07OO2ClRkCWsJ6eUQ7ReT5JPUogJLumax+3zO7QYcLVKKpIBaiiqBzSXKOPSGouc79FDjtXP8HnQ2Y47cNceW2YLcoHNDuCKZ02Ue/9DLuOTOceWxBmbOgZaYeohUGenAkgcKSoLGYpkoKPP8ozeoXUkZHQzBF+gcBvsz6FnSgWXq4JhCaMy5FoUSKCsJ1y2mJMjmJbmw4AvYHfPE1RsUIwOehxQCdE6GwEWCsC+waUG3JfnJT72YskvpbygOTLXJU0255ZMXBe1qzicHhzn7WMp8oMlExFgMeGhnDX8qwvmasF1wYLFD1Y44b1roGGRWYBXUqiWKSNK2KZ4SxCVNXgnxdcB2JUf0DTKdNF1YreiFVYx0PJJssr+UQ2lS7A2lI0CiBxYxdtStpmSbXKfPTmqRSGSWoksZzjfUF85in38BzbkyO5sDXFAgrYeUCqsMwljKqwK9XZCOFMW0RGUWF3lMtYZ0VkqYjkAmBVld4qwlXgiQBci2QHoT7w2LwtUVtqyQOxnOtyRTkM35yMQix2bSlSEdEkFQyieR3yOHPy5wTuF9MsVbUMTzPlrBwm6P1c06ZsEhhKOYqyJKPiQFCIdwAqF9CCYFs/RggNwWyO2CG6pJviio3xBI4WHaDluxiEiRlhWhEJMvnsMEQoFrVBBOomsliryYeKxYkKlBphp/uwAvJq5I7mplhJ7lhS/7MFd+9+/wzJ+e5PqCJl7URJfAGxhcd0xRrmOkQG/GCMsk2llahJpEPVtfIEyGGAukF+JcjtjsAZOOlhyPk/NNLj1ymc1+BxtInPSRWGTH8fRPH2JQ6uGvtxm2JOE1y2UdMn7WIQcetqTQWwbXcORPBZhZH7cd445LaClKdUMkLJ1zYJxGlDU2bDCWiuZSg5W1NsNrmnhDM+yXyMogfAtCMRSCa1nMMVvmxsAxak1kcH1R4GyZkXkVFbNLaTeB8hZ+uImWjmvBNZaXm1y4ssONs7t8/atm+Ujnfrq7VbQI6fZjen7GL1y/j1tLEYdLx+hd7vDk2evUq2UOTjd46U2HWL/e5vc/9ChPZwNGhaY1pWn2faJ4i+Nzm9z+hudYGBwE4OsW/jHvu/izXE4vMhQ94uVV9NptvGTxKPOLzc8VUUAIQe6OYgLFzYsXCLhMMSgxP1//c9euRysddr62RPiM4SObJ7n16BqGyb5aNBAYcILpUsRWW3GlX2c3iTk3fIqR2ODB9uMcbSx8fjxpoOQkygn2z9ZY6TW4XttBCFg8Nk8YefzpwytsdUececUW9fAEH1j5MUbFmK9e+lcslu78yi3Ue+yxxx577LHHXytvf/vb+aEf+qE/U0QBKJVKvOUtb+E//af/9NdTSPne7/1ejhw58gU5zLu7u7zpTW/ie7/3e3nve9/7RU9ijy+fjRs7PNnushIFlOZ8GqnP2I0JTJnRKGXfkQWoRBgpUJki/7Ri556AoqJQSUF3VMI5yfxUynQ25KbGOtOLAxqtlO71EqqwyEM1pm76DBUnSZ3H+d4Ugck5U5rnw5cO8tGNA6jQ4UuLzDPmXY+H+scYdiPKYcGnt4+wv9/n/geOcHi5RHl5lTyDYRKwaRJcuUTj1IDRuQZIgYgMptDosUMOCmTqsfBVXfpeQJjG7G7PMhj6dKxHXoWiLLBKEM0ZTGjIpzUqBTCYULNWtcxejFH7IwYrdXIjkIFgpBJ0agjSlCyULB9oM/+KLUouZhgFXNud5drHjjPeSchCeOLRq0TWYxRMUoL652YIZAo2YVTRBHek7DyXo32fzFPIlqJ7dUQaWmQgWJzusF2tkRUBgZh0s4Rdi9eROAercyNuTG9SvauEfCJGxjlBP6fILH/39R9kZmqDtdVXcGVzCmEtXhlkHcbzkiIV+IOCeGPApc0ddMfgcihfSclnAoa3pNiqJWwaGmhM4VCBpShJwsCfdC1N9zFHDf31Mk4o5ACyRLC1OmD2SJPtdoHuxAihMAKccahegdJQhFCeHlLacEhrGBpB7Ugfs90i3RKIoWTf4V28BG6t3Uk89nnW73DJG7J/JqJa8xDdIUdlm8euzWJjOfE8LQpqnqMxnTNohzx3eZfQc7ggABxWWmTf4JyZFHmEJtCgcsiSlLTsGGeCxm6O3xshyzloibcLpdWc6YU6s0WN8cWQ3e1N9ChBOodDUJ6KObu1hNnNeMHLl/lQ0iEfgRsZjK8wnsAqDdJhSgoTKmSa461lyBmF9gvKczEdEZELhR7mqI7FVcD5EuFP4rBdAoUpsHWFMgp/rPFHFrcrQblJ94gzqKqFxCHSSaFQWoEIBUUh8YwhDwUGSelqSms5Jh371EyC6FtkH1wlAl/CIMbbHkEegRCIHGwJlo+0yeKAQb+CNJa4LulFmtazKZQlSihUD8zCFNmNGN8pEBbdy9GdTYQOsGWJ9APsOEbsJsgkh7KbyJOMj31qivuJePUrNugUjtrsgBu+hwmh8kSC7AmEkphIUJQAl6P7KViJyATeeg8naxgPcm2QSuLyDBMqjJX4VmE9gfQ8SAo+88GzFHFB+URB61jG1WfmkakEY0g6fYJnRkjjyEsKf1tMCjYXC4qGwjU0dIf4b0/J8ypECq0EJjbIFclgNcI8LPFiy3hRkeqQPF/k9a++ncW5JtcaEeH5KXreBva6QxhHrTLDTjcnXUvoFAnnWxVKpYCt8sQ4OHCCYqype3WeKS5jyz63Lh3gGfU0qfXYDp8hTyrY1PHEEyvc8uQxRGBRzZTfuvQntJ+8CTWXcDZe4GVHv5r7Ys2z2wXzszUqM2X+4MrzHJmZxt1o0+sMsTbDSocIBc1qk3ubs+yKDcYIjBjjnOPK5hOM3DVEq4vvV9E2hu05GvEpbl2aFDLObm2yOuhzLb/GdifnobOnqXtNvueuR6jV/nzZTHbTCsvemOutKVxDcCNtMVdsAxD4x5HebSRJh1v3nWF3tMZNrQUWy1X2lw5xYZhzV+vUF4zn+ZpSNcQPPY4cn+fhq6vIjge54tnnbnDAazKbz9LP1rh/7TEaVcXj/TqDQvKCWcniV2yV3mOPPfbYY4+/BvY6Ur6As2fP8ra3ve0v3P7Sl76UH/uxH/uSxv6iCyn333//FxRRAKampvipn/op7rvvvi9pEnt8+SwdnGFj7QLKT2kWm6xeXcK1CtazXfozIy48eg0ZFxBIdOxwQrLrVfF8i0gNFskoD1mxQzrbSzwwOs2xsWQzn6acGvQQmIHFuU9gd15O4BUcaHUZbWs24+sESjH0LCoHNa4yskMOHt+mOT3i41ePEwUFJS/ns7sL5A3H/P42eTKg2y2RPCvBeShPcLG7j3LV4HJJbTom88CNfbxM0Dg8oiILyqWcrWrEdu6QJcdWUsNGDiEKhAwoTSUUSFLjo7oGLwbnIJ9SxA1J2lQUaUBadcjUEj3mw/4Uf8qStRVLN2/TXO5x48MNBoWHPgUb8znj3pjxyYitpYTK8w6dO5ybyFCWKx1m54Y8XzQRd0G26JM+FsGs49CWR//yCF0WkCp2b6uQTRekVzSRszSaKcV2CRGCyCxeIYhTg+vk5KFABAHUFNsNx2fX9vPSUputwRQICcYSaU0hDGoUYoRBrySs5JvIpsYzknTOI0gtpmqxOE7MbVNfSOl+8DCDRyNcV3LTXIPSVJ3zOyuM5nJyC6WdFO+cACOg7JGGih0vY2Fxl6N3rXLxgXk62w3UyGKnBIQSHTk6RZVTdp12UbBqArRviMo53SjE3wYxq4gWR1zYXqN9WZGnhn23TZGPM2wjICo1eOiqwLZTJBahHUIIZg5tk9WHjFaaJIUmtSB2BrhmCYclGiqK1JJJCy4D5VEqwa2LLW50+6RBTk1qYuMw18H0U/JyxJzWzM3WUUpycWUXJCAlLjXEnsf51SWSsaWZ5ayujVGbGlKLbjvShkAPHTZ1JNOS2rgAqdHGwyU5aRSynmsi38eoFD+ZdLtgLTIpQPpYBaqZ4SoOuR2RTCuEBb0rsUOLN7QYVyCNQCLQqcQ6B+OCOJU4C4FWEEgypRieiMhbktKVgp7SPPecZrdfQi34uNRiIoHUky6UopphPcckvEugreHo4W2cgAdunEAPNc6HvASdY4qpZwoEDhsIXNUju2ueynNtzEjgAKsDXG4w+LipEtxhoV1gNhNMJcCc1PQ6iqmzAllp8e4/+lYO35zw4Sd6iK/dQQXgVwxWWkRQxpZL2JLCCcAphDU4wLMeeSdBeoK8pBB+gJMWJyDfV4HtGrpSkMzXUf0CvTUCJ0iGGmdH4CAuOZQGhgZhwBpH0DcUWYK3M0LkAut8zL8cMuXFmNOOwdsLjBF4CehnILsDXOgINnLc0JCfqJEXOcOruzzxgSc59h2zbHfGeDNjbAIMUvxnE9KKRpTrCGMxBoq4YHG+xXU5IqlJFpplbm7u4xtvupVPP3gB08sQvRE9LyIxHuPccuaWJa5e3kZIx++vP0c4P83BqTbV5g2eeN8B6g2POc9RdR0utn10NeTM0WXO7ezw6RsrXOl1+Wd338rNy3P89p8+ThIPWao0+McvPM0j8WcI8n3IvM92R7E7GPG+T3o8M3sTiSvx9cunmKbHyXs+xEzzgzj7ToSs8v5L51np96lNTZGJLZRukGWrPHTxMKLUploJyAtLKfCoVkMyl/G9Jz6ALwy/Ed3B4VKHQL6SfZU38PyFdTaGv0FjvsaRuf+HByqXmJ8d8vpbTiGEYJgGlNwxBF94hUl7imqjjLWOkbLoIkKvRYgc7r/xGOKaYakUoVSL2SmPE9VDrA2rbLkuDW3/upfuPfbYY4899tjjK0in06Eoir9we57ndDqdL2nsL7qQEgQBg8Hgzzw+HA7xff9LmsQeXz71VoXXHi/x4c0bTPdCbogEFUJ/p4dLDOWyxghIZz2ymkQEApfLSURwWaNHBQhYNU2SecHlbIkj6iTV0mWK0CL7ktxZPvjUK/CP5iSFj+rBVtrgaFRjKlMMM4cSjpbqc9PxGyQln8XaGvXNBG0LZG4hL5jSY7qiQ7QrEbnDRBIyjxoZO90SY6FxJketFfhhSj6eeDaMu2UGo5Chr9kZlTBVSd8FXBnNURUJSaeCKUnaq1X0PovrOoJ2jnYSYyW2pSgZhxlZiFLGkY/1JKdKB7l9YY53rn+W8QZcTpaYXgzZeJ+HW7ZELcl60eLuf/Q8j3cP4SoB6TFJMIjx0oxiLKnXEprVEdlDM4h5yC4FSO3hxoLhbo7NLIEB4yvyTBDIDHPAMq/7FN2AuOUh+hGJzahcgjcuHeSB9efZwWClwBYK6xyf2DjFgpcxLMpYXyKFI+tZOF9QnJQIBNKfyLMypRgvaFwg0TJHv37IfCPDCy3CQSYc9a0m/bUhF9pbNE9ahsMx4wcDRrd5FBLqZYfKwAlBcCBiO0xYrmVUp2Oaiz36mw2UVAglyBoOU1dsZWWezuZJkxHbfcV4HDK/GTA8AOEQXFdTTCkubbTJVyzhDniLN6gsFKzYKYxvEJcCtNPoA0Pymo/bUozXIhJfYcoeFBJjHVJpXJzjSpr55SY3bnSxtUmaTiDHqNd2eTiOKdZqhB1HxVdUdY2hPyazCV6as3+2QWYcXTtkPF+gOw6dSsgFFAotLM5O5GU7Oz2K2FDgsEoiYocaWXTuUOsFqmzxLiST/+uyxGYWMwKZKMp9gUOQ1iQuFIwPeOiRRfcKRGGQiaZQDisc0omJPMlz4IGa8kh8QbBjsR2DqUjShk+hFbKU4yQIJXHVSYKQ80H0M5YPdei5MmbWw0YSPZ6YtgoE0hiS5SpKRdgEpHDYSLC2VaUfh7jQEd6YpBelNUNRg/GtOYc6fVbTOUygUGHA8VffzPMPXsRuDCAzYA0qDyisI98XkIXFRGIzWyCqjjx0uO4Am3S4urvFyhO30nloGzZCxvs19rEY3TYwF6JSh+xnWCVwNQ+3kYGzuHEOlRrkOcKLkEmOlQIEWA3mYBO3kpNXfGzgQWoQ3Rzb1qw+O8eL77vK+XrE5qequEsGE2jyIkbHCjVK0FpjnAU18YcyWmLLgnQ5oigHuLYg3I2pKUm62SVfl9jAIUsW2cvoXh9y9pFLvOob7uXGpW12Bgo7pym2cmxmCC/sEh6vkGmBixzVSsDhmSZPb6yTBpbDlSY32QprN9p0XIYTORviKZJc089DDnIPh+6do73a5smgy6odsVD4SOGIM4m0UBM5R1uPc/HSQzxw4TXosc8Ltvu84J5DrA9u5shMi3sPHuDK9W0GH3+UmfqQVx6a5tBMjQeuZkRyC8KCx3cf4cjm7VxaGTMe7ufMLVP8vaNfT03mjHb+A8Y5ntp9J7fOfAd3zy+zsnqW7VSxcCjkn776Mtng46yszhCFmvNXtuh2x7RKIUePzfHAzie4s7GBA146dZHLo1kOlL6H4TDn0w9fZn3wPC961TUG4Tm6w4JmOWKmVcM5R01HdJKE6aD8566JozRjtzdiv67xzGCDYphy84ufoKFbnL1cJy8M/7j2tRyvzBNUfpuslOGnAZT3pD177LHHHnvs8b8LBw8e5NFHH+XkyZN/7vZHH32UAwcOfEljyy/2gNe97nV8x3d8Bw8//DDOTTT1Dz30EN/1Xd/FG97whi9pEnt8+WyutfnIc1fZ7Ffxl7oMDkPhCUYGzLzH2APTisgORBRNjfUcMgZrJU4qnFW4VDJVlSy1+oyk5cHRE9jFIdyTYk8ZvLpPfXGaW6c3uXNulaF3kn4ScemJ+ynfGHOT2uGALCjnAu1b+uOAX/7MSxluhLjc4hIIXMFxl/OKYJON6zNcuzFHfjDl1InL/My/+g1a9QFOgw00w16Z/sCjqFhkbBgVgitJnW4akTwX4LUt8UjQbVdp7B9i9CR62FmJNzT4cY4yFissToERBpE5KlEKR3JsMJFlDGzBcq1FbRQTjAvSkc/aJ+Yo0oCkU+X5S8sM4ogs9bg573BneYHZ2TrREUupEWMkPLEyz4OP3UX9QJnRx0pkZyXRgSpOTuJknZLgBBngrgScvDrNi9qCrB2QrIdkHY9IKHyt0Ms+T5d7qIpGDQp0NyMYpFSVI1jq0C5JRHPSgSCcQEpBulCiqHmYkkdRURgN/WMh6YzCljTDOZ9uXqLkF3RGJS7/6RLFdpXxfR306zukLmGj02dQg3gmRN0QmJbElHzyUIGFl+w7gj/O2Tnf4Lk/XWL1sQWkkhSJQSaWipdSbw2pLQwYLApWZ5roGc3iLbv0BmMsjlRDmkkcIUMrIbM4DOPjI8bVEfXpbfIiJ48ceaSJF8t0l8sMlnzaO1V215qU62VOTJVR6aQ7QoiJj8612T7ZixPcfQnyBWM8lVPJUmzuyGQBi4psHFNMDbABUPLxkoLe+g7PPXmdpwYb5IcgXYK8OTF5dbmFpMCGjsFRj15vDNriHJi6xFTARgpTEmAtYgASh7AWigJpckKbUM0gKgQ28omXQ/Kah8wm/9c6thQdH7eukDmUrhVULmeovMCEknheI1oGwkmkcDrjkTYkeUXhYk2RK5ASBIhcUO0qyhuCYFDgtsbMnt4lrA7QOxnR5RjZSRCDDOscxXwEWqJSgXTgjOT8c4tsXq5RumTRicXbtcjUYkPLyPfZrWjMaNLBMlrWPKp6zB5bhLnaJMWGye9CdRJGiSZpVHBd8K/GcGOEyQ0kBruTM+ykpMkY6Vs6kU97Z0C6sYvdaCPaPfRmD389wSss+fEQF3mAJJ3x6d9aIzlYw3XHWJsjHOjY4u8Y5MhilEVIgRoZhnVNdWlEuTJGHEm4+85N7pheIbJDKtMpjaMpzHrgCZCTDh0RKEyjyvDTZTaSOptP1TBTHkVN43xN1tDY9THeQzFhJ8bljppImL7ew5GSTA14/vlVrl/dhqtQeDPo15Zxyx55x+KSHEKPoByxb7nJvkaF6kCh2hJ/O2cmUQyKFKUFqp8h2iWWoy4z4Yiba4fZ7I0oz1cYVjXDTU3DjehlITU/56aFaeYrjnObLURzxIHDcNuhBeqtiDw3GOcYphPz1+u7W7gzN/jmb/wILzr+Zsq2xrcceDPP9w9zJZ+iPb/KQ5efQ5cUrfEc3zD9MlphFeU12VQ/wCfiMzw+WAHg8ZU1doZjjpVu5dsPfxfVqMZj2Rw3bIh0gsQanAeNekQYevjqCOvpAteTOk8Pl3mmt8ypuUWiyGOqHnFi6Y0cnfpBWtFLue/0EV5y6hCHppo4YFTkhMpjXPz5JrbVKOD40gyLyxW0MniFo2Qcx08cwav4pAG84+xj/M6zT5I6iRaGa8nOV36x3mOPPfbYY4+vIML97bn9beCNb3wjP/qjP8rm5uaf2baxscGP/diP8Q3f8A1f0thfdEfKf/yP/5Fv+7Zv44UvfCGe5wFQFAVveMMb+IVf+IUvaRL/M1ZXV3nLW97C+9//fuI45vjx4/zKr/wKd945uXLknOMnfuIneMc73kGn0+Hee+/ll37pl7jppps+P0aapvzgD/4g73znO4njmFe+8pW87W1vY3l5+Ssy579unnroPIOtjOMLQ7qrAeHYkeQ+fkPzaxeeJBuOkSNLdD2FUOFriXexwI5ydMsQFz7SCW6tbPHBa7chtwWjlsMTQAFGwVj0ubx7g+mDOQGQ+9cR4zrdxHHbwmXuPXOFRwf7eNfFmynFLeJMcuXaAqoE4zSAGuQW7jh0nlk1RKUOZRxOKsoU5Lkg9iV0HUJCrj2sgHQKZC5RUYEykoqOyT5dgz7IeYedsxShROwYyAJk3dK7XKISFzgLaEERSnTiyLTAf1GXZKvKJB5D0t2KOXtlk2beos+QPFVE2iBmChovHjHQFZLEY/t6hX48g2WHfSLC1jO2XIirCLxDGenamPzQmGrdsbR5Cspl3NWcpUrE+rAPLieaShlWBBd2DNKU2ZUR4YYjHFuaDY+bzyySH5Y8s7ZOt2nJyyHhSoG0kptoUNrZIkzKOCOQY4OGSTdC5KPGBVY6rLIgJEH3c0WGGqSxQlwJuVGqY/se0Y0yyf6MfigJ/BLNsCAbF6RzCu1SSl5GklTwtlOss3ieolYWNIKQwaGEjasNZCEIQ0HHF2RlyVwtIS1b/FJOPYjpVRq87OB52rtldlqz1B8aIKWgspxQHgwxyRRpBNrzeX5rkSAoUNc8dJIhKhJbdiTGR7YdKnUUTR+tNXnuqFdComFM0h7jDk2Ta4GLU2RgiIsIT1q6Gtxjimag2TiYkVWhfzBGlC1yozIxBi4k557fQjTr+FrTinxs101KzJFDO0MsQaHQuaVoKspO0x9niAKkBSElpu6j+wLVzsnrHtZZFIa8GiCERjU8srKFNMdohzQO4ws8O4ns9tvFJIa4ppGlAOEEWUuQNiaJQP1AQ1nQjHyGngUBqmewZYfqT74Ym0iAcri+QwuJLVdZ3a0yn64RtC3Vp1OEAekcRZFBkSOSAlf2sB6Yid0MTin0UBAMLQKHHGe4akDYBTVUpOs1hPycgWwE6wcFBzYzTOhhmxF0hlAYCDXNQYxrGGyhMNfHEMR4gQVR4EKPYN4wfd/TJOMmYjsn7IxRPQNW4Ha7GKNI5usIXUDN4s8o9JoDIfELjZQSbySQwxRbkRRZhurmiEBipEPtxvibOclBj1Jt8hkRu5Rf+o83c3B5gF1VLJzoMn1mzLnuEYbvCxHbQwg9xEwdZx31D4wRn/QIel04kJEMw0nM9nxBvJ0S5iB9R7qocdsa2Y/wbcx4aDl/dYeLa7uEcykL9+2io5SNBU3vlyMsEplklKIKizN1drYHdIsckxo+89nnePkrlrl93wK19QR3bUR/tsnKwRlGRvPA7tO0rwnOXl5lJ+whmpLAZkyJId1hiFYKz9UYJgs8Zpv8vVtOMRMv02hWWdvtsdkfMMoy7ju8n3GUQkXzdDzPy5oJz15b4/3ta5jAp+dKNKaHuHiF04cOY6zl1PIs7azNjeENDoTfzEpygEW1hLWORhiyOFVDza3wy1cf4qWNKeoqZkd4jNIxiSmI80nykzOO1508zcPXX8ovXBrTsWXKfoaWkn6cMS4LTs3fwkJjH0VuiLOchy7cYBA6vubm41SISAtL1Qv+wnXxnlsPEV9L8D4OjCQ3Nr+O80nEi25psDYcslFNkEKSet/I9fgDHK3sXQzaY4899thjj/+d+OEf/mHe8573cOzYMd70pjdx4sQJhBCcO3eO3/7t32bfvn388A//8Jc09hddSGk0GrznPe/hwoULPPfcczjnOH369OfzmP+q6XQ63Hfffbz85S/n/e9/P7Ozs1y6dIlGo/H5fX7mZ36Gn/u5n+PXfu3XOH78OP/23/5bXv3qV/P8889TrVYB+L7v+z7++I//mN/93d9lamqKH/iBH+B1r3sdjz322P8Wsc3tnQGLvQFTJxISG3Ew3WXrSoub53x2YgepAwzBtiWeV+SRgI7Pa196lm9/4wP83sfu4A8fvIlI7uKSmMYwIujWSVoeRVrgSkNSPyGbSghkQW4V19plNm2V7Wm4aX6VX9+6hVHhk/ck187OY3cd3pbFu8kR5JAWGk85fufsTXzd6YvcceZ5Hu0d5LpdIk0k7Z0Qv5PD2kRmlMwKRKHItaMIHaVhwKHaFvtL23y0mAMNUjnSqsJte3hlSyLA70nG0iHTAnAIpUFZZue7RFXYuhjh8NAjAIfxJlefX33PXXx4+0/YXYHksxWaLxxSu2VMNgy4fGmRjUtVmNUU2zmDusaLwa8nFJUQIQrGmY/bsPh9ST/L6W52GRnD5WpCJR4jxqAKQ+lsCSchrzuYYlJMMhZjLadPLvLMVJtlv0WxtsV26MjLFimgSCzJ2ZP0517AQnyeHhkIiKcseV0QXB0jego1zjGeh1MCUUDdegxFQRLm0NOkqyWkZPKFs6EQWxZ9xaL3wSArePmrn+Ou2U0+9NQJzj+6RLag8KVj5F0mrzjUYoazYK6UyJTEhAW6ANWWTB8YUw9jRGA5Vl2nrgZcuTqHpzwKl+FVC2TuqLuYupeQ1aCWlMiuFiRhSITDSwWtE4rKUkGqJcGozdbzAb20RlT1KOMx2B7hYpDKn3ypjw2lS46lE302jmbMyTErN6aZvXOXrfUGbFWRQ4fwfMJ5R5xolFbgGfAmHTzBGErP5iQAHmgpAIXMDfQyrJBMzVXYWOvjkhTdt6A81FBAwec6u1KKSEIhkJlF+Iai7jFqOZrbFTr9ATLJcf5EguICiSspUALjW1Q3x5ZL5CWIj1vU2FJaN7iRwtYM3inQz+UIq0hnBC4wFFVJ1nKMj4K/myMLQXJQo2Y0pZkRlBWm5lEs11AZFLlBZwbRTZCJxQqBjHPc0MGUmiRBVQRFySIQ6K2Yubet0/4HB8iq4PUU5AUIhcwlIrE8dIdjekfAdBWpGvgrCXKcI14J7PqEfcjfn6GfyXENi1JgUTTuGGCrhnKrROWsxyAoYY9WEE+3Ed0cJ1O8T10hXgxwVY0alHDKEPQKxJU+xWwJU/Px12KKNCbcHlPEQ8y9h9DCw0kHoUWOYen2ARuuxvipKVxa8NxOAzkeM4xDmtMZ5Q0YVCu4pADfQzmB30knpsO7Y9RwzCgeYuZbqCKknDq8DYcIFPKrQ5pqlnQ3ppiJ8AtJQ8ww7CeMs4zSwRglHBpF+bpPL9BILDZ19K9u84ebY+rNMrKsMLIgG+T86gOf4A0toBwi5g333n4vl3kfiJxns6s0N6bZutBBnpYo63j8gX0s3NZnpdrgO27dx1Ij4Ex1xInyEs+e36KyaMmTDNdLeMX+A7x38xL//pFP8o+P3codxTx3iY/ysU+dJpu/yjP6k1SpY0TBoXCWU/Zmfm/tMn1XcG5zmyftn3C1f5075K2s2AcgO8Bi6SDOQEX7dJI+LjKUhOPCuILfKFhactQaBzCFIRCKciUgtz020k/x+nnFB7q3IIXFOce7HzrLL7//IQ7PN3l5dpQ/vnCeaCC5vLLLe65d5D9+6AFe/vKjKKEo7Bf6mpjCkMQ5QegRhh6vPn6GR49d50+vf5ZHl87xZPokM8MDfP++13Ps1By1KOLJbglp/y7z+jRFXqC9L/rUaI899thjjz32+FtItVrlgQce4Ed+5Ef4vd/7vc/7oTSbTd70pjfx1re+9fP1gi+WL/ls4dixYxw7duxLPfwvzU//9E+zb98+/vN//s+ff+zgwYOfv++c4+d//uf50R/9Ud74xjcC8Ou//uvMzc3xO7/zO3znd34nvV6PX/mVX+E3f/M3edWrXgXAb/3Wb7Fv3z4+8pGP8JrXvOYr/jq+0gjtcen8Edanu3h5iajdpMgVUg/54ftewkP3n+MPuU4aCKZui7nzhVd49uIi0XJMkilO3nKd21XEH124hR0r0DrDz2tY3yPbLVAjj2LasHV5huOHNqnpBOv5hDpFGYEXCTpJiMRRz2LMdsDNMylPKIMfpVBzmEAyKoeseQ3ef/YEX3fyGRr+iGg7oTI/pkRKgzG7pozVEqxDSYdSlmqRUZpOGHohN7IphLMQC0gNSSxICgFY6q2IQTGaJMpUJXbXIg2U85TQz1hc7nLlidNUTsT0XQCJpXAZoqZ5svIItflV9LLH6rkGxVWFPpyzu10B50iOl6msGLyKY3hoRJxqVOFjBwrn+fiDAtEpIwaOhdMVdtI2AHp6QHZOQwqkE58KJ8HNSYqGw+xYKrnHy7/2Fo7etcT9G2vYBcVNfp1Hr29gUSgsM7NVsl6OzQuWZ5sE3SH9OCEvDC4XFJUQU9e0dn1GWEZLgFWUbYDXt4w0mB0fUtC7BWECU50Ko2u7dE8F+HM+mWe5Za7NTcWA5uIFfjxcQviazEiePecjjGW8E6LaDln+nLRICSywtVPm5eNL5G3F2XiZ0Pap50MOh5usDfbhzggKKRi2fa72WxRjh0wsSTCmrMr0C8fYl4QNTbXe4u5oP92jT1GafZz47ogPvONlFA7KXoAUDiEsxAm6HRCWfcIoZHVrin39Dr31MrP72kwd7RLNpmy8t4XesuSViSmxSi2mofFHgjyQBL6iWvJIVY5fCLzMYRNHbC2imEhGZKjpdgvcKMXFOeGKwTQkMhWkJYnIC8Q4xxqFtCBFQnNxxHC6ymyyhBs7Vns5xbQmXM+Iejle34IOsKEhn5MQTD6SZVwgjCFvKuw4nLQdWcu6spSNQZQcKB+nQRiBjTR6mE/kRbGE2JLWJPFciVKSERQxthGS7Qa4AqwCjUAWFvIcCfh5QVYI8CW2YhmckHg7Gj+LCFWMSCwqUsjC4oYTo2erQZjJ53DnvjLNz47IC4uq+jgpydYMoil41R2XqBzt819//wAis9iSDyPLtZ9XUAOzNWb+jZY7/k7M4/cfJrueUgxGZIFAO0E4sOhhBqr2uSgmi3UOowTFYgW/b9DjHCsl7sA8cpAhnEYAVgnySPHxx0+ghERVC6R12KxAjA25koyCiO64jDUWWfJwmcXFGXIQT9KHLJg0p7pr6cym4JVwSQcwuFmBuyPAPOA4VOuzfHrA6uN1avUSo3GGNY7xszVqS4vce+hWPtl9kn51m52KondcEp1LCNKCdpyyGFVIxwLCnC4Za6s7vPrMKea/psEbXnCGBx58iOvDNmfKB7g+2sV1YqqPS8YVGN8ZcSPVJJFmXHEsHazx6e1HMBs5XlRHlzRWSIbDjKXpKXwlefraKh/b0rz25t9GVFaJ5m/w6bOvZ2a+w8oAElPH703x/Kef5lIRE1R8aoHPYXmUzc46p1r/lhfrEb+3+yoa5YhOHHNtt8s/OPRVnN5fp915hM3VB0E51o4+zb76C5G2ym53hBOCXrJBYh2RzFEuZ5CHPLp+nT/45GfpxTmP39ji3GcusZOWIHPoBvgbmk405r2rT3OoNoOWX6hQTuKc7s6AxlQFpUPSUcZyuQ6pw5UdkZ8zVB0OHZyhVS5jrOG3r32UrMiZW2xwSh9Ae39dK/cee+yxxx577PGVpl6v87a3vY1f+qVfYmdnB+ccMzMzCCG+rHH/UoWUN7/5zfzkT/4k5XKZHL34qwABAABJREFUN7/5zf+f+/7cz/3clzWh/5E/+qM/4jWveQ3f+I3fyP3338/S0hLf/d3fzbd/+7cDcOXKFTY2Nvjqr/7qzx8TBAEve9nLePDBB/nO7/xOHnvsMfI8/4J9FhcXOXPmDA8++OD/FoWUmdkmTAVc6s2ykJZIdkMSr+Bct877nr4AFOQVQVHXzB1o05wbckhu8OsPvYCHVg6SLcSMulMIlWGUJA9TIlngah50QW16OCEplw2XRzM4qaiXcrp1gehIHuwvUqnFdPOQdNEwu9CGcR3vQIFq5Qx9Dz8rk4wjVGT42tue4iUHr3N0JaYZDSg1Uy6W5ommUkxbkvngfINRjqjIyHYE0bTAFwUVleEVOSbT6BGEMsO1INsukTpDcLCPWW1RTGlsu0BZQTlIac4O+dPxYcqhIu1MPCpE3+L6Q559+gpy/hmqOke051mea6HUWW5aXOP81gFG/RJOGIx2qNAyNALpAwmIFE61drjn9Zf4r5/4WobOEEx9liNlw9OXDzCKNGUlEIDJFfg5NlRY5QhdhrjJoB732dns8chHznNqsUWjVeah9lN4PYf1LFpAv5vQ3uzTK0XEecooyYi0hx57ZJlhdWZiLHvzPUd54k+eJ9opyJoK41nSCMa7IW7KIVoWIzLoWMq3l+kXknTKI6lZbCb4/fedIbrrWT7x+GFkMfHIsBXNzrUefk1TlHzICmThUJ7AliVFZMg9nz85eydHFjYo5hSMNY+96xh3vfQivV1F9XUj/MRhHq+Qj1uYxCFrI+Iwxx0v0GNHuBKSH/B5thyzVpzD31jnRTWftRvTKOeQzjEYjKCfoX1FLgWurEkbHiXfZ1QVPN72qRwYcVO4Q3U45mBSQGOV59I6oylHmlqCVOJKIaWKIC8ctbKHOlGjZwZ4I4PbBu1rrBtzcu4aq/WQld4+kt0UBBBoRCGQRiByixQSMcpBwviwj8wFdrNGdt7BisEPBbmTiFygx4ZgYElmNUlVEm1Z/DboToEVGlPL8ccF+XWFqFmcyCnOCEig9KeOYCjJC0WQF9g6CKWYnhlgr1gGw2CS+NO3jI4qokMDdjfqLB3codrcoP1wg/H5ErKfogYpRoYgoaQSXvmCc3z0+nF210N0JJAj8GIgBlcKiXZ2GR6aRSAJkBRKIRDgK1RWgDB0XlimwDD9WYN2DvGJgsrNOf/knz+EQ/Kx9jLFriZ/tEwwNWDfHV36m5rWkR1KNUsybBEspGSVADcdMrwrQMagd8fUzoKb1diohEg1Zq6Gl0OhLK4e4RzIcYbaHsFQYlslnJRIpfFyRR5abLkgnRdUHikQWYZVcOQlHZZuS+luTTF4pIHOIc8MGjcpmjmFiEcgHf6SoZJOIoqLCIw2RFM+ZrXFOFK8/jWXiIKUtQiujSQH5+pM16rkSYrabPGOG89ihMHzNAKBKBz5tIe3q6iWQ245vo9ifY20YRHpgHxlwLGFQ9y27wCfbT9NvN2ldBXK6xFBrYIUYCJHdvOk+BxbTW4l/obhN9Y/xPn+dbZlzJtufi16ZBmblI3dHh+4eIHWTJlef8Tvb57lVXMNjnp9jod9TF8xtdymUZFI+xKC9UUeliscazY5cWCeqVKZQ+FXMb/ZY8GfXOBYju8mTnOsdaRpwdkbu5yaWeR3P9jnkxunWbx1h2c7P8uT15bZ536C1c0uN51YRNem+ZWnXkKSa7ZFBEj+0bv+KyqXWAXSQXscgAIQGGsxhYVKwdg61se9yf/gf4f2JNLTeIGHtY6iMNQrIaIIiB/Q+LdW+P67X48tOX7uiY8x7dV5+fQd9NIBM7IBX9451R577LHHHnvs8bcUIQQzMzN/ZeP9pQopjz/+OHmef/7+XyeXL1/m7W9/O29+85v51//6X/OZz3yG7/3e7yUIAr71W7+VjY0NAObm5r7guLm5Oa5duwZMjGR836fZbP6Zff7b8f8jaZqSpunnf+73+3+VL+uvnP3HZ9FXq0g1wncB8/Uao3DI/loTKSVb7T7OChyS808sUWwKriVNXO54tr3IkcNXsT68bqbF6uXr3D9YYnH6Ciuqgp2qIy4K/C2Y2dekFl7HIdnIHLqYnchvbImKl6BjRSNOmT21i/Pb9D98mKzbpDE1wAsMFWJese8yX3vLOWCShJKlEetPtnh6tJ8bawuUowRvLBg6halrhkoSTDmSrqV7o8TUvj7xgoeXCdRNMYGGoiKIVcjIpsxVCsTYIWNFoS2BiKmd6VBMWyo3UkQWkaUanYJUinIYEg5GDJ+fRx5tc9/UgKfo8uwj89h5QeZ5BEWBXLGMTihkpKivw/SJXbJBQKICqn7KrpgCTxDMjDn11Y/ih4Le1TlujDxM3SJzgXDgrI8ra/Kxw+94lA/GFLNdPrL1HHN5jahfobXcQGgD1iGMoHCWxz9zDaTk0E0B8dhQFAaVWqwW5Ap8o7CF4NjsLL0Xj3lybRUbCIZexrhpUZlE7liKKcH4UIBfGxGf2KZcNdiPJIyPCYK6x/E7Vnku8HioN0d8m0e0IdBIpkolRoMhXpKhNzNUqggbmr4EKyVCGFAS6VuqLsW1PXppwPvvv5NyoBl/yuEt5vg259BcnZ1xjlEh+bRFTIPsw+JFj10nsMpisaQbFT783AtILmiEyThiQlQGG3GKsZai4ZNVNCaU2HFOo5ThWgVd36fwYePhO/hX/+h3MLc+xXe87etYzzTetiEQCUMxTVAP2KLAOsWc71MYCyLGl5qxDTjaXONQqc2LD7V527uX6DUF5aUCuxUgPElWlXhtgx5aDAJyh0gNpuyR1zy0sQQZtL028aCE8BVqkKNTi9OSfMpDaIN3LkcnjnRGTIxurSDcKsh3HRwFV5KgwXiWvBZglMDv5bj1gorLOPB1a7iu4LlfnMfWQ0wAriLYN+6x2xNkXoAXGEyscJ7ARRq7UEX4PlZqPAUFAr/IiVYh6IQQSYQ0mGpI/4AlPCjxp3dxn1aYqSZOwtCLcSrC+QKcAiYdSkW1QOQOXuEYWcFvfuQ2GjZh/bkyc16B0I6FkzGv/+brXN6pEt3osrtW4qN/vI+tkkdjOELnimjdJ6t5RHmMPW6QaxKhA+zSFAiBSAz+YDyR41iBCHy0L8gDS96QmBcpMgT+x3L0AEorjqKaI5MMIQSiKqkdcxitWDjYZt1WccahpMRJCc7CYIAQBjs7hZtLkTLg4OENrn3Ko3UmoThWxRtVGMuUD+p9lCpjBjfmWawVHFxqcGiuzvPXNvnouE3mDNQc000fb2ioXDCTTrJWwFStzLEzi+ymI1ZdRlyMGG+M6O706XXGHKgv06SJWUlIK46FQy1koBDKIXOJyw1uJ+DbajcTFoqVdp9ETuKrz93YwVnLV99xnO3hgEeeucJ8vcqp07N0TI8/2byFr5IhtzcusrNasO/YdQ5ObTIXHOVjNw5zen6Oe4/v46PrT/Gbj3b5Z/e8hsTcjsvfQCYUi81X0SxH/P07z3CgVeeW+Xk++8QFnnjndTQS3RS053NGW4KQHjK3+FLyz//Lf+H6+hJOgBx/zjPZismZiQIM3BS1eHrUR47AVAAEzoLLBZ1xzIt+9G3UpeS3f+BbKQc+2SBjY6OHF2r8kkduLEf2T1FkBdVzllfd5Tg181bec/XlvPPGNqOeoiWnuG9uP7P5dY4emufwiaW/yeV8jz322GOPPfb4X4C/VCHl4x//+J97/68Day133XUXb33rWwG4/fbbeeaZZ3j729/Ot37rt35+v/+xNcc59z9t1/n/2uff/bt/x0/8xE98mbP/62M0TGiIgLVqjzzIORE0YMHnq0+c4EUzB3nz/b+LKwfIUUY29Hj27DSqqlCRQIQSv2IpzcQMSj6f9E4wmMoYVC1pW9IYjxhSw8OwWx4Rfe6SXb9dYbRRRmXwA2fOcSHocUk0eXywQJp6ZLFE13LiboVBWqGEYn2rzGlvmyTWrGVN/svoJE9t7Mc+J6hEIyyQ5hrRB10xCGtgpmD24ICi7JGVPB5tHyQ/pQiXx/i3WLJcc8fcdWaOpjy6eoTuuITzFMYvMGVBp1DoNERcq9JbjWiOHEv1AbrlmD26S9Tx+Luv+ASfevhubi89x6GlNYqNEyRmmtbLB6z1mgilSDYU1leoUCJkwWCzhuw4XCq4OJ5m5+whQufRHVqeeuII99xxnjf983fx7o/fzRW5gBOTdCSBI9i1FKGlMptgdzTDQOOqKUdaEaeWlskLy+YgYzyjCOsFxYZCyMnbdWM8oByFVKZqyHEKCoKaJkgSZCa4dHGD0kyZqFRlaHLiXYcoJMo3KONg5Ag2weSa9qaFfoETisJTlLTm5NF18q5HOSywcYj1HUpJXnzfcf7k049gPIlRiupFMKkhtzkOQeF5LG9o1FHD1d0ZdD9mNupx3+nLfOrsbcRpFa5b7Mkh64sJ6XmLIMBkgkPpFMHIUo7gBgPiXHCmOs0dB0/zgUfPEauMKBcUsSEfFxDC2EmSchlTlZP3sYSDd61x+NCIy7tzrF7ax1RtltW4jhI5N3QDmYwIcZhCI3244RK604poLBBS4iUKXbWYokB0A1bOzZHIMs9eXMIbFvQXJdlimdrOxGTWBRohHTI2YBzOF9QeHUIoaJ2IyaIq1oPZqT67cRWrfAIPyAtKNzLyhkdREuQix09zXC5wOiJb1KhugSkygq0cseJP5G7lAnE9RSJQxhDuesQlRe9cNPk7KrDaIYcF0TXBOA6IWgmj6xVGfxpMpC+qwGiBm46QWqFTR7Yp+ezZJXbSCD+NsTt97P5ptNS4SFHIMv4jEtUfo/qWLEiRzjL/UI/1usVNebiWmli0G0GmBcoauAauIvmd63fiZEB6U4734SEy8Hnd1zzH7Qd28MuCx8/NsZqH6EFG6clV5I6DSFO6v4M7GmGHBSQWkXTxvSq9hRJFs4QscvyuwFZCrDBgLaOj4USWkxhcRYMUaOmwYwvjDPoJxBZbUtjAZ9ybmF1f67bwkhwrFc7XOByiWsJZg2hUEZGHqSvu/vrrNE4POXVfzumbNhjJiJ8/O4V7WjLo+WRhzvau5e6lGp/+0NM88fBlorqH0RLdd7iioLegqV0X6BwKK5GZYdgb8tt/9GlIC1ya4/UFu+1tLujL3HrHEWb8Fv/k4Ddwtnedl776DNev7zI3VaavBujYUupC1I1Jy2Oe0GM2cs14OqJfSWgnKfvnGizt15zpwcPPV4lkwBsWT5JPfZAP7KQkq7fyqw98A4NkyNX1Ji7I+cDls5yJdrn51D/nY6vneWh8FXVdknZ3qJZ2EOZfkgYejZkKQsC//uiHWR8OeUEasXLuOiJ3KAOdKw0uzR7i2JGAW6b2oRwYY9l4vIeqeaAnxRFpwUoLRuFJeOOhI7z6pWf4J7/7HpAOax3Cge1rXMViEoFIHT3h+D9//b284UW38IpTh6nVI6yEDz1+HuHg7vlZKAQzL26ztbDFI71Vbq1eIBAz9DIYyIzEZrSTLsbM/UXL7B577LHHHnv87cd97vY3zd+GOXyF+aI9Uv7pP/2n/MIv/MKfMWUZjUZ8z/d8D7/6q7/6VzY5gIWFBU6fPv0Fj506dYp3vetdAMzPzwOTrpOFhYXP77O1tfX5LpX5+XmyLKPT6XxBV8rW1hYvetGL/tzn/ZEf+ZEvkDH1+3327dv3V/OivgKkw5hrazvEEaQ9y1bSo70/ZYsBl5M2Y3Kmpkd873e8jwcvHef3P3QbriJRmUINBK+ZPcfSUpdHei+mCCKcczybzGOEjyw7lqs+VhgG45xFrwdCcFks4EUFNCznxif5xGCFU7V1SuMmuw/OcuzYKk2RkqcR5WZMXskJ1go+8vhNnEsX2SpHaBxxGjJzqE+tMWR7rYTbcsjEoXyFkAKzq6gfjbl0tk41hfKZAZ5JcW1FfzfCSeh7JUJbcIQql7MuGIexFlEYGtWUTlImm/ZwJc2ZQ21urp9nbTYkmI9ZeWqK1W6L0ZER7YZg1ng89ol5XvCG8yS+5jrTbLgZnA8vfeEFgnrOww8fYLxeoTRMCVVBd6dEaxhz5OQsqD7dQZMrlxqcPrXO1FSbi3YfSInIHE5a6OaICmz5JcprKWLk0VgOeelNR2mVqjzwwHniwnLw5h7CSXRYsN2vMBqFNPGpVErYwGG1YjvpYavgO4XNYL09IPIibOCQbZAxuIpD1VJklEMSYZWjyBTFpobcEVqHQDBNiT/8k7totUb0xxGzI49+mmFJeXZllcWlFqvVTVwqYdVBWZJrcFpiA0UxtOz+wSzN1w7RnYL5ImV3NcBlMd2TNVCC2RMF3VGXUPnkXYk/ENweVkg17PgxxdBiC8mmushngi3GOkGXA0Kn6IcSr7AMCkcaafA0UnuTtJlRQrIrmL9tzG16nWeeOMkuhr/zvm/EppZSmjB3JKfwFckgxAiHqClsFeZERGZzCgP+ZoMkNpN0m4HPWtyYeNNEjqzu4+IcJyzCOfztHOEmhsU29Ca+HNKhdkcMrkWYaQ+/aci7PRai40zVJM/Gu6RtiS0AAS5QDG6KqD2v8DJJLh2jRYmueYi1Am0guKzIawIRQ7nIyY3AVmC8qCmE5saH5/B3EqhJRGHBWvyeppgJkFMJ2stxn5VYZ6DioORjKpMuBn8nJzw/YqNZgylHNhfiez7OZTijkQVYAyI1iDAiFymkBukpilDR/NAO7W9bnBRR9CQ+WPdz5MASrytsPUQbhYssZlHROyiY3pY8eXWBA/fusl0u8czmfmpvTCi/K8c9rsmFwI5ylHN4OwWm7uO6OSJ2FHmMWhtiGgFOSJKGh0pyhC7wmj7OOlQB3kYOnwhAWOonu+hyweC/aETmQAriJY9s3uPhTx+g1XCMigATeojBaBJ/LHwwZpLKhMRTkn3lHTa9OptZlZocclpu0EnLjFWBa/icqK0z3drmsZbHuXOrXL22w9gYRp0cr5dhzw7wd1NKCxWyUgXrK0QgkQaKfszmGPKWQggH7ZRDEvp5zrPdXUZbhveaBynfVPCi0jGOn15AacXoVolBo9sQDAZMl6Azt4PfThmnjs5gxLi6zkK0yE7v7zE12+E1L/4JLmxVefLaOout9zBTrnHvmXX0/BS/8Yk38OnLpzhU3sewsCxWdjjq+1zONkjKFmEd/tJ7Uf6I996/RmnxZYRzS+xr1lkZ9emnKb1M4S8LpucrrIxi+uMSYXSGlx59NfsqUwA88dglZAb0DHgS0hxX9mkkmn/x91/CSw4fmMSN1zxCCUkGOlcgQKYKMwaMwMyBxfHI6iZzz17mH7zkNmanayRZQeBpNvIea96YU6eX2Bj38LSmavYxM76Xb5zdx69sPcI9s4vMtz7LE9GQg2mdo27py9ZN77HHHnvsscce/3vzRRdSfv3Xf52f+qmf+jOFlDiO+Y3f+I2/8kLKfffdx/PPP/8Fj50/f54DBw4AcOjQIebn5/nwhz/M7bffDkCWZdx///389E//NAB33nknnufx4Q9/mG/6pm8CYH19nbNnz/IzP/Mzf+7zBkFAEPzFsYp/2wj9gN05SVZoutmYxHNE45BgrPnPFz4DJz3239jgys4CZw6v8h57O0XZ4HLACe6tXEPZAkePP8olyIxcSGRokH2ByB2573jlwUuEOsc5gVUw8DXKL3h0fR+zB5+j1Rxw5sgN7v/ArcgjEMmEisqY93t0NyuEO+D1Eq7sNpkq9zBrmmrbMWp6BNrHFAorBDYCW3UUnqJQkvOXF/C2BMlGFXEgRjgHGyUW71yhVU6QXcFwFDA0O6SESGvxYoM3BKE1oQKv5+iPHesjzT3LhsHVKivX6lx9bo6V1UVmbt0icjkuB+ELdq5WuevkZY4f3uJBt81/vXYn1fkYIRyVOKM3giz0aC302W3X2diB0lwbr9Whd67KRz9+J09e3WV9vYk1goklokVkFmHAyy0MFUVHEfYKylpQdprrFzZZPb/GMEspGUlQWE4u3ODWO1POnVumYu/GD3w6WYJVAjd2uLWCqlSElYh7TuyjS86zg21CJ8kCgw0dcj1AVQV5WUJk8XsCb0OgOgXSeQQxdOWQf/T1TxGWU3ZWpyhuTdCpYXgh4ixtDpeblEyB6BtMEJJHEgIPIouQljQDVfExz5Rhc5edXHLu6n6srwlFQnN6RPUzBc1qxsp8Rh8fLy3z+JXrDNMObuxjDytcI2SkUurdTUpDRb8v6PcNxiuhQ00cGwTgsFABrINc0FlpEcQFr28+xPzL1vjl578K43zA4bqS7qBCqhU3HW8TxHA5WcLPBbm1+JdHlFxGOQgorCCTFtGUIA1OTeRDTgtsTWK8HBMKsjqQgvQEQvvYIiWLJK5RwXkCbTzcoGC1mGGtuYMeO6wnyG4LCc5nSDMxfrUKqAU4IxE9g6qD7IHXz5FWTN5zWmHrErIU06lggZlRzG5WQgiJizzIwZeKXE0kPIN+QKgMdCUCiWcsea4wnkKkFlGZmCA7aSeRw04R7KSU1wxZqJHVyftcFhbnSWxvjG1FOE8iiklBIuhJpv7vVXa+dQmhJdIJlBZQE1BSlMIM044oPheOFt/XpPi/N5Elw5qN6Kc+7d0I5QnKX5OyteWjdwBroe5TGidkfoSKJCLPQCtE2ZJ5MaEnwIJYHSDTDNkMKJ/TyMxQzEZIXULEY2a+agwqJ/mwhlUPFwZ4YwdDQX7NZ60aEXc0tZpFKkdjdsAtL7/EhY9V2HquidQSh2BjtcbgmRCrBOJCi6ffvUzWLGGOeqgOrF+s0Wq1cUmFQZaBdoAEUaC3M6TxkTaGnTFuNiIvW4wMJ11BSiL0JNo5PZ0RvRiSD/qcKw24cO5pjrUrrM0/j1YJH7/e5O7gTm5c2Ma7RZA1HaovyYqQjc0bmGMxQW2M64bYwnH6cImmCrFFA8Eu9x5b5PDyIr6n+K/ju7gWd/Gd5XCmiYOQu48lnJx9lu1RhdMzBWpXUMtbyGQHTwtG26fJ8qe59xUfo1X7EAv6LirBr/O6k8dZGfT5sbu/ihs3Nvmt2Qe49lwPNh3Xehv80dof8S+P3YoUEh1p0BJ/J0WmQAmqW/Cae07wplfexY1ruwS+xweuXkaVBNKzUDOUU4nw+wxbkkDlDC80JlKfKceHzl0lLwyeVoS+5raT83zzA+/GPuP4tX/77TSLNzLs/Qrz4S9h3eO87+K/gDDn5OwnOVh/mBV9gHIt3Cui7LHHHnvs8b8swk1uf9P8bZjDf8M5x8WLF8nznOPHj6P1X0063196lH6/j3MO5xyDwYAwDD+/zRjD+973PmZnZ/9KJvXf8/3f//286EUv4q1vfSvf9E3fxGc+8xne8Y538I53vAOYSHq+7/u+j7e+9a2fTxJ661vfSqlU4h/+w38ITJx6/9k/+2f8wA/8AFNTU7RaLX7wB3+Qm2+++fMpPv+rs9MdITOBCASubtgNVhjKFGs1YhO8HclTDy8wyiNWai0KCXagUZnF+PB4b4nQFfzhx+8mK+fYspxEuuYKNXBoYoI0YLkWM+NvkDtFb+DByMMkiifMJjelZQaU6JkSqfXoJQ12blRwdcXudpXUeGRNj2m/R1yHRjDEeh6bozKZ1uysTOP5BVUKRqWIrCyRwiKwyA1F4BLcWDN6/xRBbJg6uoOdNoQiZ2ZqhD+V0bdlpkuWDWZBTmQOLhPIocCtSbwk49JuyC9tvBC1K3HGUZlKmF7Y5sC+LS7n08zmQ3bPRDx9/laS+zX3vOQKcebhcp+jYpPZaMgD+eFJYWQqx1/IKBKPdktzZzzLcK3HzmMJRTvgYm+JkiuQRYEYA+pzKScCnBTMTPdoHErY+ViN9vUuv/77H2PuxfNsdboMByntq3VqMmZTV3CDBuvpFNMiYf2pLXBQKku8DNLOmK3AMrMcccdNB7k+2uWRNRhbx/AZw7hweEPQbUV51mBuZMhCYp1CdyRBliO2MsozIeOeYvv6NMWuRCyCVuAOCYqyQW4a1LWALClQkSZXArTGlgVqYHFY8rwgLjvElEc9K6hgGGSO02dWqM7FmN4xtp/TGL2DVAZRWHJyqBsIBFOmQjcZ0RAJx1srPNA9holz/AxUAaYAmTq8JMdN+xQFWCfAOcKO4C7dIesrtvIKMiyIDIwHHnnDI840JrQokeL6muZKju54yMjR9HyqV7qoKYVSlsBZXO6IUknqWWzuEBZM1SNrZJOWRaWRFYHTAmcd4WKfhGkMEpFOooULoxhNl0kWUxg5ygMNDrKmwnqTiOGwLXBxBk7jKYW+YrGASBxiaNCxIZwSxK2IUVpDBhonHMsWRmsxzoC1DjQUCmTLo9/QsC8n7/n4c5bhCzSlSwphJNoKyCzO5jhjkYUkX1bkLY3QMPXUCFqOomzRI7BYskAQ5gYECK0wvkPPl/FuOIJxSjctJh1XkU8WetSeyZk53iM4ltF+rMZGs4XzFMI4ou6IUm0MgO8bDh9pM62GNJfH6G9f4vlLs0jjQzXErm0S2ADRLOF0gkkSiGPceobRZbztBGEN1lmKno9TkC4Kin0RMitwzmP7wQZKZYzHArcQQNnizkj8YYE+m8P7LF61hJIBThqq1RHbFz1aywO2ztUmsi3r6I2bGCtw3Ry1AeyGHHzBGofveY5H/+Awzz4xz9Pn55gZVjk4W8EmOdtijFARtpTBSyXZZxskwuJqjuSAhzMWf7cglQESCSnc8oIrHPS3uNEtc/5j+5CR5fb2QW4rnyQO2pxuHOX61W3Ic6rvMchbB1QkeDiEqvHi4h4+UnqC7XKf5VaFN972Wq5f2iEb/ifmF0psr6fMhj5IgWOZ1Fk2t0/yx52CxaM97jvxDM+vldnanmVcPsKLb9rH3NZV5KUGjWpITZzgkQ9IXvAvLuHpnJr+DEm2QSdOCKRHZiy3HD7ErXfc4OErVzBaIps5U8EcprD0xwk/df8DZHUBQ0vYzZBTHn6csbvTwVrL4lKT61e2WBx6LJiIDXrkpYJSxWNueYMndpeo+jmu5BCJwI0m8r7/Ht/TgEAKQMBUq0rSez+JtRTCsmsH1Cohp6ZmiI2mH0/jz3yhl9rfJDeGf4p1Bfsrr9wr7uyxxx577LHHl8DVq1f5+q//es6ePQvAvn37+IM/+APuuOOOL3vsv3QhpdFoTMz5hOD48eN/ZrsQ4iviKXL33Xfz7ne/mx/5kR/h3/ybf8OhQ4f4+Z//eb7lW77l8/v80A/9EHEc893f/d10Oh3uvfdePvShD31B18x/+A//Aa013/RN30Qcx7zyla/k137t11BK/ZXP+W8CZyyNtYz+zQZXMgzynKya8Wz6DEeqL+O5Ykx7I+bix8tsfW2DIMihAOuBCQVCORKr8Oe20fEUghysQCYSExhmjq9yefUAH712kDfdewWNgQKwAgpJQ4UoqdCYyUlrLjm/08JM5TgPsjTAFKAHBdlQMhp6pFMhwzwkXQR/VSJ7kxjMpVab7EDGc9vHkEmOsg6MIo0U+Bo5tlAYBpsVzIZh02jazQo7pknQs7zi3stc9zZ48tnj+InDdQV22pFWLMW8Im36+JctOgeDQOQFmpy5ch9jBI9093PJayLq8LvX7+ZdH7yHYVpi/1SPl8xcAuDM6Q0+a8uo5kTXL42EwrAQzXDxU1skN4qJ9CZQuLZE52A1E3mPb/AaEalLuP3uy2gFnScizA501geMpiLSpoE2eAPDQIc8du4wthOhckgGA3LvcyfVqUUlGUVqcJ5gpTvg2naX59a3GF5PsELjPJC+Jt5nSYOCevy5L85Vj6IsIJPgNASKdFRw4/IM43aJbCdi+fI0RS/DHhiz6FepJBoXfi4+d2iwmUAogdAG4TmCtZjxSUnW8NAO1EgzN9sleXSKwRMR/p2WYl2RdApS7ZHWBXmYsxTWuOuOU2gd8v7rN7CioBuXWNmp4DIBWkLd4+S+aS53RqRWoQRUozJjLYkHKUg4evMV5oMLGCFYGrYJezmxKEEVMgUqtSgJ272TNGLJcsmgyyUO3byP69s79F2AlxoyO/EZ1akjdw6ZgzQGmTtcBK5lkH2F8wVGT34f/tAyGJVxdYuXO/yBIZtzFDVBaBOicYGKJeVYEdsCpQWyn6GVR7hrEEbhlMA5C7lCJTkMHeP9HumSJrqe4e1YCs/DeQ4iyZU0RgwTrAgRmcUYSbY8kYuovKAkMmwsSMYRpinJ5xRqALJfINopblEjtMO0fHQhsOWCfAGyGgTtGOkpcJJcgihLspqHBowA62tQYJ1FFAXN93fpvKyBf3VA/aLBRYrsiMduo4YuJ9Q+ucXO6+cIrgxxAnQH7gh6POo8XvR117gl3OYp2aQsMkQ8kY3ZrCA9NUsaaYKtjGBU4Goh3tUOzVXIwhFSK/B9TOCjE4eTmnjZgwCULyF19B8so00ZGgakReQxTkgQoHwPaQr8KCPteVhj2Hqywi4a8s99wMYpBJa8WcVc1+grwCqIwtJq9ZF+TnQ0oSMjgmMpg98a8Ox0F3VIU2xU8MYFy3+/TW065uL8Iukny6g4Q48dqmfQ+aT7yWlQ0nG8v8n7nz9J8llJFMbIxwxPlyz3Tpd55U13crC1zNraBWb+j3mGO4Jh2ccVHfYv7lDsHKOV1jnh38xW/wKvmj7DjZU2g2FKqRrw1KPr3LiyxYFTCwRBwJWBY90IyuFZ7p0qsdx6JSVdYlzMcDG+m85VD1c8hHEfItNTjGoFzaNbLH6j4w/f+c188+sVWwcPcFAv8tojjrjImStVUFIyNd8gn9XEDcP1+Cb+4f7v5NrKLtdutHnyuVV0bnElUM2MsF5FVAyH7pnIaqQWrF7bIb7Q41jYopfG+LWYubDG5XETkyi6eRnVHJK3y0Q9hdLQGyU0yyFKK+aiGr/x4m8nNYbj9TnWN3v86sMvo9wIeNXJLQ5VZtB5REm/kA9crnKxY7hbbHJ8YfFvvHAxLrZ5dPv/QghohSepest/o/PZY4899thjj/8Vectb3kKSJPzmb/4mYRjysz/7s3zXd30Xn/nMZ77ssf/ShZSPf/zjOOd4xStewbve9S5ardbnt/m+z4EDB1hcXPyyJ/Tn8brXvY7Xve51f+F2IQQ//uM/zo//+I//hfuEYcgv/uIv8ou/+ItfgRn+zaO1pOW3acSSfreKn0+T19pEpWWmpYfaTlB5hlACLwM/daRTApEJcILfv3InR/wtagd3yW6UkMJijYLCYBRcjVtkBbQWOnRMGQC/FSP6BWpkKa37tJb73FG+iq0o4obGXYNiOiSILA6ByByir+jFVRZ7I3aeaqG2fYJKAVMWU4DpaFRkWNlemBRMnMLpAmkLSBXlUsJLX3uWzSt1zl5YpLFYsKB7xH5I0Y6pNlJORmscWZBcXFpCJIqR9RElHyc1NnRgNM4VFMqhhjkj4dFJylwdTTFwIfKywn/cQ14vGN/hM+55FHVBtdRjKy/jS8Ng2KCS55S9LqkSSBeTln3ev3GZoiEp6j6+FJQ8h8gB6ZBTEjogdEHhFWRKsrHdQNQcZhNkOpFXzFzy2FgtyJ2FgklhCodIHcHY4iEpsEhPcXiqxrVrOxhvknQT5ZLCOToioZ/EGOkjaiGFcOSeIyhZcj9HSoUaW1zV4Z2wyPMBhVYYk7J8YpfNyw7vsiUoeQRVOLMZcvvhE/z+1ScZRJagCaptIQedWwgtOhbYqkd0Y8zwTICrKforNfp9idGCwcNNtp+ahiQhKjKGSyHGL/CzjL6A1Q6ofIiIDVlLM/JrXNsS5IVGFQJ1MGN3+ToiqeIEIBzHl5rsKMfK9gaMcoZtnweeP8FrT17hWH2Hdz7rMbh5Iv/xuhKRgA0cs36VILSMN7Yo0oJqZ8SuyxlVwbNgjARhcV5BvZXTKQRFoSHNoSqxdUkugNzhDyVF+XPyFydwymGVw/QdeeDhgKxncSWJHEDXzzAliTVQ98ZMHxwyKqrYCyFFJMhqgqBjUQacs4xOVshrmqIsaTxS4PwC0/RxHph1Hy+3uKDASYHwBXJsca7gjtYmLz98jXd+5ma2+xIbWsg9jAYCiTntky47grMWqyRyW+IaEnJBWh2jBxIbGyhLUAqZG2wwue+0BCxykJFXffJAoDZi9r1zAyckNvIw02XSsk9WaE7cMuJr9l3g//2/jgIC53mcumeAxpEUEVGUUS7nhCbj8WcOYipiYoxcluTTPioAN21xmxZVeIhSGelAjmOyMuAplFN4vTGukARbdbIFgXUWF07kRmTZ5G9EjuvE+AMHhWTmVWMatw7Z/lhAej7BVX2SVhWxbhEFKCkn6hynwRpcLJA7Cmkkzma0KjGnSx2eiA8h+ppGPya7OWXjRRWEdLiHCtyapDAT7xPP5hjjUwqHBOciTCQJ/ZB6o4InFSvdTcarPtnFBt5ojBkGhEPLzrzHJ5/boXzTRzmWncI7JNBWMZvU2bw6ZhzXaD9cxTVTtjZ7VObKHIkWKXYCPttd5/DiFDL0uH5pm08/fon3rV1nZnmKnSaIkkXrhJdOXeKe6X9HqfVS+qNLPNZ+kpV8h0vdNiWxRZG3GI0tj42fwFW3+dpvexwzVvzcn/4Q3/fqNpHncbnbppvEBFpRDjzsrMZbjnnjwd/j3BqUxTdRq4W4SGKUgwJqlRgd1njhC4/xD7/mJQgh2N7p0+0PicePcT0LsVYxHEmeGm1TKQlCv0CrgqnZAWaqT9yfwzc57/n443zDi2+lVAnxQ4+D5SnicYYpDEjH1V4Nme/jzCHLy+b28+zGiI9cuML57QATjPGbwd94EQXAo0HV3onyoKT3DHD32GOPPfb4IvhbJKv5m+aTn/wk73znO3nZy14GwD333MOBAweI45goir6ssf/ShZT/9uRXrlxh3759SCn/J0fs8deJFII4CGlvN9BDWBcj9LqD+RK3Ht7H+qPX6TSHvOr/9yxP7o545tll3BGHiAVKwfHWBgthjzjR5MbhCpDdDJl7WE/SG1co54o4KzE0Ic5B2TO40GGFw/QgLyRJ5jHuhKgCXAD+IEUtG6ozY+Kny4xlBekkaiVjef8uHTuFywX+8S7dhQB7TZMtK6JuzLgfIXPHQtKhs9oABccPrXLmlmscPhZwtjtPb1xlcz4h8g0XV2e5b/Z57FghpCV0FmdhVHNIG6JSEB14zd0XefbGNHEWoAzM1Xsshbtcac9gpaT9VIvgvKTQ4LTDiUlHQlDKebB7mPVRndRJ0l2PsObjBgaZ5ugr0HVtfE9P4mCVxK4m6IHBSShXI2QpJc0MYTSilzQ4d2k/puGoJAOUmfSfx4+NCZwktwVWS2woqY4ddB2ycATSkhpHGcdoNwE9iWr105yKcKSmoG3GqOOGIi3QqxLjTTqIaiaDQpCXFXpQ4J1M+Zrbz6OPebz70dvxYsft8zfYUT3OfuoIV+Y7pC5jvl9CPnODFRNTLRtOn76O11Q89vhRnJD4mxZ/w1FMS0pDiydzLIZ+t4RRPnbJERaKIrWoYUGagT3ocBWJ6zuG3SEXL61T9XwG2RgpDXmoOHhoQE8q2jtVtCroDX2sl4EQWCFpTVcZRwa3qnA7hvW8xMXBMvefP8z3z30aIYE4R3kaPXBkTYuzhv4oJe+m2G5GrnLaz1zHHPehLrEjhzeeSDkWbm1zxx2XWXmqxcNPHcR5DukUciiwVlDOFBiDM5Lc5fixwRtJjJY4LP5mig0kQUkyyGEYgfMcNhAgJOXFnPJyhr05ITkryas+0nk4bXGpRCJR7YysLvG2cvTQYbQlb/gYLWEnRtYM1X+SQyZxv+pP3odt+EdvOsvxI12uXFrk6eEc6xd3iKc1Rk2ilKVSUDW4KYHYLiYfJLFCFAa/67BaI0IPJwTKgPE1sl6F2KBTB1icBBdKdKFhqsy4PaZkBFJIjLMkH/NpnR7xk3/v49T8nPc9eIK1TwPG8bPvfynf8E1P8lQyx35vm3EhCEVG9cIIcVed5FlLuhvDPg+1r6A8NcI9Z3HXJoUNpzTSL5EuayKvBqtdyAQBBe6ZLfRGwPB0AxlWKZzFH8UI8f9n78+DbUvP8k7w9w1r2vOZxzuPOWcqM5WaM1NCCEmAVCBkKGOwXd1dhjJtlzxgV7naQYOLxi5jXDRYUNEI4wLKlC0JMMhoypRSKaWUc+a9mXnne8895555z3uv6Rv6jy1TTbfLRgiT0HV+ESdinThnf/HuiLX2t/a73ud5JEIBQchklMgz++YB8Ypj55kqCAlaQxjiGgmiN57I8sIazgmirZwwHyLqVZxyeOH4gbsuMdtIubU0z288MUtZDWjd1ePWXh3hPUd3BesRVHqO1dUu4YLlVX8cbEzvRAOVgR8XsD+kiCWjwPD4k8cpljxJW6P3UorcEBpB0Q4oLk0xmB7zjrN3ULqCG/2MV/depLopGRUZejBme7NDseS5Gd/kVFhlY1vxr774Atm45Laghgcy5wiTgCNukXO9PabqOap7O1e7W9x531G+/eS9LNSneOrSRSrdBKdSlsKA+eYUgexS14oTQQff9PQ7Q6QQPLVxk1f2dric3mRnuMvD6Vl0PWB1aYvCWf7l57/KfUtvJLagOh4FlF4z2I7Qdo9jc2+mVZ3c1PzGP/8CF1+9yAd+6NcIB3N8+qsf4GtuksLkXAjSUYtSxjbkeHOEf+Aqe4/N8fHf/Srvvf8slXqM957fu3CeT1w/x3ccv5N7pw9xwzkGbpa7uzkviOdRfo6dgWJsS2RosIn9k9/A/z1srLf5zOP3UG9WeGgRXP6LGHORZusnECL+jy9wwAEHHHDAAQewtbXF2bNnf//31dVVkiRhe3ubo0ePflNrf8NOK//O5HU8HrO2tkZRFH/g73ffffc3VdABfzTueeMJ/C1BkqYMRxEVWTIz1yfWbfpLl4nu3mdxo0dzMeNUeJMLoyV021NTJbMrEe9afA0pPMNbx5CFYH6hj2x6ti9NgZfcWZtnQEYlKrmUziPxzBRjbtkcFTiaJxfwVXBSkKJ54G2vMdqLOb+1jB+AjTWBd4BHjkvKIUydGuJaknDWMh4JgiwgmkpBCE6e2mL4XAVZSsaNAFtXqNSwdn2Vp18Y88xwhV4joldGbG3W0dc8PlDs5tM0Zy9SbeT4joQMdG6RgcF7x/JOyaE/d5O8nXJ++xg+DHF5yI1nVxl0AtLZALEdkL3JsCwGpFMevyQYDSpc3ZnHb3t2izq9dIZkMcMEnqDqGcYBs52Ss6en2DY5I5mjnEcYi3QOrGRcG1HZDvFOk3enUJGjG2vkfkFzLBDe4SshKgoJKwk+1JQzAa6iUD3LqcVrrN2copM3Jz4QuSMrU3xp0c5jhGecW166scm9J1fZsFt0dwKKWQ89A4mnPl+gtiL2WhKbaZp7JT9w7Hn0cccz107QSafZ9FPsiyraWhZMjVMnHue9973K3/6178I3YvJQUSTgxhpb8dhAIoeeUjuiUzFH4qO0R9vo0lME4FoSpxVTzT0W4m0ufmIJFwZUUoGtOISR5NOaymKVRqEJX7hGbVcTBBnje5rUtGLDSaJNwfSJErOwx+BGEzLP/o0uyR3TSBVimgnXiioDBIMs5J88/k7G0yVelYDAJ5JwXWKFZdTImCkdg7LE6wBRWtR6Rn1KUg4szgs0ns71JmuVBUabNVplQBpM4qvLakgaglz3CG3JZ6G2WUBmETLAJBpb15AaqmspcVTQLOq0ZxWuKgkMGGXJvxTRv8Mx/lIFHwn00OC0QBXgFZQhVK5mICHatGjz9SaCniSVlM2AOMnxpUPogCPHZri01kUGin/xqbt52/23+NJziwzKEa4WIIxFf306zI0lcgPUtsc7h5QO37CQWRACIQXSgGUyYSOFROYOj8fh8UWJzHIsDhFElKFn/MAi7nKf6k2DyD36lRw/FTJUKSMLnY6CWIOA7zr6HB+ausLa2jRJVOIEjMuA9FhM0hgQVWLKJyTlosakmuSFEe7SmFBKRBDhtcI2BX61Cm2Fm6ni08HENBdLKQtUe4jwEh9/3T9jPMTHId56lHEg4OqvTFO9TdK50IJaibKeqcU9Zu7usn+1Qu+VBGsMUk6uUUqD8B5VeEpr+OKnlnj03bd4+tIqYqpgsFahuACN7RHCGrquIDkVUa3kFKOA9sUWRmmGCFxF47SBocWMc8pEY2VI0EmZeqVLeraBcKBmcrAG0xdUu8fJS4coJKezFS5sv0axNiTaFehhwbgwtG/t82J2i+zOkovJZW6v3MmV4RDnIRnmrEQJYaA4NjvNwtwinWsvcDLcJ5IFX9q4weVLV0nrAz68+j7MtqOvMvZ6D3OPavOh22/jStBAbcf89q/M8OLNHHfcEYRXee+JQ9w1t8BT/XNsDz1eWCpjuPnqEo8/nVFuJsjV13htawc38YBG+QLnJUU54N/sP8178lNMB1UC70l0hUgtcdtiwRNBFW9ShFN0TYCQiv1Cc3hpjyC9m/boZbKthLwoGeYlS6Gmk4/5J688wc20h7SSt7/9ODuNnJSIc/1lji6ukqZ1Tq6sMF+uc7XcpyXK/9BW+ydGkRb4YUHSqqFFxmjw42gEqVqi0vgbr3d5BxxwwAEHHPBnAiHE/88AiJQS77/5sZ1vuJGyu7vLX/pLf4lPfepT/96/W/un42nO/9Go1GKOHpKUaoMbu4u03JhjyzskOuHTW5fJb+vT+0yTX/sXRxhn05Sxw6WKfhSyux6wPW4wlwyZrd5OIy4IY4MMBVQE8a5ilGb0fYHLa9xTeRWL4GsbZ7E5OKG4InZ44+ImkTacPnGLS+kSU9MjXjFzmCym9AInFM4bvPcYI0mHCarmWJrb4/r5BU4t73NrZxaVGMquJsoN3gTsF3V0TWAihdWKJ75ykuHRAFdxKGmRwiO7AlFxHDu1ydJ0ilQlZaoRgMgCUBKbeMqB58lP3c4WERwGeTyj82qMbCvGawGlE9QDz3Be0c8CFs72uG1+h3Nby/R8zK3fWKWYDQgOK2oPDkhkTn+jQVCG+MTzoe9+kN/68nlSHbJUrfHFr17Ctzwi8tgFRwHkY4G64Uh6ln4npXENpAixMkNKiCNNV1iEFriKmhgy1vZp3dOmdXyLz3/qAbwXpHVFzSrcoEB5KCJJYSbGqKsna+RXLTbW6L6h5jVZvWQvimk1HI1GBTcw9HYkn332NBXl2d1ocNupKb7cP0uvLdiZTljMHY8sXGelMSQKDJiSfOS4+PQy6kpAvuxRpUWmAl9XVBZCUiOIeg4hDFlTIRAIL/jQ9zxBJR+y8eVvYTiKiZsZtZmUVnyG89M5O5WbBO19ZBEQOEcyldHs7nH+xhGMlLRODRFnxyTaEy8lZP2IQS9jNVXIsSc9BPmix2eaoPTk+6Cs4sH7r7NTaXKzNovKJeGOJc97BEVIoCVF1RFJj9uzVMaCnSmPFwF2BG4v4LUnj9KUMT4wCJFhnaasaXyiGHsHmUIKB8aB9jgtEN7jQ4WLQ+y2YawgjTSmoQkKySGT0Esz5LWI8dUAKwREIC1YKRA1jSw8PoXqpYL4ZknRCkFHaCWIr+Y47SjnY/qRoHl9hBuHXF7v4QTIwvLaxXkuXF/Ee4fTjsFJRWUdgtRRSocXGr0vEahJ6lDhoGJwwWTKBAe4icSIQGLtpBEqSo9PFM5YWGvDTAU3FeFjhQsVbkXzng+8QG9Q50v/6iSjvmRvXEN5D2+avE9/RXH3fJvCCKbylGML+wjh+eUX3speOc203ie+lqH6DhO1QEqKK4JwVGBnAlyS4IwjbwmCW22kr4NUGAXCgUsUviwRN7q47pAyqBD0x7i0wDlDQIjPSmSsSbdC0pkA7x1CKzCGZm2M8J5q09CuhOhBho8MSI0tS8RoiBiOEGPLP/+ZE/ziL57Fvb+BLgw+M+RbFtVzCOvwTVg+0+bQ0V12rzYZ3FjBND1lEuKUQ2iBaWqsAFtX0HbEVyHazyhXEyhKkA5nurSqU8ShJhvkfPG5i7x0bo2uGVLbN2gDFAXGOm6stSnmUvJByGs3erRW+5PpLAdZe8T1dERNT7MyVyeVKWVeobfX4v+5e5rd2isc6xckg5zN9T7LeydYbrXwtQrZvKUZN/hzix/kmbWL/M6Lr7I27HP2wef42u6/ZqXyRt526P/G/WaJvaMDrj63TSXVdHY0e+OEO1QDOUzp7KTI22Ksh6DmKLcUQhr28z1e7KzxzoXbef+H3sjORpdjq38etGJ39dfQosAWEsYClKdSyyl8iBVDslhORpkNvHz+JjvDMXeeWmb7Sh80rL/W5aXFCzTCIXkZMB9fATXDd95+L/fP3MavXvgNWvYmRbH5uu3j/w5rLV/86iX0Tsn3fMddBLpCIBR4j5YHMp8DDjjggAP+A3j+dEh7/jTUwCSx5/Tp039AtjscDrnvvvv+QIOl3W5/w2t/w42Uv/7X/zqdToennnqKRx99lE984hNsb2/zEz/xE/zjf/yPv+ECDvjjodceIsoGmzsljDXdWoValNFQPc4/f5jhqI6d67NZzHNirs/3v+85/tWlOzm3swpCgBD0y4hOKRCkbG01qdULKkfGrMhpejdGjHNPpnMGLmY8jsi2YiDAeUmUeF69cpjdYZ2vvniGuFUgc4v0AAKbhZShR+OxwmG0xgxiZC45fHuPZ4dnuXV9lnqco6qO3TCBTIAxCCMnRiFy4iMSRBn2msZWHHJlhPZQzNURRw2/s/EGVms9fu35t0LbI8cKqYLJk3QJOE3n1gyjuqM6U0DD4eqaxDvSI4aVlR6rR9q87FYo24qF5oBEFUzLMaNRDRtIMgkVKVCJIdAG37DUrxTYZswnz13gxrVd4h4s3hXSuM0wvqKhbih3InycICoCeTXHO6h2LFFXQaQQX8+H3en1sdZS1jQmdDgtSfMEFPze/r203xcS3/A0NmFYWqwWkJfYqsJ6sMuO9V6XGoaSkNlxjd1qSTKQaJ1jWhYdFcgUTE3yzz75dsJM4/G091J2Xl7B5gaTSzbXRvzM848w1R3QvRUS1AzkgtwF2HmNq0mcc9gZj08k1RuSxb7m+rLAl4JoR+EaYLXhpc0T3HP0EsmpHLtVQDWgHgTM1Wu4qx26KuTBh25y/uYbKJoBDSuJGl2kFhAqxkckZRbRGhjG1wJkUTIep6x12lhbYGIDVpI4j5wpMVXFyYVtDs/3+ND8C/w3a9+NGgnUCKwvSWtDxsstekck8XbM9K6i0ypIWwVhR0y8dJQgloLZSp1bjVvIOYkYe4Ie2MKS9CxTs0O2u1VEJUL1DU4o8AKRMjFwjTW2DrbmEYGnqINxhiNrcENBMJhIHJz2pIsR+eGQsfborqW2K3AxjE8FSBlhdsCHgmxWEXYkxYzGjQz+FviopEwlJAEOh7CWM29fI29rrp9fQO9rZDmJF6cs8EIico8Yl1jv8ZnCZRrGDp+XUIpJyk7mgZK4CSqBYl9B7nEqQiqB388IUqAVINpwqLHL2XfsIPQOX/ncCdxawdkE4sBx4u4e568t45uKi50KjIc815/Djksckm4Yk3cFtwazyOWS5itDgn2DixV+b0iApahJbCvBlRa5t4vvZhSNEjHTQKgcNfQEI4NvafzY4vICYQtcJiiVwI8LdJpCGGCTJvqNEnVPjrsJ5okKAth4qUWlssv4ZoSbDTB5ifYeL0C8IUDMjuC3MmTmAYUPNeamRiYpWgyxA4MIqigHNpB0rtbZfnWK3XPTKAXBoKTQkqweoMegXILDQwa1YUl1PJGviSDChho/SPBzVY615rh1fY/GVJ1wtUoQSK5UunSPeObHJeJKgm0P2bk+pP3ADHYYInc0l3Sb2xanae8OqAwco8Kwt7XDZwbneXl0lUG95JMbD9Hek4ikz4fE/RQnfo9O+SqPb10lfuks73r/XTy/u8ONL/dY1HUaRhDWI0QqkLHh6o6h1H0enCtJopC4p/nSuasorzFKsT+1wPf95x/mqc9dJb+6hXYe0Shx9QAbOsK+4pH4Tt44c5wXbv63fPg3Q/SlGokUPPc//g3uXJ3n4uYtZOAxKHRUogKHFvCe0yf5169ew3iDHwueevka+1evcubqAvqyRufgpjw74nF8kKGcJEsT9gdtrsW3uH/mNt5Qf5m9cpOl+Prrsof/f7K70+M3vvgSZnfM4qdf4q/+X95NMvtvceYaOv6217u8Aw444IADDvgzw8c+9rH/ZGt/w42Uz3/+8/zmb/4mDz74IFJKjhw5wrvf/W4ajQY/+ZM/yfvf//7/FHUe8B8hroT0om0qexo3lmQ9xTgL6WFZlnezle1w69yL3P7wNT744A0euf0ycSvn7/76AloFBNKgpOc396/SHi5MIk5STzjr2JspKDYkMoPSWcpUcvnqEiYLCfA4Ab4GUWK4b+EGw6UKn969jXptjDAeJQ01l+NKzTgAoQTeCuwQfKI53z2EOJORJwlz/ZTZ2pBwSjCYmcKOBaLqEDsgIo2UOWVRIUwVZcsSVAS2DW7fQ1WRbQX84oV3EcwKgrCHGHnwITiPGBsSD8KE9MYZrqNgFw4lOxx/W5vBaoCKLM3KmHo+5tyVWfaKGrv9OteuLFAbgAuhFIbSOkxHkywNSKISecTR2Y64vL1PWHh6w4LXKpfwOEQW4toBxUlBnCm80nDU4TYdxBK8xIcCbxXMB6gVh3jW46cVQeqRhSXbi3j86j20Hw3wAeRLknkbM9wagxKMDkkGd4bIrmBeVWm/NqTIFPkI2jJFBpJGPyAcS7wVEFhSJDXvKaxFpGArmk2bMRrEmNCS9D1x13C9NsXNXp3qXonfL/FNi1rwZN0KzmiyGcjrIGLBxuaYaLOkeahGqrvMHdtjvzeDpcKTj9/P5+1tBDcNM/GIqFikWVS5sdtGDSSEErlisQ2FrUt0abkVTtE60WZ0eZEjU21E5Dk2SslaEfvdCtZY9m51SaVD3ZLQE3DUQ01SOzwiOGW4Ws5SXgoQl0NsCMWCpmEzYidIKzEqtgSVmFhbpBcEJkKVDmUFLrAUhWBzs0tlTSCCkrymUVmAsI5juuAtyy/zYrzC5e0lwlCR1QKE0HjnQXtkS6AGgnDf44cZ5UlFsNDj5rkEW4X0jCC+5ZBtiVeThoqrQB4LGucl+cmA7C0RwQ1QPU+6KCbnS6ckupkRiwxx2uJmPOlmQjgSCKeQZ2KGS5J+rHEYfC/i5D2bTM/0eeZTR9A2oJQOlzikKRBWQi4QViJHBplP/I/QCuE0eSPCekEYG3wK0gpcEBDhEGmBko4wjRl2YwoU+UAzPiRJlwPW+g1Oz3TZeyHG3pyYWEdJRlFIolctOyt1pPJI5xGlQ43AhwG9b59GCYVMLbXtApM5XGahKPCNCtZEqFGOtyC2uqjNdDJVgsAHERwJsXaI2s7IpwN6D1apvtonec1iC4OMwF43iJ0hbl2DDiAQmCJiv5hG9UYEsYEgxJdmklx3QsOxCPGFdGLWLUHICJF6irQC1wqEUrhaiAgdrhYx7AY8+8kmvlFw76nLzG5GvLK/iC49AgG5QYbgvaCYiwl3W2S7fcokQM42UGUHNxjxbP06b4pnWFi4m0OHp1hOQp68eY1yRlG2PHGsKQdjEAK1awnqUMcyXt9neW6elo6pzDbY2u+xU5ZcurpJvmDwQrK6U6G/1yXck3z2y1/lI//907yYN3FTd1K4mC+sfZYrvYLznTq3JbM8uLzEmduW2fQFs8sbIKBfbPHvHkGNOmNEv2QuSLiZS1p3jvnX6gucXTiDjAKUhcNJwrjeZweNzh0LdoqqivjlrdcQN95M3PPIoeX4j/4jwgc6OCLAIfA4oyhyxfxUxkOzZ/nd8FMMRYksLfVKhJoPUVrSeLNjbDK+ff4BHli6xNLmJcZlxErY41NfOYtZ6LOqblLjGkfCAU/vfJajiz/8uvrA2bRElg6ZewQe7z0qOIsKzv7HX3zAAQcccMABB/w+P/iDP/ifbO1vuJEyGo2Yn58HYHp6mt3dXU6fPs1dd93Fc88998de4AF/OKr1hDP1Nlfnq/hNSX+3yQvrhzh7qMJ/8/6387UvXeB3T3yee952k81Bg8fPn+ITXz6LMgaTwlzQJ5KG0Gb4sURah888Joey2UPECU7HuBDWh1N021UYC1wFXM3RiT23L68xFY657+hVfu/mHWRhiJyx+IHD9xSjRJMf9egKxJckZamxYUB/c5roRIEKAxh5ZuyQ1ekRg2+L2LgwBy5GKInKHF5KHB6lQFccSnlk7Mm7GvoaIkkwViRpm2B6hJeS0aiODycNCyTsaosuBXooEanHvg9qxwfcMb/HejHNlYuLTE3n1KolKoAilZRKYGqGGmPSToUi8uQqYRiXBB1BZWbEeG0a3TcsH5rlwmCT7r5GWTuJyI0t+aqk3gvZGoxhVuBljL5W4AsLCMyqJVjVVFCMC0e4n0/UIkpDqLHSUV0zFMsOuS954NBJzg9usbO1i50T2JnJuPvWxQGd1iYuzCmGGpMZkpvQLQtmlw1JMmSqtUDnmGRd99A9ePjNr3Hyjj1++emHGWuJKyASDtICsgirwEuLMxZ5LCSoZWif0s9DklRQZg41khgpyHOD8prjh7a5402bnH/es/vycSrdnIWVLS6G85TlFINXd2gHgnsebbFZluQm4sVbt7McdIgzx7e++Wku7c5xLV8k8iWdzyzzhgd6/MC3PMm3HXqF//YnvpOl2RbXsxFeespWhPCgNj0NG7FyYkyQFKR5yCdevh8xDWiDCwV5d4b21YBlVeX+QQOVhPR1G5unNOMQk+X4tESPLcKAcw6VQVEElEJTLHi8lNy/WiUOS/yuY3BKMqoLgh2JG1ji+RybBYTKEBY5ZRZhnEU4SbBTpahoymmLX83JQ4i7gnCnwBUZPi+RxkK1TnGHwC+B2jDIFKLtEpUL5ld6xLWMvVtNMl8j3a5BUCCMxyuwLcFrz6wwdbSLW/RgPO/74FcR0rN7rcb2oEU5LpBSEg48tlOg0hCRO3SvQPhJPLqQAo+nCCTSCUopkErisDDXwG90J5HNpiQ9qsh1wGgcYo1E3uaohQVlMWAvS2lcGnKrnEco+NW/ew8zSymXN0LO1AOiaAy/4PDvllglIJK4moWhJdgruXXvHNJ74st9omEJUxJxeB471aJ6cwQ7+3jnEKUnn4kozk4jwwi3LylmQ/wdTbinzniuTmNrG3M8wn5rjeipFPGUB2eg2cUtzSCsnJhTlyXi61N1k7Qijfy8RRyP0EEFW7PYUBM4h1sbwFyMwCIjgzsscVYi90qi5ZIHH7rBVr/C3/7+z4CFv/J3/wLFfgQOZGYQucDpSRrUzEqLjc4IU9PIsSTZTmF7zH6zwSv9Pe6/tEl7r0t7vYN5sU39SEaIQ5zOsZckthUTr8e8+90Z1/w2Jg+49cQY1xtSWWnSub2J2U2Z+lpI8h3T7JZDbniDmQ3x0wX9JyRLvTF3TJf8PMchf4ZHV75ATZ3k32zehawIjBDcde9hHn/lKjuvzHP7d65zLLkDHUxuK0woSKdhJm5wtDyPXxpya/wKd8a3I2OFl45box5H5wx7KkaZjCeuvcrhr9Q5XR3zu7MZ3ArJEwNRxFw9oz0IyJxGRZapwNGojklkwnM7NzFW4ac9fkdyOKnzzkfvI6hovvL4CwSpY6lZ4Vp5np1OC1uE/NaN+9hoeSQ9Fms1nt0+zUONF7menngdd/IJuzsd5r1mFEtOL08h5eufInTAAQcccMCfDYTn6/cur38dfxr4/u//ft75znfyyCOPcPz48T/Wtb/hRsqZM2e4cOECR48e5d577+UXfuEXOHr0KB/96EdZWlr6Yy3ugG+MtzX2UEcNWZ6zS5WatTTjPWoqYHuzw43XpmmdH/JK9wzr67MU2sAUuIoisI5YGaq9IXpkkdZBw6FiR1lIVGyxLceWaxG3C5aCLpe8J5wZIxYMfhBwa7/JG2bWWB/MgFCcmd7iUj5PIQWZlFRUytnKgPPjOUwgMKXClBKxG5BVBfHxAm0ct/ZaxCsRyQlP4iztmxpiR9DxCA9eC6SHsqcYdxNsR6IVCAumCuWVEfFugT8M4rhDXxA4BZoM4ULS+kRKYY2nogxiSvBSvspTN49xOOrwI3d8iX/zmbfipgPujm9wW2ODr45O8KVX72I0rmFPelwT0lhzYnmD3EVUgozO11Zxs7CZjZChZzCuEBaOaHpE8sYRajpieC2jZjXlqKQYGar7Djxk2mFaGrmZUDoNMkX13SSyti4xASwdHbB7sQLbjuEDKUemGzyTXsUXkFyz5MsFqhcRz2k29j25qGBjgYsUHjORTPYgHhhGnSGjWg3bMiStId/z6HNMz6U8v3+Wzw1XkX0PGnw1IByCKy1egB57/HqBOOXJigAnPCIEVSikDQBPluZkNxzr6wuoXYHbbzJdrfKhD/4bplfW+fTC23j1idP0TEZyJKerzvPGt4/4wpP3UbMD/HTI/HRJUI3YW2uRbYX4kWJvpPAnVuj1HmdnvwZVTb+p6YSKcgQqM7hI4wUkmcbtH2YbxVY3IJwtkBuKXICTkk5RMBfFFLFmfnoKX1pMLWJ5P4SR4lo/JayVqJZntJfQ0jE2ywgODUn7FWwiCHLPjWcst91RoW4yKB3WShpDRxGUqNyjjaHlehQGlE7QWqJchZ2uJq8YfBWU8MTbbjINISUiHVO91MdVNH42xjTVpKFhHcI5kCHEktveu0lkUy59TXP1whJ2WaENBN2cIFEM2iUiFaTP1FFNjUrhy1+6g7n5LjdvzWKmoKwLtBN4JVBSoexkcktEGo0gmjP0qOGSEDkyCCRqCF56lJKIaoyZqSCUZ3Ssimkl7OQBv/fxO8j6CQsn+6hFQa41MvD0bPR16a5gPNIUF1tUAsnVf56gp2YohyW139pk8L3L+KpF1D3eK2wjQEc1xiua8dunCZ/tUS88SIlSChEmuFaT0nrkoCQ/EuOWajjhKBZnETH4eQmhQZcxbmEa3zKwPcLNhaRzEWE7RwxTKAzCOtRYQr0CxuL9xKxMjFKcUYhzGiPqcO8I25vGDzJUpKFfoizImkV9b4nNJepnDN/7nc/ypjdf4YVLh9gbJgw2EjIbTFKEJBAqfOnxWiG9YFMawqkEmRuoBhRTGrmd0rg45tb9DbY29hmeGzAY5WSXhjSLjNZ3jFB1yJ6sYbQiqHqefsZhLiakOqbW6UG/wCxV6FlDXgG938dsJYwrDh1L9E1BsCnIneIv/tcfoGodb/6LIS88r0ibt/HFzZOUSFamGnjviQNF1Bmx90KTO9/3T1mqL3Nhf49T0zNs9frs7HbJuxaZBAyGMYWUfO2pSxTDkuFhhSbh6q4AAyqTvPb0Fv/k/Kd5w/Rf4DM//G5efssmf///8dtQQGdfEsUFJQIbSMgT4mQfTZ0vXe1DGqAwOB/Q7g5YnG1ghOc+fZaiGHHX0io38jczGr2GsYpBGqKn4ej0NGfm5vjUpb/FDz77Vd61cO/rnkqYjQxNJVmYaTHTqL6utRxwwAEHHHDAn2U2Nzf5kR/5EbIsY3V1lUcffZR3vvOdPProoxw6dOibWvuP5JGyuTkxY/v7f//v8573vIdf/dVfJQxDfvmXf/mbKuaAb46OPsxi/SrPh6uouZzZlR6SgH/62Sd5cvsapjHD5R2DOGRgx2IrGkJJKCx5rgil5IWrq+jMI7yibIQME4/WhkpPEGaOaTniSLzPVb9CVpEELkBtSeaGJW++7yozesz9S2v8LxdLpisDqqbJyEbYzPPjb/4sx6c7fOyT9/MZcwpfyomMAEEwNATGMS4VWaDo9Rq4iqQvY/K6IBSSmcNdGq2Uy2sL2HwyXYIW0PATiZHxBF1DZc/gioB0ShIcc/AKqDQnlAabCzwxGDdJJOkodj7bxNwm4JDnnsYmtWrO4fltnuq06O0nsCgZ2ZCKGvIz/9fPsmHqfOS1b0FsNZjrpKzfqtJpJozvLlm9/RXSnUVOpDmbK57ORhXGijjx2D2PH4K2YmLO2rd4IfDWEzjIixgqAakFLzyD+2NsQ1G9aKiKkmayzU5+HG8kYWL54pdfYbA/AjUxUK181TJ7osp333s7G//2JmW9x/ViDis9+Yygug6jXsx4bY5I5ThjmVkYMr+4z69/+QGOHeny/KVFqHn0GOJQ4ysaVxPY3FOKlNCVFCsB43aM74QEoUCWBjsH+YzDlRYzKig6JS4LeHlwmNBDq+hTZqu8vC15xcxSNscwBffN7dP0JV9WS5g7DY1hCTJle7TEP/vC2zjS3KM6W9Bb92AEszPH+Nv/4C+QDjJ0pNgaDvGhRDqPNJasFYCHzrFdwoU27ZeP4GWJmjKoFORIUQ5LwsLh820uXBlxbdjnnmOL1ENHM4yoHmpwNevSPLXHW05d5xNPvZHWqErYVbSDDHRIZdeiB4a1W1CdmmGrG1K/YDBVhU8E+UyEcyU1n7HvA1RbIeoCappxa4wcBwT7DnnTEGiH35lMdlENIGgwuE/ghEdXNfFrDhKQY4mNITsc0ohThkWVnqlya3uWoG/Q8yPcWCKcRUYhWmvKmidMFTZK8CZjptVluj6YNB4FZHisNzTmxpgZ6I4TVCoxSUhoDKmIsFMJLlSI1E8arBKEmUzoyMJjGzGy9MQdcDdhupmx+N4UbMpL/2yJxpvGtO+S2LRKcEeA2wuQUYg9sURxbZOoNDz0N7bRLcGzP3cCuVuh/ktblA3Y/UvzYCepO9aDDwR4KO9rwldSRAEIJma9zToiifCdDklX0JEp2leJSokJHdZ7nJPkhwU87pDXLMGuQY1KslaA6+RERYne7eJrtcl7VQE4j8f//kSb8OC8QWiNGyXkqxq5ERBuF1ALkXGMTR1WKJwSiMDS9REZmr4MefzaEW4+O0MZBZPpIRzCWKgGeEBYz07VsxxD7VZGerRO952LNJ7YIdoYY1/p8sVXX+T00VUWTi+xc7qLlSHVOMVcE1gToEqH6eeMvzLEtkvMrEYsN5lahMNLc1wdZPTGhnA5ZCseTRLNegmD5oCiK6jdlZJfKMjOjfnsv/oKP/JXznG+Pc9oHFFLFN//yP2EUmHbfeYWm7hazief/UWKtbMU+hB/7o67OTEzy5tPn2Rzs8sXbuYYv01jv86VV3eYfWif3XKZRBuW4iHXalMULU+Jwt0ydNUYW5a896138I5/eYLf+fSLvOPRO/iep/4ZpR2TO6jIlKkgJ5GCjfGX8LWC1mJB91KFzd0BYaj52tp1Hm9vMCMi4krM2iVLaEAYy7H1gNWjR/kv7noQ4xym7TG3quyK4eu8k0O1FVObrzO/2OD0fUf+gEHeAQcccMABB/wHOTCb/QN87nOfoyxLnnrqKR5//HEef/xxfuiHfogsyzh27NjvN1a+7/u+7xte+xtupPz5P//nf//4vvvu4/r167z22mscPnyY2dnZb7iAA/54MKXhs5dgn5PMhQNSH5IWIYPMk9gCKUH7gtW3jcmjMe2bVYqsiisljSXLi+YQxbWQfLuCb3nKukTUC5SQE3+RzCNTRzUrWZnvcHVzGSwYq3A4ylzStwmp16RS01wecItpsKAygR9qUicQ3tK7WYNSYJOJeaOJJVkzZFQk6FXLgh3QiSu0ojGxtIQFSGd46wfPEUSG7mdidq9Ng/PoMOXDM6/Sf0OFJ758B25PTTwdjEeGHlmzSG9RWCJVYqQi6ICUCmEMhCH9VJJvQaVXspa3+Ko+xG9v3Ef9yBavbC/ztf0j7NgaD51ZZ3W2y4wfETwG0VDzxPmTuHFIriyHFzc5Wt9DzLRZnGvTaE/x0s5dYCX58wpzs4ZcsbQbhjA3MFb4kUMEjuG0wDUMp5uzjLKcm3NtyqbD1jXZtOShey+xc3Ma6QVGSLLXInau7OM9IAAvqG5bFu+IEVZwslVlbzYlagy4erVJXrUTeRSGKMoIQsnOVp2kZ9i4ormw0eQzr0lkAEIBTiBjQVGUWKvRhUS0KuTaolYMKnHEKmc3a2EiKCMwSU607vClxdsAMRpRtQpyhWiF3Bwe4YXNM5TLO5hyTLgj6XZDMinYTqawkWR//U7sYMRez2Bjz36ljq9B1pTU+54LV3YxDozUOOGRI0MSxJTW44UhkCPG0xEqFKSjkLlGj8YRy8s3Q0odkfQNegSy4SimPeOWYNicGPY24go9mVNkjqiiufvoTXa3mrCjUAuapVqTDb3BQFXxgcdbMB7Wsiq9foL0EgpDOS2wAXgLqVAUlRpTvk+uJslKciRpdsdwpcSpACflJEK6cDjj8IknP1LHKY/uOIJXMsIrFmE0Ppj4f0Q1MHnAatLmhb6iMmtoForRrsOHIXGryl2Hllm71WcrGmMzQ1yT3HXXDfCwNNWmyObIBmNU1/C+d71GV9b558WDWC0ZHw4JbgjyTGLqAcJ5RAlWTfxNlBOowiAdoCTSFQSFp3otpWxpBpsJ4/2Ixbf0mHlwRD9PqMqS3np10jQIJWHFEr8hZvyqRUoPQuAamjJVBLKJFyn1rw3wpcas1DHxRGJEJiAU7D+oWHi+BC/Yeatn8dMpJAHSBmTTVcJRiAokXghIIVgPsEzOE18WBFojugLrMoQBrzwY8HkGYQDeQQBoQekgwCGCAOJgMq6qBK4r8NMKoSRYg1DRZGKoneI/lmHqEeFeyVc+fQS/KNi50uTEvVuoN+3x/LMZrnSQFfg4wmiLUgopwHnYN4YpwB/rUdyo0r+9xtxeTrQ+YH1acSpcpXqkTjmAQofsPz9DZSdFlgbnJX6U4Tse0cuRSpIuaerVhMp0nZW+RvVypIrwcoTQhvDMHtrm1I9nTM2OWH5klyPhHp/9tyf49gcuEl42fGLndsKpiLiqOTE9Q7cWc/qBY2z1b5AWGRfX97jayRjsZHzvwkmCDO45e5ivbW+z355jsTLN/vgip+/bYe2xad4yc4OH7r3Mj22+F6oVyoZgMQs40pwGKeh0RjgPf+673swXti/Tzy0WRZkresqy6CSZuMTVKydZPWFQU44gSVCB5Avnr1BrxiyQUOkL1jfaKJFy5Pg25TDk9P4pXCn46Re+SHg54JGZI3zL0WO8594zr99G/nV0GHA9MGwWI8bW03i9CzrggAMOOOCAP8MEQcDb3/523v72t/Pf/Xf/HUVR8NRTT/E7v/M7fPSjH+WXfumX/mQaKf/fVCoV3vCGN3yzyxzwTdLrjBmUnq6qMB5F2GHA9k6TxZN7/M33P8rHtj7Dkxs3aV+ZYjRrsKkCo0EKBl2HDARlFqCVJFcaG0i0BOEMamQQfYHRin5R52q6SOgcyZ6naEzSLIopy3Odw+xPVRnYCOUh1gWqCJEjAXiOz2+TGchLhcYTHC7JOiG50ribAU44zBQo40iykiAxNJsj+u0Yn4ZcPL9EZS4nrQjipRHZYoHOYH9Y5z5zi+urs1y6toiegkpXopZzZAnSTfwNgnlBua14672XaO/V2L6+QlFMzB7nmm3mg5RoKDhU2ePk/Ze4dGWWRmpJ5yR4RY7mk51TvJzNU49z6OSMtGNU1ZSzivsqN9F1i3CGi/1lzg/ncGVJlBjm39Um9G1uPnaGMgBf89Re8HjpccJiHwJ1S3HP3Cx7YcnN82s0ny8ojkXYKOBab5kTxza4cWOR0awkWnMEOkLlllJ6hPLI0tHdvMT1W9vcUBXaO03SLGLUMvhAkHc9x6M9zt57ldd2D7G5NcN+WkWXIQ+dnaXXc9za72OFxURQVQFzXrG+2ce0AnxVkxGzst5l5Y4hxapivT1N0bAoZ1HSw4xEGY+eqRBFY+ILI7q1Kd5012v8V498levdaf7rz30b+fkx5qbkCjX8YoDciQgrksvNMXkjIAkd1UZOVC3o705RTGkGsmRttzP5MqxBVkK0UIQ5ID290wku8dhYkOdV8ltVDq9sMw4KoqRBb7mkOWcockNyKEeEjsrljHSoafYtl6/uUhiHiAV+V/L5//kNeK3w0tNvlCTLMUNVx2mJRxAAQexYnOoRxinXdhexwiGFwecKGwgsAhlAfkTSWxTgPeGe5uRcSHFccrUzwI8V0k2aBHJg8LEgvj5GtwuIQlykCNuOcsHjAtAji3MJYxly/soCC6VHRU16xiKiHnkCPnGsXW4ztCW2JWmIgL3qiF//5NuYcSM2Lkwzv1igrYBqwPMXjrK82ME0PWhBeiygedOSWE8mJ+eWMuD0JOFLOIdpBojUIL1AZAVRz2CspPQZr/7yLBbHe/7Ldd4we51n9o7z07/6QRobBdrmiMLx6A+/wMyRIU/85CFe+HuzlIerjGbqiCmHy1LGd9fJVzRylFO9NCK+MsQPSrb+i8MT2VmoMCJHew+RZvc9DRpfGaNXGtiGQjgQqcUrgQw1eMEHT71AfXbAb/7mcaLdDCEi3PQssuUg1nCug8sdlgHMNVBOow2oMsdQoKXCOyYpYuMS4S3hTopKDWpc4hKDjwNkFJG8YkhchtOeO+7d4o7ldXTH8uXfO8vepQRTOMx8DsKhmHwOidThKhIbSliqYqMcX1ikBnl0DnMhR44K5KEmh25b4RO/8WXcgxZlBCqyBIdyyqaGGwVRKIneWdK+IukvVqgWnoVQMzNdZ293iMg9C6VmSlXZndkkeHWOYiNFpIqZt+xwaLpNpVYyt5rzD//1e7ESknpEtRrS0BEA+90Be6ZA1+eohHtsXhA4bdns9PmX519k83qbEyuzxJmDVce+GhNUoesUzkn2hKbXgMryiN6hKiJ0dLcl2dhz8cIWN7c67JZj/vKH3s7h2hQ4TVEIvBFoGXFH7TR3B7/Cc5unyFcDBp0agTPs5zm/8Pmv8ufedA9vnzlMX46pNWLONrfZGV3mul3iYrvN6NomnNbED1rO5wm3Tc8xX339pTTNqRqLc01kpAj06yszOuCAAw444ID/fyHLMp588kkef/xxHnvsMZ5++mmOHDnChz/84T/Sen+oRspHPvIRfvzHf5xqtcpHPvKR/+D//vRP//QfqZADvjkq1QgZSHb3mlTGOfXRiHgqY1iuMFur8sH3PIDNLV+O97l6dYegEOiRhwIKVXJ/9Qbh7SXP3DxCb7BA0HXIqiBqllgnMChCCydP36LeTOn4JskAiizCWo+JDbmQjF1IgaYcBByKR+yJCjp16LbiWm+WI9Uu7X4VIQRTtSHbnRDjQnwmiDY0Rc1iNiJOtLaJsayhEFXP4ft2ePGFE1ROjVl+ww5RVLIz1mztTeNqcPr0Ns2dk/zo9z3GT//Cm9HzHj8IsBVBmihqYsh4LFEi5JFHn+fGlQV+68oiGE+jGPOBMy+yNDtiaEIurS/y7MUTxPUxYWLpkVDkmnPFPKc6bXap0NcJruZ479w+T6xNU9nLqS8bwHDdzLO2vcRwvUJVQFEIxjcjgjenzM8O6V/RyNigjILC4pVhUQ+p6EXmazHHp6d5utCYMKAeN9jyGSaX1OKC2dUu+XqThJiFE7P0r+1C7idPzx3s9wJebF+h1zyE2PFUTIFLNHhJWfe01JBKWtDQI+TYEoUOUwmIjld5X+UwT7x4hWeH23jh6OIZU5KMQU4r2s0cW3HsywrmWkR7ZwpmJDoDpS2+CYWKKE5OYnOTlmD1u7uEVz2dvI6Sjk46xezGMtevrKHzEuEswiqCrsWXgn5LApIiCGn4MeZWSDmWeO8wSlCGjiKUqKri8JEWYyFoBCGbNzvIQqKNwWZQNifXRdoNKFuO1ITUkxx7K8AcAVP1uJpFG0tzQ9BrpXQ7Q7IQMqA3C/FgEg2sIsHurR47xZBiWiO8RGUCG3qq8x2+711P0U4V/+D6B5FDh8wKmKlQNkNAEAwyXDVEjwXeOWws0DNbpPYUpiyRxhGMzESy4iAcCXSnQG/nlNMK0ZCU04LBfRofC8KLgmK94OqNOSpCcmimiljMGW6nmH6OacXYMkPtOqrHerSOldTc3ezU9rhqqtz8ZIXQwFAbEq0o9nIubEzx2pVZxLcavATRMwR5CYFAd0ukkJPJJ+NRxuMDgUsUygt0NiTWBl+ATcBHoHZTnJPMuJTZypDaOKUUBi88BAKQpIMYL0eUVqJrDl0dkg2nkdYRpoZk3VNOeYrlADkuiW4EMBzjYgtSAgpT8TAGLzTFnKL9rSHBMyOa2ylybYg9OgX1COs9tTjjgw89gweufNccr/1KC6VjhHcIFULg8bUA0c8RuaGoelwYonYLlPOowoO0E4VPGIDWiN0e8tYQVRosHlk4SCaJQd4WCOsJY8EFs8DC1QEvfOkIly6uIjop2WmNWZaIdkkwFNimRAwNPpaTKZ/5Kv6KIbiqsNMKJSTZ7XPIAnSkyGcsZmhQowy5B24kKWZitE7wcg/hLFhH/9g0ohTYYUHaK6nMVDl6bI5imNG7tc/L/QEiDDiqS/S5mNNHpnj3yd/Cefj8bx1n/9p9DEyF3rRErOQsVSStSgJA4CTNviCbeRG3eI6TzNDde4Qf/8C7+OKn/zHDvRkQM0wFMT0LSZxwdG6KJ1+8nbIe8PLgCI1Lmntn5nl8PMA7SOUeQeUQ7e0en/ztZ2iLnI63/IP/87fzppljPLZxjZpSfODkCiOxweLM3+U73vMrPOuPweEe48dmuHVtj3S6yueuXuFktcXR2WkOLbfoF5pz5w/zcncZGYxotC1vPtxj/tglorTkia8dY0/knDm88Drs4v8by6sz/L0f/FaEUkwfeKQccMABBxzwDXBgNvsHeeyxx37/5+mnn+b48eM8/PDD/NW/+ld5+OGHvymP1z9UI+X555+nLMvfP/7f40DH+/qRVCMemi24sG8JnUMLyIYhe+EAgJNnVrjvvbfz6Scep+xNooxV4fEeVALgCbQjnDHIkUdaid+OOOu3ecktIZ0kwuAblrrKMBbEuCAcAU6hhKCaGY4lu1weziGlY4ygtAE+EHgn+Geffph3PPgCQ+cx1ZKyHzA2Gq8dWV2iBwKkJ1YludbUs3zye2CYWepz88UFyr2AMgvobdSYOdTh2m6dz2d38/zvHuV4tEfUd3zP257lay+doLtXQS5kZPOKoA7iUgMpNL/78Ufo9BzWCmRmCQLHky+c5H3vOcfLnUV2r00z3moyM7/DjeNTOCNJpnLuj9f50PIz3MhafKV/ksNL22RzkspWSZyWfPIrd/HwW67QCaoUuUQKA2pyiU2mGMC7JmGa4cYG7yxCOsLDOQiJXdjjer7O3f42Hrh9lZv7XW4pj40E3U6Nl54+zvaoBfcWFJsR8Uod1nZBCbwAAoWVjsvUGQdQO5vR3WkgrMRLUCNJP6lhXMCRxi4vdi1BBslqyPF4gVfO3WTgcoqqglwyDmBcgbiuMCojqnvyUCEXc8oQij1F2HeIwmIOW6xUOAvsluRTOa5hSW1EdbbgpbWT/Ohnf4ydvYJLmxuEUuC1I4gM80tdbuwfInMKRhIZODwW0bYUPYeu5cTtgDAVaOdRWhDEksPTU1QWavza1lfRKqR6zSPzAW4lZkovUBsLbp3s0etNU4gAWQhMGTDIFaLnSDKJSgVRCdfFgI0ZjxASF06SfYoqqNQhA42ynrDrkB2Hiz0ucPgiJd0v6e4mXL4yhywMRob4gUXuWlQEZcVTHlKoniXZ8WSJQJTQ68JumWOdhdyAA+HAhR5X0eQywNocqQ2yDMhmoUg0Yc9TCgN5QSRDbju5SONsn+3WdaovKfrna+iOQ9UUlShg9p4esqKIBgI7CwiNOgLqAmS7KYWUeC3RwiEfdYQoyiIg7JdY68AEyNzgqxECj8wdYmzxdYmzoPMC1y2wY4+JHN1TFVRY4Q1vu8RoGPNCcZitQY16LeXDD3+ZL1++m3IUgpJ8+dfuxH8a/FafN/33axgheO7TDeyzVby31C4WlDUo50LKOYlZ1qh6jdl/axgcGuKfb1O+YRaRgq9A2ANVgLk9or8qmf+tXbJ5A/UI5w2jYcQr11aoJSOufGWBsBpi8xynA1woIYzwWoG1iAKitS6ubrGZQFsLWqOkpHAeHBPtbxIinEN6h5MeZwuQCbYZQX9I4IFKyK2dJh975hB6v0RIz/BYjDQCmUl0biGYSKZEUeK0QigwxtMpHPFYI6fAFx4fhSAdRjieGL9C5APmOgMoHeJaA7MV0qhq9rWgHELlixJxR0l0s410Cf0ZsFYws9hg9Lxh6+It8vc3EKUENHEJreY0Ny4v0Jrt89bvXGf31XNcfvJdjJIhfeE4NxrSzgaEheIrX3iNV5+8hJ0Ds3OEmdv72FpCTf4o9731Kc4eidi68fO81mlzLcoxHlaOzmCLgiAH60KK/BFccJPZcxY3zknm1th464hq4zTuc4Jcam61B3zmpRfp2jWisKAcxvTzl2nUNgjCb+G77v/HbL76D9DHMs7RRJYhodA8t36L9UqPn3z0HeRmQG84hRhE6J6mWghqJ1PGpSUQkoV6g161S1lL/mQ37/8dZqYPBD0HHHDAAQcc8M3yrne9i8OHD/N3/s7f4eMf/zhzc3N/bGv/oRopjz322L/3+IA/PRR5we4oY3mqR2VguRYtYLchmBrx0pUNdnYGTEnNI7UpPr1/i+N37rJ2dZ5ur87SyT1eTpepyZKdK3NQUZgGaC0wM57gRUXYEURk1KOMq9k8UhtEbgkygXcQTxVUazljH6GkpdEYMCSkYxNc0xG2oTabM56KeOC7z/P4b92FCixahqixRBWCccvictiLFHtbs7yUHqEQAatzXS4/v4IXMOpHXP7CEQBs1eAUmASILaOe5tZalaESzDa77K/XsQMBwlE0A7RRGCV49aVZXN1OnrYHgnRPMn6qxS9038FHvuvTlO+8xP7NOtdfmacZjbkRTHN9NM2sGDBTSQmVRYqUw2NFKxxRhoCW2GnFXzv5VS52F/ifZh9g3U7hqp64VnD4vk1O6C6f2XuAI6fabHdhIGNkA0ZLTWZlTjRtOX3mKG7fsLs5JN3qcWx+lX4/4/1vfYb1KzP0b1ToFwq8YcMOKbwABC6QqMww5T1qcwV7U3DrmEYVVYK8xEjP6eVbnJzaRMQge4DwpFWJKguWa/8D979ni4/+1vcgghBReKwSSAvST8yHo6IgURZVCNSeI9wtJ+absSAXCuMD1Nih9wtEW7HXrdDfr5B6TT5dcjqD9OUupvAEgSRPPIszPXQBZSaw05PzQOSWoiFQLaAvCBsWseNxMqCjHQUeLT19M+SBhWP4TYEVnmhoGS8EjO/TDMsBR8YNxlfr2GmHzyXFboQKLTJ1mCsakSnYjCinBVsYkAJtwPctFeOgV0KgMBqqSlNVkvoO9Jc9xI7oJnT3pvjZL70Ltw2lqONCCw6Ukqh9TyocAovuwWwq2at4tPNspwt0Io+oKYLdEu89JhTYRkBZk7hWiGkkRL0Q0Z/IPVQOIpP4isAuWBZHCRbHsFvDVgLSTOGlRFhN6TTjiia4cJbKUpV231K2JkatjaFAItBjwBYYIXCVADWdE+0bbK6RQuO0xXrQA0MZKigdwXDSNLC5QVvAgy0strCThsxewfG7djj2gT2kd6z3WmyWLW6f22C+NWTn9DyvPbUySThC4DdBEDDYjlEzhnykEFphtCLUlnDbIlOJyARlkqOKAC0ks4+PkC5GfnIPEWkqN+pkxyOyhRBlBT7QDI5Uqd3okrsMu9REjzU//yvfRtEqSDJHXBqUgVP/+U3m3zjk5a+u0ntV4YUkCQXYEWKUk+4KXBShZ+rYegUfSnya4/ISpSbySFuWICYCHaM9jMb4xKKkglqEvDZJVBIji4hADzwajS4EUjlcxeF3LCo34A0CBYA5XCE6NyDZKiiPVRE7OSxXcKFi60LB2VMNdh/uQAF2OyD0muEow0aT1+cDh88cXoaEQygqJePhmNp0dSKPCxVuUyCEwPQjdNVRjSr8r//iEW4/e5FH//w58vqQa2JM4EJ8kJOLiF/5zafpPr0J/iq+HODLFs3qHsNxlX2R8ZPnYrY3v5U7+lscdykLzQZv8SFH4gYXWWd5do/tjVkW5tvce7zNWllh764+5dNt8jygEBnb4RpH33gnHzr1v3ByUfKK/T+hwoKmBlHWmW2s0YxCNrpDPnP9Kk5HLDb2uTiXMeiHfNvxY/ze9euMTYGMI77Q/SmGZosp906iNGXlLXsECzts3Vjlge0fxNcqdPLP8PTuOtZ/G0qoP+lt/IADDjjggAMO+GPmb/2tv8UXvvAF/tpf+2v8/M//PA8//DCPPPII73jHO77ppso37ZFywJ8O2rt9Xn2tRW9co2KH5DbC25KqyvjYyx8jLBu8vTnNG+7+WUxrETctoe7pfLnOynSXb002aK+3SFTJnosJUgnSsXZ9AasnkcPCKjKjmY97XLPz5C1BfzEiHDuS23Neri4x73qkWcDuToNsLvj6FIYgHhfUZYpOoG8CymFE2pYI75FjiwgE/pRHSs/AhzwUrPHE43fDIcGOmcF5gfQeIijjyXs+MX+LnS/OMpUkFFc1bkFgGzn7nRl2t+v4voehxtYUQxkSLQUgBeFmgbMOVwdpPHLgkKXA7Uq2bk1xamqDynzJuWyBY60Bf2/lKzy+eYzdbgguZmvYIug4krTBua0VrItwFs42pnhq8ygfe/bNLOgOycBSyATnAm5r7PM7/+ObmV/p8PBdL/HKpUUeT4+SLmgyNAxCFmdrnDl8hHPXr9Fp98mM4/bZOTbPDbi2sUx9Zoy8KQiGEPuYhTRgwziEklipiCPBVLhDgSMczaF3KsRKsxWNIXG05IDElezttTj3pcP4SOJmBUwX3HnqVcosZLa1j8kXEQEo61G6xL65QL4QYEpLMu0phzF+XRCPLNmUJl1SFA2FLyT1jkfgKVWBDD1BqSmmND5S3LjWpS8zPGClR2jFWraI2gavcmxNQ00jrUAbwagfIa1FK4O1CaYGZdXhcw+jlM2tc7zyWog1CmVADS3yFOiqJRBjBtshbAUEOx6xaBAafCTxkadc0Jg1jddgAnBekAw8AnCBZcoE9GM9SYUpBfVqROIkVSfJug7dc+ihxoVQmBgVjJF9i9NAEkAgkaUjGApcluAHYzLpuS1sMbsCryrFjh8RKoEIJteajTQCgUwN+RLIKCLqg61Kwu2cWpIx8BG2JgllSsdqhns3qZae22c3qa9Mc256GvQkcampFNu7gt6tPRqthGjGg/GEPkCbgmoUUirNyDtGiUd+KYZpRRBNTFZ9XSJdgEw9frdASIHMDTb0yAykL3FZTjgsEVoRRprqtRFpN6D2gYygtLy0s8SwXuWmmGFxdkDz+AD9NYHNS6aaY6ZWcm482+LmJ+YJ5lLkywJfB6aqlPUEL0HvMWnYpQacRmhwzRDXaKKKAtHJiVOHemnI4ANN4l2HF5b8gQbBk4J/NyfptOfQ23aIpnOu9lvI6wUuCKgdKZDaMTWT0p2awZce0enA0OOtQHgPRQmjIW6+iYg1PitQwxR0gHMWGehJLDuCYFBgS4df72GFRC23aJ7IWb5jjfPPLjIYRoiNHFsXFHMSua8QTiBrimgvx6/3GD40NzlfA01ZC6ilFl4ak01LMimRRuJdzNRUgpmHdFcxkh5TOrwSeBUgKorkdI5IahgKjJbIWgDC8blPPs3WeocyUJRzIU5IdoJb+MoMV/f3OfqBdQ6d3uHF4RGuD5fYLjOSaoIuAoQXnL+8QWv4DB/+a7/HqB/wi//Dm/iB7/oiSMFvPH+GQMD2WkhaxlSPV3j+5Rvs7g2YedNJMgmtQylycZcP3vc0S42Qo+0f5Ojdu2wkL6As9F55lEfe8XZeXtzjXXeeA6FpuApn4vfyxXwHGymEWGMn6zBXGdErO0Rhhe2tKQZ5A60Vw25KqBX9IufXv/ICs/PTREtb3MxL+i1DJ6+x6Locbx3m3sUznHfnEEkfGQ95bv0qd80eIQg1Sh14lBxwwAEHHPBniIPUnj/AT/3UTwEwHA554oknePzxx/mH//Af8n3f932cPn2ahx9+mEcffZQPfehD3/Daf6hGynd913f9oRf8+Mc//g0XccA3T7c7Zm1jll6tSphCIQTCKdIdSFY7CDMipk6OJ9+LuXx5me2NaYSTGCMRBi7fnCXf0ciGR47Bx5JxHlO0LH4Bqm3ojSJyNNtrCUaAcwITCtq2Rq9XYSnuMOtHqL5gejpldzyNHwf4MueBlRucqu7yP527h8pOydAoKkcGpJ0qZcPDcGJKe7K+w0u/fYZqX+C6UC7ASqvHpmmQe8Xy4QGd3RrHZvqk05vstU9jZEgqE3rjmKlRzvndJt4HMFDYiiDJHD50eDv5ghv2LSM/QnUF3nt8BWwiWZ7dZ6E2ILUSNe+YqWVYL1mOh7TFEp/YvJPnN+eJYkMQHiMob6ByKLKE69rxu1dOcz1vMsolU9MjfBxRXxgxqwZUA8fK3ApfunaCrJCYQCPKnEhZyqpgLe7w+KvnyPuOTEkGUxEvbW1jRwXnnjmCkJ6ZExm6VqLLCqqQeO0xsUDgcYVkv1fn3vv3WLezuL2S1BvsEYEj4Okrp1iPxuQNgWhKxEaJmIWklvOJr/1lHlqIuDyu4KICGXnUwMOSRS0X+K85xBWLomS0nJAHgmrhGS4qTMMiuxDtCOaveAot8dbhEsny3A5Fdhg7FvQpyO2kQYOZxOiaROBKjbR+EisbSoQFLwU5Ho1AeAHTjkAKXK3EtkBegXIUcGFjgKhN5BjjxQjVljQLg6s5srkUUbPENUM8neOMQD47xX5pKKcN/VlPox8RxgrvClQxkWdIEbDQqNGvDXBbGqUEovC0fc5Wy4P21HegmgvSBPRsgWqWqB2HdeCmxSTW2nvkCGQArhHTTRT1uuDdp+5kVeT8+rlz5BLEMYe5pXChxAmBSyTEClkYnHaowlGREKx7jIGwPkZqoCpoLO+i6rAfRVzrzTKYDiASBImmTsil4S7aZ7TDPlor9NAjC3DTEdlywrayhDdTgpHDyphkOqWsaUKfI18T+HDy+eKEh1qIaWp0b4T0Ell4xNggkOA9CInUkv4w5pN/5SGYj9h6q8TVFUfP7tGeqjK1OuSuO9a5ca3Kwz9yntpswRd/7hTL398HHOkwpL9bQTCZdFNaUn11iG0oomsDRG8Hd+IQwcwM3lq81ihl8YVBjwpWf3md7Q/N4aRAScHwbdP49pDoRoldjqnNjZEh6NUAshF+tsrLv36C+DuGrO3OMTvqYGNF5gOir9+IOCEIvMP3MuTaJu7wCn5cTDxSnEU5N7GKlQJQWAGEEptERIOU4vw6Rz484vZ79rliZtjZCKkWJWVN4xJPMioIXu5gAo+LFUkhybMhLpV4YxmElurAIp0HGU8SybxHyBDpYgbXEvY6itgLZGcAviTxOXK6yZHjFfb6MFQhZb1C4GG/O2T76jZOhbg4gooAK8h6FtEreM5u8oHjW8y1BuxtNhm/2mTOa2ardXq6SyWIOHlolnQ4g1KScVfRfk7TaVepNAyHklV6M68hTuf0r1V44tlrXLy2jTGGV1/dwGQlMlBoCWPzTmb9PD9/6QLDmTZz4SwrWZuGizjdOsvF6FV+6cn/jJUpWJlb4d33neaF6adQeG75XayRzNdz/u9vfh8XRzM88cUUlayjSsH6fp+y7qhPb3FD7PAtU99L1Wg+N/4Cej8l3wnwS/fzxrffSUKA8A0GcUCe1/idr13g0EPTNKdqJNXoddnT/7gpi3NYu0YUv/dAhn3AAQcccMD/4ajVarz3ve/lve99LwDtdpuf/umf5md/9mf56Ec/irX2G17zD9VIaTabv3/svecTn/gEzWaTBx54AIBnn32Wbrf7DTVcDvjjJQg0Yc2grScuSjQFmVCka8sk0QzRqMI4OMao+3d57LELGBROeryy7A6a9MKSLCrIjUTnBlnKSYqI8cjEk896GIR4JbFC0L44Q3W9wNZDfCixVzTmrCO0JSMdolcs3aCCNZPUk3GsODS/D6MxJ+MOu6NptPB0D80iW6ByR/2Gp4gsN4pjmKHCJwKsZYqcUaiYf2CLM7MjiumQlXyPjU6Td5y5yv/8xSMUiSRTCZdeWkaVUJYSHQlkxeIDqE+n2LEl2wyQqYXMTWJUtcYVAhMBTnFpd5HikKQlB6iew2c5/6/BHXx+6ywVa/ne5jkKrSjimO3aOp28QZLn2H5ENCsY2BCHwllPPONRD/RZOrpHVZfcsbqDM2/m6tZxxv2CIPUI6RFTOb4XY1+L0LcnlFFBiqV9OmJcS5l2hnCHiblnv0ZracRcNke2mSEsqMLihKCQHpMlvLx+JyOVIoWnYgUi+/oT71QwEDGVeIyeLRmMm7A18RCdPfog9UNHObT6AhfSDYQHvWEQ1pJvB7SGkkLkoHNakaFs14nU5MuuzsTXfT4s/s0VbC/HVw2tqSGDoknV5pAEE/8IN5lE8ZFE9C1yoDA1UDgkGV6HuEAQuxJ9yGIziZ1xJGdSyp0IM1S4ikNEEcY0GHoHhcNrjWloZCpISEnTADlQ5PMGHzoqiUVaQVNWaYv+ZOKlnlNZjdi9J8X0IO0HRLnHDx2N0JEJi5zSRKlgVOSMKh4feFygmJ1rMmy3iSKPfyXE9RxL1SpreQbGIUo3aVo4i2sqgswQpZ6pUNCMA15Y2wUjsIccfq4kWigwr1YnEcCBQo7ARDBeUtRvgpeSLIsRhaDIAypJRhQFZFSpRyXmiCRMx8SXKsh9T9AI6eucRBUYKRgc1RSzklyDrAkkER0tsF6STwcEtRJzt6OiQNYtouvwpcRXNT4QMB9QJkwmUcaavKJRqSPoC3yokVLitSKbCkAGhJ0C2S0JdwMKC93diN1mjfxmjY/88FfYayf8r88fQQVdOjdC9DNVaodKslEd7wXCgQ8EwkOUB7idHD206NRSZgNcq4UoPDK3BO0uxVRlcn0QEJ0vKO6o4CohTgmKUy3yEVSuWy58aQWx4BjsaZLRCNYMaW2Rrd0qSEkZKSJXxx1KKJ3D5xmiPcQXEzNg30sRV69jvZyYPAuBrCUTCZh0lC2H0BqXROhRdWLMmySsfUUQPwDRyKH7DmxEsGcJeiNqV1NcEKBHjrJd4mJN+OUNbD1AZA6zNP315KYxeR4jMotRHpTjsXQPnSvctiCs7sGNEr9fIk5Z4gcK1q4eYvZ4D1WpU+45wp2Sq8+tQeEgtIgpQaWR4bwgeNWg+mBHcOPWt7H1zFd5+fmjDA+HLOeGqUoJq11SJzlUu4Mr0d189fOn+dTPPYWqpPzOF29HrU5xX+MwTw5CCiHZ9or50uClwFtLd2eAK0u8cez2q/zeTc3d9TdQq64RkFMblZgbknJ3mrw0jIzli2u38dbgCA+cafAvn36Jp9o38MpwZPYsxxuWt86+n430IrvZ87Rmj1Cfr+H7htnZGvfddpSt6pNEgWTphOXltZLXdJ8ssPhxidzpEsSal9s7zM0t0NTH2d8dcC0fkdQiglBTWkthLNUofJ13+T861pbs774H8NQaP0Gt/pdf75IOOOCAAw444E8U5xxPP/00jz/+OI8//jhPPvkkw+GQw4cP/5F7GH+oRsrHPvax3z/+0R/9UT784Q/z0Y9+FKUmGmJrLT/8wz9Mo3FgjvZ6cejoHN/zwDq/vbVA0hnzhniPL47P0qkanqndoJIEVM41yaoheSCpRyMOP7DO1deWsUi2ygb1Yz3CZ8YUUYwcBHglJvr/QBIqzR2LKxyqfZaOq1NdzenvV9Hp179M73lMx3L4SJuL+Twm1dikwOcOaSbmjGu3ZllesCzdtU/5uTMUsxWcAAIJTGQVtiKxo8lTeS8EUgqu7zZoupTFOMOYiDJSJDplfDXhk48dYmQLXGHp72u2hvMYq5BRRlwpyFccIhOIdY8feVSk8MIilEA7jY31ZHIlnphArvVnUUXBBk3Guwn9MGFq2TEehByrbXLP1GW8T3lJHGa5+grj4Rw7V6YonSCbzxiM5nDSMxgoRl9aYOnMLd43fZ6zSZv2quYr60OscNhuhrYK6yO8DInLgGQ2Yn55ms32Di53ICaRwgaPqStEJOkUgp2ygi4M9MaE/RyTSFonmgwvDBGFw40cUQG2IegfLidTOA6UA9sPqKY57bUGMncEfcUh2SRRAUk1wFmPyg22Kqlv5gxnFCKRFLUcb6oIJ6joHtlunXQYEzYn13+AxZywdBqC4KjF1xX9sIIbBzivWdENzhxq8Pnta9gFh3YOuSNBC1iA/K0SlEA+ZxGZInYlZS8EaelvN5HdFK0cMzsKJSK0ClheaLJbz8FLbOIRqcMqS1ZqqgiW3TTP9XYphSQrHWIoacoYG8SIXYEalAxmChwKNJR1iJRjYXbAU5lg1KoSZgq5YzEjS+g8wcCDAqM9eTyJfT6rPWfessb61TorzVm+1N1DGwHGY2YDTFVS37WEfdgqO3z65d/kYtbApZJkaYSvW+yMR4wseEegPbQMjRNj/ul7fofOfoXf/vRJXn3+BEYF6ObEx+TWVEp1u8XMXohoxFQ2FTNGMdjpE/QtydFpugiKTENYogPL0vyAynsNu1+oobzAl0ApcSvgZxyDyxVcz+FezBGFx+clXodMhxHDfES5XWISQbYSoQcG3QkgNXghyJWhf3sD4aH+kiGoSGpP5xR3e7LlmFd2V/AuJkwc125M8aV/fhoXTkxWO59sIhYUpYjw0kPoEFIjhwYXCUytBgjUDYFuOMaRQY7GRNe6OAGisDCTYGSVZs/TeX7E+FsSpISwJ/BYor2CaCkj6ksGlSpr/9kURz7eo+ikVM41QICT4EsDocbGAXQlaj4GHL7dhtIiM4e2BpwHJfEiRgiPEoLeoqYy0ERpyehbaxT9kNoXM8pYcKXfYvZ4l/1np8hVjIlKlEvIZix6v0AKRaQVPrMkQ8PAGqSSqP0+49NzmK4AVaKKEBtPjHVloMg3A2QxRm6OYQjGS8LUkw09zcM36IZTuERjhpoiH7M96CCloMTiRxFJmCO1R1fB9VPy2Zgpv8JXfu8Q2x+MkN6z5kZMb0bk9RgdKUZHHOd8j7cePcwbP/QmXr31Mn/x4RdoVEu+8sS7yWYCbA9kZPiO99zN737mZa5f32Z6oY52llFQcis0zFfbjMNnqS3EDC8cZrw1y+Bai9mmotNPMdagSmhWYoZpQa+bEpSatt/na/vb7JX7vGfpAjv5RbazG7hmCawySAueev4q54Mex1ZO8/ZT01TKBW69co7ATZRaQkq6puD5zS16U56qkTQrIUUz5o2tQ8RRiDGW33z6PH1T8IEH7mC+UXsdd/k/Ot5t8e9mrK0vX99iDjjggAMO+E/Ln0Fpz0/+5E/y8Y9/nNdee40kSXjLW97CT/3UT3HmzJn/bTnv+bEf+zF+8Rd/kU6nw0MPPcTP/dzPcccdd/wH1/5H/+gf8dhjj/Hkk08yGAxYWVnhkUce4Wd+5md49NFHOXbs2B/1HfINi39/6Zd+ib/5N//m7zdRAJRSfOQjH+GXfumX/siFHPDNEYSaq+X3sN+e5ub6PDtXZxAFjAxoEVERTV6+tM7j568gPUSJozZTMnOsR9qPCY2niGLit2SEsyViXOJxiMmDTzAw2k8JtePe6g0e+YEX2L1fM1qGvOEwVXBjyQvZEULpUT1BlHtkJpAZiL0Bh1e3WVgcMGomFA/FFMsJOp9IF0xFkc9rbFNDs0Q4j5dgNVgL2SBm/5UW164vc+ELR5BbjtWpbbb6VeTWkMalHvFawTiIKLQmqqWMGwq3DI1hymhNYnYFoqcQUYBwkiB3iATkewycFdhcUI8zRhsV1jZmOTK/y0Mr69wYThG5koqZ3IDWKjmVwwO++46nuauxgUDgqopCCurVIYtH2szUxgTxLmuvTLG2Nofwmn5li1tX2pj9FBtPphuaIqOepORxQVbpsTHc4sb6PspLZp4ecftuNGkELITkTTVJs6kNcMGQoQMrA1TquePkMsqC9pK4GNMoQyo2oJCaoiUp6xLr4Y7VNe6fvkHgQTiFK+BqXPBvzl1kd2fAfKVK2JXIzFOaEpE7GJaUXYF3AdlOncHzc2T7EhMKygScmsiAdFtTHWnCa6BTcCNNV1UYqITrvSFJrcnKVIA/XkBkEVKS1DIa02NkwqSR4iAceKzzFN0At56gepL9LMb/TsqZUcLtNDg526RZiWjV6ngnMVVQ3uMrgv5egt2e5tbNEa2LBd8//xR3ZFuYZ2v0tzoEXUltHeo3QVxzrLwQMvWYINw3/OD7H+PH/svf4G1vuoTSAjD4PKcscuy4RLoQ2wi55XPSRoATIW/69md46F3Pc9cDT9PQEYEBNXS4usInmlqWI7WgXHH4Wkbh9ukaQ+4EvmkQDUdeUQgvEU2FdhDnBathl9nKiDOHd9gcNpHS0GyMiHt11EBS7RVM1ds4AeH0LeRcm35aQGEo0pTQOZwBbx1qBCGGsgjpyQpxbYgoPSYS4B2NL1uqny/RNwzGKPCOKBgSpBkyL6gUgmVTIcKhMoseFKihm/jsyMkaNEKkB6TAS8df+oEn+Kmf+zgrj3TZEC0u3Vzmyldn+K2vrvLZcysMz1ZJj8ZQ0VQeDIm+MyV6YACFhdLDIENO1H5I6/AywiMoigo2DigHGTLL8UojnSfsj6nH2xBoWmlE5Qsjgo4l6HjU0KHrgh/69sf4r77z37IsRrh7Z7BaQbs/kZR1UuJ2gRhkyDRDDjKknZgQ+2oFjh2Fo9O4OEKIydaplYfxaCIBigNsKJGFxVdDihMh6d0VbE0THBMo46hNFSzcu4lMS8qmoFgMMK0ACkNWjiEvEL0BMjc0RgpbDwmGluhqG7sgcd4gsgJRemSgAUE4lCzFI+JlD02Ba0jcuIr7WkD/mQhxMad+MadyOcfGEaNxQVk6TCARCCrtkoSSYlTBBo4wBrlfghuj2hBfdsQ3PTEJ08MmybjK5taQU2aKmX7EzcCyb0JuX9xnqTLEjS9x52AGqp63HNulGgXceWaRWiXm0JEFlm9fpBfWObOU88aZPbbSJ5mvaGr7dU64N3LX2VNs2Jznb97C7eTUrxVMZ5pTCzN82z1nePvKMW6vHmK10mM2HnNpb49Pvah5ZafBrthHSwupw/Qt7d6Y7f2Y/Z2EMoeT0zP80OqD3CFnUBVNZapCO8lpqIB7GksMd8AONZ08ZZTmXLu8zUsv3WBtfR9n3euyt/9xUA5/lhCNIqLx/2bvz6MlS8/yTvT3DXuK+cxDzpmVmVWVNatUKg0gCQ0gg+BiYyHRtkHgNjTC64LpVvsaty3fa4PbTRuWTdttjK1Fg4UBIdAsJDRWSTXPlUPlnOfkmc+JOWJP33D/CF31lZkkGzHY57dW/HF2ROx445wT8e397ud9nvrf+rMuZ5999tlnn32+is9//vO8613v4tFHH+VTn/oUxhje/OY3MxqNvvKYf/bP/hn//J//c37hF36BJ554gsXFRd70pjcxGAz+yH3/3M/9HK1Wi5/92Z/l4sWLrK6u8iu/8iv84A/+IMeOHWN1dZUf/MH/PKXm1202O5lzPv9VHSKA8+fP49xf3AONv+gIIRgNDZkRCKMZtyKkFZQK/vEdf5PHP3mBXx1/jrQGybSnF8as7k6ztjZHMDfgSDbgglsmrDmUKpG7IUJq9NhSSktxyKPPPE8ic4Y+YSrMMKctwU2JNXD87m2urs9RI+Vyfxa8pZn0WQuWQHmy26v88sVXc/LEJleyJmUQQelRxuG8w05rXCRBSNIwJHASgQcjUZmj3sgpLwX0ZkN05Gm2xixOdXDeEbc9QmmkdfQaiqRvMHtVxMBDN8DflKi6Qd4AXykpQ4egQGYKJwwiMagpT2PsCGcKnrtylKAwzBwb8Ux+gi9kR/Be8djNU6yvT7N2rEq1VXB8acjUlRwnAmJvkdUxRaSoz2TIoSV9poo1kn4/ofQlNwZN2mmKsx7fUASypFYbER3OGV2ZxYRwoDlLcnvC2SeuMmiFrFUtaUVTNiRYmEoNrVFBI4SbWQZJiMlh83oHEQq8h/4gY2Zhllw7elsDzGGHj0GXkkFawc15WvNjxisRWiucEIzTnO3ukLMb25PRikLgQjUxfu0JnPe4xGOlo9wMUANHoEuChqSYkriWwNUEM2WFoXZIV5BUMvZChSjBjjM++/EXmK9vUl+LGbdjVCJwSoIaED4ryQqJ7AuwDjMAPS6RpURECj9lqBwJ0VXP9daA7WuWRhxSvaWOGoEcGaSZ+IrYQNNJMwLgFYdW+JZ7zvMqLvH/HB6hdjVkeXPIrogJC0+84xkPRlANqe1Z2p0qRSZJ8oSwY9B9RxBEpDOOaOBQA09Z9TAWhEYRWsfs3A5JNWfh6HUu791E7GgEnqBfYipQtSn+CHCnoVgXbEcN2AkQWqJfCDADEI9phJo0sLyAPNJcuHGQn/kPb6SrNVvbDWIvCLHQ2KRfTBHaIf3rFer3bXBwdhfbNDx7+iiJjXC1AJ8NOX3mBnvDFjvPtdiMA1zNkHQt6Y0Am+S4aoCsQCPpMdqoMjoVgIIyFkyfGTJ2nkE7Ymj79A9bsqanul1S33XYXEMU4F2KDzR65Km8NEamHtUds3DXLju6wrFmm43VaUzX8d3f9AIPvPYSD7z6GtFve44v7PCBX72P6pTHNce0r4X4ROGNJ8gdLrYIMTGlxpc4YTGJQpagdIivJIhKANajbYl3FYQxOCWoo8jWHEXTUH1qm/KeWcpCg/HkqcI7WPmfjsHGmMbVPoEBGQRQlpM4YeVBapwxID0Ih1uuIvseV9qJT4l1UFpsOcLJOoEIJ88de1yqcM5jlhTmcY00Q27WIX3GonZH+OV48j66KUkFbKnxJkcaA0qCVpDEiG6GKCYR6slGTnlLDa3BlQ5flrTaPU4e3uKaruPuTFDXG5jxiCILqHVyfBgwOiopjgjUhsVNBICoQUngJPZ/0+SvUeS7CapRcHJ+hiMyY+mHn+GjN+8le7SJQjJ3qoKpK270e+xeGdDYNNyUq2hb0B1o/uG//BbuPdWg3nqAN7WqvLbyGT72wnEG4goLukJ3ccTv7Z3nweYBtqIIaeE7D30rlcoqweom7e6dPHrwKey4oFywPDIewfMx5c0B21c7PLFwg269x3o6QhnN7U1oVQKe2tjjP56/Slif5U13XuTl96V8/LqnjAWRthw9eYPtaJVB8jJecf9xynPXaD9Xkkdgpgxn29u8kmXmo5hembKbj3jk5g1+RD7AjStb9C92OXDbPBP55F9MevmTVIXAe7vvj7LPPvvss8+fOz7xiU981c/vfe97mZ+f56mnnuKbv/mb8d7z8z//8/zUT/3UV8ZwfvmXf5mFhQXe97738cM//MN/6L7X19f/yNdut9v88i//8n+WIOTrbqS8853v5Ad/8Ae5fPkyDz74IACPPvoo//Sf/lPe+c53ft0F7PMng/OeMixxsUcFDnIJkUUpx4nZWT47KqhWR0RTgmgq41l7gPbKEgGSuSBnlxBpC4b9EDPUBCODIJokQEQTz4KzN1t8x6v7FC7Alh4ZWWwSEkvD8nyPk4d3OV3s8JlnTnPm7jYilhCWOKvxsWZnu8UYgWgLRAdUCUG3ROUWhCA7rNDDGGsDogQYG4R36DSgebjP3rkmQV8ij3gWKgM6nToulETLEcXIM1wMkbHEd0tkJsArbGbxWqBbOVxxWBGCATUq8cLhBhHikRC9kJP7aBLtqiZXmo/V2jx+9TZ8oCm6Afc3tnjXa7/AoxuLPNQ7jFDQ6ySQQ2El+ShBbGlcf0j8jKeaZDSP9rnlNddwCK6vLICTBKZEDDw6DRi2a/T3QspMUTMtlt84xT2vOcRnP/I0W3M53chgtUcEHqkdNQSVnQUOLRzhBXcOq4BYEIfxJDK463EjRctrUimIxIhRYCdX1YVjXdY4GQoOntjk5vUGgYhINi25cKzvDei6Eqe/7B1aTNRIJpKTplxoGS9HBIGjOsgRxtHYhrwrGNwdUVYFq85Quy0gGBWke5KgahFG4Ocs0bU93vb2z3P+3AE+duE+nJLoQBM1qvhRxGLQZxylbPWbyK4kzB3aOHQqyZ2ifbDJY4c7ZNMCUUSIXUs/HQMCob+sYIoUKEE24zARvLB+lI9e3mMvqmKXHLa+yasO3+Czv34r40aEEIqiEhAVEBbwiU++jE9fvZdDwTKu3Mb7EI/AB5IisdjEIy0kBg77KqEv2e03OLi4SypiBgcvIc/eCns5EoGYEXSLmDAsiHJgI6A9lAQug1F1Mp7xBHjlETabjMg0Bb4aIDN4vDhEZXtiHjq8RWJuxpTVmGw2YNybpZb3yFcTOmtVzrUPQeEwzRAhFUMHh5b2EF3NTjGLM4puJ8Q+VhL1DE4F6LFFpzm33bXOhXuX2dyrQ1fgayFbL83iowgRSFxQEtQkY6/weyDyYDIi5x0EAUQKLwOSjkMEju//8WdpKsdzWYuionjT3S9yQHRpX56iIRT9doMfeutDKOlYe2mR/DUho2LMeLVCHmgEbjL2NczxqUBojdru46VAGIcclpPRwygEpUF5XF6incGP8okxbmJJj0bgwB6u45XmF37lrVCWWK9J0hI1YxhHMVwZobo5OIcRIKJgYorsQCcg6wWiCllH4pt1fJnjjaF0EqMFerNPYca4agWKBK9C5IWcqJsiL40ZjQoGX4oIIvBVjailhJcdUb8gujqg9IIg1DgBUmssHtmokwwgvWUG2wxAa7wbgQMKA7lBF55+UeGLq2ewZUoc1lAtA3mIq3rQirKQjJZixDgg3C7RIgRl0dYgc4+Xnmh1jF/rY6KYDZWzeNu/YCVuEOwYijpkRiMcvLoyz921OXK2eXF1m+ogZpA4gsRw6UCFi+MFTja3eaPNGKWfoTGzSLq1QCktvSnLOB9T4lGRx489F6+M+d6T7+NGOUVjaUwRzaLGI6YPDIiOdRl+/h6KzLC92aEuYh7vX6Dqa8zYgxyoHMapbT6z3aFQFluEnAy7PNQ3iEAjx5asZ/hbsxc5Wr0JlR/ESM1ndi7hXEE0tFTjKi87cJDvvfVuDs62uPdqi8eupzRsiNKSs0/dYPfqDtMHGlj3h5vQ/e8f/Cy/+Zlnecfr7qW1Z3nu+VV+/Ce/g8NHZv90DgD+GBQlW7YkFDEzf9bF7LPPPvvs8w1F+Mntz5r/Xw39fv+rtkdRRBT90UbuvV4PgOnpaQCuXbvG5uYmb37zm79qP6997Wv50pe+9Ec2Ur6RfN2NlJ/92Z9lcXGRn/u5n2NjYwOApaUl3v3ud/OTP/mTf+IF7vO1MwjGuEBgK9CodlhxFWQZsrbV4cmtLXa3ZzkcZjw/U8OJiZGsCUDFUG3kDHYSsn6CiSVxbhFDj3cCJwXRhqREkPkAJSAtQkTkIbGUpWS2MmBAzGrYQDiJDgwytMi6xZWT1JNQltRFRlsmBO2SIAWfOnTmEEISDR1i5BFOw5dPpEQGynii2RHjexqoHU+l44id4WK+iFmWpIOQsp/hqgJpBQQhZiZDTkN9PkZIi9MKHzBRbaDwtckJsqtqcguRyskL6A2rjJcUQljuP3Gd3ZsLPLO2DLWc+aTPal4lKyQzNsV4uLi5jI0EDjClRBSK7lqFI3kbMfLU6iOazZwnzh1hcWqbjXie2ZkOaakZuRl8ElA5OmRWGCqnuqzsrHH+nGecW2Y3HKeb01xMt9Fzm4z3KghfMtpYYtf1EamZKB/mEk7cucQLvTVE2xBZze5On+holYOn5uhGN5FD8FrTdRWe2D7OTCNDzUJZWJQ1yEaAaZznsDZsjSThCBqNKpsHCrK5gHDNIDOFjQRMSUwk0F4gQ4UyhrANLtb4EOZFRNpuEy2m2EoN0RGoviPvKx57+jTnrx7BhCGimxElgmpvzLgXklYlzXqf7V6DfEqjSwl9xyxdis403kDeV/jQE48VLSnZygts7DGxRBhB0JOoVUM2r1GlpHPK8Fvt+6g3Uhoyo9+NGLgKlTwkqmh2GuHkCv2KRUlJUVeMnCbM80n0bAMqe6DbFqugTAQ2srjIUCkj0sJyMCyYCRy2UjLensZWJioaObKI0kIosDsR/hMamXuM9NglA7ogXslIdRUfKkQu8FrgqwJZ9XgzmW2TA0P/tRHZYUV+zSPXNYwCtDLIlwn2Usm1y0vokSNcHaNGDioxkVc8/tDLsW/uUr+lQ+PZOfJjBfLlHv0pMEIijcCWAmMDZkYjbvQsPpvMfAbVEjFTMOzNQl+SPCsI/Ahn1aShgUJYC9KSVEPSzFOGkqXDQ/7SG64ipOfzG0eRFU+eBlRmCp64OsO7fvwd9ArFq990mRNLuzz19CKHmgVuylGuK4QySCWQwwKVO3ysKUNHkBXYWowuJM7mk+8m51HOAwIhJXZvYvAr9ob4usY2IkShMQsRCo/VEpEGqKEhtgUqKvFWkd3Vorq5ix8NUWWJq2vM/Aw2zUmqimhu4lmz9cgMRkl8qwGpAWOIYok6XMOujlFbY0Q/RdZyGjcN0V6Bq0ZgJkcTZQFyoYaqBkR7KdHVHqQlTim8moxheeGQlQp40JnBzVYQ1QhfGLLFCFVKZG7xfYNPID0p8bGmsloj2DbYCOR4jI8TSAWqzJkeDBivNHAesAaRm4m3TD1AKE+wvoe+ZOkdsmwFQ4J6jtoaIgqBquaUacDGTofV2ZIVNebWpetkSxFLrUOs3ugRT+cQWLp0uLC1zfd8070sO4MfWV4SIXEQUHtJgRRs6ozRskKkjmevP8291RYn5rsYpSkeb7Kw2KI9dR2bt7h1eZHNK326m33+yr3fgVstGO4pwiDi5uAFGrU+s60G7DQIlKPdfg03thqQD7A1RT52zKgNJALtd1lLl3lpd4+xcNx2eJG/+YZX8sCdRwm15lx3nR3ZY7bm+P6ZW6nHMb3OgKKTMS01C1P1P3Tt/ZWPPUXQ8/zqr36RxqbBGM8/+vu/wb/7lR/9xi/8XwPN+JUEdpU4+uY/61L22Wefffb5b4xDhw591c//8B/+Q97znvf8oY/33vN3/s7f4TWveQ133HEHAJubmwAsLCx81WMXFha4cePGn2zBXwdfdyNFSsm73/1u3v3ud3+lw7RvMvtnjwDm4zrCbSJCRz+roXOBE5ovvXCd3DnkQshofo58tIUqHeFQYWOYjlLOm0Wm4gGRL1CFQhmP7JZ476lsaoSTxKfHZC4kFiU2kggHJgGhHdMiRWH5+JXbIa1x/ZyjeayPcRKUJ0wMx+d3OHCgzblijsFDIcJ7XCPGFymh86ANZWgQmcTWFaLrEYHAe8GwohndIqn3NCLL2RtW2FutIa2nP+UQiUKvF+iaxtcDggdHhLcOqVcKooUK97/qHI9xnM31FpKQ8QGFC9ykEeENUnusLzk5u8PVYob5oI+U8PbXPsVbimv85vYpzEDyykYHU8Z8vD3PINPsFA1ATk5GCoNJPDVbUGxqynHA5Q/X+PsvneQNr3qS7nKIdCXtczVU7jEIpIbWTI/uKKbsSS50b+J6FcbWQc8g2iWz83WcWGVQ1hmPI441IjrtMViLFJ4yL3n2+RVMHCAak8aXGY2ZNwnN6QjbB5l6wqFHBZblQ23CxFFbbGGNJYpTpjqShTseIuxUOPvS66mHVb71e47xfv0I2mUUewlux2MR6KGDQGCRJEdC9IpnqCDsSqZnA6ZMxIoTTDcdYZTSadYJnaMsKzzyubtImwqrHToQjDclB4KDbHaGiEWozBb4wmMXFYWVqMJSRoqqLcnrGtcLia9IaqnAxAY3tGgrkCIAKVDKEgsz8eUxFUYyQytPjEVVPMnaAi9+poqreNRSie5BOhNQVCXDQiCUAARFZiYGvUOBL6DO5Iq86UlsaHHasTMck3eGvPD8DPMzOzz+2AFGz83irccLjwk8NhBUB4pgt0BKgQ4ha0pG0yEuCLCijtwQCBFglcN7D5HGOrB1jzQKq0OCy5ay6gh6HttwRH1DLRpy5sAW188dYpRrhnGCWYgwuymxE4jS0T9eUJ+1UExOtm21ROCxWiCNQyDQfcf5rROUl0pUKJHHJVZpxrWApCjxDmTPkkcQVyy50ThpKYXH1KHeDLjr1uM8+cXL2LFj81LIb/z2XQRRwqPtI0S3DenvhKyNWnTLKW45tc7Va7PYvqdTC3A9z/XPVxm+sULxOk34iEEQ4JxDC0BIvAwm3kZWoMYGrEMpgYkFmomRitfBZLTtTB2Rl1Rf6qE3ethqgl13mCRE1CsIByioPm0wC56iFyIC2Pq2Bkf+/RgvBXLs8Dd2KRIo4jnErqPYCzFpiA8d5UwVmaWI3mhiRHV+iAk0hApjMhimZAmEWoKS6FAilUM6SdEf4admEDLExQplLXk9xhxM0Dd6aCNBy4kPi1SI6z3cbbOIwkBWEnVz1E6OcJI8kqgphSw84wWHLDUuHVIfGpTPEE2HElDsGnQ7J+yDHRtwDoUA51HTAt9REFqaFDT0Hr/4m2/knoVniYIxtqKhr5B5hUfXdxF4tlamue+I41vuPkO9uMojN9tMNwvitZyFvMV07RDnn/4HfPHpy8SLlsPTFaqDEG9KOqcKBtsx9zTWkbHgoXCWD3UO8ezKcSgsRQHrO006SZUT3oJz7PbHtHyFHz7xZv7P4eOIUDDdbDCyI+6evYPP3VghqRasDSrYwkIAZV1hFXzfb30XL48y/h/fcow7b2mw1K3QbQ+Zv7PGkdlpuu0RUajonO1y1EwxGMfoWY0QkBvHuMjp7Y7RWv2Ba68xhtJ59CBF7pWUTuAjycpGG+/9n4tRmqD5j2jEb0CEr/qzLmWfffbZZ59vNH/OzGZXV1e/qlfwx6lRfuzHfoznn3+ehx9++Pfd95+uqX/W6+zX3Uj5/2e/gfLnBw/0uiVkkKQFyrlJfLA0VELF6ZkW1gwgucbBoWGj08A0BN5JgqBkWjle6k4R2zFlqcHHhD2DF55hqBDA1GKfnbJOICyzukMjyhgYSVwt6KWeeiNl76k5ZE0x7lSYn22jlUUAhCX+UEEaS9pZhXIGvApwEkwiCQ+UuMMeyNDPhDipEFUmhohS0IzGuJsRcghR1dKqpYhooiKQOaiuR/YNQho0EFcUQepI2yHf+ooXednpi5xp7vAzv/yX0aMSKqCUoFSCOC5YaHVZOALXalMsr/Q5KEYcVIq2ExyvD/jB6Bz/8urdNKRnSjjyvYidmzMoXyLLyVXkls3pjTTBSxqrasjA4q1BWfjs+A7UXIGrlpS6guh5pPNkvZCrF45Q4GjuFZx+8AT5vOf8iyvciEse290mLiUHj3ucEoStjLnXfZGXPnYHpQLnJxHRexsD4iWNlx4nPOO6YDMfsrUxRo4TXGaRqWGuOUC9qMkbkiwL8cfGJLNDeo+0GHQr7I2rpA3JXC1izw4oTIjql1SuQd4QiECSzzoqKwIhBVVVZfaVC+ztbaKUYVRf48ZUncg22JWapJzUXaCpmRIVQFaDYOTx3hMtttmatYxkjemZMXP1PqY6SfCxFYPPS/qDCFtCvOAYHjJkhyX+WUGsFEJ/uSGWetRYECtDZX5EpAXDzQiXK4rM0R8l+BDsKUW3KpmdGcCiwW/WyW2Ir3p0FqB3JyqS/rRDjhzSSIz2aBswEoZCeKQWRKGeND08PPTUSdTBPl96ZInCb6DdMj53GF1itMJWDfQhyAXOhyzUY7YNDI3D6gBfg6Bj8NHEi0aODDJXVDdznJ5EroYrkDxf4qYN0d0BxazGAU9fXiJPY46EQ4KxQYwVxXQFPZYMtaB1pk+WKdqPTjGYstiNEHWlxBvw2oP1OKUxuSVPBfa4oGgIZBRT9EvKvIqvC2TmUTVLOQxhLBFCoPIcbxxFQ/HMjS3GNY3sOzyWD/zey3nP//J+Xr/0MD/ziW+jLyNkA06c2eCNp16k06uwdLSDEHDp4UW2j0wxdduY69dmJh4jQYSIFBYmJ8UzGmMqVDoWJwU+lIjUQRzi84kixTqF0wKXCJyC7NUR4etB5Tnl8xozEyKcQ7jJqGLllpLK3AAfKAabdZzRXP3by8z+xjb1jRLygrBv8bJHOU4gFHhtGR4LyGcUPpwjOe/x4wGFAGVKGIIvPekCjO+IUY+Pidsj5NsUYkWgnwbbz8haFpEG2MMt1AC0EJO0omaCz3OcEGjnEV5QX8vp3e4Bh7SguwV67IGJYTPnUtxilcCB6mwTjiWmFVBLMg5955Drzy0yiqeQRwXRMxnhl483nLPoyoja37CUuzH5+wRCBUQmZTBsEs6FqE0I+x7RF8y9+VHs5r34QBLtKgabDbbnLGJQ0M6q7P7eaU5PTZHcXuMTV6+QdMas+ZRmrlmYbzBbD9nbKpjScF/zJoea22xTp0ybGHOGhUqAyjXDYZui9GQqJ5rtQSAwpeWhh8/Rm1Fc7XWoRyFBfRbnDM0MzizVGTpP4pfwdMhqIHG4QLKa1gmKBR4Y9Lm9XMTfyPB7Jasv7XH+4hqNJOaZRy/xxc+cZ/HINMODmscbW7y8f4LOTh9fWm5u7Pyha+9qt4ttQXzOIqTCC4fYGSCc57d+7WG+5/u+6Ru06n/tCJEg4m/9sy5jn3322Wef/wZpNBpfc8/gb//tv82HPvQhvvCFL3Dw4MGvbF9cXAQmypSlpaWvbN/e3v59KpX/lD8u2rjb7X5Ntf1BfN2pPQDvf//7edvb3saDDz7Ifffd91W3ff5sEICRFhmU2FGAzzVh6EhUwXuvPMbFcodTjed5xfSL/HenngYJsgC8p1Ed8cpkl2mZcevSDuFyiiw9NitQzRHZMc3wsOPgchsznBhpTqkxUxstfFDQN4IPrt7JLeEObllQTjlGUrOdNSCViKGgsukJthT3RTeJRUl5UDBuSYrQ45qCUknKox6z6HCxxyQCW7FYbxHec8exNeTIEuYOMYKgUVKJDLfdtsbpAwPCMdhGgPPlJBFjw5J5iSlDWnGGLCcnON6BzAvIC+SoJN7JmKt1WTy8RzhdknnN+nCKS9sL3Lhe54uXp/jCxiI/88L9XN2qs1GU1MINZvSAalwy2q59+Sq5JNuuQN8j84npq3OOY3fu8I/f8RHmyj7BAMJ1RcWkyMTgQ4GIPEvLPSonLXOv77B2sM+HyrMsnJmGEGwANrdcPr9MmoYUPiCZTbE2Bws6c/jU8qoHTlCpKGTuUKnHl5YSQVwqsB5VTEYfCkJursxy47E5im2JI8SOqoy3a/zuL72J33noXorFku5sm89lVxnsRPRWKphAkAZM9uU9Qacg6OZ01vvcdmyJitJUTEbsLekgJ/AhsTPYxE6SeC4JROkZG4d0AuE92gnmFlLig3swb7EJXH1ykXi3IOiC3vSQ5xgEbiyRfQtTBjFV4Ocso5rHNRQu9BhVQlZSWNC3pOxU6pSBo17JOX6gQ+oUrEm28px0HgojcSOFMQpZdchDOWFuUWOD7zrK0n3Z4NRhhKDXTzGpxQUOE3us94wwlNKT6JSo5Tn94CbjsWCuFgOeoNRIJ8kPCsopiatKGrHA7w1x3QJpHLEp0D2DGGbIbo7KDcJYQpnxstdf5dXf8SI28fRmPUVo0dWCnUSy7esM8xk6RYM81GyPW0SpRxcghUc6gXPQW6/SfXqavExQBYRdR9ARSCewkQalcI0Apyyt2/ooCmzosbHkQNyjFU0SaVACHwCRxwUSHyikgerAEnUMpG1k6tCArHhuvX+VuYU9EJ5XzF5nbnfMiXAbMYSd9Qbnzh7gI0/cy0NXTnP98hynXrbOydoOh670qWyUWEqE1GSHNdmswgVQHK4zPtmijBVeeHAeP1MhX6xinJuoLDJD9fk+8aUB5qiapEftBDgLqnTI0iMKQ0WNmUoKKh1LsFEye7xHfSqDQLL7tgU2vmceUY+Rs03UVBOkgjJHkxExRjqHKgS0pvC1EJMEZDWJ7hsQCr3raHx0gNjJ0ZUSTIHvFZRlSRlJbFWDgOyWBuVsgB4ZorWMcNvilEbWa7hQ40KNUhIxKPFxgCgEUofk8aSZ5AVEBlRmCQqJ2guxizOY6QriqObk4TWUFCgnwdiJCimKJ4PLicZXNN6BUDD0NYxSDC8I/uEPf5ClQz22G9O0fR2F4PzRGsu373Fodg/d85Ryj8NzU1DVEz+rUYm2kI4LFuab3H3PUY4kNZojxcpL2wwHBdZZ1l5KuXT+AE8+fYJDqsfUjW9m9FKGKiwzIiE4VHLsjj2ONrv83qd2Uc7hCs9DT17m0fMrvGrmEIdnGjy7fQbRfyMnxcv4+3d9K289qjk4fZPQC0xNIgOHD2HqoKf5Ss/N5iZxEnDqyBxNrXGl4cL6LgvzDbI0p8gM7d0+W+mIx9c3ePj5q/R6Q5z1DHs5nc7wD1x7r2538dHE2B0hkEoTlhaykt/+t5/+Y9fu9U6fn//IQ3z6hUtf2ea93zfv32efffbZ578ZvPf82I/9GB/4wAf4zGc+8/siiY8dO8bi4iKf+tSnvrKtKAo+//nP86pX/dFqy2az+Ufejhw5wt/4G3/jP6vur1uR8i/+xb/gp37qp/j+7/9+PvjBD/LOd76TK1eu8MQTT/Cud73rP6uIff5kONVwrN/MabqUBiW7zRBZeJyH3TSlvzXPg4e7bI+nUKpEZ5YiDDCJYOQNR+e6bMZV9DDEJRphLa3Fgq1xSTFXoPua3b0plo/vsGsrNDfqhLMjosTTz6oMhhKfTByOZOiIC4uWHqMFfishXhcEBx1/be4p/le1iCslwrrJWE0CCk8U24kKRVhUabGlQJiAzEdoU6KKCIfkQKXDhakDHJjvUYw7XH3hbpw0mBjkjiXEE9UErpREUUFR0agqiNLjSo+pCmRdEq14pltD1nvTlMOAddFiuF2lLkrO32zwuDnEo1vH2B1X+ckTX0QFOTNBzqtvvcwwjCZGmN7ilMCUYpLo4j1WQxAWvP3bnuXwcpfvmXuS5/aWeeT37ma+0WEjnQLlWZrb47ZbVwn8DGOT8KULV9m1GfM64Kis0c0LaosR25sDatMBoY5pP/F6qrknyLbBe3wJ3/3We3non59FFA4B6LEiDS1Ho2nWtzpwqUB4SRI4RhULbY0vHOL5mGFNYZ0j8hatC6pTBW5QY6oXkt+E4ZRifNpiWxZlA0JdIEzO5MzacWl7i4EdATHDToAuJTZPiXolOtWo1FA6C4FCBZPIECcleEG/v8SszLhtQYBsw7TmaKfNysYMLiuQXmAaEtsUqECz3N1i/hUj5k4NeOiD95NFlmTVU1qPm5HkFc/qaAYnBKJpeNPpc1SrOX/52Iv8yy+8nsqGwgtLo+vp7tXwQuGajnKsUdZhBdiKwzuJLQzBGG6/b5P1ygz9mzEyc8grFvCQKwglvXGNK0/PcPnROrbQ9IsSJcBUNBUPdiNGKkt+vGTbOyoXdxneskhZU7R2cvI8RFnwErwVFDWNNJbVtRazh4aUUwq8p2xV8UnCSEVIIxl6z3yrR7ETUWYhfV+jqE0USdJ5VCQYrSdEGw4VOVzosUJhaoJKuwAxUbV4LWi9fMDBt7ThOUXPNEA4wvkcvysZrhhMIyBseGZeMaB9o0ZxI4QrA2QrJAgy7j+0wsPZfeQ1zR2vWuF13/Ui5zcOsLtb4TdX7yJKHWYkyQYhjXlHMQqRhxwDkRAe9mxfmqJ+W5/xzQCERY1zXFUxOhEinKD2bIaZi/FW4FohwbUeZA4x35jEIzuH8AKcnSjTDlRZVjmzUyNuDmYZHZMgx7hxAwLJ6TPr3H73DXoXNWtXpuCIw+WAEyTbhtqGoPOaGdR2Tr2t0C3Lq99+mefOLhLuRQTPFQhXAwW+USc/mlCEKXItRfXGBF2PdBITaoLbJqNehBAGHpM51PYIR4DuKWQ7x5clksn/gJACrIG4Akwa2o2nO9gY9O6Q0ZlZ7PEWaTejcn2MbQV4J9BpiQgENgqwsw26XcEzz9/C/a9b4QPdA9Az2IpAp5O0LOHArAg6/2dC/1iV/J6A4sIW4qZhbySoVgsWmnsc+5Yh33XLWf7fa29gMKjSTEfozOJdnc3hkONHF6mevcSBe6/TCEccd6/mu++8jUF3zAPHDrLTHTLY6RNXNaNcQ1UxziNK3+T3bryMu+Yj1ncs3V7OyElmg4RiPkdgudFJGZYjhm+Nef+Bcxw/3+C77z1Nrko2sps0KgP+ysnX8FT/JR7v3GBBFThxHK8EcVCQWUXNjekNJM3lKlJKjh+eZzTKSRbrLM/U2drpcMvJRUxhGY9zrhRtOnspH3nqPMY4cILO3pCLlzZ4xQMnf9+6+4Ur1/FK0fvmOu+56372NlOqwvEf/8Wn6e+NeebRi9z74Kk/dN1+5tJNPvfMFTZ3+7zhzsn+87QAIErCPxejQfvss88++/wF4s/ZaM/Xwrve9S7e97738cEPfpB6vf4VT5Rms0mSJAgh+PEf/3F++qd/mpMnT3Ly5El++qd/mkqlwvd93/f9kft+73vf+1/yLv5Ivu5Gyr/6V/+KX/zFX+Qd73gHv/zLv8y73/1ujh8/zj/4B/+Adrv9jahxn68B5z0XL/c4Mr8H7YAjtU2eCheQJXzfiZfxgZsvsHh4mwvtee6c2uC2e9YIW44LZw/x0tYi0WnJoaRPbjzndw4zfoXnoGxz9MyQ81cWWdQj6q0RKlUo7SiJ6cs+w26MD0G5Mf/qhdcSDwoKGRJftuQHQnzDI6WgGGjmpztEYcFjN0+yEHfZzeeh8OgqFIHDjcDnAgYSFwjKGHAG7wus0KiBQDrHvW96iZGusl5W6W5XGd1sgvjySYiRoD1By8I2sF7w2OphXj6XMzQJwiiE0KBgPJ0Q747J71CI0tO+MMXeuE5Z9xgj+JXO3ZyM2tR9waAW8SudO7k/vUF3HPKh5+9hpdahkgxId6qT6OaKJti1ICd/j+r8iKP3bVIg2NlJ6NQrjBuKYd5C54qyYUljxdMXj9OuhgR1uGM1ZFFXORG3OCv7dEJFLj2V0uMGJf0Fy4tzAaeONLh+YwebC0Qz4NGzK5RCTKRJUmFDz2y9SqtSobQpnTRDesugo4nnU8IlyG5EuJkRvu6xXiGM4NDRHZojx95axPHuMv3RHiPtKZOQVrVNEI5pVXrU35ExeK7GKJpifCMl6DlMA/IEbCDRqUUrR9EPCYzD1ENE7JlaSBiVBdHIIpTALxWUi5rlbMjR6DLPdk9hE8PUpSGDNIBQ4KoBTkNcC6jEktW9WbbqNQaJxw08lZGAaoCvQhA47HZB82gKs46Bj2nnVQ5VRuiW497OAleurdNuRfjZEpEJ5FZAuCcofYmaDukdgXjkUV5w4Pgu73jTw+ytV/lnH/42BrdqknWPLB3RHTXkGhxprNAeJ+SzLUa2RRkqpI8JUwNG4S0I5ydpSIXDTSua4Yh+FmDaEz8iEQTYSJJPa2xDYkLJSESYPYFpOtSWBQs29wRdhfCeoFYyPTsga5d0zTTGg531qAXgkqFat1gT0agUDNcs1ke4OYk3liLRCKWQxqHGJcM4weaS8VaMm/Ko0jEMInId4iOJCzQiyijbAXGjxGcOi8CVjnwUcL1/lLISUdQVN7vzDAcxy8OENxzs8oUrQ8qGJawWnL5nnVecucHs/JBfe+ReqgsZ3Z2Ewye30TWLOlXgng3Al8ghxNdLglwSDxyDAxrhLDiFma6DUigPiIn5sRoWwCRxyAUh7W7AIbXHdKXD6NYlhBX4mw7XVVxbX0ZXLesPa5I3O6amxqQrlYn3j7Q47zAVhahoGAiClqd61FL8isBNC9gsEDUDxqFCPWl+TE8jgwITOOy19sR0thD4ewThfZb4Xs/RZ9d55HcO4zOLX27gLchKjNUB2YxGbYyJuiWqKPHjXYSH4UJCdkwwfzMluTejeMlS3RtTVAW+pnGRxtcCsAU+DkEKEAL6Be1nBXUVEA1zrHSIUY6KHbe8cpWLDy3hT0nKvxwQXi0pexWs1MwFKb/+ubtZPu0YTFdYa8LBgwPiTx5g2HCEn04RYcZwGLO50uGeIwscuD3l+Mv36HvBizf7PH11lepY8tHzFxhmOctFTK+fQ+nIhiW+6vA1w8xSyZkDC7z02EXEako7daTvMyTfNE12ucRvt/EtQfzFgPKNsN1J+aWVT7EUn+CBOUsSlXTTEcZLWuE8stNgNLZoY4nblmrXcbi6ypFGh6ceXuIdt7ySorTMTtX5jm+7n4+ff4Hfee4ZTodzHDkygx0XhJt9pLUMegMqUjCuKoIwYNTP/8C1t7QG4aGahLz9ba/7yvZnPn+BGy9tcPbxK9x5/4nJOJz6/SLgZhjTRDMbxF/ZJuR+82SfffbZZ5//dvjX//pfA/C6173uq7a/973v5Qd+4AcAePe7302apvzoj/4onU6HV7ziFXzyk5+kXv/DzeC/0XzdjZSVlZWvSGiSJGEwGADw1//6X+fBBx/kF37hF/5kK9zna0JJyVJ8hKd/r8+i6WJORvghiIHhyniFrNbFdT3HVQe/I2DW07cxaUPSvzLLuTsWOTG1woqr4Y1gnIRsTy9y29QaLpZUxiVHFnYpRYDwntvzjPdbhR5CWfWI1ZhRUKI3JFMrBhsKqAgqoSEsHemoym8++SAfN6fo9ROOuj3KWCC1pBYUGCmwQiBGEm8FNhG41OGtRZGyEPaozxaobTh2zybGK/pljQudKZY3qwiTo/seQoMaemwO5a5CXZCMjoasbs4wHCpEPlFsyPGXzzeWPUpZRGzRTUuQ5IziiGERclslZXdlisMyY7gUMdQJP/D8X0V7x6zKePPhF3ii/jrkhicUUBoIECA90gqWDu7SyRU2l3zsIw9QfWUXKzJGQQU/JXENS2drhmKvxBxMcSan6jzjyynPZx1W4oysLmkh0XVLkKT0g4Q07nJzMEBUAmRg0EnAS1e2mT4+x861DYR35HMaP7Ls9IYUkQAJPp/cFpd6jMOEjZ0GjdziKo5COtyJXfSsJrliiLKc63bEzhGHqXnoQLoakt/iEUWdXIYsPDgiXYlACmp5QDczeCWwShCVnlanZFCFVGsQHqU81cU+9TYU3QCpFOJgzI2hIN+cYSyOsddtoGopogYyBVF4Xj5zkc2dFmUyy1ZnhiESuRghRxNPjrzuMXVBpVYiA0tsR+j2DEOX47ohr7x3jauDGSqVgoPTDS7cWMENQtQlw9RAIWOBV1BWAgIhCa3ECYNznv52ndXVObbXa7iaRwtHOS1xWx6TFUTtgs44QbU84XRJ3pa4UBACvlQYpbDKIAYljTQk6hTYKGRqp6DedYxLh1ES4QV5RVA2J029siVZN1XExRrJtkMMHDbLiQNNuOuQwtMoeohmges1qYQdwmqGH9Uo+yHTsx2mkpQgm8GminxJYxoSNIjcYQKFF+CFIGhnlO0KT/7vR8gbAfJVk5GmclcStErYAicc/Q3gSkzpBX6oUFWDxyFcyE67hvQWjGBze5pf/8Vv4x//3Y/Rrnpuu2vAM8/Uqeqc7WsNPjy+jXNPLPNX/7vHQMD1L8zB0BEvlFScYe6/HzE4ELH3uRpcAj1UjOcUToLIQcdgKyGhlZRyktojDk1TbvYQmcW1EpR1lNerPPGB0+ibGbX7K3RsibEKIT2dbp0nvnQrjPrMJykIiMclR359m9Vvr9I/pXCxokg88cAgpOKjZ+9mqwkLKwZhR2Amse3Oe6TR+GDy3VUcrhCPLZEL8Xt9SB1mV1I/Kbjx8SMI6dGbPUoNslSIIXjnoLAINVHm+bRA9IaTlJ2KpViaJ2DE9KGS7jMGH2i8EKgwJACKQCLGDqREZBYReOLCk3bmufApT9IwCC0IZy2v+Nmr2GuG/BmQDwrcAYUUBvWhAdkUjLKQ2tBSaaQE45LpIKXEkz475se+7wF+aeqL6EFBZAWLs00WD01B/wDbl4fE1eOsPr/Gz//eOvfdfoibj10hzxzbizMIKZF4BtMe7xV2EPO9pz9O038GdekuapsClySolqTzVA1zvUN+eEyUJ4hQ450kbnt66yMacylvP/V2fvWDn+cXuk/x7r/5bRw/tsxLeZffdY8ivSC1AdZL3v6GL1Gp5Dxy7hRCvJ0yL+lsdnn80YtsjvuMpCUbf57q3BNEs7DU+e/p9Qq89FRn60wlIVoqXFH8gWvvwekmlURx18Hlr9r+7d/7Kn73176EwPO7v/s8Wive8IYzv8+0dqFV485DS9x97P9+fhSHf6LHB/vss88+++zz5xnv/3j5ihCC97znPX9k4s+fNl93I2VxcZG9vT2OHDnCkSNHePTRR7n77ru5du3a1/RL2Ocbg3WOXZXiA4saeK6szmNPCeLEsT5znej2Szz7yWkCM8NgFOLPKkRD41vgYsX/6wPfxS++498ym/SpNUb4ToO8aXksSHBCEqSOm+NptrIp7mjdwFcdpdf4xBC0IRiX2G4dKT2yhKImSRS4UhAKw96yJzSwlTcRkePquUV8XWE9xK6gcJKBDQiGBptIrBaoKYcbBFTmchJlOFPZ4Jo4xEuPHeDwg+us9aYJrMUYi9YC6ZkoZqwl62qoW/RYsFcm+OEMorBQeBCgRh7RtdCvMDxbpXGoz+E7NpnNAp584QRJ4chuF9y9cIOtvSm8aVCOA5LpMc5Kjh7Z48Fj24hEUUwHE/l15hBS4nAQGJqHHU+tHCYwhq7WdC5Pk7QlKirwB6oMlUcWDheAGYSwp8gXBGl/zM5wSHokQijBjIw5+voLrF9sccshh6sarnQikBoRKryxXMr2GAYOGyikF+AEWWFwnYxsnGOURymBaAhWtqdJ16sEI9BThoWZLmOVMrIRtZmUbwlXePHaATa6t+FqYwg8auyJr2rKtmK0CJUw4+bKMnOHqwgcKAelQOSTJBCEx5ZwYmGDga6x93STeErxxtu+wGo6zcdqdxI8B055BruSfNCl3Zxlfr5PFiVUoiZ5dcQtMxvcdttN6us5azrntsMdnjx3An3DITck3TKdjFEJTW5Clh9Y555br/PwtSm63ZjxkYDMRnzv/CU+1z1KLx1TVgKM96hxgM4l0xY6iQcFxaxCKUuoS1SvZCQC/s373kCUe/QhC7lADUGlDtHLSLcN1s0hDqYMVBVfl8gvT/4IMWlcnTmxyo6vspk1UCHUU00yqhHYksJrfCAQRlDWFS4WSD8x9UXEiKkMcVUQ+pIiECSZoejl+Eii2znZEcmoVlKNBK25jO7zTYpNaHdjZqd6nDpzBXM8pn32JKVyBHuOyoYBaUnrChuA2svIH4jhXgEvSDwS5SR2GJBULAI1SSTxUKYBtqkRLYsbCQI3aXCkqYUStPSIquZ//qFHCOIhj6RzPJwvoKcLwtJSWc7YnY8Rpwx7WZWoahiXIY25MZWoYPn+HrXZnDEBW/PTFL7CYBl8oKm/VKIjBXhkoLDCoazEak/UN1B4pAeflujcMrwzRugAj6JzocQsBngEiMljxbDAZSVrTy2wfbmkfKEgmtUc+tiAm391GhdowrKAvCS3kt1uFfdNGvm+HrZaR6Q5BAoRaJwoUZlC2gxyMDVH/s3TBDc0up9SuzBk9SWFvxDh3Rh9o0CtpuTLEa42iygd4XNbCBXhKzFojRcKf3year0C53PS1Yj1l+qI3pjhrXXSQxH1FbAVgUgssuHx2xaDh0CRVyuIJMSFCrE2RDQj8lsCfufZV3Ckeg2mBf6JMbV7HWLdo1YFlSSgHybYTwvmljMOznVpVEZ8ZPsIzlheyM7TujOh90xOZzjipcvr3HPbQUxL8Y/e+AiReph3/ttvZncr4uNrfdylNokMMEmV3VdUmT0rcaGcKAi1wwmDUCHBVIIdZ8y+eszCqRFnP9uifdWgZgQu0ag9xYHPBkSDlIUPKO7+9oja6ZBzL3SxRclvXX2e6/IZkqCgfrjLba9c5alfvQurBb/z0APcfnKDPL8XZydqshuXt3jopevUT89xx8sPkCz+Fh957iS3lpscmn+GZ9qHWYkK7joyRbQ3ZIqAUPzBlnKr3R7WeiL91fff/y23sbuyS1KNOHd2DRVIXvXqU9Rr/0kjZbbOK+86yolDc9+Yg4N99tlnn33+m0JMDsP/zPnzUMM3mq+7kfIt3/ItfPjDH+a+++7jh37oh/iJn/gJ3v/+9/Pkk0/+sa64+3zjsM5xvdOlV0sIU8OeqKGtxxaWBT1LPPoiB06mEFsuDuZZ25ilPrJ8zx0v8LniIGv5Yc7tLlLUHJ16jFm2tGa69PshlHC9c4Bww7MzrnMy2OI389MkHcWr7hny3AuC2rOa/u2TfyhTmYzmmAXHaKwprKRc8ry+cZaTd2zwfz37KnKbgHIIKci9JgwsicgZmohiXqJSR61VMtypIBLL9rBB3la41PDkp2/jdwd34JSHHcgGlorwCCvQsaYYFYSpR8xYdMPQm9LUKgGpqiCdwGsNIYiaJDaOwVYFuyGY+u4xWIkeepbqQ1Y251meG7M+leByqIsRo0wT7lmmGyV1ERJmMZkSyNIT5VAkgjyR+Krg1OIOp6MNHBrjJIt+SK9WxRUheiipzsUUQUqaCJiViMKjmxVu7NzEC8+xss93v/kFApfwzHXY6rUYtDKmLx1g0MkpJWA9cStgTxa0dITLJ78HsVbi7wgxewWu8PhE4p0nnRGkVAlsgEjAz0Frbswty1s8+eQr+W71OZaWhmyMKjCsEvYUGWCDEhO7SXJJJBleSagVVcTYstfLEFpC3RBagVAeowQ2dERHc6rTGb3HGxSZ5vKVRS42Zxgc98QXLLdkJ+gMVhm3CsZLHr8Dq5cPIEqBKAvW3RQLh8Zs9BbguOHSzWX8rqK0Gu9KsnmHqypcpBCppDeuIvCEyiGcIpdwuajxTSpHS8v1ZB0ZW3ThaNQCklrItdmU+tAzv1vjSqUE7WmqAtuyjLMIG3jcyFLzIdkIcDCINTUjscGIUenxNyWBVgRHJaMDEl8IorRAIbnrtlUeax/F7TZRAxBdzZ7IKayEMEQkFpuC9CAHDl8R4D1SeOxYg8kQlTHpnVPIXU85AjsVkgY1ptMxzuaYcUJnp0FaidCZgXHAlWyZU/dusniwQ76pKUPQmUFZT6ggCyVSCbJbZ6iMB4R5iVMOr2NMQ2FDRedyFW88auDxFQ+5ReYWFwuKyODLABlaUCXeBDghmGuNuPv28wgFv759mtVBi1nR44Xzhxm2NTNLKaMioIg1udVUY0sRKGIhyTLF6PN1unsVRlmFYlZT1L48HhgJ5DhD1BO8EPDluGpRGFw+RuclMo4hK2GU4fIYHwqCRFI2HPZIidzWqEaJvKhhXBL3CwapoMhCwmxMsNJHZCWLn4wJOikijBDWYQ5KGomhqwLWv6uO7RUsfnBIKAW+zFEdTRAH+NITDD22GVMuRZjWDP5GTpCNCDdSkDkuFUgh0EhMKqDpkUoijUSMx5DmyETjqyHeCzyCZBtkCWbXoDEk1yVmuUrRFAgJoghwQ4unRNQ0RSKxy1XyRY+bViQiobVh6HcjxHMRa61lwr1t5t+ukDMW+SrL6BM1hPCEg4LWqCT/UoR7XYtQRmy9uIgZWFZfzOg3FCacJJI91lnn7YGksrhHFJYoPIGziBL8uMDNNvFZCWGATjX3vWyeL8keBZ7FEz2e3Pp2fuzMO6i88f+i82FP+8WCIE8Q8wJ1r4MpgZAW9jJojzE7OYMlj90a0l7r8ODdJ1g1A84P99g2knvnY9LrLZ68MUNUHdJTLX53604evnIX3/fyGXSgmJ6rsVdkdHzBZrfH0ugsT7ZPYUuFV4Zhfw6rHLthxvLCIbIwQHQyBoPsD1x7n7x2k1QbutlX3x8nEYSave0BBw7P0pyqkPwBSpPuKGOjN6TZqrA4941PQhyMfoO97j9guvlTNGp//Rv+evvss88+++zzXytfdyPlF3/xF7/iJv8jP/IjTE9P8/DDD/PWt76VH/mRH/kTL3Cfrw0pJbVKSFcremUVGYCoWOKpghfSi7hwgbeeus7nbxykWs+wNxXf+drn+RsPPMGrhtf44d/+Pj555XZOHVul00sIM8dCPmS8MofVDqsld05dZ0XNc7CyxePvP4Wygh+46yT/yy9dBSGJhgJzUGAyja3BXr2C8wFZLoGSv/zAU+jI8Vi0xfPiEPVVgVeS7glNJSmIdxWdcQ3blPhQ0LglZ2/bw1JJPRwyMglpNaGMFFJYXCBJlyVWWYapp/KYRxQjKpkl25GkYZXp4ZBKkTLTGNHPI6QSiALyKXjPt36B7zxzmZ/+0D1cu5rwnbPn+Jy/BXfUcrFsUr9asD1uopZLZlpD8tzztoWXSHzB82tH+YWHXkZvFBGklqD0mFBCLKCQJNNd/JmSWFh2diLe8uALhHOG651FLp09BL5kXiVs9Cy+pbFaYBLDqTjgBWkpjWfaDxhtCRZmO1w9dxt5GlB4xw8++BHECcGzFxa445Yd/o8PfTPZRUHezBBjAypApAZTGkI9GfFxSpM1LabicZEnjBz1SkAgSoK4oBCaldDy8c3beZnZ4tnOIs4OUbFHDxwu9diqJtk1BHNDgt0Q70tGWYFzljJUeGUxpURqSSgkU9qydO8ePg+4kTgqcczHBrdhvUEPBdU8pLEWsJCsE5wZ8WDlBmcvnMSOHUSaUIb0iognf+8Qw9k6LhfkaUQ669B7Hn8QfBiTLk+SiXRXsLY1w+efOM2imCcfrHH79E1GxJzNaxxv7bJWi6nelTP6YhObGTaPCEYzkMeOympOsgtq1lG1MJaC2mxOvx+DArVliAcWhKZYDOiUJTqW1I8UiNM5o+em8KHCWw9Vz9TykCxzfPSFu2hoR/26Ito0BHfkFHsxzkiCUYaXimI+hkCic4Osl9gyoqx7pAJvYHBLHeqO0irCxBKFOUeXeri+JkoMFBY31pQ1yOuK+khS4Ljw/FF2rx2jrAhkZpm6aamHAeF0QkdZEBJw6Mspy7UNNm/Ms7fcwmtJL46IwggiifZQ3DJGBhZxNkDuaGy1jgkVYdFDdDLQAioRO/0G/+Y/PMiRA54PytMYpYhDS+14jhx59kx1kmAUpSA8WRYwCiN6aRU9a7nyvy7ghadeywmWDaqtcM0Q4RTR0FIwQE2DbHgYZciyijQTySdKoqxmdHsTPxcyd3KMmFNs9WNM4tEnc8LPjRDDCJnnWO8ml0xKRzkXobsh4dWccGOEjxTGGMLI07wrZf6+gnPnDmFjiV+M2f3+ZeY/1EMPskkCV17gczMxfFYh4VqBd5JQRgSHakjauHMC1UzIbIHOIcodjC0gsU2N3MyQ1kJhoFFBGoEf5BNT7iyHopw0gjOL2stAhSgd4AtPulxHn99F9DNwMTQruOUUYRTpyzVT/3FA9ZrCTTWxeYwIAvInS+LbFMUFgasE+ADSY1UWd3YZjKpMZTmvPLDGhx89jTjcJBRVXl1t8lBxEVV4boqMVqtKuT3if/zsPeSlYGVUI4whTKB/NCFP5iiqAeK4pNGNEaMuOvGkpkqvM0dVzXHHkTtYrT9JdjNGjGfwM7sEzuN3FL6uMUlEUSuI+ppRS9M6NkNjscLrWyfZLkfsNXfpG82NjzRQm+to4/CHJLJm8Mdy4t2M333sHG/7lnt5/MoahfA4xWTksTVCTrrSFEVCr9EiiwcgHZ3rHaQV7K12eeJLl3jL9zyAEHBz+EEq6gCd7jG2N/sECI4em/qqNblSSchSw9Z6m7/0uts5ecdh9JdVK8ZYBsOMRj0hy0uu3dgh1oo7ji/xjaYoz+OxFOXZb/hr7bPPPvvss89/zXzdjRQpJVL+3xLWt73tbbztbW8DYG1tjQMHDvzJVbfP14wSgjcdPc4nrj5FMYKxSQnqoJVA+DEiSnhi7TDzouA1szd5Jj7M5/YO8eruFT584zZ8BG8+8RxfevY0ek3jGpbVy7Pc4zdZtw28Uvzmp7+JV93/DM+ODvPq+27yuUdvQ8vXMZrZJDspUCNBfrBAhxoz7RAICDzeOoKq5Re+9CYOHd7hebkMiUAahxWeNNVEfU2nK5kc2TqchvkDKZfnBc0jQwoSRi7BhAHCgXAOkXuUE/iWRFqDxROWQAli20JN49sSs655aW+ROmNUsyDXIWUDDtcHdLohYWq468A2B9M+9/p1Pm7O4LwkHSvC6RwlS2yoWBvUOJy0yRohq9fqPPL4ARQOoyVSepyeJGEI45h97TrPpctcLaa59MU5jtzahnlFMJ1RVCTOW3qjMeOKwWsJueTW2g63vuzX+Y58gff/2oO8eL3B6u49hIFge80gI8HcnQWvPHATluHkiV128yr3nlzlyWt3M2qOEdZhqwZXExRjS1rzE4WGgfK0QVmF3JS40FFPCqYPttE1x/haQi40G6bG+y4cpLTTVKb7DKZLZGSpbzic1TgElVxw4k03WHnyIC5tEDfjiceCczBlkbsaNbLIsaHeSVGVlEOH9hisN7G2xHcU1Z7gyPwMV0bPoaRjjhwZS5oVw4wcY6gzX2nQj1J6YcyoadBpFXKBtALhIdwzeKUwsZvkuOceLwW7ukI6s0Z9NsehGJqEzGqsV2Q1S3tQI4kdckrS2lAMbUF8acx44FGiguhKCl9nun6TxsyQqztLFENFmXhcKyDqg+uW2JpF7xlkK8MrGB/XFClE7YLpfIRYlIx3E3zX0rMljbbEznlsaJGA8oJkOMZLT3cmwlYkRJJayzAKQ2wVwj3I79QI44luakTuSMMKL7vlGq88dJkXVxd5LpglMTmdGw30yJEdkOR1hxoLyhG08wA1B1HHYXJBlgiGxZhiIQDjCHdyikxx84UZsrCFzDw+8LQaOWk3xHgJgYDbPcI7ZOHgksXnHi8cfjBRkBE7RCcnbMJuy7G+pVGHHDKEqXRMoxhTbY55bO1uZvWQg0kXhCdaStFjS1OPWbvWwlYUPpukilWfHyMWPemZGbAeF3rEWzxUoP68YXlxg/MvnkBUQPYkFBYvYHhvQrkgGY0iGEdIJ4lfhHe+/DHkay3/8f13IZ2kSPQk6ad0yLWUYOjxUlJQomNNkEMuA2qtjOGuJq1KiJgoswLH8L4mptoiq8LCBzfB5ZPP3FRAsphTxhq/ETJemmLh1iGjlRC6U4h6ADe2kb0SRjkiUvjBxGMIB947GI2R0008grSlqY88BCFeSkhCBAK/0sYfncdFDi9D8lccpvHFa4THBaN4ifiKpXI1JzcD/CDFOU1+bETlGozvXyA8skeoLSwrZGkQxyz2+CyDtS5Rx3Dc7fDmAzf4ZOcMRxo1eqFj4PJJUplzjH3MC9s7jLoH6T9SMN5MsQsx0ZFZZm6bYXdzm5ki4Edf8TIOzrV4z89/DLMkkCjmhgskNHj24g3a0RXiGYXsxCxVl1h/eozaMLhbCuwi6Esp0ZxFzlep3j3LpaUBn975Av1LOeOe5U1v7FLIqzzRv4PRiYhgryC5HsMrS37q5R/nZG2bf/3vvpX/4zc+T8WDEZNoeacLtoXj1NIuW6rGhV9fQL9hjIs8QVCSLnqOy2l6K33SQUaWFqyVn+aXrvwHpsOMvc//ELkXCAFB8PsNYo/dtkQxGnP16n/A6Se4++7/DR2e4cbKHs+eXeXl9x5lcbrOPScPMDf9p2OWN9X4n4ijB0ii1/ypvN4+++yzzz5/yvwFTO35i8rX3Uj5g9jc3OSf/JN/wi/90i+RpumfxC73+ToRQnBq8UX27nyCm9kcB5pD1qOAnSikGo2IA0v32SpSRGSzIb4CFwZz/LWPvI1WVXNkbhcVGkZZRIHGBZa0E7LSm4U5iTQCEXqqITRiz82XZullA37+uc8wOmaJn3fIuQg5O2Dc8oRJSTYQhMMAlCD0liuby5zfPkLZcCRjg4kEVjmIS2abHTZHx4k7ClkKnO7hkLAA840xF64cYHc0g8qB0mO1AOXxBtQ1QbQOyjm89ggFti7x1mMLxd6NGoOXGhw/ucYtb77Bk589iS4FP/Urb+REssv1PUelAB0qnlo9BuMYX4VKK2V9qkHpFLV0xIwe8K9feA1TNuPG2hJu0RBkEtNSbE9pplYF2oCLDLcubzNghkGZ0FjIqXVzjgRtNkQLjMA5kA1J2RC4wKKHhqRWILSk1iwQQmKkpz0W1IRC2BJlJZujeX7uiQfhYsjVvAVNwZXVGaYriiU7xbnZMcoHFFOWMvI46VDeo6QCJsaYZSApIs1qH8YXZ6gcKJiWfepGcuPGMqKIeL2a4+lrm4gqLJUDfvIvf4YPn7uDx6+eRLUMwVQO9TEus6iaIhpLhFSEwZBqb0B6Oaa/FNLeqjNSNXJv2euOEMbja4LqYszd4WE+u7bC0DYYv1BhfDJm4XDGkd0em13BjfURJvQUDYnLYqqlpMSgugE2c1+OehbE24JKOKQX1DF1wViFRNKRupBHrh7jrvktrosplLOUeYwZKWg4dCtnSc2yNUiZnip5xXee58bqEs+Ux8mkxl5rEIxLDp3ZorPWpD2YmqilUk9lzzCKzCTW93yM7pX4psXFHt0T+I6CqwVyXKJkTpA7yjgmlRH+pies5CijqdVbBKlhZApKraA0NPKMbpZAxfGq06u8pXmZn/vg/YzKFqIPat6zulOnvHiKdCBoHTUYH09SQfolQU3idICMHDYQmIEnHIP3gqwp0UmAHRbomw6hBMGWQ1uLyx2+pvASnIQwzHDUYGxRVmA/FyKmM3ymYL5EniugFSIMSOEQHvSg5Nirtrnrdats7NYQW8cR0lINMuaDHpWZkuyJKqPMYb0ABJV7MrYeWqR70WBWEnxpUQZMTZKebCC2RuiBRSQl2SlHAAgnwCvIQmIzQiUCWRG4scPgkb0CnSkKHSPKgGjXUhc533zPBWJd8NDDx5l+cI/jr27z6Y+fYYdpVMUj8hTrgGlJlhgqY41KSzKV4K6C7OS4W2K8BYfEiRxZKkxDMfjuBlP9AXM3C9IbfUZvahJoS7kOi4tDKjMBs2/xPPeFFk5ZRNnE7GyjsgKRSkIDQgU4U+C9RxQGv7KBmGuhMzURD8UaowKqJ0OO3DVkZmqTL64l+FYNAnA4Rq88hs+GuAji6znhxhg9KpB4KhU4UL/JS8lxXD0iPBgS6wGy4bCzHRrfVVKxKbee3mV8yXKtPs/PPX6GzUsCvTigcmqG3d0UUdfkSuAS+MzjL6IbCYOzc/jre1Rtj0bS4JZbK1zzgtYInnjoIhemIsalAafQUUg9dZy886OcHX+E9o1bkKuSOA/pb6QM4pzi22q4XcnM8R5J0WP0SJO54032OoYvbq4wd8jxl+75LFevz1BeeAvLi7OU13ZJkGRzmnLGUsxNjJkBxCjnCw9f4pUnl2m7HC8FDsFba8/zssVVnq3O8/TiO0hGcqIOkxDV63z3m17Bf1zrk2cl42HGE4OMT104Qz3OqFzeQMSAgGoQ/b51+c6Xn2DQGfL4uQ9wfRRy8OB/YGHpp7lxc4+nH79CLZCcfvlRLo07zB/4xo/1AEhZoZq85U/ltfbZZ5999tnnv2a+5kZKt9vlXe96F5/85CcJgoC/+3f/Lj/2Yz/Ge97zHn72Z3+WM2fO8O///b//Rta6zx+B956XdvvMn+hQCQp+9N5nQXp+fOX1dMoKEk97t8G2CdioBYhDOcEQ7EbA/JEB/58Hf4vNXsB2vUJuIex7cqu4Gc1gowCdC7yRXLm4QO+65sbj06QnClbzbeScmyRNOE96NaRx9whjFd5E2AQoHGOh0IHHeQ/eU8xBWCoM4AJFfSpHJ33yi1N4bZm9P2dgYjAluuq53J+mcU+H2X7OyhOzWCMQTce3Hr7G5Zem6bxUp76YY6YF/Y2E/glJ62yJDwKEc/g9zUqrxiEGBLYkVyGdQcj5boNj1XV2u01+9bOvxkuBqlpcBGXTUUgFHkaDmAN5h2jeIUNJeT0kalrmT9ZYvZkh8AgxOQFVePLVGrJlJk2D5pBjR3fpZDXSQYIoHGSevcDg5wVCePTAc7GzxK/Ff4XOBxyBgHRKIYwn28gR3uGEZ5BIfuPq7dTOSYSXqKokTz39IuMWOY1OJIwsLrGcOrNBOgrZ6B3ABZI7WxHh8kWutSts35xB9yX55TpB6yYzrQFhJaeSlfTSkHNxF9XzVJ4veN3xyyQY5sIM52G8HbKRNhlfayBqBVWjmWvNsFvrMayMODTX4yIxg06Nc08fIVry7F6eRR/XCC2IRtBUFbJYQLbETjbEdyRlpmkd2qKXJuytSXS3hEAQtjPydkR3SUGiSITC1aCoefSWwxpJQIUwVUgjMFlANrAUUjNzesTKcJq3yXU+99EzuCRC1g3jpiau5Az7FhMIWrMpjdkxdxy8RtIdce3s7aTrMVvhFHe+6hpFFNP+oqBytUQmknJOTZJ0nGHPaWwUUR+M0WmFdDoiLTzsKBwBRSSxyjFXjbGMMGOPmp4k5vQKybQM0GVBvG0I+9ALm8QVi/eO42WXm3GNW16zx8pZy2CzRvO6Y3G5y/XeErHMeVl9nrPbm4i2QwVQVjW1rkZZh20JVCGJREp5p8GelcjUIZxAjxUulLhQERxxVOYNu1sONPhEsnN5Gp2LL4/NgOqA2YGg5TANAfWIAJDCU1RCUAKROdKR4tHeUeLEcsLvsjOuMp8MkXWPRaJuSPL1iM/U74CWobhQp78ek69CYHJe/tprbFyc4vlDxzEzGj0tqeQeo0OktiRftIhIYkrBtbUFVJkhpiylivGxopjSRDc84qSimIJw1yMQpGXMBz59PzPVMWudOW7/9hWSVsHs4oBepUJmNGpdEW8NEeMAE3iQApV4ZGWMWg5Y/DcF8lHL6lsjXCzw3iHHnmRbo5MIM28oOh65mWN2BM4pqn3L2EYUbUV5uYqUGhFI3FyTcr6LciFYAaMcj0dqhXUWYRzCOEgLbC0hN5LQOspWwNRJz1ZW5/gbbzD7c3vsxg4RTKMAjMVFCZQOYaB0Bb7MiY8FjBsVSA3CelTmSa+HTM970k8pxEVBesVjpiSHTnfo+YjP3pxmx1aYu22PmxvTzA5i0tQw7yvspDmya/nQ+Cqvbx2gP71JQ9TpdPsM39LhRrTHeCjpWsulR26w+/IW/RMhwjri0DM8eY3FpRe59MgSax/fJh1MwyjlUmed+ncUNF7d5vrjC/wPR55iTTX40CNzbJ/vMbwpaLLAO1/W5bapF7hanePRF9/O1c89R3UzxFtDEQs6ZxRu2/NPnnkLLZlRng/RVcfTT12H2KONxCKJjaTuPTNtM2nEi5xwHUoXciPocW1lF+Og10tZX2uDPkZ56QZd0+JAs8qayanVIr7t7tO/b12utSrMLU+xvCU4eWKbRuNvAbA012AuUDAsub62w43ru0wRcvfRfUXvPvvss88+/2Xsm83+6fE1N1L+3t/7e3zhC1/g+7//+/nEJz7BT/zET/CJT3yCLMv4+Mc/zmtf+9pvZJ37/DFY79ns3smFZ0MOFClrh6+RhoKEnLFNEMazeLBHRXruOLrKEHBdzXYac9p1+Hx6hOFuzEY2hZUBLsi469gqbzlxmXc//514BU4ITt+9zlQ1xXdPcO25Je666yAvjNfYSQZoq6nejBnFCiNAmiquBFcxzCQZ//g7f4f1wTQ//eK3YbykV5mc4PyP93ySE4c7uNXXsNMpGDzaQhzUJHXD8WCXNNWQC04eWWehMqC3FbKbNPjOw1f5n898ie6RhLe/8D2YqsJZhZmKJvJ75TDCILxDG4lqSg6Pcp62GpFbTOEImSRINOo5q1ohC4/KPXba0RU1bBuCEpJgyHKlw7gZUVEFL+6dwmbQaWSIuERKRf+I4O2Hn+WWW6/z9M5ham6MDgqeeOw+qkuea6MFtl2T5tQY34FSRIRdh8k0rqIR1vPMlmV6V+ACg84cYuQhN6AVvrS8fO4yj5XHIYgQbUe4ZbAzAT4RbMk+VnvCoWF627O41MWVktHaUcZjx9Kh83jTx1dTBtUqLmkQtXKm58bcsrRDeTVnOhlhagHlaER6SGGOOH5n+wxnH55l5dI8sSoIWiX6qCUc5Iy6nlJZZmtTPCU3KG3CN9/xTVz+7ZcQ0nJtr0YRRiwrgZyPKGs5vgt7vR7rukveLcinHJGBVpnR2Z6hnSlc4ZB4hJfIoUWlKeZYjAs8euwpZsAaRwiE0zF5K8CtFsjcEgaO+SMjJIZMKJKgpNhTFNsJ+YLF1DSqYfGzho3RHoUIeEHPIx+3HDu1x+2L69h0mRVTZ7hS4eyHbyftCTwWE3s4LKAUxDshUmtsMiafcgzSGoFX0BKozJLvhrhokjpSZiV7WUokh0QNQ1DPGOkGFs9YDIhVTjGs42Qw+UBrgxpKHt44yJGXdcmXNa3FEZ2VCv2hYWc0hW1NFF0X9Tm6W6fIRiWRNcRdSz6lJr4m2pHNe1RdI0NQFUEwgNQ5bAheC1wgcBVNdryCa4Owk89lUTiCzFPOaGzu8L4gSR1iJYPlCm4hwbaHSGewrRgfxYTtnPiEYbtsUPcZr771Cr/9/MuYmXHcOdVh1wiCrkem8JEPvIzFk5vcev9NXmwvMRQ17ntgg7f+tRcZ9mLO/7sjiEhOGq81hcgFzaigHmTsMkMQQW1pTP9Zi7AJzivMTMjg1giXSJQ1INxkVMl5rPB84ulbUUWArzge+5VTzJ4YMnyV4sShDbaKmPKjI7x1GFsSiCZOlMgBpB9X2FgjehnKhxz+sGA8rSiWgklz2DnyviZrR/hWhewNmsavrREEIdU3VQhyR3opwa8mSOHRzoOX+JkabqdEWE8ZCYSDsPAopXGeScpQUaKyCJca8J64V7A1mMcVIY+eP4wQhtYYenGBqYbo1CEReOVQQuGqIa7IaY886fEaO6lAxxFhKajOlQgJMhYUqab/gYD0gYQvJbdy2/Ai6Wdzhp2A4c2ExQciTs42WHn2Op1hgZ6OsUVOPqdpzwwJv3ePeDfAbMJuw7FbuU55eZY94RHG0FceJycN4D4e9qr82999PXMrGfmmxAeePLMIDdNpxmJlSFtW6OuQ7ayB1RLGnmTgmdt23LXwA5zda/M7FxqcSRWP3LSUzQg9KkjaOQv3tNkZ1PGfqTMSCZEZEyDxgcREQDD5nvm3n34D9y98iMeuHmGmNuDb5xf52JNthqHg0Mk63c0etUbMxnpnYhx7qEW10Ay8ZcOO+dZjt/Ad33SGew4v/751OQwDdtfaNNyQ22e77GTPcbj6l3h+fYPfvXSFsqH4H/7SXbix49jSzJ/eAcM+++yzzz777PNfzNfcSPnoRz/Ke9/7Xt74xjfyoz/6o9xyyy2cOnWKn//5n/8GlrfP14qWkp/4pgf55/WHEWaPF/IqB1tbvDE4x0Nn70AbxyU5w/0PnGdVTHH7aJuiEeKTKdqjOud3buW7Fp5laqNPntYpQs3h01vs5go9BiQEqeEoe7y81uXQvY7fWjnJ9555kO0XPkY36DM6Dq++7yKPPHsG7SwePxkTKCDC0s7q3LWwSnA2xxYBrasB0hUsvToFL9i1NdomgbogKCVVlWOVpf170O+2kHfvkh8IGJGgAsdeofj0YIbBdozTgk5eR2YC4SU6VaQLluDqCJV57CBjaWrMk3tLFC098SCQlqYccnC+j67u8uK141gtsHhy4anuFYxchKtJTkebRKqkqQrKkSDoGihgeDiFGugdhQ8K3vltD+GRXFk9yPYw5vInTzG6UOHpf38S962A8MzOjglmSjp7TZzROOHwFY0vBFYUkFuC1JBojd/IsErgnIeK5vVHz3F+YwEnwQ2gqgKoaexUyEoxoAggtgrbgxvPL+LyBD8oQAasrMxzYGnIRmeaggBalp1Gje1hk5/5d9/DsB4RNS0zzS4qcBTZLKIiyOqC85cWqA09QewI6iPCBUe5pcgLQZ4but0RZWAwiUPFSySzG4yHXUwkQSpsRdMpLNGWR2WQyZKr4TZh5lAdi6zkBJHhxlYdG4JMHK5n8E4irEM50AODrUgKaWEokGmIA7TXGC9AWvCapJWiKyX0Bbvn6qy36py7soxMAiKdMTQK2ZPYliLzAj3SyKGl8Ms8+sg8yekBDRqkocMkkrFReOUYL8L4lgC0IFzzyEQirCXMPfXVAUOjYUaj2go98jgPTgvwFm8g847Mh8RaIS43wQlMDXInEDqhUgHVLciUxiQhNnSsRDWujhsstUfYAMTdnvRywEaWoKwj8JKtcwuUY4FvKNR6hhwrerdrpBXo3RLlwHpFsCHQGxZMie+n2LkAhEV3U+IHx9TuzDGDiF47wsaWSgytZpvtZHESqe0VJjWERiAGJTIQBC5FLw6RgwreGqRx9M5VOHHnBvdMrXOuP0+G5v7pLU5GYwY+IRQFGA9SYtOEJx8/QeAzgtixdnGGzZUWF59fxMxKTN1j5yQy1TRMyVLSpz9MCDC0psYsz+/xXGsRv+DxbY33gFCEbUfl8ghf0wS+gMIzvDUhSzyVTdBDwbiXsPVSnfDuNs5KbBmS3jVL9MQ2PogJMwtC4E2BWdXYQw1EPcV2CkS9SlJCfMVSJp7ugRgXWMK2wk+eRvcdR6hcHkAuqFpJcUH8f9n70yBLs/ysE/yd7V3u7rt7eOwZERm5L5VVWapNqkW7EGqkFkKoEdD0tGBgrMEYljbGWhiDeppuGBOmxgCxmBCSKCSKQlJJtW+qJasqK/fMyMiIjM339e7vepb5cKvBxqYZUwskQbX/zO4H/+Du73V/7z3nPPf5Pw/RpMYbiRB+dl/MN6ETU8WQLymST97BHBcIIRBa44PHtRW2XRFPgCBQVUVVG5SXHO3N0ypGlOuK3mMZ7I04ugHi/DxeaoTQaC+xbib+SWlINmOIAy6Cg+st9PWa6qUI14a6LemeqsjnI177wirRGzXOGqxV7N/eZ/77nqQ/HiOsoIolpbLYpMKYjPyOQkxgeiwQCw7fVEjpCULgYvCJx8rZehIryVm3zLXtgLqTIgeH1Gc1NhbEo4qhSrF3K6YbKX/7zfeReEl6Zoh+IwIp2H5pny987DafjJbYpU9Z3+LN+SWKhcDiHdAKVv2YudpTXR6z/+UV/HxC3DbUh8yefyoJSrI17fGPvvgoNu3xgQdf4/nxAZ2Fq6ySIoeOV0dbdI0hzy3Xbm7x1jMP0zCGaWmRalZXfmnpf18EkVKyft8qg4lHKsvnDt7kRxfgH33qGfbLnF95/nX+TPguLi7NE+v/KJPWJ5xwwgknnHDC7xG/7ZV7e3ubBx98EICLFy+SJAl/6k/9qd+1Czvh/xghBD61cZM9KzlrPMsL/VkVZel4cHGLchrz1dfOYSvDWxbvMu53ieY87p2wNemyde0U8++acDHZZ2+YIryh6SKm8xaVB5IDSETJWVfziMm4eHGD32yn5JUlmW9i9zVmLHljfwWagSgLVEEgKoESjv3BHD/79Xcj25bq0JAeOfRIUUeav/kLP0j7XYfkzQjjHVlb4wo4LNqUr4zJPzGHf4dG9Dy1USw2D5nE89y1bfamTXb324jdEtmLEVIh7OyTXDuXoOQEVQciD/LIsHenCXjqcw67C7c3u9yp78e+0kDJgE0kNEBNFIwNGIeac3TajsX2mER5Pv7iIwjv8VLDgcDHCpkLZF/z87ceZ6VV8JlrD1BPKvQbnugghyxmdE2gNiWHKqY9P2C60qBzpBmqiqjvcEaRBoOaV4y0piU9KhbgFQGIWiUbg3W6g5ojG7CpIuqkVHMxufAEq6iWBPW+J44U/XwN5SQLi022xwWbuyu8cqdHvVgjWpYQWcpIc+ewR7Ebz/JdIkHdk+xM54i6NXONnDyDrBdT90F0IiYHbdwzDaYvt1BtMD2Fn5bUuqZakOxdP8QYCY2IdKgJtcalAtsKmB2Fry2iEBSqJurGeDMbtXquSjhdFEzHCVVUE7UF0jmaIWGcCKIxsxyPdDZDZb0ixILFoaCXpNyUB1iadE7lszyQg5iFK1OK2jBc0Mw1JiSnc/YnLWwlyAcR8QiiHYs+7jN9KpDtrjD+eot20iIOfWoZMC1FXXlU5tDHEp+AKMAmgVB6FhYnhLamdVTQL5s4LSgLjfYerxVSKCKnwDqsUxReoXOJ1IFmVxBEhxpLrzsk7hTssEwwGjQUcwJ97Jh8uYk6XSG3JUrFWCFQdYkZ1OSLajZKJwSVVJQpiElAOkmMId6qKBKP7Ub4VQfbFm0UGDGrqW2llM+XKOcQGxa/HBCVoOhpOjhGRw5hJVSg8gqtFbIQyPkBouXwQ0E0qHCxpDhjkGXCH0jf5ErniHHWpkayNdB0gmRn/xTTuYhuMSFKLcdlE7fbRNgmsRkyPOzwD//6+6m0x/4XAhtrtC4IQ8tca8Jac4SLI+zI0nNj6hCBEciihhCjM2hdm4mn9SmNfyoQ9moaH8uQqwHpHGo0c35Ui4pOErP1zCJqUjIOCbxd0A4B0zdgHcLXhOCg8EipodvCZ0eEWM6cO8OSCI3eK6GtMEcBrzxuTmMdICVVrigrRZxmxPsleVvhGwrbE3itUGMIkUJWkvDACjy7DYCIIkKsCG0Lo5woBBhDSDTq9T3qtQbJrTHTKw3qS4rGfEFrKaf3hYRqMKWcS3GRRDRjIMVJUJsj6LVQ3RirFVUVMb7WRDlBteipViOqqUDeSlg9iOhcrjna1rBTkDvLC3c2CHjEpGTak8jdjGAsh5tNGM/x9BMP8pHjZ/C+wATNH77a51OfmEPLnFLHTMoGwSmm04yxeh05XiD4gE41k47CB0lSBDoLfdabIwZxl3xQ40vNt77nNr91+zKgCWXgw7/8Oda//w2Oul2e7xxxPGcRdaB5/wKTw5Lbd1ZQkaJzYcJyb4+9z57m8cfP8fKXb5MnBVZoBJIAVOo7GYvXWbBbHEwXsGuKH7jyCGa/ZDotcdOCW/t7vPrxA9IHFlkQCQNdIhBYwkzo/vdw30On+Z+f+3Z+5bXTePUIP3oVXAVSabyAnemEj11/nbecWWdtfdb8U1c1IYCJ9KyN6oQTTjjhhBN+u5yEzf6e8dsWUrz3GGP+7ddKKZrN5u/KRZ3wfxwXAl/efRGrR9S6YH+6Rhwfc1r0yZeadBb7+I8Y7l/cwWjHgxeOCJXmsEq4MTpFVSSsFw5zLImCZNqSeO04LXNEDTIIfCSZNB0jI/nU8+v0JxkfufEat5pDDBEukwyv9whrAhsrHIGUitC21F5zfLvLtCkxmUPWULUDXgpcMKRSkYqaPJNEBwXthSmycIS7EnRM3VAc+jYdJuR5A+ckO+UcH9x+gnozxvY8ze0C0YyYnFL4BQtjRXG+jSoCUU+Q9WPUPUXiHPmSR7bAnZa4RooGVBHw0lH3JC6GUmv8XGCxNeXPLT/H//Tct5FQcu3mBcKiwBpBfCxRhcCmgYUFz2en9zPcTynnJHNnLO+av4Y/p1k4NeVf7rwVt5syGsccFw38VcfZtUVeljuIicQ4QaoEjbkmB7Jm0FT0dmsogUgyUS1++QtPw/oUM4bH33WXS1eOeOaZb6GsmnhpmF6VtJZGLAnLzaN11ELMt7/tEX712deZjnKYgBAebUH2Fd44BocNbCuA1NitmFxqWmcLprkmagbCRCG8oVoMREEgrEcnE1KZQtQkbSdsjkbUkcFZwe5On6yyiEaMrjW2tvRkStFxFLlFFQJRCkbTnAu6SXMgcZHEJxa0II81QUlEXKNDIEwDVhmkBZcE1MSRGMiTgKwFW4sZQ2WhShBKMRlHJAuOyZLkVDJl33ZoLeWcOb/PO7pv4q49ya3tFVq3BKv7LfYX9xmnKeLQI70gSIkTDqQEDSaNaBjw+xXpRkbQAXm+QTEKYGvKVkyZRthmhJgElLeEiccbhepXhBUIsaRTRUxSybhlER7SAvyRIJhAkQhUnFLWmvEcSGUR0uETCU5R+0AoFE5CHYMIAZHVDAdN9HyNF5ZQAl6hSklj16NUoFeMqYcxoVIUylEvSkxfI3xA5wJVC0ISMx2m9JMYcQG887iOxE4kow0DZkpwMWJkMTogVI0iIIaKUdEg7oPKSsqWwZ1WZL7JtefXuG9pxEvPrTNfFRzsRqSiTTg95af+9C/w1376v6DbVEyKmCgHUwXmlwcUrkl2GKNjQZTX2I7ENQMhBn9Uo5SlXBfkNmZv2MPfm2L6Fjn2OCxCSpKpo1pQFKsNRPAo6whGM//ZQ8rgcFfXsC2NSyPqiWXlnX3aD03Y2JmnLCLMQCO+ZHEuoINEGElYERCDmEpcrw3BI6uAtA55UDNfWeq2QeWW3Agm5wXmrqX98RHiYocyVpShwbhZILxH79TUrRjXjFA7FeYwEEyNaCTET54ieE819jCsENOaUHkK40mFJPhA69oR4fk9UAKUpV5ewG5qsjsalyQQDIurffp9g+tohGyAn0JtCW1B3RE4I5CbA+xKIOyBSwVohx8qdieLZKWksWwojUFvDynXI14c3SCZyyjLJvEdj9nK4IU+dxv7rLVWuPiBFZJphFMlaVzwhRciqltDdKTxR8zan4THWUVRxMz5IXo6R/lkC381kA81xkjOnR/Qm5uSrvXZ249oZCXRes5ksaK5GfDKsWsKvuf8Nczdi2xur9N8bp8wKZEXNaMoxt1Y5vE/8AokEEWOo8Z5nv/yTQ6MA2KCDAQNqMCiWCR/8SLPCMtgPUEelHxu+xW+5b7zvPc9D/K1L7+BDwI9gBu7+3gFru8pGjUPXVhhodv4967NxmiSZIGv3rhAb2EmilTBE6LZGN1L+zu85I45pf+dqyX8n2DzecIJJ5xwwgn/ufPbFlJCCPzxP/7HieNZMn1RFPzET/zE/4+Y8qEPfeg/7hWe8NtCS8mT8w/y2b0XWA3HHDYkqp5HRIqPDx5AC8+FhS0mw4QkrfloOMOyznhx5wK3bywiJVyaz1laLEhHFtGEe5MWYmyJRx4Q2FpzMEz4VTtPdqgYz434athjLp0wyXoIIYnnaspgcE4QtCSMJWIhwAAWyoJu17HTbBG1cv77H/oEv/lbD/DSSxeQXuDL2dk1BEEzt4iRRD0syaoWuEBrOUfKQG9tyAZNnJWMxilqUxNNLdGwotKBxkqgUgk+gvxRR1QImo/tUW81iDKPzEHoQJirkeMYv69AzSqMdR0ovUBNAhjHQmvK6bk+twbzvCu9y/2rO/zrSvCVjcvgAiKpkH1NVCqWlxcoG578eIo90hR1yh27xpPVPe5f2eGiO+R6dBZUwCswQTDcGeNXDV4J9MjjDhznV8f8jR/5db5w9zwffPNBgneznXXwiCSQKo8vHU++/SbtbsnyrU12r10gbRlM2/Gw3iJkmqGLGdlzaBTOeRKvyaoSlQvCoqMxSCj2NLtmkVbq0YUg1IZmNOUfvvuT/IXn3s31rI2rBc2qYmkdxkPD1CqCi5l71xG39rrsT7ZZ9hobKfCC9kKHvJOxs2AxU0fwgnpaEzZAxODiQLFa0+mk3Pv0Llpp5LKhoQS1WaBu1rM2HuFJjgLDK5LyYBYua1sGmTnU0CFCABsICOrcQS7xLc+5B/aJU8ueafKuxWt8afwAB70GLVNyOjrmkfYW265HmUvi+QZZIilshO4HltspVaNguzHAnQFbaYqqJO1qwjiQL8dEuwVkgBT4SDG6G0MCeEc8KQFBKB2upRGRRsYOeiVhX5IKSYWCRGG9x2UWoQKR0TA3x6bISQ4dqZzQWc0oW4a9sgWlx+4pzB2HPiewLcWg3aUTTYlMQZecfiZQRmKsoBICpKUbT2g8ecydV5ZoPpdRXWyhMoHXErQgOGb38VogrBhU7KEPwUDVFFTHEllO8fMGP6cofQM1KBBBUY0iJIJaRjhRYMsMo0AF+Pidq9y8fYY7RYvaarZ9j16SoaRHIWiv55TPdUhPlcimB1ux//oCrhGh78tpHATkS2Df5ihSgVCwXS3wxTsR6shjKxDDBmIIQimC0whf4ttNytUYmVnkkSVkEb4yNE9r6mJAXGum1uFMCiHgE0E0dqg8kG44zOUJ6iGL/WSNj1KCEdg4UHx3RIgros9EYMHkAeGANCL4mWvP+wIQtDKPfs0wPW9Y/EFBc3+PN18/jSxLIhswo5pQ5nCzxp2ZR6gYIofOLXLziLoF9o+mTPbayOuKZNPDvCZdzeA5CU4T8ozgHTJA+2iKPFigGDRxtx0yeERZ08hK/H6NHwSqUU687tk/18U2DTQ1egKnHy1YePeYw4+3GH/eoVYSJnkDv16zdHmIoUGeJkxjQ6c4pPFwgnzYcfe3FtF3xvhEE2/mhIGl9W07fLj7L5hcMJhIIQTsfUGjR57DJ+awsSFIZuHFKlAtxHSOd+k0R/S/ZR3bs1RHmuOG4oXxGZKDQ/Z/MyWKPDJucePZU3Tjmkk3onwgJVuyHGVtPv2Zx6hLQ7LjURs5nWqAuDIHJuY0Ix6c36DTK/nlXpfrW6uEMxHCBqgErhFYTj0v7W8y3piQTdv4XDF3b58H/9DXaDTfSkge5OzpOVIhqV3gbWtrbO30oQhMRwV5P0cL+e9dm6NGzO5yDpFnWpcA9BZbjIYTfBH46vGLxHOCp8+c/rffY6LZ1uzEjXLCCSeccMIJ/+ny2xZSfvzHf/z/6+sf+7Ef+49+MSf8zqm94438JaLMsnXc4dh1IAj+xKnn2B106IiCdz92nf6dBkMaXFtdIY0tg0NN6AQeje/hTA3OkNSe8VTzulxhca6PQyAk+KAIpUBJOAhtoqt97JzgYr3H0ukRo16Ltz19wD96/knMIYgU9GEgOuNopQU6kVxZ3ma8sMrPP/GvOBtVvO/iFt/5V36CRqgRKjAqNNLDOCQkWY1uKVxDkRx5bn5kncbCkPxGgnubwFeCcj/QzCW+o5AO1BgmtcbHEl+B79bkieBqOuTufgM9CojaIQcCvyww4wrhHHWniQxglUQIWCpHnGodMjGGRNc0uxXfvXiDqpnxE2c/wfM/f4G6kkRLGfJmkxAHGrFkcxCYejM7VIeMTuV45jMPcLTWYGojEmp8UFB59MCRe43wgqoTEAXUyvMDH/gcC80xP/7O5/ngP3iIYBQhQPdSn2HSRgpFqA0f/5VH6T4CtzZPU4RAlpVkQ8Urnz2PGMHRQoRbL/nS1m36ac5FX3L5wi3uyi77SUyU5bx7fZPOwpgvdS8wvtclxGCGkulAs+4mvHi8BAV0o4oL0Sq3kopB2WfUSDFJl3xJokY19SgQaU/V0TS0wbRiQuQRGUgvKJylRuMECMAIyYNijef1PYSWCAd5FdObCiJnKQ2MqiZTGViMYqYLAdXOEMJTWUNtQDiJEoJlH5Me1ezagHKCp+ZusZBkfHD8Fu6US0jjSEPNWjzkbr3E1/dOU1lFEnmmOEatFpSWciBQShATM9AZtRH43DGeOvKqJFmOZmMZbYWcBvSxQyo/c1CWHiUC0RREKSh9RZiLEW42ZkMUqHKHSgraeaCwDbwUGB9Q1iKngXFkQGgiV8FIIOdqTBYQLuCDRg4UMHMo+KbBNaC9NsUMAk6UdM7VTDfbhKbCph7RhHSl4OypffqfEfRVRFRIpAvUwuNkwBlPdJjRVCX6qGI6TfFxIDQ9VkuiKyVTsYAZe8oYQqUJLoXgkEicA1N7qkjOgkF2BM6DuFRRNzPOPlxw7Y01ludHrDWGoA1NkeE/khKlYEpJKTS6dKAl0fmad/6xN7gyt8fHf/5J9rIzJMJC4pEHCfVRQrwzRRgLVuF1PBMZDeADvuEQOkIYTXpkcacEEOisdeiGKXdvSsQucKYDwVMUNePfapJ/LsUqTXpxhIwc43YgOCgjg3I5ogiIXAAOmjGMHSEEwCOdnY1I+W+YmLIKPYpZ0AXf9/7nWApT/tbPrEAlCLHGRhblDEIZnFYYJwg+QCdCTg16VGHuVYyaKa7tiFsNjGjQ61YM5iPEVEA5eyGpAOyVRLdvUyfLhCTCK4Ooa/pvpoSJR2QZItL0TYROS7wDOQR9XCLnHYiA8jX6WMBBwD4iCU3Jfuhxqio5qmKk7mO+NqW8qpk2u3gZkIsJ5x5TDF9sM9jWnPqTe+i5gqrTJq8i8g8GmnsNikTiOwakmNl860CIBDbULFZD2suWt5z7AB8+2mc6clBaMqmY2DnSPUeV5Jhuwp0vLHLpypvcfWUBVhYpuxpJoELjJMR1k3bsaDUT5P4UmhEf/o1vYf47pzSW9ol9hcQjCOjMYyOJNI62GHN67Q47x0+htiXHexnv+s67LLxtH8mv8T/80gIrSUzciDDAYqPBo/et8+L1fTDw7O0t/khR4ia/xLXxL/LA4l9iqfnef7s2l96ijEBokHkghMAffPJB/t6dA+I40FcDGmlM9O8MvycCygknnHDCCb9zTkZ7fs/4bQsp//Sf/tPfzes44T8QLSSi3mIwbXEwSakWIoIX3OzPMb7bZeJaHC9v8WZYYkv3sKXEWE/eMlxpbPEXn/gNdivFnWmP/UmXSaoodY+XmqsI4XGRJDhBe6XAIVhRY+LC00prCtvhh9/6PI8+vseHbz+JyDzxlse0Dabv8FJSjZo0CsvxpE3eMwQcHsGgkIQmTJoRxnhqFyGdxy1IeufHuJFEPeeQzlDsNQnjnPl4wtEkwiOJRzmN6xaVzUZTtAM5UggEqtLMNx1HE8P1WxfohjGhAlGCshGt1xJsleGNhCrg0Limpz5XY8qcXmfCjdsXcM1A0SjZkgGylK+88SB1oUHYmXhjPF5KNrY3mApNaSRJLvj+06/z3u+4xod//R10p54VMpxqcyhSZB3oHkkykRGMILQMRS0xg4Le8j5loRmOHJHR1Mqj2xnJ+UCoC/aPU853xrh3Bl7MF2nOearaI12g3S2oJhIxBS8CzBVstp9nQTriquJCd4+uGPPZw/uRcUGvPWE5mlD7mIW3HjD5SpetvQ5/+2NvpX/YZTEx+LrEJBmvbm/jsgaRLqEMjNuGkATEOOCzmghBtRjQQrPUbPFGPkZXszDdGkG1CEGAl4Kkr5gKSz2vELUiRIJp0+IKgawF1niIQI0D28HBvGD1dIabGMpbElEIqq7DZ7OJhUw4ynmJjh0PdPYAeEvvLs+PznA+jDjXPmYxmWK94juuXueX4rcQnbNsHQ6pg8CMBHpc4ZqBxVaLdq547fiY4AVSgKocOof21FP3HCGVpEqgvaToVzOxTwikCNQEZBygWyGPY8S2x2xI/IrBjh1Kum9Y9wXeehKhaEeHtAvDZrGMKCKCE+y8sUjdAddRlF1Dsi9QE4vpK0LXI5WnGddcOAuvZobSJTS6CYIJYWioa8Xr5RmGLzXJD8DNh5kTKwSqRCC9QASBiBWTZsqoTJFJSchnK583is23L9L8kkcUmmhU4RYE1UKEch58RCBg5RQRC4ICWQWsC/ixY7togQq0zxXsxXNEb1g+9P98Nx6QbU/dkjhv8CKgp1AnUBnFQ+tbZMQ89b03efZT5zDGE5xAT2riQYmVijLSGO+QSiDC7L4SViC9QzhP8IHQEIiVChs0h4MRzDdYPF9RXRcUfpYIa+uA8Ao3llSrirCfIm5bdKuke9+U4zsCv1eiPy2RjYRyWRFihRACNanR0wBSgQyoCIQTqMrz5Po8Sf06jy/uc3inQ28zp9+NyNYUWgoSq6iWE1zH4I4dqnLkCxHZoynnvljBvy6Jmm8yeFuLcH+CqAIbGwukRYnwFt0wlA4q55EC7Os14cEKpwKqPyU9nkA7xU0COIsIMyfIwp0x5ZHE9Vo44dhgjv4wJXzFYxJPMYC48tRbMZmd5w1hcG1B/ITHfFUxublMtdZBVYG1eML3PvAyy9895K/+3B9ltKPpdvtMppL6sMS/sAKRgtIhywAevBG4rgAhqHcCWzeX8PuOl4vncQ8aokEPmTlG8w18aLHQHmEmluiwwKaG1+6eY/1CE3HP0rrn+YfmnYQQSA6mmOMMHxkOhgUi1IizXWQr8HNfehdnt4aM7/XQBGwUoCXxNpAEz71sjuHNhD/5+CXK81Oe+8xrFOa9ZCUAAQAASURBVNM2Ez+isoL94ZTpOKfrwFbw1U9d40u9Q0QzoJ3CS8He7QN+4+ZHKM87vrj59/mx9YdZPTWHiTRNHfHHl9/KX3/h07SziOEox1WexhBEHbhiz3NxcYmFqPP7tX044YQTTjjhhBN+B/z7/agn/GeFD4F+dR7TrKiC5t7+PBv7c4QdWEzb2EHK0X6Ho5s9yo2EySRmOEmprSSzhnxkeO7Nc7xwZ51qapAEWp2SrckcMvMID7KG3MSgJIc2orprKMoItf84IQsoL1hrHKOc4yd/9EP83f/qn7GQjZCxp/CK/jDl2mCB061dRl5xYwSvb7fQhxWhCpS1QjiPrANaWNKuJV2tCffls/pl6Ym1Z3FuQrzoMSKQbBhEZPCtaBaqmBjAISOHsFAeJqS7gbBoaa9kJJ2C1GRQexapETKmloar5/f5p3/1Z/mud7wI2hMvl7yplinnNfcmXY7KFrfurfFbb97Pl25fBu9RHcv4qEu5LNCLJZOGpZFWNGWFKgLGSaQIPHpph+vXH+Y72wPetrhHIj0YgW1ISunwQuBDIDQ83aLEzmcU6Zi7tWfpVIeHnlxn7d1HbA4W2Zu0cQfw6FtucRhaTGxEGSzxoUUWjnoa4ZoCJIhEM9fSXF7e5dz8Adu7He5uLfPavdOETDLQTZ4ZXuTDdx9mOkh5YvA2ylpRx/CVO+vc8T1W+wku82xOWgyqBtOiIEwjfG6oczE7SAbPOCqxCSDBNwQLvRZq7HAm4BGIUY0oHF4GmAjcgWZrcwidCGEkKg+o44AvHFZKSCJcQyMxRIOAGAWGmy2Gx228k3gDoRkQIVAOauogEAhMs8R6iQ+CoUtZkDnNdEw2ViSi5q3JXY6zhFpIKq2oRh5zHDB7HtdS1HqCiPY40+wRUIgAUS0xQuCVopAgDzU6U5g0IZIGUoVvayZLhiA00nkCEp8rqCWtnmX11IiWFVCBSw1KeFTtCEbgIsHKIw6fBjpxgUg1PlE0Go6OdERTAA9FQWu/pnFUIzOLMp6dqMVETDgIbUZZil5okfkEc6CJdySm9owHLcL9mtZGRqAEGYgmAV2DqgQgqAuN2rWoF3OEBxFmzqGxbaOmBhcklIKen85E28ggmpqAQ3pBpyExU0+9k/PIu+7w9u+6yaiK6OcNBtOEsta8WS0SrPtGxo5DzVcIE1CFQ5WO/ExEZ71kxUxpq4KFzhQ5gerVhPJmBIWHyuEtyABhbL8x2uUJVUXwFj91iFGOKCpatuI93/oapy8dMmlDtdZg+WqJTw2icBA5ZEOgq9n95I3gaKNLcbPJyjsLLv/IIRc/sI9dSChPNbAtjaxmh19CwMuAdhIZwBsQOYh6Vst942jAjec77L24zuuvnmdiDemGpXUn56k/dsTlP39EuWKQ1iO0hIamc2SJZEo9zfES0iwQCUFUeowNFJcNorK4vMRlAW0ipJaY8I3Xu68QNw8xN45wRyV2bzxrB5ICkaTER4F6s4EoLXJcoMYVC2/JWf7AhMY7BSHSNM5q5kPO9zz1En/+Wz9FMyrRdaAzX7Hy/TU8pEnWMlBQWEMIgbv7S6gq8MrzHV77H1fI/0Ub93fmYeohtwgZZjsNLUAGArCUHvJQ8y7+riNTbdIfr+k9PWU+nZDeKEif0cTPg3cSYoUXAtc1lMttqlRgYoMQAvumRu/kcO+Q5O2WP/zzr/DAD7zO6egIOc1noloR2N1vEZxDEJAu4KRAxoLYVHgvmE5iqhDo1465s8uE8VXcZIli+0lcZhmPK6QQTI4m/ObPfZnJJ27SbE+hdrjcMToekh82WPJDdm/M88JX3uTocPxv1+dvu3CJx/d73DdJuLt3zP1nFohdAGv5zP7rfGr3dY6qye/DzuGEE0444YQTTvidctK3901EM1rhlb2LrG5uEeZjJA7Rq/jhFc2/udEijgLvbN+i1oZf3H+MIBRJVdOaL/iLf/dPEBKFUwWqUSHmPLq0+CCoFyvM1GC7nmKQ0FwacvnBLT7Vu4TyGpeUDI/guHbE2nNmbsB95w+xXnDpz93i8zuPIkuJBoKTTIsOz45Pc84MuD3tQhWIGxaagqwt0eVsbEdJh47h4vljtq81kR5krdmuuiRPlVTXG4RS4RoCjCccgagcpS1xziD8rMmmtDGL9SaLT2fsfDbC7ke0tmB4MaGTC2zwXFreR3nHt52/zq+89BBPnLnDXj3P5vY81kZsF4sMJx3e9+DLPH35Dv/os+/l1s3T6MpTCcXgtEZmgrSbU5Vg5j1PP7JFK6RQKl6fzrG98V1crA1O7xGMgxSE9sT7NcF4gghMkwY1loE1bE5S5i4uMVcrXi7XKLMYpTz//R/+KLrwfPn4PH0bqHYNydQRXE1uBYXRpDW01ZQ0qng02YQ48ObuBbZ6Pez9njSdIrYk96tDHr26yd998V18pb8JzIJfhQ/IOmAKAVoT0oDD4WOJEglBlCTHAnkIVgoCETJo9EiyUxwxpwSmrCm6DaSQlARkCcpLki1IDy11nZH2DK6cHebT44DJa1yioe0RwVNFDpMF5seKI6lgPuCuBvRdDyZQpYYiDnSjQCJLFpJjbt1YYm51irSBP3n6t/jUxn1cn56lMXVIEfjo7gPUXmFtMqvK7Qtk6YiMpdAlb54tuaW2EBsS1fcI6xG1wDcC5ZIiDwFpDfVOxaqOCGiCN9RtwzSF+KYDJRBjTfCev/yXP8zOrQ4f+jdP47OYoqWxHUF7CwQCPR/xenGesa9I+p6OdTiTQSeHLlQbigvn9xnriPpGMjt4K3A6EKTi6zsXEbUEb9mrhojI4uOEYMDVCh15RA3lYgINhTx0qImlTgwgIJWzANvNgvQLR5S9BvUFIAjEBKgCCAlK4Z1EJ44KjSgKdOGJxpoQBMLllG1PJSQhVlxWh2wczHM4arPZmkOrmtAGPfb4AqINSyQiamsRQdEegZ5WNCWMgkNIjwTStRyrFbafUh5bkts5clTP3g9khlMRog4EBTpIrBUEFZgaycG9LqvRkI3bHSrd4fpuQh0Hmp0x7vsc1TAhfFQS7TuEVzNBpaGZ3osZ306ZbDaJDmukKAnNCBECcgwukaAEznsQYiZOCAlRwDci3J2caR34yd/8ACpI5FKFOa5oLWQsXhrSbk559dpF8r4gGnuwjqqpaO46snNtmvdGoAzzcU1dzUYq050MrSvcN97vg7XoWXwVSQ28Npy1lWmBTBV1QxM1Z+JPcV+TkFviOzk+0gQDIUlZOrtDtOLgrGN7cQkde5Kq5vsefoU5kXPxzQN2j1NWF49odi3D5hSvBdNBg8Oqxc98/PsIUiCsoypi3D0L27NackmBUJL6HTEu5RvBrp65r0347j/3CoevCFgUhLhBda9A9Tz6uiAxBjM9QESe49489HN8LFBVwAwsxUFGI2rjbKD3Zkm2EEEk+OG/+Drfs/omSz8WM/nRwJ/5axGHg6sc7hS4SLBweki3m/NSOAV14MqVXc4/eI9Pf+ytGON4ff8ISs+ZKws00zUeGLyXGxvHOPsKAKKjYdPjgsO/LIiqAf5CTFEWtDspy73XmYsLlqPTpM2YOJltr5zzuNrxA+94gA++foO/+PO/wV/4gXezsNxje29Ae9DiHU9dZi46Ce8/4YQTTjjhPxzxjcfvN/8pXMPvNidCyjcJjsBefkhvaZ/9FzvM9wvue88G19eXeE8xO8REC46H5zfZ2+2Q3haQgs4VdU8hlOKt524xmfO8OFij9JrxXgLWYx6syfYFyZ4g2a+5sHhEezWnPoBmVNJJn8O3YDROuDZtsqfb/OyX3s3K/UdMe23UrifEHpkGwlJgWLT55N4DPLF0h8/83GPIGPI4QjhwKjDfrBkpxfZkZnW+f2Wf4fwcUQFjm1IVHbQ8Rkw0ytWIUEEv4IcKIzNENIfaiGmOxsSLlnLS5u++7zP8s+IqO2UPgkb2PdXIIU1MsIJPffYB9rfavBG1WVidslBXaDHA7qe0NyvcKTh3fpe9rM0CY8bTGFl4VBmwxqMVeKmxsQMbcKnhH997jL/x7s/QvnTAOzvP8emDx2lkGhlXiFZNKhp0aJCNjijGKaEDtYIvDi/RdTl3xvO8/f6zfPrXX8TlEtZgZb7P2x7YQIrAP/nzikUXqLwlSEGx5Igij10GuRfoNCeszU2xU0NwHlEI+lWDOBnTrirGVYut/XkunzpA2ZpDJYhNhARCKmg5ga0dMR5khR8KQuVQxtHYlzTiObYjh5waRF7h8GgjKOsN7k4ypGmiSwtxTD0nqdoCUQWEDQTnqRA0RgHdLAjaMxQJVimkFOhM4OJAPHaYQtI9dGSrgrzjCRbmhzk/9q2f4UsvPsKzG5fAC6gC6VjxbfffQIhAo1WxEeZRqeL8+tGsISoJnG5PuFcrIiPQueRoCLGtsFNBa96QRZJBXeO0JDRmeTZeCdISnIRyzRD3A+EAisMckQuMFBRGkPUU9BP0UTX7m5eBg9027XbN+CgQDUFmjuGjs2YoYQOpiZhzDUKZIW2JGEs6SULtCqZTQ/NURfN0gWrX7H++gQ8gYzAqIEeGTien3DfUIVAVgXToEEsZWasJQqFKBdsS1dVYC8qBiwVezzJlqramOqPxwZC8PKD3bIldSqjahu5xCdLjlECgmLgmpVYEKTG5RBCobInXAZ1Bckpw57Mr1C1D57GaVFdUbcnN4wWWp2Ps+Sbx0KHaU7AB8holHaIjiGtDO6t5MKpYcYEv3DmFFJ52ryA0A/uvNxDOIZwDIQlCoM9DtSfQpZ8t2D6ggqZuKcYXDc++fo7mtZrWyDHKPcVqjE8dkfCIvQiRQGhIYpPjjxXTkYZRRbaVcvOlBlVTY5cKiicjNDXp82HmrEhmGSVOCIhASAVjO6tfR1BHMHxHg3pJEm9L3CVPRSC/1+UrH7tCq5EzyRP8nMAOLb70uG6EzBWHjy6Q/ssCtIQv56QPGcpjSfRmTrBgrMUahROCUMJswEpAcAip8GcSshWDGYEcZxgjGF+KEUsR4Zf4higmEHhspKlqPRMH5jXnL++w2DvmQ288wINhlyefeo3RNGH8POxPFmkMaw622hgVZmM6SuIEkMxyjioFhvCNNiEoHx9QvnMZviIQFryS2PmYL/ztB6hbGfsXWjRsQueVJuZcibi6it2Zkr9RIo3FdwRmIgiFR2We9KhAmUCROMKkonKO6A/V1L+6xBe/cp53fO8WiXFs+Zo/8X/5Mr/+q9/K9sUmRztv8vDaPbq9nGh+wldeup+59Zqs6oBXVGXM68cbPLK2zjUzZjTJmQ5yTi3OcW65R5HXDMKYIvHoQuJDQNzWWDnGP7xECDOH45rJkGsD1jo94jgCoK4sk2HGA1dPM3z1FUZZwY2dI4IHP3Xc+vSAH/4TT6P+/wTWnnDCCSeccMIJ/+lxIqR8k6ARLPZeY6W3Q/ltmhsfOsNxQzO4vsr6mmSjUZAfnebR6IgzK1PEFsjCY4XjMOuwOq/43nd9nb/12e9C5RE6eMqWoUGBsZ7CBVRQzK2NUEnNMMSMJk1iplhZcnapRDnJfOzJypjn9y9w5kKMVbOaTe+ZOWQ2IvT6GKslXx9dYIpAzoPwAiEDbgBHQrOkhuxlXQKwvNcmWS958LEN3vjaWTZuLxIhsEphZM2f/28/ydxczv/0Mz/IWHXw3wj5bOUV66bPjaLNp1+5n0hoGg87DnZAhtmnx6UVJPsO7T3Pjc9Qf6dFC4vXkjiqWQ8l/uEcKw22JfiZj3876shha0VSBVTlaTQzFi8POD5sst1vIe9FqMLzpa2LvMQzFD3Pt8+9wBeff5zJjidarMhbkpALBBHqvCbrzxqFllaOmeylyIXAsEi5d/eQUWmJspiuh41Bi5/51PuwDgbDFol1hGDJe4KQCsxYs5aOYL2muzohI+aX/t7TNEZQ1IaiE/Ftp1/k6sVNfv1zb8EMBf3YQukQicCr2VhHMIGEiMoVaKtJ6xIhDONhTZAC2de0LifkpzLSMhASQd3xhACX5REHyuIaiuM8oWp66p4kKIGLPXJqcU2DUwKVQyMdsbgyIlSr9I/nqNKAjwNegI0FLoIwgqaAelOgXeDKpS1cLOm2M7SRRMLhpSVOLVu+SyoqjooGmUj5za88yXuffJlz8TFCQE9P2bQdvJwySizVYgM1UTReLxkWNfaWQRwqouOAi6GaV8iJIxkG0j64psUHSfhGHofA412NzBTBKKSVCA/BgW8afubvfzdRVePGFcKH2b01BNQ3Dk7BUWf3WKgNd3KF9IE8eJK9gIwcQ9lk+5U5JseOuKhAaVpZRjY1hMyglwKnHtng+E6LrcMOdbeFPhgzdzCkPq8RRlCfS5hcjNHbEi0DQs3qV33lUdMSmXlkYTE6QY1r1j86pu41iIWjlB50hNcBgUN6TXDMnqOwSKmwGsRSCnMxthTcfOYUUWSp5gTzrSlVJtnfmCfMG6Y6kFSGngQ1l1OONEEYgpYMx6fou8CmlYxGBttUHEzbhCogsMTDGmkkPtbIxJHLBuW8RU49QmhmVTqgaoHK/KwhRgjqliYSksVLfVprE+48fwrxUoofCSi/MYolQMhAcVbR3pyF/IpBTvG2iPwBQ2wdyQ2H3JuiBh7lJVIIgpIE7yEKiKpCpA3qpUB21eAbDj0I2LOaug3tPcfw1YRBFdNqzurfFQZMIFiLbQiYj9n/rnUWX8pglJO9mOPXExLrQGlEsBgZYL6B6SrEaILfLfFpgopjqi6Uixo1zoldRdj1zG8UFMMmQsVoF7ClI+SWwc+1iJ4qOPic4cwDh7zr267RSkp2dpv8rx/6Fv7Cn/0CUWrZO1ji+EWJO98gxIY4ySjzBOclgtmoYogk40uKhbuBcqXEnD0kDAXhZY8QgsDs0ynlBKMiJW/FpOuOhcUK94ZBLTkG+1PS/RxnNeXA4E5bmrqg9gpvBHoiKXRNoT1uTRLta771sWv8m6O381J5lh//K+d5z4VrLP/IEDsWvPTCBg++4zzXj7scrTXYv9Xh7kGTYjUwiWG5k6OMxTqo8mNkpVlaPEcWJlStT/HpZ5e42r2ftz11lv/x5ofBCYpdR2sjhtqR3KlpPmFwNtBWLU7rAb25D1BMa7zzs3tRSZrNmHavwZ/9nnfy+t4BP/6+t/CpX/46sqwpTlt+6uWP8lce+y4iqX7vNw8nnHDCCSd8c3ESNvt7xomQ8k2CDYFxrvCqQVXGSCEYTVOYKM4unOY9Z3uUt59nZ9whmi+Zb44ZDjrUqaLWiuaZhDqtcZUCF1DSE7crkmOHluA6QGmp24JD38RNJZFyTIg4ThOGC55t3yG2OVSKQdFkro6oJVRWQQuccahWYPrqHFsXwB8q5CVJXFfYbBYCK6wj1IpbbpHT0YBWXDPtBzpzGc3lnPn7+9w6XMbW4GNBaHte3T5HJ8uI50uECAxUihKWUybj0fN3mUaGfzh8lCt1n8alAntXI4az5g19UMzaPmKNslB5jQ+BIrKszFm65wrqRsHYp6zKPlSSWiqCDKADzgd8FEjimma7QF5fQJVALZE+0C0zxEjgtaLRG/Lo/CZ/6Uc+yd60yZ/5xT+FKhST45Qnz27xf3/v5/nI1lnuHixgyg5tOeC1zX1On19kJVpk0gl87WiLX918gMSVLP3gGF4xiJc1Ckk8NVzQuzTmKy4sb3Ftb5n8hUXue+sxF3oHfO4XHkXWgbIyEASTUcwlPWItz9ATqFJJMOAJRCPIU8/CXEI+dJxZ1zTNKV45vkUmwMtAy9foPFC3BE4EQhIRXE0UAnJswWmqrsA2ApVRhCBBSKSvIZYQoJaeQdEimlo6axl7ZQ/XkmA8Ho/PBb6hOQ6CxpFHLAaWFoY0Fmo+eucJbt88jReWyswmUO5/7A6vFOuksubTH7nMm/1LEALF5YTtbo/H0i3eu7DBK/dO0VAWbyPqocLGCo9gYpqIEuZcTFHnFD7gCJhBjVhNKVuO1rbHWoUuJZ21JtNsh6zVInQMwUu8CUgt8ems4SdoUH2PXWrAwZR8WWEc/7bFpF+VLNsBfkHAYB2fwNF8oDlpEG16RMMz+loDX5XEw4LQlSyqjKFu0tcR+YFhGLfB+1lmRxkQlSDYgNpU+HlHPWepU4URipB46hBmv9+IWbN2YgjRrCFLDy2ilOjDCpsqmPOESELp8LJENDQ4hR4EwjjDE3CJQSiFLi1KORoXHcOsSXs9J23UpK2CO5vzZCJC55ZGlFNWEa2GRy5bogNNVlsaZ8bcsxGDoOmc6+PveeoywtWCJJplXIQ5Cb2IcuiQwwCyxqUSuxoTDTPqtkSXnsbtAidibFtRroKeSp58/z6mUTEcz7G/20GVFXLimNqY4rTGphoRwGmJrB1MC5pfztCXQSUBdSQhNqjRBK0krhJgZ6NWvhEhpjXCW+ItEFMPBkTqSXVJlUuao5nLyK8kVIuCeA+EkNhWjBMOVVgadyxozeDt8xShQNUCM6qYLkjWvjgkSAFSIycWpEKcqtDHklprgtbUiSV9YYhvSqqhRBSW8hWBaxXIcUkyDsja4bXgqT825IH3H/JrLz/A3pcavPnUMrbU6G7JXCcjnpb4acTByw3CvSaTnkKsORo9i8cTigq9k1Nc7iDLgDsdM/0DA0JX4TebhE9O4Egj4pmI4o1DRBXIQEfDubMD1i4P2XvtEdI3Fzl6bZ9Q1ZhYQxUIFvK0i6xrIl0RvMcnTfIzER6FGBU83dnmv/yRf8rn7l3gH7/2fp7rL3HljsaPYW29y0rSwPvAizuniN40yNThnhZYFQgtSFoFWd5gNZrnbHKKw1bMlcXneKT3JaLpGa4/v8T++TnavkF2uSL5zi7RP3MUtzJk5di/vYs2Me3Vv4ZfdHTME6QNh4n+nSiSNmO2NvusyIQnnn6cVhxxZWWOw9uHOCN5dWeH+mF7IqSccMIJJ5xwwn9GnAgp3yQEPPvjJmmUkR80kH1FlrQIGnZGn+DKfa+zf5Ry/p13kCrw7v4NPvrmE8RJoFoVjOSIG9kSVTRzAOj1mrmRYzI2ZChcM+BjT9+2KDPD4csdxI6kuKg59G2ePzhHU1gOZIowjshbTtsh+3UTYQJOCfI5jRABceBRz7aJr0zIuwo98czrMWjFQBgqp7BW8O6120gRuMkct//lOkfbXQ7KNq7j2R9EhERQ5p7f+sJF5H01BysJf+S+r/DM+DIhl8T9wKNndnhGn0PsCoSpeHztFvfOrFBoTbwbSDKPEwIXC/JGQPmA8vALz7+HHzr/AjqzLPYmKBuxW/UoYglJQPVnDguvPKtJn/965cvINcdPb34vzYsTXnn+DF4Ebo7P8c7Fe/zKxmMkq1Pee/Y6LWlotSuqdEQ97qAc/PCTX+G4VcC8ZTyImLeO4XaTYVlw//Iy5y4pPrf9HHFtsF4RLzjEvCecrnE3NC7WOCHY3prjret3uTdZ4OhmDzdqsW9bHH2PofmePvXGPJ/42mN8/rOPUg0jxvMNNlLJhbOHvOJSnJQEG1jq58hasJq2efzpZ/i+7/giX3nx3by+cT91XSDGcC8bYE4n1I2K6F5AjCvKVHFMypSAk2BGs1BVOQ34IqJOAsJ71MQSpMQ1A3mI6VcNVppDqiYECQSBUgbXDoRIUeDQlSMqIsjn2d4cs/PSAqpyFE/AQTvAYczn3nyY95y+Rh00B3sLkDiCCrz91E3mWhkTn/Dl42WoAiJvUPebqFJgvaJeiEBppPLEaYTIc2rjqeYVamLothsMwwSrNEQSl0vGMlCealB3EpJth5SSwExYEs7jg0NUlqoZqBOBiCNirWEcELYiKCiXDZlucCo5ZAOHC4KgFXEUM+k4orEjOKgihTVAKhm3UgqfICTk4xh3bxlXZijtqRue6akYVcW07oE40MTHFfGNMWpo0InGNxShhqADOveoHEQRiKKaeLVgmqegAC0RcYsQScS0hk6gaimcMpBXRBMBefUN+01CtFEQPewRb5EI57CVZCkZEWN5s1ggvZHTblQsr1VERcbd7TOY3FKXhjJyNBPH89PTnI37fPTnHiI+W9JdKJkcp4RSoi2otZqVtSlbN5t4HzG1gepSSnk2pnmrxsUGWXrcSoMQZuKWKj2qtNz96BLNyyWD3XmE9cjSEo+hXI2wPY0eO5KNYhbeGkDnJTiHbBYzsWk+IdQG0VT4skSMHZRQfavAJ+C+CNGhR12Eq/cfMN/LiIXna92zKOl491+6xr2XF3n5ty6jpopaVsg64JVEe0gmEA8twwsG2xOo0ED2J+ADrh0TbE3wECIF7SYcDAlXLW6pBTn4vEAfQTz21FITPVxQPWeoDMijgB06wKG8hBJWTvfRyrO4UnDvdodnXrxEa6FC6mMudXbYPOoi3szJbzQZn28yXW/SnZ8iFyuk1EQDgXY1VTULlBXW4Y41cc/h4gZuvkaMD5Eqxs0JhBGkS4bwYkY1FlRrcOeVVcpBhlwJNM5OYUWQ7Qus8tRlB9G26Kzi0avbvHw0Exqjw/+tRt3w1//O9xNOeaDGRCWHN2L6L55D9D1/9J3nKcpAup/RTo4JVQ+ZBq4mfRSQypraaaoQI+ixP5G8vLuD6SxxqVxj8/n72Lu3zT985Ra0YtRFQeuhiPQJR7FXAzDcmhCnhlcOfpp7exMebvx1VroPY6LZ9ir4QF3VtDsJr9zb46As6LQvct/6El/mTcztiCsPrdI08e/L3uGEE0444YQTTvidcSKkfLMQBGljQEyNTwpsx5Ad9UAI3vPUB1mIxvzLi5e5ezjPYntMqqbo3FFHClEHrBzyer5CKTTEgXZSkJSeLBZ0Vkpqr3B5xK2XLhBfPOZLG1fASJyVbIh5qBWJy1kYV/zQhWe4Kc7y3vtf5bBo8upLa1Q2pnAR3nja25504KmlwXiJlQYnJWhBls6ad0yhGJcJqalwO4Zp0WSSS4J0qAWLmghcWWF2C6rXAoen2vhFwyevP0xdJ0jtUEmO8Bok3N/b42L7kCuLO3STjL1gsGEW8Bp8IAgIS4KmLonTjNHWKv96851cvLDD8twB9bTNc6+tshRn2H1JNhW4oIlKTxUMcWnZ0y3+yQ/+K6Tw/Hfb38Nz+RL/y/Pv4OVikblLOU+07/CFl67y7Vf2cQQ6vYwDEVFZQdHzKCKe7Z9jW81RxkdMsh7vW10iaRoG/DpHWZO5KOX7H3qZX3rxcZKbGnVT4jW4lsElgUGU8qm7V5ibHKKtwsaSUAcOXu7RnM/QGfjjQGYM2gWGR23Ov2OH5oN9XnvmHNIL5joT/tzbP8Nr28vsP/t+Jkcx/f2Uo0FAxRK7ENACQhZjjiVZU4OqkTVIaymHI053jlFnNXv9HjoLyFpDLZDWYwEpBF6D1AqoGC0kjMtk1jJSBogCSTGlDs1ZAGjXk/cNBBgPY47uniY68mjjUKmYNf04SV5EjKoUlTuKowblwwHTFwQtGPuUSNakqkBJTz2Yh9LiEhCVJ3Il0YKjc3lIdr3HeD6ijhSybejImJ5vMKkOyI9iJsuCEAlC5WEocK0Alcf1Aj6C1maFtzXBz5pNpBAkeY0vPEYKyD14j42hDoKuucBAzhFaHjHxdDdK6igwvq9JulkQlWp2UJ+vCbGkb1LSuMbnCj0GkQWqhRiRWVxD4hNmFeMNh8nBWLB7IPBo69DKME4CAYmcFjSuO9TU4yeGsl1TXCzQ0zax15gc1LCkEpY8Nph7OQZHPPGIOqCKQHQUoM5hwZJ3Y5w1LM2POZP0OdvsUxWKydH99LwjEwnblWI5HVOVFYnQRKlCR4rRrQ7fnx6ymhZ8KqqpWhpXQytU1P0mNtUkczkPXb3LnFlgfytl0k+ZnFa4BU1825EOLQLJ1CuCEqjck+zXqDpw9EaHnWuKehEufNs2p64ccvMfn8YPWqixJ9nLiQ/sLLzWWkAgClAfLnALEEpF1ZVUPUOy79HFzL3nzjETVq+0sBcD4e0l01bMfXP7fOBdr/HFZ84STTycFkTCokeW4AWuCAQlELXFtRSuqVD9Gh8Jqq5EFgGRCggal48YKWjXDooaYSq8D4jPJ7iWRE5yqDxi2TB5tAUtx0ZznpW3WaJfEwTl0UkBiwHuCaQPfPr/tcCZb7e88pGE4Puo/hieThi9EJEcRWzNNTjbzxHG4VKBjwLZNOH+hze4Va0w3RYI5WavayERJBQ3U1R1jPpkTWOjTb0oGN9f4ecV+k6NeWVKFmr8QoeNL3aInECs1RyP4f5v3ye/1yJkKbbUszyawiO8Y1pp6igQnEYDarfGtVOSDUHYdLjHHOJaTWzaDKqArGtu7x5zt8ooLkrsGw26R5Y6i1ke14gRtM5VCDlrC5tMM2rV5MGlRRrplN1Bl0HfkN/rYw6nhDM9atNitTVl/eomn3rpAqKSoCU/81P/gqvf1Scslnz+7sd499IZkkZCs50yGmW8ducXmT+dce7CH+J4XFHUNZcePkXry21s15Evws3hEZe6C7+v24gTTjjhhBP+80eE2eP3m/8UruF3mxMh5ZuIfpEwL0qqXUPT1zDVBKCuU0Q84mv7l3j1hbP4ruPPPvh5SC2qMEQbivk6YnpJ45UnCMmFaIA/4yi7LZyW9PyQvD2HjzxjkVBd8ihK5iNPklTgBAPdpF1aGlVNay6jXzX42evfSuEj0AE5no3EuFDxtoc3aF3N+fXb5xHbDY6POgQfqFSEWtC0L45o7ebcePMs42ETU1pqYdDOkQwtzXtweH+NtAo3J5HjMAvRsAqhA0ZU3Ntd4ivXL5JfW2D9/m1GNmEyl85CQCuIK4WLJEF6nPKYAEY5IhEIUuBimD8/JHMR29tL/FenXuWdb7/BV15Y4Gc+9n5yJ7ENybFv8VP77yNIwZX2Z1lLphy6Bg3nKd9QjIsG88tTVhfG3KhWGLgaJQJvWdng10SPsB9Q2tMVOYvphKrQLC5OmXsiZ+tXJzSnlq26x8TA6bOH/PG3v8gnx6cpPtzmvW95k7kzOZtzXW5NFrg+XSHxbiYkjR3+yCK9QG1baq3wVuNGkD8CUQJ6U3H7tQXEo0AhEUEwH2WEIHj0wl3+wUcHXIrexk9/cJXjUUIppsSPV4Q1gXi5QNDCCYlblogCklsZZy/vMh0bdMPhBuCWBdEQVAkPLR+SPDXl1dfXCIWiigRrjYzBisaNI0y3JGxEuDqggcKAj0FKSR1AV4HEBmwtqSNPGjuWZEHmEiaZoXmh4Ln+Os1QYTueIDVBBZQLrOoh0xDxjjP3+Nz+A+TDKS3rGBQJZqfCTQzJBKSC/bbGPiTo2JLqKKI4nHJndUL/ssIMLcLN7ps6kVTzBh0A47GpwBcepjlUgkhIQKGCRcQRKlhCXuOlRCJRAXStONwpmBagKw8TSewcRUMThCfEGoSASCIwEAUmaOq+Ro4CovSzcZRuhIwUoSERxtFyNQ++Y4uVZsYX/vX95KcbJH2HE5LYG4JRhEhRne7QeHmfYD2ykUBlUNOIhhfEKKq8JHITrNFURpDesag4EBoxPopxpkYJgVISKSU+lQjhcSi09qxHA1pxQWpLlEyQGcSjgvJMSrHcIPKKhb5C1JbBpqc6jBCna0giai0IVqIOwVUBrxzNMzm95JjnNs9xtGewKwLflCAFwWhiXVHaBnVXIhDEWxYTWZg6xJxDLTvcSGGUAxEw8zVqK6CnAVUKtBcEByAIiUQUGnnDEw4D7pym7knqpsNogbQOpMJ8ooZGhA2GENWEUSB7XcMdwYe7T9ENFeo3Cr7y62cY294sa8YFQgj4doRgFl4rKg8O4k1HWY+w55poqSFYQuTRcpb5IoPg6IykOpuw9PUR1B7hJMI64mbAv30JtZqRbwQGC4GWDoheoPVuD7sWe6QpK8sH/sqQ3rma0ZsR1z/XYvxbFeqsJa0cHs8fefpFtl6fJ8SaYj1Ci4pEHPLsly6DVMhRTSgs0ktCCnhAwXSjR2d7F5k5hGhjY0OwgujuhGInQwSJWzPUXYW5McU2G2TK8OqNM6zmAbtpyFNFSGciaABe2lzGNCOUDxhVQacgX+ygSrBaEr3iMLnEGYGYCmoRuDs5YvPskLIXE4IgHFc0owkXu8d89V88yNqZA0Ra48eOQu5z/6mU19otjNriC7+4TL4dkFphlJ25GCnRgwU6SxHNxz3Zs6D2x9w+FFx5ch21vI00Z9FaIqSkyCtee+kGL21/nIuNIXF4K3vDJv/zBz+Dyx2rFxc5Ege8cLTF83ubJ0LKCSeccMIJJ/xnxImQ8k2CJwAJZTGBcWDcV8g8EDx8ZvdP4MMnufbGKaJtcGPJL5SPI4cBqT3JgeLM20tefnOZ9I4ktALluuHNagEja7T0eGm5//wWyw/vI0XgfDzgI7tX+cmHf4Ofevk7mO7HPLl6izv9U/RlwblHDtj0C3gpEECwIOuAKAONBcf/409+FgzsTSNk8/186ct30FVNo5DgwG5HXLi4y+IjUw6Oenz2jUcxwiPaFaEKaA+NUUG1YgiJQpQBVXjmfc49t0gjKQhe8ezGeRbecszNuytEouJw+hTH4zZJZRFa443AGw068H/97o/xi3fegR1LzNihxh41qGnOF7hQ8CNXZzWYf+y591OEFNsVyFijksCSyJm0DX/1+rvQOezdXkQ6j3IlZZ4w58fcuvYYP/HeZ+k7wRjBjz/+NT7zz58kb2dcikesmYoFN6UlLGHquLe5wu3xkAVbsXy6ZisYxiLCVYrDnSUWrxb8l3/4WaLU8//eeg+X431uHJ/CB4WtG6hMIkpBJCVCS0LLMV1QeBVQpcMMJFrA/uYC3in0RIIM/Okf+DgtV/PcG6fJjOfIeZYWr1BWR9hBRfOmhVyy2LSc6c7z+eoQVUm8EnQOIxpv0RyUKYcjQ2ha7JwBL0gzxw8+8jIfunGJqFGTOYlNFP32CtqN8FOJMOCMxBuLIyWaesqWQBQKaT0hOKwKlPOghgI71Kh7GnXFIZuW4yolHyoiVxPULPg1RDWLzTEVmoao+NxolRBB3bVEDsxAoQqJ7gayqWb8tYXZgXyuYv2+A+4+F+FuCZQOs/GJniEUARcLulFKFgqqXBC6BpdCvSARFmSsEBiE85gkwuNIVcSkCc4FZC6QtccWNbn2RN5TFgpUICSSxkJB21UI3Zhl7tQVsvLUiUJXHqs1Uepn7h4jiIJDVJJQQt3VzA3HnOn2uX20jptXcJzjpSQoRelqQM9cQXlO5GuElOSjHLcU01pSPLhyj6ASbnyhQ7czYdxb5KELR/hxye7mWVwsEQFkw0BpZ4GiY8HldI/2YsUWc0yOI+Z7U24Xi6TzlnxNkRx4xKRkcNQCo0h0hDuuGJcTrII//Qvv57HvPuSGWcZHmjJX5JcL3LEnHcaUieSDn3gP9VGCCI5oJ6NbFmA85VsijhOJeRlUKWdhs5GgPmtoipqi45HGI18veW1/mYU7HeyrEUgIAaLYgmSWHyMgtFK8smArvPXgIT4M6HpCXHmcCnij0QcgQ0DFswaj8UVDncR89oUHSe8rkb0aMfXkewG/6pHKoyYBKcAJi9WeaOjQE08wksbE0hh7eOke+WNdapGQVF2iNhB79h9vUzzao44FO3O7rDxXYM+00DsD1KYkWfKUcYpKoZ0cYa3CtJo8emaLF24bjpYFYRro34ronrVMttWsDWkqqD4VaLQDtZ0yGsS05xyi3aKncjrvKmhmBW8848BLaAfkAYTaYQYlKEG11MSMLcWSoUwcUk6RzQYIS7RToDLB0v05RxcmTPoJ4rYnqsckVw2beZNkE4rK4rsatEQ6iQiB5MgRkOgogqlAPz2iHK0hraBuVxQXNHO/7pHDQBkEy90W56/Osy+PYSoJfUPRcNT9hC//4wdBwEd+/i28/4deZvflRcKepnX+GZ79yg9wrnmeYnqXCkdnPubP/uQP8U8+/KuMjo7YuKXpnXmac5dLnljXfOaff43VXgddvpVePce7HniU4AOCwMHOCGcjVjvfy3qnYndrkWLcRxQVxbRiaDPi+44Rk4pHosXf8fqfTUuEFKRp9B++mTjhhBNOOOGEE35bnAgp30TooEiEQ6eOB999i+3pAgEYR7fphxaLFCyezZENy7V7PXplwFtP2Ra0F1/D33oYVXhs4oEMbneJ1xzpXImOcqpRi68enkdJz984+5uoCn7zzhP8jSufYmu1Sb7f5Zk7j/L2d95k78486mLJ6XjA7dsNilLhlEObQDZq8NnXLrI4N2F7sMT09W0iD7IUyGlAOAtZ4LV7Zzhzqs9qcwgGylKRxU2MrZmblmjnqUOFW4VwOWGpOqQ60NRTgy0ifAhkmaYjJI91N2hGJRvRPFWsUMZjtCVUClV4fKjoyJx2XTDQCa1kCndj2rYmcZ6kqvFh1rIy8RHhSo1aCOR7ML86RJua3DYptWR72CNNPL4Gpz1CeG5Fi/COff7B1x/gj377Hkc+pRYV6s0S/aAjVp4tZyAB9gP1VKPu1ETW0emkXOys8uLxkFuHS/zwL/wYeuA4Fm0+8fLjdFemXKtWaE1rAgFMoOksLqQAeOHwfUWwjmoegpasmEOqvEclJEIJZA4Ejy7h2b1zPHrhLjfzRapEcFwVHNgKvWpoFZq5c0NCbWhunOeMbdHe2cOqgL5hIXP0dz7AauM2hyIjuSGpg8ATsNJh2lPOreyxfX0eU8xqTMdS8MBojr1iABsaIo9YENg6oItZU8m51T0eWLvL5ydXGESecODQzhOGEeM3W8RpiswK8mFEjcYFRbMWuNrNMigQJMLiguCw7mC0R2nNZBJDkIiOZjJnCEnAydm4yoKYUhpDrhSiCedsg8G9MXUiUKXHCWhIjS0kdTOiajl8m1kAsRbIlia0FHISiNIYU1seurTKznTCawdTVGYRzqFxxHMOZQoKP4dNBcOrhm63RBcWm3vIJKoGn0hkESEnGtcWkFWIWmMThe4XiImkWDKQ1eyNW3z5I1eRTUG2APaCQb8CMtfYyazpJhQZzUFGfrmN2bGktqIIIB28cbiONAGhxlQ2JdIRYdrkoW/dYPdXziKUxDUM8VGFqP3MUZEEnFKsuQHHoklRGT4+fAgpodH0jLsaMS0YHXSRx5D4IWGhw6QKWCkJbXjwW7a5cvYOjW/LuPn8I9iuZDpKUAseeb2kDgbRk7i+wkxr0kxSSU95LlAvGGQd0K2AVg7VD7TXx0yrBNkQuK0YcarGSkXRVhzfS+ngEcqTdEoWrg6YvtSgPEiBgCgKRKRBScS0whc5qhKcWTgiLEv29toE6UEI5DmLebog34xxywlBA3UgfMJTpwExVWglkVJQLGuaBx7hA3Jco0VATXO80JA2wFmC0vgiJlgJtkaMS3xkcCYQVpuIMHNr1Jd6uFsD6rNdiA2idPAbBv9IjFysyPqe4jSsHuyz82qEDhqZeZCB5//XHjc/0mYz7+HXHZHy1C+U2CAIqsnf+csPceYdFfgJ7VqxsjykmCjEuIYkEPYdIqsRY2i/McIpqDsKvENkE1wkKd/ThEIQXhhgNqdYExGakHRLJiFism545P+2RdxyqH+1AMxTMHtu0jvSXJDWnrIsKRc0ugr4RmA46BBkwEcCKUq0Cvgu6KNAutLhDz3yKMpZBrsVt/eGjOKcetFQyUBrI+AihVYRooR3nL3ByzsX+Ocff4B9pmzbmgdPtagoUR0Ny03KY0l9L0G8rCl6FUYLzp1f4/HHLvLis2/wr372M3ihePCtL/M3f/a/QUca6zxLp3q0ivezkiwgexMWGk1eGm5w24x5ZH2RPNliSSt6jeR3tO5XleXOrX2EFFy5egr1v7WBnXDCCSec8H9OTlp7fs84EVK+SfDB0Wr0CeNA6WLOne2j+4JaScT0PoZMcbHnJ//UvyEE+Ju/8L1svdqBOOBSwWYAJ2cHNQhUhxHqpmZV5MzP9/nE9YucTy2idKw1huggeGn3FJlOcaua7zqzzcenKdrU3CwWufXli6SNPoM8xQaJDBC6gTAIqFrxt37p/fiVEtYrZBXwQWBKhUsdauDwtWEiGozaOR1RkrSmZJWhiGfBjGbb0zpTkWUJk0lMfdxAnZ9yGCLKXLA/6iIdBC9582PnmFxWnO71WZ/fY3OyiOgbQuWQ0xLpwOrAL37kafYWulTrNXUroHYkiax4pLfFb00v84+Pr4IH3yp438M3WerlPNc5hdhvkh+lqK4nBE2nNWG0rkhbFe5WTO+UZ5q02NiaQ2eSgU9Y0QU+gDj0+KHmMI8wUc2ub9CKAgMXc+f2Gt9z9RzdhS7/5M4RpWzjI0GUQ2JrskXBB595G2KuonrYc1RIhBA0REkjzuBMxUh3cVVEEIpsz+C6AqEE0zOCaHWA+Ow8RBK0xXuJFrPAxmPR4HDaZG6uSeIF+75g1HW0z9UsrlqC0OzuDInGU65sxAyimtF4gipg706gsfQgO9EdaAk6qzleghwpikTQyA1hGiOdICiHJXC4PUUKgVqvKLIWTgakqwh2dlD60Q98kTO9I+obik/uPEjn0MMhOFfjEkHpKqpEEUIgjBQIMAQmqcQUmr//b76L//YPfoxKSIxwOC9RSNxUY2xA5R7XA68EXC3xNfQ/34MNgZAaH9cclTWqBZUJSCUwuSDPcnQG5IF8edZuJQceIRTOh9k7bAhkBxO0ELy4uT/LmmhKXAUq91xe32LaanCw0QLnCRJCrnBIzETga4+gpugKtFVovnGdSBwGXdQ4ESCzSG+gtsRlQGWOlaU+qwtDducv41SMXwpkRxG+raAGPXSEtsD2IkQhMPs1acMzOI5QbYGrNYn3qGlG6AfckuX6a6cRqSLEmhDARgo1cai6gMSwca2JP7PGdD2h0hp7pGg1Kw43m+ADphYoH3DWIpSkmOQ0oxRTK5JuSnLzItWFCeHuOeLDCmEMEQ61ZwgCIlcxLGLUqsVnEGnB5N0R+VpC48UKNQa5KnGrFYtPj0jTgvYvj/nAUzf4tV+6ynijg7I1TRtoThVRElBRxaFpEq3V5JseseNBKwiSEEeEMkf4gN4dIVc7uCqm0SmQ3iLHgDGItoNgiQQ0vhbj8hoyT7WqyM41iFdK5r4wpp6PCE2FG87+/zKAFxafakKsCAak0ggB9lSTclGgrufM3Q1I65msKkJVITIJLYNEYApLKcG3I1RdI+pA8kaNqxyTtE1zegymIo8lYjWirWF6HPHf/fTXWL5c8tO/9D6+fu8UyYfuYDyoyMFqiZ/EbL1cY9+YkE+X2ax72P2KpXcNccJgbxrsdOaEUmWNiDXBA0JiV7oEAo2+Im8WmNtj8IKiFdjJ1rGvCAZz0DY580nGj51+lo0/OMff+19+kFh7iuDRBwWqApkkxIWlEJ6iJZmeNzihaR4VUAiqFU1ys0LeE2RnDaXw/KPnvk7rxRJtBawmhNjg0kCvETPtSqpIk50JvOP8bX75155i+8YKoaGwVwKXLm1zbmlM63obd3CB+qjCN5vYJUkZG0ZRTRUCk7yk1Wsw3s9njiUlefWLN/k7/8Ov8BN/5Q+wvTPARJLzF1ZotWOe29shc5b9uqB/NKanT/GD8+/n8YfPsbr8OxvrCQFefnUTJST3XV45EVJOOOGEE0444feIEyHlmwZJhEVrR92r2DBtytQhUs96/DQv3NlgVGs2sy4NakZlGyclPg44KRiqJc7ev8vNO5doR55H2wXDynNze4miXiW6z1LmgqejAx5ov0EfwenkAOYVy80BkxB4+L7bTD72fr66eYmojIn8gPGwMTv0hQAIZOSh9gQHecuDlHQVWC8h9Qg0QTpkWfD2J2/wucn9hBiGaQOvFcoEbCLYu5LgGpBKjwoSpyxTUsa6ST2nqJWjVQG5Qo8D9+4tcvB6DzFSqMxhMvBKYaUnEgJ3WpL0BHUqoAWydFRdyXc8/irNuCBeKPj88XmcDXz3fS+zMq1Y9wVpI+dzx0+R9jJM6siHBj4aOH1lyPZxk9CUPHxuwLWORO8JQlAsyIKenDU+3Nfe43avw0e/+hinTh2wk80jcs/UacanE+6eqnkcyN0ssFSUghBLvDG4tqByYJTCuoCsAsrWLKkxpq+wRtC5OGVwL8V2FIwsYVHgBYyOOiS2xliHrCoaD3uyfUkyKnCJpF82GG53adZQlDV1XpPEimo35+5knjhKqO81cWcCjTjiGAuJxo0ymu0YpwR1aYgulYiHasROxGi3zU9+4vtIjiGqaxAB21SIEqwJRCpQmQgjPS1VIqqKLFOoCn7jtSd4+twbvPDifYQjjb8HYuChLamXBFZZvDcwlRggwlK1IgIC6Rwbt5b5V199mquPbSKlJ45qkA7TNjDVmKggTCAoAyXgQHtwhUBqR1QKqshSzgXEsQQkrgnFJIAC1/L4VCDwJEc1Io1xiSJo+Y0qV4d3gQJPXQTQs5/hTcy1/fsQOlCt1RRNMGNFdCyxroHJC6o1iS402nqiSY2XNYQUVUFIFLZdIooakSqcF0gnqHoSYx3v+/aXuP/iPi996BR7roM+VHgfcL0EbEAKR9jWpFmG3/cQzzKSEOAGHp2V2NxRBk0ma16XcyzlY6QqESgoAl5aZPD4ANI6ooaiOVejkyl93+LO4TyjvRjpYkxuiccBUThccJBElG1FOtdkpy4olmtuvvr/Ye8/g23Pzvs88Fnhn3beJ59zz82hw+0GutFodANoAiAAAiABiGKQrOiRTVljeWxSnhlbKpVtsVRleSTWyOUZK4wSRXtMiaQIghBBkARAJCJ0vp1uzvfkc/bZ+R9XmA+7rRkWpSqNPFRxWuc5dT/dc3ftWzustd71vs8v5BV7imoL/KLEW0mVaKhDFDqSmmO+m1GOJFXgcb4kcopq35Fcz8BqTDyTZqsTFpsrJndafPuFc6z+yTEXlg+4/UtzVNfmabQ9vj4mWCrYUi2uvnmc2gNHoC1GWaLKgFVU1oKapQZ5L+jvLnJ4L0dMUlyWQ8PjXgF132PzGDEnUCrC6ZJgY0pUc0S9EhNpQKFGDl+UmFjhpaJ0FleTSK1JDmcdLl4JCALMnCBIpgghIQwhEshqlpCDlcjK49OSoJ/jpCRfS4h3U6JDhbWa8uEmtc1D3MM1Hv7wHe7vrTA5PIbt1UlNDUFOkSlCW8fPB0Rjy+J79nnkPze88sVFJq82sMYgixK/L/CZopoqgo6fSXkV6Eri4wSfCHRuZq6XMCLeLAgGirSmsCc008gQJ5DYgHxgCUVOuJ1hflUx/PMhtfYsDajVFPQjS3o+IHlxSLjaJZ9k1EaWsl4S7kjmnhjz1PEbiMrzm+0nMTUojCJfjMB51IFHTEoYVdjAITqKYFxS8wmf/vC76b77rzHJBf/oP32OchigTgpEnCAzQVLB0rGMakcweVBiM0P5kCV/CrwApz1FUVEUhnpL0z3WpHdvAB5cqPj6197i10yPJ0+s8gOnTzM/1+RwMOHSmw+I5iMWJwnZdU8xGvFysMuNg20eOr5GK0h+16q+n034p7df5cn5Yzy3cuZfuvKXRUWWlgghcPb3aXtxxBFHHHHEEUf8Ho4KKe8Q8rSkG41QLQNPO27oeRp3xqix47uvXiPKS9RQ8Vd++bPQMuhhjKsUvpQcP7PL8NYFTjz+EjvNlPe39/gLH3iR7Vdr3HeCzaSJDjwKz6Vsng13kXOdPcpAobTmN3fO8snVm5g8xESaYFhirSMOS9wkwJvZjLuYeGzliQ0oC2qoCI85Gh3IM4WNDFE9w08tH/7AFV6brlL6gL1xg+FySGgF0VxJWipMIbBowvqYxrsyxGlHb7eFSQVIj8CBEaS9mEgKHj25y/kTW3zvNx5jMlHgPbK0+E5A72JEccpwc3seKxSkEu8cegjxpM1arWRv0mGwGaKE5y8//huYaQ0dOR4VjjcPn6J1bMQgrLHGiMPdLuMsRp1UOODajRY7UYAJPZG2MAmIuyUeeO5915jsP47oelTlaVCQ7ybIUYjwjueHd7hZbKELh/ABACKXSO0I95kltix4arLAbSvA8twnrrD50hJ7/QZirsRszYpXyXZOvq5xKw5bSZrfkwitaN82lB+U+GFMahX3+ss0wgn9fkJHS4bkFAIamwVyoyLVNaqkwXyz4sLKHNf2cuy0wAlwbc3x04vYkeHWZkmfmOTRisqBHnrmTqRUzQAReCoJ0dRjtWDkE+YmGW4kSc6lVAJiUeInM9HqS7fP8dIbJ6ntCyJb4ZHYtqVcULBqEYMIiUBUEjVxqIYicAVRFdDMCtSBY211QKQt67Uh40mEtBpcgI4cRStEGInKJbweQgFFqCjbsGynBGisCxiMNT5UuMKiR4bpVKBXPYPlt/9t5pB9A0qgTysqKSlLgxrP5LlhEsDAzOSxXoAEU0oQFWXg8V2B6IMeOoLKgcsRkxZBJvC+gkAjTEU0yqgaMS50mJYkjAtyo5BKYbsas+QpSsmbLx5ncLPGI3v7nBN9Xs/OYDCoQmMbnqquyMcR1WIL1S0RVyazQpIrANATD5WhvToiExGFcZRZDVlFYCAal1AUmMgjEUijqe5oimshvSdiOu2ccNHxypurJGOIDitk4TEthVsA1ddoqUnCgDCPkVueMnC4UKFzh2tI8BIbCnTTEQhPvZHRavToTwMOihbN9xQcf67PzuUW6bbHzCUwDbCjgIOvdKntlSAEm5tz/MePfA0wDLuSHZ/Q7jYpl3I2jzWwG4JgE2RZgVc44cE5XOVJP9HAtSStX+qjphFG1pFSQaIQgwnOFpR1SHYCTMczuaARXnPW9lE+48b5JvahABsLfE0TbxcIIbGRwocSeQCuLbEaKCxSCHxl0IUg2tUIHzIROfZkgG9G2LrCOIu2CQjLwQe6JHdm3Rd+sYM5ADXOEMMIVwX0n1lk6dKYO7+dUDs+QueLyEnE//J33sfSyTH3X66hOiP8coQ4VqEe6TC9azj5hySXLwOVQecjkn8vokwNxT2FjxVWOFyzQmYanwSgJM1v7+Bjj5YNvIA8nuKSZfy5DjxsiW9A7eltkiKAX3JEE4kLW/TLGpMqYnxasnKgQTqEA6klkzRFKknkJUmcEN+v0D7HncqZZAnTOY0Sjj/xwVd5cfMR7uZdws2KoPD4aUGYxzz80euUL0cMrza4fW+bi4+H1OdSVFUhrcb1ckxXYaMal6+fYrU8wbWv7GDEIa9++xodk7AfSuw0oD/uoY418KXl/vUtHnvPWZ75Lx5hOin52z/zG1RlyfRwyKVY8cMPP8TG/QOWVtt85OEzzC03+TtX9qiFEcvrXW62+rgCJlX+ewopN0f7PL93j3GZ/ysLKbVaxMMPryKVIIqPtnRHHHHEEUfw78RYzR8EjlbddwjpJMOmgk4jJ1isuNo/xUpsKPqOO3YDMa9p7FtOtQ8Jjme8ce0CtByiY/gbn/gp/uZbv0xdF0gruTKc47/+ynPcHjYoy5JkwWB0gqnGzAUlK8mAXdtCRfD8ndNszR3nc5//AKNBk1o9Zy4ccdhpUFpFiMFmASKdRd7apqJqzcSW+lwBLcsDJQhDSSVionHBdDnhxmQNBhVRwxMog5x6Llzc4OS5Pb7z6inU1zX+VIGtSVprBUHL4EgRUjK+LwkHFhNrvNasjGDxiR631Bzrz27z+q8dJzaWD33kOo99aIu/fflDbEYJvfUYLyHYlrhmRdPCuxbHaBlwYuWA3nAdj2Vrv0uiK/JOyEI4Zrxo0ElIgKMelgxrjvq9CnCYTsg3t0/SfHzIcLuN3An42vcu8tlPvYAFchPSSxN+/KlXeKVapjssmArNpIyhVhHvCMY6w7sIYQAJohBY7Ym3DHiNU5rpXo1GI0WF0Mtr4Cwu9WTDBBfCR59+i+x2xG/PnUd03o4ksxoRKWwYIaeOQEFlE67dWyWpplQ2Yd/nBFGMMiX1bot0WqBjx/JTewRbmixP8W9H6vq0QMQRS8cXcIcFYtcTzueke3WiZgnS0l0e0Z80EVWIaSuEVQSZQBaechLjQ0nhBUjHwDQRMkfkFl9XCCFRxlGFULkKLxWrp0f8n37wi/zCN57l5fvnsFbi6hJZgR2FsOCpfIAqckR95iOR0lFNApSTyMggS4EzFtOMIJBQSpT2COE5eXGX9eQA3dK89cIZ4lxRrAlUblFjQ+EVlBLhBLIEUQmUUSRrKfVHUtJhzHArQIQCXcGxWsRbRY5RoAagjcNrg9wPIayjhEMVBj3yOOlgXCDqBl9JhAUrJSoIUL0K7Ty2JVlQA57+zFXeeuME966s4q1DBRInJa/+2hluBDHveeKQTdHFeIsXAB6XOAAkEhuC62iUzJFxNItTNgZfS5AanIhYchnZSEGmkDl4DVaADiMwDqVBWEEcFlxY2SWYs8SJ4XP3n4JUE5gKWVZkEVCTiIYk2LLUpKa7ELAWCA7SkuxRh3wkQTQS/OHb4t6pp7ZjaZ8f8czKgDv3aly91YXMUrQDrroVsoUQ86OgQ0l4w1F2FYxidC4RMazKXS52NxkPArJbCb6UxN2Y7mMDBsOAsG+ob1d4A94ZcAbczHFkVyO8s9hWjCgd0gucELgQ5HwdMSlAg3CSck5RHNMEgeX/8qlfRXrPH/vanyLNIuJxilQOpwQq0kgDVA5tBGroZ9LbRCFyNyu01RVeaYLMk1hFfykkzEBtZDQ3MvZ/NJ4Js7XG1iuIa0ihwYFVEhlqZK1G8XiNjbMtyv8FGt+2eDWh1nb88N99naRRMP1rGRsvdDn1/h5xXHHni4uw7NBeIpcqJA7fy8m/UOIKiwgs7maFmEb4cQu5aKmURipPdrGNmBqCXYPMLXa9hnCgcoULAlwdCmfIbkeYxGKnBW+9Ns/6u45xZ2sJn8/SbsLDDDHO6C7knHj6PlderlO8pnGTApkkmJcDXnr5MarK4N/tqaRELeV8nFf5J298fPb6pDk6zfB5gAqh349g7Oi/vM34Uw0mKqJsJlAGaBWQYUEJbAUPdhVGh1hveePSPcQPeVwvASXYO0jRCwr99F9n/amS4IUT5OsneCV8hbmVgL2NAjWq0CfgzNkFevsTVtfmuNBMuLKxy3c3tkBmrJxZ4CfPXWButcFarQuAcw4pZ+M5bZkwOcwxwb96R+yZOZicdxSFIY6D36ddxhFHHHHEEUcc8f/JUSHlHULciHn94BRn4gfsZg3GVcTS/Ih80qCIPLYR0G4VvO9D13Aa7r28xnDYxJ4q+ac3XycqWtyezDEZ1hmLBjejBu2DMbV7AWpP0jsnkbIk70VkQcx21WWSJ1Q7AYdTQ9nXOC0oZETcsSRzGaXWiNAiC4HTYAOFm7e41Zxqx6O6ntILTOLwWmATaLwUENQc115Y5HRrmyF1TClY3DCouiQfzRI29FCTCotCsh5u025M2T08R6kULhJUNQjMbPOZSbh1ZQVx3DC52aLqKkzf8P2fuMqpc33eL2/yy4NHiZxhUU4Z3mmSjiJE5fmJv/3DLJ3o85EPXGVTtJiPxqx5w+/0ltlL2syFU6pUs7I8YGRrVEJDkkMQIo2ANceHnniFX67eR1BoksDRTSr2Bg3isOTV7eMMC4XwjiuDBbJQ4ZYcgwcaKxQmqpCF473nd1lrjbi2s8ad/gIirNCFwgkJY43cDMk6Oc2W5bWXzqKHhnZRkPiKYORIlGE4SmBV4N8OdtfTt2NLbUU0LKkIoPQM+iGDjkQmAp95zKhEKQ/KEWeS5sURq48POPQLpPuSrapk2JSYVgPdL9m436M6yIljQ54lhPuW6LGC3brkgWkRpAEqnMlvhXNQhoSVwmOpcFSHAdHEYmJBYgTxYcG0HmJbitQJVAbhVCALy1xvis8d3VpOXBgyGYDwyLHDVZJKSOyqQG7A7WyBgU7oVzUoNN4K0B5vQZYBLtYgBdrMxnRs4tmbxHRLSd1XFDWP7Ui8By8FUkIwtFQ6RM9J8qaDxBL3HVnQIJykuKEirE0wxNiqxiDWSA96aBCZwwRq5jWZglAebwRqNBu3Q0FpQigrvI5nox3W4RQEMYjMIhTkUUw6iBjbBqajcQse0fDYNYjchOBYxL5usVm2kV5QNGYeJNEH3auIDyx+XEJZMf/MhOGNhKDwGK1mKUmBZNJOKPZCfAymdIgAfCgwYUA4yJAmQJQGYseTn7nHyhN9fCyxTjPZC5EFsFCg75ZIl+CkxR+WCGJCEdMOFBuDbcpRnWCvIHs4ZNr0+EOLDzS2VIxOSaY31ni4dY0XfJM8iYiGOdP9BD/2+AMPKJzV+NChJgASKyHwsLPZ4dvfeIKRLdiZLCITx2A8ZVI0OLnbo7hcwxCS1iUi0fhc4ccWhKR7I6P2yZzqSUl5JcJLECh8FFBFIa4mUE5gMwj3S4J+Sb1d8JqZY54MrSuSW4og1YjSYGMx81EZi+qXOAvCKpQFpyReO6gEMvPE11Pq9x1YycK3xqAVXgrKIMC1JNSgfhuCqcQnEmcdbr4G44rAKWTmsAkoIen9iXUGw5mHqnU/p38jQj9asV90iM8WmBMBj//ALr1yCfeE5r5ro1Zy6k9PKPeBkUUua6q9GqLv4M6QwJT4aAnflHjvsfMN/Lqi2t0mwBOmDibZbKyzbagJy+S3FXm9DsckWkw43dnjzz5zkzS7x3/93z3Bg8GIfE3h1ufIOaC5XLByQXHz5RiZFtggwmcGpcQsYGkMPoBiQ/Htl08wtSWxKgn3xoSZYbBumQQxjYenTHoRxV6JLmEhGpKtRehCYWNNKBVlBYu1CZ/8Q/d5Uc1x+5KkGE9YT+fZGFiqUODGJRU1Xsta7E5bPPnwDsoOGE8ntI7F7N4dER1WOByRVnTn6gAoLfnHn/surlfgtGV/o097LuGhs+sAHOwMmI4yVk7ME8Uhrx18l53hNi+aHvCJf7He72Yb/Py9n+Hx9lN8fPXf59ixLtZaouhoS3fEEUcc8e86wr99YfoH4Hm80zladd8hSK1IpzG3+ktYC/WkwEqLTgzrosPIbtM7iHnz2jqy45gECrTAeMHtg+vo/ZJRepyiPvMjOKcQlSQQkCmJLByb6XHOLuyzPW2zUo0orWK11adIY8pJgF4qCecMkzDCFpIgK0iVwjbBRp5wIPGHIUl7ANMIXzmipsVOLcWCYC6dIJAEI4vNSw6+0sF9QCJKOHXmgKeeukORQLW9TiAha8SE05CNKx12xjU2e/MYJaEpyWNF/RVBSMH8esrGt7roMCcfxIh1yeFnAzaDgK/8ztP8+vAhTCR5NNvlp5/7On91/6PceXWNvO24Purw2n4b952Sn/7kl5ijpDF23OkvsTHq0IpyYtfn/bUNXpyc4Hq5gKsLwFMlYBcM62f7qOsgncdVklemC/yTLz0EhxHb/TpBWFHIAFMPCDOHnYAZxPh5oPDoPnzmiZcpCk2rPWTztWeYy1MGcYe11R4yhGv5AnpfkPQNh7bF40v3GPaa1Jdz9I7i+a89TFROiJMKseSh8EQ5OCzx8YK5pRHDnc5MpltJqgcJjcwTp1DmJa7mKAsHqaLcapIeFIw2m+xTzLo4qtmBztUV08Mpvds9slZO5lssnh6SbwWEmUDfFsipxRkPlSMYS7yUIMEGAlcTyMCi2hWMBbKSBFoijIJAUCx54onE73nk1HH72hw/+1sf4nbvOLmKKOselXnkGFzsEZmkbApiY9n5h23sT8B0HP6/x78KB0Lh3czh44HFR/cYugaj3ZB0M2Tr1jJ2L0S0JWVd4BOHSD0y94jM4esWh0UXAlDkbY2bKLKXNCo31Fam2MM6uagYljmyUKjU4YRCKDnrgChLzLxCTz2EAlsTFDVB0BfYVkSYzZJQZGFRQUUtqGitZtw9XMNOAl568Rzj/QDZBnmoqBKByCv+y7/3Vb5+83GuPL9Es5ayP1L4mkY6wDFL6TIG4ginC0Z3Ynw+xekETAxZhWiCyzVOSAQCGQbYUOEjCZXFRAJRKXTiKdY1bw5Pc7N3nDCsWNApY5cgqVB1hww1zgDWYLzDzkmknnA46LPvupSrivpKyVQ4MmVpDgpEapmcqyEUbFnNT/3sx6Aj8fMF4aHHbxjUS5q5S1MOn2vjCk+wURJsClw9QmhNUTOoRPH65qPsTsb4JmiR0Rsp5Fc6RF2AACFALyiyekR8z0EcQmnQqiJSBncM/A0565h1HpkabKJwtYAgcxApZFoR3amwHxZ87tZ7+GB1m3QvQcUSGQqEkBjpMIFHeNByFp3tI4k1wLSaeWpqEu9LGtse4RzOWbyYxck7LQi1mqVt6RDlM1Ss8Hs5gcipEjVLG6pA74wxzRomkXgtaPQcsh5z8of7/CeffUBhBf99rc287VM6y+XLxxjejii3Y3aCDp1szI/+mZvsXhNs+TmSC55bf88ixgaMhLJC39rFztcx8zXkKMTMNZFRhJcWnMdnJeaYQM0LyhOe8Bcq7AclLlaUx5v04xwhYTQOuHVvm3oc4WKJaWq2d5u8cukUVdkmVLuYwpO2S+JKIjKLdB71egnLdT7/1uPYBY2vW+ykRC41OPXfbPPyN88wH22hn6jI1wasfedZdO0FNrI5OmtDegddfACmJgkqx6ff+wanzl3FyhXu7VyE+5rWZpvlwwHFuCBbECih6R22uXrvGHkxR9OnfOS9D1H/wGvc+a4gGFTISUYUK1aOLxBFmjQtuXxpk2DiieZiTJ7zs6/8Dl+O7vBX3/MZpuOcQW/K3FKbKA45216mE7zCuXbrd633n7v/M7w17nM3/XU+vvrvs7z0u//+iCOOOOKII474/eeokPIOYZLnqChHhg5hBOc7++x8c4n2yUOWxDrZfYtKI9aqKWfiNm9YizQW37OcPHmVIMipXzrHRnNWPgxVRtT1hEspncWAveEqxHCys4eNAm6nC/R0Db8C5dhTtQXveuQBqu6ohzkvfPU8nWiCa1qMMsjYIPrxbGShJRBIiqHHWceaqvix819j8KDOF9QTVIvQPVEyzWKqTYmXgnpSML8yZFjGiHz2fy6DCN9wHCQJbtLAT4BVg1AgJh7fUFw8d4ePfvQN7hwuMNEB/XGD1y+d4VBGXL12jN/61Udxp2PEouLa9mn+XBTz2dXb3Ht1Benhj3zyFdqdlL//ax/kO3/vAuffc5/5w5Q3swV259qEoeGZYpNHxJDHF1/jJ+/8IOIRRXFf4VsBypWQSFqvTUiX53CZYJ8ak6xBNgqh63BE/Be/8mN87EdeYzyI0QNHLRWY6WxMwE8kS42SzuIB73Xb/Pr0GfKohl8UPPyxByx2h2x+6VnMVkXuwlkxauJwRlBbnmJuGdJMMV0KqRc5qQhpNjLkmqIceIQ3pL0IbIjC4SqNtgIQyFgzrQnywKFHnnokaZ/PqS3kNJYnnLQdml7S3xzjjCcoHEWRszEcsuPn0Oc8w2OKZM9zLJOkS2PsYR0ROYpM4cfgAocrHQqPtLM4ZhsFRFTIfYGdeFTHIoTCJ+DXDElRUFwKqDqC18M1ypUAPQCMRBUeKRx5y1N1BIGuSNZL3M2IK39rBf0DhkA64ijHTxxWNCAJwIKQDjMHSZgysgEuhWlVI/GgpoZwqLBaoLTCxxIXgwskQntmChtB1VLEhxWUDmEsUxNjTwiK05p0u8IteCIHopKgZjGw+S4EA4NE4JBgZxHMvhGgHOBBVgIElGHIw+/b4DDvYqwi2jQ4JOHEQ15RNUKclCin+PaNcxRGY3NPJAW2blEyAikQ3iBzj6lJ8tMxbmywlzNMGWAjhXYW5yvEnETh0N6RrAvMPYWYGpzTyLFBeoWIHL6QBEJg5xT3Jk2SoOTmcImi1LTbh7SynEK38VJAGKCMw8eOUI84yEFlAVEnoBIBGIuoPGrgUSOPftKAFNjYoQ4FOpWgI6QfUGlJPopJz8Rka5rwgUcWDrwkW/DEVUV+XEKi2ao0ohoT5TniEUtxaFHfS3F/KqOTDhiLiJU/dMj1Xz6LTQS2ppHOkL2mMTsGc18gWwZbC0BIRJIgXYGLBN6EOGOwDUHxcIisWQ4mDb7+yw/RfWhCtdEAASb2+AiEnRX9XCBxWlDFs9cwQCAqi481QSFncfCVAwlSgBcegacSsPCLBxQXYvJEE6UeOcmhrhFSsfzEJqPfiWjuT9l/7zEm9Yh8HFK1BHqupHFyOku3Up5wGQ5EF/PFPvtlm9qcQ+UWsgACya1Xu/Rciz3XJvxnDq8iilVHuLuDA5RxqF6KLCuEqCjCgOVawEHfoQqLfgD5iQSHxOxAcz3CRQISqM2PWV/c4a+88i6++41HqdqewlXISmJrknQ+YPv1knPHGuzHPTwesyAwQqN2BYQas6Zxc4Kqm6CsQ92dYAcTBo/Pk7Q9Fz91h6nRXP65RwjuDtjO3+KZh0oaz1Z85OJbfPHVZ/EO8iLAhYJXXjpNviu4cvkY1WMWPQo4fHgbZaaoL9fQJqATxZwvK7YOHfeGy0ztLremI/7iud9E1T+CsJpE1RBakaclUaQJQkXYCfGuYOl0m1o9YrQ7xRUWiaCsDGla/IuuwbnwIT6wmrHa/N2FkvWg4LIsWdYjvPcIIf619wrWOZQ8SvY54ogjjjjiiP+tHBVS3iHs7gxJkoJYG7yAH164xN89/Ayiqchbkl5Roz6X8sn3v4aSkqeOP8218gTKZ7TT08QlbA4Nds7hlee9jT0WHpvQ3084yOsEBZQB3N4/xmJrSD+OkYVjz9YJDMi6JYgqnjhxj+fvncVLyeGbiwQfKtANB9bjXUwwrqi5nCyL0LnHho7RsuRnv/sBggcGIwVn5gcMmgEtkTMKE2wAr714ju3tBYZlzICQxOcEqUM1gCIkXs+J+o7MM7uFlQZnIq7trfN47zavHyzRLxssdqc0WwWHKEajBkQheiiJkWAh0yFfv3QSLz3tKOcn3/c9Kif5xomzbGd1ooWSgQoZfqeFHocYHXK5scquqXF7NEdQOZQU2PrsoGXLGr/xpacpioB4S+IFlAo6p0aUo3nCpiCvWfJjkrlwggocO9bTHORMjSBoW7KgxnaeELs+o1whJwa36KkiuH/YJZtKphNNiGe6HOAqBxsOvW/QG56qJlC5J3i45OH5A77nz9CKCx762C4vfvXdDNIYu5kTNRTp2OFzEDKgajm6skHPVZTNADM1lFpie45EFrSiPutJQleHvLq3AeEIGQkCHbDvc0RYo0JiBgFlYPGDKVFXkGmHrQf4GISS6Fzg5y1+6tFxSW5jrAqxC5JaX+JCSauWkZqE6kCysDxl8dkRg0mDXlpHhrPXXBqPGlq8kpjIEYwd2IBKSSbPSuqPFVRvdOnYA5KTe0gjuDecJ6jPbvmlB5QllxoJs+SPlsWuRZRZhY0ENnGIUhHWY+KWxPcm+MBReoXtzB5EjxwitlilUIWiUcJgSeC1oIgdwiuchnDHoiNNpWPMvCaYOmSkQFeoCpKyYNQMqdoShKW9M0EsSRaXDYfDFpM8ASsQ3vH9f+QNblxf561bx9G5R2yXiKikP6qxsblEL0rwZxKimyVlVkPgEQ4oK0QsoaFQPiKzCiUsSAV4pFUYqRDaEF80tN5nSbuW9Htz+EDOhKOZID/ZQFaG7uSQ9kLOjmmgpCUUlvjA4MKEUaSQlUNnDtfQaCXQA0+c1BhWAVJ5uoMc+0RGZ2lCQ2Tk92G6NIcfSRAC5x2L7TFpr40rJC7WLM5NGHxA0FcxCEEVzxwTzoPreOzI450kNoJ42KPbOuD2mXn090nkA4n/csVHP36F4kc0z2fnKfoSJx3lsqRYjmi9+nbC1F0BmUXVZl08vhbjAo/PDTYrCJzEBwpVkwjjSUIzc+38Icf51gHbDzxpkWBqgI0IUoeoDEJLVOHwAppBysXn7nP1O8tMqyZUHm9n3g49JxCxo9r2IAUKgZKa5K0M9KzI5oKAsh0jrKPmBO1zFZMLsFdz1ALD4j/usf/Di7gznqorqLzFOvixP/EG4yLmK39+CRkIzv6hPnuNBWrbnne9Z4e3/sYaxUcbBBcFtVMj3NiR2hAvFolfPcBZj3EGObDoaYFpBky8QYQRBJqw76mUm3WwjAKSxYy8HuMTxbnGAaMw4tWdU+AV9oQkSyvykwIbgY1DOhcd05spZi6GnYzag9lrLxTgBao0uKbFdQJ8BLQD/FqCq8+S4jambXRhiHYq9L0BJvU8/w9P8B+87y2+kz2NboVkuSCLPU4JtvcWKTfWGWRjnB1hWzFFkKGlxeFgWNJWEU+3fozN/Vf5rpD4xKOCijt2nUyFMLWEWcX27pBmUpLUQypjkXWNmQrCVp31Y4s8Ml2iyCbcHO/gylmE8v+avnN2fo4/9u53M1+v/a71filu8aH2i7RFSmmnRLrxr7VP+Km//St88/Idnr1wgr/zkz/+v3nfccQRRxxxxB9APH8wZLN/EJ7D7zNHhZR3CEHwCzzTvM1L+Sk6Yspj8ZBAOqKOR5WKKFAMRzE37yxTq0m+8+Z5ogCGtob2yxyXNbLuTXCCaAgL9YJjqqCXt3FSoAYC6QU93cQA9Spl1GuQVg0ikxMUgu/dvcDzuyepxxYKiR0mZBNBVLMUA00tA5VafDzzTNh+SBULkrmAobHUDjxq7Nk87KBPG4LK0Do7RZSC5sfHHLy6QLZfoz4cI07nBC2NrwK8UOi+JB44smaAsJZamONpMOzX+Sf/+CPkJ0vUQsXkXp18v418OufyV07gQ4EPFDoXWG05dfyQ0Y0G7uGc/r7i2qhOLC037sxB4blw/pD3nbjHX3tpgYMiRhrBYD7klXKBl6fHacQ54zIBPN4LzK7jcGuOcFggWjOvRH+rS7yW0i0i/sMP/io7pxM+t/NuchuQpyHDvQiZGZ5wy1y/d0DZsuynCaU5xt60Sac7oCwEZQKXrpxETQRWQO4h9gXSakoTU9qIaRohpGfloR5/4v3f4/hcn7vXFggCyxsHx3Fa4o2nFAFBxxL0PN7N/CVSQ9AUBENDEHu8ADGs6PoxzX5BVcS8cXmTh86ukddKZMvjQ0ekQrLVhLW1jB989yV+6+X30strdMcl5esJ1aLGtBRqucDHjupBgqwJ1LzB5RJhJLISGC8xWHzTYZWg1BIx8Mg7mup4RDGMZjex9zShEMgcQGA7gnJJEhyoWbFOCkY2oawHuKZg9+UO8aFnGLbwgaCoGRp9gW0AWlBVGmVD5NihI8OP/OEXMVuaX/vSe9EFlAlkWQWVwbZnIy8oMYvv9oKIiioKIdII5cnLOcLrJXrTkyYS1w0QBShnUd5RyZiq8bZjxOQUJwLE2FNLDUrVqDoCbyt0avjkR6/z+s5xVFiydyciLC1Se3TsaF7IsAOJOBAoU3LW7/L+9Zt8byrYzU9T5QInHSIvESqcSWYXEmQABBLdsshWhMg8wWqGmsZkKKwMmZQhfixoPjAU+44qFNimRDiFW0owCyFqIhmPNLvXlolOOKY2YmVhQl0bHog2eReShkOMPbp0WCnwpaFUFkNFec4wXtWsmYpaXMGSpPdwhPchtmHwwjM/V/CRT77Fja+v4zPBnbTN3MNTgmOG8Sig7cZ4q5ClRAFJVmEaGgjwU09ox1QHzCJ4U8fyec/VuYQ3bxzj+IU+G7/dYbrbQQDCecJCgNKgKqSz6LbCS42X4ITDdSKc0+h9hXEVtqNJdirCVw1ZIyQPFco7GtrgDkIEgvK8IrkBNpbYVkgwqGbdKV7wzEeu89733WR+7pAv/7OnQWuqVgSBwgYOU3qisJzFyVuLV7MRTW/s7LMQSHwjgsLy4LsNdBpTXZMk54DKIivJyucGbB3rohqz5CgpQESORBdI6bENz8sPTtCtOeKpI73eQKsQcT0nWjecjHrU1j33pw2ifc+obDO4PsUAOrdYAT6zDJMOOqkw8wEuNmgMdhqht8DdVNApMa2YiQmRLcsTZzZJj0Vc/uIxgglQuZnrJrP82FP3+MJWl6rQ1EOJyC2lDfF+1t2Y3KiozsTIUoCwqCnIA0MVWl54cJxGUJG+2kLWPTKOIM2wO5p/9FMfxS/VyKuCslNHWI1wnmFD8ulHjvPlW9dwwxphGeJ+S5G3FfTNbKzNOSJOk969Ra1RoNcsZ5YOefPgRym4TuIshy/s8VfWv8bf/GOfZmdrQJbmkBmkAVE5nnjqLC+427zpt7ky2uQHH3o3eVbQ7s4KJ0IILiwu/J71PmeJyis2zBzjKvvXKqRkecnzr9wG7/nelfv/P9l3HHHEEUccccS/yxwVUt4hfOPut3j9zXfzscff4EzrgJ6TVIUkjjNGyV0+dfoB3yzXeOrcJghBW1bsjtpUicULx51xn7uBw+FxAirjubPXJc0i5KKfxa/iSbzj0dMbZGlI2qujjKSqRQjH7BbNK9JhSCgV1foYn0XkeYAOS8Tb4wxBzZE3PdYoCqFYmGtT5SOaukJGEIYWMkPeD5kGmnZRocKMIC5wMqEjCmTHI89ndCdDFs5NCJOKvUvzNF/VOAQLj085yOdRxjOZj9BnS1QDGnsl5a7n/NoOm6M1XEOhc0N1ouRX/pN/gHeK/+5LH6I4Jmgdn0B9Qg6cPL7P7n6Tzz7xOnJsOR/v0UsbuCjgpBnz4wvX+aG5O/zj/hPos/DmS12q2KOHFiqPXolxfQNA3J4wedBiPepzIoSxFJySe6yKAb1BA7MZIULP+Ysnub99SFpJXh+uYmRIv6ijLhjkRkTL5GRpCKXCLElkLFkWPdrnK7KNGkUZMRolKFuxtLzPvmzQMjmTMsaiSIQhfTuC10UeGOGLkOjQUDQ1JpC80NtBWosRGl8BSOZrGbXUkpmKjcMx584YCq2Id+q4qOLhD6zjtjZ47rE3ONHps1wbM65r6GiKqccJRZB7xNST2wgXeOhHcDgbm6EJovAEPYENLOVSTKpCVCohcUzvNvG7mnykEIHDVYagisALytjjFXg9GwnzCBCeKDTMdUYMozpZLSAfBIjWzEfiVwWq5/DjCknFfM9RFh3K/oSgJVluZgSrJVoAQ0u6LEBItFH4psRFnmhfkrVARJbaZY2XFVUskQ6UdYjCk0wd1ZrEVR5hLAKg9OAdPoCgW7J+coc7N08inaflCg7WJKYBMneMz3ounjrArkJvo80bhLi6QJTwvRceZeXCIUv5gEHaIjxmUH3JS989z6t3T1JphShKqqFATse4xXkEAqEUZQTOClyu0VZhNbg4ZOWDKbdvdJFvTPDa0zgTsSzG2GNwedDBNAQ6Be0U4V5JfT+ljGMeX9sjqc+Sic6sjbn21CLbL3ZQPYUqBUJ4wqllsqiwNUnSGeGKGL0QkytYOt7HecfGsEu6Da5hcBkgBGWmef3yaQ4O5vFWEOqMrec7dKspjamFMw6xP/sekt4T3jDopsY0K5zJyZzATWIowKbw/rNPc+/Ea3zzHzyEftwyvVlH12NkKAm3c+ojizMWAoEUevZ+MgafeohCZrM6DicMPvB4Z7DWEI4E5YOYYBpycBiRHlRI63HCoacC01Hw9mFaCImLJLah2N6dYzxKuHHtxMw95D1VWxH2KoyXKOvwahYnjxV45TC1ADKJVAKRVgSHFTgzS9RhdhjXNzx4MK0GpAWr/8MhX/2zZ/gLg0+wfz/hcOBRt0vSNtjdguBly3bZgTDk9sYSoZ+i7ln0qznu3ZL5dEw4Knjup24yPoz523/pY9igQAzz2eevEdDyOZmKcUkE8waPRKoKnXooHJVQqGNDbvTbvEdM0NJSjTW1+6BHGptXlN8HBB5bCURmUblHCiDQxCanyh0+0jRuCCYPV5SqIBARQiqifonAc/jaIoPA8PA1zXBnjF9dpSy20NMSf2uPsh9SrbfwGrQBP/GEBq7sDUjWE5rHWzz2rm2CJ17ncm+B3b+xihaKtlF474laIa0IBhnE44/SbCbk3RsEI4fMK2492GZjp08+LmZdYFJipefmzh4H+2N+YOlR3lU7wUdWHkOYmRTYGosO/tXbs/XgT/HzD65zmLX4/nnJQvJ7f2eYfRXwtJOPA/Cn//LP4acOXTmSrsQYh9ZHIz5HHHHEEUcc8W/K0Sr6DuHvv/F+9qYNfun6M8yrjDlRcXp5hwvjTf7So1/lz517iTPNAcMqJHUhwzICI5ETydlxk7MbMcFhhbIe04C1s1uUoaUW5zgZogpPdOAJtiTJxNKwFZ3mmD/53m/wqdOvIUuPnlT4wGGlpOh6yhbMrw9oLkyo1zPCwWzu3r4YQMdSdgReSXpui3O1XURP44cCcoMbCFzqKJ3k/r15HlxeYrDfJNxOcc6jl1LquuSpM7cIwpKyClC5xIcghWR3ukDVqUBJggJqtiS0jjCrUIeG9yQb/NX/w2/x+KkNNIJch/z9Nz7I1+89w47pcrp5yIfP3qcrK7qywoSKudOGqRXcv9+C3GAiiY1BuhIsBKKicgKROBwOm0iCqUeXkkYUEd+dokeG/etdDk2DWjBkOoHH7ICPJVd5JtmhtV+wv9fGVJKqmyPqHhLHTTtP3KgIk4rFxRHKC5z2PHXuFu8+eRchBUoKFk7CXnOO0WGCKyDfraH3SrYut/iNzbP8V1c/TSkl4zzkoKwjSo8wIEpLZ2FE++yExef2UfMWaSS6kFDTzCJqBCZSBCuWcw9tsnS+TxxHXN/Z55HuPo+c2yZfUci5ABfCN7bPY2rQOjVg4diAcD5C6AClPOutA4J4is4KdGrx1iOcQpcCZwRBJhFITDdETRWyELPRGwHv/fibfP/HXuPY6oDaimf5xAQ8eDdzBahcEG9IhJ2Ng2g8cVCxVJvQjsqZxDMwmJrB1Q1iCmoCYe5Yiyc8sXyHaTUC4VkbVnx68RZnZIE30A4qGrYg8hUi9gjrcEISFoLOW4rFnRIqhRpJ1KhCGIMwGQ0jiEKFVILaviAZCgSKqhbjmMlDn3z2Nh/5xFWaLoMCenmTvK3wsUB4QaVivnh4np1OnbuTOiZW2EgivWWaa9bPH9KcyzFdQb9VY7Nq88K3j9NbihmdCKB0tFJLA4+JZjJkHzqIJd7NPB1EAhdIiiJhY2uOfFghpwVyYjAHls7cmCIP0JlFjip0ZvFKQKwQx9s0V7qk+zWWTk/44IUtPtzd5qcf+zbdw4JwaEEayC1VViACiQ8DfNgg7giW97o8PDyGdIIre2vsDttMzjapGo4qCaiSgF434vadE0yrGjkBpQmRCoaDJmpHIG6FuDKEUFIpiZoXBAsV2jnsQ5699RqTaZ3ifkBvW3K1P6F2vEl5rMX0sAXez5wTxhINK3xlsGYW621bDbzUqLpCCId/+0f2M6wpiEYWvZGiUouyDlYtMixQ2exAj7dIY6BZYJVBjSpUVmJrCm88XsK9uytc/s4FtjYXIZDYQFApj8gd4djMPgChBgsoj7Sz9CrqEWiFm6sh+gX6IMOGEo9BugkORxUpqqUafr1NdbzD429s8xef+wp/5vtepDfq0CtnXVtcnMeeabAajwknBXpUIpxAtzyND1Tcz5f49S+8l9e/cQpnYH+7hXcCEcbIIEGPPYurBzzxv7/F8Q/tz96nWYjdULj78czBE3hWf3CP4u4CetxENCRlX9P7yjxBbvCBJpkEhIUhnkvZ7iWYkQcVgLdU2Zj+ssCHAqEV2ipOfmWT//Ds86zqTcBDFKHznPooJ2iXpCcMEjfzzdTDWYEst9iJwYQKmyi8AGkdS/GQU+2XecKXqAeC4qkrNLsp80spet1S1DQHN7f5wv/jS0x7+8RzNazw7EymnF7qEp1tzx6rMsgHKQvzdU6eXGBuscnCQh0VCNo+5P6tXR7cPuRE2aEmQ5SWBJFGqn/51swai3OOlcYcO995hvHzFzCFpdefcPnNDXY3D3HOUVQbXN79C3xr8y+TFne4fn+POwcjqhDiMODC2hLG2n97G5QjjjjiiCP+rfG/pvb8QfjzTueokPIOwSuH0+Cl59Klz/DT//c/SS3KcWXA9944z93DeSqAwGBURagtwnpcXXFTT9ltWuwQwqFHVZ4N3eTu9hKb0zbjYR2ReoQThGHM3fEKtyfL0Pbciud49NTmLL647nl4fhfb9BTzlgTNssl4V3BI/fMNdOaRymE3muirM4mmCATaerrVFFUVqEowJzI6yYR0L2I6CXGFYpg3yOsKZTxlEDLZn2d0qcWlLz3M7tfm2fz6Mr5QeAn5vGNlboSuu9nc+kgQvyJp72Tsvdoka4d888o5VjpTHl3dxxeWP/f0d/lPH/keW/tnGR8s0FwtOJQxyexsyVPvvcGwF3NtPE+/HSF1gJQeFzkeTOb5+u113szbPMi7HExrYCXhGLxWrKymnF6ZzlwIoac5MtjSMeyFTCYhfgyn2iNGImRXtCmUIA9h4/YEV0qIFH1XB+vRuWTusQmTVkTRqvHsyfv8xx/6Ost+SHOzJJGOSBqYn00kBHMZ8d6E/K4m70Ws1EfM16Z01ZT62nB2i65AjRV54YiXStSSQLYcviaoOhrTlEitcDUFwFIwwrck507usDcacPvBIWYSs7QwIgrh77/8Mk4KNntdfu4XP8QrN46zd7VLI2oRhhrwhKJkyWXIcBb5Ch6rBTaQiMDjAo9THin17LBYMRstGgt2VMxlvYSoGeL1jHqnQJcO6TzCvn24HVmkd/jIYY1iPIkZVwl5X8HAIzNBfBfCByHxrsbFHluHtbVDHj93l+O1XaJeymKxj1IWYz04ATH4pkW1SpqJp+GnCGlmctjSY1KJXCywKxZCjY8FMkqJRz1Y0hQ1jcxATQViYqB0VHWF6Tg297oURYgJHGVLUHYVRAYfgG0qtNaMH2iKHY0bgXPghMBowaCf8EtffIbp1mlUEaIPFeN6wkM/tMni/BjvHMaCSSKEV8ipQRYeUZO4hkdogz8o8KKaRSwLKJ2gnNdYCcp59IOS1//5Me691iXcdzQ2LMprCBQoh6Wialj27s7zIJ/jnunwvWyZAxMyCmLykWbUCSkCRxUF6FxQIyacNCkHJZG9yRLfYiQSDiY1CqPRxy3qgoHAQWCplmAyb0FXLM/v0ZnvET1VID9ucI87ip2IItXkCwI3B8n7SooLAV4HhNuSFXlA7yAmLSPEesh3wkukx4azOG0vEcoiC08w9SgZIByYqpy5PuoRMrLUwyE6UTMniQbbCjHzGlkKlA/wSpKfCaHu8WuOoG8pVxX56QBnBNHLEpl7VGGRTmIScC2Fi+CzP/Qaf/4/+gZ/4o+/iKgcunAkOxZlZu9t4T1Oeryb+VVKayjWFNP3VFTLGhfOfDdVLaRqKfJQ0zhbQqgQgYKyhKIE4zi5NEYJx4W1PVwosas1RC2BSOPPSKSCIDMgFS5RFFPJ8Ft1xq918LGm8PP8zE98jF/6H9+HDCS2pXFlRVg6wtKQTjxpw+FbAd6HyFxTosnXE4p2SKdeICqBGyt6G03SqwnmeokflbMxJGeJo4K4bXjQbyHxiHFJbiR5EmC0wDcVTiu8VMyvSi48tckHF29BwyArj9qcIrdTyCrye0NkWaGKEukl0nqElJimRNRD5F5G7fUB4U7Kp595mQ8//TucP/15dvR1nt8/yRv9NV68sk6lKkwNgoOc/vaQVlQjduAVdOKIR5eX+P53P4pNFNIJOjdTxqMp83MNmq2ERiPh+FKX/90PPE0p4M271/jaK/+Mvd4GRV6RTot/6RpvKsvuZp/e7ogoUES5wk8ce70xV65vc+n1+2xuDvDOo4Tj5/cv8k/2Huebu/+Un/n5r2ID8F6SlxVv3trlL/zffoWqOiqmHHHEEUccccS/KUejPe8QEmFIS0HgKtbal4jP1nn93gnOnR+xP/hx/vGXctru15kLSgAiXzIxljKSXJ7sEwcKwhCdCrwUHKoWpVK4DBIkzjoIFceIOfHQPW7vLrM9rZHtrbCux4ilkmNnB0ijcB5CHP/g0/+c//PNj7DlJM8+94BvfeMMWirmTh5Qlgn2UJLWFE+GJ5k/e4nxuRH9W4r26pRt00L4AA4VHoENoYoFIjWwKimjiEpCv1dD7FuKqAYSvPEkKieYFojCIwqPKgyPf/YOA1mjbMSMbyt2Xl/m19V5vvrtRwkyQXo7YXIiYMm+wo9/qsW3r5/jqQs3uFLVwQt2r3Vouowr01X2ghZ2LkZMZrG9Lvb8xv4jlGNJVQsZ7Sf4mp4JQxcNT529wumzPS7/9vfxqR99lcZ8yue/9gylCOlnmjvX18lamlPLW3zyw1e5Ol1mf7PNJJ1ipoqgk3NifsC97SU2+/P08hq+KSldwMGgxbVylX5ep54VnD7c4k7/Iud+aAfVc0z3BP0rTWLlaKkJ72ts86XxI1gtmFzrIAJmRbAamH6D8c0Q2TEUqUK8fTsrS7ANsApEZTg9t8Nj0S4beZtJkOENTPsJD156mCIJ6OscIQU//O6r/OjSZV4ZL/E/33yKwVaGRSDzhGHWpmo5RATlQOAiEBF4B0Fk0N7jKo9xCtcIqaNgBFVpGWYdGgs51ZzmUAZ8rDPHMBPkwpItS1xdoPuesj1LRBEVZMOE8VxEsQPhtMCd0ngg6FkEAis0hJ5a6dncOUavWiOcg43sON+5d5LPv/kELpKMWoKi5VGpY3ECExkQhA45cEjjZ1LYP9PHjz31rwj2B23sVFFUlsJLhFAUYYWaGqQWCCGomhJbd9zYXOLuP5gj6nl00xDkBcm2pOp44qEl3KsYpAk3tlZIlca2FChBVddAiSoNWW4QQLhTETZL4thzYn+I3VQM2zFWhmAc4YMpol2DSGG8x2HwtYBSQeAl3nryPEH7DLMYERyWWKGxrQAtDVUcwUighUJWIKYV0caI8ukGa+cGXGxtobxkVaU0opy0GUFQUq0rXCJoXSkRVhBPciYHPbxP2D0lmLgagZtSKYkTYBc8jze2een+KUyowAnqLiMJYWG+z8Ful/HtgIOVhEJH1LRHGI+ceJyFwJXUgwxXSJ4Jb3Ky2ONzbhEfCOK+JVqqKO5Iwm2PjxymI9BWYhXkvkQ2BS53GOxsJCnOaDFlmGpUEGENiDBEBBKhJkgEpikouwnsSJLLhuR9BfaDOYcPagRbnu7pFHPRMPxSizAFJzympbHKc+3WHMX7NTeuLeFLM0vgqjxeSQgFUkhEYcF6yhhkDfJVyI5LRC4IvZ4VPFML3mC6MaNeiagAWxE6gfcCu6B54e55/uZPD9lIl4gpCTcrXF1gYonDUZaCKtHkDyni+yV6pMjvGPzKrPOvpkApR7U3wcQhj/zJPit//JC3/q8L9N7sMt2tc9BZxK8ZhINavY9pBjDVSOPZ/5k2cz9wSHmvwcEvLzEYZEABvsArhe+ltGuSOMo5sbDJrutghEYLAYclzZFBxw3SmiDwICJPuqOwA4feMwQ+oliIcROF/rqDa0O8MdBdQPhZJ4voBsj3HkM4SXJvSiAF7Je88MJZiqnk5VeXKSYNBnM1NjrztNMB3mvCsWX+fcd51/lzbLuSOwe7lJFgK59wd3zIbjmlOBsSvlIhPLz2nVuQeX77zj2+vfWAJAxohiENL2ievsqwuM7f/dwep9Uf5snHT6ADTavzuwWzVVnRPxiT1EJsZ4uHfuA3eOONE1wvd/nIynkO9oaUzuI8SHmM/azBftFkVEluXN8m6huqmiBJNFPheem1O3zv2j2+77Ez/5Z3K0ccccQRRxzxzuCokPIOoSw0lIqygA9ceMCppTE/+TfPczz6i5x5zPGVzZ/l7M4utbd7kIS3s5vnrsCnkgmeZiIo1wym5nk02aAvlomlJ09yzEQjjWA7KxjtrLM3aWMKQSPKmFcj1sQ+n1i8yudvPYucKFwKP/tP3kXrmZJOUrDxcoKKDE88/RZJ3fLg1TVKBKYboDtXqSpJbb2iNENeu7pCuRBgmuB3NSIR2AS8hEgWqJbioBNgteSZj9zi8flDfvZz7yML4pmLIiiQcnbIDCaO08d3ubi2hdfwT19ZIRiBy+AXv/okYebRgec3nr/I5kGX+1Wd973rPvsbXR6cmaeWGVIX4CtNmUdcTVdJZYQPIorWLGJVJBbfFtQPDZevzzPZS9AB2FBQTD17kwY7G0voEwm7eZvnH5zhY0/c4mKnxwcfesDWqMavvnGa4XaTpTMjmicq7vYaZJHFPYBw0dGbxCQBqMggg4q4pWm1R/zqF99HFWmqroVM8JX4PDuiwQ/EIx56fJ+/9Q+epZz3qKdS/shjV3j33DZfGD9GpRVzZ8b0+k0QEhV4pKhg3SDXS6I7DeyeniXABOAig/UWCsMv/tKzrF/4dcZlTFqG+Ai8D1GnKpK4YLBXQzlJcyllaTVFDjxRYQgzmOQWq2GQzuSl+swUsRljlzJYdnBQ0VkrOdxo41JFsFchS8FSI8I3A3amnsmrLdpVwfCtGnxEcm9cUCZNnHWEmSBvOORCiUlicB7vIBKGdjpgdNhFWkdFhGsEiFIis5kbyASC3bRB2lOMzKxLJR55vnv3JJX3CG/xQ4+8HlMsCCZeUXmPsx4ZgPACbwTVYUgSZvzY97/J9esrfPm7J3GxxqYWHUmQHh8r/KRA5RXxLigj0GMFUhMmUzgIwUaIFQg6BfMnR9jbioO0SVUkmPrbcafOo7xDWsFSc0xuNDGCxI5Jr4Zc35lDSo3FUS2GiFIQDwW+q2diUusRfQHdEF+D+CFHeVnigllLZlyEtB6ziGuQzcckLcdIeLIVjTwU0Bez8aY44qFnH3DiwoCN9SW8ELwxWeGBLEhRYGbxvUiFrWs8BicEhSvxNsI2FRMRMzisMY+ZtYQ6QTy2PNzc5WayzKGrQyUweUBeCLbvrpHdU5B4/FRgEo9LBDKT2DlHGBh2ihrqewmtVsnxd425/fISyigqLFE1onwlobwUIquKODWk0wpzuolNBFV3DjU1+JUmRnp07qgmyzx97EX2x6sUGQT9ElFWmC5kc5p4YkimFdnIQiohldQ+mCOaniILkEbw4z/5XbKJ5nPfeh+mHxEXnvFKhA/hu2+d5LX/6CSq9FTCoJ3DxwqkxCGRGmRmEAKCdFYArV/KUX2N3i5hroGrzUZDVCZxtqKSbVTskR68EGBL0JKF9SFn/uiY9oZl8xcXaR6rWJ4f0NtskH0hIFuPsSsSF74d+V1aln5kyKgH6dWE0b1Dwt7hrCup3eD46UPCVkX4Lkex2yXPPUKAshDeOSSZK0ltiRUCNylhEiA/r4hWPXbeEy6VDHZAGkWSKlwzol4f4F4fcXezC3WQgWT9mR4HX9NUe4qqJqnmAvRUkvdCxEBRTmpMj8F8HhJ15+kNp8h2SFK3dLtT9g/bUBjQAhEovJli2x1cXVM2Q3QOd/ab3PjnLQgqEAVBPUAMPa1zJeJ1Qe6gtt7izaHj69d2ETiQkp5I+VrvHreHAz72R57jWvgWflrRXW6xfnaJ+y++gc0MaeXIreORkyvUziZcur/PW19u8SuXX+aDH3wI9S8Z7QmjgKW1LlEScD39Gt35HusXJdvy8/TT/4ytvTHNRoJSAiEsa+RMTI2l6HF8cRUvBMd1jUOXYUOBGnuu7u/yfRwVUo444ogj3lEcpfb8W+NotOcdgp1KggOByyWmFEjpObXQ5PiFFebVr/IfPPQCRVeReUHmBb7lKDsz8aXXs7bkIAnx0SwpYSkdku1pBocRukgh8HigX/cwkayKIfWsJBoZYgoWpOHpYsrDSxsI5fEJXHl5iXtfWeKVX1znZm+eH/iTL3Di6T592WCa1/CFQBSOkjmCrmF7r8F4M6HTGdM5n9NuFdjFHJs4bOKQkwKHQiCIpwW1tOC/+fh3+eHHrvHcI/cQgSU5rHCnSvYadYoOyMKymXbY2m1zuFdjQQxRlUUWhvCgoliEybpkogOu7i4R1QpumQW8sGyVbd6crvH68BhPnXlA1QqonORca4/dnQ4yV+ixxE4ixtM6e5M255884MRyn+AwR6WGYOh587VjfPX14zy0tsRLL5xl51qTz168wscfv0EryZmv5zy2dpdBFnJtssD59V2Qkqmy+IYnPajx7qVN1uZ7LM0NONs+QNUMJg2ZPz5hbn2I0AJTm3lTvBCcOveAYH3A3EOHVB1F69EJ98M5MhHQCCuaUUllJLYmZ0UqG2InIWRAYXBViAnBxWBrHpMIZOBxDdiYdPkLf/9P81f+pz+KSySmLilOVjSOZbSaJTq2qMLym1dO8fPDU3xje53ioMZIGrydjZQhBWasqd6osaZrM2lsJjFFnb0785TjEFGAzkBOHaOm48BV2EAgtCC+7Kl6kvXigOGuYLrgMU2FkxIBVEh8AISOiIp/7+R3WbieIxdCTDuEqYTcYzVgPMILQHBjvMCte2vIQuNyT1oGUEhW2n0iBaEBZRwKT1H3OG9xTmO7FW7OIY+XlF9vsX5lwvrKiFFRw6iEwknk1KBTTzgRhKUArZClQxqJqtSsSyIxREmJdHY2xhE7Frpjlh8ekby/ImxBzYxpyQnJTkHrMCceecTAkB8oKj/TZ0xsDdsIGE66HG5F+EWIFytUVGEtCBmCFIgYzKICrQjxCK2hEeB1iMQhJUxFm3GtQWMhZen8kFOrPZLNEtUv8bbClgVVTRCtwG9df4I3v3GC3M06fkKR82ufe4xg6ggAaUEVs6QV21FMT7ZJL3TwdQ11iWlV9HpNjjcOqfkS/YZm90aHAoVKHESOrDF7b44PG1RxSO3DJaee7NMYWsK+QKcSGwroVDRWS2wkKYqQnXGXw8MYEShUoMitYPFdDuUMUjgknmAqqGoSU1OIePbdWiwr8uMBpq1QQvLK6FE6qx6UwwmP1R5RWoScjcKpPKN2JyfoVXQ7Kflvh6Rfq6Ne9ISLBkKwViAmMxGy15LYzVKGosJhpMDFCloJphPNRnIESO849eQeT/34PcIQpPfIsUOOLGqqsMsx0V7OyXqMFQ5nLFKFEOjZuJsHacwsGnpSIb0FASoEUcGpdx1w6rEdzr5/FyFnBfbovqF+LSPYzgnUlPYTKfPvGlKtStTJkp/42bd49s9uINOcS39riUtfOMb2/QVEI4D3BqhGjKwg6hnG10PsjkPsVYQdB3hUZmjaPVY+tcOTf/QuF57axTbauFqEFJL+DYHZVeyPI3IVMbc+YO3JlPhxCY0EhCDoGWwk2M+b/MqXn2bRetqvC4oFhzwULLmCz3zgMp/48Q3OP91DliXee7x3+DKnaQ9n0udI4ZXiiadO4h+MUbuWacNjz3qC5Qp1ymDGIVWlccrz6lv3eGO/R6EcJrdEEwc4lpoNLs4v8vEzpzi+0MWNHdu39wiE5LPvu8iaqrPgIxpJwOp6l9XWDu+eO2CxXjC32OLkuWXqzfj3rPFKK+aWmjRaCY91PosSltXOIUuNa4zTnOkoY3Q4pSotQgQcb/4YgXuIweExpoHDNgTNOUkiA+Tbs+s/f+nN3//NyRFHHHHEEUe8QznqSHmHEF2TxFOPKCTlx3ImvmJiK669scH7P/gCSZiCinmzmAcg62gKoQm8pZQOPGgLYipR3oHVlFoSVjDNE1wrQE+gJjWPLW5wa2sFP9RMqoCtO0vkmcYISWtqORnvEI4M88/lpJN58rTCK8v23SW2VYN8JKgmESKyKGfp3/FEqgbOUk0SquMJhYuJBLTWpkzGChU7kg1DMe9BKFq1gpEL+Z9//SLnjh3ywnCR/GFDrGB1PefaW3NIpzEdz7Cu+Pzl9/CeY3cYmAYmmo0vfeTkFX7n8imsCyFRfOQTbxHHOV/eewTXKXBCsJ21Z7GdpcWWBae6h8yRokYO5wWqsKi640dq17lBh/u2DkLgtcfEkjoFq2qINoazJ5/k9b0dVk8P+bntC/znjUvcP1zml958F+vtTfySp9ksuH0nROceF1gYBbhuyLsbm3zh8HF6to6ezuP2QlIR8un3v8jpxR3++hc/Q9lSbN9oEkWK7156hOPHd7hdLVE2PQ93t7lv5rlt5rAejJOUeYzTs6IGuUeUHrWr6Yw8VRLjK0dt1zJYBht5vPVE5+pMNlP8gwjXFKSrChcprEoY7SvE1CF3PNI5OuEUKSva8ynmwMPI4AM7cy5MAlTH0T3Zp7inEQc10jpI54iknRU5poaZpAbOhVtcGawSWMHK2oAPfOwy+c9fpHHTsRlHSA8m9BTzHtvySASB9bhUcHwwYJQ3uH9/DaPboBxeemwoZsUWLXEKbCBot8c8+a5dtodneKOnGZ9XfPnKRdwUOo2AKEp5+PxNNm6sMxhHMC6wiwo3Lzjx/geEUcUwWGH79Rb/5Ss/gncBOsoJGyGyHZBHYuZyedupoAKwWmENaJljhUUfK3BThzv0jNc75L2YJIX8Sp3SBbhKUpYV0dijhgbjFSZQTDZDkqgijwJsTdIUlufW73Ll1irZqiTUYyodUrRqMJmldYnWrAPAKCAWiBqwLzFNRb6iiHdKwrEEG2P2c5qtERtXWgQDgUwtqqxAg2tYLt89w6gKaa5BZjQfm79CLCoufmSHv/nlTyFqjnDHEG+VuNiT1EL6S4Ki7anvVISdAO+gl4f0tjV2EFEuSL6TnscIi1h1BNohCo3xHoMFDdlWSDwycOjxlUcECjlyCCTTqzWEk1R47m5ZJr6ObBhMalH9lOgLx3G6wDuHUpY//9++wv3pLT7/K0/TXZhyELSgkpgVSBugdgXjqoFIoQrVTMCsFLKqUFOLrixeSXRleez4Nv/tT/0mm9ttvj5YQHwU/vn/+AS5CZgWNUwU4COFqYeIqSUY55AaCDSi9MAsTttLCc7iBXzqj78MBvZeqbH5WgPlNWldUrU1thURDS2jW/sESQSCWSKUmT2Ws2BaAa4hqbVyJi5m740WNyfLZKdqPLi+gHjUcl0s4D+jka8ECCuQeYVVMDzR5so3O4QTgzoULD0tOfnIiMYJxy/Y72fl8xvkkzo+EQSP5eiHLFUtorwqEE4SRxaaGXkZoH/UIX7VI6aCchQxN+3x/U/coHtsyn/1UzWy1mm8buJaBf16iOwJRO6JI8GbrzQpihCv5ew7a5oyvQCigPujJulVRZR4SjJkbogagu+93kJNHYt4vJGAQ0YSIks0UoTWElQK5yyLF7rY7whUqNEHjkimzHdSvIf1hRFX/Qp2atjb7LP+WAtXeGzlYVQyd6xOw4X0TcokLelvDCn7GZeev81P/Gef4uPPPsTdnR4HkxuU8f+Rb195mA888tMMwt8gLTU/+OmH3/ZI/csRYtaJFqs23UCCKGmrgvdcWGfryh5lVuGcA2BjGPCgB9/I74ABFAhheKjbZfpgjyoE8DjvkW8/7hFHHHHEEf//zx8U0esfhOfw+81RIeUdQjCdeRvCUPPtg9N8+37Mt+5HHNdDvvHGs6yd+TLf7K0xnVoqrxjaDlVboU0FwWwjNVEOCojGiqvqAmZJIqpZh0MniphIQyo8fipRWOr1KZNJjQjDuBXwO3mXi2fvUrwleSVYp2hJmhstZJKzWx/Qbky4Newy3tX82BPX6S4U/Fx6lt39KR+40OO1rTNUNiQ7CJFdT9GTjEd1hJxFxZZdRWOpTrQ8QpzNifZLBrnk5z7/GKO5CKxDHE/pbSWEhwZrZwezfFUiWppbyRK2LjGxpF3kPLS+w8H1hN2d45izGacSw31ZQy6W6J5hLpwyMA3m1JTXJ6cwgaZBzva9EGSJKhJ0CdZL7h/McX97jvjhEZWX2HpMWAhyJSi8xFYtvnXpBsI7dOBwWvGn//ofZ1iLKeuapXiJP/6jX2MyiTBbIUHfkzmLnoBvwZIc0UxSoqqJKRSlVRRW0YqmjCY1KisRzhMtO2jnLC/1SCcJJBHmnOYT6w9IarcJMVRW4gXYOpA7hFIIr6lVFaoF+HmC+ZhxmRJUFhU7VEcT1VLMbUk8ELhmTrKc0y+WMIFDTSA90Dhr0ZXDhXBnp0mrWGOrXESU4CcOYQSu5qkICVJHtRXTsGbWoj8fIDPBetmnFzZwaYVJIiLlaQU5713Y5uq9dbI84OtvPkakKgb3Y5ZO7FMW6xzOG+ySA+3AGuI+RKXl1KkezdNTrj++CIWj/tosJUdUM9Gu0bMVxyiQyrBBwo28Io8UsvKcenoPuRkx3FhALEraiyXurYj8ICcXUHSBMKDQktJEhN0J070aVemQiSEIc4KlDhcfPcalcZ9+YdEDM4sZr+tZLOxhjhIZZdDk7pllgoUxay9M8O0AMxH07neJDyt8qFGZwNUlwlZ4AhqLUz74iTd44dvnGR+0YLUgX4wRQ880CegkKft3O6Q+RN9JZiMNopo5h4REOAiE4cKFTdwo40YnwIcJXoPVkmRYUpSSLG3w6v+zTrksiaWDQOOkxwQOlGBP1vENT3JxxM3xEuvhEIPipZfPwXTm8Gjdc4hSgRcEY0OeGqrlCFlFqLHBBwq5FVO0BEEqEZWgtDG6PUGEFu8lMrKElSNuTagO62Rjye5Bl7IbEHclYSmQVuK2QsxQEBmLrQfsZwuoD5YEzZzwNYO957lzN8OqmYfpQ5++y6eevc9wHHD11TmWL0w5mHa49Mp5Ku9Bz4p6Tobsa4npQjDxiMpB4RGVhxyIEmQpEG4Wdy6UI61pYmVhUWKkIp1EqBMVuQqpQknVEVSNgLoXRL2ZWBY80gHeg3FIrXj+C+dYOTVk/048SxhyFuUCgq2chRND9GOO6QsxXsW4JKSj9llem3L70jJIR9EGFiXtd+ecW9zn3IU9Fsdjbl5dZ3Ir4fXD4zQ+NaX52JjpgaTalcjYUx14vLEUiwGiLTAZXMs7/MJ33sONnTUkgsGjLaJ8FjtejfXMU9MPZt1n83Xajxyw9PiYwZWA7FJA4AxMa/jUUbw24fknTtI2Ker1EX7lHu70ceKbglG9SbRV4LOcasOgM0NwLyOdWgIh0N4z/4Jlokv8GU+WtZGBR/UNHkdaevyLEB0qto7POolQCrnYRM4NmDsxZudOiS8luq746s3rSBQE4HYytAE5AhV7tIY4LShHEr85Yj8ZotoKtCcOFB9cOse5zgIbtQlpXqHnI7hu2N865Nd+4esce/ocv7l/h9PNXYQwTKf3wc3Raf8wq4vPY7KK/d0h7W799xRUrHVMhilBqEnqIeejlCzoERBQq0XMLTcpCvMvOqnfOthlKoZ8+0ExS6JyghW9iI4k3fmEbZVRr4UclVCOOOKII4444t+Mo0LKOwRZWUQJzlT8T7/+ASwJUkN9qc7c2W+RtMcMpzGfu/ckxgqSUUBaF4g8orGSowNHXgVIB1bCa5dPEQgIhEX4gCpzWA3GOMZ5nWxSRw5AHASceLbPI9UGnfYQWwr6gxpFoAmalnGZY70AGxBqR31pQjKU/OH3v8XCSs7LvzzP9b3jXNtbogpCCBTBwFFdEegcGjc9k5MSU4C+JWcpNM9lOCf4vvm7PLO8QWu94v52BzWVcE8xHQnkxFMsW3QqEEoR+Fl6S9C1iA3JlCYTmzBVbZivsXbsgB+6eI1vF00ODgWXJouA5ES9z86kzvFkSiINlw8XWdRjfKVnsa9eIazn3o1l/FJJu5uycnrI/utzyFRgJzEPfELYCnFFxoXHtjgVHPDyrx1nksaY+tu3qt4xyBMqFdALmwSZw7RyaCpcJ+TWdJGaKXFGMmen7Nkm2SjiH/7Tj5MvKkg9ST/DjmbS1sdOPmBn1OLEWo9eeoy/e+/DGOeoiZwkMSgF1Zxm2K6IpgoTCkabAcvHD+kEGdN0gXjosLmFJYMMIToosFOFEoJYSFqmZNo3kM1kpa2wgFgwyQOclFSqzmv3ThIsCGSpoAnB1GBDgQQskiyPaIg2qAzvPI6Z26aoh1SRQlZy5nDxnrmwza1Ssn9/nu1Jm+b3xkTLsL4yYDu1lA0LCnyuuFC7z+SteRJrOHdhkzfvd5A1h+5blHDoTFMGs04cAoGcWJRxHLoa/XENWYe4qFBpQN2XGAtl7AiaJVuHbSZWkgYOGyk8ciav3AvQpSecOCrnUNbgnaLIAw7mS8bygFgIyqhCpwVeOkwrwSXgtaZIurimoAygCDvUPrGPig1OK+xCSX2uYDSKQYKa2pm3wjkW5weszA85fv6AN0ZLFAKcclQWtkvJenufxm3NNGwjvcZbgRjnVI9rXA2ie6CUJ4oy+qlCDA3aF0SFRo4dRSGwSiKkQDqLl4oyUVALkQascJRtwHjCPGP3XpPuGnz91nnC34B7myeII0nVNSBAmFlxdl+Vs1EtU1EsadrDHAqLyCN8G7wV4MA5gQsFQniqUiO0InETFldy9jZDypOg5g3kkqoVIqcejMK3ZqOKwV2HjBxVKyCmoBlU1B4ds/21Ot6UkNQZPhrwm5NH+czOTa5d7zLwbVbjjCydjZjUb4D3jnAiKJdnhReBQVYONSkRmcELydJ7p5z9/vtsvjTPpTdW+Im/9MNMihqjT0pEJhENxzm3S0SJtqcRpUM5S1l6AqdIahK/X4CQCCnxZYWSHp9YgsLw5tfP8la9nI1gqhKvHEJAtzvk4hO7NJdzvvjCuyAIcDXFIJ9H7c58WAhF2PPgHePNOtdSRTtL2XipQf3NMT6QiNLRyca0lOXchw/Jh4or3zyO6AuaV1PEU4L5zoDgM5K7+/N86cqjmFGICCqqlRq6ZxFeYO8o3IMEV5P42ONDTfpaDRsI0q+A285ABAQNAdmUxmnBwCZsX0lQU0nt1oRc7hI/Jog7Y7JvCagE4Zyg0cwY3U2QEwPNBuBBQLNwsFEQ/aBh/HwNcVjgnMV0A0wVIIxBjjQyUOgcao0GPhixe7VOzeQYEaLnJIdlSbt04D1J5anGUE0VQRVTXjlGtjVG6AptJGmaQy1EZI6qmnV3PHvuFM16jSdPrjKapvzqiw+oDqb8o7/yRWQgCf/so9RWnuTpU5/EZW0uTTZxE4u2kPenPLizTxDq31NIMZVleDhlFLzM9f2/R684walwxIuTczwnBSLU4B3GzjpSdC1noT5gUibIsk63G/Hchx5mbaXN4JVX2bm/xaJLsN7PBL5HHHHEEUccccT/VxwVUt4hBMwOGroUTCtNAIAHqbl1b5WLDw0weyG9sA0GnhRtis0JToJvWDyWTsvS0zVMzZJsTfC+hrKO0kjQOaudlO1xm3PhIVf21ukNEwhge7vL7auLBNOc+gpc2jvGytkBTzQ2OfncHt989VlGUrLdjggDw3RO8qUXz/D/Yu/Pgy1P07s+8PMuv+3sy91v3tyzMrMqa6/qXb13qyXU6gZBgMHCGJDtwWjMeOwRQ8wwhELDZmJsbMYg0ADSCIQQ0JJQd0u9qbu6uva9sqqycr959+Xcs//2933nj1OjCAdhz+AAIdr3E3EjbpyIc+IXJ86J87zP73m+nxNrCa+my8w3FEdZldpcQu58sp6PtBJFyUeKQ7xpxndunkYqhQxLnJ3ZeYZjyVe5j+2DFoEo0JlHXpcIHyglnoXpmkAmjvR2gHdQYCuGsuowSvFL3/gBGlNDYTOa/hgdTHlA52xmHdb9OhtHTcKqYTTRLN/J6Y9CLtRG/PDKa+x8vMHGq2fJGwIcHJWS1WZCM0joH9bRI4fCooOSue6U+nLG9sZJlhZH3Mu60LFELw6x8x3WogP0XMLt60v0qjWmYYWyLoj6JcKEiNWMONcIqaiqnLpKOXSW5bkBvcMIlSvMNMNJiRo4dCrYLFp0lyb8SPclrn23y397/inGsmS9EPwPex9HKYfRil46u0vtEEzwWCo0O4dtBs0EmRh8KdAHjs6JKVZYDA5nJWaqYOKQ1lHbtyRtcH44awplGUESkJ8zfPh9t1i/tkzvsA2eQ2kLORjPoGsFqlOSTHMa45xxEICESepjlJod0NsSlOS53mmu0KUvBjgtyEONiRS6aRjWfFQ1oqsdyc6ET56+yg8/cpW/+fzvZ7xTo1HPeG1wEa9dQAnlVYfvW0Ri8Xs52ZUI5wvwLdVqSrVaIpSjJ5ustedJn84Y3XKIi4ZyT1EuSSZhiat5s2yhTIEDlymsZxCuQAWgrEBIsDForyCbZgR9D28q0IVCOUs5KfFDgXEO23ZIz6IlMHQcxgu0Tk/xWoazeZ/1Ym22NiYs0cCip+Ai2Hm7zfPVc9zYOoVVGm9o0cOCYGKJVi2DwzrOSmpbU0wjwmCRxuLtOAgkKhE4rXlh/QI6jPHMAG0z1FgjSostZofvsuMRrWWYI0lZzSHxcQiklggUMjF4+yliqU5gDAjB6L3XUBqsk5jQIRKHcw7TfC/wNlCoAe81lGJkEiFSjZMO6QSuhDIDV85WoKhKinnJyZUDpluG/s4c1bRC4peYOjgjMdaiE0tULah+LOPojQZVPaLVc8yXKd35KfdkB68yQUws1msyyXx+8r/+LHnDIXSXnaNlMivxSodpgPHA7JWIQkMmkBMB/Ski9HEVgcgFXsXgVxyN1YTijmY3aRLlFjER2ENFYCx/4+9+Ab8sOdqsEsYOhSNfs8hVx4X6PNfXN0FqvNLhtELWZpp10Vc46/DQUPeIhzkut9ioYDSsMh367L/uIYTEWYdMDC62pH4FgUQoiU5L3BHk1338x2P0KUPHZNx9DrKGwusImquW4bcrzF0aYWKfSDrOPnBAtT1l/31VLup9Hpvb5Ea0wGOXN/je9in+/isfnW0SaYcYlajEQMfHeTNLkMpK4lMBo0aAOpvx2BNT7gpN9ptjgnRAvqDwrSR2CkIQpSQYZIg5D3yDqEdYISlSjfe5HDtUFM+UMEoQ1QrOGPyuQbYttTOG0bdzPCkphCOve4jFk5h7Q4zSxKs+c2+NmA4y7GYVl1uCVYmuKYp6lZqRlGKCQqJDD7GS44UWWjFDH9RZMG/amaFOA7Yk3M3JEXz71ZvM1Su8u3tI+3MhC6sdOme69A4TrABhIDwwDCsxf/mddT7ROsuv3HuJ8VHM4lVFsx5w/5VTVKsBAGVRUpSGKApQWhJFHvt2F2csk6TJX7/xGSZFi//ykiFEkBYGz5tp6h+aU2yUfXqjnH1R4eH7VvnCjzwKwD94/jWYGLLC/K+eSBnmI5p+43990XLMMcccc8y/HY7DZn/XOG6kfJ9QaokKwHmC+tqQpNdBGokXSn771sN8fXsVdx3atywt7dG9VOfexphyLafQDoHjkx97k39y+33IwDIXDDkqUqrNlMN7J9C1EqEtxrO8sT9PP63QtDFzq0Nev7fCwUab+7qbzJ0Ycv/lHf7iA0+jsbzb8ji5U+f1gxX6YTgLepXw9M1V4q2Q/KRBG4/QLyF3qMjiGpJwviDMCuYWYw4aIckJjbcH8Z0qxkDiQeu+OmZb09+sUGkXpDbA61iMdgSdlKIUpDWNMBBVclqdKdNpSNmyyIkl8gX4gjQzbI6qDKxFiYIfrewwtSFXBzAIPeymT74sMXcF9/Y7qBOaP/bgS/zMu2dR0oGEYQirUYm1Ek8VqNwilUOdTzlzeoegIlgfXuDZ586T3SewucN1LQvhiEdObLDpReyMGsSlR6J97KmChleQt3JOro3ZLRt0oinrU8uRqzJcCHBTyfkP3eWdd85Se+9uaPT+BOtpnnrrMp9+/E220ia2YxgJw0SU+D60g5jMaagxUyHNsiwpTwfsBk1qD43wn3EwlLgSrNGIoUPWfUTHxxVgxxCjqLU9krJAjXKYaAzgDxyPnk74A5/5LW6LJtn1KoO4iTMg6yW5r7BOEBYlalpg44S1Vs5uCkJAf9JEeQUmTsnPVkAYrng3uPf6JfJI4oRAlDA941EdOtiWNB8ImVceleRdfvDyO0RhSa2aMNBVXnruHO37YvZsndLXRK5EJRJ/XEJS4roZZiJozE9I85BH2ndACsZZhXUzomUiECkuEeRa8emT11i78Ap//Uufoi+rCOdQE0s1nHD23D6vv3uWYtVQXTcUvqXsO3SS0U5CjHIQediao9AgfImzAnJDaCw4S7A7pLhbZdqtsJANOWm3KUyErbjZBI0RlJHAn1hMBGXgePPNM+h5iycFw4ajsufhyYKdfJGWmqLnNG7fQxoLTmA7FYIDi3MW0xQ4K3BWEumCqCYpRxpXOGxpMRUJGSx/pMfp9x9y+5VFNt/pokYlLrVYp5ClAF9T3OcRnM/oNiesRkOKPyF54f/ZIrYRTBSltighcAK8YUna9FGloL5lKWQFConyoLElcF6J0RppBUWgsFaC5wgPLG5Hc8dUGN2osjCnGN4ukJ4ir5XEkcI/SHAHhrCRkqOhLSmLgN3Ep08Ne9CAQYGLSxQprdc1JvTRiYLUYVslxmmcZyinMcWpCBd6iBZ4eY61IbYhwdawSiBy4Cil/7rm3WGb0eMNWj8wZfKbFZKzgrTtiK4a3MgxaIY44yODHJfx3hTWTO09HoxRzmKkQDjwvZSsrfGCMZFZJT+a0gkq9E3yXgAtWB/Sasjzv3AaL4wQNYFTJTiNQpClFaQocU4grcAVFj22yLsSPujIRhpXWvTUYEYe27c7pN/RTJ5t4ncThDB8/DPX6Hd9Bq0z7NJiZ30Mm46Fz8Z8iE1+7S9vkt/Xhfka1lNgLNo6RAHCOkrh0C2FjgzB2ZI//InX+aXvPMyNdoV4ogiHI9Kkze52E9U1MDaIUpL/Qom5HEHP4eoKO4DRyw14nyTfs/hXx4g8w9YqZF6VcCFg+5eq4CQWh0nGULZR1qNYayKsIDsVYt4dQ5IhawqmBZ2VXcaLS5SFhL4h86DWSxC5IxuH7N6uUAscp1qS7b0pzpSogxy5WEEWimhgsUpxdDThqbdvszEc8cGDs9RrFdqfOsP1rI+8OaVTqyArAfdGfdo64NVrdzjcXudkdY94coHlpRbpNKUoZuHF/+IXnmY4jPlD//FHaTQr6EBzTnyRJf9+vvwzTzO9e8TkB3zEj0KJJSsMk2lGFPr80Nrj/OLNt9ndi7ANxac+ed9sxUcICltijWOSZRjnUP8atcZR0uOn3vxzFFZSGsMvfvgX/80XNMccc8wxxxzz7wHHjZTvE5KHPbhryRrQaOeIkcOUFplZmrbC1XGEf07R+PaQx+/bprB1HA6dGeq1GF+W2LpD2hQ5dBzSpqGGnGrs0k9WyaQmsIZmNSGZhAQji8wk7TjnLT2PrkNlrmB9sEAsfZLcQxvLr4+uMBwafBkzyCuYUuJFOdVQIHJJpAqaKyk7d+qUmz5EFrViOfngPhcaWzzz9GXurrYo60AG4oYju+2RnXJcv7pEloWIFFoqwT8V4xqGYDMk1IJac0JzaUgvb1AG4FUMlSIj2HTIQcni6oA7YYu4VIxrIff6Ed2a5WSYcz1o84PLbxO1U/7qrU/TbI7AF+wddfmlV97HpdV7IC3FzB5MYiUHPZ/GksKfN6BmpWk1KyjHip3dJ3AeDIMIddMQ9RMmTY/9SZXn907xwJO32R37eKGil4H+wJSDQUh1JedC84jEenRFQpHB/d0jtBUc3e2QpQG1gUN5imSaUW4p4rWIVwY1bn2zQ3R/ympnxFfiOc6EfXxniHOPnqmjpME15exwrwT6CIxT5ImPbQP3HDorcT2PYtnDZJpOVRHPlThhqTrBRxdTTjxZ5xdeGZDfUHgTw/l2g9OdHc43+3z37inWD+egUAhjsEajrUBISZr7uKEmEA5fgx669yYuZmG4SPCbBQiYVkBURrhpC4DCCrQTlMJDGINaT9gKSj7ZPZiFJVvIYon1JG/tn+AxOcHcLLGXppSnQ+aGdQ6mU7LTIafP7pIcSaphzr39JW7vzSMCy9E4nE2TDDOKlofMZtlAH1xdxxrFytyQ/rCGtCBygTvw2AjmKeqO+HKAv5uTZiHCxKjYUqicoq4R8Xt5GuGsg5U3NU5q5CBGJo54GiGdI+pn5BuKq0+fp+qngMR4DlEKylBh/Rw8KJhNqYBlOdFMZIaYFoiuwTMFdqCpWEt5YcJ0p4J0grQu0VagkxwrLEIIVGJp7BUU845AWczYkEWStFNBT4H9GH/sqNcSFKCFgWlBacDWI5ynsbaJq02Q0uKcIE89jFPIYqaHlaXD+mCFxCshVJbSzOwxKAlCz0wyuaG6WdK77GFxs8DVQiCEJdjOKJXjztsdfOWITmqkSBGej/LB+QVq02GlYjyIqMgEOSgRtSpL5/u8LTp416Y0cNjlGmJfYhOHyi3WGUwDcAbnFMJThL2C4Koln68ihylr58bcGawgJjPbkfMEJBbnO6b7PkXZRv2wJCgsw6rAPGGhKZg+rKm/Ibh0ZZ0gUrzx1GmMZyGS+D2L2i2ZDmNEbpHjCVQ191/cZb3rk+4o1t5/m/7VE+RJxqQZ4wmDWffJLzRxvqOSKfJGiAnAFQadg1Cz7xOhh3CCZE6SN2c5LJO7Abe+sUDv1RBbOkRcIEOP0Zcj/DTHlY7zP7bJ1q0l7rzToLU65uz79rjRW+J5fZbrv7XK7jsN7l7r0rJVhjeG5NUQkRvy5erMDFTa2WfcF1S6DiElKgr41S+/D+s5xIUIb95yN51jXsacfHDCHa+Ni6botMSTEr+TkOoQPcrwFNRcTjWzxHmToHsAm4Isd6jFOmncgNiAcFhpYLGFCH1kZhheDsDzsRJKqRCVKeqCQ90T1IuMjzz4Gr/6nU8x8SyEs8+PxBL1Uzonh0jhyHY62FJQSkswSGi9vMf4TANBBWcNMnW8vdvHaXj59hafeOACWZyTLbXQ800OjqbUjeP+NOAHTn6dg0HG2Ys7nL28y9s1ybnOR8mmCVmWUpaK5757kyTN+PCnHqB+pTKbNBIe9fIKozvfwu9D864gzguG45TN3QFZlgPwUHeVT4/m+LIv8c7U+VLyFs2DGh9aOMPhNKZ0FpNZfPWv00aBv/3uP+RgWqMZJYD4nebMMcccc8wxx/xvjeNGyvcJz//F/4KP/r9+lgdaHW49G0Mu0KXlV177Djuxx+n/oM/8nSkbG0s8+Mgeu+oGfm8OlgxaG0DwtWuXqY8yxMAjTwVUoSo1nnRkWmGV4mK7RyxLaiJDy5J6MyGdRLAC3bmcxtw23372Af78+BN88LEb7O+3GW9XCQKLtpZU+NTbKaWrYCcBgZzieSXZzToqhrm5AQ9+YJ2NjS6HO03iQx/PFlw+s8VP/dBL/MrkIt+7eQKZCgoTknc0noD+dhMbZdSrCXkh6Zyc0OhkZJlHiUIdlERnS9L9AD0WeLmlVwT4wxIZOIJahjCOYQ4/vf4YV9M5LI5030PsVbg+OolTCu0ZGrUpSjiEc8i5HKFgsh8wsDWmTLB1cFrhpMC3hhPVCTbf4N10EdMQiD1HWkLliRLUiNgZXh+fIKqkXPE3uXfrEWw3g6ggWCiY8yd4wuBGsDGaJ29scSHc5w1ZpVsZ0zssyOYUthGR1XMyYfFrOWe6+8iqY1yEKGVJjea1OyfZL+ukkYcoNVhLtGNmilSj4BBEXZMNQzyvQAtFFGTItYx87DHa7+AfWsTUYFqSP/MnfpEwLLnW/wTfGzRROmea+ewNzvMPXv4w+/MS51ucA1dIBAK9Z6gs5AyIsJklcbA1nsNTKaYUlBpQEtsQWOUQwLI/ZDO+TF5/7wMfOoI9w+KJPvev7fDU2ytkiwXPbN/HxfNbtGoJeV4BY3n40btcfnAHu3yZG03N9HXFSOXYlsb5EFtNbSlnFPvIUrGz3qboOEqj0dIhYyiXJd7I4Izgy29cIpuE3LzXRbgCUdc4X7A5ncdbL5lfPSRMLYsfGzP6ah2RGcgtVllSLZFpNtPlCg1pgQkjUJBph8gCXCPCliUiHmMmDr0pyaMqXqugcDNDkRECORWYUlI0IVkTdFQNjMTrW/KGYmw09pbFTAzhXIaXypmu2Rfo3OHKEtnNkWUARiEqgv79Ee5WxKlYMh6W2KlCVCw2V5BpmMID5+/Re7PKdFKnNII8BBsJ/FqCE5JhHvHy1knETUfwpgbt4yQ4Aa60kJeYuk+8qLG5xCmLH3mkzuAowZMwKJF7U8xDVZwSmDHoqqVMJdmSwJWOzllB/mqEOzummPcxkcMVdjYhFDmUlXhWkQY18k6IliVEisoupA0w5ATjAJTATxNsJHBiZqlJFj2Er5FaYJHIqZsFMVckDz5yi87tKW+8eIpsoQZCILolYl7jNhVZ7BO/UEeJAvuoQd3SpGcUTklWn9jhkx+5hnCC9ZfnyccVLA66JaKA8dEsVNbakrwruJ23KI7GnFnewz8taa0VDH79HJEIMPM+YtvgjQ2uAiJzqInBeAo966thpUDkDmctTkDRDXCRxPVjlh454sznDujcX+WVv76MNpKyLJHW4VBUl3I+9sg647Utfu6/eoJP/Xf3CLOStcmYl3dP0f9QwHdevUI0SmA+wjaqs/ybAkpRoD2HiQRlVSGaHlE7IS9DkoOQF96sUZ710BXDQgc6J4bUKxmVKGXcdmxlIUpJ3EaKvWURKoVOE6+W0r045EZ+iuS+CrWNKtQT4olGtzxkM8G7XoKDYrlBuRBiA0VZKtZqu9zVJ3HCkS9H+MkEmwqcpxibJpv9BeROQuAK5MRRLESoiUDGObIE2bVMkiHBfINyzyFzixU5Mi4wbY3FYHzLn/sPfpU7RZczc7/F+Oiv8sbWHn5oUEcC80DIehEzzwbL0dtUL6U889ULnDq/x6DfgqahvzNkdDSls9im0gohdrTnakgpCCsBiS14Y7DNycdXOHj5DroeMIpTssJgckNpZrPUbw2+SWG+zCevKL79/B/hxrUtktOzAOSV+Sa3R33qteBfq85Ii4J/8ZZikK9xamWf09UNsjIn9P71XueYY4455ph/ixyv9vyucdxI+T4hCAKe/zP/eybZlE889dewTlBrZkzDKUa2+U9OPAsm5Oon+qw9vM63fvsz5G1L0DEo4cAKAmUpKgJZGFTf0Ss73GhKnvhoydfGgsnEJ556VC4MueDdI92OiOMQTQ3R6DFQFa5vrzJ/ekTa8/lP1q7zcmXA97bO0pj7EZ6aPkWuBOVE4dKQXEimacD+3gGqHSKnPrLr8dkH3+H/+o9/P1vDLq6lCKuGP3T6FmcbAz755F2+MTlL2VQUdYvfk+i+oxmMqbXHeBMYZgq2ffaGdYZxRKpgrjKa3ZldcWROkJchn774Os+/fR47tsx1x9R1wTZt3prMwbbHU3cvUSsL2gsjig2B8RxGCGwq8T1DUFhixExlGypSfA7e6eB3YsqqQEjF3LmM5hK8/+Q7vPpUk9z5lC2HjDwqyxPy3KeV5OzFVWrVETfKLkI7fuj065xrHfDawTLPH55GS8tHG29TrcZ85+YZLjSHrIs2q+19RGJwEws1hfYKQk/QqiQs1iZUM0PU2mHeGyOd5Off/QjVE1OkBleCUwVWa6QEF0hcbHEyQbsq9ZqPiEuW62MmieNcOOLl6RwmlWgLrrCMJwqhDMG8I27N4eUpxRCqwRZ/4Mrb/NLkPNl2FWM1JBki9LA1Q2oEIrNQSqwv6KcZZRvEdBYc6YTABj7SFUjpMPuaqtPsVMrZ/o+FuBsyGjd4cT2knEsJ44xWkDPebdDpxszND4n3FCfWDvHmcy6dkbzzFY2IBU4YdJGTS4/41Q7jO7NJDDunsE5RVkt01ZBPFfpjGZVqQfFyhMkhnmq+/exlLB5CMcsq0RD5KZVKwZIcM/fpbZSyWEpu/Y91EI64o0hbGiE95HaCF6eoAsS9I4RXktUkYuBgIcQpWF5O6HSH3L56EiEgTxXOnxmHnBQ4BE5I/NiSCcfypW3WXz4BQmEqDonElYKD+yuYdhU1ygkPZgYYHTtUaXG5RMzNxDDCOaQSmMzncCARkwJyh67OcpJG1xXJKbj2xmkmowCbCbzCoHNJhmAuG+Nsxm0zz5nqEW4O9vNlXEVircGLQXiWbithGlpir4GXlRQFZBWH7AmKeZBSIBoW+jGl50BDOC1IhlWEsNiwRB8Zup0xg7ZGVlLIAkrhYREEZBQXQY7BJmAcOBymY2naAu8oQKkAP89Qo4LGUpeDecO45RNupBTW4EZTWGxgpEDVNH6SQmHwlUG5nG63j3An0eMc6zlaX8xwEpJ/ViHXHuVUYeZLmHOoVCAHghPtHu9/+DaZ0rRMPPPNpwkykbR+JIWJoBx6ZHuQzfsUq5os14hSc7RTYelKwqQi6DWOMIeSpKaIPEV1O2N6QVKsVvAPS2QOMi0RBymiGmFDx+QxUIVADECWYKsZu3clndsN9t/u4uohpneECTy0N1OTX/z8Hqb0OHvhCNnyePb6eVpFygMr27TNiI2sgckMtl9AAlRqYAxkJaJvKJcbs++yAm8MxcsB5rTE7QnsvIdxYpbB1dN4oUUKR1lqevsRHoIyidHJ7HEKixuOma432fi2z1T5yNJhsoiybylORJhAzyxjWYoKIoJMwH6OUwbGOeVIETyU40pJNYV0T6O3wEnB1lKLzRcigklBMEqwkcYaC3FB6ntcf7pDOI2p9QvsPYFHRB4mmACoaYwPeUUimoaDoMbbr61wy5R00ncpMoN3LqWwEXbO4PVTxtPLLFcf4Zl/epenfvmI7/zT++k8skhwZczFRsCkP6Wz2GHp1BxlXuAHAUIIKrWQr9x5k3+58QLzT85T3WlBLQIEFSmp+d7v6CZvDQQngyNub0dw/YCzD55kJWwC8P/48R/hz/79X6OwhlfubPHYmdX/v+qM1JRMTYkVgtxIStVgkO+w5J3+N17THHPMMcccc8zvdY4bKd8npHFOkZf4kaay7MgOZ4f++xc26ZcZfdvAKywnGjGT0OPyA9/h2aOPU1/J8ZRBS8UETdQqyRYs5W9pyihC6QEnH7/H3DsXyHs58VQwF+WcePCAd7cvQeJYjEJqjW3e2VxklHXZL2sksUa4ko8v3WW30+GB8/fz2t6rDG3BpAwIS0WaaZKpIxc+lU5MPvTY9ir8wuBRrK/AB0RJ0DT8s/VzeMspR4lHM8gY5R4VU2D6dbxYkOVVzu8dYeYENT3kRtyispKS92sYKRFdiasIwmqGDUBhWGv1eLZxDpzH3f15fuPO+/GOgJpHlJTUNwVKKfqtKp3aLm4CyThge9CmGmSoxRymPi7XZAoYFKS9CvKEpQxneTVXdxY5PTdhJcgZ5x4ogUgFjCWjd2rYyHGunjHf6fPw3C1+8Z0P4KYpl/UB3Tjn2bTKXtZA4Mgij5XOGL+V8Ob+MklFslWpIa1C9RKkkfh+iYfgZGdAMQiJ44JL0T3m6hnPJ6epGBAlgEBKS7uSMIgjnGRmgZE56mEL3YTpWz6nTJX1sWF0oFn3JZfPL3LnYJNyANko4ulXVvn4J2+AkGQtzUEYUBnmdOcnZIlE7TlcD4R0zPZBDKIiyQcSJxXgMJHj/NqIe1YwjZuozCHtrGFS7vq45Zy06lMaqF6H5KyDKWTzitHIwziLWnDwjmBnWOPwbIW2DcnrYFDEOuCodLjS4l7zkVFOeCZilGusgmKo0HHBqhyyFYSgIC8UYmLAc5RLlqVgyO4LPjaRRCbFCYnzJIW0VCezsfZQ58zPjzCFxQzBNSRBq0BoifU8ZOwjq1A2JcnpkNqmo4glxClmv0d9vkPmC7KqxCrBTlqnHhncYkmgUopRE507Sh/8fkkZaZzvEKHjzPwhtciAdeTzUCwI5jZzfvSzt/h2b407QQtVlaiqQBYCoaDUCqF9xNAgqxa9W1Ddz0lSD+XJmfnFFOTWzQJ+Dzze+JU1kosaY6ro6L1skKRApYY4CEiGPvV6ypluj1qtoGn6fP3aefqHESa0nPzwEX/vi/8c5wSf/wt/jGkZQFsTqII/+EefRqqUX/r6D1CEGoWDAGQhcFKhnMEJidd3eAPF9IKmc6XPtOJTFgLrBK6YNcKcMVDkuF1wA4F3UtNoZgwCjakb2JOIfYWsFFRCTb5mSFo+ZWTx0oROd8CgF6FCD39skLFC+RYrLX0TcXPrFNbXyMHMqmN7GgKLmwpEXoKxqG2J3gTjLG7VUlhBEvusVPu4IdQ7E4Y3M4QGvd0lOCmotTvsD8ZkXZBNcBNHkgYk24tsfrlG5ZMx8nxOGfuUgWDl8gHTiSaJ2pjYAQJh3KzJ1gxmqz1ViW2XWOmobue4bgJzCfaw5MZwniKtzNbM/DrqpGX1J/qEkxzGlntpk3/6f3sM49fp7h7wwEM7LHR7JInkzveW0AOLUrMcKn9zTLFSwWhN80AwCQuKeR+X5cihQaQKK0Okm01OkDmyMKV/Fx69NyLreNz7VpPBBxdwRU7zWznOTEFpnLUwSgjrhhOfyQhyx+bX26QTSSEk3rUpup5RPOpjKiFaRYgCVBxTnKjgD8HeyWnt7OMLibIWYWa5PiiHC3zyzCGcRQmFGheoOCfBoITCThS2JrC9EIIQF+TYsI3OHSJ15J2StK4J7qTkdxQHX+3iEsdOdx3Z0ays7FE+qjld7/HSlx+kYyOGB++DRLG87DCRJlv1uOFGrNk22+t7LJ2dp9GuoARUKt7v/NbvFN+mW7vD+ZWPcq8WUDjN5vYRC906tShgeakFwJXWh/nvXvkhzFHC73v0g5z74HkutucAkEpyVKQcjKdM04y//eVnWO40+OIHr/wv1hkWRyUCW5Y0vJzz1QMycwc4/W+sljnmmGOOOeaYf1+Q/64v4Jh/Mwx6E44Ox0ynGcX+TBVZ5hqbWWr3j3hmcpp3equ801ugaTOarRF5wxLVErQ0CFkyb6YkmxXMvYDslI9qlZw9t8fTwcMIf0pVp5yd3+PsyZ2ZIWeqcYlHOhyi9i1hmDN3sketM6XQgr/2/Ef56rP3843nH8dIifUsZakZqwrThkDG4Iwm1x4rrZiitIwzj7cHS5Sho6gpvI4h831u9Rf5xf37UF5JJUlwI0E5UlxY26BWTxGe5EDWKJTAI2YqBYfPzMHIQ+RQJgCOiefhfINRgrs351n0E+bnY4wf8LVr9/Gb985iQ0P5XlZDaRXVbsyjn1pHVO3MHCIU98oaZctBqmfhnUaT1eD8uT1WG0fo2GCFZZzV2B1Vee7aaaI++L0C5RWItmOl3WdOTNgatHlc9fjDy3eQqcJPQqZHdW5srDDqN2kFMXPRhNQGKAfxsx1OLfVorY643N3BejOdsNdL8XSJ0oa3jla445rcv7rDxfYh93kJ394+j84sJpWUpaBwivnzUyQlZQhYSHKPONUYK9Cbhrw/IdgTsA1ZTxG5VyHJEeMCWzruP3dEQzs+/sAGtlliIoc0Cbd6y7y8M89Rr0pQJuhxhu6nyO6QOV8C7+l0EcwvHHHxwgZBYLHSYTzQqUOOLWpPkI0102FIaSwVoIwM5aqDEvorgkkX0tLDHPisBj2whgTNqFfFoTFVgcYxSC0gsMYxHM4sMCq1yEGOsIIPNXZZ8KbksUb1PcQgBOtoTArK24LcgvUsB7aBKR1WgwwEVjus71g6sceZ8zs0qxOyTFPkkN4UiFIg7ew2sd8ziNJgtSSd8xFa4WQFlQWwnWLKEuvPrCVmELBzNEesPVzNQ0c5XhET7U7xRwbpLLZZcOHRXX7kg2+gp2OGviM94XBNS6Wb0lhM+fjqbZqjGK81u16kwEqLrMtZNko/o7F7RO0wxesb6m5Kd6HK0SXNaEmSzFVI5328FfjoxzZ4cnUDdZChMoOpaawvUOOCo7JGElW5OLdHmSo+KI/4sz/0Iv+Hzz2L1CWWgosLu0ycIaWkFlpkJmhuaB4UhrAVczNf5NMfuQpKIkOL13NEmwa17yP6EldKVCqRhSTZrjF4q4W8ukLpQFVKZGgIbYG+pVEHEt9pokziJYZqscSkElLOFWjniCuauZVFLjx+hkpYAI4yKqmeBnHRIzAl/lFBWZFkqz5loLAVzbVblxmLJnLFkK+EZAshk1+vEn+pDrnEaEAI1MgRvZzj71joK/a3u3z3y5cIXUGpBcMoxIYeJneI5+vc//YVlnSXZhBSRJJUaUZJiB0GEAXo0EdvdfC261gZkIce9eWYSmSpbJeEdwvUsMSfWqhpbCcET6KGguh1ydzdMSuP7dD82CG6kaA7FrVUoM5kIAS2ocgvKpxnmXged745x7f+3iU2Rqu4us8H/+AN5s8MmfditmyLYC3DW3IgBZQWrzCEG0PCG0fIpKRyN6V+M6Fzb4K3l88aG4lB5Q45Kd7LpIHTwYQ//qmX+OOdF4jXW5i2TzEfIHHoRhVRFUjnkIDdK7j324bpdgrTgnScIg9LpHS0w4z/7APvcuFPTZg2LKlvyE5XyLoeIjdYTyNzQzlKKfoxyvMQvkcZ+phIUtYU6XKEaSqEBiUl4v0dhO94/MN3uPKhA2yqcDiSlsCcAiMcpIZgLyM8SPEGJV/96QuYgwy3OyHtT9CxYeOb59l/u8Wzz91H5TXB4e1dUl1wdDTlaBxTaYb8wJnz/PjZJygKx/bGJm/d+ybSh+Z8nagyW50xxnKyssJypcnjixdY7TZpOU0xTKk3Iq48fBKtZ2XdqeYcfu1huqffxxMffYDluQ7/8EvP850Xb5CVhlwZXABfevktfvbLz/HT//jrjOOEUZLy07/1LX7t6tv/Sp3RCiIudpvUg5Kl6pBLlR1qov9vt7g55phjjjnmXwvhfu/8fb9zPJHyfUKrW6MoDNv5EWmuQQrS3ON6b4XqC5rWhw9IDyq8uXWS9z12jStRSutSDxNYTkZDhIMPX7rB39r8HGbkQRWU73jn2qcQSzWW228j1hQHRYPhzTPU8wk74zoqsojaCDFUpPOCQVihaILICr62e5aju0+w3KxwolsnGFmqZYHCQL+OSBUcOrKWz6JNuJVZIpvhkXPoNCLTRLWYfVMn2BVsBSt86ULIkWphPI9uZ8AXf/hl0onPz/2jz7IbVuj1Q/K3I1zdw9Q1wjq0dQRhgZw6nJFYZdAFvP76GT79qat0VkfsJSd4OlnF83Ji581MKkoQBgWfPfk2UyIur+xzdbBMe2FKEQjIJVgBCkJpWKPPk8s7HO0HCGuxoSVSCfPBmI3hA7TrPsPhhPy0RWWWTjTlJXOSwoS8MFjlC+51Hji1za6J+B+vvo8/ffFlgmbCfY0JpYPb8QJFKSkair+59g32ZY34ZpUXJPgVQVlKVLOgFLM780+eW8eEglNVQ10qzkd7PHJpn3zO4zuT02AFrpCoQiCPZnYNa3wCPSKvZ3jLPsl2jg0l/kGJKwQbyseNmB2GQsNef55Tpkc7eoS1yYTxtYx8aFi6eJvOUg9TWWTpC/scfKdLlghsGJFahVQluYA8kvgm4K2r8/TCOllTYDFYqXBCoRKBt67YG3fJJgYD6BsOe85hI4e6KxicqCF9i1wJmAifhYUBc36Mn+U4Mn6s8zpuqnlh6vFKXCNd8pGFmX1xQkFZ0YDjpeeXST/pz0xGBoSFbjMgf2WB/YOUyk4JSvKNWw/QKBR6YnE1i8jANR0Vv6TtTdlvzNGJEtYafXbSGrYWEJ+rUs5pgn4JTYdX2llOSiZxDUl5cQHby/EKQ2WQ48Yav1QMDgVYR5J4iEOHykv0NIcgoAwEmafZ2W0yPgq4uzWPc45wQ+F8w/RdwS8VV8hKCGsFpWWWhZJbTF0ShTnFSCOkJrVVnFVkYx/Pz1gfT1FWYCoKCTjnaFczUB6ffew2L714CjXJKE6oWUCs1OiRYHm+xx+pvMbLvTVeHc7zaOOQ515fQfoWZxVBWVICBnjooR7feLnFxbBDJsZM4ggXS5IsQA4VemmZ6AhKCcpKnDA4X5DPSxrDnM/M3+Ibb53nyuo+N0Udr1JANScYlygnyOYUel9gncOgybYO+dtf+Covjhb4q3cfRZ5WbKsxP3L5JM9+c5fqcMKf+NNP4SnDl75yhWRgwFck532KhiaY+pgkoEx3EdoSRAXpOEI4gZIKlZRgBaKhcRasnk0t6aGllqRUK4binuRbT92PXXYMbET0UAMxKZkuSG6JfS51V8hHilIAmaBQirItWKtNaM8NGNw6gew1kCoGZ7j3RoeyWZL6NUQH5GGBK3KIcmQQQSZnAb09S+VEjKobsptNzFMGeRjDfE62XSedE3h9QXrX4963u9C2eCOJvVWiVxzLKxO+/dr9zF8c8qmTV7lQOeBmbwHWi/fMPGBqGucp9DTH5gaUIJMCUfNoxBPKUmFqCpEa8lMBwcQgYsneqMXPvfMhbuk5tk9VUPcc/n6MMw6nFM6vQR5DXoJyFL8t4JkE1Zoioxpe1eImKScfGfPYxQOW91J+Jl9A1jWuJdEnSioLjuRpixsalJpZhf6/QakmVLiGQliF0A7bCHBozl5YYOlEla/rCZXTBUo7ROBBKimrlrlah4k7RKQO5wu8YUmuHZ4vsXFMkDpsMrNkidJhf0vR2J6iewP23t7k/9y/yvmlSxytjknXPJ6MAmrW4+SFBe7YX+FWuc3R6IcI1ONkaYHnK6bDmA8Ev48l+UHYMrSkxistb7y7har4fKZyH9XIo9qosJ1sMhK3oVJnbrHFm7f2+GcvvsHanRYffeI88+0aRjs2No9wDqxxjKcpf/fFl/nlV97kV197iy9cuf9/Umf08ym5SJE+VETJE8E+mux3r9A55phjjjnmmN9DHDdSvk8IKz62zPi7t76CawMTAVaQJh7jzSaf/7Hv8M1nnuC07PFGf4GPntvECwy9rMqanSCAnosouwZRCla8mFo9Yz6qcnqpz2MPfhs+An/zl3+QdiVh/ajLgVfHCcFi5ZCWnvCGOEl/EFHzch7q3uNqvsLVk3tUehGpccS6xISKvND4bYvYFsgStFfgKYvwDMbTlFYjfYNOoRw6PAn2fEmZ+tze7RCkirCfkLuSw3GDd2+uYg8UXttQbZWsXTninbvnyIRAdHNM05GGHqWSlAcS78giUVTPp9zXPmK+MeLxtSmb+x5HgypHkwA1NXiJQ5SSvY0u9bNT5sIBfr7EmWLAa3sLUFiCzhQT+1zw9wmHgsW5ESudgvR1cG3Lg8E2/Z2QB1deZGQ+T2+UInYcQpScW9nldbdGhsQJx0asaFVjSi9AH0x58DN3aUVHvDg5x8Vwm31R57pcIV6wLHqS0+GY9QWBrAoe+shZvrtxD6ckaaYYDkN6kyqnvD41IXHO8Lm560SrJXcGa/za7kWEgz905kW+ewh33j2FzC3CkyxEMa6TM7xVxS36FMKg6or/5j/8F6zNj/nz/U9zeHOBai1hfxyykUlub60zfzuEmxXCExUOqh1O6U3Odg9wHcGrd3xkAqIhmWZriIoiqQvwFMNJg6DaY+pLnLCIzKATS1n3MEJgtMNuzZodEghWDHHNEuxBoCGPDe1uycAoOnM5UWipiQxRszzw+bs8PzjD5emIVj5GlBYmBU45pBXQlOAH5JHk8Had4ShAenbWYPIEZqjozTE7GN4t0UKghKCoK5wB41uStsKrliRvRDSXp9zLL7NjDGcbh4xHIa7q43yBkAZXL7F+gFUOfyoRZUlQ5hR45C0P6ylEDCI3GJlSNAP0kaC0bta4wVJ0JdFCiksCRK4ZHXb4tV/9IPWzGTaUyBLUXo4LQUlHt57Sfzei6ArkKEEJH4PP1PNQVUNxTiL7EN7IUUKSxj5FF2QuUZ6haUckSPpDxd2TLb770scoFiKkTbE1QaEChJTIwvLp91/lmfwk75QrTOOAr/3nl4nPhNiugDLnW1cv84MPvENDwCvbXdCC3csJxV7C6CvniWoZ62+fJNQZxdAgT1iKeQ0a/D4kcw4nQXQcXhXWlqasLm5Tqoep6hQc6D1HkXukD4J+LcM7cLgc6mFGnHushFPMQxr3RMBk3fDS67eZ9CcEVUu9miOcxd0McdbDaYU/zrBVEKWPdYKi1CyeHKLymOTtCk5rhBXUTk/ofqTPwRttMhniKgLvpKG7NmLwUMAoCwnX4c1fu0Bx0oHxyCOBzAxcyOhVYq7egf2jMZ5UWGcQEygaHg9/9h5hmPF6K6P69GV6eOhMsXpuiE/MK+kiVgZYbVCqROqE9JTm4xcuc+/1XfrDIeVUkx0J7HaACST1j6SsPT5ku+UzGnlY6eE0GKtRizlm0WL3fVY6Q+ZqGXHFcu9ul/1ug6VogPiGRk4cVCQyyTGjFOUriAvylQjb8rBawlaB708oYrAyxIYSq6GoSKxT9C5V+PrGA3jNAiSEWxn+oWC8VsfupfjKwzXrmNEE3ZpCOZu4tEmCEB42LwC4c63NM2/CM6+dhIqHNzKUsaZzZUptvkSOYfw1O7MFSWYrUMqitqeU5FRtANbh1KwRtjBf49mjbT788Dovvn6aVppTTAxogQo1vWmKVw2wnoC6JA8kzkrsyKEGAmcNohKgRikikXiJBKMoXY5LHPYrCZMfmczsSqll72DE5mafK/ct8MZ32hRmg813p5x5ktnabqARUvDbezf5ys67XCnnabaqDPsp13f7jMqM/jjm93/sQS41KpQMuVCfMOdqeJHH67vr7KQxvd2Mw/GUzBiSuOBj3VW27h2xNt/ixZfXOdoZoRLw5L9q5Gl4IdvDlFGqeMMsUZww5Hbwu1/wHHPMMccc8z/Pcdjs7xrHjZTvI3ypmfObyHAT13NI4TjbHnFYKfn1249wdKODkvD13mXEkk829bCxJp73UELwSzvvQ64WON/RW9cMJj47ZUn+gas8MKexTjDKNGvWUDuRYG8LcJa6mGNp8Q2eMT5ZGtIKM5YYc2dsCMIMVckospLEluRUWAzHXFpb4dVkh/hMhbqLePloGaoQZ4p0U1EVjrAsmd6rMXe5z+Mfv82N3UXeem0NkinB1oR45PEPv/txzMjDl4Js3GCiLIXSBEegohy/yFCq5C888dvsiio/e+9JgtSShwI1LZkLxyxWpgQy4d5zLVY7Yx48f4+bb57gVlmDdslURiRbmo13lpFGEASC6WGEt1AytzbBOYHKMl69tcb3+tvcencO11SwXJJJj/ZwyjBZZbQzYa4zpVsfcTetceLUDv5BgTCgKRnlFXq7Td59+TTNYcFLN+/jkZV9/ouTT2OzRX41r7NYm7JwcZvrm20eOn/IjcMO/uUOcawxcyHdao9hViEeClo6IcHnzQRGNuTANJCl41pSoxVkYBz34g5mGiLTGOt5rJ3ZRGuL5xWMtiXloSO8XIWVKW/31+jM3eI//QMv8jM/+3l8Ck6e2Ob1a2tsb5ZsJk20X3B2eYHOKcG6m+OwqGGs4LBZ51PzN/nwI7f45X/x49zOM7wkxxAhU0G4MsHaBrb08Y4s1lqctHhTQWSG6Kwk1HWEgInUqIkA44jm4DOdOkJEfHt0wL5tsbXR5dyFfVqNBB0UFIWjvZjwlV95AqcF4V7O5KRG5IKgZ7HGYKWjxKFiidOSoiGQBRRHOWlkcSsSXjZoo2iuGzIMZUthmxInfJQp6biMwJQ83thgJALGZUBama0VRBslatdQhpBFIS52GL9EaIvct1QYkFxaRBrAcxhZUHUZY+0hPQgnJSKA3AUkcz6DSBHlKZ412EwifEEiNU4J7PIEr5nQuRZjBiG66sgDD9ISlQikMyhZYgONCQqMX0LgZoHDpQXpqPULxouOsysbXDxxyKtvnCJuVpnMK/TIgpI47XDImeK2rjAVzWDNpwg8FrwhO99Y4NM//DbfOLqPfdNCepDfVfzvfvoPU0QFNgyQVcewLDCJYpwsovojgspgNomhNLlLwVTfWx9xyPi91QoUT01WyQ5C3r16EXPJY6AV4cjgbWniRYEiJuw5RCaIdcHrYZv//rUnsVgqDUNcC7ATweZuggs9Uif5+f/3R/H3R0x3G9imxIYQpCnRbUHSjBBpSYKm2k4oDhWmoUHMrD7+2QxpHVEnJbyXomuOv/ZfPsXK3Jj/9ugyX3r1MZa8PrYqKQoNBpxxyKwkSkuiuk9/c8Tc9ICHH+nxrb37KAcWhOG558/QOpMyrUpWO6COJCIX7B/Mc+LUNkGaU2YSFwqs9qBwyKlgGKccDWMsCc1uRlgv2BpXEfdN+eiP36WQHmdaR/yTZz6ETmYmMhcKZK9EJAphYRiHfPSB67xmF5lu19hanEOslrR/Ykr/FyrI/RTnK6zvw+EQJSSVcyWtxw7ZvNpGXTOIoUaTY6WhrPvI1CEyhxSafFlheqB2BW5fIKcGWTjM6QYH5wXtlxOCqQWlKI5CdMUxdQJjC7y4wGGQFY/YC/hHv3E/ruKTLznkdg7CZ3xDIo8KzDWB9gtcUUIUgnKEpPimpNguMSsBua/wRgVWSjZ2B/QXx3zljTO4do3RvZJ6r6Cc92iXMf1mE5NLkBJ8ha061OaYRlThfV94lKf/wYsgJM45bMGsYSIUrhpSTMfIWsidVPDJxx5i0ipYqzV44OISjUbE619d4cadAKkSHrk4QXsKIQT1VpUL5RLmrau88uxNFsYB/Szjcq1LaDTbewPWd/pcunSCJb/FA/Y6yS3JL+//PO8Ud9F2kUjOwsXf2tllkhd85e0xn3r8PkQJX/nWVTrtiCdPnuCR0yv/itbYupLF6jrdUHL9oI1A0lDHZeQxxxxzzDH/2+T4F/D7CC0VP3n2R/ny0V2mecbDVzaYtxM8lfFOv4WJA3LnSCdVrk0fYtCrEw4t6UlvlvPhJJPUQypLOM7IBhGyHHPt1Tn+nv8RrBNsyy6Xxvt84NRdFt4fE8uAbOvjEDgW3AQiRRjkHEzrSKPxhg4c3NjawXkQ6oKHxISznQs8V9lGTAvGwkMXAs9a6gNHdWKZXxoy2e1ApjlV7rMgpuTFiGupJKkbwjyjNpez/L4J6dRja3MVWc3wI0s61IjCEuYFZ1Z2mZ9PeLC5x8PS8bfKD5FVPUTs2Hxnnu9WL9A8kTKsB/Sv1bn80X3Goyr+WFKZ5AzmoH26z8GrXfayBg7FdtIiTwK0KDlX72ERTBKPAsU/v32Z4k6IEhY7Epw7v4fD5+Wr5xgPEpbPZtyr18ibHr+1+RBpVgEh6PU7vD1t0R7l2IlmkCm++dJj3Dtxl8cPT/OL1z5C4W/x5MO36F1r839/6jJ/7I++wD/+zhWiQhG7hJ/9j34OEVmu1U/iljQ3ZZPECd4pl/FlSX9QYeRqvLG+xiCrgRW81T9FlkYsDA5YWO4zOR0yKXwYacpXFCK1uAH0Gk2Mk0ynPpWlHjJ2HJkaXhwyPuyw05+jf6GF157y7tFbPFAsMe1X2Sy7OAWpp9GZR3nkI32JVeC1h8hdjfQy2msT6kcxw0FA0dK40iGdQBwWnLp7yMS08Wqw3KnRbw6RLYNLIe97HCxGvNvaJF5QqM0aT735EN0Pjxj1fO789lmutdZo/cFPMPXWsd4W0oCXO0x1Fjbr7xfI3FB4krIiKEMxCw41UBiLHkqypQhXydB9SWsaMK7BoZQ0uxMGsaYxPyIbWVYbQxa8hCALCcucb9XMe/YeC9ZgPA8KMZv2aJSUUw/8Cs4I/NghCxDCIKIcYXO8IEP4PhWVkEQhylmqKmOS+yx8ukf8VoN0R+AiQWsuZrDbxs15pAuQlIbF3RKkwi8MvrZIIbGeBGcQiUSN5Sw/wgSYoEQaS1QxTCJN0fDZzJZJb7c5HDQolc/Rfp1Vs4+ODQ6HSg3+YY44rZj4de66OWym0aLkj3/6LX7wsRuce2vAz/zmp7ER1Mcxqa1Q2SyIz0WQCdSuhdhQ+/CIBIF3e0zu10iER6s+YdQw1HYyRqJJZhVYgSwcxbpmkmiyRQMVUKWgum0ptcFUwQUOJSzGzaYPxFAwGCuKash8N+buvRDRL7HTktJT6KllsNdGbwkadUlSkVBY7FBjmgGynJmubC9k75UW/dsCETooHc63bN9pk+wGpOs+ulJStykXV3oo7VhMxqjCYoVket/M4iNTKOuQdqvwnV2yczUKX/An/7N3+cz776C/DF/evw/pO4a2ydHGHH/6Y79NuPY667/0YYaHmlilvLu5gKxNsE5CLBGhwdcGi0MKQVT1SGsTWIJU+vjRLuq+gEJ6GBTWM8hC4R3GKDLCWgavOLQT5KcUzmjuDtpcz5cYByGFJ9gadtkPI9xcibXRLBi6pfEWNGaQce6hIZ0HEnKjOfqOwWQaF4Fp+PilZf78HsUbFbK9iGFYEi3mqK6l1Ipxoqjtg2l7yEoXtzLCHRTI/gThFPJ0gT/U2L6dGbxadVy7jcgNLrHIwKGPcmzDw3qO9E5Idq2N7zJ8X2P9IaQJ4ZolMjFnHx3w0lNnSBYDfE9gfIU0ktt7I6iCv25YXdjFnZJMr9cgKTl1cgOxlTFOahAKZGpRSUr1ekIiE57lJc5fWuPWxj7CKfR7WSpOedD0Sa/UkE4RL0vi992hFt5ivNui1XqCWiPiwv2rvHvzAFM6fuEffo/PffExwqgLwONzJ3j/7Q7ffXOPDYZMFNSPxpxfmWO9l6HdrPmR5s/xgLjGG1uH7PTXaJzJONWq0Kq0iNOCTBicdozSjOsbByx3GgyHUzJZMpiXbJgRxlqUlIyL2fpObsdUg9n/1XKJ89UfRkRf/HdQ7RxzzDHHHHPMv3uOGynfZ0gt8X1Hqhye1mxO2mRK0W2P6IUNMusxiSsU8TIfJODqdJeDcR2lLB9euMlvvvkY/kFOgo9vEmQmSLdrvHRtESdABY6JH3EwbtBpTrlZdInFba796pPMf2KXhy+NkAamaJYuHXJtaxVKRRxC0gvJEo9D1WV1OtPaeiOH80vu7i+wWO6TG0US+yyc26duhoxvL3DnpRVM6XHvYI7QGWTfp6yF+KsGGWYY67ABlKFPLC3zUYx1UFiPcb/GbTPPr23fx3p/jmlRIZ9TqHGJ1zV87eUrVNKE+ScGtD405tnnLjG53zE3TtElWKH53iuXELvgpMIIizYFReYTJRldOeYz7ZvcHbR51Z7FOI/Ss8gjg35L8/LBSdJuQOHlmEAyyfQsX0EZiklIg5is9NkzEd87up+xqFFKg5KK5oPbvPCdy5itJQ7kDolp8+vuEeo3HX/xx3+Dsw9s8lPn7vFT/81P8lP/x39ArVYQScuLZp4X+2c5Ge1xSvrMyxETG1BMfO78xmfZ7EyxZwrwHNP9CLsXkXQr3P+RHUgF/b06ZkGCs6AEE3I8VbByaoMsmpCJOp/97Ct8/V8+zD/6Z0/wp//kBveyZfxRjhiUMHG8vKXI4jNM8xDTBjsVvHG7w9PrayRlSiAk1B3+/AFuJSfacDwi9vDlHHenNe7qKeQOKFEjEFi8wPHkI2c56LxAWhsRpyHBdcXkg4KeCRBLDpmUhCdS9ooGqupY/sCA9sMjVs41CF6NcMZC6dDjEhP4uHpAbDKqGznyMcCI2Siicfi5gFxRWotIBEnoU7GGg/GYYi5ClYrF+gi/armysknWi3BU+Oe9BxGTko+IG0zfrZNLcJ4Cr0QMSvz6bK1IjHyksRCByEFNcpwfkHV8XGoorkvKZYfMFXlcI2xkVDpDDho12s2CUa9CmWlcLsmDgvOr61y/uYAsBdbXTHerCDeC1OGdLnHGYEc+mfIgkLOVlKJE6QKnPExYIBNLHlkKLXFqtnJmpCCPJCJ3mFLSO/SRWQLaIAOFW1CkVdAjR241SCit5IW3T/LkhS1evHkKfKgEGRkKt2kQFYNyApc5agFYb8xEaqapxu8qwqbB1jOaSylVmeOVJb1+C+fN0sui3GCo4qSl8/6YUVzD8wrSniTcFng9QXjGIO63mBsaUcDi0Yi5NOao5rO/0SR6MaY2hKKd4/ISShCBQM+FLJ9bYLo1whSOZjenVwkhFwghiFoJzQdHiK7PzlttLO/Zi4aaycBj6fSInZ06R9Maf/Ivf44rn99kvx3gJKRHIX5P4A0tZJb0jEJ4krLhU0QajeW3b5xkqTtiK2pQNHxkK+eHHn6FbnOC7xJ2b9YgKwj3LEYIWNSYwsOVs8kkGRlyKSlqJS0vIl9qsTt/k0nVxzeWRjimtxvhZSXnGvs898J59FGOTi32PovOLHrJoU4V6CIm+W6LIpZUtGW+2edH597lbr/O3/lai2RLIBaqmFDj2hI3yVFWs3UnIg0TDm618cwhYClCjYs0Ls5odsZMahLdB3ungjlTIuqWCw/usfTkmBd//gJRD4INC7FCVSROCPxgwOIfhcFQU/4PBcQFVihYtEjtQX+IkIbAiyAtQAnyVU1x3kfns7VBmXq4osQ5yJzg9rtV7GCE2PFonjzB/Jxm/e4BpS/w7oREnqD2xBhbOuKXI2QmiMcV7KFCpYYikojUoTKDQiKVIt10LHymzp2DI9T9KdID+7TCegIbSPLFkvh8gK2WvHp4yE898DK311eYjD5Bo1Xl4597iO8+9S79SUGB40/8yb/HV7/y5wF45uAXKT/+Zdq3znHvLUUeKA4Oh3zw0klqpxbpNKsAnKh+gvXob9BZPsGllc9SX67yc2+8RprkmLRk7cBnrz/GHwi28iOufHieccVnlOeUJiCQGikEh+mEP/itn0Oh+NKnf4LewRkOphm/78wTyM5f+HdU6RxzzDHHHPM/h3CzCdN/1/xeuIZ/2xw3Ur7PKMqS6SjBWs3WwTKDIsNUDfdzwGiQ48mciQ9h3OXHPzLll75zix1ZQ/sF3WBETSZoA5n0MEikgKoJsbshALYsyI98duYaZEKxn9SZvBXR3rfsfu0EZz94lTzTDJM6n7/vLa6EfX797YdZXehQvO1RHHnsdipcWdHUkpT+ToCcWrxqjj8uKSYe3PTY9BY5JQ8pYxh2It64c4pwXKIp0VZjT89TX4PtaZ/SCqwz5BVH5CxpAHP1gCw6ojeusF80+fL+RfLUw0gHVYceQTbwqZcx3cGYj5x6m6flJXZqy+RZie07XFogdQFbVeouoWY8dmTK5riG6ytsFLGYJzwYDnhoacBvLGxzfX0FNSgI9wpQFlcxpIFCtEaku3UmMsCrJjRPDhi+GbAwHJHaCNUsuXawSjH0McsWieSdu6ehDniOE0GLSWOTC49ucy+co706pKpgWrNMKMFW0d4RpYNv3rmP5CBishxQz17j9eYqQsPaao8/+B//XS596xJ/RzyMs4JhEhFahZOOwbBBPtXYg+C9tG0HSUlXTrHG59ZLHdY+PmatPsD84D2+8aWHIM04tfIuXH2YaiYQuzm641DDiM7REClyJiJA39TcPepStHx85QisJRCGTAV0mlPevbnIqF+lc8my1m2weWsKCGyjwAtLijTAFZb10YjeIISaI1v3qMaGS9U5Xt87RExKPvixt4gXBZtpm6IiWDx/RNgoeHPzKr2xw1TFbAoisfijgskJj+JUSCYceiJw8wI5ButDrCyRU5iWRI8coy8oOn/HQqCxpeNEc5f765sMVI3Hw7t8s3ySw0GHcRGhZcI/2P4IO0s1urcLkkVH3AyJNgtEXCBQeJnD9nNsKTF1gY18iqoinddQKqrXpgRDgwsLnDWUvgOrqOoUqSzO+Ax7enbALyRZ7M0akyPQNyxRKWisppw/2eP25gWMUugCvNiStBS2UeJ1M9SWxfYyKMF2FaJrqRUxpp4z1+nROSq5G9WxcwrjOZzQzA1T3K7A6JLxWgVTWHQl4/rBMgjHmcYeTz93khffXWWqA1wExV4EjQJXEcRnJHqaIiY+ifaJK03kzoTaUkK7kqGSkqOvtkj+kMdYRYhMYusKEIhMkEwDpJW4KGZlYUQyCWgGMVs3lnBVibCSim+Q1WIWNl1YPnBqg4+ducdBGfILRxeIliXVcpmH71tha/NdYhci45Qwy9i8uouwkkajRJ92OCkxPYNXWmzpkfUCiqnCCoHzBTYXJCvw8IPb3P/IFu9+d4Ubby6zsnjExqttbp3sIqxAkeKtC0S1AqEkmkhsWhJ0lmmEIWYwYutahb9vLxHsdgjNLLB6uX3Ey5zh8JmA+FccyYrDhiVut6RzpmCS1slsiBsWOANJFJIIha5ritJAZJGexcsN03WFOeXwckOeKLLbPjqBoulhhjnaGLIEKnMOcyNkrhpzoXPEtBXw6Kkt3jla4J2rDaLHEkbFAt5+htQCUokaOrCO6aTF9JU2FBbfCdBqNm2VW5jAg8Ee15cFqSyZ3KtjqpKKV/L7Tq5zddqgI8YcbTlkLQAncQ6QmqyoMb41ppjMGtLKl8isJDUOF/p4zTricApBTiYLCOrYJQ1rFis8XEdhb+SIUnPx4hErc4e8+4JPn4jKRkIcjLkrPXLtYTyHREHic/QbVdBgxgoTCLY25lCBD6WBHGRhcIXAahAeuFDztedvEzQzgh/IkQjyFyNKX2GaPvRK6p8eoaRlc7PLNK6BOcncYgOA+fk2X/jCE/z8P3kOCkO6O+H5Z67TaVd5LX6FVOSopQx9s03gCU40qzz6wBpFWnDfxWUAtHeKhaVnSEYHVBsh1XqdeDDlzde2+c/f+ccEUlDfeM+8lVuWFm5j5T2UfAhx7gT7LuadowN20iO2JxMAXu3d5syaRzdz/KcPP/G7Xt8cc8wxxxxzzO8ljhsp32ckpmTYEggr2VIBoQPdHrK7U8eOFEpaTs5t013Y5rb9VbLJBTr1CUi4U3aZO3MENcG418LtSoSAJEhBVcGC8SQkinu32qgmTG1ApZJRah9ZK2iphExo7o6bfK59l3++22KhNmHvYEDgKqT1gsOaz476Dr//wdf5e997kso6UA8Iz4AqNFZ62Ks1Bv2MwnM4D5RzzLcq9HcmpHVHmRv6FoSTGCvIzxSESwY1FTR0ytJ8g7fykqQEETnS3MevpJjIIKRAeIbIL0iMZkO06B3UyY88WheOYChhkmMzgUlDamspQW7Y7U/B01SKIbacwyB5MV7h99u3cMB95w64urGIj8EoyBcFfuhjYoUtNE7BKItoTzPKBK5UDtgu5rFDyXRapexqdCFp3S2xWcrooSrSg2vDPvdfWmF3/i1qUcr8yRHDoyaPLIxoSYOIJM+8/tM8+uSfZ0P4uFxz8f5tPrl4E+kc/WlJLOtcig5Ybo7Rj1+FOw8DEhNZrChoZjG1i1PE1YCxrqB9g40c2cRhrEMl8PKNc1z8QI9p6GF8kMbysR+8TiWY8uiZl3ip9wDilODhxxo81Zvwf/rIC2zeqfEbu48wqFQp8zGRznDDJlbkLLQnlJ0cLzUkt3zGD3ikvYx6e4xMZ1pVZxW3zCKRk5RZwe52H+NLppMqwuXETcXTu9sUuUTUNU9NLtBUUxaWhqShx82vrNA8MyEOA5Sf4Zo+mRMEfYfxBK5aYrSiWPVxY4EsHU4J/Imk7BjSeYupOETNoqoOF0nKukB24Md+8CW2hiu0qjHfe+M+hmXEG/sr7OoGmW2xZWrYhyWdpwvyqqKsCfK2xlU9ROnQiUKgwEik8ShDjSjkrBliS0wVyhL0uKQ8yLD9KpNKgL+dsPzogA9/8U3erq/w6lfPU69OeP7p+5FOks8LjBWIUcbhpILYBNkXpB0PPNDTEm8oyM5Y3OmS6H0x9p8JMhcQnrT4iaBbH3PkR5xu97hx9yRe7JHUJAIo2hW8QY6bWPY+3QBPYENBFpbocQWA3qtzBKnl8tIdnkkeQGYCObE8sXiPJx/e5DduPkbfepQ5HEqDCQLOnDhENA3hjiVIDIc7PrvrVfJTmqZLUcMC2fUxFQsOXDEbHopGCY8vruOZko892OO3di8ylAGnlvYZvBkRa4WuFET1hBMrQ66+1UT1M9J3QlbPLqLmA+wbGs8zqI0UBhlWO2TVwyWWWp5RqY3JqYKR5AeCXllneuBgfqaQdlqiRwX7rzZYaRxxdLfK3PkhhyeqNPYz2q0Rkzzk5NqAyrmMxgMF7/zmGUpbhUxhHWinCFSFxU8eMkwrnLiwzt4/X2PZ7vHN5y9RLmniA0npNF7NUuxZug8mKK1xGkRN4UrgKAOjcY1Znsy+GREv14nmShbTIwamTquRMlA1bKnoH9bRoWBaV4SZIegbpqOAYbOC2qgS5QVvjU7w6miNu0mXz3Vf4C5zDMo5At/D+gVSOJwRFMKg77M45XAKpHV4Zx3tuSHFqy2sLyg6mhd/7RxRt+AoadC6MGEv92mLnDmZkTuPcRoifIWzDilAIHFCYYOQYqMgLRvYJz3iNGMhFphbY1yQgACZ5ZTO4jyNMiD7DlGFcFtiqwIhJKLIiScxP/gTd1nfuYwZL8MkAV8jc4cMFfmSxmYWuV7Q2/CoDAUSRRA7bCyx5z2ceS8EOwWlQ8RsYxKhJcIH6wLcjsOdLslaCoGPRCC8GsmLJWZREL4h+O/Dz/Nw3GY4Tul6Cq0U7fk6n/nERb751bcQzvF3/uZvkgnJcHURfbGGOGjz5AdXmGQFKyfmuLq1zweunCaq+L9TD4RRROksb4+3uHP3BdY3DLYwrLdyRGFpWIsoZgajzvyzvP5uk1ptnReuZvTLhCeXT3BpoY0UoCQsRE3ON5ape1VW6q3f5ermmGOOOeaYY35vcdxI+T7COstX9n+NU48dcnNjBedbGgxonYvJM4HsFnhBxodObfKpuV3G+R7PTc/htAEJHwjW+Rf3lgjyhKKSk8gQgUSk4r3CWCBywbnTPd545iJmVVLWHT/xQ8/w5fJROmdG3MvbpIVmpzfHdubx+InbfGXrMotdi0hqOJnwUNTkh8//IsvVI74lT7GXNhG+Zjr0kVqSLIKceFTu1ZErisWTA/7YJ57i8N77ODr4CL/50ruUNUnytsW0I2ws0LFH7BRn9YC8F5BMcwofhAHrCYb9iKAIqAwFRjn0vqOhJgxknaTmsUOT60cL/KGHXubXNx6kuKgI7kl0HFI/e4Q/EmhTQ0jJjquRn0jJvQaJ9Ujfe/+9vMA3EUGsePjRG3QvT/jG7Ssw8TFIEALPN7SHQ6ovJ/yZL7zGf7XxBTzPYY40GEu0Z3EIlJmFoYrUQlqic0d/WKVRTOklVb579wF+4L4t8lzTqAacXBjyUr7CgfU5fWLIqeqAFX9IVZWMPY+JS8mtZmQDnjiTUr+VkEQa0RQE9YLiXc1m0mR1ZURUTXALlrwIUaVgvB5gqnWiwmNnHJJWBbmD/8vf+HXe2rnI9UIzd26d0e4VskqT9eY2yZtzfHvrFHkIt9I2nrU0WyVWKsox5FZSeh5HezWaZUzR9LFViR1Z7NEAvzvFComeFLNQTmswQlNrBKjRGKEgOrCEucbtG2p7Dlcz9CpVXGaplymZ1Jz97CHVbsrWK9dpRGcR5JgFH1M6zvyRTZKGYuvpVVxfsXSyz+08QKYC6wnmUoU3n7MnUvSioHw+h2rIExfv8LEP3ebmvSVi51iZ6/HmvfupSsuJxhHbG5cQyiFLiS08Dp8oqO+V6FhBMQuyVQZKZVE+iAic70CDdA5pwWqHWatCaNAHApV6lHUPoTya4RE/8anvsRVVqdQKIp3h65K6jugFAm9icVKy3BixUjsktCVyukCYKSgcQjoqh+C9ItArJeMwwJwRJEpRdhzqtiNWPoXxqIuS3VELbwrTYGY7CXY0vfs6pO/LMDWJyNwspDYSeBsW5zmyay10I2FtcYS4Bio2mNLyp/7IC6zOjTDVOv/4yx+gIgRVXxPbgqrOaJiY0duG/tttnCexAejQMj8/xL4zpQgFW6eauGsBogTnfKwJiNSURuH4/JkbvPPQAs8crXJv0KVmE1wsQUvu7M3x1Hqf3cyjlTt2VMiDp5f57kv3wEmkAYPBFYaTZ/f52B+9wTdeeIJb+yfwp32khRKFt5az8IN9Dp8T9HZqSKGhtCAkuxstvvZ3H8A0NMGZAr9dMNiJsK9o7j+1wY39k3SfHFMpjmh0Lfsbs6aQcOBPSuK85GaxQlIERAd91jo9Ti/tcbTi88x3T1LzCvzaMsVOAl5OPFVke4Ji4iPqJbp0KCXAWFw/5Lm9TQaixBM+87UJyTAgPt+hajJUUJJnEgd4wqALSeOxArEDlR3H5I0Q5wsG1mdrfZF4VTPda1G2NOWchC2JdA4jJKJ0qMRQRj7e+QnxngU7Cyhvfxw6UcbRxOHMzC6+ux7iyhYXPnBEs5YgVR0dG56azDM0/qyJqsTvvDdYi8RSf3DKuc/vcetln8GrmnC7ZNLP8KTEZQ4nDUIrpPRwCx4ut9iJR/2awrs1phgniGkJo5SN5yt897cD9ocryCjAepq4UqCiAG9qkDkwtZQLHs335fiHOZPn6oACBem0RFkQUiFCiZAa6wmUEaiSWXZOJkl7IeVKAVqjJwZRgMQw/5sJ5VoTNS3gqztc/5Dj4GMDlLHEWUmtVWflRJf2XJX+wZQ7dw6QgUD0PZbdEs0YHvvEaU6dmedGr0/pSfqDCfXQp7swm2z52Z//Nv/yl7/H0rk+X/yx16i1Pko+aNKLJNJTpKsGb+AggpfffT8bmxPqlTbpfIYD8qSk7kXUTI2KkpyqzvH51R+gqiOUkL/7Rc4xxxxzzDH/vzm29vyu8e/VL+Ff+St/BSEEf+7P/bnfecw5x1/6S3+JlZUVoiji4x//OG+99db/5HlZlvGTP/mTzM3NUa1W+dEf/VE2Nzd/l6/+3z6JSbmX3mVlQRM2DUFnwnz3iDn6nGn3Of+RDR787F0+2b3DpTBmzftjRKbk3rDFjcN5KtJSXvVJdiOSwkcEHgSKpj9BpRaZg8wkh8MQHYfozYjakeDO/hy7psWdzWXKXUWvrOCkYGokqV8wv7rDK3t/hXHLUiqFGtd5++77+JfPP8z6vRbCWIRxTLMAs5Bi2xY3x6zxUHo8tHaPsyf2uXTfy0gFNpSYmsKFHuqpEP2iRvdyrIT+vYhsQ5K4EqMlCIfzLIVQqKqh0ZoQkRGWltz4IDWNA8PZSY90u8IbZpFkHJDXI8RqQLLmcXTYwDUMS/6Iipdz4vyY2jmDLgRRJcNYSWklg7SCzgBP8LkvvsYHHrvJ5WgPmUi0eM90kks2DhcorCaWJQ8tbVCPEvK6o5gDt5hQ1CSuAlULVau4XOtwM+0TLacYKal1Er5+/RI/848+zX/9N7/I8twRv+/hn+LPLlzlQrjH37nwdf5M5SUC5Ri5Cp4W/K0v/RCv7J5mYKokhcQrc2RpcAGILuizBs8vcKEjaudUKiWpJ8ATOCEpC0eW+rzxtdOc9iac9Sac7k7pqY/ynfEpvnxwH5ODCsZJ3hgt4IRlcWWLsD5CUSKkZLpfJdmsgAWB5uZgkSNXgUyhghKpHCI0rHQVNWupV3I83yCVhBK80uHVPIwGoyyVESwS0JwoKkeW9jBnvjXk0souF2p7zDdHTCcBqmpZbqxw0BtAPLtr/8jpu+RTifAKai7Fyw1LOmal1cNUwYaGSaug2uzxkfo6wbMpzbuKsrTUyZiOfVIsd3aX+NZbl2jpnGA+AeEohUQISxDkOAXJ/RE6VgT7BikEKjMQZwhjyZsecSsga2gowUlBKS2lc7ixQe/OglJLpXDeLH9kP2jzy9cf45LfQ1ZyHvrMbb7wp17l0sVbqKkFaSl9w+Z6A5k44olPWWi8zCAwOAfOgRpJ7D9VyJfhP3z0Df7sJ59jKRgQ1AxVP8crC2RmCGODSAvkXoZIBToTiFJiQw01i6sBPrMRkR2B9ypYUeIqllf6ZxAIHA7T1Xi1hCmGtCxIckesLVo5RFZy73qLu8/ViYdVHvrwFp/4Ay8jbjnqRxngoGmIkojS+KBAlBYvLykETEzAXlrhsBex26thHfSSGplSmBEUO4Jbtzo8/92z3H5hGekUua1ytBcz6WeI3OIyiwsFJoT7P7jDfVd2eeLJmxg80qxOmWqKlkfi18n7AToskftjvGHK4z92iw/9R9fJL4NaDrBSUrypyZ4NGL/UYPRSldtfOk1hfXZutLn13AqH96pgHM4ZdFrw2EqbE40MNUkRheXgRkQQlFxYO6K1XJC0InwtKYqYtJqRLnpkVDBpCKlDDwpEXGIrgqwbIWKIk4L/D3v/HSXbdZ53wr+994mVQ+fu2zfjBlxkEARIMIgASVGiaCvRHmk0+jz2WLZsL2skW7KsCbKXPsmWZ9keS7bHSpaGtEjlQEqkSDCASETGxQVuDn07x8p18t77+6NpriWHzzNjS5Y4/fura9epPudU96p6z3Pe93lyo0iWy+SvTHD+zUWiShlRE/R1iZ2iyqBwMKHFzzXJrTqFWyJzSgg0QoBAIYyHk4OQkqWlGWp5Qc2NcUcpjGOstRR1gWm66F8LYCyxAjCWzhtNVl+bIR2HeKnF6RlE6GIqcObsLW7pSYYLHutBmad2jnB9Z4os8fa7UNhPNqIwFMKSjlyuLM+w5E8iJwR+J8PVArTGCouQ7n6HkAW3ayDYF2WSKMezFjmIcITFEQISeOZXjzFd3kJbjc1j5PYA58ISanUX59IGMkiI7s849cgm8w92CaczdMXFOAqVaUyxb+qrpdjvIjMa4UqEkJR6GTIqGEU+3Z0qtbMDHBtDWuAY8DONtzrCWe5iuiOyP7jJ//ihn+SpJ15FG40BPAcWZ0Osp7CpxvQN7Cbc+tJNLlxaxg09+sOIZsnH5ppLb66zstYB4PrNLT72iZcY5ZaNzRLHp7Z554NbfNP778H9yt8mXKjgV12OTDeJy1OMjI9SLq6rkEIwX6niopgtl2l7VVbjHX7+xm/zz6/9Krkp/qvUOQcccMABBxzwJ4U/NR0pL774Ij/zMz/D3Xff/YfWf/Inf5J//I//Mb/4i7/IHXfcwY/92I/x3ve+lytXrlCtVgH4vu/7Pj7xiU/w8Y9/nHa7zQ/8wA/wwQ9+kJdffhml1H+N0/kvTjRKKHLDt09/mH/2xZ9j5vgeRsHkTsRRucXs5IjliSkmSwMmVMJt9b3MhA8g7D9l8/oEGIFuVxEmhTRnaNuUyg4IqAcRnjboXGGF4VpWw+vk5BMGR+asZBXyliWvKnaWK6QnXI6fXOdSMofr5TQrMXY3pT9McKXihe0lnuocY+rmJNYrMHmOjDViaPBOxGhdhlRTuDGiY3nh88c42tri9vlH2EmHKK0p7WnaXpm9IkMb0BhMT6B2wfcNamRxY40uLJQs2gVnLts3YBpK7ASMPZeiK8jqDh+99TCLd+9SDH2cxKAGhqTkEJcV1QKq45jewKNdjrnf3aXtZrys60yKMbvGJzWKU7MrfEHehbA5S51Jqn5GPiUoOSOi1RLCVRQSrA34/m9/hmkhue/QJp/bPUE2K3FKKaapMGNLXghq0qUxVaa/nrO3G+MNApqtMXWbYYXg8zdOEe5ZFg6NAAPA20rr6MLDy0Ki3OGOdoRvDW495VxpA4Hlty8fQucOYTMnU4a44XLojlv4LuRDwShzsZkkPAZal7B1gXEMNjYs3Z5hOo8oxj7P3pxj0lnh03tnSDJJLkD2A/KSotoS3NieIRn7WKtRWqJ6BgT4HqSJhRuC+D5D23S4PROCFjiB4mzjLnTwcXqBS1DOePnEEbyh5MREi2ZQJrcGleQYXZAOY9pRjR3Hcmx2i/njG9xV2SJDEDd9Vvba7EUl/szDx/nXKy+TjTUqMTz0tiXOV1r08pBkkGBdyUrFYRCV+dZzF/jht32Jv3vxUd5YnWYrq5HMeJQ3LKSGNANH5+zerrDSaeOanPnTHWQtR1QclKsJvBxZ5EQmwGCJpgU2UIjYYl2BGkrSaRcj9sUqXImwAutYcAXSCpzMQyYFRcnQavWYf9tt3lhaZOD5rKQ1LiVNnDlDFDmkkUtcGGypwH00wR0a0iWfJ18/gTlkKaYtZlchPI2QkGtL4SuCDYl6WvG+P3+Tdi0m6gm+sHSa8YrHYmvAsXYPK3OIJeFyjs0DKAxaCYSSYAw2EdDQiEzivntEfLVEESucsU9dRhj0vpeEEghVgLCkQhKWXE4vtnlydhVtNSmS1l2Wt08uo9+UdKMS9z6+xbnjK6xeq3PrfBtvy6DOFeze7zL5asJkMWAc+JTJWcrr/O2PvZdyTVPWGVMne8iGwSEl72tyxyWflPiiYOrQDtdWphn3Y2YmKmzIgmyUI7SLnjY8/dwpdtMmr2+ewArQrsWT++MqOhf4u5ppf5vRqISUitnTHbQP5UbK9CBlI2pidhR2wyL+eoQKIPtYFSePMK6kSAXa5qgwxI4LmuWA3ChCQpJRSBoqRFFQKcUEhUu6UkFIh6hfhSxHZIKi7TB/7wrHji7x+Z96J8ZzAINwcowj0Y6lVvVAZPTbLre1j6v6qHJGJ/foxWXKaYqecRhKh3C9IDY+yZ0adWeO/yWNHgqMYxEF4FhqXkzLH+PUU6qjlCtbR/H7GdvTJagqEFCUFTLef6+QkjhX+EMoAhfrAI7GHea0g4ybK7PkStLvhiid73/OKEHyVknpvETuJohi/4K9f8hhkHk4NwxFzUG2C6xSCFegM5DeV/4fESjPQ0uBRIAAXEW1VSbpDQAJ1QBvIsHBZWXLx0RDlNX416N9cWjKQ6WC7vQ2cXuO7UsV/LggL0vkgH0jbqGQwxTPFBSmIC076LbE6++PwFnXwdEC/5qmNJXTeKyPd0xy89dnwVGEJ1vw3HXsSCMtkBfopOCn/7+f4tHv2SGbC5gYW2QuUVgMAlN1Gb3FkJ9QuF/Kuby2xb1HZri+0eHm3gCzmzE32wCgN04oKg74FRrzG/QCQ/VoyHF5hI9tXabICr7tobt494ePsHB4gl9b+wI3Vy2Vike32yUzcH59k+kpn5G3gysdCiOZ8Bu0/AaO+NqonQ444IADDjjg/yl/KoSU0WjEd37nd/KzP/uz/NiP/dhX1621/NN/+k/5kR/5Eb7lW74FgF/6pV9ienqaX/7lX+Z7vud76Pf7/PzP/zwf+chHePzxxwH46Ec/yqFDh3jiiSd4//vf/1/lnP5Lk6U5Ojccrba5P32VrL5AjqTVHVHSGTqXxEOXkmvoGpeuOUo7uw46IbipKKTEnIPFhS3WVtpkFYWY3Pf1aJgxh7IOm4MJdK1ARwonzrCFS3W6x+zMLrdUm6BRcOfaMreZ5vTsLpHj4VrJkdIO9fqIUpGi4hIqHlKpW9I+yDyDokBQUESKynCEHxmcBOIJj9KW5K6zt2idHmLD2/SeOYWTCWxeMBomKCHwAkM2rTnqdam2Ioa9JoUrkQnYDMjBZJL+yMd19X7U8wQYIZBRwXhCY+sKW7fUyhHNIiYdOUSuIpA55dIIm4MSEmU1k0HErz3/MF6/zHHZYS2r40hDqj0CMUQ6lheXD+N6lrQlCSsJyXIZay1oCYGhqCQM+gHXNltkewHNhS7DooyOFMIaspaDrbucOjKDUgnXuxGtLcOPPPQCc96YDz57mOG2j9CW5U6DV1KPssz5/OA43xze4Oc/9zAPvOcSbwlGaCs4PtshdR1cctxmTmnbUG2MGBkfdzdHNSyeMiTC54g7xC9n7BytsDn2cesKVTKY2KMnXT710jR+TaFzyV0zn+Q3R99AnDmIQoIBm0q0Y+jXfUa2RCoVVhsqssBUPYzrYuMMb0eSbwpe2znKxH17DEQV39dkpYIHp1dQkcuFnQnyiRLZnKTZarN4tEk80BhfkG4Ios2MZK1L0lQMkwrf2biMdDRrWsE0rPbbKNdw9c0NVJTSmBwzvF1hPQppR4rTh5b5+fFJSjbBJoJ04KFy+NDrH2Jj1ISgQBYeNgMnMngFDHt1bq/mjLoejsgYSp+e43JXc4vxoIrVAgfDRHNIGvk4SjO8M0RtgpcJlFUIH2yuEQYkFuPmGF3eH6UzAqMNKA8bOGShZuGDPS62ZlmY3yR6qcK7jr9J3PN4/vVjZE+XedMeoiO9/TamVCN8SREqpC0Yt3xsScFI40UaWzJkoQ8FaF9gC8vf+tkPcWxulxuXq4zGAakfUDpUUJ7KOXNqiwvrLaoDl9GkxbgWYwtEliOHCmMUduhglcRVI5xDMdHVCmjJxlYdhMLVGvZySsoSCkmiPDKnYCceEtYStLQIrSmHKROVES9eP8aF5QUOPb5/d92mAisKNibKFIWPCEE3BdHVEkMr2elVoQP+WNKq9ag4hiCMUXcWdGZCuKLJgB035PjcHkXk4hcO7WYJU3ZI9rpYqxAdBVlAL/N55skWpuUjQgvCxSiBAIRjKB2J6W81GZ+aQwv48r85CScM2U5I9nBG+GbB+LKHmUoJ6hoEuGeGPPLW6wycgGuXpolECxsbWtJj/t4ya96A6LZHPAhIpiR7NBkOIzbWS3RKJYJ7DGLaQ7xusXmKNprensvwxAzcF1PcqCIcRaAlKkhJJwXzpoSJU2JPwnbO8ekNnBnL+WKOhyvXOT29xT//9GPE/QoiDPGPpTjTCY7OcVqGoldiXAUhch688ybHFta5+OlFtlca9NcDmp4mmwrwrncoQg/rCgpP4m4n5GEJjGDxvg3urt3kC584RmRC3EigBin31zewdcmmahCOwN7KcM4JbKiIFgxZTxFu6v1uJ2UxMy5CaWRdozJLXkjSOgRDg5ICa/bHJ5ECWw2QZRddz6i+cw9/tWDr93xEIlC2oBjEvP0vb5KvaJ49P09VDLFDyzjTZFJgBiMCIfD3BCMJS1+exksNQRah0giNQDgeMjK4BSgLkWugVSIfF7jKxXoSEecEq2PufHiZoGrYjqoY30VI0KlG4aG9FGs01hqEI0EqXtxbw2nXeHtrGtd3efBMgxeudvfjqutgXYOpCP7NF19l7b6jfMu77+OXfv9FzMqQmja8/e0nObo4wUKrQmd3zH13zXC7c4idzbvIxTZdcjzHcnR+gnvuP0Kaa77JezvhO8vIuMSXnnkBpQ0TpRKzpSanazPMl9ucqc9ytvHd/7frk5VbO6yvdTh79yGqtdJ/ydLngAMOOOCAfwfxb8di/wQcx9c6fyqElL/21/4a3/iN38jjjz/+h4SUW7dusbm5yfve976vrvm+z7ve9S6effZZvud7voeXX36ZPM//0DZzc3OcO3eOZ5999mtGSKk2yuhC84mt32DrVMBd7gqR8pk0O/T9Kp8/fx8nO31Ea4tp7/2cK59jbzygv/ks4VCTB5arwwrVSkF5JoEwIyq7WM9w6coh2l6PohezU4JqoBk1BJGUpP0a8fQ2j567iULzSjLPqbBLP/GwEnp5SG8U0JIjqlqTxaD7AZOlHTr9GtpR4LtI6YCFI7Ud1rozyNRATZCPFf1BiJHge9MEniJqWKJ5DzWG2i2LHMMDh28yfWjMTqvMlSdapEkPIyX4AiMUWeGSDiQqKShFYAV4HQcZa4IErFMwN92hpDLGrocIod0a0q7usRQ3ON26zudqUxilOLQYsXPFZSI23C5aDCKftaLFdKnLxHyfaOSxljWwe4rSiiFAoHKDtRZhBHIXXj+/yM52jUs3ZnBSDbGDLVn2s3INRUkSezmTczVmm5P87oUVbid1nLxgOS3TFR62IWjYEY88sMxv7L4F10kwmcWpGe462aNf8rmUhUSDkDXjMR9GjISlWk2YnuqzOLVLqh0uXTjMuBdSnx6hI0mjyBmMSmw7U5iTErPnIroWlVtiIfm1lYf48bc+Qf14wvrQxVzXyCIkGCqK1KJGEpVkjIYB/fUSIpGYf+vdIC2uVKSFhrHBX5fISNGlCmNBkStWxhFfuv0gp1Y3Wb80gb1PYz1BLjX1agW3leKVNfpeCZ8TRCFYLbi1NsGP3Hwnf/7wK0y7A1RQEExkeCWNaA5BefjWMihrjGO5tdGkKMfEFRcczdj6BKrgyu0JOsYl6XmIzEEUCUoL8jaEpTqHz15mM3II2hGDzCORIXkI860+n7hyJ0Wi0J6gFBY4+b5w53s5Ttfidlx0uUCMMkQVvCEUswpZeDgZGMdAZpDCkkx5SA15qeDC+ixbvQkGg4BjsoPwBStX2gx/O0R4DrHjIEtgE8N47FGq5ZgJhbsKlRWNOJqhPItODCIqcFKFSC3uuEDXHLorFV7eCpia3KbYqVM4lhyJX855YGaJq3EdRwgWkk1mHt7mykcOs/eWEJUrCATa3Tdd3duoMxV0qL6uYApas0NW+wvoqotRFiEUiRX0ew5+BLE/RlQ0CpDjgnKccsd4wK+/PofoCy5eOszS6gzfXLvAZj1nC4VVfMW/QVAMDb5TICQUdUFeTiiYI+YiAAEAAElEQVQqBd2pACdR3H10nfQdBYMVDxUJRktlujJluCwYW4vbKGFKAnvb4gw10gi8scAFCDS51shUY7FY5SILi8d+F87q8iTWdRFW012p03Og6WvGaUD/nKQINeXnBWZTodoFH3zsNbya4SS7LK03sa0cc9NnEBecryxjfYvvOci0QLkeqXXZytrc7vVgNgLPoqsOrQlJHkdIDPWKZXW7RdookZ9xaN0sKIygUZbUtkPWr+1hpebIHQU1p0keWtwsxxlpJv0hiXEoNwr0hosTeug9mBkOUEPNxsUFcl8jI5g4nLB5sUVDD9jtN+jFDWxF06kJxidDSpc8MBZhLG7FwfQypAYrLN9w9gIPn1hi52KFpwfTWAS5a3j2jVnO3bfDaOAj2hrnkuUEe8Sxx+hyiNpT6GaImyX7nkGhgzQGVReoHQuhYveRCpMvZvg78f6gslVgLYXVGMdFNlzqMwNKh3KGu4Lx5z30dh8QnJgbkdQ1z7+aoLshZIayZj86Oi1Q1lB/dxnZHvDNH7hEGGd88qcPU1sYM3YbdLNp3K+M41kETm6gl+7/c6YG1P6xhKUC4yiE1HSSMrkLD/3ZGxw/uccr/2iKrdfruDpC3m+p5IuUrceHHn2YTZHirRR8+qkraMT+72sr/GcN+kiBfGkMjZw/8CRPvLyMlQZPa770yiVO3XqdL61dpeW7GCM50non7z/3V7jVGvDbT79OrgzCE9zc7DCIEr58cZm1tQ5PfXYJ5SqCuiABQsdjlBdQ1LCFhxDi/1I90u+NeeapKxw9Ns2ZO+c5/8ptrl1ep1orcfbuAyHlgAMOOOCArw3+xAspH//4x3nllVd48cUX/73nNjc3AZienv5D69PT09y+ffur23ieR7PZ/Pe2+bev/w+Rpilpmn718WAw+H98Dn8cKCXpFrt8ufMJzGyIeDFCtjXZjsetyhQDJ+RWZ4E7nG/lo+u7PJZfZ96Z5qYE1beUgjH9nsd4PWDSGdE4OqRTlSinQL3hMt+IGO9o5ETKlaxNw82xPrTI2FktI2spqfEY+GVe6NU5Vtljqe/ilAxru1VUPEGWu2QliQl9xk4Jx0qEH6AbLiqVWAnSC3CUwjogrUK6gs2ixaeu3Yu/02ChFsKWBCtwsFghKLSEssSZLVh/tYU/SumOfHTdw/oW6xu0MjAS2H6JNNM4MaQAPhjtonck73zgKueTefSuhxoJjpdu04gdrg6maR4dkx+xZLnlum6CdMkl7K3UuV6aolsK2RRV3nLfMi/dOsRA+ggJD8/dYHdpElFyiCMfIyxBM+X13UXkGLp4GAwiNASlgtxzcZDIwoAUPL93i3E2JK9FTFYHuN4IT0GtmrDjl/iRb3qCew9t8WTnBF8cvoUHI8NHni1T8sc0Thc8H9fZ3q7jC81uFrJrAm4nLabqPc6W1smt5Cl9mlGUUIwcyDMeC9dYTydYNTWOzHZ5enAc4zjYukIGKdvpJB9bP8dCb8RtW8fcKGEGPq5nyBsO1uYgNc6mwdswqKhAaYXtFzj9nPJ0Ba0S7n/rbV4fzdDNmvspQRkU6w5LuotYKbO6soBt5EgJ2rEszbzAT68+x9y8i1vRdHYqOF5IpYBYGBylebx0mRdXD/PWySU6gzLtypBSmOJNdAmCGW6ONeXZIcenOmR7ZcLIYlXG8KwCJ6Cmc95x7w3aGD699wgIB+N7ONLiNgx/+dtfIA9X2H3qGL1RHZU6ONqS3ZZ8Nj5NL6oADkXioIVgFPlYBMGNHKPBSAPWokYFecvB5gZ6FlkxyAB07GHV/igVtkAjKa3GjPYCKmODNQH9qk9S+HSyKlP35uztlChyBV5OWrdQgly7EEMRKpQtmGpsEu9WSSMoEhfpCkgL9AQkD4F3NUeMYeL0kO3rk1gP1gZNzq/OsdcNMKGLzDWTix08KTCepHUho3dUUNQEolr+ygWkQ3zBpzyAVnnMuek11uI5tJE4haKnJQGWQeSitEPoOozSGCssDZNRr+ScnuxRmTHsqf3uoHEUsHsowJvNCJYK9jNmLagMcWKMlGCtwBUpnXsChqNJ4tThuLeOlIbGPQVrn2ngdV2ckcfOjTZW5VQnoE/ORFhBBxI1yvdTYjBYA9JVWCEw0iLwEBaMsGRacP1aEzyJ9Swm3x9zmtrUmMyQSo/xXAnZKnDKhkqS44cpR+pj2mqISAWyrKm0UsY3yxRS4L1WwtZzzO6Ie1o3eO/JFZ68ej8b1ye4kjd4x91XydZ8rj5zlO4gotACN/OYaLmUJ1fpbfnE3RCzo7GhJY0l5cySiYxDlQFfV7mMWjjEr/YqyI2Eh6u3GIiA3azKVlEm8zWYmBopdy+sIwrLnj9FhsdUY4QT5HScMrOlAWZqjecul7HaYloOGEk65THbG1A9nLH9cpO8abFy34A32XXJFyR7NwNEkGM9j/R0k2wy4YmX7iCfKqFkRnkh5uSRbUpBxvInWuixj7SKIvfQmaZoKHDL6Cv7QiKOwRyqkGwOCbo5VgqwBiHBSWAwI9Fli3srwJ2CeOBjQ0FR8hGuwy//wj2U7szI0RQJOMoBz5AXX7mF5iuijYJKMeDeB1dZuthm5vGEvZcCMsdDDFPQOViXzLfI3OD2MpQSmBSEcpCFBj/jwtI0WcknHzvIkqI6GZELTfpQgNz0SSMfuSQ4fX+Nw3Nz3Dm3gLqyxj/+hU9jG1Xw9/1i1Dhn/FaH/IiP6Bb4Vw1OXGAzg81zsqbDhp/yKyt/wNgOqAZtksKy3R/RrFRpnarxy594nnBdoADfc0iyAt91WOv12YoiQt8hbRishbVOn8LMcmWwxEoMy9F9LJZm/pP1yKWLa3z+iTc5enyHM3fOU656OK7AC/5U2fIdcMABB/zp5MBs9o+NP9FCysrKCn/zb/5NPvOZzxAEwX90u3/3Lom19j955+Q/tc1P/MRP8Pf+3t/7v3fA/5VRWAprMCjWt+rcsdjlejLBeM0l2GgxXdTZZcwauyzbbUqpTx6F2BByr4RfFvhOTC9WLNb6yEBQVSl/88OXce2Q7c/mtA9res0a+qaDFXWmzJg8cHhts4lwJFHf4jYdJs2Qi/E8OZJQa44ku1wcVLGOxkQat5ZTlMuQCUwaoxKNRdHbC/AcQxI60MsRY0Oy6hOvhiS3HFS0R2k5g0ThBj4qTWCkef1XDvHG1mGm9vqQCKwVCCswAigAI7HsJ6lkCoqKxeQGN7OYwuKalIvpHCtpE9VV2MShf7vBxbTNkdO7hKHL8fImsXGZrmzj9gSFMcxUu1we3MHeZo2+X+JMsYeIBZVyDq5gVdaJsjJ7cYU8BFnSTIghcepz8ugGL64e3zdS7AsS5YIv8EcF2neQgcPV3m3megNOhZZbosL/9utfx2DeZSf1sNWMCS/FFYL58g79vWWur30nb772eeJI8d89doNMupSnU7ylI4TemJNun4EMyRRcGC8gMZSqA2zNZaAdSrll5CqqzT5VMaC/IxErFj2hKJoeE+UeSTvkuTdOciQbImYKRqJCoEEZgxprZGCZnO8zVe/ipJbV1TZkAq0tvkqokrP44CqL8wOONDv8q2ffxWxzzEbWIqvCaHPI8dI2s490WN8pgW/JdJkj9OlZl6PTu6AszzwbUnQiarGhfjTjvV//GkY6XLp9lEVtuGfqBkvFBELAzfgaWWcKnbuMqPDKpePcd3KNXz9/GqoGW5XIGDI0V2yZbz75Op/aeIjJYJu/euQ5lvcqXMzu5J5jF/nE0jTp2KHlDcmKbTY2pnCOSvpJme2iun/xbQW5luS5QUqX7D4Xd1li3tDwQE4uJJw3SCUw0gUrSDwHVUDeVBhXUb/eo3XTId6NGC0GCF9iPUHHbfLUtTuYn4gotxKicUDeKaF9iedoZkwf4Vg2pUfa8BBGkG546HFBOTYMpU9RVggPdENjPEMxqZEjyXAlxG9mpKEkMy6vLB/BxgpRddErgqVXphkuhbglg9iKaX0+Jr3bozNVAiuwPtTe3B9d6HVCnn3xOBOVEYUviEOPprufBlIJNGNyxnEORoIw9OMSb9xY5F++6rG5V8crIMFiHIlYjAnHZbpHHdACEOSuxXydQGswVnAi6dCvhOwMXUwhOTuxAsJhop6jahXoWYwKsJHA8VN8J2NqUuJZH3cnQ7seuDFFFCHCEKMtSkMWuAghUelXUmeQjEUdvxQjdgqcpEBZByKD2hOYscC3Be5Ggdg2JCsuRyd6vLA6z8u/chTddjEnY7KNEp5QqIpgriLxth1Wb/YJGpYJHTPXGtMJTrClxzy/fZRBs0R4PCa96hBNeehayMD3OHG4y923b/Jq5zS6qbANjTb7ccH1Vpk7TlVIprf53JV50toOQeqz0WsxzvfQQuHEhiDKsZ6k/cgen+ucxXYtQoccmuwRa484LRGbkJvLc/T7AW4jxlwBHZRRkcAZWs6+f4VgUsPYZfVmiF0dopRHuVRw+0aJvcdq1E+OKV4wvL/1GpF22dir8Lp3lIHwiBOf7eUSnuczCHyq3QInMpAWGK3RjiHIBW7FJUsFaiRwVIHxJQaLUBJhLEhvP3Z5OSZ/IGT8lEfSDDFxiCeGSKUQwmH+oV0iVcFrQrpTIquVkL5gXI0J3xiSGChdiCBU/Nob95LmJbakZOxZROxCUaBDl7zuY/IYZzdFhiWKQGLKEj9W2KLA7kFhPLq7NRSaioFnfvMU7rxlr1tjshkjkoTCaF778jV6Mx0WZkOeenMNohQCj/JDHX7ob3yKKPH50R/7EKbpUNq1uIkhfLNPpguiCUFRj7ATgtVYI5CErRRbLtOqhggh6HZGDLoRbirQE5Ynu8s8mh/n3IkZVrI+t/b2iLYS/CxB+C7vOXOchXKNO6cquLLAl5LfWP052t4M75764H+wDhmPIlrtGvPzLQ4fngKg0apQqQQIeyCkHHDAAQcc8LXDn2gh5eWXX2Z7e5sHHnjgq2taa770pS/x0z/901y5cgXY7zqZnZ396jbb29tf7VKZmZkhyzK63e4f6krZ3t7mbW9723903z/8wz/M93//93/18WAw4NChQ//Fzu2PAl9WKKsyo3iI8w7Fx4YP8dDDt3j96XkqQ4svFHeXjvHuY3dztn6U85cvki4IksLFrWWs7TRxdEZYH1MPEiYq64Dhcr/Mxk6ZSbfHI+UVvOOGf5W9ByMktmyJ6ooTw21CpblcniV3Co429zjfXWCgQ0RP8t5Gk2tDD+KcHIuwCu06EEqaJ3dJv+DhRDnHD62w3psizstMzw3o7dUpepLkGR8v8+jb7r4Rbc2nKEnKHQdnN8fpGNqrGaVGQYbG+hJhLdYKKrrgB+//PKtU+LnnHkbFFu2DMAqZF6jYkOLw5sVDzB/aYSXykBaGhU/flPmBez/NuAHfEm6SWJeWHOEOQEjBmWPrPPX0adzbHpmRqHYJNXZwyjnCNVxamiWL62jpYoG8BP6xMUkgWHl9ngYZHe2Bk5JkdaRbULJ6P9o1M9w/ucTjjWusqzbHhhPURZOhHqCCHO3Bx//gTh7/titczkokPly7sc7Kbh0XzZ6ukGoXRxYcbkVIaQhFxmRpyMVxk424jZUSp2EZFC6iAKOgt1BFKMP2FZfCurh5jlqBPMppHB/SKymkUGxvthgXEu9oTKMRsW1q6CTkdGuDr3v3BS4tzbJ0ZZrCVSgMlYUcVViGhSDLahx2+owrPgaPzu0qbqxx9yTxIGby2C73nr5FdOk0cZJSuNDyIpaHM4zjnDyT2FwxFoaqu42WPnefWeJCZ5Go8NnNPU41RugtwVZepRYmCGnQdbAjyVOvnuCZ144jj45w0wC1pNG1DF0XbI8r9IcBwo/5qxMvsLhV4dHJHq89eJ5qySUbK3Qm0MDhu1bZerXGhI4IqzErOy0YCUYm5HUzx1y7y/ruNFJm0LCYmoOdKdjPLbKIDIwSWAnaA69ryev7cePjyYDwuQ42DPC2MuJ7QoyQlBopXrvAJi699Spxz0MHgnhWEkhDK4gZxwHCCIS1uInEzHiYbUtSUWSZxLgSJzN4S4qsJkhRBJ5la6ONUCAyCZlBZxonKBCDMt7UCPGAJFwpYUKF9RR6usJoDmxpjMTH200Rnt3PuJWSSJQxiUPuSpSTktp94TsSAgeNyQTV2wHWHdBxA1bSgN++0Sa0FuWAv2apne7g1zQoyWRHsFraH/6dcGO2ZYNjqkOhBbkfcr+/wR94E9iS5FZnig+03uTiYBpGElWW2DiGKAMXknnLte0Rcr2DE1mMW2BdSdF2KUlFVoDS+50GOAKNQSHBWGwf3LLBSQtkZHCEwRR6/xs1Lqg+lSI8QXFGUCwqXh9NM7XVJx65HPvAFlMnBrzyyZO4yiW1Mf3SOtkTJaxxuPDqAp16k9XeYcIioi+aREsh7bCP2RBgJW6mIJWI2EEGlq4s0TzRI5qukLkak6fMjefobPb44quWE4vfjCcGDNY8inGCrEkQ1yiJhHoakWwpgrcVNI72WV+eYbjpcnK6z/ve+ybCh5/9la8jL0m+PD5ClZzAzclCj6bvk9sC73bMxnMVmg8UdLZCCjTSF+iy4q13bjNdH/PzEnIPnEMFV+M51m83eNdD59kMm8jtMqWZjCtTszxWvUR1ZwxdFyE9hAYRRfjrAccnOuSTktGwghhZvIGhaAikNphCYouCwdkSTm6oLmWUPl0g32fhQchf1rg3FVbulz2D1YDGuZjVvIGQCgpNnln8koeYroECG6RYAxOnI3obHvpLZUxmkBUXU3MwOOApbOAjuhqEQuQWVzhY10F4BUIXiMJiHKAQWAfSVU12fkw5GWKmyqhCI3oDstDhhqv57U9+eX/8McnA8/jwh17BkYZaKWb6Rofx0w4oCdKFIse3hmQ1g3aKbZXoRwEyl0wlIQC7u/19jy4hCJWDZ0GGPpVKQG4Mv3fxKst7XaKLXbavdag3PZrvnaSTxLzeW2FUCI5W5oh1j5ujSyyJq7xj8gOof8dwtrM75Lc++izGFtTbZYKyC0A8iEmGGfEo5YADDjjggAO+VvgTLaQ89thjXLhw4Q+t/YW/8Bc4ffo0P/RDP8SxY8eYmZnhs5/9LPfddx8AWZbx5JNP8g//4T8E4IEHHsB1XT772c/y4Q9/GICNjQ3eeOMNfvInf/I/um/f9/F9/4/ozP7Lk8YZtpB8++w389nP/u+oUwnfffRV/pcL7yCyAbaScXuzx97GmPfWHsR1HW6xxp/9b5/hydfOYZRgtOkg1zyC6TG7RYkyBUkhudE9yngp5uHBTUZdl7v9bebCIcI6vGX+Os8NjrO61eQt7hoj4SMWBK+M5+h3G4y1S8WzPBdPkZZ2UJHC+pa5Iz2mzq3w7GdPcZyY1ndd5dTUMkbXePq6hURSm4gxewE2ERxpjFFum95wCDu7uP0CP/DJ5gRuKef+5hLTR8bsDEN24xpKWLSyiNxyb3WDxxdu4jiaX1k7Q3Ktgs5d0KByhXaATUV33KAdDNCVfY+LG84MVB0+Ye8gWnb57sMvsFDt8asv3oNRGs9Yrm/N0S5ikq4hzyXLtRKDYYDWCtOEYkeRWhfxlbFw30946M4lAJ7aO8lwvYT1wClbnB64Q4mKQQeGrMiZD8fceWSNRd3nH33hDv7Zh3+VAsNfOf+trBU1PvDQK9S9Aqdw+LrqVX5l4x5yCanjsZ7UuZ1N0nTHfMep5+jj0RSSCXfI9kqDtaCONg5+nuNHBbYkMMMmzWrC7u0qc6dTdm64REqgRhK5ZVj32sQND39PUJMDDp/qM3G4z6XLR6ilBUkksLnDaWeLz16/i2zHgxpYKVG+oKQgzSSjTpnbqsTOTgWLpT8qYxcK1IaDWc24XJll5siQXTck2fWpT8R40nIrmeb2c0cQeUKtPEakAqY0TlvTDCKEYzFhgQhyIhKuf2GSRx69RWnzbjaaPlndQXUNbl9hC43bVSghkDsGN7GYssuoUuaVzSNMNBKklQROgRPG3B/ESANHqgmc3CQxDv24SlE4bF5o4ewm2Alw04KSTZie3WV1VMNGCm0EUmUUYQknMyitEQOPtCXRbQ9hNd5Ao10JboGQgmpPIAoLaU4pMFgbEece4WiMEAXrGw3GDYHOBKUgxoSQCsXlwQRF30EVFifSSAS6p7CJwACPfdNFtqImr792DMd4mEygmxKqGjdxyTONN94fP5Ily9uPrvJbT1aQU5JiK0Skdj99qBKgA3ASw8QXBnzzt5/nNz/3drRVOFi8pmLoK4wrQAm09PhydAghoJ8EtLyQbRnDSGGUh6gbbC5Q431PZhzL/Y9fRrQgti4m9zk0jLknWWK9XWNv1idbrnA9d7EDxXe882UudecR2iKAC0sLqKpl/ckyNs3RswpxGzxr8aYytmZbvB4MaciCou4iC01wKsKtatSnFU4C1liUowlUQlhEDPM2pJq8ovejYrspyjqUWx3KYcbachubWNyxIXtAkt0rCXYFIhPsng9onB1Sm49xQoN3JCK5mJOOCtxLdRJpkMog5x0G5RLObkYWgVNymbcD7vNvw2mHpOHw7PML9BKPucYa7lbG+nYbNSmYfqDDxjMTLM5uY7fmyaKcwrHMa5+3nD5GurbEYD2h1y4T4TI2PmUnxtBicLlC410jqm5Kyc/pC5fp+S7JyIW4AOHiljWqDKWWwt326ccZqupARbD9my63Vw+DtmhXYBo+KJdR4TNJxKG9IS/faBDtVNidCZAVuDmY4d7p2zz9ygmcdxly6fBc/zjJ4RAnVOQJ+N0C0S9Q44x00XLmniXUVkb3N1vYTLB1lyQt73fyRZMK7QryhsfwqI+/ndNcT2HPoDYk1nMxYQZxwuZ2wPYLE6Q7CmEKLAoZChgZ0GBSjThXJikUE0c6jHWdyfeN2fpYmVwIxMT+WJM71DjpfveSyQuUo5AFmBIoEyMyQXrDRZw0YMEMIsLlCC+2EBcU+Qi3cEAXlP+Cw96yS7fvos8vI3cjMHBjawI/zpiaGTIyDfzjHo+/8yxPP3mJ7uoQWVhKjo+f+IxcxbhfItgtWBiG9PKEvBOhtabZLPOW04fY2xvx6LGTfOvjDzJZKpPFBUWe87m1AUZJxMhwYr7FRKnEYq3GjNsgH0A+rnBv/TCz4T3/nogCMOxHLN/aQbmSh84sMDlTA+D4qVl83+Hwiak/hmrogAMOOOD/3RyYzf7x8SdaSKlWq5w7d+4PrZXLZdrt9lfXv+/7vo8f//Ef5+TJk5w8eZIf//Efp1Qq8R3f8R0A1Ot1/uJf/Iv8wA/8AO12m1arxd/6W3+Lu+6666spPl8rJMWAV7sfxTmm+fbpGzgCfuLuL/Idn/kOZlsTnJ5tUit7WCyr0S3mSlVe74f85Qef5oHqGh+7dTeXBjOkQ8Mhr8PtZIKjwS7nN++lv7HNzmaDleWUs3qXzVGZkfV447WTmMJjZ6XOVjuGCUleSJY6beRWgXJdCKFe9rBTTVKdYrOEIva4+923uPniJJUwwR2F9EpNKgsRwbUYkZYpEsVf+u7n+ei/fhAhA3Y6Q3Z6fYhTwo0Iv14nUtOMp1yOn9kkGlaYe0uPN15VWKlwxvseKm+8scCvVu7lZrvEKPMRrtj3QsgEhatx6hZVhU7Z58tbR/jGR87z7OU7SN0AUSp4+topnJHip6+2uOPcbX5r936cyRxn1fL7n3yA1kwfp5tjpSSZyfEH+0JEv+pwd3Obw1PLfPbyXRgribXLKPdxpGZns4JHAXXQWQkbatSmRvZzaHtkheX2oM3vbZ1iO6/Rq3v4To4HNEsR23GF54s5/lvnCu8qb/KxvaPMzjXYGe6Q+4JXdxa5uD1PyU8Bh/fff56KjKiVInpvTFHcL3AdwzjzELsBOpRUBw5VE9HJajTnU3bXygSRJZMCgQQLd6xvYguHWFcRRzUr6zPE62UcaSlKKecn6/zu6mli4VJgsMpifMleVKdIR7hFSmRdLu1NI6ckE0e6rJZDtKfIawZ3bMiv+/xO627yXoBMIYlDouV78LZ2Ibfokkt+TiDP+8RpmbMz64ROwZnmOhcn5jjTXsdKuL10iJWNBb7h0CkGxQZW7I9nSCPAU1Takm5s0Z6DMSHvmrnB3FyfhdktRjspv5uf4X+ZfhHhZSxdm+XFqw/yTLrJhB/Rrg65dWUB5ViSzQp6XMUcM2gHRrFDtDZNWheEywrTVFjp4lQ1zaNDsBC1IBf7Ee3kFiyoxOJkBa5STBmXoetgfZe5d63S65cwjs+hU7usB3XikwWjHZ9pM6JRGjMuW1benIXAI/cVRQm8QpM7ktQqjDTcd98qD33dDQAuvb4A1pK3JTK1WNfgGc0d96+w8uo0e3tlRCzY2atA2Se7AnoqQCuB9Q25p8kDEGXD3Y1d/LxAJBIbaKL3GIIY1FWLKQtwAWUY5D6uY+noComXEVlNrAqE9CiEQbgCnH0hxPcypg4NuGOqy1SS8gfKRTRzduollsMmWdOhuhUxWi/xs3/m5zhUT/hybRFv21IkBeWbsPrrswTBiDgwjGeqZOdyTsRdGknEznbG9ILP7FyNN4cDVDfD8QpkDiIQiMSSCIslIPclrQd2Mc8rorhEOuHiqwG67OEDf/2H/wDH0fzMP3o3a5frgEVqw8JkzDtbazzfmcK8JWfx0QGmEKR9SXV+yPZajokEouuhmxqhDdkjPqdOrhI1NNuv1ikpwfyZIa9ML/A3Fl9m9YrP629U6YYh2wPBbrTIzakmjhFUNzRVN6IaDNncHjHbLEPD4fpgD9n0ODFZ51I/Z8tq2mqEKzQvDo7spyi5Due/cJTakQHK1yRZg7//M9+Ot5MjfIFwNdXWGN82SIygF2hkIrDrAm9gEA7IRGMDD7ewCL0/cvPRq2c5c2KdvbhMb6+CzBTOkku1SNhz6lzfmufsXWv0Ah9HGNxxTnrEQSSWyXbEwrs32X3FY9y17K6HbC40qB8bkZ8JiV4oMf/FlLTi441S8vkQUXVQKEQmSQ6HbJQU4rWIqTeGFCVLb9Fl4mbBoXdYlt+oIEoGszlAT/jk7QDGGjMakJ6p4s75qIt7LPda7OQ1RuMB0hdIo0mrEm9o8McpMkoAgfYFeU3iRjky0sRji+/4mJyvzqy7OHiFj206iKLPqCkItuEbf+caJ2q7nB/P88IvfANmmFBojUiHvPAbb+PpbgZJgklSkqbivd/yFt7+vnv5P/75Z7j9ygrKGLzIYbBrsU1DPrS8/MZt1K7i9o1tPvcHv8/f/rv3csed38DNZMj0fB0vlxRFwZvPXGZ5r4+1+8bRCMmlV1fZu/MUySDmub2rCFNwZPsCleBTxNmXOdd4179Xg7SnasweaxP4HoePTFCvlYF9M/yJmfyPugQ64IADDjjggD9W/kQLKf9X+MEf/EHiOOZ7v/d76Xa7vPWtb+Uzn/kM1Wr1q9v8k3/yT3Achw9/+MPEccxjjz3GL/7iL6LUv39H5U8rfujh+hOczt7FE08/wXBOUlOGnTxABoq0qXFdF0fCoDvmY3s/S5ZkvCv5Dqarf4dAFjx26Ar2bML6a2UWTI+3zd7ik79xP2Hgw1iyvjVDcdXl5JvbjNOQWHhIKYmSozy0+DKrUZ3aREQeurSjiEpJs9wPUV7BOWeCV/U2WmRUXIfJUpc3nl8gtYKJs3XeeMknb2f4JmciHtLZKpMUkooYs5eV0WuadNhjLHOioz62WULoAuUaHEeznLWIh2WmBiNsJrBKY7VCJRa7q/iF37qff/i/fJI/M32N//Hqh0gMOKMCJ7FYYRElTdIQ/Lmjb/Bdi89zTG3zi59/J9GcgpFPDizrCd7TfJjj00+ThA7Z7SZpx2PFqVFPLa4uqE8NWX+uTWnLIZu2/JX//glmygnzrT1+5sWvQ/sOv/svH8b4inKUsnB8ha1jNbSUDDdShK84/We3aR2P+NTyGS6Pmly81WbxcIeTJ1bwUAgE06UhO5R5PZ2m4CKR8fmFlx4hHG/gCkHVj7lxfRatA4bCZ0cqEqOYkAYDLJ6s0J65STuMuOK22TFtpMwIu5Ypv8fk8RFXbJtLtQmyUJNhyJsKt9XDvWiIUhfqFrstia+XyHc8BhUXvxFTBAm/t3cXp9whMpKcfesyV5YXkfWM9FWLGCRQg95emTMPr8ORHp2bhxgYn7ASE7YihoMas5t9mndusnahzUbeJs5jvO5XDFt9S3M2R88K3KzKQ6e3qSjFucqAztxFHmyukVtFuJIhpKE172DTAp0rvJ5GjgxeRfDA0QdZvrKEKKBV7fPj3/B5BHArV8z4fX74px7ht+6M+N73vEwUOUw3XmB2ZZr+KCRNHJJdhUOBtSFSA40C3QITg+MaTOagxwleDVRoKEUF3zLzCtpKPla8HaewZKrA6Y7JagpPBsx2BzQnjjDvK67Uu7gLispizvbFEtZIRB/8vZRuqUTWFOQdcEsFUbeKKkAVAgKB9SwgKcqWfsknNLCx2sSxhix1kOsGNZHgrru4OWQPSzYmLPfN9yi3E57/5bMok/Hk8jFsKJHSobAORkgKqxG6wJEK17F0t2q8/OodKHLUhMY9VlCRKfaixygVZBVFqz5ks6gjjGDYqxB7BQwtVFJEZlEjF6v2vY1wBelY0VluMBtuMtwJ8PKEq8k0xWFNQ43YzOscWk7Yiwt+7vQ7mZoY8tJzxwhncrKmJusGqKFAF1VUO8cKibCK0vGUzm/5iCnBctLl2x57N7//+k3Uch+WM3TdQ1QdwtIQ6UJX+5Tnx4RHc2xnzJ4MyaYl37R4hc/ceoBMSHKtqLUjjt+1zdr1FjaQBBsF32EuU3JysrsS1m5WWah1mXb6fFLfSbRZwUYuogKYBDO0ZNOCE0WbbDnGdF3KTkYhNE4pRZVdfq5zN+U8YZ0mhb8/6rHkTkLoIR2N6Tbwp3cQocfRE5McazbYZokLw8usL09BWsYd56hBRq4VhVVMeTGDxpBRZRJtBDqT2ABspUAMFChwkoKH3r7OmZOrXHv9Hm7mZXYmBc4mOD2LckOYK5CuQCuBMvIr4qBhrtplJuxjtMTGEhCYroupaSZrMXYrJKhlvKW6iXQs5qrk8oVDyF2FN20wmUdlQeNsadLCxUQCUkF2TSEyjRVy3zTaU8QzDsp1MGmOsgFuIcgU2Pkya7MBKrfoisPKAxZnrcAPCrK4AN8Fz0EKichj7JF5zLRPtLXOsTsLVkdNdk2D8tYQ2clg1gdHYD2FccR+IVVYZJTjSomLQsQZdlhgaobZU31uS29fSNmIEIDRmtGJKu7REN0b88GJG1SlpSpifrufUvNC3LKiP1WiSDRSCvBcRFAQ+5Z/8/QFHpyYopASp+wiBjk6EoBBYVBxRi4ktizxNxPiVcG/+vuv8W3/6yNc2NthMxlz/7EFli53+bmPPkfmO5Q9gUhA6gKRWqqBDzYnyz0avkuztIoxGVmx9h+sQwptkY7DbmfEzatbHD42ydxcg+7eiJ3tIeVqSPU/+MoDDjjggAMO+NPHnzoh5Ytf/OIfeiyE4Ed/9Ef50R/90f/oa4Ig4Kd+6qf4qZ/6qT/ag/uviDEGU0Tcxc9z9B0D/sp3fBPZW3p0r83g3muwacL2So8Nv4YAzpbuZ8tZx459nrwwz7vP3WLP+mSrArfo8+t/52HkRAOnNUYfj1CZIHN8bm3N8fFrJeLAxRjBK/0ZFmtV6tWEW9tVHmlcI26FxF5AciJm5cI09foQd6zZXelQjBNm7wy4f2qTz792Grfqsna1RzoyvPT5abqTNdo24qGjS6xS5YvJHDIyuHJMNACcFNmAtOliSxAyojw95AsXzjFRHvHc1gJTQuMkUEiLNBIR5fzF7/wcqR+zZwLecfoan756BlkInL0MXcuh7qACy6DwcX1NQR1fKyKp0c5+SpDrZMy0lni/vMj1yQn2/mzM1tUW440SIiqwAqSBZFDFM+BvCYLCUgi4+/Ay4gUBBmQGbm4RjuX2YAY1zgj9HJMoDIKve+AqF4czONYyTny+/fCrbPkVpBZ87FceIjhSsDMdMl0asRw1+cT5u/ipNx7BCR3qRCyWu4xHitGzc3A/iBQKW6XtxPzbCXXb2OOxyTeZ9sfcEbT416N3oPYKGBjktmGQVpivD1mc7PHyXJNR00F6PsFKyGArQ3tgApdwmBOtVJEZeNUMGjG1UkG3G6DSHGeQMTnZZylPaU3vsftiE3eUIyoeTkegNgRKaXw3xXFdutMOx943Rtx0kA8V7KZVHji3yu/tNoiKCBEY9JxG+gUL7g56wcf15wiqMQuOQgA399p8c3sTi8VxNYVWpFlOkhc4scUbGaRnKKQmGRuMEqjY8MDCEmMrCIQlMRLX0ZgEPvWle3js3G3mjm8x3cwxXs5HXrqD03JAy47YrPikNqDwBCU/QQhDmrnkOkAFOfmdktK6QymPmAg0p0ubGCuYfucet1+qYH2NSQqcSCI0eF2X2WqFm8NtNBJpFeOhh0wN3Z0Kb3SPI1ZSvAcLglZOLFzevD6HqRlEXVNTI5J+mSIQJFMu+YyDo8EGhtlqj9P+HtoTuKEgQ6KnHDAGEVhMLlm91Wb4Yp2yN2LFNjBdb/8CFLnfpikEVoETS+TYoPcUm92AwJTIlY8xGm8IJX/MnuMiBKjIUplL2U4bOLJgGDn4mYEq1BdT0JrsloPsSPyuQEuLVQ4vXDzG7FQH+gY3yhh1WwyvxbjzGjWCajtBpIYbL7d4cXYRv7A4hSHDwVQFQcXBjARF00XMFJS9mLsnbxG/u8qnrk3TVdm+MWthMJ7AbGqMK5m9Y5MzC5uUagXP7B1lfafG8HxI+owPUwKbwqWbi6hkv7NtebNNeSLi+H3bvJnN0rnYZCDL/M7Vozx8902CImX31jzvOHeNnbTCxmobMjBYfASlksOob1ARTKwo1jfbSCOo1GL24gHmUMFUfcz1pMWr48MEkwaRBfgYtFPgbBpKDsyedkiqCeb2JMdabY4cnqARXuOIvMLrL3lcHu3gEOE4TfppCWEtlWrMu+wGTxQJDI/Sdz1UWOAmYIoUm1vQlqwomD+yweXbszjDKU7qEdfb02QVhbKGWmWI47qMIonINWlTQOLwTafeQBSW+xaXeH3lCNYK8AQoQ2/bob/iUAZeW13gW4+8zK9+4l7kzYT0WIOd3TrxKKC4NEBOCWThs7I+TSlM0F/xGzE5OK7CBCHujiEvRUgpsYHFaIvNLVYKgh2DGxuyuiWtK5Zrh6hu9KiMcnAkqh8jd0bYQQTHPcJNB3F7kt5RGD5cIsHFf7eH6A1Ixs19M+JIIw14VR8hJUmmERkIBbbQOHI/AtnVBk9pbAoilDCwDCsgJqu4qUW7hkll0RhmLYjccOKuDle6c4i7ykTrFsdXeLcGECq0trz6+hLv+YZ55qdr7F7fRDsSoSWly4binEY2BELmeKmkCBWO51IKZzi1OMnirQZTtQo9mXH75hpFIKGsSBNDOdYgJDIv2BgPKStFiwkm0zJTzmkux1eYCu8FIM4yQs/7ah1SLnt4oUfn5jarNza5864FAHq9iK2dIbOHWn9sNdEBBxxwwP9rOUjt+WPjT52QcsB/mH5nTJ528UyOcixeyaWtErI8Q0cOmclIhxWkVPihx8P2cfymy5c6X6ZjSnxhdALlwIXNGUo2wpQtJoB7H3uDrYsfYCXpITKLH2ludstQ2i8iVdcht5ol0yTfCXjAW6LdSlguT6InBNf35lFTGb3LCY6Xo/Kc+cMbbFc8jizs8PrKEXZ6FbTu0STmxpUKlQmXZjOjethQ+A6NdsJ3fuMrfO7JM7x6rYVnY4pZTc+EVLcK0lWHYsNhtdzkPccusjnbZm+1jjDgpJYz51Z44L4lkPCllWlevzGF39HIcYHKLDK2KJNhMsmze8dwv2x47vX70LUC4QrSOUN1J+c7Z19i/sgy46zEGZvzQr+COppg+yWko/EjzaXfnSM9bAGHeAYiHVC2GU9dOoXMQO0KnMRgylCkLm5uSS9ViCfh0Tuv8uVPHuOXPv8WJg7FpH2Pc9O38AvNufYGv3PxLWz+ToXsgTr6Q5eJco/ZUp9/9elHEXemtN2Eh+5aYrwdsvobi7g4+FfANiWnH9qmZzymbLz/+Solh/0uZZlRrqRIZZElyMouoXAYpC5aOGz36zgjg1uxeKmlspuTj3Ok1eSyzJtfnsPJBEpY5t5xA9PMeHVjEeVJjssOU/MJ94llJudi7phd5p+Zx7CegRSc2KLXfbx6yqBXQc9KRCG5OpzkniOrCEfS61bYTMr8z+/6Iu87c4Pv+7Vv4oX6JCI2iJ6gs1yjFCSs3+Wx1ivQJcuRMMIT++kQE4spGztVbu3skSUWWwgWprt84M4VnrowT28YYTyDSGBQC7iUVcDCsBCUpMGLNFI4/PVf/hC/9f0/QxJk4BU8WNtkfaWKGTuIsk/p63usuWVGWw2wUBr2UEdSbKbQSNQ6xIMqpdMbVGWGReDXMnAMoY5JKwkMQwpHsFcuMet1KZZ7iHGKDEp4MqU2GJN1HKpvzag8HrF7o0r5JYvdEsgCZGoQuSJKKwSFJa9nDE55yBzcITh9wZqeIM8VWeZgBdhIQEniyQL1GQfHVVy/MYdPzuTMgM5WDSFynFygdIEeZ5jAhwCMzbHCp/AFheNQaozZG1UwY4fR6z7jvSpaSRACIcAGcGe4wrat4fsGX3g4QjLVWmGYeOx6TWwJ8tAic4G1BotladzADl3cvYAjuswo2aLetVzb9RlFLjOHN8niBrs0yENDMfDJXYmUOX4lRTTBjhzSimGcBHxp4zTnzDom1wS5w9VL64R7BuWESF+jhEuhCxr1iGojw/QV2igGz5Zx1w1MK5yh4Fo0j+8W+JGl0YhQ0lKeimncH5EZj2zLYW8+5MVkHu9yQftol01bYyx8pLFYK9GOxd9OSCsZwYTAdn32yhtkFZhqN2lVMzZeN+SxQuaGRdlh8MQkuS+wjZz1lRZLE/PoSJCVNJHaYrgT4m7nPP/mEl+6sUQcBiwc/zMMfBcrX6M2mbGyJullZRCWi9QZ7CyQWJ/jU4psLaVSK2g2IjaTKn5tyB1v67G12+BfPPff0CxDVQiSTRfH9TBa4JcyJt8esfOUjzuuUSiLeWCE80KTOHf41Pk7ub41gxUSZQwi1hxu7CFTi3sqRwlQcr/impvvcPu1MtbUEKkgHbjghsjUomqGxTM76GnBxn0CPuMiHLDjDCsdam/GDE8G4IL2DHoWUBKZgFNY3MySFQY5BmEgK0mKlouRCjWyuF6AnS5RRCliNUWNUqLNkNaDA9KjEcXzGWFTovsJpBajFMJo3IpPoSEv7ccUk4CUCrfeJxtJ1p6pM3ooQGpLPTcIJ0eGHtoBsZXgVSSJ0UgJn/zsPYTdHPuWjPRzEueiIZ1OKWbKTHfLbIiU0PrEt4b8g3/5Ge6ZaTDZrrC5tw2OwNsocFJQDYE4A+lFzVvmT9C9tkN5uk48TJiolhnZjF9/8yInKz5vOzPHc1c2cXMoVwIGhSHfyXn40CEudrbRsaRnY57sPcVet8EXBx3+4d/8++QJ7N3r82ceOce333sXZ2ptTKFx5H4keFEYAOrNMpW9EVr/0VbV1lpevrnKsekWrUr5j3RfBxxwwAEHHHAgpHyNoByJKeqU2r/Pay+8yZGzK4ThEtdyB5G4ZJklGic4SqAchbGWna0uvbU1BsMSTXIGWy6i7OIaRfHulODOHVb8WSKZkvspYpiBNfhXBcgElUp0vcqwljPrwMzsHtVGQqA0C0EPjeDEqTXCccHa6ia6NCD0B5SbN6jOjqBi8G8mhGGBbnhU3CGdxGFb1eiqAm8AXhHx3kev8OjbbzK3MOSVf/SNjM75CF/irBjijsPAK6PKGmUhigJwLUIKcgmO3jc/DYXFFZbNCy22BzWkNSDAKomrDJmWeLlhUXV5+5kbXB7Ocs/CEtv9Kq87hwj6hrXbdd7+4JjRuMHOuIXcdphSY5Z0G+1mOLlGlDOmj3QYVz3uODrkp55/nOw2bF/xETMxbuIiMwO+A0ZQDByowEy9Q50YL9Fsb7VZMQ4y12z0G9zUlmwkGY8aTFcsKhMci9YZTQUcKXa43DzCt556mcnyiJW0wZ5XodAWJQztOwZ866PPsZ6VqKgUF4W1ghklmFVDzvgFiQU/yKgWGV5d4TUdJsl5daVBN60gE4VBEbUtR3e3GVY047BN7vmY1MX1Ndoqbu7O43kxwilBzzLb7BKcNfzuc/cTnfD55K+/BU/EuBVDUWj8tECEGdazyLIFabGh5QOnv8xFFrlbdKn6BRs3Khyb6KC15JA74tU3F0jbgk6rRGpd2kWf3758L11TZs1p8Fj7Ml2tuHllks5WBd8z3PKG9KdBaPjAuSX+m/dc4vjCLv/i6h3kQYHdgEHZ47eu3M9wp8yj594kKBd4fkGaKnQJNjpNTh3a5l88fw/FlTGjvEZBwPBmmcm3d0iGPuSgYgM3HNwhZJlm5pKLjAvSpqGzUmUYB4xSn71BFSc2LHgDdsqSvheQZ0DNsmEFRZZDmpFFKWFXM7KC4ye22EzaRJGPlxjsLYXNLMK3FBq8oUIIRRGALjt49RQ2FeElENqwPWryfT/xXeiRIPIt+YJDJU8JPE2252CVQhmQ2tLrB5iqhxoVuNUUXffR1f0IZtfPiUMXNRKYpiZZ9+l1JNbJcbcFbj9HCoe4CabiYgLBYLvGTDrkXGuDz9XugZHkf/ju3+TE5BYG+N7r38WAOnnJ4sYWkRjcPRgOQjwUsSnTqLu0vIR+XkN5hqEQ1GYCcBWlDU0eBYgi3xd50TjnckQuKF51cK+74EvirMasd5nyTUPVt2x1dnDTAq0NOpAoR7G+1OKLpqD+yIDtfoks80AW+FmMTAwUDvOLW+iOQ1RM0Fmvc+fhda7agLznEN/yCdIUc81jvZgm77m09RYz8yMGVZ9KM2F3swpKEEy42FZOZXJMsicIiz2cxGVzoNh4NcYYuPizk5z9G1t0n63hmwwzEORNxesXZlGPGeyMQlRT4pHF7LnsrBny87chlOgTdUx7HqNzZEngKRdZUki5/xmoC5dE+VipEBMbnD28RSOPKZ2EiUNlNlbqaFdw/OwmX949xKzbpqc26IkRxvdwioBQpeieT1GR6N5+OoxfGLKFIb928wGeXT7DMPMwJSgyyZ0zO+wNauQRdAuX539/lrOnl7kYT/OF4Z1U7Jigp5E4CGMQuyPyYwEffM8rzB7tshRN8np9kXp3Bc8oNBbhlqEWokY5ZtrHNjW2vW8cKxOfvKqwocK5ukt6rgKei646pC0fx0qyssHtGYSQjCqWyvUunoHUsWw8UcftJqihpCotzs4QU2QUQYhIC9JUI2SBKQuk8tBCIU0BIgetcVYz7P1gtSVpClS8L6IzSnH6oK3D3/vb72N6aszAvJ+779mgGmbIik982EeHgrdvSAaNCn4kEFJiEWSp5lJ6mzOtRRbfXeeF527jakXzqRh9TBPXBLbscPbcIV7d6LG92WFpfQf/K2NMjUaZR08cJ657XF/vUhoWnL7rEM+dX0FbQXcYMRmWGZHQd3cJi0t855HrZFbxfdG3Ed7WqPum+Z2rV3h2aZlP/3ffzdRUDQU0pyoMxynWWtrtCuNRTLUa/JHWQT/2O5/j469c4Eirzu9933//R7qvAw444IADDjgQUr5GqDXKWGuxtko5GPHovR6fv9AmpUS66+GkBeNOwu3buyglCUsea7d2WLsNa3ETWbWcSdc56fpEwqdVNZRUxvLrcwRlTXxPk8FuB29oKdshwbIBVyEqCaLt008dlEx47o07eE/rKufX5znudNn+6WnibZ/GdJfMdbBemdduHuZ9Ry8StAtGmUOkAxrVXbS//3NWlIh7gnIwxncLLl5v8dr5eT75mbvxRzFuluIXEC9Vib0yeUUSzGvY0WwPKmR7HmgDoYfN4MbaFOl2GVvLmGrvIW9apAahJMIafM/glWIGpRqPHr7KufYqf+dtn2Q5rbOy8wDleowoK7KBx9q4wUd+9ev4a9/yFJ2iya0rbX7i0U+y1qzwxDMP8viZF/iivQt6ivHTDYbDgGiQ4q/vG4zquRpuKSXtuggLNhYcW9jjA2ff5J6JTa60G9xw5hAopBZ015q8vNkknzG0ZipUWhoZBnTOfxsv9Jb4s3/u33DPn/s/WS6miTKPk5Ud/iA7S2mzgGrKh06+yErW4q6JWxgrSKzEF4a7pm9QFwpPFHgCou2Q+0yXLS+gpn1WdpqsbB1ilFRwtcVzDWNXEO14hNWYJAGRZNg2ZHUPmxgGOqTohggFWMut7QUqhSZu+iwN29RdUI5COTVqEx3mDm+x12nT2fLgkAEkzaBKdPU43/bWN3j2+VO8eXkOGRv+199+nHkv5tWrCxCCrDv05n0Gyw7yWpVh4PLxziO4dcP4bof//coc3u+VEZ7F8wuEbwkLSZSnnL7vKjaMyNwhO1t97BFDKUj5q0de4pMv3sP6pUluVWZZPLPNxMI2t/qHufvO20zqhGIQUKRlsvKILAPpFDhrku4nm/hNg5wFNdD4PYkTO4S7Bd44xxqDvxwzqIf8g9/8esJGTpZVyKccrthJFCPCok9Ju2yFPqtuQem4TzlK0eOC9dstxlmJ3bU247qLfMMyc8cuTdvF3nJZPNvhgmmQPDWHEBIEKDfHlg22Am4koLAwp7nvgzfQkeALn7sDIQ1hllCKYHzWJ1/ICZ9T+B2Lth5yDDI2WByKKYes5CFyAalEJRqpLY6fgh8yzlxsmuLkBmslRWDxMkmSARKG4xK/+ua9/NVHnyLpwOF4v/tHYDGF5GR1mddun8EdSYQQCANiIOmsVagrKBOyYTXL1xaYPRZRSEtYyti4VkUuaII4wxlJ7LEcuSfJYojSEE9laGHIay5ZGYJmyqtPH8EECj2ImTpcxi965GNFYQy67gCCsDwm2XSJfIGRmlp7iNaC5msD5qpjjh3vszE5wSgrqAYJphCoCIavVBhGVURUoC6NcKTD2A2YyF3GNiDteMxFQ5QS7FUbKDtiOCqhSgVxVWD7hr3EYzQAaX2c1FAcUtzI29z31lWqScbNl6foZQ7OcgQ6RLsKqxTWLfDmC7w9yzB3QfmEjuCexc9yMbmTieYm/tDh7smcwN3vjPKSnFqrj7soOPPICuMtw4WPnMSrFjxyzx6Vxoj1DR/GBfce/jLX7SESr4I7aShcB2MF2U2PvSVJUc0wgYVcoFYc9CnD6mgC5YCXAQWI1OAtjNC3AyQC1XHpdEp84c1DFAslnJFEt8rY0MFGBt2LENaQLgQsb07glwuajTFTsksyZ/C3NDp1MEZDuUR5R8P6iM5UCxEJwpcGJAt1iopLkRvmLo1oXRyQ5injB1swsNhaCRNIstK+CXV6ssTo0QnCzy0TNFzGd3rU3YLylqH7ER/H5Pg7GuukICTGEdgiQw3AVEMcr4yQEtOaw4wjPC+j2okpUhfKPrqaITyJLSmMyhBW8cb6UW5cTJl8dIOz971EOpBYL6PwfKyveG0w5Gy1hUkNwlooDMIYHjp7k+7tWX7wH3w3f/7D/xt6I0bctjjLYBZd8pMhv+4ucddMlWy14LknrnD4gUM8/rbTPHjiEADmjKV4ZJvtV9ZRCrzQwUq4tbWH03LwXYeUlNWbbf6nLz7Adz36WbzlBGMVsm/QFej1Yp558k1uXt7E810WAp9Dh1oYY/iLP/bLdAdj/tFf+xDN1n9+p0hRFFhrcV33D63/3iuXEAPDjayLMQYp5X/2vg444IAD/rRxkNrzx8fBt8zXCJ3dIRurHYpCM+wnDHtjLvbLaEeBo7BIcgtJblCOIqyELJ6c4nf0Gm/UZtmkzvXVOa6szTGcdZk/PqDZzyhd90kmI7zDQ7JHKqh2DZs7lPyckldgE5dw17Co+gxGZX5n6Sx/9/lv4Zg/pF5kTJdryImQdMJDWygil/6bbT73qdO8+uQCg/WQfFxlZ3cGIQxhkKCCgqysMC7444ybexP8q995O+ffnAPjMbUTU56N6R6pYJXAjwQ+GRVPgDFkpyQ4GUYYikAwN9NldmLMbDWmvjjGGwucrkAkGpFbZkcDZrciStry4vAwL24c4jc//zC/9KV3cnOzyazoojV8aesIP/RrH6ZoOvz0R76e53/lCOF2ypGjm9xx/zInz23RyypkkYOTSxxVMHWohytyEBJhLO2ZbU7eP6RR7xHmKSqx7HTLfGbtJJ+5dZip7Yy5rAMKsOD3LP7YckzuMV1bgnFBN0/oZhl32xm8Rsy0TGipMVPhiKqM6KYVnKTAKTTFXMGZiZvU3JxNXaOuCmrScq7s8IvLd9PNXb64dBh/yUP2BJ2Rz8qwzeKpJT7w3qeZqXZRqYHJHGciY5Q7RLmDyAVBnDF5co/y2R5pw0EmCi9VoAxOX3Ghs0g3C9gTAW4twwrDqYU1JqYrUJIUdZfMeswudql6Ma6j8T3L5hun+bn/+b1c+NIc9y3cpF6P2RnUefnGApKc8oN7VO/qsxOVEVXItx2qUUa4KmkPE17+9J1sX5ig2k7JHEkiFG5o8EoepUqEVBHbSUZlskcxGGFGimY45ni5ywPzK/ScAC/IQGuEMqgcnGjfpNcYl4E3T+vMiDzycERO3LYM5h2Kw5KiaTBaYsoBJIAjyUOFrnnYkgsIClNip99ilAWIOhSeg1qBxht7lHsxOPu+F7LiIq3C5jnFMKFCBFGKHeSkJYVqapxpS2shIggsZ6rd/S9Psd99IrcFztNQvKmwjsBKQbkS0ZgY0T40pLGb0+wnOHMWpEGoAolBBoLW2TFpWMa3AjkhsWVFrRzTDEY45ORG7Y/O5TmNSowvxuhajpy0WE9gXYHQBrB4WoAWUMClS3dwc28CbV0iFfOLTz7GlWya63qSu46vYeo53iDHHWqElVjXQfZKOO6IeDPCSCisi841JpU4GSQvlIifqJKuhRgNNrIYX1AklmzbZ7xawvoSpMY0NJ0naqzcmtt/PxoRZx8MCBbBtkqoMEAJAblhoMsM+yGuKJiY6fDuD1wAZdHWULUxXlowKzs4ec4v/+rb+NjH38Ev/cy72Hu1ipWa0ET8T9/1NP/DB15krjFgfXmC87cPc7M/SbvdZ/5wBxEJRrlH0IwJahETtwo2r7SYO9rl6CMb6Db7Im7fp3+jzGg9xK3nuIuGcGgxFsSwQGmNYyytypj6zBhZc8gaJfK6S1rkvLTisRNvUVYx1fkeZZNQURkVkTLZiTmtIuwgJM4Drj8zw2inRHe1xqmZTd5+bI2Nqw2io2Vky+GMWcfogmPTAmEcjLbEnYT+1YTkRoKKDGpscG46lPsx0+4uVUborsAb7MeDX701TT/y8UoR5kRM68gWVTeGmQw5aYnvDbCZxRQW0w4opgJkv+DLN0+yNywz7vuUX+oRyhBvYQKq/r5w7gmsI7FeQOEIisyD2KW0mhB+bpO5j9xEOAqhFPd9f59Fu0tlO0V1IopQkrQlWU2hEHhji75/jtjxqVxKKKc5jC0UFqEkSimk1og4xWQGqxRF1cN6El/Aw4/fRfXtUyjgz//lV/ngvRc4PFhDjHJQEluxpC2HrO2iSw55y0NLWB5l7GxW2LxZQ2YCJwJvZMmaHqtLu6RHBMkRw5//S1/gL33Xk3z7By5x6I5l3tjYxPnuHPNPNO63aganPWQhSRYlS3LI+I4Q3BJXXl/n/BPXeOD4ApnW3Op2OdJu8tDp43T2InqrPWq+x3SpxF2z07TDEsdrbd5THvPki/exeW2Cn33x6/kr3/sNVN62QLjnUF7RhOuWp169Qb8zRheaai2gUg64eHODa8u77PZi/o/fe+4/u9bJspwnfuslnviNF0nT9A89VwtcMBYrDZc7S//Z+zrggAMOOOCA/38cdKR8jTAaxiRJTqNVITeWtMhpuH26ogoKjO9ARTB3ZAKsxWIJSwEb0mC0j1GSd37dVV67dZgwjTgb7PK7b7wNZSyPHb/A2fYyn4zuY3vcoJ4Idr9cReFiax5Cp+ymIc6epmJS0nqFvUNV1pIGlwMYz0ncBtiOT3lH4RcxvfUmRU0xXY54671v5bcvvIjBwyvnlJsdtmfmSSjx0uoiUalEtqjwjSLYkyzPzNNZC5CBwDMWR1t04uKNDL27KriLBWJWY58Bu52xvFnnF/7Pt/LQBy7T9jLcRCAyAVKjS5LcOPiJpeakjK3Hz914iGNqwPiZBu50zCtqEbenqDQ1ojBMnN1jYnZM689FuCZjw6sw9hykiHjid++n/PURWIFf3jeuLGYryIkBgU04fX8Pv1RhKi3Il0rsrjpI67DuK5rDbeyey3QtYltkGBTWFRRlyMaCI84qg3iCLJYYO6akfOZUTo7lhNyjQPH9b3yQMMiQqUHqjD/4+F18719+kgDDCW+PXAdMuIrOWplu7vKDzz3Oa6+cQGB5ZX0OcwiGSjJZyqhay+xMj62wQd61iIHF9hUEEpoOtu5yujXkZhl6nsY6CsdahMjxhg5J4tGYGZM0J9GBZro9pLKe0C9yOmGVrW6NInTJR5ZhXoYjmpSEMKwjgLnFHrMPdmBHcuvNuf0xLQy2bhA+OJklTR2sB9ooVCGohDH+Upt/8v/5OOeObHJ7u8n3//x30MsshTGkA9jtlsh8hSlBr+mj6pq1200+euU+XhwtEtfAr+Y01JhGI2VlTXD+4jG+r9PmfdMPkLu3ubR+mOhmg3GtIL7Twa0YzLzFUTlFpGDXIuPx/l3psAJSYYTDmcVd/v53fZLXVg/xQ099gAcP3eTF64cRUiCbIZWJnCM7fTbcKZSnCEYj9ACSKqRlBUOFpw1qLmX3epnoVZ9eWRCLiPWovP9eAEUARUVSWrbo05bhOYHZcNiZnUA8aZA7DrEOKVUzIlMhdi2l5zW6anENtE/1Wd47hHAE2lcEnib0U4aJgyosmdk3xBSFRO0J5Fhi5nNIPOLjDsm8xb+Z4ezmECqMZ5G5Qlf+f+z9Z7Rsa37Wh/7eMFPlVbVy2jmdsE8+p9Pp3K1u5QgSlgQIYV9jogfhwjUYPDDYF7gMc40kEyQkS0gIqZW6W51zOjntfXYOa+2VQ+Wa+X3f+2EJYQyWuWOAgfb+jVGf5hhVtWqNmvXMZ/7/z+P425/5EI0gA9PHSYdFIqRjJ2xTthTVuT6TvRYKjZFgYkkxcUwyRVrktOoTzjR7vHS9RXejhQskpZaYqoLSYg8jqEtkWLCyfMCkHzDZqhDdk5zs3EVVFIdpDR9DWy1ybX+fJK8ifLBUUFoQ+oqJrymzkEAYFrweO+N5isUaIpVsXw+RxYBjjR1q45xxEvCl0QXStsCLEkphUA3459mDPBTtM10bcN1v0bsdsNw8YNiv8tqLqwQIRsrj2KkdakHMnec6VMU+C+d7qCrsvtlB5TGpXyd7tcoL2Tm0SPGlJHA+RSWl/kJO5ekdRKrIpn2a5Yh0PcTWA0xNYHyPa73jJH1Fq9igdn7MXtamE6eUuWSnq6l6Tcaiyd10ieIhA9ckpi345OYpeq/6eH5JvOUT9iM2b3VIdY3tWo7dtwQGhAopmhIRaRQOELhYEL44obE6AiTWWawocEgYC1pxyrGZfRZOwkPnb3LzwjyfyeexLYcQAjkEMdHIiUNKj/oeFC8M6bU98kXLaFtTr3UhCDHSp5aP+RN/5re59WaLj//yKt3+CmVDYz0D01Vk5FMmBkYGLymwwlFMNL7zKHMJAQgn0DnIAkwIUgjkbBXXTTFXY8pNTREVlHXNJLbU+xYnJaijanhRrSLKkjzPeM8HzvPa5S8haj473Q4HYorRfB35Rg+9J3F3CtSHVphMaUwFjBBYB+qy4bnJWfTAIpzAix3EBkooipyiFeHHks986n384Ld/grVenV/YWeaf/8V/SvDHSxwe7vGc+m8qZGYY55bSd9zOesyWhjIr2Cfj1597g6/tb3Bl/4A/8NijnNI+dwYJphtz7rFjHKYxf+PvfRSzqHhtZpdrvkejPmI4rnN8AT709rcwVoL/9SMvkKUKv+qz1GnQmPeZ7jSIk4Lbt/eo1MOjNVoLqTL/mnZJkpSdez1WTs6g9f+5JE3TnDe+fp0sznjsnWeZXwx+99izZ9v8Sr9PsDrhzdHP8cD0X/13pLDuc5/73Oc+9/nXuW+kfJPQnq6TJDlBqJkMU3Y2DvF2crxQY0uJa+TUmzWefsdZ9ncGKC2pVDyCewKZWJKmzwvZCoOmZn9Sp8I+/68f/k1e2D8JCqSD6dqY8QVF1e+TfrYFArzUwfGQtecXWB3eIU4c9RXNP7/zGINrFcbWEMaCpUaDrTQm9+Dhs5t0fYlpw8XwIY7Pdzg+2GSw2yDJW+R7Dv94hpGK+GoNMfawBkRqsKmAlZIoSGHdI9h1+DMJ4dKQuN3Edh15RZF2Q0wg0FWNLS1feu4Up59aY+FYlyAryZXGRBqjFTu2A6XkdGuT2XafjakaKws9bn7qJJMshFzQOD1mtTEiGQmeub7N1tmAV7srNEzCB8WbNDuGsGlwuaErAjpBDkIQNVLscAo341M9yGhHgtd2agx3QubKEqmioyYif0x11nLxLbvszQdc6VuSqo/zLTOzA/buTOM/36EBmGGJLCVG5fzybz/N93z4eTzh0K7kRDdl9eU9dutVpLUYF7GfRLx8uMTF9gZPTu2QGJ/nRY1NkeN8gV9JqUaKxLNkjZzhAD6z/iiuKHjp8knUOUNuPYKRwTUbSJNQNKoUYYUXrpyEuZwwSkkVLI599sIcl0dYI0hTjRECtKJMDFfvdCj8IeZUDeMVWKdIy4inV9b5UnKCoRQce3KZGxtd4q5mtB8g4wJFgZEeUkpEEJDkjiz3CcuSybRCZBoRaPx6idVwbGWHOybA74woU4HXL9GVjKKVc3Mwz2J9yO1JE85adOjQq/BLn3gbZV0Q5TkbBx0uzG9y5uwmr106gQl81sZ1LiX7XH76EHN8jsatjPpeiRKK2YV91nQbkTt0agAfr26pRhn7ZROUgNzn1Mk9KrrkwYUtXObxx97yBf7Iy3+I9JTPMPHx44w48QiERyQE4UlIS49jswesbTdAKXzfEcgJw+saua0oTzmGeORWoRyYssROOey0o1wXEEicdORthUgEZ8/toxvw9c0G5drRepu751E0PNIlgR4rQqHwZYbxJM4D3cgJZYJXKdlM2wgtcKXAlAbjWYopKIWPLiKKpqOslcg5D2/i4QT4A0irE+RBSn1FEPerbA1DuJfw0XsP4HuGy2sL6G1LP6ihJjG0azRrEy48ssb+nmIsM6wfI3KfvO/hZxaso/LEmPBMwcFrVZIsAu1jU8GcN+Lp917DGvj4zz5NsWwY5S0ef/o28RbsdlqstRUnxlPEJkDoo5U/k0iOv6PN2mAHaYb4VcnGczOISEDkYQNFkUo2d9rc25+j9BVOW1xm0YMSmVq0V5A+KHhddUBYWo0hgZ9TxB6TF+q8elgnjzTMljituPS1U+Asx3tjAgr2XumwpVrkE0F7NmFjdRYhSpJMIlOP+qbBy1OUZ4CjqakIg0gFnVpC3KugMks2G4FvyAtBEhqGWUR35AjCkq6sEXpQHkrGIoH5gjDw8NqagSoRUxI/T1iYnfDs6pigCv/o4+co5+pU2prdTFG2JMYJ/JbC7YGqRjDJcZ6PUx553+dz/++zZI8HFOMEWSa4mQirfGResrU5w9T5Po1lw3rWRlwTBIMMdU6i5kPkqzEyLghczru+7SaTvka9W+N8weIjY+ZaXXZHMLwyy+ziAM+NOX4y59gPdNh5Q+PtSUQB4laPerOFme0gGwX2YMz1f6iPjELt8GJD5VqKafs4pWi9mbD1nhqyhGgzI9wqGbZCgj1J+ohCHcaYUxUm14fUDgpkWeAAvTPGNiqQl/yt/+YjHHv/Mrc8ny/+1ArZM7NkmaCW9hBxenQT4DADobFS4SiPJsnSnPC2w1Q01AJ0t0THJRQGb5hRu1pS8X3ivYKf/hvPYhTIRxzh+ChsVQiLxLG8csi1YgbjSxSWvfGAeW8KE2l6IuO//8e/xWBa4qoenxm/wvt+8LsxyQSz2WOjoTkcxUzWNsAY+K9ahDMxjxzr4uZX+PEH/hxKKz70wYt85O9/Fs86WgtTTDeqKCFptau051tUayFGwfxsg4NJzLvOnvxXdMs//rsf5+O/+jxSefyRP/VBPvS9T/2fah2tFHFcsH5ji+sv32V+cfp3j3WCReZqr3BmZY3zje/9dymx7nOf+9znPx3ut/b8X8Z9I+WbhK3tPmlSEEY+1lmGw5SDcUSyVMOoo1aP0WyBpxUAWkushc5LQ2xmmLQjnvPPI4RPo2r4tuYG10WT/+rEZX7ocz9AoMYEpabNkHFXHe1oKzAlqMMRM8fqZOs+IgHPGFZP7fKfPf0if/0fv5vO3VMst6fZHW1g0pxXt1uoRYO7p3kmWuPOaIaBCMi7Pqn2MBXL9CijdJIilwTjkugVB3tQVDwYSpzvHVXuJgVe3xB5cCgVURnTv1dDb4LVDhMp7NBSNko+k5zHvg6mlFgtKUOQseGw0kQ6x97NBjW/YGOhifQE2SokMwJvy1E/luPN5gxv1bhxZY471HFPGAyCsQ0plKY6a1jqTHF3f4jtgZq3+FMlj0f3+O7jL/IbX3+I7e0mfsWhOzFNm9NzNfAtznksRT0++Icv8yu3HqH9esqB8Ti5cA99Q7PXaHG5I3n2nqOIC6QUJDXF83ffy/qvfAsnl7d5aOm3uVAcoC4WfOLaw8ggJV3w+MkvfiuyVFzurPD02z7CF9P3stkYMoVlRo9hfYrFpXle2lincZjxymSKPZr0ej6N9hCvqoidwtWhWBdMz6ZU+yn7IkdPNP3bFcopdWRyNB1+rSQHnBFcj9t4fYdJBGUGSlmkdlSFTzdzyJmCxXafW/0OVkisEHR1QlH16B+2+PLPP0TYSmmdyOgJjQlgLiyQhaW4UqInIQgPqyROSYRz2Eiwn1cwPpROorqW0axGlobKXMlr4TJrJub2F2bQcxa/UWCGPjYHfeCoGMtjzTWm5YiHjxX8c2XBlGinecn0EL5DWLDvNOx/pU4QFjzy8Dpr9zrAkekolKJc1MxOQjZqAukkRcUndwGFUSRFwPnVu7zZbVNWHLrqyPYCdvd94mUFkcX3NVtTsyw9NIJnKiy82mPzKxpQqM8IuKdwvo9Xz2nNpARFziFVhBMEu1AsWzQe+rLCVhxm3rHQGfH9jzyPeBhuvTJP1jekdyS28BmeEaTLgso6vHDnDCpLKG0F6uBHgiaK3dTHRQIKhakoHA4/LxgLD9XVGL/E37HoPYs/FDhPUqqjDyVIfVygGR1oPOMQGUQJTI8LwvmU18eSTs/gyYJxVeM7yQc//CoPPXSXS292+MLrU7T9Te6NpkmrjupiTJoJdMOiIkOjkiIRDJWHk45epUp/XGG8FZIjSSPoDmq8Pl7EDCWyZkjiguEwQybgF448yxFOc/P5MU7X8GWXbD9CTMMz336VjUtNrn/+NK4wKOVT1D3KULI0v8db3/0GL3/5GGu35mh/2xiEoD0ZseK2+fJzF5h6OGa8U8eWPmUFVCVHCA26QKRHn+XsTIfUDtg/iNjPPaLRkKCd4DUTqmHGeK15dO7KE3RvQNRMMbU64aDkXcdu8vC5dT6fnmNiAQy5V1JEHm2vRywrGEL21qvkSrOd1fCB+tyIZj3GrylObXyQm5v7UL3G1NjRemBCrZqR6yqxAhUo8C16OkcogdyySCPxUoM5LNHtEOzR9IETYKlT5CMy4+NqArmb4WIwLUPiPEgdt99c4Z5X4XCzRng1Q2lLeTLCLw3BQU4Q5fzIX/wqF0+O2dkK+MbOMfI5ibkpaD+VE+8awteG3JGaf3TwNJkNqL5VQhggSomr+th5D1fRiH6GKAzUKsy985DeFU15E5zQ6FGMbUqE8rE1zfzzCftnIViL8UaG6tCRPDBF2XLIYYF/c4BohLhu+TtrgB56dLTmI1pVsrLk7q/dgGaFcqVGqgJUlsLdLqJWJ6rn+C/coNecwT4whwu9o+kWrbFaAgI/K4+yPg5itAGpNdFOjN8WFN0BWJ9cCywWG0gmaYB1kpX6hD/0Nz7D3/n5D3OgFzGlQG16RI2QcJRQ7qeUJoOZJk5ZXnr5Jv/1z/417GYXJySHo5Qi8HHap5wGvljiBgXZOwpqD/ZYTz/FLN/PsdOL/PCf/AC/+tNfYqbhMxym7HdjJmmO1+1z8aFVavWQuWYNzwrmarXf1Sy/+c++wj/9+58GrZCez+1bO/9WWkdrxcJqm63rW2ze2v1XjjW9kHbosVAJqPjLv+fzWOt4+fU1QPDEI6sIIf6tXv8+97nPfe5zn3/B/YyUbxKq1ZAw8tBakhtwuSGhIDkGRQN0oyTesWgcQf0yo/Rx1rZ+jGh7gtru8/TsVS6s7lGfypiqpWSpIMkkL+0tEVGQxBF6P+fwdp31m3WKKMA0fUpbUCn3OX5inwtPwMpjfZ48eYcpYn7r8ALfceIye6MBvdEELzHo3JLs+YwGIUmu6I4j7g4HDMY+2aFAJoaKn1LNY2plTKs95rve+w2Oh9s4A0VVME6qLMse/9nbn2OqPkZmiuNFl+mNmONZHzMKsVpx7MwGElCF4cIHNrgQ7TIVpqgwwyqBUQBHF95Fzedmb4nP3TyHO1S89NnjVM+OePjMPVRbsXlthns/N8uz6/vsywYu86ltKIIB9MsKB0WNpOqYPdHEezOg2KmBybkTNWk9OKAuE/oLHm8mbYyqQxWysI4IBbkUWFnycm+Z9aKBVI5+4GOmLO+eusMlM0/SETgEzx8vUIMcPTBgRqw+8hzXWOPjO47re3PcOmizEbdxnkfnVEwaaorSIzE+2STEiwNOy88zI/fxZMlB7HNvw+Mb1wZUpxKePnGbqBkz9cCYRz+4gYok05UR0YEjGEpEVRGGPn/g972P0xe3UWmBHhUEWwLVkwx0SaY8nAdSHtUss68RexIzH3Du7RtcOD9HaAtUVDKIAl4eLbK13eL46h5PnrvFDbUNwuEcJNMhw+UGQ+kjkPi55rs6J/iAXqc6qCKSAO+wxLmjDJonqls4Cf/1L/woVVOybEv+yp/7Zf78d/8m+ZLhW85fYxIp7uUNLmRb2FcUky9EiK869EafuV4XREnZsLw8WmGXOjp1lElJ3C6YzDsiL6cepJR1D5Uo3C6s+kOUKhGBwds8MuEH1iPIhuRTUFahrAm+snWc23ttPvPyKbY+Pc9P/NS3o2ccJlRINNZX+LmD2HKgckoF+UQxngR4px3Zey3lsmQYSTLlkcx7jNIK40HEeMcjpURYi5eBshZlHViLzhNkWtDdr/Pla+f5/K0L9AYRcg7qixP8WooNDHJcorqWwI9ITERZURRSUKMgEiUzrQk6KBG5RZWOshGwnqxQuBA1MkgURkuKuqAUAmEFTghkUVLLY6gYSs/gcosoIfBL/NDgKUf1isDNwnKnB/7R9/PG9WW63Tp3tpapLiraZkJjGNMb1EikJmqWHL7QZPeTUyR7PsaXlJ2CsuVIspDf/sqjvPDKGaQRyJHBkvOW6V3+5B96mblej+VNydbNPloHFJWAvCMpPYGxBaX0cVFAmlVozU2odlLypZDhGcfksZJ4KkdqcDi+7/1f5dvPX+KZt95mchHK3zGqsysen/jyw7gTcOHpDcyFFBcKsimFmRYgJcm0wAYGpGXtynUOhjkjWSG4PSK/l7C3H9CaiwnDEn/HEdwTuBLKzOJqjmIUML5aYePmFHu7dXq9gNx6FHWBDQT0ckZ7FdSBoT+cojuqM7YBd3tt7vSbPPKUoWgF7BwusHeYEWclsmWJC5+bOyc4rDYZ1XPWhj7nzuxRyQrSsSFTmqheoJGIQDLVDvEFtCshGIfMCqSMaD4rcYuGbE7itSEKQS071FMpRV1hMxCJ4OJj+0RnJGkzItvR5AMPNSVwTVieLenMGJz0ifd9Gial8rhCmxZbbwSQG8SoZGt9ioNsgfUvz6AnBheCwCKkZDIq+St/6sN4QuGk4NgzPVqzCaFyUBpknFHdGBEOkqMpDAlzX4pxmSWb8yjmIlyg0ERH05gHMd7dQ2QU4JTG2qMWJPyj6m7SElM6yCwgoG4wixm0Fa2ZlLe+f4+/8D9cIljvU1TBBpJYWaj4iNBHOItFoH2J8I/W31wk0QsVPE9CbMg6EW6qhhoUGClIM5+s8BimAZ8cPMh3Pfk6ogBS0BOwxrAwExLWPNxcHYTAKYdTlv27B0fn0bkpdKuKqAVE7zrG+G0Rfq7Jk5D+uEO7UxLUzdHKDvD9P/osP/xj7+Q97z3P/HKLvMh56aUb/MTHv85f/smPUmSGxXaDpU6TRu1ftva88cLdo5UfY5AVj3d96NF/K61jjOXMI6s88OQJWtN1iqL43WONSsgDcw/w2ML7Wan+3s83HCX8xpcu8etfeI3eYPJvrbXuc5/73Oc/Bf5F4Ox/yMf/Hbg/kfJNQlT1cRKkkjhjiPtj9FXJ6b9wm+RGjf6X68wvb5NmBb3unyXyYqT9CtOP/lHKwTX+7AevUGqfP/pr38p+NeQn9h/i6t4q1WbAbjpFmaUkBx7dgUYfWExHYqsRQllmFjd59EF4bqTY6s7zFm6wrHr8ra8+SXpHYY3jMMuwniNUkiSR5DWBJww7VxTHFwMudSXlAhhl8H1Lp9ZHOYdIAy5fWcFKiSosKofqeskffNfXubh8j6CX8+qdY3QaMZ03U9Sc4McvfJ6dgyne/cQd/vrnF3AlvPf8LU40J1xLRlyZn2GyU8NqAcXRN936R9W4frMg3LHoNzQPf2CNtbBBNgXKL6l9yWd79nE254eorsekCHCywtdXTtKXVc5U9zDZZWQyixMeg0FEkvuMbchhrolHML+wxzuaW/zWyw9z0PVIraVaiTnW2cU3CXeLFodOM1AhYPEM/NDSq/yz7SfwplMmJsTYAqTj9PlXkdWSuolRErSf0M+r0B5DbghOpUzPZyR3oRAC4Ru2fMu4r3gq3+Fv/KOzGDFFsaBgt+Rbn32Vp6N1Pn94FlUrGeOjcbyxtYqrOHRf4ApDmBva7Xk66xNG1RFjocmSozBTayHUJWkzww0q7PdbFLMGG0pkreD88R5rX4koej1KH4wPmSdpJiUrzR7drEJr9ToHchUXGGxDIowjTzTaE5hU8y2Np3h145f4WOVBROTQ1wxytwTl+OjnH4WWY1xERL2Qu4M6pxb7LAd9ZqMxjzY3aA9TmjNjfvvKI8zcGaEUTGSB6lTJQ00qIl69e4LlU4f0TMTYFzClCA4NpiIY9iPCSkFsAtojME6ymqRUVYHIDWZQQ3iO2K9xcyKp3B0zvFgFJyinFeMpx8WLG/zil96G8wW2sEjlMAp06EhaitKD0hn8AOaW93ny3D22DhtcurvKwbkKomqZiktsZqnYDBVbvGpJmR3lx3gLhtZ0ijU1Zt/TJTo+5uaLMxRxyD/96rM4HxqVEf5jBt0wxF8BdSCQoaPiZ9hqBVoKUR4Fxe6VITLP2M2biEpBVmj0WFJ6Dq09smnw7k5Quc/krKRYgWLLMvUNRzEtYNGnvuejdiZ4oUOPNCp1DLtV9gZ1inWfQgbkpc+a1wYhEMry2ssneOW14zSmhyzW++yYDqojKJxGl5ZhWsXNadxKzjDRiF1JtWfIahrdc8jzE4gE3n4AdyV2TnNmJqaWF0yvFOzOOlq5oPQV1je4wiFNgfN8Mhcwbs5SlZadtTqj/ZDeWgRa4J0yiJqg3C9xY0lT5nT3auxcaiMOBd1JAzl0zM7HNB8f8V8+9EVW632OzRzyC/fejRdDOTQQWWhayglgHMVGyfEzB3zXh17hI/Y0b+zX8PZTqhsT/DnDhClsbCjGJbKqCc7ElHdLslTxtUun+HpxhvbZfZxT2LrChYrSlRSRRCvNPVvn//PBT/Hk8Q3+9KVv4YX+Eu958hL5N85x53DCzTvbqNqETjnPsOxTUwV1oVi/PUWr2aMyZdjdtaTjgihI8ZQmq3qIWkjt3DHycUE5yWgqxUCBrUoaj2XoomB3O8BXFiN8zkwd8sTyFp2pmJ/41FPomuDA1YiWc/q1AKUy5HVNJiPCEm7vTLO1VOVQVFEKPGmYezBiuDYmbVXhcIwrHa7iIyseejemaGfkvsR/0GG3BSa1+ItNHnlkkZeeX+e1n1nFxSlpJsEakAqNxGKp1ir0dwegwS03SWYlulsgrUWPSrLlOuW9Puogx5LAiQXY6VNGCjvfRFiJ10txnkU4ePCRu2wcm2V7twJKEM2laN/RmEvw4gx6MW6qgqhrxPioCr7wNU4LVFag8xxTODxjcHXLuB5grypkXJBXBVJqgpUUIY9Syq1QbOYdHqjuI7RFlAIbSW6/couHzy9w5sISb+QjhmGCqzjccoX8QoWn3vIEpx9a4te+cQMzLBBTmtpsG7GRQydn5e1XOL3wJA9Pfz+CowmOKIr4wO97G7vre9zdHdLPMr5ajPH6ht3DAR/76hvMzTZoVkLqUcBwEB9NqSy2mFltUW/WeexdD6G8f7Mc3R722Zoc8P2f/VmafsnPP/OfMz3f4aG3XyAKJfEko9k6au9JigIlqkwFdbTwf0/NVIk8GtWQoizx/g9e+z73uc997nOf34v7vx7fJNy4s8c4zqiEHtVQ01poITcd5acPOX+qz+byPHpnDi0VzeafZjL+84yH5zj1YZ90R9Kagq2hYCYaMNsc8pk3H2BueYKrliQ7AZNMstgReHqE2jYQVbHeUc3q2bkRZzsVfq1oMSbkcrLApw4rbDebqPExetMh+WCIzEv8isM/tJieh98o0H5Mu1WnuOZjPEcZ+Cgp6bo2c3qfJ09e5aVb55HG8dBb17gxmGdf1Hlu9zit9oTX7ixzYXmMP9HUREY6cVgjsHshL19bxT8sseqo3vefH55hN62y61rkUyXJrMMfO4Qz6EyBNEx8SVj1aHaG1F2GDh1+VOL5JbkTHHZjmHc4SqTvOHVii7AsqVRyDscV1MhQNARl4VBplWJH0V5OiazifZ01HnxoyE5Z5f0Lt/j0zQcwSlAmEgt4wmKcYigCnLQA7PbfzeOzn+dA3+Qvfecn+V++8XbWTrRwz9UYRwFJr4ZKod4reb1YYDvxeXH8IPZbYIJjyk/ASpACIxy7qsat3SWCpMC/57P/DonwQESa75i9giglp2b7XHYVDtM6PVHDhCAyg+o6CgG2eo5aS1AJLQvHR/RHFUZ7YLVAGkXNT8iqNYqmxDkPpy1VP2bxoQNu7R0jTvuUUYmnBCIsUarES+HGbodYBDRViipL0h8y6G1LZz9jM65iTYmyCpv9CNrVwAhELjFLBd42lBXoRRXyWUPpSwbO48W4zaVJyMZOg0GvypZtcdbbpZ9U2EiXUGGKMxbGCbWFgryuyGuSg7jBnD1EICiO+ejYImOFK8EvC/p7dSrVhLyqUT78ytaD2FBQag9R1+AsTpXENUPrliObByEkB2GVx8J9bpgpVCOjKAJcoRAThYjB5o4yEFgP5NhRHaQ8vLRBVeeEvmFchrhAHLVwiQC/ELT8MZErGYsI4TxEaKmfHCGrhq6q4o62BBBG4nklP/StX0VKy8d+6gkmhyF+PyNOPXRkMKuOqh5yuF5FVXNq8xNGt1rE/ZDNQx+nIF4yBDHo2GGdRcijtQJZ1ahxiTTi6AK0DmUokBkkhWMXi1cN8WKDKsBqQ+bDy8+fItqHUIwpcp/U+QjfoYTD6aMQz7QMqSz02by5Sl4RTIIIFGjP4hoW6UOgDeWmZO5wyO64TRl52OsVpCgphSOrS/wGPLfbpr1R4dr+NOExx4ULc9za20WIo9ykzBfI0kPYCWJoMfcUZx7b4sTyAW094dd+6q2YeQt+Tr3iOEjr/H//4bs59kiXy19dQPuQzYc0KjEu0NhBRAVDaRW3e3OIBYM8tMycGLJ9o0LV5eQygKZDJRnveGaXTphy8WyfN2/UqAwT5NcXKM53EXmCTQXe2GGER+eUwO+k7H+jSWF94kIy3O+gYoutawzgDyxikPLhZ++wtDzm5OwBUjhOVg95fneJw6zNfH2CvgEP/8EvM2hFjF9qs/WbM7j9ARvjGlf22kwlFWYqOXHmYRJDes8y39kDtYj0oO4VbKcJpbEsL9WYbO9TdNps3p1G5wZ7XWHjjHwO7g2bTN6MeEf9Jlo57uzPoMcOtjS+K3HTFhsBhaWYSF67PccDK1vs7oaEfkE1SKhGgiIwOFfBnJR4t2NsZiizElUY/JsprSdHzD8x5PBWi7XXZvj1Fy4z9oDAI51U0UOHKxyiTMFKbKBwqWN6ps5kNMGNcrxBjnUKO0iw83UocorpCLnUodnfo4hz3O0tjB/AQgdXC/D2YkR5ZKJIVRBcgHRcIRg49O0x+2NYm4p4fnMapyX+ToLLwIYCMTSU2iFwFHWN3ysQaQnWkR/ksFsi8gbmxAxWQNp2kFu0LvAnDpqOdmXCou4zcQFOCAigaFlk4rj68l3+1H/xHu5+9iUO0xQGksgPscEy3/j6Bi/2eqhWFfKUqekuzUmDvVqFST1gIiPeuHMLl32Udxz/DjpzTeBoVbgsHaNBgjSCUkOUG0Bw9bVNLjywQjHOubd+yMG9Hi5QfObOBp23Huev/LHvYHd3SGfqqBa5LA3DOKXdqHL+n/0/SQ4jKnMZb1tdpxpY/tfen+VB+X08tfIM40FMnpa/q4Mm45Tbhy8T7d9jfjrkZP0t/4eaSSrFWy4ewxiHVveHs+9zn/vc5z7//3PfSPkm4eTqNHGSU6uFVGo+b3nvQ+w++XWWTg24fOcEa2szzKSwsbbH8vEPMbPw3STpNtPBR/nItSX+wjceRfRhOuxTkznjgzrBjk+4XEMqC8IhFwa0lmPKmof5nEGWjsK3bL/4TvzW25hS/wjykPV+jcvtDjoqWainHOSSXBmQOWG4R60V4W40aDZhNDRMFjLabsBwq4Zol4RBTLXIiIQhqDgm1uPU/AGRn9M8Neajtx5hO2jw0dFDHMxU+cpzHRamJtzsNeksdVHWsDuOCIsEG2rKuuYnPv0sJ0736NuA8DDjvNnDm8+4Mj6GyA0uKkAUVNWE894hV+pLHE5aHI4jdGqQhUMkju4ooRQlyq/jKUvoOVamuoxkhXu9Bjv7MyQnJDK1eCakLARfu3GaD5y5w3xW8LNrj7Kf1DnmhpA5pCwR1tFPAtJMMakfoK1FDY+EXcwe+cyAZ5dHOCQXF/f4c8dfhvfBf/tPnqHeHnGrv0B/W9AoNRvT86Acwgh6oyrGSLKKw/klfXyccLjpkue6q/D+AgY5/pZCBoL/4Tc+xF/7fb/NeX8HV8CleJlxEWGFJDrI8cYOmQuuTo/RYUmgHc4q1EjiTcDgMJljysbsUyATi0wFYiKZmrKcCw7YCRq8NB5x8eEtxoHGKxo8Hd7lK/Zhkr2QLPQQAUzVc/TshLJUjJQP9wQiszw4XVCpxASNkNJKbCEoHtTkJwWEAWooMbKAiuPXuud4x/HLLOgxs9OWT33lMfyg4AWzQt71cL7ERh4yMyhfICpH60FF5BBBxka/xfakARUHJeRTAoYeP3TuJT43OMv55g5fazyMlYqNjSbqnEVmCoNCFjl+mCMzx9NPL/Nxu0/eUnjGEjtLQ8bIgUA6h2oKkIppb8zQhOCO8iWUkDz59BqXtk9xqrnNG/1ZGlMTandKRkJR2CmoCfSUwa+UeK5AdEFJSyE00rN4BeTP+8xFY3I5R0PkVMMUYR2hcAyuVpFWYaxGVHLUBOSChZajGGrU71SIu1LRaMXUOhMSqxj2giNTKRPIwpA3AXO0zxVsgauXqEOH80HHEn/LIZzAViUiUBAbZhcT/tsf/jSffeE8v3H3IqWnsZECAb7Lcc6AUAgHlSAhnGSslHvsqyqHrgmFYEV0OQwsVAu8u45pl1CsaBgZKPVR0C5gnMU2JFnp8+LdFcKvpijpcGsTVldDVChwhUDUFaYD9lZJnZzkuoKyYG+rydqozcb1DnLgCEeGii5JTISQjv1xk+270/iVDBtprC0hMKyJDsEg443hIk/V1piJurRGE8TAw4wD8I5WLlzKUcNTp8Gv/+J53v+j97h7UCGXgpVnJhTePYrIp1yJWT+s4gJDEAtq9ZTRuIoRHFV1NwwxUE0czhpC30PljkDm/OFnXqA6ZbmdRySlpBQ5onRMMsnQSkwdJlWPTGiiB7vIr7d537uucmltmaDf4cTKgOPVA9TA8spojpiAcV4y3bUMJzmFclQDSUX4LC23Obx7gNtOSIoIG4Emoyw1qRSUmYcaWm7udsD38N8A5wmclZT1ALUNDErEOIMCXtxeYPK8wlwX+B1Bb1xjM34Qp08jiz1kEiB1gZtrQdUndRCkDncpxz0CZa4RmWV3s89UCWI84syf2Wd0U3PwGxHF0MchKKSHrXr4FY+wGpNphUgc0TAjq0oKazCeRDlJaDxyoZDSkdgC5zvEcICtNQkKh/A1NsnBSNY/M41aMbjLR+tcTAR31wPGX+/ggqPfpCIpSVeruH0BxVEOlsgNIncgQUlJ4WlCPSYe1XFaICzI/RI3rahFMfVty/TpIdo3PFjZY9CNIFZHYdMtD5MWjGY9Lm11GWYFTgqkFKjcHeXaaEV6Z8zcxRpZVsJBidUDZLWBqJQUFUNrNqY9NUVY+ZdtOUJInHEszDQ4udLh9Z0DxnsCnUJvEGNzw2CUMMpy6o0aH7lxmTs7Xa6PIFUlK8ttworPwWDMt/65f0BROlZmK0y9MyU5aCJEQstPqYcOg+RNfp1vW/xWwlqAFEc3HZxz3Fk7IO82CG2LqWDl99RMWklOr8yQFyVh4P37kGX3uc997vMfBueOHv+h+Y/hPfx75r6R8k2CFVDisNay141JJzmzJ4f8zBsfQI3Bmyth00BhEBp+6ubfZ+d6jyeCd/HQgy9yYmWPz906xanhmG4UoSeOk7qLLirIWoryYUoO2EtCqpWUkXOIxKADy+KpzxJU380+c4ydpX/Y4cwju3QnVd56/BZr601M1OJg2SePpvjg+Rf4wrWnKCceORWsg0ZakB/miELiV3KmZEohPD555SFEI2RcD4n0hNe/foyl2T53h1P0vIidao2aCKjLYxS2h5f1iBPNlFPcfn2JrK2wVcFMvcF2nlOdynhgfodGz/HE0jrbYppR0jjK5KgJ6juneWl9jkW5RW+rRjGlqM+NMLFCWgUi548uvsAn40dZdyvc6s5zLNnnWHuffFoyavahPYXa8HCZwN+BvdmIP37jfVT6Fr0XIjJB38VICxSOcR6we3uJM1MbdA9ruB1NMAQQvO5Krg0f4VtPXeJPfPbbCErLd5y+QyYtuzt1ogWNEwI3MvQaAa5mjqo4C4fY85Bigm0YQFJ4go1xkzXXIq9IglnQsYCKwkslV6+e4fr2CwhZ8pF7j7F52KE0Ai0t84tD8s2ApFeibo/54qfuspc1iGYS5isD9oZtrBa4TPK29k1eM8do9cVRcLgwFE6zsTtNOMzJZMBoQZP6mqk3Ct66tMPnosfQ0ZhTK13GuUdmqgT7ivF2hawisKFBZIKnH2+SeSVBfYIrJMJanj13iR9/8EW+cPUsP/epD+CcIsfwq28+zsX5W+wNa2QHHn/ru36D/p2A0/M3+eQLj6L6JcI4nHWIUNPvBphM4s1Zru7MUe542HEAngNjkZlAW0d9lPKXTn6SusoZfE/Aq5+5gNGWOIsQxhEODN4Y0llHIiWIQ+bKPge7M9RMxiTTfPr1C5SJh1MepAIn5dGdeAPCCKQTuFBycXXCjZt1jg8KPrVfYzXqMfIbTBUJ2+EMpVMM4ypeZYyvEurDEabjkRQ+XmFxxvG9v/8bHD+/S/GlgJuXTvKxX3kKv2/J93ykiLGBpGwKshUf6Szvnb2LaN3jVz/1OPGtFnbVQWRoLRxQmgiROIwSYA2e8hGFQAwdadsgUge5T/WLJdYCdXcUhBkKVHKUA2E9EBn8lR/+FMvLO/znC4f85ucexuY+EkstSjB+je966gVWZgZc3Zlh+6DBUnNAlEPPVVHOYRwUoaTMQljzUHslvYpHfg7spES+qOF0CQMNBmQs0NpgpyCvCZgA+wmD/REuscgCvKbDnLT4WUL7IGWbEIsgyzyuHCyQTAJsbFjpjajMGy7LKbzSUIRH03npKUXhSyglZcOgqgbh4OOXHuc3rz7GwXNtpoqUajAiqGbsFnWkKPBcjm8slbkO3UTwiz9xnLwmUZ2Q3IXs6gZZqTkYNShOhYg8wF7pcWNtntp0gqoXWC3x25D2NDp3yMxAaCAKKCuGT985weNmB93OccJwrNrDyy0/+8uPkDQqROcKYk8zzH3qhYWKYOhZ0kSwFA45Wd9j994UyTjCiyxxTaBrGTIZURwafL/g1PFDGM2jcoh8HzkpYVoeTbVZgTe0KGuQsaXc1VztLZJPGbzNHFf1SE9qOJYjrKS8lx8F1oaCsITSWE6e2WPp2ASRPsjccI5BssvOtCHuZ0QHBWKYIRy4UkDgEW+0uPJzHcpagL89YlAcMnEaEQWUsSRoOhASdIjwFFYKnIKhyylEFRtlCCWOcm2eUNihRgiFiC1+LFCNBrY7IFmRKAuumpHm+8gopNL3cVJijWX4UkB84MF4gosC8ErMSzm7V9oIQO9NyFs+PDiNEWNkYjCBOgpbSnJEavA9ib9awRwMQEpc88hAiDJIuwnipKWqU7CQGo0xjiRWiFwgnITcYKcrsNxksDfk4uI0X+rewyWOrGvwS4czJWqQc6He4uu3dzA7OcVJifUlOrQcDuoMaidZPHOGsPK/XZ1xjEYZgSd4x3un+K3P78F6DTl2dHeH1JsRN9Z3+cKrt/jw284xd7LB5BgUU/Dbr7zJ9198BGMsb9zcoigdOLi7O2ay3gRK4l6djw2O8b0X73JnNEVIhaRTcrDVZ+PGDo++M8ALPAZJSqBm+ZalH2HKn+b3wlrH4f6ISZKDcxxbnfl3Lcvuc5/73Oc+3+TcN1K+Sbh8e5vBOKUSaHoHY9Ik4/VgFjEWmMji5g35BFrzU2hPEe/lmLRACMsPfteX8CPw32b57Bcv4rkMf1wSVBwXGje5oeeojEoaeznXL8+x8NZ1Rs4hy6MQyTiwEIwwkzrF1oRWa8xPHv8CL5kZRAFnZ3e5W9PEqw1imkyGFc7MbrFzZxFVCch1yV4+hXQppFB2NbthhKUgPaiR9WpMaopXby9TKsvps7vcMB36wwiVOyoaZqtN7uU9vFxyTObsiRbJ/DJpc0KtUMxWI16s1HCjBk8fv8vJ5QEnFgbkzkNUSwrto1KDLHKSvmG1ecjQVJGuQVL4THUmDBcjOoyZnU54qNjh3miR1kzM8foBJ/09misx1yYnCZ3B4GOqYAKBG2pEFlC6DL2vaIqUjp+wXx4dL3oB5Y7AaM2zq7d5yVtGShDWcNLr8tJokY/tnGezP81D05tslMnRxV1TsGE7xIGH19T4fgKFRJRHrRkqFZw4HJBMaZRvsAlsTZrMeCOSXkB9rUvvsEKoDGLgQ245lSsaTcP2Xp2ikMisRNagpjNM05LuSuyhz+7aAaKpqCtDfrFHfngMMwCQzNkY5i2lXyKMIjyWEk4K1tdnOVbso8eWjXttbMUSdj3euDuLcZpTiwMCzxJ5OWurEXHuI3GIXokoHTaQ3DqUXN+ZopuBGgFa8MMXX8UzhqeX7/CzFgwgcoFEcvlGh8ai4LHRkEYZcPHUgL/+C2/huVdOEHgCqQSiMJhUILTB1j2CHZj4dZwHpQWqElMVBBODHht+6pPv46/++K+xVTSZtD2ElYgpS7ZeIewMUClgQO37ZHOSfVEwpI43EkwFKaHx6e/UMChMBMIvsb/zWnpiad1O6a/6lNMl1ZrjsektHprdo9h+hFbUZRAEZBOBDjOK0sd5MEwqZPslxdDHhJJRLqCnaWrJzbszzJ7uslNER+GuVXOUJxGCPg/xOYG9LLAth4kkm9qHuzX8Vkl1MUMNPMbDgO69Bn5H0Zwas1+r4x0WhHpMIqbIayByhxwX2EqAKyWeLSF2FB0o62C1RApAOISDztSAplAMf8eU9WyOKiAwPkkLChnx6APXuLQ/TxxqBtQ4oMW0N2ZQGbJrmuz1GohCY31JWBSYbYFYksh9j1IbCmvxlcFUPYwHx6e6jPYiykQQL0jyk4rPrn8akc3hCo3cLoluWx6qHHL+yUPW5gM+c+UcxTIsqAHeasrw7Cy7z3rUtgXxrsWUAlOVuBlHsepQhwmr7SEyOvqfnnRdrqXz7Cc1zLTP4VCgs5T+pTpiwSIjhwwLqrJOp3WbRCpKHZAtS7y+jxlGpGFImod4qSFsGTInEb5HND2hVCAjCThUDuGaxAmLw+KqDnLJZL7Gr24+zbXeHd7ztptIOSRXGiEEpgLGVsgHCZORxqs4BqOQIit54doDNL0Jua/42q0z5Ac+h70m7mQfHbSoacHZMwKyKlXxBnr6gHDRsL7RJsPihZbGuT6HooK5fnR+oHqUCeQ8iZ7PQYN1OTbwMA5KFFJLmok7WksBhlcjMkKiBwznHtnl7munqaqAWiRxgcK2fbg7xhsmGCmxNR/jeThj0UNwwoKn2E7G6EQQpTm3fmYOJQrsqABhsVqQzleRhWXjpXVoBVAN0ZkDDYGUxJ7CaYUDSpkj0xyhFGa1AUahbhzS6IMqYoTnoTodykCTL4Xkx0IwHtFEIboHuNggyqPPQQgIanVS6ZB5Ab6H8zxcoHFphjOQWYMxBkeICyKcsajCYZ3DzyT3ojonjmdMshqeLPF1gTUS/8CiRobajQSkQkwsz93cpDPXoGIUsbEYYfGOVThTbdCeimg1qpjDgvieomwIUBI/KChyn7vrG7zafJX508tU6xUArLXcurTG+t0Dlt5RQc9nqJMasa3IS8HNnX3G1nD3xj1eevM273hSI2pH64Ff/Nh1fv8Tj6OU4sET82gnMMahhOD84gpIwU+++3tZrU3xD6//Ir/++g2sU/xP9tN8rzlPvzsijXPCMOSRE0skWc5c+1+2A1lrEUL8a608UgqEkvy9n/88B8MJf+yH3sl3fctj/1512n3uc5/73Oebi/tGyjcJZ1dmGU5S2o0KylgoHLYQ2AiC6Yw6I/xhxOxiA600P7z6IxxW+wx3MrJSI51lzouZiArjMqII3VGgpD0F9V3UJGfrbpPphT6b4ymq4QBXBMy1+7x5fYm3LMP4ZkFw6DMxdT755kky4zMpLderHVrtQ4RpgIO1zXkWgpLAZqTliCtpj9FUiNo14HkgFHqUQ6BwDUVRSvJ6AELg+xkHvRAxEZRGIUclqZVsT4ZYbdlZb3KwuMTMwoOsTcY4X9GebXDq0Rf4f5z4DD9/5a1sXG9w5+YM66ZBkkd4rTHhtsAZQ7oc44Rjb71Ov1rl4dlthtbj7vMdzMkKNTGgXXccXG9Qczl1E9MrIp6fnGQ2GJDNaRSOtC4o6hKmMih9yA0qsPiJIVI5w1GEMRbrga1adOiYTqaYNKAbRrhc4IxibadDfamkcjPgH37oF/i5N57ii5PjGBTNk2NuRU2SWY9IOOYXDthwdRwCsaVAaoq+xHUcRanwlKJeS5nxE+5uL7LdneGtF+/wTHONn/759+Iywy/+9kmW3jlEPSchKnE1Raw1a90OUb8A35BJj6Ct2TiMePPVebJdgffwhOyyj8LRj33Yg85j+xwkTR56cBN7WbPnV3C3DeGOIYsERS3CjhW/cvgY0kniQcjd+EgAq5MJaT1AjR35WGOlREggzbh9o0OvGWAVKCu4RYfWfsb1e7NHd+I1WKORGfzKlXfxXe0vMj0z5FhV4itJfqFkdEYxUJaZnwN/FB9dfFUlAocoLH4M6ZxD1MFJjfUE+UBhFyVF3efXDx6mtJJ6YAgZcOCaWN8w2WoiFgVeIiibgKd43cXkZZ3ACQ7zBoP+ND/69qt84ouPEVZKptWIyUrJwaUmyoG3WxLqkuEcfHFnnrf5e/z3bz6Fj+WqmkZ3JR0b88BDl9ncmGWsK2SFT9ILkU5iJgK5KXC+QpUONQqYyyRZFpIEgifes4Ywjtd/4SzjypHhp5TFu20wxy2XRsfIXm6SRj4zK0ctW9ZqQq8EJ1ls97m+P4fzLGoiULqAjsIzBqHcUUtIpCiVReYC01B4EwfOYi0IJ5DAYtXiC4/J2Mf6YAtBMLJkssClko9+/WFeuTZF2CyoT8V0XcQmUzREzLZrUGiF6jgqXYeZKyB3lIeC8HMeRaQoTkJZaiiKo3WnlmBoQvSkJNEGJw1eXbBTaaG6DjwPWzrKSUb8tOJTySlOP9TH7EU4HHmssUONrThK7TGa+KhckWNx1tHyUwZC0lkYMb/aByl4/e48w3QBFXJkcApDVoPuHU3ldIGHxYWGXGum/IjjDxyy+8Y8aSeCKU2Zg1aOY+097EmPO5fniMeglEMYy3QUs2MDbCShC+ImUCistDDvSKcg2LaIXPKX3/EZRGL5WP80Z8Qub6YLJBOf9qmM7m5KdytiroCKztFzglajINQJUhfspnUS6egsTUgninzQxEYBy60+YVnl+NlXaNXu0T2sUZ87oJHV2U9A1BNmHo4Rdw29iYezAn/G4U0X5FrzgeOXuHV5miu2RerANiWuVBgg3C3wMkcRKp5+4jbhdMwg8dnebPKpXwiR6k1OfetpgnQTO7FMnSj50A98ld1kjs9+6kmcszghkeMCrcBMCgh8ZHeCdTn522ro6yV+t8BIicRDCIH0j84DRgVImx8ZUlKisgK/r3G+IwsEaccnuH0I1jL7+UMOvv0YsllF9EZYT2EKi9zaRgWa4+/V5LMRW1+tomLDwYUq3qWE6hgIfURYQVoB/RhROJwssdpD4HAKMCXGFxRdC2emcNI76lzc3iWoVCiqinRfsLYwxaoaYp0gLTVaOWQpqewbBJLsWA0RGwb78VHt94pGAEVdcOe843ytRZD5fG1zHbczgqJkctiiWNEYqpya3aJJzuzUDJ7v0T0Y0J5uIrVi694hm7d2Kas79E9HpIsewbTPYFZzeWeXC51prt9VOCu5enuf026V3e0RrbfVyPMSP/RpVCP++o9/iI987hWq9YCtMEZIQSiPpOpbp5/i7cc/wVNz67yxt8rpCx+k2aoQVgKUkjx6fglrHFF4NC3T7415/nOXeOjJEywem/tXNJNzjmGS8Ob4kOipET//ygv3jZT73Oc+3xT8x9Ka8x/De/j3zX0j5ZuEjYM+h6OYxZkmRkusJ3hiZoNrd87y7IM3eVvtKl/buUBvb8SJM8ssLi8gjcYrR+zs/VGk99PUh9DfDhnUNMWS4+5r85w3Kzy68BK9UtB7dQY1B0qAr1PKlqOPRzGOCIigpsnTo1DHX339POMgIh4f1Z/qFPShRGDpFDGy8EgPfPLU4FdiZCZRAkrfkgWCMPEQnsAogY1LHjx2j8fft8bzby7yYrxIZ9RjPGlQTCRGQD9NySMYxIph9B00ag3Ghz2oQBj5nKxnOCdY8vq8Ec0z2mlC2yALsImHygqschS9FF163BrP4KRi5ytNyhJEAfXYw6s9wt+96bF3exHX1GxMZlD1lK1am1lviE0LonGbSSeDpRxxWyAqAhNp8qHhjOwSlYY39+dwLY1pKeR0yvLDewQbD/J6usqt4RzR2OJHORcqhwylpKEdFV1wMjikdALnHKsru7w6XqSyPCFaNCzJA14bLALgfEdRh8PtJSbzBoB2mDGrxpzw+nyhGzDIQ3ZGdWYf30IPYkgMr1ydZnIhhxmBPJdjto4yO0Y4lsMxW0Ed1wnpic8yKWexOejEcKx5yKbnIQaaf3r7Sbyw4OH5PVr6HmJbsNOqEIUlO/E8JpTYNCQYS2QC8iTQd+xcatN4yxglHP2mT7DnKNd9hBWgHcJp8uos2/WTfH1Ux7ZL9GFJ7iRf3T9OfNNHpgYhPKhYRGyYnu/z+dffwnPeAb/07Of4sdffwW0zh7MWUTpqS7scZ4l7k0NGwwxmJHIFvufpl/i6Wmb9cIZs4iGEwbOCbMbhfEtmNQ2/pBuHlN8GgcxpzozZudYkDy0WHz10jKcsYbeKHIMJoUTz01ce4wPVO+AMduQzjFssPH6HzUoHKy3pVIBpWVzh+NzwONfpkBBgpxxnpvbZkVNMLw1IVzwa3phiXVPEAYkJ8AODEwLVEwilcMLw4Weucm76kJPhkFfHHXq7VUJRUg5AJBAUjmCrRJ2ZMHGS8aGHGgtMYJkqD9g3TUxtmm7XZ7495vp2B0qLHAqKsUMGGc6GULO4UCAHAqEF06t9dvMOXqKwgBqWlDMSqxTCWaQQOAevd2epLsf0DgJk6TAVwVK0SaUiOb14hSKcZmnpgNu2St53ZCOFqSgEAqMc+OYof0UKTNsxWQCXgxhalKcxFUdtUJASsD+so6WlWo7o2Al+UbL4zpirLy1ShA5VCHQlJpGQW0leSlRmkWPJ4ItV4r0qzkjMRkBZQio98OCpJ27y+z78da6P5vi1m0+wcb2DK0EBrfoIry3Qu479uI1tSPIlH+2q2M2ALFeUgWZUGG6rJQqh8LqSwAoS33Lnmsfj7zjkMJwiqSlKX+Dt5VCr0GxmTCcHvNhv4WKFXsxIPI1IFIX08AYSFZeEB1ARBR9bf4AbUx0OZY1x5uOcZDKpgJAYH+Lcw5WQZZLOMwecLrf5/OUHGI5r2Myhzu6T7rXQxmEqJe969Ca3rjzAAyeuc+bUOn/r899GMuqyXK8ycDXMsYBrzzWIYhBjh66UNNsp7U5Mt+doqJz+OACvAlXwkhzre0cVwqEH/RynLSfnBySzGXUbo+s5s8cPefkTW1xb6BOakrDpeOCExvcdclIi8pS842PqoA6ODD18RTEj8Hua9CFB/ogjzCSVgYCBwS8yLj50j73RPLv705TGICRIZWk2Jox3FW6kSaYcAotp+hhfHWWYaMn06wnpqXnKE7MUoyHBiwOIC7wiY++VFH1yTO2FLhMHH/6+bR767l3+yU8+hclCbJ5CpUJl12CtQ2cONypxzuEKhRQGUxrUpKAsBVJztM/r+1jPRxSGyhUflSnswlF2ijE+p+sxMnO41OCqHrbmIaVEGrAaklGJrStwAuM77qkJ56pNVGmPIo+cgMrRd6j0YVzT4FmuDG/z4s8e8LXPXOKBR47x5//HH+SZ91xgaqbKwsO/xK9sfielUmQXAmxpWYtj6t0BNjxqyhuPJH/gRy7yW3eu8eCJOfK0JPVyOvUG73/recZZQS9J2BI3cAgifZRh8lD7NOc7u1yaLFGtT2jPtqg0KkRRQF6W9CYpAC+/eY/ra7vc/Oot3vjSVd75bY/wJ//S9/4rmkkIwYuTdR74/ddYP2hy4/IhH/qLP8kn/uZ/+X+RarvPfe5zn/v8p859I+WbhKwwGOMoypJ4nDEapmSbDagVtGRMMfaodAXW00glkFKSxjm9wzEXL/44//T2Lp/7tKU88DAdmD41orlfkKsxFzXc2o/pHkhs3WIKSA88iopABh7GVZiebiE6ilHVAo6DrYi3fegWuzdr9NOQbtEksAnTqsu8g9S/iEnHOAlF36JzDZ6P0ApcifZLPFXgnEBPLCb1efPmcTp+HxeArUvcviTaloj8yKCxWYmISj5Vvs754XFOL08z7O6Q9wo+8eI5wv2IewdNzpzfYBRCqSTClBgPzKKAxMNEAfNpFVOdMEgdjAWNakY28cnLgm6Z032+hTuVYysS04Y916IRJfRGEXmvQtT0CdZK3KFATSTpOQsjB0MJseT2eJbasZjsEMahTzUqWG72ebL5eW7EAeODkCA1VE1BvZZRKMUX186Qf9Lyjd2z/PFzHz/KlZlNqA1SLsgxp6d3+JkXL0LtaK9eji0ih4PUYQc+CIdOx8T7PnpK0Z0JIS9p5DkHgxr+1ASTKz7w7FWeeWyNr5cX0E3LQS6wGdRuS/q9KkoKtCxoTY/ZLNuYkWNkKvRHGawF2NRDuSb2Gfj48ATRHcmposfubIVJz6M+cZRTjlaYMMgaiBLkpsN5JXLdI3imRHmSpUYPfSgY4NGzAaIIccpxs9zjfccvM7mzzCir8fAHb/Hi8DjBpGTXb3Hy7Ru8eW+V1Ye3GOx7PDp9j/eaLl/eXOUDH/sBTiztU5EJsZAIY/mx7/sK61/9UfzuCV5/fQe1b5l9OOWB6TVud+vcSJYQokTvWbw+2JqkDDTboyaTSs5hHDEpFEpLMjRniw3uZHNU78U4YLToU1YFpgElAiEkN6+2eOXq23FzJXLogxNsX26hYodVAucJ1ACixDJ1MkMLycz0kP2yyjvyG/yT5N28MTxFtZfBckZ9q2CSgywcOrOUUuJPJMZ3lJHgH/ziU7ztkbvcuNqGABaafUKv4LpehQMfO20x9RLRFChhKaTGBRC6lPRAEGUFflwijM/dfh0zLfErCdIKjKcofYnVkFuPoGIoZYk5nWOdozYY0BvO4JxApiByBVWJ80p+5G9/K8++7TaDBcUz33OF3/hnDzF7boiYBX3b52xjDZcrorCkpQrOtffY1PMM8gYqLShqPliLtRaTWgolcU3IZiSUAm9iEGPwSkdNGvKxPHoPfYlUgoXZLlEdvuPYFe7QJlFVzJSlFjhub00jx4KXX5xFSsuDb13jW77zVTYO23zkf3wHIxkiFzOyoaDSgwCDtZJ4FBIPFMWNKu4QLn7LPR6cX2Ot0uZmX+EGDm8MSvukmYdQR1XOzkoSU7LjBzz60HW6/Q439+eIFwSuLLn80SUGT9aIpUfZBu3A3lIc0OB0fYRKc0gFU3KMPFmwO5jDaQ2ew7Q8ql7Mz196jK9snyO7a2mdGzPcryCwdDdaEHnYimW+EbNv6swVQ+Qy3HjzKGfCAji4+fVZvI6hpgfkUyFr+y183WG3O8VhW+GEIFOa/d4Q5XwWyg7rgx3KgSKoxJgOpKMQS0Yri7l+aYWtcYdiWmNnLJ6XUjqFdZq8IqECMtAc7jb58ANv8Npmg6osGU88sgC4ZXGepJyVTJ2H3Gtz/eZJjNWUTYGpS/KGR3CgML48Mpg9RzDIKQofYR3lDugqCM/QqQ9JTI3J3DFMN8b0S+qLKfVWht50ZCMP6gpvWKKsI3tkDrUb4+1OcEqgBiXOKsxUG9m2mLKELKcYTZPcDdH+IbKfsH9lxJu7PvYwhe0NrJaIpIrXqOOKAvwAr5ejRw4hFUJ7qNziJglBP8Q1S1xWUrqSbF5TiwqqrYJsp8LmuAGeI2pqUpXjjS3eMGHhRId7WQ5jg6scZfro8dGUWFEXCANaKc7MTzOvH+Tvf20NqxTN0x2KPCOKMvJS4/KSL3/9efZ+sQ6549XkDq987RYX394kPPHf0QwNS5MD+pMmhzJCGMDAm/tdQs9gU0UcT/GrH3kV15Lc2N1EPXCR5lSFNCsoCkOzEmKVo2Z89O+sUv0Lbk+W+PLaClI4/s6jUK1GAASeYKZdAwe/9IkXeOXKFg0HmZREtcq/UTfJ0jBOAg6/NEN9x9Lf6nFv84CVpd87X+U+97nPfe5zH7hvpHzTsNxuEApJJdDYNMfECS88d4YP/vhlAs/x8d9+kNHlOUYfKtjKDpnTLQ73Bty7uQs6Z5DdouNNszdu4pWOd4qbHPv9fSQvMLjyp3ntK68hXA9rfVzqKGONQZILiZKCa5e3MGZIeFDBlZZ0OeWzmycpZhUi9aiUOUVVkQmfO9cFWgkaDYkND/GrEs9o0rkaOnd4BwXVixO8pZLyVQ9nS67cmWdl2WLSRxFZl7IvWVZDGp2Cm6N5HBJ/aLEzAvYco8GY6cUq3ZUxhzqhum5ovbmEqmeMDiIacwmqlSK0ZXXxgO1siiyOUDMeF2urXH7xDtaPMUIwSjQ1k8JIYP0NvPaEp9WEF/JThK2MAEPTywh8y73jAWCwuUOVkgCFCjOMFJiyJLeaOdulbwTeKKBiS6orCYnxuSJHvLVxly/vX8D8ztTJjb05QnEeXdlFz8WYHU1sfRSWSlDQ3auxKwR3XmihKwKmLVhwyZFQNlJBLpAIOvMjruZznJUlxZzAD1I69yYEIsd/54Cdr86xTZ03t2cRrxgW3tHjwKvj/BATCGhIarUxjz10k8WZmOf3fGIX4HKBsQGRl5KnkrlqjySOGGdVzERwR9YYzwZI6fBvl6zGXUIsY9M8Clr0SyoP9Fi47SheM0Srq7jgdWQN8k5GOEzYcw2KXOGiMadqI2ozb/KF+CLvvHCDS2KJzX6LhfeOkMqxS533Nd9k1A6YcmOsNbTEAa67yoEXMB2OyeKS9uczuu+uIWybkd8jXQZlBJOBzyW7zObdCOcrRGpwfglWEmwejabvVFt0WyXp0McdFqg5ia4VrLgRtztL2JsTZGFQhSZdCMBKVL8kLASdxQGTGyHeQoI1Hta32J4iHBlKB0YKKC3RfolbPar+9ZnQFEM+cfNxkqkIV7EcK4c0BxMujRbJyqMGDRFbZCEQSmAVSG25uzvLzd9qMX7Yx/PgwuIGLS/hhcpZsk0PUTqUzvGGOSO/wkyUkacRBYpetUK27yEaHDUc4Vg8e4i9KohdBRP5EHmoFAQSFwlc2yKrlgNbQceCvH40Rq/3j3J/yCWkmltbHbo3azzxY3fYHNSJpCAxNWrLA4ZrKxTxWW4dvA61gLn2mLHQDEWF5vSEIA3IWxo1EYhUIqqOqkvJuhInNSaUqMxhK0fnmsR5kErclMWTKXJP8I6H17lwdp+mLum8NaV3V+OcR02nJH4dLRyi4SgG4Iqj86xz8AMfeoMPv/86f/PuU7x2uw5C8txz57liF9kv6shawsPvXEd5jsN+k1uTBXpG0nMhcujQqQNhyTsGLS1uPkdv+1QnAVNmyDPPbtIfHTJ+xaMftHngPYYXmg1GoUctnzBRDdJqQDOeMOxH3JtpQ8PgqwwRGU6e3GPv+WmsJ0nnFd7YUMaae3t1ylhBphi8OA0OdCWhshBT5lWanZzp1pgVPWDKxNwZzHJ1e4VqJSesZJQGvH7C9GLB6qk9DjcjRkmL9e2SWzcfQVfHtM916VUitu/tkuwOOb2oqQ9h0hSUtQBlLCY1JKXPieYh5IqFaMK9cRN3zFCtF4x3FUiBDaCMjozs84/u8Wq1w9Jsj2hiaR4v4EYTMcwpc0c07dFqTRNwB6ckEonsZ5gIgp5DjTy8pCTaMMguUCTMjkuKmz5on3JytGZ581KHfb3KKLK4mkT5jjJ1zLQm7O1LhAOVOWRu8fs5WSdEBz7Cy7A4Sm0ptQQJeauKijRm65BydwLjgmKQIZXizkfPcMdZEgNjZWkaoJ+gkhKiCKsNArBC4HHUpkOpEHGGGIMLC9w4o1ysUbYlSxcOeOCxe3zsJ55ABwYpLXcKzTOzW+hhgR4WhJ7mZLvOgRiQepDGBTqzeDsl3RPAcZ8nV5dYWejw6t1tPvTWh+ksTfE/z1wnE4ZWltHeSvFCy2StgMLinKNWD3Cm5Ktbv5+36hTfN3zPyhv8xgsPcVD6OFmhqIFOgfTI8JVAFPocjBJuDfqsrx1Sa1T46Y98g/WtHu959gztZoQYcJSp9L/JN6mat5N398AIPnrrMt919iIAeWFI45zCxhBtk8QpY98SHtM884EL/0bd9H0PP8pn/6evEK2nCOHhC0VZ/t9gFv0+97nPNzfudx7/ofmP4T38e+a+kfJNwu7hiEmcM4lz4t6EcX9CI+vy3xz7MhbBn3AfZugpPrX5Cr9x6ZBvmX2KU60F6q2I0m0x7/Upm3A1n8MZn/KOwl6Q9IY+O2ZAT+Qo52Od5NSKZX0uY6IVXiHQqePmnX0eOrXH89PzWN8hXpkmEjmVICcTwPWAWtWweqFPfSYnOZQstAa8/bErvLJ7koPuErqXojIBTiIM1JsJQ7+KKjW+UejqWfJaTLYrcROf2cUuETleXJJLD7McUWhLeL1ABJL8wudxxUmEcsQNgT4GZcMj6E9RjjSdIucdz17lrafuknYU/6D3LO3aLOVVS5IX2EBSNEFlEgpDbeJQoWH+cUV6IyBvSszII9IJy1GPoQvYH60ia0cXwqsz+5STNuU+9GYsTkli4SNw2EOHyTVSC7JYUTjNV14+T+9EnXQSImJNzzRIRhGPHmtyPLjCd164TFMLpu0EAsf+dhOzptiaquEGHkndIqMSWwqCWKM0OCdx3lHDSdjJORRTCLeGFxdoa1lrVvhelbNVX2H8mM+LyTIf/dQpwisaf7Hkw+fe4IpepKdnKB8WqOM5S5M6N29FqKJksXLIuO1xUCgaT17jRCA4/8A6H/mNdzKIfPIIYs8x/WsB3vt3OPXwHscPYw7DCre/sYAwlu9+7Ks8ePaAj4+e5DrTxOUBdb9B1U5w1y1MoHI6JRGS9x+/xXTzZ/hG988ghaSdCd4yc5vPjc6wtrlItZHRvxMinnC0vZii1Hyuu8g7ort40xkHwykOtipMPV9laaVDM/8B9od3sf0COQ+iWvDgxQFRxXB7sIio26MpkUAhUKgY/EPHww/c4Bt7DyMmDv9AUniS0zObhFGGy0PK6oTwsCDoOfIZhxPgJSUiC7nTn+HBp++wnXdI/BRvMWavVwdpEUqirQZrCHdS8lQQO81kv8ls64A51eB2X7Ac9vnW2k0+nSyTeoos9HBVgxJHTUnWiaNpH3PU+OG6A9R2jfJBnycbW3jS8IF3vsFHrr4NMRBkS4KJXyWzASMnCJzC5IrD2OClBaJ5dCEjPMFBHKE2JUEmEFlBKcAFGtUsaJ5MyHLFidEQl0huD+YIC4U0JUFRkiuFMUcmnw0c8aDCF37mESaLBt8aCiw96VMVCWn7Bjc2l8B4rHT36GWCTHustO5wbb+DSiWyAOME1sH3vu1F7r42x5cmp9CZxKkAbRzOCIqRjy4d5UShz8XobMyvf+IR+pPrqGsFouOw6xIhBIWuMN/LmNyRmIkj7She6x/H3jCkacBffep5ViojnuCAN8xxqispyZbHvbSBaQmCXONXDcqzTJUj+kXANTODGSoUCmsNQhZo5TChxquXsOYz71dYyTM+84WzRJWS4W4VsSrRbUezSFhd3GNxqsfH15/EKYHxoBCO8VgjjOX4hX1aixl3dAtvbBBdS7ki8QcOmyluHk7jnMOGAn/iwAratRFJ7OOPDdmODxlI5fj0b14gV1Wk1cyuDpmpbPPKZge1n+M8y1rRoaN6HN57Hxu9MYvLPVorBS+vTyHygJPZXR55apfXj42QpkV6XOEyj8YrGcu1PZb1gFA7HlrdIk9OsGUb8Lzh+B84YKfXxmlLdkzzoYcv8b7vfYPepMmWneVuKNjpwt3tOZwE4RSuLBjfKvmVr3oEVx9kmDZBKFzDQR2wDhErnOaogtzz8E/GCFegfUcZKEQg8WuGR957wJe+toQoIpb8gMFMQemnbM80EYMScyApqgJlBSqzeAcZYpLiTAkcrcJIDWUkKaY1Xk9izq3w0LdskAaON24v4X9qj3GvQOUC6wtsQ+EmEqdAGANJCs0ICxgByh5NirhKBYockzuckwgEojRQFoR+zLgXEdYdM/UBQsJy2KcSFYjS4jvD9OwUW+QMiwKLoqgrZEOitgqad2BwRlP2Cj6zdZNXXrxGsJ9g2j6xLHAS0lJjNi3zyyPW1xepz9WYOzHDwU6fV75xG6/RYlxGzExnvNZtoaoCPTK4YUIYg0iP9uVt6CP8nO9570P8/ddf4t4w4a/e/CK/cO4H+Y1Pv0b86IjP7Vzm/d5ptOcIlU+oNEmcc+WNe3xQPMpv9T6HzuF/+cqLv2ukKCUJA4/XDn+JpWfeZOnwLNffbFJquNLb4iLHyLKcX/6ZL5KOE37sz3wbvexr/OHv+yy/fP08exsLPPb4CWY7tX+jxrrPfe5zn/vc53/PfSPlm4RmNSTPS0Jfs75+QDJMUAHcultjeinnwFUx8wbZmJDvWbqjhEpvj92NA9TMDC7yGaQhxvMwE5+DeJZwr2Dn3jRr/W2yZkZ1X2H7IfWFHdIHKri7ICcOGSn8esR7TrwFvXaJ68975AcKqbOjxP/SocYGG3q0jMIXAtwYl6WUYZ/Iy0njDKcVKrM4LdF+SeiXBI0ETITNDeN+Qq86pr4QM84FvZpgsTHCK3KiJGQ8cEgjKEtJGVq+8x3f4OuXF3j7qTscjCPeWFnBTWD4tRBpYW2uwQPs092o0nA5aVWSipiqVKj8aDfe4qGD4mhU3Fjyrs/4eY8D6rh5h8uO/r7MajzfsjAW3HMprSjlLcdu0CvaDK6cQBAgYolLCoxwBAsHlL0KJYp4t8LubUOxWeEVf4VitiRvWuSmRU8sk36XQvscjKps7jX52De+h3euXKY5ndA8M0GMFEUEchqUB045lPHAHeVI6L5EaMsTzXuIDP7Or78NcdpiMsG1vVm+cPwEc6TYYUQkDKvTMdUPjbCx5uGFLZ6eXuefvfJ2XKSZNin7O08RhV8hKX0u1Ha4eOoutzoNfuXyg1xcOsEXP7ZPuJ3hnigwdYeoOfRLAvPpabrvs9wyNVaTHYQnkdJyrDHieD3lb/+BT/LXfvu9fFVOc3L6kDS3rO1MYWSAHxWYTNMuQv7e9V/gtdFjiOWc/+7vfiff+0de4tL6aZLP+thAYGd8LvoHpDVYUhMe0AMe7Ozy9hM/z5v3ZvjLP/eDVBqSD7/9UcLCkgjLsFsw742ZvdjnDz414i9fX0aEHjIHG0pcCDbwsNoiSstKlPHK6wqnBBbwmhk2kRw0WugdkDIkSAvKPYurClzocL7GjSXjSY31vTbpoEo4XeA3C+SmxVqFzC1FzVK0BdZpPnz6BJ9NrhJ4GUvpAdn+cfJAsPalOj/5icepv7VP2MiYZFWkAOMLyprGhRJlHCIDlzts5GEWK5iKwuFwwOJKlzIUlKtQzPpk+LjIUoaCVi0nzmvYQcBUZ0SSGbAgpx0Dr0I9MHg+yNQijQDpYwcwKRKqZcld22BVTXBjiRyX6GGBUwo51mgtsMbgTcC0QhwOkYNUkA4qpPsaHulyZ7dg3PJo9DRffOkRmk/cZFYd8sKnlsnPeGgnwDiElajYsbo85LX+CWwOYu+ornphvkcWC8aH+qgGVgr0m5os9DFJyMc+9iBn3nuP4UKEkBKQDHc9UitIboV4OoNWifEVkyhAhY6f+PXH+JFve5nLa1Uu1te5XsxTPzPmgcf2uDOYYX2rzZuXFpHWYlzAOx+/TN1L+PLeCVTTILRGxT5a5ZSFIr8mUAj2dc6D9Q6vfyHH5prU87Grjk8fLnL8eszv+7bX6BVVvpqMyasa0SopOpadl6YJUsMgrxBWLJO6D9ritwsoPWwmcBVHWasgzNG0RLyUcnJncnTRHww5uZiz063hEpBVgyg8+m/WeOKZTX708cskomB/60Fq7yq48U8WKPcUTGU0c0O9PuK9z9zlmQfu8sd+9n3kpLzn6dfYyKpUpwck9Yjh0MfugdcVjLZrXPVCHm7vEjnBzdszlKVCi4hrvzRLcCEnyzUycaw8vk8/qdLsTPjk33yY97zvDWbPHjIZ17CBj0tidHC0+rKZ5kzv+Ni2xjmQsUYPwdtIsaqkbErylkI6i3mhCgsDhLaISEPpIeoJbw4WERVDMLFM15uMDoccyBbjgUXnKc4DlMAblCgETvkIbXHZGLLiqL1Ja5w1R1MkEipRwmNPrdOb1OiuS259+3H067tUDgowkqgUCKkwjYAyGVIOCyqjAqkFSghQgpKjCnvn+zg/QE5y8rxEHqRo4NIb0+SrAW3bx1clCJAI8tIjb2hKl7B77/b/j73/DJcty886wd9aa9vwEce7671NW+kqy6sqy8k7EGqMBN0Ib0TTo6abgR5gRA+tBkk0NEgCSSVXpSpJVVJ5k5VZ6TNvZt6b15tzjz8RccLHtmut+RA1YmgGPjQg1DX39zzxZceHs5944pzz3+/6v+/L4Xfdz62bu5AloHysFmAFBBLVNDxn1zicB3S2u4yfv8urziZ6fhHrCuTIkkcur7VXiPo+eWyQ/Zi93RG/+5svMjv9x9itXOLBjWOIhd/Fzqd4sca9FuHmAhnnWCHJF4r4D6S8mG6zV8ixJcnGeMRXnr2G7iSIo2NGvTJf377MEwemEEKQmpTf/eTL/Pavv0JjKkStgPbh9maXKIr47ecvc2r/AkcPzdEsT3Gzp1BZkdmpAfPnenzw/CHSNOVzv/Esv/xPv4SJM579ylv8xf/nhykHZ/jjf+EkG68dYWl5ijTNKDKxC2ljiJOcYuj9+0ave9zjHvf4Q4cwk9d/af4w3MN/bu4JKd8iDEcxeW6Ikow8TrDGsPhgwC+8chRPeHiLY+YaXX7k7A+RRh5oRSduEvg+W9FlinN9jp+PeO2Z4zg3It73nVeI5zQH5rq89vPHKIYJblchStBLgEThjCwyM4RGcPpAnbC+gOIQay9/klT7BIOMXtWbNGisgJsJMk+wFs4RppvMBSPirMiwK7Fxhs01OQZdUgyMT96EURSQFVxyH4h3UJHPyrEuqwuGczOb/N6lY8z7PqI5yZbQ1qKxGCX5WnSMxw5eZ1+hx3IouBPVSLcE3sBDZDC+7vLVncOcOrpFKw4o74v5gX0XmDv1Omuvv2sSQDjsklcdlLAQWrxShb1shEghs4pUKHLjMe/1SI3Dm60u8UwVmXqkuUfcKtDIerS6s7gtSPsK3084fqDN6/ECLhovG/LEmSv0jhf43GdP4rQDKk2LFoIDMy222hkDKry0ucIrlw9StCl/+3u+gQDSa4bn3ziKVxCMawIvMGSJAGMmYaNMNhPQlkBnJCg2ZZ2KGmM9wQONda4O6mx0qggJ1bmEG0fnuJMVCHox5errTDPkoYPXuLK2H2doke4chamYYktztzlL+YbhT55/iU++do52+DX2B+/nrf7G5KHRTjYCJt2+lnHu0+kXkF4DHQpIJR2nyOHpdVwB3/vgBebbi/ypg29wWVT5m/vfR14KkaHENgXCOU9oPk1VLTJKcvRY8fPXz+NUFGIWJApXevzUL36Qv/Ojv8n1XoMDUx0KuYNwDf/kN57CiSEMHHQnZr0/Yssd4B7Y5thKi1XX5dfax+iR4g4EiEnmTPFETH7N+eYpOIylixOAHljEUsbbjt1AD2H3zjSNzTGmb5DCJTSWd7zvLXaSEldWayS6hp3N2Pv8FE5J0DjZRMzmLM21aP7iElZZ0oYkL0J+wIcVh31DjYjGlAeCq60enmlw6nSfj5y6xq984wydbYn0LSqHrOqhXQWAGuXkUpFPKbJpBzwwrmF7XGYqHPLm5X2kDQfRA9VRuDOQhZOmL684Rugy1igW9w3Y2ZpD5pZgFJM2HPR5MF2NyWA0p1Ejiy45JHshiQK9Iri2ViS1AQSWvCjAOphA4Q4tMtIo40yEKCFIigYvdvjoR54nnIn4lVtn2FNVCntqsqUmBaJVpDG3x7ZXJxxpooIFA1oJQPHsG0dpd0KccooqZvzIh54jWnLZGxT44sX7GB7ywcBw16XagXyU4JY1pUM5B4/skL9SIOqWWPAHdGpFkmWHwmBMLc4JKj32shCTS2w7p+j2qKeGi7qEHUqmD/VZnOoiXFi/Nc10mODVEy4Pqtxqz/Gew5d4qbWEViHGsZMq6NAijMDJDUZK2ibjQmeFXN9Bjiy2kpMpj/fPr5MJj/vLXb7erxCGGRJDHkF1lLJzdYqo67J1LSA8vEYvDTHzGbWHhpiyYne7DiZEDcEUJMIaHGPwQg2u4cmHbnDr2YOQu6AlUsKpJ1bZvFKlTcCb/QbF6SFvsYJjDOKkwrMZpark0PQsu9sZN3enmZ/rs2c84oaDW4157sZhvDjl7bPH+NT1HdRdi+dZRENjeopxJvniM4exA5fskEteAeVZbBPK2yn1mxlrK9McP7VNOnJo7BtwcmabGW/IgeU27c0qmBRrXUBQvVtAT2fkoUDtxbibEnHX4AwTdCVDTRXwhjkqEkjpkP6Wi3VzRNuAbxhtS/ZWfeKxSzzS7NJjvDNgylEcCdv0E4c72Sxz4wFGpSRBiJRyIsA1KpipMiZwyEKLdgVEY2zuEDfhzc/VsUox/tUI+Y6IUk8wPFgDY6ncjhgWFE49QOzGaJliun2cLMXxPKiU0cpBBgpqIcp3wAqs74LnoBKNlAqnLZAMf78yejcvMkwCdFGR+QEb1/tsvf5ZVCzIHlwAY9FWY4TAChBDg6rD9Aj2hjnjNMc0DcZIyASO0cSqhKaICSAT3wyj1RYzjtj79TvM7TvA4Xfvocptio5irVhHGYNQLlYKpMjB1dT39akWQoQjJpYoM2ZjvI5nLNXigG5UIFAxj9XOUy+UKKiAF5+9yfbFm+yUPezhOXQZxtbw3/3Up/jq9hah4/CTf+RDhOKjPFL6Tj7f/D189w5/7v2vEgaSz/7SM/zKz34W0x6ic8P6TUt/R3DfQ/+I2skCNxp3Wbu+QzJMoAG37uzy65+7QG8c8Se+81GOH5z7/zF53eMe97jHPf7/mXtCyrcISkq01oSBx0OPn2A0iKl/9zovmU1ck7P1/AwLsUfhu2os76+AFWRLNQ6emKfV2c8L0ee4fd1B7mmUk/Hcywssr/Tp9otkmcOy3Sbe8RiZAJW6NG7FjIYKZRRIy8D+T/zGW1PsDo9g9wlE25L1C3z78ks8ffUUnbhMJiR7o3kMOdr0GQ597iQhbVtEOwKZWEw/JZnxyGMH088RkUUgqEyNUEHG3LwiE4bl4oCwCeVeTr1TZJ2ctAgymdhYph++xlZcY1+5yY9NXUUAN/sN+vt8SkcleUXwo3/sdZ59+gida0VkKccMfe4LWlzZlbiFHidKs/SXr3N7b5HMgTy0DItm0sDiCIQRiMhQqozJrMKTGakegldlkEl+8+mHKFvB/uUm/obG7Wky5WNGkt2NKTIJQhs6wyrdkUNHFgi+EaHPOhz4o3eJx4r8sy67YxcReHzpzlnSKYHsS4aJQ9HL6blFThzZplWr4sw7FFxNP3LYSUIcx514+jsgK5ZOp4StGaIwIDMCowQnWxG6osiHDn6Scv+ZWzyxeJcv7x5nvV+k2jccbaT0V9Z4cfsoM6Md2mttwsr34Y8v4WYpMrd4CH7+w5/hX9w9xKWoR1F2sHEFnXtYk9OfiynHLgvDPYZXQ1bOj7h5v0eQ5ugp+L2tJc6W2/zsZ57kvkNr5IcUedcjKDmIJUnULgCWn/x6h/mZM3Rch5lOwjgLkX5KftTgJgUYuWA0W+MGP/LTf5rG8U32nWrxo8vPsZPV2I1quAksVAt4VtBKxtzVfX7xr32ZQIAs5fx3F/az7HZpRbPYAJS2aC2wIVgDFoeefj+lxQE7aQTCZWxD6sUBtdKIxpMd7v7aMtZ1mSn3cWcT5skYXbDcWgL/locsBmiR4y4NGMoAr6xBCrQrAYGX5UQ1y63NhPnRSYT6HI16TFWOWU9qPHh6l5XFIcvVMZu3Z5H1SVNXFuRI8c1NDQvCSkxJYCqGYDnlxPwOnSxACc2xw00++7RB7Shs2YLViJFEB7DnBOSuxTOGXstgnByJRFUS0lGIF4MrQHgaMsu4qFEFS3FHE1UlXi2FMch1g9WarGyx7uR3xlqLNxCgJKQahMZMC4q9mP2HWgC8e99NvnL3PJ7QaCGQUrPU6HFrsEhwPsWPxmzHITpzsRlYR1PSEXQt7Dg0bMRz/cMslbo4WYpIMoRx0YGDFQ75bgGGfXAF17f3k44D+qJEoZrhlROG/hSULSUyxqFGNxW7nRCjFcEg4q3deS5sztMTRUxi2bxZR83nbDSncCsxbjlnqdGj7RZJteTi7jyFYUQc1ciMIa2lMAwhc9HTCrUtsUBnnCGVACEQOBRWc4xwyNyA17fnaHoFhLD4Qc5wrkZjd4tmJwOpUH7GsKcwUwJ3ZkC2BywKcqeEUQq/bRkZSz0YENxQpKWAqKu5KGaI05AeBbLUoSgTlve1+czifVSqMXeaU0hTwCLQVqDKOU4jJVoucrt9B7tR4AtvHOYbt/dDFOHLnFvtKfTIZRx7LOgV0mGH4twIoQy9cgEtJIVaQqhjtmpl8mINADlW+ENwB+DNRtT9Lt5Ik3kBC2e7zBiXWuYjYg+EIAk0hQ7gSKzrktVDbC9CZjlIiy0oGILXz8mWE46YCjdbo0lIceajUw/laVSmqc+MeWxqkzeSkFSFNF9vEoWw7PQ5UNlhdXYaf2ColkeME40YOKSuAqMx3kTc0MqiYsBOcoFsb0Bxekz1SUkcu4RfFpQ2xnhDKF8e42YS5UqyuZCoLCl1CqhhPqnGywRECTguohhALiFNQSiE72BHfaiWsVGMs9pH+Q5pS5N/w6L2CbaXq0TjEJkmuJtDWCwhU5eS0QwyILWMj8K4YPF6YrLR1u3hRA71apnmVJHGbkonz7BFBzltqRo4e+c0T2+9hZaWheU6dV9w8xvXSW91aHZi7IMzfGR+nQ/ZNV6tn2C8WMQaiU4V5eKAYCGlUo44pkscK5a4easNNUnzbS+ysv8gLbtOJRwSu4qP332VX3vnXyfLMvLRCLo9CpUZilrQw4KAjf4e5KCtRkmJ7znc2e5ypzvC2hl+6WM/xk/8KYfP/8YL7K3vIedDrA7xKxUatZBqZbJ9Up+ukEU5hXJIs93nL/2tX6GVpFRrBR69b/eekHKPe9zjHvf4d7gnpHyLoC0IMTkd+6G//BQ6N3ym/6+p9yJCm/A33/1pbl46ysZqG7SgWPPITE65Wma79zon61vI5QKvKoPEkiiPkwfWkBZiX6A+NcWleIyyDiL3kK7BjVKywGesE3pmG6VqeJ6GJEOMLcLA2niWfr+KLoBJcnxmOTrqcHu4w8gYzKaDjuoENiN8ZB0VwMbOflrlEH82RQ8VeQdyVxAuRGwMpgjnJTOliFfeXCDq+mAUXpJPtka0xRpD7b5tfmLlVV4dNqjIyWdkEp/DtRbXdw7w0Lfd4sTcNtlpwa2dWQ5U9/iNrSk+8dJR8lihnZTZWc3xhXV292bppB6Z52OFxUXgSEM20jDSpL5iJ60CIGYhqWZgBMWuJLOw2SuSVyHcU3gZaCVZuL/L1WyRYADJjEAKw3pvGueQy/zKHt996DXigeQTzmNkM4K8IhGeh0o0eerxl196F6YkmJka8GDyZ/nVwdeZCm4TKo3JfNr0MH44yaLIJxas14dL3Nks4PYNZlkgpeXW09O898ANnnPBzTU303n+Uu0Fnqzf4cX+PMOh4ovlJd52cIedbzSwezFnhUNsM/pC8cBMh6xoub1W42pcY3+1xytFw7Fam/5OzN19DSwW8zaFeCYgvVnAaadU+2N+8ANXmSoP2WnO8Guvn+SntwuMm3Uu31xm+/osd14vEBxImdo/4s6ez5Tb5qmn3uTt+2/y5p0FfmnzHXAtR3lmsp6+ECOuKpJQoIyFLGfTKZGkiv9+7Ttor5VIjlpEG/70h97O8mKDtVdjdEug1Dcza4zmTx/5df6H1x4HX4IjsGlOPPBgSWOlxL0FTwwPcEO8Nem31ZLRyGEhsJjEpeMVEI0YEXlsrxdZfKHA8WO7/MXvep1/8Htv57X1w1jg/fe9weVOjbEOGM6DboyxvQJqDLaUYz3J9DCgNg4pz/cJjeXgIZ+3XrNc2S2xb16xtQlOO0OXDCSTTIWsJCZWmXTSEiRzIDagLZlRjMc+mUy4vj6LjC0i0XjTGXrkTDZiPB8xzlFeRnVgsLGHqAhEAJXFhF5L4N3KUZlFAyYCWYAsdkmaEt/ElNWY2lSHtWQfpgcysuSlHCEl3gCEcrBWI22GTVN84dBPfLAGKSyuo3nfO18le1eRradnWP3GEiv1iJ3OmGTLZZSWMcqZWBgyy+GpXXb1NOvRHFoJmkbR3S1z/UtLPHzgCpWpmKgXoD0xEZhcl3jWIcktYtth1GkgjELEiivjAwRbmtJeRLU4oFcu0c3LJPFENG7XQ35t/RyPvO02rz+9n828iliEVrOO42qylseoYUkyhZYC4cOd0RT3BTu8sLdI4GgeOHuT158/NwmGHklkrFGuZEH67EoNRqE9gTdU7MUh/vGMX1s7za4qUpsfM4p90oMSWclR53LioWZY8SkrgbKa9dlpDr+6xeyJHqv1fWDAReDllqKJSd0A7RrSXsBQlXAMoGGzU6O0OOZc4y7zS31UP+HDj1whyhXPj5fIejnJJXDKhtQV7LwZ0b0RQ9Ug6z4HZ29SmokIV3NElCOVy6fXXiDoWzSSxDHoVKEDSbkQUahrStsx3obF+prieorMffKSQ9oacfG1WR44v0FnrUD7VsjiB2J8KQlljjAWUwTby8E6WAyN+ZSBteSOwHgeyYpPXhxTWEvJhSRyFCaUCNdFDAXhwSH5ZQfbzxkOqoySLlHkkKUKEXrIosOoCJ28gD89xgxi3HKGWXchMRgREVpLIiAXGiRYOakfNjWJupyw/PZdurJGjk/ybQ7qS4pMgTPKUcKA8EjmXIpNg9tLcSKNdR2McpDKQJZ+M19kEnZrowSLQY5j0rAEBR+9IzG1ECth6+8OmPsJl9MnthCO4PW784jQx+YeVmbIOOZYpUpWUVxwe+RHBO4Fg0gkO50EappyOcQ/1GDXZKjeJAC72ogo5U025z+PU13A9BSXrm3w3//Ed/J3L61iM4W1Bls5AFaghKaadhgyw9lHV7g9fIPRV10EGdtpiS8+f4WHzkyx2dpBzGSsuDnJUhXbn2OnEDFOHK6bNl/Yep37WMYVGd5KndMPHOSJE/v56a23EFqyMZ0w1/E4fWCeh8/ux3Ek8WspMp2kHA7Hlo3brUnmYdVjXAz4jj//fr7vbfcxyDOeefUWD5xcplAKqM+UUEry6sVV9noxVsBwENMoBn/QI9097nGPe/yf517Y7B8Y94SUbxFSnZMajTaWWqUIwPR4ndRI3lu5zRt354jaQ0ySg4Kfvf6rDO2YP3Pw+/HlkEOFFpWpIr/h5HiLI868a41x6nG40uLHHvg6Nynw1hvfgXekjJs5iKCFdhRW55g9yRvX9/PgUzcRGw/y6bUtcp0iYstWv0xuoO4OGFkfx+8z6naIhooskegWuLJPQYa8f/EOLvDL28uUwoR3L7zFM/YEzX6DJx+8yPn9t3n9wgpfXj1Ju2iJ+i6J8tFakPUzXKVJHIM30pg855/u3sdD5W3+ly88THfB5YWtA7x9f4RcynnpM8u4bsqbn5/leK2HzCVTDHnz4hJ2CDgOtX0hMxJEZpG5ICpkJPskCztD7JaLriYsn2qxvVGin/hYQL1fUCiN0ZcduOuBBEe6OAsjxGaZVEqCQsSRY7tstYrcuHkIWcn4vcsPU7CSJ77tEh8+coF2XqTbLTHcC3n/t0/z4k4f9Ss5Ox9QyIJhKypzeGaPV3dXeLxkya9J8nOKTu5SlDGzWy5bNSatOIBqZqx9dj/6PW20J7BDBYkho8j2rUl7jtVgKw7btsil5iKf2/oo/uACzVGNl+Z2ORfcZXNBcubYQTrlMbKziStzrtye4/nuArmv2D8lyEwXUc45vD3CLTucKnV4eWsZVMZAAzojULC/6XCykPOFsiXYN+D8/W00G1z452forZ4j6rWY1zcp7SYMghKp8nhofo2qTDg+06TsDWlrgS44OEqjbwU4WuMYhc0sWRChxw7NXhEhJbblYUJNoAU20bi+Q9tPiOsBP/Jr7+Dt1TYXZ6v8nce/xGZ7eRLakVtmF3Yo+TnrvWlS12dBa06c+xd8996H+dkLQ8gF3UtlNlcEzWsVzjx0l+ZUQPdmhcxzsGHKte4Uo+V10nhit1IyZ9+hJnFBM1Y+mVCU3tGnlVn0WhmZC9xRxgONeYak7GwFXB7P0I99dNGFmZw71qWwGKO6MV4nACsYVXJGSy5uE7yxwgpQMsOMHNKiZj2v8DOvvIP5Tk5AjsEihCLNHYgl2byFUDBd6dPaDmnIHWTBYqWkVh/x1z/0PL1Ogb9/450IbfBLBjGdEYUBTm7Iiy7DA5bWcJrSIENFLlnZomsWjAI9yTZwYot1BFKIiaCSGERR8Lndk1T8lN1RyOFKm3l3zHseucAv7IZcGxep3urSWS3gHfYZmxBrBVYaPnziAr/48uPkRiGcSZZDUIg4tbzOwvSQtSci8p0GRlqCICILA0zsEDbH5OuW9LigmAvESE+0sYIhcwqM8gJBP0eMDPKbz1JRQ2I9xd2daaomZmdYJB6DKSlSC8K1bLXriH7A/sUeZk/Se92hFzRwhuCrnBkxxGQCB01hrUscNagsBSyUqpyaXsVH8vkXDjIIPR77wCoXri4w3PPxFzOGiY/RAruoeaa0D1ly8fYcjI2405ylJwvkhNyYXWTnZ3Pk+Zy0LnG6Eh0anNiS9xz6PY+04WBxcRxLZhwGnRK7exU+u3k/G90Gm/0aV9YWOTE7T2l3i72mIq2HxGPIPjYk3eliAxc1K0jriu8/uMlFW+Pq+gJ54OJmATEd/OUCyaUAMfNNS4iT4XVh/e4Mt28s4R7MUQWNGoOyGWQpzihi1h8xHUJfu/TeKON8Z4LjWLpxOMn6yVyMnwOCQ0tdxscEfTeEJEK0LKqncboZac1jXCyylefkNsWLXOQ4xW4ZcHMwllI9xQ1yEq0wrkSUCtg8ZxT6NG2Jmu6StODm+gxilOC6Buu4xMmIoBqQVou4MWQywxQdnF6GzXParTKlYYBOFQO3gWSAcS1urtG1ErrioBsFzF5KHoBNwDVmspVSqWDbXUw3Q9QUeJONO9OPEEohR9lkW+VEjXw+RGxWkFMhuzuGjXGdfjfAv9VFJBpbLWI9BwePcyeWeHXcwW0JZMXC0JIekmRlwe5gRHm2SFyqMXbAOBN7Zt4rMRXe4HZ1DtXISfOAFPi//cTHmVmZp9tJkErz2BMaV2qsFfyVD32Nv3D7e9g89DKjTxSRY4HeFoyNYjseMNsrEfYM/iBHJAdQSc7AdbA5xGMfrxLxW+sv8sDBFQ4dP0Dm+jz8gQd422OH+ZnfeRMKOTtjyYz1UYnm2s1t6rUij99/mB/48Dk+9dULBJV1iqWA+588yXWToIVFWMW+AzO8dHGVJM1JMk05dBFyUrV8ZGUW6YAWAuEI2v3hH+xAd4973OMe9/i/BPK/9A3c4z8No3FKc2+Itf9G/ru/fJAnyqvMqzFm7JJnHkHoUSi4RMOIeDfl9uVtXtzwuNxf4OlbS4y7kxaOM6fXWC51cEWOsXD4TEz90cM0Di2R5TEmE+QhWCnRynC8skcrLfP1whvU9iUoDI4WnKZNQSSUiai5Ma2mwyWhiJwi1jpo7eBkMSKx5COHqOdT023eHV9muZ9ydm6LtC5ZmGvTtBWKpRS56rL+8UUGmw38HUGrPcIOUmRkEb7Ej2O+9MIxdkcV/umrj2JDxZnlK7xz3xXW4hna7wpofGTIMx9fJN6VNN7RRZxLqZZiRpHLeOwyUzLU62WUJ7GZxFhJ7it0ZnHJKIUxP/GB3+N/PPoFvuvsm0hh8FWOp3IePrTG4twe2gOUZJQnmLbCComwAndc4qVfO8HOrUVkJqHjINou/l7Iu2c2KJUSlktdfv4z7yIRLvdPXSb81BihLa7OkeWcvX7Amq5TMDkfW7vAqDxie1imOS7Sb3mk11NMPEY7oH1L/WbM7OURj++7izg0QqQSd0ORC8mtmwu4WUasDRvbFW4Manz6zjmayuVqb4b13TJ31qps7xVYjvtcvbFF2spplAxhMSLNJJ1WiWzkMOgdYSlL2G5UWC2X+PH9r/BHFq/y3Ueu0Z12GM1UCBZ87jt9B5eUJNKcCm+yPxygAosbGB460+C9j9/l0ME90l2H/rWQNFW0nSKxdtnWVfww5/T9m2RTgsGgQKdfxmsrTCNFKIvScPR926hUYVoBeUtSueVQ3or58PE6d27u8tLz17nYv8VwOmMwLtJN4dzSGn/nzpMcdNuI3CKwFOdzgnLOVGGA6hpWFm4RVr/Bk0f+Fctdhf9mxPyxIbJiiKYdtjsNttvzpLGiEYyoL0RE1mWgC7zj2DbSWIRnuOIvgPBZ7Ta4tTGNasREt0PUjkDkkmpsODY3Q32qwNXBIlfSBS7FFcg0W9erRAOPbOhQlhGuM8RYTdKAfMaiSzlOCk5kEYlFOpI08xDGsr/WBqMZpM5k48YzJEVFOi/IaxJlJAtHW2AFhRr0SlWUhHEU8M9fe5xRzUFPSRLXZ6gdsA5zs20MAmMMxgdtFVHmYxxJXlQYX5CWJMYB44lJ1oaSONOGc39inQP375GEgrVhg0vdeUY6QJqE95Zu89jKKk/df5k4dujpAmlLoQdjal4fNzWEpZR/8VvvodsuYI0lt2ALLpn22Bk1+MLt8+z2psl9hSzl5FVNVnUxgYOyBtFJkKnB2hThWZbqG1T9ERjJ2k6N7biEcTU2UthEgWNZJ6BTdFB+jNWSEQ4d4zOSHipIKFQTQt1n8W6f03aTwlbOZlzH7Rn0UPB7Lz6Aa8GkkOxVsEWFLbm0hxH3ndum0kgoFyd5UT+7+zjP1Q6wtjuN9RwawRjfy7C+JShawkSjIotOA1Qi0IMA60hMo0z3bRUqFyNUnqH0ECfoMZIFdGKQoxTtGDqmQCwUGkWvWWTnQpWvrJ1CuAoHySgpsrFlcfyQIBiiVAQFjbo7gCgHLQhHKf5UkxdWv4u3Nk7Tdj2OFnqYLRcnDMh9dxI3tKcQ6x5c97m+tczYBBhlcQ4nqPsSgjMxNs0xjiUOAjaTBuPYZ/87N3niJ67yYrfM5bFDZnNIM8KRwAqBbMc0oyInZttUDqYknRLO2KWwmSPHFqMmoqJb8PCNwE0ShDGYsUOSOpOqaJPSd0K0bxB5BlFKMMiptAccd9uUpzLccLJRiO+B502ySoxF1YrgCZyhIexb/JHFKoe84HLn5DHu9qa5uzdNvOMhCgG65qGUxJqc0bEi3sDiaYE0EtyJgEuSQbsHSIQEG4+ZRFtbGGeY9hg8ibIpjQ+MWHnfLp6O8UeGOdkjM4pyKSZe1kTjIWatiZVjHvvuBzj08AFkJgi2NeG6Ji1rdCDQ3QGvfe4CL756mUHRRTkupBqBZS+xPH/9INsfr1EdTaMrLk5kcIRgrxNNhK3UUDKfYZAbUm0ZxJbGTJHxRpU09IiLikxZirci4vaYw/4MXmAJygmys8zOTo/kpoOfBmSZRBnLw419BP7zvPPDb3H20aMEpYD1jR6yACoSkwBwBTZJuLvW5M56G60NyolIE8ELrwqeufkmv7m1ybBewisHnF2c2HTOHVvi4bP7aFQLxHHG9WtbbG7sMVUrUC77qMRSlg6P3nfov8RYd4973OMe9/hDzr2NlG8RKqUAR/7buth05UfYvNDCrX+CV6J5ijInjXN6rRF/ZPojDMoRm5c75D3JP1r9HrZ/9w5ylNHdCHnzzgrnT67TTEOW3IjN2ENGhuWKz5qXk0YeBw5u0dkq0yXk/OIOvxyfZZB7qNSS9wWkmuLcGL2p2NiuU0CwfKRIz+lTLFlmGgkPPbjL7z59ArTgK+0lbOgSpQVq5Zxa2XBoYUBySfK/776D48tbhFlGI4zYG1WwiQVXUHAVPTOxLuC7hFbxjqNv0VdlHtveT/9dN7idTnOw1uIbzx8j2LJEdkQW+9iVmO7hMlsDn86wSEEKwkbM/MwKg/w15ioj+sqBFJwhk4dGM2LXreH6k6HWzTQ/MPMaBZnz1X37OTrT5vLyPOuFZZzIko18RDJGxRaEQTmS6mLC0PikEcR1Cb6h3ne4NtiPp/do5mXe/dGLXHtpjoK3S0YDKyWlaznxIwYTSu6sTeEODKVwl3xWo7QCLIMrLm4vYuGzW5j7Q4aFafIpjywQFN2c9+y7yQutE8SpSzbj04oDPJFhFgSbozI/+8rjDLqzlIkZDxWPHtngr6w8zbXyFL/5hdM0d1p03DZ7YURrXpKuW/yhYOrRPUqxw0nnAC/1rtO+XibZZ5ir9GnmCpsJxEBydGWFxvKQpPY0tyJJW9fwhzFb5TLaCk6cvcVjy5+g6NX5337nMcreYFJTmipGBIy0JRs4VO0IMcxxN0EgCJuWtGiJapqKn/Lo/B02h3WyApivzZL5ELqWNB8RpynXL+ziHblNLRIUByO+mB/kvy0/z0eKL/PDX/pjkAEGHjy0RpY7dOI+vUvTrMezbMZV1roW907OO/ff4bXnDuAtZfQHFbxShozASod0XKD1xixPvvMyD0w1OfNkm09fOsi19iwvvnGYUd8jOWgRmc+VvUP8sYXn+b3rj5Lkkur+Ma9cXUXmoMo549QldyRMZ+wu+Xw1OU5xOUNuQz1vs9VVuKkgLjgkNcCYyVqllhSzmMC3OFKTCo9pMeKuqCOrOTaTpPsFqatwexpCuHRxmajg0dIlcuOgohSATurzzzYfwkQe2hXY2BCPPYZrJZQ24Do4dyzBdEy1lzHwAryBxvQ1yRlQ2kFryIoCkRmK1YiwnjPnjLiuXVyr8V1NFnkk35jmk37Iffs7fPaFM9ySBarlHqasyPshie8gNCRDj2J3hKiHOHZiA8RaciUZdkNq1QFq5DGyPvgGpQy5MIzPCNxdg7eXQ9KiMuuy6Heo9lOibgUZ50htGPkSE/rMXo6IrI87znE8ydTZHt6Uixoq8oZCJoJcWdxaxiMHbzJViFl/dRaspvXdlr2+i+dp8syiqwbyiUhN1ceGDnGmGY4M/+oT55gWCe1OATvzzcankSUTiiNTu5xsbPFWf56b7gyjbzhUBwqMwCEmfs5FvBuEIyGeCIGxYyGH9x67gjOT8szqcVTiTRYxhGE4lOhRFSME2TBg6Yk+NpVs5w2SVQ9lFN6U5MzBM1xc2mGQWaK9GC/N0ZUyUkJVjSl4gkVbRyVDTqmblMsZc8d6fOLOkxRSixES1TNQz/GTHH/GIALAUeidFLEAehP8QY4daPLZIk+95zammCNT6LVLfOXmCgcKbdZvNpDtEVZb7IyDlxuyl/r8rjyECR2kpyHOUMMUrMJYRWEj4uEH9nGlkLIZRPjdDJlafMchD3IGvZA3Woq9cgGEh9dMwYesL0h7LnK/xltgYpFJInQuUbGZBGr3RhD4oCENLLnK8caGfKVMCqQjB5M6ZEVJ/2wFORhhdgwq9KhdNxhhQVqU76M9gzUJNpvYbDEW4XnQCBGOB1jIMkSaINe6sFTCkTFi5CJ3+xibUn50ibqzTn8UEC5meHHCaFgli4Z84Usv0lECT1iEtOQh5BUFQuBsDbGdhKg8ohh4JHmMSiRmBHYoUJdz9hVPIB+bZfWLV5GpmdiZogSJwKQRL730Ub7YyPnI4Yv8wsUHWRQ56dYRxmKHpB5R2E3gmRw5n/C+R07yFecriGVNOOcyt9Jg3AwwB3eoFkcs13pcG77AKPhXTE0pZMlntf0wp08vM32xQG9tUs3sGKiXyxw/soAKXH7zpUtcWBthxOQQ5B//wkUEkPvQOa9w6z4Au1tddnb6nDq9xGCUcLvVYygsywdmOTg3zVt7m5Qdl4Lv/kGMcfe4xz3u8Z+ESUbef+m7+MNxD/+5uSekfItw/vgSUZJRK4e/f00IRSc+x9fWb3KrVaaU5gSVAGsttVKZwPXZsR1aesCWjegvBtTXXJRT5O5oitN2k82syk9f/jbe33sPmX6d+VLAm9pjurTLu07e4SvrR0gil9V2lR88+jq/c+m7ePa5HJHkKG24uznH2Dg4EnItOLa8wKDVR7Z8jh47wfEzf5Ff/NQnyYZD2KhRmR3R3fPZ3Ssy3+lzdXuOMLUsleZpxT0OdXqkA0HsGVTgkhVhX63GxUGElSkyMiivwLP//HEq0wH5lMtfraxyLmxzKa7zmWsw4/Zodsq4SuMjiY0CT1NZGTJSBprzXN92uLHdxFSL5NqHDERskFJwuzWHdQz/469/hKPuDt12wJ/6W1eRwF55m7oasbZXRSoX42RkQpF6EgQIo6m6gqMPrfJ8dx43UZyttPme+17h1555L8r3eHl8EJecohczW+1xIzEkBQcnyiluOsQ6Q7Udpk/3CcuaopsySl2CgibTkui2xfU9Qj/hj7//Df7+yx8hVQVGpyPebC9QEwnphoMTC4wjwRVUZrss7ItYcjvcyafo7Ar8cA83M5ytr7FvKqVUaPGNCy6iX2JoU+KmTz5VYJyFVGtj/qeHvsIwVny6/TDluMRONMVP/tY7WTrX4tLVFdwIKjLh0KFpNoPXWN84wKAXcuz8OvEgRM8LDAK/cIkbN5Z57UqVthNSOjmm0I5YshadSWpuTEhKvTzA0R61VwEsOrSYJUGyqPmtj/5LtrXhkbk7LKsxX5k6x09//H0s79+jOjWP3fN45MFfQUURL27vIxwYjlXb/Mvrj7AStciiACUkWhoaps+s6jM8GPKSPksuJZ+/dZa07VGd3uHEyU2O+Fs00wa/xf2s1N9go/8ouhATNwJ4dJer4RR38x1CbbmaVBHljH4zRA0sbtmS+xYrFFcqVWoP7OJUDeOey9c767xrZYUeJaLchchgC4ZcKaxMiXyP2tkh5q7G7UCYQOTk5MIlKoJCIAawPN3idmEKKxUzwZDMKeDVEsR2EakMbpqjIxD5JPA2bvjUdwfk1xLyOKWynFGICnR3JKNlhSJH5AKZWuxAI3SOHymSKYFKMvYNOvQ9D+0ZlLG43RTPA7Pg4TfBT0IKnR26V0tcL9fYnJrBWoHOJYPEJXAyCvWMheUWL9xaZm23jvBBZBIjNFlVEgYZaexgHIufDhmpAOt+s6kqTfGkoeimrIx2qWjF2mxAt+RTznP2p9eJpwp0pEV2Lf61lJ131MjXXbzMkg4crKOQ5HipJpceZjegMDKQurz9yTc5enCHt7YXYUMgrCUrKmwCmTU00wonjjS5Ml6kvzNNZBVyyZDPZNjLky0SPfYQSoJykLlARZqsINj2K2xelpQaKcNySJ4rPJuChHdO3+LR8g5Fm/FWbYH0URj+UpnKjkZuW/Q4x/tKn86xIlkhxcst8aGAxmCXv/pffR0JbF9c5LaYI2o4E5Ek83nfgRt88dpxRGp429JtDm0U+djNx9FlQSbgyEqDfY0y17crJMrFFgTqHTHeeoTB5cjpTaaqy6i8iuiMuX1znqnykJPVNsK3ZH1FesAiRppGmpBFHtFQcGSmxwVjSXoWfdfh+x9+mYPf1+Gf//3H6DoBzXGBZSmItYuvNEfDMWfn+qwd7XLpksIVgqylJi1Iuca/1mH8zgZmYDCOQtQVnEtxr2eIDUnF9xEO5EoTtjQCiclTRGoQGMYvB+RnPWSoYCXE3umzY6f53FaDUO3QH/gE6QjRBeEojNYoIci7I2iUQQnSIghlUKMMpCAbWoamgNACU1OQapyuixBAEIACoUEoF+OkWK+I8Mvo0gDbHkGiIUqxfYmdKyBQpMVJxbUeZ+RW0vrVGtb1UNEANlLufGKT3eOHsSnIrw/RcgZV8sjjCNPp8OpX3uTEB86gjMTKSY259nKkFJhagOmmTCmXlhlh6kx2l7d8ikWHXvcqb92M8axBWPNNwUdgsww91vyT//lV4sVjfPWRs5Sux6woxakTJbrxiPZOgo0sJvawoaVSDvnj7/4gb7VucbC7TDK/ja6tsxYO2UotxjqsiP344Q/RbL3JF98IUO4a3//RB/nelXP8fOWrIGD7azl31312rm2z78H9RGnG7EyFy2oHk2uEO8mtQRgyz+IWJodON27ucHejw+xClampEvPLdeZnq4yihOWlBqt3WpSrBbrDmEq58B+cwXabfXzf/f3w2v8jP/Xc1/j52y/yU2//Dt6979h/7Mh3j3vc4x73+EPAPWvPtwg3N9pcur1NlGT/1vWxaPPm1WXslsegE6CwWGNxfQdHOVQbJYQVJCTIeU0+UyKd8fjy5lH+5d0n+JlL70Z1ltnuDekPInr9MQwEYWVMErkIY/GzjLeSOp9ofYDduR0q+ybbF9IINnYPkAUKfE3uKbpBztgaRknCzTc28EsevgMqhxPuQYZrywgheLG1xPp2lXjQ4MePPMZcrUhnVOG1tWPs7FbI65bcm9gE9IyLu88jbbgIDVYpXAEWQZ4abM/FAt5QUtuIKeV97p85w8kjC6TdkBl3yAG3hRdGeFpRLrbpp31Wd2a5vVuEkcFEEpVq3FyTjwX52CXd8bj22jyDXojRk7aVLPApOSlL1R5WgZWgcosz9DBFhS5BYfkSo5sO33fyeT565hXKbsS81+f09F1c1SeQGQWV0ss8ZCEj9TKSOggpEApCnVO8KrCJQIwk21sNbCDZGZSIU4e5J3MWPhLxPX/0Lgt1i+NpdEHAcs7tnVleXV+hkOUIJb5ZiQvz6ZCpmRFR2WNlrgOBwW9E3Fcu8eTMOpmwVPwct3yc40dmCDKwPcVrl47RHdb4wXe9QTmI8fwx37fvGbqjAnFJ0r1Y5cLnj5BuBGSeoNAY0dhXoV8NWbs2y/aVOq1OgUIt493VqzxRvcmjszf47Ssf5NOrD5AEBe4OZmgNG/T3PLoDj7RnuLY1hZcLpHBxMosbGVAKG8KxeAsjYcmVPBDEHPA9qlMRcpjj1oYsVX+eR079eQLV4pnRESoyZ7jl098O8G8M+KHliygM0gqM0KQjyVdbJ3grXkKkmlK1jxekFIOcE/s3edu563zHg6/zJx75OqGX8ttXnmSwqIj2h6QHBG4pI8IhsooeOR/8wKu4YYbxJCaUGGkxrsD48HI+z+H929RMggwFyjcUp0r0dBGNQPU17lXBg+FNvv/YC9xXu8vCXA9vMWVwAjqHXHKrEE1BuDTk2OO3KdmYvfUq43FIkriMhz4316ZY354mtwKGFtuTiFigBdjcYjU0ZEQ+cDBewGy5Q1kO8VTCdBoT5BrjSnQgsEITiBSpc2SsyWdCrtlF2vEMhIK8JBgeL6J8gSczpopdKlNdjh3fJbUZtzuL9PIAXTX0Rz4FJ2WUuUyVhhyc3+Odj1yk5MdIY5ElgToIJvBJco9MKoIg5vB9m1hfYn2FMBLjOgR+TigT3PmUW0WX40fX+aFzL1AvDyhOZeRuQDZXgrqH31fIvZzIlOlTxKhJNbOo5sgQpISFo00Wz+xSDBMqpQizK+ldDjE9iShrHFcTqhzrGLajCptbVXb8Ci23wNvnb+JtaLyLEnfbQXcKSOHhWouXGsqtHsvao1cZos8q9JLAd2N0aiE25J4iPmV5oNpnWkpO+yOKbkIuFLkforIc30mRqcaJDI2mpTEoIsoB5TsxtbaDK+VEOOj4kCiEmHxWGS6n9m1RkDnKGmpyyLVnlxC7EpSYVFT3U5J2THgX0h0LkUdfVGHWgCe40V1gbbXGncrv8dLFN7n01izbO2V+/o2PEmiwFkTVokOPWATkroNvHKbzPs5gBFmGs5dz8mSTSiXiye+4gco8Pvflo1wf1rlj6zT8Lg8GtxneFcS5wfoKG3q4RmC+aXmp7KUIz+AULW4goeTCCojjYJVkd6tJx2oKvclWh800WoCUk6BUkwtUN0c7kupcEV1RpAcCBqUie7sLRMIlshKUnQSqeh7CcUn9EDnUWCyOUCjrgedgHYkbGlwnR/l6Ijw4TMKrhESMEmxuwVdgDDLwJiGp1kC1yHiuMPny/X+2UMxkZBONIvH+EO3l5P0+OgP7zRY55bjIPY2f5niOITlQASNxU0m45yBrdRLl8sbXryE9jVcEb2iQ4xw10pi5Bvq+Q+x8/hpqdTwJrc5BdAXdUYl+10JiUV6AaIRMLZQQSiI8F+SkDS5o50x/qoMzGrPppIT7AtxU4MQSoQJSWYDcoRvFfNv0o3xP+H7SviVVmnj6BmFxk0ZxzFQwYHN0EctfxcqfQealyRZMbvnIfSeoFGOqhQh9X0I6Stnd3mO+XubD95/gx7/33RxcrCLlxH4lBcRHBK522FefhMMXKwX8oofjKbS1GCmJ0pypWonzp5Y5eWKRxx48xFSt9B+ev27v8D/8vU/y9/6XT5Pn5t9531rL/3rrawzEmB995mP/0fPePe5xj3vc4w8H9zZSvkV47fYmze6QY/tmKATe71/fTHfxbgr83YnP+aVb67zj7BGMP2Qw2CQslsnSAaVih/2zHfa9+zK/s/sgcix46eZhSAQ//tQcrY1/Sjry2bz5bqIt6I18NocOd6Jp8pLkmY0DrBxOwLeoUMOxGPOwpF3vAxVsCWqygC8VbiQwOSQip1gp8O0feZBnv3SRo0cWSTcdLvS22XRLfLJ5gg+86xqLlct8qd3FzQypZ5GhpLBpGR9NyUPB8WqVy7V1SCXGh/JKmUjnLM43OHtsga989cdpLtzg0xc6JOuwcXOW9vRdlhcqdFSF9xSu46aAL3hTVPGyDLM3wLQkX3jpFKUsw0aWXGtcz4InyVA4icBPIO77JJmLtZrIC/ls/zTlIJmsl0eGwwdvsvzAgDd+5yTjnkfxWIvLZ2c57zV5ZXM/26vTfN0eZfMTpzj4F5/FVRFKa16/vky253B2Zo3yCLCCgQcllTMcKkZNjySXZD2PxDOcXNhhM6+wUM+pfn8f9arL1dvTWGFw6kP2uh6lxTE6VnhtSS7NN3NAJPVGHyskY+PR2ylTmx8wlD66MstuVxF4ijyHXXWJ/Uef5uTDfYrPFUmXXF4OD/LP7TFORVdIJRSVZi4csVqawUSaYNuSzoLKJEU/wgkTGsGQVlTBFznX4jlk4vPflF9lbC2+Djg+f4wvt65DbIl2PVQOWipm3THuUFHNcmRgsQsZchcMApF7mJ7kz37PF7mVKmoy4429Gm/1jvBbv3I/xs1Ibgdke1MMD+VcvzvLYT3gbl+x+sBhQEI3YXn6GQwKKyzCDrjUXKBLETQU3IhwlDMd9igtddi60+AXX3uUH3joZT7/xn3E2gcHosUcby8j3HXYWK8xVx5hqyM62mXt0wdJRIgbC0Ri8K5o8ocnLVd25BM0UuqFPZ469Aafe66BYwW9rgIPrBLYguEbl48zuuvTb1fJ+oosyhkeK6MihRgYijuG+mJOGMSoYoSzL8M6JXIhuN6chiTA9gWQoyoJOnQhl4jU4I4EQ+HRHs8gj/RxKg7XS4cIuilv79/g0rUFRiMH405O5N1U41RSUs+H3KJkRlaQmJYk7OeYWBDXYaEwRkVQ84aIUsBf+ujzPPfjizjNFLHwzTrbnsNuWMIYQVDqsnZtmit3lxglIUJm+HWDM4aol2GUi3Ukcd9FBt43sxxAWYHwBUpBqTaGKUk5zHjbgVUCJ0PPW56/vkS0YKgfAdFysG1B/loPOR+iXUEaeDg6521H13i2X8AEkvBgio0l9a0We/0i3Z2AzX4VuZlx/tAGadtjpHw2TJFWVOLiq4cwNqBjQt60Z3joxBrNYpnN9flJPpRnUXGG29YkruL21B5+KWF2bkiGT7QmcSoZQrtk1oW+xys7y8ztu81nVw+w3S/Tea5KKF06MwHFrQxXSLS22HHGX/ihF/EXEn7q5x6jPyzyl3772ynYjLWtKTAWlIFcIUeCf/zpt+NriyvhxQvLJEOBGuWISCIMrPbblOZ9dgbbKLEf4xhMVxG7ITaQtPQ047LAqUwsVYwCNvvHGRVi9MDBTyxTzQgz9PBXeiQXA1bq23THDjgaGUhEQZH5ltA3bLTqKCv583/ly2x6HltxnffNXaezOkWro9galRkvuRR284k4UXDxzku6a13UzQTnrELXJ1kn4i2Dve1gleLCaovEEZjQIZ+SqD5I5WJDiRga8tkipuIhhGCcWpy9FC9OwLokXQ+3nyN3x2SZxQkUYqRxhMIUXUxpopCEaxmj4y7jQyFuN0W3DNRAGMhrIDyFjHKk503yVTKL0Rm2oLCpxqaatOaQhZasEFIYWhhF2HGKHcdgLSbMMI2A8M4QtdFHB2NG75+lmLnwVozNHQabJUzg4okRTjHF9DOsIxmfXSA+UcBvZYh4TBQomAK5FuGvRRjXYmcVecVFqBzVlRhXIVSGjTXDdoA9IDGBxXV8ipmh7U6CpK0jkVmOLRTAWuwAsmnFlcs7pOSobooVkqwsMeWA1BiC0GN2oYqjLKujFhWvhyrkBCbEEZqyqOHwDIFaJ40Ueaq5dHGdxx4/yuBOEbteoLpsePjUMov7p5BSMl0pYoyhNYiwCpQr+SPvO8Ov3r2Oax3mShMhZd++Bp7vUC2H5MaAsBhjGY0THNdhYamOH7hEUUox9Pj3cetOi7XNLp3OGGM0/8czyhvrTYRM+a/PvMCUO+ZG5wc5Uj/xn3IEvMc97nGPf4O1k9d/af4w3MN/Zu4JKd8iDFTKnhdhxL99fXVPEnRGuKnFAv/zha8zO1fmxfifsfGq5eHho7Q72+RFS6YVhyt7zK3tMe4ECBUwV+5wf/URfnKzzqgiKBWbqEyQ5gHPffkIfnuMXi5gtwV7X5ojyzPSu2Ps2ZTI8cmshzQejbMO++sz5MJSUT7Domb/wRmkFBw5u8Jeq8fcUoO+yXilu4nTg4NzHb79/jcpelf44U7Ir6+e56I+iBQOHhnFOxlpRfOVp34Xf8VhblWT3T7KqcYiL11dI1OGpdkq3/ldD7N160GcF34BkymEMSTNiJYjiBcc6k7GS3vL7DhV9jU2WV2bJeoJfJOTJw4agTvIKO1vsrgv4c7WPFkKqpPjDC2pUvztn/s2jh1o8uaxAHxFIfFw0FhjeOyH1ym5CbNHRnz67z3E3naVv/ChNzgVdDkd7vFnr3wv3/j7J5mbF1SDCJwB48jF3PZJmj7/cuc8Bw2MrEGWNZ7KWDy7TXezQVqTaEcgRx7nyhvMMKC4VWW07fGrr+xj2Ckw/SPr7AYNxKUCw9RBRJYpG6G0xSYptlhAV13q/gDbU9imolcqIE3Ord11dtuLHJi5gRKaxXqTxXNrdLsBf+6HbuC6muc27vKxKw/w9I0DPHV8ldebs9iBIA8UnmMwyoCRGCG43VnkyJpmmXl22yUKvmZvt8y8GLGbSSooNrOEjWFEOpXjNSVOptHCIS/BJ19/kEdKGxyq7xCEGYOnLM6bltIlDxHnOOSMhUcJzd3I42/94h8lWB9hU4dgxiB3BM+2lqkeG3Hi5AYbbx3k5Xg/JrQIbUH5/In/7SNg5KRxqQBbrSLLc2OKaYY9LlhK+yxlfapBzm8m59m7XeDz+jjOWkZWtzgji9sxWGmhZKjYlJ2tOjuNaaYLXW6tzsACkGrcBITnMRIWG0Bx5DBoFml6AY+WrvH+sx/nZ34HcHxkRYMn2Xewyepmnct7cxRDgY0k0abFrTqoDIwQlOKcoaNJCgp7LGFXz5LFLpmFgszwhERLME5O3pY4VuK4kHqgyyBTSZJL9lcjbhQrjIRPnjpcvTNNsRbRNRVkqtBWkCOIpYsxHrkvSOczpAdSC+QuZHWDwHLIblI4mHKw0OYrF+f4lbfehp0W2KrCGRmkJ7G5JNeTbbJeOWBOj7n60iwityg52aBK+lDsdOnOTwIjhZGMujVUbpGDlKThgmOQmaFPgbt3GwhjmL4bcWRph2vPLJJ2ff762RfYsHU+9fWD6EKIUgXYHaHKISK1KAfe865Vrryyn9vtCt23ijxyapX979nhN75+mp2Ds0TzIdONNm+rrNEaVFmdLnMnrpCPHIJqTG01Z+1AmUHaQA5iZpeHKJ1iPH9Sk+tAHoAJXPJAIasKqya+4sj4oBXakQgvh1rKf/viu/nZ5kk2hzVKz7hURj7GgWRfgfBOB2sdjMlZPBix9LZ1QplxZnmXN1fneS1exOsJ6h5YISZf8XyyNZcpRZBlWATFiub8sdusXpwhEgE4ik2R0RztsLd/EbmqcMYWlVqGiy5O4iBwMasOfpwR30gRSB57xxZf3XaJx9M4wwxvOicTLvnQh5ZhOErwfYsIDNYrYUvQTCsUFpu0kml0UdGYH7DdqU/yVXHouzFjNSbaq6AXM2wzA+miS5bSdMi40qKTW1ztEh8w2JKluGMJmhpbVYwCg7Sgiy7ZVAH/ToqLRWqFKPoILCIDHI3uZTiOi7cGopeS+ROhQzoCNxbgunglRWHaYZwnZJlFuAIpoPbGEFyJEGAHMdm5yiQI3gFhLV5LAwJTUsjAwyYJRoPMcjypiLEIIXF9j9zXKN9DFBJMp4fINX7oIvsxZ853uHu5RDdx0EqRzxeRV4eknSFSh0gt8JY9qvuG7D3tYbOMrC5wI4ubSbRXIni5TXzUQwqLsBaJQsWGtFJGJSMMOTqQmAXDwtk2G/kU9roh0w71osf4egcbOjhaYMdq0qSV5ljERHi6MWbgDci8Nk5mSUNJVgshcHnrxjbnTqzQ3e1z6+I6X/vMZUofdujO+cwGI44Wd5l3fUTyC1Q9ycnDf4SXLta5cOku58/vI1gtE6eW+Jbi3LetsHRwDiEEaZLRbfYJHAWAxbApRsz3fGoiQIrJoDQ/W2V+tvr789I77z+C4yiGoxhHCTxP0etHDEcx041//1bKVLVArRywvFTHIv6d919/a50TEdSdiKHxebb51zhS/8x/xMR3j3vc4x73+MPAPSHlW4TroknTG9CzMUv8m8Hgb9z3Qf7GZ36ZQGUTT/Y4JUs1twYhrVcjsps3ULUyxVrGkf17bK/V2PV8Ti+02d4sUJMp10ZXeHVrGTVI+cDCEuv6LlkcEuZ90lzijmHp4Tal9kGmnUu05B6vDQ6TZwGZdTEKxtUxfbHLQngCMx6T55pMjPECRcl1CB0HF0s/SVCpROSw2m7QHhZIRJ1KaYcjc02uzC6imxacDLsLH/6+F/l49zGGWyGNuIc/yjD7FIOyJR/0+Cef/CrF9S9y8OFLzPwgrH/jAEZPAimT2MBA8sqt/XzJPYCpC6bbI/qOi1AS44JWLtFhj9qNJuNAslqcJZcZNrG48aT1xEPz+uUFLlxf4IFDr3N6usnV+D5kBlMfvMteGlJ0U7bTMmPfpfU7HjvfW+BM0OFiaw5dNLiBg/YVhT2FLGfMBzH/9//qt7h5c46fefY9RP3Jg/7c7B5+ZUgnqDJb6bC9Nou7J8mmBR2vyILT5UZ8FBNq7KyhOOix4cwy2pnYgmTiEMghYsaAgrynyIqWfNrSVgWq00NefmYe56Ecqy214i5OJWJ7EFJJYz6y7wrroyrNOw2uNXPqjSHpnsK9Kem155GHW7x3ZZsvP3ITf7vI8nyf+/b2OHffJv/o4nvZjcs837zBn1zcZWa6x+1omqgrOXRgh0Gk6I0kri9pZ0OqYUKxphm3BSQKq+HuaJpHP3CLYVVzqT+FVpKCm2NzFylByiGJddnIPNqrRWrLfWLXo3DNIW2O6KyCW3T5fHqA2c+9j+ultxgdqJNXQKQW745gHM8jM4v1QBQgu11HkDAKBIuHu3Db521T2wzyMruNALvnQMvhR9/2Vf7Flz6IigQqVmg/4tjRDb798AXu7E0zW+7wN/7B9zMMJzWjpYfHgCW6EiCEBKPIEo9La/twA83feP37KTUTjAG3obCJwTgps4UOWyrkgR/YIL7isaVreIUhfWYAcPKM7PiYmdNjiiLGFT4m95h0mVqCSobjCowqkEQucqhxFeiiQCGwYwfr5kg5otuyjBOLtDlhwZDMeTT2d3Bfn2IsXKSbcvzULsPMYatfx9EgtYMyGVNpxEAUiJYdsprk9u4MP3biqzydHWYrTPn45SO4lS5Th1NG0mPcL5OPLNZORN8brXl6uwleKUd0BNZISsEIGgHjRBJUYsZpiMwEd1tTEIIuOZhQIjNJr19gaD3Gro+2ht+7WeOgs0uhnXJ4ZcDDpTbBdIDalxPLKnnFBRsixilhN8PzFdeSOfbcAOVa4qse0XHNK60VZsIhW4NFnJ5g9qEeMrWITFAopyS7BVCWzcUQdyXD9CcnMiFjDi3sMqiX6MsSMrHkjiBfDAhuj5G5w8g6iG5IMehi3BmsUniJxOgMbXNMOaeTF0hbkJ7sYa4XMFIgxhm5MvijDAKF/2DKT1x+ioIf08gN0kpkJshCgXU0KpK4ESQ+5I7AhIJ6f4hrBG/dmKcYaQbSx9gMZwS2rBjnGYwSdDHA6RgKscXTDTp5hD+wqPt7FPamqM4rpg4EfOVLayTrfXhnQuJK8kXIZyaV2dkbBhMI2lkZWXVxbw5gv8dsuU89TOgXKxjl0XN9ilXDHD0udGaINyUbd2dQmSGtBOSqj9Ig0oxOS6J2DDwkSQoCNwBRckhlRuhI0AaMQqYWGRvkIxksauLnUoojAUKhTYquuqjYkq2OUNai0hz6BioJuhLg7crJ385AYhxL7qaY+yTqtQT2JNZxMeTYLMWmOZ6EtKCxCGQisULgDwTf9J9ilEW4CqouYpyDtCAlwlWIzCAqAfRihHKw9QIME6SwPPX+df7MT9zg5uUSf/3HH6MQFQl2Y5AG35d4hRxTEvRFEWcHTGoRnkvtM3cYfNdRZGIxowxyRfkLLaJiiskVdtpBSjCeRBccrFCIHApTMbXDI5JEsWvLpA3B6tNjwvUuslbCCz0yObHESd9CJrExOO2cshSMXk0hB0HwTQuQ5pVLd/i+px5kNBjT6Ues3d3ikBBksYdQk23JQE2B8yGs3uK6kgzDMXcHPYQjWVgocbnSJMlhS7RYtEu8sfYF+jyPvPkOPvrwPB/7/E3GruBr5g736SrLpsCwH1FrlBgNE4bDiHqjhOc5jHVOIKBWKXD/uX1kuabdHaPkf9gFv7bWZm80opQUEP9fOsqLz7zF5Ssb/OtPvkIeVPGfyAlETt07AkCaj5F4OM69Ufwe97jHfzruhc3+wXEvI+VbhL63QxRuk4j437r+1LlT/OSP/QDBfInKSp3/5sTjPHj0IH/9xI+y3N5Pt2lYG2xwt7/E77x5jl+68ShChqSJj0wNJhUMxwt0WhX64wLnz56mtn9E3HM594NreHM5wbExo/0lHn/vb3DuzAs8dP4u+TjHSDMZKjxNw+/xN8/8Mu8KZ9GjMdYY2p0brG/+Q7bXW2xc3WRztUk3SzFSkEvDBx64xGK9R+Y2+diNB/ni5jEWZztUloac2N/i3W+7wcNH2kS7BfRY0mmW8D2JJseWJLExDIcRsrBO0Ymohz3cD3aRfoIpaMg0zsjwd//372G/2uK1iwfZ8gLKYcrsUgNvxSMvgA4kdjGiXM5oeEOElQgfEAYrwTgwMz3kz3//09CTzLoDqmmLuWNNinMJv/z62/ipC+/i66v7qT3eQWwp/h9/73He+ZU/xj958wnKtTG+dEiN4GO/8A4uv/wgN5N5fr33IL1ln4UDe/TPRxgspfKQU6UmYRJTClL8KMftWNw+LIZ79OICaQFuxNMYx1ILhyihkb4Ez2Adw8F8nf6Uy3BGMT4UMLWvRXS9SPdiid3VKokXcGp+m1MLO5xrbPDLt+4jTRTR0KW9GfCLP3ecaFNyOW9wLalxbLbLT//Xn+Xc6TWsscRDh5qSCN/hif23yJMSG9sN9qZ9bNmg3HXmD22yeLRLVnMQwMAovtE7yMg6XM+mCfdv8tTRN5gp9rGpQqTg9EFGkpmlNsIoiq7GWoku+xhXkBnBe777Kn1TZDercH2vjlAGcSwlnhbkniXragZZTksU0BLee+IVSv4YLFhlCceAFUhpkU7GI8su52tHILB4KznOfrixvchf+Mc/zD/4zT+OU8pxUpdCDG9cPsKU2cPRAuGCFR6PHl7nVKPNR49cpaM0H33qJfQ+KEjDysEWSwfaFGWMHFvUSCAywbgf0HGKZPshD0OyisQ4AmkUYSXm+eQAYj6j0EmIr7qItsKYCtJYnNTiNTPKSwn+2FB1YkJrWB6tUW0mnLJrlGQEFtRwEsVgXEV+IMMci7HVHF0W6EASIdhrNfBGRUwgMa7EWdLcbc+g+xKVGPZVOhwMBzxxbhMkCKXwkpylyh7nz6xRqqbIcY4Vhm1T5G9/6bv56ufOIfYEZjvl8IE2tX0jGkdGiKmEejX+/QFgkIRw3LL0oTa2YLCupN8M6TULDIIKXpJO/kkrjZEKL1fIXKJGBncweWjOcxctLHkB0oLP3W6NOPNY7U7z2atHeWN3Bjkdon0mWy8pZIFLWpBIP+Yb8TImBWKLVoq1SwuIGA4ca/Jj93+NA4VdSu2MxnSXlQObVMtDRKARnmFXFGnpKmgBRuBazfnCJgu1NnU/w8sMWeihKw7aE9gbiqCZ4SaaD77/GMfe1aJ8ZkAwM8LTCXJkOFFsshCNeaCwhUpc5Ewbd6NPYSeGogOuRBnBa+M5Ov0Ca9sz3NpukJUsmT+ZaPIQdJCT1EAHoFKLExmCqQhpE7b2pnn25mms7yJyQ+rnTEmHlauGk+tlZBajrMBddDkWNDhVX6QkQkTHJY1aiJkbHH+oQejGzO7vwpTFrY1pDCOCvmFvq0rsFUjiIsKRiKJGFzW6JnDcAid9ybGpFjLNkUYhHEHdTzAjj68+f543XzvMXpAjRil6lMI4hnFG3urTX6ljnJBl22UpHvCEuo16JUTUNTKD+gCcsSUMxtQrY7xpja1ZbJqjoghTcUB66LLElly0C4NDLuNFie9bZAyetijXYqqK0ULA2PMY3wRbz1GZhTidRKBkFpkYqBhs2UBVgxKIXKMcAb7Le99xlw+8/RbKM9g8QiuLiBOyAsRlQVKVWJ1jfRfKIVQqsDIHpSK7nSLKseysB6i9lNLVLs5egvUdTCSJtwXjloP78jb6+QyZZaANvlJMf3mLwlsdlAK9XIZyGTU9T/rYPlRkEe0BbjfBKCjeTfC3MsrdEWbDYf1LDeyixVYs+ZwG36EYOExPlanNFBFSoozP3/zJD4JrQWuaX7yDvD1ACIuxOdYFKyXDVoTjKrZHEf/kM8/SPlCgh0fUKrHZWeHpnbO80FzGr/2/eKv597i+7hOlAr/gEgQuM4czrALjwdXxc9ze3OPpi59lPXqau/ZpLu59ibAaoeoQ+g4L5RKHlhr432zh6ffGNLf7ROOUncGQX37pAh9/7SLWWnabQ3ZbA6rlkKn6fzgj5dmXb9DJc66uN7m5tgvAzaub/O2/+jH+199+jmQQkw8SWqNj3E7mqYaP8TOX/iRvbJzitY2jaJ3+pxsG73GPe9zjHn9g3JPBv0UoBNtYZwDy3xZS0uwy690b1O6f41SljnFzfu7uP6VduER82mM5Pc9zBYkOPGSi+GsPVPlXn1wnnlKQg04kIfvBXEJLj31Ls6zMtegsOWwen2L+HYZd6TGOJPEMlCoGkWgUGjsTEVhJx3HQOSz7Xa6vNjl5OGJza8jJI1e4dbPI7tYeG9s9stxS2lfFSovUljwRaC3QHnz1xjGQlvvGTeZnmjz30hI/9JOvoBV8b3SBBwvbfO31fVy8/RCbq02WxDr1E0W+vN+lGPn86OxLAJx6qsPvvnGGjd0iowhEqYhjLD/3uafYV+9y6c4J5lfHzM0VWHwAbu8MUeWM5GTI8bkOO12Jk4BKLKmbE3oG7Qf8g7/86zQqfR7PAj4brdAbWmLh8NHpm1wa7MMayQ8f+BpRo86v/8PzHD20R2dUJHccjk5vMRYztBsZXsfBfuNhVD7D5T2fK/V5Fu2QO1NzlLyUAwdjlrw2L7NM1w1QymBXYsSU5FJvjmLPUsvGLKyNSaKcnTdrvPfVt/jU0oO48xHWwPInY+7cJyf1tX7ODxx/mWvfOETmw7VhAU+naDnxFxwu7fLB+67zqbVT3McWl7tTTC0bbr91lEV5kfc/1cfxNYGSPHZqh1Yu+MpOnbe2pxFG8/rWClN9n2xcZXa5SymEP7v0KudnLDWvQ6XeQ82k/PCpC3x94yhbU1XagyJPVl/i7Qfe5JMDwZ3XZxDZ5GHU1zmDzGc4CIhHLnIgMUWFFSCkRLsOAx0isDx98wClYwYHw8w77jLa8NCpy2CxRBoLSuMIfXGFxWqPqBXgv9xHL9VAuiAM4blt7p95jS9unaK2kHF7VIdYMDVeYjBOuHs8xe9phBpjfcnp+++y9o1ZmDUYacA4vPzxGt/zkGJnXOSg12XxkUt8/tZ5NnbrPFhYReSCwc0q8QIMlzXGt6g5i5kFZjRxRyKbBuFOsioqvZx43iHNC1xcWyLf82DPQTpj3PkIZS0m84kSxThTbLsVnD3Dg2aXx4+8ynpB8InuI9jMRRmBreWoJqjIgudg6posM5iiYThfpDycnIzrgiRpWVrPTjEOUsJMYV1NuifoeB71mS64fYr7XXaVy3p/iu5uncwroIzBTyV5VKDoaFyVk98u4LdGZEiOHujw1XQ/akYzHQ7otqYxVtItFCh62/RifyJGANnIIW656DmP8cCHssAoF+WmmATUALKqQgcShMbzcmTsoKSD9QRkMMp8yBT/+vUzeF7G8rldNq6X8HZLqAywgnTRIRlGXNmdxul4+MWU6Y90Ua2ME49s01jsc9LvkfcDfuuF4/Q/VGSU+mQ7Cl8liMBixrDQGjBQVay11IVmrj/i1htLqLrFyQTCWCyGZMmhvJ6iDdjSLGP3AP1T16mUEhq1IZcHNRrZCD1yOFxv0R0HyFTj7ylEK0VnFpkrkAYhJMcHm3z/297g6eYRXvn0aRhrvrP6Oiunu/zm5qOMEOiCRLsGqS3BhmbhQI/2dpUSfRJbIM4k/sBiGgqdaAaDjGFoyIo+aizptoes9nY4cP9+tmxAZaPE6aNfZ6+j6CYD/sQ/+BrSz/lnXytzstJmtTfDfHcPKRN6Y8XcUofdVgPXS9H7PJxp2KSBpclMaYAShu3NOh86coPEWJb8lC9Xz6NWDGlLQm9MolKU6xItKXxtiGamEVIy0AEVN6fUzxCpTxqMCHouypPokmL++C5P7H+D3W6Jz49OAgZbCLFzAdpzJqKma7AyRxd8dBBQiXrobYGVFiUsXjfFGAevrTG5IJ9zKPoKiSTuxhgjUQicnRTRE+S+QmWW8o0YlMANEv7sj77KmxenubTW4GZzCuOPkGOJGxmyxkTEF5mZiFpWTKqioximK7yoz/PUj83gvjLGQzKKuqhykYLjY1d80jkfIUGGEoxBZDnKWiSQC0syO7F5hjs5VDxMI0DhImODk2akBrxU4klLkiuifhF3XmPXson4FuZ4FzUi9DhxchGjBF0sg9E6jhxybfRzuEsFzKsWMxpjdIZTL+IEijQzWCnZ7vbIMs1rb26QpCATwbW9aUolAUYjLJjMhexpos6r0BSoTBK6HtE45vsOHeRr1y8ijKBXLHFoZRpRfYqr/YCNrYPkoon1JAJwPUPTyTk/UyIsTuqPG1MlwoJHqRRg0gRXSQqey85Oj1s3tnE1HFpqUCj8+/NRABozJZxLAsdKur3JDLa10SaJNaMzAd5LERKLNMcZmps4agXPXmcibVqsjYH/8M+4xz3ucY97/OHjnpDyLcJ8sEFiBngy+v1rUfIKm83v5J33S66+/ncoItkTI2Txdf7M7IvEPyR5dvoDXPxEDxNqqt2cK4wwFqomoh/N0hqUSKwER2ELUCgFLEZtbk7V+b7qdS54K9T9lEq1QzursmkbDBG4LycIJbFHcmSQ0xEl+qMaC4sBJw6/ztLSNZ77cpXmzln80GVqrsbMYo1eAF41QQwUN7dn+fzwOGPj0h8WCIoZl9fLvHFhnmRaciWpIBzLg7NrPCB7pIUC16zDONmiqzwa+9bJdo7Rvt2giEOgNKf9Ab80cmBkEVOTk1ihc07s3+avPfm7dLolPvbi4wRC8IH73sfLFz9DPx8wODhFs7nEaHcXs5fjW0NWkqjQ4rgZystxEHgq58XeQWSmuCVm+I2Xz/PXTn+dTVnm2c45Ppi9SXU65kx1i7vONH5oeKi8zdV3R+wmx0kSRS/fo3HrADS7dJ2AwWiGwFecPDXD+564Tdoesrdaop8GVMop765fp3Gyx5c7h2ADVip32evUyIzBKsMrX9jPge/ZZjecwf2a4a0ri6SPAUWNXxzzvsU1ps8KBtLy3OefxAjBcOihxoZtW0VmAcGO4BMvfZD9yzc4ODvg1q2cLz53mE0qfPf9dzh8sEPJtTxzs8gvPfck63enKNUHbK6vsOnAE0mfemWPP3f6dd7T6NLTguvNZb5r5g0WTuxSiiyFBC6PFrC54M2dGd539A32hguQKVQKIoOTS2s8HGzRqwb87Te/DRkmzKkCI5ET1mN20hJj7RNpRRiHFNsxQgn+4uFTXBn8Hr9rj9DJyww3QUnB128dJlkKQAhMXVG6PaZ/0qL3GYo7BR5cGfN6ZczdvIgvMg4VO1w4GyJemKNbSXDXC3ih5cTKOre/skAyEvh5hOm75IHkq0vneeofnsRMOfzkt/8Ovsm4m1WIXMtnf+UBwsTQMyGkFiS4Y2cS1qlTlLEcO7CKP6+5evUoRkI9tWyOXYxWbDFNaGIalYj12jzacVFpBg2Dn7ik0mV3vUh6XfKqK9h/tsONUYF9S3vcqFXJRxJVtEhr8G544CgK5/u0TkjEQCItWG8SKmlCQVK1EFuETpnUTTuEMqNUGuOYMaV+j7dNneS3si6pUWQWHDRix2IdcKOc4qkINZXTer1A0Idon+J2oU6ZjLs7FVZFDRyLFdAcl7n76RluXZqGkInoICX+0JLWYzxfkQmFQJA6iqwgyUoW6wqsI1CZJrUeQlqCnRRvmBJsdTFTAY3SCO1HPHjsFjPlMZfuLmGcGPoubg6RLyEXuE1JmviE02P2dmuk5RF2qEhyxW1d5M3Veapen0BkZFLR3gtx6zm4EiJLc6sCJ3LI4bnNJW588nsRsUILB+0aCh3DyE0RVqEzQ+45DOOYzZ09srJHeSYmaYDjWPyKR7xa4NamRzcP8Y/FSE+QXQ5RJsMYgfYmlb6dF2d46b2LPLXwFi/Zk6iB5cmz17DA0pEmly4tTRprCgonM3j9nBoJ3Z7LQm3M6cdf4uu/dpzWZhWpJZtFS37UnVgQJEidowNFJ84QzR59x5Blhob+dg6fyNltVsmKipKTEZYtLwYHGXs+Z5Y32b/X5CX2k+FwfbOONQEqAjk0vLlbZjg4xqeaJ0jOeFzemeexA5uUHM1zF5ZIzhq8vSK1nsto3MVOKwbHi2TLLsWbEYVtzajh0A+LDDNo3T2B6wpsu4BxBFluMLlDZ7NCe1+V61+ZorCRoq2DynLygiUL7cSGk4Pq53jNDDsvyR0PEeWQSLLAIgNDcT35f7P339GWpVd9Lvy8YaWd98mpcuzq6tyt7pbUrSwhgQQiY18w4ICNbWww9uU6wsW6tpy4xr6YYIKNSZYlkIRQbiF1zt2VqyufUyefs/PeK77v+/2xZfj04fCNgZx06xlj/7NHrVV77Fp1zlxzzfn8QGpiX+BlgjgQaBxBoLBKkOCBK1F9OUUpPV7l8SX+0Yyp+7r88pnbmXIxa4MaUvu4co5IQBYOURhEJcIFwXjCpSgQ1pLXKrh+B68dsu+4z87QJz+fUGo5in4fEVWh0AyXx8eVqjW8yOK0wOUFFBaDR15VuFDid3LcVIioaETH4pylkAI7HSLbI2ykEMKSS0HHRUxM5iRVi9QO9hjEtZyy52ECRcd1wQ4o+pK1U216R+pErQHujEEqcCZGVRUyD0AKtl7b4jvf80H2HJ2haGjycoYONX4lISxbOplP7Fno/SCPHJHcNf1NnFuZoHW1xZc+eYpDj04TdAUYQTFTZ+/iBFPTj7J5bpbLySVOv7KAMj5hyXFzY8jlWsbKdsb7eR3b8Tpf2vwMb5l7J0qXaeqI73/4fiSC3/7ky6ws7/L6ew+yuDDxFbXWh146xcpuhx95x6N/8N7JY0tcvL7DMQNSTQABAABJREFU9ESFg0uTAARhQHR3E3sgI7lmqQ4swjQJgia5g3/y5Nv462/4PMudOR7YV/uq1IErmy0EkqXZxlflfLe4xS3+F8V9+fU/mv8ZPsN/Y26t9nyN4MsBnsgQjP7gvUutfw2uQIiMfmmdm60ed9aWOF5q8s833sLP7zzKxMQ4XWTibEywFiN1k7TqM++nhLpggoR2OuLuxRvs27vL5ESFtVEdPMONuAJ7EnY6dV7anudaa5KiV8acivCvSaJPCUoyo1aPqdT7bN8ccn3573L+yts5dWoPH/3lw5z6tODA/BR79kywuGcS5w/wF2J8RlR7PUo65ZDcRfUFriUpeh6qHlPMebw02ssroz1oL+ZGVuJTZw6StXNqMw3UgZiWdeitNnakGTnHrnGIRpf4JtDLmF7c5Vu+5wmkLeC0I8kVu5tVcusjrcRaixERI09Tqg/JXJv33/ccETmikDgDkYjZt2ebT/X2cno4zWfaB3l/4zxru03UnpircpaPLx/m6e5edNWxb8+AtzXPMKmHHCxvcaCyS9Nv88A9K1Tu6JLcZdh7qE1RSwhv7xO2M1TH4rcMk3uq7KtOsVRNEKkgK3xq4YAZr8uB0g5H5TZqVPDeg1fQRx3hWx39t/pwp8fBHvzYxDV+8wd/jcW5HfBzXCnnkclVvGEF262SD0NErpDG5+bZBvJqzBvry9xTWqWsC9IRbMkmaSVEyRSXS7Zuljm/1UAAuRV0jMdOZzxeP9iYwOkC6SxPXTvI1sUl/u7ld/He576BP/3Z92CGmomR5R3VNd64eJMnnjlKkgUkuabvLfHC5/81X3hiD8JI5MigMsE3PvoyNW2ZCjPKCyOiSoJMR3z3e+7nW3/4c1ij6OYRopshBZCCHkjWR58h23H4qUH1HHM3S0wqn7ypkblBxgXNYoQUkvJqgH85onaq4Lc/dQCVSy4tz5MPNJ85czv9smLhz7wyHlsva5p7YmpNQzPMCVPJ3JGxyNMGFhdp5MmcmbkBP/ncG/lbT74LEHieZdgv0etECAEyBZGB13VUVi3VZ2HylYI/vedlvv3kq1SmBjQabfKFGmGQopopbKdYG7AbVCDzUImAqmPPG7cQczneZMb3330JPZExqPl8prVIdS6l4qeknoJagQsk1nnkZZ88g8nLKaYHtnC4siOwKbdPX2PS61NMWtI7HZYAckdVxiwc6nHnPWtkPY/b5/vcuLyNqGVEKmdvYwu9k6H6hu899jjfvOdlPFJ2RxXSBrhaRCqbLL+0QG8nwlspKJar5KmHyTTDIuDq9jRmMkTmFmXAGI9cOUTfoG0OTmCEIil7mHCcxqKFQ+aWycqID3znb/B9b3wcJxVGSrxk7AaK9sTsRmWW+zM0ogSvbijPJDhvPKkR9AW2B3NhRmUmI08VSd9n+0aVz/76CXZvhiRO4iYKsiJkWvWZC9u82NuLEnYcn40ku1MyM9+h0kgYUSVMLaV6l3AyI6lCYnOCbYvXz8kWI5yvkAPHjdVdmqJPtd5nMugzF/YJyymHZtf41oVnece9r7I42+NtrzuFqSiQCuUEeS1AhAFuWOKLP/UwO4MI4RRSSX7r1x/m08/fwZnXFin2KOweR2Ozjb+TohJDO63Ry6pcWl4g6YY4qUjnPGJt0P0CmTumohKVNlhf4XyNCBSNakgxKhiNErY8STXaS5HlPNY5zmd6JxkqjZ+Pm0nFruTMl/YhfI/dG02KnYg8lhQHS8RpmVRoetsReU2R1SWXggleSRZ44sIeGvsHLCz0MP0ekybAD0NkuYyfaKSvCBoRR+66iB7ARDCiYYfIAwnULUVNMZr1iHOD7OUkqx7PrxyiP9dEej46CrHGgRUIJ3A4vH6MEY7gxoiJbhuZxl/Os5cYKSk8CLVESIEfClTXIUcFIrdQ9siVwpYEWc1SiHQcr6sEFDB9X4/6niEfvnmEn375ftJCU4SOdDIgB7AgnMCEaiyt1hInviwJLjncgRnuvfcmf/NHP8kP/f1nGe1XFNbgZRK33SXrtbBTOa5u0f0YIg9Xi8BXmKJADBLCcy2ClT5eDAIFZU02IylmAoSx2MKic4PXS5AbfbzIsrvRpLOjcE+E2Bc1/pcyGtWIQjo8X1OrRTQbFazxeO1LB5k6ELP/B1Oa31PCTTRx+yzhkQ7CGoQxeB1De7vHqS+cJy6lpJMa4Ryda1UoAuJc0EkteI8QF0e41C1TYLiwvsWZU6ukuSIaGILYcnShiZKSahjwXffeyWRQxtoCJcGUwPmWaF1yLe4C8Def/yAvtD/KP3j1p8ciYMBXCsE4aGJypkplssSZlQ12ukMA2qMh/8cXP8aH8o9z/4f/zh/UWnsXJnn9XQf5ujfdTrUSAVAqBZTuqjAx2yM4lFCasyyooyiWGGzXCc5W+We/9n5ep3/8q1IDrqy3+eEf/xA/8n9+iJ12/6tyzlvc4ha3uMV/mVsTKV8jbKYNRiahcH84HvpCp8WBoEaKoldfpvNKwEsXVqnN3sYTy0NwMJ23mNnTZHsnZu++adbXupRUwpV0gp4O6fYqTJQu8cMnPgcIbq7+ADdfnUZNprxcm0Pd5WifbTAY9TjdrTL3co0D+VXErEc2VeXk4grXK3UWgja7fZ9RJnjt+gFk5zvZ2HqOfUd8CpfQ7ybMzEuiqmL/4g47N6vsXezwcGmTl1+bpbyTEZZzcBY3AUUm+dyNE+igoLIvwb/oWL1e5m2vf5Lff+0g2+cmuX6kxt9700cZDqfQQqCFYSQFifQJg5yoVhDelsIbYjYHVT7avocdW6N5vIdLh+SBxS/HlIqUKM6oqA5+keKXYpJdgUw9hFFUihRPaX5m40EOlLZ4OOpxsLZKNjlDHHio1PBdC6/yxdEBHh/u466ZNW7kkzxUucrIhTx74QC18Ah//+RT7Ng6yam/zi97n6IS5gzbkL/go4RkcxjjAamSHEhbbJ9rsBJMcLE/SyeK6HZr9FwFk34X9uoG/te3+Lkf/Dzb7TK/8+oh7j26hu0JpqcGeDsO6Vui2BGVMpwS/OqF+1FeBIVFdiR7j2Us7RuhlGOfHvK7WlBDEnmQlRQDXWa2POCyKfEzZ46xltZI2hZ2HIoCpyCJPSZKKU5LzKpkUPEZUrAgNml16qShRuqxSyeuKWRfI7qWVjjg3FoblzgKz4JzZIuKVqvO7EKfQSKYliM2y3UcEh14VEo97tPXcQh+8wNvpHRPAoHljpkFnnytyeDKGqNeCVkT3J3PkpNhPXACpA9Bq8CMcpSnKA0U2lnSNKBIBLohWN6aRCSOvRPb7C33OBXkqIWCxWCXRmPE2993kcsvzfH6yml+wz2EiUOcl7NNmT979Iv89OnXcTBqY7Y8shUPl1tILTIHVdGEmUIlDp2Op1PyTPH0q3t4z5vO8Avf//O8tjHLr517lGoeUviWuYUuK9sLFATovkUIgd8YEUhDfTnnvuYubztxnfnv7/CLGweJhwFpV7JQ74AAjEYaicg0KpSYsqD/WoXodRlJ4GH3WILrObWpIXO9PvlGiTyV6JUMaRUVlbHSneVObnL14gyjkU+XmOTOJqoDV87uwVcGE+Qcntngqf5tkHqkqUd9qocplekN69i+RxIIxEhimuCF+dgJow3lRwc4G9P+aBMXagoNUhtEuUTPeDgNTkFeOIKBwTcGv2zJe4r9c9tMVAbcc/AavxK8mVSGZL4HYcha3mRDTTKKaxwadFg62kKuaM70qpiaRiSWcKTQRYGLDPsmOpy5MoUsfJZ7dUIsoZfTr3sU8w5f5FAy5NUQlzhUYRHKUa6n+L7BlDKGpsTk/h1GI8Gaq5PUPDwHupNi4wIXGqJSyvCIhoPrtC+Xqa6PCPalPLIn56XtCcLZhP37N9ibt3hX8yz/7JPvRZQkRU+MpbShpvAkTiiUg1/9jfsRUiCU5NrGHMnAxy8cWTaWzHbn61Q7KaGXMSr79Gshw47HUy9rep0yqiRAW/z18crQ8bmLnK8cpD3IiaYToisp9Wgfql+Q32Z5dnIFPxqyd2qRz2zPYqPx1F9WKKZnO6y/FDBKS0jfYrXDjsaNMFvVCAVrNDha2aIwQNWQac0nuodoXO9xsrmJMimjbZ/4wiW8qSpFSWEB6zRFpCjPlIiujTgys8r2ap1dWSaTFqUEzhPEiyFOCvweoB1UHBQWYQQIhxoJtAKFQY4KjKehrIlDi008pAAbgShFuIHDhZZGOaKvDUWekec5uXEMAoMnJdZBoQRmqUrmJOU1iywsW5+ps7tXEVwu0FZjhMRWFTJ3mLom2Ezw2oJishg/7hKAGDf3ReBhjKOvqgjlGHRC4jftpTiwQ/WZGDWIyWOHvJYiKyWcFyHcEJRk4/uaOFNn5t9uozox1uzgju6lP69BS6LlGG9gcRUfIRWFlAjpI4xgMAjo32wiJjOyusSGPlGk6MYJr5xe4fC+SSplxcl77uBLv/MS/Qs5lamU4P2GQSJRoQdhhglBZiDiFJfG46Sp7hC1pskWK4iKxY9ywmyKrDdCFzVc7Wf4xDMvE9vfRxhLRXrUGuNUoqgoKNDcs3D0K2qih/fu4ULpEtXQ5/SeNkoJ/K7jL3z9wwBc3C6Rixo7KyF/+7d/HRV6/OUffje1csTdJxc5fXmDL7xwidQampUSU/Uy1SBk/9FtSqUMEQ3I8hTfC9jY7eH7ksMHZih9OSa5Uov4zgN38s+jy4gHHQ8GtxFPZOx2hnz08TPozvg6PLln9qtSA97carGz1UdKSac1YqpZ/aqc9xa3uMUtbvGf51Yj5WuEoQkZFILU/KEy/o7J/51nN/8qAxOQnNtDZi2nrm0gax77TuwQqoKrzx3j/e+/j3ac0fAdv/bbTxHSJcwgDjR+xedNC6/nte0ysZnHbztaqz7Zbsj6+QY4waLu80NLj/PFm8fZPOwIooyT1S6roeL+uWX2lEKmdY8XL+7laG2D9jDgtgfu57aTXyD0rvO5T/we61cqSKUwezts5mW4R3Ln61ZJBzkTvqC6bVFGkJxRhFMFyaxgZ7eJEJZnP3mcC5tNvuOul5j1e/zF973Gj//cn4BccU7P89fuPot1ghtZnSvX72V0rIZMU1zdsRo3cecF7YOC3794G8VQMRMPmFpbJ067lOoxWZriO8Ml3aSxPE/S9xCFwx8aet2Awdoe7rUb/JP9v09qFb/UPsrbvv4sL9/YQ10M+ZbwNfazycn5DT508SjzCz0OBW1+buVB/J7lmWfv4i/ve5Dh8Nu5564lfrv1G3hJwLBsSHcDQiERBnytCdQuK9slTqYdLlx2iJHH81P7mW5nyP0DamVNbt+Nv/Vh3lJ9meP1XY7Xd/nEqQP045xsq0lqFN6mj/QsZ70JLnd8toXBBT7WczCyyESw1HwXffE0Gxcr/O5jxzEZZEFAZkO6iU/PhZwezDO4YGi1psn8EK/cRywb5JTFlTTliSEKiYwctXxAWlM4oJ14bF1o8rtXKnzz+17CKcfb7n6VlzrzyCJnmHscmqhjhcArNKODhmTB0cqmKKddPn3jGM12gokkwvdw+WkOeR2UgJqIEQOB0ZI0CdDVEn/rfd/Nd3/iFxDa8bp9C5hhjo/AGQueQpQNeSTQ/ZwwDyFWHD68zclDG9yo7eX+pRVesVMsNAbEeYnCSb4pPMew7/Py0wd59zdf5PoLE/z1v/wRTl3cz0SRstrUlBcSbBHwz596EyW9wWiokanBEKBHDq8zHrnHWqJUIrIYJyRmTuDmDJ+NTvDdU09wYWORx1eO0fRjFkttAjq0JyN6ukqnW0dgEUojliNif0TyJc0Ts3M089s4d2OaF/QSM3aLYD1l8c+uEuY5w8RnaqFNnDbwwz7+HkP2GizUEraiCtyM2DimiGfmCVd9dOII0ww5LJDGMnIBhya63NxYoMgEmzc8WrM+pGMfQWkyo9j0kJnlF37tDVQeieknIUFLIlIPNcpwuUJaMEOFrWicE5i+RE5bikJy068RYAnyjMJX+MOCdz56mU+8dP84qjUYTxG4Aox2qEJiOgWqV3DmuT38m+Ct3Nxp4AQQKoYzVWSgyXdKeKkjzwNWV5ocesMaflNwsbpAsC4wWoCU5AmknuTalRou9xAyRSpBJ4wY5QGVmSH9QFJXCSg4ILtczifAwETQxxWW3jBEWocTkkIqoqURyVoJ6Vn0wEAgCfMUd9lSe7QYR59PgTdlGZRCgnbB7PQm3+df4Ex3iuc2DtK/UeWpJ+9CnCgoQh9ZtdBNcNLRaypUanAmJxpEWCxGOgqtkYDVEpOBSzUqKVAFNOdTXM1gI4VBsrpTp2xSKNzYlVVTmFDQdo7o9oI07bP3yBZ2TfPa5zcxEtxAYuUuj31kA/3yRd7x42dImx6r15q80NrHdr1EueOw2kPZjE6vAqHAY/yzoagpPA9eax+m0BLlGbJMkuaC0y/tQ54UvFRdophMqBUD5DCHpSqFDy6FXJbpxXfSyFOubc6TGYUrLG5goG/wXEFe88EHEVuS37R4RYKXKDJnkKEkiA0utExWuxgpoazoHfPAejT8mNxYPN9gSpChGRYx2odECVTVoxACYQ0qNsB46kJM1NC5xnmAMdg4odhw5JcCzLT35YQcB1KAEOSBIHAWVTjIHUWgiIYWm1mKPMGc7RMJj37J0Y4jdgdlZGrIbq8x2kkJY42jQH9+iD3ogVDgKYYLmvTIFM45hq/PKL2Yw2SIXW2hjocIJSltg/NCbAlE2SMXHioWmNkSRZHjjRwuBT2REVRzzISGGynZdpezl1eRWN78ba8HISFJaX/CMnylhF9ucudDC2x7Z+iUMwY7I1SSoboZVhYY3+KCEOcL0twjKGWs9FvEnQpdl3Lt8ga1MMdvpBitUEoRhB7K5EQkZLnHuZsrPHTbIUb5Mr969kP8wxsj3D2CN/yeIEwclWNVTjabvPPAQZIkI1+d5amtJt/glnj+sVfJSpqNJcfdt++nvA0vnV/BF5I7jy9xYH683qOVYklP8tTLPhjBi8eu8vDCbbR2B5y+tEapEVGvl6hXI5RWTDea/LWFb2VHdfneQ+9iK21R8yusziteoYUSkmb1vyyy/f+X9fYA6wsqJZ9ms/RVOectbnGL/zW5ldrz349bjZSvETYGTbpZSmq8P3jvgZl7uWvyM/yHT73AC/Y0RZCRa4P02/yZ/U9TEobHNjShfYDv+5NvYPXKBi++fIML69v4LkVFHlp5hHKRRw6+gnOOZbdGt+qTvKbxnUUpizUC3xYsNTvkYhqZpCxNDThxaIV5L+NA+SaBsPS7ClGX9JKcUWp4x1uWuXotY2lxRDJaoD5T5rmtlA09iQ18ShXYzkrUKjFeahCJBSfRMxl51YMdjUgtDzz0Hk797tNkwtHraEwvQg8ySAKe3J7mUO0I88GAn335EfZ37qBWvojS0BmW8EZNksMVcj/D3ggJ2jBaLzEoRqx1O4yKDs0w4a65VT52/T5e3FlAGIEoe9gcrBOM8Lhyc5LazGkA1nabPH32ON/79qfY3YnYXvHYZ+B6Xuabb7vMron4wMW3cGNUw78m2JvP0tnuc/Chv8+Oa2PzQyysz/PS1h5MVbIQ+qxvJ1w5tUo7e471QkFkMGWJTgwyERza06BXb1MOykit0YVHz0kKNEOr8ds+pp3z6sU9rO1O0qh4tGLHHhszJQseXljl8dYu3/oNr9BrhfzTL72dhprn7736Hjr/pkSmNWIGsgRKfpftbBEtc3o7klODPRz32rSKkN2tCVCgrUIOLMfmt1nJ5xCRIJ3MUZFhvtFlaz3CczAqfL5wfZHFAwN2Q48kE2ipcakj0oLZP3WTXi9CvRrwndOnOdy4QWpzPrG7SHtb4faD3R4wt/QSTnicCDo8fe4IXr8A68GKY8X2OZ2tk00ESOOIfcloM2Z70EFOOUgMx2YvEy9IchOQLHnY7YIdETA0Hg055FC5QM8Jnlpd4HvvfoIvvHaM8sgxc1qyKIYcLm9SfiCl4VnOXxCkmU/jbEL/Hk02DPCx7BRL1NrrGAzJZE5tUyDN+KbdKIfRBt8HqwVWaETiEJMx2pSJjKJrfRpLu5z/uSbK34c6XEBL4OUKHCRVw/RtbRbvaLGbVjg2ucYj99/g9O48tq2IX62xuq5Y+v7XiDcCPCmI0wBhUk40V3n96zb4D5eOsfWYZvDABLotQCf0kgybKVSpwF/McfvAXABfS26sTzOYaNBu9xkMfPoHFSoV+NWc5qEe3W4FeVHQ1RO0XtHYgwo5kZJVHXOJZjAEJIyGFtMYR3y7WGCNBAF2CEZoKo0eu52IOK3y+XMHwJNoB6ZjsGWFZwRWKEzosC5AFeNY16dPHcJJh62D8x225pFMeCgMpRWLLgQvnzvOI4efod+vMN1r0+tN4QE6LdhYqZAshIjcERUZ870enX6FnihRchn7F3ZYzuukTqOFxfdS6EmIwMYwMiVY9lBDg5wQWCMQgUZnYKTD23WoAmS1xLf84ON8+LWvw6g+KEvz3jYnyiuc+pW74fs3ud6a5Prje7l0doEiq+LqBYHOMc7gtVNUN8b5Ej0fIoTFbxekbQ+RGcgdMi6wnkbHYJXEhpbm9Yyi4jFzpIs32eGq3I+zkmjXgIW0BKVYYcV4KGJ1bYGZcpPVJGWwLCkuljCxAQ/kjmLp1QpXnmpj+yN0XLCv3MKWFN+0eJpfSe5n/5FdNq6HJAOQSpGqEsZ6qPaQ0E/IU81ux+ICh/IN+ys7vHHyEpfunqVWTVjq5VwK64RzEjEQFEPwkKgi5/3HLrFv4VH+6bNb9JIawjPItYygZfBHBToTyGTs2hHDHBMWiELBVoIINAQV6rrFlCnInvExKsBUFXN7e5w8usliZ8THzh3Fao3TBuMksmfJtKMQDjPMUIMCqcaTHBKJA+Qgx3keYghWO2RhoSiQUiBHOSIU2KkA5wlQGjcZUWzlZNMhhD6ULSQGKQuUp7AHGxQbAw5Ub1D2Um7fv0r4eUN3wcfdMQGDMmQxo0eamKaPt7ZJMBohKCGz8WfSRGRH65hmgO71qKx2GUwF7N5eonS2QBuHK0AKH+cyrBLITKGubWEbJWazLqoOyaEyJaYYbLbHkfF5ysXTryJUD9crEL4idiFJHJMuJUwebDGTFGy+bpLsWhk5KpBOYOaqZAdCnIJhHOAGPk0RoHJB58aAH/iXvwi5QezRFHtKqNAnjTNEXmGmSNnpeGQrGWlWcKXz//APnq9D6kOp4PLdjlrPQMlQr2gCrfj1jz+HezKjYR3X51vEGArts7Le5oE7DxJ6ktmJCofmp3ngjr2EwR/WVX/33r/AN3zp3wKCzU2Lm3f8uxde4fpgyIuf3eTtb7gNGDu4yqWAkzN30pwYN0ueb53hqZ2XObB4kv0nprjn4CL1WvRVqQF/9+lzpNYRO4Pve//1A25xi1vc4hZ/bG41Ur5GGCUNhumIwoRf8b6vQubmppifqfN8c408duybNLyptIMSDvafYXBqiM0N+44usnBsgRcvrRJfypjeE1Dy9R90FIUQTMw06Ad18rty/tobPktJp3zoxiMMOiET4ZDy4h2sv3CJVuFTzRqcbns80txkM6+gGiE7nTKXttaoz5eIZZ/KhOXQAw5x4DYaVmBXcqrzCXloyKKQA36fU2tH8fKCUA7IH8l507uvcvmJE0QbAm8ouP1NC1QTyblLc9Snhqy/GqH7hsmOQK4u8sEbtyF9hzf0uGPPKf78N73IE8/t5/nV/Wxe9UkbIaVLOTK3eAOL1zakFcfN7Y9QmqkwGQzZG7WY7KfshDUWD3SJR4LagYSd6yF5WfHCzhzGjb1K3X6JpZO7NIOEcj1hu+3xy198Pd/7vhc56GXs1TlX2tMsTWxTaWR040n07Iu40jojJIePrXB9ZxIUeKllbbODyiSjRHBto0BIha4OsYGHjQBvHE1rQhAVw4nbFvFFmboe8jOtYwQipyVKfO78Q2zcbNHuBtx74BRbyTxBnnHdq/Hi7gLlesYvPfVGZqY66Bi83ZTDkw2+dLBAbkA2pej7Gmcc9Xqf3koNFUhMTZKoseOjWFS4h3PkawJlLBPNmK2eJTeanf4E+a7EGUd/INi7b4P2ZMTn3FGydY8wSCnMeCw6R2NQdPsRogQyNHzT8cvYwpEYSIcRUij8ZsHdjas88sDzaBxnluf4f37xHirdmGYpphzlROVFrm7tkE6NZY9FKCgiwY2mwGbjYv+gXeWZ0jHSu33s4oD8RolNF3B1pUlzZDiz/BCv1gpsrvilj3wd//qtHyGMhvyjL76bOJKsJDXuqm8yjKG7q5GJJRI56VZBpkPykcCVLNcqE0z1c1RJwUwOyxIUOE9iLKhEYAOBswJGgqhnmA0z9PQWlWwPP3bwJcwHJQ//1p+jYboEnQJRGU/5YFIqXoZOBDqA73jTJQ5WMv7y+17iT/6bpfGNtNCkcZWi0AjhaGfjdJPbKh0emd/mxuE6nzh9ENWWqK4jmxSUlCQpAjRQ7Pr4mwIXWHLj4QqP6kSLbsWj1PQYOkHfkwzjiDVPoK1gar7LqKhimhqTBZh4iLOSPgXG90ALTF0gukA4Tl/yvLHQdqrWZ7Eb03xDj93PLCAQFL0mOkwxTo8nTaTDKdB9QxG48RRCIcmrEluTWCRgQFqsD6IMdiSRwwxpxDjZpGagq1Am/7KfwmGnDXoqx1kfGVtKw5zdTh3P5JBBZhRmW9CojOhnAcu707yussqF9X1Y5xhOhRSZxjuaUf54hdw4ttMKJ5ubWAHk8I53v8xNF1F4mk03wWDRYbXDkxkl5zNzVVIOu5yUq7xs9zKoKxJvfM1IF7BzKQQtsaGH8RXCl+i2obAFxoMciSgLirLEhQ7jC8prBm8oKKqOqBQy8ASluT5zsy0qYkRuFMcOX+fiy0vEdUuuBboPIhZ0kZi1IaaruPlYgyAsIUM7/nprKTcGBcWBCt5lxyu/tcTEkctElYSVbpOl0oiH7t2iurDJz3/kdoz1yaSHUx5xLULnOaPEsa/YAekI/ZzjpU0O2QEXR4usmVl21ifxRxLjWoysh0gt3jS868GL/MnbXyLjZZT3LcgRFA1DcUgiUoM/0OAcLtJkEwo3zPmmr1tmejLhQz+yFyNKEPmIqiPd1mQ7HqIw+OuG205uIsuGmbvanPxSm1fONhg5h9ceIboJSSTJAp+gn6JSifTG3zO+QggQoaZojFPw/DhHIUAKrJTIwBtHYEcKYR0GEJFHPB9S1BSmpshrmmrLYKxFWUVhQdRKbMl9CHWG4a5HNqnHMeaNEHyHX2h0r8CVfURicMMEfXoT734F+2qoskdSGl87xD5FN8G1OpgDNXLPolOfdMFDjBwiywGHasWwvovXizGjCDEyFBcTdkyAm6whhKDcEWyca+EGgJRYBEYIhHE8t7LJwswsR+urqBkoViX4Guf7xIs+Rkh0z2JVTLmbUJquofsBo7URpcIhhMNdHWEmS6Qqx1rLpz/zDL2WB84yMg5PK2bLX4fsvoAoBBYQtw8IXptiKtfcNTnN5FSNM9c2cM7hJFxfGaCmQwKnWOhFfM+993Dm/CqBkTx4/37m55pfUVMdmJpiNqqQpAUNP8Q6x5VsgCvD9jCnWRtPg3ha4XkKrdUfHFvWEUJIpPaJlkrsOTj1VasB0zTHKsiMpTtIqFbC//pBt7jFLW5xiz8WtxopXwM45yBWkPgI90f9wdOlkHroYQ4MyXPJnfMHuZp5dK3PnrDDc/2EPC8ISv44FlFIXBwyHwvCQlBk5g/OtbPbJ1wBLyg4Utocn990qVjDZiPk+XSDoDlN9VrOtRE899RhXqTJ7qjM+/e9zI2dCa66Pnr0LH8xyBH4fOo0HG0YZmZqNMuOlV2BjuDCeoPNuZD2+buxccx6p8HUe1scme1S7WaIXYnsO2ZqNWZMibVTi6yHKcUwhsDgdRzrSRlbcRSZRzjV4/iBl/j3vaPc9y0r2A8cJl/XZJOWUiwINlNUJiC15HUNQUY3CShcjSfbh7nnxFHk6u/xbOUA/Y0SppBjR4MoWO81+eClR6irlNNbCxyIe0wfvMBQaHqrM7z01FF8GfL3vu2zXElqaL9gX7XH7WkH761bXPr3b+LEmx9DiwIzIXimskR20lK9UJDnDqElKMGLnzrGA3dusL0yg8NReAKtHa8mbeYzgTEpzVoJUXh8qX2Yb5g4z8h5bL1pjS/9zkPsXPG4e981bm9uMNUx+NmIH/uNb0T3JZIcTwuSGY/qlS3KxzQz4QQHHjzDpc8uYuoa38Lc/A7HByVeHtbQzYKiJthanyGSBdPHthmtVolzhyahenKH0TOHyYzC24J42mN5c4aqv83eu7b5prtX+Mc3HyYdlhilmploRDeLSApNb5BRiSxMFDjp8Vw0wdvsBj3jkSUeyQ0fuwQ/+PYnkEJhEfz8px5GXTOQ5bz7+CXu3nuTx8+9hVevbeCUwAlYmqhyaTsZR2IWHkZaztr9uJkAEyhOHL3OTq3C1MsDbvYiXBnCYQ01GqCAtBrztz/5VoKOppeHPPje81wPp6nkOQ/6Lba2aiTSAyXZX2pzqVXDJhLrWawL6WlFMLD4UylmPcQikFIgCofXt8i2JS9B+miKnM65Zn1GrSbT25Kb1QX2T3Z4U2OFp9b3EHopIjdYBXpoqPgp07pHZ1Ch440jii8nVfIlj/axkOq+Tf65uR0okJnCrfsYDBfXZ9hub/PSU0soT1IrBrihj1/PMVeqaCPJwoJMaGqBwQ8L+rLGRGXIXQev47dmONuqU00zenkFpxxJLyRQDpykLFIGhLjCEo4E2reQCZIlTbYgsM1xI06IHJt5FHmOkIL+oMyw67iyPINMCoQe35wJIfi6O8/x2ytH0ZkaNz9qI2xUwxlg4DAKrBBIM55GMYFFZwKRO3Ri8VKHHIzwH8y4uryHidYQ0/JQgxxVOLIy0BP40zHOeOTDAOMMr/v+azRnR1ztT3Pjc3PU37rLL196lNFamQcWLiEHDpkJpg70mSyNuLgxiygsauhY95r0PnoXzI//316YqHHu9AFsoDnVkqT7C1zhUe44zr14Aj2tECaj1Qv5vdWj7Mg6VmlEZnAFCCWhLHG+T7FQRfgalVhMRWKMQA5yqIENGDdTcsCAKhzBtZRiIAhny5QXc8oVw0NvPstcPOTg4Zt8OJa8LJbQZhwfK5Boq8jzsdPIVkLIHAaDEIpgMoNmAW3NdG74kb9whZ9f2ce1GzPsr7U4pDb4+qkrfOLGEYy2XO/46EpKgUe2qxjc1FTChOE2qChn5HkU3Ro/+sx9/KW5FxFqxOpohvPtSXr+BLJhwNOY1HD61SlWG1We6U9zYPIcF19bZFAohAgpTyVkpTLKFrgsR/QzZDnh+957FmMEr94/xdlTDQg0/W6ElOOkOboOgcWbT4jKBVudCleu1NFWoGKLahfowpIDrqRJy+CNinFVpTRWOYRQOGOxHmANbHQRbpw05YTA5g6ZpoCP1eNkMlEIXMnDJRl6GBBPSSaDJq1XMkwBqp2ggxJBmJJZRTBtCc+OKKIS5ZdzMA4vd4Rdn8K340QoIdFDS+1TOyRvGmJsgA0qmAAoC2TPodtD1OYAEJhqaVxPBB5yvyLLHbIfI/d5eC1NMasoYolMR4idFvlcHdesMNroULIGQqAuSEKJnzuEFIw6kkJakgjcMCO6USA9nzzQ5JMlyssWG1rEpYxGo0/qG1Q4iUrH8e8MEqSTqPM9dhfherbFqXPrxDrAm3NEsyClYCfdh5e9iLECT6VUwoQ0G5KEPrZwaE/xbe+K+OIr45pGAHaihLSWe48v4UmJSQuKtMDk9o/UU0II/sJbH+Lmbpc79s6RZgVFbfy0qS41xliUkigtqNUjlPzDmuye5lFgyLA9hR/08fVXrwR/wx37uXF9m6lKiSI3//UDbnGLW3zt4tz49T+a/xk+w39jbjVSvgZwzoGXI0WKs3/0on1tp8OL11Z5474V/tTrP09SnKSqY2bFgEtiiouvLnP1/Bp3PXyETppgQ8fXvedFnn/uDpxZYJhl/McAwLXVDqVtRS8s8/GPvQE/jbn87BKbB68gFx2hjLjRnkUNC+7xL9G+XmI9OEAgDFfvb3AxnGA4BWuBo6RhMJPzdLtMuLTN/eUZ/HqVcm0dOaP41d96mBPHWvS+UEa0umQliEcRrW6D+iChGAmiImT5/CrD3QFCCkhC+vsiytsjCgf+pmD62C47/ToHZrf4yNUTXLy6lxsz00zVurhelcrxbTKn0C2LtAqnQBZwwTUIvILuIGRteYJvzp7h7vee57HP7Wc44TCb4Hcs1leITcWHLtyFvy5QoeW7HzzDfj9m1/n825f244zhibNH+Q4bsnO4iZSWm/0G15+a5odPPs2lXpk8nkFHbX7tI28gqyiCmQyEJW/4qFQhpOVLF47wyP2bdGSJrC7wjENnhrTnIWYFTX8Dax3lSHF10OA31u/m0WNX2VNr81IOdX9Aw8soBzkLlW3cZEj4eTlO/ADEVIpIMoILhmfyZwm/dZ6hC7H7YshC4jRgfbnO7mqVsklJe4pKLSZzEVVtODC7yvnTR8gyS9kPWegnBDbHWEUpGdCcLBgaj72TbV5YnuX06iSPmAsM55pMBn3OXV1kOZkG45hdaBBbhcsEatvy8//Xw/zOI12+/Y7XqDNiZATftvccmTO0zHgaaH1wDG/wGkJr7lnaoOlnROsVzvdaFCXAwlsfOs6r564jrcBJhZ+P2OxNMbuTI2LDfXddxx6QPH/mELs00fHYGfOmhs/jS+vUKvDAHdf59CfuJigysm2NPmaZVkO0Fjx+4zZM4COKnDNqGqs1QWRQAw2+IK4owpbDCYvA4cUO3cqwFQ05COPwhmCWC8y84qcuPsD7Kitc75YQwtGKIzZbVbx1h0NhPIVncopA04kUR0tDBuuw0Ve8/Se/F6slXj1DVVLIQuzQUqm2yK5XUYFFIHjl3Dx/5QPzqFySa0ulk5Ilkt5GiJda/LQglwLhwFU1vbc67CuK+lyXss7xKwVFZpEjUDEYJ8lqYIQlawWUSTBaYoQlLBuCWkauArKyxJZAjRykI/7Eg8+zdbbKJ0cnMULRsoru1gT+0FI3I5wKmTjcI5zI+OSpg/gopB57oUYywu+P5bMZDlfSSOmQqSCrC5x0aJfjugqv7dBZSnhXh5V9e1j3Jti7sTOWxGYOgUBshFSOFExNd9lsNIn3RvhbBX41wzhJLwtJazCrBlx2Fbq5z6fPH0dIKPcMf+X4F9DS8tH4Lq70D2Myy3CfR14JsGXBXKlHnnvjaaSyoOg79HSC7DlGKyGq0yL2PCbmd5mUOXZXk2cKPA9ZCLSRaKFgkOOlZuyKsQ6V5AglsWWNGBVj8WzPELYdBkeB47vf/wrf+OhFfvxfvpEX/IhyM8EqxcG7Nnhr5So3blbZ3q5hp+w4UtlphBBMlkNGoaEvJKIs8YyGwCPJwY7KqNtHBC8b/txfPAsza1z8/L10bk4gpzQl2eZLSZNXVifAWYz0uefEMjYKeOXJGRIVsHikTVIDuWhBCn7hzD1MLI64ass8VF/nodvPcHr7rag5D5sEmJrATOdcyGb4S//gGwjfOuAN0xe5+sUAfxCRK0tD7bIVRCS+xNvJEO2cYqfg13/5Nqb3xFxfbwKW1IMiGjeppiYHVKKC1UGdwBgqOuX0J+dJ+hI0iMBDRRYxTLB5DrFDFQVaK0RhyTKDqwRYJCKUWGGRaYy/G+NCCXggCrRTMExJVAWrQGQOOR44Qu5CUSpQnmRVxZS0wgqHKwxGZVx4ZYZ/+JvvphdXeejgXk5/9AI4jdGCpGQY7vMxTY/8YAPPgdiK0amPfiGmKGLYH2IbFdJKlVAEyCTDbfXwM7B5QvTcTd7751a5NNrDudX9mEOzMJgm1SnBh7tQtrgVC3GMyi22GiCCEFM4Dj28SbSvzpeCaXhWEmwUTDzfI71S4ok3HaVog78kKF9LkNZRv5iT7PfHv9NiHzuoEpc83E5KEUJjskynP8I5SzAsyAcZTAmsM2AEYZBxZG4eAJF8kKMzZS50ZgkmMoZJSHMkUTl4oeLa+v1Ibwjh90FXQCAQVpEVLQ7vnSIvDFdu7LC90+M/1474lofuwFiLrzWrOx1q24LCwb2HF9jtDpmZqCKlRAqB0n/YSHli50s8tfMEpexOtoXDBsVXpwgE7j++j2uXt6jpAGf/aAPoFre4xS1u8dXnViPla4SRMSTGkfFHfzEPtofkvYw/c8/nqagCrV+mm8OlfI7+pqLVHYEDay2DUUKtFlMVknLJYESMM3/YnLnj7n2ET1RpDwZcuXIbnYspaMm/eOKtuA1Dpb5LKMDzwOYeKjc0LjsqjYSz3Sk2upM458hjwcjBhpPEkeRjnTO8WR1gs30DN29QEdT6fQ6JDqeVQrkc1fXpf7jMr8iH8XOLiC1veuOJsUwxKSB1kFuiawXDO6vM75lg1Olw7+QKanEZkwlOnz9KsCFxqc/9f+1VLvz6Aj/71i9hHhV8x3d/I/moAASeM5RsTux8yquOyY/5TP25c3iFIcwLkjBFWAueAKWQQ0d0ycMbgJnNeWFzgttm2kgn6e6U0UNLMSW4/egOlyJJwx9ybnWBINO88GSdPL1CfOafcujYAsvn/w0z7+3iz2a0F6rkdYc+LyhqAUk+xU4eYY7ESJ0glzP0jZhakLKnvkMQGLQn2bu/xnuOfQYBfOz5u4h3y5iOYKHeIy/qDJIciiHn0xKysDgE6d0Sr+G4L7rMi61pWqUO8629bA2aiMhReA4TgetKup1pXHeEFxYMUh9ZFpR0xvumr/Hq6m04LI2JKRoTMWWZk+UBcsow2g0p13JqlZzVQrF7sc6B/Rd5/D/MIKslLqwvIo5YsIJ4EcJtg3SGtCeoRIbvn3mZKdvnXm+Sj+ljPFRdR8eSrpF830/9aaTtoKbLZAg++Atv5if+0rMszr2bbv4ibqBxyiJLOTu5RQtB4UvUTkyR1pnJWgx353jp4hHqh4dsbcxiRpKk4eGPcsxGwF989FmqtYxfv3wPGIPQEG+FfIO+SKhzfmdtL4VXQcYOgRuLGn1L2MvpaQ8bCoSWFCsJpubG7oyswOv5xLlFIhACnBXY1GNoDFcvzvGFvYZdWean148RLifsnCpTTg3GOlRumZ9b4wYz3Lawzp7ZDnv39fmtD9xPdW2EqXiQCsgkmpxhr0zZz3BpzPxkQrbtkxWSPAInJT4OfzIh6/rI2HHUrnPgSMzF9jzXbjYIgLyeYkqO9a0a3btALg2/vCZgIS1Q8ssOoVBADllhsMJhlCQXCpcHaFmghxlFO0RuJZw4sc39Ux2Gb0h4bCdjlESU6il3T1xn/co0w/UKUsCht68gtKPX2sPOSoAtjSNLUQISxqsc5LhYYDwBI4NaTVHzFj105Eoh2wVJ0yeeb/DwGy6wp97mdHWeyuUUe0WSbDcJGzHKSR6q3uQzvkeRKbTL+OK/O0j0J3PatoS3WJBHCpuMU8G6rRrSQiWJebTURgIv7XS5MSzwRznxtML4EnLDnN9mKR1yLXfkhURPDDkQ7ZJ5FrYsaqD4hm87hZgYUGpIdEkylRl2EwElhUkFxhp0YtEjg5OQVhQikDhnEb0Yb6SI92mED95OgZIOGwjeePcKzXLCiTfs8vjlgww8n8R4xH3Nv/zIEc6+OsewpdHKEnYlsgCnBLvtIfvnptjppcgcisDgygnShmS+YziylBsJ5ZlNRqli0PUxsaQV19D3JsRTmpP9NS4808BR8NpoHikF5SUwfR83DHBVwb7ZNrmRrG028aqGc9szHEi32UiaqFxjKxK1f0SxG2LmHb7KsK85ygdT1isNivs0ct1DZoKtxSlcdYBsVzAliTMC6zyefm4PejnHSI3vGRIMzjq0MPydn/w05Sjngx94J4M4oFRkdFSIExZXOKwroKKRMiCbDUgOCjzhEV12WN/HSYkQEhtIZGLxdhz6yoB4toxZquBd7SCCAGUkWklk7vD6BVY4pFQwytCBIvUUTjh6BcSLGiEFSc2ncmOE189ofTFC4Ajv9Jg5vMjG1S2cMeQReB2LjRwi16THF0gP9gi3Yqw1KCGwdY23Ywhih14e4oymqEbofkEYGZJOysoLOclUjDKG3ClcUuBnjnxLUBlmZLkB30cEPsoKjCcZRjWuX24w1Y/Ye7jOTtzCCAhzgyxGNFyAPm7pb1TJ2x7KSKxvKaJxw0F1Hbt7ArJ2hrwSo0NB72YBWY4Ig3E8dS8ZrwCWfLK0IEzrNL0qNzc6tDsRS41dlvMGUSVFF47ijMNGNRonSuS2xy9eepjOCYnoWZqvjSOpC9PEr/nEruDA/mn6gwTzn3gwBfD0lWVu7Hb4+juP4XLH1ECTJRl7T1aQ4svCfwHa+8oS+0TtdrbTbfqjaWx1lVhmf+za7z8ShB6T9TJRLlHiv/7nb3GLW3ztcks2+9+PW42UrxFsoTCJ5j/1e/8jj71Cm5wsV3hBjgX+6qvfyny5x24SMeh1qU6UsM4xGo3oDipsdhvcPr2Gk/dQCf8wUjmKfFq+weV2vP5z0JFoyXolo9YyOAy2pyjalkoCJxeusb47S00mjOKCaKVAGkl1mPNCLIgLH7YVfiigsUM3GdK+XMeZgEWzS7oj2HvbIiK6yWZWUL6aop1DyBG+V+B8zcE79qDmfZJLQ7SFognJHRHXVY89PZ/PvniCuSM9NjbrlG5YgsyiJywXRgvsfahNIA34UDoU03tlLLjTRUbQM3iBw+1kGGmYC48zyp+j0s3ZLUJK2x6BMdhUkNVSXOGRTTpkLeMfn3ozl7b3crS0husH6GGK6KecsC2mZ4bUiTn39GH8+7ssvzaJHfTZ3R0yN0yYnrB0nyxh79UEMicve8iGYbSkQUl+7jN38s53vcpwOkTInP5KwIQaoGJLUSpjsfSP3vyy6hB01SBvCr7rT/0Wn/+1Y2zKEGUzdrfv5WI1Rj60Bp+p8je+6wv4ynD6hTnqr5vn+7/r7Tw56OCulNFpQb5HElufp6/sZ9g3+EZitzXOSoZ3CnZDx3DoGPUihIRL7W0KZcEY0kyxMdcg73scnNxiJDTz9RFToourSZZO7PLgQxdpPVbw/M0juEhThIKKLLADg4t8+t2Q1EparQDbEfiF4FefPM5b91T55at309nnE65L9EJ5/PerCGf/HaVmihZDRptlGtNdwmiVHAFWIgpLVHbkaUZmxz6BZjvD7BpYE8isIJ8SiMyQa8lnl9/MmhwweSPmgXtusLNdZuJAj06o8AtJVM+o35nTeknhraUsXB+RzIZwpkTRTOjeFeEMFPtC8rZB+gqjwFUlVioyv8BLHHbOUBwrMDYiuxhw2faJQkOQj5iudFGJxEqH8yXSwrG5LtsXp1h+ZpGlB3ssX4zIz0pElGBLEiEU+XxCURZMVts8Mr3J4+52KBTV+S6drRqZleiBZd/bV2DGY7DTQG4rJqsxyVCzMN/hNdUg2xFM6BGbusZ8aYAfCy6d2o+VPk5bVOogcDjfIjYkCQHJSGBzicolRUXgnEJP5Igcqv0emRcwNz/kI+fvYmu3wcIDLbqmQqBT4o2I/fPbnFEVRCq4+fwM0cSI3k4Z6oqiLMYRsT1H4YPKx5HSxgeHI5lwSB2SWEXUG1DZHlH40LunBKHHbLlHoA1Hb9tie6tMNJcz6CqUDXDGgIDWzQAXKGzfxzc5m1sBriKpk9C3EY3GiLAo2Nyog3XcefImv9I6SqRy3LqHMGP5pKgJHp4/z06nTttplosFisggwoLb991kxibEWFwt4Zy4ndFUxOJ8m56q8pPvfJx9lTY/86tv5MMvnMQ2fCgsGQ5dUuMmnKcwJUce5ajcxyqwSuCNDF6vGH8/OuADv/FG3nr3VX7n3CFqB/uMjIfvLBtXK1x+9iAiMxSeRfct/lBgMWDGN7tpZhG5QQhQeYbXN/SlIEhyopFBookqOcpCz1YRoYcxBj/1eenZBXaemUQZQe48YmmQiWaiNKLTLRMPA6rthG9/0/MMC5+fev6dDFoRRdbjpe4+XugdAgM6FuR7HMJZwjhHaph5exszVGz3yiRJA50YVEfAPkUQ9xGtEjLPMK7Aq+X8wAefQij45Z99Pd2zDr0ZYXoWqzTDYUA5yimMYKbcY9ofIIcGEyqcMRQK7LDAIFG5Q8cOVXbkSqALi5JmnIokwOUCbydFpZDeMQGhJq0HSCuQ7QTXFuh4nNCDEBA6jJbjiWg5lhE75ciaClPxKEoebi2Guo/fKVAYXvriRShZ/CigGBSEuwm7D2lMRVHUBNl+H789QaI6MJD4RPi9sR9IpZLDxzucSZpwwxI2h+w9vs3KFyd57XenScuGeP864th+TOagl6OqUDtiqBrH+pZhNBKIwQCpFOWmxlropFvsmThMS3SQoULWcu7+sTVkSSCEYcOb4rVnFsefYWDQnRzVk/ibOfERjSEjOtOnum4wKYh6HRuGoAThlM/i4iQXb65jraBlE1a7HdqjlGvtAbVoSKATCqMQIifpDRgmmm2bcPG1r+fcSoiZLBCTwMVxKp5VHh987DFexxEe7M/S7ozY3OkSRJrJRpnw/0vgutHt0xnF9OIUrGVae2QlyTe8+XammmOxrBQCIcdrm/+RkpqgIm9jqxjQN0NyvnqNlM3+gH4+TkIbxl+9SZdb3OIWt7jFf54/KtS4xf+ShJ2AqBXgFV9pa3fO0bxnmfqhNq2tKiUhqArB3c3r9LdLHKpsQwK7rQHOQcdaCl/z+BcPMFv7Zg5NTaGUxBjLxmaXsxdWcZ0MgyXPUqKlXd7yvQOEVLBgKO8vkBVL0VdU/YI890BneMGI99bvJB1mNHb7mGXHB3/9LfzEjzwKueXPH3uZl2//Jb7xm6+jX9C45zzeeP9NSvs6DBqrLDerPHz3Ol46wo4cWSKoTg7xyz6T9QYTb96k8YhDvzOnfERjQk2hBL1GH6573Dg3R3a9RG410jlqwYhJ2efJzx3iJ1bv5sdv3sNaEKG8DGctykq+5/ZXub28yuDzAYnIWAx+iBd+4T70sCCIISigLAwqcZQuR4i5PmqqS+WURLUkn7l+O8ujKmlLozJHMEpYf/wE38wNBk/McjTd5i+++9OM4gr9rs/NtTalSomwMoUeQnM4hLM+//iOz5HeZ3AVi0hhrVXhcLPFt06/wKONSxyyx6nfnKbTLdMbCXKb8lie84XOMZ7qHeLB+mXu2XOd0fWQqXaBGVnaOqAdH4YelGYV89O7vKe5zjtqW4gSzDxwmLe8/0EueV2cELjc4lmDiT3WtuoEzmGUBq2JQoepW5YrHj/2774Th8GZDDcV88LN/cT4WOUR4WDkI61AZyByR8V0ma70qd69wz9afjtnJmdx2oEzfHz5DEk9wcwaqksZzEtk0+IpUJmB1PHM9UX+7rOPcjmbwviCN77hNH/9R/4Df/OH/h29uxN+auuXoCr4xmMvc+c913j34VdZP20IegYdW3wrKJIAuppao4UY5exxm5zULZyzyKzAdXJGecGmSPnAvd+D/0s++ya7hFHGwaUt5vZuUwBt64EUBHdtkVQMVGH+7X1m9QATaKJMotYN3o5E2oCR9jEWbCDJIomHN3ZZRJLadEbRK2HPRDTKKdG0z9Z2k8ks5qnGAeJSgUscnkmpR9tcvVLD5gqVCNxQ0cwHOCkpcFhV4NKE4HqEeLJB0IkoLcbo0CAGliIS5B6QGaTOeO/bLnLb4TWWllr4PrwYH+a14SznbZM/882PMXlynSMTG9x78gqH9t7ESWj1mhSNgLwe4bc0egikAp2OJ1CGsyWcFYgMNA7loN8MCEqgnOT2k9c5vTvHy6sLbLfKPFBa5tDENvdNrbCY9Dg8s4r0wEaC1bOzXPrMAoX0KJTEpQ45AGkdtrCkdcFon8doyXH49TeYqI3w+o6g53BljTfMUTIlqgwxRjJ4NcL2LK/8iyVWzk7R7pXAgs4scuQIg4xwB+RA4o0EzYd7HDzUQWWCZm2IERLfK/AbKcGmpl7tc3Mq4Hw8z0v9vXSPWPTEiOFMgKg7lo5tk+Y+u+06W9shwShlX3OD/VMt5udaTDe7jHbrlLOYf/gbb8SMImIEzdIu29bx9kfOUZrrQD3BeBDUA/RUn1FDYQKNDT2a6wWRL3ChJthKqaxbdAx64Ai8ISu1Er909k7igaCyZ4dOFjHIfQYadGGQGhoH+9Q8SVHRKC0RWYaXGkpSE22nlHoD8lAxKjycKsh6gqxQpLnPIPX4W7/zDWReQN4YTzvFuc+l0SwtUSZeCEgWKzjtoSsZrzt+lsXXreHPpOyZbnEkbHO41MLh6G0F9Adlzm8vkhUaYcE5QWEVoiIIRhZpHaqfU7UZ7VYV2xMoLJFfML0xoDbIQDpEBjbSlEPBKPHJjSKtR0zMpKA0KreIYc4/+Il38be+7xvZMFM0qkOUMRzfv4bMCpRSVHcVMrEwKghaluqVAu9mCeH5OA+EVMh+guxkCJtgnQUMMBacGilwWkKgsTWFixQuN4S9At8ZQqlQAwN5jrAF0hlEYsapVk4gWwk4g6sp8kgynA1IohCXOGSaoTKwZQ/nKeKqQqUWGVs8FaJqVdQAgtUMqRSUJDfySdKJgGIygBKUyzmuGlKUfGzPUT7VRb54lUJkoAT3/tk17v/RGwQH27zzXTcoGMtwXXuIu96DlT75Zbjwa89DoPCnSohGiF92jPBZtZNs7lYJlMQV4GWgd1LyJUu2z8P2BYxKhCMJGVjhyIXBpimUFXfff4zJyQrHDkyhGgWUBdcGu+xbbDI1P8dko0cQjaiVUiJPUCpXmFi1DK61efyna3hf9CivKA48G6AqHYwGJx3bF2PKVpGOcoJQk1nHU6evc22t9RV11btOHuWb7jnBnok6WZYjckskFJ1eRn+YAGOXiueprzjuye2LfGz1ec71XmFodmilq1+1GvCeAwvcsW+O0NNYe8uRcotb3OIW/z24NZHyNYCUkoWkxk5vSJOvNLV/ZOXnWHrgIkv3w9/56bfys3/js1zarPDEC7dDJLhyZZJZk9Dt9CisZSOwJLOaaFuyd2EWT3tIYDhMWVneJSsMacVgtEV4CoRDZxUQliSOiFOJTjyq4TYPHbvGUy/vQ9oMFxukLwgOZxSnInCK8HyGOmWIZno8Huzl1Wf2M9VsE7YcXgIHvnWbRCs+9LkBj777Ms88uYjujVAuxWnY2lF8/Vvu4Ay/yqPvvsi10izLy2V6+SJT010y4zNxdkR0I2ErXkSkBWKfQT0v2H2qSvvJ44yOjPjYqZM4LQjbBUrGWOtQMRyKOvzg3Cs81ztIEWRs73S5ubGXcGaZIwc3iFerFEIhc4FILPJyBWyOjFNKawpvr+ZydxonBMJJKnpErGb4sR/+TgbVjHvefJmT5YyPZtAfBmysd2lt90jqjoONDY4fWeHU8Chns7/Gw/d9jCc/eRwdF2Q1j4XZDrWK4YKSvJKXaK9Nsf9t1yk1DEkxoh+0+c1z92OBj937S3whmeHSaw2uLs/CbEKtIulEhtvDZc7197B6Yh5nJeAonKZcgCscyl9jtM9QOWcINwWxkBw83mJws4rpaOTJAm/S8cDBFk++cBirxXgFKNTcf2KZSDlEZNCJ4URtmadunsBlEulZ6tGIqSyliMucShdpdWqYwiHmDSSWM51LNDwPpOY2T1Ddt8rRo7vcPFMHAw/uXeZxcRDjaWx9nM/6rcefpWpjnnVLNKdjvvR8g8D8Jr0HprltYoPg4t38i9+5gCoc+PDO1x/hix9+Hjk0dKbHNzdbazWOvOEq3mQOJFjXZJAnZInildfWyJWlpGLKB2PWr0yzEAwYEFKRCaHNiEoD5g/N014XOA16KkHUClxo8FSK6tWxnsBNlSg2B3iFhcJ92Y8iMSVJjzIzWYfB8jTNOwaUqjmyJvj06TtIDlrcvQ5vok/4RI/6Oqy3J6GpGJUk/arH5nqDQmpMbvFXM/S6I29KZBCx1t3DK/U+RaHQXcvU8T7tF0s4LRk2Fa6kef3UNfrrE1w4v4AWjq1OwEPHLnLP5CpH3rXFD5/+JvSuZNYfsJ3VmNzX52ZcQQLSCBgKciHwENjAo5iCYlLCuqXY9LEhyLbBn0lYCre4bWKV6xvz5Kkm6OT46zlvvOsynjAM50pMuwwRgM0cGIGt+qQlhZIgUpDGYRGotMAuCUxNctfkMn/hgcdoHavyE7/4bVgpsL4kizyK16cUSwXRqZyXPn4c8TMxNne4CcvwmEfkLC5xgODSzWmCoaB8qUAYS/RQSqnmuONozOp6mfdMnGJSD3ksP4adtITVAluxHC1t0c1DOOzI3qzIN0u4luPczgJbu3WyqqbR2KHkZdxe3eTl84dRUcbdS9cYxBWOv3MdMXBMNHbYH8KyDQHHqY0JvIOCantE80gf057DKxKGVxVogROSQgfYkSEUkqwAjAArUEKh1g1//k3P4E8M+b/OPspcJBgWIYkL2FI+pZmMfKTwBophosZpP7kjKxu8RLLV7iE8TUEZbQ1OeYiRQArHqCkJG46//7vv4/qNBmJ/iixnqDlDVtHIGYmrafK6h4skJi/w8oza0ONEY4PLjSlm11OW9BDrBIXzcBKKHEKZo8sZhQJpCnTuYDbDL1lko2D/RofTKwewA0FRM2SLgkYtJbqR4DlLXwiSgxXiBjSzDsmugkmHF8WUPHBynD7ktCNNHXYUYjua4c0qvVzQODzCFSlCy/GDg8DHdDOc0rg8wMvycaqUVmgrkQjyYYytlhFYTCQRK13M8SmccIhuhugUFJ7ASYnXGcDAEomI3FlEYijfsPSPR9CTlAcZqSqQzQCEHidw7fXJax7GjEeo9fkEYTKKRQ9VqPG/S1njCoOlQAQSVIibMFgnYGAQo4LWnojCN/ijlOG1iFfPHiSdqyEnc/yhgcIQ3eiRNmoIGRKPAoQQtLdqfPG5GVQ5winGzphWipUgPB9bFxgHXWuYnJ3gib8t4BFJ+AZBUi8xP1Nna6dD0cnRmcBVPPJpRVoVuIrG1mokowFEHixVkOsFzsL588vskR7lCcORYJdUl3l47wHObm6RVz7NldUT+FKTZQJRCLI0QV4e8OzN04y2+qjFkPLjHXrlKvlEA+kVCOHhMsf37b+HU/0VpqaqzE1VxhMp9dJX1FXVMKAaBgDEnQR/mFFIxdVrmxzeP021HJLlhlGcUW2W/+C4108dI3cFa4PfxBGzp/KV5/3jUAkCJqoV1lyb4j8hyb3FLW7x/yLcl1//o/mf4TP8N+ZWI+VrhMkiGMc1iq98ArIx+Dy5VRRO0isUf+/D346jzYnbNvkTdz7O6Y15fu/X76e91R9Hdh5q4Qcpvadr7LtjiYrvE0Q+Siv27p0kLwyuVhAODHFbk2wcxrWO4/RTFEPHcMNH5Qblyhjnc2z/Ni9cniXZhWvPjfixg9/Bh2q/hit77F4UlN7tMftwwtkLe2GoaO00mQt6oBRXL82jteP++gpnX5zEvCzQVoKSFAqqnuWHXvhJfuARga8ctXpM02+ynaU0SyMqtYTbDp/kmeuXmX62QzbnIV+z6OUdisMT2MBjYn9M94UqufCJllvIAjxbUI5ijHNcHDZpTPdoUSUxhkS36F3xWDzRo9AhXRcgRg58i/UleBBcFgT9glqkMW/M0QclYpTzwNwltDKYYgqtE+bDIQf8AeGooN0XbK/uMowThrWEmUqHEzMbPD5xG//2ycs8/L4BRxevcXW0H9vwuTJsciLaYRiO/RBpItl2VVQhGZmct+69yBc3jrOvusNHbtzBx5+4n3xZEvk5vgjYMxnSq68Q1Hb49PkHMdbnW37vT+BPJtw8O8XRICa2Ocfndzj1Uo3yrkEbUBXDwv4c9SPXeXrtKPcdW+bVf3k712M19mGkhkJkHFiaoNkuqMU+01nMMK2wPJygdrhH6FJO6FWuMMfq+hwtv8+V/hzhZIrREl0uyGKF1pa0CJA9xaJ6kNfPPcFE6Q187so2N7dneftbzvDAxAr/8Km3YZTFRopuXiao5OhhwbTpohfg2SdL6C8s8lppL8OFCFF2eIlEGtjdNyC/UxA+mbG5M4GqOSgpcDlVlTAQmrAc0F83yMLy9KtX0A2PK7+3j7fNv8zujiHPBKYY30BJ4dgXtcnrgo4WhLoA7WHvSJG+Yf90l+WnI4z20DsglUYOC/yuI8hz3MgQ2YS459OjjBCwcXmCfuLTNWVEy+CVEg7O7+Aix/4/tcG9z/dZW6/xu8+cpMgE13qzyEGBTc1YiikFUmrkyLHv9ilaYcS3HDjLz7o5zMAnEBk6VwhhKXLB//033sydr9/hVLKfrAHeVgFGYT1Lz9e0kxJmEDK9E9NyE/Q3cuqVEWtegXEeonDjtYTIYsU470WtZ+SNL0s3C4kagA0lnozJ64KuF9LQQwgUk1mfK88vcs/rngUc3p4uP/OBd2JDhUNgA8aekZBxCs2Xl4GFlhjhwDOIEHZdicIprm1NkWuH/LJ/IpsSuEaCMRp/ZMkHEp0plDMUcUCSK0KvwBbjp9SXlmcZ+RE1UyAzh3nGUjmeEkQF57sTnCytU5eWxsQ5HqvcQ6db42j1Js/s7mdvqcX+0g4kEqvAFYYrV+eJw2DscoktMhOkmeHmsIkcZehqzIZt8N1vfZqb5+t4RvFUe5KdoIJBstMuM5I+Nveo9fqElYC6TLl+SaCswzhBEWpUZijEOG0nDQxBIZBaYnLF/j0tCulwkSA1gp6MMDnM1Ub86P/5IeJC8WM/8u0YoygigxpZlA9oj3aWY0sK1bLovkFHCfEoBOXwd6DIFbv4OJuhkhw57UBCXnj4XZ/AheTDHGcgPD6gNDtCjwBfUwpHvLozwdNn9nI+nsGPLVmhaPkl6l7MsGIxokCoggnZZVANsI3x+lVkYobDMkIYfBMjdxWj1KNuCwZRhTQQSN/HH+bYvmXhQA+LZHQpIOsoRKWCkgHGCZxzZBJEAs6MJxSvMz2O2xbj68L5kM0qXMmS3A/RyxBsOaSzCOmwcYbCYjoxNHxUO8WExbiJ4ilyDFo63Mhip8d+EUwBWUakJL2iwN92NDs53cMCGxeIkkDkFhV4OClhaLHBWFhdhAIMFBMByX5Q2yNk6KMvdclun0ANHLJTIHOHHBjyukanoAYpwct93IxE72SoXkIReJDGuH6BC0NEMkJmFnfuJjjNqz/huBDNMPWnDM139dj6nWkYllCjDCUTTJojCoM1BQiHC326m22qCw6xkZDvlEkKn4HOqEQ+7V6BTCTBpQS3MyC+q4mJBPlERKUkiGcreNLiZYbUWnqv7fLbp7e47a0z2ChFyzKjfsaP/9on+bp37+XuhRusXovoFhOoDILAR/UH7I76YBKyWojreQz3aVxpPEEajARCwXaS0MOw0u9zsih46OT+/2LtlQxjPO0xM1vl9qMLzExVASjysXPHFhb+YDtakheSI+WcKf8Kh/x7+PjvPM9d9+xj776ZP1YNePryGtdu7pAnBdb8v+Du5Ra3uMUt/ifgViPla4QqmhSF776ykVKVCQf8LhUV89nBfvRyxJ0Te1hsXAIEgbGMJjW7rRFOWN51z4tI63hmcIiNnR6ztSrlzoj6RIXZ2TpnL6wS7o4jeQ2GTt6lzSpyOB6FR0NmLalUJMLx5red4/rFKfo7HltZi+euXuCbHvnfeMd776fnPcXVnZf50X9SUEwJZu/cZtAO4GBOYR1LOicIHclkj2sXQ2haOOBRBB7RRht3p0BZwe2lCk/tHKJo1dlvHuTV9BUiPfbGXM9i1EQDNraQQw9RgHaGrOSwFc2b77/CC+dzzKka/qADjQAhLXN7NkkpeFttl2d/qMVnfzbkthN7GfxeTn9NcX53jiCGTCjCWs6gLkgOFVSGMbKdE1YjSn2DVtD8gW32r23wUm0/FW2In7BoI3mutcTDnS1G/SlEYRh1RiTGksaOgauwsdngXQ+9gmwavnvvKbJpwT965X2s5xPEzucLo0NYDEWWQZaS7XhEfoGzy7zOX2TvgWc4mm7y8ZfuxH8mZ3CsjjvkYcqCQyeX2dYBLeGjTllsYVjZqaF3q6hMsJaP+OF/87v8lXdKxDue5NzaHtZWZ6lGKeeen+PPnrzIN73jAge9Ie9qHCVNy+TTFqqWv3L8QV4etpjRHa7uTuJLgzcqkSY5sfK4sjXJ5NISN1s16l1LpV+iKAcctC12znsM7w2olQrSwkekDjV0SP8C9YlJIvl+blz8RTqtkNRoyjrBlDN06HCh5Ec//y3oWsK/evMc99U+Q1Zd43OjB0iP+7giwwYSPIG0lqlA0o4T8CRISWbqVKZG1GcGXL80RX8jwPWgtidnQwqKksd2PsS6lM5Omd984QEm84Q3tT2OLwzQQvCcrVAWGbFNiIyHVoZgr+H+k9dQ0qKvpRyd6vL4K3ciDUgESIfu5IiBoShJXK5wqSA3JWRkyZEk61XCmQSVCe6rLdOp+Lxn/1n6kcLXPlElRRUFw0GJ5WVDnsQYJxkcrmF2DNFIIAvLrFdmI0uYnI+RDXC5I9ceVoJMDbVVg809znx+gtHbJAsLLfKdAJErGAowgvagTPmsRmtYz5toCk7uu8pGVGe3M4W046QcLBivQPUFwjjsqkG2Mlw1wknAOYSU3NHc4D889SCPHB8xM79G+3yZ1qlJ5uUAiyCrBWyNqkhPYv2x9NR6DmccToAsDLIQWAvCBchNYAhre5v8w5feQ/tqA1mV4MAE47Sk7IUAVzJEWxbdycfNGDletfA9iatajJLQzBGphyoZkoMa2bMwG3Lxeo2qSLhn8iY/9fTX8SuPfpyZcMShuW1u7NY5s7NId1hmu1qmtjfBGQkFiFAwHNQQocBaQTEQyG3BY6Vj6MghOpqV1+YIPUHgDLMTAzpxyJ5qzum8gQCunG6QFFWkMfR9Ry4lyGDs5bAamX3ZtdEvEKFGSkla0zhjcc5hgLuDLt954W14nmNzUIGiIO9W+YGTj7PHG4F2zC9us3xjEemAzI1lwliMcMhBjr+bU0SSvClxfTduMHQ0xtOkRQiVIarq0M0c68Cs+9iBw2WWcGjxhiNu/5YVMjQz+S6nhnWiVsHmVoW/f/MtFMc9RMlBLhjkPlnukWc+zXrMcEezGPVY1nVsJjGxY7ox4GCwgR0KNkchuQQtcqrlmHSiBLE/dgqNJJ1siqcuHkCXYLU1j7ebo6wl1wXYHJUUyMRBAMeqa+BZKl6N9TtrbL0WIZKMZF4xuG1iLPoNLUVdoFsFgQabCvAC0A5hU8QgH69ICol0EizYGsjdbLzyVjiEcNg8J40FWWEINvoUWiO0oHyzjD+QFK2Y+prChhp8SbhrQVlMqFCxQzqHMQ6vBS5tUzk7AiVplUBGFfxM4KQgnpUkhyJqF3MYSFQvJnSaZFKhNkD1M/yRRfiSfPyDCilAjSzYlEBKTAZx37Bwt2DmtXXWz+xHTDUx9JiurNNbk1jhkbf7uNRRzlvU9mtmvCH3zV1hNZnj3JX7EcLQasUUdQ0xBF3BqJvjIo3ezbA9w1RlHa0kw9o0jCRGSZyQXLzYZf7tMBhYXrq0gi0sH/30A3gTu9RqHlN3FhTWkDkPWW6StQw4DytqiMAgc0vpckFWduRlDQj+5r/6GO++7zYKZ6hEITevb7N8Y5s77ztApRL9kdorM4agpJk5MM38XGM80aogiHwK6/CCPyyzn9+9yqfWTvP22ZxAal54apNP/dxnuf3kEh/859/zx6oBp5sVGvUSST+jMLcmUm5xi1vc4r8HtxopXyPEWGJpcf8/iuQFP+GZfoWuCVBlgcss272U+7e+jnL1jXz0pd+mfLjDuXidQEqGJiQ3koWZNurLBp0iG+92A2gHoshh1lIaBpQWE1q1m7gaFLGCukTFOcpZjIDZoCBShpENGGwV5OkOJ+4Y0myWmRDvJNm4g4P6Nxkt3eA7Tj7DIA+4MaqQ9UpsFhGHwyHXdyL6oaIxN+ID3/gM9fmCn/zSffR2YasTcnT+p2jIPv++8xgXV9cp1wJeP3eBwioup3UefMudPLbcR17fRRqBOlDw8Z//q7zr9/8ZX0puZ/hGy9v0ZeYObPH7n7oL0XMM2+OR5E/uzPKhF+9gYrVNoxzQH5TQRZ/R+SZ0LF5osVOOZr2HtyTZs7pN7vt4ssN3/28f5hfTh7m2U8ckjodvv44KBJ+eryA7HkVH8uHn7iKIGiBaLOydIBmmiFFOqZQS2JwT0RrVkym/uPoI+9U26XaJyWnNxf4s3zh9mn+5/iYCIYljy1uD15hpJtREn+Py/+Af/51f4/ve90kun5snacfouIn1JPfsO0PWldhWzkO1Dp8YDtjqV1DTElORRKUU85rm7PZNHptq8Pb91+g3IzZuTlMY6KaS3/rCXfzU8S9wNZkhcgpTT2iUBH/mTc9wr//tvPT082y3NFutEn5JMRHOsJl3xysfGyWeOHAASo6l+TYLU0NsVpD7BaYXoNz4evMksA65B9/2jl9CKcELz3yBGy/cAc5Qr474O+ffBiWHLAxYhz894h3zTX539yIvXTpCeWaIOyzRw4I8kMhdiVUOXzqak03qlwL2mnn04ogrvR6JFrh9ipubUxQmwgo7lvjOaOKG5cDhp/i6w6/yb//VeylWPdb2hLzUnOKpm3Mcre5QKccMRIgdjUhqDVbjJuXakOOlm/jCkFUVO4XHG+7a4tnnJsceFiMQOByQ1SQkgrr1/z/s/Xe4bNlZ3ov+xhgzVa5aOe2cQ+/dQZ2lboVWTgghCUS0wQcHDtfigtPBBONEMCYcjG2ZIGOMhCRQFpJaaqnVQd278855r5xqrcozjzHuH9UW5vKc4we4vvaR9+951j9r1pxVa86qWt98x/e9L7IDsZeSexI5Z0i2PObGNpmb3ODW2Zi9OqKeD/i1pVexuRBgCg7ZIMdcTWGg0YGDmS7Rn80InkkQUU5ntY2dcflify/bgUOQw8X+DEplBIMcb1efdmsC0Um4/9B5zpzbgdnyhp42W4KwW2Rnucvhd79A5Rqc/uoJhBFc227i1SLUukHmlqxgER4omeJEijgMqYUOcUGi6xqEQGJZCzw+3z6CsJI7JgUimOfDvTtJY4+eKVFXOZdMFanBCouUGp0pbCBwBmZ48+70QdcQQiCEhRRk33IiX+I7b32S5Z0j/OaX3oTpWdK6QvQ05QVD676A3iR4zQSVS4zIMVpTwCIcByUtE0GbQbdAVCkipyEtWopll4ENaOZ1Lm7sBF/z+2duZXGlQbvk4o3GNLcq2MylmUriWZdGYcBWWMdogcoztOsijCbe8sibFnYaVJLjNi1Zz6Mx1uP5l6aZqcSMeDFNR9GPAgSGpirh9CxO0eAcs6xeXEcGhlqjRXdjFOsIjDZQ9pGuYLLg0grbGONircEAVzOHgS2gkASuxr1sGEymDJKAJ6NpRkSCP5bCdYOxFusKjBTI0GD7CTIEhEFUBVniIrH4QYaayshCn3TFJx+ReL4kFwJSgepJZGIwqUb0E4wVxE+UaEx3+O3eXRydXsav5GzkPlOzErFTIcuwfb1O/3qArYEsO6SZx0zWoTo2YJ9rqKke5xenmDzS5aFDZ5i/Osn64AiyYPDHu7TnFe1lgYsejtcIS6OacmBnEysFjbGQXlzAZBqRa7Sw/P1/9GWm5gb89m/cySsmQoSwnE4NcVpC1zxETyO7OSoSBNctzhLIrqJQg1S7BKpPJGuQG5zkZa+SY2VkN0MnOQRD43Bv1JJsKAhzZGiRjsL2M0Sssc7QD4bAJwjlUIwsFCDLQToQKIwL1oJwJLI3ND51tEQta/SIGHa+ZYbylZTkyDAeGylJpwpII8lqChkXSHaPIqRGH6yTbKfUrkbYLCfTlmzEI68VKKyFiNRihMQRgJRkfcWljQryegqb20SFIu/9uy9xy9s26N1Q/Ob334W7EaHTnKhWRp1NYTRgZ6NNe75EwSpU2UO4EqefY+RQRN7xWJu8tw1OgAhcxhstav4EqR3lzGoPk1vEICPbEsRbgki4rOYRFF32yOtM7+tx48Y43bhEmmi8OIfMwSHDpBlRmiBLBcprBm8roVZI2JzzAcv8So/P957nF//B+9k3O8rP/PgfsDDf5Ht/6EFe+6aTf672mpqoc/j4LJXxCmfPLeOeVEyMV9lY79Lvx1SqAZXKUIC5b/wAcZpxV+FzNNMWZ7aWSfu7aW32/so14J7ZUZIoZbs5wPyv0E9/k5vc5P+Sm6k9///jppDyLcKS26NZiujkf9YFvqHavKXWpiBy/lDdTneri5tInEqR2Yn9vHr0n3Ehm8KRz+A6f4Nz7QkS7VKONNVGmZ1zY+RJPmzVB6SS+AWNZw1FFTCRj6LXShi5gBQWjcCRwxGSpvs6fPdOys4XaCYexAbPl1QaVfq9mEq1QK0U8IqdBxBKIc3TOJElnCiyuDHCci/nqUd30M8lLpad921htaHmZBDBPXmT6dFbAcnIaJncWowxiNEiRTcnECFXTYG33n2Mr//O0+h6DS0Fb/9XDxNpTZIG6K5DKuH213VJ98ccihZY/lIVEQp+/cP383jnJH4ro5RYemFMZRAyiCVT7W3itSKiAXrUMtJOeWD8CvJcypO9fWRGsL1VxDiSrOeib+To4wLhCmRfYfChJ3GylLlb59Ce4pa7D+BZST5uWK9WaBRrHLArXGqP88yVvVwsTSJKFX7yux7iA2cjPrpwJ1kGbwxctPWxy3u5/TafcvGVNBoR1lr+wx8/hGoo2CFx+wYr4Mfe9hQvPPMTOFtfI3FcMtfFtRLdh3zMcGDHKhsveDDwIRqja13237vJ6XgXWnsUNgzrX6ny7fb9kCtEz/DPv+tziHrCue4MW/0uYmDYWK5i+pA7LmVhyRMHKxXepiA1Aqkt/TGPhbgOK4pk3GdPucVL25N4hWEixozTIzEOly8dY+eudT79B8exqUI4hl/60L24b8tQLjgVkEWB0Q4jz+7hqXu2EIll4esTHH/XAmHf48LX9lIK4OF/9L/xj//tZ7g6v8kULt7A55roYcuKrJEzOKSIawpZzDGZxCqNcCTZtKRY3mLnCLjlBMIiYaLYW9pkZ7nF8605DrmrPFhq8pN6ku6gQi/3CQeCsak+jtS0dJmtsMRovceJtx7mG59YZEQMY4dtbrE2RySCNHSozBSItgzmRI5oWERHsapG+djKXdwy2GAp2ODB2fOc2LvERy+fACcnbro4S0XcTCMCyVx1nQs7ZrEGGmckS4M+hA3Ork9hI4XZ1phVh1oeQmKxHTAFF+sKRDfAdAtYpcgdS8sr0xQlyv6AE7UNPl0/geenZKnHVjjKZL3HltYYBOEOi/UETGrieIC7w7DX2aA0FvG4OAFGksQx+dGcFj46T/noYxV2+9NEysdWFE/2dlL1Q1ppCVdrutUBQZbjbgT45T6h9kF5jJ7cYuvFKiCQicRkBpGmhL6DUJZuFFCKuuwaDzmzPUOlnnBi5zKPR/sQcuihIByJsiGJU8C6BlmwOO0cO1D4uYYJmD2wSTMusbI2DiKHgqVQjWhMbfFrZ+9GbjkUDvYouCk9U8QvJsSJZMQZsFUKEcsWU88Yzfps5TWkNNATCC05PrFG8/IIk82EBTNOPy7wyLlj3HZoibeXVnkuquHJHAdDf6SAzUB7grAjER1DFPvIvkAYgVEC4QmM52CsRW9GjHhVXvfexzm5f5Vf/927sSYicHKEjJBWc2KsR/HQPI5KiKxg0RRZqY5gHImRBiNAWEmWG5RWiKLEKoWJQFrw+hovzSjs7ZMOfLorZby2YXS6Sx+fYiGltd0gG+SoNEelFqHbPHd9FrWxg9pkj1GVMFbZJJ2M8XYo0rGAHY02C5URnlg8SL9fQBc0KjfUgjYrgzFy5TBZ6aLLEtsLiD1FUEsZtCWuCyKVTDdaBJMZ69dnyeseWdGhvVVgq1PGSEnYLKAygzYaOQDpW/YebiEk7Nvbot5IAHj+g4dob5WwvkTohGA5x4s9bMmHnqFMzHa1jFqNUVsKvByRGxzpkrmCZKeDzH2crh2qH1aRxi5WDM+vUgIbeFidYaIcRw3TqGyUgyvRvoPwFBqDkxtybQmnFaaocMOX07KsgNxiRgV+KImnHJzlDNVPIY1JfYl7e0x5/4DeSyMYDLmfkx1qgLaYisLOjpDNr+KmGpVb7FaCbeSkvsA1AoRCAnoUVu6bRIQ+jevb+ItdstEEdmbcSMeojIdU9s7QubFOwUh00WNwLSN6wfJjziux9TrV601KxiJafWzJpX+0hgpreJ9ZYMfBGTY3+9T2D3j1X7/C2T/Z4OL5AOGWGZuo0e7EWASb8w3Ku1zSfkYlz2hMxOSZwCvFDKyDV7BI6SGFiy1B7kSYgoMy0Bgp4ocZM3VFuVpgvhmChsV2RqvfZ2vT5cwLN8heTqn6LxhjSXNN4Dk4vsvU3BilcsBYrUC5PPSpm5isUCr7FAv+N/dLuxkvPbbI8dt9jjQ6fOqcxS86TM02MMYg5V8+/6EXJYRxRqXkEXjqv73DTW5yk5vc5K/MTSHlWwBjDAvjTSIbc37rR5gurjE3/Q2EEGzaSaQeEJkiTj1h6sAG/fWIhZUtDhya5uzpOdbOj+JXt8jznI1eDYulty7ZSBbYb8cJw4xy/eUnUxIZgA4sRhgCz6J1juykqL5GlzxwHXYXJnj1ju+iTUI7fxqRZQR+ibtfe5xdR6axLyszmbA0BxHxSoMZ+RM0TMCj5x7Fu2GpzcacnNtgoVXH6yneOnGZciXh2VaRjazKWyYv0Nj7eaT8Z3ieQ6XgIi30/JymLhKkAblNafcifumDf40f/Xv/gdItHXLXZaO/xL2NKzyzMYvBZ6LxGpbVpzgw22SJUdI0J++4NB5dIzCSk+++lYnJGixZ3NzSXi/ia0NqPHLjsLQ9xhs6ls9+PXrZpFLx0qldJPUAW4H0ss/jP7aLwr4quiUpNjpM7d2koHMGSU55tESlXkRbi7SKc3aSpX6ZC5/bxZN6F15X0S04XPrd/zdR82fxr2bYwEFpWCv1GB2Jue3gDSZq+8nii/juHqwRyBxyRw1n75VCGMPC2h9jnQ7jOxzGJi5RrEh6A8XhqXm2/QDXyZmudui1pyjZOZLwLbx4RmKqmk6vRHXXBiuVCruaq3RXA177zoscOrxBbiSPPT/KilqltdohvRZQK+UcPLzMysoObGQwnsCxgqqKEVrgSEOnXcBbFdTdkH2NTZ7e2EE84WCM5Cdf92FW2hUe/uiPs+PgFLv3bXLpxafJhYOULr4ToaVDmAWQeBSkYasTMpJvExRzNpZHOf/PRsh8eM09Y7z31pOcfXGeziAiUYbpQ2M8+8JF7PF5xmZdtv7EYeKlkDe9ZoF/v3+EhUvjZDHIIMYZ8TBFj1vmOtz+A5d55I9uwR1oLt2YZvTQgKwjMRMv3yOZjKwkGfRLOOlw2dgiedORBc59eRqd+ayvxKjdkF0zMIDmScHoVY2witSaYXqHZ4hzB60F0kqyWKHXFc/lU9y+5wpx3+FKt4w+mmLqFnHKwZaAkk9h3GFVV/A6HtmUwL3cJcw1NFOuL0xSrhvkuqSkU1QpQ7c8bFgkm/LQQZHzG5o7D1zmuXgPnqPZf/c1jk00mVYxp1amuLWxwI1Zl6STMzrWQrkW/31rHBQtPp8fQ0oNTYWcqPKGgy/xYnMvodJoKREIZFugU4UbaLJZzdqLPdr9OUhCqnXLtXAEN6tjLSQTFm9PRn07o9Mv4oicsWc3cIOcrVvnsKMp7oqP0IbcE2Se5LI7xs+8+HbiCwUmBx16rgJjiPseT4Wz5EUoX8sRUtCbhZ1H2nQv1cltjqxCtqAIQ5fuZhF7p8afTEmv1smki9vVWCGYKHU4MrrBE0GN0K1iXE2+IXnjyTPMldp8+toxtpIS126Mg4LgfEZt34CWqIGCaHw4krTw2d1YT9POXIwv6IQFgqKhmY9zfbHEfLGKCgwD4yIH4M0NwLWITGOMHHr05EMzWqHt8DwELlIbIp0h44Q3vfYy1WLC93/fS5xNxpjwIqyN8LY1a2GVHx17DFcI9vghbe3yT9fqeAWNG4NKQaSCXgN8bTGexPEyhISw72Myi8YluxpARaD6Fn8gcHuW6f1tdN8haWakyXDqyPgO0neJxgN8mSC1ork6xiGnSWt1jM1rRV5fv4QuKJ6/vIdEF4eRy6HB8zWtmseoDNFlhy+39zOn2qxu1rheHCEdKPT8AL/soXsCuU8iLipkBirMELmgMd7Gr2bgCrxyQr+nkHGOTjNyJfn5D7yOykjCmaVZRm/PCTAsRCMYCSiBIxRK55g4Q1R8cCSDVg+TF3C6KWIAbhijwwFiagynC8UXeygNOgrRh8awiYYIbMlBlxzcrsTGmqzgkO0oEm72KS/GCHJ0KLEjDlaBVAF5liE0CCXRLjiexB8PYKuL3WGQezNM00E2LVFikMJFGQfTUIQTkPmK2p4W224DOVvECQXW0wgth94vu3zM+jCNRpjheRdKoq1FYbBCkEz6DPw6DCy1gcBNcpyViC/+2l7u/alNOs+N0ulnhLuqpLpHrZkio4jECPBdTE1hZES6lYGV5BUFrsKJDNb3WXl+BX9nke4CLF2ZIioXaHkaGUn6vZy5A5MsrrRwByl7d42zduMGXphRLgiaH23RiRT5ByoYI3EDUOEwElgWxikvaHIbcfTdezl8aB9hZ8C7btvNP/zo1wCJFfC//+If8Q/fdC9JP8HzXI7ftuubNdfjF2+w1OzwuhP7ka5D7ihUyefgoRmKhaEhSjhI6XVDxsbKKGcokHz22os8Ki9yMF7n6laJgQjJgeX1NsvLLXbsGP1L14GnLiwyv9QkCBz8wPtv73CTm9zkJjf5K3NTSPkWwFrD9x39BCWVkoYOrl3k6saPs3/yX/HK6X/FZ65/N9du1Pm2d57jPfsuspRV+MgfHOaN/m1ceXoXXiPmxe4sv/r8xyn5OdbkJMJnwpskDFOiKCVNc5Qj6fYGhGsBqWsoDxJWBuu8Ys8a8tReJJbMt0jrMS5GcB1JnliWtmrYZhdVK3D01p0cP7Eb7+W54XCQ0moNiDoR/dYoQcXDfyHDXdHUVMTI4So9r0c/rKGMIMsEW4My224RM5bRRJFkOb7r8MAbT5BWBY+J09wWzLOrscVmsIdPPXuWvffC9/7q1xkXPV5VbZLn72B/aQ/V3X1Gcp/O+vdQ9D/O2cE0SeaRZALP87GHZshHJRslQbcXgyxiizm4YugFMFpArOU4VY+33PbTjL3vJT70K5/BaRTY2p4lD7pUdQwbAs/J2SiluCNlRo902Htrk8VvTDNbLFBG4bsOJtPIFZetDLqdAqvtBC+EQssi68PrnSXz/ODoBr8T34obGLaImAlCyvUeJZ7AtB9nceELuOMhycAl8Q3OjEsmDbuPraHjJjNTYxTce7D+K3nz5CWuTX6Et77lOYyFR7v7aU8exiwLSCQF836ub36N7fIWVjmE5SJz1Q7pUoCpFXlxMMPudof5/gjhagM7V8DmmqhTouZ0GXUHbKk2iQEjBKZgmap2GYQuXppyaXMfE8UBE3FOuaApdnP6Ez5SWXIBxyaanJdbJGGLI9/zh3x0/tWUFhVecUC1HYEvWTOSvCDQShIPBlx+ocZ2fwLldBC9LSZrI9zhjQGWYsGnMFUgFh0IBE6wyDsL5/nsRw6TTs/woccfYrT0cXpRAEKwtjbAJcYXgtWNMi+MjXJpaQY5kMjExS8a2raELBnqIuVy6BOXJZ2DLlnXQZgC1joEpORCsrE4SrB3DG9KYK1GXpPgSe44uMzmeoMoLg5NCgeapAiyZTCOGo62JIJC2zCo+3zo+fuoLWeMTW9QjDV6WtHJLNFeHxMaKFWwfYHyLOSSdNQludtgRAe9LYk8SzkDsah44G3zPPLpgzBuSUbAuC79lUm+vj5Jud5jeqbFrJOT6oxV4/DV/3Qvb3jv05AAmaW9XcMDOmWXL/hTFEspO6srtB6fxipDnPg0SyWktLzsgIqpu4yPbOL4FlGD2KlR2mkZJD5hCzZaAUFV0kt97D0hsydavGHiCl/5xGH67Spmt4sqWOq1ActXikib4sbDDg+pHdS8JZ6T0FXEukiUQ+5biBycTMKoIqwq1HlIx+FyOEch0YgufN/9z/DU8iHsWEhvo4hXzLnxtXG2TQXpCEqllO2wxLwY5Q6u8oN3f5mF1hifOHcXMjX8wPQ3CGROa7LAVl6mF5YQEmSqMa7CmqHXi7s/R3qGHdkW/bNVwkGBbI/EuArRkvTqgp//hZPs/uE+5X0pVZlQuiCYePsqwVzEcrtMuztC3CwgwgAyg0wSPG1QuSS1ijTL8XLLP/3NV3HoUJ/3vPlJrkY72JVsYQU89uJe9u7o0G4XuHeyR00GuCIjWJGINEf1hqK3xSI9idUKr5Awe8sW/kjGqW8cxa5ZZJBhL5fIxhXSGIQjGAkSHBVRHdVMvm/A8nNjhOdHScoGUTBYaYmVS1BoUx3ZJipZBoHixvUp/u0f7GT60Bq9qzVk7GAZGrW6mcW7LaWduzgFQbzpkw8k+UjKsZ1LLFPBmjFSMTTeXRwEmK6HsRoVZshOTqtZ5ItfOERWq9BJS4hCjJOAMA5J3eXsYIZg3VI70uczZw7ywMwSf+v9z/KbOJw6uxNdcRHGYvPhNbWpwXpVZC9DymHrgjAWmVtsrw+uS7ASousSKRxUM8IUHbQ3dK+1WHJfIWyO9cCWfIwSZCsxrjbIPKOvoZxXMMIMvW8STfm6Id4tyYqQaIMpSfz5FDlpcVYMhQyk7hJSx6YWnYG3LhB1jYzBlCQYi04s0jPIyOItRwjhQNWFWhErJSLrocMMUc8wiynuOwJq3+4y2Nb0Ix+bDT/TIpOYK5pTv15GiyJZO8ZO17ETderTBSrHNAtfPEPw7CZUMhz8YapdwcUbSCqPt1CpRWkJgcQkPcQrJakrUIGLq4YJRTbRTO+ps9HcoqBj9owYovUm5bGMu3Zdpfwv+vzqS7M0O2WEgMZISBAJTCqwwqI0CO0wnUgG/hM8ubxOUtvHyX7AC1UNUmAGlp/92OO4UwGvaozgOn9aLhszHHkz1mKzjJlGiUPHdyCUwBiLlIJnHj3HEw+f4dvefz93v+44APurk1TSgMUbtxA7Z7lxcYo0TLlxY5tPfuwZfuQDb/xL14EzoxXa3ZB0kBFm2V/6ODe5yU2+BTB2+PM/mv8ZXsN/Z/7yfYQ3+Z+Glf4aI16I72Ss5xWwgs3oGgBl/xAL18a4+ugo03KAL3JmnD7lUZdK0SefcbGOy0gc8/mvn+ZAeYPXTV6gUsqxMqAxVmFqtkFQcLHWsji/BesS03WQ1iVvO1y5NIrrhYiewd0YrjQXAxdHqeGsfpghBxndlQ4Xzy+jcw1AHn2CWv4e5upnGHQjcm0YGa8zGgdU4pAdYp2xYoVer4hRit97/A5+68UjfHphL/tH11nPgmFrb7sPwKGTu3nbd74SNyhSdBOsBHWkyzPtVZbaX2fMG3C02BlOD1vLLXKDY06XH7vr3xOMKNKCg9+LyBKFzDTT/jaHD1ylojY4c2WVx85dx7l/lmhHGeM4w8Ix17gDS0GEXN38MfpZwokHj3HygQMcvvsxagda1O2AZC0g2QjQWPBzyl5MGPqE2x46NbgIovaAXqdLqdCE2CJ7MNpIkbHB6Sb8ndvvQAjBwPws79j9d/ClAauoVAcsrdaZXyiSpZLMGsqVgH3jazgxOAMoJDnH7rnMP3vwc0zN/TBLa2c4/cI81y6u41pJvQyfbd3CE4P9HChvkPqrWLlNmmqmZhrUyiUmJ/rkVcGtcyvsGt2inMZ4HcvmcyP8ykdez2d/705On9pFtxsz2Fsk3e8wfaRL5hvSUpmyyvCsASMYiQbMNbrsn9mmMNWFsqIc5IieQ7CSMlLoUi30OdOboRe6DLYKPN15no9sHEB7BfBcnAmL6ElEx5KkEh0PPTFHpoq0+5NIC9YFD8Wrxnaw2uzhlwJGxyps6gFdlXFucY1bazUu2RFaG3W0p4gm4F8/+lZ00UWXXExkKGzkTCYRK50Cn72xj+5XKsjcIh1DoR7hejkEkujGu3A9S2VHiNsWeBsSt614sDjPfcUlijIkbjlsddo0L/fIm4q0oijPRvz4fU9yz6ElCnGCijVhqDHSxV1M8M9ryMD4AuknuDLCKMW+48vseMcG3/N9z9BdD3BchbPu48aS6ddtUz2SDhMjFPDGPnIm5LUPXOTQoQXy6tB8VLUlU6Mxx9++ijw8HDPSvsUai8wFg7jC/I1JLlyYpj0oEeaSrK5IlgJ6azWSQYC0giyUxFfqMJBEkcfx0jrhDw2wNcuaKNIeU2yNOuQ+aB9sQyAkbFxvsLHQ4BVHrnPy+Dq37V9FFyxrgzqXt8dY65dpFyu8YfoSVTfi7d/xPMen13EbRbbWd3B7voVqB2jPwwiJ0hYFqHkHecaHlqZXVPRcgbI5OpAYB1RBgwd5YEkbkAsPHAejJQfGuhgpKCtBtitkrNqldFuE2JViAsNAFPD8DJMLOh34vcdfz5ULuykbTaFo2RyUsVZw9sYcvdAFJUAKbOCyaMbJGCYQ2Z7LoZFN3vP+U7znnz6BjBNMcXidR8Y7TN67xY4f7VFrROwPNiiZBJs7bFaKbDg1tvI6dgJ0y8UKifElbiZRuYMTG1QGJtXYEC6dm+STXznOi5dmOOBtUfdDxoIe/bUJBtrlX/70O3jhxZ0ALKyO4YXgphY3BaPAeBbtW0RmyCKH5sUaS89MIJRAuxbbF5gVge1a+mOabDZh18waM0mIn2aUR2Iah/qokouWEtP2sMIgw5ztKGDNlHCqOZv+ONZTOH7G0Z0d9oyuI7Kc3BOgBFFV41XAK0ny5rCDYbVXZrMV8I65TX7kznn2yi4isNhJQ7aqoZdDHA1Td1wHN7XUwxxv28HNwfiKzHnZyNhzeOMPnecdH3gOvagYzCs2myW0kaysjyCEBE8NE7GsQXsGCsNuJ7UWk467mLohc8GUXLTnoX2B9h3yQEFBIQyIXKAigwhznDDH6aVIaRCDYSSxLbm0XjVCf8YHo3GVQyYMOApyixaAGZoYyxRSKckPVuieHEU8Iii5MTMntikfSfG7IawPyIqWGIe8p9hs13G74LQs2gMvGVB6vklhOaO0nOI1E9yNEDHQyMoo9tAcNKrggx5TbOoaqt1GXWxT6CRIIVFSQclgmhmmaYexxUbhrabkJ+fp3rOAKnrIconscA1d87FxBL0QEaXItRZquY2JYjSQ1j2sW+TpTxzl7Jf2wnUFGxHhchurLFYKwtxjfmNA1psiGG+xsejxxUvHOF4Lh6KtluTFjLG7CwipMLkGC9Jk+GHK0zbnwgNzfFZE7Ng3ygM7x3HCoSuc8EAXHfJdAaVK8M2a6/7De3jnXUeZqlc4//wNrp5f4aXT8/zG7z3Ko6cuA/DIHz/HuVMLPPPY+W/ud7QxzZuj43g33sjjH30nvVZt+D2LIPkrih+Hd07y0CsOUGkUKFeD//YON7nJTW5yk78yNztSvgWoBWN8Ye0oNS+ilHV5NqpSd3d+c/uVh9/FyqmX+OBjc/R+fIbthQO8addx0jjjo//y7/DXfvg3iPoB9+88xzt2XGJOxtR3GbpZguc5eN7wbWKtpRcnOANwHIlvMqpxh73NTU7HUyShIG1ZYs8QBxlIELlF9kKsFFgkWZKg9VBI6XV+ntHqBt/xjkd4/vL3UZ+oUG0U2Xd0msHIdY7du0xro4Rtn0CQ0Lza4MrTdbJdAbMjPYr3F5kqvJeJevlP/9bNVZTp8++X7qeoUgqFMY7WxjhQ3o/gaV6Iytxb6LC1HeBpy6vGZggKHtOzgmXHpwjIPMPTOcW7Y4KJJt6FPsvfGOFG3KM6UmXb6aPSASqKKCQGXIc0DIjyZ1noHMI4cyRJQsGRSCloUaTg5+CAEh5BXeAImAzaLA2mWOhtoXIIXJdcWDZ0DTPukhrBr7/x+/nwIy8R2w6jbgGtDbn20fYYr5z4bfpegFdL2XQlT11osPO4wmT/ENI+bjXDOi4gEKHmeybOUKoPuDY/xZZZYbQ0gU4zkijlwtoJrlc7NMod0opD2V+jxwxLy39Imv8UrWYfccCSGouowspylYqv2U6hrHNms0U2n2ugiyWsFsRzhtuOXiO54HC+X6fbs1jp4RU1ce5x/dEpxl/TIq7VOHFiiecfOUoyIfAyyYhqE1ICaXmuvZPFsILrOWxpF7YFcQHKnqC6MyRqZ6As2ZaHyQJMIHnw1gP80akNhLZY5fG33/UQa2ttMiVxsDQ3u4TX2vhxwqbT4/iRN/DHvxxRilIGRzL8tqa3R7FxbQTTUBRlFdEIEQsOBw8vsXvvFu67Mk795j6q1qdUjlnPahS8jNX+u+kHfWrONuUrLfKJ4QhHZ8tnamLAU5dn8MYjGAnJlisUKxn+jgKv3O1SKKR0tgSyFWKrDlnDRVqBVC6p1aAdEArbiNBRiVxasnsyfmruDH2T80RxlYvOHoRMcXdqGq+MGCwX2YyGJpPlcpt3nrjB+0eucaEa8Nee+j6sH5Ni+dr53dx253X+ZPk49+y6Tnk65dEvH4aSjzCCKPZYXBvhH9dbhBjuOnya1xY3uRKk9LWDygW9QYCij1RFrANXt8eHIw+v6+CvanJfDseeAoO1QyHhSLjB6as10jhgvlDk2GxCZ7uI14nIBx7dVmUoAvUBY/ithVeSGJd9u1YZPbbN39vzVW6pbOOElk+dvh3hy2FEsbUUTg7wxjTpwy5i00EHgtQF6pKREz2KsxGrmzWShQAnF2TDuyuEVJyyI+jMstQq8+Z3n+FGNELiewhfI4SloDLKUz2k0Jz65N0wcFko+Dg1jfYFf+8/vQcxoYgzj53+IuZlfwU9VoAsRCkHkYNdUDRtmd79BZ5cPkxcluD5CGNp3Nnm6y8cIS0pfu61Hwcr2etv8OhIxvXrDfwNga0Y6IPTVQihsUKi8qEgEBUFWVkhNyR+rHE8yyvvf5E7jywR+Bm3l9u88Sf+d6pTXe49MM/HuxP80597kN862uTa5jTOIMa4DjZPIRU4mUFVQMQCm0FnLRiOeu5OKaSawNUkmYsRFr+iKYaCisypixY3GMEQYHoQtDKyUJN5BlPRCKXp4/DlG0f4wsLtKGspuTG2mvPi2k4qKmPnVIu4ktGqVNneLrHy0hi64EBPIxzD7vIW3/6miySFjGpS5fiRHguXpklNjvI8KjWNbmWQZOR1l/17OvzEDzyOVPDdz34vzsWIfHR4TVybs2/HxjDRxY9Zu1hjoZTzhef3st0qIFMB7WQoyBccTD9DFB3wXJTjkA9g6taIlW8UsY6PkCCilKLO6eOgCw5ObpGJRSExJYlFDBO8rMDWfYQrsb5CDQQUHKxnMWTYKEMod2gMqwSDGYktCby+xhbVMGJbCYJDMWYStgYVsrBIJDOC1MDFiLDhk285OF2G/5M6OY4xZG6AW9WIokB2xXB0KclJZ1zSCQ/juqidU4DLWjNlEFUQVUP9MxdJeyCtxvU8bvu2JpP7O8w/n/HCI7PIToLUCWoiR287MKHQYwF5UWGqgM1hq09adzCpQcYSYQ0ojYgrmNgl6XjY/OWx4iTDSsnXn7sOxKhFw3NfyAhGNNHaGIvzivrrYo6e6PDF7lFwIBup0Pa7VHY00GKLQX/YldZtR6ydGGGQ50QzlqfXVzl5ZBeNfsBmJ8YmIDzBN+abuO6flsuOkpSVjzGGzBoeOXuNi4+cgszh3LUV7j6xkzBJUAXJ+H81rpNIy0IQcuOZRdLtBFwXxxW84hW7ed1Dx/7KteBmL2S+10FsSg7u+KvFKd/kJjf5fzCW/5IR8j+W/xlew39nbgop3wL4UvKa8lUaQYcbgyqx9dH+oW9uf8s7m3y+vcLVCzOc+vzr2D1SxzniYazFcVwGpRIYWF66g/33nCPvK2qJoSdWgINYa19OxBBMTtUxvsTJBFr3KVRCju+6ytktOOPtxgqB8cQwVhZwreD7f/xhfv+f34P0Stzz6ltwXxZmnghnudVb45SpkpUUe3eP0VxuM7XD48iblyijaW61KQYO3VBgdUQx9zBLgjSb4uTMD7Fndhbv5QLn6Y2n+ZfP/CHj5Yg3jJ1jPh7DPreLcNMwk+3gzTu/yI/9nw/TeNNPUTQp7aDITPHruJ6iWouYyLv49wkO3fBxailfWL0T0/N5Z+NFXvvK57gQTUEOvtJMjJfp9TtEHQ8jwPc0ZWUI9nXwoqMs3Fin3pjEjkg2GjWqhwZM3bKNa31M16HkWQpjMcQZm8vbBFLSbhRoxylO3UGgUBlEGwkP7NzFqY3TNEb/Gaev3UWDd5FlX2fc7/GakWt8Vh7Cao2TWUI9Stk9zOKNJja7g9QfYBzIheDRzTl6fcPS9iS10j7Kjk9zaRsbabp9SXu9AjrnmbV9zPQ6aBTtToFUa8IoYboQIkNDt6VIWzn1csamGGW2NMX+YJLP5Bsg4ep6E3F8mzfPneHM0hzbvRLLiQWlkGKYtBK+2EC9uo1IDY5nkX5CkkHi5NTGgUGKLEM3L5Bbw4zV2I5DrRuiHMg8zdhsjzAxbA5A5TkiyLCBy0eeeY6516yR5pq3TN/Ba6t38ZHf+iomThkZK7O41EanOeQ5Pd2j2xngKAfrWPwNQ3leEpct4ZRClCUHXrMfYS+y9rwg6GTstE2iEY9qqc1aZQeXOxPcPrLANzp78LZ6FGZinEFKNUyIFiNEIecf/Os3sPuuHtWDfRq3R+SholCOEElKIZScmq8zf/EtrF7IyCJnOI4jJTY2wzZ7bXGi4Ur2oFzDVC1209K7UuEL3UPcdeAihWpCpRLx0IOX+NTv7mfhRsDWagUygcgs/efKZLsFn1DTTCUJk+WIrOQjcsvK/CRvvO8stV0R77zzefpRifRQwBPbexlpbCPXKoyUQ5IowESG75k+Rbcied9rH+E/nLqP5naFNHeoTHXpWyATnB9MoT1DtlVhcTRntJ+QhC6tikFIgfUNx/ct8MoTl/k3v/gu2v0RvtwrYRo577nvKR6+dojuuEFbhVGWP/7Qbez5tiYPjl3hqa2dPP70QXYefwyA1zxwns88dxvy5UXd4ramODXAHdf09xRJL3jItsSOKvLIIu3wfaik4cETF5md3eI///Ft+Gs57hj84VPHmFQCJ9fctmuJ/VtbPLts2BAFZB6Q5w7tqMBIsYeIFffcfob1rRpXxBi5dqjSpdUbQVmQqcEiEIAQAtMOcOYyEu3hiSKdNYff/eADRMUSdodGpBYlBBevzBIXfYy1tGURjIAY9t19ncWlk2Qoil6MOmPx2vnQe6YdQiHAWkFSlySzhg/88MNMV7pMDRR7RvogLBGwvF7H+PBP3vsEd+5YIfveGp/43EHmz1eQbo7k5dQYa4bvRQUYBz3ioo3FaUZo3+JFhuLEAJFYUs8lqUn8YobTF2zrEgURkvY8yuWYhlBstga41pIXJCowSGuxA4HwwO2B1xLDUbS1kH5DUeoZlLJsL5ZwjidUrlfhRoP+7pzx/V2sSRnxIxzP0jaS0dE2Eye3sEsgNSTTFbyyJjNFhO+STfvMqxqd1KEdlki7UMwt9HNc7ZD24AsvHqfRHrC1XsdOFLjzjhe54/gl5peqvPD1w8iih4gNeSFDxhYTJ0jPQyYZhciwcrmGshZhh/kpjuMxuadKxw9JsJSS4XcWGnLPwVQc8n6GcMEEEpuDGuSotkZkFteXKKuRUQ7eAOuXkUKDIzCOIHMsRBqMxNkMUQ0NSrC5NIbVGcrtY8aKCFdiCgrrD1NyhMnwNnPyuiIb8+jXXTLHMvqNJslYCVwXOxKglEugLaavEU6RPd/T43Rr+N522hYlwBqLTRLOP1rjzjc1OfeZEmy0cMUI8SBm8J8kNs8xfYETRXj9CipS4LhIIfC6Gf0KmDGPILKogcBuxHBD4o6lpKGHXQ8RXgGJJHUsttlF4pJvS1YLXQoLOe2+y2JlmmeuOPgXIpgpUQoTbF8iKs6wK1Ja6AhW1trIOEBtR6AsK9Uepr3AT7zrTTjjAR/4F5+GzKL/L3q3tTZUKwVOL6yBdHGTnMsvrbC9NWBqdhTpe8ztn/zm41/aXOPp/hppMWFv4PCKO/aya+8EU9N1ypU/H638F2W8VmLfxAi7J0b+yse6yU1ucpOb/Le5OdrzLUCSxVS8HlJYrl2aY2BnuG/ir39ze9b4OCKxjI1u4/VStrf6XLyyRpznTI7X0FXQRcHkgQq/9dlv5/mowUu2yrExF2MMSZSSJsM7lChNOb5vnslqi34WcEMXOFMusXOijyMMxpcIIdnsD8dtwizDHijyix/9PHt/ukOaaLQ2AHyps4v/4/oDfGlhL91+xLUra0gPotTlhcEErbqDnABd8MiNJpcKqwTpCMSjgkefusQL5xa/aVz7775yivanG/SeqHFbcZl31V/k/FXBJdvhcv9zbG7cydvu+hAf+vA/ZHY04r65TZTSpNk5QpqkwqW4x+HB777Gvlv2kS6UyRZKVLoOR7KMzbU2URSjlMBzPdoEICTSSvbtusxm6nBh3ePeBw8xPQdXlydo2QCZS3xc8rUqec8jiRy8TPCKrEPa9Ah7Gbm0rK1tc+3yCjsuaOauWB6KG2gruLi8xVL/NHE3xN34OGubP40nf5n3jVxhKa0yqkPiyKezUWdCnWV25P1Mzy6h49Lw3BiDtYKHzx/ld156DQ8vHMY0A+ZvbLLZ7FMsKIqZIliRTCcD5rxtLsej9HyfASdZmN9gx/4bFFVMOcmomZjjO9YgvY8ju3YxuafCJoLG7IBSPUJ4Fpt0qbgJmadxqjHKbWNyyLQgL0iM56Ijge5JMiTfsf8pbJqTt13a/YAwknQyj64s8sJ/3M3Gagvl5iy1C2hHsH3CYXI2wkkNK2cmcLZBrUv8co9vLF6gVIqplWKqosjaWocr55e5cWUDhCA3BsZSkoOWrNzh4QsPUzk6QN7lM+eneB1LbSvB29eDfSG371Ucq62wZ2qDUtCl3S3Q2aowuWsXWiqW5xs8snqQA84azWaP3azz4pk9jE9vMFlc4OiD59k1mrG4MoVxwPiWOHfpND2yZZ98OSZc73Hj2gSttAGBg3UlQkpUZHAHOV6S45jhqrUpO4iqxHiW1c/M8jufP8C59TKzQZd7Dl/m7a96jkopIvn9Eu6CxXcyhGtoxiP824++lsP5NtO1kF/7vo/x7vc8Te4K+v0ay8sBnfkKtamYsSCh5mWY3OG1d15irNZjIBSrRnM+UnzgK+/gd5bv5nSxQrJcQmcuWIkWDtIzCCCJfLI4QKQe12tVpqI+lW2DH6bggzCCFy7MYXLLz/2/PkTeVhgHqkHCV545Qn0hJChHaAds2TC4OscDo1coyYSTjVXqpQFf60/xfFzjd5+9DV0SkGlkZsAKxLZC5CDj4Q2/lRBsgxta2s+VWL84QrYZ8L+9/hHee/IZHth9hWQM9CHN9OEuvtQ0ZMJjKwfI5xzSsoPpC4gENlUYK8mMQ6PUY7rewcsFE6Uuhb0hQSPFzQ0ytZiOQ9IwkA8X31GSTCusFWRliSk4iCTAKyekMxYnt6jcEmUBwjHgWqbdLof9NfJE8apji/hzCaYGE7UOctkQV1xMQRBEkBcUuuQQlGPUZJ8HRta4rzCgUYhYuFGj0y4T5EV+5ne/jbyq2O4WcaSl7SuS/ZbW/Q2SmoMNBLgCoRxsYsiqDgqJdR2ElJBqQJMpD5PAYFAg14ok95jd2WT60Cpf29rPZ68fY6q9zV5vk91OE8YzdGApODk2EtBRTHoDJmt9KtsxbpdhqkonI74Uk7Q01zs1vMBS2ugMYx0NlM4PmCg0kVnGpY1Rfvf63Xxw5X4eCcfZnnGIipq0YPBuxHjtjDwAmWvUZsJudwPPSZmsbuPGMdrmVFILrkK5mtn9bYq3Z9hXQFZVPHFuP8+dnePsmSl0lkKSYvIcmYIY5EgJ0hqkBTKD6Apyk6FdB+F4yMBjZPc4s6MjOJFFZnpoFpsZjAdW/ZcfF6uG/iBSK2SWIzKL1Zqsn2IH0fB3YmhKXtg0OH0zFLlijRNZxGgB2c8xZz289RwncXFLZUzVh5KPvyVRPYM3MHiLCabkgu+hOgaba8BB7h2jdNdutK9gK0RtR8iL2wQL2/hX10lLPp5voCZwpR2KhID2fJIFn0t/XOKWgy2ksdBuoaYaxFs+aUtgDzuIbyvDPokRFvoZmRiOvul9FfLbJhB7JrBFH2FB9vtMHuozMheSrvdhYRPmN1G5IA5yhFDDUZ31EJU59PZVaQVVwlKRoG1wmxoRpOBa3FzgLmvic33ybo8oTnFPGyovWKpPW0bOu9yrdrO1FdLcChG+wJQE2sCt3/VLLKw1/0ztJYBzy+vIdBjr7Q40TqRZvLFBUAwYn6gxNlr75uOPjIxzyG2ww5Y4cGQXP/BDr+ENb7uV1Fq0MTzxtXM8/fjFv1QduN0asLC4Tbcb0+qGf6lj3OQmN7nJTf5i3OxI+RYgywy/+QuvJxjPON64m+97//v/zPblziidjYDtZkCcrhGlPtmLGW97660IKfnln76LF5oX+Tu3/hDv/ekP8dVnH6AQRIy+8iTwcgrJy2JFf/tx7p86TX+2wh98+QEGa2N85cYe3E2JCmO8zRw8xfj40DDzwOwE3/9Hb6JaCzneugW7Vw99U4BBWkAAsXExUY52wZUScsnDf3ySL3OU9zz0LOudLoPckucGR0m0JwmzjJcWVjnbavPC6hrf96a7ud0e4tL1R6hmAzbS8nAOvSRhPKDc2AAk5aCNMYZPdI6xV6zweGsXPzV6kXNb46z7dXItOZS+jXzjGMVr5zCjmj0HO2ghKN8oEJgWu+eWWb06SbFh0X0Q2rK0MoHSHqu9ScYnI3bt+QzyIESVERbjKqLgMdA+bGW4cc5Lg518l7zKzO4y3Z5LLzNsrHWQmcXIkLvlJAcnp6hVAq4vb7PZK5EYgXA0bafOqK9RyqV79gAL/Vm65Ywz7k6C0pOkcpO5sU9z/8nv5HRzkVy74MLrJw5x6rkm3mpEd9+AXqtH3E8RnoPfU4zlLQ7tWWc9rmIJsOPjtJRlo9nj+Mlr2Nxlc2MS3fCYm00QDzyKt/RevpR9A6O6jEx2WVgY4bnnVrGnq3zsR+9kd+zTWdwiJMckDnnoEhRDPJXjqRi/CAWVcGpsjldM30CnAnmjR8koQr9IpZBgtwNEyVKUPmd0Hbc89N34veePMXK6j9ezxBVLNie48+AWF15y8KTFuoaycFhc2kKnGY50SOKENNX4JmW8HuJ3Nc0FCzXFifef443qLP/yS+8Bbbm3d5bClOXY+Ku5dKPEofoGpmeZ7GUsLO2i1YVKrcf6+hjv2/swIhfoo5MUVAA9h0QUue3NCwwO+sxvFOlmRUa8Abos8XJN7ozjqhxH56R5DikYV5H6kng6ABdsmmETjUwgr2cgHXANwjF4hRyZarLU5Z///j2U2j6vvWuVl86M8aM//iRmQvGN8zv5wqXbeO0tN/jiqX2I1PLRy7dw4pYlNpMKY/dtIv5IYA2cXT2AHxl+/+N3w7jg8voOfD/ha2cPIZMCdSFYTRx+/bffgKjVufZcGXmvBi2gJ7G+pd0vkjeG0Ztv8q7wpcX96FqKkILt2MM0M0ZHFmmuHgErudLZwckTS5worvMzP/872PUyP/nwdzJRCmnO1xivt4gDh2IxoTdbYf7UGMXbMgaRT33F8MHfvo8sU5j5ADEHRgpEojGeIF/y8CsxclMj0hzrCBzf4lX7eBuWdrMMPmSpJBeKK6ujpE5GQWQ8MHGDb0wfYHlQoR1N8+3uJa7NLaO2poiNJHAyAj/kwfJlHnv2VlbKo+Q9RTCS8R2Vc8yUM/7PZ1/JICtSTzOWhCQt5Hixg0XgBBlp7kABtFKMVj029/cQGx62PYzHVS2JbzSZ77LLDfEETGQDbFnx+pMXOL86i/2Kiy99bGJJRiXW5mhXIjyFOhcw0+5SeoNhKbc4XY/eVsBnnjnMVy7sYUPXEYHgpz73Gn7lsXtZrgh27moTGkU7CShctmAERsdIV5B77lAIlAZEilAGoYZeUe1WgaKbI7oKupKpPCSb16jrJdqpx+qhKoduWeep52fpBAV+6I3f4LV3XOTnH34DL50fZ+fdXXTBcjarYRgKceVWmcxC67BE9xzkpkSv1hAmBSHxOindC2WqGxp9Q3G6sJPRPSEX/XFu3BjDXZUgfIrnu7RGXEQjx2rw5yPyvRnzvTrN7TKVyZReV5Hscine3WBb98hcgcDSH/ERbZgbbzK5u81McYtr2iUpWgpbZmjKVHGxrgOZxWIR7lAk09UAmeTY3JJKydnVLSZHisMOs80+drSE9VycFMBCZpHJcDEi8+Vw9EcqpMjIFHjGRZRcHE9huylZzUNnIBOJ8Cy6KMiVxRYUR/asoHyXrz1yKyqzGAUkGhQ4GyDWDd52Rp6bYZwzIKXCxgYQDHakzFhFFmsyB0yssa5C+y5BccAHdjzHb7TvZVu52FKBpOojOxEq1pALXnhkkvGpEOtIRCIQZxdJyy729t24bGOUQlsBgwFIkAUPM9Wg5AeItZw8BCdwsZ0BrOWsnUpxxmtIKZFbIXgehaeu4noOWmxDP6Ts+whP4voCqYZiajJbRLqKPK/jljTjUYHLK5LcKZBHgtW4TXC9xrhWEGseuv8W3nDvbbQGCa1iB7fSR2+UMWLYEPZzv/dFPvgTf1pfCSl46tIiYV1S3QQtLbt3jjA2XqE2XmZcCgpFl7MvzNPqDLjjzn18/623cyq+yuRUjVqjRJTmKFfxyOdf4GP/8QmkK/npX3gf9z/4Fxv1WVreZnGjTZxn31ysuslNbvK/JgIQ/xOM1Yj/9kP+H89NIeVbgG6UEl0skFwIWP2O1p/b3tOH2Wr3oa3ovyImXrRc315mdWGT8Yk6D+x6LQ/sei0AsW9ZSUeQaY7jKKSUBMU/jdILuEJqLdtZhbwoIZbMr0+ze2mLoCcoFFImdZGCKmC0ZWy0yKfe9MOsdfpMZg5XL66z1QwpV4u0wwYbWUhkHJxBzvn5JrNHKtx7x0FW2yvctfdppqczkm6KNRLPV5iexdvUjFWKZLnhpa1Nnj3V4fn1Df7Ftz3ER9wvsb7p88kn72Y8LVG2M+wQJUbEEXK5j6+fO0KO4S21/4jb/yVOFD+J6Pxj1vp/j9965tUoAR+597u48eyzBBspZgBbvYR6xTKebHHnyTMsnTEs2hKDqAA1hWNdElPhd7/wXbzyxBHSNKAgQzbCURoTfab2tOm3x6FgmF7YZvt6lau3lzl+1+O8K7nAb7e+xLXVbaznYI1GCod+nJIOUhbnt9jOBsTlKpuqyE+uv5K/sfM8a5lC+oY/uniCQ/sWiJMycleKVh7X04BOf43TOz/Erm+v0XlyjPB6HemUmFhpohOXgdYsDEK2+hEiC/CMQFwBdWtOO/XZVZpjudcnz/qY8S6NIGJj1SUPYatSRGcj3OFeZmEwoD1wEJUypYmY/JpCGQ255G3uRZb6RZY2xtE1TZKDtobZYodeWmT+d6cY+ZtttCyDtDiFnGfDveyvb7Gx0KBQCqn5Cf2jo/TOpZx07uLsxEvURlMS7dBzQ8wxTTZfQgQB1VKXhh/hHpjF6BY2N0xO1Dh81z7c3OC6kl37prh0vYmMLe5AkF7X5JdKmLpDhR7FWoYXh5jEEH5dMHmbz9TJ3fzrC29GmDO8eedZfFdiKSKOrxJtjXJ01yYnR5eRyrIyXkV3EuKeoJJpTo5u0++VOGNdkkBS0hnL6wHdThHVzpEbGuEpjJIYTyCEIhvNyca8YRRpXyL6MdIFb1uhSwK/FOHVLVmo6BcUs+4MaytPEV8u8rnnprG9cd79+Yt8z8gy90+0+OzmUbxSikiHHgyf/fqtyH2a+WSUskw49MANmltV3nL/ed45+gw/85vfjrnuIVMfMRnheobWVpH2oMgHPvQ+CsIhqWucVBF/qYTMBCoHAziTOZQACQcqfa77K2y0SygfvJKm7iacjXfhhwaRKrrVKreoZUpOzkpWZWauS+pKHMeifUmcuRyc3cBaWLwz5Qt/dBs7NrYZne7R+WxGadNgAkn/fpc0yxGFGJ25CAQjh/tM7WxxY7pMdNpgSorUUdyiNhHjkpLa5OsXd/Ej/+S7SJyAxGiKSy107vCVyiH0AYd6dYXvvOUiv7lxO65JSDyHbNJijeBvVR/n/Gd2oropzz82Q21HyisPXmR6NaU4llGqJvTbReZ74/hrFu0KRK6ZrHZp6QCZgvYEkphkxbLnRIdOwaeZv+ypkApUnpPNSaacPgYIF+b48soBXn/ndY7IK/z7a/cjs2Gs+OT4GqHjIjVgBRgXt6PYzB2syBivZcisx+trV/nYC8fJR4dpTgJLpDx2Tazyw/d/HZ1J/vnSt5EFEmUgK/p4SyFsJ9gdHumIppTHTPg96rWY08tVrFSwleHrnLzmcbS+RDKbMf/CLNGmJTnqcak3Q1QJiIsOI5MDlKsZiWLcLcuF5Vl2qRalVkiSFTBhgqgE5LsVoFHa4rkpWVgcmkgLgzNhycMAtRIjthTp2QJJIeN6pUHTKuy+DLlSwFY90AKjQBcVxncpzhh21dtU3BRT8rCOhv0unYmUit/l/WMv8kI0yvPlHeycjkkjD6HU0Kw0EJidDiaziEEEsUEQYxyFYyxZNQAJKgJcd9i5oiGOMraaPZzEIjyFwGIkwwhrCU6kEUmG9Awq97ACkumAtCIpX+/jpwqkpLqrTrac0hvE6B0ljGPxugYnM0STEsemjNZCrttZ3OMJYlmRLjmQZwhHkgearKDwVg12zMc6EqMgdwDHQVck0VGHzo0eXrkMvS52KcUGHrmyuI5mv+xwUHd48ckqnf1VvFCC78HiFmnX0u4W2LhSQhBDrnGNxo1ismeuokSBUBXI51yQAqIUkYPYHuB6lmR7gKxXoFBCVPt47gCWXUpul12/kHPx/6iQNTVIBZlG7yrjdAyyoNDFgMq+TeZ2xEwYn6+fm8O4gsh18SsObuIwOVljZWELbTUEHrunJM2wS2e5iBsE1GpFSgUfnV3g6L5lTjcnsDRQueWJ+VVOL65wy46ZlysiQW8lwtUS4UhKrmCyUWFjpU0p8ChWfYyBX/nVz7MVJrzzu7d53C4ws6PMG27ZTbVeJG320KnhhedukAuQBj75sWf/wkLKzh2jNEbKNPsDJkeq3/x9Oz7LEyt/g5p/iPtnf+cvdMyb3OQmN7nJ/z03hZRvAYzfo3NHgIwE5RNrf277ztGM0YMZzqyPOzfBpfkWWEH/5TEb978+ljJYAdox3+xCEUKgtSGJEkbHtjk82WfhkkdeUsRjDhlFSLbRWjHn1En6ljhKvrn/gekJDkxPcPqZa+SZJk1TAA6XF7hy2WdEx8R5iblqhYMnd7G03uKhVz9INlKnaQSB30L3B9xxYi/tS+usqZQD9+/n8pOLuD2DCDNW/TZxmCIbAXmS8czZ/UzEAeVCxut3zXL/3t9gxF7g7bev8cKFE3ieolz/YbY7n2HBRLxx9y/zVHYfY75LuRQQCEGhE+N5TZIxTRIJjs09S6O+wcVoF1ls2NWo0yspmmmOCjUjxR2YgebSQpeqP+CH71vE8zT/4HqDV735GkWV8uRv7EXHgtgdngPPc3GkYveeMUxucLUh1YaolyBdh5yMsYceZ66kEeo+/u7kgOubGuk4KGvp+ym6M83bXv1VRkpbPBMXyJTLDWk5VFtjf3WNT32jjEgMYZwiM0tkLOthyGohp+1Yyn5GKdeIbs4zv38LJ3/wDihIFhfXEFriqt/lqecOcujAAhmSlayGq2KeW9vPxuI6ppBjdjvooot93gcDxqSUKglhXiFOPbw4pqcrpEh6xifPfGQo0WEbp2TAWsZlzMZmhbrtsdttkpZ9ZotdnlueRTQH9LoRTt0hMpLXzp2lb4vcddcNXlqq8KXOXfiljF4sOHnrPJNuxPWFBtqTuLtc3vqd92GNIR4k6NSw2+ygNbhAnJRpSYExgpWszu+t3U3WEERBhe1+lffunGN8soEuBly7PMvfuOd5fFmgXCkzNbjK9fkpzvbmOD29h+tBg1q2TD2oI5qSVjz0HrpyZYxbJpd4973LTPubhFWfIEy5LEbJZzR6QUPgYxVDb5xWjqoNQ1GNL0lrEutKpLQEpZjEE8Shg+emqNgnjjSq4JFN+Agy8lXNIb9HXQ5XJb93z3l+f3s3/l1LtL5RJQhSlqI6jjWEucvffe+XWLtaZ9/cNsVqzrf9rVN89JOvwg01+YjG7WcQCqSE0WdbbE/leFMBbz5wEXN1lCddCxpAEKoA5YcoD3ZVm/zErkV+5eqtaOVRaHTA8SDX6IZF9ATWkcyVYtq5g8gs0oLKLYxoxh5qE5cENTdCYWnMdrgsD/IPv+2zRMLl068/yqkPj+NNQOqOIjTUqgn96yn5TI2DxRXunLrG14/s4vGrUyQ1BxlIcsfnA9/5MMVSTPW3bmPMhpyK93FucwxHCnQo6LUCVC3n+mdHeHh2F1tZwN/f/SiPXt3DoSNL9BOPn59/PUfv2qS/4uC1INsOuHRqL9n4Grtll8BPMD6kiYNqGRiVZAG88RXP8MHrDyLE0HtBVCKSpsPqcg1ZsAg7FGqEK/Dbkl17t8mtRAK9tsf1xVF+Z6SM3ohJA4VwwdEdDp5c5dzaCEmzNozZEZbKZIfP9Q6wz28yYnrIehfP9Pjrb/8qf/DM/bTzCliDclxs2yFLFKmQzHnbbE2MEKce2nHwmwmuNeg8J1MefqaJNosUM40VBotBhgorFCazrDfr7Ky2ef2bnuGP/vgO1roBM46DrIDoG37puVdz4OMdFlbHcLOMbF5x7WKF8uUtKBTJC4p8poq1Emc1wxlYpJ8htAQhkLmh2NFMbzVZHoySW0WxpdnTX2Vxu0JvbQxTLCH8BGdaDROZXId0zAPl0zENslxRcDK6oYff63O0f4V7917jdYeWCKRgr9/iicm93DKyjSpoXC/HOArlBYhUYEs5oi9RcYKXCVJfgXZ4w3ue5yufOIrBx+YaZYcdNs7ADBcoDJT3x/jjsD3vIwzIHKQxQz+dKB+atPcTsikPJnxYz9BpjpCCMMwRuUaIYScJRTX0rGlLCmsZKtR8fOEugl2gY3eY8uPooeFxUZHMiGE6UcFFSAVCIOTw8y1ig9tO8CsGEVh23zHHJbVNvhUiTvVAKjobLn/vt+9i5fAU+ZglHwfvxTYiMQjHxWpNaixoDcaSWTv0ixECmSmEo1HaxYkYduFo0M5QZIz7A7RMkUkft1KhfqTKG++5zsJZnw1dpb1VYOytGetfG2HfwQkucBVbzJCfSBFIvJ0W55jPTLFP/cUdpCM5VrmYsiGobDDtz3H85F4+/NgW3tMxdjvl7KNb1O6sALCuTvFMeJby4ms5s9pj/4EuB2YEZfUQ//aJ5wHLd3zww1z8Jz82rJe0ptfsUg4NfqNIqiwvDFrsv7xCxRo8pWiubXH1+gq6k/OFp16gd4eDKr3ElTDnKN+OUpKJ0RI79kxw9fI6QigOH537C9eB9VqRONdcvdCk04+YGRuKKc9u/jJXkgIqucF9M8PrcJOb3OQmN/n/DTeFlG8BKv4I973vJQyK/WPv+HPbd1fP8+DfTNnt7eSL0UHyK5J8G3rdkChMCAr+Nx8bpzFuotFo9H+V/22NwWhD7mYEjS5ZOhxp8ROJKBkoKfB92lFGai3jafrnWrrKtQIzO0aYmR0aob155ALF3TljDOi/4R+wf3KE6R0jXN7YZn67zQNzrwat2TX6KHK0xt/+7gd47DNnWBp0yJAMtrpkdR+B5NV7d1OqBhg0TmKJs5RBqpASnumucH94jci3BF6OEyg8z6VW3cPH1qr8gJ/hiZhfPfZVgsojbC6mLC5so8KEuA6f2jxBNcxof3GC976nTXcgySKFqWlu2T3Lk49dRwLzS5tMjdcZrVZZ23bxRJEsl+yvbPL60QsILNfuGKV5rgi+R9T9dWwS0W0biqMVfvpfvp+S7/Gbv/J5ojDh5O276CfX+d65JyjVMrZ672QquocvmTNs5y5hGGCMZdt43J2t0Ik1SeoyWo349LVJXryyj2OHFwjPj2JzS3sQoVNDKjXbWYzTcImMJqrA/pk662tdxqYr7Jio044ThLLkQnB1axQbKf7kw3fjlFxUP6QUakZMjQ4Z9++/xNyrmjSjKquf2g2pRrmwEXl0dRHN7ThJG5lohLakwkWlOSJNSbqKe/ZcY0SFXO6MUslbtMMKu9w+t01cZaoQ8cnVOzAZpFlGSSWM1zfIrIeQki+u7+VKOIMTSPbXtpn02+QEvNDfyXitw9rlAWe6S9zme4yNlQlKcPz4LN3OgHrjODvfOMYv/NtPUZjs0l8u03d84ikH3RY07Rzl2ltZzR9nb12z1s74xrU6eXaIsH2ZUT/irolLFIs+b7/zGgjBH5zvI9M+bi8jTDy+dmU3l1szvOItV/jPm3dw1/hVJkSb+ahOZSZlzl+luVyh1phmTVuENphcIDKGDlZS4KAwApwxSXUsYnO9jC4ITGxwhANCcseDOc1zLUYme7zw2DS/+tU7ePXbvoxA8Dsv3EI8HlP4fEDFgC5LRvWAzHcoOSnXumM0WyUO7mqhhCCTLrYAceySb0nWUzP0vVGS9l0ewvGYTDL2jXa5fGUEvKEQJRTDtpRQIqVmshCjyinff/uL6AnL6ed384X+JMGOEOtBomuQKX74p/8aecslF5p0poC/I6Juu4TSx8kU3WwY5VmSIJOMR8J9OMIy964Wz396nNvuu8Cnw/2gBY4bo8crmALsr2xwsLCBPgyPPXoH+BZdNKxkVS4tjnPH0QV2VposXq7xvW89xY89/EaqgQGZMtILGWz56F0lnn54N694aJGfOf8ODs1tc//kDTpZgY9t38Lrj5wjUoqjI9t8+D+/iqWFUWwUYl0Yn+1xQ0+iuw6lZkxcKBAqwwfPPIAtZlhcVCSQaYIRis5AYfEoyWGqkTACYRwOjS6xrgMUmrtuu8rvt25nY61EVh2DOyW1M5aRrmZtq4Haa7DnerDbxwrB3NFtRtwBLVPgw1+c4N1vPoPNLScPLvJvLvuYTOD1FH0d0z1f45e334iuOjzkzvMNt0Li+NgUQikJ8gynIwjagpYsYIWlvz6OmRCYksL4Q8u1vA4vDHaQhAEzI9vY6TpH3nQFv5ozdqyHfUYTRw4LN8ZQcui5EmwZRCdHKEPgaXqBgxMJ/Kv5cPzJU5SURfRjtAcWSSoCeuUSQaVPltZRqSBbLbNaamDjInQUSI+4XCR3XWRsscpFJZYLV2b42c+/if5yBdsZfoYO37rG9FSHQAocBKmRXNmYYkehxy2H1ygWMkZGI+ZXJFY7ZEGKN27x1wVuIae6f8DmSzU+9shhilmIK8RQqFACbS0qyVGOS22qwrH3XaO9XSfqO3SbZYQwgEQiUbFBdiLkIIMpD2E1uuFhe8lw/KanMfHQM8nrG5LAYnwHkccIV2ACidqGaNxDhQpbDKCUgKsxwqJWNG4GDha7FZGMulhHDk2FXUEuXWq/38NHMXa3z+mXQnLfw8yWcVoJquzT9V3SxAVpKawOkNpBihSsHR4r1UMfHWFx8xwrhtHfjusOzXfXB9jARfRSUGqYdjRRIryjiBikyI7mQL1HEue07DQ77+zy4h97mH+nsF5A1mhw7qUl0n+wE371Cr622Cimel9G+FgZkd7Jcy+uERwT9E4GFFuW8pTkwK0NgjSl0PTIoiLZszHOQkw/yJmbDsgKV1nDMMHtrHUFascUe2+f4If2PsivPfc8KBBW87Wzl3nw2AGyNEeMB6h2yC1HZnhpcY1ukvLpZ87zQ3cdpz5WYX2tjzswEKVsvrhB7YEe7z34OVLzGQbJQ/iux7PPXOORRy9SaJT4wb/2ao6f3PGXqgVHa0WO752mVv5T41rXOYlhHiW8/5s9b3KTm3xLMfRk+B/9Kv7neA3/nbkppHwLEKWGF6NdSGEZabp/bvtBT1Lcucbe0iE+dGqV3FUMTEY/jZFCfDOVB8DkCdJItFFk+TCm2FqL4zoUSoIFWqytjfJ0bwbfhTQz1GTKrpEBixi67SJaSjaihNz+2TldKSVu4HxzaK5RPs6bxEcJcfHvO4hSDlE/oRentNOUfpRQcBVhK2Ki5gOKZrNDoAQ7nTLfSHPiqosoeLzhlUfYsXsC00lxekMDu6oqM1YusjTosCkGbCYNZkRGLs0wJcBzKJUs52OXI0HGqU6FK+u/yrcXf5Rr19aIBynj2xF/88jT/KunX0eEz5NX97C1GpBnCqEMr3/oBMvXtmhGIat+wtfWF3n7kYNcb45yvVNFeJaJQo/fOX0/jUrEuc/N4qx12TVbJen/GttrNaLuQ7SwfORLL/DWVx1hZnaUtaUtlhe32Lu/Q6wM0krEpuRqa41sAubzMUo2R5c0g1KflZVJpucW6RtIjMO75y7yj489yj/6+jtwNOTa0jcJCzsNcT9DtbZw6j5ZSbDlh+zcvZdCOeDt77qT3z59igtrp0lrAV2h6EQe116aIO87lCsG4UTURQSdGeL1C6zOevjdCk5NsPuV11h/aoqkaliIRxiEc7TahtAECCsxjmHH3g24IQhsjzvUDL93bRR5wfJdU2dIns84fU+Vr7T3sLZSZfUnJ8itwaBpvaZP4CXUfEvBiemEkiSsQGoxImc78njf1GV+6fxb2RhU6Xout63HbLldpIR4MOySmpiscd89+wgHMY16iV//uR/E9RS/+Mc/jM22eH67RhhqLDkrxW9weeuLTB0tcPwJh95Vn9L0PEl8L0oNeO1b+rTXdiIEWCyXv/plNp7ZgbudURjRvO2BFzi93OOTZ4+xvjnKw/UKb/ROM59PMzPS5+4dCzy9sI87XnGMZpwS9SJO1Zv0OhYnExjHoXpvhA0V2ZJgMynhbjrIABh4pCXD3Gyd3f5+Tr7+c5QrMc98eQ99KXnf6Qe59LWjpDaDQYjsZ0igtDthV2uTV56cpyAsH146ytn5Os8XZ/Erliev72Z2usWSGseVGaKQMb5jnSS0yEFAwUkYtD2eaO/Gtx6e3yHMq/RHfVQCJvKxfcP2ZJGO5/J0a4K/Nfki971+naVTI4yWt3ihupP1czWEhcQ1OIFC+i8nfjlFzi2MM1sckF/38f9OhBCQCo/jr7vMg+VrFFTGF81Bbn/bKre/KueTX1FgYHa0STVIuLxWpukXSY1kpVOlXGoRXyuiR3226z5feHI/9Z1NvLEE93rGleUq+YRDeVMjQsPIvoT6/oxNY1l9bpYXN2aRjuWhwiW+o3qOhazCR9JbybUgSxSzU21e97qX+Mh/uIfl5gjzZ0do31FFjEkQIFJDdLiLGBMEjwbowEEd22ak0sHbUPTCFOsHzM5ssWNnh9NPH8CNh8mBlWKEIxSh8fnS1YNs+WClPzS4rENWhmI3p0qfMJxAuRLTCZETigP3LL387Wv5+Oduo2WL7Bgb8KmXTpCOKNwcDIZEpCgnJWoVMJHD48vTVJw+vaCMAMy4g92wgEBGOWbU0G/4OD2F8RS5YwnrGfKoxAs8skCwb/8yT5+exVFgXYXMDP1mCUILuaE8Ygj7PiKxGEeABKdYRLsBKhnG+hpPk+3ycULorbtkZYMX9RGpjzuTURnVnN6weNLghrDRKjHzlRaLviVvKFSsMJGBhoNVoGKGKTt1yfPhDkoDKBMhhOCrv3eQtcU6X5eS93z3GX7qxdfQcRw++8ztvO7Oy+wIesxNbfPkvMIUcsqNFHsL5M+5OGdTTCpxMphJHHZOTXJ5tYPxHXLXxRowwqK15cHjOzn9YpMsgGg7AAe0B64aikrSFYjUQjgUJrQnSUsOBUehAolWAuE7qF6Gu56glcVGBmUUIteMjwy47+6rPPfCNDfKdUziIaxCeCW8fh8jBBQUScMZjvIUBcIBYkOwMECEGUlcgTTm0S+dRdcCdEOBcbFqmDQX/ZEkeTPY3KGwkZDVFd62ACVxrEVLibUGkQ9TiwSABpNlyGKAMALZzbCOQhQCUAKZMzRGz6GW5LRDhWnnPP1ShVZngqCZYGKHNEoREy3oJIgvrhIsAMbi3pdyYG/GhXNlLl3eQOcG72wb5+BO4qJh85ltTm9e4fju24geLmKaKX6tjKNzTDOnk3Wxj+3mQH03Bw/twa0UeNy/iO3uIIwyHrhlB49cvY51BT/3iS9z+9wsN26sDz2r5sq89e23cuXffZlodUCuEsS9kiBwaExU2bl/jOsvLpM6FlcOmFI9lAJh2+RmnHPXVsniDJNqwFIqB3+pWjDJc85ubTKdV5l+uSNlvHg7QeezjPj7/lw3irWWzy7+IiPeFPdNfd9f6jlvcpOb3OR/ZW4KKd8CLK002bzYQPiGF5LL8MCf3T41/kEmws8gS++nY34Wa6roNCPsxH9GRAGYLHjMnbjI6cXGN01mjTFIKVGO4svnJ5lWBcKkgHQt33/7Pfzhf3yCs9lu0skY7YNRkijN+f+2O3OUxFGS//J0B8d+isvZGnrjIJ7n0Rgt4xdcKuWAcuCis5xnTl9je3sRX7UoVV5NojVPXb2Gbl4l8QzungGpFOxr/CTx2rPMjD3IUr9BIcmYaBR4//0n+IP1qxgy9ngdNuMaGIGjhqunypnmhk6YH3gUZZ9ryWV+4cKPsHBlAkcbVhYr+KmlHMXkBzSiJ8lCAdbSGBPs2THBB37srXzt8nX+89XzVAs+tXqRp8NZjutlbA6ZgORJxTOP7cNb6mMziequ4hW/i+ZGgklcfEeRxhnt7ZA8SbhwdolBP6Eyfoqj4yCU4ZkNQ9gdMFZtk+BSczJsvYvvpnz1D+/iDe98G1fqsBR/g7899QQCy75GTNORGCVoxhHtaYm77cC2RRdj8lGHnqe5+OQ6rV7EtRsbXG3Os9Qtk9YkIFhIG+zfs8rZ87O4qoK0A9xwN9fOFRlcD1DFjImHujzX28PMG1psbddw24IvbB9DtCL6m9ukrgulAOlomtUC7/u7p0guv4+33/sd/MBEld9p/yzNpEiSOkiV43Vzrl8tEy57qKrHd/34l9l5a5ffWL2b1bBKIDNcCfU/knRe06dfLPK3nMdJjcNGt0qSeeSeouj6lIsBxYJPEsYM+jEIw5WLK2yud7jznoOMT42gXIfnzW5kO8YoB4EGYzjfNhTHJTe6JZYuFPD39ZjYnbDUrVNQb2XfyCs5lf0mz8UVIuNw/nFFslZES0PsSdyxGL8bEn/BpZjkZJ5H/7Yy4TGPeNPFlIt4YzuIEfyjf/RtFAseD376l9Ev5dCWWEdw9OQS5PDsS/shMChvOAqgewn5hOIL2SIXnsqQ669FpxKb5Nw7eo03zF3i73EIV7jcYhqcHWshtYOqp3zoU6/m3bd/CE9ovn3sGl96+v20Tm3wK7/wGX5879f46fNvI6w6zLk9znVHaHckehUahQ7RYpF8W3J5bgJn2UX3BaVXNemvz6Lil9M3QocPXr6VplGk65aT6SZ7docsbFQI7tjmtbec5mMvTWAygRw45BWJX83oB8OV8eyEg7hF4p3IMcbiSkOUu2SRx3zSYHfQYjWtsO/kBhdbE7zj2AWeX57kgSNnyZRi8d9MYDZdWjMl4k2HfC2nsBaROJZ8VPP3f/ARfGXJxl0uZfChT96GutUf+i6kCs8MCNcczB6B7yWEL4KpVNi3q01FGna5fUxm+O7RG3z/9Yc484mDGOMQ9hWibIedAeXhdQLBj/zSF8iLgqf6e/ii2k/w1QB/pktQcRg87oDNCXTM7MQ2rzlylnOXdmK2DVYrGtWE03GDqor5zNPH0NPuMB1GGpCGmVMDTFnS3KrQaZVJdloqz7WY2tcZdkMYgdYQ3Mj42m/sJDk+SV6y+KlFLnWh5BOcz0gbGaO9kKY/hmlK6kFEdyYikw5WCWzVBSXIbU5wyaDLCtNwyQqa2lNt3B5k7yoSLOQM0oCraoIz53YiDDzxsSOM1ZYZubQPv95DeQ66l+MkghSI9gaIqETYKWAnish+hredkR4F40pkKBiIEmZO4mQhLCh6IuDc8wpd7VMLWtx27CLzxXFWqg3kssBRHriaSAm8XoxBkfsS7TukBbCeIisZjK/IA0V3xeGpj+/BKPiT1V1E9w5joJ2NGOFnKGWpjw/IpcHRQN8ZikADQb/lMhiUcR149xvu4OE/OQ2pxgQChBxWWgWffj/lyrkVBt4MbWHQNsYogdAGYQQW0CUH6wNTdfyBIHQVMrXgKWTgkCIQgaRSr9Acl4RFidPKkM0UlWoeOH6Judk+xjq02i5hT2CbAXkoyK3FTmrSCYfGSyndPWWsJxCRxnqK9ivKuM2YA1tr3PH981z96igXntmJGGiIEtLZKtn+UfLlDbz7cwI3YtBqEFw3ZI5ArYFOM5QA4zjD1UhjAYvAInSOzXNknKOiFCe2w867wEUaQePxENPpEYxrijsk3QWI+hZVCmkPBCqPcVCITCB0jpO72LKAIuS3O7QLAlOtQCBQsSC6Zwqv45IHlvn2QRbbMV/ufhLVL2DzHDLNg++d5LmvnWZz06V59f/D3ntHy3aUd9pPVe3UOZycbs5XVzlLCAmEkEDYmOAAGHtsjGeMA+NxmBmP49g4jRnbOI8D8NkmmGSMEAKEAkK6Clfx5nTuyfl07t6pqr4/WgY02F6TPWPfZ62z1uldu3fXOd2997vfet/fL8viHy9xxx9mKQ9W2Di9FbPpMJ/b5E+//c3s/onfQFhYVl2+8uUTrF5YIyN83ILH6ECRHWMVOkttqnmfbiclDBOGJjO881138is/83Hmiyk9rQheVIHU6WGwdzMxUOCo5+C4km7YFwH+W6y1/Oun3sVSCH9w9X+kkhn4e2PBei9ksd6kGyfcuGsLAM34AjnVIdXHSU2II7+WpPnwud/icv+3IIF67yrKmf8+XZaLXOQiF/nnzsVEyj8BTm1+CrvmIBDkD32jdZ5wdqGK7wZgY65KmBcoV7AcNTD/VdnVd91yP93RZW5ZymKNIY5iGhsdCuUMmVyGY+FW0sEZ/FQj0hgX0ffQ1ha7LrFTDjKRBEWPwte1DAHoFzUqrOkHCUqVmCz9MacXF6g3uoxuqRJkfS7fM8HoQIF4pcED59ZIww69JCE1n+Ge+Tq2BeKQprwW8m9vepDz/gAXWjWuqcDwpYLjI0MUnSZTtz/AXGEJf24frbZHrygRRvVX314sixnwspzuVnHR3JWrsbqa4bVbz3JCTiEyQNZjabPCHflvo5mLefKJv8YlwhYNxf2rTGyt0ulE3LhjG7dct4eMdKht9LBG8uqBYyDhU7VLSTxLdrVLHGsSEkaTFl7hfQj3QRzny4xXc7zskq24Bp5+/CxLZ5ao1drceFOVjJEUpMWXY/R0l2jdZ6jS4PnWFmTocWDkNMu9g3iZfZypz/F8uo25lVG+c6TC7fqVPM4DoCQitwJmAKMEbk9jjMYpGrSWNNshoTWEUcLyfL/M3GQVUkEz9HnZjdM8s7iFeqfLRBDyzjffw3/4ymsgdWjWi/ze7G1UhtqInkNB5pFig0m1wflmHtMThFUPOeji9CTrYYW/nn4Z3zV0BdWRMgLoiJtZba4hk4jicpv2fEBmR0x8aUjTVBmdijFrLiNum2Xjstgb4JKZaZq5IjtO+8w+LHnyhoN8yzuew4YSk0iKlZBtO4eIU02z1aNS6pc7t5tdTj5+ho1ajy2TgwyPVamOllhPBjBOg4Gig1zX0E04fSpgJrwRfcaSi1Y4vTnAsZUqN4+V2TJVZfeOIc5GixyLBoiNIs1mSDIeRhnyxYgpN8MJK8isbCJbKTYwTN+Rxx2N6A17/PbsHeyoBFxVzJDN9EuvjUxJBmKsdtC+5enpUa4fnUcnCiMdlAEcQTQiEY4LvsNyqCm380hraWaWuad2GUee3I0JwBrLNjHKuahGUrCU1yNaY2sIIlIBlaDNa/ae4isnJnnwyQku373MD+16ivm4xP0f3Q9dh3BMoUZiVp8cRCQGXfLwF+tEaZZkSFCQXVLfIo1ABxbVsuwYWmKzNc5ku8MXD+/BHHfobSimknVuGJ3l3uhGwk4GlQfrWVxhEDIl6/WodOoYX4EDK/U8OAJrBfvTTT68egUXNocoZno88PRe3nbpSd75ii+wuFLmkbkAHSvcVsLDn7iU+lKBp2eGUbFGigivEpOd0URdi1dJGRiKODU/ieoYZCJpuz7kBcVNj3orS7zDoSh75Gou7t4VQhmzEGb4jcMvR9byeMpyfm0blXaKRBDusWTPa6QEVIwQPlIZnutWeG3+DGuZPKq5je95x4N8pH4FrdggpmMyK6CfCdmYKvNUb3ffycyN8YyEaownE7rWIVNO8ZbyuDFEVZexj9QIMz62rSkMJZi5CFk3WCUp394iL0I6JkC2Y9AGm3OIC4LUSbENgwjA5BwUPiqjaeks/mKCG0G2GHNT/mlufe05/t1H3oQMwUkt+bmU3i2StAJiCUQzRWoH4aYkKy5qIUAbwbwYZmupxYWVEre96ShZt03dK+A9kUMVwXRANCMcT4Exfa2WZkIyKggnPUyaEAUKvy6QCThdieqlTOxsMJ/Jo3oWEoMz5LBnzwoHLp0m0gEXnhlFOAphfXQlQXoJwZGYRElMRqELLlJIRALK6r4z0VAWB43pxChhKQ5Djzz5/6+FTwCuYdkEDAy3kI7Eupb4gsR/weD0BFJKtJIoCYsXNpBJ3K/KKChsT+OkFisExhdMn16BSkBSzWIFoIDIQpIiMhLHSowBlMCk4EUGk3VJCz6+F+IbH6xAFgPCsu0vdgQKLSQiTnnwqR3cEM3x/IURYlkg57bodiVKSfAUpm2RSYxYjzDbsthU4ISGONN/TZ1zkJHFyVh0KSAczaIkyDWNzfpYTxEfLHHj6DRDQYcnLt9G52wR6ecQQx6sNdGhQWgDXr/qxcYJGjBG47a6iMFc37ZYJyStDqrdhZwL+QxOqUDcCmmtJmA1dAROOybTlaQWbEZidIy/r0i0R+LthJYt4pzWnPUM9ngXb6RKMfVZJiLy+td7Vyu0tGzoPG9+91PsqG3woZ/ew9Fzq1Ruiln76xCMJeqGPP3kaba/Yic9A2PW4ZN/+gBXXL+T3Kiit5HSKRgePnKKQmSRChyhmF3e5Du/5To+l8lQyPgozyXI+Bgj8DyXypYKs701FjYrCOsgBTjOTYSxxlcu5UJAZTDH1GSFYvFryY7V7nP0bINLSnV+4dgv8Z+vfu/fGQd2w5isdLj7sv2Uvi72GvUrKG8FRw2ixEtjsqc3m1w+1v/dkxkucpGL/NNA2P9LXHv+L5jD/24uJlL+CXDl6BTe7tPoxOGmnTP/4L5bnh3k+b3rCOuzUQwplnMvGT+Z18y1tjFY6HCthW4rZm1hA2MqZHIZXlUpU93yGE8fq9DTll4rRjsgfIlX6MBqDgGMXVmhVMq+5Ngba21WVptMdmJyhf6Y6yiaGy1OHZslTta57ubriRshO0cHeOzoDM0LK5i5Lo28i9Z3kuz/BHrVJb+rSfZwzCWFNjeWm/zp3F0cLD3Lc5u7yM7FbLljjeqOGk0O87bL30Bi78DY+3nyhZtQGpTsV6TcMvZLlFd/nPH83Xzswgd4U+UoP/vIN1PaGeNuWgr7V8nIX6ajEuorLWpLeWRqwE1xdi0ihSAxhtQaMsEmj5+4gGqsM+B2EQlI1zDbGaK1xSAKFrMZkYy4TBZ71MIZbKoJw4SV9TbSUQwN5vGRSAHCaKLerRBkEcX97Du0neX5dR5+9vXcu3Cc5sQAZt1jX3mZ893doOEVmR2cntlgX2WKu7bfzrFkgdSHxIVMr4e7EQKgPUMmDbGOS+BpmmFEWvbIl7OEqcBmBNa3pAMpLeOwa2odbEoYu1gNK0Jz6xue5SN/dhNLO0OinmBprcgPla7llHqOmZ7LRLlGuyJYtBUK14fU3YDyUJvN6QHWbI767pi15Tq+q2h0Uk60pigVl/BPtZgUG1QHQ+o/nHDkN6f44BNXUdrdYnqwipEKtGaXFzFbvIKFyae46tY5CiM9FrqSpO4jraTWyxL6CdlsBs91EFL2b3i0xnM9hiqK0mAOg8XzHGLPobuZo5xPkGGIbnfRqxrtlCh2DM1r1rHeAGLY4+6rD5HEKUemZzjZi4lyA4DFHYNe3UXWO2wZXKWQ69GNM8goRTZaONbh0PAsJ/UWjnWLGEdwfjRiZmaBRq1DvhBwXX47H888TzLeb4Nbi0pcGj7Fw+4hkBJhIKBHo+LgRBJpNXElQ3dIIXZ26OwqwUqeJZ1DmpAgdeiYNYznoByHjVqF77/9ATpGcj4uMyB77BxYIDwUcr88yJNf3Me333yYP/+9q9icL5IdM/QqitR1MFLjaoMVBu9RS/MmSSoFq/URTCnFaoXJGkQoWHg6z6+//R6OpcMUMiH7rlvmFxbuZubCCKO1FrGnsKkgsQ44EAtF4Edcc9UZeo9GrNdHcboJlSnNqimRprBteJ4jK1dTmy5T80pkFyPMJZp2T5OYHo5wUUoimiHt9gBffuhSzOQmg946QlrKO7osPzBCu5MhV2zzsdn9rK1kUFKDSanlfXI6obUUIG4Fp5XQ1TlE16G1UeQ/nn49akVjanmcCF71y2/FMw7JIDiJQFiDEIJMJeGGG07x+DM3YKzkalo8EO5iYbbMDzpHSTYVjWYGa2DKpmhX4TY1M2tDNM8WcIwgzDhYIxgqNijLkK5xmTcZ/Drgg9O1+AkkjoHAMqzaLHRLyJrFWsPwrpDj4SiuMHz5X22FyPSrSoTFuhITCNwLEV6jTTph8CYFelpSLPXQNY926OGt5Wm1BdnNCNmSSKWIlaa7N8BKyCQJti7RWYXVQM3HJhJWfF6xY4HPrG5FGBir1LAK5I4O/pdyeF6XpOfjIrBhSv5EF9FIkL6k0TPYgqQXaTLPGjLWIQ0EBkmmmSCSAyjVJmmHiCjFCWNOHtuC7Qg2TAF/vkcsXZKSQlcFfknTq3oIY0iyGjc0aGlRXUmmnqCMRDiKyS3DLAVLmGJM5OTw7xX4DY3JWnybktMJntZYpUkGHJxViVwV/ZYVR2GNwCrJk0+cxbRjbMFFW5BZBxVaEgui1SP2XOxmG1PxwREgBFYK8BRoA7UO5BR6IIeRFis0thcTB5ZCo82An6NlJJ35Jn7qYUouWIkZDkjyDqstxb2f24ENFIW9bfZsWeS5aYUNs9gEgtUYtxsSjxbAc5ChwaYW20txO5pgKWZ9dYAv/GmZcMPByxmso3CUIBEKEWryG5qpbB0h4ODlSzzx6QpOL+1boRczkPOxUQRSIZrdFy2WRV8rRSeIpJ84E2GICGP69s8x0ZiPzXhkzgvMCx6ILhxIoSYwi6As0NOIKGL52greCylhTmNthDfn0I4quJOWcm6TxUVBtW6pVVzMhoOIHaQCm2R5yNnOG17xHHOPeDz4V1sQ1SmccQ9tW2A8HvzCIyz5UEqHOPK7T6NXYx76wnGu/deX8K7X/zIFJ+HHP30Xd8W3MiFizECGXCbAkYpCLkNGSUplH+lIUp0SJZqpHUM8vrqBi8JYj4XVEm/8tb/EeIrbvCEmJqp80+uvIJfLsL7WpPRibDacPcT+zBJaCIZya39vbPfciQUanR5JAJODJaq5foxl9TQV2UWwgLF1lKh89TmLvSHedfSN+FJw79SOfzB2vMhFLnKRi3wj8h97Ahf5nyenbuTnrv40P3PzX3Pm0wf+wX2/79XXkhlJUWNg8xdwnJfm0lbjHHcWzyC0odnp4voSN/D5236ceMuTDPkNMmmPTNeh4LkIV2I9QXmgjRqV9EpQk/91Y0/fhSSN037j/4vESUq9VqO069co7fhBjjV+BuhXBJ87NseFZ2exkSDyFccXFjmwJ8/w61aJv5xS7xUIihGxTXnb1uvZDD9FNcribUR0ll1a2qeWZPjkM8/ydPNu9mQ/w9ET1yL5WmtP1tvJ9ZOfIDYb3FhtctuO9/B7l1/AzedIKz5pvJNd+3eSy/o0ml2M62Glj3WyrM72S2yd0WmOln+Jj517Hw/O3s/0+hfIbMKHPnEzR1YmOLs5wHJ1mODtY4T7KqiBErXlIt30caIwRVpDFMYszNYoVwpUJ8sEAzmGx8usrbRpJd9Ju3MLuVzAzn1jmIGAleODxI/lKZ51mF0YQAmHZr1NJft53j76Wb77egeBJUljLBarLGFoUa0Qp5MitaEgEkbLLUZKLZZHOizsrfH0/DzDWR8Rg3Qs7qpD0vHYVm2Tt11UT7CwWGZmySdbgtz4EK7N8ltXfIo/OPgx3PzvY21KNt9kPFvn5W87jagkDG7WOXjdOQ7kZnDaBkyHseIcNtV02iGN9Q7b5Q5uHdlL53BAKxewrnMsNAO0C839hgflLpbaZdbaBdbWc3zw3CvwvFGu9c6zY2KVUrVHIwwQsUQmUHYj3jvzUdLEkCQp/+XMw/zkib9mdMsQ17/2cq684xDFahmtLVobum6CKAo6G0202yBTCaktNiluCKK8pfhtBnF1HjcWPP7wSRYurBOR0pM+eadHTsSorgFtSHw4ta3I4bU8iWzxS79yP9/y+mehKLnEtljbKNJpBNgEnFjz3Pw8j3/lFEvLNWqmTdAzCBWDYwg6ms+d3oHTjXGaCW4IrmOY+pzu24c6YHDpjljs7gjX0VhXg9LIdYvsWbziCkqnqESjjOZzGwf5SnuKDy1fywdXr+OFc8McPTbE9MNjHLMjbJUpyaZFK9BITKIwViGVRCQaGWls5KI2UtwNQ7CusR3QBQORRESC+lrAT/+Hu/jIwjUMXdOiLX3qOs/ixjBH1neSzUdIAzZ2MFpgI4UUhhuGphE5Fz2uiHb5bC2tgrEUvJjnD4ww3lolO6fInnOwAtqpZr4b8Pkzu1hPSjy2tovYjwGBtJrqZJf8li6lHR2GRzt0exl+8hfexjt+5B1sp01WpkhjUaFhZLBFRTYxc4ZOU6EzDtmVCBVLZLtvFSyzmmAJgk1BvVQmKaZc9epTjO7cwLqKpBqw76YZXn7pDCJIMb4lSn2+p3iel9VaLCwU+UpzG/JoDvdMwOa2gCQP8ZhPWJS86FLdP2cmlooXopFkRUK52iXbjQjWDHKmRaoslogD336u387WdSEvKWY73FJepOCklFVMZIuIrItRFpTEuqDLAi0TTK+J8iPSdkTxTAdHpuiMBGFIY4XcCLhx8ixuD0QMRgkcX+N6mpu3nEB1Q6w1yMgwOrGCyKaUJxp8dnEHq80BtLDUj3qUmx2C2nbQkKY+WoFNUyyGcEwQ78yRlH2cqP93i+15jJ+lm5c4iSXcE5BcZ+jIFNWI+s42tu800xEhzz45xfm1QUwblAPZg00O7pxlsJ5HxoLOpMAUPdK8RAoBBvLZHgYNUUKz3qW01VDcrqlONJDNFIGLFYK1OEMqIPIkA7vrBF5McUcXbP86YsoZKDigLHGi0a5CCQe3lyItvPyarZD2qzRUaiCfwwoHohTVSkDrfgVHT2N7UT/J8KLYNE2NU4tw2oZkUZB0UuimGGtxtURpkBpEt6/vI3oa1esfqzGdY3HBQVUjrHIR0iHe6xHelsVZDbFWY7ICmVj8uqb8XI/MsqFxlSSpFlAViWvBbRtE16CSFKedsOWaTRpphrYJqM0VUVb0XYG8F1uZXAWDFUy1QH1Pljjf12nxEouQDsaRWCWxgYsUYKREpwndnTnisSxJ1X9RLNGjs5yHAQGeRAiBsGC1RmxEmFSijoE77yIcD5Rg6/5Vousk3UtdwlOS6tPriKYhzUAaCOzWBsudCl0rWVrJgetiOxHpmTq+K0EpTp7s8TcffpQLj18gXepiHYH1FG8YH2RH0GLACTlw7QUq5Tyvu3I/P/Ytt1H1fQ4/cZaT51eY32jieC7KcwmNxsv7XLZvjGzLstnL8N2Hv43f+vO7SU6vErdiLiRdZMalMlzk0SfO8ugT50mSvkadEIqQIY7M7+D5jW+Mq/6WajmLcAQzmzVemFv5qmui61zJsXiSaXMVQhRf8pyXj+6jlQ6zs3TNPxg3XuQiF/l/DPt/0c8/cS5WpPwTYHapzW/+zBsQgFv+h9/Sy2/cyg3T02x6isuqHkmS4rpfe85rS+e4PFtju+5Q74RsHx+kOlTEz3pYa3lsdYo7d57i9tFzXMhvcmJmLzJOUc2YwStGmE8skedzqvmNKyd+4JIJHMTX13pZ6NoZhhLNhAw5rZ9hZKqCchRe4EOSEEuP0BlgvmP5mcvfwru+6RfJpgZ30kXh4AKu+Tn8gW/m1Tfu5w9Nj3rqIxYsceRizsxxPJjhBe/9hNdniR+75quCt3/Lmc0PUXIaPLv+J9z3+Rz0BI6FTDVHkPHodHrUmiEd5VEpdTHtFP2RNudfPseT7gU+cXqScdOme04xF5UIa1msp+kan8BLMVbQGTfYgTw0e7SaPlXnWgqlGYJ8hq4QLK/V6YYhGcfFMYa0GdGLEzLZfjlubaOD6ymMG1I52KWxniMODEfTEfZtrTJU9Dm7/hCVfIf71/6Mf7njduZm63ibKTrXX012rEbmHGIlQVnGs02wlvhSH3cYOt0af/Tdb+SF9VUeOHmeh8/P4LqWsyYgb7rUwxymI/ntL7yeb95xCiUl3rzkbJhlW8NhSLdRcYiwljMbI2QmU8RWxepjA2SOx8w3fdI7U5SSyGSBXClDEidMz2+iMWwbPY5OJfWspC5zFH2DHaix9OAouSsMnbwD0vKtvau5ZO8oR1+YY3LrrXQ2v4j0G/znp1+Jty4QwnLn2HHOjZfR65b1sM17j30J3zHcet8MPzmwxnrHsqvw49yz+T24cQ4ZXYlAYaSk7LUo+oLVqEmmUaQ6GkCugxoEZiynzizhZxy2ZHYy2G4y4DYwSpB1IjZzOZpXZSkV2oRWcsMVq+xwY+Jr17lvps7HH9qFHhd4ocVdi7hifAY9chWdMGZ5vkaS9si3E/x7u+Q3MrRvVsy2RvA6LRIrEEWHTKfHFTcs8sQLW5m5SmADnyTxaGzmGJxaw5lssXk0h5uWMa4hKKyTNVWMtmR6deqbGe6bO8BcOoAQFnetQ7yeIdOOqOQ0v/Ll66hs77K2XO1rH9QlQ26Hugt0UqRysJ6D6BhkTpI2FSrQaNX/rAqtsRmJ2AQdSJSnSREMTjapL5RJ2wrpJcjIgoVMIUTHCiMt62mB7ppPa1eAxTKrywwV2ghgNS1xw44ZTp7QxFZyzaHj3HrtaXodl6u2nuOzz+xnpVmkuyNLoC24inHRJbpJ0asFnJwtIbBYbdDaY2Uzi1A+IgGDxg0tXHAwXU0zdEjCDH5B49YUdhkYs8hZSVzun0KkA+VCj6Fqh2XGkA684uanGdjS5Fp/masOnWGuVuW5kxPcvWuW177yNHMHtrA36XCmNcZIpcZia4jQ6WEHHYwQWG37ziZdizSKEMVGnGdU1WFF4tb6d87ulhLNsqWQazJyTcizj43gNFzKAy1+/k+/iOu7vCtzjONhjo+N3Eqy3MQo2xfR92xfqHZfHv+FEP85jRhy8BqGt+44wgPiIOeODHLd1U8zWgbVDvr2tVrjdi2Bn2ARzMsKajPFrWmcEKpRi/J1CblcxCNP7SOXRoheyH3vu5xcJsbYNlq60JUkw5D6BhVaRAHsaMhBdZ6nz1+KCB28yOInlrAg6SSQlGAjKaNPtlBSoH0H203QAQQWUJrYyaKExZRdQDHbHWUks4g0JZRw0J5GmASrFcJxiEYc3Ef7mh3elgreWJ26hka7QDLk4i1LnGbI+kYetwzeQIJbSvHSiFKxy6YpkEqL8QSukKQ27uuA+Q7Gk30XK21oasvOXXnOPdvFpgaF6FerJKDCBBoNRCiwvofwXFJfYQOnL8JqU4RSfdHiVFJPDcIa0JbQ71sHq1aK204R7Qi3p0mzkqTsYcs+80v7cZbWkIMC40riwQxiSOPIJlYaTFaBlLjrXVQnxgaS7BkYn0rQ29dZWR3CnrOIeher+om0pg14fnkcz2h6XyojhCR1FJ61/YWVSGM9SVwQRJdOYpbb5O+ZxzG2b73cDBFZB2EdbD6AOEYGRTJLCYYYjxDGEmRWYHqK2mIFPxcjA4vuhSQFD3/doncGGL8HwqA2uzhKkIlDAscjalrKXp2XX3mKjy58ExhIJ0NM3qOQ6fGLz7ya7vM+SoXQ60GUEB9LiHePIbI+3sw6YhpEu431Slx2zQ6mhvfw3vnL8KXhkuwi/3H2c9w4OMkPtcfpdiLiWONkXHLFgFw+QGvDibOL9NoRnQsNttY8OjXJZj3HaM0wogyUAy7NDvRF/ZWkUetgvk4jRVvN6blRzny6iswbuPnvju32bh9h99Zhtq9uUAj8r2rfrUbPYEjopIvEuk7gfE1jZW+lxP5BySsm/sdcgi5ykYtc5H8lDz/8ML/+67/OkSNHWFpa4pOf/CSvf/3rvzpureXnf/7n+aM/+iNqtRrXXXcdv/u7v8vBg/94+k4XK1L+CfDKGy/BZl104PCzP/yN9sdfT740yBtzq7wmM8tB9Xa6re5Lxh97bDsAp1ZG6DkaL3AZHCtTeLFNJ5dsI6c0gdthIKjRaHRQvk9GwmWjV6O9HtqH3kD3qysqf4vnSZQSLxG3tQhEspML7WHORhVunvocrXbIxz70GNZzMbkswneRdYHfzLFr/zgDO7fgFfJcf+MFXClpW0PPGlY2n+aSQ1vY/oZz3PbKY0w/OsnGkUFsxXDlpU/yw+MP8mN7P0cYh8ivS5PqVPPX04d4oT7OA6vXEJQGae12ELkYT4UszWyyPLNBc70JsaSc7WFNTGsuw5/+5/exuV6lszBCd/lSRltZGgsBaaNCuTdI9+Q42kq6qYMRDZQGJ+uCnSLjb6cwVCTxXaLAYTWJaHZDknYLpTU21eQCh2zWo1DJEnYj1pYa5NUyY0EHOZygC4LlbgnPERzP/BZDu3tM6wEeXBrD9V3Oz66hbd8FQvdctFaIsiIYAplxMBY0AjEtyCx73DK2l0Ixy+2X7WNC5Im2J3QnLR84fZCCq+ntVvTGHFbHAj720H5O6i7dFLaqBD9KcRqWdquJXFYsr+WIY5dXXHWCzNYeYeSR3zNAmvfpGcNTZx/mFx74Vzw/8yydTo+w22V4fAkiS/cvAuIVl5nmIH4WKk5IgZRKrkPWifnMo2d5dmaRRAomBr+HibF/jcCl0S6TFC2psuwYXOZbR49hUkNBeAwXO0yWm+Tzi3zoZJt7L8T81Qs/Qc6skJdn8YIIHViibMTY+CqjO9vkBAxqj9u3fp53jjyIv9Ih2+zQrHdZXu9wrLNMKdikojpU3A5bLlvB9R0K9YhCo42X1ZwMcxwNS3x8fRcHLl9k/rkSsqE4IFZ552Vf4vV7Z5kaHWVy6xDPvHCW58+vELkKf6PvguGWBV5F4jp9MUmjBFt3rHLHLcfJByk3rm1w+/oppu5tseVDhuyvV7APFVHdMtYTSM+yb+ssTg4Kub69eeV0l2qrSZi6dKIAhj3crqZ8VY9db1yifKvlwlaHOAdxSWBCyS981yeolnq88jvO8frvfhop0/4qurTQEhRqBmdd49QMnoH6UJ4UxVC1wZousKoLrIoydYo8/+QWcidc/u0PfZzvf+t96LLGuaRLt+fy+W/ewYXFYdaXCnQbPu1TOcLUIUwdNqM8+VKbsGyJBgVTu3vkXI3jpWzM5PBqmvV5H25wUAMGYSGsZTltJjljRjk7upXaLVmcGGTPcuLEBJH2cVKFbKQ0DnvEKw5hU5FIje32q+GwBps6JKlLKlxUM8F44LSgezbDsXsnWF8o4uZi5oo5TuQHOZaW+NbLDhN92Of09BidRPJCq8LjzW28fvR5/s3L7uXfX3kvg14b1wj8JogI8CwqssieQCvJyXiMFV3k6MYk5mgOnViMMhhlMb6g0Qvoxi5ZeriJ4PXveI4no0kebE3yN51xPnt6F0iJFSA9hUDjuglBJcXdpkmKEm3BJJaBiRajxQ7fc8fjpAWPnVtCJkZCsgEYaZCxxium7KmusbOygScTJnMLfNNHjvK2zzzKVVMzFGxEwYaQ04SrPfx5g00U3V6BaKaLTC0i1Hg9Qakc4SHxl2MqIzVOeSNkdnQI1hLcukU7gnAowiko3BWQkUC1NNoRmKyDHc8iBnxsKiAVyFRgrMPW0Q12ezWmik3OmQlMWaEwfYFTEyGtQUqoyBDVDJGtHtI35EyHYMUwkW7irPfww75j2sZ8nqKMCJyU6mzE9vIytc9kEViiAY+4oPp6YxZMK4FEY4zF2hQZG3Qrxi/kyN1Yxfh9q2gjBTpwMO0WQT2FXoLAgiMh6Fuem0BC4JGUfdJMX3w6sgk6ioiNIXVVv0oDixUWYSUEDgQCHRjUShe50cXKXL+6xUKqFXKmb60tY4mMBCawJI4hzTrE1QBjHWZXDDPPDhJuuCRFn97eKsYRCCGpP5OltZGh3QuIy7JvfZ3ziLEkuovsNpHLm0RpimwkyMSBfSME4wGBb9l1XZOBgTY2SaCURwQeWEtwqkX2mU30dAe2JDgTCbYOXmiQRoProgeKuCEUp7vkjtXYeftBZMWFsoefCFafyHLo3HmyT3QYGmrRkhVQ/co9R7UYH6yT82PmGoM4AwXIuYhUoxyF7cWYtQZGOHSAeKZFIsAEgvEtVUbHKsz+zZ08e3Kc3/jwK0iW82S3PsT0hWV0lHLFvnGuPDRF1nNZX2thLWQzPvnAJ4xSpsYq5DMBmZ7LWHGI73nNDfz+976Z7RNVRioFCtmA0XKOieE8n/rEE3z0w48y3ThJIYLgfEjlpKXR6Py98Z2UglImIOt/zb1xOLiR3e4Gu911HPHSmCwyXYYyEkv4D8aNF7nIRS7yf4JOp8Nll13G7/zO7/yd47/2a7/Ge9/7Xn7nd36HJ598ktHRUV71qlfRarX+D8/0a1ysSPknwuFP/+R/035RkrK+dCmi18YbHkObl9Zd3fPwQT758NX0xlN+83trLxkTQvBXr3obs9M3cuTYn7OyUMWMahzdF/3bWihxXfUkx4LdXDZyAWsNfSW9PnGoadZCol7y1W3GaNbqXRbOfAuD/i7qIx0eevgEH3j/w6RJh7gIcU+Qlnw8R7KwUOMnf+nbuHBykRtu/hiraUpZOByJr2Z7uJf9E1n+c/UL6J7kOb0PYQ2d7BBTokVWpAhh8Z02UqqX/G2PTO/lwdXtvHb8IGtT02w/MEPvcc2l0TJJN6HXDInrIcIRGO0yNVlnvhMQ6iWqjc+zdeFKxnI5GtE2us0N3NSysuDy7OI+hr5/iUQInLahl+RJSgFrPb9fleE66KxLzzPM1RqcOjrL6kKNeqdHTzo0GiFxGCOVZGW1Sdjq0oxTpteGKO9rUdsoUKg3OXL/Oldd3WPKgXZQYhfrgGB2qQ4akJLYKyA8ixA5pIqxicdKp9+fbk4L3CM54nfAjFxl995xllWL3VctYIFtvQ3Otov0Xh9ipUDd59Fu5/E3NLqa4Tf+7Cre+7ZHKRQi6rEmms6R+1KX9NgBzuolvK0u1X0dzlUcEq+A72gW0xyvGTrP9uF3sm/ybjAON+w/y1+n42RbsF530cZD7IbCYA+VtSz1CsTnAwpdy3qjy85KBZskPPqliKfXLiHeBmZrgm0JXAdqoQ+9GJNqqn6PkUyLepJlTbosxKPsaWaRcpFsLuW6rSHPLiRM7WkxszzGPhuT7RkG83NMjp5mY7nMG6tH+PTyAbrdkFzOx6SSQBmw4AjN1n3LPHnkCsyaoUeB9ppi5sg4i7cmnEkGSDoWoSXrawXcyZSJ0Tarp3+aohA0m23+/E++Qvp2F9sWiEaIk5EoOU6h6DJy+VmePTqK9SVBHDM/X6KVeCTdEt25MkL2q+ELuYhgIqRWqyB8QTbfY1K26KUFOj1J0vLJ9eDNNxznwYUrIVK084bBbhd/qItFYKwgv1Oz3JKIQGKyPbKZiMnRGupWw3Q0wr43TvP4o/v7DhoSBm2XQ615TqyMMnZFlzsueZaHwm0c2LpI2/arS5zAglDEq1lueOsT3L1rE3ZtkozezzWji9y7eIDPf2wXjeES6ZpHK1A42xJMF6QSxFqyp7BBXLYYDEfqW/g29wjDZc2Hc3vo5TU6q/jJq77Mkd5ODj+/l1YrC8YStgvYnCAZ87CexkpBrVYizSpcI/B6lnbFw19uox1BL+uCkHijmn2HLrBUL1KXQ8QlwdjDXXpbFFZAwe/w6tc/y6OPXYrG8EJzilfnjqOs4Wd/7I241mfPwAp5L+QyP+SHhzZYTPIMe3UWw4AffPt9/MYvfBNhaLBG42iNMQKUg1WGh9d2kRiHnY1N8l2NtpawGkAKwoD2XVQ7Zu+hBRY/n2f7wTU2kxKnkzwt7XP/+y5DxBrlShLbb8vy/ZSgEkID6p0A40lUCDdfKtmzd4PPnT5AVISnzCQ3FZdYbeXBddAmZeTGBt8x9CQXogrPyEm+79eeYZvX4nBnhPL+BmefMsjEMlTdJPI8krxE1UHUe8iMxctYetJB5mD7cMSxWgZb8ikomI8HsfgMHajTiyTtrMIfNoRhQDoETs+lu03gNSzWFyRFiZwoo163RvuxAJXPYsOY6+44z1q3xKbIolQVM6JIsg7WgL8QkZZSjHDw3YhurEBDqxNTkoZ8xhBFDjKUpK6LsLBJjtO9EWqbGWaeGMK2Q9ApMgvxcA6bU8iOQWQF/mYPN5D4sUALh8GsR7WS5XPDyzS9mKlE0XuyA04O47rowMV4BikF1kCqE1RbYfMGHUuElKSuwG9rUukgpEtSddGOxTp9MWkZW0RsEInGBAqnk6BMjF/rJ2aiQOH0YtKSIhl3MK4i/7BFGHDbtq9fEgg6+wJ03serWdKii+yk+E2NybggIR10UY0e7obG3C/6wvELAumCVYLEN6T5DKqVorMeynFwZ2MIU+xQia4ruHzfWX74F48Qpy7/8lvugOU6KvDR1pASQcbibiaE9wpsQaKCBGkt2vS1fdKiwmkaBAJ/NcY1CfGQS+pDaTUhPRvy/OEy4aRiemOYu3cd57HVNr2m5aqFJR48sZfINdwwX2JBLiOqFfA8RKuBrfWQ622su0R+UGKMIUkkVhvmlutICW+/7WX8wD0rELsUrqzxwPkdXHrNb3Bg5Ld45ugsTx2f4fSpRaaGSrz9O25meamOch2KVZ8XTrXZSDVO3fDFQhvRa3FbPovvuf0EaWpwhaBd7/LE8wsYY7js2ts5uK9CbzJhmHE6tRal0ku17f6W9VaHTz97nIzr8pbrL0dKwcnmUUZJAU2cbuCo4a/uf9vwdUxmR9mem/xvih8vcpGL/L+BsBbxX5mJ/GPN47+Hu+66i7vuuuvvHLPW8pu/+Zv81E/9FG94wxsA+MAHPsDIyAh/+Zd/yfd///f/T8/3f4SLiZR/ZvSiJS7b81e0Gy6bm6+itFmkOlj66vjk0Biz65vsmJhjd2GAsBfT68bkCwGe79Jth2wupcwvjtNoh3idEIXApimHnz/LmcIQ1915jo50keql/bytVsjGepN2q/fVbakBL+cxtGWArZODbK7ey+Dwp7DBFrozEa4D9auqZAoFCtZnfn4TJQBXcd5ouiZLS1iuKb+fhdl15mptMp5Hxe9wY2WajLqez3R6/Nl9d7P3Dfdy4YKi3crjyK8VYylH4TSyRF2XvVtHGNw9S06HnN9d4vzD13Nnvu+mYq1Fpob1WpmFTBn9HV2+XBmkEC2wT5UhEWzIJq5IyHg+wnMQTspavUSlXGehUcQtSKS2xKH9ag+zSRKskhhtyOYyaC2wRhDqhIX5Go3NLssrLWZn1nGsYbFVRC9awjHD7n0LzN3nkQ9Snnl0iqUDgmOf38HoDTWkFFwIa3QmLDQgyfsYa6j0JGnTwxnMsr7ad1EqtCzSUTS7MfPLNbbuHCZnYTPKYI3k5PoEjThgKNtibcVFRXmwmtq1HniKOrv5uft38Mbri6SF50iMZfF8FWM6/RVOLUiNQFYiMkOCQMekZz0unVynVNT8/Ds/S9LIsNGRmMBgew7JEY/WtjxiWeMOGEQE9dAnc9pBahjIBWjbA9b4+PNHYWOSweGIjVGBChTzT3wbXm0vnbDJwUTz5sIhfm9thkQ7FCZ63DZ2CnPyDnZll7m1/Ah7/S8xtDpJrVfg2qtXOPPoMJ1WjYpXYzQI2RJHPGMztC+MUZCafQcmeNmhS3j+hQaxiVEuvODkCYXG6QZ0Fz3Uhk9GWM6vVog6HqymIARuHdZXi3x05fs5NCBYmqvxns89j/YliVGI0FLK50m0IWyC6yp6j1/CtrFlNht5pvZFHJ0eIW4rItfBRgbHf9HKPCchNFgrwIWJwiZzT3wzeSem64Tk3RbRksOz01MIB0xekywHuEGXzVnFqBGA5RVbTzHzzE7owWC2y4Cb4ucTPjd/EN/ROIMp/v6QznoBFcKPvOMzzDxdZmt9kc8+dR2ff2QHmT2bRNrhTG8ER6QkJxRpQWBjeGxtB3ACawWlOMJgubJ6gc9OXMOb3vA4OSfmw09dg7ECGUPbBmgNh5weXjaGrOX0+ijf/p//Nbe89Um2jDQ4cmEE3005MzcAut8C02k4bC0twbygvlzGXQGLBSGI10GmAiEd3ETSO1RgMy8Jy6AyOaSKyKiIt5efoVdy+Gj9AM/pMfZffYLnzhxCJg4VP6a6pc1tA0f49NIBih2PKLB8aPUGsoc6hE9nyDhdzhuLAH7l376Nf/s7H+KjC1dztDXOdw4f5sbLnuGxw3uxbYka7aGnyxhfkJY1zTiDAC6cqoARSAM6gCSjcXtgBlI+3biS23ee5Kf+4D4aJscWf4WS0+MvP3gDoiVwXUCDFRI0yNgynqlxdXGGz9irCGOfS7dWue62G0gyD+KOxIQDlo+v7GbxWc3c5jBkXKwj+Z7XHGZPdoP9mVWKosNHl/fys1ufZKff4PxmnroKiIWiPNVgszNEUpSYICHzwyHKDTFfGCWfdNmzq8GZ42Pkts2RFHM05hU73QukQyUGgybHT5WwSZ5YBKR5jbQuCIHxLVYZjOuSZASikcN7RQ11PNdvpfEFjt+jUoK5uSKpVkSZBCEkGIPJWJQxmCDlwtoA+WwblRqiRGPKkspIm+nlEYSnsK7BOAIcSUGGGEz/OiAdTEYSFh30kIfFInsa0wuJZIKJQpxcgPIccoGHLyxdItJE0K1KSqJJaIf6yZOsR5pJcCNNikFGgrSTYDyJEP1ESd9PuG/ZLAQIx0X4YF2BRaA9ibLQGwSRd8mck1SqIZ01B2UtQlisk0JisTg4iwaT6X8mBBZhIXUtKjGongYD4sX+11TZvihuqvtaIYEk97qY8JCDPOuQX1A0wwRSjRIeOk0hl8EOZpEZj9RqqHoYoVCqwMpqjrDnsrxZQmddVCbACokuBbRuLOO0EnhyDW89Im4ZRKeLHCqgB7MkeQ9ZzhJmFepsA6fZYfFPnie7owLaYExA4hWxhS5uLSXwQ1I3YtfACsdOZTnWU0w0DHJ/Fb/VT6AhDKM7R9F2kM1HT5EmKXKhQdRwkShE4CEqJdLIkFqYb7QY6xZZ6fZQHUmQ9nghsFxjDMvNLnOrLdpRyvTcBlZrxoZLZAOXZ04uszrfwsRZostjhBNxpLVC2ItpNEPCMCJfzHDVzbuxRrCw2SZJLHG+yXoY4l8WMVmvEifp3xvfZTyXYiagks0gZf/9O1g8QLPW/2pg11+yvytdDhR3/TfFjhe5yEUu8j9Ks9l8yWPf9/F9/+/Z++9menqa5eVl7rjjjpcc5+UvfzmPPvroxUTKRf7PcHJzgQ09Rm9DUF9aYcfkS8VCrJ8lkzW8fd8dlJxhus0uGysN1OQAnu8yNFpibjZDD4NxJVtGqpxaWyBd2eS5T6wTHAxQt1uuyM3jqJd+STJZH8VLtYcinTLbbVGseuw8NElj81+yuzzNy14N92xsIzWGbJplpKkIAocdO4aprTWpFTwe7e6kabPs9dps0RrPc5Ce4k8O/ypVGfEtt19DqRrwyQ99BB3B3swSBw5tUlvdguN8LZHS64YU5gxKwq6XV+jIhOZ8jpzR1LKDGG1JhMUmFpEkBJUCpYkcp3YKtJa0BkMOTFZ5tDHLmZE2xUaXTCjJuQWKkWFpw+VMx4eWYTDSCJ2AFWitcTHITowwijRMGZ4aZHj7MKdm1rG9FBUoitUc0WoLVwnSdkzuHHjrEdWrehx/bivFsTrpjS7ppubsrxfJrDdIc2N0XxORXlkj51o2VwqY2MUg8B0X2YuJopT4dB5tLHmnTStJ6XZj9m0dwlEOw6JCN/RwPU2Qvw7ltRG/32L5kjyiIJGOwCiLzlvwYWGpx1q0A+EGWOOQxnAsDchGEabpkSYJjjJMDDRI2pInj0ywdluAlZIv1YfZk27QjRX5KZ92pBhackn/qoF3eUwyVwXH4g6mvOv2Wzh5bpVOrU1sj6C6R4h3vAoRKJyRiKFkkxsLJbZnvplj52doy5BUCO7e+jZ+a/lX0VZijOT2wnGOhS9jbq1GauCeszs51RpgoNPj2bkJ2ptZYuOSFLocEwVKlYitK1lE7KAzMDJWoteK8G2PX/zK69i5Y5XDD+7FBC65vKIHfHT+Gq5xZujeW4SRlM6iwunGBGsJNtTE4wYTzrOw0CbtJBBIVE2xa/8uLsHjmelVOiOC+EJEUotJ10rsP5hhas80h0ZCnnh+N1HsYn2HKGtw2pY4dpmvTSL6+T/mjhQ57xWYCGDVO0lTSIKRNvc/MYS4O8Lxwe5Luco7zWfzu3lV5XFi6xDYpP9dtbCoK1gB3aLDd48c5tbCAh8vHuBjR2/EZDWxFowV66xqj1QWiccc1haG8R4oEnUbbH9XHW1cutIhKSqscTl7uMqdJ96KQXH7HV9hpVumJDpMvXGGa/PznIwGecWBk3xpZTfXlmd4trUVJ0h5/8oedk6uMFpq0Rj2OfXMHq5mhq22xmrq01MeQ6WUhVyWOK/wOg53F4+ydHOV+GmPv/nKVWAEWEOIRy6N0BZUT0Gjhx7JI0oC6fdth3VTsbSWZ/sNs/wrZ41Wz+VnttxNdFOEqrcpb1nCCInIWDa7WdaOBJzKTWA8h0qpyXo8wHd+y6M40mCBoaFNQu3yyAt7SZdy/HbxLt4af5lMC8IaHNy7xuHzY5BYTNnQbAcIafHW4762qVBgLEammOE2E7e06JLjySdGuONukFZTsxWe+vIYJ45txWk0ERkJ2pIUFMmQg9NRvGnr0+RUzOCPP8Tv/NRtvHB0gZmVP+GuvW3e4J7lk0O7WFoY5/UHT/MHhS30XhR+3pwuwfAGAKNByIX1YeZaHn924nJem1+gZTKEicvZdolsUXD5cI6nd8XYXBYx70Gk0cLjsfMDZNsJ+27YRHRXOfO5CRI1iQnamEMOfLGFd4lLN8gg8xZVB7RBdvrJDGTfBUYaS2gUMpFgLFZJPn94F7u2hdQ6BbzIolcUlAXadbCuhNTFpIqoC7l8BpukSNclXN9JWDnPxskCSkV0dlr8NY9WHLCYlOmkHo6XYkKHJCsg6yCMwUYxJAki62NaXUwuIM06eFJSyAWcPbHOVpbpjSoqJuTK753lfLvHs09cipMNwI2xoUUJ0XfrqbjIKAWp0BmJE4FSLuQN1pGINCW1sp8Yo+/CFo24iNjFCTUiFYjU4vgCtMAVgt54Bu2D3wZblOic328xciWyB/75FNkMcQKXtOhhpcDbSPotRa7FaosTWaxS6Bx4fsLYZS2cmQydF9z+hT3W2LSfxBVGYq0GV6GaMQ4SlcKy2sYPvHOYZLCI0g3IZhBao6SDTCVCK2TPgucgrEDFMSzXoN1GFFxMcTtiqELUC3EbbdI4QWNQcYxqh5hsHqUcIMXLJEyv5Di9kiPSwJpDq7VI4ofs3b0Ft9oX+Z3akmdjpQvXTbD0+AKkKaIeYQsBZn8OZRSrtSY61RQrGd5w7SH+y8PP0H2yyM476gyGl2Pzgj/PPsTKq/rvyZYPSQLPpdlNCJshYRgjogSnaYhVgBUWu0Xj+gptLFGsERKmtg2TxClXXLoViaBkqowGExSnMixNx5w6vsy23X93BUnO9/i2ay99ybY4voee7bsm6X/8BeqLXOQi/wyZmnqpDtPP/uzP8nM/93P/XcdYXl4GYGRk5CXbR0ZGmJn5hx1r/3dyMZHyz4xGu8i94QGSesJV/laGJqovGU8SjcGiO5rN9RZe4NLrJpgXW4Bcz0VYSyAkKMkV+59gKNvmweM+Egcb5bj3nl14QvKDV7/0tcMwpt4KabWir20zKafiea676a/5i+U/4pVSgFJkyTA4NEh2Ms9GlDKcuuQKWYaGCrQ32iS9lMOb21mPCqSlJjsW5/AyPgqP4UKe0WCcyw5MstRoklpACYRqYbFU8hvIr6tIWWysEffqKCvRmRaXuJuUxzSPuMPMZl2EAN+T0O1X0lSyim9/0008OvYfWQzL3Dn8PHY5w1laNKSDGAoY3ghIE43spGQ6ORrdFLmZ4tV6dCoRKIvWGmsEUSskDTLUNtrMX1in2eyiWxEIw8piDdeVjI6VUMrh9Ml5KksxIol49dQxjmZGKXpdart34x/VzP5+EwTcnt/LUuM8t5VPcCycoF4sYJf7ApndEtieRHYN+bog1QbRBeEYEDA5WUFIgZsKcpkQhKDhb+DGDsdvcXDWJElJQFFybSg5vL2DCCzNkiLcPE4ishgfrIEbJk7w2LmDqJ7GjfKsLyvcUoqa9xjOZvkXf/JWaDvkD67zL19+PzqA/KhPc8kFz+GAk6OYZHl8LcJISeTEqNKn8MVN1HspqXAREn7sLY/wN7Ut3Dl8lA+duIZkUeLuEv0WCSxow/GnL/CG3iWcGnmE6wpnqXQcSrrM+TDDve0xZsMCIhGkseIrzZ1kVjrszJcJbjzJvDdMSp1HPnFdv6w/cBibqNDc7BEiaN5T5XB+FLWusVfFtAMXkUqc8Yh0ziHcLLIy3MGxPRyvgNcFHSV0zTru5IO0PnMdwnFQ2nJw+xBubNm3e4qTKkRWapgTBhtGGGLquUVKgx2ee3IXPeWR+grHpgipMFFKy3UxKFQPMJpLmiXctz/K/qDFsc9dhvtgyMAra7zl1Uf5jfariUIfL2f4WHoL140/gzaKnExYS3MkoxE2cRHVmHo34IorznGru47pGN4+9jwfzdyMrVkcRyAtGCM5tjyKlinW77f8iTFYjkooNGkIXt30b9JON5n5piFkKlFDhssyc7RTj5tvnub+5i6uys2xvqL47eHP0BWWaTuElJZ7Vi9h/+AGHR3g+BbrWJanK2zfu0Yj9viRVx7lZXtXOT+xyDtemEIngrFOzN7SKt/3/JvJuCmma5COwDM+omwxHY3Q8M43fInMkOEPPnUrpiigJ6EDv37/7Xz81j8gEJqlTIZCEOI6iho+79jyECsM9e8jZyzW8zl9bDsmMOwqziEjg3RTFhOfNj6vuON5fu3730T6ygBdBG08vKJAOgJfQygcRGxxBCQtQWexAMLizSWIooIUTAzKwJbBDjNrZdR9LqVTLvdnD3HwhkXu+439zJ7egixYssEmWvugJN1xgd5qyTYsA04HX6bUvQDRjmjvzfF777uDXTs/y0ZGcUVukc3ZYbxCl3bqojMSERm2V0PaixV+q3M5q5S4q/oCzz03ykKrwoFDR0ifdphNKqQdxe6RPLsmxzjXiAkHWqTr2f65N0nRboIOE0Z6dQIn5LR/EBEZWEpIT4bYgwPYgkum3kYdcGlriU0EVlh0ILEOGNf2hYwjibMaoosOJmlTXxvlsPCJ8wJTsIyKFktRGddLUC1BnAXZVVydmaZdddlYKqJ7KfFslQufnyIqCeT1XdxzGulIyjXNiNdCOpqvlBPqXYeJyTrX3D3LY0/sZm0xhy24GJ2gcLA5hbWCHhbtSOZn17np0ll27pljfrPM4rMV3EmFCfp6KSLnQ2yw1qAzor8tNugMJF7fWtsa0Reg7VmkK0DqfmuPAKxEFBxkx+CebyKlJJx3cUhICUBJhAavY5CdFL3TIGKw0vZ1lNrgNfrtM+GQh/EVQliiwOBnNNoNEE2LRGCjlE7qMpWpU/AjFsfXMY9V0AUfJ7T9KpeSAxaEcpHNFsGGhbxEBC5JVdLb4eCsRjhSYsMuMhOgOiHFwwmi3sbrJiTWIhynfyFB4DYiaMUkuTWSoocULonrkASC7uWDIAXy46dx2ilGSSJPUdrmEzcDQuviJU2CxTaimMM50uO5x5/kHT/7HNWSy+NfPECzU0N3JP6uIcKza6gwQXdCOnnJQMOQ1FpcODbNXbdcSvuKiPuPnmF2eo3w/l2E67t5atsFajbF2AAQjN00iOO7PPDIcQ7PrTFG/3ysNgylWYnOCO684iD5QsDEWJlelMVRknazRxwmbJkY4NEnz/KX/+l+CnfOUGxN0VovMjP9d1sgL6806PZiWjIlH3jsHO6Lyub8q9jofISCdyW54Nr/wcjxIhe5yP9TmBd//rF5cQ5zc3MUi19zDfvvrUb5er5eZxP63QL/9bb/k1xMpPwz45svv5RP/ZdX0Zne4Ka7duIH7kvGezYhRtOM2qSRJo1TGhsteuMVKkASJ6zMreA0u3hykYya58ffdZIv3vNNHLhjC0+MLJEGMenGN360pCvJFTJkvv41jaY4dpiXDZ4hkCmH21vwo0FCT6CF5F+8/mbOnVrk1JEZ2vUux48t0NxsQWI4OjtBkricag5xQ1Jnascw63R4WiwwHNW5s7GXex47hu4a0IL3ffGHCOLn+OJzl/HON35tCoWcpXpgDeM57J8qUWw0cDOG2PF52kDU7NHthkRpX1ezttnl/sdO85p3nKTs9HiquQV3rUl2rMVUdhMdF/HiEhvrbWILk1GGZdtBtQw0Itgd4lQTkjhlfmmDsBthbZY4TimVM9TDqF/CrcGkhk6rS6sVsby4yYVTc2QCD6+T8PyDe/nRf/kpMgiWcv+KbZdt51ef/BSrzU1WrvpT3vOVYYbrWWJj0csBaPNi0G2JfYuJDSpM+6udORcfFz/wEI4i6iXUGz0qXpddxXUWmgmV0i70UIq7blEtcObrZIYnEEGdgp+gXYmTSWln8igVIbCouo/T7a8eD2dHmNN1mnNZhjqC77zlan7lgUdJtxhWKXNiZRRciPwG1h/DagGOw83XHeLwc88ircDEko8tafZ1E9possFBpmdvx2YdxnJ/xBfnD3D2wghXdrOcOTrHuVNLuOUC7WaXpdk1Ws4FmiOaexq7We+ewqnPc9odIdOL2QwDBjYj2r0cdreG61JuLO9ncfAJTCrRZoDnXoiJY0FS7ZKiODezQupM8Rv//s9495ffSjLgsNebY+UrI1x2zTz/6sCXubd1CVHikOIgGgGB8VFRQlxwaLZaPHXfFKnot3wNH6iwvB5itWE+2sSkMcJLwTUkwmBiTTOGVVNgePsm3hFL6oEXplT9iMVcjvp4gC5b0g44cUggM+zed4rDjx4gH3YJreb6XRe4YvsS8cMeajFA+y5CaG4tzXK4toPxfIPzrSrlsTqh9chkIr7voW9iY36S/df+OWMjDU6/UEanXdwwg3EF/+H9305us052Zxe13CWUhkB0yXk9rvEX2DRZntyxn/JcDBb23j3KWqlLEkgCEVMSIRkZ83Pn7yI1ioVKibZw+XdbXwBg+XABqQxJLInMHAcKmyRGcdzs4dmT27l81zS3XXaWiZ2rLFpNoRSTSVNSq3jm7AQPnT+Eu6lBSKxjsbLfskHskoiYci7ic2uXUJAxf/jd96Ctz/uO72fWHaQbe8xtlNk1uMl9a7tZ62Qp+V0mMg20zuA4BmRCPojYOjLPZsnn7PQUohARDLT4SO0gQ26TTV1g4GATayRyMSEdlSDh0fExuAy6UcD80QGsABVZRFfibCpEoHnvj3yWH/ndb0VnFBaFsglx04c1Rfm5HmnJ57EPXMbhD1wOYYrICnQORCvGRBaEJNM2tAdjMPCF5b0csMt85Keu6gc70kG3LD9y/jUsbVTZEy9xy+37+Mvf245ZMTCsece3fYmjAwVeXWow9bspA601OpN5jvt5MtUYYyXbMy1GnS5PrW3FVlzOXVhhJOfwI5f8Db+7dAun3C1MjdR5za3P8dd/uh/hwcn2Hsp712kcHcDkfIz2yCchTSdLa2sWayW5TozueDgtgy67SC1we6ArglLSI142mBWBzXioSzXtUkLq+kSRR72apfzliPCGBN1WyOEu79qao5ge44nF7dTOZUhNymrY6P8r/AhViZGJQdoM554c591vfpxOweWDrodSksmpHju3r9DpOpxJh5k3w9hNieNIjHCQicEowVO1NQYlfOWJS1gaqeDXatSWfM49Mcb4a1coXbJJNOux/KUiwhEIa1GxgVRiPHCjfuVfIlOE1SSXlLC+Qs6G/RY1Y1GCvkZLK8HLRahIYiKNzEpMFGFEFtVOkeUUORqTH4vYKJUx2y3SxqhzGkcImtdWMEUf1RFYZajYJqLk0X3xuoGmn5DBsBFliTses2sjlOIQ202hq0FYdMbBuAIbuATzBhkboiwIYdCqSzrqYhEET3axBQ+7y8LRCIzbFxFux3jGgIlJ6AvU21wWlMRZaOGuSLpZgS15SN+Foo9IBfrAKHK1Re/SHM5KwsuvX+C5ZILatjK+csg9XUO1ezh+hjSEP33nXnbfNUVlBBrtCBLDRLXABbUKAlQKxa+sIg9k6TQN93zgIR6vr5PN5olSjUCQtEIevfc5Hq9kkW+OcJUDWN6w4xKUgUdmV1k7kLIUSLiySP6IJdvVpErwjgPXIoTgxlv3kaSagaEiaytNXCDvKk6fW2LpfA2aIY7b5JJD+9g6UfmG2Mpay8JSjdVmh+OdTfIZj6lqGc9RrCUdzqc7OVh4HVL4LPWWqXgVAuXTSbucbJ5nf3EnWSfzPxdcXuQiF7nI30OxWHxJIuV/hNHRUaBfmTI2NvbV7aurq99QpfJ/kouJlH9mSCn54+/5Ds4fn8PxnG/I4sVW0yPkwfDj3DH8b+ksW+obLXTSF4itbXa490OPsHxmATXVZT30CYkJxwY59KZLeGJpnZ3ZedYa5ZccN4r6N+b1RofU9FOUcZRQr3c4fmGcuauKPB1uY7taZzYZY8m4rKuYuXaTvFK0N9o0ai1G0gHiOMXLuuwJVigFPdIzN1AcyzEyWiEbaMY2imzR/S/sjtEBiC1vuvPLjI3WOPzAXjAKay2N5i9ibYLt/ghv9l9GadRnMreLR1Yy1LTH8plJ8CXKUUwvNvoODI6PtpAJPP5w+nZ2VRd44sJ2fmykQL64jFOJMJ4hV9/Jcq2JMILL3SpnXljFqVuktAyWQ4KixqSWXNaDJEVqi+0ZMrkAYUBkHJwgw2vfdC35Up5mPSTshOgUlHT55V/4dlohDNh34PlFpOMwWh7gN/74+/jZwz/PyFiIn1/ngXsugYMhxoJ0BFbBeKHE2WSz3+uflWgHhvbUGTUhS8u7aXdDioUcvbDLVZVZJjMtxv0mS+UDiDVFeCCErmD3wwnLokZRQG22hPDghXIdk8mSDFtUJ+HEuUFAYwKXUpBh/6FnOM1xXjwAAQAASURBVNGYZEdphS8+VWDs1Tnmzjb5w1f8FU+dqGKky6DqsRklWOHiBi633XCIXz/9TN+VIpasHB5n/05LGpm+habO4bcTtpZWcdwBvuWaZ5EPXIuNNfXNBgqLMZbJbcN8ZU3QiAIsimfMNnbVQtxRl4fndhGdz9LolMgEGidr2Lw8y1vueiU/P/MhJGBamuHBiIVOmfqYolTOks05VP02Q8MxP3LbA2ilGEwkP/rYLr5VrWOUJNUaYSVRI0e21kbnY4Sr6O5KqCYx66eGkL7ECstb3ngTf/D406jUYsuCdmSIOwE2iYl7BieRNJ+uUP+mPN12gFkzqAjSRJF6Chkk4PQ/Q2QsNu5x/PlVnM0dFGXE5MAGFzoOD3xuG6MHeoxHLVajLCJ0qBbP8LbrlvnCM5fw6MoOOqGLeE5TuaWHxLBjX43efeP8+JHbGYsj1huS7JAhzWuM6zK3McKkP4++kGfyig32DW7w6uuPc1SMcH55iB0ja2gvQTVayDjlQsswdbrK2haN3T2E5gIfO7WP3QM1BDCzkaObuEQvSjSUvS4b3QKTlRpvGznLdJjl0sIGj27Zy0yzStbTbNlv2dCDTHc8Fp8eQ2cVsXC45/4rSDck2aRH8e4ms7IMwwnlD5foCoFjBZ0W5Jdh3QyyOVri333pFowLE2aVUGf44d/7Ni550xmcvGVLqc6rSy9QMAlm00UNGZKupFSFEzMjDOYa4KRsy3WYHq1yvjuEl9N8R+UEDePx/sCheCyhca0hyMecPzGF28wijaLWy6O3dFELGZyGRqYOfjHmOTWAiWOsdrEjPlY5rMSSXHGN1qWDWE+BEXhxxL6hVXKdiGNyHPFCGSF7EAQMLnQZGajjJ4KMm3Lf+/ewuVGEDBB2WLtlCPWIizcZUR3r8Oh7H6WwMoVX6GKky3tnbyU+7fGR3YtkvxTS7WXxd0Vkb9AcXR+nljzOis1ipaRTjOk+P41OS0wOtRkMQgKpycsur7vxGK+58TRyAz5Tn6BpcoztanDO8QlWIZd3EQMZwmIGUwKEwc1HiKaHDSQijNHlDDpr0IFgpZ6n7CQgFTKxqGcl1WqDeGcR62jqiznKhSZODWQzxR/WHH3oaVbnL2djMY/wFSkGvylQqSWcVERBhlyhB+uwvFbiv3ziBkaH2qzFWRxXM32+QuapHRx5fgpCSyoFUoPogXEM0ggGSh3+6Jc+zfRMkR/8y7sRaZ5tsokXxBAlbHvZAk984CAqtlCIcDspaSBJyz5OLUZoi9D9FibhWCj29aGsFpghD6etwRjEehffC7Aa9rxslZknqiSRi3EEnmMJU/r/l6ymO6RIfYHYrZAdhazEuLHGoNDVAOtY0BKZQtLII2JAhcjVCFn0MVGK/pzAKUjiMZdgVJP6Ei1TdMEiIoNSPtZzsMIglYfJaaznYJKU4HiMlC7OSorqJiA1cj5Gx4rWDQNgBOLZFYLFEKv118QKhUAYi0o1JAm5liZ1FdK3hGshSgskDsmQR297gJMVPPahAskrXUxG9FsTfdF3xOp0cUYHMY0O5x5cIbPLRbcj5JDPVdfvZWFxg970Bo4FayRJbJFKMrvY5MK9z7PWFrS6EUIKet0EFacUw4iDpXVORB7Xru6gOpHj53/7Hmw3glLf+Ut0Bdkowl0zMCz5o7k/56ev/EHyL8ZhWhvqG21a7ZDllQa5rIcIBblGTLan2HNwEseRxHGC531tQUoIwa4dI4yHMYV6jkLGx3P6gvqpCbHWkpqQzy/dz2+e+huG/YD33/ArfH75Kzy09gSvHLmB142/4n9ZnHmRi1zkIv+r2b59O6Ojo3zhC1/giiuuACCOYx566CF+9Vd/9R9tXhcTKf8McV2HnQem+oHJ1yVStNZkjKStDSpr8YqSk48scuypaca3DzE0PkCvE3Lh3Aa23cNkDGtaMZemJC7I7AaXjCzylupZpIyAX/rqsR1HceHsEo98/gXmLqzwWx94F0YbEmtJc5OUpeVt1XPscLv8VUPwgtpJLyOZPbfCsHZYq/VYX65z95tHaY0UefbUcX5s6xdwhOG+2gB+5ibiRJNzFDfJDr1GhtQanjk3/6KeRQ0pDDfedpIzJ/YTxicJ23+IBcLoFsruOBVVoB21+LEX7ibVkrvEGUKd4md8wjiGFGzgYJTi+77zZfybT6zxOTOKd17QuKqHKXYwaxnSmiEQkgwS4cGBneNUPnscE8PEtipbxBxb5SBxkjIxMYgNY5zNEBtqHA2+76KQ7N0zyTU37CWTC1hda9Kstdl+cCsHDk1y3csOkiT9hI5yBJWKJsh4LM1t8PL0dfzFiSbLZypceekFxty7eG485tn1NYSEt1x3Ob9wzwM4Pch7Hv7wGsdzJTp7elRPwtnZDW64skAaa672N3hlYZWFNOBPlGTgsZhwwGHkeBurLWY15N3FW/nLwY9jpMN6BLn5DK0gRXYN3WYWJQwiSSmXM9yy40nO62mCk4q/mB+i7sGOqyuMOh0GW3mCfIg/YTmjt2B0ijCWJI6JphKsFXjLlqApWFis0dYxjvEY313hyPJ5Pre2gxt3rhI2HJ4K5+ku5Gn3YsayPiPDFfxxeLUc4ithDSEEoShxqTvIWHmFanGNeTvGrBmiNp7y5qsf57n6OKVqgTc1j+ILy73npxBRgitTrpjYgYkeZfvwvyPjr5LDZ1Jpri7VSFOB0zU8eH4Mtb1GT3lEiUS2BcoRyHqT5iAkw2P0pg2uY5FaUy75GGNwmgYX0PmUNNTEKUSzHRwDQkjcnsRpdpmodshXejQ7Tt+GNXXZtXuFMw1Bq5tHZjuUywbyGdIP7+dZqak9FVEJVhiKupg04nVbnuFP5+4EwOSzNI3gZdV5jsyMI5RlS73NZGWWNw+dp6lduNznUTnG7oMrqKzDJaWz3Pe7u4AKh64+z9rH81xz6SzXb59jeaRAe9nnyfJWlC/Y6AUUd2wglzqIBQGzRYZqLV5x4FreuOMqWumjHNPbmTk7SrnSpjo9R+GhHL8ydS1COrxp69O80J1i0q+RlSFlRzDRS7h2eJbj8wdwohyXD24CcLS3k3ufupw0lBgh2LF1hZff9BwDfsy/q7+Wy3bO80uXP0R8g8t3v/vtCK248fLjtDIlsqV1Gj3BW1/2JHfk53nw/i18yinjV5u8tnKUTt7jyxs7+eBP3s6Y6TJ8zSKv/RfH8LPwM1OHeViN8YHwcl577TGu19M0B4ucbw7xQ2OHsSKlqFIyoyHymGD/1CJD+R5nO1VaZws4gNKWwiUNIsfinPZQbY0w8ND5Xfi1FBlrYjy64w7Gc8niIqzCIghLEnowuz7ALcNnMUsG66TYNkgJetEinokZGNNck7tA8UCHUye2ULs6wPEg5yeYQkRyJs8dYxf4QHgJiesgaaOLHt/1qqc5VRvh0af2MrgxRzQUsXZNATWtyJuEmbbPfKNCJ3F583UP89gnDyFNg+qWbXz22D4WFnNsrazT7AnWrWEFn+31VbALHOuMI3BwcfB1SqubI6hZkrJFFwWJUjh+AkYQzHRIK3XMnWVERuG2FYd+cZyT718jORkSt3xc4XNwfAnbhBNf2YpouQRfjFGpZXi9wd3/4VnOnRjj4++7FmFTEBAZiwgMwlhsS+IO9gjP5jDDlu//1keoN7OsbOT56/svYTUsMP/kpciuxqYRpuziVgVGJ5iMR5oKBsZDhDZsGWnghoKBtEuRhBMXJhCex/PHx5Fxv13LOglEMZgcCItb9kmsIHUFomNxEgGOQqu+PZcyoq+HkmjEQAIbDtZXzNcG0dpijUQoQWJE36ZcW5yJlCDfP+d8883P8uljV9BddvBWY2JP4DQNVknkcg/Pd9AZhdPRfeF4ExEXMzibINYlekZhhmOQlvqIQ2G51xdmjRNEbLEliVzp9qudkgSvpxHNEGklzhwIRwES2l30ZoxTjZFqGDnscPcPr7DXX+V3f3IbomUxAoQjQPfljURqEFL2Ex3diMzRDXTGQSx2cDZb5E2Cu9BjrebQzPuIa0DHPkp5SCIwBttsY4eqJKJMutkE4SA6MW0Tk835dLM+uC7S9ZBrmzBcRZQKjCaCpbU2qVLYvKKTt6iyR27LAtuqdWYWBplUOeZWN9hYbZJLYLMJpighSMnMhYgYwozHB4+v89Ty7/Dp1/4Q0E+IdMOINE5ZDu/jR9/xFzz8+m081c1RDtapRDmSMCFN9UsSKQClYoZCIWAp6rC83mTAzTA5Wiayeeo6hxXD3L/0OZaaOdZVX2doR36KU63pi649F7nIPzH+X3XtabfbnD179quPp6enefbZZ6lWq2zZsoV3v/vdvOc972H37t3s3r2b97znPWSzWd7ylrf8r576fzMXEyn/THHcb3zrjem7OgQyy5t3fCfDwRRRNEeSpnSaIWsrDRqbPa5+xaVsrjXxt5wgr9fIWIiHHHJOEWM88jJhyEtecmylJHEzJGyGzJ5ZAcDPeOQGAqJizJqW7JUxBsGIG6FCQeZCm5qzQjJQZNeVp3ndW3+HjVqGcuk4O3dMsJZaJIbFv6qx/NqfJhXfR5hrklY/gA0GCaMrOT6/isEyLtss6Tx7nH4lRmpdppM8Atg22OFlr72FIOsz22ywfmIAEsN9rX1MmIhMxkHrFGkl1hik1lTKeXaXhlj+dAPhGBqNEG9ryGi5w2K9RNiOSDsxSkqyrsf/9/538vTTMxzaM8qRh05QGSyQRAkXzqygA4Gz3kQFipHxEjfecpDlhTqlSoDvKjrNLh9+/8PMHF/mihu28+0/eBfaGJZWNxio5iE1tJshoxNloiihaHP89o3vofzNBT74kUdZXW/x4zfuwfEko6MVnj+3gKoliBC81GEmyWC6llMnp3i555ARknYzZHqpzvZnBrC3ruImHifrbfxUMjiXMDKaY+lkHdMzXDe1g988X8TSt+8cVw79umiJ9RWJ1JTivm3rZKHNDtvg4WQfJukH+5GNUGmAKwxKdhkfTtGRxkpJo9HlbLqC8g3WSAbQDB3okawGxCYlFRdYD3+NBxbuhHHNSpInaiiwPR7uHae4kWepVMP1JFE35EK3hZCWsWyLZuKTzRY4nmwjDlM21ooIbUkHBbcUZ0h68NQzD+OUEhzHUJnvEoYK6cYcGtrBQ7V/z5O1Cd5QqHOoLLihusb7Hn4dj5+C3LIgtz2iHvk4TkKcujgOpKMFvNUER0En4xJ1PBxikLBr3zi6Z8l3LE5qWXM6JCLGLRh026AcB+v0b6aOnd5GMqpYLwUMhwqcJsKXVFXMZ/71H/OTF17FdLeKetJgEkG6WiLcWye6dICSWSbYHrJjS0Q5mcc+ZxDFFNmSvOvpW0i0xXENAkt9Msu7i+e5JtgktAr7LU/zxo7Hbx2/jGuG53jnyCm+56eP8mRniPf96i7ynSKuSenWBGeODFLLBKxcXgRPMUwL9aks9kCb5GgMpTwb7R66HtNZ38Jo1eHs0gSpcgiXPUoLm6x1y4wcS7jzpmMIY7hsaJGiiKmqhGfCQWoqpC0tkerwlyeuoTL8ZcCy1nHJul1qmy5Dt03jXN7iYw/uh9Tyb177WY7aUQadGEPEt77iGc6eKdHaLHP+zBBjlYiPzg9iA4fFgQLjoovvRQwWmnx+YR8v33aG2nyezRtK1M+7FHctcjyaAAyFzipFE2OfE3zpxGW8/W2PEQUOFxaG+U/xTfzyFV8gTh0244Bs1OLK7AyXVNZYurLAB+4Z7+s8NEAaiV3M4q+FuFFMNyM4pYdJJyBT80iyFtlOMZ7Fz6bENsUoFyEFlYNNNp7PI9yUu/ed5Ht/+Vn+03NX8tgv7EamDr17JZuXgrhRIUcsvaqLUC5uwSCUZWysznYxQyQD7FZDUgMRBXzvTzzOt45Osz50ljveN4hNY4wNmMxv0KsVSTcDfu2Dr6JRCrBG8PzmrhcdlzTnHzrPI/FN+HXoGE2ukHAnz6NdTXclwBgP3XaRMsFYl05kcVVI3HaRmx4GlzAVVE2CNS5IQc/1IDQIoyhuJlx6/RfY+u5J/vw9JQYnLCUn4ofHH6Dj+/wyg7Q7Lpce3EJ7s8dyJ2F9vcDc80VUN8UmMT1Pk7gBQeCSaXWJpKJ7EpxWSDzkIK3i1KkxHBKkIzGpQUQGi0YsNUmmhkjyLmJ7Bk95qHbK2eeG+KkPvpraso8JDc8/s5ULKwM4HYPNS1bWhgl2WmwH8jMGHBeZGpyOBT/COj5W0HfqMQIDWM9ijQBhsb4DjiAoJ7RCH6kF66fLeMstfCelNNLl1m+d5aO/fk3/fPyCYPS6JnvKy9y29RQPze2mszFAmjE4oUIgsVIQ9PoOdRaNJUUYCQMBoiJJ8XCbmuiIR7MdoOMs6Q5oS0thtoswoCTY1S6y26OzL4dse+Q2NaInCLsN5ECA8T2EFAgUVnkY12en59BubTCaW6ecT6nshrXjqj8PRyLzWeiEmKSFRIB8sRWqHSHiFNFLkbvG8Vc6yPUItwDxgSy6KBFKIYoFhDZ922YhEQ6IToxNwSYR0vp84a8+jazlyYwOkTY6OEmKtAYn5zDfDEFlCLSgWZD9UjnpYpSl5hV5cinASFhY7LFhF0lz4Gyk3H5qgDe84Vp+6N6PY+ttpCNID6UUCoKZJOQ1n/kF3tm5nUuv3o5E0mi02b//c+SchFeMnKOx8EPsqV7OgC2yMrvO+eVVgmKWXYODL4mzNjtdPnvkOE8/OcPu0SF+/p13ktgQKwISGzEhp0nal+C7EdZaLivv47Lyvv8dIeZFLnKRi/x389RTT3Hbbbd99fGP/uiPAvBd3/VdvP/97+cnfuIn6PV6/MAP/AC1Wo3rrruOz3/+8xQKhX+sKV9MpFzka8Q6Yc30MK5Bmr4I7U13HmLXgTEmd4zQ66YILHe87momdg7z0U8+zHLzHv7o2etoTQna1vK9w0/hSMPk8F99w/EPXrGdh+87yuhEmTRNcRwHL5BQ7nFkcSuNiU0GVcInFscQqkB2s0et0aU6McD+3TNYQNiI5eWnOd48xYe6L6fsdvnZX/4CVeVg+AFOrHyQppMlTXfQSVM204i0BL//yCv4vise4i/mrqJdS/Glg0uIQZCzcxTG+33HC2eO81N3fZLVdoH3f+RmOqUEhATHx8oWxDE/8O67KVZyNFSIcSxWWAJfcFl+gbxIyK/2ONPqEKGRiSWMUsbGqrzu7irTJxfJ5wPKQzmS2KCUJb+lQnu1h865rK63qQ5lsUIQJ5p8MUMYRlw4vY5OUjabIa6raGz2WF5tYi0MV3JfFVsSWBzPQTkSrVNWe126MgVrse2Y1ZkNdCtCWLCxJYxCcv8/e+8Zbcl1l3n/9q588rk5dc5RrZyTreiIZMsZGDDYmDAYTIYBPASDxxgG2yQbjHMO2JYly7KVs1otdUud883x5FNx7/1+OB7N+IWZMe9aM3jx9m+t8+H2qapbVX3Pqb2f/f8/z4wEAfVdilzFY3SgRJZqtu+Y4JMfvZyn7lqH621kud7FeA4bN01w2Xmr+MT8fQyMlRkaraKOQ7uRg0yyaDfJ+g2yI8B3kG2FFRsO7DvD+aoEMZxt92FHUAwiFucyxNq/YG7uj8nNS7yRFIEGy0LkXA60pin1dREYXrrW4/iTTc6cyJO6kuVVNp6XUKMBoUVZRegSpIWUmtVPZ2cbs1AFCcVKDmFtY8w7SF8uos90cNyI1YM2dx0bpjIjyYmU/OVNrinUufvj5/EHzz3IBVdt4yW3vcD8mTKZtrArkucPTPKx/MuIllzq63O896K9GOcW9kz8DA9F76NUjFlXXKG/0OWoGiARDrmlGM+xkUrha1jpS9FGg8mQjs2J2RX2RDHDxsHojPn5FlmpZ0ic9QnsZRvtOwhh2H92LWnRQmmLSKZ4wiF0XTauWaamDUk349q+Y+xzh6mJMtf/9Nd48tmbSY3LmQ1ruPLS0zQtj83+ChsvO8VQqUOtFrCzfZLPPn4NzjW9FfqoavHE/Wu45NUrtLXNTZV5TBn2tarYtqJqpfzy6St58L7zsTdI2t15Hv3Gah792jgdb4gdd5ziZaN7eSjcSiNysZVDHAjMaEA87kIj5eGjZxh9wMO7YTXaCJAgjKZbDrCFxfEvbOL9ehhRVXzj0k+DEETa4ktTV3CscoptF0zzzNPbOD2fcaprE9gpY7JFtCAZ3nqW9VcuE0eCQtmmrWwez7Ywmmuyb6nAtmqTl197kKODE3zoYxfT6trUn7dZf8kpUk/hWpphmVDVbaaXBojOKELH5fRTm5Aio1iYZnxXm6rdxiXlieUR/ulrl6LyPiKw2JesYi7pRzYtHq9t5/H+p/mrT92BtC3inGCzrLG3s5oRu4WI6jgND7kSkTxpozDYS2GvlaNjMeR2mB0tYWKBygtkbJBhxoqbw3IE2rcoVbqUS21W0jwzizl2rF7BcjTb1jT4Vj9Up1K8JUP7mM0XFi+k0bFQwwqRWUR1D/pTcpWQ37zsu9y9fyvJK3IoZWE9mBAe9jEbQBrJwKMtlJSQl0weLrN1Q42z0Thht8LYRbPUmmVyo21qAdhdgUol/kqMsTyMMQwXu4xJyc/eeoh3HrqUU+0J2o0cwhhUzqKtC9hxih2mWLEi0w6BStCpg/ElUZ+DTDKSlkBLkJN15g4ZjocV7E1luirBXhBkqaQ57yJDwcCEz3H3LIOihDs8zoe/NopzYBZLRr3vYl+iuyngUsq1SQOLpCxJmj6mIHk+DlhqSE4v5hCZAd8FodEI/LNt5FgF5ULnlgLe4wYhDbaG2vMTOP0ezT0hKgcm9inXU9Kqh+jatEYgqKVYqQEJ0jL0bW+RK3SYOdJPIgLwQBuNiAUi1KAUVidD5C0sneF7mlbeRXQSrMUUaSQ4guKg4uTUYO98U4Oaddh6aJ7bL9mPowVOSZINSMxRDy1iSHum1cayMJmAOAZtoW2FZVzEksH2QSNwWhGOyaiXfKzQws47iE6KtGzICYwRZAVJNOwi+jReI8ZyNUY4qIIFntV7tvoeJonRXsBsziLMKtzT3Ih9SlKrCZxqjFkL2YEOKkoQg33Ql0csNiHNwLGQSc+Q1uqEmHmBcF3CdWV2vOI48UqV2SSP04mwhUEX85gwQoYRph33xhXfE/9NnGGYQDtdTJRhLIFRoDSYhSap73G6neDaGmlpFB4y05Aa2knA0nyAKRnyxiVrR5TaKUYlRI02c2fmuGa2xJF4kXYlT+48jRIWP732u1yklvjr95zi0x/ewC/97u0kccYHvngBQz/+EA/MrmUleIIXnnmOrcuvp28k4D9++dsAWBZ86UfvYPfAagCKvocGOq2YJbtFmmm2unWK3pOs9W7DGv5xvvbsEww6Q/zbr1Wf4xznOMf3c9111/VS+f4nCCH4/d///X914s//Sc4JKed4EaW+V4UL2N9LtalUi1SqPaUvX4TGSguVKiKlmevCkcYEg/4sxbVNPHkPF+d6sZgv1L7IhaO/9n3Hv/iazcxPXkGp4r+YmuNJB89NeGByG5ckN/PR9POIsmD9UJFan4Wd81mzeohP/d01nHfRHLrQwRJv49MLNzErKsyJInK8d3wBLJzyWG7+EgtZxI5rFZnIQMKx1hC/+8HX4scgEEhZZNyOEQgy/d9d8EcrTRQtRnJN5KCm66XoLCOrx4hCHlWwedlrLqfbjREiQ/kGkSpq9XnW+0vMtPtZqJeJ83VuveIAT9y/hsf3nmDbjnE2rB9ianKJMyfncPOSDTtWs/PC9Wx+ZDXPxNPgOsycXSLuRnQbHabOrvRWIqXEKUq0ttlz0RqEEASeg+/YFPMeaabpRAmZ0kjLwnEktiWwLItGFtNOY/LlgEBaLC02KZfzRK4CF3SqSUu99q58auNWPKSlcWzB7/zSK7npqu3MHp/no/c/Ca0USUppneGyyzZxycWbGJyocvDMLPZpB6Nc8KEWxXjrPHJnBMLqGSGSKC689hkuLixTC1w+qwPCrRmR5WGrlL/57CTy9Hk0WidRpys4awNMatHfX2Io8RHGQgB5BgjDDsb0ojS7rTyVYsbYqozF0CYRNlEqmDtcwmoZuEjBguaB506ybc0QuwdW82AdnpxajdIW/sgkF4ys41TuIOFMicFyjdWb5nhk1mFuepzTOclP3XqWZsvn0aO70cKQ4XHBRJUHZ+uIZsZCWuCZrZrLx/+S0dYCPyb28757b+JRAkorDa7qO01mC0Ticd0bH+LAvRsZ2rmMM9xlqdCH1gKDoLHY4Z++tpeckGydGOTg8SmyPBht097qYS1mGAVOasidCQmGY5afraJcl7hok7kWlptwOhrmhlVn2NceZ8OVDfZ+NM+NO47zDTZxYGY1u9efgjQjVZqnk1FyQcrh2hAVr4PROdKSR9pVIKFQiLlibJ4/PnMhOTfhRwdO4KM49NkBJu9TrP3KGKfbfdiZBTakG4pkDy0gtc3AhUsMLMJLCvP4ecHf//l1+DkH4Tjs/s1lHn5hHG/ZJ4wF//TkadJ1F9B3X0i0yiW2QugYvAS0D4P3D/Gh//QhVpSFJyQNDXOHi1y8QTE40EQta1oG1ndThqohdSLyswkTlQa7g7McY4RmfwDK8DL/OT7TvICFYDfXWXsJnYAwcFgeL5IEAkGGKRiuGDuOL1JqWYGZbj/aFfiLsOBU6VYi1udm+Ks7vkHH2NzfWMegV+dvvngjUnuIQCBTxWe+fjH1sIDoWpAYBkKJkAJjWTBW5rlkjOv6TpEai3RYEByLkGj0/QZzSxOddxGRwjQNZ6ZsgjYoYQhHQCCRK4KfufC7bLi1xkNLG1kM+jn6xBCFyYj5pMSkrBBNOnxubguNPQGFOYUTKsJ5zd7P9iNHLdSshd5gIaRAByntaICf+sm3stSfwkaLLA+q4PN3n93Dsw+vZfJ0ETsXkskUVS4jFuGMBcKVOIlky+YzzOoBqnaXG95/gM+84WKEsrHikHjQ4/w1U9x+4wtIIXERTH+lSHqxjRVpdMlC2BZZaKM8jcmBX8no76ux1CniRAJbGUyqsRyHuGAhPFh+psSZkwK3vIVYzuGGmrAj+aNv3YT5pwCvUCSstFAzbeYXDaPFUfKOYvMbTzO4scEXf343TixgxUBZoXQKmUALFykNKvD58OkrqOomx7pVtDSAxKQpzvFl3LZBtQ1ZAJEUYHcxSmCMQ7sVs36sSlVq6lmCZVso32BciRASuyWQaYqVajqBhesqtJG027leRY/SKMv0EoEMECmEbSM8B0u2GCw1mRieY6Y7jE5g7ao27RUHjUNzpUj9cIAugNQa4wsefHAnF168wFi1y0y92DOrTVLseoSIcqiCQzYmWTe2QGO/S9zx0b6AGOy6RlgSkWb4N2YULwyRC8ssHh0iC2zEQKEn9Bgwjg1WDn9W4fs2puoRSYFJQ9I+B6kUbsnBRBIZWMhcQJqlpL7DmeOjOInGWlujPx/S7XOZbvlkaUQ+SZFaIvMeeHnQAlOQkGXoag7ZTRCpRm2qcmZmLZXTTZLFOXJxRFoMkK0MnXahmyHqDVLfw7HdnqiTdjCRwpRzRMLglkvQjnrX044gTjDVMpmwKYsQEXTpdj2i2CPrWmQdyPUp7Oka4eklzLNnyWLDnJjl7qlZmh1FrpORHWtg3Z7Q/soY1xdnOL1vlMnpIbK4zZFnTzG+eojuvmF+4ek7eO1PrqU98BVWFiw++dmHec07r+p5YAGp1txx30d4+uW/hmt5LLU6THca6GaCsRKSJAVziLKVkamj9AVvY8DtkhMOyhj2Lu/jW3P3c9vErewsn6tMOcc5/t1gvvf6t+aH4Rz+D3NOSDnHi2QqwyiNML1B+r/E8HgfhVJAxygW0qP0l2LuWP0sff0JEefzTFjGFZpNY2/5Z/vWV7q90mEtyDKF4whMakhiCxnanJldxpQlRgnWFIc5606iE0W56GGMxeOPnsclNz3G6VN5lusZF4zMc9ehDXx3dYVLgibHujnu+dZ+uu2Uvm1DOEIyHOWYabe5cP0Ez6optG3YvKqClGWUsJBopHPei+e4Y+B6hHg37330WQQOma0IwxTpSwgzbN/ixLE5LEsy1q+xvBXsZUU3GeT5pfOYPyI58MIG3nrHI1TWL3HHlhnu/doOZqZqbN40QpD3yRV90q4i7CYgJDffuocXjs+z+8JJ1m++g8NPvYk0DFlY0PzFE69nbMjiNW/+j8xPN7n9jVehtSaMUpaXWnRbEX39BcJOTJJkxFFGO0xQGoQUND3FShwzeXaRjeODjK/qJzOGzLbJKhoTWGS24sKOT+FsgO5LmXVr/MnjX+PMgOYPLryOXTvGOTbXpThvoW1oLXeJ4ozVawc4ETZ4z6MP0O3moD9DOgItBXZD8MqX72Lq5CKzz83QmpunvL1nIqqM5LlT6/BeGmFJTddYPPj0Wa7Kj1K0xygOVFgzkeOFE8vkfZcrq1sYHq5weKFNSZT42Wuu5neO3kPiwXCxzYL8Yy4bCXis9SXKTpdkMk+8GODHKUlBsu2iCf7syw+RcyUf+4U7uGbmZh7vHsYk8GQJbu/bhqsfRtxcZz628eM8U6aP4W2beem1H2aoFHKsWaE5mscuwo23reElm3byyftOYTViFiOP95y6hW/tstm2cZQHDuUJOpqp5jBprsKm8+aw3ZQssdl1/hwTu9r4ScLJqSpOXVIo2722sdQwc3YZ4wlOTC4R5X0S6SBji6jiYq/x8A7VcKQgW7bILXZoRjmMdMgSiWlqjlLh/pnV7CrNIASUZIilJAb43Y33sHftCMnZMcLP+BxPC5z38hXOdCpoKZltl8j3S8qVOqt0xBHVz4+MH2DLpknOTAk+tXQJXz97HhrNLRv3ET00ykfedgmtK31MMUUYh/4hzeYrGhx/ZIDwiKFeshkpxtSWArIsT2OHA9NVonqDy3cf5fSzq8iO+dz+ivu4bP0x3uvczJln+pi60KVTK6MnAoIFgyMtfu59P8qPv+vrjLptllTAdRfvZ+RIE1zDj//kPUjbYNkGy4FDj68jiwOmJ8e4IT1A3u4lmdTv3MRT25bZmK/x8Ow6Ls9VGMq1OCaGqW+VGAv6hhtMJVXsBbhk5BRzYQEpQeRi4qLNwtwQI/lJfvzWJzgR5djstHGyhD/8yMtBOthxgsk8xLoFkpoBy8JpgVawf3GYOJEIJdA5ibVgcem6GgBD4ynzfRblhQwpLdJmgeZWC3c+RaIY+XCb9pqUNGfhtCVZoNm0+jSXbp0jh+LmoSP88nO3YxY8qs2UZlQgeww+cu1F6FAgczZLW+qMHrTJGYv0PsXyhRpha5zlFLM1QZY0l2ZreJjFnu9OIlDCkNkCa7zAc08nBN0UUkUhMJTEDPGSpJP0g2ejLcGlJ44QXjBPn93luZmRXs/obol/a0ylsUhDSFqpoKMVCYbf+vOH+fM/u4zFhRzJ7hxgMJagu9FFGc3X3vJZ8lbKu777Mk4eXgWpxhIWOlOIAcADc6FFN+uwsjjDzuGjnH1uA5mxeeslB1kp2zx7/wUcWYEYiVt1uGLnOrzyAywSky7lkH0epmsjI0kWKOpJjoZVQY1KRk51QNs8vW8zuVodcVZjpxqKPv13nSJJEsRIAVXKUEULrxDibs3IvpMjyzskLhw7Pod3EsZsTddaJr/bojHfh4zBSLDbgJTYjiQhoH5coi2DdgWWShGtFIFEWQohetVHmTD48yme0yH1JJ0N4DgON9w8y+GHyxw61kdr2sLvRlh+hdgolCvRRvCeP70RN1Fsf+0ZYuMwrcvISKALbq8FyDKUswY6cOnWfbTXaxuztEHGGWnVp5MZ/JbC+a4ikw3kYInEFQT1DGNJkBbGdzCeIcHgKomlBCrIY/ISYoHyXKSv0dIiLljE2sM4FqlvIcYU9i6bLVvatL0cM8kmRAsy4+E8t9ATExJNnLN7bT1SYvoLGDfEXopwwpTFUxWMpYkqOdSUR4AiGRSYFR+7UUcup0Sbq+jYwq+tQJiB7YBWeH6ONM3Iyj56pU4pNehmglQp/bvWsdTtorM6oR5GZCkkEveky/gzc0yfPUV9vkCWGBCQZRn1WgPHd7nk1eczV2tz8t7DFO+YZXZ/QJA3FCsx3RWDrSUmVWzrL1FdO8xNl53HPU9+hdnnbWTOZdPoENYxQaZNL74ayc33/S4fWfcuamGXs+0GcRyThQlhN6Ud7GAh3csGeT6esCjYDp60McYwFc7SyjrMhPM/sJBi9DKm+1mEfzPC3vivGmOe4xznOMe/N/7l2fI5/n+JUuBkEieFkuv+i9s06h26nQSdKHTWxc0UW5wGURoQMsZfLbycOe8vqfhj37dfEmfMzTSYna/TaEZobWg3Q8LlkCy0iJVkOW31+r+FpFAI8D0LkWm67Q5pkvHlT5/Hr/zWz/FHf/QKfuz8J7hm4gi/cMnDTKajfL65jWOnhjj17Bm6zRCU7v2OVkRpSrBH9rP/H3+F/f/4q3zpD34KKW36B+8mV/kIpfxtL56nEIIdA6/h9HIfJtK8c92dNBrvxKzyaI36yFUFjIEkUVSthJK9QDBdY/7UQ/zc5n2sBC5x5BDnIkpWm1rOUC3lWLd2ACEE1VKA63tMTtU4dWSOsyeWWDU+wMVXbuK8i6eRdpfquqdpDgW0qh6fvucSvnKsSu7KP+BXfud2ypU8xkCaKmr1DrV6F7IMRwgcywIp8L436LWlJMxSwizDrQYUq3kq1RzL80285RTsjGjIkA5KnJbAijTHD5zh2cePMzU/S1RI+K37v8v8fIPcujYr19u0X6Y5fmIGoxUGODa3yNmpBYwyCBeMAmzwOlC0XS5cv5rudAOTaL741HY+sbydD0xfilvLUC1JqiXuEQcrJ3ny6BniQPNMp05Lx+Qx2AoqQxWuXruLo9l+nkweZrCco9jvY0mLndEWvJPbaS1rirYm0CmVvi5qzJAMSLTr4AzlINSEjYyVVpcrRneDUJT6QjquZnN/H93MJjEOG0SdQ0+tpi8/yvt/+Uc5cmKURRWwsbDCtttPMfH6aX7yin+ktryIZ1koV6D7NPUwj1KaKEr42rdeRqHZJbcYUf245oGl7bgvWyEbCtnbWst3V7awPxln/okBwldFLL4k4bbbdiDROG0FjS7aEjgYdGJQmSCzeuX1MmuSuRaZFPiZRrsOquBhbAelLZ6dG+f5+VH2LYzxs4WHqR5dpr25xJ/9+qsIkpsYP/xBhpav5JU/fZKXvuEsn35hE+NunWtGj3P12CluGz7F37zqHjp3reZn4n5uHDnCsrG4YHyGNO5VrP3Mqkd4+Q0nWXV+B73BJ8mG8RuSn8h9ly/c8UlecvMZpGMRJwG10OEjB8/jEx+/lmTYkFUgGbE4+MEB5j4ywpsvuZ4w0Lz6pgOMliN+613fwuuE5JZtnBUb5UikK7DjjIvf/DzP14c5GI9xJhlgqNCFZohtUi6rLrFzfJlDgcMTnTx9a+cZ2Npi8zVT2EbyzT96KbXnBzFC8p3Pb+WJlTWsC5bxZEKqJZ++8xLEYIzsj5FCYknDRmeF6wunOdYexliCNKfpeArjRGy/tsa369v4+vIe6qnNX//t5XREgealHs1LfSovzGG3BfEhg/LAaNA5w/3hWmLfIs1B6hnu+/ouFhOLw1GOKLOIL6kQri8SbihiXIfWWohGHSzPQ4UGS0N5JkGEXbZedJSfv/QxKhqmRQmjJd3QJ040IqNnrIFGnPJxTzlYWhNvHyAp272Jrkrxz0KwmOE1DcmERWq5fHF+lpteWoBljTOrsecFluswPljD3xKQ9buIso/Bx+y1Ua2ApOqSlAQ6pxm24Z+e3sNdB3fx5Wcv47IPnYJYUXXbjJRDRMlwsOixpKGubOzVkvKGAEbLyG5GmpfogoWNJCiCFgIhDJs3zWLFAqkEWkhkJugbaVOqdAk2ZjSlRNWnMfOQq8RYjuILna18on0e9eE2WoMadhm4coiR8UnQTzNablB2Q9ZdsUja55OVPFQmaRWKhOMuyXqH+qiPHUusGQtzROCdTjFxiHKhds0Y2fnDqGoBf0pQHW9S0DB6SROFJh52iQddulJR32Qxv8Nl5KaMVWvq5De2cWoat25wltKe4KZApAYTGUTboLWFNJpgKcVraawEvCzCyAwjFJmU1Go2s7Ml/EYI/ZLvHtlAFghMaMBAdaJOjMbk7e99QYPOOfgDCfn1EQO7mzg5iSm5CNvCUqAjzdKhPIuzVbKKg5X0Un+MMhgEQgqSIxb1vyug91uMHUrR+1dISxqpMkSUABmpm9LZYtPcaqO1we4o/D4fXAsZgtBgZK8dRXmCsChJfJvUc8jWaJJE8vzd6zh51xidIYlb9ZEnG7hKQZoQq6SXeNafRw3kIdW4rZTSmi6vuvppXDfEbSqCMKFQipEZCCOxlEEgEQisRogeyaM7vRZYep8aCGNcDaqdgBfQUhqaCUzVWTq+iGoXyJZHEUsK3e4iGxGW1vyn2x7kJVecYMOVdcTqURgqYZdtNp+3iUuu2831r7uGd/zeW6hetBkxWOFzv/ZWluY2s2vbNIG0Obb/JE89uo8sTfF9yanZr7Bp135uef0Bdm8fZ7kWsmNoiLXDRezVXSRdhNB8+PTPE/guLSshqRo2bxyir5Knmc5S1zm62RICSGOFKjzKndOf4ZaR6/mxta/lusHLf+Bxou58BN3+IKr5n3/gfc5xjnP8X8aYH57Xv3POVaSc40XiNMHEGozh/5WK/CKFYkDkpHRViiX7iM08ibb4u8v+no+d+hwDXpeqG/yz/SxbUmt1mJqvYQcWxmhs28Jzer3JGk0jbRC1JNIT9FVK5D0XC0G+WiC1BEmYIGoOaJhuVVhXXianc1ySP0nXuNz78dWQZaTCUG90sRGsLLdQnZTTU0sv+oj8NwJ3J4G781+8zmQJdqtJ1lcXGRiMePMbEv7yMy9Hehalsk+cKFTNJxApYddlsWaRKMnB0xPkzmY004A7a7tZm6yQZBn6vx1YCPKFgKibkMUpWggsz8ZxPNqZS8PYFEZa1C8uI5RARl1mHhlDb34eIUBaEiEgyxRGSIZHygQ5j7l6nShOyAUO/f0FAt9FSklBuHRNjPRsrJyL47kMj1S4ZPsED7enCcsKIyAuapLFiMnlkPyaAYznQSyx5jSf/cJjvOwdz7N/YIK1J5d4JtqEdCXtRgcTZpBkWMKQZRbGU0jAtBI8LCqlAFHOQSemO+/yiROXYgx41RTHMuBpolYO5VuYnM2zp6fIjfYziYVjeUSO4fDkPKdmZtD6aK+lvhjR7GYYbfBsm7AV0a3USGPRq0qwJO2iha0EceIRVw/gdQtg4IGnj3HrpdsZLHdIlU2x3GKgMMSVA6fQCLa5s/zlrqeIjMa2bd73xk9yYv4XONJ+lnfufJIKMasc6Kx+B7+c/Q3doQ4rxxqsHh1FSkEu5zFa6Of4/gZSgDSCxj1l1DiU5gSLUZW1uRVqYQ57Z0y6ZMECfHnhXkQ8gFQGC4ExGiUMjifRvoFYo31D38aMRs3CdAXzZ6qsvnCBk2crGBssoWl2HFw75fDCCP/59NWIkwGZJ3jmXsP7Khezff0sz3znca65UlAdT3jXKx/jM3M7mc8qXFCY5m/rm7ipNMVFl+/l9MmbuOolujfJ0ZLls/38yYVfZWSwAcDJ1+RoP+nxrZ9/H64QnEp8/vTMTsY7CVFRo4zHvvw4T917AV5bY6UGM2PIsl4p/0oqePUrL+QDDz1JlEo6loeoaqrb26wc8xA5D6kFytHE+ZjTssqhRzZxy0sPgTG4VkrniKSy3uB6irydsb9VpdjRlMeX+Zlf+xbNxHDgsQlMqpibH8ZRMfRbdE5ILt18nPue3MjZ4xv4tVc9ym/vfwnaExiRcMv2Z/jikfN5Ih1ifqWI17KgDkPdkJ+6/SnG+xscDAd5enENv/2LtzGYeCzcrlize4nGSo7FHRVK0zVMA7IBhVIC7WqO1MYJdIzJLESaEXUd7glXMeq0edktT/OPd9/Myo4i0hGY3mI2GENcFTRvGqA8EyNsjdvWbHYXGU9ictqwM9fihkf/A14K3VUBmV9DxLCw0yHYH6O8iHRNDmlZzNxaZfgbS+QSl+I8RG5MPGBw3BQhwR1JeME9Tdn3iaZ94oLCK2fEHUUqBKboIGINtkFaFiLWSDLsLCMtGU4NFLg028+p3Fretv4+JrMSV206y4HHB3B2Z0jXoIACkqJtsHMKkSsQFi2E5wIG41qomkVmJJ9f3kzFizmcDGLQKFuQVCROR7BzeI7QOPTpJieiPO0+H33IQecshqoNjh0ZIpl1aNbzVPwIZ0Lgb0mZLK4wUfGZFJKn03F+5PVP8sKxrRhtEFKh+gzYIJRCBTZuZhDaIJEIYSGTXoKMqeYICz6ym1KaVwxZDZrdItNHx7n42mM8dnJXrw0n0ygHlCsZMx0uKk3TRZA9Wep5vNST3vMhNTj1mGTQw7i9KGfTEShLIw1oX1AaSfHnFujoKlmpTJgZ1IkmhUPLtK6zmT5QYLI7gOWC7RqkJxAGrHqGSlOEbaGEoBEF7H1sM1JqHKWwAoHJ9PeekZKZsA/bEeA5yCh70ZtF2wahLUQiyNoJttAgbIZWoJMmPQHPBm1BVnZRro2IFUQKW0mCnE9dGqwUpOeCY2Fcg7+QEfelqDKI/oyw6+LMJiw8M4rIDOVciN1pY4cpZAlGGZKtPo72EQt1pGfhTNbRgxbqlf3U+7rYHthJTDVr4AxZ1I+WMEoiRa81SmtFvitYqa3gVVz0Sojp97GkjXYsRJQS+B5Ga7RxMIAOeh5WvWe6wA81naog67MQccLzB/NUygm1QQ/xfIAousjBImdrHY4uNahuXM0GI9l90Xoeb4SUyxXKfe9i5dR9+Okik8fOkGhDYbCfmYU246d2oCrns+/Bfg4dmmUm7BBc3seG6mrKuSMspZq8HeOXM7ZsHGLp211c4GTcQggwRmEw2MJHGEjzTzM+fISn60cYysa4asNLf/BBIiC9G1DxI8jgtv/9xuc4xznO8e+ccxUp53iRdpyQSoOSYP4FIUVrTbmaZ3i0QuB7BP4YuWArE8MPIITAmJMMeXOkevaf7ZspTb0dE8WKqJ2iMo2fcxkfG8CyJFILZqc7zD3fx5kTg1StPqTroB2L7eetIs57tCseOucwtr7K2NHf4g2bnuYN61ezxQs5329y5MRqotCQpglxlhFninIhh2NbjAxXv09E+V+x0j3Im9d9Cb8TMTVfxikmDI0vICPIWQ7zcw1qy23SeY9tFy5T2p2w+/qEdx36ScilCGPY1xxnLinyxOJaxoaqFPK9Ch8/Z1OuBJTLPp4jGBguMryqD8eG1YNL5IQmXqxTTAVZHnLHNbrpcO/0RoQQSNmLrE4yhRIGL++Sz3n4noMUgmOHZzh6cJo46aUm5bAQCew9MMkDjx0hihNqtRa3rd/Gb+25qpcCI8DP5Yhtg5I2o9tX87Vf+jkG780oH454fv8Mfff+NPXfHOWZv91BakG7HtJcCskbC+0IpG9x1fCaXrpDJomrKSiD4zv84Qd+DFktUA4UBT+mnA8Z27hCYiRRbGPZDt1+n876fpq78rRNSiNMWBhqcHrPSd518Jv88XNP0+/t5tcuuJ47H5shwWCEYPVwpbfc76/QtH3qZogz92zHbxqktrEKGY+sCKTubfb0s6dxXEnJFrxu/V5uX3OAbqTZ5k+zK5jibFylt47fQ4iAjSMf4eUbn+a8kU+x1gWDQSmfUZUj34hot1vU6x20NoTNL/GXb/8vBBe1SDYnpCYjf1hS/q4FXUFghSwkBYw05I8rdCDQOQFSowc1xpG4OZ9SOcfWNUP8/E9eRr5/nou2H8cfqqPLQ+SqEpSiuywYXVODaorAIHXG9vwC549MsnPNNL9z84Os8rqgBCof8vzXnuKrn7yLxYkhtLBoGSjYMGZ3ecvgfu6onOJNgwf5+pFxxoo13vLm9zPgtCkJxbv+82twD3o4IqM3zQIrn8O9qMWr9r6ZP58+n3VOQsfP84UnzmP9zywR/GhId9cg0tDzdjASr5EyMC+QnRKdJMfs/AKImJluhfUOrHclq940j9cypAUbHdi0hyXFsRpTzw/glTVV2cGzMtZ253n6uTUYW9GsO5xe8HFagsdOr8WPXR5rr6JWz/O1ey/HSI3QmvPPP8lnr/86d970LT79wnXsm1vDF37uy9x+4XF2d5YonBGMrbSZyNVQHY+io9lYXISWQR+v0jw5TrK3wC35OnuCBWrv80m7AZ4n2dU3w0+sfYSbNhzA8gPiEGwsREdjhEZEkiwMwOmSeQaV67VTbnEXcEXMjqFptCWw8oLEaZGWU/L5FdI1ktYGgez3MXkLnXMwwuGZpdU8W+tnYb7At57biL3gIB/yMRWX6R8doPULNtn2Adq7PNJ+GzvO0DkLYRmuPH8KAAuNrw0/csc+XEfjOBotBWf3GrpzIBZDnAVDcFKgjxRhXoER5EaLrNozitXvEttQOTvDWnWUvocbfOU9u8gf6TJRXOZEMsAbS8/zE289RnOoyPSZCt26xV2PbMQViobW1DOHpWQMNV7COBZZEZKyJsxnKEdy79w2vjm/nbPdKgiNUQZnW8xPv/c+buw7xPnFs9wyfBK9bFMSXdr1Ct24wEKrijyRkn8uxjutCYcF+RHDXG6WRzuL/OPRCzh9skDndJGjR1exbvNprCDFHQnxEoVogWhJpARtaZSlaW8IaG33iCsChMIoDUkCVm/CukatEEUBOhOsu7iOlClCGQwGZznFX1K8YccR9kys0GwOofIWOrAQWYrJNEYbUCBSUDmHtATdiyziVR5Z3kZYNovtEivlQdI+HxFqAmNw2k3KixHekwn2SoKMNO6AwQsyxssttGtIbY0q+ZQnEl79E3vZcP4i6bIkWbLQWpMJg7UQIToKEVuInMQYkI0EwgQhDMoWCFugXEluS8KGX2+QPy/BGAuhNH5HYMIUIwV2SfYSfFYSnKmY1DUY32AFgrfueZaX7jqJkYKobNM4T6J2QG4ppe9Qk3wzwSSSNf01tCPQAvRsB+oGbLvX/pMXmI0lClfnMTkXs9wlGvNobKoytdTPA4d20am5YMH6V9eIZnIo2yILBOQ9ZD5ABHkiwJ5pktgSuxRgZ/TEIwHGdXrVrQZkuYhVLSKFDY6HsQ0YgSnmEIGH2SSwz2vy4Rcu5IPNCzlwYCNtNCqDeK5J/egi6XSHBx49xlMHz7LpgrU4fUUOztUxSDafv56LXrqb0TV9vO3X7ufX3/15duzop1QY48lvvYxnnxyl20mpLbWwYk3ZcvjKS/6MW4ZOcX5lmlXePMnC1WRdjUwtTi3XiVOFMA2M6SBJCTyHjgBPJvgy5ptP/RN//cKfMNU99QOPE4V7IfbA184JKec4xznOwbmKlHP8D7SzhNTriQ3C+n7RIYoSZiZXKBR9hkYqBK7FsJ9Dijyu1Q9An71Mv7dATob/7NgC6K/mWDNRYfVEBSEg6saEnRix6KFahiTyCJYUVuqw/TWr8bynSWOF67i0KjZ2n0VLC1Iv5cffdC0A2tqCEN9CG0EYFoGUKM1wUHSThPf/xmt45uAkr7p21w98H746+fe0lvOI0GHT+hbT3YBBE6GlYtPEAOViDo0hinO89NVnuHjPDCP+S3hi0kNP2nQvNsS2g0rBRBKSjOnpGkPDFZTqmcBmSjE3vYwTfAkTPkif/woCp7fKpWTCT92wjef3fwL7thZ3PbOL7zy/A15Fb6AtoZD36Mt79FdyeL5L4DukqeLY0Xk6jZDLvyekBMKBjmLmxCJmvsNebThzcpnlToj0PF5hrWJyeplRPBp0KZcCrr9xNxs3jXDVunGeerJnTHrXN55FepI41SR9DosLDeJSSi0+hnEzVEHw+h0pj33exqvLXnl+1qtm2HXeGq59xR4eNU/yqvFnEYBV9/i25VC3c3zoP7yW1z77GYxy+NCV9/Gff/UaQh0zuqbFhk1n+NJ8HpVKptICGyuv5+PP/hlC98SOl126nXi6jlOYpRhGfG7vOL/9knsZf3qABya3U/ITFryQQrFCox6zsNREJRkv74cbyi8gMSym9/Cp2UsY9ZqspJuorX4TRffWf/Z30WxtoJv+LX7hWR58tMp9D3+K4YEZvC11yvlfQQhBs/YufvvwJUw3Xcr9DezX2ZiHJUnq09yoaDcd1g7ViI1F9myBicMWOmqT3xISb8iRSolvOezeMMqa1f3UkwOQwWqvxYYLZjl+9nI27rF46KhCp5qnnxglrQocejGchSzm+v6DNLIcC9MVjrwwgVWNGX1Nl6m/LbCkY4oPBcyqBCOg1Q24few4x+IKBoGD5icHDvPZxi4mpwMGy+AKSWuHhzWvqS9W+cuVq3Fdw/NzI4hJDyME35GaPYWziIZGFmzWDYccOLua8cemaE2FpLesxTY2VmbILEM67ONIeOMH/gkT2PzxIzfyyZd/nZXY5eG923ByLtrq+e2QWtiZxWKrSIbgrq+fzx2XPw5lG/8yhz/4vVvJsKlM1Lj+bSdYa2ZxsghhSd79wJvwSin5RCJswfqJGq0owO8IJlhmVg3Ra1IT7MjNMXuoj7TqcrA1it/OGM3VmY3zWK2IbFUOkHzu4WsYHmtj1iXo2QLSaFSW8Zr+57nBWeTy0UW+4l5F2j/M7NVgXLtn6NmVoAXJlAuDgONiKzgTVvnHY1cwUawjM4NRgom+mEKxwaTMkTi9z3xwKiOThigniQsW86eL/KH9EopuhtrvUZ41oFJ4SZtI+ewaXeDRmSG0VBTmEsgEaVHy5dd9lFYnz/NPl+ksWWz/sXlWb53HPrsFg0D9jY2e93GNRAQWIBCJAiExvsFup6h2yPEwQgcutvJQ7RxL+yoYy6ZRrTK45SDn+8f584/eyI+J1/La607AFovmI0XmlqpsC4+xfKuHFiltJYn6a6i4gBlt46Z58AxRn4bTkqV2gJv6KAWVWgtTsRg7f5HRoMEOp8uNhTk++vRu6st5ZOZQMIK2DZFwSf0Ir5kQ5QTxOk3muLgNcCYddnGIUHgcX67SndlBaEncrU2aC2XCrkA4BmMLpG1jjABjSFf5pKtcRv5qhijvI10bu6WRmcCRCc25AqYSogoOI0MNJtZ3mJor4K92yEY1oZYcnR5gTdBi2fYJJxyMpeg/1TP7DVzwBgtEVyR0OgndMUUmfIJrUrJ7XURH4y4kpBM5jKQXOW/Z7HxdgycOjSEWM0gkjoaXnn8EWylSzyH0BPnEQhvB8OoWa9cus2vLEu99Q4DJAfmApGphBTbGdjBCoROJVHGvMkMKZDvBsiRZzkNISWVVg+JoF7NLcTzoo/pcitdKIE5xW11IfDLThm4ROzLg2bRdiz3bpvmpWw/g2JqrH34VnR0Bfr8gWePhnYnJPx8TzjqUHlRUVzVIigKpDbbS2EkvzS3JuzDhkmhF0DR0ryjRaVZI+x101KHwQgflOAhH4NianJ0iMrCVIOuCDBXGthFC4XUMUgtEashsD1eBihKMEQiTgeWCMIiuglwegUa1IminiKECJBlCSNxYUOpkJK0h2qcc7FSgh33MyZVe5HGmMUrTWqrx6N3PM3HHJbTKDkmiue+hI6wbLLNh0yhDo13GR1aIqxGX3PjThIvfptsMScMMk2lUR0A3xfveatc7tr2HfbOvI04NURrSLy1UlFEq9BKyyuYkjr1CIOrkPY8kXsctxc9RlAnf3KSYTVJOdA4xkVv3A4+RznGOc/xwI0zv9W/ND8M5/J/mnJByjheZD9vE+d5afGIZTp5cwJKS1Wv6MdqglUGp3qfCkpKisdCm9+8AN+WOYasZMm/xnx3bsiQ5z8VBkHRTkjhDIujUOxRSl1RkVKVPd66JJSSDlTK+tEjSiG4zonWppnzcQtjQVvq/n3Pni/RhCI1HruJSW4wwaURT9xJONq0aYuPE4L/qPtSSIV6oj/Hun3mUidGEdmx439ROtCW4+crtNOodpGOx2Ojw3U/dznWXxyzPvgGpH8YNLWRdEGkXi5SoafHEg4cZHatw/gXrsCyBMoa4E9KOMwqVg6zohOGBZWTiM2ZJJoYilpxPcMPOB5g7k+P+p7aQtfKcnF2gaBz8vM+JY3OcPrXE6EiF0hoPy5Z4noPvSIwrsaUFgCMFSZZxdn6F+mILO8qI2gmJJSDTtGc71JYizlYd4lZMKhLqtTYAf/5ff4KXXP2fyWxF3EkRgQOOQWaKQyem+E7/fbjSYJolVJ9kb+0rlJvXomsaryFoOzGdQoxAUu0vcapd4C9KZzBC80cv3MDQlU1K+YTyQIE7X/5pxsl45JlRuqtsvDMaJ87jqjxYCsfJGM1Los7H2bMmz17TxZKwqr/C8ZkGkdXgieMT/MjIYT49t4PwmE+fXCFXhn986e/x+4fu4vmVOeIwo90Mubp4ExXxCXJS0tXH6Xc6nGwNcfXgIXLyP1Fb7iJliuc7AKhMs7zYxJitDI1cw8HD76BTH0XmJTsHF/mRa88jTmZ4su7z9OdGWWXPM5kNUymmfOcTv83fPfHzfOTYOKVmwkXrT6GNxL3pKu6/V0Mnj5kso5YFGotIGY4cnSHIO0wn+2mZUfZ3Bhjf69E60WDiZeNYaYpUFuGiQ7AqIWm4EEExbHCB3aDldfjVn7wDkNzwY09xfHmcrJQR5H2smsXUySIjq9vktEJgWGPX+YNfupKltxZZZerceXYzU08PMpkWSPvh4j2nOfrt9Xz0u1ex4EC4x+tVeGmBFAKxovgvn3gVH/6Nr/Dl6zRNu4K/NqZ9skixleB+7TRZ1WbpbcOUv5uB5aMdQVaSZJ7PsccHuOi+H6UvSZHLXWQE3hIkAxbCSA6mPqljYSzB7OF++ia6BHbKrW87wl8duRaSDHOmn+Vjc4x5y8zN5bn/5Fqs1CJZo8i1bTDwqUcvJwoLJBH0zabUSxkv/52f5Gd+9H4eOLIak8FKs8KX77qcvrCGJQxqxcJOJdLq0s1yWKHhA5+4maibUHRjUkvhNBK+/g9bOHu6yBvfeAiRaKwMdNBblTe2QaYSoQ2FjfNkxxSymcdrZXz5vvOZ7Y4wxzDS6g08uu0cO5wFZmWJ/sc7yMglMRrf6eJ3fJwTNu1uhWRVQstJedPlT/DEt89ncKTF0qGEK65dYDBe4lG2gFYYpUBZGGlx2zd/hkG5wMaLF2ku93N2ZJDFxX7yMib9SBHZsUClCPd7onqSoixBc41BbJMUvwtpN+lNwAUgJQb1oicLjs2mrbMIYbhpzwH+4W8u4cnVRepZgBNKpErBSvi1gzdx/dhpvnNmC2OrVpg6UyTfbzAnLUSiwdJYHY3qWkRCoBW4U2381HDkyQm+O9xk7ehe8lbCbVfv4/Of3YA/ksPJ2QhXUuhz6aZtjFDEGyA3Zijlu+RMRjf0KK6NKKiMYiOivuCgX4Bw1EIMSExmEEpjbKuXfash6GTEsteKmg47lJ9ZJF2VEvVPoITmmmue4cTDQ/gbE8waxalOH9P1PlIJOvGII4V2LT505+V40pDkbHTQpXQkRUuBJcAtB2RSYSoRMvbZWZ3lYG2c84ZmeHxoI04txekaSAxSgrEEHdfj3qUrMeMWDEB8JsJeDtl7ZIS1g22eWVoFBZ9IKqzEkGUOG92Epi4z+hsR3dRl/psWVttg8gZlTK8SxbbQORsnUlihhlhhaw2BTVZxWDjQT9a1qB/NI6VFc6zN4N6wV/0nJSIS+GjkqRA9UepVmOVs9u8r88AlEyzOFkiqAbtPepwqdchcsAVkbQvTdJHzLgemdqOCDNMNycU+qAytDGLQRUUWVkcSORnKz5COh9eQOInhR286zPh4gw++7zz0subQRyv4xYSQXpS1TgUy/d4DX2vszCCVIPPBJAkmNOhcDqklCIHlSIz6XgxGClZgITVkJ2cwfQHJ6grpikd0MI/XTHqRzmmMrHUJay0CpXrJP0YQrXSROcmXPv8EpZEC9Zkl5qZXGC142L7L2s0X0QGizKGjbWT51axe/afMnF4k7GT0FX3SwJAv9apcXavMclZg+kQV22py0dBanjp8jCTJSBIFQmNh0KlDq97hj694Bcq8hwWd58LqGTqdt3NJ9dp/1RjpHOc4xznO0eOckHKOFzH+IdRQBsLgBYt0l4aRUqKVIch5rF43gGVb39taYpTuGfhlPWHD8y6D7glsb88/P7YxxGFKoxHhWBZaaZJU06yHVNoucbNLPpME2AzbRZJY4eds6g3J4vIjYKAbaDxlcf2ukRePO5U0KLs2c5nAWBIlBLKZ4Wv+l1nk/ytuH7+Z561nWVz2GR9JmG3l+PLDe2BAcdl56zl+ZA5lFCeW6jTaAzhmLZtXR/h5H9NtYTQEpOTtLothmaWVLicOzwAQRSlJN6bbSAjjmPF1P8Hw+Bybhm/iQL3BojpNQ1tscn+czDxMI82j2z5WyfDkwn7OUxuJdZ37vvMCC7M1du9eRZZp4lSRKYUDpEJ8z1yh9wG3lSDsJCRRxMmcgyttCtKh2wyZrbdIlWZqpUVeGeIkY2pmBa0NUgre897X8xv/8eNkVq/dK7E1QgoOv3CGNdfUcPOGJeWwZALGtOYd117M337uYfILCWE1Qg8qpAUT42XSw5L7pzbx+jVHqK5uk6qEsqtYs26In7znF0kaTzN53xjFNEE0UkpTowyc3IM9sB8ErLYfp9O4m7/58bfi+L+C6zmcOT7L0lyTuBBhZxlPT5epNX2sWRBln3rsMJcscvWlG9l/YgGTSJ46cJbtG0fZPuYhAZevUnVHuaDyLPsam8kyhVYa9T8IdpYtGZ3oQ2uD69rEkQ2Zoq1yLNujDPQNcGxyDW7k0N/tcHF5mldfcwivOMTeldN8PlyFrGQ485JtToglNM7WCb7zzVPItmDAXsfx5Sam5JAJqNUiFufqPLQ4ipGSqbDK0vMViks1Gq0If30XdTRH0IKt1hQvdDZhtGGs2sKiy7AFWBIEPNfYgGMLnJLENEOkb/P+d7yEpG2ojIb87B8/zj2fGOPQQ/0cv2KUx7NxUIKp1ghDSzVuWneIfJCymBvG2axwZx3OPJES9w+gLBCqQ/3oaqqjDbJlj1+48Wlu+eprCaVP+FKP1hab1Z+cx2tpRNJi7OUx79h1Ge/6h4MYCd6ypnwmI646hIGPIMPKvjfxkIBlaLseJrFQeVhVXeCFxiivHDzCiXgIXeriPauQkeC+L+yhIj30ZfOEmyvkKg36Tneo+aOgQdmCrz66BVOvc/WuOYqthOXM4wMfvZm0m+HmbIwAe1qSLrlUsw67XzPF3dZ5DPe3eOLwNpKSi8pbiLbAqnVRSpEJzcypEifu7aNwVYi3HDE43KIpBsDVeEmM0TYGWArXIfvAshO8xYTp+waxbwkpFmIaRyo9IQWXocEVLGcAT4I1mcGQQxrm2DwxxV5vI3/19s8wXq7xC9+9lWTC4lPv/RKBo/idJ67hzs9v4ILbPK5bOsQDpzegpUHbClyBSCWLDBPFOYbKNWanK5Q+bqGzDCo2RmSYgsdiIcapCtrDDn4s8a5uYN/vImMXg+wJM54kakR4tsAUBK/5ne9QO+Ny8NgY46tWeOLOtZRnEhr3+EQbHLymxs5lLDdcFg/5fNPZgjQ2x9M+rNGYrOPixoAyBGcdhBBYnYQs7+B5GSoQGCsiWyv59jcv4D/81NMoQHiatOJhr61Sbqa0EQwWSzTys/huQjd2CUOXkYEmIjOctgbY70yw2WnwpvILPP/CBp4yNlZTIisSKzNYDUPmpaSmZwJbji1qFoAhK9ooYhw/IhYCK4Kjx4fohHkqOmbIX4YpC2UshAWU64i4Dwlk0qBzkBXBamtyTYPSGgtFKn2yULG8GODMCar5iJtHTtI47eI0s55XiWtht5PeM9gSZMpGpGDcXupUVrAQ2mFmro+5uQFUwYWcQvguxjccnRzhY3ddwpQq0hmzUB5kwzaynSIjjSUVmSPBEWhXYsIUkemesBYBmUKmmiSymTk1iHEMTi0h2jVGa+YExWnVS2rKNKBBgnYthLAQrQy9SvPhzha6Uzm8tkEFiq2izJTWlN0Sy+4CJBpSRXYmoawNOtM9zyBtCPMG3wswWiO+l/DkFDqYSYORDsU0ZaC8Qm1WYS2npFqi9uUxvkYVErJOjNXNsBILY0uE54DRmE4I3YjIdZFo6LSINWT5PIHrIqSATghRhElsyOVxOhG61sKutchesRpp+0ih0VMr+LN1SBUtHRNIifZsTDHoiTOZwsQZ1lIHp5MxdXAKu96h2+rwstddzpH9f0T+yt/h6c4aHlxazyXXfYwd4Zso5QKsQcGxoEu7G6KUQpBRFi0ato8toZnFJJ5Bo3tiirRQZLQ7krBVY7wScDwaZsidZbqzExl8lW62mcD+l/3iznGOc5zjHP9zznmknONFrh3byssv28vtlz3F7sFNbNgwzLr1g1h278/E9RyMNoSdGMsyVPI+Rc/CcXqzdln6LcTw8wjv6n92bGMgSTOUUcRJhkoVUSem044wiYJaQn2lhUAzNFzA913KfXm0rvH3R+/kx296kh1bBlm/o59rrv4oHz9yGa3uSQ6FVb7bKbO3289AtYxUCtHO8FOBK61/dh4/COv69jDSN87vvvcG3vyLP8mrP/5G5LKNFdi4jk0h51Eq+ixGIZE0HDw9xanTS5SbBawuiBAaqcPpZh+BUtgODKyqAtBohkwtNmmHCY2VLotLFgsLuyiVy9ijV/Cdzmoe7Kxl/brr2bLqGCMDf0+5v87w6CKBcxdGKybPLvLk06c4PrVEs96l3Ymo1TqE3YROMyRJEp5uXc+3T+7CSQ26k6JrIVEzZN/UAnvPzjE5W2dmcpEkSiHRpM2ERiMm7aSszDaRsvd/unvPGpTrIAIXYwuSbQ5pWTN5aIHTXxqjfsIlnvUwHcFUMkQw6nDjLzzB5b+/D9cTOI6FZVlccs1WrNiwZ3AeIQ3nrVvENxlZ5PLr997N715yB8eyKtloQsENsVJFc7lDbqtPzkvw3ZSd/TuQ1ghZdhFzUzU6rQg/55EvBywlPu/Y8wRySdL/QozoZkiRUuxLmEmOcOO1O7FSjcgMzxyYpL7SodspAQLLvoLZTpV/PH05QvWqUDzPwXW/X2cuFH1K5QBjDCdr4xgMp7sD7PZnOX12FEzGmnLEYGmFE8sVZk8NcOLUldx9dB9dHYOvOLFvnEB2sUXI9rFe1GiWd+iiyVTam4Bog7HBtgQ7q7N4ZOhMoF2LzHN4aO8pzHCvZYyllBcOjGAALMELjTKLxmVB23iFGEPGulXLbGoYtBaIwCIzCZ2uRjkOrWLAnnWaP/6TSV75zkN4zRB3IcWyMxZLefbNraMgOoQP+RTCkM6yR3vRRbY9XjW8jwfe9FcMJ5L8mYSw4/Lnf3IZjz8wxuZCh7Tj4M8JfHyMSFGOZOJAnX63zt3BB/nMuz/AV9/wF6S+BluiSzb+YkRO2RjHAdlGuxqRCT77to9y55v/krdsfRQCWJV1We10eePIYUYuikjzKUiDTlP0qE//6AbqdgG7YCiXNWnOoPKghMJWGQOygYtmtTeLcDXGBhDEZUFSEtj1BFY7fPEj1/PRM9dgDydcuHGS3/mPX0PlepUmetAlTDOsnYpcRWCEQHQk/7RvB/6JOqqj2LluitH+GoNhhFBgJZANuZCTmIESSb+PW5f83Q2f4pNX/yNBMSQrQeJJ7jm2C7ctSBouBpBdhap4nNBDyPEuuweXKRjJLXKaXBb3fGAAZQzZgMve/Wu44ZXPsyN/mtKRlMq+OhPeEjrVCEsR93ksHXcpPughM5Cu3/P6sGzq28D3CqSry/iigGV8oicGcNd1wRik64AnEa7EsRRepJFDimwkYOwawSeObONP/sPLmdw3CI5N0spjzSpoxHhBSrxiIw+kDAQdrhw9yvqheaoDDYY210nLESbqYs832bBqDjkjCfys5w01Jrn0rUc4f/tJtt10gjnlcSiu8mw8RPn8iLNpxAtDCSrLOLKwQKQ94j7FcKVOspjR6ubIWrBOT9O/0iLX12FsyzxnukNsGRjhpa9dBOFiKRthBCIVpCMWqSeotCxkF0Qo0FqhcgIvMohUY4ymrqqMbupyed8K26ZiikYQ+BH5IOStr3wQu67xGhov0YgswziGaLeFUKY3+QYSJUgSDW2JTuG7S2v5jhzkoYNjyAiE6bW3BLMt3Nk2MlSAQBiJjBVaZ5BoVN5BBxZaJchWB9FNULZB24KB/jq3XvsYFdFAnRI4z2QEJxLsdtrzf/IFtisQ0kKVHKKJPFm/h7GsXquXJTF2rxpGhBoZalwtsWNDtrpCd2OJmWtLLO5yaa/3SCaKyMzgzbQJDi0h2nHvC1UYgm5CrdFktD3M6uVhVNRbnBFCIhphz69E9fyNjC1p5izM6gKOGyP7UuhzSGwLdV8OWXeQmaGzYPEPf7yLz/yXXSRJitaaTtxF5X3wLQqTXazZDqbRgma75/+CACGwgwCbnkcNnRgrUXhzS6STc6ioC2kCnRhqbUycgAQpIBcrgv01iHsmuF4nRcheNZPjSNJqgKkUEAUXbAttNF1jWDg6h15cIVzpcujYInfdtZ8D+86QqSFob+Hzz5/Hkf2DfPzQHp6bOkmx4jOUr7DKLbGjPACAIzSjbpNLNp3ggs3TDAV53FSQT8BkR1nOfGb1BJldICi6dFpd+qKN5IFYdunoMyyEz/x/Giud4xzn+CHl3zqp51xqzw8P73nPe/jyl7/M4cOHCYKAK664gj/90z9ly5YtL25jjOHd7343f/d3f0etVuPSSy/lQx/6EDt27HhxmziO+ZVf+RU+85nPEIYhL33pS/mrv/orJiYm/i0u64eSsPt1fm7iYQxgdJMgV/6+9zudmGatQxpnlKp57O/1aEdRRrHU2+Z/ZugqBBgpyFJNa7lLY6WNZVukcUK92CWpGRr1Bq728T0PpTQD/UUiJ+aGwgzlfMSqS3fyiQP72JCbZK3T5bHZH8EWReayCmvsGvlKAEYijKJrMsI4wRhDbbGFMYbqYBEpfzDt8E0X/hxTX/0Sie9w2+phzupF/uRttxOFCfPzNYZHKxghMJahEWsmF+q4gYPT7a2iz833keYlcjFk50Qf51+8AYB2N6XWiLECh3gq4cQL0xw+MEOhr0i0fhWP1TcQKpf1Z2+mqWI2pa/giv5JCv0RuzwXlVqcOD1PPYrRxvDE/pNU+wskiWJlucXpMwtsvPJBlrUEfFrtf0AtXYrXjum6hjA2OFqQWj5dqWn3adxJhZHQXG/Rfzzl2PGpFxOOHMfhVbfs5K579nPFZVt4dM0z1F9w6J60oR1w8GwJ5BJWxeGUuZyqfogfG3kKgG+MXYhtjyGEYHSsH4TLr37nGq4dnOKJb1QRNwhOLg2Rps/x1a8/y69c8AhqosTMPWWe9deAlIi+JnZXI4HtQy+jJNbSic4nSxMQPSXYtSxOtCqs7e+nvBTRmvVhTNHeHrCp2ua5hTO8dLMLviTzDJPzK6hMcdedf8g1twxwLB9zXf9vctvIATpa0mp0mZ+q4bg2lf4CrWZIpxnSN1TCdW3CMGFDsZ9j9YgNo9OMDbRIlUPZUWQeuKUU0ZV0XclAuUCpBE6YkfMS5nWVVuSQ922MCrC1IU0Uy7Ul6KZIN8B2HUpFn/E1i4zmlpjp9LE5WOFENo7OOXRtiVcUpJ5A2j6mKUlGoKQXaNUD/NQQeJrSUMZtN76Eh74xzpWXfIGJtwi+9tebENhkEyWMSikOZ6QdD3IRvEwx8lSd5rcdVCj4i5/4No5UPPbtQe69Zw+ViZDFyQFEo8t4/xyn0xKf37eDvmiF1iEXIwzLJs/xI31UdsT8wc1f4sNfvYHmiod/ZUb2jIc44vKW33yET0TnU7IUhQHJF171MT7mDnHfzK09j5AMopwknRhEdG2UUDz2XIkbLgy5bO0k33xsN6eOjGBWH8EAreUcgxts9riHuPblM/z+Q6+k/oiPrESISxJCLIydkeGAFhQskCVohQ6zukS52abk97G41CJ08uBKrJZGPA+5NKFlVYkXmmy+ImTNxjq23yC2BkAJ0j6Ll7zkBewBj0ee3oLe4BDPCtrbSpRLS4zvDVkyRabtElQ0OAISgYkSVNlF+xK/CcN2B1doBq0a7TQPCNKajZouIFc3cKZTdGSBV6Dh92HdWePoNf1Uqx3uvG83Kw2fR169SMFVHHf7MP0eH7ntI4wFGcXf7HDs20NYGN782tM0hGDR5Pi9911EPDmA7stQaIzoJcY0+wSm4qFDgWxr8CyM1mS2RJS+NzDSYHcUJjXEnkNzzBDUc3z+01dxzW0HeO8r7+VPtl/B5N/3UXzGwmiBFUeI2GA9GRHlA9z5jLXBEqeWBml1c4z2tQkGE9IgJn1KsetHV3jO7KBaabP8YM+Hq193uXbDNCNHa1y5XWFZETVcNsqIa/ac4ItPTJCVBN1UkOUMCkG8mGNi7RKOLOOGXVyVsHNgjrmoQvdojm/s20mQmd4zI42xuhryEp2TGAxWIvFSA1bPD0XbEio+nOiSHvEwuxLwLNJ8ke7qkLmwzBGrj+GCZFV/jVUDC2ypLmGFKdpxkTWF2axRowmi36CyXjyT8HoeWDIzRH6GK1yCSkx3sYCwXAQKgcCOQywD2nIwgQQsBAKRaQZP1lHCJhnKIUOHfDlENcBZMYQiIR7yWDOxQjUfsnPTNHuf3ohQKeVaHfIOwrWxIzAOYEmsWCOMwESGVCgcLRDKoOOE0vaIDVdMsfBIlflD/WhtcGKDFAI9UEaXLbLlGOnbZBHIs22cRkx2t2HxeIDpSKxOhjEpj3zyCaKtVQzgn1rBOE7Pe0hYiCQlsyzSEQuzpR/TyVCNFjoAKQX6sE08EZDlJN5UE3d2mcT0KlRdIYkxuGlGFjhQ8FElG78dk2mBxEZoBW6vhdAAwnEgThCZwmSqd9elwNRbRCqlWHZR9Qxdb5BJGxwXK85wF0KY7ML4MHaQI5UQ91skRRvrUAsHBc2QeKiMXlWBbojfbROFGSoHVqUI3YS//aOvY7uCm9/wFroT+zFdiVEuL1wmeIWtSIRisdVhutFBKY2UAYFQYBmMs4V33no17lzMRavvZdj7C05HLo7I4UqJ7XsktS6o6yios4SxTxa0yf6H6ssfFJUtIOTADzymOsc5znGOf4/80H8DPvDAA/zcz/0cjz/+ON/+9rfJsoybbrqJTqfz4jbvfe97ef/7388HP/hBnnrqKUZGRrjxxhtptVovbvPOd76Tr3zlK3z2s5/l4Ycfpt1u84pXvAKl1L/FZf1QshQ9i43EwiLW3e97r92OOHF8nuWVDrmCR5BzGB+tMDBQwHP+95UfUkoqlRxaaJZXmnTjlNpCi+WlFmvWe9hjKbZRZK0uupsQRSmlgRIDuZi1VsLL8zPUk4h2llKSGQbotx1uzJ/i1sJRLgx2cur0Msa20Giydhfnew94rQ1K6X+VMDo8UsXKeTR9QdHP846XXk6Q85ifrfHkg8eYPLXIjedtYMD1eOU12xgZ76MkXGwlsNGYky7ihEs26TI82s/6tT2flrxvY2no6yvg5T3azS7d2gM0l9/Abv4Ti0mRSDlEUYSfag61HqS/oxhrRFjCY/L0AiaJUVKgpeTEmUUqlTxJmrEwW2fy1BLP3LOZgITAJPTZLXIKhqo56iLsmdUpzXVXbWHDtjEo+LTGbNpjDrrPZXZrSruVonXvZhljuP7a7bzn3a/n9373NVzitDn/vLOEG8p01xVojOZQlQT6bSbDGpXWbhItyRQsHynRbicv3lNlecw+neM776pgHkuRrRglBBPDC/z8xffz0PJmnp8e5XF7LaPXxPzIGy6mHR1kXWmFDeUlit33k9R/GUf/DW7QSymK44x6vfddsCM/SaULadJHaa2kcgHMhnmOhfMIS+IFApFktOsh7VobaXsEla08NfkRWsrl/tYGTsf9RHHGwnKbKMlI04y56RUa9S5pkgEQhjE5ZaMjycn2IFoKnonGiA2ktmJ27QYqr8yonV/iDbcEjFUnGCq0cN0MU1L8wxd+lntWbuTnjt4FaPBcmlpjcoJwuEugNHRChty7yLAxCJjUuCsZIrOw/IisrtGjLqpawOR9Et/i1LoBGoGghaChbT7wwSswGka8xzl/0xleddtR+talyMDDbiYkUZfWdwyf2TfE3UfLhNrG8QS+lzHoddk9MMfOoQV+4tZ5wqLm1hueQ2QK2U2pJy6jpTo1FTC2ca5n7qpdUtFh3bZ53rrzIbYPzPHGPY/gnZEEWwUg0HWLxX02Whue6xZpGc1iAgcf30TlO2cwzhwyiREmwWQKSwu6Y4L/2n0Zv3DkVv5paQ2lCxf49Mk9vONTr+ONn38TncMV8DP+w68fZK7Ujz7rkxqL4JSD/zces8/1wbIhOBySf67NhDPHtv4uU7U8jZUySSFFvO4Y+aCDN9/FbWSYHHSv1ChfIalB3Wamnufr395ASaf4uTZJv0Fd38cr9pxkaFWLH/vpB/nH3/84XsnhgkuOU+lvcGapn3jFgSrgZjgdTTHrMPDICjf6D5FbqiO05s/efSNf/9wuzkwVwTZgG0xfyrZbjnLTrXsZGVmCMEOEEVJBtqfCb/zGq3n7T7+J9KmE0pEmbhjDUMyu1VOM7W+jdU8IGK7EXPvyU0xcfBYQ3PfNMd799qtQd1ZAKWStSWMoofUqw+yPWTQvy5NaknSo5w9hhEYKcDJYeXQAY5nv+YZopNIIT1Eab+IYKB0X7Kku4gqNL22sV7hs/KNpLC/GnuuikoS4q7HnDOpswEKUwwsNyYqH7Eg8odA6Ri+nnIzG0U/7dB8aIHegzcCTIelwzBcf2cznnj6Pd//CK/itMzfRSh0WM5+vT59HMNBG+5qkTxAPCtKcRFcMR46P01Uu+SwmndMM5OvMpmUOvzBAZyWP7rZxtaYWFrBD06vuUKbndaND7DQjMwqRaRCGtGIjQ43VjrFMjOykrLSLTGxu8+3aZi658RjVsQ6eG7OqGDJSzXjVm/cSDSfY0uD6UByKyVkKYcAoSJVG1UMWN/43U3GDkRmZa1CuQUjQlk2j5BEFAhWAcgUahdAGoxSOL0jLDhqNFaak0xamCSYGp5MhUk2sJJbSNLMA41iogkN3UNIahdTpJQwJ0as4sZZT/NMd7G6KyktMzkEgkBH4gyF2kJEfqyNWIpzFCCeRCM/FApSn0UWXeMQlmnAxQgIGkRn08Rg7TBGJxmiN9HptXIieECM1YNtIo9FlF9YNoFdVwRGovEQ6HrIZ4JxWiHbvHmIM8uwKxhiaV5WIN+ZQaw1ep4UXZ4hW2GsX3DhEMuKAML37bEu0kRA4CJViorgX8YZA2A5W4CMcF+1aaEdCpLE8qxfpbGK6m2yaWwokgx6WMsiVBqnOIPBxvCKB9skuWkW3HdJNY7LxAhowgUtYdNAYWAgxp2YxnRiQmFDzzY8+ytpP2xjhY5cTKmN1Pjz6AB9/37d45KvP8Zn338XBZw6z0r63F28NOLlbWDVQ4RUXbmX4okcQQCBiXNPG0dMYran0FxgfOUjJW8QKmhihiJLlH3xwBNQX38zC/B5W5i/+V+13jnOc4xz/3vihr0i5++67v+/nj370owwNDbF3716uueYajDH8xV/8Bb/927/N7bffDsDHPvYxhoeH+fSnP83b3/52Go0Gf//3f88nPvEJbrjhBgA++clPsmrVKu69915uvvnm/+vX9cPIkH8ehxpPI4xhjT36fe85joXnOxSLPgPDvUoVz5aEmUab//1qhhDgWpLF5Q7zM3X2759k1UCZVqPLu1/2Sp4fP8F9e5/iBDWGB0sUSz4jI2WGB/vQuTYdBI2sS+RHfPL0u3nHrgbnuSN0G+9EG0Mqf5FG7StoSyJSiUkzsqxXDu7nnBc9P35QgpwLrkVMxNT0CgvCZdPWCe766l4euvd5lMr4wz+4A4AoTnj80WP8499+C6M00tXkTiuMq3CWILfFo1AMevdBWsRJxtDqUSwpOXNylnzuOENrahStFVZ5q+l0BL9x921ICX9w0QqtJEMvHuVrkyt89EMfINm9G7Vd0ve85hW37KFY8JHSwmgolwoM9JfZ5g3jmAOs9JXJk7JQbxFmHTxjg51jaLjE2aSLfVphba9Tn+0nWNPAdwWHPj/PYw8f4aprt2FZvcmUNhlJnLFu9kbUC2dob1ckBUHhhQhRsbECyXI7Ya5tcXhgiCNmiE033w2n/ntVmA5A9NnoqoOoZOzYPIf/6Freetm3OT/f4I0Dh3jb47cRnS1yak+R9VvHyLynWOvkcUWG419Blt5Dkm2kNt8A0WRZ3szwhcDRW7GlYjEdZG5tGe8g+OdnZJkg56zg+x47Vg/xZH0KDDz22AnWb6iSRL/Ga8aP8tS8y1j5KCuqjDGQZQqVKbrtmDTKqAwUyeU9AKSGvJQUJDSMxx+evYYP7XkUTZ47G2+h2a3yzcVRhqcyctvuYGu1TnimZ1rr9Sf86a/9Hhe97w/RxyWBzHri3zof5iXJkI8Mpmg/U+TOj61i60UWuulw+OkR8tqgKhZxzgY3pTiUsKIKGAnCMgRJwHHvPCYPnGTnrkVKgzuZbN3H8aM5aqGN5xtWRA4hMryFbi+JxXH4yu9sQRvF9V87y+Z1i5y4qYodaFbCgKFiiJSSyW0lPnj4WuRwlygxXHjJFFvGljk8txqxzpCVc4iOQTg5xsYbeDrlrqkNHG+sxngWdvNSvNJJolbM3/7N9Vzx+mE+8F7N8LYuV94yQzgTYEnNmg0dfvZXH+LZhz3+fv/L0DkP8hAEGRVPU3Uz5lr9WNrm+HIZs+STjsesum4OUbRZO7LU8wcSApkZhAVWNcZuKJzYxs0Z1v7yNCdag2y6r8Gj3ynAbTaHnlpPn7VIsNBl8byANO/AGFhRSP9LmizfX+K/vu8KyLm4gSJ7iSbOaxJh87nHLsE/FDO+SbAoPS7a/ALH943iGkWrFlCIBee9/DBSai4rH+U8a54zr6nwtY9vQmY1fvnDd2OM4VP/6VLe/usOH3rMgIFGPuD1257CJuXuYpmZuIq1HJOVPIztsHBRnrEHl3o+CanGfi4hWW0z84TEOSN4x4+9iWh9EX8pJSs7WB3Fn2qDtxQiOjFSKgQauSvF3J4j61o4lYxsSaDzFlmiCVohupJgWjnspkb7FmG/g7+sMa6kPmxI17pct+o4e5/YjaMSsmXNIdnPkNtksZJnOJ8ysDth4T4b5QuyYUm+7eMJi5OtfnaKJpktGQgiogWbVeWI6Fc0K8LCayqkgvpOQ6Omufb8GXRqsayKrAo8FusuP3vgzUgHVMfiujUHebS+jbTaEwRwJdL0IstllPH8d9fgtuvsuTTP0qNV3IZhx6Zppp4vM2OtMFFOUfleq5dIFbnROqQ+nBR06wlG+RhLoPo8craGnIvTUWS2h0k1tw8/yz2zO9hRnuR0NMRhOcET8QjHVhyq5Q7JakF7XmI9Dx2rgBYSq1on37bJtCZ1U6xlC3uDhZuA3ueSD1o0V/fjtC1kpEkcKHYshDREwyANuFbM+nXLbLhgmrsfPg8Z90QfkyqEFKROL33GSRUjAzVcT1E3RUBgLAt7pouzIrFyhmSigPIsTGpIfIOlLWRHYxzDps3TVAZiHjmwndmTOaLIRz0mEI6FDDNkI8a4FirnI5WN29VkKHSosIM8WSXDrse4UmKMjSZFZgY8F9lW6CxGKEMmNFmmkeUAU84hjYEYlLKxYkGUFpGpQloRQmu8hRSRzGJ1Dcn6HOGuPGo4j33aRosA//klTDGHDDWshEiRR1USRGwQrkOWKOzARcTfa6+sFGChDpaNdGyE0eAGuF6OVhrjpSlCApHGPxKT5FPUCD2hxrWwGyFZqsjWVjFFD5EYzJ5VWEiINZkPSIHcOEamZ7CWuugkRSUZObsXpa2lhVlIeWP5CN+MJ3C6mrZTJP2NBOvrFtFiSOL+IpPNExSMxaTyObP8d1wVvJXGcpuzI2UCK2KtHZGQUHJHqNUjpk4vMjZ+C0N9y1TSU3TdFm4zQA0rLOsHa4eO0v0YINMLP/CY6hznOMf/PYTuvf6t+WE4h//T/NALKf9vGo0GAH19fQCcOnWKubk5brrpphe38TyPa6+9lkcffZS3v/3t7N27lzRNv2+bsbExdu7cyaOPPvovCilxHBPH8Ys/N5vN/1OX9ENDf/mnuCC9F8vegGUVv+89z3PYsuX7xZWzZ1dYnm/SbHSp9JX+l8dWSjM7Vaex0qHbyTi49wzjN23HkpKhYoVbzrucgxunmUk0xf48lmXhOBKhXOKwAHaRubRFpjVzKmCk+ha0jvC6H0PIIk7fRbju10jCFC00SZjQSVKMMai0V3WktcGyfjAxJZfz6Atcpjsh7TM1TrQkO8/vct+d++i0U86e+e8rOHNnl/m793yD2akGJhDgCuxaglQCS2sqpQD7eya9qVJ0ohThWKzdPs7yfJPRNQX6+wyZFvzS6HP87KM3seP8SeZrBUr+j2D5/4WKlXLZyBTf3rCZM02D39Fc8rrNvOWOKzl9epG9Tx5j3+NHWL9ukFe87jK2rHozAFNDz/Kt6c/QnlrCHXeJKylOTjGz0KI91yG3nOHsr/Cm/3gnndjjb+95Kfmow3MHznLVtduQUpIrusRdiUZT8D3iTsZVoo99j5yl5Em656+j3U6I6jYr+ZBQD9Pvdpgfd1k730sWeG7hLLqg0Lkc9s/muGj7SUp9KRtHnmHQTrAAA1w2cZavPzXE4imJd43FThkxlJ+mT6ZYubfiFH6K1uwKS/MniXNfYqTSq5y6ceAoUhjq6/PkH09xGhb15wLEWMKmgdMIIXj1DXt4at8kRAmbtj7AT7/tKQ4rgcSw0b+EQO1k6/pXsjAvsC0JBrQxJGmK58Yvtq01Gh0cR1L1i1w6voHXX5CSF98G4I0bXsX02jm++8IJrigNcfLIHI+cPIW1RoMQrN2yhGVJKqccxnYdYumZMWrdPH0vaTJ9aAx3W5eLJk7zyIHzWJkq8q27S3Q2WBgpSPoEdhfi0EVlFn4myDotzEBAFvRSaRCG11+9hBCCOHuO8tjnaE3u4T3veTnLZeiuruAEMYMZeO2QVMqeL0ZX8Z3b+/mDb+/jfm8DY16dt333JvZMz7L/4Ai+F9PZaZNJj0BGtJ+w+PqnVuFeP0BweYisJOR/NWa0r0bLN5w9NIZfSfiZl9zP/MSz3PPYj2O9dpSnvziPsjweuLPNQFVgEfKpj1yJjDKMa9N6zuMLey5mpi4YWbFIrIzOcQjHbKJEUHcsBgoNpq0S3XaR7T9yCFspWi/k+fXjr+PV1We447oHKQwt8tUHLuPqK4+Su0hwW/4EJ7pFfvedr2bvwmquGTrO81eO0D/SYjFzKT65TLesKaYJerSL6fNx6zHX/OJTTJQjvpnfTZgWMArWDw2wXJrGWSoQLAkeG76QG+QjlIe7jDpd3nDhIT72iMdy1Ie/KMmEzRvXPoUUmueb4zSCBpm2UEry+3/9MEE+wmj4xfc/zGr7DH+du6HndQLk24KcF/LsybW4EoIlg+NltAcFumDTyglc7WA8w/TjA6STeRzh4LS76IkAywgsbSBMsSNN5lloDZbbM7xsuJpo9QShMpjAQiYZb3vpF7nzGzfR7hQJ1hjceYdaItGOBTaYvgy6LpY2uJ0E3TBsH1E8F0eYWGHPaUr9MftOraLS+X/Y+884y47yXB++qlbaee/OaXqme3KQNEk5B5RBIggRDBiMwZhkE2yMjX1wwBiTDBhjbDBgkggiByEJoZzT5DzT0znuHFaqqvfD1iuhI+I5/h9zOHP9fvNhumrVqrV69QpPPc99Nzhl5QQPikFa/WCyIK0cqZ0x0pLML3QRZhq0sNECnETEcHeRx+IBVq7yOXSLxoQwWqhwIDHAipV1EnbEncdHCKIYs+jhVg1hwcKuGw4+MogtDCatWFnIMbWjhGhYmHxblFRLwT/8ze0YzxCt3MeRn/SwUMyybF2RhZJHJgixVzdQjQxWpLGbhtABbFB+hAw8dATGkeA4xEIRLHewijZC2nzkntNw1zVIyYBet4IjFdNkKIgFgtjDWJJok03m2y3qp6QRQtDYmCJ9fwNwsBMCK4DW0QRyIcSJJCqZp8OH2DOIpiYZGySyreGiNHZTc/LyCepOCrPCkL1dE85FWM24HUxSGiEkaIPd1BwY7+bmu9cytiOFyGmkH5NcDLENhD0WZijXvpVIg2M5xB0CO1Dkxktc9P49+MZh3s+zsznC4lSOpB1Ch4Ws1SASGFfg5hXD2+eo3Z1H7kuhPIlJWiA7iPwyCSnQRqOSFiqfwcQxrZEUOp2DZQoTa5y6RtQ0VtQuJxNxjIglxpGEWYFVEdgm09Z+8QwysCFtYy9BZrfCmYloDgpUVxZZj5FCYCmN1VSopiI2EjnaiVmqIyyDCmMcaYHnYtIJzGA3ptEkUhpHW1gCkBZJN0ngOdBjYU8soaIYp+7gzikCP8ReLOFJD0sKvIkayvFRuQTak+iMh0G3s4006ISNWD2EkNPYjovOpqk3W1jKxpNJQBPvKXB+90Fu12vYsGWSTfl5Htzex6mzZ9JRuJF5baNMzLGFDMI2rOz4Jov1ET69+wyW1u5iWKX4nbVnYRJXsVRcYvzgHInM/ZC9j5zMkooa3PChe7CN4s3/8FJ6ert+6fuR5V1HrfkZkD30/9LeJzjBCU7w28tvfGnPT2OM4a1vfSvnnnsuJ53UVhifnZ0FoK+v72l9+/r6nmybnZ3FdV06Ojp+bp//mfe+973k8/kn/w0PD/9XH85vHMLqId33YxJd//ZztU58P6JYbKC1odmKKVaaBK3oZ/b9aaQURH6AX29Bw+eRew4zeXiBxfkKSiky2STLTlmO7M6yVKrTqPltq8CgG9Qf0tn9NZqNCNWQyFg+MWYCr+c7uF1fRAjB5296O2detRaddYksG6MFQgjchIPtWL9WRoplWWSzSexKTHHnNA/ee4ClUg3PFQhp2H7ayE91FsxOl9tLg7Eh4VXRdozRIVYyRhv95PnMpDyyaYewdTP7H3srM8cm6e79a/KpZWQsm9G+T/Dc9Tt5a8+PedOG23nWpm2MWTkWRUxS2HzgFbeRGq+TOlyhs9o+D6FS3PuTXVQaiscOzPGDW3c+ObVsZ4p8dwGnO4PKSFResK1ngLAVYdVj0tWIvsBjzfT7+dJnLqTzgRoYzeBg4ckx0ikPHcfYliDXk8GPwC5CelUfG07dSG+qj3gugWlalGtN1ifmeUHuOM/JHyPhSowx3DD+PUQ+wuoIWL9+nloqSylKkz13lnuWthFjo4Hhnhras5COwkl6TB36M7pL20nUP/jk70+FisX5Gq3Z62iaJC3jkHVqfHNxK+ntFaKhDNFgjrAjSb2cJmsHHKkeYLizgNQaYWBkxSICgSsUjtBosqjglbiJ7TiWxJUSAdRrLVTud3mwcQkPjp8OQLPh06iF1GtNyjNV9jaO8qNGLzc1eoniHt5wyZl8+g9eyJtffCH9gx1848e7OKvrKFs7xhkZbZcbXvaqh7jktP1ceul9rB3ppvLDZfzBq37Am8++iTv/YQtDywq0VncxeHoNJxfhjsRYtkQGEeFsgFWLUQ2PRGhItASqAUNrBI//4fU0cVBYOO5JrF42h23HNBc9Gk6K5qkxzYEkXl8Ho2+7AHvIRTQj3EgjGw4H9g3xwq4DXJ6bJjHns+uOLpYaKXLTku6vLjEix+jdeZTFH2YJphNUH5P0WDXMqT5XrtrFnwzfz1Sykz3JToZGmvyotoHKsEP3wDhbLzuEyCtM2kPJLLWBdUyWtoEGYQxIiRoSWHsgMbaJbG+edTLB1bkshS8qju9ZwS13nU1zpoMt6ybYfG6RgdvO5OX5x/nAS27hLS/5Jpc/6yDnb53iykvm+fe//TYd2wIuKkxQsAyjdpVTzj/Iswd3cX7XIU7tnODAsUH8JFSv8cg9GhK3FF2fLOLc2iKeyfOKVWM8t3OG886fxkv4ZAo2Z56+DvGAIDUFIjCo44aD+zaSz0SkhKZgK8btHvZV+okiQxgI/vXIuXx/7mS+PraFGQUSxesu309nd4NQ2ISWA75DNZrAuAplC/AMb/3w9fzBx1+PdixMwm0/sKMQxwdjgQzA2AI7UnhGY8UG0GhPIqsRyoLAFZhGSEQEsSLudNj2kt2cdMlBBlfXsQ830E+Iepqq5Mu3XoqoG7zAsKVngivPvQMj2vawRmt6L5kC2q5KqSKYg4Z6JEC2Sywmjvbw2PgAhcMB0z8agqLkWduOIkcd3A02dEriZYq6FRLtk9w7vgo/cHn07mEO/o9hmhNN1gYzHJjNsW54nGzQ4EwzTcfqCp+vnsGnZ86gr9OncfkMIwOL9KcW6XhAUNgN1dkcKi0wacXz165mRSVHVkSc070fa9pl+fIqPZkqfU6VC086xMozJshmQtxEi86kDzMax1Jt8WYp0VmJ8qA5aBHZEc5EE4RBRAJlu9S3dFJekyPoTYBnM7EwRLTb4lizm6Z2ibBJpSKIbHbN9WAFhk7KeCHYDbBrAtXvIIOQ4lkCEdN2DDJxu8RMKRLC5yXXPgBSI4RCS0PgKcIUWEZi12PmgwIH7E5+ML4JOeyh0w7Gdp5w7rKIHAuBRLQUloL7HhqlPpVANmLsOR9p2xgp0VkHYQRWBHYY091fRRqFFca4iZAd0TAH4n5CT5LZV6GvsEDQG2EaLZwIrFgQu7D+ojHeMHIXf/y7N6GS7ZKhuXMSyCDmWW8c541fv5Nl5zcxnRmQNtqxcSoxVlkhQoNdCbBKLZRsX2dIkC2DDBXoGCFoOwgaDWFETIy/pZf08gyy2iR5IEa0BN60R7JSx2m2MPN1TN3HBBEWgqRjo1ohZlkHVtIF2mU+xhhohCAEalU/4pQVRBsHaWQdjGxLBLnawhQKyFSaxqAkMDG6HmFFMSaMiJtNaAXQaGLVG20RfMeibRBukAtVkuUWl61cRsIPsDs70J0d7eNxLFRPmihh0EIwMaVYoUPspYALckeQ0vDaqx6ie3OOKh4VneKzXzuZT73pEv753c/lUztu5vxLNlE50M9/3PYsds6ez4X/VuNzB/4VkCQ8m0RaAgJbZalMJhl7UHPw0WMsTv1qC4YJbzV5achYmV/5neoEJzjB/0H+uwVmT4jN/mbyxje+kZ07d3L33Xc/o+1//vD//4tl/iJ+UZ93vvOdvPWtb33y/9Vq9f+JYMovIgxjxscXKJWarFs3yEKtyUy1yVI1YM0v2dYYmFuqowVopQDN97/5AIXuHC/U7dyvfd5ROp99gPmFbpTSxLGiVGwwNz+AZfURNUEGAif62ZdtV1eezRes5yf37CcKQswTAmqBHxFHimQm8UuviaeRtGken8a4KYIwYq7U4DnXn01pvs7zXn7Ok91GVvYjczZRDPgRq0+aQs30oos2qWYnc/OVJ/umUg4Zx0I1vsnY4ynG9t3F4uxJ5Pvvf7LPQNNnYr6HjuEqrudw2chz2Tn+LzTDmN7lFpdu2cWhh7exfagdPMwkPFat7mLfviI4Ns1q64lzbhhdO8TVL7+QZEKw7qTlHDgwywP3HsQyBhlEZJCkHZdkJkHvpMEPbLRrOP/Cp0pyGg2fhZkiQ6v6yHVmiJXGHC+xurfAOacMcp+3xP7ZJcAw16gy4Cu6cxZdrqHcl6e1cAlHWisZHcgggHSqRVM5OEKxNTPJreEgXZ2f5Yfj76CxQtFNES0UxdkyvryJnWNwyujIk3+vi0t1vvnZO9n2rP28bL2mprsYcSs41nHUBsnQlhfwjh/uRGlDUA8pxmnqUZH+rrU4HS5hscrHP7AF2xnk7GvPYba8jx8+fA5nnPtyFscUa/kCYRQRhjHpjEddlWkaDxX77fNRD5ieWqBRDDiwe4pCc4G0nSc0FqctOnz3yE42rRjg4jVraLVatJSkrFJ4lqJiLJTSrM4X2dJcYtJNcP07Lmb58nXAO4jjmPGTP81cqYJTVJTqKayEpildtCvpsy2CSYNsgo5CdDaBSks8bfizsy+lkB+F/F4A7j52OqtTZdacNM+OA6tIFCXxbok367N2fT+1tEasTSPmIwgVYXeCz3z9WdTmduGv9pg91Ek6rBIOOHTnMkSVmOj7PswpSDlgGzLbbI6Xu5leluO01DSP+nkcO+L1m/fztmPncKA4wIbuabI+bGkd4C2vm+H9n/kdJIJCLkGlqhCWgoygY6iKaTnMVLrozabYvHWYh+84QHLBwzEew4dqdF85xkU9Y3zykTNQJQ95vMhQvolA0Gu3uK9ZYO/tK/nBTzajVxewZw2Ll/2QFxWOMCkMd81soDbm4Y7EmFJA70kt1uTm+NCpN/Pqr1xJbTyFZaDrgQr6/hJv/N6lyI40tg6JIpeg5rP/8CSd+zVzaxt4L25huYqFd8c8eu8gp58zy2zT44XXHOS+8Tr3fXktsqnZOzvE7oVhpNY8JxW2V/E3CI60NJ/58lZ23bOFv3r391iRm2YwXWQq7EIITXR5gH1fgXpO4E5HEEdoW6LsOpgClZPSpHfUcKSFbSBCYTkJTLDIy//mUf61fDFxU3PJ/ZPsO9qDtdjEtRwuOrfC0QEHa7zKaHQu30/PtFfK6xKmM2Q2jHN6ci2bz/kyk/UO4gciTBRiq4ADx4Y44+RZkqkAJ4jZc3iAz956Gt2yCpHgkcnlyIWA0q4+HF/xrocv4M/OvI/8hVBrJkkOBGy4+ijZks8jrX5mZtajQgudsMk0l3j0s9sRbsT8Wf0U5pt4os7hxRzl1S5R5NFTn8dJtdh4apGSVcBZF+PerUEL3FCgY8gUanSUs7SaRaQvENrmMy//IiuXhdze7KXXqTK+mCG3TDFw4TT994bs9z0WWnm6N5ap7coQRA4iYaObHiZrE2QF3sEq9OdBQcMVNLdoEitLmLECQlk0Sikyh2p84VUXo4Vh06sWaFqa7qU6PxrbgLRj0rU6cV0Q9/poXOgMMMLGrAXzmMAKBc6xJsayUELj5QJMUSB8UBi0KxGOhXEtYsfCsQ2q0Usj1OjA4NYMtlEoobHVEyVOCRdtDFILlmaySD+kvDyPytlYLQdhOygZtaNzUYzwbIwUDI3OU1rIoMMY5QtqLQ8hoD6eZ90Zc2y++hiTRzt46IFRtAHLGEa3LHJh5hjLwiqJVEhfxyJT9U6kLTFC0DdUIWFpCgM1xMGBtpW2NBgHhNYQaoQC6TqEsY/tJUFIpB9gtEHbFgqDbQyy2sIOFNqyUXMuNdsm4UmGRop0dAfsfGwQFRaIhjpgvkxrOI0qKdLTPjLUOH6E9kN0PcSWEhNGCAW67qMKCYxJo41BSIHo6yCYqpBotTBhjDtRRCc9HM+j9LosmZ8skJxNYGxN5LnoqiLR9LGSLmGjitYhKusherOIdIF4Yo7phQo6iDEIrCjEGIFyJMZxUBZIIprGZr/fRed9HuYaODt9mBJJBk59L2n7D4lbn+XIvTms2RqeJRlvWTzvTf9KPutx7lAP9/3nA0id4t8XDJe/1mJwtJeE/XySSYeIbzCUbHDOC216+i9n5cahX+nVSMteFnQabTrpUQr7VywJOsEJTnCC3zb+rwmkvOlNb+I73/kOd95559Ocdvr724mFs7OzDAw8VXoyPz//ZJZKf38/YRhSKpWelpUyPz/P2Wef/TP353kenuf9f3Eo/1fSaoVMjC0xNVlEa42UgsfCJco0eHhukjNZ/UvHqBRrpLMuzbk6JoiZnwxYqAZ4qfZ5vm7rpxhNTvPD4xGRuppqM6DYCjh0bAEAvxljNwVtKTswwb1gKuBd8WSA5NiuOez5EFEKSHo2xpgnAykq1kj3V0/Cet7zz+DQ/fs4rsFxXc67eAMHHzrOsuFOEomnXxtf+/6f8OX/uIOo4/3UH+uh1ayg5TLmAkFp8SnhXsdziZA0Y4sN2yooabP5zKeHoZZ7f8f9xf9kRe+zsSyL7o4+yseTlAODo0LOP/ONVA8+ysL4AlGkCIOYZ111Jhs3zRAZuPTa0/nB7qu4pOsgd+x/MV09L2LlugEGhrvYt3uSWrFGJpsgYUlsS1ANQ5YqTTZvGOCRh4+TSrt0dT610hRFhumJCiuWGgyN9KAwLI0VGdIOg7kkzxvawK07DyNCaJqA977vEj769h8wO5dhaG0DFR8hMqspJJtIYZiLMlhC09IW+5on8coNn0BKxaC3yLKEZvm7y9z4gS00GgHOuluxjqU5Xn8vW8QXAZifWmRptkq1L8SgmIsgacEKZwmD5NT+lfQnjjM1XcNKJTmv5zo2Zk+lppssW9/Hsb0LxL7mb/+1nxcUNnLNuknOWvcFtF0k1IKj5c9SrV1JsxUwPNLN6qV/Qfrvos97BeVSg1YtwPcVwgKjNC01x1zcgzGCn0TH+aexB0hM2jw6+kbe8h83ImKNxOAbm4TlE4ctLhp8A4sH3oEBkraDihULsxVu/d79/OQ/7uKSr8+xbIXF8aMFHpgcJarb2FaMnfTAeIhyAKFBFyzitIVWhr6up6dkV+N+ZH2OwWXz7Hl8GcoXJGwHrxyz/rIhktvfT7ffz4NzKygfUUTDWXDggekL+cPzzuS+xJcoWh4mLTnrms28ZPV67rp1Pzd+7jbiWotkjyE/GDDVSGIdynL07AwJOwIh2Onb7J4aJkKy+/gQ1zp1bHuALacd5aMnf5add30UXWqwWxyhuW4H286b50efOJViOUdHKkGkQ0qlJoE2OA6IjMfsZJLxGzqYeX6aZL7Fip4JFib7+cDN5/C6Fz7IvcVBrus8yGOZFuuG9nMwsxW7YvOdf7mU78QX81d/9GlMALVY8r0966lFWSq7u3mYLmbX3sW733sPb3vZ5W0HlTjGsgWxm0IEAh15aAna19z94HGEk8JSIclkQGeiRfkvPG7765P5iRrhT177MFesnOKS0Rmu/uBqYisk9UgVP5VDxT7nPPAyZJxAWTbru5dojnkIA//+72dx7nPGcH/kYF/eFtT20oqcW6KaSdJKNNE5B29tQHZBMaMyiEKGxkUZ3LvncCKJk5TkjhQ567xjnN07zyemBFpLcj0Kt+nyjjftore7hd85QrJ1iNLkRdyfrmPC9j3V7q6x6uJZ9jy0iiNihscz5+E3NYX1RU7OznLdmTv4/H3bMf0RzZrLyL46L1lzhG/cfAb1YwoLSfFwHmu4hcRF2BrTcnn84BD+oAfaEOUktrEI/ASJpItbltg+NLsFCDCJJEY59D/e4ugg9MdZylNJQj+J5SiiMc3oUJGXrdgNEv7u+GXUXIUIHIwweIc1iaiX0373fu47+SjNvGY4X0PFNn+191zu3L2RjqElrivcw/BAibVDi9wxuZI5K0kcZXDLLSyjELjgSJQFJmlwZcjWV4zxwP4hhJLo3gR6GJiWOKqBVfPQlkFlXIQtEJFh4fYsW087wh3hCCYB2jhEzXbxevcPA4pXCpwHDANXT9K7yWHS6cJpGUQD8BQMpyi2Ehxv5EAbhBFIX7XFwC2B8QxaStZ09zLWmkMrQ0JY6JSDWAhACIQ2WJUQnbKQtk1Qy6BbMdFyC9rSsQghiHM2/nKBVCGxm0BYNqXjLpbW+MtThNM+SxMCIwVOIksjrCIMRGM2shljKQFCM3UsyXg1x/JmDzqnSTshCINVVlRWWGzuDtne0+TOgt1esEwKjAKdlhilcBu0HYMAL7IwKdl2aHM9dMHBaINQpi2EbNvga5xihHZ8lCsRSC694hAjo/OUkw7zdh5/T554sIPWqgQm2YH14ATOeAsRxIjpEloprFaEcFyI21kuYiHCStjo5X0IKUgeXEAohVIx0pZIYQCb/GKA/c0lgs4E8WgW45RI7Dc4KY8o59JyIc5L7DDG8mOU76Klgx7s5uCxJeymIHYF3V1JslpzbLxC1Kqj8incUKE6beaDHEon+MYfn8/v/8cPWe9W6LFblKJ/4ntvejmbL7uNx53liLN83rb2Hl5sRlGx4k51DOsNOeS7l1iRzBHFmp7+ArH3r1SaN+B5J9Odfx2v+bPr8dynl3P/Ih4p3shYvZfP7ljDstTX+dI1L/qVtz3BCU5wgt8mfuMDKcYY3vSmN/HNb36T22+/ndHR0ae1j46O0t/fzy233MLWrVsBCMOQO+64g/e9730AbN++HcdxuOWWW7j++usBmJmZYffu3fzjP/7j/9kD+r8Uow31eovQj0ilXFQYUxpZop52OZx+HLjwF24vpaC21EBqF6EMBoFBIi0oFNoP8N7MSmw9zai1BktKkgmHVjNicaoEgF0Dry7oIokKp6H4yvZHT+cNCHcbAEq3nQDsUPODH93JJRdsIltIoWKF4/56l/uWrSO875OvZe8jR9h24UaymRRTE0UsR/A/J7Z0dOR4/duewyf/85M8fLyfVgs2r99ErrPFGec+FShRSlGtN3FT72R01SzPWnMqw6ufvgq0cd0ZJKxRli1v6wAFUcyx6T6+ctwhjl1e3z1I6D/MnkfH2fH4GFb7+4PzL91KriuDcJsMBzNoBMXyo3jxs4mjtvBuueJTLrZI5uq0wpDQtRFBO937Q//0SnY8eozV659e9ewlXeIgZGZsjuHVAyxMVVFRyNihWfxQcenJK8l+oG2JWu8MaRYK6O6QNb0lqvpVjMUwVu6gkAzwowQnd42jsEhIm+ev/ykxaWGIkHS6DUpzNgulKpelfbyTI+48ePTJbtvOWceWc1cxLVL82fE81TjBBwYvIOu9kxYZCtYIa5JdzOg6li+5ov+lVEtVDu2Z4GVnncQ7H9yFNyUIhnPcd/A2rtv6edYtNzRVgopJUWMYrQxhoLBsi3ziNNbF3+H4sSVKeppDR+fxcknCpMP1LzmLKHkrR4JOOqwWPxh/EBVrGipkaqrE7a3jZLAxocBLKgTQaGk68i9gFhdf+URRhjCM8f2Iz3/4e/g5jzPXz9Fj+/x1fCn1cg6CGLuu6UqnmXEFodVAVyLCPhssGEgmWL3KsDC1kaopUJr8DOnm3/Lhb/8jtQdi5GwDJ2VhWRInhLvvOsB870lElSSJfkX+iE09tnjllk287k1XA1A972Lee/B7SOkw1NlFd08nlXKTjkKOhoB6M2RpMY1fV+SOx3z9feew9Y8PccfCOk7JTfDCdQ+zKTPNl3dtY+9jJ7E+dRWf+JFh4vEBXvbKPAtKAJJjlU78mTyFjibatkiJQWr1Fs0gwrEtEgmbRNmnlUowmK5x/coH+P7YVvJ2k/qaFo8vreBTt0WMnlLlLQeezZuvvZ0bJgcpFn1G+yoEe3uwoibf+sRWLjjvYR787kZ81SK/LsItGYSG+/au44p1h4nQ2LqtZ+NYMSoIUbaHoxWxZxNmLSwtsQNNfsxmMFVmMUyTywcUc93EjyeYEUlKymEiSkLCJRxKENcL6ABEQiACg/HgwrXH+Oh1N6G04MJXv4TZ3Vm+vfdkLAS9z68Q4pJ2fF51TZ7vfGWGyaCTXBji70jRyLlkZYlqdzeEgtrWLKlZjb/C4WXbH+bqq46ycyYNJYm0FGPzXejAZmj1TlTNxY6OMtbo4/H9iijpQ4/ACMNFyw6yvWeWw7sGCFtZ9u1N0zXV4JxXTuI0YrQQdKVbVKdstnXN4RsIqjDaM8bBYAApFIVGg1omz2K/oHNGoFKS6l7JwKoSV/XsZ9HLMB0nyaxoMBgVmQkV0reQytBYIUlXE5g4xmnG9I+Bykt0I4GYkcSDkqrVg+5coqZdFoIMxTBN3Ae6IokT0PFog0Rkk0t9lOe9xKKyP8m9d62h0vC45+H1KC0pHe6Gk6DVSrC8q8Tmi6c58sgQ8m5BZU+GSElwDeUFG9G0ERoKmys8vrQW0TTtgNCyLCrlI1dY6AcCjI6xYo2zsk4804WJIqYWXaa+vpLKaZ3oZQ5gYRIa1w2g6DHwJZ8wrON92PCakTv4584LWJzpAGkTJC1iQtIFRV++DFGIOxcSixhpWSjHILRL7FnUtWaw7CFrASkrQa0ArUWfRC3Ez0W4PTHhlIeVi1FBBisCudBCD7jIRozRmsaQwcuCHLNRbkhvZ51/f/ut3HLnaj76+dMIhUdpvwVaEO8uoxIdfPXg6Whp4Yg6Dm2d1pqd5vtfPJPwrL3csmcj3kkB3CsRUUzcbWF3aTQCdzBG2LLtuOMrCAzaNmjLEJkYtxbjAs1yC9mXRktIExE2XJxGjIwFJukStQKcagsnDomGspQ3dfOllsfm+aM0VyiSnTXiWYtoysOuKWgZhJNExnWkbFs5i8igjEEmZNvy2BhMFCHHFpHFMnYgELJd2mNZkpZlkEphaYOtNekKuPUYWy1gr3EIs0mEMbhK4Rwr0SjIdqlYQiATLiaTwTgOxoC0LBwE6Q6XY4/NguuQkg41KyZOWDSjJHLKAl9jXLjrG6dw3stuRgqNP6k4vmecrlMsLvjjWb5936m8+P2vxDk8gdnYzXkDu0lv0Xx/8zpK31rkgzNvoHd4Ha951SW0uI9C+iXk0y/8td6LjNHsqk0TaZdsssmu0vSvtf0JTnCC/wOYJ/79d/ObMIf/j/mN10h5wxvewBe+8AW+9KUvkc1mmZ2dZXZ2llarXb4ghOCP//iP+fu//3u++c1vsnv3bl75yleSSqV46UtfCkA+n+fVr341b3vb2/jxj3/MY489xste9jJOPvnkJ118TvCLicKYylKdQj7J6MpeMrkkzz5/kTUXjPOCTb9cnEwIwfxijUbdx8u6CAWWgO6hp0RqV/R/liPHv0Jt+nwwhkTCIq4HLMxVKJfrWFVNoiko4BG3Pk9I25JSmaeiGhtOHkE4ApFOsvOrB4F2EEfKdlDu1+HI/mm+9O93gpOkp6eLcqnBnp3HOXxwBqV/9lgrev6SUPbhucu44IpTePdHXs4Lf/e8J9sVMF6scWS6SrW8lqWlGko/3YI7m02y6SSJY16L3/gyRyf/HcYdxnZ3k5MxoycNsX7bSgZX9bH34Cxzi1V0pGjUfRYX65SLRd5x84X88+GTWGpeQeS3bXwByuUGxWqDeiNmqtKiHMVorfGeCDJt3jZKOpV82nxSqbYjxf237uT+m3eDDtp6FpFicKSAbVvtmnUJQaDwB9IM2oIuy5Cwsrhak3BjWpFLwUkz2epgupUjaT1doDgvfod0JHjkG6uJFwc5dmyMvAMJW1F3U0/26+jM8b7PvJ4/f9EZZJyQbdkpbHmUpNB0iBrNWovRrg661i+SHi2ijUbaFmErpNtL8ab37IBL00SjOcZrWSxhsIThQLmbVuLtbOi8hlrdp9UKOPkNH2TLX36Ey//o4/QNXsLKkUtIJG4i9BW9/XlGVvZxad9lXJ4/ykuWXcXq7kN4KZ++7hLHp5ZIra1Qe1ZES9oE2iY0kuPHFpkcW+BtX3yUv/q3h3nDF2/ASzjkO1I0Cwl0Ls3WRIXltk9Nu0QFjdARdmhYWqrjVAXk0jQusKlvlmzcbvGDD72eiYXXkBUthuQM797/Qd7+dzeSPE3ReY3Cjhok6gFYmuqKBEfGS4T/mqSfCn7N4uJrtvOaa8/lhS8+h+JClcVag3/7+m3IVoDpDejx0kjHolhuUdIxYRhhtKY8l+SkkSJrVk5QHO/l64e3cXi2hxsPbuPcwhEGrAqnLByheLTKytHlqPlLGOxZw9E9M/QtK3DS6WtYKGRw8gErN0yzYuUUQSug2vSZmliiOlehXG0yGBsGZypcfobNnsYgSULmoxx9hbVYmRZHJ3r42thmjs138fad19Ar63zptG9xxYqDbFt5nG0jB5Ghw323bsdUHBITPtliCbcWkSxFzEzluP/hZcjhPMrzSKQEV7zmAHMvHWL+6iz5rd0E3TaNlRYahRKApSmFCa7qOsAbBx+E/joYOHZbH99ZGuDWHw4jAPNE1FWIJ95t2vIESNqlhwYI8iGXv2Ina/NjOHMVOt7Z4OqenTynY4y+8pXYqwISszFR0W3rorguUudRkcHS0Bi1WDw3SWO94PhJHTSWXO58ZBTtKrRRHN2XpVG0SVhgZWJyKYeJhVMpLgn0QsRziyP0d83y8uWPcXrqOK/Y+gDSj0jMZHjW2nm2VIp897FT+Nh3L+Smuzdyx/R6NnhzNBcSFOfzhKMCI0FLSRh7ZNYW0RlDPR+zfXgRNe7y/JU7ObswwQs69/BodQV1HI4ySFL4WJEG21B+voOQoCwBpn3fdsohVrmFUzeIusSpupRvXE5gJDmnxYb8LLVOQdAliPKCVgIqjYhr//zlnJLQOEGaySN9fPdoP2+97kbWnL8Pp6uBm2mxzptjbzWPzEj6kkUC7eEq2nohsUBpm3adiWLaX0V0NEuUAJ0UxF47QKqMheipQ8LGCHBWxAil8fMKWSlhtxReyWDX2se0qr5E11AZiIkbdar9Ge5fGOFLi6dR1CmU4yGkhIQFQYRoNLBmytjVEKcZ47ba9uj2bAUxU2XFQAe1OMJKOQSBpqpb1CsLyEYE2rD8vBrRkosVGiJjYYUKaSwc38G0QlJjDUg62CUDlSTStXEairMGjlMpeoRNG7sFesAi7LYJex10HGHCkHiNpDFqYUUCtMYYgxvbGK044nYSKJf6VBplSaKcTdBv8e5vn8kf7TiPOx4Y4onCo3YZvWxrxGjbIFIO2rLQQiKkQUQgY0GysohTjnF8BTUfK1ZYkaY1kKCxKY+ygaRLbCxqfgI9YYj3xmw7/yijZ03jVcCtC3Ds9h+iEkgkEkggUDpGD2ahN42QFmjI+JAwBuW3oNnWWEkIFzPSi1rdQ+BJjGNjRwqRShHNeBBGmFKVuFJFKEN2IULO+9gLCjMxhwSsxRC7XMcszKMbdfaPLcFgD3TmCVMO2/p7SDig91ioKQscAVKz85HV1OK2C1yzkcRKupgBSV74/PNzvs4VyfvJrcmhO5P8eM8wyYcmSTzawnLLjO/0SGbu5sXX/IT54/9E0n7uz3yPuXd2F79z29/ywNxDz2jTRtFp1+m0G4Q2ZLL/D9hynOAEJzjBz+E3PiPlE5/4BAAXXnjh037+mc98hle+8pUA/Omf/imtVovXv/71lEolzjjjDG6++Way2adSFT/84Q9j2zbXX389rVaLSy65hM9+9rO/st3b/+tUqi0O7J+hqyfL6Jp+hBD86SnvoxVPkHZ+mUJKm/nJJZr1Fqedu5qgHrBsuIu3/PVTKaHGCMplj2qtQqQ0/YMdxEqjo5h9eydoxiGxZUjaDk7qOhrNT4PIk3Y2PznGqvWDhELgAtYTl3etVCfwIxzXwfXcX/mY77t9H7fdspfHHxnj8ueeyn237+HgznF6lhVIJH72OCNDPVhFRa1UxXYl+Y7009pjCRUUDb9FKuOS9BJ4CecZ4zTL78aLH4DoAeqVTYhiN4nHff7yra+lI5vnte+6hompCj+4ZSdBFCIaEZ1hgVSXTWdhBV94+cvx4xrTop9HHzjM8id0U44cmKZWbLJQKFGrBQT1gGoixv4Fd4JCbxbpWkwfnSdWhjMuOYn7b9nH8nV9bD11HX7gt1+HdVufIPQMt09fxcXLHsTB5UczyylW0tiOZrM3Qtk5jsDQ6Zz6tP3k9EmUnZirXrKfY/s7aXhjREYhEfgq+Yx5jeav4J0jc8yPJbDti7AyNlpvwnFc1q2MeHXyRgww77+K7uQQhUI7GDCStmj2eagGNIzNTRO/D+YevnLgWu541SuZHF+k1Gjw+n/+JnGHjROC6YS8Y5BAd99duOkXsn7DAH/8kRtJGJdk4VX85HNv5qXdtzCU/CQnp8/jfbfdTTmZJjfQgBCwQQtBIiNxPYvqY4u4RYu5x+dQ9ffygx9uwO3sIrVsjn+a3cy2xCQTx3PgCjKmydrcHJM7B3F7UhgEnYkil5+a4aUrr8dgCNxXUw/eQUVblDtSCFuQHITKmADhEUfQzPj4a1PIrIV4yCGa1DCvyG5P86o3XcnsVAkVR3z3zt0EB2okjYWfqRPmfFzHRrkWGhASjJZ4BgpWxOv/+HscGsvztrHr6CnU6E41ef/+szn72AQ//Kteeq87xKeL/8bvvf2FfPU9u7j95p2MnryMC6/dzufvuZlJ1YmJBli626NVnIK8w9ysIay0yCwk2Hr2JgYGchTO6+TGfTVkZNGMPN664VweXbobs3ea1adXuGHbLSz4LnvjHOuSFbwhxS4xzCMLK+mbq6JTMdIAmQw62UCUQwQud/1wBSNvmmDlshkONjsgNPz4zvP52Nu/x9q+Rd740SuRkSC9yXD2hiqzP8mQzSXpjjbw+z03YSP4uz/9MX/7xdOZmG2x8JkR0rLBugseZP/iIJXySkRK4DpVcr01cmIlqwbHePf3TuPBHatwLq6SWVZm2x9M4n5/JSedPMYPbzifFw5fAmse5D3X3sVd6ZX853+eim+BkobkmZNEHYM499pYk5J4W4yQmsO7upnodViZLOJ0hGx2Jpm0ViJSLo/MrufxrESE8OBSJ4ErELHivvHHye0w2KfotqtPNcKqRdgti9se2MitE4beM+Y5dLwH1SPQBUG1qiiXJEdUJ+nOAqLXgrrAREmiYkTGL5Nc5zD/SJpEMaYjUeHPH72SVEVzdu8B7g43MdhZoWWnKFkeVlcLbVl0u4qFx+c4flEnI4cMqu4TJQxRlILAoWO3T3qFz2rPJzSGKwp7+Un/alTgYQRU13okApsoyPOS1zyfNb9zmMV6nn9/wS4ATs/O8zb/Knyvg7JpstVMk5AaX1nssjegrTSmFOPIqC0+6tiMZJe4eOU+bt+5nOmHewiXZdquXgds9FpNfjiiWhbguiw2usgqcCMbZ1kGq+HT8qvQLCAsOL7QSTghEfUG2dNbZF+lOVrs5keHT6ajVCMnDFpEJNwaUd1F5T3Gip1kkwGtbAolFKYRIso+Vsoj3LZIWdiU5zyUJ5HSIOsOtoqJtcGbCYn6Elgu6FwCrw5EhmDARqcEVkuBa6F6chihkY7AGPjuA6s5sLPAQqkHZUlU2sM9UESWY1Rg0fOsFmdefZCpB9PslStAS4Kudq6pp2MO3T2E2x0iFiSyptD9AmE7yMdh1ZXTrHv+JJ8rXYSsBiSUJM7YiBioREhhESYC3GqEHWqolyHpEJhunFILY0lwrXbwLueg8mCEQlSaWK0AFXuMZQYpHcsx3DVD71VlVMHhwKQNWmAZC5G0Mc0YYbWzYmTGonvrEPM10Q7qWLMwXUEkJLrRFlIOogiZsHCCFnIG5PJeWDlEbXGG7Fz7xiiExBiN7sggytX2Io6wkGu6UTmJjYc+uoDXkUNMLSB8hVVu4tXTmB4bnU9DymWXO45outjNECM9XFsQa80b3vR1Bt0Yg8VSOc0b/vERxmMLJxB0p1u88o37uO2tp5H58TzJcsRPfnAOWW2ID9qoTo3qhGWXT1BKvohHjlzNWRufmZX9lvu+wnwjxXjja9z1nNOe1qZ0gw3ucQwad9TnX4/1PWP7E5zgBCf4f4Xf+EDKr5JFIITg3e9+N+9+97t/bp9EIsHHPvYxPvaxj/0Xzu63myhSCAG2baG1RhmD3wqpVVtkckmEW+LOib/Bsa7l0pFfnh7aaEaYSFNvaD76H69/RrsQILVBKw0Ghlf0EAUhWsO73vJFKs/qxbcMoYiRziqyAwefMUY666E3DuGPz2Ovb5fGhGFEq9YWqvt1SOUT0PQpBgFvetmH2fPwNGhNtRH8XNHa7v4CrgWeK4ijZ/bpzqboyWSw0LSqPl7nU0lhC/MV6rWA0DIcOdTPKcNpunN1ujpjYi9JsGSRSVwIQDKZIJ1ukUw4SASVqs/cQpkainoQcu6ydqnTvtpjLCzUaLTC9rkQGmMbPMumObMEWhEsBLjpn68HlMmkaFaqzI3Nk+1K8ecffw2Zjz8VINo5exjlaqymhYzAKQtSlkuCMlIqvrV4MWE9QWhgaMUSa7wxSlGSKwbPfdp+qvUFhjoM2ghe8YeP8rW717M3BCkMYTX4n6eFEDaefh4L+3eRsusMrfiTJ9suX76GeMkAgkwihSVt+pZ3I6TAFV8kKH4OJBgbtg68Ctt+La/Y3g1AHEV84a4d7cwBY6NcTVS2+fbtq1m/cp73/90FWFaNoMvCdRzsqsZfageqHHMyGyp/y3C+m73RvyOnU1StJK2TS6wojGGMoZGdpMOsIxe2qJLBtTRx43McP3wNppYj7MywFGZ4/+yFaLttQ/qe5/yAe765kqnkIFagceYUVlpT+0qOymub9PQrNva8gPO/dyelVprnLt/BD8qrmf9KnjgXEGuDlXPR6w1yQ4CKBI2BBPPNTmQscSwHKSVdPVlUrLls+3q+k0/S0AEdI4ZcziKbS9LRmcQKFcayEFKDcFmsZwi0ZLYzR2F3kStPOULSUmgNO+9fTZwNia+1GEwd4V8mvsrkzgyEMUvFBgOtCi/qfwQ3FXP742fTLMaYUCGbkuUn97AQzLFsdZFNF93LxnWv5ju1SdKOZmamk2rJZSFTJwoEcSPNuuYCxJqXf+Uqrr3iUSwGONJK89j0EHHLR5U87DDESaaIEwXGmhk6jUEnDO6qiL+ZupzcFS2sOdA1MPMR6/KTdCQMZ605zNHFBJtPnqZ/EC66fpI3/NEf8EJzHoH5CLYwUDkTcZFkb20T5w4f4uzCJM8qTNEye/jQp/+e47338mdX/gAQfOfxU/jkI5dCZPDqTdYum2dFeZHPpc5j/WsX2BGt4WJnnqjks3b1B5lxUxSunGXlwSkeGh7FHZhD9SSwZjTvfeXnecfXXkXqNh9SNk7D0HNGk1UJH2qPctNDKxkc6mQyaPCon0FmaqxOLnBg8DDleB1CadSci7lmkdd96mW88KwHuH/3KFIZCCKsBYGuCOL+NHasibo9tGMRJQWN0KZVS0AlQvigtYUw4Ew5eMsNelrhLBY552VHsIRFh2xxZFkfkSNYmC7QISvYoYFYE7aS6ETMseeWye+2Gb29yMSrV5K+FxLC4DYEMXXWpGdY3l/FEJESkLVaRFmJeeIWlhmNuOKyB7lrfoTOe6vs/qeN/LRhW0JquL3MN3edQn3dVj74gu8TJCPyhRAciXYthGlgYovlzQyTec1lg/vItAJe2LOHGya2MTOYAQG276JEnZ7VNaoTIaIoaaYN2ViTcl3CnI1co8k2mvjFDFYIZb8TyzE4nk/3mwTa8il44zxwZDnZqoMWEX1/uUjpawboIOpOsnJtjaVqnclaGmk5xIU0JuEhViZZ6KvQOtSHdMBuGWQlRkobrAjhhxy7OQEnR4SrenAbmlgpsNqZGZEOUa4i7JT4nZCYKrP58s0c+uEYekXEfr+LlOUgHEPCD8meHiNrmjDqoXN5+57sWGCEQdsQDae56hU7SKUCbvrSGZRnUxAr8ATJIgRZgVYWKRERxDZCRuiuFE0rwA0FIgSraZCWITQCLS2sVlvrxfgQlQzSctCujak0kAkbLAurHuEcr2EXQ/w1SebW5nHn06RmisxOpnj4R6NM1wcJ0hIRapIK6O/EzJUR1XZgJs5lWFwEmXfQsUJ6LtbGFShHIo7MQRRjJSz8U/sJJufxpuqYjhQincIZGUIvTWJV2plJwoCx3HZARkgUoIa7MSYkrvvIZBIRKbQRSNMunbaCAFNpgKUwGQ+x2E3Lq5BNeUjbpigV8YqY8cks9ZWLOAJOP3WOWmiRHk9yh+ljr9/L5z54CrkdRaRqZ0BhNEYIpJDQodnjDbAwmmN1R4mJme9x220v5+KLT37aszVrBIu+oKvTf8Zz1w8eICvbz7shp8Kv+Vp1ghOc4P8Awpi2G+JvwDx+2/mND6Sc4L+HKFIcOjSLFII1a/sZWtbFlddsQ8UxArBtyQ+OXsPm9DSH/J38+PhKLlmx/ReOef3vnsv9dx7gZa+56Ge2CyGoNFsUK02iKMZxnHZ6rdGEsUV4YAkxWkD+giyikWW9GBtY3UfPYB5ov6S0Gj5xHOPxqwsIn3rWWj4JqEqLvT85gunMIpRBOD9//929nbzuXS+gWmxy9mUbn9GezSVZ35ejWY8IAgVaPpkV1WiEbXvpZovZ0uWsGQywsiexf/gbGNcm0C4PPXSEs89eD0BfT471aweI/Ih7jyxSqTRJ5ZJY8VOptktzVabGlijOlwH4nZedx7ddl5M3L+dTu6YQCIRjc/IpK37uMQkhGFjZQzqXoLurQDqdelr7pu5RnJ4W5ngGGUFqNibwLwB3HumcQ2/HnRxqhNhWzPrc7eStSQJtcVJ+3dPGKS1egpv/BwIg3dVi9egiSyqJMoJjRZf75j/Pts4X4NlP7X/m2DwP3bufRhSxftvap85zYiW671ZAIu32iln3QDuwdvujn+UTL7qBTz56No/OLeMl3/gKNz7/xWS8BADP+tG/krEtrEhiNxSv27KJ07av5d0fN9QnG1h+hE7HbFvVz7d/sg+ZkTzr7Pax5LMJ5hcs9h8/DukI7QJCcXRPgc7lOcDwYGkHolLAszvxM4pKRw+hPp2hZetx1AQ9fom/HL6dhws9vOPYc1iWn2RMZGFFHXOXQcQaOw6R04a9jxzHKJCyHZD7wcWv5dD8VYTAxj+t8vG/PwdRqVLPxYyO9DDttjC+TVQSqLUuvX6LxOYWFzxnK0ppaqUmSMGqFf185rt/wt/s+Twyb3FR/wZUrPDi9ocBGrAkCofZZp4HasuZiXMUJ/tphFN8774tODLkD6/7EZ947BKe33UPa51FFs0U/9DxXIRysXJJWpWQymSCypRgebUbs9Jicb5GcqBAMp2ia7CDS3/3qyyVBBOlP0PxUYrVJM2aQ0eqQbNWwXY0uhZy8L0D/PkrTifv1NBlzZSr2XPzMHHLQTZ9TNR2vZEtBweHrttD7GRbs2mpmKXQUWOoo8zs2R7e7Z34lRp/8/dnovMZ9rc8rh0+wHq/TG/aJ5CGSsnnwO5p8tkfYkufB27ay7HkMdwi7D6whmuusQmDaYSEN15/Pld9foJjlW48O+ab95+Jdgw44NRq/MX6m/nTg1cy1yxQWsxiEJw6ukjWpNGO4OFaH54Xs+01Bxkon8E35hT1CYtUvc6Pvz6MI5rI2CI9pZh0Bjh1W0SgFfFnUxR3dGOpJazdRR5fhHd95ggytPlxp0N2uEbteBorE+MfTeLuSvCNA+eTyC4Sd0JyUSKstrP7yrVNSscdtHGgrnloejl+y8GUXHRYQ3gFREthTIjvQvKRBt41IfHXkmy/aIozCg3eE/VRu6uDaqKAtTzkyNQgy4BELiDZqfCTNrpTkD2zhLW0jkvXb+Qbex9HRpCsCNjRYPawx2WvOc6xOI02khumtpHPBlSNwmiLhkixMTXNg9YwmbOamO+6aKO49ZZlbD1vnsMLIEtJCmcsEu4boriYZN1QgNe7yGjPIoeKvTDfxHg2UyrJluAAXwo24RThBdajuAaUrRDaQnkCURIk84r6mggqNibRDkBFnkto2dBt4dkGuWSQStPq8kg1FP5Ahv7kbipxiv5siQcPLWvXuDg2VCKoaILBJGbYY0n34DsJjN3uojpSWAsBZjakuOgivRDhWIQbAzI1hTWmcCoWpgFuNkDGChoh0pLoRPsXKhSkDjdR/SmCLhs55OCN+VyY66Z8yh5Oe94OZioZ7vvYZkQ9QnmaOLYgEDSn65RnR7lpxqJ5KCbVqwhtic4K3M6IuClJ+T5lY6HzDtp2sGsxntAsDCT5jwNnYe23yR8XNPBRvRbCN0gEwpL4AkRSQtXHaA2WBZaFHWnwLPB97HqIbEbotIdIC6ymQvdlkT0e3X01zjhlmocfX0arkOfIoV7CLhvVISC0iLpTmJkGnmdjUh5hxkIlk23XoFghwggTGBARWklMNo1Ku5i0BykPNnfgd87BYp0EBpnMobRu66cZ0X5HqIUQScJMChPHyGKLOC8RWqIHHcRsRJiQCGljJV1EOolWMfHcEk6QQw64pPMu+DFKAyvS0IBvNTZTmsrz0M2DfOq6H3Hc9qgtS7FOFhmrZyl91SATMUQGtwBbn7UFO2iLV9/iHaDYTKAPZrEvs+nP+bz+LTfypfW3cOxQDZmQvO99L8H7XIJlYzWsvm5aF7VIJp/KCJVWPy1jYRD8pLwOR/z2fyid4AQnOMHP4zdeI+UE/z0IAVIIpCUQQuA4FitX9dLVmSGRdDEG9tR7aWiXPfU+/v7uW3/pmL/72kv4xBdez+ln/myHH2MM1YpPsx4QPqHp8fI3nU/s2QgR49UECdtidf/P12SRUvKXb76USy9cxxc/+gcAFOer7HnkCItzpV/rHCwf6SGZdtBGoy2JaPlYOZv3f+o1v3C7My85hcteeCb2z6iXsW1JR9rDCgOa9SZY8snslqFlnawY6WawK4frC4TzdpzsK3n/828ktl0ix+aDN33zybEe2z3BZ790D9/4waPUG02Ki01MqJH6qeXX6ekypUqD8eNtC+ZTz1rLK15zMYPLOukeLGDZgiuv30bnT7n0/Cye87sX84f/8HJe+varn5GNk06neft5V2LFYCmDvaRZ3bcNu/NzJLKv5ZKuA1yzdhe/t+5+8nKcYpwmJUJc++nlUUtLdWwjGbE8OlyJV3RJCk23FeDHSe5a/CIfOfi8p21Tr/g0aiGN6jNXzqS9HGk/5fAlpURKyebuD7Gle5a/ueAHqIwBLXnhZ74KwMOzP0Q1LXRviL8s5k+vP52skwBjeOOLLsb1I6yWRivDu268jcLeRV5++gb+7m3PBWBissjE8SX+4dCtXLN+JyuWLXDt6INUxxLM+xnm/CwzwQ6mDs2RdNM0V6UJ+uAt338OJ51yPhJN2NLsriQZtaqE0mZ71zitskOq35DqrmCMIWw0WfgxbNq6kla9hXhiyT3prSclJTkJdx3aQKPHJibGS7vkCjbWEQ+5M0VywcLVNrWqR0/esGJN+yMuihWBHwEwMNDNJ571Fj5+2ptJ2gmW5quUFqq0VT0chJJIY1C1TqbKPawJNrF6aIapiTz64RThfXkePTCA12mRbkbIULM8UcQkLHTa4dILNuA3BNmlV+DvOZfKtMfyU1fwsrdeTc9IHxhDECoaWmAHAt+XjOQdqs0EoiFJhprsQMREswmWIEpK7rl5OfOPJbhzx2q+ccvFzM6fjgzArijEUoM4aJB063Rn5jj9lY9iZQIs0yQuCf5k809439qbeO1LH2WgewIRKEpTKQ7ODmMfSlIqF1hczFBRmof3d6Jjw/HDcxzcPcEXPn43d968G2usgWhFdC/0sTLYh2ikGN/dSxTBuec7vOv26/mT21+MiR2cBZBa0YXPeJjhyL5BJJrFpRxLS1nWdT3CTHGKD/31qxiMaqyxythuTFreSmsmQ1hNkGzW2Lurm2cvPUDQIQkKNiYtufQ/rqUSaW6eHMZbVyc6UEW0AvwdCRJC8Z5HL+X2pTWMrS+Q8GIK6RqJWQsrAqcBwdEk650ZKisdrO6I7Vcf5syte7ju3DOxGjZ2VbJrYgghBLJuEx1Iga8hitGBgkfTyClFJhORNoazu1vs9x1WV2Yo7KxTuK9OnyzS4zZZzCZppD2Ge+fpyDS5eOgQg38actHFg1y1cg061c5KcOoxUUqTtQI2nlJmk9uk3/a5oOcoL13xEL3pMto1EEvi2BCagOMf7qedHqC44WOn8uOvbOTUXs2n3vVjtp+5RLeu8K8feBZf+/IpJG148bk/whR9sNvioBjN+EgPwXSKhu9w5129GGGR2V8k6jRo1yATmqliAXtCkz2SJHMkS2BiIkugGi7FSpZ6I9nWWokVYX8KYxlUQlJwGmzMTGE7mr674rYzTwTZ2QBKScypNmYEZrNJ6rGDlhZYkjBvYVUikker2BNljB/hbirT4ft0zEtylmTg6nne9aEf039+C88SiKDWtvrVYGyJtg1hj4dVjJD1tvW168OPb9nD6pV9xL6AokRJhXEtlOsxPZVnfjKPji3CykFe9Ow7OPOKg0SrIlRKIGPB4/ND7FwcIKjZUAvbGkGWIEpClNSoDRorr1H9BhVqUiG4kUHnbBq9Au1aeFJiujL4IxnqvUliFaOMIk5YIEHYEknbljhyDVFG0lyXR+XctuWzY1jXM0d6SGLFApoKGWhEYJAKTEqg+j3CQhIx1IXs6cSy2lopMmg/E2TCwVgWJoyR0jyRaWKgqbBCAb3dhD0pRLWFmJrHpDQqZaO0wkgLYbffoyxlcCKNu28GMom2xZyBcLFIshq3heu9BCqdoj6YJurLUPcEURQj9i9iVeq4kcKqKCzhob/bxx3vPZ341i5e9bxLKbZyHDYDHFTLGPuug3Qs8GNMFHHJtWfTWchx2uVbeMNfvxBXLye8swdVSXJv0MfjTifGCjmwq0QYa1qlkD96xSdpHK4glGZxtsnLLnkPSj2l45ZJbCadeh0TYZ6VuSJBFLC4UKXReOYz+AQnOMEJfts5kZFygp+JbVusWdvWQpE/lRedyiRACFzPxjaDvH3/KKd0HGd84r8mJhf5PjqIcJ/I0vjd37uMf7nxfkRsI7s9Xn/FOZy7buQXjnH1pdu5+tKnsmNKczUmD88xfbTEitXLfsGWz0QJ3f5IVRonYfOj+//u1z6mn8ayLCwE1aUarWbE0EjfT7VJjGNx8OAMO3aM09ObY/Xa/nZmzikSf14wtOmpP9lqvUXDD5G2IGN5RH6IFgZhPfX7uvDiTegw5pwLnsr+qFZbzM3WWL6ylzPOW8tb3vX8XzrvRCLB6edv+rntl5x3Eh/5/F2ouP39EbeeWqU6K91imbubFcm1HPVjBp0yFZV4xhhhs4Wu5BBdLZLC4lNfuoBXJ+8mmAWrRz2jP8D600YJg4gVGwZ/6TEAhEHETGMYL19hrFKAssQKwHS0S5/SjkIkFSqSPO+UYU7uXcaOsaOkMh61o3O42iYSPtoS2JM+Skp2jo89Ob6O57nolL+hcTzJ1xcH6fcqVB9agfE8hpNFQDBdTPPoXfsZ6snwYHcN6cTcNT/Bh9deTaJQIWEJshWbR+sZcvmQ7NGIeDbFmpPmufxDP+Qb397InZ8cRYYOCxMlZidKqFhhjObb338Mq/mf3PDZn5DszJDtrrOY7cA73iA2LqmFBjRcvACYC5DzITMLFo88cIA/+toPiYTg+c9T/Hn/2/G8p2v3RMowXa2j0aAjEAoZQ1BX3PqJbUycfJyWzlE80oXd0IjYsOuRDbz17d+iXkkxWUnRmdB0hmW2bt7c1ixyJXF0H2tX72ZwQ8C/qBfw6jM/Qk/HMLsffznBYp2/fewyLho8wD37N/CBVU1OHRpnz/goOpTcOfcQWT+BIwSRI5COxCwY5m5JY63T2JUaCHAqPnq2xSknVTnnOY/QoS28UcWOM6dZPZtmeUeFdfYieRmxPT1D6YKYQ3vPoVjKsuxZU4T74Xglz+H/7CT+XJbQzaBaPtMTi6xe1YeWglgrvAyEgxHnDqzhcQf8VpZ0xSMTxnTk7sbp2YplK/SBNK606Zio4BBy/4O9OHYdSwv0goeQmkorwVLhW8wdPIXT+hcQwiADyc34vPzsezg418feWyGXinHdFqn9k1jJAaSC6ZlulspJmgdSpEcqaNV2HjLDmvd8bhPNbgeMxKpBpS/F+o4DHJlYjfIkdiuGjEU5lcXvcrj+9+7FbybIFiNKzh7sloVpKRaXuvDQRLEHx12E3wTls/0vxjmW6aQSpDj+tRW86OxHkJ5mq4CVuRmmJgvYccya6hhHE2uoKJvAFvxx9Ch//fkreKCymrd94Ha2v+q97H7wGGdu76Z+5xHy+Yg/evtd9P9VxN3VLvrVPHHF5qR0hb7MIsXhDr7+6NkYAZ++9wzSnoV8dUT6wxbBVAVrSHLSZeMcERnSRKxRMV9c1kE6meU7R87mR/+2iRWd80g7gel3aHYo/vzCb1ESWfYurqA/LlIZtQky8zQOLKcx34INBtEJjXwGea+NiRRYNv6gh2tZRI5ELxmM8JDCwlgSg6E16OEvE0hjGBvvJvA92DILd/Ui0NRUEmdrjF+wMbagldRo5SKkxFhgV2KMK7AbioHHfCZWB/gTAivWmFAQ1h060hEWko7thqG4hlgTUX2kwKKVwDgSEUR4iwHOTAtFRNCfJOhJslSvYQ4tp1Q/nwdnakilCHqSaDeChEb3WqhOh0LnItLSDK6rsPMLIaokEFmH6agD0TQ4RnL60CTDp5b5zmNbadguKiuJDws8YeE+btNMxmRCB5Wx0K5BCKgNSfKTBqMidCGJTCZQhVxb26gVQSskSljYrt3OrPFAJ0D3ZIlqitDySQj4+gOnUGskMa7EcgVmsobVSKNTFiIyYBuibhtnSeGEQGzANWhMuxTMdTACwlyaVLEK9RaikCNdMzSsJsbzENKgEiCbLYwWxFkH0d2BnK+DtIEIdDsQZYUKuWMMtaIHU2+hVIzWqh2ALECYstH9WYyRCEfQSkjQ/cjjSxDHpEpxW0jfjmj22FhlHzv2qM170AvzOyQT9zsQqva+Ex7f+vFuNq3v55xLT8ayHHIDaWrjFcK8x8HZDlb3lcl01KnP5dECmC9iawGejUraiEZMvWXzwB17OPviU558FkxE66gbj4SM0KHP1a/+GKs683z+06/Hsk6sz57gBP/tGPOEivdvwDx+yzkRSDnBz+VnPRCPHZ2nVGyw6eRh3nHqp/jUW/6RQx0jeHui/+39GQNHD85RLjaplOoMDrczT+yhDM1GDLEhtUJj/5oCwSs2DlCvNFm2svvXntOpZ6/hrm/vwrIM7/v8M3Vd/leoN32mpiokM03COH7y51MTRRoNn2qlSRwr9E+V6Pzog29h7/xONvc/Jfx29qmriI0hYUm+8rm78X2LZMqjUHhKv2TLmavZfMaqp2WRxEpTKtWplFsMLPvljku/Ct0dGX7/BWfyxe89xIbRflznqWtn1fAuVuEjRJJwfBgwJH+GxEzox/zDO6/gTe94kG/evpbQdrjpW6sY6F4kO7wIyuWK3tc9bZvOrjwXXnvaMwf7GWiticKIG/Zv52jjJLJuN1vPqnOgFJN0I45VjrCh69l87xrN7WKJ7tQq3IRHT28OWwiOHZxHC4W0XS5+nsutNzcJnZBX/N4lT+5DhI/TlRvnZSdr3v/P5zBtBJ9/0XN50w+/zVizE89SzB8VZOeqdA91smzDHH9w1i3cO7uSXD5NIesT1F1qUx38x+e2k3tDQOXBHAcP5iDfYsOp83SeHSD+3cJFcvTQLAd2HOHSF53Ba976nxwaL2LHIUk/ZvrQAtZUkZwn8ZdJ5oqLmDjCadokCylMJUKUoKkTvOuPvkjKtQgGk9z4kOEvLhBc8tV3s9SKue3aPyFhuZRKNerNCBMohGVIdMYEUYyREVQE00dyBKoLpZKoZExrmSSMC0zqTipFC13zmP9cnujxRfTgLFEUsm/ncdZvvhdRlGza2uBP5Xex5Rwr+ms83AjoH8xjLwm+eugMrjzHxhUjdMgGYc0j1B66+yh2sQ8Za+xYo47UsJMp4q4EzVIaW2jS+YDTzy1RStdYtrHCTQubmdmVwjqoeP/Lf8znP3cu95QHuC6weSjs4e7qSgYvnif8iYNSNqmVZRZXp9C3e9ibFdcNn8G3b9mH8gSLs1XKizWuuGY7u0pT9A2XKHp5tgxnGciV8bTmzvQycrkEaskhlQlQWvL8Czbx58+5lGf/0fsJK012H1zPyi1lKipLZl6Drbij3EuPWWJkpIuy8shYIY3YY7SrzqTKs275LI8Uh5ArDWPbMyxL1ZjdNwBCYDcl193w+/R1NQgOO5i0j8h28Nb/8V0e9VcQOvuofLsDtxlSWhdxnE6mOtLoPli5mGa6oZkW3bh1h4emlnNN8iD1JZf7ymN48QoCLdE1ychoi51HOzFa4/TCZW89zIyVY1SX2WdSvOxtN7FSWozPORw1BbwVMQVvnlYzS3POpWx34pUMIqf4+p2bMTOKjt+rcVFfmbQAcfoNfO2+s5lfPcDRbocjY930b5yh36vxT5/awo4vD9J1Womt76xQXx6j9rR1kYrHBshmZugfKFNKp4kc0EridAYkZUxD23zjoZOwvE6aIxp9NEToFIfLA0AASZt/eu2XGU42uH3SYXwmC7UWFz/nAMtWVqk1PN75jRfhDPggIHIsvImIuNeBWKGWpZGRg7As7EpIOlthdrSANe6QaFqI5S0skYFA8YoNDwDw9+VnU7xHowQYxyG7MWDD0CwaQbjDQTdshNJIBbZnEEhI2qjjFgMBRLsVoYT6eRLPxBxfzDPe6mZYNVjIdLJldJbH9lgsqD6MK8F2aIykSe6rk5gx+CuaCCNp2iVGe7OMXPEgqZ2Se+9fg3JsrBx4nSGibBPkXMqLaQa8EpVWP2e+aJb7PricUtZw3uAY0+M5jtd7uPyqffQWlji80MGDM2tQniFKewjfQIcg6E9RtwWWr0jUDFoLVMFCHw+xqzGxEFhaIlwbUQ+w6wFx0kL3JSlnBMnAICKFNxsQRxZxt4PXJxgarLCYyBD2gJwzaB2iavMkxXJ0BLEDGgNSEimFC0hLgjJYaHQUoSyJeEKDJYgVwpHUhgNMj0P6EUPYlcCs6CFIN0jMNDHNFrIZgCUxjoTOPMbrQS1WsOYqyCjCmvYx8wHRcgfjScil0K6NpSE15+PHdcxoX1vk2NcE2RjZk8ZSEilBKIPSmlC5qMFOhI644U2ncdlLKpQfL9KaSGOstj2YTHsY12ZHqUXPSA+d/TletG0DHzh6M5GbYu99I5zx/Hs59wWGeHwL2zb08ZG3fpkwiohTFq3VBZL75sl3JrjrRzvYfs56vCfE+k8uXML3p75GM5bUpjMko4i5hQo/RzruBCc4wQl+azkRSDnBr0Wl2mJhvkqs2h/5W5eSTO2uIPjff4K2M4AlnmOjf2o8cZKN3hETdAq+sPejvHDk87/WuCNrhhhZM/S/NKf/8U+/x3+u+xEjqwfYsvVnlyT9uggpqZSbmMgmm/rp2uN2GdXaNf00az5r1/Q/2ebZSbYOnvG0cZIJl8vP28jsVJFysYHvx8xMLDLQX3j6/n7q7UYIgWdbxK2IOI5J5351zZhfxu+9+Dx+57lncnjfNCZSGGMQQjyx/yTlxoMkRdvuUolnBsOOH19kbNLlY+9/Nl3DPTgLh5k+7DE33E+5e4iTuqaY87/IB/uu+1+anzFg2Rb1MMPRuTynLU/wwtWb+ML4d7FlzLcmv8cfpa9hdd+VHMg9iDaGVMqlb6iLQneOK5+/jQP7J+kb6GD4is/xutMVNZ3jzC0bntyHnbqYj+94kL37crgNwUd/7yq2blvJioP9HKtM0p1pUp+KSOaSbDtnHXLF3WzNTzLg1alVmgz1VjlS7GB+Po1fknR7ISPdRap7XTJV+Im/hlbBw5IOxijCSHPwkSOAYX6+hog1kWMRddjEnQmsgos4PEtqrkU9aiCH83jPLdF7YIBj1UWUZyG0bK90uxbOXIszTytyw+4P8s7TvkxLuZz+ec2jL/tzIj/C1EJEoLGTis50nUoUg7ARKsJbUoRpGyTEGyO2XDBGUJMsK1WIZ3LsPNrD+C0JTCPiwS8/ykt/cpBUX4GTn93J8y+ewS9I5o8lWZUxBMbw8K4pLr1wLSvuS3LtuYZXX/BGahjuObAKHVgIDIVigKkJjDKIUoA31yBal0J5tO1CtcDEAiEM6aEC2d4WY7MDxA2fuOixX/UymG3SFbX4ztxqvrR0MpYUYNZy7Z/vIqh5nLdiP7c9vo4H9QpU0yaIBMaxMK2Y2FccPTzP1S86ndWbjnHh8FEW3CSrRv4Y6cUQCtasreM7Z/LioWu5edLDGMElZ6/DdV2WF7oZm5ojE9R47LEN0HBwLehN1Dh98Dg77ljOwXKJm/7tFOZPE2RX1LkgP81kI09e+uhkgnPOF7zhvH/h0d4v8acVhVUXqG4QSrLQYdE51sJ2LIJhh84u8HYEJM+IyNSbqIbNeSP7CR/qYPqsJiah6CuuwSzNoRybN5z3Q/7ii9ey5ry2ne7VK/bxxdk8S81ulCVYe8oIexZKMGvz6r+4g6SjcJTix0fW0usUuajPZyQVUFQu7zx4Om9fcS/d14Z88wsbmPlijvgiG0saBvIVnnvl/ex87Fqi2KaiNMKSHK0mOG3rfn48kGBzxyIfPbqdpcXH+eLhzczc6+CFAdU7u1l6rMAFF09iE2LSLonjbYHloF8yct4YVW8r1eQk/bYmKS2qQnF0sg/dpcAScJKgNVUiu18SZzxy2QYr3TpGG8bnOinrAm5V0azYIAUmgt8//RhfFt0YJFJA5uQF5qcH0E4C4bqoEEyk2XD1IR5qrMI0HGJPErsabz7CJA0P3dXLhsuXSNkhlghxky2CmoeaC7j8dw7i5G367RKf23NmuzTRBR0ajLSRtkGkMxBrEBIn6SLTkqCWJGwFVCbTfHlhLblCzKTVw9g9PRREhJYC7QpELEkcq+OmHEIExrJRriEsg5s9Si5bZuOo5CFtgxIEQMXP44SGZNoiPxxRlwlGOxdpHO2EtGTzmZNc2LGP2aU0N6jzuP/WEfo3ZTg81oclBaYhiVdLVEYjO0DNCGzfILXA8gyRLYlTmsgKSJQUUYeDiAXEGmkE2BY2DqIaIxIuKmlBM0QEAfaRRYKBXixjcJJtwXrTERFNRoiogWvnMcmIVHdAs6gx5Nplmo5CGZCRwShDHDTbGimLNcyW0Xb5UC6NyNT4H393KxrBez55BfKQRoaGqOCSUAmsch1mi4ipCkJaGNtAfw+iK4/uyBHWF2G6jh1pnGkFWRuhDdKxMEEIWZeOJageOoS+ZiOi7iP3T6JLES3HI983gLEEQUq0s1fDGCudIBq1uOkTi/RsdJDGgh4LsaAJm01Muu1s9Dtv/Q9e9uxtyNkm2R0tvElo9LuYzR5b1t/Fxz/fy+6bHwMhKawoMJuysIwk7s1RrkY8dN9x7rttDxdeuRWAnmQH79vyRc5484dwYoPuMlx31tYntbpOcIIT/DdjaGvZ/Xfz25+QciKQcoJfj4QlyXgO8ol0rbPPWM2NNzyEZUEcxz9TF+RXRUrJi3//AmqlJus2PhX4eO7ajfxz4iFQkFr4r/vw/1V5xR9e/l86Xi6boFFuUF1S/HRyTS6bwEIgtCLj2tjOr/ZSksmnqFZatIKYfXunWLPm55e5CCEII02p3MAoTU9X9uf2/V/BGEOrEaD1My2dG9V3U7BsWkZjuVc8o33i8CxxK6JcCXjtn5/Fw7fsJjYSNSEoFIocUKsZbJX40b41XL7h0K89N8uSvP2j32JvK4ntaHqcUa4YvJSvTn4DAdy3uItXdnyEXPft5AppWs2AMIyplpukswlO3jrK57/zdgA+svcLeN0xqOBpmVurVvdz3V0b2+pTyzUXb2sLA9sOLCx0sNRI4xyaw/IsRk8ZYqX9NupBkS2pHTRrZ1Mz11H1Gnzt4c34G5IYWeG0S8bIJpv0ranyuF5Or1dBJ2xYqKOrDez1nTiOQ5jSmGwDp5wijMBuKlTSxhsusDxpceTADCa08cIGZ29ZyfF9s5hOF90SIK22g5OlePPAu9gVvAEBpKyQU/w50tkkzUZAOuXQzHiQColsjxiJMBbC9oiqneAFGNthxZo5Ls4fgFBTn87jzqaIjmhEUmEaFkiBmqlRm61x754R9PxLWXn5Zm659S7SVz3Od79eoFisccuDu5jqVYjJLqTl4ElD6Kex4pgeq4FqBYgaxEGMUAGWZSHm6+gBGydRRVT7aeAy6zuETY+TB0IcYWiqDlRe8J67L6N/h885vav48vGjdPc0ef2aO5EIPrT7LJ7dcZS0E7F++Qx31DeQOVRnfzAFkcJqKbRSLC5W6B7IcfrgLIMioEMawmbIYP8d7D3+A/7y5nGK4xne/epvEDVegjIwXqnAMjh1xQjHdhwhE2uivI1UIOoWvu7lY3/xQszYAmJxgu/vLnDxGcfJCcN4lOTq3G72zxVYPzDGVxcHeUOYJuz4Gq2Tr0EXE8S9Mc5xMLkUxXSVbikZXF4mnwmJFnv58gcG2ZJbollJU1g0XPXcXdzx+FoiCaes7uTwIQ/lSvrSNZyyoTtZZ7ivxERngXOWHebbkwVEU3HoSDfNsyq8ZMN9eHVJNSlZ1bFEkHyQS5YHjEW9jBAwGSbpSTf57g97Oef8Kfh8TCiTXPD7jzHr58jvjunr83G7LMbuzfLX4nc4PFWieqiLv37HVzjHPUq/W6FrdYO//5fnkZj2sWQNa1mOyvYEMwM1Jm9J0jc1Q9iZxF2K0SSRmSkyZykuPvMSPv61bzKnPCqRx6Bp0RItZFkjoiSmNyIxCR3birhdEadeOENkBJ6ruOvxjfg6RSnUBEsWQgnCuQ7oPMJgRXMsGMCVmpWv9Fl6TzsbQGiJtAybrniMgZEmDz1qIasOanmAsCDoLeA8GrMzWsHNNy7y0pcf4w8uuJ3PcA4T783R311jzB5g79wgm3JTZMqC0LGJpUZaEmWBSkismgW5LBgFDYXwEhhpEI0Qu9iiUU1R7kqjux20hEUn1RYYjwwihKgrQaRatBIFrHQK0WzRSnRx4ECNP7l+jt10QEKAY+F3WOjIJjLguhphaQyGup/goT2DdG6qc+h4FzOrc+w72I9u2eyYHWRPmCOc0shejXIlViXAJG2chRjlu8i6aQdfHYkSBnchJFEFIy2MEWgUtq8QCIxtIxGYUGDZAm00Qgh02ibMZbEaBrtPYKPIyBAtYpZEgLe2E6fSItA26XyVy88t8s2vJdG6XS5jtKTWKVEoMmMxQgrilIMbKpACUgm6R+usS823A7g9TaoHkggEUV4ipjXCTaKSHk49gjiGZoAKYwhCZLGIafjotMQO7PaxLDQRWqCjkDCXIB5MYSwLlmfgzt1ILbDnfaQAL24RT0wjOjLQ3YlbCpG1iHChDMUFTADCFViJJHHdRhFj+zEEAULFGCW54asP0a0M2cjQKjYpN1K8588uwblcY++bJhLtQFVyRQ/dSMpVH2V7tOIIt+FzaNfEk4EUANu2Sbt1KjpHItniD/7w0ifbxo7McXD3FOdeuoHUTy0WneAEJzjBbxsnwscn+Lk0W20XmZ+mb6DAsuVdJNPtFM/+5V1EnQ5+p8Nisf6/vc+TNq/grAs3YNtPRRjecNG5pI5Kkscszttw5H97H//dJLIeUb2FbvhUSk+ds2bdp9nwKS3WWFyoUSk2fqXxMpkEgYmIo5gde6do+uEv7O9YgqXpJaJai3TK/YV9f30MfhDh+9EzrMtdLILYoSAdBrr+8RlbDi/rQhroG8iz9YwNdK7oAdfGTnmMnNxkwC1zSm6clPhfKyPb9vz3cdc9R6k3ImKlKXgeScdj0U8z72fQAurKR5s6URRRqzSZm6tSrDTQtFO/tW6H+JVZSzVMYs29mo1v/hAnvflDAGy/8UMY0S7XctNP2bfXSiFOaIiPtXCmNIdbc7QaATd/cS++Ocw+3yFgkWqnx7NftJ9P/u1X+fhrb6LHbXDSiOZ5L55h23afPrfCRNQDngvLe6mtzvOSt16DbdtUT2rRuEYR2hFeKcYKNKIekU6kuOSy03E6u3C8FKnAMLfuFua3u6hLW2z8q93MrI6Z3QI66bJ75zjTjetZmMjxwI9WM7GrD8u26OnvYMtpq7ngii3IsyJK6UJ7scGASlj4JZ/83eOEWYM/7vCs3ATnds4Qp0PiQFGadcgM+sj+NCLrAO2PTqTgoqs2c96aUc4fPhnHfyvHDg8jpKSzN0+m4NK5vIBlS770kW8iOmLkcp++rhkORj1tgUht0KEm0jG54TJXnXQ3cqkbIUBoQeWooT4myWQqLBuoEg+nMbkkbwpP5Z2/ey0bty4nKiZZm5vBlRpbap4/uocbP7yJmw5t5oZ7zsNKhbg1gdCGzqSFNAaBwHNtMtkU+cwGYifg6LHtxFGIJZdRFDfw3eu/zjtedRuPTQ8jlUJow+HqAgCjy3uw4xQ7+wexVzcRWYUXCQbTOV75khJvftMdrFhbIkp59KZCxho9rHBKZJSimc3wghfP0RQez338XUxMjzCwegG12ie/rAwiRAsIz86jleGNl93JbFxgpnUxpiPDaLZI3DA4U4LhzfP82/X/zrNPephRrxdRTOBWNXfeOIBKaO6qrOCx/ACP1ZaRUH7bwSUlORLBhvQ8Vw/OM+SFnJJrcnhHjlOyAr/i0qUX+dzSIH/2j6cRa8HOG/qZW5KQNPzOJx/jupF9vGT1w+ze0clr3vMSyjkXPZLjrkc9yof7OXvlbsomTUa2AMHkWDdxCsIDi8hAgNbEQH4qh1zWy1l//DiZZgWpJWjNzB0j3HNwFZ++7+vUJyI6rSajTomJKM0l9gPkbxon7GqQ3OnjNCOcQFDryfDG7iN8Y2qITz52Gs1KByKyCKwUn9pzHr97+ytIj1YR0uVPBu/i7P5jnN13jF2lfii0kNJCaEGcWKRvbZPAcVnTO46yfYQ0eLuTZB9Mo2KNhUWyG763tIkfL21kd2KQ7WetZPxIjvvGVrJvcpBDi30IX2JCCyNA+ArhCPzBJyx8LNkWX40VpdAnTlpYoUbSFlnVxmA3JfZUA3+ZJnIkTg1sX2AZiSq6qJSHiNrBC+3ZzIoU/zy7mu/MLUMWWhgF3qJGtgSWLzBI8plFup06g9kSay88Ri3IUfWSfOCRZ3Hbno3EnUn0oENuWUg2r7A1WA1N9vYG2VmfVENhhRpLgOjU4BhsLOyqQRoQEixAtmKEEBjbapckCYFQBvwqMlYIIdr7Wp5HSA9HKAoJn0w9pDGVQG8e4OKX7+Wc1x5BdEhqZLjxB6PoULVdghQ0Exp/ZQLVlyBc2YnlOeiEaTv4KMAI5mb7+Nd7zuPjD13E7NFOnLF5dK2G6fAor5SISgs6sqiBHKo7je7IIVWEFQXIpTqy6mMt+m3Xs6SNAEQUI0KNSNrYSmAZhbVYItFQJBdjbEeibdDSRWlNVGnh7ZrCqUfIRo3E2DwsKVRSMDvXQ7RoY8oSvCQqYZHv7kQKGysE2VI0qz6kEygg7LSIczbTIo+SAo0hNIqpqRrVhRZWDNIYtB1Tny8yfnCaIAiefJ4W63W6N85iRxFyUfDX7/v2k22f/tAP+NjffZt/+uunfnaCE5zgBL+NnMhIOcHPJAhj9u6fxpKCk08axn5i1X1guIv+ZZ1PlossX94FT9gBL5Rr9PcW/svnkvQS3PUXl3Kw+FG2Dnzlv3z8/9NsP3cNfSu6iJRh8085GIWxod4MmZqpcGS8yNpK81ceU9oWKtFeaSouVH5h3zgKmTy0QIgmkXxm5sj/DgYIggil9dM0piqV3cTqccoIHqqv5eqBZ4rNvvYvrmHD1uVs3DZCFMT8y1ffwHe/cB9nXLiJdScP89ihYW5vruGm+ha2hNNk3Wdm3hhjMMY8I8X4ZW/9FEY/Ib5ltSf6tQcfZLzji4xmIwJlk7RDFnyLIWc9xcU7mBpfwE57REpjO5I4/P+x955xlhzl+fZV1enkMznvzuas1a60u8o5ByQkoogGgxBgMMEGJ4yNsTHYGAzGNskkA8IChISyUEBZu5I25zgzOzmdfDpV1fvhyMIyEsF/4xfjuX6/+TA9XdV1evp0dT/1PPcdYTs2Ukres/KfGBue5b3f/wHGV2gb/mX3WWhxIbIlwJI1/rnr2ufKm2Z1jbhmkfNreJFixguplAJuFfv5wt6XEweCGzf8K153hUvO3EYdRX/PKGroRApKozBo7XDfzAow0LqozNRUO3ZnJ4vXz2989uYIFVsEnoPIGOotFqkDISoMKFTq2EZgAs3SsQXc84jCnXWplJrZZq0gZecJVUR7HDB+fIZtHQNs+8xLsGoG6/LGdZjNJ0l1JJnf283xjMX+b4VYx3xMSxYrBttzUE2K3p69DHXMp2JcMpmA5Z1TfOtAGn8EuteWWXfeK7jh/Zfy3S/fzg/+4QF0yedv3/U1Xn79hVx95RfQRvFHf/gxgtBh/YZF/PiJHXRnsvzZmz/D1vv3MO9N7VQnkwx3NhP3GsQzjdVfO1Sk5jexfsNOdAuYowZEQ/xYlV0ck2fPUBfz50+y3VoMArzlBwhmT2FwqMCGpi7u+7HNha84jBSarbO9VOa1c98nstzylRt4321fYDZZwXEdmvJNTCdLGGFYf9lC7pp4jJ3zDjEwsZbFhUGOHRhj4YpeuhM+GkGnW+PixQNcvWwXoZZ8e/LtwKXkmtJYlkO0wMYlwjICoT1SrsXVl26jMjVDR0+VT9zYxZf+5SV8/I++xB2jazir4xhX5g4hgSe6+nlotJ2HWhwWZMZwk5oQSfPJdY4d6sUqQtxl8cV9p7F9uo9etmNkM1tFC9mEj7BWcTA4SDDlcWriEPYsdEUeaauFu76+GGtVgWfuWMb2R/tpSteRFxuiaZcoB0OJgIuaxqCWwK87zFupuCpd4+hATCAsWlOax3+vlYlkCn8qQ6Ewxe7x+ZgOwwUnFOhzFLMK0sdTdFzazbbKFFYFnLqhKVPipL5RpspZ8k7I1x44mWNDizFJQ+2cZvJPBwghSe2skGjxiIXivq+dzhdu/Fc+8+mTOXbvQuJcmvAhQ2FlntPX72AsTuES0yULpPKtkHe5oHCA1qWz7HD6IBasscZISrioaYpPlBYTWQG5IQWVgNmTEwhH89GtZ9PlKtq6Sjw+sgBfuigted8H7+Wf/+5aHGeC7EsrSK0RUrFywTRHHukhTCkSkyn8Zo3l5uiaf4TdxR6OHe7HkoYwtjj5pOWEQ5v58fE8geNxeLqD3kINEYM0AiE0dlURpRyCvESUAhxjgS1JN1v4UhPbBpmWJDWENjBVJlGKqdoh9ZMdvCcspIZYWGjLInYjVKuLyWnsQU1ctHhqop+4JHG7NLVxsGsWzphGuxLlWHhZSMpGUHvR0gL7DtXQIo0JNEbFxLamMJPDljVKJo9RYJcDXMfB64vQ/RG1B1K4EUS9AcIXeE9H2HWLqMUjkhptNFYokI6NEQ0hX41C1CNcBEaH6AicugDbIB2Jl1CsyQ7xOAuJMw5Cg4kESoFxHUqVJH5HTNPxGXQuiRXFmG4HY0DbEuXElNKQnNSgNcYRWPUYWYi4++l16JyFY4e4uFSb08QpcDwgCImTDl5LE8a2G/ONMFCpQT6JKQcYoSEMEcJBZDIQlVE52QgMTcyguvJQDzGWhcpZCKNxKwoRK1TSw45jiMHsHUAohXFs3IxFlPawpiJKCUFONjJ1kDZCCb7w0Vfz3du38eM7dqAiRTRbxWqyEDqkemEa1WWYPbENqymDCQ2U6mRrCiMFjm0RZpOo2Sr79xzn4J4h1qxfQrFa4+tbr+LStQHffqSLepTgvgf38q63XEBbew5hW4T1gKc3H/p/f6iYY445fmmEMQ2XsV+DcfymM5eRMscLYlmSRMIhkXCR/0lB7D9qbrTnM2ivUXM98d+QkfJitGZP4rT+r5JwO35lx/ifoqung3++4/f58t0fYP7i7ue212oB5WKdqZkqdT8kCn/xAsfzTlmMVQyRvmbbM8d+5r5PP36QwkyZerFG57z/HrHZf8cYKJQDZgt1tPrJDbRWuZKsFPTaIOTEC7Ztbs5zxavOpKu3HUTj9ze861KWnzAPgNUL9/GVY6fx/cET+dz+W1+wj1q5TrVYey5zBOBb9z/E/sNTSGOII4G2DVoYkl0+GS9guJIhMhYHCu38uNqPlJJE2iVWBksrbKVRkULpht3maOm7bBlax1j1K3zwZecjAI2g263wByfdTVumwm8vG+XkjcueC+gs7W6l2UkQ7xBEvmSm2yKdTpK+YJTL+/ewqGOKnHs/g3YT33hiE0ZbPHlkEeO1DI5UJKRmz1Sei5v3c137DkLPQTsSF8lX/u52AMIRG39/jkqfS2GFS63HwSRslBa0tKQRToSslSm6m8nYbZRXGFReog80YSnwqpJaRrJi7TxO6zrGRe/bRuqGGXq6pgEYGJjmOw9v4+/ve5g1TobMyiI97ghn9LbSayVo78ux9BVVRMLldaue4HODJ1KLHbK2pjSRRChB78I6qZYc7e0tvP0PXs+n7vpDsCTRYIktd3+T2dweplMHmd+zk3XrFpBI2nSS5p6vPcyOhw6hBPiHMoTjScxRh/qEjV0G/BjlR5gopFDPc0vuLOLkNLaZwYxWsPv6OP/KddwSn8UPD57YeMER8MOJIWYrPsdmCly4cAXuRIIP/9Nr+djDVzD8pXZ+9/zHedN1CRKuS64piUjaCGkxM13G7sjgreriwbH93DT8A+49toJHty3gqekuKkWfIFTk5Y08cPAK5NjfsBsbLQy2pViRfrBxcdpQMyEV7RFEDpHvEiUE0rHIZm4gmdes2eTzzb+5i1XZfRy+bwWXF8/gC+9egzISAxwe6MEf7WF00mFeOENLskpvqsTSBRPohMCpxGhhcThsYt7CaeqWQQRwSLZx3MqR9Jax0J2FGU081Uy1XCddCFkgLF5x/Xm07CsimyMimeZouo2XdI5Ba4xJhtSmZjnRW0Ic2rgJQXtCsqgjZnJaMlnUvO1lZ3Bs1zziZIaZJ/J4M4Zqfx55UYb9Ey0IoBA4nHzmGj5y2aW4YwGyFJEswDpvDNuKSWwX+PelGb21kzgJ2jZYCYEsVpGBT+tUjCmCPWvhdzpM6xR/+idPsfrsQYg1XjEiWlXisbMW8IOR1ezweyhYDrUz87iR4uhtLTz5/dVwyHBZ3x6u79qLcQ3DtksuaehaNo6JajSXDa+a7oHQ4aV9B7l48TP8zfQZ/MOG7/OOpQ9wwfw9lOsakRnmQ797Fysyk2wv9rFntosH/mI+zoSiZbdP6MREG2qkziwxlWiFb+aJBxzCqktc9HASkuWLm7EKFs6IjVPThMrCOJI4YaFsQZSQSEsSN2WhNYNBggFnLMQ+Nos3XsMKNUZr7LHoOeeE3L5SIxBiC7QlMBkXSwtkQtC2YJq+jnHs+QGiKOC4wIoUlWIGISxwGjpm0mnYC9933zp85RAqi9u+ezK6FaKchVYxzkSIQWMkdC+o4aQiUgfryNhGWRkqoUelmsApGVQEqmijahaRI0AZZAz1pEKECiUajmRCNTLYDBJhCYxvoyKNiBVWOSIxHCBqmvamEkkvomNpGeVWsGtw54/X88DdJxIZC22DcCymzmlj1gsbbjwTZUxssJRCuA7R0jzh6vmYWDeyRmo+TJVID4TYBUVqqI5xLCwjkJFBVhRyfArn2CRKGaQ2jUwbpSGVROTzJDTYAY3AjjCQTCDmd0NvNyaZhEKN1OYBZBWSpEi2tKKyaVTGIWx1sdvzGK1B6YaDkZCNz55IIYsKEyv89iyz63oorehi4tReZiyf6myFsFhvnDttwJYkl4Y0bQy4+qwdNN2hiZd4oAVCCkx7GmEMji1pzSURrotO2IwfGufHP9jMM7uPceFvfYav//MlfPq2S8k3FbGNIW0LZmfKALzyTWfgZT2CasBTD+/9pZ8j5phjjjn+tzCXkTLHC2JbkjUrf75AaybroWTjAWfL+GEuYOXPbTMHpFKpn96oDWEYYeII17bJ5n9xPZg/+LNXcc9tO1FoSuWfXfoydHgSgUVLU4plK385O+ifhxQCFUbERqBUjMOzJVrOhej4Hupac2rrCwdBoCEGm84mnxes+3dcO021lECFFltGLTjhp9s3SmnE87Jh/vkHDyFDifAkV1y8jO84R4mMpnIsxaF5HTizFs6SSZblxuhONjRjWnNJEkJSKdSJahHTEyX6l3UDgr3THyErCoxVvs669jfy+bdfydu+dCd/te0M3r12C69fWuJdJ3zxeeMq16pY39tH7rBAN1lM56BUrHLhggPkrIimtioDU9tJyCx9q6ZQJsW3xtZgtxseLPSzMFHCt/+M7c98jeqEi/E6kaEPoeGx0SEAgpkEzlAV2ZUizloQRzT1pzlz02KufsM5PFz6J5qdOvMuVTz4aAFRs/F7DZ0Fm+pMndizmD1JcMb5q1hcbOGNrQ/A8p1sfPBlfOH+Rzmtta/xEI9kfdsKTjj9Vka8jfQtO5lvfHMzs5UqyUDRzyxn5ffT57Xy/qOX0Juq0LXhCLJdsP4lk0wN/OTaX7VmKcHqZtha4KW/t4MsLkJq7ritRBTfiV9X2BiEVCxe089pl6/lpkf3UQ8jwpkkiekSwrfAKBxjqI/UePp4K9YKEF4Sf3GM/SAEtuHKV5/BDx4/gF8yKGFAC2quzdaR71AOTqBSizjloi0cnJ3Hq094iI6TbS5rP04sRinMvpau9hxRoOluz5LpepRkb4HbtpxBXPGYnE0jjoekw4BSrZNUPtkoKzLNzOx9Kcf6q8xTDnk7wCD43sEF/P2OP+N1zSehmn3yecAYaiZFbBuctE0y/UriwgdxhKAkFKedNchrzj8AbGXt0vW89bevYPSybqomgXBijo0sZLzgcvbZVXxtsS49wl2JtXiDAU7RENqas9sO8eNUO6ISErUmyOoqA0eHmZi0cTMhnaxkx/ZRCjNlgvnNnP/S9Tz12D76oqeRa1u46sJdTFQt2vMlpoYsnOmAJ6Y0fdrDHDYsObGGwdAqk/xzYQOtH1hOeu98woMT1Kbr1Ba08dAj7aRUxLvvuow1pT0cnz6FWIzxoy/cQsv8ItEjTRhLkVhUo8OD2x/s5fChReTThrqvkZHGnggRtRAlBFGmSpxtJgoC9MkR1993DY9feyMf+9NdvOyqPoKywl7tgYBHZ5aTTwfcvO0kSk+EtFQNYSaJSrvUxwPufmgB05ky49kECsGkl0WnAK34oy/+K6vymt/TGm0ZPjp8KstyBRbaNW4K2ulwZ3lZ6yCJt6S4aWINH1lwP8XY5a8+uJGJYAmWp/FrLt50nWRThOfGeL0C6iGZ/RFFLw0GdogCC5pGMHt6cRQUpUfc4WKXIoxu2NKahIWxBGGLwq0DkUJYEqEjkrumsOoC6dpo10F0pBB+DePZiCFYuXKQwUP9RKFE2zZaSvSkwBvx8TdD67wik7qT4/d3YYRCOhI30MiZCLvVgSZJYLmIuuT9f/0mWlMF3IkKLQvHOZJqRRZDVC4mPRzQ0lejbXmBzGAr9VwKKhLl2pRHbIwNKQ9E2WAddjAWjZLEUCFCja0FfkqQqIAo+OBZSEDbFjgOxoQN0ecgJk5KTIcDUtCcDFjuTjGSypMoBVTnQYxNJG28REP83ABYErUmQzhZwSlp9EQJnU+jbYNJNayLRbqh7xG1pcHkcI6USO0H248xUmIEyEjgTNdwpQVBTByE4DgIIxCqcf4UbiMZ0rXw+1LIssZzXbAlohpgggBRiZFKkZqtQWcLGElSW0Su3ZjTNJjFfYiBEUykGw4+AnwdYwuFSjmIzhwyENTmJRAOhHYT13/xXl63dgmWNuhqDaM0p68con11iZQr+f6yJOklJcQDHnGskZagmJdYOZtZqUg6ScSYRAjB97/yCDf+aDcym0ZiqHQnqXo2pyzspjOdwJWNBYw16xazbFELB3aPsv2xA2w4a+65cI455vjNZC6QMseLUq8FCClIJF5cR6MllyRYGqEjid2zA7jyf26Av2HEcYxfC6jVIyxHkkn/dPnLz+K8K09k6zODXHbliT9zv4uuWs/sVJmzL137ggGL/xcsu6FboAJF/B/sm7s7vgRA7ue011oT+jGOa2H9B52c5/ovpDB1TelFYkypbLLxwPofBGDXne/xeM1n1boMv//my/jeZ/8ZE4NbkVwwdjm/95afFhO2PIdazSfrp6hU6kzPlJGy4ZYwHiWIrSTDlTyrmxQbly9i12ff86KOBVprhrcNYe1RSCOp2xZxW5p5S9tpSxxAAM1+O+8+9BTvz13AFW3HSEvNhnmj3FNYxPemVvOatp287MSXcNWayxmtlHn/X3yRymGFk46or25o4ihXsuyc7eyfPgWnoNEr65x/7Tm85+SziWPNB1/3j/zevR/jiZu7QaRxazFGGpIzIXq4RpCxWf07h3Fch762DzN5/GaeqbXwxU238HdDx9kYfYRNnT0sWtPDhkXnMTV2Gge3DDIxXuFwOmAiJZAjeVqa64R1i0UZh9fP204QCkYTWZYsNrS2QDDzfPHBaHU/fn8HC5c9SkiIjhLc+FfbseKGd1diXpbf+sOrOOOSjXR2t7F3tsjDDx3DiRTuQEjoi8bLlesgjUIv8pj3dJWrXr2T7U91sLnSz4ln7WS//00uXbKYxwp97JyYDwgm/Dyv69rOgzvXEBR82tbVWda/jeG4jTZ3jO31DG1Ohd1PHmHw8BhGS6qFKmdc9ww/2nUCgdd4manVM9jpMgiBM2OhIo2UglK5ykzF5zvlR/EevZJr1j7F97evZ/x4Fy2bpvnuzGaalwW4toUxjZcyIyCfaJTcJTu3Upl5J1I/TksUETVyD2i6xCe5rJUf/dbbOfVL/wKuJiF8XC/L9r02v7/4Ecp7M6zYOkhlLItUGt+VTBRg8ngzbhlMRdKUVkzM1Nm8fzlre3xaMu9k18G7qPmaYj2ipTXPH//ttdzz5F1ce8nTOELzlNeBfKJIri9Jqu5TSw0ykcugym3UzBZC7TEmMwTao/DMMKfq+SSnAmbzkvikTpqGNOkrinz89FsoGIe/+vQiKvV2ajGYYp7lbxxh6/FF3LljJcemFrM3YSFXQml5gsREHasAzmidclYgPYlM99HzhgM0VUOKlQT+PJu6cmhOGn7/jx/kD794IUHU+G4KrRmu56kPJmjdF4PQSL8GGQepIB502G+n2PX9TeRPOYrXLMhkfSra0JzWeIKGvoQRXJbezR+OXMqYdmm165zXdAxHwOnpYZ6pdfGew1cy9l2JfSSP64VEyqFV1FC5doozEW6nwIwpVLFC/lFDeflihIEd1uPMc0Jye+ooGbC0eYTaVIaJiRYsL8IJQIWgMiBci4Kp0urYoA1GGpzQNLLkHAiaJdK2iBMaN+EQNBtOmz1CpSvH+Gw7xIbABa8QUxiQOMOGsja4DuiiJqoHkHLRWmJhoC4IjAs2mHkh1hGXauiRP6fC9FATzrIYKxuh03V0bOGmEuzesZzxERsvCnALgiAnsY84kI6RZYExElkykALXh1qrhRGaWjfIWdEoN0GjA4XtuuCAkhaGhtOYcTS62SFqcREWjIZNSOPQZwo4Q4p4viRuNriBRAaNOSlOKawaCOmi0x5OUG+4exkBcYRRkqjNIjPccJ8LJTgGRGhwZn1sNMoGRArpeOT6ssj+WdwJ0HEAdg6tFXHKQYYG5VnEG/uxsJBBCKqGjiIsbKQRaNdBuhYmCht+hUGATniIdJpISRwFKpeAQKHbmhAzRazQYBwH25WQ8Ah6suiUi6jHoBSiUIPRCrYj+NaDM6SLM3jjVQSCHT/Is2Is5tiru5h3/jAdRZ+9swlyMzFEmlKbBBIQGKquwJrfQiYqoiQQgd9q4c3GiFgQ5Xq56KwTqY5OU6/Gz93XL3n5qUwev4uBQyNoreccfeaY438SA/w6lNX8GgzhV83cnW2OFyQMY0aGZhgZmkGpFy8x8RyP33/lcV591Q5uWHvd/+AIf/PwLImFIKpHSGPwnF8uzvmhP3sF3/q3d3HV1Rt+5n7nX7WRf/z+e3nNDRf9zP3+awjKJZ+pmTLBfxIqjqOY8AVEaJ+3T6iolmoELyKY25yHzpWTKPeFy8iklM8Lotx94I/4603/xNf/5A6+9jvvpuQHeEVJqmzxpfdd94JBFADXtQgCRWm6zKH9o+zeNojWjXH3WT7FOMP8TJZk0sHxnJ/5kBhrzWxHgtriBH5OM3lmFjPrgBiioBPM6gRddoHbNnyD5fM+xTfuWMvAbJYHtyylohMU/QRDtUYIyrEs5uebWNI/D5GzEK7D5ac/g++HiHqIm2glt7jMy65+ivVtg7Q0+xgDRmv6e3v57m/9A6cvSdOfOoqxBRlf0KZsLK1pcgu8o+9RAIRI4jX/NYsTowxUM2weXMiewSNUCj6JuBHgKlfz7Ns/wb79YxzL1PBrmvF6H3bcxd3b1/PQ4SUcO7AAdchmaDBNtSJJuzWSKUGp/jDDsx8nDPayqNNl4YpB9oaCUk2iZIB2JSgNlsWKNf2sPXUFnd1tAJx4yn3IQCFiCIeTCNdBd2apLc4SnthM/0urNJ9dRSyKWX/uICqh6U5NkxEhZ+YPckPHYyStAKMkgbDwMmtJWhaVQo33Lf0sm6cXoCLJPxw9l3dsvYatpVN57I6nGRucQuuYmekitCl2FfuoZyRuS4TWAt2VJOxKwXHYse0YQRBRqvpMFios6JqgaKf4t6ObmNzXS2tU5x1dT9PrVpntTzJ+pJXiTAbLl0ilEdxIqfJtbLuJnZVDWMKwfMM0nxpax7enF/L1o6ewbNV62rwWnnjj9YAhqRXGl+Q7BJ+783y+9tlz0ZttEkUfC01uccgRv5slq0Zov7DKvMtm6OnNUNY+dx9YxE2DbezdeoTBYpXQs5EZByktdPY1nHPyGDsnWogRbC50k0ylWdE5QVt3DTsD6VbF2ouP8mTQwbYox70zC/ETLrHU1Eo1BttidNLGGgjxW+C0JYfJW5qBoJW3vPNBIheUC7FqYdfmFTARkRw3HMPBtDnETS5CCoKcIf34KKl9Jay0YOoCm/HXuWyb7uKRkX6anCopJ+QrB5ZybDzFojUR06/JUZtOYCJNNhFQGfbojitgDKJbc8KFPhtOPQyuRxw0M/uPPSQPVinXmxkcaGH/E30IFVMOBWUNoZS85Duv4GMfeDXNQ3X+7LYzeWjHfLaV24gNzEtU+WTfQ/Q8E+KPtSHiAC0ichnJy6/eDo6g+a4E+Y/6ZP+0jDIW3lQEGIwFxZkMtb0+yfEyibESK7qHSPoKoQwyFljVCLcYIkNDLID5TRRXJhCeRdTXQn1hjrDVo9yfRLc0IYTASdgoF9TJFrd/dB1Th1uREVghBHmDVwwIlI2ShqppfPcsJbCzHskZg1VQWBGY2BAnDbFn0GmNrIMdKQqHchTsPPGURTyoSI5GCN9nrDvDwXSacEUCtxKRKIckywISAuEoSFokIkBAlDRk0zaXejnioSnczdO07SjhxArtSaw4RkUhWBZ4Eu1aYFlox8adjbCqEUGLy45qH5snumieMMhZw/k9+/jsGXfSlykgQ4MIYjofKeEN1bAxBH1pIgt0GIEGr6CxfYOSEHsQC01tfYrps1JMn9/CdFMjK8b0NqGzLlbCIp9Mc8JVi+l+TYiVBGUUShpIWKi0S0P610F7FhIL0ZQksCMEIU5nhKU0Do2VTSEEzFbQoyOYoILoziBTLgKBakmj8x7xgl70iUtgUQdxdzNBZwsU63i1GMtRZG/eixycARdkqY67fxS/XEMZAZbF9LDHo3ctpK1Q4fTOYzADybJC1ENEFJEZDzAxjTcEA2RT1Jo9VEu2oQKModALJgkqnWTtuj5UbDh+ePy5+X3dmYtpbk4zcnSSXc/MaaXMMcccv5nMZaTM8YLYtiSVSSClQMqfnbXwxhWf+x8a1W82IxMldu0fZnqiBBqs/8K382dlD/1HflWrQ45jU6j6TE1UKJV8Wtt/koMS+hFxrLBs+YLZJgCWI3E9B+tZG+1/F2v9dy5cuYuXtj/C9up84L0/dzzHZndwZgYcSgBMl6sYCyxb0pd/8fyYRMol9EMOjc8yM1MFR+DXfWxLcFy5dLolNg/Owx49yrpTJE0tuRf9TJHRBClJ+XXzaX0qxs4lkLOCfccPkVjYyDzIWAEFI2nr3sm/fuZ6vnX7JsRZPlKElFWOJR1/9Lw+X37+JrY8uA9baBLlVnYcOkIicAlEjQ+vv4eTm8Z5/NA80l4/hakS9VqIl3B5aMfT7BzbQ+2mNB3+CPE8G5nvJUo65PIRn9xxCTc9q388WltFVrqc1lyg2S3yyDMPM344QVfns+fNgiECZuqG9thn0+U72PHV+Wx7EBZPGl79Jw/xx4dfx8VLj7PqykkKm11EWtDVtZ/DEx8jRYVS+TNc97o0aUtTNi7jxqI7jPjLWz+Ao0Py+QzlGZ9sJvPcZz/zzLP5J+coStkY42IFMTJt0bzCMJGW7Bvq4w9XrEJWj3LgcCcyFoy5TeyrxbSrs8nMFOhKljhqPCSGVYvfxkPyXqZnKrSne7n53C9yvHKI7/3j97BLFn8/08LiI7OY0CBti7GRMl/5u9OoVS3yxSrlFRa2XePk1hGma2kc1UktjNHGYGKQkeEN9WvJnbuZ/fvO59PVPXz09T/ggJXhjasf5gNfuBYn6RIP27hhQ0y0t2OGkeIXyGWu47uTy7ml2sO2ry/h0KpWyAioCG7dd4j3LZ4lm09xxYYRHnwoQzobI6SBItz87RtxBdwy3cKXP3AabotPGkW6X1CpGMgocitXUtu9l9qRHEpEsHMP9WYXmXXYcP4qsi1pBobqzA41sWM0w53LFzAvM8vk1ibWXXaQCg45N+BTT57Hp8+7hS67jjKSrAOm18atOzyydxQzz8U9XCNqSdB5cCf3fqKHsz5+lNc3j+IKKFxzP/90+3nIQBD1aS7qPsz2ZzobpSyOROcM1GPsQ2WsWtRwpLE9sjNtlGoanCZMsQptmgN35Rg+vJTBs8d4UPWDluDF1FWS1t1F0qHH8OYU5rjGtars7gz5g7WH2H7/fLSxiJUhfahKedTB5LMk1RTJEcX7r3sllaWtVE6BxKjFuz7xXTrdGpurvbyueQ9fH17G/e4CWkWZiakMD46sxuSL5BI1HEcyljb0dpSxA4UyNlaqHRYEqCOTCGnRtKVG2JuifkBz98QatEhg2yFDowmGjqbxhSZKadxZhalFYJI4MQhfYoUweZpDnHDwKr0EYYQQFnqiiCyDlZAoKUmmfMpZDysWiAJgBCQdgjaFmaxgpETWJXFWYVkO2paQlchyiHYssA32vBpGCZSvIYyRO0P8/gT1hUnco5LYShGlIixlUR2roFamUG2C+oIU3mgEyiCm6liWBQK0LRBFhWi2mZFw+PAYTeMBUewjO1pAe5jIQAhGRuBHIG2MJRCehTAOcjLCnVLUVkIkJX9/8Hz+dsn3MJ7NG07dyeoFUxzpTvPNH68larUQtkXTqKHYDpElkVqTeWqS2SsETj5DnGqc08gzSGEQoYK0BY5NdGYXA3ZEHIQ0Kws3skjhIJ0CqSYIpYvUCmNbz2aXKIRrIWsxsSOxjEEiib0kQtSJ7RSqw8WphxAqhIrBaOpJC5HTRJ3PCt96FgaBnc6g/BBtS0QyQVwPsKRDZjjAHD+ETtqYBa0kJyrUlyfReRdRrmDqGozAX5JB9fcgHxugMuYieiTBDFixxhiNlBbSEsiBAqI/j4gbIu16YQvVUAOKrHSoddkIDWOmTkt7nnx7HutZZzvLsmhvb6N/ZReP3bmD+295mrUblv3c+XqOOeaY438bc4GUOV4EQaw01lzSEtU4YNfsMKuaesg6v1y5zS/D8Ogs+w6MoIsh2WziFw6K/Lrh1wMKU0UKsxXgJ+LAjmeDEEjrxa8prQyhH2I5EqUkQS3Adm1cr3EurumcZpFVpyt/9Bcay1e+fgb7X9LE1LE1fP0G2D04TqgUrhE0Z5Mv2k5Km4mJMtPFMh2deU4/bw2pVAKtDXvr3eytdTN4V559x+6jMF3mylef8aKBFF9FeDkNgaY3l6AaAsKwZayZ85ouprUjQ07uoxw/zS0PnYhJ2IQZg5NQoCULcjku6z/3eX2etGohF527jqnCYTqjd/M7X7iNOCc4sncDNx1I0fHSLTzyyFL+8tSlDB+aIPAjpCXwYpv4izEm1tgJC+uIYnxNjXpnkqFaF/63Ij587xeYv7Sb/mv2cE5CAXBR/yD7H2rB9yOqVR+AwMDxRA3LCnjJRYc5tf9pRkSSiWoLx57q4NEdCzl/41aU4xG2gnjGkLZigqYt3DO9hFPyB2hyKqxOhCQtxbHAUFWCXVsvoy9fJ9+ep7OnA8+tkMlnn/vsPbk3s2DJ5ziybwKpDCY2OJ1V3nP9A3ipD7NpyZVYWvLY9pO4yfwLapPNQ9Yyrm2/iIFDj1CrXMqmjtsZkTk0sNU/iq9iasUK01NFOrta6MsswRuz8WY1pi44+ZxlFGYrDE9UKI2OETyaxzIBlgiw1wmcdMBZbUdIpkN+kLqGsy5dS2trDkeOk3QsmryFnNF3OUy/DpVYz2bVznG/FYPB1TZRHsQMxJ5BaEN7MuLpWjMrgL0jqzlcq+LkEiQGDGGvg1U2WBnZyMCyLWKd4FNr7yKBz1+ULiHTXyL1bAB8Q65A3wfewte8j2GEINEegSuwpGFt52J+FB/AOiapHLA5MHKc5JIs+Y6Ibd1/yyUTX6Sz/eNsPfp3jBxM8Ninu1j1knY++va7GB2MGVFpFtgF6n6SonGoKYkykqm0jbZdnLY6hfNc3GMO1eVgu3X6lU9poo27HlzOxS95mqlIcVbvce70Ye+SmH2v/TKuEMxcHLI/EOSQIA37t7RwY8caKutSTP84Q5zKIJQFkxp6BKrF46G9Pbw2WMdhxri/0otMSgQgjcRMhiS+apjoVcTSQ2Y0daU5N7WNzwer6Gg7zthMNya2kWFE+zdCRt7ZhGyySOZAmyrj6zKI8SSJpeN8YuslJN2Qvz7xhzjS0JosMbPFIeps5oHvLCOep/HCCMuDyLVpqYfEwuCqKn7chPRs+lvz7K03gm6ZJycRzWmo+ITVAJIQ5T123r8eAzgC4jpoNNkCTC8yICzCjCIxa9DCJnIF0g5xtIfAUOt3SYwHpDJVVNqlvaPMPjcLkcKkbcCgW5P4TQprV4AMYhxfU89U8NIphFJoCbV5DrnRGGdJSG9XmSC2GTyexyShfK2LHQoSgw4mMtT7mqBYxq2ExGkbpAAPotNirHsTuEaRnlVUllvYEwHCt/AcQRwY6hgmJn2stiassXGkJYh1TCgjbGkwWBgagQipFEaDFoI4qal1WkRtESbZCI7829BaZE7xhS+v4NxzjnPft3sQtsCpCEQUg5TYMxGqxcUa9Vm7coqDd9qMXZtrLB7FBhEbLN+AAq3+XaNaIKSNXOBR9C2MX2dahyyK8wQ1B2O7aCMgBgyIep2w2yEZNKzTZSQwSiFxCGINYYKoyUGIdrxCgBidRNRCvGbBTHeR9GiJYELgpZqQ+RTGaWimWEGjhCcTNAYm+9vh0BiyFhOpIsxrITEeUTu5B9PTjHzmKNFgiD1YRXXWUOcuYebBXYgslO+LsDwbkbTRBR+pDBkt6Kj7HMsl0YpGTZsUCNuh7itMQRAtaFjPO65DdabCeKnK9NgsHb3PZg+etoyH793N1qeOUK3W8TwH31dkMr+4/tscc8zxX8CYX5PSnl+DMfyKmXtLnuMFiSPF7EyFmeny89xX/i/yqR0P8juP3cQnt9//Kz1OECompstMz5QozlZxfsnSnl8X0p6DCSJK/8nFKQwi/JpPHKkXbRtFEeVSlTCM0drg10Pi6Cd112taXoEQDlm78+eO46mDe3H2Bmz5m2W8cckVANx57BB+1lDNGVLeiz/MZZuTjE2XqRYCYhXwyjeeSblQY2BkgjXeCBvTR6mMtTMzUWLXlgEqpfqL9lWNAmI0lgtqLCLoVITtmruqRxh+6DL6En9Ixv0qH3/TDXz7to0YAbELVeUwU04SBj+dOSOEYN7CHvZPZvjYtx7FnDxKlDdETYbtUQ+/+/HreGDHclIJD9sGxxEYFTO8YxzGLDQ0HvKlYFTWqS5w8OcRARObAAEAAElEQVR5JHdO89htO7jp8w/SU3k5T9ZaeaKa5+FiL/PPGeS8K07g2tecBkDsaSbXlBhfNcva3mspBN2cdHk3mdY0Mp9jXuoTbB3tQ4UCEwnmLa0BCUrOcX589xV88OY3MzvzFlwrQBGTtxTHhy6mN/0myrN1JsaK2I5DR18L6ezzA5i/dd05EKiGC4Vnoc+zsKRgXusUGdcjmXA47cQFXLAyy8nzB1jVfJypyucojx1jx7EfsjyawHNikk7EE2oL9fMHmfeOH3PT7gefO8ZyN4dVAzmued/fvp6PfPntWLZEa4mIwI1ASI1cGyOVxA01xrfwbU0q6yGEYGq6QqHi40cRxijmNd9H0KrZM91Nk1Oj06mQaq5iIg22QlsC5Uo+efNp7DvSmJ6PjhlMZOGv0ugM5A4b0uOGZu3hdMSkswne3NXN1wfX8uPKYr516ls5s/kwg6GgoDX3z7TxF9HnuPfASo5ta+LwtlbMcai/x2bP+ARS2Tg1SEdJpNG4R8ss4RiXdx3lw8ffxeExRc/MX7LzhyuQU2U+8q67OaVtkJPbJzlh/hQLc3VMQjMYtBAbm5Fqjtaggq0igqqHzinCLk3YI/mX376bkXozeU/z0GMn8sl713PFvdfxncl+Ws/cCRKsZxPQLCHIWYqDcTOpisRTMQeS8zkiFhCfb6PTaYwDTUM2uc0WYsRC9fewJZogQZLkrECXFcpT6DhgyZdniF2X6fn9VBalCTOaU85+kpaa5veXP0r76vNJf6CI0+UjtMEOBAu/PczKfBlruc1v/eUTfP7Kb3PCGTtZs3qKUxcdI5n2GQvTjEcOi7N1Tlg9zI1/cSYT29u40NlKe18ZLaHJCll7xSGGBpI4VgmFoFCus/exAw17Wdslbkog6wHSEpgghChCRGCMAgEGg1WLceqAr0jvH0PLECEEQluIGkgXVMYHE2PN+KT3x2QWFxGWIZ8M8CJFYrZOciIAZcAIpHCwJ+skp2pYtQg3CMgeLiPrIe5MhB0aHFyMUXSuLiGAfKJO0m0InMqaQMcCWYmx6xproED+cAyOQXVIpKNxMwEJz2Argz1aQ8Qa4wq0I7CrClmPscsBpDQmk0CnbExLC6oaoIwi0TzD/DMGufiyp2mdN4WJFSZQyGoMWlFpdoh7PNyCQDgxuVyV44VmHDfi2I9yfPXPVjM9k0RlXVTKotqu8WtFnOEimS0lbn3qPj510w7+bfvdtG2fxLgSKQQ6ITGWJLejSupglfYdAU0HVUOwVttYscayBQeb4cZCwL5dCxDpBKQccAXGFRQXZzE42CHIKEarGCuIcLRBux5+QuD3elQXZoi1xrQ346zuQh6L6XlGkXlCkygZrPFZnH3H0cUaWgJBBKEmsiTCslCxxrg2IpfBxkIcmkaEIdYzY5DJYBa1IyyBqCvsp4YwxQIjIxnG7k5RH0+AMmTTCV5//WmNFwMJ44N1MvtnECrAoJ69ZhrPg9lxjTNlcGcFpZpPOu9ihCGKfzK/rztzJaQSjI2WuO3WLSz74t+w9kuf5KpXfPJF58s55phjjv9NzAVS5nhBpNVwPpGiYYn3f5ktA6OUazG7Rqd+pcepVn0q0xWqtYia0iQzv7rsl18lfb3NpByB/g8BEAAVG+qVAG0MSivuGf4kj43f+Lx9ojCiPFMmqPlIKXEcG/EfypC89GvJtHyZXNt3f+44jgdbiBYK4gWGE0+Zz5HpGR4bG0Qpjf45scHe+a2NB0YB1XKEUobiVJmH9h3lw999JX9462tYvPEkFi7vpKMrS3W2+qJ9STFCZ3aa9uwsaWYxzRG6WTHjVjh2aBTfD8g3pUhlXXTKQifACjS+coh9h4R54RKkb96/hYF6GZWVpJoiMmtmSJ8wA8rgp2HdCU8RRRH1qo8QUJitc9u3nwRjsKuKSiZCOpLEkCI5ZjBFTaJuMEEEWjM+OM3Gvge4r9zNaW0jXHrSU/z+H1/DyRsbtT8yYfBDQa0uaGlay5UrH+H9b/1rvvajP+DWx/+CKy4/iYHZdm5+bDnf+cZ62jbNMBpLDgcBs8dKxM8YBgqXI0QncWTxsbddRvrYy+hY0MbBfSPs332ckYEJJodnicLnX0vnXLiGoEkhLKjlYKzSio6uZknH9c/tk0q4vLT/KpJBlVy1ihNH+BULWdGc0T5OZ65Ie75C7B2jZdU4TxxfxGPizufaX7ZxJaUVMLVKc3xqFoAlK3sR2uBGEVbSprRSUpIZjpebWd//p/zgE9cQBTH7dgxSrwccGJvhWFRlphoCEikSfPPiv2NiexOva97B61u2s2b1EO60xJ62UDWDVYFappkbnzyDgalZvLiOKnoYZdG36BgqpQmbDV6mxBcO/xV7Z7azovntvMw7nQvy76VSTTPPhxIhR2PDWa3DSFsgpObYbAcDe9sY/zuDHgp4+NZtiCjCChVvePdLWH/OMk7YNMh1v7uNLwytpc+b5ctjNyNa3s78ec2cceYkMypBoC3cZlh30gxRJgHK4itbT+frR09l12AXsi2mKevjj1swYaNbY1a3tPGN6Xm8+RWH+N7nb+eKNfu5bfMaPn7Wzbx58WY+dM49JLZNcdb3r2UkCpiOQ8ZVijYV8IPvt/Kdz8wnGEujI4tAZLBEiB01Eh6EBfkJF3EI9q6HeirELjnkhiyyOy0+c8k3OO8NO9ApG5Vz0QmLmcUZ7vQvIL+kTOgpUvNj3tD3JPEfge5wqJ3cQT3fRELFkJRMWs3UlY21N8nifIF52RLzs0VsV1MWLoNhC/NaIxJ+EXemRO5wiXdvfJLmbICpu0R1yTNHu4jqMX5kiFMu5cVtEGvwJCQl1AMoVNFKoRwLLLAMCKuRRRQ022hj0ELCRISog1tQWL7Cm2jYuhvHIGYj3MmIdB3anRpTwzmmJzPsHuxmdmMLcc5CRAowICW6PYdoSyKljaxFZGZimp4+BAPjEMcEIqLYIVjXfZz39D6EZ0IcX6OSkjh2UMpuZGvEMW4tJrY0UVqgbIk2kE7VcTIap6YQWkO9Bo5CJwR2LcKuGtyqxAkNftIwcnGe4y9ro97nsf6PdrHpt6bJlC2qgUNku4BCaoWIG9euTkrqC8BPG5q+VMP+oqB8n40a1lj5GLRGKoMG4qSFzrvovINpbkJ053GsxoQgMbznaw9jRREiMhghEQIsXzV0WGKNFRvcHQoihZKgtURGIBJQSLmohERZGqMNJtTonINXAFwbq66JPU2j5kejjEGnHHRSYiyBsSyMl2TT+atJNHnMRIIQAVIgEbhGYw+OYhVrKFuABdJzCDMeuh6gc0mMNFjZHG4miaho7FwKa9sg+KCMRGiNU9U4Dw/QfLkk9ypD/iJNZGuKkc+b3nYFC1a2YZRGWxZxBE379tOx7ylEaQylQRsDSDJHDM4sbN4zyKpTltG/ogfH/ckCUEtLFjebJJY2X/ruQ+gk6GYYjkp85fP3ABCGEUE9+NkT8i/ITH2QcjDy39LXHHP8r0b/Gv38hjMXSJnjBVGxZnKiyNRk+WeKg/5fwEwK1JhHW/3nec78v5F0LUxNEQaKWsUn+zNKT36d6WjLg4aZ8dLztlcrdSYny9QqPk9P/iNe9M/Uan9G0Z94bh8hRENDxbIQAoIofN71J4TESZyPtH++NfeVK6/mzW+v8scfyNKayXHBjf9CnALyijgZ/8y2tm3zqjeeRe+8Zl775guxpMB1bWZmakTKwa8keP2rz+alrz+Txcu7X7SsB6A70c2HVz3FR1Y/wzmbTsbNRDjZAJmKiKKYVNbDtm0uftnJ1OYL6r0GZzbCJAxCQ0a+cEBtUpaotwv8douJSpruRIkmWaP98RIv6X+UrA3lUo0j+8aZnqogLENUqINrUVkqoD1EV3y8isYuVEnmfaJFrfjNHtmuHDMTVTwvzWm5o1zbsoM7C8ufd/y8ncKd9nCmPcL/oA2cy2VwHAeDxsiYOOsQp5MctLvYFfRQjHyqJ9SpnaAwHUfo6dnGh254J+PDOWZmfLK5FMWZGtPjRWrlekN4WPz0PWjypZKxa2uUNqVwqoKny7t+yoWqObuEM1oPc1LuKGHdJZy4ivJwijiULMrO0Jcu0pYosG/3Ako/amX3Dxc913bjhgWYZXUyq4p8etdDAJx+/nJkpBEadC2ERaAwJJMRT+37OMmUQpcDtj+yn52bD/GjYwfY31LhUH0WIQTz5x3ltJNHuf3dH8cSEmMEs0dasSyDd24R0R4RaQNaN6xwx36LrqMhndvqnNV6gMpEC+HakHhlROeC4wwemuL2b3yIB2+5iWP7mpiecXnoyF8yUMthD7fQInN8/p6z0BqaM2W8thpmoI6oBIggxgwVkTHYWmJpizd/8GV85Is3Eve8n3M7jrE0Mc6BYiuBb3POtRtoWuiSkT6xMdy1ZzkdaZsyWXA0tdEcO3ct5J69q7li2VGUhtpkE5ljDumnPd581gbaVJkVi8cwGtK2T8XP4qEQNKRfNg0cJfX1SeqVHL2Oy7LA4ca/ege3/tNaxg+2Y6oKlTIE2Fz92z9CuQotaFjDasjNWnTkR9jcGRI0Qa0bLMtnUbbOxb81zm9/6MeEyYaIqGlz8MYtPnPLq/nud1ZR6HyMbKvmQ/PvQCxsiJmGSY+iNsQzghv/4Xz++E9eSX0yQZ5Gedv4rM2fP34ZKoLT3CNUtEVzcgTHK+IXA3qTkzS9KSaZrTFZbaYau5RmQJgIpxwhci7CGI5fahP+gSb1eQWWhO48siNHbqFP2ONjPBCRwKk3SktwJJay8fbOIkKwAoOMNPM+exjnjoZ7lEVjEWQicnEmDLWZBP5ACumDTjgYXyNjg4xBNXnEF+fxmgRxc4JEe42ufzIsfGOZ1IFh3nzCbfz+m37M7/Y+zab0NO/pe5z67hR2YIiTNnHCQlsCgUSnMtRXtjO7sQ1Zd6BsNazJhxNYBladPculbz+MM08RtzfEYoUAq25wigqrJSBOGkQYUjwhz+idzdTGkxRLKbTwmN8+hYgMUhnwDcQKe56gc9Esuf4q2pHYFYGfyJH6w5jsB2KcZTHu6Zq4xQVHYI/VG5kvWRvtwg2/dzkTsWa37+E6is+99ytY5TKyrhC1CNuPSU7FyMAgQk0+iul42iCVAzbPWjYbwiWSMGGIEzCrixhXEDdJtAMYgbBtVNomTjooIdA5GwIFvkYWfbAEGtj95FF0eyfW2j7iTb3ULQMqglhhKbAPDaNGRokcG+HXIQgxzTmMtBBeAuVKSHqkA4Wz5xjJPdNkn5rAQyCfTfm3lMEMVXEyColB2ZJwtsZFS9/DitU9WK6NiBVYEHn9rDq3SNfqEm0nH0GKZ/sRIBV89eZH8ZIeUlj41ecLxbcsaSPyLKoFBTUJsSBzuE653PgOTY3MMHF8hiB4vjj93+98G5ff9w4+ufOPX2xqfR6fvf/v2T56EU8dP49yMPrc9kJtG8XqI79QH3PMMcccvyz/O2sH5viVEytNcaaCZcn/84GUbMkhO2OzaF7+V3ocKRouDMax8FX08xv8mhJGMWMjRR5/aD8ve/O5z233Ei6uZxPWQzwliVG4UuH+B1Vdy7bwki6WbRGGEdVSFaENqfQvH1SyrTzXb/gE0BCtNZrGSqAQtOR+vv7MG99+IW98+4VAw5paWIa6rJKa0AghyTkuj+8dIZl0WOtaL2rxaNmtnLvoDkBy7vs8vva9T1I0JeLQYrZcJJNJARAhyKwsIlxDdTaJ40rwDCnHecHx9S6eZGaqEVC6un0HD1RX0utNsf41Zd76imcIlcXU8AyHth7Dr3Yyf0UXZFzwJNe/agszQ4J79m3AOGBOrtPp+BxL9uJlocnKUan7GGP4h4EzsTT4PH8cWdsjGdpESiOCn85aMxia7TKxNIyUW9mUPM6QyrM8MUX+osUM10pctfh1AORac4yPV5iYKtHakeOcS9cibYvWriZsW+L8p3OgtaZtSx7Rf5yancUWFoucc39qDImUy+H2DjJBhUeLHZzfs57yVIYnRmY4cfUARZMhIUKqLOW83/0STU7IaPHNdOc7OXFeD5ct3sXV7fu5Z3oCuJozL1xPvudmisNFcCWuzlOuGsyYy87eNjad9xi3bltNxY+YHZvhSGGMsDPHXmafNy7bSrCwcwvX/s1fcWSmC2IInmkmsWqacBTWtQ3Ssr5Oxh2lX7Qw6eTY0HGAvU8sYuN5h6joBMvdWR6/r5Xe8Dg7y19kx/gK8qvPZKB5hsklebY6EdcmbD5yxTMkch/CTb+Wle/8K0SmhEkXEQUN03WsVg8r43H8yDQz1hc4o/YMhXSC8/zv8Lnyn3J9z32M7O2gxtfZ9NpRclJzuJphu9vJHWMb2DW0hUXFfUyMr0VLQdAsed9jV9Leo3FmLcIWsELNio4O+t39PHRvO1PfbObHh3sQvXD9V9/KA2/7DI4t+dtvbeaKCy/i9gOnc82Kx7jpjnUUpw+Q6BHEx22yXoU/vORBypHH5368CeuiWVaPz7J9xzwMCRAQ3NlLxkjCTEOo+tpTH2DWpDk5WWPVGT5f3WcxHCic4RB8qC9yGVnfycaFQ4yrPF7V0NlSIjySwxFVKrUk1bpNkJVYmYijLb18/s5uTj1ygOqDTeg/Nbxn58tozvhMDDgsGBwmlZ+lNG340t8uZ2KBYfq1SdbuLTBwIIEeAj2vinEzaEfi96Zweqq8e9n9PFhdRvVGixu6H+Bb4+u5sGM/dWPxpb84g3KpBXe8ihISoSQiUtjJVuLJGsKAHRvihEvrlKa4qpF9IZVmdlcz6VDBVIirBEGLDRbYdX5yTww13tIQp0mjmvKY34nY9sNG4NRqPcBdn1/PunfsJLm8g067wo7pHuzQQyiDU9CELQKJwtMBsfDQbU3Y3T6JzgrRsEf9h1lyB2q4QlGvehSradq6ykyFkigBbgAyUMSOpLSyDRlp4qSNSlrsbFnLaaNPUzIOm3pm2RN4WHWDMJLYVmhL0r62TG9HkXroMBC24dWhJhyMk0JnFeJNkmhnGjFjIUNFvSnEC10QEXghx0e7eMNLXsYb/vgJNpwyjBSCNe8b4MCD3dQfTJJBIg2IhtYqSElsAqQWSOMQZQwikkhjsGoKKUGva2ciI3ADQfEED30gxlTANg7GDrF8gRAS1eZCwiZu8jCqjLEM41MGx/VIRB7aNejlLfiTdcR4BanAaENirEoweQjdkcZqk8hUBtWSJrIF2rHQDtTbXJxqjByYwg9jjCORSLQLli0wAzHxlMapxbgTFahG6KLP3V96HGE0pikBTgKUYsczZ6DP8sm7rZTQhCkblEFWNDuZ5R1fuZn3nrbx2QvqJ5x3xRqeGh5HJQT2oMbp8Vh98mK6+xo6Kl7SIxABtv38uXOoPskHF+1gIsoAf/mCcyA05vaq/zRbju5hzYLGtlA1yovuP3YlKbWH9z71MhLmezzw6k+9aD9zzDHHHP8V5gIpc7wgYRCw9cnDuK7NZVedhP0zVtx/45n2yU5CdyL1Kz3MvPntyNiApVm+tO9XeqxfJdl8knIt4MiBESYnC7S3NwGQb0ljO4LQj+lPvIGiTJDy5pN0Wp5r63oOTR3NeOkEjmOTSifxkv/vJU5CCJY1ZzgwWwQ0D193/c9t8x8xxiAFLF8uyR6v09oqUKFiZHCafGuS5rbcz3RCEuIngaDTWvu59ch+sDXH17sN9wrApH3+cd3dWMLwtpHLKLlplAbXeeHSuotXt3Pge4CBh/et4txTjvL4/atZddLDNEsXLQ2FrEvPwjY6etvYueM40vIQaUmyHtDkGIQHIlREeyX2yTEnLD/MVNpmYJeh93gRFSumJzzcSKNSzc87vufYWLZFZAxB/PzAn9YaFU6TVhEqiomVwA5gTWYCB8Nrl3+J0dnvc2j0AkLRTHPHezAHxpmeKDExNMPGc1eQSLnMTpZJpn466CWl5KPvvIoH7pzHk3qSEgYredZP7WdZFjKneOpYH4tTM5x20RrWn7mcbW47ffJDePY4tlAsPeVWFueOUzMwU/8M3fm/RErJ73Rvp9sOWNj10HN9fvmu3+ej7/5XpDSsPnkNf/XM4xgXhJYMDS5Et6TwOtK0d+TxhsGrQmpB4388PryKhKhxTPWxvPOrLMiVOTrajZZgl6H2qOCEE6cYW9JBLelTq49Q25DAqim057C4vURi0GLNslGO7luF69ZZtN5nYMpl8dBxsotdpsmwT3dTGG/jNes/iIl3Ynvnc2B8H24tBp0iXtyOHp9oiF7GimTSZWJkiq5MjVJ7ip5Uic+PvZ9H9Xre0reZLXSQsqdRMuRLkydQ0S7vP/FdLGnv4djmV3N6eYI7pmp0pSucc9U2ZE7zuQfOpAVJctygcjE7C0M88fQVnNz0MFuPpHFqVZRnUAKQEiE0tgCnLc3QzDGu/8uXw54I21QwySbMIo8LO/dwSnYIIeDORcvwxqvsri/kmnc+xK2fvhC0RNsSqQW2grqsc8H5E2w51MNJqxsWrFf13sKt/3IKYloxnZIs+K0p1i4YY0l9gkdnFpNNh/QtqUF6lMTSAqkg4nG5DG1btJ1d4cK1m3lgcDnTB9OkpMR/PEJcbDNVSqKzDkeWJckdldSCZjhswFKoTTYH98ynVghIqCJiskzQ55IcUKBD4tuqbD1hESuSE+yJu5g1Cd7Wu4UxleDMZJkrP/F9Xv2SK1HDGhOGRAhkWx5bGrxKiLRsCDSW47DshCn+6E/vYGg4wdfdlSjtsf+GFqTJIUODJUDEYCmDerbGUdoR8wdG8NpjRoJmChM5lNNwrbHfOp/jP5xiwbl9HKjXuLnSzvGjyUYgLgfO1nGstgThEhfrGYN2I4Tn0byxyLy2WYZam3C/AjpUBDog6rZwXEVnW5nWoMTEbIbi2la0Z0MIwrYQJkYKgUm4KCRPlU5ntaP49tN5ipkc7TJCaEPsCqTn0L2gRLtTZkqlcWsG1e6hei0Gt3bj2SG9Z44SE6EwGClJHogw88FPgiyFJJRBN2f5lz87kc4f1Imw2Drdy2ff+F3e/8SbqMyTpMcjjG68oFu+QlTKpCqSYHUL4oDBnORCYDCiEcSyqxosiU4Y4rKkutBDxhZM1cjt1+iEAO/Z0lVlEJFChDEmjLFSSbRj0XAgtpCRh4zqyFyGuFolDgwJJI42MFEhyiXRqTTSshrfJUDlBNU1GdypiGYfklEMxQrCBp3P4Ns1avtqTNwu8WsJ5OAkJlIYIdC2hcolEblUI9vLj4nqNZwdivlLE5RLs8w6EjT4bmOM+wcK7F1d4ITWhc+7B19z/kn8642PMz5TJTWsaO5oJt2SJggVSinS+ST51sxz8+C/syrrI4Whzam94Pz374xOvYtU+EP+4mLDp/acQnt0Muctbjw/1eMZvnrkdGq35DEjMb87/R3+/p2v+pn9zTHHbwLCGMSvwSL4r8MYftXMlfbM8YIcPjDOrqePsePJwxR/hpDm/wUGZov4cdx44P8VcsEl67jmNZs499KVfPbLb/vVHuxXyOIT+jBRTLVQY8sDu5/bbgxIyyKdTZBtStHf8tu0py96XlvHcWjtaCKV8hoWit1tZPLp/+cxGWO487q3svP172T/W99POvXLBcVs26apo5mrV17K56+/hK+87rdJpr2GTWU1RPwSccYPnHQhSANacDj3k0mm3RpmZWKK5d4Mq+vTDacRDXn7hQNJL116PXYAdt1Q3N3JHf96KuWy5KVnvB6JwBYWPf0tXPCKU9h0wSoe2D2In3YwrXmOjGxixF9LsrOOsSXtEz5LThlgV76DxUumKZ9g8aDYSRxrzmw+l3rUxbtWXPO84zuWhWPJhk5J+J8yqKbOxyufy2VLh/CbLYSSHB3rIvGs74ZSPlun/oQJ5XK0HnP2pWtYckIvq0+eT+gHqFihYoVlge288Mm9aNMKXnnJOqa9GuVsyFA0+4L7nZewOSM4yCoxQi5/gNb2PMtalnJ7cS0L7RoLrICVrU9SNR4aSVfm9OfaxvZK/mFmGXdWup/blslkWLSok4mxElP7J5m/dBSSgqfUIurlk9COTZBL4nXm8C6u45w9y8a2blR0BGF8tBHkzAies5jfvqQCxiBiAUKTLzYxNtrJys5xutJlhnkz1570BNed+SjZtM873rCBa/rfwwmJN9PVd4DOZWUm+1O0r4iQXS52tJchv4nxapod461sGQ0R7uncM/h6niz8Fp1NkzixxM21QDoFtgATkUralKeqVKPTGNUOd9QXsSA/izECIyDRFJLwIkJLsC43wBlNB7l97BPcuHuAWmc7lZTFiZkjXN11iHf0DNHvFXCUQ9AhqXdJqnbEB777AN8/Oo+426IYWOhDCveRw5yf38UDd3hoLRmfSSPTFcJ7NKY6gwli/KqDVg7S0SS9KoNhjq3VDm687J94Y+87mL9plDuG13D2Gx6npXcUoQ1KxZQ6QrpWVXhmsokrlx2iqA13FrOcd/YhEkGIlIITl5Sg2fB4aTHDxQSnJg8xc1MrhX2n4+fzPKUW8ORMPwQGqxbyshOf5qzmg7xyyVNMPZrE8qo0PeZSmnVQkYOODfE183nLDUeYv2gZZoHNxPwc4pkE9TCP6clhLIOo1HjPn97FB752CyesPErL/hTdg5NUY4sxP8sSp8B3xldhsChozR3T7eQ/UaXmVJGOi4vEijXMVkiHNVK+j11XxDb8yScfI5muMdLpcmbLcd7b/2Pe893HecXb95BpC9EIZGgQWhJmBFFWEssEQSrHX3/2ES69dDcj461oqVhzxX7++KLv4p/SydVtR3lHx3Z+p+cpeE9A8p6DtNx0mPzRmKYtZbypkKgJdEqiHcH61iHW5odZkxtGGcCyIJli+HCO3d/P0GR82pJVdGBT2OgSdFuEeYHcN4sVS7xyQx9GC1CRYUBXmV7WRtzrENkKGWuM5xDlXEzCcLDSjidiolaH6Y0WUb9C5QVUDPYA6IE6cUITZi2kkIhagPIcrKrEPlJA1BSiqY3vfG0Z/3zoDN68+HFKcQKZtDEJtyHGYwxCq0ZpUTXEHalgjwW0lhUdD1Xp2DNDHMVEaYk7a0iPaQhDnCggrNWRgcaJBLZtUW+xiXMWRgvEjE/6QLVhR207GNmwEdY2EMZgaLgAhTHJhEN2WdOzoq8GqQ2Jw9OIXUfQlkE7Ei0FoqywfYPQFkQxQhmMI1GeR3V1C3FvE2FGUsIjVjYmihoCx7FCFUpoadAJGx0HDbHj2Wmi+0bZ/S9PwqHjqIkZ4qQEB3RUxN52hOEnj1CtPD/wYds2r9i0CnfvBLlnZmktWrRkU7gIju4bYu9TB/H955cDAbxx+c3kUq/jhI6vvuB9/TmCu0kISbMlOHvhcf74yj947k+n932Z05tHSB8NsUuKZ769B63/D4g2zDHHHP9jzAVS5nhBMkmPTDpBtilJIvF/NxtluDRNIRFTTxmKiZ+9MvLfwbs/cDUf/svX/MqP86ukrS1Pe28eBGx+6AAAURSjYkW9VmNqbIYo/tkaJb8KhBB4SRfxXwiICSFIZ1Ok0knWNG+iNdFNJp/EDxUTwwUmx4u/cF+9uSbMtI0p2hQKPwlADA/v465qB/fVWmGfg5WM8VIRzakXDqTkMkmcSCA12HaMTMSopSF26hWMmRbG6EIIi9aOZhIJj9Ggjj8vT3ZNDy9/7Qd52bULueS8ccQKEJskTfYE71z/AFav4i2v+xHXveYhpusHOSO7jOaBDvyp568sSCnp6cjT2pKm6T/r+ehGUGO5N81ifxKrqpktJ8lbkrSUTM/8Keeka5yRrNNm+5xy2jL6lnQivAR9S7vJ5FMIKbFdC9t94dImgK7+NtJ5D5WB2gvoqADMy76djllJZ8ElDu/F8SzWNK3hwp43EiGIhMDXhs7Wm/mTR6/j+ju2op5NDd/mt3Nl7hBnpQco+MNA41oITcTI4CwHdg/jdMTQFxA1K4TrEiUlQ1NHubfy23zh8pv59kVfo7t/F+PFLzOuFUfjmGNhCiEE65Z9k8/ecBUvOW0ZdqKC8QSVUpKHjy3ikcFFFNUMJZPECMOOuJfTu17OAwe/wZD9SXq7p7h+ZStTtXkMTbvoU138rh+hQ0XNT1I1Lm+467scKj3Nvw17PF5YRN+Js5iMhfBsImWQtodMCwI/YrYS4FT7ebLUQ3eyzHCinY8uaeVA8EFq0z0cO95DU8XGIAi1zQMHu/n45of5+pZV3PvQfHLGx7J9Ep4hb0XgWIStUO8wZPKNlzEM/PXTl9Mzv4QIJanhkKP32hTGm3js4V7qTa/i7CWG6lQbKsygEiAcG3xBXJfsuGMhB6ab+Nafn8R73/tVBvYXCd/fxqav5nnVQot73v1DPv/hm4iuqUFXzJvXbOaa7mO0O4YOy2FDpkyHIzjptHFM3iOYkSzKzOKJmIJM89mhU9mzezHHxoqUDmV51ZKneHn/M6RbayA0pupRCTMM7m2FRVku/9ABSqtcEltzOEI1HIdmXG5bvJK+5Xle/dZTedcrttJxQGPVFdge/ipDkpDkwkYpy2Vv3c2i/kHK9+Z4tfFxPl7hr994CtbtMX/z1TNxDNw4djJvW/A0X3v0ET7x4H2Nl/pYIwOFqRjikk/sh0R5h0d3d2MhmacLuKJxHZ+VneR1F27mLz5wRyOGGxs0GqNAOwIRwxhNeNJwwzXPgAthc8i6tiG+MHwuVtVmaLYJAUyXkiQNZIxExBFSSJRlKEsLvz1F6FoYaUgR4YiYxJRGxBE68IncmKg5QTgG3XqaXCmk3uMhI4FTAbukaRsGMVLG2A3dFWNDZCtE1cJ4NibtEbRbaFtgaUNpjcVje1ayc9t89hfa0GkHr+RgT0hkSdCnZuhPTBIJjygDUVag+h2c2YiWAz5Rq8fkNe1EzQIci6M/Wso1W47AZskHP/4aRKSxnrX+NVqjacQ7wmYP1Z4AaZBKEUowOombESRnBfZ4nTgtSB6N6Nga0Twc4ScapUgqaeOGdkPLRIJdUFhotGsjEw7CcaibEKFBhAYRxM+K+Wp8BCNLMgyvaWTqacBKQcq2cHccw3rmCHLfMI6wyR6QJGccgrzb+F9pgYwj2D+F5WUJViyi2DyPwO3ENCXxXQvfgUAptIqJXY32JNRriECBNASxIZisk909hbNzFrH/GNnvHyG/u8hDX3uc7335Qcrl5zv29S9sxYsMjjKsyTQj/YiDu44ydHCEQ9sGmRqd+akSckt6nNjx57SlT+dnEVnnUNCKwQimoo3P+1tzYiU3bLyfMzctxJ+XJE7A5a+ZcwyaY445/vuYC6TM8YIsXzuP3/2Tq3jfh68hl8v8/z2c/98QYgS5ooZaHnLq6hd/oZvj+SxdMx8TaXZvHyCKIm76yo/53lceZmjfKAe3DlCY+sUDD/8dCCGQUqKV/m+ztU+lPAoTsxzceZTbv/bQz2/wH7BcgQgchKe5/N4PAzDkC8raYzpOcsBuJYxAxZLWFyspE4KkJ5FaYtsRYk2ZV/U+SMV/ivvLLvcVYbr64HO7h6HCJCxOXDaPNZuW0bvw25x1wRDnvHMHzql1ItHDtfO/QJSCjB2Qd+p8ZudX+NyPHuX4eIF/uvexnxrChy46nw+cdzYb5v2nUrTWWynRjBXOEB82JKZjHn1wJSUVUdUxY5U7MWgEkLWXsHXHAA89dpDv3foUtTDCsiziMMa8gFP2+uv/jhPf/HfsOHScru521q+dRzIrSaVeeDpr676U7mXQubDKULyZVCaJ7dic39LGbr+NmpbsC9r45s6tzN6a4vi/SR7ath+ADU0X0mwFtFoB0kzxg0O3cMqtv8ees79L0hHYUcQnznodpy8+yPXLHmbVRZ/jtX/0PUpxluakIGvXkUKzqukIo+V/Y6Et6bdtzl64+bnxnXfCMj7yyku48z03gAVULYLAJYotdk0cYGNyiIxUZKwY17FI18pYwpAUPr1N5/HS+SfzPXUGG5sG6HBLrG0aa6xeK4hqMceKLtt2LWLr/qVcv/FDJEOLdAj5dV3QYqETNpX8FPVaSL0SMi/RzWyQYFFyiow1yEZxGaMzKdS4R6Zuc24CMH/FQLUT4UYIR9H6kgxPTK/m++OrOBYJvnPsJJp6C4jmgFxHkes3PQ5uYwXdPwR3rV5FoU2iLMF0LUdpdiEO7+HErj9k03VbWX/DAB3XjWFWtTZWxTGoQDNcm8cXfvcCjg/MY/ujR/mX27YSG0klSrPMWo8ygpFSE0HNxW5W3H58GdsK3Xxw2xloDBOBw2MzHVz70m20yUmsesiCmcN0FKv4ww5RMsWyV+yBKCYTOFzYdghHaE5ftZ+lV07y2R3n8oGnX8JFzhhXbXiaz+05F91p0AKiAx76eAqrNaKeTrB9aIr06f9CuhVOe8M+7Bi8bI2OdwvG+7Ps+U6e8KjkhGydrteXyS5MUuOrrO/3qBcyVCccNuaP869fWsvy7CT9TplFjkVvLuD9n3saKwnSAuM51Bd1EC5rxUtk+fTHL+Tc338nb/3km/nHN5zFP377bHY90wXAwWordqgBQyw0hBq7DDKAWeVQ1ZoHi32kjU9i2HD3e9exIXMYkTR86hsXcflnX8U//kMf5CBwJEHaZbpDUrxwHiLdhHAdRHMCRkqsah4iL+vMa5mm0C2RSuHUNbGnyCx1WJyaYM3SYWrzEoiKhFji1A0i0LTu0bgnTZA9cRK7bxovUMR5C6ZC8AWl+TbKtSDSBL0SEQmMbTFxII82BjGlcA4JRCmio71AflmIPtHBqYFd1xRPbaZ8Wo7yQpvyeg9/eYbpCzMY28HkE+w/5uGkoMebbmijKIMyBqEbQq1CGkQijepuQVou6BgyBh0BtYYQa7KqyQwrEmUBSRvHF5h8ksoSm7AWYKTBCMAWjRp7Zaj0uARJgTEaPAEZG4zC8Q3CsjBSEHQmiHvTOAu7CFe1E89vRixzkRvz2G1JZC6JGyrMjsOYsN7INMl4BDUftEFKQS4UJA/M4NYUMuMhowiam9AbegiW5tGtNrIWICKNLFawS1VIOOgFXZgFXZjWDCKTIVWJSA8E2EY8d47u33oIKX5yL9basHhtP1e89TzOf+1p3PDqs3nywd08eONmPvbnN9Pc1UwqmXhBLb6o+mWqY6uJ67c9b/sX972SO4+s5pt7LmTzw69iNM4Q6CybSy88V37i79+EG4Z4oz6l/dPs3Tf4gvvNMcdvDM+KSv9a/PyGM6eRMscLIoRg45nLf/6Ov+F0ZVbwmctnUQg2dp7y//dw/tdw+vmr+fHtWykX6tx585Pcc8szJJMOJ51yNemmJOn/hnKdXwZjTCNVWtMQ0TPmpxxeflmklOQyLnE1YsuD+3nTB6OfEkV9Mb509RW8+ZHvYQnFbNhwN7rwtNfw+OABhoo2U20eQmkEglW57hfsw7IE+USCar2CqiYw25JMdTTjiw7G4iaUkQz6U7RlYGRyhihSCAVL+hsifwejDPP7C9x820m8/IQd9LVP809PfYi92xdxrGkecdlw4soxKtU2YldTjv2fGsMJnZ2c0Nn5U9sDNUw1HqI9D5okUQvsLvbRbvUhGWOaIkcCw4xKMS48VuWSRCrGxDAxUqCtOUccKaJYPe//dNLbPkmkAUfwe5+/hXv+5p1oIVBOxOHCzAuep2QyScfKCj22Yqx2lKAeYjtJHhl5O9sLbTzj9TJca+KpvYOk6gZZUTxw937OO3kVy1pfxXhhEik8cskTOVZ/P3+wssgPxlfwxzd9n1T5Lla2LuI9/S9HA25Osr3YwdINR1knx3lwegn9qRIBt9HiWAhiqjpNq/X8B37Xc+jp66Zz6XHGdveSIGS6niBsho8eu5TzW/bykTXfBqCzuoHTvXsRsSasbmFJ97tY3/Ievjm4gTVNoxwqNzO/PMl4oYPkqOExfRxnJk8657G6fz5NyiIKFK98zTn86+duhlkBoYNORBSmylzRdT2DqTdi2ZrdpQEyxRJjkcKtJBiZcekUl3BK6p28b90yPn3gTEwuychMlnSzxXHbY1HC8KU1T/CRvz+RrVaE3Cd5/OpexP5p+n9sI6OY7Kd8jlwzH/uIBMdm+Zmv4NSzVrFn+G2cuXAWa+Es6dmQb+7OUg67ETVNVK4RLEpg+lxanppFS4kRgpm+JCvyCYZHr2BL6wL+aO9BhNRk3YCnCr089dgCPrLxB2ypSz5xzyXsG1yAtaDKmc2CoSN1nrh7DZ952yMcEUlOa3qYDZtKVF/2NJ/65J/xsUOXcHRrMwvyExw5NB/pWwSTNrWVvaxbdgb2Iz7lbIJUSZMZkGRuGMaShtjAoddGDQd1AX0rJomzNnJDiaF6B3F7yC1/t5C2zCzR9YKxXBPjo6tYNlakf2GKLfcpDj7cCztswnqOyh0hb7lpBzNWyGdG1lJZlKT/jdOM3N5DWLHQUuBUNSCxPJvMcEAlJZnpyrHjljRbHl1OcJOFKRtSojG/C9tqWNnmDcYWiPGAq7dfR5soE45FpJXCqtvc+uBpRMsNdiVEywSjC9eQvbCKmY4oHpmPiDXWaIxJC0CAhtiyuKd4AjXlsEhNErckYEIiLEmUMExU2xk+nGV2KIdJuA3hWyMgiMF1sLUmfgxyLwkwuQTFjT7enR6JGYO7s0TU7WLcGGwbWZDIWKDzCucegfFo2BFbElHUDBXytJ88i+WD5YORBi82GGlBohHUxjRE3qWt0Qb2bclidbiousTQOF8q62BHIToQSBWTiAQ1aSECjfFDqie2YYTAjkv0FKY5vKAHgyF9WIM2hAmwfI0VGPw1SWrlOt7RAN2TRFR8iBVxwiHKurgzIeSSKAlOoBBCg7RRmRSFi7oxmQSmDHEeZG+WMSugvj6PWWNwJuu0PlrAHZ0i2H0U0d2MzueQy7sQB6cwkUG4GulakLcw+RiVN4R+CZPpQXS1ozor1AemsHWMXfCJiz6iOYHJJEBbmGwSExsMBpXOYvIRvjDYLS0MVWq89HUfZTrdDBr+4JVnM3pkmo1nreSMM5ejtcYIRYxG1UJGJwus0voF5+PI38z+OrSJ7zM/eSUA05UjJOQAllB898/XUhh5HPG2K0j11EmSIoqPc2Dyo6TjuzCJl7GwvZGB8okPv4oP/Mm3mFqe5/Jvf4eVbTnueNf/3hLqOeaY49eDuYyUOeb4GUjhcO78T3PB/E8hxVxGyi/KGeetwvFsCGNu+dZDTI7PEsUxy9cvYPmJS8hm/2cDKdAIfHhJFzfh/j8HUf6d6959GSeduZRLrjv1Fw6iAJw3fw2dzbN0NJWItM3X9zzBqaedyHsvupnViy/mgnMcVNkhrtnkEy9c2hPHGs9UQRqyXpVsyzSXLx4jZfXSYlWY705TCKsA3L3jAJEn0QnJooXtAHS5K9getqL3Oezf1ct4Kc/A/jxXn7mN4zrHSDrLxdmD5NIDuEFMsr38C38+19lAZDyUJdlZmY9Ku8QJGy2KhELTZBtKxkLImGW5l9I3rx3ha0RNcfjwBFpr6rWAOIxpCATAEzsPoAKDFRmIDW9/SSPlW/o2Udliduan6+z/nW3RKg7FHrce7GNqZJYwjOi2Zrm44zDf2H4KDxxezmxBsCCdxS2FlIamn2vb2fQ7tOffCsAbWo5xUXqay9sO4zghK9c9a5esXFxgwm/mWCXFmy96iN978hzSqQo5WWdNsswqt86+0OFHtddSCbYSxhPPG6MQgt+76AYuPPcQj5/3b3xu4320JQJ2HZvPPaOn4TiN4MuChasZ2LwBx9+Al3oLCJvXlt/OS6ZW8pqmz7JndAHTHRk6d4ZkjtR57O8fJHfXboKJQT644/V057M4RpDLp+lcXkP4IaYYEtcipkZniOs2Bypd7A160F6R+zO3savcyaGpDH+/9ySOVu7DoDmtbYi44kLCoNKSepcg6tIMTTrcdd9ilLiM8pZWKlGSyu1LWPqoiw3YynDCPVNcd+ajmIyLSUgeGRnn29u38vUvh3iiId1ydKKD2uEORCTQrkXUnUJ5BpO12XDVcqLmFFFaonNJhmSdgfoRjkSf44K+vbSny7x16eNkpEHqANKGA2E777nkPn7vtY/x5XO/hsoeAmOo7UqRaYrI9odk4pjRgRSeD4dqOxk52InjKo5Pt5E4GJEZCHFLilz+ehJtF5EZiciNaryZEG8koPLnzYyV0kwWs6TdCIsIP5Z02WXiBHifBvVQE3JRJ47RkEjwo6d7aEpU6W1eRm97lhPOfDt4aYjSRNMOphbiGIsbLrqSUsFlXOX5xPxneMXCAYw2WG4S1yoTFyqYch2hFK6vyIWCVCAorsgTdaXBjskeraNNjBEGFZbxghC7EuP4BreWIPOmAtW3CDru9knsC5EaWsbreBVNckBR8pP83cl3cds5/8a1K3aRmFAkpgUyYUMpRkuDCSKcpMsFzft5ZcdW9k12469qZXp1mvLiJHHKpvPa47Qt9VlyTkOzxy42xFnjdYJQ1KBSp3lrBEJQ9SXSlsxsNGhpSASazEAAWPhJC7sqkAHYY4qksVGeRT5TI+VWEHXNRC3FHV9dQMFPIX2NO+LT9P1J8g9Mk/7xKHJCIysW1kwMsxWk1rQsFwjXsGDeLEaCwCBcCxVL4pRNJIE4wqpH2IEi6EwhayADw9lnHKBsmhAajGWhLQ1+hFAKbSkkEoVAdSUJFjnI2RgrFijXwck4ZCOBjCXJsQBZrkPCQVkS7VmEXVkSvgujirg3Q31hE9oWqL4MRkpMWuJqG9GWRfR34CWSzzrr1BFhHWMUDT81g/Fj7LEQOSuQEzGJiia3t0R62wRM+cQn9hA3e0SOwcQx0VQVGQmE0vhCUO9OExpDuKoL//QFxCfOQzcl0HmXcEcJETZs6z/7vYcZH5lleqKxYCCl5B9veR/x/BxRS5Lb79jG8NFpXoin6ln+tbCKz4wUUapRDvz0+O/wTKmXvSPNTI/m0Ab8oRxj1RaieIJtw+fhRneRkREy+O5zfZ156kqu+u0ziToMuHB4YJo1f/Txnz+ZzTHHHHP8DOYyUuaYY47/dhzHYdXKPrY+sIeB7WMII8iv7P1vC2D8svz7cS3rv/f46zYtY93Xl/2X2r5vwev4i703o3yLT+/7IW9YdSoAv73+rUw+8iBab0FqQXcq94Lt/SBiqjCDjJoQCcHjH/oY8DG0ViStCI0kaRUAmBwp4bcKhIB1i+cBINU2Lk0bClffz8e/cwk7ts7jHe+4nduPnUS0M0W2rcrCU9/G6/u+y4MLp7n8pGPAX/xCn82yXFo7HufL219NadJFWRqVMwgSODTEq5ulpqP1JtqzJzYauRIdxgwMTCOlJJVJPNtXQ6Pphr++FYHBSMOC9gzXnL0egG6Zxym6YL/4usArlv6Amr+T+ZlOtHJwHJtQeHjUsGYgriWRjmbFyW2MVH0GqnWUUo0SI1Vi5+jVOLKLdprxmGGpO07GWvVc/ycsPMI9Q4+Q1G/gvO4ypXKSnq6AZXaVPreKAbQRSGCT+w32jX0BS7isn98oITLGYIzhgo0reeJoQKgt0ipi20wS3xbsmmp+zl47jDX33buRqUX3Eg19kPOX/CWZpEuPOp2u1lZO7TtMKUhy6nkBd30uz//H3nnGSVZVe/vZe59QuTqnmenJeYZJ5CHnJDkLioIRFXPWq4KKejGLWcGECggiSaIEyTBMYHLo6Z7OqaornrT3+6G4eOcFjPe+eu/bz+9XH+acHVadml6nzqq1/stMVAlKgpPcZ3hd5wY+52bAn8/IQJ6vfO1TXHTw59Ali7DsMbwn4Ndbvk3PjOmc3bmOSMFt49tpaJb4UYL99Uw2VvoZl1W6vAwo6Nyxk5Fps6m02ehiyJuPOICmFbPQnd1QNqRbCpx9VRMXzPjcS9fr+9+8nR+99yGiowEpuPfJzVjjJVK3d/LFCZ/M4glG2kPUgMYSAX5GMeOUftQMTf6xLBNWE3MOaGPjzlFkKNgZGa64YwNmzon85PjreHPnU0Qm5KJjd7PNF6Skz85qHaXQZVUiYuvuZqz2MsFagxqNce67T8fXMY648ClWNw2wczjN/M4hWqaPc+y0tezoS/Lgw6vQjmLhtHGmTfkpP73lo9ijPsooVp3xDOt+vArHtbC8HI2tHuXAoief4cjmPsIoAifElpK22/bgS4PVmGbxfquo2/d3LD1smGnes+xJPcm2TTtZfOAhrH20G+PYoCSh7ZFKKN5+yjHM/cUoCsE+i8ZI1AvGxxWOE1KYHqPQIkj2gKsVxhU4niEaKhFMsWnf+gKRM49gTxUnVuJDX7qDz555GIV9pyAycayCIYo0xgIiH1lU4Br8RsmyKbs4fvlmfvbUfmz3Uzw9vJCDOnr4mStelL4R2AWN9AO8mKG9eZzliV4soZlXGWCzmUJ1ZpogDAgbDNtNOyPBHtZ+rxWlNcIo8ANEj8vYYkH7wwHWMIzdaBOutjDlBNQFZPsiTMalon0c4TAx00ana/+nEs/5qECTrI9403l3oRzBlz51HG6vh0nGKCZi0F9EpxRCa9SIV5OaiQsQBlU1WIMVnFyBYdJMhAIzPYGnImJ+7W/Ur1fY/SW05yHQkPOQRfC0IPu0IMwonh5aRqAtdLymX4UCowwqgtbfjTN2RB1SKfy8wI0cTDrAlEOEtGhqcDE7S4RKoITELgIxC9OQpCo1ekoSIyFuGbyREGICQYjWNdFXAkFswEeEESblEq1uoVLUpNZ5sHMME4KUAjyBsOxaeWUhQktRK8exJSqCdElSHaxS6YgRNbtY3QWkEIj+YURLAyYmkQj0jAzGVSDBdwxBu42pBlx+3VounHsHZS04/PXns8sWHJOYx2PbtmJci9Wds3Bnt+ANF+juH+fG3zzJfocvfJnf3lWJs7vYUCtzzD3Hssb9Wd18CU8XfsjdX1+FCgRaGI4+eD73ljdQFoL7CrPZPzGCFCNMqH2Z/p/W+/AFx/OjT68nqoQ0PjKBKml+u+/jnHbmQX/VfW2SSf7H8K9SVvOvYMN/M5MZKZNMMsl/C/sdswCMBmUjLJul+8/+Z5v0L8W5C/dFmAjpRGSTe4vzbRgbgEyAiIU41ivHu4cLJUrFOMKHoPwnQWgpFaER+EYyUt4EQLmnRLonYPqIwnVrmTOP5FvYGEimzhhh3fc+itUa49/vP40df5hK2K75xiUzSMZOpN69hIsbRzhyylkvs8GLqmwrbCHULxcPFiJAl0GXBKEVESUg3nA3xkBkNBt3tFAX+1MwwolZhEKwZ6AmVJvOJkm/WAKmtSaKG8IYREZw+9Vvf2leo+ViTUB5/M93F0vEllLf1EJjaz1SSppTtTaYnz7mEdxZBZoWjHHYYUtpbM9Q35YlCGsCLTtHPomjtyPCR1HZz5No38jKWTuYNeXOvdY/btohNMcFrcojkyrzw+O+ybzGy6k9ZhoeK6xmlh0Rl1XAYJsqUVQrlwr8EK/iE0Wao1Z8ggvvP4l/611Ob6UThCBEs2W8lsHih4ZC0xhVmad7xwgvrN+OVwlxYhLLWsdHZj3NJxb8kTee/zAf/urZFGLgWALLeAQYWhoKRELQ0zuOEhGXfGhF7df2mEWobJ75ToZd3+zge5sO5Cd/OID1D8xD2xKqgkfW7GLMH6arlCQlysyztrNoOGR62whu3GeGPQhFj/HhIXq3RhjPUExbzE+t3utavekdp/CDm69AegZVASEt7McS+Lkqj97aweM/OoDd3zoApxKhvAg5UqVpQQFhGWYc3sujsR4WN7USCIhsg4mBqy1E0eHpwlQGI4feKEM1CGlQVRyhQUr2sXN0JBfz/YcPZ8PgfBA+gdZUBuNUdQqxs4F749O4vroff9ynGZ0dJRYLGVufIl7RpKIC585cQ2VMs2e4G2OBTEjOO2wTdp2PiAzW91vozjUwWkpx7eajecu1Z/KeT1/KIYs385q3P8WUE0awprWgGxqx2l9gn5OHkLYhXvcbToj/hjMWPUafsxb2txApi3x9iD0W4afiWEsyfHz69/jes7PoTxsmKhJTCfH3xDHYBNk0+f1SlJRGFgPsvE9qzONtl9zGqumryKYcau7E4rldM7CVg52PiG8rEuutEjmS2asrnPye3cRTQa10Iyf5/P73ccacHj523D087bWRsavcX55N2NeFAUwAOikxwmBsmD57iHvH5rKu1EJXvImGwWHsR4eIkj7JPQFz8mOc2roVvztBfGeZRE8VVRXEhzUkG4gSEmFZNG5WyIqFIEBlDRHgBwEyEyPKKqIOELoWXE12B9hFQ+AqnISpdZyKgXEshG0jpMA1EpN1EacoVMuLneS0RPoGpEaiCbVDJGIMFVvZGZ+KaYkxMdsgIo2xLSbaFbayEBqc4RBnsEr9iIdd9rFyIVGoQEAEmDAiTFsEtiA+FCCjEHfCoIwilrVf7JoVIUzNSwyOeoRhCJGuZY2EGmE0iJpuiqxo3D0hiT2azMYAeyLEyRlSfRGiBLPzIbHhKtLXRHMEZrUhdphCxpPI+jRB2iJfqWLyE5ixPLJrBDVQQQ2W8CNT06WyJELUrh8CrLxPaNXKstAGg8IpGowlifyaPpDREB+D1MYydj7kp8+uRCCIC0P27IAdXWXedcODXHHFb3jDV29k/19+FGM0Oq4I6h2e2fDKmiX7Nb2WDb0dbBpoZ+1YTVctmTmXhsffS2VbEwjIZB1mT23HIJkIY9xz9SI+dcGJvPOSN7O8/caXrbn1395Pa30cqxDCRIWexo/xjccO4+dPfunP3j8mmWSSSV6JyYyUSSaZ5L+Fsy4+kt//8kl6u8c58vTlXPLOE/7ZJv3LMaW+SAQoGRGGIdaLQZPtlUFEWSL+TJfmkbCK11ArU4q7vJSxANDv1xMaC0PtQf2F/mFUweBk/+Tyf7npMK5XFfqH0rwwG1q7BfYWjQk1MuNwSMcbKJcqzF68ivqWGylbu4jKW2lM/CkD5ze7P0lc388G+1TOmP5ve9kX6YCOpnH8ljgmafGTM65jcOQantvwJq75soWyFOcc/adyqGTcoVQKGB3bO6gEsO8l/47UtYeNRGbv+H/FDfFsTRe5v+qaA4TBBhr9X9IQm8Xyzns4e2EtEOQom/bGFhJxm5hb64qRjR3A4Isp4sLkEMJ9aZ1c/vOUyzfS2Ph9pJzC7iDOdLdKXiew7RhKXkhQvBYlYkxLPElX4HNb73Iu6VyHEgopfCBGGNQCKbZrc9DcOczfcQT3VB4k5RYolOsQAhY01LRoOtoyjKzNcmv3MvRWRXNujKRToLmjESF2kZARlUizLYjTcugohRPr8HdFxPaBODbNra0YDM/veJzzHr2XlmoZa3od4ZCNkQrLtkgNhlAW9EdZ7HGbnu5pxEsjNCYqjPYqeu+XjOyOcUjnkyQ6JIdNOYddia1s+DfNqBQEporthYBkT3OKlU1Hv+wzWLxoKlLomlAoAiKDIyVoKPTlufjkJ6g6KX528wraUhEHZbfT5TUz3QzzaP1MhvYM0TGq6ZltAYbQNRgZcWT2aJ73f48tfPpFgcEggy00jRR4/ePnsruY5McHN7Am8Vuejjrp2dUOUkK+zNYdTegDHHZP1NGW9hliGl8cnkrdcwKkpLV+hO6uBOOFaWztrRLEFU2tdfR4Wc666H5+9Z2TMcZQ2uAStVugJeP7GBp22pyy+nmmNhf41fdWIeo1r3/PPex/4CCugHwxzuy6CInAsQTXfOxebsy18lTvUry32zhlCylCzjn7eZoaU9yxdRl6hiYMbWTVwwhJfI+HE0iMG2G1S+Kto0QPp2uaHMOK/U5YQO/Pnmc8lERhjN//2xJUVCLul2G8llkSxuK89TOP0pwsUamPcf+1i0h0ejQqsAU4WLSJEvcNzGdOaoQwkUHGgJjAYCGqwJhHV7yOh+5fSLKhiCpENEUOmUKJyj0+UUOM/qFO3vXIceieDI5TpaB9HCuF5RtiBagm4yRCgbBdUveOUjy2Ae1b5FZEJByfcGtAckwQ78tihYZyMyhX4ikoVy2+8uWTCWWEHIvIzYxjC4kY9xCEGCWJOm3cAwXl+xQypKYR42uUsjCRxmgQ+ynsHOglVWRewRZQnkYkYoQtMchXiIpFRFxgFSNM0cNP1jKBDCAsgTVYJLvHw/IlKuswfX4LTxUrGAeIQEiJ0QqjAxABQruMNVnUDWmMa4EC4UWYMEKFIaYeJIrQ90gYSd4YRBChKgIdGtJ7iphyiFcuEex2sTa5RCWbMAoRUYjfoMjt0wKbxrEHNaIaIQJRC2hOlBEtjUReWAuOuAqIsCyHWDyOsSRa2TXhXCXRa3YSLO3EODXRXOkZMBrhw5HRdvoDyVOlRiq/jeMgwBYICfYEjFctlrU28vzuAVA2gYZtO3qZO3vKXj5iXmYuYTWGjgyFSu1m+NnP3crdtzyLVAqM4fyLDsG1Q6So4KqQ6ByB/IygOFHlqNWf4sHHPv0y3/PY+67g2uBWrnv0KVQ85KvXHUu8v8rnGr/EzJYMt39uUjtlkv/hTGak/D9jMpAyySST/LcgpeQHd330v0TY9X8ry7JtbCztISZCbtz1S/ZrgNkNF1Do0VgFG6fp1ZMG7YSk3Gm44tCHEMKQK5doSNXy3OtUGd9YtZ8KgVLFw69XNE+pe2n+eUvuY834NM6d1w98hhE3QEwIwozFomU1vZFEMs68ZbO4ffet/HrtbRQDh+sP/CyZWE2wdpl1F0VdJslNwN6BFEu5XH3zayBea2/amhnHYAhjz1BpPpTQ2fv9pOsTDI4UGSuU+dqtt7N5ey+fuuQ8yqUSgaz9OqoCw/1feute85LZAlFrmcCNCCIPW7n8OYwJKE58F8tUIdpFrnAL9ZkzXzo/f9be4rmt2QswppdqMEQ2cfJe5zzvj2hTIAg2sG78C/yod38ObdzFw6OzOG42SNWElb2Gwdw1NMhhQmE4un0DMedkGurfhJC1si0hBOI/dbr4wgmv4WpzCtev/yULGjs4cMrhL51rbM0Q2hbbxtqoHy7TUxjEUTaZpgwx92TWVr/Pdh3giBA10k/7+VXKYYxbxH68VkzQ2b4GrbL4VZtrFt1Nt6zjOucwqs0lRDGDiTT5dkFTU8j7lt/L1zJHsapuJ+uvbaZQjSMLmvKopDRgE7bazNq3wCmrPoRlK07t+QjUjyMbU+iihVQel7/m2Ff9LNLZCuNDKYwlCOIebjaFHhjjgNU9nPTGzQAE7jKiXCfHJ/vIxwe5fvcyGhvLPJTZzML7s7x5v4M47swdTBS+RWPDV/C1gx76MQqDEYYldokJE+BrRVc1g0pEfHxNnuTNK/jud27mde84H/wI0axQdo72ri62ZRroGmzCiXlMuXWMyI+hVMSUqRM8+0Qjvbk0g2onqm0q+WREnANZuN8d/OZ7VXTVofNe6H17rbV5UFV4dZIvnbsKlKRYX6W5XKZuVpEpdgRCU5/VFI0BDb4xOEIyrNs4Pr6B206azeBdHeD7/Ow3x7DssN2M5+LcN7YAOd+gxwSqEhCrGnR/D1G7zfTmAVZclueuPxyIVoptj3dw4Fu3M+WuOD3bJyASGFvypo9v5uiTu7jgjDMJQgvsBHfvWsAhs3bybM80jASZMnxqy2r2S/bzlWcOItVUZmhdK1viM9HHVAm3CBSghQBtiK/tpbdjClagKOdSNN+eo6SLTKxohP4KSkJidxHfa0AoMNqQO8Aiu8MjMoZqxcLOxgiHK6h8hfSOKsXFE4hpjURxTaUcJzE8BoMBqXQGMOiiT67eo7pPI245ZDweI2qIaEgUiDV4NK/O0d5Q4ZlblmB8SeBBOBiHmI+RBi3B8iMiIoL2NKJ3gqZ4yJ52B2I2Mh5AaIgXNc6oRjcnIB4R1CcJxz1UUCWIaapG4EQhxpGockB8p09Qi/MRlkN2PPgCVrtLddF0lAb8Cjoj0WMKLAuTdDBxi2hgFL/BxgQGqwRIjY1Ej1QhoclPkZRbbKJkQDwhMZZERiFzVm1mydFd/PS783D3eFRvSeG2xBGWpNpgExv36dgFEwkbXI+shHDMB98gdQS7+rA62/DLJQI3hU7ahGlVK/eKOZi6JAYJpSp2Qz1ePo/wGl8M4Bu8qoepS3DDo6u49+Z9wdeQlhTqwd08hh4fQVYrzPhdSF9ngaw2VBJZpG3z1W/dz7e+/Lq9/IOlFPHQpeyFVKq1DMH7719fy9CRBitm89rXHsqWia10ZsaQQjNqJfEEGCkIhOHwQ/6N97zvRE4/48C91n77h09n4moPhwdx84aoPo6T8xjd2c97P3sDX/7YBa/qtyaZZJJJ/oPJ0p5JJpnkv5XJIMqr88ZZF6GEwUfxg21Pcf63dnH3lh9hciB9Sad+ZX0UgBlZwcL5PZw7cz3nztiA5k/p0SXtUtY2E2GtQ1F1pIJd0iyf/qcOQDuqbRzWuh3HrgVbctMNpWkWxWmSoxZn8P1dTPTNIN8/iydGH2dR3QD7NXUz4v0pY+TO3FQ+130sPxh8eYcvl5CClURUa2np8dj1NKTfRVd0OH47JGfu3QWovjmNkYKCV+YXDz7NsxsG+OlT93HaJ29AKIERYGxJMrm3UPG+rXlWze7i0FlbKPl/vq3lTdvP4PmeWTw88ih5rRkNIwZyHwYgijRetVZe83/TXZ3GG5+rctYf3rfX8caGb9NQdw3JxAUYtRihIn7Vt4IJEydXqZUonfbI3fxmT5y0VNQri6mWT3/5DrYPvIZHd87C6OKLeroGo//0680zwzeyMbqZh8a/tteeUgrEnhA5kCA2rujZMsTgnjGcmKJv9PM870MucEjFLuOGNUl29rcwlMtwfOZEYIK5nTnQEVQFxcEEbQ0V4jGfVCKkfeoAzc39NE0ZZefuVn7du5RDOiv49ZK6phIpytTLxZzytp2c+Z4dHHDROJnFFn3V9Wxd10PkQdjcROinQGqaF4zxjv2OeNXP4/1n7EFYIIVANaQZmwKJRXX0DTfXromBittJIj2F2392CgNrz+bR0mwun/oQP1h6Ix/+ynV0TA/I5T+B0H0MjVxG3J6HI2qdTmZIaLQU2thsGr8ElCEs2wwVA6oxh452gzVNEWVcfNeiL1jA3Y8cSW5zM1FXktImhdhpodDM37eP1aetoLF1OcV+Q2xc49drAhVS7b+EFumzeN+daB0xMFXh71SIHQ6JZ2wiEUDoIAJorcbwJwpc87bj8I0hMDDUG+f1x53ON742k8BY/DY3jVYrh19W2HaKptU+B12wh2KDy5uvuYlVq0JKvo26sIS6vIAjAkwQUE1ZJEujDK1JERuLcBur6JjF9mc6yXd3sOzgRShTKxvBdmmeWiEMJbo+hk7FMZkkt167gg985jwGH22i6kJuS4oHXpjN15/fn6lzxii/kCLRJYhvVmhqXXA0QGBAR8QSCeyekHhXBWdXBFIh7Di6GqGnZQhcC8mL9wUJhWaJbkgj8xF2OcI1glK7z/jiCmZwhGQQUX9HGTUREK/z8ZMhaiSEVBI7H6JKEemn+qjb7NfKxCoKe9AQ6/c5+sItnHL6FubYE6Slh5CABamMpv7gCYop+6W/PR1ziCo+cqSEbQJed+wTNLt5wq4Ewa4UYRwIo5o2ihEIx0L6GuW4mLiNbo6Tcm3s0YBEd4WmJ3LEAokbOrUObFWDb2UpnpDGCUpkXsiTGgpI94dIITGurHV50oqx5Sl0UhElJdW0wmuOk5vlIBBgDPHBCjM6NyMrAm1qGSEmF3HZmU+zZNkQx560mTwaE0aIMALfRwUChEAHVVKBTf0ChXWaIL6vRgQGjIDAR3f1YuXLGB0QZiRBNg6RBqMxSiGjEFksEeVGKRyepdpuIFci8jycrnFSm8YJxycYnCmJJKQmQhr2+KS2DhLbWaXloRBGDP7aQdgwRHzrHkQQkXZfWaw9FbiosmR4vMyGDd1EVQ1KMXtuEzf88p0APDt6LyARRlFvFzn6G2WkK5FaYny45gt386Mf3veytT/84fO4dP/HOf/MZRgvID5UJdaVY+3PnqqVWE0yySST/AUmAymTTDLJJP8k5tXPYKSSYLCUYkbdKGF3jIfW2pTbPLw2n9mNja86N+UkOG7Rep4ptfDH8TYak3/SGwm1g68typFk3dYeSirCKEND+58CM2+f92V+P7IPOyorawemBFSW+IRzKrxl3tvpG/s8EoOF5uIOQzlMUw47mZWd8dIa28ut7FvXRdwOKHsehcqfOvuUgj50BoKpEUG7oaP1KNLZD/PeI9/Jj97dwO3v31tzpaUhhVZQ9QyzDtKkloe0phMYp/asI4C117//ZddhSf0JXDR9B2dNGSET6wQgKHyNysBKIu+xvcYKsxsQGKXJaagCNmWqlQpGa6IgxOiXB1Ju693KOZ3PccbUZ/ld949eOm5Z00gkTkUIh4PbPsGJLRnqYhWSyqe7+GOe6N9K0g15utLOSJBkezXDZq8VW0UYoNny0NU/IKSoZaX8pzvyPX03UWdKPJer28uW4d4R9HMDND1aJlVUDPfn6Nk9ipOM0R214RmLinFpcJKY2BMsiw8yLcrxvv0Px45fQFtriaxbwkSCTb2NuHUuxgkJhUIqg45sjjp0M/9+7C18ctlcTpk6TMW4xGb44Go27VzMB+65nKs2X8Z3B4/iqdIyOuKz+eW370MXK0hXIQKBsCw+9vG9s4f+b07Z/0qsZAGhI9ACUZdlbE4TX/r1v/OV71/J6z5zBX1DGQbGy6xdN5ftW1azPD2dm7ftwzQ3ojPr0db5FgQNALjuaqIoSauSdNqaIoqqNlT67+f8FVcitA2BJIpL+g63ec/1h3PRhQ9RmBEnyFiMFcvIqsAeVMR7NanHK2gfZCzi/e/ewFEH3cS6ui2kOg377DvCBRc/xeKZz5POPsOzlQaOuHg9ibYC0WwX6iwiRxE2Khr87djTPWjx0bkq0hekKiX6fMlAIIhnA75+9520pi3eeehy0rpApzNCTzVNUVjYMzxOOHsLd333Bxx43FPcX4oxXonTX8wydVkZdVAR4ypiKk21Wkeqs8C62xeibYmfdig6DVz58U2c+sYjOezQGYgXOxV/8YuH8emrjiYvHKKkBWjc/grOSIRV8iHrElsY4HREUCdIJqrEqmDnglqL3gmFdmqlLIQaezxAugncdTmswQKqEGLiFrgKK6DWQSZrkauXVF0YqxdUVjbjbC7hKBslbRzPY/+ObaRNSK4tRtWRxEYDMo8NQlri7C5h+2Bj41QCVMnH8iVCaexRn9SeiGRjFdGkqPgO69ZNxVQj6qpldFh747lSHbYrKR3dTKRqXWx00kGkBfFKEcvT7Fyf5pSOdciSRpYEYdIhKvuIUhW8ABOGJPMKOeGh4xZBKAgTEq0kJqq1wPZSivFWh7IrIYywhE3Trw2ZZ6qIaoQRFkYqFBDqACTouEBZLqGla+LaCVXLUmmMU5jj1uKLMQvPaWG/VZtwikWcXETrc0WeemIasSbN0cfm+MCXnkMoi2hwGD0yjlUOMHUZSMQhHqOaaie1OIHtpDANcQIJAomINFR9YkcENO8/Cp0SwhDKVegdwuzuwfSM4NUpTMYhSihY34va0oP0K4Rj47SvH6fl9134fSOU8WFoCFOoIkxEybaJ7FqajsAgHcWSpVM4+MC5r+gjpA9Kh0zkK1z300cQ2mBb8JWvvZGWliwAhzQupM3JsyDZR7NbJHIHue/+jxJLKIxd06G57jsP8/VrbnvFPT561mls+PlHOfLIRQjHIqhL8+Y3fu/P+q5JJvmXRv8Lvf6XMxlImWSSSSb5J3Jy2waOaN3G0kwv9dPyDFoKu6WK1Vpl1aIprzovaTeRH02xx4tz9R1H7ZX5ExgXX1tUtcXN617ATyi8jGLF0mkvjWlNzuE7B/yUK5dfC8A3DluA4wQsbiwipaQpfRk7/TjbvCTTs6dz5bJfc9Xy7+5lg4XPw/2z2TLayK7eORzytS9x4Q9rGR4ffuJbxBtK6ISmrnXspTk9fSV2Pbccx+z9xXlKex1EEHmafz/sPdx3xaf47E82E1oCIwXzpmRf8To4qp6jG47lAHcQ7a8FQHt/BFNBB+v2GtsXXsa9uQX8dvgQmmSaRilpVxZP9S9BKolyLKR6+W3xwplnMc8d4YDEHuzwq69ohxCSM6e9g3Nbn+as1mdotuv40a5ryVdjBAKeqH6ffl3H46VaVpABPK2Q8aPZ0T3A7+5/Bt8LXlrvxLqnOaVxIx1Wfq99tq/rwozkiW8fI6YUUimUBZl0AiFKrEj2cGBqJ1NiixjZXEfXbTMoPF6HZVmkG64i29lF05TFmFgMVXk/Sxe+wIyGQ5hmzaM1eyBVzya/M0UUafpKv8AKnqJYcbBdwLMZmFjHqac+Tqa9yM6og8vrn+GPG47j8Qc2EghJJCQaQ2trhqVz933Fa/UfKKudt7/pSYTtgQCJwPM0J33ieuasmI9OZ6maEuX8evxylfxEma+uvJSu56ZS9qEkNXVuSEqOk1AdNGS/R6VQYUfRIacN670sod+I8UO01ux640fAgIgJRELyoDWTnzqLEc1ljIpwEkXSiQIN630yWzzqhyVhLM68RUNMmRayuTtBvE5ywbWb+chV67lsyRaOnPcsmfTVBNh4KsZ5lz4CnkaMKsKUwct6XPb+jbz2m2sxx7RSaU1gwohIhThWhJSGsVhIQQccePFOjnvPTo5rynNAssLanmlEo4qpTX3MaiyRkYKLj9xA4sGI1c07OK1zLUHoMnZQLZtLVD3SCwLOu2I7PYP1VIMExq11/tFxh9OW/RtbNw0iMRjPpzykWf94Gl2sUJphyK9STLwphtOVxxkq4tkhekihKpoGu4BTCPAbQqj6mIqPKhuMZwgSmoY/DpPeHSCMxPYi5ESIBiJbYZREhQKFRlR9RHOcwsI0emEzIoxofaEWcImyMRzbZfCpJHVBiDkrQW6uQkaSzBabzA80bXuGSVtFoqYQzxWEUhM0Jig2W8QmNE2pMfJ2Bq+Y4c7bVrF5y0weeHh/pIhqArQaSkUbU7VQjgS3lhlTE7O2iUwc47jcfeUCbvrAUkIR4tcbjCsJPI8w1KhSgEIhtMEdDwn8EKc+jk5ITMbCmyIIlsYYPsmmeqhCtzsYJdCOxCQshFJI20b6BulHoDXSEbVMISmQQhHt8gmtCPNipzkRilqZrKplgIw+ZnjhrmYSjkYqjdJw/TcPIOtKGuaEtE4r84a3PMKBB2+nkAioZiRCyNrnkU0SqSS9v22jtDkOdRn8xR2EL95DpIAzjnye82c+SUvLKNoo8DWyGmGKARiDMx4gcxHWWIjUGrummIvUBl3wsIoeqeEc4oUevIEJTGTAtXHmTyVYPhuvuR7RkmXViSvxQs39D2+kUHi5YPhIqUQYKbYUNrB2bQ/akTROqaOuLokxhrX9r2Nw4sssS1dodopMjeUYL0ps2+ae+z/BrDmNKK2QBn796+e49E3ffdke/8FnP30hmTktaMdh2+5x7rjr2T/rvyaZZJJJJjVSJplkkkn+iTTaVeJWiEPAW8+6nStfSGBhKPuSc5fs86rzhBCs33EUO7oKfP/Ne9dzB6QZ8X2U8dizp49qu8JImD+t7VXXO3HW2Zw460//TiX3Z7FzFxCh7DmvOGfNYAsrpg4T+SGvue7tWBY8NR7j6F9dzX5zhvlMxya2VluY5ky8NOd17/8hYQkefG4b137qwpeOz5vTVgvtG8GaDV08td1gKY0JILIlN3/2za9qe+g/jjFVonAjlrs/Tv3XiPwnUbET9xr3zoVvB2odf0rFIYbHVjJGiE0tI8Sy1EuCvf+ZRQ2LKE9UeLbYgeTVxdPS8fmc1P4+it4jNKdex5MPfp6o3yU/J8sF7z6EX2+dyWsaHqQ/THD4jA0oGWOsOsa77r4GOVbhsd2buPYDNfueLnbSGRunx2vC9zcyMXYpkTwUqQ9G1sWxlMMJZ+xPS10SoST7H76QgdJCtowJQNKRmkdpwMU+ZpzWlhxdI0PMaGoBoKGhnt078+TzNQHHSMJ4vkTr1DSJqBFvfYoz37wZ32jWVHw6Ng3Su6OeOU4re7yd5KIERy5fz82PrODaXctZc7WFKXnodKymq6HhkkuPetXr9J85ecbVjL/9Sh75VRPdvbXgWjgRcfvDL3Di6iTH7/tRbBHx+Q8cz8BQHePjRc7qXMqNDy3m4mPXAwaNwZgBqsUNTIw18ez4VBLCoxq20v37s4nF1tDedirPFOaQqp9H0NWI8KF1hkvMCfnFpT/mzl2z6P9DnF3jLQzqNDIXoHyBkZIXdB2fvX4q69bOwmtWxNI1jaKiVnQYj/7xONe+sJrvHnMLsaUhTTN/zjc/sAr/+TjIgKmHzeNtDyxET5G4xyn2Gbd4rtmjrO9HChgJ47TKIhVjc9ApZbSpPdBad7QR5Cy2Gk3qrI0MhSFPF6eR2Cb50JQnsGSE3eTwRDzLTxbOZ/y5esLtca67eTXG8jDDNjoWIqQCS1A2AStPfoTbf7wAtINQAhUaUgNlht+apFpyESqkaV6OoZ1Z0k6ZVGeA2xKSjap8ZMEjvPOJs8gekuPsY17gZxv2YVMhibOhgPANwjUQBWCBDDXOQJUoG0chsUIIxotYQwUiBUyrB+VgP19ibKYgEclaENO3yJcVybQk2RQxfHgLmYE8JhbHrmhkt2Z4SpLwwDTGNqiHqzTkLOyiZGiWz66GGF4yRJQ0rrYwCoSGjX3ttUCFEhgLxkZcwiRECQF+LfPB2ApkSNCUJKpzMWNFhKexJwRRrNbCWEkQQQSWREQGZUkSZU1FGkRokMWARCCRpQCzygYMzYcPMjHWQCgUQTVEaRBaYFU1oQQIEdJFjhQxzZmanZ0uqd6AoKHWPtnWGl9pZNcoyVwZA8iWLNV0DOIWuLUg5KUXvYaz3rqeww7YReXoCp3HdnP71fthlxN40iIYLhFLxDFKYYbHIFFXa52MQC+ehukew64U2T/bTVQwdMTa2RwqqpYBW6GqkHRtjOWiQokwAjG3E4ZCZEORIPAJR4tkJiKM1ijgoFMGOfriXm780lx2djmQTWKmNFHxfI4992A+ddVvCTWcc8GXue7bb6WhNYPj2GhjEPEIE0lisXGKhTqEEBxxcK2UtBr2MVh9GknEBWmwhOFrXXPo6215yb9cf/07edfbf8jaZ3eDJdmydZDDjryK3/7mndTXvzw4/4nPnMV73vNLig2Sr975GCefuOqv8mOTTDLJ/59MZqRMMskkk/wTaXbmU69KJGSAJ+MYu4RyAzLpKknnzwun/vqS9/Hspz7Fyo69NUoinWVFopt3TXmC7sJATcuAv12rRtkzXzWIAnDh9KMo+RbYFlpr2lYOs3xlF7vzJWI6pD+M02DnOb71Ay/NKWvQEp7q6d1rrUXzp6KVJLIkdz+5iY/84g9oUdNGyf6Z7kUAyYbvEs9+Git2GgBCtWPFT9+rw87L5qRayCTfDsZlefZPgaiqN8CavjeQr2zfa3x/eCBf2Hosn1p3Mr/Yduurrts18TV2lu5j49CpiM0CZ8hDdNfscCvv4mu7juWeXasYH6+tccOTjzO6ppmRLW1sLr3w0jq/GjiQa3YfRWt8BcNjV+DqXuzglygn4O0fPZOrr3sLb7niJM685AjOuPgwZLSW9sRqFjR+luUtP8aPQoIFHk31E+x4fhqXP3T1S2vPmtpESkqEF1Iqe2zaOsjwUIFi0UNpzZ6uUba8kCImJI4SnHHUViZ6p7Nte55ib5ydu9P0lbJYScGQJTli+RjCGKyGJFJIMk0Jjj9lxZ//0F6kI72cc2a8kZNev4UVBz+NqkLgQiHqZt7cL+HKACk0h521jc2DPfx240+ZGW/m+gcPZcNYmlyk0QYKHjzbfTZbt53NajXBmJ9l7UQL2zZX2LLhAcKixZORw89X386Xj7wJ01hi3Cpyw6qrGS3ZPJ+bhkpG1Dt5Skkfq+ijKj4yCmloKiKkptEuc8TsFzg6lWPMS/FMYRlCK348fhTudEE1UMQRPLGphcqFcYwT4v1bhs/2N+DFI6KUxmvQTJ/dyj5HbeWhsdn0+Vl+vnN/CtplIIqRdWBdMcbBN72ZdZcoej8YsLBhAjfzQ8rBg9x24QF0pPZgyZr4piRin+QEl52/DqECIlxkL0yMpbGQ2H1VjCPR0sKf10TbskGCWBaUJNIG4Xng+SwMe0g0Fpk2bZDph+TJzo/IeBGN5QqtiTzT46MQChqSEacetZlDZ+7i7AWbUBWNnpKitKQe7ZhaCYhRYCROKAhDTRivlVhEIWBZiJgNUiC6q4j2GDRZRIFBliNEBEHDdPKVBqpPtuB02wwvSlFtKCKSY4zOFJQXOFSVC3FN+XSn1i7YC4mMwW+zEDGfqBVkACoENDy3fjrVmCHEoLXNjp0SPT1C1lUgFiKolUGJ+R7evnGiFoeW1VXcvMHNGeycRqYTIGp/N6Ia1fb9j6ywgSJ4EU41QkaaMGnhjCuWtPUy6LRgHx9CXQJVVyVYDNqK0J6GSoA2EhNFuF1FNCGRBBIu0jPISogqhcR2VZHdARKJlAIpBdqx0baNTgtKJxjMmT6lYobrv3U4Q71pUvUhVsxGtDYghYMwEm9ZBmEpLCGgNY2f1FQaFEZIhCUIFrcTHTaHJW6ZhTEfNxthLIMVGtyiJmsMVqSx/Ai7FKFC0EojqiHSOMRMEhLZWuaLEFgxm2Pf2cesZQWOfEs/djFATFQRhMiEy7998FcUylXM+ATF5/dwyVGf5nufv5l8rogUgsOnNzOtYYzhZxrQrkDb8Lo3HAFA3J5CrtxKOXCQohbglr0OdZT38jFfv/ZSPnvN+QgpIARdDTntlC+zddvLNbVWrZzNca9ZSpgyTGzPccGHv/9X+bFJJvlXQhjzL/P6385kIGWSSSaZ5J/I0VOupKItKsZGiBCEIdSCfyRhcGFqCUk7RADGE4QZQzL9X5+AePnyU/nCsm/y2UXf4/mPvJVjO7dwUNsugmqSGzYu48HheUyrjlCfnArAY92PMz5Xku8UFObtLeaXSsUIEgIdEzy6vQdRijCeBiF44pr3/lk7DIbx/JUMDx+D1uU/O/Y/09D4UeZP30G88XNALctnQ/+RtJp72Tp4Mr/f9uGXxnZk3oi/Nk389ym+cuNzhFHplW158XtDoWBI90XE9vicMKMW6FrZPAdxV4ZL5j3KSPl95AbPoa84xOFv2EDrYaNoGaNarXWrueOIK/nB/h/jW/u/lYEwQ04L9kSKzD53smjlLFbuN/elci5dfYhw7LVEo6dR9LcihE2o8yxf3Ev3U1NY1b6b5LD/ko1zZjRRF6tpPnzl2VMYObBA2Jjmqe5x8lJTEincjMdQFDHhCXIDLlJBodElTwfja5LYjsMRKzZz8ZQXeP27thHaCiKB8CMO3O+V9Q5ejZI2TLHGOefYjVxxyWzSsQo/et+PWZwZottrYDjI8P2vLoHCCF//zRBfvfMBGvrgsmfP4YVKO/nIYcg4NIsIuwDJasSl9TuoGoMCNq1t4be3zGZVuoeMXWLB1CE+dtAfqWqoq6vj24UD2dDdzMZSCzufTMPgGDIIELZG1edwSyFmwqXeKXDJiQOkLc3ChMeC+Eks6XyYvBcjX44xPRYQl5L37L+ZJbOGef8XTuLm43/Fz1fcxEHzd0HRRkewZ2Kc5a293Ll5Me++7wIe2Tmfe26cxdJYkbSqMtXysRoClk7p46NL7mbV2zZSmLiEh2/fgooUF3/5Kfr9OC6KYkVwzw1trHm4mfYDejAJh0ohS4Pp4cxP/56lB69Dh5rRfRSlVpdvfux0pAGddSHm4M7WqKyg+/ezOCe+lpMqW1m/fSlBRWEqginTxmh2CzzXN42P//J01nZN45eP7sedmxbxqz+uQkiFkALbh9C1MNqHMMSYCOPYlLI1EVpVCTC+IZqSwbTWI9Ix0mWNVQ3445few7yZRQKr1v0ncgSyKhDFABWBE3cYXNxO1wUzKJw6l3BxE3ZdH42zBglLNq/7yEPIKMIZ0ahhhSw7IMEyCipgtACRQESCIGUwNrBPByJWJZWcQKU9RKQprbQYuqiB/EyH0tTaK+ZJVEWjAoNJxxC2Q4QidCVhTIJtQyJOsipI29Vai2Qvwm4u4GjYMtBBcSDDSG89ohKRLFqIkovUEoxBRhrLD6DsIyODKEVIIZFCUtIadyzAHiqTGAuoixQi0hh0rUzItQhjgsC28EaS5BY7CGVhEHziqlP40Ecu5sPvPQexZgBtAkwUghZUEgbp+MyZ149okOiYoNqpEIHGHi+RCCrUK4fmpM2cxSXEoUkmVmcodyhCAUYIQGH3V5A5D6cKMl9hIiwihSGlFLIhiWhrxGqqI0rGGDNJBpw0YczBRKbWYjqKkEFIPFfCGS1ih+DlfX77tft471lfpOh55DPrqOsoUBAWaJg1p5l0OvaS72ge+Xd+/aPT+cmdB3Jv/1Qe2zCTWOzlvvmQQxZxxx1XIG0QBtCCSy/+ER/72C9eNvZj73sNieGIyLXZumuMQuGVff0kk0wyyWQgZZJJJpnkn0g61k5C+tSpCmVsFjb3UxcrcUb7X/eL/itxwfTX0VNN8kRxGvaAIL5HcJDT+pcn/h20JNI0JJLUJRtx5RSMsXCayvhBI13jjRzcXkWqmjZLS3wqprlCZabm9UckX7aW12yI4hK/UyOEwK4YvvWWE1827v/GGI0xIUaXMObvUzcTQiClpKQlAihpixe8x8mN175or2o5ntQuiVsEuT5O4D3+iuss73iQpU1fYPOzH0IrhWxyedfZtfbF9Y0Sq7NErfgGdLiVps5NzGocZr8l3XzqnN+yJX83AHHLYUa6GYD5TV/nuUqWrdU6MtW5NLTV7bVn5D8JxsfoMfrzP+eR3svJxqbzoQXHs3zuCG8+5H7edezdFLzhmh3NGWzXplds476hRTBeK3fQOqK3wSaXzfK6j76W7qKg67l2bvvM/kwNunFWWFRb00TFRg7tP4nLwqfotMtUQs3U/WYSBRqk4NyLDvqbrn1nchFTnTxtdoHTD5vN4UtngAFLCX78nSO5/fbXImYpvJxLC2VilQgZesytG0dXJG95djWhEdhOSDpTxXY94jIgZiK0MVTKgmfXHM7GUhMScBQ8V2jDq9jsGNpNqC2W7t9Hz54MhSHIbK0S5otQFyKnlBETHoXRFL6EiWrta5MAKvIndOnVfGDmfYxubXopiOZVJd8+8Eba2tZSRlNCM98ZZtni9Rhb8NzYLg5Ob+e8jmcx2iA8izsfWclY2cEAjhNy9aI7OKxxKxurbTxbmsk3BvfhthtvA2mo5JP0mzhPVdIEXoz1OxO80J3hlLO6sOoCiCL8rgS3vnUfDprfxbz67ThFsBxREzYOIdIGPyMplpsYb27Aa1D85u5DuOeWeRwx/Q8oH2LKx7IjEOAVJWv2tBMFsGV3C1+85Rh2DDbX8twkREoTRRGVcpUo9KnEDePTLEyLiywGWKUA1wiIJ8B1MJYkFwNr0OfUA69kfTFGsVVRbFVgQoSpldJEFohSiFPVsCPC9AmsxyL8DXX0PteJGnf4Sv5YVELSsK1CctsIzo488c0Sopea8iCFwCQFKDA2qLxG9qTwqw6qYsAKCR1FMSfwE1CeHTK4IY3uK9TK1hwLkbCIWpL4LTGEluhMEj+hMFFIGBii3gjteSi/zMxSN6sP34CvFEZArFcj/IigrLBHBQaBNAahAaFqvsIGZ8sQSkuEZRGsrEOGYHlRrZP9cJ6gUiIyEClBVKoQpWywLZy8ItjtElgCXJugHCdvSbR0qfNsEjsnMGMFArtKabrN6ec+wYWXPcJJhz5NUK8Q0kZYCqkFyjeEgYVGEO1yEEONNOqpVPaZQ/6INrx59ZRbJCrnoXqKyLyH1CENRYUoepRliLFjGEcyvXOApa3DdLpl4gnDxMImSoubmYiFlEIfr1hBhbV8SSNqelgIQfdzvfzblV9mcet2VnTs5tCDNyKk4OMfOXVv3zGlg/EnYtx8/TzW7TiUeak+5iT2vKKfyWQyPPyHT6IcVRPtVYKH/7CVM8758l7jhBCcfMwS/AaLMGXx+i/c8Df5s0kmmeT/HyYDKZNMMskk/2QcWcdwmCIrq2SskJl1eT5z4Bl//3qOw9mdX6Xon4YxNk7J0Dn91TsA/VfxwSXf46JpP+O8Bc9y8IJtHNm2nchEIGplCHOap3HPxW/lt6+9iI8e8K6XzVetUDyiSqgMQsC8uSmOXLXoZeP+byxrKkXtMRFNUCz99B96DyubH+eeiTY2VlMkqBIWP/TSuSMXzkIZiKFR1hGvbIuK05Q6gyfqfs2xn3sS55AibR21a59ys7znrCOwmUuzbCHT8gcunPM2ku5SZjaMYruSvuq6l62Zik3hsLpfMWX7e1DieOob967tl87+hEBoNPu4RfZxRxmYuBfPe4Ivnf5uxr0OxryFJO16ADLZBAWt6Wnr49CmDejjAkhUQIFUDhP7W3gthrs2fI0G4ZEfizPem6WkJFppCAQz2qfSn27HkZI1E/sR+RpLQHNbhtlzX10k+ZXIuLOY3vhVpjd8kbSzhM+97SI+8d2zeMfnXssz41P5+YjLYfv2E8uEVNe7qBFJ5BnOK57CvRNT+dSyx2l3qxSlodxcYjTVxq+G59Hvu4xmYXxWCr1yC0fVbWFdOcvzlUae3T0dk3P57uM/osWeoKgTnH7JKCfsvwwhNYiQkfYY6WRA56I8BQsix2GPZxEaTU8okWaA6Y7H/vVjGKU5747z+eq3Lya0ngGgberZGCMItMV4xqGuLoJ2n4+88Xk6I82S5hI3HPMjbj/1Wyw6dZgPX3AOn7r8WFJSc3BbH02iTDFy2VVpZkulE92p8BOS75y2kglP4Bv4cmkp9adEHHHyAHWLJWe96z4sBUQaWRXc8JVVHLjfE4TCR1ShNNWi5FQQfTkIfEzMJoVDmFJESRgv1XPfb/fjo9+4nU985XaGTZxd1SbCCQdbgOUYrMigMMRGIpgIEbkIq6yJd+dJepIQQ2VxHWFTnOoShZcBkGSkjyhXMMLUsgJmJtCOxfjMBNSnUGkLaSt0PIGuj2OEJpRFrIEh1NAwth/QssPQMFQh++gQznCI8gyLZ3ZjYjFkoKnbWiQxVMCtaFAgpEGnwEgwXkDrD3ZgqhBZASIsI7VPNJjAhBY6Gae9bpz4dklsh0ViY4FEfwmnr4CJWaAssBVBk410JFJZhPVJjBAIYzChwMQdAifGqkvHcCsBst8QxkKqmQhRKqPHCug9A/iBRyVGLcOk5KOloLSgkWBFB6EyRA4IZZGzfKgE4FdRw0UsbaFcF6viU2jwkJGp6aVEkL4tQAuB9GqBqMxuiI14GOkStaSxslmOPHYbuq3KztY0140cRDFu1zpVVUwtkFEokC87vPVtB/PY9gznHb6Zwz+3lbZZ4yyODxPvEchChBqsAhqjQir5UaKRcUyhgAkj0hMeJl+gFHj07s6wpbeJgSDBpuGpJMcj1HCArASIYomg3aHS7CJiNrl6C68phidqgcodd21kgTvAvNggh83cxOJ5rcyd1bGX75g+o4VQSbxQY43voSWWY05sgDB6uXDtf/CHhz/BzFn1oARaSUb7ixx7wlV7jfnE+89ARYbYsE/fxuG/yZ9NMsk/HWP+dV7/y5kMpEwyySST/JM5Z849jAUp1lU7CbQiLe1/eM2FDUuYak6h2iSptEmWzGn/L7D0L/Pw4LXc0b2U7flm6mRESmZRqv6l87Mbm9mn9ZUftJ/6yHtJPuCS2OJw6D7t3PSZt//V+zoigUDiqIZ/yP50OsPrFz7DIYk+Tkz3UkK/lOVy5TtP47Sjl/L2i47Dcf58qdS+MzYwJzPEvqv3rsNf3HoRs6b9gfopa7DselrjM3nD7K/QKWezp9zGYW1XvOJ63V0We/a0MmVm614dmgBU7Ajc5nuJUh8DYZGIncjTQx9ha+lpto2/gVMXP8ypi36HlDWb6xpSVMKATc90sHXPfFI7LKytHuwpEKtoklttzj9uXz519ikcdeoPqZ86hjGGeNc46S0lss/neGrzFoxT+5I0TJF5s1uZMauZD3z81JfZ/tfQmDwdQZbywCxK/bO49uNvZaRoU1ok0K7k19tWIEwLCd8i8kN0zKalsYlL536CGU6FpDA0Jg39bpo1xueFKMl+DbvYtqcftGHLxtlcdOebydoRWdvjnQc9iCxKZs17gOXZfs5rfY6fbFjMmZcfh5xbR3VOBhFmKG73Gaok6Clm6TEZZiYq7AgELiEpQhLSkFWGc5Y+TWL9FG4ZbuA13/gpYRjSNrWDemlxde/hDAdp6lSJdy18mgtndjEjo5iSGKU9WSRl+7z+wKeYtnCUnomZ5LTFhFYM6HoCbdFiVblyyiO86Yp1eC0ZZn+llycfa+Ox8VmUoiSzVhY58vQB1tJCcWkHy37dg/xQGtEUYAKLO69ZjegZJr19BJWLcLomMMUSYqyIVfaIpMAZ8VGFCHc0IEg7aFF7mt3zZB2bn59KkJFk1uXxx/LYNggjCeISKwJnVz+pXTksY1CWIh3BLz/4U8K6AKNrpSMiBFmyyFaLSD/EWBIQSA1KvvjkDIDBau1GZByctMVxBz+LGA1IPzZO+p5edKWK8AxuOSD1UDd1+T7yX21BTESodAbjJ6mPCoimkNDVhDPLRONV7K0+rWsihk5pQ7dMYBXzZPwS4rn6mi0CCGD4923ExgyprhBZDDGRQRQrVFMVgjCgLEMiXxOmXcKExFUSYhZEIcK8GBixLW7etS+39u2HkJLGZzRaaLTUxKoaZceJpmextUEgUVJBzEHYEikVSoAQBiPAzK+jWKeQqlJ7KIk7CCWRXkTD80Wc3XlSQtAcQnLMJ9nVjVY1EVgThUQxhaOBagTpCtv2NOCMGZ4ensbGnnaeG+zErgqEUYQixIQe1TrNznWSA+eP0BH3ee3srXzukzfy+S/eyj5X7kEqCykkRkvc/ir1gxFKSUQYIXwfyj5WsUo851PNww8eOoiru44kJ1IgJfFAkx3TJCuGVHeF1LgBDemhEqpQ+/9YlZK6bIGkqtCoCjw1MovjT3y5+LplWWSak4SuYtv6NI4TIXD+oiLYT35+BZe8bnWti5fn4+UCjj70M3ie99KYg2d14E6EuGMBu3cP/l1+bZJJJvnfzWQgZZJJJpnkn4wUkjnxM+mrJOmwDd878Hv/Jev+sbubyDWEMcPKOdP+8oR/EK0DvOgOjuvczCFtOzh39muJtTyCEPG/ar7jODz3iw/y/A0f4Dvvu+hv2ruz/QnmtD9EPHne32P6y1g8rYt04kxaG36AELVbZTzu8OG3nsB5J+/3F+cnrRMY8Zo5a/ZfDgYZbdiv7duc3Hk3SXfZy84HQcDWNV2UyyHxVOwVVgBpzSKQNkMmjZs4gbQIiIsIYaKXja2rT6J1hNMXsOX+GWR2dxLGFTGlaCsHPHPTh/jgJSfUxiZW0pJeCtWI+CZB1h8jMTDO7tm3cO3ufbh8y1H8YcsMnn9yG6sPn8++B8z7i+/3PxP66/GqDwPQM3oFEpAYbBnxyE8+B6FARILI1ZT9AK0kdgQLl7bzmhNXsrx9GagDiBD0BzGe0dOxZcT0xCgtboHFnV1YZdB+CKOK7mo9GFhf6SDVLUhKjxBF1TgIS/Hu7X/gso+dR9BaTxCLKPRNYee2RtrDMfabs52too5pVkBCheTHwEHhoDimqYvdoSGyJEwYvrDmZvzKz3CF4ZrWh3hHy4MckNpNLFZCY4jQpC2DpyWREdy4cR9aLshz6rHb+dnYcq4fXYnEMJduDsr00GaXWJIdQWY0x8zcSs9Iir5CM4uSEzw6Og9tBBNRnCPT24hTYSTdybKPJDAJgbYVtnRo/OQ4QlqYxjjp5irz23s47qRbaTp3Hamt/TS9UGKm4xL58Kl3nsTHrziZyg0u4mmHEZ1gyZt3YLVmifSLXx4tg+V7zGrKYS0o0XhEjqnL8zSdGOJJyb2Xfx1ByPg8RRgTeFLgKIfMrirus2Nk1+SJRYbM7hC5OQe7ckwsqDDSOo3AEvjCZvH0MtV6gzCaxJhBRCFh1kL4II1FPkjRtdpGOoASiHQSP99BdvUQB56zhnnLulk6ZTPpiRBjDLa20NomnJ6gEqQQgQX9ovb/zDOYNpdKJ1RmCfzpCQITIWyN0zpKYAeEWZt4VeInDVFcYLRB16UIXEEt8gFEhsHBWkA3siWiUmtPrMqacmecgVPqmagPqcZrOinaVmhbEVkWxhGYgofxaxpSUitEZwPD85oppzS4ChwXk4whUg6Wp/E3DlEa9xAZC5Gr1rRWpETIF/+apCJMCEZOtdmTbmRO2w7yXTH8EYvRFxzsisAKDUIqIiGRhQq6GLG+lKJkDLcPT6/5KQRuB/S2SZRt4+RDLKMQqTikHNAG/BDCCHSE7flYpQqJ8XEWJ/qoI4+y7JpdxoBlIe1aKaUJPFRkcEJNZvoEcSdgyvE1vSsfxbYv2dz877/j1uvve5kPOf20VSQSFkFpPtPb3sHs1m+j1F++51x22dF84eozX/zRXBBoOO6YL9A3MA7AR998Ik4YYlc0jz2y+W/ya5NM8k9Fm3+d1/9yJtsfTzLJJJP8C/DaeZfxWi77L12zOFQmStXa0bZnMv+la78SXvnXzLaGCBIGCSxo/q8Javw1CJlGyPRfPT7wnkDrAdz46a94XilFQ/PX/257Lpr30b96rGUrkpl4ravEi0wUi5x1+0+4ZPFKDlBt5IdztE1poaXj1TNuKt5ajPHwwo1MS59PvPos+075+SuOjYKa7oQz4LFi5Xxs+wkGf99Gadzn/juf5+iTlgPQ09PH1ie3IbAhUMQmRvGVYudEK4fP3oY0cM+lc3HEBIWxVxZlHMndzE92XYsdhKwrLuT7R30HAB2NwejZKAyVuu8Q2sfRH95KgGKeGUAXH+OW88/lzOt+xeGn76RnSwNRbwK05pgD/xSwmdr+GwAe3/ZuOtz1ZK0JFjolBIbeWR3INQaTtEmMw13DRzIUVgkCQWWOT0aWiUmfPWN1EAvYI0a4T+0k21cm9AxmWgt+uY1tQ73MWzDOgZkJlLAZjTT5SoKh0ZCWhohcn0WlDZBgj2oOa93GRJRmMIgRDNWRbh8iimwak2PcVayn76kGjjz0BRqlwNOCB6O5bFj5C8xKwwMTSVrjRVJGc+naM3jHgWM8M1EiVBGzTurGS2c49/AtrGqTNNpVfqVm88uxBRwa76aKwlM2H93vJzzsTyP7uYDYWB09sh6jNcUDQuzWJt7Q8SR3/LqDP/5sLosPHeCxvha0GGMsbdN4qs/oIwKzJ4YSgsS6CarHOFi2RlsgbAijkKChSqwqaFMeu/sk/U4a244In4zx/kdO5McP38q3Dvox77zzTVTTOdw8FCYkygtIjxQwkUbELajLUDcc4V1exY9F6MAn6M4iDdzy/FKKsQT+URPUb01AMonOl7FCMJWISBtS7VXcpir+aAayfVSGWpFbFb0N9UjHMPpQHdZYCZ22UdLB9iNMNYYODFZJUFcOKSYtRGSBFHgmQkQTxAZClDRY1ZC6nVCeHyKeDxg9VBKKiIZnIQojpFKYlgbCgoe0JNoKST8+gTuSxR2OMLkKbsxBJCMMDg3PeYRph+pMTX02z+h4HV5YxYk5mBLohKJ52yijs5tBGZAKYWxMax1hKcAJLEwsBoEPUYTQkigKKEc2E0s6aSmWkXVJNNS62cQtSrMC2BQnShrkUgv9qEE3SdxeUJUKBAYqIUFLKzIM8OvivPU1hxLNTSDrWhh+WwyjDF3bmuFNdQSf7MfyA0wYUU04kEwgzTiyFCIxhELgRrUgbiGnGKpmKJYFsm8YYyuM7RBEHrZywA8xgBCg4gGt8ycYTMbpTjYzL5pAmYjCzCz6171c+56fs2VtDx/499cDIKXkwvNXs3jhVCTQ1zOOjpr+stN9kUNWL+FXv3s35531TUxUy4x5x1t/yG9ufT9T2huY2dlEz+YBeneO/tVrTjLJJP//MJmRMskkk0zyP5xK+VYqpZtedjw3UCLVLWgeVP9P7LBjR4JlWBbvo+lfOEyvdYHc6FlMjF+OV/zRP9scjKnpHOgXf73pG8ux/GdfZd6cR3km+DJ/2P40bsxmwcpOpHz12/aVmyt8etshjAermN3yEQ7ovAmlXrkF9AknLCc2oan3FWeetYBPvPkBpDEYHfL5D9zAlR/6MUct+iCvP+hzmECidQSuQyXXRFRUFD8Nv/r9ftz0vgWIAHwHXvuOY15xr6/v/AH7pwaoTwV8dv7tPLPrxSCIcImQNY0HmWXfjq8zc9p25k3diM69DV38EssaH+Gc5fNIuRXkMQGRZdCW4M57NrxsnzPnfhVbzuVbtx9HX1hHf5Ql12YR2gIVwQyT5IcHXsGFbWW+vriND554B/vFxjg4NsSd3bMRVgQYHsp10XlUF4msVxPCLIYY2YqpTsOSEt9I7i82sSts490/Ppizv3o0HdOq2JZGCsPxhz5N2vyIS57ajhd4FJ0msgY6rRzdQSNb/TpWHridBmmoGrjosTMIK5KnSzY7QkH3aJxPrDmBCx86n6PnbeT82T+iMzXIFGuQQ/Z5gSlWnt5iOw/1T6OiFfNivbh6EKFC1lYayD4nuKWykGHRhOqcQrTURdRbdJVbuObYbdz0znM5+vw+5i4aQ2ubTc+0sWjBNoK4xXCdoctpQB/ugqswSuE4hn29PWy4Zh7BngLl9pC2R0fpuMvH3abZohuYdu4o0emSw6/cxpV33s0n73uAN684mc9dcDRfOOdGGm0ficC4Eq/RRQiD1BGUA/TgKMXqBIP3a6aGY2i3VmkjIoEcy/DbH95Jw0gTUXsWMzhG/Y5xEAqBzT7Luzh26no0RVSUx2qEpFVEPx4y7icYKqeZaHOxKgY1UqHu+SqN94TopCFskmhXgxXREXVhXIWxJMZSTP9diWQkIRS4tsLtCnFmVskfFEN6NpZfpdDuI8OIcnOVwlkVyu0SUywjSxpHOYhyiBguYhc9YnmFPUtQ7bBxQotEzlDnadJuSEPzOEnbwSoZrKoBKSm7Mdz1IyQ2TuDuKZLqKmBVQEuXUL4oNmsJ/ISDn3EJHYU/1cZa3sDgyS10H56g2GChJ6pE3UMIzyE+Ikh2S05o3YDSDlakEMLF9l8srQo0osFGOBaiJYFoS2MXbKwQ7vj8Eu54fgVbRTPkBbohBraNaYzBlAyqrQ4xox2mZNGdjahZLYwnJEYbvJKkFDpUxIvto0ONiUKk5YAjiepcTDIBSExFYByXUnuS9hkFRsIMI1GWqNmiYgDlcN+PH+ENR32cB377BMP9owghWL58Bu3t9fx250YuuPl6rnrwnr/W/dLe3sQ9D3wYy5EYSzAyXubmW54E4Kgjl1CfdKHi/YVVJplkkv8fmQykTDLJJJP8D6bqrWdg7G0Mjr+TcnnvL4+jxQrSM6TdVy4H+a/GsjqoT36eAX0cp8x58v/Jnn8fLhKBALzKQ//wakFU4r5dJ/Bk75v/7jWGeocY6BnCGMOh3/k+SkTEVIAShr7RHqbNb2fGolcvzyr6OVbUr+Pothf4xe7b/+J+b738BH75u/dwy53vZ2H7fMruuSSafHRkiMo+D96yESNk7ddjITnwxMVI2wKp0LEUyd1VGj83QGaDj6U1K8/exguVl++rdZ7js0XSxmNGLIcAmq0qAFImsVufRrb8EdddVTsmHMABkQYEWAv53ElHs9rZTvGxOEHSQqfjbB8c4amdf+qmMZz/OQ9tW8hi61kmtjdx98gS7h5dih0LMUmN8g1+NSTudPDGRd9n2dQPs3ViKmUsJozDIbN2/yerJY8sXsYRh20mFRtDWBIhBYk6D1cIUlLRqirMbRjFLwjU9gor60u8fcXTHDl3E5etepZymGLzs814JBgcraJCyXS3zAKnlwVuH+v8egYqMcZCh66RejqmVXnfjtfw/PgsZreFfGnZ3Xxu9S0c2VBCCIGggQ5XckT9HtpiBY6bvYNBJ2SrBwuSYxxaX+HoOduYXvoJt923P5GptcH1tYWwLE6YtYm3LPgjuwe7uGHNtXzz+x9hONFI48JRTKjYs6MNUygSC12iqkWp0MTIcVDZ15Bp6mfnD6fiNydprLrMeWob2rYR2jC8oET/cSkeTC8h5UTskxhhqV0TyT3xpi2cc8smTmscobGtQpCxIDLolMEYcDo8YrN9rFiMzLhhxt1Fog942N/rh5hHmAKvmuC6jy8maEghixVUrkLVD5mxoJVlSzt5a+c7mN11GabogoqILa3QvGqEk455noFtTQzvbKDaHEcohbYlShviqkKsxUdkImSxQGs4TNrNY4QGC4wl6G9zEKHGtQ2JbIW6oErufofYiIWaEIi8IdbrU01LgvMEtEuCNxpGl8ewWgXxZMiigzex6sRu1Iw0Hft2cfgh3bSWcrXW2qGmUHGoJgxx4SOFfDEjTWOPaIwTw0IiHYW0BEIAWuNoiRAGpQ2qGqImJsAWRC4EyVpXJmxQ2pAej7B1hNWcQRmJiQmkE5FMgSoKZBHs8RBZCFD5ClEYQtUDI9DJBFFDglD6iBd2ULdnjNb3bMN+KEZ8bRyTiWHqXLRr4VVfbGVvK7SsBe2NEBTeuoTReWlKBYuRfIyJkg1CYEKNqFQRpRJ6zzB09RMNjoIS+BXD5ucbydk26zdPQZmIYsmhPOCSW5GgGFYJdETfk93c8uMHeeahzYRhbf+Y6/DHvq3IXTl+eccDf9EH/mdisRjXfO3C2rWuhHzjC3fyq589zPIDp9PYXkexUHlpn0km+Zfnny0wOyk2O8kkk0wyyf8MIhQ1Zx7o/r3OhDrEuIKW+pe3Gv7vYmHz+Zw884f/z/b7e5DSITBxLKPIBS/8w+s9vOeNHBLbzjL5AMXqlr95vtaabet6WffYNtbs3EUUjwh6bB58fjalZ+ZRv6WF6XM6sKxXT/Ppzt/N4vgYi2MjLGv460qcWlqyOI4DwL7Tr+Hnv7sSy1JIJSEKIQiRiRiHXnQwV/3gHVx93aXI+jjCsREZBzdtg5TIJQG/aTmMi3/TQ7lc3ut99fetYE5sB7MaD6RoPscTpVmo9A9eGiNlCqWa97JLSoVqeQ7VugXp7gtEHNzcx5J9uonqFH6z4pPvvYfZsQ8wMXQMYZRj4/BVRFaEtHJc9e6bOKF+A8uSe0gmK9jtFYKMYMQO0fpP7bGbojfy/k0n8K7nTmV6LEfzD0s1nYuqxAQ2328/hJQYREgQkWHL5mMZiTTdQcRQ4DIzKLE0GMCaqPKHoSx+S5lsWz+bwwxbPRcSFivjVbq7GukdTtMoJTsqTTxT6kRGNhk3xEFhOZpKKNBG8PuxTlbGJ5gXh6wVZ2n9CoL+2TSqHMOBxJG1L6auFXJ6/S463BKVyCDweHznDFqbT0TMK7Dm9qWUK4KYDKlWGzgmuY0F1ii3lufz2F0DrPnuQ3Q90sSuwkpmrsijSwq3EuLsGmLK2AAXXvgAJ8zfwuDKFCMnZ5kzuweUhmGPUTmLUsqm610x9nvTCOUwiVdwGCzG2VXK8milgYdK8+jNtjLi11o6f/39d5Gqr6CyAQdf9jSn3bCNLe+ayZ73NVP+BuSaNWiDY2w61wns3RVU2TAx6LL2sXqEtBBGIUoROq6oSM3JZ+5LY3M9C+fNo4FWhF/k2KN2c9L7NnHwKb2onETlLISyMLqKpSRCC6YuGyI0Froq0Q2auqifWYcViD3dR6QMwhd4q1sotgRcdO1aGtsKDAymcMdB+RrLM4huSFVjxMYNNVUfDaHGW5jGthW+dqgGaeqyRVqa+3nfBY/y3hPX8okPPkOhFSKpsUJJacAmNyBocroJh3OYfBnHD5EVHy8GkamVmxCEOIFEaINWstYNLYgQfoRT1Oichx6u4odVyEOEoRqXRLYFSpC0AoQ2TJ86wGx3lCipMWkfMZ7HhD6kktjZJFZVYxo89KEhqXKZzJMDpDblSGwcwc7Gka6NZVmU968nrI9hkHizbcK4INA+E25I2fhUgxC128dfPYUoYRNqC/1iMEhIgbRsZDyGDEMoechqCH6ENOCaWqBn8JlWrv39Efx64wrMFIHeXiW/MEM5LfGBDfdt5Cuf+wU923sBCHSIpQVyTEEOitXqX+UH/4OVK2Zz5pmrajo3Xsi1n72Dt732e2x5vosdWwcYGRz/m9abZJJJ/vfzL5x8Pckkk0wyyV8i5i4nnX4vkR4hk7x4r3MHzZzOczt7Oe/Q5f8c4/6FcVPvI1f8IQ3Zj//Da7Wnjsb3niNCkLJa/ub5Silyo3m2PruL9+98CpEEEhYz/BnMfcGmY2krM+ZP/bNraO8uOpSHBUxJL/673kc6nebejV/gqg9ez66dI7zvE6exaNmcl84vXzWH3z91FYPD3yIVfAmAvuLNvGHNLVgvKKyy4MhvfZ/H3/dOTOUXjOc+SdlAoG2SRnPC7NOB0/8qW2qdiWqaMZZVT1vLQ3z9/UNc/ctvss9+m1mcLAJQqWynq28ZrmqhTB6AmBOjMerlmg3HUjE2bYeNMf7HeoyBXz7/NBeuPACAi1cegLN2F7Lpp3Skz+Q1h6znpkdnkTsgDpaPcCW5jM3yZRnWPVFge9cYGzbexDHHrcD0fpxH//AgL/R0Epkq62UjjZkcFauOD953NkSSA1J7KBnBEzet4JA5u9ijQ16T3UlBW2AEtjRYwufAed1sK7fhR4qzWnZQjlxGdIb5Ld+gMn4mKElCCIYiQWGogcYwwa3Pz+Vtp92LMTDsx3GVJmFHVJF899JfcMYNV7DxiVkkp45T2NnAvy0/li8u/B2yyUFLiRIG44Xsc2ieD120lUs3TUHuDIg8TfVnPg/2tLF9djMozbipY0QHWGM+YcrGMgISEqQBA60N4wz0NZPrz/Kbqw/hsLdvwSyrfXp3b1rIrtEmTFmz/zvW0V7IUfRclqRzWImAN8x8lje3bsO/Ey74ziGIG+vQIz7OtjzCzSC0pJiMIZREZ1wm9omRKGuGegsc8pr90JFhaHAcz4uIRlLcdP5+nPvuZzji7AIN6VFyo81o23DkO9fwhx8ejPZCem/PkI5HhF2SyHbpfiDL7mdtUk0O1cAgjCByJOFrsvxy7VIqgYUREBssUZhRRxloLbq1duHSEP+GIn+IgREbscyjb67D1G6fMLBZ9/QMqhOKidBBqZCc61IthsSMRXZXQJS1iAYEImfIDPQSlAPkkUsQ8Ri2G6PQEpEsRARhibRvQGsm2i1EqLAMRC02lgpwYmmcYojcMURyo4epBmAivOkdCMvFV4bIMUxtHKNgLEyzDdkAEdeYCkgTIas+ulqldGoT2o4RrSphrbeJj4OVikM2Q2VKhFVW+K1Q8eIo14CWFIUgWhrH6Ylwd0HQ7GLaEmgCwmISEUGkDbauQjoBFR8RRETJNEYFCE8jghAjbWQfqA4bnZDQK6FPIQqKpngaMyLBSuG3C9zhPJVto7z25KuxpzXx6S+ez76t03lizzb8pOLunVs4e9HLxbv/MzdsOheptzMzdT37T1/KFe88ie5tAzz70A6IKYyUBGWfnq5RLj7+Gn5wx+VMn/7fL9w+ySST/M9gMpAyySSTTPI/nPrsB17x+McuOY7h8QIz2hv/H1v0r0999q3UZ9/6X7LWosY3E+lLEMiXWgz/rcTiNpu39xG2xJF5hVFwefMSotWGlYcvelnL4/+bRrGBuBUSGkl95sy/y4b/4ONffP2fPV8qX03arqXwz5xhcc/Uj7Li8W9TbdZUQx9T+BimciNZKYgLzY5QkGj4x7RoHHse2PM47PA3ERdVdmlFcqiRL9x8NCMTgt98/iTCIMNQ9T4WtHyYTz/2EZa1dLEz30ShogkytUyOa7t/w4UrD2DCq3Dc775NpOHnx/6ClS2d+PsewKaftfG4dpCWIfIstp48n1+dex6nv/EX9MyEyx96gC3HrWDulKt4insomyeIPENrbAcxFSDiedqmj9KWyvPMvQv58KPvRAZltmzp5KA5AxjVxMU3nk6qFHLbpddjhCGbqLIk3gdCc8qcb9RaydoLGMv/jILv49kOoyE4CG7+9qVcftWhjJa/ys/WHIIzGvFwZzOfnH471otSSAawK4bGjjwVFI0LB6l0J7GX27x33qGsfc+NTDngENJTD2bNrBt478iR8EGF+EwAPQKlLPQLKabkkmgbCjPijA1NQREiKwFhKoasauQjDo/57SycMULwaJ6ZTV2sa57Dfb9aQfzJMtWERaWQpPkNu9jltuPKYR6tzmR7TyNPRGUa/VEOyvTiCIUr4MhzhjnrbW/is6/7Cts31BMARgpY2IBBoBzFPlOH2fVcM76r+Nb37+Pd7zmOe0dvo2FeSKk/wgHu/+NCfnvvco7/xNP8rudAkm6JQ2YPoDe9wOMPTicIFdEuQ3bFABUvjiMEQRlMrgpP9RMdOgUETJSzeJuqCNelSZZQRuKUB4hWNWGy41hPpNCWhaUk+ikLjtZEIy4hLtPnPMHuB9KUzFKkH3D3o3PJn9zFDfdNw7JAhBovLlC+wfgQlIF4DDOvg2L/IIlp7QgTIfyAqikjtUAbjRaGoDmGZSS6r4C7u0p5LhRjNvUYZqzuo+3tEZX+kG1fnAHFEl5MUkgowgZNfV2ejPKIVUMqgYMYA6XAuAKpNSrUWN0SXIEs2xQPns7ICTHoKlJ3S4n6pjGG/Xoc7eOkDUHFQkca5tswDrIqsYQi0qpWDtcHdfEAJxPhdFYZzjRhRscJKj62MHBYI/6SDixVJfxmLwqDrBqiRAKhXKK0IRyPYRyN3xhHN7ow4REfDjCzOhD5CZiYIMyl+dBHbyY/J0G1XeFWIzYNDsGiV/cp5/3kK6zZuj86tpJ/P/MCHt90GwctnMU1X38jX7vmt9zykycxArQEGUV4Abz+tG9T15zi1nv/ejHxSSb5f8+/SlnNv4IN/71MlvZMMskkk/wvJRV3mdnR9Bcfwif5x1HS+buDKACL95+LONHDWpCHFp+6ljKlfJFVRy2hqe0vB8LiokiExhIapey/246/xBduvoP7BmeQ1wH9nsRylpFIJEg2KWLz8qQX5vjl9l21wSJJ2VrMvNYHkfK/RqcnZi/FMxYaeF6n2FXOMjTRxuVf6KExux8LWz+CEIJ5jWs4c+oa3rXwQU6cuY3WffvILhuhpT7Hp58/iY8+eRGBDghNyLZCDwBj9gy6CzGm/FYSBbJWyqIkF913E4l5SfxmQ5DQHPO9WnvyefNnYUsLZQT3XbMAV0OhkmSfbBdhIBEGHu7yOOiL9zHYex5Tm39OZ9vdLFjcw5xDu7l+eCG35WcyFKSwpUYbiRVbhLAXsHXHvsRKn0QpF9f4TLE8NvZ1cv117+DH635HY32R3ak4QXQIKjB8ZffxnPGDN/DcaDPbxhpZtd9GWhrHKa/LEGjFdatv4YDmf+f4zgv54P63cNC553JF+Q4m6K6ljrTY7P/Tcc77wEYSLcUX1V5FrczdUkSWRCtDKDV+waO4KEV9xaLxwSwDP5iFKdaz4rQ+TnjzSl7zyd+TH8swnMpQ7lTcdv9BHNy0kxPbuti+KUPxScXu2yTJrT7f3DSfPYFgbcVFKU2T/Vuu+ckz3P3cAzz4lW8xu2MYHU/VykJMSH5NDDVawWsuc+/tz3Ph3Vfx7/fnWHz6IxAzEGriahzpT+fJjx7BhxvWc9W8+1iWKHPFO9fzzg/ZNO4zQushfUwIm6jDoCxJzFXEfcGMHRHKD1EhCCMQjW1YDfUEaQdjC3SUQG5XFEbrKbbbRG2SaR/ewYEf34nVZRMbcxECHvIOJz86E7yQSEjy+SoP393O7usy+LYHWmAFhoqfJh9XCCWx3TiJ8QCrIUZpqIvxeTFELIbtx0lUQqQUeFkHW8YJkShpYYWa2I4K7lAO4hUaGht46OFlrBlqJtq3QMwPUJQxlsY4hkVTe8jKgH3ru1CjBplI1+4P4wXCSGO8gOTzBZJbq9hdmoYxGzVqYGqKwgkNnOCs4aiF67h0/4dYdNBOZKCxKxoRGuyqj10N0cpCVQXN946RGNFkTY79m7tY0D6CEYASNT0aY3CmBYStYGZKqqunYr3DIn2tTWWewmtSiBLIqsHZWSBKC4QQVA9x4MUyLRlP4VhJdP8EUbWMBFS8jqCpgR8+9whjw7lX9SUbH9XEBgUqZ7GnnOUN37n1pXNXvO80/rD2czz03FUcfsoiokQc4yqIYHxwgiP2+yRf+uytr7r2JJNM8v8Hk4GUSSaZZJJJJvknM21WB+864yhUV5yUr/li65EsP2IfGpvr/6r5qcYbsOR03MwX/qrxxkR/l52/2fIC3/zmKRx7zTuw3KdeOv67y1+PjiQTgyk+9vw+yKYHUC1raG65nZg7kygarXX++Qc5eOqvuWX7KWwoTOW7Tx3BRKdDpm6CzesHufRj1700rsMdIYlPSnrsU7eYT686ncXtEcsSeabE8uwabaUhUaQxVeA1U/cFYP9ZX8KYDGATf7RQ+ynaF7wwWuTH7z4PNSGwxyQ9mwsAzJ/XgYPASoVEx7aRK7+Lw+u7mOsNsv7+eWgBsxbvpOrG2HbU7aRih9NT6MFKhLhuwM8fOozPP3IsM+0cI36C5emaVswZb/wa0xM5XKGYZguMFHjCcMrsPQC8Zelr2bx9Bl0b5vOR17+FX6/YwfULHuAH593ML54/mGe6ZlCu2GzePJODD9vGV/f7NXX1Y1y58QvctWU2H3jiRC58/qvYNvihoc0dZ0p6nJQeZOXrfD5z3wvsOdcmyI+jAx+3q0i5SXDZe+5l6kf6UStzaGnwE5LAgV99/Ie0NRa5/avHc8yi+3jrtG4+8o5fYmywJgQ6svjej47l4mvehPdYPal1HtaT0DxW5NIZG3iw2MKWwKVBlYg7c7EQaAwGw0kHbUIYAb4m7B6kuh4y/ZqkqlKtlhj4VhE5XsYpKDoWjIPQ5DZPY9rUJPNmtxDmP8RHf/hGLvzeB0hN28S+qz9IW2aUYDxCFxXekCJ9TJF3/eAPnP75R9AGOn7Vj8prnPFaS14UFBc4lJdLZDuE+07A4iLRvDLZI8c5um0Lb+58jKuuuAnLN1iFiNCG/d7WhRECtauPLZ9P8dSH08SG8qSeHEMZEFIhMQSddUTpJDg2oSWwNvWT3F1FbRtGYFAVD+kLtDaE6ZpNyVGJX2dRanPJL0lROLmV0mGKNx19P28++hnyI3MZmmIj3t3GsecfjExpZH3AIa391MuI1x/6FCIEoyQiZkEqjjARMjBYxQBnrIQdgtTQeldI890hcqLMXdfOZVFLP5FrIeoF+AFRFELVx1kzTnxPCW1HhK0xHD+EgXGiEXDKHlmrDAiKdS6VWTECR1G5T5Dp7kXtESTTYxTdJsZLWVYesBm1vh85FuC1OkRpC5Evov2ApmyBvNJEGJAClMI1EaZrhKhSwUgDCvBTrL7vS6zr7nmZH7nriRdQQYSIaqK9X//FmRhleP/3bn3Z2FVHL8df3UJ1/xYiAbgSbMkD920gDP9xnzbJJJP8z2WytGeSSSaZZJJJ/sn86Nkv0hK/hete+15WdZz9N2cRWe5Ksm1//KvGXrflbIajCSQ271t0x9+0zznHNHOdP8CcaS7TO5peOj6lvp6g4OJ217JheidSTGuo1ZqMjb4FUb0DI9I0dGz6m/Z7JaaOvIbv3D4FWdKYxQZZhcbj+6jOWc+H7n2Yq474MhtLzWjq6EzM59Q5XwfgqI7D2NB9ABkxzMbWNu7e3EEmHpJway2i58zpQImQMLKYXpZsjgTYGmEbDvv9t0mKJHLARwWGT/zgNq687FSSWZeBkybYVU2wfttW1p72c2bHL6d1+zOM7dvPTtNOo10m0VITgn588AY+NeNeRsMYu7KrqGzR3Bes4r43Hs43vjPIvh/6PNIO6Q1gmm0Y9AUD5Rgzsh7FKOA3Wxcy3S7wg0MjUiKNDJchox0gYFUD/Ojom7EAW9pc17qco9p7aXEMW70UbfEqyhKMmSzagF+2/g979x0lR3EufPhX1d2Tw+a8WuUsoYQEElmAiSYHY8ABjAkOGHNt4wA4EQ0Gm+yEMcFEk3MGESWEck672pwnT6f6/ljDvVz4rgEDkkU958yRtqe75p3e6jnb71S9xZObJpAISN484iKarrmEGz0wOgyYVsQRnai+IFa3gdfocdUdBxB8bQMhu5+KRI57n3qaaMDAUT4iJyCjKHSswRihmN2UJpZsJ5urQRiCQNZHeOBUWZidJiYei3tG89imDPtN3EDSKPJgZxWh8WfQvHkCMfMsbM/n2nt3R/pAtkiymMIggCMF4dEuI8evofnJciJLDN58cxQ1o3ro3lrAGQzTtqaTWV+P8Od7/o7bnyTTmef4ZZeQd1x81cBAQVE/sZf6ZIaV4RouOv+LhOd18YMnH+OPLbsQuqiHjuBwvKoIqr2bcquf9nHjEA0Z/IUFrJYCVBt0G2WMtHrxkVQEs4QqWxnwyohUZNhj3jp6ny9n/hFz2Likn7VL2+lvz9Byei2NDxawpIES4JUEGBxRhjGQY9em51l6Uz2j9u3n8KvexlWCiw+aS1AY+K6HmwwOrSLlesTSBn4gxOC+1QQ6bAp9EW7ctBNHVDcTCbo8dM5LmEJx7p9OJFLikI1KAoaBBch4Bt/yUQjckAWuizAkyhAYRQ9hFMEKIKyh1YQC/YJwZwY7C6uytWS8CJPMFpYP1CFiEt80sacnEW9245VDfpxkdH0HZt4l115CrKeZ6oZBMtLHGZZAqDC9s6HiTQP3VYvCOIdvnraWlW9X8sKDjVjHFPFNiTs6iGmDW20hV7r4jg1xRWGXCCIdI/ZKM0YkjCjaJPsLFBduoFg3Ditr4Td6KODc+x7l2v3mM2rCyHeXj7/5vlcxswofj6u/fThn3PwIePDE4g385n9/4CQD5IVCBSWX/+0r/OkXD9G5sRuZU1x09t/46e9O/j+Xpde0z9z2smLO9hDDp0xf+ZqmaTsA1+vDdlu3dRjax/DI268Qs+5jYryZdd03fupTsbrcDJbwkaL4kY89d/bXWH7hedx/yjnve+7Hk+ajxNAIiquf/O+kTi7/xNB/VPpjx/w/hZUg2qYIZgVn7jKBCrMWOwHTkltZtCHBDQsO5bjSFewZX0ev895pTuWyi7h0+VLdam4+dA6PHPHfS1abpskNv/0qDaVBqsMVPHPgKYiATyKeIVk6yMVf2gMrqzDziucfGUoINTSW0VtI4A4GyPRFeegRhy8evC9/urKJ9kUJBopBNuXLWZepxO8/m32jdxGUPiMCDlMn2HgTChgNaQ4/7iUee2wVRlEhBSwqJHgoG+P3T+/MpBKXkJBUGhARHkXfRCJQFHG8HMhGPCxk7GxGDmvFkgFKpeC7o5Ywvf4qyk3BzHCRzkKMvG8yM7yJo2qXc97MF3jjCz/ixQN+xvBfXYpIS7A8/BIQpgslQYyl/TjHZ4iMyiNtgRWIEcj4ZPOKG19oZKtT4PI3akl3Bgm4ks4FZ/HE4EiuaZnKd/d9FivWivIUdthHDWTwgwJnXBnZqbVYNWU8+uxuJM0iUih8EeHKex/mB9e8xUlHHMjX998DtXUAfB8ZyJKNxhlIGPSOjuDNDWOOMImeaFOrinRsqaS7vYJ9vrIOz8vSHh7kqRvzpLPVhLpdArakryVHsdMBu4yDZmxieKifjkeCxFenUXaRwa1j+MUZX6bnB40UBiop3VKk/PUBypYVcZaGcctM/K4SolsU4S4wN8GwXdp4ZPlUknhE+4KUP1PkpKqXOCb/AqM25/nDpY/yxcN+QaKum0OPe5tRP0xhZGzssEK5Hkoo/JhEBcFPBJGlccIxmPztfgQKS/iUXq2Ye/ESQlEbEQni+5KC4yA9E1nwwLEh74IDS/L1TKzvYdh+60hYLhHToxjuwBh0GFXVzmbbYIsr6HXjeCNdhk+opr4yy/ARHUgzRLYijBML4DgG5It4AvLKoH1yjvRh1aSPauT1hxoIr3J58rwpiIKBkgIVkKiwiTsujDM8jFsveX36SDa8VU2qI8gzt43n9qv3QpRFiG/KYQ2A6VoIz8e0PQ7fcxUHT1zHt49/nYDKseJ0g8izrUMjZLISaRsghyou9KRjSBXFfGIlOdtGOnmIhPAjFuWtHk03LkOseQsjmaWwLo772Dr+/PN/8PdrH6Jty1BCs2VzH9KGiGWw1/TxJKXE9BTKVaza0Paez4zdp4xC+CAdxRML1/G3e8/luC/titub5pWH3+Kyc/7GX256iqMP+w13//3DJbM1Tdsx6BEpmqZp/+FcL8fej15E2rP4wy4HsHPNnts6JO0j+PmTj1BXMoeB6RFS/cd86q93aO3ZPNJxNY3hqZ9ou1/aqZfjm66lORfi9Pu/8+729XaIRjNLtxdhl0/gdeZMHc4tj7+FcBWj+hv47u+/yG5//j5337cnxqoodzxXxQlXXk9eBTms8b2FmPuNvbDt16hKnE1T2X7va9txPVp7coiix0nH34D4mg9R6OuLcE7qfiZW19C5rh/P8Fjw2jpGNFVS0bgGK+DSvynGVRc/hvJBmZL6nVJ8rWoJg36YRPjbqMI1uAwtv6xEkNPGTUKYt1IqMjzMPHxTIl0f3w0wJf4nFm5czIUHXYSBwADyysNRgqCwMRAIBEbwAMzo0e95D6HoV3DzN1MQCUrNBIaMgSu49x/7c5cvCeeLvHDujQz4Hs8uPYQ/Pr0zFVUBegYrEXkTmeygJCKINdmEgoLsJXnc0QFs2yNipZAlJoMVJrcyg7uu3p2rv3U3Iy+5H5EwWLVqAV8p2UiVGWJJoY4bjwxzc88ivmK+xu23TaQ20sUDan9cqxwVAAhw06bdaUj289iSydRnH8baGQaT1bi3DVK+vhexeRDhuUy9tYsn7t8NLwBt2Sy7jGvBVILepUn6RAAIcOvm/TAmWgjLQqQLGH02tiEJSx9cDx+PzuGSyGyX13+TgFcNfMskEC3ibB2AUAQhJDhhPENh+i6eEESPUURGp7B7JeabAWQhR3TAY/QL7Uw7oo+RlgM1DscevYVbLx9NIDBA+NAtDB+XJxCE0uQrHHB0O/WvdPD8C0l6JgcId4WpXFLEDJYjfFBSUTUyR8+MIrfmpnGmvYiME2RjTwWxpnHMurmZ255poO63qzEQyGQEO+hAqpVQqU8mVUa03easDXvjRCyu6JiCKkjWLk0yWBsl9WKcHz51FPOPX8X6dBmOMpkx8klyXVG+cPQKzlmwN5muAJFHXPygwMRkYJKJH7YQ0VKUC6pEkWsLs+6mKlAQtMAZkGTLPIwsqPoQ3iywkkXGhDbS78ZRwqBlbQJDBol4NspxMTOKkkUC5bvgKBY+UcM+u5WwtLmG0NN9KCWIV9jccOg/2NhWzgV/n4473KRYEiCQAIWgrL5AcbOCvIuJwo4EEKYBvRlqnlDYz2xAlsXoM4rYM0u45+6F/OGuJVQkA3ihocTMzLFDK/D8/uuH8Y3L70P4ip/+4VHuveTUd6+n8mSUiKvwe4tsXt8FwIFfnsuLDy+lo7Wf555Yiv3UUtySOFf/7nGuv+FZ7v3Htygt/XDTMjXtE+crtotCr/52EMOnTCdSNE3T/sO5yuXkEa8yNdbGAy0tOpHyHyRnNzNh5y28/egE/tg2jzcu/Oan/poTyg5gQtkBn3i77QMXUy8UDZE81eb6d7dXWXGaHYum0NhP5HV2njyCQyaMpr8vyy6zxwDw8tevYN9f/ICsimAagjaxJ9MariVgJt9z7MTav/yfbY8aWY0SEqFcME2qb1F0fwO8okCaAnZVFPossmMU593/GH857RjuXvMMyhfErRx4QYQpSJyQZiCYYEG+kYRVz1G136G9+XIGlKTcLKOq9i3c1hRr/v4SucEwnjRA+uy791h+9eNjARhfO51C7yuo4jP4CJI1mzjU/Nd/tpWWXgilFxL758/h8nv523Or8PxNgCJvW2x0FTUmzByxmUPOGPqWfu5dJ3DczL359s7zOOG1szBKPXa6pgUx4LBbQyeDjqAoQlSaWS685ygqyrZy7NeeZ3GqhlcGavlm4nXs8RIvZzEjlmdew/cJRQ4lLabQWixjRs1WtvbFuObk2/n967uxvHUa4UCBYG6Av701Dz/ic9Mhz3LyDScyeZ+NqPlB1v89QOWTDhW35SgrSTP9oDd4s2sSLA+SHmuhUh59fhlOmcH4vbbw1nMjEQZ4UuKUhpCFIj//+yNEMhkefKyOtwujyRXLyHpB9tt1NQ+9MA5TCkxPkuh2cFQ/KhpACRCeoiB8AtKnzsnQZpQRLnNQeQ8j5yNdxYLHRvDG4p3Z5Zq/U16W5cViLbJckukI89iCMcw9tp/engBXJvYg2LGYF1YnCa/JYTUXSfS7iKJCpguocADDdmleUc78n2ykPefxRks1L3ZPpaI0BVKx2anEysfoOmU8I69azGDUR2UUidc8vJ0NRlV0c/Lct3koO4XefIiHN46msDVMpNcmEPDxidE+KLj5pd3ABNFncn/rSOp6B3l97WycyYKaFwdxgxJXBDBEGOkI/JBCugLfUuCCCll07GphLS8SsUxUzCD0ZheqIYmVk4wc24VrwwHBTdxjTiHlCoxoGKEMRNbHDwTwfR/LMrBdFxURdHSUc84ph5EKZKmsyFP+5Sxjjra5YvNUjo9soTaaxccjMtIhlkyzbrxN0ILi1qFhKp4QCFfhZwqghurIKM/H70vhzijhtF99iXO/dyfeunY6NzgEgyEGawOcc+3Xhj5TpozEFOBJwcaOwfddU7s0NrC0dQOVfgCAuvoqbnjiv/j+CdexfnEzppQ49iCyMknBh4MP/R3Tpjdy3e+//i+vV03T/nPpRIqmadp/uJCZoL0Q4W+thzMu2rGtw9E+gpBZx8yKVmq/lOLUpm9v63D+LWbwcNZlb2DQMzhm2n8Xoh1X+xCji09jRY7+P47+aM4/77D3bXv6/Mt4Y/EmGuvKqK1OfsBRQxy3i4K7kVhwzvumUcWiYb7xtd24+eYXwfYQEoYNttHXUEGuGGBpqJvYrDBH7LuAUrNAOjyTu6Y8iAd85eWDUCLAoceMZ+HcZ8iLAMsLjVwx4S4AtvgWARy2OBnMvgxHfOcPfOXIFUwct5WLr/siV/3oNCaPH/aeeELlf0R5vSDLPvaUL2mN5/T9x7Now528tKwVZUhiAhLSQgC+UkgheOqkwykpmwnA5NhWagMDNAQHqI9lubdvCnuXb2Ck0cWKYhXHzlrI1yasp6AEGa+LpV01rHOGklB/fHpnvnvqNQRDFbRmlrPEHoYQUD+7jVmZo+mP3Mw5Ry0AtQD/nze9hzduoiGaIStNrAR05xI0JrO4+5dz4sEjWRp9kOOr1iEr13LFEyYL4uNY3VJF8yv1yMowhVEef1i7LxGhkLaLwsdPbCUbrcMrOrhxyc4Tunnt6uGEg2mWJkfynd1e4KUvOGQXhUAI/ICBiYHnAb6id0KA4qEW0UXgPGsxJ9tOj52kJSKx51qEX3MwbY98aoAv/O5oAvs7xOqKRGpzyHwENx/mJ98+iubZCm+cxfmv747wM8QSHcSaXbyQjT+QJ/JomkJDGCojbCmzaIqU8MO6Z2hOBRgZbmWBPZ323iBdS4ZjZiCSM+ibNwKjfwA/X0CmfayUx/TGDgIhl9buEmqCgyzNDceNWxhmET8sEAaY0ShGawpfxlFSMNgUwl0SI1HZy/BYD1u3hJGWR8UhfdTFOnlz6xicYBI/YJPIDuC+LojYMdwRUbxSCY6N7M/jJwPITQOYRpjv1L6IKT0e6xpL3okSMgx8aSGkwg0ZCCNE0JQI28N0ffKmQgYNZE6RHLDoM3LUjwqAyFEckCy14pQ0ZuncEiNaapPs6qdpQw+5CoGXK+CHQphSghCIaAiUgHyBgOvgeVCyJMc35l04tBKTZSE8D5VzKCkGOPVXv+epa38BwPjhlazY2I3wFLc/9xYn7D3j3WvovDMO4PGRS5g9a9S728LhMN+76GjOOvxqKDqEjCB2KgdlMYQSLF28lS8ceDFPPHbex7puNU3b/gmlPgeVYD4BqVSKZDLJ4OAgiURiW4ejaZr2Hgc9exbHVL9Nc6GUC2Y+vK3D0T6nVq6fRtAc4JKl+3DtQTcR+BAjKD6qq168jbT1Gt+b+QtigY8+fP6t5tGgbJLRrzCq4pf/576tLQ0g4OCFXyabD9BUMYCThntmP0LSECwr1jLCakMBoYrniQaHMdh/Kd9YsoZQUOG5gtt2/zMAizYPp4jAxOesc88CH267+ioAlJjNuIb7P/J7+TiaO3r4xS2XceWJd5L2DDblfSxLUWaNp7zsOnq65yOFz5OpJsaGuihgEjdccgjqRAasQygz0lT6r+ADm22fHy/ZB+faBrIpSeXIfq678i3yXMaLfd9ms18DQJPRRtL8C7vUtLCs+1yEgL5CkIKKcnhJD19/ej49uTit65JU79JPc0+cyKDi4NkTCMRv4dTqFYQNF4Vk7xdPgadBFsBpDGDkgvhBCAwqjILPl4+4j+eWTefwGSt58dUJsG+GniUGvTdFKa/JcepFq7juvj056/AneCA9icHbFam3mnBNiQgHUaZAZYvMmfAsHcEptLwVwBhQuI0Wwa/YfHPqS/zt7BkMdiQYbDLIz6xg54619I0wSD0TwOpJUhyVxFEeffMM/ISNchQYgro/diC2pBFFha8UQcA2JcUI9H6lmtmHdjG+dJA+O8zyoyqxS0L4daV0VvQTidZgpSHSMoDIO/SPkhQCBURpJYvPu4mMo3g1V0qmU3LBlmNBgNnuobYKopkgVl6h8OibIyDkU1fXDr+tJHh2D2Wil+6nghwQ2sSqOeUsvHMMyW4LgQDTIJBuQ/WFyVQE8aaUI7JFRHcOM+NRSAqkCyWrBim5oUBNSYHNPQmaH6qn/M0swjLxAgZYEmG7GI6P57sUQz7FhiAly/JIpVCmSS7skKvyqR9jkFtkUHp4gYH7TXb+xgbyw2J0r7ZIvVWElVBYKIcKvQYsMCRIY2ilpXAI4XmQyoLv4TsuQikwDQiFh5b3lkAkzNbdk6y//dds7e7jkB/+BSUEoVKD13/z3Q91Pd1w6QPc86ehxKtZGiI0spyezjzC8xGOhxkP8MSTP8KyPr1l6bUPb0e+n3vnve077ExMGdzW4eD6RZ5uvm6HPNfv0MVmNU3TdgC7lA4yPdbCoeXLt3Uo2udYp3Eddw5MZlRTF1nv/UPk/12H3fIdzLI/ML5yAdcs++8bnc7US/xtzS7cvnZXfN//P9twfA+BYrC49F++XmXVI8SlxZ9H/53aZIrN3aX0EiBhCAQwIlgkWfUEJVVPEw0OjSaJJb7OT5te5SvVb3PzLje+21bGL+WNbBOvZYfjCRfPhKIXJojk6YHPboDwsJoK/viDyzASryGFR13Qo9JQ9NlrWd09nx+t3xNLuuwSa6PgAkowseTH7Fz5RyY2rGCn+htoqL6VV3KjubZjPN/fdCjHjXiVQEMeL2zQ1VvGMW9O48jX7mXr1jhjzVYGBk1ufG0+961bTVX8UAw/SToDcSPP3Ggvab/IZXs/ynf2fok1V/yUQ0oPJaKAkMFdb21hfvxqpFA8s66cXf/wLezVIeRAHgYL+FmGirYKsMth173f5OHTdmL/8sVMmNDCUce9yMY/lzF4k0XJ+DQDBxj8YNUBXHTag8xu7GV2ZQu7nNLH8X94HjPiIiyJHbEo1MV4MXUYq7cOxw1ESdcE6DvKo254H2WDPqY0sJCE2nzscIq3h49i56N72Pv6TubevgYVtpDhIKLThCyYzTYNP1oHawdReQ/T94ZW6QXsEKQPCMHEElb0NvBs+zhevKoC8gW8EhMVgqqHuwjeswSZz+OhcMMGbkkCRjUQwKCoIGEJ/C0hbj5kJ0RWgQKjSxDMGMishygqzAzMWrOG8ybcS+jJIOb8PMGwzV7BzYSnC6qPyDJ6zCDePnD6z5/kxF89D4bE95IE3W686qHVcPyggbG4E1HII0yD4KYs0hcM/FeS118cyZY3arFyAfomhrBjJkQMBAo8HyV8vEwOwhYy5+MEFSgfPA/TFagmk/TSGH4oSt+dMVQuxLpiI/2BKPGdDHp3q6RzYpD8JIkEZKGIKBQRAQsRDaNsBwwLyktASKRSgEB4Pn6hgO/5CFchCkXq1sL4k8/n2kvuRpUIvAgUrQ/+DGnNLiVdfO+oz9N/eBjDp9SB5+L2ZYn3F/nVLw9HuD5e2KToKA4+7H1rAWmatgPQU3s0TdN2AOdOuooXtn6T8tDO2zoU7XNsz+FzyLYcQcyMURos/8B9fD+P77VhWqM+8Pn/izQKzI1spcsPEPDGvbv9ha7LGR3sBKAl8wpNid3+v23Ul55PV+ZhJlb99l++XiC4E4XYDxiufk1os01pIoNlGVzRMpnvDi+SLP0ThlUPgOd5rNo6ibh0GV//ElMDte9pq0ftwpsDBQqeReWobro31PGjTQdTEUzRnK3krMkf+XR8ZFtTHZz4yqU0xXr4bu1LjA1DXEq6HTW0cg4KXxj4wMZ0lEDQx1MefuYXVDduAGBt732s6r2c2zaPo1NWYEqXduqo3m0TXa3TcZVk/sAWAhNc7n55N75Z+Qivbqljq1tOSeF17lt1BZ4oMDFRQACPdw/jhNrNRAQkxCALN83iB/su5NX2Ft7KNIOv+NoT97Bf3WSebtsdqSQyX0Tlbfx4ELFR0FjSw2nffpJptX08vW4YHSNMltaPZFR/Nw8XppC1HSYEunHXC0RPhBnznyNuDCKFZEq0iyceKeOF1lEEdt9KYUUtuOVIJIWEgR80KdaVEl/bzsCWKMvrXFb2lpIdSOAGDQLZImX3DeIdHUQIga8EhuUj3KFvKw0pMd/OUvmPVsIwVANSCBxpYAqBFbCJnGeQH12NnYJsNkE24zH57beZeWQ/lScHufa6QymNRUm4acTmJSgRY7AuRDAXx827KGmx91WnEjUGCP4ljQx41J63hsKIUtwQOMOrsKVJwFGYg3naemrZUNvGGd96ks5CmKcGpvNI306ouM9vm/fGUSakfe5onUJPtJyS73Tgn9YDBQ8RiCK7c3hRCVIQbSsS2VpARoJ4IQnSRN5vE2kIAT7SNAl4HsVikbAnEb7AlRIjU8ARCaysi+UIigUXe2yEYsxBGB5mV55ieZBwMIQXACM7QH0oRdQrsnpLBcFjwqTGpYj9LDM0+sP3obsXZRmIZBzfKUK+iDAEJOOIdBYFSF9B0MSVBobnopp7qMyX8qy5GWNcCdHRIUYUJOeffi3jp4/guFO/gGEYHHDVlWxp9pHleZb86L8wjdC719Uf7/0+x+19IX3reujY2MONv7yfP95+Oqec8ieEgkLK4fgjf8PMWaP5rx8f/ulf6JqmfSb01J4PaUceCqZpmqZpn5Vsx2yCaoB87Dzi8a99pGM3bh2Nq3KYCEY2/vdy3325VTyx9SuYwuCoUS8jpfGR2t3UejCutwrUASzNv86wYJKZTU8jpcT3PTa3jiAofI5aejzDEj1MincQMxo5ufFQHOVRV3IEf1qxG8eWtqCAFvEFJtXe9J7XOOe6q3murp2AoaDT5fb5P+BbG66nz80hfcmrB13yvrge3fhTnupey/eHN9JQfcVHek//m1KKh1bOp8TM8IO1e3P+2AfZI57DVjCgduOengxb8wZjwibfHP9zAqEJbN7SgMCnyowRrl8LwKLNY6g0c6wtlvFfLx3AzhO2cmLp62zJxfnt5V8GIQiNT3HUIa+Q7VcslU0sXD8WhOLuvW6lJpDhuf5hzC/ZhAMsGKxkTqIfB0lrIU5tYJCg8lltl3LWM8cTj2aJ4NO7xcILRcAHc0uO4NYMteM6OXZyB+u7Enz/hCVEpaLTU8Tx+NbbX2DXUZvZMljKQy+O5dvycTavS/KNb25hfJ0ko2zWFAX9ns9vdtodAgpqPcQIl4wnKVozsGstfE+AAQoFq9chzizjmA1vseCWCbgBCzeVhf4M+YBLw28lkyenePviBC3t5RQnlBFY0Yva2kei3R0aZQNkhEBaBn17xYmcaDBvZCtPbp1ISTiHKTwKx/VS1tjDgYe3cuipfRz81zOpuT9HyYFtlLptpDYGaW2sQj1iENmaoZg0MevrIFtASQsZjSC2tkE8gbTzeHaBrfMjhPvqKUnbmNkivoQjv/s8k+f28UJrI739MY5uWkZBJvnx4n0Yl29GCZPd5rQipOIPF+yOG7ZItvuE0z6VdYPcfO2jFAuCo3bbE98KMm3fHhYtq8fvLpIaGcFMxnATJiXrHZSroFDAlAIVDuAIRbZWEF7WS7AgULaDZ/vkawwiWYkRT1AIS0R5BIQiG/QYW54i3RGipyrN4L5l1MUGED+y8Da0EvD+OYpECDAlhIPg+kNrl8h/DsAv2EP/JqIISw4Vz7UshFBDpVUQuE1Johs6CRV9hCEoGVXGVf/4Pnte/jekD8pSrLjk2+9JpAAUCkWO2vUC7IE8wlMkmko461fHcsFP/wGOi28KhGFSWRXhvrvfv3y89tnYke/n3p3a03jG9jO1p+X6HfJcv0NP7dE0TdM07TPkAOCpzEc+MhKciwBMo+Y928siE/jS2Dc4ZsyrHzmJAuB5q2iyBI8NLGderIMKaxUd7dMBkNJgeP1m2oom59c+xMR4G3PDm6hnIRt7f4Sd+j6vrJvCMKuX5cUoW5wADckL3tP+BZfey4tP5Dhu+BL+MPUejpu1ilEjqvnNzKMZHTE5e8JO74tJKcVtXe30EOe8dX0f+T39T7a9BqUK7FW6mQnRXqbGOykxgsTqtlBWv4WRDbfxg2kP8Ltd7+Pb0+4iEJoAwLC6F6mP7kWw7L9H76zL1ZHzLZamqylk4tw05w4W5eYwOZklODNFvhGaJrXxdk8JXZEydk+s59rd7+TSmQ/w8mAD3U6UFb2V5FDYKA6qP4FLl+7HM72jOO+pQ2iQBkuKCcaEB7luzi2URh3iNUXKTVADBkoJvrjvYg7YdyPt8SauDszhJycuxEUhABdFQChmNrQQNYo0xAf4zWEPUH6oZLfv91Bd7aIASwy1VW3C0X9dTcmIIl88oBl2ssiMTZKsXY+dLyJMwAAhwRg7Gs5tpTVmMGzWICPm9mCrPEbRJZZS9H/VZsGcKJlHJb/+1oMc1PsMtVuamVjRx4wjNxGc2wbDFZVHpmk/OMCu38uQEqV0O1HGlnYifR/rtK0EhGTc7EF2O7KfnGeiehxiF7fhzXJo21hKeiBKyQNZSiiAFSCWA8/1EcEgMhrE9R1Ubc1QUiFfxOgvUv5ICtWxnrxbHJr+VvT4x1X78Pubd+Leu/bgteum4qeCzCnpZFRdF2uz1UytamdUrIex0W72PGgtVk4iPEAIjj18JZ60CURsGpp6EUJQFB6DdUEyNRLZnyW6qp+y9S4UXQzXRjkOjlL4QiL6cwRf6yTcZ4PjoSwLVRsnkjfwUSh8go6PFwTfEIh4mFUDkF+WJvKUywnyFc4c/zKTL+zAGllPMRomLcAVQ/FRcMF2EL6PEAIhDUQsAtEweD7eO0uzmgBDz4dDAWJb0/hGgHwkiPKhb1U3+557HbHNGbygS2XCfF8SBSAUCnL3gvMxEyGUFAy0DHLLpQ9x8km7YQoBhoFI5RlY1cPPz73t37qeNU3bPuipPZqmaZqmfWaClU9QLC4nGd77Ix9bU3kLNf96t49BAgrHG0rCbC2GsWWBlZvGs++I1UgpmVK/irViAisyRa7bPI+iY3LD1OcYcC1qLY/VToQS0U2XP4Mpkfp3W87mizy4dhNUWMyKtFFuFJibbANgUulkbtvjon8ZXXng4w0eLgxegp+9EUe5OOYkSsr+RNjv4MY9j0OIf/1dmrRGIituf8+24ye+wKzrLmYg73DonKHz9b1pdwBw1v7fJ249iOMLrlu1B3NqFjIl3MWEUBEVFTwzqPjS3Sey4JtnEjJfxnZXU5L4Hn84xOSRDbdxyf5PkxJQazl4ymPnahfrcUWmJIx6GyLDfPbcZzF7JrYyfHIXo7Nprnxz96Hfnl/kvp5hTIylebsQ565np7Lbrs3MqzyC11O3Ywof2zfYP/YW3W6BlKeoN3w6/SDTZ6YI395M0Dd4OLmQQlFy1qa5+LluWl6rJNBrYXhghyQcP5U3f7iIr5w9j8eWbsTLd6KUGppaohS+61E/uZPbLhqDtKB6VJpdDu5i3uH9LB0oZdUbHqsDDdQ9neeXo17jsHQFK7tqSIaKVN/dRjNRjIYkL3bP58VLk8j2TprObuWYwTfoapY860/GfS1A1vcJjnCxhgOWyWCgQLlKohwfKRSgIBHB7+kFzyeY8jDeLpKIZgjtF2JgTRXS9kndNQwZShEoC/Cz7x3ERTf9g458KWNH9LOIOibkezDtABHbgbAkPzyMXDJIa79FQFi4ysedGsKod3jGH0tyYxoyaUzPwKsM4vT0svM5nUyc08Hjd46n/94wbjiE5SkC4Sj4eXzXgaBAZDMIZSISYWxpEPINMqUeQdskVwtsBlF0MZTDmsVVTJgxQLDUIWfnCQRNrJHDyY4IYr7UTCjnIAwD6QNFGyQoJML3UZYJERNl+6iigzIMhC9QUiKLLkIO1VPJR8OofJaKl/oQlolrwQu3/9cHXiu2bSOl5Ht/PpIr3ryRnisjeC2DGM8uRRkSHJ9gUIAPKxY347ou5qdQjFvTtM+OvoI1TdM0TfvMmEYVZmSfz/x1M5lbECJKNHrU+54bPWw92dxCvls5juUdt5LnKsx/jnBQykcISSgUpl3sxdTIq9yRmk1emixOlTIsnKJdBZkeTNNrnciete9NjGQKNkJKlPK59+7DOeOrq5hY/cE3Y/+TEILf7XQZzYUOppdO+ljvWeX+QlAM/annqH7M0EdPXn2QhWd+8JKuJ06+gpl3/ZC0E+bXUx7BFTaWZ9DphrGED3hUVAxQkSwFDv3nY8jBo74MfBmAeH8/XanpxPF44qv3s9MFX0NVSjxDMTLUxfhoP0uytbzR1sjXxz9Dl624tHkeyg4wtvRUbtj4INYYwQNPz2D0oZsRQjE93MrGYhkgKTEkru+Q9cMkDYcWO4AlIe+6bE4HeTxfyaTqDr5greaXfftx6Yy7+fHvvoQKQKgLvONm8Nj1CwAoVeDGYijTwM+nKSYNegthxjdkmbpXL8N276dmWICUH6MhluLylt0xVxmElrfTkUny+1HPcf6j+9G2NEbGnAN7+Dj5IlbjRqwWEyNgMWfTKjJJ6N0cwl+owPMISYm3SVAyroO0W0OdSnHuJfdz0elfRNkubrqApXxA4AQN3IKLoSBfEJiZfqLVikJHLfnqCImij5ETeIEIl1y4H31fjDOYiBL1C5x/x0n4ShKXA/gBAYMe+RqTTdl6JBsICoNVbdXItIEXtVBRExWIoHwByTAEfEpG5xBSUTLDZeAun+BgnmJAIfOCXFiQa0oQ25hD5VyMYg46BjAsE3v6SErWumTqXcxNAqUq8M2tGL5F+51hbq7ai7YtFYTiLYiOIkZzO1HqSI1pwlu7mXDeY6jIrIdwQZgKkIhCEYkYSqiUxZEIfM+HzCCeEkjfhFBwaCSJtPAyGUQ4wrzSCgzjvSPejvnWdXS8thEjJfC9An2/jCBfbyBWBemwYk3bIMJ2sUyLnXcfTcv6fvoHMhww75dU1yS59f7vfeylzTXtA/n/TKRua/52EMOnTCdSNE3TNE3boRULL9AxMHTzP8yaRnvOpqlkwnv2iUZmATCt4Qxe2XAZGRVEKvGekRtfGHEzD66ZyHeaHuPXG47g4i1zqQkNcmbdW/xo4S7sPWkUk95bY5bq0jjXfOcIUtmN7D97F6SMfui4y8PllIc/uGjvhxL+Mnb+rziyjNLKpz9+Ox/BuVNXMje8jCczjewS7uKpzEiSxQw7RXqYHO3mqNo17zvm92/9niVrBrn26HMJWiFCcYc1fQlWuzDZynH5t+7iksX7Mm30ZpqsHi5dNo+9R66HHps/rzqQO94q8L09H+UPt+/DnH33Rry0lK7mAkZ9gZf6NlMQSRb1NrDP1vUsnRBjeP0gFy/cmZNnrmBJppqo6ZL3DI5KthMwfEbLEP8YHMaAE6HoG7iuh9i1j8CrlRTj4CcU/DDOhSc8irM8wEUnzaGj2qD41QrMtjDJ5+KccdU/aDaq2SMRZGshxPFXfh0/6mCUZFEBC2Mgw1FPHcY+ozdwzTF/5+gxJ3N47dP8ecPOJHA4rnIx9/9lHAEzi7XOJB0toc9tRPXIoaV8XRdT+KRbSgg39pAbSHLlyZMJOC0UC3FMVyEtE18KrEiQQtLCNmwqsx6ZBTGCoxyE04mKjiaQzaGiQYQLhXUxKn5nY4zI4zSW4ieG+n+mUIJvpQnbYGYcVqxJcFNLI9ktYcyVGVTcojQSxystxQ1KbOUQ7i4SXNHO038dTsV8RdtrdRRn5zAHXURZHRkJfolEOT7do1KY63spW+qRrQ/h7jEOZUjMdJFQS57SIuRVhoFai8oegSkF/be6GCPShFf3YAgDvACFrkECNSUEzTBu2CZnF4kpiUQNnTcBGEAsBIMZVDoLsRjCEIiSMjxL4g9mIJfHMEzwFFKaqFSGdbcv5NYJDzBsdB0z9pzAM69tYMuaDiIZCb4LwsAuBinbDHZ1BGUIct2DRIt5Sqvq2O+A6bz0zGpeeGAxmJKOjd0ctO8viA8v5Wsn7smBe79/ip+madsvnUjRNE3TNG2HZhijkFgIYfKdBRfQEOlnfvkKIqaJEFGmNi15z/67jlyD8vqRZvX72vriuJW8tGEuB/W/Rkuojgsbn8EQguMnvsG1W4P8cc0SmjsTYJl8Y9oSfjrrH0xpeh4/83O62wNU/7No62chXHI+lJw/tFrMp6hgb2Wga3dsJZhSdg0r+3/M9FAHPYTZKXYoK/MriFjPUEBy5JSldPUOUlWeBCDrpLj5ok7CKwf54g0X8Mgzv6bodlJQFgAqdDB7Vz3IxD3upRmBaUBrTzmhUauZtFM7BwRXETc8Xu+vpfELXSxavYVnT/wuhz91BnkfDqhdwbRQP99bfwBLtjbyzf1XAXDh7lEydpHDkls47Ya94BHFlx4emnI1LthLqZfBWJnhO9Oeo9+IcEjZ6yyYPom2TC3nHPQkQenSmTOYPCXD2h82oQpRVMhGjC4yom2QRAxmGWkkMCxUZN9Dn+Lw0RtIGEXSR4eYE3XIKQcweLqvDs+2eLhvEgeNXs2BJUtZ4dax5/HP8MJNu7FgURMq70KjgxgvUV1AQsGAiypAbk0JOVdgkiAABKVPQYHtuRjSQAxrJCoEfRa49GBsLiASOQxnAK8rRcayCDseShlI18GJx5CbBbLZhoZexNhy8IFInGI0TbC9iMwXOapuEwtLylHpJEZnDrryiNFVqHgp0ghgZ7qwpAfP5uloqcQwCjg7RTAsE+EpTFdh2wKVy2Jms5ihKPmZUdya+FAC01dDrxsLkSsRBDtymAWJHzKQUhLMOvjLWgGJGw1jBgJYjo3o7wNTkJ0bo+CWYi3sxeotYgqB8D2oiCOioaGlkbv7IJ3DL0kMrejTUIryDWRfHq/oDdVXYSj/4inFn350J4GyMLO+0oDYtQ8oBd8DFOGgwfgXcqyfYRJuiWBlXFQgjFi9GSNqEgtKBgayKE+9ewfWK6GrI8XPL3mItvZ+Tjlhr0/5atU07ZOii81qmqZpmrZDM60GRjZsZkT9eobH2jm8ci1Khqi2clSZ3dj2xvfsL0TgA5Mo79h91Ctcvf9TVFqSkAGlpsfkkhSH1i7B8S1G1gyAb/FER+PQAZkrUcojSvFTfJfbTsvgg0SlR6nh8ljbHzhw3Ns0xk9kbCDObo1H8O1JfyLJMColxIH7H3vg3WPDRgwz62MUFPTZFHJFkqEpzKo4g2mlJzCq6mqs6tdpbFrB61su5ex7ToCww/19UwgYPlFT8u3nj+aeNTtTmcjw4MvLAfjtzntx2qhXSQRsXAyOrduT237yR2xCOAji8dMwI6fwg7Xz2Xm/Qcwrgvzqucks3xzl+UcrOLfxdRqmZ9k73sy9HTuxNDKW8OU2U0Zupr0Q5ffL9uT36/bn4UWzUJEAhiuxOkJYd6Y4/tglIAUP9NTym+ZaftU+EqMsRFB6ABSkic8/R0fgs2diK6FomtmRDYQtmw1eLceVruYbs1biV0gGQhUUwj7eQlDNCjHSJXekhZwF5CwcV2AAAcHQCjVKEQo5RMw8ftFB9faBUpT5gqJfij1iGGPmpkjbVRiDA8gQ2CVpYn3rcfMFKDgYOQcj41DVDmJZD74PwhsqvmwEg6T7yvjjA9OYHeklVpAYUiAdG2NTH3QOAgJRHkdEgpjhIJm4IjMxhOwwkEOT5vDxcds6CL3dQmxZP5YH7tgKRGkUJYb2CLZmibRnEdkimaYQxMN4gyn8dB4zZxMgQqE0hCnk0DLICERHBpHKky6CXaJITSsjPX84qdokhKyhaUeui4+iaAhwHIx8ASGA1n5E0QN3aASLchx8z8dzPZTj4rk++f4iL13fwgsnpgkv7+C0Cw/hjw9+n6q6GPn7+6j/cxeiJ4fMuBgO2ONq6F/Uys9OuIblDyxABMAImggUOB4BG6y8x82/e5Z0+qMX4da091Bq+3ns4HQiRdM0TdO0z43TJtzB31on4quh+gm+khhG/b887oPcuOtfcNXQN9Y+cMem2Xy16VWcVIDFB93K7TsN1dFw1AAKhY33ib2P7cnoijN4MVPDY6k6Dms8EYCg+2cSsoverqE6KPXDXqR/0GSvp0/monwbf1+3CAApJQ/+7QfMPXY2P7rsZGKJoalPI0pOZUz59wGwjCpMM8IP9jmGnxw+n4PHrWC3kvUcmWih1Qnzq70eZPfRq+m2I6zrXvHP449j38qH8L2xVJfey1mTj0ZKSXX9eqrqWwiH92BY2fmsHajj+IZ1jAl28bg9jUvsPXiiYVdAUB8Y5I9dM1m5pY6Vrzay5cBGVH+BhzdMw3m6nIG7GrnqpZ2xqnJ4wwr4ZX1U9xnMnp3miwuP4RfL9qHHKidkuJQYWd7K1NNsl1KaKWArxZL2ShZmI3SqAJdPvp9d67fwzHUTSBhFJGAJn1DChsoasoE6UrEAzIXZZ7Yhdi9h918N4DaVQkUYVR6hWJvkJ4+ew85n7EohFCZlB3ClhM4UwnURDNX7EfgsXTQTUV6GMA1kf5bwBrCDjZx+18vM2eVlil4RFbdQwSBlbpSavk0ErCwlMzvw6sLYJUEWdFVz042j8SNhgpEoIRFGZPLIjl6cfBq3roLs+ApSjQmsUIjAhjxBz8MTLn73AMbmHvyKHNgu7kARu78bX4Bve7C5k7LHN2FlPYTnYxR8hBEkMyyMSIZRvg8KgjmFFY3h+/5QPSMEhgJlF6lrEchh5QjXID82RnG30RTKYggEnswhfBs1pxZP+SilQImhhErrIMrzEFJCwEKZAoHClxIlJML1MHIFREMFNFZzw8/u47Rjf0fz4jYcBa6QhBZuAQlWxsYKRvEAvycLfXnklg7GjivDigZJDPqYEmTWQXqKs8+4+bO8dDVN+zfoqT2apmmapn1ulMWquHT3B/71jh/SxKYWbKcN06jiuYahP6t2a/gRcTwSpHiz8w3GmqPw3fVIo+kTe93tiRCCg8e+8Z5teRUkQBHTmvrutsceOIt0VREQ/GLxwxw/ZiYA1RUlXHzJye/u906B3w9y+Oi9gb3pT29hXeeeVAfTCBSeCHBu9Uv81Gp4d99kspFjk3f9n7HPr1Oc9PaR7FPfwIFTn6AuPICsUpRWPk9z5w8x7CxGUBFea/LrC25jWqSXcxYeyJZMKbYFAdfC2RCDpIcIOXznN4/R5Tq40gIFo2QHK4q1jAz1YDs+33vmGKIbw+Arcg0uU6uX8aM9FxCXHsv6o+S9Ir87ZQ4D58dp2VJBDMX3vv44i9c08OhrU+lTLrmR9VxTcgYnX/8ghd3SVK/2AYkn4ac/e5ivfmUOuz/+R9zBLC/cWEe6O8TKdRK/pgbh+shwCD8/VMjWjUNhfQvyOz7JWTHuunZXBt6K0ndglMhSiEgBpqCfKgabAgx2VzK+phu/WlJqmdy2ei+C5a0kCgbSDBAY7COULtDf3I07LYo9rgrpuKjuHAKByuWx2gr40kV5LvESKC118bcI/BUeaXsDuWEhKhYXEI5LqrGAHY6hYhZCgAiH6ZtTg2rroWp1EeGDnwzh2g4FJ0cw5xPxPBwhMAYKhHsVvgS60uRHBHAPrid2/2ZK5oTIN0ewRwX50rk93P6NEEaxgGXbSAEEgmBIEApVX4boGYCUDb6Hsj0cQPT0IWQpKmjhSwORDEOuiGtayOpq/EwR6SpU0UWEglAoooIGSMHg+k5KLAenIDhk/mzuePht7ILDhnXdvPH6OmbPGfOhrz9New/F9jEaZDsI4dOmEymapmmapmn/hoBV956fbeMEnk09S5cT4ner7mCgOI/fzfwhB4w5YBtF+NmrrV+LUjZCBN7ddvwJJ3Dzm7/H9U2SkdT7jvF9nyc3TcEXimml36Wu9Jv/3/ZL4020tFzBMwN3gpRUm738dtF+pD/C4KLmvg3UOyN5/MADKQkP4/Kl/2BVtgFTePzs8Qc4YcoKdkuYLGgfSy5WzehABg+4YtoTHPHISHwZRMUElHpguXjEqR9VICgVnidIxDJcuXwvjCdS2LvOBCSU2RiyQNqNESfD6r5xBNyXGRVRBCvbuG+BS9LNcNzYVUSnSl4evoVdJ29hjxlrKXqCqOWSej3ISQvuQwYEQTPG1r3z1DxVxDAkwodb/rAAceMh+BjI7FZSw30C81ziK9spFsvxUYigonx+B+l5kNgKigg/HPsC3rmK3/xoLsYSj8wu9aQGiiRDBQZHh0EVMCc6rO2uxhcGaxUwWZKrLsd5vJ8KV6FiMbyeQayNg7gVbchRDfgKZMDED0p86SM6B1HpHAHpIZp93A0KZQhMzye+NkNJi0NO+rgVJl5S0jczibVZEBSAEkhpoZpqGfB6iOQVfm0JhaoIKuqTyeWI+0sZaK3DHV1OIZmG2VF8D+of6yPt5wmEAzgvuBx313JmlXWzLlOKWV5KLl1ApVME3H8m8gQY0kBu7MIBZNBCuAJbQdD3UIUidPXTNyyKNzGJECFEb469pq3i+JPWcvsdO7H8jgbMdAZMAz8cQto2qmjTuaQFAkGE43D7xfcx49ideG3ZAL4PPzr37zz70s8+fEfWNG2b0FN7NE3TNE3TPkEzqqZy7tv7cfHSvRhdNgASznz9LUbccjGvtm78l8fvKP5nEgWgaXg1h6Y2Mbt2M1/0V5HPv7dmTM5pRYmhZadbsv961NBRE49iWCRFS38C4VgcOCPKn/edjFKK1u7j2dKxP76f/sBjX2q+kKvfOI9dh13E5u49cL0MFaF9GR7pozE8QLj0BUYGc4yMpDlnxiKqMkXOuuA4TGVgmIrdv/YWxYCNGFTgKmRQIXpClEibVsfknpm3M+JXnQz7cY6GZwyG/7aXM+c+wYXTH+KJw/5KIpjCMyycbIiVhXoUkGsJc8WTj3Dzy09TGjaJCpPRyW7eWcx04ohOzjn+JS48+mnGD3tnu0JIk8y0BEXlIfCGaq9IA3wf4sOIbSkl+EwYpzuCIU2kZTP5t2so7KVwMRDDInj1YbIiRG7QIOWCcotQ3o/axaDp4DYQMKaql1AIlB1A+BIcCzNlQm2A7926jPPuf55AIg/JKGErTOSVbtzulRR3aoFwH9HhKSK1No7ox6rNkY96yDdNLBXGjVoY+xl4ZSbCNAkpk0jaoGydouKlNHVrMqh8gUP3XsIrv7qJ809+DKe+nPzoCpyoSdFwKBhBqK2hvX5/3NIQodWbGfVCM+GFfUCB9AEFStpSBNr66Bjucc91w7ln+Thu+PYk/LmDJIqKIAa+YeLzzy/1PR/hOFi2TT6bpTAyTmBYNaIsgYhEsXEJr+sm+uA6gi9sIhHuY9Gro/ivqw7lmyctJLeTwlMSL2jRFwmgDBOk+OeoAR8lh5YRWnznMoKr2/D6+slVWOx93OUf7WLTNO0zp0ekaJqmaZqmfcIunhnk2e7ltPYnMCQ4ngQEFyx8iifr//8jLT6I66XwVYGAWfXpBPsZOnqnn/Oz79/G44UGXv/D1dz/0A/efS4WbCQZGEfBbWd61Z0fqr0fTH2Q4qg7CEYORBpD58d2tpApvAjA2q4bGF/zX+87rjmzgZJwHhhKRoDDyaPO5Ter3wYc4vljeb6/ixBhuvoc/H5ARlmz9jH2mz+FyypW8cWqs5le9WPiufPY75md2CIb+e76AzisYh2b7SQHnbGSu3+9C8qHCTuv4OE1k7hx9hPETcmjB9/C2ev2oyycZnpyC1s9GKwVJKSgwxOYnk29GeCGC6eytSVJRa1N47yNsJsAYDAXQggfqzRHab/ktK88wD25Wax7tpKml4sccMgqaoYPcOf1u5IlhlAGOC6TvreUqTNT9BglOI7LgAjQmivB8B1+9eiuBBflGJXpJhKXbC4tMHziFprzpYS2pOgxLIgVEEELX1lDX8cqk0hlgUPLNwDwhfM28vCPp6AKRQJKUvq4DZ0NWOOLzD92JZ4veLBvJqphkOwah3C7Q9FUKEdiv+Yjii520AfTQAZCmMqgsKZzaETJ4AAzTmkmIH12HtaB2NyJ60oiAQszEsZNF7FtwFSIEWPIl/aT7k8hXRuj1cSvCdA/SmE0+wSKafqUxeunGgRVP2JLhNTEcgJvZAgpcBvLMM40yf1uENFWxPMUhgBjbT9eQOLZHumAQSBqYGQFvuNgZl1CuHgDNq5dQo8XYvZeXSx5q4TgYJGoZdKyTyWipoy6TR57jqtg2YJ1pJoHUa4CU2D2FbA3deJES9lzn19x1W9PYPpOIz/OpaZ9Xm0vhV63hxg+ZUKpz8G7/ASkUimSySSDg4MkEoltHY6maZqmaZ+CFzfcwV+fW815hx7P6OoJ/3Z7fX19zHviCopeGKUkTx18KqPLqjj82R/Sbef5asO+fGPyF/+/xw9mX2BD71AB1+Flf6Usts+/HdO2dsY3/8CqpVvxBZTuUsYDV373E23fcVaxrOXr5FQvj7bM4qK9bn/fPr7vc+Yj36E80MkRE45g1rAT/r/tff+3R/L6w9MwZYEDflbJ/uMPYUbVUA0LpRRexzgE8PuFExioLZCXFpszpUh83B9VkerNctCpS6g+xCMqPcrNDCkPpho50p6BFx4qQrw8V0VJIEdYeCTJ88sTTsIyDFK51dg9JSjCWPEig40JUgMGVb9I0dpWx8j6FhKWzU2jn0MKeDFdTqyQRgjFLZfOYG1qJLKYofHHPeRTFl+2FnODuws71fWhijkaUr08cu0MBhcXKKkoMmtcD7vOH6B6dg9fXfYV3KyBkyngvZ2mVA6DzgKyux+Zy5BqKlCYO5qrjnuUukCaS06aRE9nI0Y6DcUcthmAnUZiJArs/cNlHF/1JpYB57y+LylVQ/uKNMVUKRMP6mEq63n2quE4Cw0CjkcIAwkIw8DAHxrN4bgYcQ+iUGwuIMwgKhGhOL4SW3i40RBK+ohYZKi+iV0gmlzNxH27aQuW4qwWyN/lIG1A2GawK0rECoDnYisFQhBQCi8SoHh0CWXzbAbPyqAqw7j5AlZeIX2fgqeQUuBYYMcVfgoiBUUASSAE5V932OWIAV68bgLNbxYodQMYvsKxDPr3H4ZvCJAeh88eycUnH805x1/Gsuc2IGybIuCNrQMrgPJ89t53DGXl5UyaUs8X5k/9//ZT7V/bke/n3nlv+9achikD//qAT5nr2zzdcdMOea7foRMpH9KOfOFpmqZpmjZk5gUXcPChb+F5gp/PvBfTtD7x1+gtDHLkC+fjCUVAulgYPLHv7z9w3+aeH9KTG0oEVMfOpr7s+594PJ+1rq4BUql5VIQdfnr/PK456y//dpvpgStod8bz0+VP4RVtfjPhCf62cQpvrhzFDw79ErsPm/Wx2971Oz+n24a6w7rpfKKR2Ig+rjn4QUotl7LEFZSHLHz7DW7cWsmq/lcYAAqOgSODJEMevg8951nsdukmTmtYAQjeHEzwQmo0/Z7F8TXLUcAw5dBqGCSkw/Prq0lWmOxf2sziXCUHDH+ZUCjM5uaxLBuMsay9jDsvmEpwTh8//PYSOttg+fIRmBNcTmhazcMvjeWuJXOxw1kmzN1KS7qG2rI0e9orePntRsaPGKCpuhm3vISDIpt57LcjeOr5KkpLUhx8TBv9XXGWyxKekOMwSypxIzBz1Cbe7qxmSnATzRcGkH0+ZYUCedcgEi6AMiAmyWRCSF9gKA/hAaZB/Rf7OfCodvacsRWEIixMijmDK1tHMrkqR28qxK1fGk910cMrKvA8MExcpcAw8B0PaQ4tcawCBiDwcvmhmiPlJQxOKCdXFSAoA2SMAjEimG19BJd1gOuR36mSoNVDSdaGhQqUCZaAnA0Bk4KvEP7QLZFnGkhDEaqDwpah+L3aOH48jLepg7wBZkZRooaWSAaBROH7Cl8KhAJlSvz6KgwUynNJ5XMY48rINIVRMoSIhpEmeGEQOZfTq5rYtGQLq59ZgTKCFAV4o+vBkijPR4ZNJILbbzmDQNDk57+8j2Q8zK9/edy/e+l8ruzI93M6kfLZ01N7NE3TNE3T/ql2bIoJyXYAmnOrGJn45L8BLg8lkdLHEFAdziClyXELTuHOeX96374NZReTLr6JkKEdIokCUFmZoNRzADjxoNfJ24OEA8mP3V5v13GE3de5fNVeRKNQMC2++fIh5Lck6FFh+vylwMdLpNi2TW5kARkK4SuDYL+g4aB+moI2Lb7PwOB36E8bXL91GnMrN1FX4/N4+1iCAYO0CgIgJajNkrX3hdhwWoLNqRKMYJGJ8U42F5Jc07wbh1Uv4RtPHoVIGxhNRY4b8Rov9ZeSxmePQAsd3VMY3rieLWonZpYuYLTZwW0HzMHdOJ5fnjua6KDC9KBqfCdNP1nMHWv3wjcVtbsVOah+PVcvaWCfspU8tXUCyUQ/z95Zxx7jChy823paApL2siJjd+pm6uxW1q1MsvCpMlK9CcT36qBPYmYU60rqMAyPVq+eymGrqB8dYP3GMOE2B2I2ogRUMUwsm8X3JCovcT0fhGDNY3HSs6PsNWMrAkFSBLBDHsHOAr/50xeIrPcoy/fgCQthuijPQUiFraB+bjtjz0nR/pxJ2+2NuANDdUWsoANVBRoPXEVqQYK2tlEMNJiog0tJuzlKN9rIgo90FUbaoejFweqFiCCTh7ztIRC4tkdtNI0qxOlHYCiFcqHYLIgoH18IzKyDX1GGOXkMIduD1i7ozaBMA8Nxh2qehCxM18WXAuX6+B1diPoKVDhIOJXDWJ8iUAzQPd9gtLGIlmdLCRc8vECQe/o7UcEgIUNieB4B14O1m8g3VIA0IRQfylPFg9xy+yssfWMTGJIvHnE5D/7j/VPXtM8x3wf8bR3FP+PYselEiqZpmqZp2j/dc9SvuWzZKSSCMUbEp3xqr/Pcfr9nzyfPwhKKkYGt7BrfzCNrxzE+djGj6o58dz8pJZPqn/3U4tgWhJC8XogQl0U8ZdOceZRxZV/6WG2taZlIjAxhQ3J42Sp+07YbqVyQnkyEXx8awA+kOGTYSR871kOv+CvZYgLRJ4nV50lP76G9M0h+nEApiOBTZ4LnK/JegJhR5Esjvs59za/RabdgSImbEeAI2u9s5OpUJVf/5DH6ioqfb9qHXUrW49m1uL5EBW1EKoyQirQV4Niq5WwcqOGot/ajobqX+k0nsrh1NP6yWfimQXBQEPQVJu67K422ZWJIqcDxAYFFkenBQY5oXMoflu6Bv8ImsTHO3ocvZVp/BxuXhCjkTAZVggwlbHzTZsnCStyeABOe8ZjIGzy9eAqyNczOVSt44a1J7DJ5JfJ0l8tnr+M3D0zj/itj0GpgdgAIXBNQitIKD9kJSHDT0PXTIOc/M4Jzr9hCwbQperClahiYcbJTfKJ9eax8lnmnL2L+sX1MCfvc2lvHAx3TWOGX85OTn+eyPRr54fDHuPbBGaQWSKbMaie/WwmNp+S5rOxOBAaGUpx62x60J4fBNJ+MXYZfWwbSQ+w5QPqpIPkuoNtGDAoMRzFQTOArRcZSyEYf0zUIdwmwwTMlMhlF4SMVICViWC1e1xqE5+EZEmlaCMCVEiklMhwg4Dq4HYMgwPN9REUEp8JCpBRHnr6R2y4dAaEgrp3CKTFJnTAZjHIi960k3qEQnkl4bQegiO49hmtuOI1kPMoX9pnE3X9dANJgoCPHV756LX+9+ayP3cc1Tft49NSeD2lHHgqmaZqmadq2ceYr32J+xQsY2Ax6MeJGnqAYycFjHt7WoX2qisUCL7fuSZEwBwx/BimNj9yGUorOtkZMFBk/SE3Vk8z40z/IWg7mZkh0weI//PhjxVfIPU++/2RwLeb85RQ8FUS4ivW/PvfdfVL5t/D9HFt6vkZcONzWvTuTyw7niBFHvaett15dzY9OuJ5sWYR539jMDw95iZgMYAiJAtq9HAKF8EL0+y6XLprHzMYO6kv7+eOWuazrrGJGUysojxWd9fhvluCEIJAWBPpA5lyMTJH+sM/0r7Zz4oTXeaKtnlWbS7l1j1coC7q4Co7948Gct8sSFvXWMWFSMzsHemnvEly0ZBbC8KleJeluhYbEVg66oJ+o9Hikv5G3eppYcWGeUKXB3j/oJ2D4TJet1GUr+N0js9myQkBXP4E1AwRdge+5/HL1W5hC8Y+2yexbtomrdp6EY1goy+QPi5/AAcJC0F4Mc+olpyKEojr7NvVHDfLdaUspEQpDwimLDmdsRRcb8mWc2fQqV2+Yy1UTH+TuzvH0vmLiLPTIUEbll1N8f8oyLEwUgofS9fz1jZkcVf86mXCCJ382i85pWcLL84RbbQhYFOtLEDY4xSyJ11MYngtKEY859AcjeMNiGEWJ7bjsMWITSzqqUNYoJBLPhGJfJ5FgEhGN4rs2qrkTmbGRKDBNhOuAGRhaoCefpxCQuN9uom5KD6dOf421S6p59KuVKM+nR4K3ewVMrsNv7iH+xiCxogmuh2v4BIouDTNrGDdpJEd8c196Bwr87Kf34TkeoIgkgzzx+E8+Vl//PNmR7+fendpTder2M7Wn64875Ll+hx6Rommapmmato1cN/caOlLLWd59BK4qMjE4QEAuZk3zeMYNW72tw/vUBIMh5o98/d9qQwhBh99EjDaSJb8jFBzNyjP/i1mHXwQI8sMFr28cQ28uxFPpMVR4X+cnux3yodrOpC4hIQP8tH0Cx+7/Cs8umsr3d16A552OYcQASIRnAFASXQfAz4Z9cFszdh3PoV/bnb8/9DYPLJ3CKV94jXDQxxi65SagplPwV7MsG2BiLM1Rk5dTF8wzqXIJVmEPzm49lO6sRU2kiOc7uMPyiC1BimGHYpPDfnuvZWJ4K1c9eyDzGjcQM/K81jmFgXyMtHqTMlw84M+nPE+1YbMbndzWVUVfJExXfYi6mMcBJb3MP7KFovJ5IWOxc6iIAlKJNgKZIlf+fSM/fXs/uv1y9ohtpUuVUBNYx5aWPZGVCmJNBMpjzEgup3pKmqgxNHVrUkknk+M9TL+ll8UnV+KHQijHwLA8XCX4R8covECa87/9DPvHm8n4Jt+cvBt9lqCsRmE29bMxYSFkigu8yVSG+3hwTin5MT6zJjfz9CM70b8lTiKXwrtIEI75CHwKysC1DO48ajTOLSPxzzaYlOynMMtk43ILMyJxusrBN7EsQSrWgvVWN5F+j+TcLN0dFYjqJF7UpvofA6zrqySEDfn12AQxBIgZNUg7wtAPAYzhDTiWQBZsWL8VHwjYRVxp4JSFwAzQ9PByJh/mMSyQITWhDE+BoRQxZVB4rZvw8z0EQhbK8/HDIXzHxS0rpTC8hJW2w9pHl/HC86s5+at78OtfH855P7wP4fjkewscfuBF3P/Yx0scajsQvWrPZ0aPSPmQduQMpqZpmqZp21ahUODRzTszLdpHTlmUSpfyirsIheZu69A+M8s72jjs7tuRhmDdmR+/Hsy9657kV0+9RM2Ibv446THuSY3gC4ktXLhuH/4+//11aD6I43Qx0DGTS1rnMLtiE0XP5Nh4F1b1Egzj4/0duGjhWp5+agl3V62nrKqNy3c6mKk11YjA9Hf3Wbr1YGx3HR1ukAnBHiIijEMDe918CL4JsRG93DjzPqZFHHZ56ESmj9jKiWNH0bdxPy7869PEd+vja3MW8kTnaFa01GOFXS6e+Ax1yXMo5TyaghLblyx3hqYDNcoiAUOiEFTIEFnl8WYuxuhglqBQ/G1rA4fWbcYUPpct3IdgVZai7zMl2U9vT5DvjdvI2wOl3Pz0SH562Is0BaP4vs8rRYkpfe5vm8I3Gxbz/GAVXzA6+PV5DWx+YxKeDdLz8YQkH/U57cm1HJTcTNY3OW3qPLqTYI32KRnpQ4uk900D5XgEPRAu4A0tF+xIScgMEC4W6fY93IBB/dUmnZFSKk7vJO0K0lc3QnmY6tgg+1Qu52+P7E1sai+9q0OIwQSn7/0gf/nteGSnhLDi6MNXUzYnRJ8X5dnvhqlNdDO4vpKymkH6t1oUUklEKMjgzAqC/XmsYDmi6CLCFp5QGI5CCYXvC7BtyBWgowcrEEIUCzSeYTPjsAHuvmgi5oJ+ZCaPAwhfIQQIBUIO/aukgRsw8ZJhVDxEcUQpfgFUxCToFDh4bA3PPbEWlSwBJRgzuZI//fVbH6t/fh7syPdz745IqTxl+xmR0v2nHfJcv0MnUj6kHfnC0zRN0zRt+7Bucz1BUxIUPrHE+UTj39zWIX1mjrzrVhZ3dAx9oRq02XL6x/92vWfwTgZTP6LgC55N1TEn1s3Pl57EI4f99EO34biD7HL/xexev4HNg+XcNu8IovEvfOyYPizPG2Bz29DS25YQRKRghf1FrtlSYHysnW+VraRbhDnkjhN5+kvXkxMRDBQTh63n+U3jcZEMN3M83VXJETX9VBiSlK/o9XMoZXLSPcdyxv4vM2CHObZy7VAxXBTLs1UkLJuCl+PtQiMHJTYRlT7NhRCP9k5kRngNImAxIngIvriHvnQ58yr6yXiSO5bUcuCktQwLRgF4ZDDKhHA/5UIhJKR9E1c5LBss4ewXjkEM9DJ2fTcs86E/AE6AiQdupXO9R7EvhHdWKd/ccwXfvvdwPAG/+OIT7Bzv4LIfjKL58XJytsAPSzAMiq5LZaMLPQJMm4HxYWYc1k7PW2Fa740Tmy5QZyQZX9/JmytGMqjihGrzUIBin03TnW34aw3oLzDoh/DnWFR9M84Yt58r9n2DovLY5NoszFbyWvMYNuRimD1txDYoUqtNaHdot3Ym7gFYCE8hjKEaOlIplOOijKEVh4SQFAoZrJyDVAJzMI2ZKeAA+ApPgBQCWymENAjwzugCRaopRkgG8SoqCWQclFsgVSgQq6tE9qcwXAWJBEefMJtvf+/Djbz6vNmR7+feTaRUfH37SaT0/HmHPNfv0FN7NE3TNE3TthNjhrdSLC7DcxYTjh6zrcP5TF20134ccOctKGNo1YnfLDuNc6fc9LHaqkgeR0XyOHzfZ4zXR8Cq4JERH60N00gwIWTz6qoJ/PXgY4nGJ3+sWD4qwyghZI7A9poJ4iMQTCj/IncPPxAAx3HY9cbLoNKhX4WYHPBRwED6IQr//NM+qyTP5qZwGC8iGFrDwwCKCo7c+1UCymF4LM0L6XL2jPWyoK+JyzbN4pKJT3Huq0dx4+630+OFUDg83zqKmbUdGISZFOkBbqU0fivjqqbwwvqz2dzVwVemrcXF4NbucipCGX791v7knwnxo+Ne4tDJG6mSiown+Pn6A0EFmL5nlsGdy5h/zhoOL11HmXARwiBsB7huaSmPZsYwLjiIp8JUTWpnr+QWbHx+etlaXj01woSyFG8Fy6hwbUqtHJ4K0fx0kPVvJZk6I88hh/fSu6vFKY/tTOYVgfVqhhVemMw3OzEKHVS3FWhfFSLZ5+F7FgQk+AHCro941aHnlV5GHt3CyzLA3c9PIttkcOQxbzO+vpulm+sIj4gQm9zD5We+zLJMnJfvHuStXWfjCYONN5tUro9iAcL2UL4HhgQECEkoloSYjxs0yMQrCT++nICUICClIDlOYW+QOAYE7aGbNaEEga4CEhfZmR4qqhu2KDdCeBu7GRA+1FYSFz53Xvs4e+wziZ12+ogdXtO0j0QnUjRN0zRN07YjweAUCH56KwZtr8ZXVbPP+BJe6tqK5ykisuvfblNKSUBWfKxjhRDcesiV/3YMH0d97SsAuG4RnyIB87+/0bUsi43fGiosunTL/Qz6aSRQEp7H2MTRdGduI+dJDkh4XNwyjWMqVzMq+BXKymZQEt2XDnkRA8U7KfohLN/hr22T+d5O92OHLmHAOZJH5v8VRYjD3jyK/xrzFDes3ot53lK+1LASGFrpl/R/EW5YxP5Tb6O9/ct4ai0CwWsLGlnw1gxC7RDzFHPiA0MDKgSYUlEf6aPfLac/F6A0WCAiCrhK4AkokQae5bDn1D5O4V5+vmZ3kqFe+tqi2NMtDGnT51o0jPKoN2HAztCSqUSaFjMjA4w4ssD8g/rIiiCWMKit8Zl/wVZW3xOj9eUEeWlQ9geF5Sv6lEXUcME0IO4g9jWhAuS1EuV5RKRk2Z31rLm3gYIw8EeXc2H7UTTt2UZJWRZHGSDAAarCDqUHxjEHXEJBxfCvuGTzDsc1vcbz3cNZfuEISrIOIWkghEAxtFyy8A1EIIC/zxSKAzZy5Tqito8932SXr/az4IF6ok4tnRX9yM15outzgINhGih8VLYI2BjlJZQZgkJzK9l5Iwl7cb5+wu/xMXj25Z9RWjrUd5RSCCE+qy6saTs8PbXnQ9qRh4JpmqZpmqZtL97suJ7N6Vc4YuSNmEbk325vScfXydkrmVz9V+LBcZ9AhDuO1v472DxwEUWvgr1GPoOUku8tuJ6XOzZRsH0yxRKi8SxV0RRnjIwwyryfKuFSGYoicNjijaY/txhpSR7sGsej7TtRcTcMDFZAzsMMFPjbjTeTNAVKwIbBcqaXZAB4oyAxfEEiZ7OBMCODBerCLouKEWaF8wSUgS99TCQF5bGsv5SaeD8RHPqEgQIyTpCIAeVWDgBTRakyBEsKPqVmnrRnsbEnzGt/Kuetvw9HZRU24CofH4UpJHaJx0l/aOGF86tpaUkSr0rBFgmeSR6JCkm8cTVkJ8QZvfoZsrOSVO8LZ45ZiWeG+O75++NvHKCi0cavhXRVAJWUHLv3ekoCBW5avQeXTb2Xs988lMw/MjSsyuEkx2JHLPyYSSDlYhQ8QJCqsShu3cywZVmIhmiP2mTmW9TdD6FcEfyhqT6+ryAWASkQBhBP4MaCqP40VjKCGsiBkHieS8azkXuPZ1dTEswVGDt9JHseNZuq6nLKy0u2TcfbRnbk+7l3p/aUfW37mdrT95cd8ly/QydSPqQd+cLTNE3TNE3bUb28ZRzgUZM4i9Gl39vW4WxX+ltHYAlYZQcY9CLsO2rxe54/bcGXyHom1aKfw0Z9jT3rjmJx6xcYsDdRJmyShkdGScol9HgOp6w4ASOvkJeEMBXkK0IM7K64/PC7eGPzaO7pHM9z+9xP3CqyLG9QYxW4ZusM7l2wK8F2i9DmIoVhIb4wcQHf2+8tEgGHgDS4dWsTq7tH8OvpL7GlEMEx05jCp1IJXsslmRUbwANKZZCokGxwcsSlj49g96dO4bG9/szyXBU3XToR9aQBpS5z9liPiEpeWzCKrdESCpNKMKcaxErzRII+0VyW8ya/yjk/2YlibyXR11pJ4kPQY84pm1g4YyZ1GzpZdHs5VleRYsJD1UuCUUFNWY7SpgLRyZJeJ8KV+77CLetG8qIxkU2vN3L8hBc5Y+eVnPPjeTSvn4h0fQia5MtMck1BVNYj+vByIgUJ2RwGAmGZKM/DEwLpuniGiTIMnJAiZAaRjgLPw/dcRDKONExIZyAYxFOKvK+wx8cQtRWY67opa88hyxNMmtnA2ZccS2Nj/bbphJ+hHfl+TidSPnt6ao+maZqmaZq2w2oqPZ/BwiKGJ76xrUPZ7uSVwBKKnG8ghfG+52+adwcZO0PRK1IeLgcg6zTjI8giSYgiMSEp+iF8HAqZPHUleTInSDIvj6ZQInF9xRXL98LBIB5z2e0fJ/DY4dfzQnYSW7IlfLXmVV4oG0HPYDV+FbgDOR5dM5szD3yDPqV4sm0ks0QvLXITd6wr487michQgNNHL2RlppyWYJC8DDLD6OYpO8j4sGJ9oZRp0T663SCRqEvCEKz3Smk7qJExp3RxVux1LDtHotTjlvpDkOkAftwhVpsll48RVymunPMQ9VaQZ655iRtb6/D/YvPcX4aDLXn+z+OIPF1gSUUT7nAI1ric+KXN7Dmug2sWzGbzDWVsalGUbC7wyO9eYkE+wvBhWR5cF8ZuLPK3rl247do9mTVpMZd+/2a+9aMDkWYjdp2F74OUBsWjx7L/Hg/w8I+nU9I8gPQ8AgIs3yMtJXEA5UAO7H8WpVW+jwe4vYMIIAhIBYZpEEURXZ3BWZMjFxN0Ta3Cr07Q++JKlk/6CYH6EvY/dme+eu4Xicfjn1kf1LT/VDqRommapmmapu2wGhNfpjHx5f9zn2e33kDY+Q3LcvV8fdyTmGbwM4pu26qqWcuq7ospC6WYUnf5B+4TC8SIEXv35/EVv6V54DJKyWOJTvJ+EptyxlY9xNJjwwAM7HwN8yIt5AZLMRoz9OTKWPm1X7Bo83TispMV+TLKRIbFTh0P9E9hWFMfvcUKnHIQ+QB+JMeDG6cwurybl3uHcei4rexXCjuFB9h7xAC3dY/g3MVHIpvDeBUuTx16A4OewYhIlmrTZ2TABIK0F0zyRdhiC/YLbmZzfZRN+XLeSA7nyIaVDLgxjGQeP22BDQY+CCgzcyQMSVwaLMuZHFPTQvn5irqmInf8YQLRgksoNshx+yxhyjE57hmYRTIW4tItu7E0OJqzb3+ccUYn1y+aBkAEeCHbQN4LYZogtiQxioKVa3fhW2/NxjQ9DM8j7blIwkgEsWQ/jz9+NMFdDdJ7gTO5n98fcCvPrBvB43ftQV+VZPiIzax5tY7I0izemBKmnrmasiVF3rg0gVsbRxxqULi8C+l5WEIiDElASgIZhd82CEJStAwyiSh2Ostf7l/Mn5dvwe3sZb8xo/n5704iHo+9r09o2y+lfJTyt3UY20UMnzadSNE0TdM0TdM+1zal72dOtMj4cCeL++5m56oTP5F2Pc/jh8+dwZZ+xeEj5vHlWV/9RNr9pJimyZTan32kYypiB1ARO+D/3Kek9Fus+CbMv/uXtKRMfjd/OAAzhw9NHRr7z/3es7j3fh/c1mEjrscvpFk90AYMYAofVwRAKPxkAYI+tmdSKgosypdSHR1ECI+rFuxKdESR1/e6i8XZOm7smsja3moyy+LMmtHPS1Y90jZ5fL8H6HUshgX6eHaNhVNTwlFVXaRRtLg2ZUaR5YUYu1kZjvzKAJk915LclGf+nhley5WwLN/IXsk2Di7ZxBFl60lPfZ4BJFGheGvxLI5fHOc3Y59i//ganpUjyfgmhuKfyxqD4SsMCdKWVLRIlFPAGijgBCIwRg6dBAF2d5L1Xi1vXFmNOdiPP1hGwxcHWL1+BIHUAG6nzarWJo6cuBxpK4yUSyGSJO51gJTkpcQICLIhi1wwT6xDEZEQshX0DmAhUN051Ko2BPD6og52f2oJyoBoPsc+B0yntCZBd2svhVyG4SOHkaxPEjCCzJw/nrETRyOl/Eh9SdP+k+lEiqZpmqZpmva5tl/d7/hHyxlsLZZxUdMR/9/9rlxxOIZI43uCs8bfT8D6v7+t//OyX3DAyBfIekFuX+5y6919PHbpOR87zkyhk+dbDqTXjTDCamPu8GWYZvRjt/dpe+aYj5ak+SBm7AyIncHusTR/33QIGwtx4vYxbPraV8hmsxz91I2c8PLJnDAyzzlzfoVlWQBsLbuG5xZuZvm4JN1ekH47TGrA5PXTd2JBagGWWcnshsv5wS1P8pP9riZlechywRerejGlybL8Tsys+Rv7/vkqnj7qeorApmyMV9zJfH/u8ygUYVEgTJHmYhkAfXaQgFkEBa4SyEI/zfkaDnnoWxg9FiLnU7U8T290Nd6YahBRpDQQHghAoED5CFOSGRFh7oGvsODFnXGFSe3MLZjSgXgao6YKa4zNG/cPI76pE7vapDhOMrt0LTWFbkKHVNBfEaWhvZm0FKAUUgqEsIhZQUwRwg700xOVVAz64CmEKTHU0FLaBkNLLicFKNtDpFxe/Psb5IUAoSgtlbSu66ZQVNh5m8dvSzJ6wnD2PWF3dt57CoGA9W//3jVte6cTKZqmaZqmadrn2rDkWL6bfOZf7ueoApb0SasIvdl/UFty0v/dbmwqnrqLtlyS8WUdPNQ14d+K84W27zIl3IVSsCRXQU/m79SUnPJvtfmfIhKK8/UJL7xnWzQa5bHDPzgx9Zf9vvX/HeWyV2IUrp+jKjKev357PPAdAMY3ge/7FN1BDgiUAhAqEzzfOpp9ajdRaWY4u34BN64Yxw+mrGVKqIDKbiUZb+O4JQcxz2+jfvM6pu9i8+jNldx8yD0srBrJ73v2Zrd5z7BTbS8Pdkzh1LrVnFL5GH2e4PG+Edy/cQL1gTwb1lj4r5RAKgg+hKRk7xOWMq96AwNZuOcHw+DtBH6tInRwjo1r6pm1/9ssK5RyyJS3mFfTTkMixYzaLgazA3y1cjHFr1v86rZdcR4xCLVLfKBQoiArkMLHUQopJYY3VPAWJVCuhwpaEAxBQKGEQNlFQpZJpsHCaqjitBPms35FK0teX4soOqS6U2x8ezNjpjRSVVf5Cf3WtY9MqaHVnba1z8F6Np+rVXuuu+46Lr/8ctrb25k0aRJXXXUVu++++4c6dkeu8qxpmqZpmqb9a0s67+GZ7huZEWxlj1HLP9RUBt/3ufTe23ji5VZ+dPp+HDBh5sd+/fbUYjb2nkCnE6NK9jJ35Ho9neIzcsuqx7hx85NkcjDYksTssnBGOQhLYWQVlpUhKbN8c85y7l5Qj5cL0VOS5EdTn+MHKw+mOtZH18axIIEWj/Jh3dxy0D0Upc3PV+9JQIb5VsNLfG3pMVw58wEqzQy/fWR3+vwYFaM99qhbRXtblGWXVtK1Lorpw4Brc8WChWzpUdzRuzNtGysIvF5JsAC+hPG7rOLbxzxN1BQEpM93r5rGqpVNiNn1qBKP8sG1OCsUbjqKmujS9FgG1Z1AlAXIegYytgXfH4+yTKQhsSXkRya47+KTGD289t1zMzg4SMvadmKlUWrqqwiFt88aQzvy/dw7721+ycmYYjtYtUfZPDNwyw55rt/xuUmk3HnnnZx00klcd911zJs3jxtvvJE//vGPrFy5kmHDhv3L43fkC0/TNE3TNE3TNG1HtiPfz72bSEmetP0kUgb/tkOe63d8blLYV155JaeccgqnnnoqEyZM4KqrrqKxsZHrr79+W4emaZqmaZqmaZqmadp/iM9FIsW2bRYtWsT+++//nu37778/r7zyygceUywWSaVS73lomqZpmqZpmqZpmvb59rlIpPT09OB5HtXV1e/ZXl1dTUdHxwcec/HFF5NMJt99NDY2fhahapqmaZqm1jQ8PwAAGZ1JREFUaZqmadpH5/vbz2MH97lIpLxDCPGen5VS79v2jvPOO4/BwcF3Hy0tLZ9FiJqmaZqmaZqmaZqmbcc+F8sfV1RUYBjG+0afdHV1vW+UyjuCwSDB4PZZcVrTNE3TNE3TNE3TtG3jczEiJRAIMHPmTJ566qn3bH/qqaeYO3fuNopK0zRN0zRN0zRN0z4hSm0/jx3c52JECsA555zDSSedxKxZs9h111256aabaG5u5vTTT9/WoWmapmmapmmapmma9h/ic5NIOe644+jt7eUXv/gF7e3tTJ48mUcffZSmpqZtHZqmaZqmaZqmaZqmaf8hPjeJFIAzzzyTM888c1uHoWmapmmapmmapmmfKOX7KLHtV8xRatvH8Gn7XNRI0TRN0zRN0zRN0zRN+yR8rkakaJqmaZqmaZqmadoOSSlgOyj0+jkoNqtHpGiapmmapmmapmmapn1IOpGiaZqmaZqmaZqmaZr2IempPZqmaZqmaZqmaZr2n85XILaDaTV6ao+maZqmaZqmaZqmaZr2Dp1I0TRN0zRN0zRN0zRN+5D01B5N0zRN0zRN0zRN+0+nFOBv6yj01B5N0zRN0zRN0zRN0zTtv+lEiqZpmqZpmqZpmqZp2oekp/ZomqZpmqZpmqZp2n845SvUdrBqj9JTezRN0zRN0zRN0zRN07R36BEpmqZpmqZpmqZpmvafTvlsH8Vmt4MYPmV6RIqmaZqmaZqmaZqmadqHpBMpmqZpmqZpmqZpmqZpH5Ke2qNpmqZpmqZpmqZp/+F0sdnPjh6Rommapmmapmmapmma9iHpRIqmaZqmaZqmaZqmadqHpKf2aJqmaZqmaZqmadp/Or1qz2dGj0jRNE3TNE3TNE3TNE37kPSIlA/pnYI5qVRqG0eiaZqmaZqmaZqmfRTv3MftyIVQXRzYDt6ei7OtQ/jU6UTKh5ROpwFobGzcxpFomqZpmqZpmqZpH0c6nSaZTG7rMD5RgUCAmpoaXu54dFuH8q6amhoCgcC2DuNTI9SOnJL7BPm+T1tbG/F4HCHEtg7nI0mlUjQ2NtLS0kIikdjW4WjbAd0ntP9J9wftf9N9QvvfdJ/Q/jfdJ7T/6T+hPyilSKfT1NXVIeWOV+GiUChg2/a2DuNdgUCAUCi0rcP41OgRKR+SlJKGhoZtHca/JZFIbLcfbNq2ofuE9j/p/qD9b7pPaP+b7hPa/6b7hPY/be/9YUcbifI/hUKhHTpxsb3Z8VJxmqZpmqZpmqZpmqZpnxKdSNE0TdM0TdM0TdM0TfuQdCLlcyAYDHLBBRcQDAa3dSjadkL3Ce1/0v1B+990n9D+N90ntP9N9wntf9L9Qfu80cVmNU3TNE3TNE3TNE3TPiQ9IkXTNE3TNE3TNE3TNO1D0okUTdM0TdM0TdM0TdO0D0knUjRN0zRN0zRN0zRN0z4knUjZQVx//fVMnTr13bXbd911Vx577LF3n1dKceGFF1JXV0c4HGavvfZixYoV2zBi7bN08cUXI4Tg7LPPfneb7hOfPxdeeCFCiPc8ampq3n1e94nPn9bWVk488UTKy8uJRCJMmzaNRYsWvfu87hOfL8OHD3/fZ4QQgrPOOgvQ/eHzyHVdfvrTnzJixAjC4TAjR47kF7/4Bb7vv7uP7hefL+l0mrPPPpumpibC4TBz587lzTfffPd53R+0zwudSNlBNDQ0cMkll7Bw4UIWLlzIPvvsw2GHHfbuB9dll13GlVdeyTXXXMObb75JTU0N++23H+l0ehtHrn3a3nzzTW666SamTp36nu26T3w+TZo0ifb29ncfy5Yte/c53Sc+X/r7+5k3bx6WZfHYY4+xcuVKrrjiCkpKSt7dR/eJz5c333zzPZ8PTz31FADHHHMMoPvD59Gll17KDTfcwDXXXMOqVau47LLLuPzyy/n973//7j66X3y+/L/27j0oyuv8A/h3uS4KooDAYiOIRkFFRLciqDGOCF6rSVCD0UARrRkTMalK4la0tYm0uesYjUhAIgm0EWnAVkArRKXooKVQoICCwlQYjQEvqCDs8/vD8f25AeOaalD3+5nZGfacs+d93rPPLOzDe4mKikJubi4+//xzlJaWIjg4GEFBQfjvf/8LgPlAJkToidWnTx/ZuXOn6PV6cXV1lbi4OKXvxo0bYm9vL9u3b+/GCOlhu3Llijz99NOSm5srEydOlOjoaBER5oSJWr9+vfj6+nbZx5wwPTExMTJ+/Pi79jMnKDo6WgYOHCh6vZ75YKJmzJghkZGRBm3PP/+8LFy4UET4OWFqrl27Jubm5pKVlWXQ7uvrKzqdjvlAJoVHpDyBOjo6kJqaipaWFgQEBKC2thaNjY0IDg5WxlhbW2PixIkoKCjoxkjpYVu+fDlmzJiBoKAgg3bmhOmqrq6Gm5sbBgwYgBdffBE1NTUAmBOm6Ouvv4ZWq8XcuXPh7OwMPz8/xMfHK/3MCdPW1taG3bt3IzIyEiqVivlgosaPH4+DBw+iqqoKAPCvf/0LR44cwfTp0wHwc8LUtLe3o6OjA2q12qDdxsYGR44cYT6QSWEh5QlSWloKW1tbWFtbY9myZdi7dy+GDh2KxsZGAICLi4vBeBcXF6WPnjypqak4efIkNm3a1KmPOWGa/P39kZycjOzsbMTHx6OxsRGBgYG4ePEic8IE1dTUYNu2bXj66aeRnZ2NZcuWYcWKFUhOTgbAzwlTl5GRgebmZkRERABgPpiqmJgYhIWFwcvLC5aWlvDz88PKlSsRFhYGgHlhauzs7BAQEICNGzfi3Llz6OjowO7du3Hs2DE0NDQwH8ikWHR3APTgDBkyBMXFxWhubsaePXsQHh6O/Px8pV+lUhmMF5FObfRkqK+vR3R0NHJycjr91+BOzAnTMm3aNOVnHx8fBAQEYODAgdi1axfGjh0LgDlhSvR6PbRaLd555x0AgJ+fH8rKyrBt2za8/PLLyjjmhGlKSEjAtGnT4ObmZtDOfDAtaWlp2L17N7744gsMGzYMxcXFWLlyJdzc3BAeHq6MY16Yjs8//xyRkZHo168fzM3NMWrUKCxYsAAnT55UxjAfyBTwiJQniJWVFQYNGgStVotNmzbB19cXH3/8sXJXju9Xgs+fP9+pYkxPhhMnTuD8+fMYPXo0LCwsYGFhgfz8fGzevBkWFhbK+86cMG09e/aEj48Pqqur+TlhgjQaDYYOHWrQ5u3tjbq6OgBgTpiws2fP4sCBA4iKilLamA+mafXq1XjzzTfx4osvwsfHB4sWLcLrr7+uHO3KvDA9AwcORH5+Pq5evYr6+nocP34cN2/exIABA5gPZFJYSHmCiQhaW1uVD7bbV98Hbp37nJ+fj8DAwG6MkB6WyZMno7S0FMXFxcpDq9XipZdeQnFxMTw9PZkThNbWVlRUVECj0fBzwgSNGzcOlZWVBm1VVVVwd3cHAOaECUtMTISzszNmzJihtDEfTNO1a9dgZmb4dcHc3Fy5/THzwnT17NkTGo0GTU1NyM7OxuzZs5kPZFq67zq39CC99dZb8s0330htba2UlJTI2rVrxczMTHJyckREJC4uTuzt7SU9PV1KS0slLCxMNBqNXL58uZsjp5/KnXftEWFOmKJf//rXkpeXJzU1NVJYWCgzZ84UOzs7OXPmjIgwJ0zN8ePHxcLCQt5++22prq6WlJQU6dGjh+zevVsZw5wwPR0dHdK/f3+JiYnp1Md8MD3h4eHSr18/ycrKktraWklPTxcnJydZs2aNMoZ5YVr2798vf/vb36SmpkZycnLE19dXxowZI21tbSLCfCDTwULKEyIyMlLc3d3FyspK+vbtK5MnT1aKKCK3bk+3fv16cXV1FWtra3nmmWektLS0GyOmn9r3CynMCdMzf/580Wg0YmlpKW5ubvL8889LWVmZ0s+cMD2ZmZkyfPhwsba2Fi8vL9mxY4dBP3PC9GRnZwsAqays7NTHfDA9ly9flujoaOnfv7+o1Wrx9PQUnU4nra2tyhjmhWlJS0sTT09PsbKyEldXV1m+fLk0Nzcr/cwHMhUqEZHuPiqGiIiIiIiIiOhxwGukEBEREREREREZiYUUIiIiIiIiIiIjsZBCRERERERERGQkFlKIiIiIiIiIiIzEQgoRERERERERkZFYSCEiIiIiIiIiMhILKURERERERERERmIhhYiIiIiIiIjISCykEBERUZc2bNiAkSNH/uCYM2fOQKVSobi4+L7n//vf/w4vLy/o9fr/KYbHxapVq7BixYruDoOIiIj+RyykEBHRY6GgoADm5uaYOnVqd4fyUBhbkHj22WehUqnu+sjPz39gMa1atQoHDx5UnkdERGDOnDkPbP41a9ZAp9PBzMw0/hxZs2YNEhMTUVtb292hEBER0f/ANP5yISKix95nn32G1157DUeOHEFdXV13h6Noa2v7SbeXnp6OhoYGg8fZs2cxfPhwaLVa+Pv7P7Bt2drawtHR8YHNd6eCggJUV1dj7ty5D2X++yEiaG9vf+jbcXZ2RnBwMLZv3/7Qt0VEREQPDwspRET0yGtpacGf/vQnvPLKK5g5cyaSkpIM+vPy8qBSqbBv3z74+vpCrVbD398fpaWlypikpCT07t0bGRkZGDx4MNRqNaZMmYL6+nplzOnTpzF79my4uLjA1tYWP//5z3HgwAGDbXl4eOD3v/89IiIiYG9vjyVLlgC4VRh45plnYGNjg6eeegorVqxAS0uLweveeecdREZGws7ODv3798eOHTuU/gEDBgAA/Pz8oFKp8Oyzz3a5Fg4ODnB1dTV4bNy4ERcuXMDevXuhVqsB3CoO/PGPf4SnpydsbGzg6+uLr776qtOaHTx4EFqtFj169EBgYCAqKyuVMXeeVrNhwwbs2rULf/nLX5SjX/Ly8pSxNTU1mDRpEnr06AFfX1/84x//uNvbCQBITU1FcHCwEu9tcXFxcHFxgZ2dHRYvXowbN250em1iYiK8vb2hVqvh5eWFTz75xKC/oKAAI0eOhFqthlarRUZGhsHRPrf3PTs7G1qtFtbW1jh8+PA91wwAysvLMX36dNja2sLFxQWLFi3Ct99+q/R/9dVX8PHxgY2NDRwdHREUFGSQB7/4xS/w5Zdf/uDaEBER0SNOiIiIHnEJCQmi1WpFRCQzM1M8PDxEr9cr/YcOHRIA4u3tLTk5OVJSUiIzZ84UDw8PaWtrExGRxMREsbS0FK1WKwUFBVJUVCRjxoyRwMBAZZ7i4mLZvn27lJSUSFVVleh0OlGr1XL27FlljLu7u/Tq1Uveffddqa6ulurqaikpKRFbW1v58MMPpaqqSo4ePSp+fn4SERFh8DoHBwfZunWrVFdXy6ZNm8TMzEwqKipEROT48eMCQA4cOCANDQ1y8eJFo9Zm69atYmVlJUePHjVoX7t2rXh5ecn+/fvl9OnTkpiYKNbW1pKXl2ewZv7+/pKXlydlZWUyYcIEg/VYv369+Pr6iojIlStXZN68eTJ16lRpaGiQhoYGaW1tldraWgEgXl5ekpWVJZWVlRIaGiru7u5y8+bNu8bt6+srcXFxBm1paWliZWUl8fHx8p///Ed0Op3Y2dkpMYiI7NixQzQajezZs0dqampkz5494uDgIElJSSIicvnyZXFwcJCFCxdKWVmZ/PWvf5XBgwcLAPnnP/9psO8jRoyQnJwcOXXqlHz77bf3XLNz586Jk5OTvPXWW1JRUSEnT56UKVOmyKRJk5R+CwsL+eCDD6S2tlZKSkpk69atcuXKFSX+8vJyASBnzpwx6v0lIiKiRw8LKURE9MgLDAyUjz76SEREbt68KU5OTpKbm6v03/5inJqaqrRdvHhRbGxsJC0tTURuFVIASGFhoTKmoqJCAMixY8fuuu2hQ4fKli1blOfu7u4yZ84cgzGLFi2SpUuXGrQdPnxYzMzM5Pr168rrFi5cqPTr9XpxdnaWbdu2iYgoBYnbX/aNkZ+fL5aWlhIfH2/QfvXqVVGr1VJQUGDQvnjxYgkLCxOR/1+zAwcOKP379u0TAErMdxZSRETCw8Nl9uzZBnPejnvnzp1KW1lZmQBQikRdsbe3l+TkZIO2gIAAWbZsmUGbv7+/QQxPPfWUfPHFFwZjNm7cKAEBASIism3bNnF0dFT2QUQkPj6+y0JKRkaGMsaYNVu3bp0EBwcb9NfX1wsAqayslBMnTtyzSHLp0iUBoBRniIiI6PFj8dMe/0JERHR/Kisrcfz4caSnpwMALCwsMH/+fHz22WcICgoyGBsQEKD87ODggCFDhqCiokJps7CwgFarVZ57eXmhd+/eqKiowJgxY9DS0oLf/va3yMrKwrlz59De3o7r1693uibLnXMAwIkTJ3Dq1CmkpKQobSICvV6P2tpaeHt7AwBGjBih9KtUKri6uuL8+fM/al3q6uoQGhqKpUuXIioqyqCvvLwcN27cwJQpUwza29ra4OfnZ9B2Z0wajQYAcP78efTv3/++4rnbPF5eXl2Ov379eqfTeioqKrBs2TKDtoCAABw6dAgAcOHCBdTX12Px4sXKKVUA0N7eDnt7ewC38mXEiBEGc48ZM6bLGO58H41ZsxMnTuDQoUOwtbXtNNfp06cRHByMyZMnw8fHByEhIQgODkZoaCj69OmjjLOxsQEAXLt2rcuYiIiI6NHHQgoRET3SEhIS0N7ejn79+iltIgJLS0s0NTUZfEntikql+sHnd7atXr0a2dnZeO+99zBo0CDY2NggNDS00wVle/bsafBcr9fjV7/6VZe3tr2zIGFpadlpuz9069+7uX79Op577jkMGzYMH330Uaf+23Pu27fPYN0AwNra2uD5nTHdXocfE9P9zuPk5ISmpqb72sbt+eLj4ztdVNfc3BzArdz4/nssIl3Od+f7aMya6fV6zJo1C3/4wx86zaXRaGBubo7c3FwUFBQgJycHW7ZsgU6nw7Fjx5Rr4Hz33XcAgL59+xq300RERPTIYSGFiIgeWe3t7UhOTsb777+P4OBgg74XXngBKSkpePXVV5W2wsJCpXDR1NSEqqoqgyMi2tvbUVRUpByhUFlZiebmZmXM4cOHERERgeeeew4AcPXqVZw5c+aecY4aNQplZWUYNGjQj95XKysrAEBHR8c9x0ZFReG7775DdnY2LCw6/yofOnQorK2tUVdXh4kTJ/7omLqK0Zj4jOHn54fy8nKDNm9vbxQWFuLll19W2goLC5WfXVxc0K9fP9TU1OCll17qcl4vLy+kpKSgtbVVKYAUFRXdMx5j1mzUqFHYs2cPPDw8ulx34FYRady4cRg3bhxiY2Ph7u6OvXv34o033gAA/Pvf/4alpSWGDRt2z5iIiIjo0cRCChERPbKysrLQ1NSExYsXK6du3BYaGoqEhASDQsrvfvc7ODo6wsXFBTqdDk5OTpgzZ47Sb2lpiddeew2bN2+GpaUlXn31VYwdO1YprAwaNAjp6emYNWsWVCoV1q1bZ9TRGTExMRg7diyWL1+OJUuWoGfPnqioqEBubi62bNli1L46OzvDxsYG+/fvx89+9jOo1epO+wwA7777Lv785z8jMzMT7e3taGxsNOi3t7eHnZ0dVq1ahddffx16vR7jx4/H5cuXUVBQAFtbW4SHhxsV0/d5eHggOzsblZWVcHR07DI+Y4WEhGDXrl0GbdHR0QgPD4dWq8X48eORkpKCsrIyeHp6KmM2bNiAFStWoFevXpg2bRpaW1tRVFSEpqYmvPHGG1iwYAF0Oh2WLl2KN998E3V1dXjvvfcAdH000m3GrNny5csRHx+PsLAwrF69Gk5OTjh16hRSU1MRHx+PoqIiHDx4EMHBwXB2dsaxY8dw4cIF5dQu4FaxbsKECcopPkRERPT44e2PiYjokZWQkICgoKAuv7C/8MILKC4uxsmTJ5W2uLg4REdHY/To0WhoaMDXX3+tHOkBAD169EBMTAwWLFiAgIAA2NjYIDU1Ven/8MMP0adPHwQGBmLWrFkICQnBqFGj7hnniBEjkJ+fj+rqakyYMAF+fn5Yt26dcq0QY1hYWGDz5s349NNP4ebmhtmzZ3c57pNPPsHNmzcxdepUaDSaTo+0tDQAwMaNGxEbG4tNmzbB29sbISEhyMzMVE4x+TGWLFmCIUOGQKvVom/fvjh69OiPnmvhwoUoLy83uN3y/PnzERsbi5iYGIwePRpnz57FK6+8YvC6qKgo7Ny5E0lJSfDx8cHEiRORlJSk7FevXr2QmZmJ4uJijBw5EjqdDrGxsQDQ6Zos33evNXNzc8PRo0fR0dGBkJAQDB8+HNHR0bC3t4eZmRl69eqFb775BtOnT8fgwYPxm9/8Bu+//z6mTZumbOPLL780uL4LERERPX5UcrcTh4mIiB4TeXl5mDRpEpqamtC7d+8uxyQlJWHlypVobm7+SWOju1uzZg0uXbqETz/99KFuJyUlBb/85S9x6dKlbj0SZN++fVi9ejVKSkruemoQERERPfp4RAoRERF1C51OB3d39wd23ZXbkpOTceTIEdTW1iIjIwMxMTGYN29et59O09LSgsTERBZRiIiIHnP8TU5ERETdwt7eHmvXrn3g8zY2NiI2NhaNjY3QaDSYO3cu3n777Qe+nfs1b9687g6BiIiIHgCe2kNEREREREREZCSe2kNEREREREREZCQWUoiIiIiIiIiIjMRCChERERERERGRkVhIISIiIiIiIiIyEgspRERERERERERGYiGFiIiIiIiIiMhILKQQERERERERERmJhRQiIiIiIiIiIiOxkEJEREREREREZKT/A3kF0pVV7dXwAAAAAElFTkSuQmCC", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(12, 8))\n", "scatter = plt.scatter(\n", " filtered_df['apparent_zenith'],\n", " filtered_df['SR15D1Dn_Irr'],\n", " c=filtered_df['LWP_Corrected'],\n", " cmap='viridis',\n", " alpha=filtered_df['LWP_Corrected']/ filtered_df['LWP_Corrected'].max(),\n", " s=0.5\n", ")\n", "\n", "# Add color bar\n", "cbar = plt.colorbar(scatter)\n", "cbar.set_label('LWP Corrected (g/m²)')\n", "\n", "# Labels and title\n", "plt.xlabel('Apparent Zenith (degrees)')\n", "plt.ylabel('Radiation (SR15D1Dn_Irr)')\n", "plt.title('Radiation vs Apparent Zenith with Opacity-Based LWP Mapping')\n", "\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 20, "id": "d3dc6dfd", "metadata": {}, "outputs": [ { "data": { "image/png": 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8Tvt+t33zXL95b2644QaVlJTUKq49APWTn/ykzXWGYaiUlBQ1bdo0pZRS0WhUJSUlqX/9619KKaXOO+88dc455yillAoGg8rhcKjzzjsvbh+TJk1SgwcPVuFwWIXDYVVWVqZuv/12Bahzzz13v3lVVFQoQF1wwQXtOo+NGzcqQF1//fVxy5ctW6YA9atf/Sq2bN/rN2fOHAWoBx54IG7bV199VQHqiSeeiC3Lz89XJpNJbd68uV15XXbZZcrlciml9nxPZmVlqXA4rGpra5XNZlPPPvvsAd+Te+39HXzCCSfEfXfv/Tzm5OQov98fW97U1KRSUlLUiSeeGFu293v6pptuitv3iy++qAD1wgsvxJbt+95r7+c+Go2qrKwsNW7cuLhjFBUVKYvF0uZ3ihBCdFfSIkWIbuboo4/GYrHgdrs5+eSTSU5O5u233471gd/r9ddfZ+LEiSQkJGA2m7FYLDz11FNxzc/3/qVyb+uLvS666KJ25VJVVcWPf/xjevbsGTtGfn4+QKtm7u31ySef4HK5OOecc+KW722+v29z5ylTpsQN9peZmUlGRkab3We+afr06WRlZcVaSAB8+OGHlJWVceWVV8aWjR07lg8++IDbb7+dBQsW4Pf7230ueweZ3bu/vd0QioqKWp0H7JkxIzMzM/azyWTi/PPPZ9u2ba2at+97zyZMmEB+fn7cX58P9f7s+5fZ9957D03TuOSSS4hEIrFXVlYWw4cPb/UXyqysLMaOHRu3bNiwYQe9F+314x//mFWrVvH444+3OXZKV8l3xIgR5OXlxX622+3069fvoPudOHEidrudefPmAXu6Zk2ePJmTTz6ZxYsX4/P5KC4uZuvWrZx44onfKcdTTz0Vk8kU+3nYsGEAHXavYM8YGX6/v9WsSj179uT4449v9RnQNI2ZM2fGLeuI+9GR9/lQz6k9xo4dS0NDAxdeeCFvv/32AVtfHApN05g0aRKLFi0iHA6zZs0aGhoaYl0+Jk2axIIFC1BKsXTp0jbHRwFYv349FosFi8VCTk4Of/3rX7n44os7dFyQvd9b+17XsWPHMnDgwANe108++aTNbc8991xcLlerbYcNGxbXgrO9rrjiCiorK/nggw948cUXsVqtnHvuufuN/9e//sWoUaOw2+2x79+PP/64ze/es846C7vdHvvZ7XYzc+ZMFi5cSDQajYvd97v/vPPOw2w2txo8uC0H+9xv3ryZiooKzjvvvLjt8vLymDhx4kH3L4QQ3YkUUoToZv7973+zYsUKPvnkE6699lo2btzIhRdeGBfz5ptvct5555Gbm8sLL7zAkiVLWLFiBVdeeSWBQCAWV1tbi9lsJjU1NW77rKysg+ZhGAbTpk3jzTff5Je//CUff/wxy5cvj/W3PpSCwzfV1taSlZXVakyHjIwMzGZzqy41++YOewYIPdjxzWYzs2bN4q233op1h3n22WfJzs7mpJNOisX94x//4LbbbmP27NlMmTKFlJQUzjjjjIM24W9ubub1119n7NixpKen09DQQENDA2eeeSaapsWKLN/U1nXfu2zf895f7N64b3N/9p3tpLKyEqUUmZmZsQepva+lS5e2euD7tveiPR5++GGee+45brjhBmbNmtVmTFfJ99vu1263M3HixFgh5eOPP2bq1KmxQaU/++yzWBef71pI2TfHvWNYHCzHvLy8WHfCg9n7XmxrFp2cnJxW72mn0xn3MLk3r29+Z30bHXmfD/Wc2mPWrFk8/fTTFBUVcfbZZ5ORkcG4cePiunN9W1OmTKGlpYUVK1Ywf/58MjMz6d+/P7CnkFJTU8P69etjD+FtFVL69OnDihUrWLlyJevWraOhoYEXXnihzTFy9kpLS8PpdB6298q+25rN5lYDDWuaFveduNe3ndUpPz+fE044gaeffpqnn36aCy64YL8Dcj/44INcd911jBs3jv/85z8sXbqUFStWcPLJJ7f5vtvf93koFGo17fK+sXt/h7fnvXewz/3efXyzoL9XW8uEEKI7k1l7hOhmBg4cGBtgdsqUKUSjUf7v//6PN954I9aK44UXXqCgoIBXX301riCxb1/x1NRUIpEItbW1cf/AqqioOGge69at48svv+TZZ5/lsssuiy3ftm3bdzq/1NRUli1bhlIqLveqqioikQhpaWnfaf/fdMUVV/DnP/+ZV155hfPPP5933nmHG2+8Me4vdi6Xi7vvvpu777479tfI22+/nZkzZ7Jp06b97vvll1/G5/OxfPnyNscReOutt6ivr49b19Z137ts338A7y+2b9++wLe7P/sWr9LS0tA0jc8++6zNwSLbWnY4fPbZZ9xyyy0cc8wxPPjgg/uN6yr5fhcnnHACv/vd71i+fDklJSVMnToVt9vNUUcdxdy5cykrK6Nfv37tHneio5100kk8/PDDLF269KDjpOx9z5aXl7daV1ZW1qGf5e9qbwFn3+/I/RXf2nNOdru9zTFn2mpxcsUVV3DFFVfQ0tLCwoULufPOO5kxYwZbtmyJtSL7NvYWRhYsWMCSJUuYNGlSbN2gQYNIS0tj/vz5LFiwgOzs7FiR5Zv2Dn56KEwmEyeccAIffPABJSUl9OjR44Dx37yu+8Ye7L2y9/dYdXV1XDFFKUVFRUVsgOu92hp4ub2uvPJKLrnkEgzD4LHHHttv3AsvvMDkyZNbxTQ3N7cZv7/vc6vVSkJCQqvlubm5sZ/b+h3+be3dx77jSe0vRyGE6M6kRYoQ3dwDDzxAcnIyv/vd72IDp2qahtVqjfsHY0VFRatZe/b+I/vFF1+MW/7SSy8d9Lh7973vw+njjz/eKra9f+2GPQ+SXq+X2bNnxy3fO0juCSeccNB9tNfAgQMZN24czzzzDC+99BLBYPCAU0hnZmZy+eWXc+GFF7J582Z8Pt9+Y5966incbjcff/wx8+fPj3v9+c9/JhgMtrruH3/8cdw/YKPRKK+++ip9+vRp9XCx77aLFy+mqKgo1mz/UO7P/syYMQOlFKWlpYwZM6bVa3+DnR7IobwXAEpLSzn33HNJS0vj9ddf3+9Uq4cr3444h0Nx4oknEolE+O1vf0uPHj0YMGBAbPm8efP45JNP2tUapaNaAu3rpptuwuVycf3117dZJFBKxaY/Hj9+PA6Ho9WgriUlJXzyySff6rN8uK793tlI1q5dG7f8nXfeifv5UM6pV69ebNmyJa44U1tby+LFi/ebh8vlYvr06fz6178mFAqxfv36b3tKAAwePJj09HQ++eQTPvvss7iZXDRN47jjjmPOnDksXbr0oLP1HKo77rgDpRRXX301oVCo1fpwOMy7774L7Jk6G2h1XVesWMHGjRsP+F7Zu27fbf/zn//Q0tLSob8zzjzzTM4880yuvPLKAxYSNU1r9d27du3aNgePhT2tSL/Z8qq5uZl3332XY489Nq6wD62/+1977TUikUibM0Qdqr2DEL/22mtxy3fv3n3A960QQnRH0iJFiG4uOTmZO+64g1/+8pe89NJLXHLJJbHpHa+//nrOOecciouL+cMf/kB2dnZcl5Rp06Zx3HHH8ctf/pKWlhbGjBnDokWLeP755w963AEDBtCnTx9uv/12lFKkpKTw7rvvttkcfe8D7N///ncuu+wyLBYL/fv3jxvbZK9LL72Uf/7zn1x22WXs2rWLoUOH8vnnn3PvvfdyyimnfOcuDfu68sorufbaaykrK2PChAmt/iI7btw4ZsyYwbBhw0hOTmbjxo08//zzjB8/fr/NutetW8fy5cu57rrrYg8I3zRx4kT++te/8tRTT3HDDTfElqelpXH88cfz29/+FpfLxaOPPsqmTZtaTYEMsHLlSn70ox9x7rnnUlxczK9//Wtyc3O5/vrrgUO7P/szceJErrnmGq644gpWrlzJcccdh8vlory8nM8//5yhQ4dy3XXXtXt/sKebgMPh4MUXX2TgwIEkJCSQk5NDTk5Oq9hQKMRZZ51FZWUlf/3rX9m1a1er6WkBEhMTGTRo0GHJty2H8n4+VKNHjyY5OZmPPvoorqh34okn8oc//CH2/+3J8c033+Sxxx5j9OjR6Lp+yK0K2lJQUBBrwTVixAhuuOGG2Hg1GzZs4Omnn0YpxZlnnklSUhK//e1v+dWvfsWll17KhRdeSG1tLXfffTd2u50777zzkI/vdrvJz8/n7bff5oQTTiAlJYW0tLTvPC3rUUcdRf/+/bn11luJRCIkJyfz1ltv8fnnn8fFHco5zZo1i8cff5xLLrmEq6++mtraWh544AESExPj9nn11VfjcDiYOHEi2dnZVFRUcN999+HxeFq1pmjL9u3b25zZa9CgQQwaNIjJkyfzxhtvoJSKa5ECe7r33HjjjSilOryQsnfGsOuvv57Ro0dz3XXXMXjwYMLhMF988QVPPPEEQ4YMYebMmfTv359rrrmGhx9+ODZjza5du/jtb39Lz549uemmm/Z7nKlTp3LSSSdx22230dTUxMSJE1m7di133nknI0eO3G9XwG/Dbre3ea33NWPGDP7whz9w5513MmnSJDZv3szvf/97CgoKiEQireJNJhNTp07l5ptvxjAM7r//fpqamtqcmvnNN9/EbDYzdepU1q9fz29/+1uGDx/ealyTb0PXde6++26uvfZazjnnHK688koaGhq4++67yc7ObjVLmhBCdGudMcKtEKLj7Z1VYsWKFa3W+f1+lZeXpwoLC2Mj8v/pT39SvXr1UjabTQ0cOFA9+eSTsVH/v6mhoUFdeeWVKikpSTmdTjV16lS1adOmds3as2HDBjV16lTldrtVcnKyOvfcc9Xu3bvbnMHgjjvuUDk5OUrX9bhZMNqa2aK2tlb9+Mc/VtnZ2cpsNqv8/Hx1xx13qEAgEBfHfmasyM/PV5dddtmBL+j/NDY2KofDoQD15JNPtlp/++23qzFjxqjk5GRls9lU79691U033aRqamr2u88bb7xRAWrNmjX7jdk788WqVavizuXRRx9Vffr0URaLRQ0YMEC9+OKLcdvtvQ8fffSRmjVrlkpKSorNIrJ169a42Pben73vi+rq6jZzffrpp9W4ceOUy+VSDodD9enTR1166aVq5cqVsZi9s3vsa98ZNpRS6uWXX1YDBgxQFovlgLNd7J1p4mCvfd8/HZlvW7P2KLX/93N+fr469dRTW+13fzO4tOXMM89UQNy9D4VCyuVyKV3XVX19fVx8W5/Nuro6dc4556ikpCSlaVrsc7/3fP785z+3Ou6B7sW+tm/frq6//nrVt29fZbPZlMPhUIMGDVI333xzq5lv/u///k8NGzZMWa1W5fF41Omnn67Wr18fF/PN2VG+qa3vrHnz5qmRI0cqm82mgNhnfX+z9rT3fbllyxY1bdo0lZiYqNLT09VPf/pT9f7778fd30M5J6WUeu6559TAgQOV3W5XgwYNUq+++mqrYz/33HNqypQpKjMzU1mtVpWTk6POO+88tXbt2lb729eBPhd77+Wjjz6qAJWent5q+zVr1sTi9/3+OND1OxRr1qxRl112mcrLy1NWq1W5XC41cuRI9bvf/U5VVVXF4qLRqLr//vtVv379lMViUWlpaeqSSy5RxcXFcftr6975/X512223qfz8fGWxWFR2dra67rrrWn1W9vf53J/9vS+/qa1Ze4LBoLr11ltVbm6ustvtatSoUWr27Nn7/X65//771d1336169OihrFarGjlypPrwww/jjrP3s7Bq1So1c+ZMlZCQoNxut7rwwgtVZWVlXOz+Zu1p7+f+iSeeUH379lVWq1X169dPPf300+r000+Pm/VPCCG6O00ppTq6OCOEEKJjaZrGT37yEx555JEDxj377LNcccUVrFixokNaGAghhOgcu3btoqCggD//+c/ceuutB4y96667uPvuu6murv7exxdqaGigX79+nHHGGTzxxBPf67GFEKKzSNceIYQQQgghxEFVVFTwxz/+kSlTppCamkpRURF/+9vfaG5u5uc//3lnpyeEEN8bKaQIIYQQQgghDspms7Fr1y6uv/566urqcDqdHH300fzrX/9i8ODBnZ2eEEJ8b6RrjxBCCCGEEEIIIUQ7yfDaQgghhBBCCCGE+N7dddddaJoW98rKyoqtV0px1113kZOTg8PhYPLkyaxfv74TM95DCilCCCGEEEIIIYToFIMHD6a8vDz2+uqrr2LrHnjgAR588EEeeeQRVqxYQVZWFlOnTqW5ubkTM5ZCihBCCCGEEEIIITqJ2WwmKysr9kpPTwf2tEZ56KGH+PWvf81ZZ53FkCFDeO655/D5fLz00kudm3OnHv0HxDAMysrKcLvdaJrW2ekIIYQQQgghhGgnpRTNzc3k5OSg692vPUEgECAUCnV2GjFKqVbPzTabDZvN1ip269at5OTkYLPZGDduHPfeey+9e/dm586dVFRUMG3atLh9TJo0icWLF3Pttdce9vPYHymktFNZWRk9e/bs7DSEEEIIIYQQQnxLxcXF9OjRo7PT6FCBQICCXilUVPo7O5WYhIQEvF5v3LI777yTu+66K27ZuHHj+Pe//02/fv2orKzknnvuYcKECaxfv56KigoAMjMz47bJzMykqKjosOZ/MFJIaSe32w3s+eAlJiZ2cjZCCCGEEEIIIdqrqamJnj17xp7rupNQKERFpZ+iry4i0W3t7HRoag6RP/SlVs/ObbVGmT59euz/hw4dyvjx4+nTpw/PPfccRx99NECrli1ttXb5vkkhpZ323qjExEQppAghhBBCCCHED1BnP4AfToluK4mJnV9I2evbPDu7XC6GDh3K1q1bOeOMMwCoqKggOzs7FlNVVdWqlcr3rft1DhNCCCGEEEIIIY4wCqPLvL6tYDDIxo0byc7OpqCggKysLObOnRtbHwqF+PTTT5kwYUJHXLJvTVqkCCGEEEIIIYQQ4nt36623MnPmTPLy8qiqquKee+6hqamJyy67DE3TuPHGG7n33nspLCyksLCQe++9F6fTyUUXXdSpeUshRQghhBBCCCGEEN+7kpISLrzwQmpqakhPT+foo49m6dKl5OfnA/DLX/4Sv9/P9ddfT319PePGjeOjjz7q9LFuNKWU6tQMfiCamprweDw0NjbKGClCCCGEEEII8QPSnZ/n9p5bzc5ZXWKMlKamEGkFz3fLa72XjJEihBBCCCGEEEII0U5SSBFCCCGEEEIIIYRoJxkjRQghhBBCCCGE+IHbO2dOZ+sKORxu0iJFCCGEEEIIIYQQop2kRYoQQgghhBBCCPEDp/73X2frCjkcbtIiRQghhBBCCCGEEKKdpJAihBBCCCGEEEII0U7StUcIIYQQQgghhPiBM1AYqvO71chgs0IIIYQQQgghhBAiRgopQgghhBBCCCGEEO0kXXuEEEIIIYQQQogfOON/r87WFXI43Dq1RcrChQuZOXMmOTk5aJrG7Nmz9xt77bXXomkaDz30UNzyYDDIT3/6U9LS0nC5XJx22mmUlJTExdTX1zNr1iw8Hg8ej4dZs2bR0NDQ8SckhBBCCCGEEEKIbq1TCyktLS0MHz6cRx555IBxs2fPZtmyZeTk5LRad+ONN/LWW2/xyiuv8Pnnn+P1epkxYwbRaDQWc9FFF7FmzRrmzJnDnDlzWLNmDbNmzerw8xFCCCGEEEIIIUT31qlde6ZPn8706dMPGFNaWsoNN9zAhx9+yKmnnhq3rrGxkaeeeornn3+eE088EYAXXniBnj17Mm/ePE466SQ2btzInDlzWLp0KePGjQPgySefZPz48WzevJn+/fsfnpMTQgghhBBCCCG+JwaqS8yY0xVyONy69GCzhmEwa9YsfvGLXzB48OBW61etWkU4HGbatGmxZTk5OQwZMoTFixcDsGTJEjweT6yIAnD00Ufj8XhiMUIIIYQQQgghhBDt0aUHm73//vsxm8387Gc/a3N9RUUFVquV5OTkuOWZmZlUVFTEYjIyMlptm5GREYtpSzAYJBgMxn5uamr6NqcghBBCCCGEEEIcdup//3W2rpDD4dZlW6SsWrWKv//97zz77LNomnZI2yql4rZpa/t9Y/Z13333xQan9Xg89OzZ85ByEEIIIYQQQgghRPfTZQspn332GVVVVeTl5WE2mzGbzRQVFXHLLbfQq1cvALKysgiFQtTX18dtW1VVRWZmZiymsrKy1f6rq6tjMW254447aGxsjL2Ki4s77uSEEEIIIYQQQgjxg9RlCymzZs1i7dq1rFmzJvbKycnhF7/4BR9++CEAo0ePxmKxMHfu3Nh25eXlrFu3jgkTJgAwfvx4GhsbWb58eSxm2bJlNDY2xmLaYrPZSExMjHsJIYQQQgghhBBd0d7BZrvCq7vr1DFSvF4v27Zti/28c+dO1qxZQ0pKCnl5eaSmpsbFWywWsrKyYjPteDwerrrqKm655RZSU1NJSUnh1ltvZejQobFZfAYOHMjJJ5/M1VdfzeOPPw7ANddcw4wZM2TGHiGEEEIIIYQQQhySTi2krFy5kilTpsR+vvnmmwG47LLLePbZZ9u1j7/97W+YzWbOO+88/H4/J5xwAs8++ywmkykW8+KLL/Kzn/0sNrvPaaedxiOPPNJxJyKEEEIIIYQQQogjgqaU6v7tbjpAU1MTHo+HxsZG6eYjhBBCCCGEED8g3fl5bu+57dx5Pm63tbPTobk5REHBq93yWu/VZcdIEUIIIYQQQgghhOhqpJAihBBCCCGEEEII0U6dOkaK6J6qfZ/SHNxEz8QLsJg8nZ2OEEIIIYQQQnR7xv9ena0r5HC4SYsU0eHKmt+kyvcRTaF1nZ2KEEIIIYQQQgjRoaRFiuhw6eYsGqLFJJpyOjsVIYQQQgghhDgiGGrPq7N1hRwON2mRIjpckW8BFaEK6gMLOzsVIYQQQgghhBCiQ0khRXS4FOd07JZ+OO2TOzsVIYQQQgghhBCiQ0nXHtHh+qfe2NkpCCGEEEIIIcQRRf3v1dm6Qg6Hm7RIEUIIIYQQQgghhGgnKaQIIYQQQgghhBBCtJN07RFCCCGEEEIIIX7gjP+9OltXyOFwkxYpQgghhBBCCCGEEO0khRTR5SyteoZXtozh+W1ndnYqQgghhBBCCCFEHCmkiA63veonfFF8HL7Aym+1/ZaG57GaIljU7g7OTAghhBBCCCG6J0N1nVd3J4WUI1A4EsUXDqLU4XmHN4Y2EDS8+EJrv9X2Oa7pBAwLLUZSxyYmhBBCCCGEEEJ8RzLY7BGmsrGZK596nQbl5SdnjeGiggkdfowB6Q/hC35JSsJF32r7DOexbPWuw2Iq7ODMhBBCCCGEEKJ7MtAw0Do7jS6Rw+EmhZQjzKbyasqavYSNCOtqy6Cg44/htA3HaRv+rbd36cXkWXfjsno6MCshhBBCCCGEEOK7k0LKEWZkfg49ktyURxswafbOTqdNua5jMaLlpDo6vrWMEEIIIYQQQgjxXUgh5QhTXtOEJ2BFhd2c0XNYZ6fTJrs5i8KUn3V2GkIIIYQQQgjxgyFde74/Ukg5wvTJSeP840aQkuhidEZeZ6fTJqUUm5p3kWlLIcUm3XuEEEIIIYQQQnQdUkg5wphNOjPHD+7sNA5ofeN2ntv1Hpn2FG4dcGlnpyOEEEIIIYQQQsRIIeUIo5Ti1eWr+aB6LWcNG8HpeSM7O6VWbHqYRHMtvVwDOzsVIYQQQgghhPhBUEpDqc7vVtMVcjjc9M5OQHy/Vm8p4fGnF7HlvXo21JV3djptml1yDzqrCIXndnYqQgghhBBCCCFEHGmRcoRJdNhJtNnxWOCqwmM6O502pZhKcVm9+KLOzk5FCCGEEEIIIYSII4WUI0xhXjr/uP0cHHYLGS53Z6fTpgK7m6ZIE8OTjursVIQQQgghhBDiByH6v1dn6wo5HG5SSDkC5eekdHYK+7Wqbgu63g+PuZl0e9ceFFcIIYQQQgghxJFHCimiQ1W1ePntsucpTPLx0+E/wmZq//TFC6vW8qcNbwAGF+dESPSVkpZw+HIVQgghhBBCCCEOlQw2e4RRSvHhho3c/OlrzK/Y2OH7f3f3OpY3VvJWSQONoe2HtG2K1Y1J03HoIeyajx3euRgq0uE5CiGEEEIIIUR3o9AxusBLHQFlhu5/hiLOjvJaHn93Gcs+LWdJ1Y4O3/9xOb3JdycxKbsnafZhbca8uPJLRvz5YW5/d07c8kGefK7sPZBhSU18Uj8Mu2kkGqYOz1EIIYQQQgghhPi2pGvPEcZhtmBZ30JPp4VLC8Z3+P4LPRm8OvkGrCYTmtb2/OH/+nwZPn+Yt9du5E8zT44t39K8g/9WLEQpDZcpwFZfwn73IYQQQgghhBDia+p/r87WFXI43KRFyhFGjyrykz30ciSRZun4WXs211Vz5Zw3eXj10v3GNPr86GEwgkbc8ix7Bk6TGxMGoaiOWe/4FjNCCCGEEEIIIcR3IS1SjjBZ2Ulc//OTsNnM2O0WAEp92ynybWJE0nE4zd+tuNIQCBA2DCpavPuNMVs1wj5avfuKWnYTNKqIKhPZjjADHM0YKoKuydtUCCGEEEIIIUTXIE+oR6BevdPjfl5ZN4/dvk24zUkMTZr4nfY9NrsH9xxzIlmu/Rdkrj56LI8uWMqo/Jy45d5IMxYtjNIhakRIshVKEUUIIYQQQggh2sFAw6Dzh0boCjkcbvKUeoRp8Qa498dP40528cu/z0LXdUYkTyLZmk5BwuAOOYYRUQf86Pxo/FH0T0tndH5u3PIUq4NEi41gtJEe9mpaItUdko8QQgghhBBCCNFRZIyUI8zGlTtZv2wbn33wBf9d/xUA6bZc8l2DcJi++5gpS3cXc/e8+fxl4aL9xjz52QrueHMO9324IG65oRKoDVqpDLqpCfVgUPJF3zkfIYQQQgghhBCiI0kh5Qgz8th+DDx7CJyWycpQOQALqt5gTvlzbG5a9Z33n+J0YjHpZLv3X5RZsGEHzS0hlm7eHbd8gKeARIudJEsAQ48Aju+cjxBCCCGEEEIcCQyldZlXdyeFlCOMyWTiwl9MxTkjhfzEPcWODFsedpOLZGv6QbY+uP7paTx1zplcO27MfmMUBhgQjkbic9NMnJo9jnRrC/mWOiwYbW+vFIY6EibVEkIIIYQQQgjR1Ugh5QjT2OznkX8uwLswQFWgAYAxqSdwWcFvyHYUdMgxLCYTmtZ2FTJiGDRoQUJJBk6LpdX6Sv+XWPQQfhVkceU/Uap1MeWuBZ9w6gvPs6laxlARQgghhBBCCPH9kkLKEcbbEsQSsWD32TkmZSAAZf5iPq+eR0tk/1MWd4SoYXDF7DfZHmog6lCk5LhaxeQ7B2HTItgJoyhGEW0V89bGDWyrq+XtTRsPa75CCCGEEEII8UNhoHeZV3fX/c9QxMnNSmLMqb1pHmnwfvEmABZVf8xnNfPY2LS2Q45R5m0iuE+3HQB/JMz6qio0NHSl0d+V1iomzzWDBSWDeK90OOmOH6FrrVut9EtNJdluZ0JeXofkK4QQQgghhBBCtJdMf3wEyspxYdQFMUwhAEaljMdldtM3YcB33veK8hL+vOJzRqZnc8f4SXHrEqw2bp44kXlbtxFuiXDa8EGttjcIYzYUdl3hMrW0eYynzzibWr+PgqTk75yvEEIIIYQQQnQHBhoGnT/Qa1fI4XCTFilHoGBLlAyzm4LEFAD6JPTnlJyzSbKmfOd9m3UTGmA1mwAwVJQS31YC0T1FkRN79cYfDlNt+AnorbvtNAQ3MTylFJOhk+wY3OYxEm02KaIIIYQQQgghhOgUUkg5wuyoquOdRVtwlyZxRZ8JHb7/np4EBuTZ6JO5Z+rizc2r+W/ZM8yvfINwNMqv5s5lW10tfkJoptYz77SEVpPlrMHtaGBu+d87PD8hhBBCCCGEEOK7kK49RxiP047TaiUnyY3FZOrw/e/0VrOzpYqWaJAz8kaTaE7BrFtJsWVh0nV6ejyUeBtJTXGS4/a02t5uPYY1zSsoCnkYGt2NoaLoWsfnKYQQQgghhBDdiaE0DNX53Wq6Qg6HmxRSjjCpCU4evmQmwH6nKP6miBHFpOntigUYmZLPVX0nkedKBSDX2YfLC36Hru1p/HTnlCnsaNrJK7ufZW2jjQLPGXHbZ5p2cWHaJlZ5e3FKzi+liCKEEEIIIYQQokuRrj3dzMqSUt7dtImIYew3RtM0oqoFpfZ0rfmifi2PbXuO6mBtXNyWplJuWvUEr+xa2O7jm3UTo5N70+t/M/KEjRZKvO/SFNpGua+By+e8yK/nv0VxUzWfl66J27bS38Sv12xidkU/RjjLsJsd7T6uEEIIIYQQQgjxfZAWKd1I1DC4c/7HtERC5CS6GZ2T2ypm59YKPv7gA3pNnkv//FPp6bmY/9vxBiW+RnQ0ru17aSx2feN2KkI7WVYf4MKCSa321ZY1leX8aelCJvbI46ejx1PtW8z2xpdItg3mi8YpbInuxh1JJMc/nuN7jYzbtjkcpNnwsNWby5qWPB7LTqb1BMlCCCGEEEIIIfal0FBdYMacrpDD4SaFlG5E0zRCRAmqKOH9tEh57A9vs+7LnQzapVDXfEJPz8UkmhNwmBpJscWXLXIcLpKsOj2d7W8Z4guHMZTCG9wztXKyfRhp9mGkOyYw0VbI0rIi0j0pXFY4loKM+FmC7OYAvZM3MCCphFqfm2yrs81jLN1VzLqySs4ZOYQkh73duQkhhBBCCCGEEN+VFFK6EV3TOCYvn10N9fRLbbsth9VhQUUiRF0RNq/wMHUAXNLrbNY3bufEzKPjYg1VSbZ9N4lmS7tzGJ/bE5fZhtu8mogxEsOoRg8vx0s1vZKPo6HWwpamcjau/i/njBrGhceNiG2rAF2LYtGiRA2DbY2vMcr+81bHeOOLdWyrrqV3WjLH9+vT7tyEEEIIIYQQQojvSgop3UhTyMsHu5aDMlHS2ECKo3VLkj4DctmyaQvuJIOew/YMkTPY04fBntYFiY+qNlIRMFHqq+Pqvu3LQdM07PrbBCIN+EPH4I/W4IsUETB8WE0mrhk5hs+27GJHQzUue3yBpqezJ7nOo1hX30yFcrPbt5xRbRzjojHD2VhRxVF5PdqXlBBCCCGEEEJ0cwYaRhfoVtMVcjjcpJDSjXxcMQ/sTURCZhy2ZiC7VUxWj2QSs+zsrsulcbHGacPUfmfkcegOqoNuPJZD6z6T4/kpwUgRLttwTJFKMI/EZRkEwAm9+lDn89OogowZ2LPVthPSRrGh6XN6J9SRaR/a5v5H9cxhVM+cQ8pJCCGEEEIIIYToCFJI6UbKm32Eah0oQ2NtZQ2Fyf1axTjddsxuCzUNCQTCVgLBMA67tc39jUgexKbm7Qx19z+kPNy20bhto/ccz9KTUZn/QuPr1ieflKynTF/Pk2sa8a52csbkoUwc3huA3o50BlrL0AiTYE09pOMKIYQQQgghxJFKWqR8f2T6427EobtJdPqx6lFyEhLajNm2oQxvXT19CnYz7pQV+y2iAOjYsOoubGb3t87JiNbRWH8DTU2/QSmDxmCAGQPzyfLYaDbqqKxrZntJTSx+Ze0rRIiyrjGbjU2hb31cIYQQQgghhBDicJAWKd2I3ehJXeV6lKFhBNsufhx9bD82bH+XPmOKcCYceBDZXEc6SRYPfRK+/VgkUaMOX3greriYp3d9wrPbVjEqMZ+rhs6kb0IuxWktDCrIjMXnucayuHo5XpVEdWT/RR4hhBBCCCGEEKIzSCGlGxnVIx1LRhgVMpGeYmsz5rO3V1E038Hm9RPxJHq45M0oZrOpzdjqYCUG9dSGyg4pjwpfEyl2F1bdhDdcSnO4mqCyUuGvJRiJsqqilOqdQX52nIOTBhfGbdsUaUFXfgIqylEpow/puEIIIYQQQghxpFLoGF2g04nqAjkcbt3/DI8gaTYXZquGZoVEa+sZewCOmTmSYKQFsyNCY2MTSu1ZHvT9B7/3/1Dq6+40LRGoDkSoC6p257C0ahe3rHiLRzd+BoBVNZCuG2SbDe4cPZOL88bSM5RKeV0Ty7YXt9q+uuld+jtLGO4uxhLdeghnL4QQQgghhBBCHH7SIqUb2dlUSUO5DZTGwqLtnDdoTKuY/iN70Wt8OUZ2MoFKHbNZpyVcT3PzM9h1MxbbcZgtewapLQs0UB3UKA80H/TYSim2NJcRNiIAmPU9NTqnYyqmaDG6pS+YbJxROIS+WipvrdrAqJxsNuyqoE9uGjaLmR1NtfxjUzombQL9U8spafqQEekXo2lS7xNCCCGEEEII0TVIIaUbSbV6IKBBVCPD6Wkz5r2Xl7B1aT6mPCdG1EQoFOH1skX4/L05NrWAIeY+sdihnl4srVnL6OS+Bz324prN/P2rdyl05fDw0efise6ZMlnTndgTb2B57cd8uu2vLNuZysm9hvCniwpZu6GGP3/wBSce1Z+Lp43GYbbgMOdS7vPz8a6BeGy1zJQiihBCCCGEEEIclKHAUJ0/Y47R/g4NP1jylNqNrKmqRvfp2KoMHvr1Oyz9dHOrmHcf/ZBwTQu+JjOOVDdWq5kMWzIhWqgMVxNR/ljsjsZaqlsilDYfvEXKV7uLKPHXsWz7FtyaFdP/CiBKKYLBRRQ1zSWsVeKy+6loWs38kvsJe96gtLmZVdVlKKXIdiby0yFTqAkn4tetZLvyOu7iCCGEEEIIIYQQHUAKKd2ITdcwTOCoieDf4eWj99e0ivE3t6BZzSi3HW9YJxSOcFLWGIZ5rOjU4o98PRXxU59/RXm9j7fX7DzosV0JZjQULqcJs+XrwWv9weWU1vyMEdb5nJ57Hk+ecC3l1VZqvWaUKY+UoR6qTX5C0SgAmh7FYlZYzRqT0qZ994sihBBCCCGEEEJ0ICmkdCM6e4oR4QQdLclGVn5qq5icXungdKGHIdocREPDpNsYlXYLQ1Ovw23Jj8U2m5qxucMEHHUHP7bJisusk5yehKZ93ZwsjIuwiuBTVvp5hmE1WQirFD7eeDJubQaORp2kFis6e7ZJNu/iJ32+YGxaMRXev33XSyKEEEIIIYQQRwQDrcu8ujsZI6UbGZ6dhTnNh22LQaLdTLKnCKVUXGEjp08Oq+uCKKsJTVcY/5u2J8GchqEccbF2axSFwmqLHPC4hmEw/0+fYyoK4r3ABBO/XmfXDeymVJyaHbNmYeGuXTgsFpIdDkwRjW3FNZh0DW8gSHKCE2voP0z1bActQGlAJ2KEMOvWjr1QQgghhBBCCCHEtySFlG6kNlxKZlELepGNBi1EedlKDGMGJlNmLOarDWU09rGTUK+hoWE26RhGgNLKaYCf9NQXcdqGAVC9S8fa24FW5TrgcSNGlMqaJgyTFePLJsqK6wiHIuT3yUCpCDpeLHoawajiwaULKavzYgTgI/MWmowQmgGBYAQSwGw/kc3e7ZSE3IxIG4hJsxzOSyaEEEIIIYQQ3YJSGqoLDDbbFXI43KSQ0o3ku3rTJyfMTmUjajaxeWs+up4RF1MUbUK3uLHUh1A6mEw6exqlKPa0TYnGYpUDQsqM1XrgHmA6OhF3MmE9THO1iatm/QvDFmHc79OZ1tNOgaajazrvlnxJvWc9Y3oWs2nrACoDKRhRBQrqG1vITk1Etx7LB01f0BSpY0WTifHZX38I31+wjrpGH+dNH4XNKm9dIYQQQgghhBDfPxkjpTsx7GxvzmFvAbC51hTXVQdAOSxYTRqNfR009nOiaRq6bicn432y02fjtI2MxSZlOggZOo6UA3et0dBIxoVqMZHTJ5VIRGGe0sSndTt5eftiEkzJOIhS3+IjENQhYnD8iC2c1r83zhIDV3GUpkY/Jb5qHt32DuWBELoKY2V37BjBUIT356/n85XbKamo77hrJoQQQgghhBBCHAL5s3430hRuoTIrRKauMHmDVBlGq5hj+/Rkrl6JL80OGry+fC3njh2GxZwBxLdeGZqSw2e7vKRusbAgfTOTj+nf5nF1k0ZKfgKlNFAzyo+zMYmgUlSWmYi6bLyRaWG4Zzj2kBuj2obP7qDcbsJf9wnmgAkFfLF8B4WDxmLVLeTYTfR3FNPDlhwb48VmNXP52eNobA7Qq0frQXSFEEIIIYQQ4kgWRSfaBdpKdIUcDrfuf4ZHEKspiDWgQVQRSbLB/zrrfFNOkgfLp9UYVlAmcDktKKXYVf83NlffRjjaGIsNtyjs1RYi1VFWrSna73E1TWP6FeMIn2Wnwd6Er9CPFnFgNmkMz6ygIljLpqZVzBgwgIl9ehOxuIkYIcq1HegtYcwtEZYv2kq4zuCnhWegiPClN5coOXHHGTusF1MnDsCky9tWCCGEEEIIIUTnkBYp3UhJk4HWolBmE+YaP2l90lvFNLdESGlR1CcobIk2+mVloAjTHPwSpUKEo9VYTB4ALhg2gkbNx5QhBZwzdmSrfX2TRZloqYXENDN6MVhVkP7H+umZks4w53ZSbGYSrDCx9yrKvdV8VZyEuaknJouOHlJk5aTg8ThZ71+Npjfg0MIEVcPhuExCCCGEEEIIIcS3Jn/a70b6JqXTz2NHOa1E0xJI7ZfRKuaUC4/Gn6jT4lDUR4KsrCxG16z0Sf0dvZJ/gcPSJxZb6EznovQxzJpyNFkZngMee9GiTVjXRNDL3CRl24jabRhFFkyRbNyOQsrDXv69614q/aWg13HOoCncO/1n2DKcWLNcnH3xeMx22OZdg82kk+NIJC/hjLgxXsKRKL5guOMumBBCCCGEEEJ0E0YXenV30iKlG6mP1FBV6kUPKDBp9Ml1t4rxNrbgddnRlYFCw2XdM5Cs09Ib9plp+PUlX7F43U42LNnF+aeMobBfFv9dtQld1zl5ZD8wqogG3keZxlPyURGeuiCesRDo7aOlNkTwUycl3jSGX30bz+38I42RnZijBdT6a1lavpB3wruw9EohGjDonZ9GUcsmdrZ8iYafCOmMTDkllothKO56eS71Xh93XjiNzKSEw3othRBCCCGEEEKItkghpRsxolb8foUrFMXkj7B63mLUz45H078uOmyub8Lsj+LYYmB3W5mU13u/+ztxaF9qt9Wx/YsyPlBr8GQew+ufrwUNjurbgyTzfCK+V9BtNQybNpidXzWSnOamujQIDRoNoyKsc9XjthSw/C+9qSuPcseD03h691rCprV4DR+nTZ5Mom4hGoySk1zAMM941jauIcmShPGNWqahFLsq6vAGQ/iDocN6HYUQQgghhBBCiP2Rrj3dSL0vhHWkhrLsKUBUlptaxXz88iLM26sxEk1EEyws2V683/2N6p3L7VdOY/rJw5g6bSjpiQkcN6w3k4b1IdXtRLcdh8lxFmbHDDwTkmiYBI12H1f2PprjBrox0KkJ+KhtbmH3bhPNUSv33r6AQCRMH2cBPxl1NpdPGMXqdzbxxz/MpqUeTsmexfEZ53BCxpk4TM5YLuFIFEtUx61bMaKtB9H9ppKt5dx4/N08/dtXv+WVFEIIIYQQQogfFoXeZV7dnbRI6UbSLU4cW5MIh2uI2nU8qc641igASQluqpM9ZNmcONPcDMreMyCtN1xM1PDjsfWLi09OdnH+ReMBqPf5WVSyGzSNM/1DcNoyWBuYzgBbJpW+zwhFDAIRg8ljniapvol3aoaRYLVjt1owbDoKsKU0UZi/iqCu8VnDVga6/sau7VVEogbeJj+7zRt4vuhpDMzcMeBOejqzAXDYLFw5/SiaWoLkZSYf8Dp8Nns521btoGJHFVf+4fx2XTulFL/58kUqA3X8cfhlZDoOPCaMEEIIIYQQQogjkxRSupEn3l2Cd0sTtv8N0Drp7F6tYo4b25vyxZtJqSrHPS6FVJeTqBFgTfW9KBVlVMbvcFl6APDJl9uYt2YrV049ir45adjNZhpbAmzdUcXNDbM5/fQBvLRjBZOyCrGGrRDQ8TVGMOk9MBnFRHQ7PsOKpmv87MrJbNpYTr9zitkZ2EEwGsQI+PB7A4QbfJh0ndQ0F/WRCHa9mUDURqmvOlZIAThueJ9W59OWvMH5JGSm0HNAbruvXciIsKZhMwYGa+p3cpJjRLu3FUIIIYQQQojOZigNQ2kHD/we8ujuun+bmyPI2PyeJGEDBcqis3pxbauY3RtL8Xq9fJFjZlnFNpaWF6NrFlwmFxZNYdW/bomxZkcZu6rq2Fa+Zz8Oq4WG4iasZRE2LS0hz5VCktVBH3c6p3gKyf3MRfpnGhbHHZjsdxD2ahgtoGsalspm2FzGyu2lbPN6aPyiB9HPT2b3zmoivjDhliDKALNJw6VbSLUoUqzf7jpYnDairgQsya0H290fq25Gj6bQHHBQ4Mr6dgcWQgghhBBCCNHtSSGlGxnWN4fjR/dH6RpoGuXb6lrFHDV9GOb0MFqyRiBkI8vlRqkmCkw76W+tQ1eNsdhzjxvGhVNGMmXYnpYgFU1emhMMfD10TG4z+Y50Tk4YxRBXT1btKKKswMtOVz3eRj/zSpcRDkTw+gNowLa1u6mvaqJkh43dzQlsCeczcMIwPl64CSPBQkqijZA5yPvlX+IzcnDoCm9oy7e6Dru3VxE0FDu3VWMY7Zt8K2REaQwHMZRGUUvrApQQQgghhBBCCAHStadbeXP2Kpat2YUpGAHDTKSNIsL2smp8tVYsn7eQMSqdvsmpaBpYbMegVADdtGfMlKhhcPa/nyZSZuAnyHljRpLicjCyIJeVa4sYMrYnH23cynPLv6Corp5Uh4ZhUoTc8EnRTgwtgGFS6JrCpOtc8dsz2b61lPv9y/H5vYztncFRhbl89upXhJUiZNbxa0EC0SD+qI7HkUiUpgOebzgSxRcM43HZ45bXltYSbgmgLDq63r5aoc1k5srC8ZT46piSNaCdV1wIIYQQQgghugYDDYPO71bTFXI43KRFSjdyzIRCehdmoswaSgdDb/0G3vhFJVpaGuG+LspVDc3hIJpmwuW+iYTEO9A0GwAtoSDNTREiUfh4xzYqdlVj0XVOSu5FRqmOq0wRDkVoag4Q9Ec5uf9g3GvAvhX+W7QFpSeirBCxQjAaITUriaOOGcTMXoOZVeji5P7vUx9+i5ysRDSLCZ9SZFnSGOnpg12rZIvPw4iUCw54vg++8Sm/fOI9tpfVxC33NvigsRk91P5pkv3RECvq17GtsYTdvup2byeEEEIIIYQQ4sgihZRuZMiQHowc2gNMJjCbwNT69upWHXSFYdeJppoxlKK4poFGXyAuzm62EMiGll4GkfIGHrjyMVZ8+CW+QBBvIMi63eWs3VJOS22AbTurSM/04DvaSfUkCycM6kuBvRlNU5h1hfGN2Yr1RguhhiJC4e1U133B6g1laA4zM2aMwGqzkGa8yiXJK+ht3Y5Fd3Ig4YiBAqJG/HTIW9YUoSJRQl5/u6+dTTPjq3eyfqvBkh3l7d5OCCGEEEIIIcSRRbr2dCPPvLOMzxdsQqGhGQo02NlSRC9nHtr/ZvIpHJzNF++vpMcnOtc+cT71DX7uf3sByS4Hf5l1amxfhlKY0Ik4oqhICwF/CEyK5aZFJIyqpWlHOjuqasFrUFLbAEA0EgUNarxebhg9k1znq2S5h5Fo29PKxRsM8Y/PP8es2/nNCckM7DmKxrCPkEXDkZfC+tJKPlrdh5nD1pJgTjvo+d563mSa/QHSPV9P8bxu+XaKtlRgRCIEA8F2XztfNMhuXy2+KDREWtq9nfjhKSmvR9M0crOSOjsVIYQQQgghOozqIrP2qC6Qw+EmLVK6kSS3E2e2C8OkUDq4Bxg8X/QqG5o2x2JM/jCqwUd0ex3/+nwFUWWwq7Geal988cBusWBvUiRu02kxN2PKy6ZG9xF0VZJQ2Ey620pitpNQCiTmuFi5az2FznLyi7x8uXIzj74zn0n51zIoOR0jvBGAUDSE1VkPRpigMoOmM35sHxIT7Vh8YS54+CX+sziJP757OrkOE1X+VQc8X7vVHFdEAfhy6Xbw+yESwYpCKbWfreM5zDYsrjCpuX5G98ho1zaitWikhrLKi6mqu6+zU2lTfaOP3//jv/z+H+/T1Nz+FktCCCGEEEIIsZe0SOlGLjhpJCcfO5CTqx+COhh6vp/msIUE89fFhozeWRhuG0amTs26XRSPbcSnolQGWlBKxVquBKMRLHU6mldR9ZWF9SMaWPjBAtLNNiYOG8pxZ49h7o4dmDWNHlY3W0te45bxS3ndGER1TZTljRHcVctxHV3Bc2VZXFw4EUwp2HqEsTUrolYXTUYiwd2N+DbW8tKGeahxCZiUorLWhosN1PjXkuEYfUjXYNq5Y/nolSVUFlczcvrw2PkcTEs4hJkEHOYw2c7UQzqm+NruxtfQQ8sIhFaRlnRbuwf7/b60BIIUVdWDBqFItLPTEUIIIYQQQvwASSGlG9E0jbDfjzXiI5xuoliP0CPqoCboJd+1J2bIqAJC5/egJlXDjB9lUiR7HPTNrKXK/ykZjklomsZ9L36MN9HAbNIIeBXKCUqB94sE3tldztwe8xjsSMZapQjZWsjKcVD6ZSo5ys/4cV+RYGlk3fZsKmuTqQ5aWVVbzHl9BjE6uy/HDO3BphYrZRU7WbyoBRU10BLtaEqhAJMWYYN/BLNyTj/ka+BJcdFY5wWLldq69nfRaQmHKKuLoABU13r4/yHx6QMoDSXRrJIoaGcR6/uk6zpmuwlN04hE2zc1thBCCCGEED8EBjpGF+h00hVyONw69QwXLlzIzJkzycnJQdM0Zs+eHVsXDoe57bbbGDp0KC6Xi5ycHC699FLKysri9hEMBvnpT39KWloaLpeL0047jZKSkriY+vp6Zs2ahcfjwePxMGvWLBoaGr6HM/x+KaV46PF3sa/QSfxc0ThfI8+ZSU9nZiwmxeNAM5kwwjohZWdoZhbOBI1x/T5lR8NTNIe3ALC7ug6iOmGnhq1JJ+VzA/eXEPRoKLOBUtDD7iKlMkq+0817/83l3/8dy8uVg3n1/TFUWSbR3N9M/7RqLui1kymWWkwVNmzlOSxYt5FgZDe2cICQx0Io2UpabhJmH5i9gKHRFPVhN6cc8jX49IO1hJta0HwBQg3tL6QEoxGagyFagpE9FSPxreQljKRCOxmn8+x2twb6PmUkJ3DKxMGcPH4gqUmuzk5HCCGEEEII8QPUqYWUlpYWhg8fziOPPNJqnc/nY/Xq1fz2t79l9erVvPnmm2zZsoXTTjstLu7GG2/krbfe4pVXXuHzzz/H6/UyY8YMotGvm+1fdNFFrFmzhjlz5jBnzhzWrFnDrFmzDvv5fd9q/E1s1yvBpmGYNYwVZq4oOI0Me3Is5t3XlmB5o4yUjxpJXRhkS0UNRZXNrN7RkyTbSJzmngCcd8xwDH3PNMo5ZivuCh3COgkVUZJtFu6/cjo9clOwWiwk5boYM74PFR4Lzakm1ua4eGVrOluWj+TzeVNoLipk3gI7T72+mPKmZsxaLf0d9eSbDRwmM6YoVFU3oWmACWyYMJSbSGRH3Pn5/CGaW+JnF9pXXXUzZosJk1mn/4he7b52n+/aBeYgyhwkEIm0ezsRLxotpcC2Cw+rUarrtfgwm01ceuY4rjh7PBazqbPTEUIIIYQQosMo1XVe3V2ndu2ZPn0606dPb3Odx+Nh7ty5ccsefvhhxo4dy+7du8nLy6OxsZGnnnqK559/nhNPPBGAF154gZ49ezJv3jxOOukkNm7cyJw5c1i6dCnjxo0D4Mknn2T8+PFs3ryZ/v37H96T/B698+JqzDYT0VQdGkwEB0W4fc0r/LT/SQxN2lMgScxNBpcds7KgTGBoEfrrKWjVJzEwdWZsX2MG5aEtAoVGbUuAhEgEi8+MCTNGaZhX3l7Fzi/KaNZ8PNG4mMnD+uHPgSSbH6/Txm6vidHRHHyBCIt3usgyOzn16EJm120ngQZ6W2vw2P306dmD9dvLKU9SmC06hqEYMxLGOtcR8L9HgvtnAITCEe755wcEQxF+85PpJHvanhp5xgXjePlPbxPFYOuGsjZj2rL4yyKijXYwIMlk/w534ciWYO2Ny3IUidY8NK3rNemrb/Hzq1fmYDWbuP+iU7BbpHejEEIIIYQQ4tB0vSedA2hsbETTNJKSkgBYtWoV4XCYadOmxWJycnIYMmQIixcvBmDJkiV4PJ5YEQXg6KOPxuPxxGLaEgwGaWpqint1dRoambsyMTeY0A2F1WJtFWMNKlRyAsEsF8FsGzaPxjnT0xkzuY6GUF0sTtd1zOzpmtGSAU35VszVPjR/CFPAz86dNfjrA2hRCAYj+LwhUlt8uBJ9uOxBBnncjBmdT15BMsZWP/VbG3j909WsLC9hzo5ktmwZwoYtIyhKjODtYSOSbMWsm7CbTESSopRER2CzT2uV/8FYbRaSM5PQdB2vN0gk3L4BRYu21mItM2Mtt5CWkHDwDUSbFld9yV+2bOJvWxZjdMEWKUIIIYQQQgjxXf1g/hwbCAS4/fbbueiii0hMTASgoqICq9VKcnJyXGxmZiYVFRWxmIyM1tPZZmRkxGLact9993H33Xd34BkcfpdecSxVNTVc+tN/QhBcvig/zjqJxODXhYENn23EXNeEkZWGYbNg89r5qORjbO4GeiT2IMteSJ+EniQ57DhtGr5wmP6DS9hg6YNjl5+kk6tJGBYgaUcu2yssBBvCFH7poWFbC1Q68OdCyG1CpURZWl7C1qIq7JVBolV+sJiwjjRTk+ViXqWF0tVF1NfpoCAaNfCaFSYFpc11BBt9nGZEsfwvb6vFzG9+Mp2oYeB27b/FiMVq5sQLxvP+C4sYPqEQs6V93Tdqq7zYlUKLKEymH1R9sUtZVvMhVQEboWgzGl1vjJRkl4MHLjoFXdOkNYoQQgghhOhWZLDZ788P4gzD4TAXXHABhmHw6KOPHjT+m9P4Am0OerlvzL7uuOMOGhsbY6/i4uJvl/z3yGw20VDahFYTRVPg9UR5/M1l3PfSxyxevp3Ssnp8zX6MnbU4NlbjywpjcpiI1vXD0TiE1xY+zQvbr2P2rlfwhcN4g1Eifo2WKid6SIGCYydtZVbhSjL6FTPx+P5YM63UDA5TnelD85oIbncR8ZpxhxOYnl9IrteOrdnAbjVT0CMBc4JOXmYFYwYUk5oaAQXa//rRGSaF7ovSN9nNUe4GGoKr4s7P6bDGFVHKaptYsaWYqBHf8mH0xEImnzqCM68/jjUNG2iJ+A967fJykzGHwGWVh+vvYlLq0QxzVzHEEcDoop0ji8vrqaju+i3MhBBCCCGEEF1Tly+khMNhzjvvPHbu3MncuXNjrVEAsrKyCIVC1NfXx21TVVVFZmZmLKaysrLVfqurq2MxbbHZbCQmJsa9fgg2frGFSNREyLATNSAzKQGX2cJLLy/hkcc+ps+wnmhKYS1uxO5VJNltNEdcfD43zPYnCnBsUnxR8z6biioxwuCo0Whcn4qrwgC7hewmP55wGFORjzOnj8If8VOlWmhMDKGHo7iKDZxhFxNz+7J4+Q6aVRCVqpE1XTH818Vk9A5SV5+KP3gM18yYjt0bweaLYPJHQIOoGTLTh3BS9mVkumYc8FwffudzHnlnEau3lcYtf/fROayb+wUfLP2EF4re4uOq/Xfh2mvyUYUkpDuYOXXod7r+R7pU21Gs3HIMa3ef2AXbo0BZdSMPv7yQB1+YT4s/1NnpCCGEEEIIIX6AunQhZW8RZevWrcybN4/U1NS49aNHj8ZiscQNSlteXs66deuYMGECAOPHj6exsZHly5fHYpYtW0ZjY2Msprt4ffYK3ty5FcNlQZk1wtUOhhyXwI3nH0NudhLDhvSgJhiFzBSifTIYkZnHHy9/mqL3N1PtihLymvHW2Gkoyqe0oQlzM5hbQDmjmLMDEDb4586J/HPNsby7IROTScdcrUhaEqZwp4eoWUOP6FzaawQzBvWjzFKNxR7FkWqisU6nqhwmZgzG1OTi1c/MzFu6G5tXYWmMYgmAYYKIXePLnRWkuk7Gajrw9McjeueQm+qhZ3pS3HJzgpOqhiCJTQu5KfU5+piWHHA/0ajBm59/RZMKU5ib/l1vwxEtGlHkqXSSQ0664ggpSW4HFrOJJLcDu01aHwkhhBBCiO7DQOsyr+6uU58kvF4v27Zti/28c+dO1qxZQ0pKCjk5OZxzzjmsXr2a9957j2g0GhvTJCUlBavVisfj4aqrruKWW24hNTWVlJQUbr31VoYOHRqbxWfgwIGcfPLJXH311Tz++OMAXHPNNcyYMaNbzdgDMG/pV1SHy7B6NfwZVtxnNvHQm5/wkn0Dr950JTaLmdXLtzHnv6sIWqIUrd3GYBJJadaIFhugYKcvk+odKTRUrSehHnQ/+ArBMcqL/VMbjesSWJWSSIJXZ0t5NebyZhJ2BAjX2fAOtILVIDXByY6mGmo8QX499iN6Kp0n3ptBxZJCFjeWEAqEURos3LYd5dTR0DGsGoYF9BaYmTUOcztmfDl/0gjOnzQiblkoFGHrlmqimk7YX4NFM7CrzQfcjz8Upq7RR9gGVbXN3+UWHPEK01O54dhxpCW4MOtdr067vaiazdsqMJt0WvwhEg8w3o4QQgghhBBCtKVTn3RWrlzJyJEjGTlyJAA333wzI0eO5He/+x0lJSW88847lJSUMGLECLKzs2Ovb86287e//Y0zzjiD8847j4kTJ+J0Onn33Xcxmb4eZPTFF19k6NChTJs2jWnTpjFs2DCef/757/18D7cBPXJxJSSiWSy4QgpzvYaK6FQ0N/Dv7R8A4HFYMYcr6D1hF0ZKgBGXH4XjmDyiugboaAHF0OxUxvfPRy8w4Rsawpdj0BK0oswaiTsVaUUKLReW7yhBRaLgdFBfUYc+pImMMRWsDqzls4U7SWgxke40GNAniduuOY5fXHEiUcNAM8Ae1Uhw2wmkmDBl2mjuoYMGGAZh9328s/sn1AR3x52fLxCirsl3wGsQ8ocxlMKZ6GDICT+iTD+BJM+vD7iN1WzCSNAJu2GXX8bO+C62VNdw58fz+NNHC1qNXdMVeNwO3A4bSS6HDDYrhBBCCCGE+FY69Uli8uTJqAMMSHmgdXvZ7XYefvhhHn744f3GpKSk8MILL3yrHH9IfnzVFBzPwpuhxWBSNNTZ0Qc2E1Ym3ihbzKWFJ/HGwx/Rd2YpSwr6Yfg1TpgylBqLon5tLZgUvhQrpx7Tm8m9xrHBKGNe3Xp0zSAa0VE6BJLMBFMjhHID9M9LZXlyAhGbGbNmoOl7phreWlrJlvVB3BUBtruuYdwF47BH3dhtFgYUZlBRXUz24Fp2z8lCj0AoHEFPAMOqEXXrbA5EsYWLSGn8jGMzLgbAMBR/+PdcGlsC/OqSE+ixT3eevV545jNKd1bjSXJgbuqLJ//3JNpS24zdKxyN0iPBTW3UzylD+nXkLTnirK4qo1Q14w9G6IpjzeblpPDwb87FbrNglYGFhRBCCCFEN9JVutV0hRwON3mS6EYcDis76iuJKhP4ozTbbOgG9EjwMybFh0XXGX7OGD5c+jlOI4wvYqO0oYkNlVW48ltwW2qpNJtZX17LuB4RFm8tJWI1Y2oES34LmKzYmg3ARCgV1ljLsaUl4GsO4E7zENiqE4i6cfmdZKUnUeW0sqA6iHnNa2zYUI2veBS+oRHy+raQk1RPuZFJMGwQRWGvNxFxKkg0CGtO7KZeDEg8JnZumrZnViJN09APMNtS6a4aDKeNjIIMtqdX8NoX/ybH3pc/j5qFaT9dTR5ZsJRqv48su4tMV0KbMaJ9pvftx/LdxYzOyMXcRaeRDoYjmMxdMzchhBBCCCFE1yeFlG7kT+/O5dP1pTjNGnrUhONLCOZbcSVlcPX40eiama1ltax9OxN9YZCak1IwUJhNiiunfYgl6OPVupH0Medi0jWMqiimiA17RRClTGhhg5YcM8FEjahSFPZIJ5xUxtaiejw9k/AXuEmKWkjfHOH40f2ZW7YbX6SS3U3LMKdHWFeZSXmVTmZjBhf1H40xNsrcxTsxolE0QBkwqm+AgoTBTM++lnR7duzcNE3jN7NOJBSO4nbaWp17MBLhH58vxXVSNrOG9+SEU4bxQv3LWMxlbPU2UeY/jZ6u5Dav29KtxQQawhSpBt5duoH+52QcrlvU7W0sqmLF57tZrZdw1tDBbGmu5uGvFnFx4UiO71HY2elRXFHPX5/7BKvZxJ9uPh2L2XTwjYQQQgghhBDiG6SQ0k0YhsF/P9qAP8eEHgGPrwXDkgC1VprDNhL1EQCYdtZh3lmDXqXo2T+ZvkmppJldVFa6SbNFaKpxUd+vBZvJzCh3Jmu3lGKtBNtXCj1sYPJGcCTZOXvkIK49fjy/emkDmg4JZjPpiS5mDB7AjKv60RIJ89ZrO9jdCLXv9cTVHMXSO4FoXRPRctDz+mI0bcUShGgYlFlh9mj85LhLwB2g1r8Jp95IgnVA7BxtFjO2/YxrUdHsZVVpGbqmccNFp5NgsxKqjRI0zBhKIxgN7vfaVTQ3ErUqTGGNUf17dOh9OdJkJCZgNZtJctqxmc28uG0VSyqLCBqhLlFISXDacNqtpCW59ttCSQghhBBCiB8ipTSU6vxuNV0hh8NNCindSLA+jC0MnuG1XHDaQh6cfyYhL/Tpk47nf7OTuGwmTA4DWy8Da2EVH727mqLt1XxSejzOkiDWaXV8sbWYi0bDrAmj+NvaWuqNKFGPHb3aT1JRhGFJyfx+xjQAAi6DhkugzNzM5H4j+LByC2N755Jmc5LkdrCkzou1Zwrjq3dw3SlTuOuxt4gqPy/+dyEbSlswAM2koXQI64qvykpYWbsMs9rFlXnNDMt4HJMeP7OKUoqoUnGzwuQlefjZxKNxWi3YNA3DMJiQNo5ltTswkYLb4tzvdTM5zIQdIVRylBZ3CxEjilnf01IhHI7y8D8+JBox+OnPT8Jut3TwXeteemel8JtLTiTFYcdk0ilwJ6MpRY7L3dmpAZCc6OS+G09D0/a0chJCCCGEEEKIQyWFlG7C6/URcoGplw//VzbWZBaQ6XBQGtJIdNnZvraIzat2MmzSEN54by6ek/zs2O1g8fwvaIkEsNijOGu8GCWKtd49U1LX76onUuJFMyv0oIFm0jHrOrU1LQTCEewWMztqKzH6KXbvjPDvfy3CboOvCvvzSf0KGjMNVLFGCDOJBQEmDOlF1sAowWgEhy+IgQJNQynwp0HQDflpGVRFs0g21eOx9UfXWnfj+b/VK/m0aBcnZPdmYHo6R/fNQ9M0JvTK4/1XlnD//f+kV2EGv3rqRxyXNo2QESXJ6tjvtRvfsyf/LduEqY+fR9Z/QnZCImPSCgDw+0OUltSjlMLXEuywQkokEuWT5VvISfcwpDCnQ/bZFWyrruWpxSsx6RpPXnQm6yqrCfo0NlbVdHZqMbouBRQhhBBCCNH9yGCz3x8ppHQTtVUtKKVwfhnFtt3EzqYCsk9LpLKygVXRYjY+vpzAznrOPeto0nK9TOy/hUhtf5KNodSFfaSn7mTKeWt4a/1wqN8z4OquTWWopiB2XREJG4RdZpKSHDRmapzzzEvcevKx+LYEsYYsBM06KkGBC/p6kniz0s/mhgZMNjPmUJiSL4azZWclJR8bKDRcF2wlpSqf+nodFTWwNmkoXSPF6uSO3pcc8FxLm5tpDgT5z+p1pJgcDMvLxmndU+CoKGkg6AtQvKWcyp013DjspINeO2UGUxCosJCdmURv99djpNhtFqwmnXAoisUSP56GUop5FetQwNSsIYfUwmHDjgpem/MFCU4bD91+dru3O5wCoQgldY30zTrwLEcHkpvkYWTPbFJcTqwmE5VqF5olRA2lHZipEEIIIYQQQnQeKaR0EzabGVsjBHQ7FluIKdOq+HCpE7PJTlOgmUCyg4hFZ3l5FeXrs/jon27Sfu3l4hkn8fvX52E1+7G7QkSiZlTtnnlrj58xkq2bylkbqceI6qCb8aVaqQ37idREeWHjKkIpNprT7GhhA3OSwojoWFsWcXV2hLvedGM3Qb+0DCbNzMCwg3NII9XeBBpsDhpbdAxdoRtgCoHDaiIrxXPQc715/ER21dexeH0RKS4njm+Mm3Lx9SfQUFLD+iVb+GrJVvoOyzvo/nJTE1HJkOb38MD4c0ixuWLrooaBrulYzBCJGnHb1QSaeXTLXEAxLKknWY6k9t0soHePNMYNyyc/J6Xd2xxu1z71JpvKq/n5SRO5aOKIb7UPp9XCrSccG/s5w91ISnqAVEeog7IUQgghhBBCiM4lhZRuwrBAxKmjaVYCWVaCLhcqGEI5nYSDUXrY7agGg2BLBDQzjc2JREPD8Ycj7HZVcN2I1QTqrDhDEerYU0gZOjqfzHPzKK9sxtQE2XPD1JlaMOwmxqfkcsmw0fzKvg00DcNuQgsoLGY/UePfWPx2Qv5jaNFgm28HjtACvNH3ueWSFXxelceCd4cQDeyZ1tjAIOjRMbQoul7Drvr/I9lxLB77UW2ea6LNxrCsbIZlZbdaZ3daOfva48nOS2Xk8QN5dusS7CYL5xeMbtVipL7Oy/2/e5PdSS1YvIA7SlpS/PTHDoeViy8ZjzPBTnKyK27dxqbd+KJN2E02oip6SPcrwWnj6nMmHtI2h1vEMDCUIrpPwei7ODHTg8W0mR5218GDvwdKKd6Y+yVRw+CcqSO67BTNQgghhBBCHCpDgdEFBno1VGdncPhJIaWbcFqtRK0Ke10Usw92r/cQbbBg1hXKpLHFGcKSrjGi2QwOK00TLLg1J7U+H0EU86r7sbymN9a8KI6aPR++h5/+kP9WfElSS5hwTw0jT2Hy28Cic8UJYxmVnYupIUJKbRBflg36GCRm+nA5f0xWWh0XndGHVxetpqXJShQNr6uFT5uH4TSZsHq8tFS4MIVBCyp8FoU1ZMIf/IK60MdEDd9+CykH02tgLr0G5lLcUs/clRvRNI2TewxuNU7KisXb2Li+lKoeZqLJJlR0T2Hnm7ZuKOOB37yJ3WHlny/9GJP56wfvlXVb0XSDNLuLdFviIeXY7A3w8BMf07tXOhecNfZbnWdHG1mQjT8aJjf94K2C2qvKm8PqrX3Q8rrGV019k595yzajlGLKUYVkpnaNQXCFEEIIIYQQPxxd4+lGfGdJSQmkRU3UJYVxVgfZvS5Ci8uG0gBdAzN4PSZKPytC+fz0awxibmni3dcsWKvNrEroRVBZMFIgPW3P22KNt5KAHVw7bOj2CN6RiqzPTFx95iRGFeYCYA5G8aXqoMIEBmgUG4nU+keRk5HD5VN8VAZfo6GhBv9uO9HUkXxVVcRoSy1aTS4Wn4EpGEUzFO7iMKahLjISJ+MIRUi0jT6k8/eFwvxt/iKSnXYmKA/zXlnCsAt70y+1jkHuMW0ONnvs8QNZ9OkmtqQH2batGqM6xEefbeSk4wbFYqKGQWOjD18gRENDC6lpXz94T8sexUflayn3NlPsq6WPO6vd+c5fvIX5i7ewZOUOzj/zKDRNozkUZNaHr2LRdF6YfgE20/f78cxJ9pDosJPs3P/AvIfq/Q3VVJYnszzkhwkdtttvLclt57hhBdhsFjJSEg6+gRBCCCGEEELsQ9q1dxNRw8CoCmMJaIT7GaSnNaL7w2hRA2VE0YIGpqCBS0XRAiF6pZWSZdvOUkslQbtBituL1R4ixe0lXOwD4DfnnsrJtigp/iDWjTrWNVYCDX6+XLkTgAZ/gIm/mIB9QoiM0T6MBiuhZp2mliAAJt3K0YVuUlxedppTsQZ6cm5GE7mlYWx1iRhmhckfBXSsNQYmh4lAxEGW+3yc1r6HdP6VzV42VlSzdFcJa1dsZ+f6Er5YuZ4oTSTbI21us3ljGY2NfjICFmxeDSNs8OnyrXEx+QXpZGV5SElyEg6E49YVuLJo8Jqp8hr4gm0fY3+0RBMRl4YtxxnrcrSrqY4d9fVsrq+lpLnhkPbXES48ejhPXHEmI/M7bhahHkkeDEcUp9N08ODvwa7dtaxYtI1lC7cQPRLaHAohhBBCiCOGgd5lXt2dtEjpJswmnaysJMrLG4j2N5hw8ldsuHc6hlVHmTWcdQYRr8ZJlx/LW68voDaUyubaXBxpdhpsfrKbW8jPrSVaYaLU7wQgL1Pj6lPeYF1+Jm/t6k3Rv9PQdRPN9fUsKi1iS0kt7+/Ygj/swhuJkvaVQdhuwzh+zwNqxNA4tuBOfrXmz/jyg2wrX8apg6/l/neXk2HRqHCGCTsVWDU0pVHoSiLBbmFO6Tp6u9Ppl5jZ7vPvlZLETycdjctqJQsbhQN70PvoXEq1Yga4B7SKV0qRluUmPSMRe48EFtVUogcVJ00bHBdntVsYPbY33uYAifuMkbK4dhE9U4up8TlZ3vAVQ9N6tDvfgb2yyBqVzjEDC2LLhqZl88txx7Llgx3832/+y49vPpn83hkH2EvH0jQNq7ljvxIyEtKxO8pJdHWNQXXTUhPIzPCQmZGISaZBFkIIIYQQQnwLUkjpJjRNY9KYPsxZtR5vgp+G0gz8Pc3oLRqGRaGPbcayyImhmzDqYfMnmQzMGMw2w0+wWbG9qid9P6mkuiYRrXrvXp34zQaFw4sxvuiNrpsxkp2sKamjeNViLug1FNUQJRwxo8I6uCOE8wNErYoSbyO/XvIhDn8zOeZymja5sDp8VOaW0BwOYUmwk+pKoKW8Dt0HoQSNTcVVLCrbznM7PiHTkcFDYy84pPMf16snb7+1imcWbOSKH00iMyWdTNLbjH9yx7vs8Jbxs1+fwxOffElYB82pU+8PxMWZTDo/unV6m/uoD9Vh0gzsljCNkUOb3veL9cWUr6tlUXWEn0z7us/LJf1HMfWPn1LuMFjwlxf5+KEbMOsdX+D4vlw2eAyVkVqm9mhdzOoMiW4Hd9x6amenIYQQQgghhPgB6/5tbo4QjY0+Zv93KVW2JoLLrCz+aASuZgNMGrqhoyVHMDdHmXneODx5SWhpTpoiIWp2luJ3hnGn+ph4xnpsepSUdCehUIT35q1ncUtPPm/KJbrbidllw2rSSUi0My67x56iQ4oFc4PCVKfht1vwWzSef30RG7aU4o+GWROqpJREzOs1tv83lbuvWEz15lLqidIzMxkdDcw6JkMjIcVBrf8rBiQvJNW+DOMQZsJRSvH2mg0s2r2bSDRKY6P/gPG1wSYiKkpLxM/gQifO3nD0uAxmHjUoLi4UDPP+f1ayYvHWVvuYkXMKZj1Kqq2FYm91q/UHEjUUQZOiKfj1tMCGMlhcs5DihAgtvaNU9Apzw1P/4TevfhQ39XIoGqXS6z2k43WWdLub8WkD6OVq//gxQgghhBBCiEOn0LrMq7uTQko3Ybdb0C0a5jKwlEZorrJhqtGw1YZI2OnDEopgChu4Ep2c+rfTcVxbSO3KEhLWerGW+SnILeW/viEkT2wgOwzvvraMDz/exCebe7OotIDqchdhXaFK6giX1PNlWSX9MtKYWNAT2wgf5rwQppCGc7PG9jWVrJqzlSHJWWQkpqB8LkLZadjsDrQQmEMatbVN7GipIewwoUwa5kQLOTkeWiJBzDpYTCV8Wfefdp9/RZOX11Z8RbE9yOXXTWHCxMIDxv+k8Ex+3u9cCt09+bK8hKA3xM7qOv66ZBHb6+ticTu2VvLhu1/wxvOLUSp+TA2n2UlxXTZl3kT8/rxDul/D+uVQmJfOqEFfb1cdrOLzmgVE3VEGDt/FqceuYgdVBCORuGM/tGQxP//gfVaUHlormM6wsqqEF7d8wTMbV3R2KkIIIYQQQogu7r777kPTNG688cbYMqUUd911Fzk5OTgcDiZPnsz69es7L0mka0+30dzow18HulLoIYVRHyXi1nBUBNHDBqFVoFXVcfMp93P5S5dRZWoiMsACbidhh46qBbI0jLBOKBAhJ8vDCUn9+b/gesLeCLqu0yvLQ3Gqjs1q3lNjtBoEE3x4loUIl2sEwwpnM+Rm2dEnlfBZqYa1zEzGEhvWlCi9fuLgy78ECJdYCHnMWBsjRO0aJgUzpwzj+KkDeWzHPPyRcUzL3E1xUZiBCWHsNstBzz/TncBZowZj0jVGDOoZG8B1fxItLhIte8Y82bY1RLTMQmlqiNmbN5LictInec+YHumZHpweB30Ks9rcpzNSwNa6aqb0a/94LgCD8jN54Mcz8bjssWVptnQKLWMZbXzFL/rPJ8sSZH4SnN7rWizmrwdrNWkaGhr6Qc6xKwgRoMGop+bQxuIVQgghhBBCHCKlNAzV+c8I6lvmsGLFCp544gmGDRsWt/yBBx7gwQcf5Nlnn6Vfv37cc889TJ06lc2bN+N2u/ezt8NLCindhMlqIpRgxtwURQ9FsAc0gmYNb087pqCBd6yOZ3OI8p113HfN0/iTAwTHZ2NK9WCtiLD5i0LyVD3R2mxOvmICR58wmE+3vkFuVQ2+JhPNDjvNhTbK/GF0DF44aQZvFq1lc0UNbHOiNwFpGuZUO6n5BlbPLgp8yZQHcqlM1sjN81PjrMc2LIrH0UIoMYUEIwFbsokGU4CP1m7mlNOHolSEKFF8W6ewaKGfxjGrmHXO0RiG4t/vLCMQjFAwJIOlu4rp7U6muLKBH00bS4YngbNHDzmka7ai5nU+r57DwN4DWL/JhDWoM2vocKb3+bo1S0lJHQ31PjZ+VYJSKq6Y0hT24tfLAcX6xrJDvmdZKfEf+pZAmP9+5iMaSiBJV7g0SAvrvPnFei4dPzJ27J+Pn0BjIECq03nIx/w+bCyvwmWzkpeSRHFjIxHdR1mLNH4TQgghhBBCtM3r9XLxxRfz5JNPcs8998SWK6V46KGH+PWvf81ZZ50FwHPPPUdmZiYvvfQS1157bafkK0833YTDbsMaVOiGhmHW8FmjGBk2wqkWQmk27Bs1mge7MbLTiTZbSdwQJC/qJm+dmcQNEczoBMpSiQaimMw67761kpdeKqWo3kM07OHqi05jl7+JiEsRtmnYgCk5fRnTNx9X71RaelgJpJkoGJRJQ4UNV9UQbhx1On2y0vBkuKnclkhkUT5pFV5GXl7MpOmbmHHKCHyRKIZZo7naR25iMpcX9mVoaiMBez0Wi5mMtEQAfIEQy9YW8eWmEj7euJ0vSyv46KutrC+qYGtZTavrUev3sWD3TgKRcKt1ez2781PmVLpoTtyBjoY1rHPpkBHkuhNjMf0HZENdM8GSaoo2xRdLXtj1Fp7ECrLTm/BGDzwmS3vYzGZSXA4sVX5euO8s1m0/j9dXncz8TdsJf2OMlCpvCxurqokYxgH21jl21dZz/wcLufudjwmGI+C14i9NJFDu6OzUhBBCCCGEEF3UT37yE0499VROPPHEuOU7d+6koqKCadOmxZbZbDYmTZrE4sWLv+80Y6RFSjcRDkcwNEXUaSbq1lDhEP1SS9kcyiEcNHBsqiOSm4quaZjsZsjvQWBpI0aDn0iiHffmOhxDo6htHp574D28CTZqp+o4LGZ8wCfGLsIpCktagFCLhaINpbxqbGdh8XbCIR8qaiZqGKzdWU40AUoX65x3dCF/vWgwV9/7Cl+FmigtjeLZYibYoFFWYePEmVnkfOJgfbgec5PC7bBREBrGPY+twxwN8dQfTiH9f602Epw2fnz+MQRDEbJyPWyoqCI/MYnS6kaeveEVHm0O8OiHt+JJTgDg5/PeZ2V5KT8aPppbxx3b5jVribgIRcPoWgLJXhPhsMH7C9dz3kmjYjE2u4WjxvaianctSemJcdv3TsjDqq8hGLYwPrtg390fMpvFzH1nn0z5UUexY0Mpo0YXYmRVkuJyYP1G156/LVrEjvo6fs4EjuvV6zsftyMlOx2kJThJc7uwmEwMy87EFbLRJy21s1MTQgghhBCiWzO6SNeevTk0NTXFLbfZbNhstlbxr7zyCqtXr2bFitbjKlZUVACQmRk/lEJmZiZFRUUdlfIhkxYp3YTdbsFp6JiiBo5IGE8wTEZeHRFdw3CYsJRFcO1sweQ0oyXZCRgK044abLvqMDcHSEwJkVdQToPPT4vZhFbbgmWrRrjZSshnZu66IjLNZUwYvIX++btZX1POc8u/oLq6BYJgDiosfgURGNE3l2OH9cFqMdA1jQ3FFTjLQ7Q0+cgaGuG0Y3Zy6uANJOgWUjJcRIeECIzZ82H7qriCxooAtdU+1m8tjzvH4f1zGTs0n7yUJDxRK7U1XtICULKtgrrKRl76+0copVjx6SbKVpYSioTZXd+432vmNvUEw0JTQyZGSgTdouE34gfz0DSNcaeMYtxpR+FOiu9Kc3L2JBLCo2nyO1lbfWhde1qCId75YgPbq2rjluu6Rm6vNPocV8XS2vPokbGUIbnxM96M7dGD/KRkCpKTD3iMUCCMt+m7t5Q5FB6Hnb+cdwq3T5+ErmvUBQIYNkVdB7TYEUIIIYQQQvxw9OzZE4/HE3vdd999rWKKi4v5+c9/zgsvvIDdbm9jL3vsO17lvsMufN+kRUo3oRREPRZoMbBGoxg+DYfVhWZSGCEdU30ApzeC6u0gCY3I0CQ0lwX/tlqiWgBbZjPp/eqI2v43/W9UkVvdjD6nJzt6hkjYFsCdEcHsjZC8tYUnzB9jWByEEsDIA2ujhttv4sxThvOzS4+nzL+FN0v+QEHCSJJCGiFvBN2ukTe5CqstQk52PV9WVOCytXDx8KU0+FzMWbGBr8pXEnKAbiicmTVAv1bnWtfk4//eXoJScNNZx4DZDLqGM9lFfXUzrz2xgOQaH94pCZzYo0+b1ysSjtJcbuAL65SubCE9LZ1yk4+XWr4iaUcCp/fecx3CoTC/+dmzREMREj12xkwaGLeftdtqCVrNrKo5tOmPF20t4rlFqxnSI4u7zzix1fr19e+yuEGnOvQevZMujFt37tAhnDs0fjyYpmY/DrsVi2VPyxXDMLj23H9St7OcK248iTN+dPwh5ddRclxu8hxJZCUkdMrxhRBCCCGEEJ2juLiYxMSvW/W31Rpl1apVVFVVMXr06NiyaDTKwoULeeSRR9i8eTOwp2VKdnZ2LKaqqqpVK5XvkxRSugmr1Uz/4/KprGwiP6eCquXlVDaOJtrShDkQBUDzh9CqmvAHDMJ1jTSOSyaSm43R0Ezj3HIqrIqwU8Mw66SamrjtTx+xbv0AElNu4LH6T6lbb8VYnkCwxIT37DpsY1KIKBOhfAv2rxTRiEFm4p6uOMGoDwMDX6QJTBqRRBOaWTF3wVCSeoRYXVXAkP5BHPk1DHCVE7XpPPLfzzlvnIdICphMUTY1v81YJrQ61821NWzRG0kImbGadTSLTtRmZeG8DVxyw1TGTRlIcNtObJkOkuxtj83x5EMfsb5XNWG7CZWn07QpjM8VRg8EaQoFYnGV1c0Ekp0YUQN7Yut9WYMWVERHO8TBVIf1zGJ8nzzG9Wl72uSSwEjmFzvxZno4/yD72r6ziof/bz4F+Wn8/JoTAAiHIlTuqCBaUcu7/5rLqZceh8X6/XzcvU1+IqEISWlu+qanMqF3HoMyMr6XYwshhBBCCHGkMtAw6AJde/6XQ2JiYlwhpS0nnHACX331VdyyK664ggEDBnDbbbfRu3dvsrKymDt3LiNHjgQgFArx6aefcv/99x+eE2gHKaR0I1dOH8tD8xdQWudh8ClmijY70MNNKJNGIMeMoyzMyNH5lEUNNvUPoLkinDp8CPP+vYiokciOr2z4Ci0YVo3sxghaUoDeYzfxbNnLOMY5aXY68P0zDCqErSWFpKQAgYiJBCyYfQZa0MAUVQD0cg0n0ZLGix9sprl2NcpuwayHyJjWwhi3lxRrObtaQnywyY7PGEBDQyJ9++aS1qscd5Ifs67Id01t8zxfX7ueGhUgGDLxxsoNaHYLymyioSWAyaRzzo8mcXzLWL4qK+eY3vGFihV1K6gMVBJSEZISWzDbQpicLhoiQczVGmelDOaCfsNj8dlZSYwfXYC/KUDhgJxWudw8dgL3frCAwYdYKMjyuLl1+nH7XV9elEP4uQrWFbphzIH3FTUUCkX0GwPShoMR7NEIPpedo04Z9b0VUULBMA/+4hVCwTA3P3AB20JNLCktZlNdDTMHDPhecjiQQDDMax+sJjvdw9SJnZ+PEEIIIYQQRzK3282QIfGt7V0uF6mpqbHlN954I/feey+FhYUUFhZy77334nQ6ueiiizojZUAKKd1GOBzlpqdn0+T1YQ5omJeZSTsljL49iuZXmPwaIbeZoo1l9PnpBJbWrUdFohS/vQswY0Sg2ZxAS5aOMmmYk8OElImgrqjX7OQm1LO1yAMuJxiK5B7QFNlTaXRsjWD8r0VGXa2XDZvKKMhPY1N1iPkVu9AUaEDehFLyh1cRbdKI+E2EG7wMSGuisdmO15bAX04aykdFm/l/9u47zI6rPPz490y9/d6926t671az5N5tjAHbGIwh1JCEBAgJpEJCCymkkJAYQjGhBjDN2OAu27LlIslqVl/trrS9396mnt8f69gIW7YM5mcQ83mefR5p55yZM3dm7qN5dc77ipogFg2xseOC5z3XN65eTt/RcUpOjXv2HCPsAx7Ip4M4Vcvhpi9/hymrwkdfdTHXLJ9ZjiOl5Nu9d1B0ypy34kJ01UNKQZ0OI5pAeAorm1rQlWcTu3quT/nwOK7rMXJiijlLng2m7Mr00Rvu4x2XrOK8zgW8XHLTJQ5/u4/Y/jz+U1mqf20RjprkrQoPjh/ikpZlxI1nZ8csnNfM33zw1cRjIaqOw+3dR9jc3E56fisVz+Wo7WJZDqap/9Jjc7wJHG+SsL70edckCiHQdA3X8VBUhRXNzVy3dBlzUi+cz+X/l+ND02zb1Yuha1y8aSGqEqSJCgQCgUAgEAgEfp39+Z//OdVqlT/8wz8km82yceNG7r33XuLx+Cs2piCQcoYQikDWBE5U0FGf4g0XrWBb61bCW32kBZoRxfVKTEwUWT1PUt+URfuMyfTgOLH6EHIqQzTp4yQ6QEhKI1GkBF8qaMKnsanEiUMeUpEIJCVXoZTXCZUk2REVMylQLbjj9ifZs3+A+We388PBo3iuh+74CA9Kecl39q7lh6PLWTyeZs2rMzRpPfzkwEr0aZU3Ot/m3MQ0TraJWiXKpz7zU669cg1nrZp10rlunNXJdz7wZv7yljs5OjyJb9sQ0ZBPlwPe2z1E/2QW34TRYvHZz0gI9h6JITUF5+AYna/KYBoOS1eez9hAAV1ROGfF3JOOZZgaN77vcrbct58f3fcU72pLkUzOJJ19YuooBwr93DingzXtz52t8ov64j/9lMKRSXBdkHB4bz9nnbOQP931HXZNDfCVyHZWpmbzvmXn0xyOMzlV5OvffJQVyzt4XJvg+wcOsLShkTltacbHMyiGiqq+PAGD/sxHcf0snXUfJmqseM523dD40L/eiOf6hKMzayDfumr1y3Lsl8OCWY1ce9lqmtKxIIgSCAQCgUAgEDijzLypvfJLe37ZMTz00EMn/V0Iwcc+9jE+9rGP/VL7fTkFgZQzhKYq/NHrzuXTW7ZSGq2gXZ4gc189NT+L0EFHwdCiQJXH9g3hjqQQnUX04x76RBF8cCoqHV86hj2nAX+FTqdqM4LCnMgUxR0hhFCgXAMEzufGiPx7F+akxJtS8RWJDEEeQSxtc7DxCHY5hzccI2R7IOGppjbee/YDRBSbXHEVtxVjNGlJ6nZJzKJDaUph3UccSkMm9lSaoYEMBw4NPyeQki1X2XtihHdeuYGR6QL/9pMTSFXgPr2yZduj3cR7PNyY4JqFJyerbSl30pvNcP2l66mkv4ihWhztXouaaiSim7jSP6m9lBKiJoOlGrJcY3gk+0wg5drOs5kXa+Gcxpd3iUjHnAZSBwYYLdvgS44fn+CscxYykCtTrKj0uyUMMU5PYZLmcJwT/VP0Hp+gWnOIbEzCuIeqeox2j+BmykwtTVOsWtTFIy9+8BcR0udSsY+gKacuZ2yYOjydR2qyXOYfHnmYxQ0N/N669b/08X9ZmqZy1flLX+lhBAKBQCAQCAQCgd9gwX/JnkGuO3cF8aNVOFTin//iR9ijPlJT8RQfIcEL+0jHRRnUEGM6nZsW4F7fwfS5SZTGemp+DH28SKR7mohpYSoSMgaT/xCl+7868UMaQlFBVVAtj0TEwGv0sFKCarNAsX20uMbb374RP+ah19uE6gR4EmH7dMWnuKbhIJekjzGWm8SXPlNOhNCkg56pYvY5XN71L9Bfjz9tcdVFy7nmqlXPOc93fu57vPdrt/OF+39MNPUZEBIUgVQEP/7yg+z+xuOEJ2rEctCSTp7Ud5aSYCn1NDVFSRs2YeHRELew0g6ts2PEQ89mkpZScrRnlK9/fRvS8XnTGzayaOGzmaLbwnVc2baGuP78CW1/UTe95xLe+ceXw9MBqEhoJt5pVVSYDKFUDP5wyXlsaJwJMK1Z1cVb3rSZt//OOWRLFbSaR7FaA6Gg6yotiRjRkPGyjE0V5tNfGt4p2xzc3sOO+/YjpWSokOfw2AT3dfe8LMcPBAKBQCAQCAQCz+//ks3+Ovyc6YIZKWeQPbuOEw85lKUAJMf6JxDJMMIXUCwhQiqysY7BgQJSV7AGJEsuXczRb+1HMQuEdAVZn0YKmNs8iUDDTMDx4lxiXhFtqgy2DUKgR3WE6VNtt1HKDsoRldplFtJ0eejeQS5YvZZLlyikVyb4x9tvh4hK9SdpetYnGXPqMHsVmroqyKxGxXGR+EjboWo5hOvCxKXgqitWEI8/N0hxIpfFV2GqOkhvnw2OjxA+CJfHtx6hUHLQpI9an35OHo/3X38eQ5N59tzxFLX0EiLzqqjaeazsyDO7W2H7lgNsvGQmqdHN3fdyMDNI24J6Fja0smnj/JetVrnr+WzddpT6dJTVK55bueeCK1bx5PXdbN1xhJ+OD3KZv4G5ShMjlQFiRpiL257NyaJpKpvPng/A9BcmqX+gSEUpsmZJK1o1xFkrZqO8TMtYas5RfFnA8SYwtc7nbi9bfPVTtyF9n9bZjaSTIaxiDVV65Ms1ktFT14YPBAKBQCAQCAQCgd8EwYyUM4RtuXzz649iCoXUkiqqIfBiBr4Ong7S9RGKhLCJXnTAlyhtkr0/PUCt4IKhkUg6oCigKkz2NtA7lubOoQXklkmk8PAVHzdpgC74+Dffj66ZqIYHs4oYZR9pSqqqx4N7jvLTH/fwlqXnsCjUhIyoCEVS7Ijxd4eu5tNPXsXunV1UKzrWtI5StNEnyxiTRd5/zb+w87Yd0GA+bxAFIBozkTqMFdswwosRQiAEOLbHW/74SprnNeLURVBq/nP6ttYnmNOR5ttPHOCH313BN2/eTHFKYWlfjPs+tYVPv+8b5DMlACZqeaQiue4t63njjWe/bEEUgN6+CX54+y6++q3HcH+m2k65ZlOszJRfnrWqEzvj0nvrEVzPZ3aiDkzQ9VMnjZ3cOYbwPVAE/f1THPFdPvu1rTz2ZO8vPtbioxzK34svPTrq/pb21EeIGmc9b1szYnD2lStZvmkhzZ31TPkVMsky43UFwi9DsttAIBAIBAKBQCAQeKUFM1LOEIapsXRFJ8oxhddcv5gDT+X5xt792FKilD0IG1Co4iYUtIpEK9cYqxSYWgx6o8barjGKxTC5CXBTUbrzaT7w+S5Sbxgg4ZcQroX0NULL29B7sxzae5yReRXsYpTwzhC4KqFHwoTbotj4NKZi7H28h2/dfD+aC3UtWQpLw0wcakYf97EjDrUIiDaI1WpI6YPlMzBVIr8yycPdveQrNZKR585guOasJfz0qaNEpMcPdlSJhKqUXRVNgceGRpCawJWQibhkylXS0WcDMtsP9vOxrffjVySoKkpNMqsuyXceegzXdhG+xLUcAD645NVk7BKzo40v+Nk7tstPb91BKh3jwletPK3r1dWZZuP6eTQ3xtGeTgRbtR3+5qt347oeH3/7lcQNA00oxAyTkK6hoqBloVKrnXK/b7npbD73n1tA11h84RKO7+0DVVIX/8Vmgthehd2Z7yHxaQktJq434CveKYNKQgiu/8PLn/n7zv5h8mUXBUm+XKEx+cpl1g4EAoFAIBAIBM5kvhT48pVfVvPrMIZftWBGyhmkULbI5yy+/O87ePCn+xB5G8XxQVMprGmiuL4DX59JKYKEWCxBVyZK4oCH4kva12Sx5zfjNsYxMy6aJ9F3GsQfVxFKBISPd2CSWsHm+/9+D3OiZYQn8TQFJ6qgh1Va5saIaQaRyNOZNHwfI2VRqYX5i3U/JTQuUFwFRlQYVxDjCn5IQWg60tBQfMCXFDKVU57nn1xyDl992/VcvmgRDVo9N350Ew1zG1EW1XPrg3sYPTiMMVagnJT85wP34Pj2M31//Mh+BgZz+AKkgEKjTs1xuPyClQgBqgJ6aGbmRNKIMCfW9EzQoOo6HMqO4/onz3QZOjHF/bft5ravP4r9dBDmxYRDBm+9cRNXXPJszXSBwJdVfGkjgI7OeuKJMG3taQAmp4okuj3kUxWGR7PP9CsWqnztiw/x8AOHMGfFEY4Pjkd9fYyb/+FN/NcnbmTF0ucuwzkdhhphReoaFicuJWW0s2vyP3l8/O+Yqh08rf67R4eRvoInBaPThV9oDIFAIBAIBAKBQCDw6ySYkXIGueiCxSimxaOPHKHqKWhIatpMvhQz4yHwZ6rbhFWshKQjYvKWlpX829e+yyGvGXFPCT3Tgzu3mUR7lFXnHkGxI/QYYSwh0DSPWljBqDpE6yJsbu9Fd+oYamyj6EeIrp1mMDaFotTTLXPsdpOEWsPIUJHaYyl+cstmKi0e6X0u2hREvmegOi5UFYTrzgQsfEjtLzJ/RevzzkYBUITClse76emZom9YEjUl7bMbaA+rNLTG2LltjKqhEMr6jHM/W8cVLm19DQBvvPQs9n97nJGmHABeWDCYL7BWiSOFSs2FUq5Moi72zPGO7DnB9z57F9U1EbqXwNsWruOaWcuQUnJgYgLfqWFnCwhDB/mLXz8pxnjDq+5FYBCPvI6xyQLZYoVsd5VSqYo1WcMPKaDAjoMnuLa1DoC+Y+M8+UQPvd1jdL61C0wVoSjsu3MP8WqNN/7ZNb/4oIDFyUue+bOhRBEoqOL5r42Ukh/efC+F6RJv+rNX05lOwrgPik8q+fIm5Q0EAoFAIBAIBAKBV0IQSDmD3LF1P/t6D7Hhhkmi1tl87+4BpJAoniQ0baOYLravUpwXIbfGp1wd4zX6LCjNzP7wLR+BQDs+gTI7Tmp2lulQlU3GNDu/MAtXbUBOV/Eti+y0ZM9UhCvm7uEQWR5+cgnSUxCaQuvZIXKNo9y67VH0fTp2NcaicwfZfNN+9ny9k8SwB6pAWJLMxQbGIZ/U41UQEOlMY1UdEh1NpzzP6VyZ2x49QDVSIrZU4C2s8Km3vRlD1xgcmOL4nhEmx6dQhIdjeSi1Z1/gV81vY9HKFgZHsgjXp/GgRXhBlVU3rqEuqqGqykk5SHxf8s8f+xHj/VM0jKn4i1tJmzNBhAMTE/z9I1uJOoKuthSxVARV+8UneWlKlFgohK4kEULFjijYKROpK1Rtl7itojgSX4exkfwz/Zas6OC1N2ygvTNNem6CXRsOUD5eRGby3P6dR+lvDPGnv3MJhv7LP+6rG96DJy105flLKVsVmyfu2ov0JZNDGVY0tyCOCBSpUR+NIqXkqZ5RElGTOW2nLqEcCAQCgUAgEAgEXhopBfLXYFnNr8MYftWCQMoZ4q4t+9ny4BGshM7D25u5dt0YbsEjNi3Al4hskXh9geJElAvesJw7IgP4lsM3PvNDJD5C05CeinB9ZDzCsFKHvttjaG6M47F67JiGY4GKhoiEkVGToUKcLxTrsFGYrdoMP9HE29+8gQNjh5nWBUZbGL3VRPY4hFcV0cMuTX4ZFB1afOTVZaQVwy9IZDKGVBUquSoIQSJ66nK9kbCB5Xp4OYMbb1jNhq65mKbGl27ZyhOPHGXiqSHUtEJuUZQjD89h9cVnn9S/uS5ONOMiLIkoexw8MMTuySne8IErsUsW9W2pZ9oe2NNPrlDDU1XqGytcvWAvrdElwDyaolGSoRA7RgbYlyjxD687B1VTf+FrqKt1LG/6DwQKQihcvmkJA7+XpTEVozEd58J189lxVzdCwrL5Lc/0G8sWuedEPxsj8PrlHXzu338Xz/V4/L6n+NJdO+nNFsiVajT9zCybX1TeGWPaGmBObB2qeG7y2FDU5B0fvZ5yvkLHghYqExqNtQhRX6NmuUxkStz8/UdQVYX/+JNrX5bgzksl5cy0oZczeXAgEAgEAoFAIBD47REEUs4QDzxyBGH7eIZOviToq5wgbDZRU5SZZCCexKmBHw0xuGOKa8+dx4E7d2D35ZFmGOFK1IUmtmzASYQIN4Q5rIUxhiWjOZ1kxcUXCn7Yw1BTmGGVXKGdNemnaBs3uXOgE5Dsu7+fg4fGUdwqrq6g9BchbLDH6cDJ+qhFF1wNJe1imA5tyWnsaZ1aXQxpQCg/85J7onfilOdq6CrtjUlqjoVj2UxPl/jSvzzK8eMT5AazCMtByQr0UhRlUjI+USSdfjbJab0SxkuoaNMexAzM9U38972PE7r1KCaCtZcuZ97TJYnDyRDEDRTFY/HqRlLmNCljFgAN0QhXLu3ikR0HadyR5dN7vsum9QuJJn7xJSyKePaRLJVqHLy/h1g8xGsvXMnReAFtc4yV9S2cv3nxM+2GpvJM5csc6h9HSsktn7mX4YFp3vfha/jLZe14vnxZgigAj09+i4w9hCo05sTWP2+bpRvmP/PnhU0NvH/p2eiaSn0yynimwJGeMcIhHeUVCGRMZkv801e30NWS4n03nh8EUwKBQCAQCAQCgcBLFgRSzhBrV83m8YP9qFIwu0VjQ6dFXzLK0ORMhRcZ1SAqcI0ImfEC2e9PsfHSNewZ348rFKp2AV+NEZosoSZ0tOEC9hwVD42WH9hoRRtFV6k16IjhCkY0RotZx7GJtTw8WSBa8xES9o6OEb//KJRqiHmzEI6HNk9haneaJ9QQsRMqmipwekNU6nT0gRITq5Ns/ttjdMosOz95Fr4EuTSMlPKZF10pJflSjVQ8jKlr/MVbL+ZTn/oBX//b7YQ27KOtlMb1fOREEVGxcOtT1O+AhnSM2bMbTvqsDhwZpZrUUKUgZPmsX9CJdqSf/UeHkRJ69g8wb0UXO0aGeHykn1ydiqsb5I+FuKztY8/spzs/zq1DTyJn2fghjUh9lHDMPOU1OpjbydaxB7ii7nrmNc8/Zbtn2ncPc/jYGELA0GiGjS2dTFxR4u1L1p7UbuOSLkK6RtI08X2fgf5x7KpHPldh2cKWU+z9FzM7tg61ZFDzYvi+j6K88FKmwakctz65H1UIrtqwmHLVxjBUEGC7LtovMYPnF5EvValaNoPjuf+vx325SCkpFKokEuEgCBQIBAKBQCAQOEmwtOf/nyCQcoa4/OKlfOnWRzCrkvhenx/d3US+w0GtekgBjGcpuAayQ+ArkmrK4N6JflK+D6UKvm6hTHjIgkdVjxEecal/VKd1SRP58jggURyX8IkKIhrBrbn8wwWX8Jn77+N4ehSWakT3m9REjXjZRigawnJAVVDG8yRLPqVcAuFUkYZKtQkyHRqzN7rMqk1z/dwnSWtVGj5uMzb8Os5a8QiT5QRNsWsB+PQ3H2Db3j4+cOMFGHU6H915L8VZFRL9MylejLUx6u4sUnZ8ZCyCHzWRioKrKTiOR/hncqMujKV4oqSgZV2am+Jcd85Kzm1r4ff925FAYWIm/8jn9mxnslSmrj2KOC6Ye2kXd4zcxzkN60kbKZrDCWJamJZSDOFa6FaBiXIfLfHnD5J86sC3Gf9YnNuyX+MdH7yC2pww67raWdH+/MGOprY6lLRBteLwZ1+6g1s//g7ObZv9nHaqouBO17j5G1tYeI1K9KYTbFDXE42aTE3kicbChCOnXir1UixNXswPBob5994vc07dKj6y4q0v2L7sOoxGSuhSAQHrl87ig2+9mIa6KJHQqYNOvyrzOxv507dcRDIW+o0MRHzrO4/znVuf4JJLlvEn773ilR5OIBAIBAKBQCDwWykIpJwhDvSNoUzb4EnSqzuZqNTQDQ0/5IIvwfVQyj7FBokIm9gplaiiEWrxqO6ZxlQV/LgKmsAPm9SiFtFSiMtXLeF7n38UWV+HFCCEhEqVRDJJZd84/f+0jbnLHUauaEc8qaJEFIiEwXKQyRioCq4Bqc0tfOkdryZVH+dv/uk2hipTVL08mWoU6UODUiCueJy3ME/X2Q2Ml/KUqzX+5+EdzGpJc9/ubgqFKg/v70OJKwxPFwipsGFxO+HNTaya08md3xuEp0sTCwFSAzuhoOonz3rQ60OoMRV/yCK3r8BTe/vZsHE+miqwazbZiZkyvTcuWUlvdpr18+vZc8deDokDDI2MoCJ5VdvlDJVz5Kwa1bBL3PGQto/juKe8RuNjKdTRmRlC3/zP+1HfOo/+6Rwr2luQUjI4mqU+FSUamQkwFEeLkLXQHMn49jFGJvO0NSafd9+O4yElOJ7N5CH4ySNHuDvfS9n36VjQxCc/fePLFjg4VNjHithxjpcs4IUDKa7uoTUJdFXF0FUmciV+8uQRQobG6vkd6Kc5I8X2MgwXv0PSXEXKmAcIFLX5Fxr/vI6GF2/0a+q2LU+RER73PHo4CKQEAoFAIBAIBE7iIfB45f+z8NdhDL9qQSDlDLFmUQfLzuoiO1pkrGLjJyLMS8bYXbXxy9WZRrZPrV4lXPUIFQXLOtP0rHepVKIkx1xkxQNA2jXQdTrb6uk/PAR1CYRpgONQd+kyqo/3kIjpTNUKhPUKs1vyXLGgn+8ZV5BwFAQCEQkjimVQBMrsNHpIwzVURoeyqJeq8IUh1kxMIt8TwnNVEq5KU8inJi0mM5torVvD4KjGg3seIRENMaszzdGBSc5ZNZuOhhT7vjGC9+QY3cU+tFqBntAwY30TCM+HXBFtRZqyCblSlVrNIRp6dkaGhsCSDroyUwq6sSkBgKtooEkO7T4OwJVzFwALuOdrW9n10z0wXqPwFo+aOxNoWZRs5rVdK/n8gw9gRTUSiSSN4bmnvEbNoTYmjX6chjCuELx55RLWz+4A4MCxUb7w7UfobK3jL37vcqSU/OSr29ALNeyIgRvSuG37Pv7w1eeftE/P9zk0OsGK9bOYt6CZ+oYY1176aZzMFEbNQralmBzx2Ds0wprO9l/6Ptuf38O8yDiGZlOnT75o+6hqUC26OJ6P4/gIAYqYCay8lO/XfG0Pe6a20hE5wgptP5bn82T537h0/toX73wGMZpC+JUSWmNQSjoQCAQCgUAgEHil/OK1WgO/VuIRk3/9uxuZPb+JoeEMtUKNXM8kZslHq85UKFFqFsknJolO53HmlhmvKzGaAsNM4FkCWaogCyXMkQqxjE9tKMOTP9mPNHR8XcX3PRJDOaqqTvdQga/0byPyp40cb17AA9vWYkUFtaqPUBTQNXAlUgrWdDbx56+/lH/+zO18/LO3oBYfRMaTHFyxiHqvQqS3Si5bRwiFA9vn8uEP/i//9Z1DLJvTyusvXMU7X7WBeDhEQyRCyDDwR4vU3dHDvHQE99wIhahH/sAEvqmSX53Gb07iWBK1KvELLjXHPumzanAN6kZ99JoCLXEIq+zqHUIP6aBraNrJ8cWNV63hmt+7hIYVnXjDEXzSAJiqxpvnrceujzJyZR3hNy8kFD71Eho5KpDqTPQgHDZ45+a1rHx6WU80bKAoCqnEs2WFh+3pmepEhkItDWnjuSWHtx47zt/ftZUvPLyDltYUuq5hKAIsB9V1cWIuEytMRvLFX+i++nl7sjvxfAVfqqT0F8+/0jeRoVBxyFYs9jzVT0TXmaPHWJluQHuB/Co15wTjhVtw/RIAx6vN/HRqGXdPzgV/Gsse558fu4efHjv6spzXb4qWRBzdFbSnn39mUiAQCAQCgUAgEPjVC2aknCFcz2fHkQEy5WkUfBad1c68RIKhLYdQVAUfcLriuE1h3Faw2gWiXaXlfh1twkUikI4LAiKjNRSqZPsnkLqG3RzCmM4h3RpNXYs5kakikUw6FlWlEaeqU615aAKkUJCGNjPZQFNBU9GEQltTCtV0iCTyhJrK+HhIRWfQTpI9avC5uy7k1dcUuf/O+dSGc5QW5DB0jdecswyAH96xG7PoQ9Vj5/0HmByYQvqSd3/8TQztnOB72x7BjSg4URXRPJMQRShgRwUKJy8f6Wito0mGyboOftXjw1+9C8VU0cIqVkWi/8xSICklJybzxGc30fv7PUhVct7O1QAMlMfYOrEbtVzBKPgYoycHbH7e1L4KbsRAKbm8/0+uPmmpzdzOBv75L173TDlgIQTT5/lUpuIonqC9Hq7YOPs5+7TKDkNjWRal0s/ua3kHPY5DqlJhzpJ2cl31p8zD8lJd1nw1+wYNxmu9LGi/5EXbL2hsIFQCrQSJWIjR8TyPHutHP6FywzXrTvqsf9ae0TehylFGyztZ3frfNIUaSRhtzI53kZVtHKtMURY2MeO5JZjPZM2NCVRdJRYPvXjjQCAQCAQCgcBvFfn0zyvt12EMv2pBIOUM8eTRQb5wxzaSjQe45KIRHrkrz9C8dnwVKNVQImFUS1JdGqflqId+ULJ8QRTpGzxZ7ac6K4opXfCfXnHheiB9pPDIX9BCYZaH4Qp6BopIVSDCJmvrltHvW0wkcnjTM7k/FMdHaDrS0JCaCpbDxNQQD/fcR+ESQXk8RnY4TF6xWZc5wsb6YUbnJzh44BwWbN7CrZ8zoGoy9kQ/o6M5bv7Sgyxa0Mz6FbNwHZ9ZbWnOet+VSClZd8kyVnYu4qoPfxZPE6jDWWJZhXo9xHhaIpQQRtGnKR3DdTy0p1/apycKmKqCoSlUShaZ7mnmb+giPKeNcnkQvW6mVPK3TzzE9vFuSvfqZLtLOI5PLBIjps4sq/j37m/TUxqkoSNK5CuCTN0QuZtKpOqfv9RwSppMCVAUwb137uPyq1aeFEwxDZ1DfWOc6Btn+39vwclO4F4a4arUYS5av5/Herq5eu2X6SuNMVSZZHPDUsbyRcoRh6Fi7pn9hBqi6GENK2/Rc99+lpmr6HzTyzODoTXczvtXruLw9BY6IkPcu+MoZy1spyH1/OfcHI1Rt0MipWT/wSFWbJjN8XgVoQhGC0W66lPP22/a1mjQBFPOTL6Y2bFm/nXNO0F6HBn/DL7msaChiUT4pQcUXM9HVcRvZLLZhlSUdEOUFfNaX+mhBAKBQCAQCAQCv7WCQMoZYlZzHc0tMdz6MuqAi2cLrGyFcgqiUxJ7USs+JdwWjemICZrD9v/ZjvpwFpQm6uZVWH3jOE99vYlyVgfhIcIKtbNDJLcO01RSkaZKNm2gTBdRffjTC87ny9v2cE/fFGoFpIQwKjIWAgRoKtLzGD5RpVi9haHxc9DGo0ybMUx/iljERY/4qCmJn54mVV+kdW6O0e4k9Z0n6B25jWLJpPf4JI1tgt5UhYd6T/Dmc9dww19cTUyfWUaTjISohHSceY24uiS3a5h4rgyLW5jV1cTO+w/wwy9s4drfu5hNV67i3AsWI0yF2z73AMeO5LF7BWs3JnkyFUMJmZS8mRhqb3mUql9jJJ+nOGHjbJpFw8J2ovGZQEpIxPB8Sdf8RhrPaaA9kSCWOHXuiusuX82Xd9+NEALp+c95kXdcj5u/+wj5XBkzU6CyP0PYj7Bn/lz8qkJi0xBXA7f03cO0VSCuR4g3SK5esx2Vpc/s55zNC9BUwYHdBWoDMXZvGabwF1WSiecuDTodnnSQUqIpM593SMsQ0216R3fxnZ/4DG9YxDtevfGZ9plyhahpYGoavu/iRkEqCt3do1zzqtWkkxE0TaUpET3lMR/L/j5jtYNcN+sNz/xOEQo+Do40CYfquWnxIlY3vrSAwvhUgU9/8X5mt6d539sufGkfxK+BFQvb6B2YYv2yrld6KIFAIBAIBAKBwG+tIJByhmitT7D6/Fa+1N1Mab5KU73L2PwwynAG39TRMiWkcLBQMasCKcIUjAR1iQrSNbjp3U+ycc4wd85bwA8+up6IO4Ee1olFPYq1BjCVmaU/IxWUYg0UBdd2uHzNIiYmimw71g8hBeNYGT8Vh5qDWq7hqQo+Cgf3zWVloZnF87q4b/dxnHIdo+MrMR7w6N/dwNQJk+mc4Kyrxjm0IMSyq04QbinzB+/6FxrqY/zbo48xNJLjtuJBbM3jvrE+blq+itcsWIzqAVLihzSqjQIzqgOCuroYoYkyD965D8dymR7Lc/DYKHfuPcKThTHMJVGcAQO/5rJ9ew+Lz13IiUKV9tWzAPiD+VdzZGyYb1pPcNAYwwvrHByc5NDBIZYu62B5XRd9lV5insrmTUu59MoVaC9Qheax7cfwQyrCkdiu95ztpVKNzavmkMmXsVSTI2kf31AoZcJMixAjxSYcx+W8hmUcLQ4zJ9pMj/09LunYTc3rA/4agKsuW8Ha1bP4i395jPxoBCmgXLZ/oUDKd4//NeO1vdSHVnBt54cx1Sjf7amxY6iN9NgSVs5rZc3Cjmfab+s7wZ/+6E6aUjF+9PY34zqAACElTS0pbHwaWmK4rsdUsUxHOvW8x72wdR1bRpKsrp/zzO983+e/vrGN4fx8Vl74BN869jBJcyEb2jpP+3wKpRqjhSKZWhUp5W/crJTxqSLZYoV8sfpKDyUQCAQCgUAg8GtGoiDlK58GVf4WpGI988/wt8icUBNr6lrZNGc5b/nANSTbMrQtyhKxiijFGm5YITTioQ/WUMs+RkFBb04hfEm3rGPYjtCjpGFsGuu4xLwAbCWBlDPlk6Wu4IV0ME1E2EAzdVYtbicTsajFwTIlMlcDx0N4Lr7nojguMibZr6/lz37vWppzkrasJKrr5I+b7PhsC9NP2ISsHH/9h2/jnu9v5NCuVr5/zyYs2UVDMkJ2osBfXH4Bb7+knYtX1VNTPZzJCqPTGf7sb79Ec0TQnAijF2qEh8qEFAN1QQvVY1P0HZtg6wNHWLB5MQvXz+P7d+/h4T299PdP0XdiCs9QUR2fcrZK8/x6REeMUPNMwKHBTHDurCWsmtNCZNpBK3voGYfP/ONPAbigcTXn1K+itEVy5x17OdH3wlVsNp43Fyem4hmC196w/qRt+w4M8kcf/CZ7dxxn89xWuh8+QmjEIzTho0oPNy6oHYtSKla5sm0df7zotaSMGNHkBror7RyrPVu9xrJdLNdj0fo48cVVus7VSNdFufv+A1SrL5zH5We5vst47TEiSp7J6gF86XLrwN08mN1FbznJWBn+8q2Xsnrhs9WAijULKaFqO/hIEokwv3PlOs5e2Mk7bjqX+kiEdclG/B0T/PCf7z7lsQ/lxhgsZxmvPpskt384ww+37Ofxna1s27+SyWqSonX65wPQUB/HCwlcA0amCy+p7ws5kh/hq70PM1bNvWz7fD6lqo3vS8ov4ToGAoFAIBAIBAKBl1cwI+UMsWdwhJvv30G/q9E3aLE0f5AbbzjOWF8Pd2WXIy2JFw+jFT1C4zmqHTbKsE11UkfxPb5z12bumr+c/KEE7eWj4OtMflnDbzGRTVHUYhU3YuB4VfS8RGo+R8YHcfwcezIT+CEwchJRrqHoGtJ2cZoS6OMFWBLiqi44fOibbP9pmaShc/XvLefmf3iM6Xe1Ij2bhf1ZKqM6qZY8mckkSr/OrMiVfOAtX6ZWtbnyz+oY6biTzFSCJ7ZtINxdYss3eikNZFGiJu2rF5JoMBk/Noan6LTVJ+gfyCOkQPo+9x06RqwuwtUXLWd2d5pdjx2lVJYMSgupq+QqNQ5NTTGp2zw5PHLyh1u2MasOyZxBTfqccKaQUtJbmuShiROkNie4LLeErlkNL3iN3nLVJkYOT9G9cxCrePKLcE//BFO5Cv0hi4mHHDRDwVVqKBEDWdMol3VqIzFs7+TUTe318/nfkRtYHH92icsXv/0I+w4M4NRsUp055jW18LqbPkvFcvncLQ9w+7fff1r3VMmyWGmO0qxV6LXTxPQ0EmiPGFh2GCOVes6MjosXzOWhWcdoiUQxFBXflzx5fIRCpcbwZI45HQ34NQlFD1t1TnlsX0qEAPkzqaoa0jFCIR3f05hd/wbSus05nS9tiUtmughlDy2s0pKOv6S+L+Te0f08PHGElBHhdZ3rXrb9/rzFC5rpm8qyaF7zr+wYgUAgEAgEAoFA4IUFgZQzREjTmBRlptQS2rY8O9DY3Tcbp9aMViogkPipMImdk+g1G3dc4s4JoUwIhONgHCqTrcUIdeeQiSgUKyiKhq8peHEdX3goEzm0mIlfqaEZHtnJMtNZi9Rxn0JUYuQEUvqIchUUFdWS+KqCP6Gz/7bD+KtqnHfj+URbprhj32NYbVGkW0X4gvyxBEpKsOzsCmM/TBKqGEz1xnDCCr4tGAvtRAjY8/gCalkXG4/IYA49pFDfkiCrezjCp9wZItxjYwiBH9JRbR+pCJKL0lx45Uq65jbiFi3uPzSJVbMRRQshwbIttk8MUwn7DNZOLhWcqI8RjYWoSIkAynEVKSU5u0rBqVJ2XcZ7Rvnmzl00JGK8ZuWy571GI6M5DjzRz/Rghunx3EnbrrhoOcWqxaNDw4TCOotffzaPlXbSfJ+Ki2D8ri5CLXH+88ATvGPNOuY9XaVncHuWof/JMJas8Z5/KZOKKajaNLk9w5QGHbRYPbFlKuVSBS+mkj2NJSGe73JLz4eouS7rdIGmeyyK1gPwhs4rubjxbB7Uh+lMPTeBbc/oFI/sOAK+5PXLlhCpizMxVaJmu5SqNkPZPI8NDzPeIKhVTx1Ied+y88laFZrCzwY7QqZOc2uSUm0K5Lfomz6XvkyWpU1NL3pO/8cwdFqjUbra0qgvUH75pXpV+2oazDjnNC562fb5fLbtP8G+vhGWHWuhq7nuV3qsQCAQCAQCgcBvFgn4r/QgCKr2BH6DLGlt4kMXnscXD+3AXTWOdSJEORampkWIDVpoEwVkxUL6HkrRQ++1YAL0qonQddK9FSqFCuZgBcsQaI6NahiomTJ2Rwiz5CGFjmpL0DViZoJb3ncr1uJGQuEoTPuoUZXWszoY3zUEvg9IRMwg4qo0uC00RhI8tbPErI0hrIIk2VBjScsg2YfDTFWTeHU+kWqYJcvySK+Of/vMXUwlFWINCbqizYyLQfySilIDpEK1IYKbjtI6r53H9BrVTBkREZiGQqFqo7dE8AZLWCmD129eRtfcRgAiIYPyRIGq5SCEAgi0pE6kNcJwoYD79PupLyVP9g+z5solpOpjfPHrj1Ao1wiNWfQeHuHqpWuRUnD3zY9x7+BRumuHqLSoNGlhVs7v5A1f/RZCwnfffhMR0yAc1jGkT8hxiKgnv8THIiZvf/1mbnJcPCn54a3b0Y6EqSYdVCnwipJqrsrju/pY3db2TCDlzrv3IjJgVyuMFYpUuYcNF9zBrm8soQg4LgjNRNRbVFKCkPfiOUGOlw6wZaKClJJIuJlVik2MmVwlmqIS8iOcODCB3+Jw7pxZJ/Vd0NLAyoqBLNmkkjFiiQh//rZLsG2XsxZ1UCzVqDvuYPXXSM0xTzkGXVFPCqLATLWj1oY4I5M9bJw1wSqtnoUNLzwL6OdFwwbN0ShzXuYgxPx4M/Pjv/pZIl3JBClXY3b9y1OFKRAIBAKBQCAQCLx0QY6UM8hlsxfwrStuZP6ChSy5Uufcs5cSnXCR6QRS+mg10NQQUhE4GyP4r4qhdClIAaojmDO7EWdJM878ZvymBBRLkC1g5qqojo/mSZSqjZQ+xWIWt+igHJigGPdxDXCQRFJhfFUghUTJl2hryXH+DUM4lRI//vdj7Hz0KN//7ihT1WYS6gTmHIfY+ir44FdsfvoNldyRKpdcMUbUVKhaDoWpMtt2NbNKGUXJKWgVEGYYqyWGGCpyYOtRlsXixA5ME+4tEG5J8Fd/93quWj+XWDlDODvB1psfZmo0R7Vscct/3Ue6o46WpihC+iiuS2TMpqmkUG3wKWkzMyV2nhjiI3fcyx999TYmxnMzs20cj0jepVSooQqF13Ss5UNvvR4GSnTemaPueIWP/cNtbB8c4JAzxUFviv0jYwDUpaK86qqVCNtncjj7vNfwR5+7n8++7+uolku4uYzdolNt1PBMifAky506lqebua+nh2yhTN+JSTwN1LhOWzJBWOtC12L87sdWozSq+IZK99FRonUGVhpqpxE/aA51UB+qENFsvjK8gS8PbKIvc9Ez2w8MjnLv4W6+/dgetuw+RrZYeWabYejc/Nk/4nO3/AnxuhhCCC5cO5/LNy1GCMGhgWH6p8aoxjxGMqMv6f6WEpzRGrFyK/sOXcAjOwWVl5gjZWBgmiNHR9n2+LGX1O/XxchIFqfmMDb+8uV3CQQCgUAgEAicGXwpfm1+znTBjJQzjJSSd135avJli5XzW3nzPT3k7TKETFQziqoLRLMJpoocV7BSPrqhU2vQORD2iWoCM+uhFKtIQ4eQgZKfqboiAT9soGZLeLYPtjWTVFYBTVXwpeTAyBiRbBFUFcIhxk4Ipo9EcffmZoIlkTDWkiSx9gTTW2PIssRtVNDcKrKi4VYkwz0mu54Y5NK3d3DsFgMjAvPDC/nXT7wTxQRUH8UT1GbHiA1WMKNhfueSs/jk/UepCpBRg//95G00z2ugmLKxVoXxoyrxVISHtx7mqYFJlFKNtOaD50PNpVSxSO6eRlukoJkz8cXJSpnRShGh+Bzqm6AW1fE0WLCglXlL2hgq5vnoI1sw0ai6NVTfY3ZsnEJRpy0RJxWv4nkK7XXPzh4Y6pnEdX2OHxnFcTwURaD+zOyUwzt7mRzO0L2rl8bOImNpEykVjJyNm1DxJy3+d99TPDU6xoZiCne8iogp2LbG+FSRxXMuYvfWem7/1uOEih4l6ePE4oRTGlZbBicEFdsmYhinvIdcvwZAoWYw7cZ4KLOQ/InjnLtgA8lIiIJpUZnrgOrxpTuf4PJ1i3jnlRue6a+qp65c1Nhp4pxTgoikU3tpOUoUAWOTecbGi+zIgp3q5uIVi7hk5fzT3oeHj68LpPGbGUO2FZ/+TI6DU5Nc+UoPJhAIBAKBQCAQ+C0VBFLOIGOVIu//3LdxDlZ575sv5ou7j+BmpxAnJsEHxfHxkmGoeZiPQSqmMbnExfAlbkTBVySedNBzFjhAJIwA8GfWuQmhoNoSmYghNB9RkUhdRc9LUHzQBZqhQjIBqgK+j19W8I8pKIaCb0uSaxsZ2KhySdNC7hmvUd1/ACogpi18VITQQUrGnoqTmJOiVh4iHAtzbHyayPoMlUfjmIrAMSSNE2V806TkQ6qtDvP8ORTyZULDZZ588BB+JIzR1YApVN73oTdihg3Cuo7i+mDqXPF7a7n9m09g75sGz6fYPUEoYeIsmFlykqlVcYUkkjD4w7ddwR/91bexHJ+hqkU4anI8m6fsOIxUCvCaJmLjR1m9epjNr30c1++iua6A9GEiV6Dj6Xwi7/rLV9M6q4FZZ3Xwu3/2Nbqa03z8L16LosxEbT0pGRvJ4JVtREZS15hFyUv8Jg1P0eltPcH7u87Gcl2q3QWQEsWWaK5P2NABGBqYJjuSxar4Mw943KBlUQPd4TFcV+eiu/6Z7a/98Cnvo5AWJ6x5mKo3Mw0E8ApQqNZIRkLc13+QmlFjfrPHfGUhq+a2nfY9Kn2VXNxEVXwmfy7h7uR0kcd2H2fj6tm0NCae01cIQb5Qw5cSpTaz/vPExDRw+oGUWZ0NrFzRyeIFLac3XikZKD+MQNAZPe8VL5fcPTyFpUp6R6Ze0XEEAoFAIBAIBAK/zYJAyhmiaFl8c98eCtkqtVKFj//wdnwzgrUwTmrPEOgmIluEaAKpq6j5Ck7NxnxUBdcheqyA1lvFmKohUMHQ8cIGREMojo/QNKTlIPEQYQEihEAiDA3FB6mCqSrM6koztr8Ano/vuRAzUXSbpquS1JebOXZ+jfpajVnzUvzp71zC37++G+n4CN8hlLPx7SJexeZ4DkTXGGrRpZzLk7xQQ2mbIHt3BKEL1KKP2D+KaoYQisK+Bw9h92WRnsv0kQkUXUcoCsq0R2onlIcK0NXKnPnNNCsaRSERrf9GhMVYlZkErMUTU6T9FPnOKACz6lKEEhr14Qi54SxxV1L2YEzWEEhWNrbwkc0XEtMM3r/yw+QrglXX9BNPVDHZR3k8gmcr9DVlOWt2JwDRRJgbfv8iPvXfP2W4b5KSZc+Ul0bguB6PlPPYS+o5y9Xwl1d4ajwMeUFk7TSZSBjmFLly4UKuXLiQ0rk1Xrf1KIrjo2RsRvunmdWe5ro3bqSzvY6v/+NP8FXB6952Hg+KEeyygfQEGfnC6Z886VCvGwx5ITxXAV/wN9ddSmd9itFqDwsby1R8ydXz1vCaK654SfdpwS7hSwXpCbyJkxPfPvB4Nz+6Zy+FUpU3vnYdWatMc/jZgIoQgoZkBLtqY4RUFE3jVWctfknHj4R05jfX0dV4ejlGKu4kB7LfBCloDC0jrNU/bztP+kxbBRrN5K802NLYFCc6HuaSNQt/ZccIBAKBQCAQCPxmkijIX4PsHb8OY/hVO/PP8LfEk8PDbD3WT+vqNrTmCmJslDrd56qVC5F1MaQKiiMQw1Mox0cRxTK25SKFh9QUhFAJT9moEY3y4nr8eAipgBiewlcVkBJpauRXpSk1RXCqFp4AWqO4IYFnCERdmTE3i3RcpOPghwXmapfUJUVGqHGwNEUlNUDLgoPkq19mtLKbyfOj1BI+qArCl3hlCywbz/YYeGwAfayAPpyjodZKdW8DRq6GOV3DCYNraghfIiQ4rsQYqxDunoaqA+Ewhqnz5vdcxIoNc9h6z35G+qdQNYW2WWkc6dLYOA62BE2DeAxUhfJqDdFqIaUkFFIxFRWpSL7z2Xuo7htEG5pEUwVWzUUIwfLGZmalUjiWB0Lw4Dfn8+iTi2hL/hGZnjS54ynGnPJJ18p1PcafHCWl6rz+tWdRKVQZH5wGoGtpG5FUFFfVyC8poUU9REils6LSEs6xvqH3mf0c2juAankIT+IpgqI1syTnR9/bwQ++ux0zHaZuqcKyC+O0JFKQV1HHDJYqHS94L8X0Bq5qvZ6VsQ40XyHsa6zomJl1si93P7HQ3bx+3k9pMre85Pu0LqyysG2URR0jXPo7Z538uRRqeNkasmjzn4ce5EM7f8ju6YGT2tQ3xHBdj2jWYV66HvkS118eOjjMvXc/xW0/ePK02guhU3ZrVDwbVYRO2e5Hg4/yyQPf5JHJAy9pPC+VUvKJVqBOO3Wi3kAgEAgEAoFAIPCrFQRSzhBr29p43dKlbMxEUQ4aqJMNXEADDbEoufe0krmuE7c9hdeYwGtJzuQ/USW1sJxZt6MquJ31FK6cgz2/Di+uo45lUVwPZTwDhRIiX0KGTYy+KZRMHrJ5ZK6MUEEoIH0oj1eQlRqyUsOPhii5MSJLoii7SqiHC/iHdYQr6X54nM996QilpjDluVEEyswMkmQMJRJCbY3jlEDLV2lMhnj9lWezKX0xRsLATgiqczTcc7uQuo5UVX7w9YfIFYpI08CXDt7UFLXxCfYd6mf340e558sP8PBP99HSXsc7PnI1iibAlcRmeRRWN1P+cJzMG9NEJlyUkIMnfXwbrJxHZriMEQ/TkI4QcTyaCjA4ME3VtTmSH8aTPvM36WhJlTG/nXt2nIWuduA1COw0VIR70rXSNJU3vvkc3nbTeVx30Rr++T1f5uO/czNPPdnLzR+5iYYj0/RvOwpfMpi7KcqS1jT9R1LY9zSQ3fnsTIT/y60iVQUEKE9X5FFVBUUILFklq4/xlR3f5b2bN5GYCtH0SBnvi8eZGM6cNCbf9/nJiUMMFGeS4A6WtiBEDxKFqBLB82dmsaxMXUy9OY+a5zJQ7udIzxiWffL5vZCWUD3nNgxwdt0AixtOnk3Skk7QEY3Q1phEFQpCgCJO/ooaOZEBy8eo+Vy6ej4tdS8tz0prW4poIsycBadXYUcRGhGtk6je9XSFp+enPV39SfkVL/1pb0zS1pCkPhX9lR4nEAgEAoFAIBAInFqwtOcMkQiFeNe6tXzr0XvwHA+iOosuW87/Dv6Ic684Rq4c5sQ7EshZTXg+CGpQrRFygWJxJg9KOkpoxMapumjjZYSqzizpAUSthhQK5kgVvzWNr5dQChZG2YKn36OtQpikoSGjNaQAxVfwRzRCB+egXjFC53gHibldHL9vF4fGFdxpg3iuQqzHQnoeUoGmzkayk1m8WXHkJFC28UI6qXgY1Qd7qowWUlnZB/ULmjhiDSGjYaoFB91xEWEN1zRQLB+hKOx+5ABaA7TF69h48TykvZN0uhM7qeF6UJyOELnOYtnafpzVGke/3Ek0VMHxXErVKl7ZwXV8xiYdwufMhUPj1CoO2x46zBOhozw6eZTrOjeycO0aMsWjNG8aZPXC4/RltxBvVsiUbJKp576An3PREgA8z2P/QweplGr85xfu5t/+43d57U3n8eOb7yHUkOLAI6DKIrpQEEBXfSvj1SOE1RRnnT0PYgbC8girGosWzcwambeolaGJPLPWLubL33+Q8vcjhC9U2aw00tcziqMofPyPvs7Nt33gmfG8d9uPuHugm4ZwhC2veT1tYgcfP3YRtgfjfpnpQpnmujht4YWEkn/Ox7/RjGdFuKP/Ti49bwlve/2m07pPs7bDD04sRxE+bX07efNrFzyz7eIrlnPW+jmk0lEulJKiU6POjJzUv6MpxvhoBidbYNeB3Vy7eTmpROTnD3NKwyM5SpZD/9D0abUPqXEub/sIQgh0JXzKdq/t2MwFzStJ6bHnbMuXaxwbm2LV7Fb0F0jEezpi9RGMBpOmpufmkAkEAoFAIBAI/Hbz5czPK+3XYQy/asGMlDNM1fMxDAMzZbJk+Szqoy6Dh5oQwkfGVayIh5It4YU13KiJcDWk7yPxMXIljAMTCFfgJ8P4SPxqDQ8XLxnBrQsTmahi5j1qc1JUFjdQCRsI20NqAqEonL1iEWTzMJFBUEWruPTuyJG5rUrvj49w9G8fJt68gIFiGIEgfsLHzNpg6CiqwXs+dQNdr1vBmusXQWnm99NDef7pv+7kf+96HGG7qCUbUXIZmSxDUxQtGoJYGJJxnEQItz6OEICqoK/VSL4/yod+8i5mdz2BLPwjjx/8IVrVxqvo2Bkf5aCL7wtqZZ381YKyBWXPYU5ziGjKpa7OZfFFczjeO0W1YFOzXA4dGsGVg0SNIxQHMux8NEuxGCE/FUPWFCpTOukWh0hLBczKSddISsmBpwY53juB70mcp2d0qGWbO/7jTkYPnuCzWz5MZm0aR0ps16MzmeeT732QD1z8AI9P/jd3df8HW+7eh/r0TIiooc1UIAJ27TnB0T0DfPdftyKPepRGqmQmixy7pwdp6vhhnf6hk8svPzF9BIkk55TQlSglP0LRDuE7Cr6r0JR6NkDw4KHj3OvabHGznBiaZtvOntO+P3/av5/KwSTFfWl27Th+0jYhBHX1MyWTNUV5ThAFYOnSDnTfI9xW4+DRLN6L5Hv5eatWdnL9tet40xtPL/ADYKiRFwyiAGwZ7ebv9t7L0cLEc7bdfPdjfOJ793PPnu6XNNaf57gen/vuIzy++zjf27Lnl9pXIBAIBAKBQCAQ+MUFgZQzzIaLlqCaOqGaip2pMfKTOgr3phl7MIVqCcz+KZSxDFQrSMeGqRxYNtg29SvqUU0dhIIoWwjHRRg6aiSGCIVRPJDSh2ye8JFJzJEyYjhL6MgEiiPRCz77nzgGpgFCIFRAUSj1WpgjVWS+SHl4kkO7ulErPr4CIl9E5gvgeWhxg3xMZU/E4YmhCrguolIDx2H7wBDTrRquLpGayolijn7XotqVYtnGOfieg3QclGIVYYaprp6N09WIf1Qn1ZeiOd4A6mxQ6pnT3EmsOYXqa5QaEvC45Mi7U9h711CshikUQ7ieRFcSNLbU09DZwtvefTlrls8kjPVMhYsuXUrMKDMrEqOrKYpdrmEKyZEt7dzxrxv50Id2siRZYkHDNE2Rk6vTHD44zMf+8nt85EPfwfN9Nl2zjs5F7Xz0kzdx+NGjDB0dJTee4xM3Xk2LamIUfMZGEnT3bqakvYOEPMCD/+7yr5/6yUyOGN8n3zfJlz9zNwA33LCBec0J/OM5EDahdWlaOupQdBWQKK6HGT05x4YZrpCqK5GIlwmpKYpyFlJlZnmN6vFH9/2Y/NM5WNo769CjKlpCxYjpNLa+cOLW/7lzB3/2+TsYnS6wKNoOBQ1R1Lho/fkv+f5+8JGjOJpBptCMldWpVqxntvm+zzdvfYLb79p7yv6mqXPZJcuYP6/pJR/7hezNDHK8lKE7/9xAymAuz2SxzEih+EsdQwA128X34YkDAy/aPhAIBAKBQCAQCPxqBIGUM8wjT/TiCwhFDMyIQTgFbhRcXRD3plj/6hNEulyslgi+7+GrDoR0FF2jNAuqC1tQpUCxXKQqkJaFiIRRPIkiVPxUFJmKIVQdxTBw2utw5jWhVX0iCLSChSJBMTRUJQ4hA7UpikjOlDWef1WGG9/6MEsWD6IVbdT/SxZaLOMWKrjZCsmyR4dqgKkjFQU/EqIgHEL9RbSqj1J1EFUPJJi9eY481oNScRCahh8NoWgKqlARvo83ZjNx3OOrn7iX48dnIeq+wGRPB+bcFP/4yKvIJBNkVrWz6uoilTpwCgZOQWckX6QxGmN18yzO6ZxHOhrhnGtWklsepzgvzOZzFyLH1/L41vkI0c6HPnEt85e0otsKWlVg+5JsuUhMrzBUyp90jYrlGsWaRbZUAynwfAgno2QnCrznM2/jsrddwC0f/R7H7n4KVdXwQwpKTOcnBxp5771jjGRmkx0x8FyfRDyEcFykoXCif4qxkSwtzUkuvGI5ra11NMWS1Lc0MVEo8/effhNIELbHslWdz4xnbLJApKIQ1lwWJtIAdITriWkuRshBFZBxK2StmSo7iiJIRaNsaJ/Nxz74Gv7y3ZcDsG/6pzw6+p84fu2ZfQ9O5nhgXw+TuTIT2RK1TI1oj0N4ssoT206d8HWyfDdPjb6JfG3XM7+rWg6ZhKTSoCLyJXwdkslnZ6088kQ3n7/tMT799QcYm8w/327J5Su4rn8aT9KzajUHz3vhPu+Yv4k/Xnohl7c/t4rQdRuXc+6KOVy0dO5LOu7P0zSVaFRHarCg/fmrBwUCgUAgEAgEfntJQM7UVn2Ff858QSDlDLN+3RzOPncRN77tHD597zaOxzVGzlcZrktRHfKZ3qcx9/U2tfYwhfUJisviCCMEoQjagQzaUBExkccvlamtMfGFi1up4PsetbSJZ6q4bXVYy9oRcxoxV3YgdRXFkaTTcTq7miASBkXFc210KVEXxtDftwo9apKcA62tVS6/5AghWcD3PSQCNBXpSXb/YCezn5jkHDWJ1A2IhhGGipapUn/UQggVqargOMQPZQmFdEKxEMI0Z2bSCAGagmI7RCwbT69QPZxjy4928a3/up/sZJFvf+sRDo2OU3bDODGBExP0NbZweesClJKGUtYJqRoRXecTF1/CW+Yt5we37uCue/djlDwMVcGyHfIl0AhzYnia73zhQfoOjiBsF8/UAMFIoZmebD0jxWerveSsKq7v4TNTMndwcIpqscLwkWHyk3lmLemgob0e13IZ65/CCGnoikIkauBKG/9HR/jq3zST9cKYpsZVr1lDQkj0ssN0/xT3/Gg3AJuuWMn6q1YykpAcHh5jOFNg3YZ5nH3+QuIRnfnzm7CcmSVF05kS1RNJ/GyEy+vOAWBZw19ycaoBv2TQWI3xNxsvYXaiDoD/3PU4g6Usj544zljPFKVCDU96TOQ+AbUv0JO755nzvXXrXrJ2lfVLOlk5r5Uju0fRLYVowaN1wcn5RMZGstx+6w7GR3Mcn/o4pdp2eqf+5pntNdshU7PwQip2awLhQzb37LIpVdfwhcRXJM+34ueu+w9wzds/x2vfefNpP09HDw9z/fs/z/s+8s0XbFcfinJBywJCqg6A5bpkqxWmC2UuXTaff77xVSxoaTjt456Ko4A0BLPbgkBKIBAIBAKBQCDwSgmSzZ5BnhoZY+f0KNGazc7bdlBeEMYMKdhFA6Psgqox9lSCoWwrkUSFfJeKahggaoCgVjRQLAt9JItQFCK6g6+A61pUZtVjTlTR3JmlFNJUedX1a8hZNvc8cBDXhNHDA+TyNlIRSCmxqhXUnAP3Vilc1sSas+ZS725gPN9LdeIE05cnkX2NxPZkCY1XwXZobUpyZOsRImED36nhtMbRwgYtho4jyihCoHg+5UVhIqNVSivqeU16DlvvPgCKAFXBVwSkTOyyjfDr4EgeVVXYcP4CkvUxvM4oqceL+JEK2Q1RVNdjxEox5OZR3ZkI6vFMlrZ4gtuePMhU9zRPPNjNqOagCoXWfptb/vkePvD313HewtnsvOcgT4g8kQi4moEfUlA9SUJpZKRo4z+9jKY3N81Htt6Lur1EImQQS4To6mpgcjSH43r0HxsHYO2ly/F9n5EWn8oPHiPmQkzXqb9wO9nP1SgyF1RBLGZy/w93YWk60i8jLRdN+kgp+fxn7uYn39mJ0iApL1VojM9UeelqS3GwZvOTB/bSm4SPvfVypieKaAcVvMk0l1+9HABDrWfDQodRZZqL29bSEpvp7/ouKxJJdh5ViEwr/GTPHhzL46Y3baJZFyg4JGxJfrpIsj7Ow84AvQ05KmEHIQSXXrSEB/u3kpybIXsiddL9+/B9B7nztt3UajatF8bJ+BW6lGeTqtbFI7SYEcaKRaSh4WlQLlcpZst4jkc4YsxEhoVA15/71fbt/30ULW9RLNlIKWeCbi/i+w/v40TaZdCbOu0+AH/z4P3sHR6lfsrg9RtXcP15K0+r34tRKj6aI8kVK5RKNT703m+QSIX59L+/5WXZfyAQCAQCgUDgN5eUAil/tVUkT3ccZ7ogkHIGuf2pwzx2zwF4aBwclws3FNl0fobv7J5NZjgGUuJ5UcRoldBwAddNY2TKgIavCMq+hZZzUDUFI2RiH7YRyQYMKdF780gF/JiCWrNJtIXp6qznB195EL0qkbaL0TOF67vgSqSqYKgRnLiCIsA9VuB4T4Zq2mLa9SlpC3FcBU2RoGvgSfB8yiGVsio5USiy+tWreXxgDDNiUlnmou9VZkr9Aua4RWF1A7HjJY6e6EFmc/jtjfghHdcAPV9DlG2USg1NVUimIjQ0JVBVhaUruxh6dAw9H6Jpi03zX02QnQ4zXcsjNZC+JO/W2HNimDt2H6Y1FuOKC5awo2eYvh19ZAseB8QIYV1jWXsze1qOI3WbUF0FywrNzKlzfUKZME42QhWXH3xtG8qcKPZYDftoHsWHeUvbMEM6C1d1kRvL4TkOX/3orTTPbeGBH+wkvKyO6HkJGjp0ivtKTORMlGIWEVURJYeMa6O6HkpIAykRjktra5JK2eLBh46ABLUqCJsGjYmZQEi5alOLhzBdiefPLFfp7R1DTDmEFYfGyLNJVc9tuYxGfSff786zs/hDPrnmdWzPbCMb28XyRBThpblo8Rw2nT0f1/cZK4eJahV+fPNj1Hr7+PMv/h6RsI5uaPzfd2lbaxP+0gUcnyjw1g0n50iRisDxJTXLxVQ7MN1xVKXjpDZf/vu38Mkv3MG9YydwGnRywua7f/AlHMtl/R9chAcIXzKVLdFQd3KJ4LCm4JoaWsXlkYeOcP7TlZNeyJUXrODO4700J2OnHUQB2Np3nGylSmlCY/DnEvueSqVqMzFVZFZH+pTH+vN3X8aeo0P87us28ePvPUnv0VEAjveNM2fu6ZV0DgQCgUAgEAgEAr+cYGnPGeTaVUvZsHIuakwSqrNpnyPRVVCnVRRHIAwDGTKRnocT1xGGij5YQHge5XlhqnPr8Wa34rSmqTtnHn4kCS1ppK4iEMiISbHLBMvG3jPK//7HA/i2g68JhOsDElkoI8sVqEtA2EToGunZjdRNghqOIudEqD5Uhq9PkL6nROuhE5g9U2BbKCHB+mvXY756BeElbbz9befTZYbZPK8dMQzS1EBAtdGguL4ZaWrotsCxPWS1hjR0FFeiWRLhSUJCImoW0jAoRePsebpKzJ07DjG9SMMKSTxTI53Is3DpMCu7zJmEugISmKyb08G165fxrks3MCtmEpuuElc1lLYoiy5aiKrNlLJdMruVuWMTtNRyLGkeQqt4CEdC3kefgv4v7udLf/UdvvD73+a17Ut53VXriMRMhk9MUylb9B0eplSosOV7O9h2206O7uqlWqyyuL6Zz17wOv7jnTew6TVLOdozh9yaNpxwGF8VENJw02HK6RBePIzWmOCi69YTjYW49tp1xJMhZs9r5sK+BLffshXf9xGpKH44xIKFXXz4pktnKuUYOur8Mv55Be4aezYnSUSOMn7sISZ3TOMWfFShoAudiGmwYVkrb75iOevXz6OlNYkiBFmnlSmrAelE0XQNRVO5+dLXsi7XxMCeKSazJXaO9dAzlmXatnlq58lVe9o60jS3JmlpqyMmxqhTi4Q4OalqbmAafUsP83dnWL6glbpYBN3UUTSFQs3GSgjsqCBiPLfM8OZNC1AdD8WVPHT/wdN6ptYu7uT2v34n3/qz05/xIaWk5nm4isSqOuw5MsAnHnyQvkzmBfvd8K7/5q3vuYXP3rLllG02LOokIQz2HBxi5aoOhKogdJWQqZ/2+AKBQCAQCAQCgcAvJ5iRcgZpr0uwe2iE7NwE7fMtdo9dwND3hqlNO0Rd7+myHzW8rjj5KyOYT0wgSjVcTUfJqYREHNX28eJRxr0avqIgXB+/tR6BRJkukjhcRam6SFUhXy5T7YqAJ4jnJL5tP1M0XOTLSAF+xGA0VyRZL9DygtGeGu6Ug3rUQwlZeJGZF0Ech3A6TM/4YQoliy0PHeZ15y7ny194N6Gwzmv/8T+oqj5+NIwbEviaglr10Ne3kK8IRPfYzIcgQVQsnKRBcu4savcdwlZVVKHQPnemUou2r4DZFkJVDPAl1piJObvCtrsLKBXQNYWR0TzxFSZvOWcNAH/1we/SXbJomtPMx993OUuXdnCilOHT++9jc9Nc3v3HsyhlDnP7nSG0Qg03qtNGmGMFBfI1sG2oKMxtqmfzq+ZxaM8AjuMxODDNnDVz6O+bpGXtXFav6GDLj3cxsvcEc5a20RadWdoyP5kmPKpRGGhCaBJRdYhETKzWOL7tgudBqcbkSJam9jRrVnex++4EmaESPdkxsj3TXHnj2YxnSlSjKsNTecJPv3xPTRTQJlw8F1L6s7M4SrV76Gjq4+zZ83jDutdSb8a4ouVSOiPt/H3uG+yZ3k/ipwu5ePNS3nj9Bl639CtYbpkrPxVHSkkoYmK4Hp3xFIVClSPdo9z14AEatlq4jQpNG0+u9nPuhYtZddZs4okQx0YlpvBQ/ZNnc0QSYSzh4zQKLl3SyeKWZv7si+/G9yQ7jg+h3C9Qtedf2tPV1YjmSTRF8Jrr1p3WMzU1UeAbX3mYZSs6uPzq1afVRwJN8RhKHupTGmMhi+1794Er+dvLLj5lv0KxihCC2+/axx//7qXP2+Zo3wR7Dw+RL1Z556vXoeTK4EvK+Sq0n9bwXlTVcdgzPMqKlmbiIfPFOwQCgUAgEAgEfi34CHxe+WU1vw5j+FULZqScQbaNnuBEvEixReEw7TQkmhhyoNSpYzXo4EtkWMOdp+Ev9HGviGEuVFH/IE5s3EJxPKjZCE8ytymJqDj4pjbzki5BkTZ63gbXx02ESV/WRaVJQ6v5aBM5hCcR4TDoOsJ1kZY98+eqgzI1U/HFdbNguNjNCqXFSSw/DPk81GrUlpT50ejdqKkq8ahJKhEmGg+haiqlSZXh1zQyusqg3KmTvn+A9CNjVEYrjFaq+KpEWha+4iN1lWprmKF8ERmLgCPB85i/qAUAp17FM5hZqqQJaHVRkUxPlojlXMJTkqiv0DM5zaN9/Tx6736SXY2kW6Ks3jSXlSu70HWVr+/ZxdYnhrj5yUdx51zPksv/kave9DZEfZjG9hR9RyYITfnE0lEUQ6O5Pc25G+aTmSpi6CpdXfXMX9jCpjdsouPVa2le2cX+0SIDJQdPgW0/2s7OJ3r53vd38PVvbcNzfbyIgWZJFMulWrK4dt18zu5qRs2XqWVLbL1tZkbJ4lVdvPYd5xFe0EBV0zAaEyTqoiQaYqiOj+X7nBiYBiAcC6F3m8zf0s75TctxvBxHJv8SmxhLZ3+I977+Q3TWzySaFUIwUp7g0JRGd1HFCOu0taQAMLUQiVA9ZtggFJl5Adc1lb959xVoNcnffPVO9uwYBE+hoRrmTX94yUn3rxCCRDKMEAIfEx+Psn/yLI6W2Y34f72E7usS/E/3dgCMkEEoaqK6oJckWt4nl6/w84pVCyVsoCdDLFzcelrP1MCJKbZPDHHPk4dPqz2ALyXhqkrKD/GP774Gx5TIimRi4oXLH6s1D22qQltd/JRt1q/s4k3XrON3XreBfLaEdH2kL9mzrfu0x/di7jh0hE8/9Aj/u2ffy7bPQCAQCAQCgUDgTBIEUs4QlutSqjikRw0SZYOzm1t51znriOgzy11UC6SqUHxTnMr5CuFDBumEA66CWa7hCYGSryAqFqbjoA+VsHUPLVdGy1dR8zWEp4Ou4dVFsTpS+BmXyJiPb2rYYYn0n853ooiZnB3TBZSRSYxSlff/1Q24KqjbM2i7ymTWtpBdZqCcLVE3xxGJON6ogf3DBPOqI2yOq+THCuTKVT74lZ9A1ic6KlENA3PMIjxQYN3mJaDoCAmeKlFR0Ko+QlPRqpJ0UxJFUxCxEDUE+3afAEB6HqX5PplFkkqTju6bZN0orzrvCHFq6NZMno6P372F99x/G+955A4eu/8AjgRb55n8FSkvStg3afaSfO3+Xfzr7dvZPzjJrHVdrL98KbWqg6h5xFpTRNIxrFiIx3f1MT6Wp1is8cSeXv7yE99nwfxmrr5qNSPHJpGej64I8CS+afLfX36QrbsfYnbXEHPSSXxlJgAkTQ2hqZTKDkf2DSItG8+1OW7a9E1lqBSrfP8rW+jdPYirKkznqziOR2djAj1ToXh8is/f8iAAEyN5XNfHrXoA2N4kNXeIsjNIzLgKQzs510inOYuophHTVbKXOHhzZ75GpPSR0nnOvampCtPTJUrSptyiopoqV1y+4gVzjgw4YY5ZLYw7kedse+38FaQiJue3zWHkxCQffdsXuPXm+5jVmsbwBZoHd+04wsj0ySWQly5oJeJIIo6HY7un9Vxps8Jk1xr0r/SfySnzYhRgarhIfqTMYKZAcyRGyFKY3/TClXZCfZPo3cMY/dOnbGMaOpeds5jZHfXMWdSK5rootsOc+Y2nNbYXk7ULPFZ4FEcpsrDxl68yFAgEAoFAIBAInImCpT1niMeGBvifx3cRjYZYXp+nc/I2/uqjhylU40SnQDgSEdGhReKHBDXVpDzZTGOliP4diWI5eM0xZMiAUo3ux3pRXZfa8gb0fA3NdmeWp3getZYk73rHhdyz/xjqhI/UFdRsBRQFfB/FNMHzUDQVKg4yHqIgHJgugCMJLVKIpVwqpkv8nDJinU/lgAN9cWSdoKfXpCe3m4dv6+amf7yWxw4fx4+BKlTCSRVd+mipGAfvewqxvBlTKMiOJijbyEgIz9Bmkr3iM/Qmk/qHHMCh278TOA/T9hCOh1B9kgWP0b+ro2nTJBPJBH54pva5JX0ao1GskovaaFDfUcfCyzs52tHN45MdbGpcxIUrmtAT87lq9jq+tWUnS+YdI2rPRhENvP6K1dT6cxTjW5jVYfJwcjmeLRkZz1ENm/T0jGCrICcyVKsO7S0pCsUamWmLpIRCuo70skaOWHnMEz7XvW6YocMpFE3F11zcxgi6puGGVBTFR+ga5TlRbnMO0n/fBBsLs5geLoAEX1eYtbIVw9DIDGWRjgTHJfZ0IKP7yChIScEuAxDR5zOn7oNMjvn8zV/9kFkLJde+uYHW6GU8uf04X/2fh0gvTVBa5NOfKbF/apQLWuZRyn8E3xsilvonVHVmxofjeUwVy8TDBsJRkICfUJnQprBdB0N7/tweQsSwpIEQqedsu7R9MU+2LwZg/xM9VEo1Bo6NcV19gqglyNpVvr5tLz9++Cm2fvEDz/RraIiTlhJdKBjG6X31zUrVsW52J7MTKVTl9OLOni9picTIixqdyQRLOpopOjaLWptesJ9fs1GkZGosd8o2Vcthy45u5nU0sLCzga5FbZSLVdLtL0855LxTZKpaY6QkOZ7LcBFzX5b9BgKBQCAQCAR+9YKqPf//BIGUM8SyxiaY9EBTSNQmeOieCIObDIotCtERH0wFRarMvW+Y4QtbGFcVFB+cpiTGcBVfgJ/SUatgdYYwiwaIMDJmPr0sRyBtFy+kUG2LcvbmuXxv137Umo8+7aIWPYTvIVQVfB+EQCCgOUnorbPJ5EpoIRMrq2EPxqkuieOlVKxBHW3URwoTPB+1WEKTLrYP1WKZb376XsykSqXZRHXAnKjgihCWoSKyZbTjGdylzejTFRTHx45q+IaJNFWOVy18T9J4xTiti2u0r0yz467ddI7UEPdbnH32EqZiFfqnS+SPJAhfoxDus5ClEEvTaV6zfCn+Qw7JnMBYUmLnt4+in6uQWVAC4IHprYxpk2RFJ2tkhsHtTxA57yCvetMVtCR05OQ4171mB4rwOWG1U8y34FseX77tMXxTI6zBm39nM3PnNKJqgjUXL+DJnhH86TIJCZHlLWjHpnAdwaHHG+mt1rASBk5UITVaw52o8OidB9ATPjJkos7xuGTt44R369zz8Ho8XUPGBHbMwI7MJF/d88QxpO8hpKT74DCFQpVyuYZ5dhXjggxPZnazLn0WydAajk31MTKZo8AQq/LDxI35ZDMl8mEPVwnToUXpyZYQtgZIfH8cKS2kX4Knc72++2s/5NDYJOti9cTUCIXJMrbj81jPIT5/c4U//uO3P+/9vC51Nv25J2kNRZ93+/9ZvnEeG169Bt3U6RmYoGqAUvYRUlIrWie1rVYsivkyipRUyjXC0RfP/5E0Q3zqnMtetN3P0lSF5QtbyVdqNMej/NGFG9m8oIsL5s15wX5KOAy6ga+e+mt5z5Ehbr1vD12tdXzi968inIiApuFYpzfD5sXMjrajW61Y9hiPDPTzznWnl0smEAgEAoFAIBD4bRIEUs4QTdEYH7rqfHrHMpzTuIG0vZWBWa3UIpJHxnrRhwsIxUEPhzG2mJgrHZKLa4hEEr+gIBQVqUqwPaSp48XCeE11mGNlFPXpt+KYSWVFM6qq8wd/9U1KXT5mySI0UkbRNfxSGYSK0FWEOlNhJ6qovDa1mJ13H6Vac0FXAQWjVMOJRRg/3EDj1kkEEs/10A2IzTLIZCIgfQQCFYm0fRzVIzRcQpcCpasOpVhFlh1CfXmkZSMVFTeiAxJhuyjTBRIPDJJrTfG2r72Os7rO4R0X/i3lkkW8OcXqzcMsWF3Pd744RduGHvLxBSjTUczhGqMDWbJzd9I85yh7PqNQOSRwFYXWg3Vc3rIKgPMbN9JT6qfZaeTmfzlINptkkcjTtu47hBem6D9e5uEH5hI1qhz/yTiizuJRoZBOR1kwt4n3vP0CWlpS7Nlzgr/8758QiYX4yO9ewaz3J/niLQ+yu3eEtUtbmR48yKHjYbyIjp0CbZYNORv3QZWC4uNIm4iiYo7YRD2XRcsL5Pe6lJvieAkF9clhskWX8vurxMMmiifxgImBLJ/5xG0sXt3FzvII0WQYy382+FBTJF7axGzuoDkyj4jeycWXqfxr7kkGakX8QhFzl0Y5mUesVYgnP40v82jas7MYposVbMth/tpWVBFm1y2HQVEQfRq6eO7Xz0M/2M6Jg0Nc+d5OYlqckmjiZ4v65qYKHDjQw9i8cZYnFzJL6+DhB44gFEGiM4WhqYiBClrBoyUSOmnffQNTlOqihBWBETq9Kje+9NiT+SkhNcrS5MWnVQJ5OFvgzr1HcTyPob5pZnWlOS6KqJrCRfNOPcND6AoSQWt73SnbdLWkED40J2MoisI1N21ibCjDrPkvT+nj/tI01WiB1jaT9246+2XZZyAQCAQCgUAgcKYJAilnkMtWL+T//u/8Sx/8PlPf6eZPbnk3Bx55AFH0sCM6Pfk64jmb5W191DdVOTTehqIbIASy5iIsDweNUKaC05oCz0cqAALbcPBDGsKXlA2JW9EQcXCiAl2RM/twHUAiFAXf1BFnd3BweAqJRBECzBBoPtZcgRUDvWQgfR8QeCGN4qI0VsVHUQSUa9Q3R+mPFVE9H4lCdfZM6d+6nhKxRIQCKkrRwU6HkLEYImRAzUd3PIxYCSyf8nCVT1+3jT/42wacdoFSMLClRcG4i+6JCJe+u4ojJ/najpVUXAVFCOzyTJAmM1lE0+pRdAnZMsOjgv7BDPNnN7E2vYK16RX0Hhom7yhYaozj9xmUnTJL2l0i8RBbfjQLYdkIbERtnBuuu4E5c5opFaoM9EzQ3Jx8+uVcIIRgbkcDdfEwfdUSU5Ua6+bO5zVXlfnoJ8cQuofX5RFfmCfkmVSPaSglE1H2kYaFGItw7LuXcONfX8C2ia2UCxUqWR/F8Wmsi4GAv/zXN/G2qz8DmsBTFZ7Y0ceFN6zBPtpC23gnZ6c3PHM/rVzUzqsvW8ni2c0sqOsC4PtHtjNcziIB161il0Ic7R8B4Ee7Rpkslnj7+V2Enq6ac05DOw8NVhndOcx4m4NR9kEBv6zzu//zBnzpsS/7fRRUVtZdx80f+hbVikNy/lrqz2+nTrQBMyWFuwuTfPyGz5EbKlJ/g8H0u3JcVNKxpgtEUxHO37SQWs3lP576HjLrMPesk4MWvuOjSIkmwSrbxBPPzb/y8/one9na+2MikRDzl2/CVF+8T1TXkZaP8CSaJjg2OU2/UuLAyPgpAym+7+O5FqLmk+w69TF+smU/PX0TDA5O876bLmBqNEtxuoB/mvlbXowQoKISUsVM+aFAIBAIBAKBwG8M/+mfV9qvwxh+1YJAyhmomCtx9MnjAPzrZ+/EbqwjVJhCsyWxY0XQdAqfN7DOngVKDkoWaAp+SsM3VBzj6eShPnhRHVF2EK6LioovbUKKjtOkQ0XiJ3Um5tWReNQmmi2iANKy8GoWMpFmyLMZPtLP+286l/z+fqaGc+BC9LEySpeGEVWAmeS0TksUrzOBN1QiOlJCFsvIkk3qeIX8Yo3SfB3VC9O4vYzn61TjEZgqIWoWmm1gt6RBEWi2hx81UMItiKU+YlyBBp9Hpx8g9WEH3xKM/bnB9tsXYTapzJ5l07WmiO8qMOWijVtkR4ss613N6DeHsUd9lJIFNRcxXuH4kXHmz34238Wx/UPYLVEcTyc36JHfYvI/929DaW+nbWkL1dIE2aNVfE9lMDPMurPm8r7r/xO7YvO+j13L5a89i3/7wOsolWrc/8X7yU8VaWiM0meqbN15lAMPeZiDVRL1GSJLFWRWRT+qkPairL5kMcWRLI9+fyfVOQmeMiTf3lfk+j+4hLHRLHduPUJ4fgt/9ckbiMbDaIYGlRoYGooE6cC9X3iA4vwYP+od5ryeE1yycB4AMXEvbzp3HyL2B8+c62PD+0nEisxS2mmeamd7aoS2unps1+P2PYfwPMlFS+exqHUm+enrL1hNpmeaqX2jWFkdbA90wHHRDJ2yO8mJ0qMALEhcgi8EUkAmY+MORAjPm5khs2t6kM8ceIiesyX133Som2zh8pZzUUd06hNh5i1tQ9NUNqydQ/3adkoTJa596zknPRcrz5qFLsDyfTz19NZtThyTjG1roy5Rh7nqxYMoAJqmUmeGqFgOXckE/X4J1RIo/gsfU4kYYFlEoqc+ztK5LZiqSmMiyuDxSW796jaklGy+fAXLN8w7rfG9kM5ImqXGHB4oHeehEydY2/Yy1VQOBAKBQCAQCATOIEEg5QwipeTbd23ngf/dDbEwnnQZCilYC1L4iiRycBRhpgBB1YhjPziO9DyE1MBTEZ6O8H2EpsxEUUwNNVNDhk2E7SJMSXzQJba2Dj9fRTtWwTinSOlAGs0NIQwdXwHPMIk4HlbVg6KHsOD+/fuZHp5GWi7S8bFb2lGlTixsgaEjdA2lyya63sbWyrA9i/R8zFSEXJtDpdPA1wW+BkrOAlfQvrCVvuEDKI4LiqTUAkbNRdFAKpLoQAVvwEO2SJxZMbL1YziqQg0duynE/nwbqf02fVg0r96EOLdKSInghgxKxQoL1jeReVzDaayh3VkFX6JLycbN80/63FedPY/8j+/GPFFGq/dJ1kbJ7I0QX23y7r+/kW237eChEzvx42G2H81ww5Uq1ZKF7/kM9U0wVSvz+f/ZQu7IJPa+Xprb65j3xxfQHGtk4itPMTFexo9FKVtJ1h2pcGx7mOqYQarJ4PJLlrF37wDd3aNkC3nCyQhtyThLF8zl9g9+A6tUY+n6+cRTM7lGDEND8zwsMzyzxKdURZOC5NEiyu+UeKq2g0t4+oXcegC8IXC7QZ0JjLxr9eU0HdzJ9cs284379+ElpggnYxiayqq1afbnB4km1Gc+m/kdDXzw3Zezd3svj0+P8/DWIYTv84F/fAOKIoiJRubFzkMIgSHjtLYlcWWe7XeN4t5fxx/820zJ6jojgqGomNmZykLKqGBOtJOJSAZdF6hPB0Zu/p8HKUqf+Lw0ne3pk65TIVehAkghGOqfprkp+aLP1Kq1cynmb2DO/BdOFPuz4mGTtYs62dc3Qu9ohs7WFEazyaY5nafsM7NM5wJ2P3yEN775olO2W7m4nUuWzGHe3Eai8RCdC1oIGSqLVs867fG9kP0T4zzweDeVgsWmjUEQJRAIBAKBQOA3SZBs9v+fIJByBunOTvGNWx/D7c4jG+tgYAQvruC5Ei8RwlNBLVepzQ1BawV5uIQ6rUPExGmD2CfyOLaC/b0QdbMbsJAIXyIFSFWgZFz8Rkl59ySJhghWzYchg/T149gPNFKMz8XonUApW9TqQghPR6t5RMMhcrn9+LYHQuBXHDzDR+iCUj5ColQGVUMdhkjdOC1RjzFFQVFVDNfDj2noGfBbANfHUWsYx/JMZXK4aYPK7AbcpEp+lYbwBcluD0eBhmIYF4ktQ/gywibT4Rv/VMSZsvE70tj1Ku4hm1AC5syeIhK22TVlMjGaYnyPgSZeRfmwRClqkFBQJz28fIUvfWsb7//di7l9bAs9pQE6Hm9GGbARJZ+FNwxS11Wk5xvQkUiz5fGjjNVsrDoFqx5CmsFo/yR+rohUBPGYyS2Hd3Ds6CAcnEJFpz2V4MHJMYam8yxe2Eg2W0Y2S5wi5LZ10ZgKY6WrZEez3PHdHfRmchQWZJAFSUU4HB+bZu90D3sO9OLoUSpKDzdmirS01iGEILGwldFqDao+iqMi8lUUQ8GMeezJ7wfeBICIvxfcPjDWP3OPLWvpYlnLzDKfK862GfUrnLdyZrnKXusog16GfbkTdESfDWI0t9VxxbXraO0d4Z4nutE0hTmLZgIklp9nqPwwAPPjF/Pez7yNiamb+Y+P1RBCp9XYBMC8RANfOvdNXPN7f4WbjDI6OZPwt2f/IAef6OHYnn5WXr2SfeUpMmGHZGOCnGXR9jPPR3NLiuZElJpl0zX7xUv7Doxl+c/vPsyG5bO4dGHLaT+HtusylM3jKj7XXbwGI6XzQN9xdFV9wX5/+OdX43/wVajaqasDjY3mmRjNUcpXufHGTfzRX7yKqdEcivryVLIfqxYpD5cxclCbqr0s+wwEAoFAIBAIBM40L8+/vgO/FlqicVrWtSAciSIVhGEishZKzUXLlFFQIBqBhgreZf+PvfuOkuyqDv3/PefGytU5TffknEdxlCUkhIQkksBGZNsEg7EBBwwOwHs2YDDGJJtgQGSBBRIgUEJ5pMk5p845Va66+fz+aDGynhL8/DB6oj5r9dKaW+feOrdV545qa5+9Je5bGuYKyUpgfURPepb5TbPIKEDVPLJSEhkaoupCsQr5MkhBRIh/chKFoFBIk7tzHq/efC5SiwgvTqC3huhZiW7bGG5IR1cDixILUFGE8nwUAUGjotIWQsmh2mARBQHacZ/un+lM3W1gNiRJtGYJlSB7vEZ8KsKeUCAiIlNHSEikLMzmLP6CRjQPrAmfxHCAmQuI4rB83XzK6zpRmoEx5jKwbT233vpPvPbPXkZlQ5IgM1eU9uoXl3nXTY+iZhSeqyEqDrnJEu0NaRqiEFvVyFo6KIVCMDg8Q7Xmcc/4bnbNniJcqqF0m1BYzAw2URkSFE9LmrvSTOXKzOwZIj5coeNEkdddso73//7nQTdASn624wSZGQNZ8hBegJmMcemN52MWFBRCUrEYsXkSa7mLSgoWLm7jU596PeectxhXCB595ARpAxa+eIbklSVmtRo/PXaYExOz6J0ZVJsJBYc9W06c+ZykmhOg6URxE1WpoXwfai4Tp5s5O3k5Y7Uhds1uwZfdCPvFCGE+6XOmlOKBY6fRhOArb3wllyyb60azPGuyOGvQlYg97edz3eJOvvZXq1jdYPDF72zB90MMmaDJWkqjtYRD+2f48i3bGZ5souJZVHzJ8eIXzpyvS0loCsKMTdmb2/Jz9uWr2HTZSjZdupKyCklkYszvbmJ+eyNt2eST3j8MI2KNCUTK5ujRETzHe9b1tO1gP9sODfCj+/b/ymsQoOL6WIZOd3MD1160irFahdlqlf6Z3LOeJ4R41iAKwPIVHbzpzRfzznddCcA3P3Un3/+3+zi2d+DXmuMz2dDawTlXL+KSa1dw4blLnvuEurq6urq6urq6ut9B9YyUF5CMZfOFP3oVr/6PfyAIQogUsWkHbAPDEUQtTWhTs0QzFo6dRtd0/EaJmasSmwpYYk3iBTqHRudjtSbJpQyYmuuyI7wAFIS2hj5VQ6sFqESEkAJfFzz64BHiY0VYEcGoi0gmkTFJqhZx5YUruGBlN+/8Yh9KKcT4NDHhYgzqxMdDMuvnU9k1gJZJMNyXIaBMWJhENGssffklbH3YJVISqUC3IkrrspjKYNH5y3FPThOPezieR/buiDAbw2kUWDNwOsxhVkNiSYWVruAv7ifTpHPTKy/hp+88RHXSQWkafUcLBELh9EBBNiIsBzthoElBS7nM0MECfmscpERJgVn2MAxJRuuhyjQrl65gecspxqsB7Ghn79cdRBRx6Ys3cHVPM98/NcP2fYO4lYhMJoZvGkS6gwg1cjMVDnxpD+LkLFLXeN17XszmGzby6Q/ux4xg6BfHiYKIcMSGNgOhSYYGprnuxnPo658mN1Vi0p2kxY+TbU2gKi7JqsV/3LkLqyox3BrebJX7v72Fq19xNpqucdEFyzh1606UVHjrJFqokNt91k8u4d0bruQrvZ9ipNaPAs5pvOgpn7Pv7zvIzY/txgo1FjZkeOtl57G6u523Ln4V/ZUx1mWWP+3nM4qKpONfYdG8bto65qPrEiE0zm15Nw/ff5QDB3txHJ/7fzyDKkuUUhjGE8VZi65LzHEpA7FwbouPaRn8zVfeCkAYRaRSMXYfHeRnu4/z0+1HePNVT2TTFMsO4/kitXyNT/7hV2lsSvDF+z74jG2Qq0UHEURPaaP8XBoSMT5ww2UANCbjvPn8TZy/sJuN8zp+res8HSEEm856oo3y+Veupu/YGPMWtfy3rw3QGk9yedcS/n3PY3x3/17ecs45z31SXV1dXV1dXV3d80J9a8//nHpGygtIzfX467vvJbyogQiPZLNHS7VKqr+CFoCK6aBpeI1pvCNZvHETZ2GWCEWqPWCeMUu3PgsxhZlKUHE8ZM2FcgVVKBH5HnolQnpzX2IDC5QG0ldMOB5+1iZIWsRSCuF7CCLmJU1ee93ZWJYJhoEwTWTcJuoL0ZwIhcI5PY1wakSLUsxb3Yod04jKVdzJElt3DmLlQHNDUt1FVqwfwjJDql1JDogyQy0F3K0FxKiGjCASIH2JVRBUCg4xp0oyXoFQ0b70FN/5wp/xkTd9Hv94Hik0goYYDQslKOgqVYm3p5C6ZEYKfvTwAQYPFlB2glpVJ0jFCVvS7HPyfPyW+/n9eZewzFhNVktRrrhEjsvs0DSabmDE4xRzVVpTW1h7yRGkDAmF5G/+8rsMr0swuzGDkgrNlMwM51C+j/IDth8Y5uc/34/jBYiSRxSGKKXm7mt4ml/85FH+7mu38ol/uoPBwRlmx2YpDRXI/6LGocd0VBSjs7EJEIhQga6hVhisuHIlmj63teTErn6SMzVahSC8Rsd/SQw6NYo7xtj56EmcMKIaeFSDpwYQZitVvr1rL1Xhk/dr3F0+zr8+/AgA3zi5h88d3sWuoX6KM+Uz5/SfnuQ/v/kok+MhmfSLWXhOhZMLTzPuzAAwOVHgtlt3cvrYGG9588VkrSZEoYwoVqjlTgEwWMjzrjt/SqhHGAUHyzafMjdNStbN7+AXO08ylSvTNz77pNf3HB6iHIZEVY/A9ZkcL+JUnzlIsnFpF42ezsJ4+tdYhXOWtjeztH1u+1AmZnPegm5M/dnj1n1Hhrn13+5lavTJmSsnjoxy63e2kvsvv9NfalvQQufSdkzrV2vn/KvYPt3PuJnn4ZlT/9euWVdXV1dXV1dXV/dCUg+kvID863cfYvexQfxjBfS4xrnvneSiv+zDSxn4WQtRclGmjvQNjBlFdqtHpOsQRZgjNUJfIlyFkhplN0Bv84iSGsrQEOkkNKURQQCmgbINiJvIqk9cCdJNKSrtjXAyyyVvnqV5QZlYk+TDX387mibRdYF0PSjXUKaGKIc48RC/MIVXnEKEkKzCS96+lRVnjyNiGjKmkxsZJ5X3EX6AmO+iWwGN+jjmUJ6hljz5VRLX1IhchUJRa5BUWjXmNSUIxwpc8Za9XP7GvaTlOCtUSOBHjAzMorkRRjFAeiGnji5kZHYeXY02F7/iOE3zXYyEwgsj0OYK4Qo5lxHTmo7TuaCZCNg6OsKDgwM8ONbHvItbEIFD4DukuouEoctt37qPwdOfZvmGvXSsVgjbJj9bodIYUp0vUb7Hy67bxMobViNtSVhz2PvIUX7wncdo02xiYzUiKUGTaKUKYTVHmM8zVs5z/Ogos7kyjhK09MS48UU76VB5sv0Gf3/pVRiVAC1XJtMeJ/vi9Wy8fv2Zz0llIocXeFRKHg2HY+h7FFolRktzmiCIaDJjZAydjJF4ymfs89/9MaOlYZTnsWlDK2ZjRNuCuXFeFBKFEd/82G188u1fPhNMeeS+w9zz033sfPQk+/ddwg++sISR+zxCNdcYLZtNkDA1mrJxzjprARe+6kqsrEH7ugJR4x6UUgTRXNBNJk1QCjv21EAKgK5J/uAl5/Dqzet47ysuftJr65Z3EZU9qHqomI2yTBzXf8b1lEzYLG5rZN6vUJT22dQ8n9MTMyj17P2EH7xtN/d+fxu7HzjypONf/pe7+OZn7uGOH+16yjk/+fZW7vvJPk4cGvlvzfGXgsijx5xkQWTzumWb/q9cs66urq6urq6uru6Fpr615wXEMnWW+hlyraeJai6JRQ73/9tadDei0iJIChDlGsZkieSoi8gIzHNmYSRFtj3H2ekJyq5J7JRDxyUNWJf2kk9kKBxuQLoBWq9D6HmY+SqRZSC8EGUJ3MkZSi0a6Br+jMe931uNLx0qFyX55E8/xXvf8BLKxRQEIUJFCN0gkTeIuQFet4Y8O030oyql7ae576PTTB0XqEqC0NdQpx1MQ1A9L0ZhqgERRTQenyY9kycYbmJipkpYqhDZEq89SbXTQNka41uHifVOE3oCJTXcSZv157+XjRcLBnv3sPWRkxApCBX5ss1Pbzufxnk61cJpZk8G2LEpxu7YBwhwPRQKYZrkc2WSfSX+4k9ezkxYQ9ckiWMeG1Z/j16zm8WvnGLhFQVO/qQVpxjnvjvXc+VLexg/3Y/yPfRIEcRDoiBCm5/h9GiOPcPjVJuTYLqEaRvlBqxe1Mb+kzMo00Aqhap4qE6bcJ1Jw5oEZjBDeTqGykumZlziDQ6ONCiqgL/77p1EoQc1B/10iX/6wkvpyD6RVeHGLdzWFIGusb7YwbFtp+ha1sLf/tNraG3PUJk4SCmYJms2POUzlm+cwgcqgctqI0Wp3MFLm5YB8BdrXsR4Ic83/uN7RFp0pgDqJVeuJha3OPeiZfT2TpIKUyQPeow8MEzHtQ388P6vsO/hUTRNZ+a9Je66/zBycQdLXlolYZ+DUoqDPz/OBeNxtriC2XKVVGMzlaCGO+Pxo688yKpzFnLB1esAuPr8lVx9/sqnzL0xE0Mr+aiYiaoGmLZBQ1PqGdfT4sWtfPBvbiDxDFt/funQ2AQ/OXiUl69dxaqOp3b3+fJ929ndN8ofXX4Ol6xc+DRXmHPZK84i3ZjgrMtXPen41HgOLwwYG5h5yjk3vH4zQ71TLFvzzB12XC/gbe//FkrBVz75BizjmR/7OW+QffsHCYdsgqUBLHrGoXV1dXV1dXV1dc8zEYKI3/62mufDHH7T6oGUF5B3v+Zi3nbHj6m0LiT4t9Pc9umz0MYMRKSwRqs4zTqxnI6d95FRRDTrYO6PQxTnnLUT2JrEjrkIAUY6AyFEbgRKQRAhoghttkTU3oQqVJBKYOU9jIJLY1fETDOIKR+EjlYCeXqWE3HJnuF/YtdXuoiwQGoop4YIQYRgKBMmXHBc8AP6d6SINUSIDMh5AnMzlCbiKDuCWIQTSdwpjaSVxJtQWIeKiKpLlDSQFZ1kvyBKWRizPjguD310HnpLnCgWZ1fp6yDhte97J/cf7ccshESmjuspvOLZvOwlV/Lp93yPwNlL2XPZftcBorSN9BRCQTg9Q6A14vbNEvohixubWJxt4rv7HuHwTDNBNcSdkURKEugNzDoex4+sYCZopaYNoBernH3OQkrbT1DxPVIlHT+MSIw4uMWQwPOxjo9BawN7e4dBCnTLRNclZluKCgFBn0di8RRnvecgnq/xwGfO4vqbdnC/sZCu5eMM7mplaucEeiDxWhKYqzsp9s4QpCp0L52r0dF3egqyMcJQ4bkuVsxg44XLaOvIAvCitldyYfNLiOtPDTL85bW/R2z73cxyjAlxO9rDq8g1F2EFWJpOayqD9oY1NFgWiUyMh473ce/hk/zB1WfT1pJl9/ERyjFB7nSeR27bzoqr2jl1/ABhIUHEXDAwX5jCKTiMHUljvnycSqWfLb84hO+FFAsuoDh9Ypjbhu9iWe9yDmw7xexU8Uwg5ZnE4hYEEQioLshy5XUbsexn3xLT1JR81tcBtvcP8VjvIB3p1JMCKXv7R/nRzkMYUqNSdbGfJYAB0HtgkJHjowjOetLx1ZevYHbHKTrWdFAqOXzt21vo6mrgxhvOYtPmJWza/OxFYR/aeozBA2MA3HXfQV72ko3POLbJWsiNV2xiaFBwwZrFz3XrdXV1dXV1dXV1db+T6oGUF5DPP/ogvf5RKpFJphZg7KkRBQUwdDQipEohDA1CmLnQRpQlmT4XQoOpWoxtTgsJPyBsTNGxfD6P3TKKK6o0HJuY69YTBUQJGzk1g7BtlFKElk5jS5asbeAsncE1FfEDRaQbkDkUURk0uGVyMdN3F1HKB8tCBQFUqgSZBLGDVYyUTdTYgMjnSLXNJ9R1zDU1vFe5FO9MIWo+siIJJRSqCeJWDfojKl0liutTiCgBlk3z/oAoFKiCDw0ZGJoiNOOEWgYtEEwfTZFeWOYj37qF4uoY1mQAAgzT4KzzfLJN42y+dDUH7z4EwCXXbuSRx/rIz9YgUmiGiZ/UCSxB9Pg2jWrN494DfZwa2oQdn6T3kSQDe7qJlEl8g8689fO492gvwdp2Elt6eeiHuzGCiIwXUE4Y7N47QKvUIVdCyxWQrc1Q9ajk8piFEkYlIjA0Km0JZFVDzHpUXQ3TCIikYtEF4yipU9EsliwYZ+ZoG8IzmDTioFvke2f5zJ99g0xjgg9970+x4xYtzSmGi1WwTU7tGQAhCWpP1AqRQntSEGW81ocTVZkfX0VHrI32hjQ7+wNaO/LocZuO7rlWx8fHpzg+Oc2hiUk0Kfm92RJ33LGbQ7U8TcrkvCXzaWxI0NKZZenSdl5x/dnIyGP/UQtKFZSEU/t6iTeZOG6NFdceZ8KdYElwK29816vJzZT4995x8p5HYnmMNruZ1edn+b0/T9DZeeGvtEZCW4KmIcs+7/vjFyPEfz9afv2aFTQl4ly4aP6ZYyO5ArftPszB4XHaE0nMSDIxU4JniXnsvPcgY32TDB4bpbnziWygzRcuY2S2zPJlHYyM5Th5eoLB4Rleed0mfvbNRzi6u4+3/PUNtHQ9NYMIoL0pg5jbRUXKevbsGqEqXNSxBdnVgh2/7lf/JdTV1dXV1dXV1dX9DqkHUl4genOz/OjEPqIWj0QUoYcRibSOVwzxihVkOgGVkKjmEjQlKa6NIWMprNtcYpMux4qtbOlfQGeYQ9MFW/b0Elgaof741hJNI2pKQxiiQhfpBYiKQ2THqPXPcnpwGjtmEt9XRVUdhJjb1hFZGlMPOnNVYAEECDSCpjhRKkmQ9WAqj9acIqkn2fSiVdxz92HCSLCpOMKuWgN6IIlsUJFAFXX0IYWqeST2z5KwNCKhEcYVRBDZEpDIYg1l6FB1AGhZ1cOVK/+Q72x/Ewtsnb5gI0GkEcyPMc8JaVtzO/2z93AivWFumioiu6iDRaWAvQ8eR6kIYej4TQnClM4vs9VMU8fL1WjNTFOuhCAloSsRBlx81mJe97Yr2PeBbzIxlkNGcwEYFYZgCKh5VKoOw7qG7nsIKQnKZfSEjTVUQXoKhHj8vgSaArpC/I0hhw5eReCcRNticff2dVzwzuM8ev9aNl90CLVE575PrCdISmhN0NGWpX1eE+bj2RfdjXHG9p6Ghe0IXSeKFFNTTxQyzXsTjNaOszh5NgLJnSNfAxlyXecf06B1cHy2AAF0hgWmjYipmRJeEPCxnz9IGCluPHs1S1ubuf9Hexi6/Rh21uC+Ppf9XcO8/KrTvO9dk8xv+ii6luHQ8GNM92ew7TxIwc3/cBunUjE0I0XKMGnTCvjeNtac9ZeUCjU6l3SQSsd5z4duZFX7IkbzH6d5+cNk0s3AIvoHphkYnOGC85dgGNpTF0rcQJZ9/LiOpj17iaia47F1Vy8rlrTT2Z59xnGtqSQvX/fk7Tifv38bJyemuWLNYhZmGrhv30kWtD19oOOX3vDBlzN0Yox1Fz+569GRHaeZ2d6LfMkGlm1YwBt+fzPNTUmkFBzecZrx4VlG+iefMZDS0p4m0ZZAhRErV3c+6xy8YIzp8mFs3cZOecCzB17q6urq6urq6uqeP5Sa+/ltez7M4TetHkh5gRgrl2hMtBKLGlkcwh6thKsCRIK5LTqNKWTNRxg61XMShA0StJDSTRr61yWH8x1MT8Q5XWthft5hQ0sj08emMDwgEUeg0JwAryuLaowh8yHFFSmq7RrZGQ1RCjBnIwwfRHscfPD8ANVgwUgJlCKKIkTVgShCBAItAIFE6QI/axJ4ksrCLJMXNhOrFJi3YoZdj2o4noZRgNDQ0AoRMqEhcoJkwqLsKISmoTkhSgiEE+I3m4iqRMYsqHlQLNPdmWTH5DiTdoYD422UUzYNpzwu7Gpj77ERHvjpcl7zB1ncRm+uzTGSn9+6k8psBfV4tsbiDd3sTenEhKAxM7flQ9ckbRhkVxzhsXuXgKGhai6JtjR//J6XYNkmV61azI9v2YUQGpHvMv7ydpSmaLl9Eq3i4jTHSabiEIEWhOD7SN0mtqoZ/WQBp80kdn0L127s4Ce5LfgVgRzt4nTzLFrcIt7j0ze7gQFpsqSaJDycRnNDgrhi5kUx5l9+Dm9ceu6Zz4oyNIQmSOkKO20wM1khCIIzr++a/Sn95f2EKuSeH0UcHm7j6ldrfOmdP+XgYydZ/+HzuGCtzQbzHNTSbjaum095pkxl2wAqbXHVTdeTjsfYNrEdP1cmpqVoX56irStLJn2ak4f7uH/H9/ijv3o7TbaGXQigKYNSMGrMZToFfkTSFWQ1Dyuc4tOfupOJXBElIpTjnqnzUZpYyN6DjxLzKlz1Ko//9dW7CAoeibjJ2Wc9uR5JsewgUia+IRHBcz/dt+3u47s/3M7q5Z289x1XPevYgcoUB/P9XNK6mrQR5/xF3QC86uw1+FFEAZeu1uyzXqNrcRtdi9uecvwHt+8i0AVf+saDvEIP6WlrYPHCuS1E8167ilvveYBC/hBf5+nbTnc0Z/inj/8euqbR8Rxz2Ddrc+up8zH1Rv6xvR5Eqaurq6urq6urq3s69UDKC8TmeT186IoXYQmdz37gdqKkRf6cBJ7u0/zdSUINVNJArwqW9IwQ/aKd+E0+mgEs1qlWdYJI4lV0Ii1i++EhzP2DCF1HJVMgwGkCv0XHqOkoxyEyQAiBdnY3ySMzVLdM4GxIkdQCwmEfKSFob4C9o0RRBFJDi1sEVRchNWQIUcJG+DHM/jJ2NslA3xT4EY6yefie9YRVAURYwxrxIfDjEuNsxdLLzkHNVDn+wClC2yBIWyDF412JQsLGJHKigL8g8fjcA27Zc5BDk+swdYdICsK45PjgNKEbcHrvQpY0v5M3bOpju/5xABpTNo0NCU6OHQdg6PAQ2cXdmLZOqVgj2zDXreblrz2fz31rCiUmEErgNcRZcl6MPVtP8Yvjvey4ex8EASqKUE0JwrSBEhAm9Lnivw0xLrx2I1v/czsqCElqkL5yJeUtvVSrHm45IjdTwDmRprVtCk/XaTNOsKT1BOn3TRMDziov4Sa1nLt3nEPqUIidqgEh4WiN4elZap0usV8WTW1K4Zy9CD1p4e0eRnk+Sj6xxWVRchNB5NFuLWVyZhdxWlhtX8y3fvL3+I7LI7fsYv65N/LYHUW27TjJgu5m1q+bx6IBB9OOiOlzmS8vesVZjA/NsOP4GLWT03zi729kfLSFf/7O9yiWBrhydoS/fdMWRBUIQ7Ak5bKPHmkILeLRMM6lMkSJEgOlWXb0jJM4OInR7/Kl/307n/jBn/KLXa3c+tCLOKdRJ7lukDHdId6osWhhy1PWyI/u2wcZgygKaW597tonyxe3sXRRG5vW9Tzn2NuGtrI314shda7u2Mi165Zz0dIFNCXjvPXmH7Gnb4Sh6QIfuPay57zW/0lriuM5HnpTgu/fv4+mdJx/effLAdjTO0JYUhwcHn3Wa6xa0P4rvdfSbAup+GZWNT41oFNXV1dXV1dXV1dXN6ceSHmBkEIwUinxLw9uIaZ7RG0x4meX0YpzxTXRNVTNh8ineItOtlCkuTpN60sjBk82Mru0GZXTiQo+Qo+oHh5FrzggBCqZACFx22JUF9qIENqGyzRunaawPEvQqJErVdCCCMc1WXftak7s6GNG1Uitt3Du1xE1H2EaGKk4QamKKFVRcRNZrBJaAoqgmzpjt+ylyQ846w860ZsneGRvBoRAhAJ0CQpmhlPkUzOQqxEPHYQnMWZD/MY4+mQRGSqClA0ywm2SxK/TWXjlMFpfgqGRGr6oYpcdknmDctxAVn2K40XGhmdZMm8hdsLGKdc467xF7Nw1CJYBQlCbnMZqb8RuasU0dXpnZ7n7xCmuXLeIY6tCsoNZYpM1hAbXveFKvvHvD3Eg6xI7MY4uNUjEkcqg7YHqXBegaoiWy+FlLfYfGZ3bDpVKEF/cxdDEFKpaxQwizFxI+31Vdi85TOp1LsqFI78QxJtbaNyXppQ0Gc1ksMd0PBQimPtdabZPeySZ+dRxPpkZ4q+/8GZM28CtehBGiFINaRlIJUin4mc+S4uSm1iUnGt9+74/yvB3W3/I50bvJNGQoDgVEu9p4thQkdQ8ncGyzkSDomNRGzd97mwcawSlOUAS29JpzprU4gInJal6AYd7TU7lOjHawPV9Ai9EmAoZCRq7EkwUQozAJ5GtUM5Czk/SZBq8/g8uZuTIfeS8KcJ0goGpCkopmrqytPc0cf4la1jX0841Z69k06IuGhufGigZmMyjmRqNbWk2Lnlql5vAD7nrlm0kM3FWbuzB8QMmxgvce/8Rztu0ENt++pbLAJe2rsHSDNZl5+qkfOEXW9nbP8pNZ6+nO53mlD1Dd/bXb6OslKKQBhUzKCcgkbZYPP+JIFFpxzTZ/oBU17NvUxrLFUnHbBLPcg8AjXacvz/3yl97nnV1dXV1dXV1db996nnStUc9D+bwm/bs//X9G/bwww9z/fXX09nZiRCC22+//UmvK6X48Ic/TGdnJ7FYjMsuu4zDhw8/aYzrurz73e+mubmZRCLBDTfcwPDw8JPG5HI53vCGN5DJZMhkMrzhDW8gn8//hu/uf95/Hj7E1Ok8pXKZQOXJxPLM6/Jxl3YQZeNEKJQb4Q55iGKZ6fsNjvxTmsqohR+LYQ+bCMcie34PeksCNInS9bmOOp6HPeqhlyPMfICs1JCTs5hjMwwsgqEbWvB6ssSOzrLnXx6iU9fpfkcPkbSpXLQApEBIgecFoAm0U6OYBwYQI1MYAwVImcTbMtQqDqHnk1EPcd7Zh5F+hPAjkALpKmQINGr4ukJGAX5jDBEohBugzRSRmg4xe24bU9nD7Pe4YcM6Dv/QYt8/jmA8qHH5kgneefE+7KZwLotFgEKd2cvnzuSJyhVu+8zPMXWBSsRRMRs9lSC1tpOGpU3E4ib3n+7l9sNH+O6jezAzFSbPt5ldbSHdgFs/vo0LL1hGtqoRNiQhboNtzxW3LYWYxRAMHSEEfhjhnpUATSBCxVTvBNNWQG5FmkCPCK2IqBYx7pj0/rCV8ufi2BmTvnsXcuRnixj8QTePTa9kyeosHZUC9pSHiCTpl5ewkPT3T5PPV898TgxNEOufxj4wQFr4Kb4AJwABAABJREFUNDYluOF1F5x5/fbbdvGFz9yD4/ikUjrtXY/SkthC95o2kg0Jxkset+w5zM/Ge7HiBql0DKUUO73b2F+5i9HqQQB23LmXXXfspsn1WLKsHcuay1SxdJPWeDNLmnu46sZNdCzTSKQj5vW0ocdMrGwSY36C5rhDUUkOlKvk9TyfOPd6Vrb0kExrLL8+Tl/lJDdevJ6Pv/U6Xn7ROlrSSf702gu5aMWCp10fr7piPddduJrWeIxqxSNfrD7p9cFTE/znv9/Pf/zj7XzyT77B9z97D+Wyy6HDw3zl5oefde1tbFzEHy+9ho7YXOHdouMyXSjz7Vu20uPEuPmtN/K6zRt+9cX8OCEE8vEAoucF5B2HU1NPtEFed00jjecXWHuB/4zXOD46xd/ecjcfu/2BX/v96+rq6urq6urq6uqe6rcaSKlUKqxfv57Pf/7zT/v6Jz7xCf7lX/6Fz3/+8+zcuZP29nauuuoqSqXSmTHvec97uO2227jlllvYsmUL5XKZ6667jjAMz4y56aab2LdvH3fddRd33XUX+/bt4w1veMNv/P7+p7UmEsQrAdaUh94bEtwZ4t9ro43NovdPonwPRYTS5FzgAIFnxAgMgUrFkAHIQOd4pUYpIUGfK6oqghDpBtAgiSwgmivsiqaBLhEFD2PGI2qQoEuUF9G3awzjM9OwrUgskCihUJoOQYS0rLkCtpUKKIX0I6iFjA7MoBoSYBmcOtrJQ9tWQxghgwBNhiBACcgEs6Qr48yPxVHpFKGtERog8hWUaaAMHVmuIaUkNhPQ+8kpeu8yIQKjpnjlRYe5YnMfay4cI/BCgoRJIhOnY14Dx4+NgqGD1IgEXPUH3cRTFlQdhJTMnpomN1rg0IlRrl66lNduWMel3Qs4b+d8evQZ4lM1ZLnGiT19PHjnQS5PbuXtf3yMRMZHqBAKRXB9KFZQcQsVs1GGxmU3XQAxYy5gpSlSExGpXgetUCOe87Dmp0k3Sqx7AuafVySQBZbHUiglQQluWLeK1k4buShESMDSaB0KWNg2TseFS3nt+687U2z2Rddtoq27iXjSJtOUYuHSVlofL1QaBCHf/NwvuPOW7Wx58CimDFiRmGJpfIYVZ7WycHUP88+ZT0zXqYyVqd0/xfE7T9PfO8YdhxJs62+g7M5tC7nwZedw7g3nUJ7xmLi/l5ip87JrNvC5T7yOL/7v15PPV9m7v5dCCTxN4+TJCWoyxKs6dGcSnBUrUiSijMZnDt1DixZHmy4z71KPcab453/8ObWKy/y2BqR87qj3msUdLGrMMj1aZP/hIW56/Rf4uzd9aW7bGaDrEl0XGLqGkIKWxiRve/Ml9MxrJB7/1euF5Co1+vN5zJhBAp1U0mZ+c8P/rw5BUaRI6gZaCKaSJJImS+f9l4yUlhOYi0uU7d5nvIapa/i1ALzoV3rPXb3D/MNt93NyfPrXnm9dXV1dXV1dXd1vj1LiefPzQvdb3dpzzTXXcM011zzta0op/vVf/5W/+Zu/4ZWvfCUA3/jGN2hra+O73/0ub3/72ykUCnz1q1/lW9/6FldeOZeO/u1vf5vu7m5+8YtfcPXVV3P06FHuuusutm3bxnnnnQfAV77yFTZv3szx48dZvvzpCzT+v2hptoktaxO0t6eofmcWZ4uJXJJEFscRfkiYjYNh4M1rwJqsEJoK5/IE8d0O5oyD05VAoQg8D6dNJy4ihHq8QY2KkDMOYdzHywiaEQQ9bciESeOWCdylcSrrNMLGGKkdGpEnGT9dIkp6WKZDoM2le4goJIyqgEIInXg6ReD4iCCgFtdxm9KIYg3TbWNgS4LQyCEjqLYoKq0Sexpq+7PY/ZNMN+fnitsKcOelsSPA8RFSoISY25ZkGQgpEFFEKCAUcEKPM5U32He8E6REuh4SOHZ0lK988X6wTUQQYXcIfjFwNyuuWsS+7xbwXYV11gh5w+Efvv5T/uV9v8+bz9rE5HieqbECHDRpX58n8bIqyfGV9N9f4I/eNEIq6bBzocWRPRoCAbYJEnRXoQmd2ITL/i+eoGf9fIYfOYKtS+In8qiZIsIyaZnfwjs++AruOf4YD/5khLBZoYYUI0M5grgGCm7csAaOnebkrIlUIaiISd+mdeNJhtjIsrVPbGWZKtbwNQ1lxagonWuvXc9YoUJNRXS1ZXHyVZQfMt4/zczkPLqGl7J85RSf3D5FuRzy1xsvIFiU4G/e+j3cSsTo6CzJlEVMg4qXpTFuA9Da00w1buO8RBLoBSYnZunuacNuEZx0hljbuJCbbrqER/ZsZ8fPxlC+QpMQyYjj+wtEq3t4+dpRRtwmekcq1GoeIyM5VCmkHMYhyPPV5L0cvPsAa85bzHs/+doz97j3wCC9fZO84vpNSDkXLx4bz/P5L/wC1wsJdYVZctl7bIpyvkq6MUn3kjbe/P7riKdsVm5agBUz0TTJymUdxGLPviWm5DvsmxlhU3M3pq6Rjlk0JmJ84I2Xcmhqkr/88V285bxNrGpvfdrzIxXx78fvpxJ6vHvFlcS0ufcTApYsbOWgM8q8rgb6/BJ9U7Nnzjv25QrFvgY6fq8CZz393JK6QbIoKVbKVKouiecICt198AR3HztJV2Oape3Nzzq2rq6urq6urq6u7nfR87ZGSl9fH+Pj47z4xS8+c8yyLC699FIee+wx3v72t7N7925833/SmM7OTtasWcNjjz3G1VdfzdatW8lkMmeCKADnn38+mUyGxx577BkDKa7r4rrumT8Xi8XfwF3+33VkbJLOWBM3vWQ9d9ybY1YIJlVE1NOCeWoMKQWabSNzLiqMUIRE6Qi/W2PF0QFa1kY8XFpCYAtSJ2tooQAVobLJuVbCXpmGvQFOpwRN4rUnUQJmNrbgthnYYy7ND+UQtk5kRIiYTrjMxJ12MQIFeESaBF+CFSGzDYRxGxlzEMk4tS4bhEakGyCGuPbGcW7+t/VEEShTIiNFfCxATldQ0iScLKG3Z6lmLYK4QdiYQM87iCCECFQQQhDwxr94Kf/8gVspVFz0hVkeLcyj+NNmnNMx0BWagEw6RmNTkmTCprElxVTFIadb5G9rgMkCMplAxGw2btzF5HTATKmKaevcM3SCsbsGKA3nEF5IckUVq9NneXOWkz/LcfO/LaFpjcexIykwJBg6ytTnatZUKhAq9BmfUT1HQkVgGhiGjt2epOyW8HWN/rTgH9/wVZx1SYQjuc9ZRpqIbF8VzdawDY1sQwKj6euszTRzwFkEpYDCPpNt1iKq3iQjuSKZ2FyA4+qr1jB6aJD7f7ibXFTlc3/3Q9quXUVDa4Z/eO/1GHETv+TSNq+Jr33xYcbHVvH6P7iQ8eptlB2PL5y6j9mKQVULkKHCbIxhWDadMoMRj0gb6TOfyVHTozSVxsrUEI+XYfnc8duphi7vXHY9Z523mP+4bweepXHllau4+tr1fOijN+NHHkZ7yH8MnIOQYBBx8+1fQC5II5c58GAEIQyeHGd2osC+LceftBbe84HvoSLF8NAMf/YnVwNQqtQIogikonvxKIneBtoWLSH1eNFgIQQXv3TDU9ZVIvHc2Si3nN7Dz4aO8JpFG7lp8Vn886uuRQjQpGTv3lFOTs1wdHzqGQMptdBnZ99paqdqjLRsYklr+5k5XXPBSvKey7yuBi5fuJzFbU1nzvMNhR/oc5lJzyARt2hrSmFbBpb53I/8x/oHmfaqbOnr5y2Xnv2c4+vq6urq6urq6up+1zxvAynj4+MAtLU9uXtEW1sbAwMDZ8aYpklDQ8NTxvzy/PHxcVpbn/rlpbW19cyYp/Oxj32Mj3zkI/+te/ifdCo3w1hUYllXE5va2vkZEANCCTQmCDqyaJpG6Cu0mofyPTQ3wtrmoZcVN7z5BMs35zCOK7YdXUx7WaNsWYACPwQkXluc/MYYWs1HZOIoCaLsEGbjRNmQqKQRAhRrCD8kyCQoGu3QkSCZzmN4AhIxiHREvoZyXGptMapXtNK4x0O2CnINLtZgCVUrMnDKIdnj4DkGDaUAts2iHZsh6Gmm0h0jNuagFllolVm04Qj8GKBQlgGej7At0DUG8hWWXrqE0lARp9lm+0MraRoMUAASahmdY8wwKI6w4U2r2POVuxFKEA1WCDJNWFGIQIEUTH/P4t3/difjQZItM2/kayfu5aXrH+KV/1jkvi+cz+TtGawlkumhKZTjc3qinQMxi1SsgKwFqEqF4lktxI4UELM5wqKDSCcRpBDzslhxEy2TYGiJQiybjzHhkzpWxPEFbm8VE2i/3UdDIiKFFkEQBuRzFbZNzcP0FMqFKNLwIwP1qM38uEd38oniq01NSU4dGSOM5hrNqyjENiXGChjwZ1i2qpP9tSmmwwqlmsvYZBFNMzBjJsL3yKsabYkU485cEGrv3kFys0VWuEeZqmjsfGQ/l19xIQDLl3Tx4MkRvGqcUk0xXJ1irHYSU1Rp0TZTcBtxFprYdjPnXLyUm7/4ABs2LuP00oMsb1rH2zs/TT6wuFVfR6ZpmvmLupj52QbCRS7+tMbLX3cBw5vms/rsRWfub9fePkJdAIL7Hzh6JpDyyI5eQgHCgPNeeoz8kQU0OmcTRQpN+++lH67ItrF/dpRlmbltN7r2RGDjDedsZF1nO+cveObuP3HNxP/qGKWjNe4qPcaffHAuC8/1A+7bdZJCqcbSea28aM0SHtl9mlrJZc2STuymVtxWh4R8+gANQNw2+dC7r/2V76VZsxlQgnmJ9HMPrqurq6urq6ure95Qj//8tj0f5vCb9rwNpPzS/1lXQCn1nLUG/s8xTzf+ua7zgQ98gPe9731n/lwsFunu7v5Vp/0/brJaORMYaGrJ0NCaJn90BNkUJ1Lgd2YQsx5avoiK21CuIv2A2IECUVsDWjwiG6+QfmSYbH8X89f2cHhgCqUgMCWGAi0yiI/6RIRouoYS4DebCBUihUBIibOxh8TOfkSkkDNltEINO2mj6/bc/6E3NDDiUPKIalXyPWlUVeGfnqZ7NkGipYw8VuO0k+Hklgz+PAO9QyPRVKHmuohkiL1R4Y3aFDY3Y7Z7WD064khEeTxDqreC1AyU7yP6R0FIHt56lNO/OMr8td305ov4rs1EEoxuaEjHKAUOXqPP53b+grFCJ5ohifIlNFdDPzqKdm0P6tEyynE5/VAjOx6bT9PagII9jmmOsO8HXTjFBRTiPlGvgV8w0akSb0oybUviQz4CDalViBaaxM/KUzETpHZlIcijmtLIdMTKC1sxj1Y5PjJDGJo07CuSCTVE3MbNO7S0S/L7i5j7Cqhl3UQxDaFLhBXx9x//MSenl1AwXbKGIrIkUxdmaNpeZHZ/H39/0+f5m5vfQXNzmrt+uIvefQMIx4Wq5KU3beb0NTEeLB5l6lCRcMEUetcUP82fZFW2kY5FLVhxk3/4whuZyhVwu0JWZZp4pfc5VKChuRGu7/JQtZUxL8XO2+89E0jxlY/TEBKbhvZMin2l/dRUBUP6JESObbuHOKdBsXRjF8xGuG5A0S/T0HOag8U0V7QqUobLbC1Bc1hi1rSxUw5RFOGGNnnfQ17YQceyjjNrobkxCY/XXnUcB4Bq1aOzJY0GREoxM51k4kQD0844YRCiaf//y0UppViT7eSizYue9pnSlIhzxbLFz34NFKX+CsqDYztOnznuegGT+RJxw2Djok5ODU7z3Tt3Y+gan3n/q0hpGcxinpbMuuecZ7HskHh8u9KzuXjDYvp2FFmxsN4Cua6urq6urq6uru7pPG8DKe3tc6nt4+PjdHQ88SVpcnLyTJZKe3s7nueRy+WelJUyOTnJBRdccGbMxMTEU64/NTX1lGyX/8qyLCzrVy8w+du2ubOb1AWXU5l2+OZPt3NiYJLybJW46+MFEUFDAqSkujlFlI1h7haYgzlEEBK1NzDrZRkZmKVaMNFMnVzgo6q1ucKypoaTSWKPVbAODBHFDJJn9TB4RUhQNYn0ECUVkVTUmnTUqmbSe8cRQieza5yO+c2M6wYEHlL4xBo8ShOAGzIvUtQWKPysxXSuSmoywqtJPBUiNA1tZAZvVStFx6R0UTPmVI35J49Sk+0w7GPtqiEzHuV5TVQW2qRPVZBBgFIQphOIssPYlpPYEpTnYhQceoZzFJekOfsla2iajrhj1zFicZ/LFy2mf6qd5Z9bzLfe9m0iFVJdFMO9pMyL3L3s+Vk3Sur85w8uo3VXgt9/ZwsqjFEoCmpDAjVsgx6hfB8MnWrNw+lOYU87oBT2dQGNPQ7Tj8awLBdCUKkkoRciHJujN+8nmi6hmtNYrc2orM0FKxezt22GiYEJYk0SdlioQANAIJCjsyAV+eZx5FQJ0d1IrcvDnvCIhEDEDBRw9NAQb3vTV/inf30ds5NFcD2IFNL3UN1x7jx5Ci2uaIs3oK3YSUOmwIJwNW++4QqmZ8osWdzKY/ceppirsKqxieHwfbzjAwX+4+Nn8ad/dhWBo9GdGkcLHbSWLDAXYPju6QPU5kfEDA9Dk3THM7TZVbTI545tZe778VbGBwfYo3v80Vtfwzv+7MU8qj6Do48y7Toc9RqpKZ1jv2ji2Np2yiLGpqBGtltSnvU5WJzi3sMDHN7bz0f+4DqklNS8ABUDFUEsr4iiiJe99rN4bkAyZVMuOTz89U2kTZtFC01048mPQafqcc9P9tC9oIWN5z97AATgx6eO8u0j+3ndsnUc+eJeTEvnzz/+mjO1WX4VUkhSVpJZyhjSeOK4FIwWy0RKMTCVY1VPG5tWzKO9OY2uSbSJMeLlPGYw9qzX33lwgE98+V7Wrezi79759HWpfmnv8Ch5x2FH/zCvPXv9r3wPdXV1dXV1dXV1db8rfqtde57NwoULaW9v59577z1zzPM8HnrooTNBkrPOOgvDMJ40ZmxsjEOHDp0Zs3nzZgqFAjt27DgzZvv27RQKhTNjXgiEEKxtaeehrae45YHdTIU+oQqJJgvIqQKy7KBqVdyeBvx0HG9tI2FTAkyJCBW33rqSTz32UraPrcPPO8weGkIFISoMkVN5QhEixmeQFQfheERGgDIVaNFcBx8l0TSJMiW1BWloTSAMHSUF41MlVDh3Lb8SUallqKzJMPuidspRGbGghHF+iphh4lc93GIN5ftEVQelSzxdJ+yf68KkDI1gFBIni2SOTGNUI/RcktSpAFmLqDXpEIZE+lyBWRFFJPRjvPydj7DwpfdQzVUxlOQjN72YT//Ry3BmHYQQlGbibLAu5yOXXMlVF6wn1d0CySTWqMPaaJS2c31EMoHIpCinEyT1Vjakl9B5oJ2uGweJdVbAixCRICqV8SemUKUq0okIemzstTXKd6YY+rcshVkTf5WPCua6qEgvQCAR2ThaU5pIaaQPF8k22WQaEkz/fAyrN6T0QBktm4L2BkJbg5oHjg/VgI4A9FCh6SH2Qg+leaR7FcLVCea3oS2cC0zWHB8zbpBe3IYwJZtfeS7hec0EALkY12fW0ja6jNi+Ri5Nb6ShIcHSJW14bsDtN2/hB199iM9++ha2P1ojFhuFSHH88Bg9PY0M35+ieixB7665rIxHfn6A+N3TZI5UYUlATfNYnFzF0kpEplBmav8jXHHpahYuzpA0swQubNt6ktM7fRSQNEKUTBEVFdm7a9QGNOR4SP+9AenBbpjQ6ChLEkM1Jh8aZHIkD8DEeB6pBCJSnL+pm0rNxXMChBNSyVURgUL6UCXkFTee85SOP8cPD3Pv7Xu47duPPen4/r4R/vhrn+X7W7Y+6bgXhqAUW/9zHw89fJx77znM0X2DHJuc4rOPbuWOvUf58s+2MVUoP+saDvwIIoXnP9FdR0pJe0uaxmyceS1Z4rbJO15zES+/Yi4DRT+3jeKyOM78Zy8Ke2hgjKlCmYP9z7yd8Zfeecn5vGLNKt5x0bnPObaurq6urq6uru75I1LiefPzQvdbzUgpl8ucOnXqzJ/7+vrYt28fjY2N9PT08J73vIePfvSjLF26lKVLl/LRj36UeDzOTTfdBEAmk+EP//AP+fM//3OamppobGzkL/7iL1i7du2ZLj4rV67kJS95CW9961v50pe+BMDb3vY2rrvuuhdUx55feuklq9hy6iDO6AyyFmLNb6esFLgBsuqjTxcRpoFxuIpW9VGdGqZWo+esPGsvmOROdzXuFg2miwgpUVEEQuC3xTALcbSCwFvXRGGmhuFL0kcFtYRNLavT7MaZSfmYUz52MkatEICAIIqIzBoyGUeZCcQCjfwincDSsA4IrCM2nVETjfNt+vMFlBRIw0BIid4VMhWLkX5oBG2ghD1QJH0RTD2mIdsz0JggKgZEto41q6gsTxEmA6JCjmxfDYCoGmAnq5R7G6l2JOjqaeJApcD+z93FmnU93Ds4CJrgjz76bWiXfPWNv0cxX0NIDa0SMvCdBqatZiJdERoRsjvgVa85n/bmDLUfTrKzsoD4sQBjZhah62BpKC9ENBqEMUkiWURMS2QkQGoYI1VkXCfS5FzrZ11HagGXXneSkdEkR+5uxsg7uA+P8OPtUwgD3PkmQgna5qUYn6kQxjSEYyBdD6UUx90CTiZOS36G1htmOCC6kNMKPymJuSabL1lOMh0jaRnc//ODOL7CXNbN+itWszjWjr/uXFYlWrhg9WLOWfsnDA9M88P7DzKQ38srXrIRyza44Y0XsG3vray/9qdUnCx+NSJIwLGBKSYGpuk/2Iafy2BPPl6YWUKDnsCpOmSn0jRbaaquz20/X0qkBazwdb703vN45Q1nMzVe4PjxUb77jS3EWmKsOr9KRUq6kn/K+z/yGJWYRvpLOWhKonkGflni1DwWtTXz5y+6GN8LaenMAnDx5uVc+8Axju7q49xLVxOGCi8u0BRYroIoIkgaaFKxfu28M2vH9wN+ePMWpJS86LoNdC96os3wQxPf47s7H+Lyc3YzXrwL+PmZ125cvobNnd38729+C+EEoEuwdB7o7ePnx4/TFNgEkz7z2xq4+uxnfuYEFQf8gNzoE1154pbBV959I2GkaEjGnnJOuSPD1NnNjLvPHhNvzCSRuiSdeu4su9WdbXzkhvq2nrq6urq6urq6urpn8lsNpOzatYvLL7/8zJ9/WZPkTW96EzfffDN/9Vd/Ra1W453vfCe5XI7zzjuPe+65h1QqdeacT3/60+i6zmte8xpqtRovetGLuPnmm9E07cyY73znO/zpn/7pme4+N9xwA5///Of/h+7yf1ZHSwbvoRlEYW5rR7ElRahJZMlFxi0S28totQBVc5CaRC+ENFyW57xzB1jbM8FIVxNb2pYjx/MITWOuSIqP5qq5bjO2gZmLIGWzqDFPoRCjsskg3uoSlXTsqoEsB3hjhbnzQ4HmBQRNAtpNjP4IVRI0nZjFDXVwbKrHMkxYCmc0R03TEDEbIoUKPBA2wg4pXNBCdVWS9M4Ef/T3D/HgB5czOBvnwt8/h3sfOoooesgIQOKldVInXdTjVY5GTjbwHx9ciDQCFl/Vx4GkwfC3HyPuSS68cDnZgwUiTTF5ZZJaRuOkO/t4hSSFkBLtaISTsQkbTJQhWaEPMeZ/B/gYsymB3qyx8q0jzNyVIbdFJ5jXgNvWQrQ2id4XUjtiYJQtfCtEdwKKN5i09cWoyeJcf9soJHIUu7Z00rNhBLttIc5EDpQ218a5WsPuq1G6PMvQWRHavS7RrEeUiiEMHaEU+aqHsz6LMesw+tctNEmP6kIfpWu89BUbOXV8goGhWa6+Zj03/N55PHDfIVads5Ddd+zl0Vt9vM1tZBZrsBpM2+DURI6fbz+KtfME1125DkPXuPgl6zjccQu9Io1m2hy9cz7FhM1ApURrdxMiY+O0CZzWDB+/9yHaRhSeLzDHbJyKYN+BIWZHZnCGNdAUq17cg1KKk6WvE8QqlHLnovshsuLx2FfXkr68ylpzBeONg3AuWPdPIewEgaUYL9eohSH93iS5NXle1nXxmdofmi5Zt2oe/QdHGR7Jca6uUW3XSZ90CQwd/AhQyME8f/u6f+Pzd78fIQTTE0V2bjmJEIIPffZ1pNJPBC52zt5BkNZ54CMLyY0nuek/c7R0zG0nlELQnc7SkLYZVArlh+w50M9Lr1lD0jTpsVPs3T3Imu4nBycOnx7jxOAUL7t0DVJKXCFQKHJV/0nj0o+3k34613at4uTpHP7Es0f9x8dzhGWf3PizZ8UAzFar/PTEEa5dtoKWePI5x9fV1dXV1dXV1dX9rvmtBlIuu+wylHrmmr5CCD784Q/z4Q9/+BnH2LbN5z73OT73uc8945jGxka+/e1v/3em+v+Er+7axbd6d1M9xyBzUMPtiiM8FzSDyHWIkjb2YI2gOUnQkyU1m+eaNw/jBS6PfHEeuWt19h7oQc9VEa4PmgQvAgTWrEvQkibM2vjNcVKGQNohuuUQT+hoMR8VlbGmMoTlgChSyCBEKVB6iFjZiLHdRYQ+0ckAK1fAMiwq52XQSx49PS0UNkTMRnHiB6dITEVgJqgM1RBFnSAlUJokjAk0F2oFi2tfuoEdR8bmuq4IiV70EaUyYdJGiyVBlhBSEu9qwg0UygtpXlxEG3cJUhpaVWPdmm4euX0veBHxXoVegYvnzedrnkNUcxHZzFyGga4jFYSWwfTxZlqvnStiKgoa82OzZFa5VA2P/K4kUcqi1uxjdZrYsx61Fptag0lsuAT/rJG+E0r9EdLUERUHdIkYGGPmVMDM/QZLznapbl7I0O4hiJswM7dFKDES4q8TzLu8jdPVWYKcwj4+V0/D7quQvjBBMOyD0PGkwj4+CXGbpes66Ds0TuQEiEix+9HjHHj4BAd+cQjh+JhxjcHzKxxUh0j1lzmXjQwWCpQzCkdFfOozd/GOP7ycbDbO3cfbaW6bz6ubDmCs8nmkdy2OiJgczdP26kk6UgGHDy7mJw8f4jKrBd8NIFCEQnL65DiB45MZc0g2FDjr3LUEqspkdRsKxWRxMUY6hutITh+O095jsvGyPPGcQtWgsiJLo9S4rKMdY2k7k5Ml3KUex4uDHEsNsijZeWYtXPSiVZimwbqzF2AaGl6jQCLB0ObaUJdraCWHcuqJFuftXQ28+i0XY9r6k4IoAFljPqOTFdTDGsqHXfce5Jo3XvKkMSvPns++radQhmT77DhvyF7EGzZt4M57DrL74dPotYg/eduLzoz/k0/eiuMHVGsur7v2HHrWdnPyxCT/JQb8nNZ0tLPYamJla8uzjrMabMJmnXhP4jmv+aH77+HB2VM8NtHPl66+8VefTF1dXV1dXV1d3W+VUgL1PNhW83yYw2/a87bYbN2v757DJxiLisSX2wTzUuQubsU0DRruGIWYhapVEESEto6KmcQWtzCuFEd/FlIZlBR/KFD2DKKYIIoZBHqEERrIWIzINDH7JxEVF1WKE7siTeBoaH4Fc0hHZXWCAoSeQugW+KDUXBBGKA2r14SgBlGEch2EF4DUkJHAHK8yUOrjvFet48Shw3idCeInhkFIgiWtxCdDpJj7SRyv8OnPXEuyVOCBh49SzFhEGvhZSfpYESUFxWSI2xYn1i8gDDAFNFw5nzFvmNNTPSQfc2hrTnP9+y5huq9Mrc3GtwVaUZCqKF77ss9hRwp0HeX5RHYCK2HijuURpRLxRYoGM4FSiq7V7RybdJjNJJkZzJDVa8hIIAyTfKGMPF0miidQcY0gLnG/BbpjIPMKzY8QloVCISo1VBCA6zN4cAgn5yJ0E1kLCJsyaLky2piO8eMYzRe2czJVITb9eC0NpRCRRL8gRSI2QOFYK/rIGCJSIAQf+8KdnNfaSbXkkJsp8eCWk2BIRClAKkXkRfS0JLEbfQ4WTrHl2xNMVaoIP4BKyLCRY3yiQDYbJ9ruM74hSymI0Xe6kUhXJDWdxas6Kd2bojxiY4SKcKZCY2uWV75xM7c/cIh03OLFL16LGwV8vnQnQVbyH/dt5/xF13PLzZsZO5ynPTmJE4SEUiJsk8yqXoZnv8W7ru3m379zFU6rRSlm8op3XMmipe0IIdiT/wWliWmWJW1qns/DJ/pY1dlKd2OWq27YcGZtpLI2FKdQDWlwA3Q3wkyZnHPJ8jOddsrFGg/9eDeNrWk2nLvoScViD86WuWBlP9tFlkhI5NM0dbv2Vefw9Vsewc3qHM8cP3P89NA0E9NFhsbyZ44ppTCVxFWCzOMZJ5EfIsIII3yiRkoQRvzzbQ8xHI3y+ks3cX7byie9p+0LXrtoOedduPQZnwtHT43xs7sO4Glg8NxRmrUtHezID7K2ueM5x9bV1dXV1dXV1dX9LqoHUl5A5EyINAWZQwY0NBPHJ66XsBM6rtIQjgNCguOi95UZXZ1iZut8oIxq85hY0YORc7Esg6AjjZ+U+K5CcyICE+K9HiKI0CoB86NWjn5yP3KRhr7LxBk1kEMSJSsgQkQYEsXjBE1z7VN03wNdI9oM4QVp9B/EMIZ1vJiFqkZoJybo/8YuMrUC1lgZNA2EQGvKEHkRhi+JfI0onaCwOIYKIqLhKn5YQ2V1hKujexAkNJSpU+s0iAPC0MmPzrDhkos4eEeVcETQVMjRnJRctXwRf/WvNyPckNrCBIkBD70coPy5bUFC14hMHaRC4iMmptGA8RONfGjbfNYM3sM1r7uAA5+fIj/Uij0dEuIgah61BpNQKKK4IHmqQGWBhYgUYkYwcZlJoi+gaRIQAiE1xLxmlndl6e+dJDAthKEjax4ISbbNI9ZcYybfgOyb4XjVIRkJzMEySoKKxyFu4++FRmXOtQSWGlFcJ0pYiMEyQRtYmRjThQqRJkEa0JBAVmo0bWjjq69/FwcLp/jsO+5k+PAxhKnRtbwRK5Ngw5puFi1swfcD1N0TeN/x+Pf2K6g2WahF4OpzRVELAwmcrIZwfS7yU1x60Rr2TuQpVFwcFZFOx5is5ElmPCwZ0WLNUKw4TA77uFVomx9n8hTEWtIsvijLZQvvp6YUVnMFPyFQAqo1j/e862tsXLuQpqYU576tQFesiqemeeRkP19+ZCdru9r48A1XnlkXSileu3I1PwkG8WWIsASRrxPma+x4+BhBEKLrGsVchdmpEsVclcCPMK25QMrhyXEePthEd1uElSjjVTRSTamnrL+ffH8butCgFrB00ciZ44MTs/hhiOsHZ+ZTKzv87TtewoFTo1xx/lzdlKVru+k/MYEWM860Z/eCgGMzowwtOMg/Hj3GF5N/RlfiicKyP/j2Y5w4MoomJRdf8eQgyy+NjBeoVGrIok+QcJ92zH/1tvPO423nnfec4+rq6urq6urq6p5f1OM/v23Phzn8ptUDKS8QJc+lvT1NtjqDWfEJgHNXnSTTU2FwuIPJrXNbVWhKEsxvxjw+hrVzAsOfAt3A7WlA6ZIoaaDnFDIM0MuCMGMiXA/b0fCXzQO/RpixCGcEIoKwLKg02DCgSPUXiQxBYWWGMPSJykWc1Y1gaMgriti3BDidEidhkWqPECdddFMQej7kikzNFoiZJiKThrAMmos2Oo2wYziLEtTaNIJYFs1ReA0GEYDv43aaJAc8QhUQSg0Z6bgxQEUoJ0RlU2z98i6MjIEhTPRA4YU1HhvdyZIVbQzsKSHLHvgBfspCmwGCkCiKoDmLEJLaSGEuc0HTCE9WsbQEU+UK5rwaaVVGO6hBGBHGYPJFCnOqQuqkRWzahDU2ufNC4qd1nNY4QVwgFASZOEZ+riCu25LkeDaGPHchV65bxLYTpyjd248Q0NmmGNmpg+ahKUEw6SKrJYQvQAiUbYAmiG11GLPmEVVHkEFI0NNGlLDQ8x7FYoVS0cGpeEQqAiIMzwcBM7LCYHWGOz66h8FjUxA3IIiILmhkqFDhwR1HCbtdZu/N4U/kIQgJq2UyjQJrSKdzQQO+F1Cu+dQaNXpO1vjUj/8GgKPf3oKyJbqUKBXQ4P8v/nr5Tgqu5P6HLuBjX7mHzgVNXPXKzRy+9xh+yWXpkjaOpRUjO1/M3121kpt/tJsgpuE2gIgUFddlz6FBNq3uYaV1LS1qFUvS60l1VFjX1c7FSxc8aW3M5Cs8uOcUKhlDmTqRJkhcVCbYZtPW3k3gzwVSuha08LYPXk88aWNaTzwahZKUcimOjsRY7AwgbJ2ZidKZ14fzBR442UeiNYkZRTQ0VXhd6418b/t+rlm7nGXzmjlhDLGgNQvAnV9/kPu+9yinNrWR8z2W9LRw9XkreOXrL6R3cPZJ24rilsmfX3c5HzzWjy40DO2J1sgA525eghCwZEX7Mz4bztuwgPGtRbQjBkd4aiv4urq6urq6urq6urpfTz2Q8gKxfXSY/foE0VBAKR4SrzjkdyYIZgRhroWYniffGCPqyqLlq2gVDxWAUIooDBFODTQTO+8jVRyBQIQRsuohQwEaCMMgimnonmJ8pEhiwSJmEhDMBytdw4ssQl9imwphWIhCAX04h0xZBFGI6K9Q+L12HMuGhioNuoYYKKNPFpGmBZpEpRKQSiBiNtHwKPpokSjKEc/H8bLdmDkPLdCgViUyNMx8gLA1EjMuMoQwoWMUAqodgkgTaMJAJGKEA2XMJTr+umYum7+a3YOHeXB8N9e/+koe/dlB7HFBeUkGP6VhhBppU+I4LlG5CrqBcAOEbYFSaIGiwa1x/mqb01uLbHpxjh236biTAq9Ng1+2Xe6tIhMpelLN1B48RfyQQzS/TJQvoY3q0NAAvg9SoPSIchQS+SG7e8eYcSNMQAURQ9ss3MYUQSjR8hWwJGQSBGGEMA1kwUHOlnAKFayFcZzQxdI1tOkyygsot8aYnCjiFl1+eusOrLwzt50ojBCBIpxwufXzj/HoA8eINNCqHkoKSlsnuPz6tRyXx9gljhMfl4SWidBCDKHTdcUow3vmI4Xg9L5+EkcLBGmBarD4zmN7ufGctcyf30SbZdGZTfO972/j4g0O8xsj9k8rzl6zmV2HPQxd46pLVuGOlti7vRdj3RQ9y3ewfXIxB6eWM5U/AZYgiAEKqg06ri3pC6r84bv+g0xN40Mf62TRik4+dMNcDZLJySL3PXCEs89ayKKFLSxZ3Mr2nROYBQ+WhMQSLm6Xwfv+/hXYMfPMOlq8qutJ62o6Xyau6SQtkzCnEBmFeanHiD7O2NAsLe0Z7jp6kh8eOMwlyQ5Wrp1HrO1RPv6z+yhUF5G2LVYsaOPRdJzWx7NYyvkKURhx2iwTxOHA6BhXs4KFi1t5259dRVNT8sx2I4Celiw9o21oCNLGk2u3bL5kOZsvefbuY1sP96M1OdS6TLSm585IOTI2yZbeAW5Yu4L29FMzb+rq6urq6urq6up+19UDKS8QZ7V30jpgMlQQSEciShUm79EZb+qiZWEWaVfRxmbQxwoEbRmc+Uk0x0Ur6UhL4q2zcNdqSBFgHBVII8K6tEx0xCA8paFQEJWxigGBrVOwbJhykIYksUGiL1Tkk3FKToI18/sIjwTUCgLrxARNiw0mHu6GQKAQoARC1xGGQrjBXEFbKZDZFEFjChEy1y0oEkSECMCcrtHyvaPIZhBtCcxpnVBIvFXzWHFlH6LoM3x7I9KNsIemMfKSMCYRsy5UHeZfsIj9TSVCB/afGGS8NcvMDzTsJf0Qm2sJK2suzovBV/CaCy7lrtt2oyYqIDyQEiElUbWG8n12HJhgR2+OL7/xJST2vZHmayfZlrqL4l021g/ndiapzkZwQFUjUvsconINzYvQGyDyI2TRgTAEL8SedTHiSRzXYzY/S9OKRvJhgCxXqZrQsk5jejCBclxUtYK3aQGaB1rBRQoXoWuEfsACK0G14oNloeVrFBenUZ1JioMukQUHOlyyQxpmda7tsopBTMb58a4TyIyONVMFzUQoRTBWpPfOg6xbnGVh+2Lu2vEzKFagMYuKm5ijHt0XjfL6za9lwZpudFNDaDDTYvOfj+5nUVOKL43vYvYCm0XTMe79xTGS8ZtIrt1OprnM/J5dXH3hX3L3oRN8d/t+FnZk6FrSyqpzHkLL9OOGSc7rCgltSRSBUQWFQAsESEnfTAFjukIhDPn4e77DR770Ftp7mvjhzkNseeA40ydmKZUcli5p4y3XnMPDR07gTZlYlSrVB9LImknN8Z9xTY1MFfjYN+9F6hLpgUxa2Js0tEt99g2c4OB7JrjwqtVcduMG3CDg8oULuL3vECqdw63No+iU2HtoCG/SYbBQZMfhfm7iAq79wyvoXt3Dqd59TFYqXL5prr7J6GSBL/1oK7ap84n3vwJd+2WNFoUldTQheZba3M9ofLZEV+s0p2sWTS2F5xz/syPHeehkH42JGDduWPPrv2FdXV1dXV1dXd1vheLx71vPg3m80MnnHlL3/4IGO0bmmEfDYY/G0RCBJHIdlGkymatQcX2E6xOmDCZu6GL6xV04DSbRdB6qAUvMSbqOjtGwcxLd8xBrQswNHsY8B+kEyNFpzP4CIlfDmHHBD1EyQsYCxGMh4YCFOGUSCYGwBWs+M4OKWbhr5jEWa4UtI6gwIv6Aj3FQEduto1SI9AOEbqDSSVQ6MVfDRc59UUbMFUtFCJCgbANjZSupJoWeTWItbMORktCTJOY5mKKCdHyUpqHQkNUQoggmZyhVK3jNBlEINCdpbs0Ss23mdTUhvBDhhciEePwtBVf98eVULpiPME2EbRGGAZHnn5lb7PA0sqTIzk/z+jddTG/hBOWfxFCDEhubjgXwihu2Yccd5s1vJLOph8qqZpwr0xgrOpFNzWCbqCBEBSFaNSAegu4oQj/kr266ArczSWTouO1JpsZM0mqWmKUT6hrSCRF+NPd70nWiZBwRsxChhlKKyPcJZIQVGmT3zKIXXMpLbOIjMHteBhXXIfBhcha9UsVNSPyMTqjrc9lIQmDMeszsy7H3a/vZFMSQgIhClOvSsDJH9+UzFPMJDF1gxy2UFic2pGMfnKW4a5QP7vwmRyunmdKKnH/pcq69Zh2bN69jorqasWozR/pXkkza3Hv0NPcfOY2RMZDTs2z9yRJ+8J3LODWcZTT3B7zqkt0oC2QNtBoIP8Iar9KTTpDZ2IwSJgMjeW754v1UPZ9bHznAvv4xvLTG5Zet5OZTj3LHyF6smEFoCayChcobpJXGeP/UU9bS7GSRL//jT9nz8HGkkNimjt4eIdt9iicjZsZt8nsCxo6NcnT7aZY0N/Gui88nVoW+AwHHt3SxfsUpktkqh46NsvPoIFEpoO/U3Hvd9vVHuPVrj3B+ooXrV69gedtcxx2lFEdPjrH34CADgzNn5pMxE3x43e/xobWvIaab9A5MMTFV/JWfDT2tWcr9baxaPsRbN136nONvWLOSGzeu4ZLFC37l96irq6urq6urq6v7XVIPpLyApFsS1CxJzQ1R6ThKRQQJDT+hU05L0CRhTEdEEfbiMtpFAqVDaOhUTzg0bJtGHwpQhsQdsigdyBAclYgoQmkh+AH4/lx3mekyqjtN2x9VUBtAdYaYiYBUr2Dgvvkc2HcWoq2JsKuBoCOL5kVIN8BPW6hQJzAV/oJGwrQFtoHyfYJiCYRCCcDzEV3NREvnEcVMlK2RfnMDgZ/ALSWJSjNUeifRyy6n71rA4S8upDwTR8dAxGNoBW8usyUIIB5j9mSR1uE8L3vlQ1xxieLf3vtq3v/7l/IHr7uIqD1BkLUpLrIoVWzUPpvccJliLCKM6YQJE0kAnjfXBlnXaTo9y+LEIDsfPskbL/5fHP7GBP6gQVQSICK8isHYiUYiX3B6Ikd7Nkm4MkGw3sadChBSEKUSqJhJlDa54q0X4NZ80AShgsgPqC1vJHflEkqrmwgjk0Jexw0UiXlJxGQOOTKNcHxAAILI9RntG0VaFrQ1I1ubscYryFwFPVcme8olMVQlORQgaiHK86BSpdY/RbwSERupElrGXDaQUmgqQuiCTGeV/uKXqBQr2EmbytkdTMzrYCabpFCN8c0fbAMgIyVCRQhTRxkaL1m2gzeu3krH/gK3/WQvl162kpaWNGvMzbQVO+iK9RA3Dd52+bm8/oINfOsffsb+vhkObPMZ722mOp4AVWVDzzCYCms2wChHWMUIK+dxzbqlrKw2YFckCEm16pGwTG66aAONmQSrVnTS1pPmoYnjHC2McvXq5XQaKaQPcrpEYTjHZz7wn3O1cP6L3mNjHNrVx5FHT/Hxd17Hu2+6mFAPCcdDCj1xBu02hls0WjqyrN+85Mx5PYtbec0brmD6cp2j6UY2r1vK71+1ibMXdaEB8+c1AtDYmkJqgv3FGQ6PTtA3nQMgZhnUCKmKkAcePfbktW3ESBo2QyOzfPYr9/PPX7gH3w9/pefCBesW0t29gsnCJmJy4ZnjSin6KqeYcMaeNH55WzNvPm8Trankr3T9urq6urq6urq6ut819a09LyAH4hVqbTrJUgAhoKA4TyfKmlgkiY/U0J2I+KFRjEU6slXR8PoFTJ6sQkknv2QxUYcOoY2RDygeS5CaCtGtCGXqqJxCheFc/YYoIhiskF3tMLYzAc0eUV5h9xeIj5dxG2JMn9tIrCTRKxECAUKRKBRQKxVGaQZ9vEiYMFBFF1FxEUQIW0dUQkQQopY2EekmTS+vEW/wGH8gi5aOiFXK5IsS9IBA+uSW2aROxZBZG3sSZNXDylcIjbmPtzAMhKZhTznMi+Wpmif4yde3MDq9g6HZebhpHQzB2QsG2XzWIe7PbWRgeJqGqQiScUCgpIlKakQNCbSpMjiK1e29fP2zD2OUFMqO4UoD0yoiEgHFYpLdDy8mCBxmakMMZgy0y3yMJR7R1aB+EAdDI+hpxuuUNFwzj5bv72Ts0BBRWwOPHu4ndSiP25ZA5UOiwMWoKag4eEMh/so25Olp7JRNiINSCqVLin6IqlQQMQvVkCC0dbQykDTRauFcS+Rw7p8CCek0iy9YRH/JI+/6yNCYy+IJI6JOgchXELEE6QaJUy0TeAFMV0gKyTnZfkYa2lneNtcm9w3vXsk9xx5gcGYJ3S3duIHNzn9twjVs+gsTnBwcp7UpxaLGA/SkCsSzc/U6Llq2AIAtS3Zw3HMw/JA15zRx0YvPYnnzOXz42zuJJGQPFhCGhFoApsHdd+1nKi5QHTFevGoFb3n3VYyO5Xls6wnGzDI9SY+0GeNdK66gFvhceMFiDi8e5uYvP8iYHzIzWsQPQqIwelKr43XnLeLGt17K/KVtxG2TQ8MTVI9HCF9ipxrRCjpeUSC7mqhpTzxCNU1y1fVnM7YN7jpxhDdfeTmrO9vZtLSLTEeKm649G4BrX7uZq151DvcePsXATJ6VHXMZKamkjRYoQiLU/xHc+aVUyiaVtGluTKLrv1ocvFRxaRg1aWpp58ruJwI/484oPxr+PrrQedeSP0eX9b8O6urq6urq6ur+XxYpQaR++9tqng9z+E2rZ6S8gFx7+WoWXtLF6pWtyDBEZBJI20T3BCKIwNDQqxFaPqCYT6IeDpmcXyO8UMdt6MCL20wvTuAssKh0aVQvDAibdRAaovj4YhBi7kuelMiqRzasYt7pov/vCtoxn3hfHmPaw9rh4rVaBHGBpgGJBKK5EbpMjEYf1W4QhQGy7KO5ATKVwFneitZVQxgaJGyCvhlwfPxQ4ccUkScJsjraBTaapSGkxGm30JwQe6yGNeSSW2IRKJ9Is+a2qFgWQpPgeNRUgofv3cA997Wxxetn6XVbaV9/L+lMCYWgKzFFq1GiPTPLN/7uNrS9Y4jpPEzMghAEnU14C+eybJAauz7biQIiTaBiBpEmidwYatokdBQ4IWYtxKpG6NMO2USZQGi4ayQyAOkFIAQL2xu5qnUJ40eGEX6AzJe59cdbMUoeyYEyqcES+kSFX361DlpSSMNApeOEVQ8VAUgiTSOSQFMDNKYItBBZ9ghb0iTaG6i2GBQWmtS6LGQYIGouCJjqm8YbnWV6YwLNCRAVB1F18HSgCMWZJOvnfYxAgdJ1EqdzFA7oPPDvixkOm+joyQKwpGsZM1+3sR+Y5RWrlrG0xcQ5btO6bIJUa4H2jrlaNFurjWx3mhj240/6/J531kKytsHFL1nD5Moktw9Ns2tqIwOigyAlkFGELHsoUwMJvVmFW/bwqy5hi8WbPvAt3v/RH3Jo1xBUQtqUDcDZTQu4sHUxH/7OHXzuJ4/w3r+9ns4LFlJe3kBRk/QeHT0zhzAI0TTJJdeuZ/7SuU44P91+CBVJQh0SJ8vEBwTWlE9bZ5bFy57cLcfzAq5avoyZUy5/+427cYOAf/rWfXx/10E++d37zowLwpA7HjjEnt0D9I/OAvDY/UeJBYJkoNGzYC64kiuUn3T9bDrO/3r/DfzpW694UkHaZzMxU6Iy5cJARNKwzhxPG1kazSbmJxaiCe1XulZdXV1dXV1dXV1dXT0j5QVlaZSlUisjTJdjwqXjgzVaGvo4ee8i9P4ZolwZTAO/JY3vGAQVA/vnJbSmNLnAYGqhjuZE1KouugkyigjiIXYQIfQYtFqIcoWoVoNaDd3UGNvRjruiE8bzxPtziAhUEOKfcEnFa4DEGKlAEILnE3ssg8wbiFwjpQvjNFZBDORQUUTGm8QoO1SNJaBAL9WIpksUHm6klnOJbSzCAgNrlYuWtgl9HXs0QBk6wlfUuuKkjxYxch5CSFQUglJYdoBuVvBSHif1RvSyRmJJM056HfOaWwmPFjA1j213raB3tI2Rrc3YfhHpRyg/RKaSqFIFUQuQFR9ZqIHnEXqKuFODJoN4tkLxgAa6jdIMZBCC1CBm8bp3XsXXTtzJwg0TlKSNHfn0q4XgR+i+x+pYM8pRYFkQRqDpaLM+KgqRtRBiFpFuoFkmkaahoaNVXPR8Ddwysr0JpRQyiBC5CpEukZHELkWEMQNZDVBUUQsS1Dp0jFJA6PjIKEIFAeWRaQIvILzURjweJEOTpM7vQQz0IyOIlEKY+lxtHD8gdmCCvr4stcUm82IxAj/ga//0Yzzfw8zBpkWd3Hd0OcFsEf8+D0v6LG7vAeDIbIYhr4n5DQkAdu0fIF+sMtI7QehEFMfKrLt8HglD0RN7gDWZMfZNNhHGdGRchyBASag0QPJICZTijrsO4rfEyE/leOm6ZWxa2clVL92AUor7hk9y/4kTPLL3GGpQ8Ff/64cMiSq1dh1DxUhl5jrhzM6U+cJHbse0DN77D6/EtOZaDXfGkxiah3AD4oM5Er0FzrlsBR/+2KuftP52PnaS73/9EVZcvoSq6+N4AVXHJ6V0dEeRCOcetz/5jwfYcsceCptamXFqeNHcFp2RwWlSloHVlmDF0nb+/B9vZdf2XjaetYA3/ekqnNDj/KaNv3IA5ZdKnsukXyOVSVB1feKP35cfedTCMnZoPscV6urq6urq6urq6ur+q3og5QXk37//CKOpCl2GjijlkaaHpWnESjUMR6HNb0A22jQO5ogNO5hRQHxhA97uAG+DRPoR1phH+pSL257GmlX4jRIlQoSmocIId2kbxslR8ALoTFEsBojH65/IsoOyTYKsjXB8rAMjaOlGcNy52ieeBy7ogwa+pSGlhu8r/BVt2N0xgnvzMNkIhRxCKZAacqoALSkmz29A2hFLuwYp9ybxG7MEYQRxi3Sfj640hBDIUEEmgUIRRg6yUqOrZ4qJiWa6jCL5iQayRcnbzj+PdR03sOvnh7BqPyV0ylSbE/Tumwe6wlQQpeIQuKjJGRACtyOLOVNBTuZAatjpkI7leeZfP8rEwzH8w1ki08WbEChNQjqFSNnMW9WJkdOQRkSLUcav6CghkJUaSiniGZuO1jQgENkMxC0sR0HNRYURkalDPIbXaGO5IbISYE7MINy5bSl2ysCddRC6BpoOng+RAtNAuCFYgqaGOFN+9HhHpLn5hdUaQila1vcwcHqGhp0eQcrAiBR2q85EYYZ0sYYfVdl+zwEWb+imf3cfejZONeYQXaKj4zNyaIxTloXbMszivysTjKyge0k7J34wF6ipndb5ZbuZ0YFpHv3gLG5C48Mz38PLfZ/4yjaS3Y287Iq1tA7OEmtIsql9AjPaglQneOPKkMN7roFKFRlpRGFA1NZIfDQgTJlINyJMa4QxgQgEKy+8i77BIkf2fYh567r4/IHH6BufwYgFWJHO0EgOZ76OUoJVly+iY34LhUKVj37iDvoHp1nemiYIIszHkzdqcdDjAWFzSP7yFOmy5ANf+qOnrL9CrkoYRGRCjffdeAnzGjM0JGNcdcEKcmMlrr14NQAz43mcqsewUcG1FBPlMqsBd7ZMrlbDKkn6xmc5eWqCUMCJ4VF+MNCPpkkWJObREWv9tZ4L+bLDlF1kyp/hoZOLuWbNKgDcqIavfIp+Ya4d9u9AdfW6urq6urq6uhc0Ndch9bfu+TCH37D61p4XkE0XzydYJJlYXiTW7DD1WRj7SgbRV0QGEXqPhAaFtzGLn7YIdR39iiwqadM4WGXJnlHadk5iHJ1AL8wSzQ/x1wrczjhR6KKcGsJxcVZ04a+dj1gTZ96LRhCVCQKvQhD4+IGDqlbwTSie1UYtFaBmcnNBASkQUqDlyhhTJcoLJZObTEQtwuz3yKyIYSUj0DVUzIYgQOga2lQRJSVhpDFyqI3ggTYa4jaaH81lwFg6kWUSH3MIpEL4ASIIyV3ahdtlMdqfxisLRk8340+lyXUafOWRnfxo1yFmp8tz2RwI/C4IbY0woaO3JBB+QNeFNi//k0a8yxdSWd1EaWMTaBKFopRM0P+AwSgJ3EsMEitC7G5FpElIKLAjgmUV7qg8ivYTg8nv6dR+FiIqNpGKUNW53+fKpe0MnZyY+5fouERJG2IWLUubiFyXiBCVz+NnDILkXIthljSjGtJE81qo+XPFYaNUDNWUgShEeD6aUmhCIKouDdkY1kSN5h0FrJkAGTfRWhqhpZGhvf2IfJHESBEzChGWjq/7eB2K6YtS4HqUig6fuPXPec3330zpbT2ENZ1wi8S8vcypiTwLVs9jxfkzLMrmSPYVmRrJsXrVZURJid8swZh71AR+QKY1JMgLZvscKkWHmT3DXHTOEo5vOcH07n5EIcfJgVupBI/R63r0Hl5HkAipzNMRudLjWTuQzik0YdDY1UTs8VbBsZSG0badzo2HmcyfoNGK8YpFazi7YR7VFg1zpkxqskJy0CM+HmDMenPzCubqpCxY1s67/u5lxBNPbIEZsYuIThet1ad6RRvaTUuIJZ94PQgj+qZm2XDeIpas7+aAO8utR47gibnNWHffc4DBI6M88uhxAJq6GomkRms8TmdjmsXtTQBsu+8wsncWZ7pKb+8k8YSFb0tyOowfs1gZW8HUVED/5Oyv9Vy48qylrDg3hTU/oBKvnDneZnfy2u63cFPPHyLFs/9VcPL4GB95//cplau/1nvX1dXV1dXV1dXVvRDVM1JeQD7w6ivR79U5eeoI6dcFjP8ogXs8wIoiVMUlOFlDLkkg98cRbRZ22sa+I6SqFCidLjVAX60VESmMUR9zqwk1fa6YarEKxTJ6voiRShAsaifI6Yx9SqIfKaBLiZ/UCZoS2MNFpISw1SSKNKJYDlkLkEKgqg6YJkLMtRwWKsK3oey5xN2Q2qSHqvgIJVBSIiwDNInbqpBtHo2t0+jw/7F331FyX+d9/9/33m+dvn2x6JUA2MDem3ovlmPLtmxZiR3HLU6cxEnsX+zEjn+J4jiKZceJZMlSrN67RIkiKXaRBAgSvS2AxS62zu70mW+79/7+GISKfhGdxE0KM69z9hyenYLZxX7vYh4+z+eh8eQwwoLTTrGuhDTrB9WmKSLJQIDTE7Tu2Y3/wDxKKXqOR1oR2ESgteZr9x5iS5rDmyzRrfXwFg1JpYe3qKCRIg1EnTGqcpIsWENogUVhijmkTFGLMfH2AofTzSjfIFwPdcKgx6D1DkXSdPiHdzyI7z/E4R03kTxiqTouO2+pkY3swmn3wFj+3e99njFHICVYDbLWxhZCJrcOs9ztIRcMAoPqrUMAdltGsMkSz/dXNRss1pegBLiXVkcDuYJPe60FvZjqch3VzRAIgvkunuOSFhQizbDZpe0vq00YK2Mzi60KyqJH0/XwhsrsuGoz+VKOsZ1jNB6MKCmFdny6l41RHC8S5ANu2fF27rv3y6yeyPFfP/wojQ3naP5UEV1UjD3o0u3EnBTf4uANBQp39vB+O4FUowUw5fH0H5wgafQ4dGQFcfoabnpTyMtfU2bpwBYK8yuIREK5gA0UWaCwEtxEcPl1mzn6qYfp7B3hpmsv41R1HWkGu9NRhBC8fc/13Di5gZ984BR2TBMkOVraIjXMVxsAjIwU+Ge/9locJSmVwuevqf0PHuP4158jGg/xCxmv3bmbl23b9V3jNZ995gifO3iMG8IxTh+ZI/It3FhCXrrP3m2TXDy+xO4t/U4SP/QJCgH+kmEx7nLqzDKbR4Yohy5Oq0vX5PkvDz9JoSkwniDThnB1C+ujvfziB76AIyVf+o2fphD4nD2xwGq1yQ23X/aC50IUZ4zOl6lM5Lh7/e7vum08mHyBR323X/jxPwalePShE9z37d/6X3rMwMDAwMDAwMDA3yxj+x/fbz8Ir+Gv26CQ8iISOC5BVRKdzTNzcBvdERe7uT9WUflqGzsfo5cj1KiLn2Xc9qabaV5ssbpynnt+5BiXXXOR+7/k8dyjW6Hdpfj5JbKRAkK4iG4MysFmGSLJUK0Y0YjoFkOw/f+r72hJ68pxvFSTD0PC5YywCgQhdJsgJFZAWlS4sy0mPjPTL6wUcnRv3EL3oiXLQhjy+x0p7Q5RxYFKEa9uCasCVYSs6NIrerhrPVS9C8YilUJnGcKxWClACGRm8ZdjhBWgJM5aDKGBtqVY9LhQXWam3WPkVRs5e3iGysEOfq+L9RWJtv1g3XSNk8ealLo5dOqjzi0hOlF/TMkKRDciiFxsJmkvSsLlKiJwqKsNiClDS/k4VlPbk2dys0e50mMt6HcgJBMlRJJR2VojnW+x7c1XcfZzpxHdBOH5nD23igjD/pYdx8FrxThrHexim6FAM78mAQE7N/bHq1pxf0WzUti8T3V7iJMlyJzPylIbfA/iBCFd7FCAXGpAakiNQQhA9xf2SEBISBoebitjfLKEujSa86qNl/GhhSFqhVn8q7swY3jqWx7RO+6hkF3J7JcPsrzU4IF7D+H+WII6m5JeKTGRxnEUF+pr9Lo+NlEMe02s76Kt5n3f/DYvu34rB7spsQZdtXSn72H+/i0snn+MwnKblT0B4ekmSb5AUlL0RgQ5XJaSNsuuoDcieHJ+lo2bN2CNwTtzFl57EwDr8iXGbInanS7RxSJyfx2pLbMLDVYW6lRGi3zgT75FLufxc7/wMqTsF0Ee+OzTNBcjhCyhFwJuvnYzL9u8gyzVnHjuApt3TFAIfJQQ7Nwzyc5Sha3bxlm3bYSRfD9M9+0/fSdveP11DI30M2Fe+dabufGll/PDv/Nh2t2YA6fmePlNlzFx3Tjddo10OEeiLFuCkF69zTo/4O1X7uHdn3+MzkoPr+KihCCKEn75je9CZ5p/9B/fxstff833PBce23+GZ++9iDKW5MoU1n/ntpnOBQIVMBH8+eNCOuf3i0fZ/9rK5YGBgYGBgYGBgYEXs0Eh5UXmDbddwWMfOYDT8fHiLmu7KwgDpaKLk2qstVDv4Nfh1OwasaPIRhPqFQ93LCOqQlr2CdZa2NRgjUW0utg0BWvROQcxlMcxEiGg5W8gvVPirnQxWUT4zByqBUkvofR0DcIAKRRGSJCC9tWT2MDFOiCaCU6UQSeheGSJ/GszlvUo3kraf0ffifCSlCwsU5yx+DmNWPBI2hJZ7/TzP5otsnKIdV1kkuGmFntpLMOb7xDMNcCC1RFOXVN6dpl00xiHFpYojLvctG8bX6pPEw9LTCiQscRqga2EsNpAZHXCaxVut0378VFULYY0g5yLsBLfzRi1MUZIerk8Wd5BCo/cEUNyi+ZdZ+5i/IAiazpUr9bkblzBxGM4az2sBD3sse0VNXbvnObTf7+M8R1M4KJExujWcbqLNTBOvwiiJVYoZKpoNjbjTLRJjQFjkEmKiBLAYgAVxYhTCdbzsQpCT5HVOv2iUpQRV1u4rR4GAZ6DTTNEoYBIDMIY8kN5zjoe4WUC2YNunPKHv/JBLpycJ4giSrpObialfF1C88uKez/3DPd98RlWVzs0d5ex20LcQxnhk5qspWiPhaSZ5s4tV/NB914KxQZaBv0NQXmXO8af4KV33c2JZ0PavRSnpCnec5bH713GGy6iS6B0B3opphCSlBXWtciKR22+AfkQ4wmWTEb2yAbGF1dZf8fE89dFqFw6xyBpOnRm2lRvdPGXDSOn24R5nyPH5rj/wBkE8CNvvYWR0QIAb/2VV/Ktf/9x5IYVVOaw2QpOHjjLs/tn+Oz7H+aqm7fzG+9+G3ft2kre9+D6//GafPboHF/8xnO8+VX7uGrvBoQQDI+XuOOabew/Mcvrbt2LtZql5WeJtk6AlggLb3/TzXz53mfZHvh86N9+hXO2i5rIo9J+ka+52iZbWgXg83/2yAsWUq7cNcV4OUdnoc0f/Yev8zvv/FGUI1mKlvnohU8ikfzDy34ZT/ZDaJdabU6vrHLj5g14qr/NRwjAQuYNpkEHBgYGBgYGBgYGBv8qfpHZvnEUz/aDRoO2YviRGqP7e3gyh3D7b5QQor+2t50StWNM3aFhcjx1ZhNL9TKq3sHmfBgu43V1f/vNpf6s9nXrqF9bhm6PMGwQjjTQeZd0yKe73sdvWUSUoRyNjBNIEmyv3ylhHAlef/zEFizuSgtbzIHrUDaGbhAiNvmoXbq/clhrtLT94oILXBaRXJWgxzvI6Tlso028sUK0bYis6ICU6HKO/p5ggdvVCGP7IaxGg+8jexo/VqRnu2QzHc6fWUA9sELlSA+rDMmmPKboYToRstamc9YyPNWiWQ2Jxj26V0/2i1HdFFwP3fSo/uk41T+ZoJfP07ptK7JcwMlA1x3cRUHXV3g1SM6HHJqZYoO4BikEyoJFMHNqhGOH70bXBfH2MZJNw2QCVm7MY7K0nxkT9egMJ3R35NFXb6YWeFDwENYiuwkizcBabJQgHdXf7pMYZNQPnnVciZUKm/NAScRaC3wPU84jK0WElP2NPYBNNY6UjMU+eT+goy3tepcjj5/k9IGzrF5YI3+9y663dVmpjhE1emzYPEI7EHQ2DxFvLrBu5yhTI0WsI9FDDqXL60iny7ePfwhxOiA+MIRodhHGUvCavOTl36az/t/TajTQRiNKPp2VIqOFsf4GpoZBEqDKOYTv4SSWtGiRw4qbX3IZ7nwLp5GCkLSSEOM4PPz1Y89fF7WkgyobZGjpbZdkriF/ukFFBKSJZvu2cXbunOD667YyNJx//nGbdkxS3FxkbLLJ6OgaH/wXn+b9v/lJ1pYaZJnurwKHfhHlBZycXmJmbo1TZ5e/6/OOFAwXQ9ZaXTIDm9+QkB+NiMvgKslrX7OPP3rXT3HjHbsJSz5CKlRsSTspCMiXA3QxxOQ8zp2vvuCfv26iwrt+961cc/Umtu2YQKp+t03BKVB2S6wPp3D+u/XHf/jYE/zLx77B544def5zMtGodsxgS/LAwMDAwMDAwA8ui/iB+XixG3SkvMi8/6OP4g7lYaUJtTb5rgduv5vEFkIYmYTlNWi06T47TWXjKMubSux/ZC8bG9OszRcAgYliohFwMgd3rtffumKhsJDQHHGg3qAXG4L1DuFJi6pFeDlNe2uRfF2SbgiIrUN4uomsNfp/fs5Hbuxhx3q4JxrIzCCbEelwASfnkn3F4E8Y0riEKktwJE6WkeQkaUERO3lMIyPc1ybb4+KeilBNH5N3Uc0Y7TnY0IWlOkQJ2fZhEBrVBem4WICOIn94iayiiHeXOHRmjnw1wziCtVt9wiUX4TqEs1VEGJDEDjs3CA4cDVC5LrKbgtMflxJRgkkysmoB6xhUHsKVCOs6lI5kOEagfUPz9ozwpIPsWYrdhNqHl0CAUYLwfIuSB6+4eyNH4qOoRg+T91CxofXYRWQUYbN+AG/x/hmQgvJL9tDbKMldtp7aF07g9Gw/GTv0QQqsMZhCSFSCYKGHjBNqAvBdjBSgLLLbQ1sQqQE/REx40O0h4hS0JjUZzU6X3HxGs6554DNPkXQjgpxL+bZdHDA9njkRkz/ToOhKrr9tJ50nHmB1q2XkWMqvvPoOfv8fvAcvMuwIFti4ZY1nLyzTrU6xobyKS5tF4WM9B79kGVI9Eq3odTS206ZTUlw/Oku5cj+fOvMyGlsLyGaM7WZ4M2tYz0NfDcuqy8S+IeRSk7FPt1n+mV0oT3DPy05w+COl56+LybDCr77sHr5x/zGOPzFLWsjwV1OaUY2n7zvIy996Oz/26us4sX+ao98+zZW37gKgvtrmpNfAOzSBOwovv3YHOtP82C++jOvu3M1lV20EoBclBL77PVcT333rTqrVFlft6c/UzJ5e5MQz5zjXaLCUdhkq50it5tHsRtrZCljLtqn+Susk1VzMW2ZvSXEWNMmCIK2AMQaBwI4VsZkhDF+4Jl5v93jo+Fl23bmVjWOV519j3snxCzv+7v9wf6doqLWaHGxf4Ee5GgAbpxgpEJ30f/tMGhgYGBgYGBgYGHixGRRSXkQ++9ghvva1g6TnG6gowyytUn/NHkzoUrl/GjU2jPEdcB0E0G71yDUiivN10pGQeTnJ6stH8JqWwmNt1EKL3u4h3FmLzOewRpOOBASLFqtcyAS9ExaRN/T2hgSHWpRONshGQ9RzbfLJpdW9Uva7IApFgv2SbL1GtIsQtCHw6e0ZZXGljeyGqEiQDPv4J1aRTg4QWAPWgWzNpTeTI/IUbqqxBY1jJbm5HkIbUt8itI9VElnIIdweXinDZCHWCoQxqE4EShHmXJJbc2TfiMlKFhEZ/CVN5ns4iSRdV4L5BrIX8eCfrUcmksK5ZUyvi/I9yDKskJiJMiZ0EYurpHtK+DWL0BqnA8pJ8J5soJY8sg0TDE8mjFdqrKz4SE1/3azO2H7Faf7sd1pY7eFdWIUwAE8xXhcsCdnPpbG231GUZbQunqP00y6tD4zAUKkfzhv3c2qwFhvH2Lll1FgOqQVYMKmGsJ+RgutAprGZQVZCMBaztAz5HEL3N/50z6wyVPFwli3ZiuaZh+tMFl0yDbUkQ/oCvU2jazmEOwzAmQ2r9IxhJbG87yuPo7UPUUTnAYvs7OT9q4dYWdlAMpsjKVRx3RhrDJ1ajsbaGPNHroB6CztWpDQWscgFrrgi4A0/cwOPfuQgNux/L5y1Ls6ReZw3jOEuWp647wRI6Fw1hONYSn5EtuRTHPnOaI+1lo986xCz82sMr1rWn9NkiQBjePc//SRX3LGXP/6P91JdarD/voO867P/BNdz+A+/8wWSqQ7N9S6bKyX+7i+9FYBWvcvKfI2xyRL7j8/yrs/ez/iWAneP7+CHX389udx3tvocOjLHM8/OgLXs2j7B5997P8cOnKN7z3qmRsuUCwGulDRONzEtiwv0egmf+OoBHnzqNBe9KuGV5xnZBN2vB5REDtdxaHW6/cKXsRQmiy94Lnzp8aP8lw8/TAbkQ49PvfMdjFT6XTc93cEVHs6lsR6An7ryelIv4+Ub/rsAW+Wg6BdTBwYGBgYGBgYGBv5vNyikvIi8/zNP0JYZoTWY0EWPFkmmCiAgGc2hci62oNDrA8K2jxgtkKsUkNU23nyLzuVjWCUxjkH6eZIpC+UiiFY/JMFx8GZW0TkP0qS/Htw6tF5SJN7lk5VyVD4/j7vY67dzGQuuQlwae7DzS7i5DdgZgXBdzFARk/coXOwSbR1CrqY4+Tp2A4jAwfZi0qEA6wtkAm5DkFVzZCWf2hs8nJZm3ddiRGZBa+RqGyMsItGgwKla0DEQXurUAII8SWJII4dX+rv5Uv4ImSfxO5rcjMA6EWnewRbzmDGLnDesG9tI88IKOnBIIoGJE4QFdIoVEiTYwMEEgjQvkcZDafAOpLR2lLCBgGFN2e8yua7OhYk8zZUiUluUkhw4uAnbtghjsJnG9nqIWLHnLTdQXepi6m2ElFht+muOxQitT0ncxIOCwLRjrKMQSYrxFdHWCdTJBdxYYJVCxCnCGsAikgwb94tAOPLSyJaFwMd2Y+xQCaFA5KB7m4uzZCjOaSjkuP5N13LhxALJ7lHmH3yG8FyT5K516JOWC7OrTDQLzCY13CVFXM6INwWIwyuki5pXvOp1tDflmas2+MzwQxid4aQpIs3IYvjM772DW+7aS678VVrlAr0qnD+4BbVviPU7dqO9g0A/Z8ckGbLksmNqkd5Do5xKZ7BbhmlfNYL1HbraMJPbxet++Ibnr43FbpMz1TVSx3DLq7cz1XR54ONPkGpNWm9hjWFhuYkGnrsrQ7r9ro2402P8wxdpvWKId/6Ttz7/fM88doqvfOQJzh6f54IwXCzVaN1f52K6RCgc9OaA4XyOV1yxkyv2rOf6a1a4+fptANz6mmsI8wHp7jEePHKe4+eWOLW4Qn0pInNBGBgSPosrLZbrbXbumcI6mjMnVhBtSbns0enEuEKSdz2iXsJk7jvjSP9/MjWYTgaeIBMpntufz1mLF/nCxfdQcof5oQ2/9HynyrbSCL97w2u/+zlsv0FTmhd/m+bAwMDAwMDAwP+prBVY+/3/99oPwmv46zYopLyIXH3lBg7787ilgKXzVdxmCJkBKZBRis6nrL08xBIy/oE2I5sL1Fd6UMhDIClsTuBoD38tw8kccIuoC5psMo9qR9i8Rl5MkZ2sHwaLxTY88s+kqNQjdzgFRL/jQUnoxBidoWR/FIZezMj6FbbetsyxJydYmCzintPkTxtGzrXoaEO2DEkE0ZsnyK20iWol/HpGWnERpQy9NSFreKTjHiiN9iKcrsZmGqkcqLUR0kEkCaqp+sUHr9/REQ15tC4vU56OkbWE51aXiUYEIvJwY8BanFaEmq+hyyGqHYOE1/zQbdSf+Bizy02UEAinv70IKVBzK9DKY6QgWE6QApo3QrAAppejebMHqcK3CQtnR7Beyny9TLJFkq+ClSH1mg9lRWpbBGdWEMKDLKE3vcC2N2/h9IdPIrRBeA7WGEZuichPWbrnNjBfa+OuWKSjIMuIRz26632G6+MIpRDNLsZ3sIDoJYjAR2Ah52GyDKMAbbBxChKE0VgDxlWoWYmz4KDH8wijOXZ4ntQYFmtV3JUazogldSzWwmOPnqZ3VDAclfCrKd7KElZ3EVqDo1iZX+XHfuQmrLUc+MNTnIubiFyAdfrdMXNnVljY28LJBQSAXjMc+vwwfxrOMd/7PE7bUDqxBHHWz3Jp9NhV6bF8yxqNh0YZ2TTF2kIdGRXw2nD+mYjfn/kEL3nFFbiey0SuxLb1Q5yfqVLK1tj6tmfY04DDH3exyuH4c+fRor/tqfusYG5llc3j4/z422/jyH2PMvLoPAuvXCR76SYWV5tsvnyK2199JVfdtJ3phTpHvjaLCjKKjo9f8vj4s8dppwmHe4u848rr+IW/fffz1+l19+zlunv28hvv+QpLK02ePXWRrz5ylLRnEXkQUvCamy+n4vs8duw8416Jf/qqX+ETpWf5g+WHWVvt8v/+56/zu7/6eiZHCpw/s8yJh0684LkwOV7BBhIyw/apYfJhv7Bp+ouz0Tb7n54tylXozOBueuGCzcDAwMDAwMDAwMD/LQaFlBeRjtTI2ODULV5XQalA4duzmIkSSivcE4uoan87jejFtCoJwbwBA3mvx0/+1GP82b+4g14rxCYp/nIb6zmYrWPo9Q7yR1ZI/qCD07E4K4AQGGPwDzfxz2ZYrUkdhTdUoDfmQK2FKRRQ0/1VsyLw2HzLRXa9epGFKwLOz5bQQYZ7IiWKMpRwMZUSQltEy8Eu5xB5gcQiU4ueSBDjKcoFt23Z8PEmTqZAKYQQJDmIR0MKZ5rgeaA1RoEIPKyxaL8/YmQzg2xHyOkWjgEVa1QnobtB0t7nMvbJLm67Bwguu3mVp89+itWVfs4Ll7aYEAb9TBEBFgu+i9/UJBugt0OiK2CXJaKkEUsOOvFpJYLltS0M3+DTy3rkvt4iU+A3M1JfkE2CPZ6BkAjP5djReeJTPkxVsHM1SDNM3mXs1iVsUbF8roGvHVAuxlHIVBPIhGCti/Evff3+pe+PtgjfQTQ7AFhH0xsfIpAeWI0EsnUjOKkAR+JvqWCbEe6swvgSk6bc/rqr+eJ/upfW8RapFKSRpNOSlOs9br99Fw8nZzn8+HnMWspaN8LIGAVgDPd+7Nv82D94Dd/+xmH8+06xa7TDhbAEGeArNu7bQiX06KaGbLVNOGEhU+yf3km9lyK1pnJ5m85xjZAK6l1Eq8TotXWaD/ksrkgK1SZiTGJCj1bdxzeGD/7Bvbzhl2+nGrfwthkqDzTo/uTD/OZzV1K8J6Lw0R4kCbPPne8HGltAKnqNGMbh7MU17vh3Vc7mhvmD2mMs7Lc89/AcxZzPO3/x9QghuNxabr5pO6VCQLcTUxrJ03pqhdP1T3O+cYbHpodRMwl33733u0Z+rrtsI3PLda67bAOf/tYhjCsQ2uJowIVbrtpKvdWjGnX5md//BMeOL0BekC+5+J6DFIKxLeOcn6tRyL3wUT5RzFMphqwmMdONJgvVJuvHK4z6U7xq8udYiaPn77vW6/LeZ/Zzxfg4r9u5+/nPq8Al68T9rq6BgYGBgYGBgYGB/8sNCikvIu+463oesMc4WT/P6tocLKwQILBeCTFcRlSbjP3xbH91bztF7cgRPzIPvYz1O5YpeV1ckdLtCYTtj8vYTgpJBp4ieapC7+UTFN0A/uRoP3BVSoQfIKzFdHuYdRXQiqAOJvMRaYDdMIadryGN4dQXypjN0Lwfcs0OolEkGZH4J5egkMdRLiInKScpWeKTi1O072GkQZ31oCtwapLRby0iFzt09kzgJhJ/Laazp0hccMmf72ADD+tKzHwXVZLgSbzFJq51cc8tIhyH9EtncO9cD1ahA0Vzl0u0TkCSYsN+dsieW2LON13CqzfRXTmKkJfa1KIYAVhjWXvFKE4jRGQZhdMRxnNxqwJhBcWkRXilobYyhkk8dAEmNzqUyzlyqy7NbywTVzTtl0jKn07AaITvIhyHTitBygyZ9zGlHKIXoaxg4UM50rtyrFUthVaKDj1kJ0ZmGtMQpMpF+QIRCUwlj2z0kNpAYrCeAwioG7zYoid9RJQilcCp5GGhSTpcpqMcfBkiTQc6GVjNRz/1INFiDQW4OUUcKOKCg+/Dhg1DyJ0G54sd3OWEVGdoE+PkQsJthmXfcPrsEsWhPCbTqLgKUYCIUkzg8pLX72My9PhimhJ4a/z07z+JzVz+7GNvxvQ0aV6RrgjIgaknZFM+tUoT5cHmVy9y+o9zbNmzkaWDs1gpGHtZzPJzEzhFn987/hlSk1GvadJ6yjPdTXTigE7sszmIcNqCL33gEVr3bEJGls46OH+myrZN66iMlFnr5HD9DEd7jBXyOI5iuPSdzgwhBBMTZb752f188v0PcdOrr+aKH3qU7ZOPspBexSN/cpATT5zlyHMz/No/fxPQz2wZa6b8+J4d3HPtDvAENrM4jqSSC9g4PkSlGPK3Xn4Nf+8/fYZTF1dIpEH1BNvWjfKr73gJjqNIXYegUmDfrdte8FzYvWOSn7rzGj725acpSIfxSuH52/7FM1/nbHuFf3L5q3jNxis5sVrliYuznGvUvquQMnbZGDMnFhjaXPmrOq4GBgYGBgYGBgb+ilnb//h++0F4DX/dBuuPX0Q+9M1nuO/MeRxXIsMAuXcciKEXk+QVphggUoOMLEIIGgebpJ4DQpAYj8987BbObd5IZ3MIUYKJE2i1UUdmsLUu3tM9ZMNS8DNMEmGSBOE6dEcdEpFhlYMUDqYsufKGeQKbYD0HUQj7nS1pSvOo5OTHxml/QZF7UhBUHYKuRDoOItWIXowX9Sjv7OAOGZwwQcQp1kI2qUl3ZHS2SzrjChsnZGWPbCRPd1ORZNRHOg7peBFbDEEI5Ei5P94TJUibIVfakGagNVmrS7iYInsZznKT4YfqlA9lgIBGC7pdvviJXZRre6mtJGD7hROkAkeBlAglKR/qklvVeDMNSAX5I+Cd6uAfWWX4vmXGP7VM/ogmPx/hNgyX5caZDAvo+QR3rUNuscnY2TrW8xFhiE1TjDaQJISBQy50UZlBxBmkGQvdMqdGxmneZMgSjUz766ktYLspjOb5+V94OSPlHGVjEaa/1MdKsMNF7HCRLOcjUoPqpMhWjFspk0sMZqyMcBXuSsqwDJAmQxogtTQTsDkXM5nHbBhHjk6hcdm0ewqAa0fPsOHKKq5M+4Umx8UVDcZuifAmJYdOnGH99gnWXVNlrNTFVuvgu+BIdl61HqeYo7BpjE7VY+VYgVI5JnVSMqVJ85LGtIftWKznkAYh6QmHbY6h93SRQILq6X5hz1HkMp/yUI5XvekGJsMhSm4ObymPbTvMHytjtMRkCpP2AEu02uTDP/8mNucc9p6QfOaTT/PNrx/mi+97mEdqWzmWrONVExu4ff1mxoKQkSD8rmvPWssnPv4QF2ZrfOp9D/NfHyvxzbVtPLJ0OW7ep130SJ3vHLdnj87xzn/+Kf7gt7/As4+f4apNE4RKUnJdispjxO8/v6Mkt+7ajC8dRAI2s+w/Occv/ZtPAXDn7TsYEhnd+ZUXPBeUkmzeNsaeYpk7dm7Ccb+zw3g5Wganyly7vz75+nXr+emrr+HmjRtY6rSev19nqYFsRthE/4XPJ4BjT03zq69+J/d+6OG/1PMMDAwMDAwMDAwMfD8NOlJeRJ49e5F2O2LX0CjWl9hKADsniCtlRGowxRAVZejhHMmwS+5IE4WLUZJzJ6ZIOiX0lYp4xCfX7iIEiMlxrJR4kQUh6JQN1eNLFLQBJL1xj/bVo3hrMeUnFlDNLnv+qMlPBQf4srOFh/aP4GpBnHeRtRSEi21bDBaiuF/UiPqbZGyhgFWKVAhmesN47S7+9BKKPGLjRsxkhixonDhDtTWmEKIiS3Bxje6mIloplBXgO5hWD7XSQAQ+lv6bP5NzkPUYBFitMQBGI22GzQd4MegLGcJV/eBX3yNSAbNH5zHNHtZTSCvAGoT6zptRd1WjRUxScSFyMJ6LSjwcoTFJgbRt8chIcSidSvn1X38ti+dW+ZeNTxDlPJyRBLnB4jzaQ/h+vyslCEDAG/7+y/n6R56mpyKE52LSjPJQRDi5RrNVhLZGJBaiBGEsQrnYJKXV7tKdXyPuRIjQB8/DCtHPQgk8ZBiSlFxcrUkrLqFRJO0Y4gRZyiN1QHS0janFSEdAL+LmV1/Dns2TfNWfofqFM5TCNq1qgbX5GkmS8dQZj/nL8sgdgq43ia2EyC8fpZdl7L3e4pYKzM6ucttN0xx9ZggqBazvkRUdfuGPP8NPbbmMTi/FFgt84teu5YqXZkRbJ0idGKEFNGOINVIYvOUOzz63heEtFWqXN1CnPFzHgfUj5EPFlBrlR/7u1aybHOIfTfwQ1lr+6fF7aS8fwzZD3GUXDNi8QqxGIAWPf/ZpXnbN1Zw6cZHAc9m4ZYQT+ZignIFrebR1hG+/p0bvYsbWdR20sTiq36F0qjnHTHW1//OhNRcfdLivfTvXFjYx1I0h59JJv5NFUhzOk8QJZJpzpxdJFyJKPYepdcN005Tpi1V2bRqjF6f82X37aXQjlOwXxZyuYbXeH9HatncDW6/czKt+9KYXPBe+/vQJ/sP77meslOe3fvll37Wi+fLREU40IrZXygB4StGKIv70yH6mG6v869tfAYCuRWSh4qLucd8zp3j5tbv+QmfUp99zP8ePzrP87vt41U/e+Rd6joGBgYGBgYGBge/t0hqJ77sfhNfw121QSHkRmdpWYX6tSXvMZ7kT4CUa5RfobAnxVzIqiwHGaZMOeURTAVPaIar26BVC7OQw3kKDsuMil5sgLBYJAvT6of4Wnl4H7RraU0UKQoI2qJUOSksc7WAdhbKWtfkCv/PbN9KpeohCB5sPsMUAPVXB5nI0ozxmt0CnEc5SA5Fp0k1l/J6D1GC1xBhBWnJx/AAZ5JDtlCSSuNoS5HpEV0yi12fkZroAiHZEWA2QmSBxNWFP9wsmxiARYC3NbSOw2qZ0MgJjEEKSf3qG5p4RuhsCjIDwXBuRGYTnYV0H4yuSehdXG7LAh3YPWQyxIsH2RL87RSlkYpBFj/YuRW8bFJ9zUbNwxS1V6osB87FH5grUloT7HjvI/k8fp356CSldeqOjNEdcSmIZT+v+amIsQipOPXWGV/7QtXz83V+HTIM1bHpjHek5zD3tYLVLkqSQZSAlJueh6hmPfXuaoY3DLB672M+1ECBU/++MegurXBzjIC/lrjg/dTn+l+Zpn19G9xLMUB7jSkQuQFyoIjyH7ePjrCsNoe49yQ//vYcp0uMb79rJcHAVpw6c5dixlJYpYDc7OF7ImFeis3cjZ0949OQ6ji8fYHgxZuXQHVyz7yIUQowjsY5krd0ji9rQ6/QzbVyHfLCBysYh5nvzqLbFDBcQ822stVAuMOpELIWHcK4XFI/uZSjKM/PcNNF6n5Hr99FQkjTVOKqHEAHXTU1xKLOUDvRIthX7eSTWJT9SwloY2TTOs7NrlPI+yZl5HvrkE9RzAvm1EptfVmDTulHmlCK3LuQfvO1uHNXvMJmt1/n6yTPoGghjEAhu2LWFbrXKT1xxNV84eIDmsKVhk+ev1dV2jF2qgjGsnprjZbft5sSpBYyJ2X39Lu66dge/9sV76aUZuzdN8NSh87i1DJlZHC0RbgrAe/7Nlzh/YgEtFDe97MrveS40kwRGE5qdiE989tu84yfufL6Y0owtzVSzEnWfv//p48v0VhOCke8UC3/yV17GH3/2UWzZ59C5+b9wISVf7I/ACT0IWxkYGBgYGBgYGPg/16CQ8iJy06aNPJ3OEUcZcrVHc7eHU8qBC2kBpOcirEQ3YvIrLZod8IohNh/0n8B3KccQlyvopVY/98Rz+rkaSYparLPuA4voooN0HVAWNxGUTnXAcRCZRqSa5V832BWL9CzGaqzpd5AgPUTPkIwo0qIiGQpxhkPCegwLNYRfwgiJtIbyUw2ioRxmfBi0IppySRIH0TSEfo+ebzG+xDEuQimCrkCdWCEt+GRDHkIKbHLpDZvor/lNxwrYiQJZI0KtxEhjIPQpLCU0p4pE2yD3TAOEwGoNRuKkhr03baV0rsix2RBd62CTBBFJ9FZFd6xC7kJEFgps4JBVLLqYgTRo4fLYqd2YvINjLOlVGeLKjP/0zDdxH+shjcW026hvtQhmS8iVNiZK+iNDAgRw4L6jPHFkmmzMIZhuYrVl7cmA4OWKXtPBWo3jueC7/e+/AdnNmDm6gBrLk+2YRNR7CATWUdCOIJ/r33eti/BcRKbYrYZ5pjsDlQIIiZYGEaXYwEeODIGSfPoff4zNezfQ2Q1PLG5nU3GNw3u38Jm/9RNMTA3z5m9s41sPHafdhbjcYnjfEKYBKjbctH0D9124QPeJORDw9LfGUEkHUykDAtUzfGVimTM/VGDiixHuSosL5w+xuncTY5tGqJ7o4s01IBeCBVkZwp528c4ocrs7nDq+xKv2XY5XSXCnUr7ywHOUghKX747YPPaHXKhOUPF+HrO1QDZikRmoFMJcnu7SCtJR7Lh+K4+fXCLpJjhxRn2+zmSi6RzLyM5n/Obn3srslgalfMC64RLQH+n5jV/7CLPzq0gpydx+10jL1XjLmtVaB11UJKmk4XynI2VqXZnaq7ejcy7zjTaXj4U8+eBF6knM49UV1m0a5onzsxhj+c9veh1H7z1FWpR01jmEq5rJYn/056rrtzJzfB7d7vJC3nTz5Tw6+wSHP9ng4/P7ee0rrmFivP/6Z7oXyehyoHaat269HYDXXbWH4kGXH9l3zfPP8ek/e4KujtGjLn+ZDcg//g9eRdSKuOXVV//Fn2RgYGBgYGBgYGDg+2xQSHmRsNbyyNFp5ufqeOfaFBY6RDsU1jfkzmcEixFcXMF2e7iN/lYb43k4gYNoR+zctETlVsNzBybRJ+oQx+D7/bW7vQSWa4jE4Ci3Hx5k6WeQJBnMr0GSIXV/HMYSYXyJFdDaU8EF3JUIr5aBArXcQi1pxOYKTtuQlPP4M3WkC1iNcSVuV+HFMaaRQmpxuj4il+G3DL35HEP3z6GaGYyPoz1BNhygpmuo+RqiVOzntRRDbJxBFIGUWE8gIoPcNYW9eBKTzyMKeYSjwGriEUlSlOSyfgeL1ZpsOMcjj5wi7qUkeYUdyuOerCOMoLlhhGhjHpFZjO/Q2ZKjfLxF4dkebk1AGOCmBuop0kB6XpJMeOhpi7cezIVLY0KZJne2Cb0E7KXCT5piXQEFRWfPCFZLnGaC2zXU948ipjVsAVYCTCtGSgFWIFKDMBqSDH9LmaQR9bskUg1SYV2FSDKsksgkBUeC1Zx+9gzG9EeWLODWuojUgLHgKjAGKwRjI3lKb+rwzZktPN68jGxMMLl5DM93ec3PvZLHnpjBSInXjOhUWxAn+K7idbdeQakc8tnPnwcDWqf9rgghUBlYKTi71iAuSeKcYPvNa3S6IbNFiZBtvBCykQD3Yg/pOMQ6JirnOX52CzfsPoTFZWrvBkZ3+KgdMaOHFbvCbVT9c7idk6yaOT71wHPUtno0NymcjsCrCTquxDMGHWsai3X+4T9+DedOzPPENw7z6r91Iw+/689Iy1CLMz760cf5Oz9zN3F2AW08lAxYnK+zeHwNYSx6wsW7mOCXfb6weg7pwNceO8a6yQqFls/uLZPPX69h6KGHQrQriMYKdKIUL++BmyHzLpm2bB2uEKea93zkEYwn0J7ASsAX/NI77mbuwipLnRg1kqdU8F7wbPAch9dfdQOnPvUAed+jUs49f5vBILD46ju/Cm7au5mb9m7+rudY7kWoVBP3Ml569Y6/8Dm1bssYv/H+n/0LP35gYGBgYGBgYOCFWUR/kuAH4HW82A0KKS8S+5cucmqmilM3JDnF+Etc0v1d9IiD0/RQ1sWUQhzlEk2GdKYcSicbtAGbC3hWBHRH84jrfQqZS3h8GZumiEYb1Ysxa3UoFzGFAD2Sh+Za/821hXjTEO58A2UdhNYY6yC1Bk9QPNNEBg5GSkTBIws85FINGRtc7WBcifZATw7hxAIL6IKLyGKopZiSi1Eujs3Iz1iyxRxuTpBtK+GeyaBagyQCNUE2WYEowmtlyMCHwEdU6/1VyMbgOhLVzjA9C5U8st7FWgOui1jooL0R4nUewWIL4TrEo5L6jZo0H5F7KEXNy/79oxShFPmT/a/D60hE29KdzJA9S3NnGW8tIz+XIucaKCQUc6j5gODrLqM3r+Ld3ab9xYD4mQApBNqTiKyOyDTWUVitkY6B0CW82Mak4HU1xvcQkn7wrXX764UF2CTDus6lUSMJ1jJaj+m2ImyUInoxAFap58dPtOdi8wH+Wkrr5CmYGu8XWIwF1+sXntZa6LxC5/PIQsiP//oPcUHOcO79D1Fb61AoCu6XD9G5cpz/ePhxuD6H+2yCdX1mcprRfEDHWD71Z48xNlHEBh4YS3GqSvdcgOj0MNZFxQEN32KFoLMp4Gj7Kuy+JrKUYo0mJ106N29FH6mCzYi356ncvcbwuhY5k+CuWTbumGD8wjD1ZJ7zp7us5U7xuSfXeM3OjdTjCqPDedxnoNCLaG/Jk64TuJMKf9oF4PEvH6BjJR95933Ul5soz2HlRoc4lUweceiYDs3421yo/0ey9jXsf+RWNm8Yxi/4xDql5Lm4OcnsbklSMSDh4pk620sVdjgFbtu68TsXrLZUnl4hLnvsesPlvO3l1xIWXD791GF2jo/win07+eaZM5xaq3Lk4hqdPRYbZPjnXEgkH/nEE7hdy/mZKsZ1ueKV1/C9nJud5Yv3HeCGy69g346NBIHbL7oB1bhJ4FiQhsvL6/7c8+Xut17LZw8cxZ3wmW01ufYvcVYNDAwMDAwMDAwM/J9uUEh5kQgdFwkYBUOrLfYls7RvgUdO7SUtWvzza1grSKcq1K8OSYc93MjiPldDAmJqiExLVCIRtt/hgjHYXozoRUgpiTcMkzkZstkiHMqTNHu0xh1q1+VwNwvG76+CNkgFUrkIx4PEoJKUTKeAxqwbwkktFov1HRCG3ImV/hacXA7rK6Jhh/xMF6lB9sAUJbItSXFAQlbQNLflyJ2oYzPT3z7TSoh3jaLdMpxeJYhSUKLf3aE11lpuuHIDRxanURoMop8ZkmlMN6LUsBSPNDn3r3dSPtKBTNO7YYx0l6brCoI1g/vEMjbLsK6LNRZvsYdbN+hdEwgJlXMJ6WRINOWQVhxy021krYVwPYTjIo1PuJzg6H7QKfUMHYboXAEzWsTpRohEI4ztd9A4DvQEwbG1fqEk7Rdw8CS5fx6RTRdwF9fR0TVAgFLYLEWstsGRXHQ0phIihED4DlkvRljR3/BjNOgU3CKiEyGkQsYpSIH1HKSx2CjBGy+R5F1iJfCqEe/8jc+QL4e0z6YoY+jM1nmg+Ri373kTRBadGPz/9vf738VMXXfTVt77qUfJ8i5CW+rJMGEFbD1DRinheIDNelhPYH0FLclaWqZwvoe7LMiUj3USmAyJyy75LGZ0Sx0/n4EL3jqHa2/ezsxIwrnmGm03ZPaMYWlLkY83bsLRAZPpMmnFoXwyob1VIDPoTeafDw62CD7w3k/Sk1AYLXPljdsRyVmyZoJZpzmcHaGWXotAcm464Mn9Z2m0erzr3T/FvQ88yzVXNvnCf77I8fHk+ZVvUTvlyNMz6FrEwaGz3HPjZQAoR5Kfa+BPp0z97QJJphkPc6zON5leqLJuuMSDJ6dJtKGsINnSz/zJzYO/pAk2OgjXcP11W1lbaXLr3d9ZVfzf++pTH+XhpxNWOzV+9dfeQjEf4F7a2uMIxZBboqUNWwoTf+75csvEJMeLs3SHfXLeC3e/DAwMDAwMDAwMDPzfYLD++EXiitEJlBBgQVcFZ48M0zsOKhYoI4nX5dCVAF3wwFWonsGfaWIvbc4JL8R4T3YZ/9oi4UqK8D1IUzAaijnIBZicwFmuoxpdhnev5+W/8hqyjZV+6GzP9O/rSITvgtMfEdFDOXTOhyjBtlPUhRpm/RC64GOFhWav310BkCbIdoS/2KQ8FUMhwBs3pAUH2UqhGdG+IiHblDJ2f6M/lhJ4yPWTuLFBCIVabhK0UkSaIaIUjMVYS2pT9p+fo+dDFsdIS7/wkGZgLcKCNBZRj1l9/VaSKzfjD0nybkpQNWTTBXTo9peiJylWCIwP3Z0eXNPBvbqLWqfxa4bSdEb5VIopBKTrKtjRCsmIh1Upstbm1MWAE89uIDrnIroxIk6RqUVvX4c2WT+DxXGh20N0ItAGG/c7SjAG0YgwTYfsgs/tL93Ntfu2QLONaLSg3UX4HkIqzNYhHN0PmDVrTdRKDeHI/mpoJRCFXL/7xXOwrsIaA8agtqdINyFwLdvXlQlvcVGJRQqQSrKWreJag8oM667dzqz1mHnwPG8q7KR4yiIzizQKrxphezEFX3LrSy8nt7lCVnCIhl0MHlnPQLsDzTaddoLfzfBtitfuVyGCJYs84yGqHh0Pwm/PEDx+jsrTy9gly0jaZUu4QqAz0pbmq596mi/8osP8J3aRa+9AGEm+JxDn8gTzAbNOk8Z2S2dTgHEsScWSlRXWGqzRXLi4TPONXdI3t3nLr9/NS1+3j9+59jXc3JxgyASIeoJZ3cGe8T/hlbf/PG9543X80BuuZf3GEW648wKlzR/mF37fUhhyQUgsEjWpuOb2rQxvHeaWOy97/nqVUkKWYTPDwvQSH3vgIO/8r/fRrLbJDExfrILT7xwZKxYIT3jkp11UF2Qe3vCW6/jX7/wRFs8scfqZGf7Nb3z6e54LN94wylW3rDCr63zqE7/OoYNvI00vAFDx8vzcjlezb2gbw17pzz1fPve5p6g+scB4R/HyvX/x0Z6BgYGBgYGBgYG/RlZgfwA+sC/+0Z5BIeVFZMeuSSgo4o15FhYVB7pbSX1DNOqQTuQQOkPpOrmLi5TuO48zU+9ngUhBYU/CVa+co7CzjdAatO7f5ruYzZOYbVMondC7aQPxnhGWnpvhwY8+QOnsHCPfOEvpwBoi8BBCYNs92DBGe2cO67vYJANtscYgHJewvULrnoCVNzlkgcZ6DtGmMgKJQOA1NIkEhCHyBFI6SKlISwovtMicJr4ihy3nwXdxSh3CaxL8lTZ+PQUsVsn+ul8FaRaRlX1srOkOKzplTeKlaKsBy/PbYI1h7Asr2MxB1lrIuRTRkbhPS2g6NO/YSP01u/pfU7tDfV+OlVdUaGUORsfI5yJkOyF/IcWrRjj1Nt5Wi/93I7JJBZ5Dd1zRm/LoDimMTXE6KTJNsb0GaqGGLOYRrgNa9ycLrYVhByElVut+N8z6PM6wwQld1q8f4Y4fvQG3ECCERUjZ7+5xHMa2jZClGpJLq5FdF7oRNDrQ7sGljBJhLLQ7iLV6P/Nlu0Zck7Hh2g38/Xf/OEPX5jBGY6trBDqh8sYljO5AllJdaNOqdXnk64cJLrZwoxTtK7QviIYc9GiOZmr54PsfYr3K4bYy3NgwUslDN+vnuljBRFdiHIF3zpJ5XZxGguvFRDfG9HbF5FYT/JYBITChS5pXnP34GNFMnumPbkDi8Z5jz3B+m8/SkwHzpxrItkH0DC4SJSSqDW5D0Bt3cJRh795zFNfVsFGMTVK042E7LlkcMLvWD2+9+OgyzS+vMHFcw5cTPvAbX0HJAvm8z6tfcSXbtozx9ceP86lvzXO8WufhRo3LbwQhDdI1/KsffjX/6hdfz/vf/dPcct2256/VNMnIUgPWMndqgU3jFRq1NkZY0JaD0/NcVRlnnZPnjfdczTZvlNJFHzJBY8zhk08fxQschkbzIKDTir7nmXD9xr/Nq2/9h6QXiwyFz9HtnqDVPvr87ee788x3Vzjdnv1zz5bEUyRKEuq/XBPj0UOz/NY/+TiPP3zyL/U8AwMDAwMDAwMDA99Pg9GeF5GP/L0fZW65wb/65fcxXe2grxpBBxLRjZHNGHetS+1tJYwPetsQ8puK3IU2ZBpvuIk7Ct66mO4TKemVOZjJoRIPEzj9TpeipHIqxfY0WTcm62SYTopnDMmEC8IDv7/hxwhB4A7hpwnaccD3MFmKyDIKhSYzG8cwZYdkfQ510SJTjc35IMAIQ/0KgXg8Jh138WZSBAa3pkiWJbKSISIHm6WoXkrph3s44xbzlTbxcx7xaIiqd3GN6o8zXb6BaGueRNMPNzUKVwZ0ryngLrXxahnNXcOoZo3e1hAbGEgN7jFD7I4SnmqSBQnplhwys+hKgGp38c93kDeM4luLOm6hk6KtJJkskoXgJzH+ZRmypAnqoBMwiaF0LEG1M5xYkowGZON5dOiSW7iI0CAujU5YC1PbR1k4V4V8rj9y47u89O8dYWRrmwdnb+fRp6aJkoyhm7ax9uw5sijDuj4AcxeqhJ0eutnFOgLbTTFWQy6HVAphBaqT9LtypAJt0MOK7GseQmtmplrormFXazPTLIGMOHfyPCvxJBOdOegYjMmwKXR0m7nrL+A9LkiiFONYVNsiugm0ErxOxJ3XbufoU+cJnZRrthzisRObMIlEAHddsZXe/YdQi5okb/HrEd28wowabCAo39fEIyDZUKFx4yjFZ5awjzY4fW4D1//w7fSGMp7IV4lMj+5MhmlrdE5iygK7aIhtxi/suZEPfeARGlsEm7Yt0pY+w3NVTJaBUuS15e7sLu49eZZlpw3AchajA8XIjnFOr0aYZsrc7CpT64efzxpRFs6QZ/+xmwnOa+q5Gl5FEuTzjEzl+Y/3PcoHn3mWt1y5l9987Uv5wp8+xJP3HSEcGyLuROy6ZRf3f/MornTpFWw/hNfAqQsrRFpz8NRFVtZaDJdDtLEsyB4n5pf4uT/6x8gNeV6z63Ze+9rvnZEihOC6rZvZ65X4yoevZt22Bvv23fj87d1UsNDThLLw554tBgG+Q8Xz/1Jn1OxMldVqm/PTy9z633XoDAwMDAwMDAwMDPyfZFBIeRH5l++9l5MzS0w6Pl6m2NyZZTTfYfrAMGrVQmTwDrSIduSxiSDLSaw2IAXxfhCbM5KjAfgOKQI/EYg47XeY9Hr4NQ+RpNhUYEMfEQFZB6TErXexedlfTes56JUaJjeBV1H06goCH+kVodejvMNQOhhiChI3zkNaw41drBQIC5m16OMFwl6KdzyBWgOb88gKBbxTCjkUERytYmo9dC5P/Vtj2NBFzvXAaHTBx1vIwFPonIuQLtJP8G2OLDGESzFK+RSWLc1NFWTeYMcKtHcViKcgPwPJ+jLWkRhHgpQ4CYSLGSJJUO0EoRR+02H0qMeGa1bxenWWThexykNlhqwE2ZZhsnYX/cUurCWoEYuKItbVctQTH5ELUVLSHcnjZJBctg4RZ6jZKirSkPS4+Ow5RBAifB+JQJqUoVIb2opbR67g9AVFu92m5jawvgepxi6tgAV/eRnrBYjQp5+hEsNwGXIBthv1u1eEAES/4wW4avIsC+cL9BYFeqnOB/71Zzl3eYHsFoV4aRFVV0TKwcsJND5y0tKthaSjedKSZmSqSPLsRbK4x8TBLtaBeBKcbpc7797Dg198jKXpJRYulDGuB6HEKsm3nzzHiCjSCLo4mUHLlK5SRJGD1ApRa0AvRgQSm4DspMgQEl3nircUecPUS/jV/+dDPHdwEZF0oWGJtwUkIxq1qnAcKG9zCVYinLqkdb1D41tDlA9BycuwQjA8UkCXXdpxyoNPnWbuTWvUz7XYdf1W3v6zd/OH/+XrtI3md//kPnZvneBX3nEP1lq++sBRtHLxTnvYJzPyBOQtnHtbyr/e/03sYUOcZhw83e/6mD+3QhKn5K9YT9aK2XTlJp745mFyrktQ9rl66zre/pLreMf7PoO1cHhpiban2bNliDOLi+hajHPHBW6++gDEggf+fZkbbtjO1p2T3/NceOTgNA89eAh5s6K2c4j5pMYQIwB8Y+EotTjiSOMiL53c94JnS6UQUNsi+cTqNMsf+iJ/9JNv+AudUS95xZVMTlXYedmfH247MDAwMDAwMDDwv89ans/q+36/jhe7QSHlReSZ6Ys01zp0TlfB97n5pueY3NvGnFrP3Fz/jUv+szVCVkkncnjLEVZbEDBxe8aGmzuIjuTc+SJGuDQ3Fxk6vIpz9Hw/xGR8GFsqIoIALSRivoMArKtoX1WmeN4ifQ/b7CC0JSt7NP0M2W6hRP9HzSrJ9GPj+OsyhBAYDLaYR0rVX/0bZwhPAaIfNhs5YDM6wx5ZyUVicZagc/kGVK3LUNTCKJ/2kkIuNXDSCHdTQHYuQ1hLtGmcsGsw85rf+M11/MF/WCLePkx2vokwoPBIxiXO5jaFsiA4kkd1DcFMrf96NwhEaEitgy56OKlLtGsC/2IDvXEM0bOsnclT2JGQjAegfFSscapgChZvtotYMZicpfzGJiIzREcS3OMSE7pYB5TuMeRFrFUKBBcShAWtE5Tm+U4Rm2bgu+hU8LU/2geXBbR7q/zaz7+Czx56hjONVYYWC5g4QxUKIAWm1kAMB4ggAG0gyRDNNlYK7PIaYngIYUx/E5EQCGDHdedhbR1nqxPYKGPpYp07fnw3p7+yhJkwbJQj1Fot4paCrsakJdKxAiL0KD+9gWebM4jhPFbkMekyrZd5ZJs9dr7+airDee55ScgRf5HpBQdTrcH6CawrqVdgvONhnRhrBLogiYcUkGEwyNVWP8w2ligjcJThyt9aQxah2jmMEC+l1HPweoKkIVEFl8KsILtgiT3QCt5//DFMJcVZEcRfzLN+ugtdhfVcZKbJ5Tz2f+UgjuNQtJKnDpxHawNWcP/ZsxwotBFNw7pWxnijP/ojhKC4KU/npMtNe6/nyDOHsR0DwNCaxmyGt153BU/sP8fPvfw2AN72j17Ds0+e4d99+CHiQNJLNP/sl19FL05pm5SdU6MoKRkbLdBa62KPN3GVpD0Rk51uIkctIq/xZYpVksZql/lzy3Dbru95LuTLAczU4W6DtYJm3Hn+th/bfDtPVI/w1k23fddjGmkbRyjyTgjAgf1nyfa4GGupttt/4TMq7sWce2oaL9PsvWmQtTIwMDAwMDAwMPB/pkEh5UXkF3/sDvafusATJx7F2JQT05toL8yzfMjH9rpgLBiL1Jpg+dJmEaNBCJafqyDHJOebG2nvdSkcXiReXwLHuVTaNKA1ptdBrx9GGBczaVCeQ/WNExAbijMNTL0FApAS40nSDgStLkYbbLmIGC4ipEAogZUSoS3akzidtN/9ESfghmhHQKaxQmI9B3+xhdwCuA5iWMAMBBXBP/7FQ+ALfve9t5Gdy8hVYtaMg7hugsvWb2T6iTlUFJFvwmZ3LzdNGR758lPUrqrgxpLcfA894qGvjem4HlKn2P2iH0DravbdeZri3T2e+sBevIU2uU5GZzQkGSriNDXufAv/1Q10RaBvzOOLhHBjRPZYCa0TknyAiLs4Z7pEj/uIqxW9eQ/VTrBSEHkJ5adnSdYNIdeFON0UU8yjh/LIc8ugbT+vJoqwmQ8bJmiIHHrRI3MT/uh9D7DOFaiKxEiFruRQkUFY299a1IuxjoNIMkSUYLII3CKyUrq0NjnFOAIRx+C4HDu+gTUxhPH6YcFrW10ORPei6hPEJ8u0h3wufzqlGpTBxmRd8FZrZEM5iBXGtUhjyXzwQp/CqSJ7d27l5l2X8aHPPsnH76tTLk9wvuyR99tYnSIyi5QloiRGO0BkEKpfMDEShh5MUG2NjVPcFuSOLiN0inAMbt5w5vhh7j31DG94+61cqNeZOdDDtHtYx8POdZBlDxzD5miM6dsy8od99hQCTtk5CHz0UBEuLnP86Byq7JGzMFzOU8z7TF0/wcG5BW70A162dwcyg5dt38ZVW6aA/narG3du5HyjwV1X7GLT6xw+9/H9gOU3br6NfTdex9biMK/au6s/sgNUlxp8+cNP4C02yfkOE5WQdRPl/+F6vnPPVo48dpa11TrKFQxXfC4YibsK7VoBp2fJFiVRKnnym0d489tuf/6xj33lIHNnFnnD37mHG3ZuRCiJ/XAGo4qbf/6q5+83HETk/XlOto8wEfYfv5Y0+INTH8GTLr+2+6dxpYuMNKMHUno7Qt73W2/5Xz6TvnTxY5xtn+Bvbfg5fuvz30YfreI/ssD04QuDQsrAwMDAwMDAwF8xe+nj++0H4TX8dRsUUl5EXnXNZbzkCoefe8/nWOhqZr5Z5kxpGLPbJ6hmyMU1mBpBdGPsav35QFmkpHM45tTFIbr78jgmQrguUgvM5BBmpAiNNur8MqJucOZWwHWQhTzWWsY/twKdXr97IvTB8yDNUN0EUW30w0yFgDRFxBq5J0HPykvdLA4qysDS78QIXDApblNBqjF5F4zBaUNl4yLqKmh9KyRekngiJfHBhoLGtQqnth5vqE2hFNPNF5hZW4M4AqP6uar06M2uIC+uEeYkKsjhdDNkZkhPCMw2gehJpAYbeOgNLidPjFEOm8hWRFCLkBb8DAyWaMLDtZLsfgXrBWbaMvTmBu66jI50aY27DC9FdO51KcUd4kM+yVIR1Ypw1hpYKejuKBLMK8JcQsd1SUtBfy20ErDaRTS7l3rjLDpwEUpAqpGeJfUEK40unXbG6FhIolP8coi70kHHGdrzEb6P9VzWbR3jzh+/ig88+izB001MAkJKBA50u/2fBeD0F4exY0VySQerBF3dY1SBVSAMpBVJZLL+4fjfOmUAIy2FveOU5paJZ9dwegZTLKEDjxPfWkL/rOGh/adoaUEBB5VKZL6IqPewCqZqgoV2goo0WkCaJhTOBHQ3GEoHU4Tb/5lCStylJjZKOfKvhgk3Wuwdm9Grz7Dj1btZWGmSTPmEc4J4o6C7x2P4mYyhXJ5fuuYO3t17ivblXa4sT3Dq6f72GtnoIITk3NEFfvcLv0qcZjzwiSeh2mZoMo9TVRhfE6y3/K2t+9hZHnv+mnvq2fO89wOP0CLhi/owb7xsuB80q2CUPN6a5eDcDO98+x8yXizwbz/3a8yeXGD+2Cwir2iVXRZbHQ5/7mm0Ntx2904++Nh+rhudZHp5jWTUZWjPKDPNNuePVcmkxBhDtBZysTeENBIZwmon/q6z4Csf+BadVsSVt+xi743b8TxJtJZBO8Na+3xRRwmFQOCI7/wqkEgkAiUk/aoojE9VWLlY4/VX7aEQ/K/npDxUvRdrE/7riU/yyIl+FtKvvO4qbrvriv+ts21gYGBgYGBgYGDgB8mgkPIicv83H+PxJz7D1DU9FhaHEMsNurdeBkrgJHW8VYUIfHQpJC5r/HMNpO13nAgpkO2E3FwP2Yn7K3FzAXgCmw/6QafnFhGmPwaC72GiuB9aKiRWqv4bNAMiTjHW4D93EjKDVT4Yg82F2E4Xd7dEd/P94kmtBaFCNhOot2G4SDThYD2X8voq7lDEypkSJnBprhUQOkGHLirRRNLnnf/hFrp7PTpTeZw9CV1dhp5DZ8qy8VuLqNRg04T1t6Z0Oilv/dXX8MTJC5icR3dTCGsGf1VT+JN51v8MRF6FtXqArdawtkBrrEy8qMnNrWCxbLt8PSdXukidEc73i0VRzdIKK2Qb8tQeTwknUjJPkVVclBODcDGegm6Cu9BCtnqYtQbS8wgXfZZevZ3yrEBFEj2cQzQ6pMMhTq0FSvULTp6PcBUyyqCTkCpLoBzwHaIRw2X75jhzb5lMC1ToIV0XE2f9DT6Zpr20xsjoELoWgQFrLLbSH9ug3cEqidAGt9qlsbtCMC+wvsPk5mFeXb2J6dMPUru8Qmu1R3wN5M8m/fXYQYgRHm4iWEzXWC32yPkOopdiVX8rkNYaR0nCkRBvViFXLbnH6gi/gMn79MYcjo1FbMoVWVtqYRzBrbc+x7cf2Ye4z4ORiMwbRS3UwESYfEhaySFm14imFb/y91/B1m0TmEDxrSEPdWWL5H2a3PGMLITwbILKZ6wuNLj40Az60CLHrusg2z1QCpPPAWAmKnzok0+RrjQ5d3yRIwdn+fij/4I3XL2Hdz/5MAeiC2wuDH1XIeXMzArdXgISzHqX0uQK+BKU4I9+5yFsfD+3/uLN1NME1Y2pr3VYPLeEq+D0RIgJJF947AS2a1hdafLw8dMcTFfZ3znFL77xLnpXCN418yBxDxa7PWxJISIoLceMhC106kEGG9dXvuss+NF/8BqWLlQ5M17nw09/GrG1SNatITfn6TS6FIf64bL7KvuQIuDK8neCXytekX+y+x0oIXFl/1fEb//+W3nymXO88u7L/7fOpFDsopldoD6/DpnNg6N4/c/ew9Rw5X/62IGBgYGBgYGBgYEfVINCyovIgf3LnDtTZG2xTFaBtOgTVlNUbFGrPaTnY9eaNPcVae9cT+lpj/LBWn/NsbUQuAjHwYaAFthyHt1LESt15Eq931kC4LmQaaS1WGMh8CAIEFmKTdP++l5fwUL//5JbXyKDACEkZndGbySHHI+JGy5ut4tjHTD01y4nGb2pIkoK7nzVMZSC+//wSlrNITjkkuAijkuE5yAyjV1y8LqCkaTB2t0Kd9VBRgYrdH/lr7Fs2TbHa39imhPnj3Pw2z9DrgV2sYHIMryxcdKhjMD3yF8ek93bIDzdRCBwGwn5Z5epXzeEmnfwZhssnJknGS0TbSxgC4rSiQbVN+aIxz3Csxb9pE/3qIssOYxNW1TiYkbKrG0YxmtkFJ+Z7xegPBfhOoTzEfXrJf7FBsJK0vEinSkXZ2UNofodAdYapOchIoPt1BChj19zMHmJzTT+hgixbh4Pi20FpK6LNCk2ilBOwuTuDJMpTh+bJzg0j60ZRLGI7Ud5IMMAW62DBaciyDUzhOMiM6h/aYmPJXO4vQi9r0s25LHzxmVWv5hgAw/X0RirEFJw6s9OwnCKkRJl+6uYrYDMhV/9g89xfH6Z8rYyyYNVaKxBURGNhERlSS+0FHBYVRI/6zGxp059JSQahfFGSu/HNdnTAf4TCWJqmNZEnnjHCMXpRd7wozcD8MnPP029qhmed6j6mjjnIK4CedGStiM+/9gR4rOr+AsNph85ia2UyOV91ESetekqtujjuQob9Lt4TMEnMjG//fSHOHZ6hfHVjWzcM8zR2SUu3zgBwA1Xb+E9n3+clIzzz5zlD75aRyQCa6HRSxCZYfnROTbffjnxs8u8+9c/xRt/9m72vaLJkdNnEZnliq0TuIHmY5+boXNiEbes6TZizq2tcXG2S6ub4igwwiKlJahqNq42sT2FKwTlXJ5r7vruAseJI/PMz1S5sDnm0MwMgY5ovH0dwdYSJ/QqN1Cgndb5w9PvZqbb49XrXsYb178UgNOnLvJL/+mTbJoa4b2/9pMIIRgfK9F2DYv1Vn919f+CzGQ001MomRBUVr9zgxIv/KCBgYGBgYGBgYG/HDv4t9bfBPn9fgEDf3V+4m138aM/9hbCwhBGCMp7Esao4zYTlOOC4yC6Md58G9VOcVd634l2jmJYbSIuLCFmFsF3sSZFCInKLLLa6HdHOA5ozfOXpwA9FCIE2DQjC8COl9DlsN+5ImV/HXKakKoUs8eHcUE0KWlv9uhcVkZ0IqyxmFxAWgmxDsi25uzxUdqrAXEvh3UUtivJjoTQNMhqE7oxst0jS2PSgsfIYy656ZTCTIfR/SnWVxgpqS0GNOs5zs2McujwHNZ3kKUiua5HttwiKgia4SRnz2+keZ9Frrax1hBvGaa7a5hosyLL9TfctOod1GKVaCykuzlH/ephjPURmSRYBp1z0YFDkrPkvTqi1sOt9nCaMXRijBRIz0N6/e9NOhoischmhJqrohbXwGgKJ5voUIGkHw7rOSAE1nexaYb1/tvfgCU67XP2/jGSCyl0UkSjjWhHYC3GK7K0Ok6wI+WWl+7BzSTkc/0tSqlGaoPoRKAU1hp2XG/Zsr2O1Ro6PdJah1Y3649r5X22+GU21G9E53xIUkRbI6MU2U1JZtr4MxmZ0Jg0Q8YZbjPFJJoDxy/Sbaa4dUMxzPe/rjjBu9Cg9PQKxUOrVE8uo1oJtm557F07EIlFGEF2zmXbN2tsrNZIXEGv1cX4AuMJXCdEZ/2xpLkzKzQ395i9mAEKvcknGVJEEyHJhhL1RgRFDyfvkvZSmF9hyJOokZDepgJR2WXXaJGe1BTGCrz5J27l9459gZPMwFiPUuLwp994mn//xYdodCMARko5rhwbxd0uiJZ7ZCJBuBlGCbQj0Hmf06cWOXf8DOdua/HcufP81w8+xmXXbcddbeJ0W7ztTTeSyDruUMzo3kWu/elTbPmleb69MM+FaqPfkSQEWShJNmZ0J1wu6FFOdKd4rjtJ23R57qmzABw7Mc+3n5rm0FPTzF9Y5c72NkYfyFinCwzLMgKPXOoCUEuXudCtUUvalNzvrD/+2peeZinp8ezcAsmlka9f/73P8acffIhfet9nWe10X/AMMtEDZGt/GxM/wUznAs3M0khdTLDWnxIS4Kp+/f7E6jJfO3uSzJi/iuPv++YX/vizvPT/eQ8Hz1z8fr+UgYGBgYGBgYGBvyGDjpQXka8/c4qnj88yOZ5n+exF7D7wbY/KgTprF4uIZpNuVVO8kJA/cw6basTUJDaKELVWfxNus4co56HdIzjXw+ZzkMtBPg/dCOG72Fj3g0oRZMM5fGuxWQIYpHCIowZ4EkF/nTG2H5iabR4m+HaEvpCicJDj4KaSrJLDWawBAjuWwzoS90KVZw9McFRswZYDRK+HzAzxKz2qWzyGP9rDu9hD+A71vSHl6RTZMujJHF4zw23qfoipsdS7Y3zmE2WKbwI74RCHJYKFCBlneHMNvJkqravWEe8PCBoW4XvIVOO3LZ0dClvR9C73yJ0ChMRWijhd0Bl0NwVUGm2mLtZxKoallWF0KMlt6zJ0dxO9Yql9YQSRGVSzi0o1N792mhtee5Evv/cKpk8GBKsGXXQRbYGz2kGuc9CeRfse5Rt20j62RhIq3JUu0lhau0q0tweUT0YEyzHCcWidKxIEGpv3kfUudNsI1Q+MzRLJETHJA998rl9AyTxElCAuvX8tlQJq9SbC95k5aLnuR1ZYYBM4CuMo8DzsWMDYMYdqu8H5EzX8iTGK5+dwO5pE9cB1QYMRISZ0ECKDVOMkGY6RRFMBwkIOh64FhsqINAMPVK1L6bk6egpkroi10F6rEC6mqMzDWMvckVEyE+Os1GG4hz+9Snh6jdTz+Uev+/fc85YbefObr+PLnzlE1k2J8xr3giB8WOBXLdJJ8ROLLeYwxQJkFpKMxeklOHyWfD6HCAK+fP5xElehtg5x5vQ891WOkyjFldkG/vnfeSX/8h9/GCEF6u39791X7jvEoVMLNK9JyN/qM9maY26mQNQ02MAHJek6gtpPO5iKJF0HtUcbPJc/T+u2IaRVTDfqnHoupdcJWS4OcVd+muVGkbMLHdyKjwkENrPkO2COC6yE2DosnR5GpBY/dti8ZYw4Tnnvn34LYyw/8XfuIucpikWf+84oWo02Y/cl7HnNPnaN9rtpNoQ7qbibCJRmKvzO6uR8DMNPrzFUKuA7Dn/7Vz7AqTOLRBsd4qLms0eO8bM3Xf89zyCbnQNTBz2DL7ZQi3M005Cetwx2KwjopQlR1+HXDnyUpm5h5Q/xmi3/eyNDP0jOLq3RSzPOzFe5Zsf67/fLGRgYGBgYGBgY+BswKKS8iHzt8EmWl1qMrxrIByzUPUTRcMeWZ4hShRZdelwKilQOuA4o1X8TLPt5FkLBxNaI5fOynzC61sSELqLT7Y/mRAm6FOB5OYw1EMVkq20koHwHq8GUXdpXuIycv/TjZUGXPNCW2HFRxxLcSszI/jnQGWJkGOEHGE+irEP5cBNVjVCRhpLqjwJY8K5O2HpXnaVegdZWHyMFeRVQOt7Baxis60KWYUyGzSxxQeD2UpQx2A4MTXnIvM9532DPduFS1wRAMNugPRogHbcfM+04kGmMYwgWE3LnU0TgIzwXu26EXCpIpMUKsMsSp6HZfuUi3mjK6c4EZtmlNxvS8B30eAd10eA1NUjB0EQXpTSlSpdk2CcpKOQGlywr4jQT8nMR1ZetxyoXJ6dIx0KMAtWIkY6DLrgIAcakiNUmNvDwug4676NLPjKK8TqXslXSDHoxwdc0jz93CL1rBI41kY5E9DRCQHN9BZMkOGtdkkVNtZ3rdxKJ/tfX25rHFgPUakSvlxBULV41QeQU2nUQ1ukXXXzQTkYcOpgy5HoKA+icxAT99crv+PFbmXvmHB/+wwexlQKy1kQKCcqDWGPKApvzINMgNEnRoisBNjYI288DcRE48wkyLJLmJRcvLnDvhx9l177N3CEv4xv+KTp7BCKfIPam5B5QsAZe4BCiUENFVJzRA+xCFRslCCORViFygmRDkTRJefzxU+Q6GWZKEc66nNi+gqdhNJfDpP1OjSjNsAIKRxRinWX59Cg5r4l2BMZahLUk2mLzgpIfEY3kYU/KsfwZtu0tEbSG8LVird3ACoMOPMpZzMnHtrHy7BpOoJCbJdZAlmjcFLyVHgLB6WwL67oOr7x9J7fesQvPc7jlph00Gl2Uzji+/wIWwcYrNnD68AVKruKe67fjugror27+1d3vYC1psKuw6flzJO4m+CsJBdW/Ns6cXcH6Dn7D8rKrd3PdhqkXPINk7q3gXQXuVTi9KvUkBARpMoo0IIzh3/7If6ZRbZP/d+MYz2M0H/yVnYHfD2+/61qOnl7gVddd9j+/88DAwMDAwMDAXyNjBeYHYLTnB+E1/HUbFFJeRDZePoYdU4j5BWh2kcWUoJJx4t15bLOJThQ2jdB5hZMalHKwxiIsmCQBpdh6fY3q+QAygUndfmdDQ4AU2CwDJbHrR6GlscqiFvtrlS2CrB2DiPGbPfyq6GepJEn/TXnJx//2NDbnw7aN/fW8qe4H1EZpfwOQABvF+I0uUvgIm2CF6K/xkuC9KUHmLJVGjF4po1JBYBxUTWLbPdJxCVh66/NoxxBPeHhzLcrP1ojmHbJvbmAkP8LxxWkKWLJhD3+t2y/05F2c1Q4WEIj+qug4oXQqxqn3kB3Tf+1KYbGI1FCZNsRFgX+6RgdLLBSL9WFkL6JXUKx9sExnG8T7PAhcwqMzZNbyzfdu5NDXhpg9U6L6tgKjdBi6ssWSnUQ+PYOsJ+Q3lXE0+I01yAzpiE86HOK1M4qnO+SaHkPlLp0woLlriOL5FlYb3NUuxoAp5+n/Rz97RmYWHWfcvG49T57qQq0FfoAV4OU8sl7a/yESkv3+KHlPYD2FNQbb6WJKPkpA4XSK1zOIJINrDFk1j1wx/cBiI0hGPVTJxa0aRGqRWLJA0tuc4s0L/u0nH6SpupTaPUQnxnY62DTrF6hGC2SVoL/2Wkm02+9o6pUlwvHJP34elaQgJdZzEZ6HX0jY+O8CLj9xGY16l9UD55FTLu6KptKDtBRgkgyRCBZOrRBnmondE+iFFp16F+m5yDBAhwE6kAxdNkk96o/thIGLeiom8HyGbsrzsf1HSG8Y4WdfezuloX5OyM++7Q4eODPN0WSNK2SFyTGP1771IBcu5vn4By9HWEEp9EhVwmjYoe4qxqONjIc5vnV2kYWFlOe2znPXjZeRnjlIo57nix+4nd5iiHYl2lrcWBALg+z14LaY4JEA01UMbSpzlS3y2DeOYOOUX/7tH+JH33IjAL/59vcwc3oJXMXUVRvpXTXFVTvWccur933XmTHmVxjzK9/1uZOnlrFBSC3u/wLce/kUz51bxDqCLYUKk4UCL0gEJGIUDwdXFQkdQ+Ak7PT2clZ2CGs9zj13AWsM+w7tYfSO9ewpbfhLnnzfP/vnLvKH+58k6AgOn5zn1mu3fb9f0sDAwMDAwMDAwN+AQUbKi8jP3Ho9Xsnj+HqNqLW5Ln+eG7efJbfOQUcShKJ97QRLP7GT2u2T6G4McQJxglfy2LKny8zTFVrLeXAddl+3HTVc6I/6OA4iDLH5HMq6WGuRnaQfEMuldapOP0NFjQzB6qXVqWEInousG0AgDBgBWdFH+C6qWMAajQn7xQYpJdmwS3uXwrgC4hQRJf3tL22FrlmiT2v84w1EktDNUgwG4yicuSZGCbKCi2Mcxo7EhHVBMuZTvXqMgzOwvFjHFAN6m/N0p0roch58j5yXUJ5rIlwXE7hkm0awSuAvdxCpRmQaPVwg2jmOjMFKS2edwiqBNIaoaXjq8B6S2CWoa4aOJthGm/ChBcqfbFA8lZCNlbCuIo5c5k5VMEGA2/OQRyX1Z0eQrRjZjZFJikgNwWIbu9ZDZhmqmyG1gUQjDbgti5WSdChEuLJ/JVtBVgmQUmJDDxv6CG2gkKN1W5lkXGBVvh/SWwixrsQ6iuFGB9GJMFGMiRMSI7F5v9/R4rt4s3X8TorXzPA6IIzASkFH5EgcH6IEVuvIeo+xYz1Gv7ZAONtEZJrGBoW5PeMddz5Ifg6ac22yBU1jb5l42MGOVTBDefRwnnD3MBYLmUFV66hEEK6AyQd0xkG1u9gk63cSJREbXrvChrvXWLMJN7zuGrJMY26Y5vJrZ7nz2km2eyW2KY+xwMONMrr1LlYbzq/UmW91EalBRgmgSPYK1v5ZQCsnUI0Yb6VLcmqRuZKmFgqeOTzH5p7HPdfs4JortqC1YXp6mSTJ+K2/81p+78dey4ZujdkzVU48N0bXjGCRWG254ubNuGcEWdPBj6FbiTjXqRKJLkQZYWx5+KlzLJy1NHoaawSelxGPSLQvsNpgPDDKJV0IWP+O9bA+T2jmOTs/Q01nTM+s9AOjL3nN227jrjdcw1VvvoLq9S47941z2y2XUVtu/k/PkWqj28/j6S+55qartuDWUrLQ8oVDx3n0zMwLPnat/Qlmqz/HWvsjhNKn6FocYSn5GVdMTXL7LVcS5DyUq5jOGR45Nc+zCwt/mWPv+8pYy/BQns0bRrhi57rv98sZGBgYGBgYGBj4GzLoSHkReXZpnoOn53CFJNk5QVLXjMglTGyQuX6LvSiE/U6DwO+HwV4KjnzDzyxw413LfPXD63n4qxPYrubM/jPIUoDtpv1xlyxDTAxj8wEmSpBaXQqTzTBSgNHYMMRKCZ4DUYKU/VqdVQo7UsaOlCFKcVMLgQ+ui1WAK0kdUEh0UdEdbSGLGbnYR8QadEb7jwLEQoxQAtGuYovrMDkfU/ahEqKWDeHZGupCRJaXpGTItTa9y0fpbS1gapqZ1S5xzqW7t0LhQobqxggLGysXmDlWQhiLmRrCDBfAibC9GFnrYZMEnXPAVVgPVLWBKuaJSx4l7WKKEmktbidDyEtjQaNFIAOpkFrhdjNEYhCBD46LowS5hRij/z/23jvMsusu033XWjueWDl0V+dudatbqZVs2ZJlbEkOjAMmeAyMPReGMMwwwwA2gz0zZAa4BMMAZmYM2IAztpwtB1mSlXNotTrH6q5cdfLZca11/9hleTQ4CWTsC+d9nnqkrtq1a9U+56yq/dX3+z5FJl2cbrdoTfJcpONhlUYGCifWyCzGhMW4jXEl1rGszZdxMJQWYlASU3bIKy70BO5Ku7ixzgxW5si9AWYW7rvrGCrNEEGAdRRgWT46B8YUGTnA6FOaHFuMfVmLkyts32BzA1FCZbJOU0REL/PYuTjJ2uOzSAS208VS5HkInWGDjNLpPl2nzD0HduC0EhzhIj1LOBfjpaBREJQQmeVstMLoPQuITIMjsKUZhAbVztCTCqM1QgisAGeTy/hLYnRFce9tk/zC+z/FW37wpTgqpDbW5eUXbaXxik18rnMf49sU6YEIZR3mhz2yiqCyti4TKIXAMhxE7NzdYPWpYdJVh9JsF/oxDNXwz+SkNufYJw9g73CxL7mKz99+mE996nE27hjl1lOncRTs2NSifrbB44+X8adSDAYJPPjoSeQpj3hcMvLSEVyhODm7UmS49C07Lphm95l5zt2yyMz2Orv31Di1MoQRkjE34FBjBREbTMnFdhwee6zBZcMtrrvxSzhymHf+1YuYqz1zL3j+jRfx/Bsv4n/ccQf3PXSYracs+S2nUI7iF//3v6FUKcZpMpNzonuGLeWNhKp434o0pGMhol7UQv9adDv2+xXlo10uCHdx/a5tX3MPSrMn0Po0SfowYfB6VvolGlGVy6vD/P6/fhWVwOMP7l3kyOE5Xr3vIro1xf4NX3tU6DudDeUql41N8dILd1Crht/u5QwYMGDAgAED/rlj19++3XwnrOFbzMCR8k+IG7fvYvowDD+V47U0Kx8t8eh7tjC7ZTPxTBmrDKV0jYnPnqV+6xlIUmwcY7KMxoqHNg6NFQeMwWY5utcnO7+CSZIiM8RzoRQg0gxpAUcVoolZb/6RshjlWW0gsgShFIyNFG+5RvRjZLNXNP14gt7OEsmYTzJdIy+7qChD9hNEZKne28Gdj7C6yGkQrkcy7BWVwMYiwgA7EdCZciFKQGv6G31EqYzUFq+jSf0Itdqh/OgS/rkO3W2W7kZBWrOkozC9Y5lNO1fBGM4+4GOTYgxJrfVQ802c5Tain65neCjUfBO50sU718BUS3iRQ9DQmFDgdjNITOEAEYLW3pCl6yv0928gnRnByqIq2gYeOBJhLVoJ3K7GiQzGz3C0RdSrxDtGEcbQ31Jmy+5JyA0i07jtPiJLScZckqEQAhcTeuiSh1YK0U8QmUFIBz1cxgxXwFFIHCqfocg1yXNwXKwuXDYi00TjtaKBR0qElLywvgnrF84cXAcVRfRKAtNpYrOMvBdDnMOaZfWWo8UolOMgHBfrSGyWggPaF7iRQGaWk+PbEGYJi8XJLSrpYlZbiE4btMbqnHjKcv7fb8FOjyByixUWp6Nxm3HhxpFgc43Nc9IFydn3lzjxyTF6jYBosc0ddx3m+bVXseHJm/jw7z/Fxz98kmghZFN5mn67T/v0MvLYHMYVlPo5ylqcqSEmt41yxY+dYV/lHKI1RzaksFnC2ESVMKB4TC04eUK6ucKJVoOh4TLKkbhK0u8mdOf7lLbv4tJXSpSnmfXqyChDdCJopYgUzKLLZfEuXr7neaiPVEhmBWlFcHxphd5yRCwE3QMdnnpwjcbxOV592R5+8YduoFTyMbIYhTNKEs4qepGLxaXdq7J99xQvun4vQgjuevwkH//SgafdKZeWJ9n0eMSONUVz0uHMZomUX5lZvXvlYd51+sN8av62p99nF3tYRxFGxWvP9D1s36G7y+PEXaeZrH3t0Z5AbaIiJb4cISejn3j04oBm7DJaLeG7DifONlmYbRAf/RNeueV9hKr79OfHeZv/9dT38idP/iDa5M/l9vgt4cD5BQ7NLXHXsa/t0hkwYMCAAQMGDBjwT4+BkPJPiLljK1hji5pRbUgWc1onh8kmy8RTJfTzFOZKFzdOifeF5KMeNs8R1nBmQfHYimStpzF5hnWcIr9ESkScYpMEm+dEdUVvOkBIWdxs+7IQN6xBuC4CMFmG1RZcBYFXvGELAWRfQvlH20SXJCx/V8jK9SWUBpWaIvxWKmQuMFPjTLzMsuvfryD9DJIEd6EF3T62H2GxbHCqKOkizy5h11ZoXxTQ3+BisSTDHmakhqhUcTIYfnAVJ4fayR61kzEbHj3HTzzvs3zfzx6kVmti+gq0xcYJ9CLkchvpeAjlEu0YQW+fRpZLaM8l3VzHeAodKITj0r5ohHSkhBNrhLYgBPGMIBmV9GdcTCBIRn30ZI149xS22yfaEBJtHyLa4JFXBL2tFOMU1RJBS+Mud0hKGcdOL2ElRd5JP0H2MpzliNwHmVms5yBKwDUp1nVIh12SuoPTjHFaMdbzwFpkYDFVg1VgSi5CCkgSDBZ3pY8MgvVxDnC3ldGBxDoSkWbMXNVDlySqVIFKSNTuo9YynIMZ2UKzuGl3VDFKpCT4AUlZkZYsopfgNQwiFsibJN5iH28hRrQS8pmxYrRqbgkWVyif6JJtcMgmAgh9VCMjqwh0RaE9hfG9oi0q9Ag8w43XHuPGSw5QuX6FZNpwdnaN6a1jXHbJLlKtOSrmONGf45a5h7C9CKIYf7bB8GKCqSuCXDPiSERumD9RRxvF/IzHJf4ww5ds5YrXX8u1r6qiqw7Gl8jrPI5fX+PX776Di/Zv4nd++/V813V7qK1YxoOAOxtLHNAJV7/5ECOX6acDnNGmqLDWli/ccYhPfPQhKm3N8FNLbJrrs9UvcfDsMlrB3LRi+3cvUN6ccdHIMLRz3IbGjcGJNX7fUkklnWad2774PI6f2At3n0M8sUS7F/O2d3+a//djd3D/wTNoY/jjP7kNc1+beCnlgX2Cx7doPnPq+NN7xqNrJ1mIGtSdr1haZKePN99hy1DhULlu90FeduUj2F7Oy7/7qq+7B+n8ECuZQ5IeoOZWeO3WPVw81eXS0RH+00c/yM9/6n2cPzoHWc7IzJMs9R4jzk49/fl3nLsHR84TOKf588dufY52xm8dL7pgGz963VW86QWXf7uXMmDAgAEDBgwYULjDv0Pe/qkzGO35J8Tv/b+fJvENQVkiM0131zDdTZLak22CtQxTUtgVn95Wn+aLqwSHu4y/dwFyzcZLO4zuipi+JGL2iRHQurixlgKkQiiJ1ZrOthA77BKctmRlgbtqMaZ4qVitEa4LWYZwgkKYWC1cDFJIkAK1SSBGIfBjnE6VcM4goxyrDbIbkzmGxqVjlBoZ29/QRbmWs9MuyYkctZbS31ll9YZxavevUplrsckbob9hGmMNTizobYDK4RQyib/mIHODUBKnD9MfaeNWA4KKC59b4dylJVzfEKU1RCCwvagQBYSAKMZ4Hmayhqn76MSCKmM9STThFzfG1iIdgdKKaGOZwCj0Sh/rKmonczaNt1C+ZMkMYx2Ixl2UUOA46LEq1lE0L/DpTUtGnsjQoVtkzKxojGMZvX8NNORoUDl+BWzX4q1FCBwoeciFJiQxedshHQqxUiDjHOE6YAUWQzYakj7P0sWlcs7gxg5CO9jAhSiFkgfrQpmQkjs+/jDO0CRCg5AO51oT6EkHIwwy1VhHISx4Yz6j09N0oyZ4hcihAZFnUKrizbWx7R6qocAbwvmDHDsjwXGwIx6ELrbikParWGkIZRn1kRX8kyliLKVyskU2WUP1LUMH+yghsUohlcDdAOM7YprCUPlsi6zp8kP//oUk3YQ7F+Y4Uc3Rqabspci5fuGqMgYElNJCaHOaKcunVgAYfySktT1j+2iTf3PjS/n0p5/ghTdcxM5LXspP3P3ntE71uOD5FTrlcWp+icBxcKTkjjuPEOU56YrGWTSc+fw0tSjghr038t7aY1ib4JV8epEAa+iHkmONFrK6jFt28Cjz2BPn6Tcj3J4hOdWlsuW1hE8c428e/hRmeoh+HkNZoBLNxkVDuWSZ2l1i4pWHSJOc0x/bzhc/3qIaCIJ3Pohz0TRb3zqCAHJjEWWfqa3jbKqVmO23uWB8HIBuFnPH4gksARvCorY3z3Nss1tcJ28Dq+0lorJPn4BXPO8gP3nNf/u6e9CZfCe3ts+yO9/Aqycs06Uem1KXRmOFx24/j7fQR+Y5OA4fWnolIydzXskQL17Pm91ZuZx3HruIJHeQpxb48e9wfaLse7zykkFbz4ABAwYMGDBgwD83BkLKPyGuuGob9sGTjKicp2SD9g4P60rCxRSZaWTk0t5fwWvHuIsZ4bm0ED6EoBEFNG1IT/qgc5AOaI2sDYHrYPoRpBkT7z9QCA0I1AVOMdJjDUhVZGrkOTIMQUq0BzZJkLkpbrSNJrnNIU3r6KMwmaYoV2G1xgYu5sg8evskMs3xFyImkgauoymVtxDXa2QiIRtxMZ4kHfXI6w30aIo5BMLC0CNdvLZB9RKwBu2tu2mshWqJfhn620KyumVjnPPBX9oKYYD0KcQfKMaT8rz4vqKYtFyjO5YSzhnymQr+UkLldA+rJMlOF3cuQVtJZ2cJdwHyDSHaE2wZP8eNlz5Kpl3e/+SNZA44ucDpxJRmxrHzEdaRjN7TpXVRlbgmKCcKEJiyT7Q5wOkuIzKLl6TkyqFfq+L3+2R1j2Sjh9O2+LPLCG1QvYD+C0aIJqB2MC/ahYQAz8P6PupRSf96i77QUjtnKB2PwTqw1sZDkyIKR5HjYDsJzpUpPK7AaPJVB9nLULEBx0GaHOoO/STApn2E74PnYjyF8RUoML0ebjsnn6rT3VOndLyHm2ToTh+Gauj5FNlcxpQC0s0V8AXVO+cppQKjc+xSAnUHO1ElmgxRjR77X5Vw9IvQ6xh6K6Pc/GdXsnY0Im/EDO2Ah+88xrGj88xOCnrzHa546Ql27zjPl5Y20wl8rJSIbo/+I6e48iev5YnDhzFSgjYsHRglF3XSMcmv/NlfsGW0xAcOnOHX/ubf8XM/8N2srnQwSc5F2zezZfvE06+5DZtGEKFiYqzC912/n1sOPcBV5ZgPnZolylNca/DrIZ1uTlqV2MAB15LdGCIe1czddp4P3AcTm2vMPnCWUtuyddMhsguGuPNkk6zfwck0cVUgtaGfZriuYuFYl6nedhydUvbGeN5rLuSWv/oSaCid6zA1WkNrw3S1QnLlTkaHPOYPn2Ns9wSdPAGgl6fUnWGsWCFYH/dZW2xhrSHfMc2i57G21kAJg7GSYb9XhEp/HW6fc3mgt4H7z2gurh3lTOcpjjR8djge1SciysrSFgLhu2xUlxE7PhcMjz/9+dtHx3n8SxeCgCm3/1xujwMGDBgwYMCAAQMGPGcMhJR/Quz9fofmdU02Hpvh6E8fZPQLbSQKmSqyiouMUmoHW+TSUO04iL7GSoEQkuF6wpAX41iDUC7WGnBdcCQMVxElH3tm7umvJazBHs1gWBSjRLbIBkEqbJ6jQ8ni929GpDD90fMIJCbVmFIVTgYoo1FJiuwlmNBFS0m6dzPR9jLZkKKxK+ALf3wZk6NN2otlhLHYVofKuQxntkXYUYS/ZBC1lNajBrMYEmYe1giMK4knqoSzjSJUNc1I0FjPpzoXU/rUWajWivyVKC7EE1NU+GIsVgiEo8BzCRoW/7ZGIQqdbOL0MsTICDZ0ccYj1KMCowXadwgj6Pe6lM82WNlc4Z7qPsK8D/tixHEPf76Pu9YjFaA8H7PaIVhKyTyLkAqdGVQuSAOX/rSPdkaoHWoxVFUsbhhHOwFuX9DbUycZcQlnI7w0LUZHmprq/edIXrENqwQ2A3K9/thY8jKUVE79gEGEHiMXdmk9otFln3QtRgiLgcKJ9CIXouJTjesgoghhIat5eJ0cF4haMZFwWQkiStpg+xGoEKSDkdDeU2No0QXHofxUg3xIYft9RFOB6yEA0e4jM0OtYYt6beuAAqkk1nEwviKZCpCZxYk08ydDvEpKP/MxvmL2xAi630OOCGJtueuzB9h92RZe8qI9/PXBLyDOK9gKvcUKwtMIz8Paou3o5g3zXLlljPluH5nl6JamuVQmftxFxX1OPnQcx3NpNDq892/uobHWRaWao4fm+LH/eBOV9bDW8ystUgVLF8zzRafPi79Hc9PFB3j/rTVMFMNClyjNePEPPZ/MJtz52WOIwPCC3Rdz313HWH5hQDRmOFVtc9k1mxgL5xi+6AHGRi9h+VMjqNWMoGepXtyBKzRrX6igI0HFd/mBy95G3Oiz/Q3HuP5f7Odz77sLxoeh7AKglOTGl+zlwS8+xb0fewimLMYJ2P19YwBMhjVevXkb96/N8XDjQXZUt9BY7WLLAVZJ3FrIcH2E4w9OkGiPXTNnv+Ee1GtW6eY+pu3SbwVoNmLpcHRuhRRwWimBBDd0+LFrXsjM9vFnfL4QghHt0k0y9u2Z+Opf5DuMLNc0exHj9a9TCz1gwIABAwYMGPCPgF2Prvx2852whm81z0pIabVa3Hzzzdx5552cPn2afr/P+Pg4+/fv52UvexkveMELvlXrHPBN8OFPznJ6yeOSkRZpxQEpcOa6mEqAdhTNF4wz+qlT6NGA7sUTCG0JZo8jM03aFHTPObzqdR0u3SP5q7eOIX0P0+4hvhwuqSRYUThWAKxFtCSiUgVrsXleuCCUwgoAgVW2aFuxBvR6DornYAXI5aTIWtGFkKEW16goSe2oobvZJW54nD8/Rla2CGnwmilYKM1l4EH0GWBc0Ngwgxo2VA6ugrW0r5giDwT+oirGhlyFROM1+njSK3JM0GApwnK/LKR4DiAKd8rESDGCYiwyybEUzgVrIalI9HQJ21WUJ2JYkThRztq0y+hnVkA5iDM9npzdSvqCPgwZSg2N80SMLXlk/RjV6SIk5JNFBkf9oRayFWHKPtlkDTxBMlkiP9MlLCmCZkaeGpIRH285BQ3i/BokKXlZ0b5hjOB0zPR7D5JuGkXlCpsXI1ZOJ4GLNOVL2+QfHUWXJIvRBNtuWuXcE3WUlIy5koWTi1jAHIdkNMR3crQncZY0sq+JNwR4TUvciBGZonqoi9NyMVkXshyBQS6sIpOE8TMWHJfu/s34no/Tzopr7SrkShNTLiFaRcioxYIBq9ebg4RElEvF/1uL19ZQCVlKx5jeKyj3Kpyf7RShuSWP5RtnqJ7ssLljed337ufya/dQCz1+948/w9zjk2S2hxP62H7RwpRXXa6cOUmzuwltM5AW7bukKyVMaIknalS7bbZtn2JywwgvetEFHHryPEt+yr1HZln+9Y/xX/7baymVfJqNPsZacptzbrHBPfeXGelu5KqpMe7oNBBhQNbPuOZf7+VX//TjmLqLE1lesLKDW5JTmKpA+Abdt8y1Oswt+qy8+3I2XTkFsk82rFB9jRtohNYoA1GUEWwt8ZmF46SfPs9jdxyhUgsxUQq9GFUbe3pPqGxRHBo9hZGa0TOay/Q4w8FX2mWuG78KRMKlQ5cA8In33ofSIOab1H64gvKglwRoK6lIOHZmiV1bvo7AoRzSSKFTF+k67KhdyYHW/QyNlfBHykztmeaajUPsuXrH3xFRvszbf+TV3PL4Ef7Nd339PJbvFH7+rz/FkbllfuX7buB5u7d8u5czYMCAAQMGDBgw4B+Bb0pImZ+f57/9t//Ge97zHqamprj66qu57LLLCMOQtbU1brvtNn73d3+XLVu28Eu/9Eu8/vWv/1ave8BXYVN5hjT0CRqC7kWjyJUe6ZCPLbuEZ7pUn1yF0Rrty4dASsKDK4iscJLceetmHqxdiHnEx+35iE0Zdq2N9H3MwirEMQQB9KNCYLC2cC8ohcmywsGhJMKATROU9Jn86HlslCL6OVZJhBDIRg9TrSBaUeH+8J3CrZLleJURbB9EZqg/uIoMfKwVSKFInYTkRdsoPbWCWutRer5L9HgAgUtpRNOddjD9HjIMQCpqJ7vI1TbWgnAU2cwQrf1DTNy6hPCKv9ibJEGUwvWRpAxyA4D0vEJcAWwSI/Ic4XnEF29CdRKysRCbJrjHW6xeGjD6iT5ebGntHgMpMM0GIKAZoVGIvqU95pNdWWdYBMi7zhSijOfS2zuKtj2ssIULKEpx2wnasQRLEc65JvO4dK6pYqXAa6QYF+r3L5B3WyAkySVjdC6vkYy7BLNZUZ3sOtBZbzxSEudxwdoFQ1R7GXpIkjqC4+fHqba62BTCjSME82vEnQh7yMK1FiPBKsHQ3ilawx5eI8FKixwP6KU9rOvjtCJErqEUFOJZu1tcS4DAUD66iBmvoayDGB/GhB4sNFCeg8FidV7kukj1dPOTRUM/QuQ57tkG0vjkZUnklVhqKf7Hn/0I//Y1f4TINVIq/DMdrtgwzitv2s8f3fxxKudu5Zde/0b+aPoH+dlffD/5lItzcplyqOg0M1RX454ZYng6IHq4h6kGpGMlwk5KPhySjXrwvAu49lVX8e+/549odRP6vQRzUZ3EWKI8L5qKgDhLyX2BfajGhnyCmy7Zg1e+kXt/6n+SVgJCx+OS5+/i3vc8gXk8JRr3qcxGTNZLlE82OP/iIYS2jNzrooxEBiHzUYXVtkPgOMQ6JdsIpZLGaWSkqxblS1b3CP78sYe5cfcWXjJ8BfuvvQCDAEfh+erpPeGx+CSr23uIK0CcGMHvgs41yimOmQjG+d6Z73n6+AePnivGtywMb6/jqoDAS0lyh5oX83vvvY0/+8Wvvb+HfkzacZCOoR5YXlzZhwAuL+/goh+cZ8+uaURuuO+uo8zsbTC9cfjvnOP5Ozfz/J2b/z5b4DfFgwvn+PyZ47xhz6Vsq//dr/9sOby8TCOLOLvW4nnPwfoGDBgwYMCAAQMGfOfzTQkpl156KW984xt54IEHuOiii77qMVEU8dGPfpTf//3fZ3Z2lp//+Z9/Thc64Bvzln/5EvpJRrLa5eH3PUBkJY2rRsBawtNt/JZF1UsoKxBrKeWjTaRSWEfxvP+4yjUvfJjPTlzEsf+1GYXEOAqTZ6R7qhitCQ8XTSTWGIQQWKUwuSa/cANkGufEEsIUIbVkObILIBGVUlGLjEB4blFli0VbjVKyaJAR67em2qDTGNnuYPsRYmoCYSzxiEs2VcZtxCjpEs3WwM+wnkJYS3CmQ7ZrEh0qpFXIbozVBqs1UimyqSEy1yNLIuyQR++CIfxDywRLMSKQRduRlBhHFiGuK40iuyXLMaFHNjxEXnVRkUHGhvDQElIXdcciszjKY/TJBBsnRe4MgvLj59GbduFkmn5NojQsjwrGhyuIboYt+ww9vEp7m4eWYDaMYEMfZ61P/WSOs6Zx2gl4BmENRim81RQZ5wxv7tFuDJHLHqU5yfDtbdyGJd07gxsbSGzRltTpgyiRDnm0m2VqyQqyKanMN4pK6+E69BNaa11yY4umpk6OP53CnEGkmvKGUaxNyV2BXcvZPj3C460+Yc/BVksIW7iQhDVYJUELivJ4QToS0ru4zvDDyxglkYtrMFxHuD2MV0d2I0RWhOkK18NGEQQ+mMIx5M61yTcP091aRSrDZLWDHDmIrkpsBupcl9qdq1z4U7s54+acvkjjLXf5qbe+h5suu4TA87DdJjZO6CcgEAgXDq5cwPddPM387SegXkJPVqGxRrAYI3NLUCnxN+/+Etn5BqIUUioHXHvxDjZfs4lX7ttNWPIAGKqUUKlFZYLJi0O6e2Pan2iT1Ieg7mP6OZnQPPLZo3jdlHg0IC8pnhRtNmwfZ74So1KLcR3CnqZjMqYr4/inHK6Z2sxdD56g4wKJQCznmDSlNlrjopFNPNhc4Fi7xS/+2PfiOw4qS8njlIuv3vH0nnCVs5Xb772HxkmDj+SeO47QWO0yNlkH4FRnhXefuJeXTu/husldzKUZ3kgF1YsxH58juKHETLlJIysRnSqxa+fU192DgnQDvc4p3LZgddVyyeQ4/2rbiwHY97pNAPzKf3oPTzx2hnrF4/X/z/XP0e73zXPb7EnumD3FjvrIcyKk/PLrbuTo4gqvvnLvc7C6AQMGDBgwYMCAAf9/4JsSUg4ePMj4+Fe3YX+ZMAx5wxvewBve8AaWl5efk8UNeHa4juLA0bP8+a0PsLJjmGirg+ykTLVXuOin5zjy3hmEEzC+kJMdX0JaCZ6DEHDZ1CwjYY+XX/ckizeP0V0IUbUySb9BNm3Rb5xG/uoq/uG8EDzWhQ/jSXS9VIRQnlwAY4rw0TyHZN2ZkBXhrVZKRJxj0xxafYwSmLFyUYscpZg0gcVVZJQUYzcihW4P22hTbpbI1xKC8z2stoWY0+vT3lon2lXBmc8IOyHWU4RPnEGu9hDrYzt5nqFJ8HQJR0Fad0lHA9g5RpC1sa1OUVVrLXrnNNZTqKNzyG5S1PlmOfmmsaLieXEVP0qJNteRroPILDbIEAb8tsYGQZGNkefoskBvyRg+FVH9ZIwZLdPeCumIi29AK41IM+pnBFSqJNNFBa3qpcU1yTXGkUhpqCarcMoi2w6yH5FaxYWvmOPJT+9AaE3tYA8tNPmOGqLRQ+QW4yqk52KTnKjsoXGQnotMKVxAgUd7osRoo4OTZ+T5+lClkoxub7H5gkVmmyM0D1aQDng9UBksnF6l0kpAZoDFChBxgrUaWQqLAFcA36N53QS6pEg9Q3iuDUpiQ5/EDzBTIWK1jTPXQGoNSVI8j9KsyNzxPJxmhBASva+Gl/e5/jUPMTd7H63dN2ER1Do93CRl5/5t1PdvYsutZ+icbdJcSPlY8wmGmhHpSrtwAAG4Lib00Ss59/VPk+7agA1crIHmiwTD90i0a+judtCfb+ItrkK9zB9+/FfYtHWcA42/5eH2Z7ja/zFKziirnQghBdXJlO70Ie5bWSW+0yA9Hxu4yMkKo9fNcOAPjpBunkAm4I14/NbBu/E2W9zHPKKN4GyKaD4m6I6G9BcbqL6gPdukl2pE5tD9zCjDt3cQmzXNTsxjHzvOd//4fp6/aQu+4/DU6jyNTS7BCcPJJ76SZfLAnz9M7WFFWgtRrYTyZJnRia9UHR9onudA4zwlx+P5Y9tBQzIZEsymbNgySm4N55MNxFnK5750Lf/53278unvQtqlNJIcqOJnDfHmNd99/K0J6vHLDHs4tv4uJ6i6ctodqtAm1/qrnWOn0eNvNn2P31Bg/e9N1f5+t8OvyL3dfwvb6CC/dvOMbH/wNyLTGYLnpol347iBybMCAAQMGDBgw4J8L39Rvfl8WUbIs48d//Mf5r//1v7J9+/ZvePyAf1z++NDneM97HkMtGkZ0ykrD0p2wlIcyHj11Me5YjO7DyosF8uo6o+9aKtwAZY9PvfMSrnzTOb70wMWseSFep4NVDk4sqRzyaN5cItpbxjt3BtGLCyEDgegnuIfPgXKLc1kQSmGzYpwnumQDeA7ho+eL7JHlBmJxtRgH2bkZsdwlHa8gs8LtooersGkCtdRCKgez2gDPR611UUutQvjJcmwUYz1FOjmFCRXJlhKJ1DiNmNIjYH0fohirFAqBu9TDXcxxmwmqn0Kq6V02TnQ6Iuy7CGMKE0WnD9USIk6L8R4pERjc5Q7+agvRSyAzyMAD1yV2DMq6yFwjogynXoNWG+x6sOapGPmxFfT4MI39ISLPkSsdbN+gZ0ZIpz2C2OIttPHO9FC2BFph+j2C2Q6iXMVutMgbJbxbYSUYzyW1gtZiBRyJ6XYQ7Q4CgffAMWS9jvBchOeA4yCA+pkMXcsxjTaOgKwkkdUi+0ZWA8bGKtTHKhx78DhMhkyVVxj12uRa8shiDzVVKUSWXLN970Yev/1xVDuGwEMEwdOJUsJxUCOlQjCRMPJIk2RrBf98v2gEsgZOn0PumIZ+ipUgpSxcTK6DjdOiulmpYrwHEJmmdLJN+XSTWz4/yeFLuzhOjnEl6cZhvG7Glj0b2Ll9hmu7m7nFbTP2aI0dfsDsoZX10N1iFMdiEb2UGcendmaF5rFz6KEy3ReOMVWJSWsh0U6PTtJk8vA8Ni6el61uxMnbD9He8iBadVnpnePUkTXG6yXIDLuuOcpYnPG87Vcw/4oh/mr1DtIJhyGl6C91ELGmvNwCUyWVEUZXCGYtfgTZfEKYWvzVjLBiWPWrZDXBuV6CDiVIQCts6CFKHlpBO4r50uwxXn3ZXhKd87Y7P8Xqvxhm4r3LVIZKT+8J1ZEKMgPVEZAKyr54RvPOSyZ340rFZcObeHJ5AatzBArnkgne9ObvJtMa25TIxKFdcfjtL3yJieEKF2/86s6UC8Ym2OKOsW96gj88+h6sisiyKqPJEXaO3EMneYDl5e8naUXEifmq57jl4FHuOnGG+07N8h9veCFKSrQ1KCH/wXskwObaEJtrQ8/Jue45cZb/cds9NIh58a7tvPXF16Pkc7POAQMGDBgwYMCAAd+5PKvf+FzX5eabb/5WrWXAP4AjzSXedeQxkpGMwE8YdfrMJA2+a/dTdGdDuos+0ZoiHnZIpx3SbSWML4uckihnYXKIjyeXsDRSIaqUsHmKTRPkUBWhHPy5DL0+0gPFDbPVxTiG04xQK61iVMQtapORxY20qYWYko/1FUIqsjGf3r8aI9k3gcxMIb7oDNHuYAMPsWEcWysVOSZSPu1uMaFHPjOKdRQ216ANwkhKR1vIXo53po/xbSEcjNdhYgibpgitEaGPkj7hYgcqFaRfpryQUDncIVzrILXB5BmIDGd3H+/JM8jcFLkvxkBqcZ88g5pvInoxeqiM6ltUX+Ot9MmrLtGIT1JxMJ4LpXB9XMmj8skVRJwxtrHJn77wb/nPQ19AJxGm5GMcSWXewnyE6bSQCx2EARxB6bF5ZFpcb4dhopuHMD2LiBNELyZvWWYfGCuCfadGyfbvRF+6AVn1wKQYCUanGEdgJWhfYaTA1D0YrePmEmetR3C+Q9aMaXZSXveTL0EGCtlPOP6hYe75830c+tut5KFC5qa4JnHG0cfOkm4v8mBwi8cJpUA6WL9oxuHL5paJEmpGEG+pF6NSxmItON0cd6WHWu4W1dhBgNUZNvQwjsQm6dNNStoVJGM+YjUinst56vYK4VxGuGRx8+I5LJTg9o8+zC0//re4Zw8Sj/VpDkuWXxCSj3hY1l1MUmGFJPnAYyyea6LaMd7cGjvPnWbX/BpZVaGlwj2XYn1ACEy1xG/9ysf4m7+6i9Yj13Ll6I9w5liJd998P3c/cQakYPnsMM21kP3DV/C6f3U5r/vll+AbS/r4Gc5+5h78WonEcchGfKRTpr6SYSZSRJgRjiaIOUl51wa23rgPXVp/7VQsuBRyt7RoZYlHPZxuxvjFNfbsnGLEKyGBE6dPwzjI3LLr2t1oXYgUn7v9AdoXd8lNivYsURNWFloAaJPxvtkPc677v7H6QUb9Eu1dHo1LXUpXjyKEYLXdQx/wEUdLlGVIJfQ4s9b8mvvQ6TNrrB5uc//dJ9noLZC3PLJ+TsvpgXDQ1qdU9glqFcpV/6ueQ7iQK0skNcdbcxxpz/HTD/45Hzh993O0Wz53bBsbZrJWQSnJkZUV0q/hshkwYMCAAQMGDPjHwFrxHfP2T51n7UX+nu/5Hj760Y/ysz/7s9+K9Qz4e7IS9wiEQW/rcdWLc+T7DLzkFB4pS40udilE5xm6LijfpwjWMi6/Zi+P33GIfKKOOAPyiME5mCIbCteVuKHCbsiJugLRShk+m0OWY8p+ccOfJMhSAIgijHbdlVC8bixCQ/DgafA9ZGIQ5RCva3Ee6pCvWLQqY0dqaEfgLDURpRLGaEhSbLuLDENsHEOckG+ZQW8cBSVw2j3IsmJUo+QjI43fVZRvbeGspiBKJGWB7yiMzqHTITihcaQPFDkTuC7h2TYogdUgLGAM2dIQXtkisrxYS7Y+EmJl4Y4wAu1L3MUGtlLC6fUxQ0M4FozvooddRMODfh+b5yS7x5ErbcZXzuH5mqkNXcKmYOm6GpNHImw/wlEStI+eqKKHQ4wA2XXQay2SizfCsEt3s6K1c5ThB1dwQhc9WYzwOFJgPIe87IBjcGMLeYIphQivjEwNVhYPkQkNarED0z6ZA875BfydkyRTJXJHcOL4QXSUI2LgGoi/4GGFwA5pvCzHMREyi0j9ACeiyFMBrKPAUZBpRFoIINlwCRXl2NUcM6tI941QOtlEWIsFdOghk6y48OujULYkkNop2qHyrBBprCG+YAw9VDRNVR44B5UKVklkZrC+IXidxzt/+6MsLnWxkWao2yMtn+beTsT4Bw+jehrhl8AYLAKiCJ0JyC2iXkUoy9404+Bjo3jG4KcdnHJMaVOZ3uEIXfYwnmDrBRNcuusyZsqbUFva7Nk+yVA5YHWty9nHNtI8FfDnq3dydul+nrpjCNOIsInP4myMvGmIbiRxI4trJBxwsSLBXmCwNY3wDG945fMIt9e4/8nzkFjsRIo4E4IB2S2Ew/6UouZ7XNof4sDvPMWvvSzjV/+fl1Hq9bGJgDTh0bNrnDi5xAW7pjDPi/BfAFZkLOhhZvwh6iNljM15cuW/UDMPUBJNTqwe4JLpj+Jg0DFcPjEJwJHzS9gcQKBXc1714gu4ae+ur7kP+e4qKs4Jg4x99YCnlj0QoJMuHz5zMcYIfuM3nsfqUY/wylHef/JhvnvTPqpu8PQ5VABZqRDR3nvyi1w4uotDrXn6ec7rt77wudw2/8FsHhni977/lbzpIx8mt4bcfHWXzYABAwYMGDBgwIB/WjxrIWXnzp382q/9Gvfccw9XXHEF5XL5GR//D//hPzxnixvwzfOCya1c2trMQ2dnuf+MJDi8wKgcxhF9OsdKiEAjW12Ghyp07o0Q1uC8Yif1S3ustSPyvsPE7UusnQdZElxx/UU8dP8h9FyGCHOczEWmFkaHMOMlbJThnMyxaVa4Dzy3cLCI9caQ9UphFWXQTyEMEJ4HUiBO9HCSBFt3sRLcc02EBTm/hliWmN76GMh6ACxKoRab4DrIToJR0L90EtXK8M53cM61yDZU6e6bJDjVxHtoCTZUYKgC7Q7WaKQRIO3TIx5kGTY2qEoZAgebJFjpYipVDKtIIaDqQJxSKCgCEYYYnVNqpOhWG/QKUkriCzciMoPbzcAIrOcXVcolj30bmyxPS058osb//o2dNNpVoskyzRs9Ju5fQOQGjGH7f004e8s4cUviYDEzJaTy8bWHjkB7ArWSIBpdvAsrhBM9qjvazN1bw3oCYxVO02LzoiZXivUqZ0shcCQatysx1pKpnPDcGmhNmsSkuyZo9nNu/ZtDkGss0B53UPUQmVjUuTb5FhfPSQmGMrppBaUFuuShen2cqWF0boumoCjGWjCBi6PBiS2luYj+/io2W6/NFgJxeg6Ta6SShcvIGkQqsOu5MdZxEUpgtSBYjMiGXMLzHWzZJ90xhhdpTACi1UdfnfHYp2dZ2D+OtVOM7DmFfrjE2Jk+KiqEsHhMECyJwi0lKMSaJKGycYzpqTpeXoHZUzjlGPmFGDfNyCjDkMRsH2fV19w7f4LtrSkuZhPT4zXe8mM38lP//T1ElZxSX9Fd6/KZP15gYo/CVZoss5DlJNOKxe0GFbmUHtJoIBoWqK6HOuFghI83JHn5d1/G+299BKsgL0OpmiBiB5Mp3ChHKJeNj0UoHO4/eoSRSxfothZxxCuo/EUHfIVswfOu3smmmREO3HOU7BELJUn2hMZe63L9NZfgeg7GpmjTZdgbYbEfItgL1mNo5wiNtYjLL9oGwOzcEt0LMxCQPAAHDp3He9nVf2f/efL8ItP1KmU5RZgKvNTnptFf5J1f/DAGzXIwwRPNDkJY3vn2u8mfbNP9+S0s+jHjQYWXbtj99LkunhpHDqcgLdurG9lenmLcG2d7aeZbsnf+QzmxusaTpxawwvK5c5/hNdu+G0e63+5lDRgwYMCAAQMGDPgW8qyFlHe+850MDQ3x8MMP8/DDDz/jY0KIgZDybUIIwfbSBu7uL5GtWsp5FfuRnExUsSZBLXQgiWFlPeCz7PPoJ+9DL3YRvsP3/+4aN159lI9+cQ+f+9BenlhbwUoJFQttg3QcbCAQaYZyPbRV2NCFZq8QJ/KscCfopLhRtuuihRQIx8UisFpjPI98yxjq7DJUy4jAL3JV5FoxzmMMQlva20rUTraLc/k+qhWjuvMgJcmGKv2LxpH9nPFPnyXdEBS5Kc76pJoxBAt9EJDN1FCNHlTLYNVXhJQoKtwR621CyY4ROhdXUV1LkJtCCBEOjObQ1lApoyeGUMtNjLHgKqiWkJnGnW9ilYNq9BjaFDPxihYnj0xh5ytgGnRvXkX4AWeO1yH0iK8IkdISlQzhYgQWllWI7/TIUhc8RT4TIps9bJpgShAcXabyWB/VjEnLLlfcuEA2mzOvq0Uua2yA9VEkd93VoQ0mS5Ha4DQyRh7KiB2FaseIMMRmGSxHxYjWSp/mfBtEkZ0S/mWPdHoMHUjSUY/R0Zhq1RAdr0JDYAGpLaJSRkd58TjnGjBkdRe12sUqB5KU6ZtWOefWwHUQShViRpaBEkVDTxyDEKSjIY39dcZvW0P6AbbRAiHwFjow42HzDBEneIfniC/aRPn4KtIY0l8UZDsV+U6BcQKeOLcTtxby+n1PUdvf48MHtpM1BHapUXztwEOuj/iYOOXkw8c44zqkfoCsKWw5JA09ZJxixyawBrK1DuJQzKHRE3BdUXI7u7bKXefOoyz0SjnBpEU0JNfsvYzp5+3mgdnzHPjVOwjOp1SO9vFCB9VJsSInviCksqwxoYPGxQr46V//IOeTLiQGGQqyVZ8wsYWryHHQExX0dyUML/jUmy6jL1nGLzdZbJzlyv37OPzwKd78gR/nha+4nNZalw+/4/NEY8M024Kw32XLXV2ueFMhRkjhcdH4r9NPT3Ci8buUnD7a5KSNHJNajp5Z5pWX7OFdxw7AetVztk2Tepa3/tmn+JnXv4iJ4SoAf/rZe/jj2+/HUYL7fvHfsrc0hhAQBhWcuqKXapZaIWF1pTjVumnjRd4WOhsV+0efKZCMhzX8kRiEpZEd5crR7+Lf7rqBi0emv+n9sJMkvPvxR9k1MsrLdj7TQfPoynluO3eC79txMZur//DWnpLrErouqY040X+AhfhiZkr/8CDbAQMGDBgwYMCAAd+5PCshxVrLbbfdxsTEBKVS6Rt/woB/VK64dDO33H8IMdtDrmlyLVh70QbIc4Y/cRQlJHktJV1LMa0UQh/rKaSBvdNLjFRSNm9rUxqL6WQCb9MUdnkVun2STTnp1jL1BzOcM4VjwwRlUBFWSvSWCWSjWzTOUIxrCATxuIO/sJ53IQR6ZghbL2GirGhj8RUCFyklJvSKEQ9HEF0xTfXIavGNaV1kcGQZCIG7mFN6YhmnmdC5uEzjpWOUnuowevNRZDfBIhB5js1yZNtBT0xB1UMtRGAtQgqM40CSYLuFEKQyn8qRLr3N1eKmddsUVknU4VlEnpKFlviCIZxRh/CRWeJLN4Mj8boa4bvF99uOGLuizcQ1LdY2DnPkyCQqXyscO1kKyiErC/qbQ2TkFG4dYxFK0n53jnO5j14sBArvaI6c74JtY6eHqHcMrPbAWnobXR74+E7MqsGOlIpM3G5StCNVSoWQUg4gSovrIIow17waEqw0iIfKiG6O8FxMmqJONLBagK8Q+IgwgETidC1WWoKlkP0XnqSkIu4/upk8EFgBcmoY0c+xUqByA8aydskIZrSKe75NZdWCzln4fBX7w0khWjkuNs8Loe3Lo0GqCJddu34cXXWh3EckRabJl4WvYCklu2GI0ucj8sjS3+hTOWYhShDGoBYtu05PkuycY86MkBrLZS8/weZxzeH2Jg4eD4rxLQQ2N8hqCVcJRLmEjTOqJcWiBFtSWCkxnotzPsU5u0yuSwSb6gyNDjNaLm68kzzh5x/5RabUKPmxEOtJ1A1NqmaI+z55Ejkhub81z8YRCM4Jxm6PcC+tk7R6RLtDkApdMeAp0EDfcOjcIt0xUDm4jcLlZRzASKywWCmwNYtopszdvUycBQRbQtrTFt932HXpZqgEnJhf5T3/5SPMzbXoXW8xOoQLXeRTTVZXu0/vF54aRjtTWAzgMRyWeMnGLdx76AwXDI8AkIU5UhbtWzqGg0/M0Y9SXCH4lZ94JQB//rn7yauWXFtWWx1OraxiXMGJToO+n5K7hm47RXkGR8HP/fd/jWwbxjaOfNV9rOoGCCzGwvlewhfnjvOHB7/E8ye28Lb9Nz59nLWWI2eXmBiuMlJ75s+jB8+f4y8fe4TRsPR3hJTPzx7ji+eOs6Fce06ElOFSyPbNo0S6w+Wj+5gKNv+DzzlgwIABAwYMGDDgO5tnLaRccMEFHDx4kF27vvac/IBvD+++91E6Xs7GTBDlkFdD5HrYjwxDCKC52YE5gRn2sNZFNQQoy9/efCF+KLhvdpzJf7nMyr3jVFcTWqdaxCl4i5pkWmIB11UkUYrAIhyHfLQGozWMtahun63XZpz+koew4HUtaANCY+IY5tfo7i5RXtCofoZxLCKjqKAdKhejECJh8gNPYbFgLAhZ5K98OehWWyqPLWE9B+uXELlGNVKcTopwHIwj0dUQ2Y7Jp1z0jip21eL0l0AXzbpYixAS4a2PIjVSrAoYuW2+EG6+HBqZFFXEspdifFW09igHmeZYNwCbk4eKeGON6nKLuU8okuUKcyOjyK0x3sk1EiGL5hgE7lJE/eEVnO9yif99SPB7DmCRcxLTy2CoSq4M4nwTkef0L50mnakTHlvFX5EIKdDjJRYrVYYPRTgpkPfIrusizma4iwq0Ra51sNYWGSfjNRAS4UjIc7xjC8UYjgVd8gDI+xHbL97KiQeOY7XGrcVUr4bknE9nKeChB7Yhowzby1FRCqst+ldtRWqwrS6l8xHCUfhtgdSGrFRCnFgk2VQn2jSE9gzWlYg8Q8jClSKkKoSsShlRKTF1SwMkyJziQapVChHId0k3VZnuN6i+0GHpqQDbiUhLEr8POA7pxpA8hz0Xz7LbPcfZ2S08+KUXcaayyJG7QuzJ88VjYEHUyoiypXbVKK1HkyIAt+LTumi8GCXLiuyW6lwC2qByhWwr1k6f4ZAw8HOv4GjrNGNDXRpzEwghELklejhgrhaw8YIGa90FTC9Dne9hceltcIknLWFaQbQt1VlLnmcI5YG2+FIQGYMOFJ4R6NgitCTzAUcghEamGudRQffJPhjD6n011M5dvPmG38QYy+t/4TX81VOHMU8+xfbREqsPdgk/2SXZVsVKj2izIInSZ+wZR7qrfL65jy3lHVwoJGMjVYYqJc4fXWB+0yRbxoY4M9crDjYGE+XI1OL9HznlfQxYgXXgg7c+Qnqyj3UE5xcbZJnAWkWeptT8BA+JdjImNn7tZrfAcam6BitzNgWXsJI2WYjWeKr9zPyRT9z3FL/xoVsZr5T55K/+6DM+ttjv0tMpeWSKLJ//o6no+3ZczEylzg2bnpufYZ5yGC+XEaLMC8ZvGIz1DBgwYMCAAQO+bXynBL1+J6zhW82zElKklOzatYvV1dWBkPIdiJcIhoYrvOaNe7j9Dz/P+YWI6smYzpQgngopL2dMXHcBJ/1T1A4m0EyLdplSwNI5h6fO1miecHE8S37KoeS3qe/tcfTUMGrVMHRfDyEdVL2MkLoQOhDIlRZgkY0elMsstWv4W7ukywYudDFP9pC9BHwPVa+SXplSeRJUJ4a+KbJXOhFiqYl0PUqRxFgKAcVkINfFDiEBSzYd4LRyFB5OXmLj208iNEXgqZRkW8YxI2Wc8w3yrTnBI2eIt80QuAHCV5i1BmQ51nfACDAGmThULlB0Eoupu7gHTiN9f31UxsXRCv9cF//JOZAS79B8cbOvDaru4146gzE5yYLD2U85JP9aUNvSxH9ckpi8aJ8Zr+MmhurJGO3nLG6dpDK0gGpm0EuLt1IVJ81AOiSjhnjHCHlJku0fZajdw1tJUS2HfFLS3BUy+mgHZAOzrQyTDt4Xu9gsLdwcrlOIKOuiiVhew6QZCIkJilpdPVQi2zKM04roLzWLUSqlKF2WUXtJk86jFTrvT0nVKLIHKipChG0pJPMMwisanUQ5BMBvxGSlEKedQBTTvmCCeIPLxC2zOIGPafTA6vXGHwfhrFcnR0kRLpvmxeNtDAILwtDcP4F5SUr3fXW6so5Omqg4o/HiLdTvP4/bT5F9n/Zan4Mf38muq2aprozQj2d44O4ykZlDCVs4UgTkZYVnM6quZKnZhU6fpskpXVFGVAXxrIOxOW6Wkk5WMSMVotwiXY+TJ5Y4d26Ni2d2c/YzG9DDBjEHRkF6yiO9WtLaFjIy0qIlqjBSgS6Y0MU7l+F0DDLSCKNw+zkqa2J9j2QsxOvD5nnJsjRkQxRZOVZgjcBpS4TK0I6D8IqpKAtkf/wg5BakQArL1FCFKM35qf/23fzs2hqjGxdZXIxYbZRxT0fYuBBSvrD4Se5eepjF/ii7K4cIooQ/esv70CWXf7F3O0986GHOfvEou358J3fOzxWPbZRTDT3aImXbzNjT+04qDNUF8FuWO1qHEbPLCMehEfVQFnIDrEBN5lSnFJ/7n/exe+8Wnn/TJV91HxNC0OvU6emM5kjOa3ds476ZA1w+su0Zx91z6CRJP+dc1Hr6fVn/wzzx0J08+re7qQ9ZxnfXniGiAGyvjbK9Nvr32WK/KiXX5Q9f9t0AON+g+jgzGU+0DrGlNMOY/9UdOQMGDBgwYMCAAQO+83lW9ccAv/M7v8Ob3/xmnnzyyW/Fegb8A/jRl11MEDT50IlH6cz46HOL6M4aOhBkowF6aoS1jzWxE3VaexQmzyi9YTtmU50XvuQYF+44x9ZSxLH37GSccU7PjnDo+CjZyBh6eqSo43EUlaESwlXrLhEKR8hCA/oxNLtEp3ro3KP/YzUarxum/bIdiOFaIUxIiRgRyNyBwMfppailFioMkEleuAZ8fz1fZb1uZv2/liKYFaNQuYQ0w13tYcsBdmoEwqJOVXUiRJIhWz0qn+/gzCdU7j+NEOtPd2uLm3gJ2BwrBFQc9BFg+xit1w2jN3ooRPHX7EoZMVInbKQIYxCyEHSEBek4uJ0M/0wTHEkqcxo3bCHKQ3q9kLWLR7HjZfRU/SsuF2PIPi/x3Jz4Yq/IdpEKpEJ1+4UDxkCQOJQOLSOiBBlpch/SHWX81RQntnjtHGsN6qTG+3iC99ddrBDIUgjjw+S1EItCdGJEL4Z+kV+DtZCkiGYX9/QSsp2RGYEZH8Yag15Zo/WxiHO/O87KrUPgu+SeKK4TFjwXO1rFjQwizvE7SfF+ARJFsNDFW+oiHAeZWqy1yBUfm9oiE8dz1/NxAqySWMfBClDCIodU4aLJi6pkUTHkP9Rh+2Wz9CcCUt/FBj7hUox1oX3lJOnEMNJIhBD0l8p88fw+HuzmPPA3tzE5XSe6bpjudaPF1w19mteGOK7H7GfnipBh30VePkVrysfOpIyNLVJ9ZBHdk8g0Jy3lxWMQKIa3jDEyWgRsf+KHf5chs7F4TmYG4wlkYkkXXF5y6GF2RmEhkmHxVzSlpRwnMlgl0W4hOnlrMUiJigzeXAvzwUcZ+tvHyT1DEgqMKMaokIIoDNErhpHNwwjHQXhOISAC6b6AlZmUt77mxfz2D7+CeiVk24+P09g3SWLLOFLgt3rUhgrB62T3KKvpCs10njFpGe7Mcu7kEouH5nnZdfuY2jTK7su3UHJ81JpCrSkunD7P2IY6peGQqcn60/vO9p0Namc1QcvSeGINgUSkmrlDJ9k00WRmcg2pBJOjbdS9fT7025/id3/qL9D5164K1m0Heg7n5vtsLo/zB1f8CG/c/pJnHNPrpAhTOJi+XPdsske487aMw48uM3RcU4rcoo77W0iqdfEc5xv/5eXRxpN8ePZT3HzuM9/SNQ0YMGDAgAEDBvz/hXe84x1ccskl1Go1arUa11xzDZ/5zFd+V7LW8su//Mts2LCBMAx58YtfzMGDB7+NKy541mGzP/zDP0y/3+fSSy/F8zzCMHzGx9fW1p6zxQ14dvzV5x5h9rhB+h3SKZfSCzYy9OgSdBPcRoqdnoCKj9PNcVZiZK3EVVfv5c57HubUE5NsqCxxtD9DMlXi2FoLqXOCSEEisI4D1mBqIWu+gwpcjDaIUoieAr3LRd3VhxRkljI63KExvJVWv0qwIac8UcJrZpjARTzkIkaGwM8RvRibZgjXgVJY5HzkeTFe4Xlk03W8Rkw8FdK+diPhUytUThYBrRiDzDSMDyMdBxwfe34BtdBAzq6AtcVNqJCFWyVN0XnRHKNGBZvektK5R7F6i6K5r0b9yT56g8WZd3BKE9jRHLliEI5CGIvOMlQYFjdmvgtJVtz0A0gXPV2GWkg+GoCWvGrjAcQmy4Mv3MZseTP1BxewCx3iMYf4jMd41oKbBPZBA7Zor9GBR7TZw1nJ8OYi/MUOddPCzvdIVjSm7sGMx9gDbbAC4bowM4l6ZBGba6J9GyidboAQyNxinRyUW+gnUIxZAQi1HgoMMu6TT47QrpSK9wFoD7soUL7BKoV2FVlF4rYBt/g8byVH+qq4qU9zrKsQvQhKPmKtW1z3XOOkkrzugwQhHYRTBL0Kx8EKUYgrcYpOUhgeQuhOkYmDRX+fy1Vbz3J8eYysrHAbGe7ZFdxexFDPsuVVF/HY1jb+Ax02TFeY7edkFQfZM+RWc+rx04xUHFrjJZrfu5G0rsmrCa2mweutQbMJjqK9ISDVHo21KtGwwrkwofJIAyMEuWNpXj5Eua/Z0PiyxaIgdwRZ1cHqHD3iYAKLs5Jwx7uGuPB/j/KUo7C5RvU1zqhEpwYrwLgSoyTSARVphCsRR+bW3VEWLTJs4FFKCwkxCwW26uEvGcZ21mg+XjRg5WMB9CK6PzvBB8Mn2XrXNN93w/UAXL/tIpYWFok6LcTpPunkGJ+44wjPf+XlvG7mh7kgvJUjK5/kyuAEtdooG976UoLyKFObRnnzH70RgB95zwfxm4VAsFqqo5OIJMtZbHaevga/fONm3vaRJlhLqVrG1nMQUPbb7Bieo5d6HM9GaJyp4cxbtMnQuaWx3GZs+isZJVGc4TgS11GEbZdEG3peUjxdhSDqJ7iug+MWDrXWQ3OMPL5CXCnaqBTgVv4DG7d+gcrwImsypzffJM01vvusf9R9Uzxybo7fvvVLRK2Ey2am+ZXvufHvOGD+TzaVNjAZTLC3fsG3ZD0DBgwYMGDAgH/erMcQftt5NmuYmZnht37rt9i5cycA7373u3nNa17Do48+yr59+/id3/kdfv/3f593vetdXHDBBfz6r/86N954I0eOHKFarX6LvoNvzLP+7fLtb3/7t2AZA54LDpxZQ1sw9Rw775KVHay2+HO94q/mp+cYrWxGvP8MIvSpXbiBJ//n41gXnjo4zaFPK+Z+aANiXBAmMPTAGrZSQvZSbD8CxyBaXfKVwkpvPIWUkvQ1Vcx2BZHFeVzgj0q2/URGJZvl7oO7cRoG6YVkG8rIRFM5mNAdd6i18kLgKAUYRyC0RaSA4yBqNVo3bEdogXvHccxICI7A1AOwRWCmtRbSDBFn2FBAv4+wFpPlhfPFc0EIrDaQZGiidVeIQJUlQkHrnhApBf5aim00CO4wqO4OhAcIQXZpGXkCxFqCXWohcoNQCpGlmLKHaPfR9RDrWZxY0n7hCPKiCO+E4ehfbGT4RS1mX15D3ZpgSi7CcVi9bpR0Y4hz1yK1vRnZcgfCACamEeUQ67u0r6gSlpYpneqQRIpwJkT0NaIU0JkJ0J5k6JQFpRDOeluPzrC+g+l0kbmG4Sq2UgJjiyrpbrTuSDHYsIQUhZMk2qvQ1iBLMZtv6nD2lgpECSZQCM8p6qNTXQTAChDdGOFKpLTgVNH1MhyZRZRLYAy64iHGhxCJRnUyZJwj+wloC+tVxwCkKVKI4rmVpIU7pRUjPQ+xXpV8rj3OscdnIJIE2wQiclBVH5Fl9PKMx91FWs9zoenidXLKL1U41lC/Zw3XeiyfWsaUXJovm8Q/E1M9kKNdgVQBYnwI04vRgYMXW0RuMUJR0uA9vIRIQdYr1BYEYxcPsXaywZE9Jdom58vRpp3VCOs5SKA22iPplXDnW1ip0DJDILCeiygFeI5DFMdYR9DZIrC2THkuQ0UplRGHyFcIXWQK6XGBdQ0ylwiKHCOhwHb6nPjkkaL9JreovsEkoPqGMHTZvvMrDTin7prl4M3zZO1hzLiP7MQceOQsAGP+BC+cfBmd5CCnsxUCu4EX7d+LEBJjE3LTxVOjvP7ii3jw/nMIICtpRGyoWZdtk18ZS7li5odJyn+KGxtKu2t0FnpgoO7sYf7YLKcXx7ALglPRJnZKl2r1DPWxKlE3efocZ+bW+JFf+Gs8x+ET//sn8YSDTDN6STGKtHC+wR/96kcZnxriZ375tXzsA/cx/+AsTpRSbVh+4lW/x1985i1INcmrX/8D2LHH+K3b78ZNNOZZ/jax1o/4+OFDXLlxIxdNTn7DY6M042SryXzU4z9EL2SsVP6ax7eymDO9mBGvwQvHvuZhAwYMGDBgwIAB/2x41ate9Yx//8Zv/AbveMc7uO+++9i7dy9vf/vbedvb3sbrXvc6oBBaJicnee9738tP/MRPfDuWDPw9hJQ3velN34p1DHgOeNOrL+cvH7mdxMmIG4rxp/qY4TpirYVNM7Cwc8s4K2sdslpAe7VDIBy2XLuZ4/MNfDuFG2WkVYX/1Bq204XxOsJ1yIYd3NQi0hzhCkRdsPr8MjIRVB/skq+kyIMRIqzT31nhnvNjVEoxW8+2aZ1OUWcbUK2gkz7eyQSxuQ6ZjxYgWu0iDFdbTKuFrZWx9RCjFOFaQrp3BpFlDN16DrXSh1wjSmWszqFUQmQ5ea+HijNMXtQg27KPjPKnhRO+XK28PjaSzbuc/+MQ61SgpNHDIcaTyEQjz6+Rl33shRnpK0Kc2yP8j/WwSVHzW0isAi0VjlIIncP5Jmx0cLsWkzlIAYtPjDJ3sI55oyRsp3hLESZJ8PoOpiOQjxkS/HWxIsXGLYQrcLpF7khwYBEZG8xQiX7uI6fKyCyjfqhHZ5uPMAorgW4faSzxzgnMkEs2M4S3uF5LrTVisQkIbJKCLkQmSg5mPaskTX1GD7fBWuovMKBzzFCFvOojpcJogVUKaSXSUFyDdg+VpBDnoA3W9wrhSkmkcrCtLrYb4TYUSSKQPbBpii0HSCQmTbHTY9DpIdaKr40E0gSEg1XFGJbXVqQrbuEu0qCMIbpiG0QR0nEpnXMJkh7+ms/c7ALpOYt+wzakcIo8kDSFJCGYGyNsC0SqCdcMumwIujFpxS/ye071UdMhG2sNXjF5gE9t24GdixFWIq0kPdAi7+eYVodMf2UkpWQlndQwvLlJ5xbJeOMU3lKMRfBzN72Cf/sbB9CZYGyixupqh9yTaAV5CP2NIV7L4OeG9OB5ZF9jEajQp1KPyF2HSqlCv6NRGkCSjYZ4mYBWB/IcqSRSukz+fpf//ME3cvnWr9TufiH/NFM3JZx9/3CRp1JR+O3kaceEq0Zo6ktYyY8z4w1x6vACritJht9BPzvB9uG3MlMbRmbFS2jt7CjVfoaXC8T/kfuqpEt3q4PIYd+GYfosgrBM+hU++5ej9Hc4uLmhlCle/PwdPHx4kaHREpt2TT19jk989jH6aUY/zXjy0Hn8kkR0LR1ZiC1ZmmONJe4X//7Qn99RhEW7RePT3PGlp8/lBy77LtmEd6+L66hnPdpz15kzfPDAk5xqNL6hkPKSXdtpRn1+/fN3YGTRNPT1iHWGtZZ+nnz9AwcMGDBgwIABA/4ZorXmQx/6EL1ej2uuuYZTp06xsLDATTfd9PQxvu9z/fXXc88993xTQsqRI0d43/vex5133snp06fp9/uMj4+zf/9+Xvayl/G93/u9+L7/rNf6TQsp7Xb7mzquVqs960UMeG6YnrDURERvURIlAuoljFDokTLOyQWkUhx+6CTp5iF0yUXMrrJz/ya6XYPpC7LA4ZLVNc59IcZZjbDlEOIYYzRC9yG2tC4ZxqmFbLtqknOcxzub4z6lUY/lIAKyPeN4aylmPmfopU2aD45hG01sr4+URYWxzDOG6z16yRAizpBCITKNdRWmGmK3TIKxBI0cfzUFATrROGspGMBxsMYUN1JZBkmCoxTGdyFJyC7cSD5Wxju6iLO0PiayfocjSmHhUslzUivRgUEFJZy5PrJcQpgUNd8AwFDFKRuc+/qYskdv/zheWxOc7SCyFEdnCKVQCWTbRvCWuzi3CZzWMO6qxGCQ3YyhDwjSahn6hSOm9vAq5XqA105I9ngEgVc01cQCoxLcjofGQUWaeP8WcB1KR5aQbo52iyyQ8opCphnGUQhjimsCRb7KWIggREQJ5DnCcQqholKCtMjsMIGA4RFEP8PpWWQ3o99NOPiRUXTNku6bwl3rg1DoyQo4AkJZjOIoCY5TuF5a6yM8paC4zmlWjGZZC7UKeshH11yMzYuIHc/DKoUNPQg9cCQ02giTMvPqmByfxY+VCycN4LY14TzITIA2eG1DPC2IZkr4HdiaBNjHYoRxMb0Ye6GLrw3dfWNUltZgMUBYy8gdKcgcYzRuK8ZpK5TRqJU2RhRLDoVis9/gMw9eQjTiodIO3mofmzikEowjcIRk08jQ06+5mT3jPPnoLGudgKC/gn+uW7iEXIf/9I6P4v67nQx/ZJV4tYPNc5TjQWIZeyQlrnvE4z4qt3A2LUqpjMWWJJWpGGshtiV83yGJc6SyqPMt6KZQCcGCzCNM3iNdyfmr//gxLv7wNirDJf7qgUcJ2x52awNdAmsksmMJL/hKyOpy3OHdJ5/gohFonQv525/7I4SAn/+AjzcMCEGjF6EKcxBuz6XdiHANHDw2z/Mu3QpAnH6cX/n+Rzm1/Hx+4Mrv4qfefxBrLBurNTKdY3wXnQkya+l7Ljf88LVs3bfpGXvXvj0byb/0KFLDhvEa3oQkNZp+JcZaQ6v6YV7ztj4Xjf4AQgjGt42xcHQeUSkVrrbsmXFfQkmsLOq7n60j5ZrNm5jvtLl6ZuYbHiuFQGjBpZNT3Lh3FxPlr+1GAbh0aAdvvvANjHmDn5MDBgwYMGDAgH/6/N8agu/7X1W0OHDgANdccw1xHFOpVLj55pvZu3cv99xzDwCT/9cftyYnJzlz5szX/dqPPvoob3nLW7jzzjt5wQtewNVXX81rX/tawjBkbW2NJ598kre97W389E//NG95y1v4mZ/5mWclqHzTQsrQ0NDXnf3+csWk1l87QHDAt5YPfv4ka4+UEcBoxae5L8VvOojMIBY9WO2w+yV7aT9yktSXOOcacNlmZpc7aM/B25SzY8NhFu/aAq6LLLmkZRArLZx+Bp6Dqx28Bc38ky3GDnQQ2wJ6F3mEDyUIKRnbUGft9Bo96XKuNYSMJJ0rJ6kcauLnPnkZXvv9j5BeVuKOR4dIPx1gzmVYKciHykhZBqNJh0OkFuiyi1WCvKJQpw2iWoE0Xc9VcRFKoas+va0VSk8sFzf5eV5U0ur16uQwgDiBNMPqHOH5mAtr6B92IIPsoyX8Ix2M62DGApy8hohTxHyK/azBLQ9jxlz0RJl42BCeagACEZsi2BVwm2uIRIGArOaR4xCmObYW0N9UxTqKbLyMu6rwWxm0cmyU43y8hWhHiDCENMeOOphAYQOFmagVrhJjMKlGKoP1fJIRD4REBR4iM6At8XYP53wD7+wadscQGLcIAo5SGF4PBl1rIJQiHlP0rhpm+I4VZKKpPZpCs0deC6kMB+RzfWSc47YShCkqkvMxB3JbjCcZEFoX7T9xhMxN0eDkSuhl62NGGusZch+E0eQ1B5tljAz7LCQGsd4eg6OwUQ8pFUu3Buz+lYSlL1ax7R5CSYLzLWRl/ea0GaPbbaST4akKftuyvLxCpeLTX2qDFNgrHZy+LASllwaYj6RYA3Ktix6tkUyGqE6EsoYkypCeB1jyYUV49zmyH+gxdo0m/9Q4OjXkoUL7Bi/XCG3xJCycWWZqS1Hf+8TZJUzNQXRC2tsmqJ7qFC1JUtA82sK9tET+yHHyNMfsnEYA1pEk4z7hfIzMNLIRYz0H0S/alnQ/h7/wyL4/xxcGaxUogQacboSNc0S9jFGSbGOVXdsWmT9e54zT4s8f+QLXbtnP544cJ1u5kLX3QT4pwAjspMPU3g1P7xehcpkMXXwBqVgAWcJxyuyc+BlKQwZPjbK08hDBSqGkJErgJCAsTAx/RTCQssrzd3a44ZIJ7jl6CqkirIFtl0zxAz/6Iv7okYcRgJzSxFM9XvOa13DkwHl6nZhyNQDgI/cdAAOZB621LkGYo4YSYmlpJPM8sPRBznVrVKo3chEXU5scwm4cxvYysJba5DOFCekL4o2GTELOM2uTvxHj5TI/cfXVWGs5eGaBsVqZyeGvPn9rreWTB46QpBkXT058w3MLIdgQPneNQQMGDBgwYMCAAd/JbNr0zD+e/dIv/RK//Mu//HeO2717N4899hjNZpMPf/jDvOlNb+KOO+54+uP/tw7xZe3h6/Ha176WN7/5zXzgAx9gZORrtyXee++9/MEf/AG/93u/x1vf+tZv4rsq+KaFlNtuu+2bPumAbw8j9Q6lUoI7DxOT5zlcrtJZ83G6AlFThIs5x88s8oI3v4R73/45rBKc+MxTeOMlFq+bZmWsxhfC/dScFbLcQhBCXWHafVTbgEnxVxOUdchX+/R3jpBvCxBxBe9MRtV4hHsn6Pox5vldOnjIrsHOlFh+1TZqJzS6JAj3eDypxtDTGf2NlnC+ipkapl9WVOfakBj6G2uUFgzGcbFaYFZ75JN1nPkG6QYXkTj47aIpBm1JpiuUDq9BpvBOrWFnW4g4KSqKMwfhulgswlhsmmD8UdApticht4hqGdXuYtMElIMu+widIxyJzXNELAiPraFii9WmOF+eYaIYANtKsdUKQki88zlYg8ygv9Ol9MgcXjslr3hFOK41hbiTZaiH1rNcpCDzBFndB7cIt5Wpxj27jLMW0ds1TLaxSuVEhLvQobO7io0c/KYm3Spp3jSEe36I4IDE6WSkG32cTop/Nv7KEyRJsUC0bSPxaJl4e05wLgZHYjwXJxe4vsbzU+QT56FcgkqIm1qchT5GUAg3rsJ4RXiv8lysMuB7WGc9UyXNQLpIx6V+ok/cMQQHmwjXJes0UGsa6zpQLsQj4Zcgy0i7iqfunyYeVnjdBmSa4GwPpgpxVsYC90wDcWIZvWkMMzOOVYK02Uf1Mqw2mIMaeUOT0icFUoPuJ1htCwfNQoaZHCINc9yWAQRSKWzoIqOUcE0THfAY+/4OvdEqLUfR3F8jq7o4JzKcZka3qvjFX3w3/+tdP4PrOcjMoKVAK4W1GpQognTrIT/48svZs3GUX0vuBQvJUI6bSjrjkmhMIRIPXXHwyopwrQF5ighC5KSif6Uk+F+afCZHWEE8XDgu9HgdEWuUqxDTlmu/5wgvv+IJHrhlI/9zz/W8o/EYnzp/jJ++5JUcMif5lJxbDxa2yGnNkll6+gdPxQ147cwWdvMJ7Ag8/pclLhu+nqHRrwTA3vfQGYxTvMzCFVMIk0riue7Tx5T8a3Gs5q5Hxvij/3E3tBWe59Lv5DxyyxFKWuNYweU/1IaJw3zi06N86UNHuPyaHbzx390AwNbhYR7IzyCMJY0zVK7JIo+esJDNs9QY5qPze5mPjvCO6y/GZHmR29NLCsfRrmcGjmgskZchBaTm7yfuHz63xB989E4qgc/bf+LVX/UYIQQ//V3PZ67VYe/0NxZSBgwYMGDAgAED/jkxOzv7jImVr+X48Dzv6bDZK6+8kgcffJA//MM/5Bd+4RcAWFhYYHp6+unjl5aW/o5L5f/m2LFjeJ73Ddd4zTXXcM0115Cm6Tc89v/kmxZSrr/++md14t/6rd/iJ3/yJxkaGnpWnzfg78+P3riXP5v/NE8dK3FqeYSrdh7lgcMXYhyBG2lEGNKYa7B45ymqP/8C2r9yK6JnUF6KEgarBY2lKuVyQlATxAZELLBWYj2FcCSynyHRhMd6RHvqWBPidyXZ5kmC1Xm2jAlOr4IajvGcnPaFdTLXZ/zzc4THG8S7J/jTS67FIWL0wZzyk4J4c0BW9kjGFMKrEm/xyeoCYS3ls7YQH2o+rcvH0KOTjL7vFCLJsaIQObK6S+3xJWRm128SQWQ5VgqEcLFJihUCNTqC6fdpXTJKvKeGf2dGab4IQt3whmXm/0SCEeTTVcxIBTyJWm0Rj5Xxjy7ieR5yrA61CkJKRCcHbYvRGSmw601DbitFVwKmfmyVqBTTvSsEBE5rXXRBYNudImvEldhSUATtjg7h4JKnGqeb4LSLHAVrINkxjPYlOQ3CTk75iEYGJVRikEmK7LvIbkA+HaLSNv6pVVRmMHGCXGsVd8K+D75DeLaP63QIzvVRRpE6hnClyFTp9AXpr27A+blFSFMMAYQaa3xEpkHKoq45MogkBSTpWIjbTAqXiTaFi0ZIcF3cPqjDLUSUA4I0FgiloFIuRpGyvGgech06e4fpiiFG02VIMvBclJSoVgKBA9qSlz3cjgWpCNZiyDSmmyCUxDiCxlXDaLdCKCN2XH2ew3eHxViXKoJcw5P9Ym2eTzbk45xbQrZTRFwiCx0iHZC3IyavWKT9+RL1o6A9RX9LHUcq+hOSAyOWew6d4tGjcyhrUSuaPIBKReNcH5DNTWIRDGmHq67ezfCmEdq9Dm5aQmkoty2mZ9C+xEqBKblI5SO8nHDCUnmlobUUIkROVBI4/SJTBQT5kI+/EIMC8bqUa64+wcxIH65d4C8ahgDNWkWwe+MYb3/obqzV668LSNs+J5KEY+dXuGCmcNS8YOK7OLPwQTLboBk4fOSsxLrzXDpT/KB6w8uv5M67TxbPw0CgKy6jtTL7L/nK2EsafYS09z6Gw6tIxBCl8RoX7JxmYnqYs402+YSExNI4kDF8qWHL5ikqtbNs3j7BQuudROkT7Jx8I9WOYch32LJzCveREhv++ijZuM/pf/Ewd/U3kiJZioug6XYoyUsKGXuYko9TeWZ7nACQFoQgt38/IWW0WqZWCtgx/fUdJOPVMmv96JsaITrUWODXHvs4109dyE9ceN3fa10DBgwYMGDAgAFfEyuKt28362v4cqXxs/50a0mShG3btjE1NcXnP/959u/fD0Captxxxx389m//9tc9xzcjovxDjv/WdEICv0BbxNkAAQAASURBVPmbv8kP/MAPDISUf0TCtd1cN+PSfNFhdH2NlXeVGGq3UWu9wtrve1irOPvYAuK6kN4PjLPxcJu9r9nNQ39yjsaWEUScQ6bJrUKPlskDoLwROgnO2SXWLq0wOmvYPFqm1IxY/OIcol6BXLPcMiz/7SFqMw5XjZ4gdBM+NHUNtiPxlvqgDe5Cm6mP9QmuNcRHh8ED47m4WiBWNdZzUZHAOpBXLZ0LHFRb457PMQ54sxpR8pHSIx2qkPkGTUr1ZCFSCCiaUtb/JYRAyCLbgzjBBA79PcP0d0p6W3xKn9KotTZrn7cMj/XYdVWDxx+xdBIfIzI6+4ZxVgsBgbEaZrSGaHahExd9wkJg8rwINV2vQlYnF7CjFfpnLN335th2FzwX4bnYXCNcBxHWMN0utqQAB5unOOfXMAsNxMaxYryHIn/VOg5OJ8cKB38xQjg+TmxA5Igox1/LmHhXj4V/FWDJ8M4lqF5WNOJICd1ecS2Mprd9As8JCQ+3CkEjywh6SXG94hSspdUap7LBxbNVpOthUgchLbKbFCKEkRCnCAs4imzIR0YZbl9DpVTkU3T7WEcUokorLQSuUohBYk3RFmRdWQgk1iKUpDTbw+3kOM2kEO78YjMzjsHJDf2yJX/eBmw1oHIiglYOZt1lZC2kGvdQD719hO4Ol0xXsRRjKUI5CClw+kX9sB0vMTYzzNpaGxnlBC3BiGfZ8aoTtPIRNl92nhOrGxG5Jd82QjpmsTiIWoZsKH71N2/GHlrBTFRRKNxmhjyckmcZYrh4Fv7V++9h8qIp/Mlx3LhKpBzQkAcSGVtqp3oYT+K0M2SWgZBEPYeRzRn6uEFhsIFD6okiQwdw22nRTBVr7BmBuMwDC7l2yVNFIiUXVMZAwfLhReq9LqJexnoSGwlc13nGmMrG8ha6Y3/Bu47/NJ1HBHedDzh58i7+5A2vphr4LK12ketB1bgOw0MhNjIcP7HE+BXFeZSzB+lswYg9lOUKE7vrvPy1V4AvSK4JkYe7eKtw8gtjlA+Nc8U79nDFn+7BWsuTs9+NwHJ2rYrLCC6KleUOGxoeS/MZ3prmXcdymiagXunzkg1FmG6uLGnNx+sWYzvm/7J2Vj2Pkl/8eJPimfkp3ywTQxV+/8de9Q2P+193P8ihhWUQght27/i6x35x4QkW81luW+oNhJQBAwYMGDBgwADgrW99K694xSvYtGkTnU6H97///dx+++3ccsstCCH4mZ/5GX7zN3+TXbt2sWvXLn7zN3+TUqnED/7gD37Dc7/73e/mz/7sz/jDP/xDrr76al75ylfy6U9/+jlZ97dMSHm2TQkD/uH85V9/kYdOnGLblaP8zr/6d9yu7uGv/vtHaUZxUZGbZojRIbSW8Jen8dttequGg2cWSDcrjC+QysVog9Ua0ZfkJQ8ZBljPRa51KJ/qw0Kfk4HEOb5I0I2xYpV0JkRGkDQ7jOwa5fnl87RTSy/1UQl0988QzLbQaZ/wiTXy1IHZBFur4pUVOoD+zoBwLqU0lxGPO/irOb3NCl1WBBlUZnPCmRhpPawnoZfS31sl3jiKcJapPLpaOEOMhZKPqbiIlS5CKuxOh5gq/a3DWMdiRYauSKzJMI02vZblhp86y/7vWsarSu66OWD1qjr9HVVqjzVgLoRuDKEPzQ62n5DXFMlFY3inejinV0EbpC+wSpJtHWX5EYFf7SJaS0XlcD9CV3wcIZFSIpRTBOhKjTAWPA8ZZ3B+GbtxBKs11ljsuttCZAbjSJRSJGMeajREPr5MMhbQvWQCZzECL8E72QLHpQhJycEYrBBYa2nvr1E9Z/CWHEBiA4FtNIvxorwYSRo/N09SKSFjHyElaqUDzno1r+8hUg3SQfoGqyFc6CN0EQaLLG5ojZLITq+4Abem0JxqZXJHgedj+n2sYxE6Lpw8KsBacBKQpmg/0Z5CtHt4B84iJoap91KicZ/OlVP0pwVeJ0d1EkSu0ZnG9nrUb05IfnIDecVFr14D9kvIUlCsa/0aAEhHsnnPJJ3HTiPcom555rp9PHFAs7AyyaMHJynpBURqCLoOo/vP0+gNs3e+w+rCFPHdJ6Ed46z0MJsm0DbDhC75yEbcboZMckSpypP3n2TlzAJ5RdDbMkF5sRg7ETrH6aSIJMP4HhoLeU5WL3GiOoT/vCbiIQcvEwS1gHa/ELvMsIcYN4j5HP3hmOTlGSkOy+Uqdlai1wKWJ2PGRciwbpG9IoCDAAInNtR6KdXQIzM5870HMXaJDaWXMLf0Umbf+Rjuroh9lw5TXlfkF+cbeI0MABsZtlwxzNpih9W13tP7juNfjeNfzYf+61/Q/dxjdOshv31snj0/vo8zownOTpcgN8itDlfesJvGcoe7b3mcy16wi0VTZsiNeaTjEDkG4RmqtZBdo5MccEOkUqhuDasFaerw4JPn4WIYGiujK6rISmp1KTtfsXrCermWm2ItZCb/lu671+7YCsD/x95/x1l21Gf++LuqTrq5c5yclTXKIFBAiCzAIGNbGBscFszX7BJsr72svRjb4LQ2XtZr7y7ZmGAwYHKSEEI5jDQaaXKenp7ON98Tq+r3xxmEhSSQWLGw/t3369UvvUZdfW5N39vVc577eZ5n69gP7zN+yerzORae5Mqxs3+se+rTp0+fPn36/P8n1p4uGP0p2MeTZX5+nte85jWcOnWKWq3Gueeey1e/+lWuvfZaAH7nd36HMAx54xvfSL1e59JLL+XrX/86lcrjZ9j9a/70T/+U973vfbz97W/nPe95D/V6/Uf9Kz2GH5uQ0uf/PsF6j9YJhwd2NviFP/gw5VGfZNtq7I5D0I3Bc3EHi9goIz4xh1rukrke7YUWm1++lR1uk+BzIcLkGQqyHcJqHyMcRJZXBwdzIcKCeXgWXfKgJxClAioSmIKDaGusWiY2DstzHsHxCLdbQHo+4eYxmJ/DqTuE1sEJNBQE3koE1hKOScJRl3jEQcSawskQbyXGug56yCIjQ0OXqRQy9EAV025R3N/GOh7xumGC+QTVSZBTk4ilJrYK6doazoNt5IpHekEFN8wYuC+kdnfE4otGWXpmkeFjLkLCyW3TjGHYv3scjKF4qEVW9fBmQywGOjGi3c2rmgOfzkUVfCrYVQU41YZUg1SgDepUA3wX2e5B4BN4Eddev8Cuwgb2fbWEf6KRe3ZcD+E6mE4XOh2QErFqDDcWUClDt4ewULnlaD69IApQ8CgspchmikkzjJ8Hf3ra4i22cJMYXSpiU5M36TgONo5BCMY/cYD5V22kuE+jMk2mDFKbXAQJfDAZ27fPce9nphCl4HtV0olGl32ktnlgqoBMSWSckAHCEbhZCmEE2kKvi/ADUKebfshDSkkypLXYJMXOtqAgITOYJKL5gq2I1DJwywlEmiJaXVACJT0MoIsu6UgRp5PR3uSTjjhsvDEmCjMQYNaOkZwxhZNKjLBMXLIWZ89mDt1/NBelXBfjKzrnjVGdjTjy5QdPBxILNm1fw9DkAItpmciT9FaKFJTCSgGOYE22zFbhMH/IUjzWJIkzDBYR5UJOstZBRUWEkWjTQXYjLrhoI9e9YDufe9dnsYnF0RpT9PFjg7tvCZnlj90+t0LBA68REWzJ4CFBM6zghCHpoqbXi7AVsArUYgidBFPwCdcOsb86ikng9sYqOFHEjSXqxmX+4osfoTzpslIPQAmMyn9+5HLC7geO8xV5C8fSu7lmpM2zJ9bw11e/hnfcnjJsC7zu6kv50N07uHD1FDtu3cvCmQUQUHu4x8MrS0yOVLj4wvWPOX/uv+MQQhtkqIk1HL5xjuERl2zF4JwdMHBxgXv8WSa/Ifnih29labbBnksuYtDrsXRyhIyIZpKy/+GTnLdtDZ8eKSM8B/XxFkYbWttq7PbymmOTGqSwuWJSKfH95p3MagwaBLSyEBh8zH5/GGGS8pn7H2bN0ADP3rzuCdddvnEtF6ydYrT4gxt7ANaWx/jzC173lPfSp0+fPn369Onzb5X3v//9P/DzQgje8Y53PG5I7Q9jbGyMyy+/nI997GPccMMNdLvdH/5FT5K+kPJvhAdWjvON5ZOEZYk3a1lsN5k90UMM+tQqJWS9gy0FTK0eIrlgkN33WGp3LyK6msrkIC2/SLPXIoi62CjGIsHJ8BcHyCoZohPmua7lACfKENqgEBgpEYGPGyWQxThZiHlI8pU9Z3Dsa4PU9sTUtxZxewaLhvVjpNZFlwxeNyKrlbAdjYw1g7t7tDbVSEr59IApukgtyKSD3p7RPe4QDkLRGDAGZRTBqRh/eZ7OmTXmXzWGu6IZvzHEYjEKaIeAYWBdk/N/fok7/nwS91CDZKoIVmA9gZqcIJjscv75Oxl0e3SHfbQC4RQotHyczMGUPWQ3ry9GSvBcCodjojNT/IUY4XnI4umg1STBPb6S23mkwmYZq17nc9UrljjzRJd3f+EyZLUMFmy3i9X6dEWzzq1IUQpKotud3NqTaaKtI5iSi7u/nRuX0gwbGZCS4EQLkPSuF2g7gJqxqHYRktNCSkcDApy8walYj2hfUKOyp4fTzTAFDxMm4GowlvvfuZpkwsdtpig/yCdhAoX2FSLSiMwQDQu8g11smrF03RRDd4d4KyK39KQaUfDz9prvTro4IDJNbbTMynwTKR3MmZMEx9qksgvG4nQSPKXxxgMya/PXYK2MCRQrWwLicYfglCE42YXQ4rUMjQ01ONlDxBps3iQUnNIY17Br6TB/8t5f5i1XvwtcBXGCzBTFQ02Sio9sxHlArlL0GiGvf9O1fPpte+idJYEqyZoatlRElh0e+PI2vLkIM9ckGU4onc5dMZlm9gwHfaWlsBNqu1JkN8Ui2JEs86tSYBODCDJ804GmRa10cuFKKRCC0pEQudTBtkO83V2KmwRhGGB8L29KCsB6Aishqyj8uiUZhGygyJeOnMfUcJ3jD6wBafMBoDCjmbaZnPeJogZpsUo2GKBii6p6fPRvb2RWLjD6G2OMFM6k5m/BlSX+2x+9CYCv7N7PZx/czZHlOrfHLfSwg7BQ36K44YItbJseY2yw/JgzKC0HiPXTiMSAsZSky5oZy8lOmyjMSCY8rr58M+evWsXyfIPtV0zxzZkiB3SNyfJRluUESgq2nbOKhUYHHfhYKVg8sUJlbxezJBi6LBdEUgw2sxhfoRLLPmeFr888zPNWnQVA1fOYqKUYYyk5T83aY63lUH2FxUaHf3lgN9UgeEIhxVjL737nazTjkD+48Cr8RsbqLZM/NEW+T58+ffr06dOnz4+fUqmE1prR0VH+6I/+iCuuuOJpu3ZfSPk3giMlQxsc3HJMJmJ6hw0Ty6dwtlXpNNrYJIGlFV70ivP5+317MBuH8UZLjOyMeO3vvIS7Kw12f/IozlIP4+YWE1EqohKQ9RiyGEo+uuSiZmIIQ7T187mtXgRJXntrtabTHODkrVNkpYSxKzswD5U9iwhtaJ1ZwW9bZFdjjYuVlswHJ7NkFR+lwQQCXVLUzy5TPZqhXYE7k9AaVmRVQ3u9S21/AykdkrNKuCc6lI62cRAQZYhGi2S0QHdUUN5jEJ4gbcKpr5SxxpIVFelwGX9eMrqji9NOWP65Ih+buZTrig/Se9hFF8hbcxoZUjm5gOLkeR5ojQg1TqmE2/TIBj3iTZLgeCMPY0XkwornopOEdBUc3TnOTUxw7AEH2+4RTw5iBbhHIgh7SKUgCDDtDswtYnwnbx1yXHAUnXOGQEmCY23cdheZZmgpMJMDyBT8VoZzs4sIPEYCw2KqUBpEIciFmt5pEQhgfUBXBqiupXi4i9AG6+XVzSozUI8pxpK4JFGOxPYSpLa4mYNIUohSgrlOXiedZriNlN6agMJciM000nPIhMFUgrzKetKjcDLD9npELYlwHKyjcNeVyOYjKARIRzCyZw45anELICot2ktFtKeQVhJPesTjLlInFDtFhh7Mv0fNlwrk5QGFr6V4u5ewacLK9WsRqSV9sM38XDtvBwojKBWxWqNQOF2LMQbaPQyCnhjhHf/zK7grBl3IQEE85lGZicgKJWgqzEIXpxEhuykUXOhECClgUwfRrpGMWLzFbj6Z5LlkBclf/sst6MmxPPx0xuD3EiyK1DMYz0UkFlFvwkoT0ozOEcG65AiNCOJmATEZMHCszfIaH2kdWmcOIRJNd10FE1hO3D/MjDvE5XODzJ67go0FwckuR6Mu6XQVuaRxvS4yCPIcESk58PBJyrUC/+msN5JoONBs8c2Tn2P2M/eyQZ7LS9/4Ss7fNoguNHAmLJkBJBRrJV7/gssYqz5WRAEQ2pJWPNzlmLRkOTzYpLCUorTBhoKV+3os1VpMvnKES37uQt566xfZfXQTbk9x3ub9ePelyJ7ggTsOcODkLG4zwSpYboUoCa5VnF/OLTwdnWIUyAyENnQbHT57/L5HhBSBxGqJtZoke2qixk1HD/P3993DhROTvOz8M1kzNPCEa5c6XQ4dXUYFgr/5h/9Oc2WJX7rw1Vz7ysuf8GuOdmf41sKdPHvkIjZV1j2lvfXp06dPnz59+vxA7OmPnzQ/DXsAPvWpT6GUAuCyyy7j5MmTT9u1+0LKvxHOHljFW595CR95+NPMPyQpLLpc88ajBBXDLbunWapLKPh89dABTqQreIshyW3LLMSSW75wP6/63RfzpZO3k25dBVLhzreQVoLWJEMFRGTxjjUQYQiNJkjB4FiFpRPLZFUFjo8zWwejkVmP6l2ziMzCSzycrkZ2I1SoKS8LnDMcHB/0QwGypfOQU20wCiyaLHDRxTwYVMYZmedgVkoU2xFp2aN3xiDFeh1pfMLrLE4iKH0Kygc1NkvyQNdeQuGoxnoORnt0ly3tO0r4QP2qDfSmfLxWgswM2nfQmcfJh8b5x5uehx3TRCMuqqcRno87J5CZyW0eiFyUchzkQhPlSczqQbLBAnqxg+yE+RNiDKbbQ/6qYmI7LL67xxd3T0CUEW320KuKuG2LWy9CtwvkUza5FSavGZaDA1hjIE2p3buM8cGZb3P9G09RHTZ88DOXkA0PQaIJDs4jTirMYMbKMYFQLfADEDKvfT69p7TsI487FIuWrOiS9bp4cYJ1JXbVBGpmAQTYNEUZHxslCK0hTBDay0UkbcAAKs+oyCZLVG5ZyfePxbouUht6lZTemgLx6iEG72tRPhIS9xLyO3MLRpEUXNxmjI01adfPW6IKGhE5IARapNTPqeE2wZTA7QlkL4ZyCakNaiTEDBr0hIDZMsl6QzqiCY5bopWQv/j/Ppw39iAgjDGDVZSRiMxi4gybaRCCE60OR3cepnQqZmJfiHyhpnOWR3V/iEljlHZQnQSSPBi2es1ZNL/2EBjN0OfbqFIZp5shEVjXw8Rtikd7LFYckvEi/mJEe5VCnQCMg3BCxJJBNHsECuIgz2mxwL1fmkBFCcoVFFoGnWTogTIkEI4KksEx/JbEWkOhaSDLaDbmmK4FnGiAk6hcPBuNmbq+y8lvlDGOxAhoz7XxE027ERIYn9+9/zM0k5Bhu5u5L0UctHt59guXWfBnOdaZQ60qYdr578JeL+H33vcl3v/Wn3vcM2juYhenqyneauitkkSDBnVGQDyv8RcShmXGqcYJfv3qd3LvsxwaIx62DDoVHDy2hqmlhDhW7N51gnUv9FB/m2ItuJnGOAq1rDnw7QN8JPgGh246iCnn5xNKMliq8utbriTTS0TpAbL0TBYO+lgss2s7bBn4Xj3eHbfu547bDvCqX7iMVWse28gzEAQoKRgvl3nFGWfxZx/6Jvfdc5T/8AtXPmbSZM/cIgXtMO1V6EwvoUcSji4fB55YSHm4dYAHm3upueW+kNKnT58+ffr06fNjpFR6tPV6dHSUTqeTv6H6r/hRmoV+tDqDJ8Gzn/1sCoXCD1/Y52nj4uFz2RKfT7KvSDBYQncm6bYg1SVEtYrwAx7+1gGsTiic6KJ1iG43Of9ZG/j8Z99LZTiGJK+ptVIiwggbx2hXoks+1mjsdxtwrKW1EqJXDRGdM0Xz+ROo7UVkVVF+gaEwqSkNxiwcHcYJU1Qrr8f1H2ohDrbo1jWZIzGuROsYWl3USgu3mxGspJCB00xxV0LcVkpSULg9QW2vZvT2JqU9K3gLLeQuF46CjXSeV+F42OEqZkOVdUNdnnftEunmcUx1EGkVUnnUjmmqxzXxoMfK2UWiEY+k6ZIuuyQFH10JEJklrQhaGyVxKUG3O9iij900jfFyC49INc5im8KDJ/EOLsBiA5ulmHVjhJuqzP/MBCtbJtD7DCJJEQayCZ/5l0+wdLGDaHUR9W4uumQp5nTnEFmWW3x8H+H7yEKBwuEm5YMdfE8jt/l8TZyLt0rlVcqeIstSzEIDHjyOWOmhtD19LQ1xgvVczOQQ9sLNaOHgtCXle0/hLnfB8/AGh/F7af78drrYbg/hBXnNtOdgAw/b6ebTR1KC4+YBsr6HmO+iPQfrOlAtQamAGapgrcFr5lYgJ5UIpShVC1ibt+xUhgZRYzVsnCB8H5IMG2WkoUccjEOtSlKGpCqp7W4x8FBM+UQKy83Te1CUPhdR/kAT//4QMo2/6OM3Umr7Q1QvwcRpfv3AQxQCWmcUCFUXvbIMgCqXUKUiwUyL4pEObioYuFNyzcA+znvOcZTno1DgKAhcZKWCKJVp7ppDeC7CcSjMaGzRQxcVyVjekIMTMHY8puLmwchCWEbxsa7C+g7PecYBRCcEbYiB0hk91KhEVRzcJYO71MFEMaroYIc9VCvDTEf86Ys/w/XPv4O0aHF7hqF9MLQHVi5qIXpNRo5qTFHiDVUoT6cEaxPUkMFKQIGq+ZQmyoxuzhDp3zJdrDJWqHDN+pdRXTVJbWyUVECvF2O0ZLpSQhc0pqARPUPsmcc9e+I0ReIjU4keKFI5ZLm2vJZfv3YjMoWs5pE+J+Yz0TF2j4T0hED1JKqjkBkUshhfr2DTlNm9szznnCvYeHYBL46YXD9G5nkEjYzOgUX+4e9uQh1o4C/2kKfbowojJaIU5lt/w1zz3Wj9HYLDHs4Rj0L66F9zt9+ylwfvOcLe3bOP+3e5cHKaD173Cl573gXcs/sY37hzH5++cScrrd5j1l66bjWve8ZF/OYVlzO9byveXSOcsebMH3hOP2v4Il429VyuHL3sCdfMdBd50z1/z0cOf/MHXqtPnz59+vTp06fPD+fIkSO8+MUvplQqUavVGBwcZHBwkIGBAQYHn3qWHvyIEynGGA4ePMjCwsJj1Jzv+o6erlqhPk8eVzjcc3OHDpJGMeOOm2qUCg6ddhFhlrGxprgMa25KaLQtyakYtOFjf/kFimtgw3CHA7Memevl1bMWlLb4J1sQhXnTSKgQtSo2jEi7PaRzOgQz1DiXTzPw2pTmssvGVxxm+XiZwgc1MrUk42W8pR5Ca/Q82CTl+MsCgobH8HdiZDtGlzxkmFE9koErKSwbjCNIiwqnYxBC4UaQeoJ0wMUUFDR7NC4qEV+WMPLFJvGGQUScUXkmvP6aE3jdlJv3noHVgFLEVYUNJFlRYpXEW4iQwqG3UeSTIJGlstdQ3LtA89wKWbFANCjx4hjGBsF3EQMVqLexrkQWfTi1grQWWSyC0MiTK6SXrCObcHFvTZjJNmAvhtKRJiw2IQPrSlrXeHgHY4TjIIXI220KQR4MqzWm3cZGMaRZXiMsApJU8bkvr6c+OYRd6hGIBKQkfeYYIopR964gNdhaGdsNEe0QG0aoYgETpcRlRVoUqJUM4bq5dON7edNRHOeZLo5DVgsoDZfpKoEQEtIMJ8wrnnEUNkuxaS4OBXMRbuhgAz8XbnQ+WVGaNcjFlOqDpxBJihGQDo4grAVjSA4cYvOmbRx6aBabZSAl7YunKew8hXOqDr5HMZQ47QxvIcRd7KIHi2QjVVhMEElG2biYuQ5J1qV7/hhWStwVn9bmAnQalLqS3mqH0sEuCEFWEMgwgSRDON4juSp2TBO9IKb0lQJuNeHE+4cQIRgMRGneBhUnoLy8DKkTIlyVi0Kei9D5ZJMUAuMrMA5jG6d44Ssu5T/98w7ESpdED6IcD3RGaTKvmuZ0I5NZEAjHwRiJTFIEAiUUSS8j2lhGBxn/+aqv8ayBo1w+cJR/uudidBOEJhcdFxKGOwmNuz1SJYiKGZMXtFDdlORoTLZWYKVgwvFYfcYA61Yf43jjXr5zCrZW1/C8sYv4/LGv06ivsPfoKQ50IkzR4Q2XXsZ9H7sRa2G4adEeHJhZZPOq0UefPUrh9SwyU4iwS6Fjae1qU96+wvRkk5WVKouzCmsEq68aI9rVprXOw1EpVnu4AwntjotcbmI3jGKy3aybOgprRyj4Llppwq1lCvMGWc+/d6kDQgqssNTrIXtWFhgcr5CYMuPeeqbmjhClGUX56F9zUTem0+4RhfETnqUF1wVgdKBMIXBxHEnBdx+zLnAdXnTWFr516Ahr1p+Fe6rKui2Tj1n3r6l5Fa4ae2IRJc00b/3Yv3C0vcSRc2f4pQ3P/YHX69OnT58+ffr0+S4W8d1Z9J/4Pn6aePWrXw3ABz7wAcbHx5+WPLunLKTceeed3HDDDRw7duwxFcdCCLT+/v6EPv+3+Ot9/4v5pXmcUKISwcoNino6iMh8tmQrzNzkcdXzz2FORSz+0wNQyLMj6j2IF3xoGyoFl5VyIX+3vBSgfQElQWd9gcqOGNlL8jYWyN+RVx7lkwY5Y3DP1VQHW2x67jM4ZXyiqIBK6mBg9tWjuKciBh+yqFDTWS+Y+swc4XQFlIsYGUa6Hqkv8he2FvTGfLRrCRZi7KCgscZH9TLS6QJSWIK5lOBgj3iNQGpJtr5GdzpA9TLaRxL+9Pcvw62m2OM9rCvorR9i4bKArAxuC6a+1sACi88MMGisB931mtq9bdzEYouCtGoIt9WQqaK6axmhQUgXigVEGJGNFxDFccT+U3l47Glh0YkEUzfFaN9y6oUGoQ3FO1s4ixHTHziCnqpiqiV6l22gdNvx3I7kupixIcSxU2AtwoIoFLCBwYYRNkkw06NE31hGTRnc4gDC09jAwtaMKC0jCwP4h5u4sciFney0ZQgBGkyWUHmwi6slyimA52HaHWSmIU3zQNrzN+fCTi/EKRew3SQXP0oFbJrl7TtSgpBQcFHG4jZS8H2Em+fDOFpjew1sUSKsxWYZerDAiqMJemE+dbOYoc9oY7qn/+xZLrm+za49Q7nIkKTgBbg2ICvESJ2i5psk28cRCxnCWrSBgZEyM2lM/dwiI/d08WZCemtL2IEA3YPoTMXidaMM3BdTnclwZQlKp0UQ8gkRPQ1mQtB5nsPqtQsc/sQInsgw41VUapCL3TycWJAHIccxIk4RhQDhuKhOQlpVeF2wSM6/bCPrXrYZv+giY5uH2iLypyFw+KcHnkU5nUF6Po4RxIsFkAZVtpgYrJLogRJWG0xqGbmrxYFnjaA9zUpaYGTjHJ2Tw0QlSWk54YzpHt0RqN9URaUW08uwA5Z4lYu/dohQgHGg0wjJjgaoykV8bP8mOg8nzJzR4F++fC8LxxcRWNxuhiuKtNMEnZy22gpAKE7ctcCbd/8zH3n7DQyPfm8EUkqJ6hmwAhnmQlCv3SOpRyzMl4gyTXbAwwGWBhS1MGXdlkNEmcuhxhpcZYhHB5FejBSCJDtGbVVCtsuyXO/SObNC4/wC5b0hw/eAcBxco7FxBkqhluH5awfYWT+KYIgtzlqkzYWmzD5a7L/70AxNk3HznkO85GcuetyztJvGFB2PyZEqQyNlKgUPz1GPu3bX3Dz/6657aC21OT/UZNnjT+0AdKKYku/9wF/e7TDm6OEOUVagsTZ5wnV9+vTp06dPnz59nhwPPvgg9913H1u3bn3arvmUhZQ3vOENXHTRRXzpS19icrLfTvDTxKlwkeK5yyQPDWCtoCUC2F2FsiSZHueMbD9ze2d4IJgnPlsS3Jo37hQCxflXn6J5MKR+chS3oxDFIoNbqyzvXUEuZ9RmerDSBSkxIkUKgV8KCAMPAMeTzO+NaX2+zZ6P3Yx92zrWxCOsnJugWgYRC0y5wPJlkvJRjYp7CG0pnggJ2qfzR4aK9FYHaB+CFQtC4nZsXse8EqKmHLxmgr+cUJiJUT2N0+4x/uGQi39hiVW/0ODjH7kQmUmkVoTWIWwq9LkBUy+bIQwiFvZswCiL9gzd9QpnRZOMZ1hX5vqtlnizXRCS0tGIZCzA73o4xQrxWEjQihDFIjZOc3tPI4ZyAeF7mChGGI0FujWL50ncnUcpTdXAVchmAkriLYSIhYjktTWyrRW6dgOlnbPYOCZTBjfLsEqSugpVK2KlwLQkMjNkWQ8vzAgONzDbR3MxQadYbVBHBBaJ9i3efBtKJUSxgLUm36/vUDi0hL8QQqkEvpcLP/J7WRNybIAkULQ3CsxOg9tKQEiEAF1wcHpxLsx4HiJJsQ70tlYon+yB0VjlgBIgFDaKEI6CTKNHKyw+Z4jhu1ugMzCW3oxk/y2ncGRuQXJKlrIT4caVvPJaCZI1g/S2FQkrFXQFhnaEeEcyrBUILD1Pkj13FeaBkOFb2hQWugSzIdlAAdVMCVf3cEPF6Kd7OKqElB7S8zArLUTg5/W5UYy8LUEUR7Ha40C6Br8qiJWlOJtgVW4NsVEEYV4jbYxGJglWCoRSWCXw2imyk0GS8vCX7+bhyYTbswp6tIrVJg8IzjI6kz6mF2PDEOu4gEAgsLHFFBSm4iA6CcQJIskbrfyGZsc3t/HZvdtxjkuGr1xmYbhId1TQ2CaZlg69zxcRMxlyuQsC4nlwfB9HuHgtQ1oUZMaSJhlLCzXSByTThxZ51faz+eQ7PpOH/q4u8PJffDa33NbiWKvOyZU2blcCltSmkEjiO+b4+evew8+9+jJ+7U3Pe+T8GSoE9I6dtl1JyZXnbWRiUhIXH8J0wW+Q1xGfarBp+SiGgDlbJh3QxEsest5DLbRZloJa8d/hmDblgTY2SgkOL1IYhNLhHrguNtU4ZQfruaAtzTTj4dYi48F5+KqKIwOSAYcktGTf9ztKp3ntdNp5fNF/x/Jx3vPwTVw5sYWz9TTz7Q4LHUEnShgoP9auun5okO1Tkxw7EqPTLs3lzuNe95sPHuADN97Lzz/rXF568VlPeI4XPJc40pAKlmaDJ1zXp0+fPn369OnT58lx8cUXc+LEiZ+skHLgwAE+/elPs2nTpqdtE32eHuTOUfRtITLSuJ6LM26I13fQD/okD0Qc3T1J5B+n+4YamQD/gMFZ0Tz7hT7Xvv5+5k643PZ5h4fvgtaxDkM1h+VujCkV85vfsgfSRRiDtYK46OGtGaItBeKBgxQaXYQBGWVEezuE9YP4LYfM9ak9EOLONWlur6Hm24yWV2gOjuPEEhN10WWfNFDoIlROhbgrluw8sOdoerd6BI2E0okEkRgyR9AbVngmw0kdJJb1G5f49j3bkUEZ99gyyVAZbTXOUgsxPoBwwEUjjMBfEWgVkXWbxEM+QUFRWc5omxJD94TIxGDbXWShTOmYoXigibsS0t06BCcj0BZRKiCUwqQgZhr5TS/fnViAZNClctchZKQZ/PwC+C7j0xH1RYdUVhCeg7y1i9pWREQZdHrQ7SHKHnawgslSepetRlkPIyHzh7HSUr5rBlksYNMUZ7lLmiWIYkDwGRBuE291RnjfqVzQiFNs4EE3hCCgt2kAJzTQMnlzDhYbuPm6LMtfRK5LPGjorHEonCihGgYZZflYgqew4vSgnqtAZ2STFUrtGOIMAg/SjHS8jIgynEIB4fvYokSPFBi5o4U0NhdxrIWSh1OP8jYk10Enlh2/52CWOwjfQ2owhDS3VaFncFKLcSS2k6CLLgaFTBOaD3eQBQ8mqjDTQBiD56SIlSW6LypR2J1RmsswqkM2MZDbj5TMp4DSGLBYDzK/hFGCYL/G62qcdkqSxCjPz/fouQgrc0tPJLAVkChwFb01PuU7T0I3gTDCKIfq8ZjeZInOmgLuSkLQzm0//lJCOKARQYBNYhxXkfZCpOdhGyl6qIiz1ELOLZONVRBWkLqW9lyN8c8cR/YS4lsKiFeIPHen4HLg5iLDBQeiMLeGCcHQrEaPxxwdyaDtoFIIHdh05jSN9Yu0Vj/AhpLPedsMH6pnEASUjIsQAlcJjNS4VuYTSEDqSTYMLbBysIh1JF/8/AOPElJ6nQRbdLGOQiEYGarw8IzA6ymiSGMcm2cUFy1Hu+spHe4SlnxkJhluKnqLXUQv49znnE3YNZRLa3nOywIe+PYe3IOnGP96M/+ZK1pMwWNABPTooAcrqCjjtvkZ3rn99UBuP7WeRGtBxqMnRN76Wy/m6zfu4jd/7fEtM70swWBppxHnb5nm5ZefzXCt9LgiCkAtCPi951xJ9+JLWDhZZ93Wx7f23HLfQY7unufrYu8PFFKstbgNwFqyBe8J1/Xp06dPnz59+jyGfmvP4/K+972PN7zhDZw8eZKzzz4b1320Zfvcc899ytd8ykLKpZdeysGDB/tCyk8he76TEtXLeEGKFXnmRXFDF3Wzw+LtPtY4qHEPf79LUpMMbq1QO9Vj27lnUREuH/7WSbZf9RD3f8pBdB0O7jmJGR+BYoAtu8iRDD1vUXMNLJZkYgJd80mtRg6WcNsJ1heYAcX2849zfeEgf/Hfn8Wp8VH8mTbesTrGpAQzXbQJcdZJ8Dxs0UDBRTqSwvGIYtzFui7BlhAxbagfGMCoIkNbF5CeZV80SVqpUZjTFE8kRKPw4V3DeCccrBDEE1WyoSKlA8eQpgczCct/XWJlSwU2gNOF8oE25XtXyDZVGV1exLY1x39tmuxqiT5gkAsxnXOniDYXIe4ycCKEXkK4rkRxbx2LwdR8mPIQp1xsowNGo12FANzZFeR3798cxVmXdvjFf3+Egw8WeP/fnkNaBDWrcN7bJStYCFyIFCq2UCtjhosUl1Ksa+gOeaQjAVaC0z6dl6IUpaBBI5jEDhapr9GormFkNETtqiH9Yt6o0+0iPRcCB2ENrg3QIx7YDGstsqVQBR8QWD+fjnC7DsUT4KQKhEV2onzCYQpU08U4FpvEMO7TuqpMrdVBFFys72FchdAW6ymsNRCGiEKA7KZ4bZvnpxgDrouJNTaMkWmWTxlYS7aYYXWWf41S+A0oHcmQKzGlfXWCuYxs7RBOqBFobGYonejRWy1JRwL0SBWRZoigQ9B2yG4PUaGL0KCsxcZ5pK8cqGE8idEJvWFISw6q4pJpQ6YygvkGzCziDdcwYwECQ+PZ0wzdtQTWIh0HjABjEWEMPYvqJtg4RQ5UITM0759h1hXoQKI8SB2LKSq8hkOcaZC59USsGcGerKOVQERp/n0zuR1MVxy8+Q7JeADKyVuhtME/kiIycpFDWFb/WpvWf6/gbkhI6wKEoJOWaeyydKZDgoUqWNALETtu3c/8vlOU/z/Nec8dximtzqeTujHhyZDWSofZTpvlbsjWLaN4eZQSY7rB8MgyS04RK2Fy/dAjZ0+aZLDUxhYLZGWPtOzwP2++j3jEITEW5YCVEqUtplSis0mSPFjG64YMvsDw7y44h/92504K1QJnX7qZ/Q+e4IsfvR2pJL0hzfJLPGyxwNA3U2RXIhs9ZGkSyyIA2YBE9743vSGEICtJEvlYIeUZF2xg9aohpsZqj3uOXj62kVWlQSYLNXzl8JafvfJJnb+lSoH12544YD1rZ4iuZmWhQyuOqfr+467TRqN9EAZU1J/47NOnT58+ffr0+T9lcXGRQ4cO8brXve6R/yeEwFr7I8eTPGUh5U1vehNve9vbmJub45xzznla1Jw+Tw+vedUz+eZ39jA/v0K2e4mLvr0VO+lz1467UHnHCs74MN5dGQOLSzRbMa0o5ZN/E3HW2LO4t5KyctsWEqXQBYEeK+MaIMnwhyOcOKIVkVs7BLz+Nd9kT+scbv7MBGqhDYAol5ATkoafcHtpkvaQS29a4iz6eDMCJ7JkqwZxBxS96LRFQ1lELyEeF5TuPYltxWQDRdgwRDovSecVBUdTvrCLAIa+1qO9pHBnewwNp9QvKFFfrlBUCXZRolQV98gihAnuJQ7OKknvK4rOYBnjgVsXMDBItFnj4BEdA2MTMiEJCgYTFFHlGFHRmNEUM5KHgrphlueVdLu0L5pCV1wKk13ktEKaALmS4LTzH8LC7iXC0QLFBQGVIknBJShmGCOoXzpCOuRTu2uB2jMy9E0hYrmF8FyysQF02aOzyqVyqI2IMgorlnTEyfNmXee0EKFY9eyI+v4S1oN4XGBLMLkhIpkrYe+z2CRBBD69M8bwF1skoxYbWtymQKoS8sRC3sKjTueFxDGi2UasKnO+XqZTGaCxkueXADgNH1FwwVqshGT9EK0zHDbXZ2l8ZwgTpYhEo5o9wGKTFKQkKwWoI/PYUhGi+HRwa4wonJ6KQeT1YZ6LVRJhbN4aVQhwY8HQjh4r55RwQovIMtwTDaiUQOZ2LOMIrBIYB6zjYAsuaRKQDRrKu1PiAQ/rJQglwRh0sYC0Bisl4aBFZRaVVLDNXNzQVYUzu4JUDqbZQ08N0d5QJhnxSQsCdynMrVCn73FtsYDINNlzNiHuOgrtENuJsGFIun0CmQowEhVmuKlAaEs8kGDDGGssqaOIz5qguRpK9y9QPtgGz4ORQVQKWQHCCZ/SosGK3EqUZ+gYUILNFx3i8tpRvrz3HEbPzZjzfXAkQ89MMLslI59KCact1hV0fcGLbngGs6Mn6A0scmC5x+2zt+BMByR3zJFJwcO37yPF4msPRylGtU+90aPm9Di4czWqFyOsYHqw/MjZ85E//CeCOw6RrRqGoSGSqoN1YFA49Jwkb/pygAzcnkVXoVgLWXXhCa7YtsSZYzdxxc9dgM4u5VkvPp+bP3sfK7MroA0rUy7hmUWML3BbguptEu1ndCYdFq4Zw29DugWuntj4yH6EEAgXiCypeLSQ8v4v3cUD+2f4pRdczBXnb+Sh+Xn+x11386qzz+aqDesRQrCu/L1a5AMnFqkUfSaGH78Wb7HZYbHV5YxVYz/Q6nrWunHu3XmMBd3ij2/8r7zzmmsJ/Isfs67geWRBfp2BSt/a06dPnz59+vR5CvQnUh6XX/mVX2H79u18/OMf/8mFzb7yla98ZDPf5f9Uzenzf85sr86tw/fTvnaR9mcFCknckvzmbz6fe//yNoRUIA1nbhzjof2n0GGCDROEFCyEIafu38GW1hGOf6wMTgFZsBitwc3zEWwnJjklcWJ92hYCH/n4BdQ3TUAlwxE2L9NOM5Izyhx1i6x0isSjDtmQJV4bUN4XoKxLOOhzZPU0g7tjhDSkAx7q8DLl78zlkwxANOGTRFXkEUFag3RZs3jHCF3pEcUeQ986TNEJuewvV0hGPL7zd9vRPUW3pFFJjJyvQzMj3VMhrRew5QJju0LmytBd5eDGCm9sFHd3Qjbg0VznUOgkXDZ6mMPVURJjqX5rGSFdqrd1kZ6i2O2RTgni4SKyE6OLCueEptyNmXpjQveeAnP/3AEL0nVJhz3sUoJYVWRyewNR8XDLEtWKyaoOstUjvilDzqT5TbmxkGmshHjEI/MrjN3RwOuAdnqQJMjMIqTAFDz23DqGMi2kspSUxW8kLF41RDqkGFg8ANUyslyicLSFzSJsHLDwjBLlwxlD+w04CulIbJJhrX7k8Ydap3jtb93Dtz69jjtn1qKD/J1zaVQuICDQFR9pXEbu7BLer/K9BUUwGXhuLr4IkYfMCvCKp4NqZS5mWCVISz6q3TvdApTbbfA8SFJst4euFZFK4jQTavenqJUQkRr0QAkZx6AcwDK/fYCrfn0XwrFsOHaKb//+NsStNUzRB6ERVpGMFhDagqNwV0KsIxGNFuVTFqEcshEXf1rRjBIcSS72JBrrK6wUSOlQPhiSlRzcusqtUMbAQAU7MoD2FU4oic9bgzhZx5/pYLIU1xq0VdhAIYVEdmLAoAcluuzgJILAD+jqkMEHIvyjYR5Kezo3xlsOsc+sossglgTJxjGCI0tYAVpkuMpyXbKXO/7zKmTY4eTOKk7B4BU8Du8o0DlYRpRcZKpJSi6ioJncNMozN51D076JXfYgnz31HNJi/rwY4MF4niONOiXpI1JBWDYkVtCuF6FkER7InqHT/V7rTX2xnY+tJBkiSynMaTZunmDu4BKep0lKEoxgdSxYmG/huDXU2oza2REL+ERJyMq+hOEpjRCCiTVDuBjcoku0o0f7bJd4rU9ccbCZJiv5OL7BHSuSTcUMbukxOPBoG0xWE0TOY8NmxwZKdE91+Mo/72Dr5Agf3fkA3z5yhJWwx1Ub1j9q7YETi7z5bz6H60g++c5fwvce/cbBvoUl3vS+z1F2PN58zTOopYKzL1qPepxg2jPXjbPaL7JYajDgHSGMb39cIcVxFFddtok7jxzjj3/2hU/m+P+R+MTOB/nYAzv5/edczcWrV/3YHqdPnz59+vTp0+cnzbFjx/j85z//tLpq5FP9giNHjjzm4/Dhw4/89+kmyzL+83/+z6xfv55CocCGDRt45zvf+ajaZWst73jHO5iamqJQKHDVVVfx8MMPP+o6cRzzpje9iZGREUqlEi996UuZmZl52vf7k+JId4HDnXmW0g6V1ZaBmk/nxAL/+53/jNUa3emgW208ASLSqEoJNVaGIADlUgwkasjkIaG1ClSruLGDUZK04tMTA6SZl08vGANC0D2iSdsCr5jgrBoAx0UY8Hd6lE2EPC4o7LNUGyFqc0Z01jSm7FPct8TYTaewlTK6UiIZDJBYpFAI5ZJs30D3wmkwgtrDbcZ3tyCMaO/16DYD0rJDNlwkTRS9eUm43+IcjlGNjMF7lijtr2NKPtZYTBM4CSJKEJGmfDijujfDzQz+UkywlGENFOYjztRzjCVt0lmNSDOc+YiJb8+zbrKHLFikjXEadWS3h793gdK+JkmzQC8L2Du/igOHPbb/ep2X/d0Jhs+sUpoxYDXB6BIjW5YwWI4vDVPetcLYTYt49RQ7b/ObdkveenNgBmf3CYJFS/loiA4cbBThz4c4mcjbc5KUSimm5HSRApRVVO9u4+5uk92TkOmAcPtasoECVglEJ0Qt9ygcDykcjxi4ZRYxv4KeGsRYCwUfXA8xUIVCgV6m6HY93IpDpdrF+g7Wc7BGkamUsJjSXVfAXZXgOBFRVsVKB5FqMOQ2pWKQv06MwV3pYB0FaXJaYMlbaezIAGashhyqQa0MmcaM1rDC5OtSg/U88FzaGwNEJ8JGMWFZoIv5DW1SczAVhVHQ1QFLqypsvWIln1axFpRCBB5Uq6TjQ3TXFalfOJhXN/seFjCei5NC+vBcHjicmjyLJY6hGyItuM2M4kyboGEQRueCkrFonZHWfKwr6ekM7SicmpufT46D19LIXkawGCIyC50e3SGFCitk04NE565hYdShcLxNcGQlt+q4bi5G+T5khsIhQ0GEJCOgA0n3wnV0L9tA6XiGSVw+9Qfns/hAAdPQyOUuJk5Zv2GEjq5gum7eNgRYR6CUy0f+/tv84/tuQalNWBFQLVTRZR9GBhg8YxX3jh0mkW0qvqTguLSjFCMFoauYeu4shY0R6WCR5Vb4yPlz/Vtegt44SbZ5AuWBE2WcvGeG9oiDdRWlVOG7isMbLJ3zPeIBy7wqc/ferXz02Nl86nOvY/+uKgd35Wfy3JF5wvllegsrqFNNRlbqlLIEr2exnsTxHC7YsIozB0cIeorFlsvth0896kwUKv+5Sr5vIuVnrz6fc8bHaC13OXZsibLyyBJD2kr5wzd9lPtuO/DIWm0tcZYRG02UZo85d2996CArvR7dOOFbn7ybD/3Xr7LzzkOPe0YvzDZxM3jR2Dbe9IwXUin9/BOe5//t567jtt95I1duWv+Ea/5Pee/td7BnYZH33H77j+0x+vTp06dPnz59fhp4znOew86dO5/Waz7liZS1a9c+rRv4YfzZn/0Zf//3f8+HP/xhzjrrLO69915e97rXUavV+A//4T8A8Od//uf81V/9FR/60IfYsmULf/zHf8y1117Lvn37qFQqALz5zW/mC1/4Ap/4xCcYHh7mbW97Gy95yUu47777UOrxay3/X2JzWXH16BKdlYC7ZsrEtRbm7nmOHjiFqRQQ7S4AB256gItfdBH33ruH8HiM1AI6GZ1PNDi0aRKx1kFETj6d4Pt5Ba0s5BaNtSOoE4vIVn6jWwljxvf1iLaME3gVWoUE7SrskMCZ09i7HYgFck7gHNIEJzvEgx7FVSm+9kktWCHymtKRQahpRKuNXGtRQwbVMqgUQOCEKcTgthLSQR9ZG6Szapjb/7pE45mTeKst1bvnEGmGLbigHIw1SJ3R2ippXRRQeDgjq/p4XWiXoLc5IDjWxWlrtGO5e3E9dy5tYmBdh9rJBNKE869osHLExWqHrJWgFy225BFuG8J1qwhH0qg5xEuC2soKU2e3KU8ZyusilpdrqDgmfADuUJN89tsTWOXkzcHdGCtkHuJrASwmTRGZxiYZqptPysiki1hOUe0MFWZ5SKvrEMU+k2s6NONRnFDnUxOjw5SWBe0hm9uExitkBoJTi3muR5QxfnMDmgnWN2SuwI0T7Gl7j6iWwbHoRct//e1LcCaGUL0uspm/drLpGqKRkrqa1sYxzpo6xtEvjGEcgRS9R+wu4vR6ISyiVDptKhNYbbCuJBsqoDeMQy9FxgaKASJMsa4DjTYk+bSHTdNc6bVQOdrDSg0aijNt1MQYJrDowRIqFbTiAlHmUsgClvcPYE9PR6A1otuid14Fb0GQDvuIOEMXJCK2SHxss3la3Cniz3aQicYam9tRrCUpWPxmhsky3ExjC/5poSdDNXskURdhB/C6KUaBbXZpX7YGicitO1WF6lq8Yy1M0SedrOCEKf6RBukqgR0tkgUOUgiEozBZhlQOohuhpMTOx9S+lKKDJlkvZvk5AzCU4e9wcLuSpCTxTupckEvz793khVOoiuIuewJbAuPmeSxaKkbGK5xx9iqmB17A3xxai5Epz3vlGh4WD/Hrr7uWfRNfZf5IxlCq8KQk8/KXnY1cGhOK3koJmVqOHVsmDBMKBY9bv3g/bphBJ8IvGpImdKOErs0wviDUFkKNzYBtkl5sCfyE7ryHMxJzxuXbaDltVl9Yw1rLzlv2YeKERBv8iwPKZ6RUZYPezTWk43HZf7iE+//5fsThJmZDDUcJehdEjz4Uy5JMQHo6I+V1//6D7D0yDw5c9YIz2Daymp1f28U35g/grvFpNXsc3tPg4R1HufDyzQBsWzPGX7zppXiOolZ6bP7J7k8/RFxMWDQZuy9pUFoHk2uGH7MO4IortxEUPc4+a5pKqfIDz3MhBN6P+ffSuVMTfOvoEZ69ft2P9XH69OnTp0+fPn1+0lx33XW85S1vYdeuXY8bT/LSl770KV/zKQspN910E5/5zGc4evQoQgjWr1/P9ddfzxVXXPGUH/zJcMcdd/Cyl72MF7/4xQCsW7eOj3/849x7771APo3ynve8h7e//e284hWvAODDH/4w4+PjfOxjH+P1r389zWaT97///fzDP/wDz31u3tTw0Y9+lNWrV/PNb36T5z//+T+Wvf/fJDF1NldiguoYN45Ap6y4evgMWgspx25+GDE+isESRxkzYURkFari4XUNvYpEJQZ7MCP0fRyR4vU0IklQUQJRjGk20WetR1QC3GQA3WmTlgX6eRVWbdacmtG5RcNXmDAm/csMYotZVSKs+QweShHWkgw6LF6+hjPm6tR3CSyWs0ZHOXKkBWlK56xBxPkgYk2hl2CxYC2d1QF+5OKt9PA6HdrrakTTHpw/iej4SNfCyAA6ismmhzE+mG6I7GWkNdBFRbq6gLOcB4TKriStCDKV4nVT2ldUEOs1G70FWrcXoFzEDoxw7z6FM7NA1rYYXCgoRODiiADjS9xWSm02JBp1WXn+av55f4UNO0JmHh7DsRpKRWwvYubhEo5tgePkQoIv8PctIFwXtCYZq9G4di0jH9+NYwW1Ww7R2TLMyjOGGRp0cBYN6tACNtUIR+EOQTdzURmYgosYqoBycBJwmwn+cohc7uTBpUqCleitJdw5yfglbZZPDeQ2HMdBBA5GZ/laBGQWdESSabwMdLMFUuIsKoT0KMzWce7rceTQalRNk8QGv56HowpjTtt/ACEhy057EB3wPYQ2uPNtKPpka8dIXYU9uYyrNUIpZJRiU52LSytNGBlCOwJnbQfzi6OoT4SIA3mYqtAGqTJkEnDfrg2Yls/SQJUh2UVkGgIP0YlBSuLxPOS2fNgguxGdNRUG9jawnR6kGcakMFRCRCkyztDSySdogODgCiJM8qkOk08ECSGwSiEEOPWQbKyKk6XIxR7xiKK7QVA5Ab4W0NY4nTTPuJGS8sE27fEUqQ3esRWM55KNlvHmW5xWnTCuRkgX4bmIJMWUXGQnJFmvedlLbuOsgVn+efQS9t+3EX+mBZlGQD7B0+vx9U/egXzBWpxGiHFdVDdFlxSFYkb86hlK05v5n9+8i3sfaLBxe8DxL97P4o1H+NhMiz/+8hv4jv066IC5ZidvogJSJVjZM0hQz59nm2iyLH+uz7p0E0P/ci+nhCVugEgzfCfhzMt3cs83zyP1wXqCgiuoDjc51hjAL0Www5JucFFej/t3zvFw6wQveuZlXHD1mXzt47cjikX8LqSHO5gFQcko/vJ//yr/e8cDdB+YxWkmFI3ALQ7y0o3bHnUmOkYgM4HWJhdnji9SCfN8p1u/sJfx9YPIB+fwWg2m12zk2uIYD3Zm85arf8X2jdNPeO5e9owtfH33PWTa0uwJznj2+BMKKXuPLPDJm3ZSj2Nefu15T+F0//Ew1+uAA0ea9Z/0Vvr06dOnT58+fX6svOENbwDgne9852M+96PGkzwla88b3vAGnvvc5/Lxj3+c5eVlFhcX+cd//Eeuvvpq3vSmNz3lB38yPOtZz+LGG29k//79AOzcuZNbb72VF73oRUBuNZqbm+N5z/teDafv+1x55ZXcfnpk+b777iNN00etmZqa4uyzz35kzfcTxzGtVutRHz/NTBcv4siOF/DhD07gLApqbZeX3vBs3v5nryQfExBIz0UDfidjcHSAZz3nXEaGiqzdNsTaXz0TPTpId8MAnY0l6PYwQLZqGBUUkH4BEcY41sVUSoDAWcoI7gjZ8+0RmsMlTE2BLyhmMYgEKzJExVDbremtKpMGkuLJkOiwy/3RKmQnwWvEXH7uGowwxBVJZ32RZreI8jVRzcUYjdWGYDYPWHU2JxR/M6FweUi0ISXcLog2G6r7ukghMYGLSDVOM0UZF1twqN2fMfRtjbvkIDPoDBjSIoihFKk0ItOMfWGJDR8/xnkHD7LGnEJEMeLUMtlcl6ibR6JK10G5AQ4ewnEQxqI0SCMQWlBYVohuiePLY+gCQG7vEEmGGSzntcYln3DTINGGAdLREigFpSJieIjhe5rITGMzjemFFO4/zvThef7d27/Ji6+/F+kHiHIJMegx+sIOy70a2pMQJdiCT1ZwSQY8rCsRYZLbW8ql3DJkBd79DbKiiykExKYEzVy0MJ1uPr0RpwghcyuYK9AOGM+DyVGYGgHXAVchBweRyy1sN2FwS4dom84nSDwXWynmgoO1CN9FVzzCjTXSNIRyKX+xWlC9FAzIxOC280kb4Qq8qgOFIN+7sehT84hmGwYESDCBhjCCOMFmCZkW+M2YrOlS3ZnSvrdGd7aEzDRaZ9gsw2pD6kq6E2XkfAvnwCzuzDI2jrA6wzhAnILWefaI7+QijO8jPA9Rb+cZMKcnXNAmFy5cB+H7eM0Ed6lDsqWMnSqybWicaLUhGoHuqAApMcUiDFcQUYJbDwkOdzGuQrqG6371DrJJg8aQKUNccxDdFHohxlcYJcB1yCZqJOOrkcbyF9+6jqGhJv5KmmeqnA6AJo5y21I75MwXPMToGYu4PY2fSJxGSnlUYTG00g7tbsJ0MMjPTF7K/JFldBhzfP8p7rr9Tq69rMFvPPcszls9SbGSUWynBDOS5MEKaaCgHSLnlnnvB77MPbMzbDhnNcPnrUNFYDVYLE6SkfQcVGQhy3+GQmUZjVa4cPVhXjC+i6EjlnUPDbDrznn0siI66DHs1RhfO8LA1BCV4TITF25jbv8aOg9VMe2Y//bHn+eeTz1MUnaojXa5/vr72LxKsK26+lFnYqGncHsgQsvOh46CL5D1Dt6JJqrewdme0rs6oHlWgblayu3uAm0Plo3m7V//Bt86bVWdr7dp975v2uU0L3r5JZwxNoZSDumMR+meJouzK4+7ttkOMcaw0uz90PPcWMPhzi5W4rkfuvZH5Yq16xj2izxr9f/dKdM+ffr06dOnT5//2xhjnvDjR814fdITKZ/97Gf54Ac/yAc+8AF++Zd/+ZGkW2MMH/rQh/iN3/gNrr322h9pLOYH8R//43+k2Wyybds2lFJorfmTP/kTfuEXfgGAubn8H5rj4+OP+rrx8XGOHTv2yBrP8xgcHHzMmu9+/ffz7ne/mz/8wz98Wv8uP06EEMycMhypN2iqjMpsxJ/u/gjnqDUIkea1r4mgMFpk21vO5GfLU/zLX3+LxcU2E2M+xw+corlqALcTExys58+vkAgp6a0pIIZk3nwSughcbKWCEg02XtxithCgb56HhSbupUNc+uoZbprdQhw61G5JSW1EOlYmW/IpzvYYOthl4TKXrOpSVR433rcfJxYgFeO3tciGi3QnfDJrkd285SXdVMW6GhyLaEnUUQenqonKLngQrnFw96YIpdCexFtJoeBhnidgTOJ/JiMZtoTDAr8h0KnFFjQr15UY/WAbdzmBw4YD6Sjz7iiBrufv8Bug6OdNMmGM1RpTdHD2nsTWKpihGhQdTMlDNUPUncuoS13oechEImyXsecYFhYS0nYHEadkZxYgU6hWAlrTu3Q9/nwXtxFh1OlAV2sRFsxXTnFnZZDhKed0CrfFCJ+jt46iC7logtUIK+lN+4SjLoU5TTpQwuvkYoqtFCEM0b5k8Zll2JtX9ap2CrUKZnklF0LaHTCGZLhAfOYEwlEE+5tIIHMl0WiA19EYz2JnF0kXDSf2T+DPLWBdEBis4+fNO2GEdSTRtjHC6RKFagH3UBvjKkwgcAKJFjGxbuAbi+l0GZ6MmNgYs6cxAdUyNk1zq4/rom4pYnf0sA+FgMAIQLl4J1v4xxKIawzuzyCKSKYV7qFF1KLEDtQQSxGjX5D01gq6m8oUkgaukyCiDJCYAR+ZSNRSOw9cDTxMvYF0FCIIYKWOdRxMsYCTZaBPx6ErCVKCFEQTJQbbgjXrDTe88gt85fbr6EwJCgtgBsHpZWih8p8ro/FKNQqjK8TzDjuOTXH1q+7nrjvXYtOM5lXDqFuWwS2STVZQK12cQopILZaAL3z1GbjHfXbs2E75ZITjFrAmvzm31oLnsmrNCI2BJvWDFVQvxXgKt5PhzZR53epXEMk25uLD1Nbuon7kFBPPXUvncAff9zk8v5eH6h12Zjt46TMd7KYWpYccVEshA4FVDsKGCAu3nzjGoR2SS/YWmH14DgHUpmMai2WSKOPgjVOYzICnEEagtCC61eXXfvVWTCq5tXgp2X1tvkELpMXxBY04pNE9jPKbDI6OU5/wCCslVCIo90KEoxASknUV1von2HB5zEk/Ym9rljNq35se8Wc1fkOTLsf89l98FSkMqpMhXIUcTtldbNJeoyiWK6wZPcls6MN1hntv3Edr9TQF12VTeYh3/+ONlAKPP339i1Hy0e89fO1zOyjdXaewXdEqNzhQOc7tD67lZVMvecwZfcXFm1g9OciqicevXf7XnOjt55tzH6fk1Hj1uv/4o/1S+CF4UlFxPBrR44tEffr06dOnT5//FxE8Ui35E+WnYQ9www038PKXv5wXvvCFj0R+PF08aSHlgx/8IG9961t57Wtf+6j/L6XkV37lV9i3bx/vf//7n3Yh5ZOf/CQf/ehH+djHPsZZZ53FAw88wJvf/Gampqb45V/+5UfWfX+F0XdbhH4QP2jN7/3e7/HWt771kT+3Wi1Wr179uGt/WjjzyiWO2ZP4X6mQOBJZS9m58wTNCycp3T2L7GWUNq/mK7MPcc/dO2jurpOmGSd2z5J4ZcS6gNJMh8Jcj9SAMAZDER1IslUBQV1i9zcQUQy9iEg4uJWrec4LI77ha/SKoqjKHDowyrYzlzmyPEhqPVoXuIRrNKrjIaRBOhHV2SYqLtOTKemRZYhD0uEaXgQCRdCG1ipF5koo+VhfUdm7Agm0l2to6zOku3SbloH7M1THQGYwVZd4zMegwXPwNmTQM1D0CeoWHYCIY5wll0x5+IsZS5cMETQyKvt7HL6wQlYWTBxvEiylmIKLzQwizkjWD5JOFkgGFINf60CYYj2X1ioFQqKWe/lUxx0OZtDLJx2Ex8KxvDlFeB7ZQEB3cxEbuPiLE4haBeMrqESY0CFZPYh3/LvvaAsQcODLgxzZVsG0WohCAdNsYYaqkPWQAmSYgRX0xipkFUnxZIRIcxEFyK0Za6ZwAoV/tAcnI3ShglzOIEnAdRBKYcsFhAbl++iiorxvBduIEJnGiRVerYSuucjEIqamUPUV5EqPoO4gsgwT9RBphjwtBIHEn+1gMHhzHbKCItw8BY6iOJ/gNzQYmQfiug4D4wn7bg9ApnngapICoF2Fow1yycM4ErTBSeqk1XEyZXHbAqctECutvB65ZSFNIRN01pYptyVOZjD1jPqzyzQuWEtpucX4P3UQmcbXEutJRGaw8nsNZFiwrXYuKGaatOrjtMBmcT4FIiQEAWnVIRkKiHc1SNvH6D2/RyEy6CTPuBCpRYYaKQVZ3EMuNjHVMXon82mlxQ+ViQ86OPUQk2UUD8X0tq/Fa4FfjzHKofiaOrapad1TQ55QBIsJSEEy4uAdBqdUxPoBIuwhXJfMCUhOpqimhMwircaxFqUS7v3EAR6cuAt/S0LpRMJ971vm1OIK1pNMbB3nxc95Fbd/6iZ6VvGN47tpRi7lJYExuc2utdFnuuyxanSEda86ky2jo2Db9D6dIKYkbjlDnkix1hKHAcIxWKkQUvBzW8/i+PvuYFSnDAx2Cba2Se+sIY3FE4LpsUHWlsc5XrqRXqFK81RC9oWDDCmN8ssk5QJrN48zvFbxnU/McvDwBAM7L2XguVuZKjxaKFcrKf5SRtpMcI3ASyxWCawxZKkiM2Adg+/FbJ06SSP1ecCfZs3GSa689FLOnZhAR5pjc3VKvovWFvV9M5xHHzrOwWyFbGCQgaEK05V1PPOSSx/3fJZSsHHNyJM6ywe9MQa9CaYKP76w2cONOic6LepR+MMX9+nTp0+fPn36/D/I1q1b+bM/+zN+6Zd+iSuuuIKXvexlvPSlL31a7uuftLVnx44d/MzP/MwTfv6Vr3wl99133//xhr6f3/7t3+Z3f/d3+fmf/3nOOeccXvOa1/CWt7yFd7/73QBMTEwAPGayZGFh4ZEplYmJCZIkoV6vP+Ga78f3farV6qM+ftoZH6wy4ZYoW58L1kyzxT2JWrZkq8sk60bAGNZctoaTR7rsu+MY7ZlFbGZItKAzMYozE+PNtNDdCLtcx4QRzmyd4EQLL2kjivb0DbJASIlFcNdH7+e+xYfJRi16ncLMdDn0rSFu27cZE0oOvKSK1Q4yAe2CSbq4x0PKd0ekviEJwBxawaw0EGlGPOSjXegNS4TO7SEyNZR2r4CFZEixst5DNLsUDjTxDyXIyCISi5WQVhSyl+Hun8U/Usf9YAyflmhPIjKLfyqidvc81QcWiMuS9rBD+USC7MQEWyzKdXAii9OIMUZjhkvI4RrSKry5Ls2zCnQ2BvTOGUZPD5IULE5bk9ZcOucM075oAjtQJq15dCY9xFIHc7RJ1spgYhgvcxl4OKQwZ9DjFaQGFWkmrlsg3lYkXBfQOm8EhMgnHoTE4tNwh+lcuposDhFGIHcfQe09jjpwkizq0J108euGqQ/uoXjroXwy4XTtq2x2EEmK0ILa4QxCi5UKu9JEGINwXeKNJcTpCSTZ7LL24lnW1xbw3YTcqEGe3eGIXF9WCtcpIhONLrloR0AY5/serMLwILZSxFvqUdlxCrncQ5y2DxkssZ+SFh2UU8QIi3Ucjh0eIrUuJoox2Wm7j5Q4zS402hgJerCKRWGrDvguuhLQfOYaspFyvs8kRWgD5SJ6qEz3nGFsSWJGFOEqS7Ymxkwooi0BWcnB+C6m10NLSzpcxpQDZJwihgZyHasQIJTMbTxJgvXdPDRX5EIjSYzVKdJaYpNw4uAQH3zXpZx1xGHqjojqkQQVGiyGzJfI5RZojXOkTjZazKetJiu0D67BlgKEkDj1jOJMD5VYkgEfXfbQWmCyvHmncCwhWE4IFmP84dNNSK6DcBRIhbWW/dOw745xMpULVWnRQcUaR1tu+cJOxG0DXDx0Kddf+P+xdfNZmBpoE7GouvzZz/0D5S+c4hcvO4+vr9zJyH0psmsRCNKCoCAt9XbIQ3fsZ8/n9tFZibn2uu1EviCVHiv7C8hmD9mNaF9ZxEktTl0jeppnrxphwZ3i7z/xQnbtW4dan7F0MdhY49UM3VOz3H/XHka9S0kbEh0Z9HwDb76DjFMwlge+vZcdf/cg7UqZxkSZhcImiukoS2HIXHiAe5c/Sy9r4sUWr6MRsWayVsH4gmjAg0KA03FRVqJcQyA055bmWO0vE6cuN/z6Wl6wZQtT1Sq+4zBdLTNcKBCdFvb+NXfcuofl7Yqw0+MPL38Z/+X5/4nRwdHHPZ+PnVrhj/7uK9z70LEfepZX3SF+ds2/5/LR6360XwZPgqONOia1nGg0fmyP0adPnz59+vTp85Pkv/yX/8J9993HwYMHefnLX87nP/95Nm/ezAUXXMA73vEO7r///h/52k9aSFlaWmJ6+olD96anp1leXv6RN/JE9Ho95PeNUyulHqk/Xr9+PRMTE3zjG9945PNJkvDtb3+bZz7zmQBceOGFuK77qDWnTp3ioYceemTNvwVeMHE9hcWzCV2Hw07IvvokFd2meKhDsBAifY9WFeIBj/Y5VcRKiJUSJ1PIOpiah3JcrO9hR4q0bhihe0kBZ66NOmBoXOiQrq8hfQcrBQQ+VhvK0lBcBjZUSHs9nPkOKM3JxREwCpkovAVBod7D39/AtBOMFTjLbZKSJds8jV4/hcoU4YRLUpEESwnVfTFYgRAS6btYR5AFDjoQiG4PlWhKy5YsEHSnHURmwHMIFhKyDeOEm6o0No8ilQdpSlaQSCPAlaRDLtaBwooh6Coc63Ls0hq6apl6uIUYH8duXAWORDiC8poUN+qxfs8RJlsr0IqQxxbwWppCE0qHWnhtsNUy8YBHWnFpnlGks6UKxqCVC9qiJZSPpBRPWZwwF1GMJzn5zXGcfW2K+1tUDrZyQcKY/CZ4vIoeLmEGS9jRKtJapJLIagWGamQjJbKxElILRJQgtcU5OJdPTEBepbvcgGaLyLRJBn2c/SfzwT+ZiyfxUPGR9iAZGS5wj3D9rx1i82UdrBJoT5L6sLJFkA4ZlDUIBF47Q1iBUHleCFGc38xXCgjASMvw2T1M1UMZQXHnKcq3H0YmBisNTjPEBi5WWnQIdmI0z5UxBjE6DAPVfJ/aIltdlAEKLsmKxSiBKipUZklLYDMDUkGmIcuQUYq3GBOtVcTbLGawROVOzcZVx6FqSUd9UifBRjFisYFjBSLJEO0ustVFIPLJFNcFIfESiy7mAbDWWIwx2CTFWQlJCwLpOpiRGkv1QbKTBicGJ7JIbXAVmIKCUoDwfaRyUdqj/uo1mDEBWNAGoTX+iSbOUgeyFJFlWAydTw3T+uoYwgoyn9zus9jmwuoBsuFyLpxpjRACYSH1DWN3RKhuPoXhhAk4ira0zLdCnnXpxbxk6sVsWL2Zt/3lL6JKEpHA4v4lDu88ytF7j/I/fuvjRBLkLMhEI5s9HATVJYGwEgucbLX41t7DPHD7Abz5FroeY2zediQMXHIowGlnmLICR3Dz/s+y6vxllBAUlOJnNz3A/3rdx+lcYiBM6O1qs/u2wyRdQalYyO2cwzWy8QHiIZ9ffMOVbNo8Sq/tUH4oxD8QER6Y53OHd/OtmUM82PgaDza+zrHu/ZRcB5UZXEfRiBJkaNBD381Q0kzPFBGhYJ1a5PmV41xbnCE5UeL9zb8lMzHWWsaGKrzlhqt566uvploKHnPmnn/+eoZ3xFzKGBeN/eB3Nj74xTu56Tt7ed8//3TUDT9jfDVDssCmyuOH4/bp06dPnz59/h/E/hR9/BSxatUq3vjGN/K1r32NxcVFfvd3f5cDBw5wzTXXsHbtWn7zN3+Thx9++Cld80lbe5IkwfO8J76Q45AkyVN68CfDddddx5/8yZ+wZs0azjrrLO6//37+6q/+il/5lV8BckvPm9/8Zt71rnexefNmNm/ezLve9S6KxSI33HADALVajV/91V/lbW97G8PDwwwNDfFbv/VbnHPOOY+0+PxbQAjB2dtWs+fAArah6c1uxP3OMv6ueYgSTJri3LLAwPNKhAsrkGZYK5DlIk4ElYdmSRfbCD/ADBcp7XeRbQdRUojMkHU9SrNN0k6E8BXCZMiBlOirz+TMK4e4s7SL4kcWcAB3LkUJn+qcJJnU6FGNnZLIdolOpcjgyby9RkpJOiBxlMLpJPht8OsZaINRNrfXuSoP+JSQlRxG74hJVo+gN4UEyxnSQtC0CGMx0uYBrmUfLSyiGeN2QWRgyxnSCBZfOE06nOG0Nd5yijfXRDmW5sogpiTJsgDl56FDKmmy6hVNBuYWODxX4YqfP0ZW9bn5jrXEUiFOHxRJzcNrWYTS2IqDLkuoQOcFEyTjQwwei5FRCkhIdR6w6ntoX4IAbRWO4yBFiaxUwkk1CIkpKKL1Qwgr8wmbZoy7QSPLVXoLAbIQ4GcGuX8FE2tMphHy9MRQmCCUhDjDhjF0QypzEpstQ5qBozBKYlyHaF2Fyp4Qa0FIwd0fWMuprZMcPFgFEaEyi9szjH67jhwpUJ1O6RxYhgwYqECzjfBduhsHCBYTVCfNG3F8l+VDRUS3gy4VENIirUT1esR1h7QsYLSKc9Igl0JUJ0JXA2QzRPQiwOZWG2uxBqS12DhDaoueSEnPV6CayENu/ry7DiQyD2s1muKJHgiLM7uCvyZDrxkjmUkZC3rMPneAgbsTBue7jxz2SUURdIPTDT0G0gxRCLDdEBGlCBnmIkHgIRyZB+YaGNjTw2uEiNRAL+HMa8/gnjt248x2Ga1LomN1elesp+A7uKnBaI1CMvTpeRDQ2zaCW5Ko0MEMFXG0QYQJ0nEBQ1pwkFGKE1p0USG6IUjJPbeswmm0MO0u8rRFCwTjxywiyl8PuArjORgraGGh5HLvwzO8mNyCotOMiwbWsHe1YvH4Up6rYwzdnccRTIBwyQZcZOaj6hlxCCMVnzRzOGNqkuedsYVvfvB2Tr5wAl0SlI4kjLRShDHM33sKqh4qNhhfcHurywtedpirK/czUOmxd2YrZxRaPGPjDLv2rGf9BS2e8TOwft16XvnrV3Hnt/bwwPF50pqPdWExThneMoneOwNLLaSAtasruCMOV02vp+hVqbkTrCmdj+cfzyulBSwvtFFlQbZOkKYOasnSHOyiXcuBYJh6b5BdM9OoruDzR8/mZzbewKB/PueO/j5nrH/8qUWA//Tff5l/3wopVALuPjnDpqEhxkrlx1171aVbOHByiedffdbjfj7KUv7k27cQOIr/+OwrcOSTfp/jR+KqjRtYbPe4eM2qH+vj9OnTp0+fPn36/DRRqVR41atexate9Sq01tx88818/vOf54477uCssx7/32mPx1OqP/793/99isXi436u1/vhTQQ/Cu9973v5/d//fd74xjeysLDA1NQUr3/96/mDP/iDR9b8zu/8DmEY8sY3vpF6vc6ll17K17/+9UcFyvz1X/81juPwqle9ijAMueaaa/jQhz6EUurHsu+fBNZazrtqlF1319i58yQb3VEq1ZBdOsbEeRbGnntn6Fy4Dr9rQDn5u/1CMB47dOaa+bv50sGbt2SmjXTKWN8hHXMY+vQCSb2V17EaAammt1igXPY5HNXJjpWxpQDrwcBIBKGgHpbQLQW+xV9RmMin1E4wroMuKJJhgQwy5EKIODSHKqzCoFCpwW3njSTWWHTRobW2iNfKsyFs4BCPFgi3p2RLAcPHGqRtCUohdIKzGON3M1hsomsV4oEAJ9QgBI6QBBt6LC/WCIcgLUvcRsKz/MM4PcVCz6c5k2CSFFnzaS14xIfHSKRkabYKdQidUbKNDpQ9eiMCGRvKtx3HBA54CnfM4ewXLzNRa3DXLdtQrsGWFdlCihSKtOohpCALFKpjcE91kZ2EdNUg2nPQrot/chmnl+LONBClIv5CiAgC2lcMYw/VkGkucMjU4h1dQRib3wQryO1XeUWwdR0wGqkcTJrm9izfxcYpaEO2dZJ0UJHUJEHTYIWgV1fsf6AKCGy1BEJgsxSva7BuyuD583QbSf78NLsIzwHl4GsfURDQrmNLDlI4mHqGiFKS4RJB0wASNZ2SBmXmL3LxlyJGj+atORiJzDKElHnOiVIYDDLVuThkTP45Y1G9BO/hChQVajlEBw6q0cHG8ekWG4HTionGBNIVIAWy2SP+pEF2LRNqhd6ghzzdI98bTghHDcGJ/O9F4EEYY5PTe3MUUuffO+I0t065udWneLiODrvEF6zFObbCPf/4LaxfhCQjOt6md/E6TJzgdJJctNEZGIMoBhglcD0Hawym7KEHy6h2RjZYgCA/omVswJOYQCDS0xNhSQKmQLyuinNiCeuo74WAR5oNl41x5Bsn8lrqgoep+PnUi4FypQDAqaMLvPfff4jjiy3i4Sq6HePGTUSsMRhEJBGxxZtvk5U89LBPZiT1RoqKNHc9dIgdX74ffXCZ6E0bEZEgGXBAirzWuSjRFZfMt2TbEmZHivzTkcuZvuAWxjPFXb1VbJ1f4M4HNjCUpbzj7TdSdL5BLyrys7/6HK7/lWfzMz/7N7RGO3TPFExeMMCtf3oHbjOhtW2I8rllblRLrNYOESGbituYKp6uQRZgtCFONDIxRJOa6GyLMDGDu7t0Zodgg8dKo8L9y2/nvSv3Y7Z0sbGglRylGS0y3P0Nptc9ca5Jrxtz3z1H6E5J3vfQ/Zw5OsqfXH3t46696txNXHXupie81myzzdf27EcKwb+78GJGv9ty9TRyojeLQLCqOIlTEDBosP5P2VtGffr06dOnT58fnZ+WaZCfhj08CZRSXHPNNVxzzTVP+WuftJByxRVXsG/fvh+65ummUqnwnve8h/e85z1PuEYIwTve8Q7e8Y53POGaIAh473vfy3vf+96nfY8/LXzkyI188rZvY+6R6NSloiRnvGQzD921D0KFGBnE1MqUViKWL61R3bWAs5wxMFTkRdev43PHDtALq/nNa7eLOLKMPbMKroNft6SeIp4sEpzsIQoKYwTpuZP8+vXPInYy/nrXLYQXrkZVIl71zHs5dnSY2x7ajJktomYUhRNdig8tIDKINljqZ1UYlxFFLwQ/pDtcwrqKuKCgKiieMghh85viMKa2p4eMNdlgEbAMrWlSO6eNPuFxxkXHuSnchv2mJK26OHMhwgpMtYgeK5EMBeh2hAkkJg5ozgyAJ0hHPZpnVEgHFfFAiRcPPsjy54axXYPIMigJ4h0Vesd84qkKjC4iTUoxbBFmAYknqe7NkKkhGymiawXceox1fc4ozeJ6Ge60JpARpbNbnPw7H5UInLpPMlnFbcYER5YRs8sgJaocYCYq2EBhSi4ySvAW2igvxZ4WEry7YtKh01alGIwUSFdBL86bbqSXZ3i4Ko9JMRrSFCtVLqIoBU4+uYCU+EsxEze2cJoalIMUAhuludDiOwjXg0yjtIDFBoGb4hRTzMAAot5GlALwA8DkOShpHn7rOxZjgdEi9VUeXltDwwdjyY4bsotcigua4tEEr5mBkohqBZvEsNTAavLn3mpA5mKO1ohiESHArrSQQyXEbILfTLBAUnBJ11Qo7j6V/3lA4cWK5hWTmIGA8p1LuPUuQioUPk5TY09boHRgkIp8+skCxQJGtJBZlgtPUgICO1pkUkhWFprEViMzi2i283YjD2RBIcIidngAKwQ0Oqh2hLeQYntRnmdSLiF8D5OCdBzao0X8qksWZRRnY4QFpxFiRtzT7VGaxjYXaSxOJrD1BliBWhTIWiG3b2Ua6/nIQFNfE3D44FGkkYDBanPa0pIg4ozdt+7kjlfvYFf3y0y8xeGuXzyDZLCMIEZImVsniz7sLKLaIv+Z0xnNjYKRRUVxrES60KEVR9QaAjE5xs9cdQd1WeQ7Hzk7t9khKAwV6cUWAZREhleLKPsR73rwGkYG2hw4PE59YZzCSIVKuEQUpAhh2bHnYzRubPOSN1xL2VHookGiGJwocc6VW9nXbhEP+ZyYjxn4dszizoDa+Y+eBOmFCamwrCy3EYHEnYXxDT7xvoh0ehi6ApvkFcnNYJitwXp2LpzihZsCQu2Q9jR/8Zef5pnXX8T01CCXX7zxMWfun7/zc9x9+0G2XL2eqWdU2T4++SOf3xXXw1+0YKEd5k06T6eYshw3ePeev0MgeNc5v8Xf33U331k4Qkl4XDj2xLbdPn369OnTp0+f/9fZvn3745bMCCEIgoBNmzbx2te+lquvvvpJX/NJCyk333zzk75on58MXzx0D604o9wTeKGhOuBw7Uuu5HN//m1ML4RexJYrRjk83SFLC9iKC01oLDepnvm3nPnH09zzRwOItsGmGSAgTPLMCkdCUKazOsBvZNhml+4z1tBbX+Z/f/V2/sfrn8WIXYbN82w76yjDQ5Z61CL5ZMZkYxZxucPyio81uTzpznepHU3JSpJeu4hMfewk2MAFB3rjebVv8WSCWgnxkoy45pJtkNjEQfsCT2smhxoMmS5BkiJ6UBJtmgMl9MQQlQMdlPXJphQi03TX5e/CZz5oa1A9iQotfsMgMkNTFvn2rq2EYYrjtMFxcLYpGLHog5ZwxCEOfPyWxoYZznITx7oIkU9P9CbLWFdiuyk6dfjSf7sEU3YQ20IKLwgZUx2Wv1Ui2ydx6zHxqMVpJYjEYAs+QimiUZ9UJVQeOgaOg1FAkoEyCKmwNiEaHCepBniNJplSiCTGa3WxIm+/kYEhHKwRZPp0PkoHIVUeXpuJ3K7iewjfx2QZVkmclTi/6U/ivK0GsKODee5GGCEsyNRgOz3ivZaj7x+FapC/NoREWEGie6goD+SMx4pEFU3pVEZnVRVEiHu8TjJWIqiDtZKEiDRw6W6q4i3FBPMxuuQh0wxRq+bCQBwiHQebGjD5DbnphXng7WAFU3Cg6KFaEUIqkm3TFA7MgaMQjovRhnC8TO9yibQp8WIFMwOdLVUmb2rgxwZNlk8UPZRgPHk6qDePmDHnrqbrOyjXw53vQpJQWVVj+Z4jZMZgqgVkL8R6+QSQe7KJO78MqydJSj6qFeP4Dm49Qqzk9dI2zZClErQ6kGbEAeCMkBTLBIfbZKuHYL6DKXjIboYUAi0tvSmFMFA5IaFUQAqF27UIegjfAVfh1SC4QDBbqWCWegib5QKcp5BhCnGCSA2up/n67D18/P4X4DQEw9VZVGqJh8pwavG0rUlDAsqQ5+DUFCNBC2+uSknFSKmwB9I8R0gbOvsKbLlkkdsje7qp23DqnBi1W1E9ZqiMdNDnxGQ4NHdXqRcHoAs66fKaZ1/Ilc+ocdfil3GEZjac5fCn72Tt2WtY2XWCwYc00XkjPP83t2HeuJX/dfIhYmEpHzaoriHaG/Hpw7fwlgteTpZqjuyfw/oKA7S1Zv2qEWaOr/C61c/if9e/AuUEcaaLEobKYMQq+edcOXkuH3jxbyOzHexd+AiZG/GZXsinvroDz3e5dPs6HOfRE4wrp+qsrHV5sLXCBy96EevGHt0c9FQ4dmgRHmijFbzts19maLDMf33JCxkoPDab5Uch1AndLMtDg41hgjKr9QAXDvZFlD59+vTp06fP/4+9/46yLL3Le/HP+747nnwqV1fH6TRBkzRJMyNpUEQSoIBkQBIW4XLvDwOWfS/RMlgYY2NjEBcMRkYkIXKQFRAKKGs0OfTMdPf0dE7VlU/ViTu94ffHbsaWhezR3AFp4fNZq9aqPqdq712nT7279rO/z/P8w+ZVr3oVv/7rv861117LrbfeinOOhx56iMcff5zv/u7v5ujRo7z85S/n/e9/P6973eue0Ta/KmvPmK9vLqxI+rZGbSaHRcenNk/w+Of7RALS/gDhHJsPOby5SYJUo4yHiBROWh46t4Mv/PE0waiHzD1Eq45QCqsUYpjhL29iZiaZOL6JHGms9Ii7G9hKzI5dp/j8iQI9t0r6uyOOfKGJ+2bFFw/N01wrUFVD0NLULlDmS/gRyghMMWLQalATBikEyYTEKonfzZEpeGfXSRoeNSUwEzX8rIAXjyiOVChqks1zMesfqXHi7gVG1ZDgtRq1KLC7FfgSwgCrNVYqnA8qdThP4mUQrhcUEwEmUgx3xNSO9akfzjDWRzcU3qUO4KhNC1oHUs7e3cSNHPe9dx/1RxbRzkCjigg8hClbcKLjq7jAQ2UWvwkUGrvRIVqH5125xSuuOcb+7+nx++++BS8VEEjS7XVs6KHSFjYQrL2oCrkmfkzhpRb2bEOkBSxvQl7gYgCB0BblQK0MMAsTEPilhUMprAoRqDKwNr+cVSIA4ZCewgoBWY7LcpiswkwII8AJuvWC1vEerhLh4gDhwAU+hX854NfzEIFfCg3GIJIC60mktgjvb9KrBTZJ6Lx2ionaGsG6I/yAwFYqDG6YxJ4T+FsZk0dGpCcLsskAb0szunoOmRTEvRQRBOA7nO8hBGUmiDaAwOFwygNtYZgiM41Ic8xkA7WV40kfV6mC1vhbGrNNoboOhKXy8AqBKeheUYP+oJzcURJRDcvXoxBPjyJKC3bL4M3HSKegWcUUAZt5QaMaYAqDjXycGyGMxhlBeGoVr6YwJNhqjcp0ip3biR56+MaVVivnSmtPoQGLsJLoRIfK2S4UmmKuiWg3kdIvK5kFZVOTlngJeH2Dq4RYzy/fG8OiFLTCmNx69C5W0TcKPFXBRUkp9KWa0XxA/4om9c0R3/5Onz/Y2MnsrxeYukc2NUGc5IhGVNqGlEJIaJzVSBeQxQKVOfwjEgtkCq7cP8f6pR7CCowneep3r+KhjSGTl8BEChf6RJ8ryKcijG/pH2mSTYSENyT4lQLXqeAEzN91keOL6+x9tMqJiXna/oDT3gwveOPtPHZsBREHiGGKTD0ePnKeW563i5mWYu3iiGBjiAnLY55rrXO29yBH/sry/vd8mlE1IJ0KePDsInPViKgeErUjUBLz0oyFuR7dpMpVjVV21JfIkoxPrXyWaxsTPJJOYbTg1hfv457FFVws6CYZk/UvtbZ+/w+9gkt/+knC+Rp/ePo+brd7eMnclc9q/Z5uVxCXW8sDJ4l87znNSVmIp7l94nYC6TMVtfiRl7+IQ8uXuH3nrudsH2PGjBkzZsyYrzXi8sfXmq+HY/hvrK+v88M//MP81E/91Jc8/rM/+7OcO3eOT3ziE7zzne/k3/ybf/P3L6RcuHCBd77znfz2b//2c7XJMV8l33r1Pj5z4Sna108gixSbaQIdcWFHDf9iB2+kqUzPYZ8QVM93cX2NM47mZJ3O6ReiHvwEdDNcvVY2pngKkRWQpNgUbATFRBU5ypHCMjXc4oU7F7n51gFPLt5Fd7GAdYspFPf9pwBkF7NnFtOybK7XsS2LTFNU4hBKsXV1xMxTZWaDVBCMLHldUExGCG3xuyleT2CqNVRSIJIM3i2pfeMavaIOZxUb/1Xh/IT0+VXkX1fAWKKlFFcJEbqsanYjiZ9pZCVAVyXh4gh/uU/nBU1MzYfCEvQM/QWL8wT14xou3wXuHwnQoyGsdal/MUHGEUJUsO0QZibAOXSaID1QqxqZaUyzgmuEkAAXUgaVvZx6tM7zmxc5f19MfK6PTHJkmpPsm6CYCKFrEFpjKw5X9+h+y3YaDyTIRoyth4hhghoVCBcQLmUEMkX2M2wlRGUaPduiqBSMdtWoJjF2a0SwkpTWGOtAgguAvAyTdb5fNuNEcTk9oHOECKm4CtmtCrHqoQIPl2uEp1AAykd4Hi6OQAnEcqfM+ahXEYCyEicFzliigaN+YcDglgrtYsBGo4YqKsRrNYo5D1fLiZaGxBdGRE+tQbOOyAvCbo71JIwSZBQjKmE5uSFEmU0iFcJohBDoqoduBgjj453dwEs3EI0aRmvk5SwYUWhMCyY+kJLXy0BjOWuoOIupBaA1wpXWJTdKEEmKhbLNaqKFuDxtoSNB2Mnx+iPShSrhdTsZbY3wnlrGCS6/xgJqNXSjCuf7DCd75NEErYFh1/4mqw+fRCdJ2Xa1sQkO7N551NoW3tkuQpsysyYCv7BQAeskruIhrEPmDmEFKrUIz4d6haJtYaNLsKVxrqwAt86g2+XwkVdG0kAs6O9rIAJJ/SbFdOujvMpt50Q0RZQCBLDVp9KX2P0OeUTiqq7M9nEOISTJZIhvHQeu2eKG/VdgsxZClVMrLvSwVpAeaxPEAhsqvMwgrSTfDv2ZAIRDnIRkn8DMZyB98C3RpuCex89w9IOP0X7zPHKX5vntm6hsn+YLH3yU6au2c7LXRXqSux8+xS3P28XCjpDhR88Qnx5gJqd4/i+dIDCn+ZXDe3n8C9PEJz382SZ2d5tOkWJCR7HN8evD++lfBZUvSNavaDHMIy6EGamdxIUZh//6s1zaMU1vPsIJyY98/zew8b7PkeeaauB/2Zp7zW37+P0bd/H5Syf4pYc+wucffZQrX/V9zO+Z+Yrr9FCnVFT4ZeOlQijCXhnY/raFq3jFq68neA4zvD61/EH+7PwXUMLnlfO38MjKCu87/ggXii5v3n/Dc7afMWPGjBkzZsyYrzf+9E//lIcffvjLHv+O7/gObrrpJt7znvfw5je/mXe9613PeJvP2e2uTqfDe9/73udqc2OeBXfuuBrZ0HR2LXPztyzyu//hLewcTjHc3iS9bgEqFU7UDGeuy8hbWdlkowTXfNutPJn2wXDZ1iHLu+XGlhd37Rpuso5thqgbF5i+cy/b9k0xWI7Y/KtpAm+aC48uUvtUl1gW1K6wyKpfhnsOc0arIaOLIXktovuC7fRumGLt9ibhUCEzhyxAJIKwU1a46ligY0n/mimG181StCMIfcTlSlV9f0z7yIiwa8q7+1lOMuMxmJckcx7FZITs9PDPrCCGKeHiFt7KAK+XInJTTmv4Em+oMVWI1jawepONO2p0bq+TR6YUGawlMyFbh+vgympaahG2FoDnkd6Vk96ekU0aenskyZVtBs9r0b+qiqkHuKmI6OaAIss4PZziF97zYu774wnk1hCkQCUGr2dQI4N0FqREWodUjvqGpHNTiyKyZJMBoyunKWJFMd9CDjNkoklmA9TWENFLGD1vlo1v3s3wtjbptZJQgBuNcJ2tUkwxDjfRIJ+q4SYa2Kl6GUI7zBArCeLcGqysEwwN/rKHnqziJKBKq4vLMuRSp2zFiSNcs4YLfdzCNK5RKwUUW1btSguyElM/7gg/7zi1PM/ia9uMtlfxNoZUjq0iV7fIbIoOYXjNFAUp/qkVZFKG4opKBVsLyzwXKctsl7+58PQU+EGZTeIgeuxSaf8xGpln2KoqLU1xiGzWaDyyyfDbHemrJfm+Bdwln6Cj0AsTKPwy18W40jpUrSI8r2yTkgbnSYJORnSxDwJEFBBt5PzQ978S//AFRJIhbDmR5KREpHmZsTJMmPz0BXKhSTJN54lldF6Ugos2pc3Kk9g8ARzOV6UIEiisBBuA383AA5VqhDbUzliqiw6nDVnTw4yGpJM+zi9fn6xi8f5RQf+NAQduO0PYKXBa44zBac3Eo+tULibw8ZzO6Vt42a4XIo3BeQqnFAQKZbuIYwasRX2rhNMa1UvxuglGCXixRky0uffPNvjk7z1A8JIE/wVDRG65/HZBWsHm/jo4qDUcYaZxnsApid/Jy5wX4TG3ucnk5zyO/PUNzMuA1WmPCw/McNCNuGWiwYtffg2v/47beNm3XIebEMQFNPE5+sRFOp9LsY0ArwIEHu3GCIVlKh5w282n6e0I2HbTLpwHiTGciRIK36I8iakK/FRREymerwlCzYreQNgh51cVZx/zUMLhSccwG9FbGpKsp6xs9P/WdffMsEMtipk6qWg/Ylk+t/4V1+jDW6f5V0/8Jn924TNf9tzCQpsbbtjF3itm+IZvuPo5FVEAEn2SisqoeTkTQR0pZSmA/i1+4TFjxowZM2bMmH9IRFHEPffc82WP33PPPURReQPdWksYhs94m894IuVDH/rQ//T506dPP+Odjvm7IRA97HAT++kmf9WvEstHaW06KlsWRQieZNQYku6vEnUigkdXIQq578OP0rt1lnZajoa4UJC2IegkyFSDKTAtQbZPM711hqmDIV1TxZ7qsnaiwjWTP8CviPcxHXfYNFV65yLkgRob22psv3ODU+vzVJ/URKcHmGqD+ppEnCmQokc6U8MGCk+VuSU6DIgev0B0YUDxkhnSiZDwgQLZ6eO2V4ninHTNR2WGtGUpdljCkyPis13yXRMUDYWfXr6gzVPkpEQVIaIr6MkM7Sn8UYaVHtm2iPqZjMoFgxsZ6k8VuGqAqdaAfplJEgfEeyps37lCe6rDx27YhQ2hcl+K3WHxzxSESzn+lsEfAMKR2xAouPkNp9lx4wof+Pgk+f0aK6u4aIArLDbysY0IlekyY6LIkEOD16njfEmuPZI9MfNHT5INpkurBAJvkGE9QbYtZrg7witAqBC/l9P+9CpuDtqjHLMZ4qYnSgvP0mpZpezF6L0xcrmHmaxi6hGDXQHRakalI0vBAgABilJYcOWkhSgMLs8RUQhZVk57OHDaQjUsw0wFuFxjrUGOUuTJDF20aS3nFM/3CNYG+J0EYSweDp0MsZ6g0m3gVIynIhDldAOFxKUZLilgOCpFPedKgcQ6kA6RFlSOrJaWJl+BUhQTEXqygue8UhBxFvf/09x660U28yrHjlyBt9ak8vgaMowhCMr2nctNQCIMcNqAczhjkVmBUD5ylOME2M0tZLPBz/34n5XiC6X4oTyvFHu0wZ08jwhDnO9TPbqJbdfp5zlyoo5yA4hCqMQIY5BJgROgqwo/szgpCboFLpKYQCIKi0wK1u6I0TVBuCYI1wpkqpH9lGBFlmHAMkEekJhVyW+86c+Yn075kdZr6OZ1nACVFwhriC4OEMtb/NHPtdj7R6/nFe/4KJ/6j5fKiSVrSSarCGGJLgywT7gyN8daTKBQvsE+GXP+yQxZCymqHpVZgUgFVhikcwRDUf48kYA9DWauTukcs1hRVpm753nc1D7DU70p7H0xpmlwgWKJhDwKyIqA3vIMc9fuZbre4DXfehM/8cEP07lRkz2Z8fu/+RmO3XaG9PPniQLLrjdbTn8i4C9/93pOvG6Ox88u8K3XPszkKxTXXLPM4Y9WKCwY4egOJP/P/Av4hZUPo2Z6tOIRk40hvnQc6W8jFR5zu9cwK9exWbQQQqKVIGgG9JOMdjP+sjW3l6X85Bc+hpGWZHsdMxex87adX3GNTkyGxTHS6Zc9pzyFv71BMAq5uLLFwdrc/+dzwn/Pvtoe3rRwDwcaL6bihXzTziu5bWYHU9Fz3w40ZsyYMWPGjPna4Fz58bXm6+EY/nv+6T/9p3z/938/Dz/8MLfccgtCCB544AF+8zd/k3e84x0AfPzjH+fGG298xtt8xkLK61//eoQQ5QTAV+BvS8Id8/fHU+sXOXlxmvk++EP40w8fRk76zGYbdM+luP6Ixn2Ca265krXlRQZRiKzVcAODN9SI0IdcM7h+knRnk/jcgPrxEaxuIFyF6pEmNu9z6qEu7G4hXMHCnmk2H68Tf0jTOzBJvhLiMkeqDKMrA+SUQRQWPS3or0sqJzfAr5bTJdoiej1o1vDWC0zNw3kKv9AIHKNY0M5SCp0hioIDbzhHfU/KyT/ZxubRKsHkiOo3Grb+OKTWjXHHC4bbQDhHPlvB90ak3xrhLSkqf+FARQgH4UZK3pTMTw6IPl1QuBgTCeqLfml7KUwZ3CpAZo6857OVT1Bxlua+EblSiCVF9LmA8MYM7/WO5FTAoBcSpgm1a4coG/GP73oEIviYuo6d2y6x8YRP6nycVyA3elgPXGMaFyqU8VFDmL4vZbArYOu2ADVMcElMKCxuqQ/9DBnEdK+sYRsx0YbBViOkk0SrA9RKgrug0LtaIAuEUrhKjJifRcQRrhEhhcfwqilMxSO82CfZHpAvhERFjMw0VoDLc1QaYrGlhQeJnW5CJUSdXYYguOx6FBihKaoQO4eIY2Siy4riNEeEHn4VbKCZ+WwHhI+peshBWVVsJqewgcRfy8FIEAYnvXLipNCIwMf1h+Bs2SRkLKQZulVBbJ/BG2joD0rRo9DYNMXvREh8SDLIB/R/sM5WZ5aVEzX+7S0f4mdPz1NMVfB7KXiXJ12CAJekYC12MERW4vIY0qS00HhldTJFaS8TYY4oJC4IEEVRVk4XOaUX1CE8r7Q8aU2w1MUFFcQoh6kWYqTLryo0eWBxWYa3McL3/HKqph5hm01k6OOMQ2aaomHIp7xS1MCBJygCgZkKSbbXqZ/YQviCyB+Sfq7g/dl2vvVnLtF1AS7wsKFCWw9VOCIzQNiCzVMJf/7QxzjTPops17AbAj/M6W2bxOyUBJualj9kdF1Ocm8V1wpQqS1rz4VPv6IQURV9v0/wVA+BII8kxhckE1A700dcyjjfq5PVBHiUU0sNR1F4BFsOllOymYisIehbQ6MDzofZ3TUSq59e115wYDcfOPMEKhGYQPD5py7ScharLSdOzoISxEerVL5N89qbD/HhI8/j37/ki1Qiy2e3vYit1VmCgUBby0ptgJ0Q2HsV5x5fIGkHTM11eOP15yjSCp94bIH+pRXSK6eQvqS3I+PExXUKrVnuDGhUv1RM+fxTZ7l4fID2NdGUxq/AxWSZybisOc70Kp/96GE2Lgm+/ftezM0TVzIfTzETtv7WNfyKXVOcPr/OROu5FzfODu7hYpqCeIzbZ6Gbpix1B0xG1a8zF/OYMWPGjBkzZsxzy0/+5E+yZ88efvVXf5X3ve99ABw8eJD3vOc9vOUtbwHg+7//+/kn/+SfPONtPmMhZX5+nl/7tV/j9a9//d/6/KFDh7jpppue8Y7HPPcc7Q0wEYx2ww3pFKt7BHMJLD10EtGxIARRIviVH/o/+L7lX2P4yEYpfrVC3PaA/i2zNE90iTGYrZxwLYXAgyhCihCbeyThLN6ehMkdGndBo/emHDq1SH5RkZ6yYArym2qM3tIiP6M4vTGNahfYOR95IUA+uYZzQ/JtLcK1BCUkIqyWY/+XNpG7FIOXbcOSk7ZivEcKJjo5Lo4xowFoh0klMsuJOznhNsnwn2yj+scSb2QJl1IGuyJEJYL1Cv5JgXfS4XJL7VyfflhjNBcQH9T89F1f5Gytzu984CrkSa+80MOVDTBZhpUKsdYhntG0dJczD9Z5/gvPQE1w9JH9ZZbFdMHuG1cJbrI89Kv7YTqmvrdHse74/Md244yExx2v+qFHOfSReR5+/06ynU1MLPEKhTTAsEBuJQgt8EYCIRxFDVqPpXB+C2Y9ZD8B38eECv/MBm5BoOshybRPdHoTpRTZwTlEphEyQrZDnDNYBf4oIQ8yevtr1M9aZCYQgz7hyTXmjhtkasqslHqFZEcDmQUQ+xB5MChQCFRuMYEHO2YxjQhnLEo5kn0tCD3cOVXWLCNwukBUIpyEfEMg0hR/o4+tRwzfNIPc5xP/gSVYF+hM40Yj5HCIDnzyA3sJujmiq3HVANkvp1GoVXDDBCElarULG33MlbuRk80yJLY/BGvJaw7hEtRghPMVjQ/G1MWIzatrmOdB7XgHvXsWBJg8Q/XT0prjLk+8WFuKOL6HsgonBDIrykkTKXGBD3lRelhsWfGr/KAUCkajsqJZSURegJCozgi1LcNpMJ0tbDXECIcODKMrmlSPrIMQZSyYp5DKg1yTRgppHf7miGhtRGPCkjUqqDxCpOD3csJeTrCZowoHRpJeiHFa8sXKPu4/8jzIozLnJlAgFC4SmFARpQMKXRBd7HNw30Wu+paUj/zeDRy4doUnHwkwgMwt6VpIdU9KbiMKZ2kGBcWyQ4gYJ8EJh5gfYQceu8NpLqz1SXsaPKgvZ2AtMvDQngMrEMaxj1WqFzQrhyaYUVvE50aEEzEbXmkLExocmo1sEYAf+ek/4/6HzzAhZLlW6Zz5LgxqFbLpkMKGTKUjnBV8546f5ie+74+4/crTjNqSbm+GSysthBCoAmQBf3DyIfznhYhPaNRTFnGt4IDbYL4yzYT3w3zWPsZcdcjjnQ1UKPjMf30Epy0KSeh9udVmslahQkCgI1535QITUx4z4QTX/PtfwHMFP/rqe3nfv7wOgDBUvPG7X8z2yjSHn7jAH/3Jffzjf3wnVx7c9vT2vu8tL/w7Oz+cGS7zWH87Tw3hzVcU/NI993BsfZ0fuPVW7tqz5+9sv2PGjBkzZsyYMV8PvPWtb+Wtb33rV3w+jr98+vh/xjMWUm666SYeeeSRryik/K+mVcb83fO9B1/A45u/x/TLDTdfuInb7zjIajripz93mmhlBZkUDDb7/NSff4Sl4+uoQmMHKVJnvPbmFUb7Ohz5T9sIFlP8Y2l5ce1JqIQUkyGjtkecNch1k107T0Jfc7gqOXPmDI3nbWdw5AzCAt9iqAYZGwQMTUTNZogeRNonfdMEQoUUazHZhETEPnEHUBLrQVFzFHhIFROfEVQuOWw1xAo4+QczeNE0Bo98e8TkXVv0ghj9mE9vB0ycT/CFJBw6XJLin+oTXIxwUVA2sSiBP7IMt1dIr8rpK4/WtRnF/XVUJ0MKgVUgCw2NBtL3cekIo3MCWZB2FE++s4oTAj2b4rb5jI5U0DrAq2Z4WykmjNHLgtGZCr9rXwjW4u0p6OgGt75smd4xjwcaN6JbMWHHUH90BZlk4Hs4pbC1AFF41J+SVM4VMBghzBrEMTZJCKuGILCMzqxj9k4hQvA6A5iYwNQiRB38vi5tFJEqg0KzgnAFvEt9uvsmkE4QnymQljIDJ8vLTBhA5VWcpwCBSjQqt+AEGFtOkrSr5BMRTjiCi+sEnQyhDC4MMAq8oqxpLmt9BA6JrVURhSObDXBLAUVYEG30EX2Nl+c46UrxxYHcHOItD8p2GyGgWccNk3IaRQhcVlyeWQRx/Bz2+v3YuQaekCAFxhWMrqsxebGsrxaLa4CjMrOTv/rpPWVg72NnoF5HrncR1SoOECYoc0KsodjtE6xLlPJwhvJnyfLSvqIUeGWeSXnAlKKdUrgsLRtycNgoQF7OXlEnz6NHohRsJup4DpzWBFuGzuuvIj6zRePYALmV4DzJaCHC1TycBtmKUP0Rrc930M0t+tdvR4gyoNn0B4hElVXR9SpyKyHdO81Gu4k5FLAtWEct9rCRj64qlBbYRJBteGSm4E9/fhEqB/mun/oEVgkuLs7hIZChD7HG+Rm2Crbi6C0oNnYpth9J0WFBEfnYiiC42qGEZenBHo16zAiDVZJsrobfSYiUpWiXk2DCCpbf7XPy9gPEWpJNRggZYpY1aqcrs5mk4/VXvJFt1av57Z/5rxz60/tQ822CqzNER+JWJLahEEOFqvpc//KTeNcGDO4/wM3zO/HPLPOy7zpEq6ZZ63w7V4mY5r42i+tdXvGqffR2a+57/Ck2PUX9xJDkRSEX5F4KbyfbZ7fx8/9yL3/9Xx/gY8MOJPAHv/Ep/Llppq7bQaP+5SfX26/Yye9/7z9irT/iyvlp/sXdH+d37vsjihFkbfjU++PL9eWS88dXGIwyjlxY5Od/5kNsrQw5e2aVP/iDH3x6e91+Qm+UsmP22dcofyVubuznwa3zTAQtQHDFxATntraYq9ef832NGTNmzJgxY75GOJ5uoPya8vVwDF8lZcHCM5/TfcZCyo/+6I8yHA6/4vP79u3jM5/58gC9MX9/BF7BK2cm+NgvZHzggU/yyT95gOjb9zG8dgKVWqJzIWIw4KEHT6AupaAzQOH7Adm9Q3a+osNT8QzWOfJNvwycTTRCSLyhQfqC2e0bzO3dYuV4hDhlCOOEhif5yZ95M29/+6+it1LkhkQ3NV4qEVsSuexRPd0jWh2RHhToKzQLeQfbEYwueTjPkNc88ukJXBzgZQLXMohQEyQW6QQ29unvb6E2M4KRRUc+J57YR/W9l5iYzBAGqIHzPCqrGqcNMjIYm4OVUIkwgWC4MyadNtQezHjn6ZuxEyE+PulNksrnO5jpGr6SoAROCKwnMGclJ3oNrM4AjfMkYr1DcZ1gtKvOsd+qEg4rqByC9T6DkzX6e6swITGxIst8Lp2c4aYXnueOb5E88b79JMrD7xrIC4wwSOFDJULlUF3SBD2QiQbfBwT0h+XEwkaH/lQdyFGFIeorvGoLVzhkmnPNzRfI+iFnnphF5BplbVmBjCDsWLL9kkIJ+i+N2bVUQRYhNhzgogCpA4qKppj0ibqAlOQNH2nK6YMgSTGiwBUKlTtMRSDDAKUlollDnV2C2UksZVaIMJcnEaSCRg1bs4heQfThHrKbQ1baNxwQXhFiswCxWZTfJwQmCpCeV06kDMqA3jJ0FvqvaKLnQxofPIu9ciecWSsnO8IA2ZcYKUqBCsDziEyX85+fxilQSQ5iiNoLZtmVNdZ5jqCs/dXTCn9dIDyJzTPEoJwGwhTYoigbgaTEVCsQKfRCi2B1iKsHiEwz/8aC5c9XEJcEQkqM/JscHIvoJYiJOqo/KgOHtUPFTYorKqjj6+Qqx5gCZRxSQ086+t+5nR2/exI50AiV4fcKrHJIHBaD7A2gP8A7EFI91ic4mLK4c5aom8BIo4YpQRbhAkXRChFbPXAW16qggoDf/zcvg2zIyJTVvkZK5PUBe7fNMrprhYs1hbhomP5IiucJVAQmlMjE4Q6HiGM5hbGMVrt4vqV/XZvRTI2pLyS4XoHdVSmFJ2fY+0ObXOpEbJ1uIZDkbUEyLxDS4gKB04bj993N5J3TnHjsHCLRuF0a/9YBYugwjwRk2Qhfw8ErO+y8ZQnhHPd+vsojJx5n26skH//wFdzwmhm++y3fzV3fAJ1ewuZqn9WVHm85eA1PVS7Q8TxsQxHFiqAx5NGtT/P41kd56IkFlj/Wxr7IB+Eo6grVK+he6Jdi3v/A6Usb/PwffYZr9syxdeuAT118kiLV5DMeWMGFP9uFdAYKw86Dc/zSn3yOz5svQl8grEfW/29ZKc45/u9//Wd0uyP+3Ttez8E9z21GynS8l93qBNsrBwikx3fdeCNvu+GGsSV3zJgxY8aMGfMPkquuuoqf+qmf4k1vehNBEHzFrztx4gTvete72LVrFz/xEz/xjLf/jIWUF73oRf/T56vVKnfdddcz3vGY556/XvoNhv4pKhO76LuYzG3StadwkY83yBFRhExTRFihWAjxL/iIwpI7x+NJm7PDCZp7IpZWMmxFIrt9RBAicHhzOfUrN6kt9ykeTRgdkRTnJbUiZ+Y2yS/9v7+J2Jtin4R4MkFvtWgey4g3HFnbJ1geIDoj/L+u42/zGFoPuWLwkoT+/gg/VQSJz8ThnN7egLQliLfAKYktDLYalqGnocLr5GjrQQ2IDKpfUNR98kYFOTL4IZh6SPWamMl0yMqjtfJiQQqSCUU4TKnfnyBHhtFey9Y3eVig8Zcj4lFpA3KbXUQU4vke+WSE3V7Hf2qJfQc7FFXFidMTJK0mo/UY5jTePWvYeQ/hoGgHeP0Mr5si8pyi7jPcJxk6xSAOkAWEPYcUEj1RQ65uYHwB9VLsyRsCf5iX1pJ2DQpKUSvwoBmjNiuokcNbyaBWgcBDaMvO+UVe8YZHsUOPX1x+LUFHE54fIBxYZ7HVAG9gKSqAAim8UuSYaiOKAFsMGe0KyWcl3mEDWuB8RV4tLyZDQGmD2sjK7+sXWNWFuILLTVkV7Ps4T7B5RUDrSA/hC0RucM4iBwav0yO8VIoIeqoBOMRyh3RZMrhtgerZFNms4qSgmIgIFrdwwmHbZRWyF4bcct0pPryvjoskelIS9vOnG33CnkEc7iALjbCm/MUoHKM0pqr6pHtaVB4bgdYYsQDTIIyEKER4HlZY3CDCehnCauxgiBpm5dTKZINiYZLg4gYizZG+RzHRQBQaMRgh/AizvYZtdYi3jUhWQhCC1rUNNu/tP908ZPMMpCS+2Me0fGoPrOLiENdP8JMUb71P/oJySmB0XY3mOYudqJGGGhNUkf1VXG4RlEKMC8p0Fr0ZIT3L5Imc6298lBNygaISg3DodkQ6G2JDD08JhBYo5ZeiUAqqKMAXlNYs0J0a9VfOs3jCIM57TDzRwx9oopmQ4c6AoiEQRuCdDPBWUqgKcs/Dv7iM2OHBXB3XrOAmNES2tEIphdom2PG8S6y3A1K/wPohJhTEa6JsRfIcxexHWcoN/+Tn3srbfug3yXRIcHKI6wXgW8gdVkj04wnTbx3gWU0lS/nl33o/2U2KJx+6jfs+6/jcZ99H7YZpjp1bpT6EHSpm27Y2ST8nnwjYuL7CRL/Ot93W4GxyCuhTby5xXNaJLvlYzxGvG2o7I+qTNaSULK5s0azF1KplovswzbHWcTFZ5yPHP0djJmNBTXH/mZT2vZdbrDKDw/GqN93MUx94D8m6oX11SnZ/lV0HZp9ev4vCcOH4Kklo+fef/AI//rpv4MqZ6efs/PDk1iE+tTpP1Fnmzfty8sLy2dNnuHXHdrY1xlMpY8aMGTNmzD8InCg/vtZ8HRzDr/3ar/HjP/7j/OAP/iCvfOUrufnmm9m2bRtRFLG5ucnRo0e5++67OXr0KD/0Qz/ED/zAD3xV23/GQsp/j3OOjY0yX2NycvLZbGLM3wFVocg3PMxVHv2zdf7P/+sIbjbli6vTnF8dgjFYB6PlAWooCDKLMBatMzaKnaR/voH6y/NYrRBxBEUBSJwrEHdVEA95LJ8KUFlEsZnjDKSZIIksnX5OY3HEcMkj+I0Ef3qDa69Z45HNq1EZpSVjOKR6PVReM2Dts23cUo7XHZHGiu41NSYOgz+Cxqmc4a4QnMCEII1CZAbpMoK+pYhhNOkIqusU/6hG8CGLdJZkJqB6vEtvp0LPBcxXDMO7axSzTWQ/ob8tJBgUWOmjMgnGohvgnXBUnkzw6q3yd36U4oqiFG6URCaaYqHJxC5464+ewEnJf/z1l5AMfPx1gfKqDK5T1M4NSadjVGaobORQaGyvT3bDFA9t7KV1X8bRMzugEqNyidAar3AII3CDlCLs0X/+NKYqaD7WwV/t4XzJ1rVNGmeSyyncDk8GiJrBaF1a6qQAY1hbmeHwqV0sH5vEBh7WK3DGgIDCV4Q9h3eurIDOL0hEbkApZKrAE9hqhcZJRzp0BF2Q1mIEqEQgDWQTEWx2CZZ66Ik6LvRQlzYxO3yU9BCNKm6UUNQFzUe2GB5oUukqhAajHPHFIRZbZqxUQ7Ldk5BnRKsdlFDUjq4h4yo69DEVD6+bI5WPjSNsyyfqOpyFoNng4P3LLPUrRJcsTq0ifB9hSzuQyi0uy3G+D9qQ7Kqxc6bD2lJEPuNT3DlD5WyGt5WAkLj1LjQqyMEIAh8TeogiwUmQQiEnWrg8xwUBwXIXV4kQxiELg3d2HVp1XBSCEsiRY+WTNcS6wekCHHTuSZ9u+IHy4tp5IdJA8+ENRFpAqsupDU+i51sUFUX9wYtEjV3UVwpUo0Vel1gBKorASUS7PEfZbg9hygkhZ0KG5xTBUBPOGXQW43CM9lSQxsMfaoQUCE+UbUXWlYKMH2CDMgNE5RbnF1w4vIzeO0DmTUY7Y6LlDJtKkhWBbIPKy8kjhYSRxjlHrgqE9lBDQe4rhJQIc7kFCjjz6W3oO1OypiW7NsJfsTCwYAFVtgSF3vVMxXfy+c8dQ/U1KvTQZxtlIKoc4W0MAcdSNsH9v1PFLeWsDSaYTbps/cU0npdQPbbBYiXm4I3TDJKMLLHcfsMu9u+fJVi0yFSja4rVZMTv3rvJt998ifV8N70ndjKpKsiLGh3Cddfs4Uf/7Vtpzza5tNLlV3/vs0xN1PhX//Q1AEyHEbc1J5i9tsWRjWMgLF5lCE4RL1sGVzSorhVs3z1NdyvllXco/vLtASuzVfwZwQX93/7ICAKPO15+kC+uLbKoB9x79sJzKqRs5o5KnOOLHOs0H3vqJO975DHOdDb5v190x3O2nzFjxowZM2bMmK8HXvrSl/Lggw9yzz338Cd/8if84R/+IWfPniVJEqamprjxxht529vexnd+53fSarW+6u1/VULK8vIyP/ZjP8aHPvQh+v0+AI1Ggze84Q383M/9HLOzs/+LLYz5uyRnmkf/q2BwT0BMQTx4K0tCcOLJQwRZgosqCF9QncnJYgmPeiAM9avn6SaS4ZMpcQ5CgjAWalWE8nAyJPuIR++GkMlWna2iT3Sqj1rP6dwRsmrXEHGV6XiIIsMNwYQ5qqXZvneV04vbENYBAiUNUkpqsk++Ud6hryxZVAAiySAzeLmj9ZhDFpC1AsIkwey29G8RZBcEbilAbPao3NfBe0kFQ5PRvCBdSInO5VTO+OgVx+a5CuQ50l/F1as0zmxiViS6EgMSUwvYvKVG7Qy01wYsvGFE70KVS/croo7EJdnl3AxHeHqT4RacPjEJ0kOfsDRqPfRKDEVB1pB4SYG6ZBntaREMEoQQCF9haz6j8yHvX7oNHVpmbIbLDd4wR8QBes8sYrOPn5UXeFJLvK0CtMaFHoOra+TzMX4nRz2aIn2LQyCUoQgccqRRxqA3FZ/9patwRlOpDPF7BdSrEASoZowJJX5PY2OFn/lYXyKVwvkezhrEIEH0JXIixIYGpx0q06jEIguLzRxqdYRAIFc2yK+YxE4uEOQWmeblMQ1H+J0UYUChyKam8FKJFQ6XFaj+COEH6KkqwgevkyIyA3GA0qWdSmQZ/tIaRHFZEzxIiXKLCwNsLebhk/OIlQGVM4OyCtk6bKCQsmzh0RVFGPiIIMC5lP03C771W+7mPx+6g9rZEUXLo5jQ+BcznBSIOKJ73Szh+pDeFSFRD4p6ghoWqDAo96EUMi3KoZL8cl0yDmUcVnm4ZhUxGOGspag0YD4kWDtXCii+KhuUtC5DaFdSZByRXzlDFhgaTzqEdrhQYCYn0dM1nAc4RyU1yCRHDDJ8GZOPQCZFKRpZV4ohfgA+bLw4JDo+pOUN2PPKNS4dnmFwtrRCda4JiZcNtTMaFcVl01Fbo9MAz5aTKMKVopwILfEtQyb3RGydcwRrBclESPf6kNaxDJk44jWL0oYffdvH2Vyq8id/cBt14dicmKC2NKI7HWInPUZLKSE5yUKILBzFwxJ7IiScl7gDkvi8weYGV/GQ2pWtP/kCpx7b4g/+3UdRDnx/EplaZOFgqCEMsFrjRinDuzIu/uIuQmUwuUTKkGhtC5lbUIbvfcUtPPLoOdR6hmlKarWI6Pom6hMD4qWIdG8NPYiQTDPt9zl3/iL68Cxim4cMJWdOrfPBe59k6/wah3754xT1iH3veOPTa+69nz7KfR95gpfJG/ip134bP/vwRxj1BD//spv45b/8LFIIkJre+oDHHz7DR7tVqqcvkncsSkrWwq0v8eP+67d/C8v9AXefPsuL9+5+Ts8Ps9VrmK/ejSc9RqaHcHB+Y5PH46XndD9jxowZM2bMmDFfT9xxxx3cccdzf9PoGQspvV6PO+64g8FgwPd8z/dw5ZVX4pzj6NGj/NEf/RF33303jzzyCLVa7Tk/yDHPDG3vZ77d4kI4QTeucehizvmoIKl7eNUQNczw5hTbX36RQhsuPdZCHFpi8NBp5F0HsO02Zk6gMo1wChFF5XRYLUY5CNYsipTa45vIwmCHBd4TCaMbErxEo01BKAxW+8jT6zz5PvBvKsA35DvaxBYGn0gRxxOuvGGLx8QMCPBG4J9P8NYGWE8w3F0lb3lEWx54At0MCJeHeOuC+LxDrGu0EQSRpTqd0t0ek9UUxYTH8KoGtbMKtd7BDjaR2uH1M6in6NkKxZRAe4L+npitWz2KlqJ2ZEDjqozZWzpU9w95ylxD0fapP7wCUYTwAuTGFjY1vPdXr8I26/jCkM5GeJsJVhq8xRTbG+ImG4wWYpL5ClNHF9n25iH+AyPSS5buXoELJEJbVGeISFP0dB1biVFKoc5tMHnPCqPdDaSWOGC0R2HrEW6YUUwKjLWlj8NzOA/yCR9Z8YhPpsh8hMsN0pb/N8ILsDiEc5i6X04cbCY4K1GFw0zV8CojdE8ikrJi2O9r7MAiLWRtj8pShjcqML5CSoWcnEAYjRwM8TYLqKQkO6r4Q0uyrUbtkEGN0tLCMjCMnm9pHzfIBJLvDQgfgviLBX6wSX42RG6McMaU9cOtOoQ+IrVlcIqgrCEWlNNBzmErHlsuwkcivBRpXZmdUglw3bK5R8uQUHllpkUY0k6W+dBvXIvtFfi5RBgFw3Iah8EI53tUNgxFs0K6ENJ+8CLeSh+kwgXe5ZYehfMVLtOQ5+WFbxTgahVcs4pzlnyhgQ0kWEEhC9g3TbRU/nzi6cJoymrkwjDYGaGEB5c0Yr1bVmB3C6QbQRiA1lSf2ijbirIcL00IZRNRGFye4fK8bA66/HrXH+kTH+6yWfM5urWNofTL10bA7kOruEWP/rRfLhZSIFsSPQpg4/J2pEUYgxMDbFHhZS+4i7OPfRKROfKmJmsL1OI6xbYmJooRCHbs2+Laay/xhx94IdOzTTbPLpdZPyNNOuETLkmEUgR9h28S+vurbF0nwYPpey3xxRwdG7QReKkjm5Wcyx9gFFxExgq6GSYog3KRIAbgigK3sQlKcLVY4uTEDlTXZ73fJBMGoXyctshQ8Z4P30f9+BCVWT73Ww/yz/7RN/Dyaw7wyIGjJPsVjYbgu9ov4g3738Tv/fl7WT5ssXFA/MQythlSrA758GcPYz/6GKxswQq86KodT6+5t774IHmmuf2lVzHVbrGvVkF5Ka/dfSO/Xv0iZmCQo5zZqToz7SpzQ48nlcJPHc6zuKWED95/hNe/4HlPb7OXpPz5Y0fYShK+7wW3PGfnh6vb38LuzQ321XcyHc7yR49+kpHT3HPx/HO2jzFjxowZM2bM1xZx+eNrzdfDMfxd84yFlF/+5V9GKcWRI0eYnv7SceOf/Mmf5M477+RXfuVXeMc73vGcH+SYZ8ZC/HyWX/Uwe26Gjx47CAca/PgN1/JLS3/KpX5RXtxGVUgF2lrYHxGeCDGFIbzYR3k+0XSVIMpJFm15ESvLXwM5KmgeH5Yhk30NxuIqIXpqEjNXI3E50fkKotsnbhV47YABBcMFx9ouh1quMfGpjPjJAelJy9KeJma2heoneHjoEJK5kKijidZzRjsiRjthcjnBaYfIQ5qfyvGH4KQlIka+cIrOKXDHNOFODSck4aqkaAjCxQQRW2hGiNUqRD6dG1vYGNL5ANN2mAmLGln8rZzNpMK5x2cxTiIrHtRriFlTZm9YR/UOMANJekjj8h666kM6hOkW3sihDi8hjEOkG1SfUIhGjdrVmvrtGtnY4MJvTVI9Y6lIUFsFOIuOJYMpS/NEB9es4XyFdAVsg7zSxu6ss3aLT7iR03xiCzHIkMDgdp/4C1tlKKgVSG0wjRC5solAQrNWTjgMh7jQI52roAYpbt5Hz1SQQwO5JpADWnvX6T7YwpgQPA9wpW1Fg8oE5KXdQhiDkQIRSjwLKldQ8zF+iK1Kll/UIloVVCbqeL0Ul+dEJ/rYWh214SEyTX5RY+bLRh9zyiKUKcNoKa1JXFqDbdO4dhUqITbLkJ0eItdY53CTLWThMAF4cYjbMQUrm+CpcnKKUlSpLg7KfzuHs47Hz18D/RFKDaDQeF1X2n56XYQt3+deokH51JcERgq8UVIKLbbMwsAvrWAuCOBvao5HBqbasLSOLDQunsXWK8SHl8murGAqsrz4zw1Oa/CD8hiNwDmLqYf4Ww4xKm1g2PK1Hu2YxNcOJ9V/yynyfWSqsZ6EoihFIinLqubLv6P74w5LXoRTFT71F1cT93MmTZnNIs8KRKpptC3MNHHSQ2+z8KIh+tMh7hFNMMjK6Z7ukNHnaiy/cYvqyzQrT8Bgl8TflNCqIZSHsOUwzFkvYvHYdnCwtN6DtEAEHnIIbipiOO3jfAW5o9LrMPQa2HZp29MxEPu4SBFtFEjtcL6iYA+/exbS19bx/2QR/PI9Y6VAT4TIocNbd4BkhxuyMFNnpZ5jK46JHRtMy3WqqsfZ305pVkLQFqE1KI9Pfv4wj3UWYWRQPUWvPeITU0d5vfF48oOSYl0gQk1y3Tw2Ulxx9U6WfYm9YhaWuiAVv/GfPs6v31Z6aBd2TfHt31dmgznn2DO7TG5GrGQX+e3/+N38wm/9NS992x78wnLDnfvZ/OSQTwQnwDmCy97hYZYDYJ3jAyeP8uknT/Pk6hq9NP0SIaW01A5ot6ooT37V54dHtk7wxdUBpwdLvGUXdNOUMl/5q9/WmDFjxowZM2bM/+48YyHlIx/5CO94xzu+TEQBmJmZ4V/8i3/Be97znrGQ8jXk0Onn8+cPhUy2MnaOqnzn9dezUKvzooUr+VP/cWySkYUxJ/9Tle6ddb7tBXs484Tm0nIHz/dRhUangqKoIMQQMRwhpMAaH2ctttZjRii6UYhJc5RnqS0YdofTpL6mdnCVmflNtr884ZEzt3F+s8fmtgZYUNqST1aJcNiKor8W4+IAHYfUZgcsXL/MoYd2k7gu0UqKGPrYiQa6J/Fyg44VRa3CxjZJvFxQP5NxcbqNLCzx9Ih8KqJ1uEBYx8rzQ/qv3EXt3ICJuzfLxpVC0zzSw/qCoFth8+YIIcFWBcNtEdJ5VL+QMP2iLXbvCekcqUGzhot9XF7gT1uar7EsL/pkuYfqJYQbGnFFiDgHsl6D3gCnBCq1hP0heiBor2g2LxhYWsfTDTxd1uDqZowyjtbRIWa6gcoMrlljx3ddIt65yfGPX0FnUGPy4R717ghvcQuTCNJ5hX/eIHpldbB3ZA1RCbEYjBLoqQpKC1SaIwYjSAWyohDaMjoYEV2UuFSjTq9jI5+tcwEuTxB1idMWajWylocyjiKEIPRwmcYCIi9QWwOolzW+yJRiqkp1MSMYFlhPUjR8ZLuO1BYbOirLFoSFJCX4XIBrBTg9Ki+kt9eh7Qi6Q8gv21W6A6SSMEpQl+c4nOeVYoCUkBXEiwYTSFQ/g80eGIebm0RIVb5fiwKELm049Tpyo0/RCJFagxS40KNohgTrpbUMB+7cEm7PDP4wRAlx2bpDOW0ySktBsR6XokccI6TAjRJcq4oocsg14XoCl3p4maV+JqG/oKjoArTBIcAaQICv0E2f2pE1wo7BVHy8NMBJiTUZhTcin2sTXqhi+0OklOVUixCEq30IfKQnsEOL8FQ5veMJZq7bYFQErKUttr1/CWIPcTko17Wq6IqHnw8xixsQBRBWEUIhZYqR4NIccOB5OGX5+HtPMnHjDpLBFtUjEgTIfITUFi91ICTvPPYaJo+E1HxB1u+jTi1BFCDUBLtu3cni2mZ5Z8QKCjuJ5yz+0IFzVC9ZXO5Q0uHlZSNOvJbi8BnmKZ26oHbDHLbpI7TBWtC1Kk7E1Bc3CKoWU3GIGUOwcwsTg5AFrUqCNxKEB6vc99Bp8gMVGscGWCVYGgx4yK0isgTZr5JXHU9srPCRM7/EN337Cv/5Z1+Epw1VEZMJyWu+8w5+4S/uhomI1vZtOODME+ex1nLy2DKP3n+KN7zldoLQQwjBLnUPUi5yfsvx4h3/jl9753fwL9/+B2RpwZU37OLBc5fwC0NRVZibp5hs1Hj9bdcAcKG/xe8/eYjeMKNeD3jpgSu+ZH1/+OGz/P7v3c0tt17BW7/zzq/6/LC3tovJsE1V1bA4pvwKF3WP+aj6LM84Y8aMGTNmzJgx//vyjIWU48eP/0+9RXfccQc/8iM/8pwc1JhnxycOLTIc+GgsBzqOztlf4XceGvDwmb24K9rEx1bJZc5ooc3UuuKma+bY2tbi7NlVxFoPtoZgQFZDTCCRDpyQmIk6OIt3fpPOKENOBpjhEIfPqw6cQd38Co6M1njFi05z58zj3LcxyT1/tEy04RMb8FNB0HHsm1sl3zmis9RmdLLA7LFIp3B74PBkm6IuaJzJUblhdmuDPpL4dIIwkuSKNroKtmKpXJuShzHeko/xMno3tKmcSfDS8mKssmwZLfiIQpVTBc4gRinhSkHv5nlsLcJ6GqcBK3C1Km6UMVyUuEMVKhM9gosWF4W4aoDIC7ofVXRXWqy8ehbtElp3b1G9RSM+fhHb93FzU5iFNoNZQZzEFGnCzD/vwq6C6yoZn7hvO9e+cMC5h5r01zzShTrOGaInV7DSYZsBwoDzZdlYE0tM11LbcEgt8as5OolQOsAmIJoNZCXEdns4TyIDn3R7CzNVpcgM1RM5xCFSSLxE47AkdUeUdonO5eBAxD5OVcAr7+7LKYmdU/iJw0mBpKwnNqGHcwavq0EqjIqxlRiXXA4slRJvYEinfbytEVIbRK1KoA1kFtf0sVETPRHAyJQ5HJnBO7dKMVUvxQBry/riIMCNMuxwhKrXyjDUiSY2Ke0rDodVEm+QlQLL5fwS1rZwtQrCGITnlSU+aY5QCqzBFkXZ4mMFrjsgSLJyOuRyBqyUkuDMKiot8DoZTkpQEjvdKm09vQHZribBsUv4unxdRCUqxaeJJgQRqjcsa5qrFUwzILuhBl9YKdt6ypcYcTngVY400bF1nOchp6Zw9Qouz7CJIT7WYXRbHWXBVatY6ZAiwoQeuuoTDlOoVMrJGKkQlQCE44kP7Ma7osBYib/ZhXiqfE2doz8Rk+1VtD+dIJyDXCMeqsI5g1gWuBgIc4QuM4zUekL/wQtUZq7BWXF5TtQinSFc0phAki2UE0B65MD3cJ0M1WiAtYyuqHDh2EZZ8WzAeLB7xzIXjk4z9akEL5P4m5BOR+i6hzA5Qgqm55apeUd40fwEH/3CnRCUuS1r13q4Jkw9rAlXNLLiE7Usv//A7SxXMqq9kEZlSPJwlbP3TBPURgzOR2Q3GZACG3mMJn2+cPEScqdPcnWdmUSyPsoJJzIyVjjcaFJMS7xNyy/+5JtYfPIiXiVEGPAbUfm6AXpU8BsfvJe/+g+fIh+mPHBxkWtevp+9UxMcSQxdcwW3mk/w9l9/Hv/8dd/Izbfv48K5dT6ZP8jJ9AymFpBPeKR5RjuoUgnLOr6FWpNv2n2Q+85d4GzRpVYLv2R931jtsXh+g9npxrM6P1zV3M6/vf4t1P0YJSTX75vn8f4K1Vb0rLY3ZsyYMWPGjPk6xPH037dfU74ejuHvmK8qI+V/lmbbarXo9XrPxTGNeZbkuQUr8LuCoU7pdR/j2v0RD16cQ7drRFGfbVGd5SBkm1P88X/4S7YuriEGCa7qoycrBGsDPHLoCYgrZZ5Dd4QIBM4GOCnYeeUCxwtNcWCOQ09Cvu08y7ZPwCRhPMGpzasoFuoUIsdEkuqSACG4dd8iHWu47+MOqzXKJVhbYRmPwZQkjEHPTeJczuTrLpGJPhtXV5j4C423leFfGDE7M2D+tj7D6TqHj+/EKImJJUXbYxCFCAfOEwQ9iNcc8a0WeyEjP1aggxBEOd0QNFLkpZi8InC+ZrAnJt/TRO6Aq48sIowlqwuyeZ+oUASbkmKxSlgRRJ0R0VJG93CdxmaBEI7NBahtKlrLgiLSJAcEx+QUaU9w5dYSL/+RDV6z8yQPbtvG7//5jbjYhwywBm+5i4gibKA4874pKq8u8HenqKWYYqoC6w7ptxAVh8oFXqqRCCwCvWcWEylcXuAin7zhoXoWU/GRgQcITM0jnQ3wzmjiQ0OEBT1RwZ+SeM2IME4YPW4I9muCl+X0P1nB6zmCHHSkEIB1As/zsBVBPldDWEHejCApEIVDKEm8mqEDH11RKOlwkQfGIhyQ5HieQNcU/RvniE53EBa8YYatxaheueo7ZyGMEe056I1wEpJtAZ0b60w/SmmtcQ46DtVPcXEM+eX647xA1CoIIbHG4AqD1BrrLOn+BsG5suVICnC5vmynsVjnUFGICHyEAdoNSEZQqyEH5TSKVgJPS8xsG2+1XwoUXoAalFXGLklxoxGi0Lj+AH8QMfveFWStWlqMhqNyssTZMg8miimmKyQHpmieTHGVgGI6wiUR2VRE69EuKFW+n1VZn2sjD7CYRoyNJXQFMtFIe7leWHg4J1GmDMJ12uKEYzQXs3lTQLSV4gqHKEyZKB2EiOW0FIoCh0tTXKHLn0eXAsTSX59kMpCc/u4KXldgKz5GGgZXxMiG4qrWecI9dQ7WD3LPuTVcXFY+y6iKyyUidXiJQ3hwPmojnaX65BA71SCZCyjHgRw6lggE52e3cfziFk8N5ujuskysWHRFks0r/FRjyVGpRcZ1RrkHf2awb4Q5Buxsr3HRm0DuzREzBeLxDvGlBsFmgnACMemhpWXj2DLtR1KkGbDzJQLbzjg6nOVzZ66i3hBEYcFvr7yH3qGMjT9oY2fr5EYwXZX014YUjRqhB3lW0Nsd8hm9zH33dNk7NcGF2avIrMfEVM6hE5d4x3s+wo98x0s4+JJJ/tnPvxvvIw4VRoh2nQqGuVrw9PrtScmpw+t0NobsnG9w+/YdX7K+J1sjWBuSrPaf1fkhzR+laT9Fw/s2oEG7WsFXish/VuV9Y8aMGTNmzJgx/1vzjP+Ccn8zWv8VEELg3P8G0tPXMQu7thiccjSfSDmz4fiQdyXf8R0XUWdSvLWyjkcMNAvtmO6pDdITl8px/TjC7GqQX13HO9Qi0xZ5qVPe1ZcKhUQMRpjpOmJoWbk0oDlj6TUszf3rJKf207gy5Z5Tu9ie9ljYOsfEnXs4fqmB2BCMtkmCfsZfvW8XbqTB8/ADi5dkjBJB5V6NW/UIVy5PPBCytVqnvTNBzUGyo41MDfHGAHHIUdys2OwFyFSjck10LCVcNeTTAv/SFqPrWyQLEdElQ3bYx05E0ErRO9v4WlA4S7QqqT4BRcWiegWi6aNMgDpqWHliGhdJbOThBJjAg9mJMoy15/C6FUQFAuqYGRC2wNUUulAoY5FOEJ53mAcUm8cVj16cYP47Qs7HbZ56qoW3OcS0QsRIo7TA1mOENnibA2zsgZO0dwzofb7OaE7SXjLoPIR8ALnG7KohNiQu8BBhiIsEoz1V1OoAtTEkWNcQ+TAscIBtRQS5RF2wZK2QYCtDbQ0xWYhLQnJfgtL4t+acWd5OsltRu2iJ1kWZWyEFfgbCFIjNAbot8I3CHwzwiRGeh7MWudrFU6CbIao7wkmJDhXe1hCEQM0r8sAHJXGtBsKAlX3UUherRJnvojwEDta75bRKYei+IERHFUbbNFEHrAJX9/GSAhZmMd0t5OYA0hTbaiAciDQtxQVrkJ5XCg9hUIonAM1GKcA4i2zWEbUqLg5Ql1uamGhRzLTwhlkpusSlPUpEcSlwVGKEp7DZZTvMYATVGILSCiaFV4ZsCVEG01Yi8HzcVhc8hQsDkpt3UVQk4vEuMs1RmxLSHL+XQxxhwwAnBK6fIIYZ0hMEXYPIHXqhyuh5TRqPdZEasBY3ShALHsWKxI9CXJaB9Ig3C0gNUotSGBXgJNhIIXsGmRmELyiaEWpps7T71CoYASL0ywmjgcCKMlspnfJLsaVn6P2LmNYrMjb6QyYWpthY3GA06yORCAPlDBEIA8OtCpPLG+y5usN1rzvCh375akwm0I06thGAEuSdiM9+5EZ0Eya2DKZRTisd/MIq3/zGpyiGjo8cv7qckHEWUkd0qSDaZ4jjlHyhYO6bUpSC4VbA6JKkiEE4STrlMUnA2SlBNquY3tail/fxXcojm7sZhAGy4mhVhlTmYuQtAStbDdCO2YkaN73wAJ/6k3tJ5iaoV2LiiSqjoiDwPJJhzsnBJZJ6DTw4efYVRFKV4sjRS3zsk19AP+nhFTkqMMzMGbz5kItZwQcePcobnl/ae9rVmP5aRncz57FLy1w/N//0+h75HtXYZ6r97Kw4g+SjDJNP4nnzNL238rzJGbZHDdpyPJEyZsyYMWPGjPmHx1cz5NFofPUTv1+VkHLgwIGnaxr/tufHfG25/ZorWFX3wxdCRCLJN3Zy1YGfpn30ZxhuDXH9Af2NGPuxC2RaozwFlRhXERQvb5KfKaczEB7EEThRtpZYB6lDyBwbxwyVZNf0iL17j7LnrhFXqX/G//PAB5mqDlGLBY/8eRvfP0EjuIp0wSPbaZCbKcmTPsbzaUcZwiiG51JU2kdGHs0Vn/UXh7SfylGpI3kwZjAZUDnvKGoBwUJC9s0R5oil964qRZizcNVZagcEWw9Wyaox4WIfb7GLPw3ZzATR2R4mF2ztnsKbKLASbFVhfYF5UmNjQ3whw98w6AlD/bERaEtvX5Po6CrRI0vUrwxY+F7N2f/YRM80SNqSqJD4XYmvBW6qiVaWoiaxgcFiifc4sjwizUNwksq1a2w7uMR//vhNZB/dRCZbeJUAlRlkFGGaFRgVZdBnJEiXFcNzDbxzA9w3W4o5h3pA4j9YILDYOjAQOGGxCnRUCpxmQhGMfFx1ACsp5JRBpBZs5Ni6LqD5RZ+iWiFY3MRJAV0D1iNrV1h/v4fcIQi9EcZ56EAQjezf5O1iqyFqeZNoI4NKjOpbhEiwtbgUUgpTXkAjICuQ1lKzI3IaeF7BG//pAzx5dBcP/OEOvI0RdqKK2Bqh6wIrFF4iERUfsbIJRQFVDzfZorJq0TMOq6C325LMCmY+VSByDUoiRxnoMhjYrXcQzUZZWy0VLs1woUM6iQgD3DABHAxHyDCEOLpsEbJQB3egQA881CMecpCiMgPaYIyFZKusMg4CMBarM1yvV4oT1SrWg3wuIlqU0E0ppgL8lRSndVkzPRqBcwgpUXGN2rkEsT5A9kfl/pXC2TIIWO+fRAceYqOPf3GEG44QSiDCGCgIUpAXDdIA7WYZmqsN7rSidz3I5Zh4AAKBMNB6MsW0gTjETrcxMw0IAWcAhYkUZqFCLD1kAcLzUKKcxBEbPeoPjCi2R4iJNSoXG6Q7Y2pnMoTxGd0d0v7GOosrG+i+T/9ABRuAl0LYHmHyKlaCCDzSO0DuNuzdtcJUczurSw2EcOBJUAKVO0QOnpaI3CGnCswQJt++xqfcNv75jXfz0d/ZjvPApRq9s1razpopoV8g/3LI4rYGO2c22XFXl9HH5hjV66XwohSPFZconKXzsjr1fJLecctMbcBrdh7mQ5vX0TkwwejjNa4evoCF63dw8tA9eOsDdu+c5MzHHkchqZ0d8uGHn2R65wTDxQ7XN+f44volRi5H+holBflyiwoFb3zRdRz+4BN0j6TQ9tFNn3imwtkroPFAn0jmbNw2fHoN/+FveTHfcHEfnzh5gtt2bP+S9f3aO/bywJHzXPuSg8/q/NCofBureZOQq2kCZ9Y3UYhnXLN8dniRjyx9hhdO3cz1raue1TGMGTNmzJgxY/4eGF+WA6Vj5itpF/8jxpivevvPWEj5nd/5na9642P+frmtZWkfeJS/eMW1LD9c5bu+/YXMtxu0Jir0z1wCwKYJ69fPUUiYvLtAVmLsbom4KqX+sR7h+hAtGmX2w9/YJaTEpSmin+NqOeHkHBfuEVw40eDszhs43nyKO/rTDDdPcehTc1TyPnqHT259Gqct+abEv5hjnaVzTYXsYo6stqg8da60QlQ8ENA+tEnRrIFxdPZWyLYCKn2JaDjC7PIPacsGoWh1BCYjeLPEdGr0l2tUVkKcAGoNpj69ieompFM+xhP4QgESUynDOYXn0dupaXU0/jpIo5m+bcQWFZYnquSqzcQnu6hOip8loDXq2Ca7v7GKvBG6v2IoJhS9a1tUlg3ZhGLhAxfwn28Iv8lDdGM2zsUYv87Bm57iwOwSS7dN8OjZOdqHh3gobCDIaiHoHH9zgFOK0V4I7+3jQke4NMD/M4doB4ju5ZYWJ5AdhYt9bFjWU6vEwaTDWQvCIXKDGmUIP8QZg13r0Lu9hQ4UIg6RGRR750AJWBsgtEE3I/zU0r5vrRQbfI/BrQvlC567srY5N1AJYWeMyARaB6hejugNEMZStCJUYVC6vDAWWOSumGw9wBUxX3zPHmZvHhFe3AIHNlDIUUY6F1DMR9SeSsH3SlEA8DdGODzqj1awBwqiLtiOI16x9K8I6B70mf7cOiLNyukEBG6yAXEVBkOk8rAZDPe3iFYT3N+0UBkLWVZ+7iRCSNAG9eqE4I4c01PkTwmsldhuF/wA24hxYYC0Dhd4SOPKKZCiFHOc1thCIy8WuCLATCrWv32O6KgiOrNJ9PilcjrF97Fzk8hqXFpgLIjAxxUFphEx2NPAzyWeEIhEoz2J8D3wvFKgalTBOVTDYC+mMEqxuQYB8Y6YYDKndaxGlClcluCqMTaQxIsZ4oJDOIsYjlBrFjcRI7yyRchULZU8L605/R5UFA6DPLOEiyMmj0vU51YoFMjJnFa0iLfexHmSfKS5+8gZhsJQ0Zb2Y30G+xXCV9QbGZsrtdIeNlGQHvQ5NNrGVY9tMvdiWLxvAnGpj2iGOFHmA+k5jd2boE741AY91sMm991/LQDvu73H7F1bLH+2BVrjopjJQ5atzTrJ3g7eAcHSmQq7mx2CmiOqFwzWAypbBToG+5TF3JzSvBvOPR+kFMTdjOdtXeIz+ioGi4CB33z34wTTp1DWoZTkwtQqxf9Zp9iIsX6Vxy6sMNWx5FJw6MlLBMpR250Q1ftYJ0jyPmJT0Q4iXvrNN7Bt5yTHzyzx2OlT6M2M5nSTakVycHqet9xxw9NruJSCW3YucMvOhS9b35+6sM7J4RD/yQu85K6rv+rzw2Ja5b6NTxBufpTX7/7PHF1aoeYH7G63v+L3HFq+RGEdt2xb4NTgPGeGF5nwW2MhZcyYMWPGjBnzdc9nPvOZpz8/e/YsP/ETP8F3f/d3c/vttwNw77338t73vpef+7mfe1bbf8ZCynd913c9qx2M+fujri7R9nOu2Dukv1RFb7ufjVFMrV1DTk1gNzepVz1Wd8XojRRrC0IXMvvSJs/ffQ+P749ZWvOxJseZy+pdGIAAUYnxPIOoWqq1iK4DmwFDyYP5Iv0io6mb1GzBVd94ke2v7nP0Vw+CACugvyemlmfsGS7RO2kxO+LL1geJXmiStz2ixRHBRsLwioD0oIfqQLgFRUXgH3PIRwz9PTX0jR7hRo3+LsvKAxK76vA7Gr+vKaaaZcCqCBBegK1HBGqIuc1iT0VkjTKTIFyFYcNn+YVNJu7pMj/Rx14ImJpPOA94XgU3P4Xbv8nZQz6ur5FKkO1WaN+D6YJ0zidry9JqEivkSGMvgRko9EWfxqkCJxQPf+IAW9c2eCjZSXJtRPOiBF/iukOElmTTMcElA1mO7IWIwoEoyiyQcuCBZE8ds7NKuD4ifKmHsxruNijjI61ldY8FEZEtCaqyhQyqhBe3cIFH0E9p3T+AMCDZLglOjCCI0O0KxVREsJkikwLvzBoYh9AWV1E4U9bx4oH1QXUTXC3Cf3mC2m1Z/ew06qSmfngZoS0m9jAzTfzVLv2DDWJpCCdSgrMGESouHpnjxAZEQVZml16uXY1P9aic2ALApubpxhwA8oKinhGfF5hGQNgprTnJCyRRVaOP1/A3B9jRiOzKafxOhkzTclolKGubi4UGrl8QdIqnQ21RCgAXBlhPILMC/WAVdbXBrRawWSA7aVkzXAdhItQwQyZpablJc4giGI4AAYEHwqECH+M7ui+u4/UFNvIpqh6BBOnAzDZxlQAXyjL742CLyjGD7BpsPYSJGkXq8IcOWThEniKyorQGBT5OCRxw7cvPcuaJSbYeqCPWuwjfp8gVo/UYqURZZS0lRAFaagb7Y+JVTTiSuHqOq8WIjQzlCdAOVXU07xjR/a3LeSujEVQiRF6w9oYryNqKiS/6VI+s4kQERzNM7JXV0dKR5YagM8DbyjAVhd8vMBMenWaAaeWYIkCMPMIHNAv+Js//P85zTb7ME+d24VYVtluAADcjqc8NGEYCKpLiwRH6ziZyR0Ix8AhyTe2HYOWBEFeJ8LXAyyyDQYUTh7ehn+cIjkc8le2kcc4xPBsxuEbRPt5HVxVRT+NvQOWcJZ/O6e70OXp8G//58B0cv2aaWiXF3xrBFVXSzoh6J+faV+yneZXksYtn0b4huNhB39jGQ2FSTRgFJNZw27bdnAvPAVDrRyz2h4z6Ka/5xuu4RM7SAydwzmGN5fZLO/hXv/U6lCfx/4eMkg8eOsq7PnY333TjlfzYN7746cdvuXYnW70R1+yb59kQSAvkeNIhCPln33AnZzub3LZ7x9/69Re2unzn7/0Z4PjT73kLd0w9n5pX4UB9z7Pa/5gxY8aMGTNmzN8nd91119Of/8zP/Azvete7ePOb3/z0Y6997Wu59tpr+Y3f+I1npXWMU+b+gTDMj7GZfZ4n+3VOuSa3NqGofIZ/87MdHpwNqOysERQZrckmt14Fh96/jlrukqseu9Qmp++e4silOeLlc8hKUdoXhIDQLwMyKzXsFdsJGiG5P2TnXR4geeNtF/nBhyJQMFEXzM0tsnvXIjtqDnudZmubImlbjKvzvTc8yh67zJ/uOcARLyJan0AJyertVcJln5HnEy4PwQZEFyRyS+Kllrwi0b4DCcmOKqYiKWYFMrOkuaXihtQvDvFGGk9K5EyEQmEm6kQX+wyf72EmFGboIAcnQCaG1ue2GB2oMVqI2KxlVFdSsiHEaU71YooIQvJjTZKihbkxJTyzyaVeC+dBKyqorySY2yVRz1KximKuSm01wX+fQ3V8Wl5BMuXYalW47/RBwOFjKWRBtnNE5YkU0Rfkz2tjzvugIZ2fIp13eD2D6ltM4MiuadLbFyFkQTeuMrWWUT+cYFdH6PkAE0riJXC+oLIqUGlpoyh2TSKHBdbGbN6ksLHAKoHXSVGFQC12mbgmp9jyMVsSVIAVGjczgfEEaliU23Igu0XZYoRB/27BaNJDTmp01SPZXiM8t4WLA4SvcIGPUlUyT9LfGtIuNiHLwTqG18/hrXXx+3n5/9XQFCNKYcNTCOew1QiXZ5jtbfSEZOsVHsGqoHlfgTTgrKW9v48KHXZXgE23kYohw2un8FeGtD57AfygtPtMtgnXUhSleMHl8T5hTZnHUo3Av2xfOx8wfJdHsD5CJBolSruJywqkUjAqw1hdmuKGCWpuBtmo4dIMckPgfJySZDtaFCpEOkPRgPzKJkbMIaSHp6qIJEe3Q5Jpn2BTMzrQpnrEQRASn+6Sbm+QtD2CgaGQjqDIkSoCBCLTYAzHD00T77Zkq5PE1iKFI7hNU9+2yZObLSaPSIKVdWjVUNKjaMZUL/ZwuUHOTCAcGGfAhTjPYZuC3iWB7CVlXbgxoDUCx+CApTo3pJtExJeqpfDkGyQCCoOcCqj0NUUCLhBkUx7RWgFewMK+Rc6fW2CUB5AJovsT4hcPWAwqnDqzwNJkwGzex7Wq2ECxO1znFc9/iMWVCT517BaSbT7hNQnhZIGdgF4Qc+rCNupZF6IAb22AmgwYbatTLDcJq0PiBxV2q2ArUqT7AsJzKTZQpLMerakKg2KAXkuJXYb36ADRs6zvipmoDXDr5Zo3WY/xuwW337mfnS/Yi6gVmHyDh54aIPARxuF1M5LQsrY7Ydtkjf3XX8HxrUMIBJsnBgy3Ug4/cJqXfMNV/OrvfxZ5YYsg1zgVsG/nJ1juPcHnj72a2/bv4+C26afX8kfOXmIwynj8/NKXrPG1Ssidz9vN1Ez9WZ0jNgbvRrsR2kVoLAutBgutr+wH3hyO0EODDSBNM2IVcdvkDc9q318LLixt8tSpFe68+QriKPhff8OYMWPGjBkz5h8s9957L+9+97u/7PGbb76Z7/u+73tW23zGQsonPvEJXvrSl+J55bf84R/+IT//8z/PiRMnmJ+f5+1vfztvf/vbn9VBjPn/Tm46pDohUIY15/PXPXjF+jfyUH/A1jaHrk4xvZKyuZXQ/kCf2t0dXFGAltz/boG9cQfhhQ4qjLCDIW62gojjMpjSgW4p1GSEVw0pvnCS53/PSS6tV3n4KZDFHqySuPtzOvcpngymKA60mJjbZPTgNPXDCiccF0bTrK7D2oMx3jUaUYlBW+rHDWIzwU8svRumEVpQO1neKfcHhmpuGG1TCGpIAzrQyFRB6DBTljz3sEd02fblCYSxOA+skygc/oOGyv0G0Uvp3BqDJ4ku9PCXtoiWcorpmP7rQs53KgRDQTatEI8XyFyDFXhDS/aKCtEQ4rtzbEviLWXQ9mg+PEK2HNVbRqz7M9x53TEmoyGf/f3rWT01gUXjgggvg7AP5A7kFvVHh9Bsks20ifoOmRscYCoeUoOJfWwscaGicjYlPNTBs4J0Z8zOt/VQey1LvzWJtWXGxeTjBUWs0C0fHZZNKViH9SVCW/IFifWh8cWU6GQPVThERbHtxk2y/TUWPzQJgMoMOknRuybJJi3VtQycQ+YFzvcQqUPagOhsjlM5WhgkZbuM6A9YuzMkWvSYPDqktTCkcluP5Seb4CxOSpQXku1r4x1dg1GBzSwu9hD68pSIsaBTyDNEbgnPjdjzFynXv2mRxVvnyLsRy8enGB6r4E0UhMcLTBSgvAbB4pDwfBfrCYQwZb5JT5AJhd5dpdKTxKFfWnmcwzWqsNlDaoOeaiGdpj3ZJVlS2Mv1x9SqiP4AkWSlgBKF0C8Ahx0OoVGD0EckGdZZiqka6XyV/AYDRqI7FlF4ZK1p6mcLqucMVjuGVUMy4zPxxXU8fEQQ4G+mOE9RbGXQqoKnwAiEdaWFyBhkmoHWDE+E9Bd9wvUtnDa4hkAfi0gfVsS7hijj46oRoigQYUztXEJ0aQhKoudLUcZmBr/ik+aWyE9RjxVo4xBaQ2FwxiCiiB9/4cd5xfxpPjR5kA/efyfpaJNgX4R7UuJvZZjzOQCuEaAn6tiGwvUFWkNwqEI1gywtQ2fza2J6MuS9D9/G6FCD2rItm5pwoASesKAdsqsIEkG0XmXpqRp2pMjnM5bXdjBNRq8R4HdGyEpAfS4kuWYLf8oy6AXQhurRDg6HEj5CStIpH6EdemlE/nxHvNWFWwO6okExkETbc3a1NglC6PsTgGT7K/dz54tu4P99/+cZ7D9Heye4l7fRxz2EhtRakimPPBVsXsx43/Aw7QN1rpme56FjF1BO8tk/vJcffsdr2R510FMduus1RGFYWu4xGj3IPU9V2ByaLxFS/vkr7mCiEvO6G7/UPnPfZ4/x57/3RW7/hit503e98Ks/R9iA7UGHQAZI+uRmhn6aMVmt/K1ff+LIEjOPaXQbwqrEOcdGvko7mEIJ9VXv/++bP/7wQxw9sYRSgpfc/uxyZcaMGTNmzJgx/zDYsWMH7373u/nFX/zFL3n8v/yX/8KOHX/7dO7/imcspLz61a9maWmJmZkZ/uIv/oK3ve1t/MAP/AA/9mM/xiOPPMKP/diPMT09/SXjMmP+/mhFt1OPb6eSfpbXiCf50KjNUx+o8oYXXMVfPnmGVz5/NzKcR1nL44fO49VrFIWjtbNNtybZmqzTTtdASmwzhmKEhDKfIQoYXa34hlft57G/OkOehTz5hXkWDyvynuL6b95kuBVRfGyLVFY4trTAp/76IJl2xNrDc46iKvjEhStZWPIxZzXV1XX6B7fh5wKdZ1SPXsK0Qow3RaULVlqMtEgLXgbprGV0lY9INEFHciBb4kwxx3Q6oHYkQSSKbLaOCySdq3ycL2k83qHy1IDklhpyxSJrpb/CKYdnA8z8FOHWiGi1z/rVLczQsX/XeTqdGs5qnDGQZhjZoHaiCy6g/nCOKxx4HjpVyBMhSLDbB8QfWiXZ7mGnHPpkBzGMyPc30NKhlCvzXQYpTCmyoAJ+Hd0ICHSG8hRFFOA8R/1MARZ617Qomh6tz10gXBwgGjV8HTE83ub5L36SC7sWCJKIIiuQGoK+Jp2+fJFXE0Q9EJ7CKEHjMDQjj83qf7NUuQLO/tU8nnWIQQK6zCYxzYhkISA6v4XQBhDYYdlKI6VE4LC1CHmxC3Nlpo2NA0Ax9VdLmFiCqaNGBXpDgidwMiSPyoBcoRz1PQP6D4NxEn8KiqWszOJx4JpV0AXCgeoWFH3BE/9lllyE2Pk2YTHA/XmMMA5lfdTlml+vN0SlMLxhAbU6oLI0KqdSCks2oZjMJSDKCRLnwBiENjhtUNqgehmj0xXcixTmQYvKqpAXZXaHp3BBmZFCpVIKh40arllFGlsKQHmBnaoRJo4rNlI6gyq9VOJtZtQPrxHYCOl8RKppH7fUFjWeVUgkmAJRaNjqESUDiqsjBIJ0R43aYQ8nBVKIMoxWCtJdbZyv8I3DT3JIPIxTOAXVZY2XaFyjAc7htQqqfYuQqsweMwZ8n9A36LURMjW4CxInZhELDndxpQzVdQ605qBdJ5SGq4INPpxq8gNTJELhzTiaq0X5vpECLSTSU0hXho9bX3Dk0nZs6tAtiSgcg2ya5HpL4z/5BEmKt9fwz//1PTz04G4+feRaFk+1+Pxv72NTLbDzYM6lVYHf9coK7UshxaOWrg2Y+78u0XlnTnLlBOEoph2ssH9hlTOnZ1jNQ6hVEErieaV1y4X8/9l7z3Dbrru89zfKbKvvvk9v0tE50lGXLMk2cgUbB9sJxRBqwr20AOEmIeEGJwQTTJwbAgRIYiCYFgIG2wFj3LAlS7JlWb2eotPbPruutVedbZT7YQqDr51c2zHYD9m/5zkf9pzrjDX2XGuNucd//d/3RRpB6SBo58iBxKwADUHUNOxq9PDS42dCgkDxXW+4g5e/6ginL6wRZPCi1mFMMuQcOeUChBOB6YREKdzd2cfpE6ssXRrh7st58tFnKbo5Koqx3mGNxb1snY1fmEYpj3Rw5MgF6iG87sZprtn1mZv8R09d5okTl1is1dk7N/3p40pLrHXo4P+/iOF9Slk8hQ6OIGUDgH2RxxYOJRVaJPw/f3Y/z6+u88+/5mUcXpz/rDHKfknkJTNEXNNZ5InNT3L/2oe5pXMXd8+/5gu/Sf0185LbDhBoxaEDi1/uqWyxxRZbbLHFXx+erwyz2a+EOfwlfv7nf55v+IZv4EMf+hB33nknAA899BCnT5/m3e9+9xc15heU2vOXJ/LmN7+Zt7zlLQB867d+K4uLi/z8z//8ViHly4QQgploJ35Zc+p39zD31AbnvvooX/eKp/mam7+Hf/rNf4Bzljd9yx286g23cqE94RMPPc9gEjDcF2M3cox3aO/Q9RCXS6RUkBYU26B3KOK3lp+m2QpYvGUH8c7dJJuXMVdyVjfquLEhtrZKJVkSvPzqZ3j45NWs7gsJe+Aihcwt5qoW7tjwBRmLJG7XCZ42yDjGxRHBxCMcRJe66PGEzSOzmOmYZE1RbjckJw2eFnKv5MYDJ+GqkvFiRPc/dKoLYT0u0Tjlkd0U4Ryu5lh9QwO5otBjiZMeM5WQzWuCzYTgUg85DhltVxxd3s32PzuHr01DXKW8BBsp4fkrECQgmzgtEVKS7xCE42qTl/6aRuQlD/zOVdhbHGrQQ44HNI4ZsJ7eixeQ3RGNxy7jlSA92EavjUkuj3EH2shIEKSW9tMjlFN4XaWW+FDh4xgRFggHCgOhYigCyCQd57gSS2TqMLECAUEG1grU5ghlPMVcneayYKoVsLxTsfJ3DhD2S8JeQX3J4oYFwlduvmUkyXbVkZlDD2zlNSKouiPCED9KobRgYHRjB+ViGmcvgwc5LpFrY3wcIaMRvU3wq1NIXyXKsFPjjhT4hwvyjQA3E+MbCeUog3iI90AjRgqB0BGucBBH2CInI8ES4q2mtAFOCIJQIpyk6ERgDNFyiUBgG5LNI/PUfu8CiMqXpHHBIB34qTZcWa0Wd1FFXIu84M+XN6MU47JDqNaQl1Yqj5haBFNNGE4QucGNhkipKKZrREublXfJOK3e+xe7zF8foi/BUNSoeYO+lBKt5Pi6RHUHVVR8PcHUNc4bZGER3ledGaYEEWKVQBlPOR0hQg1FWU2wWUXfmkSB1vi5hGBSonXJttd3ufxuRV6fg9RWxQcE/opDNKh8UwCdeQJGvPFvn+Q9v3sQgcFLic0C0A6zaw5wBFf6+KLg7d99LS//ext89I+uQY4ymk/0yQ7NEK/liP64MsCVEp1BsS3G1iV+vUtej9G5wc05fPrntxpB2q8z1e3hByPkpqLxD8YcONTlvicd+bQk7dZ4xY+v8pIDd/N/P36CcFCtCeH8pOomQsJzJU5JwqDOeGx5XfsMf2v7SZ64dZG3P3AbanOMr0U45yCoJG0mFuQJ1OoF5uo6+akmc+dWue3NF4inLI9f2cXm6YBrr93G/n0L/Mbb7+UDz56il2bMJPtZnN9G9Myj0B1S+5pruenaHVz2Ezbiys8oizM692+QpaK69sZAGBBGASce3MWMyyGqDH+vnNvOAw/s4Hv+j92f0Y3y53yuELxTJ1a4eH6Dpcu9zzpnrGN1c8S26SZCCNLJu5mM/xtx8noazR8AIAivIhISJWdwfvLp5/kfBe593dfeyLNXVth9zRxSSIZlxvnxkJ1Jn2c2z6KF5HB7z+f+z18BvPiW/dx1877P261/iy222GKLLbb4m8vrXvc6nn/+ef7zf/7PHD9+HO89b3zjG/n+7//+v/qOlL/MyZMn+cVf/MXPOPaGN7yBn/7pn/6iJrHFl4ZLoyYPP36QtRMJPi05/3Cbfn6JX/rjj7N2S4OpJ4a89+zj3PSaO3n4wfPwocvoJGLq2sNcauds3rHAzJMbiESCDgCFFwJ9KqX5rIbQEFxRvPK1d3Bw4eO857Jl91WOMmsyyCakh2eor2dsu3mVu45cpHemzUZQwzmB7qbUzwzIS0l5aAetG/pssxOyfoiyGjk7Q4gnzaq4WU+JCC1R32DbAllI5t/v8EqTbvecOz7Hi29cJneCSanJm3WSCyOEMUxhCMYlwUhh9s9S7wvMROHzCGWqSODRdoltCdL5BD3nkJmj2GdYnw4Rp+eoL1soLCKKkIXFTGqAR4YlxUITMU7JFwL0kgYL2gt8rYafDcivUygXoh6URGeW0XfH+OYQ56pUksn127HtCBcOSZZL1KZg6o6M5aPT6FKiCosNLNGGgkRidkwjVg3Ce6Z0nyN/Z5XHv38PYbdPd8c02nrKUOCFR+XVrigaZOjlTXwtwlPDJrD7FdM8f3wdWUJ6QJOmAcKmNAcZwlpclrP+bYuYNiT9EnFJ4WIBHvw2RfKSlOJehzuZ4mdbhKVGCoXtNCoJWBwhjK9kPtZRxhKnFZGq9GHhrTlzezMmH0kYiSn87qoooDeHyKyAUCOTGn51HQqDmAQgFDRqFLN10vmAolGlFb1o31E6e8cc/4+z5P0Zih0tdKgReGwroHmxxM61EXlBMeuZzDqiyxCbsJICCYX3DuE8MgwhL/BxSLpLIp++QHRmXHVuAD5u4LVAhRpfmCo9Jw5RvQkijqoY6UAhrCfsjsiyJgfzIVwwbCYw2oiY7J8itBrGDoIQb0tckVeJS5MJ3jm8gGLHFLJeIxqWgKC+LiEMEULgohBpPd5brPLYlkBENaQXHP7a4yxc02f45DxdHaMtyMwgB2OctfiRRTWagMc7S5lKzjzXYLauWL/KMvOGdUa/0iE3CUoHiO4QGYZ4rdnYrPPOdx7EpWNc5IkGoNY8ECKcw59fIrtuO6KZgBeIiSfd38I2BHKk8X2BEA5hBWiBer6Bn1yGJEKGEW/7/rtJrk0Yb1OM5wT9x2uEq7cyDg7g9EmCrOqU6jRH5GUDrGP14Rl6r5+j8XwPVW9SG0q89sxvG1Hu1oSPC3woKaYkph4SdkuSK0P0xJK/W2KaUBxQeN2g+76Q/KuanF6Zpz4jOLPa570ffJJH7zvJKM+goTj97GUeX/kkwk2QWc5Marjz9n082n0XlwxcyrfRyUs46PGPW4gVPokJm3G1OMcx6YyjXgsQm46HHtuPpcZ7P5XzjbM99s38RXLOdXsWOLh/hovhk3x8bcBL5yrD2ZXlTcq85Mqlzy6k/OqffpKPPHGS//Nr7+B1LzqMUntRoolWBz79mFbjW9mvr8K4TZLgav7Zqw+wmWbMNeqf837yyWfPce+Jc+iVS3zVrVcBNaDF5UnGr5/6bQTwjjv/IYvJ/zj158vJ+/7saT768eN877d/FYev/uIMerfYYosttthii7857Nq1i5/5mZ/5ko33BRVSjh49yvLyMkmSVN/0/SWcc19U/vIWXzr+y7GnOd1doLOtQDu46evHZPkMzxcFxY4QNSzoXRMx+KNPIh7rE/bGlL0RC7WI/mBSeUkgkWOHC0W1YVQCYQzT798EPHJhhsceOMnd/2gf+198PweuEcTP7OH+x08QnSxg4rh8cp5MrbJ73zKPPLyPxiOXCNczpFAwXYeyZPqGEb0/nUOu5YjU4KnMZPEWnaWIJCadS4i7UAwNWUeQLFeJJXrk8LHimfdfQ72R0rvYIAqreBvvIe7niBKEF0Rak8xaNgcBVorqDS9ADcfEFwuQiuxAGzkyzB+bUGyXFJ2YuF8iRYkwppJzxDGMxvh8hNIOkRU0PiFhVwOkRNRjROEIT0q8khjd5u6//wTW5Ryd2kugRlwZzeJ7TVykcfWQsjlNkI8QSnPxwjzjA4pYFMTnqqjmcJQRHu9SzIeImsYZj77LMjklKS4JcGllHgrozRwfanQJNlTgBOVUDWlBIGm8vMeVA0tM96bpDxRiFtRaRNZwNMYjfGEJZgNe9dXHMVrzyAcP4AONr+w0EEcmqJtLxKcyGE9w7ZhopcTNxtidM1hX+dIUBxvU7ztNkAtMoivZja3kQea8JLyYop/rgVDY1j68BDZHVNWaKsLZmapo5NMc8gKxOItMDWVDorxCZZpo1iKkQ7cEIqsRXUmh18cLQeP5OsMjHUZXC4Ily+hIiI9gdCgmeSivijUbvU+bKbvCglWw1iV5Jkb0i+pcoMFY3PYZrPJI6/DWIIzFlSVCq8pPqBnj5lro1QFSem799lNElyXPnd6J3SypLY3Qpo5aHUCkK0kRAh9qbCBQpsSXJV4JwnGAGBiKdo5baFFEEtuK0RONGI1BZjDbJhpZJlMeicC0Qo7/6SzP/sIUsjeitrAOOoR6HdtMMI0AdaWL727CVJOiXUdvjHn86QXiRp+DO5bZ1+nyialrydcayOEECgNKIbSmxHHpb9WxYZ2Fj+XolQv0r92J8NA+VyLygvjUGumLOwjjkYWhfn6DbN80Lg4QtQBvLKah8TsL2PzzD6Fm/VsbqG2O/L0SlQmEl5gbYTzzTh7bnMHXDyBzj9eeroppFqbyjBlnSCQ+HUOtyXgmZ8VLqE+onwvJd0+j5tsgKlmUCyW1JYNXgmhdYpMSFxtcaVl+9zRTi3Vuv3GJ7vpeOtvm+ZrXXA/O84FPHEeXHjdMCTKDGSt84Vi/9zgfed2TvGjXBfbFmjP3v4bURsSrBZSOIEqQgeb131LF601flhRO0hhW69JU4wgrVza499FziO3T/Oirv+rT6/gHnjvBu544SmsmI+isf7qQMt8I8BubMBp/1tr/ruefZSke8olT53jdiw4T+i6BlAgx+ozHrZa7mQ4PI4RkeTTgJz96D9cvLPCPXvqSzxozqGmQYDTMJzV2htdjvaOl27z30gmkEMQy+F+4Y/3VcunKJnluWNsYbhVStthiiy22+N+HLWnP/5AHHniAX/mVX+HMmTP84R/+ITt27OB3fud32LdvHy996RfuP/cFFVJe9apXfVri84lPfILbbrvt0+eeeOIJdu/e/QVPYIsvHbfPSp7brSiMRNVi7vxqz9LaV+Mnl9GTktqpIXJsic44zAvvbu8sGz/3CQ58neP00hRuto3PCvQgRwQSRhMEApcbrLRk2zXFrpJscZbGyydkwSzF8w7jSuqjEsY5u1+R8cDqLkaLEdGRIe5BjY0Vw8MtYhEQrhlW76shZjz+bNUNYhsh6YxGZg4fePKdiqIWEWSOqw+tcfFUG6NLeje30WOLKsFtRvRcROvSOrpv8FJRzDeJDxmyUxFWWPRGCk8nFK+pInVdAcJYir0R8SdTfKtGsuYYz0j239LFL3iW/CJuWSJNlSSDB9+oQ5oirMNFAW66hqmHyMChJlVR0TdfSIDZ0Mzs7nPL3afwHo7+2QGGIqYIUpobKaznDL52H3qli77QQ6gQEzuaZz3F99QwMkFdMISXs8pPpFNQv6Vg456c9Xc4Os0IMR1Coch1RjySiM0BvlVHhgqExNY0ZdxAeIjWhpQbguFA4y+WdO5fp9WQlLtmMEZQtkOCzCGUIekV+KZldmrMiGlsDF6CfjggPyHxoxCaAlp1XCPElzlOCHQJQdfgkhpeg+jnREMJpsA1Y5wUpGc96vQYXxfYKY0cpLgapLctEj+yBEWOdROCKMIbg1uYRq1tEro+vqgz+1SJaUDWEZz41UUSv85oksAOCVrh6zV0aaA3IVwOMFMxKksRlRcqeugwdY+QTYS3L3R3eLAZvqjid8X6C34nWiOExOPQuYfhCJ+WCClwzlReJ5Mca0vszAxEIXac0twzRkaOZNYRJoZxXyEE6AywBgoBocAqz/iGJsHQozb6EGiKxZhAJPg0o39DE18LCNYzxMomDsHg5mlaFy1ohWlBOuepnxhR+/gSTgWoQOM9qLUBSInfm+AjjZQKX4tgtQ845FwL7z3iZSXFtZZTH5xm6acS+nlCOaPxKiY0FsYZ3jvy3Q2KOQ3ak8oRsXdV0RJASUQYYTsNZOnIE5BW0Tg/JD6xjo8UZm+H7Ko50pbG1xzMOIRwjBYVmZvCX4Hady0R/2ad3rWS5WSO42sDXrZjFpU5hBBYaZmsBbQ3e5CX5NOaIvTUA42JHdN6iBCOlWNNxllGdngGlUI4cchC4ip7HPBQBp505xRiGBA8L9h3V489N61yb38PppWTncsYWcOP/ejXEbRinnvmErVEcXxmCY5ZRC3BS8GJn5/lzv90nidOHGR9R4jKoLEnIjgzweQF9SRkPMwAEKspQWqozdXZc3COWlBy9OEB4XrAq/7pgb+8jLM2HOMLT2BafP3Or/308fMXutgoZnntM4sjAEXbVWtbvZKxeO/AF3hv+XNhy/ODJX7l+d+ioSVvuenH6KYpmTFc2Ox/7vvJNbv5qjuuYnGuxXSthhCCVy68mJW0x4FGh0AGxPorNw3nO77xDi5cvpqD+xe+3FPZYosttthiiy2+zLz73e/mO77jO/i2b/s2Hn/8cfK8sjUYDof8zM/8DO9///u/4DE/70LK2bNnP+PnRqPxGT+XZcmP/diPfcET2OJLR8PH2HWFfdIxyQL+/b9YZM9rJMllQ3xuSLQsaMo6ppkzfIkmOaZQjw0oBxmTyzXMVEBwKUdMTJURjAchEFLCbI3+9U3qk4i584IPvu0Uzdc22XbjYd707a/l9R98ED0y+Djgwi1NiolAaI9ulaTXLVIMDZ2TA8pailsfYD4k0C+p4bRBCEdRC7DtiMaJPtpZyqslRijKTsCd15wje/pq1uoJADYShD1L2YR0xjK1OsKj8bHCxJLNrE1QM8SnejCYQBIjyw5hX4AApxXkAcNb5hFOEo5ztBYMSYhUjhkosilJYzOrDDqdQ+QFTlTXROYGFzhk4JEmwTQFyeYaRd7B16eQqSF/KuD049sghu4nZhgcVEjhMbs8ovBEPifsK4gkwkr0eoqoRYgPpWQ3SmrHI8Y7A1CwuDggbEwQTsDYkx0LcAa8LYmXSohjXBjgN/q4ZgxZge4XqMEYrzXKCS5ns6jnNNHGmFbeJxw7oizHJBpf70DsKMZjHvuNXQQ3J5QbIfgSnMKbAkqBW5F4E6Bmp6h1Cvzekv49YyQCXWsgPehxBFEAgUVojW1GmJk6IivRWcHw0AwyStFZRHBqxMbt0xTzEVNpTuOpDbyTsGfbCwUsC5cLzAimtmX0LnpUIQiVoKi3IdUI6bEaGKa4nU2Ci0PMlGVyl6P+zBCpGjRPALsLwqkx+nFD0dGIWCI38xdSqTyEldeNN6ZK6/Ee5z1oheyOCLQCL/BawfQU3hjE5pDi8DZEOoE8J8ws6ck6D/+bOnlfgBlCvUY5VScYpCAVqKoLhkii1ywOA41qYz65bprO4z2kAekqiUzQz14w/IWwb/FRWMmRSvBKIEcFojBICe3rBL2Vnfh0gtwc4YTHRRpZWsS4QMQxXiic8IRhCMMCYTy2axmstzF1sKHEhzEMClwc4Z3DzNahYdj/b5dQl/uYqQS5MiQ+v4nsjxG1GBFHhOsZkfWgq4QXFwlUbgg3LNQLJvMRjCRhT/HSHz3HR37jEM0LFicF2SFJhEd6jxCeSBTUw1twtedBCOSwJJ4voKw6xDwlteUhphMx2m+4kk9xyK9y3+/vZ3j9NLYRUD+dEWwW2OkmooBsKiTqF5S7IsZXQ+2ExUzV+PrvP8OR/SvUT2X85vtfhH70DL271+GG/Rw+vI33PvAs7uoR6YGY5CWO+N4hwnrEhSHz8yOurEYUfYHMBf3pDjNmjHeGsXE88dg58qysrnW3TxZCcse9rD4zhWztRouAet/BX5Lm3rVnN588ep4jYZuk364UNcDNL7mGc+c2eM3fvvWz1v47F3fz8Uvnee2hF4xrRYvKIGjj049Zyy7h5JMMveJK+jw3Ll7NT7zyFczXq3u5955J8TSBnCMMtnPi5DL9Y33qGx7xmqocU7iSwk2Yig2B9MivYP+Ry5e7PPTgKWY6debn/scxz1tsscUWW2yxxd98fvqnf5q3v/3tfOd3fie///u//+njL37xi/mpn/qpL2rMz7uQsmfP/9xU7ju/8zu/qAls8aXj3DCljKuIYARMBhHuxAYvm1/g6fsuQi3BKI2crlFMHI1XzLK/nHDZGi7Pd0hlTnBhhVo3w2sJJoAgwPkCMTR0ThS0dncQz63yzNIa8Yct/IszvPZbJbdfvZ3nPr4J0mMvlYhtba4ME8zzNep9QdAzqGGJHniGuyNcOI28oCpZjB8TX+kTjqpOALMXurMtbFgVMf7oY7diCol0js6xHKSEoqCYrlM7O0AXHq8swnri9YzCg40lrhFjawobBzSfWkcWkmIhoVioQQnFnCXsQ25LwtWCnd0+h/ef5f4iZJDN4OoRcpDhFIjuECYp6f4m2eFZ4oHGa4WJNe3GhLt+cI0HP9RkuRcwfSLFO8GH/uOLmOxKcFnO3Hs2MO0YLTQ2FqhVjau1CBYDGBYQ6aobqLTsaPRYX2iwemcTYS2Nh3L8BYmcU5RBg+6TE5IpGBXVZskrgV1sY4PKzyToZai0RJbgtMQHmvbpkqQdkKUSs9DBOIerhwwOauYeyZAWvJPY85qcFmYHlM2S5PgqQmnyxRm0AXV5A+8hCCR7di7zwK3XEvUtcm2M1RJbU5Tba8QTCUrjC4ealMhRxviaJqYT0TgfEV8aIXRA41zBWEiSkUTWEoTQeCFwAryUiOkpnIT1wiLSDK81wgvufv0T7Lxmg3vefj3LowZyWLB5pIlKS4q9AbYlyXZ7mmstgqBgvN+RbgtInrPorsAKAUWBUxKhNeQ5Po7Bu6p+KAVCKpwEb01VQDEWURpEnuPzAmcteqWPGGVVClK9hpeS/HJRvS71CNuMkJOi8jjRCqEU3jrU2NI8PcSWBR7PaF9C46JF9lMoSlqfdLhOgt4YgXOIIKiKMLnBOyBztCeGZlzglSBYMNzxvWf5wL9/MRAhc4svLdlsSPOhc3ghIYlw0w1U6ZG6RDybUr53wszsmK/7mePce98Rzj8RYxoBXktk6fFJiKs7mg97gr7BWksxF2EWaphxgbgwwM9PIaXGO18Z6SIorp5jdO004YVNmucm6NQjRM5d7bM8s7YTfWOEShpEK2klSfM55JbpzEAM17TW+NN3fwp5wWL2zCDmHKpp8e0aLrcMblS0nxxSbusQxYqPfeAwlx9tcklNEy+l4DJaT3Zhbga9kWIDyWhfHbeiqF0u2fYnOdnNlanvpbLNDXKZg3PL1C9k+KzguZOX4ZPP8Rvv/ATlxOCEJXIB/kATPtJDOGjepKglI/6P6+/lX12ZIe22KOZrlSxMVB1w8zumUFogN8eIzRGlGtHeuUH3RBODZhPLu37nQX74J99IHGgCrTi4c47dScKJP32OP7xi+YG3fAMAX/PGm7nu5t3s2D3zWWv/w1cu0y9yHjx3gZdes4/N8ihXstNsU4f589yfQMU4ErSQaNHkudVVzvR67JuqPE7WJ/fzwPJP0VI1XrXnvSzOtZjp1D7d0XFscIb/ev5PuKVziOva+2mqFlhRSTK/Avl/fvb9nL6wzslTy/zMv/6mL/d0tthiiy222OKvCfHCvy83Xwlz+AtOnDjB3Xff/VnHW60Wm5ubX9SYX5TZ7OfCGMPS0tKWvOfLyKPLFhXCzDetsfauRXTpCEvJcxcuU7QCwm7OgavnObnao3nKUMs9w3GGEeBWJzAnKWdjODdAGAGlwbebVfTrJMV7x2g04eBcndGlNcarjvvXp7hz6QJf+/IX8dzvHIPMEPyZ4cSrpsmNJrJgE8hbEbonEbYkvbaN3mzil3NwgPYIlyBNJfExLqY+sQyVJN4QZGUE2pKsbiJqMS4U9K+qsWvfGsnmkI3HAryQTPa2CMYlYTcna0mEdfRunSbcLGg8uYLKHOZSRO+l28m3CaQT6BG071kD79jxslXm9YCr47M8OYig8LjhCCkFRBE+y+ndvYCZSbDLEp1XSSCNaYcPFfZZx9SlJVy7hXQeK4C5nMVwg+yyx+UGnwTIwuOcrzoFREkxHaLXbOVJogI2T03Tmu4j3j2krEua23OKjQBbBERxn+/7ySfQoecXf++VjFZqyFRUyRtSogc5wdoAF0jMjimkEzDIiHuenbMNTkUZ3Re38aXHNDXCFTjlkKMSaS0u1JTKo2KJCGOytEWyWnmC+Mwi+iM8gt5gB70/C2kUI6QK8HGMq0tG2wtiKyt/k3aIsA69MQY89XNjymZOeGmETSKkLWk83aN1KsHHEdQSpAPXHcJ0A6ElPg7xErIkJJ6LIYwQhaPeypDCEcU5eikn3V6jmNaMdke0nxjhlSR5vEcYTWPWNelQoS95RN/jrEWUvuow8oAEEcdgLU4J/DUziKUckRmkqxKH8r1Nos28ksSMJeQ5eIfq53jrEN5g8gJ3aDdBd4ys13Fa4GKJGoEXEuHNCzEp4OKAsFvgjCXvJDSXHMJOEHkJ1qIHKdY7hJOAxxtDsJ6jjMR6j6lLykQwqifU4hHiOsf5/g68UIgkwqPwWpFcHkFWIJTETTVx9QixPeOaf3iGk8e3Ub6lpH9BcP79Id/8A4/ztuOvJ+imCMen73+xNdTuH1YdOkIQnRuAVEQjAa1qTDnKKWZihAdpoIwUaInrJJgdMXpc8m++553smeqyManzrz74eqT1xJfGeAV5O0CurZF2txEkjmcf38XqL10gdlBsjChespPg4TEitbgQ8iNNiouVb41aChEPjbnkWxx6U85orUZ0fAWsoFTVPl/YFwpkpcMFCnY44hsGuEzyGxeuI59JOXlxO2JfB50HnCbjkQ89QjdNAc/sqTn6z6wRPSWw1+6htX+O3KzQK+ukIiA/pWnMTQjODyEMQEgOHV7kh3789Qw3J/gkYu5Ow86v6nLezpO+2JCelgRLgu6c5Md+7X0sTjX5l9/+1Xzi/Hkeyzdgm2IjLT+9viut2L3/s2OKAfaMGkzWUtp7Ks+SnAZG7iRXV3/6MbdPX09D/wumwgZz8SL/8sPvZXk0ZLZW48W7d7NSDHl8PM1soHi1EGxf6HDdN85zYbLKoEwZmwnOO8YmY094DW/97fv4z6NL/ME//zY6jdqX/H72v0r+wrUbDtIv80y22GKLLbbYYosvN9u2bePUqVPs3bv3M45//OMfZ//+/V/UmF+yQspzzz3HLbfcsmU4+2Vk+9yEYc1y3dx5Hl25heLYBqfDkit7YphZYPrDZ7l8fgMZCsbbQroLltYwYTppYGVKcjFFBBHjm3eQnO4htK42s40aItG4ZoDeU+M7/t5rOfbxhzjVWOLxqM0vv+MB8uaQ/i2a2qfGlFFIZ8PTH4LZU9JranwsCV2EnhHkbUjOTJBjh/IO+iXSlrhIk80Y6lcp9ty0giwFJ5+4GuEFzjnMXAORl0y2a4pOiN5Wom7wbOhZoqcd6d4aOIgvpejeCLkxYvpxx8adc+gDU6hujqsrbKKpPW9x05U8It+WIEzBY4/vxmWSpaen6LDMcDkE52F+CqRAlAWth1bJdtcZ3jKDRNE445kci7jv7LVwYYQuLEXH4aTA1SJ27lpn+oYeXdtg5dkp8vmE2uk+8dyAuV0DrlrY4CP3X4fqgugNUUGAKBW+FqIHOflswvOdfTR9FyXGRMMN0CUuUgy3l7iuQGWOJMoxUxqzCS4MKKYC2JawkEGvO0QOHDv3TPF4c5PxHkF42TD9zISgkaFKjzCVa47Qimv2LXPtiy8x6Mc88OQBtFOo1RFuqoFPoqqDI81xpUH3J7j5Dq4WE2ykzDwOxpR46bH5EKFDbBBj2yFBN0dtpsg0x1mHjwKIQ2wjRqYFwvnKp2Q8we5oEQyqYkXRqaEdiDjCCbC1gHt/5wbaQY/u5Qa0JGpikbkjWM8YH2qzeXWNLBAsPGqpPSfQyw490FgRoccTXH8CWV4p2KIIEYf4ssDXQ4p6nViWCDxeCYpdtaoIUpZ44xCNetWdUpRQr8FGD6E02ljc06eR87NQ9/hAUXZivHFo6RBGoCYGWwsQzlFog+gN0SMB7VbV1aIkItA4LVBOVjKgAfjQoUoLpUU6h1OOsQ7Id7axX1UnGg/pX+gipEKmJSI3KGMRE/DzU/jlddgcomoJbk2x8UyL7tI0CROc14gI3v/zu9FnVxBhAFrj4gCEIMGycpNk4YEAmg1CnRL0AAQu1AhbFYewHteM8JklujhCpgbpAnwtIZ+VxM0c6wUiNOQf7lA3g0pGJTyDlYiZSYEaeZyHckXht89VkqvRGK084R4LDxaVeawTRMtjfEtgpmKiCGJtufO2Mzzy3NW4WowvCwaHmggEtfMpzRMjFAKcx22CSwV2oNCfqvOfNr6Gwge0XxLy91/6Ii4nJZ3RhM5YcP7yMjuCBnn3CjiBwrFmc+q9Jj/3fW/C1hThYkQ0GZM8VEKzAYHm7DOXeMt3vZ1v+cdfiwXGcpp8PuX4qUWOpYt0ri+5tijYe+0iH3ngeYrMAHD1zAyH5+eJ+i3uftmNn9favzdscanbhaLya5LyAD0bMc1fFDiEEFzbvurTP7/h0CGeXV3h8FwVv+zEDkq/gJVVB4rzjo9eOYbBcmnSZVvc5FCzyd76LO888yH8VRt0H9jG2ub4K7KQ8vKvuob3/cmTvOJlh7/cU9liiy222GKLvz62zGY/J9/3fd/Hj/zIj/COd7wDIQRLS0t88pOf5Ed/9Ef5iZ/4iS9qzC9ZIWWLLz/1+iLl44ZPfvIWFCvcvvsoj/YP4VQNbRxkORuJZdQOcShqT40IT6wzbkZw6yJqkFNcNYv1gmQs8YWBrMAFGumqb9/Vgxd4x7l389XfMWD00edpnWxwZdeNuEhgb+6AdoxeEdIejTDDEbVDkpXVKeyViGBviZo3LPzmELmmkY0EXaTYSYlXIbacMJ5vI0JHfrrg0C0brOxeYHOpjco8PgnwoaJoV73kS70pCtVGXoS4a3C1HBtrwhHYQIKz2DggzARuusNkv8Q2JDiPMgJXClwCducs0V0j9PY+jz15kCILEeevYFuO8uY60UmD8CHCWOrHxiTnU4qr53ANR75Lo0YQPjUh6TjKuIPeKHFxVXhaPTqHms4ZX4iRpad+doQPQ9TXb3BlpcWzq7s4eGSF1ZMxrpMQpA5vBTISjK+fYbIQIQTkB2aob6bkg5jffOAG7GOGsJti6k2Ec4jMoQrLZL6BSzROK3YemePy/ceJ+wPEpCBY3YSaAOmZPpFR7xp032C9xicRVnu8VqyttehMDRic0bDWwwuFma1TLNZgLiI6tYG6uIJo1PBSYBONEoBxqKGoUnqsQ/QyZJgyurXF8IYOs+85g0otbqqN9BK0xscxQkmQJZWrL+A8wZkVaDerQoeA8faA9qm8KrYAZZmwvlJ1WgCEfcPCfV2KhQZZlCC8IBg4JosRw+taBEFO82mqbhKZIeo1fJ6D85WEyFedJ3JYED96sWrGCAO8EmSLgsb5EpGWiKLAKwmdZtWx4UBsyqqjxRjExOHTAuEHsNBBGEO6MyZ5qofupbgkRuQGoSWDm6YJzkJtxUCziiZmtVcVQ/CIrARkJSuqg69ZfNdXxRY0egKqUKTzkm27r3DbK07zngfm8anEi0ryRSCxUYxbrBGul/jCoJdKhr+XILaNK0kXiuNPzrG2MY00FqsV1EK8kqhxDqcE07cGTG6tET+6jitiECVCSqIFi9vvSI9LTEODNSgt8QcWkFriB0PU+oDO7ZLQw2XT5PzyHKu3xuw9sY4fpLhOjUmzQ+caj39ZRrkWsuYCbCJwzYS8HRHWDGrWUWxrMN6roHTksUTXEtqpRs8E1JKMnoR8m0SkDUwk8IFCGI+vhcjcIyYlToJcl/j7YoKRI1xyFC/x2B0l19htfPz9z3Kp45nfN8N1exbYWB4wE0acvDtG1SbkJzW+n2H7Fl0WeB8gbUi2HFMPS7wBby0i0uDhwY8dhyhgkIY8fW+TyREI8oDwvGF83mCe3aQRBMRC8SePHuPXH3uUlWLC62++iltv2/d5rf3X3b6LJwerxDurgsbp4QdYzlcRww+xq/WN1WfGGR7ZOMW+xjzbkmled/Agrzt48C+NIohVk7rqAHDi4hrrn3Ts2t3m0F3buH/tgyxlJ5iNptgeLHD0SkrkJYvTzS/2lvVXSqtdozYTc7LfqzrJvoL9XLbYYosttthii79a/tk/+2f0+31e8YpXkGUZd999N1EU8aM/+qP80A/90Bc15uddSLnlllv+p+fTdKt99svNtNqPGJ9GGsuVXNFYn2Z6f0b3Skh8boxoNVH9FBUqhBUkqyWaCNZHKKkJjy8jjUDU61jpkdbgJVXqSifEFhP0kmGl3+VDv5axeiki6mhmSsWQEvkJC65OUSr4L6s0BzluukH9xjFqdozJwYwVcV3hxgEyzzGFx2cZNrCU1+8kyhz19wzZXAt55kiL0bUxLgA7pQmHlmCQkyxLhldJstM10r6g4VO8FJi6pH1ihLRQiAkyTqmJHvJczHBPAFLQXMqQ6yVKBSQDT970hKlEnBMUnZDJKMY2I4IkpDjSwXYa+CMD4meHoDzEIb1X7MJMgWsJdOYxUwq8JyknlBsSP93Gh4L6izYJ92ScfWIbthFQG1jyaU//cA39RA11xtI6N+HsoRnabogbG4qmgNjTrOfo/YLmmU3sJYGp1xFRgO+0Gdxbx62OIVHIhGpzWHgmtTpoMHWNyiyXj62STodEQRVj/dA9x2l9zU6yvMQFijwGe4vDn9QUpkZ8cYQepKTHQn71t78GPSiI7BK2XWOyu4lKc8JCoGRQFRmSCDfXrAxuhxmqNAivUWGC2RVSTBUsZhlxmWPPjPGxhhJkECIcmEihrK9ScrICXxQQBOAdMnewOaqMYPdLim0ac96gM4EeppUJshdQlIj1sjKHNRZ5aonaUkSyNI/sGSYHajgNRkvA4eshZlsHdX4VlCQ/0GT9jdvZ8R9PVQUpFEDVFVIaXBISLyt0+kJSjaq8d7wQyI0hDEYIIRFK4axDaI23BU4FmOkaygjkYIJc30T2coQcYa7dA3GAqwfYHdOkjYK4m6N8iPQOl+e4JEJZA5MUjENsCpwOgBLKEjV2BCPAQdQrWd7QnLx+jkKHRCZFaFl1wyiJzCwOV3VyKIHGsu+6FTafaleR5FJycbWDmumg+mNcElSmr9ZVUqRCwqkaWgpEqwGTHCYpPsuRMkEXE4q1DNlUlNMxauIRrpLTTBYbBP0+7rJncQILieOff+pVCCmQo7SKgh6mlPk05dUaOWNRwtBdimhGouowQ2POQ/l4iLaC2rJkvC2if+00DVdnZgRDN4vM4JN/dh3J8wX5YoStC+KNAu8seuQRwwmiP4F2DbsnIn1FicgF6vcDtr274Jvf/Grqy473jR8jiyxy1OCeM6dJheXU6gYXO4qdz2jCsSUQFqKAciagqCvKmie0Bq4BfxmQgkPXb+dN/9ff4iff8t/xkUYIQ7SjII41+XFPuOaxmUSVjje9+mZ2LnT47QefYHVzzMQWfPjdj9F+ZsIPvfkNnFj71yynj3Db/E9Tj2/4rLW/0Yhp7KiTJJW0JwxuoufOcW3yF6k/D2+c4h2nP8L+xiJvPvKNnzXGVDCLli22J3sB+MSJc6xcThFZgBSSm6fuIlYJh5o3EF3Z4I+e7KK0wzrHj7/rQ0SB5Me/7pUESn2J72xfHK945bX83NpjPJEfZc/TC3zLjZ9fd88WW2yxxRZbbPE3k7e+9a28+c1v5ujRozjnuPbaaz8rQOcL4fMupBw9epRv+ZZvYd++z/0N2ZUrV3j++ee/6Ils8b/O37/mNlj5UxobKe966HZ2vlrzxmtewzt+7pMsX5ngGwFeOxrThrv2XMVSeoFxOMK1ZzELPaxUxM9exm2fptw3h3YWvTzAZyUMx4SDjNmvVzQODVl9a4bNND/0H24mrP0t/tWvvYPSeFwq2bw8zbwe42NPOYkoJxo5Y2Doybox/roE0Qgrjw7raDx0CbKC4axD6QAVGGxZMunVMSsa5TyUjrIdQFkQbBYE44Bow1FOBUx21TD1GJtIrHZQWtRKit20WFvgZ8foXh2MxyAQiUGZKjY13MgQXjNYqbH2TBPqksa0I4lr6JOGrMxR3Ry1nkPuqijgfsn0H54lu3GaWhYRnTP4JCSdRHgPrh7gYo0ebyALQ+dUj2w8DwJMJPEaXKZobkyI1nPsOciubhI/1WVwyxxeSxqujf6T5zC2ThAEBN0BPlAQWkQkkc0G3nn80gauXaNoJmA9epxX/hW9FNcTrL+8SVBG1JZyirxEni1YPGkxccDmkQRKT7s0BAW4skCsrGNnWwTpFFJFsHc7QkjC3EGW4WUM9QTXruGyDNNOEHmJcGDrAdJ6UALhFa2RI+3X8VrROdsl29ZkfLhNbTlF91Lk2ggRakwzQSoQSuLLEhUGgMAnlT+KXYD2mREyLRnuSWgseawCFWqK+To+LUjOdvFKIRGV/EYqTCcC68kWPcIqmhKy6ZR0yjIz1oR5wviGNj6UFAdniZ5ewkuJVArSDCklfnqGMKMy/pASF4XI3OIijW8liPEYF2i8t7h2gh5JPB5Zb1QdK9YTjgGhqm/EvccLD0lA42SOGI0pY4u6NKhiipsJ0nhMK0Ss5gjjK4mR8zgBqighiQiHnqKoOnhkaQmfGFJuAzU2COcR1iMzA0rh8FhX4vMS4T3FvOLmv32Bj6+/gujiAGY6lLMxbrqOmU5QgwyxOcHVQkwjRCIQhQUJNgmYX+gzOJFidEA+KCC1eC3xkcQkVeFGISlqHn15iC9ytu2eUG9klCiQVYHE5yXCOkwnxE8C5CWD64K/ogiv9IE60coQUZSojYh8ymGihPENCrfXkiyFEHom/RIfBbjbNfKuIfqZkt4tAteGzkcg3ADrqpQlkRZI71BXQPYUYsMxE6wgsdw0u52BSqnvrBP2VrnynqNYJShevMCgkzH7bIFQGl85M+NqmlgK/GKAvmjwWYq54pDGIVTIiZNr/OT3/yZpHCDqEXqqILluiCligryJjTRBTfHIkxd48vQK/+In/jY/+Nq7uP7kOX7jHR9FPT/midUTADze/SBKZjzb+13u2PbZhZTp6Zhy2pB0qlv6xDmUnMfwF38c7G/Ms6s2zc1Tez/n/WOtWCe3BStZlfQzKHLS0rCRpxjraAdT3DnzCgBOLB3HC7DW88SZy7z/0eMI4Ltfcju7ZjtfitvZ/zIzMw0mviQXJcuD4Zd7OltsscUWW2zx18OWtOdz8t3f/d38h//wH2g2m9x2222fPj4ej/nhH/5h3vGOd3zBY37ehZQjR45wxx138AM/8AOf8/yTTz7Jr/3ar33BE9jiS8ee9tXcUPtB3v6n93HgmgFf+7qbuGn6RlrhY1xqBai02ni95rtewhtu7vIb//4CG3+s6Z8aYMd1fD1CGNDdMdILhAoQSqH6GawPEFIyeLzJ7P6QyaSHw/Nf/zhjYD5GeswRbYyRk5IdM5r+N8+wZ7zKlVMNbC2gyKHz+zlBlNP9WzXiDV+ZuCKhniCbLer9kHBsKWp15HSGVS2kFVXxwpfEn7qM7Ke4xSbxqZx4JSebbpPtbNB6ypK3JXJzAh7sdILaGFLUY8x8TP35Ll7A4EXbQMTY1TFFDK2TK6jtkvl9dZbyeeyMRK/2EShE4YgeWUI3HWUjQvYBL2gf6yPSkmSzD4tzSCPAS5IbJdlGC2SAKhzFx9rkj1lMH/y0wIYhQenoPFfQPjxE7bWM8hq6n6NP9dGZp/V0H11KNg9oRKsOU3PVBp4C0wppHu+S/R1H+GGBOmsRpUUYjyrBxRCsjvGtGKckk22K2lJG1DUQRyStGlkA8aZFloKBDmiczdETXz1HIHANRbavhp3VJKsGH2q8cYQ9iypCKAs8UDRDBIIystSXxqjCU8zWWb41IewZSAtmHy9RpcEYi5ik2NYsQikobWWqKsAZQ5lAtqNF8/lNZB7iyqLabCcRIitI7k0RSYypBUwWAmpnBsggxscBYpQRDDIcVQFHWgVOUAiLrYf4RGFigSwF0pXkU4J8W0TWGRJeMLSOGRpXUpKup1xoo9YGWARKVvIYISoplA0kWAlJiBMSEQb4RoyvJ4jl9crQ1TmEc8goBCTB0GBjhdUaP9VCDHLwHqkj1MggTIZaG6EDgSwshIrlN+ykdWxA/Zl1vFDVCl2WIASuLHASCBRqlKGGES4WTG1rsNhRlCLDJwprQ8gMHoFUgmB1QOQ8PorwKxvo1Zx7/us+KF4ofFmDwIG1FK2IxvoE4QUiLSCKcd6y4+Aag/Uao5WCWm2DoqkZZ2G18NQDaLSIu55oqQdK0b1jGlsLCOoKuwMaR54jVRbnHfJcyZ4PXakKVlrjvUEYiV3w+MQT24za0ZIyyPGDIU5KVC1BtiFb9ExuluAcyfk+bucsYmOAcpA9Dv5FJTKQ6InDJCCtBAvBIMdrjfdF9Rqkktr7I3o3Sl7zQw+wXfb53V/8b+y98VZEJoh0SGlGCOcxheHKTM70IwK8wDYVTic0VUCeZXDZV+87lVDeGhD1HOiIdFLgk7j6QyI3+L5HtSy6PWLv7k0Gp+sUvxyzgsd2JR/4wFP8gx94NVf8+gaxAAEAAElEQVQtzvDOt32IPFFEnYhv+/XfYnzsZVx19xlqN7Y/59p/adSnFJZ/+2cf41dHn+KXv/suxvFpZsO/iP01vqAUG5yenARu+6wxQhFgvaOuK3nQ8fOriAIm/Yz/ryqm06nhQ6rPiZT4FyR3X0nqmY/eewztSpJYsHvxc1+3LbbYYostttjifw9+67d+i7e97W00m58pSU7TlN/+7d/+qy2kvPSlL+XEiRP/w/PNZvNzRgpt8dfHSnqCZPdF5g41GN+3zu9OPsGfdJZ55KqYdGqK2YcG7N8heOzRo/z3d3Rp3DPGZh6BoHAx5SFN55G1Kt3CC6SxVdys86S7WyQbOaXx7Gkf54RaJL1qiieKkEBMCHOPpEQrha5pWnsKXrx4jnf96Xa8D3G2hBLCtYL2gyXZrjnU6YK4N0JbD9YSjm21IZ+OGB+YxdQTmmcMIlTIzQnqcg8Kgy4crbUEEwEtT5jk0AlpbBshC0m+GqCW+8gwRAhB2vEk3uG1wISCuFeSrHnKtsDunkPUNeqjfRaWLzD5zgbNZwvwET6dENYs828seCzbiUznmXpwHT0o8aEmnw8ZXxfROlNikgi9RyL6ESK3eDxYoC9RWYF1FhcodArhpsU/GjBqaeykJNgs0cMSkSTUrhhoaOSFkjTSZHMKPbIUzRjTCghmEqJ7AsRwE9k2lM0p1KRAjQocAuUFpfOkO2LS3SG1UwNU4RE1z87ra2wMUoJGwc4b1sjMdsJTHpFZXJbi6iFrbzxIMAnRIxDGYb0n3RXSPO/w0iOtr/xDhMDEimjTgQ4wypE3JMJ4Wo+vkaQK4QXeWdRgjGu3QAgmCyDyGgKBizUCSZQZ5OUxKnXYRKE2RgC4doLc2ERMdxAWVKlpbEQMbl2g/cgGyjhUWUBhIUnwRQ7GkC4mLH9tA+9h8YGS+lmHzKtY6OmnHPnpjNrREb40hOsZcuBwSYDbsUB2x17sxjKN5waIUYk4fRHTrsGOWdy2OYKVETIv8BubyCTGTbfxsx1YG8BgUsV5O1cVdqDqKOkN8DMtGJaAwDYCnBC4tMBub2CUolEITDOitVRQK6oIYYGvYpaFJ9+dIKxAliFeKkQcoYxHGEn+sQucvRiB0Sh1GnHt/iq2uGYgAzmYQLsJqkrSoRSsXGyj9BhXFAilSGcCOiNH3rKkeUo9KyEUiCxn4YYRN73hFL2NBh//lYMkO0tWLjTBVOaoTAlUL8MlIUQRalIw9eyI3pGqqGgalpNmhmeyWQZFQv2REqmj6voIQbojQQ8N4TELytMYOIYH5ogubuKNQU5P4SJFMKiRixRxOUSUICeeWEpEfwyAHecs/ccpXKFof9TibY5UCSCQaYmLI4SoYnxcTTDz4i7Dqxr83vgOvl0+xOmjq9hbJqjpGumlMfFsTFbk5IvQPpOAGiOQKG+REwOxJJ6KGEcGSoFILTISOC2QUuBbQbUGKAGBopyEDJXCWsFUWPLiFz3P/StzxLs7SKlxk4K11UGVSrYYsn12k8HFgOeey5lNY9JuwnJpPmvdv7C+ydvecS8knnTi6MqMf/e+93H45gcRPmd3406AyifEe7x3n/P+IYWiruvEKgbgzp07efrB89RdhJKVF5FxBikka3aNaMazXXbYNdOmXg+pByHz7S++PfZLjVaSeENiY89i9JXp47LFFltsscUWW/zVMhgM8N7jvWc4HBLH8afPWWt5//vfz/z8505F/P/j8y6k/MIv/ML/9PyBAwe49957v6hJbPGl4enee/itjwxZuieCfoR57yanb7AMZwxSgJ6M6HyPQ7o1pjcjilgiAkV2ww7GO+sEl7oQhFU3wJ/nwiKgMIi40t5HoyHR3IToNoMLDWUINSsQFzYo50OG37rIrTffyPLoDJfNNH4KUBDUHfmNmvCTE8IV0MkaWWs7qZSUckwtD7EdjUkdRWJQq5Z8v6RsQDLyuKRJsXeG4OImotmAOMZGHll3+LjAho7GzpJhu8VwrcHMO5fx3hOt5wQTi/eWol3D1TX5Qp22LZhcU8dug9qKQ40DgmHOTefOsfiNYx78z/sovSXbvYNouI5GUHZiip0N9LJn/aYa4/11tt8zJBwb9FqGf4XHXqxRpgEulLheTrZNMJmtUbtYEuSWOLeIzQl+zuPXDdGpPtRiXDNGOompaUhifG6onxkQb3TJdrXIFjVoUSUXDVM2b5ih9XyfelJQrlbR0aK0pIsJTkkIPeGywTRr2Nyw49XLzN1whX3/LsK3eiQnDdvVCr31JnJlhBpMCJIIIROKGUXjkiMYGzCOZBhgvMQpi9cgjScYl4hI4WsaE2jUqCQeecbCEg4r/whvPAyH+EkBQjO8egfZnCC6lGO0gFpI0MuQeUmUQTxnmRzoQG8CUiBVAK0GWFv50GqBKgWqAJFnCKWhUYeiBOshULi0R9FSuECgJ5LhHsi3vSAleRSCoSMcKfzUNLZtkEkM4wIXhQgVYmqOfGaW2nMD9AtRxWqtDxtDFKKSH810EGkOWYGMIoQQmJpH9gocIHQdYT3eOdxwiF7awBUGt30OihK9PsG0A0R3jCosSS9DKEWYl1ALEb0Mn+fIeg0fBLhEsf6N25i6v4vvDfFaVKlQkQJjCXoFotnAjyeossRbW3m5lB6dGzAlvrsJ021EFOFDzbiooYzBlyW+LCj3HmC06jCxQ08lZKXHbauTTCSTlZiPP3oD/c06xZTg+W/aRXJ2SN6tekfjeUvRA7lZFadEFKKMpXXcMdnZQhYwffWQh9O9FE4huxNEJiGupHAiVDSfTxGFpBx4kp0p+Y4A1gOYB9IS15nCh4pGz2OeLFATQ7qrQdr0TOsA5T1+mOGPpax8W8jsH11Erw7xCx38/DS2pnE1jeo6nBJMXZdx4OWXmVExn3j/Af7b+2/BRJrTwx7xuQniXA+zNiAUHr1imMsajIZ9yAVilCPrIKfqEAWMtxniSxmhHhA/ugl9j2vAYH+T9iVXGSxHAXq+IB8qVOi4Y+oMNzcv8uRsnZ73WG95z588wf33nuDH/s038JprLnLX7R+nu7SXY8++FFNKUh0wLOufte7/wh/fh+0Z5LKntZAyKWo8cn7Eo40jvGnPJq/dVT1uV22Rb93WZzA6yqMPH+bW22/+DAPWTjhNW+9gd+0AADsXOiRKM9OuOlTuW/0gD23cx43t1/KJ8lnEwRHT57fjnSfOFQkK+RXUkuKMZeQteQpPnr/Mi3fv/XJPaYsttthiiy22+Gum0+kgXugyP/gZJvsVQgje8pa3fFFjb6X2/A3iUPtrSPx7CZZz1GZOkUeMbg4JeiAzhRARuh+yZ3/I6NB5cmMhd7S6BZOpgMFtM3QugkBUm9dxjmvW8LWAcHWIyAy5U9z3K1czfMqQ3tIhrods73S4NJuQ7wzY2K95anSRH7z1Vt5zZonmjhH+2SbFdZDfriBIYLdHf3JM2E0Z7lfkNzZwzzlU31FMBwinqF3ooYcW0ZquIoilgO1zKNXAK4F9eUl8VYH/756y5VFLMatiBltXOAG9115NcqpLtm8KE0KxUFDOVhtclTlyE6OHsHlYsHE4JFybZ/5jddZUhHQDmjcYNo63cLWISdmkdWGd0kTULhXIuMb00xmtK4poWIKQKKG5dL5NO/IEPYfKHTYSDK6KEEbgQ4nFo7sFLi2I90vyszWgT9aJCBpNjPH40QQRCAhD3FQDM51QdDT1k0PsXo08kiMeAAgxkcb0QVrHaFdE2QpBKxpHN0g2oMxKAFwjov9cA9+B0MKVk3XGa5J8TRJMBtgkxBzeRtBN2f6uDdJrp4k2Ja5jKec9YrmspCGFQY8KnHP4dgh5JZOwrRgpwYYeHGweatI6NaI9M0EGBYMLAV4LRAHJxZzG48uozJG+aB/RJMcEsPHKKcK4zvRyju1U8bHEUdX9sjlEawtjR3RlhK4FSKeq+GLrQapKUtBuku1rsXl7DT2E5pLHBJJoQ4B1eOegNCAEUmlcUeB7A5wTyA3D4JaQ0T5N58ESESdgFXKmgx+l+I1eFQXuPXT7iMVZXD2uigFFSTHVIF7qV0lX/QFuroNpBrhaHbE5wc43cYFGFyUqNajMks3WUVdGlR5CVSlGamxg/IJxt/cIKXCxYuaeCRiNMwacw2mwtaoLQnio2shEJbM4c5n01m24KEDUQuJegi88XkuEEmBAWkU4lsikhg8UclwSXtigvj5BWsX4mib5VRp/xeMuRqyvTKFLaHf7jH9zjsIIQtsFIcgfK2BKIQZF9XqVBiUENtbYmkT2DbWkYOJClHf0X9Qh+vAaUlepNmE7R7cc41nJ6BUdlqRjz+oSkzzEi4TSWhjliCTEKkm0MsalOb1v3A4eXDrD/KMDRM+AscRH1ysTW+fw4xw5SfmhX/wwTzyynU/+6mGEcQzPhqx0O6yvtWk9mrJ2d4d8b8IP3H4tO44E/N5/uofRMAfniFY86zIlSh0qszgFeaQovaUYGPJrA4IzA2Z3rWMaUAw15dQ06JDF+YT++XW4sIbePcWVvE40LrkQznBVfZUsmsI48LJa4ja7I97xBw/y7a/7KpR8kj03voh/uPulvOfxP6FclOysH3rhreH5d7/xUaSzPPjQ85gpid3uORwuM5tkfGJlD1kZc3nymcav/+Z7Bly5MAfd38a238neWxr84Le9kdtefQMnhuc5ObpIomJum76uMsy+us6B/VU88iO9h7mUrrMYn2ExmGa972gt1FkaDBn2U8Yi4/TlDa7Z/cV9q/OlxnmH2CixTU3rhQ6oLbbYYostttjify/uvfdevPe88pWv5N3vfjfT09OfPheGIXv27GH79u1f1NhfskLKe97zHn7yJ3+Sp59++ks15BZfILvqt9J7+iG8XMEFJYs7hqwqjzACtIBRzs5jd3Ldy27gfSd/B//aAa33DsnXRtTxmN01EDDZHtE7HNN+qkeSanyooZHg0wLXbtA75XCNDB8GZBPD82qA+artDG4skRoaPuaXf/dZau1dbHTH6NBTICEWhKFCYHFFgo8CGpOUhQ+uo86XGAvFS/biEeA88UZKZDbJkib1+ZJBEVLUNXYKmteNMUsBqkxwmaJYVJR1Qf1iSjCypNtrjG6ZxTtwSjK8ea7yS/BVR0UpBHLsqJ3VFDUI++DrEZcGO1n+VIm8PCHq9VCypL4rxT6RE6RdCDU0HSLWyCLACpDOIOoBu3sDRmkTUTqkFygP8w9m5DVPY6nA1hRr22BqBYbLdbAeKTW1pSHF4SZBBmVeElzu4kKNboPOuozlDurnh8jVknQ+oTXOCJ7I6N5UR687giuKbGeM6jtk4fBhQD3zDLIMryDoZhRPhGyaHZQHoT/bR3VT6sN1hPP4GujTy7hGgmwKzCtK9EMSvQqqW4BR1WsiReVfJR0iCNFrQ8Rwk2LfDFkUoKygfaxk/bY2vbvaJBcytr3nPLgcaRxTR0cMFyV6YsB6gtUxZa1Kbwo2DflMRHmiQK73qvjew3WEEUztLrn6ZUs8+/795KmBwmNrISIJIAoqr43CIqwjGGraKwU138d0I4q24O+88ikm44BH3reIMx5Rr1c+LVfWq98njvC1EIKSwDii4yvI3gS/MFMVOV6IYPbOffobfB8EVWyylqBCJjsUan2e8NwavlHDzDYQpYVIYw/vBCUJT62jshIfRQjvkcbjw8qc1iuBCDVWW3RhkK0mviyrIqIH20oIljbBWMTmCD9pgquhMgF5Xs3TWqT3eGOZ7Gzjmym1e7tYIxDTLbwEcXkVvIMdLUS3XyVBWUvULYg2MnghmjnpGsJjjv7NNYIuyMyjCkAI4nWPHAeIIKjSkkpZeSqFIVgDtQQfB+Q7m0gBwkKw6anP56QTTbFDImQVQ44QFNcH6KGn9fCA9OUN9BUYH61hRh4bR5iDTXSvQE4KFNC9sU3eELjpSuZiagqEw9RD9CCjfWaIFyFIhcpLfuL33kUeC16y5yRP/9cFxuMOedfx+P1X4SN48d1Ps7q3wzO93Tz63Fmufdn1hH8/R/7hAPuwgKyGkDW8sWAMvh5gOgmBzbnj9UdZmWnxcHmAybtWmfqRCHehjljJSDLHiAllWqVK2fMCPVIE0xmpCOhldWSnRrCR0tzewXnLzoOz1BYSHh7P8vprf5+9i4uE/RXuG41QjZKGrjxPfuOPHuI9H3oSJ6H2+BLDr5vHBQHnalMcCE9wWNZYPXGGo/+gz0++7Gf5yXf9KL/6m/+atZFBXFrHT9URxnH193+cf/aq53n3mV/mjunrUUgOtvYAkEQh8uAFLs9c5ML4CDPqChvKcuvUDezebPP8yj00rol46MwFSuWIULTr8f/3tvRl456PPge5RW8qThy7BHd9ti/MFltsscUWW2zxN5uXvexlAJw9e5bdu3d/Rjfu/ypfUCHl137t1/jwhz9MEAT8yI/8CHfccQf33HMP/+Sf/BNOnDjBd3zHd3zJJrbFF86gXOHV33mBT+0sOfdYmzf+04/w/B9ciylC9MoIu95l6egFpq9srzaUUYzYEZLurTFsKJrveR6/PEGoKeSuGUw7gu4Lxp+TDJFE+Kk6+w5Mc+EDT6OPrjB7xzTnz7QRtQC9WNKalKzJIaIliWxE7ewE4SG7ohA9w/quBvEmhLtqdLKSnbNd2qM1zl/uUFqPMxlhtyTYyJCRQpshjb2WbXcPebjYDksB5a6SyfEW4RVojhx6aMg7kC54ol6BtAJhHOEQvC/wmUTGUZXSszZBbxQUdyWIXkT9HNSlQ/YKgqUeKlIQKMJNgxjnyLyAKyVMTHUdrMcoy+pXL+BaAdsfKPDS05kbEfdHtKfGrK9NQ+YhLdD1CIYOFylsorFNhZ8SMJDo1U1kWXXbDHcFTJ8syQ9Moa6k4DxztYvkm4pxb4b+DdM0T27SvpwRz0iKkcIngsEhTacraD43JhxafKShN6a+fYaucGQdQX3FIIB+WlJEAXccPsvtNx/jwz+7l9WzDWRWQlqiSo+vN/EC8j0SVkCtCHwicSZHT0ZY5xHtFnJS4I2g2DuDma5DqFAbOZQW0xDoCSQXR1hXogQID2GvoGaouj2cR6cGEMiJof3sCKs8ghCMhVCT1yFen+B1wer5NkHDMJrSBNOe8pRE5qbajCuBVTDZEaIvdGneO8H93Q5y1XDw8JDrrz+HB56dmyZbjfBaI73AxyFCQLlnhiAXtB82lB2PqLfwGch6ghEWea4LuloqRaNeFS3KEteKkC989nwBxVVzuFYNuTpAlBadWaTX2BDUZoosXVUg0gJKj5NQ1DSRKTHNJrrRQNgMnKsKN8aAdYgR6NxVHkJSY7VC5BlqYonWJtB8YU5php9McDNNcIbO0x433UbECTq3kGaf9jVxZYEsPT4bIrSimK3Tv8UT9B21KzlydYhxAVm9zWSbAi0I1gvQCi/ABZUZsgksKqwjnMA3KgkIQuC0JNgoIFBVPHRP0hIZm/0Z6ierCOfKoFRQ/0OJHIGIQ/bu6pF16wzEPHp2UhUl0xLpbPVZ8Z5oNUdYiKcM3gqijQRGKVJrvJLYUpBd1aDeHYKA3Ar+6ZPfxDiPufkN5xm/J0RJj987Ia5l7N+zxh6zwcX3TZGlhou3dUh2G4Z3xpTPgERiFUSFBQQ1KTGlY3Z2k0PXXWZ/sszTx/fgLuRs/JhGzSqEFrRCRa1VY+XKJqYW4ZzAHKtRjDTzN57mUjpLakJsIySdEnzPt7yCl7/iWv7Tpx7hj548ykf/4FFevGMbD4RLjFZn2XOjYbRYAHD09DI2FpUHS2FJLqaU0wE7kh5rqw169w/w4xGma/jk+x7n63/rZzj5VET7OzWtC3XK/ziGN0UICeiAd7zl3fzjX/77XN3cx2xYGbPWooAkUWhVfcyurjfYG8dc07qGi71ztNY80Zzlg2dPYAJwwlZ+MF8hnD+3SuQ0PpTkvc/2ltliiy222GKLv4kIzwvdyl/+eXwlcc8999BoNPimb/qmzzj+h3/4h0wmE77ru77rCx7z8y6k/OzP/iw//uM/zg033MCxY8f44z/+Y9785jfzcz/3c/zwD/8wP/iDP8js7OwXPIEtvnSsZSdpz61x1e0RrexWupcXma0P6E3maK5OwDsOHNnJH/zIe5DZiHBR89JX7eTpzYCxLRGZwTtHfLrH3GaBIsAuTFWyg0DTboQcuXkPb/r2u/i/3vsEamXIvvYmZ8SNhDbkkL/Ac4MdFO2Sw9fGXLiUMT4sUBkkDw2pPZsx3t9BJR1m2l3MiYgrJ2ZY6oRsvjhB1TW1oyV6I0eMJ/gywNVr3PTac3wy2Et81uJSKLIQm0XUFvtIU5IvaYKlAVP9jGy+TtlWyNxSu+KQoxJ/lWDPwVXOnZvHXR7jW3XEpiZIAQ29A4L6JUXcqyEihdEWXP6CMZFk9FSMCFIQknAeNm9r40RJ/VPrOKYQXtFbbyD7GnnKoBdz7NjTbo550//5LEfPTfOB09djY2hemBB0SiwC14phUuK1ZPqRDYSX6EYTVXpkYZl6lcdmOZcuSWxNkyeSbDWmvaxQF3psW++R3TRDNA5AQplIypojfm6dUkt0AfVM43DI3pByrs747JhBOuKhhV0UQuKtQ2QW8hJfWsRmDXFfhKkH2Fmo9wuCSYHfkcJSiRICjMPh8Os97LVz6H5WvQFLi0sn1C4EtM8a3HqOFbaScEhBfTbFhk3K7R302giHR1FJWlw9AmMoi4wQwFtMDYT1jE3C+FwT6xTl1QlloZg5tM74wQIfGKwuyPfVGRyqESUW24P1uWn01SXRg4qVmzuMs4hu0sEdTojWDVFWIhZmKRoxzNYpC0vYS5FlWKXpeFlJ27a1mbz6AMnxTTQhbm2jStGJq24MKz16kNE6bvH1EC6voHoZPgwhSRAedGZQqcG3arhIIyYpZQDBsESMcrAOPcigBJdIXCyRkyE+ihFZhk0kYpDh4gBamnIxhrhOPLH4uQSzPiKcOOQUZM0pzFSd6Q9dwO2ehaQqDNJPkQ5cEsFwTHTsCrLdgSTGCtDNMfPnz5GbJkLU8XGEEgHCK0y7kuBgfSVByavfhzAkvTqktlx5KcnRC+8D7xGmJF7VlY/LOCM7EdG5OuPYw02aj/RBSmjGMMnp3tQkWC/pPD7EBTWKbYLigiboC/TKGOE81AJ84cB52s9Y3PaIwcs1WMjnNSIroBaSH1pgtCjIFhOKxetAwD/+T4cYHAZRSp5p72Z7aDBtS7aoSIM6TxzdyXic0H56k4nr8adv9ZhbIvInJcQZen2I25Vg2zEitfh6ZWC7ttzhyT/ax+pVNTwaryVSSHxeIKxDuYDv+Iev4pfe+j7KzRG13Zu4a2PyluS9T96OWY/opJehU2LWUv7sT57k3g88Q39OstlJGUQFD91/hv4+RaEi3GSW69xOxpOcRifChgJROsIgovOpHq0bd5A80eLCMx3MjCFvB0RP5rjSMvy+59gWRQyf3cHM313jl469lb/3Oz/BH3/HVTTzAaVN+bUz7+TelUd50cyL+EfXvIn9c9MsnjnCjoUmHRZZ4ms4md3LwvAouUowicQ1NAfqM/TWJighGIxSFjtfGcaud996gHP3PUWQeL727uu/3NPZYosttthiiy2+jLztbW/j7W9/+2cdn5+f53u/93v/agspv/7rv87b3/52vvu7v5uPfexjvPKVr+See+7h1KlTdDqdL/iJt/jS080ewRebnHp6P+vdPo1PfT3d7gCjPcVUjeZ0m3UvWFUhxB2Cj6/ywH3HsduncC/bwfDlB5i57zIiLdEyrsw0xwUICVnBZn/MJz76HI0Q3HgCgFwyHNy1Sc/OcWx0NWVoaMk+Lr5A8xMtpItZe6VG2QB/OkcqjdltWLvcpO1yxMggnxkwLUeUBwX5kTk40aYUOeVMRFzr8PQjU8zPjeke00ymBMXOkNpyinEZK3c1oHAs/mpGkBvMbJPwQoGvhQjjUIOcfS9f5/rrLxIczXj+3AImCYhP55RTMdmcpJwVTLymdSYgnQ/IpxUiK6E+Rb69RvPJNeov0cQu403f9RzGSX7pt+5CnivwjRiCACZZZQyKxA4VNEN23Dbg0PVd2gdz3vWe62lc8bhIo/MRrld50dipOiIr0BMLWMKVETYO8NbxzMMHEaMB9Y1l9FxMdKGPcgJ/awdOZOAFaslBBGq+oH9DjckohNEce3ZvZ+OpK3glEWmGMJ4dMw02T6/yRLiXrFdnempISw2QRY6zFqzF9wZMblxkZmcPHongjIMQ8t011CHQpwTyuMNLSbkzQI1L4tPreAFeS7zJaZ6qIYTGzzRhfYxYTxGF4fBXX+G5k9fTLxNsKJGFAQm2FVPOJuAc6twE4TxOeEwnp+xEqGs9YtHjP66pnRgQ3QQcq0xqfSkprppHDwzJpRRpI4ZXb6OQAaIO4pTjv//j2xjui8n21cBq4nGODQ16po+6CM5Xniy2XkU6640BWAdFiWhGRGODmpQQKxhPQGvMehewqCBBpRmyLLHbpkEn+MTj6zEuUJUcytgqw0dW8qi8k2CikmR5gsAjGvXq24NRCn0DpcOXFrsYoX1MoEIcmqwJ9m1TqNDDuw1qqdIcDQ+2uGPuNMc/ModdaKGu9FD1GB9oPCA2xwgBvhZh+wJlLMJ57CRFJTEUJbNqlTDL0TXLRLbwzRhfV6hUVHI46wlSBxa89fisQDpPdNlBmODiCJkVYBxEAU6JKmY4d5AbXv2SM2xbXGZoApY290CjBoUDrRhdU0fttrSfHNP//RqTuIEsC2745pt45jceQQ1ysoOK5OERUgqu+tYxp2YXCR/yTNq1ysy1KMiObMPVQ1oPPI9+yVWQVNHpLgfWLb4jyLIEH2aosab1Cdi8WnNleopRElPrDBA9QbGekz2TEF6cILKSaFAiX96CicErR3u2wbA7xGnFUyf2sbYYIVxI92v3M/PhC+AdNlRcqml+4af+mHwzo3VnRuvbc7rdBBs7ZMvgujHZrg5JHqBTzxU74Oo9CxR45mfqRLWU62s7OToYsuxzGk8t8H+/8128/A2f4q6vO8l/k9/C9AciRFYSLrSJfMRUuoeN1bOowBK+MoQPa8BXfjF5Tu1cn2/76u+l05nh33zDv+Jf/5d/y7hVMPimdZ5afRaH5Wj/eQAuLHc5fXaDK8tDLq99ELXtOHuPZIxNn2v37SVpxvQnGV9929U8/vRFlBCcW93k4M6vDI+U2bkm9ZFjHnj5bZ9tLrfFFltsscUWW/zvw/nz59m3b99nHd+zZw8XLlz4osb8vAsp58+f59WvfjUAL3/5ywmCgLe+9a1bRZSvJLzg+L27KI5up92RvLe+ytSaxjQCxgea3HXNfkauxCw08R7c+T5ybPCrPVQ6h2nGqDCBqFZ5HygDoxHCWvwow6vKI+Oex8/hkwizc5ZTywrbsTRmz6PeWdDpF+z6BwXPDbZTvEYTPB4Qq5LJHQnp7THhWY2YzvGJZjiJaPTyKvozzxCbGrlp6V/TprxukaQvqJ3zDF3MehKTFIb4VMaOzQK3toGMEuQQRAZeeIJtUwSNiPWmRFhBknmIQ5YuTzM9P+HiyhyiBdFKgQigFAK9URKvlSRXErKoJLowxooG4fIEHwhyIfG1kOJYQTD3wobYe6g3SW+SNAchQWmgcGQNqrhVJVG9jOMfr/POmcMcldPVpnPToPoFmY0w7fgFyYLANSVuOEYTIqQGoaChCfo5otQ4oUiWcwQBaChCCDyQg7IhRSSZPzLE7y7Jr0xDHPHo8VVMTVcb9Ok2ql5j1417efroMskFi8QQX3aYRoTIFYwm4DzZ1RF+mDC8KOjMbsLQ4MIAs9qhSNokz68QaoVwgv6b5oieLlAzNbxSCOcoIsXgcAKBxEmLO7KTXe84XW3cu5a9i2d59pn9KKHxia48bIzDkxFs5KjM4b1HipDm0RzdNfj7PV4IROJxWpPe5xCyDY0JeScgGJfozZTw+BUoStQNC2Q7tiFKSTlV9RVufpXEJZ7mk1X0q7whhdsV8v4Ce776+Lg4AOex7Tqsb4JziMEYv2sWP3GMtzeoL68jpCRAkGqLNlV6D84izy9XMpuZaYSXVdHBO1xpUJt9vFaYXTNQWsqWonfzPM3jI6aOpZS9deTEUGxvEk00LqlhphOCyxOKWQHTluDiGJ0ESOFIb5CEpywYS3RylbPbNK4ZEzxzrnp/1hJkPcZZgxiMYK6OmBKQNaA3qjpCBFXxTAFjT1SrUywE+Ms5qihQI4hiCZst5MThvUMPU2RpK+NeBNFKircj3M45XKzxQYBEVB0ZGyNkluOswdkC6T2BrMx+hdKVTKgo6TyWEnctstkgfmxCHBQI4YncBfRmhhcC2avh64YjN63wpm8+w7K/wG8cvwbvhnQfmUdIifYBjBzKSZRQqIHBhZALxdTZkvJvD0knYRVNrgRFUzOV59yx/zQFmqcmi/RvnaZzSVBvBPiNfrVOCpgKE9IdAePnM7ppCl4gDOTTMWKsEXiyq1rwfoeXVAlJQmCVwytFkcckZUZzGLB2KcRfDEgyj48j9GaJLD2DesA3f9dLuO7ITt5/4QOsiee4dnoX9/7wkCKSnDmxDMAD7zvEG970BH/n+of51O/fWUUtA+uXhrSiEFmWJLnDf6Agm2lQ9iaV5MY6/tv9/5KF7ZV57MHFWb7xl/4u4sqYR2cfwo4VuQ043NxRfUaEoF2PqScRUgoEFikskQrZMdehlURsDlI+evIULoIgDLj94K6/hhvd58cdr7qO//LcUyzv93xq5SJ3Lu7+ck9piy222GKLLf4aeMHc78vOV8Ic/oL5+Xmefvpp9u7d+xnHn3rqKWZmZr6oMT/vQkqWZZ+RuxyGIXNzc1/Uk27xV8P54QkuH48Ynx2xcSAnPyLIN6rkDxEozj59kQUF070+vm3wmYOpNsx6uDVHng7xujKH9HhkGFB2apCECGvBO4RSTEY5olXHt2ps9BTuosTKhHh9Be0traLEovEx2L2GJCxpnekj31UyvKFOfFkh+hJJjNQhfn4aRiMYZcTHHWZ+k9oDZyFQTF59LUVLUVs2RAuGoJmRnY/pvXiO2fGYxT8rMN0JLoD0+gbhKoQF5E3Q4xKnYPWxOh8+cyOuFdFa3SA0f7FpDlYHJP2U8uBOiusjNtsJ0SVJMRsRrIypPbeKSj228AxGEb/5o9dhg4DJTQLf1uRXeZLzBWbNE27mdF9Sw0WS5IxA7ldwU5c9SyPW72/gdYBQApO8kLBT0wSDAjmcIAsLNYVrhEgLOMinQ8BjiYg3sur3CQSjQNMRAtFuoUoPE8H4UwEdu45+ooffU6c4H8BcndH+FsmxLjfccoCpVgxrXZI0pLnmEYXHb9OUK6ZaCKQgfdkcwoPdCFCPQDbfIsgl0goYW9TmGJoKEWjUUogqQmxLgq1iZdM9bYrFGGcMQe6ZfXiC+3/Z+9MwSbO7PhO+zznPHmvumZVZWWtX7/sqIbUEakQLEJux0YAHe6wx9szYss28HiNzMQILG9vYr82LFwwCJBkbBMYSEkhoV6vV3Wr1ou6upauqa6/KfYk9nu0s74enaawBjyVZlmRfeV9XfsiIzBOnMiPrxPOP3zLfRvYLnvv1ahCiZqvnkY58vNKh5wW7r1H4Ox6z7xoiWy2cLvH6At2uEax1EL6PcRbbVqg0qBRDcQTtmNI5ZFbihEWUmuTFbeYmpskagnI6ZjwXg9NIA4vXb6BriuKZMS6WuLMKF1jUkqF2XYf80w1sHCLiCIoCmea43hhvXBKv9cjmI5IdUzX/9DMYj7H25XrmJEKIKjWlbEjytkd0oU+w1qlKdQIIBiVFKKlvS+KtEcm1Lt6iQ6+VOBxe5hCZxusN8XbGOGMxLUHtjQbzb6A3CimlJBgIrBKorFIbjJZixPkMGUc4Y3FaI65uwg37sRMNpO4jN3JUCcQhIJBRhI18Ovcl3Lm4w+BMQna1gYssjFMoLK3fXad3E0wcMujVFDkwOOuwoUIWtho2WYe31Qe/Gog6Y2FmogpmHWco53j3Xz3CwvUzrJxuAQ5Xlggc1uXU7tSoawGsSYyE8vpJ7rKC8ydWENqCEggncIMhWo7Y8As63QJRSszQAykxgY8sDE5QtWgNHYTVzyfwLSrMyD9VZ3IbBrLAn5T0r1egYrK6jxSO4qhP9FKXWDU5SMKLUIUxK8f91+/nc5c2sYGkHBcIFEI4bCQQWlK2DSbROJMjNnOIYhr7J3jX7/04f/Mv/zrx3Hluaazxxtffwi991qf7xTEGUCOL0BZhwAjH858/x8KRaT7z5CrtowNmcOSxQBYO6xzCQJ5KFNWAxx6sk44y/FJg5xXff/89/P+ev0rXE7SuDkA6ogMLxB6856l3EsV/3F7zxY01fmPrJSZqEX//2Fv59OZnWU0v89DcfQDcfGSBv/S9DzA3VednP/gZJrw+1hk84ZNEPj/8nXfjK8X7XzoFkeCOowtM1OOv34H3X2B3awh1gRWWlVH/G72dPfbYY4899tjjG8hb3vIW3va2t9FoNHjwwQcBeOSRR/gbf+Nv8Ja3vOWrWvMrCpt917veRb1eB0Brzbvf/e4/kYvytre97avayB7/9bywIUgnDYQZwZrkW7PLeHdZ1p+5nez4NsOdgnoE+5zgxv9pxEe2ay9fJHs0H7EIscv0vSk7T0SgJCbLwERVPkJR4JxFmhjZ8uHMEDGRMG5NIKxDjjMmlkqyqz6tpMffOjriXU9H9LY95MoYfdIRXTbUd0cwEUAWoorRy8nJDoxDZBbRBG8zrYYJzpK3NdIoZGhZ+ME1hLWs/Pt9iDTh8HddYvBiwsp/FEgXYp/aImu1UEoQbu/ChU1ku0myNkI3JFt/7hgtByaupPRuosC4lOxYC+eDmfJAOlSukYVFZhrPVcGnVkisE+QmIZuvUz/TAwXcM0U2iFDlAFNX7Dzgsfjb23Tua2EXBBuuyczMiLuuu8jxqwcY7k8gL/DSAq+fo1KDcAoX+jgJeVMgvOpd8+FSJctvnOxi2hq1PQArmB0aSk8gnMNaiwk90v4E+g81S3dvMzo9ZKcTUyyEIEHUI05dXkdHfVxZEH2HRN1Xkn8gYnyfR/kC1NckNgqJTwc0tjdwXoD2GhRtCDfA6xR4uyM84cPqFm5+ivhyBBKClzaqC85WnbhTMt6vqG8UTD2XogqHGFvQFufFuCLDSItIQuSwwPoKuatpfjbFNAIoSty4g5MCOT2BbYSYXa8alPgSUSqcFDAcIUpTBeHONigXPLJ9Iclz13DtBt6gZOpgRioV1vOov+BoRAbzWsHghQbBSor4lTFmeQYx7fDlGPMxjRiNIAehJK6ZkC/UsJ5H0JE4ocjvWSL61DXoDQnLoLJORRH4Hg5HuS9G5gHpjE/Z8pGjCH9DYmtRZaMxDqTEJgHqwg5mnOL5HnIiqmw9RlRhs9bhnEX4HroVUT6SIQrJeCegrEu83RJhLE44rDMETymcykDKav5vwQXVUM4piRt62EzDxnZVW+37uEziAgh3c659OiLQilKol4emAmcMxioaUxnzNqc44LO9ISimIrKFGuHakOB8htAlyLiydm2NwFp0I0B6AqV1ZQPLHFePN3FKonwPZy0uL2CuhFkf65W4fsnwoX3EWwUXJ9rkkYcd5pXdqjdCOEc/inhseBBGEs4J7E6IVQIbB3hpVfftrANPILTDKEH7SkoZSGpppaYpWgFuBEI7rIB0FBIPNd41Qx55zC1MYidCkA5XGGSkuHl6guLIAT558lmCySbWOJx21NcMuiYo5yW1RohbDBGrJTZWjNOCd/x//gHNfXX0ouOxcB9snqN9uEkxkqSPtkmnVRWqPLLEWuMVJa1aRDmaw1x4M3ce+xYsv8zU9IBoa8xoK4HS8vn+Mp/8yF14haW4LmHi8Q2mF45xbmMXk0S4UFBGEcvXTXL1mXWcB1Z/aerb4fYEM8/2KJ8+y8eWdjnnHccTjr6+inU38ImXnuTfPX+Z119/lPX+NpO1bayZYD46wk+/7+N84oVz/PBr76AuQuQQTry4zupOj31Tra/jqfefRwhInsnZd/MEbz544zd6O3vssccee+zx9cG9/PGN5pthD/8JP/uzP8vly5d5wxvegPdyiYS1lh/90R/lH/yDf/BVrfllD1KWl5f5lV/5lVc+n5+f59/+23/7JV8jhNgbpHwD2RjHTHzbJp5w6McTOk9NcNufvcBOOSZd61IayXY/ZywMJ587ir7T0Drew8sNblOiNod0GgGEQdUsspUhhKOsKZRyqLFhcIPi2PXLrJ3rY5MYdh2jGcXMI9v05n1cKHludZ6kcRnO3Ur7k5sE/SGjexuw7BNd05AXhLdGZBcMrjTIospsUGHM9m0txCgjuhJhY4k3LkmuFbgoYLyeoAJNLmJkKTh3Yg5xriQ/4iO7HlJL8gkPZcBfLcAYGKW4lqKcjBBSkO9roLRDro3QfsT4Ncs4XyAKqK2X+IeH1K8VqFwiFtrIyLFw7zqnXlwkO9Sg+blVvJ0h/QdbqExRv+ZV7TGm+vlMPTom6Pq0T1ryVcvKzhx3fu9JXv2WM3zgd0b8fnov3pzH9KDP6Ol1nOfjZqeh3YTCILXF1CRyUFA7PSBbbmCaPuGVAptEKGPwj2qKx1NEaLHSAx2i6z5iM2H9DxpYKzAxjJcTlDNVTsU0rBRb2GkfFgVCOURNkn9RUrsscMeWGR9u0jjdQ45z8C1mOkRPeRS9DFEKlBYQBTgbY+oRwfqYsh1V1bfWwSiDnQHR5YD2BY031MjIYtMUoS1usknpwN/tIweKcn4K3QoQuz2iM2PSJbCBhzIlthEjBISnVnA4zPwkZiIhXQwRuaZ1OcdFAdEgp6jHWE9SHGkg/IPEz17Dnb+Kf2OdcivCTjlsAnpbkv2OhHyMbUZ4BlwQ4o8t5qSPGCus0bhhDtqB9FGu2oeux3ilpna5QLxcfYxzSFc1z9jeAKEkwZlKmVOP95GPDPghgweW8TTEV8eY2EONdNUys9ZFWEexbdGTCW6xgeqWKG1BqUoJZh2eBvf5MSKpYbYD3GZAzpiJbvUcl0FcHVZav1LR7KQECfLEefR0Czc7Bb5PSR3/8m7V3uMcrhYSXjWMY8WwEWJjQ/1KHycF5WREtlynmGix6o+Y3MowExEu9iqryChFDMe4IEBLjaw3ELZSG8ntPqqsFCvO84gmJXNHO6y80MCWolK8OGAE8j0DVEdhS8POQz6UjvQTIxqDFEFl9SoDSxSHrM4mnFit0whzbnloDW9nk2d+4SZEqLDq5dcOSiBebo+RhUGkZaWoUgqTBHilpihHhGEDayXr/66NPRcRap/RdQl/55/+CO97z2OcjTycAZTkvR96jEacYOKA1Gic9HAO1G6GwCdp1pjWHuOf3Y/8gCVYq+HpIYf/1vM4IXjid28ldjnKy7CdCBP7pBOW2k6H/oTCjj2mXI27vuV6NvpDMmlYH+e8+OxVkkFOesRHnfCQwxwjJf/0Q9/GMKwzMbZEuUENS9549Airv3uWaGWIbgSMjk6yfu4qrpTkmUHIL31FMxkn3GlanHW79AY9wqmQ/fEyxxq38nz3U5wofo/G7Ax5eZAbZ/fRtLvM12YJZMJHnjlDpjWPnrzA4aWpKmDYgafU1/nU+88ThD4Hl2ZoEhN8E+1rjz322GOPPfb4+hMEAe973/t45zvfyfPPP08cx9x6660cOHDgq17zyx6kXLp06at+kD2+Przlhvv58NX309q/S8efZj1vce2DD2BLgXf7HHNbVxg05tiZk4AjCCQTsy22WyNGtQB9/Tzzz3Sh1DAqEEWBnk4o5hJkTZE8t0btdMHO2bNwbBbfCGzk469o5NElRg/nlLlD5z47KzFyPqc4lODvZjQvpqhth/YlSd0wfbDkvJqDbkZ0YQuBACEIuxa6OboVUC6FeHOwtRBTW1Nc/dg+vNJhfYHKLaGxDOcSwjNjhvUYGnXwFOQOc3SWxvoAPB91qMHwlnbVcDGCvO3RP9pECkHQc+TTAqUhiCyiKSnfENJ4cUz6QoDdGLP6+zXk3QIk2MRDuZL0zjpIw9SlMTLRmE9ahA6ZPKEp6jHJOtQuWXYPJZxZmedossuZC0tEqkQJQzPIGU01IDXY2EMCtsjoHKuhshxqJe3jBdp6lDN1tpdiJJaF3jaz3zPg6kkPlxeUccB4MUIVEFyxWKtAQX7dFHYyofQcUtSYUClHvmWOz65qzL/fJVzwsOcHTHTH4Bzl8hTSWPb/8A47vy0Y6glM2+HmCvyXUtL2FEJrVK8LRYksNHbcgeZMZUfKDWRllbchoZjwiGol+laH+lWDQ+CUJL1xivDkCCUd2WQMsgrcHR72yOcSGk9v4ZzDRgrhK5TnQZ5jYx/dDChqEI8s+WILLzeoQY7qpqgkxIoMfyCrUNfAZ6BrlIkib3uUdUltN0VcdoS2ROz0sWmKzEfktx/AT31GE03G+0PijZL47HaV5YHARAI7IfH7ASWOYl5SG7wc5Op5uCwD30dICVmGBORA43UyhtdN4mKF65cIWdlxAlxVW+tXzT92TSAajp3XTBKuj2k/0kO3QqQFZ8AqoBZC4OGt+UhfoUVCOpcRbjnQJUp5mEChjMRR/QzEcISsJXi9ETSbVcvQLfOQG4LtFAKPzqvnCXdyUB75okchHPVrLUb7fWyRE23l6E3LUMfkOyGeLAg3M7xuhhoLaDdxukBNTKAyg202kHFY5cboYVXj7Bx3/blVbvi2Hb742zN84ZFFwk7V5ERWIE+UuFgyuG8fzjmEFXj7LPnOBKqnEZ7ATElcx7AzrNFZb7EbWKbbOfIpHyEVlBYjHVgQ2EqOALhyXNkUjQWp8HoZ1hPI/Yr69AgrBduXajR2q3Bc6ykmp+oUcyH9B+ZQp1bwrw25fHqdequG3OxSLE2RzwV4qcU3hnhLkl8ucedzZhcTRhd3QXuYyNBxCcZKet02CZXSSukRrU9OU2tvUVy0+KcNZmGKlZbhujsPgie4fXkfoZD8/u9/EZEFyJ2UeD6lv9nEKYF6Aoo3CArf0HpuGxnHtJsx3TAAAcZzhMe3KfsWnMBTkrgW8+LzV/jw73yB7/j+e7jl7oP8yP/1Zq6+tM72gcd5sX+Z10zfzVy0SCe/xnX1C9x8wxpnXryPi1c6JMUUvcYu737855HjSTztuG12lke7qxjrKIzGWPsNOfv+NI7dsMCP/bWHiEJVPa/EN5dXe4899thjjz32+Ppz8OBBnHMcOXLkFWXKV4v8Gu1pj28CJhJFuGFZOTlLGcDybWtIVb2wtcphvZBiJqHYX2Pi9l2at2xx880NQmLGLY/a56+S9ga4oqhiijyFP9YEm2PC9TFSCFQvpRn4vPotDe58W4/9t9bACUzo892HTvMvv+8P+P233U9r8ijjtmB83RTZW6eoz2tkYSinAtLFBa71p3BRtZ5LM4xy2Nk6yZamNhIQFAy/Z4L6jQWRFoRjgRIS1S/wB1VWSO4H0EpRmxnN5zepHV9Dlgbrg8gLRKERaYZdK5j+/cvUTvaQvRHR+piyLdDSEHYc8ablVtMkeLIge6rJaNCk9b0jpJ/BOMeNHY0vbjP95ArB+hBTk8THBRMfs6hYY6wH001kADKzMM4oE4+iHaIjycfTY/zf/+G7ucYMKrME53M2r7XQh2YZ3buf3o11tCyx0hJtGSa/0CHsGnq3T5A3HHkDokaJmAfuVez++iRubQK3OQWpxEUeNpTY/RHhmyD5y+A3AuLLGf7IEq6NsccNWx8UFPvqlEHAaEcS+R7CWXCOwBty030XaN8yJJgqUUWOHGuSFy1lM8T5grLpQ1kghiPs2gqmzBBOUCxNUM63KReabL1pCddqsnNng84DCd1rHsboKhNDSMLA0rinoHFHhlrrYBQUcwmNjZDZj+4gZZV5QRBStIPK4pPE+FtDgq2UiRcz6tdK7GSdYjKmmKkhhEClmvZJg1QKji0j9s/TDWr4qWCiNmR6rovc1ZQTCbaRVBk5zqED6C1CWffIFxLK2ZB8LkTPtdCzLcYLHru3+KitPqLbxy9hcNME6cE6LvIr1WKSIOs1nFLYssRqjQklV76jicoN0XpGeGYLd+EqYqeDboSYVoKTsvpbq9cg8Jj+xDXUah+X5Xi7I+SoQBWGeCXDSw3OWfwdjVWuqkpOAorZGBq1qsbaAXGMyUaIbr+y+DgHqrLrCGuZDVcINIgwAASH2w32yQ2+/Vtfwt5vUR6gFNGWpdjXophvVkOwXYNIDRQG4SkUHiIKEJ5Caosc5lgpIA5xw3E1AfIUot5ANur0V2u4QrC9O8Hw2BTUIghDGjcVfOuv9BD3LVFbyZj9D33u2l3hjW94hqbpokRlsZo73CU4BK6jUJseXFG88OwRtj7TRqYlol4juLyLf3ED5QeIfgraIn0PqRS1mYLv/9VPk8gNxHYH//wYvetTbIeEykFU4FyJcEPe/vd+mw+8+CLBUYj2N/HakyRRjTvuvA63b5pi0qesS4y0SM/hdSzqmqAzI3AfHSCvpaS1Pnqn5P2feBUf/PT9qMseLpMoaZk80Of1P/4cYrAPsTRB9+4A61uUgcceOc3v/d6zfPxffZ7NJ9fI1we0Bwb/9wZ0jyfYVoSLA1wABAITKnp3T+Ain3YY8Rd+/DtIDzVIJyVyc4TLLNOzDX7o//xOAM6cuMb502u8+MJVABqTdW66/yif3lrn8V3BlXQXay1nBgVqNMNHP349H33iEv1RRl4aeuMUbRzGWKRxXN7osjMeoXIwA43R3zyDFCEEnY0ef+//+k3+/a98+hu9nT322GOPPfb4+uC+iT6+iRiPx7z1rW8lSRJuvvnmV5p63va2t/EP/+E//KrW/LLHME8++SS7u7u86U1veuW29773vbzjHe9gNBrxfd/3ffziL/4iYRj+v6yyx39L+pnH5568Hu+SwuaKB244S7fXZtNOEGxY8o06cX/EvqHlxldfRRjHC+8ekGeSxT7Yiz2EkLiDzaqFpSghTog6GuEkzlNIHKnuszE8z/Z8gxWZIOZjlILwhQ6PPdHipTv/GXP9v4LK1tB1Rz/3iO6sczjdZO3RHHu2Q3HDfpiMkICQEhcowilDpgUuCBkf2EdGjXzNYAcWoWVVZ2wc45Zk8CqLGwfEHYfL0yoHQjisNchRSXCpi7MO3Q4omwH+RorMLF5fY9oCM1UiMg06xhs7Tkx0cHfUyKc8/IFj9cwMMh2CJ0Ep3DiHIkdJH7ltEE+vU+xLKL+QoFsBReIT7o6QtgQpsIEA4RHupJSTNYq2QlmNCwR2O8B1JaYhyWYDAMbLNWpXID7dI7w4wBsYiuWQJN9BOsloukZt3EN3Nf0tW4XTBgIvddSu5fhDA4SkW1NY7UNX4vkGmyqC3QKpwZSGbMnHPTiL/4JCfMGggjF6C+SuwG1o1m5uks04zLrDSEHaUkS7IV7uwA/QNy3h+hlkOWbaJz69jhwXuGYCQhFvxmQ1gTKCrXaDYL+i3gzwSoWIFMFNmuYNhu1/JlAbfdxyEycDZHeIv9pBBCEilni7I4TysDMt6I0RziGHGdQSnBD4Oxml7ygW2njDAn+o6d7nIcea2oWA/nUBonCUbZi6voOUltGRNnotopBQu1apZFRuUalFWEHQMXgG/K6FKEAMU/xTK0yeNDA0yIGGMGTmhI+NJsgPGIRJCTc1wlgoy0oJYR2cuYz+npsp25LwqSt4Gz0AvGsd9Nw0rshRUkAYYRIPl+cI5xBBglBe1VJkKzUBzqIPtOneVEfXFS4UyFH1eP5OAb0hQnko7UAZmGmRd4YoY3DKopIEK0CUBaPLCmlKLA4nYVyO+DPfdYkH7t9mfC3gkZ1DCGMRUqAbHrbmE62VmIkQdIntj8HKao+dflX7naYIHChZWem0gdLhpKBYSignAm46fIKjfs6JqR5n1+cqKxswd+8aE0cGHLjpEldONEguauJ7CzrPCMyLA6QqKKeXqD8wZvh8iBo5go4luDLEVzXIS6SQuLQEZyHLETJCXdrELE9jEwVGM+zXuXh8lumDA8YbAWorwzzVxs14HP5ftpBacOmX9lHO1rn46GXaoSB6U4bdkYgwYqkW8z/9uQd46ekrFHmKTS1NOeDBv3WCzajOH37hbmYeG5HnDuoJ2ZEIdUAw/5EC50t0ZsguR8gbU66rrbG/3Se7sAUIsjc3sTsweU6xsDjBr/3Dz5KnJZ9/7CWaFmxuoRaDJ8kaEOzk9G6vgarqoyWK8eEW/+app/ixo7ejtEPEPgAWwfKbbuJHf+oHAXjDd9/J7HybW+4++CVnR8PfB+4CFzv/hkIH/NrFT2BPL1G8OM/QFYwjzf/82g8RRJKd4n/nkdc9Q8vv4W36HKxNcE5t0YwjfO+by0Lz0Sc+xZX+kE89+Tw/8mPf9o3ezh577LHHHnvs8Q3i7W9/O88//zyf+cxnePjhh1+5/aGHHuId73gHP/ETP/EVr/llD1J++qd/mte//vWvDFKOHz/OW9/6Vv7iX/yL3Hjjjfz8z/88+/bt46d/+qe/4k3s8bVBchudw2eJN3JqZwyf/sDtjA5FCE/gpRqpDUEQ0reW/hcTkt4YaWL8zODPGLLpNlK/3Nrj/dGFnMV4oAoLoc++Q5NsbK2QpZKl+g79Q03um93k4lOSxz9wkLY3xP1hSn/t90n+xjzj0Mf2EuTjXfKt4OUmrKpdw0tBWYWoJeBgWKsx8gR+Lunf1MBmsFPGhJmljAUqEzhtGB5M0Fai9+WUmyX+2GECQXp0mubFMSDQzTqipaEdkx+ZpH9nCycEjc0Rg2Mx1IDEw5UCqyBtOVSo8DxNXGoGz9ZpmhzRCKoLY+MQRYkMPHSo8HLw1kEcFPgY5GJBsaHwao7Rco3mxRQhBKLUjI6CyhyqAOsD1mI9wAq8oQEc8XqJ3ymRvayytihHNqVoKkF2IUD7HkooaFjkboaw4BoKNx+jxlUts64rUllHbkKy3ScwGiMjLBasoIwFQdOQKYW92VKcFwRXLU4orJDsXmiTtQXmU7t4YYTyDV4hcdJinUG3fEaLPkEsSWdiwtV+1aoSBpiJOgLwUoHMSrLlygKVrBT4LkQKy6u+7TSte0o+/M9vxeSgXJ/6SwPkTY72n+8w/PcSs141FQlrUaWBtsL8rz58CIwXI5TC+ApZVheL/sgwnggZz4RMPJmRzzk6NwS4SOIJixz6dE+18JIS3YsRvsTKly+4ncMbapprEheD3BwQdUOcAK0c4coO3jjHKsgONAlyiZISSkvaG+MdnEVQRzcs8uo2TpSoogQMuh7gjSRWQbjefdkmRDUsGaTIcYmQEqGAlV2E1pQzCdJRhbEKgUHjihIrHPzPmrrN6J6Lcc4CDj8T1fAiCqAwVR7JYIQQNcShJbbureGUoPXimKBn8DoOs1ogzAinwRlNen7IU2aJEI+ND8Y0ujlOVnYmr5dRToTYSOFsCZ7EKwwuyymWGrDQwDvVq+xPOJwpQBikMeB5CE/RuX8WkyhWRm1uTLtcvWkatQPFdA0xKphbHiALRzH0cMaBlVz7+ALrOz7oDJxBnL7ES7/apH6hQN3g8HJBUEhAY01eDcLyHIxB1Gu4wMc3Fv/yLumhOWw9Qgh4+rM3Ez6+Wv0H53lEJ4bk90xRFD5BXqId+LsSWzr8SFALxgxsHYdl5eIG/+T//LeMjMRPHUF3RDI5QtQs9UaKqVusJ3FKYpWithkhU03RUAQDw2gpIhAho56i9HyGl1uEEznWd8y8P0aHiq0bFQuHp5iYqXOls4PyQ0xSoxikuLxEWQh3SmQhCLdBx4IpDer0mNGN02hjuHxilXAro5j2YN8MCMujG51XbC31ZsQNty7RaP1xu45xhpV0i8IasNt0i7Mkss/ykQ5XtsALBW1tmW0PsM4jWtC0bcaPHf4C7tjz/N2Lr8W8KqLRn2ZusvH1Ou6+LJq+xStzpuu1b/RW9thjjz322GOPbyAf+MAHeN/73scDDzzwJXbfm266ifPnz39Va37Zg5TnnnuOd77zna98/lu/9Vvcf//9rwTQ7t+/n3e84x17g5RvII+un0InJdmdFv+aID0dsrw8R3wkofuJa+j+mOEPJbgbM84/P8fNJzfJdgskiuyZBLHYRHRHuPEILQWi4cNUDVMPYWeM3O1xdTNFxjBcjLE2YDut8/h4Gu+JXYrFgLnZXdZ+3yefsyQHBszuaDZczPhbYsSljAdet82p9X1s1T30OVFdbGYprtTYlTrx2kaVubG8iJMeGkENH+cJdCKwwyHRJYXvlzQ+tUPu9pMtx+jQQx8CVgROCMqGh5xOQAmCAyMevP4iT20cRD7tE68ZtCcRqSE8u4FzhvhFSbAMyQ9k+Ect2784jQxy3NjHaYPzJCZJcElMGSmizQHOGpxMUfs8Ri/A4FACUQ1Xgp6M8XXBoaNb2HWf1NUQxhF0M5wDHQlMrJAGZAn+ahfSEqIQOzWBa8cMjwWMglmS0IH0UAcc6sYBNzzXY+UDNbTwkKmAwmJCQTrrYz0Id3RlrdjtUxsNIQ7RSYyd8HCrHtQ08ppEHt9C3upRnA/JZmoUuz7mUw7P9JE4pLD8wJ+/nw++5wmyRkjeFoyXPEwM/nZBtpxQu1yiFyZBSqK1IclGRtmWRJsBt5gV+o8VOC1AOBYP9Ln62CTipRG+kBhPIkpNfTKlfrtBeSHb/0RUoakTdfKFFmqfw38kQ6xpCDXlkXmcAuubqsXHGILCoj0JAoItQawLZKjwhyU28JGfC6DnUE2B9i3hsApEdZ5FeBJXj8A6vFqMdhZZaoLtIUIqnADpBElPInwPsgIzGuFbjdwMse0a1pfkty0ix5rdxpiJF8cM759DGEf0mXOvDFFwYHWJ29jGFVUQrVXuZauOwM8cwfleZW2SAmkUwpYIJZEHHVzVCCHQbQgGFjEucL6Hq8WQ9qoqYiEASz4N7uWfiW54hCNbhS/vDkBqZFgDqwgvDTi13ebcY5MUcxHUFWqnJFgZMblm6d8xj55JkLnErY1huwd5gd/vM3zdUby1HqI7xOx2sIsTyNBDWokdjXBhRG1Qct+3nmB0Hv7+/347ermOmZwkXY6RaciB1wxJd6e4+sw8yu2CM+gIbCMh6Guk8vGkIw0CbKQJuprgXA4jTZE49ESMZ3WlBGo0cKUm9VOSbY1zBtu0sOFAQHcyoLU0Rbw+AGNpXR6xefcclz90EH8zg6AguTZEdQawbRj+osD5eTW82x2wmg0pFxdAOPxRxnADnvwX17H6QBsU+AMLoxRiH389Q6QF3QdmGC9Vv4fa3Ii4XvChF26n0dLc9vcv4l+Dx/7ZNF7mSDc0ceBTNBy7dwWEJXzH0nU8+ukXSCOL6xlkGEHo4W8IYid58I5j2Ouv4/j2OtobszrogzYI6ZEenSA6OkAX0StnxKd+74t8+H1P8p0/dD9v+L67gCqx/sTWNfATNrI5Xj1xO3PeDt829wwvTPW4OpzgxJUlgs40n+cmzmaP0xwdYLwzSdoo6XQSROx49W2Hvu5n3n+JN73hu9l87kM8/NBrvtFb2WOPPfbYY4+vC+Llj2803wx7+E/Z2tpidnb2T9w+Go2+6hy1LzsjpdPpMDc398rnjzzyyJfIYu69916uXr36VW1ij68Nz3euEQWa+YVdhodD3vSmk9x46zncaockiThwywEm8gT1Gz14qeDalsKaEkuJrvlgLNoXGKGxzjA8GECu8cYaoW0VrCkE4VxMPidpyzG2EOybWqZ24zT5kRrX7j1M/PZ5Bj8xRz8MaDXGyNKRpCHZcJpzT+1jolfwd7/rcSbSEpQHQYBQCgIfMT+HWF5i6mTJ3OdTGpcdJlZYSoKz66hxTv1qRvLULvJqgcxKysU2+f6YUgVkbQ8TK/ytAd5mH9Ub89pjL/HdtzzLG2dOIQqNyhwzJ1ImXhwT52OiawPC1THq+Zz81xVb59qYVozOLXZnF7uxidvcxiSKYq6Gcg6bjmA0JP8WSx4a/N2CZG2E8wXJjgEBMwf6vOFHXuDeB8+RT0HeFpSJhxplhCs9vE6KlQ7rgfB83NwkIoyhniCtIFwv8NccoJCFpfk8PLB+gcGzMXpyFkQTCxCoqillrImvDglW+2AsTpcYzzI6UmewpLh7eYHmiT5HTm+w8PQ6bqDpXa2B9KldGuJf3UX1x/QfPASJDyLjs+45su/ReMOSZK0kWSmpX9VQl+h5gW0FeLsZXr9EpiXOaERpmH1yRO8TCa6QuLIKxX3++BFObC1W2SCeQh+YpTgwhS0DxOWC/q+NYDjCOUN+vcJNK0wngMtVq5LKSqSxqEyTT/jkCehY4o8MXq/A9XqkjZKdN3p07nSo7T5qXODKAlyJKDVhJ8ffGlbhsEqBqqp4hXU4IZCBj4sDRFkpbdxEA9FoIP+otrgsIcuRoxyxM8DlBcHOiOhqD29cMrWikM0G4XpGuJ4SdqohYWUVdWAt3lqn+n4pkVJS1DzsZA1lJQQBNo5w9Zjs8CR6tsbg2ycRj/mE/8YjWhuhRiAKh00CTM3DNSu7U3W1nlDMJnTu9yE3iFQTDXsE13crdc++CVwtrPZUGMreCFkYrCurOmBPomXVwCI1CFvVBKt+CYMhdHu48RjyknArRYwLEFVYs1Q+Zn6iGgQphS0K5CxMzg144HVb/MyvvcAt0+sgJPXzA7CGsqcoxpay5pPvj+i+MSFbsvTvnqM4OAVJiAprTHyxi9weEm6O8bs5wU6GZwReN0dkBdZWVicXe+w8NFMFZjtHWQcbSXQzIDk8QHUG2LzAphlilNM6UyL7Dgd4Q/C2x2AcDgEGRGmqnBlPMfnGaaRyCEBPxZhWjeGTYxq/tEF0MccVOVBZ0NYeEqTLVZ16mQh0KDiysEXol7DkSBuCoYuIl8ZYaRHa4fcNSeAz0agjhKDR85ldjvHuMfi3KKaO9aqAXgHJtqO2Yuic77B1tUPY6BBPv8RaeBKhFOGuIewZgkMl0Y5+5YzI0oLhICXLyldukyh0ZrAWZv0dTn/+WbY/NMeqXmBzZ4rVy1NsXZzg8VO3c7kY4oUppnaeR55/mGf7M/zgDU8T5oKm+Oaz9e7sjhlbn5MnVr7RW9ljjz322GOPPb6B3HvvvfzBH/zBK5//0fDkV37lV3jVq171Va35ZStS5ubmuHjxIvv376coCp599ll+5md+5pX7B4MBvu9/VZvY42vDXzjyIP/30x9hfSPEa1juuHeH/+/ZgK5f4qcZGy90qJ3ziEqY/JaE7hGYfWOb7iAkPzPGbKYE/TGjhTrZUoLcHeOcQ1og8KuWkbIg3RFMp5oXHjvAKIgIJhTq/5iksyXpl20KKbl/4nlGI8dT545RJIpiqqC37SGUpJV22Lxkkc5hpWPwmn1EHfB3C8CjEAJ/N0cCo7kYEwqCtYJwu0AgsY2EzkMBxlqiyzGeNng9jWlIwpFDWChChUkk2b4Ar15gm4alo+s4cRNIifOgXU/59ree4onPH+Lc01PUVzOGokZ6tU1UE5TzMwSDAvICF1QXhyCQuaF/axs9GdC0jmRCU9xrQUhuG1/hwmCKrIQo7bO7XefyYIrBQYWwMP1oXmVQ5IZgfYDKNKYWIYXEKomqGeiMcFIweTKknIiwvsPv5AinOfuZfeihw0w3q5pbrSkjDychvrBbNdh0xog4pJxvky8mJMMA5xmenz1PcIdD3+ATaE3+nMU/vQ5SIuIY4Ry+sYRLbVw9wsSazm9t4c1JnBcghGDyVInTmnAT4tM5wdVhpdJpheh6wGgxwMYhrpviMoOoxS9baHLOPjaBDhReUA2a8BXCweB0nXHHoPMhSiloJwT9GD1lcU6RLU0TbI6wjYjSOQIL/sjgxikGheilSM8wOJYQbmYIXSdYz/FXBxjhI+cDOOxQlwtkn2oo4vtgDLoRYaNqmKIyixAKIT30ZB2iAJVnWFOitvMqMysOELKJS4tqje4YEQRI4RCFQ3YGIARJFlNbteB7VcyJACclUguc5GVFjIdwDtXto4ys9mJyVBIjBzlFFLD+F49QzwaYSxnSCbyhwB+CLASkKbYRIQcpJDHOGohC/EwiU0e4k1eKpxGUq/VqcONDeH+N4lN9nNGAhwsVqlMgtaqUGaXDeR7WFngruxTTc/ijksKWBJ4H1oKEoFtiIw9pLSIKUZ0cpzW2062GokC5kXGqt8ChiT5SjZjcB7XHK0uYzA2b59pcfWkK0/TJb6wzvtVHbQVEZy3jpTrSCFT2pQGmYlRAaXDOYTyJKsaARZSamWXNVnEU6zQitYRfWKM4PI+a6FF/+wrm6H7YXUdEERjLeKZg8niJFBYjfBSO2cUa6ysdiAKsNaitXfSij751muDFXbrzHvmEpP7CkDjN0UjC9QxdkyBVNUQKBDp0hNsaF3gIbcidz/qVKdJ+wmSn4PHRzehCMmUqxYzzq+P4r/3wtzL+jT4vvrDNe37l8zQWS9r1lPu+6yIf+blZBAqpHaameDpfZyaVvOa+AzzdvUq2aTCBD57AD6H4g4Qo+uO0t7EA004ovOrFwzOfOM6jf/AU5vU+zmlGVtKLztLNlwlzj6nxPB97bpFASh4rPb7r7ue4Ukxy38Er3DB9kvl9O3xq1OS2mWtsj8b/jU+4r5zdxg5rbp1blhe/0VvZY4899thjjz2+gfzcz/0cDz/8MKdOnUJrzS/8wi9w8uRJnnjiCR555JGvas0ve5Dy8MMP8xM/8RP8o3/0j/jABz5AkiS89rWvfeX+F154gSNHjnxVm9jja0OeruKf1/gdgdWaX5+4ndFmQl87vDunmfrYJXRq+Z4ffZDjcof+7+yy+6nLTNyxj7XliNbJFUgFKguIr+XYokSMSkw7QQ1ThDa4HHznaBcp649k1JeGHDv8Rb796Ks5v3gHHzz/Eme7m5zI9iPHI/xrGm6z5EcM+WHB9n+M6Twace6Jw0h/iIgEys7ilKBoe4wnDEFnSHhljWxfgo0n8YYWvJDi4BROSagFuBmLaSni5zTCCYyU1OezSuIvBVLF2LmIIMqxI0mmfWwuEEph92VkLcN9N2zQXCyQB2P0Sg26gsENNYpJQdixhImGO2cRgw6D2Rqqn+B3c0wIu29YAKVofXJAVloYjVGtnOz7QKxIwrUB17banOm9HqcE9dkcGyik9NDtiBJFtDLAuai6gJIStMY/YNBnfUQAVjuEdeCgc2uCOlAwOzmmeK+kbChwUFsz2F6OGKWYQQ9h3MtVn47B7TNVmO/FAgK4VOuQLsbk602mN8aE9RT6fRCuUmUIgXCOcLPAtGsQFXgrKaojGMx5CF+hw5Lx0TrRroORoJi2KKtQJbgkxCYe+WzEeNqxsD5EhGEVPjqssmukqx4DC2o3hdBHFpCNG7iFAG9cIMYWk1lkT5A3Fdb3EKXF1qqLfrE25palTQ7cvcknP3oEsZNi5iJG17ewB2IOyi7uUylikCH7KaYdoc57WCFIF0OiyyWeLhHGoroj1O4I24yxfnWBCkAjwUYK27BQOGTX4IyB7hAbh6haDJ6HH74cDKsUVoBp1aAoEJ5EGIFtxpX6Y3YSsbVbKSWsq56naVYF52YW4UtM5GMnagTnt0EKmse3qZ/tIaVH3nSIWqPKLhmD1IJ0fw1pILam+tvMMoTyQMHCRzNcZ4iyDldPIKtCQOuvy7nhwXUuD+psnZ+gnGvjDzV2NkEUBbKXotolOneIQUHUH1LMJYhSUX9pu2oGUgo3PYW0guLgJPLUBozTKvsnLwAQpQYB8Wcvs/uaFr9z8jr2fXiGy9fmIKqe08IYoutKarZArPqEa6BrPnLoEW9nBJ0MlIeOgOsXEaXBu7hRhcoqSbA5Yud1+1jY8Yi1ZnQqpbMBLV+g5yfxtweIdh3vzAZ+PwN8aj1D6geIvKj+fi7l+PMGV4Lp+1hfcezWZa6tbmHmJ2GYIQcp29/X5moyYGJZ4nY1ToY4JXCeJL1pjmKpTfLkdvVvN5bZD6cEmcCIHhJFEIy40J9it9dE9CWz012yD4fIwkPicBaEFWz3htRbMdt6C11YhFRMNRIe+sE/oDE5RBy4E3fexxlF2RRkdUE6W7DzxJj+hQWywlFMCaK1MUJ4zN60y/DMH6tPDh2eZWauycHDlbT1qY89z5knzxMcDRDXlWyYNtcfPcqav8LtzRVedUPEB59chMhxx10vsjqaYKNo0aiVLC5eYyXfx2reZD2d4kdvXPqKz6xPnb3Ae5/+Ij/2qnt59aHlr+rc+3/j7AsXMbspWxtbX/O199hjjz322OObkm+Wxpxvhj38J7z61a/m8ccf5+d//uc5cuQIH/vYx7jrrrt44oknuPXWW7+qNb/sQcrP/uzP8gM/8AO87nWvo16v8573vIcgCF65/9d+7dd44xvf+FVtYo+vDR889yilFxECWpZ8oTxIG4kNIZ8NKH1LnCR8vNNlPJEjQ0doJf1WSePTG3gbQ5zvEXUisDk2LxDtFqwPYbdbZXtMNJl+i+bAkV12vjVmKc+4f/Y47/tpSf/m6xlOnWfeL5hqzHOxNKSzAqsduvBQ2pK9KqTbb5NsetSuAU4gCosaOZw2qKkI01bog1Pk+yMO33iV6yY3+eyH70SrWhWqGgtCDbVuTrSmwcF4LsY+qyDNcb6HkA7pBH4BH37qPo5vHGLnTI26Lshf6xABfPZTN3L87FHWhy10PYdIciDepnlozNpn92MIERMZsq6of26Imfdw9QRRCoKegwMpwjfQF5D7uI7kpY0E9ZoS/3MCedlh2gHOVzQujOjcr8j2KdKpiNqpMaQZYhTAZIMyFowOBTRPl6jUoE2AcCUqL+i+bppiSiH7guE4IL7WRQ1BOYktgdAHoRAIioUIfWCeyaRG2BthfegteWTLgrIbEw5H1IYjRt/qo+s11OdivH6JVzhcECDaEV4+xrgIMh8aNWwnI7nWR0/VMIEE63AKylt8/JfAO9nFehLbjPG2hihpGdwSAQJXatDm5Y/KelFMxqhhiQ0VJB7kBdGpfiWxsw7KEnlhE314FtcKia8N8QYFtuso5xrkvuOhh09xYP8O66cSnh/tQzcDhAPTlAz6NcKDHjIF2wpAF2ipyWYalA1FdEEjHRD4dO9sMr61TvOCxS8UIsuxnoJSI1c2sFZT3DCPX68GdE6HMMxfUWXgwPkSV+iqhjqJ0WFE0C2gtBRH59CtkGA7w9/xqgGKcFUdtLXVEMb3QEhEofE2ujhAKImzAukkrtcnygIQJbQV3jhBaEG0UyJKh8xNZflQEiOAUldBtrl+pZ7YaoMTgh1T4+lfvxF1touyILZGSAu2XWP3jga1C3BoYUTnnM94YCHVqEFONtciSuJKOZHlMNXEZiVqV+NwCGNwo3GVw+LABT5ozdafOUARhfheyehKddxIKZHKsnD7NrMH+yRTBc99vuSWbzvH9qDO+Y1F9h1ZofP7AicV3R+e5OCDa5w/ucDEv0qQowzlHDIHh2ZmPifvBMTLNcZXStx2D9VPEdoSvrSFmawjfA9rDNc/vMTzvz2GNAWgzoCH3nGa3jDhk//iLsKLhscfebFqCRtlkGWQFxgnEH2HLDy8sSG6UCJFhPA89OIEpi4wbYnY0C9bnSR6MsFb6VG/0IOFgmEZEi6NmL82Rl6TFM2QcNdhAWEN/mbJykaX537/Bfrv3SL0ILtjkQcfvBeX/x7DYYLZ9EBJwhTcqMBXEXe5hM/oHbKjEa0XRtV6whFFKYtv3mZ7/o/PiIun11g9t8XJZy5x8IY57n7L/dxwzxEu91JeHH6UZGGRdrgPf+IJPp8FzDmPkTLYwhEHN3Np9BKetLRlSSQcF3WTFTPDA/sWedWRA1/xmfXvnn6Op6+u0AiC/yaDlFcfvouzyTr3H73ta772Hnvssccee+zx3wdlWfJjP/Zj/NRP/RTvec97vmbrftmDlJmZGR599FF6vR71er2S4P8n/M7v/A71ev1rtrE9vnIOze3jhO2gUkdtn08/ETSaDehmVcvIyJDJnHE6Rsxpgh+NmfZHbF/MCC92qotYBPgeZFUmCoANPERvAL6P2vVQpxdZ/q4O4feWbO7M85H33cFLZyXd/Aq7r1UcyDWz+77I0I+5OncI54F9zGf+5Jibl7YY3aJYrTewv6UQgUe85VD9FF0PaVzKwDrKWoJuhhxpnGV6vo9oa8Q1QbiVI4qS7k0afcgnnw6wSiBHBv9SF7k+gEZCvtAgXO9ThgLr4PzuPPFoTPPqNsEnGpQ3NujNxOi1hKSnKRLF6rc22Ddn8Z+1RKqgGEnExS3EFpTtOuVEiFMlumVpnzaYboq/swO9AOdV9b+q69APahASbyfAyxwax+AGn9FBR9GGpV/bQA5SXBigShBbQ3bviRkd8nHZFK2rthpWvHQZEXmE63WMF5Kc3iE4vouZmSIYZAhtcfWQdKmJGuXIso6fakrlMV7pE6gq66KY9CmkQElNsGZQY42Z88iaNcTdyyRPXkJmlW1q6ScHpMc9Vj8zi018xhMxcW+I389RRY6/OyBo19D7WpgDJcknt5FjQRFCuuDo3TSDKiRB7hDKq2qawxBnLE5JRGEwsYfvHE5rtJH4oxwnHGXDw+8WCMBO1MHzCXoaVThcoCgTQbQ+xnqST33uZm6YusS1sxMoK8iiADly2MJDYPB3BKJex9Q8BvcY9FRA81FNuDrC76QvDzRAL7bAWdRuF9WHcrayTMmuRQ5zxFSDIJeYVojQIEqJ6I3JGjHhOMc2PVw9xAjx8lBIE764garXMZFHPuETXN7F6+QIBGJqEhsoyskEdWULMRgjagnOkwxvmCDYHJMM+jgVVCqT9I/2KsEa4ktd9NIEKq8yTFQBOvFR2z1EmiOVBOVhvQCaCWQlTopK9WQs/omAMgQ3HiLTEhFFOE+zdGgDszvPYLYOkUDXaoxvbmOTGNsMkRtd3GhUtQQd3g+ewjY95GYBrRpuq1uFmwmJE6aqggYaJzXyvGV8yKKbId7IVIokY9h6rsGZzy4jtizXtc+xsH+LCXq4SCAf04gUXAh/5Qee5KHZFT44s8wH/+kyrhHjcouwFuEEvSDk6HU7XDsH482CYBtUEEIckR32MaJAvWgRMxEXXnMV9/FapZCKAvTljGac4mPwVsbolk/qQf28RGkPq2JoWGY+NKZ/DwSnUvxUIfMSkwSVrctYGuuG635oi7Wfiap3YKzD1H3CcYoJPditkTzfh32Oa3+wjCwh3e9VDVKtkuS5lGK2zu988Atsf/QM1jp8GbBw8xwfv3SV3dU3MXXlKm6kkFLiBMS7jlo74MAw5OnGiCTJSPaP8I4LwlFKvjHiwj+OsO0Qay1SSi6cWaezPWDt6i5/70OfojNO+TsPP8jrF2b56NoWF0enCD1FbgOcMLTrY7wpwbBwrL40SxBP8V2v+gUGyvBs3uSWaJMXsmnum58nCb5ya++bb7qe9c6A777x+q/VMfglnDEjVoTjycurvOG/ySPssccee+yxxzcZe4qUP4Hv+7z//e/np37qp76m637Zg5Q/otVq/am3T05O/ldvZo//Ogq5QH5sh3BHEG1GHIxC9t/Q4lKqaZ/Msc7iO6h3S+yKhzjjKI7WueeumM8LgZBQ1kJCW1TvtpcWmwQIKXHL88hc45Sic6mPv2lxE5KwPuBEtkxZi7jzDTdwudwgHzjOPqLIpCNaz1D9FBkFJLIgPR0iLxva31rSOTaBHGnCXY1wlqA1psxDRB/Cq11EFvHZxi00T2oGMiRqWOTYEPYzos2C3f0xbjlGDUqC/pCgX1TWgzQnHCYo6WGHGdYT1K5khFs5FAXeKYvLA+z1VYWqGmnCQDBYkGy0EuotzfK9q2x+UjJeEQhX0H9wAhHEDJccTjmi1ZJow2EyiWo5slbVoOOfcBAL/C+kyF1LGHqoRkg2ExKtgL+do4Y5GEe5P8HLPQgU0RZ4gxHByKuUGa6qckUphHYIrYnWx1UjSlmi6wFusoHzJemsQs763MYq509PYTF4uWU846Nyh5MCryNJvlDSnZ8kGKWYU4ratsUFAbI5gdjcAEryM4LhpxVuStO5NWZypOg1Q+rHNWqjQGY5aksje2P83RZsVyoSURZkTVWFedarYUQ+4RNsV2oJ14iQgxFWOLzmPCCQhUPUFbYRkc/46JpEOPDGtgre9SQqs5hYMZ5PiNIMuVPiJLz4zCTnVyTUIuxESLEQI6Qk6GjUtkUUgqLuQT/D2wYZ+0TbBjnWlT1slEKpmXyqTznhE10aY1sB3es1tQsQDAs4uFiFE2+OMcLhZRo3GFFO1shvmKHQlsalIQwL9GRcqUqMgazAtQQu8ok3c+RGD5FZ7L7ZqkmmFsAoRUkFzQbaasr9DfRsrUo4P9/B5TnCVSoYpFdVHluHMBYTA9ohS4ep+Xh9hROVXUgUGuG9XNUdBdiwslwhAOdwgWD31ojW1YD4cpXXYwvHtSckijVaUxOsBAqRDTAPHkMiETiSZ66B1lgvQFr38u9cV3Y1F76SzO6owrscgFQ0z47BWpIXfYY3T2GGluTqALfbxXQEO39Y44f/ty/SvyHmP77vGDvBJL0yYp8e4IKAvO0T7grELESrAtpNbN1H9MbQG+P3Datpm9X1Ju0LL1WV7Uh0LUSVBbvf1cTh8Le3iBykKxY7UUNdBTEc44DHfv1Wuus18kmP8cEEf2eEXQsR6uXWo+kawaBg4pMjZFrgamFVY50bSCJSmRHbiJX1OUSUguehCoNd6VSqvu4AdyDAn9VQd0T7ewyYwPM15sGM2Zkdss8H1C8PeOrZR6q5mbHImRAtHCtrm7RnO9x07zXW3zmB832MkgTdkskTQ14c7nLH/We488ENRssJn1i5l3E9pLluMCshEwPBo7/7JK/7s6/iz/6Fb2F+vsUb3nwHv3H6JMO8oBmFbI1P4efv5fYIjrX/Ms/3nkGoSTZ7+7mpO8X6cMz+6yfQUZdElQigo0OQDe5uGg6HV1+2FH5lqfefPHuBqzs9PnvuEt9x87GvwSn4pQxWB7huwdaZ7a/52nvssccee+yxx38/fP/3fz8f+MAH+PEf//Gv2Zpf1iDlr/7Vv8pP/uRPsn///v/i177vfe9Da82P/MiP/Fdvbo+vjKV6QuM6RZRHiOcNw0HOi2dW6QtNpEpapSaZapAtTJBdXUVc7lcVnp/t4Woh+VRMeWgW//RuFZI5GCHyEhEGuChGKIOLFd5ETjeKCYWh7hd4VzOKXcVzv3GKa99WRwUhE9aitGPuE+vYsqT7+jb9hibedpRZwKD0cUqAJwFDtuRRS0ts4JBTOWJb42SJSB29zTq+V4JR5JM+rvBI/CFWZrhrkvhCFx1JbOyj2xEi9PAzUTWgCMP0Z9dRMqjyRpoNPCNhYJh+2tC7MUQYR3025UBjiFgo2ShrdF9ooC6NEYAJazSf3yU9OEFtfYCeDWme0+i2R+euRZoXc0wb0mmP1rkC9YgPPQ8bWAa3VxkPRoAoBEJ7FLN1vNTgFQoXeNhGTO1cD683pmxF2CSp3vlv1UAKdN2jmAzo379I82JJcGYdMdvCaYPUJX7P8eYHT/DGA6f4WLmf37twH8Fyg92mwB8apLH4Q03/fg/Tc6TLNaL1jNqZXdxEHRklMN3C9YZsvhfMVJ1C1An7gkFeUs7W6Ly6htrQTH48Q+UWYQF8bBLjjEXmMPmha9hI0X/oOmzsExQSoSRid1hZQoxFNmPyRKHGChNKRG+AnvJQuiTYtIhuhg2CSi0hHC7wcH71+4xkifBdlZWx08MIQz6t0HMxKjU0V0cMj8LW3T7tz2Q0nurhDXJUP0Lf0cAFIc4FFEENfzhCBB79m+uMl33mhxZpHf52iRjnSCOx4xRb8xnfJomPG8TYVcOIJEJphzAv/+EZi7czxCUhUkg4sohzAunAKomuRchYQeihowB8D5nmkMRV8LEqCVYrxZjxJenRKUa3zlB79AqeCPA6Oc4YhADrK7wRVTXzeIwSssrFmWvBsER0RjjnQDhsqekfq1HfdMjC4EyJbgdEY0OUgkyq4ZOwFmcdCMVg2SeQEdFOicx1FRorQCoPEYCse8jhCGcdDocpS5Ae3HwItbqLzHJcloFzCClhnAGCfD7B75bYVoSNPYSQUI/ZyUN2ezXOryxw6cX9mBisUGx0ZnGTlvHN0/z6L07xuYVtLj3XRKQDXOSRB5C95gC+VoRJwc13XORcc4HuZkz0wiZeLcZd7FJ7UmGaHl6/UtapzzQRQwNOI5oNEHD1IyE0ItR8QLRW0D4+pJxtI6TCeA7ZzXF5DlkJjRhQ2HqEKkwVcPvRqwy/+xg7a22mBtuIUQqBjwp8xtMxrsxJNg36gof3UI78rpTaZY/0k03C4wHbk4sktX71XBqPcdZCHDMMJRNCcoQay4uX6F6pI6UCQxVuHHhMLrc5NtniyTNdBsd7XPniFNYTuHbM6OFpDo0kjS8Y6u0EgMPH5jl8rPL6/J0DD1IaS+kG/NTz72akJ3jzzPOk+ce4f/I7OdH9Zc73MoqyxlS9xlbrg5jJgkdfup2bD1xirUw4KvociU+RFQ7HX0PwpaqUq7td4sBnul77U8+sZ9ZWyJ3h8qCLcw6LRQn1p34tgHMFQgT/2fv/n/ylP/NaVM/y6ldf92V/zx577LHHHnvs8T8eR48e5Z3vfCePP/44d999N7Xal742edvb3vYVr/llDVJmZma45ZZbePWrX833fM/3cM8997Bv3z6iKKLT6XDq1Ck+97nP8Vu/9VssLi7yy7/8y1/xRvb4ryf0d6n5feQtKXE05OruPAGWLBTk901RuzpmPDPFZCYYzAnGFxJ0ZwzdHsQBSvqEmyU0alBqXLtWNet4VQOosBahIYgLTGjZ1RFqOEeaKiyCItSoiTGH2GWiCVc8j97rmwQbQ+rdDE4Y0kKhun0mfnsbc5clW4/xBhlCe1jjo5uO5eu7nN83yWCpTn1V4VmBKwVlXVBM+tgjU6igRbvfQ6SVxcE0QkYHA5ovDVDdDKcCbC3EhCVqXJIvxqgC/MBHlBqZG6QU6ETRvV4h5kccObJFNwhoLmT0fkOglUex2MIba7xcE17tUruwjUt8xg/NMDoQY/DxB45kXDCaUAyXIvyBw48Cyrqjf7vESoe89nKVbE3RfdU88VpO7aUOXvflEF9fgXVIIyobjBC4Vo2d22tgLM0nN4ku9zEHFigPzuJf2qxyIqTE79bpzPkMa4rtFxTJlV2GC5O4dg0bSMKrGRGW0W2K+npJOucwscd4f0S0neFKi51toQYjilzSvWser5Qw0BBJZj6RoqdrjOYjBvfO0H62hxVQztUZ39rC62psMyR8Pke3JC70CbcLomaGM2O0NZXKsFnDGU2wkyFKizesanT1EZ/6DRnDJxRcGsFojGzECKHIWhHB9pCpG7aJHzBs/2Eb97kckZfYVoyfC3KnkUNH/fwQb6RIb/RwRqNGBQiDnlLoaUWtP4J1iwTM4ixOKfAkOpFkiwnJWkHjJYu3+XI9blFgjk0iaeOSEQxtFbRqNfGpNfR8G4NDOoNKDcZTuCjE1Dy8QYEYZjjto0rAltAdoKIQ0Ygqu1P+R5pHhxiM8IYp7sgso6NNVC4YPDBLMe0x+8ltvM4INyqRg5xkU79cXG8Q4wKZ5kipcNKrKp0RWOvIl+tVhbMeIkqHyEr8DVPl6vg+zuY4XeLysrIPWYMqJaNjk9Qun6f+yZMMbp2HIEQGASLwEddJ2AB2ewjnGN8/S7SW4SlRDVCMxU20ELMT0BngnMUFkmx/SPsLWzhPIceV7ccpQW894l/8kzdgIwU3DpHXa8ozAaNWjWQtRxaCa7dEDM9O0e4OkMahRcbw9kmIfUTf8cA9p/jOB5/h4+42Pn35TmQc4LC4yTrtR8cvh9MqyMeUZyJEGCFn6rheAcZVg4+JmCC3qE6JtRaXFa9UYaN8ikVHeiQivlZH5bb6nUqD8z3kTgfVtfjDKltHTLZAa0ZHmuw8NE3Yg+x8l4nfDWm+cYDxBPI5jbw8wjUVakaT9xWFsEzu1Kr8nTTDKc07/9LDmGKTa2vvZ93v8zxHAYcsLJ6WrMxs0rm6wfY1j8/984Pge6gDAm/Bx79zh/FEzl//mz/Abftu/xPnhRCCYXGc5zbfyebaElHbIxRzSO/V/O5L76YVpMT9g2xvleTNLscmcsK65vzWJK9x51hOtni0f4Tr69u04nsQ/4+XE1d3u/zd3/0YSeDziz/yPQTenxyQDG1VGb05HvB3X/hX7ORdfvqWv8K+ZPpPfK0ZfwCX/jay/teQ4au/rDPxxPFrnDi+Qr+fccc9h7+s79ljjz322GOPPf7H413vehftdptnnnmGZ5555kvuE0L8txukvPOd7+Sv//W/zq/+6q/yS7/0S5w4ceJL7m80Gjz00EO8613v2guc/Qay029xbr2Bn5ZMHW/gRQav4SECg8odQoSkhWZndRc3By6q4ZIS4ZqYpo/VunrHPYJsvo7Jx9RXS4pWq2rCWR2gtKP/bMLVU9P0j0bEUcjN/+s1VrcafHGwTLOXkSymmGcTrEsY3eqzc1+d2mZG4mVESuP9YYF1AvtigG8sIjMIZSgfNsjPSzrP1jE3hOQTPhP9HDuIoGXwZxzOKYpSoTJBlgb4sxHl7BzqypjaS11kv0QWBpsITBQxODCBzC3+oMTvlxRTktH+hObpEdQSdOJwnsDmksEzNcxhR/1givpBj82f91DdFFMLkcMCv5tDM6HcFxJ8cQNbFBDPEHUtpIb2S+v0H1jEKwsoQaYCuaMQThBdGkOkkNav7B9liel0EFohfIWVsrrI1AbtUak1BmP8LShnG4ipaWwhiU5dRTTalPsmCHeGiOkWRiief+YIT7y4hNzN8J1FjAyqABAvt584pjxHoRMYGMTQxzMRyDFlU+GVHmJhHt3UxOe2iQYCPMXuHTU2XxPjPEnYUdS7ASKKwRf0b4Pk1Bi/L8ijAH39Eru3+7ROlDRObZJOGAZzdVpXd3Fphphu4SIBO7sgAshz8H2OzW5ywxvXuTgzyRe3l2BKk32PInhGEZytWlNkbBHC4c1o5C2gXwyQcYIYGbzM4jwQvk+0aZj6wxInI2w9QqohflIwcbCH2wgokEgk+ALTCmhctfiDAm8ksEkAw6yqzh2OQBtEtyC8ZvFSBYFF+KqydViHP9K40MdYiWuEuDjASw1YgStLyqkYQoXa7FQKG98gHFhPVoqN0oCzeGmObdarTCKl8HYL7EQMYUg+45PdOE+8vIP6lQ2k8PDPrIEQ5HWPckLBcIAbpIiZ6SoXxYkq1Hc2xkmwkYRRBgLUuCBIC7K5mMhJbJqhELhGgPM8ZLtJvF7getXQNFzv4fbNIJIE4QnE9+aUH24hN1YQUtD65EVsEqGiCBmGmCyDWoywYMMQegNMq0b9RA9ZOjDVUEPUa1glEUWJcB74sHB/h+2fAV9b+vcsMm4JxnWLWypInskQnoLARwqvCvj1oKiBqhXknmDl3DT+yGCaIcVcjCra2JFFru2itnogLW7SIbdzdHMS2duoVDkCov19dvszuNk6utMHDAwKXF1iyoLuj9Yopx3ehz3EpRxhQRYlFCXC92EGTGGxk40q9wiLrvssfmqMN9AMl2OC+wek2wKes7infJRz2MKQ1DN+5l/8Nr6w/OwPfid5v44bjolODzhzbp3ffO5ZLhUPcMPBs4g8xzlVWWikZPeYIZ/1yMcRLkvxAg8hwW0JWid99k12cDf/cWtPNi549GPHKXzJa157A+/+3HtZXLxE7+eWWPzhlLX6Mi8890VWzu1j+ttmWZb3k/efBCNZCkd4fsbGYJqaKgiEYWdU42K2Q1J+ksXmX8NTySuP1R2lXNjcpRb6SP50y8/h9gSnBzvMN2o8sXkB7SzP7Fz8kkGKdRmCAGxlP8R++Q08R47OcvMtS9x6+39ZTbvHHnvssccee/yPiXOOT3/608zOzpIkyX/5G75MvuyMlNnZWd7+9rfz9re/nW63y+XLl0nTlOnpaY4cOfIVe6P3+Nrz9MY62kD7RUv9tMFLMor9Pu1rKcnTlzA6QNY8Ci0xywIxHqImQ5xUmGYV3NpZCAlus/RHCe0nxhjlMKHDSx1OCaw1hDOSeLHE8wzlesztD1zh1Iu3U/uXPVRm6X5vnSCPsalk3+cGrM1F5Hf5mCmPuhjg3tCm/EwAx6sMAiToRCDPpMhLlmx+CuNJbOIohGB4ECbjHCMCGq0x+QsxuuHheh7CgJACAsvwznnKhqR+fItkHOIVEHcFRSyIr45wgUKLEucnmIaPLA3tkyllQzFZFnR3mnhXC9z+jE6viQtA7QxQ231cqCC0MLYEK2BrDaLzIOYcymaIy128MKJ2bUx5RBF0QZWO+JLCeo5W1mP24Yzu5Tbbl5v42xmisJUMP/Yoagq1miH6OeMjNaLNMd5al0bfo/NgjJABLCe4qxbr5fjnuuB7Vd2uL3HWEp7exqCRcR2ZlgTbGTZ++V3g2CH2WfIG6Nwx8SmH0ILx/jo29jgye41kJuPqbzaQF4YgBLYVIbOA5MUBZjnE3hZir+S42CNcCpg7uU3RqVXVx7tjRGmIVptIA1Y5yATNnsMtzsLKJiCQqYcMQ+Kox9DOwXBE51xM/0Wf7mMKuzCFOZgTfn5E+FwOfo2indD9CKinwZs0tP58yeDjbcYnPYSF4cGI2kaJCzzKus/g2ySMFdHuNIKIYqGG+aTDCYlbtEQbOfbVBTaxyLOW5FKIrQWI3JDNxJjFkOjZMf6WRm300DOTVfjvy+Fd6eEp7EYfv5MhjMbPLGXNRzmJ7I0JjIUwRA5KCvfy44YBIo4QvqqGkoMcqCbgTAnUZILrh7hA0r+ljjdyiLQk6vooA6OwTtPbrppkRlm1kWQKF/i4yIfOEJcXiNQCDrGZEYQGUwuQz13GWhDNOrbI8cc9wnkfnXqorES0mwjPI10IKCNHMBTYiRa238dNTeGPRDX8M+DtSspOUeWHGINtJLilWdiqarQr5UeB8H1EfwiDEYGqbHfuZXtHtZahFvT5Mz99nOefOMS1lUm+Zfk07x8sg3UEq32CTKDygN73lXRfp5j+PYcceLg4xt91eIFDaMszXzjG6TMHmbpjG30qxw67ZEcD6hsKUThs4iPbdeysD/MKua4RzsdONmG7D8bQDvqkskWmFd5mj3y6gdIvW8tciX+6h35VHVdsoq7qqnlpMGDyQUexLhhaVbWaNTy8Sx1wlqknM8rr91NOJ8TbBcP5GvN+l3EeUzQDhBX4QcHDr3mSf7nyem6sr/DWX36Ef/Xn3gSBD0HAb/7Ok2xMZvSDgBfMIk0kCIHzBLLnmD6X8Be+83X81ic+xnB3jEw06WwLfyEm+4Cl788ibl6C11T/Dfzbf/kJfvf3v4iciOmOc84X4B1osvhdfQ7fENFo55wfjphlgbfMvZ4zZqsKJ3aW088eZP+RTba35vnFxx5m/5FVko0Z7L4UIxsoGX/JeTTVqHHD3DSzjRpK/emvD759+To2Lw45FE2yKkO00UxFfyy1TfNzrHb+N+LgLhYm/h6ErwXvy89SmZ5p8n/8jerNnWH2DA5DI7rvy/7+PfbYY4899tjjv3+ccxw7doyTJ09y3XVfO7vvVxw2C9But2m321+zTezxteFHb76dMzuXqaMBQVmX2LojfnqI13FYl1MuS7yOgWcLbNZDEWBFhLfawb9kKb7tCI1Pj6j3U+TKCFVrE+4WOD/ANiLU6jb1fdBzIfvKLl983/WMp76X/j6JbmyjCksz14S6JF8H/2LBTMMwxlL/8DbFvhB9X42SBkEikcMMf5DiFYpyoomdAb+QTH1hTL4gyEchrRUwfkTjrgHlpk90tcDVHHnTAmBDx/CWGqr0kU6Q729ysL/B9rBBOReS+yHeYg0xzIiujpBZgL9o4SWoXSwp5uoUfoAIHf1OjdUnGnhnJI3+Cs5aZLsJDZ/yjSX+b41wXoBdnIVIQtPhOg5PVdW2RllMP0KUGTryQIIqBN71EW6siWYy8u0mZqYBw4ygVyDHJeXROtH6EGEtMi0pmgr/isMJUANNNuPjGYm/P8Hb1pjD8xhjsMohQomVAi/LCMYlIimhnxNvdwj2TVPmBW5QUP7eBOZ+8Gov51pIgVSOBn1u/cEVtBCMLyVsf66FGubIAiY/vIpfwuiQJZuRmEWNOpEhg4yluSFnwzrkBWpjxODGSbztIflczODwFPOfWIe4VQ3LWnWIQshLyOFVf3eVj/zSEv4Qds9EPPJzR3D1iKTswmWBy0tEbnEtR7jgoTttxFaKHhnGTyvG1yoFCKXFTx39wyFOSvS0BL8gCEfI7g5yTYORZEdrGOFjPEVwrYe8orGvNfC4hp0UEU4jHAgpGR4NSSeXaT+yipdZIMP5ITiHqYc4JQk2BsjuGKRExDG+tRTtGqIvqrDVyAdtUNtjRBzhsrLKvZESWViEUDAegHLIAxEsC/RxSTrjoRsCU5M0nsnwnY8qDIWA4rZJwhc66MTH1kNUd4Q3zBG7IwiCqkY5y7GjFJQgOrNZ1SxLVf0OekNUt7KNpRM1wlFa2V4soBTxlRHBVo6NQ5wCMdlEWchmAtRmVRle7le4MMYszyMurSLHGZQW0azjRikiS3FbuzgnMGWOmp2gnGlU1eupBzhsZ4jsDvBbmmDCMbvYpbMS0pzNaRyw9HdDfOlA+NR2Rmyut5AvCgx9pLC4SGHaPioDgSDLA8rco392P3ge/m5O/FKPgCYIiZypI7dTKH3sFpXKJMsYLfg0Ny00aqx+wiP9lhjvkbNVq5CQFEuTWF/i5ZaJ9+e0PlWiRo7O7TWG19c5dH7M0o8MsKng3JMl9VGJVAUuiXDjFCkluuFjfbDNALeqGJxv0joXM310hjO9Xb79b36ef//515OtNHhh6Qg33Ptb1XAtCHFobJZhL6f4yw5vy0PPKsRQ4oQAHHy04L3bT9G7skPUTfGsJLGSsluil+r4PceTHztJWEu4/s6D6Ppv8+Cb+3z+sfu58dgCp4+32C0aHH3zmOunJtBuzFvffBcvbFhWvBVGWYlcyhiNIla2D3Go9nruOQrfcv1nadWfQexvcNvCLxN7U3/izZTZep+ffOO7SaLDCPGmP/XMiowiLhVBrvjW+f1s510O1P5YjZKO/w26PM1YX0RM/iPwb/qKzsSttS7v+YWPccsDMcv3/CQIODTzu8TBXmbKHnvsscce/4Oy19rzJ5BSct1117Gzs/ONH6Ts8c3Jm/bfzmdHj/BYZwTzBakYUi/rBIdmMHmGrtdxtZDBPMhOgGwY8iCpJPwnB3iZpvX8Kl47QQ4d+UwDWzi0NDhygm6BKA3ZZWgPM9oqA1eyu6KIdkomvneEGyvyIuSmP7vGXDqm+N2AU6uLNN+ziuga6DlkJ0T5BagEMxNCGGC8BH9L0b+pTv38EKUtvjaokqpCuPTYeb6FKDWR0MTNlML3KZDEV3pMFJrxYh09GSGshz9ICcY+FHVkoMgPtIhXQ4xW5PtqmGJIUqQ4MyY7IBjeGlMsgdYewWZI0JQUC3WCy53qYjRXxDcZOBySdSJEVkAzwPNL/OYuznq43DDe1yYcgNAOv5/jjR3pUkD5bJ3O8zFZU9LQGq+0pMeaeM9sQiyJL42RBoSC5eVd1s43KFsBZrqO8ARWWUTd0ZorEO2QTtfHJBHBtT5qPKJsRy//MTsoCqhFCAPWWISxgCGbKRhON/GExJ9PaX1uSHuwQzBpODxIscrxzGqN8c0NsBp/OIatMf55S/C5Edn+Fv7xEqs8TLdgEIR4/SGql2MDSXx2G/KUYrlNw3l4wwDUCOcpCEOspyjmYpxJeP73juByh8h11epzcK5q/dit6o0RPsWNEQdmB7zqR57h9AtLPPXp61G2ZHBCQBIiagJvUCK1IO4IiqaimNTYBGQDyrt9ov8wJD67gcvrFDdMk7oI54N4BtTFECd8ut8SEe44kvWCcNtRP94nOreL8CNcokAGVXuOrn6WykI+3yLMdRX6GlSWHIylnG+icgOehxOW0UJIsNoh2hlSuhAX+ah6o1KWlCWmESP7EfZpixM5ybohm1eYOKC2bdi8WZJcGBFtlPjpGDMfYiYmMZM1vGu7KC9EaIto1BGDFGsMWFM1/dhKnVJMJxQLNaLTW3iZQzhHsJsiJts4q3HNGumsR77QJrmYEp5eq4ZBwuG0Ibu+RfnqeVpXOpgPNXBJgAgDJFVzD5dWcYeXQAk0BpVE2GZEvhARDEtspNC+QGxnYKlak6Sgux7yB7/5AFvnE3yTsXJlArW/jjcdM3/HDmsfmUPkjvA5j32fugpKYqMAWRikdgwXBMm6oHYgo32sS/8DdZxsoESl5ugeqiEzwzJj/pd/+lkunZvh/e+/F6MCiC00Q0QtRlqH83y6k5b5YY4LQ2jEyNJUeSUTCeOjgtF9NdofGaLbIS4J6MkpBqdy0suK+qld/MkYgcBleWVkMRaMw9cOKwVWa8KB5oEffYq1lWnyj9/Kmd8+iOvXqA8cWR7SVW3QBjcYYKZjhsc8hmcM+TBAiyZhPkYgKgumgyjy2Go6xssR8WrAba+5npN+QFFqyqs9rm0OGGyNQEquXOuwdOQy83bIYuNbuev2A/z6r83jOjNkczfRuW+aw/UO9WSJj45+k8ufLOlf0Mzs76BqdUY7iiKyXGLA0bzHbLMkUnUurba4uLHJd989TRL+cdjsMP0Eq+Ys/vglZkyJUn+yHvnN991E7Pu86sYDDKMjXBptMhv9cQOgFDM0pUCIP70t8E/j6e0PcaZ/gu9eeisrlwesXtkFGbB8bx2BRcn2V3W+7rHHHnvsscce//3yj//xP+Zv/+2/zb/+1/+aW2655Wuy5t4g5X8wetIiAhA+qDBi1HaMA4f3QzH+oz54gnLOR/k+jZ4iTUIGsz7+5R10EjC8ZwHhKRov9XAtH3UxR9kAshwxHFMuTFKWIxbb29SnC+7/oVMsBq/h8soJ7r3zM1weTPGOL3wnb6g9xcHZnO7Dbb7wB7dTDzaR9YL0oTq1L5aUQUp+uImZB9FqUf+URCeO6IUtkoc1o5OW1mcMxYyH8QNMKAh3e4jXa77v0HH0lOAPn7iL8pOW+EK/eofWKUaNCGEVG3IGL8kxSiGMA0mlftnNqgtfV2CkwzmLGlqm795kpEJ28jqBBIYRNvbJ9sWEV/u0FzvcYEY8119GFRa2h6ge2AcSyvkJ1OVtsryBC2NKZ/FsAYWldlEQdSWigCLyMM5DN3PMUGLrCYMHlmicHeCPNSQJ8cKQyT/bI/25kpGu4WWK8FxOV0raD4x58HsvkT7v8Zl/7pASlAiri4N+iiDABQJHga3HFIdmyBdCLA0ai5vUH8wZbBlcV1B/ukAMLcW2obgE7/6HtyCsT6ZibEvi/AA3XeIWG4THV1Ej8J4qENsGxiXj2GOcJ6juABN46MU2TheYUFPfVEitqywMgELjuj34/7P333GWZnd5L/pd4Y07Vu7qnGeme6LSzGgklEES0ogMB2QLh8MBbHS5h3hsbGMOHGNsc5AOB5zA2MAFAVZAEgooz0iTc+rpnKor164d37TC/eMdy1dX4nIlwBhc38+n/+ndverdu6r22ut5n9/zHFyk7KY4HXD12i60t7jpFmgNhcW1NLZRO59cIkE7IrEG3pFmQ/TmhMmtKeGFMbIM8FpijUNl4JwlXB0RX/FsNZuYJuRGUr02IL6YIyqFWhbIBc/kQIfGqsNORbVYIA16XCFecI00LpWIkcV1JUQxKjfYCoLBGNIA4T3F7hZmvkm8mqPHFVUnxGmBa2h8VRH0xwhjaLQT5OUtyEuE9ORHusSlghHgLHlTIyJFdHkLQoUeBkxt9JFRiMw9VayYTEk6XzCYVoKZDgiHFcqM8SLARwGu3UAbj5f812BVY/BRiD95iOxgQjGlkIVBPb1SB8vmHp9t4aoSX2QMX3GIcjogOL9B7Fx9UJcC247RF7eJ0xYu7eATi/OqzngxBgEIY3CxxgdNWFmHQ/N44almQ6Jz65i2It72EMfgqX82wgDXbXDlbD02NYlafOT/NYNqbeCtob0rY9nVv5/SOlyoQas6U2VcUMWashvjpMVfaTO62iS5tIzqlfjpNuX+Lj4QOKm4KX6GYzdvsjqcxcykhCsZOFClw1uDVwFYy8L7L2H3LSBXt9DDAmk8Jgko905T7tnEzGrKBcXUgz2SLEH3p7j4r+qRNX04o2otYMOI9GLtVMKDGBfke9t1qK2xlLnC40F5ELDx+BTRjAMn2WO2OH9hFtUMKFsthjdMEQ4E3/7KW/jQ8nP0l4ZQUAcN123XZHjsAU10dD8vOXKY49cd4Qt//BRGeiprKPKSxpGEpcLw4X/xEfYduINme8zuuT2MBhPUbIdT91yP+3bN0mXH/37z36ChWzi/Sne+T//CInFSwoZEeggqWOw26cRH6crHCPxt/PJnH2Z9MOHA3BS3H9v/X/ciG3OhmgYBN7sBiZr5sv2q04y567ZD7O90+LHH/pDtaszx6ApHGx1U8npa7e9HunWC8P//DzwfvPZxVouc2egTvP4l38nf+PuvZ/eBGWYX/wYASn65oLPDDjvssMMOO/z15u1vfzuTyYRbbrmFMAxJki8dSd7a2vqq19wRUv4a8WvPPMRkZpG2ucJwueTbv+t+3rd+F9V0m+DZiuSZDHWkRbSeUoY5jWaGFIKRD7DNENkr8AKkgu2XRyye01hd4qRACBB5gcgKyoHid3/hjbzlZz6DaMc88cgZvF7kU0/ewNOTRcQKnP7NFntvlgQnLOO9DvcTi2hRwKpFfmxELDLcHk/0bIANAoR1iM0e4Xaf5ElJdUkgRYq2BeOjMbLy5HdqbnvZEjfNLDHciinXPOmpNUDh2wmhUahn+qjKkjVSIpvQvFQwOKbr1qGsAhy6l6G3J9hA0fv6/bhGyIumLnL93CpP9XfzSHqIZGYD/1hC4BVmf4D7torH/nMLM5AQCZxzyMxS9mJEXxJtj4iGI6LVCdNNhxYOqwRBBGIMVjhsqPCBxU55sipCZ558VrPVDWlcyUh6jnEr4PR6ipsHvQa+tJhYoUYVtyxdx/SRe9lqtBCLU4hhRdkMkCjUIMeHCpso1OJuXLNJsRBRhgZZGZI7LVsyQUiI1gR65CAIMD1N1QnwPUmYGeyeimw6oOpAsKGZeVDU1jzrSC9VqGEBCFwnQnmFCGN0HGLDiMnRDrtvu57h71+CCnyWIy+t4ssSggBRGJKrE2xT15NF1Bk5k1mJPOyxq45wwyFeqJUVueH8lT2s9afIToFMKqRXmOMt4mcK1HKFbQR47bGxxOyOCCaS6Jokn08pDiS0zrc4+I3P0wwHPPH0LMIrooFHTwpsK0AYQ7LicVGE7UpsM0LlE8ob90IYEF/cBlPh0hAfB9h2jHCQXJugS4cHxMo6ejugPLEXUTj08jbKeFwnrbNEOmktPDiJzjwuCaHbROQF+YE2RBF6Kyff3SA8tYQofF09HCUgYHBzF9sJwLq6AaYpcZEiHDhcGhCMM0R/BICb78DGEK8EYmEaWTqSlQJxUiK/Lcaej1DSImxdTyyFxI4z2o9uM7ylS3y698WxLx8piCOaW3UzkGtJ3C6DetbgpUCmKWKc4a3FL69DVlDcOI9MQuJNQ+vzlxBFhRzkVM2Y4PxaHTAcBohWk90HR7z2ux/lkQeP8NQz+/GrBn9ujMoqnlufRY7G9eu+YCCJEEUJRUW4OiTa0JSLMWiJGFZ4IzFBhJkLiEUDUXgUHu8Mj17eT3Ih41SwiJcOFyi8E4SPXkYGDchzfBwhtcLNT+N2TWMDia4cthkgjKf9oS2S/THRmQyfpIR9j3RQ7G4QreVo6xjsVgR5Sv6SBtOPTPDOU8mKbKEWnsJl2DAdHr/nABv3zJOIAWXoKVJBOIH+Rsz7v/MmFj9ZsP6te1Gp5sTuBT72i59DVp5ppfDWI2TtcBHO4xHMjSPC0vLcPZe49tQGqpnAsCA/MkO6Z4Y9Nxziiccuk5UVWh1m89KIpadPMbIVK7cIZq/bz5037uLS8ine9Rv/iZfc8nVsOsXM3jHNxytWPr+HcDFl14kNRnv/iMH6IQL5QUq7yiT/MOMjsxz2i9y4b9eX7Ede7uLqqIuSjrEdkARfLqS86777ePTaNX7ojjt403xKs/qP7KvWqPoBqP+AEBGhO48oruDSb8LZqyh9fT0a9yfw2tmv5xd+5Cn+Wf8yT73hY/zo//PNf15b7A477LDDDjv898/OaM9X5Jd+6Zf+3NfcEVL+GvGe009ybaNPd2IhjlBe8kOv+ASvPflv+N7rfwmfl9iLMJpro5zGvayAMEE9bPHtBm5sCddzOreP2ZhtInoRxZxFINBrY3xVIfsTfBwxLgKeu7aXlcd3s/SEJi7WWX/xLZRdTfPqkMuPtFg+1eKlP7cFm0OipQHlm1LSjw/xGKqpgGCQozYt2zemRFse000QDUk/i3HJCDm24FRdBRw4mn844Pz9DT76/Tew/KEu4qKv73xHYGYDgkrWTRrGE/QnYECnAdGWBQ+qElRzbfTEIEIPUlFNOyb7LM+dWiA9bThXzZEcq0hWS+woRBoH/YLJJ8E9YcHlYAwbd7ZoP7GNNZ5oXCALg+nEjI4mLKApzIT+bEA4hmqkobQEA4PKHNHFkmCcY21Fw3gm+xOSSwNEGOH6AdljbbKGYXa4is8kfuhpLUdcGkVcXboDp0DEDWgJkkYA1wbYbkr/cEp0agVv6naYxpkeraJi465phs+3UKOQ9g0FwUmL+2iIrDzVrg7l7gbNy0U9krAYUnVANAzde3OCzEOa4AZDGE2gsnjnCAcGv9gBK/ACTARFR/KK646w9JaYJ9/7NNJZvLV1d3YaI0JNkHuYMrg58GslJvAcfcsGrzr6FH/4c8eY9FJkXyGGQ0Sni+2mDJ+vHRbDgykyABE5dK9AVhZXCoKNDFEFZPMhtivwsxVKeFpPjTkWXOMt3/o4YxlzvrePpUAxOimZf8+I6KlN/OIcgTGUi21cFNbhq1WJLBwCj+02EGWJa0S4oERuT7CHuiQTA85hI4W3DjnIUGt9bDdBjDIIQuS4oOgE6JkE0YxQY4/UtWuICRR7m5QLMdJr7IE5tHUUB7t4U2F2N+uKZOMJe4ZkzZOsFuBhvK9BcrGPa9SjR/6FbAphLWqjxLZitl63i2AkCTIJjYJRGOH3eOZunEE9toH3BrTCpApV1tXH+mqvblfxvs6ocZrw/Hr9/es6pAsRF0JkYcF5SCJ8rw/eI69tQhSgejlmbgq/UaEQEIYEYwfbA8iLOih1kuMdyHHO3r1rjDcDnv/cHGVC3WRUWjg7xu2exQcgJxbfiCAOwYPtJkinEKVAeIjOj4g3M4Y3JOjCU8QFxXxMNSVof2aD8fPbfOIX96NuVkjp61ybQMF1B3HLA4SzCOfBe5wtEUFcj0tlJVILZBhgjx2G0RjT1fh2oxaarIdAIpQmWClJ1j3xpT7eBmy9scv05yeQF1gFCIFJPGKgOf7qayzEY5780CEuv6bD6LCn8TxMPeTJllqo1U1uSA+zfGnC6sYG69c1UIWj++yIEE+IZFw5sJbW7AK//Le+ix/44X/LKNG85I4j7IpD7n36HOM9hv26yz/+qW/jHd/yLoSSvPqNNzEz1eADv/l59h+c40y4zf5Whx88dohHBv+E/DbHL3xMsXXbLs5seegEpEUMhzVyusSLgtJkGGG4XHR58vx16ClYnAlpxOGX7Efbo4QHHzhBoCx37zvLbHzoy/asZhgihCAJAvap9/JUscnQVjR1k1AuUNDABK9ByRg3+g+Y4rNEzb9DmLzlT9wH/+D/HrKdaaopwfu+8Ax7pMSXFYv7Z3nVt9+OlPLPc9vdYYcddthhhx3+CvCOd7zjz33NHSHlrxGv2XuET9jT2GRI6Ryff/wWvvnow6hxE4FExDHVaIyNBEI7ZsIRj5+dIdnIiTZzioUmZu807gMl3U5B4TzRSOCSAGa74AUkEWYqwYwN2+st1rMWJnaIIMe1Y9COaqaNe22b7nUjnv7lA0w/uoX2JcX+iOBCiYgM8hUacTnDK4gqgbARWmjWb0/Z63qYewuET0B4qsiDseiVDN+DTzxyHe5YQjIO4ESMbUUMDlg6FyQmbqDHJbJ06K2cYJBRtVrgQExHiG6CzS30I0a7FM1TGbP3bVPeGLK2q0vDW4ITJYM0QaWKKpHEF3Pyi23ElEaOC4bXpZSH58jSGdJVi4sUxa4uLlJ0TxfoF8Vsv1HTI6axZamWBI1LEh9L4os9RGnwzqIGE0SgSZ1DZgZKjwwsXJ3CLoZ100kgqXa10EOY291l+fwqcVTishQdaIpRBUJQNRWdJ1ZgbYhrGXyjhRxX9biH8WSX2oR9CKcN4UyBaUeooSXb1aGxYfFa4kJFMfPCQV9DmAGqPmQLKXDTTWxZogYFvqywSlDsbWBSSTmXICv40C98mld/yy3oYYnIK8r9M0yOtEk2LVHf4BvgZgWsJNiWJBzDQb2BHFrkGISQdbZEI0U6i7MVrp3iiwIfBajT9WhWsdBA5vX4ih6XRNcKvBLY2OMOVHQ+uUn82S3iOwrSpCISluFYIjoGFwjISnxR4kwJcYRJNXpQkH+zBdUm+fVt5AqYxWmUVzglMIsN6GqcFEwWGwhjkZVBT7UQ4wKRV6hLI4T3uGaMUBq9VTK+MQIt6TxSEvRyfFYLe+PjTUxLE63X4kG+GICdRZQV+V5BtG4IVie0ljzJlkVagdVgUkk1E6JGph5b8+C8Q1b1qM3wcAzOEg0EYb9gHGnEZkCpFMYOCXztRhFaE6gAJ0u2b21QzUWEmyWtM2O8Mfiqwk43kCWo7SH0gem9yKyqnWtJiosjyEsQAiEkQeaRPYMYjSAI65Bb4xDDvP6+ihccTpMJK2dbfOQ3b+bShXlEVhENKrwTECiQCic9auKJnncU0wHhVoWMIyQKgUJNPDIzROtD5KgiOefQpcB2BINDDic97cfXkVGIezDDX2hQXq+Q05J4u8IJULsD8AU8H4AQyLUR5khCuJahSoMLJaYZ4aRAqgaltWhTosoIHyhGN3eJVpZxiUbYCj3IcaFl9tMGpCLYLAhGFmXABgoroNPJ6b4k56nPHWW0vw4CNk0QTqK2FWYhpuct5cQw8Hn92gKiNFggC2SdxRJFZMZy9vFLnJ8xiKTBaG+Dp++9hHpujajQXPjmlCDSDLMK7z2ZcdhGSL7YYu91e7j5iuS5Z1c5dfAg1zaabA1D8jG0WkPKsqTwCZWFdN1z9/X3UmrNR7Zezv2XbyFKN3jo0ot556FX86r91zGY5LTTGABTPkabMyBASkdhngXe8GV71t990Yu5sTvPsekZepM34/wlPlMc5OXdf0TLxvz8sz/A905/gbbWNJPvRAiNlLNfts7/J+u9LUxI/b5YVvzzp55BZoY7//Ah5g58Lzfe8SeLMDvssMMOO+zwVx3h6z9/2fz3cA3/31href/7389zzz2HEIITJ05w9913o9Sf7HT9/8VXLaSsrq7yoz/6o3zyk59kbW0N77/0VbLWfk0XssOfnZft2ebq+Qd4pncYH4d4CzPREBlu1HW2AYxf0cTHQOFZff8Mjaal6CRke1pkHdCVxwpZ5ylkFpQG46A/wg3H0E4Z7gtpnS/YvdAnnbTY/sIQLws2xiFMAnABk8kiG+sDig1J75UpU/EG7ZdVZM9E5AdnaXxhRPuNgu08hiGI8x5ROkwK4zMxYaqwWKo22ETgCcneNIU8WhAfNkzOAVJgZhtgHPMPDVETwehYl6oToUoPUZ1nEa4XFIsprq0J10bIIMIFAT6WROsj0qAkPlnRPJkjhWOdJs6BbIT13fkkQmWW/NbdbNwSUnUEnUdHpM+PCQqPXAwReYa1DZywmAsGOdOCyDK2is6ap4jBphK1qdF9RzkVE2+PcDjy4y3SZwzMSrLvkjg7Rq41KBYamPlmPbJTFXzbd7+c3/yn9zJciWi0x0yyhGpPE5VrdF6309BM8Z0WcaAwJ+cZVRVTjw/Y+roWfga6fUfxfJewMiBAb48ZXD9L0CsRlaNxrWLwMkWw6ShnFOUrBNH9MfpSTD6raF0DkcaIIKR/CFAJvnSEY4+THlsZPvneJ4laIWloGM+1cd0UU+ZEvVr0CXpgJy+M8ISaB373KA+vdykzjVQKLy3ueg1rIVU7QuZgpxroSiB1gNWSvKvRWUA4dNiGhokhWivRRjDpO1wvAqW4en6ej39csTrfIf74OmGSUs0GaJr4lNrVEYfoq5uolW3klQ7ucIgcTxB9j+w08K0U3x9C0sY1XzgoNjWiVCAh2ogwU02EUFSdJjItEHFYjzfFgmyfJr5a1Y4KKRAOvIJguYfa2MYvdOtq5bICKYmuTujcu1m3BO1dgGaC9QbbCsh3Rfg0ooglVgvSVYtydQW4j0NMUyODhNkvDCCKEFKRnM8ZHk6w0iOTBvnRGcKNHCV1PQJUVrQf2yI/0CS9VtTihwCcp2pqlI6Ql7YgUKgLG4h2pxZVlcTMd9CbY0jiuslcSNieIEd5PcYDVLumEE1NeGkLrH+hvUjhrOepL+zGdJqosoes6rYxoYM6KHeU4Z2geX6Eawb4StXOoGkIWxsouwefBIz3Nmlc2GZ0tEG0WaEnjuSaRW9UiEDjsgzSBKsiwkrjhcdpsO2QcsvS+foG2akCvEcXBZXxtYDXTSmnE3wkYGQwCphrYqRAZPUHhGgzxGclYpwTPrJEcXwBKx16PKqdfFc26T41QI8rtg8llFHKg585ibvqKYOQ4IrAdiTBhuDEi67w9btW+dy+Wyk/tIXfHbLn4DSTT11AOhCBxFcObz0+qt+XB7Lk2QdP41uGcG/B5z/9HPHZPnJckqxE7DkdYiqLG00g0Fw7fZXGbAPnoD/MuPLUMkv3n+ffLWzTM2/k0iMed3YL93xG44crokYOssXhVptm6MmkoVfmzE9ew73PnuGG0Qb7eIT/+NE1nruS8ePf+mqOLrYpBv+cKVHxsv0Vc51tutFLv+Ke9fGnz/BbX3icVxw/wOm4y2r2v/D3b7qRvY0X8+nl/8yNyWm6qkJiEHoPzdY/QYjwK671X3jVrddz4Y8fgwrKhYBsUSBswNu+/Qxh69049wrkTuDsDjvssMMOO/wPxdmzZ3nzm9/M0tIS1113Hd57Tp8+zb59+/jwhz/MkSNHvuo1v2oh5Xu/93u5fPky/+gf/SMWFxe/rPJwh788cjcgTWrbvE0Ey77Dx86/nnPvfRZfloxu1EzuSmCcox8XVJnCNSXllCJ9esLs0wPM7oJyV4B9haD1GVnXx5YlYpgh2018aYi2SjIlOTa7zMyeCzxx/y184fw0ZrfD6wKWQsajgMFgGn3HELkN4usjxjbA3xVQEVHtM+huwVY8iwsse+7NEUYS5ArrNd5XuHIAywp5dBo9cuz7rm2COcPFnqJzekDPTVFGAiksVSBRWuDxqBKEF6iRRUYx0UTgBgbpBPHQUXVB9MeEBMiFlPCujPy45sJKB7EhsDcI9re32Dw7wc62aB62lJcF8uEr6MMHcbGmcX5AfGUIrRS55Wm+JGf5QhMzrSm3RwS/M6FzMGR4MEH3S2wrZHBYMTg0xfQDBcnYY1shKI/aLPBCQqTwSuD2O4LdE3gwRYkQlxds3drgF377U+xuhuydH3Ft2KA/DrBaIDSgPHkXorMTsBX5Qpu2DAm9Z7sZMv3HfV73j5+hM11y30PXs/TsLD4p2XxdCx94dv/eBJVZfBzSeE6RrCvktif9UA/wMDuDmhR1zoxUEDiaz4yQmysIB+Xx3ZRzMYNb2yTLhuGNAa84cgX3voTizAS2RuAEYlAQtgXhQkH2sAEt8YGjHEl8f4hTY+wrY8zdTdSTnugTGYxzqn0J2fULBGNBMAE9cYTbFdI4fCCRSmFahmzWMmm1YH9KtF4gcsuTv7rA6jcusKe8gtCQnJvUQarH91A1I4KNIVQGYR3hZ0E8KvBVhIg9IgyweMLVAX5jSHFgGpN0kcaRLmfoyoHzeK0RFrSWKCsQWxNMGpAvtJn79BhVWVwgkQ6cljjhiK9MQEIVdAhWh0QXCkhjvHd1BknlEaGuR0zzCm+hmGkSXx5RqZwo6RJuZkihoNGASCJ1QHNTIo0AV6JCSzWXkq55ggrCkaec7kJq2TzZQJmA5LElovPbpM8PAIHvRjgRICYlsnKocVGLH87jG3HdwFMapAAXSkS7gXQel5f4tAEWfBKBtXgF45unaD87wU+ViFFR1wtrDc4hrMc1NWrJIFz9vF1egFLIyoM1DE8m6FKSXvV1bkvD4m8PkZctDCQEApNKWqdGiDhG5BXRtQHRYIKvDG62TXX9HhKXY51DFIagVxD0CmyeMfmVov6Z9q6+e6IE2zd0CCcF2ZxgatVSCZAjgx4OEF5At4XezukfhYZSoBXByjblbYfwzlLO1K6duNeAK5uQ53R7McP4AJeX5gnHDu0sjSVP4RJ86Hn9Wx/mN//Vm3D9usZdjxxXf+s+xPo2y9+2SLOIaX5ijTBpUY0L8I7SR+StgIOvukLxGYluOa4/fj0PXJa4qQZ2IefM6ArhaEJVGW5+yRFe/fobOXn9bvbvnebeDz6Bj0qeaZ0nkAI2mnirCIYRzpV0Dg44PneCH3ndm/nw5c/TK5c5ebDkb524i3/9uSHf+R0fpNP6IDccOsCppb+JsRYI0PHX8+z6Gp8/1SPUFS8/+hjwN79sz9o/02WqkXBs1yxKSAprONQ6hhCCW6ZuZ2OgiIXEIYiCG/5UEQUgFhLpHB5BJ5dMRF0jv3R1hmtX4NiJ5p/XlrvDDjvssMMOO/wV4Z3vfCdHjhzh/vvvZ3q6bgjc3Nzk7W9/O+985zv58Ic//FWv+VULKffeey/33HMPt95661f9xXb4i+XG7uv4jOohs23USFFowecebHLhfQ8gwpD0isR9fkw53aaYUeR7JGITvPAvjClY5LjE7unSTEdEUlNEFoGFmQ7e1odGG4KfChFhRU9IXv8PHuHDH38bzfuGBMuWcSeG2wzjlmDhdw2Js0yuC8jnA1Jf0nh4hIo1+YUpplc1GycDRock0kbogUcGAjOlMIsxVd7CaSDxBKlFS4eUEBwoiR8aEz10jWx/CxV26ln7rYqqoevGDi1xAvCirgBulOS3SNTpEjXMCE1F2Zxme71NebRC/QeBUCFm2zJ34za2O0Oxt0O/1HTlFpOVEOUg9hobKpySiFYTpwXZ2YxwO4MgID46hek7ZBUgjaBKBY3zQ6JJAxcHlLMhcX+EChPKOx3RkxOwEjuMGW9JbEsQbXpUDqLfQ64PEFmXLG1yZXCAamOClw4zH1N2JXZgGdysad4nCM84EOBLR29rxMZNEd436TyS8ei/3Uv2joQ1M0XbGFQQ075iCSYZrhmjq5LhgYjR/gZFx9G46mldrMM2nXcE7QivVX2QjEO0ixBmgq9KPJaiAclSRVQK4tWCJ64cIRrl6EkO/Ql+YQpROeTQEoiScbeBuraNR+DSBNkf1+0m1wz0HPoJEKWrg24vjpBpE5dECCQuELhGAKXFa1GPy4SK+BpoXWFVPR7kyxGzh8a0Zq8xXOgi0wQ3KkBL1NoYfXkLURnsgWmqfSn5bYrWZwzaAkmC3M5RoabqxqjS4tOI9sURVaqQL5jxatGuBK2wStWiQxKgjCNZHlF1Q/y4rloRlUdWpnaHXTdHY82iJ4pif4fg4gZBaSj3tMhumqP9XA8/HFMcmYW0g5oUtM6OmSxEeCOgNLhIIfMC4TyiEkjhcZHCtVOcs0SvKXCXIkS7IlrIoJuzuLdP7/km6WMDqtkujCcoL5GRBgSVDzC7Y8KVMdHA4lyOEBIvJdJ6hFQgPe7aKtXtB4keX65fiLJErOXINMWbqm7z0YrOMyOCUkLUYDQT0bg6xpcVfqqJ6DRQWzkohTcW5x2q0wJj6xpgrRkd0PhWg+TaNnpjDFsSUYW0tzLGRyTB0AISvZGBn8DcFLYV0Fh6wVwjNeQlhQ7Rgxw5zBFZndci47Cuqs5zyGpBRU1KYl8QrefksyHehtiuIrk2wk/GKBGBKpBbI+bvtxRzkvLmlOjBMcIadOmppkLipTFOCcRkAmGIzzLkXRPcp2OsgOjUVeafsUwWY8a3LWK1JrumENTOnPhUj/zaFjjP7HtXWP3B4zSn2lAWuEYDhCAcVOx7zQzDj0fIX1MY79l6qccmCpMIjLvG+cHvIF68SFbAu37/Pm582RH27JniM4+fY/bkAsqvUbRLRrkn60TEkxI1gVc2zzIVZHSa1xOkilt238WpzQf4ple9mSPdRVQpOXfmenbtO8u++QP80+9+I4tT9QeTqPm3OXG4ZP+eH6DRGtEzC19xz7p5/yK/+r3f9BUfm4r2cCROsUIAAaU5S3/9fXQ6f58wPP4n7oNbqwPSlQKpBD/2T9/Gj3zsw5B7fuc9L0YJyTv+/tdm391hhx122GGHHf7q8tnPfvZLRBSAmZkZfv7nf5677rrra1rzqxZS9u3b92XjPDv898H7l97P8tRpmre28Y9rwi3LaG+CvnkX21tD4p6l/YWczW8wpLcO4FlL9JymmIqRFSBkLThIME8qClM3QzghsWGJ7hX4NCZcKzH7WsyEY5qqYNPMMtcYMnm2RIw9gSsJfIBICoYnIvS2ZTuOSLHQ1EQbfdQhhVyJSK/kzK+HeBT5rgBlDeET6+jVIbalsDc0ERZsIblwZo7ocoFog+mBcYLxm6cRlSB+xmCbESYUyInDBRbpYTwFUmvi3GLmAwZ3lKSFR1tPPtdiMq9YmM052Vjm8cUbKIME13ds2xHdeJPNYQNnQspoBuJ14rNDJrEkXJlglUOkAShBmbVRtsR7jxpUOCeIhh4pSzrdPunqkPV+SBUEpFsVujdBTkriz3uqG0LoT4MQqIGBDZCPh+TtEjWRpNc8aX/Mwbdv0X82ZuXxGVysqZp1aKLwkF71BHoKN5vj87K+s24c3oLXCidhaypgY3OOYNUTXFtHCc1CIBASJtMproowDVUH85YWh0W22jAYI0cZspwi6aQU/Rwfh7hQIKZSnExYvz2lfd81GmczXvJtI849uMB4LCidx/XGCOHxUYBXDjtyVFKjRtULTg6HVBoxPVWPozQiwrMBolXigxLbCJFOEI2hiMCF1GNdIYRDSzEdMdwbICtDp1cRVBBNLNVih3hzjTe+4xnoKD72kRMsp0cJpERlBhGFSOvxSqNUyOC1nmqvRVSS7mUgjeugVQHaClCaeKtC5QYqyWR/k3hpRGAdAvBSoIysq2+dB1nngQwPRURbjsbpAS4JkJVD5oZy3zR+BuI1T3xthNnVpQgU4z1pfV2xRvZKhHFULYWsBF540BJ0iCtKbABBZQCPEx7RHyLyENFI8IFi/NgUtqOoQonsCKpOhXtqm0bZx5/TRBeHDF40Q7ixDiqqg2NDjY7b+I6HQUm1q0tRViSrE8R6H4IARhlinBGf28YkGr3Sq4OFrUUE1RfHlOS4QvYLPBKMobFeQVHUtdijHBp5XedrLSIpSV4qKB4BtMblFTZ02FZAZzAmWOkj0rT+HbsoUErgn+8jdYTb08CPVpF5hbcetV3VQpfWqP6E5OkJZqGLSBKEsXVWCyCKErt/AU5fQsX18xdaMjrcRDpJMRPBpaoWRdMAY0Fs5oggBiVxeU72hjbFyxrgHGluKb0kWi9Z7LbInGC4bBHWglKE0wV+A3RV14NTlDQ2LM3PneMXv/G1dNYHSOuwMx1Ef1hfT6DBe2Y/3kMYz4nbj/PYM9cAkF7w+7//OCZQBN0mOMeFU6sUx5sI57nr+Gl++3dvZbmZ4pvQHBkevvc0cj7l5+77JJNJzpEbSopwQiv00E5xmaT7+k1OTC2jDPxfzwz5P6/8e370llfyjpu/B4D77znNuSeuceHUYVq33cWN+06w2P2vH0xKO0CoCa9+0Rk6coORa/+p+5d3A7LBP0OoBeLm/wMhBKUIKbxCqn2cv3aOd32izV3H7+UH3vAnCyk33XyAR+45y3U37uHGlxxk/rEOa1e2wbn698Q6lN4RU3bYYYcddtjhfySiKGI4HH7Z349GI8LwT3e8fiW+6vj6X/qlX+Inf/InuXjx4tf0BXf4i2NPlLK83GK8nIAQ2CpHKIG9aZ5ibwvrDSaSTO9aY7RLUu1W6FGFScHGsj4YaEGwXVJNebLj9d1+23TosavvSgcaGYSowlEWimfW95Lbij2tEWLRIwJLdqcjlQlx36MubhM+vUZrbUxDligrCaKIqZNNWC2gVyL725QHBHJ6RLpWISuHdw61beg83SPeMGgP5bIivM+y+54BN7/+MuV8THGkQX5dA5sqhocSsoUIhCMYWlRuUdt1uKhXgvmpTfQFR7AiGN3aYfJSjfAV3Btz+bOLTH3DJnJ3TrArx7UcupWQXhkS9kcYvQGTIY3Ta7TODpA4fKdRv/BCUu5psfaGeWINwdYWoqzYddcy3/Bt9/Pq732KW37wKqJrccLVzTWBqmtjrUMu1+KFF57CSfQFTdkMcElAtadL/xUHKdMmw4ue+HhBsDEm2JwQrxVEWwYXSzq5paFBHmqRXz9N2a3dA/HIILxFRAl6EBObErWd4VxFKSqGAsq1DLdZZ5YEayOaz4+YfnhI52yJnVIUtyW4ZogIY4qZFr6VIkKNzAzCgG9E6FFOvJyBsOAsVmrEsCLol/jFadi/C+EFIitwmWW8rbGRRnYa0G3BaIL3FhEGCBfglxXiSIUfjBFeIY1DZBXFbknRknUeSyjQpcckAq9AWEGjNeRV3/gYd7zlWVRqKXzA0vMtllyX5UNzSOPr8ZFAgrUMb4nZeGOLqqVQVwWiB+HzUC628HhMpMgWmkz2dyn2dKiaCtOOwDnCSz2MtPiyxOFq9xMeyhImFdZahgcSijlNFYKZSzAzKdVCG9dsEK17bKRxNkcubxFc3kKsDRCjMWxsUxUTJgc7BL2cxrMbTHYpiq6i81yfznMDZFSR7/J4LODx3sEkQ0xyqAwuCRAOXADkGvVghs0yZGXYc0O/dms4T7pdCzG+qvBlyfhoA5EX+DhidOMcg7v2Mbl9P3Kqg1+cxZ++jB+MsPNtvBa4NKgdHWUJgBtPapeHtfgwoGyHuO1+3dzjRC00CYHwntf+0OO8/C3P4qZSGndq2q920KggL/FBgO3E6DHEN0wQQVALW8a+YDXxSCfIF1OKuRg708BLQTYtGd48XYs1YQBRBEoj17fxWmJDzX/p5DMzDmszRBxDkkCasjmdEwCBDIg3LdI4dObwSiDzegQMfC3GSEX8PEQPjAifLamubeNd7eyLA8Xf+2ffiUpDiEJ8ELA8bqOEpJpO6xa0VhPRbuJlm/4jU4hGnXEkihI5fOE54xFpTLiSYRoJu47vwsv6PQ3nWX5wleSQxScBPgkRUwm+GeEF7N41Yn0jxgUvBNaujvjAr9/DH3/kETajCeOuwZweEKuCWJXsPb5O0sxoHstpy4Les1NkpwVFr+TXP/kg//i3PsbWcML1J1t83//yGN/yN05zTT7LZ1c+CoD3nk8/e5bffvSf89Daz7BL5zgEM2Lwp+5f1ixjqzPY4n6gQghBFB7j2eoQ19xbObXxZp5ab/PbT13iVx/7Oc7213nP+UeZmPJL1tm1q8NMO2Zmb4d73vskR59WLDxc1U12M80dEWWHHXbYYYcd/gfkLW95C9/3fd/HAw88gPce7z33338/3//938/dd9/9Na35VTtSvvM7v5PJZMKRI0dI05QgCL7k8a2tra/pQnb4s/F7v/ZZPvj8kN5USiML8TG4OEBpD/2KcNugnETKgElU4fGwy2BMicUTbIwpU0H4JgXTBdt5F12VhGID51V9aNAKhEMv97CNkN/8sZvYOHCIW166yniljT83BgeysIwOlOwTawwvllQaZh6bEH5BER8vSG+BfHQYZ56FdYEoFMmVKfRYYFNLeXIK/WSJHnk8jmxBE687wj2G9FUF+/ZsohSEQ0XwhEetjxCrBeWJBtqCKhyyNyJY3aa6eTd6y1E1ILsqaTwnGB9r0vqWNZrxgMEftlGRQe8tGS4IpmbXcKHA5pLxMEUcz1Cvs8gHHJ3rPfm9jkNRh7PpNqgAWVX4UIGQeC147fc+ymzS56O/dxsrrSadvMuBbJPhxYhRN0HkliL1xFWC6YQEqoSRooosLhtRTrUItCZ81NC5ZkAYhntisvmUcxf2cHJ5mZtuucyT145gkwC9lVG1W3T3DChPx8hZ6OcB8UaOdpL4VJ/G2GF3tenvT3CZwt/u6ef1+EPrwja5VdjCEY0r0klJslpQLLaJhg6hwc6H4Ju4zBNc2wJTB/kyznCjMVa2mHq8gVQh+XVtetegOTshfz4DJGoL3NxU7Tyxdf2scuBQlN2A6bkRB76px5MX91J9SBIOShhV8MQWlNTBr9ZhA0kkC+woRlWgRwVieRO9bzfJFqhhyURK7pk5yJGgR/fcBHmD48FrxxhuNMjDiMT7ejxFSAg0g5tDTAuSiwM6f1RR3p9S7GuT3yAxgaf7/IT4wjrVfAvXSagiSbA0QmcWbSw29Hg8YmUT323g56cReAQeXTqa1wwWS9KrcJEEHNqBCAOaK47tlsOZAmkdjArkMMNRITeGqFGFSlv4JEJvZUw/VWJDgbD1oTgaOMyMQY1q0aGciwiGEqnq5+adx7SA0IGF6LEBapTDgRbbPq0dM8JTtQJEJ0IPKrIDDZxS2GqANOBaHVAC6WrhQAQa99IbcJMMJy2qUsilNQg0woOvvTl13kgUUR1fYLIvJR3VDiztgCisHTta8cpXniF4s8SE03z21CEK7wl3ZTTyClVYhI2YfqiinG8SB31EWIesele/xlJKgu0CV1Xo9SGTgx2Gty4g8PhQ41Y36uuJE/IXzRJYjdAN5o9u0WhnXHxknko7Nl8+y+ynryGso3lFsn5nFxsJcqtJAwsapO9TvqSLetSAd8jRGKQgvCaIzpQgQwqdY5MZwLF+bY1/8wt/hEoSHCUC2O/XGM4sUE4JImNwtnbOTfakJKOA7ZMdumcnMCxhdho3mSB1RDAVYwuNa8fc8+glQgGmcviyoogd1TAiciXeC77n+17L7z/8DEvRiLnGGjOdPpOrCQ5PnuWIZsqZj51ld+LZvC2Bh4Y0v1UgXMXKxQVGhz3tuZxP967nzD2H2bPQZ2FtD0GUcFX0WekNOb5rhZM3bTIYw799/Bgks6zsHlKUll//7COMbZfv/8YhkV4gMadxMv1T97Bfv7gEZZuXz7+Yk9SfKyIZMjRj1ifv4+HzLWzT0o8Dnsue4A/v+89cmWyxmU/4wROv+OI6W2sDhtby6QfPMx+GBJUn6Rfk3lEVFaNBRrOdfPHfG+f4Pz7+GQZ5wU+/6bU0o+jPa1veYYcddthhh//m7LT2fGXe/e538453vIM777zzi/qFMYa7776bd73rXV/Tml+1kPJLv/RLX9MX2uEvlt949x9jraP5ogAXOISV2CQk3i65/sQ0j26v4DsN3PYY93TM/C1jdOmoui3CvkGNKsAzujyD3qugADdxeOOxcyl2aRs9mIBLEeMMp6Gvj1DlCU+d20v3Pw8IZMT4VQn5rhRLQbjURewv6L2xReO3x5hcIEXBDUc3WN23ypUbJZtoqsgTrfUgaVNEkvTCGFUqMDn5tKLoCJI1j5+ETF4V8YTZS3Y+RFnApETPXqWYj3FJfcYXUkEgsMKhvEIUDllZ1u9K2f30CNkPqE+zHteVxK+cEM7ljE3KeBIwOt/Gr0a07ZA0ccxNjxjvC2kcgvEnYOXRq0jrkXm/HoeKAtBdrGhRdRXOC5TQlA81OJcucOnXu7Cqac71UJmh2JPiAoVpKlwU41NBPhMRXhyhtyVVIEjXtqEI8IEmObNOPq3pThve+nUP0T2SYz/e5JEnDhNe2KRqBvRsi2hg8BNJ2s8ISoVtSPRWgc4cgcuQQcDmfk04axi9rsX0728TrsnaFTEVsXWyQfrcBlVb0lzOEEKSzTYovaIxgmCrhyxKXGmx0mLnBHrLEF3qUUQxYlwRnVln82BMr9FEpSW6VOAlclTgwwAXBfVUhQSMYTQfou+MOHJihf7VkEsbXfASZS1uVI81+GaCH+f4NEB+oYfYNw8IyhRUs65nRiiQkqzTYNzzxKnlxgPnuP1bnkPsF1xzU/zutVcyUDFTpzJU6XFaMvWgIV+A5HyBqDxh7nH9EjXyEDnU5gixsU3QbuCkAatwUYANDdI7hFB4YRBaYWPQtxZ0NnK2n0/Aa8a7JUSWsFcSlB6Hx3ZjvJDYAOSUQG8YfADSSVwUUByZRTVCouUJPo0QeMpuhHICMc7IpyPK2QQZlKg8w0uPcKAmFqssMq8bmERe0ehWZIVmGMf079iD90OqS03MIKMdrmBbCXk3IC0cZjqiONqh9eAqBJLBi+eodgW0TvWIBrW7xDbrDBjZSBGbfeTyBky36zpp56gDehxCKnxVEZxeIZl0MAttXDOk8cg1sBbaTaQx/Ou/fSs/9vuP8ebXP8dHzHXo+3JcO8TMJYRLvVoDmcD4TBOnBviZFLkxQBb1qIy0Er2e44oS4w3ZoQ6yApkbnKnf00BAnhG3UuxY4KVkZbQbMVVhOopyv8CciOBTHhHHNFYL1g8XTBZh+qMBepxjO23y62ZJH1uCSiLiAPHCgdtXhv6LY0TliJ7P0asDfOhpTzWJuik2DqkuXUSGBd0FwbiapnWuRKQx5CVWw+DoPgQC06pDeNEaAouQGhcpJnMx4UAgKovCU3mBLA0+Dtk8GhBsOHapDJELzqw9xPEbjnJu6Ry/cfpO9u5d50JvF8FSRnB6ibmXHeP8OEdUFXEWMX3THpTrMRuv85pXXeC3T78UKWDoEvyNBQuHtvjeW97InvAkG4MxN+ybB2bojb+LZ045dnWOICJFO47RqeDOYwcIgz28dPE7uLr293B5yRP3Cxbv2mTf/pmvuH99+Onn+b17nuB7XnSe7clDFOZtxMFeYhXRkRWxMOzrDMgnDprw6Weup72QYb1lPf9Sm+5oJkAc63AwSuHakGxSMUwLhE/QleA//con+MGffCsrkyH/7pkHuW12Nxe2eljn6WfFjpCyww477LDDDn8N6Xa7fOADH+Ds2bM899xzeO85ceIER48e/ZrX/KqFlHe84x1f8xfb4S+OViPCdDZp7hX0zk5TKo22gka/wPVzKExtdR9nyElMHBnkLsinBNlCQvqARVaGxmaP/sIUHO0TTjQ+SfFRE7dXwbMTFAo3yTDfakgflRTCIWSINR4hJaOoDQaiyDDud+GYR/UkwxOGeJQx6oV84f1TiOu2qS62iK0iHkwgH7HxyjadpRI5qmrLlTNYHeAB01DYM03Krmc7CFnoDVFZiQglR76+5PlHUxpPbeDjCBM32HxTk+bDjlgIdOERztF5rmRXZ5ug0efR3zlOdcAQXAqwhxSVCrCxotwOaeiKzIeEgwL1uOL6F68hFysu7tuFOdJEbaTIcyuIKIEoRHqP3h6RXg75wMfuoDUpkEue6nrB1naX+Rf1GSxPEayOAFBjjwsceqNkeDIlHXvQOdHEE20JXBuCa324PEB2WqhAY1pNBmnCe/7TdbzxW88wfMYS9Qz28C7CoUGMFX69wk9ywsEIv2sO10mp9jQYNC2dC4Zsl8JMefKrTXbdk9O8AqISeAE2CVG9EeHSNkWrhbS1MCFzieppTMMjXYoYj6FvkELDhsPO19kH+tI6xe4UNbBMv3zApX2zBLd12ffpZQYr0y+IKAphBCIvCVJBdWmTohOzPOzyhXuOsPVYgCgtPpA4PE5YZNLAJSG+FZPPBMh1TzgoyedCgomFZgOBoGpIfA5hT1M9Cb1zms1jKfY2Sb6kGemYciNGeE/eFggkjSsFasOgpkJsu4WPJ5i9FdEzAxhkyP3TuLk2tefEIuQLbgulsN0GFQXhwCNwYCMGL25SnIyxjyh0KPDeoSpP4/KIYFDWzg6pYFhim3XuS2upQL86QKQJ7vEY2wrxYQDz0yibkkegKxDeM5nTaC+oZiJMO0AgyW/wyCHEFwNcNUGMcvJ9LbLbJe2nJFUhUK8ekekEm4ZgZohHkvSR9Vp8ySqaT23ApEAIR/PsGGkdk8MR1e6I1nNjZOHxRQVZSTnVQeYCPa5Qa9uIJIaNPiKJ8e0GlbboK5v14EwQIMqSaGmICAO8gLU372Pu0Q241Md5z8q4xdaDXcZxk9tnz7J0b5NyNaCcr0ervMnpH+mSL3hkVsCgQEzqbB3fTBBliQ80fraJCxRh5sgTQXRhE7HWq513AM0G1UQRTRWYVQk9Q5kr/HzAjd9+FtMRXPrlEJdlCCk4mKyz64qn3BOy9WTAaG+BPWnhCYMY2lo8FUAUYTrQf3kMHhYf30I9eg7RajJ381HKlQ36F7YIbirwp3Lc/ZJwYAmv9vGmztaRXtJ+fsT4mKKxWuBCjbu6igpD1JRnsLvF8PoOcd8x/dSE+cUu20pgX3BVtM9UhG/VsNuje4bN58ZsBxtMbTi2Z6Y5vucqMrbYFJTWHLlxL6e3RlyzE3xDc9uJl/KF04/yyhsfRASa5Oo8N0W7ed9nhlzNApzKOPGGG3i6/ymWgrNce/w1REWL3/i5Mc553vBjXeJORBxopBD8wOtu5+PLP8P7zkz41D0vIlk7wPDZGdav3s+P/Ng3ftne9Ss/9wHe/8sfg5kGK/+qw/ULy/9l8or93R9FM8JayT/7OHQnEeOX5HzDzVtczg9Rjg0npxa/ZL3TyxuYmYhX3XWSf/O/vY/NIwXVTwVgLVM/rWh0E7z3PLp2hQdXLzMoc376ja8jN4Y93T89y2WHHXbYYYcddvirxWAwoNlsIqXk6NGjXxRPnHMMBgPa7a9t//+qhRQAay3vf//7ee655xBCcOLECe6++26U2pk9/sviZbfs5tHqIqv9/RRhgPOQzUtEELGaFzAcIbfqul47LplUAW6kMGGIGtStPQSaqNUi+b2AsAll5FGlxOUFalggOm0Qkmo2YLhLcOPXLbHy0G7S4Swbb1CcOLrKsbkxn+8fZK7bZ5Bo2umYa2aa/OUhL73tDMe3VnjgZ+dY+UIHdyBCCVe3wAhBumHwQuC7TYqFBCm6iGaEdB4beNRsRXdhRHOgGa82ScYTAhfzdd95iYtP7Sdes5RygpsKCNczug+tY6czZLuN0IKFZ/vc/rYL3Hv/DejcIc6BMIbt984iT2ZcOZEy58c0psbE1+rAVm8Fzmiktaz+qylG0TTTrQJVOEQAvtFAWEu4OcIGfbJ4Fnu5Qg0rXJ5SlgnF7gzWA8xiF9spSFcLfFmQLzTQpcTLCu9BZ475L2xT7moi0w7MSlwSU05ptm+dwQWCbqdJLkPKoh4nAuqD9yDHRxpnFNgX3BJKIFVAuTtga0FSzILydfZMtGlxwkLgGN3YIFnxmMqAc7hEs/yKJq0VSdUJCIa8MNYR4X2IjDSuKCDSVEfmAXAb62y9boFgO+cNr36Ul+sNPvfEUbi5zXbZQGa2zlNRdTaGLcAHmtZzI8wwYOWZKeTGELvQpJzR6OUBYixr4W9LYxe6CBHgFzu47ZJgqyDILaICkwucox4/qUo8KcWBWU5danHh3y8y0CGmoencv0z++n1s3BHRvGhoXJNsvahN1daozQyOScoTEjWdkH4iRxcW2hK3a5o0GTPJBTr3qMLihWfrloippw1ChEilkE6iz3rkGcNgd0w+GxFteYQXdchsVtbiha1rdrOFGNHXjJ/uYIJpWt0h5VRC3HeoiUFYj0TiIhjf2kBu5gTWE2wWuDTGx2BTzehkSHzF4qME0phqT4QrKkwSUkSSxamMhcubrNkp1IYkujLCz05jJxYfKHA5LlJk+1Kym9q0n46h4UnP9DCNDnZvo3a3PLNKvF4x2q3oHQlpiy7N5/vwQhgq3pEdn6G1vI1QCoq6GQfva5eFFFRzIbbdRbENrh4E+sQHj/CMOsThA+c4/tYxDzx2HboXYRa6mNAz2R8DAi8kOjN4pbGRQlmHH0xwsxE2CQikxMYhQWFpPrcOUYgoaqFQtpoEq2OoWpAXYCxB3+KkwCqBFwIrDDoM8B7s+1NW73fIWYWda2FfkuFnPeU3tkjf4xGlQwiBN4ZgC5pPDVHrJWria3Gv3eLZhy4gvYXhGGND/ErF8MPg2iWUBrTCpBoZhtCJ0K4exxTWMDnWwR4VHO4WlB/sYyNJuVuhf3qbq3JI9DNNsjAEa0nXIV4e4zKNDQKSvTGTTY0q4buPPkufFDEROA/Jri6XHjiL2RghGhY7gU++5zEmt0/x7y9/AybXiN0Dzg+X2HUkYPnB/ein5+iGMyxn51gZr/LwQ58nW+oyHkxw3vOH9z5BerDD644fYVe7BUAkmxhTIGhRVhMqG7A9zL/i3vX7f3Af5Yv3EZ1ep7eWEJ8QFO4iMXsxbpk8/yzee8TkO5BFzKFeg5+5/Rf5F09+FNQWN07Nf8l63377jUxtlOxNUwrrqU7CqIgBmNvlufVFh/k35/6IU4OrfMuxm3nlwjFmGxUTu4H3M4gXgoh32GGHHXbY4a8kni/ekPhL5b+HawDe97738RM/8RM8/vjjpOmXjhrnec5LX/pS/uW//Je89a1v/arX/qqFlLNnz/LmN7+ZpaUlrrvuOrz3nD59mn379vHhD3+YI0eOfNUXscOfnVMPn+Nys4t9qaasNPkcTPZDdzriQDTDymAC1mJMhdiA4UNN2hcNDMAeC77YYlFugUyhGefsOrjK5YfnwcQU8zF6VOCcY/Rt8zDyFPOOo0cmeNlA7h5wYnGJTdNkeq3J3pke8RtWiT84YXj1INyqWYhG6NATT3tcHDA60gLnaI8rpLVEmxV2VmFViJmK0VmFEBLhoIwEbZ8hY4fb1NhmSDEHehOefmxfPQISSIKRwWZ9EAk+0IhGEyEUqjRsbLX4g3e/iKITEadDqCw2DKjeLMn3Q9AvGLcE7j/vovn8NtM3TTjwsnUO377GxYdmyZZiwjBneCjEXT9NOAwop0KC3hgRp8SZxC1NSK+NEZsDpq/Ctb9zjI2sw/S4PjznHY0ZWlwrQhhH9PyA7FATPZZ1WGQpEU5i51M8EpEXRCtjGqcUPgoQe2Z5+unrWD7VwB+qhRQbgZwIbKQQLkJ0WwwPNHHTAc5VLH6mxEeSbFaTHfTYvSXSGlRR4TueZC7HbTaws1NMAkG5r4MelwyPRQgBpuNoXfS4wqG8wEaSfP8M8QCiXknlK2g0WfzciM2XJPy799yGmADXIuRkjK1KfBjgpcRTYrVEZhaiFIKAxvkMm6q6aWk4Qekmdk8brUPoZUjnketDbCPANUNMN8J6ix8ZRGkwGlwqIAhoXe0TpQIpBBJJf2aGbEYSXRtSzUd4LRDWw6TAWUt6MSfbGxNfHiEu5jgSjE8hyaCscDhsohGhQIw9RnmUcwgBZTPANxQiK1FZQfdxD9IiC0urN6Z5UVB1QrZv6hBtW1pnh4isqvc35xgc1qSrntKoOoA4jvChwjsYzytaT/WQDc1kvyafDWheGRAsbeHThPzoFF6HxHqMDiv0+gTm2vi0Q+vBPk72Gb1kD2kWYD7awJwIaJ3dJj5TYpqz6OEYihLTSsn2TZEd7pAd0IjC0X5+k/higRqW+K5g844YHWiqI7tQF7fBalwQYQNZB7lWBsKAcqFF2VW4vTPI9UmdwRKEeFNBEGOnU9KrJWJ9iEgTfF6Ah2/526c59VPTnLqnS/WNKZu3N5i5D0KToCYFeizwpUDqoB4JC1TtQnEOrMUrCWWFGOeEwxJxeaPOYCmrOtepfsERucEOx+AtvjeAVgO17jjzr/cjn1xDCsBUiCBAfWpSN5kNwJzsEDwtqG62lGtTFC9VtJ/cJBjXIb1CeNrtAH1vCZ02viyhLHFpwBu++04+9h8+j3uuj5AauwpVMiLKMpySlLtnYKqJlwFWggypp6O6EdsnEybnBUG+RPfJbewVS/d/zgmEQ71tgQsft6Ak3sN1oyFXFkuG/RYzc7t59mqfyYLg/YPD7DULyJ4hOTdgfGWDs3GMCgPmpxqsrUzYHo/pjUO8bCAqsD3Byn2O137Dk9zWOMNzH3o1AK9Z+A5+/eov4MQGam7M9Ns2ufLAHLse3+a7vv3VLLSaAAghyN0iul3y1ptX0Icf4kF3C694xTd9xb1r5RunMXOe9vQsD4w0d9vHsb5220jRwvsMYySNvoGxYSPXrGYjzkyeweoNPr/xGNd3d39xvac+fYqH/uBRxKBESUlwVmD3SQSel7/1BCdu3c//+i/+iOGg4tj/tMV8I+ZzKz/HyAx49a6/x1xy4s91b95hhx122GGHHf7y+NVf/VV+/Md//MtEFIA0TfmJn/gJfvmXf/m/jZDyzne+kyNHjnxJD/Pm5iZvf/vbeec738mHP/zhr/oidvizs7Lcp3xtwngBZGqI+yFF6QnHmmefvYRoxbh2GzfXIBQQ/sEEu1sy2RXQeWKzDgG1Bn9mGfmqaTozjmN3LnPh1CKMBNJJhkc7LGbLuD92KO+48to2eZkQZ5qnB3t4rLcHHRtUZYke6bDLjrn80QZaWcwdmic7e2l8tODqEwm6qQmVhXGFLE3dMhR6xocVYpJQpZLJvEY4jSoF8YYhvijZ9aY+owM5wTWL22ggC0e8a4Q1JWVH15W2MyXjF4V4jjD9fAWVZzITEp/JcIFisjdCI7EK7LwmGQxpfsrRvDKmOqZp7xmw5+Q1YlmQnBhxqTfNsx/v4IVA9Ua4icRPJ2QLMWYmgVFGPMjrXJpOCAeAz1h8z6Nzh2sLhLF1neeUJNoOcKmDK2OiK9v4ssLvncPHCV6DizxLbwponlbMfLZCGmg/vEKVwGvetsyxvWtcOPcGilGFF6BkgG0FGOVINi1ChySXtvDFFFiPQCDHhjAOCZ6uKJf6+H0plCkidARjT17kuCDC7WqThiPCyDKaeLJZSM575HqJ6aRUR6ZxwtBYriAMEN4Tbg0Q3uPmp5h6bEzQFZhhAktr5N2A4tAMyaURhAFW6PrAWhqkFARlRJkWFHJCe1QCFcFgDEsa2WjgpawdBK2AoJeRzYTIkSMeWqpEYqdSfCBQmaFxcQxRRDAB11CYwJM+t0WzqBgd71AuTNN9umD6cwOCqz1QilTO0bwyQFigknAmJRpXaEN9SL+0gT06T4VHjUuEKSCKQSmCtQqsQExKqAwi1Jh2TNmWBIMKWdTOEgBRGpyAajauG2CMQxiY7AqQViAyh68M4XLO8FiKaQRkczGREARjRbAFtt3AtxLsbAfhQOQOMXFIqXFzXWQY4GOPj+tDeTjRSAeF1kQjgX4uJ9iylLsNwcUt5LAgcJ7xoZRqPsELh5kTmG/U2O0m4UcLpPE4X1G2E8KRo5qJSTc8waSkak9h1RhlBc5awrUJU+tjVGEQjToHyPeHCOeg10dtD+gOOtBoQFsiKksQG1Y3Bd1jFdlMgD4paa6W6F5AsDnBSY/XDqvBFwVUFuEslLXbykUBVA5RGuQgp4oEzbXxF98Xva2ddr6oQCl8EiOvbdRtRtYgnEQ/2kO8UEfsPIiyQghwWQGyrpwOTvVJf2fM8NYA+0Ljjq8cIgpBCoJPlHVTkXN1g1FluO4VB9nXSPBlhSNC2rx2O+QWKz2+GZAMPcV0SNX4L7lOEi8gHkG8Igl3tSiCFVwa0FkUJHpE6QJuepvk4h8ZCCRUjrOfHhDubZE2hzzYfogrwUHiAxnnXYfXHb+TJ/7PjyIub6HaKd/4ztfw3ovnuf7obob/9wP4nkFUDrTERpY0SbhjfpHQncE5gQwN1jk+9/R9GHmVqUMDWkGKnQwYnYnZfFjzphN1HfE9ly9ytrdFmV7h3JWSz9y3SCRez0++6mle9rLDX3Hv6rx0kzguWWaG+dmcq+MpDtkeAKHeRxJ/A1tbQ0TRRNiS2bDFwdYU+5IDnJ8Ybp85+SXr3fDig5x7donbXn6cj9x3htUZYFlDJfnMA8/SGSSYnkRUgnsvP8XeuYjH+zCo4NYZx1zyFS5yhx122GGHHXb4K8nTTz/Nr/zKr/yJj3/d130dP/VTP/U1rf1VCymf/exnv0REAZiZmeHnf/7nueuuu76mi9jhz86b/tarec/gEez8mKlHNEXPMY402UaG2crqw433CJ8CAipD2U4QpUWPDE7Vd+p1Q+IeLVi7boqHHz/B0skpFu8pQCrsVMDxOzb4/DMLiOsV+6Y2mASKyXCMjyQkhsoq4gdL7Bcs/kTFyW8Z8sfZMZr7DLEyXHk8QVqImpJoa0BZOHxZ4J3HNgL6+5qkqxIxqZg612N0oIvTIVIJqpsCUudopCNMU5P3Q/AwyCNkXhLnFf2bZ4netkoUGBy1Td+FkrBvyY/P1q6QrsFHmmouQhaW7ifr7A6nBRMkC9evM70wYvVDTcZOEJwUZDfF2LMVgztjJscS5j5ZkKxOGM7E5AfaBIVDCEvnyJjwToO9fobyvTHhwBCeKggGGhMpwk3F+LUO6Qsaz4xAgt+TQKgpZjXJaoGXAllBdHFC1QywkaTwMLxjkUd6CWHpmNguvikQkwpZ+TrHIpS4RgBViTQKtVGChNH+mOSqwwsHzrH46ozWoQmbvzZFuangWUkoQ9ywwiiI5wqCeyXB9oTZjaKuorUGU3XYumuaxWcmBJ2KaqPAlR6xPcIHCtPWZLMpct4RPCHxgxy3qwFC4NKQfFdIY9UiTYEUFtNMEKFGuxfcf0mBqCxIiWg2QCkwBq9rgUwZB7pCWsjnA/TE4ZTAppJgJUeWHiS4SCOtQBcOvTnG4wh6Se2eyCeoFzJzvLVYV2JmmwgkeE85lxAEJaZXoPo5LgqolMIUMREGkrgOtrWemc9cxUTA9Awq1OQzES6UFPMNooGFsaGcjzCJxCmPbWiE0rjEodcH6IGpv18KMBbbAiM0g+MRIEivtfADgyo9jasV8WoJszNUextICxSGzEqUeWG8SwrMVMraNzQoZwIWvlDhtxzZWkQhIyY3J8hBSTQO8CcWiE6tIYcTqqkIp6lHYHKYn6zgrlmGmxLvFcY1GO9N0BNoTUKEMXglce2Awa0LTD24ggjqUR41GIOSVHvmyPZ1aXxquxYjrKszRdIA0xYEvTpUtcolv/7v72LYbBP/QIZLQGeeMjCoVGMjMKkCKbChRI0L8B5VAVMx0ivcQhehJGb/LHpUMry5S/fBdRCiruqe7UBW1eNpWYlopIjQQqTx1iN6Di8EwlpkFOFMVSfeAzhP8X3bTP2vQxh50mc3GN9xAKE1dryFbu/ClSUSUb+tTkfogUaEAac/dZZvf/trEXNdgvkR9vMZ3hialyqElFRKv+AE9KiJJ5CgckvVhv6NMVMm5Pu+9bX87Bc2cHFIKLaRVUJuQlb7Q0QuwAdgLOM9UxgE03NDFk+sceVzhyjKgC6GjnZU18/hveXYwgLjoy3OXslYvnaFO195HRc+9zxbmcUmHu0c/+CtdzHofpJrVxcozYT1THJ+eZN//XtryLuOkhPwDbPXMTh/mjefeIbZF1d4lyFkwn988nFWhkNuPtTABjlCanQffucjJ7iy/ghHDs4xGhUkCF78ksPELckPXPc5QmH5vehmjkQ9InE3c+HX8a5f+WMqTnP3t3fYM/PjhO49FB7e9tITCCHYyBRU+7HuS5sDD92whx/6P76DylrCuQSbG8IliapgPC350PseRV/XJfGeG7pzHGseZHUyhXID2lr+N9uzd9hhhx122GGHv3h6vR7GmD/x8aqq6PV6X9PaX7WQEkURw+Hwy/5+NBoRhuHXdBE7/Nn5uz/1TcRPL/PxzacY61lKF9HoVegrnqAUCOOxzYC8VREt5ZS72vRujgjWIlw0RfvqGgKPfWXApIzJjscYO4MLHbapEQZspHmyPE71dwtKE2BWY1bSJtOXczqnM7a0xnQCov2OzkpGsLti751rhKcPEz5bMlkBebqAUsKegGItAG2ZLCToUUUcgB4IqkjQfWKTxtk+ye4Jm3fsw8aSIgvZ2mpSTTm2qyaqERJkFecf3IvUEic1ygm2L0whrq/oPLpNZCK8VhgMKgJROTqPXgUtWfneY9gAfCNATQwrd3XApyzdl1BGgu0/huL6BsH+kGv7Zth9ZB3TjvFKkC9qiBTJWoEw9Z1ppXLchzPKbY1NU1w3RhbUB1ctUaUjnIApBGoIbkYQlJ7o3DbVDQ3kRFCEdQDs7t+fEJxZxmuFaDcwswlSxZzO97BfjKgSCQikDFBDg5ECGzqi7QEyt9Bq44JaRAp6BdkhiXvDmGR6gljQOFdhQ8UkSogzi08kLhQI6yienKZs5whb0dwQ+DDAxCGTwymWHJONCcYVclRihw6yDF8KNm4RjG5osPu5EmJHEIckZzcJtjJkq8NgxtPYEOBE3fxSVdAH4SCZ07jDXezSGNdIkEEIxlFOx2T7QpJrFWG/JH22z/BwF7TEJLIWogxMFmPCvqsPph2HLwTReEj1FljdM0NZpczfZyCMGN7QoGUNYqOPnJTgc+xsi2pKUM2VqDEI6xDOf7Gy2SQKvzxEGYub7uBCiXcOWUBgJL7dJOlV2FiQ9EoINE5L9KZFmYp4qBASRGaQpcHEitHhkGStpPXwFhpNsbdBOa3xEoTz6MKjcoOLFFKDaUqkEbgAbACmFVDSImqM6/9jHEFvRPfpkLXXtfHWooxDWpCVw7Zj1FZO8/MX6ue7u014oSTeMJR7wXkBqmT0uwa2PDKQuFZEUsQkn1tFGo+JOgR9Qz6T4EIQUwlucRqxvFm7OmT9uggpIIlqsaIw9ZiP92RpgWwI9JNriDQBIVjfaBFf8RT/NmT7hKbxry8gS0txbBHaHURpkQbM3jbhc+v1z7332ETXNdG9AJoJwXZOdqDF6FCTzmWLCCJIghdeG4+3DiEkXkpmD0/Y2FawPEGGATbP69EqYxFK16NCUoKof4e4I4R7SiZHO+SzCnFsmnC6ib64VTtOGglIyWRG0VqLkEiQkvf88sfxxmGyGCUFvrAQ1qOUeljidYjerNuZgl6dUVTOeig98fuu8p5JhBUOYRwqHpIVIUMTc/ljmmgwpIw0my9p4ufbLJyboBuOvFJ4L2ifHnLi6DXueegTDJtziJvmWPrCMv/7jSe4sNnjtr27+fZbb+SX/vn7OXP1HN1GxuFgltecOMQfXC2ImtsEocXs73Pf85fpbTs4Nc31J7v8ndu+FXFwRG/le5DdjGd7H+TkzHfwhgNH+LVPPcjTQ8/L74r5tb85YeXiH/Khj9/O3l1d3vfRx9nYGBIXnt72hJW9V3j9gWXAszRznkujWbrymzn1/Cof+diTOF9yy52XoXuR3DqkUly3bwHvPS0ds5VnLMStr7gnrvdGjI1l7ioMlSEcw8yRnHRPyZWyiQeOj2/hSPMgjckHKKIV4mo/sDPas8MOO+ywww5/XTh48CAPP/ww119//Vd8/OGHH+bAgQNf09pftZDylre8he/7vu/j137t13jZy14GwAMPPMD3f//3c/fdd39NF7HDn52//9qf5ewNG9iv63L41dtcOtPAeUVTWhbv2sU1U1J0ArZfHBMe8TSWHGoikJWnbAV12Kx1dG4Acxg2twU67CPDiI05aDyrcYUk6Hhum13FoLi2fgP2tGF0KiPZcCy6IduvaqITgVKO9fsTfuvsK6jCEHdpSG4FQmlEIJiZt5xWrboy9NwFgnLCt/2Dx/m/nnobVQL9l8wQbEyQGyUmdiQbDo9lRcbo0sEfR8gXqk3LrRTZ8hRBQJVKsJLknCf0AZg6FNI1AuKlIaISOOnJ9gZ4pcBZxk3JVNwi3aqt9cOiibhnjCwKyquSiyt7wUPWi5h7apNyNmTzTbtxhaHziMVqiRcVbr1C4bEPSsqGY3yjIp9WJMsOtKoPmsYRPwSNZaAvsW0JhUSs52gRIwoDRUnVkqAVUkrEICN2jmxsifdMWCvbFLMalXvCvsPjyRdDqobHTmLi9RKfwPbLmjTP58RbhjxRDNKE2YUR21nC9ic62GsxSdFD4cl1l6LbQG87ooFFGI0NLP3rp4iXxqi8JJYR4YOriKs5xju8iHC7EuTVNQgU7PakUxPiO8ZkaRPRT5GZJpwW2GsTTBqSTQnSXEMnwvfq3AqvLLbbQGyAdA43KTCHp6B0TPZ4tk/GVF3JzP0VVgvyKWpni1B4ASS1e2B4U0xYjal2a2TpSd87JnjAY741AO2ZHHNM3S9orvRxcYCeniKwCptb1KCinJFMjkj0dknVDJBbGXKY1Y02SiOsrUc3nMFHCcOX7abqKGbOA8bhTYWsDN4BwiELgw0i9MiDA5UZrPJU0xFOBQQDQ/rcFsGVTexcFxN3cErTuGxR5gWxQEIxq/FaYHVA82JB0dV4JShbDl8qiljXLibnwAuiDUvjsiW8NiZqWMRB6GmFLBXJsgVAbY4Ir1bk+1tMjk2hSnAKbFtjRIMgtAilMHvm0GNPdG6AV2AOxjg80UZOtq+B93D1JQF7Hp5FXluvXW9xA5kZGs9t4SKJNLXbRwDpM9sUBxp1BfJ4UgtfehrhLBulZeqX1+tgWueITl8DFZNetrTXFcIG+DRBlAYvHKt3dWhcTQjObJNe3gbvaCxlBFcnCBUghcTIOivF2wqQ6GEOpmBXd5mJ3UN2rg7EVWGIs64Wd5wF78A6jJYMn2xRfbfCvkThVtv4UJCsgEdgS4MUAoo6+0bJKfAeZ2pR5OxjFxAzU+g1KPYEBGeLL4aZOiWwcy1kblHjsv5Z05IqiVj4nSWEEAxCh17eJl3KcHthb2PIetYiOd9hYy6m8pAtBESjCm0ck0HE7qBEOkfroxtcupzwyn96mcYjLezFIa25Lr3tMZOqYn1cj0CdvrZK67Yt3nLwGW4RBZcffx3ffMv38zNXLtJobJHeMOCPnn4M0VK4lRYvv+lFzCcdNvueT3/+ZsI7lmjoK5ycgY+dOkM/K5hNZ/me/a8hsu/lM9vT9C8qrkwtE0xHNJWls2U5sG+aNWE5u32QUk14ZriXK8Mp7nzxEUb9nERrhBAcnH47rfBG9u07TekcLzmwGw8MyoJIaIamYNdX2BMXZ9rsm53GhoZRL8dGAjOUzM9McS2XOOf5T5ee5PT9Q775QIkWFZfzK9zU+YvcqXfYYYcddtjhLxbh6z9/2fz3cA0A3/It38I//If/kDe84Q0sLCx8yWMrKyv81E/9FG9/+9u/prW/ah/ru9/9bo4cOcKdd95JHMfEccxdd93F0aNHede73vU1XcSfxtLSEm9/+9uZmZkhTVNuvfVWHnnkkS8+7r3np3/6p9m9ezdJkvDqV7+aZ5555kvWKIqCH/qhH2J2dpZGo8Hdd9/N1atX/0Ku9y+DS09fJvUZnYuSpacXMKFGZZLhfsFjzRHLdzQILmzTPpWTXjVI65n/5IjpZzIapsA1E4hDDu4aUj7YYfqThnKgUH0FlcZqCYFleylEa0Oqc5ryKmwIyoZEz2q+7S3Pcfu+y2x2YooTIeViSNFq4uKQ/IYEOU09xiA8ZsvUOQ9IXDNEpYoy15SJIdqo0AOLMwbvDNHlbfJ5TTEfwCQklQXBigfjMMoTnihwL7YUh+u8Ar9o0PdqwmF999/jsLFC6BCmU9Q/abP9xt21m2AsIYkYxQI18aiqzjURWYnQlu7NJYmskEbgYxATg14tWPhsTvsxRz4VYtshepyhMoPZE8Cswk+nBF4T9jxWCHAeryRKG4ihTCNMu43ZdpiZaaKtEr0+QOYGEoXerjD7uoyONnHSY0NJ69k+CxczxLaq3QcjgxwbhIAgd18cE6hScGlM94IgrCJsrFGTiO7HQpbWW6ytduivdRkdtbitErtliLQhXqloXRqTrAxJ1QBhBTgIxiWytCQVZNdNU7yiRTXfAR0iRzkySRFxRHMsmW5MaO8aMXvrJvntnpmFNcLRANkfs/BH26QrDqEkVgbke9vYmQQfh/SbmsxafOFQ/+VtKZREG470aklypWKy2KR36ww+iXBVDtYixiX4F9qARp7ShJR5QlFFlKXFnStYfF+fICkoj1nKt+T47/aYhRQ/28HNtBFSIfpDmvet0HwsR7oU04mxkcTOt/GxBqkw8x2q3e16jAbwSUijrzANTdHVkMb4QDPaFzJYhCKx9K9PqLoaqz1W1U1JIqvQQ4vIDdVcEzPbQsQhrXMjotUROgPhBCYRuLCkSgxlZKjS+ufPK49XEG4a9MAx9/tbqOUehhITAkKRXvYEOiEvm/ijHplL0stAp015837cbAusQ40MQkjAY5sOm0j6t88xPtbFzbdxnXr8anzbLH5xjqgI0ZknXS6IV+qxsux4zERZqtkuvpmAlghrQUukqV0EvDBK5Y0hvFILKN5a9PUBR79nHT1dMvWZNSjL2r2i6tEfTl2A51dQ9z2HePwMHgdJhJCKZF0gnSK0dcCpUApGE4KrQ/AOnxd4a9ATC9aTHWjhpcePM575SJPs7AttQs7XgtztCsqiDhlWEnN0N26mzcIfVTT/fZPpd28ze+8mUw/1aD++QbAxBOnxuhaKwIPWlAemEGmMbDbqa1ASvQh7/4Vnz8dDeImiaqX4XV2CsUUIKHT1xWsJx/6Lr9drThwmWS0BMMuSq+NpemXC+p4e4z1NTKIJTQkxCOsJC8tGv1G33BiLu2y4FB7n1sUGi1uOpvR88A8fYmmrz2NXr9EvctL9Ght6zlTTuPWSf/MLH+bHf+8TBGnKWMbM7hnQnu2xd7HL3j1T3H37SbbLHg9feYTnPzbHc+89Tjp8OcY6FhoNZmeavOhFjt+6+jucyTRSWSyCyhoyW9EbZWwvD1i+sMkPvfwudnffwG9dfQkXJrNEkSUMNGNnaNzQ4cZbDnJg8XWApCoNS4MBP/2BT3FxfZOgCjGFpK2/cqiJEIL/7YffxMteeZAg9wRDy2Cjxfp0m+sPzLF3T5d0JkILgYy/m6vuCO3kDX+R2/QOO+ywww477PDfmJ/8yZ+k1Wpx7NgxfvAHf5B3vetdvPvd7+YHfuAHOH78OM1mk5/8yZ/8mtb+qh0p3W6XD3zgA5w5c4ZTp07hvefEiRNf7GP+86bX63HXXXfxmte8ho985CPMz89z7tw5ut3uF//NL/zCL/CLv/iL/MZv/AbHjx/nZ3/2Z3nDG97A888/T6tV235/+Id/mA9+8IP87u/+LjMzM/zIj/wIb3nLW3jkkUf+etQ2d9p0eiMat66xaWfoOsskb5NsG7YnFmkEQkLr4XXKo/OgJPr/zd5/h1mW3eW9+GeFHU+sXNVV1dV5ZnqmJweNRmlGERQwMhYiXGGLIAzGlmWu/RNcrgGZwTgABhP9GAkEBkkI5Tya0Ugz0uQ80zl3Vw4n77jW+v1xmrFFuBdZEjK+9Xme81TVOfvsWvukfdZ3vd/3Xetx7cvP8Jq3HuXewQj3f3gS0+4THSuwa4Ii8kgu94ZtPc0C4SRe2CeUOaXVXPxSQNeTiL2SiTLhE6P76KU+RcfjYnOWqJETbGYMrozxHodyVUAmcBaKbkFTdkikR/vaKtEDKZ/6d7uZmNnEz3xUUlAMzND3oxpQBqD7sLO5xg69zgPVGfxWishLxE19yoen0blg0HQEJwPSWkpDiksJYI5wdYBzBk9B/lwAvo8uQHeGK8+EHlNVD7fvHPmGT/44hHsMckMzozY5n00ziDy8ZgUbagRiWAiqOEot4EwOQuIGIdkVIa6rcYnF04KiKfDX3DBJZKDwugbnacxIhUJXiTKHTIceDirSdK5X6FMS/5ygbEa4hqWYjMFC6+4mLWeo6T7SChgKDDANR+V0F++ZNYRx5DeMD2UacuifIZ0DafGfCsjLKvmoIz7P8NgLB2dywqZAd3LGruoR65KljTqbepRspo7AUZvRKJfhrvNwHbCJII99/DPrlPUYcSGgvtBh4soedqfAfcHSPu9jGxKhNcopjBxGXVM4ROhQgxJpBNHxDLvSw2YZQkr0xU1EYdDCMOr7ZBcFyWgVXQjILGEfvH4fIxz5pIfolcTLJcJZOteX1AYZwXjB6OtylnLwzoN2GnehhvM6QxPbP5/8KoEpSmRhGL2rQ7HLwzu8iMwNdqyBXusg1jvYiQbZVEzcLtErLUaf61NO1BCjI0PbFO2QvYx8rE60UqLKEifAxlAWJWrToIRAJyWFLJn47Cpl3cfMj+Gd3kTIgqBVQXmQ1R2bhyTTd4FKwM8s2bgk2SUJVwpMKEnGLCYukQ5whrxaUj2Z4mRG96qIfKBxVUvRqZJNBHgDgddy5KMKwilQinQ6GhZmhMVIB1rQvraBc47w3jVkxSefjYhPFNjdI/QiQ2MtR7YLZEfgxkaIL+QsfneNuT9YQdQ8TKgI2hYQFK+r4n0yASPAuKEZjr1UwNAe0s9Z/E8O/5phAg12eD9MiahUcHnOyOnBUNGV5rjS4PwQfI/oQgfdyclGPLxeAVIi2xnG8zDNCjItsWMRtrOO7ueoTkIxUUMNBqisgMxcKugUIAW1iqbrCTAWWw8RQqELi3euBeccttVFbHVIdlQAH1EUCKFweYkDyn3TBJkgH61BX4KxuE4Hl+d48wZlLLKAuGFoLUygVzs43yILhR/7mJpEOIfLCkQU4vKcD//ChxBOgacRGvLcQ3gOcb1P+Dt9VAEDI3EBpBsBRU/T/nyTmhy2G05cPs5N5at5b+dBvLxLeapD7wNPcuXrr+DUZMk/u/vjvKQ6w2NHTnNtY5H7759gNW9RjK9j0ghkjR15k1N/0ObcDR4ukHzh+Gmy8Ud5ZOtpxG0N/EObfPr4Z5mtLtAbFGgk60mPamTQzrK+1uDgt53mB99wI0cvFGwst1k9usZNL9qPcT0Otz/PyyYFDyZ7kVhwjn/zx5/judVNntObiC/dxadPn6LYMSBL4MKzx/jE00e5+tYZpJLk1nzVqXDlwib3f/opbr7jIHN7Jnnnm1/HfR89RS9NSb+jz8rsCufObnDd0f38i5e8hrnxJk+1d7C0cpBH7jzHK77raW565aG/rTP3Nttss80222zzTaRWq3H//ffzzne+k/e9733P+6GMjIzw/d///dx5553P1wu+Vr7mQsqfs3//fvbv3/8/e/e/Mb/0S7/E/Pw87373u5+/bteuXc//7pzjV3/1V/npn/5p3vjGNwLw+7//+0xNTfHf/tt/421vexvtdpv/+l//K+9973t5xSteAcAf/uEfMj8/z1133cWrX/3qb/pxfLMR1Qqbx2LWRyFVNUTPQ0US6VkOnbAsLnZQVuIKQ3hFwuUvuMD5++u4HYKn72qidzjkuOCxzuXgC6QncHK40h9fdOjMIxt1tNfHceVpQpGDtIQbBdo54h0ly0mMNRC3Hfl6hfhsQXXNoOZS3O0Kf0nQOeUhv5KzedInPmTwm4b48RamX2KtwzMdGB/Daomph+QTAdlclWqroDrVpagq1tIGOjE4BbrdJ/0vBebGklwH2FjijMQGl9o+EFgl0IMCq0G11uiemEdfZrBWUT3eJlxOUfuauFe3mdi7Rr7ic+6BOYqldaRvaZ+JoA5mNEDpHCkc5eYKxWSFrDaBMYpqJobFgkSizimEZ3BSYYQkLgqEUcOJlXDoTo7Qkqyu8Vf7uEhjfY2u+lz12iv4wug5ip0K748Tgk2DCwOEkMhWBzcSoIwc+ozIoQLFaYHMHNlkBXNwAr1lMBXFxkFQiSJcFTgFxfWG2kMe/ohB96FyBpTRCAtFXbJ1MGT8gYTLr13h6T/bga8MdgRsI8R5it6plMpWjntWUXRTWNtCTNRwziIHOa4P8/kGdknyZHue0XIZWfFRvsJGMfqgwZ1IQEpsIAjOd5HuUmFFKaQX4ryCYjJG5zkqt0wc6rH/e9ssPzLCMyfH0ZmDrETmgLFIZwnWS7ylLfxSk02FxInALHvIm31q1w/IBz027h8nWBaodoJeNSg7QBQOfA8HmLEKojCY8QDdSRBhAN5wVZ/VNvgetllBSY1MekPF0iBHL3ewzQa6kyLbA0w9JFozRJsWZxXxqS3MaEF0OMNMjGCsIW0qgqUCnYPaKkhmhsofW1Xkk8PY7+rFHrIr2breR/UVpbY4JSjHBPUv9+hfVqGMBGXTo3Vzg+ikIGxlQ4+SUJM2ob3bw8w45p/qEy+lyDlJmfk4IzDKopxDOMf4PYv0dtfIx0JKJ3GRwHk5JrKkoSGfDmieDUkE6AFI5QEF3mbKYN6BVFSO9SmmA/JKBEg8PdRzJpc5isk6c6d7JE+3SE4XUA4NhVGS/BmBkJLkLot/uWLqn8GRlQUqv3ABTDmMVtYKPA+yDDyF9SWiU+KvJ0OTYClJZyK8tRTlFNlUDd/Yoa7JOGw1AAf5aIQqNFSjYUuZcAghcH6ADEu6fogZD1GL64jU4Co5eD5CGBCXpLJaEy0NKPfVUadXLxnX+mRzBjyJto7KYhshNHgas2caaQTJoxrXsqRnDGVHosYgXagzmDCMPFNS1DyU8cA6jCthBXAgUkdyzQTxaoef+fW38q573wueojg1wNvwMZ0eYw9OUQSSZNLDyyVZAHtv2cGusE73JQOefPIEQaiwjQAvK8izggkCzriSR544xfkHcn7wH9/HvsvPIPdq7vzF3cxU+lwc+GSuxsWTiuX5CqawIByTlQrW28kDW0/yva/8FOONPh9YfgnzI3V6ecZ6u88udzVv2L2bE0ce4+kv7kMox8qrjnJo73Wks+M84nvURitsJst0jEPhEMbQMwF3nz7GQ8cvYNXwMfjj556ksAqmHLKEaMVRNAsea59lOm4Q/IWFkK987hk+/b4HKPKS137fC/ntn3o/blAgjSOo5kR+gWnmvPH117NrchTrLH945i7WNrZQawMmvtjYLqRss80222yzzf9GNBoNfvM3f5Pf+I3fYH19HeccExMTz7db/8/yNyqkvOMd7+Bd73oXlUqFd7zjHf+P2/7yL//y1zWgv8hHP/pRXv3qV/MP/sE/4N5772V2dpYf+7Ef44d/+IcBOH36NMvLy7zqVa96/j5BEPDSl76UL3/5y7ztbW/j0UcfpSiKr9pmx44dXHXVVXz5y1/+36KQoj1JWVi2ighZKkQBrgqtnU1UFDGIDLKbYhZmmDywRn2+Q/Nmxb2f2MnY0yHOFJRWUC5b0l2a0DqKpkM3QAgHpcBfH8arnOlOILWg+iLJ4OmC4LDgYhShMgM9R+2xi8RTwGwDU0p0MyO5wiM8WMEctwjhmJrv8KLvOMVTT+3hnCrx9xkmDqUcOzlGb7KB7A2oHO7gryd4LsNbkohJgUdJPcxQuQULzljkckGwucT6rgM459BXtFhfqjDxpEaWDmHEsK1GGpK+RViDWvMQFXBS4rUS3CMJ/htSvMCwdaECQuKJgiAsMIVAJobobAtrSsoxH9GyiKyAXKAzhxuJCeyAjZkaRjj27V6iqChOLu4gHYVok0uFD0EZKUxVUVY9YhcighwbNzBO8MTnTzIeaxoLNVYDB7Ekrxho99HWwyFwWiBwODFMCpGFw7uwzubfm8XtnKF+MSc+OqC6JOg1LP7RZYqpmNaVcxRXOKSxiMRAYXCXV8gHsH59DbmVkEeKJz82j6WkG/lIIyh8gSpLvOUEnGUw6mOdw6uEMDNG2aiiNtp4nYx7L17OTLaBHZMQ+OAFuGEmD95tOeVaSB5WsAq8rXWsk+AsRUMTni8RWjO4dgQbe3hnVwhPtHCFJdEhupsjtEImDhf5lNJR+BJdCrI9o9illGTCp42HPFjQPTdB7SNdqiblUHqO48Es4VoXmYLzJE67ofHraIRIU1o3NuldXiFcF0TLNcLVDJxFU8Wtb2KGLzicVuSzTfSygMhHlBaSDDwNzmJdQTruYaQgPpOgDqdQWooZMM0qeiOlnKyRjivK0RjPBNj5CSgMlVN9nHDIbkptoElyTbLLkF0zTAEa+4AhnQwQTlJZdPQDQzbqMXF8gLQSW42wkcfkA5blF0q8Rk5LNJi74SLefsPWE6Mkp2t4J1fwDy8il0KYHsEb5BycbHFsMEU+kKQHG7T9GJ3GBMuX1FKtjGyHhws9nIpI9jZRDvIxzcQTCcFaxuatEUVV0VgysNWm9kvLiFDyyp86w85/nPDv33wlZBZGm4hOF4zDlQa0ojglWfx5if/OlPTgNOG5Fg6HcALSFKII16hgfQ+vM6AYreClDiUUIgTbFMitlKhXDgt01iLbyTCmu6Jw0iGSAhN7QxWiG4qShHUc+KElTkUhvedidLOBzDJEKxm2GGmFdRYRhRD6CGvRmwlOCNwgHYpswhm6l1W5OlqkfVePdBDghMKfrGB6FoqCzcUK7dvGYTNlbFWAslSf3aR0CicrFPUQURjS2CN4JvnvhZuzHcLC8LlPfxF5xIdE4E6WEGukEOQipxytIg1092o2FwTL7z/PykaCeMSgZJud+6fp6gYT149wutvnfXc/RO2RiPJKw9LVgufUJPV8k/1hnz4xlfQchxojdHqHeOpUD1u16L5lvjLKZFRhTN3AVw4/zP4bNhFKkJ3fRWt/SlYa8rzk7mdPc/3YDP/uZ56gZ2Lq39bhcOc3+fhnL2Pl5LUcu7DGa156JTe8eIEPHL2JvFR0pI+Tgh//6MfRTmI0KAu5UaAcWIGzlsJYaJQYBJtZ/y+dC6+4aQ+nT65y4+1X0F7vsnpxA9vLoIDuIw3MnoDv2ftiDlw/w08/9HFGVYMXj1/Hc8vHefSRL/H02tNY+11IuZ3gs80222yzzTb/OyGEYGJi4hu2v79RIeXxxx+nKIrnf//b5NSpU/zWb/0W73jHO/ipn/opHnroIf7pP/2nBEHAW97yFpaXlwH+knnM1NQUZ8+eBYZGMr7vMzIy8pe2+fP7/0WyLCPLsuf/7nQ638jD+oaz79Ach49doBxXw1aAtYxkT0hdDdtQrDGYSOGUYOX+MYKnM7ZOVgisZWuhSX1vj2y1wv6rpjj3qQ4uVUx0UxY9Re+AZOwuQ/VCiqkJJsM+FkGqAK9BOuKjJQR+n+y0JTqSErYKxt/V5/E/XaA3FzFSdFFBSXgkR7mC7/8/H6exw/L0yb24myZJjlkuPFFSNqq4SFI7nODCkMH+Cnng4aSjWAvYOlehMpVhYo3qlAgJsiYxL4nJN4ZffL3JlPiJ6nB13gMTKmQvRUc5sibxWhn5ZAWvZXAjVazcRJSW/NPQtQFzqkcpfXJCgl0GtQ/80znRxYyyIli5vUHjTEhYVXgtg9Ga8rIQN+qwy5aRCxtc/X8cwW8K1v+4QdfzMVWFKCwUJWXkk+7wcAK88RhtUmTQxQ0iSC1l6di42MWKYfuDdQ5dgqtFQy+QwuCEHK5Y43ByGOvqtQpsxacpAkoxAAvCWawHRV0QbSpUBulsgVnQxCi8GwvUqqP52Rbq3CY29Jn77i6Z8bi4uIPOPkm0Al5LDVNsSkdlscANLC6Ohpm5SoJS6KUOIw9I+HZLhEGUEXiSkqHhb35PhJg0iLVymLIUezjtUZo+6XxC+PAAlw5brZwvEaUlWfP44m8fwu+VBPRJ3TDmGePQK220FKT7x8kaPtarUglS+jHkVYvUJdmXff7Jf3kOFwl+6T9XyLo5QijAUNZ8ssDR3x/g9WKIDE5KVDcDA4lOqdUDTGOUHdflnF+OMCE4M0AZgahEWC1weYnSAS7L0KtdggFsvHwe4SRyUBIZC4VF5xaT5ngnl3BRwOZrd2BqQ9PgiQdzdL9AZiXZiMYrhoay0YohvSLHNEB5DisMyc4I60lq5wzBakKw1GXkmi5mskr/0QiRW/KawPkw8/mUsm/puRGq1R5lrnHWYH2F9RSmFtK5aZIy1kxvdhl9oEtvxKd5xCGsQXoZ3R0ByYhGH+wixqC3GiOiJqaiScMS63ts3tzEX8oYzMWIwuIoh8UlLREq5DMf3sN+abGjMTrXCCGpXasIbIetM5KipcEa7GKB/MlVVL0KWUH/4BjeUpcoHSo/7OkV2DtLsnsEtESnBTo1qMwiSqASYeoecjMdRrzvtAxeUmX0vTlSeejOADbbuDSD0ToiSUEZmlcaZjZbnF4xEPi4wBsqVda2hpHXrc5QGdbuoasweihh46mYxnhCrx2Q2JLqkU2SN6c092csv2tYNGW5HLbf+R6tF86gCoeMA/KwIFjs4a0W2MDQvcYn7BjE8gZi7xhmdhydGkhzWO+SeQq7BnI6x53WOF8jJEN1TzVGJ4q8UiJO9pguJ1BrW5TGIdWwiHm21yFZCFkII/KNdXRf0G93ieciZNVwd2cf2YpPXJ4nXRGMrLe5ZvcRJhoH+ZktSVRm7HQRvZGEX/7YZ/hX3/4a2v2dPP3Ma6jONtg5+iLmx5r89KtewoefPMytC/P87rs+Rn5ugOcs6X0RR/YWPPFlwWDtImlekK71+If/9f0MGJ67xQBQoPPhTymHHy+1VNINhxHR1gMpBAZwmWRgDC/517+DSgS/+oOvZWqkzsMPneTYZo/TF1tccd0cV9xxkDN/+jBLTtB4tGSsZ3jjG7/IH504wadWlhh0PFReZWTDEiQ5p44ufd0rVNtss80222yzzf/+/I0KKffcc89f+fvfBtZabrzxRu68804ArrvuOp599ll+67d+i7e85S3Pb/cXv/g45/5fvwz9P23zi7/4i/zcz/3c1zn6vz0WF1sgFNr2EQ0Pb3HApO7xire/gpeO7+Zf/ML7yccjwrUUsWpY69eHPikjkvXbppl82QnyCz7NuZxHehXUikFPOOyGxus6/G6OzAzEUF6yYd5arJOuVpAOXhWdZ3Bhk5PPxfSuruEdyuk8qBFpRuXejM61NYTyMTcaJh5d47lHp/HWJRdGIpZUzMgXN/FygRIpzsUYT6IyCDcNfWOJruhjph1lRfP01g7sbp9w0ZHcVkcccrz42uOYx0MW+w3a/ZhsXFF6Dmkkqj1AdvsYEWI6EV7dI9wqqc6mzOxcZy2os6c4xskHxpj0O3zbP3uWh1b2cvbsGI0fatFKa7S9GumeEfLaMBbXTMdkawLdKfHSnHJG0n06xNQ81hZm+fhnprnjhsd483d/gY984mY6jCEcOCTSE4QrBVY79HofyoJyZhyFBZURj9XIIos6tk52+ShRUlKOhORVHxN5yLxEZ8PWHt3KhukuExWCtRJvqSRvleRX1kllwdYeTeVMg2xKEq0PU138FYG3VpK7iOLZmOq5HlHPkUchLpLMX7PCqWd2Ujtv8XqS9m6BbQjUlWP0V3p4/QLRMTDZQOUWtdIbxibHOdFFgZ87Lq6PE7g+vgWFpUwTOmIC2coIXQ8rLKZawQaa/lSM67VwaYHIDHqxRbYjQo9ovNlxkukq3vHNYfKLscgBpE1FRQmsr+jO+qA1TpTMXbnK9JzhdGeU4N4ePTRnRQPtLF3PJ/AzSIfPIc6ndyBisFNTP2WwWqFygTdIiL5yAet52D07EdKx8mwD5afksUZaiygdZS2kdVnAyDM9sCCtgzBAD3JGH2mRTQQUYzHFTIPK8TYyd3idYbqLTHIaRzK2rtNYAVaUiEGKFY7WleO40BEul1SPd4mOKtKaJDrv0N3hdUVFo9ch2+ETtTPa9yoqt1pgGN0cbObUjyi8Yz20EwziSVpnR5CtPmHWxzYrZC84QDodgueh+ymtj2mirZSw6MJGH5EW2Llx7O4dJOMhI19METUf2U7pHoyxwjFx3wZG+NSP9tl64Qhm3EAhcFmGqFeQoUQ4xeaW4u4vNOi/SjD7wQvYesxtL1/k9jcd4+nlST71U/OsPqpBKVRSQNqCsqT2ZIqTCiI5TNNREnFuiezmWby0RPYznCeGrTjOUY5GrF8bMfXBZYR1FBM1TE0MfXp6Q9Nkt9UZepG0xNDryJcwDksPxYhBiisKmJ/G4pC2xKUpzpOY8Qr6Qsr+H9ik+eqSfY922LNnnZYIec/SDNWPpSRPK/Qd/jDi2EAZxxB4lMbinKV2wUB7QHfaQ0zWcFmOCHwaR9rI1OKkpOwUiN4A+vnzLVAy9KjFNfwvBQzSgqtv3cvxp8+ROAdlgSwEgVPEK1C0VunuGaXUhnw+ZzpXlIOYmoMf+Scv45d+4aMc7qQ4JZl5TnDVCxdZq9Y58uAMDz6wn3CjYP2RKkf2j/N7z52kKhpUNwI2r1Js7dhkuWjzM3/0X4miVX7vF+rUp5t81y+MI6XgRz7xUdpJynp0kcWL6wg1jGnPNz2+8rmDvORVjnjxSi4+t8jWmU0aD7ex11Qpqwpyh7IC41lkLjASxhZL3nzHLfznxx/FSoHzDQKBa2mYAEoBqcNqwf/5B5/khr1zfO+VV3Lu7AaV0Yh3/cldtNY7dM6uovbOMHZLi6mbV3l4a5VbRl7Cn55T2FxS2hLnK+RCg3AQfIvO4ttss80222zzDeCSBeC3nP8VxvBN5mvWrr71rW+l2+3+pev7/T5vfetbvyGD+h+ZmZnh4MGDX3XdFVdcwblz5wCYnh4GH/5FZcnq6urzKpXp6WnyPH/eXOav2uYv8s53vpN2u/385fz589+Q4/lmkax1sN0BLreETzi09DE7PYpazoroA4bY9viet97Hwi2bJKMh6WRAOhkRtOHW8VN859T9bPzZaVyZ0Dkk2WoGFKEkHVf0dsb0Zz3yaY8Z1WZatzFCICIDcxknzBRPfa6CfS5BP96m8+kR8i9FjHxoibFPLVI/3aZsOioXIVcNPpzfxB89ez1LJiQpfYgLbN6jvzMgutjD65cIBLpf4m9oqgsD+qeqJIdjYlfi9Rw6Ae+kxZ0WnPzMPP4JSXVZQmGRA4O/2sdb72HiAOH7JDOKohkydtsAryyZv2yZmZ2rVCsD5LUVKv/QseetWyRKc/KZca6+7RTTlTZRWTCwAWXd57rXnOdl1xxm5DMnYbmH1ytRiSF8LiFYzqkqQ3j1MB742Q/OYHNHXB1gPCgDib3UTuC3C8KljHI8QlZq2JEKxWiMX2iUFdijLVR3QNjuD1sQPDm8BALdLS8pU4ZGqWhJvqtB0dDD1NbYQ3Yc5YhHuKGQUuMZ0C7F73SxkcQqB1Yhlh12Ewh8Wi/fhWjUePBPr2TjYh2jJDKx+C2Il1Oy4yv47QHrLwvYuq2BtQaX59Ab4JKMwbimv6tB8tkaI+0+/gDIcshLrC9Zu9Fn9eUV8n8IoleillrozYR4KWX0Cz1E6UAIRB/IFFY3MGMhzpMke0cZ7B6lmKlgAkcZSzZunGDjBdO4QGMV+MsJg/sUk6LFVclFgie3sKsJd/7pzfxfX3g5qfZh19A7x4SKfDzGBRITge7kuDLHBJLe7hF6N+0kvXxiGE07KLC+jyws6Rhk4xG2GiC2uox84QLy1BJmq0U2FlGORtjxOuFaRv1wl8pSNpzQj0U4rUBIsiumMDvG0YlFpQJVSloHQvKJKoP9E5iqpLdL0bncZ9AU5JGPWo8orYdRgvhCSuNoHyFLuvt9Olc3KRoNOmcnsL4CKbDO4KcSF0pcDCI0eKsp0cU+0VIfv12ihMb5ClEaRu9bQy0zTKcKPJQBmRvESovg5Oal9hU1VBFFHuFqhpc5SBKmPr9JuJwSLQ7AA0KHLAxqo41o5+AEQnpEazn9ecX5F1VxxnCxW2dFR/QnPOTVDRo/7KAooFq9FIush0WEwMc5i0vSYXtcl1i9pAABAABJREFUklM9vIkTAlsJcTjM+gasbiAurhE/toIts2FL2aBB7Y96xKsrjO9dh14H4V8qdCgJWuKE5rGVBfpzVVzoY6sxLi8R/QTqdUS1Ao0aIorJ98+wsmOS02KClaCJMYp2b9i+li7E7DzY5vLw4nDsvofTw/YvWwmonkwIHzpLfHSNybvPoaQku3KGYjQE65CtLqLVI69KjLtUwCmHRqrZdZOszyn6P+BQ351RaVhe9pqrcc7S3V2S7PAxkcJWAoLVPuFeg5gWVM+AebxAbQwYNR669u/5nrc8zoiEMNZsTnjM3Xiay2fO8ZaXPMwbX/sktujz5P3z3LN2iNaIR+PaAaNzY6RjfbIa5E3D/uue5qoXn6S57wzrZ0/z7k/ez1Y/oZ0nZLaklWZUrh6q7qySGAS1WoM33v7d/NCP3M6//tXv48TTFwi2CvyVApFAuFpifAi3LC+9YoEf33WQ6ycm2Tk7ilUOmTrEQGKcQ2QKcokoJOWEwIxA35UcX1zmjpddwc/93Bs5eNkOosBjq2hh5oY+KkVf44xg5UiVp94veVnlRozxuaw6yoHgOWZ/pIWpDigL81ecZbfZZpttttlmm23+O1+z2ezv//7v82//7b/9S+62SZLwB3/wB/ze7/3eN2xwALfddhtHjx79quuOHTvGwsICALt372Z6eprPfe5zXHfddQDkec69997LL/3SLwFwww034Hken/vc53jTm94EwNLSEs888wz/7t/9u7/y/wZBQBD83VmZ8jzFudf6GCFptHKctLRma6w+2eK3W2fIpgS7dcJdZ27ishsucOTCHKYmQWqMElxXu8hzH23Sfrgk7PZIdoRYKVGxoX4Eoq4g2VljNtwg9gucFQjf0p9wEDnOPh6y8J2WxoMDlr7oGJzZoNgoQVichJiSweEKedXiLxWUDzmqK22s9PHLLqbdw7ywifU8yhLKSkk6FZBMh+RVyamzE9RPS/KlGm4kQ+YOV5RUzqW4FUs+GmOnJHJnQSl8dGIRxiEECE/jtMIEEfGpLVouojJecPGpSVoXI/oXQ1YuNJm+aQPb1bSLCpvlBIe/knLzzHH+/sIjPFTu5aFoN9GVBQJLUE/Iz7QZXDuP5wrk+QK/MKirCup7e5gVaF8Iufu/XcPqygReUZCPaPzSYDML7QFqYwszWwfn0Bt9nBQo5TFY6QyjYvMMlxqEK1EbPeSOgLipSPMA6zmsAusNJwjeZoErFfmYT7RmcKlDFAKFoKg5dGLxtnxs6OP8oYJLpgZRGGy3TzJawQYC16hy3RuP4MUFH3t/SPBsCx35bM1M4S87VKdk9CstRBjjaj52s43KMhAQn0vIdldIR2LcMyWlFqgSnFbYsToVr6Qe99G/ZZFZCLEgrUiKmsbv91FCgBKoTorzDNINj0MNDN5yD7XVpwwkvUNT+CeWqbYy0l1jDA6OIR3IQUbSlWyYmCvKRVbHxumbgGilwDRLgrOKRJT43TaGAGXrVFc8CAz+8TXiVoa6bQeuVkE0GxhXYPoJQihsqMGXpBMe4ZrBbxXIzS4U+dDLQgpss0IRQFYDZ2Pqp1OUFXitAhMp0gC8zQxlJesvrDHyeB/BsH1BDobRyLqXUznvsM5ROZ8TbBlKnVJGEtUrUGlBGfs4JcFpakf7EFcRcQlCYOMAISUUBSIvKZwgPxQgB6AHORhHPlGlbHrI3oAyChHWYSM1NGj2NMpKiDMQEinB2xyQzTXxNhKcApGmBFs9ynAUMz2Kd36TLLZ4q5bGQwnJfHWYelMYRKuL8wpwPiItkbml96IJxKOL4Bk2y4B+4tEaBIz8SEnn/Qk4OUxVyvNhsJK2w1hzIXBFgfD0sGByfhU3MTI0gi0tjuFrp/ZcCkB6+SjSREQXNpB1i9/vk6cOhx4qPeII5ymyhmP9WI2s7TE666GiCoHtIzNB2b2U4hP7EGpUYri4NIGbyvGe1Bz94D76FUlAF7mq6deqlNUNbBCAAMewFU8IQbhZkN6wk+DsFmqlhezmqI1NTKRJpiOirMRKS1nzKKslMnOoQYFQiuU5zV1PH2P8hR28Fwsef/9hJo/P49IMfbpF/9Ym4UVLdUPgxkfxWcHdFNCrhQTKsmdBkzzY5tmHNin7MTObObXGCHO3HuB8fZWVfodBWSClZuOWBleNrnD9jous9RpcP2X51BN7sL0QuZmivZJza9MwcpGX/v2jTPxogg+MV9/Cqy7bx9lOi1984ev4kyNf4mNPPgEOpBFsrHT51Mon+cHdlyOFZGbvGOfabapLBeEAwosDBv0awXrKb7zn7/Mf7vwYvpR88MgzmPhSu1/oUJ7FrwzoNRWeMuT9CsYCY7C+UZAUJbHv0aiEvO2NL+D77nsUd0uDn6m8hPHKFOurH+O6hd9ns7bIT6wLVFhyxdQzXLnvMMcvjLI0Monn/0/78G+zzTbbbLPNNv+L4ZzjxIkTFEXBgQMH0Pobc57/G++l0+ngnMM5R7fbJQzD528zxvDJT36SycnJb8ig/kf++T//57zwhS/kzjvv5E1vehMPPfQQv/u7v8vv/u7vAsMJ4dvf/nbuvPPO55OE7rzzTuI45nu/93uBoVPvD/7gD/Iv/sW/YGxsjNHRUX7yJ3+SQ4cOPZ/i83cdVwmxEpx0mEqJa1pEoyAbLNE96tCF5sLGJJ6QLA4OoFIIOgVClAxGNV95dp629jj34n0MZnxwFkqBKwTBqkMZi+7CnpFl9gUr5LmiM4gh05ArOlFJPhkz/SMbDEZ8Oh/1UGlJtmuMZGeVeNWRTWuyQxrRHiDjEt11Q++IzFDkJeUzJXW7gRhrYkYqdPYGOE8ihSG8oMFadA8G9zQZXc/BOeSYxV3I0UHB+HU50ptAxoZss35p8nUpLacSgPAQStN5RuHHETqBLNdoWVCfHLDrphVaRIhTHknd59hz0yydGeG2f3yEstRk1ZCptMVIdcCx5XlsIDB1CMcNW7NNZE/QPDtA9ww8UOISx4XNKbQAfPBWU5QFG2hEfwCDFOvH5K9s4M54qK2cYrFL944a8VfcMF45M0hpsJGimKizqSVBkCPsUE7mjANj8RfbqHUJY3XmZ0Zpb7Qpn+pRVDRqvaR3sI5KFV7P0UhyZCunrGocFr8vEWubqNQgdMDxT03QSkcQWz4YgegbXJDQvjpk8uPr+GsOM6kxDUG+b4L2HkW02MMvA+LVjLwAr1egjMBF/jCWNjdcMbuI3xuwtBaCDrGewnX6ENSGxrQMI4l1JghWUupPtJG5xSssujssAqikGMZWpw7Pj5EdS5YPJ9q6m4F1zCxvcf6ROYqXxtjnLNXTA4oRn42DMWqjhvt7VcbuWcc/vka5a5xqDnrgkJmh+dwAG+bIdp9ysoErStTiKsyMYqsRqoStayLiCwWyXqWMJC4OcY0KoijQmwmd3WM46TEoNGHHIpOC3oxF1B1WiWHxS0OpBKXIsVWPasfh9yyySPA3B1S+sjVMN5poEm4YKhe2hokusUdZD7GNEIqC5hNbSCkxSiGiYFiAiDycUhiTEPQN6vGc9nURrlkQ9h2m5mOqISIr8dYHeBsD9PkWZ35yNy7QjD06oLkRYSOfvO6jghB/4MjGIshLND5OOqKjq/Sun6PeywnX2oSPbuGe7nD2x/aTxIIw9dBrKaKdwv6ILAwp6gphgN6AuJ6glCWMc0ZVF/WBgpe8d5OHPinpfKKOFzTB19ilNbIrZ9FLW3itFGcsYjAYtvlsdRBaQ16Ac7gwgEqEHfQJTqxBs45rNBCz0P3sJnQtThqEHOZSu1oV3U2pfjClUvTRRuJ8g1tMMFIOU4PSDBk0EN2MUjqMp7A9jVMKoT1mX7rJwr6LnP6Tec5/ZQdnP+zhXVwcFmvmZxhMBMSrKb3LQthlsLaC1+mQNwzB6RTdlwhXUiyMAqBTQeO1Jf6TGb1HGiAE9YfXUIMc96TFfJePOCy5sLaE8H2qR3N03CbsBchSgK9xnxWIfYr8kGPq9oBXPXWIT3hP89mPvJJXf/sV9JK7cGc2WZRnGMzV6PgJDz2+k4uTNaq3lrz+tlW+cnyUoxfm2HVohrf/+Kv5vx68l1ZrHR2GdPuzPHy4yrXfdpp6o2D/2EV6+Rrn2x1A0C8MP/iPXs5dH3+OwSBDKEE0mzLm76Xfzzl+apX72CCf0jSO9gk2FK4oiE8YVG7odhJ+9CdeyX/46T/lqT89iX9w2N7lmgZfCxb2r/Ps1iS+LkirDpEMlWyiHKa+/zn1wB8aC2vH5TftZXc4wRfuuR+vNiD2Lf1ORlUrXji3k/WNJ1lKRth789V/26fvv5blwSM4DNPRzdu+Ldtss80222zzP8GZM2f4ju/4Dp555hkA5ufn+bM/+zOuv/76r3vff+NCSrPZRAiBEIIDBw78pduFEN8UT5GbbrqJD33oQ7zzne/k53/+59m9eze/+qu/yvd93/c9v82//Jf/kiRJ+LEf+zG2tra45ZZb+OxnP/tVqplf+ZVfQWvNm970JpIk4eUvfznvec97hl+y/jeg6GdMf7pk8w5F+7sk3kVHZTLBVATycAVXlvhPXEAJSe/mCfw4QOYW40tMVXDuD3xaFwO8V5U4PwDhkBZkpmntcUyuF2R4nC4nuF1ZgtDiCgFmeKktO1wpkNISXadITu2gKLq4UUmxp4Z9fEB8sUDqAcHJLbggKSclbEisNBDVkCpE+wGU4Go50mhEajFVAQ7SUYELFP4GuF4blRtcHCG0R7aSc+6zAe6miFfd8RhPlqNkl0wMVVoiLKhWAZ6if3VEOHwv4ZzFCoi9lKlGm/5mwPpERD8qCYOYlqzxvkdfTKsZMaY7vHD2NJ6yPPztsxw7OoqoDAtOslSY0KEueJR3C0QhIVZY6YZRvQ6sVjg7LEYW82NoDXMv6RLvTThybhdUfZxWJPOG4KRGd6pIoygp6O+qUox4Q4PZWCICjXOglzKCAohDlHP0R3w2q4reaonX0+jlPjiLSH3aeyuowhCugJmoUFSGj70500epCKl9ZCdh9ck6pdYwVSHbGRCcWGP87CbFVAWRluAcpS7wVnNUKGHUodYH5FMCPQgYLHiorqWxkuOaTQg85GqL7PMl+iaF9YNhEkthCM6t429miPEmZpBAXrL14hmKyYBwpkL1fIqwYCsBTkmysaFPjDc5CpspJtII4wiXU4rRKjt3LDM726ddGJZ+cwTT16z8/Rn6V0QEFwzOF+SjHsV4SHC8j39yDR0HlBMN0p112tc3GbtvDWktsiiRcmiMKrZ6KC/A3zKkY/5wEl+vUo57uDCk9B2VJ1YQSYF/oAq+R9CDIpTYqiLolUQXACswwiK3uoTPXGD6qGJw1QRBFkElxLUH0OohuykohahbuoeqROd7eF2L8HyEEFgc6VRAKUapH2kNk5gKA1lKXmsghMJzPi7OQUh0quncNEI5UmIqFaRx+Osp/R2K6Pgm2Z4mQaJJp0vWb/cJlhx60+B7HjJzsJ7R3RXib5UEixmmGWPaQwPyMtIEpUEYS9r0iU+mEPuUBvieKuuHxgnP5phHE4SN8VYH4Hnkic9+v0dhNXUSeo/6uH2CyddK+n+UI9QWLvLZvGMHYcuiKxG2EMgzF0EInLvUCgY434M8h4lRWNtAJjnIAt3LyOoasTKC83Ok6mGqAbp0wzQeLaBZRTuHWFxBZAVlL4W0ADmMZnbVGGNABJbuvphi0yM63Udd0MhWylS6TCRTxN6StC+REz5yzce9OCCfBXnKYQvL9HcsUxtLOOdN0pncQ/V0Byox2vqU2QCnJe5SpLn/RJ322iiKLq4sqZwZkF4zS3FhC/2eDVwS4e2fwq63YbqJ3wECiyotBg9XVNBfjGj4Dr3u88n+Y2wNLPXZUX7rXZ+iKC25tWSDhOWBI9MRO2/cormVMKWvZ6qySUKdi26U958quFB9iKi6AolETlpeeqvPg49v8e73vZRDepHX//QbecH0ON9/1bX0i5yFRhMtJbWZGr0lg8OyPDPFd87+H7z3Aw9w34MnEE+3qPQKyHOEE1gMSkmEr9FaEsUBjz9wEpUWhCMR+ZwmGOnRkKOcTWqUiaIrI3S9T+Fiwo7ChXB+ZZNgYJnbO8mOSpP3vuhtFNZwRXMHH/noo/zaw9cwfW6WH7zlPNNhkyLRSPEi3vfgeTZ1xMYTJ0kHOWHs/y2fxb+a1Gzx9OZ/AaAxvYtY/9VtyNtss80222yzzV/Pv/pX/4o0TXnve99LGIb8+3//7/nRH/1RHnrooa9733/jQso999yDc4477riDD37wg4yOjj5/m+/7LCwssGPHjq97QH8Vr3vd63jd6173194uhOBnf/Zn+dmf/dm/dpswDPn1X/91fv3Xf/2bMMJvPUoIVA5hy6N8MsYk4C+kVKdGqJxvoXsZvowQrTYqqIDRFHWLEwJp4NxlM4S9Lgs7z7Pp7UE6S3DGEKylOA3dRoRIJY3dHbpuqEaqTHTp9ysEa45wWaC/JDl4xSK9aoXu3oDwhEVvDajds8bg4CjSQOWJAUIq7EST7q4Gdq+HaifYpsZfd4iVDPKCVjVm7LEOTsDajTVE4QgvdtA1xzV3LNJXkgsPNlDXGIJzBYQ5+RUj1KtdJte28J6ISOWlSRaOQhuSnTWgIK8qkglN5Xwf3TfIforoOC70R2j7EWtfmkGuRZD2KEZrlD0fNymwoUcrDQj8krV4kmIyptbqomzK2MMrDOaabB2oo/Q01Ys5ZDlJQxBvFFhPUkZDrwB/fYCVDtVN2fyyx+aeCsHyAJUabOAx/pEc1S4QDNNwlHEI5SEsBJslyimMHq5OCiGxoQTpyF1O8OxFzG0hzreosyvYQNG+eZLBzgrS5Iw83MI0fGRRIVw2DCY0wi8hiinqAf76FlN3pCydn8YisKHGVEGfzYn2zGDFFkhF3gSdDyOOTaNEeAI3VmEwXaXx6AbJvCad8YhXu5QjVUSvR/sDCRtfHsMXCozBhT4yDHGZobengYvG0T2D5yAPIJtWVJY1iGGxc3BZHWY91KBAlg5XFpBBsNInXM5RvZyV1YBPHbyel40exnYcMjcUMxJqBu0k1bNgn1nHP59S+gIVheBp1FoHV5GYYITegSbeakq8liGzEkyJyCyitPjLKXJBgRaUgSKve4SdYdqPVJqyIWgc7lF6DgiQYx5+x5KPSIrIEqwYBJaskkFWoEtoLq9jZuvYzRrZjipm0iM6ZpB+CA42r/WpZ4Z4eQMdTgMK2U9gtDL0DLEWIjUsJDgoTR+8kHilz+5rexw9P45q5YjRkGI0xEmBd6GLKHJqh/vItQ7Z1ZOotkJsCqwQJBOGkU1DLhyeM5RaIgpH3lREq1DWQgYLEd7AIDspzlqEUoTrOVOfvIidGqFshKRX1Sk8zfRMjzuuOsXdv9Ib9jJVI3ZUM7CKrbzOgVeuE11boG7J+OgjN1KPtxAqoKhr8vGQ2pk2UhiELUArQCCUxBl76TEYFlXIy+EFwDnShsREkvDsJmqrN/ysNMP3DYVBZI7R27aoHeiz+N6AYllixmvYUysIT0McQeAjpYAwgFBDBnLVw+UOvbxB3e+xUO/xZKARxhIWHlw1wuqbQpACv7BklRqBGQwNp+Xws9ofCAz+MFZ7vYOZb+C0R/2pTRIVo1KL9D1cNmxRLHY0KaZq7JpbZu09EfuuH+fZ08uomqT0HZtXw+RnC0SksEIyWonor+f0WwY7W+Wy+XFmZ8c4/OBx7HiVVm/A2d1gulCrQiVIubmxTO3M93DrzJt45OJjPLrxLBuiw+HFRZQHthwn6zvOqxPU9qS8evcR5LGSn/6ji/ybf3ieehjwzNoKa/0egRN4ucUJS/+2gtdf/ggPHp2lWj/A6EiF3q4KQasgbCcE6fB94b3lSn75rW8gigPe82ufZtDrMb6jx+LYbkQpyTsBF/0BkSfxPIsSlonpNmWzQ7o+SZhZ/u+f/EN29C2v+p4X8prvu40ZV+OxLx9j/cYKlUZEy0ZkG+McP7/Ey3Yu8OWzG3zgiedYsRNIr8+uQ1MEkfe3efr+K1k61uH+jyfs2DfOy//e2Ld6ONtss8022/xdYdts9qv40pe+xB//8R/z0pe+FICbb76ZhYUFkiQhiqKva99/40LKn//z06dPMz8/j5Rfs0/tNt9UHMm4ZqAjgpZD+JLiCJjLQvZPTnD6yHG8asnlb1zn6Mga6w+NgyuQpUBlAQvfOWDqDT06awa7JWk+ktC8v48qBGVF0b0qxnnQyyJaZYxzgigwON9iGg5RlIimodWOGazFeCm4EGqPb4KweLcV9JZmyPaMI70QWUrC41vYQFMsjOHt3kQvGfJ6gJjVyNDCcgsR+tRXQvxzOd6ZTcIoY+8/vEg5H3BqcTet/TD/D5apVC1Hn2kyunyBjWNj7H5tnyc/Fw6TmbKc7g0TOATZRMQNwXlWno6RYQWqIdb36KSa8pzBTEm6WYDs22GrxkYHphtQ+FQaGQ+kezm3MUpZDfBbOaqjgQqqMqCyYgjTlKwqLpllKurnUmRqEblAhiF+O8NWAuRmH6SkF4zSOV5jR6c1XJF2DlEoKHOwFqsh3dkgeHoZ77IdQ1VN6A3l67nBd2CVIJ0JiR5fQ2/0SQYphe9QGkzo6B1oYCoOtSrwEocZ9aEElVhC3efg/2+V4FmPLz13GbEf8NLveJJHHoCnHqvSO2jwTuVDJc96D3XFDlRmqd6WMWcGHDmsyRaa2E5I2JKkoUT1S4J1UB2DQ+K0xAYa9k/BSAPWBsNWtLxA+T4IUL0cgwOh0aml/uwA+n2cFohSIJ2geqJHT9fwMonTCmcNLjMMpqsIY6ls9CgCx0U/4r0j17HzlhW4P6P+SJuObuJtRgwOhDS/KNEl2GjYHiOcQ5YOUkPpFzhhEUYgl1vIpkf1kKDzrIHFVdzMBM5T2DxFpg5tY2wAalAihEIJCcpDJDmCHLtaorYG6FVJsjvC+uCSHBcr+lc0iWsF1esd+YhF/nFB3lAERiHG6ojMkmuHt5Xh0hw5KBGyhx2pDpVBWU7l6NawkHKFQ7xS4n4tJOgJbFVSmyzZN9/i/LEaaqsAJMmcxlmB8yTCCmzVR2hNfGSD/oE6rlBEaxmNwz2c1ENVzmYXjyrEASI32EYFL3cEXYfxBarVQVoLno9TCtTw+fK6JclXIsav6vOvXvhlGrfmPHBxD8n9GaQ5n/mT/bTvkJwqRpjZ2+Kya1eQxlJ9pofa58OiwkuHBcP2CyqMh1uIj3cw8zOoNB8m7nT72OLP2/xGhka7nkL4HuCofPEk/dv2INa2cNaBvaTc8TTWOaySjBxqEc4WRNfVyR+pQN0H4YZGyZfae1wtQiYl9WcLigCUFyKcg37Cy6+4yMJ0wtmRDR5o1XBWo6YTXFuABP9En8HOgHgNpse7qDHNsbyG9TW9cUf14fPISgXPaYwVqNLiUktnT0j98WEbknCgcgcOtp5tUts3y6G5BY7WjtBdgM2disZT+VChkxfgadZbKxQvitmxNMvpTsbnLy7ByhLRTIUgdbhQE8Y+7adKWl6EJw1Lj49y75ce5Ybb9vPWq1/A7pFx3vu5L7H2hMfonj4+jqkoRus+fu7YIVuYcYv80AX8H1J8+ORhnl1fZd2us/r0Enkvx/mO8YVN2k8U/Mu7j1BrLlMeXaXSrCCch9AS8gThHLt2TnJg1wzL59b4o//8OWTsc8MrTzFT2eSLjx1g80A4VCOhkcpS8RMS47Ew2qPcd5Gt982wurnF+GiVWjOm3034oX/56xxfyHj5ub18xwtuoV939BqaE8kOVgbPYG3EYsuCp1AVyeUv2fm/RBvNu378PTy6Zz/iuOBNrykJ3Kcw5hyV6g8jxLdWLbPNNttss802f1dYXl7m8ssvf/7vubk5oihiZWWFXbt2fV37/pqdVv7c5HUwGHDu3DnyPP+q26+++n+d/uL/L/G9P/QSfvOezyKaPtERQ1H18M0aNT/k1b9wNb/xtq9Q7/ZZvF/hWitUtEJ4Elk6yv2GV888hy8LHn7oMkQkqL0ow7vc0f5iTF4L0EpRSIGSjrPZONJZ6hdyqhdK/K6h/7IKUwsGXTEYDw7dfJLeksfp5gyqX2CnNP6yRbcywqX+MMq21cFpiRwLUU9Z5KajHLXoTDInVuhv9oZJJ4MxiqkK5A3KsOCpM5pn7Sz92zT5HLQ254jvAjOqad1f4/zIGLe++QRP3jMs9slc4rcMorBUzncYO7RJa8NSVsHGAdJBvhox+HxI/8UG0Q7w6m3CJQtFSbCrS2+2SvvpiCfO72TdVchFhYYpgQL8knJZImWJzEqCfg5InBLoPsMED8cwclcpZOkQUYjTgt6BBqMPD3ChB0mOmazhfIWXlxSVgOTyMUykMemAybOnyeZ3kBmBLCylkwgssoSoZXGVCq6dkm8NqNw6w9aNJWtRlXA5YfTphM71MYNXVDBbEW4gMBqEkrx+xzGCScexk7Ns7ojZFDGZEMg0RXVCatWSylTOqf0B/jpYT6CPtOkkGuVKZBqgVjJUvyTfVWFwVZVgS6N0TtaU9PdGuN3TjI8UuBMJzte4wMNWPOwgxTUqlBWBzXpIUcFfc6hza8huAkJCHOPCEHBUXY/4tgG9j0bItMDLQDrJYL6CzAWDGUs25SOkYWP/OKP3nyd+tke+4FOMBMTPWbz1ApEVaOMQQUChQGmF8yBeyqgdT5GLLcgLTB7QK8bBrOBMTjrnI5MCubKFVQJTVpClh5F2WJQphmoOW/MIUoFoDZCdASiFX/OR/RxnS+IlRf/ABMpk5K6HuTsCTxMs9UHLYRU/zcD3qJwv6V03TuPxDmKji6jX2bq+hqkr+ish9ZN93LImO1pDRgZpHKKE7miT41uCpN7E1kG3EtR4iPCgGI3pXueTTRl0HuH1LH6W06/70DJDA91LajU8f7ioIMDvXmrnqWjymsYrQEg1TOvxNO09AVIr6qsOYQ3+okbWFHxbj46FQV+CVghj2fviVV5UWeL8yihRpaAUkkE/wlxewdc9ONzCIkknxpH4FBc9/JMlspkiPG+YvOMcMlPYOBy+Vtxw+cOVJcL3EcZRefAsTkqEzUEqbFkgqjE0q0gpuPCJaeL5nNaxKkiHLEFWA+iXOCmw7Q5aS0wwfB/7icO5AjHIEdrjkfePYV/e5vCFcVQvQzgojyvGz3SH7UFhE285J4oy8p7HxpkmTkhacx6mJnBliXRDxY/KcvKRgOB8B71mKJVDJyVIgRrkWE+SPBNSuTzmkc89S952mEXDjo8vAg5mppClwRUWuwL64ZyVdI1oqk5ZGxYHBlVFfGwZU/PZNTbK6S8dxRyMmVQD4mnBuemEP/7i3eyMYt646zV8+rn78EtNmSzQGPT53puvIR5d41O/m/P+RzKSrqb3glFGKmu89ZpDHNlo83R6jGVjyboJfm5IPjDOE0+WmD0FnbVVkoZDNhQ6s7iah9f2QEuOto6zlnbob/URgwwY+gZdblvcr67GGYnLFF3fIVC0Us1CfY3exhXkG+cQTlI2K1z5umt5wauv5uzyGs/cUJJXFA911vh+6ZFPGZyQPFeMcI0/w6B0XDG7gxMnTpJ6PdYfOwuv+ds7d/91bF3YRM2MIIUgVCWDjXcgXUkmxwgrb/5WD2+bbbbZZptt/k4ghPhLAhAph4vXXy9fcyFlbW2Nf/SP/hGf+tSn/srbjdmODfxW8KJvu4YP5J9hOj7H+ldmCbdyxq9aR2eajxw/weQ/LVn7TxXON3ZRVz7KSlyeYxV0Vcy57gizUYvBiRh1sECNWvK6YqusU39GofqQj1hQjsujRYyV3H3iSupHUkxd0Z5K2TW/Qi1M2LN/kXPpBPW65JicYlBWiIou4b1nUD2LCKrDFJ3JJkYa4nYP11GYug8iRG7llF4MfoKrhujEkkwJ8kPj5HVJ77mc3mUCVyvRqUNoqCwKZC1j7MYOLzhwmqnxzeGcSgqKikLY4e/WCk4cm6S0CcV+Q/HyBP8BjTovyQtNcTRACUV+XYCOCuJbc66/dZEjazP0iEh/JaDa79DfXyK+RyCXSrpnR8h3VKg+12duZ5Xlh8/gghAzXgdlMWEApcHsNaijAlUIKDJUL6f5yBY+ES6QWCUgK8BXlGMh+XiMDRVpA/R8xPquOcbcgMFSAJklG/cJOwXSaYQB06yRNwKKfU1uffEcH+yuYrqa6pdbxBdzui8P6E4Lomcd7lSJFQa55vjQbx5k900DOiseWjoeT+fZuFshVjcpd09yww9sMT9o8Xv3zRGe6eLyArPep618ymvaTL2/g7+WYqZrlOMhGYp4BUDgel3KuAH4fNubP8PGVyo88L5DlFUPJ4eRui4b0NtXx1SqTDyUoAcG6lUQCtdPEb0+Lkmweyepf3uHYCwl+YyAMEQoQXyig6lHlKMhyS6Fy0BuWWofWgVgx99PMTeXrHUK7IpEeh7UKiA1Nhq2IFUWc5QRTN6fUIaOIpYIr4IYa1LmHkxP4JTAxCCNJZupkE54dK4LqB4xROvDaF4bCNR6G99ImGiSjweozT4iDhAqoLvT4RUxaE14ZkCRWNwX+rgJDxoBMrHkdclgboTKcQdC0TwO9eMJ0mqcshBrmsdS8oojWYhAWsKWwJwIMZMCNSggK8i6PsePjONwCKAILV7fgbQYDTZSiETTvUJTP9pHZgZRLcimLWU2gKiB9H2cHcYoG09gomEkNg6Ec1S+cg4XeJQ7Rim1JbywjtaWm76nTb8b88T9OymWJGdbo/jC4O9OyY1Ped7jutlncT1L0CmZn9pCCsfHTlzNVq+KvSIlisCmDqfBCEGyGeMjKWVJsW8C//AiarkFnkYMUlyzjvM0lGZYiCoKMAYlPUyWDdUl1iKEgs02BBoXh6RLkjT0EYXDWodea4MfgidASoQeGlbLdJi+Y51FHDv/vGrhno/N8flPzdP7gZBGrw+tNkJJtOdD6FPsgEozY37XKstnRlg7UiV84iRixCO7YhJb8bFZgr/YprczJJeCsMipXMgYTIZ4Wx6uNHhPnCG/bjc0qnTWOpweMZS3zlE5tTVMahJ//vkxjF2WG1BKQdcZikaBu6Re9WxJvnsMb7nD5XPjrM036VDSWq1zX2uejRfDkcklzp0ques9j7Ly7lM4Jxn7wdsYGYmYGRnjJZM38Zmzx1k+U8cpwd69q9z/9K9R61zFG1//Dl4l93K8coGf/aP3IgzotsSM14nvP4GII4rLm3SuGUcWEBmPsBcDguKJNR7bPMPtl13B7I4mK+c3OfWFV/GGf/ZtGP8e/JE+plCUfYlQlmozoXAaT5S4cYtTEt1OuefDT7DU6fPDP3IHriPQOXSeKXj30l3E+3L60mdcLhMH4/z4C17MLVNX8n+f/n3CsM/5pwZ/y2fvv8zJZ87TTXLGP7/K7J5xAi/GiCqWBC3Hv9XD22abbbbZ5n9ltlt7vgrnHAcOHPgqtWmv1+O66677qgLL5ubm17zvr7mQ8va3v52trS0eeOABbr/9dj70oQ+xsrLCv/k3/4b/+B//49c8gG2+MbznP36a3Cg6Y+PD1A4fFl45oCk63PupXZRmjsqONXpj4+ysbPHSyw/z0H1NDu84QF71KK1mI6uwKR1YxeLFEcJaSrjQR5+IcLmHzgWBV9BtBXSKKt7jGSauUQqf6Jzlgc9cxuZtVZ57ahfhZEraCXACnATTVcPUnVaCq2rsZJ1yqk424jESrtA6EZFMhcOEGyXpTULwmEQGMaJTkMwEWCXxOo6wU6Ke0vjH1qFZwFxIMVXBXS04vDrHLZzmT3/6NlyS4AIPqRX0C0RmsNYxWK8SeBl2wcPWHXbMYPo+gwMKfyxnQS9xZqRKNqbYeUOPWBc0s4y2mMBtXsBrZ0Sewxv1CJqGflEiTvm0rhun7zuqozXKqSYicjivhC2B04rM+WR7NZULKf6WAWvxB45yXA+jJnoWkRWIyCJyhyEjaXpkIyA7AhVaTo1MkNwMwRJUj1mKZoBA4LUyipoDHREc8Di3uE61KMj6PvlETNkIUMs+lSWB2NeFxQD/eIruJJw4PsKRE3MoDPmIx5H79xIXa0hniJbh7rXLmV7NyWREZDsgJGUzQioPEwtkJ8UKS3eXIjxlqT2dIJQGBKLepHKxQCZ9jizNcvCOs/C+AtETEGik1Lh2j+hiAYFE+QX5iEdZC1Ftn/iZHIsZFinGBFudCvWupFiTeMYCAv/MBtb3YKpOOFuHjkBOZpSzguoLFep6eOPk4/yXpdsJziQI48APcJWQctwjv0FSrMVEJw1SQVq08Vdz+vvGkY0If7NEex7lqMSOCnAKszCJn5TMfnwVGWjS8TqIBJFahA9WC2ysMb4m3VHD82NspHH1gMQXlE2P5sc6uLEGwl1Kl7KG9lVNuvt9rAbKKo0n2lCX9A9qwnAcZQIIfbozlvhchol8wuMtVOZw1RCLxGjASnRpaewf0DlTASsomxW0lDhA5CVeDi50yEGOXG1TuElsrhn79CreuQ1KISkWJpCJQ/Vz3HTMYCokXCvQuaVyIUOlJcYJ1HJreGw7qkyMtNh30wWM9Dj6mXG8ZxVRK2Ruvk9jwbH0ZBOajuVC09h0nEgbNNKhkeYg8Knd08eIkNblMfGpFK/lcB6EZ3oIISjmGuRjAcFWF3zv+SKCW98ceiL9+d9liVQKawwijoYeMloPlT4OuLCCGyRoz8FZi31lDfFcjBxkOClwhUWM1BCVEPoplCX4PuUh0OeHz5e4lF5nA4W7GOK6y4BAGoczGXgaoyW5g5Vj46w+20RtDNDtHJmW+G4TahGtfaO4OEDkJeJMZ/iacJbu9ZPEHzuFAJQRw0j37oA88CGwVI6uU0xKtCmHKqKyxG4ME5/yyxrkUUjleJe0JpBTw8/j6GIC9Rjnezw3OMn6t1uI4N4Le8laMU6mpF/2OPhDq2y94DzZy2Dri4rJ/Q1O6Tb/8Z77+J1PPEA2Kp+PyBGm5NPv9Cg2ThOsf4G/97ZX8MiHDzMQDFvhrENaAUrimjWkGCaSqcyQXhkj7t8AHM3zE7xgfB/H136dL70xJD65m4tO8J9ffwN/es9R7t08jtAWUKioRHkWLQXfe+0+/uSeCyTpUBXYKQ13HTvNY7/1IcKLAarvQAmi2RPIQKGsYpB4HFtbZlJc4AVTV/KCnS3E9AqPV771BvTv/uVPki9MECSCZe1TFJZw/KM4s4Lyb/hWD2+bbbbZZptt/s7w7ne/+5u276+5kHL33XfzkY98hJtuugkpJQsLC7zyla+kXq/zi7/4i7z2ta/9Zoxzm/8XrrttPw9nD8EnJ5D9Emsc7aQCwiLLGkUvwTxmmJhd46Zbz3D7i4/CYI7lj4wiDtWJdI7vGdZeVMGsKVjS8GSGuM7SP1QQPOyjU+h/puDhj03RunkHphphqz4uVDhfMLq/x57eRZY/AIuXTdB/pYdKSugpdKjpv3yO8M8uIkwJWYlKDFpBu5T0rxG0r3JMPCnQsaG2d4s8zXCbLZLpSZySOF8iBxajPMJ1MPvGULu3CD5t0AywukoW+Xzk01ej0z5yqw0Sip0TyMKiewUChTSacrKBekKhLubowynVl/dYuCEhquZM1ttM5yFHz46wVUQstxosn5hCD4aqEdfLMM0R5Fc8opeuoccKvCdK7LqidXlEOdkErfB39Smf8Z+vDJcV0FsOKwxlNUSGHvmYRl5KH8EXCN+njCX+6S3i9RKrp1GDCsG6w2WS5LXDfWVjMH6mixutgBB0Dni0rtR4646xrYILnz6Pr2Hc9pGpJZ0PiY9apNDYfoR/eA03UBRNn2Cpj7fcJts7NTT3LDXZbROM3p8QLRWsrNdIl3IqFwryHVXKCKJFB+EYUeHhKj4mNrRf0EClAdXzjkE4QOQWLykw1kMtt7n/ocv48pOXMRql+IlDJNnQp0N7BD2FcYLxWxY5sraXfNSn/nifipIw2iSbrlGvpXgqYY9u83QnhDBGGrDKx41UKJoR/hmHG7XDlJXdAvVtimWqPP3gHNVHQmwMVnUQzmKcwQaKMnLoKMLrJGzcWEW0K/jne8h2it8FzyhcFOAlkvEvFfR2ge4p/DOb6H6BrBvsSImNA4qapLuvhtXeJa8QMI0ItZIjC0vzSE5vVqH3OPJr5pBK0d+viM+maDuckFstsNrR2+PRPOzhIkm2W1BUJc0Hc1SWYxcivNQyet8GYisZfgiYHtJUSOdj5KAgiQWMCeSzLYyOKRo15mbXaIz0OfzALNIotJOUNYWpDS61Ewl6e6qM3L2MWtzC+Ro5cJhaSDqhCDYLdFaCdah+ihOg0gIEaAfFoXH6FxKMgjSTlBisgpVOxA4G9A5HyKzECUHgZWQ98I8UrM9X8bRBDRzBxQxhIR33KOejoSdO4VA5ICXB6U2s1qR7J4hPbuBMiWCYroS9pIiUEqIQKwRCDaN1hRsmZj2/QqI1opeAHvqpiE92sSMGU6+gOunQAyUr4FLxaViAKrA7HflVkvCwHBZXpMCNN1Fdg3MOeam44LIcZAWnBUk35un79iFTw76rlkjH6iz3RpC9Hmp9i+pjKaoSgxR0LpvALfZx3ZyiHg+PARAWkrxPnA4NuSvnWghAdxVOi+E4Oj0YJCDA5BVk5hBKobdyAgXCCIJ2Dq2hGfbZp1cRLxQYJwgf7GOnIXoM0sV1rtXPcX7eYzlowljEo+4UHTzaPZ9GW9Mcjylnmsjc0JjdYKOEIjEsn1qlLEo+8Z77sdUIIQAtkHlK8xWTtNZrmLqh8WyLqFWQvshD+Hqo+nEB9Cy/c+E+/MUXolJgNWPvO/8D+vpNLCFYhzMCU0iSgcdlIxm3jl/Oh5ufpWylQ7WUFsRTFbQnEdf2ccqycHySv/eCAzx48hmyzGdC9Hn87inOjpyjWj5Hffc6C9UNnv6uXeRZgR986wxnjz98apgoaMH0LWVp8ON50PPfsjFts80222yzzd9FfuAHfuCbtu+v2TG23+8zOTkJwOjoKGtrawAcOnSIxx577Bs7um3+xnz7m1/AgStSKnM9hDEMxn2eOTfPshvn517/Mpr3LzI2aLFzbYlTgwk++/hVPPDFBbxuQeVkjxGvy4TXQa0Yml9qESymBMdzxn8nY+TeBCcNZQSFE6SFT7Ie47sY5QRlVdBdsFy95xz1PCH2MtS6wZQSM+ZwjRLb0wzmKxSzVVyoKRshDlCtPu7xLsHJDWSzRPYsat1ycGEV0fRgpIoOQ/TAIhM7XOkVChMpbFXgph32CvDWM9S5AL2ucLUYWVNIz0cqjfU16biP9Ya+JVlVob0Y5SKCoyXBRMGBl13gpXueZW5ylQsnR5FtQTXMibRBW0MaOcp6QXC2jZCgTQV3NKJ1eoTibIjuCZRR+B2LaShEbskR2EBhIo3zJIM5hyhydApSC2zFQ3cdupUg+jnCWAY7K5i6AyTSgCoV0VKOq4UUDZ/pewtGDhf46zl+4A2VBa2MctRiGmDrJf3lkqxWYEYd3vKAYDGhfjhHpkMLCR1m0C8pI0PvKolrVohnA17y+qMku6A37SikRfVzVJIRrBmq950neOY8om8JUolcL4YGw6VFZQ41cFSPS7zExzlBMeJTne0zt2eJcGUdZTUzj29xcPMi8n+Q1rmyxIwE6M0uqp2w2Jtn5KEeE1/YZEe3jWhUkHGESh3Zo23cHw54QfUsY7PFUF0QaBhpMNjfpH2wSn93iFsJ0I9qRpMBjSChttrl0YfmsHkf41KyuCQPLJ0rGmRS0fhEn/hYOXxtBZp8ocnghln8FIKBQBiGBqXGUYSCwbRmMGbImwoXADs16vgGG68IWH9DQDGlhsqJGpQhlKEbTv4AUUJZ09izPvmopmho4pNt9GILW5Y0nm4x+5FVRr+8wfRn1igbAb3rBMmVEG5ZdFKiBiWjT6UEaU6QKaSnSK6cpLd/hrKuEYVFrfXxOxY+niAX+xi/j/Phxa94khtecJjJ+Q0KX2C1GD4f1Zho06AyQXRmaIQs8hLv+DIUBmtKnIQylFgFRVXSvqJOuWMUF3hDxZBydA94tF9cp9uJydKA9HofaoqlxwP63QHNzR5WSZCCz/3nPXz4Fw7S+y1J+54q/S/5uJ/ZIBtVZA3B+g1VNm72sS5j5Ik2srBcfO0CrV1NwtMtigNT2Ik6LhwabwopEEoPFSp5DklG0Qhw/T6kQ5XJ+Z/dR1nxIPBBCGzTp4w0TkgoDXqrj9MaG3rDdKBWF9p9sAZ3ydTW+zzonZPD4x7+Z2ygIDdQlGDs8Pm+LMCNZhgK/LGS6w6eYufsGj/wnV/iR370K/gTmu7BaazvEzhvqCryNGWsSSvBMAlJDHPHhBAIaxl56DztHTVct4fQGnwfcoPUAVJ6MPLfx+SQ1M6uIZY3iPIeI4+1aB7tU/qClVeO0b66QfCFFH/Tx/Y13T1jFFFMus9i10vyYwUHTid0T0TsuGzAGxceZm+0Ac4gBeiqT7DaRpxa5Oy/9bjyDQOqUYmnBdrTzO5sIPoJSjjkRIF3wMCrSnQlGCYjaYUVDiXB1aqIRo2kl/JPvus/MdvpkFcLvMSS7/ZwnmC8miFxWBQitFSAyUqfUIbcd/EMpfMwgRwaBeeWt+y/nHe9+ZVIUYK0pGe2yEeX2FgZoewEPPzMAsu7NFuVjN0jIzyxsZOlbJRzvZm/pbP2X8/WRgd9fhWx2oILa8N0qm222Wabbbb5GyAYngq/5Zdv9QNxie///u/n937v9zh16tQ3fN9fsyLlsssu4+jRo+zatYtrr72W3/md32HXrl389m//NjMz3/ovIP9f5rZGj/J1Cce2ZtkIqvipZn6sxV0//2nk6oDB9CgL15zlyZUreOaJnYTnz0Ho6M96HP29MW5+8wXsRx2jJzuYegEU0IhQbRAjlqyi2Lh6jPEjbUJRUgAiNASiR/jAgHOzTW655STPpfDw1gyzeoNlUxsme2CJspywAqlfpWj4qFRgogivF+LGNbEt8DYhH2hWnppm9OdLVu+OsB2BLC3BQCJSg/UExlfQ8emdj6idS1AVicwtZQD+ZgYmxno50inyUR+wMEixzkKjSU5JuDzAzUREryg4o0d4emkHE2GX79zxLPd86gDuMsme+TV2qDZf+KLHelkluXEnRnRwIwPsgYAbrj7F2YfmScuAEo0NSnJrkVqSbIbEduhJQTNjYqzAmDpFXaJ7JSo1Q78GXyLTAhNJ9MCBUciRBtb30AQUoaIMBSoSeMcSwk0P+/oBeiPHjtZwvia6YLH1PtGXNojaMXa0Sh77KNXCUKJdjPUlsgD6HtlVk6S1AJkXmJEWM/M9brvuJE9mMzwTjBO0JPlIAAhkIRDGgnMEp9dxo1WYreGKAclYDd+Mko0G5NMVhAVvtUdtxVD2LStxHbWjju0mvPylzzB5S4+P1G6m++U6TmpUkpCPhESbbcrIx7U02b4mOk8I+pJUK5wZ+pK4Vsag6/j8n8xhrcQVOdaXbNw4PjTA7Zc4TyOcRJiAzoUGfChhpdbE3BISPibx10vS/U0q54d+F5SWPA5ROIqqT+1kgXdhE31xExo1bANM3Ue1kuFjtUPibyhckiCVj6sE2EwgtANncUhkt8RUJGGboR9ObiEvUJmFwMNLBd3pYUtIHllcWKN+r6FoBqhWjhOSxtMtZGkpxjwG+2OcJ4jO6eGEcEeEqQRMTC7Tf1RhR8bpLkyCFUPj4ccXcZsdzFQTqTzQPsJE6JbjiUf20Rzrsro5SjqpkJlFJ0CS4WQV4aD5WAe1YwqztApliet2MQsNvC1D0BqmSXk9h25oXDVEDEqEEyRzHrYSsl5qHr5rP0XbpzGR4uYVI5cNUDVH1/NBSyyQenXKpQLXjFn9XBVEiee26KU562+YxtYtogLllo98MoXIo1wIWL86pncypdJxSOUjfIOzKW6QDlUm4lJrz+XzaOtwKx2ccrS++3JEbLGTFTjXQXgeam0ASkIUQeANPVRWNpD1+tBHx9M4pYZFNqWwUiL6oB7KII7o7x/HTFcRvRwnBaJZx7W62Lqh+PER0IrwNxJuf9NJXnzbczz4yH42zlQYmBi3BSqUDK6fQx1rI6Skc9UoKEVvb42xtTYAg5kK1Qu9YeHQGvwTy7hiqAoSWg3Hn+ZQ09RfY+m+18cZizBgNxwYhzuZIJwAYylnY2rnDb3ZCtWzW3TPZZR7NSq2yFVFcFYyqFf4ld+8leh8B7CsbUk+eWQni0kTpyD0vaHKbrM99KTpSRYfuhY/XOHI8XMMBhlnn7mAiGJsL8f2fGxb0XtOkXdSdDWkc6hBx9Wh6pjTGzilcEnJouix9o+v5ce+/3JWXmi464vHSUcVrbbAUwVWAoHFbkVUduZYE/PBpy9S9EJwDmcdRsDLX3mI6lQV/7EA17McbExz36c2yAqHU5LupQLcdL3GrTt38tO/ewt/PH8tu83Mt1SN4tzQ1cgFPsoOFVVKfevbjbbZZpttttnm7yJLS0v8xE/8BGmaMjc3x+23384dd9zB7bffzvz816f0/JoVKW9/+9tZWloC4F//63/Npz/9aXbu3Mmv/dqvceedd35dg9nm68MG88w31xi/cpMgSVm4ahFhDY9OaLqv3ENnR53FtQqN3Zt4g4LSh61r64RLfc7d3+D8kw3SzggyjHGNCq1vm+DiG2NWv93HRgJvs6R6doC/VlAMSrpzmqShYCWket5n/ImEmaDD9ddfRNQdY3GX0JbYUjD2YI9rn13kpmsvMKEH6NQRlBANHNlVs5jZOqIQGOHAQlcGmNGA7i0ea9f4mFCi45LqfIILGBYhsoJsj8fW9RH5dA3pFGpQDBM9ogACj9IlKCTR+T6ql6IGBSot8bZSlAGxLuj/aczGvaN0NkLqfs7MXIvp2YR0ELDx1CjlQGONxJOGn3jbo7z5/zxP+zsrpF5E+3OjdDcaIEuwfaYvX6O2o814cZFwYQvdK/DaOXLE4nVBdS0yd8NJjZBYKXAG0IKiEWB8QVFXFMKydqti6/qhn0WU54TH1/GOnMfmCQoHrQThaZySBEuO8Q/nRM8lTDYaqIEj3LT0XzBP68U7wJc4IbBK0h9ElIFGDwyqY9D9DpvE3HvkAOfOj+KvC2rnDBKN0TCYHhrDAlAaRLuP6CtUGVJdNYR5SNADsJRehg09lFCIep3+dJ1stYVc22LpSIUvnruS9TRGDXLUoMDVYvw+bL0qZOsNPsHZgmBxQHT/BTaeKihrPjZSl0x0Zykmxjna3c1GvzFsX/AlKIXKBf56RlEVFFUYHLSoH9Ys96YwgxiVSJwKKMZrRMsO1SuoPr5CuNgj21Ohvyui1AWqNUCmOVQijB1QuTIl3XtpUlWUpAsB+YRHZGK8ROKUJNMhpScZ+9AW4x9o4XU9Ch90J8ffzAiXEpyz5JMRRoNrWLyuQV2Kea49kqFWCvRqh2KySjZbZevGJul0SNH0iZ8TeFuOYsxRxB6tKyI6ezVro03KV3hkSuKf7xBd7OOfbyNyA1JiYk1/f5Ni5zjJXI2iKWk0etQrA7xLhqxFDHQ7KOtACoJlgwh9MBZRiXGBT7lzkmxHHS8RqMxhYo315LDAZtVQhVEURIspldMFs60+I9/eZ/INGxSPCrz5nPDyPsu5j5qzYCzOV9idExitkN0+B7/nKFf/o+OECxGVjmT3e5aZet8qLpE4Kdi4tkJnZzz05fAg3x1RXS5xcYiLAtwllZNgOBEtx2L6CzGDPU0633E1W997PbYSUkpJMhdAFA6TfLQGJ3BpjnAghUT0UmyS4rp9XH+AG6Q4JRFphhAOF/uYKMA2qpixCsmERz7qEy/2EeH/n70/D7Mtrcu78c8zrHlPNdeZ59PT6YaGhqYb6G5kUBIHNGqCxgGN8XV6k5CIyesvGqPRoERNTAzRCGLiEEQQFAUBu9umoQd6Oj2coc9c59Rctee9xud5fn+sDm9IohEcMG/qc111XftU7Vr1VJ2119rrXvf3vkNcUeBSQxFp8kBjtSP1JbmSVKHjuck0D50/QNbQUBriz1zBdfv0j7WoYoWQoKIA0YiZv3+ZwZcepncgwgkQxhBUFqEUKIXwPKyuRUXRLqiWQCiF1BJvM0MojZQSkeYUuxLK2Yhg6Ag2De1nx5S7fIrDHlYJgvMZrdYI1dYEt5U0Nq8hBmNsd8Ddf7tLhMFVAirHP3jTXXz3V9xJ0IxASUS7werGgMn1MWdeHPPAymWue+UN2E6ImQrJWj7pizzyQQPhaRpHM/yeRWUW6Sz5VABZQRUpnBdQdSIe/+2HaD2+wnv++Zu5fSnkF2/4u4RBSBRUKGnxdUlbF8TKJzUX8RfGSN/i4gATeuw5MMu9ly/QP5Hg9iYcu2UfT5zsIVOBN4bpxyt2nU946/WvpLSG0GtjlhO62+Vf+rn7v0UIQdAIya6bp2z5fPs//WrCeKfueIcddthhhx2+ED7xiU/Q6/W47777+Dt/5++wtLTEd33Xd3Hw4EGOHj3Kd3zHd/Drv/7rX9C2P29Hyjd+4zd+9vGtt97KpUuXOH36NPv372d2didN/ovF1Qvr/Kf3GwaHb6B1pcB6MMkCVrcsXiOErECblM5XGmh2SU82WDl8iLKlYVHSnL/KI9duwfkh+YKld0tIdn2J9SE8maKuCfzNinyuwZEvX+KRdJGqAF0JvGGJcOACQeo0pSdoLIzpqhiWHbpnEcOcSbegl3nk2woV5OAHCGMo4pi8EZD6Hv2vthz63YzNEz6txOJZ6jvC0nLblz5LMpXy8H03Mn7YEV3ZxhzUHCdjy8yR90uoFHWSIbhAQ1ZCVW/DJWFdvVqAiTUmy3GxhygF1ZOC8JGSi7fP8/DX7OePRgcItnOunp/luVfOsP3XI/arPicOXeWg8XjfL96C3jJcNtPYyGGkItiuSD5TsO+rt1l/okP4lMAqhXMg1sBeDMlnIZ/2aJ/L8YQE4+qL3krhdwvGswGUFrm8xsx/cWy+aRfZQpP9dp3BZp25IYQkWw6Rfo7MTR2MKQVSeQgrMWmOtzXBoogWfHrjhNLPcJ5AFA41yIi6QybNADUYwaam92TAH2bXEw0m2OtiKt8hK4s3qIjWCyQaFLVXTwmKAw6vH+IkOGcJLvfprG8ghcC5GCdBlLauuh5nWAWj7SmuPrFIdNMI8RmFGwlEHICtMJUGZbGVg7JCVAKBIU80xayHGBr81IO2Vwd7InGFQXoSf2xxWYm/kSJ3B6T7BSqx5PeVzETreLf6rF6bwlofPbEoPOyswx0QbC00yTuK2d+7RnhhCLvnKRY6ZLsUe6JNhnkbvVJgfImUAk8aMiUQlUOsdxHWIaJ5mOsguiv4XcPQd/i9kubJ9Xq/Ew6VW0otKVoaKsn0yQHWV+jM4CoPF9ZjaGJcISeW7IDP9u0BjXWf8OmU+GSFLAQ20sjCUoaSifIQ6xnqXBdmm1SN/+qqiOrGoLhEl+A8jc4AXXLdoasobZltDKnOW8b7QkwnRl3q4ZuSsh0wPjGNeqaHSCLsgXmGJ6YRlUMVjrITYqzF8310ZhBlVYeHVAY8Sft0TrUY0V8OsE9P4OIWJAGDO0LieUN+LcKVBkyFHzjio5rsikSaump4fDgm7/jEp7aQvkfr2RSjBCbwyBZeUPMmgA9rL1UcuM+HtqYSOb7n47RCOAuLbWwYIJTGamq3SCFIHrHEl8ZQVIgkAqXqMSDqMTOoR2jccAxlhbMOplq4qkBqSRn7teikJcYaZFGB9bAKVC/DRQEIgegWiP84wC628FYqnnloD36nYutamwM3rGCbG0T3zhDhEDl4QYzZyCmkh/UlQTcF5/C3U5KVZbbfcJjWf3iqns3zfTCG2qolcKHGCkPhRdiHwWVZnVlTGBwWigpXFKSzAWYuoXl2jBASv1+RUSK0h1SOzo0pRvs09/aYbk+YP7HBfNrn6f88x6tuX6F7KuRcOoNoOPbv73Drrj30/vFX8ys/8cHP5sgMU8u17Qlvf+99LI5LfKfYdXieZyY9unum2H0mx40K2s0MHhBoabjua6/x0TtvpH05Ju9IOk908VKHsRXLS12eOXmVd/3i3+XjG2cZnXM4NGWuyKKK0kpQpxifOkr0YlCHKtyTgIR//eEHOH5kgeByhbxq+fTmJcJGzuGDy5QjD7fdwG5L/vVjn+TfLz9E68yQ9rzkq+889pd/Av9vMMYiQs3o+iajG5tc/8YTX9T17LDDDjvssMP/7niex6tf/Wpe/epX80//6T+lKAoeeughPvzhD/POd76Td73rXbz5zW/+vLf7eQsp/z1xHPOSl7zkz7qZHf6MvOcdv8dkT0kx8Nm+FpLu97ny1By77rrKv/vHX8e3fPU78J/eYOO2AKY7pKmPDBxCONJWxNwbCzZ/LaZYTMg6MF6UiKggeTZj/ld6YAdsful+VNOR3Rzjf0wRjR1VYPD7Bpzj+fvafPyV11EuaKRxhEHJ1G9t0dmoL/S1nTC9a8S1/iyu9Cl7W6SH55GjDL0SIkNBuSDAOKIHK/w7DI3pMdvdmLKClWvTaJORhRIxU1EOHGYQs/p0gJqU6BlBYbz6DrkDsIjpCOUEthHgXD0/v3ioy/aFNuVcg6rpIUpLM13HVJp0JWDR63L4tmUu/OJBGICiovQ1Rgk+Vh3kjJvBD3MYC9TGEOmPGHyzT/uTE+Zf30V5jo09CYUIaF3JMKEm2j9k6uYhpx44hokF+YJCXwMnDPryJlJV6JGlsz1icqiByi3OOeJTGeOXzLCU7iZ8eYE6O4VtaJJLJS4OMX7dCCQqC2WFWZgmNSnZkTZ5x8ddrEhvFywdbbP79wzSQGdrC+tXVO02an2EyypcUBJvFDihCJ8toSoxWuMVguknB+SJh24neP0clEAf8fDXKjZ1QPJkH6+0qIbEeWCtz+C4x/TTQ1TlQexx080rfOM/uMzZjRX+w8kX4zkFvkFOcoyC8PkYb91jtFuTT2nascIfVtgYytjDtDTR88sIBObgHC6QCFHvV/7AUlKw/fImxTxUiaMUms2FadqNFZi3OFuS2wxPSuSVEfL1BdyiaVzVVNsh0fkeIq1wmz3KA/uQUrF1oYO/NcLrDTCdiNGJeYbXKcqxYrA3wLvgoccV0pM4a3AzbeTaNkY51LhuZUIJst0J4eoEkeWYdggjjSorZGkxkUIEEa7TqN0OQiBHOdOPFTgBBgkzTfRWifMsxZwiumIQezQCh/p4H4Ggmk4wocR6EkqFKC1JGWHGjmxRUYWK4Owqn/i1E8QLsP2UTzgakC14OE+RNwOCc0PUMU3/xhi9OSEaeqjSUiaC1uUMYRUmkCgHqnRUkYSpEDcYoqxBloZgqUuRNrjQP4A+u8L0rgFT8Zg12+bX/+A17FpOkRsbiHHO/tf2uO7r+zzx83s4+xNtqoWQ/i1NWHCEyyHdV3YoOiBsxczDGeFqSeveIcvfsg+UIJ+TCBxYi7QSIo0LPUzTR+UVXjfHTOu66QZAwJ0vv0g2k3LplyJUliPjqBZQBLhJCv4L4z1R7SyR7Sau1cSlE8wkQ0QB1lpEI0IYg786Iji3UVcYG4ty4KabiJVNkvt6MO2QScLBPRscW1jBrGo+88BxNrZbyLJCjUtIK/AcVLVbDSzjA02sNQQX+4TPGtRByaVvPcqhX19CIHBaI/IcPA8vq7ClwOtLnCfq9bdbSKVxlcEWOSbS2DjE61mkFfXh0VjCJ9bZ+4k+/hsDJustJsMWGmjdssZi0iPpFDT3Zvzir95EnkTYaYdqQOJql9b5y1vIThtXVjgD4tkxM2sTen8thMEIlVdcfmaFqOEzOQb0BohexTjyaoecduw5sYZ99hib+zwIK2SqmX0WJjNtehtDzr/rPhYPznLkxCzWSKpS4ozA4nF943peFvwST24cpyw8xu2AsKEQAt5z/6N81eRmpjKPUVRwYP80SatkPH+R5/J9LM908M72mSwLxm9RBC+aZs81x9721F/6+fu/RUrBroUO10qD8xW+93kbh3fYYYcddthhh/8JWZbx4IMPct9993Hvvffy6KOPcuDAAb7+67/+C9ren0pIeetb38qP/uiPkiQJb33rW//E5/70T//0F7SQHf5svOTVx/nD4mnW8jYL1YTGhYxw7yYr6xGH7p7h9iMHeaQwnCUhuhgQKoc3EniVo7fbcUvjKrd+8xXe8d4vx2yE7P2Dgt4rJXZKYH0BQoH2edErT7Pn0CZns6MEfcj3aUb7Y/yNEfHEkuNhkUz6MZHsk0HdMrOrxfy+VY6+bMgTl2Oc1MhWgh7k+Bf6ZLsaZNE85UyG2YTk0452lFMeCbANaO6dcObMXoIqZdcda3h3GNY/asmlIJ+DA9kW18QUd7zqCk+9bw8CMFDf7S8LVFpR+XXI5atf/SSfNDezeW4Wr1/iLW1x+z1nyG5oYGYkDz5xI09ePUx4tEQ6S2+3h8kll5nifG+WzShm4/YGyk7YUxlSFxA+4hEdBxE4tmab+McsfFqRL0SocUl6scHMN/Ro//KEcFOix6J2IWyOUf0JStTNPaI3onVZUimFExY3NY0wAgxoaxjtj1DDDH+isGWOvNDD7p9DCIUoK0wzpDI5dtqAFXh9h9MSUQnKKYe3VmKXM9SCJexLdNBgctQnOrFAe83RmxRUvqAIBPl0RLRZED7Tx98ewXSrFi8qS7kkSHUTE0lUVe8f6W0+2y9tM31vVbe9xBpyizOOnmqStwK2LnrI909w01P1BWFeIpRGOQ+RKYYHJWhBPu2RXMtRWYZLLZWsXQxOStKOwBsJTJFTzSQI6bDSw3kefg/8gSPdBdVURPf8DN7hnKEKSfZksNQkPQhhAHIBoidzytUGNJtgx4g4YnxAY6Vh7tQEUTqE76EzR2O1pNfVEIFNNJPrZ2ksF7z6NU9x4ZMN1gdTOE/jjQ2jIzFVZw4TeYTbBoEl2pbEqxXpgsY1JGVuKNoh4UQgHCAEalBgtML5HjKtUKUhE46q5TG8GSZHPdofd8RXc7xeBsLHzQT4maHamiCkxM420d0JCJCBIPFzJjbAHjdsXsrxHvDx/ZI8CfA3S0SVo1cHiKqidVLg9SqSyynCZdBpEq4VVIFCdSfogUN4AS7QtRPKFqjtUe0C8QNsojGBJRgLquN7OXbPs9xx13P85s+8iNbWKpXfxo9ClPIZDQRWDqkyUDMeqpkRXhnV4cVG0jyX0z8uyPZJ/F6B3y3RI4eNbb3vBBLKekQpX4zZur6BziXhxRHhygAvzzHtCO0ErhL4Sc5XveYRuMfxzv6rGL17gM0yZJLUuRRSIJK4DtrNC0xeILVCGoOVCumg0gq8oB6rUQq1mSLTCgYFwq9HomRnimqU1q1UxmJxXLw6S/tKxunn93JxYzfeVk55wpCcGeAt57jtkt7r59CFwTTqfbm8eZaZsh7dc0ogF9ukewaE6yNMWaClrENti7IOuB2VMNWEMHwhpBYcDhEG2NmE1tObuF0zEPnIysIow7qS7kt3AZqmztCXwGWal911HikqHn//Li48tIApJduvbCMjQTtT7JvpAFBMSihL7HhC45BlsGwo5ub5F3/zDbzz8f/IyAsh1OAc4aZCVg5XVlza2I+6A5xyfOzkrcxsG9Zuqo9zFoVrW4rlHq47wm0P+afv/Rjvv/5buSnZwxObK0gDX3lkD1ausTjzL7j9df+F80kHO5vibIIzDrOdce/SJdLJBLWWsveo4saX7eWfPNdg2TWJb5DM3ztm4USXlx/LuTru8eDz8/zu06f5W1/6xasYFkLw87//A/z2r3+SXYfmOL5n/ou2lh122GGHHf7347+GvX6x+auwBoB77733sx+PPvoohw8f5u677+Z7v/d7ufvuu/9MGa9/KiHliSeeoCzLzz7+4xD/TRvHDn+5vPHNd/LMo+/jP33KonKJCBX2cIM8mgDwY//h2/ihH/lVHr73OfJZgSwUMlAo49BSIoQjSkrUsRy1EtQVkuse2nmsfHNCfE6jS2jtGRDoEjsAmVY0r1BnVUQR4ZTm6OF1zvQXkNIhlGHwkincFQtJi0+Wt3HtN8+QntxGdnLc7lmUBhekpPM+amRY/E9LuG2Fe5lH0YuwVqC1pTmdMlpv4K4qqqMBvdWYzqs2uXY2ZPk1mvHHLVOfXufZtRmEdDgrsbGPLAzdGwJaZzOkaIAS/N6v38kk8/CcqMcThOTMI3PccvsGZ6N5rj63F7PuEQZDuq8U2DAkTjKO+xu8vvU0l/NpzjzeYerpNeIvh+yTDYIrlrVrLQZrDveVGmcDXKRQY4H1FcqrcDichHipxEqH8G0dRKpEfWEW1fWnrh3B6iZOC0RVUSWgsgq7JtARmDmBiSTRZ3rIvMS1Y2g2QCqUEhQtj2KvJRl16fpNcPXr0kpFuqiIRxHt3SPy5waocYY50ESHIZPLq0yOR6S7fKySKAOjJMSIWZLTgnImILkwxGFwe1PcOUW8VOCMxaU5JgmwQhEvp1hV1aNAtkJouLC6wD/68A24j14hnGpiQo2wpn5KoNGFI00EneczVG4ooxemiApLcnGITVPsVIJLInQO/soQr58xCSG7sYW36dG6ZOuxrYZEFLVQmL1Gs5Y2qDxd7+tpickd3gaYVY2QHvG1lGo6QscBphWST0lE5TDaovsjhKcQvoTCobuWSoOaFLQeuYZ0kqd+d5HJlo9JFLYw6NUx3pRPuifGGxekRwraZxUu8nDGIpHknZjhrCNYz5GZBC1xnsIqx2R/gM0EnVMZDkG4kTLa69E9HhNuO3RvQuAUOI+i4RHumiBTH7kGZrqFc5DPRzgpaRyawB5Hf1UyeWMbaDH37yr0JESXhrQdIytHuUei/W2CoUUM8jpnRdTjLs2LA4bXdwhW+yg86GiKRJG3NY2VChVH4BxlrFj9qkWEcNzaWiItYp4ZHcCMM4oVwcJgm1x5mCjEBY6NfoOP/8w0rl9x4z+/xsZHE/LH+rAlEFoz9ViGFRH5Yov1VyXMPJgSLTvmPjig//IA9dAmzgSICtIbIpprCq9f4UYVMjcoTzDR9XnJCMekCHjs/gOc/ZWQYdenOriAt97HCXBaUoQR/vYAdF2XrJTGdHv1OJ2SWN9HILCmqrNVANEMca0Y8hxGdR20bQS4g7sR55bAWpxSrEzmuHLfPoLtEq8wiMpSaZ/uLS1mL3frNcQe/vKAcTIFFkAwbEDrQhdcCzW0iLkWxWwTu7aF6ma40kJZ1OJPWcJGvz6eVAab5fXYkpLoqwXsnQclkVogJzkWC42AIvDASirhE24XCCTLZ6aZ3j3g5jdtEe5t8Niv7kKnljJybKqS1WGPi/et8plPnYXeALdvjt4oRM+sotoB1839J3aNn+Pi9iGMHyBDj3xeYBYCxMoEnQuSDYPxBemu/XREn8kfGkxbM/PxKyz+iwq51GDtFxw2kORpwT/99++ld+IqQQjFMGAwucju2St43ut4yz0/yM+d/HHkiS7nf+8oKEFz07E2GuF2eez7/fOsH9nN6keuksbziEDhb1YE15XQn9B7vsv4nCOf7ZDsMl+EM/jnorXia7/p7i/2MnbYYYcddtjhf3te+9rXsn//fv7xP/7HvP/972dubu7Pbdt/KiHl3nvv/Z8+3uGvDk88cJpnPjZi4XpJcShhFIWIgY9W8K4PP8wf3X+K8QMX8fsV4XTJwm1rXF2dYzxq0Nm3wel0gQjD9pUW4+sD0kWfvCXZF26QPzJXt72kFr+XcWFjCm9cUmQ+sgJhwPgOb95SodHC0I56TKRHdN8WulcyfpEmmvVIz1fI1OCyArNrCvyAyS2LaCnIvArddxCHjPsxo6FkUHrM7hszONdClo5+5ZPeXycsZ7HBhh5WC/JDLeRn+hgRgqC+SBAOZ3R9d3lOYbctLtKIYUTVBJFL9DCHOGQrX+QPf3EfN732Cq980yd48F23sHZqlqN7Nlh5cUrvkYCDvVVu+N41wrM5c8/k+CYhyNdxqo2rStL9DV7urWKf9Hio0cGEdQWpzBwnvvtZDrgel8KjTB0Y0Rs0cGODbQe4sIU/LhDWkSwkjEdAGKDSHDFIUeOIGzpXWVreAxWYSuNvjEBJ3L55ZLOFHGeIqkLlmsHxKcSSpTdfUPoeyYWCcCUlCQJiZ5h+bcX2H3YoFhKkS7Cy4q699zH7Tdu89yMvJZubQyKofFFf0OkACoO3MgIhwNP4f2QIygnlXINyf4y+tolBQGoJ1nOYpJjIp5ptYgKB8QX6sTGCBi4J6mq2yiCyEkJFkYRUDYFXSfyVCcPjEVaDLB3O2bqNRWryGR+rSmRhyXc1kAV10GjpaCyNqBLLypeHWKvQT3jkVyPcjKhdPZcDglNXwJRwDuRyiDUJZkqiixwTKapGQPN0iiot+vx27VRotKlijaoM7WdKileHdfuTqINNB3oGGeUMT0zjqjrLRAqYeq4gvDRAVjlCtwAQxuGNDMMDHuk+TdkJSNYGyLzEdAKyAwl5XNB8fBU3MJgDC2inMFMalYEaCaQWuKxClo6q7TNIWsRpDxFJZFZhmzDem6DHFdk5TT6OsMpixnVNNYVXt9ooQbBd1nXBWuCO+fiftmTTEcWowqs0OFD9jPDqGBU3kaXBGUelBd7YorbHuNEL7hedEF+asOfINtd96WUEcGFtlq1Om5d+7xKTZxJO3jvLqPSxUmKdRI48Kl8y3I7rkQ0rwdeUwhGUjuRiwdbLBcYPGO+RCOHjR5rD71nCOWAqxESKmU/3wBryIzN4wsdNtXCBT/LsFqP9GjPbQmceH333S/AurOJ0iZf4yFYL3SgI5g3j/RH2vRlIhQhU7VBxDrexVe9jM23KtoeJNH6vQBa2dup5ElLqgN6iwnoOWxlUVUEjgqkG0VoJKIKrI7Tw0Wt9wssON0kRysPMxLSeHBBvZFRJiu3EABT7W8jLQzqf6jO5KUFODASKwSv2MVkeMf3IFfCo3WyOejTJVHVOlDF1GK0xSCUxZQXWYnNQkwwRhnU2zabCGUk2lAQv1LN/7Fdv4bobLnL7/3UVb74i35PUI0Gew3rwXX/nl7D3Pc/U3op8xmNUGpLmBLmvZPloxD95aJ3ezC24sQZj8UrL7ANj/KsZxUwLFVRYIRFJxZfcPGR7DPYfXIJB3Q5WXLa0bsopDsbc+Q3XmJ9b5bGNVxAGFUklYOxxeG7IQtzk6dUtPvD8Eq24waHdS5xJNCbSzM4kDLdLrJDEe6dIvuRJ8mAL+dEF5Ixj6qY+04e6DD+geeIHOsRfdgQ9BVfGVzHOoMROU84OO+ywww47/O/O93//93P//ffz9/7e3+Pnf/7nufvuu7nnnnu46667/syiyp85I2WHvxr8q+96FyuFpv/aKaQnMUqAgqZf8MFPf5LUGWaPZLy4c4X1qTbTL08ZnfPpPt1k/3yXW+ny8Q/dSrypSHcJHBovhbV0jkYIXgVUhjM/7OMHBuNVZHOW3otC4quOxr5txq+Q5FZx+T+2CU5m9L41IjvhE1zOsH5AMMppHC/ZGrbAOWRusKElWi5RhWXyap/eGxaZedzQsAWTcYLtOTam23RGhiCrcxkm0/XvfMu+q1S/cAzRGxGlIUy3EarCWQ98hchKrBLkCx42t4jZACcdzSfG+D1JMRcQnRujxiWmneCUz+VTe7j5lZdo5RusjUMaZsCPHH+Ghy7tI8sNyICTz+xFFgIXxqwt7GVyLCF2A6LlCeUdCfev3EK8WiJKWQddBpLDM9v8wb+6k+nOEA74pMsR3lqJkQXx5RGispRTcODgIU7df64O7wWa5wbopMXqDYuwV2C2BFYqrIzxF9oQxRBobK6RoV8H7DqHKC2tpwxOZYhsghylqEWJSiQD1WIwO4WTjiKEbA/cfNMVnvvkAsGGIZhOSRcTnBLowmJuGyJ/rwdKkh2fId8XE62BlwqEk5hA0r+9Re/GmOSqw1UVYjxBlCVmd5PRgRAXStRIEG7nyN4QGYSorG4fURtDtK9gSO3MMdC6OILC1m0p1E0kRitMJCk7imJ3A9OMkFWJpRYo1KQiOyZRDUMgKkxLopYDog1LNWNQSIoFH4o6VyboRdAQlE2F0W3i9QplJOFaSjUV4toR1gELHaT26hwaJLoLyYbF7FtAGoELFa6borsZVaKpGnUgbLxaIl5wHSSXMnQJqnLISU7W8akSgR4L0CAygyoqnBCEqznCOfJZDw1YLWidLfB6FXrgGC96tC6lVJ7ATx3q+TFKSfKDLdgyZAv1iElyLavrqK8ZVChp/ochumtwOkCkAtUb4OaaEIeISY4bOVAeYc/gpltQaNB1ZpHKLKqwtfNhmBLnGeViqx4tecGtyCRj+v4ctxzhv84ixo5Rz6PnmuzZ2+XlLz3Pppzi3O/tR+QVgTJMHR1z7cI0ax9fRKo+4uIA2wjJjy9iBwVl4hFsOlQqEJ5F98bkjQ7D/R1oNfDHBf6wgCxHGIPt9ZFbFYxTXBJgbj6AZ1ImnkOkjta3ZvhDj8tnZpj9cBerJVJUSFHRulOz8TsBnhM4U+c+YU09RiQkojvEnNiNCzR2VKG6EwiCujo6zWsrrRB4vYIi9sG6euSmKPGTin03rnMtbTKWktamwESizgnJBdneFmE3BymY/qOrrH1VHXjqIsnoRYskSymd+7qMO5rBdU2klFQHOohrA4J7huQfl7hVgwg8GOagNCLwXxAZBU5KZF7hygpROewoB8AaS94MsEIQm02610/TPpuyeOsGu1494Kxd4EI4j0orJi9qoiYKG1gm55bYt6fHm37oApO+z2/80Iv4+u94nPgGxzs/fYi4J+leDRDG4iuYIJCXB5QbA8qDszTmUoppxVfe8yjXtX0efO8tXP9PDvPc958C5xj+TkRwaor2ZMBdNz2PbvjsP/i3ifa8inf+wTOUocS4lNVsjXkmpGbCtN9gZW2GKvZQxjEsSpQUOAsb183xwL0et75um8mUopyB7XHC/KSP9GeYO9bh2Jft5vLps3hTPX77oUdILjpecvf1zO+Z/ss7ke+www477LDDnxX3wscXm78KawDe/va3AzAajXjggQe47777+Mmf/Ene/OY3c/z4ce6++25e85rX8LVf+7Wf97b/VELK13zN1/ypN/j+97//817EDn92Sq0YHegwnlZ0zhSIwx7OCPLfNOy9+wpVGiCvZqw/q9neM8PltMFo8oJ7I1eUI4+LV2YJNiSB72qXiedonEupfIvtxDQ/tUa+qWC/wIyhOqBwWlAl0J0P6Y/nODPYwowEsoLGoGLrhkVG1ysa50pOxM8jGgM2rx4EKZG5odIOL3N1Q8VyQbSSIj3BZBghfEH7GlT7LC62iMqRnOsydbRkgxmuW9ymagxYvaDxSoOJvBfeOAuwDjnOqfZNYz2BlxlK5SArCU5fxU/BP7SIzCqctXXeA459B1e58fASnb+1zeYDAQde00cKaB22jGSbX/7MS3jqgzPINMfNdFBLGeM9DRhZwotjPnN+L/19GrVZEJNjBwFWQdOUeEXKyLXoFzE0wJqQxuObYCxW1KGZz54+DxJsM6KcCajmGuhrWwyn5xFCEVhD6UtM5JPPR/j9iqItCEqPKvZxOJLNIWKkcZ6E0lHMhOQHNV4Z4zuYqBAdCPRmStp2tB/v8isrr2CX5xi2I8b7AlACLwNCR3yhi6wMVAZ/LWVwImByrMHsQ4Zxy9J4ZoXgGYvaniLZDBBFBUJCFBJMKsq+RW4YJntibKRrUWVsEdbWB9ksRw8z9JUB/dcehlBTqopwrQcF2MSjSiTVdIgEosxgmxEqs7i0IDw1wiQJ63dOo4qSME1RHUt2uMJtW7zZnFYzxx6TDOQekjNbJOdHVI36DjmtDqDqBp7SQFkiUoUoKoQQWO2hC4tTgv4tAVARrRtUXfKCHPWQ3W2CUOMlPoPrAqwnKBONcBLne5TTklwLEI6pe5doPGsom7NYDfmNCu9ixGhPBEWOkZbe3XsItyrklsT4ChcExMsTEALv7IR82sffqEVAHKAtZSXJD4YU06q+WFYCXVq8UUHeUeAEKgVjUsqpGJUr0ilH2B0hS1W33DQr3HqBGhSgI8xcG1kZSqkY3NrEepLpe5dQwwobaQh9RFRX4FJWGF/Qd00+8QMvowhh9Q0+3kk4//IJJxauMXNgwGqxRZZrXvRNV1m4YYh533XM3r7J5kdguwoQA4vTiqoVoCQ0zxbkcz7xhRF6pU813YTFDjKtkMZhARUEYA3BeokrijrLRwmchqzloQdQNBxhNUIfBZHX7UPGOkQaUFiPK5M9zB8cwMXNOrBaq1oocnx2XCa87xTVYgM1MrgoQlUWTIWREqcFeArnKTQvuBnyEjeeIB8fc8tbr3EheQndkUfZmGWy20PkGTOPjWg+P0SUFU44ZF6glnvM3n8FrGX9rv2EUuHSDC0VyrQhq/CGKXkjgQdLKs9DTjvojhCVAR8IQ3C2HvkBLBWjaUf7iU0QIMIIE4GKHKIS2IlEbacM5zS3H9pkfnbMqbVpNs7Mks9L9HaF3OujU4vfLahaIc5APtY44xishFR7BAvMsuumFZY9MK5BGXp1M9nCFHqtD5UhCDOs8egObmH7WpOT6QjmBmglwElYL9l76xGeOr7JLz+umYodcQw/custfOTQKsrBJHiIqjTcvcfj3+75Cq5lT/Mvf/EZhLBYX1JMKrKWZnq2y9TuDPPQFCsfvAcZDYhSgT6vuba+m8UowO/E9AaS8S4oSfi3H/o0xx/O6G+P+Jvf+4a/1HP5XxRVdQljVvH923fGsHfYYYcddvg/jkajwRvf+Ebe+MY3ArC9vc1P//RP83M/93O8853vxJjPf7T3TyWktNvtzz52zvGBD3yAdrvNbbfdBsBjjz1Gr9f7vASXHf58SSuHsopkqSJZKqhCxWQP5I8r1oYp1YUxYnsKbWfoxtPkRgOGsDtkK2+iZnsEVYa/HTBz0jI4mCAqS/P0CKccK18e0IgU/lHNwveVjH7co/3wNsrMozOoVgKGb8jJhWbqOzLOn2ogdzs4bVFjRxVBvKfk6Z9tIxoFSIWcFIxe2kZIn2A5Y/bhbYS1CKUxUY4rQIYh0WpF3Jygr5YEu0YkryyYzUY8f3I3caPEpgYGtQPBqBYosMah0gynQRhBp1+RbRXoc5t4vQJ8DdtDXOQjiwqRlxQHWlweLHDFTREczDH3dTjzK23Wvy7i4f4RQm35kuZ57GFJGibIaY2ZatB+boLYMvTubKEXBf5yTnSmh1iMcY2A1vEee5I+B2a2eWJ1liqXiAqCzKImpg7pXGy9kOeryfcLKgQbdySAY/Y9z9L+0DrpXSdwgaBhJxRjn2jLUXQi1KjAWVvXolYWM3SYuQBRWYLtHJVDebiJf02QNyQmsZCDnY9QFlRvxPCixc21SA87JgcU/qbDG1qSyxPk5TpYEwdsDpn54Jj+m44zWggRW9vIcYksLf5mRnq7h/wDiRrVF8Og6rDVSUEVCqqZBFUY5PYQZy0iUJh2QKUnqJYHxtI/HNG4mmMaGtWdUM0muG80lE8pbKqxsznBBYUqHfrcJi5LSY/PUNyUoFJJokZkYw/TU+S7KsLAkSQGYS1+Bq2zKcJJRD9ndNRn+6scsm/wPiQpE0n05JDwar/OjVhsUXZ85FaGycdMf3iD7XsWGO/WNE8PUN0JwpaQWrAGF3gwyfErH29iUalFiKp2dEyKeuQsinBJRLhWYJsGijHihMalDUQlmBxvoQpHeHVEtr+BEgFIgQkUyoAtHV4vZ3Igxl/P8FcHBLdkiLlF5MgSXa1QVlI1PLxRifMk4xdbshsTko/kND7aZXTDXkTVRhQOYzNcOqD3N1sE3Zzot3qoUQnKYA7OUMQRlAYTSRD1/6ETFcW0Jh6XteCgJCbSpHtjqvkAfX4ChaTzZE64bhldCnno7CHkKGTvYo+s0ow3IybdnMmyz5Vf8DCTCtPwqKba6NJidN3yqwuJyRy6XyDTEmUcpTAE2ykqt5BXdctOniPLChEEoCTDl85TTUm6N0fogaDzSJ/NDxqqEzGDI5Kp3hAN5Hun2HjtAlYrylaAvzAFWz1Ic0Ag/qs9TCnAoddeGHFLM4x1qMV5RCOE/gTyEjOdgFbIVgKNBDfXwfR9Tj80IJxUeLlFFpJgU9A4N8FbLsj3tZGlwd+YYJuC2U9cACERnsfCp1bov2SRqKzwhhY5Kml++jJ6XDA5PoM/UHijAjseI5SuW4jyHGGq/7cqOQ6odk/RODuq91ElMaHCHm0Td1KcEzTEBH85omhpTj5wkMFDPk9yjPHxgDhyxC6jPGggdzCTcGlO80s/dDOiq7EGPviTL2Lh+x120OCZkcT1ckwSgyexnkBN6iwolRYURZNBlfDgtmNucIi4eg5bWKpIwNhRjTVWK7I9Dc73DrAva/JN1+/iH/3Gh3k6XENqw0sax7hhSvDy6dexll+gOzjFwi5HL5JIA2HL466je5G7nsKXjv/7O97Cb9x3mfzpp9FDSdkAgSIsNVkkONHYTTjaoL8xxljNS77kBl569w3kZcUwy5ltJl+08/ufFWMqNte/FFxOa+rnieO/9sVe0g477LDDDjv8pWKt5dFHH+W+++7jvvvu48EHH2Q0GrF///4vWMP4Uwkp7373uz/7+Ad+4Af4+q//et75zneiVH3XzRjDd3/3d9Nqtb6gRezwZ+erv+42Pvjox1l+0Qxiw7Er2uKZ2QVGd4XwSpA9H/V2R7U4R+VLwknFvuMX6F4MGI59tquE6199js3Tt+BNCrxpj2pPwOhIk7JpyKcFxXWL3PrdzzIKGlRzPqqvCIb1z7cG2h8v2f03t7k8maEgwjMT5u7t4Q0tk/1tnkv3oaOryI0eTmpsOyE6u4H1FIQ+NlCARFuFDSRqbYCISuxMjMk8XBjiigC6PRrrA9b/4xSV7KD0BJFPQLepGgHCgS1LpFYQGpACddKiGwa7MAUpiO4EO9ukbGjitMRGHk4LxkXI5WKaspIMHm4zsdA4N8D/WM6+9hY3ffsVsm+2fPRX9xENC/xnNHQFYmiQyjEQDTydI4clZccD33Fz6xrHGttEkcFfzxGFxRnA97FJiKOkiCSql7LrjgNcObmJyEqcrit0nafIbp7BNiSqgP60T3ilAkBvTJDDcX1HfN80AtCTeozBeIJ0l6RIJMJQu26MoNXLqLYFphLY6QCLQvgK31foHKbPWPpHBckDIzwZIIIWTnYh0IjKIH1J67Qhn3Wku6couiXBtRGuCYOXCvRpjzhrYD2BaXpYKfB6hqlne4wXIsxMVIcZCwFaQ6UYfW2MOeThfcIQbpQEfYtIEowTpLMJwTM5XppjtyuqSkMpoKzIFxMqPyTf00ClDpFV2Mwj9A3yvGPYVBRjQdk3iLFEDRwbr5ql+fyE4OqQYLkkvJCRz4SMF3wCa/FfHFE+OqLYE5GdmEEPLXJrgBynEDka53PKuQCx0Uc4gQwkweGYkfQQV7t0nh6QXT+LEA2yWc14r6TzXIlM63wLOxNRdiTtpzYRqkRPSrhBE20l6PURqgphVNFUE77zWx5j2Av59f/8CvA8rHJYX5DOCsZHY7zdMTOnE4yXEy/lBP16zEmOc6yvyds+VeLD/hSdOJK/XhG9NGBwEioVocaWyinMHgttQ7oZk790hvmHc6RxyKygano4pWg+P8IEiipR9F88hSwcYddgqwRhLVU5wVvaopI52dQ00VZG46yhmhPYZbjUn6VzF/yNbzrNs6cXuO93j3D6o2CEQ3SbiDSj2t1GD0rEyjbm0CyUFhsInJbkB2YIVsboYYFTPlY6lHVIY+uw194ABBBH2NAjW/RBSVoXa4eaLkEKh2cE4VBRBgIvsygnmb3XkC4qgrUJrGwAArE4V48IIWphwjrIMoQfIJzDVmVdx72xhZCziKIA51DOYYWj+537kBsjmk+BEoLlX09Y+O4e+f0N0jig0hXpYkj4zDpqMKHaPVWHw1YvtPB4EvdCxXF4oY8FnDNIqDNQHNhGiFue4NKsFnuEAO0htAIpwfNw0y0GL6rHAFtj88L4nKEKJEWQEPnbddRLSyJ6E7SS5OuCc5dbbP6LCIUhXqqoBh7lcoxNLOr1Mf3FCdVJn6nfWkfuXuBr/u45pjsD/vP72mSrMbQVulsgpxKywiBWu7XLNw6IXcrA+syOttiYXKH94pi1h+Yx/gTdH+GAsRY44wi3He3DPsvbA9Y2hphZKBtjHtrcYqvc4vWL5+kWl7iWnmNub8IpFhAOJv2KB1dXOCT28RXX7+LyUzkP/PLjuNvq6mbpJCZ09MoS87oFPsUq0w2fosi4Ydc0WydXOPXoeX7gVz+Mt9jgn/+tL+P44uxf5mn9zw3ntnEuo04PK77Yy9lhhx122GGHz+EnfuIneP/738/p06eJoog777yTt7/97Vx33XWffY5zjh/5kR/hF37hF+h2u9x+++38u3/377jpppv+xG3/1E/9FPfeey8PPvggw+GQPXv2cM899/CzP/uzvOY1r+HQoUNf8Lo/74yUd73rXXzyk5/8rIgCoJTirW99K3feeSc/9VM/9QUvZocvnG/5ob/Bb/7GAPGHV2EjZ6gS9B6ocnBWY1IPSUE5TpEzMU5IqrkQ/4YM9Rsl5wdtWm+YUH7DmPJeTXBxQD7fASlRlUNUIMaWRqtgv77K1E9s84F/8wZ0Tp3DsFWgrow4u7lIvCvHXgjRXoETrr6w62XcdudZ5m67xvt/4Cim1KQzbdqXc8xkTDrtsXXPQWRVMfVcjh5kmNhD9cdYGzMMfBqZYzwTc+3BFrufukSkM8z2CGUcdJroUUm51wcccnmCMBbz8hJ/E8SVHlzn4+KQarZTh7sGPmbBY/IlEWLgsFdAJoZnHztE1lVMz28QZ47txEeVFhUIhHS0OxNU3GXftTUGnVl67VmKecVgn6ahUjqH+qj5lDz0kRPHmct7wCk20rB2KeQFWeiQ1QQklFMxxXSA1Ja1pWVs4aGcYP7BlIqK6sRBxkcjhBXEawa/qPAnAtOJEBZsw0d6AjkygMCEAU6CCQTDIwnjvRYnwO9CVJUooPBjnG+wvmTjS2ZoXa7QyxbPKlyvrk22vkWkFXJjANZhAo/qpn14wxyyEiqfvK1olQYpJMGWQm04/BVTh+UimHQUJtQULY+pboaNJPZohlv3McIgK0O6L6Y6KBBSoEYSL3U4U2InKbYTgZRMljXTn9kGCcVsm2rap2z7ZIfnKNuKcL2keaYgm4LBWoS3bYh6ks6zFa+be4aV/hQXTh3Ed5BFGs/40GnhjyYsvKePmwFEwU2vusSrvuYM73vopay9y9J66BrCq909LorYfmWbyQ0NvJFi8NJ5Gks5N99zjeKK4MK5JgxTXBgQbpeMbvEp2xphHMW0xux1kHrIYYUoDTK3MAfVizW256i0QSU+yeUCU2QEnZz5zoiZaEhYVlTGw0lB2fZRShFuGILxiCIQtOdyUk/UI3yjoha80gwv91DNCM5rirmcft5g0ICkkTIaehQtQRUpZj4DwWrOONKMFhNEbxMceM4hKkcxFaJzgbc1QnZHVJGjmk0op2LkxCDTHBl4tYDXDvAuj3jd655g36uH/Gp6G8OnI7QJmDyjeS7u8Ny4Q9mo83AYF7CooOfwwh5yWSGqiqIo0U5S5hZhHLqX4YoKKyxFW6JGBf6kwnoKkReAwPkatzgNTjDz6AjX9OkdT/BycPNN7vn2TWbnL/KbH5zh4g/dwKGfXMI0Aya7fFxR4hW2bqGyDpTCNRKsdYi9c3U7ztJ6PdZn61BUJxUUJWY0RIZhLW4UFWXTkh3wEHsSWo/WWTVsWsLOiM7JC0g9RX7XPMlSjpI+oqxQl7fqEOAsr7NXXmjcUe0moRG4QQ8BhOe36L76MF5RgZKIJ1fqTBfqIGgh6owXoQQiy3CTCcl5D7M7QmwPkZ5XZ1RFEicVUc8gpwzDZBq9q0IVFaprEHtnCM9DUBrii4bRQoi36WFyh+gETD1foJ83mKaP33a86OZVpKuY9rusPj5PuTAmTHOU7cCowCGxC23ohMikz+6PD2j1DOrLx0w3mly7ACLy68wdT7FaZvhPd5nv54ihz12vP8StB3Zxb/cyFydrkFxkLprw6NUr/NaT68RzCe3WAGnn0WND2RCUk5LNrs/DTw4ph1dpbBiOPaq5thusV+dX2RNtWs9PaFSCx9tjjKdYngxJzq/x3JkNrhxQiCylKKsvzsn9zwEz+U0CoTG0SaKv+mIvZ4cddthhhx0+h/vvv5/v+Z7v4WUvexlVVfGDP/iDvOENb+C5554jSWpH6E/+5E/y0z/90/zyL/8yx48f58d+7Md4/etfz5kzZ2g2m3/stn/mZ36Ge+65h3e84x285jWv4ejRo5/z9aWlJX74h3+Yd73rXZ/3uj9vIaWqKk6dOvU5ChHAqVOnsNZ+3gvY4c8HAUzGhsm0xluQDK/z8LqG0XzID4Sv5V0/95sUnRBRSfIWiECwGUX0szbtK8s890tzHN2b4t1g4X0Z/nKFeNoj3raAJd0FM7cOCWRJ6TTzQcrWqy2dxzXgmD22Tf9cSGMu59JaG+KK9myPCy8/hDCO4MKAj16+js7DPtZC2QqwnRC7XYH1qOYSqkiQdRTxNYj6IAEhFUG/pJoOYWgZHtGgJXNflRJ9asy1i/Vv7+amcL7DOlePfJQgwhBxWrNwpouSmvj8NmXkYRIfBiNUMUVwZYg+P6J4TYwncxaObHLxwUXCcxWdzgQbwvlhTPnXPDa2dnP14UU2j0qCNxr+3ps/xdt/503ka359p9Wv0LHBnzPw2hbe/RUqM2SbHpm1LE/PUQYS0/Twrmyi+hPsbAtxUKC2cmRQsHDdYa5mXcqtMcPDPkUrQfbB+hIseKfW8J8uMEdnca4WM+XmEGcMdmHmhb2hbroxUUC4AVVsyRdrG7tT0F6YkC/FWCFfeLrASYVNPKx24ERdI5x4VMrDSwKE0igh6+/xAwD81REt5yMyg/Mko1ua6AspYmuCC3zwFFmU4XwfFwj0BswlQ/qPBJgkhKaEokIUQ/wParSpm2DkpMA7uwJZgZqfBgOjowqnJdI6sqMOOZQgJKoQ6NWK5GqJCxVGS/KWo/m8QZWGo+Eqd97xHNtBwtOd3cSPCtoXNyANEKGPa7Uw+Qi0RA9LLj00zd5j05gnBN5WVjul2h5OS4Qf0NgSTEztDCKJEbEi7TaYn7nKytMRwzgEJSmnIpILQ0ZHmmAd3vwYOTeCqxFyyVEdmaZ/m0Z7lmB9neD+EWKvj9QhMje4yrFZTPPL77sDs2KxmQIMsrDIwKMKBY2zI/Qgx+0OiBcKihsNg0djWs/Y2rGQ56i8gGaCOx0x6XiIeUO8WWGflTT6G4yOxGQLESoVsAr2ZkPjUlEHpdZ7Uh0KbCxG5/iFQVaO5oqlNGBEhdzYhtDHqwQm9EkuFXgrQ/a+oY+cgV2rA7b8Dv79GccWrvGGr3yKO3sJ/yV/Obvmenzq/XswLw5xv7+BveYop6eQUuJnAutRO2NKh7QWm4QULUUVaPyJwQUak/jo1Ie8qGuJjYPtHmq2ja2gihze0gC7q4nLHJ/+0WnceJv2ZcimK7buDPCFIFoBmcQY7SGMQRiHCAOwdRiO9ELsdQdwZ5YQL4zPYOv2JtkfQ3eAmJtBbQ9h6DDpHJSCkhR5ZRtnLMvfonBVRuRWSOY0XlrnzEDdEIQQyMBh06wey5ESV5l6fMr3kZ6HHpSE3QmqlxKd2oCivsCXvsZmdRuPsxahNW6S1q62PGdwvWDmrEI4i0ti1CAjXJkg3xFQHlCku5roQxXtsxmNAwnh9Jhsq8A/KwCNGBsUjrJyTJ4cEm8LkDMIl5L3St79c9fT6AjWegtIJUmWCkZlSOULvEQhLo5x7QjXHbI+2EXk+sw5yy3Hj/D0WUcVOYITJeKKAmsYT6/guhXCwfD0Fr/0wQeZvtFjSRYUObx0d4NdTcX9z6/ymZVV9GCar7htnemZEd0qIe8ItIObblhnKlS8bNdrWWzdza8+8ARlXOEQFLOwdG2dfScr8rYgf2XdFrbqJnzfd72Wd73jEzQvGCoXMtpOYd9f9Jn8L4ZB/gjKlTgx2clH2WGHHXbY4a8cH/nIRz7n3+9+97uZn5/nscce46677sI5x8/+7M/ygz/4g58dw3nPe97DwsICv/Zrv8Z3fud3/rHbXl5e/hN/9vb2Nu95z3v+coSUt7zlLXzbt30b586d4xWveAUADz30EP/yX/5L3vKWt3zeC9jhzwfrHCY0CB8kEkpJsD6EWPLX734xv9z5fWRYUbSbhFXJ1h7N8FIHdnl0jmzRKCaUW45hEeLLCjSMDyf46RBRWKjg6vocu/WAwikmPY0MDJNdAuVg3ysH3PAVayyMBjz1oX0c+dur4IMNDBhNuD5CPTYmy1uIWCEbMVUnJjvoEfQaxHlB+HCX3oumGR1s4ggIV/pIpdCFwE0Kgl7F/GMlV7+sxfzeEcNdEofA+Yqypehdn6BKaJ3Jaht+FCCHDpf4OPnCyENet1WgFWKcgwIxEnif0qQHFcIKTFviPJiemnBxYwFKYKw50trmG178EB9973HWWgm7v2fEaDnGWVfXpJaC8bUG0YolfEojEdgQ3vSd91JVitH5BmJeEG4UCC/ANh3lfAvjBK7IqBZmiLuK1995jN/90GOku+o39WGvxAlVr9UZKByMwQVgEejKIgwU9RACVkAwqLCxh/HA+RaswEkoAkm0a0iSBWyth1gvpHGhwDQ0I6+ibCosYLQj6jqEL7DNEFeWuL5h/Y6AmUdG+FsGO9vEkwp7cBe9OcPa3R7hhmC63Kz3GSVpnvVRNqeYVcyUQ1rlmK48gBXU2Q2+ZLy/g58rXOEw4wnpjKTjLMx0YKqJV4DLcirPYI5oii+1eA9ZxJqkaAiCfh1PnjchXagrm7sv18gMeLLDx549xtqRKUaH67vr179+g2d/ZT+qEphQMTrcIRxK9MSSmja//zu3I851UcWEYs5jcrhB4+l1dG+C2TeHrECPQeW12ypuZsSBoTFbkReOUatJtFEgrCBcntTi3jNdlA94CTKHzqPb5Aen8Ldz1HYD1wGpAnS/3j/NdIzSmkvnGgRDhw0rJi+WxE9anDOM5zzcRNPcTpErBVuPNtnY3SBZM9jYQzqHczEqBBELrA+VEpiBx8yHSryNMZQ5zcqgt3Kcqdj+a5o0loSFh8UiLYgkwnoSKyHfK9FjCbqNa8S1CGbKepROCarFKZwxKAVfcs9p4iZcLFvIhuVF05cYBDGDNMIOGvQuT/PGL3uCJMlZftwne2XIxmcg2xaY6YSy3STYHOE7MEoRrCtKCUp7BOvjWmzbSnEzU8jS4toNSPM6I2S9C8MhGMPoxgNUscO2fGQh+fAHX0p88RrS5XQ2K0Seo02FUxI5rkfpxHQHW5V1kG6eI0ZjsBbTihFVjtgzB5eX60BlX73Q6iPAKkQYIXDY8ZjW721hm5JgJUNEIVJrrLEwGIIUdP5oGTk9hUsnuLyEuWnQCruZ1a6SmRZVVaDHOQaNCnX94hYQnl1Hb06QZX3zwsURLs9r8aQZInsW4XnQ0ogiZHTbFM7zsftmUd0UWRhcFOJvZhD4qAdXmB9dBk+z/WUH+Btf9iRXwyk2/mCesu3hBMjekOQkJB6ElEymm8iJIBSKqhVzpdL0/AhzQjL3rMG2JHJljMynkFaSX9cCrwk4lFciEo+NQUXvuYfALCLnD+CCAOHVI1lTx5ZYi/cgU0slHJOZMZtJD48Y02+zO9qFlhs83etiAkvlJNP5mNE86OdLfCTFjOSvJRvccSBlamaG42++kZ869xD+Vu2wLKc0jcDj1fdcxytecZS33v8+hlGM6zuOnthPkVV4AtT5AaX7428UfeM7/jPPnVrj5uPzrP/+FYq85C3ffg/f9Hfu+Qs533++KCw9awhV+3/95B122GGHHf735q9Ya89gMPicTwdBQBAEf+K39vt9AKan6+a8ixcvsrq6yhve8P8GwAdBwN13382nPvWpP1FI+Yvk8xZS3vGOd7C4uMjP/MzPsLKyAsCuXbt429vexj/8h//wz32BO/wpESCbhmzWgYC5q0NGmwO6d+zlh/7eL7F5wEcXER1S1ncH6F7K9KMjhkcT7JsSXnP4MZ6K9zN8roG4A3ZtjfHHkrzlU04FhNsR5VxOhkIKwXAUo2IDgcEWgql4TC4VG2FMuTdEeRNkUKFCi8stTgsEAjnjQVegDTSXcqSpBQ4hwBs7wm2DjTyU1ggrEc6hjEMtTHBXfISFaB1cQzJaSBhdP41pqBfaSxzO05SzEWqQYwKFvSnHe7C2hFkseB5EPsIPQGvSfQFySVKFCa7SrA/b2Dtyipdbvv3FH+Mnf+7rGJ5tQ6tizmVsbcWkKwFyVTKpFAM/rKumy1pMMYWmv5kQbBWARQjDwYM9fuc/30pjfcA4bAEO1wzJjk1jBTQfuoQqQB7ustKD8xc3sElI58kJwhi8qyN6r90NhcDun6PazhEFqFEOnqBYbBIKEHmBkAqdltAICbZyBi+PMHstjYsSb+SonGTVTdF+8ZD+YxHGgagKKC0zx3ps+wHdcYtgS+INLb3bwHYrZj5TtwqJfkomJ4QbGS7yMF4MThBfrZh60DK5USOlgsqCdQxvatI6nRGuGfJmm6ViHpv4FJGsg1AldM6V2MBStD2cNSSneqy/7gAz5wqUkDAY0TkF9FPo+disQS4VyTCjMTakiwH9wxonNa1LjvZF2LpdoDJJ/w6f+7iBIK9oi5RqI+HK43uxnYTcGIbHfKqmR/hEVt/9lzCaCRH+NPEpw/BEByU1Oq1dHt3rYhAOf1iH6zoEh/b12NXpcfnkPNf6MZN9Mf7YoScWPa4o5iLMqIEYVYi2xpUZXmmY+AX+uMJpj+y6XQTdCtPWWOXId4UgXC0MdCf0vlKQH5GU8xB9WhNONNVMSKZyvBVBUSbES6DGBlfUo0NutkMRCMKvGDI7PyJ9bBp7U8ng6xTND8ZQhFhf0nh+ExdEeJcEZraiaIfUMyoOmxe4ZoAJoP14UbseEh8kKAQoD9dp45ohrrKUCw2mZ8e86ms28F3J42stvHmDOCy44c3LnH70IP/sh7+GbU9y+8I55hf7XFxdJD7raH2dR/5bCcr4+Ne6yLSqq5kTH5Qi2OxDmpIf3010Zh3RGyPiGNuKEZMC2o06e8doXFlStX26tzgoDDIMUAXkMwnjm/cQ9x367BIIQRA7Mt/HRTk2rVtvZCvBBRrXH9a1ys4hFWhPYyqFCUOUS0HXwa2MxgghIc0+W//dvm+I6zRxcYCwBVYrEAIbalRVv7sQWQ4WbGXqv7mvwVT1eEsYoq3P1ht2IQS0PvQMrnCMb1pA9yvUyIDJEUJQKYPfauOqErfRx/k+wtXCKlKQnEpxlx02CGFS1K/lAvKbFutxw3FejxVVFdHJVdTfMpz8sQ7yBosrDQiIL22xcXyB9GDIsbKPW2pBWUJW4I0tpJJkPSVPSlp755mPNrk8iug7h214qMsOM+comx5llNFYNZie5dP37uMNb7rIY1t7GFzr0BF9MJbtkzHODyh9hzSOf/m3v4b3Lj3IqfMjMulxvneKWbHB4rTg8qiDwnH52k1sj9u0gpJ8VuKkICx7iCzEF0MuZAmpFDhP4nmKN7eO8X9/193MzLY52V1CXlAkRcrMxysa3+ZBUYIEaS0vvf6Pt6OcOrlKkDnO3H+FJC2RCH75F+77KyOkNKNX4Zmz+OGbvthL2WGHHXbY4f8w9u373PPnD//wD/PP/tk/+2Of75zjrW99K6961as4ceIEAKurqwAsLCx8znMXFha4fPnyn++CPw8+byFFSsnb3vY23va2t31WYdoJmf3iIxEsBk3WJn2EKaBwlHMdRCF5xpTgh2Qtx9V2gBGGcFAgK4EsBe0wJT0cMDvqE6xUeFccwvOI1nLk6gA3tYhKBYFXMLYhvqjQUwVuKCinLd5Q0GbCWCk+cel60rjD0oMBjeN9KivAc4zvadO6I+TwdVdY6cds/uYiOgejHMpJpFVICUG/Ytj2KJoKvyhwgzE0fEYHPfqHNMlzCV7fsfSBDunQJ6gKCqfovjhi1x9skB+aQyCoFh3qxJip4xXpEzGtW1O2z7QoFpvYWINSFB2NSQQmkASbE8opxbF9V3laLTCtxmjP8Q/f8nus2nn+cGM/1YWIFx/usvHV63xs7TBL6x0K7SELsJ5EFBYrDf7YUXl1tozLJf+/H34LUysbcFcFLmfyTIieWKQViKKoLxhRuAdgeN2A1JvCSkXQl4jcUc20iPyctIqoEh81NDhPIV1V1zfHikJJ0jlBslVRzcZ1S8bYoPZLTC5Qm6CMQA6hPT8kmc6IOinuqqF1bhkpFLd/9QUuXtvLyU83yWOBO+LhvXoEJ1OsDw6BKB3R8wNEIdBL21SxRI4rshmNtAHh5RKRxHXjiSfxrWZ0Q4vW+ZSilNhKI5Sra1mzCkKNzC1CSKQFb2uM9SA97jFUjvazBS72a9EmlnXw7Ec8grUBqp9hZ1rI5ZLBwQgpFDqvkFaQXBZUscQb5bi9gtiWSA/EEzCpIlRskXMj9KmQ7nURk10+/toQ1ytwpYZ2THV8N2EpsULgFmcoWz7ta47hQUEVOHRuUJXg4+8/zuHdMdcutXFthVOC0rPoLMfNtgjPbaK2RtjdsyAVJvHpnvAYHQooOh6NJYvzNJNdCln5FInABZJsqkBohSgiWvdmZNcKRK6wvsAJcALi7SElbeS4xEYKKyymodCjEtcdIK4X+HtLnJEUsxoZl8jcoAtHOt2gCC1i0ycoBO3fGyCvbzI60ahFAQWirBCZIV7KEb1BHWA604HKUjW8OounEyGW1vCmOgg/Zzjy+P0PHMM2Qp4az+HdMWHS99hqN+gHTXYf2cAtNSgFjJ0G4dF9MmTw5Q2yb/SZ+nCFGkkQYLTCRV6971UWrTTJeoFAQqeFsYBxyLQAYzCuYvO1C2CmmP7UFvMrfUyjgetlVM0EpxRmrkEmMhplCVmOXTLY47D9kojOE11EGOCcQOQlpjSIuWmk7+GMpXIeJoHsRfMkn75cCwklEPgIJ3Cjcd2aI+qGGkyFWB5iphrI8aR22OmAdCEkuLJVCx8WxHQL1x0gRynO88DzYbsPSYzrZ7iGhzYSqQT+akEyBhfFlAszqEurqNxix7269r3TRoQhFEU92lMW+GsWG5UwrXCuFq5BQFkgpAENoqpf49GW4RM/tkB5JUcdy3DGw0iLSiuK+Q4Cx8VsF9OtFO+TK1BVVC1D4/oKrkJpYmYaTc5+pomVEjst8TsBnMzwtlLGr+iQdafIXzLGioLoJZvcpxfYfjJGBhab5iBgfCYmMBbpBE4KfuW7fo1v/OG/wb9JnqAdC44tNBhU29w6ewOPXlsmTgqWxw5hBS4SlInEKvgXD7ySQ70xL37RBt/8pQfxlyyUgjDxONie4sFPn6ehJO/++Y/TnoZRJPEaCUWa46Sr/05AHPr/03NvUZb1KNxWSlDUgr1TEoFgPJ6QJPFfxluAPxGdfCsN/2Wgr/9iL2WHHXbYYYe/aP6KOVKWlpY+Ryv4X7lRvvd7v5eTJ0/yyU9+8n/42n8/nlq/p/nijax+3kLKf8uOgPJXB+scK90UOQQ9hGyXz/i4j3SGqThhvNzDW+5jD7dxxhBes6zfPUMZaUK9SlM5Pv2+3cz/4QArFGLo0LnE+j7OOpwSJIsjNsomGsue1gZNL2cQKny/YlAY2klF/+QctAW9QYifjuqcExyyU7B44ioqKOgPZsjmA7zUYaWjcXlMtcshtGRyY4W+bHFSYLMJcjLGjX06szA4cwBVAjimjo1Jr2rEVQg3C6ZOgnAav1dgPYGcCdGbivwjgjtf+SxuZcRma8JTW9Mk11LAMW5J8sUQb2wJ1jL2vm6NYp8gOC0xaZN9L3Zc1BU3J1so3/GB9BjCSprCwJMJv335VYiyRFS19b15ReAKR7IhEL7GaocV4E9Krt05TXJLH/e8wkx5CCqcBNcIGBxrEa3kyEDQNm30YsL26gDlubr5xjmoRH1cFIaZm3psXp6rK3dFHfYrcotsaKqpCIwjnYJkVLFRGtQ4wmDqUg/n2L5vhvHenPLJnGhpExEbTBwyTiOEShnP17kSYR8mo5C4yhHKo1xoMLmuhUhL5n53GWFKKn+B4QEPb22EKxzlAc3kWEJ4RVIuROhU0D/moQdjGhcNznPYhk+4XiAzg7y4QeVZ3N551KgiXAhY3T+HFJp0rmBqkmHiAOMLhNfB3GQopw3RL41R/RzXCCl2NQg3CmwSYAJJ1SmppgRq22EriJ5OGX1po870eKVH8yoEx3L0MRitaMbWRxcCr9cmGDuirkCOh3hOM5lSEGlMElC1PZwnsYHDehYnJTbyyAqBOFRhroTga8LVjGAjReQGvTKAyQtZGnmJa0SYyMP5PsFAYUJJOmOINkpMW6N6E/xJXQm9+MyIyZzEeQneRkWwbDENjyIoGB4OoBBst5rYToQ/UgS9HJlVZDN1QK9+9gryqYr0jhm2L8xh9jncuYDwiRzjv3BRGAeYA/NwdgsVRhD5FLOK8Q0dGudTbCNCYjGhRkqBjXwkom5H8hQmEsj+EBcGWE+hRxViNOHTywt8xz97lDcd/yQ/8fCXYSuNbFtmwx7t4ZBDd17hwPF1vKBi6cw8azc59hwec2V1GhN5lPMt1CBHKIXxJKP9HmJqmunTA8qmh7dRIbWHNAZXlHXVtrGYTkzZ1AhjqY4IxKsFqpigH44wOFJp8VdHqMxiF2P0sKARTKiiEmsVF96ywOLv9ogKjRMOlfjYZoRDYIxBhJrtmxTZFLjGERq/fwaRxLg0x1pTC022dgWiJDKvcFIgu0MI/DpfSEuE0uQvPYx6fgnZnkIJjdQa8hJpDDYO6hYioPNsn+6ds3X+i3VE3RL+q+PE2LqhxzlEHFCQ4wtAgivKOrDWGoRzjA7HJANACcjqCnq/7NH81gp7d0T2r1KEpyCOydeHWBVRDiO8wiKNZf+bNzk/bXBCEG0rJi6B63fT6F6mf2CK7FpBuDpGHvF4ei4jFJDNKvJpzaLUbPXHOCHRmylz0mPu0IC+COmNmuRXEuLVHLX1wkiVdfhnDPKgpTISYeEPn1ri3G//AduHFY3A5wazQH9zwPq5TQ7PRxTCIcZNnIBK1tk6WQLLJiIzPo2VLdaudvHXSpSpcL2ST9x/Ci8tuXZ6BVEY3FXF+M4mnJA88ofP1iK3s/UxyJjPCdn/r5xcXiXrQOtMBaGHEwL/6hbkFV97y//D75//2b/w8///CiEC8F/yxV7GDjvssMMO/wfSarX+1JrB933f9/GhD32IP/qjP2Lv3r2f/fzi4iJQO1N27dr12c+vr6//Dy6V/57/VbVxr9f7U63tf8YXJKS8733v473vfS9XrlyhKD63Su/xxx//ghezwxeOFAInLRpD5QvGsz5KSJpLBZeCIbIB8ZpFbJU0xISe9YmXMiZ7IzrRmBN+j98/rYnaKcO2Ql8ukKGHnemw8VIJznB0/xbblzss7N+irTKKPxKoOzLSSvDR5Zv5h7P3YfY6nDQYC4MqRo4FshCEywGjaprb73yS55llvEfgdRXBRobMK0TmGH2fo8odahWcAxCoRhNTWe644SLLn9mLNxII4Mi3rLJ57zSzt45Iz0kGjzjQEjXKUVLACOwNDgIIZMHVywmDsY/UBmEsOIiXC/TYEG7lRDcW6HaFVYIRIeNVyY/+o9fRqxQ3ffMmp0/Nkp5SmO8ccah1hcDexPTMGNHzcL7EyyxGKsSkeuHOLlAabCg4EG0hVpuMrwU0zpeYtQw5NuS7E5wSuJfO0Q0tUwc2me4s8MjqMnHXwrj+v9WbA4bj3RBC0RDMHtpi/dwUsnQgZD0a1QlR1iAqh5OC6NMXYaqNnMSEaym6rMcxcFBt+MhnC2QSkl9fYW5rkp+f4b7/0KQqt8neNEHNSdKrgmIlwI3aNFQXPS7x+gYX+shJgQC8i9tEzwzZunuBcneC1BUWRZWOCS7lFFPgbzaIlkpU6nCeoGoIdG6xnsTsnUFeWoY0R1cKN/HwZwR2w9J4eoAYZehJgRpqqr0ziOkSMQXFS5pU50LG1zUJugXR0gSTlJQLDdRdPfJJhzgHcaNH8Mac3nmfzqOSyYLBmYx8qBBjTaY9nAeylRGMLKqsnTzOOZwt8ddzsgNNZG7wBoJsSuECQXR5iMgNpuOjcsf2yhTH37jG0x/eBbGPFaAAmwRs3dli9jNjXCPG2QoxznB6vr5Y3KpoPtvD2xjjNwPkOMNpiW2GBFHFoZdtce6j+7Fa4YTEeopqymF8CcojOzyFaWjEGthI07yaEnYtRcdHS0kVBwyemafoBISrlqBfYvwA5UnQAj2qs1CcrwgPpDhVEp7dxKYp5UwLLVXtLmrEuN2zyNzUF+lSYiKFv9xHbg5wSmKjCAKFKEtimXHo8CpRUvCy7CoP3ncD850xy+fnudqdZXuYsDpp05jPWFtqc+wrruHpAp6NGK9KzGCICENGRxXWcxgf3EJIzwuggJYQgCCfCdATQEukkfgjw8LHN5DjkvHrPNjwqSYaoWohUmaG6MI2wjqqmxZpXG/JkyZzR7oMV2M2ZMjVr55i4TOGxtkhdqqJMLVopoYpMq8IVnyKRoA3cnWQb17W4b5SI5TEuXoMToYhQD2mA5AVWGORQYAONVknZOstN9N8qk/r4WtYIZBxROUpZKdZj/kkEV5VH9s2XneA6WcGiMogxhVOAFrh4giZ5TjhoNHErvcQg2E95iQlaE2+GGPmGqiNAnwPqzVCK5QscVrgpgNcRyOCAMqKN73tLL/6H19Vu5wCgcgd61+xyOK4i7/i6GVzSAVqeYCrKqYeWwepcFJQOMdNN+9jdMZypeoTdi0bbgTzHZTwcK0mg6sJ6bkGx264Qu++mGISUuxSJFcG0FCITKDnS4p+hswMtt3ARSFnR2NeFx4n3qX4yKURzase09f28P/cdTNnyns5xxqXT7axIaAcSEHYLmhfDweORhw4OIdOLXpSC1zXFiZ8xU0HWT55tRbACkOVKAae4mNPXYQkqEdGFTz4wGnuuud/rFh8ammVsvVCWLADqno7IgqwTjIYDGm1/vg2gacurfAj7/sYr7ruIG/9irsA2FzuUqQFu4/8yW8Od9hhhx122OH/Czjn+L7v+z4+8IEPcN999/0PlcSHDh1icXGRj33sY9x6660AFEXB/fffz9vf/vY/cdvt9p+cD9Zut/nmb/7mL2jdn7eQ8m/+zb/hB3/wB/mWb/kWPvjBD/KWt7yF8+fP8+ijj/I93/M9X9Aidvjz4ZBvuHipwL86IYos1X4PMQFlFFY7SilxOidIh6gVQaenUKmh+lJHSclrvu95Hl7aR29lL255CpkavMoy/6kR229w9E4u0k01oVey3GqTXA0ZywLPh3EWMhwIbFzfjVVdS3SpDqOt2hCcD+memsXsDfnyPc/xi24v7asWmRusrygPguwWBMJQWYvxFap0OGtRmaWUPnHXIa0A4dg3s82zixP2HO2S3ezzzOn9OClACHRh61rSGypMoZjdM+Hod2xy7uoiS+/dj+jWd3pF2CZZKVGVIb/g0/9IQnGdpD9pMHtuyJW1NlVD8F+W5iknijftP838nj6zBww3mTPMMiR8MEVUjnQ+xDmF8+taTazDVZbrT6xz2w1X2ZpzPHtxL6un59HXVqA0FDMHcJFmcf8G46akbPo8du4qVbOuRnbdHBsGeE6SnJ2Q74nwh47zn9qPTmvXjnAgnODuVx/jEx98HIIAqSQ2jqh8wczjfeKzQ/K9swgdILRDZgV6WEIFqIjylMbi8M+vEUxS1CkY5/NM/ZGlJQuGuwTOlHUbilY0nushtEQEAZ4BIxWdT22y9vUxhQ0Y73HEV1qIytJ4akgiBqT7plBS1uKJFC/UNad1G8m+Kco9Hp6BtIrwBpbWIyu14yQOEdZitQBrWXx6wMy39IhelnH2kTmCy45QtDAzDdKmwAWGcdEADWbOctehc8y3R6hF+M3eK2g+XBBslHDOkq8FuOskbm9FWUjUMEcicVriJhlyvY8Uks6dOcNJgtiqsC5k/gFDtG6pQg+rBdJW9M8rtp+fxkmBqzKMNKg4ID3YIsw0+X6PcDOnjCeE13Ky6YqqofC6FWpUQGXQucVpj6rtocYFxcDSbyW4yEMNS5yqx5WG+wTOr0N1vS1QPYdwEjxFEQt0N0UGIMMQI32S8wVijyCbFWRzHhiHHpeUgUbl1I6JFix8eZ/qcsXGIxpvs8AtSIo4wL88Qk8c8iDYSmPGgiIOCFYn0BvW+7qvKOdiimmP625d4eh1l7n//iPYCP7od/dAUWHGknC+ZPfigO0tH45bxoGPN1+ycbFFWIyYPFwieiNc4lMGhuE+H9tUzH0qZTLtQyFwgUBaSGWF7taNQiI3MJpgkwRtPSgdc9sTTCBZXp3D7hkimwLXDSn2tEiYsLi/y0LQZ6s/h3AlYiwgEey7NyNaLXGqDnd1gUbkBTM3j+k9puk8ammeS1BOw/wMbrOHxSD6Y5zTdSWx9nB5Xsc/G1MLnkLgpMQVBXKzQqez+NsVft9AZRAOXARSSFyoIe7UB/a8Yu7BTUyi0SXkLR+bhIi0QIxyZOBDVSHyEjXKsQcWURdXoDI4pfCOTHP5GxaILxWgBUKHsNGt81xGEeNfbuEKRf+EZfqR+vs+/O+P4I8mFIzx531e+5KT/NbqCQZ5wkK+jXAOKwRVYHGVQUxKpCrxXxcgT8P3vPil/OF5y+a7z1MdnANPY/bMoLoZplk7+ByC0yt7kNdVBFcga2vckRYdvYF7Zoy2Enf6Wn2sjjVXf2If4QMjqmaPcN8i/WqbQzfn/F9ffhvPlRd4aP15Gs0cTyUUkcAhUKlAKUNvXOG6lvBGn0B7lKFATvnEmePB33midvYJAVrirMAaeH5ri7Z5oUnJCj70G4/8T4WUjzz7PMJXXHtdm5ufrRjlGtY0Ls1Aa779rh/lN5/8yT/2vP2BT53k0sUttjdHvPUr7qLISv71972bqjL8g3/7bczvm/ljv3eHHXbYYYcd/r/A93zP9/Brv/ZrfPCDH6TZbH42E6XdbhNFEUII/v7f//v8+I//OMeOHePYsWP8+I//OHEc8w3f8A1/4rbf/e53/4Wt+/MWUn7+53+eX/iFX+DNb34z73nPe3jb297G4cOH+aEf+iG2t7f/Ita4w58C4xwXzgyZesmIdCnmcLzBI9E83qji5r0HeW59Bbc7JOv42CSgNbVG68snbE5mOD1YwBOSG49uMoo1Zz55A2ZO4bYmhFGKN9TMm5TFA1uM15sE7ZSUGFUpspWIqgXJqQH/5UMvJ7i+ophR7P3tbSQG/kaE8xUut3SmRrSSCZ/4gxtIuiWiBNkdk89EeM9u410wFLc0ic+n5NMehbaERX0BiQSVCmRluOFVFzCJZnNWUK7Mkl6Ma5t7YbCNsB5NSiz2oQi6kvtn9nP4JQ1Gykc4iZO1NaN3U8LUmRFBMMC9OGRbzGK2oepY+gdCwqpAKQeFojgOH9GHuHV0iZWVFk/85k2cDyaEaymycujU0Dsco8cWowWicFSzmu/4/j/AIfjd8zcgZ1Imj3jE1wTSQLA8orreY+1kjBilFK/2aTxqaEiNd8Egx5ZyKsHGDaSxNC+lWE+y/tdjOh+zyM0UJyVlKHn0M8/jrQ0w7YhyzzRu9wxmysPvOBDjumnGGFwJw/0BaEGlPZKNId56RfeWuu1FNS1Kd2g9JpGDChsbbBhhfIHKDHOPbRGP8/8/e38eZVl6l2eizzfs6cwn5oicxxqzBqmkKkkliUECI8AgTEu2ZQwYui2rb982vWzfa7ev216rfbF7cNtuMBgDst1GDcJIBjEIIQmhqaQaJNWcWZmVU2TGHHHGffb0DfePncbNBbuFL8JcOp61YuXK4WR8cYa99/fu9/e+yE4bwqh2WXR7yHHG3JMDtt81D5GqR0DyChODa0S4VoiJNUaDLA06N4jMwGwCj0fM3euIB4argybGK6QXSK2xnQZ5T+MiTVBCHrS4uR7R8CO8Ap0JfFuSrSiyOYmyguSFgtabh6i+p7QBt8se58N97PGK7EoDPRGIQCKNINkvyZox3a/kqGGKbUVsPtph8UmFKg0sNHnk7FW+/PkjTNIGunSoXOPDAB9Ygs0RKq0owhYCS3FSMz3ToZqf0dxRuFjXd6mFxMUCH0XYhRA99dgIoq0KVRicqbBRg/z8HOmypnF1SJB7JrslNobg+ghZlCgxR5j2UEDZ9kSUqA1J1UuQhUWWBtvTSKGxvSY+CdHG0748Qd4oGXzjMp1LOSYIQGscljKCAAiCgmKvx+h186zc2sRHAeBxeESaUU07WB1QzXtkCnpagnX1pjjWlPMR+ZxkSy9yX+9VxItN7r5vzPN+zODajP2POM6+a4N3nLrEy8+u8tmnTpIsFEzWBQv/2YzsCYdaT3FVA9XWiCt7LG5KiodXSbYMs4UIF0mE8QwvrCG1xIxydGGJLt0CY/BlgTkxhxikjJOAuVsVzZ2UciGBMsB1PbPTfWTVZG+asndxiehbSnrdlKlu3amNNghj6tfujsvCzDWRp8f4F0F4VbsORN1MJZTCNTSqMAhjsQqksf9uxCfQ9a++FgN9WTtUrCyRZQS9NuVDpyntFNGMSV4ZITYPYJzWj1ueByUJDywoQTjy+HYCYYSapThfC5xMU5AB5Vqb6OomUkqQgnInZ+7XdpmdSXBSIj31SGIUYc44yvdYgi97/GajHgXyMLndRtzXZvhgTPu+Ad/12uf4+f/6DOUbu4ifnsGDM8xSA99KkHEIIkfrkuSBJrdEl3/wI7+Iu5ZSzgcwHWLm+sgcbDPEKYmRYOYhWss4Nesz/NlXaDjH6HWr+Guizvh61kNQH6tl7pj/GGQJfDJ6ltkHLvHG10w4cjzi5vYuti/oBovsXvdUhQblSHY9OvecXx6w1B3w0Y91+L+/ziB1HZj+A+96nH/9wc+RljlSSESg6rYq60F4nPB1GxOAkuzsjn/3iRcwziIMoBW/8JG/AkCe53zHiR+CyjC+PWI8zpBS0GrFv+vxSaUIU0hkXUevtKS/3GM6Skma/+E58kMOOeSQQw7548CP/diPAfB1X/d1v+PP3//+9/N93/d9APy1v/bXyLKM973vfQwGAx599FE+9rGP0W7/+12fX2t+30LKzZs3eeMb3whAkiRMJhMAvud7vofHHnuMH/mRH/mDXeEhXxVaSiI3z+CTniDyHJSC1R/bQBew+c55RGLpblkagwoijzwiGaw0mVQB6bUFPnvqNG+Yu0ygPM7Xdbqzh0IWj6dc22lxrJjymte+giEAHPLFHmhF4zbkecXCJxxqbFlZT3HzbZSp0zCjPfAphBPJejTHT33hccInJK2iZKyhsbuPmkWIWOI1yA2JLD02FDTGJcI5lBfMh1NkDDg4dmEH4xRT1+KG7dLc8MwP9+onohHXG5jMQmqRleBgu0kxiKhyiUsEJBEeqDoaGxS4A9C+JGqVFIkgnxfkGtQWlAcxza/EiIdLxkcT/ttXv4l46OkKz5H+mEvNBYKpRRUWNbO4hqqrR4VAZoaXv7TAzo0ev3n1tXQe2yO5PaO8ewWVeZx2BBdLkpfWa1fAcx3sykI9gqMDDt7YxiaKYCpp75V4oRFAnMxQQ5ClA+FQWEapR8+HIAN0YZl1JWEuME4x+MY15p+rgxOdkjSaBdVajNgyqIlBookOPPnbVmkcn1Lc7CATcHMwvitgdsLTPD6H3s4xTUu2EtMYlAR3VQzbCUEuUDsVeuKhcpSJRGYlrigp71/FBxITOsIdR7OfM7VNqkITDiw+Fuyf6zO+nRCPKhwSoS202xBZpLG093KckVTtmKFqYy/C8G5N41yCrBTWOryVCOVR1tHYN5Q7ASKV2GnAvd+0zdV8jqiXI52mXG7WbTVXhrisRS+TqAxceGfTpgPUcApK4nTIC8+cpFIeopAgExRzAp1a5MygS49Xsg4XrWcpcLGs3TtViYmh9eIeelQi2k3iqcQqmH/JYRJDtF/VoowWuLk2VawIJ57pXV26z+wz+YIj3psi81q0UJVHWYHIPO31GcmlfUQlEUe66NwRpBVV4FBZCtZiOglWCezePn65QTSyBKMS149RFXWuh7e4VzNe+eE5sqMxjSqvK7Wv7+EaEcLXTi+1keKWm0ilUFbUgceL/bp1ph0gLcgKBoMOv/r+N/O+v/oZkrsqeguKnb+t8Kln+ErIJ/fOcPX5I7z1O15AKsfOzyvkVDD3hpL0akz3sRmTY4bp/1uisoDGDUvRUrhII9MKFWh84gnHnnwhwTpQ1zSqrPBxSLnYgPkEuVmxMw1R0wp5UmCVxAHR+gCrQrKBRW+WBCc93AeBspz9iV0mJyUqF1Qdj0eSDEtQnls3u1TL0Dpw+NJgG0FdfXx0kTKyRIVFG0A5ZOkxAchRjtQBXkqaf9Nh/nlAfrUeAYmeuEbUa2EunAAlSV7Ywx9bQBmLm84QSmKTCNcM8ekMJaLa/RNQv9e8x8cBoqjqxqlWCyHqtqj83CKN7azOVQk17YszWlcr3Fybo3/qCpvv7+Ezh3lzRLUC/kFD7zMpXgd1zos1hDJH0aSlC1In6H18n3LfobZygvMgB5ZkVLFwcpmN25sE0jH5WcHi5VtsyQ3SC0dJrh5AoKnCEDpdSukx8wFSgC0E7zr/ObZ+ockoWwAp6TyzCf0ESlsH+FYGnxf41XlmfU1jHarUYZ3n4Cc9n7h5m18f/BR/9SN/me8/+538/KWXeFHeRDiJ1Qak4Jvf8CXm5ieMx12kFPhRhhrN+Nkf+Q3McovqaJOjo5dJg6NEScXupIuLJTYO8O36s+wljDdHv+e5d7XX4eWdPfrNfyeSxHEMVYXP6zrzv/Cn/wkqUPzUv3ovrfbvFFNOLPZpBQHnjywAoLTih370+3HOo5T8g7lAOOSQQw455JA/wnj/f56OK4Tgb//tv/0fbPz5w+b3fZZeWVlhf38fgBMnTvCFL3wBqPudv5on4ZCvDcY5Zn2HMh6Ze8Y6oeiHiDVov2VA87U7ZHslLvWYIRTPe2YfUKz89G1aTx3woz/3ZrYHLU53d0mylGCQI4KS3bsEVSIRU8+tyRxPb53CGMn86QG25SiOOJo3SsKxhaiNjwKCicH2GowfXMIFAcIIyg54B4O4yeScZNwLKNaalEsNJAoRBkwebSEHGpNITCwxaz28EDg82jgaVweonZRnfu0eNoYtBtcbzP/ve7QvZnhnYZLiYo2NJL4UqNyh8gqnFPlAYqbUdZYepAd9YPCiTdZfQUwD7n30KvfcfRM1hnhfor9tyqlv36B/ckje9lSzkEgbfOyZe8su3/eeJxmfT8hXGuTLCTZSgER40IUncY5f/5kLPPmh05h9x/DTc0gVoDLIVyPSkzHelHjvkKVFFBKZ5VBZbCyp+gGmo4kmln53jAMkhuNfHhJMy7q9RQl0ZnFFweY7VjELbWys8LHChOCmhuTSAT7LsbHCxopyPyKPNK4Z4Pug2o7+9hjhPAsPHXDfkdu0OwX5asjshEIVhmjsUGiavzkgfjbFpwWzfAGlI9TmCLU9RI7LelMtBFgDWhFOLOGsItqc4lrgC08rnFEta0zgmR1rkOoY46CIJIqSxBb4hsCFiv7pDO5TDB/sIlsV586u07xiaf1KA4xEp4Zoc0xzq6RzsWD19DaP/dmLlJFi0gwZPpxAoPmWueuEiSXIHNmypmxLSHOSK/u0Lh7ArKJKBIP72wSzAm8dojTog5TdF9rMNitcnuO9rPNAYoUuBD7UuE6CjQOcNwjj6syfyuLaIV6AHmfIsq6cdaHAKYFwAj3zVAE4Z7BLvTqc1DrCFJSTjF63AAFIByQJrt9BhhGNWwXxbom+nSErw2y1QbYWkc1D2RCU/Qg5KVHDnOjmLkkzY/z4Ku7YPP2XSpKhRA8KbOApO4rkuW2Y16TvbWHOxsyONuvKXqWgEUErRmUlelogZgXI2lFl2zFilkOW4wJFMCho3s5pbud857c+R+v4hOdMi5eDHtXRmMXXV7SXJqRnFOqeghkBJhZkxLSWc+buyTn2fTnLbxpz5NQIe2EFe2aZna9rcvNdbWzo0ZHCK8AJiiZQemwg6kDrJEZqhRpnhNf3KeYdpqEpOgF+y+IqiRrkJOtjgo0hxWKC6cUc5Mtc//xRZk8JRFHQe/KArAN79zVoXNlFbQ0IDzLKsaRaDesOF1U3J4k7pzzbCesRGw9qXDC7p8vtHzjG7GQDV5b42Yz0v7MU1/Jab4tClFQQarx1BHtTdKtFMCpBKWQc1VrJuSPoOEEu9GuBuDKI/QlifZtsTmHbCS5SlA2FCQWmHZEuh5R3LUJlYJbhD4Y4AaKyVJ2YL6gH8X2JzXOCz2e0ypzmtVntxOg26wM1kvxVT/tLEvGs5mOvHIUkZvl6iW1IPHX+SNGN0UlI9cgS/83fucl/+1deoL0oQEmCUUY+10I4R+PyHtOV+rNjI7AB+MAjWh4d1qNNVBWq00bXDeOINKOuZQJ3bJHWDY3ODf2/t8PKL2yjNiXVJMGGmp+78iX+2aWPMjr6Eo2lbR5457OYtqJqwCdfuo+nL55jc2uFwe4E0hKSiDwzbCwJ4v0xx/6blKlMmNoGdx3bQniw/ZCqpfG+PhZUo/T3PPdujSeIwtMQvzOI9q6HjiPabUS7xXhrwGBvwt7B5Hc9/vUXTvDovSf47rc99Nt/JoQ4FFEOOeSQQw75j6Ie+/+j8fXHnd/3mfobvuEb+MhHPgLAD/zAD/BDP/RDvP3tb+fd734373znO//AF3jIV4dxjoNxxviIouhLBmciRl9/FG+6lOsW+WVLUs4QskIoS3ZfiIoUi8WU3t4E2w14YX+VV2cLpA9DftoT3lMwu6xZ+fBVRr8BT167ixdunGAyavF5t0q4X7FgcnpPpsjKY7oxVT/GS0c5F1GsOKyCKhCk5wx3nbvGtzz8DMHUoSd1TWV5Yp5ipQnS03x2xnBNMLgvItkrUb0GJBFCwOVPLqH3HeFuys7NDv/8c29HXzJENyx6Ci4J8cbgncOFCtOLsbHGRwHeGOQyMCdQOsQutDHdGNuRqKbARSH5eoRMPBJQQwmBZ2t3gWkYo183QC9U9OdHlKlk/scM5c9EpFsdVAiTozHpakQyrG39aacWMZbOj2k6g/IeZQTNqMBGEtfQiNLjQsfg0T77b18jfWwZs5ZgjsVIYxFas3xzyl3jAcfnpqRVAyEFVRwwuNnGO4cJBR6PixTZ0RhRuXqzZSHZtThpoTTEG2ntXMkMMquwpkIaj6o8kwcUYTelkWUEA8Nr1jd58Ph17jq1TjAWBAeC8IpBHEzw0xlOObwEmk28V6gSfBLiQ42c5IQHGqUlzjtUaVBZQXtxjIqbyEJRmCZZFTCbHyF2BlipSG6Dp2ByoQBvmQZNhqdb2ECQjyLUeYFfUrhvUFzfXcHnArUPpkjRWwOyRdh/SDG4N2S36qCNQQuLsB4Tem6ZhEY4RUuHWaHO8JlaxP6EbMEyfFDSemmHcGuCUxXFgqY8M1+/d6IAF3hEXuFMSb6gGNwXs/uaBjbWVJ0YpyRYizea9FhE1QmIt3LCjSnh1NJ5ncbf1cJ1NDhXuz3GKZUs8MpCWSFHM0TpCCd1sKqJBNG+ZfRgH6oK63NMmDE94nDSMDodsP9Yl/J4m2hUkWxm+GaDbD4kdBGuGeOFhACWv+6A9N4Ws+OaOpkCpHXYRCK0Zvx1JxC6T/BshKxm5EcC9t/UxbYDCENMP8HMtXBRQDCoaF4eocYlFAWmEeLLArG+XQtMoSI66Xndo+ssmYIrg0W2ph3ckmKvtcCVwTGGrSaZ11QtyVSEqFNdTENipCSfwf7HO2z/731GSwmTswHTkwqfQNWRWOcR1uNigfN1niizCqFUPbLnIbp6QLA5RAwnpCtgYoELc8y8xcwl5Mcb5Md72H5CdqJPsdCkOIjrEaxJCrOMznMHHP/gbeSsqsVXa4mco+oq0pMJO48miLJ2PEjnUaXDKoEbjfF5jo0l5WLE9veexFtbh8Iag9BBncMxy+oMlaLAC4dvJnWg6izD7e7j7vx9nbpdj5i46RS3dwDOISpHERpY30QeTFBpiXKAVpQLIUVQ1/D+28dPj8Z475FpSffjMw7mOog4JvmTkmS+InmLZXq6Q3a0W4+5KIlZ7TG4q8ktvciL//Q4VVszeHiB8fl2netjIW9CNhjTSgxR4gkCh04UdFoE4xJ3Zqkej4kDnJaY2RSnwYXQWBzx2afPM12/AGstfK+DjyLcNEAIgdC6fu7CAJFXRK9sED/xMuhaCBxtDSHLKZYV16OcF7dA5An+2QUu/eT9yKwOjX5yfJp//cXXUU1jVo/Pk/QaiMLgfb2O6e4eT105hR8V+LjkdtrHaSj6AooKMU4RoxSTFb/rvOuc49LeLmXPM+J3Bu+//c+/BaEkwlmkc3T7DRbmf7f9+NrOAbfTCa/u/OGMRs/y32Jz992k2W/8oXy/Qw455JBDDvnjyu97tOcnfuIncK6+S/Te976Xubk5PvvZz/Lt3/7tvPe97/0DX+AhXx0SiBoa0/fkxiBLMGuO6o2K6q4S09Kcv+eAL1xaJr6RITdK7vsLE76xe5kbzT4/8dR5Pr9+hrVsD/GyJLrpiJ63uGsxwnr01HDf3AavDlY52trk6R9/hHDP8Z57jvCz48v4EJCKoq9AJthEkB2pkJnDFXXjyNsvPEfQMry0c4biICAwAaAxsxzX9gTjLo1QsPVAgNewfGUApsInit6xKS7WlC1NejwE65je18Y1BEEjBKFY+JCBskJoD4HCBQpl6zGl9rmcIhZkX5K4ELyWfM9rX+St77rOT//YvQyN4tHeDZ5xx+DugqwSNH4ZDqYNWu8uWT03JCvgDdkO18dHsVbzM088gK00rc0SWTmKdoAIBDhBuih46L0v0P684SufPM4Db32V/mvGPPnCQzgLwdTSvDqlSjzbb5tjtKBAGS58IiczFXqQo28Zsm6IXwkpCgXeobzju//ss6hrFTcutzh6n+EXn3yI5k1H74UDqoU2SIGwjujaBJHliN0xZhnytQ6m45gdCei9YFBZTvzFlOBUTu/rLVdf7PHxXz5DsNCBZYMJDV4rdFrVAawr84hOjKumqDxCD2eYboTrNalWK9zwADk29RhIYRCV4+TbXsG1Okw3OjgpMKJCpAWdnQA5N0eyb1B2RO/MHkuy4vruaYrjYGKFbWmyqxXmAxVR3zJ4cAl0QP6gofN8hTYxe2/qMHh9HVcR7gpG0yZPfegscqxYOVfywKM3cFqx5TTn5nZ4tdmk1dkj22gjhGB2JiY/FlMsaeJrOcEUgi1LIJuY+04ipzlOCmjEKB2RXM/xBdheVDtPCotzFjkZk51dRqiA9pUUPbPgHT6U7JY9oihHbpeIylP1ImZHYrwELaFa9KT3ziM8RKO6ISTaK9GFo5IOm82gpdBTB66gbLUQyhNFDtcS6FsVwkQoJ5FCU8UOd2oBMW5S9kOuvCjgHOixA6lwsUBWHmE8dQeWg1GKGmYk2znykUVGD3RobkiCVFLNx4i5mMalHVTl66NNMcLNtSmWQ8KdfWTukMOCcr7NoND8/IceItgf8Im3n8OFCv0GSbuTUTUl+6KBawu6zRShPGkQk7UC0iJCrMLGP1qFoqKzYpicVkTb9RiRqgS9mynjZYkIgzrA1Ho62xkqbuAaIb4V4QuDnddM7++iT+Q4G5HFXcoTjsb6gPDlLexCDxfMIQdTnGshvadai5mebdH+0gxRWfAel8TQjBGdmNY7J4wuxXglsMdCsn1NsmMRziGyHDlK63X5gPhqQeNKhmlpsjedJvn8NWQYUtdm1cIKKkSMCximOBmjJLV4IgRSSJwQiJ0xrhEjrYVZhoijWoTptNHjOkwW72vXkrFUTV0HiCuBX+7hKoeJJfG4QEpJuL5L3pqjWuvAV4ZUzyj0vYLqBY+LJX4xwb0iEUFAUEB8q+Dx86+yddNRnJ2jON4heDlDywg5NGS2YuXkApOv7PC/XHkINR0z8i0EgqwXo6/uMX7kCPmxNrNT0P5yiZrV7V2ZbHB7vMrbH7uX7clTTNcLfKCg00Rs1a5XvEci8NMZs1M9WtMK0Uzq/CzrSIIGbTSPnlrGSctH//sRsqrQM0E0k5QFjO7OmReW2cU2n/7yFUa+JHAeRP0aB++OkC+LetRp27N3r6I8WhFvlOhxgQgDBBYfB6TTjEYzZnv2WyRqkS9dibBDTxAKVvrN33FOftu7HuNH/vK/BKAxmfL/+Jt/4bczUsbjjBdf3uDBC0cZjmbcunbAk/4a3/eO13/NrxWMuY51E4y5/jX/Xocccsghhxzyx5nft5AipaxD7O7wrne9i3e9610A3L59myNHjvzBre6Qr5pAKR5dWePZL75C9+UME8DwT1iyBwP6OiPswZXn54nwCG/RN6e8tLfCg3f1eXb9BLbleW3vOp/5Vw8T7QnkJKO6LGnsGsqFHoMHu3zikye5cM+LvDg4zkNn9/jCk33C+dcxOXITWVbMToa4cMBsrkN+VNWjXtIhpEAqz09/6m2s3bfNrbc2mPuIQ92piwz2K9RMUfWh6IVgPNMjmtNyyOhFjXqjIDotGTwwR9XSCA/JrYz5Lw4ZPtQmPReQrFsIozr/xBj03rSu8gQoLYNrLaRzteIkJJiC5WjGx39ujYOLkqUHcq7+1Ar9syXMgXACNawIdUE0NcjAMhq1Wc3HbDdLJkbypZurtHZyMFC147omFI9HsPgt13i1WsAUAZuvLpEUGb03jVFmxqwX19kkaY42gvjmDH0gmX9Nxre/5wk+/j/fw2gnxFUW12iQT6I6+NQLukHKQ6f3UKuWtftmbOs+p27v8GVxEhc6dGHqO7nGUB7voXYn6HGKHmaUQUq8GRLsOXQhCL2gdW9O/8GM2VafYCul2phS+CYz3ye9UJGftLQuWkS3g4tDhPUwLoC6zUSVHhdJ8jN9fB7jzpW0P2Hqf6dg8+e7tM5WxCKlKBqocVG38UQBotFEdhPiqiLuOpaOTrkVQmPPUMagBiVkOTqK0ANHisR78FYQjHKirQI9alD1Y2wA4ahu3bh1oUO8XJFQ4JGMTYPCB1ivKO+uGE2a9IUlu3eR1hWLHk6IXhogKkfj0oDxG48wW3IkG4LAeHRe4YYTkDOS5jK+6bGzom5acQKaAS7qkZ1qkOzmxOszXBIxO9fDaomvAvT1CdopcB69n8LJFghP49IB0jjSqotthfiDAhsIisWAypWIKqe6fwGGBcHEAhHlQshcmHL8YIvtGyEmL8jW5uqq77FkeszTe6UgOJhBVZIHbdrXStrXqlpskAK9l1LNJ8gUols58mCCKB2206D5wpjkyhhBExs3cRKCaYVZsuh9D1kFoxwfawIVo1aWEAdjGEyJ1wX0JLtasPfEHPKbHFJ5xBOCwbDFyvfv8eTW3SyGY0509/FAUmaUn3Hkn1XwiMbFASIvibZmhFse81iT4ngTURq8tci3WWzHwRc10VcmqO0xXitEWiCdwCWaje9epJhXRHuCQGpk5UgueR7bW+d2VpGOc+KXdlCjDCUVgQ+ZtR3h2IDWtVNExiitwDjCCwOKRDE4rxkeBx95esqClthAQ1WgwhjR68M0Q4cB0UkDTfA3WlR/9l6iz17B73h8EqEa9abbT8fIMETOLAynYGoXiTOmFhEmGUIpymaAjuM680RIfBxSrTVxN4fYQNZjRoBrhUSbGYHw2ESjnCcs+e0qda80YjpB2jbZQkwjrsO8/RFNtJsT6QmTx+boPDNGWlh4OuXtX3+R9w/PEKczfBwSvzTAn0pQuUMcTFHfchp7aYfpIMCnCb7YZfeddxFWDrIA10lYuDjFP5lTnFtCZx5noNqxmAl87OrzyOkeft9ArwmVrfOZjYEorEcfRzOiOEA2E8qeZvZaT34hJv7FjGDdcHD5c7j+bXaOP4ggITowdG5ZTMfyPQ98gTNv2OXf/NPH+Vv/60dAC0LqCaYKCO/XnHx8i9sskP6GpVjr41qevOvJXgxItjwyDAC49Pw68/dv8c+u/FMWQ88v/Ohj0K/PFzr4naM9SeNOFor3zPamPPXCT/Hwa9+LCk7wgQ9+gY9/4kW+6zsf4ZFHT3Pu2AInV+a+hlcI/45W892EwT2Ewf1/KN/vkEMOOeSQP2T8na//1PxRWMPXmN+3kPJ7sbW1xd/9u3+Xn/zJnyTLsj+I//KQ3ydCCB5eu8VBc5epb3Lu3D7j0rHbi+g2Jmgqrj9xHDmviUJD2Y/Z3Y74+194C0kKjaUZcTDDlupOfW+BUQlaCqqOwocBGJjLDC3t2Hi2j7FDfuQXfoFwMiS+WqDHJdKOSU9PKd7ex04hvNqpM0lKxyhvc/BCh2LO0eoWJNsFajRDFg4RWjbuibFhSLQr8EWB/vaArfYiF15zm88+fR/FoiLc9wS7M+JyD1FCMCkxF0MW/s0eovCodgNhHMJ6hKnvOPokYnOpxcr+PoHdw0w7yEnG+z/6EP0XtpkNFelLDZI22NsadSzGLEpGDwmScxNmp9o0Luf0fyvjs/kZ4hjGMkRVU5INi3eOmxcSWvsCXYIJ4Z7Tm8zoUhRhHYI6gWU3rO/uO4/w4AJFejSg8+V9AhfTLCrC73K0V3MGNzSuyinnFtBlPbLjIsXBOOFffPohghuOi40F0l6CTQXRYEa8nWObEULXORwIUJnB9RrYtQVEOoNK4WLF9FSMHgqqYg17ZYx/TiBW2uyeaUGjTWPDovdArEFjsyKucsxgStGfQ4UesTmEpT4YR9V0eOvR04rmVUP7qq03dVIxPttn92RC53YJhQcvMQ2FWVKoPMS2FTZQbAxOk93aJzk/o7jRJR5WyKre1FYdRTUX3nmne0Re18giBMHOhOVfHeOaMeVSzMFDDWxcj2alJuAz189yvr/HxdkSYuYplMYhqNqSpBSoPUE0DJBhiNAV7SMZm6ccXkqEgehAIJTAtxJ8EpGe0OCg/ew+qgCEQIQKAZRNS5FI5FijhxVq4qiSOpRWoRF4zEILPchILk8QZYUQCqoSXQF7OXJmiPcC1P6E4GDK3DcKVhsjnpfHSO9vog4yqAzTQczmxSOIqELMjYlNQAXoWUnrhqVKIrQEWVjC1OIaCd7NUFsZ1XwCrYBww0KgUE7hFjvom0NcVtK4bInXU+yqxrda6JlFKI1zHUQ2wsYJXmuckigna1eLUmAd4faE1eP7PPz9t3jp9DI3G4BycGDRWcndxzb49V97hLH1VLnCI3BHBTv/dA6Jxr3k8Z0ClKBYCBjdnSBKgU4hOrD1qEepwAFW4rsxdlaBMajNQV0DTIOlX0qZ3Nug7HUBaG57lPWsrM1Ij8Z4FdJenSJnKSPTJ/chPlA4CXRb+IUuYpojnMBXjiKK4YpAVQX2SIT3Cjk1MPMoPHsPN4jGBkmAqEqKlYLZkTZBYBifjFg9PaD3PTHRFzUXnztO75ltZFrhswIxzlA2qMflRtPalYKrM04OBjAZo+fmaicLQBxBI6Y1NLRCQzkAr2QdJi09vpdQBZ7k1gjpgzufFfCBxs93aCpPcqNg/y1HSe7eJgoyZNfhbuzR+qGMKI8ovzDD5prB61t8YOdeBlOFDAvkjX28kujru/hek3w+4qm9MXFVIUqPL0pwnu6z+wQ6oexojHXYOKSMBS4Ap3w92nNzxuMPv8L1LybMrkv8qSOIMITA41a6yOGsboWaC1DNCIHCdFrsvTbCP+DpRBlv+VOXGX1CcvFn76d1okf/KynCwfD+BiYSHDxqEHeGtMVkxnRS0Cqp3yfCIzQ8Ft3mO+99mi/8307wk7Nvx1VAIVCRYLoc86633s+v/PMvgve8/MwN3PyMf/PM/bSSgiK3CC/wEpI7Ysv/keXjc2zfGgGeX/6g4m1f/yucO/M+vvLsTcZfus6Hr+1y5md+gFnTcubupa/NxcH/F1LExNHX3vlyyCGHHHLIIX/c+aozUobDIe95z3tYXFxkbW2Nf/yP/zHOOf7W3/pbnD59mi984Qv89E//9NdyrYf8B/Dec3FvxD3vusban9jlPe/9Eu/9rq8wv5AyNAkHYau+G2kgXW5y8J1dEi9o3rSo1Zwffu2/5oV/scDBecd02RAMDfLAM7qrw+4jd8ZFPFz//BFe/vBZrl5ZoTrTJV+A4v4YhMCFIXk3RD4k0aLEO4HtWEzDkyUCFzqcrpNeZ0sSM5fgiww3GuNGQ4r7ZiS7Y7wpaX3jhFkQY2OBcjCbkzTPj1g7u0twY5vw1QnpecWZ756w0hwiGm3EfBfX0rhOxN5jTbx3eO+wSYivBPmBRG44hFQQB+RTyW6rh1OCNEjYjefZNz26NwzBnsOuVdhzCicF+Wcj/OdjVG5Q7y7Ze2Of6YWYYi1gcraFiT0ogQ0FooQb//RuiucSWm+e0vymEa/9S1cYDDr4UBPfnhJtzzCJAldSHG0gwwazZ3t88Jcf5+bkNEZYXDcBLF5KpK1FkWI+4NPZaZ6YnWVwIyC9JSh0hChsHXSam7p9xnm6l2+TrA/wYUC51CBcjWncP8af83ggyBSMI8wNhUpnuMQRdBJ0KSm6EKwbjv7UPp3K4BsJ0kucFoi9qs4FaTbRFQQzT+u5bfRwgjkWYZtxvXlLYrSJkM6TDz3OGXysoJmgRlD1IkwoKDsaazTjJ+YwucZJgZpYUAqz1EWLGOlCWpdGJDcn4AXFUoyZT2rni/dkqzGzow3CiSS6HOEuReSbMYSKa+NFVq9XbP73a6hPxbgqYHK/Qp3LUbZEVwp9coloMWb15JDFrQHxniXeM5Q9RetNGeqeBdxyHzETxBOF6zVwxuCsRY6muLIguZoiSs/uWzrMTjfQeV1Vq63CLrZxrQhZWYRS6MEItTekON6jWuoQ7ma0L08ICo/0mua2Jd42FL+acGO4zPLrN+gNt2h98Ta9z2zSfDUjaweUrQBZgsoN0V5BkBpsoKgWE6b3LpAebSKtJLCG/OsExKau7BaCoNDoXGIigVQhvt1AVY7hWxY5+MYl0tN9ZOlRhcVLgUsihK/fi7TbmNUWRgPGYlsR5VoHLxwlmuf1KvpbHCdnuzS+UtC8OWPu7hE6dqiBRDwr+OS/vI9ff/997O0sUPUbIEBWOfeduchSc4PB/Qm2ochXJeFEUM4FVImg+RMTop/KEVOP97Ku1+61cN0mvplQzGm8VzSvWVzTIypfr9kLrlQn6FyIGS8sknRmuNTSPD1l9k5Jej4kP9KAKACl6owcKRBxiMos4cqE0x+ZcNePDWFmSY8HyFmJnFXMvVzh5hNINL4bU8kAM5bk+wHxnqdsCvYmbW5uLKGdYvyaVSqT4vstXD/BBRJKU2ccKVXfxQmDWjwpDcqYOuTWWJz3VAsRVTPCFnWWiitLnBbomaX18h6dVw4QpcOnddisn6T4qqrrnVFQWJI9i38hIbjtqD4SgNNM/0mC2JL0751RNh3p+R5PDO+ic2xGsdIgf2gNKRXlWofs/DyRTdhasxTzIa6YgXN1SO4jJeljnsmZhPSoxo5nTB7okR4LapHXQed1E1pyTH5DY88do5yrG9eE9chuC/1AiXmwxfK3NvF3d6ERIrVk6akCbsR8/XjMtz/wKg98x4BqR7L55TlUFKKdJNnI2Xqjohgq/rdX3sD/+JVv5kp2EiUEwtTZKV6rul1rGtCoBC/+9N3o3BAfWE7+1AG9DxfYRsBvPnMN0gyRZnz2UxfZvN3Fv5IwebZPVIGrdSq+5/GHf9d5+Xv+xp3cOCk5d2qXY6uvBWBJCNQ4Y3pzj9/4yDNsPbnFL33gya/1ZcIhhxxyyCH/F+A/dcDs/5XCZr9qR8rf+Bt/g09/+tN87/d+Lx/96Ef5oR/6IT760Y+S5zm/9mu/xlvf+tav5ToP+T/BOMeV3WNsfyWgm0+5urWA61siXVFMYlwa4pszqk5AOBizojLkZ3LKRkikNM+cWWGjv8RkqYHoOfpfOCBarHjw/kt8xN0LU3AaVu69STjMqe46zfhaC/05g9qwiDim7EfMTpzGD4fM/fCQ4O4e47sVrmVoNUve+w0fZ73s83O3HmF8VpAdlcwNFZ3NCpk6zn1qA3Mpwr3UYnSsTXRuyMl/fIP9exX8eTjJAasPDnjhyysclIp7vnXG+458meuix/907Q3EUwVaU8aK0VmYu1hnQIjxDFF6xm+LiJ+McJVGWktyYHF5hS9zbBWTrYTEBxWq9CjtmaiQvZciVF/SvzSAEuZOTVg6mbH/gSNUzXq8Jc4hmVqqhuCtR15leXWLL33iPHLLM/eaIc9sP8Raa8SNzWWMEKiWRlSGYHtMeMswO9VkckLV4Y17HYLSU612iYcF0V6Ji8CECmkF7bggNREoQbxb0XtxwuTBJaZn2mgnwTj0uM71ELsZXlNv0hAsnUqZ7LdpK8N+VL+eNoKVh/ZZm+1yxRwhrixZ2xNvWdobQ1RWMnWKIPO4XBFtpoi0hLh22ng83lSMXr9MueqRu1PkqRhuSFwzJhx69KdGeGsYPbZMUEG0awhGFfrqAC8F4zMxYSqR3lNWCmFc3cxiDKKVgHBE+yWTUzGmq/FaMD4d0np+F1Fatr5rhWIxoLEt0BNDUEDn0TEBllxDIyyRLzhsoevaXiGwPYs9ArmOSbMG4YHlyLpguLvI2f4em89H9SY9kAxe7uMLhzIVIqywjRYUGlnkddNJFOJFgYhaiCDAJRolTH0Scb4Wtsp686bSinosKqCab+K9JdyZwP4M14yoEoltSGQ7RiqNn1YES4boYUHn4Snbky7RHuSLCaapkRNHuG+xmUVQZxFFY0mR1O6FMK8dU3m3gW3GBGck4VXqJhnZwAYCbwyYguwNCrexiO2FTBdjei+W6NxhQpDjjGBSQRQjTUU2pzG9GFGldehqokhPN+nODOa8ZdvHtF3O6x+6wb/efJS1d2/xLd9yicxDvOfRE/jix8+BdHRfkzJda6K2R5y7sM1bvv9VhjtNbnzwNLqA4ZmAfF4RDSTFWpNwd4qXAtNUxAOLzApcK0b0e/hAkR0LGJ3r0rrtQYPOoYrrpq6XJ8sk01Wqec/tJ3voYoZ8q+D4G7bYeaaF/mCGchJXGggCXBigZiXVRZjKVeLE4ITk3AczqobCN8K68lp7gq0ZqADyCjVN6f9vOSzO084t8nlNOq8RVwNE7BGBYPbGszQ/cQlRGKpZSrB1UFcPRxGkKVhq4UNpXJYjta7HJb1ndqQFAoqoidIl9Jqk9yxgWiGNnT1UKcgWmoR7ZT0KNJpAUZKuJFQLMSQhWmmCYyVoEJEAwM4ExWcUwTf30P/S0b5cIo1gb2uBQE1RWiD3prgHjoNU2E4AypC/dYXGIxn+gzneWMyeZ/aDkvKJGPAEuyN8uHRn/khiYuDHZzy1s4LXgrwlae1kENdNV6QWcWBQ+ZDuN0huTxawgUVbBzh6n5+w9E2v47N7ko9PNL4UpPNJ3aYlBWXDcvQ1t9nbbeG+0iMXDXqjAh9rbCvAzUpsK0I4wS889XredPw2t9bnCI3j+Ef2kHsQbeXYXpvpjQEiCfFasXV1m/vS1yCswEZQNgWL04A//ydfxzsu3P27zsv3vv4MSIEvStyNgu38JU40HuXq9dt4pRAC3v2tr2f7xoQH7z/+h3GpcMghhxxyyCGH/AHxVQspv/Irv8L73/9+3va2t/G+972Ps2fPcv78ef7hP/yHX8PlHfLVEijFP/yub+HvfPIfELs9drA8cOw6bze7fOhfvw2fSbJEsGovYW+WxMs9RssNVD7D/tI+v1Uc5XR0g91LLVTqKU4mnPuOTcpUEWwJqrancylj8Muepvac/frrfKm8hweXVnnl6YuAJ8hzjn3dPpsfXURUkmBkkQ58JZBWsFt1uLt9i+P/qI9zIfbkKuFYEHYcKnaM72sgdhRlEmFMQCwr0qlHvphQfLTPJFeo84rh/XPsH5Nk1W0+f6nPM+9fI4gqRve0UCUk2x5tNWnX07yVIXYHNG5J9PcXFFtNzIZEOo0qPL4RQaDxCwEuEqSrASJzFG3PsZ/ZgrJg7zsW0HmFKCvaSUn6xSbBfoGeCA6+OSI48Ghl6T67z7e+5ylUy3H7M322nmiyfvkhWrcNF3/0HNW3Foi2owgaOOGIDjwgCHZzZicbjO/qsvxKgd4aE9zcrMdG4hhWF7CVxTVCjvd3SEcSN4iIpyBaLVzgyY6FaNekcT1D1nt1SqcJSkuwV5A9IBgNYhoU7OZtvITxSaj6mpW+5uWNR4g3SjrXR8h7ugTWUy1FZMcV8nKB+uwecq4LxtwZr0gRvZQq78J4hg4i1ATil3dhsVdvAIcTRBziQkF2doXGWKCnFjXz2E5CMKyFiNblFJWVmMWQNGojscibWwgkIrpzl9o5dClRY0idI9yrEDtjfOlwYrmunc0NVUfRnE8J2hXyQDC90mAgmnzwyUdRcxFFx2NDgZ5IypYkOxpRbWmqXovZ9T72oMHB6YqqK9AzWTsxpCfcHWKbmt2v7+NCRfNFyfytBCEVvt2shblbkC4roj1JOFGgBV54ir4mHFX1z2MtFBX++DxKS+zoAC/AJQqz0iaaemZRxWw5Qs0U7XHO7JqgvNTCKIl7OKB4shbHPKCmBWQaYR3lahOVGpSHza+TiEqw+GRAkDqyOUV42xC+XKFUiDQwXa6jZue/uIs+yIkijc0q3KcqsqMBoorwyuOVx7YiZGYRuYF2A1GCyCrUOMfPMmyzQTCoEJVn+NEmZ960zoMLt3lxsoQ5Bl9/9hbneilX8gQhK4KZhziEQDHbCpG2wkcRW9Mj3F6fcvPqMtmqItl1FMcEJhSEI5BKYVfmsKsNooEh8BllP8E3I4TN8c4wW2kQzCTRVkowrkVLPcmZnGuRL3iSnSnxVGGWVjDe0jyW4qscO4mQjQrGdY01XuISjc8KCtHG7yWUrYJgkOEbEUEpAAn7Q1ySkC0GNLYrqAxBYdh75wK+rVHrEC94qpsBDSsxxhGlDu891cOnEY0YM98iPdei9+lbtdigA0R0x5HiPcL7+ngA+PEUPymQScj4tceZ+9wGhIJkNcd1HNWrMV5L8qWAxsBCKEBrsJZ4P0clMV5qihYcvNpA3zSULwVULQnCkJzPcasKnVk6L4wROkD0u7C9S/Pqbl01PSmgFaFuDOhNS3o3DL50iEYDkeckT2ak/8UcXoHwguJIm7Jd/wx6DFiPzBUirhuvgv0pw7vn6bx0gPQCG0vMdZDe8sLPzuNXLa4ZwHaOa0XoacYv/6PfQvQ04uuaTB8WDOc1LhK0LntUrOhPStyWxhYpdjekWgzxFtRUYiNNMR8hlMBKyQ//6tcTxZL5VsoggHDL4BUEE0/ZCIj2U8hKZs0I0jrDReX+jsNHcGFl5fc8L6+dWsantVPn4EbFr936Mu+d+36u7g3oBBqnBLNBTsdKsr0U7z1CCPJZgXee5E447SGHHHLIIYcc8kePr1pI2djY4N577wXg9OnTxHHMD/7gD37NFnbI7w/vPR+89BzDSLPmPCu9fYYvR1x8YpHjqzscbHXZLhOWvz3g9KlNnvvYEXpnHWIpY/hhw+hKl2k5YjGbkvcTZCnhdkT0+IjgMgQz6FwtiPsNXvvwHg9+85SnXn6QxoWjmCevocaGfKnBaCdhdk+Tsh9gehHCgMgF07TBR15+mHh0nMbeDHdiDjuzyO0xuW0gF2O4qaleF5FmTXIbMHAtfKKo1uaxDYlODGq1Yu75jP2TDTaShNtX2uRTRXMjwyy2UDl4EeAU7L2uQffqDHo9pJDYZzroKxrlLemSR2SO1ragOjNHNVc/1ipBOa9obZToQuEqAS2I/7Rn6aWKIw9O+dSPnEE4h4kiKhWSnoPGMyVqIvi1T99F/DrLpY1FfKYwRYN4WmErgftIk6oR4iOJW4pIbZNg5Al2xvSeHFJ1W0RXUhiOcKFC5BVIaneGkwgp2LvRJUktPpshPHjpMa07IYfe4aUDB8L7upZ4kqGsRVgYbvWYzhym7VH9EhspRJYx/HuGVr5OdmYeKghtxfRkl954hHqTR+zbeh2TFN8IEUpA5TG7ESoGIUMaz+/jvGO2pAhGEnO0i3euFiJCSdVSiMIS7DucdWircK2Ioh9iQ4cQmkoVhFWA9AIvBQwmiDDCxyG2GRJNLEw9nZsVeS/AzDcRacnSz13DLjfJHlgjSR3NMwXOCvJhzNzpGVkeYtaadKop8nWGgZmnLBTZMCRZL0iueoIrY9II8rvbiH1B2HP4wBLupGRNQZzXORzhdomZi5BWIrpN1KTEGQeNGJcIXCDAQxEa4qkkm48QCkxLI53AaEWQW0RpcYFkdrqPbSXY+YRkr0KVFdW8ouoFiEqjZhk+DJAftqjIE7YFo3va+EASHFQEY0u+2sbON2tBRkOlC+LNBtIJ9u9N6Gx4ZGppffwWcq9CLM7jEg1SoKdQ3L2KvDZA3CqRWtKcZUQ7Jbtvjpl/JsMtxFCW2KZCZ/U0aDwocKMcOcqhLAl2HCq3zBYDaCQ8VmxxX7LDrck8Xgs++msnGc0L9k52SVcD5HaOTxRBVj9f0a0JOBjbeX7pnz2KQjD5MyFDqVCNEj+oQ6v1xEAYEI3rMTZblgQHOdWx+vv4IKZ/sUQUhtlRjbunpPPrOeH1HJNYXO5pXp7hBRTnlpFI9n8rocQw1hHhN7VoPrdP99UU5jqINAfnkMMZZqmLbYcYb6ATEY4rglGOmOVE6xXu2BK+GeDjClEIivkGoXFMTyqmSxDvWqLtktnJhGgvRTiD8IKqEUEQIIIGxUqbaHNc1yXneZ2TggBrETrAVxUCT/cTFzGJRpeOaqWNFKCve4JHU7J2D5tI4sLBeApVheh18GVFaAThzQl2EXykGLdD8s8phLP4yDO6v4u6bLEziZYlZU/hui2S21OmJxrYrqbz5C7ylXWi1CCQhLcc1VyL0BS1aONBpo7AWlZmO+zutUm6mnBaTyw5D+GBQfe6YIBZRrA5Ijs/hzD1sUothIhGiDeCcFSCTLHdABfVFd56f1SPaA4NgSzYf0yw/MOvomeWzW89joxiNtbnMVVI98KEuJqw89QKUUMhNlKEtXhvcaJ24hixQKhGmEKy8/pVomTGvIlp38qxkcIZh4gDyEq+srWJzjxFF8LZvxW5fu/zshCC3lKPcVqyu1/xL547wnsfAIHAO4sNNPui4sr+PpWqPdBlXvI//IUfx5SGv/KTf5HOXOtreelwyCGHHHLIIYf8R/JVCynOOYLg34WpKaVoNpv/gUcc8odJ5RxPbr+Abo/xzrO1O096vUJvGx7505dQA8v6//oneOjsDeJmwbf+yScZbbe5sdzkC4vH2S9Wef0rY3Yv90FbDh5WXLinoL0tCKYWIRRF7Ji/Z8Jb//IWP/svH8Y0FE/fXGd0PqL3TEXn0piD3iLqFORHYpACNfaoVoWaSvz1mIlapNEd1PXEicaemMc1App+THDLoMYByStD3JdAPmWpQk3RCsm7Cv9gie0bsi/EuFKx1+rwuROnCY45Gi9I5r84wsSCyV0d+i/uUSYR2ZEOelYxu7tN+6UMYQQST+eFCU4ZfJiQLzbxDoLUIgJBMSeZLmvcm5eYnLG0Tsz4jtYNfnr3ca7+uidvCZSyTE41EEON2oZsOcBpePLUKUZbGvV4hEssax+YkgQpnWMprz64in8hQqUVZTti8Pgcc1cdVBlEIWGp8EJAr0vWhdlDS8w9MUA6kJHGBpqh6CHVjCi3HHt8m8WzAz7/8msQO02cF4zfKOl/2aEc4AOqwNE/2mfYAFOBqiQEluhAElYwWXSIiroNJxAIp7EmIVhLSRdD4rkKcywkSuI6/6Cy9ciBUAhZb5rMUozekhjt2X/nMY7/7BgLEAa4pH5eogNDflwibES4D2riEN7XY0dhgJMSHypc6piuKEK9RPczef1vbEEuBbLZRztQU0fvoCS/cAQxmhG/uEF6MsQ2NMHAMhuHSGFIV+F0lFGWGu7Lmb+wzQOddX75yoPc3Fqk+UsVjdtgwgrrMrRr0n16k/R0D6FCVOnBOEyiKRYaqFv7rH5ghllrU64uoJyCKEJUBisDyibEOznhTk5YKJDQvDajXE4QpcfEEk3dHGXDOqCyczVDbR3Acxn52UVUGGKVw1O3BU3PhET7Cj8BHzqqPphG/ZkPRwXhyGBaChfVQQ22GSKcYuF5g4sE4e0xSMXkrg62FWIKR6AlVTsgGoM0ApIAL8AmHhlrpkciZqcibPOOOeIgQx1M65ELqaCo6r8QDooShKgreycZARXTRzpc/fwKa60Rrzy3RDB13P6VLr8ZnebNf/8Sf+/P/3P++vC7KGcaaQXBTBNKiXQONS3BOIRShEVF1he4Ri06VVOBCGCyHKJKCCbQ+OJB7QoIJ3U4rHHIuQ5Zt2C2tEByuUBuZ9hI0nrmgFY7QaoQEwtMSxNOLPMPHRDcm7Gx2SdbColvh4gdDcNpHVwdBth5gZIVMpVUsSLeyeqGoEYCXVN/3kYZttvAasUshvh6xdyXZ+QPzeNLS/KCRcxK+i/MkMaRL4X4WOGUQBaW9o6lWG4jhiVC1W4oLxVUBiEk5EUtljabiHSGnuSIICAcGWyicCUU10OkEOgKLBZflbW7Jp0hhKjDt6UkW42ZrmrC22Nkw+JGFnEwo3nF4UxIumdJOjC9qwdaoTcdswt95LwEN8bmi6jdHLGxhzCGcHdcB8O2AVOHAjeKnE2xQPdmRtltIMo6AFtKQIEvayFGpBnVa5ukr3MI06R9w9I6XWI2S8pNBYMx3lTIQGMmEtHpYyOFzCuE9ISFpv8hT7ydA7D2M6+w+Z57mG72ufcbL6LbhuqlRh3YfTNncrpBOA2pugFSgA1BvjDD3jLkjQhzr6ZzM8Nf30MszXHP6WVeuT1CWfCB4satHQQQ7ntsIDh6dI7zx/79QbGPfcdr+djPfpF8ocnefn0NlR9v09wuQcKTwy0Gjzd4w30nEULgnMcai7O16HzIIYcccsghh/zR5KsWUrz3fN/3fR/RnUrZPM9573vf+7vElA996EN/sCs85KsiVIoHeud5+uAWzbQkPRnBfMjpN+3widnd6NASv7LH8GZM1A3YWWvQO1ry0hOnuWEWEULxtj9xjRvby+RLCe4hz/agzdanFulsFUxWA4Lru9w+SPjxv/0gg6ttYjti500V0aBCZiVOheAs+NoRgvboyiOAeKPOKog0lKsx0hd897uf5JkvnOLG823cco4owAuBMA49lfiLAcrOmB6JcBoa/RlSOYIkRRUt7ERSrIcU/ZjiAUvvyX0QdVVwuJmTyJLdty4TZgnx45vYD7dRUNdregfaIbOKaMfWmxoh0LYOO+xeytHTAvWA58jCgK9cOUaPggv33uTqmUWenRzDOWgdGOIh+EyBjGh0PO5lD1cDyp7EdCLsoOSet2wyPZEw/HCGmhnS+5Zp7wqoNPmxBirsEg0cKhdIrXjj2XWiRwY88aVVQOGdA+vJ5wLal+tQyrseu0V7NefqzjY3Ns7ggpDqvozwVXCFotcbsN2cR6+0sHiCGQgDMpOYYxnshBC02PqWE+hMEe+XoCwSx9+8/yn+0a2H2M0SmrfTenMUKXD1TkjGEUz2mdxzBHllC5fNqFZiio4mnwsI9kdUczFatxAVIAWt2wonPbapUcMUvz8kOpCUZ5dx7ZhwZimbEtMQmNMt4uEa4UHF/gMJtqloXS+YLTdBSeS+R+Ue10wwZxcwK03UzJLNSc4+tk3cNGyM2jzYvsbTg/PM1gxJVHA82GetOGBn3KOY6+FkQfLcOsEohzlwC21a61OqtS6MJvjJlN7TFWa+jVcSFwrErCS+voeXAc6BkAq1M6W9XlIc7aLHBbbbhKgurNGZQziLcB7SnGqxi2mHCCEIC4ualfisQqQztr6tRf/pkuTLEyQec0bgdwJsKLFdULcdLIMPBdldbWw/ontxTDAxeGOQSGw7vJNdY9Ezi0pK0ryFO7KMSCxMK6peiNcSj4cCFCHVIyHBBlRHYybnE4JpiTcO6SzCOnwSIY2vPzvOU86FNDJTt8ioOofHLCYkN2Y8G65xeydkv2xgp5qsA+35Mc25gkBCbyWDj1aIWGMWOmSrTeJLE+TtLfRre7AvaD2j8Q8F5HEtEhU9SdoPmX9uTLoUEmYC32vVrUHTDFnaOvC2E5OYBPnUGJXXbTJiroNQJdYbXDehmIuwyiMzAynoqaf1iiE4UhIWvhYKnQPhsY2I9DsVRIbmxyJ8LlHW1MJSpJFaIZ3AH0yxzqArj7yrgV0IsQ/nrIV7DD7XJl7P8dJjWzFic0B029TimQHvPKahCTYKUBJXFPWBXSuErMdfnLO1u0RrhJLgVF2VXJSUcy2KhqbxbIpSHpdE6DxHtFt1pfJshncOARgMBxeatG57lu7Jmf+zY0a/EpN+1BPvG+x8hBIVMo5RpcBbcKFncXsL/dqIZtez94mYaikg2jlAeIVQiuZDHnVXycEnPHQDiAWLH5tQhZK91yV4XQu2qoSqrTFqhj7wEGpm3xwjThkOWk2qFzzGDwh3dK3XSUE+n2DOLKO2M4IZ7H/LPPO/sE4cWG76Y9CVzM6skKzvI6YF8x+5zMF77meNKffM3USe0fz65QYbo/4doUlgcyg69WeUrKqtMoDaE/Su7nP/9x6w+9KQu970Zl754iVc3ADrOD4SHNy5cmreMuTTATYz0P69z83v+5/ew785ssVmz2KcAcD368+fQPDc5nMcOd/hOx+8D4C4EfFXf/IvYo2ju/Dv+U8POeSQQw455JD/5HzVQsr3fu/3/o7f/7k/9+f+wBdzyH88pbPcMlfgJcWL148xtzTCWsm9fo/boz5xboiY8Zn/4SjqXJP4fSmJtuS3NJxynPN7LN81RK8FyCqAfc96o8eZ1QFFuoAe1nd7EYrJRoxxAnfektydEgwrBAK7pHjdN97iU6+cJppJXCIIpyDnDKovcGVCc65g8CcdP3rhw5xqlHzbN17ie//iD5IUEoTHuwqnI2bzkGCgExMPC4LSsPMhg1zUVM4SHqnwByCuQnFMYxsS228h8YjSM3hzG6MCzNEK2y44nU8ZrzcQZVVvgJKI2WnofG5KNM2Zrh2vL6iD+kZ76+aMIKvgSU98l+HcmXXuMzdZe3SHC7bBCx9dQwwkvZs5IpPMliNkXzLY1RSBpWkt7WsTuj4lzwNeuHKccauJi+pKWg90X87IlMMrh9zfpTizhAkdrckIXVr+0qNP8MQ/+25EZUFDddIhI0fVj1CTGZ//yDn83YKb0yMEd/q3it2A8nY9DjBKFwnTCTdXFDbs0sejpMBqR9p26CuCuaziSHLA1eUl0m4L101QTnD5K/Mkn7e41yZkSUg7vNMk4h3e1UJUerrB6Lhj5TeGuPkuQgX4hkToEEGBJqgdJx489k6uhyGcOOR4hp/l5PeuQRwgncdEkNzO8D6kaipEaSl7IbalMV2NUBXxoCSbj8CBqhyy8FTNiMYtQdkDPRPc111nPkn5xfFDTFSM0I7GrOJoMGA773L1oI8sHcKBz3Mm9zRJrglku4Gba6J3UkRWom7uIgqDkBIdlbjVeYZ3h7QuT4l3KxgMQQj88jyyNPjKIHWIbyukuRPm2w1oXD2oN/lNifYCN54h5hKEAB8G+HYbnKNca+BVAjpH+AJROcJ1izWKWS8ib0dEWU60U1HNKaoGeGlAaVRmUNsjkApzqodpQLqsibua1VMD3AsJs1zXQaqNkGBcka3FdWXx5QPUOCMvEqqTAdmKx7cMVQJIj5zVrhPhwTcjQMBgjCoCiO7UUqcz6LYJDgzBJEffZ1DLFUdb++y+0KLf3Geut8+R5pjIFYQfkKiGRk0sRZUSZEBZEZ4X3Pu+65xa2OejH34N02mT2Dp8x8BLjdqxleZEu76u8203sFKg9wzMt6iWmkQTB1ISuBDXbWHCAuUlciZQk4J0LqRY7BBMK2RhGX+mSfX5CDVVxPdN0DsWnytsq4H0Hr+/hcqO4EuFCQXEISb1SGPxRQVegqtdNMpI8J5gv6IxynnN2VvMPt8kTUOkdXXbVRjguk2stNgkILgj3ppE4uZiglGGlwo3n6CMRIYROIcdjWFU4YscpK5DghH183Z5ir62h2o3ES1QcYgcpggV1MLMv5098R5lobHtkaVHBq52iSR13bdrJngpyE5ENG7n6FHBbEXhZynimQw732S6EyB3htCICb61QfzZfdJhg+Xvz1ArgqopSH+qQv2dFiSCsiNqN4ytG26w1PXWc5pgK4UgoPmxKeW7O8hdRXxzgmhYbFOiU2q3k9KIXKAHOciAcCCp5gISaWrXmhYQaGxZooMQWVko4dc/9QjhGyxnkx1CU1eSR4GhsV2C9ZQ9QeQgur8ge7GJiTXRruHu79zn1J8ecma6zz/5uZdQj8yz+MQeCEE49Zy4u8e1mwNMAntVwc31bS4+9YtED32ec3P/OXPJa3773GyEx5xN8MUUBrW79+G7TnD52UF9bFwqafcKmpH67ce0eodu30MOOeSQQ/4j8Xe+/lPzR2ENX2O+aiHl/e9//9dyHYf8/0ggJDYbMfQNTE9gMXgheOrWCabPLJJWcPY7r7F9s8PO6wNiJMp6qvs9pw92eM8bP89eFrC1ljCuIlLpKFNP7wVLMBhQ9DReCZYf3Gf+IcGlj54mzEKS5hjTkQgJf+rNz/Py1hqrH1rH9JtMLyyhJ5ZCKdQoRAhJMUvIyGlEBZWXXHu1ixOO/CGP7HjUhxKEFmTvSJibTameUHVgbWlwucTeEsh5weIv72ObgmjLU9wyBIVGBiFe1nku6V1N1E5Aa5ZjtyTDLy7WYxBNVVcEC0Vw1daNI8YSDXJcHJMtgVuc4RYEeirIr0XsP295/qku1abiQmy4Ik/hUoX3DheALOqbw2KvpP3BEnFS0tw0BGXAAw/d4Oqto+imI35Os3e+j1MQDT1COdoXb+Jx3Pr+YxTdkM4VwZFzM/Z2m/zPf/1eXFXiO02q0NfOHCOIvzSgeVRw8CdbbJserSueNPIIC81mjprVFaRGakSaEz/SovNbA4oggU5EpAzTm01c6Akjw3TWxPYtzXZGcLneKH/sEycRO45WCLOFLtlZiZiUxDf2EUkDjKE4G5GfiKhOL8DyAqKyBEOHntWZJ9IJnBTgPF460lUIx4KqJejsBkga6EGKmavvutoYshMRIFHTknA3Izs9h0vAdDzifoOvJO0XHdIAwiNnFXFlyfuC4mwIgeU1/XWEgAvddZ6fHGHeFixUU5ZaU1wGj81f5TcX7iEsprjNjOGb18ge6LP2oQPyBTDNEPnqTv1zxvU+Wc4MyuS0b0ryfkxgQpQDMZzA7R18kiDiCCEUxBo5muETjdeSOrwEkAG+E9XhoVLg1Z3sizDAd9oIFdF+boycSUwrQo9zKAOsskyOS5QRlG2JLiTWQpB5gsITpBlukCKHKX6hR7iTEQ8mRC3J+P55bj0TYTNJ1bIkexXCQ9XRqByEFbhexN45TbEW03lhD9FtIgjxUnLlz3U583Oqfg2VQA9nuCBENGKksVh8PWKhJF5KfENBVpKnjk3XhpZg/v4p/VW49r/0+eHvfgwZBvg5S9kMiPZyROWggvzsAspOeOD0LWwkec1bXuXyF9cImgaHQBhBPDCkZ7qYbki4nSPHHqcE3lrEYEyoPabfIBxmlIsKO68oOy0Yl3TGJYGxxJe2KVdbeCQuDlDO4goJIZTdGCkK/HxC+3RO9hLkkUJ81qE7EbO1OgfHi5DW9QyMrTMyALcyB4FGHExwmeah9XVe9/AGH9l5iGDmauEnlIiDOvtndlcf7yHcrd1ECMXoqCK5EaCaAXJSkS8GJNMKISV2uUeAwAlRt/BoXX+VBTiJFAKX54hJitjewzZjhPagJT4voBHiELgwItku8BL2P5dQXAvwL0loeaR1iFKiR5q8JxifS7ANyE43QShmcgUWPc2qQCpFN8p440/u8+MfeQT90lWOtHKyX0yxTkIocQL0rD42IeoqxKoHWGi+ktfCWFGiewmdgynxSwnRgaVSEYPvO8KRH1kH5wm2J/h+n+nZBsnFAd0vRciBYLLSR1WgX1ynsT3DxhFukhKq2j0VVPArzz/IqZdThnkP0XBkCyBcSN5RCC2opOCmWOD4kqS8vEt025M+FDH1MS6vR/DMQgMXRwghuPr0TXbmu3jRIFvQGCn4W9/8P2KriqN/PiU5/lMsbb/C+/7qO1hY7tAKIr6uOMPPr18knEguXtlkeOOAaFC7U8KXG7z5ngdYiDp/+BcOhxxyyCGHHHLIfzTyP/UCDvmDwXrPXtUlPpZTzQnWd+e4tdfnqV86gY7AWcHOb83hX4ho/GrAeNRkOG1gdjziFwue/vGjfPbFs0wnEXLi0IWn3ZpRTDUyM2Rn5pi8Zo0TPzgjPmLJsgBuS/JpgP2kxgvN3vWQvp8ivOdC4wZ/duVTzG+OEI36rpzMPUWhWIl3GRvNU1/p83P/5DU0r03Qn7HYmxKR1ZvOeFjSekdB+78qmB3T+E6T9DXHMWdXEDrg9nuXcJ0QlTlkKTDdGLTENgN8WaBCg3ACOw7oP2mojnuktgQHGWpSgLOIMMYHinI+YeFCyg/9pV/mtfddJdkqCac5+VHP9H7JRreNOqoY2S5Pb9zNizvHAI/uFxyciklXNT4U+JZFKk+cW9TMIqTg1L0TrGiwcWuVu+Z2aBQl0QSUrZtXXBLhIoWaWaJRgU4t7/ivnmPxxB6jsUJYg7h4g+59uxinMFKgRimn7t5kKJsULsA0Ba2bJclWTpkldd4JQKgRkefI6gFxVOJiTZVrpuMG3khsQzBUhuw3N9Cf3iX5jCC6PSK8dUA6NEyWYvSBYO7JCeFBhlISEUaIosDnOfELE9qvOrSTiKLCalsHTSqB1Bo1q6Csd1ByaGitV6hhTtaVVMsdXBwhS1dvpjyEB+CkwiSa7EiT7GgDnwR0XjU0blTMCJn6pK4TFeCEB60AQTkXojMBXlA4jfWSmYvo+oJma0q1KWnJnNG/9Fz7BxH6+ZzsNQ1mbzuKEg2idUtxtEe5EGPmInw7RhiDaMb1eESnhem1IAqJM0G52MQfX65HWpzHNhTFchO9O0HvTpHDFCMNXimqM13EYozSEWQFVBUYh8osAoFpBviFDsmepfOVffTtAzwe1wjxOEQQEIwd4e6M1qsp7Wsz9KTChY5yWZGe0LgA6LQom5KqJVBpSbhfQOGYtWKKxZDowIJ3dU5GJ0J40CUQBhBEYCHcFfQ+OwJfb3pdI8A1Ikhiqm6M1xraDfxcG99uQJ5DWdWv4e4+VSJodAfo3ZRJHjHME4bTBBsEmFaAHqSIskIKiYsUtp2gUMh0hhE5es1wrDmipQrmeinhgWV2o8HsdgPvPMK6377DIYyv3WWugvEUvz9AbwyJNsaoSU5zY8zd77iKOl5SrEVMLvTwRYEsSlpP34TA4VsO4QReSpyU7MfzlCTMv3nKsT+9zeK3jUlfv0Z2d4+yK1A5SAvKQNkN6hadon5NZWHqPJPS0JxVbF7qcOnlE1gVUMQKUVSoNOf49xxw+i3X0AcztJG/7fQJhxX9G3WOCEGACCPMYlg7AStDFXmE0lBUiCRBSInqtJDzcxAEYB2iMPXzIyUyv5NlY+u2HBnFyChCNWLiWyl6I6X5eEn7HRPCRyxuoYdrJaAUFx7eZOW+AoRAZZZwprCJxoVAw2EbYZ3hUsDGXp/2lQnDL7a59v9sYRbPwr1n66rvUVE70erJrzobBYhlCqWpA2OTgPg/93QfntJazRBeIw8k0bVmHaiiFL402Iak6sdUbVGPpFlHtJkTbaW01sfYd3b4+g9vcPZvQm++Irw5AOEJrmfMvjhCDGfIyiMcTI/UjVHCO6yizolqKPIjLUw7Yvv5BtvPtXjl1+ZRM9C5qJ2BzuFKw+Kvj0E7dA5iZLBFWY+7vQJXP9zjiU+8xL/5wBO/fX7+ttN3Mfdl6F4yfPpLVzjhNKJyCON4xWzy0Y0X2Csmf0hXC4cccsghhxxyyB8EX7Uj5ZA/+rTiBdafz4luOWwzQDhBe3zA4kxxdd3iRwUiL4g3ArKNHjbSiGGGIOWZm+eYPrPAwtN7yMKRnpc0RhnkDo8jHHtMt8H600d58Bsuc9+3XeUJeRKfaxpTD4Fmfz/mrnsLsrctwMsHXPmNNosPb7Duz9OcCVQFLoFp3uGp0XF6jYrMa7zyiDWPepPBPa+QWxC+5NDf7dCrjvZKgb0Y1MJH0+KspNvICPfA9ZtUS/UFt0SDdchhSeVaKG1gLJktxaj7Zyz9mR02/9IiaFlfFHcCxFwP2dQsNgaMbyTc313n06eOc3/nNrN7Q27RpRKO4r42WbbCW177RYLWJT701OvZvrVE51YFHjYftAgXE8xlBNuO+BXH2SM7FOMO6UFE1oz4hLuLbmHxicQoR7Bf1Bb0Xp8jnyiw1QAvQ0QnZzKIsJFidrJNN/ds63lkKdDS847/8hqB0kQzg8TjhprGxoRgkCHu5FWIO5spHUvEz01IZlNGc/36zn7LorsGP1B0b0uSuZjUGPJuQGO/oEigWGoQp54wEwjjwfp6YxRpvAmgLFGuSf8KsDiHacL0ZEiQKhAFwoNwjqoBLpbIKEIbSdUKaW1BOCgQSYNCFkhHfbe6gPa1nHw+JFrPUeOS4GCAEJp407P/QAt30jF6xNJ5wmNjgQsUBBbhQVaC9uUZL/3mMfr3TxDW8QPHPs2vXrmXW/ERXh6ukuuKkU0o4xCTRohYE2zV7x3TEeQ9Qf6oxwU95n7BEO7X9bO+GUM7wjdh8FBEY0MQZabe0HmHV5LJPU1S5+g9nyHjEC8VXsE73/EEv/n+e8lKVb8/qUcQwrQWgvK2wjXAxTGtSQ5Kopy4k4ORInohpx7cIV+PyG8EiKpCzypUpQhKhxMJxekO4tYEqoqyExMcmatzKNoRogI5s3UbUlA7vJL1lHytCUhcqKiaBlF6ov2S7P46zdZ7AVOP2p8ipEJW4LqNOuvFUwdetJowmiKiEDdJibZTGo9oOq+fsLiwz63dPjs7c+wlDcI1j8kidG7Q4wxdtGqXBhLSnNZzExr3ZvS0J7OG9sww9+yEqC3J7tX42w3KnqL9Qkrj5gTlJBaPNhU+iUEJRCvBlw4fBVTScvvSIicae7y6MUfj+T18WSIQBAQU3zAhcwHJrwrULQ+RBGOYzsH4047pK8dRUUHzpQHCK1TYIpg4dG7JFjVhZZHO1ccSQE4yfFqP6UjnsXHMR/eO0o5LTFuiS486aklOe5bvH/PSLCBPBaoX4auKclGjc8iDinh/Ckqg0y4+DkFJkqmHRgKTCT6s86h8UdYiinN1PhWAkHecTiFuPKlzVGKJaQuCTNYOlSTA9iP6Z24QnDCIM4Kt7CjRbkHrRsbjr73C3JsL/v7PHUPeykleTTF9j1Up4TnD1HWQleKiXuWF3zrC4vpt9MEMMS2QAqqFHiARwiNchQtrY5Z30Lmc87Zv/ApfUl1wFqMC/GaEbpTYq7UAm0xyku0CKyXSWITwJDdGWJOQXBzgTx/FeYsoHDljAmP5xu+/yHetXuTo9wmG36H4az8UcuVGSOvz26hJgXDgzq4S5NC+7ZFHS049eJUvP30BK2HsLLYd4IqS4V6fF/9fLWwYIN96572ugMoiAo3zjrnf3Gb4+GotHgcKbx3LFzLGT0lcLlhZ7QIwHWd86ROXWNwsWb8/4cee+BLf9dDd5E9ewzYjyBSPL52nHx6O8xxyyCGHHHLI/z9xKKT8McHhOciGzB85wD7doGc1C/fsc/6vXyf75VPYcIp4Q0Ly5RlFrmhdrC+skyenFKKk7Ifc1d1k676YcktRCQe7AjHRsAqDC4KlZxy7z87Tvv9l3vGfPc0nXz5BJzaw7BA3LZOswaW9gOFqwsb6Et2FEf5tCrEOxB6RQnnUk+dtPrp5P48sX+UmC7gVhf1+hxCe8BhEO2CbkttpbXVenh+w5Vt1VWgsKXoNdDzFqwBZWZpXJwjpKVfmEN5TLoeEN2LcJKeRVeTtmP/61Jf51LVFCCWi8shAY2ONTersgGufXGKbLpNVTf9ITn6lReJzTK/BwpcLmt+TcvdbX2XLNNn50VXEpiE8Vd25we+QgYBc4ZcsfiqpegE3j7b4M697ipdvzNNe03wlP1a3cHhLOMhRhUFaX499eIGaOdxSwOevn2ciQoqGZjVVpGGAXC9hKaLfnHDh8T2E9Sz/d+cJ4wirNWIyw+0P0KMeXjcQFVjlsXcfwadXoKxDT2dtj+gZ2oVjkkdUEdBuEEjF6EREvtCgagmsBVta5Cs5NENkWRJvpThbjzJ4qVC7hvJMLZCFNw+IRQvXjPCjGX6aI6IIL+s2IJdbyAuUh2quroNxQHDzgGq+X4s/1mFaYe0smmsgtjNkBjQCtHMkmWc0Z3BCUC1rjuVbpEWDrdV+nWUhQfUkb37sMlpZFoIxt/0cSRNWz+0hYs/9P7DP068/j9MBkTWI3TuVys8NQSmKrsRQ1vkpwlC2FcJ5pFB4CemqIJ+DxvUZcmoRnTau7RArC4SFYHBvTDiVtF6ZEowrZFhxsJ6QdCuyiUYkcV3tHGmofD3xo8AoqDoB1dE5ot06o8UrSbXcJbqnIjyfI/sVs6dXcGHJ5FRQt/UMQ+J+SbzvEUJiY024MSXvJsyOBpiuINh2CARCKURp8B5E5Si6kiD1OC0xvZiy4ynbIc2NALXtMQ1Ba7duVlKlxXQTZCapOgFqWmL7baxpEl+XOO/wyz3KtTYbmz2c3SbJCiQeu2rZeaVJr5Mwub9H89KI+HaBm2lEI0EYg13uIbKMQMAxYZkLSp6/vISXEPUM8VzOvmjgvEEUDinAO0O52kSOIBAtiObrLJFGgG9HTI56pkFI6/kWnVenhPuGsqnRXuHWOshnW9jTOWrokJXDO0/vVUNwc0Y18WAVXjbx3YrirCHUOckXBXrmCGaCsiUJBgq6TUhzRDpDSIFrJjht2Ht7iOl4zDDA9iyz85Js1OHgyx1WijmKqQLlEblBbw2QgaM8uYA1EVHmsYsdgu0CO9dCGIv0HoRDxHEd/ozEa42IQkSnjR+M7jQoccepVbfr2bJEzhx+4wDb7aAzge3F4D0m0JRV3TBlG57kgZITi7t87gsnucff4vHHn+T5v7+A3SuhvQCzmOw5UNQWExfVWUCFqCDLkEpBu4mIFH7mIM04+K4IfVBrbyaGciHkK//qDL7awnuPObVA9fPzIDMG9zRZ3hrhdRuqCrvcQw0zXCARRUkyDFFC4Tb2YTIDKbHv6MEmPPuFhEffcYu+SBklOX/ie1/m/T96FLvUQpUW3fz/sPfnwZrl910m+PyWs7773W/uW1VmVVappCqpSrtkSZYtbxhjG8wABmIaGjM0zQQ9xAAdEz0xTTNDNDCsY0PTo2a84EVeZMu2FksqqUpLqfaqrNzXm3df3vWsv2X+OIm7HT3MNDQY0NwnIv/KiLw333vf95zf93w/zydCipz+csb+3jy97oxZ2WlqtT247REtNJtvT+iYKcFXd5DdtPm88+CwaNfEyLyWSGNov7DN9Mk5wl6Hqqw4vlqwoWuMiphfaGqL1+/tc/WNe6zOd1mTBufh+sEQ0WqTzAz584If+yvvRUvFIYcccsghhxzyHw+Hg5RvEzSC4ws3YXOP/e2U9M0Y+/qQS//9Mke+q2T0kMJvt0mOKfr1jC0lURmorKZoS9T2AY/+4CaXq3dBV5CsZxw8qRlct8gYhBdk84rzj99m6cwutVIMpynO1sydmaJfFozWAuZHjRxzfeko0cdr6Fhs4Klls00RrkvcowUy9rw2OwH3PTIoMRlNy89rBfgYvWYYPy+o3xZj35C0V8eceXqdtVeX2bvVB+XJz3SJRMn7/8CbHDm6zy996sNMfcTuO+ZAQHfN05kaqmXHF3/5FFeqI5iHAzp3SihrlJFMziakGyUgKIqQ4akAFRlU7EhXc5bXc/S7S8ppyOKju3zyNz7EqTc3kYUlXHAEuUDgeSjZZ9pS3NvsEV9RCGPZ2B0wnC9ZPrnJ+ZUZNz59DOE8kTL40GLbGr88oFj2GK8gXGLO51z9ldMcec86dz8TkG/u4dOY+EaGnRYUW1N+sb4ILqBcEyR+HzNowcEE62t87huxowRVWQoTsa4fIugISpFQtzTnVjZ4/5Nv8PlffSe7kxi/OYVE4/UKpiXwgAs8vmgal3wao3Zm0G0hygo3HCOjEDmpmRyFwYsl0UaG3s+x0qNGNbRSfKAJtwomj2hU5REHBS4NCLdKqn5EHUI8aZ7CCuOwXYVREhMJTOCplSXcHeGOLOJbMcmBI/isxMWQ6JKoX1IeNK4O5cAZT5TUFIki9I7daZsqCPntV57gnUtXWQ2GBMIzaOXsVy2CtKYeKKoFgw8l4ahm8PwW5psF9uEjKJtiQ8tsXtC7PIEx+JN9Fn9lneCgRuh+E7FIQ4SX+MohrMLGMU5McYmimA/44rfeQTixkNSostF+Sgv2gVwy2B4TZhnlSgdUSNkNCPYzlAuhhulmin11DvVihZrmCCzdN6dMHu4SjAUqcsSvryNlgO5pzGqX7pu7dK54Rk8soFqKoh9x8HjE/MsBshJEWzkeS92W6GGFqDXRliE0Cissp35xRr0UIvNGrupCD0mEERYfaXACYT1WA3i892SnexTHUsIDw93n5jAdS9ENme9PcX3NwaCPLzSzcwnx1qzZmqqbBio6EfWZJXYOQjYrxUQKxqVk+719bLdGDGtcyzNYr5CJIu/FoAXlQoTsKKLX95vtFu8hUk31uhXooQEpKRYChF4iPLiNqCumcxKxp0iuh833IKrm97B2lCsJwaiGokJ4mHysT34hwllLclVBLfHWk26WoBSuJVHjGd57PAKkoFwW5Oc0NjG0byvqrmT0SEj3qwa3ITnY6mEXA3QhwdeIwqC8Qg8NxZEBRaoQQYDamxHd3gFEE7uxD4ZiQYCPIxwgqqpxoMgHQlndbD6JOG4iQlLga4PKahxTON4nGFXI/YzZz3YInigof8Nz5NF13vfxm7RlySu/c5TP/tJDfOi/vUl/KWD/dojcnmDdMi6SyLKJyEgjAE+9EKOGGdkjS/SnCdQWdWcTZQzBjbO4+UY0KwSI2jML2iRR2PiCjgaI2jZCXA8uUE30MQ5x+QQXaURZUc+18InGLnYxA00ymmCV5GPf8RKfHr6f7S3DX/trR3hn+xpH/8wuuQio5mPUZoZTEtMLEWWIfR4OPuxonypZPHrA1UunAZATQ7Q2IT63gO86zn3HAetrinS9JjrIIFRgG9GLqCw+jQhLRzi1CK2R3rEU9QjrmOk0Z397DMD61XXK+7tcfO/DzJ1JuT0a8zd//BP8+Cf/b6jhlEik/NWXf5W/+a4fJFSHt2SHHHLIIYf8r+RQNvv7xuFV+9uEyjlGmSBajaiO9nATDzcqXC9mMe0QjvZoj0pkr0YnJTKscVXE7PEek8c14YZhfulN5EugCoeyAo6BvOLx1mNbnrojWHnXJkMS7n5tmc6BxZ4QiHc65r68RbLsWD2XI14UGCUpnKYqFNZIqiVPMvPIBUNxo8eG8fIrMfkAAQAASURBVMg1yeJ4hqgN7tcV4RWL3Df42OC3c7q/MCE602Yo2qwcscwdGzOaJtybzRNUzQDDK8k3b56nRQ5LFjPhQRWrJ51Ils7tU5+d8c3bR2mtK9SSw96XyCBBVZ7OrZw6VtRzIXJkqLohMpUs/SdrHO1NufLacdSgJrOaQFmEUZRtgZJgpWsaKKQjbFXEUhJei6jbDpOAqgSTyyk3v7RCslARJorj81v8tZ/4DHcP5viv/h/fQ6uURKOK3qDmT/6Zr/O5505y79Wj3HrlJKreodZgF9vUbYFZBNON+Na9ORLtSf70GJ7NUN8aU59dQU5LkosB1RWI12uEg7BuI/rw6Huv89JnHkYYTVUFeAR1pUEHeJ0QSNV87gbgvKe/5vD6gYvAOeS8xm+bpr3F2sZdkEYkB5LqSIqRXUwqCO8OmzX4MAApcKnAC0fV05iFOfTdIelBxezhXtNetDRAiKbKQyiBiyVFD2Rp8KY5zGZ9y/TRiP6rQ3ThMGfaJLrkfmeB2ThC5jm0JM7DxXde5a36CIms+MI/u8iN9mmEg7LdYt+0OB6PeEdvm+vry8RxiQlClG2x/w7L4NUZB+9aIH1rl3gIxBJ9d5fuGxNkmCAChawqpIpQxuK7cTME2BvjbcXww2cQ1oP0WAlVX1POSawWREOHi0OsL5GVRSJwyoOS6LxATWqKVYtLQ1wIJhW4SNC+kZF3I6bfDEg2BMGsxC5E6AyCUmDawDhk9vYjtK9N0JVFbOfN9+Ed6a0Z2SNd8jkDwuKQECmcrVGmhY0lSDBthReSqq2RpUPUnmSjwmpF1dHI2lO1A3pXd8keWcSFElEa4ksbUDqQHmQXPaqJpyXR0pS9OxGDZ2akrZr0oZyt39xn/9wx0rsjPCBK07xOg4hoWqN1gH5fwrYIKBAsLW5RW4/xGp8rdALh0GBiTbWUIAtLMDQEa0PKtic/KWitC7z0CONI7lZYm+ASGD0Wk00Mj6xVVGsOf6xkL+symFicUpQdTbWoqROBPJCwOg9IOBjR+8wO4sgiYSLRoy7EEpVVyFHWbGCFIcQhXglMJ0IbQXIlQ4662CDAB5a4dFSVY7CWUY8Edr6NkhKhHS4NqR5ZwSpHslHSuwsu9vhIkA8E4VXTxLyqxq0DHi/kg8GEwO0PEUoi2i1KbwiyqnkP+v/xDkaYRj4ujcddufkgCil45N27nH684utfOsnu8z22j/e4eS/iYC9ABIq3PrlCvu6aAVFWQpYh05BAKJyXqAp0biCJ0FGC78eUj2bkH4X5H83xDujGSAt4sNIjrW/kwKFGCM/J8+tsfa6H1W0WXyhQpW+ahEqLWekSvXyv+b8M2ohxhuimDN/VIr0zpj65yEOdZ/lnf+6f8BuXz/MLsw+xdr1PcUvix+C9Qe/lyNKitmeY0x2cERRaQMcTL1W4yOO9wPYj6qmjf6TH0YeucnxkuPeLS+idCdlii9jXSOkp3icJvlAiH7ym6u4ElYSIQHLxI/8p3/ziJaSacfr86u++/u1OzOuvr6G3+/ypP/YM2d0D5DiHosYXJd+6c5fyKXM4SDnkkEMOOeSQ/4g4vGp/myCEZ3eU0p04apdC17Hzx48jw4CZfp0ffOI+r/+TVT70py4jlWf4xZSde31EWTBOepgFxeX9VVTlcRrGH3b0XlX42mIjcLHDdBQ7VY/JpQ7P/eO3MQgka39OMJYx8xdWmFnBla0RAo/w0Ns3DIcRMrW4SJGd9ohQ0L7j6HytS/1YxuTpkN5XStpvVKA1LhA4Z5mck1w8l3HswpjLYoXbnz3Dva0FJirEhYb4n+bkx0L8QcnkfsA0T5i2Yx65cIcbcgVdepSIefLD19nkYUob0StnrG6OWT+2QrjvUIVFANG0xnQ1NgJdNFGL37j8FE/5TfxMMOjPcFPHZtWnblvWv3eecMMwuFEjc0+gDe+PbtJZnfEz6206qznDG4sI43grPsK7v/Mur4ll9M6EZ87c5tig5MhgA21zZK1wEo4t7rNw4R7LSZu1jRXMUGP2GyGirhz9IGS9BaXuQOXwKzPcMYm6oLDXW9QLLWQvRW2DXirgRomf6+I0+D3BKxvLyKUcaTS3767ws3c/ih0GOGlIE0U4NVCUmH6EwxNYh6s88bVt0jTnmR9e46u/cJZqVCAAZw31UgfpHfLggGgzR660KedTks0DhNI4pQgKRf+GwUmH6yjc0KEOZnRuaEyiMDEIBU29h6VKNT6SuDAgf9sSYSEYPTXAhZLR2+eIdmvKZUfrWxY59bTzMVt/rE95PEBsK766/Sg/YL9B5RSjYZuwNthE8p6zlzmWHjB1Ma/tz+MqQTmSFJOYwAry1RZBFWI7IdN3LxO8OEWVBrE7xJsaLzQIj3WO8TNHCGae3uUcvTPGT0ZMLvbp3wLpmuUGby3R/QllV9H/2jrSgDmzBBHYOCLYzVD3d3CthHouxD/wrITTmrKvqPsBrfUaHLQ3HUymuE6MCxwSiVmIH7THCHwo0JWiFjUSgdEOF2uMduja071WEowjokv30TNDdWwe5SXpekV2JEBNbePOcI5gVDyo224kvkpAcWaJaiEgmDqEl4xPKJQTRHuS8DL4skb0OwQHFa3rI9S7I9wPSXytqSvBcrRHWFYMbwUc+eZdJGAHKcqDnpbIsoY0oRzEtGPBN8fHOB6P+OrPniX6UEFvoWQ8akGtEE6gZxa/WWBTjaxBb0/Z/t5F8rMd/MsF8XpFOCup+pCd6iELT/euR1pB1Vog+s6Kze05lPF4Z1HWM34kIT8S0L6W07lXg/bN4V8qgr2SdlLioxDUg5hHu5FEi9oitGH2XRJkQPILI0hacD7loeMHhO19ZvmA4RmFHHie/AvXuPvNJe680UNVgiqUhMO6GViGFhs1rWI4T6Ud4ZjmZyEbobOvKrAOlMWeOoK6td38XRDgpjMCHTzweSioH8S4hIA4BufwvqlpFkrhrGVwriJMDK2Fgr1bLd74+T6ytIBBesfupQBnLKLfYvL2Ltm5Nrp2hNJALvChJ9lxiKwG79H7BXk/IiCnfComfMUQXx9RnpsD5RDKow5mxGtTnBREc47tV45iIgl5STTKsCppRNS1RRnfVGw7h8pKZOnwdUXvdai6Gt9J+Hv/7BOw4mh/akT3kTG7m4ob908gh57F3TFuKUWMCvR+jhQH0Ek4tm7guCBVVSNKxmO7EdmZiJHPscUc5WdiahsTFAb91cuYhR62nWJOwOjJNssv53gpiEqPyyuqIuPLL/8UC8c+RKs1x4lzywAk3RSCkAuPH+Obd7b4pa++wY899XDjUVIKHyd03kzo/OH438u9wyGHHHLIIYcc8m/GYWvPtwneQ7s3JZjWqP0aZSrG/QGjtMMnnv4G3/fEG3SXcg7utBjtxES7lmjfgIKgNBDDpmpjUonpCeTAIEIAg5gvCbsVynguv3CGK/kShQuag1ytGImUg3MRoyXFdNri6c51luf3+eDjb/D0uatI77CVx1SKwikGr80I18eELwUE9wQmBKcMbr6iOhLil7uUF1fo/HhITcDkZp8iDMnyBJk1bRTR/ZqFX19D3togeW4Td22KbQtuXz9OdS9lNokxqaOsY7S2DFozBk+OOTF3G+krRGnwzv/uk9tgapmsCqR2JEkB+23evHoOrQRHege0woqvfesk87Lg+BdzVl8omo0UBA7F5qU5Rj7ir3/37/BfvuvLnOjuk+xU/NZvP8Kn/ocnmGzEDOKcb1w/hzUhZR7RWa6YXpQMnwh58s9dIdMhL5Qn2fyApjqSYUcOvTVC705x7QNqKUDBiaM7mKsS/8sG8WsO1emA0pTzmkJo8nGET1MIm/pdYSzySwo7lshaEhw4skAjrMUHkuiDnv5359RLAcILZABnTm/xtmNXwTmKqebaq8u4qI1YXcAHErc0gDRCZYK61fhN1LhsIize43d38fWDdqTa4rSkThV6UoNWiAdCVduOcBJ8S1B2AqT3tK5P0GNDuFngfdOUgfR4mvV8Ko2dSqTx+DjCtJsGEWoJuWBn2mbrTod8FpMvO1TpEbGkIEQLSyKzZqvmKyHpJYOJPeGkpn1lQmgKFi/s4uY1xWqAX5knf2KVqqMwVU73rqd3o8IVJcJ5/Cz/3QYevMfEMDolCHZH6Lt7dO4UyMr97pAiHBmiUdnUbjuPLA1mvoU5MsC1UryH5O6M7rUcZQSzkxG+rBDjGWI4o+4l1BFkRzS21VTrRJtTwvVR0x4kLNNHO/iyQO1MH/gcBLL2qMIhPITjsqkzBrASNTXE93N639pHZGVzABcCpMQLQbJR0rpfk66VuFDTf2NKslXRWqtxxxehFSPThHhsISvI64ApEYtLUx45ts5DrW3mVMb+w6vUp5awK31MJKkGQdO+JAUegQklB7dbvNNkvK22JJGgaGsmVULfFyTrEjMXIDwkYYG0IKVAKk25EFGnAqNqlNeNWDcJwXmEgSBrGlYm13tsf2kZsSdYWd3n/H9+m/hESTT0hENHuFGgZgVqNEO4piFIzDzBz4xQX1FQevIBHJxXjB+fw3QivKipz2vqcxozr6gXa2Y/FjNZiDi6usfHf+hbTI4K/FZzuY3jEhOCDUBXHl9VBLe2UaOcetDUzDsFo6OQrYZNlEoI3CxDKNW8v/KikdDGAUJKvLGIJAFrkK0UpMI797sNQEiJEKKJos3P4WWTs3n1k/O8/mtHuffyAr6uYXeKjwpE7vHTGW5qoZQgwIYCG3lEBUfecZ9gfgZDC7UlO79I0dYkl7fxW578+S5qr4U4foSwElhfYJYNSz93k7nfvInY2seGkmJfYvY8XkG5FBHYGWp3ipzV2MiRHW83jUOi2QLySYTtp+iJRW9n+FjTvqpofUljno4I7gv8oENZa0wt8Jd3KF3J7g8ukp+KkeMpojIENzW8rmjJCqSnfbekeydHTWs6txzzX9onm4nmvVDVeEBvHeB29+mrnP6FmGK5Rd1NKPshWVZidcTN5+Gll+7y1pUNvvXVqwC88vw1tne3WDxyl3d/18O0+wmLj6yycHoBn0RU/QD5cMSb+1v/Xu4dDjnkkEMO+fZC/Af059udw42Ubxc8jOuQ/jaIsW9y9DMFQrC73+XEYI+drMftnz5OPu9ZqWcEhadOU/R6RDDylOdAOIswnoEs6J2fkL8EYkfQU5IyliR2jNjImXx/RR1p0sjQCTJIFbOzEj2VxHc07SM5Ix/zc/eepiRoPCsTiDcKojsHJB3H6oVNrr6YUPdaiNog7oJPE7LViLhTMPrlHi/vHKciQhnXDAVyhzYKVesmGuIcQoCPFLKUOOupUwgCR9EJee61C+SFonffYmybbClE7dXogxrTTajbQSNaDBSiDdp4hBU4CXUHFs7vk1cR17aX+a7Ta7xn5Sr/8Ge+g7rQdNZrfCfGacGrK4v4WZ+T3VdpxzkHIqaaC2h/eYKfWsavJnQ/lnPl3km2ZoJeUnK+tcsXl7sE90DHlpYsWMwn2M2Ao9+3z3iomHy9jawM+1mKXYF2L+Ovf+9v8H94/sMEL5WIUBH3RyQrOTNl2VRzxCNF63qFlTE6M1gtH6x9KOqWBA+TUxbTgvgOFC9LJv9pF67LJg7kG6HsoF9gB4swnLD2ahu6EpzBt0NEJBC1wYcBs5MR6YvbyL0Su5xAv4fQCuoavb6PWWxjV+cQTiBP9/HrBWKSQTdBWY1TFhl69BDcgqV7dYK5l5F1AgBcKAhmIEqLyh3xDOxcC1NZfBrQu2qYnVWYqSQ+m/Pq5CgtX2L6HtvSuKFAFZ45OSP3AR88dYeXbp6jdd2AqTGyYvnZEap0pPct8qJn73GNE4I52hSdlKSYItWMyZMSuSPpP3sLGbZglmETSVgpyqLEdJPGlZEVEGiQkvrEAnpzjPCiuah48HGAPbEMUYCQGm0sYmrBWuxiG5kbZG4wocDOpbhYYPoJcloSTh8oPUIaV4XxCGPIVyOqlTbVUU21lBKsO8wJSfudFeO1FD/r09qssP0UjKeej1BeUJxuM/f5u2AsxBF+PMP32gilMN0IPcqJtie4VoyY5cTXJjCax6aaahCR7ER4rcBYRJpAmSMcWJrox4lwH7ni+Hz6GHaxDRZcWTB5uIWom5ab+ZdHpNf3ULOS2SsJ1UkPnQSrJN6A2hfoSuBijZcVkZtRlAk+Us1mimv8Gz6N8NJhI0U5F6IrT7zXbGLoYY2sS5TziPuGOgjw7xTotiW65VGVJag0XivEgyYpXzebFuoNQ91O8b2QbFVg0wetUViCu0PSn5bUD69gV9rIagY5FGsx4zsdXpjrMzA54a+VvByepR6H6KgkcFAnmvJIG68E2dGQaOSQmUHUsPzbu0zPpNhQIrTCF81ARQS62WC5cosigORBhMdXjefFTTNsIFEe/CxHpAky0M3wpN0CIfBKQOm4+KcO6J+1TNYkG88FSK3x6wVOWUQY0J6f8tATOS99aZH8zBLClwS3RqytrVDNx2gMdUsjkNheiD+QzUbZfVCbM7ybUJ84hu1pvBPoYYGYHyCcwz9oOxLDEdXxNtNzc9ApaF8GYSzZiS62o/GBgKIZallTYY8toG6sIyqDaSvCGZQp+LVmsFvOCZg6rJMIBflTCdOHE7yCZHMMlSFRFcNLA+5uxtjYIvNmYLsw3WF88mHmxzl380UIVeOY8UArQYcJ2W3F0adirhw16CpE7WZNVKlw3H+1R+dkD7+jiZKQV75+g89+5jWq0lG1L1PpM6xt7PKXPvHfIOKkcSvJmnubO3zx8hUuvnf59/nG4ZBDDjnkkEMO+TflcJDybYIHjAuxG6Y5qDlB7/UJNgl4Nv4Il+6+zLCaR6eSYJIzLmNUURPseeK7mvmLe+x94QjdWx4vBeZYwB3RZmFygGsLfFRx6ugWJx/bRQnL/PUrfDl+iP/yzOf4e5/9KPbZhJUz+9xZHLBQVly4eIcD3ULIRlgqLHRuzGhdGyOTiu/50btcfN8eP3XzPGtlStldId4uUcRNA8mnc1gqeOI919liwPpbR7CRxynwSlOfWUaNK7zy5Gf6aBMS7nuoDJQeEHjl2R71OHJqm8nlNtbAq+cuoOMY1w3x7QfNJwq8EHzkyCW+vvUozgXEE0f/rRnVXkD6AwVJYPizj79AGlX8jVgRj0rG5zvIOCQdG1qbwOM1P3XnCeqdGDvrID3Uyx3CfIo0Ff6O4E+/80VUK2cDwR//sa/ytf/7GezAczoZcyYokV/TpG+CFTEjP09+MUVPLWrRYVtQKsXLzx6BnRh5FJbSIY9+7B6v+GPYTwncDyxRRpp6pYOXAaKwCC2pFtr4ZcNsrnkqrieWYCwIC09BSv65mKAv8CH8xMd/i8nrXb7+uYfwcy3KrkTmzdAI74m2BcJJ5FaOW4X5X72DGlbYuYTWAc2hLy8QdYVd7eFSsIFHGYcuPFQg8gpCSa0V+WpIb7tGlSAqTbHapuxLhJCo+1NULundbiQLlbYopzGxRO8NoVSEa13KR2ocAWMiylFEaGsiHyCcxESW1f4+XgjalLw+XMUtOcZPS8wsxBFSzkcEmaVII/IXIlQPfMvRWhhjnUY6j1AKtW8JZnXTkrI3xvVTytUUEQcQhYRTz+woyF4H20/wgUAWHmkd/mAC3RZeK3yoEQ98CHpzjFECnCaYVNg4QhgHStDaLShWBiiTIPOacJjh+yHhzFLOKbwTCBTVYgc/H6CFxMxg+ME52jf6zA3GbBUD6hb0N3IwHjUqMP0EhMPECjGumgFPO4UgwBsLgYDxDKktaneGE57pI304puhd2kPMCoTR1G0NtcHtHyAW5xASTvS3SZKc9bLP5usD/PcKbpcLJAuWWShpbVhkZhGVw3Q1emSwrRA/LfEC/vGnL7L0w2NuGfBOYkvJ5GSBLw3BviRyFVtzR5tYT+kQYczSczOyk5bZuyT17Qkqj0jv5USVBtdUc8s6YG5tRnDDoCjYW+khf22F8m4LoZuBjqgs9Dq4yoC1MMtB6yYOU1ZgIzq3Lc7kJGtlM5CQAn1f4NOm2cnPLJMipdIRL9x9mG4+QQczZObhdk1+IiXJHFLI3/VseAfzz+/gkhjfTRDDKSpz9C9NIE3xVY3otqGsQSl8GiLqApIARq65CDyIqKAUsx9agSqn96sHUFUQtRFC4vb28Uo1/45WTNYiOsdysk2HcA5hHvhLoiYiNNmGqOuQlWdue0j0TkmnnLB5cx5dCmwMYuoQ04LePYuP2hQLLY7+/GWoLQhB6+v3GL3/UaQpEUmIkBKkZPZwBxFo2t/cpDMuUUc10yKl7TJEaZAOdA3We+QDc550HjWp8IMehTfMVjRlBWXXYR5VxL/m0CMIDgTJ3RkqjtFJRe8bjtYbU/x4hihKCjmPape8/A+e5Kkfv8T+Vxapy5BHfnSDX1s/Sn3vOJEAVdUYJZrXJo6bzbsv5dw9GiEeC0j+u2toRyNOFoLJ7YjVQYwNwRnLT//Dz+MRyH6bI8sX+MaXx4xeuY+8PwI1w5xZIj5Skv5mxf23LmOfeT9K/esvCr/52j3iOODswyv/a24jDjnkkEMOOeSQfw0OBynfJjg8olaoARgBInb0X5k1MssPZkz7CUFuWF6c4lsVO28O8KEgnMzIhOLIsfuM7s83K//OEf/kNmHV5PJ1KphPx9hRl+eHZ1DC8Rcff5ZiLeXnbzzDX3z6q7yYn2K9XGS23eOdj73GvUvLLD65zaocs7a2irWNuNS1AsaLS8zaI65cDtkp5jFCIuIE35aIyiAqcFpxMOky392hU5VQe4Qp0aMZszMtWtOiaatYGSAr04gDtyb4tQN6ewv4xZS845pV9KWaKDBoHDo1TNMUWys8nmirQBiLjST3fzFEn80pHw4IK0uyU+O8wk41c+s5pVBUJsGFmvoo1O+2DAvB+Swj7pQMi5DaSqbflKQPVNXlQBIfRNjlmvYPDfmN/+cp3v6xFxi5mLRV0/taQfakoqUNI6fRRwrqbyxw8KUB+sqEDhMO3neClXKfbSeYupiffOsjzBX7zJYHLH4wY9qf47bu01qaIWqHVx60QlYWYSyqbp5yZ/Ma227STP07hnhLUXQFohMgKoEuPHpo+exzF/meD76C/9opuD9DCI/HU7cg2iqbqE0s8aqpghW1ag5HvQ6Vjol3x5Dl2F6KXVkA0bTyeK0JREEZCwhTfBRSLmlcLHC1Q9eScN9Tz6f47ID4boYNPKYvCQcZy+E+2/c9G39gmc7vVAQ3KmQucV2HvR5BKqmGAbVUOCEJVYieeaKxxyHQWCyCoWsRxY7ZmQi3FpHsweR0i3oxwAkPsSHIYf71HPEJy3g7JLrhUZXCRAFR7pC9HuMzHQZXM6hDxhf6FPMgtKDsOQg01ZEeth8hRI3dsiih8ZOM8mgPiUANp8hZgZhO8UpSnVlsDsOlYXpE0Nmq0HkzhFGlQFmJ1Z7iWIKJdRPDqiy6lrhWs6EVbZQULRi8NEPWglHRQ3lP0bf4MsfZB7LZdkSwbylj6L91wP67F0l2HcmuQUQBNg3xtcEmEbJlcalGhhFeRehjE+y6wIeaqqtI0gixP8Ypj6yhLkKqf1Ahv9/jKsHXJ2cQyhMuGXYThZwVDK5MmF8/QEpFtdLBthNslDJ9ZMBjH7rBxYfv0b/Zx/x6m43v7lEUIbLv0CND3Q4wLYUqPPF2jrAOIRU+BTMnKc9GRJeamA14ROIQViMcGOdgyWFEyMFjiuSnE8LaghTI2iKla/7PoWpiTsYglYQwQN3eo+qnpDf20ZMcrxRm0MKdWMFJQTiZ4Ne3KU8nuG4L0iZK5b8RUWuJUjU69chWgMUgCw9OoHczop0xsqyRRU01aEGkcYs9/MEIVdZNNMfW0O82sTRfM33bHMPzIUc+tYaykunTx4muNS1f3i9gUoMTu4jK4WYznJS4U8tQ1cg720jnuPrTLTa/scj0oA/hBFeWiCjEW/dAVhvx9V9K8KGmv5uzsJST+Rq5N8Mup8S3RlipGKxPUaWFWpLensI4A9t8DtEOUJlk9SdvN8PDZs6Nii219pQLEWf/qzFh7xa3dxdBNlE9J5rBuawsvqwebNKAPbaIiQOyFYGLBVUCwbBCC0M9Jwl2HNVCyqksQD0UInyLqq+RmztgHV6ARCCnNabVopxpwqnBZYqvvnoGtSDYf3ufucsgrEdeuktwYRHuTPFKIUaSbK8kaIfNNl1JE59KYlxdc/nl2/zYf/adnL14BBEGHHt0lWllmO1dYDncIDlznG9mO8ye6LPw9RHiyZTwPiyv9JHyX38RenNjyE/9w88jBPzX/+0fpdWK/u3dWBxyyCGHHPIfH4etPb9vHA5Svl3wjrluU7foIk0YVExPx9SdgJ2rIfExiZ4v+OP/2y9hW/A//PX3MRr2qPsJpqXYViGmBSJV4C3ywDcr8K02UWx47Y1FTqUGMzb0RI71kp3n26w9NA+nPP/5n36O/8unP05wxXLdLLD38lFGxwTDrIUTIL1g/10p9bUQXWp+6cvvQOc15vSMWvYbieQI9LRC1g67kJA+NiM+V9LanKF3xzCconanONMhvDmDpTm018j9mqyrUSdn+PUS19J4L4hGUASaO88eY248woWw8MI9qnELlT9opDEWXRjUzDGxCb6usI9Y/ERSXY2Ze2ybR87d45dff4afG5+j2pGo8Zj5pZwnjk65emMBd73F5EYPERfInZre1/coj2mKt8foOwHZCU3vyJhrr53AbcfsmYSFoKD2EnCUKmZj2mJZ1YyG4Ioa53xzqHae/q0J157uU+umkaQKJcXRFvtPRXz57gWiS57px0p2/2gXeT+kfb8mGFY4obCpQo0tAo8pRFMPq6B8NCMQBmvnMcqT3BkRtiKiqcdtGqofElQ7lmBnCg7sIKG2OfJggt+fwceXsLMY4xXy5CrOVLhQE92fNBERIZoH5ACph2WPHXsefd8a33ztIj4DlVtad0pMTxHte9Bg5x1lJ8Qu9wkKQbzv8VrwsT/5IufTNT7724+wIZdIdxxSxfhANaLWQGATqKVGHghcGFLHgnLREYwlP/kr38Of/ZHfJBeKwBuMUUTaUNUCWUOyV2AWAkg8/pGK2oD8Uob526A/RvMkvALvIF+QpFuOwGqEdYS7U9r3UoYXNKavkJmD6Qx9F1y8iswNKoiasGgUEE0tNpXN9gMCO2jjHgh/VWaRNsMf6WF0iS5q0rUMH2hcJBtHBqJxwvhmkypcn+CTABFJwp2cYBIRDV3jd5jPOa5nvHpuwP53zNF901AfmSdfDvAOopHBzMeYfkTlDOmewSmJtALbTTGdgHC9RuERlUdI1wwTF0JcqHBa4QKNShPE/V1opxy8JJDtiCDyiCOO/Vc7hN2S0b02fg5sJ8T3UuTuBAGoaYW3nmhnjJqlDH3MvorYf7FL605F55rFPOqQmyE+rJCbFUHmmnO+a2IgOx/qkJ8NaV2xtG5HCCkQPUfvQ9tI60l+JSFUNfalKXiH6Ev6X6sQQuGixgEiixqhDGqcNYJTraHXxmuN2BsSVAa/O8Msd7ArvUauelAijG18RFv7MCoJ7zr6vx4jxobs2AKzoyHlkiRalRz5bE7d1QRT00h9A0UoND6JGmeTAC89vhMjHNSJR+wXiKxuBii1AWfRRU37TkbV8+gSfCgpl2NsZ4V42yBrRzpyuDhCFeZBZbKCUDfDDS3xleEP/tWbLJ+v+NV/9ATX3kyIKtlUEgvRRIHquqlQznLqV0M2/3yI3fbwNotyhnBaIYgReQ1IfBISTB10U2TtcUWFWQqgmCCVaoS3ZUk1HxG+NiIZlSAt3ajgE0cuc/e9C/z8V98PWoISiGmFObVAeH0LH4bIvMKEcHAxJFg3hCOPF6CMJbpk0dfBtRUulVw7HTLYDIi+OETOR9Qn5lDrU1w3xWuYneowfkix9NwWN255/GCOajagOus5trSHvz3A1x5RVIilk/idGlkZytNzmC4YURGudOHOtNlIoXk/oiU//d99hde//Do7YZsw1nzPDz7Fk+86xa0oQ7oW1cFlglwRlC26zy/x/f/Z0/z4d763EQP/a+LwXF3bQ0pBlleHg5RDDjnkkEMO+X3icJDybYJ1oFyNEhKbVwgMdT/EzQkid5LNb2xjfcndqEtc1YyrmDoSOOmwsWTWill6zwa7nz4FKMS7U6r7KUHhGQmFXpFkmWLx+QPOz22QvUNwYmeH/jMZZ/tbTL3nT37si3z+0hnWbsyT1A6DIBumCAWidiAFVVcS3qd5urizQ/LaBD2fkz15jGIpJvSWeCdDVgUf+bMv86I9g+l7yuMpcj6hXmlTrFiyRzooEbD4fIHILMHUUHcitv/0Cr7QBPueZCSQlScYWmwFjA2b5hQ6EuAcLlQgwJnmID49OmB2MSRoZwhjqXox7/++yyyl+5TnPN84OI69Cz2fYW4H7PziMp3CMN4FcdwRHK846CS0WzEREfItS7asmZMlvUem7G8OsEuSdm1YiBufgfAVURnzd3/yB7n46C0meRcSsI/VlPGAKIJgzxGUHls1h+e6rxg9npIfBR941B0Je4b5FybU8/O0ywqEBC2Q3lMsR5hQki0pXCDAe+LPZUTXZ7izmsAGKF2h39xFOcHcn6lYuzqP2Y9BlPjZBBd7bCXxwwl+VmKuVJiTAcKBjGJmqxFeesIbWwgvQStcXiI271H9hQ7i2gCfST5//22Egae7O0VYKFRFeqmG+WZzpQoF0gm808RThVaS9j3L87ceZjifcKk4T/TNAHVQIgKNCAPqDtigiTWoElQhwAnqAWAluvTsvTHgl9JnuPDxNaT0xFGFVwqXWDwKOSpJbymKExG2AlF7wt0ZovbEGwW6bOSr+YrGCUXVbmIGrasPtg6MwYUBKq9pXcpAKWReN9s4D9pTfBzi46CpoA0VfpA27922JltUpHcmSOtBKtJ9S9FNaA0F+XKIlI04NdmVRNslk2MaVYFwgux4SOtWQXxrRHV8DptGzFZKkjs1c7MtPvLRq9y5+h3Uuovse3RhQYQoQCKRBHRe3CLySfP0oDRgHUHtCTZyfFEiyor0yi42AfZz6jNL2HZAMKlg5wBfW4R3oGrUkRq/VyOet7j/jeLGnTnEL+fo3ZzgYot4BDYNEWmE66ZYYVEWkJJqPmD6xZoXv7BKvdzGrqa4SFEGgjAFNamQJxJ06ZtKXq0gy1BlFzWE9LpBjR0+DaH2BBiQIPegIGLpu4bYzZqiTpCXBdbU+FZI2RV4FZDcq1G1QdR5M0BJkiaK1euAd6iiplpoI6RsJMqqIKgszlrkqMZLjUt66HseNamp+4bo9QLOK+I7M+owAFwjgnXNppeNFGK3QM5ynP6X8RxBMQgxxqFvbaFGTewJQAz6eATaKJIbU7SX+MKRvLaNX+1Rx57e1THCg+t0EX4CSje/W/szvBDUTxxneL5FYe7gTUE1NeijSzBeA2i+P1si0hY+KxBS4CZThAlxi33cIEVUhmiYYzsR0oOX4OKA5M4eqm5kxibV2CAlulVBUYFWWO9QmcJJjdMVshDknzTYvyzotnM8HuE9orZURxTcVVghUGWFdw45zIk3PZEZE7xao13J3k+k5KnARpJZt9k+khkka1PCnRnR1hSXBsggRNQO05f8yJ/8PFe+ssy1T7bBZ9i5PjZR6IkgXIZkYURxX1LqEJ85zFGFKBXjDwY4Y6i1R1gHVY3XGmZZE5vqp/hWxLOJxS1aVu8UPPfZ13jtpdu8dOs+rfcuMv/VEXIuQFpFtjbkueASP2SepBemv+e6vpmN+SdXvsYziyf5+LEL/x+v/dvrY8wD30yZVf92bigOOeSQQw455JD/nxwOUr5N+MqnX2K+PSH+QcMBNf6hmsW/G6AKwcbTbZwLCbYdP/s3PoK7kOO7TbMDeUk7ybh3aZWL1+8ijEUo+KMfvc7Pf/JRbOkId0vCMEYKz/bTc+Qy4UPLtyhOaGTb8y9uXeSPnH6D4aiNnHkGr41xQUAc1ri8iSDgPAvPjRE16IlHCo00TXOEjyQ+kNSBINkGvGPQzbhaL1EScG80YPQ2ReumQPQF+vIu2WqCkQLT1gQXJPLxmplI8blAOE+yUSB00MRw+gFzj1YcW9nh5hdPwz1woUTnBpNKspMtDi6CJ0AEDj0U6M0KvV+gJh2605zdvMP9zR4knr/4o59nZ2eF+ZUZ2b7m2ZceYf6j+5hlh0ot02QFXyuCCmKboW5O2bSLiA+X2H6HrduLnHr8NgDv+Mg6X3+rh0kkxTgg0p5CtRB3E3woydoGNx/RulFQrTb1mLIWyFKSrHvSNY+X0L08pfWGx3ennP0jW1w9OIUrIZ7k+Bv7hIGkda3F7R9ZxC1bsrMp8V2D7SV4p5g9HNK+JzFKsN2ZQ7csVTdBbkwQRYHYNnS2RXMYAuROiTjq0IVFZhWBqaiWY3BAXSPaLYIwoFyNiTqWWlQwC9C1p2xJXCCbT59Om9qWiNCjLARbgvKcwyswsUDMHGVPcfXeMa69uUw8lqR7BsIAH3iqBEZvD6BqhieqECSbFomg6Av0xJOuG9L7GUd++IB2VHGMA569fw5vNEJKghDyEwnRTg02Rb8cEW5b9t6xApGjU2pEWeKFx8kIm0j0aELneka+mFKvJkxPxgQzyeKn7pHcnoCOmXzkGA5FeCPD+xq5n5OfX0K5AKEkLlL8S/ts3dFMHmpRjh3tTY/KHHEFNpKUCzHpnkNUhqqj0XlFuBNiuxobQLGUkqyVKCWoFhOKkxH5uT7Lv2wJpgGf/advQxYVMQ4hAoQWzSBCC2wiEIljenGJYGSY++YE0hBR1HglQUmIgmZbIgyQRYk5Mkc0NdRe4L1toifeN4fIbot63SHrHDN29JKC4Lhn55ZAjyyqHhHvVXhbY9MYoSU2Vej9ivzCMr6j8O2D5vU6mLLxI0exocBrh20bNCFhkiF2CrwViMrSeZdj4Ym7rN9fRF531O0EGSlcpch/NkUYQakCpPRcfGydyzcGzHYLfAfQIOqK4ZMpzkt0ESNvj/HWgvBIBzjB5H0JxUOKwa/ViMIgIo0XAhsHyO09ZL8NSYSwDjffY/tjLdTEcrSc0nl5zFsnF8lOdamvRfhI07pLM2RTgtlqRLqlEFojlCA8qLBaICYF4bRAFg/ad5SCOMIFCgiZDTz5apv2rRxZ1XRe2cTuWqZPLOFtjihqpBdsfWCJhee2kUmKiBOksUwHCl1YPv9LDzGX7rL5RoSSG3jTOLYINJ23zyMWcsafk80GlLH48RThKvRugI5qXCsEpXDtBOIIMZ4S7M6aQUwYoguD3xgjTrebgZSH6nxC2rG4TOHXO4j5GCs1e0WLcZ2SrYboYUU5HyK8Q+3nqJUFxP4Y4Txs7NMTC+iNGS4EMR4zUX2kc3z4wk3e2D/CTt6je8NRnJ3Da0G0MSEIy0bYi0ROSqyUDJbGYNrNRdQY5PYB1flFbtw9wolLO4iRQ7YS6uGU+kyX4cMpRoTMvTxj910xlBWyFeJr0JHGKkEtG6F3dD9ndKyNs57rt3e5EAacXpjjE8+8g7957i3iGxN8K6U+D3vZlIkp/meDlDeHm3x98y7DsvhXDlLOPLTEyaNzRLFm5Uj/39YtxSGHHHLIIf+xchjt+X3jcJDybcJXfuUleL+j8+6c9h/N+dboGNFoitARJsxxJz0yC1h92z5Br+D2pUacZ2PNH3v6/fzM1lexmwIxL8F6PrXzCFMlSCcFMpIoESBujWg/olisxuypFLEqeOGlcyyfGvPr/82HcVohfEE4NnhdU9+RSO9xVqJHlnjbgDOIW3tI55n80An8uZDiziJh3mwVCKHJVgLqwFFdXaZ7zhBikLVn4eiY04/f580bLdpfmiDCEBmEBGcEnQ8WuKHE70ri35C07xqMmrH7ngXi+zVz795nO0iYMzvs/swcelTRGRiOPTXkm9VDuIEnHziEBXs3Ib00g7DiWL8iLhJWF0dcny0iS8e3nn2UODC87UdvsDwY8YV3PoRaLRFI0rgij0rEgcOEgqC01DX4jmV/bQE9C/jkb32Atz12BwdYocA7PrH8An5QMtZnKZSESqDKKelNi52HrB8g6ubw6wREuzXtawahNWVfM1qdJ54fI6QnKzQ6s7gSXG4RUpAENVVtsfMG2XXMPpAS5P3G/1A4TCfh4ONnEJWlfm1Glh+jTlOCboIaTZCu8R2IdgucJ/2RiNlbFXLWREiSrTHB1hgRRYilFsxKQOCXNMObXfRRQ3zFESxluL2UuhNRLEqqviIRgqBSzdN46Qn2MsJcgAS1N8XEPaBZ2de7HitoZJlSoN/p+Esf+yK/8lvPcCdfwXse+DMc0T7U3Wbd3guBWHZI4VAS6kmIrBVSenQGyd2M8cU+dU8grcbNSYI7Oe2PFPRvQXVD4axg7nXL/hMhvTfHqMzhj6YUrQikRJYCYWgaX5KAJHGUmUXmVRPj0Rq9k8OcRtQG8grfThCloXtXUvYktqtxe1VTV1x5fF7jpEff20cNZ5QnBxTzCXOv7TN8YoCfU6R7jkTn2F6L9tqM6TaUFwNGTy8RPTdh/9aUKNttnBdpjFjsIkdQrYYEmUf5EK8FxbLGmwovQ4TwGCzVkRayp0i2cnwUNHEL2dRd68xiRY2PwiYO8kBiGh8LSbzloR8dE4ee3775GHvPJMy/VJOsT3GhBiGbDYmsItqvcAvtZkNMSYof6uNXcqotGlHnFGQO7aslOrMsdEq27jftSBxMmCm4pVfJFmP8H3YsPO+QM0O2qjFRm2jLIzuaLrucWdrnRriECEPENEcIT3g2JD4w1FKjRzWyrBHGNvE6BK4umD3WbTad2inaNFXAQoC3NdVDC0SbOUI2Mad81VPNC+SC4ice+yLqey1/5e73U/iA5KDCdxQ2CVBO4JRCFw76bXzebM+4WOPLEn1nG5eGFE+skN4rkUJhOwGqNDitiDMIXx83Nc1SQqiYvG2OsGjiUyLLEWVBPAqYPNpH65Rk16FCRftmietJPvQXbpO2C8zfb7H1YoiIomYIEmuMDIhOANRNpCiKEF7jM4ParRCzCr89hsWmPUs6j5AKa2qE9Y1LCQhmFjGrEUritaR4agmLI/mVTZAC11bcv7/MlVdn7FzuE+Q0w6rKEBzUhLoRBlvnoarQsxmi04W012zCLC6S3HdkpwXt5Snvcdf5zPWLBLsWpEfNami3EKrCCfBBAEoyziOSZxxEETiHTFsI1wz3pQWjU3RQ4K1F3t4kfLRPcitgcjZkthRiI8UP/teXMZnlhb95miDukv6A4e4/c/iqIMkL9rEcKQqyXo8f+NFneMc7T/P5168xPNdl8Wt34Ijk4Z1j/MS7fphj6RzOOarCEKchAF0S9rcrJt78f73+p4MUj2d7Z8KR1f6/m5uMQw455JBDDjnk93A4SPk24Qf+/Ef5iy9tcHy4x3aRMjURR86Myfptiqcd9BzdSxVPfvgtfOrYuTLH7KBDcd7wCzuXqbYt1fen8LXGv3BZL3NquAOtBCEE4bMzoq9OGP3BZboPR2z7PlUekjwvmdzs0b45Y3YqoliJyS8kxJMZVU+jxhaZKVyo2X97B98vmP/1AJGFBLomCzTtK/uUq22K5Qi1n9E6mOCcoz4QbP7FNtZr+q9KyrmUvc6A6oMR/Z8SmNrh5qAdFazKPe6XPSotEfOS1n1POdeITH2g2HmjhztpmX1DU0aOYKfkxGPb/IE/8SbTS5JnOU6AZ/muob6VUnQGqDrkb/36+5lLZ3z0E29wsB2xYCYUuyEjm/C1ndPMBxOKXLEUjJi4FJMo9sMaM2jjvWdyMiQpplx774DwjsYlDjHSfPW5h3B7BZt3uwibc//VNjsvLyP+RIVeLshGirIboSclclyw8vYh5sBRtOKmVWi3RlqJFR7pBfGGxKQB/pji9svHiaoa4QWm14JYQVzgJk3dJjRegWBaQxQipoa51yp2Vzq0L20Rr4+ZRS1IgV6C2FCYVKFKhw4CROSZe2ZCvdWjLAPIKnxVMH3HEfqXphghmrrVNEJvx/TGHn40I9cBdlYhbQvb0cTbBXJYg9O4MERVHkYZrfs5Km10vb6X0FqrmJ7UuFRSdxx60oy4Ze2QX4K9s11KGRNOHHUsQUE4882gKhWMz0qSO5a7ZsB4GjEyCaLQUEu8bLwU2dke9VLQxCcqCNcOaL+0gVhvU088rha4hTlsKvFCMD3VpnenJhpVcFdjui1cYNn97qPMf21IIiNcrlCZwKQBYV6DFrjFNmiJ2hqjtw6wC23ssXnC/RwnA4xVIBQu9KjC4HyNsOCD5oDXxNEELolo366ZeYlpa+qxxMXNEMmcUtB2FOcU4kuG8pEuJm/Rfm0TJjlmsUWyVxEdVNTdEJV5Bq9MkZlBuhovIkhihH0QEwibamOcQ2QlLgSTRCCh6MUE2zFeS+Q4h7zk7DM7HP+uPaITNbmJkM95Fl/OwArMXIrtp+iDHDkswDpspJH7GSIUeBkQ7M0YPZky6ccwtFBrHILxozHJxj6nTh9w52AJ5wTpnRmmsKibNcQh7esAGryle8Xg2gkyFuisZpZ3+MIvv52DvE2exMjtTURtEQ/NsfC8x6MaF003BWOYrcS0bg6RUczCmyXRe0vqbg8z1U1NixRUJ3ogBWpWI20Ls9Am2TAk6xlBx7LTCggyjdgq6Gw75i7l1AOBa2lcpJHWEe7k4AV+rotTEtMNkGPb+DKUJsxDhGw2ReSoaKTdc20wnvHjHdpXPPFI4uOg+f5Lh7cGxrOm7jhOyFcUvbuGaikimDrqtsLFAZP7LYLjhmzcxkcCyhKhIXmHQn9nzs7pNvqewt3vIdZ30VmFW2hRnV1AOkHwLYcfTxAugQj8/gEuDZGTHGEc4sGQont5gjcGH4dEcyXuSsTs0SWQ4HXIke4+33t2jcniDn/ra8t427i6ilMJZi1DDyf46bQZBoYhfjJFRiEg8NaSXI+YnQyYbLS4ensV346Jr99CFRa8xxzvoxZA7Fq8d7A3ZXSnw6MX7+NXH0YasK2QuuURFbQ7OY++8zYbb80zXJPIokJdUbSDgnIuIt7zjEO412qxKbvMPzPFHpxg8q1thA0RQuCsBwl3XrrOxR/7CFVlaLUj/uE/+AKdmUDMdfELPW7fGXGsvwDA3/4rP89bL9/hL/2NH+Gxp8/w0tZLDCcZr9Z3fs/1fqfY4Bfv/X0u9p7kA4t/iO/86KMUpWF5qfv7cbtxyCGHHHLIf8CIB3/+ffMfwvfw75rDQcq3CccfWeXg9RZu7KlrSSspcccEYloTHoTYuqI6kNx5YYXihGNUJPhUYiPBzt4e0bpjMu6SLcgHT1rFg3eAwDtHHUXkrRamr9kTHQ5ci9xGtGxBfUch705RU031fYuMn0jxQ0vyhRnmrMGnISZunvxZHcCJGrEpERV0vj5CrytMWxKM5e9+TRlG2K0K9wsJwkiWzu9y5Ow+7ZNjbm6ew7YCbDFBrGUMf65g9Ms9qgsw+46Y2TsEBxdCFl5vNlPay1Oy30xol2PMgYDTCdvvlzx2coN/9C/ez729Oewz0Nms+fHzL/LT33qS1msjbCDYbp3g7lyKePYcf/0TnyMoPV84cYE70x6X7TK9UY84N7wt3uLF2XHWqjZeGTAelGd2RrKwPeJatdTk/pGM2jH//CffiX59GxM75MMBxx8dkc8JsmELv6eRmaJ4SNC9UiEMfOhtl/nMr55jttLB7ffwkcSIgLkTByjhuL/VJRoKbC4oFiWEEmsEXoOaeuosQHpDuGZQiwZqCMcK0/PoyhHnFrnwYAevqDFZSGfomtaYJEUcjB48rS4hTahqSdEJKeuA6OYYlCSaCrwFOS2RD1wI3gh0YilfSpFKE70KdTpGyYBwNyOpa4ZPLeGlQJWW+khKQUjvrSEqN5jlLqajkbXEOshXPDIRtG96xGSGDzRf/NQFdh/rYNuSsuOJRhAMHcU8UGsmpwWrteXgHyRUH5RkLwboxypkLuhcmWHDFi5WCNO0gnR6E8r3aEbdFeZfLHC5xZsKF0rGpwPab+3RuVUgnGoOu9binaN/0yK8IDvThgONnyhUbdHTCuHA13XTQqJkIxc9fxQChRplyHGFWVWk9wwyCvF4TOQItirC/YLyzBzliT5yVBFvFkgpqI8YdB6iS08dCLyr2Xv6CH7kcLGD3PN9/8lLfGbvGXY3eshZSXRgyM70aW2XWCWQSiPyGeGkRGQFwnicqhDOIQNJen0PYT0uiRCA6YRUKy1sovESgoOiEZMK2RyKJVwdHePKbJn2es4gKZhWES1KXKjQoxqXWmRhcFmOlAofgzYKaQ3hRkbwNo0PwMaK7s1dXNmmvBggvCSPUp79ZEL5TomaGnw3xWYz1DciVrZK6GisMcTrBQDlosD1E0wsUBZu7S/jUonRkNz0yLym3G7Esv8ygugUTC70iEZNjTUOIlGSxBXqoW3Gl043IlZAZgbTVtTtEJW75vfBePrPHTD8sR6ffP09HPnNmvBYiO2CDR5EuooScZBTLqWEVmETcFLhNKjKQRBQP3wMIQSydoiqAimbr5tEEDdbQMO3pQyfmufcP99FZAW9FzaQwxnlIMYdXcRrSTxRyP19xO6U6WPzzB0EqJln8V0Zf/qHbjAbCz6z6aGsmspnAeVayNq0y3DaYbGbkw4ldFL8tERnGXpzjJIRotfBb+/C7ggvx1DXKCkZvu84nZsZam8KUqKGWVMZPoDqsZrOL1v2n5rHhwI9NRRBhJGCSRXiZlNUDbKQuDhh0hV07mS4NEYJCQgq4VCOxnWFpfCg7wQ8v34e0xEQNLEzpKD3dytufH3Aw71rTPc01f19qAX7X/TcuTCPwCOcaG4844BgCk9fvMLbn7xH90TJ139uFdHv4DMJXUXvpkFPK3Qdcu3uEptfXWDwtRFVOMIiSY0FpRBKoNdnRHHMX/o/fh/HTsyzsz+hGJVIAC+Q0xI3zfirL/4af++ZH+byS7fZ2x7z5os3eezpM1ycXyZVlznV+b2Rn19f+we8NLzHjclNPrj0w3z/977j3/1NxiGHHHLIIYcc8ns4HKR8m7C9MybRFTLwaOF4aLDF9fo8yTeu035klcl1CaKm42p6m5bLRoKzdH7ngO7xEq4pfK9m/wPNkzGiirIXEO5XuAXF+NSA0Zk+Tx25RtiBW/kC+0dS+IEMsxdjD2KC845+Z8p8a8LotyN07YgXM4ZnI0RcwyQm2vJQW5wSyJfHhJf3cb2UY2VB9ukhbr6PiwKE1ngH4qaFRKLVAY/92HVGdQq3JFYLioGg8/oYJx20FOGBgbRGKA9W4gk5fnqHD7znZdbf3mX3TkJdh9x2beRuwqUXz1PtQds5MiUpkj5/b+0pzh0ZM3sjRCH4wBPXEGdLvvDFx3j9k8c5c+4u3DVMvOb2lWWY86xecyw/ZvjO7m3+/uwZ7KkYfwm8VgQTT+/RnLmXpuTtBYQVmBZIr1FGIZSiFoIv//px3vF37nDtSwP8yAOWeEMxeyhCZJKFpwq+Z+MS4+2Yz5vvYHo0oO4Lzjx2wNnFLXZ/5W2IKzWzkwEoh7OiOQxbA7sHEKUQBRz9ZsbWGY1MPF4I9MwgCkPdDRFIZFGDEOgDh1MCpyWiaupXEeDTmOj7U5LVITLyKCTm9DKiqNAEKK1xWYa1FViD91OyH0yQt/sI6zHdGJKAYKdAaIUDpBGEGyPC/RzfC6hWoqaSOm7qldN7Je54hLQa2xcUD9WIayX+JQuZYet0j8lDoHeb+l2PAGkxbYHtgBAeF2vqKynDNyNctyKdq0i3M5JrJeMTAabfQpYgQod+uEDGnt12j2Bs6b0l8a0OCEH7RkW0luEtINSDJlePCz0mbWI9dewJ18eo3GC7KS4KyC5EjD7Wp/WqpHWjQiBwWuOUwPZjvACbRri4AiXAAtMS2glhJqlbTRuIjxTlSsBid4ftY8sYr2nfqlGBpjgxR7JnCXKJu1cTrU359ODtmDMaPbPUJxYwgxKRRGSnQ6z3dNZKXDtg9GhCeG9C7/IYaT1OC2Rhm2aZyuBDhagV+niGrMOmwUhL1Pas2TwATK+FS0LMqmHXp7SymNu3FUZqxidqFr86QoQhPjXU/Rg9y3BKUHcUahYQrY1xtsbcCRGrEUKDLjTJesnB+8BLie+HmIeOkAwdeIkvasyxOfJjbcZnLSL1LP9OgQ80XglsAFY7gs0hdeyh08HGAcFOjnl6AXE7R4wsph+gKo+4uYYOPZ1yAJ0U0wmRpce+buGmpjQdZMtiQoGJJKJUiMoR5DUiCqC2WJuz+z3z+I7AUzHaiekulZjLNWpcMzvVJb2dISclQaDxnQQvHdmiAi9ID5rXXGiwnRi1Pm5yxrWBOIKsbGIygeTYrwzZfUdM3hNEPkTuDhFFRbRlcJlBzg+QRY3TGRJDNaeoRxI38KRHp+jE0fIeGQp8L0FMSjygBVQywo0i8mRGb32fgxMtxPuXad93SNtD7E3wo0nj0/mXFyKtQQjSGyNct4fc2GtkzFLiRyPqXshkdw55VOLFg20kbRmFin/8m49z/eAkLbeOlwKbKGwIo3f0aF07wPfaiK2mmU5XFtoBOJA4hPaYrkdvgNrLCCpJthKTXh3SXoGT37tG9YKi3E8QrkRIz9a3OrR/G8Iiw1QaWRiCUlPhufbaCda+cYLZNMGWe3ByieT9U/yXC9QuiKwklHMEN1L0OGay6qkWIup5TfvTt/FVhPOO7s0Z3/UTH+e1F25x7PgcaRJhOgoA1RJEe2OoDXufu4d8tySea+HHBYOlPgChOMaZ9gmWos7vud6vhp6WrFgORvgHLUv/SymrmigM/k1uMw455JBDDjnkkP8Jh4OUbxM+91tvkC5WhMrgEfyhhZf5W/4i/tQR3MxC6em0Mt77/rdAOl66cYrJTU1wZZtyTREO5vAzg2k5hPccsRMC6ZFKUKu48XPEcGPnGIvFBNPzGAfjJKJ9z2L7KXOPDzlx5D5vbh8lP97FGU31pCeOJ+A9TsakayXhZg2ladpALPjAMbqTEhiDyEp8N2hiHTs5YjbFDlL21uBTf+8DZL2QURrSG+0QuQq71MUNBPL7PAd+AWE8WIGsPaYrubk/h/g/LzLNOjgfEp6v6DxVMotDrJVUXYEuFbiQMAOTCrbLDmlYE3Ytf/jpV0F7Li+sMBsGxCs1s47A7mji2wF2VzExlh0fczfvofclxX6M1woXgNGC688dozyREo7AK0/W8lTf22agNLpw1H5INRqRP+9oT0tmmSFw0H1um+pcyO57Vtip27z62wm7t0I4P8WpObwQ3N/rMf2Uh90cF2jGjyWovYre1QzhoY40SgYI57BBjp9IatenlxSEZy3VekzdT2F9hlgyZBcW8XXF9Mw8JhN07udI6SlOd9CtATp3mK/VhB+uSOwUnYVYH+KHM+TaRiOlHE2xiUZmOXLq8JsDxFxG/FyJCyRVP8akGhELnIqRTqAqgywMcVZQdxXjR/tky4r+WzX5YohLJTYAua/oH8np/nBGngXUtzTVsQgvPMJ6WrcrqsUYURratz2jhxv/x/APCnqvhYirBt2f0T89xL8nZOvFOaxuoYtmA0sEllwrFA4nBL4lKE/1iG8e4KQg3rDsP71ItGcYXCrQpaXshRTSkT0mcCEktyROOnwCKisRYUhxUuJCiU8FKAtCofcz6n6EWU2p5iKSfYNtBU3NsPEETlMsKcolhcQR7htMS+O04CDr4OomxmQDz8LKkMlFze5uTHq3oPXSHSgr6hMtZvOLoCxxOMSHCvugq9ULEMMpHkPZX6JqDWhdn6CMgXbjUEI2slmZVYieIPhOj/rmhHJjDhN76vmE+PY+s/N9ZhfnWfidTTpzJet2iWgM7cuG9kseVzYuHRAIpTD9iGDUQlpQuyVukOBFik2h9XBO7+Qee3dLgkuGrN9H7ElQCisVWhlk/uDonkbMxQdMLkbYjqaymvxFRUpTEy2FR4zKpnGn8qjdCr2+gzkXIn9IUN7TpP99ycrJLaJzM9YOJH43RuocJyzjJwb0rjscHnfTIeYdmhpZWfzig02XzCL2S0QQgHPYAXipiHVFlNaEP1ax0svYX+9R6AC1O8OlMSLzaCMohccpgVeCVlly5swG6zcGTOMOIq+bQea/FP9622ymxAEuEOhpzZGvTZvaXe8pTwzwtkaqABFGaCNgMiaY1FRnY5bf8BQnIybfUzNbAQOUTtA+XiDGAVWm8N5z/F0V+6nH7EC7pbE+oniii1iSdJaG+Lse50L8unmwQ9w0isk4xlUl4V5B1ekjpGxiYUEApkaIhOB2SLo5IzuZUCeQSIfTgrXRgO69HDc/QA1zkttjxg+3ELFCrCwhtYKdMXiBMg4byOb6oQP6l2qmHzCYuQAhW7hCUc1HBFsZYZqzud8hOFYTu0aMjAAKQ6ozTBHjtIasZLqcUEeCra0unWETg5S39+FshOiC1OAnJShBUkjcVxXtrRGzjsH3BdJLZBJiywCBJ9mp+OqX3uDmnRFPvPMUhTF41Qx78w8fJfy5q+Ac+QtbXBmvUxiPjDS1bVp43nnsKP/7D76f4/3fG9lZifp8sHeVlqiobU6of+/Gyr+KD/7lf8hkUpKkAc//nb/wb+HO45BDDjnkkP/gOJTN/r5xOEj5NuHcwzdZvJWxHaeklFwIx4BHr0CuNOVqzfSbmvs3F1C+ZLweIpUge6TP+KMDkk1PlHlEJVh+1ZAGGhtLXEtBFKBGEu9gGifYkUTs1+gbApPEhN/aR85yrt0/ylv7R6EF/UhQBSG5KYicoRwHtCZQRw5fljDzSB1AJ6Y+3iLZEogwAKUw7YDqlCK6PsWf1+i+IxgMGF1rYbYVbVc2zTBZQf6eM9hj0JqbEL4VUPYdyW5NlFc4H+MrwcH9DiqOIZTM9iSz6wOmHy4YvCQoj8XksURZ8AEcP7PL9HIbH3hyL9mtJaGzHFxOSe47jh+fsvpHD/jUzz6BnhnqrmZy0nI3SHl1tki7nzErBnjRSE7DHYs9SJibVczOpxglsPspbrnGh5oFOeKhP3mTr/3zZYIjgtmtCG+aTx5RO8KbFvVExeZOzK7qMHo0pZWPcbdTXE8zvtPB3LNIa/AHE9qvePLjneaJPGBThRIAjrNP7/DkD9zin9gPEwc1k2qA7CqwHtOS6Ps7dF/cpzzSIhwa0u0alVukUI2IsZUiTYWrHPu/s4TZSqhzg7Ie0w+xg4Rgf4qfSuyJBXRVoo86ppsDLJ4430Pm4EKNDzU+FEyOaJxW2FYPvRsiD3LcXBvbCYk39lHbM6rHY1yg0dsH6ANPEGuKMwlFEKJFTetlA48oZAY21tgYZqcDqEDW4CRkRwNUt6L97C7+jifYCFn74UWqoxqfVUS7mnoRfCAoqgBRQnjf0n1hyqm/fJ/tvxNRbpSI8wu4lsbMLMwyqlZEsaARbYHrOKRpKomJNDK3lMc7BIWg93pO65UtZBEirEHmDiUDbKIo5gQ2VkjrCfdrsoUAaSSpCSGSDB9RpGs13VfG2Mdb5ComH0Ska5a6C8JKor4lfHyP3a/0EXnRtOx4TyfKiRixV6WEL01QM4N5IsR322gtyB/qI0YZQsimvliKZqvANxtVKIWYzpBA1m0zuxIS2hlFV1LOKfRIY1LF+J1LgAdXs/c7GvEBqPYC4sEIc7bFbJbiypwgauEAvd/EepBNy5Y0np33tMlWJI9M92l3a+TpCbtPp5gDjUsdNnZELcnJx++z9sJx/ANnTP9CRXVkn7vVHIEtCGcSWQNKojKH6QWMzy5Rp57V39hA5hXBVYd9fZH5o7tMRcBEzVO+GTT1zvUMMSvwLQUWrBbIUYWv6iaKZHzT9mM9ZqDwvRQ9swRrQ5ytiHYt/d8SmMc8O6cSktxgtcbkKaTg+hHWSlwJpqWxmkYi3Qo5fuI+7q6jFc7ITRuPxAuJV76pM1YeXdSIjT0EDh9EyLwE77FlCWeWyJ5YRdaOZC3HFxbiFNc1qFwgnSK9VzHeCPBdRW00lB5T1oSpocybgdXtr3Qoj0UkWyAPUnzb0vvGFvqjAe39GcFcTF4blh73zG7HZBu2GaYIAcgm7hZLfKgRrhERizjCLnQQRYhpFejtKd1vjCjf0aP99im9D40wF0I2fmYFkUREU0v/lQNaG4YkNBRZjKsNQqrmZ1Ha3404RXsGYT2y9BAKogNHtFFi45DXXp4j3HcU1wPU0S7qtRuNCDeOef3/tQxJCIsDqCo612YcvCvBtBXiZrMtEnoweU7xmQTKGm0lVBVsZZRbEpGVxKMZKjOkZ2dkSURUNT8vFYS8+fYeT80v8Iv/4hvcfnOdYFhTdUNk3KI/3+JgbZfq9T3eHK3xE//FJ3jx2ct8xyeeAEAKwXtOHv+fXe8nrs/URmy5Lu+oc5b+FwxSNrZHlPcydCjIqf+1N1kOOeSQQw455JDfy+Eg5duEZ69c4uDF4zzxwcs8dHydoZeAJOxMce+qubi0x9YbbR4+swnW0qksVR1QHh/ggaInGPdTZFbhpcB5gXOgpMJLgS4FqoZQ1Dx09j6bN+YQ1xVqXnHwaJveayXxRs74QozKGhljeXyMzxKKUiOkbWp6X94EU2L7XVwU4DugLrRx9w8QpWhy+LUl/u17iMIy+tF50l5MdBfCmcNpxfzNDDnKsasO2R+z9N4Z3fmM0Vs9Bi8pOncsquWYBjW+Jdn/7uOouZzQWVJZErxVc763QTFdQI81esfhIsdf+b/+KkHl+afb7yFDM39szMrJDQD6pydUdcQH33eZS7+2SmvbQpmRnYgZTGd899xNnu5u8VvTs2Tnehy8nmJj6F9tqnqFsKiy8S+EdkY4iai7McVmRjL0RNdLdn5WUURB83RZCtxjixgvUFZy7eocGx9cpJiPaW3PUC9Z1J4lmuQIJLtPtwh3Q/rf2sccjzHtpvVBeAejKUQhsz2HPRKR32q+hpIeJxtvpsoc5cUB9bYjvT9F2/1mK6Fs2iK8tQj7QFI7L8lHMcYGjfw0LymP9hCyqXBVXhAQ0j4GZ9+/zldvn8QFHqccJtDUcxE696jC4VQjeLUiwCyHiKUuaIGqPHoimT6UUq4EiLpm8XP3m9+r0VH8Zg9fgW97grEjGjbOiqrtsTFMT2uCXYkwAkKPlJ6klzexmSRE6Bb9N0u236/wK4LkmzPaa5b9xzuIWwFiBMmNAh/AsXNj4u/rc/nrJwlqwBhEWUFW4AJwUpBsSeo5cMqx+IptGn08yMI3GwtOEaxbphdiKAQurHBI6p7GSY/TEtfx9Hq7ZPUxHGDCmtlRUJVl8JVt/MaY5aOe6YcWGd3rcLCY4GJBnAk2w+PIbxraty2u02L8xIDWlRHZOKB6xSIXDCrzCKdIL+8xe3cbocBKhVtuo2qBKD1KBo2DQ0vykzH63ojAeWwnxLZiZAa+HzMTEtNRxJnF64BkoybcmSKt4vipMcGXFSotefR/t8uNs8uMfnUOcwJkLhoJ8ThvNl6so45AhpK6JXGRZHA+RzhY31/C7DqMUhgLvpYw9Vwzp1AP4hF6FrLzfJ+WqBgEvnFjJBqfN0/0vZDYNKTsQlBV2F6EKCp8HOKnAvu5EhG3GQ9bmEEHeo4ovIerKiqR0N5SCOcQQjUbPAiE99TCYssC4ZoWq2CYw7RAzjIAko0Au+MRcyGj9gqzsWlqooXF9gJEIZkdE+gSAuvJOpK6LcgIafmaadHC9DRqWmMSTTSx/+OQwptmy8ManFAo55raYmNQexnhfgenPMHuFKEDPB5RddBbU+yywkcBC79huDJe5b98/WPwbMl0y+EOZoDHO4t1MWYzIsrAFR5GE6LLB4jrXfQnAopC0k8MH/9jlxnd1Xz6L5/ARQEyy5stJgTa52TvOoO6t0O8McFLIHF0XtsmuluQJzGBLcjLGGk8MnEUhKjS473Ehwk28uhRhQ0dKq+wQYDPsqb+fO8A5ueaTScB4VsV81/YZ/v7lkAqhNTQjhm/1aV3a0x0s8At9nBvO0F4bQfwTaW0bz7XQCCkorXjkQaqxYTg3pBqJeDI2/c48eNTrt9ZYfq32/g4JFyv8dkMEwRUC12iUUX2OUnbC1yRQxg1g5hrQ168fQ0ThNTjEkKBFw51fUzR7SJ6FWzO+NjSRX7+Jz/D689f5dxDS3zHH3rmX3m9T6rv4jdvf4uDvM27whlLZ+ebFiv43eHItHwR8LSjd+Kc4/v/i38CsUAfGGqhKcqaJA7/bdx+HHLIIYcccsj/X3I4SPk24Ut3TtCqBM/dvcATq/cJC4mXjurrlj/x51/jwuIO/6fou5tWG+nJ0xBvQ8hK+t/I0DMPoxkoxfbHV3jiHVe5+Y/OIHKHWdToKbQ3LflqRD1OEV4iA3jswg2ySjD8SpfwlRnTcyHCe7J5kC2YO3ZAUYRoDGocoaY1WEv9aAsOMmQlKOcrkkWNmST4TowcTRF1s5fmxrA+m2NpwyDtg6fmzsFkRmA9Kx/cxu1LykQRjCXOQTEfIiJHLUFJCT7AhzW+FgS3LKznHLs65eE/ssWvvfQkfuooFgS/dOkp+r5gVCUksmD5+Iw5XQIw6yrco5ZpqfjGZx9CGEm+GmFjj8glsvT0oxm1lQRHKnjJUnY0yRp4JXHCEd2bUF1oY+oIF0mSUYbfmbL1Gx2CiePMiSmblzW2GwMQPR1T3KkpB7B2bsDx+/sM+x3aM8doEIGpmc9mOK0w/QFmMaG40EKdc4iXDQiBKGtwFjeZsP1swE9919PIBYt8swbr0JlDOI/b2SfoL6CkRQqHCA22E2OXuvjhlOxMj3Qtx4+mnPyeKQ89vsulb53mzjeWqAYJtqdBgJx61JaHLCdLAo4+tMtgNqRC4AZtlJcEU4tJFcFOSXxf8v9m7z+jLUvzs07w95ptjz/X37AZkREZmZG+qrJMllOVVFLJARI0Ro3UQNMwwg4tmsauhjXTjRVOwDS0BEhISEIqWYpSeZuVWZmVPjMyvL/+3uPPdq+ZDztQL4Zea4AeCbrmPmvFx7j3nHPP3vt9n/f//B4fSmysEKGom4asRxmBWWyQrUA0kJhIkz3QxxtDf/8uqe6RqxajU11Eu8Lfi6u4UCErCIYSpwUCUTNYAsNKY8r26WXcuERoMLHBtg1i5km28rrW+aDi+Ml9Lg6XKdcbcLPioyvX+dGtJ1HjHGkcyy9EmFaAX+4iQ123B1WS/hsKIUFVHqIYr+vX42YWPa6nHJQI8Z0Y2jBrOUxDIxzYyLN+5ICnnrjEr/7EOiYBFziyBYVNJaK0uEAy7qcsdMfkX49wZxo1y8RYxIWKB/78Dd64+ABWR+T3RSQ3JXJqGXw0pWpq4s2UcOhwyy2MKCk7IeHQooysG0aUgKCuZhYGoIUcZPWUShQgckMalxQEhBOHUxXxtQE+VlRdjW21icYeoyoe+WMbHF8c8sjSAN2+wf80WKtTKeMcMc3rqKCvoZymn+KFoHPJku861AnHa1vH2c9bNJYqmFSUQQKAP5qjL4aEsUBYj9USUximRYjekFQ+xSuLTeu/vYuBWJAMSsxZi10L0NO6iczuTpmuKKpCkx+LqBqCxnBaA1S9QXS66GmJqwzVkS5xZmoQc0dQ9BJsJKgagnC3QE0qcB7uQXftWpPRB1Ma1y3phQyXBsjtCXgQVUi0YQjGBtNL8JFGZZ5iSbC30WVlcc6teaM2JKTEa1EbJ0IgQlX/jcKgnkKLAqx32E6C1BolJOnlYW3EthL01hAxy6DfwTSi+rsHuEhw+u6I3/nfvMhGs8HH/snD+Fgg3Lxu8pKaaOJwgUDsTZEHM0QUQwB3p0fxKiQ8EOQHnuFNXU8IelsbGPem8aLtCe1vh90DjW8kiDDAupR4YwTG0Pz9GYOPH8OvhMTnr2G3FPNfisE6hJCYToTtS4zbRc4jSAKETxG/bhw5nAAZh3gtWf90yds/dIOXn51z99wp/GyGLhxRVTD5YECq5zSfvwMLPYSSiDDAz+aQWXy+he+kuLCBcBBGhqRlCGNF8SlB/49aOt2CtpoyWjwCQPLGAW7jACUEvP0oXlS4WFIFAbNU0Nq24GHhrTEH2V3Of+gJihOLDEb76MwSDSpMbhBpgptM+fyPfpknv+khyrzk7OMn/w+f89t3Dmi0Eo61+rzxiXPgYH99xjP7V/iFH/0SSwtN/vhf/C3IYMhbOz/EwAjee+Qn+MLLI3zdRo2NJbGBLD80Ug51qEMd6htSvj4k/c+u/xJew2+w5H/uF3Co/98omBv0uETnjn/z84/yP/6d34MNPV5YXv7UGm9cWyOIHXE7I+4U6MqhZxXVWszo6QXGD7cRXuGX+0QzyTBNcNMSP5mjhzkyh7IhsAq2ix7DsEG1InDnM5585zVsHFB1AxaaU6q2Z7ZqMVWDZOpYcxnNX+wQHziq5Ra23cC7GLPWYfjODrYRIqegSlPHCVopPLJA+cA6gwe6CCMZxyHzRY2sDPnxNvn5dUyvy/ZnG4z+tmb3HzVxXuIUZF1D8+I+rSsHlD2JrDSLny3ofs1QbCpmRwNezo4RLnmiwGDaIe943w0+Gl7iztYKA9Hk+Pfukb5zDkisVzy+fIvqZsLNSZfOsSki9rgoROIZV03+yS++n5dmC2yWHUyrhulGE+oNd6KQyzWsVXpHKC16kNO4OsSXinnW4NQHct75B7bxpUNOMuQ4Y3jLYbTGdmCmEpKzgApZeMc+VdMzPR9RPRSx3ByzMJmz+skDZGlpj31tNgkQxkG7heh2IG6y/Mv7LL02ovexIc2rW/U0iBaoaYZ6Y4z8zhj1PRHZ+9pkS4r5qS6j9x2nOtYiP9ZAWIe9VHH27Xe4f/k6ejhFT3L0HJpphlkMMRFgKuYHAZ/8648xfyVittFithxg/+0UTOnIFzTZyQCpJEHmkKUH6/FRDeStdIUIdA2UtILxu9aZPHGUohVTLRlkXEHkCBolwdShs9pk89KRblYEhcUkDu9gNo4YTWOCS3sEW2P0xpDGmxOCjZBoL2J4vs34wRa6a/ngk6+y8tIevVdmRAeOXjJh+GIBOwd46yDwBMM54LGhwkQSaTwq96i5gKUKu2CwUQ2HLbVFbOzgcDXnxXqE94T7JcGwwjQ9tuWYTALKKkZYS9GTzI5HOOko+4Ldjx5h+s5jFDQZfqWL25fc2xdhlEDe2ObVP9fGBDHCS4RKGT21iGjU4FrhPWUrolpqQKhwsULPQVegC48yjvTiqI6kWYcXUIV1IxGhRu2MSd7aoXo2w96SNO8aVr44QEQtsvNrNYhYQNkO2bvbY7/bZD9t8Fq+yLTQiKLCeYs1GeruHnI4oVhs4JoxQQ7RyBBMLOnVKSMbMZwlOCPQlUEvlRA40I5yXTM/5bGa2pwclcj3JfCtnnLJUTUkMrtX1WwdtByjkwrpE+KLKVZXNcekEcHpBv63NdBv9zTvVLRvVsgowDdjRK9LoENEYRD3NvZOOJhMcdZAqPFRgJcC0w4xzXozL8IAOinjRxLMAmQPWPTBjPyIYPKONiYKULN6mkYA0nmc9JTdOr7z6MO3+D2/7zm+6ZsuIKYlQtQ136i69tqGEq//7WNbIrICbwu8mFKtRthE19DbQFGl8l7EC1RWoAYTnHd475B7U/rhiCTKOXJ8iMchowDZbCKjCBd7MNC4UxC5AHtiGddKcb0uwVjgpMdEKT/7dz7IF37ygdrcCUJUr4uIwvpnTD2z/21Idm6JcrUJWmEiwf43rZGvNOl2Cmy3gRxpDu60yV+L8ZsSMcupfEHZUYglyD/UhjipOT06wGsNOoAkhv0hXknwHuED3vjpNjxvSC/to4cFan9G460xMrfofYMcz2E8w+U5bjbHG4tPY8RSH5kb0gs7BNOKt91/hQ88/Ar32dtk621eGh7l9eE6L90+8evXHcbgnUMkMdFBSbmQMD/VI50KGo0msrLUszkC6wX9JOToWgfTrivLj59aQFHD14VU/NRf+2XeevkWw3FJ2k7+vWf8rStb/D//b/+Mv/YnfoJuIyIcQTCFC5e3+PGffoYLl7e4eHWHfF6ihOentpf58c2TfHX3V/jLP/pJCAUuEDB1YAXf9UP/K1lR/cYtSg51qEMd6lCH+gbX4UTKN4hU5Qgm9Wa0gWPuZD2GPve8+sZZnrl6lhPlLivJHA+oaYXPHQ5BkQaUxzXpiSVEFBJNBEWs8EG9+VelJMg9PlI0785593e/zmtXTjBsh1zYOUnDGZwrCLVhQh8fenoXJnyovMHnzpxiKhTnH93j2tdX8J0U0VBIqRA2wC7GxCPN0ge3uPvl1dra8w7R8oTbBrEZ4ZXApFCl0HplRn6qy/RdK0RXdmm9dIALA4qygZc1D6NxYFGiBfJetMJa1r5nj/EwoWpE7NsWepxw9UqPfCcirBzXfnmNIitonhpxdEHyhjvL04svc7lsYZBc+alTNLXn8s462R+xuH8hQaiak5B6LrLAzQtvI0g91eUIF0uQgnwBGnnO6ac2uPCxdc6cu0uzO+PVL5zBJgpTea4Fa7gjR+GVNo++9yKvfPEM87YmvnBA2U+InhQc6w+4enWNwbRFPo7wscYKibACqxTFWJJOYeHLBfmHm4TvKdAXDbkOSWYBhAHYjHBrQrHcQHYlxgSYtB4Dz0/28J0u5Q2FjyXhiw4hcqpuTLhl8DrExRpnKra/Ijkt93gluw9hAVkbNtmdgPjOABmnCGU5cWbI9KUQMRiTrywilMamNUdBOEvR1bhAYOL6MN+0720Qfc3MaH99j71vWcc1auhjMANhBf4jC8h3DRH/Zoy+aGisBLhRvb2ZrUnKlsTGjmzhXtOQAjOLmLmA1NTRC9sApwTxTs0FMa0AGwlaZc5b148yPJXS/9wEM5/z9/7qOxiuHIHMUi0lDN6jaD47p7FZUfQihBaEU4NNNLqVkfzhEfbvgtvQVI0GjGd4pRGRxiSKZLOefhHeQKBROHwKB3uaT/+9c+jAEkwValjibEl5NKJxoNA6pdhyjBcDZMfj4hqQa7oSl+XIucRVFh8oOi9PUZMxrhWx+KqtuTGLKXLfkN6d0JoV2GM9vJBUjdp4Mz0Ntya/HovwoWLy6CLdl3bAOGwYU6108c7hYoVdb+MTTTAuiW/N0QdzsrWUhQfnnGtvAIq+nBP1CubrAWUakH0gIb1VsPzZEaI0kDmU1th5hsogyizFtsQdrbmq+XvgnNpmOIyxoUZ4SbLliEcWqxxSKaoNxdZBE78S0L7oCCYFLqpjZyqpCKMSLwP0VkVy1WDjiGqlidq3JAdghwJTelThcIHE9dt4JSiVQ8YKMbd4D74oobSo3QlBv4nXUT1ZowTVUowMw/qabzmyxRg5gOZLBvmRgPBDY4YbKdEgobs0p1qf4T7tIVSIQGKVp/XGgFtRk/0nm2zd6SFLA1KBAxcHCO9RpoYqYwzCWIQS+HmOawTMHghpXAI1qpAewqmDThPRaEBZIkyFlzX7xq81uVD0+NkfHjO+HdeQbiXASXyomJ+uuVHztYD5ekzv+YrQNBCivkb/7b1WGI+JEkRfcvS7h3RP3OHK/6tJeaDryE3uSS8eIHOLykpkUZAf6YETzP4fbZJHc9QwZPqxHmYrgkaI9DnBxdvYfE7woSbBCxPELKx/qZf4vEAlMT7P6wlFJUBIPI5yIgBLcn2E6ywhiwPiFyeELwwQ4l59dBpC1USMZyBrQ1JoVUcwnSXamnP7tQ63pj3yy5Yw1ty81WXroEtrL0dgavaS0vhmyuxoA9GNmJ1OEVLQOtphNp9TCoeWCvIKkaR8/XNv4M4dITsmqDoaczGvJ42cxZuK8d6Qn/mxLxAHmq986g2+8/e8+995xt+5vsfm7X1Gwzljf4unP/oKV189wn5zztPvOsMvbYxR3bQGfrPI5rzNdtFkUIDYLIi9wMQerQRFAuag5J9+8jl+8Lve+xu8OjnUoQ51qEMd6htTh0bKN4jU0CLnFXIc873f/iZPnt7mD/2l344PNA+dSNlYvUryfEkzqdkBYmKQs5JsOUaU9ddg1pkRhYqsXdD92C4TvwK9EBdpVFFPaOUNzSt3TrLjW1ihSOcFARXhfkEwL8myAJ0pmm9VXB72aJ+fE697JrdjnPI0J0OUcNisBUYheo6FZklQCPTnK/zFDVyicHcrZOUIL/Uoj4eUrbqVQjmBykqKhZhSNznXvEMoFa+LNaoEZOnr9tilgLybEM4890U7PPL2W5RO8vEvPUW875EHhq9fP4btOILCMKsCtvfbjIM+nYcsxfWUvQ82eaM4Qm40WS+gDBW3Oy3yeYBpTxj3e7iohmU244JoYNl5rot9USKCgqobk1yf48c5V3tLlP2QLdPhYH+Nsw8e0HnY8ie/4wv8+I+f4/Pjh3jt+eM80bck/51l76td4reGBENDiGJ/liBjiygtOixwQYOenzOcLDFqLmNkgU8ce781xvQlT3OH6D7HV37mGM45ZF5x+rt3eejEbX7yvjXmfyql+1zG9O49tsj9C8TDOXo/R17aIEqbuKSBsCDCkGi3RN/YIawkRjb52ueOMthtQhgincIhCLb3kcMxztUA28Wnco4HFS9dvY9432E96JnB4wn250SBYPZQCTdSwtiSLZa4bQj6nmyekK+GdF4ZMH2oR+ONAZPHFiBQjLY79F6eMn0hojqnGQtNtixRc0E8kHUrTlliojpuBB7hQLxuEVEEAorVmOG7FvGRQE8dna/cwSvP4P1rXNtbYH6sTfWRiNWP3eTapzqI+wJEI0IXgv6XBTuP9xEYym6I1QITKcJJBWXB/EKLaH1KvGfw1lEud7HeI4IIoTReOKR3yNG8rredCIJ9j4gbFCccjbuG9uWshgUvSHSzIOh7xCzGNSI0mnzh18/FEQbsiSV0J6KyHh8KcA41NDgqON5COXCLFfNlQfLSALu6ho1qo9FKgU0VZbtJvFuRDBxWSXQFMmhRrMyJ9gryB5ahG1DqirJd82eCmSAdGoL9DOcs6XbBQm8PKT1vTJe5I3uc3j8gHFR1ZXArZXZcASP0KEc0G/WURdJEb40Qto5r3APnIMaS5I2Q5ImKmdT43GMDiQlBOFXXqB9YROEhdEQDh49DwKOnBdXQE72uaw6qMXghEcIjshJ3MSefNnB3FLqsozmmoRBpgI0keSeoI2M9xfyIpv+ywbdTfBxTdetplGQrRzhPMKkbidCaaFgRjBxojSUkeseAIAE7DpB5xEf+m6/ywg+vsDtv4NUcFSqkUQTjilEe8cN/7VvxgNMGmVeo0bQ2LpoNbDtC7czuTWVoRFmirMTPIb5syJdSUJJgbnF4rHMEgcCnGiEE+mAOWpEfa7PUG3P8d4yYXJoz+CcdCCQUBiEk0WUPap/hk11cqKl6IfHWhNZHpmxvNpAG9P4UceUuupMinWDx0TlRz5Cu5hTiCOwOQEK12CB9fgPnPI2XcmbHE4QKEPuS6Aa41KMbDr02xQx6uKpABJrAB6jP78Ln5/ilPn6hDfMc8gyX5TXXJI7x3uHSCDmaQVjDZ4u1hPjiFqpygMPHdU21TwOkvGcsNxu/DlO2+/uUax3yR5aJd0tu5z3SKQTRjGxFEocV7Ao6nRmyTMFJgl7M1ntXcI0Abx1VF4KxY++JmIOrOe13nkX988t1VMob3vNb3s4nbm4RTsFEjsaJPqlVxPoOFz5ZG2ZFGHDi9Ar91c6/94w/99hxzj1+nFMPrrOTX2Dp9D6DJEAvv8jum08zm+Y0Qk2ShEjpabsGW3lMU92PjV+qJyJzX08VahAdzYWd7d+Q9cihDnWoQx3qP6MOW3t+03QY7fkGkVMBLgzxUpPnitXlEe14Rtpr0tfP8APv/DqN5TkzCVMhwNesEgqPDT0u8FgEOjO0Xhmy+ZMJcm7reIjzyMpitadc0Mz2G6S5Ib0CydcVXHGIyuGmimPtHZz07L2rw1y1sL8kKP9OyMGNBuffc5VTPzjAnqoXnnpSsfCvDigv5zTX5jAtEGWFHGdk59vYdszsvMWEHps4vDQI54mGlsbNis5rEx5b26d4dc6xvZuI2KBnjvw9OXsfEOSLEO2W3NVtnv+RY7z8vxyhuVGQbleEe3X1sry7hdvdoWxIhk+uoo5LinlIMDFsmjZvTlZ5bbzGqSfvkh0VlFJyvHnAQJzAe0kwlqhMMysi9kWLtXft0VuZ0bwxo3FtivYBJkw5GKYorzjY6cC+5Pve/wL/y+/+OPctDfmOj97gEXWT5LphVDU4enKP+YmE6ZMp5VqT2SDlseUNjq3ssLA44nj/AN+xFIGmcWRGeHxSn+7vDQjuZngJR+/f4Mw7rxDd3EcKiaBk8fdlnPjOjGZU0o4KHBoTg43AdhQu0FSLKUw8zoKTHlXWG3Ybgy6oa0zjiJ/+W0/z8suncaGkbAeUCx51UuDPdGGhiVhb4crXlrk1TShciLeSsimRWd2u4QKBFwJ7KcKFAhsL4i8P6HzmLuUdT7idI43Hzw3hgSU/3sBHEhuDCyTiM6BvFTS/PmE61RQdR74o62pRKTCJR4QOYovE883NV2lMLN5bfFkS7RSkNzJMBDIHee+k3waSreEC3ghcrKnu66Mf6tDQM0SWw6Sumq1aAeP7E9TumGi/wMVQ9kNMEjB/vo2Zdnjgg3N8N8C1IsyxRWw3JR7YGgYpBFJFRKMCj4QqwGkJQhDc2kHf2kXOC0gkC60Z/ffsYU9UlF2Ndw49rGhdK4n3PUEOMkpg5jGJxoYSmZt6Ii0OkXd2EZs7dF7cIdme4nop5mgHF9aAU5cqfCBQUwgnHhsHmF5cn5RLyFdSPL42NqOK1c6AaN/RfH1A+9k7iO0hbj5DeAFRyO3PL5H5AICgKvnyz51H6AAQdUyr8KAVVUdTtiT5coiLAyhL/Dwjv6VYi0cElSN4S3FwrYNPBCq0NZB0RVG1FThHfqSB/lbPieUtoi2HUwqbBgjjEJVBe4sP6ye5bMl714Kowae5Q720QTCramaGgGBUMF9QFE2omgKfF0zXLNmKY/5AG5E0UEGAmlTIWYmoHAKJnhnKtiJvS8TOkPbrGe0rBQuTIcXnY8xnmgSfSwibFXFa0joj8P0OtFKc9GijmZxpUfVTbKQoF0KK9QZVL0Q4B8ZRdRKWz45Yf8cAkQYQaLzWiFYLlhbIj0Q0Lw6ptCPrayppa6YKoMY5aIV2Em0EclQgpAdnEcpQLCbIfFxPeRiDt6BGGZ2XhnRfndN4ZYALDK0ncjrNKfG+IaLgu/7iFe7/7fs4Lbn8wyFX/1HM4CWNiCXDP3SK3f/6IfIz3Zr1UlWomWXlxy8RXrlDJT16e4baGrP4gTuc+b47rB25DBu7VA+sUi00cZ+e1eNq3tXXX6gRy4uQxIgkQYQavzdCDzPkOIeqfq419kI4soQ7sgiBRndj1GMdmGV1dNJRQ4SrCgKNayXIJK55VkrSvjZFzSvErID1Er1YoU6WmEsKMTfIrMTMK2wnQBYlCy8OaL81R1SW9PltFj5/h4d3QFYGWVYI67n76i3uP7OGqgTxAcRNzfs+dI7f86c7tbknwM8m/IW/+328+5se/Pee8fPhlHJzlxvPvcV9yftpRRUn17c42XuTje0hbpBxcOuA3e0hQmhWkvfhyjVefUtgGhKTShweZQAtkKXns3du/Tqk9lCHOtShDnWoQ/3H6XAi5RtE0UhCFNDYgtJUjCqHV4KZ95y/f5cjakp4tOCtYgHwTD4oERdiglJSRg7hIM4U4dgzX2nhH85hVJ/kS+vqxd9+fZJ19vE7vHn5BKaUFEXApeunQOWo0JJMHY2yoHGjRHQ1thmjpoCW3HzzKOUMgtc20PvXcSuLiCpj9izs9Tv4OEKUBhdLDt6+SC+paK6PMTc8ogfRJYEJBVJAOHE0b1dclwtkpWK31SW/36AqWHpoyuU3usShZr4O80WNv92nV83IRyHRyFIGinR6gBl7hPHYcUX3nSVplHN70KS/dQDKs1100M6Qf0aTxlOOf/OA8GvQvpgxOxbQf33O7FjIsafnFCVgPaLhcArypZDGHqCgs1HiF+tJjcZCzq9tHmF9YcCbl8/wT7/+FKv3H5AWliNP7XPp+XXCiccshgS3LL5MeCjd5Jd2H2PkYzbGPcwkxMSC0Aw5sbXL5UEfUVZ0PnWAbXZ4bXaazsqQohMQDXJUmDP2EUYIPGCcxOoIk/oaJloCQYCYzXHe4xZa+DTFxxE48LJCDqd4ByJoIEqHTRWT+0PmPU3VdQQbfaL9OcF0Di1Hms1I10qCuaG0ER6Pl6KONDVaSAfRsMAkCrXrsIZ77RnQfGULNSooz64QDDzimGeQgyogUBWP/MGbvPojJ8gvO+TUICONALJlT9l1KB8iK4XPJCvbY4KjggFLmPcJ2s9toCuLCQVOA0owftsRZqugEseDZza4O+qx2Y/YPJKwf7tJfLOiN9/BlwXqTEDjpqb37Bbq7pBqtUH+nuN0P3yXoF9w8KlFytcivjY4jdPiHvTLo/bGmON9wNdxqFDj5L3aJF+zErx2+ETUkREc0yWJ320RvdzA70b4WGATiR5bwk1DMfF4BWKW43pNdAVWGLITKS4QBHGEaSX4piV+ZY/wpqHoNpEzg0s1LgKde0olcKmsJyFaMbOTAdNjqjZr5hHFuVXiW0MWHi8Z3WwSTQVy7tBbY1QSI9IGGIsrS+hHlKXiQ/0LpKbi6Ltm/NIvvwsRKZI7Jem1Kb4y5Ks9Bo8nIBwLLxew2kbtjdlpdsm2mvhJQL4UcXMhxM0y5IpFWoEsqLkSDuQ4wz87xnQDxFjW4NVWSDRxoMDvWHSRQxDgtyeAR8wqIudwozFuYQlUfaIgpOe3/pGXufhSh4ufXECuJuQyQE0M5sEmww/3af38BI/ANOp6cd8OoHTkvZCqGeADiW0n6GnJ6YUR/+2feZbNu00uqA7ubYKv/suHmJuQXDTw/QZUDt8IkFagJ7Up5iKH8LUBQhJQrfcRWYE0jvd+55vsXu2wceUIeIPIPSQxphthG5r5iZRks0JKi5eSeo987/ul5D2ALYTtnHGl2Xs1Ze+liLKnCTY00pVY6ajeFsLlEBGGxEMQkWb/qR77X1umvSWQUiCTiJNvn9LYvMvFj3WZX/PMb2hQCvtIQfzODHFLUV5W0KjZP0LIe9/1AlEUCK3QN4ekV/d5xzvukPzugn/+pYfYOdWv64xHEcE1gwoifGlg9wCflYjlfm08SIWaZ9iDIZQlynl8VeKSGCKN72jczhC5m+H2C2ScQFHg8VBWIEQdN13pIz00r2TECxGz1RYECpGXxMOcxc4AlwvW3z/l1qcW8UYhc4sJDMo5kB41MzSmJeWNAXJcMK0MtttE5CWkESfPr/N7//Rv5Q/85X9BxQ7z1y/wsUsRv7fzbbTW7jDeLAizkrWj/f/DWuK0nRImIf21HknYZjUKEXJKT0v+6488wV/4uVdxhaEs6qnTy7slmweGX9u4jCzqZiI9sWgD4Z4lyB156bHeow9rkA91qEMd6lCH+o/WoZHyDaJgWo+W67Hnxa3jXPnUMW51F+j7XT736VNsjSQvZWu0Z3MqL9nrt/DvjWluTZmLVr2ZmOfI7YKguUh56hj+Rkk4Mdg0xiQS6cC0JMPNFmUh0JM5ZSMkrAR5WyFbhvs7uxSvt9m4L8Q8baiuJZQjS9Wy9HxG7lKEgbStOP3+A54d9UkvObhq6kV/HJEdTYhv50R3M2YHDZa+vkf+9gZWaLIzXWKGtMQ2MjNMgpR51ESMI2TpUFsF+T+OOHJ3l9l6guh2KBYEg9/eQSSgXrVMdxXNSzm+kghdj11H05J2ZCiagqDKCLI57aBiYlM6asbtd5xkZJqEo5ts/HyfNJ9RpU1U6QiHlrt3u1jlOR7NmBlFfqKDQiNMhqwcVaqwhUFajZQeESj+x7/1Pey1I8q2ZuRDvv+PfprtF5YZvdqjKQy2ZwgvbuOfOMmiGLPQGDE0KXYk62mawtPYnJFnAVUrwEcBqhUSRFM6awOuXTvK9N193AtbfNfvuci3LN5kUgUURiIkFAsasVlHQZSRYDw+1BAoho81SXaheWOCbUfYWCCokzLeGkwYIEpD467ASE//xSFhrlAHU+SswjvYfz1mfkFSLYBIwQW+/hleYBKJKEEVAcGowKSw880rqIkjmGnyE10ab+xS9WKiDGylaOiSMg9QWvDp649z5IF9st0W0VAgdYBtO4qjBhFaMifQM0Uw8qx0ZjTvm7IpQhq3KlpvxrjCEI4rRAWYks6zd4kXQ6Yf6TFoavadxg88yz+zRxJXcK6FSzSuIWmtF4gv7aO3piAlw9NNZkckrUWL39G0RwPsfgeT1NBPbwqCgww5LyiDCh3qe5+jxbYigilY5dGDAt+O2fzuZXrPjkivzqk6bcItjd0GZcApUCVUrQCFwzU0wljibk6umvisolKG/fc36D8fEA3qKQCXeVygEJUnO5oSb80RkWB2qgFeogsQrZKkGjOuAkwjAiGoYkVyb3LBSdj9VKuu++1akCG2HeN7bfTBDD+e4HKFON3k4miNvpvTynMuvnUMWQmS2572zGKSBq5VkR44ol+8zfxIwPhcn17ZqL8jwwamr4jHAuHAoYlLQxEofCKhYQlmDh8IwmFOIRSbxQruuMR7TTKy2DSEVEFREW1PmTyyCI0O4fgu8kIBJkB6WRuFSlJpePLBW3zrh69x8NL9XHELiEzRO1mSuR5Ce2xLkqcW0W/jY0nRVUQjR7A7x8aC5M4M20sgjYleu0nu4nqaJIJhEpAEhqov8YFg/ysSNndxq31MGoC3zFZAGYkuBMHYoPYqfDtEzjLUMMNIyaufP0Gqyxp67Hy94faeaKckme8hH1OUb1noNahaGm88cjSpJwu9Z3wyxCaSxmLGycV9Tr1jwOKxkFs/4SCvW4fEMCd9+4h8cBRhPKIweOFr40gLiC2UAr9v+PQ/Oc3+9aieXBOijspoDRslfiKxQ41H1u06qcDnJVgDUiJub0EYUC60MNtwkVXSdoETMe1fe4vphx/Ara/AZAa2nuTyiJpnMp5BEiGFxQuQTqDiFDeZIeIY6QT+YIYIgpqjIur7F5XBKlHzUvA1q8vV01bCWPw8Z9htEu3nuFaC2NxHLKb4iUTkHmUr5CyDmcFLiR47qk7EwaMKh6SdQTeJuWsypv0Y5gXEAYwzPvGzz3G9yri8nnM8yqledRRzy4XnNjny4Ckmg2s4J/hn//AzfNtveZK1o/1/5xmvwoBjTz3ImYfr1qDjoWdJbqBY5OwD67SP97DeIaPahLt6sIeIM26OSjpC4RFEpmZGRXOHDT1BUt+LDnWoQx3qUIc61H+8Do2UbxCJwiC9wAfw1dfOIJMQrwE8577tNuv3HbD3+Q7/+vZ5jJW4gYJlxXSlRUCBCAUyTfGyQs0NhQhRTY+zkqIXgBAU7RpuOXUpVRiiImhuwun33ebCcy3MvGKykVDcsLTeuIk9H5E92MF1Q0SZI287Wmtj6KY0lOBdT1xj+1MR+3uag+fBb+3A0VWSuSb93DZYT/MFTfLilOhSRra2ijqS0/7gEDLP2dVd4kbMYL+F6wm6r+W0LgIFYCxisEfxUBvhBY2dCr+q0PcZMkLEHLo6J7tjwUuSdccPffALXFIhn9y4j5tvNGkLw9F0xMakQac9Y3PS4urVHvG0xBcZ2coiQmuqpiZ8LSB9fEJzPcOdlexuL6Mqh7S+/uyaAfFBSSoLkk3L9VeOYuYhPhbIyhPMLDtX2nC6xD9QETwD8c1dhFS4huT6eBlVWqpKE9/KmFmBtLC7v8T4TIQYF4iwBCHRM89DKxsoJzioGuws9vn8M/fxpfn99CNovNugA49rajJfokyMU1BJT+ipWTsyJhzn9+paSwQS4Wt4q/MOUVnCuUf4ioWDDHF9m/L8EdxCiqimiKIgP9bG5AqXRuDBpAoXSKqGxCYKbSw28OAkRU/VVcFdTTS3zM/1sKGjWtZkgSYuStp5CfMAJxTDgybhSzksp8jYYJuOybqF0OMKxQm5Q35xDZVZ0tM5z19fQYWWledKAp1gtSE/kqKnHjm36HGJzA2Tss2dootIHHFpwIJ2joCM/TNdquMave+welrfPJWiWm8SzCB7rU/0Qk701gzmOYo6wuOEJl+MsEdAeoPazxHG4bOcanGV+bIC4SmOpghdR9I2fusC931xB3qOIrWUdyDY1fdiDiAqh5pZRKSQpkJMLWE8ZNxeplyJSC+MSF4eYIXCH1kBoSh6ETaOCPbmOKkZv09hAktyo56uwFTkhSPaGNBsBES7IelmjpjO8UpSHusjS0u4O6dSYJeauOMdgq0pYjxFRBopFNNbbcLZhOd2T+F+VLMbHSGcG5zylF2NdBLfaSBHM/SdKXog2f/ACqufP0DtTUm7ivGZNl7U0zxIgU0lUnqKUuG1Qk8KypUYEUS4hyBcKChcgosFhdIE2uMrgU8kQZbVAE4PftbDroHM9jHNVs22DiV7T4V8NjnLu964RRakeA2qaeF+gb8T0rxQR9FEq4u09UTcbFES54bk6i7eQ/7gCq4haRyMUR3P/i3N3/4L72BveYG9j4QIL+juCVoHBW6/RBYaPy0IIo3e3iHUnuL0CmFW39NdpGtm0bSEeYaYx9z42lGckvgYxGSAmkyRaYKuSpQx3H9yyItXziGlougFyElOvHmAKAysLxFvGExH4wYRN+I+iy8tsf9lQTCscFoi5iXgSa/NqUwG3uOtRA1nLP6bKfbJkPDyDN7bwF/2vPXltdq8MGVtpAQBvsixtxOmX12gakpE5O5VFGlYM3DlXsWTc5CXBBv7qIc1AwI2rqRUpUK6itZnLyF+VxtxB3x2L4UcR/iyhDyHQEOaQG5rM0BQR5W8xI8mqMIiVIVLIkRW3nsvFkqJ0PdsYe+gqFDzEl9WiPmcotGkffOgfn1S48Zg5wqfwa3nerjMICpTV0wrBXj0uMRGIUQBP/Cnvotnrt3mI4+e5a9sDnBfuAuVhSDk0sdeY/FMn+lH1/m273uUT/2zN+h991GmL0fw0l1IE37tF77O8mqH7/j/MFJuXNrk5S9fZK94CfPkj3F5tsyS2uK16RGePqaJ+gmV95S2jmPG7ZIgGjFxCcI0UcLTP9bl/lPLfNnuMHAFranAeo/6jVuaHOpQhzrUoQ71DatDI+UbRDYO8MbhIwmVRUvwJYg04uarCzTiGWJbs7+d4C20tusGFIDyfofAI7ox08cXCDcHBNsRgRFIB+G4otSaaFS3Wix0Zuzv95BeIYTn1purVAst7HhGOd1lWkiSwtA5qHjHg6/xzMUHGfY0wfIM0bbkzwVM84CX31znytI6jfU5Ylrg6vKFGtyjNcIbli5Atd7DFgLZiNHVDK/BTwR3vhTCe5coZYKWEFxRZOsBcqaQgzmzc8uUa5BsWtKvWZSUzL4jxKae6X2ag4MuUTur2RilYm11k8QFXJl3uf3UArevNdg/kjMeR/iXesjVnKXH5iw9OuDN+AguEIzvDxFAcVfQdJ4WBbtXlog25/hAgRf1yXnTUpaSMLGMXYI+Mif66gx/oknz5pxiSfLVf/UA8bcYDtZSdB/SgcIsNfFHDaMiREhNIisiDCYCgcd7QTwUMKygqhAmQJWagU957O1XWH1wl5/8B0+wtDHhvmNTTn90xC/4R1DKgdLMboyYPhUDIIxGCodQkmSjRBuBb8cI4WjuT+oWjkDjVhfq5ou9Cb6R4COFP7nC7GwH4aEzc+ggwB5x3PehA25+pofNJNIIXKTQU4fwFkqDxGGTAJXdi2wosFLU+6xmxPSogkDgrwtsHqEl2BBsAsI61P6U6Xvb5EqQKo19o2SxGvIHv+ur/MTL30o2DTm1vM8LrfMEjYrJeUnnKxVyb0LzdUt75tn6bWtEWz2kNYgEFpMZMrTsqZSDb18g/uyE6mMTErFDPFlFdqhbXzJL0Q8JqwixD25BUZ0OabgpxesONZkggg5hZkBAFllkZOspiKt36ve/1yAahRgN2gHaoZQjumEwWxHNyyXVMYj2NU5opAUnIJjXlbx6WCK0QDUVU9/A9hL0zJNcK9A7c3wnwd87czZJTHxtp+bpNBuEt/qEQtXNXAbmYUzVCYj2HeFBjrYSWVrISlTlKJcbBN0cMXQU2hLkoLzANiMmT6/RuFsR75ZIK4msQ3hPNXD4NYGLJPO+wsWSeLtAjQtyCqJIMX6iS7InyJYimrvjekNegRC1CSk8FELhDVgrcAFMTgWk+wZRVQTPAycUrgmmJQgGdaV3clDitCD/YBs/gPilXXy/BzFY3cTHDcTOHAgxUUglQ/7+D78PmTvc/SmuKZjthygJVcthE0G5K4nHDis90W6BmFqqbgLG45KaC9N/LIc04eBzbQ50DCYhvuYxCigd//Knvhkd7CN9jsxLnE1xqx2qeIq2dTUtsq7J9u0YP5CgJAqPNVVtmnaa0GsjDqZ1m1Ag6b8tZ3IrRY8NSljUtCCPFCKJQZR4INzPiPbB9FPolQQnSppTkK9MmT68RuuNbWQTyospuu1xeYBzEnm8T395SPXQjDK33Ffd5u7yIsudGYuLOzz786v1g0gKfBgwP7OIkLKOV+l6ksR2Aowy6K4g6QiKqxW1KyipFgIqPDOpa5MkChECsDNoh6BSuFfL7ZMEsZXhh2P+rRnipITRpGY4KYcoSkDWkyxxyvhEk9alIUjB/JE1Gl+/Wb/eNEEqhXce4WFyXwsfyZoPlaQwywh8SYhArkP1PoX9WFVXH0uJjwXpjQm9Vya4Zsjw4S4/8ld+hYkpufLAFc5980Nc+OwdUApfGfA1t4ujml9cL7jvDzzCTw5eYdbOWKlqOPVsZ8hTT58BYG97xGB/wpmHjtJfaOLnOV7sUtmMza2Ej117H/vdFr+vsUN57YC8rOg06vv5+V7AQA0ZTAwDmoSh5Od+/I8A8PSf+vsEhUCkHvmfGOsZlWM6Yfs/6f8e6lCHOtShfgN1CJv9TdOhkfINIh8IbBSAd+gwp0oTvAQVal774nFe/5UF+r0cdEwwdgRaY4cec7xARBbh4aH3X+KL49O4VotTf3OKrCqEF2gbUvZiTCowCdwcL+ACgVOSUGZMb7SQQuHDgKXHxrTGU8LLEWpfcLbzFvvLTZ6fHKH4YIX7KxVipqnQPPvSUeJqBxvE+H4KoosPNXkvQDSbRNMCjitspBn3G8TbwO2A8mcjzOsF5XaEjCRWaNSownmFS0HODNWJPpFQVL7edEvlCKYl+gWPeY+huVngLMjxHOGBImRgBHjLQ0XBC282yJTEf8+c/NUGK6eGlG9EbLy9x3f+hTc4OR7yj/7NKaTyOAmmXxBXM2Y7IdPNBlJIKB3TYxHxoqORluyoJvNM4vqOxu0E24VuViBjS2mhMAHBxw3VNwvsOYk4uoYdhRw/usGOa7DYmHBjusD8lKLcsOiRpPn0mPnXulDWJ7buIw7xSMYzV87y/u5r3Mk7zD8Q8Z537LJ+foReKGgN5hipkY0MGXXqDYsHuyxxucCfaJBMZwgT44RHOIsREUEa1SfJcYBXElEUFCe7xAclwmnCsaHsaOR0Dt02v/2bX2Z+Eu5+/D4oPOHUUzUU4e4cOROE8woXQHYkQiqPysHr2uALRjndr+xw8MFjICx6u8DKlCqo36caQdl2NG9U6BsSzlQs9Nq0b2zx/vNvcPT4Lo0kY5o2eP3rxwmftlSpZPC2gPQVSziAaGuGEgHxaIa2oGRtTN3f3YUAplXEwXrCLAxIQ4UwnjKVHD+yz5mHtvi1Z84zOpISDTzNWznJ2ohT33WX1189inphhBx7rJT43SEkEUmvgy8F5XKIXOmg9ibYhkKWgqCwaCnBwPKFMeEzFUJBNI1Y+/kRs3YXEwnUpCIwMO8pwg2LwBJtTplPIqScUQQ9qr6is9PANseYfkI4LXChxK6nuP2aZVItNZA2RpZg4pqfYAPB+D0Nul8oMAtNhPXo0QxZGGykaD484uRHt7n+zBHMJU10bYbMQtRgirN1c4uVFvu4p7M4Yy0cU/4xxUu/tIYfVPfyXZ5wfw7eo1st7n5gGbohzdv1Zs6mAbLwtDY1xlcoEeKBMpR4A155OlccjU1HcGuIS1LEvMRvRbAC+aLDJpL2C2PUdoWSMD3Xw8YSmtGvP9NdqAmv7SDmJU5KVr4icaEkvFFPIshxgW2EzM91YG9K8UgLH0hcLBEjR5A7pscSAizZUkq0NaPx5i4+VGQ9zfz3poSLFfZTMabtMUdL0s/MiQ9CCiLMWpdkuFNzOpTAtVJmj0Y0XzcwK7GdpG7MKi22ESKHDt+I0Hf3IS9gVsBCBxopxuZUCyG3L/Qwaw18zxJkBnVrl7TyiDiCOMQvdJCD2a9HOcKrAo56qoM6ZpbeGNXXd09Q3FU4o/CAns6JfE6y6hFvA9kUBAeWc0eHvP99l9i62uDzn4sozi9DGNQTjfd1kAJUBbKwCAlOKhAaseR57MNDvvYj//vyY/LLHtuPmD+n8XE9wSLbTdy/GkFDI8qDummn3UCImOnvLEh+ags9HuOVQkhRG0YSTFsjVAKbe9BM8UWJO37iniEFs1MpzTcicB6v6mp1PxzjnMOcXURZVUc+wxBfGaZhyO6dBq0fHZOYHG89IgwQ1tP75C1Evwth/fxz3jAtS8qG4sbeASf0Awy+bY3Op7fxFkQaIhFsZhPcQcXdT99Anso5Gw0ZRAEiUFSF4Uf+1I8TBZIvvrSJ9/An/sJ38p4PP0ychlTXz3KfeBs/8qd+EVUo9IPQ/9E2Mg3wgeDK7V1WVzp876mn+LE3L1HsJpQLkt/x/kfw3t+LSNVGfGUsxrr63vMfqINswH//6p/AOYlxjp98+sf/k9YshzrUoQ51qEP9X12HRso3iAanFK27nslqgD46wU7qhoxqUqAFTB5xNG471j+zB8Dk0S4mCZEVLDTnaOFoLwxY+rtD9L5h8MQii1/aqmtjlagjKl4gjaMqQ5QDaQU+k+QNQXhQoWYzbphlJve3Wf5yiXWCTz//IPM3mrSOFQzLFP1wSThxOBsi5xVqYvBpjr89QyHx/Q7VikPcH9HeyHAjS2VnmKMBxmaEmxr/+hzyOT6MKeeKYDTGdSXV/QHBuQn8ssN3BVpZjq7usdFtkX8nxF80BL2Alc8XxLdmqFiQrYcEE4drOP7eD76bD37fDY48kFGtVDx54jZnj97gR575FpaPDbg1fYD9/RZ/43/7KPd94gr8Do9peoQErGX2Gcv+MKJ9fsp8K8TjUFYhr8yZpSFiRZC3JHpa11VjJJMwIrKGhZM7VFsaecriF0rE8QLzjEIeOO7r71I5QegNeaF5dG0DMY0Y7PcwMiCYGOYdgeyleGs5MDGjmyF3995J9FTJ+pEJ2ZJhkMHBF/tkZ0MGokGgHOmeZxD7eiphD5q74I6sIlzNFJD7U0QaovcdotkACaUyxDtTMJaeGyJjyTwTBDsz9IZBjDPIKp54cIOfuP44pQ0QWuC9xwuJCxVCSnxhcEpQhYLIeIKxuAeQBBsq7FJM2KkA6D4w5OBijLgXe6hST94PieYR0YFn9UuewSNzFl6fcuVWl4ffGWLv5EhvGW2GHLmUMzoSEJ2ZUqy0IW9S9CPCecXigweoTxmk97ix5/rWEqJhGU4SbKXxUjM/v0pjuyScw7lTm1QzTTzyxJElyOp4Vn6lwZtvnaBc8FTf0aP9yQLXjpF71G0iUuCjAJEb3LElVNokImDSBxtp0k2LFDAfRcT5rI4lvZhiqw7hzGNXHVVfo3YsPtBQ5IjQI0oLlUFMJnRe2qE4swBhyPxdx0nvVqjc4ZVHjzKCwRyvBIOz6xAKwpGrG2wEYCTN10GESQ1fdQ6baqYPdbFpiNqGwYsGkQnSA/CNGHV7ghrNCFJJebRNdTIlWBqC9HgHJpOIsUFOCtLrFS4OCbam+DhEWI0KFBjAe8qVJi5UFEsReuaJNjJmj4Z1nKwU+FAijWXhlSnCCSqX44IInXqkALTABR45GJJcH2BXVvFAsJ8jcLilDuzMcIElvDxA9DtUJ1eQs5IglzAxMJ7VjA+R47oxuhI0rwxQiSA/2UZWkrKjMalCTy0muhd3KyvkcAZpTJGGiD6gPUJ4pt+WY3owdAnJzxhOnN2iOiHZnPdQVmDSAKs84Q1JMAoQtkIV9fSfyirU3X0oDWr7LtaEeGPBGMRoijM5ZU9RtQSqk1IsR9igNi/aGwpl67YfGYYILSlWYqqWqlve7kjufrbL5Pn63q7mFcI43DUQfdAzh4hzmFeYyjN60bK2vM+N8XEuBV2yn8kYXH+EnbdS4jDFDCtiY2rDcdWAsLiFEB9o8iNtqnOK6nhAo8q4dG0R39lFTLJ66uioQC4Ket9ccvv6CaLXN/HjKTJJsFRIDyIvkUkMWtHbnzB6YAF1aYCMY4SUlI0Q1Wogd4fgJ+QrTVwvJNyoAMWt715FOYHVgrIqCO095hMev7aIUgbiBJNo5gspURwhpaMxq1hu71HOLDZXlL2IaGcGStHYN7j9PYqHj6DmFukVNg6QSlAea5NPS0wcs/v+IzT3SuTOjDC3LN1x/L7Hr3Dx/k263Tmnzm/yK187x92v161jbz1zkf7JJWygQAg+86k3eewdp1GBZjYx3H0lqnlEQOOmZWIrimbEbOQY7k0BeNfqaa5trvGvpjl6XvC1pdt8bvMyH1o/S1FVyMLjtCAO/uOWgf/w4j/nYNqk25jjcb9uzhzqUIc61KEO9f9vOjRSvkH07I/8SR77h/+AhSDEvZZiAxDGg4YqDln94yXF33BUdzQq8LR8htoG368IVE35f+7XTtF6OcN7B2KOcRVB6SFROAWy9ATW1A0vThHM6zadg2VFby9ndaXkibXbXLtwH1vvWeCB1h22h6uwqWjOHNVUMvuOJup9BeZHmwQHFXa1gbozRjhqIKIOWOzNyV6Gogyx7YDOVUNy+Q7rjTlV1mTQiBHUUZdyNUEVOUUngVEFly00JEE6I35fQVYEMIPoyw79gMXPIqQPEFGER9WTAEWB2KooUs3uTswv3T3N5gosnI+5vPsI8bWEy9dOUa0IvPQwyRHCQOURSzkImB8LMDdTODKj8cAU88wyLtHIrCIcZASTnIPTjXr6o3KE2w73rSXNVkFlPHvLbbrfO2FNTbh9eQ19kJN+dgu6IV2dEUnD8CBkkLXJ002WxISR7RK3DZlKKRY02ckm8tiY4E5J77MTmgsljW+yzExEpEuufLzHyz+7xvgvNagWwzpW04F08x5EsvIICxQOpga8rOGwzqFlhh9neOtIrUMgKJc7/MU/+xlWFif80T/3IcxFiZ4aSCMoLD/z/NtwZy0+9rhS1swCJ3DUEa5iMcVpCGcW01R1lEHVHB7bDsiXAty99Xl/e8Z2ofH3sAE+gehAUhxpEIclZhwghhVjmix3J8x1QKXuxdhW5yx2ZywsSXYfTDEvZYR7M2wsmJ9u49ZKmg9PKPdrc2D7Ukp5rMQKBcpjY42II8ROBUi+9JUHaB6ZUxaC9HbB8MGUohFRLkv8rSbHTmww67dZ/OiUnRcD3H1rCGPh9g6cWkcfFMiDHAKFNR7TqOt4C12hdUh1pMl+I2DxC7uIwoJx+FCiRyUmjevTd2qwpzIe223hhEfnAa7TQBUwPRHgQkXzVllHoWJIdgtEmlJ0FNHY4qUljCqMaeEsBBay7w3Qv1IR7kxwrZRqsa4hxzqcgWwr5vzJ67z4zIMI41HzHIzDaYmTDpxjZGJeubbOjY9NkNsNzHIDEUbIgxnRhQ08Ap+GbD3WIN5zzE95rNRIK3FpiFACrCMaFUwUIMFPJaptMR6qToS0MDu7QDiNideH2BMO33f4PGDejpjcXaJ7R9R8FR/iOiFV5JF98AceJ4boeYnenWAiVU+QARxdwVdVXX3dT9HWU7YDkhszyvUOJvQcEbfZPlghHOXMH14EQHUt/nwDMWlhhWD3uRahKfDfZohfkwwfkpRNTfPbdnjymy8jEPzC8x+gKhXCeoojnsa2gEjiQ10zr4TGmQopFCJUeAHiXAP/8h7kOb7fhvEEPbTI3CF9hVcSG0uSzTnVyUVcYfGu5rtIYHp/G5tIojsZS+cGHPmWOaO3pVz7n2X9HYV7N2IBkeCRd9/G71W89vl19Ltj5JkY92yH0ThBdbd5dbtLY2cKviTZrdDTqjZgVwOiA8voPcvYVIGUyFMVhpTZNcP2hQr/rnPIyqJKS+99t2i15sRRxczuMI0jZBziK4vYG+N1XfcsrUNg2Dl/hPxkRHrp4F4EqIbRVjsHhOMZeE/x+Gmq1QQbKUxTEc5nlAtNnPZMH1uh99IO3lSIVhPCAK817Uv1hEux2ma2GCNnCd0rWwTOUcocwhD5aBc+kyGUButqE09KTEtQxo7HT96h9HD+o5t86ecENvZ0NgvCiWV+vIW/O6DdG3Cqe4XdXs5Lv7JGb3FIGcQ1RNc7xpHiD/7fv4u//pd+ASEEjzx5kqX1Ho8+fQbVCbjdm1N1A9Tck6+HbByMcRK8cxT3GCmXRl8hC79I618sI28oBp+8yuyZdwEQ9yMGeUEi5X+UEZKVJf/iJUtuT3Di6Bb3NW9TmJI4iP5Tli2HOtShDnWo3wgdRnt+0/QfPs95qP+ilSQJl/77H+JXv//7cXc9XnhcAAyHTM4kfP/6s6z2p4RLju/8wRcYLSpsJFEtixQe6TwasEshvhEgZEj+0Crz+3r4tS5VA7yw4DT9IxP6xYQgt5QiRI81Yjxhvl3w7MtnidYmMCj4r771Lb7joxdIViuaQhN81mM3BfKnFXJeYWOJ3MqRU49fXYSjK1SLCQ/3bqJ2S3wFqvCw1Kcz8ayvG4phidqf4voxk2MheuQhihHGowuDKdu4fg8xSxm8vsDeRh950RPsG9wlSfVAxfBswN5jKenxOb7h8aMxfmuGEobe4xV7ZRP7lZjn/vU5LvzcaRprE+TQ1K0ODsK5J1nzRJnAOYn3ErMsyZ5qsHXpJDuvd6nsnGw5JH6iIn1K0HqvIAwtohKoSkDsCE7n+BbYKqbcjygvePaIkNby+LEbHGkOWXxowAsHx3nu4CSrrSENZrx8eZ3toMF0UeJWLViLNJbw7pSgLFCLFrXgSc5I0hcVD925RZ+MxQcz9no9JlVCZjRTH5Fe2MXLmg/gA8H4iEQWAxhNsMKDlngkptukOJVQ9RJ8FGEbMcorLry2gIot5WM9Bu9do+gFeO+R7QZPLG7RbWdUSmEScHhcIvCxIhpXhOOSIPO1GRAIkB6dObzwiMoyeaiDtBaFZXy1T2IERcNTNjwomD7YQO9OmYYp5aIi2ne4pS4H4VGEkKQnK/TMcv+Ht3j0B6/SiTPcJxRVr0FxtI2PIhpXpxw8t8LsYIXJyjreB/iZxk81OjJ19fPTOer8DNuIsIGgEiEbt5fxQYBAEI1A6NqgjJVhLR/x9He9ztk/sMGxp64gR3PU/gRaCXtPttj+lkWq1ENRoXenpDdy0mtz0s0Z6e16AqdqB7V5GXtsEtaw3kDhPdigfjZZPFVbU/YjZBiSfHOCWW3howAbSKQV2DRg+30p2+9PKdseWVk0Ae0bGe0rJX6H+vOGOk6hDS6+B1b1dTW2sPWTMN7L6czHXPjEEbKORE4LQOBNhQ087VcHdF7aw+Se1dd2SV88wB9MMbYkeOUGeuMAEzqa/Yy4UdC5PaP/+pTlL09Q3qMKj5/nlKmgbEnUuMBEDhs5xFBiRhHkEcJa1NwQHQiCWUUU5og52DzASUF6uSTKZlgt7r03j55Zio7HKYXpRJjjC2AscpyhCsPsaMTO0w3ylRgxnuOHM6LNEWo0pzzWwbZjkltTWs/epHh+Qnh7l3B/TrA7R5YF8feWqN8roF+3orhKUiUKs+5xQqJvBaz5CU988CqqaSluKYK9vDajckv3zIggrWuLAVwoMe2wBp4ag88z3CiDnX0wBrIStEIs9NBRi+L+Bcq0Qs8rkr2SYFKRbM6JNifEWwPkZJ+qNcbc2+86ZxncarJ/rcv21RWqpSY+DvGmAltB2xPfZ5j7Bu/4tmu4luTuep+vj85w9ltu0HpywN7v6GM6GpIYFUb4JKg35FISDSrGTy1jU4koDMmbWzT+1wOC/YrW5zeRWwcE17cItsd4QDU9StRw8/G2wvoKkZWIqkIIhdQab13dVpRoxIYimIJbX8ZVBmcNsigJswIRRXVt890p0XYFztN59i7dT+3XzeMlJFWIjKPaQJvliMEEvzFCbh0gxxlyViAqix7mTDsJl15fJb/dwN9NCV4sEEriigLKEooSlRm8BNE2pGtTdl5p8+l/8DDj7ZzGliE/JShbmuwkjN/RYnt3FXv7+/nEX1xn68uOn/6zj7G9eQKUB+cwuWHxxAIirg2ktWN9wjjg+/+H78J8uM3Hxs8yf1LWdfFa4Sy4YYGoHJOdGQAv7xUs6AGzmxqEQE0tq3EHgI//md9PRwRI6/m55177D15nzExFJQwoT1lJrGozMjv/J1cvhzrUoQ51qEP9X1OHRso3iL7+5Uv80k98pQbA2hIX1MBOUk0/n5LTwAlFs2NoHJvyyNveYnhGoZdzQm0JhaNcDjAfTpn+YKMGiLYamBM9lu4rCCJPkHmoIHIVD33nRWyscRWkewXxbkl5R7H9QoP5i57o+R2+/E+7fM+Db3LuxDYtr0mua+Itx3gpxmmHGIxRBzk+UrhEYEyBnGa8eauFG43qTZBzdT1pqNmdRvhEYxsaNSpJt0ukV8jS4OKIeb+DWYxwSuALR5BMMQGYNKZ6KkR9jyUK5gQXtwhuHSBvDHC2oDy1SHHfElv9Pi984Ty+TAgqxcpzhv6bhuEsIbpzgMos0Z2K1isF050O5bqBSQC7EdYp1GZAtRkzfnUR24qY9xX7RZPJsTbNUwY/kUgDKneoEZjPxhQXQ2Qm6e1OeSi7xf7XmnS/XtAeKh79QwbxdJudSZvNUYedjQZHgxHrk102o5Sy65n3FaK0NN8c0X5tn/TVnEbLsPwnc4pWh82f1lz/qxFbn06Zn0+ZP30Mf68KU0kHCzW81WmwsUDmDvPbJLM/soAPJFhw4ymTfoO9Dx6j/JaTlGtNCBSqsHzsZx4lFyWRMpQLIQfvW0DOcrCGnSsN7v6tNZq3MsKpg0AhLKitMSZRVKmibCnmiwoVmbo5qCFJdgxLXxuz+LUJ4cshzkriczkqh/SaR1lPsAuzowrjBXpqUXFGspnjtx2jzYRxGWG6Eq8FdlEwUQkogbocE++UuFaDeGOKnUxY+uKQ9EAQVR7b9JiupRKScqwg9HCfpNOZgPfI3MLc4RSYVDNfioimkOw6krJkoTll9kbIjb+zSLarCVYNurA1Q2htgfbtCll5Zicb+GYEnZR0s6L7tV18q1k3gYTgYkG5GELHovcm6HFBmFWEc0cVC3RuMe0A09J1NTklasXhlMcmkB+tJ8jkokRVHhsLjLS4ooSywlvP/EhMtdisQaChR84d7R+zMIlwzQhRWoJBiZceG0hEbtn6tZC9uI1QgmIxxs/mUFbEV/bJ1lOUETT7BUfePmPp3SVv/60T0sYErAVXEf5Qhx/+1Wf5u//0kyzcOUBlhmC3QiSW93/ga7zr9KtENq+nGlwdm1GZQAqBEA4hPdIpXByghKcxGaCNxSiJNwKRS5xWBHsGtT9DzirCW3NU4YlMSZAVhAclPorxrRSKEiEks/WQsq0pWwLmc3RR1O87hHAOQakIBhXCeMZBl8n5NaYPLqI3pySXJthBAEOBn3psViFzR+uiIXleYTNDdsRigPk4JtIlb/3qOnqaEW6OUVtD9K0C/fAEleeowuAihQtAGgdS1G1NUuBOpYgkRsQx9YcjcAtNokFJfHeOnpQIIcjWGsxOdTHdBNOL8Qq8tKR3M/SopPX8TdztXW6bLpM8xqx28FJhI4tozeh+aAC355RDy8f+6bsxUZP1qzucm93lwbO3eOC+Oyy8rIimEU4KvNaIIKFKBE44ymNdJAovBE6BVwKrNexGlMc6CKXQu1PUxgFiXtK+UiEmno1PLrD/3lX2P9QnL+a4gyEoifC+blg6OEAnJfERjx5bdOUQWiGERE4yhFJ1jC4MCaymeXVEtZRQLUTEaYP1jx+w/tkR0U4O7Sai10YmUQ1grip8oBFKEb+1TevLN0ne2kfNDZUIKNZa+DjCphpnLCLQYGwN4J7NmawLep8ccfulFfxUwNUS4epq5qWn79L401u883e9Ci1HMBF8/GfGeGQ9/eM8BALxyAKi10EqyS//o08ikwAVac48sP7rz/q5f4ETCzdYC6bIWYbw8OUvXqAdaLpa8+3f8RgA59pv52euvq/+/khJ0Ih4cmkd7z1xGJDHnlngGWU53/fXf5I/988+/v91neGFJ4hBRZZOWHK2uY21W78BK5pDHepQhzrUof7L12G05xtEv/jjX2E0mLN4tI3PbV31KQRIRe+JXb46OcHANym9JhCek6vbfOVCRXc2pRQKrwT9keFgq4sfOiYnFaoSLEcThh9poP91ha8krVHGe37Xa1z/6hpyWmLTEGkVIogQErr3Txn1UoqrLS6uJvy9v7HK688scuTBlAOzTTFImbwtJokVvZcUeWgpTsV0budwZQBCcPsHFulfAFeVSDzeV/hcsHOQEDy4iPraFpO3dZGBpNOoyLstsl6Aiyw6dLQuDeoT3P2I8J1dZCmgaWHsqHYdjTcO0FZThppwwVGtBlRKcOvRowymBUlvClFA0ZJ1POr+jKd+yxV+7hOr2ERiY0U5cNArYZzWDTNaMztdslA4hDU0hjAtDFUQMCod41srtG5U5AsepQrKVUmcefS+Z7oScr5Z8Jd/4Dm+54cfQQjFjulz+/Ix9mVC6x0ZgTRkWRPREdj9lNNPb3N33OXRzm1eXngMv18ROIs4KUF7LkxW6Z0uOLu9j399zpmlGX/77tsIxlBNFa5nsSiipwUiM5TLIaICuTdH/tSM6nc3aO8XSAtIRfOWIRhZ0sDDnRFqVuI6TR5/5waLoeNbHnyLN268BxUFCKXwoWd7kjLfTZGVQxSOeLOgDDLUzgHlyYW6btV5SlWQdudk9BATcCHI3BCNK5qva6anImZ3OtgAIi+YJg46HjEX7L89YWG3xM5BzwzKGPxMkBWa+X6MsA4WHEjP2KbYWCEBPbbQSkinAWIqEELQjBwD5ci9Rg/BaYXvGdKvl4h/LVFmgm8mYC1lAqYncYUjmBXYRsCJU9c5995d3vrqGuVcM9sOcRdMPfEkBTZdRs8c4cjiwpCiK1AWbEtilxPErCTvx3gB0oJZ6FA2DF05w8W6jnxYT+NOjjQO24pwuyXazelEQ9zLIXbNMX5Y4HoesyzoJnO6+5YrzS7mWITZsRQLCeVSjBIKaUBmFXpvjjIeORdE0xn5UkzRl6gbe5RhjG9q8rUUF0pa16aIIMZphWtEyLJCO8V4JcGnASftBtUw4anfO+WDS9c59ZkDfvbKWUwr5Ex7CxnNCAJJGBqqzICWdPYPuPyPNEXe5YEPXOXN6DzB3pR4D9I9R5EKpm2BjyXIOlombEA58OSfMPD7LSr1uBzEkqBYW6hbbloRqjTkqUBslvgwJ9wPMa0I245QOxZXZiQbOcKGRNcP8N5CmePzivDihOrYAtmqBtGgWEiw3RAXS2xhKFeXENaT/uocayTEAiszJKAmkvhWQdnyDE8pDrIWX3vhHPf94S3MJMfOZlStDmpSwmdiRBCCVnhRIvMKHwqoLEJrRD/BRUHNhGnPAIGYFzCfIVsNlExAF6hKkAsoFyKkE+ShJhpWkAnSSwU+neLPHiBnFXJJIVYNFAXiaoyII7zQoCzZUOG2YeOgxWRtCdHxPPDQkAc+dIemnLNTtdGirh0WStW1w1WFshJvLNKCnhqEkeCAlUXKtZD0wh7B7gyRW2ikeOvoNiZ89Ik3KLzi7++fRTgIdgq0Vqh2E5vneKHxlUEIQXE1I7pxBfvAUXxl8FleZwWdqxtopMB7h7B1s5VrKPTYQJgSbB/Usc55jl1fRDiP3dzGHV/G5wVVUxKWkjCLkEBJSXx3wDu7d3Ef1dz+1Gm4so2fZ8huG5IY0UiJDnJWP1fgjGF6s4m0Jd6WWBw2UGx+7n5414C7YoHOv86Ixvsc+5++na//K4XwHic8oZK845veyVdHrxA0LK9efA4dniXuRZy8fwmA3bsD1sQSZXtIVEW80hjitOT1X32NMI556p3nCO6ldM4tLNNpn2U/fQE/s/SWW3z3H/3HPHz/Kv/DH/4ITgEC/uUzL7F3d8brN7b5o9/5HuIk4I/9/K/y1Mmj/In3P/3vrDMW4gb3d5rcmu5zpDXkfLJNKue/OYucQx3qUIc61H+QxL1//7n1X8Jr+I3WoZHyDaLf9gPv5e6NPeITCa5fQ2RxUAWa8SsN3H2OkW1QSU2RhZwSBdHDY+YzxdF4DF7QXNrmS5e74DUylLgIBoMO+iBiffEaWSNmc9bmk7/4dpqvjolGYyYPLRKXJTQaCGEpzxjckmAWLnP1jmP0ikEFjrX1PptvXSXZzikf1AR5ApVB3xoiZimQQr+H396l/7kZYmhRQUBxsku+2CJYbeMiT2spp7wckZ3pErUN3/ZtXyIfp/z8L34AXRqKKmByuo3eg3IhJshAVZ6kKLFfF6hjgum5LoomwY4h7jtOPXSH/fUGN+MOjXSGed3j5iX58Tai9Hz0gefZu5YSTWeM1nvsv6fJ0a/uITNd3yTuQXi7s5L+EcPutQbSWzrXZ0yPJbSzGVkV46VEG091RhJcBrdbsPeRJqoQ3BJtdrYSjs5uM35L8VanzVNP7cNqxPGFGdbDtu9QeU1+f8oPtz6OOy649MwRXowFpqGIZgrxRM0YMVbw+PmbnHtql+9MbtCKLT/z1pCT78yYr0veNEuAQN4XEP/yAUKuIitHY2OO2qlovDhG+BCgbm85yAl3a1aHyG29ka0qOqsTtndj3MYK6Y058Z0ZOE/7SMXS6X0OjkSMvtBF3PZkLcX8gSWK+3vEN3NkO8LpugVovB2SnRbICCgl7nwLPa0oV2OSq5rpniT0jqoh0bc8YlFQNS0rXxwxerpLvigopWDlywOioGShP0VfM/gRPHRzhzv/Zon89QRRWJQOasikk6AlTgoIFUUZYtw98qp3qFJgck9+NSbJC6QAKySTUKFKT3S3Qu1OiTbH5MsNenpOU+VE35+xYgsePLfJ135uDQLN/vuXyB6MSO8Yqo4k2XMwz/FxQrYSYo8tEY4sQe5pX6moWhqXatS4wmsNUmKFxymBnhTI3OCVwjQledomL+cUpcY0NY0bErPvSG/lXDvapTzh0JEl2CtRuSXZy5ncn5IOHMoLpBNIJHI2x3qPmmfoiSCYSuwgQ5YOZz2qAh8HJC6jKOt4Hu20ht0mMc0bM5p+n5VfKdgdtrj2wQ5njw64/nITPXeUaxGhr9myBlh6R8X+qwlOC9TBnDKL8SLCZpKgApQk3XLkCwqXAPMK3xI1WDUTiKxCOAFWkAWeuFkg2h51Q+BUjAurmvshJdHIomzED/yur3PwkuVXPvs4tuuQDY0soXW3orFb8eE/eZndZ+DNL5xGT0psVTJfMkwfbZIcaIKRJt4rkSPBbKm+NoQXoDSiqttmCBQqg7ItkTNJkHnag5I0LFBvGJ794mlGWmIaErPcID/VwbYEjf2MYKMONntNDRDWGidBegepQn5lG1cawCOOHwEd4KdzZk8sgm/QvJrVbTR4TCQICsB6ZKag9GidYzaWKR+T6N0x0Y0K87yvv0+RQuxJjFSUezFBZrEiIsjraNdXLj7EreNN3j56k1NH99hc6yGvgagseI8a1te+7TcQOkAVYFOByixqnNGYZthsXkdXVtooG4DzHFSan95+G3ejFvvLAZ1f2iO+NcYHGpfEyKU+7vrtGkr7b/+NHM3Xd/HzrI4/eQNRWH8u0tdxoazERwEcK0naFfaLE7wXMJogohCfl/iqqnlDUuIWmszvT/E7FTK3qKyCWYFoWKJzAB71rMQu9xDjGR6QujaR6lxcgHAeMZpBWeDbMS4U9d9SeTp/ryScFPXvSlN+8Y//LP4IcMNBkcNKim4kMJry0O+8i1iIeOXTBvYMNy7v0F9o8Hd+6CdxAZz7/g/w7OtfQOWGZKvkWt8j5wXuEy8xuLzJn/+x/47NfAMT3iU8FRLehui+JW6rGVvXrvNX04RWO2ZcFOxtzpD3cuwbuyP+5jPP8NKdLV64ufHvGSn7xRQflASxJRGex6IB2pvfgBXNoQ51qEMd6lD/5evQSPkG0ZNPn+HcO4/xZ1/+Z9iVZr14A6ZHurBd8ZHFF/iF+f0I4M29Jb718csEumJ70GDVz/BOUBQOPc2QQQNhawim9I73dkre/wOfwnvB3/rr34EclBS9gDKIMLFDjSqE9JRHFXfTDg1TcuyVbYo3FfkDHVTWJFjvUD0ZYgIFTRAti5jfy6FPDKIj8WGASCKMlgTZHApDfLEiXznG5MOa4Kah/OIMmSS0Xx8TtUt2P9Dlxv4K4Z7BCyg6ApFIxo8vYxOFkiVFT6JsTHdSIXYlvZ2AsusQvYBWPGU9GfFfvfdlfuzOE8znEfu3JcpIVA6qgFe+/CDn3neFxjXDNBc07lpc3kHPHKxkmEnE6sWcKPecOLvHCnu8trFC2dN0xgX+lqMTTdhb61C1NGrfIzeG8PJdxDf1cYFgPpL86J9/gOjhCuUl0VbI+8+9QrUOz87u56F4g7eqVW4VS8iOoCcCTqZDsv4cfZAzXQoIfUSpAualZj5N2J80MHqXRtdjPLyvc42Fd2e8NjrJS59fIr444/y332QqltmvVmlfmhEECUIGxC8PgGW8q6txxXhKo+c4dW6PN3dOYIYRJhQcDELuFJqD2T5rv6RqnkOoET1B40TBmdW72Pdv8eyfWUftt1BZCAjC0rB3vFGPnVtPdDOjeqxJOLSsfm4LPfPsvW+FohfilKF9aUxxtEMVQthxVC3LypcygjAmuVNBovA6hOWI1kpJWxUEyqI7FS+/dJTZzS52ppDG4eYFalBvhKpUMD3Tp1wI0LsVRgjAIQoBwhGMFPOeomsKUPUofzBxZP2AaNsgpMBLgfRw4yfOstR8hY2lHnvK8bi8zXg3AamwnaSGskZgE4GLQfo69hAOKoqexgYQTkGWombebE8YnVKkByEIQZBZjBL4osILD+M5+f2LdQtPvkSyP6OKICw83S/uEowrvFsgaXuKEpJXh8hKUKaacFRS9ELi/YKqHeKdJ74+w6URPtJkazFRpjDpChKFGlaE0xzXCTl4bI0sFnReHhDMBVU/wfQS1MYIXe0zPtdgdizglWGPN/9mzOxMh/l7Fgg3pnzt5ilevn6K5sCy/XKEXUipjmvcMCZuGgIDm/vHiBKHO76GUZ6qJxASorlmHlhsKFA5iIZCJiF5EkIiCXUNAxZ3IqpUMr3P03yrQAQBKlcIDXkR0D5d4K8bsg+HBJf6JL9mQUmkhmTFcOqDMy58vYlbiCkaDtePUSWoDERhCaYlMhGsmgmTO01cGoGQpI0BUuRUTjJOG1S9AK+hM8lpnx8zFjHJc4JbnzjB/EEBPU00EKiJZ3bMI4cgInCZxTUaqLkFV0dXwnhE1J0yulKBdfiwjjgKEaJGU/RuQrnWxKSKcGYJn71Lud5Ee42eO6T1CO/xOYiJQ/Qj1Lslqw/vsXcQcnAnplpOCcYhKnMwKLFxE59GdWObEJSh4/XXj7DCDmfuO8C9GKEu38aWFpKojl9lOcFkRtULyM6vIrKK3ld38YMhCEFxfw+fRpSdmODVHSgqRmf7vH59FdU3pNsF0mlIk3txIY0PFNx/HG5t1w070zkohS+KOl5jHd6Y+vryFarfAKdro7EVsvhAQbJoKHcNxRcVYrFfc2aKClEUUFn0xgDZiEgx9WSQFAihYLHFZLHPS9cEzec8druCfgu50EEWFd7ausI6jQGHjwNEYbChwi12UEYiJw45tchGgihrIKwaZzhjYJgihMGHEVlh+NpXLhIbw2CnS9qW+GGJ14o7N/ZYWesQJyGX7it4cXoB954UPmnQo5zx6QZiXvJGLJnd2gbAM+Wsy9m9P+U7/tuP8pN3bsDukArPjf0BhTPklWF9IBgkDlV4/vbf/SS3G3NCBy7h3wPRdsOEjVHOvFK8YNexxyyVH/4mrXIOdahDHepQ/0E6hM3+punQSPkGUigDeqqFkruIeU2kTPfm6CXBr/3jR/CmnlZ48e5xOkcmuMuKKo+ZnQiReP7f7P13tOXZWd8Jf3b45ZNuvnVv5djVXd2tTmrl1EqAEJaIRtgzLPCrGTMe6zUYhteReTFglg28tscsMNhgEUS0JEASyi2pu6XOoaq6unK8+d6Tzy/uvd8/fm3NYrCXZ7wIAu5nrbtW1alzb514z97Pfp7v58X5A4i3pZhNhfcFS+u5LhhYeV1O+QqFdQJaFXLH0HxvweoLy1BaXOnAGdI5j6IIaEU5/sgyak3VeRwe9DeGpHcKSqmZsSPEjTlsOyI92aHY36ZzuUJf24a8RH3R4pxA2FqtmCwNuPfV69w4MMX44xl+4eEHDUrj8Qcv3keZezQTMJ6s7QmJpnXTMdzr8HMJixV//bXPM3635BP/5CTi5XEGtZlStVMGlKxOa/pPRYTLjiP7upxf6+CtlRSxxHmSi4/up1fMoUeQrIAsE9pnUqL7JjgHiQ/rj82QmoLrX+pgfMPwrSHhCw4xkJS+xA0LRMPH5pI8hlAbRA4uBF059CwMG4v075yFquTXf+F+7n/TFn/v6x/h2vV9rIqCva0e7TDj2WvzzLRSXlqZZfsVTfTWiPHhiPm8B1WL8UjQkjljG/LUWNKzDcYixKG4kUfMfHkH72zOlYUG3DuHySqKKUWwVoGoEJ6piwlKYBOfcr4iv7FNe3bAbJmzmiXI0nD8xAa//jtvZvXWDNnSEK9I0BNH8oqKwUzEVplQWcloX0jnmW2m7Dq9+CB6OyW+FZAuR8gC/NhgI0OuRK3ctqCHFaqC1MsQVtTWqKGpXxuZJltoICdDklVHc9MxaQpKFTOqBJ40tA/mbH64x7UXKl7x3g2uZLOMqhnoDqE7qIsTmU/zXA87GGJ8zeToYawPeRP0GOY/P8ApuP6eJvt/ZRsBBFmOLC3SwfBQSHbvMlMvFYhJBSse9x++ztD5bPcSxpdjcIbpx7q4hRZCKAYHJcGOqzeI1pJcGBLaksEDC3VmyI0dkBKdWoLFBJmVL28cK3RRUC41SRc8mmd6eGOL0xJRWsYH6/E6OzUmWhuBcTTPDzCzDWzbIoUG6fCHFXqYYyOFzVLim2NkaXFRgAt8bBLiZYLe7SF+z9DJB0zymMliSOgbnLM0VgSgcNLRvX0WOx8RTCmW/BWa757g7XPs/HDIqdeu8fQgYBx66ImDxw3/+rNvwbs1wGYThDciizoE44C+v4B/pYuIQN/aQVqv/gy2dVimMyAmCpEX6KGhnHV1CHI0hL5m4DdR/Yrpq47ssCW4VaFHFS6RWGEZLMBHP38KO5G4uYxqFlgtCcYpdu8UVSD56B++lumHr1D5ATZS6MIw+8gOk+UGdkrQfLGLLAwcl4jIkS34SCfQmcRlhuIcQEGrv012tMW73/gSxw5v8Jn2HB8/fSdhnBMWEjfwsb7EyhKZG5plH/9WietKPJGhNzKs366LmKUjG2ryGQ86tU7azHUwc038FYN0guTSAOVChJYI7RBTbXwrsVIiNjZxSmGjACUcVTsA6XjNN59HxoL932D5xHMHAYv2YkSUIDZy5KSgCjxsO0RLQ26Akc+LT51klJ3H/5YR7vEcYcCFPjaJkePaT66CnIXXrdN7JEaIlzXDQpDva2M6EXprBMMxIitpPzam2JrBX4rQF4HSgbFAhc1zlKn13jQicC/nZiHAVCAkrirqHBXAoan6ObS82vqjFcPrEWrocBsC13YwrMNYyTPcOEO0Emg3EcISv7COa4WY2RZ5K8TbGCFLR3o9onkjwysNaVIiywJhDHg+tjJIZ7GhRkwKCAO0sJRa1tk0zqHHFufX2T7OU5AqGAwgimCcY2cEwbjiYOTjTi7z4tkC9yJIITDWsWf/FEkr4od/7nt4dOMqf/fXfoP4fE7oSfACooGjCjwqH/pJHX23EHRY+Yl1bn7Z59989FdJ/td9qLIFgDOWbpHhNPRNSSJCotxxrdtHSWgeizi+d/aP2XwchqV4FWMl5zdmCIhJZPint6jZZZdddtlll69hdgspf4nQUvE/738Xn3n6Jxid8EBZfCUwiSMfx+hJiXCOUZ7w/OAQO65NvGPJCw+oxxbGizH+xgi9VaJGtUHk8u9N8cG7HsQB61GHTidn8Q82OBFeRzxQ0X8kRGJI+hAFBUmQk8+GFFGC9SXpjOPyjU2EckSqYM8VxbAIGFPSOD8iGxQwu4wNB8i8QGofdAFxiJubYm/Uo9m1BGcjNk908J5aR/SGBLcbDt3TI5/4bJ1ewsQG40vsBDB1qGvYStmfDLhnagUjBb9x+wMkY4seFdjCMdz2ubA5zbmXpinTkpmHUsZBjJdLZG4QVBx/2wWe+4XbcfMCWYDzJVYZwpWcQ41tLIJeoIg+P+LZ8x2CLMIrwbsumN27xTCeprsWobezOvy2ITCzUzQP5UgkRkDZCMmnPLKrdZCrd3WL0a2MRzdmMKv38rkv3EmYrHLsO0Zsrc3yH59a5NYLks99bp5mUuJGKUdH51nUIzr/QwjTmg0RUEq4YvegRcX2ZpPLLuH8yh4272gShmOyYQBByPLvXENNg8st7qCPeE8L/v1/XiALyrIuqrzwxAzt1hqGDiZUsOWzvj5DWgT0HgxwLmfpI9uMEo8rV1rsBNOIhiGbTTCmR1q2kaUBX+HfGmGiAJk7OvtTNsOMXCTc+NYFkosV2kDrYkpnY0gV16f7OjXIWCISh4ortPFIpyYM35SgbgqCFzTdsskVM8ck19iJxhaOFy4cZYiPCB3a90ArirkG5UID3U/xhgXSM5QNME1AWVzuUJlFWCin22A3oD/CjSaI2MdMRbgOFDMednmIvlSyf98ae6b6PPZTR7h4eRaUh/AlSnqIfsVkr6aKFUUG0bZFjjKyJpjZDt5YoHspemcCDsRMB39QgRP1KfbWAKmgnGsTSof/Hkt2GigcBJpiWdC46bBJwORQQLhS4kIfKtArA9x0G+EE1hqSszsEPYd3eRupNLYZUiy08DKHsJAte6TzHqYdI1cFTkvUxNKd8WlspKjUoYTGRZLpyyn5piNbbiLe32agFEpUvOXVKzz0zmu0zzT4rScOkHc0M2fHOFOBFGRHZ9HbQ8ILPm62gffAmPw2Qfyra7XBZ7rF9FTGZL0gOOPYeGWnfiw8QT7l13YoVVFNG7QUuL5k789eR2WOeLVNujfExR5yo4syFVMrinyxjQgczbsnjK7FqHUD2gNrUQWUVwL63gFIM0YH2nQuGKTT+MOKvA3Cvfw1NoyfFKjlAlE6XBSS9QK8xMdWFeGFbZJLO7z2uy4zc3zEC70QjcOgGOwT2KhWDPeO1trdzmPgr1kQjtvePuLOQ9f57d97DVWqIQmxbY/+nhZvuOtpzn90CtEfIW506wJbEmHjAC8zZAnYWBLkVV0cMwUuq0NPxYwHcYAsHO6IxUWKAoXfqLCNgNZjVxFZhYyj2pDjKZx2WF9hpcR0VB3GKyasX0roz/o0p2LksP6c2L67Q7TqEWwWNL8T5o9s43ag+mSBmJ7CepAvx3hbKZ3nbsHSIlQGO9ygeqPGHJGEDwvUs6CKgmL/NChQ1wcISZ2pIiR0e0BtrBFS1GM6lUGEIUJQj/tMcmhUCKPJrkesXFbMZSWiYTArmwgpoaitUy+XeRAIJvsTkhsj8tv21GHt4zHhuW1aM+tU/QibgRfPoCYZQnrQCJFRiAt9ZFZBUSGkwmLxdlKIfHAOlEKmJc7X2FBhGz7Slwxub7J8wrJwR5/V/5Dzuq9/FQ+9+xV8+zv/Rd3ZZx2VdXz5Cy9x7OQy2lO8YfkI878+wRUWGyiwAmcN0UhQloYoqkcyi/IMXjbBVQqcw312FX2khRTQH+dYrz4qNAlMXIUPCGOxOMYdw1aSU1mLlpJBkQFQMaYR5jgnCCqPfe33I8LX/Wkua3bZZZdddtnla5bdQspfMoLQxx+WCOHAUzgrEGuO6NU5/dM+wjkmzpIHt9O6OcIWmq1xAyEcd8yt8fiXb6f5JXBzDra26tDascezlw/UY+CJwsu22T6fEO2tyBZhdGdI83GN7Jfce+gyqpL03hLRutjjetTB+oLpuRluXO0h1yTXU0syLhDbPcQ4I7xRYOIMJSQ0GpQdD2+S4qzFacHGU7P0xtP0dloEviNbzolv7FD6Ho3IYYXBxOC0xsSG0kKwDSZSjERMf+TxhxeOcPHyIhMd0P86weJHJnhSUpQlk6U5kkN9xEXDhe09pEuSllebbUTl+Mon7sDv1WpLb+QIxoYi8SgP+UxVY149d5VL/jx/YPfihgVuZBBWMP8xzU5nisHtHjZwSF+Dg8BViFKShx107nBaYBFcaO+DoUGNS1yUIO/cYdTv8OXfnoYoY9D3+cxLJ4j6Me9/68Ok57s0NiT9MOC7//6jXPz9iFOv3ubz8RG+PDzCAb2B6UPxuCU8YRn2Qx79tZNs3K6QRwXFCR/xjMPvegzvnWX/0kX6X1qAQQu7VsJXAxsV0Ujyim/tMjgHplTcefslTj91nN/9xH28/qHrfPzyQaz08VZTGI64vrOMeOEQKvNIbwPrGVheZPsVLawUTD/bJxgWBF+6gAgNzra5XfW4WYWMAsnkcES4WWL8Cn9cYDwNwwIUtOZygvkSed1DrRk2vj1hMu+jA8hWNapVsVa18YKK5re3aO3tszx9G49+8AJ6Z4IoK0QQID2PfLmJvzpEap+yrZAWjANhACvo3dWBwuGcYvu+BaZP9xAzPiQxIvQJZi1FUrC0p0t6PUE1NB/dvIuVL/nIMVhpIfSRlcV5Djkx6HFt8hCBD54m3+uhtyZ4hcQ0Q7I7p6laPtPncvyVHNtu4aSEqQScpZrxGR3SVJOoDkOW4CYFi2e32G7soYwV3W+cYe5zOeHQB19QLkwTXZ5gkaQdj/4dc+jM0CoMJkthJkFXgDUYochmVa3xlVCEEilAjxzhjsWu5VAI5KSkXGiBcEzagsZmRY4GC5VTXDzT4vCpDuc2lrA+qCxn7VUR3qBi+vkJYa+EQuBf2aGY9civ5ASfGlIphWwViPsUyesrdFFSvRAhPAceGF8hTF3YqyJH+50VPVfhqZJq3oNtwWS5iTqYI65LiCPkdg8GKVJ6SFVSPgytfoq0EUIbvG6O0xI6dc4GvqZ1MwNPYwIYHY2QaLKlJjo3+Log2pcziQKcEERbFmskaEtjqWA41FRS8xP/4i20bh8Tv2cLnKOYkTRWQWLBWcZzgnxaki0n2FZEMJS89GXBtUci1B0jqnMjjBSwHLKUdSknAWXhgdRIB873sWEAuu5CkFpRhqLW9wYKmfuQBIhhjign4Cvk0GKvCMyGYP/yFk8+ehyVGqT06xd+UUHgYRoKsxDUpioJOlOEccWCusXwdJPO2RQhQkwAZSfE7GuSLrbwXuizdUNjdMTmtYQZ30CWUc4kmEQSP9eDSoOvQSvKQ0dQrQm0Khbv32Lh1RkXf2ERESeo7hCUxCUhTDKEc+hXTpNfHCJ7FjdO4eXCaD0maEBr3OwUQtXLm6oBoimw1yVyVGc7OesgCmCc4np9yHKYbyMOz1KlGv/WEBtpyqhB4K/hH1WUXxniCo3c6NcBuloicCA1ojQI83JXVxLW1qmiqovGiUEcK5CfNCAk1hdkeyRbXzdNOVORpzGnnr3CxnieZx6/wrd87xtpJz7dogKpcFrw7//DF/gb738LAE/vfIR7fuAcF355meHNGBd6uNIR2brY2YwUxlgWogdZPvBbXHkSrBC00imUAzmuWLm8RXLJ4AKItwXWd2incEVFFYJxjlBolBBsTAa869M/hxKCj7/9+7i1usSksDw4fxTZ/JY/6yXOLrvssssu/w0E9aHPnze7YbO7/IVjOExxowynVH16KwRVW6JXwOsVAPhVQTVo8O7923zxcVCyxNMVg/9YMP/sFbyBpOrEX51tc0i4GdWy7NRS7m3gdVdQb1AMtwJYd/Uics0gPpYyeCamOAJv/9bneebmEZ65so8ZJ9Bf1MhcU05XqL2KntK0rimEUoiigNICDlE4jKfqNnQFpR/QvyXQeYWfGggChvcsUd4LxSSlsgJvp6R3t0ZPBCLPUcP6z6UVpJOArzy7SP9cAz/JyA6HMJVgnEKkJWG/4DUPXeb0u/azcraJd3OEygx4Ctkv0C9GhDfHzG4ZBoc83MShfEtatOFWyL37N7nrlTt8/g23M74QIFe3wIIa5uAsrS/1cRqGp/ZQJRLVtsyc2iJ7JKRzesLgRIRvCjaOz+B8H3mrxHcl4oKgWixh5DM50iY+KbnvDSvcOjvLnkMbqOmMndWQl0ZtvCnF3/jJc+Ac//AL76Lc8ciXlnBPXOHaZxaI77Kc+l/WeN/7P8/lD03xkWP345xkMu9wPYnvBYg9HrbRwDkJz3nQlIjNAS7LUSbkzO8u88a/fpGv+86X+ML5g5x9/BjOCB648yofXb8dbyJILpfgoNzymTqbI8cTxMTDpSFynGJkA2UlQiqE75MttEkezMkeabI265HPGIKtughYJR47Jxx7ThfYzmy9qQ0F4+s+dliiViTN7RX8ixJmQsS2Y/+brzN1vMvNfIqiJUncGLctubRz+WUjyv/54aJTS9WuGN4dMP1ojp8LhBF4XYeRguj6kOhmyejUFMEWbH+zz/TmNGKYohyIccnMaERn75j7F17ks9uvZM3MMshimJ0w0AHDYxGzj45xStC/s0G4WaHHOQ6JSisYZTRu9JCjDLG8QP+uGUYHY5x0JFfG+FcGkBncYIydTiiP7kFmOdGkIFkoyYtajyucwG5IGsOCIYo9X9hClmCWpog6lmJscGVeK6QzRffuBCkLyldroqsJ6uwEIQU2iXCiIs4LbAWzk3X6kw7DhZi8I8imBZ6D5LkKpMYWOWnbUCooPTi3toyQjkPbt3jpKy3OXnol/fsX8EcleqJIGx429nDPj8CAjBuItmIS+US5RkqLjB1yp8A+X5G+yqNvG/gdSdkQYOvwUuEkRkmcLzi4f0A6CegEE9a+YQluhjjtaByp4BMFYpzXoyJTLWQjQGmP6oUeuhli5kNcMaFKoJhS6Js9MBohattMmUiGh3zG+3z8rqN1pTaiGaEoiwjXVC+rfQ0ihOnDQ46+8yaXP7HI1lqT0npsn+mwciyAUiP9CaJS9Sbcgc4c3orD6YR82eFfSqHnyJyPu+BhZprIfkqYZojbNRc+OANYaDexcYB0FjcuSA916q6NUY42hvGBCJlVJGsVriixwiLjADmiDuBFYbYDJn7A+HqCKsDOtZHXUlxRgqmQzqGLGJlbqCq0Nrzy6HXSC4tMVhOsTpGeglGF18/wdwxCSZyDybDB5HQTUYLzxwit0IXEOUe2r81cv0c+nIAzaN1iHKZIqbnvwR1WxwHBXEG+WSC0B0qB1ohGApWh2rJYpRFpXWQRQiCCgHyhiR4ViLLCaVF3gmQlgexT7JcMx5rkWY0XR/XYqOdBmtXdIlNtqATesEI0Y+QwpxRDBIrKnyb9XB87MDglEcbhtIczJaI/hGaCCHxM6CGyou5v8WrLkOpvI77DIdoKvhRQ+B7llE9ZVEQneiTSMtmMufz5FmpO8D3f/w6Uktz72uN87uPP46Jaj6wmJf/r9/wcsrCIh57CWwJ5wGJXRZ3T5GBfp8FIZfytv/02lJLAAscf+Js8+qnfI4p8vvWffCs/9vOfQjnJj/3rP2RqXGFxmJaPzmBhb59VF+ALydH9e+j6OU+u32I977KT1h0pT25d5OThiG6a8Q9f8YY/y6XNLrvssssuu3zNIf+8b8Auf7KY0OECqJI6SNMqh98oSbdi9KRCpxVzfp99+7aZOvokchPmoiFTYUpVQWAmiLahaHs4z8Npj6qjEf9ZyZoAhSXzZxk81sE+bvCvDcA60JpYVYgB2Ovw6pkVdAU+FU89fI747JAqKemfEkyWRzz0lot4cYBUHqICM9fCLM6QH1vALS1gvQDv4ipqY0SYCRQS2UtR19ZJzqxjIotxEuskZdMhD+SIxBFMV3jGojPQmcB5UOz3aTDCRhUSiwtetrNIoKp4/sN76Q1iWp0ddN+gtobIforeGmGnoewo0nmPbM6naArEOMPEhktJQukEVjoWHuwCYIsKZypMICnbfn16LhTGFxgFVSaxz+cEH7qFWuvjdy16Q6CfDQhu+LRvaZK1HJE7RFqRLkTYjsfUgZxWI2NuacD6xiyvfGDA9/7TK1Qdn1/49b/GMzf28bnBIWyqOXpylb9+/HFe8YYdkmM5wSssx+Mt7j5yk3tffwtngFJQ2YrO5y7RubBOdH+F0LZ+TF4OGnTOIXBYKcgnARuTBufMNFMH+zgHr339S9x2+03uP3ETYQ1eL6tPkDPNwVM9PFMhRwqVG/A8ggGIUtTjERHEr7KIvkceWsaLlulPrROfrosAsgI1ssgoQg8KvF5OcL1P9ELG+FZCWTny+Zj+bW1cpSjbiuf0Hk5v72VYBeSxYvOTLTYfbrD2XIkAbOTjIh/XalA1I0xYUMwHVE2f8eFOXTvUAm8iaD29SXhpC2uGZLMVKrGgJLYRUswoFo4NGOkEPVY8/oUj9IImj64c5sZkmo3vXebW9yzRf2sDF3jkexP6t0Wkiz75rE82p6GskJWFMAApyadCZClJVh2Nmw61lYKvEcMUkRaIXopT9XjZfHfEm9/4AnFzjEwNbpxSEGM8QdVSlNM+TgvKwCOdRJhWRPf+DjbycFoy/dSIcr9j/Fqw7xpj7ZA8LBnvl0g/JF6z+MOc6Mt9VCkIRpDOaKpAUR5p1XkVgWayxxKvZ8iOZvXrEsaDmFEvZvCRBGEMapBholpJqydwz63LvK/xFZqNqh6FsJbJ/pBsnyJ5APQ/SJCxh7tZ4s6OWLma0BX1qIk3cqgJFJHDWZDWIUpH82bK/XPXOBGt8YZD1wh0DkZw8OA6+HXRgtBHjMfMNTYxGylSKFQ/xb+2hbEl+B7SSrxBUauB05Iy1hQzESJ3+Fs53tDWit3SgHbY8mWDjBO4SBF0Ld2nI174+QP0ziXoQwbz6hwbQVulBKJicWrA0qnLHHrPVexiBbY2/yAkVAIqi95joaOJFixaBSgLVVdQdH1s9HJhIS9Qmz28bIjwNDaQGF+iS4fXLZBZhZE5pDmUBbZtsN8h8PfkGF/gmYpJw6NrmkzWfMJufXltwHFgHHZU4TIPJ0Wd7zH2eeTyUdYuNUBJsqPzZHcuU96+DAikETAqUekYrMUJcMqR7a1wOLJYYkJBuRBQTUKksdg4QGGpbIhvHQmGQvgMVIJpBRTtgGq+VR9rSVl3nvgh2bF5SEKIAmwrBiEJdjJEb4QcTFD9FGkdXgnJMyP8KyXBlaoWcgkHng95iYzALM7Uv7N8jcgyZH9C3oHBPTNMbmuiEKTDGNAQ+Ij5aeyBeQiC+v0pACEw7YhyT6d+zK3FxD62PYd5eAobOIqZAJIIaSQiCJicTxiuNpHnIi7fsZf1mTZr3RGTUUbSCFFK4aQAJcHXnH3yBi9c2+axpw/wpQsnuLbdQuAoGhpHxZYyvPEb7uLe1xz96nrg6B17CRKfeDbgwxcfAeMQuaV/RDFckIjCIYzDKseepR4mF1Sl5JkLq1w4s8Xnr1xhKoiRUqCVYDFuc3JqmXfsv4tDndk/u4XNLrvssssuu3wNstuR8pcI6yyfXf84rW/JuN52gKXzQkl0z5jqosZu1l0mD77mIifzbfDXKT0Pz6tbt+/+ph6PzZ5Cn3YEpYdrRXVXiq8x3sv2ltxROBgJHzzBWME9b9rm+tPTyClL9raE6hpMzlquXw14x4lnuXQt4cj0FJc2twle2mY2jvnmtz3P8c51bizdxeb1EJFXiE4LJwU2chQLMeHmACscreUBb/qmZ3np2SXO3+igEGy/aY7inKDYF2EngmYhyRUsDnJ0Ac46Kq/epFhPkD2tkcMOzVzT2oDoeokaVjgJlSyp7pbc2urw9oNn+VJyEDnOEJOc8bEpkvu6jJcTusSAJWuBWs0pkUyET+UMQkGSVwxPtolNTvvmBkfecYsn10+SnljCaVHrMg00HuniLhbIMqN/9xRmysOMJf5WgbYG6yvMTAOrSuxck2SlwNuAnTymUY7YKWIeu3ySN9xxje5GzPRzPQ4fl5yVcwzzkAMHu+xPeizpIa3jBfv+YY+BSehVEa0wZ/+iYfFX+4zv9MkSD51VUEg2rrTwvQGFDfBfO6L4N/WpuZUCEykQlsE4pFv5CFHxvd//MV64tI/LVvOaV32FL117CwqB6Gg6zw/ovz6h+kaf/qBFcivHdgtAoCqwSiBmJdnlCMaaYtrhPIcoLeGoIN/JcdLRPNODKISiwvgCiUOnFjUpaa46ED7BCnR2HNZz7DzoMcwiJiYgU5q5bxygsxI3Cqi+WCEMTBYiwr7Bb46ZeSZj5+4DdF/dILkxRE4c2gqMJxjdNkuQjyn2SPwDGe1f7eNECxkqFvelrA0WsDcsC411LuYHCDckh+7d5KWtYwhtcQ2oSs2td7SYf9owdbYEp7Gjsg7NDT2sVBTLHZzeQxV7qMphS1OPzSw2kN0CHNh2gpmK64zNQLA33mY+GDC3t8v1nQg1G1BtjsgX2nippDg8j9kLwaBADisUknDkIM8xnkI0O7S/4igWMtI1QeIqytiyc2/A4pdKJnsVo4WAqXuasCMJBobhEUBC84agamkmM45sClpnDY3n+4zunCa6bnDCUQ2mUdMKE4MelURXBohxwTe+7zKnjq1SlgGfPH8f3rVthvs0OEfUz5iSIzbPSpwKoRFDLFGhoTE2JJ8fs3EvpCcV0aMCjEU4QaFCpsWIqheyd7PHU/sqxsLn0nCGtnFUMw3Eao4sKnZ6IWKS1YH2nkQ4h4qa9A8FOCnwr2rksMLXJa/+xvO8cOMEN7ZnCdYLXFsyWQpQhWX2ldtMLjlGNOsOJwtOgnOCyXZQW24OC9yxEtkVjD/f5NQD17jY2093ucXCSxN0UaK26hDSNJb4Y4vfKxg90MZue7hBDt0uNiuQbYH7gxw31UREArZ7MKqwXgchLNHKGOtJ/G5Rq+zP9RndM0c1b9AuJ3+DZO61GVkZsr0+Q6c/IFxMKbY0VHXAM4WBKQVdiw00dqqFVaJWGnuOIlFUuYBMIadzhK8Q6csdi0lA2DNkiUR2PEwo4eXuL/8+H7aBdoQoHNaTGOeDr1GxwkYlc2lKOJhwcdBgLD0ING4MVirU+iaistBKsA2f+detMqkOMUiO0Xx6HSklSIPzvLqzUQNxiAkVpulTCI3/ZUl4pY/Iytq+NZ4gpcRWtRWoDkyHUpZMTrTwdhyygGilYHw0xt+rkQ8LrPMwvqy7lpQE66g8gYh9hFKYQOKMRVgHUR2W7LoewyIgkAI/NcjSUCGZf1hQdnzUxJJ8cRPTGPOh7if4VCcm3DtNMt1gmOYgBcZXtXp+kBG+4DFe9Wg9u4U3NUOjlzOcTggPt3jis2dYO3OL/+1n3ofSin/0//4gpp8yGaScdJsIewjr1/ky5YLHoKUItx1lS/LcxXmskVjrsOMKAkm/n9HQITqNiHzBocY836hfT6zDehR3l1122WWXXf4K8xfqk/DHf/zHEULwgQ984KuXOef4p//0n7K0tEQURbzpTW/izJkzf+T78jzn7/ydv8Ps7CxJkvDud7+bmzdv/hnf+j99MpOxWt7EP+xQUYGMS3RVoJ+tmG4MkWWF56W87ch5DjnN07+yHyscV/vTvLQ9z9x8hfh4jn1pgBVgphtUs028HGQOsgSVS8KGwCQtjG5CZ4Zu0GE8H9CdadH9yBTZmkD1K9auhIgkZ/qOAdUbVrjy7TPQTQk+s8P5a3v4wy/ewfZ6E3wfUVZY5zDOIU2F3BnBwjQcXOTgqZSjx1c5deoasjum8h3ZQoBpBajPhnhfCYiuTHDSwg0P/0sVclJQNgVWOJxyTPaHiGaJbFWoyiKyClGUYA1SR4jHfNyNgAtMo7p1p0rRCRnclbDWnSZamOANSoR1yP6E7ICPKCXt5oTKKUqnWB1NYyNNdnyWpJNx4rU3ONDaRhcSnVHbgiqgsIhQM3dEEI0KhHWokWF0WCLdyzaI/pDw2hC9NalPerUk2pvhpCCZTXliYx8/8Stv4//3M29mdnbMP/nHv8P37T3DoXCDH9j7BO8Ul4i1IXUBsWf4t7/7DZy5dYBeFXP+xWkaL2UkZzOYgsmrZnBzMcFijixSdDEg3FvU4w1SUiUBsj9GTHKuXFjgaNDjoJzQkJKzW0f43PAwnx4cpRo1MK2ASknUIGV+ZpNOPkYXgrITUoWQnNshWitwvsc4jkhnEspOgPEDqjmvfg61wJ8IolQgAw8hJcJaTOiRLSRksyHGq4tS0tOEm5Z4u6Kxapj1Bpw6cIPD8SbTdkAZeLTenGGLAGfBaYk3LlHdIWKlwk9zvB7ISuDlDhmWlAlUgSN/RZN2x+e2j004+IFVpp5I68yYwtHfiRHCMgp9zvSWiEcOLWsNuFX1AXoQFDglGN0V4+KQeN0QDC3ts33iq0PKUJAueUwWPNI5jawcxheEl7ZpnNvC2xghkLV+2PMRWtWFTU9xYbzAoj+mGXZpVzeZ3jrHwj19HGC9EitS/NUR/laOKi2ytPinb+J6A1zg1ZtcK4j/Y4HYkMyctJz6zjFTwwmicIRlhb9VEhmBFBqVO9RmhR4KZCUpmj7pvMforpA8Mci0xNvJ8C9XzP/mDrrbgzSlbHh4mxNUb0J6IKZ5YIRLcqqlgKrhUS63CbsFzZf6pD+XsvqLimjZIqKQ5v4cdcOjfb2AIkdOcpI1Sx75OF8S7pSEmxlGwtgG7PQDHv/9WdJNgRpW9IYRjgphHWIwBgeVnq1VtZ7GRSEujkArwo2S5pNr6I0Bzlm8sOK2u25x9I4bWC0oWz44QTrjk3V8xhea+KOSYGOCt5Vy8vWXOfz3VqBZ1blSONQXHPIJDy775HnIyseWsTnkNwKGT0eIixY9LlCjeqwxvLiDKgze9RxlLXascUUBVUnjRIGzFlEayIuXOzM8nK1HP0gNsgSrJGWoGN4+BU5StX0mx/awPbfIxY/tY3V1jrytKO+GQRXSdQFDL6aMFXp1Gzdy0EqQSVx3QiiJNOAVkmAIAolTPkIFBENTdzkOM0R/Qv+gx2Svjx37OCWwL8fNjNcaGE/jAp/GhkP3KgwVVgk6t/XZOBaS3u7Ymg55or/Mxe4MefGysUm8bNmZpLg0Q2wP2XiyTd8PKeck0tcI38cqhcPVz7WxUFSUUz7FXEQ126RcaCCHk1p3HEe1NllKqCq89Z26w0oJ9PoQsdLHLxzTL2bE6wW+K5if7iKtQYkS62tsXsE4hTSjbNS6ZAAxrK09VkvITT325Ql2ek3k3oJgNEL3Uigt/pUuYbfCH5RIJF53xIVHzvHk586QFxUu8WCSwThHGIsaF0glkZs5Uy8M8VJH0gmZBBp3Y0CxlXI5zbnSra1E/+mTzzKR9evDBR6vPXGRuQMWX8o6p6l4WcUuHCp/2eZk624kpy1OwYFmG2UVU5FHLANuTTb5xcsf4Wcv/Balrf4cVjm77LLLLrvs8rXDX5iOlCeeeIKf//mf56677vojl//kT/4kP/VTP8Uv/dIvcfz4cX70R3+Ut73tbbz00ks0m00APvCBD/B7v/d7fOhDH2JmZobv//7v513vehdPPfUUSqk/j7vzJ85v/+xnuHz2Fq/73of40u/eZM//1MVqSdQq0Jccr3z3dT79xB6i2NJShvKeb2f2EzeoBn265+YRhaB7YhqZGVxpSWcDkoHAKoF0oEsBBVBZxh1Fc6fARIrwdJ90WBHmXTZvm2X8pZDsbylaV0eMjjVZMx5TzQk7H/OZFltQVRgjefjMEq0Xc5pyXOssJzlqUoC0yKc2CPoVVkvU3kXOP7mHqeljXHxiD66t0Te3mPnELap2xOSB5br9uTLoawJXGKQFspzmSwPyfQ1cUmEaEv0/DrAO0psJZq/Ge0rjspJwJ2M7baCOVuRXGnjb9Um3MJZ0ViCloDgf0rniyOYMi4sebsaxkgqmRiVd61E4xd2dl1h5bh5/bUypGwyDIdXhktANGV9o1CeZPvTvWeB993yCl/5wD3uyEVdck/FBjWsU2FASrFdknYTJKYmdbVKtSoTQDAcBU1NjktwgKsEz4/3M9nPE3RmNVooDXh9f58KFfXz2526neXufd3z3JSgEOizZTx+N4/TGDMKAnHLoqGLzbR2m5DX0jCXbEFjjGPd9jIRyaQq0wru2BcZizsOZX1uiu9Hi8SeO4C8pHu0dIZ9o8sgxmdU0ejA+2aHbEwzPNvFbOTITeNbHhR7IOmcgWHNYOyTacdiDCkyDyZ0JzWcrmuM+zAvcvMEMQOCQaY4LI+LNDG8oEKMSpCTYrrCq7qx628kz3Dm3RtcEZJ2AlfVZhjshdy0t83SyXqtKxyU6Fhhr0CVEKxOqdgS6wBu3eeVdV/juVzzFv716NxtnOlRNxej2Fv6KQSGwOPxeRpUH+O2cItJ4ukK3S5YPDpG3LIGf41UFkzDAasHgsKacEoTr4HkB3sDQvz3CKsDVHQFVIMAXVG0Pb1xCHED5n6O6HLYpsFj89RH5QcnVvIm6z5I9EuCKEC8Ff5ySfnNF89fH6BWHED6jEzGjJcX0JVtvlDyNLXPyWZ/mTkzzC5Z3f/8KRw50+eVLjkuDRcQZxdLmNlY5ws4AWTli7aFaHqK06LRCZwKMY+PN82iXk817+LdnrNw+g5gIkgtjmtcd6loPBjnkMUGQgbIUEpwGl5cUdyj8Wwb3PMgTkju+cY0Xfv4wa3qGw/dscfvxW6xfa7LymXmiqxnOS+jvd0RrDucrRo2AQJRsNwJGRZvk4S7tnQn6XRJpfezK6OXRmarWtXsaqgo5ynBRAMIRrmXItXGt2Y0Faa75tR95kNGR2Xoc5OUaFhb8kaMwAeZKiJeUuI5m7lgXoQyrezqkN2IEDrEp4ElN+b1jxJwl/60W00/0SA8kiO0ckebke9tUDQ/hLDRDyHJsr97cikEGnRZkOcOXfGzHokoDlUFIiWvERJ0cNzNkuNLGhgFFJ0YMx9hIISuDHgtyP6PxZJfxnR0UAWqYM9rK6d4d0DAOETtcILGhh/UVclojb45rBXNhsXumsJ6gjASytAgBnirxfBjbGNmfMNnfwQYgqpI0NqiRrTsorKAsJWKY4XQTJwENwkmkUPQvd9BLJcNBgG9K0BJZGQZvUrS/BP6tFBnHdUEp8LHtCKM99Ejg5MudNNi6O0SrukBmDDZQeNtpXci5LUFYiV2cQUwypPKwZQmVgWajHkMyFUIKvO0JqhCYk9NgFXK9R5l7VOdBBpKCAFlU9ftI1frycGQQeR/naZRz2OkGNvHrjinqUcbwvGLqjk3CuzRbH5zDTHnkeULr9Gr99rYWC5CmmLkpPv3CWbwDDbybCjkuEEpQNkJEqBm8U1Me8Wj+poVYY7sT0lmfWxt9ZGXZ2BmitGJlq8fgVIdwqQGFwVsecfcbHKufb7MRDEjWDEsLDaYxfMu3vpZnwkt8+vQFpJAYKXEWnrxxk3tvm0U0egihyAzMBh1m/DZa/OVYO+2yyy677LLLfy9/IQopo9GI973vffy7f/fv+NEf/dGvXu6c42d+5mf4B//gH/De974XgF/+5V9mYWGBX/u1X+P9738//X6fX/zFX+SDH/wgb33rWwH4lV/5Ffbt28enP/1p3vGOd/y53Kc/ac4+dYVBd0zS9TDXNAemdzAoiv0RxTCmdyOmKhSVp+nbgLGdo7f5BTAzxBcksoLRgo/UCqsi8jmJnQ4wElo3C6J1gwklamCY+D6z6ykmVBTlmOTghFHs03hwG/kcNKZLTp7oEsQGIxX7R1tc2OkQj31E3MKTEQuPTygnE1xW1nP5oY/IC5QxqEwiOu16Xn2YcvDBLebv2CIbldw4fRBfCNS4xM5Po1JZ91WFmv1fyfC8gtFcgr9WoscGMwaswFSS/jDEFxVq25G92iLPBsSXt3FKkh9xJAsZUwzYqqbqEENPEw4tgcqIVy1jV28c47Di+sYMMoLpQc5K2QYpSMsG8XoJVpOZFk9u+uQHBCFjsisRwkgwYNqSw29YoXu1yfO3jhJuSPz2iHHuobu23nzMhUw6Hp2oiR0VWCVormq+68h52rMFf791gFHqYT1Jb9Lgy1mTRBZ8eXiAO/Ieqyttgj0pd/hj+kXMnv072FY9whUkJa4JjakcGpA96+HfWRAnltH9DcR2wPLCGpfHPmrFYKcateFCCERa8NF/ewIvsoimYSm+xvOPzTE6HOCUJN0XEZ3vUc0nuNu2qApDlimmnx6A9EmXQqqmJtookYUh6Ie4vCKaGYNqkL+hQbNX0lzfxtzUbB6eppwdEt4cYKqA8OaQybyH16vIpmLC0xu4xh6GtycoLO+duYT1crpaUsz6bOTTuFxy5gvrOOdQWd09YTWMjswQl4O6Fb806NUSdVSQrsb8w9kHWcs6dGSf5otDklQgG23sMMVMJ1RDhapKNIre7SGddsW+xgpXXpzCJnVexNz0iCrz0NowvDtBbXtUiaBqtinGBn+romzWmzFnLTgPZwTDkx0mSxEzFwUur/M6OkcH5PcKWl8aU+mIvdNrZGseZz9ygDJsUSRNvM9tEbkNyvvbmD0KdcFC6Rgc8yimPYrjs/jnhri8ZLwvwWpBFTjUTsav/MarmZoes/2EQ9iMqhFj9gcc27PDhaKJemJIeGgPxoCalKhJRfOaw98o6Z5sMJn2ELlGyZR4KSO9FGOnWpQjyPfExFcGBN2KAIfnJFnhg3FIZ4haJXaPYrB3ioWbO8R5jhc7gid3KAYG/jHYQmOkZe0t8+AE+V6BfVqgC8O2l7DVTxBbDrE5gVghS0eyUGFf5cg+oZBVVY9ZOAubOwhnsMpDCgcjDzPXpDo0i17pYxbauI0+5RqUDY2acZRh/WtKiPpnxDNjsumItOhgxxNe+MU5vAMRO9E00XtHqLMG85KmWq7Qe2s9sDw+4RXvvEFPhVz9ZxFsp9h5HxP4+HIIeyVl6uEiRXrAMpmfIVrNCLsDrHLYv9tEfEognhe4wQicoJyOqRDItW1cWVLcu0zclfgbKW4yQk981GSAy8a0r2q8bETayRi+K+To9BbH3Ra/M16kUAn5yT1wZEL49BZUGUILRFkxXvQwkWDPwha377vOS4/vZ6wlVT9EO0N2uI3pF8x8YQ2x2ceWL2ff3DWPrKB5bMCR3jpnb3r0js3gbzpkYcCl6P0BcqEieG5E9PQIvjPGhhKmLdmhEDWqYMurx/u0wuUZUpXILEKWph75qXJIM5ySWN/7P7uNfIlKS5r3b+P1BNX1BnJHw61N8HxUp8JNHPh10aMu14IYT5CXVyHwsIMRRWOK4WMKKLHTPk5JiDUsTCFKWxfphECUBlcWUERYX+GEQlYW3c+4besmi1+fsTFsYyKNLA3NQYopC2SaQ1nVXTJVBVIyOqpRDctMpKEw2HSMmIpRBmyTOv9o3mP8pcsEk4q47zOez4gHMOmP+cNf/QJved1tfOjffwHSgvhgg+deuo9nzk6hwxKn6sTtI/sW+LF/+U2M85JD40XW3poxySXX8w1EBbNJwnI8zdHWInujGe6c2std03/j//H65Kd/9KM8/ugFfuAffxP3verof/sbdtlll112+e/H8VVhyJ8rXwu34U+ZvxCFlO/7vu/jG77hG3jrW9/6RwopV65cYW1tjbe//e1fvSwIAt74xjfy6KOP8v73v5+nnnqKsiz/yHWWlpY4deoUjz766F+aQsr7/8l72LjV5cWZJ+h8/YCDWxtUkebK2Tkmexxf/tRhXO6oLJirD3HuYy+R5cuEgxHagiodN852IAKMJTtuyIYCGzqiHUVyraBqeuidlGxZ1u3lSmNChfeOklc+eBWJ5dG/sY8D4Zhh5uER0xch1262aL9lQn9tlqlnJ4z2B0QrBr1pcFlahxsmIW40gSzDWYe0FjyFTQJGgwhLyXi1qFXBB+bZ+IYOxhN0LlmEBekM0/EYcTIn+3SCa8dM9oWgHc5JitIj6wumXjS0nwG/JZC9AjwfKRXxLcPi/Bad5ph1v4MqDU47Wn5GlmqEzukdlQgFb3vvGf7ZI2+meQu6JuLCzgIbXpvmUoqoSsgM1krWdpqEZwWTVhNTKfBsbX3IBb/w0++g/5ghPRogfQubGjVTolKwWuFd2mLPuS2iO+aJ7j7OM0t9xi5GFxXbwmfga4yGpJVx6vA6H918BYGfI3PLG2bWmH1dwaH3rvJSEbOxNcW20syGGUpYjr1+nZeW5zlw9zaZVVz4vCB9IiL53jHlVgNnNFc/f4jxfZbkkevIIMC122AtQinswBBNG97y7c/z7JcX0b8J4RGfdEkS3sgoD87hjQXDjZjiWoSaAht4mEhTNjX+oEKPyzp+J7VYTzIWDcgdplLkswGDszuoUUrc26Fq+VSeQo9LdO7wJhbhCcavVIQvStL9EfFGgaws//jc63nPiWc5EG6jY4PaUxEuZHgHUobXEuSkfn05abHaR3o+RRQijMOEAmEdt262KV5ap9Hvk7w4QQ0NSA/r6zo0FVPrT4Ug7wjKpma8YDm4Z5uPPH83dklhtKAR52hjcU6SlCnhadCpQm1u1cGWUYzdsfROBFBYdN8h8pLG1TF5RzGZD5GFZDwTko8npM96iMsZ3vYAcTLj+jPzuHMaGiVylNa5DA7KvmTwyiZx6dN5akTnhYJ8MSW8PAJADCZ0TiuqhiJ8cRPaTaq+ZGPQgnCMnGSItCDbl7D8QJ/tP+gwkCAQ6GHBwvgy/XQvTjuSGyV5p6KYjkAIdtZb7FE7zPxOiZWC7GBCuZgwjgOS81tYoUitR7XtEwwt1gc5AwJD/Kk+9rmcjQsRg2ETmW8z6iZ88iMHmH+8W+fDhOA8qJxDpTmutPi6AukwxsBmSnF8ipXv2Uv7YMr9D13hwkfjevRieaF+Dp1DWYedCnFhiEhC/FGFSA223cTEAeVcE4dDZiX+lsFUIdaX6MIS9ipEIcg6CVhoPLdCvjNheFsT5ivGtzT5GxRir6XxjIf7gsDel/G2B08zNZWiC8dNuZfKGbTRyExRvVNg2pbgdzTqpiG/XaHOCOiPsFVVB7BUYA5m6C/UG2/yEmsDutOztBKD0IrwxrAuDO9PUDs+3pPrWGB0skPzhkR4lmBLk686Zr0RlIJwusBsSKoQ/PMWfdngjMI2A+x0i2gjp+ooejdbrPRnSaMmRit0WuA0bL6ljbdR0fyNK/VtDRTCCXQOCLizfZPlI7fYShXdYobG4yuwM8BNR3iHDHnVJjEF3vMZy183YdKI6N+cQowk+VyM36+QBTAeYfoDzLFZpKvHHYumxhs7XCYQeQmjCU4I3EwbMZwg84LEd4T3WEY7mvTJCLWicA3Fg+/d5rFflqACiELcy+pklEJ2h/V73FSEM4Z9d3RZO+PBZoGblOTLHWRZoXUIUmClgKrC+SHKUhdcQw9ZOmTlaP0PBtVx9LdDyEtOPrTC/HSXc/92gWx1Cru+DWWBmJvBWcuRjZiJkmi/oDQG4hg7yZFxRPRlwfiEIn40JZgINu5fIrkxRPVydCZB+/zrL36aoHyB+FIXYR2N2Oe7XvsjzOjz/MZnnySbB+EUl4oha70RP/WbD3Pr3Dob53dwgLrTAySR8uhmBaNxQCprQ9L/HZ748iV+4v/7Ye5+xQH+8T/7Fj77+89STAp+499/cbeQsssuu+yyy18avuYLKR/60Id4+umneeKJJ/7Yv62trQGwsLDwRy5fWFjg2rVrX72O7/tMTU39sev85+//L5HnOXmef/Xvg8Hgv/s+/FmwsG8GPe/44NmPsfiegvM/Oo3YlPTWG5jX1gdu4vOCXGh+9v9o0h5e5vADR1CrA8TAgILhSoKdjKA7pLGQ0FsSKG0ILnh4Z/vge1hy1HyIzAusdrj7mtz46JDk7oDcKsqG5txkkUOmy61qDn8xpzdMGKw3wUqGR33yJQ/TbNCZSPDqOXC3Zxa33UUaTZloRC+rOyEaITvJFJ8680rGWRsx4zA3NhFZA5WDnAikk5gG3PYDF/nyc8fxRvXstnSCrCXAtxhpESPFWPskfoEYSaBu73YND1t6vHr6ImerRcgl1VRYhzheLsmaMXv2XGX9hI+rJJeDBibRCOe48bF93Dg8z+C1jtlkRGv/JuNHXrbdZA3myLh8bQ5hBQqHCQDpWPc6qHiEpSLsWmyzpDwFxhP4wxKTlggDaZYxaO7gU9JcyJA2I2xI4iSnV4V85zc/wWvvvcof3LqDZ+VJWo/v8L8/fAcyctwfVJzPE65sTSFzwagKyKs2z6V7mT0y4mRjldJJzkztR5aKNdeEdIwsAiwhqnLI0MP5mt7RAJ0aglsGs9jBFzmf+vRdVM2KSkW4YUBjVdG7Z4F4vaR9aUK1EeEZR5xVuMhH74xppCUohagsejjENmJkCUJIXOAwG5pMSPzZKSj7tRY4FGx8yyFmwzXKpyz6PRJfVdjnDLSaeCODP7IA3Jau8/DF23j94nl2tpt0Zse0WmPMK/psbrcZS59GmdKcyrE3xmjfEoQDsgOaqgXB9ogTr9xk/de26KcN+nfNgHB4xqe5pjl4qovtlty6Modz4LSHnoB4NOSz+l6GBwPINUZ4WCkZTgJA0DwzRA80ZeLwigrVzSj3BghTjyaJYYbzI1TpUKkhwDHYY+hc7OJdd4RbBXFZ4LRGJA2y7ZRqukG1FJPHHnnHozEpMHMR6XG/NsAYzeRQG6+XM/OFbShEvQFXApk6vLTCzIWU8wVedwRxAzlTIUaiDumMQx798nGUs9jQoHJXW79eDzufp7YFlYZSVkjFy7pZj/LFAJmmqNEEOV2QVkk9gnNshhTwjGRYapyAohEyzA2yV9E8PwFrOXnvgAsXJb1gD8nE4V0HdY/FPepQWyPY16y70IoKjo4QQmIdiAmw2CA71KCMYmKvh+dZvPsr8ueaWCkx8zF0DsOlDYqTexCFITy7QrWnhTcxSAE29lETA8LhMoP1JdlUhHRgfEEVScaZoHIO0xKUMzFqWKC7GWpQUQ4V28dnUHsqmi9WqJuK5A0ZB2ZHJMOccddHdCukqrMrBKA/lWD2V6jVEi/PedvGdZ68egJ9YwBZjtgQzP7mkJ31qTqA52XUzohwFFLtmapv/40Boh3j75TQm+CyHCKfYCfBa5SYROFGCQdu9ohlwbppM8yDeqyyEqjS4TdTnLEUOsb5Eqk1/o5htBhSVhI1MghTYkNdK5IHkmIhwDsuCY96rPYOIdeHVBE4AdUNOP2FGSY7AnUsry1H0mETj40XI7Jjms2jM8QfaPGq284QetusfHoKZ+uCTDUTo7opFZbuG/YSaYXq1p0eO/fFmECz+FkFg9HLo1khwpNUexo4U5DnFrWVYW6CTCuqhQbeVsZTX5qDyNTFAQsgcK4OZK4tQQqEpvmHmwSv9zFRCy9RVBsZwY0dZFHhwgqhFc5YzJ4OMithmOP3R1itYKYFCM5cWyTfCmAsaDY1wWKJiST9d0yhf8eCbSO2etgsQzYTDsVtLj+3zaifIxFILZHNCHLL4AFJuQdGb5lCf2ZAa9sRbKSISV4rnQUMXgVhI0PkdVj1SMDhpTm+7z1zfPD3nyC8JZAGhBL0JymNOGCQvbzeEVDFdZ7X+VublLbi5mSdvtng+mSN/fHif3M98h9+4fP0xhlfeuR8/TrNS0Ra4IrdXJVddtlllz91djtS/sz4mi6k3Lhxg7/7d/8un/zkJwnD8L96vf/rKYlz7r95cvLfus6P//iP8yM/8iP/z27wnzMSMNSn9eSWI/f0OfNYi/RTPqZdQOUQniN8X8xtxVGuPbqBFZDNKqpE0pkqqaQCJAcXu9xygqbOeM/r1vjEV+Yphh6qUbJ/OcVWEgqQLiFZg+dvLKISKJ8D7hV4K461doTKfPLQZ/b0mPDFC6SH2yQ7PqqwVA0faQNcEiDzinLPFHKcke1vwcV1vEEBuaHoRhSrAVXPQw3GqJGh+fQA04kJ1g3VXIQaKj76h68k/Cy0KEGArZMKcCUvGxYEpa/Zuh10KkguFvhbCmEdpqm5YBe4VXSo5lo4BeH5DfhyQXR/yNyDOXcdvUpuPY60b6GHog5PVSnX44Stcw36NJjNLG5/gb/RJ/Yd6ZxCbDp07ihmBUUTwh1HGWn2fduEc89Mo68NsUaQvyJBLgrCnYzs5Ay9B9q4gz6LXypY7Dg2AvhPf3A7N491iL80IX/A0DlR0owrDi3f5Is326ydvpN8PMZlgrY/RirYv7RJ+PRxgrsnLKghl9UMFT5nxktIYVFRl8H7W2gPOltDVGrxnpmg1zxI6tEir5fTvbtBI7CEgwnptk8xFNjphNFt06jCYaQluWVBW7QzSEqMFFS+h2c0KrPIssLJCmyOm0xIDhlutfcSntjEmRDTBJVaRDPEWoG4sQ0S/ImjGXnoJhxcvAXCcOkzy1TdHq3LPuNDTfYd3yCaNZw+e5hopHnzzFm2Z2sbi4kAIfCNj+lMUQVd5ltdeqs+0mnMFKgXHXJtQrlU8dD71/jF0w+SHDe83t3ipV/ukJctFlnjUjGPGGaI4Zhw4oOUeImjKiS5L8HWr72yklSFRSiP4YMd8mVNfL6k9zca6CKn8Ycl1g/rQN/5JpSGSimGh5qUTUV8PSdczRCBRxH52FggghAhFZuzmk4jA0BnBgqBWZqCULHwosUezhmriDyRWOkT3tSIyuHyHJxl8oolvKFBXVknuFnWYwz7A0QZ45ISF3hYTzAgojyc0OhvoQc5elxy9pH9RN0UNS4Qw5R9nzacv61Zb5A9y9QzHoQGxil2FcJ2STUlKecFs1FKICt8D0wg66yPwlA1PfqvmyGsLM+VbUbrEZ6AvGMYL3mcfN0Wl1b2M7mtibASHBhTUX6zR2AsFkH8rCVvCYqOxGrLqdkb+NJw5AdTnvhH+/FLVQeoBj4iK1Cbozo8OGmgBhX5dIi3MURdXEE0GrhmSNX0mezxKWYV/sBijCBv+zj98hiGg/SOBWSrgzcxiEkBE4u3WhKt5sgdR3WPRb7g8VjvEFc+N0fZCVHFVayQ5A0BviPsVniPlehhgQsUruvhF45qYQo5SQFLfytAHFmC02vgHM7TOA17q1t016dxCPI9baRxOGuJz29DXtSP8d2K1SqkLUZwxWM0jBkMQkwiCLYrin6FCgUz39Hn1voM+qUK/ysWqQxGC6rQQxWStdEcrX5KFUpk5rChQpVgh7D3b1VEUxnV767Rf6IimW1ilWTPm1NWJwnZO2M67+2SH5li7qmM7jMe3pWMbp5TJYJMSzZuhMQpeKd72BmBliEqq3CjMdm8R2MUgCdRg4xgZYgcanrHQ1Ci1g4LwNOoUUHe8tl5Q4Po8YJ0sUnYFYhE4FUh0rdUqyMsBlWq+rmsDGZ5DnltBdeIAVBpSTGWXL82h2t4FDhUDCIvEEFdJHVZgSxLWAd7YAErJqidEgINmztgLIWbpb/dRElDoh1PP3wcPWMYDGIW2YbAx/kKOc5wo5s8f20FmjF27zym4SMPTvihf/qrdEcx//Lf/TVs0xFeybGdJkpq3OwUXLqBG49BCCovZjhwtGSJLQTxnhZCCD7zqdO4rMJXimzacTkZsk3G97z7QYqO49HffgGxWRGtlRRzAd92350cbk1z71KTUFlCqfnY6q8z5c3x6tm3/hfXIU8+fI4jB2a5cG6VZisAIEk8JsOUKk3/lFdBu+yyyy677PJnx9e0teepp55iY2OD++67D601Wmsefvhh/tW/+ldorb/aifJ/7SzZ2Nj46r8tLi5SFAXdbve/ep3/Ej/8wz9Mv9//6teNGzf+hO/dnyxFVlL2LW3VxuwI2h8wfPzrT7HwE1vkDiY2RI1L/JHj//X6v8bf+vb3ICLL4GhI/6hmeECxmTXBlTjhaDdzXjFzi8OtLdYTQWUNZDk6q/jmvc9gowACH71Q4RYrpn9owMwPZkyvVgS64ujxFSZ5wGAUkWWKypOIZgM/7GD8evTBKsv4xCz9IwliOMY/dwvv6jp6Z1CHEhoDpsJsSLJzEfrWBK+XYkLJ8N4ZRiebOA+irQJZwsKvT4iHBcYTjOc8bCCxWhBX8Df3Pc/XnzpDYytn7oUMnRdUCw1sUi+GVb/kuQ/fRnPT4JTEaYmNNDbyef39L3Lg5HW+fc+TfMfS4ywlfbyuwPiCh77nCQrpEV3WyDMxZRBTLrSRsx38RkX3QsKkqcmm6ttSJY7FV60y97p1rj25tz7Vv7WOu77J0Nek065WjjqHP7DMFkPa8YTZxogjv7yFWckIT+dIY/FGFR//2DH+cLzMOdNAJwWTM6tYW2E9wdCEbFZNqlAwM2VoBBltLyW8MmHlhxUvfbzDmZ0ltChJnc+o8Ol+U8Tsa4fM3dfDVdSPRRzSWKnonJnQPDtAFyUmUtg4oBIeolEirKFqQjZtmE37vOEHniS6vaJMQmzsIQuDswanHOVigp1pIg9NEd/hyOY0W9c6yIFh5sMrND5/EW8wot0cQRzgjWHmkW2aKmO0TzIsYoa3IqwLIQxQo5Tm07c41jnLVp5QokjRnNrbZfKs4PqHE6LpetNchQKkoD9ssX4xphvHjG+bJ74UInNwynEz7VCdSBi9xufUlZSrnzmMGQn01ibHbxuQ9b06bDLP8beH+OMU76YjyVKEEURrgmpbc/rCHhYaA6qxRkwcwVaJPzKYvYL8cEix6CEzg5UOZxxWQtC3jI4o8gUYH/Ah8HCBh+xnOAEmVDhbIvc71MRBIQi2S3COoreFOHsdb5BT7sQ4WWeI6FJQ7mmCFPXPSAKUrYsYQkW4WGM69cbRGodpBqhS0npum+ilIf6gxO8XhFe7lAsK2wnIp4OXlayKPJZ4gxE2qgi2MiSuHsPBMLltmqItmcxpKiRCVVhhqYRFTQzhhmHq91OiCxJnQ8q+zyM7+8kjBZ7EKA9v4pieTxFaEN+ocM7hsAwPNdimgacsWhjCjsPv5rTPjMCDl7YXOOBtsfYLmnhzAp5CdieoG5t1p9NoSD5rMIGiigRUBXY4Rk5yGI6wngLPo0p8ZAlGgXIOlVVQOWQlEIVF9wvEJEeMJphA462NWf7gVaae7JMfExT7HRutiAufm8beyNjz4DpL32Vxt09hOxGiKvGeuYKclLVCWWpeeHKZyirQkmqhRX6qg7izBT2L8OvNqW1HmHYLr5Fj+0NcnqMOlWT7PYrFgHJvG9tpYBbaZO0G7Rf68JyhiATjicNkENoC7erXj15MaSwPCL8iEBcdcjwmmPRZ3L9F2VCIyqFvVRhf1uaaQGGVoIoc0WrB4HOK8bNQfm4brz/CvzkG4bj3jjV+5FeexH+zRmiLt1SyGc1gPB/VEDRmJjQmBbPtCVeOz9E+maMioACRW0RlUVlF82ZF83pO2K+Y7A0opj28WwNaj9zETQqccHWQzXYPt7JOfLHP0u+M6EznNE4N0bcVKCMhr3O5nBAoAabjUTmDiT2EUlT3HyO7/wD5nUsYBSaU6COKai6BKR8XBbg4rG04WlEutrCNGBoRMi1BSMzcFNVss+64DENkacF3WCkopj2qaynN/7DF0m9dgfEEF/koKxifWqL3zXcxuXsJugPcsNZanzy5QVPn7O90WXq+z/6fGRC9OEaOMtTOGJHluPkZXCPCWUevTBiteVjt4UKf3uYYax1SCnQBRjqsD81GQGkt/+oLj3F+dQt9LUPnlpkXS2IruZUOeaZ7lV5pidQshR1wefQiz/UfxTjzx9Yhv/drj/IPvu+DfOrXv4wSELUiAHID0vMYFX8Fjid32WWXXXb5K8PXdEfKQw89xAsvvPBHLvvu7/5ubrvtNn7oh36Iw4cPs7i4yKc+9SnuueceAIqi4OGHH+af//N/DsB9992H53l86lOf4tu+7dsAWF1d5fTp0/zkT/7kf/X/DoKAIAj+lO7Znzw/+4O/yurVLd74997Eh37l9zn8v6zzL2f+Ez969rX0XtUgvgJoSwU0Lke09iSoU4J33v0FPvbY/ajEUDyfEueWcNaxVSUklGRG85ydpmlTlKqoKsmJ1iZuqgGe4N695zn9OxorfWwZIr9iqR6SnFeziGcdgwMJXpzTv6NNxzQRWpLP+SgN96vTnPv8HvKjCwgMwvdwmaFsShhYjGeQXoHMFGFRn/ba3gCV5bSe2GRysEkeCvyNDJkV6LaHEAXWxigr0aklbymOBNs8tHgJEzm+fHg/XAxwSoEF6RTCWfROxfhKhzxJKFoaVTq69y8gEVw52eWZa3fyvuoJjiyt8e9/93XMvtCnbGrO31wi2nKoHYso604EYcFOBP3nI6qJppoWCANGQ6QmvPbuut35029pYD/bwHgSG1ka1ySNiwVcvEnc9ZFAYhxvf+sFggXJRy4d52/+f16gaGh+5Kl3kM37vHb+SQ7GY7qTGV7VvMhH1huIRkm5PM1q1uFaOktHj/mGB55AYLAovJ2Csq8YXPbpHp6mfZtm7ldShu/16S1Occe3nOPJ3z1BOu/j4SGcJO14SCNx2qOUDm0VLpT4jYLFoze5Vi2Dp/G3M1ifkGxA/2oblYNrgVUOQg+b+FhPIbtjyIdsdxsI31JJjWjnBGsTkIKilTDSPlU0QU0snlWovuXafIubT88TrEzYO+giKosVBtIhN/4gxDvmEElJ3B5hgwnX/48WjWDCYC0mazhGx31mvpijPR+ZVygr8fol0Y0MlSryhYDRKOQrV48xu5hR9SSqrGg3MmRc0piGKDCkoYeYSnBCgPBxWUWxEuKfFDRuVkRPTgjnUlbfHsFEom5Y2i9NmLQTVGYBixtKJh1BtuAhjMO/ZTEavEFGtJKjb5S4wRCiEDnJEVWF831kUZEMx+z0pjCRIJ2LwVnCSyVWOdIdhxmlBBsDtGpg2j5V5dD9AcIY7vlrXbZ9xeVz+yjv2PPyhthiTUlzxWKS2kwSrGYgoVH1yC0IrRFVgDdw6FJh5loUi0281T77fmNIKHoU8mWF6k4fxilys0d5YE/dKeAUz6fzaOUYFT46fzmocxRi+5rW6RGiBFSB0QEuUOw9eAud5qSeIupbZl6sSLYt3ROC8SGP4mrM5crH9jXfNf8Ijw+XCMwE4QRXNvbwmXbO8PdTWAScxevliK0RohEzeEWb0YkYHh1Qzk5hfUlyf4V4socatlC5pZiui0LC1IryYFQRbbwctN1S+DsF6tI6bbtDOK3YzvfWxa/ugOFrEvLXQHTORwRgdjLa92QkB3Liact2V5CuFaiVAWaxg5iKoLTQHSICD4XFbfQgTWGqw4zos2VadTBxZaCyyEnJzrkmrqoQpmL2wR0uPnsEr1+Q72sT3hwh17rEv91FNWPcggJf0n1rTLYQURlXT7BIgb0VMdyI8J3BmDpc/MCRHebnDNdXZ3BWg++buF9EAAEAAElEQVRRTfl1YLMUCCHqThxXsPMxyUZzBr3RRQpLFpVM5gNGIoQpwdy65Xx/islmjHd4xPRjA8ww58jZLjfzBbz3WKxSPJfup/vmOZLT4EZAAa6s6kJgleFvbDO9IMg6CzjTxdvOEYFGLswhpMSubULgY2xJZQzetRCxH8SKgKJWy5udEWKpSbmwBNYh0woX+zgcTEpEQt11VFSkDzaY3WexlY8/7GPXBa4VYSd5XWwTArAwSJFo8DVOijrHRUik51HdVHDEAo7CVsxfqTAzTUR3jEpL7LgHGpL3VaSXKnI7RXxhB1Va9NmbbBwo+a2feyOHjt5iXDbR5XadzVKWtSI+L5FhgDmwh6ooyEZNDv6H68hhCUmEeHGNlSsbvOktt9P+mY9ju5aFzOMXvv9b2ddus9kdUwwLukLgdB2euzATsX9mitvbS8yIJjdvjLjRsNzVPsRCeDvqv2DtOf/SGs7XOOeYXmxx+I4lAF7z0O185XPn+I7/8fV/6muhXXbZZZe/6ghXf/1587VwG/60+ZoupDSbTU6dOvVHLkuShJmZma9e/oEPfIAf+7Ef49ixYxw7dowf+7EfI45jvvM7vxOAdrvN93zP9/D93//9zMzMMD09zQ/8wA9w5513ftXi85cBax3WVdwoP8vhb9zmu5bOEArHj975MH//p94DRsCwbl9eu7LOyr0xjXOO3h0B3/ngY9zZXOO3127j2hMdjAw5EmxzNZvjYLjJ2auHaYi8Lj44mAw0Iq/ASp585ARhuoqQGhsH4FlKFFsfbjLzxIDybSHVYkjlFxTTAuskZaJwWUB7dkTb69G7FSCURGiNNyNoXppQSYEuJQcO73Dl0jJVKCEDVeQIpWie7lHON0jvmKLcW7D4zBa2FOx56zY3Hknqk7exQ+Vw/eIcH+EuVpY0w5aPOeqRtTWNgcN4AlEJbCQZz8MZscChoxtsv9hCFJAue5x57BC28PlXjy9zx31neaJ3lFlZEAwsj/3BnUhjiTaKehMsJoh1gximFNdD/Acc9y1c5Kmbh8FJMqEZFCFaGkb9GG0E5YMzOB9sUBKsZzhrqEJBcajJpAWf/tAB1Fsck3TAwp4JIhEkxzO6echqu8Pb/TWaIue3Nw/jOhEYiVWOpzf3c2ZzH2FQcOFyxvd+y8eYVmPe+a2X+MTn76f/xhgvqEi9kGQlxfvZAbOjAfKYweAhFgI4P3g5gFPXXTpSEK4BYR3O2vqmbdafX8QNPGiBED7b0x5fEHvBGEQhcEJStDT+hkB3U0RmUJnBFgJ/KmO20+fqXIQJNDvfuIeZL1mckKSBjyr7SCUY7Q9ZcTN0TpeUiaWcrcctdNBBKM3c0S4n79lh39E1btya5cE9l9Ha0FuYpWsNYrtDNaNxQlBOh8hUkukEb2NM0dKkCwHeSHLsgU30QsVtB24QD6bZObLAqalNGke6PHr6FL/ymwfJqJBNTXzU0u21sB6YQGO1pJwzlH1JsOkYj0Imo4DWBU2pfbIZS3ZMMXVgByxkywm5aNUfeAaigcX5gplH+oS3ciwOIVUdgNlMIPLBSETokeJhDo2pbnnIvkIbR/KWJqMrHaz0aD52CVE6ymWDcA30+gAhJYfftMMD/9MNnLjJL/7APFmukOmEcFCR7W3g9hYcu+sa62dmSLsJDkO+ISgPtQg2S6pIIwqLcHXehJykmEjilRprM8gLTFMw+KYW0bM+zjQgq9DWw2rYMTGesPSDiDIWyAmYwFF5gtGxGBNqlBegt8ArLJ2lMZ1ggPxcBLJWQ9sTOfFhx6AT49+oMCbkf3v773CsNeD5S3vYmEyjh4bGFcHWRw4T6kv17ysNW/dGzD+agrW0v9IluDXGuzrBvLqF2inQ0xp3chF3pt4El74DLdFBxfTxLcpHQrCOYk5RihQbhkz1h7z3X5zHb1p+7ycFmzvtWn2b1kWKxFjixhjzFtj3rgHVQLP2RAu7L6MaSuy+DiKXyNwiTEmxaGjZiirX2PkWpUoo72xy/YBi4cYaw2FtdtH9FKebTMIEt2BqNe6aQJYO3S9QqymiqBBKoYqKYsknWADZc2gLHTXBl4Y81eCgUnDx4gGmFrswNDhTcW7jGFeuQOSXDA5KPK9EjL264OAcwkCwA961OuzZxgHFUgc1yvCFj90p+PSNA9wMNcPCZ7DRqIsLIsJ0UkSRc+3cXg69ZovcA60twcaEydG9yEFOsOpwRV2rsJMUOWkgZIPWzA5eMyM766N6Y0QkX840AeamcIFHORNT+Y6NvTHqtGHhaoGTljRyxALcbIKQCoeFm+tUp/ZhIo3pCMS1LcavnqFI9hI/t8no/oCyKVE7BlVBqTXpqQ6t0130KKs1zMJBUdbjYxaqloenJC7PcQTgwFmJbGqcHeNwqMjDGgta8vqP3OT41NM8N9rL7//sGxBxCNQ/c/PLc3SDmLNuGTwDgUerrRkHEXkUYEc5Xm+CEBKRxMiuQZQWISWmLND9jO954B8i2wGv+ltv5tPXr7Mw0+Hm2U3OXHyWX/yh34R2hJ3vgJYIYPjIDldf1aUxBU/1ruCo+OLmTQ6Jz5MWpzjRevCPrUPe9M5TfPKjz4B1nDy5hztO7gXgDV93N7dudintX4FV9S677LLLLn9l+JoupPzf4Qd/8AdJ05S//bf/Nt1ulwcffJBPfvKTNJvNr17np3/6p9Fa823f9m2kacpDDz3EL/3SL6HUHz9R+YvK9/2L72I8TDmf/ydO/2yPwSsVUlWs5THFfD2ikVzMQQp++2c/w+l9mkGzz9X3TPOaf3eDmekJ737XGf7NT9+PHfh0JiO+efE6v/3BB2slciNCuQKrJb/2j14JUNtPCoOQGlmWuCyDZUujoQk3Sty4XtQLC80LGdYLkNspQT/AH+dc6y4zWLPEt1uKKkSICucbaHbwBhOkdKTrVT0jnpVgQbXb4MC6HD0qsZnDH1ZY43BA9+JMHRiYGVxD4Q0t1ig+vXKC7/vbn+Sd/hV+6aOvZ9yRJGe20LkEX0Os6J9wvGnpEq9Zv8onP30nvbLJwAqiHUc6U1G2fdZ+/QTLx3sUd2nyW03MQGLLgmRiUVKQ7GySyWlMoPDyiO/7+o9weG6H33z8VXz4zANUUvGxX3gVNhZEXYcOR7hvrF+HwVMFKk5o/s8J8THF6XKG5FrK9jMdxBWPVngZVypEIZmLR0yUxwvZPBVnGViPX/7Kgywva8K1HCHg8qU9OOeT4jPqFmRW0RYWE1ncgwlHD2wyFU24KGfYHDWZ/80tdNew+mF4yzuf5HOPHGMYzuByQ+VXFFOK4FKF8EJEUeFwpF/skFdh/ZxIYNqnOCh4NtjLgXmDd8nSmB3RS5skvkAMKzQCpMTsi5l+aJspOWHl0n5Sq3B7FLqRUcqYztwANc7ILkXY0qBHJVNnJmQzJb27EuwdDdwTY2R/yD3fmvKt330TgWDNvcCrp6+S54rmlkKMS1jtkT8wBQ7CnkVXFisEw8Mh5ZTGxgnLz7zE//7+TyGBK6nHghryL6/fz+HlCxz1S8xphRQFMldUTjPeeLkAWFrKdt29Vu3JGSxD/xUaTzhK7ZAmQyY+2SGfMCt568xZjFV8rvsAOEsVO7xBiepOcFMJtlSIzKKFh2uF0OsjOk2E54N1GKmQnzXItiD7Rkv7ywatKwobU7VCrHJk8yHBVo6JAopZjZuKaX0lp7fVQbnrjFKPspLItCTcrHMLshMZgwcUr5jaJmpOeG77OGI4pmjEuECRCw+rJTYACog2SySKbMZH9yBNYhhlqKUUeY9GH/MJftGhr+Rs3ucRkrNp6iyVSRaSzXgEeU65DM1LfaKzY7LlkOwIKCUolWDj7DTJjGPl6Q5lWpK1LVP3DHA+5JVm6XMpsnJ8fPluvhgadlaWSRqQL2dMxglev0IvTuESHznOaT/Wg1kJ6xKZVfgrJY275wie7yP+c9eDzaCZ4EJFOHRUHUuyf0Tr0IjhhyvKymPzwYj9g5TNrRbjg1O4AGaOZcyeGLPZn8cutAm2IuYftsgcjr56hZt6lqV2j/bUhM94J8ien0YbDyEdTpjairUzQlU58jYJQ3AqxCxGqHhCtDhiOMioW2TqsSpPluSRz2RR4vZ5DG9M4RUlIROsFyC0hmaMiyGbifH//+z9d5Rk2X3fCX7uvc+HT59lslyXaVPt0I2GB2EIkiBoRAKELEWOyJmltEuZGXGlkXgIUiNpNTKjESnNrkSJEg0IEgBBACJAgPBAo71FV3d1eZOVPiPDPnvN/vFK0OrMiKNdiUsJyu85ecpEZcariBfv/e73fs0rFX5UEd+yNVEjFGR1BbdJPHTlkKsSOfapyEDfXvhaQWtjh1bLZ6I9nIXxiqOxZjGej9frAFvgCXQ7RBpHMNG43YL55pCFZITFIcvatignHqMH5mnlCTqOSE6vcpfaIWxpJjcdrz63SLAmkM0WBBIiH/ZGgMB5DnVCUvyeh+t1MFagUAitsc5gdIXIMvSxDqbhEezkpIcTts+CLGC8chQ11syulnipxGlH0G1gGj4Igbc7Qa/MYdoRRUPQ/fRNRkTkTQ/ZloRFTti3hNMK3UkQ0xJZWjAWl6YgJa7hIwqDLGrbVHcmZUqEwJFcmqKkwGqLqSr00TbhlT7fvXCBrtI0RMEn/bdhowjPQnrPYYTv4e3W9j6ZafB9Nu/oEG3VndwuVGgFwveQRUGwq9DtAC+/zUK1E8g1ziguXt1gOAMv5wOeefYa//pnfoPhm44Qr6UIJVCprrNhXMChmQ7CSUod0PF9Ti1OqeyEtLz2fziH5LlGeB7WWl54/ArjTPODP/gQTz1+mY2dCbvb4z+I8Wcf+9jHPvaxjz8U/BdHpHzpS1/6d/4shOADH/gAH/jAB/693xNFET//8z/Pz//8z//BHtwfIoqiYjoecWf8Vb7/NTf4m+/6djLl0GIO/eaQ+NUMkgicpdGM4KkGx+5pc7N9jd/6Sye5560bHPijY9xYY23BE989xxfe+VaqROJOgG6EKFt7wl/sztLJ65BNc2tAfqJNcnkHBhPCS4Z7On2ue02yUhDfHOEdjolulAhV4UY53u6U4GibNXMce3eJlaA2b2GVxGQF4sw8Qkmcrkg9iTfSyLSqh3opwTkmhwOsX+Ld2CHwC2yqcbGkHwZEpUNqg44sUvqoUvO+s19meWGLYj7g5EOrPL59nHZe79o5Y1FOEK1FpIsBM4dSmgdK+ut1xXDRkZQtRTCumG1a9OUKdygnfX9OerlB9liI7E+RngQnMd0GstFACkm23sRb2uH+Yzf4+CsPgxVEqUNMwUlHEcYEexrpGcRQ4jx45I23eK44iFiTGE/RaEu8o5r43g6/9IsPI04rpg8pFuIJN7Iev/ThN/KpL9xJ67RHpDUyUVgD/vMB3AuiAjcOmfOnOOHqPMa5jDfNnedQNOZYMMcHx48QVAFCGV751EG2RYN4RTN+sc4oSBcFVU8RrzQJrmTg6oAll8q6BQbwpo6qzJj/8h6TP9dG9B0ir5iRE6ZRgnpwhPukrRd3WJwK2PvUIsI54mMV1RzYaYXVBttQTO4VMJ4h7meYVoBrCsqWJD8DcTYkeTXF+Arm2iSHpswriXaC8zcX+eHFy5RIhNS1zcRaZNqkfRm80lG2PKwvkKbCRA5hHMfu2SB3gkA4KiAOCrzM8vj5Exg7oTne5I2vX+XF8yusX27hfIVyOa5h0H6AwxIHOQJBmXgUVYgKNeWd0DoPvQsZzcMl9zTX0JXisaRkGDfQbVCpxssqiiVJ0BeoCoqlCH/H1DvZpUGEgLVYP0IXMXQLZMNRNQV6EiCvWmRQYJt17aqJFOVswM4bQ6RV+KmgM7vGQX9MFeS4rsJVBcN7IvyRobxD4axg9cYck5s9hHB4O2PKI11UBQKHNKBySzAowYILPMIRtZ1HOIqVJi7xKYbQUQVl20M3PfyJJexqNoougdKUaxLpO8qDHtHrU+SmpdgNyBYT2usCI4AI+jc7zPl98lsapT1kBekLHuaMohordDfAf2Wd3Z8uuPj+gyxslkybPkUVYBtQdiXe4Rb5UoS3tUvnZgWZo/YqCUzDpwplXWsGkFcIJRCjCSbpEPmCsW+ZrDcJJhluUJ/3zauCfjKPcAp96iCb2TIL0VUOvDnjertiuz/DdL6JzMbMmylmW7H3VUHrvoI1v832pw+CslQRGFPQvjLFdRKqw12CNUmxEYKQKCGItgsWv3OdmYfHnH9fh8FqQvOqjxYhjdEempLWCzn2hiG6T9A+PaL/ZBfteWgFjSYcvMtx46kxclogZ9qY0meYJUgcRlM3pg0sSTLH9G4P+6Ak+VJI0E+xcYSdZMycqbjr8GWe+Mw9qElF54mC7bfNUC5KhDG4JKZYbNN8/ArCWrKZmOlSwjuPnefF315gabrDpeBQrX4JBFFW4V/JaK1vc+XILNfcLH8qfpRnvnKSzmXN9EBEicQTBmcFYqaDS0Jc4BhfbyLWc9RU43pNXGZwSqE3tmrywIHIKoQzNDYHTJZjZs9XSO1orpaMVhSpB72tEWqU40YpapgjCo23OsId9Whd9RFTRfG6U2TLgrJvaUYGPjuC+RnE9hA5UpgDc6g8xRYF+KrOHOokiNs5LBJBKAzSgp9ZpG9x1mJ9hVvqQSCxvuKwX2eOdHKJl1HbBp1FKkHlS/IjTbyNEUkF+VwCfQ17U+x8G8oKFYZIA9YLUVVBdSAgHghkVVG2E1RLoozjLW+9m6vPn6MZ+iSHQ3RZEK1Nmdw9S3PDYNMM1/CQheXS3i7Hkw5+0aLcA7lyhknwIgfDWo0yKUuaQfDNOeSu+47gWgFVZZis9jnx/a9BCMGrlzZJKw3Rf3Ej5z72sY997GMf/17s39W+RfBPfvqj9LcH/ImfMHghSFGht0OcGMJeB79vcX79drui4Kt/5zL3vf0surKko4gLN2fwrAQcwvNw1pE3FSv3X0JfOlXXHSdBbUnQHrODAnyJ5yxuvo0RfaTRkMG7gpd59ceXuHFPk6+3DtG4Y4puKlQlkFjkzV1sW+ElgrTl4YzAqzRoUecwVBqEpXxrj6Uze9ivl7zuPVd55cIytz6xhw1g8O3HsIFi8YPr+EVYL+oqTTI2WB1hEg/hIBgZjh5b5/XvvgRW8Knn72T9qyFzkzFCKOpVhENoj95Vx4XOAf5Vv8O21yNsaGws2XodLH2+YKHKeNt7H2M1beMdqHhxepT0mMQ+psCrbU9l35KdkvipoGg5CuWTlgFf/vIpZOboPddHtLoQKDACiaJ6qsv0jpIjJ3bQT/p86WMnGN+T0HpxQhgbnIOOn3PjiwfYfWVM8XyD8N4BaRVyoDHkt554CHvnmCZDjv/EFrs3Z9l7sYt00DjvKOZh/sQOufAoHVSlhwokx8MdWrLgvlbGh4JHSO+KabyYYUPIy5jWfIEocsgdnVcL8kVJMHBoUyGtJVvq4HIQMeCgObOO+vQuWdfHzwStSUYRJah1waHGiKMHNnmp7OBMXPsmx5LprSZBXFEuehgjOPDRbeRqSXBXgn/V4NY8/DnB+7/tHK+96ya/EL+JcytNFn6zwA7B+D4i8Hj69+a58ZmY7/gfLrAUFERK4fkScb+k3ABRAtZSthR+s2B2LmNt0kY4hfEd0lpGD3Z5pejgcJSVQXqOaOAQOJ4KT/M9Dz3GG7/vMs88tYgoIqzwsLFCxJZgZcyNQyHlVg8nHMnuAO+Uw5aK3BeoDEzuEftjmqZCOwPjnOatKa4KaD+3g3WKMobigS6Nawpvz0BeQhCArxBpDlLg9ce0/9uU0ZUmrd+wFIvgPGjeHCKNIz8+j8DHyzWjOz2CAQhtCbYq9sZNJhsBKU28iaO0jt3Xtwg3Knr/GpiR7DzbAt/ilxNcEOBNDcI5KBw6FuhQYWck0brG02CaCqkU0kqkdpiho7gZ0L/QQs6GVE0P51t0oDgVbtInxtMBTgBWstDdI4t9Ln73YUQuiM45hAZna6J8Z9zCGJCFZfZrO+grmq3sAIH1kXkGVYUQ4Jyg7MDwUADXwc8yYgNmQeJlBrMckZ6cJdjLCLZysCVVx2PoBN7JLsaHxlOriG4PmdTVvr4sMY2YSiiGL3RoNEpMO8SfKtJYQSIQfU1rLsf3LLN3TpFfSvF3AuRcjD1cv8bXfi9hVuwycj4ZEcKA0wqnoHkpwx4KEV2JHnp4tzNLZKnBGOTU4YuS6pKit2FwLzWRWYkqhqTNJsUKRFv1tSR5YMrGsx3k2ENog/UEab/g6rUWzgzx0wwnQW6GTPIY4SzljsamKaqqiF905MdmaaWaZrdiMpwShhXmrQ026LDz9RA74+H1h8jCII1CluBnkLzVMt5VmMhDTgqqO0poKNYuNvn875yi8HzcW0Fp8EaGxnqF3AWUIq0ULVUhLMwvTdl7tY1TEpmZupq4kyB2hhjfEa2URA/nTD5aYrcNQnkI67DtBNmIEcbiihJvVOLykvRkgi9lfb45cNrQ+/oucpKhnIf0fGwSEQ3re4HtNJFJgBxUhOduIZtNWgyZ3tMg+gcDjHCIaYbwPZSUGGtwUiACDzfTQQiFqAxYh7MGW1RsXF5Cn1UY5WGdxoUSKQR5KAhHFXKxw3CsiBPDxz/zCN7UosqqJly0A1khWz6RCskXJa4RIowln2niTUtkbuqNhlBilEXf0cD/xgjnLE5XmJkYf08Djhe+9ArNhQARCD6yfpnpXV0cls4rI0QYoBs+phmAc3z/XXfx7PYt8olDlIZn0y9y88YMm4Mt/uY/+GmEUmw8EnHkrgV+4k2vY3Ho46iJdSEVZVWTQw88fJTt3THjSfEHOgdpbfjwEy/y+lNHOTrf+wN9rn3sYx/72Mc+9omUbxH05luMBlOC1l/kH/7kLzAZ+8gAhOeDF2CVRmQFWEurO4dUknNPX6MY5+DD0T9TcPUTM3UgaFYhPIX849tM/ZBqXaCrAj93YCyLOyXSGRAC1QvIWgo/yxEaQn9KZzbnjmSXI2cGrK8v0FrPOR+dYHBPg7nfG+IlHnd++0WKosWl32yhFzu40EM4UbcplBqBxWQBZUtx30NrfPt3v8ryiRG//Nll5LQgPj+FOCQ0Dcgz8AKU8BB7IMoS6UEVNwhLh8s8ZKEwlc8Tn74L9lKUs7jIx+Ua58u6KLlyLJqMN7/rRX73mftZKMYI20b3PDypGPcjnv6tGZb/9B6vnruLwZU5kmZKvyPonKvtEbY0RF5JGQo6p1I+cuVBPvyTJ1A7FYeSV8laPsV9rW+SWjjqQM+kIN6oSMeO6jmfqVH0ntylOuQzODbH3poi2NiGUuNNNHfd2KK8S7E0HHE5PskjnZfZ+6Rk0lfkRxXJq31Mr0l8yPGm1zzHsKlpqKJWJihFHEkWVMqdUU7mIIw1e3+ih0s9TrnrzK1UpJXCyg4iBN0LmR4SNC9N8ScFJpS4sG43ur0eZi+ehb8YkJYB81/MsWOHnlWMTcLkiGDtmbPEdxi8YQqVRZoS7JjwELjFBCEl49c0ODDK0dUOSRyRHvSR65rF9hBfWmb9nPCFWXbvFCx7U8oywbNw/VqPgYu58vQcbzp5kes64sV/fYTJRoK59xBG5mRHY2QlePOZK7zt267xyx+9l6dkF31UEmSOgW7xa7/zFgavzPD2H3iS2QNDpDM4JOWMpHnQ0YgEOzdauKzEdhroTogTAY0Hd6lcC39g6J4rYCIZr4XIwLGw4eOPNeNFj2Q95tbFGSbTmFw0UCKgfa3Cxj7DRxaQuSBfVhQLXZY+26/lD57ECYltBbVyIg6Y5BZ1S+P3HeV8ndmKV6u1BILs3mX6i4KoMyZ+LCX++i2kduzGIf/ox+5Hr6yAtozvDgg2JCoLkKnGOIUqHXJaYJIA6YXEF7YxkaI4Oov1BcJUhBe2EeMCu7iAVkVd4yws3sU+DePRvpjhtKVYdOg5cKe75FlMMiw5c3CNr0UPI0rBn/qRT3H/6VUM8Gef+tMUJiad9Yg3DcnlPVwckFUxHBd4L41BW0RaW3Dq7uES9Z4m4pCF2ZjtKCC+2Me2YpLrY/TbW4ghhC+DWHNU3RZlq4G/fh0ZRHheAzUIKGc8yEtEUSF2BrgoQLQSqtAhC1mHsUYS4Rw2EFSRIDJTqnGIaXjsrHc5e88NRkWEWvCIn8vx8wnlXIu+a1D60BpsMN0akZ8UhGcmpK9GiMpSHmxC7OEnFV5cUo4TlJGEeYVIS1xVsfYPOiy8e0L6QoRfGtxgjBhPsaMc8+Y5dns9gmaFWxdkfovmjVso61BpBnGIuj7GJDF6McAstcHPkMIgjINpjjk4i5tmNB6wHD66Qf83egzaPcTZNvpWhfuGpfmGKXthgFpoYMcFJDG6XTcaecOKzPbwXIlZ6AAjZCui7BR8wjzA2pkFyk6Abtfc9cIoJ499/NAn7/hEv7nLseubXO01efmLbVw3RHQ6lErhZQa5kVHKkuPfeZ273rnBTTfDl193jPmPrSLDukFHjKaIVoLwfVxZIasU1fbI7o2QQ0HZC1CVgws3EP0UkZc44eFWlus6aSUR2qAsyFFBuTMkNg4tM/a+2iB+foC5VXGbtQMhsBaCQV5nrWQljFPwfVSWY5pxbTMVEF2dMr4rrtu15gMaW7VyLRpohBBYFfBzf/XddKMpL6wfp+lnqNiirY+vDabtoS6Pca0GLvZAgRMSNDi/VgW6ymAChcwMc09OEV4JCKwx2KrEpSVIyUufe57WjzyC9ASHFmb5s3/nR/nQF59m+x8+hlMe+oEVVFkf96Vb2xyc6WB9g2pMcPoqf+bOc0xNyP+4/B56j0+w33GCi5MBP/Xp3+VXXv8eQl+S5ZaoHXLlxg5aG978ljNcfGWdU3cs/IHOQX/sf/sgL61u4YWSb/zcX/wDfa597GMf+9jHPvaJlG8R/Nhf+150ZRntjck2FCLPMUmI8hTe1MerfJAWF4S89y9/H1/4jcf54m89WSs5ZMjLn1ihehJEUCFQmHnFrMy4/vXD6FnL9E/OcccvbCAsyMEQDi+DEISTnIlfIXMLWclkN2Jzp8NXLp4mueHR/xcddv0FIs/gXS4wCx0i7Vh66wY2Lbn1aY/JOMTdbqFACEyWog+10b4HEzj3ykHsxyTfeG4JzDpCG8JbOc21CuH7FAsx4a0pwvewQ4GwApFppBJYX3Brr8fWK7OMJopofUS1VyIQ2GZU77Rri23H5Acdbzp1ldfNX6J1OGcraXD1uYdoJxNk3MaGktVXWnz1S/fznStX2N7rUb0a8JqVW9zsa3ACmQT4fY0/VlTjFk44wskY0WmjplNGbzxANLCoqcUKcJHA8y1vOnSDexbX+NBH7sFJQdWLKZYSipmYxpYjV4rRQx0WvzhgdLLN17/wEPJjOT/2R3+Pv/VX/hkvPH+YPTXP8uyQK26F5l4BXsBic8xIBjx44BraQYHEU4az81soE9CQObGDaT9i/oZm9Pol3nTvFb74zCK3bsXYZYe0Hn4ukIHD5Rq4rXC4MaBa7KDn6xycKvCYRh1EIsiXcrI90KHEObjVaLMkNK7p4dIcYSxsppi+o985iDhmwcH07XP41yoWWhnXB7PkewJ3XfDPP/oQyydKrl3qIVsCfSAm/YEx8mPAVJAuzWJ2DeNvBNjD8MGnXo/4bAvb9FFjR3w9I+xXDO6bobd8ldbsJmb1KJ1hQLEi6D4/4s2Hr/H5l15LupPw7Ffv5JF3P4sXZwxm2iyurvLlr9/B4EYTihJut0iJVh0+Ov1Ih/AhSLYNzSsV1gmIFMn1DKkMorSEwwmlr/in/6+3UHQD3IkIuaWYLHl4OxpRGMKhYnzK4ZRg96GE7rOOcFghpI8oNXgKpy23dmY4+bp1PE9QGEMYVaiexvZDkGClQGqHO+jQBwzS1DvEbi7g+I8PcHaVV379MMIFtC4MsXGL7F5DcUwjXYC/FSOynLraSmH/TfClklilcFLWSgClCPdy0hMtnBA01hUSCQKKmRA72yS8skp+qgsWfuvph/mpg5/AGolnasWJEI7hesLBr+0wSRPS43N44xJ/kGMzw+TMLKER2BnN6J42otRUVtVkSseDixXyG4bmw2P0qIVqhfg7HhCRbkc09kowjnIuRHsVyeYI4RxV4uFtFfiDMWI2xrRCnO9DqGqS0BnktRx91mFiR3xugigh2EoRE4U76mGlR3x+m/jkgHQ7JH0ioXilRbHYxFmHTC2yMoiWR+NByVprluHFBHUuIhwqRo0Szwick+jKQzdLkvURQimMH+I5B0pRrSh2VyJ6BzL2XvLqUFNAWoect+zc2aMR5LR+c5d2J0f4AioBYQBxjMRi0hwZK1zo0UjADw1OWvzIQ1egREpjLcM+H2LWNXXKa30+IRxzeodjd0+5UErsxpDyZAOd1ISWFRp7dYIXStxogsMiN2K4J2e96JLd3cKaur2s8Y095GM38ZfmsVEMgcTbMFz9+ByXTQ/RjjBGI6rbpEVWILeHWGXZ/No8xeUuh9+zRndHQ3j73iEA59CzLeTGENluISuJqATx81PylZjhMY9wz9A7HyKqlBJB6PswTHELbeT2Xq1Y6TRxHuy+5xDJP3uR8f1dBo908Ndz4h2Lrar63ItCZJpBXuC8mrxxm7uI5TlEo4kUErc0C9sDvBCigcYqD4GHSWrFmPPq10QA19fmWct6qAVNq1NQTiJcJsk6HumBEBdUzKyXuIbCyy1GgTMCpTQqhofvOcxjz6wi4wD/ssDve+A7Nn/4IMXBiIMfHuFNBeLAIca3RvzwD72BP/Ptdd7ZzMosP/uPnkA4i1zdxR1eBODJ8zc4ee8CnqewpuQbjx/nd1ffyPvf/Vn8yykUBSKnbjnK4a//5d+ArKyrxnF873vuxxjLH/3ZX8U5h3zc4+3vOvsfPe9cv7iBqQzH7zr47/z9+as7+FpgKosx5lsqB28f+9jHPv5Dsd/a8/8/7BMp3yL4xG8+yZWLm/yR97+2bvnwDTiH6SbYyK/nzEYCznHk5BI//Fe+hzzN+cqnX6BoWNy9krlrJVWeYBpjWn8S4m3N7mXF4B05catk7X0tDvx6HxX4tZTZgfV9ei9MEbOzkG/iSs3ffvq7+IHZV1m/3sS0YtLDMc3zQyDCJSHZqMNj/9s9dNVNspsWFh1C2/oYrUXuTSju6hK9ssPklmN7qcXmCy28iUYmMW6cod7tsXmxQ3zL4jxgmhNu7lKdnsG9PUR+taKUhiDwWFiYsricsaBLjt1xk4sXl3BKojKDQ0AzRhpJvAUXxvM89sU7ePrSXaxPm6ippv3ikEE7QHZituwMXh7y2GeOIwtDM8h50ztf5sO/dwpTCZwxOAJcUEvJlTLIf1OjHUX4M4ZOCOO+RRXgTYHA8ZWtFdb7CUhBMLV15oAErxCEuyUI6FbbyKmhWAoQBlrriiMPrTOjCo7dP2DpF6Y4afmtZ9o0whRhBGff+zydzoC2X7FlOhz3t4kQHN7Z45f819A8/jRPXD5GdDGgda4ksgU79zZ42xueQbgJv/PR19Pvz5HdoeGQQ/glKIVMIvxphr83JT1rKfuNeud+6qBrqJpN+mcl3ZeGyMzQ2Z1gjQeVwMQB1vdwQYFuh0QzBXGo0UGEpxXTySyTCYgNQ2c6wXiSovS4fj6keX0P8xqH94DHdhUxlzjkxOJtl4hxiT8IufzJU4yWJPMHSnQhcUYReALT8mk/tcmnf2OZ61/36S5PsHtzhC+XhJempBcch05v81J6FBmXPPvXu3jX1/FeEyNzh3VQFhLmQ1hL66yZcUEZKQpRUs2HDJcl6BA1tiSXB1CCmW9BLHDCEqaa6ekOladwgYAjgsq3+KOQ7hN93GIX6STGE6AMOoJqvk18o8D36x1tpgW6FaMalrLTxE4lpfDpBo789mdIWZC7ltY/k2RxgjkyhzcoCVqa5ikQVY7seLQ3SsyDAnc9p3IlQnvojo8cGvIjPbyLW5iDTVwjxgUSEwj8tRSZ5rXlAouKHNGNEWamCa0mZpzVIdOOut3lyBKY+oa6tbbIy6sHkJVFavjwB19L4wM5eSCZWZqyO2gSjx3CBjVJ1wqwnqJMC8I4wEQKJHiZxgQ+WTtGXQih0pi1kPZTN7ANSfrwIUwYIHKJzj2UcOiWpEgyWk+mOERdnd2JkGVKebiDjX3UKMfMNvF3U/RsgmwGGK/EBh4Ld++RPrOAoK5DdgOHIkeNcr7yoRUuZHdw6cIS/vUUGaY4r2I28ZgQo3zD1u4sNzfbeIHhzMkdxjsxWzuLaF+jlEU2S4KpxM21CSdjnE4xjQCRl/gXppgNwbCMwRpohAgpwFMkT+XMxX3KN0fElHTvHbG5tgAbAtuIcAJsWoIx+Jml59bJTJOGKMFadGYpliK89QDn5+ycX0CIimC3oNGoyI1PoQWbyRymCggvQdpMoCwQNsIpUSuFhhme1mgfVFYir6T0Xgk48totppMOVeGjNPQe30Ts5ATpNhxepJqlJrShVro1BWVbEJxfxbZb+NsTqCrKo01Kv0VUjdk+1yG8AjQS3NYuQK3+cBbbDJCZQQvDxpvnaTw/wdvJiddTmtdKZKcNScLKvdfZ+mRdRS6mBcLWgeXGV5jZBjO3LE4JkheHuEASXZ3WNczWQqVxt8ksp+tmJNFp3j7nbx+LNrhQIJTgHW++wEV1kM2NFnk5gyw0OpRs3Zuw8I0c6QSmE+HpDKMUeT/Alg5lDdHYItct/WNtuDnGdMA6xcrMGuNbCd/1HS/x3JPLbM8fhB/XlGdK/GcUxUcUwaBicibG+YLxHT4zr4iawDk34k+86T4KrXl5a4uHVg4ifYVengcJsrIIAa+dXyJuNphXEe1rN7n24l2oHD70+XfQ2bxOMdOgccNSVhI1dYx70BlorCfpHZzh9MklPvqF57+pzPn81Rv83H/krHP1lVV+4m1/C4Tg73zkz3PfG0998zEfgSss2nO8PLjO2dnj/5HPto997GMf+9jHvx/7RMq3CJ578gqjYcbGar/O6ygE+D5kJY4I6wmktjhVr8XWLm+y+vJN7GCEnDiK3jxvfP+rfPzvnIWRQXwk4+oPnqCKHG9Yucidc+t87cYd5LMerg8iDkAKTJ4hvToU0RkDnqJ8xSN9hw93VfTLmOmxEFdI4vUBTCrswhzjcYMy0eD28Aww2639CeMJQgrKtiXwISsCbLNg566Q5quK1ss52cOzbDQP4h+qCDcm2IUOaeQR7t6COxX+sZzxyQT3RYV3PWd0w+NjPMjs0V0OvLHgpRfaSOXj3xxgswzZjnFCoARkIuDTXztDM3HE5xxIR7El4USBPdBCxwvM+Ft0z06xjyhCU9A8M8aLXF1B7Xl1QKAGGUrkMpQ3FcFEg644c3ANuRhiPjdPoT1kLnAWyoZk56aPsA7hHMmNKfHNKfkBRTHXAqcJNwVaSLysDqBUhWVRVEhpOR7s4gLBTzz5A0SNCqEkyhi++vfu5Ef/zlcJHBzz9piakHDS4Oq1LjfMHH/hle9B/8qE8K4ppVMQWaqG4cSJDfobktmlCYObDVqPZrQeV8iJB77C+h6600EccvirU3SYYMN6o7b74gQ3CWubSdswXZGkJwTtpy3BXokrNTYJ6L/uEFXbJwlSJkOL7Vis0BhACkEznmCmGtGE7ECDeKOod4zbFhKLHDnUjSH+9RFOCQQSX0dwy/A3HvpdXv/f3OSZS0f4nz78A0yPHMRhWP7FW5QZlF6AtAPs7i7iVA9rHC9emWPndQ3GRyy9Y31SLEFcEb66RbqTo2ZDnvraLOaopHzNHNGtevGXdYA4RBzQCAzT45Ll3xgQ7mRUSUBxdAYAEzU4EmzzF3/wE5xfX+Kfvfh65jpDtsbNeuP9SBPlKxYeteimY9IRdK7maDNBlQbmerUFAUO7WzB+vEF1JUYsgvSg316EEGzsYwRMjkma16G4HwbHurgbgvxAQP5Um+S6oKpCVNMwUm3EvKP3OxPsbIV3s6A406GcCTB3LyGzChdK8BTKQvzqFmpc1ovHcY7DIxxmaOFRdSMGd3epZickr1b4aQmdCCEcWIGJHR/8528hUpLKVyTNAO0UYcsS/2TJ5pM9Dn9K48IA20twnqgDNANFkZQ4Af5Uo3qKQjuSzYzyUI+q66MjW2dSaMdkSREOBcvLfbIwpNQJ4dDReWwXMQFXlCjhcM0QTi/hZWCdxTRjZF7hjXKcrzCtiNZ1i+sVCK9J0VRIC04p/M2CYG+CGGbkVvLihaMI5/AmW8g9TXkq5upbe3RvGFqbfW4cPoi85XNgd8jebovxBUO8PCE92KJ3Z592mLH9lXmEhLnXDpFFzvqXZxFlbdlgItDDtHaVLM8BCuEc0bMlYaDRhcL/4wpecdi8CR2BkWDLKSo1tSWkEAyva9K3tBmkDfQemFRTzsUUR4+RvGsV8UKO/mIX40uqeIxbrYOk80GTnes97EBRHU2oAke4VVsjld9AS/AEVAd6iBu7WAXR10rmzoyQFsBhfciPtGmmGhoNZOWYdRnaaBCC6Zkm5emY7EyT5EmHP4YwtwjrSFJBmhbED+3iexlM41ql5fvYPK/bcfZukk99dGZRI0nrXEJ5oEWykRFtl1RzEaQGL81IbzqII+i2QYJux2AsMqtQuaVseJjji3iTiu6X+oiiwGkLSuG0huEIAh/R7SDCAJvmiLgmvgQZtiyx8zFOwZXRAdbtIrnvcKFGZhUiczQ2BONZgfJ8qljg7zgoBIXzUVVa2/Wkwh9WxAOJtI6yBcmu49alBiuLOWvrTZ5unmTmqxsUPydw+PCaEu83AlyWIzKwAqRygADrGB/0+PGf/ZesLcKOK3nvffeQ3HGQ8doeymhsJ8EKwc/+zK9THLTsvTmht7xI/GSFKxRxNsQvC8pDy3glyC2LlYJo4hC+wisM02nOP/pfP8ub331X/f5bKEP7v5tdLp1b5Vf/n1/kL3zgj9Cdbf6fzjrnnr5SE7RC8tv/7Av/DpFyNIm4NpkStTOuTj/G2dn//j9mrNrHPvaxj33s4/fFPpHyLYIf/OE3cvXSFkdOLuLisPbPO1PXqQY+0yMJrUtjuknE3/3LH6LR8ElHad1mklmmqyGvXFnClgVmJkS1Kv76D/02X1i7q26AMZbZi33Wxg3sDHUdsRRI4xi/sUXrI+uoOMbh6LzqePS+Y0wvJPgjjTdSdRigJ3C2IipGiEZC8LDBXm6g4jZmNAUkxWyMnWkRl4rJmxaZec6SbEpGdzqS5zeRjQbFn+oRhxnBUynt5/tk94cEmyOqow3UCxpdCPL7I8LCUPY8gvWK588dYX6vzcmdlwleWcO2YxwSYQ2Mptheg4anubO7xuaf9wg+p1j9RpNKGWRVIZKK1skR5Z6m+2WNOR1wo98jmGrChw3f9lOXef63DrFxoYHTBqktOB+vXbF9qsnckzs4AQGaC+sHYFT723UsQTqiZs7iwyO2PtrDOwTje1rohk8xG9LbshhCJrsNwkQjKocsHf645M//lffyd//uh4mURZeC+NGQ1rMZFKBGFXYScWvc4JX1Uzy8eJWHZtYYs8yNpQQzUzcdBbtT3FrA9MGY+Kbm0uMzPP7sd9FO+6y9egBlS1QuIfAhltj1AfmRNuV8AycdXjHFyw3KSho3HK1nNYOTgngrw7uV45oeuu0hphk2qJm8qhNgWxIkVNLn9MwqL0yXqQTIeYtYzSgHApyPzAui7QKmOXnDkT0cYApJlfpQTRB5hZ5NKA53UcckPFdx39tuseYCjpxYRUwtUeYIqwwiD+mH+Gcb3HiygYgddl5g5iQ3y2WGFxeIhpYbo0Xe/3fP88rjjkf/Hz5qLyOfiQmwDL7Dw0lN9E92ETqgPHuQ7syQXdmk+aym/bUClWmcJ7FLLfKORFrQgWR+fsiJuR0ir4Ln38jf/O4P85P/84+SL4PKQ/xB3apkhUTmkHZANyMa1zJUmtcZQu2E7oUcveuhQx+0w+zdDpP268+aZy1ipq4FLzsKhKPqBQQDxeLxEZ3S8Oq1BvKqJYqmBGsBemWG8YqlPdyFvQJvziKtRFiBf32IPtjBebXCRhZVbUmwDqxB92Kmh3xsFGEbFXo+IPUTWud9ROmI1hzpUk7ryQyvK9CRQFjH+FrExy7eS+RXbPziPIv5lFxolJWI0CNu5hxd2eTSM20qL6hth90EJgZfgJOS5n1jxHHYvdWi/44VrCcxkUdDjbnvTRfRpeSrv/wQk4OORuMowdaU6MvXcM6w8Z47mHu+wBsDBmzh0L0IHafItQG+tviJwhw7SD+YoVyob5nexFCpELsc45/XqEFGdG2InxukE7g2ZG9tkB1zeL7kcDoGv5YruBd9JpVCbW8TT3Pyw22GL87SD6B7a4rMKyaXLJPOPNI6hFQMHmpjA0H3Sxkqt7jREDsp8Nsd8HxsEiA9QZaGBEUMt+1VOgalJXgeTkocFotP1C0YhSF+UCILQ3RjSH5/F+UL5EnIX1bYwwL9dG19lLokecWR5i1cSyFGU+KNPYY/dAynBMWWRWHJHziKUAK32segqTLHNz50jPJhCdqgRIWeCXCRj3QOJyVqpsKKEo0kWMuIb+QYGWJVQPulHeTt0FZVlrSvaRrfXtB9VwX/eIotxG21Q20xmmwL/o1Xpnj4KH7QwN/ICG+meJmh7HqY0EOUlvHVLqIqcZVGKoVteJBNyRZj1KRkcL8HeZvuq2n986WqrTjW1apPIer3M8sRxiCKEjcaI5YXQAiklIiJQUYx177sUdwTYwpBXFqcL5GlpvPSABco0jvmMYXDKzUuVVhPIsoKp3ysNvhTQ+cVjdoZEa57ICJE5bF5Iebmxl0ksxYlbmckCYvEsXww5eKZeWwskc5hlaPqhFSRoJjzeC4dkk0FKMlvfOjL/PBbj/F7//AiAoE7voSQgvDaGP/lIS6apffoFHmywIRzyE9bopku9x6e47lnVqmadSB2oOsMp0QKTty3wuFjC4RJiA0lFlhpxN+cWfI85z13/Y/IJAZf8ex3/n0+8dTP/J/OOrMHZnHWwWTM1//1s//OY54fodyYOw5ucKr5Pf+pxqt97GMf+/gvC+721x82/nM4hj9g7BMp3yL46G89w2iUcuBgtz5vPY+8JdEHY2wkkbmPWW6Tb08ospJDx+fpLnZZWx0gAx/vUcU5fRcsp8idLZaCjNQ1+IkTL/DDv/1+zskJ8st7yEzASMOhWlaOtSQXCoRUCFnbc8JLYw4Ox7z9DRf5tZ9+Hd0XpoxXIorDCZH1MRt9pBcweqJL0IVqUiDSHOEEsulRzcQ0rmfEmwAKUULvcYe/U4EAteHQsxEseNglR7wH+mCH4R2CxstT6Ct6vz4lO9FGN31koRFzFVnH47mrdwN7EIcU8w38rQDbjbCtiOJqxvlfWSH/s2MO3j1l/KKHjqAlDhIezWi9pc/1L80TbGfk90d48yXhbEXqQhbflXPwap/1Z2KY9jBO1PKMEA6uDKi+OsEKxcb6HOLODK/y8TYluhPhAkfhHHce2uCvf/6z/ONzb+HK84tM7m4zd31Aa7Vg50iP4Vu7HP7kDl5ukcahE8Xe3AI/9jf/O1p5ydvOXiQ5Z1HzPqn1YKNC5Dn/9AvfhzWS5y8eQxWfJ3jbg3in+iwFGR2b8cqJFu1QUD4zJbyiuXElYuftMwR7IQ0jKZYbaK8kyA1uqEk8gRhnuF5IMCxwpUEfkHg7dehh8TqB2AYTeejAI7liaH8dRGEp5hKs9TAtH6cdhI6km3Jr0sYFNbEyXVbEOxWYAOcMqgzxxyXBlT0EjmA7JD0SEn6uJFtu4/dLbCskvXOGtjckO9rh5qhNNFNQGUn7Rs7opIfagurdLRppzu4DsLvRpTgi4M0hUz+mGneINwReKTkTbjJ5TNLNBkizCEphuzHDB+bQ90yQtzR2BsRAETQtp793ldXtO8EX4EnyO2YpPEFS+ZRdiTRQRY4Sj0Eas5O3mO/t8tTWAUzDoIRFZSBLRzYnKWYkOojJjzmkknR+eML4FwNc6YMx8KiPbfrkKwFVQ+LbnMbWgMKbBQvBnsMFUB1MaD/jcMWYvXsD5qIJ3336ecRxuHZrGf2so/F4hl2MKOYcupEzeN0M4TjGm1p0U+HaATZuo7REVI70zDz+TEh0Y4pXOZhapMqReQKeoftEir5s64DcGGwISkFQhJRnAkxh8AqFP7LEWiA/26Hzjg229qDRr5isJBjp8BE88rrznL33KrOjRZ77+GFs5GGNozga4JTEJD5iNiPolnTOFezKFsURhfFgdzZit2yQ34rJO+DtTJEjHyMUNBOkq9VfrtDgfHD1a62Uj12eIxhvIoWtw1RXKs6+/WVuvdzm6s0TqHGJ8wUmDChfc4iF3jaPPHKBm5+a5dZaj/j9KXPhlOCSo/FEwdraHC3fIQtBMRfVeRlzbdKVBH9q8LSH1A5p66ljp98juVghkhhrNJMHQ7rrOfr+Y1TjlPAbN5GFqUms2EdJibxc8t1/8lleLGfY+b3boZ7OUnUDfG0YHwtpXitJz8zQcSlboonX1LiiwtvJCK5GbP3uIsZJgqqis1tgO8D1ITR8/FMtqhseLvLwRiV4Dn+osZGHPzIgRd3sBrV1cpRR3ncUe2Ub2U/R7YBoV0DUYvCIoPdKhTSO6csdlMywnRhPBiAqitmI5k5ZV6XnBaGfUlU++AEbvz1P+4413LioLWOerKuChVerGqWE2Q62FeKkwMYB/TfME+wUJLsCb1Jhk5DWiiH9mkbsDhGtJp6x2MkEt9KibIbMvmqIXthADitsu40LFa4ocHmJzKq6lcc5ZJbhJlNc6EMU4qYp9DrgKpRQCOMoZhPy2MOvLOH5HYSMQXkIV2KjACSYpqSSAkVNDAknkIVFDkfIrER6HjQTgpd3MKeXUP0xttVBaAvaYCKPSRZinORwe8qP/aPP8HO/9n1sqwYOsCpBOvAzR7SjyVsS2fMwsYMo4HP/9GsIBGahTdWp7btqoUXlw8wnU4RS2C8PCN/dIfA1y8vz/O1f+7/xrmN/HmUthD4mKzErs2htefG5y8SLDY6UmqBwVDh66t8SKe97/f8EUuACBb5Hbsx/0KzTbkVQlAjfB23RWuN59SgbVJLACpaThEa4/Pv+nLLU/Oz//AmEFHzgp74Xz9vPU9nHPvaxj338fwf5h30A+/hPgxMnFmi3YlrNCBeF2LJCNTXTZVFnCvRz1E5KuTXizCnBqbP/C6vnXoY8RzpH8+V12gdzXEehJGyvRjz3y3N89aWjgGR3r42QbUSjjTUS3fBxQlC1FMH6BH8+QfgeeApfFMhrjqf2jiBVvYhM1nNaNzL8SqD8EJEDucPsTSh6CmQ9DPupwZsWiO0JcmsKQhDZjOamhqU5Rq9dZq/TYcENeec9r9COU5wUBIWgedkRTT38ASgpUAsFCIPMK1Ye2SDG4PV8ygePow/P4401vgyo5iLSgzHpnM9eFSA+FnD9Hx3Cb2R0OlPsdMJwt8mrv36I3m8WMB/jpgnqiQbxpmBQxuxUTcxsQJUEmJkEMxNhrGVzxUO9scJ02xA3uDYTUQYBbg5IBIi6ulaEjsf6R9m0DXxRkncc1Yyl4QrGUcDwmE/eUlz6vhniWynh9QEYQ+fAlHLGZ/tok6evL2NvavIqwLZibLeB6YVoITFKMi5CnvjEEapnnqAnRgSyoj+VFC4iuzrFLRuUV1G2BXe94RpveP8lmvNjZBuiNELSQCYKgpBuIUjWxkQ3RsQbU7xBhZcKTAvG94UI59DtgPHdPWzSJNzw0UsJwW5GtDYhuTLEJJD3YFM12N1pc2RliwdPXiG7O6dsBVTdoCYl5kPSAzHp6QXyoz2OlAHH1jKSjRDbCCh6LUb3L4KFZTHC+ZK/8rE/jk4D2Er4nh96jO/5wcfZe8hn/qU1yvNjtjd8/K95NJ4I6f61Eb1fXGfmd67QfWEPlWva0S5f+uUVXvjILCKKka0W3rBgfNSS+CXxMc3gf5hHH1sgfsVylAFCGKquhkGBKh3jEyHeMKdsO6oYqgY8WR7iyrVZnvn4YaaPdfngL3wnsge2LXCxh44l4QRaa3XdaO9yQedCRnklJj5Yomc0pqGwnqBMBOmCwoYCE/lwsyK8soM3KpHa4U0salyiRgWtS1v4u5rRqMFTr57ka5unGE4TOKbwD1tEpfFvDVn65ISZr+8hLChjAIcTIAsJDoRf54PEew7fKfSgT6mKOjfHSqySFD2fqiWxqs5RcgKKnkVUtVWu8izB2CAKiwgcyVxOZ67AraaYnR3kaIK0YD3B6uoCG5szXK+O4TlB7/k9mqsF1vPQDYHzBaPHO4w/lDC+HuECi41KTNdQVj5feuEuXnzuOMKBt5ES3NgjvLhThx07x9KvXsWvAqqeT9Xx0a2g/kz6HqbhA+CcpdcZ0Jqf0D/cYnjcor0UsTdBe3UWx3c8+AzvuPs8S2enDO+NKKwPKcQfc3DBQweaO779Gvk9BdKBjiSje2fQMzHpAYcJHFQGZxzCmLo1JfBxxqIbkvZyRhg4/IElngjE3Cyi0cB4jskdbUSmcRPDS586zOq/bCJ29hCjKa7MSG6k+FNL55ZAzLSpZjyytuLqYIbroxlsXpAda5IdSDAiREwswaBiSsBoYQH39lnyh3oMsoB2NyfILflKl513r9ShohoqzyF973boax2w6w0Lwr0M1XW0nrpF68nVmqzyFcUjEcP3+phI4m1P6b7dceSnKuLTMa4d1wGmnk+1kGBbMWFL8O4/dp64oymLkOxC+G93uoRECIlQkvJAF7PcQStH2ayrzauWwIUeZS9C9XOccUgHc6dSRBAgpKxbbaRANBokN6Z1ZpeAYFJRzsSMj/iYMkdKhT7cQ0YhMgwQugJjcFLW/jptYDTBDcfY7V2kcTjfQ1QO51lsN0fYKU6AU9CbK5HDnLwDJlHIoqJqh+iGhw69WnlxO+QZvw5YDkYFaloAPkVHUfaCOlxVQlaFFNpnWER8enQPb737ErIAUQBO4LTGCUvV8iD2ayEPtRKmbi4SmKPzCCHq3882ELnBHVrALs1BHBGFFXHgaEYKIQTv/5G3wMYOYqOP2NnFSUGmLOtLAb/93Kus39zDCxQKQeL92727PNcIbXCVxirB2YeO/QfNOqVxiDjAZRkuS/nCrz/2zceSOKSRRNx74CGW4zt/35/z6uUNvnjxOp9/9RrnLq79Bz33Pvaxj33sYx//n9gnUr5FcPeDKzz45jtotkOcswghCF4tWPmeqzTaKSq3qKJCCMFg58ssHryJUHVlr6s0Dz2yxZ/+nudwMw38NwaMRcgzXzzIB6+fZaxDqlxgJ7rOtwg98vmQbDmibPuoWHPizQYnbL1DNxfQOiD5xm8dw0iFCb26tjXNYW+EzkcM7tXsvlkgco0/FrjBCIZjzGRatzVQ77IJQJsIlIDAI1rPWfjchO88/jLveeAF7n7DNbIDCmRB42KK0ppGmaLKgtctXahbiZzjtXdc5rvf8ywPfvuryNjVA6SkDicUjnxWYroxsiEQz4XYRkRQ+tj+GLZ2ccWQhec0rrTYUhC+XBA8XzH9nZCvPnOGL+6eZjvpwqG5Oi0bAXmJ1oLU+Zgixe2NObS9w0ONG4i+TxV4ZPMe1YLk5Nw2veaEy2WPVAps5CA0pNuG9q09mmuGMKgg9jG6XuAmImdR7tG+XtK+VTIfDOsd+pZFFBWqXWJ/UCHrFkyCSHPXd91iTzbpfENy+acOcf5jZ9l78yE2v/MEi9sDWsmEoJvTmMmolCKyJXbkyOcBI7BpgawMyvcIS4tAUvYi8CQmBOMJIllBqPEnhlIp5Cgl7xh2HlR4jQyz2MF2EpAGE4EJBcFYcLjTp3A+x+5cQ7dAmgrSHH9thBrmFIeb5Id6NL4QEHxZIKSgMXXIdkw4hnBgWHu5i59DkUW88Ikz/OYvvpkTB8c8sHiLxkxBb84SjBSvc6tY4+PvVfi7JaIo8ScFei5GWsXTN+4mPAXBaxzpYgieItIxBz47ZjyIyEufiYnxNKhCkl9sEgpD62pO0C/x9jKIFChQ2ynZkqFqO6bNkMYdQ86+cxU3ozBLHkYruP0e6UAwXZKMFyzeqESkJarKedP3v8ShN24xWQ5Z/cEGN34kxsUh0ZYmGFrUEJTz8FMNRYlpSTpzKd6woPumAZ2flsjQIgrJ556/j8+/fC9FBOoOR/B2DU4jKoGTAhP74IH2BHKUo8Y5YjxGjApcCZ5zOFUrjkzskS9E7Lx1mSyw+BPN3ls8dr8rYPAmizd1pIdhfNxhEk3zasH8kxOCgSYoHaWQ6EXL1a8ewFUWKouNFflsgNCWy+cP8psf/Dau9A9QzjfpPzxHdihClaBySJdCJkcixqcb6FnN7Je3OPjhIcGOJL7laMQpfpLR3LBERUJ+Ypbi3kXkXA8522N4/yzFjKJsKaYLEoNGbYxR4wLXSaikQVcFo6cc2VpENo5BSFzgyI800W2vFp5NNF/9+FkufuUIrecdWy/PsfPFLtVMgn2N5X1/9Wl+6g2f5m33nWd0TFG2JbaqEJVBFSV5V1B06maUWGWcuXOEWWhjkwCvX6AerauuMQZhQbRbtbXSr2hsa5yrr72vnFshl4vQiEHWi3U3HOPGE8oZH6zlT7zpGT7wHZ9lmSnDaYSbaSKCmGBgkeOSeEnjegFqAH5hca0IW8Q0N1OqzMdKgZcL1J7BegLrA0VWt0JZg3W1OkEoSXowIHtDjIjBNX2StRTnHB2v4N7TG/RODWCcMnp9i+uzbbhDo1sevXNDlPUp52Kc76FlyPKPFiQnBXQDwpZDHVY1wVBV/BtWxc803qhAjjK8vMSFII8UKFv3zIumRshaiXTrKz2EUrVFJ88hCaCdgHYEOwX+doEJA6b3zjJ+aJbpqSY2CfGMh5Oi/j4h6sByIXC6Al3hGjHF0dm6/Uc6ZFpweukGjeURtmcQ/SlcvQkO5lcmyLzEWo2cFDhdoCpX1zQ3Qpx0ZMstnLF1O9DuAIoKGwic7xHcGOPv5eArXKcmf6QEi2KtmCFqFuA7hHCY2MNOSuQwQ45SHBakwTUsRfP2uRUEiM09jA82EJheQHWkQXagQX4gITxmuPeeJo+88xG+/UffBsCPfeC9zMw3cZMpQgiMB+M7EuK+Id7TfOLr52j2YsJAsdhu8uXPvMRkkuEUWFWrEAklMysz/4ezzfXhHk+uv8qxf/nT3Pubf5Xr+TZLD50E3wOl+JW/9bFv/tvSGlCCTjyLEr+/4PrIyhyJUCRWMttr/f8ydu1jH/vYxz7+K8e+tedbBB/87ScZjXM6oYfQFleWSKVI379H/CM9tNZQWNCa4/e8BcNF8rKHiiVe5Pjhv3SDZx+bwb+yzcr7hrwglmm/1uAtaKorHk4Y7HgECGQgsKHE+BKxV9K8w/Dab4t46XGQA0N2b5cnD7QYv9On+WRIPh/QeW6PoFAw6yMnCj8NqVKBi2OU8iGOQRvKwy2qRGEWPMLrE2TVxIYWIQNkVhBu7MB0louDRRaWB1zrz6OEo9IeypSwVWJwCGN58cM9GkuG7GCL8eUWn1y4k2EeYgqNP6kYL1R0n+hjDyi8rInKHFYa3JLDv1Vy4N4+GzOKyUYPfSbB7vqIPACtEZ5EaEtzmlGe8wnvMKxnTWxlyOYVXmqxUYzrS5qLFcNpTX687x2vUs4HhHcFPHH+RD1Imrq1IjEFhfMoYvFvN1u1JIgcC60Jf+Edn+MjL93H5o808X/XI29a+r+c4XtbBO1Z1ooOG0cV2X0dzP2Obj9jZs7htqhrq6Vl5Qf2+PAvPggVSKmYroBfOUQiecs7LnLu9w4y8+AWKlL0yyajdkLmfKLNFH8P8BKcnLB0Yo5Xx3t1K8U4xb8wQrQOgA1oeAX9eahihZVQzQQ0diru+L419j4RI2bAhT7ByJIe1Qgs7QsF1z43y+i4T+frQ4IbA/o/7jH7jwVCC1Th8K/ugZXMHr3M2vkFqp7EKQhHaZ1Z4CzTQwnpIUvREhgFA+Pzouxwc2uZbLdB8kjAjAgZlx0Gp5skz9/CTzNEHOEaMWUEVU/Qr2K88wrhKUYPNGntmrruVArkGIbjNkFSYAEbSj6XHUcFlulrE/RmDyE8/LSiTCTLz1asziQoKzDKcWdzwEu9gNudNlincGMJFqwH6uomzQsD8tM9sJYgqJhpZmyc1KRrEQTgvNpCFO9qpLCgwLZV3XC0lBC1SxpeRR742OR2VbAEERq++9ueQXqWR5+/h3IvQDw9gM09VNOnvDNiejJC9CHo58hpSrEyC5lB+LVtQw5KpBVIJ/Dm58lnIqwSBDhkWqEKReUJTBuKtsSbOPyRwHkwORZhIkWQgSoFBsGFJ47QuDQmzLao5lqYoIE0rm5HUQp3+/Cd5zCxwgaCqgUmcsgJCCnQnkcQpeAc3riica2kmInglSa2LHE49EyMazgahYZpHdqqO/N1A5FrEK+lhHsFuuHhCYfaSyFp4MKQmQdKDpza483RK3zt116D2nHkMxqvBZWv+OSv3U3noGM68HHKICoQuaGaT3DtHnEC2kpe2TtItiiINyE6YpgMQ/x+QTEvMD2LurHF6R/I6Q86dUDv7h6usHR+F+xciEUgJgViPIU0p/GAx2Rb1BYY53CeR+XneA5sI2JyWBFf6oOxdLducPq9huMHN1hsDzk0GnB5ZwbZbhL2S5TOOfJH1hDv0Gz82hLp823a3gQKxziImcYxNpNgDU4J5r/eZ3KyRbEYk9yc1ov4qr5yiUmBSXyqToj2feRbe6i+IX5ql2K2YLStuPSrMXHTYWOftWwGuy3p3bQ09gr0KKMUKeF6gZwU+Icqfu/v38nolsRzBZ3FMV7vAHZssZWrg2BLjcFhmwF2WpBcGiHfVrB4fEJ/dpbV8/P075wh3rHEu4ZqEOL5DjMcIcIQKgOmwrQDTCeq65QbMfHVETb3SF4dooZ1pkrVjVDTEhX4EEY4UxOY5X3HMO0QmWloN2E0RWJYnS4zTRvITOONBNL3sOvbrLW7uEgS92tby+RITLOoQ9tFZXBKEo40IgygKLFlBVWF04ryRBuhHeNZS9h3+LsafyihZ5lLJhzwhkyrCCsEBI58BnxnMblhfhywtiRBS7w+ECcUD59ErO2QH21jAvBKh2gU2MzDBQInBO7EDC88mdLMrzLJHO/8odcDcPYNp/nqx54G4/BKh1HU1idfsnttj6AVYXPN889c45nPnKO70kMvtsE53vfWMzz6yefoVhqAaVZwc2vA6ZUF7vzwXyVba5EcnvLao6s0QsPXor+He+AY4ov19bO/MfjmHDTZSymHKS8NP8dD6TyHG/f9e2cmX0nuXFnEGIuS4j/JHLaPfexjH/v4rwv7RMq3CH7gux7g5toeJ1bmkGmOiUJcltO5HwbJ7bBBkyIbMY9+JufE/b9CEP0T9LSkJOCv/PcPkr1QIMUWburYPjqPd6mCCxJ5xBFcGUFRgHXIyCfYLbENH6xi9KzHR18QmAc6pDTw0PTjishorBfWO+0tH8/WhIFoNIhXPbyBpGpJVBLiNxo4KYkmFpeUIBQCibh0FVptOLqEUT6eBK0Eg2bEk9VRpstN4gsZtuFjqhK3N0B2OzghcUntea7aHp/95D2Mz3appKR1cws/c/hbFVZIkl2NGWqCkUH0DPJ4QO4aDLWmvFwQZQLOg5dW4AQWx+BsB+FB6Bccf/smJpZI2yarCqwPVVMibEDZcFzoH2Jp+RpsGj6u76F/tcXhaILKLEJZUI5BEbG70+RIt48bKrzdWiYvvITG4ZKzr32VyNecnt/lx1dexN3h8bN/+3V1hkOukRQUczHT1/XqHUgtGIc9bHuKLiwkhjKX2MBw/M3XOLd+CE+lCB3TOufID0p+dfgufuYnf4tLaz2M5/ESh9k4NIPRipnnNIQBvvaxgaJ7uAXXRrBZIYxAavCnlqISNHTOXq6x1sfLBHkrwQskB8sJ1T0J6aUSZiTNzQr5dE50dUq42YBdh3/GIB9NUYOSua9GSGLKAwH5SoOZL67ijUuOv2OVQTNg8+YyrijRkUHbjOlxH+FJrNK4RHBuucHd6XP0fyXiR37yOZ6c3ENzccha3+fq821MK6Cab6B2U2QrQSiJHJeYQyFhc0q27WN8hcRilcW6nJ1jXd5716N8Nb2Ds71Vnkvuxfo+03EDJVJEEVItJHS+MaKxVkAmkKlBlAnlTIBzAmdKWqYg3rFYT5DPQtZxqNSAb3Fl3RWsWz62DeOjIc88fwc3zzdpkBN+TqJnLDry0YFHYEytgDJgM4OqKlwo0LHDn1r0FyXduQFSLOMvGBpxjjCOZlEy3gjwX9GIENxcgjf2aQwrtg8Kyihi7pkRmXRU3ZAwp7YJhqB2p7U9Rlqa1iLCBN0MqUJBsurw7IT4nMb6PsHUw7visFKSL0rSZR/vSkV0uOS/+87HefLVIzy+dRjTDMkPNjEzATNfX0MUmvTeA6D8uq1nqGmd28M2AsYnAqwS+IMMsyTxbElw01G1Q5S0tC6MqO4LcL7EoLBVQZA7tB8xEU067CIEtC/lBBmMzyREmzmiMnjbu5g4pDyxiEQSXd6jP2lwZWeey79+CH9QYFtNXFJbG/xUM931mIwE6V0RpqVIroypFkP2zkiiPcmVvRkO6h2Oq00GF2eJdxxlo76eORGiKoFDoc8c4vnzjgiNGIwR07K+wIsQ68cIH9zQ4vIMAVBkkJc4JQGBlxXYfoabakTp09lq1CSBtbTJeOvJy0wDy54yCJEjU4vIKkRiIAKzCM5TzLx9h/GrCafvusUTa6dxSuC3LOH2BL2jGLcFalzSeWwDe3gB2Wij5RRvL63VE2GAajZpX8qYTkNcIhCVxRUF3qalUYCYgrYNzAHB7Gdy8iWPPG5iT3mYQBGfn+DvaZCK4bWA0WqCa8WgJE/+/EmKywNEaeoqYl3XEhenF4k3/41lJ0B8vGDvCznln1HI3CInFcHIITPD4nuG9D8YIz0fN9ut648nGU5ZnGggxnmdPZNq5r6+g3EWcbv4BiUoj8wQrQ4RQkBlEErhv3QD2YuQUQPRbUEjwk4yzJ5HuG2xQ5CtBsIY0IbJeh2+LQqNNA6jQY4zXCNCjbP6uYytSQmlEEFYq1+S2oKGFDRWNfmyj8oEx7+WMvujW3ix5nS4w6vFInKqsB7opkQMUmTiUZ5dRGQZKncYJfAmEnQdoivzOqRaZRYqhRoWuGUQlcWfbnP8LQHx6l088Mgd35w/7n37WR797Ev4jYjKufp64gn8zGECAdphBdhEIfcka3aK0mAlOE8z00kIrOX89Q3++N/4IADaWrrvy8nCmlTshhm9xGBQLHzfDVpfupMLT19GV4bpOCWIAza3RmgrYdimEyz9vjNTHAV8xyOn2RtMWZjZV6TsYx/7+BaCc/XXHzb+cziGP2DsEynfIggSn7AV4AA924L5Nsf/wnk+u/EQ4Z5gwd9DtBq4SU5/rc8nrn6S6TgD5eHFlpVH9jj/XALOces3Qg4EtzCnDuCHDnHGUM16iCiqn0xZ/MxCWeGw+EFO5DXxb5QE/SnZqQ7Hgm3S3TbhSwW66WOdY+vNCTjB3Kc2oRviFIhEIaYZVBXC1bkAwUijvARmJHYIttskJMXs5djcoZTgatZlMPIZxk06oQ/Swx7xkcqCUHXV6nyX8UKArCrKtKIwAtnW2EDijMBPHJouwvp0rmVM5338W4Jp2kAYx/iZCNUWMJMSFRq0xAnwco1Kc8qlJtuH2lS+x2F/l2wmYMtIpLXIDPwUwg1JdtLDVArPc9z6+gFErlgdB3iVxR9pTOB49fwRDnrbnP/149wa9PAX69YNuafpq5DXnt7g5558OzMbJX/6vudIm5KymzC6ewWT+Sw8Okb0p7hmu5acW1AbEomFdl0til/xmZ85TfZgjDtt8FNF9wlLuRwQjh3+NcHHNl6DqDQvi2X2ygZir4JYUXYVQepgmiGF4pnPXMQebqCWBXmzhy59dFIvCh5s3+S6OEhjF7AOG0kccGV1GW0UzkJ4aILsKqLfhdl5zeU724T+kAPvGpPGAebFJi5LsKEjm3M4p9GJT6QrVu4bs7m2wzPXIVgf0xhvcez4LptPt7h14AzxGnj9EU89fIL4Vp/h1Yhf+/j9/F/f9WVe+lcd9C2Hh6aRlPguQc72cLpEaIknfeJdy3a7S3uhAiuRGjACELTXU+yq4Gce+RQNodn9oYTrv3QE5xRpGeACQWti8TJTExtKgOfovJQzvidAlo7LN+b5yuNnsUogjUBoQPh1RWjh6L9+mel98+hOwOF8jfIVx/BDDczlgPA+jQ4S5GpFeVggrMBUoDyLPJfhrw+xJWSLPUweECrJ277nPMdOrtP/6AKXimN87rMP0trSlMOAcJwinKOcjxm8q0H3ecOB7h4zRrO11oO4hxEa0/aJxxPKuI22jqCqMxWML9ALbfwCjGdRewVVo83Mx3K8Uc70ZEC1WO9wCwc6EuhYMTaCv/Tuz/OG+y7w0JkbPPHUD2Nm2yjpg6wXXTNxxtLsOterBUweopshthNilbudxeEwbYXMFd5mnSGRP6KJbk5wyuFlFjdf4VyAiySMNFZB3hU0hUPkFbKwCKdqMnKQ4pTAxiUiM3Vuk6wtZGbbcH1znkorRGVRgcUkPjoAUUmyw21sU5GfgCI0eJ0E2ZMI61Cl4SufeYCvPH8v2dUW4bBEGmgwJQ9aRKsZlQqwyiGswkhBmgtEOkFICXHI4P4W0iq8UjD8znnI2sx95CKTc+D8AczMIZSE5u32plGFKEvwZtDNEDnJGe36/O6/XOHt/+slQr9kpTlASIkTkumSR3RWw70VozIhuFbinV/n2kSg2hk2SRA3NOXQx3mCYK/A9gdgHeQd8H1UVmGnOSIvkdTkglMSFyl0DHJqIMuJLgwRUYA+PIeVATLLiV/aRa3HpMspipzxDy2TvDTBaYcIFaIZwKAEmRPOgqxySifBGUTg48qacPKu7yBGJSrwsI0EM9em3EzZuzTDzJObqLSkOjyHafhUeYA1JUQh+B4u9HFVrf6oYoGUPuGwqu8lusK0Q1zQgDhkfHeP3isTsH1sVpNaTkqoNGpYwETXqpLcgB8QrBrSrsEfpbWlq9QIKRCVAedovryLc47R9xyh+UIKCFwjwk6mqKwCbRG3Q2hdXuD2qjrs3YGyAr9fIg8V6IEPVpBVPrmUVJVCVCBv/0rkUcwkuI0UL3ZUAZDXJKwrLZ7yaDy/QXGq/jyp3Qn5cQ8VCIQQFGuK3aLL973xJGfvO/rN+aPMS5ASXWpe/8YOn3YTZOEwwmKtJIx9ipFhsyyYn/VZfqDD1cE22aLgc6NNDimFRfDxr72EsNSfb+FYv9QGC+mgyWcGK/yR+65zbdyDNODuM4cQL9wA4H/5c7/En/uFH0XLOgD4/oXvoe0v/r4zU1loPveZb5AXJXEr4L3f+/B/kllsH/vYxz728V8P9jNSvkXwq596mo9/5SUub+zVJmkp+cbuIeQUyq5G6BLyWhbc7cU887M36mC8suLEPTf4oT//Dd78l9ehFWGkIFgd4T9zjdivkEoz/+gQ6UXQbKOWOwDIXNcSdqFp9nzIJFLFqJ2SH89fYum5IaqoCPolugXj0wHjMz75Ugg3tqiEgbTADYe1ySG43ZzhVL3D6Byi10GFMZmJEVODUAEgmYiAW9MuabP+HmEssrLI+R7EISQRVeBjIoHSiunRBsMHHZPYw80oVADLiyOEqD3+6WKAjbx6121UkdyaoIpaok8hEQ/lCGNRwymyrAhvjZkegmi2YKXV5854jXuOr2JtXU8sHJQdaqXKnsIEfr2jOPQQlSDYARtJqqaiavi4XR+VCo42dlG+xXl1DkWwlaJ3Kz596y7W+jN4rYJ1UbHnl7QPDEgPh1Q9j6oXo+cCRCEQpQAtCEpJ6zFFq53Tm5kgrGXvVoz5ooEnffSXE5Kxxp9Ygt2KcK9kutrAjEPGV33EyNJ5eYQqdG2dgdu+dFnnRBhQB2JOfN8NqpZFxwJZSfwbEnuwQowqrLa4GUvzyoTtV2cZ6SZoTfmipXxJ4Ga6jHuLTI626P6ZlOiQYe6PQ/HWCNv0UOu7NB9bJ7kwAl9iS8lv/fN76G/6YB2mm/Ca77hFklT4zkBWQVnhpZawb/HudXS/T5CPDvLSZ++k7TTZyHFr2EJlBlUaRFEhC4OoNAJB2Lf4tyK233mYW991gOnxmNE9TWRpaFwd8Znfvo8rxSzPZAfIVjx0S6LmNeWlFghLci1D9aeQlgxXJK4ZIMKA5qajsefwCkV+S1I1BHlX1M02UW2/8jJL94LBxT4YTXNa5zu0q5yqJ2l2p6idCdHaEOcLdFLXvlIJbK+NmJ/FNCLETsXutIHuRlzvLzKaRBQbdY6RCME0b5MgxyO2/sYC44cbuLBi7X0KfUYzWK9DSYt5SffpbWYf3cSICOdJqiSoszecw7aT2n4jHEJrmpeGCFPhjSuEcXWGTtsyPQTpoqzzgww4JziwsEMowXoVwa0hyY0h7Zd2iS6PSU/Ns/T9EW955ypeBCaAsueRLYXYUIFnsCFUDYVxgiqQOGnwNxTj72gwvK+NUwYrS6znGB+JGJ1MEG2HrGpbnohCxDSlCCXB6ggnQaYl5QPLiHaXiIxodYDxBAU+wVJJ47tSpisxNPeIr49R05LOU2s0nl8lOrdKcbRi6aPXWFlYY3Fll9hL6e1NKGLFRtJg77TH8GRt00j7AdYITKRqEmpSIa1FTHLkOEVYgWjEiHaTIFMUy3EdoBpLTNsDJRBWIkNZt58IoJTgJYhGAo0GYpLCeErREQwOdHlZHWZv2mLo4vo9VIIisJjYR/7umN1/7KOEJfMDXBSw1VzEKEfZUvRnWhSdCBuHVA1JfrxHfs8yZi7BlAXOQXFqnuLuZWw6xU2nGCy9+RG2U4cOO8+DwPtmtokzJXJ7iBASNa0ItnLEwFJNBUJILAbrKcywqu9recl3/ulHOfLQANFrIpu3/69RBGFIsDZAFBrnHFXbR3dC8odPIAtxmyTzyGcUTsLWZxPs7qRWWq5tw2CCrjKKubi2QpZAGEAc4bpNVJJg5ttkBxqUTcBoZKuFiusNBqFrVYowFmENYnUbZw0m8Rif7VEuxmRnOhTzdcAtqlaK4tf3MBd6WGrVi7idwWJmmjV54nv1v80L8m6CmwvBow4mzi3xALbP+LgzOXtFQm4DIq/CaYE3FgQ7hvnHp4jKIXNLmoD2JTK3QJ2lkx5RWN/DBHWIc/zSDYL1Pt0n9/BK8DyLWZpl8zHJR37hK3zw7/3ON+ePpNsAIbFInnvqMvGRKVXX4E01LpEMYkPVruuiN5Xj4jPb9N8I05OwfS6l0Y7pzbd55wMnURXICqRxLC/McehQi8+/9//Ctf/m73N36308d+kIz60d5BuPDHFlic1yvvHoK0gnWCx8ZnPFvcfrxh5rLV/40KNcf3n1fzcz+YFi8VCPF7Z2+Xu//EV+/Kd++Q9kNtvHPvaxj31862JfkfItgj/ytrNcW9/jQDtBjvPaFmIdLhQEcyV+PoUUpDEcvOMAO9sT+lGFs9C/HrP6UhMxm6BPzKKPj5EfH6EGKSaTqHZGsJWDvp1HcNZHvFgvrmWlKacRfZ3hug1EZZBewL/6wEnSrsf0eEh+KMFUAqyoiZJGG7GUEOyV+Nf75MeaeBujehhWEjffRpYWirSW9WPRLQ+3Mku0OYbtEWbSwuraG141autFlBpUZnGVwQiDsALdEFjf49T3bfJdZ1/gX1+4j9Vzs5hSsN6fwRqDrCDeKtGhhxAClZbIzGALQ+PhIfqcRH8xxFKHKAaRo7/cJtk2tFcLdg83eSFaoTGfw4N104cJBHnbYZM6xNMfVDjtiPsW6cBlDhE6qkjiPLANh94JuP9PnefSY12uXaplyW57jPQF6dcO8Nfe/il+b+sEj0+PYBA070rRca3AmR5PSDopbqb2mbOtwCncJYd7G5RaQVNw/McHLC6M+PQ/XMbuTgnORizoW+wNZhGlYWsi8VYlcxd2IfRxrQS0xlmLmhQ4CWiLcwX+rYqpa/L8E6dw75hiXoqR2vHiZxY5cHmKnCnpzzc4fnaTwfNt/ChD9j1kWsIEvFGFmYOBjJEO8r2IG0ETAHUwhWGF1/Jht8QKi5yWiGnJxccT1NwszlmU8DjyZ0c89n8/w3DawG8JRscSpkdDihmfz07fyDsefIz75q5xZmnC6IbiuettyuMJZTogWasIyhIbekjPx59apLFUkcLeI3ChwPiw/Y4GRnpEo5L+6Raf659CO0kzNChnSZMAS0mx2mR8xKJGPlUM5JrB6QSnPTwHINgrZ/n+b3+VRz90J6ahCKaG6J4BVs0gAo9wqOm+XDA8DYM8REWOm2WCQKHnLeHzPmLWZ25th0nYQXcUlYCoFUBa5wYEaxXepaS2qlzTJG1Bfz7B8w1n33QJYeHl6R3khU/RtfRuZgRfmTC9r8H1gweINxVuSeK6CuspROjjHFgFnrNUplac+M7gUkN6R4/mWoVrJ4hCoNsB3tghK4dJFP4UnKp3p52EIIPjsymx9Nje62DC24SQg+RyH4TjGXmYb6weINc+EovQmqIrMIGiaAiEdGRLgs5VQX7Y4O0WJNc0lZtBBpbJHQLdbhBu5zgZU1Fi8fH2NE5IJODlls3vCGjeTCiUItxKST5/i+geweojC7SLDPO0oFwKKIYTRKSwTYleUriLIWFf11kSUUSYjOiXGj8xLBwbUc3m7FQeQ1/jlRXC+PViuzDIrMJOfaSViGmBXmliYp/GjQx/nGOtwcYeDHNEltMSOd2DGVtpFxfU9cIkMX5UUOUaEoMwAjEBFSjsbWLFKFBSomdi/tjPXmV8M+bZ9DB7ccR1M0uV+QxfO0O4XaC2cvRzjp7TyDkYLTVwB9p4QYmrJAKHkhphIT/ZoerMMr87wY0jGgcqDtlNvjGcQwiHqyNY8dOC9oGMyaUGlD7u4BzFgkFNxlTtkMXFAdNruiZZmhGjN3XJFxVlJ0akJSzN4ZRCYWCc1yGxr/TYeKkBuUbHCjWtEFIi4gibpnUwuXUQeHUN81TjNRTT0z10UxJsF8i9DH3IIa5JyAtEs4FwAtFtITyvrvptCNy1AW6miTuxDKOMYFDi7+XELw6hMDgkwvdwzuHaCVJ5MBhBt1NXMW/vIrd2aa4URAe6FFcr/Ct9rLGQxPVxQm0Va0d0XhzCpH7tUKKuFy/KWv3peVgc5vgBXChr14+zBAiENlTXJHsrPge8FG0leenVbewZRLcqvKlheN9sfdymVnU5CeAQxrHz2gA3F+AvSGxpSeqHkJUlnZVIT7G0mOLvOo7Z49z3htN87F99le/9k29gZr6DC27f+56bsveeefS9lkbSIJ+XRCNDYAVOuNrOYxTdR+tgWO00BB6NuRZHD8zx2rsO89S5GwRKkkQevq+IZD2qPjz7EA8f/SyPLF7j2Y2j5NRkSTFISZKAU6eX0dqwMN8G4O/+2X/O5/7F54kaMZ/c+6V/Z2Yy1nGrPyRdhOA1I5559Vtfgr6Pfezjvw4IV3/9YeM/h2P4g8a+IuVbBI+/epMnL95kVFT1TpeU3JmsEu0KXtO7QddNEVpjjeXMa47w0//ivyVuNRC+R551KSdNHvv7M6jrI/K0SX6qQ3nnAWwr5DUrN/l/s/ef0bZmd3kn+pvhjSvvvE8OdSrnJFWpJKEESEIiOoDpBhs3YGjbNOBrX2javnbbfZ3wcHOxARNsgwGDQBIoS6ikKlVSVR1V6dTJ+Zx9zs575TfPOe+H9yAP2vd6jB5tu7HGfsZYX/aqM/aqtfZ63zn/83l+j7A5Ls9xnoKFCj3MUJMclRS4IMBqn8nBmHSpwXRvyKQ5w+jQIpM7uxQdDxFq5ESixhI9Nkih8HSECAJ0KjDO1ZsDT2GpaxhdnkORIZOKXitn/vYU1SoRWrPvt/rE5yvmPrFF5QukkUhbbx6cqWA8oZK3Ui6zHktkeFQs2ITG3QVVL8bsFHWkqDR445JwbQKpBaEpZyJsw2fjbI903WMiY4ywNdC02aJYCImulthXFK9/5gAfWXmQz2zcA8aQNyRuNMTfXEOPHcIpihkF1uAPK7xxHRP64+rasgcH711hKtqcKPazNp4j2nL4I4fWCqEUTVOyEIzYm03J1+H674cs6C20NPgHUsy7J/jbSb2ZMgIhHGUHRl3NdBIyHYcEE0e3mbKYTnAnBsgbO0ynmoP+JdSVddTmAJlYXCnJ7g2ZPBVSdTW9V3fwdooaYmwc5XIb16lbVfx+QbjhONRYJ5wYdApr5Sx4lkYj56gYkH1uBu7W+GFJPhdQzEV1Zn+mUZ8S+xDezBn/vk9LpnSiDHXJEj9tkJVGNhp4k4Lhg/NUCyHZW1tcv69DFWXI6RiHYKvfxLoImZYIIUn3+ix/+BoH/v0VTv/ifXzsMwc4+2qbf3X6NoY9j+j0hOZLI9RoCnmBHKcgFVlkSeYFex9f4+DyKkobSDV+3+FbTf+xFuWcIjUBkecYmJj0RyvCXsHigT4uLlFXB2w8tcDo2DzpwZhgFKAThxP141+deYRT5/eBAZUDqWJhdowLJGVbUcgc/+wqeitj4/YOycGYVM5S7fPp3jECQMYG+VhBM0px1uGQGFPB5g5qmiMKh7cjcFrwyP2r3PdIn8YeiS0lg/Um0+2QcqLRfWi9pPAHHl6p6FyytF6aoEY54ZUh8StbiP6ENK6IV6d4g5ywnyKnRc1uyArEaIKaVFjpqJoaPElybJ7pnbP072wSbzga647oYoU3sqi07lhxVmGM4MzWPMExSdXyKWfj+oKWVehBhjiXop0lTKd4GzvIpHa6qEwgCm5tRB1UDm8nQ+QFNhwjijHzn96s3wNPEPUd/k6BmlI7BjwFSJSR3PtNK3jTEmEtcpIh0pzyel4PApqWvOPjDQX5sxFbX5pBTkGsQ9VQ5HMhyeEO+z4w4Xv+0Tm+o/UKW9+3yI1zs2w930GcrwhvJjQPjVl4YUIwsJhYUTY15b42wmiy5Q7Gd1gFeUdiowDZaCCVB2mKW98ioo+IKpIFhSwt4eURcntM8y5XMzp8jfMVpuFR7HGkd86R3jWPN8xRThLfzAmmFc+Mj3IpmeO18QG28iY4S7QjCCcOG/m4tsfE+kxeUyjjCJOcSRmjKpBO4T0wYnSbomgJUI4PvO91wmZJZ2bKE99zAW9BQCNAHNmPWJpHDaYMfsUSvTIgWE/rmuwZTfTukGDgEOv1dd8FAcpKZo+ndE+kyKR+P9xwAmmOKqd1pCXLSUcKL7aIyqATU0eIigI7nuBmeiAEcjCmLFPSOUnVdIiyBq3ns47w5hg1HOOEqd0gYVA34uQZapih+nWjjPMkcnuEEQ6bJMhJgvUUzjlUWtXXw8ivY0GzLTi4jFuexXabuMCDqOaYCMCdy6meHdH+3HWko3aXpBlmdR1z4yb25k3MZEh0c4oqDdIJhKwdIs7WjUzOWshzpDF4qUOnDikkJlI4LWmd0wQnI4wTWCepXMDeKEdUoCqN9AJsM6BcqAfeKIFt1fBmGwgEkC9K5DgnurRdO4C0Io8kZQNMnpMN+vjfOsDe7fjZn/kIv/D3P8b77v0p/EbtliHLCTsVlZO4ts9kv0RXoHOgtFTBrSYpC6JTsHlvSSl2GO5MuPDGDWa7DX72J7+du+9c5uC+WaSQOCto3gJdPzB7lPvmbnI+W2KhN+Dbf+QbCeKAD/6V9zCaZoyVIQ0Ev/A7z/HnfvxX+dRnvgpCkhfmP1ozaSUZHay461vPIQpDdMbw1g/8b5Rl+V94tbarXe1qV7v6etGuI+XrRJM0xzpHUVbYUIMQVMc9zMGS2BYkJsT5PtYZXvn4q7z3v3870vfAwG0P3sWXL9Z2ZVkFhNfBVileLFGdLnsGjps6xoUW62kqV+InKaKsnQpoQW+my9kDmnQJisgSbEYceNMGmx9pki8oog2JKBNEVqEvD2B+BpTAdRsgLMJaXJqB0ehJDnGIW55HmBJXwahqYscw3xoz3SiJr0yhSAhvZtCIkDsJWIkxBlOMEV5AGGiyKahhxfHLx7j6lUU2LnXZs2+LtdhDaQ+wGGdQ/RRZGjg6Q6UFXgFCSMJE1eNGLVDCw7kcOagwPiR7PFSoMcc0vcaU8SggqTo16yL0iXc08rpj536LTsBFISiFFSDDEqclk3mB7yz7O32W71ll9Wyb/PMOHeaoIADfQ/qOra2Yj/zaA3yldYinLg25+odN5t9V0FqoWPYTDh/c4lMHDuOSGlIrE4sooZrzcAMFAtSFkjfOHWRr7wzm0QhTeGAl5z42h0gTnO/Xi/4yY/JUFxdpRodjglNj5s8JnFb183mBakmKsEnRrP/N6noL3QeBpYp63PjRKeF5x8wLPgLJziGJHXm0cos0YJsB070NwqHBZY7m+TXUIEXc7cETAY1eiS0VYmtcN7GETfQoIw/gO77zFE9/4TamZyJu+7brvDg8Qj4NiN9e0l2aMNrscujea4hxhraW/TPr7LgZPvyluwkmA6K0IN2jKZabNJ8f/LERAmEdrWspowcims9u4k5IzOPL9F7v0z7rUNbDSU0+r7g56pA2c3bykMlEoSIFzmN/uYmpAlqvJWAcebLN5G17qFqCUoJE4NYifufMA9AGVdYOktUvzqOtw4SCeGWEt5nRPpGgZ3sgAlgsEVNJ62TJ6pwlOdoijDyi9+2g/yDCePXATWgP4QdIBzJ32EDy6S/ez8qlLqMtCT2Ya44JZckVZRCZpFhVgMUpi8gtemyxfobEIocV3rTCrkwQzYDW+RHTBUEUN5CqrCHWSlD5lnxfSLheUM1Y7G0p7RcL2legXGoA4BqujqB4gqph+cH/13fw8FuuUB4suP3hK5z5ygJKlnjfalm/to/uy6uUhxcRmUOW0LI56XpBMR+BsThPgu+IX76BzAuSu2dRaUa6J6Bsesh0hDesUIlDO4HTDVQqUanChj5ymoOADx54g3+ZLdE4ewMXaMx8h+lTXRqnLeY69FZWWfwLFU/9udPc3JzhC//kUZIDMcGpDZLZPTScj26EmNhn6nWQa5LpyRhlQ4JHSrzelJnVNTbnFjFBB38iMPPNev4Tivo9kRKnHF5mCbsZ+aQBSY5JM6QxbG8tUL0Q4o5Btg+86xU4SXVMo87Lep4kQGaW9sERg4sRRdenumuO6PwWnjH8zufv5crBefhqh63bR4y24prhYgXJ4Sa6q1j+tmuMCVl4KCXXOwz6Pi5rYDQgBKvjHsV+zfwrGxRzihPHD2AmgrAx5eTaHkQBqrB1dMoJ9CQDBG7qsEGBwKccepjnJLrQjDfmcDMpDKeYhTomFm5M8fsG52sY5DitMGlt8HAWisRneqXClhKsRSiJmOki04S8o1GlBzHYXoQwjvSAQk01JhbY0AIOBinBcwPQUf3GxSG0Gsi8QIymWN2CskL4Hjab4l/aQvkRoutjpQKtsQ2P0SMLNC6OUDspxpeo1ECjgZtp4ayDRoRKcpJjParZGG87JZrWMSX3x0NAateHurKJ833cfIA0FlKDMrder5S4soR2q65svuUoMb4gXdLEG5a4l5NmATeSdu3KaSqkXxAMHSqrwICRDq9fUsx6NV8lsYBC5dxiNUHzlRWc0hD6dUPRvI+XG4K1gvKFirTT4fXProBUiKqEEv7BX/8t/swPPc6XPvMJnvr+LTbDJUbjFjkS5wTCgfUlJgA9taAEmYoJphIaglah+M6/9FbWt0ZMpzmRU1ilSYUh9D0s/+FY81q6n6cv70MJyz/9x9/LD//s9wFQGcvdhxdxCH7vs18hLy3ivmXaw6w+XPo/yDlHtjqh32xSPd8mSi0I+MznTvD+9z78n3V9tqtd7WpXu/r61O4g5etE9y0vcC7foBUEiKICJ7jwQo+3/PmzRKoiTerTMacV4R0xp1+9SLIzxhnL6186S3ylQHo9rBPkkaV5qWDuwQHz+zZ49Z/fiQ2nCF/UkD7P4gUWwa2c9/aAteGU6LZDVA2P1rpl87DPUHSx3wJi3Qdbkd4eI8c57vI8BP6tTP8Y0qQ+/XIWE2m0VjW/JawXeU4IyAzSWqZrMdgpNvQgbuLmAvKlBizH+J86h6xugVVliR3k9N8lQQuWPyFZPzOPqBybb8ygSos0GW7q8AONCSXF3h52zkOicGs5eArnK4ij+oRQUL+WqqJzviC5S+O+ocDrGto6RxpJf2+EdRo18bFtiU4ctlHghEVqjchNXbYQGoK+Q1pHEBVk1idbGrP/lT6nXogJO2Ombz+CazWwvqKc9Rlqj6mOmHlTRTbJ2PsNKcc/LxlsdHnl2R76gIWmAQsuU6AApRFVfSra3jchu6bp3l/B3ggx1KiPOoQG5kIoPMBhdypav7eJWY5IFiyDt3bp3MwIJhVOQnCzT2O+pOjsxSmBnhjcqfoUrwwF8aRCZKCnPlhBqS3TRw1SOGaP53jjEicF4aBADQu8YUr5iIDjEdnNDvZ1xeJLfSqvxOYFCIEaJDRez/F3MsafarBnMuQCMXfcvsF2EHG5eZDDf2kNqR2zH1pn7l8OiL8zY/CMprguCRcSBiogFxm+dag9AeFnU5AapIHAR0QBToDcsWxftiQDgXeoILpWQiXQCbQuVoyORKw3uoxnc8QbgoVPThg/EVA9JbhtsMVrew/TPFVX8VbWMjmkEKVEOAMDTRSX+FNB4QxVL0ZMUvxnx+i5ADeVuNCHKEBKTRULDBKfKf75jNPNQxT3eFgrUBNJeDxmIiQ4MJGq+RNphus0UUaCsGxttnj2xBKT93mIwHLH/hXaXsJr3aOUazFqaPGuDxAOTOSo9mgwcT2oYIyaThFhSP+eLjOv79AYZQjt4bSHFQ6cJVwZki8skO9t4NoWF8D0mI8+LRntBxQ0z1i8ocVKSbRpGW01eOaTd3HPd11kvBPhPI+842Ef0Ng+0GygC4fNodKakYrIFgKEBK9vyY44yBx6WIJwqEqgrKZ1rmTnkYh8oYOswPo+LnN4GRRNg9pKsKbEFQXKt8xPU3AVIsmAgKqjKBZDvDWN9OrNp6VuAHPAE09c4+F3XOG3V29n/Ll6g3bqzGHONfYzTGKYjjnw2BruoObyjUWyRkT64g5lMcELDKrUUIEe5lRxgNNjgo0GAomaWuKOwUUVhYyxB+aRTlDsD4lWMjqVIDkWM36iTfczG+SXNeqwprpRgZBI4dh7W5/k5R7TeUVxVwCihzo3xJw09LZHDA83GI3mbu3PK6adAi81iIMeex/uE/mGaN1yYukAG9vzGM/WsU7nCC9O8bIWnZ6jeNVyrZ9THi04nx7kyis9ZDOBCbibGzghkGGIEzDdE1IsRnRPZQRbBUwNolXhtIKphDyn/zZL1HQM+x3yRQ8TKcSVAU7A/P4J/XVNNVFsnOuQjW+5dWy9+RVBgGo6vNVNbOaw+xdoXhxjtURfySkPzxOulrSKCpVoTBP0ugVTgB8ggqAeZgkoj8xSxgKRU9/fbg2/lZII6upiF2hMJ8Ib1jwgjENujRBpgYuiuqlAilswX0Hj3IB8PiO4OcY5B5Wp7ydQ/7e36oCEMQhsHZOpDLK00GzUMV1bD1Rql4YFA7KhcFrgFire9IFTfOnX7sTTFqUsfee4Z3kVYcG/OSU/3AYt0KOUsq2RWLypwS8sDsfkmOTu1iJpr0E+LXFZAVIwfE+H9P6KTqAQV7skZ5rIMMcVdYSLqiQbJdhHfoN7pjEPPTLkG0+e5fPPPUTan4KoSI710EOJLiU4gTKOKhCoSmLbIaefucHv/vxn+fTGNtOy4oGH97HcbjP0tuvvtvgPg5Bs+37MToIxgl8/fZzvv7eGxI7GKZcubOIwtGaG5GsNnO9R7Z/j/d/75H+0ZhJC8Fff+w381E9ehY7Fu7qJyErWrvf/cy/PdrWrXe1qV1+n2o32fJ3o5a9eZeXmgBs3d5DDFDlNaY+n/MDSa3z3wkmYJpBm2Mjy2uPrfOHqazXQtapwSYYdGcgF0sB0fwMxO8OhHzSsfinGro1wpYGioIigMB4Mx/XuPElwvoe1Fj/cxM2VTN5UIlSAuBbjb2msFciJoftqSudUjijqOkdhHQqHGmYgHEIpdGrq09WiQrkK2SzBGIKrY/TlbRwGF/lUUYhXCJSv0IUD38fee4hysYUIQmQcM3ffJcS0bs4Y3FbXF1u/gkognIDIR8Q+UkNvIcF8e0HwnRU2VNhIY5WgCgzCQrBTIrMSmVUgDKqE8GaFfEZgrmmWwiEL7SFyYhBV/f8WJSOYMXROe1hhsbai6GrKtsJs16d0OgWXSiqreOW3H+CV7A5sM8TMNhE5iLxEpDkSx7vfeZqn7jnLwr6ct/7YTSq/ATcLzDhDbo4oZyqkb8AzRImoQcClwGmH0ZbuI0O8H7bEVUr4LxKCCxl+kLF4Z8bW989RmQyX5uAgOl/SPuWx/GLGgSs3mRwLmNzWwkY5TimSfoTeSmisZUSDjNZnchpfvsxCusl7f/gzhM84/PMFzlUM77TM/1bIzKsVwk8wHtCsT9TVhRXiU6vcNV2heOsyyZ6IyZxgukdgN3JQCqTENAKKvR2CjuTogx/kyPeskS3FLDYqHm1cgdJxdWuO9XGb6TUfM5XEXUMwYxllPp0gJT9sGR5sMmlo7EqP4u69mE4MgY8MfKyoF/cz98Pe7ylI5pvMf3qDnQcbbD8aYgKJU4LG2YSj9gaTzSasSWQmkKuKZTUiCA3pnU1KmyJ2RniZQpj6q6Kn4GUw6TdYevMKKtL4o4r49RX8a1vIreGtCtoe5cFZdKnxdiqcsZhLBsYljXJCcAWaZco7xDnykU/elZRNQTavwfcQcYiNvDriVTrEOIHtAd7ZCfkcPDp/jftn1njivrPIwqAKC5o6ctLWJLdFFN2AouORL8Q4Y3BRgOcUZcfHZaqGXypZA1v9AJEVqFFK4BJalzK6nzfoayHlTIyXSGQhEKGPNzDo1GEaAqugsD6vfOguLn9hAT3K8demJA2YPX0TlRXonSkqt5SxT77YxAbgX+3XG9dMoUqP0eP7GbxjL489cAWd5sRXUuZeK/EqgS4VwoE3qsHRRmuSIz7W15AXmGnFb/6dBxHzFc6TVE2P9K4ejRcSvNWcYGVKfrjFCXmAL168k1Ob+3j4nhvcHW5z59WEoq3o3xeTNyU3Ap/hEUfRk/j7DWGz5PC+TWbvLLj5tnnK2SY20lSBw7+yRfzqdfTWlGBrgkzq65yQklESU+UKYRyy08bsb1DN+4RHCw59YBVlJUiN8gJMrMkHCsqqjqiJir6O0OOS3okhvdMJ/lZJcqBFIXxaZ8YEWykyF8hCEAc5IqtwnsJuSrJBAAZe/sTt3DixjJ5AwysRqnYsdU6u0XvmJtPzCjFOkYVBpDleVLEYbyImqua+eF4NpMbihGPz3U36T4WUcx4iqKA/BJuxsG8HMU6QQUD4xQnLt21imgKZ51RNiw4VTTlk7rY+1SjDIRgnbWzs13+DnoYoxgU+hWviPK92zuUVRdenmPGgKxDGgXVMDoek+yPs3jYoDdrDaYkZDKE/xBYJVAJZgDesQawmgq3vvZ3kaLvmzpgSkeT4K33iU1vISYFTApWX9TxEQikqCgpEI8btX8QcasNMi6IT1QBbJetWyLKubQZqaLO7FU1VsoaoO4cQdVRX6Ppzdqp2sVkPBHVcMFYp0+0AFQkOdobsa49YDod0W2kdRZISccvBUgWi/v2eJF/wcDhUZtEjyeqpTdYfXqqHPaKO+0Snx6AceVPWw/fVnCoV2LCONNlOi9w4Xv3iMl95ZZnPP7+Hc5d7eF5F641Nul+6ydynbzL74k16z1xF5hYqRyOrSOYsZWBYf0sHr+kx7qekj4x5es8ZRncPaEWCXqQJlcfFs6v8le/5l8hPGvS2JNhQ/PznXvjaGigMPLrtCBvd4E3vP83CoW2sBBHHrNspAFcurPGeB/9nvvG+n+Lm1Q02p6/xzd+1wsz5PrI/RoxT4mn+X2nVtqtd7WpXu/pvXbuOlK8THd03x/krG0RaIcoCCjAi5PIfhHRvK7C5Q2YF0oPRF+H5y5dwVZ0bFkHA4jtHXPt0hTUO0+4guzFD24R7YiaqQ/zVEU55qEHBoUcVK3/o07y4DjjE4f3IUHP/kYAracn2SY0cFvi5ReYlzGZ4g4pkbxuBR+tqhpjkOECNxlSBhyxvVTu243rhqBxkGfGCYbzTwUUBQgdkjZJgu8Q1fWRbEoiSUSgIxjUbwxxewp3dACH5lm97hRNX9/PE0ctsHok59dBe1FXDwY/cAvWZ+mTMRCHjgQ95hR079CioX4t05KFFuYJiJqaxWoGziJtDhDC4mQ7+BYNsgn2XJIoKGm/0mdw1hyotykkibSgSD1kKTDvCm1qcFDCcIJdjnHHYRLL5lS526HN57zLJXxaozRA7yXCTBOdJghuWF35lmZ1OwK996j08tHSFNAlxdxj8j4ygADnn43sVVoMswvrb7QR6WLeq7Htpm/VPxnyKO/CyArFuGHiKaatBq21RUiOEofUWD/8uw85nDd2Fgve8/Qyf+JDPqDtDcM0Dqvr0FjAtyb7FHbLZhO0zEXZ1yjN/9U20m470HtBlTnEsQF6x2LNNBvcHBM+vE58d4O4NscIijKEjM37tr/1b/v7n3sVx2WVm7w4TocD3wTmKQ03ksERowedGL3HFu4vydsEv/3/eyRMfPIUwhsEvzrEdSwpjeOIv3uDokxs0v7nk3BvLvOvdFzAGnn/jAP/7x78DL3HsixoMBxnkA/C8mr2x0+dH/6zlZ68sIT4u0eMKUTjGd/t4W5bOSzdorju6tzv80COf1+w87FM8WTKTaK7PLOA2FaOHZpl/dhsZeHh9sL5FpRJ/7KhCxXq/jUsFQoLYE2O2QTQigpGl6GiKbgs1mdDeKBFNQ3UniFVBtS6pZkq6H9rmBdVDPjSFhYi8W3M8TORjQkneVThPoDOHa4TIhTnSu5tYpfCkwZMV+xe2sArSOU3y0AyTZoTpOXzP0P2Uqxs8OgHO19iGjywsVjqGRxS9CwLnFIKaw2I6LWwjpPniKuWBNsM3+8i1CiqFtyOJsgo8RTmjEBZKX5LNeigrqTyoVITEYQPF+KpEdfv1Zt84snkf60vUdkEn7aMnjoEv8Qa1E8dGPmiP259a49SlRVKpESlYH2b3D0jGPtUowglbV/duKfAVdGog5dpXS+TDAVsfuB0b6rohSnt42wkyr4iuVfTf2mXS8PGbht/+0AM8NNvl+MtzBLeXlJ6m5Y85/OgO18cz3LRdLr66hIgUg26TR4+dJ36T46xcxri6ycsUGWqa4q1PUEXI8MEQPEkVlYhU3nIm3NrMnt+kvTXHB//ZCVaTDmftEjYTCAsVjmmzix96iEmGV5YkkUI68HKQqzny5oBqNmD9GxeJVnPyvRH5XMriC4LGbEExGRGulYiJYfJawNx7JlRG4k4VtA5Y3n3nJbzJiC94y3TfVLD+j3xkX9dMqrxEzAQ0eoY3PzXgY7+zhEEio6iu1d5X4Q6FyFijhhZZKuTNFFkq3MURYp9GNJqYqiDasfT/YcxCtIM1Bl2FVLM95GRMNhhhUkDliEzCvIdNfdQ4Q+CwrYjR/gbxVCARGCA90MD50LxsiDambD/cpGwL8qWA+KKob5xlWbc1xTFoVdeVS4cuHDItMIFmemePsqmpIoczJY5bgw8HIorqQ4bRhKoV4tohXgmKeoDvZtt4fsnhd6yzMZ5huhhQfKLC38ogilDtJlhHGXjI0RjKEkoDaQ7TtGa42Ft138aCFPU1iYpSWUpfI8qY9FqD53fuojm8gadqd6B2MC4Dyqag8CWuMnhOI4oSXIVTPrKogbNFS6GHgm3f0Noa1swYW1dplzMdXClQAwFICgEmjZGRxsQ+wlPI/oiX0zvo7dniY288Ru/KFXy5ihnlUFn8rSkyCACIz24yfnyObElStQRVR1PmiudPb+FPKrJDBemwxYtrl3jH7T0EgtIV/NgP/ArZzrAegH37PGXLsTMq+KWf+S1OLAS8/cHb+MHvexsnR1u8ck2RZQHaOoI7HT/2A2/jD3/l8/zcT/w7RBhg987yfW/7+7z5R97E4pEGC4uKrYGHDCWnXr70tXVVXlRs9SfsWejULKJd7WpXu/pvQMLWj/+79afhNfyX1u4g5etEF69tMk1yLl1cwwV+PQgYTfmNf3Q/cz81RJQlKE24VhF+1Wf2yDxDfamOwRhDvlLXxpLmNE9HyCTj0P4+B/YPOJcepvHSNjjwS8kgb0I7RIhxvdAbjolEg+hEi72398jPrjA9GFP4luqIIZkNEVWKSEpcS1LEgiCTiKoCC6pyCM+7ZdNWtdU5r7CpZLwSYz2F8BSisgTrBm0Ezitp5zuMb2oaY4tptzEKZFFh2zEuCnje3cbjBy9wMB6wP97hRtJGnQFso45wJLWlWRQVtnCEX/JYXhzTvXfIieeWkIWlPSghLSgW4hpkG/rIscT6ksHtMdm8onk0Zc6bUAqJVZa8Z1HbObZyqBVH7AqkEzihIa9QxiF2pohhhfJrt8jjbznL5L6Ypz95N53LGrXRJzy/U29UPR+1kbC61iCpBO1DKX/1f3sOLRz/+JWHufhHy1gCxKwjjDKKQtURJ+XhRJ19FwaKocY5Qf+RNo2ZCbbj0Tqb078sGVyLaB1xtMdDLv3AHlIdERzOecfel7jx4QjvM9u04xSnvFu2dEG2fwaVOzZWOvzYT73E/+P5d3Pn6CrDP/TwPcn6232S64bWl/uIbAEiD+OD3kogK9DnVjDLMwTjdd7356+y0J7wLfeeYP9oju/8lhP8ww+/mcFq3eSUPtTCP1UDj2WwQ+S6lNsVcuB47qMH61P6SYG1Cj8M+ORvP8n3tJ7h6kBj+iVf/vcHePufucGvf/idCAWirMgub6Izi419pNJIB7aj+OjFDvkXRvhjx+TYDMVsk9Zin87xAXpSUsWKakEiNyy6lBSHFI/feYZRP2DwxQ7ttSHNq1nNt4kVD99zhc28wfpagzJrUXUrxl/qUi5q2jYjeK+POGzY+UiEk47RUYlVIMqA/Q/7TFvruJEB7VN5TUwc4GIIq5xk1EVF4CmQlahBrwsRQku8flFDNoWAwCccw9iznN1Y4EBnh6e/ci/ZcoRSDnWphWrA1pvAhnUtdNXycL7EdRu4sGYqWCGQ7ZxkbxN/R6DSCovDG+QE/YjJPYuUcxn5MYfEh1ENWc4XarebCQR64gg3qtqloupqYyslttfmne97lXA+4SPeYfJzPWbPWnQhIHcUh2YgAjV16FRQ+YCDIgbhCf797z1K9KUB+rAHETy0sM7+b95i52aDFz/0AMPbFaICORUIqRGidl9Z5WjvXKUM78dpi+c06SGFHnjEwynFUpMoKRm5AGMEZtDk7GnLqKNwTuH1HYv+TZa6GULC6o0ZWAhpzEy4WXW4MFnim4vjrHy2QfbeHk4KknsXkdOY7GCH7qtDXKRACCZ7Nb0zKQDOOsQkxfMElbPc19ohDSPipKCSAtsJ8C9ZvGYAWmC7Me7MCpPrisAYnC6QD0jKoSZbaoILGNwbAQ5VFMRJfb28791bnP34YZSwWKnwtWFhbpXp0z2s8rmguywvp5z196Ckof1AgX/D4t9MkVVF4SlGlWRzx6eoSpjkKGewFtTrI6q1DO/wMo1LNY/FzIVwMwXls3ptHhqOjSdbFB1J68wYf7tAjgSu3aRsGqLpBlKD2h/i1grm9wywt82xvi4QCDAWo0B7DYp2TnRzSrA1gizBzjQJVqaAIFhvEG5VBCMHvTb0k/8wuPAslA45SnESVO5QowI1yel9eYvJHV0aZydUexcAh90c1YOuZgNRWWj6lAd6dUvQzQlimmFDH+UcZaboX1SUymD/MCEwEjE7U4PQK4PDoqTDzc7A2iYOh0tSGE+QjQbOOUQUILTCaQ8X6lvAW4sSATqtBy1C+kh1a9UqYKXosJrPYEJB3lVIAeGpAdJ4TDteHScqLaJw2PCPoc0VrZMDRKMBZYE1lvH9M7hS4m8lUBbk7RCZ1gwulVV1PMnTtJ8dIdZSArHCHX9mwE4ecCluI1MBunYWAgjjCJcn5EVUR7tudaIP0lX8vmOuOWaYR3gq58HoLvZ0Z4hVSHZ9DTaHcGQfpmWwEeAcv/jcJfJxxBfPXuYdiwfRusNdx36I5OqXQcE/+DuXme/G/NxP/Ea9tpgmyBUHgcfVFzb49vf+DD/67H4+uPzDFJMpK2dvAPBbH3qJX/7DF8md5a99z9v5s9+8y03Z1a52tatd/UntDlK+TtSKA3b6U47cvowbJwgl2f9ojPwf1wk6sFE5XJ4ggxaLwvH4XYtMXptl89oONk3JBj52NMIVlujaiCozbK23GKUNbC7q07EoQPbHuLJNuOEQM90agjcYkaUTzg3XyYbLiGZO46tjioMz7Fu8yZnxfvTUUc15iMohpcL5EmRd4+mSHIxFhCFimuKKso4oCIkTHliLiQJEZfBNBQODZxxJ3KXYF6G2JphQ4fXTuk1hlKLMTbayFge62/yF3nmEcJwZzNG/q4t7IyK+c8QTf/8sL//SfpIXAkQEZbfBoX03OXFhP1YLVGJRCiqpKWMF0wmuF4LtUc3FqIlBb2Y0WxNwjlCWmLDCemDamiyVqETgOwdSUy21Qas6HqIk1jj0tMK0BDsjn9QLiV4bkh+ZYXlhE9uvqK4IhJa4dsxwEqNuTslTn3NvzHH0jh2yuYD5781ZW4lZXh4Rho5x6jGcRFjfQ1pQ/ZpXOtheZOmv3uCVlZjJgsIWgj2v9GGloCF28CYgk4L3eqd5uXGE1arJqY8e5Ei8hhdUlL5CWYXQAickXlJPm52TaBQ/ePglPvPze3BYhHGUhaL74pDwQsL08BDRWmT2RIbrziIngnJPiDs8y53vvsFL00MMXtvgt198jDuX1ll3XQozg2j7DB9vYkyEWQ4ZFgL95YDhuZDgqqVseyibYJuW9FAHXSn0VkaRKn7hV74BuTiic27Asj9i46hm6NoEI0uwVeGyeiAkpOIHfvo4DBT3v+86f/P5b6K1kZJOPEzkoYzA5hJaMVZItt6+TBGG5MsFWagAQeJCeu2EZKtBdHob6xo4XxMtG+YOjui6MY2PN7n2WE64GmDnI5SxNN+8SdqVeEtl7ezwBaISSOGoFi07FxTutQ7BlevIIMafZiRhA+/xFu3+lMQFSAS6cDgLRhictOhJRTip6lYkKRCxJt/r0WmMuTnpoDLHUm/MRQBDfbpuLN2vGtK7wQayjhUYkLauVzW+QHmS0YE2nSzAaUF8fYKyEiskVVOjrIJZRdAuqOY0bqhpXMnxVzYZ39Fkeuds/btygbAWYUCPoH8HLI0S7rzvGgh46s0eL64swpzA3IpLOO0o5iJUyxBtWG5hn6C0GCXxz6eI7RR/WjF9co6VZ+fw3lqR72i89SlqT4OyLQBbn8pLCcZheg1Gty2h+wY5qah8hw3AmxpMJyRfCHFDzdqwDQ7mcktfzGHCEASEGwlboxhzqsXOzTa6aXAtQxyWzKoxIre88allnO+hsrqaV18dMXlyAYcgnZN11NCBiTUm1HVcUTlQESYz2MOa188ss7GngZQOP3Jsv3WW5bMpojRwa9AspUZOBJOFHeLjOe42j/X3342NJN4EzNTiKcv+ZyTWc/DJnKtVh2R/RHVbj4VoSkDJkYe3OPfJOfQhzdm1GVaMwN0DFsH0aANVbiJXh9ANcU6R5B5PP3s7sgmEAW7lSh0PkRKMopIK59Wb/PK2CCclQWFxvsJZhwkAKalmY6q5mNYnriP7BbKwZH3BlRc6eHskbq5L2EnJSg/nVV9rllGjDCsFBB5qZ4ozFiEUNgQbKVTu8AYJ+uoQ9s5ihUAEPsLTWOfqOuKZFs5VqNxilUNQx8KC1QRfWFwcIzeG9ebf1Kwrs7GNsA48iFYDjCdRKKyjdnQYg2e2GIZdaGrkfokY1sBYZwyiKBFxDMaAcHXbT+hDp1nfK/IK2g1cFOAqgzAWNc5wUYCtDKI0qNTiX97BLcS4MxXlFyv0Ptg60GGUxThlaJzfopprIKMWIinxpzWfZbpsGLdATyXOE3ibJUoqqCrwfEQ2Ye6Tq2x+737sosRRok6FiIbGcouTVpa4ssRPdV0jbSt+6MevMswNf+MzT8DE4EIfozQSgW1omt2CmQ9ZiicF8dmUbH/A8p/d4vrf60HlaAUTXOz4/Ruv8eE7/iaf/o0vwdoOQgjsaIzI24iwBsGnoSRI62uY7yuaccCpaztkPQEWfvSn7+Xd/DtE4AESN03rNUUroBUpHrh3H0II9hxe5Nq5NXrzbV59/Qr/8leeJpvxQMLTL5/bHaTsale72tWu/iPtDlK+TpSXBqUV+29b4s/91W9i/domt/3PguPDq8iyQs/uxSYVcr6HXprjyqkVWjNNtLWs38h58D0bPL91lKxvodfGbY04dGQVCsnEl6z9mxJhTJ3hnnqoalrnu53FNSJUVME9GrccoV7po66X2G6T4UIXO40QqsQ5hywMeietgXmVgaI+VXRJBkrVCyUF1VwTr8pBVogCXCxwSlHtWLSyeI2K8TisF62tsK40rcB4AjVJ8A5W/N3bPs/JZIburVO6JI2ZD3fYvpZz4PuH3Lf3Om/YPUwDH+k7bBnywmvHcNN6sSonJUE7pao0onI4XccIVD8hvjJGTXK8YYYpBatlt94g7nW4eYsbKFykkcOyBvIJgUTWPuqsYv8HSi6dqQGDVUMgpOPKzgwcjegd6/OW5eucfGOJPm1K1WB82Mc3muZNAWnAL/7zNyHeWrL8nk36N55kuO8mh8OM0DfYNCTZyEgXIpwELFA6hkseb2wcQg8tblkgfYcYCKTU5IsBjbUpSjsejlf4hsMX+ac/9RSp0LT+TsZf/x9e52//k++m8cp63a5kHcL1aq6vqPjXv3QPfdVm3xNXOL6yh/ZaSON8SfpEhF0QZPd7NF5xiIHEG+SIIOTe79rm4LHLbK5HPP0P9/JH1T7Gj8ywfmKW04N58kQirKFz75jsZkS4lvGe73+dtz5xml/9W+9i3fSQqwXVIYcTYA+mlCstbFMhk5Lo+hCzaZi8ucWlmSYvbd1DecgSX8jYP9vi3oeXefmZNyhGBZ5XkcuAKtf8tUc/y//7N5/ANHxsQ9I+voX4ypT+E7PYsEWmNQtfBvGoxGkHBoaDgJ4oEQ8FFIEPJz3wFJOzY7JfqmgehoVJRvKMY9Ro4AC/TCheamCkYvoXFSIaQ9HCHzrymQqBxa4UyETS3CvJ8KDhQVGx1Bizb9+Q7S+E6ComazVRFTgjyHoaEzqUk1gl0EmF2MoRaPJMM1iPafVKLqwuoFODLBxFXBFdK4gChWsqnKsZDWpSIqua02A8i9epaF2Lia9nVLFXR0+EQ3g+0VZJOqtQE0Xs6ralyTTA38nxdkoaFxLs7Azhdv15YcGbVvjGYnVFGkdUTqIw+Nrwju9+jkI2Ofm/3kOVeci4ojIlIgerPaDeDKoSvFZO8VCXLPMo5gNoKTKleOa5++ilCX5iCbcryrZGqFvujcEOhAFiUlFNa/eNKgtkkhNftYQ3Uqhg2FZkc5oydcgKnJakrkt4dQeTdfE2UpK3LZC+AH46hWMOeSwB6yhKRcvP2DlnqVo+qhCAI3zQMpF1xLDydB3fkILg0gDhQqxfd7c7HBQ+3skbfPS1PSQ/M0PvroRpHpDt8RFrKdaziEJDaRCeh/rDEeO3dWm8uo6+WlI+WHM0ADpDQdKAKpJILGXDR/UNaI1AsL3ZZjuN2Se2iQd9upctD79zhf7AJ1lTgKJ42Se8MAWpEapBMDTYUqA2x4g0Q0xzsAYhJCLy8UrB0h/cwMxEiErjSkUhCwJXoFwD4ySzX5pimh7RTg0wraxDr+zUbirfozmfU+6LCFYNk3UFylEtddGTEuHp+vqPI7T1cMW1G7hek2rGZ9Sp6H15CoXBzTewHR9hLOWGQucWd2ugUi20cTNxPdDyJC7WWGvJDzWhERBcuAlxDKFGBj5YhyzLepAiPMgNrqGw2a14kXPIyhLKCduXu4iDHtV7OgS/m6OdxI2mdUXyreE6WQ55gahMXStuXM1NgZqXUllEWUFeIoVEbI1I7gmpOh66oyiXGzh/Lzs/fYbWj89y2+EVCu1z+uph1NoYFmfr90Y65LSq27M6kmLe0jrnkIVD3BoOCk/jyhLb8qk6ESJV+DemuMKgZwqcH+CGEicMsYZpo4EINWqUQSQQzjLrTdG+xEYhphsgC4tILd6wIpEeditj+WKIuV7i5hXzpWVlMMSdiVB3QZJ5XDVDPnXzOL/x938frK0BxjtjWk+vMnz/PnCS5HBFfNEinONHv+NN7DuyxM/+yufwxg5hoQjg+Ivn6hhWmkMY4N+3xDvf/hDxHbP85D/+MD/+fe/kifc/xObK51g+vMC//8SrGC2QmcEGksVW87/qem5Xu9rVrv4v6T+Uwv3fqz8Nr+G/sHZhs18nqhQQSErr+IH/5dv5qV/+QZb9Ac463ty6AsJHNhsgJArLV0+eZjA7xAqHHwoO9BKYpIjKoLOK+BGPrPJ4aM9VfuapT+F1Ve0eiQIYhDUjQ0qcuMUbkREf/DsnODo3QQw1ZqmH8EPSGyGysKhxBVrUnILAr9tvfK8+UZayhgYKUf8OCWnLEkQpcl7iYp/bGpc4mF5CD3NcaqgKKBs+2axH5QlkXqGSAuNyhJSUNzx+cfMhBqLJ//qrj/CTr76Lk6v7MIsCpwSXfjHmj569m50bXWyniWtE+MZSThViYBC5QTqLmwpEUdV8CGMoggKMrTeXTYH/pKP/QINRHjIqQtTbHY2DY9RCXldUurpb14aOvKXImwITKFrdER4FsjTkPcfnTz3M1tU297/1Gt/72LOkWwHG+YiJpDHrY+amNNaaJG/aR7nYJPcU4VLO8c39SJuiX3RUQjHKQxplSWdbYOWtjSagMsPqZhs7GyAKiU0VVaLxywpnPMKBRWhN8O4SfcTwhRvHGB3oMZn6fPo37ubfJQ8QbE2xrsIFHvbAHM5Z9CTDDB1Xh3voV12ure4jnFpEWTLztGDPJ2DhvM/seZCVw0zHkJe4yrB6chlxOWLqKfzlgu7tE+7/i2cQacF0J8R6uh6u/dwQNcpxHbj/nuvMeRmPvesCyispQ8vO90SM/juPynk0rqcEgxxvVJDPKMqZmNX7ely/exZ7Iabzep/u6wPM6oTF/TOYCMqW5hf+7iP8m39+J3/jX76LmWbC+n3LTO7pYaOAmfMpvYuKzgVLfFMR9uED7/sK79u/CJmAVLDzSoetfpudS11CBS7Pa9ArjtGBBm80D/C2H75J3o2wHkjn8MKE+K4J4rEMJRyNpyaUD2WYCHRiiS7kqBtjgqvbrI173FiYYeSF2IYmnZdcutHDrad4q1PiNUu4aXDCkexzFLEj7yqqhqQKJRZH1YJcKT72hYf5F594PyvJPDJzNSNiWiLHOdmMZXREMTO7Q3y+T3xqvY4/TDJUAN/1/a/zaO8KMrd4kxLrSUoFzhkcULUU/WOSG1WXtbiByGsHw/YT84weWkQYKCOBc1AF9XfdSuhcKmhcSvjsxh18aXAbF6YLOBHSUwXv++BX6SxN8QT0vpIwe3wE1nzt2ieAh3orpH2NPywRBhpnBoiiZMFts/fYBuPHJUVHYxW4wlLMhqAEYjJFJTnWVKjSEJ1aJXxjAzHMMEENTG2eHuNt5KiwQsYVRRfkvIMDbSgs/uoQt5Whzk0QryfEq1N2Bi0unNzLzE6BdzzAqA6i3UYYh04ccVAXugrjiK5OIMmwZYFUik4rQepbbj0pmH18gO073AT8oWWchZhSYA9aLv8FiRoY9LBEJSW2yDGloiLgyvccZqdcIFo12MjgbVfYLKN5ySGsxBWCnXvnMXe3sGFdbT7pN1nb7vHsc7eTGc21ZJbffe4+Xvj4YdSH2pSfbSNzgxxVuJHCtGPktEBPKsR6H/IUR1nX+AZ1HEw4kMKhxiUIiRhkBKsJeaYp0wobeogIvPkSlWWoaY5fCGTg13GWosT3DOmKpFgfEKoRtudjgrrW3RkLnmapNyHZp6k6ISZNqLTDYfH7Ehd4ZMsho3tbpG2Hf3YNb5TUQ4mshKyoDwbKiir26qF5w8e0NC4rkNd3QIGbTBFZAboG3f4xNcPimB5tU8UaqyzWGkThcFWFWWzA0q2IIi1MO8ROU5wUmMUeVctncrRJcscMIo6Q2kOWFiFlXY29sYPb2IGiql0+StQx2MkUNS6QViC7AcVhSb4UUO1rMai6rNkuV9MZ/I0MmeWIa+tY6QiubBNcH1BEjmBNEK1LvCGULUHaczghsEAVe6x/153037aEdYJkqY2MYoJDBXLOkM1H2FbAJPSxs7fqure3OXQgohPvMF5TqNEQl2boYxNE7nBSIjxNaRVpW0ChIAwINmHleAlJTh5LTCHIpiHCCT58/SXKqgKp6mGKFrz7wB01sHwxhyXqxq7U8r/89G/xqaff4Cd+4N3MzcaUyhJ6EM53IAwg9JFS4M+3+fG//W2s74zJi4rBKGV2sYMKfSaDKU88eARR1EMUtGBtMP6vtpbb1a52tatd/bejXUfK14mmk5z1jdGf+Nm97dvZHP0+C6S1Hdk6TFmxdfIqk2sjko0m6BDunOdsNqGoIoQnENOMb/3hV4laCT61C+WDP3SG3/3f7yY72CTom3oC5+pRo0gzhKzYcE3Sd+zQ2QwY/1GFmxTYqt5A25aqGSNKYEKN8D2oKsxwhNAKoTUijmquhDF0X+9TKYgeSxkEHYSzOC3RPU0x9ci2AoKGwSaWytforQxZGoK0QgqByRpsTHx++auHeUpc5S2HTlJEBavVDOmPRHQ/N+bUT1icGtB9pOCOd97g5PljDLebiMqgDAglEK5EJFUNw80zgvM5jEvKhSYPPbBCek5z8OiQsfPQwhBWFbcv32D1cpuxauCEQFUWnI8uARwOw5nfaFEd9REeyEKhtjXk8ODiDZaXhsy8a8IXPv4QcRdmW47lTwtEGJHOGsSmZOeegOpgTOfzFasbGxRzjrVJG+dgpj9B5gXWlQjpYQLHwhfHeJ7gyA9f4un8Thg0UNO6yhkgHIIrKybPR7x47ACfb93L9N6AcCOHSwXX/maEvrKObbdwjQAXBKjS1nZ0VVfD4nImG03i1TGInHR/i3aRo5Xjrs4On4pnMHaG8MVNhDJkZ3KeOd6lNT+i8Q7J/ActKAt7Dd1swngzxjmLyxx6KyOba5AnPju9mGMPr/Hq6T5nTs4zrGKEdSyctkRrU5KFAISkfYclueQTXPSYHJS0tzX+liMYlUxJ+fhHj7NztybRDfZ8fALAwT3r/IMrb2HmbB/OO0ysEYdBSIM/ShFbCd6DDR558yu8xff4wscPoMeavX9+iIoMdrlic9SFuyy9Vzbx4wr2epSpIpqz7JlNuH41xHiC6nFBMae5YjtUK5ojzVX8z/vIwpAsebROWcJJiZAwurNN0fORqaV5PWdnvc2+I1uY2Q7+ZkJemfpvbXtMNdNEXTPoTNcRKxw6UlghoYLGdobxAmzpboGJDenhiLIn2X6TxnmKdjwin7ZID7QpFmKqro83cfzBFx/lqfe+welXlynjehAqNhPEZEq2z0P0HU56GCNB1JuQrKcpOi1kJQl3wAagncNFGjNXcuTJ60xuaDY/0+PmpIdLIZAFuIyHoy0OPTJhNI75o8/fhl0ShBsgypxgoMBJjC84ceowQpSoaYkTgp03zxFdTykbPuuTWWgK8hbgO8o5RekUtjJIT4Nz+BtT/LxABAHkJSI1mGaIyEryGVG3bhUaIRxWWcz2BNmRCNkA58hijWpq0g7khzTBqGDmI2sIFeLu6RHkGbmFeL1uAdta7yCPOjD132PVC3C+xHZjomyTUdmsG0dKOLV/L/I7x7RfMURdRyeeMMwDXMvS6jqc0lSAFBrXbjA9FjC9N0ZOFOtHGxz87R38LKZ9voLJFNeIsJ0WelRipWL7dh91CZyGYuKz+VqX1QOzVO8w2CgG38MPJNoUhNsFWSciP9DGZSWR52HiuspeLHQpjswhkozotRWcceAJyociRvvm8LcEasPgbzoUIWWnSdELEdbi3mxxhwTSSbxXDJQVQiocgvaekvf+xAW+fOUgJz66SKM7QIiS4Mo2TklEp43VgqGL6dye0h/6NJ4BEfj4I4G/VuICD50Zqp7Gvz5A5bYe0gY+Lg5hYxv9xhAbBOhWSKV81HaCV2m8gcONUqgs9eSG+n7qqTpCpGo2lvMUIBF+7Z7U22Nc6NNf2kPe87GVwGxbZNyApoPVjdpxo+shXzCy4Hs4qRBS1GwUB1IrSOu2NOFpZAWlEmjnEGmFrMDfW3Hg0VU2T7fxbk5ov1ZgvlUQhzmkU4QQ6PURev0kztcc/qZHufDGBmrVYj2YzgqsJzjwz06Bqblpk8f30Dm+gUxDJo90UVUFoSK/JrE3UsRtrXow5uq4GlrX8azpmI1UsL7tY/0WQgkmjRbRpI+qLK7dov0LOVWscSU4TyIDS/9cE+eVVJcDaCsqK5DW8vjMYVo/0ucrXzzP8a8eZebNdzHEh9CiBxrrG7AWURhWS8unnj7FO99yJyIA5Wny0tF49z7Or4xQk5y5VzfZ0+sA8Nf+wtvZ2Jlw+6EFTj99mslch6vDgvfNt3F+DRs3DcV//y2P/Zdcvu1qV7va1a7+G9WuI+XrRLHvsWe2g6n+AyK52/gAX3rtvZxdX8ZV9YlxmRX0t6ZI7aGUj6ss7kTCs7+6/DULljOWc9dmCTzDlazJxErUHTmi2UCFTfyb03rR6GrInfU8gsiwmreZOh/VMciswlsbsHB4GxM7rLD4Y/AyUf+bUGEChahunbJZi0uzekGua/CiDuHR91+k1AUXDzSZHqnIY1GzWqRElgZhHAKJaUfYMMCKeiCgZUarLNj7ByXhN2Vcq2a4Y26NteuzVJebuKyH2TuDvKPFAz97gcHBJuOtBjbQ5Asx1paYtS2qjTFumkFpcJXFNEOQgmwpojmX4ix4ueGDc1/lu+Zf57GFGzw5d4G7ZlbJm7IeVJQ1eFEXdRuEGpXseXLM5JBPsugx3S+pYgs7jsv9HjumwXU7y4H71pHO4NvkliVbEF0t0XmBnML1i7NUz2vilRy3HyqjsEYxPq8R2yMOfWKbAx9bZ+m5MbrhUVaW0Lc8tfcqMhdQgZFgBaitKdneEDMRvPw7B9jajPA2EtTlPuF9BbN5gvZMzc1Y30Gs7dQsm2aBzVO8q1u0/T6N/ggVepRHG+D5fMNPneQH/+nz2AcKZOEIRhKz0CXY06KzNIHQUfmSfMNjZH36Rcih+9d56K7zMBwiJikkGf7qGGUdheexk8d84p/cx/QlhU4kekOib0oaq66uFjUG3au457Fr2AMl+q4JrdMBeetWS45fn2SnaUHnC32Wfm8dV1RYYM99Uz4oVkjT2uJvpWLfEzdYuH0dfX0L79IW9nLFhUGP3/9CxszNkNlUsP7FJW5cn8edyrDbZQ1mDD0Kmqhnp7z/4Gu898hl/vGPfAzZqqgoSd6A659v4b9hUNseVy/vZ244JdoqCXYsZlZTNCJKL8Api4mh7MHkcbjxXo+X9u7DzIRo6RFeHhDdmKD9EKVLposOUdi6itsTmGYASKQCvQ/8xIJ2eOMSWTpGdwmyfYq9H0+Irxdc31mgjKBYiDGxrqGU1lFljk/Zo5SBwL/ZR+9MEap+vnFym/ZL14leT4mmJd4NSRXWw4PO2RT/xgjn1bWtRU+ippZGM6MxkxPen9V10VVJwy8wueLk+/fwO//oMT7z0n185fQh0hmPdMEj2RvjlapmuwA4QXQjQQ6zmnnSrvlKthVSTT3CtZyZLw8IN0qUcXj9EiMsw3sb9TVnkhKc20E4h5UlDvA2J8jVHRgOKdq6jp28Jole8+icq5Blm/jmNsFcweCthzDNEBtEJLfNwKLk4Xsu88C3r6IpsEpQjBLMfD3sNWUJpkBOCvTmhHK5DfqWt0EINlYiRG5Qeb1BLBYdrRWBZzSHFrZ4c/cS+8M+UsPkozEYUTedOVtXeRegJhqRQef1Ed72GLWT00jHxMUI4VWoSQlSIFOLOnMLIlxCKSRLd/RZvHcb735BuGmJblYI67BVwfiAoujB6MEGSgY4Y6CoaueEVw9UTDPAWQfGYCcZ5lJO0Y5A1qwdl+Q46+pBUaQRSiFPG+hLxLpEaI201NGySCLjFjsrba4Plth60yGuLBxmc7WFmGaIvKiZM0ZgXhtx43yHXIbk+9r4mynexhTGCVhLtGWY+cqE8HpSs4Qmt+5jgYaiRHCrIjgApPhaLbFpBZSHF8CrBwzWk1jhcJWtOSuNGO00en1CcH0EWY4cpfUgsbBUSYktJLZSlFpgbYnNcmS3gxplyElB83yCv55Dp1U7XzwJWuLyvLZc3aqBRwqc0shpDlLQefEGsrTE945gKlATV4PAtypiXVBJRTkfYbWCZozwfUgyrv3bl9FJPaixGvIuIEBn1a3ILtiqxNuY0jo5QY8ktpI4ralOZmy/cz+6X9TXBVeDkUkShLFcv2D4u2+8gzNRr3Yl+T5mp4nQt2qcPQ1FhZwWHNszg77d0v3uMY3DHjbSuIsxgbO04pw97RFf+PJHeeTdT/O9f3vKwrfHbFPyyGNHaA1ivJsa/2qAyOv762N3LPPB9z3A3/itT7Bqi1uON8GJc0O8xGFin8E9AUee3A/Ab/2b5/jHf/ejXDi3hl5oUe5tkSw12L9/FllZlAUvtczMNP4Lr+B2tatd7eo/n8SfosfXu3YdKV8n+mvf9w2sb424+7alr/1MCMnZN2LOmrvqHLd0xI0AKyFsN3ASJpu1i0WOHFQS5xwm8tmSXVLrs17M8c9OPEbrV5vIfI3wSh9TONpxwWTdUAeqBWkuea9/iX/9+UUGf+ihXAajhM3VDmamdqFIoVCNABemGK0QztaLQyXrRWlRQn9ULyaFoMwcX/rlO/ElqC+mjHYqMCBIcLFGRBIrocwMXqEQZYUSCtHrUjrD2Z+OmN4zz59rvsAjjTVeny6iz4Y0VlOcE4hGhAkcU+vjzyWEVZ9Utyh1SBlrfKnqjR8GG4XQaqICHzfv46KAp1+9m97GlOx4yN/6H55HS8dmGLNoh1wezuECibUlIitwqo5YYBxef8rs2xKy5RKTKxZ623zXYy/z28HjuNJyJl/GFxVqVCHSjCTLqTQIBGogEVFFcNmn+54J4kmLV1gaRzKiyFJZQfqagLBmaUjhyBbbaOvTf1ByanuJuDB4WxJRUmfwqwrTcURHMryN+vMUSpEvBJiZkN7VbX7y517nqyfn+dVff5zmeYcLfVwYYD1J1bKEKuHtey/yxukZONphM/ORGxkf+sUn6bxlyLWqR7TpUJXAdEO6B7YYloukQ8PinTus0qTlMgAO7dng5Z9brts0fAVNqGYjGtmYc5/pcu9bc4KwJIxL/JHkwG/uIIuKfG8P0/XYeSDlQz/0mwyM4f3v/ip7VcpvLzzG73zyKdxRgfMUzjS4c88Fzv2Rj7ACQhBS8swzt/PSFY/xvXMkdxhICtZ+XiOcY/GtY64910Fujvn4R9/E5FIbYQy9PSNuP7pF6nucTDrMnV7BpL16M9J1hH8BthdDVkrJOPWR50Y0kxyGU8TCHOWsz8rbqb8HizmseYSzOcmGIMYhfE3R8xBZSXxqSPVAgPAcIoayrVHdEFkKROlwUuFZQ95SlH69qVITV7ur2hbdsDQPZvQ32uhmUcMiHUTXK7qvTlCVQA9gezlm4Y0R0ZVB3aAVaYrDc1SbPrkqiCYFsrTgDMr3cJVD6Ro627jsQWjIpjlYSbBl8DaHtFzF5jeFyEIjK0fcL8hORFzpd1i5v0e7MUamlrEXEngF0uQszm9xcTDP5rCF1BanHE5Ysjld1/vdukuHWwXj/THlXIRKHEFW4o8sVVPQ/PKIcCtn810B+aIgWIfWixOSAxHYPgS1s2Z0JKAKGjRf38Tv15t9JwUytyg0rasWJxxWSXq9DQ4cXOMrX53BHPNIFw35jCTXEiEc47jJne/qc3le0b/gYN4ne6+FNMH7iMO/PCE71Kkhvd0QUdS1uya2pPdAdKaqv5stj0rUTBmnBQ/1bvBEYwVpHBdmFii+1TG+MUOn77BpiRhmtMaS/p6APDS0TowAS7QxYl90CRsINhY79Aca44ELNJ4pEKYkl3UN/UN7r6GfFqQfbjDeI8i7Hu0LBi9xFLlkdCxA5SGm6wgv93ELHZwvacxnJLKB04Jyvonup3WzjRM1v0pbSFOkr7EzbbxBAj2LVRpxI8L7SMXR5Q06B6ec+MxszWHSCZN1OP6pNtGbS7y8YpYcb1EwiOtBmMBhnSVIczpfWkFPE4qD89QXTQHJAA4r/JUSE4e4bguxtlU3xeVlDY6VEuF7CM+jmPdRhcTOBZiXL5C/7W7K2QA3meJf3UH0xwgh6mugqiNRpBkiL2GU4rRA+GF9b7MWruWYuS5YKBd90kWP7rqG0QQbeNgoQhgLnsI2fFRSAqJ2pigJQuFMjo0DpHM10yUr6kG2pymylJ3PLCB0gDXT2hViI45vdbFGEp/awd5/BKsl6vQ1ZAJ6dUhxJ4jKknUUXq4Q1yeIThusw43HZHfM4Hoxye0+LrYQa6puG91V5JEDVcd6HSBlfd/AQSXg3L8+wiuP3MGBYAeFIb4+pTg8S7CWI/ISlxfgx8w0Av7S33gbnzn+MhufX0OWloXb+6SxJtWCygj6zyR84kXF7FN7uDiZpYgs7/rAfXzl2R2e3XsCgHQrwLup+Mrvvspb3ns/WVkRNnyyQYZwNQwYKxDG4l2eMDmzBsCXvnyB7WHCi1++yDe8+x4Ov3KeB+7cy7XVAZHymAqL8yRnr2xy55Hl/+Qa7IvPnWV+rsXdd+z5//n83/zcx/jw6gn+6RMf4AO33ft/boG3q13tale7+lOp3UHK14k+9txJLlzfYv/eGRZnWl/7uWslXDmxwAFZD0yMdXihT6MZMtoe1eNCqb6W66YsKfe2OX4hZPNwzNX+DNHGElk7Q02TelEyAXmfgnardqWUFW6U8DtffYjq5Abh/gblCR8QTKYxLIFrQinBtSXuiqwZI5XFVSXkAhmFNXw2qIcUjCYI53AJ+BrUQzHZCYfqK0gLXCTqoYuMKJYcjZM5thmBMMjM4BoReB7SCaorAfIeyK9EtC9Y1OUdHArXalBpzYyeMr4WIQclsioQSxHlYpMglzhTogZjvEl9MihKB0VJ8+SUaqFFnnrkiUcyjGk2CrIwYDZMmA+HWF9ifIm34yhmPEwosELg98dsHm/w3p98mbGLObOyl31hn7vb67SriliWBKIgndbvkSWvTymdQIQSYSz+ekZ1waLPhRSVQj6Ysj70ib2SwJvWPn0tUZGk9GqgLUcLLq8vQg4NA0iwzRDXUfihoXV7n2KsaD05xfoOdIXaTjn65IClwwn9wZjwZgKNCKE1VAaT+MhQ8tR3X+aB7jrnnmnw0H2n+b3z70TdmCDWPdafm0M2PWQF1jPMzbS56wfW+OQ/fBR96TLXt2J63zTlXd0zZM5jn015Ue5HawPOMnhohuLIHOGXbnDm4hzXPtdhKJZpLFW4XKHGRd0eFEiKRcnhF89hf9ix7ClmVUqofNrLGd6gonFiDW8759CjN1lb2YeZK1ADR+l5EEqC5ywH7x5ylXmc0qALrKkHBEngoaxAbE+II0kVxFBU/Jnv/jyH922yOpjn+APfwbUjXZae3UbmjvyREH++IlEeKZLhVkk32majNUtxtE2QKSYHFDZyoODyHsnRakw6aBPspNhJhe41MaGk9eUtOi9uIt5osf87x9z75A0+lT6GTScIAvB8sgVBFmsar0Lj8g1Ut4m1DcpQYZzCpYrkQgyppMo1ni/qxqnzU/S4Il30Ictxnq5jDqkh299Fb0xQmcEbF8ycdxTNgKrMCDOLrCz4fh0ZmW8xvs8nOSBQY43fr+tNk0fn8YqirmDdqfCsQeUVaiNh0leMHg4wb1LoImXOSxnlPssPau755m2wO5w9cZBpKXBNhREGKUKsBuPXm2iZZBRzPXbevgfnQFWWvOto3pgQ9MdQSu44eoMjB7Z58coxpJSIXgOnJbIRA6BuTEnftEx2qIvK+7hOjJyUeDlkyuF79WDI+QrlCq585RjkEm9YQKdEDgJmVwyDectK2qG3VTLYF2JNRetLOclzgsoLCPoFxe3LSKtwvZjKFsSXphBJ5GxGuQCNEwYyizUFS/9mRLpHUumUO1pD9mvDIS+l4WWkRUx6MKJ3aQfhLMIYUJI9p0rGx5r4+KiyxNsyHP7GhGSkWdUhJpWYQCCdYHQo5AG7xrntvWDBTwoufnRfPdzxNVUvQBRDVFrRPp1jCJkea6OG05rVUVYIBMV8G6+RYrYayEaMcAqqANUM0KUjParQiYc3VCgrEUIQvH7zFsy0QPaabN3oMNYhuqEoSoEZOpgkTAaKeD1j7tltkjgAaRGRxgU+hAFyNAFjiTcLimbd1FOJoP5c97Up3xwh/0jjSkM5HxGv3qrkLUqcvdVSI2UNWM0Lqm6EtIKyGzI9GJHPKUw+g3d9gPT9W3dXgfA87HiKMwY5TnDWICsJjVsA3GlJUEBR1DBhaSxFp3ZzCt/HeAoX3oINGwelq+NKVQWBBxNXt8xojQSE7+MQ2KqqIz/GsvipaxTtAHvPAaIrQ4hChAHPOKoApo/O07pWu5ZkHFMe2UMZaMKtrB60Ljts6YhP7OCyAoqC0ZNHCEZQzbYwbYksHGpYQ9ONDtFTcM0AI0FkVd3GszyDLCvsUo/miR2aq816SF/meEIgaOBmPBgmCGuwkY/xJfcXd/LiZ6+yOl1Da4jvnhB1SnJazMYJ3FvxqZ+a5wMPfD9m+hoKx3BtzF/+hkd5/fWXEc6RP2bhQ03KLOPY4gx/64EDFGnJD/z0b1JJoKpB0aU3odzf4K3f+AAAew7PMbm4wexSu65xb/uMTckj9+5n/8E5LlzaoNkNePSeA//J9de/+90X+Ff/6gsIIfj47/8YcSP8E8875/idjeMIDX/9yx/aHaTsale72tXXiXajPV8n+sKpS5xY3+DK2s6f+Hk1m9F81eLKEpfmTEQJQnDn4/tqCiASZyxqUtRNAEqjN8YU6x6vXTlIf7XDj777EPc9eryGTrr6hDFPZH2K52mKpsJ5PqOvZuQvGcxWBp7GhVAeNri5krJXg/+YFsjcoAqDtKBDv7YrW4sIg5qVcuukzQWa+x87x8Hlq5hth25F2BmFi0IUXr0QrnI656a4yIFWlHON+qSsKBFJRnvk+Ne/8I387E9/M7/+995CdG2AutFH3KxrK9M5zVPxRR5bWiHwa+u0Wp8Qrae1Q0B7COtQpUBkOVJ79esMgjrPHgUgFNfX51jb7FI2PT6f3sEwauIk+JOKeHHKw3/xMpGfoBOHSguy/ynCb0suDOdZ3+rxzHO3cf0fNom7BS2VUq1Lhmeb2EmCNTXbQlhH3vIQzqJSQ3VVkguBTWH6qubgz20RnCyY25sSzg5xeU6hfYK8BGnJxhGtdkIc5UTnh7Sev1lX22qNCD2CGNRTIduby0TdFNcE01Ssnm5z+lLM5WsRoiw59KYb7H94g8MLp5i/7yb24YQ/UgtcPN9BKEs+ATuryRsOMZoSbJVEWwadOdCSoKuZj8c4KakOzmHvjhn3OrwnXuNbWpcJNy1VABtvnWE0I8iWZ7GeIDncZvGBFLvcwhpFNRYwzigXOpTzbWzkYQ38pX90kouFx43K8ZEbS/zYL7+V3/mZB0E63KammPokRcQoj3C5oBpPmM5bNt/c5cLbl/nW736VygMTgndjgtURqIDx1QiaHuxpo89nHLrtCpXv8eu/8m4u9Rf4gzceoiDANQOGd7dxkQcDn2src2ytdDAmZ30YkG+FVLMtPB0hlYec1pwBb1gx99E+7svbhDLj7d92BdXwUUIjppbsQJNyoYGZaXD+wj5O//oS9nqK1y+Qgwkr720xvq2N2lG0Rh7+Qg8/MIjSIvYmENTztekoBHOr3nuUokcZ0fkdspZATQ0zL04I1jLKQz2K2YCyGzB8cAabT/HPbVF8OsClKfr6FrY/gOEYqoq8JSk6Ctt0iJ5BVQ6ZO0TlsJ5CLwh06QjXU6IbKW976iSMEvyhRW9XzD2/g3rdsjFpMc19uu8SvPLiHTz74r1MvICyoTFe7SiismBrl0bnK1vYIqP5+hbzH71EdGWEkxIbSiIStLZUy4J3HDvPPTOrPNy7jk4s3saYshtgPY0D4vWS3usjbCNk9MheyEuUqWrnjRKovQ7Zc6jhkPH1DknZwC40aB9f466D6+xdndDZKGnuGEZFzKnjR7HHY7ZHLa782fvZe0+fQ/u3oRkjpUKIOlbX+eompi1JD3vQ08wcSBDUEUg1ytGDiuBSgt6oeOX4Xm5kLT598TDrgxZbL8ygMsH6o736Zh7cAmrmFd9272u8969cQ/VibNjltfxBLnbvZWfQqauS8woHtE9mXP9yBz21eFPHl188iumXuP6I0lWoDKwnSBc8SpnROjVi9os7yI0RNk3BQqUs1X0p0TtTmv4GapJDVTfDOCnovLJD76WMcFsjJiP09hRZOtxghLqyxuDhDluPNOgdHhE1C0QoqebaNOMJOEsy8HnqnRewOGzoY5zATCc1q0TVzTCFLxBIlAyplkNEUSHyCvoV3sdzKk9R7InJFwJcM6pjgJ5GRCE0m7XTQwhc7Nfg88hHZwY9KcE61FaGjKKa76U1eLcYQe0mdu8MyQN7mD6wh6rdoIwlBoNVUHQ9/InDH1mMc4wPBhhV368UAllY5LRAVvVAkKwEVbcwVWlSv8ZmA/pj3C0np5wkt5qKAGMJU4v/wjmmt4UIoMgtq4M2q8MOamrw1kd4F9eRUpM1BdtPNHG+xAkoZ0vSuZzZ01OEpxBxiOlEEHi0ryS0z+TogUamDrU5pBhMkUhsqOqHqtvBRF4ib92/xUIX7+Q1srBksi+kur6DPH+9Xj9IAZ6iWGyx9OgBbr97D3ccaBN7DjHfYL41ZLY9JlQFnjBUW/DO7+nS1i+ANahpxS/8k09xmzfH6ESP5FNLFKshamsE4ymuqji2NMeBbpvKByEFeJLw0jpyrkny0BKPv/kuAN75tju59+693Hn7MtWtKFdZGS5e3qRS4LV9lKe5frP/n1x/PfPsWUwgqTxBXlT/0fN/+OwbKFnwF+9+nh9/8GnO98/9X1nu7WpXu9rVf1rO/el5fJ1r15HydSI3IzFNgQ7Un/j5ehXRvjmqiffCMjoUUG6VXORVkkSBFNgsx1IgfQ/h++iiJNjWpBs+zSLnLhHzsdKHXhPyCtEKMYHC7QxBSjwlKNoN7NosenFAeXqKyLaojsxhwhBTaGxoEZ6k2E5wQmM8hfWgMz9DmmaYSQ5enat3RiI9Dyktb/mOy8y0DB/5vWOcO7VAZhvYXoAVFt1P8SuB/0spxT9PEWdAjYHcIhohohXQKSU/8tPfwW/+089SlpsgJG6mUy+ukKSzillZ8vRoL/17NPqlEjm09eYqCnBKs/OeQ7TPp7goRJQFQijKVoANNV5hEEj+7YfexNL8kJtvzVDUeXiHxQWaN/38FWbDhM5DCZ/+2bciheO79p/jocYWR8IRf+/8Ab760/N4eU7bT5lRE26+tg810dBQ3FBtGnkNhFBtgZmNsDMFU61p3pwgKtAritgUtKcZ6VaTYrvECQnTktYHt1jrzSJOdZkUHZgIZk9eRvUTbCtG6R5qRtLqFIxPzFKtK8pDGm1LRAWduyyLe3M2L6aoKuWp/+d1zr9S8s13XKbTPs6HX7yXL37mPp6r7uGD/93LnB0ukWxIyofmCV9P6xpe40hnoWoEXB/lPP+rj0Fuodtm9cmAYCy5NlUMTs/xh/98Ebk9QmY+drmF6cSgoez5XCiP4kWSlthick6AN0VWPsp4yI0csWUwDY1Bcfz0PL/0ax+k+3qfmIK0ZxB+jBCGzm19VLbNpS9pslmf8NqQciYgn1/kJ37r2/G1w0gI1lKcs4h2A88a8CUyqMjO+Mx2R7hpRTLV/Myr34JZ92DG4u+ANxVU7RDRdpTDgPPjWV5rH+Hg4jql8Ym2Kpy0KCdp37T0H1VUTcDzKJRiutdj/+19+g9vcuL6EZobgmwmYPJAF3uggSkE51fmYbGBN6wQFqINjS4gWFN4paHKJU455OoOm48tfa3opuo4RC7r0/zNEofAG1tk7JDOUsz4ZIs+nZfH2ECht6Zkd7bxRxa0Jrw+Ip1ro8K4rgWfJPVwUSmcJ7EdgwxB24rGqqEIcpz2aTyfsPzggNnHp5wu93Dh4ix2oUXZVTRXLFU7wKWC0tTz/fjuLTafnufsxgFEWMcHLA7poHVii8GjC6gEVFnXiDcvjtBTg704YHJXi3DTMb2rxbgZU2rFtZe32X/XNqvH90Dg84773+BsepDxqYrp7fMUXQ/v6pjOyT7pfAsin0Z3TPpwE+NJqi1FXOaYFHCS9Xd2CW4aevOOe+wWp4MOxV1TkuWYMvGwA0NjdUr2ZBOcYEu1OXR4m35lKBp1tAYBO490qdo+VSwoewJ72YIPzmlsu0P/vpB0vyC6kvAvdm7n90/exnbapPmpDt1b6cppW4GpsO0G5CWzs0Me/+ApQlFx5ulFrl1t8UanQdD38FU9GJFlXXmLBJkX+AMfJwVl5aPSCVV/SnhJMV1oYWYi0I7B/T6zTw8JJhYRt7E6pdzXpuoEyDdK5GaFuCCQkxzrDK4AqTVSV0jrENJHrE6hcvUQpNvGmZJiKQQHOysBH/zLx/n9X3mSQjZYaBZMB23CvQWN/Vkd5UlDjK2Y3NamPbgFMQ81VVOghxXOKLwrA6oKgpUJ0gjUZkaYWMoiJJ3pkhxq0rg8wTmBDT1U7iGsxUhRw9E9h9qeIhpNgrFAXTKEgwoqU7/uMKhZHw6EVEivrgvXWVnfE7IMnKOcbZC1fYJb6DKrBd6kQpnaCSY8harquFguHV7g1ZDeqqo/l1YT/BAhJW44wl27WQ/xqQdUB26bsrkWkWUWaR3pkS6dL62hJhqVFAhj8K8VuLzmW1klSQ5GtG9Y8lkP4wlapw1RMUUEfn14UJQEfUMFeKMCqyyDu2NURyIxiDsE1ocylsjCoEcJ1d4OQX+KqCx6nFN225hWTHx1gvMUwtS8MDtNqQKFWeqRL8W8+JVL/OU/9xQf+fnPYS2YQcpws0netSxEUw4GWxQHNO995xWmkykHvbeyfnOem9OELz9zFu9yQNkBtR2hLpzH8xWH7zvIx3/ti/zCT/5b3AfurqNWhUNYS+uiRbuMKikhDHjfe+7nfe+5/2vrpb/7Q++lGQWcOb+GlhIhBVlWcv3GDm9+6PD/3/XXbFw3GQqoGwn/D/rIZ7/K3FLOkj8mxee5zZ/mWO93/88v9Ha1q13tald/qrQ7SPk6Ub63oioKTPQnp39/+fA7+H35cQjqVo9Kaqyw3Hi9rMPM1AtBkYHc77ADicsMXuloXYJAS24EV7i4OUtP1dR+144wkUbEJdxqLfCPVrCRoVb+v+z9d7Rl213fiX5mWGnnk1PlXLduzspC0lVCEmBhZEBEY4zdDbgbx+7GBhu7wY3dYBMaN43ISMgSQgHle5VvrBurbuVc59TJO68853x/rLJonkOP0UN+tvXqO0aNGmOfs9fZ5+y11/rN7/wGi9M++VzE+Ogstu8jS4FoS0IrkbaOEDkmkMjcUo5iTFE1KJBluKIArwGehxWOze0WV043edUPXMBec5z46RoCibQloiyIdMal3gRRktIuwEtLXJJgTU4502G43OWf/6v3se8nVziQwPmf3odGQOkwwqAywWdfvIOXpmdY/EtbrG51cC9HgMN5Gucc8d6Q1qUMlVtISnAOLwqRvsP5VbPEpQuTXDo3wd57TnHH4jWeXt+FN3C4uRHbRY1OmLBaNogXoBUXbK0GFPskp9ZnMA1HYKBo1TAXfRoHSg7fs0nnvRlpHPDZp+9AJNUuV8PmuFxTSsk0MQiFLByjvW0m332FmV1DHv/DWYL7BeKEI9I9zrTnSVZvcmmJwllLfMcs3uYY0c/wyoTavQXZPsNCdJ3eL83gv0HAdNUYse9tXT70O3eyfbJGVIeLX17gxY8d5pw+QKPcZlR65IFHf6vJwt2KN+57gc3zCV85cy/qog9Dzf6dKzy+fye2EBRjx3gzx/iGzCtxzxuCt455/ks7efETuylVBjXNxFc3EFYS3kjI9k7RvDDESMXi6zfxlmLSNGJ8oE7ziq1yBUKHnIxJnWa5aPNnv3wfk6sDksUIYT2iZ1cgauAacH6c0b+0QNjpsf76WcKVEeMjbfQaaOthY4fyBdHVMQQR+HXKGwOkNJiZGve/6gLBPseX1gVyrGAQ8Bpxgsf7dxOtG8KtquJ56UCXNxx9meeXd7N/ZpV/8o/fSXdeImcV8nUpylj4XIhTgNCMHlxCnU2xF31+feMtdFYhn5FIK/FHkmzapyljylxy6G+usvxzuyhn24yCogqMHIAwhnJxRLOWYS/k2PUtbLQDYsBBkMXobYHxLGqYgbUki5OY2Qg1yBgeaRJu5gTnNwCw9QgzWadYbKOcoD7fJ9tyuInmTbWJRJqUYraBkxorHKo01OMUveHYfFNE0daMZkNm1vr0DgUkseXKVwrkTvCmLM2pku7ENFmrRN7MynnyK3tonTJ4SwV2LBBagCspJiR4BWVEpaprNbGyRhyW1K/0Gd3WRhWOqG9JqOFaEn8j4bnfO8hTzaNo6+GFGa+6a5Or1w/Qu2JJ5gJsTVJMThA8eg1VZNipNmYn9Bo1KMFeFriIilB1GeGGRBmF/BZBuhVRWk2wOCRNpkGAzPv0m7bKl5AQBCMOLq4w0i0Geh5RCqyyxHsi/I0SCSRSYj5qkL0hZmkWpyUqEMx9ehtRWJL9I5KRhzxhSff3kKsTWA9EbmCUIvKqZU3dnfB3z7yVZpCyWJYI4/D6grzh8LuiaoNxVYD1YHdIzWa0n72CaUesN1swoxCZRIYh0YbBSUs6YTE4ug/OMP3EFi70sZ0QMS7QRYk4lJBdmULWcnw2MMagjcD1U/I9ddxOjRUeGh91KatCTRemEc4x8YUV8CTNxRFTh0YM7miRLyte/3ev0v74PAtvGPOFj+5F1moIA1p6YAtQilIr1JV1VFPgOg2GRzW1k9vYaY/+uzroDUn7iQyX54ixwIstUinclIccVhXFIq4UiEQB4UaJJSP63FnE/Ay6lyGMRg0SkFTqyazANkPSuQBvZLFaEJ7eRhtHWffRKTjhkHFJkPgIz2IVCKsofY0JfaR1FJGH9CVWKfIZSdTLQAtM5CNKg86qFiqUhHqESxJEaaAsuevVPf7Sj13gwosNfvcXDrL9qkXqV3OEpxEBNOolRUvSvy1i4pNriLlppJJMfuoa2St34fctJoTRDDQuawoKlNHEuyZxDY98QiPKGkXNIVOHCAXRgRz5sGDLSxh0QD+lmbomkf2suodHVRi7KCzMTCCSdSgtMqqstjZO0QTIbkK8o861U6tcP7/25zuXCqSfk41qlfpISchrvPi1WWrNhE3ToZj0SX3DgTt3YF6wZHszhBOkr9rLQ0f28vnnHuP5G+9DdgL0iQ3c/klEBuMDgvalITq1fOwPHue9P/4ILxy/zDNPXOAdf+k+5hY6pBg8a7jr2A7+hx97E//6/3qM9Y0B1vynd1UvXlqn8KumP+1Vm1nOOd7z8M+y4SxFK0BMzCLe6GiIjPmwImXKMgMUWt8axW/hFm7hGwjH1wtE/oviv4bX8J8Zt67e3yTwOyPCcojz/qKs9Mff8mqe/tAL3Hj8OgCNLhyqtzn6LYf43KmnAIFzljKSFKWHn2S4yAdfIwy4UrA1nCLZ8pkoM4QQiJ2SbFWiixwhFdaUhH9Lsvfjy1w8O42UEm0kojDglzhPoyj4hUc+zi9876vJOhNIa3FKkqd5FQLna1xJJZkWEjcYI7XlyNHrdHe2+MBX7mT1DwNUXlZ6eOm448EbzOyPObmyi1q3DyNX1TF6Htmcz/hwh/pTQ1r7BwS1grBR4nYOYcVUuXhFiRc7fuvDb+StP/wpPvHMa9jz8BVG1yehHyOTHJyrQhnrXcQorXzriCqPICkQnsIGjulOzGtfc44nb0ywdKzLRTfD1l3b1CYHfOilewjrBaIsmLhrE4Tgfb90J//6217LKKsThinO05hdU/zZZx9mz2CV/fdf4fRDU+zTm3jPJ1AIhAHfJZh+iBsLVDtF5zVUDsGWYO+xTa5fnYCZnLgXEOQltXmDdCWq5kO/2n2OzqwxuHMeIZp4a2Om04TBYy0Gz7cRayO80nB4cQNXwlhbPnHyMHtfHjO4GpIOCh7/+Vmat6eUoSbPJVP1mH/4s3/EH/3mMUwm+PQf7id2M9gDivkj2yy/tMjm9gTZfaB7IHC8891f5P/8/Jvoyg7BjRG5gvNMUyKwDUnwCoV+KifvKnQ/pxjmWGdRJdz+8HnOfn4vrUOK+IE2XmGpXYSi5fPQ33+Wga1TOkmWCIR1pHcZ1LUmjRcShKqsbdl+KNckk8k6gy8b1r59ARtI2ucc1oLU1ULIm59GDBMwhvq7Uvwpw7mX9/Dx5yfJj0uK24bo8zX0SLES7qB2JUfhYSlRacaxHau8fuky37LjMldzwevuPcuHLjxAGFtae/pIB6mWyCREGonqFfirMTIp6L+1iVuVFDWBBKwSuFpAfx901sZs/+MWZt1BTaLqYXUejBzB0FDr5tU54kms79H80jr2kTn8YU60XcBI45qSsu5XrSluSLJf0++0QXl4ayVOCLKj89jIp/RA5QJ5BDLtI7ZVZW3QAhdoWNlE9hqYhQn8gcGfzfCnJOn+OtFKjq0p9MDyZHQbrQ+MaHiGcu8OZJijd6WI+wXyREQAFDdDZMczEXf80HVyL+XEnx3CWYEoKj/M5sOzyLHFtBTjeY/GmsJ0IgZtjUygdb5ApwI3KcEZRnv86jWMQaQC4wf8/nP3s+Vq5Ac6OCXQcXV+9R9eQBSWxoYlDiR2JFHW4UJJnkYEzTG7D1xn+tAKT5/Yj5aSO+47RysccHXcQqh5kIKN19dQRiCMvNliI7g9WuW8v5vo2hjnCfoHA0wkUaMhptvEG5sqUNuUiDSBRR+xK8P/t0NkXLL7lRnyM3VYy8nzlCRdpVho08jA+h6yHoEQPO+HFMOIbreBGBd4Hgx3OvyeQxoHDoqoWvi1z4yoXRoj0xJVxGTxBFkYEMzU8KzAvx5jQo03kjSXtzAzddjugxB4eUDpe5UF8myA6yicMbgkxqsLXCIQVuGVDrsuKJuK0cIkE9fXq2t9nuMGY9wMlHc00GXOvrpgV63PxbGPnPC570c3MELy3G+1EQicNbiipHNqhGsaPK1xWUVErb5zDhmneL2M0e4Wvu8hNzycbxBxghpkhFdH+OMxqgArHS4H0axhWw3GO33ymQCR5zR8n0IbrLLIjQGyn2KVROYleI7RosfoSA1/JGhcK6sgWgfCDxEKnC8R1iL8lLIV4jRIquBdF3o432f/vi3WVhqMRQQGXJog0xIZBYi8xCiHsw5VOFzowe5ZbJwgLq8y7GpMKeltRwipmXpqE1evVXPrGLI1KHJBeKIH4wS72UW0GnhZhn5+DTc/SRkqiilNkjq279iHlIrOywX6/BaF3yG6OqDVLwg3DKbmSFab5J9QuL9ucQq8TqUAVOJmc5SS2NADoBYp0rKosrSURliLFIKyEZJPBlhfIkY5e48uEkzUiLdGFNN1NkXOYLnNaFKwlTSoS5+3HHsPn3r5PAN5FU8J3FTA0s4pJhcM16XAAmXHcHZ7yLU/XeXV7xww8WKN7skMUwishvG9LdpXLFIbDt6+A4CvfuE0Lxy/zN79s4y04Z98+jHaYci/+Pa38bWnzrN2bZv2dIOH7/uPq1EAlrfHiAkPYRx/9sWX+Ctvf4C/8z2/SneYkeyq4RcO6+DSpT00duUcix7gF1/4MV7b+TSFVTy86zRK3RrHb+EWbuEW/lvDrSv3NwmmG11CM8RT+V94vCivMOqMcBJUXFLPIfmRIScOn8DSBCzGGbKlOkG/QPgeMjeovsH3S6SE0swjjMXGCUpJjvy9IS/+7b3Ido5dA7tjivSKoHl4TPD5iDgLUImj9fx1+vfvwE5K4oGm1U0Q2wUEBSLw8IoYqxSEHg5Rhdaam1r10MMJB55AzRVc+eMm/nKG80W1g2dL3v5jZ/GjjPO9SYolx8r2NMZYhHN4YZ1Ft0l/McTvenz3TBVSp18lOX58BwiBtLJq/gD+5BOPMLvY50Z/N+24qIJGazlb+yNsqyTojijaASpzSKEqSffNn4UM+Mn/8fMc3rPCq3oTPGM7WOMTe5LXR9c5E+zAGvi+fV9kvDrNJ/+Po+zbs01vKLBdyeHZZeIUyoZHuW248K9KLi3MUZgGZx+YpeVS1u6cpn3ZMLG4RfeGosQne52HuiawNXC7Mk4MZrGPK4LjDnsZUJLt1Um+5aMX+cSDt+PtiXEWdvyq4cIRcIEgOBezW6xyI9kHPYO4luHvsjipQIMuDe+++zk+t3GE9ESOKy221aTX7SBupPzwPzrPzp099szlvOcHTnO5H/H5Rw+xXtTRNcPayxOIzJDg0WiNKW3AW/RlHrlng9/9XUvNGyBnE7734PN86toxJl+3xdYfBkzlPb77N8/y63//FdgVgRAK0wrxBimDrYDtyz4mUTQ9Qf8ehRh56MJhG5phWUMIy+C6xb7KUFvN8DeGuIbCDQqMkmwemyF6RrO92SAMcmoXSppPbBHftwgIhLUEvW10YEjzkjAcM3hSYO4KiS5tk853GIcbzL9fUcyllJNw+4MXeea5u8iiqtpVCsGLv9Xk3Q9FXI87LARrfNu3PcfTv3aIfq/G3ekKOHhx2CJcc4znS8Y7PBrLPsl+j3J/yXbmo7YcRVMgM4fqV1W1sWix0VJENwqEg2i9YLTbUMpq4S66FnJLUZfVovP5FSavjpGmJLlvBloeJhD42RghDF4OradjslcI4qkO2W5FfLCNmWtUFgMMfq+kXFHYkULWS6QTyLREjDPcCMJzm2zvq1FqwaisM9QeQUsR9CR2XTA6WCPqg+lEyMRUtcmpw50QrN+u4PaMtku5MWyjALVQEtUL4l4Eturp9lYyGudzth/sVO1SDtJpn9pWTrie4V3dwASC/gM7GM974Av8jYy8pRnt8GheKHBSYDzFuRtziKSksd5DFXW8ZlhVB+eScTTG2yrIn89pTGiy+yytNwxwZz3ufvACR+66zh49Bk/y5JkZxl7Ata/OsHWpSfuVGekhMM7RueBIJ7l5nQkwJ33WNyapDcc4CfG8wsmqPjZYK3D1gu4rWjReLqjNF3CgYOnOIeNxjisd7oQhiAvK3CA3ExpZAf0cMT2FK0pEXoK1LH7E8Z7v+zJf29zLee8YwiTc8/FTOGPozRyiyEIG+zyMKMgnPRpXPeTSJA4PNS5QWJyxyEKSzVfNVrIEtzTFYL/CWx9V2Tuej/MU1tdQOvzT68g4xy3McPShU5z7bJ088rEHIKsbovMJ1gwQnlflYKU5sj8ifWSe7PU+vikRWhDZBAE884Wj/K3veBSD5fnoIOuAy1JEkkGWI3WKcAKUIl/yyPY0aDw9AqUQpaNxuiBOFSJOq2ydLEdsdZGFQAp3sxpZ4hZmMM2A8a5aVdNrfYgCiDwGt7WwYYd6zdE404NWA6QgOLdJ3nTUrmd4fQf9YdXi06xBECAsGCXwrqaYjsNZSbgN0bbFdEJEZjk4fYP153bgBQVyegarFdqvWuxwVQ6QtA6hLCLLYL2Ha9QQe3Zwou74Z3+tjpEBggKnFEWZ4Ddr0FSYdoANJSqq7gXEKc6USKWhP2bcVGQLHcL16rMstIcTwJU1tFHorQy9VbXe+L0Su5zgPA9GFl1YPL/EuxpC5MO4qGx9phopZVHieX0SIRHSwmhUsaOBj0oD1FgDjsIWDIYp49l2pfqa7bB8viDYD0KD0RqrQu56peFPXjiOHk9XZJR1nH7hGq9MGnxwlKCMIJcJDx3bSe2A5UK/y8mWIAgNRQ2wUFuNGR6eYH7k89C3VBkp73j3/ew7OMeDrzpI3+QoIelEIZ/53Ake++QJzEbC0sI0u3dM/SfnLx0qXF5tvV44vw7AuVPXsbWA/u0hC18d4wJFf2MediRINYfkPALwhMGRcWscv4VbuIVb+G8Pt67c3yTYVdsksX1quvj6Y1l+jqsbf4m/+17Dz/7Jm7F+DbRg4b7L/OXdx7nwm01+5394mKLTJr69Se3RVUyngd7oo5zF5gKJJc4t2YEGZnrMzh1z7Jg8zrNNH+9UUSkl+kPmnxgx3F+DtiBql8TnFWLkkHWDCEuUKPnQL91JVPPY4V/kB37yWT77R3t46uJunJaQ5pXvXEpsGCCtjxDw5XQfNpeUgBIGmVW1pMaDG60AXyteUb+MfaTOhzd2kQ9ctQPWiPCWEpbvaKH+TNDKYTLK2ROMeaYsq5zdPMfvGkzu2PnwBj/6LZ/nzPFdfIHbERZuf/3tfHr3DTyVc+FHD9F6QTD92XVEVuIcVbOCUuhRimwUKKBWT3hq7V6cjFgrJ3j0j4/yw/c9zWq9xlfq9/DgM8s0XhFze3uFNX+CqJ7zUPs6n9c7MaFAbxpEbik2NDIUuAs+vXqbcrIBmxkHdmpOnEyJ8RlMh0QTGi81vHH/aZ4ftPF/z+HNgM1LKk2d4OyTO2k2c+K7NPWPx9Xfu15WQ3/dcFhdR+0MybOCzWsBdluRXdHIXokdw7BeZ/i1iHwscYsaFXg46xBG8mu/9Cr27uvxD/6nLzA3UfAPvutBNo7sxHQkrc8MMFseLnQ4o5mbuc6P3PUib59Z40bsk8YhMyevMbXYI31dQL4VsHpsEvftltXeJHX7EuV0iO1WoYoqNShRsLMYcZ0R19faRPEWtRc9pAkRUcpGUSM2HrlViGYH77TEG43wYkepfawEOjXG15uEnmTtwUWEBG9osZM15j9/HVePyOMN1FaGrTcYvLmF18oRTziiJ2NIC0wzIMhz3HYJE4a5e7ZZf6KDt5rhJgSejCimQ569f4bv+dcHyTohP/baL1F0Pa6JOvVTK7z4c5OIHTOMTA01AukETnts3tNCtAukdYTthLCwJINWVVv+cp/e0SbOafqHJXJlgGsq4oWQvKWhyCBSNK4qTDfDXh+gmjdbvPKCYNbgPZyw+UKTIhTUJMjSgRWURuJvG5JXDAmed9BsIYzDKBjPS+qXR8hrW+itAeXOKZwMqoptZys1RFHSWXPcOAiV0s0hHKjYEa2DTApUw6LuTOkOGnSOD3FzBvdGS6chWT/fZj3ykLUqFHeQRTz1e3dQSoULACyq4RNuF9QvxGSzdSwgDWQTGlEX2LxGPqHJFryqdUZDNunROZkSrowoWiG0a6jE4L3QxVvt0ZoWENfIQguZxQUSCoXRBmsloFCpZq03S316zHAzIskU102Na+ttmiomEgUkJcUNgQoLPE/BWDEeebCnwBVwdW2Cf/OBt4KSpLMChKP91BbBxpjxoQn8UY94r2D0QJ32SQ97Lkdcd8i3WpJFTbha4C56ZEGAbUrMgsXmHfwtiWg2cFs9jLQ4VdkNj19e5E1Lp7nSP4hICxQZcV/T2t1lo5hHJgXlfEC4bRA6QNQcNlc4A3NTa2xdb0CpsF6dMhKYCIrQo2x6mOkmXjehbNco2yFFS1N4mqnnLyOCAFuD2TckDDcEV9dm6L5KYUNBa9eI2jMjkvM+RH5FiIQB9SeGlIcCrl2Y4J8PXsFT2S7M/R4vXtlJz4QECurzGW5XjFv1saVCDQ0uy3G+Rz4t8FdTWs+nJDNt+vcrvGFBvJFhaj5OK6yoiBSUpJhQeKMM0weKqo5XpD5FwyKR4ARlqNHLA8RNW4etaRgniFodEPgxTD62Cu0G0vOxi7MVeVIPUMgqS0VYrAzwNgqclITCYkKJns+YONDjVLeN1BnGiKqxpxZAWVRZG1qBr7DGIZOsCs/Nh5SRT6kkphkRvLGJeSIjEwGkGYEIK6KpNkH+nCbZV8Ofg3Ba4EIPud7DpjEEIdF6wuihOZoXU0zLJ9y25HXwSks63UR4orIROQfCIcMAjMNNGlTDILTD7TCol3J0UX2PzStizIUBo6aHaERI63D9IRgDUiGTAplphHFoL+C73vgLmKzA7Jkim9So2ZywkxMFlmHmo53AL/8lf/+7x/zY9btZHrSxg5R/8IP/hu/+qbvxL64gLKipNj/5k9/KIMv437/YQJ37NCqxCFNZeYsR9O+SDG9mRW0mqzzFo7zpLY/QCEIahPzqd70LYwzf/9+9j8Ew4cj+Wd7+rnv/wqz1C5/5IudubPAb7/0OlKpsPPPzba5tDFBS8LqHDgFQn2xzfdpS7C2RX3CE2zlJVzCnJsiN49efehXc77iwNcuDe+rfkDnw0SfP4GnFa+478A053i3cwi3cwi38p3GLSPkmQaRiLDmK7OuPXej9Hsps0+5AONsnvlwtnieulPxm7ZWoRUejmcAK+IlBJgXOz3GewtQlKDBa0s1jdt27zXi+zo+/7Yf53asXEWnGjjePKIaC5ac061/1GW8HOCOQUZXMb7Sgo1L8jiFIMk4+4ZHbISvBJB/6+P2sPZdVTUAluGGMnGhXjTvjGDwf5xwTckw9LvHWC3SvRHQ0Litw7YhT2QLSGe7pXOG8aVKOQWYGhiP8RkJOxo4/3cTfFARexsBZdu1eRhS7AYEYJ7ziwRMc/9pRwidj7EOCU5/fi/M1whpGaUwuBS4UtHsDGqckfmgpC1lZg4xD5Bm+K/hSMceV7SY3igavqV/hg8mDiENjxlbz+T86jLs3p/Vwyky7x7dMDJjQjnq9oObnBIyZe09K7+CI8YU682aO7GGJOmjgSzX0tYSwHpFEkklRR402IPYoXQ0vzZAJzJc9dhwXrIs2Rx7Y5MQXZ2GxxL6cYWqaqQsd7sxLlsKzPJ8voooE6Slm784ZfGWOzTN11KzB+Q5nJIPflzQvWKQKuGPXDT5vDyEKRzZbp12zZKsOBGSpZmMjAmHpjX2sF5DsaKF7Cd4LBbJhcEJSJAGDs4v8YjnNrz5n0V8JENKH+Q5vfOcpvuW+a/z2v3oV7ZkeyuRkHvyzv/ZGerdP0Nq6TLg+hMkG2qUkGwqSApHkeMOyyszpSN72K8/wlNnLwHoEl2JcqCq5fKDxgpJyraq6tVKgLkqCjQzJmHAjrRRGwxLnB7hGiNoUyMJi4xRh26www3y7RzdvIFzB/E9c5tmru5HFmPBdmsmpMZ2iZC13+LZEpDkqyRHKJz4kiZKC333qPvQ1jcoFqrCMYg85qKGcQ42A0hF0HROXSkQB8WXBXWwgl1Ke9JsILA2b4VZ9kp0lrS9lDO9oUYSScCNHjz3KjmLng6sUShF8NcWOc8q8ktpbDLve0mVz3uPqmsYKMG2B3HKUDZ/xrjqtMzkqGVflGoXEGY2KDU5qtg9rOoMcbyiqytMowHkC5Qw2yRC+RowNZajQ2jDrjRAXNPrigMZMQGwCsttgc39Isi2ZfM4jJyJ7McRkCbOPFvQP1ejdacAqwODPZTgtEStVyKz1q8pXmTmccIgChAHjCwSO8V0zmFal8piqDfkrr/sKV5Zn+crZg8i0JBgNyBoR+AnDAxJvrsaBhW22r4I0lqKuKRsQjiPypmB4rIMQgAFlBOlmwMsfm2ZydpOl2/u4uRy7omhlY/zhmHwtQOUK6STSScQ9ObNTKeNRyChvYDyHLB3x7hplXTG9liAbLWorKWtvngFS/FWQVoLvEe0aMBmMWHt1B/c4eCrCc47aUkz5g5ad/jVe+rmjiNQgooCyXatq2qXixV+6l2N/+wZlU6MSj7GZpQwM24MO8WHNeL+l/kyCSh1COMoJD3qCRHn4o0Z1E3GgthNEJ0IgsR6EVy1Yiw0EeQ1UbhCJw9U0ydGA4HQGic+FwzuIfq5A/COPxnMZwzsizPGAdHUS3RaVTafhY9uOjbc2MA2P/GjM2lM18rs1TguK+Zwvjfcx/NMGZ15aQIZgVYCrS+gOIS8AgatFuLHBP7NC+qaD2CUIv7CCjSDbO4kZN0inNd56SD5RQwswSRcxU8MlBWJzG7ndQ7zxtop/lqBHVftc40JKPu1Rf3YNAdj+EFGPoNVAjROcBedspVQUVA15UY2y7mG1QG8WGEt13poqz6aWDcifcFy6bYb6rMPhk7cldkeIf/ZmMm2Wkc74+KnCKYEYjas2ISnIJ32Ozq/wHX/5Ga490uRP/8F+iKIqeDfPsd1N8gML0Ciob+a4uQlc6Fe/6/o2JAmiLKg9cRnZmSONBEUd0glHuq+JXh5hlqZxnkQUFhmXmMkaoykPKzJ4MoRWgf+FFAYxZrKNMA4pAkjySl2aKHRbIpWDAzXyp8cIdTOQVQmsZ1Gjonqer3BX1il2z6EsdE9MUr+tRyYVI+coxb2cOPkiG+MasnAopShrHs3d03gnVgB46MgCvqeZ9jT/9Fsf4ekf/zB5ECJLVzX07dI0rgjiXRWT8uNf+XkWJ1f43KUX+bkjf4/JuTahp8mswyqBNxVy3ztu44tXr9G52Ob2fQus9Af8m9NfZuZAn/v+5Hme/86fA+C2ffNsd2N27ppi1031SmeyQbG4QWexQFiJyBViYxrn6rz8kkWfaPIb197Gdx/6xlQhP/bUGX7mZz4CAv71v/ge7jq68xty3Fu4hVu4hVv4j+MWkfJNgvW8ybCUFO7P39IvbV5jT9imsBJ5V4Zbn0QUhueem+QF9oKDQzNXcKsONcwBhwx8nFKYTkihq/aMucYK760/zqXzDf7wVz/FyQs7aB8bkX73JBKL/F8nycOMJAiJuhnccBQTmvGxaQ42rrBerzPf6DNQAvoZRQHPy3mCzU2wZUWeLM5XfnmANK3qTZ3lzvo6xx/bizQKEUVVs0AAJgz47I2jCG2ZiQY8+8dzOCuQPjhTUI5Dhsfb3D5zmaXbuhghiWRBbfIm0eQc1vep7R+iLyTkJwSPjg4xuh1a/SHd5Ta7Hlqi7tZhmDPxsRR5Q1BmFpdXAbtuuoWzDlnPafuWj/aOcqi+xu3hMrNskEyVCC/GbqXsciNSmpyemuENU2e4bGa4p3aFjBU+88Td5HPwd489hnr9Ln7j2v0Er1tBNQ1ZTyMvOrQR2FDgh0Pmj2RsnmuiX1AUuUQmljMn5tn4TAsWg+pv9PqC1nsT3r15nt7LHh9+VPCD7x3xiV/2IC+Y++MuzofR/SE7d25zrr/AqmhSCxNcaQiuptC0PPSqS7RdQnE1xhUCQwMXS2yocYHCNRXjmYCf/YNXs9mrkwea1qk+sl9ZF7AGh8YGAnFZMw488pFjIU0pewYbtXnld6wSKEs8oeh8ICf8qkHN5ZhM0Xhupcp0QJC0BOGNHOscw0EADqxnEL5CtUOWZrrcXwisk3zqp/ai2gkEPkFdk+Z1XA1EXmKbIVMnBWprgNcdgpJksxH1ocUVJc45zNI00myAcfgjgwsc5boiLB3ejGQpGHK6KRh/d5PFaIOpzoj7377Ky8/uI8pisqTAG6W0z0s297b4zp3H+dTx2wm2C6QMMDuXcBKizapSPIwVg8RHFY6yJlFjQ/3Zda7NTXD35BXe97d+ma9+4QAfufxqmmfA73WpLZck8xGqsNTP95HOR07mhEWGl8L+vRl3vnWNj71/B3EXSDIu/b4H7xSYtsA5MDUfnMQB2kmklNSvQylznOvi5RNoz+GuK2Rewy0s4OQI8HFWYmsamZWIyQ4gcHWDaQbIFEaXGvh1Q7g5gEKT7p4mDZvYS4qwk5O3NTgPv+vodyNqIiGbEPhRgREW5RVEr+wTCEXvI7OV/S+3mMgj3dPEqsqBYX3ItWPqZIJ/OWFw1xRRt2Tv3DrzjT7RUsFndt9N0fIILw+Qm30G99QY7W6hRMHWiqK2s0+8OUV3qSJOKWBqA6Zfjuke8RHzhtpjoBKLDQJEIPGDgsG0x/CIjxOCu77nOl9r7CdZiNCZI9OK6XaM7xtsLWdkwQUSMUiQha7yJDo16Bmcro6hE0V6TCK6KfakJllpUeMa97475rxs450recUbTyOV4p595/mn7/texvsDJk52kUpSBAI/F1hPIJzjUz9/DJTA1H0G5TwuLFAuItgUjPYJTN3gr42Qkx7p7pBSexR1iRgGKBKoR/ipo4gN1hPUlkvQHtmuFrKZ0Ty+TFE0EdMtzA5H/7smaX60S+10zoW1aag5/IFHuCWI9gww3TooD7wSa0oobRWOPO/jPBiOJTMvbMA9c+A5RN3wtfEOtp/rEOYWe36ELEGUlfJSRCHO0yS3tZErjvZmxiArmH7yMmWuGLxmDn8jxqEoa5rxfVNYJfB7OfVVR8V9BJBX2TI6ETjv5g202UAMxwgcQa9ABD4uM9h6SHFsCVEa/BMjpK8o6z7lVIQaZ/g3Rri4QLgGohmS1wX5XIgVJa3rFpkasvUa5CmtgUAnkNUdOgEvE2BN5TcNPLxx1SAnSwMzU9V9S1iQkjzWuFIwkk36907QebaLLcuq4ScHuWooFwPyRoB3ZgCehO1hZbERAsqSxtkBycPTDPZX7Ue1iwnh5TFSKhJ3UyEzzsGTDOY0shFgrEfWMdiaprG6jZtboGyFqEEK3QFiOEJMtEFpavsH1F7h6H6+jpyLcEriBkPUQCLjEpcniEJjywI1SvC3xtgJg9eJydZC0qaGvqQ7/H7+9/f/HkVh8ccWYS0YQ9o3iBKQcNfC7r8wE7VaLQYX18AZRJLTf+0czdOWNz5wGIDL23WE32LjZ0Z87ws/BY0aP/3Bv8m99x3kO995L489c5YPfOZZCgm75ye4fd8C0/Uauw5sUgtzRBBTFAWe53Hu2iZCwTvefCe7liYB2HFwjqUvbBC/PaP4q5KdJ+d45Xv380J8hs//6QtEfQkDeOjN3xjC4+S5lSq03MHJl5dvESm3cAu3cAv/P8AtIuWbBP3CY1CEZPbPH7t36id49MbfYVSG9B5vI6ZbCCe4shqwd3GdQJVs1Gu0iiHUfYi5KQuHXAjKpsBKePPiK/nEZz7I2T9s0L3yBEy06L+xxssvzgKCeVaZO7/NeM88xd4GwURGdk5B3edwa43bZ3ImGPMn8W3gG4qFJkzWcNc1MvKrwdrXYAyiLHHjhGJCsLhrzCd/5SgLD43RtWYVpDvOwdN4G0Ou9pcQWL74u4fprbeRvsUVIE2BTA1FTdP4ri7fds8lMheyMmjwxaduI9k7hd8roe2zVbRxm45sq+TRU3dAJlnKRkjjuHF6mdlXlYxViggc2ZQmPDMEY3FhgIm8mw0FjiQN+fGFZ8mt5HOjPbzu9af5P7LXMlHTePWQthjxvYtP8b4z9xApw33edX7r+v0E1y3Hj9/J7J+t8Nu/9TCvefd95N413McF6kpBuahwDYVwoK1kcSnh8S9O45xi6imJzBRGKq59JoSrXcR0jfWN2yk3L3J/cok33H8Nex984vPHOHs+p9er4bICdSWlmK+TpiFf/sIcRRIjjjawDR/dTZGJofGanUzecZo/+ZV7MEKDsOQH6/DyGBMo1DiHRDC+6HH+qRZ2vo2dsIRXB5Q1SbqjSbQ+RoxKTNQk6pYobaEDtGLkUFB6kuuXm8wdgYfTU6w960PpofpAYijbPvGRObIpiT8qcL06m2vzjLMIpIDIQ3ia0Ouy1++jpKVFzqfNYcqGwzY9OkuC6WGTs+k2QglsK8TLS/TmCDeMMYFk612LRB9eBa0QvRGqzKv3thHRXjDUHhlwYaLJVCMhQZNqn6NPJaQjjy07z4M/dJUrZyZ551u+zOcee5ByNkAGEnG3obnh+LMTD+GuDlDbGgKLsxLGlfoLwOEISoFMDOmERkSGxvWSjXslS9FF/uhjD/OcO0A2Z6gXMf7vr2CdoOVVDVMyB20E3rKi+IJP8ZLPmb7P+qUAozX5zhBvZUiepARrFhk6hBGIPENmPiIt0GEMuUa3S8bHBPJSG3lxk1yXMLnnZi4QEHg4JyE22EBhhIccm2pxdmGEN4yqdpu6Q/VihPYoE0ntSo/4rjbhKsgVhSwNapAihIDFiJXX1MGT2FiiOpbSKK54TUJnCdOSsuHjDQyFl6MyW4XPqooQklYgl7fQWyPacYavIlauTvGR5kOsZR1MICl2RPjXe0Q3xkQXBHnk4+eC7jM+/miIN5EgFjRTp0uMJxDCVbvlmSUeKOSkINwy+LEkbUhWRZv69Ig4rdPxE9pHUprLkh4eWQahyGC9pN+OqvNeCEpPIGsKnQts7CiaCn/sMA3FxMkM2dJwFcyRHHnNh7qlvOJxYMcyu+68wclikRPP7qR3qsmnPv4g8T0hCkvR9vG2S4LEMZj18KwkDixia45gMIQwAOFwvof1JVlNkCmPmSeuIjZK/LmAZNojy+rV9f+FIbWNIeQlZv8cRdvDKUHakhTNjJmNTSYWBqgf9Fj7bVXZUEYWPTLoGzkMS169eR17m+Z5fYTMaFanfILddaK1HK+XYXyBi3xUUjA1GDLaIal1LbatcKHF+ZK01CS5ojwfkTVLenfltL+UVPkt9ToOB1lG/YUBpS8pZIDq5WS5xkWC4EyPxktdZKNB6M2Rd3xQgsZLG7jroyo7pFFHBB7GgR45yjZoaxBTbWwosVkfoz283gikRBQ5JtKARkmHkJp8tg6eAilwmwkyyatzuzQkB9o4TwIelgw9zgFZEdO+xhsVeJllFAqoqeoahMMlGXJziJmdqELUi7Iie3ol3tiQOUuvCOiaEO2FGAUiszhjEDi8x1Pye1owyBBK0ztWY/Mn5gmXExZ/6xIuDMiWmkTHLxDN7yXb3aFxOkFbUb03Db9q0ROi+n19TX05w2qBmSwIajn5zhrRdobQCkYpqjeqCniUgvVNBpuCfn8SuWKRVCS1aNZRuaN+bhv/xhAXBmhrSBcbbL+yThjlNLycsQnIuyEiyfmZH38f9tomcleTbKKOysDbUow3B+jUIgx86o8f50e/7dWk5Rq/8dxHOf5TTVze4Mjfu4YtDaan8G+Myf7wZT7pTTM41ebJ1QYHLl6tCCoFf+PDH+M1a0dZ/dIaaxtDVA5zS23e/orbAPC1ppXVOX18CWHgqwfP8Lqdx+htjxgWBX/0iWc4emiBw/vmaHRqNJtNXt2/jZlHWvzI33grG1mXye02mzPXObe2CQKO7J37hsyAlza6cHP+27979htyzFu4hVv4bxOiclv+F8d/Da/hPzduESnfJLg6aNHPA+JSfv2xB+fu4Z7pT/C27/h5XH+NfLEKwAsWMr5/95MEyvD+paOMEsO+A7Nc7MU4ZxG9AWpNEe+YBSfQZpIfescfUzv9UT72e18lNob0iiK8t0BqS6YChHPUGROvG2RpiMo689ygaVPuqF9BlA64k3KpSbljmtDCd/2NMR/7zZzUNBBeRaSAw0lBvNunNdlna63OwUaXUjqEsyhrIDUUEx7BVQ2lZf0F8NMxTLQRpcXqCJmW+IngSWZYGAhmgjG/88T9qMcncG0wkQUpuLHRpgw8XDOCSwFzTycw9sDBehGTFjHt6YQ7/udlPvqZB9l1bgDW4nwPmVWDbWksZy/N8gNLLwKOX714D/3jbb7nXU/yZLKHvmixvRywkXn89Tc9zVYZ8L+eewM30gbN0yXl2CEGKd/9i8/jhV/CnXoX8qzEdRUyAJHnVb0kkJg1BmtNXFFQNhuE6yNkbqpaaimQNQ0TEVxS5M7SNSFD6zHWU5w7aVm5BC7egqZPtlCnPiGxpUK6konGgDf9k1OMr3h8/A8OM5H4fPz3jmDWFUrm4DvUloXVbaTvkCjksFIzEBfYtGC0r05zbUi+s40UGunnJFMhybTE1gRT7Zgd9U02P9zAxQopJb/96w/w9n9+gsFLApdbnCcQUiEpsX8DvBMZ+mKNVlbiRRDVU3Khql3JbobLU3YdLrFoDvgjPvP7BzDtGmaxiX82pnc5pbvoKKbrCONIZ32al8aIRr0K6NSOuc+tVZk3RU42P4dar6q9y2ZAI87Z1eqjXi14dmuWbwue4blf24Oo+9gbdUJVMtlLmD+8wo5OzKc++wAiK9HjkniiiV3zUFYwvr3F9CdHGJOib3SRTmEXpnCyaqFyYdVIbltgp0NsYydCjnn5C3Os75rEvAKOfPsyl3+6A87DKkfeDKrKVKUwwtKaGjL7igGx9FgsuriupHtqGrSHbFS14s2Gwe9XdaOlEITdGAT4awlF6VGklq03LzCxnFDbGGHrPqXhZvizQy4Bl6mIz0QQ75ygfu1atYve8HCprEKh5zOSgzVq2yA2Rox3NihD8NMcGeaovkNR/e7hekn40nWEdWx++wKmJQCH6UoSEzAn+wxGk5iGT9ZoYyNVNRllFiUkykJ6cAo/lHiJBRcTLzY5ubKbIgJTr3JXxgdblFMByWKd+rJFSIURAbkscU7TuphX1wMl0L0UsgI5P4FIPZxn0f0hohTkvibDY/fkJt0vW7JMoZVDBRmM2hA45Iaiv7sFFxT1Uwp5JxS+JGiATsB6FmskZZZSNiL2NS9zLjxA45kuKh4x+1fXufPIFT7+j96ImX+Rjec7pEOfIgiQhUNs9VHrBn9L43er1horJaEDUZTMLCe4NEWO0ip/ygmsL3HNGs3UI53Q2CginzVMt0fIJ5YZTdewzQBlJXgaE2nylo+KS4QUWJ1RP9nHDEcMnjCwx0cEIcKAGvlEf+KQYx/ncvzEsOR3eckdpqYNRRkwuZhTJhrrSpz2sc0QhURllnYzxUSacj5CCAvasauxzTumn+f94nWYlsTdPk92eZvwxrhq/ZFV65w3LGk1S8I8Zn1qP5tv34sVjs7jNxDKQ0pFsDqm0YgQ2zHe+Q2Mq9qLxDiGThvadRpnY0xLUtvu4bYyioWIesunsVBw4D05z39qGhsEldoOEFmB2dNC5AXejQFK+Qg/wFqLnWygkhwvtpUFyULRUMiNAmUFwvMINhMECutJnBaU7YBkQhOuJ5hQQiKQaY6IajBOQUrMdIRKS2Y7AzrBiMONVR63t3Hpn+5h6kNrtL/cwwpL961TlB1FcB54YR1P1RCPTJLt9knvb2MaS9hIU042aL48YuJjl7jxA0eQz5qK7FqPkeOKSBEWwn6Bv5ZiI838cIiaNBTHmrhHM2RmwfNwgYdLM9jYqhrvZtsknTZ+d4sgyZFzGpFoytBjcHsb3c/RY4tr1xnf0cZ6HuMx2NSDWFVEae5xaZBC1KB2NaM328QKh8gtwy7UV0qcL9lKSra2R/TEB/mXzw6RmQ/1kuXvbLHw/i5MWHRvjO1o/tUnn6FVSlpnBN17dzPx1DWskpSewtcKXygCTzPbqvH2Vx5jslX7+lz1sw/+Tb7niQ+Ag2de3uJVi5ZLnRKnNQPTpzQVmyGsIww0d4n9vOPQAwA80z3B45vPs+vO3Zxb3mZxusXepelvyAz4lRcvoaLKXjYz2/qGHPMWbuEWbuEW/tOQ/8/fcgv/LWB7HNGPI4bZX3xLPRWw7+AieB7JvCKd0sjFhNfX13lNtMUbHroCQtCZ7/C7L/8LRC3A+R7BVok3cvh9gyks9XaNH/rH7+GOVx0m60C8x/J9D3+ZH3noMRq7QUxNsvPYCH+HprgBWV3Tn25xZrSDwmqu5JNQFBhbZZPEDdha6eKSlLCVkxxuV1kEANMTKE/y0N+4zHf87ZMsb9fQ1zdR613MpGPqwQE33rVA8yK0LgmU1biiwIQKG2istZBk+CNHeW03v/nlV/ELX34TN25M0jgy5G1veI49i6vIuOT68gzLr2rglQHtiwY9tojSIa1lOFon8gumgjE7Wn2avZxyug7tVlUP7arFaFFXnBxPMzaaUekxGNQp1zJ21/tMBgPceMz6jRoNB3u8lNuDIZe6M8zWh0yvJ9j5jNveuMLBV26wcHePhx44SbGjQXJwAhuFGEAWFpVbXj4XYEuHdTn5lMbWA1zkI6YbiMkQpUt++L2voSg0TZHywcEeHhvOEM9pzt2YwG1V7QnF3hnswhReItj/3SPChzTyDvi9p1/LYxwDP2D4whqNnQ3MdA2z2MYsdRjtD8g2DXpcLQyQsmprqNXQCThS3LCLOreCEBJ1oE5yoAMIkijixtYEN55uUZ53lZWrLhjdL/jwxu3U3wPpnU1sU5BPawh8YhuRv63aoZ1ujXFCMhp6lAuT5HsmcU1L0Cz4ju87yUHtOP+lA/zZbxxDWvAvbSJW1mEYM5oJ6e+P6B+okU945O0AW2QIY1Daxx/D4HUtNr5vhu3XQz7pV2TOqGD7XIuvfvQwTy/vphw1+MIf3cXMxZjD4ytVQGQpGCvN0YPrdAcas1WgcoswluiFEqxDjUswjovvCol3RvQfnMW16qjCInOD9QUidzQGhqBb3mx5EVgR8Na/fJ0feutxdu1e4aeOPsEvf/hTXPg7x7jy1w5RNBTxrhbxjjaUDluGbJ+aY2Q6vOE7r7Iwl7C4MKzae6QA38cGATqx6NRS1jXOUzjn2LFzRM3LEImj/WxM+0JGudgifu0U1nPI3hjv9CbuqqrCNFt1zESDYlbjtMYphYwibKBxRpN4HoWvMe2IeM8EvQenKLXGoTBUYdImEFhfU0w4wqsx/kqMXLd4yuBpy9TckOlRyq6jy5hIVQ1CWqEHJa4w1I8vU3/iMpQG166THJyimAoxTR81GJNNCUwkCNYTZv7sMno7o3vfJNmUj9fN8cYG2w6p7SkR1qIGBSopCbs5xgd1ZY3gzDpyZAlXUry1MXIwRsYVeTJ4IqB21DIo63zxuTu5Ld5GxgoSTb5LVMqI3QXCafSyxdShMd+lDKo6613ROpGEVnvI5vQk268AqwqUsOii5OmfOkZ0bRtzBUxR4kKLdRabZ+jlbaKvrZEsRmQzPtaUlKFEjyzBaozcGqLGOc7T2NDH1nxs3UcIgTICPRaUuyeJj81BVqK6Gf5miizArwkIq2sLhUHmFj0wSKern+HqYGqY58dVu4ySKFli905R3LsHe3AHL/3bHTz7a1OoIsViqeuSHUe7zO/NiPd2SPZPYCJFPl1j2PIov2IYJBr7fIyQFuVZ9je3mHo5x0lFd6HN2NYZ3jePrUVV0Ox0G7dnnj3fGvOO77/Md/33F/EGBRNfWmP2Y5cZvHmKwWtnsZ7GBpUaJ16QIAX3vWmT13/ndZQowPdw7QhZ9/BHFnEjh60e3tUR9X0F3lzJvh8ZsPRwCM0aMs5RgwRpHMIJ9MYYvTVClQ4hJaZWw9Y8MI54RlE0Fem0Bl+hsxIroOIQJVbBcFcNbFVPPzrSYeOeacxEg8HDS9WOXm+Aq4W4KMBpienUWWvMIzWkfcFot8AEiuFrpxFTbeTsDHq7yloSJQilaa6VHPiZUwQrCUy2wavyj6QOMPNtVC1i5785gXdihTKPGd7WwE7Xca06edvDSKpQ+FGGTTU2kZjrDjtZqyZJ53BZDkpVrXe+h+gOUZtj9LVtTJqS7Wkj7Bibp2AsIq8ICIfDGoNMHTt/cZml/+UKk59anKDNMwABAABJREFUx9+CMK7stEgJxqI3xrjS4hB86uOnqga+zEGkaTUjJsOH0V0fPVSIoUY9LHFFgdwSqG7OD/30d5D8OxLNOqwUDHc3wVja5+GfPfIIty/OMrFt+a433Mn3vfuhvzBT3T43jzLVZ6ihA8rSUHSgmHAYCbcdmAcg0BqXFrQa4defW9cRQkjCVojeH3HsoW+c/cYKgVPgBJy7sv4NO+4t3MIt3MIt/MdxS5HyTQDnHMVQUaYephT/3tfzc1tIFOmDMTbTRJcSVktJzwYshSliepIjr7mdRj1ApjmmFmIbdRrLJQCDfgw7QQjB5voAb6Ngcjllv15HSMfcuEtpfdxeGN+pcI8q3AuW3Gmeemk3K7tCunGDqcV1bDdBZIb4gMcr3rrMqWea3NiUBFeHUJTgHKIREY4Fq1c0xR11znxmEXmzecSZPg9/7xXOfvwu6ucHgEMKCfUawslqoPM1OIcuBfa8xhyVlIkmnIq5b9c5npSz3PXgVS7/zBxsS8q9FqcEtZUcr5/hfIlTCq0sm3mIk44nevvwpcbLcqxUUDpEVi14/WHKSn+SnznzemoUXOhNcVhcZ0aNaDYTMBPkI8n7P/I69v2VT3AincYPCnbW+6xeneYvfdeLPPfpPcTuNFI49t21xifLY5RzmtbXSpSVFA2NtzXiK7+7B+0s0hV4WzGUJSQZWS6QbZ/Qt9x+cBFdSB4P9vKG0UWSSHLHXz3F8V/ex1RpQWsCGSC6jrBb8tULx1Ajx8gF1FcKxnMtxPIlzFpJqSMCLINds2y9po5w4M8b8qiNCzXOVKGzQlnA0ZwcI4RApw5x7gZzj3RZlXeBFQTbjmJSs1LMsWBW0EHJt/ztU3zQ24/dDBgrzexsH3e6ZLwnItj2aL5gMPeUZLsDMFuUL7fYWBHoscFZwegNi/yt13+SIw8MKSR86BO3ofICkgw1SlB+Tqk140kNfvXZaGtJ0fEhgtDUKH3wUkN82Mc0BTtH69grKSQVGSKbAfbiJH7kg4Pu7Q2uBXOsDn2Uc0zfvUW2S3K5bDNhDEUrQBmB9DTUNNGqIOjB5HbMxt11hk2oxY5kJiAaUREcSMItQ7ieUltx5C3N4F6I5nN2vKXLC2cPsXlykWfkUV5z8Aq7aikrRQOfvAoBFeCfWiF4dZNObcjyjRniackrv32Fq5+ZYiOooz4eI6dSrrxC4R6rVnKirxH5GKSoqobTECVygtgiLBR7JjFeHWUc/pVt5MaYvBkh6g3MZIiJFDvCbXo7KvsBhcFKARJsrJCRxuVjhHM4LcCCCTVeLig6jqypsE3J+G5HWezCYXF+hCljhIT+sEF723LpiUnELMjCIaXAKEGwkeCtD/G8EHlxSH93E9UJGczWkMYSXo/BOKSVqFGOHhs6JweU0wH5TISXSFRSkO8QbPmTRFdTtBOo4maOkvbI7t2DvzGkcy2jbIXkc3WOvvUS7d0pV8cTrL7Qxn97ye9/9fXEx9ssHlhDxwLTgKk9AyajhPOrswgDsy/EbN9ZZ/30DNkuhd4cM+g4Ck+TljVGURNGJfhNnI24+nt1tHXIKKF7o0428kh3KILradWeohX5bLv6XwMNRTEfocYGGRe4QCBTU9XeWoOdaWEDgd4YkS12qF8rwZa4WsiOvzwmKGPK4yn5AHYf2uRSbwYTeAitKD2BbSgcJf6NEabdRA5ThLG4vMCGCuVnmDrYoaA+A3O2z/lHJynmFcG8YuEzPQ4d3OJKzbK+DKIZ4JRfBZWes/jHE2S7wG6kyJHBCI/R83V+5XceQDUFrV5CNuHhj8A1I9QowXXHuCRn5Vmfrx2fZXZfi/bjF/G7OQ5H4ws9go0UthOIY1QIE9d7+HXJ23/kOkoaLq3s5up1r1Iaaomre7hAQ72G1Ird33aBaMayvlrnxnYLp6qQVKyrSBUD2UIbrzuurEbOoTyFTQtsq6rUTqYVlJbJT16AVhMpRdVQZw22HiGVoAgF5A6ViypAuzD4RUgZgJdTXU8EqI0xdtcUXtMyUgHMC9RjBcGyoPO5bnVfkgp/zWHqIAtw1iCaNWSryc7fuAJJwsbbPMxiHTFOaD+5jSgqJYUsDGJ7TPtzF+ndsUCwXZLsjFDrMeO2JigsdtJSjgWkBSqX2EBCnqE9DyEExpTg+6gsJ9zIkF5APhNS7i1prpT4FxJmHltHZgbbVpSywB+CPOMIriVVbtRLI8TrHP6mqaxReQmepHYtI90T4FxJSaWGRUk6DQ/PU1zudVCluJn7VNLs5FWl9bTFTUbc+/qj/ODwLL/2gYuYAGQiyA7NEC73qRnwlWa0FWNyw8aNQWU9/L9Ba8Ujdxzk0to2b779IMM0A6+qOLd1R5oVRKGPywtatQDsn/ut7+kcQoqUk7FCR6vUQv8bNgcudRqs9/sIA0lc/D8/4RZu4Ra+eeFu/vsvjf8aXsN/ZtwiUr4J4JxDhBnaK3DO/ntf32oLXM3nvu1T/Mh3HufME3NIUbLDS3l0OINQmo/89ld4z3//JpzWEGf4gy1MIwIBW+P068dau9FDWGDd41P/+BiRN2TjkwU6yMi2MqIgYpx5iMDHNwPscIYTJ/Yhc8HU3g3EUyVOGpL9cNeDA/76L7/EP/re1+HP+pRXi+ozN4yRIudzv3UP8V2T1J7aRnoaZwzW11y4PIfXLfC3E0BgBgnlvgW80iGAZMrHv57hjKF+WeO9eZOtUYt986t85sIhti9PcnF2gV2TQ/Jhm4mZbXSWI/oKnMUqBZ4kqzkiXTIYB6xcbzH/qWW+7Z+/yAf/lzvJ6j71uBrgikjitjVfvnSA1klFw7e87W3XOeanfHwrqmwVQvLCtd386B9+K9t3tpDKsTKeIN/2qR03FFshy6emmdk15rN/eg8ctgQTBXnDR4YKZ1wVgmpBlSXWF+QNQS3PEFt99Egwuq9Fa71HPEqhNKyvNfnYS3dxzxsvsaPe5QWtQElMI0CWBeH1Eb6fMG5GOOnw1xIMJbUXU6RrYmWfwkQQOqy0YDVYh78wQR4rRJxVvn3nEHGGq4fMzo9YazbQJSg/YvCiQhwxlHVNdK1k6oEByUjieWPyesAXfnGOJdln4RdHzNdGfPHwQfrdJupGju2OSW5rYXNNdNFx9dIUOi3YHDeQwwwHTH7iGp+9MM/CgZjJpYKz42na44tgLfd99yZbF2pcOB1QtiVOAw6+9cFjfOQ3H6fYMYFcT/BWe1hr6Xy0R7GvTTMek09axqsetlOn9BXRMIdLCemBlKAlOPjedV74wCEmr+aMT4eIBCamUmYWLflsHX9jjBaC/j6P6IxFlhZTOlQhyXYa6qegtjLGdBoIa2me7ZEvNihbPv6gQOfQeDKnWLL8sw++jnlX0jc+njZc6TXpjwKi9YJgI2a4s4kuHAGaYHrA3qXLbD8dcO5Uh9/4lXsR1rFjvImOS9gE8SGBzdIqHyK9uTtuHc+/vBPZ8JBdR9SzOK1wyqtUWgZMzUN6Chs4ut8j0C/5aGEhB51lkEvAodKKSMk7gsI3mIkaMimxfrVbagKQDQc9gfUc4z0Kb7OkeSVhcdBjbWS5vqcOCorCkYUaG0mkqWx/rV1D/E7G6skO+YEFvH6JlzpUVjJxISWb9BjOe4z2RthQUluD/gN1VJZSv1Ti9y34oPMSeXvO5tEWRJbF5wqCXk4x36myMJyjnAzwJZS7LEkkyHY38JY8SqHoFTWyGcWejS6nOk1yDZdPTqAWHH4q+LGDXyaUJX8yuofl4QFkAo4SoRzEMbWnr2I7PmZKM9znY6VFzSQEY4MbKpio6nMpoIxBFA6v67C+QuQGKz3cbIPm+TFeIrAyoDAWmRnEYIwps8q6Zh1ye1Atelt1yEru2X+ZN737Cr/z0bs4rWq0DmZMTKfMv/pJXqEu83/9w1dWdpTC3MyEUjgk0kjKyYhyZwc7SvDGApHk1YI4VuSvHhB8YMQ7v/0q55/yMSc8uJYwnJ+gthLz0niKLR0QbQ6x3Yx0sY7eFRBcD4j3TRIlGcWUh9zjkKLgk88eovGDGQt/auhsJxx4zWWeXr4DU6QoT5FO+5h5j+KKT7GtWLnaxB+NcRbwfVrPd78+yInSEp5fR/g+pdF89v17aDbGrFzrIIQgbyiyCYWzgvq0R7ivzfCiJRwV1GdLTn9+EbNdVgv3RoRRFrcWVmG5DY1QChFXSgs8RbFUwyUQdg3Cg1xniIl2RT45qhp6LXGeQhiwwiElOAlhZtAFyK0S226DzKt7uxPQqsiy5VNNfuPx1zFMIxo3oPPPLyNEZclyAsZHa5i6xrS96nxuNRFSINst3HDM9CcvsPKTd2Fmm8SxIrx0rrJLiSqzKbo4oN4dU0x2SHe1cZN1VKIR3Qz/D7ZwEwpxIoWWw8QO52lcM0LkFhU0KitYs4b0PWwjwtsc4H8oYTQ/RXK3RzEfMXF8UJGdykcA4WqMmZlACUE2kbH0vmuMDwWgmsi0QOQFohmh4hynBJSmui8pR/1mQFxgPkgUOcZFQDiTMRoGRELg9S1gubDxDhZ2rePMe/AzQV4J3YgDwZxQDEYJL19ZI5GW6R3t/+DM9QvvfRtZUdIIA54/v0z7gqtOs7rgxIVVHji2C+0pbF4yMd34+vOe2H6cr21+ldIeoQgKmm3vP3j8/zd48Pa9fO7UcfSoYLg1+oYd9xZu4RZu4Rb+47hFpHyToHCO3FEl+v9/IR9ZZFzw19/2DIthyq7XX2Y7VVx1U/Sve7g4piwzjHFYT0LgIU3OuF35bePBn1cq/4Pf/FF+/F+8n1w5LpxYRPZikDnFIOf0H82hzRblQY30PKzShOsl7nSIKh2DLYFKKulvUSp6Duxijs1TbswnzEQhwjpIYqjnMADvqqBc7KBPXwNnMWGbF//tLGExhGEMgEWg+jGi0QBnibqWwhbEnZIwFtwzc41wV0mcalZe3ENjCGU/4KGffZyv/cLt/MtXfR7zOx4//d+9ETHKEYHG+aCso7SC+ilH55e61OcL0u0A7dWqtp5eNayUcwI10tTOSYItsKHlpQsTvPbO6wRNVw3AJcjccMf+G5yuCSb8MadX51jIHY9/cB9OCv7g77+eqVaNtUgyvWuE3xrT3dHE3vCwStFbmiSY0Ew9f43wEUd8l6R90asGcS1ovtxHThYs7ZlGkvMTdz+Kvsfw4V9/kPXRHGIocJ4mP7qE9MC/0CVNYtT+CZwTbD0SQeCY+VBJ7VoCSuACTVlXUNMUDYvAEdsICoNIM4gCnBBwk7xZurzJ9koDG3mU+yZozfaQBrwx2NAx3K7jTxpm/2bGpWen2TZtJs5u8PLP72G1PSa7nNC8vIkNPRyC8AkPcd7CsEQWILSHGpeYdg3SgqLvuHa6wT99992stWaJwiog00nB071DvPHeVS6d1xQdgcyrxYoncsJhAVLQO9ZkupsgDMhwEn8YsiX20Th4BbcucN0BQoM2kqAHzXObRNGQi+/dialX69xsELBneUxzZ8Enzu7EKol0CrRGlbZa3CDI5+tYTyBTS/MrK4jSoYWCjS7OGKhr7FSTXApkZhFOY3pjhh+P2NoXkt2l+f3VfZT/pKTh+mijoXSVOicAs3OaqaVzzO4Z49c0X/yfdyOWRGU3cAryBOEc8qJB19Mq6DbPK0IFkHmBzAzOGeQ4ZrBvFpGX6L7DBori0DRCK0w7RDYN4dihMos1GcGSI7kS4aRCbee4po8YC1wIVgqkscjE4AJJGQmEVTgydNeh+z7NMwWtzTHetOH2+zZZC33y3CNo5uzyu6h1wWCjAQ52vnYZ6RvGa5rxuMZoRlUWuylN0SsJVmOCZbDtkO5RgUHgrRZsvmIKUytJF0L8bcNoh09W0zxw/xl2t7c5/uwe3KctVklsw0PkFr9XQFug7kgohabWLXj60X3I1pC+qxPcVlA0tsm1RMUWsaUJI4MXOF4dbSOF5YlzMWuDEiEkooQiUoTdEo3ABT5YcNIRyhHTz+e4ZIwdOkTk4cUxS/s3uefBq/zpH9xBa0Ky5jcp6h7BRox/aQvlFDRaOF9gAoHyQTiH2o5xoc/o/p1Ep9eRtSY0I2xRcN8bLjG3NGbhcMzzQ03WVmzbBltnm3z4t9+COrGGisfoDbD1CBPVqkZqJ8l3TKJMiScUYmYS26njpMCEmlyGyKOa6T1P8+KX9gMCJyDXktpfUSxObZF8MMRMtBHjmOTQVNUAtqRwMkJnCYEds3OmS2kUGzMdgsWSUmvSSLMuphgv+GSvX6Dz8hrGONbf6uOvdZj74x7KJLhhCbmFogDfx2kwEwGikMhBCuMYgoAzF5bQIvm6zazwwPjgd0u+/8e/SqOZ8v7feDXDoo5Xjhn5EaamUf0YawqErT4zLk5Jd/mQt2idyxClw4rKspbVBHJYEHShthkzXAop5hvIG0P8xOCNoNSWYDMnXM/o74mQSlL6ErKS8Y4Qb1ighq7awJAw2FunecMgjCR5qoYnPZSwN2ubBYKbpFvfYXyHl3nkuzt4cYZyAquq70l2NWmdNbgQgk1FMVXD66a4LEdICUriRhLV20LcMYcQHioHQg/OO2RZzQXOWBQKoxXlVBNvXFYqDOMq+06So/MCV5aIEvxNQTalSOcD8ukAf7myoNk9PsPb20Q3MlwB4UqKkILW8SH5hEH3UsxUC2Es3laKCwOklJUV11hkIPjkF0+ynRlmwyHX3QT1ZopfFlVo8g2FLj2SYo1fO/NK+ncqxKalvSFxhcPetYdGKLAB3HVsJ888f4U4K/+D89bvfPVZnr96g7//9tfRXx/RPD/CSYHbVUdL8fW/Sxh5lLn5+vMONQ+zlW1y1Uzgz/VxofkPHv//DXbvnMQ3UGpFvDn+hh33Fm7hFv7bw0336H9x/NfwGv5z4xaR8k2CMtOUORT239dRbfuWjjVkiY8SKdY5fuLZ9zDXGNCb8lHpgMZUB+Ms6UIdighNSDAocUh0/ufDxKFjSwwORXSe6KKTknJ+ArfdhSJGDx1SgdoscKMRrtfGSzP8ixJhHWL5pod6qkPtmuLsWLCRN3BxTvO0Jcy3iJObTQFdjWtLKDU2oKqEtA5vY4Szuhoak0opM3V4nm3h4Yp/RyI51v76PkwjYvqz23zx1BHmdvVZXW0RXspRrRA5dLw8XmTXW1Zp+QVuoUCbvGpHMA4RF5hrHq5fwlmHazeoLxSMeiFi7FCRxN2U5ZYzIdQz5CgibYMIB3zo6Xu4urrExrKGZogQ4K2n7F7fZuGOMcG44NS1/aT7RsgzAS6QOCEwgBrH6N9LSc80UPsyTGLIZiKGO6BohkxcnmbiYp+7X3ORoYko4xQSEJ7ATYDF4r82QSuLxOHtzhl+usZDr3iOky8sVpL8yEOaylJixz2U9vmxtzxJoAr++Gv3YE+OIc9wd04zLgwUFlHctGYsZ4h2CEGASAqEENh2BGnOcFMijQEZIDfH5L7F0xZTc+RLirzU1Cf7yKOS2rakXLXMvyGlZ2Le9Y4v84Hv2I0bWIo5jTs6g0pBnJM4a3D1EGkdcawRmIrACWrEWeUPV42S3rEWUze6OK1Yn5sijR4i2n8JuWtMuVonnBlx4SufZ7BrGllYmsdXkUmJ8DQyyVDdGJqaYiyRpUEAalxgJjVkhl67wWojIPycZDbMcA1Jc3HExOEuy0mTzr4c6SUYX+ALye4T2wyLJiL1aVw39A5X+Qymo/H6DhdohK+xFuIdPq2VFJmV5BMBFGOSmYDGRp+yJohSUC9lyNISbqaV1SRUCE8SjUuKms/ZrxxEbyoGz/aR2seVDtIM0gxbFNXCQ1pEWSCSHBt61fsFOOug7iHjjEJIimkfvV6gegVOK3RksXlKEYW0zsekXgeEokxr9EYhoUwRCMItKDyBnTK4oaZUBv/sdSa6Ib037f16Q0Y5bQguCNxLQ8KRJXjE46qZ5tRGncU7rjNwEZHOiV9qsu/ABqOVaXCOtedm8DsJ5TlFMe2RLFW3scZL26jrI1QpQQhc3ae2UuDGYzrdgt7dbaJNaF/uE8/69O6ulFhz0YC6l3HwW9c4++g8tlYRX9ZXqMwgG4b+tkfeVNhC4wU5Sa+J8wV1lTNcCJgXfUb3GVZPTJG0FXfOXOUjgz3UVUZ+tokUIIxFp5Lbd1xm+WsTuPkZ4qmc2tku0XyT+T0prASkKwLZHWM8n7u/Z43dRzbYc/eAH7vvWe5YWuNX3/dqHj+1D5xFDTOsr4jnA6QDkBgnKBW4ugeeIluqU9+sznerJK5W5w++fC/zx7c4dWOWxhsG9K5p1n9VMhx2MC2FaEZVSK0TUPeRqcFpgRwkZDtrhKc3EYVF1CKk0FgHOna0n/IQuaDRTlDCQLNGtqOOvzoiXYfzz+6iWHUoXyASiSsLSBTesKSMPNxQMJGmvGnqZcalx+9uvo6kHlKPHFszAcP+ZJWT4zSisHixY/ZzKdIpyj0+unS4TMDGzR15Z4nvmES0fernQJQS1+2havDOv3MWEQr+9F9O0F+rE42Am8qK4Tii3swppaZdT+momPJrOXK9ixWC4NQqstlABgEOmPrqGNMbgNFQlrib13wdmyrvaGzQA8PoSKuyve1qU0qQcYl/PSa8mV4b9ArKtoeTDjsc4q8YlKiavbCWMlAMd4Y01gYgFbUVgxAC5ynKnbOoYYyMMxyWbNrDhpJkxsfpSVRXILZHiHEKYcTgjXtoX6OqMffBzytFJ0IgamHVwpWm4GDiI6fYfOteinadcGUMuqSxaBivBIjeOjZoo9YSzHwH227hnENd26BsR+iSKo/E82jfFpNugt/LkWdCvLHATLcR/RG1rqTYLBGZRZQWl+aIQEBRorvjqpAm8KoQWy3xjKvyYkJJHkkGecGHPvkciXG0949ZFw0KV4WWOxwTx4eUHZ9f+xffytXQIz9cwA6QH6+aJUotOB3k/L3HP4ldHZLnJWcurvHY8XPcfXCJif9b4OyZ1U36ScraYMTWjT5qcwBS8PYfeiV3H9lRvV1FiS1KgvDPx+xAdBBuD+OyS+oSCvGNs+Cc2diiaGhcYknk/x/o6W/hFm7hFv4rwC0i5ZsE8romSCTlA3+R/3POsXj3GiIes/5ynTt2DXEIjrSXOXd+F3PyBj1rSYYpFkgnNLVLA9R2jrIZeIprz14hecPtfPKTL/LimWWalyvpMlrj2h777/G48LGA8o4QeZtEDnLsUwEehrFW1DYSwCFyDydKbBTQulLy87/1ZvKTIYFZ4y1veZn4EY/GE6t8/p/uBaVxvk8w2iBtdjAY5L+zkfRHuFajqr8VkqlOg22XwVaV12HzjKJdQ1hBcptHeSni8qiGyKDl0srD7iwTbszXjt/GrxwpcUDqWcKixOkIlOQNr3mZM/Umy49O4mxJ3l/iy79eR2qBUz7Ub+48NX2aZ3xGDw7whglzH3AkSyFPBnvZEV1FuGq30NvOOPv7bd7ZvMwXXjxEkGl+4H/8HH/8174FORpj6xHz+2fYeOYconT4NzK4WnDg7hFf3BOiegGNF7cRmSFSOQ+EF3i0eQf9wK+C/qSgyKGwGVfumuKx3mECWbBjtsvGYsLlzy4hhCC4Pqw849bhMkNYFHTCmHdOXUIIeDbYw8W5Cf7Nn/5j/uonPkZ+KiZYTZBWgAOHQCiJExqhNU5JXDIG5Xh5fS9NvY4UVUBsf7tGsVvgdPWPTGFTDas+nPCINg21uT4Hf/wav959Da1gGa8W4CdgX7iBUAbxxhbWdbD4BHGfMlcIWYIChMAqSbqzzsYbZln0+0xMJ0RTORfvneWF9jPIF6b5jjue4eTuHewv1zj9b+8gUookBGEqI6nDoYYpQijo54QP1RiOgDitpPKBZjQV8MqZRV78sxfw6puYXQUyqLHnrqtYzxJLARpq+29QXJpEDGJmHxiQfarNaCEAB3rN4bRH96Ed1K8Oaa077EyLQpeYiRr65Cb9uyYpWgo1MrR/8wb6qCJ4lST/SEnpS+JWir1kUNYSSkUcRpS+wmoonWZjcwJkgW1EWC2QaQFpjvA9MBZ5OECtWwrXgCzHFTlC6YpwMYbaVIaaihmnc1g/JJ8TSAfCZrz1+0/ypS/tQz9m2V5sYYXGKUHRCJBS4yT4qYIh5LnASyRWuarl5GaYpC4dSgvimYCikDQuZ3iq5EpzmrLrozLBXfoGa60Gc/6Ak+sH2XvsKpfYjQ0FKxemkblFTEniSYkaOlDg93NUUmIaPslSyGC/x9y9WxSfDFHrgmjdEgwtOi4Jtyz4lRrk/KM74ch1Lv/yLM4PqoWcV50XJvII1Ijgkk+xX+JtOaZv20bvgKtXpqk3M0opCL0c7u6TXlrEdAo2dkpeipfAOUYZiKTAZRmEEd50ShloikiiV0fIcUn7Rszid20gZgQXP69AKDznePTZQ/zg/iGFU9x55ApWWr7l/jOc+EIH241xWjK6ZxERCQqnMXWBUT6KaYoywkU+7Us5IvRxqgrSpshZH2niJzVRtoX3vSXbV0OSVYMpY6RXYls1sqZCZtUiXZaG8NwGAEY7/I0EAOspRFagpcSllmJKYYuCK6sTHF/fheiU5E1oXElwZyVp7uPqDrvVJ51XlFMBtZUBM+c3GL1uDlk4ZhZTDtU2GRkfuZ0zDANmBhlCgViUyKT6HKw9MoW/JZk6kWJrDlEK5DWHy4EgrOwsnsYfKYLTQ2jVEUGA0xoThgy6DYJZw1jUcJ5AJgXhKlCWfPi3HkYEjmTJ4+6pMcMTmvzZEcLG1bXWVxTTLbyihMBHxAXCVgQc1iH8gHAzA1e1CiEFpXbIOMe2gipLRQhcoFBJyXh/h2B5RPPiiP6BOsJYlBPIrWqzQDgBnodKHCozeFsJnpDgK4rAMtzRwJ9oES0HcOYysiyxSiEcZC1BczuAEMxcB7nRJ297NC6mFK1alQEiJdu3tZh6/Eb1+xlb5W8hbqpcBNOfvsjo/t0IJAd/eMDcnWPOvX+GjeN13MYQJSV2a0DZDJFrXdxkB4TBaVk1opmCqQfGrF2aQk45as85ykBjPYlnmzgLrgllTeJ1c8qGQq/3wFpEq2qqc7iqirsdoS51KTxFeZP47A+HLK72OfLakt78GsuXa4T1AjcCpCJ8aR1adS6vRExHIXHdZ+bRgqTtEDW/ugukgvLj1xkc7yKnm7ia4jc/8gSPPHSIH3zHn4fO/t23vYaV3pA7d8zzlT94GuGqWevshVUuXd1k3+4ZtKcIIg+p/rwA4MsbL/PR5adxpSF3MaNi4xs2A37Pa+/m/GNXWL/eo8i/cUqXW7iFW7iFW/iP4xaR8k2CYF0icose/kUi5RPLv8ttb7+Oexu8791HqO0SXP5cgyfVMYKdKWe/Ms+svU42SDDOMl6QGBHRLA3BtUFFXCjBmTM3+MxnT2Cdo6w5ZB/wNMKCDurQKMiCkGw/6C8Ymv2YB3at8rnlwzhX3dSj6SZJXBBc3caNDKooUE+OsVLzxUMHOPfkDhrtIXP1bUSrgdi+ytH7LM9cahLNRJjuzRpJJGKUgAOJ5YGfytkXfZXHfuJebCkRTjDR3iTRNSY+P6RxPab75nnILelhw/SX+8SLNZ773+6ksTbisw8cRmjHtJdAmVb2IgG77YDXHz3Pz+i3IJKM4WofuFnNqx1W+OAsjZe3kTMz8CVF86sbUKujBj5lJMmCJiIfVcqGRHHFHOaXfnEP2ZFpmiJj30wGo2pBRKi5dmmTvC6JBimz+xLGCy1G9jYefOUTnP2fpmkfX8daeOVPneM1D17hid8/Sn9upnqfNjfxVEJmE/SM5cOn7sEB/7D3SZbvX+f8U5PIgGon27PYREAUUTpHr92mv1xDSuidAb0d8+gffBWxNKZ/2LB03hJs8/9h7z/DJLvu8170t9basXJ17unJCQMMMAOQAEECBEAwAcxBogIpi5KsI8vXkk1blq/k/EjHR9mmLFtWoiWRYhKDRDFLBAkQIJGBwQAYTI6dQ3XFndda58NugZePr6+PTD9+bN55P6K7Nmq6qnat9V/v+3sBAeMCMSoZJbpSsh0qd0u6T1fwujHUatjAQe6q4lYzZACZKxhnxMqwhWlKnFZGMJaSj6owUeFYv0Wn0yR7s2HiSz3UMENmZRuIXvJQmwXKkbzpZ17gS394lCh1kDonq7ukO2usv3qcdNrlzrXjPL4+QzpyaI3FPP+1MSY2RojONHsb6zz5H64BGyILizIS5/Zd2K9eLgcqAvRME7RldvwsAxolJLXQGKNxlnqcOHYRRjH+Tkvj+i5X1mu445pCSWpuTDR0CPYaNBacsqJZuDF5rQoG/G6CFBUKT5LMBNRXI8TFJVxrUDur5QatLtAhuOsabymj8VJFvs2wdtsE/Y7BO3MZ1bLktyvsZQc/F4hhgQ4koqkZ29uhl1UQ2RASH1ELAYsoNFRcLh+cZDcbiPMJVimMLpBZOaQEwczNPV71ttN86MFplpfaMDIYKdCVkLmbNG/Yfo6P/dENWCPwDdAGFVhSxwEE1gGVWnIpUcaix0OiW3bQvamCMwC/I7BW4KwpbMXFMQkqL8gzD6+nUVaiz7nc+ZozuMKQ7QiYEJq8ocBYZG7RviBuSaQHTgbSCJIdLSpLCZln6NxWZc/4Jj+88xtsvKPGRz96N6PtAf4wRWaa6E5JMD3AfS5k/eIMow84JWRTZIiBRPmlu00Ll7TrU5GSyrJFWKhcn1Ft5tSujblytsFbGueY8Ec8mBzETBq8SoFt5+wJ1hlon6l/OOCrn58jjifxNgxXTk8gM8hnPCqXBML3qLRSnnjkOoLFmB2vXWDt+QnqUzmt3jx2scshd5H5IkQqzalLrbIuvl1FtCRUPQI/YiB8pC2jCYUjcVMHMwBhBGK9hxjGFJMthO/yztuf4YpxeEbM0KpDcX1I8BMFg40Q92mNiTSmIzFbG0y24goCiTvSWCXL+vGioBgL8NJykNu/LSM4FvNbD78JpzKklg7x+xnxbR7Zy13kgsFeqDK4u46VoFJD4tZJr8TUntrETA6ptQwzJmGUG9K6W7bO5GWETQ0z3LNDwuWCtTe2SbZLUiGxN6UEn8rZvH4Knca0Hlsr4ccVibcSlTEfIG8GiChArA5YfEQyfqvAjiibdqRGRCnad9CDgsKvwSY895Vr6S+G0F6Btbx0lDQr6IaP0xli6j7FXAtvWMOubYI1WFW25WhlS6YKpWuv/dULrL/9GrQSuLHB6RXoho/2JcHKCFFoWi8UxLOV8vvVccgDgdtNyrgNElUYTMVH+5C3XaIZB5lZRuMKryMR7RrW83GjssVFVEO8+VWohFjfJTpQVuNqz8VLLM4LCzir/XJw4jggDWhd8kccp4z+aQvGUnt6HnPdLtZPu0xdD0XuYVUdm/UplEQWBfL0EjZJsOt9xFgD63oUrRqjesHpP/VRaYq+t8rwYED1qQFuJJBWkIUC6mBsji1y0n1VtAUv0pCWMSKhFCL0ISowFlRm8HsF2pPUYoPWMROHv8aDp24l0lVkMkJYg9g7jTPfQUQpxBlmQjL9p12iI+Nk4+WAVEB54PHoJvmlNfzpOod2TuFXXY4cmPu2ddVkvcZkvWSfnHz8fOmOtJaLZ9Y4f6kcpPjtKlR8qq3qi4975eS15EazHn8BSZ/rWv9jqo8BxoKQeuCxog2jKPsfdt2ruqqr+t9QV2Gz/9N0dZDyXSK3oxG5xUbffhKxNPoqhZFoBMOwwm//y1vQ53IO/OYC33v0MR7q7+LsX9SwUmCMQVw7gAMJm4Tc3pojWh9y9BUHOXhwhnvvuQErBY8+8mUql8EqiVaKnr+LeD84GznNX+sz2uFisIRVixMWqF4ECFJdIJstbDHA3BlivzhCKIW8By6dmsURgmSzDv4QE/rYxOPk05OEOsX0LEIpqHrYwMNqjahVMEbTnFtnFGtUFmM7BVJrpm0XZkZkGwY1sIwf6xPvrSA7FcTiBYI8x+3H6KrGe2wP1hW4o7hsoDEWrCbOJCeHY4StEclAYFwBUY4lQPpglSlrG8caKG2xgUe+rY68MsBxJM2TIZWf2CD6pguFRnRdhC9RgU/1XB8nKDgQDBF5jg3LxVahJLqhUUPDzu0rPDLZYvl8nxv7Cf6BDH3CQUmXr//FPtqzMU47x17cqsodjcg9iPKc1+48wf0r17KrusGXH7qWC2v7cCoJ4UYXKwVBqEgxJXcmhjSv8Pe/+TZ8FTGY8qgkEUfecANnuqcYftRFGmhcLDeXrVsUYvcGFzbmuPZVVzj5kevITyUI20cN8vLU2wsxQ0kc+4jAIhuWuBOgZgtsDNGvFMQhRJUKV1anOHu6ij+dkdxRZ/muAPfRlMlPriC1wmYuFokIFL5tMwzGKWoufhRRjUHNpzRPDVnb06CnfaQqEEIz9lhCZ08Pczxk+Zd2sOzugCsdure2cKOyvjI5nFC7X4AR6IqHlQIhBGvH6yXcdKsmM1geYdOM3Fj0WI1ovIF3Ica5EnHmdEBtfpJbf3CBejOjLUbE7hjWdfBrGscKxIEIIS2Tz/e5Ug3KquDUxQRlZbBIEurLZXVq/UJG3lRkdZfR0W3IpwcYzydthChjkAd3wGyGv1dwzXvOceGny4GNFlVEXbHSaZZNPIAI/NLNFYbQ6UKe4QynufHoFZ68sL88ua55iM0EG5Y1nWe+up1N22JR1hhN5EytWLSrCFoZweQIQZuV75lh9isSYwW9TRdVtWR+CfQV2dZ3Z2jRyhC8sEFwZoO0NUsy09z6jJUAbAXYFojY4A000liq8zkXnSle94ZjANyy5wKf+J3bsJOABRMKDAJCEAmoUYbfyYgnA4w0FJMujBu6nsew6rO62kR1U/QOj6Lmoj2FLYbIkUbGAeQGaw3akxB4pc9fWwQWZ5ST4jLco6hfLpuM9BM+/uER9UBz8WKTo69eoCosQXCS++pHqVz0Gb91xBObO5kLNtlT3SBJfYwLhQ/LYhx7UxmHaUtVOqvqkOUKN1L0dlbhpGLf0Yuc/WKV5/5yluV2ndk7+hgka0WVwStC5GqA2kzRLYG/khGHtoziKJCjjOB8Bx045AdmcBbKmBrKol3B0VuWueWehAee3UfFDBiqEH2toL5u+Jl3fYwkVbz/B+4mM82ywcaakucCWGkoWgFuL0O4Lk5iMYEL1lD9podx2+iKYvx8FyezFG1DWvXJAxdrFCpQuH1DURPUmiMqBxP8QQBP9jAXUs5mhq/cuIdzk7M0NgRRoCkChZtZvOE66mSE71aZenhE/zWK7PUpKrawrBne5uEvZqQNB7+TYWOBaXrYekg2FWIaIdUzV6i0NEfftAKepN6M6K41oVFFhyWo1a6sIuICrEJHAulD6wcKNn/PQ3ou8TXbwBriHT7BeszgTp+xR3T5/il0yTwqu7pJg3JI7p7vIHONFqbkhAHKA4RFB6IEz1YC0EVZx9yoIrXFzXXJKxECrEFISTpdAc9hOG1xIk39ck7cygCF2T6BHBZ4yxm67dJ4epN4yqGymiIs+Cs5pJr0UBsZWdyFTjkwFgKKohzYSIkVFtFsYIfDF5tnBAJ5/Dydky4PfaaNPNRn9qdGnP7KLqyqU3noHHJ6HPoj2Ogh4gyMpai3KaZ9nHNDQKKPZ4xerVBiRKUIwfWQRuKc7uCd3wDloKVHccscej3HiQqSbVVql3pgQoQU6KqPlAK0RfsSZ6LG6VMbhM/PcH31LN3HdhPP+ahc4E000P0E5TqIjQH5VBV0wWC3wokthS9wipJ5tRlatM5JyIiE5l/++L3/P9deG0s9LAaE4LpDc9z+sv0ArCx0MQi6G0N27JksPztWEOWWgzWY8i4xNried977a7zxLTfy43/vdX/DVd+363c++Q3m5zvIRJMN0v/2A67qqq7qqq7qO5b8b//KVf3vIJFuweiyb4fNBuQc8Je4MbyEtwH2nIbC0nhmE7A4iYEwgCBACMObjjzDgU+dY/LJC2xYjfE9Pvwbn+fssUu8/e0v5cTzl2k9liISg8zLUyu7tk71SkqwkKISi5OAkA6imfPen3oAMg1pRlGvYiouYmD45z/2Xv740t/mJ798A0m6H2Mt7SNreAeGWM/BOAJnfAy0ROi85DcUGrKMfKzAuM4WIyXnxokZzMpRdOaWJ2iuwkqLzhV2uoZpNxCxRkYKmdnS8j1MMJ5k9t5Nxr65SutkjJPo0qZtLMKUToR7xxeY+RcxVkl237QLkhQbJdikPL1klGEm66QNycbNIJzS2WCFwN/IcOoSKWLIMuQrYuy/ShFphogz8lrIl7+4B1uvgVSQ5GRVRbatQdFqcun5bdy4foK5a5Z429xJ7m6dRo1XiXa2ED6cDidAZWVTRBRDpYo7UUHaVSZON3jv7m9yzV/O03mkQfDkOv0DAdoT5BXBO/9OjhA5QiTIKMM9vULnSkDxUYG/nrP2+j38/c/ex2vbVV666zRqmOP1NF5f0326yiFvnV/9yT/jfYceITVDejt8Ct+iQwmeh0wL1CBF9HP8oaaykqNGAuPCMHbojUL0ooO3mWMWHGg5jF+O2fnrfdxKgftqiF7ikrUKuLLJ5K4u19y8wTMP7kX0MtyNEcItMIkFo6mc7tE42+Ur89cRDSy67zL+pOaos0jatMSzDqOWJdlZo2gorAvaalJPYLVGNwOKmQYmBExBNx0Hr6x+tYGPgHLQUFic5U2KJ9ZYpYYVAplLpkhp5Yp+HhJGGl1TxLsa2CpMvnOFW19xmle84hRu37D96wMa5zIa8xnCSqTromo17ChHFAa/lxNsFGS1gGhXA9PXJKcVIrW4A037SkZtWnPvNU9zYHylZJwYi3AlOlNsPBpikrQc/Chdbq6xiCRFxBn+0pCb3nQBG3jlwDL0se0G1ncRWY50JKvHZwjXLFNnU3TgoCwEazlnPjvJs5+eYuyrLoUjyX1J7koCFaNyjUpKx4jNLRRgPV22yBiL09E0nhphlMA4gAQrLE5d03NaVE2MX8uRhSE602JaxkzLmMnZIX1RRyUW4wp0ILEeBPMx1SsDqhcGhFcG+J2EdDwgmhnHOe+zTpU/uvhKPtu7mc2XNdF1iffcJbzj55HPSKqfiHHmY6S2aN/HBh7FWAU9VkXXXdKJChiDinMcK0jnJKPdkpVmwAunZ7l8fIzJPUP+1de+lykLO7RmttElb2hOdGY5eXkbT67soqfDEnQag1CQVT10RWIrAlMAxrI8qhMMC9zcY7M/jvEF+cDH7pggkSE7Gn3mi3EWizYXeuP0vAa9SY9kLIdRjryoMcZipUXklqLmoes+ulUBKUm2t8lnWqg4R2z02eVG/Pq5W/Bcw+qwypW1gCunxrmhPc/eap9rxrq0JjYBgcoNMgeUokhj1OUO/tpW1M9xMMoihykyymAoMVmIHOXIjT4mH8FGl9qfLjP2mwMaTyaIwuKvW2a+2eHQTZeY3bnOnu2reLeGUHFIRh4fefZGvpbuw7YMTiqImzBqO/Sm5hi8YQ+m5hOMJLUop/LRiOqvjgg6KePfXKf9VBevrxF+gFAu2gPRqELFJdkeks41iAYepx5tceb4GOtLHiJKUZ0RFBrZjREW1HoP5/Iq/vMjxhbW2T+3hgoNNs9x1gcUQcbavWNs3ugjegPslQWs58BEC4nEbt0/hO9glUXmpZNH5aKEtrrgrUY4gwxnUGAtiFG01URjEXFeDuCNRfg+IsshNzQvG6wvMZ6ktiYINkEHAqucMoYzyhDGsu0zF9nxBydpPrtO7XIKgYc0YD2X0c6QdHarRksKRLVSOtKkxCqF8D1EvV46eQK/jCcJ8a3DRWPKFNMZD3dXwfaJNWzoYSYaICWNXQYhCmjWMbUK7vGLNB84Vz7OGGqjjPFhBu0WhSvJKw7DOQ//RASjGKIY0YkJLw4JrgyQUlG5OERc2sB4Au1LdN1F+wpRs1hPsOZLnNU+j/3uLMf/SZ3KqU2abkpdld/T7JhEbxujaIekUyFmrEa4nDPz8IjKpQhdk+QNybntNWg2oOozO9Hk/b/4Gb7/9b/GiWev/H9de2XdAWIYI9KMV9y2nzQt3U+33XMDh1++n/2Hv+Vm+ebaWb64eJyFOMaTPh/5o+P0Bgkf/ZNHvuM14JED26g0fEzTJbvKSLmqq7qqq/qfoquOlO8CWWvJq4ICi5HfHu3ZFeTc321SkSnFeIjbyaDis/SHii988yWsrrmYLAYpca0isR5mQhJs5AhXgsnZXOmjVAm8651ew1+OyGoFDHPcliS8WxGvBeh6jcIryJyC8KtDXKWZbWfo6VYZnVAS0S+wrsMdd1yD4yiWn00Q2ePsmV7lTdc8ySAPeFAfwEQK4Sn8mmHUAylkeZJtC47sXefE5nbMagvQ7J38eRrz92P05xC+B6nmhsYl8lqNs8UMUbOJHWqqx5bLhbISvPsf3cN/XL2f/st34D80oPpMDJXyNJFRAo5i9bLLx8/s54HH9rC9d47+8/OgFKQp1lQQW2PI3Lf4o5zW2IBgs8CGIdLA7gPznIvqMBohE8tMHrPn2ozHtu+FBUVhXf7sgy9BWlMOpKSA9RhPGVRiSRcdhl2XW996gj/beCmVIoJC4mSaXiLZ5nT5hjgAG93SAuBIxhlRUxlc/h4+/ntP0VrvYhJBPh5glU/3pikq4YhLF65gnXGU1shRjiAnWC1IJxx0HYJNg81SfuMPC/avlfA/mRvEIEHNr3NscQd3myWeS+vIdkBhCryR5pb/c4lL/9cBelhIysW3thKUQmQWkQqKzOfKD0zhLFnG/nJEUO8TrkmcMENmCm8xxVnNKd7aovrLERQx7/2xpwlblo/+55eCW8cMI4KiIFUuxhb4i5r2Vwas31VF5YYihHV/CtV08SKNf3qF1Te1kcOx0nGQWVwl0E8ptAMyzkoIqskIfIM2CrNtDCPGyqFbqrFVByfx0JGH3DaNiQMGN1TZ+8YlHhw7QHewyER9QLpgKBAULZdF2aI6OWRHZQNPaEY7Kiy9UKH1zCK6GkAYbrXnaKTvYI0p5x4IwmVDYROo5+Q3OchMoJSl2orZv2+RpT+fopYmGK3LiE7gQgJiNUYYg/Fd0m0+lVVbni5vOQqcYcGTcq5s6FISRuWARVi7VYmcYl1FOG4ZdiolfDK3DLIqp74xjY1zpnceIxK7ieM2SkPsKlRXo0OFNAZnaBC+RWAQ7SZ0CiaOR5gGrO8ur4nW9HcZNkWN6eOWPdkyertk4/E6GMtq0aCtMpYrVZyVIVktKNuV0vLzUT07QlhLKnNEIMnGQ5KGi/Vdqucibhxe5o4fOcNCY4I/ffQ2RAp5YFG5xRlkLL91FmE1c58ZoqseaqOPv95HTzfJpkpHkm74uHmOOwIHTXyNRkwpBq5L362Sb7rYiuHf/NXruXRximyPRt0ypLPaAK3oZHXi7R6O0GRCYgB3LSGdCxAGrJXgCoJFgxjkuD1L7LmMdvts6Apaedzy5h7XvnTE8eE2pLX0l2pUtnjb8paCxicGMNRUViLyyRoaML7D8MgsVlnCxRR/pJGJRgxT3CLnhdMuo3aIROK7hvTBEHWtIdYez6UT1EWGO5Ein4+wFb98b2QZarGDdQT5hIty3dIZ4AoU5f3Zbs/RnoNzPoU0x/gSEZUfOZNZipYivNgjPNuBQYT5c0t9t+HM47NU2gm1AwP6yzBV75KO5chrHaLjddzzDsOdLsaX6NBB5oJcxFRWNX5Pkq4aEB7V4ZAilUTXzRJ0YsTSJt6axKYjXCo4QUKwkVLbJth2a0LeDAieEBQnBSiJzA24kre/9zEmd6R8+Q93c9NNCX5QwGyXp8b3YWIH/+IGBQHqGgdkBX8AtqGRCCgMWgl0xUUkBc5QYwKHjddMEZzrlrE/YUEJnBTcQY68MCghquN1iDOChSHWV4hRWnKpqiE4qrw39FLQLrZd8kXShgdKIIzFDDNE1YfNpPyukqocjJiy/tkqiQ5ckt1VvBHkDQlHdxGeXEGOtTBpgsg0Ns2wZqtxrMjLe4eQ2CwDWfK1yumy5fKHavjCofr4ZaJDM7zxlvt42Q8tsvRUwEf+2SzOer9krhi3/I7VmtZURi3qsrRUYKbG8C93aC1b4rr314saqpdSZGUAoY88s1wOcuIUkqSEHCcFuuqRBAI8gaMh2l5jd3KRC2/dw8j4eMO8vJYvcGX5ha3rHlZa8tChcTZGLqwRSogP1BAClBas3jDJ333NTbzl9ut4/c98Eqsk/+IffphPfeXn/ov1l9KmZE8pwSf/5BvUqgGvf/VhvvCFZ1hfH3Djy/Zw00t2A3DX9CG6w4hX1c7TGfSZNk0uF+bFdZwQ//0dF29/9REWL27w559/Gumr/+7rXNVVXdV3ga5Ge/6n6aoj5btE0axlNGfJvG9/1wZ0eUvrNPfWz6ODEsqmqz5oTbFcQQwLpOsipSRPCp7bnGHpPdvIfnqSo3dcw/VHdlCpeAw2yxaExrY2uukixmuYsQa2UmX9ZIOcArGZoF2LqfmgJLF7C5H781hHYUIXoUtbsvJ9nn7iIgDb2nVUYak+JSACsWCQTY3IDVbC0GnAIMGmadnm4/nokUD1C8TSRtluYSxveMuNmFqA8RW6GhI0YaIyJAvqHLhuZ7lBbFSxtQr3fuQZJq6fQ8wbikegf2cLv1bwkh86iaW0T5PlfP1L+/nkv74JueQBgizPwXcQrQaysJCXMSJ/JcZfTrh1bQFSvQXW03Q6bXguQFYq4HtkfR9jJEVYJ9s5VtZ+xluL6kIjpYS4oP9SRXKrQ/3uEbN7uyxNjPP4cwd4/PA+etdNsXOiwcZahV974B5O7prG+F55YhiniI0GFf8IN96yF4kglVXyhk+2o4mTgqkF/MsfeZxHvj6HDUK0U0FP1tFTbRoLhnzWo/WOhHA1x9/MEUOFkYqwEmOVQJiSpZF+HP7hB9/Ov/3wa5E9yY/s/Cqvuv0Mcb/KAIvWpSsHwBlpnFijBgYVC7yeQBpFeB5sMSBfskx+LIEHJFJowt/epPrbPSq/00X6LtL3eO7pWZaWWiwvN0GUmfp4uUBkGqUtelyQvCnAmSzbZzQW6zmsPTXJzM+vEfy6Ibd1ClPwr2++hbGuobqaU7uicQpZOmg6ETYZ4K2dozqxgQ6ccgPplBsIG0UIMtypADwXNSzQ2jCzY8irZ06zPGqwy+vifi5F9DXhUsTIuGwkFcaKEeNySJgbjKOQucHRBSItsPUKZqaFznMYJThLHZz1AcqALVySgx75tAILaSC5sGeCh85cy+N2F0vzNbYfjrGBX54eFxbTCMpNkOuSeILzP1jFUJ4gIyVYyfPL06huhOqMSkimLDeStjAlQ8AxpOnWkEeAlWBcydwbR4xf28N5Mic9vwqUDTx57GF8gb+Z4w4L0jGD9Sy2qfHmBzgGTKhIWoqiLsjrgrhpMNeliEMZyy8zXLlPk3xgjbQdkjcDnhhs54lkmsv5OOFyRPj4ZRrPdmmfGOGsD+kdCBjNuPhnl2FpA6s1JvSR/YLG2ZjuwSrWg+6iQ/2xdYJuQudVO7D1gHC1i0gsFheZgwxcRLLlAEhyZK6RowyRG4wWmImCyrYI0SrYTOvksQfK4FYy5rYv8c2l3awNaxRdh/oJjbSWoJphC8GYGlIRGTID6xlq5/uI3G6xPzKIYqrXx6ihxUpJZTFBLfU564zjXhOxZ1uHx09O4QtDIAvijdI1525GqCct3sgB6WGFLKuULQjP4g0KqkspXrdAWLjptiX23bCJXzdMHdgkcHJqQUzgRtQqilcePEOjOWTTeFzUNeaPzJDV3DKKkuSIJAOtMQqio3PgSESS444MIimQcU774DpjN64itMFum0D6DmbcJX9Njc3b5khCn/BCFzXMkbnl7IkxTvzFONEox9lWIJcKhKM41Frh8NgSN85dYfrwBqrIqJzqIiONt6mxOmf57gYLO8cx23ysstjcIHSByDNEnJDXHWS9AV6A2OghRynFuIf2JKO+wzCq0ksqZCs+wncQfw0z15a5gyNqExlzhwr+1k8e4563nORz/2QfejUH18UMR4TH15j7T+cIey5i5CCjBGs0IkoQ2mAEyM4IJRS6AvGcov/SOirVCCVwcsjqDkVQOjBNzS+Bq4EL2pbIIm3Kxrq4dDyILMcGLs4ghbygvwuKlqWynJZutmGBKLZYYnmBKAqEBFvk5CZDDGIchoTXb4CEvCLIJgLWX7Od1Des3rMdq0qoOFvcD4zBbvFwEAKpFNJxwJXohsP8myYZniuQiyvUvvoMJx5qsSjaqD0OIsvKA5DAx063y/uQtZwfNTg2Pk2+o12CeocJcpgwPFoth/4YpAZGCbV6RG2yix0OEVLgnFzE6AKhFE5sSjenFWhPUeyboLt/G9HQByVwfU1QLVD+1vBHW/KKRNecEo6bpUgknpHIXoYz1FCULJZIWT7z0W+W/3YBjvzWcrnQht5oqzVw1xSyUcOrVnA2YvZuHwPgnjce4eW37efgNbMvPu75p+b56G88xNe/dJnK+ga91aD8eysYjb6zOM7ieo84z3E1VFz3O7rWVV3VVV3VVf0/01VHyneBhBDkB2Ks0izzO1xaWmbnzGcQQjASs0TZMolWUOTMvK1P50JBdjknS3IMHsIkICSjfszqoI6zkJEds8SHB9zwkr2sjwq27Z8BoL2rTb67itQJhB7Wk+THFmk9uY6UinDRZ/3OOsJYrp96I5WwgbDPbMHqypOu1q5x8q1KZX88xAhL92yb+953E0VPkBqJM4xKkKmySLPFeChyyHPWL1p0ZlBKIYoMR0lmZtuIrMDWQ+xwwEa7SdDPUdKQ5prv+ZGX86k/fgzdLi31Zx49wdTjmwyf8xkcraOMQz50wdEgfAD0KCecT/GvrGJ3T3HDrfv4xueeKReZWYpwFMYa8mYbFWnSvIXNSzeAkIL++jjySxabFygpGTwKT91TQTY6FHuncYZxWdkcp+WCteIBBu0Klq+tkoxNkh83PHffHtpaIRZTbo+hMRVxacVQTIJtWNgxVVZFLq5wx71dktFF+st91CBD9IYwFpRObGkRkeH3f+lV6M11MAKbpWQH2ii3wnRxmcZjhvyixsszRGYphhkXj+ekO2F0wEFOtHB9Q+Vsh4nfPE26a5Ibbr3M7e+6wiDx+bV/ezMq7ZWLeVX+HZ0kxRqBKHJk5CEjqC9kuOcFdA16lOH0LPQAPLKWi6NTiszhLe99gsc+uIMvfGgX9uB25OV1hIohybCmwCYZeApdQLpmKa53SwhxTRHVc8Y+vMTC2R34pzfgyCa7r3h87NzDpfPIQr0VkKUFYGAvqJMu2abLj/6tx/md33gj1lVYa1BRipUaTM7uXX0W5qpEzwfUzxZcvDDF5MEh/eWQYqdkfI9FXSkYXNcgz6q4xsXkgrCmeff3PM2/fGwndu8kzuoA0enDcFgeUO+cQHT62KygGAsB8AKX/q4JiopHc2lA9eyAbDKk16wwudinsxhi8xw56qN2thB9Abks25Q8n3yqglNUuPxWl+1/5YME1/gsXJxkvKaQuoRJk5RgaVwFUYHtjtC6juNqUumjhGT3DZd461tPou7UvL9+Hbt2jLj0kMEaSVBk2ElL4vaxB0J6e2pIZXA3DCrLEI6gf7hN3i7jZQDKCJJUEQQFyTaLSjL6qYvu9fF3+1watlkqGhgEad3FiSKEySgcB1vxGH9gEZUZRte1ESMXPxG4nYRoyiFtS5arY/yni3fS/ECH9ukVhrHGmW7gEaI9hbdsaSzm2HoVLQzD/RXal1OMKftarVsO0mRqkaGh9YY1lk/MYbWEyFBYl4aKuH5slYerVdK4wdj9G3jDgLv+zUm217p8+cJ1rAybJCMP3YBgyWIdsC4goTApTqIZHm/jeaXNRPYT/M0ecWWWtcMTPP/wGCdmKrR3pkTaw8kk3nCId2EdaTXWHcO6gqzp4Z1bIVjpEx+eBcdFpaZsy8oMd77mPNPTQ559YZZFv8bkZoZ1M3RXseIa7h47SdNNOFQZspz7LGXTjNVSfCHLgZqUEPrYmoeubrV+KUlel3gRgCW5fxxuLHB1QH5oDrkZEuSr2JdLKscy1OURUpd8nHyywuYrJpj+5HmGL9/JpbjBlIkQLix9ts71+TL9I20Wn5qFSUX1odNMLawzePl2uLyCPy/xphQLOz0mLVhtyAoBUYJ/bgMlnRe5H1Yb5OaI6skNNt94gLnlJZzaJdRYgRtmmE1QxqIud7FC8LEP3E7QLJg/GbL4/67h1CHtaSjSMtLabpX3/94AfIWMM4p2BcfoEhIrJP5aVEJXqeJ3oXUsxrnUwe0IhnftLr8Sqy7DhoOTg3+mB7LA5DlFwyur2JUshynagiuxQmKlwHoeMjfl64vB2Rgh6tXSvdIsoGehEmCkRBhLvKtKPl1HrGQkUwWdik9rz4DBsIbZ41NZEay9Y38ZbfR8bFYOFa1SoEqovC2raTB5AVIwvDFg6Sf2gLCYZIAyPQDWn6zxye/ZQbHgYL0CGjUMhiKJUP0YoS3DQxMlSLzqUFnXUKuSugW4FdywgbQZRAlGFwzO5Bz9OynP/ucaIDGtOtJKjCsQgLeeoaZd8hS8QtBTU0x+6nmigwHFqyfJtcJWLZXNjAKNG9YJzsTkAmTolgMWVzF133nWX3cQACMsf/Slp5idT0pXUKF59z947Ytrrt/8/EOcuLLCz7z1Tl7yhqMsf+JRajNN3vqWl7Jn3zQAj91/inMnF3nVXddS3WKk/OmJR+nfnrE+O+KZkY+pZOXBjVL8+Sce5Yd+9K7/7nXgb/3ZQ5w5tYzfDmi0Kv/tB1zVVV3VVV3Vd6yrg5TvAllree/hB6iqDJmCr09xYf1X2Dv5c7x85hf48NM/yPlPTnHH++Z5x10nWEqr/Kc7bkYbja1UEFsmlv/49c9R2ZZR+Yt1xFmDDmPmxzaJc8Nzj53jS7//V1zoRdAFk4AYZti6YO7IClceEwgkadOSzvgQZfiBS2ENDCJwXUSSEbQq/MYn/wGz29oArC73sMYiVjeJIw1CooocHImyEjlIMcqBpLQq2zwnSxzypkAlDsK1DKKURjWgkqbEm316d1TZ466ye6rDUnWWi1fWCN8ueNuHvk5bDrm3scyTrV/mvvUbqVUK5rqSbiJYPjaBdRzsKC0Xh7ogOdRE1zyqGxHr3aR0tSDKKsYkRbdr0Mlxcsvr9r6LP3I/Wm5IlSpP8Y1BBQG2n0Cliu10kHmfsKKZutOw9pVquakHbKePdRzGPrKJMLD69jmczojAajydYzY0NrRsrkVM+oZekiN3WkS3zMQffFXBu/6Px9H9N/P5P/27yMJiPEU84eELSRYb2heXGSxEOL5PPozLiMy8y8yRDvf8v54mXYbP/dZusorASrAasJK86mJcMJ6C6RpiYYAXhqhRxtliG9/o95gftUgTB6fh426mkJdxGZlI5DDCCoOVdcDS2jMkfiCEXGJyQzrpEmQWIRROq0o2nmIzzY6jPcTb4P7PjRMPE97xf53j0/94B8JaRNVBjAqEEbiblvqDPTZvrYLncuXN12GrPs3BeRpfK7P5e1OJO+3iGYuoSbIsJ3v+CnZUglnt8YTNV25Dyim+9HDJD3jR35XnOJHECIfIC0j9oLSVjztUw4JMOVTaCZVUUD88JPNderscdK8KosJozGesltGaibChixjFSFFgk7iMkimJO5nAUlk9qqVBGkHuW9yeQz4hy9NcLFKXYNfN2QreKYuIVFmx7doyruOUz9s2QoL1jGKugigcbCvAtjTR9Sn2m+X/xwqJyDQmUAgry1PwJCU9OoVMA4wAG4L0DDVHE+UGGSqei2/ltt3PwNfL+08qfMxIkG13iTyJimFuYgXv07USPuxAMu2jQ4HQWzzXUDE5toHyymaT+KadCFEQ76rBUs7Cco3GrKGf+Qze7jLz6oK7ph/nC797C6y1sYGD0AXc6NLPZ2g93cVb7KL3jGHrNZr3Wzqvk6hUYUOXdLqGYzW65mG9gHynoLNLMflNy2hKEl7RmKaD3BhQCSMGSR1RaESaIxKHpU/NEE+HuEgcB2Lj0KtUqIiUH7rrIZ67sJNzD7VJnJTvnT5GxclYnagx1AGmkKUjpTCMDtUBgcFSNF3cyFA3ERJLOnDJmyH5lIMTCdxLPb58fB8H/vllPFng2QIVu9T9FG8uYRS6DKSDv1RCkVU3RmQafyMnnfPIt/k0Tg8RueXjHzrK5Etyvucdj/Dwxhjmww5CwpXJcUJHs/FCnZtv7NNWDsoUeB0PL0lQoigdJq0a2rOks7USreFolDNi4yU7mH2gjzBglh2y5yrYwMUmYMMK/o0C++dddrwqptMpoyvZVA1nlFG7HDM8Ok4wSpmYzZm6YcjCco31sz73/dEBgpf42LoHXsnvkLlGIJGOYfJzC8hdNfonmoiihKU70ag0TuQFVthyIICFSojMNZWzHWRUkK8XHPvQNpbvniqbtqIRhAHCaqwD65dDirEKztoVnhscRKSWN/zdx7n4cJvLFy354W2IVKMWFWKYEVzqIPsjrOti6xVkkpcOJ+Gi4wzrKGqnM9S5PtL34HSX4YEGmAIvMmjPwSiQ65uINKNoT2HrHs5iHygPS0xjrKwQ12WlcLBp8J9OKaIBRTLCeE16M5bqqQyK8t8srIXBqHSXiJKHEiwJ3FG5fy9q5aDFWlPeO9iyKUunHJwEPjZNtqI5toTRui7q9ZLxeyXR+QGDYAxnmIHnlUB4KckvlkN0m8cUeyeRwxS1OkImGUZIxu5bJjs8g78QIYZ56S4Ja1TPxsiirFs21iLCEBvFnHt2O/Jok7SvKXaMl45QazFKolJNzfGIkpQcl+naOq/63DIfOn0DG90GQsBYPSW0oCzYXOPGGkeCWRqgs5iiUcMZwfh951h/3d5yoGlgcSZATrqMXYyR4ltxmUIbrIXCGI791bPQT3jX++7hJXdeW37nK8mXP/4IVkr+3ht/jc++8KsA7PTGeXLjEqdXd2DqLiun6ljXQbuKD37wG9/RIOWmA3Osrw3oGkic/z/w01/VVV3Vf13WbjUx/i/wPL7LdTXa812gK8MlJoMRVS9lPmuSWoeN+CwAobebYx/ZwVN/Mkc7TKnKnCknhkKTDlI0edlSgOXhRy+zM+yw7/UD7DUelblJ3vWjd/CuH3klMst48i+f4dR9z+MtDMGUcR0ywcJXfWySYeKE8EqGuypBCaqhx2iUIVONijKEhiTKuHRiASEEOn2YtvwDqtkQ2R0ihERuLfjsMMbGKWKUIgYxNsswaQpSMhz4BEEOaYrr5DxzbhGAPzv/fn7tqX/J5r3TVIMUUbWol8dEFctqdoxZv8/1YQcpIBm4mBRsX3DXkbvBkVRmYkRe1kWKagVXaCaWzhCMMkgMq2lK7ghMPUDUa8jxFiZwcJUDacZG8Wny6QbFeB2UxU1j5F6N8H3kWBvhudhmHeF6eFMe6cDH5gXGc7COg0kSjOsQLMf4KzHuao4ZGYKFCG89odka5+c+9PeZuvl2ZvZci9NQgMTWfKySXHq+xbNPjpFbw/h4Dek7UK3gGhcVG8Z3bHLv2y+w4/AGxjMldNdzkXGK9jMeb+zm/DUzvOIXV5FuaWeWruJlP/4azMwY+RikLWDcIse8EsZaq6KfDfnQX9zOA5+6HrNhMI0avUNVrCMxnoNwVWlf9hRCg1GCYLEgmMjxZnOyvR7DW2ex+9rlKbrr4NdCvIkKp+MZuoMx0rSCWurwpccnyKcb2FYNMe4i3K2WDQX59pAEgQldZBDgDjTC95BClL9Xr9KaadAKPWxnhHNhA7PQgTBEBB7Gcyk8QX87PNq/hmzMQ4cORVjCF2k3wXPJpirY53JMkZPXFY3JAbVGTNjMuO+jtzO5bUDruhGVBYOzoXC6DkdY5XpnFTfPcTZj3KU+YmVYNoNIifUc7n7FFUhLGLG/PARtUaml/nxC8/4BleNrDKcUbidm7nNrOFYz/fIOBz56gbd+/ElSE5A1XXTVw7ouzbeN8K4zJXtHQn40QW/PONw+z/SpBYzOkIUBBFpaaIdYu2XfT4sX7y/aF2RGcvLkds5dmWKz55JXJEuPzVCEDsaXZA2FHIJYUCWjA8HuYsjqP82IrmnQOzLOYJ8l2qEpQosOQDcEBlg/Mc76iQkqtQhvn49vDKYt2DB1znXGWR1W2dhT5Z65Fxj3It7z0w8QhgWkOVoYnBeATKFlgS0KZL713EcC70GFujLAuJKxZ9apP9elP+eS111kXZfPwxUU1bLa1/ouNvSYnI3KU3jlIImoqBj3dQP0TIrxLLnj4AU51kjWVhUf//S9XPriNegj04y+t8XCSosk8zh1YY7e0EV7shw0VBXJeIjxwDoCUw8Roc+um9Z45S8/h8xzkh0B8Q3TVPalTG6P2P4zyzQnM3Z5HQKTQ1GgX5pTvC9k6bZJOnf4JFMBbmdEemiafO82GKvhJSCMUzpJlGBhfZxHn9zLU6e2caixTrWV0pwZkWVN0jGHT/zSnTzwl4fIMsUTp/bib9pvWyEIyiFY5coQf2GA6RcU6xavt8XWERJ3qPEXCqJ6CqOYStTDfF2Qn7eIlZTKZIKp+riFQPUTgkt9Ws8NCZY1drXg0I+vk4+VbWJpyyG7pYYQBqENxXiFzpv3Eu118H7FR7yvRnG+RbCpsFmOiAdMjCX4W6+lcFQ5LbBAUWAwZFVFuqsNroOfSUTXJ2/4WCWwo3gr+gZHf/Q8L/+B57CpxhkWEFn2HB3RdcYQUpRA9IoHlRCUKO9vQiKqIVQD8FX5N1ESW2gsFt3wSni1FAjPR2qoXokIlmOqV4aoQiCUC1JStEOig+MYZyt257ugJKbilzD1rHyvgyDfMUZ64y78SOC4Hmuvn4G0jJeSZlBoKmc6VJ9ZRkYZ8bhExIZ+XsVfA3/FYgRU5zMazw0Aiw09ROCXfw/XJb1uG8lUtbxXGYue9rjiT+D1U3b/ynMIfGQQIKQsG4CsQQD5VJWs7iC0JbzJw39zFeE7mKZH90iVZDaElfUS6jzKqZ3tIYTA+i64LnZQDrn78xWyooJwQyrnN/Hne8hMo0YFcpCRrMTYGKTUdM6GfOih2whjH3T5FogbEoGl8JyylQ/ACkQ/YvOtu1j7Zy1Wf2o7aIN7uYebgJOA9Mrvjs60zyvuOvTiZ+F9b34lv/RDb+DabZNcOH6ZpDPgj3/ls/zYW36Zn/2lT6O1xkYZxBl5/K176eH2dhpfDrj0V3v58odeUjKojEXl5jveb3zfq27kn//o69m2d4xDh2a+s4td1VVd1VVd1f8jXXWkfBeo5bb56vJBGn5CtRjwQhLQ8Ha8+PPTX9uDlBGf+50bGZ7yuPS1AIzFa1S49Q038I37zgAw0x7w43NPcfq+KivPw1Pn7uNvf/8rueveGxj1IqQQ/OavfhZxaRnpz2IdD+f4BUyaIcIAASUE8mJZI+wGHtkoAixCCkRhMIVhdX4TgI3ev+XOlx9jfXiIj/2b/WXERTnYOEFoXbpQpCjrIClPvq3WYATFikVZ0KnLjftLKr4QgmeW53FNxqdXbiJQOYV08LSlGd2MJ1/gWN8iGfDVL+1DKYEXKnQ/piJzarMDRNou4+HaEBwSTP+DDPdzmyxWpyleOondGCH6aQmcdRTuqEDlgOPQXT6Jad9cnt5Ji5itQitDJzHKutg4Id83hZ5tUlXrNNt9umELVjfL075aDVPx6F43hQkFo4M1dj+0SS63cupxQX2ySRZpoq7h5smLxNtcVrIZ0JYslfzFx6fZ/NBdLJy8QDLt4W4WCCtwYs1B1im6q2xO3EjenICvz5eDKytYLMY5fnY7440BN7grKDdDezWs1VS2j8OpJWgVFJlHHqUUKxEkOdS24SSa6T9cRWhNsmscQp/OG6vsu2eRjT/ZjgZQogQiFhq3b+h+qYW7O6fxWyOqA0v87yPUzgI9CJBRigw8pG955rkd5E/6pT0/yYnvkxQqw0pLxS/dSjZJwXMQG4q8W0UYAYWm+Y35EuAny6hL0QjobgwYGXBGYMM6Ztwgq1WM6+CmObY6YvuDlv5uyBs1dGbRSUbYqiF8H9KM5FiX0JcwaiHOjai1Irq6QiUoePb5a5kslqhlKa3Tfai2sQLOPTZGk4Sv37e/ZG9YymiEFFhjkMqybecAkZSRHjGMyQOwvsQ9v4ZsuIhcUzu+hsosjDURwxz7kxE/v+MZCnI+6Vmy1GICB+oe4etizMkWZgWczYTg/AovfccaP3TjKT5w/wG+GW2jtVK6XPT0OH46QFc8ZF6h5WQMeimj7f7WZxAy43H39mWWVhz2JueYcl2Wt15bkRmMFEgLtqIRruTipQkaD3UZbkvw0x1lNbKBwrOU2xrYN+hw/kKLIvPZFC71qiYTLrXVAjV02ezXynrgSCI0/NHl20mNw643XMZ5R8Fbd53mprEV3v9nr+Yb7n6KdoVirILX13BbRGUyhSdqOKKOsQUizcjHHKo3D5k7mLK83iCthqhMoC6uQFa6rpImCG0RwNGfuMjxC3uxvkYGZeuZh6Yx3cNKw4lPvgyVKlhP8RWgFb994o0kByHTLtP5Fm/NgvUcnEtDzI4aAkjmmgQjFzu9yVeev4nRPo+87WEthLWU1efGOW1m+OlbPocvDDtlihhlZPcJVvIJ8rakcSKnfjbCbnTIdowhJktGQyENeRO0ZxFIHJsz1urwysOXmB3vcfif3ce7/8H/QTCW8YobTvLsp3bzwQ/ezKe/fC3rGx71i+cwcxNkbQ81zHD7KVJbZGEJF2Ns6CFHhuZzmwjtlrDTLf5PYF1szcWbcUjvKxBC4kwZwppD/Gy9BJ/6LjJKwYDcjBic8fmdR19H7dYhjSc2GdzUZDBsEMrSsRQdqJFN+XgbsHJ2D8msgt2C1qjP+LaUd/yTU4SO5qE/Pshzx1qY3CDUX1c3l8DV5MgcugrjR3Le9ZPPEc2e5t99/lVI6WAqgJUIYdl2qIvSBcF0AzYyRF3xjQe306NKMetAJ8IxFhyF6aZk03X8JAcMxmiUFNiKW7oTCoMexrjdAaIaYsMAIQVuTDnEMQZnVJSxoMCHsSp6qlayOXJdVrD7HqQ5MjKIiot/cRUdCDp3zyGFoHbFEM06WA1Clm1KIkmxgJECJRWiVsU6Dv5qQi+qo4YlA0VlBq9rSpCvKCOPwlJGlFp1hO+RbvMoDk0SfPF5iHI6CxX6ugaHqrTyVegPMFvuH+GXAzyDRvYiyIuSebIfRmsTiHafaHuA9iza2WKvjOItOKL9FlRX6xKY68iyBTAt0NagHYMYZZjxCjIzGKnJHR/rCsQwx10fIR5zSYWFux2QlrylsDZFSgeLIWkoTOgQdgKSWwtiXOQ+S3LQJahnDKuiZEMlFqtA1D0mphovrql812G6VaOz0i3rogX0lzrgKp54/CzPPHIWXKd8/u63nCzBeMBor4NzdoSLwtQkItcoa7jp1r3f8VrwQmeTFRPx1NIy7+Tod3y9q7qqq/rfWN/9ZpD/JXTVkfJdIN9xub5Y5oiYp2FTVnUTx9//4s+3HeiB5+FELt/8xDUsnm4jXIlFcO7YPPHuFvHOFp3NOaZTy0N/Mk26LBnO93j4Lx5n1I8Jqj6vf+9dtLa3sGkKlNEPESVQmPLkS2u0K0inapjQx/UkelQwdkMXk2VoaRESDt20C4BvDppcMYq1Gcvo2gnC0EV3uqAs4/eESBesLl6E3gmlKFc3YPwKYqzJ7MEd1CuljfiRlSf5nSc+z1S1y6uap9jtbVD/aoJ/JcEeU9wz9xf87n/6YZ5dbmGEpHVI87J3X+RNf/tuDtw2xvWvX2HswACR5thCszEKuH/1CHk1pT01YDONSrtzZsBQnvpttSTpekBjLGP6pgxHC4QXIiKNjQ3phIctcgg8il1NdCCRLcE1N59HFBpZq0K7XjpECsPw+gm6L59CaEnez/FrFbJ2wMy2Tf7gt3+BjSvLmGiDepbwurEzW6tei/Ecjn25RrReI7GCLFSYtIQUWgEXuk2eeGCStV6LInXBc8u/resSeSFxN2Rjs87jT11L6nnliblSJP0IVRgq1YSiDkXiIAYJwoIYxSUMcaOH7Q5wRjlkOY2JPq+aPgWjBJvmFDWvjAcJyKsOeRYgq4biBYegVlBbSLBnC2RW4HQscqNAxrAxWcdeZ8vX3ffQbgNVraKSHE9E2G4Xm5TvwXDogtSI4YiZ4VkCb0hjX5Vr7zwMYYCzMcCPUpKg/HcLQFIOdwQgKwHjTxdIPBpnBdGUIJqW6BlwJmz5PreSvK/IVgtsXtA7HHJmMIVrC04NpugtrLP05xXi+1PcnqY+X9C4XPDp37+RX/zZe3hyfie66WJDB1MNy42K42AzwYf+6JXge1gpt2zyApEbiht207l5G8MbZjCOImkqRjsVo7kK81/Yw7/+1JvpD2uosMBiaUyUVvzLSxP016sIA9XnuzjHFGd/b5wPf2EXK72Q4KaQvB1SjJWDJNlLiV7hMHenZscNMYE1BGsZnhihrEAow9kXpvnjn78J74sZ9ul5qqvryGGK1zMUjsAooHAQsWJz5OJcLAiPa7LDI+qLUL0ssU6G9gxFo+CGg+f4yb/3F6gMhFMjuxiQS0t1Y0D9P/dQI4nqS6SAT334ZnbUN/nhPY9S255y7Pg+Do6t4ynDq+58DiEl7tKAytMLuENNIAqqezLsURcdKKKddYZHZxDCw001YJHCcNPuC7xp7EGcTCB7EU5sOHe6iXbAONDaFrG/vQifrxB2c7wBiIGiO6zgKIMsFNsPLKJnLfGcJalJVGLRmx6qLxBZeUovi3Jd03psGe1bCh+SGZ/h/hZnvzzHyrk2yXSIwMEbWua9GhuHffq7XQZeQM+EdEZ1HDfH61m8RYvrSsYeG6ESjXB8rFDIWCPjspp+tA2+71e/yk9/9FP8vX/6dX7up/6KbY0UY+HkuTlUDj/81mP8rfZ5XnbwDGptQGe9gr80QsU5apCWAO9AlRvZdp14e4NoR62s/I1TvPUY4Qqs54KAfEwhqmX8JNEK1ahAY4Kw7tAIBxT1AI3BDkY4g5T4SAMaNfKJkMqqINB1kht3Un16DbG8QWEL0qai8B2cUU51A7zzAeqEwslyvLMDKjPg7FC0XprzD37vCcZe6yIAUa0gqhWynRXMVvNNUfe4MtMiQrJ51oeu3Yq+SLYoqzz52B5OfH2GZOgSTwWofTB4MiI8f4mi6WHHqwgN1ncJBwkiyUn3TiJ6Q9Tpy2W7jCybdJASJRyEdKDQmGiEHOalY045DPfWGe4rOSJGlc0zKjFUVguE5yIc51v3K2NgWL4mNnQxgUK7kFUF2hPl8G89K4HnUoHrkI1XMEZjypoo8mYJfHZTcIcWp6/JWpJ4wiGeDcrBhtZkTZeiItGuwIY+IjOwfQb2bce5OcXacsBKrQZ5UcKswwBkwGv+xXkacwVqkFJ56gpqWJB8yWJPxzBK8UYp/kKCe6UPQmIGQ0iT8tBEa4iScoBkTenEaVcpmgHaF6RzDbL9U2hy1HIXUykdmUmvA8ZQVF06Lx9n82gb77LB3VBULpQHQdICUUQ66ZXXdSV63RCcdvHOuKQthfN8l8k/e45bunl5ACDEf3WlPBqVQ388l2TvOGa8ibua8ch9z5cDPEcwtmfixd9/srPIxk7DYK9ERQlCSoLQozZdpz1Z+47Xgofmprh2bpo7r93zHV/rqq7qqq7qqv7bujpI+S7Q/MIytUaM4xmOffE6fHcvL51814s/33HjBUjTcmE8ytBo9CBCZil333UNuVculOeOhvzm793F9reCuytg70tmuPbl1/AL7/73/O7PfQSA3okr5ea/04PMlqdmxmDTFDOKMLZAe9Db1cRoQ2YLbv2FK/zsV77KxL8bUKnXGI0yAB7qj/PLD76Sb/z2AZxhzkgDrSYEAZ1tdeq7EmQA1pVgDKbql0C98SZCAVHC5jAjiUra/f/5Bw8RfMDgfdDjoLfO3c5ZOt02m9eFjHadptf9Ub7nFQ/z7z/2bt7zs0/xjh9+nkYrZnzbgN13+2QzAXf9xwsoMcSOIuRAwqWA3qUqdsUjf2GIiEvgbUkHLWtGbehispQIl96YYfvcGEJJyHLSOdBjYTloygtEYnA3ExwHxNlaGWP0Pay24DlYrZm8r8/YN7ps//BllOsRKUvacshzy/H71lg/d5Zic56Xscggc5FJgTAWaQXbbxC8930PsONQTrCWINOy3hgsG50m66sN5PMd1MIQkWlwHKzroIaC2hlFY8VSd4akXtmwkrQdnvr8cSZbS4SJxonAjShbggoNgxjRGSAn2siJcURWUDR9vOWcMMyQwwQ5iNABGA9UasmbEu0riisO0ReqFELiywKxrGF1iNgYYleG6Ct98mlJ9EhryxWhMSZFDmOIE/xGXsaLfB+yFMe4SC+Cuo/3/QL/LQ7Kcbjw/CXAwGYPvxIQhB666lAkI+j1y4NQVW4YikkXEWc4RUaxPyHZlzMTb+LJonQN5RqtPcRUAzvZxIsEC5fHeGBpPzt1h0JrHKuJNz3kMKVyrkftwjqq7pGpKmR5yW7oD8sIW5Qg6zVktQJGYltNVLsFW5s+tZbhdWIayzlMthndeZDFnzzA4o9MUYxJhhfrPPdUm/sfm8VNoCpz/tGPfQakQ/GxBmyUJ/Hx3gZF6NDN2uSn6/z4Pz/Jb/6dj3Pn606iXYn1JEOpUCcs177kDK3pBONL8BQ3XH8ZVViEgIGfYX3oy5D5QRP1wjrWUWhfIqQtN16+wErBqFVh/fWTbLxiG/05QWXVUFswuD2LDSxCSB569EbyQvFzP/ohgm6GxZCHglEKteUR1s/LRpOqRjwxzStaF5lwh9zYmicMYx6JZng2bvKR546gQ4OztImzNkAOEzjrIXIgS7G9HjgKN1fUTnXJP2VZeXaK/HKN9/7A/fzAe4/RngU5MUY8owh2FQgpsY7i+LP7uf6tS3i1lMZnU8goN5ESstyh4QwxiYNJHTzHoK6NcCcTZGGRBrKNkFG7jJiIDJJrxv96v05RFRSBQGe2bKPJUryBxcnAJiH4AqRlxhuwz1sjjxz237FMNisZXuPS9vvYwqCVZXB0G37uvjjcxYXA9rhj+wK3V/ooofn9f3crX/yra1mY385vffR19Hf7DKIQbSWjoUM8KVl5bYNkR5ti9xTpjhZiZMohUOCi6x7JrnoJ/h7FEEWMDoxBZkouibUkDUXjYB9ntcP6IUXezTCrAyorEc21GOIIU3VxZwPSfW2KaohUitCC1wFRgHWgcili8ksdrJRYVxLGPtWzGVYIrBAEqwUTX+ugRgWLqsWnNl7CHy6/gvujWYLJdbAlB8gYjX8pRiYp/rkO3mbOzsEqjpsx5nVxkrgcHlA6hqyrGH9ZhP8qgZmUWEcw7AdsXA7I1hWVxy7gn1xEaL1Vm25QUQ7Vst0NQIySFyGm1nPKKFC7hnAcBBK0ReYGacqIV1F3MUEZ8TOuRBQWNSoQCHDUFhy7dGQ6m0PMjilkc4z6uQwZa9KawV8Y0X7oMs1LQzJltxwRDjQrEATkkyU/SlHB7QqEKauX8RxUrpDZVqbedbBKkRwcx4xGsLaJ043x+wVYicwM1ZcHKN8ghSkHIFAeBFgQ0uG+3zjM7N4InBKgnM3UiWfGMKMcKXPY7WCnPIrtLbJ90wjXxfo+Vkq0zsuGOK3LaxqwlGsGqQ3qSgfn7Ari8jJio1sO8bA0VsuhS7qnhmlIikmXyske/rrB7RZl/bcUCOngXxkgE431HZp/OWL6SwNmP9vHWxbIYYFOU9a6UcmdqkqsJ7jxPb/Gw89e+La1V299iNnsY3sDhOOhghClJM8cv4LIDXKY0Ha/Zfw+2p6htgz1eYtj4TV3XMNP/uwbEBUP40ne+4Zf58fe9u8wxvyN14GPPHaOT3zmCS5f6fDChZW/8eOv6qqu6qqu6m+uq9Ge7wKtrUb82Qdux5kqKL7m8/2//P5v+/nSQh2KAtlPKFRawhOtRWc5L7v3Jra/d47HLp/i5+/8Ee76wX9Hy6tRuXnIW9/5fXgVH2ssSVQuZHSyBZtrK5IQHM8vT5EKjVAKNzbUTwwwTkAa5bzmpgPc++U7qTcjRt+ssieLWLiwyktuP0AnLRtWtBXlia3V2CgBAZ31nWzsrTMx3oOnNBaJqYUI3DKTXsRgDN1On1f/1G/zL37gboJHU/KNiBoRK1ENJxHk+xsMtlUY256Q6YxGWK4VH2AfT72/Tpw73HbrszzZrTIbNSmMQogA6Vm8vqZx0TA7GZMPXPqxj+cq8CQiLz1zcjDCGoNtVol6FVbjFrffMs36cB2kw7jX5/xNE1R6XsnuOL+CvxGxPmpiZl2oBlhtyBsh+WQVfzXGHWZUHh4i0hQZephCIzOLkRnDCzF5IrDkGBye/tphipqD6hZoNAeOrLPj6Dw3P3uEhVFAVPGxaUDRcHGEQWyCaCpEqrGuRLg+1lX4Q4lz3uBelrCZgFDE11QBSJYlN926yMnLFYYdi594KN/DFAWi3QAsojcCJZH9FKdXRXy8whffdS1kGgu4w5LFkYUWERrkuUV0xeDfaWnIhM1fGjL5yYjRhYDuZYnQAaJhGW9HDJWDjWLo9ckCj+ogAQUXRlOM13IwFh1FxA3LLYdX2RwT1EJLsd1gMCSjFBp1aDh0lzqIqo9MDVQ8hKtK+7UxzL5hhb3OKg+fPIwtMq770CW8gx7Dr/vk1RCiCOIEC6ThOMJYHFuw0pngTZMPEq0ETF6/g5uuW+Wr7xcIzyB1xt5fWeDYBw4zHHeZy6/grnaxRpfOrnyrechatCfJZpt4uSVreRgJblGg1oc42iMZV+VGxQWlLEIa2k90QGs+c991ZEcDXF/z3CNNbn/nFebuOsXnz1zLmc5Oth1O6ayP07iUcG59lsXtVziW1Nn5hivwwHVgIdk/RuOpZf7qF7Zj3zxH5rrYwPD84jaKQCCEYmWtxtL6JNmOOosHZtl5/VlMv+Q4iMRQhJS2fAE7VkeohwzdwylDJMF8itspsBR0vAAsLHenGdzpcXR2kZ97/+/Tf2aSX7rvbXgX18kKxWS1Q8dpUamkdHdUePKP9nHtD80z6IaMWc3vf/AOmg8OsLGLeKVETI3hbqZkukDXJWpFop4FqV240COvSMKTS+jQxayNIwJLt1/FcS2DRY0oEsJhQXsiYdlrYRGsOlV2egOuefULPPLM9Ri/HKJUvISXm0uceOoA+iVOWaM8gP2X+tjMZ2AapbM2V8gJSdbVOK4i3tvGeuVgpagIVKpRSUp6SGBXQ7wVMBLCRYGTGZyew+6393ElVOOCvT+4gulZ/Kd2Mfpyi3wqBWPJxlycCyOGbac8Iikcxo4b2kqzaQyPf2E7S885fCHazwcevY008zB1wYcfPcqnnEPodkR4b0FDjMgmPFQYIAFtDE5hyCYq5eH8oKByrouUCusIsqpLaDMI3LISfmRxL0s0IeqFAJvOQ2EYHDcM4hrOMKe1LeE1P3aJ+5/bxfrnCuIbWvg7LAwNIjW4iaW4+SB5DUytbE4iLQg7OfG+cgolE0N//xgNkRAPE84tz+GtZiQ/P6T7tABXIx23rLQvSjeGuz7EOor+UHDijdP0jnvoRIMtSLYVmPE6uJC3ASGxicEZFLhW0JjK6CWVcqiR5AiRosdq5G0PZ6mHXBtgWnVEs4qVDtq1SMpmLApTcocskBeY0KGoOGAM/sDiDPMSwqwUlYWI4d4Sym3rNcBCnGCiCNEfIXwXzATaUxitAQ8pwesMscMI2S+vHUgohCqH/a5CjYqS+ZMaaicswbkO0oIIQoqKwuypYowoG5p8n2pHIJMt4LaUqE6ESDKcesrf2vY8v3vldhJPIlwPMT6O6W5ijSgdighOPzKFaPnYOMZ99iKusQxu34MtLMnhGkUmQQicuMC0quV9OPSJJj2CU8uoSCCEAGNwnjiPmWhiJ1v45zYQYYC7NfxSJxagEiA9D9VPsCoDEQBgai4mlKTTVTizAblXtge2mggEdqNLdeAhA4kIPbyRoFKpkKQG1vs4YwEEZRQ68wT/+N//Gd/4/X/04tpKWstfjzz8UYE0Eu05bN85yaVj8wgL1VrAT3/fb7G23ONf/dYPc8tojAvRKs54jVvvvpbMEbi+4oGPP0EuLVIq/s67f5vf/9hP/Y3WgX/++ae50umhpzySrPhvP+Cqruqqvmsl/jqi+b/A8/hu19VByneBVM2BYwptFWLC/S9+HnfLNhpR5JhxD6+TguOQ5ZZPvv8L/NMP/j1ev+8OANK2ZCVq4nVcFIJ9N+zkZ3//J6i3yk212/IxmSbeUSeRHqHngi4p+xaQ2hLOD2GuQpoW7Jqd4PdvfQ+nV9b50hc+zWAQ8+QjZ3nLD93OxqhJdyzCXK/wv5hhfQ9IkLWA6lKOE0dMXpOx3BuBMWCq2DgBnePokpmRj/sMfcvPf+GrvHmyzePRRYYxfPWvDuPkAcmwQnNZoGamsGInD59WIGHq1D8m7XwYdEE9/F0S5x4+9vDtgOUl0wO6Sz2kVDSPrbGpNPUDGj9LadZSegOFNbqEchoLWUE0G3DfU0fYcXiSqJMhuymFI6nGEeGtfTaHk5gAtj23RL5qGLYVP/uLf8qxr/8b1pf7mJoq4ay+g4xyqFbKE2ulKEJBMitIl2FQcajEGWmhyCsJ33zuANvdC5hcY0VBd1NyrggZ7LnEtjurLA6aJB/VZMMqbt1DSpe86mDqClmdwIkK8omwjAMkMUIU2JUYNR2UjQ8WJu8dY2I6wjwq8RKN0yvK3LpRmCgqIbJZirQCU/fxV0dYYHYhYSPxSttbosERqNgAqmyhMBXixZwrcRvlWXa+d5mnHt2N/HULQYjjW8YqHUZ5rbwZVyrowxLxZGnF771W075SIDYs0nGQPjS8hJU7mujFlOQ/pHhLMLd/GwvPz4OUXHvHNTzbS8rTXelgjUBqg0gKnCQnHSjkKMUOR4wWPEYLVarjDYZpjlAFJopQVYnIDSpOsX2PPc4m1zRWyaqKmXcf5vgHjiGSHKsc3Ipk11LOMRxMTeIMXfKJJnJ+A5FahOuCKEowpqsw1QCWYgYHawgDaduldryPShRj8x2s75AeHUe2NFlXQZJjnTKmV0iLjixf+KX9vPErF3jN5CKvfek893z0PQSVmOq8wliYb7Z5drSNS+k4VSdFTBakuc9LJk7S3rXOE388QdQPsaGgEOCNyhpZkVk+9OevpElMUdHY0KX/wjhOWH5TWgt5XUBQgAPVzQQ2BdMPRYxf8pAjDakhmJdUmpSOHByud+apuwVLWZN9N6+gHzIoKxENh0onY+Ily2Dh8p0F5z+8k/54jfHmAPORAdMdhc1SjIhoHvfLgaOvKAJF+8iA8R1dFkWlZHT0YrQoYZ5u0+Xa/jqdj6X8+997OfmONm60jLACIWDtMw2y21PCqT5vf/OzfGjtCEGQ0H1NQFbNoRB8b/c4qw/PoK2ld8pH1DSVUcrmmRpjczHykEZnDiPlU71UQm2lsUgMJgUjBJRpMUSlgruYI2JDXqmVUGZdQoCzumZWxkhlSV8I+Mo3j3DgjjUaFwTJwCAcl8KTW001IDVlXS7gBjkLRUBVxRy8Zp35E3V27hly0s5RtAEtsA70XYfZ22J+6J6HiYY+H/3aa8oBhADdUpgo32JWSNyNYfkZURLZqlObjxBRhkgK0Bav7rNrZo0zw0n8pRyyAqyhM++h9vokuWFqrsOOqRXGF5usBtvo7g0QoaR1PiNYzzFKlBGaikClCpmDYyRGlfEVsOAK0glFsaQQuUt8qYbuxnSfLr+TbJFgKqKMm8RDkAJdq5Fvb1DJNth/YION8SoPfcJB5pb0YEj0Eodm3uWN205wKhrj1NgBpO9jheW69ySc+OokawtVTM3HTW0ZRcwtMi5gaRPSnHxnWXUrPR9rbdnAJcAOsrIKvPjramFwBiXTI296SCMgSXCMwRn3iCc9RH+IE1tMlpdtP4061hGoOCNpOMS7y7jL2Okc4QUUzZDR9hqtp9cwY4qsocoa5qqPVA55VZacp3FJ/eERw+sncYQDmaFwBSoT6IaPtBahTel0FJLwzCayPypb1SiYTBMq64Js0UU3Q5QUqGodncRlzEeDzTKIk/J7pHzFqD18ETU2xugZhT6osGmMXO+D52IbFawjcDZikpt2459cxNmIyuYla1GdAepmh2I+wBqDKDRWCpyswIsGxEd2oJs+1UsjWtVF0mOCXmUS4wnslIdQEicpKK6sQbuC1RqRW2Q8otg5U0bBPEVTgpaK+nSNepSx4bkIV+IkMAgMXzl5itceuqZcLyUZtsix7hZ8X4NxBCeeOF8OM4VAG8ux7gA94fFPfvVjLN9kCELF63Zez6vfcIQHHniBrJeidYFUZez04tnVv/E68PvfeQvnLq3TsSlH9s29+N8H2TkeWfxp2sEN3DzzK3/j617VVV3VVV3Vf11Xoz3fBWrPCKKZgLTtMfte8V/8/MirAuxggM0yVC/HUm5EbaE59fh5sv+Phg5pBdYRSLMFrQMm58ZYvLDKP/++38QVGcpVWGtIp326r5gl2lFHCFFuBj0XmWustJitE6NXXruXH3vVy5ChhxCCtCjPcPZeWEL964jKX8aotR4Ulmtec5S52w5RVZLK4gaDJ7eemKMQwxgRR4hCo60ur18Y/I5G9AzzF9YBsEZw9vlpTi1sJ/Msc16dH7njLC17H29/2XFkAc12BUsJojs+v84/evMfc83ueW7atvytvHeaohY7+MSYDUMzHoIyCE0J4hOCfLzC6NA4xlOoXJIsxxxf2sSxCT/5dx7gPa9/hpnJHnf98GO8+QcfJMxzhIHK8+t0lrvgSJDgr8R4i0NUN0UNYkSaguMisEwcXufI959i3/fu5Z7v3Y62EmMlqZRkNUPRCqjsSAiUwb0uY7VwGBzMuG5sibt3nkL4CTKzFJnFpCnZ3nHy6Rr96xqMDrbo7Q9w+ineeoSzliBGGnWwWTYYGUHQOMWzD++i0YwIOjnCEbC9hvU9pAGZavRMiJ2GfEcLXfPRgcPB6zchz0tnTZKhBikYQ16TFAe3gR+A7+NIgxQW/4rD6P46ouoiJPi1gusri4iMsgnCdchfEdL7wZBr/nSBV961wh3/6iQTb1th9acajP5JRj9W7PzbXfa5HdrbM4rMUGuE/KeHf4Gf/vV3M39intRaikaAqYdlS4spYyudvQEPTu1DV2W5KN7KX7zunS+lmKmiaz6yVcXZNlHWDRtQI8PlC1OcOL2Lr50/zF9++gRrXY1p1bHNCk4Aly9NMjexxrvveJAd7TVUurVgthqyDAFYrcmqElFItCvLjYiibOuo18rqTQnGlRQ1STT0CPwMXQ/oHx4n8y24EhMqtBTMujE73YLtbsabdpzlYiChNiJ1c+SpDpejNkbDIAn4uZ/6CLsvnKayGvEjP/M8hz89Ip32sFKg8hx5yeD3DG5qCY53GOZ9nIsd9j53Bl80SlhoYTEO6JZABTleNWPW6SMp4wtuzyB1iowynH5O3rBYF/KGYKaSsqpdlh+vc/yz28oK8hlL8I8tzpyl5caMeRF77RrZuOJ9b/0S733dQxx4VYwdjLBphpHgdTOMtMRtFz1WYWJpxB3Tp6nfmGKtoag5DI9Okh+a5fv+6Vl+6E0P4g5zZKapd7vYWqWMxYSlC69QBcNv+Dy8MUs3DnjPzKNkbdh/zQLb9q/xJ+JlnL+nimlapE7x+gX5pofOoLfgIQJNUQUrJY3zOY3LGreT8bbt3ySrgnGhqFtywAYeJpaYTOKkoqyAdSQqU+wyA5SwCAO9+ZDiiuT5+ye4PBIkLYekqjC5YXq1X4JO59cJHjqJs9ClMjXk/tEunh5t48BNXVqtDqunC1478zROVL5uFsr79bLL+rkGuRBURinxpEPWcIkmythWsDRA9RKyRgkDFVICZUTEVrxyIxo4UFiWuy2ahyz+MAKtsdrgjCvyTR/XqXD6hW384S8c4dnFvchYEyzl+KcSgpPrmEIj4gLtCdKWKAeOhcUagfBcvL7GHWicGGrfmEd1YpT0qS5CuFAySYqxgKIVkM7WytfUVQjXJZkOGM0oZGgQ65ZQlJ9BG8XsONvhvWPf5IPv+DNurazyfa3T1LY7+JWMWjuiPd5DCosMQmy9gq77ZZVuZ4QyFluvYptVqAqs55UQ6aJsrMNaROBhswwhFY7KMWHp3lC5xR1sRQe1QehyGJjXFBsvn2A460CzVrYmKYWoVxEIpKsIBgIvgqwmsVmBHKVUz/VRlSrWDXESKKpuWVtP2c432CPIxgVmpokdq5DMhIx2V8GxmNAjnq2Q31yAMQjHwTiCoh1iHRehFOnI53c+fj0bVQfbyugebpTOuqJAKoVN0tJtl5XDN2vNVnuSKYdBShBeTnC7thwopUkJ7W2GiOUN/PkO/vkN5MQEdqIO2pTrFWMovrZJ8cptpEd3oF1VxuWi0tnnrPWRcYHz5pSJZztUPpnib4wwLiSTBgIJnsS5fRbTWUOdX0QhQShUZ4i72EFGCeGRFUyas7IYs+NlEXvskFYgSXZJTE3yk3/y+RfXS0vnV5GejxSKvOURzwQM91XYXO1t1UUbFk4t4KwOqJ3psZENSBuasZcusM4JrLVIC3WArWgTSuFscd/+Jrrp6C527p/koWMX+ORXj73434+v/w5nkoineo+W3Jmruqqruqqr+h+mq46U7wI1gzEO/MMT6EJx07Z7/ouf3/2Wec7fH+CkARdriuxzUWmZVZI8/3YLqOpppAUZG9zwW+6WlcsbRP0Y0AgJxihkIaBaRVSGZUuA6yBct2SCZAVFpr/t2pM7x8mN5U3vuBmAvWvrXDGGoKZZjSuINOPu117LQysdLt/fwYsNRZGVDAwB+w7v4MrJVRDgFBIaPtm4T9YUTGSK5pTP4uUQYSzeWoL2qjiJZmF6yGr/BdqtnLF6Qh4Ktu+b4Uf+xTt58NT7eeXNfdqB5Ldu+hIL5z/Cf7CfxKZ56YIJAmJpkZGLziShH5UbCAtIhR5v4CbgDlIKz2B3QNP1iBXEsxK3rthnNriteQFXaC68Y4LB8fL0fu3KZ4iXVhCdFOsq3veL30tNCH7t7/4RVhvCRkCe5ry0cZl7D5zgQ5/YzjcejEkHORg4lUwjC0Hs+niXHazyGG1WaTkrHL88y8lTezlwwyV4pIozY8gqZQZeFAKhBIVviK7zSjChsWWd5zDG29lCzrQwGxHGh8VOi7FkyOKlMUzVKReIlRCSbhlLcSW3/+YC/aLOE3+4F9Xtg4Urq3UYawKlPdt4Du6wAGlxLnegMOhFw83VC7RVxJMP7cbd1FjXg1HKrv0rHEpX+EsoX4usoPmNgto/jNDSwS4JHv71GSLjU7zO52BjgamghxAOzza2MfUDA/SFKeZTwQsX1nnZPUe5fGKez//lCZJWFQqDSDLsuEPW9pAfqOFOKLIpSRCVkSRVpNjWWRrBkFgrbG5JYgFeGWfxYstRZ5l33fwCse/wG6deTve8V74/4pyoqnjh6+Mc/bFTfH50LTfcchk+sQVqrIaIUYJwXDAaJ7EYuRVbSA0qFmhXolsh2m8STwmK8YDaZ1fx1kf0X9sm2dNCBg7uwQp266OsD8/xn/9xlbv/4MtgBF84e4D215fwTmv8hov2XPJLEY19GRUn4+lnZsljRXMiQTpga5K8VsIrZaxQ6wlyMESPVVFpRtjJ0KHD5JGci5sCHUjMVpzHCosYKBCatkpZU3WcMOW691xidDzkhcfmUIVBuAVZ4CIyeN8P/Cg0A/yHT2KMpvLaAdIWWCuRrks/D8rXwrUECwmPme3oRLH/zZe5+KHdWCsZHJ3C0YL6yS4ygf6uKtmJgAM/uMwNd7d58Nk2NpDk4wqx6HH53BjbmmtlnMBapLLoqo/0gvLDWWgmzm6wtrvBCx+d4+APrvELL7ydfds3uG3yAr28wuLmdbx6/AWedA5x5I0LfP3Pj5YOEws6EVTzjNR6ZTzCkQgr6NbgY8u3Qr1A11xkIZBJhsg0eU1iAg933r44mLKuYPvMGkvGwxGG0dBifU0+lAyO+Iwa0HhB0locUDRcRKiR3QFClwDw6YNd2k5EjMNf/MkUOw71mb9Q5cCRDb6Q3oQ1Aq8Hcn2I88V1vvjFa+m/scVkrMlDQdYUiASyXBPEGW6SEWi3dMxJBysV8biL1zeluwpBHhiWRy1aSwVTN3RZOeOgdE79UEb/UgqDHBHB5rqHc22NfJtP8/kharVXOiDychAptMRdM2RjLsaAqiucjiljlQJsbhkcncI/kZXxxL7FSo/4hjq6OUFeDVDLmwhr8ZOyBlpXHcLnVlg7I/n48Ea6/gSmbnF8y9TOVXbNdgiVxUEQDz3iKzXcbMCN7zzH+c95DJ/ZhHYNPVtBjHLkHJiLCplYnLzAKg9zqYdbq6FbVUSmSwdNkcHmCOp1sIbdN1/mTBSSpCF53WA8iTPIsY0mojAUVUWwNMLdiDD1EFMtkFvOM3wXtMWJDEXNIh1BOuZCT+Fub5HNNnDP9UunXVYQ7x/D5hYpJc4wp/7YOtWmhxgJnPWUZH+1PFIzYCmQK13Us0NEzcUqhQBMzSPZP0btQh+LJQoqZImHkC6hC2Sd8vfSvGxBi6LyMMKUrkYrt6CtW+BcLQ3OsMBbS7Du/83ef8dJdh1k3vj3nHNj5c5hZnqyNCONspUly1HO2WCwMZi0Jud3zeKXtLAm+yV6FzAGL9iAs42TbEu2cpZGYTQ59nRPx8pVN55zfn/ctowWlt/CZjPP51N/9K3qqtvVN5zznCd4iGoFU/aLGuQ0R/YjTK2Ckxj0xVuQz8wjlMJm4Ny7QH7tDvSOKbrbOox+rgNYnGaEszWELwasp3WkjHBWIsoPzyMTgVntseX528gHGSdnyqBLZI7EObWM7EaILEelKeGuDtg6eW+IHZvk9b/R4FsmfphLf/OPQIJMLH9x1wO84/nX4VQCRBhgpaC0FKGVwJqAPE5wUAhH0e0McaVTKGnPSaam+nzXRXeT54pO//tRmWZ+rYvwC+tmOfC49sbd/6Kx4EXbp3jpNReyc/PYs9tc5yKMfQwrwn/Re57HeZzH/43YUG7+b8f/CfvwPxfnFSnfBGgOYo6lM5y2Ezx9JvsHz+/wR/ix33qU9/z5BHlNY/NikogQzF2yDefvhaFJLO7AYioexlEMOkPu+tgD7LxkM9/3y2+mvDvHOhLtu4WtRSrseBVT2qj6k0WQni47RYPH34MTeLiVoAhiBV7y1n286WcO8vbfe4JgrIpXDrn9Iw/SPNckHvdAKnLfLWYmxuD5xYTKGgt+gAh8pLZE45LXvOIyfu0D318M2tKc0qqmdmJAeTHGDDWm0uGppEYU5ggJXuDwxh9+GZf+QIcDWYmesdzRGuHR2hfwqkExWC2HmG6XmWoLW7TEkqaF5UWY4iI1NV0tvO1Ad5fHmWqMm0LqhexPJnkqHmc67PLhJ67ns2cu58iT2zDVCsm3jTGz9VPEwxyqZaK5Ou/9xN0cW2ljGhVMrUTUHsBwSNZOkMKyerpHvxMhrEIIRSJc8qpBtVrYuKitzFKXZlrhls3z/M4bP0q8XAMrMI5AA8mmCm5ziNPPcYYJeQh5FWzoYcoe5fEK8e4R1gY94gmIJi0d47N6uI7J5LODYjvMi5YLbSDRHP7zWbxShhfE0B0gekPOrVSxWQ7DCOsqRKYRCLzaEJVrhOciOy6f/9sr+OufeTmnH7sQcc5HlzN0p8+Re6r8weuuL1QHgL7IgVdKKoHBlylxz6BXA5w1ifton+bQ4wUjR3no7A6OnZvkaDwOyy2Gy+sIbfj0+7+G8H2clSbimdNw7CwiTrjkkjm+8w03IiJBsGDw20Xor40TJl7YZ3jVnex9ywlEqrFZjk5ijCNACuZ2rFI600cFGs9JWfz8CToHXUSzDXHKngtPs3lqiXuP7aV/3wQPPHQp0biDHvbQpghJtlmGtRYn06hBTm+LIqsLspogryuG74TkzRlIg1bgLyWonsU7kOGe6yC7CaVoSG24wpW7n0I5EnMu5zv3P59b/uT70bHEWc0LwsaC8CzZJwXvmL6f75p4iJV2QNvxefTCaX756C188amLcTYlJOOQz0h0JcI7vURw8jSeG0CtTFYvsTZRR2QW1Y2wEpISyFQgVwLMmRJJ3cOr5SRDOP5nFV71hoPMjC4j0pRyJcYZCFQiiCZc8pKD9mTR9DLlEScl4q94iA8IpAQlgUko2YgL/VWuK50mm1Bse03C5Bstg31jJFMBxhXgCrTr0Hhhk9i4LK2HyO4Qd7FH4+mEeCLhyUfG+eR/3AWBB+WQuC0ZNCwiLyqS/aGmtCOnemOEDUZ5ZnWaVlpin7fEq2tHuLl6AhO7dFYrKODiC+YZ2dLHuiDjHDJLdNZDJQJMYX1cu6WLuHGIk1oaBy2laMBoe43xJ5aQqx2sB9XxIbv3nMGNQRmF8SW10hBjfXq6zInFCZb3gY1ipj+ywOijK2SNwk0lowRTDnBGG8SXzJFtH+Oi608W4azA/ffv5pTdQvWlZf768eeTVYq6eplqRr+2iBwkqDNt7CmJ7sTIToKKQFqItpexroOVFPWyaYZxJTZwkNLBXethsegA9EwZZyxlxw3zaJswvHqOTT8lGJ50GT6T4cQFia+nG9Du4p1ZweQZJnTQlZBk+zi6VByrWQ1sYLGOIPMEg0kKdUNehF3bWgDW4AwSjLDI1OCfcwiP9qjdfoTKIwu4Z9tYa7GOwlTKSOVh44SnwkkWGqMFAeQ4nDk8zV2fu5zf+s2X8uT+CX70jtcznDCksUS0YbSR4Hqa/lRA7hrkdELybYLhm2rk9RCRZchhUjT0xBkiyTGOJK96xOMlROAiHIXNc9aerJAbhd+M8FoR2rHoSqHqQQrcXo7sxMV9JokRUYpxHbKREnktIC97OJ0IfzFCRCne2R6ltSH+akT5ZBuV5Ih2D9kdFo1cumgkkrnB70Je9uhc0WAw52EcU7TRWUv5aJ/g7ACRSWyvB90e9IfoPEEAuuwV4dIfSUj7kGjwTvSLezQUhLfYaNhzHGS9hiyFIAqFq+10MXGELYeoBMT4CM7kOMJzcXsZ2VwN4fuFhejMKlIq3KU+ye5ZCAPk7CTS8XAeOYm1ltG7irGM1QVRHzQERpVBSazvYaKI4ZWbaL14kt6WGkcPrLN+bEDwyBrOkTM4B0+jTXHO564inRzl8Kc2ITKDl2nqc3DqgQbz51psnS0jrCZtGH7jC/dx28cf4SPvux2bJFidsuearQgD4fwAZUDkGsdzaEw20MJgTE46GRJ4MVvdJheUVgm9BOG7GCUhK+quhSPZsu0bTT//HER5zjPDdQ53ms9uGw324ckq0+ElxQLa34O1ls+eeR8PLn/8X/R553Ee53Ee/9pxnkj5JsCDjx9j5ViDldMNHn3o7D94fmzk37N37Neo1X4EO6qRUoE2WG1I4hTlfOMwEIFh+0VrOKEmz3Lu/Oj9fPg9n+ALf3YHF127i9VnqhjjIFKDAKq+g2hZbKwLL7PWZBuBk+5/IU/Vg5i8HxN3hwBcuPmH2XP5y/nKf3orEkGlGnDdyy9lYtckTismz3LsIClaC3yfHZeOI0oholImB7QCMQ52NuX1134J270Jk8RkszVEN0ZGmjoKN3TQTsaM06Kri/pkRxYsT8XfSgvF7YMKW7wmT0ZPsyLnIdOQJFAp4TgCpMT6Eq0KCbV1JDbP2bN9lgt3TlGfrZKP+ThKsmXrGFoKcqkYGI/IeKz/3ihP/cGF1B6OEUD5cy280hswYbmYzIUeeaZ58uET2IMn4PApTBxj4xiZQk3mdM+lmG4fax0sivFKHz3RwcuzZ4me/dEOvrh+CbOlDp7UjIV9rNBkFYe0Ilj8vt3osouKc/IpQTaZYaZT8sAhC13SwMU+sUz1awtkVYtVglUZMOo0C2l1oDAOiNUhshwWj1KITARn5sfY97PPFLaXPGehP1q0z4zUsbnFeC4q1YhNGRNXtxEYtl84y7+95A2YiiIqKayUiFNDpOORL1rM0GAF3PiW03zrjx0gvCBjOarR1BVER2LWDbKbUf9il5m/6pHkDmvdGnnsEpc8rLHIXsR41efgw8e57UP3EnUHMDuO2DqDrVW48rqdXHLddrIJr7BTWRBJQUiuj1dYcz0WSjWskogggNE6MrMY36FUuQKzR/OEqfJwv1FUhcYx6WhItHuUa153jEotQz7hMv5wh/qDCdnmgPRNFdJXVSlf66CnG9gLt/AXf/VDfO3un4dvd8hLmrwEWQgXXLzA3HCJ2hNtRh5Yofe8TfSu2YTThuFmn9YlHmuVGbrlMR4/ejE6lwSe4DvnHkMYcIRH4opC1t3rkdQG9J7ymVKaCcfwquvP4jYhfq/PD0w+wN+98k/ZsmeZYEeP7TtWGDy/RH5BnXhuDJlrhIF4S43V1gg0B1QON9G1NvGkxOkLZAYiEzy1NEPPLWO0Q6flc+DJcVpnHcQg4eqLn0HkRUMLUjGclnRevpv11+zC1HxUkOG+SeN8i8FoUGhi1yFNfRaTOl0dsJpVKG9LWB0d59ot81TmLE6e4McJpbMpKhesdmsMl6sIIxCeR7DQpXRmnZc//3He+jOHEFkXun3sMCabKRXEoBRFU5jNMQ1BuKYxXxNEB+vMMGRUZmxSfUye85rpU7T6Zf76T29mMAyxg6yoqM0NIqP4Liy84ae/yI+/6G5edMEJkusi3Aj8fWs0Lh8ipI90XPzFiIqXcM0NBzE1jRUaIyzVSsLRtEYrdVnMx8lcH5EZxCCj8kyP8c8uI7IcnSswFoGgcqxF9ejSxnVccvSjNYgNj96/jU8++jzW1quM3teh+tQaeQVUbMBoDIbSUgJBQO3QEKOKzAd/PcOMVbDDCDssVAQmDNG1AHd9gDNIcRabDG8s0rKyxCPbY2k9U8eM1zhx5gK6hwNMpNBlj7wWoAY5sj3AWM3ai0ZYev0WhnsnGG6p0NlTQeuEZIdBZAKZW7IRQTrhol0QuUHlhuqxFJkanPU+pcUB/nyL5VdvIp2tY+plQCL9oLhGSUWw1CfeXmd40TjxrhK93S7GKc6NuGV55p4tPPHAZv7Dj72Qhe44qqtBW7Zetsqrf/Y0V/6biGw6JGjliGaAWJfQF1itAQWVEBWGiChBxhnWc9AlFyMF0dwoRhdtUL3FMulAoSKNzAzV00NkZjAC8pLCicENy2RTI2SNEsLxEEhUXhD41lfkJYfmZSGi2yU4voIxGonCjzJk4CJ9F1MvEdouTjeFNCcrCSTQuzJAZAZdcRBaUz7WRVpB+4oy8eYS7pikPBOBMFiTI3OLd7aNTRK6F40xHPEoXdVj5PoOnZvKJJNhsTgTFsGsBD7C8woFiueBcgpSHRD9COdsE5VYZD/HSolJEkhS3LbB2A3yXRb3XZTEP7mCDcPCQpQmuIMUVlaLamhTkDii2cYpNxFRkTditKb3gm34LUlpxSW6eIrhlbOs3RiDtgWpIATY4v9nZhsIxyEzVUqzY2y/bo7F1hiPfLnLRz/+ELf/wDshkTgdCQn8wf/7UeafWSjChxFs3jSCRaPHyljfB9/DGEu1FuIhsPUqelySoAiFIcSQ62eKwZE1WF8VWSvW0mkPnx03WWv56Yd/im+7+8doRuv/5Fhwod3lTLPNI6cXnt3WzxcI1YAkf+ZZ8v7r+Mtj/5EZ+xs48U/TjY7/k+99HudxHudxHv8Q54mUbwIcXbsDZ8FHngiY2LL6D54XagZZej1CNhg+rgBb1AAKwbnjS6Rx+uxr3/zih3jNd3+Nt3zrA2TG0lzvs7TSZ2mhDUB3e4BQMXk1hLzIVFH9CBHFEMWkPvQvm8Q4gk3TjefsRzqIyQdx0aICSFUmb7+DxfkAjWVu7yzf9pOv4FtvvZLXffv1xaQgybBphh0M2LKnjU1SbJqhJyVSKK654BTfde3dnB3eg2PbNF+4meUbR+i+aopLfvAYUy8+RfjoOnf9p900s5A8VVgHvA0VzoQ/wlk9QktXmA0M7dTjktecK0iUJIMgpOuNg3FwhUK0BuDKogWpavnRd70CN3AZC0J+9gU38u6bns9Ss4Py4frxY1xXP8FqUsFfT6kf7CItkGvUssEN3wYGyAzh2QE7E5fWgcWiBcH3C7JLumSDgIYskS1lFAtKApAcSyeQsU9FZIWHfDBkZfcojy9v4r1P38JHPvdiur8+ggk9tA/CzVADg/FBRRo3yXEbMaKkMYHC+pLYWNyOQcUamQqsNBgrka1u0eYAIAX//j99rVCklEMIPE6MNTg1UePh9TlwXXA9Ah2DVJjAIx3zELkmGQvIV11W1BbCi6b5gd96K1mUkIYOYiUvVCuqhKhWkJUyoiaIdo5xsrObx798EQ1iQmlZikZY++hIUS1qFe5pwcoXQ+QBFzN0sImkLIrsA4Tk9GqfV7z9ZuZ2TyEDH5EXLUJCKe7+u8eYnGrQn/HJXY3oxeB74LosRJN8/tDlPHZ8ByKKikyTQYxVhWd/PDc4HcuxbIRT+QhpXWKiCNHuo0sOk+M+jmMoH26jFtbxj60QXJWiXu+jX+Wy/3XbWXvRKMJ1mBqroLVB5jlaDskCyKuGx07MFDWqUmzkJDhIt4S3lpDVHXRZ0d/kgHEwwqVbXufgO2f4rQdfRe7ZIoBUeRgFWliCeY23nuCKBEHG+MiwqBNXig/+wmXc9pHtvNXbzzv3Pszk12IatymGL55muLlRTBjzHKkNpbtXkPOrpDbCe7qD9otcF12xYGBu5xr5hIZhgs0s996+hcRWsK7PNY3jlM9pyksaGypEJjChT7q5zORDLdyyxpQlJrAsNuuc7YywFlWIpgNuW9/Dzz/1Gh5d3cYTH5vl4tIKP3D517il9WQh4c8lfjvhwMd2c+enr+DUkTFEEBR1xkmGe0zw5O3TbLmoyfhcipUKSiEiBRv62MBDey4dWSd1XIJuzuSRgC2DNrLU5nCvxi/e8TJkq8bM1i7rm6usd0fo2jLpdKkgNY3BaSZYVZytJ5I6l3ln2Bo0kUPLzT/0AE5QoifcZ5WvE7e36C+XefSp3QwrDmQaJ9IEU0OU1HSMT7TVZ/yJBJUqupdO4OZl3OkaeehhyzlyuYuz1EW4Lpt/qIlPytqDHof+qI5cXkcMB2hfYo2mdKRJcLpbtF6NBlhHkU+XcJJCaeb4DpN3LnHd+n7KrbS4HjsOyvVZekeVc+8QDKYsRCmmXsHUy9img0gFnA44/dk5tPDRvuKabz1EOJ6B4xQtJ1oj0iKg1hj97HcQLAzQLsQTknRKkTgO5MVl0hlYwsUMHEVedUlHArJRH131cfUANb+G7LcZ+/wpxGBI/6btxNdsw5b8Z9WS3mpEPCHJNlWQWlI+FmECl9wtBkNOK8KZX0X0h1gnIzg4QK13cb2EvuNQChIwmqShsInA+7AhuDNCJYZ8olZkXMRFEK91FTosrDFKCkSii7p2zy0IYyGJtlToz5WwmCLc1VEIq1A5RZg5RaOd1RnaKdSAItdF1lTdIa8p4rlqkQOz0UyUa9DNLpAznKuiFmJKBxcQOse6krzi4y+kuOsx2imUKGpoKD++QOVAG0cLrFZc8CND7E6faGuNZEsN40oYxgQnm6RbAi6dWOCGmeM09rbIxuvY0EN67kYwfKEitWmKzfNCnVKtIkolQGAm6wipyH2LXV5FnlvDrreRG1ZH6W2QJp0uNooRvo/sD6DdhSTDei7eWozuDQriU4Auu7SbLna9jUgzZJZjpUAHkIUCNy0yfYb+LBf/+1Ve9eOnir/dQr5lvFg8cRW4DuupplQL6IgE2+xw9wfv47VX/gKqLPFSQTpmGPaioqHIcbDW8ujn9zNlwesUgfm4Cs938X2XkakGxhOIDiy3R+glVZK8hrUXEacZzga/YRRMjFbYt2/zs+OmZnyEpl5jR+UMv3Lgt/6r48BzKx2cyPJjL76ed73i+c9un/RG2eE0ucAXSOE953ceWl3HWoG2Aledt/6cx3l8s+DrrT3/Jzy+2XE+I+WbANfPbOaj8TlMqrhl64F/8rXyjCCdCvCXU3AUW7aNEZSDZ58f7kq4L55jdFfEllXJXZ96hCSzPHH/MQAa2yReM8UdgnUtOsqLAZYAawwMhoTLGtlw2Lt75jmfPRSK3FU0B99YFRFZTvfMGmm7z9MPHuJTH7yHB+44xGu/83o+n+lvvK/r0Yt3MNi9hreUEr/FR3zC5eoLVrl5cpE/PvIqauv7ERZmv7zC5BtaTF7XxlzTZXDPNroLO8jiJl+5/0JUanE37EXXTr2bhvoPTFdfzl8efS+vqh/k151Xs3WkCIsTxqBbVWQvKuThGlRe2FrCF/VxpESERY1lrRrz8dufZt30MYFDuuLjjCWcTcawcQfh12CQYpMEWUtop4uQ50XAoFJMz41RqgasPXgE4RRhtjY3JMPdGPPDNEa/QOtsUsiopWB/extqycF2XYSrsMZQvWPA8quqDM8EbI+20KmdYpCnZK5l/FOnmUCQj1cRGZSaioEb4nk5WalMNqKoOh7xAnQuHyevGmw9h1iw68oOZ4/rZz3vYluKDBJMDNpqgqdT1rbW2b6/TBYtQG4ZUQOGOkD1cvSLodWtITfHyGMeSVuj4pQP/cc7MN2oCJYsOZQoCA6bZeBIpn5tyIn3KZbnQ+S4y9A4GOsgbc5orURz5xjOyQWINTrRPHHXKGxyURZ6lZCSALfss2nTKKbVI8s0ylGYOENIieO5VMdrzG4ZZeXKkE2PLuPEDsQJwnMRacDwbECwnGM8DztsYnJF2PMwUcKTDxxFXWCpZzWsFQTjljj0EEmKd2qd6akOduigejFig7zburDEwYnNJKoIhu3sdRl/dMgHf+cL7N23CXUsJzy2TudlRTZQpiu4zyyTJ0PS0QpCSLQL668aw5QKiTyV4oZlwpzOt9ZR8wH5Ekw+do7BjjqVJIZ2H71rE7rq4B0+Q7vtslYuF9W1sUYAZ5JxTv/eJFe86RyHj17IYF4RSM3A+OQVsGmGMJbw+Doi3wimbsf4SYIeybCRIh/JcZYV+ZckP/iTd/Hnz1wHSvCmH7mHz/zRdTTXajxzYBN5SWyc34I8pGAcrGHuheus/meD6Gj0kqB26ZC2rZDnUO+uMb8wQnSoztCrsjVZ4ewXBB95ZBurawFgN/J4FAaX/c/swsgm4bC7UStUtBzNXBrRHwT0rY/QOWQZalBGD3soLZGORCQ+XislrShMzcF0LX9w8tW4xwW59FEJvP53v5MQhdUp1vGxKEzJQWSGmQsXOWFrWAEzA8NXows4fHycty2f4JwzQTsMwRjqzhBEkVvUj12e3r8D4QrykoPXzBkd6VKTCT3rYuMeWis8qdCNCm7eRHeGmFoJZ3WIyIuMHaMkmy9vcySZxsxY4tQHV5LXA6y0ZFWX3r4Gsp+jhppspoEbQz5eJkgzbK5RvRRTLdFplXCGeUEOjFTJ8yGDC/2NsO0elbtaCC3AdTDxWNHIpRVuW6A3Jvgz4y1GflVw789sgVZhBbP9CIYxrusy/oVzmHIZt5tikxGMlfRjzeh9EilyrLJoBOF8hGmUCyJTCWQ/w1nqkFFC+AopwW3G6HSI2KGJt7iEz0RYPyxIoGGG39JUDncI5hXhcgpxhkkzGGkUFhTPBTTTH1hDRAnCcThx5wizWwYcuH2Oaiti9boaIoLKGoViUsqiotgPUEuD4n3afWTZRWWW3BVkFR/6ww1bbUFwWgEYQTzmUl7OoR+hrGA44z2bveE/vY5cbGOqIWJmGpFnCCQulnDCJ68EtG/eSmX/OVQ7JZ4oUVkBm0m8pSFBNEALgTsAtEDlgtKxnHiujPUkuXXJHU14qImz6qK0T1Kpc/edc5SyAT6CyHfpXzRB6VQHE3qMHEnYXG7jSs3Fu5bYr0YBCVGEcD3QRfgsWiM8r7D6CFGE1+Y5YpDAeBkZ58X9HQG5RpcdlJYFWWzNRgNQETZrhkNEGIISiGzDwpymWCXBc0j2zJI5ZUpjq5gRB69pKZ3s0720gTO0yLzI1TENlwd27OK7Xr+fs0fHeOLLo6jlNrpaQQRF2C+OYv/H92PK+0ifGUDJIwIaEn7uez9BxUn5Redm3N/NEVkOlZCJC6fpHWtCZiFNIfAQvsNwkEDgobKUaFqAkSzFDf7u327j5IPvov2iHfh1SdjTbN48irWWO778NC+4dR8Ao8FudoUr+DKn7Lf/0XGdtZb3/sUddIcxYxeMcMXOTcxd2yieM+cYdSI8sYS1A4SoPPt7i3GdnzrwBpQUfHXb7D85djyP8ziP8ziPf4jzipRvAkizl1983qf4xVs+wf7/dMk/+VpveUD8sipMjkCthM4y9N8LnF3SZa4unaV5yOexrzzB9a+9imCkyvSFm1k6tUJ9zxK6K3GGBrer8TKDaZQxjQp4Lkq4qF5Csjn8h35cIdCeT/L3iJRcSrQEx7dMbW1iN30MgKgdYY3GZlmxSqdzHjt5BPMyjfgdTb4qoTng2heeRGO4abCDv/iTt1J5qk06WaWvy/S0T9cELDma+OWXUpPv44lnLkAYwdfdTKG7ictn3keSt7hloszr9vw+PzJhiHaOIZIUMRjS8BRECTZOMfWNphdPMZitceLAGV78xip73/EQX174FAv9c0RJl7jr8zcfeCGPHd7B8fVxlv59Hb28jG13NlYGR4mzJ7FJWgzCs4xDBxZpVFxsHBe+640VtV46zu/93HGCso90BQbQ1iAebDDxmCK1IXgewlGU7jzL1l85xVX3LnLw7oPErR6yHyPjHKlB2GIgDRLPWKbrXWbqPYYXGbLrYtaDBJVD5UgHrMY/5eE2FY3xFHKK5hhH8LU755i7SZBtnQDfYYoubys9xebrTyLCEFEKCcm56seOoUs+s+0WW156mr21k2ghaO0p45QFzfkmy4tNRC4Jjjeh2SlUH1mGHcYsf0ghBzH5uTaHX1diaTBCM6qw9lSDdV1HpRoG6UZAaE54aY5MBDIVVGSKfY0k7Q1YWmxx19QSZ3+wyr/5tbdA6CI8h+lLt2FdlyTOMCXNyptnSbbWsKUAWa3gpJJwSeAYh/TnU+LXbofpUUw/xuaG0lSVxusMFSciyDJUCmLbZlS5hsw0n/n4VvK5IdK0Mc02Zr1NdlLSyioMo5DSwQjR0xhf8ekvPMFX7zxIGmZM3tZDZQk4htrpPitPlHD7GeF8D2dYBD7PPCiK6lxpSULFcBTSqyIclWMCg9eK8VczwrMx7moHkgzVT5Gn2xhj+dqZOf5i8To+1L+mkOC7LvHZCp3n1RhpQLfto0su0WRIbiXKyuJYBdxhDqGPCHxcx8O1ASaDfDyHVCKGgpXVEn/6o1fTvKjMm973CHIH9HQFU/I5dWYzcqNeW2qLP7AEHYswmhvffAx2CPI5D3uNy87aMsZAycvY+avnaCzGlFYk5bMOmVcQukOjSMa9wiagBbbTQTtFaG/QUMW5pDUCjXEdvnTvZfzEy19O1PGwSmFdh9JyhEqyYjKYZJgHBYPUx2iN09OojkTmFjGaUjkhKS9I4qCK9hI2v3AVqhrtC0TgI2oOL3/bfrRv0T6kxudWu8TeQ4bVlTLH4jG8Ryu4+2sYYTF2g0AUOVrZQpEgwSjFWGWAtRBIzfSjEdWlIcIPcFciSDWqM8RZa2GdgkTCGoJyyg2j89RVxsymIfElOxH1GnlQpAJb39K+pobfSpn6/DzVI12EAb9HcX3baESRg4Ret1qo8FQRFmpLPq6ncdwc6aSFuqscQOBSqrTIPRBeztp4Ye2sf+kQ9/2bMRY/PYrWLul0hWyyAjpHhiGmHtK+aRPDS+qk03XCoSVoWdxKQST2xyQYiDdJersKskymBpUZrKSYtGc5ZDlZutG1lSXYmQ7BYIiUCtvrY9ptkhLkJYHspzjNCD0YFueF68IwAgU0yojNQaGGpFCBmIpFVnP8sRRFxvjjfby6KfK+hMSWA+QghWa3aMipV6BexelnBbeXa5wkQ8jCfqVEsfhQOdSkfmAdmRiyqofsx4jusOAUfQepJE4zwo41UJUqstmBwEUYQ152cDKBkwJKIlOLjDKkkRD44Li4Q81grE4+UUcEPpjimMouM1ROxyAM0mhEuUQ6N0KQeji9FOIIBqBLDnnooBJN5WATJzIEHcPYTT36qc9AB3RPVZCpRtvChglAEEC9glWSvOaSV5yNsFmFGh3F0QojcozVRZOPAOMpuldPYxsV8lAVyp2vW4PM11OcC/WSNbZQ/7hOkYcSekgpERryi8usvrLCyotKjNx5jplPnMVpQloVGAXpzj5rgzpD49CcB5trhAV1bhk7jEg9ifad4jz58hI2josME0+x9/SQXaU2s96QK25aLP4mbdi9aZxovsfqapEVZLIMYS1SCeqbqsxcNEXVQGU5oZf6/MrdNzL/SAVHedTvPMlgs4MteWzZM8V8u88jB8/S60cACCHx5CT3Ht/LY8vxPzquE0Kwd8cUbtnl+HqT25468mxDj6P2ciqbZcleAv9F4OwLpy+ir0fYWf2nx43ncR7n8X8Z7P9Bj29ynCdSvgnw2P4l/uQn38D7f+R1nOv+0yFlP/Enb2fX6Dy0utj1Ds1zbZTzjVTYm8JFbqks434x59DXDjI5O8plt+xl6/Yxfusdf8TZTzgIqfFXIrxWQtocYiseIiwhXRepLWtXBwwKAfdzEacwiBk2/77/F0TFR3gOgSeozZ7jXb/zbdz6lmuLFSzfgzTDIliMLHtuK6PePmD0/edQSlGrSWpOxtVX/39850+9gOrOCdYv9znkTPPYwhwPntpOYlMO9Y/wH9u/yfTrj6KlRannJuHee/Qv6EbHeHD1L/jUR3yUFdhGBVMvcdVrn1dIsZXCKkVe9ckrHub9Ob/5/X/GfGPAx85s5uSyQjxsydclxlM00yqZkIR+ij8iab53M3ZuAjk9iRAe0cLWYhIXeJjAZ2g0C6fWCwl0uVT87aWALClybJKgQm3zNHK0DuPVYoDvgY0SyAsLlONZnLUO7FnjslsuIi2XEEFAuFK0EFmt0dJifIEtwaZyj/FwQG1Pm8ZcB29iiJcbRjPB+BFJZR6qJyVdpRFxjttKcZspn7ntZtpJWKSAGtDK5Ym7dtLaPwYbcvSVhQbiJomRms79ZfLfcTn7V1NYqQmOrKHXh1z7wr1c9LztoCTCblxzs69PjDLUPo2XWvTILKP7HbJYMYwctp/eRN0vY6ohSI2NY3AdPnXwOoImBE24ZuIY276vqBdum4gPnHiAk/4qvzr2AFe8IqQyJrn0yjmCGx7gr+Z/GDlU5GWPeMovvk/fw+uD3zJU+zAyPcSP1lBnOxhHQeBzzffdzPhon1m3w7TXwXVSSHJQIHxJnEle/46n+f6fe7pQ8ziKc2criJ6D6CimP7PKRfedIXM0mTUcObiEFxR2gF2/sMgFP3WK+ldzdNeCNshBine2ixpqprZ3GTlgiMsx6VhOc7tgreJRL/cY33oGshzhukWNrisLeXxngBwkWBT3re7kSGuCx1a3kPvFJNxf6zOl1/nC6W0oWzSoiDzDdCWltikG5xsWI5lq4hEfW6siGnX80ylyzUEOi5Ym7QuENngmptRI0C4E1RztS+LVAKU1TmIRKZBZjBAEXcMw9om9oq2nmwWciepMVXs0wpjlvMG+rWeJGxCNwegNPa64aYk0L1EeJsUSv+eSbiqTlQXWFUjHR70gwfo5uhYiUDirA2QnKiZCrlOsGLcSRKUgJQkD7DAhzUL8doYzyHA7Em9eIdqKeBSSBlgfKvUMbyon9Rx0KBEmpRQMedHIWTZvP4c/PuTMY9OIKOS6Nx5n9juOc/m+E5h6zMiFazQvq2/YXGKsNWghyH2wuiAtc0ewmlXIjCiCoTwXYSx+NyFpKPq7akzsW8c0ClVaqd7hlz/2aRp+zHeOHOQidx1TdohHQvyVAeWjLXAMNsyRGzXGeryK8EKMJ/BcjZ6rks0WZHt1JMMELtZ3EXGKzDICPyPwctjnIepVUBIziJjqnqXWGDA23aU3G2DXO8hBSueI4shTW8h79tnJrwm9jfwrByEsuSdw17t4awNKKxljx2PKzZTC/WRJZiCZcouwc1eShwrjgo4T5EYbjFAKPAfre4hhif6WKrbkFc+FJdxuSriUokfKdC8bo3vTKDIICoIjSiAdYLOUPDZ0Lh9F1muIMOTs8Un6gc+VbzmKyjO8tYjAS2AYb9hPU+R6F9XsYashOnCwrtogejQyjXHPtLCeA5VyQerloPIijyl88hzOcqtoQ6uEGF+Rh6Kw81T9jUWJjUwPKdG1EiIpziXtAbFGCReqZYKugSyDOEZ7oMslTJohrEAISV4WDDZVUJnFuAZdBScX+HmAFB7WVSjlU14ofifaFBKc6eGsdYsWpN6QTljiieVNPHF2hoX7xpFSQuAUlshSCL6HloJorsy5N2yi/bzRDSVl0XYm+ykyBdcJipxa18VsHidczVGJRUQpNk6K/9vGn43nFTYenbORrI41Bu1JTCXEWxigcoFjPURmcFs5ynMx7SGOBZlDXjPoMUUpzHj3w6/i5LktyLERcB2E56GOLeDsP4qoBGhHota7iE4fDp1EtAdsH9nFn5y9hD9b3Mfe0jLdS2u0v6XK+lKHqB9hk6wgtx2JJ0APIp655yBPPXaCYWtI6WCGsw7qb+PieEtSslGf+sFhoVDSFtoxen2IzooxlLaahx/ZTPfXRjn2C1vR+rltiF/H97z5Bn7vp9/IO190LT/58pueXchqpgdIzJBOeobM9J/zO5eNjbFvwuWVW3f8k+PG8ziP8ziP/xW46667eM1rXsPs7CxCCD71qU8953lrLb/0S7/E7OwsYRjyghe8gAMH/mknxv9snCdSvgnw4+98KUnVIa24XHP5ln/ytde/4EouLg2LDBAErfU+afoNRco9d+9hEPtUL1XMvXg3N7/mSn7pj7+bV3z7dQgp0CJAxwJ5dgXnzCpiYwXO6o0VI6MxSpJOZPT/nvIEQA9jyDJ6691nt1ksOgWjBZmUXLv5zzl6ZInXvvDXMYEPQiIqZSyWtOnxx196N4zVYXYCJSxWWCJr6RnBJz7wIa64cQdzLznN8y49wqn7trL80AyDPR7X3niAH5i9k5/c+xWMyFHyG4f+/rsO8tc/uZNP/9nFfGFxO9mEw/LzPOzyGnKlzR0fvr9YjRUUnvUchBbIVpt2K+LAEUHyZJnFE6MwzJEdg4w1KjKcuXOKXAuiSNH4mzW0lBgp0PWQRmMzVEN0vcTgwhGG0y7rS+vYLC1UKVGMXWuRrbb47p97LVmW0+8nSJ2hRhTJFktzj8QOs8KXn+c87+dXGX8tLHgNXv19LyR3JMZ3sV7RlCEQdOcUy7fU6O8qkRvIjMB5UiNPCSYOQ7Ue8oJXXIHfFfSuSulflvLAgZ14eY72AQfaV0sOZpN0JiXtC8ZwA0nSVgwWisA8G/gMVwLyvkD6DsZ1iJIy1ir81T6VZ5rkiy0+89XbaZUSVGyJ9swgN3nFRMtzsdawNj6CMGCrgqAtaAQRoZtyejjkXGjQVZcrf+iViLBQ6wz9CtG4IQ0NL9x6lDdsfQZTDumdbjFR7zFT71OvLvHU/oRBW3PXVx+g9XBCKJ5GlDKMq3GbCfQHWAFOZAhbhk2s8Zr2E8jHE0Q3LlZBJbSnDGOVHg01YKLSoe41kStN5CDFbRk27Vvn9CBEXGQQvoewFm0V4ZKitOzgRRp1KCOdCJmYqLG63CYKJKakELqw29iyBUcWbVvGIrXFuim1+hDlwMQBn7nPZWz98oAdH4bab03i/mkN61XQoUNWd7n6nUtk41WMEoVlLZcs31chzjz6sc/weYX03n9+ysTPJ+z6oXVOvtBiBBjHonPBz//QR7C+x65v6bHp2j42FPR3VMF3AMv4oz2cjkZ1wOkb7KQH4yNUypA5ihSH9miF4XRAc62B04v53m//Et/yqnvIpg3yqh5JVfLZ776YeLLMyukReuslhgcbxLlDnDusJVXmdiyR1gzpiKF6Y8aOi1pUJ4ecO1rD5gabZsjQFpP1QJJHDovfMc3Smyc4+YubOPV9Y9hqGTs3RVKfKCY/vQHS87FOgGjUikyaOEetCoShyBFxIKooMhRWmo2QVkt+VHH2oQakAqHBqhw7nXIgb/BDt3yB0tcEC51xIqs4GdW4t7uDG5PDPO+LD3HdHY9QGs9BOQjPxYklxtOo2BbXHAnH8kkW9AiHljeT1MvFpK89QPZiSDOSuke06CNTia1XuemnTvFwMsuDwym+2N/E7Yd3gC0adtxuRniqiy8zvFFN821VLBLViTDCkFdcvNU2N199EGnACeDiPevPZl6Qa1Q1Z9fIGlsb64zWelS2r/Dizz3Dt939JDu2raOsxckttpLTr7pF7oTnkAgf6ymcVozTzZATY2glUO2E+iPrjD2whsgNMs1xhxaLJPUVkZ+Qjbv4y+AMCrLVeIK8XBCf6e4JbMnH+g65A/G+OexNUzhjEnc79HeWoRQgXAdHuDT2d1FGEW+v4PpgrMEaU6gAXR+iHKepKZ+Ji+wRAesnG4QiI6yl6JqLe3mKfiQt7qO9PiyuQLWMrZWRKy1Ed4BNY2Q/RfVT3L4B16JtAu1CdWcFmJKHyTSyHKIiXRDTroMQRUNaWpEMd41BlGCiIaYcFJPtLCdDowNZZKkYkEZgfQ/tC+yGxU0OcsKlIVSDgofZyFvxT1iIQcYSrwX9KQcVZWQh5DInJyWpClSsqZweEm2pk85u5G2hGT7mMux4RGmAHpWYsoNFY9tdzHoLOxiiukNKyxmTX1qm8WirOH76A+x6i9HnpdQbCQwibFAc+6oTE57qwjBBtgbYbq94Pi/URhIQjQbUa4W9VBSEs62XUIkGp/gedOox95Uuo19pY7Umu2C8OJeUQJQGzI63qfoJi81JZJqjo2ERDoxABEGRrdLuEu2dLrLKXAfpOmQNn11bR/nqb+zhyMlp/vRztyA7gktuXaOfZvSWO4gsJ7AGYSFB4jSqVPduwgvc4v+UgrUCf8GQXDKHrlepxZKwE6PinLpyUEmOzQxvf+v7eNXLfpNDi0+jDoAzv4Z3ZIWH7z76Xx3fKSnZNFpjpPIN5cmIdxlz7pBt3hBHPHeBKyViuqQQIv0v3+o8zuM8zuN/OQaDAZdddhl/+Id/+I8+/5u/+Zu8973v5Q//8A95+OGHmZ6e5qUvfSm9Xu9/8Z5+A+czUr4JUK1WeeDT7/pveu16Z8jZ7ixuPUXHDhNTo3jeNw6DB+6/kIceuQCW2rz0ihhnI2D0gqt28suf+rf86W98li++/ysIKSHNYKQCxiLXW5hMgxKULu5y2dYzG3L1byBLM2yU0Fr7e6siSqIdgUlhfn+Vd7/591lCkgwSxGAIlQrkeTHoHGR8+K8fYPPuKRZPNfnND3yGoQGHkEcXr2bxcIUXXlbnNXvvoLta4sn7LgIhSFSFiTSiLDMgo+SmKPUNIkXnhm4c8NDSNnY1t6FufpIdIz3y9xX7n6z3QLlFYKEtgnqtsYhGA5vEDO8+zOgTLklJkE6UwVrcToowsPr0KBPf3kQLgWqn9KuKbHMdU1J4gVPUeoaCtO5gpWXhiY3GoK/DUWS5ZXWlS9zuka+3scaQCZfqlh695SrkFlsUZxI0EsLvDtj0ZAtjQetCQfB1cgILXs+QxpooDlgeVhGAOa5w7w5Ik5ioHfHUvUdJr/TYdfEyFvCe1ESnYfl7U4QUVD5eQuQWr5PS3+Fz6tgsF/orZCZjPVZFXfESHPjVfRjXJZ71wYVok8Z0IkTgIVLBiDvk1pf/Dvc9+lZkL2HH5iYnVkYBkKUS0WwZ+VLF+N517KQlUiHpmYBaArm0oBRH7j5cTKBzgVodkN4EWd/B8yxrZwLEIOGhT+6ncVXElnKXZlqifYul/9gY4phloTvKWlLlyi05zxwAfzUBoTDNFi4G2ygxPnaa2993EaKVYpHFqqjrMRxkOFpijEAKy+Z9a5z9/HTROOFIHv3oLuaX5rjmFx/FJgkoRe5J0rJEZga0Jklcgq7FqSo0lsxKRGwwWoOQZKnED31Uo4ZxJINdDZy1DusrCfkOiXElORapFAaB0BZ/k2F1PCStB4iSYaqaMLxpO+VH5lGLbUhSnNsg3jOH0ILeNk3t3g5qUyHq0EYyd2mLc50t2BFJXk8ZHe8iLwqpvmNIL60z/ttr6P05aAcbhoTRkB1/fAyjBMvfsovpUp+OCRkrDcldRWYkOhBYLciNx823PskbnncKC0SbUq7beZovnNjH/k/O0Nw8CU2HYU/g7Y4xQx/lQJpJLm6skNcNQgoODKb5iVfdxt6bFvnZl74M006xuWZTr8fCxAhZ7JIrB+9ASnPbBFZa0k0+elxhSx4mSXFH68jcFI0ZWYrcyLLp7nHpjgv8qZiJi/t0q1V0WMcIycTDCdlkgDO01OI2F12xwH2Pj4O1ZL5H46I1lDX83nd9K4wLNo232TS6TsNtsVkdZ35+DIWmFym+5c1f5XN33ggCgtWYZKSCE1u0K4jrcOfqhaRGsmOxjZ+B0BYTuqQVB6+bUj/UwlQg8NfJayX23bhAR1dYTEfoac19f3ExZAbpKBzrYlMoiZigFheT66CwVogso/xMk6teu4g7cMgDw/jsgCuuWeSue3YUlpkkY/SqPq8YPcx80uDoznG+9f0H2eF3eHg4w84X9Xjo7yBzJSP1Nvl0BU6sIsMQf7WHjWJ06ICFJPSpxJAnit6lRSNUeKJL77I69eMR/dGEwfVl/JGE/LGQeBrUOZd4EmRkUJHBlhWDS2qEHz2ClZJkewPjCi6/5jinFqbQVUGrCv2tJSqHm1hXIV0HNTQ4nZzS00NIM4TrIsslbMktMi7ynLxexjS7SASl3SkHVmd56D3bUE91SY67UA0QgcWmKdpXiLJfnNNhCeX6oOWzSXvatyy9fBTkCJveP490PHAgmgopn2tiSn4xgdamqJV2KO41uSCvhRhHFDbG3hBdDzFln7RUwri2mJgPNML1QSi00ji9IoBcZTl0huQjYdFUk2uiOcVgn0Pj3j7O0NI4nGIRaKtZu6VB9QmLrldIN5VQR/rIHPAdkr2TZDvGCZ5eIDie4XxIk8z5iHkfoQqivrDeFEQPUhbXosUhVgnyio9qD9i8s8dbvusZBjrk9x+4HJkZhOviDFIM31iQ+bok3GZ5oQYFRBJjsYUCRhXB+eSGwZ4JgvkB8lwHtd7DZCC1QRiDt9wn2z4CFkb+9DRnJnZifcGWX3gSZrZAo4Y9dqYgZfJi/70ji3jlcjHGcV2sVAjpUBmtsk/VeOaLEKxC9GbFQ0/t4OXf8zjtL1/D2ZMrdAOB19Wo3GBswGA9Jhv3kK4iHynCl1u3biGZciifqnDJo+ssJwanGzPsRIjeEOM7DBIfsDz4lRWufVGVL/zZChjDB3/5o1z3gp//R8d3x1fW+fXP3YnnKP7o7a/DUZIjvYNUbQej+2R5G+WNPPv6WyauZnM4zbbypn9q2Hge53Ee/9fh/xRfzT9vH17xilfwile84h9/J2v53d/9Xd797nfzxje+EYAPfvCDTE1N8eEPf5h3vvOd/917+y/BeUXKvzJE8Sqvfd09zF3WBc8h932E/EaWibJAAtWz6yzeOc8X/vIe/u0bf5cHvvwU5VqJ40+fJXc9qJagXkWHblFhmuawQZxcvvksYkPl+/dhRLHa1ulF39ioBLLiFh5vKVlbXmTvZU/jiC5SSPJ+n8xmrL5+B+7Acu/9R+hZkFh69YwTmc9SXEYc+1Eo+Sx2h3STEuOVPiOLKSOLBieBv/nsi3l64XI+f8/VxP2Q0P8GeXT5LXtYvnmK5UumGBtUecHlT3NpcwFbKUJ4y9uni6BFJRBWQJF9RzohWH3LBKtRscLqtzTGEYhUI6MclWRkVclKt4rAEucOMsrJKi7WwMp8E5OkBMfXEGkhVxZ/b78QgOeSuy4f/NM7SRwHozU2z3F6GWkKu644vaF7BpTi8e52nvzKbk40duK7CqEzrMjIFER1SVyRhAtDJr+0gu4r1lZqrK3VSNMAck0eJ5gsJ+oNqfRyWklAc1iiV/WxVjCVdfH+cg1ygbWacy8MicccmjfUWUzH8eZqGIqBMUAc+QhrcCKN0zeUq33M9YZ8nwOBzytffoDL5tq8affX2Lx+muVTZawUxSPP8T6S4H95wPJYwLIX0o4DggMKNbS4qS3+D8Nu0coS+kw90Ed6KVJqPvSOi/nrb78QozVuPaD08T7717dwvDNJeq3P3A2rZCM19MgI3zqxwvfs+Rpb7xpCrYL1i5V01Y9xTq8SDhNUN8fmRbUzQuFqzY1xlcPvrXP6izVqMia62Cfa3EBvm0JPj7C4MIaxihPrM4gwQIYholzC64LTF+h2jzzOEDmcOtclqUhS7WACVRwA1gIOjlVQqzLY3qBysMXYgR6iorC5wUhRtC55oAPIK4r1tFiR1FWB5ybc/cHNoC1kpqgMdV38lkFIix3JSf0QqyTRZ3KsASk0z996FDNaTCLCxDIVxMQTLl84tIfDi5MsbBsDabCOYOjAZVedKWT7joRYsH68hEgyvCDmeDTBQjxClkviqiQpKx7vbkZrSZo6ZEseNTnkyqmTCM/h9a99hO945VcpjQwxCHSs6A0ChlHAXhXhhBnBxICWKvF973onv3vXzUy9Ki1UV75PZ72CjAyWnODQMtNOm/ITEdU7+tTuGRbHlxJ4q0N0zcM0Stiyx/KLR9FZRuwbWq+bpXompvJkn9LnU7Y+PeD5R08yci4urB3LEcFajidydu49y8te/1UaLz6NvSkhvKbNh5avx/qgjKCWdOm4MXGa8Ls//XY27+1i/586T3zHPo4caCD7A0R3iL9mEOUcqwTaE2Qzmm4aEGuXE4enEF+X9VtLsNjBb8ZYq1nbXWXkLRE/8cHP0bUlpt11rg5Ocex9O3B6AY4rIS/sYSLNcE6nTPkdnt84hD8hseUQ4XlUaj6zrxiiPYtRGQuDGr998Aqy0ZBsLCQbCXnd6w5wXfUMrx19mhfVj/CFlR3UpWbO62JSiGqStCyob2tRampsNSzCo/udgiDY+O6dqsKsJs9OXskMXg9Gl/v49RSjM3IDsQlJwoJML64NYAJJVpEYF5K6T/yeGrkHbj9FSEPnUxGl+84Sz1uCIxHBwkbrWJwijEWkhtJqhB4fK5pklEIEAY6f4s856KsqZHVFXvUYbK8zfktMWSZFTpQA60iMMJjeADNeJdtUQ3WjInjUmmICbizGkVhHIuIUKyxGyCJDZ6N9zYlyrLXIJIMohjhBDVK8cwOM1AgMTmLg2Vt0QcAKY8Er1CjCgg4V1lrSEvQvrIAjIQyxSqEdhduOcZpD1DCje1kF91xOFmjEhn3MeAKtBOFyih8pvG4RLtzaW6W3o4I1msrTK3gLbUwlwH2jJvpeF/ZJ5LE2NtGFbadaAd/fUPIUmSTWGrTJiCc8krkRluQ2FhZGWG3VSCfL2EqA9V2ykRKdq8bJx6uYSgk5NY4I/WIhJc8RgU86WiG5YivacxBOYbW1OseZb6HOreMudCAsFYoeBFYpvDjGxglqqY3KNbvet8bO31nED0egHCC0xgiBjgoVaGFNBXo9bDSENCHbM411HRqzdYZxiqbEYMSlnKWUz6akN3hc9rbrMVfOkY+Vi7BZCmKmOlJivFooychAZoLhzSl6z4D+lTluo4IIfaznsPvSTYxP15mdaSAzjUxytl9Vxuws2qKolFg5s/ZfHd+NlELqoc/OyVHUxrjuwtoeAilwBWCfu2rrSpc9tR0Eyv+vvud5nMd5nMd/L7rd7nMeSZL8//+l/wInT55kaWmJW2+99dltvu9zyy23cN999/2P3N1/Fs4rUv6VYf+5k5w7PcuZ/XVsnBF1hs8Jhc3LEjKoqJB+c8jn/+JrLC/3Kf3VvVz30kt47dtv4NChBXStVAzufFkET8qNCgKpcHLLHn8J33tusJkxBlsKGKx/Q5GiFeSjAWpLgyu3TaHcB3n5Gw/QCz0OHZI4QpIGLuWOiyfgda+5ik//pzuIsDyYbaWfl9iWDlGdPo3xKlObRvjlT30PlSTmRVfsYue2UX5j/4OIXHBB2ObaqxdZODtN6LnP7sNDdxxg9rYm8XTIzPf4uLnmiU9dCOMuNl4pbEtCFGRHoMFRGK1Z/JExjJWMuvOsfspH7x5j7YqQkcchWLcgBFnNo7vs0XECZsUiXitBraeIVHPywGncbgRCIY2AxHLLu1/LV3/xk5BrrBCISplao1xMgJKiaUn4PsZqSpHmmce3MbXvBNUH26AUZ+/eR3l+iDnqE78whZpC+pbh7oTgmRyNRVZLCC2wQhKdqIGCoB5h2zlmYJFa4/guquUxiHN8NyeZrFNSEu/3HeKXbka3wW97qASSaQO+5ex2j2BZQZhgVttQKiEGMRhw82LwH/uGmQsGaFeRn6ty5NQU5Yc1x6kQRYI8NYWkWynIMkZuz7G7N5PtryGUxblkyExape2myMiQOTC3o88hXS8UP7fWGO33CP54gZVTZQBsYLjqjc/j3l9fIH+lQltFphXb4ibd9gg20MQ65f2PXcT8ZTXG7stwJhzSxhjq4AJIRemKIde8ZJHP/NhWEAryjOqmcZ68+yhRS/F3hy7j8X099j+xG3upYnzegBR0d9cpH9UsfGAKlyZW57ipxO8anP5GcGJetJJILLnrInqK1ddNMvORFawQNG+uUv/bTqEE6gpEailPwYXXtjl9EjC2mKDqHBFbpHIKC4gELOgjlrVDPmxOkBiE5yGDAJumMBrj+BY7ZSGOsWmJl4w8jEaRpxSkIdBXPo6yiNGICz+wQl1kBD+uuat7NSNPNBleXGF6sskzwSYwhrSekIYCtdinedajqn20kQx8hQ5A+5KFow3e8qtvxxrBhVcf48FoO2WTMv36RS6vLnAiGeWmCw5xz/pOrqid4anBVlw/48+X97Jj8xJbRjo0U5/O4yHJkkIJFxl6lCsZF17X4e5JiPuK8PEh24+tsOeGZZ78pXFS7aL3bIN2jLE5br/ICbHaIDuKtRdPkfsGFWa4AmSmiKKQ7/6B26mMRizOV/ntG15KlGlkN2Pzpj7OtKSsI/qfLKEf9zlxwSz5rMVp5Kiu4Tvecg8VLwcvpyGXSLTLA0f3YdsBX/an2F5uAkXIalAa0HKDYmI8ptG9ECENzoEhjhaoRCOExcYJxirkqw36Fp9TfzxF9kqBQ0bTNnjwY5tYeGAEJ0whcyA3pDYirSvicsgbp26nXE3Z/DNtPvHjV9KfK3PFztO87NLTPJnNcveXM0zfYafX5ZnSJFhBun2M1afq2AsXEYCrLKeTSY4vl/mzJ67hGtPCZIrEeixFY1RUTi1K0a5BYLCOR14q9sV7ZhnrSJxeSv32k6S7tkCtgjyeEJ9bYmSkTDYZ0r+oBqMGZ93irEc4kVsoQL6eGWIFeqsku2QL4ckWpUMrrIgaBJJ+FFJLY5QMEFWF8bPC6qgE0aSH2zXkI2XU2ZWi0WnrKJU9mt7DCdWTSwjp4UQJyYpkNatiPAdT90ln6gRnesV9T2uC5QHIQs1iuj1EEGBLHtb3EdriyJDphR6xdpC5wR/VBEEPi4R6FTuIEICQaiPc1qd+uJj0DmcqmLEGIsoxY5VCXTTMEJ5FNpwiZ9uDLNS4nRRvXRVkaWHwQRlLVvbIx0NkYhg9IGhd5SDKAXlF0rowQCaW0WM59QfW0aMljCepHWyRhQobuBi3IGqcSGOsJq2CF+ZM72nTvqxOvKqLkPFy/myWmLW6uCZpi9QQnBvCSI1UlfiLv76ZdCTAET2o1QCBMgZpJCqxYAWm3YUwKO4FQmI7XVR/QLKtTr6lgTy+hrAWYcCdbxf2n0wjSwF2GCOcomLalEqAwQQKKQQmy8hrLirwINdFFlecglIYU6hhrFSwEa6d1UMUHsZaFs6uMXLRFGNH12hPuESP1BgdX6FzYgu5EJx7Y5OuKCHeMM7cf+hi85zh/Dp61cWkGSoCMQjgTAmExZvJqI3XOBs1MY7CDVxe9z230GkP+MzfPgRC0DtumbhkFuYijFAMnP/6+udopcTvvu01z9lms4fILUX+zX/rQPE8zuM8zuN/ILZseW7sxC/+4i/yS7/0S/+s91haWgJgamrqOdunpqY4ffr0f9f+/ffgPJHyrwzza4onk0nSToa18Laf/pbnECnWCqxriTsJcWYoj9ewFpwN+08gBSbJMYELAjw35aLtixx4sEjWF47DPbdfzL3mCn7mxud+tusIxHIbU5l4dpsJJf1azqv+3UOoQHH6l2dZW2wALma0hEhzoms34a5GlBp1Xv6yS/js++7ASod7mrtZT8vs7S0y/0e3U5sdpXqgSq3sUveq/Pg7X8wzZ5cxTxUDbt9r4yjNRL33nLDZM0vLuJ0UFRsmZwLGG+ukN86z34yzQh3hOohhvBEamILvURsJuPaihziXjPAKZz8fkDei/Yzakkb4DnnZR2CLkNdWiBSgbBmVZrjneqANQSUEqZ4NXxQaHrzrSKHmkBsNKett1vOEt33/izh6/xGs6xYSZxxeOnmIE+MjjF7f4+gTF2J/vU3jgUXINSYZZTVaJCjnJNLBTEiibXWMFAwvGWPsYIrXF3g9MC6oSEEyhHKAjPJiUN/JqYVDrJTIiRRMyNFvrRMsCpIG6LKg8kiT9reF4Fnilwmy90ZYz0WOjxXS9H5cHF9u4RHvdn2SxwTlzxmcNOeOuy/myw9eind2Hn+QACnWSDAZdmIU1lponWN6xfE3bPvUvQGDpgsanMRgXcErf+VxHq7v5B2zn+fPDtyI6STglcBzMb7i2H1HaOFQ+eCQse8YclHeZHhqBJKCXLp7OM56GmKEwAhJPDGKNwC9eZK5KxfwX2lJj7ng+OApRKVEiOHoyXWCMYf+uRke+/RWasczskafLM7JZhs0rupgl+rYfhmbLSGMRYYhlYViJdpUfITv4HQzyA2OI3GGKWYiwM5N0b60ijebIVwF80tgLWZuhsbEGk8f2klWl5hAYBy/sJR1BcPpoPD7Dy3euQEjT/TY9rNrJCbg3J0ZIJ9dNXb8nCwKUNWUNAS/n9HPfMoqo2UrJBMZ5AoxknJkZZxrLt/Pidu20rQeb2ke4I7weTS3V5GuojaZFi1U1uL0ctRKG/Ic089YTqu4ViMGUGlrjIDg6TWWXjeDEwsm9q2yy1uhJ32u+bZFHhzM8bzyGc4+VOEXZu8kUTnztTGkMPzd0qVcOrXMQAcEmw2tqs+wX+OCK8+wdmgzr/y209x46wq7VxN+/3M3gFRofIafTUhjC67AObrw7Ep3umkK3yqEkFw7doQ9ty7wud++jOWX1uGUANcnyiWz4z0CqdGbJLWTEZ6jaNqAH9p6O8t2DKkN0QEP3yrOPj1Lvy3Y1WhD2+I2UtZzl771uPqmQ/zRy28h/04XU7PF/yMAmymEUGhpUGmRj8NAMThXBWsZXeiTl4NitTxJcfoZ+YgiecKh8qEe+WyDr3ziCva8ZJHb33sx7afGi/DVfoqoKWSrjxhqrLJEFUMjjCiplPVqGSEEve0O9y5s58VHjnHfV2q4q10yachHLFlNFvkTjmDLlGH+8Tn+vHIBLVHh1SNPsv+zs5yNGvzYGx7jb++/lsz6iKag3ukhtCEd8TCXOIgTHnnZgVwTWlHYaLTBui7CU5i1NumWKk5axU40mDgsEV6E3uGQNiy6rxBdC57F+BIjbaHOOAJSusg0xwpJ94qdJJOK/rjE3ZVRPpNACjZQKCHJQwe3Jwj6FiPls8HM5qyms17GyZKiuSXNcJYjgqf7zL2uxYHuFKot8VNDuDtg303LHLstp9MKi+u1sUjPwwZFYLHxJUYIVBcuSBawruRMeZIkAdNySaddvE6GjBKsUjAsQkdzUSIY5GipyVWA7A2QgxSBxZRDRKrRrlfUhlugLxnsruOvRFQPtiHJ2OgWx9bKmGpQ5I1JKC3mpJfnOCtZYeMLwRkYnHZcvMYK3L5GRTnOMEJXPHrXbybaPY7SAvf4KkPfZbI8pObHZHtD+utF45YQAkphYX3s9Ivv0FVFmLEF0hxre0T7fKgovKMpRDn4HiLL8Z9pI2KLHUQbyj+BNbpIXg48hOPhPHkGd72PcH2s1qhUkY2FiCRDW4Hf6iErpUIdRPH9JHVBuDwo7JUtjduYI++1cGsWIRTWdYsMF1ksaEjHKeyZaYbqDAqVpYEP/Js/5dNr7+fokSV+8N1/RfDoaewg5eShbfQuOEL6EoVNFRZLtAMmViyLE2XSiRDVHzJydIC7YhmbN2QBzFxdg7iP0gZ8iScE9371EPEwoV4JWdCaP/qrzzP1xlVcf4TsXK/Yr38En//SkywudRi/eJSJWoUbd28FIPT2IJ0LqPrPI/D2/jNHjOdxHufxfyXMxuN/Nzb2YX5+nlqt9uxm3/+Xq+D+QSOstf9g2/9KnCdS/pXhXa99CS+58Yt4+RoIwa5L/otwWmGKusa0WC3vtgYYY1hfLgJin3n4ODJOkOu9YsK0zePn/u1dfOunb8HNMpIxh3xHiln9h4dWMDlKRwu80eqz25Q0bLrwFDdcOk9Zptz544buFot4MELaEo3ZEfLTfZyBpqv7/Oy7/pal7hCTaZ4+uRkdK7JIUotSsu6QaNCjdeGQ9mqfu+88zJ/c+1BBCljL//vJN7AtXOGBg7v4lb//Jw+HMBgiteSlV29Cdfu84O2PcPtVm/iDX3oBgxNLaFdiR0o4rQiSlJb1uW70BBPugHvPbQdjKZUyHC8hc9xChovAhA7BOuRC450bILONgb+1rK/3QQl0b1BYhixs2T7J4TsPFV5zXYQPZpnhs3/9ADbasEQJibWapx65gHd/98fxTMgHl2/gmvfczAd+4Yvgu8x+70l+99G/wiQj4FjkMUXpRBcEDPc1iCYdjA/ls4WaSDjFxUlYcDEIzyV1BDUvZkdlnZPJKEkE1EKc4xYnAXtunZFVh46XUnEKi4nIM3pbfGprG3ky7UERRrpRJ4l26bV9wqyH4yhsJyomRe2UbGhBiw0/vMVuGkekOabqo+KitUIMHM7lKUa44AhSR3HqZI2j/+4iZn97wCdOXsHpw9PsaPaw+RARSdg8wcmnziIMTJoB2YjkMT1FXcZFXWdiONSbxBwXlE9qhFTo6RznjhWQgpkfbdIlINnuQqVUEF39iC2vupQnHz5OdsZyRf1BHuV5dF4p2fLpZXojW6iNDfnRzXfx0elbOHUyxEpJPjWK7CdQ8iHJUUbBUKOxWE+RqwSyGlpatJuglEF5eqPlo5hci2YX7+acqulgk1HyENxc4KiAZBzacxITWirPdBi7awEnhWmZ89BHFEKn2CzBGsuua5c4troV75yPcTzSmsMNN5zg/rUdbG20OdkbpbGlSWR9yn7Cux96Ce7vx4wOhgQ1wenHykx+/jTtazZhreITT96McAeApXS6jdMtWimEdpl7tAUXa54MFOHJAWiDURo3G9C90MERmjE1JDQRv37iVnKjWByt0zsoeeGr7gfg1x4so4QliRWJidhV7pD5ijNTm0mbGutIXvpdT3L1955CSMNV4z1Kf3g5qpdw/M4Gqeti7QDZH4LvYwIPYQqFGUmhOjvx5AgnXz7Ku/7sXpR1+a0nr8bIMiA5cnCGvXsW+czKRawMS9T9IZvCDv31Bt6ooSwTxJihIjPUxS36vRnsrgEcc/hYaw/b/Cbrukr9xX3Mf1bIKMc4DijQr4jJHhyhtSPEnoVyH0RuiWKJvyQxgeWFLzzF/R/fVpxG8RA7VUNWSlSP5UX+kVA88YXLeeJLV0JukBVdkK7rKSLRmLKP7PYZ7vNg1HLb0b1c0FvnS++/EhM65OMe5njEz629lAzFpNsjHXVYOFDG3ZOQjvu86QX3sXaJ4fLqKrP/707GOsusTNZ56Atbmb6hieMaVMmijWHkpMR6Cum6+D3BdGvA4dkRVGZxnZxbX3OA+z47Qzsr0bthM9bzqWQVdKMEcUaQWKyx5KMOiSOoPhETWAe/bcCRJCF4Q8FwFDxPo546C2sthKPwsjHSWoCpWJZuHaPxdMzYV5YQvQE6aSKqIWMjCs+vk/QEYrSxcaza4rrrOOSzNbyTA0y1yvyZCm9anyc0upjc+y6RU2Zsa0R2dZcnF2roio9sDwtPa6OK1BZjBXndwzmzwon7yqhdGwGpWGzfMHJFBzuvMHmIbPaLJhcs5bM91CBD5ClBIIvtQiGilLSmaF83g1iPnrX8SAeclYTgqWWMkEglEVmOFAKdZYjMgDYQ5wibUNsWE+2uYCZynNQSPJUhpUPrulEqpxPSsoupK8rPdJEYjLQYYXAygfUUro5pJQF532Hl3BRenOCsD7D9onnKhj7CccknSsj5NiLakLdlObig+pZkWwVvrY/oJRD4GGsQYyUMCUQU4dxfV9YYU7QraYPfK/Kp8DykUmhXku4aIbpgHH/doNt9hhd5lE4mjMxHrF3TINkSkFdGGfnaPEiJXVpBjY+gTzaRjTrMjEGcYnwHtbgGblExbXONtBJ7agk7N4u18MatP8zM9z8fEypknEGm8Q6t0FqL4SaL67qAZfSshw4dksmQ5qUZyICVWxxm7jbI1OJYwUuGE5xxI17zhqvQjsOtr7uS5YUO3c4Qr+Qy/6UnCjuZAVPpFzk3GpI4xQ++4Z3OteFLdzxDO0/otk5RLvlcuXWW0HNp5j3O6q1c4D0fIVyWomUaXoNA+QzyiMO9E+yt7iR0gn/ucPI8zuM8zuO/CbVa7TlEyr8E09PTQKFMmZmZeXb7ysrKP1Cp/K/EeSLlXxlc1+Wn3/k63vf//DUIweZd3zj4tNZkYdFSwJxgdBjSjzVWSlprBZFy4kyrSLhv9gvh8O5J8Idkl23m8u/dyl3BIbaWzrE+UX3O5y6vdBh2B4h+jEoK//BDtx/gI//xNs6Wa6y/OuAryQWMVLs8dWKSbq2E1hnJcIA7BFyH4SAmyzS5NVidsXtmmWoQc/Cz0zTSNiVr2LllnMNlS3Y6BmDv5nHOnDzDy259kLktq9z3lb2QFwxmr/+fseQcuC8FIQGP2XArj62FtLXLgSdmMcpgtcXWAkg0tlGEyerQ5U+PP5/d4ys8Nr+V0A7wKynh81cZPlYmWW8UK4U5TB7VtGu2sHBkpgjqFdBvRdhMk4cewojia985wSFjEdZiqmUcY9i8e5penGENqFq1qAZOE8SBMe7/4q8yUqsj11a58tsuYfkNq9y17z52XdHi3LzhcG8SW9aYwCWZKaMDSRB69ObAGUJSV6gcnECzKVzj7IMe2TAianYR43Wuqs+zqdRlxm1zW/kKxLqif1UMqWDP54boRhk/he5yDXw4e5XGhAHVMx1kopGZxrgKohRhYW5LkzODccphQtpXrL3GJ3iiz9S5iChXxXcmDMJRqF6C2TaFt9hEX1Ev6pGFxpzzkdJiKFals4FCrCUsv01Tu2HIpb0jDH23aBqRRdvN+Nwsi6cPEjVzBkkdbSXxjQL/qxrrCx54aBfqax6+SDFBgChb5n+oxg8nV3HEfAxroXmbRFSLAMK84nLpJVt46OP34w4Trr/mHJe/+m7wDGuVBn/3mW2U0pjeqkcv8rCuJBsv42LRAlRzQO4YnFJYhP1mRXOLHGrcjkJoS6ZgWHIZDAV4pqgV1UVzyvrjJfSUgy8NwrHIWCCMRaliYRwsuq4wgcAawQk5iog3vg/PxUYRywtjhGsGrQVKC6zb5md++yTf8fBeHm9vZRh5cMAwckNBjuy8uMnZrMYgcUiXLV/58HY811I61qZ75RSr6RjjgxUQgsZqjHQtthLgG8PRT85yAQvYLMc7MF8oa7ZMMHtbzvg9Oc13VWGT5S+ffB67ptcBOLNcRa7GtLXCF4aqk9AaVJkdbfK26SMcHjTYK9e57/I9LC2Mcf3UE2x9q+FYtoVekvKxP7mVeEsF0a9j14e4XYvVDlqlRDtCmj9VZ+Svq+SDHP/gcjEn7QuaB6dpTo/xS7e9gFI3ZtymiDznj37tldS/c5FwT8SWepdX1x8jPlLhri/t46q3H2F1JaBhYdj1EZ0YXEM4k9LfDmeiCUKV8ab6MdZKAR/aO0Njf0z77RlBOeXUM1NUd3goK9B5ikCD4+GvAq7FuJbFdRfR7GEcydqrdiK1pXGghfUgL/lozyKsQcU5ar1fNHmN1RGei+wNEXGGEZJ6HBWWzEHOp951GUIYqEjG7++Tj5TI76zQ3wozl2cETwiE61BaGKAyeP/yddhPOHxg0ypTD60Qd2tFO0zZcGahxrk1l36tyKcY7LIEJ/Jipd9aApniOBKZa0YbMd/+s4fRRvClz16AQOI0B6jFFq4A1c8ZTIbkNYGMeuCXGFlJiceKPC2DQRpIKpa8JGiZCmP+sLBoIXDaCXWVEo942FwRjSpsHCOGaZFh0hlQ7nl0qlNINwPPKXJFqiWsUEST0LpMMXOii8wy2kmVz33kAspzhnjdwZYcwl7OqftGWHx4BIElqwb47WFh/UwycBzKTszbv+se7vrzcZ5cnCCb9amfSItrU2KYvvYMC7dtRzgu6eYR3LOF/W+4rUxpMcGmElvyMWUf1csgy8nLLjIDWw2QqcUKKM8nqFxgto4jF1pFOLo2hcrFLaqYhShaoJKtoEJLOlcmOCsxsxGl9RR8h8FciLcck4w6pJtKRT254xDODyk9toDINdQqqL+JEb5HtsfHmcswCwG61YeFTrGIoEaKY284AGOwZR/jS8RyG5lLhD9K9UG7QfDkmHZMtnsGMezDMCMfCfF6WeHcyjWFBxJ0miFcp7BhJUlBSGWG3sU7KbUkSoOuB0SbBTrwqT3VBa2LGmprEZ5XvN8wQjjj2C0zmHYftdYtGo0yB2vss62GYEEqbL2CdARm6zRJr8fxh+ZRZUW2ZRR3vslwc5ngbJttZcFZ1yH4vCAIPbr9GGF8hA9WGsRAUF4YAgLjK5qvXOEXfvSHqI4UNtThIOGpJ87QHyS08gw0qL4kXNf4FZdhnmON4egTp9l37e5nx1eOknzv229iab3LKdtnvFp+1r6cmxhtc7RJuH3pa/x/hz7NVFDiz677D3xl+T7uXH2Il0xdzytnXvDfPb48j/M4j/P4n4Xt27czPT3Nl7/8Za644goA0jTlzjvv5Dd+4zf+t+3XeSLlXyFe+70vAm0YnWkwPjP67HatLc6gUG+U3jbCb3/vD/I9u98D2rB6WvPoPUdwqx52GGPyHKstaWkTK1qTh4qxsYR9lWVeO3KSDP2cz6zXSgzaERjLufkWAGeOnKPfGmKVj7AOr6gvsGUs4gttwYHmFoyT0O8MIaxiayWIE37kR1/KFz58Pw8+/hTfvutvcYXmg5WrWB8GJJ0+ayfW2SzWWVr38OsB+88tkQewbdsyQsDzbz3AoQM7SLJTrHV+A4tFBD+AqNcQUhLlET+8/1b0UHLx08uIWKPKASJK0fUyMinSBq+7YJY7PxVy+uIZ/MddgroAf0j/rnH0irPRkGDRvmCs5GPPZJjxBrSHG6tzMH9kEZGkSM/F7RfKkM7xdQpJkEXmGpklNE8vkU2MITwPEAhH8cZ3vIArXn01W7eNU6sG3Hhzi+27pvjPq32a7xU88u6drD/dIBwxWO0zsZJjmwnWEdx68V4+/Pgz+C2KsF+R0x1VnLi5hvukJcsKNUyeWy5UbW6qLLPohnw5tjQOCpIVj4kPHsIkKTbwGPtzQ/jzZ9E4ZFsg+miF7r4JGo8sfYNE2Vg9e/Xkfs5lDY7oLazpANUQRK8tER6BqCULhQgUdidHwiBG+R6dy1LQAnfdInMLAhQWoQyhC7HOMFGOdDQq9Is62bKLNRYbuoQOBIFDdNihn/rFGHlU4SmQnmXzpnXOludIfZfM1zz/O5/kmWiaH3zxm/jKiV/BF5rf+fSN2GFUKBgaZe7/0J/x5ncc58TDJa64fshxFdDoC1pPZbh9S2fB47Oru7DjApEZetsCavMap9UHIVi7pMTsqsAaA5lFWI1f9wnaBi3B7WYE6xk9pQqhmOMgRbEiPlgL8NIUsdniRCASg+omqEaKGtaQSPSET/zOScQXQ1ofOkq0aRyvuYT0AkS1inA7PH/bYe44dCUCGL58O+v6EW4cn+cDZy9HKsvoMckFrzrJ68dOMTAOn3/NPha/6ONvT9E/qLlo6hwP/bsZMJMEXz6FUA5YQxhD5nmYTBFvzlCTOWfmalSWV78R2iyL248XgfeXJdyLDcfZzOLBcRrjXSofWUGtjvAH564iky7fMvcwB6I5tgRNFDl1Z8iXf34P46fXUK0ew1KN68snAPir5i6WuuMIKRheOEHwtWNFLpAFGzpUX2X5/es/zvDqgJ/6uW8vJltCoDLD5tI653oer3vhY1zVXOEDt9+IVg5K51w+OMNoucN9/R187PtvJfBSxnZ2uEQ5zOyIuMJb4Ygzzrz2efkNT7M7WuFv75rlTG+M7596mLJKKKuEULdwTqVM9vpM7E44uj7F8NRMYZfsCcKlc3R3bCJsCbxBTjyqmH96BNBIYxm/f4XuFo/Vm8aZ+toSTi9FdTS9rZZaRyDaw6KCNslR0xXyXl7kSWiDPaSoxBlrf2ARSQKOS39K4qbgDXP622LKJ0qYB/pQbhQTcWPRTo+33HqI491xHnrgQmrLy+Dk5GMOaiConNEsdz0S7WIRXH/dk5z+8haM1gghWWuVkOsRYjog7go6WFreCMpIGvefg6SYQCvPx0wGeEJQeuQspadzVl4vGY4KlBBYB0pnBujOAt3v2oLwBW5b4m626GEVkeWkysf0JBfVz5L8jU/vUIgSLkYlBYkRuFzz5jM8+sgI690K1imhkrwgVIxFrfYoHfULybAtmpK+74cf5O47tnL0LsGgPSQdGeXph+bwO5rcNWiRYqVAGEGmU5RfprYpZf8na/TXJapUpjoeoTcIDWktRw9sxneLal4jLXZqlOGky3B7mWSqUC2QakrzXYQvi6apiQoIi8zAyShUGq0MHQqslESbapTORSgpsb5DFhRhScaVULL4nSF2YJittViIRnAfsIi0UKGWT8b454Y4A0PYzsnHSwjXw1mLigDdJEOFIWQlsiUf5wKNdS3DskEpQWGYFeB7mDRGLTYLi9PsGAwjHMdHeQH+qY12nv4Qqw1CSdyTK3SvHMdpaGYvyul9RaBXWwV5DJg0hbBUWH6UKpQySiCyjNrTXWSsEbklONdBRQ28sxG2PUBFo8gEpHawWj/Lj9jlJvaCzQhZRcxHGxu/LvEpgnyt52ImRpC9GDNShdEKJhri9nPSioMfa2wlIJ+oEPsue+snWGo3KK25JLlBKsXYsQHNKwIIQOgBarmJdVwYrfGXB9e55+z7+fLrfxwAx1HESU6SZYxcush/+O57uPvEDKdsSLluOXAPYAx3fvzB5xApAJdcvJmLjOEzTx7k4NOL1DKHF197IYkNaOsAQ4Pbz93GmXaFJaWx1rK9vJkjvVNsLZ1v7TmP8/hmgrDPlsb9b9+Pfw76/T7Hjh179ueTJ0+yf/9+RkdHmZub4yd+4id4z3vew+7du9m9ezfvec97KJVKvPWtb/0fvOf/7ThPpPwrxWv/zUv+wbbcGKQGhOD5W5/PbH1rkW0hDUZJPvRHt7M4v47witX+dLqK07cILRlu8pgYGeVo6lMTKaH73EC0IHCLQQwbQo0855Vvvwm7JeTuE/eSYikJjQQmgxg71MjUIPspeszhgsvP8dP/7osgvsTL3/o5drxgMzL9G6SyiHsFL/ieszz92IUsJU32XHk345tqrLVuYL2fkk4aNsse50yFLU4HCxjrcDYrBpjv+IWL0UnAJdfu5PB6k9bBMYLTfZY+leOxgpoeQ1iLzDS67EFumNsywciRId5ni2YV6zg4OzQjpkdztYZqFyoXr6UpTdR564/ewMf+5gH6j58kU0VeyOlDS1Ap4Qxi1GIPqwRXf88tPPypR7BrnULCn2T0spzcKyFzjckyfv1vf4SZC2b50Mcf5BqjOfzASQ4/vcDbf/CF5I6iQp2XHr6at/zEi/mOH/0AcZKxA49WzzK9dZRDnzzMRCsmHvOQVqAdQ7AG86cn2WWWkGGJxEhE6DJ/2wi8ZYXhGZd47yi1kxHiYIafaWxukHHK1rjBU72iicJoSSgsKrNFMCLw/2Pvv6MsO+p7b/hTVTudfDqHmZ6sCUqjHFFAGZBIwoDJxg/GJtgYG2OMjQnGcLGNwcbYOJBExhiTQQllIaFRHGk0eaZnOqeTd6yq94/dyA+P712Xu9Z778vrZ75r9VrT0+fsPmf3PrWrfvX9fb6i1csXbEIwNNxhQnbYUzwJ43toZRBeSpo4Oc/hZ9Z5RyHjDI0hOKeI8ixaS2rNEKcYoNsOxhVEVYGJc6AtStI5CmbJQDfK2x2SBBsM0JhtEIdJvpuJZaTQoS09RKownmLZlMiUj3UEyxcYnjU4Sbg74IP/7Ss8+/qYeiUhmfMRSQxJiqwEnP3nT3PzP63DHIXDT7a5bucMr3/XVSzfkeCNpIg4o7Ug8MdTEBBvL8DRJnIhd3eFGwewt8yBJY/6rlcRmaQ4k2KUhZJPWpf41TQPsvMdbALWc0mjhGS5h1tJkEqhuhqLQXU0P3r73/GuY1dwpDuAt0sg4gx91CE8q0IcZ9T2r4C13PhHjyAm4NapnYhaRvJkwNsffjapMTieRWBJTjZcZWc43W/Qswr96scxQ2W++qntjD/Y4lWvP8Br/+EpHl56gi/eug3SAkK5xG2DMQKMpTMYYJ8nGQwSyt8sINIGVkh0xUPsmQSpmLQl6rHD5PIo1oHF+X4G7gvpbetj/okWz73kceJU8OvD91ORCY617IrGiHyHtGtwuimP3zLM/t+vAIKl4wGF2QSsoHDpcQoDCdGXAoSx1IoxfaNNNnsh1uvx7JMP8MT9DjbM3WfNsMjXJk/HL2parp+nvaQC2Q7ZddMadrYNSxtLTF5dZPABxcD4Akf0ICLSNOcdTFOgnhDcM3sGL//1T/H5yuUszAzxF91L+Pg536O57KMfXsTrZvifjrn2yoNsO2uaf71rNC8Q9izZ9hJuw6Ew2ULFBhkFTN84yNg/NbGOglRTPNTGb2U4rbyNMKq6JMNFsmILFeXRqaJU5F3/7Wb+cfdO5j8cQBQjYkH3yVFG+yy218YKj7RUpX1mGaUtlZEuQSvCqixvb3AkUa/Ni35nPy9fc4D5EZdX//MgwnWxSYLX87A2dwx8/a/PxZ6ftwg+vbgRX8cooxFS0J5W2DNq4CrapozUoLMUtCG1EdIKHOFhmiHEEcrzoRPi9ICuQ2+tS/Uo+WI3jHCXe6hmhsGh9HiPsTXLLDdduosetiQppIbniD3cs7KFpOXCzyKOtYZqiaP7Etp7FY7W0O5ClOSJKwUfb7qLM9smWduP55XoJREyENx87xnoQo+sXoFiQOHpWTACmaV4WpMOFNHdiPiMtahOxqGFgMhNCbuabNxj7pjPgNMBBMaBBVWnf7NApJbSw3MY3yWrlXB7FgNo8QwzOndxFTyimsIKg7AGYV2slKBANNoYa7EjFVScg2pFqtlyaYtDj0xgBIhE4uuULWaWy649wsfufC76cAAqb52Jx3JnhL8CxbmItKuJBzW65BJvG0GutLG1Il7X0p6uww80atnFcQVOO0Q6eVuLlRI5v4LNNNGQwmYtHFxEUAALqpuQjVShGEAvRhZzMH3xSBdT8ejrhXQKbl5QN+nqGciLHFoI5DPf5S2f/lQL1UsRmcVqqD6VYKMUgN6GAiYQpH0KfB+RpqtpaAaZaOiEz0BYbeAhljU2zsBYxPAgTjeCgk/WbiP66ojxQUSYYrwC4cY+VCciGyigEsMjxzcg+wxmRZIlCUbnRaptn4lBgl5eQUQpuBnJRp9yGeaJuOgrf0T1w5KrbzgdHad0Vjpc/4K9bKm0qO+I+Oh3rmfH+E72iJsxyuGuh/dy/vEpzln78wWQYytNPnvLg0zvWeG7dzzJ6VvHyYgBj5SYcTFL1NiAcHNG1+n1bZxe3/YLzBhP6IRO6IT+9+uhhx7i2c9+9jPfv/3tbwfgta99LZ/97Gf5gz/4A8Iw5E1vehMrKyucf/753HzzzVQqlf/RIf+360Qh5YSeUS9OSMs5JiW2eb/sy956Ld/9/F284Y9fxNNPzXBk9yRuf42zbzyP+x49Bt2Mv7r/cjrjlshT3FDejRaC88a/8p+OHxQ9QsdCnNBYajM40scpF69H9W7jp18dZeVlHWpK8+3ZMZJQILu5BVqEKdu3TeN4GYIFvvPD24jGmzwsz6cue7z/n+5kyBMYu4ff/rU3UC8PcvxYP3pAEAUGUxb8xZ3P4WWn3sdN8+ehOwZXuNRkB2thsLbCBz6b549/8dG7ed0Vt3H0jjKHkxyGpC3geIjMIHoZbiB545+8kG+9+ePYFmDALjdY7y4SHouJZyTdoIZRAplkxInmeTecxZWX7eD1576bZLkJUrJwKEUvdRCOQkwvkHmKQ8dXcLUmWQUXTuzcRNiLWTAOJuvAcouDPz1Ax1HsPTiLtZbRQk7j932XLExyiKiS6FTTcjXGE0QHG8RhyuS+OWwmUL7CCS2p0lSmDQhLe71FphnECQkapzjCLV/cxp4vV5hu1+AsSbyugrsc5ilGSmGM5pRz1vN4dpB2o5TP/P0ehchBOwI3zOOaRZRihaDluxybqZPgIpQkKHVJVhwKQ2tpNxby8rUFoWTuSNGWxkhEbTCHJw7vmyH0tmCdfCHSPFUz8sNOzncpFLCtFGEzrHVzcKOUWFdx3lWncd9Mg1aYMeo0GSr2GCgI5p0RnLJg9o4h+qfz6Mvk2oSrqwt88mPPZ1+2jztu/hVe+9ab8yQH1wUpSHsZH7z3eZRmQ4ZkA6c6xVL8GsrVtSye8RBi3iKloG9LTLsgMQ6UmgrHSCgXQQiyjeRpHQLoeuCFyEIJb76LXX3vTpph0zwZRAQB6WgBhEA2e+iCBSnwFmOsowhHCqyZ6LKcQbMjOGvhMPu+WsN0q1z14f18ZmoHsj5E+GKPV115O4OlhIG4zebzjjJc7XB8oEr46hANuF9IEULQG3Z48FubOf9N8/SMwzVD85gbF3j0zn46DU1NZrx/+nzuveUc5GsE6/9pP8pRmFjnIN1CgZMH93DYGaTR9tC1ADUlaV1RxeJQkOtIhgukhxvczyi5lT5fIpkxB+soZv5tCx9jEFu1fP+imwBLW7t8d/pstr/qGGdeOsM9nzqd0Gh+9PFhZh4o0Hehxp/tIrBsPGuO9mGXMFU51Pk8ie8J7l2oclq1zXWXPMnMrnOYfxRspcjS1ABDA1MU/JiClyGwICQGhzCt8MTKBNNLJ+EKQzp8jP5rG/T5HQIZ80D3LJqihI4dREdx39wWkpKDakqeXNnMOz58KfN6HKccYuOIYhCzpzfGgNchmOxiyx5yKcLMgF1nUN0cmOkshVQOdbCtFvgByxeNUN/fxjuYF6VwXdqnDGJGAtThHnogQBZ8ZKQZGulycnmFWX89st3FGoP5cUj4yX7EnjKkeYpN5vkkZc342DIfv/L7vHH/i7Ghgw007iOW8F4Pu1OQJh6jd+ftXlbJfCHvSayFsOlQXteg2yoxuGWeFSNwMouVGZQKeSuHtDhCM+oKfuedD/PK29excsF6VDth8EfHMUNVrNUIHEgyhNYUD7Qwo/1ImztSjAClFKpj87j5RxOmmwHz128AY/BDD9k2kEDzuJ8nryUJNorzVqOO4InH11Bop+CrPBEtihANML4LUd4CJJIEU60Tr1E8GpfpHbCQeBSamsaGAqV9CrIMYUC0E1RQgv4SMsyw1uAEkoV4DLvOMH2Ni/ag9kQDJzJIU0a2FQvnW7wpS/0hSNMMGaWYusJ1MnTo5i203R42tcimQ3HeRxxbwl3s0b1wA7bok9Qd4g0DOB1DbffSan+fRShJlilEmgOghYahSodXnPswVjvENZfeVofB+R6yF+MthBQPNEj7KsR9AWQZTiqxYUhhsklWcuiO12ElwU0NxaxNu1pHxC7SitytpxS6r4SYnsd4uZvOm2yiVmJsKQAUwpPIdpR/1lW+6WJchVppoho9jgQDOburJrELS+D5uSMmjNDb1iKOzmG0ASMRqSatF8gqPt6RFWSWpxVhLGQa/1iHrNqH00oQxuTJd0phkxS7uAzFUg4HRmC7PcRqUdAKgW13ULUKaIMbWbLUoJTCBDLnnRUCrO/jNwxCOcwt+Gg/pbYYYg5M5ceUOcQfneWpTAiEjknelqKU4lUT9zH0mOGz3jX84OanqLmCaHaZr/zgVEZf1uG+o6OotYd5+Ngc1nPplgz7n1PnpV/9ChjL11/3Es4e2gDAcKWEQiAigxWGLDNsKRnKhUnW+AU6A6+g2HmQSlDBkBfpTuiETuiEfll0+eWX5xsz/wMJIXjve9/7v5z4879TJ8bRE3pGSabJPEEaCJzVS+PX/vAGvvHUX3DdKy7ibX92I9VaARV4ZF6+OEw9h+WaR3l9i7L/IBeXptlZmGF347v/6fhv/qMboBOC1pgs/6D40qPgxTz1ubV8/4pT+PLrT6PXGEF4LmbdMGakD6dS5HtfPp2jB4bpGJdtWz7O7d09THb7eLIziqMsP9tSWp5s8NV3bOOhH06QJbmN1yrLVFTnk595Lvu+cRKOAiELDKuUESfBmJVnXuP2UcGaUouzr5lBb+4n29iPUKuAwFIAnsPX734Ph48uEhNjXElWcjCBR59uETV90szB70+54LIj1Pt7LLRCPnPTvUjXIYrypBAbRlTqBZTnIVwPHIUql3jg1j2k7TwK05qM0kCV8kAVk2R5K4Tnsvn09YwN1QiMYONYP35fAV1ywZWkvRjiDEeC4yusK9BKsPmc9ZSrAW4pwC+4NDd6ZAWBTSGpSuK6A8KgAonVGVmc8flPvJ4dJ69lYbpMNN5PcLSNP9PDPdLAqZRRnsMNv3IeD3znIeRdXWg62EzRHfVpbJWoXoKIUoQG2+1x+kWHOX/DIuecdwy1pQdJQnfIw+tqFg4tIaRASIVQEu15mDTFlDzUcYtJFTaTyMH+nMPrCzAC0/MY296FisCEPVQhw13rICplqFYRlTLWd7mntYxzxgROX5lgOeUnUxt44PhG0rN6DAy7jO1cQCiQRcPlpzzFj45U6FYdVta5vOYvbqFbVlipEJ6HrgQIT5EsBRT2d9Dzlj2zAaX673HyzvVccsVxkpECyUkFGm+oMjy+lCdptlz6rg+xgYs8TTLRns57/I2FXoSVgmylh+xEyHaISA02hSyTdDY72MAFm8dwq26MH0m8qRbu8SVElOD0DAWVcjyr87z1B+mEPsVqgjWG1z3/ECddeJDk5JCzzjqEZzKOPV3koYUJSn7M08vDuMOa/os00c4Bwl5AGHpk0sGZS3jH9efwN7+/nad7g+zvDXD0p4JjXylx89I4RzsjSCS4gm6fxHbDnOPT6kKSsnPDCpcPHGDpC31E40WSIcW2312geYkiGgmQKUTravzLQ+cyeIehshtGvtYgqhYBSEuK4jerfGrguyxqRdO4NIxkZn+No18cZeqbg7htjde2zO0aoSg1NS9Dza0g5xuMTS1T6CagM9AZ/p6EI/9Y4YvfP53v7a1wcCyl75qQaMcY7ZP7yYymXAu5dNNe1vQv5MkyqcljYIf6ac6PEQUxax85zj9/6Lu8bMMjLEQFKqJLMyyvJo3kLoPvv38n1nEgchChQ2lNkc5ZYzQv3UjvtDXUf91ySmGWAadHu5ZSPNggWA4JIkXWDgknqkQjZaywuD9N8nHOWnpbC7S2F9GeRMmEwOmxc2g3Jz3rABxIcJr5whgJn5o/l69O7eDIJVXoqyHqVaR2OP7GGqZnsUmCcZy8hllM6URD/F8vfhURfYi0iGgGODjc9e1T+OM3P5f3vOF5KJsnnAghMK0OdqWNXW7gzHc459TdbL3sECN9bZ7/kT357r+x2HEP2+2iyhmvuvEBlJAUJdDNC7My1lhjSQcL6FpplcJv8YsJg0GTuF8hU4tMDdZTYDXRMMRjkAUZthfjH27kMNTFED2X8tWvnUV3RiA6SQ46NSYff40l2DMPSytsufhQzk1ZHeflchtpgYoPmcgdlH0Fvjx9DkkzxoZxXpBBoLMUa1Yh0F5e+JGNEGdxieKeWZy5JiiBkBIVg8jIk2uyDK1ACInbcJAmv99a1wEvf1yqHWRiV2uL+dgjjMVppYgsb3cRicYJNc7xFVRP46yEWN/FFnzwPIzjcPzeOkIbpDGIVDO9f4A7vnsahx4bpx0HGJW3F9owxukkqDBD6BhZ0viNBH+6gy3nLkphJZXDHYpTXUrbG5Sf28XfsYDxoHvaGLavArUyKtJQKSGkQtZqSGTuBFUCowRkCfLgdD7GARowlQA7MUK8tkoifYQBrcjjj9MsdyvqDHV4Jr9eymVkqYReP4ZC4caCZOsQeqiSA93Jnztw8wx9d84w9vn9OWfK90HnCYNidgmxsAQFP28TarTz1iEnvw/hqrwdUEqQDnLPYYgTrJLIzJIFAhVZnMig2hl0Be5iB3V0IQflpilCqBxgLGVeyMFijKHysnkcY7imNsmd39uJLjj5ZsOBOZxeQu+HPh9841U8+a8nYQ1kiSKTlng0QGQCkQmshlfc8RnavQ6zsw0effo4i3EXfyXDXUqZm2+is6OUZYTRU5TdU/Co5a1ZWrNr+XE+vOcTPNXa//+VOeQJndAJndD/23SikHJCz6gTJQgNMiMHov539Pp3v5BLbzyf8689he6oRzTi8sr1D3HxjoNIOcJPukPs6g2wuX7Dzz3PWsuxmTZCa6QxpKkm6iUc3zNHFLnYYh7HahOBbkrKaQnZi1FRiiK36956y3bC2OXobD+dyGOHXGDmqQp3dvs4nChuXqwQ+i7xsRXsoTZppCktCNwlGOx4eQqEFAhfImUZLQYxooh0/8PaetbIxZw9+if84IFL0ZtGYbCENjbfrQxjDJb3/eHX+MYX76dgOzjthGAhRCQZB9jErByhNVzjghfu55yXP87Vr34KqzX33f4UrqcwJoeKCs9DGyitG0EqAZlmYLjNa970NWya5TuomWX/I49zbPc+pGOgVuS8q06lUClw5Ogic7Mt/v1ff8quBw+zstRhfrZFasFmGW7Bo1j0yUYdokHYdf8+rONSKHqIhRZWQXsNpGtcFjZJUlLW/XuMXg5zrog2PPumfyYbckBrmucGKMdFJeC0Y4QSuJ7DUtIl7MXUvt7CljJkoDGjioGnejiOk4NNez0oFui7PIcHJkYxNTlM8zXgeJauzHddhbVYITBRgh2qoIcr2MDFfSDlzLuqnN0+mbHBZzGYeVghyIqCgh+itg3jjThgDMl0Rnw0/7coFHKwoBQcXGyyp2TxpGD5i3XCnk/c8jl2yQAbt27A3xrh/+kc2TsWaKceT7eGEcM+r3rr7ewYWEINCpLRCtlwleDKIc569iaETvBn8qSKHx3YQn+9xG+94hJ2/+sIoh0TuT5PHdtAcG6TpKzQJZcb3/IgN37vIV74nkcRYyLnVgggTTCVANGNcw5LnGElxCUHnSrmrijRHlFYq5GNTt4K1Inz2FYLIs3betqLDv84fRHTUY3CdsPgeRqCAtbCf9vxPd5z2bdQN3XZ9YZN/Ps7zmPX58/kwMIgWSaY7VbY9AcRreuLDHe7mFhy5dBefutd97H9xgbTl4zxu09ey5sPXENka8hggJv+4Armb6vAKhNp5VfWYtIUm6YIz0ekmjNPm2ElCvAXFIunS7K+Asmyz3kXPI3Ty3BXEm44dxd/NHQXY9UW5aOWrKCwXoksgMJ8itex/P5fv5Bd7VGOpQUOpXUuO/1xogM+M48XqPS16d/YYeRkzW/85WEWmxuwlRK2VuHAZwbRhyXm3DKm4NFu+1TqMQtf8nj80xsZUl1kLWL6cp+FcxXOqS2OqxoPzG7G8WzO+XFVXuzyDJ3EYetDe3nVJ+5iLgkohxmNWzw+8P7nkooEk8aQaWp3HUJNRQgj8BrgNC2LgzXc0KJSQbShiggcLizOc0lpluBkF4RF+A6i1SMb8Vna5NEbcIkHC/hNB9vfhxmuUz6WQauHjDoMjiyxdvMiL3zlI+wXNYybn3cKAUZbvjOzg+wRn2oiycoeIvARvgvtHjLVSAROM0MNt3BGYob29oGypMJCohGRIQkEmTQcO1TAHlxNGRMSm2UQRrnLw3VJxitMvq3A8wae4Nq+PRwvDuetj9sKZNcb9I4SSVXRa0DPaDomZdBv0XfbNLW7Z7BAtLZGtLHC8qkFok19vOX7B3jPRx9kw0nzyNQgjMUUHKxQ6DGJHpb0ri1js4zqT+eo/XASPB/hKXaMLkCaQpLmLCIh8vFXyhycmhoO3T+KqZYRpSLScRGNLtZxiMdKzF8/hIwypBU88fQmdGSw7Q5ipYXSYFc62F6cnwcENFrIhSZOp4EwlmjIpzusyCwM/sQy/rUmwpi8ABUIVE8gkhz8bUoBDgKhPYJjMX2PtJGhBjTCD8B10Z4kGi3TPXc97cu3IPAJFjJsvcTyyZak6mJO97G+g1ES0W6jFtv53yhKEb0YEWu+/6WtfP7PzuX06CAnOcdxlrugoHNSH40LRsn6SgQHeqgww7ggl0OydUPQlxdFjK8IoxJxHFB4UCGeWkSr3MWBBes5UAoQg304uOihCtTLxNvHiMfrxOv7YeMoIvBRvp8X0cMYPViiddla0pEC8XYfEccUTjIQpM/co3W9CP83G7c1Bn+qjZWWZE2VaHNf/vsFCMehddEwnVPqhKcNY9cMQa0EvvdMu5RIMuzCMnJ+CenmLWAohXAVwnHIyj5IiVxqoAw4e6eQaYY3HxEcy12izhNHUcfm8eYt6/5sZTUpkPwzstrSTGbyfxud/15rqb1klkNhkf61MdKCCRSy08UKgTw2j1hp8eLnXIr/tCZ9EKznUWz9R6apsRZjJVff8l4+9tc/4gs33UMrjfDnu/izXQ7vnWXFjnE4LdIVGyj7HgEOHhJjLFPhDK20zUw49wvPE61pY8LvYfX0L/ycEzqhEzqh/6o60dpzQs8o7uXxj1ZBSfz3Cym3//AJmitdhuY6+a6PgQkVE8YFWmaQHzeu4uUTV1MPRn/uebMzDX5y7z6oFrFC0O3G3PP5e7j7liconGSwcYqJU+yIhHGH6nKRbnceLKTNLna4j7sfPJVbj+1ENuGV/+0H9Lk91lfmOJ6OcDRbg9wHxBmgEDrDZDl8bvTWRaqex7dv+VOUo555TUPD3yTV85T885/5PyEEpw9fx8LBRwgWIoYrPToorM6wvR6dk0awxtJq9Si7XexTS4DE9EJ+9fJj/NX0CLYnUYNdPC8kGy3jRBnnnrYOz3NyWKLvQZrR6CRkJQ+/XCCMEjaeO8/Y+gUqA5tpzYEd6iOZX0RYy6k3HOSKC9/Cd//xVh6+fTd9Z28m6sUkBpZpki52iJs9yiN13KUu9dE6nuPkbBctKG0cYNOES+v4HIe6hvrBlPmzHHrjEqsc/F0Rwlhsli82hONgqoKfHDjOWJIgL+wxWa8jawkbP2wwSYaJYvbcux8hFIEF4YFNBEiLt7y6wytB2BwIeOuxbSSLPseICFEAAQAASURBVIe7/XgrkBx06G3McA6DLuVtCAKwUYQz3wLfxQY+gwNF3vVbv8of7vsmFqh0LNGEgzRQ2ldg8scB0VYIZhOUY8hCi5AW2h2EI2DYxetaEALtOwzMeByRmnJ/SOi7XLhpGw+bh7EBrO91ePTHO3jeDVV+75Nv4re/9gg7xufZWlui/80L9NoBf/ucA7zjz4qoct5HD5AcTomTlCceOMDKpEchXcTpgTcpeWjrJorblkieKLKruZ7D8SDjboOlPSOMFhrI1fYN2U6RUZzb46MIUfAgMXnSjLZU9jdy4GJGvnAVgnSgTFrysYFLJqDRCJier9LrOfxfi/fztbt2IrKMt7/uBVx1xUG+/JE1rN3Y5PXveZw9T43zvVvOoE93OGN8hhTFy6t7uHTiGB9+zTWcvX6FG9/0ME1f8tLX7uGOR3eQanh++jD6+YbH7jyV9piH1AW8NlzeeYTf/O1dvOMfL8MKlS8uleSrT57M3Z8/mXa/yBdlrYyV1ysoa+o7XWZGM3715T+hHGj+wPkB733Ls9FozJoAKfNdbKEN5/zGYR6eG6U9ViKyirF6i6MLETpM0dMJ609boHxyh4/9/gay9RHp2FrcUkpyzOHYPePE24co1KYIY03nghLl20OCakovcrntrtMwF+XRs1aApzTDqst5tWm+VXQhAWKg0UBJn+H3J/xw5RSqMuLC3Yd49FNjqLMCpp5fQsaSic8sECwmpMTg5A4D41oODwZ4xxKkVHhzHR78/lZmd+6iKx2MsRx/dpnxW1vo0RJpxSMe0lhHUYx8QgzF1ELb0veoQWaLiNjSXS4gCxGLk2V6cYBWKcKTiIVFbLGI3B/Qf3sLp5uRlAxuz+TsiVRj3TxBTAhFOKoAwVNDLUa7PQpHDFJ6iFTT2DlAeTEmmM3js4Ve5XIGAUYKGB/II3pX2qQrPl+842KGh5o8umcj1//dHTzxkRLlR9rY/mGENMjzlljUkOHyvI8c4+++fCqqk1I62CJeGyAyiPoszdOKyCGBUoaTTp7hcTOItZBWfIIkpTbUJRUOwbZ8vAHwugndkoMAnj57EPP1BGmAXv5ZlYGTF+1FnlDn2ATtBRjhYQWIriIcDZi7cZBkyKEx7eD3IJ52cweFyVtK0gCy/gCva/JxyxqEzRfJ3npNNFOlu7mS/45uSvMUSVboY9O/x4iGRQpwehaZCvwFnTseuxHGGrzFHjIDtxFhK17ujsAiVM6S0iVJGigs+XvLygHVQ4ZwYwDCUpzq4M6kUMsLDtaV4DuohRYizbDSxRmN8danFIKU7t3jWOEgtUCmIFMLKFCW7voqwgiC2QS0xRgNwhLOB5ibJIXpmDqW7OkV6PYgs8gwBG2Ihj26O1ziukffvQZlHaxHXoRSCivyNhyxmsyTddvIxYxopIj38BRiOaKbFBDCI1njk540Rmmyg5hZgEIRmyRk24eR0xmkGrnUxVtOwHdB5+ezcKxHVnPxo7y4Yqsl7Mxi7jzxPGycPAOgxZi8CK8N1nOhWkUi0L0WshsiHSd3Ee4/Djs34s60UbMrSCmxcYr/9CJ2pZszYjwP4zvINL8mcruTwlrLyFWnM7d7Gju7wN/uPIuN1yY4jS5GSES9hm40cGo17GKHJ3Z/j7OvPMj4SJ2pgxcR9IGrwasougNtJCHS0Uz6d7HOvZyobBGAIyWbJgbpZLvoGJdIr2A0JGHGyJYD3Dz371y35vlsqWxiS3nDLzxPtNEPMb3PIbLLUZXf/4Wfd0IndEL/B2XtKgvql+B1/BfXiULKCT2jbpLhRDkjxfX++5fGORefxKG9swyvqZEj3wyZLvHZi/+Jzx/5Ov1eSN0r/Kfn1eslEmXzSSiCzkqX0Yl+goJHWlWIVoxNDPYhQXYgoJIETCMQxuBWfTrCYLTEOZ6gtGS6XSeoZ4ioyHXDT9DF5atf2ImjLdaXaCBrJTjNFG+6TYKgsdBiYKzvmdfku5vw3U3/6bVaa3HvWcCdThi4sMtFL5nlnu/0M8souuxxys51pNby1L0FbNgEQApBikM4H+C2MlrtgDua2xhMQoxSiMBBOYqgv04nzEG0mZRIz8F3C/QEmIpD5MLvfjvlLX+5E4o+g19sQGoIe4LxzSN4vsPgWB/Ly120sKxd20+f57F3aoV2L0GGMZ4xDNSLOEripQqdGs5/0RlceepmvvYHX8ZbmCVbU8QIja5LrLIk/Ravu2pBD3xsptGOREU+plTkBese4bGR9Yw8vMiRdinf0fV8sp9tFCqJTSyimCERmE4GkUUJ0KvMi3hW8Y3DZ2IsDBRjpJQU/QT/kIsVq2yMJMX6LtTzBUg0UmBRJ/zzow/gluYQEraetY3d84urPBWB1QZvXCKnPBydoVODcR1EwcHGIaW1Icm+AtJaOo6kL5MMVLqkxqFa6bF50wQXZwfQVrHDm+FjZ95Nz1qKxSL/9NpPc2zxQzzduoM/vPgh+sUSY67h126c42//7bk5y8RAKR5ESsnOC07K051aKW4rJenzaN9eJV0rqCM51qkzUW2w0i3CqQlmMEAmMUiJsgACkZnc4l/0wZNIz2CEIC27uEdb4Eis6+W7nmGMHsyhvrgCuwC1vU0mtwzwD3supBR2MVYz/VDMTY+sxbEWYwRbtnU5aed+1j13lh8tbqSVFTmlNMtNzS3sNCus3bnMzL5BCnUNFlpZwMpcPy9vP8R1Nx7CXgC7v7kGs9TPj37zL/AE7I1K/N3e09hwSYtDd9fAc9BYvvPwpbgTBicTOE8ZOjvXUvzxUUhKvPal5/Le+39KL/Houpb6pg7OSg8lICt2yQplnG4K1jBbKvPEHSdRetFjgKUgI9TcSl4gyyT7vl9C39WHtv1sOOkYb/z0TTzwg0F+/Benoujl7vpOjJAO6Z0WcUiw7/goTyyfyutftItPzJ2bRw0vO1x3+sN8/amzOUQhX9wEEq09TFBm52kHuKJ/D3vDQXYd2MSX/2Yjrk3Jtio2nTRDq1Vi5eQioyttbMkhHtLIVJIVNWmrzNgP9yGKASrSZLWAB/QI/arHNZf8lG/cewWTzy2hHLAShJWILCMrSLoXDFJ8YDlvK5AOKlaISoHhTQ0u/dVZ3hPegI9l5oKAdd+YgVYP7UsGvzFLNAJeBDIx9PohaPaQlTLWUVhy55MX5I4xtxvhtJvI5Q5m+3rkSoKYN8ilGNHLsI6D1WnuInNcVCphtpFzfMiLrBOP7sO8rJ/Xv+AWlnUJJruoTQVUG4QyaCPxhGBYGVob2lhnlTmBxGtoTODgdHOf060r6+kPehyy9XyMsJa0ni+KTxudIpQe9YUWh7XAKkkyMYhM8137yYPj9G0PcbsJ7tMLCKNBONgw53O4gwr7/ICrJx7ih397Bsb3cdePsnRFQNovGbh9CX/GwYz2ITSIUjFPIFMKZB5Tbk2GrpSQjsRGFlNQLI1voC9t4TUNCIlIDUaucjeEg/Q0fjlGHvPRTs45woIwhsq+FcLRAG8lJerzUYnJX6/vYRxB1Cfwl7M8Ur3ukGLxFyNENybYkyD6injH2nlrVJbkCUEyyIvkvRAsOC3FSq3C0acnEI6hT0V4FoqHm5T2LBFXfUBipUBXHNwueXuWFMhOiMws/mGBlypEL0JXCthCAdIM240RxQDTaqGFxoQpgw+2yAb6cTsp2hNEQz4ytRSiGiLNUN0UIQXeUojVKWbCIy26KBEj0rzA7s+38dsZREnuqirkrbYEPvGaKlnVo/jgJKZeYPraPor7BJWH2hRmXIrHFhADdYjD/LqtVqHTwWqdF8BUHqstHAebpthKGQIX64BAIAYHMJ2pnJsjJCqMSXWGKAa5o3QVett/93TOQTEW1gpEdXNe0D88A07OZRG+R6PRy10pYyPYJObwvRGiEqKMwXa6kGr08jKUizz1yAhUtnL8yQF0ySdOQ3yhWFus440eYyExlNyIiedP8+5Tr+ff/vJvSfo8jFQEJZ+EvEilhEeqDf2D+9i6+TA/XTnIvR97jL/86Ad/ofnhzyS8MxHpboR38f/S807ohE7ohP4r6kRrzwk9o2bUAyWwrsDz1X/6eRQmPPcl5/Dbf/J8+vvKaBe0Kxnr+wZCCFwxzViwBCz9p+d2ejHtVEOSQZKystDmjAs285LXXkw6LvNJhesiMofZIwMEpogZG0SPD/K8V19CWvXp1lzCPg/tCvb/zdW8ftt9vP2Ms9nkx5zmd5g8vgbrOPmuYabpWQ2uIpsYwDltnP7R+i90HuaXn6Y03YZUMz1bZXxilnQhxplr4nYs9975NI8+dIhkNm/REUpB4POpp65ASIMycEgPMB3XeaK9BjfTbNw8lE9skwzRi5C9ENvu0jdS47mvu4xspMrGzcvU/ZQHb/Ppjfm0NoIYG0YM1plWIyweX8Zow/yxRWIsuuRQm6hT9B1wFTpK2fvIUdozK0zunwVAZhYZG26+5XE+/KffIEszbCGg2DRMzHkY32Idcltx1SPbNoYNXM55wdn0P6gozGTgONx342Y67xzj8IcH86jYKAez6jDDJDGZo9lYriNSCZFi6dkuRmuMsGy+8jT0cB+FPodSMaZe6jEyvARdTRi70NDMXTVBlqVoRyA2TWAqAaZWQJwbc+DGAl9vHKau1vEXF1/OzSuLWAEGgWfBdjqURrqYJCJtJeiezdN5jMV4isn0ZwseSLsRvfkVPK158cZHuWH9k4RGsNmb5+RgigNh7qT6GexKCJ91Q+/lms13cPbYl1nrGCyWNK5Tms379CkVSMoF0iTDZvfzB2+7H8iwSUxpqkntfsPQ9yROB4oyYzEpIRxN8aAmWhcgK2WE76OzFF0rkU4MYAIfjGVDyVAfbnHyxklkq5XDL1d3TbEWL4rIPIkuKGRskNqyvrHItvFZXr/9fmQnBzSaIY3shJgoJm4bCmWB9AyjlYh6IePFA0/xwvoRbhg4wDffvR1nXvPOP/0u6/vbVKTlfX/6Murfg6XdOSzRItDby7g7V3jug6/h49NnsMGL6PYXeLy5HSt7zF3kc/AN60FblAEUVI9HlKcyGuetZ+GCtXzyfV/HPxxzpNXPiJIMFyQjr2+jy2UoenQnfBZPL9CtWKaeHsQb1vTLNkUnYfTICsJYhCtJM5co9NC2RNZKaU9VubszweTTY2SynEc+pxkuGtcILq4vEBZ92gNFPv7OH/FbV/2UymFBcNwhOCZYV17GNHwqXt5KorUhqReI+jye3r2W55RW2FFYpPuXHhYXykU2rZ3jZRMPcdmaPfTNGaKaR1p1cRoR2s8QsUBYN7++AKskacVlo79IRYactfEI1gVZgchJyEoZphgiKoqVkwS9kwroegnqZdKSQ+oJdJQxc7jID+86F6fjoO4vEZ9S5Mirhmg9u8Lxl66jcWGF+oGEYiNl4fIhFi4bhkoBoTUiTpHacOH1h3Bdg+MaZGiwKymim2A6MVpJBudC/FaKSFb5HHGStzp2w3zcVgqrHKRwsH11Dt+/lU3VWabTPp6nn0YIQ3OfT2IcUhTffsE6fCxtY+loHx1AWhZkwpCWBNqzdEspaU1wz8JmblnYwXRUR3YSZDdBDsVcccc01w7t5fzaYS4uH8tZVkohlYN2JdqTDNzapby/iT8dIkoBlAPMBo3VCTbLSJuW+IEqd35yB8oKXBQiNVR3LdN/a4PynhB/pp0nbSUpUcmA45BJQ2Gmh0oMQtscRN6NcpZJJqBnMTWdg0aVxZlt4iynuA1DX72JR0inXEamBjfUqOUespcg4hTVTTG+pHX6AMmYYPlChfGdnHtSLuA3NfGAIulTuE2D2wV/zzLBbIi7FFE8HrI6iOVtNtrkRY2VJrZSxLqKoR0pgxvb2ARMxyBmlhGtFmopQQdO7tSQEqRA9gyymyBaXUQ7j9W2YYi/OWXobU388wTGcXC1RgYBtq+CzTTCcXEjGLhnGb9h8PfNYDotBBlXbj/ImSPH0OUAPVAlHCvT2VAmHSgQdCXFyZiVa0dJtq8B311lwkiEXn1PnS6m0yFToI7NE+yeo/zAcZK1BeKNVXQgSYa9HPobd6lctuoe7IUQx1AtQqWUQ3mVgsBDOHlqnF0/gVkziPQD0AbT6UGWYjeOQxhhu12sAiUkWdkn27qGuKhIPY2olJHVMijJ7Lkb6Ph5Udxa8jZTpcBx6QqBXddH5gtsmuL1VxGZxoQRJo4Z3GL5vU88hbdlAGtK3PO9UzlyYASwiKUWcSNked8i37ziL3ne6BQ7a8fZ6M+QLb8RGSuyakBcdukZjTAtjFlBklENAqKixBMpgUhZCA7ypr95IzPhsV90mohwtqBq70P6F/7CzzmhEzqhE/qvqhOFlBN6Rq0kJi0I0oIgKHs/97ND+2d5z9u/zE2f+jEAnhU4HYPbMWRh7l7pc7ps8ZcpyfQ/HRugVHDz3R0pacwu8+WPfp9/fPfX8O9I0LUCouAjgoDSQZcrzzkbPJXvCqWa5T6FHXSg7tNbX+SrX3wrAEZuBuHSaPhEKzZf5FuDdQWpsLzrzddy0rO3c9Pn3roKL/yf61vzX8QWA1TR4bk37GdmuUiaFhDSwTMp6VwDJ4qJdYE3fONu1l3c4aK3dEgSge1JdEEQG0Uncwm7Ctopd936FADKU9g4ySe3Uczi4Sn+9UNfwT98jJLKsMJSqrTYcUiz7sEWKukhXZ/mo4oLX3A2V7322Vz96ksZLPgUVxI29lUY6CvihglxnBEK0L5LvNpy4oYC0YNk/wrNh4/y5KPHyTJL1o4xx2L6f6KpP6QpHs/QgcSUfF7ygZfx5194K5WjIQqVT0AdhZECa1b7vV0H60uSXoj0fCyC3z5/hMJ+j/4HXFCFvDVHKX7nD2/AlgOSksONa3bxojWPcubYFOWsR8FNGbWK7umGw2/byGs/vRdml9EFB1Nw2LRxFls34BhmUUxUrmGpQJ6UBKxZyoGmQ5u7uGOaqPmzHn03h7qOKMyaNhaLSjXCGEwv5NS5ea6rPsUN9SeIS7v44vR5/GDxVB5tn0Nc/EvKlft/7pqw1vLdf1/imzf/JR3/A/zD585nsdnBJhnSWDwHHFdx+NCv8fF3TsDsCmK5hTQa1Y1IfUmnD3oNj7WFZfqDLnJXHaH7MN0eNoqwNiMbqWGqAdlQBYwhFccwqWRd0KRcjpF+gMwMthfmu6mNGKkNqqexnsRq2OjMcFnffmJbRQ/1kazpY/C61cQKo1mc8ZjLElZ0ymLH4dfXP5pzVrDIFNQ8NI7CnkfzOF60ZPFcB1XwaLf7+OMnr+MD+69h39wwjadHae0e5Eczp3BLYwg5C/GQB02NHBripL9+Gu8Lu3PeRpo7t2xJEQ266LrL3NWbCLf6/NW9V9JMHPZPVjn2lSIiCtEFDx0ItKOIB10WwjJhXOS73zqPwqM9bEXRf57FSAfdS8gK/WRtsJlh5YACKbm1eC5Z2SUZKkHBpbxN4BWhFxbhdUXmrtxAqS+Pl+6bjake0viLlscb47hLgjXFBmqph2hH2KICAamo8Acfv5r5IxVko4BJM3S7Tfnrmp1zTV4xsjvnFC21mHr1ENlAEYRChQqVCUQzJAsEU8/fRGfHCEdafbzv4Rv43OFn5W0VGVQEjC3FBMLS2WTobTBUDkXIpTa2G9IZdYnLHjbL6Aqf6X0DDH1LUDuQUYqaBFnK8Okpuuzh9jRKKpR18NqGj77485x73dOQauh0IeywbftRlDQobSl/tZQvpEs+wnNxwgwRJ3mrg5I5OFPrVQiqAJ0XF4WSeQtKweXG33+MM8IZ5n9T8acvuAx1fhk77mBTgelIRKw4lpTpGEEzdXCKYtWB0wUMJrCwNkMsGVZWCsx1KnQjL481jxKGty2yptRkS9Dg+aVJnp5ZtwoFF8heRFaSZEWF1wghivOvTg8bp6RWIYqVvABuJbadkbUlQioIwM4vUHlokdqdC8RVRXd7P6QJzmKLo785wd63j6H7K6jYwkANUSzmaUJezm4SVlBaStk0tohwBFZIZCskq0mysqDb1ERLPXr9gvZ6j8ZJHq5UCJ0XaazQmO0ObivErM/ICg6zvzuErhdJKyqPkVN5S5ZxJE4I4Ul9NM8fI11fJ+srYop+3t5S9LGORCy3UEstTLmA6KsyurbFS17wGMM/mGL43yZxl3vQ6SG1pv2sDeiBIB9f0jRPsREgE40MU9LxPsJ1NQo7JbWhDuVtXVbGTX6fsOROVQGiWsZN3Bym7nt5kanTY2L7Ci+98kle/yuPI+YXSUugxz3CTR5+y+B2NaodMva1DIRCD1XzCHIpwXUQjkR4HlnNR8QJzpFFkn6P5edsoX3+emSqGLyvTf89y9g4Qaaa6nCUn7PBPkytnN+DpUSUCqhaNW/ZiRPwPZxGC5nkhWqZGWS5kINpVyGz+WSmjJ1u47VDvIUeTqVKtKmEXuOjPYfmNScRNB30cAF7cGoVlLvauiry1ilHuRhPgoQ0ytsgleeiKiVaiz6brprnI1/5PCdtyB2H0oCz1AMlkZEhXOoC8OYdH+I5g4uMk7K0/Ch+L78/itVbdUUsssbpEYiQoVqJZmeMy0p7eWHtMbZfu4wZb3O0dwI2e0In9F9Jwv7yfP1X14lCygk9o4aNyEqWrGQRnuAjH/4uf/OxH6EzQ9hLMNrQaua7Xb5SBMdaBJMtwl5eOHlWscXV5Q7bgv9cSCmXfAq+h222sY0WjUbI5N4Zluda+MsZcU1iuiGEEaWjERvHhqAXY3sJxw8vEZ6pMQ5kPmjvPz6Zx9pfIjSanlfOifyui/Uk8VCBLDM879JTOaeX8dX3/itJlPxi5yEeRGeW0aEVrr3yEEFLkaYCspTzT11PZ3aFqBNhheRbn76OoR0eu368A7WYgZRoJQmFh7GSqOdiHcme+w4CoByZAw/T/ByZ6RXiRoxuJmShy5hUvOi6J7n01bv4xMc/TYEWZnEJ1U7593+7nx9+7UH+9R9+zHe+9yjNKOPRxydxA5dCX5FK1YcwRRhQOre2GwzGBW1MzkRQKt/dMwabGoIlh0LDI9xQQUYW1dPs25e7WW6+9Q9xYo1wFNKuTgCNyC3VysH0a2xdgjFIpXh05duUZsHrWErTIk/1cF0qfUVUZpipFLlULHONM82h+/oJ4h4TlRUmdoxz03O/zHev/grf+ecLyUbKWFdgpCBrgnQNXiVibdEjiW6lWtGkdUFaE1y4fTM2ywjWano/FchQY+M4X1R5LtLRXPfTGo7J0yKkUFghCL9YYUCkrFWGIJliyO1wrNPPzup+HvzhGj7w1u+ye9eRZ66JdjPkrluf5MF7m+joeqJ2igBMprFG86rnn43Ry3zj2xOYrsqTW8II48Gf/PGLGD5lieaFimon5ZzSUc4vH6F/MMUzq+kQmUYstrCuwqg8stm4isL5KbF02NsbINWFfIcYm0d39kKwFl0QZGWFcQApqHTg9GCBL//TpYSFQdY+a5HWqUNQLCIAtOCxe4dZni9SDDWOsGwwy3zoDc/iI/ddQGcppDuT8J3PncGff/JavnZ0B+eftB+VwIH6Jpa+4HFgfiRPI9ECicCuaD7+kRs5e+8y5164DxxLPCywrmIsNqz9+pOM3TnJ3OUZOjOgwHqCrCJIy5LmnSVe+Zuv4P0vOBt3MUW1ejj7ZvAbGTKDcK1CK0HmCpbnBxg8J2ZsY5cz3zWPEXkiBlGCiQwoh6QX8KOfbEUHPsdeMEhvUxXtCA6NbSbqK3CwO4T8vI/fsLziA6/m4w+cQWIkbqjBWL5/+7kwucomNYYg1Aht8FoGayxP7NnIF995IXRD5GIDJ0xZOFLiT3/tclQkkFZhB6oYX2KlxaqchSFTQXtbBa+lqR5PKE3FfO3282jsH+Spx07CqJzRkhoXZ1nhNRzGbtUM/gRqh3XOb4gzitMR7VPWsnJRH81nDeBXUvp6bTaK49R399j640nsF3q4yzEyTPNFsdYYZXnTg2/k2+I0bJbkzqaC4Zb5bRTShNIniuA6pGsG0evHkEmWF+C0QfuS7rYKulrEei6yVMRIm8f/JhnSGHQxIJroZ+cFRxhe22bnS5fxtIM54hBtytsqjbBIa/mTJ67kRysn848HL8YtabSj6J3VjzAKty2gaRm5bQbTgCh2SVIH1Q6RYcrUo/3cObeVdqgouG1e9OyH8vZCa2G+gVrqYBT5WJdk2CRFjwqEsnhzKcL3EZUyYrCEcWQOWY81LIfQi5G+hxNpgv0rFA42yfoqqJbIZ0xK0N5aQlhLd20NM9qPKflIrRGug1QOTgDJoEfqS4wH7VNHUYmDE0oW+sdJR4apPR4T1mJKxyIyT+TMGSWxdR/lJLjNhLW2RbFlWV9ZprlWkcU9tDTIGFSUvxarI3AN0ZBD67R+uuuK+dhrV2OAiwVMySerF9C+QFhNWWlm26OkbyrSeecgjSvWYQZqiCAALFnZw+oMIQSlIy1UL8sLDxacTBBtHWb+0CDHHhjjyNRakhEPWp3ccRknUAigGGCLAcL3sYWAbKxGuG2II7N93L9nnFvv2kS4sZ+goXE7lqCVIRA5O0UKZCfG9fLoaptphL/azuh5mGoJLzQoKxDVEtlwXmxwIoENPIKWRCoXigGiWmfq5jFslGBXmvlmThjl4zfkrTxphvA8bJxDicXcEkblY7EQ5K8HhRjoxwz1I+plXCVQUyuI1YSyxkXr0JHAWoPKFGa5ReXWfcg4yXlhq3wb0QtRiUG3Y4QXIKs18L38nmEtJorRvZSlVDGTlHjO2/4KmeWuJwoOoh2hi4YC+T3elRWaSZl/++qlfPzLlzNQyq9NYSytZoS1CUpYbv3iT3nLs97Db4+cS0mltKzPWcPHWPqyz876Bb/Q3OiETuiETuiEfl4nGCkn9IwGhhdI+g0IqJe6zM01EUIQxSknnz7B7/7x8+lf5TG4noMo+Fig24kBKAVnA/MIb+t/OnYYJnSWQ+ir5G4CKeklhsz3cEOJf7gNws0ZKr5i7+PH8h50YHZmGjZJEmVxlWCw5j9z3MloEs9zWXJSrLXY1cWdERYdpXRbIQcfOYIxltZSh8E1/f/T83D9yGXcuvwIi03D3GTAsfYw1i9gawHPv+Zs/v6JeZyRGole4vDyKIenBihHGjXjYMkBhiq2VFREw9SwjiROV7eHHAfhKmySA0aRefKF5wVM9qpMaknLOPQePpPo9EdQ9QCRFpASnjxwkOmji7SjlHnfg9QQLfeQgQueQ5pZhBBYrRGriwoZGbyuQbIa4RhIrK+wpRxkirPq0nHyCaPUloP7Z0lTje/77Nw5wVO3PIUQAu1JbMFDhAkIgzyaIGoFKBRAZKgQtq4dYP/heYqHehCnYAyFYoBSltSB705u54a1+wl2OLgtxYDb5Xfe/xLe/mhG918OoZeLuGEX3YyxSjL55QrOH2UgYMI9Qq/1ZW5752tYWryaNesHeMvz/hIRBIhKnEdiFvKdVKIElCTD4G/xGJ0pcKzRwZQ96BZZXvLpMx4FF2RwM0Xl89zxJ3ikuY3xhTZaG5rL3WeuiWq9yMteezFJohkZqyFiA2GCiUPcYsaLX3sFxxfOJpmeyGOXlUSGXQpezNFsiakLfEpxF467bHAjpDWUlpdIWz6iVML2QkScold3KFUIaMuTrEUgmI1qrF2ayaGMTr4za4MASgFeMwHlIlKLDVPSFcOw7uVRyVHK9EQZ3VJ41RIyTRGey2feeR5SOZQHY17yB7u455trmNzdx3KUUpvvYMoCU/WYiod5Tu0RRgpdDvVvQW7QFL5r0V9qE50/lBc1k4zO/gnkUMZjd6/hfZ+4jZd96SriMhx4+wRj31iidKSLwFK/fQl91SD+V3cTnXsKOIrqUY0bC6J+j/lLJhi+5SAYi0hyuKXsavSIRC26REOWwnLI7uY4zxveQ1v4ecxooHJHgVWI1ZSM1p4RVDXBP2rQXu6w8yZbmP0tYgFyZAC3ZTHG49+/dhGJZ/CdfG/BnZS4aYRsGOontVjZV6d8uEVntJInowQS+gao3pa/r5/JKodbJ9fh9zSFoQQhDLjgdkKkroGA9mUbKX/uSexKj6AnmH+sjrq8R7EYE+7u+5kJiEqtzcoOQeW75NwFa/JYb3J46O/9+m2sLa3wgc9ezfD6ZX7nxl30VyP+8MGrmb+5H7fUZfjxhGg5QeQlNKJhF5EJwqBEOlZHLXfpjhYpvq+M9MBUQIUpTqeXFx8GyjQ3eBR6LumOHrUfZeC4UChgrEGGSb6cqxSQSIbOWKBZSnnk4Q1MbFrg8dvG87EjtgRPGxzC/BoXAv2hhO9+cAcGh67nI6ohNq7gRnkRYODOGKeVECxCb6hKwc+Ihwp4SyHRZo/H/v5kXvWnD5EBwXD++c8CkS+UuzGYElbaPPIZS+9ZitquefSRFKN6ONIgehJ3TqyCR13ILNlYDacZg+fgdlIsPUypi9/tsKz6c1dSBUQvpnRoBooFRJhgikWk5wEWTydMPT6ArOQFaDPioBIFqUX7eeEm8yUqEhSXsnwhncYYV2HKAfKxDDdy0Ac8xsfbBE9L6g8vonpxDqAt+KiepnnGAP50G6zJNw+Ug9CrsNTVMdD2VaCvSu/cNZQONTFG8NjBNczXqoR1l6xgkOsdSk95+EsJUU+TVX3SioMTWdJiXti1noPAkpZdVGqII8X8sUHiiqV4uMvkKzew/p/3rl6fAtIiNNvYchGKZZJBn3ikQLmvw839Y4TzRWjHYAJEOyGcKBGdO0D1QITUIJMMEoPTTpCOwmBRcYKVORnWuh62VMAMVigudnB1k3S8htuI8qJOp4f0V8cHbTDNZh61HcW5e8ZYhO9i4jhvGVq9R1rPRTSaiMVlbL2MkQLpuDmbaqWZu+raPexwP7RamCjMAdWHm6jZZl6U2z9H30Iv/z2Vcv47lQLHycfvVOft0GmWF0iyFHoxxlrINAbBzHyRp9Q4d9+2kdPf+QCPffwibOageyDDlG6Wcvc3f8o5N6ylKpYp+TFupug6Ga7RqJ5hcaHFyBZNpEPu+taj7H+4TvjeH1D4835O2rrM4/cPcsZlU8TZNAW15X86NzqhEzqhEzqhn9cJR8oJPaNr1p7C9Zc+zEsu28VlW07jTW+5mt9527WUSj5CCNasG6C13OX+W5/MWSepQWSGztwqcLX4UkTfpxHuKf+dowvwVA6Os5bluSaN+SamHSJji/KCVYBcARmlnHXRVkSWIbKMmeUGv37tkxQnPMy44hUv+j5f2ncJ7XCSA3GF+8Mau8M+jCfyHdsowWsbiA3V/jK//qFX8msfeNkvVEQB2D6yE+EIotDh3e+/htt+shbheJBoTDdCrHRwuj3SsiQtS5LAYsIEGgInNqjYknRcjrXreHGK7MQ4q8wZ4TtI30cGPtJftTyvHcUbqHLBjTdyZ3c993fX8/Z3/Q5nbdhNIRhFpCkq07jRfpKVJitH5/KIYgmNNGNxpUejHTF1bDEv0BjDyDV/wq17zkU3Etz5Ht7hZejmPI2sv0Rc8zEu0Mt7x0WoSeK837+z3MZZXVC+8rWX5NvyAMbQWV/GZFm+WHVc0shgwxDjOyybNWy9ZIiL3voQZ37oKWyWYXsx5bLHq99yBU4s2DGwSKmcMnxpG2LoLLm8+e7v8c5zXkiv6II1ICzObAt3ugkjEQU/JXAzzhndjnK3cv/tlo/98Te443uPUyrkiRYNWWL84gSb6RwSHkbQC5FnRZz96gn8A7OoMId32mKBTBsOPtVHmkqEupSFXj9fOno+jsnYsXMdwyNVJjYO/tx1cd7FJ/GsZ28nTTNUL0XFFuu7VNdajixsQtomnv2PHXyjXKJOwJ0PPoVxLKpgOPLYKFUTUSFifTEicwVGZ8ggQEiJE2eoSGMUoDM2VxeR2mCy/0gZEYUA1V9HYhFJijX538oqEDPLrHtWi56rkaaNePwAhY9Ps/6pKUzByWNGLcQbBkFKOm6Z8ZrLhz+2m4tfeYDCIuC6KOnQ2hBwxOkjIGbuxyOkPZdoWSEPLLPmzhnOenQ3//aiT1NflIze16V6OOPogX7e8vIXUrnOQzUlfqfIyuVrwfOhXqU6X6ScdnnWP8/zL3/yST566T+SBCB8iS1Aad6gy0XsaotaUrLIDD756i/x1d/4O16wZheyrKj3NNv9Nr+x9TFcMoSUWJ1h0gyrJMZThBtcVFUjhcq5G4EgONbMk6yMxTqS4FAL7eSFC9vVJDVJ1CdRocEoxd3/eD77Tl2D8QSuDTl5bBpdkOiSQPc5RI7Aru6+E7hYBN95bCd0YzjeYuv4NP2VNid/eg8qBNWDaI2HloL2zn6so6geSfngxd/kHy78MqaSkVYsWRUW0hpeIrAqb3sBMAUXXXDobhOcOzhFfKxI/UlLfNzBlgzWI2/FGKyi1/bz6l/7IUNxAypFKBcY2TOHiTUUwQzXMNUybjCA9hRKugiRpyTRX0X0V1neWUY5Dm5XU7iHPCpWG/AU2s8TSITNU390BUzDctlL9vKtm3fyD2+4kvkjI2TlApljcXspMkxwOnHeAtO0DAQ9zpk4RH+the91KV7QQjsaLTT1/S3K1xicI5ZiIUVKS2dLgD/RY+elh1jzxsNEyvBk0s/TaR+lkTbTrzmJpWsm0EN9qEyQjLi5+yBJKX+3ie4qhPBygGsicYqG4a0R1vexFlQtIjl5LdGmOsbJnWI2iemtKxHICiIGEQO9EGeqgTAgfX+VtSHydhrXIdkbYpSPVYCEcyv78RuGYMnitzQqg9JUSLQNSE3O8qoEZK5AtBO0p0ApetMBS2XD4t15e4kFnLkuwYrGzSSlAx16a2tE41UKuxcY+OYTeE/NktY8tK8QmUY22gghsRhsEkMYMSxn6A+bqBVB/56IyqMN3Pkwd0SRt5fJVKMdTWt7lXjYz4vzBR89UFgt5uZJRW5TIz2f4lSCSBJEGGG0IVtYfKagQbOHf2SZwVuOQpo9MyXwuxrdbJMJhewajJIQuKAEJslwujEqSZGejxASo3POiCj4ZENVwi0DhMM+ulLMk+awePsXke1e7qJ0FLbbxbTamJIPpSKiXMqvYa3zworhGTeQKAT5/7tuXpCfXUK1QszsfO52EnJ1/DCw3ACRO+GUEQzfM58XZAQUuwYxNgTl3B1kbd4eKzwXKhViX6GLCqbmYGkF0wsxjiQregjHRUjJwVvK/PjFE9gPJDz0rhEWnAypJE5L481nlPeHLM+u4ArBmN/gt179A/7o135ExXi4yzHucsTy5FO0jM+KXUsmAoSEmckVZm7vQ88ovD6HwZO7zHee+IXmRid0Qif0/y+yv0Rf/7X1S19I+dCHPsS5555LpVJheHiYF77whezdu/fnHmOt5b3vfS/j4+MUCgUuv/xynnzyyZ97TBzHvPWtb2VwcJBSqcTzn/98jh8//n/yrfzSK47v4s0Td/Lra+7CmB6bNw8zsW4AyM/x009P89m/vYUv/8OP2bd7GjmzjJxaot2LnjnG/4hD4jgS4a3uyABHDi7Q64RYxwGpIfAwy8uYpWVwHXq9JLfxKkUhy1jrTnHdedtJ65rNfZNcUDjOnVMvpiAylrIqE6KH04lRcYoNXHSjTWwMSZzxzc/fxzc+dx/tRu8XOg/WWIbXr4FyiV6tSDlxIY4YqpRo9VJ6kc4X5FLkRYaSR1ovkDRBxQYVGZbnK8xNDbC4UoYsY8dZE/mxh2tkQ/XVfm83n7SlGXG5RNeM8EBzIw+ubODOmZfxpQOX4xWi3MVioC9bASvQYZLvXBYclnsxew7OogUcfHwSJJz1goM0XMOKZ/FMSGHfHKod5pDbRheZaFRqifocohJ4ywkyMXS2+RgJOraY1dagYjHI4YLtDqoTUjnSQPzMhqwki+cM5JNAUqZYT1J4gleM7+KVE7sY3tnGFgNcz+VXf+1KRM/nc589hb9/4Bzu+NxaOocFP1nYwJPJNC/74Rd5wVvuY+PlUzlroeAhAo/qRIIjLZ7SnDZ0McXSa+h1J+gsdRDkEFAch6O9PvzLdQ4QLAb5a+qFjFwe87Uf3Um0f46sLHILPWCw/Mmvnso7X3cDD8XncfHAXt6++Q4u6N/HXT94nEd/cpD7b8+5Njd/62E+/IdfY3oyhygfm1zCBA6m4pNtG+eGt08T6SJlCevWN/NEIyEQWYYcrzGyNUAaQ9Xr0bIBh6cG6LXHsb0KamoJk8SYJMWGIWpqGSfWqOUQ5STsLCwTqIyNwTJ67RBmfCBfrAHoLIeFzrZyV0CmCdwic/v6KboaV4ZIAdm8ZFwklBsz+cJQSZJ+H10KEMLj4fsmkAgGXtbC1QJVLiMdn5efcxfvuO427v/heh743ji2aUhmPKRwEELQeLrIn33hBfQ/0iLYO0/9wWlEnNCLipQ2wauuuAO/ZQg6McJz8kSmJOU159zJiuij30k4ZW3KaQ8fxDw9kzM3sHlrAKA9yJRPWFd88+atuDLjvNMmMYFkas8wFkGWKoTJxwkR+KhSwPw161g5fYLirQZ3uQnSEuxdINgzmxeAVyGaotGldGCBwmQbv6EZmo4hs4gM/LYlmMkoHOni7FWohRYVP+ZNv30HyDhvo9LQ3VRB1eJ80dUJ0WWX9CsJhDFtU8f7s5R1v7uP3n5Fb9TQW5sXO7wM0qrAlDx84zHutCmJlLLuYI1AaEjaEnN3P8bJCxgISVpWtDYUqN7d4ZPvP5eFbplOp8DC4yO873cv4u0fv4KHGcLqlD/522/yvCunuOq3Dj0Thfi+l97FF6/+Fz7/K/+CN7mC0+jiHl8AnaLiDKeb4WSQVR2MK7FWkgx4ZL5DuCYgq1tsuYDxXQQqLyBZg221EW1YPlxlf7fOH//xd6j92Sx2pQWLDZx2jGx0EDOLiHaYf0b8jIlgkaMzw8SNgNoOzejOBar3HaPy4BRrXzzPsf512E01wkerxI/XCJZd4sdClq7s8X/tfZiSEfTJmK1Om/EPGPRgke6pVbKaQ1LO24iwFqIYkWiY09hE5wXXLCNNy8wfKJNH2Uhw+3F6mmjYJxwvYDxJVnERnoup+DgdidOV6KILWYrsq+esmUqJ7pCHDUNss407ElPe2cUzGf1OB+ewRGY2v+6Ui/EVvS0Ooi/LX5/OJ5wqNajEoGIBmSauS5KlKvF4CVsuIipllMzhqPguuuySjJZJ1tbwZluoKG+taZxRp3HuCLZWxhZLKOlSf6KRt6okEcUgYWKwSWJcVoplKkcTZLmE7a9QaFv8HnROH6W1cwyhPGSSF6iNBbeRoBWURzuc+rz9jA8uI2TeqofKOULWAccPEOUSuq+ACQSqlyKlQHxzhZVPV+jeVcJrRrhxhj/dQOiMwpFl3MePoo4vo3pRnmAj8jHbug7R9mFs0c95IrPLuIfmkdKleXKJ5TOKeE8cydk3+aQFejm81TZbiKUWoq+Wf/5/VnAX5MV718kdmxjwvbzwYfMimel182tnahay7JmlgUwyjCB3lSYJRCm6r4StVxClAvQiKPh5MQly6HcUEXmaeKKMnp6HKMLGMTLJyKpFZMHHGo0Fbv7k2dgsn0+JWNB+gUPTGrKhIqYa5McwBoGPJ10CJ8MtX8lfveZ6PA3nP+sAL3rRB7DxbQjdQuLgVcoYKzj82CAre/sJEwenZJg9tvgLzY3+79JmJQefn9AJndAJ/b9Yv/SFlDvvvJM3v/nN/OQnP+GWW24hyzKuueYaut3/sN1/5CMf4aMf/Sif+MQn+OlPf8ro6ChXX3017Xb7mce87W1v45vf/CZf+cpXuOeee+h0Olx//fXoVRv4CcFS9CjWSrR1yMzP80SeenKKT/397Uy3IzZtH2PzjtE8CrJaJlrs/g+O+B8KApd6Ld+dwXNpzjWJMsBRlOsOJgkRno/wPKSGwwfm8pYVbfBKkhtqx2nrBG2gLDMklgG3yrNLkzy3vI/BxdOwYYyNEkyU4M02yVoxRmuiMCGJM7L0F/tbK0dxw5uuIZ3op7epjG30EIUcKnnfj5+mqw2TR1ZwMtBYag2b232twSoBDjhTDsEel3TaxwYuz7luJwCutljfyd0aSZJPEjshqjnHSdlfs5IUia3DTHuFLJKIgSZWSKyUNJZUzuTIMozNMFJgjGV8pI4FpqebkBme+P4oRRERpClyKcltxdbmBRmdx0/77RjjS/SAR3fUJRxxMEWH42dlaAlxnO8aFioFVLGALJd41ztuQAoX6TuEp66le+562hf1506VnQ4zUYOFxwdoxx5JqGgdL+cT61UJoci+0WLvG1Ocexagk+/Uja+Z59fOv4t72ydx7CcD+U63AKs1w5tCNlUX2VJdwG9/m7T1QQYrt5J1IxqzK0S9eLVpwXL6BfOYNUMYV4HOIEtZrJWI1sJzf/MqEBYVZ4hODxGlmJFh0qjKjx7/OlHicl93PfNZkcxRxELQSzXTk0t85ysPcPTAPMeP5oWUmakVVCdBhSnWUxBYHgzHSS2cc/08utfFdLsEr4v5qy+fytBwH6PVFoGfYgY0f33T9Xxz7mweuSwBR6IHyghBvuPcboEBN8nYuf0IVuQATfV0irvURbVjxGq0q9UGvXYYUfRJSpL5kxzC8zq05jTt1OUj/3Q1crAPf02V9lyR/jUG4UlIM4In5tArbUzDMFfSfP0nozSVi8iyfHdeCM5fM8nztz7NSy6epDUMW3ZMUdk9j5AKWa2iYkOGxDMtknUDxCeNkJZ8/GLIS8MHuGjiEDsmjzH4UAw2w0YJQlvu+OhmjLHs6vXRMZruiqCeuYzeM0fshjmXA4HbjHBS6E4Ybh7cwZv3XsttK2uoXzfJLTNbed0XX8lvv+kqtAywWcyWHfNsfHOXwqzEln0q8wnV77jojqW8e5Hy44s4jYSR8QZbdq4AIk95qYSUXjWLAIKFlGApw4k1jasMhhS1FEGlROyUuOnL51ORHYg10ZCleUk/5195LE/vKnn8+We+ChcUEWmKWG7Tm8yYPWMdcy9fA1rjdAROyyKEZNPnDmKNwdiET/3eJfzr351FdkghhEFg6UxYRs6dYmDtbL5bnmpkNwWpsHWHRw9v4DP/fAnCSqQQRFMloiXLGVsmkZ0VbCKRwnLeC47zojfdy/kvfZTGoscHX3oR77nsSmScc19UO0JpgQ0yOttSZi4rYk2GsQkZYB0HL4HyPKS2AFIiGx1UGOX8C1htYShhR0bZuLaJaw1MOVBJkVmYt511e9g0y6GvxqDmCiwnRdKGRxj7JL0CfpwXdr2pNvvNRkqPC4IHfUZvSRi9LcENDRaFKBT5+O+fykemL2MyqrEvrnNH92RKuoNILUkFumsgWutDsYgtFDCBQjoOIHNmjLEgXQhj7EoTG0ZYV6B6GeWH5wgmWwCYooujBcbNo5cxgnTYz11WYYREILXNr6WRFu0tFS744BRr1i8hraVUSHjWCw+yfnyKzM9Q2qDrCnO6peilEGvQBrXURjV7WAky1OiCg9NL0YHGSJuP3TK/F6BE3rpYUlhpyALoXLSe3vZhmpeux58LKR7tYOMUWm1kkqG6Cco6UC0jPY8VSmAERjq5E8TmqXKpL3IeWcnDaWfUHm9QfaqJFRaZaWRicRoZhVqMG2QUhjqUHl9EpzG2rwa1CtZREMUYXzH3nDEWnzVAVlB5Op8FcTRC9rI8VS7VSN+nMplS2tvI06SyPB1KJAnGUaRDNcLtg6jZJgaLDXxI8jGxMNNDGnKYa2LIBlwWnttHb0sxZ7VECcJY0CaPlPddSBPo9fL/EwKR5Z8FG8Z5mxespvF5iGKQg2p9L3/9Ii+UY+0zLXNYi8kyrKvIrMkZaHEKrQ5WKXTJy1kvvRDrO2gByUAxf64xWAnOzCJisfkfDlDHQZT7aW0rk/XDQLpA9rYW7kwXdyHE37vCP733a3zmv70PD4UvAtzgMnZuHKfmKM77rf0opQlEh4JcYGydpVgtUBuucMqpXbZu1/SNxTgFy/7dT/9Cc6OfqbXybhZndrKy8ML/peed0Amd0An9V9MvPSPlhz/84c99/5nPfIbh4WF27drFpZdeirWWj33sY7z73e/mxS9+MQCf+9znGBkZ4Utf+hJvfOMbaTab/Mu//As33XQTV111FQBf+MIXmJiY4NZbb+Xaa6/9P/6+fhk1HJzJ7u53UUKwRfX93M8GBsuUygE7dozzylddRJpmiMUGWIP5BaxbQggCKTDWILohy0WFbfcgzTijupnHxBKm9LOkIMH5l26lVA5o2QhhewjZR9tEmHLC3z/5u3z4kn7OdUforbwBYy37Zl+IHXo4t+h2QpzYkkqD67sMDZboNHr4BfcXPhc7zl6PcVZ317oxNk4RXo1d9+4Dpei2Iu77yh8DMD27wjveehOz++ewgyUQktK0pjyZkPr57vLOczcD4DgOqpfh9NfQrS42S7FRRGU8ZcjfxRqvRKvp8IEHX4Cw8MLxFvhtcBx2H18k6utj7rU5g6a+K2HjmhrjYzWecBRpO8KWCiQEbPV3kto7kbMtdJyyim7BphYByKKPUpJgxRBtS9CNAnY0Jhg08JjD2978Of7xs79BuRJgoxhrLYeePJ4XZaRk+soCut/Fn87/9uGvVOm1Iw4dTZhc6We+WObi1zzFjz955jPn1PjQPrmPvieaIKDvijZ99wf8ztvv5ILKIgw9wRvClyONR2+rS+G2o5xx0nFmKgMUSKnUN5N1f8LUdIWlxjL7nj7Aa/72iyRRwrvnrmVsfYdev0tpbxeMxSqIhU99NOaidWfxqYf2533+vp+fjDjiuW/cz8Ydbb7zWJUzTj1GZPqJyj624KKBB+/eR6cZcspZ6znrgvxvOHd4ERnlO8npRMAXFk7nQ6c/xkw2xK3NGzj6pgrUU8r7Nbd+zuPZrzmNHx56EACvL+Zz73oH17zv7+m7YxklHKyfs07QFqplnHWL6OUCex8oMvArRcKVgGNHhhi1LcCiwx4izMGj4bo6VoIRAte6TJ65jVOfXGH26T4KlX5sp0dqAlorPjtvmGb2b9di/QCn0YUkxvZCdv15hYfLo5zzvUnWfLDJ9Fs0omjw+gOM7iCNIFkQ7Ns0gF+O4HiCTVMGTg9ZyIYpviJF/0uCMQ7W9xjpn2doXchHX3wqdksREGid4bguuuhzbGUj6t3H+acHz4ZUowYdUCmUCqzbtkLnUQFaILXACgtFCAJN3TfU3ZTZ5iAKyfxUif7dS7jtDrKe8eq/eponjw/x6O1bQZJHXnsCXYkQaYbILKIvYP2nMw70Btl80zy7d21gZXMF/XCJtckiwbQmmigRDQmykou/2MS9uouZK9JrKh55YgteFJJcakhrFmEF3953BkOuyWG3/ZIdU5McF9W8RcFz8Jw6G195BEctcro5yNSnyqz75yY/+s3tyKl5dpx2hNZxj12HRjjzFSvc5ZexWGTB8uvPup14u+Tzr7s4L7oGPmSW+YvHEdYy/mAzB8YC9AT67oQjURm/5POeX3kezZMHCXqCpObitjJuX2oT6JUcEFz0AYtRiuWXSMo/Bb0D4q5k7vwqsgfubIYMIkSkEanGjzVGx2QFD5WtLkKLRWwaIzMNi22mvjHCj2qC3k0pYntA33mw/G0D3R5kGrw+RH8N6fsc6jgMOJa0JCiWYhrfGUJPuKhji/DTLq5byQGkUYg71UC0u/ni1VqkFDSO1XjXEy9B+AbTdth27xzHTg3orRWIzBIP/sydYFA2T5MhCylWLWG8yv3QGlEuYRBQCcmyADezyMRClKG6EWo+xmkkQDGPjLcOslTIF8tCYH0XGWs2ja3waGOIM0cOs2vWQ1hLC4+7No9RLy7TPjCKjCS1A4JkMqCnfRobOlRCFzmfYeME0emRbhxHCRcZS2q7Qpqbyzi9gOLhFOs5z+yAyTh3LwhtyYZKEGvc0FA+3EH1NCLNMNZAkoAFjYeSGUW/S9Ouy1N2hCArWpz5Nq4CE3WJNvZBKcCfaZMMFRDawV8IWbNpGX+95afxycwd7ScMXbzbDJQsld0NZCt3kITbKmR1H1mo0P+TBmm/h/U99PwCwveRSGQ3RRc96CXIgofSMufDxBrrOnmCUebljqA0xqbuM+N6MlHHVyIvNEtB6UBE9dA0driPaJ1P51QHXZIUZ7vY0X7MwjJ2bACV2byA5rl5W5qrkEKiywGyFSLTDNuLEK6DSdO8uGJs3hJZDDDlErS7EK5uTuSTm/z+1l+Gvgr+kUXwAxioYtohRkFn4yC13VMIC8bzENZAuYD23JwFE6d54SZOsI6Tt+cpBVpT3dsEKyg0DcffN07ysUG4rwTAG17zE06/YZFQS2aNw+HFD3NR8AI63ZiDpkI96rLRC3GFoOoNY4Wg2Yy47+5B+oZj9JomhQ0Zu26f5eH1ezjrkh2/0PwojB/Eoomzvf/zB5/QCZ3Q/3EJk3/9/1q/DK/hf7d+6R0p/081mzmPo78/510cPnyY2dlZrrnmmmce4/s+l112Gffddx8Au3btIk3Tn3vM+Pg4p5566jOP+X8qjmNardbPff1XV63yQs6uP5ez+n8LpYo/97PR0Tp/9sGX8MpXXQSAUhLKJSiXOX5s5X967DhM2P/EcUytiB6sk6q8rcVKSYDk0uedkS/YwxBvoEitXsJ08ojX3oLC8S9gZdX23pIFhqu/gvQvoFC4gVLpFTz7edfmu29RliegWENqDGmUMvn0DPPHl2gtdX7hc7Fh4zCqFeG1DDIIIPDRvp9bxdMst/6u6vN/9UOWnprFTe3qRE8gI43bTgmW8iJGrZ6fT6foYqWgsH4Ip+hhOz2wlu1nz6MN/HptP0dmJ9hx5lEGNi9RGdqcw1SVw3NffpTm5WsZfDxi7W1N6qdV+Oqn34TrKrqTC3TCEBvHDA+X2br2i+zYNInuptg0QTirE0cpEWFM6oG7lFI+2qV+T8TLXvFjnnPRQ7SmqtjMcPTIAgDleiFvtXFd5g/M5akGUUxlV4c134kY/fdpws1FkrhAtFAgEnDYrxM4KeZ8kyfSAPdPHUSXNPMvGKXxKwNsfmeL0980w/ATDoNOgicsnrCcfd5RRGaQK2CTlIG5iGvKT/Nse4RMXIE/8Hme+mmNuNkjcX/EUGmFNQNdLqofJRCaldML/9Fe5rj04oAt5WkmdqxFHm/mbBklOPviY/zz97/D+svvRftPMB4Zwp9cwuu3fAyMwWSGXjMkKLgkWcbwWOGZjcKnHj26GucicbXid884l5o7y6AT8eazX8UGv443V2BoV8yt336ED7z920hrkNYyvn6FoZF+ancuU6w3EVGKbHbovWmQzvnrWHhNjZ0vP5K33i+5PHDbMFFXIZeTPIEp04jUgufkbRVHF3E7EamnIQWE5QMfeZyrzm5ikuN5saTRYfG4w+1f3UZSdEjKMk+4qlVASoRSWGO4/8oqN+7cw3k/WOCF3z3In/7bhbz7fefyx687h9LhiJb2UYsR+D6iZpg6NEDroE9kakRVF+c9K6z/+FHWXDrHww9sZOPaJm//o29zxfMfy2O/gbToEQ8U6MmtYCyZNRi9etOPUjo/KOY7uyYHZg7sjxCHIYokcSJoJYqBcgMtDFEa4D0rw6oMBj3e/9or2X//Jk5Zd5Dtg08Q9Tlc8OLHeeFv7mK0v0WpHKH9gHuXNrG22ODItYMUWk0cJ6V6zzI6zXA7EY3LUjpnCpRJGL1uhq2VZaQhj9aNMmwPgoEGxWOSgYcF4Whf7rRzMgZUzJv/5h6EMWT1Un5+Q8nLNj7IKzY+SNRX4voPHWT4zJjikOX3Pv5jzrhujvVnNnnTP9zHq7Y/gSimqKLGlAxHH1tH4CbYgp/HDzuS8mJGYT4DKzHNCNMLEVEK1mKXofDjLu5iFxUlFJYjpDH0PzpL9fByHotbLWGLARYLvke0tY/GeIFjLy7TGClw6r/cz8DjXYYeb+HWBNWjPZLREtFEmaiqMDpBxgm6v5KDTV0HVa3mDoI0ZequGsd/XMd0fdRugTwmEEIgXRdRLOSuANeFJGFhaYBUKrQDwjN4AxFSgQgUI8/P7/UiyZD9HsJmlLdphEpyB4DrkHYdZFNgtEIaWBwaxusKHK0pugKn4YDVYDTWGKw1/Ma3n+QNdz7OWc+ZhiiHWuM4UClguhVE3EMEPtZRObeoZ3GmG6iFBjI2iEisJnalZAUXXS+h60WsEuy/p0Lx4Cx6BupOF6VBS4FCk6QuuiRpnu6ijSEcdDEC2ieVcBdDbMHLXT3VAk4b/Mlm3t5WKVFYErgrq2l45eJqS6zMC0GpodrsUgg1E6e1Uc0YYRX4+TUjHCd3DGFxI0uWORzeN0byqM0/d9riHFyEMMImCU47QTUj1HwbObWIu3+OcEMVxzWc8+7D7PiN46xZu4zVksZsldZYhe5JNfyuwCQJNstoX1Cg9H6L3ZHhL8WUD/aQc0sYa7BS4GQZKswdT+lQiSxLsEnCyv+HvbOOluwq0P1v732svK7fvn1vu6TT3XFPiANJCO4wDO5BBx1g0AEGt2EYXAZnkBAIJIG4u7W7XLfyo3u/P/alM5mg84w3r7+1eq2+daROVZ2qc/a3Pzmhm5nHrEQv6oFqmXhRL6qVoBohbjtDlPIIKdA6Jh7qIhuqkpY81HwDUwjQfWXcyKd6fYOu62qYJMMEis4RPYhyAZLY2gDLJURPN82jFxMNdyNSg8S2zInAgyBABjn7HXEUuAqjHKs+6qpiBnqx0VTWOiZcF+l6lqjJ7LlGJ4Y0QZTK5GJJuH6Ezvph0qEuhMK2AuUckkDZ3+DU5puJJEFXS+hy3gbcStsC50wY1vbs53FrHqR72Xby2+usP2ke7UpqWnPv/n7uGy0y2b6KuCvHtzYfz6/nV/LVnafQ1/Uuznrqy+2xNZr0L5mjumaUgkxxJgwzezVvfcw/8o8v/dc/6/7IDZ5JPSuRiI1/9j3VYRzGYRzGf0f81StS/iOMMbzxjW/kjDPOYMOGDQCMj9uq1oGBgYetOzAwwN69ew+t43keXV1dj1jnd9v/Z3zoQx/ive997//ql/BXDSEr5Lo/80fX2b9/ht27pzjllFXgO5Bq2n+GZcYYQzZZtzMtvgetyCpZlCID3v7lV3DtjglqOUUQp9x58w6kAFXvkOYcytV30mz+GNFUpKEd0UrpIbs+BoALvO7dT+TTb/kustYCR5F0EpSrUCYj7kS2evjPRKmcx5Rz5HfUbTie65Km2ipIHGkbbxZQ7M7bIFBA1SOMmyIbIXKuAX1VULblCCDwHbKiRxS2SWbqyMBHd0LOfXo/BbfJcSu/xIVT72Zoqka4wnBi5Tnc9pW7AfDxWb/rdmbHVtoZ9l8l8FFIFNCOyFb2kwEHM32I6JE6QyuFKASIxEE7ChGl0Mrwa22EAVUzXP/Gk9h/Uo6uiQRloFK06qAg51vff2aoz9TtLJwQePkScxsC/PUlWOOQjYVgBFGtzXJvltOLk8zkXG40J2CM4ScHr4DuGGkEG541h1sx1LI8j/7Cndwyewar8zeg4xa9AyFZwbPl891lfvCNR3HW7C6mp47j9E/Y93B2pon2XG758QgXPe9BjIkYyU/xs5mjGTp2L9mKIdRMg+mzegmnJPklKQfbuxlsa2YKthJ18UgNRxmkAIQhDD123LMB94VrMOEDmFZEZ67NfXft4Umv/CldR3+V6+6pcs4JtzBxcB47vatRLc3O9k4m2pbYPWnU4ZLjT0BXXXbkt7J32wS3hA0u6N5FgkuaCwA48ZO7GH1/npksD3ik93fzzA9dTl6FfPcVZ+IIg45iTEFDLkOlGoUCaUA5mE4HBvooSJcsNFA3tNe0uefVLyRq/wQlYGTtRpT3M5JIAg6xkCA6ZFIis4RoaR5vvoCOU0SrgyPy3H31Ep59wRYEgst+2GE3vZhM4zqKwZ/twZnVdgDf0ugltqI670TMVx0uXHo/jyqOcssFg9x/1xLOe+Ym7oiHWXTxBOqHQxRknSm3jFIKpCA+7QjUdBtxcNoGYXoL0n83WLAt9UInZPDWMfToANvPWM3m1MHLRwwuquFJifm+wlGCs07by/o33EP82yK1XMDxz96Nq27gZ6PLOaE6zXhpBZ2GIKciTujbz0nde6FuuCcawGu1mXlumeJHGyAVI5+ZJXMEY08e4UVP287YfV1sVUvslz2McQ7OIm/vxTcQVwVuC2iHOHlDXmpcV6NzPs6eCYQ2mFzAF3efznCxzp3jIyw5cjeBSHjGqzYzvKFGYUPE6gvmaM1W6Sxro+fse4wUfOfSU0CcTCVoIrIMHXjIuRbBojyhAlxpa7B1hElsZoOWAlMuEPf4NIfLlA60EUkGaFQrsdaFqkBNaTAGf6yNSX2r8phL6dTyFLfXwYBaKuhZ2WRytAwpOLNtZF+GiTyr1gtjpOsCwpJfxhA2XUrdxtoXOxnTtxtUCUzHvoXNFYb8gzVUJyXdUmFnoR+RCvbv6qbn8gb+bB0hHCbqRfLzESpOoQNT/9jN2Hw3g+/dj0pi9CLocjuYGY/oYBHVhMTLiAYkSSnmTLrYNGuDn8kgdQRuRTK8uo4UhiOeOsP9t7VJksD24AqJSQSy2cHUm3bw7CqSLpf2ETkqW8CZTclGFCKBqL8bilBfbPDiPKIh8HcKaGuuv2qEyjEB+AavmNC8N8/uOyvI1VDY1ER1MpyGQMYSXdDonEenHFPaG4OjcGshqhHhzTokmWTp6jqTD/jIdkQWx5ihXhuqnHfJzUR0SoIwZ9h7oAuzNMCPO3i1GBHaGty04KHiDBWlkBpq+1xiX8GIQcUZMl8CGZKUA8LFBZKeHMX7J3G0ptNbtCHx5ZQdlX5LVOcygq1NikdkzEsfag5KKUS1AkazZvVBll42T9eJTa69/SiE41JbPUjX5g7rXzDHkcdu4ZrvbuTg1BBCC9TWgwilqLShvaYPUWtjXAcnSTHVEoQR3mQDmWq0FLZKOBMIoxFAe0WZpKAo7qjhtAyV8VkbZNLfjZaG3ESEclqIJLVqF21zcwoPTNI6bhn+WN2ev0JY4sJoRD7AVHJ0llSgEZHbPYWSLiZJkNqQlYsw3zxkQxVT84h6BxOnyHaEIEaGEdSamKiMWdyDdm0rTxpCoRaiGiEizTBSISpFq3RJU+TkDLpgA4YpFRFhDKmm/+tz5N+U8KoL7+FT159FI3MxmeSjx5+ISUE7gm3fupRXvv7lvHnTtXx7vJ+THcMZ39nC814+S1DKU5us4eUkQSmgc1CRZA61yTw4Gbcv5IL9KQTeUspuEeVW/9xbqsM4jMM4jP+W+H+KSLnkkku47777uOGGGx6x7D+HnBpj/mDw6Z+zztvf/nbe+MY3Hvq7Xq8zMjLyXzjq/z4YH53n61+9lgMHZnFdBa4DjqGVpX9yW+kosmKAnm3hEGO0JvN9lNF4BVtnHF/gsWhkggPXDjA/38YJXEzgolONlF206imkEhH9fkLk4meeyuf+/vvosk3lzxKNlIJ2omnFmiz7y9KjRbNDpsAtFxGZxhnqQu2fgyTlte9/6qH1Lnn7E/np925DGINIbNWw1CA8z97EOwop7TEXpMSdbpCYziFJsHBdTnv8vx1ap3lNL1fdt4ajH7ONlRcP21wBYGJPnvd/byuvPmUEkyQwYwksM9u2M+LaWC93nB06t5/3rqfwjff+BJ0m9A93M+N6ZFpAmFhfuAbjKgIFfXd2UJlGIHjju55kj00IWLixnJpooPI+mRHkd7cQUpGbjVFHLKKeJJAZQiEpH9D0b4SKyMgHLuHcK9gbaZYNFtEGqsUmzdRFOZqTqvu4emaI/p5vc9m2V+M/rk7pZxM0J/KIOGV4/STX/WIFlYq/QA4JDu6fBgFrz5xFpx5TUzmGhxu085OsOm6a5T98A2+79D5MIJDNlLk0TyOZpVz0OViQ+L7ip/++gemDild+9LnctfVOfrC5i5c895tcu++HGJ6HCROajZD+UoXSsgYt4+GVbRbQ/HTdDkalbcXYJaeoxGUS4zDzy61cPrmTx56ynjd94GlMzdS58JIvUs/yeE5GHcX+7eOs629ycL6AAZaOVPjKT98KvJXtW3bzo/HPkOQUQV7aGePE5gjowEGltrWCYhE9PYsRhnhRAVFUnD1bploZhsrNAFx6x4WUBsrMTfpE/SWCg3OIxMHphDascbCCSTLwU2SSosOYKy5/FI2JERobPBIdIzKrvjJZihtLTJLY5g9lcxpMn8dMs8T8aYZTCgfZEpepBi1efuoDfGz0OLZMDbG2fwwdG+LUoy8YY4rlwML5qiENPFSYQD5v39daCyElJBp/PsabTYgrkkX31nAf2+SYgTF+dMvxRLsyenyPQtCif0nMgNdk6waPBz61lH+540TmH1PBHYP5C68nRSPcmNlCngduXoxapxm/LGDyXYvo9+b40WO/zCu+dTHpKAu2EZ+h7+/i/T87C1nO4SgbFKo9l7SaY+DODmPn5AkumsVxMhr3GkqTgigTbLqrhyf+wy62XirY/Gtb975jbIjt7hDEcI5K6ClExM9ssK+tefsHnoo+WOT5f38Tpy7bQzVfp5aWEMLQeGJE7sqineHuKiEbIWFXwFzBErm1LodCFiA6CRQCjDG0l1XJopiesT3UnzVMdnuK3K0xUuKFCSqD1Ue12HFjDkJB7Blkwdbem7aifcxiymNtJIrznnMbs60y0x+r4k61cbQhm3BoHz1EdXqM2GhMGNrcDs+1QZ6xZu/EYjwzj0ytisIfzDN9TICckOiTPbyDNXRbsOjfDjLx/HWoDEInQKYNKBQwnYjkxgpEKVoICA0N30MrW4dtECwbmKNTgdajDOrfLJnthILICKrVFuOXOqhOZhVUgc9w3zgveMtd7A2r9Dsttv6qilvQFNY3qd9YtuHhSuAUJWFfHqkcssGAdqVEZ1UJMOT2hAg3QKXQGXQo7B6ne7RNZ2CQzLPNWjLTPPjZQZLlvXinJfgP1Jn9Mng0KSZN+q6aRnUy/GlNY10v2aIIkWa0NyqKB+z7aAKPzIA/EyERNG/zUaElqvAc2wQj7NdIpgKRCYw0trVmNsGZaWGMODRxkbkG1TEYKayiw/WYP76MzgvceWltSo7CiVOS3hwSB6UFaqhMOtKDChNMI6XRzCGUob45YOlRc2w8cxf7D3Sx9dP9SGzgc2lJnd5tDebuDajdLfA6KdqTyHyJpC+gt7qHfDEjvxSYtnYr3VXEiVIcDIV9LbLeIpoMpymRE7O2YjwzNgdFSbypDqaiLDEkJVQcTN4lHKlQ2jwH3RVEp4Nph8ieMumqbqi1SZMQZ7JllVQLAcxkCTqKodmEShnR6Szk0XSQaMhK4Du01i0it3UaJ4oxUqJcHzGYx9TqEIYL9esgcja/xYiFBBVjcKdrOFPzoDW1U5aj+qtk7ZY9BiHsb0sph2h3LJmjFgJxcx7Cc212i5JM3+zxtOBBpinxmn+6ll++axnrnjaNSYw9FxLNNIYPfviHdC3rxtk8weyuOkWj+c7WDicsG2BqusWerT0E/kks33g1reZBFg1PM7+7ytu/+NI/695IyypNMcJcu0xXluH8hyy0wziMwziM/5/w/4y15zWveQ2XXnopV199NcPDw4ceHxwcBHiEsmRycvKQSmVwcJA4jpmbm/uD6/xn+L5PuVx+2L//n7Ft8ygfe9/PeODWPczsm8eVkqg/R1zxmen644QV2ErOREqMNGQCdGbAdYk9n1UbFwPwwvNv4K0nXcvpp24hMwZTzWNcRVa2tpi4leLUQGYL8aLJZkx8x8NsNiZKEUYgpSQXuGhtCNsRSTukUfvzWnt+h7VrBmG+Tbi4TDxY4pV/fzFSgJSCFesfTqq97cNPQzoOBb+BasY23d91EK6yrQ4LCHKu9UD7OatuSVP8SuEQiQIg6yfRbBYYvedMehdVkUoiHYf923M0M3vTLYQNBpybbZIh8bpKOON1nIk6Zz7uaL5w69PZvvcYrvjNjfjDA2w8ayNfvfl9qEgjJ+cRC00CeiHQl0oeldhaU+1KTjl59UMvTtnZ8emZBstPXQt9XTizLcq3HsTfO89je5ZBapAxqMjwzTefyD23d3HNL4Y4/TFLCcNfEWpFtdCit9RkNMnRTH2m4yKbW8fy+qM+BnKE3uI0J/Xu5dmfvwXVTDC+x7FP2ImXSxFqGint595s2MGZOVJDKWO+t4DblCwPZlgezHPusiMoeh7enELVPM7vfy57flwnnqvT6VGIegtqHW74xQiv/U7GfM3jyY+6lZ7BCZrpXtpyF9p3aaUpT3/ho5i48RWUOoPk517NdVdvpj7VwLgKHUWo+Q4iP8/eqIfRqML4Ss2WlW0+O30HnSTh2W/7CiIx9vw3ioIbctsV9/CkVW+lPcehKt75mSYfesv3efVjPkkSRZz1nk2cfd0ERx8xTm5LincwQRqJUa4d8GapJc/ihNZiHwqSdeuWPOycnJ3rY/6AA2GIW7MZCQhhSYruLhbpvRhfYdDoKCbMGdJAct2elZzQ+wxIEozjYHSGO5Snb0U/6eJBTLkAvofodXCdjHYjINhWZHenhC8S8jJmVkbct28pcerwwK7FuCvydC1JufhvtvOat/wY9o3j3rkTOT4DeYVcITCBgxEGs2APkALboOG7pEWX2ekuRn84zG8ml5PvbjHgzCLyOcTxPtUnd/jF7Co2jkwR9mQ4vSl+V4LnZ1z51XPZs6ebsCYJ9tZofiPlhhcMsGXRctIdJUY3LWHvTInXvu12jKsw+YUKVM9DFn1kvYPJu/b4gKxSICsH+PUGOT+ir9BEvUOTCsnzX/gSpgZ6eMJjt/HKz+wkHuwmrPgUrkhxrsvgDsP5H38pJ7331Zz64Ut461deRDHO4SK47EdH8+17TiB/nY/yUoST4ZcyCrkW48eWiWotdJbhoxi+ax6pNeHRPUyd3Y/IBXYg5zhUd8zQs2OW1es7+Ac90rKP8SRJyeW8p+3jjMccpJhA90CEEVA7yUeHLqbjIroiup8zR+xJQlfzy3uO4fY7lqMKGV3DMc/7/F2sPGGKroFtxAfqmCS1RG8c23yHZhsRJcS9OZLFvRjXQbuKcMbF3Q7ehEGOZTgFx9bEKk1+XFDcByoxSNdF5zySapH8REzmSdLuHElPQNLxKGxqAYKBFW2e/IYdPGv13fT0hJCBig1agTev6dxa4Mxl+3Cc1LayhBFZLPlieDrv+e5zeePVz+DuS4cZOWWOJ777XpB2sG+UQrtFVKKJu1xax1aQndiGVwM9BydwEoPMDLrg0nqURzxcpLx9juqdY6jEYFwP3deFwkMfVDgPtEkaYNCEi3Nknv09LW3JMN0x/haonjfF0osm7PctcJGzLYSG5ooi9eVV2hUPkWn0AmFllCVZswCyQFA4GFG+a4by3TMLYc2gMkvSm2Ybb7aDSDO0ztBdRXSlANgqdKGFDUJFIBNNedMsMoqpH9OPszTFm+ngjjbROY/ZAwVm9hbpCI8wCUALzEEfkWCvc0LQHnVIl/sk/V3IIyo4eg4zW6P0wCyJ12a4lLJhUYysQGo0cV6SlnzScoCIM5ypOs7+abzdU4g5S1KYJEFX8kT9eYxrK4iRwtrJUk35vhncZobJOaQ9BTLP4KRNRNJBreig0pSoL8f86hxjF4/YYHgpwXUo3rnXBs8qCe2Odess2GyoNVGxASkpPDCO22zZSQrftQRIbG2zslAErOrJZJklnJMExILVR2C/K5kmt2kMMdcmXj9MNthn7USeh5xrLtjf8sggwLgKmWpEqUCadzFhRHs8z7QpscxtcmIwy2kvOMAPX7JxoYlIY7oVr15zE+lwD10/3YRZ0cfMZ/uJuwv4Bxs0GyHlrjxHnLiTSPyAleuO4dQzP8rfvund/HLuK5x83oY/697owdrl/HI85tXX5XjMD7/2Z21zGIdxGIfx3xF/9USKMYZLLrmEH//4x/z2t79l+fLlD1u+fPlyBgcHufLKKw89Fscx1157LaedZvM8jj/+eFzXfdg6Y2NjPPDAA4fWOYw/AWOYnWkStiLSMGFs7wyN4xX1NQHquD/d2iOURLViXD+wMmHH1sO6WvPUl5wDwIaeDZiWRP82gTiBTJOWc2hH2HalqQyvJghakiydIZl5NsnsyzHpzocOE4HOUrTnsPPHdyCloDM2RWd8hqj252ekAHz2+6/liBW9eA/s49xjh+mvFOysmC1PeBjOv/BYfn3X+xC+lf5qz0HUGohGyH+cqyl0F60XPRF4a5bgrF7Ke3/4hoft63nPv5A1a1bw5Cc92hIsSYKJInbu7OdTY6chcx50VaGnize94mts2zqK8V1WHTXCM1/zaI4/vp8l1UkClVKfaJJGCVEnwnEV2thZPeLUvrdFD13wUXHGr6//e848ZRVf+vKLHv7ZOQpKRdJmSFpvkzWaC9kIPuiMF194Cl2bNJXtGpXC+ECVyoYGx140xsUv+wIHEtg7X2Wuk+dArYei6hCohKJ0eOnG71Hy+/DcXnIqIUXQE7QxSqBdh5NH6rz5TbdTqNYPqcee/9rzIU7YPlbm4wdP5TvTR3HlB47hlAIcpfLsuaVFnymgha2NHD5wDD/+3BXMHJylfPsBO2soFSZJGZ3dxvFHfIOLj7+dARFRFJBQQDuSRq3D4EgP8dxifvmei/nqh0K+9sVr6AQuYNCVPG5vgXWFcXpUi3W5Oe6Kd0MKOoF/v+wuDvZFOKHGhBKPDKUMbncZwpNIMqs0mJ1us/nO3cxNN6zKK0047thxHlU8wH6/n2iJj1I5QCB8l2j1IoTnIoOApCcgC4DM8OpLTmD3gVN4YP/jePGTPs5P3rEEk2Ff7+Q0ZBkaML5P5kgmBqp25jOwoZimt4K/a55bv/YmXnzmaQjsjLZxHJY+5zguePG5OJUCwnWsZWVeE4UKvVPTtSvjx+86mW3tQT63+xx+ObuWC9bcxyUbr2aJmiS+dZ7xezQ3XDPEt1+/AX//LMpxUIAzH2Fi1xJDJlsY5IBRwlYhC2Gtay6IjuZpS24kKEew0oarzGzp4Wv/dgJp7PO2rRdx4WsfYOApLUIjoD9bqFWWJHGAazLM9hQzE2GubhDMaoI5zbeuOpP2bJE0TRFhbOX2aYZuhGSOgDiDKCUpQVZSOGFGz32G4fw8YeJQLkekw1WMcagFDlOZx464SLa0n3BVlSxfBqeEdIoEiYtQkqOXjPH1l/6YL731hwSb9tK4yXDHJ1bjbvbozdepFjtU822OUJsZvmmaHALpe5BkhD0B3u2zGBeE5xBX7cCuuaLIkjUz5GSTndfnKV7XprBTYEoFnJxP1Ztm944yQX6CdijBQO/1bZvthOGMoe089qwt6G6r4mjenSO+XlOqNPCOiZCeoTCoSW5wcYQlsIXrWPVdmlmSDshN13D2T2B8h86xw4jU4IUZVT/iiOo0lWd1yK9PKTw3wenYJhe3Y6gtV6QlD1P1kdLBuMpmpEQZ7nZJc3UejKEx41Kv+Oyjm7kkIOyWhF2SuKjoviek9/6E51/yEz7w6S+Tr3ZIB6q0VYW77l1FKiHc2YWOYf9v8+QSw4Vfvh/WgmiF0Na4401yW2eRWyPKd9fov2KSrkaKVt248xqnoUFJaudUmX5594JawoBUSMdaq8Cg24J6ZYCkK8fMmQOkFWuJwnXxG7D8Sx36ftvBPb/FmzZcDVGMGJ1GYDCdEHlwFtmOMHMdtCtt0K603z8RJzjjTYwUNJfkUM0Yp50iNNSW5IkDe/+k447NgPFsbXlW9MhyDpX7W3hzICNtiaKZGUgN3lyMbEboguYtL7mL0zccRBaLpB2P+o4c89vzyKZgclcXv/rGidz/4DLcjq1VxxjSjsudt62n2VXl7q417HnNWuLBHKqdUNjdQC5K0a7AGYmRjgRX4s21caebEKUwW7OEXJggfHtemTQlqeZIuwskS/pIByqH2m5EowWtkNw9Y5BoWkMeo4/vZvL4foxWiPvbOOPjOLsnKd94kNKWeWJPgtFI37eEiWCh0xibNSQlOkkxnQj/5i3kb9uFaLdsaLsUmHYE2jbvCWmVJ8K1wbFJOVhQumS2ZU85h3JpEQK3HlK8dSe4Vgn0u9ygxFFEXSXCZT0IKdFSLhBdCxksvo8Qgh9+9CgGnBBfJnzt+cdAkiKkQPoZUy8d4RWffD6FreP2/Juc4+idW5l/VDeqlrDvzh3UZuYY31ohaS8jcB5DufA4TjjvOFz3zxOoG2O4p/Ygbakp5zvsa/3pjLzDOIzD+D8M81f07785/uqtPa9+9av5zne+w89+9jNKpdIh5UmlUiGXs8GSr3/96/ngBz/I6tWrWb16NR/84AfJ5/M85znPObTui1/8Yv7u7/6Onp4euru7edOb3sTGjRsPtfgcxh/H/j3TzE03KOQ9Vi7v47iTV/D01T/n1vEWLz/+zD+5vbvgNwegqUEKpFQYDJ5n8zjWL/5HXvbaElMTKZkRlHMeswdqoA133LodZjMcY3CboMNfEeuaDX/DHGIEy8ur1MdaSKHAzyGkIIky4jil3Qz/otf8nku+ya6piGDZEO/8/Ev5wocuJcNaX6L49+fCiEIf6WCENiAPTvLmjz2b08478tDy0qIKWX8VISDbN4UEtH74L83Go0b4+CcvoNP8Z+LONNp1ra+66dHn1HnMmy7gm5+7FoCDjSYlYaAR0phqcPfd+6juTbml6wxOOGovacOBVkjYipBSoo3BFPIkriDrCjBSoOZCVKrxPI/3/tOzH/GaegcrjO+aAKXYu3UcuRBySNRh6fHLKBcD5MIPphEwfXYP3R4UJTREF1k6Ss5LaCU+VTfHwU4VDRxdDh72PLd/7ySOOu8ebvjSEmhF+AOKlQMhciBkya0P3aw969WP5Vmvfiz76tfxoXu/yDI9Q65PkiMmCSf4/qd+Re/j+qkP7SVLBYtW9dM9WGZqzwSF/Q2O/2qd6z6xnNmnrkA1rRxeCcNtYyNsWPlKBkpFTHorYZSy8W8+TPd9k6jY8Nkf/pxyKeFj7zmXTfcuRiYZpVKO8waeycra11nZfQnbDtzGjnov5aDDNXdsxj9hnpn+Cm3lLFxQDJf/ZhPX/OQO0kQjfJ85kTK8vJfzn3wst967GwLJer+GLzQdpUjKC1Yx/dAsqhYGiaa+XJFLG3z3bS9lb+3tDMtJeswke568jPijRfIsPG2SgVqYSc0y2j2K4CYPWUoRiUY4DvnZmGe84Ty+9E+/4MxnHQdxBFqTlWDRmmEGgh5rJ+jYymkjJVpJcsMCZzJjfnwR39vWTTMK2D3Ryz8efxnOTMaqW0fZqvsQvse+24vQ7iAKytZ/J7b9SDZjRIwdjBeL4DgkRqMW2mJydx/AVPIwNsuDyRIUmplWjur8LDIQTNxR5QcbBxGZ4E0zT+S0yigfOeYqbtw3wi3XbESmGqkUOjIQpwjXpXxTneicFLdj2Gv6ua+/BV1FaMYgwDWasScP0XXtQVRsB85kCUneIQsUMtLMRDke1b2HVcEsXxjuhrEc2345gjw/5cBVfXaAJm1llhE2+kdLMI5BaA3YDAvVaFJstYhHlgCS4NUpx39jG6otufXnwwgZI1qgpSTNu6SLCvhA3NFIKdn35AJO2yWtZty2fzGLbpvCZBlZ2kFNJIgwgFSydGWLtDPGqqNr3H7bCkDjtg2Lfp2hnzfN85bdRdmE/CbZSKflU5yQuLOatuhiruZy2W0tpncVSF2Hag80YgXaNkjhOvZsc13cWgMTp5BqSrMxJp9n41N307klx/qeMb6+/niOPm4bm7NVOFsMMjGYXsPskw3Fr9hadOkq5FwTpxUjjKbrgSphJSLu9qgO+WSDDovcedaUJ7l20RBOx0G7kEUheAGPfctruOqjn8XvVajtDeZKDV79jJ/yy6l17Lp3GabVoWMcfvO5EY563RTuZJtOvkzSnceplZFxgpqNMZ4k7nJRcyDClCQncBJB6oEJBVkgSXQbx/URrktWVaA1zkwL2UnIij6iuwu/5dLIIJNAnGKilCQHNb/BxPQw3yycCr0V0LZmOE5DpLDtNmZvDeEX0b60kxCzTWSUUNk2S/3sI0iLivnThnAnQ4QwVG8etZXAQQ7hL1h8hERkoFopCPBcBz0Wk5uJoZiDpr9ArLqAy5nZVm767TJ2belGpYbOSg96Y0zORdwLoh2SbICGCanevHANk7ZKmHZI/byM5qRDbktCZ0UX6WQHb2yeH/x6BVcmmsnbuiwBoY2td1YLP1YCG+gurHdJ5nI2MihKraWnGBB32YYiU2tgJuaIBnLUTu3Ha+kFlZ3BFFyEI0FnqDgiIKKTCzDFPF7NVoqbILCksFSWDJTS/l5mGSjHki2BbxUmWYZutNCuiywXaK8eItg/g2zHaCEQOkNWu1CZxqi2JbDTFCMXAmuxYe9IidQQHGjD3lFwXIgNaX8JOdQHQOuIPLnt44iqtZSZQg4z1wRjmL+xwmzsMOCnmGQhT0kb8CSvW/NLvvvBtWgh6azvo3r/FPt3VehLJtGdCHI+xIbNtys+/5oRHvv0WV78vkfex1wzeh+fe/CXvHHj+Zw2eMLDlmmTUpIh2m2TOJpS8f+DkdJhHMZhHMYfwF89kfIv//IvAJx99tkPe/xrX/saL3jBCwB4y1veQqfT4VWvehVzc3OcfPLJXHHFFZRKpUPrf/KTn8RxHJ7xjGfQ6XQ477zz+PrXv26DDw/jT2Lb1jFaYUKYpPQOlvF8hzcf/U7iDZMEzvCf3N5x7M2NENhZZyTGaK7c9clD6yRxSq3tohVEccaG45axe/c0Riq+/slfEy8CIwSeclDBWdBahpF9SLXy0D7OeeHZ/PSDvzz0t5SSGEWiXGYm/rLmpVtv3QVCEi6QJj/7+vXguxhg8bLe3/86x+YwjoNb72Ck4PwnHvew5YXBIklRoT2Bd8AG/RXzj6xlbsx/gVzyLbLODxHuY8C1rTvvXPd6KiedzDGnH8n1t2znR7+6j0aUgBSkSjG8po+1Kwd507mn0Ypb/F3u59TbLcKFex2TZOBY/7U30wGtSQoewvvD4rTHPvcMvvVPP7c1n7UGVEoYKTG5HJ//99cTJ6kdJUqb56E9+Petj+dFR+/GzQJ+uO8YZhsFXEdzhDNC5B5AYOjyjjr0HEIImnv76euLePY7tjB6sJ+05BGR4CCInUferI2UTmfo377I7vtdJnaUeMsn38md186x7sRFrDpB8arSlWgjaXkv5egz17Htjt0AbDxS8eMnDpFphxT44gMvIO9s4kc7T2XTaU9nZ/EajKc4MFen0IhwGhHh8jy9XTGOgBPP2Mum+5eQ+YKpnWP87dO6SPzHc8Mvn82x0yFDw5ezRJ7Ip0fnqXcCysvrpBHggxCG3sECSatlffQk6FyeRYuv5RVv3I8s5JAlj2+MHslGd5TapA85gRNFkEpwXERk1RGkCQP3T+LfHjD1tBlKxz6Tyc7N1LSiUQpQ/R7UgWmJzOcwsW120V05otWC3MwCBemADiO+ecf7+fS7f0aSZFzzwHZMsWiJjmpAddilEltaRgBGKcsRaINKBB//6pe44icb+dfwZHq7GnTnW3xl57EMvC9mdn8AQQvKHiecvYI7fnQ/ZBnScylW89Q6TXQzBZPaGllhlRKqHcN8E+k66FoD0Qk57W0nc838DmLtk/YZSksq7F5ZJD/nsCQ3w6eOupbRVsAB8hxdmMZfnnJjfgN4LrodIXI5TGYDSB0hyGJD5ko64wFd50wysLXI7AM5BJApyd903cTK50zy5X89FQwEg5LBI+skcy5zHRfnmhwvf/mDBMIgXnED//z+87jjVwPcce1S8q0OImrgHMzQi3KoRJDmDDoXoVNY1n+A9/z0dHbd1EVamKKaa5HvGqMx2gOe4LZXrQNt6O7bxxNePMGe7QNcf/UaEIZUgRqYo3VigeINPs5uQXKstbylsopJRsFAOJLRuzlBZ4q4WmBrp0Lj6YLbTZX6cIGgE5KWAgoHU3hNinONJmo6xFMOUmtUmJJ1l0mTDDXUZPzeAgIIl7g0dsToOEQ4DrKQt2RKmi7M6vuIvER3OZi2RrTb9OZmuK73BKZvq7L4pwfYnlSpXNLgd9m6ohAjjEA3m8gD88Qj3eRbIcIIcBT5HQ1mn95PtdTGG4w5vjAFShOa7Vy3aDXZaAUkzByXI1dzEULw1Oe8nIoeQyifb152OQI4tTLKq7Y9GVVSuEVF68Ec3nUp7E3xRI3WIg+v3sHrpMQ9iomnLMUrCvKbJxDbPTKZkfa6aAnyQY94Q4rOO+hI4EhBJh2kNqR9JdKyj2wluFMNyvsbuLsC0oLETEXIJKZw5DT+83z2zHv8Ztc6VprYDuazGG8+orWqj7TLQxcUOkqg2oNB4zQ69jxWCn//DEYHpMu6SAZLyFYLaaxKwqQJop2hc6klZ4bLOAvB583FkqwoKG+LbcV0tYwp+pg4QWWa++8ZZnunQ2oUaZ9CZgWcrSlZnJLWU8rnJpxwyjZmrwvYJfoRSWoH9FlG8UCb8U0DKFfTe+0ECGiurTD+6GVsuGGKZY/ZxYozJ/nx7AlU9oZ0hos4nQSvqa0KSUoEisxzIIrtuRUWSXryCMdD1WI7udCVw6k1SPpz5LbN4o92kFFGYbiLQKW0NgxT3rED0+pAb55w4zBZOUB71jajG02kUkjHsbZHIOupYMgQB6YWZlSNJXXiGJSDzDKYrZG7vYXorpJ2lZDNzkJ+W2LlqtpYxcxCVpnR9rMyWWaVKvkAb8eorVcWGQZDbqoBMy3axy1DCMn8xirdW23+mfTdQ3ajwuQ0Q0GKwOGkp4xy7xWLaHUC3LLkuFNmWHzzvbz75U8giFJUUMB0EnTeRSHQ7Q6yr0DXxSGPO+c2pnbOA898xLX1jTf9mNmOzxtbP+aWJz2cSMl0yHJ3F4kKedIwfO9Azx+8dziMwziMw/jvjr96IuU/5l/8IQgheM973sN73vOeP7hOEAR89rOf5bOf/ez/wqP77425uRZKScrlnJ1VlQIN7Nh0kL07Jin1dPHLPf9EwXk0Fyx/4p/cn+jEdj9BwFWbPvyI5UpKO0BMUnSW8awXncUvvnsrwmRs2zJOtqzfrugLpLOI4sDVj9jHspFuOt0OwVRMp7xweiuB0YYoTP6i1++6kqxh64kfPfIahO8vzCJDT9/vz8xZf/QIN//8LhAS/EcSJCN9VXTBxWiDCAJwNeXuAgDf+9ZNbNl0kFWnLOHau/O86PzFHLtiArHIJ5uHLOeSJesBOPK4ZWwfn7e5IdqQuYpWJ2bTrnGmGm2eevHT6M5D7PwSHbh0Fl57ZvRCFoFEZHaWUs616V49+Affh0c/42T+7SO/sKGAUnDBk4/ljZ978aHlP735FgwGmYEWEExAe8pQEAmaA/xq7lTieo7YwMolHTx/H/NJjgsXnfGw54lzMf3K+uyf8ZoHuezXa9lh26PZu/WR77cQivuvqDA/Og+E5IrP4rELgppmOMr2URcpFAO5EidfcDQ/+rh9Db3JBzDRFVYNpOC0oaeRK+Z446NsxohIEzJtUJkmHsjhz8dkTp4ffOsoVq6d5tv/egKkGcGATyv2cTKDt9BI0m6NsOPSC1h6+hHMudcidxRpqCKzy2ZZ2bOP1EiOOCvHTXeFNtxSazojVaLa14jnTkfGNjRwf9LNFZNryIRCGOh3ppmZdiHnQX+BzHUQcYKZhthNmZmocVLfhZz2s6uYD3M8Zcnd3LBjKXQ8jI5tDk7eRwiF9mJMT4IR0s7YZgKT8xlc0sfzX/cYavMt7rlvNz+5ehe4DtmpgiWDBU5cshqpra/NJCkmtR94lLh0jCIZiah+eYwLPryfohMTJoo7m4PoTojpy6isbLP9mD1wmYdJMpxygTQzOEah0xjTbiJ0HgrWvgGZPW7RQRUDlh3ZQ/lJffi7dzA1WyZtOWTVDrrk4O4P6W+2EUnKe755Lhc+/k6uZYiJtk+aKYTnIoSDCTvgSLKeEozNkjqAZ0tbPtk+i/wTIhZtbSKVg0lSrvuXgE1uD66KSI1HfzzDRc/ZznHPnuQNT7qAys39NF7s47kR269ahLNsP8UrQ/rOjdl45ixj/+qydGOTHx98AgNnTPCuC39mv+s3n8KPN58CGQSVjCWvnmXkpg53PrOfUzfsYX7SY/+rBgHBhjPmWXPROF3ZHNvHurl1eIBgzSTFgYzSXT6vvOhyPnvDUyjcmpCUJZWtKSgH4UiWJg71fAVSG0S5Kwko64QVuRkWHTfO3M4enE5CUsjBYx3+4S3P5IQj78f8LocqS1GxA3N1xKxAlArMnF6ktj5g1Ud2WOvBQmCmiSLbiqI15HxUoJDNFHSLQk+El0H3ulk2r17MQMfAmxqEuy35KoDEOKShZP7FbQbeL3H3TLH3uSsZ+cUU2UAJOd9CNkMC4zLYP0vOiXGAsgzJusDU7XfH8VzO+NvbuGNuMQO/7TBzVT+mNcvvkrxyMqPw5mlS1yfzS0Ruxv1Xz6M7sSUq44RweQX3wWkaKyuEIx7HqAPEJZ+w6tB9a52Zc3uQBox2kCZCtGI75m7HOJ02opgSLyqRVFwUBleAyiTF8ZTWgG+5pnZE18tBexklL+TOncOIDAQZuncasyskM0Wiqkehp4JpGrTvWIKkUkBEKSbuoCamyU1IkC7JQIHC/hiT9yHRttEpNYgoYf7UEUwxR2Hc1kdjBKITkjoxUTUH0xHB+DSyE+OnGm9Fjs6MS5bzMXkHL5onWNeGWDM+m6fkGwTGii18DyMFmcxYes4UhY2z3HddgU4okLFVl+R2t8itKGKUSy5JCbVL5b6DiA5kK/qRpRyxk2J6i7iRQeybQs7MITI7keEYAwfnSZb0IuMIOdMmW1YlPWKEwrYxRLMDmaazpszcBQWCvQL/xhnCWYObd6mtWkRrJIeKDM0TllO8d79V7TgKnADTCTGFHMmiMmrHQes0rBSJF1Vwdk2A52LdbAqTJLY5b3oOE3YINywjd8cOq1rL5xHuwrW/bUPlpecRVT28g3P27yS1VjAhwNjzX/o+WbNJ/sYtZI5EnrgG44QLihnQrkS1U9LY4+B+n/7hmCe+fDdCetz88yIozZaojy8/9WRyAx7kXIyuPaSKW1AzEsTs3dDP+jXXsU3C6y75Cp/+D9dzgFzmIJuK7sojlbdJsomqnMVIw6DXwBpGD+MwDuOvCr8L0/6/jb+GY/jfjL96IuUw/u9gbq7FP334Mhwlece7nsjfvPBMOp2EyT3T1EfnqXTl+daWp3NieR/3t+7il7sWcdGKk/7oPp28R9KKOeqMNb93uZCQLNyIJwYGhrrQjp2dlkpQvXmO2ild+Lk/rCI6+7Q1vH+wm9YiLFEARPUOWZyy5Y6d8PI/38r1+Ccez0+/fj1ZFGGERAaubcT5Iyqmd3/jVVy0+BKyNONffvP2RyxfMdKDOxOBNog0gTija4GU2bl9nNpcmzvv2sPu3Ypte8/jhONOoXjsT5m9zZJIn/rUr3jf+54GwDmPWss1126mVW+za7JJqjXJRAujvEPPFzZjjOccsjWNrBlgdNskvlQkmcG4tkryhS896w++poElfbZu11hp8pNe9eiHLT9p8SpwbyDTDkJD7kDGLtmNuPhs8vm1DPd8n93tbhyZcWT1NopiP6GWrKusfth+7r9xiMbrNR0D/YMNVqyeYjwtYYxgbGeFO6b+naO6HofnPGQJarZThGtbLP4jisEQxyz9CSAQqovBpZr1p67CDzzumL6et15wOT988Fh2TvTzxmt+xRcveALVXM6+x1uvp7+ZYKQgP56RbwkGii4//fHxSCGgHYJJiWoJjeEy5ZpA5Ozzr17ez44dE/z21rswR2c2v0RmjI32MLVuHjDc176fbbfsRTg2OLZ+TJX77jsV3xPEc1bJ9P6lV3JldSkf2HsRQ9EEjemFgW2qMULgOo6tGo1ijDIMrbCh2Vc+5tXsGD+bRED/36X8/C09NmNAt6EFFAsQG5JmgfnVhu792jZipCm12Rbf/8LV5Is+b/jQ08m7Hr/cewvLnlrisYs3MD1eh0aEdh2UAYFAK4FBcdvcMHNxnsJ+h3bscumPzsDtdFiSbEK3HYaPTfAaOco7DnDX0vWIRNPdm6O5eQwdGUwkrOqq1cJ4VvWVLq7ipYblR+5n1/V54nQfYXg2842ArOngJwnR/hlKsxKVBEx/qIcPbDyJyh1N7r11CaVPb+Wyr5xlNTRCQJpgwgiMIVuyAjVdR3US3JYik9AzUGegUufg+iK9OyVCuiRmgN2resk/OIokIplQrC2Mo4UGJajNtHnXS15Ie+tOOm3DxBMXMVSOqN+lcFc1KA3uYPG6hOMPeFw1PsADY8P4XsIVD25ECIN2wJkIed/fX8nfHX8RtbDE9VNFjICu5iQmhqAccdWWZVQWtznmNffjN47lhvYonRuh95aQTbuH0f0Zpf0pIrPKsJ9uupZ6B57/NGtvFYCTaLZ9sot3/PIm0tBnZk2V9koXf97FaUak+0q4c4L7bhlBOguEmevZOuBmG6cgSMolOotzFB9og3AwrrGDx4XQWaO1JVN8xxJtaYzJDIs3zPP0Z+/jOz89E/dHJaadMu7iXYz7iwkGFGiD29MiKQpMIAmOmSF6oJ/SeEy0rMsqAcsey344CvMNVp28m51JEWEM3zlwHKVKTDOx52QqFMdX9nBfbQi1KENpg+np5nP/eCYXvPA+Pv28VQTSDmBJBXHkkjYFXiCJPB8TePiTbdvUFhmGbh5j5yKX4naXQCX4KiATGmkExgMxibV/hDZMlFRDkJEUHaKSorR5DOodjHAgzdD+QsOR0fQX5mjoHEuKDe7esdi2xQgQ92nwPDqrApLFkI4Xcfcl4Eibe5KlCEcipIOqtdAlj6jfobG+jRM55PZLhAQcF91sYzyXYDyiub5Ip2/h+pCEOFMN5o/txviKwtYYMwUm0wQjMcuOGmVuZ56x6WXoeoQME0wHUuMhtcPBAyXm6seQv6qDcqxlTXiSJI6pb/XwRmNUR2ME1I/sorilTt+Ns0wf6/PD2jH4d0iK+/faa+EShUoVYZ+LPxqjUgPNDqYTIYREOAoTRZh6EzkuMb1V5FydXKuD6Soh2glZ4NI5doS0yyHNa7qXzqPv88j8PtJ2B4RHWhWkGaj5HGkhh9sIEZnB6BjRU0W7ChGmyLnmgjVHY8pl6M8wXQXEpj0YDJ3hbvL7ahghUGFCcnAKDSgDptlCqAWfUj6w+TRaIxIDvm0otNKXFLLMtvsIge7Y5h+BQcUp2VTNEh+ZtRU2TllC9bZRUPD1uaO49+e9fO7Cazj6NftIhgxDx9a5+tPdmGaCCNrWCqoNxlGojm0akkBUlczt7qVwjmFRpc39dx7gzGPfiXAlQgpOfNRSgl+3WJ41aDuSW6sPcvKZ6w9dW4XsITY+qUm5pb4c968+afEwDuMwDuN/Hw7/BB7G74VSEseRuJ6DUpJSOccb33Yxw4NVipUccZiyvd3DbJpje6uPt191+Z/c5y9vfy9XbvoQH//ii37v8iTO0GECUUqzbgNsc705jOcQuwonlUgDGxf/YfVEoVDghI3D5FzJb374OgDS1GZA3HLlfX/Re/DStz3ONlJIiZSCzJXonMt5zzz2D24jhODy0X/miskvsPLIJY9YXiwFuDMt3LkOKtLgOAR5e3P18tecz8tecz7Li0WcPU3Gtm3AzT+Wf3nvh9E5F51zue6e7Yf29bkv/Za779zL9h1TZK4klYLOfEh7rnNonUwaRCcicyz58/nvXcLytUPkCoENqWtHEMWccvoRf/S9ePnHnoNwFENrB1nxn17XkhWDrO7uxQ3BiUF1NOeddi6q9Hf4+Ys5v2czT157Hy878iaqYjtN7VOSIZ7jPWw/balIYsWQ9CgXUvQ+RZeI6U8jTKL47dSX+PS2hyuftHTszekCCfKwz0L1IZS1YPUN9/D3//Za3vrNSzhr+be5cNkW3nbGFSRdGjLJq771CwBuG7+WFIEu+OicS3cjgSgma8fkM2yWhwERJ0wf20f/9Qfw79rJ5d97PQC//sW97Noyxu2rp3nC2rtZPDTN+T330ZzMMRXlmQxLNJ2tZKltdtBRTNyneN2tVZb2V21+hoBr7x7gCOYggd5vz5E0HTQG47qoVoKIEys11xmmEyMXuL3AW4poBxS04tbZdaSetL/yWYZwbIZFe7iKN+ND4GBabXSc8Jlr3k0cJ3TaEXOzTbTWPO+V5/PdD7+TL5z4OnJOwFc++guENkilbPNUZtVQJoXdBwfx9q9i/owett4zjLfDIbhN02nnkEUfNZ7QmPLokiGdwYDO4iKPPf8oMsfFdMJDZI72XRiuEG5YjAl8RCFH5georjKq2+HIvhKNRg7ZkQih6D99gOlTqmRln6zgMfVbBaGhuVPxg1+fRhj5aGmVWEhpq6OVwp2JcHwP2YyQ7QSnlfKKdTfwzpW/4fnvvgvZihCdGCkVOjCYYh4dOHRMnmZmuP2mAZKeIsQpMwdn6bQtaTt09Rwqs3W0o3dEzO4r8tN/2YhTKbLhGIeP3nIxH7jpSYjExZ8EaQzlVsRvf76U+L0u3mjC5FSZqaky5e4GOkn4zXdWEt2ap3cmIdebUHbupD1apFnI4Rxos/Wqbk7btY3mgCKpOHT6HU78zAuZrymoNzHtDqJmAzn1uMIVhr+/7YnslL2MP7GMyAxaCTLjYuoNTKtN4hla/Q5hr0PcX4L+Aqd9cB/LXrcB2YD8jg5aOajebmRXBR1Zi49Qytq2WqG1hRUl0nM477wpxlKFdy1U7pyi65ZpghlJMUwJewVRl2Rg6TSVfMh5w5sZ+gdNUBEM3ddCS0Punr3kNo+DickFbdafPsNqr01VJTx+aBPPX34L0knIXKtK6USShJC5n1YgTtE6456bVvPhlz6Vl7xjH+/5we10DcY2nHTao5X1oXQbZ75O5bo9BLMxQkq6b5kkWicJkyqpyYhnbKaQs7dJ2A9JEVQA8ZBrQ0O7KqQrB2j3lkjKAdIIdIb9HrYjCCNkPcKEEaYdUnbarCmM4bkZS6+M7ZfYYNUKgDjS4CyOET0JJopR7QTVycg8qyj8nQXGa2VUH9jHsk/vxz2YkJQ8S3wrG/Qe+xI5NUdQzxBaIDOB9H06a8p4dYMTghMrjONCIY/q8pi5LU9rt4cMM4QjiYtFDrR7GKt3IWLoLc9z8Vm3MfKyMcI+h6w7QLQTpm71mLjFhWaKzhIyadBCY6RB1tok6xJElyZaK9DLhzArFpOWXNKcIBWZtRo6knRpP6arbIk6gNBmGwnXQRc8kGDQmDCmMxwQj1TIci5oUG5Kbj7Gy0B5tikvKzj2s2uDKSmkK6yiU0nS5QM2qynJcKLM1icXcuC5yNQmdhslyBb3Ea7qJ924nPayvkPNabnReaQx6DCyeSpa24ygQt7ad9IMpxWTVAKEEAhtIExsFotYIJGzDNLskPoXT2Ei2wCoai2Mk6O5ZoD26gFu/tpxyGuKvOPJZ3D1F9bgXOSyeXcPO7/cjUwz1OgsaraJUsoSiUnC4hW9JN0FErcHf8rhLtnD+LAHMka5DjgOptnh7su2IMIUo2yWyztf+DXG908euq7m/VWUi29jV9TDkV2ThFHK5T+9g22b9v/Re4jDOIzDOIz/jjisSDmM34tyOcc73/UkpBR43kOnyUXPPZWtDx5g7VEj5O4c5MM7h1hf3k+j8ciB7F8KsWAd0koSzdmq4p//5p2cdtZ7cVLoLPI568jlvOCsE/7ofj73j899+APKZoKEjb9MYuY4dvAqXQcdx4jZBlft/cxftI//jGp3ETHfACHQSiKVONRGUyzl0Npw0y07iBzBvQ8eBKC3t0JScZCxofsk/9C+5prth0IskRhtSF2BzD30eQ0v7mH/VI0Vg1XATsLWOzGdTGMK1tNw5V3v/ZPH/ZSXP4anvPwxf3D5O19zIS9+63cWVCuCu27cy7OfeCpCCM4olVniP8iywtlsau6j3+kwnz3yfEkbHSZ39TN05Cxu5nLZtacwq4YYvUkhWag8/k8R4Oc99QSu+v4tdPeUHrG//4xCOc+nLvkqlWd007PuINtnepANgYwkumT3X3U1WoGcbSEx+DmXxGgcCa0wwTgSNBid4TU0Wc7DFCsEBauScb02L3zqzdzuV7hjZoh+r8H2j62F0zxGcnOAYMd3igitEVIQ+xLhpkzHgic9+ww+8g8/QSQZ225YQX1mlGpvh2iXZ9WRQnPEMXsprtzPnd/uW1BZZMhcwNjeGapDk7zmbd8h2fwMwjDCSEFnsEh5OrHnmOOAVORnIC5mpJ02Yv8kSMEHXvNNNi218vw1J0wzX+vQ3/9wa1r3sh4y30HOpwhH4bRilG2F5cavnYSfRQRlzehNw/gdjcn7RG6ermCC8Qd9VMXQbAVUa3VaLQdvIVSSNLWvz1HMvXuQDxx9LXddMcQ1v1gFGnbtreAmCdt0ideWNRsHD/DAnjVkQjLWM0thWwAZSAzoFD01jXEkczuXUpDSkjRRApltvzBxjJqpoZMM3DaaKgIYTussctocWxzjV2IjRriIVFPyGoi8wbQ9NIKPPPNxpE1wo3mEkBD4GN+SgsJ1SYqGvFZUjhhncrtDlPlEnZi+vttRrfUoN4M9eSSKxd/cBwMlrv/iCjxi0lGFavoYaUjJIR2FSR2e9IL7caRmb1xlUzzNM06YZdv+XlpjdVCK/ff0YlY3ELky3pwmzRQ33z9kg0sj+yGJFBCCDxx7FJ235qBXIhqKTreLO98iKpUwSuNqjcgMKtEcPDHgxadfx4GrR6jvXkR+YB+Vexr4ExGiqwxIOwDUBuHKBWWYAdeQDUtqXg9nr93HmU8ZwxGC4qZp5Lw9DiVAOy5OC1IX1v66xa37+7nrwWU89yv3cPql9/KvbzuW8J5e0r4KOJJXvvNGzj5hkstnRhhJ91LbFTA83OG06gw3de/j3gOrIIPPX3kei74TkckIMdNCOQ50l3nuO6+leFKEEZrjX17nC9cvoVzoZWq+RGVuOyJpIRyHsKeIChOKfofl3WPcOdVLlneoBE1q9SKL9mhGSy3S4xzkIk3rBWVy75xFlHyyQkBzqYdyHIyC9vpBZCekesu4beNxsarLnEJkgns3LyPRLs3uiO7RhcBeDCbThHll39NC29q1MrvMTSTaCtosmYrC32mbpryDNZzE5nRIpTGDFXzhYwo+MsxIuhyEEYgEitubVHZrTC4glhEy5yNERmunj+4rk9QSRK5DNFhGOwJdytCOJOnxKJdqeG7GosV1RtNZdLuM7irQWF/FGXVw90lKgxHxrbPIGxrUTxkhqyr0doNrwL9FkBV9lFFIY5CxoTjdwegMpIdqhZiuAiaKrbLCVahEQTtBtlOE72MKtvGmtQqkk0fnFIgOhULEdOpR6CTEy7pJhyuUtzXw5zTtJQVyWyah1cGEsc0xaUWIhbB0E3ikI304002E59jgWmNtrWKgihemdEhJNy7Gma7jans+ozUL6fFoz7VfhTiFwMM029AOcVptNFhS23UWMoUsUWQKNsdKAkmlQG11he7tIcQJmSMpzcXIvTMLYeGLcMaamMQwfk+e/GPbbHlnGfKeVboawCibZSUEwmSUe6oQJng1TSYzbt+5mOOWj+F1h8SjEuFJ/Ekb7k8+h3EFqZQ4SvKSc/+Jy7Z//NC1YG+0mJbx8WSKSBP+4cu/wpOKK3/4RvL5h+5RDuMwDuP/Eg5be/6P4TCRchh/EEHwyIyPL/3rbzl4YI6uvjLvPf2fWf33H2VHeQnetv/5L0uWaRsuZwwHRx9qaYmG8oSuREYZp5zVj/dn1vT9Dj2Lqszsn6FnUddffExCSetpdjzWnbbsL97+P0NKifBczO/s0clDHuR//eyV7N87Q7MdgRA2xHUBv/nx67hzbBOnDD+khnnzJRfwfv0LcsLhrss3YYBSOWDd+sWH1vnaL95IFCX4C3ktUlo7RiqEleM7ylYs/09i3erFLF5UZt9EAyczlB11iCBauvg3LCVGCJ9WOAICfB5pjzJhwifeeirPfvEDXL11A3Ehx2/uWENhtEZQmkdnGY/vf+PDtnnz517Imz/3wj/rGLfctoP7rtvEpiUn8K2DR5OZChtParJtPiXwEvbUdrOm5xw+/cQ6n/jlDSAUpUXdtGKbJZOlGUJYqbose1TvmEJ6HiuWPpTf4iW7WLN8nAFnjp/84nEciAVPWFvlsmCCPe1ufJXR/Hcf42SYLKNzSZkXnXcN908PcdZ5r+Mj7/h3jDBsu2uQa/evovzczkLoa4bI+Sxevw1zdgEuryLaHUzZQ8/VkELwjNd8FS80OGmCg8QkhgIBJB1wHUxo23akJ3DrNtgRR4HrMnlgnu4DhnigyIN9VYQUnPG1f6ARG755zN/w8y9czUQ7QXiu9eBlGtnRiIWgUCc2qBroShGlBWFXSnuxy8CmhLCpSGKXNHK445s54AA+cLWXkbY6tqlHCHoHQ557/O3I/TGTDwSoVoyOY2RboBsdKq1uMj1IXoTI1DKI7Ts65BMXEWbgOEjpIvISXEW+BQiDDqC1povyvROHLuqm2QKtOe/RW7n2cmtJdGd97vF7uXZ+Bed/6VaufMnpaE/hro+JMnAeABMZkrZAJVaZ9DsyS5RyZMrBxBEiUOSFR9l08NI8zXqbPIZQKfKVDpmWrB6q8q1XPJcnnfhu4mqOZr0XP98kjhWFvQLjCGKseoYsoxl55IOEehawccU0+9Jujlo3xs1OxQ6ma1N03VtEr7d5S54r+NC2x7O8PAdRYpV1C0GXy06SFKdG2bZjGV7d4I3Pkax2ccan0ISIMEELQ+aC31Dcs2+E3P48u3d2o3fuJDxhBNnOcJvaqtowiEIOEyW2Kl0ZUB5qLOFpH7+VJYMOd97Ry852le41DeZvr9qaYKMxUwFBSxMXBXeNrqF70yTmfRmPO+IgRQmv/PBdvPd5j2LziSOYxQk3372ec06YZChX4xP/sJ4dl3ZROd+w8R/q9B09ip5YhXEhnuomKjYI9nQwaYaJE/TiXgaPncFVEW3tcPWOtbjL+qiXDIu/sNs2MzkOZGDimO7iHDknYfq6PqikOLWU4kibl/zzb5gZy/PZj11EZ1lo82GWAFmKiBP8sTrJcUtQbYXQ4IQZyf0z7HpRldKldYJKEaEiRCmgcRCefdxtCKH51NyF8EXb+COKOdICrB2aQBvBbLuHvAZMhswMmVqot6kWUDVbm26S1FYhr+mhvG0eFWlEycH1JEkto2fdHBPjRTq9loExStE4okD17nFkI0QFArwAITRed8Ka80fZsa9MPCco7FJQifA8QdzM0S4p5ucLdNNibqbImmc02PTFgPqROY5/Zo0DX62SRAnFfs08tgCnvaxE2i8xJiUfJQQGmisD2j2S/ERGuWXIcg6pKyk8MIlsdaCQJ+urklYDvF1TkKaIROI0QkgSzILFpHjLFM1Tl5D5PrLssaxrlFAZ2niYdgpIRCtELy0iM/DHmzZrRgAIRCvCyN81Cxrc3eOgDenaxRjXIark8OKE2oaMLJ/Sc1WT+vIu5i9YSfXynbixPpQVBAY8gR5ZTEaG2jdply1UIgMY17XWG7AqLqWgXEDMzC3MjAhy26eRW6esMqS7jMxK0InQUuBmPuERy5H37WbyzpCZZxZAGIRyMK5tZiPwEUgSmfHMv3sy5597NK994sdJOikDVza53j2RM1//Q4bWzrC31oU/Og2FwFrTOhEzSzz6D0aIMCbBMLpvmqElVuG5vnoqP9i3kk6maM/lKeUE7sH2oWv+YRzGYRzG/y84bO05jL8IkxN15mabNBYyN/pv6rD4VxG9u/6yauE/BJFkiFQTdR4iEbI1AretSYrw7fu+9Bfv89v3f4Tv7fwU337gI3/xtj+9/x8RjqQyWOHT337tX7z974NRCuM61jKUPvQ6/cBFCBipFnCaEeuGH0rD990cpy05/mGkR393ic++51mcvnEJ2hMYF+b3zHBgx+TDns//D6G3Ukp0K8J0YmiGNvTufxF++M8v55vvfTbrjM/k7mmy1N40CiEQwqfe2oQnDS5QkZVH7qDZojEh+NcPH8WD15YxWYa/c4Y0TWlmZTZNVPnSnr/88/8d9m8fI1fwUbmAiekqPgEvXnMyR/TPsKpnhp8euByTTbBm+BR0LkAHHtVqjsBTrDxyiJ6+IjrnYhCc/bm7GDl+nsET23zkO68/9BypPpLP7zqGz/zmZLy64GUnHsNHvvACPM9lX62LuTgPkUY6DtJz2Xh0jZOqezlncAdXXnrPwsBUUpvMkcxDMUgg8KFagiSldVyBpF/aAUBXBVEogO+z64E9lsyINJ3lVZLuHO2VXTTWD2DSxLZWhBE6TWGxixGGtKcAi3oR1TJZziUaruJPtdhw1H6+dPNneMeZP+LtZ13O8//1y0wemGVm9yQitTW3RoB0HFRi7VxGaFQ7RgtAGOKNMUdfsIPSoyQyX0A4AYTWFiSlVS7svXUXNJvg2eN59j9to/lAgF+K0QaMTjFhiGhGCD/gxBVH4sRd3LVzuQ3l1JAurqDqMXJ8BjkxcyhjQrsSIwRZDoQWiGIeWS4hB3tALrRoGGgNC2SUIDsR39+ynnfcfxFXH1jLN2fO4vivbmf4vRM86ynX01Np2NeWpjgSkkVlsgWlmm40cZMEEcU2qHO6Q3cxx4Uv3s5ZfzPJxe/azd+8/5M8d2glYeQTJy4ve+oZFIt5RDtEtCNEkhGmeTr7utCuze/o6m/a2WpjeP+pJ/Gxlz2Ke5oVlnuzKAw9sgVxjEkSnnDJY+jpq5HmJcYVdAYdRCrZe65VagnXxeQC/ArsurvA2vN2Ut3SIT8a4foKfyf4rRDPZBihLcmbV7zs9BvZ86sR5na5MBPihBqHjMyXmPkGJorQSYqIU9AppKGdDY8T6KQ0r63wxI2jDB5l+GlhHc/+4Fa7PAwxxuA07O9Zvp7y2NNuQwtBqh3q2tDShu2jBfxSi2WP38X559zLg0dW+LfRNXzotnPY8oNu0sRl9toqtzxvDYWPxMi2RiXg1jLERAORU4h8gHAdRK1Fr0rok5KKyGj/pEbP5TtxaiHkBAKBUC5SKbxaRPOAojHjMZnvp700oLWqwNzeHG6gUUJz/DHb8Z0Ez0nxXYPz6ASSlMxkCGkJEa01i560n7m3D5P29zP/glWMPaHb2mcSuO/5VXbXehhvVUgSgyPnbTZIvcUF77qP5yy+ndcvvYqyF6MLLrIVH2qGUUKiPKuEkoFPNtRDeMQguq+ITAwYSJuSaN4SQ+MzixBRQuaAcSFzBLntTUylgO4ukuZdMtcqQnvXRhx5zjR9azOyahGpDe0ulwOml1pYQrvg5mN00bB2zUHUgTymWmTF8eP0XTuHd3+GYxT1nS662IPwy7i1hNyYRi/NaC5RzJyh6PQKChMpEkmak3RGCoR9DtII8Dy0sVYtlFWHAuhigK4WMIU88WAJYzK8qQ6V3+yCNLY1yX6KbBuSHhdvpkVx8xjapMRF20HeWdZLZ1mfJUN1hpicQs/OQZzY93hBnZIWHdKitARDNseLXn89L3nZzcw/KSBohHTfMwMjfYjuMqJUtKnVuRxiqobYtIusWsD0lm3vubCvSXRVkKUCxnPtY+02NBrI+SZGOURJTGv9IMVNk/Z7lGYwVYNaDYYHMYsHyLo9axVLUmyYiwQB0neRnmszeGoNaLeRScY3v3MjL3/DV0hbMSLLcGKBd2ODqz9+JI9+9BbE2CxkAFbJVFtVxlvST1hWVqXTXeY1z/r8oetcX66bTx37de7+xnrytwUkVZcgNeRyD7frHsZhHMb/JZi/on//zXGYSDmMvwjNfdNkcy1m904DUPJchBSoTFBvtv+n9l2u5Nlw3FJ6Bsu87UNPO/T4CctHmLggpXZSRri777+07+qfYf34fSgU8vx6y0f54c3v/i9t//tglLA3Pkqi5UPKjBXD3fQXA0SUIbU45JP/U1i/cRiRGkQGcaqZHKv9wXUdV6IjO+jDlcjsf23ivlSCsBPT6djmm/+IduuTlKQkkIZq4cmP2DbrKmOKASZw6VKQ2z6Fk9pZdFmCnWY5+8I+frNl7X/p2M5++qlsXttPo61QkSCXFHj0onNxZYwyhhun76Ux/Ry6u2M8bZBpRrPZIYxT9mwd4x8+9Tx+fet7uPKBD5Lr0my8ZBdHvmoXpWr+0HO84e0XccX8cu7yewgXad70zHOtCskRjE+U2X7/wnmYJAhtmHh5xJYbu3hi714Gh15NVsnTWVql3Vuh3SswErpzB2BmHppN/PWC3mITU8ph6i1MrQ71JmuPX46pt0lXxcgUWkMFjAIZZWgpbX6D44CSiIMJwcQ8/nQb3Q7JHGyTlAOpNPhfG0HN3YcrM8q6RbEjeeG7ngzVog3CLOYR1TKmVLBWrsygPWkDE3WKyQwjIxNs3L8feX1C2nGtDF85duZ1oR5UBD4il7MztcrhOz96MVd8cx233L6aetCFMQZVrSBKJYSjqPSUqBZyxPUCTl3j1DVqTwrt2FavNtuQ88F1SKoBqW8HDBjQjoMe6kMO+syd3I1BkAaS70+egggTRGa4b+siuoMWb1p/FW/fcBW/mVpCf6VBd67D8hOmbaWs71s1RSqRUkEnhDCiHAjE5CwiSjD1DlN7JvE7P6P3tJfy3cYpvOx9L2SsfhlRwyNueOycmwHAlAN0zmV+dYX9TxmkMxLgtEEkgn3t5ehOB9NuY6KM+T2GvDbsSoqcXdyM++A81UVtsi5B2JL4gWbidMnU0R71dQs11Z5LJ8ggSQBNV3eTzDjc/LIlyChDRikiclkxXGP2+C46gYsp5JDtFHc6ZlFhntxoB9MxVPoi1r8ppHesTuFgZEmrOEakCX7JhoLqdoKut4AU4RhyR2kSIzgY5xkcaPHdf16G9OoYIUk8OO7597DsGdvwqm1OWbcFN2sw9wWHV335Yv72HRfxzy+7kK6hmJP69rKxdIDTVuzie+88BfGmFFmqIItFOscO8+B5cM8tIyz68W4qd8/Sd+MsjvRIMw+ZU+BIhOdRNz474gK10QJuI6Wwr8min+zDaVnCUrgOxpN0ndUhV4VH/e0YHVXAKEXUHRC1feLYZWZLheGLpyntrpHEiiQRDP6dwYQhJo6RGWRSsvox99K3rgWZRLYVZnEC/Rlb39+PDBOEdrnh7/sZLs3xd0+4nPyrIquW8jxa67v4yfjx3NpcAQc8dN61Ncs5xzZ+KWUVZYt6McUceD66kgcEMgMcx2ZxFHNQLaONor0kjww1mZTI1BANlTC+i/Zd2scPoFoJMk6Z2Z7j6KBB/+oUPIcs51JfnoPEIZUK7QkyAcpLqXcCHrhjEOO57H+wyNbL+olrEp3z6cgSRDEijFCdDGfXJIXdbcDgzCdkHUN+SmMyQ9jt0ClDdWuEkALhuqSLKsgwxZ2oI4y22SNJbMnc7hLGdRBTNdJqQGtVFyoVCKHAgMop/CbUnQRVEsiqROccUJpFnT3485ElVLW2Id5xRCytOi5b3E062G0zm4REuFBZ4rC2MMG6wgR+d0LmCEAQdkl0IUCXcoCBJD5UBS7CGD3TgMQGxkqlkJ5v84TyuUP5KHiuVTQlCV6UULlmuz0210EoB+k4iPk2jE8TDubxJlMKHY3TiHGEVa3R043JBzasu922SikpEEYQ7J0lrmc2QFobCEMCIdl01WI+86XzEfMtTKuDaYWkArxyF7mZFN3bjejrQjgOzbH5h11PPc9FoNCBg3EU3/71mw8te8cLv8jFG97GHTds/i9dqw/jMA7jMP5fwWEi5TD+IA4cnGVs4uGD8lXL+qgYzZoF+8jAUYtACLKSw4PbRv+nn/MTX30J3/3Vm+ntf0ix8MlnXEzhPo/Cgx7nnfzA//Rz/N+GiCJ7s9UJEeohsuHOm3awd8ckE+PzaCU5MDr7Z+1vxcrBhYYMjXEk6R9R10opSRGYwEMYieCPrPxfgMkMUZTQaceHFCm/g4ODzlyK0qG/+uZHbCun5qHZRs41+c4N70Z7CoNAOYqh544x4NXYWN5PQOcR2/45OOslnyOLFqTVDniJJuf6THcKTIVFNJpavAU3SMlSDalhZrZNWrE3vLf87DZuvfxuhBAYcTTNTp4Ddz6Zda/9BOte+wmiKOL0y76AMRqjQOcSkgXrVtRKcVLDog/PYbLMNm8kCVHT5cgTZ9kSOgwsHSUa8jj7ok1888tf4++ffxvdXpvP/+JmPvezq/jUrdfSk2uxP+3BeA7Z4i6yKMS4kkc9/kSaj1PUnpDg1lMK4xHeXEZuXxNchY5jSzKMLMJkhvKykGa3QaYdguIEUU4wm2vh5vNM1kMm587kjkuX8pWnH4t3Z8SxZ65jzdFLMYFnj18IMnehSUiA9iVaQfG6MZKcINoRcEL/GCpLMamtP5Weg05itDYIV2HihW5r1wFHsiyfpzORccvP13BgrMc2XkiJkQJjDPu3H+D5y1+J7A8Jopjqngi3YxDGhjri+/ZgfB9vNsbLHIwEacCdaEAnxKnXcZ5aYPv7NrLr7RtY+kuNyFmrQ1J3WVGaouDG5FTC45dt5tZ39vLLzcdy++hqO7OSGmsViTLQGtlTRXZXOObMIxdaYFJwbOvQ6J6MufJ3+Mazf8ILXn89Nzy41gaEasOO+hQAeSUwSjB/msIdbqLIMIBKBUeePn/ICoCULDqpwd52H32yRVlHiKPyPPbXs0w+ucpPT7+VVkvRtWyCzvqI0tIaRtjwUlO0A8uS1yZYYhYCLAUqSpBJiog1b/nsrzincRPl0TnoKSFxUHhc+pFh2r0uwm8ycNE4tTNy6HljQ4aNwcQJJkrIB4nNixHGDgyNwmhJbqzJT+b6+ORjjybVkrlvFMgaDgjBEz7yIM9acS8vWnsTplnjEy8+g1T7dEXQurmMvKsEYYdgaUyPapBlioP3d+HvmiGr1SG21i/RjsmJEdKSR9DRVB6ooRIDzRYCQ8MXhHmXLJAUvYhBt8He0SJS2F8/LTNUfSFHyPMwFZfkVkNzWnDVt0cwuQAVAY4i1Q6vvPqZdJ/epJlVOH3vGCcP7ua0wd1smusnLbTpHNFrq+Bzkyw+soWTEwz2TpG5Vv1XvCOg64YCE+f2Y5Rg5taAy6Y3cNX0OrZU+hCVEiYMuWrTOu7bN8I9Y0vIagGk9n0nMwhj0NL+nQyUSQbKCAHJXIvUM4fCqI2UpKUAoRT+zinCboNKwJ/P8GvG2uaSjObiPMGssUSSI2n6kn/bspIH7hhEdmJknJA/mCEjUG2r8uopNhgM6qwcHGfZGbvIXEHu9pT6AUjrbYwURFXPEqeOQ2HrHOWbxqjemFCeSSk2Ba5SZIFDOgAog5PaW1K98NlmgYcu5TClnG0gMhpRbyNrCy1yxqD7SzSP6yc8bgjtuuAIyrkEN2cwmaRz5hB9j6/Ru3oWjEFNtqk/oHEOTFnrrrGEoykGSMeBJMOZ7+C2ItxajNNIkYlhrtHPV286gy/f9ihm95fIb51k8tgK82f0MXpuN6lesNMsvF6dD9BS4SbaErBmIeHrd5XHwhLK9m/s/8PY/v27CY4o4Xc+YKEUpBmFO/eRu30Lzj3brbrPce329QZ6cgYwiGrFPn9PmayvRFr0cWKD8VxEPgAEzlST5ooibeXa+xHfBTTpYAUntpk1jjaYyVmYriE6HW6/5sFD19OJRp1S9yykGn824Rl/+6+Hlt1xy04SJO/42/+6gvQwDuMwDuP/BRwmUg7j92J6psEn/vlKPv7ZX9NuR4ce/8DXX8q3bn4Px55uK4w3rFlEXHZIfcXugzP/W46llC9ww4cex1fePMGbTvq3/y3P8X8SF/7N6XaApATHnvtQrWC+t0TsKUJHYhxB9Gdml0gpSYsuScneBM/8CQImm64h5xoQxQSl4I+u+19BnGnCNDtEIgDUa7tohpcxoRNuDY/HcR6Zv/Pct16EMIbiQIkffObXvPKdT0QoxZHHLuEbL/s33jP0W3pFmyubx9CMJx+xPcDWO3Zy06V3kP0npc3JT/0nq4DObLaAyGDb/BR/c8OLWFmeZrgwR1+uyVQInr8UnSSYKCIS1q8epwk3X3YnV3zzOgBetuRDhN99Mg/+6CBOI0Wkms9vfhxaSmRvB2egwdFXmkNkUuZnZC0Xt7ngldcaIQTTT6/wmu1P5aV3P4vpTh6GUp70qLsQfoeTVu4EJWjqDH84oiN8rptdw/5OldJQSFrNka5fxkVvutBatvoz0sghLUjIDHGXIFlsMwFsg4fC5DxMs8WBaEFabhSdrEgyWKQgCggDOtWMqxl+/dXjqE8WSKt2AHjMaatwfMXi01aw5M0pTseSJJkrEdqgahH5dsjgz7YyNljBGTIsO2YOJRIbKFtvQmYwwtC3uApgB/XtDqSaOy+9nUvefAcXPOFBessB1eEuzrn4KJibx4QR9/x2M2Qu3b/xkC2X1IFOt8G4nrU7ZBlmdg49NYMxsY0aMDbDxZ+KkLUWcegi7u3YK5+CnjOstUiXcnTtc7n+tg00I48odbh/dpAszvPgm3vo+1lmG1KUBClxPMfaJCoFsnIBc4IDjoC8j2mEtKdqzI3NMhBE1objdnjHyTfy3fO/xJfO/CY6twuAnhWDGE/RMz7NwD+PU9xRR2qDzAwfeHfC2kc1EbkA8pK7OZknFu7ht+NHsL9T4Yz8AU7NH2TDBRFj4y6hCujaXmPlyDjd5TaLN46TFjVOIkm68jTJcfPGVQgpkWGyUMlqZ8p3tnJsunIYE+dQmYvp78aU8szc5VLaWyeZL7LleyvZ/JG1VF4WMX12DzpLMWmGEimNaRflRrzq07t59lv324DQNCOeV/z2DSOYzGX3gT4o5OzAutXirOMmOSFosdqLyF1as8GccYI3l5CrpVYtIxUH7u4iqTt86dIzuO2qlRhjyPIK3WrZwV+iKe7PUKnAGMFTX3snRhh0ziPuLePkKzTOXsaqI/Yyn7k0U0V0UINjs3T8llWr/K41RUWC3sEEIR2ymkPp/lnyBzWlnRGtvhAC+Ni9j+KurSPsvL+H67cu56q9RzDdqvDUK8eIl/ejmKHnSS10ZlAy5oS1+zjikzthKkK2BM0BTfOICsQZMoz49e51XLHnSNppAEmCdD0mRsvoeYdtty3GaBAYZKYRUiFR1sKGwZ1q4sx3LNGytIQII4yUVq2CtlkczTZSOUQDIbV11rqlHfC0gzKCVKQLdhZjM39mDFunB4mFg8k5ZL7CrQvc+Qy54Ab1g5ScTAhEytL1cxhh221ox4dsbqkSxKscWut6bEOcchCBQ2mwg3tiG3RmQ6JXdTBDMcWdk2hpyAa60DkXd3SWtLtA0lMgCxRGCUQ+j4wS1HybYH8NWa3iTyY4HVt/LYGVuTGK2Txhr48Rgsa10N4M/nSELuYIF5cgn8c4ClwH6brIYgHtKKvkANv25Am0ryx5FRtu37WW23etxRR928pTdkkqYDyNMzELYQSuS3rKOrKTjwBvYV9GL/x+2P0jFSazb2RWzZMtG0A78hBpghCQt61BJknItAYpEJ6LlBJHeVYdWK0gqmWraImstUw3O4hGGxUnGM+h6ihUuYyoWzKZKLb7dl3aRxrqj5FMn7OK2bOXMXPWCurLi2SOsDXd2tCp+KAzdJzwgRd9EYB9UzP88/Uv4DGn3E9+FhQSPdPmxhu32JMjSqDdwTiKRqP55904HMZhHMb/Mgjz1/PvvzsOEymH8XuRy3lUyjl6e0q4/yHcVQiB4z5kR1mzegDjC4wv2Hnwz1NQ/FfQVTiCU0Y+QuB1/297jv9TeN0Hn8X7vvkK3vmFF/Ohr7/y0ONTY/PUppqkUtqAOeeRgax/CG49RnU0qhER1f64YiP53SyY63DkUSP/5dfx+6A8ReJKIglx9FD+ylzjIrodGHENqTn4e7d9/puewMd//AZWrBykq7/MU154Fr/e8TE+9YPXAXDEinv54u5H8f19x/LZLT9/xPZpmvGB53+eT77um9x73aZDj7/uXd9AAzKDMDZoR6OlxquklIOIfc0KoXbZMt/Pta1l1orTVSTrLpG1E2QzAi3IMkO5u8hU6zfcN3UxQXWKo1cswixUJw0FTf7+2J9T9Ts8rX+KN7zxCQQLnvFu30dpIE0QgW8l6Z7D0N/EPGbJZlb2z3DnNR+jXRZ87bYTqbULXLblSMbbRWxUoubesR4u7t7Ms3vuIxWODXp1FD/6zm0AZAdd0nvLTB8VMHdkjpn1vq3FHulDVsroSoG04EI7JMti5tcLYk8TizwoML6CJMUTcPLGOdZ+ZB+tNxcpvtCSUtdes5kIw67RedasGESEDYq37cObaOLPJeiiD3kfuks8fs0dfO2GjbQbHo5nMI0mxhhrLVrSx7cf/CRXht+2eijHBkwvWtlk9YU7OP4JDzDothiUgrIvwfMgCBCOg3FcjP7dZUuCdlHKwSg7cJSlIqpUYPZsH/+mMXSW4O6dRbkK4zlMr/XItv0ud8Gwa6hAWvEJqy6FYpHyzoCPffbZ/OOvH0/9Q9089twdLC8eJKt10GCl8hiMFCR9BVrLS7RWlLi5cQtZ07aA/E5JEyOYvvnNfPFbJ3H1P57H/SbAVyk9QYtluZsBqC7vQ2hIEoXRAq0FcUmQOppi4fm88xt38d3NV/Gl627AnYn47ivXkn5A8dXHH0mqJZkR7NvXh57qIyvlUN+FvnyT4cI8q1ZNkBVBhQanFZG5kqG18wBWEdXu2FrsMGHuKtfaG/IBMs2Q7Qh3so10HfvZlnx0IcfYkYILl08xd24BpA3jzmzcDBtOmeOMR89z8XMmKFQssfXb7w0zvn2AxnCO+i1VTKNJ9z9E6ErAfbf1o41gZiYgHuyyg01jMDrD6di7LyFgZnMfv3jpKYif+bSW54iWl/FSYZtPjEDOhPhzGWTaqjSGNF++6jtk5TzuVA1vYh5WTXDLM5bwvf1Hc3c4TNc5LURZ2+zh2Ia0Cse2qGTNNi/92C7ygxmiIAim23TfPUd+Zw3R9iB1OXdwHxcdfSv3v7ybT5z6My5Z/RvOXfIg81FGfnI7r3vdVawrTLCpMcSm+UXcf2YejGHtRyZIc5psY4vCiTX2fWoJeC7RuEcceqQ1n3RyBpOmOHM5nDEXOafQgSLzFVnOAyUwrsAohQ5cSDWqFSMwFDbPMnDlmG03c1xAIBspSAXGUL2rQdy9YK0zBu0ptCvxZmJUECEjawFzmgZzX4xJ66SORPsexrUftFEC7cB9966gEfq0Q48rrz6BuAQzx+UQQiGkwnRCkkBAqUjnaBfTWyVZN0y7WqGZ+tTjALcBHQG65pC1BKKe4cy2wXVQzRCnEaLG5hAZEPjIfB5TLWFchVlQdvgHauTHQ4p3T4DW+F5EJejQnWvgzjSojGsOLl9KI99HWgrIig7Ns1ax/zmrSXM+0rOqGdHo4Mw10XmX1soqjVU9yESgYm2J4npIfixDJJCbSJFpgtBABk4ttflW2tqPxFwTEoPMBMJRCCltkK0UluCSErqrcMRy0mqJaLiM7i3Z30KtIUuh3UZgG36k1mTGKpC0tpXPh1QuWYZQClEIwGjqRy0i7C3SWlKmlYa09s/Y3JdUPxR6qzPIUk57zDb6f6bJBkCkBjyJyC2QNBpUrDH9XbYBynEJ51r/g733DrOkqPf/X1UdTz6TZ3ZmNkdYliUtOSxZBFREMeecwzVgzglzvOo1XBUzCigoQQHJaQPLLpvj5Hhyn05Vvz96APeyGL7fn1/v9c77eYZ9OH26uk53n+pTn3oH/vNXd/Kcl3+LG757Aj+44xTUDKtWK8VvvnsHAAtWz0VlU2jH4kUnfuSv/m6YxSxmMYv/qZhN7ZnFIZFJO3zgXRcB/EUn9oXzOtAzFOmdjUOzBGbxRBz/Z0yURxH4IaFO9NpCQKHwt0dK/+GBj7B21eUJBdj+y19r7djJj7l6k3d8+tl/d9//EqQQMFFDayhP12nvTBJtbPc5hP53qCvNiW2/etL9V56whI///M1YzhM/g22kqVcdVNPkNrvJ5asO3i6AdD4DwiOVe/zcbbhrH2baIrYN3LSk1JL88GtOWuwY7cQbiug63GN5YYT56T4AzKqHipMfrcKQ1MsBXd1p/EbAQ+Pvw1WjNORDGPGpdA/UGVqW5Qub1/CK5eu5bFGddx379YP65vxhmPn3lhH5HDoMCXpSOHtLnNE1QkZGFK06D151P2EqTd/qMUoyyw21RZgdmmsmljHPqrFv4Dls37ieYNKnUsliNkKkFHj9iUeLnnRpvW+a8kkt1Pok6X0BzYwiXw8QjcS41Ny0l9Qcn4m5Bu5YhLY1shGSGmri5wzGjs5wzQdeAcUf8yL/OsLDBaf/4VLe+ZOrseMYEStEoDmyawW7Fm5gz+YMcuMQ8YIelIoQrgOx4hi5j655BW6SCzDPieBaC1RMrr9Jo+dxE+UbGz/kHOe5ICVPecVWstJA2zE7Hxwm9mHrnY9gGElBMa7XMTMZwnqchGNEYAQzLKM4RgQBOp2GWGEHLtlyBbn/ANmRDKRTpFVEWI0JdRL7LbQgLipiM8Aqgy8li8/ezt6JHp635hbEcVku69rJvvNa+dQrOtBxCL5CmwYL14wwSZY9TQMZKAa68hRTPjpW6EwaaVm0dBXYvL7E/b9fAsdKSpFJm5Uw+K/dNYdvbvkQxy/oJ96pERfaNE+U1DcVkTEIV+Ck1uKKDKbwmVYx8/et57PXPYBhwKffuJr3nHA6u1/fQzObw9IBgWcSypi5ooIvTY7N7OcG60hkI0zmTqbBkfkhHvKzyap74COVBK145OFepFAokUz0RDATmSyNRPZQ9mhfUefSp29lyLfJx3UkEuGYICDyAyYHDKaqmmZD4nkuwoJ4tUNTZXHtNHp7zNipixlf78LTQj75yDw+f3UZM9+NOF5QObGH9mu2I5Eo30dXGmg/hGIepCQ7Ucc7IMnsrGOoGY+QWKEbDcJsJ04uhTAlX1l3Ghed/hO+8Nlf8+4XnZVI0Y7MIoB7y0tpz3v8/I8nkI7roCqATlbNzJkCWzPm68bhpN8s8Cou/DBEjkzy1l/fyBHzwmSyLOEzg8ezMF9lqVPmuull9FjTPKt9P4VPulw3uYxPzL+ZamzzsYuOouK0QRShtEFqYxn3IoVjRTh9iWwtta1Jc3kONJRO7KLlrhFSEwKhFLEUaFNBLIhSEhEKIqEgZaKkxmBGYlVMYw9Xkud1HKODeiJ3swWQMG6y22OWv3kvAzsWYMSCyBXELWlsCeaOGlQ8iBXN3iKpn/tIpTEKEtXbijXqYTZcgmIizYodyUc+9UKEFVEcqRMHmsbCxNNKKw1eE7saMffsQfaOzUFbBs05RZQB5bEs2oCMpbG0wt5sgyWI5rRhN6vIgfEkacoyE/lKI8CoNBGIhEXiOEnxq+6DVkSWwFvailQCO9L0O1WGunKYMYgwxOvN4xWzGKZMPMoiDVJSXpqmfXM9KTBEEaLWhH3DqM6+JJY51kkB1RB4OZvcxirZ/WnM8SpxMY82DYymgIjEt0VKdNqBlA2WhGbCJNEiuZZRxsbyg6RwYxkQKSxDwlQDOdWYYdDJhLGactGxSsY2y0JEEbqQhnIDYdtJMUSppIiSzRCXK1R7i5jSZOL0Xlo3TZH1AqaO7sbcMknGD9FKJ0bImRTCgIVehXuLNsZRHvoOB1UOwZWU2zSGaSG6LKK6T/ohfybeWfK9D15LvLgVaypE9rlYo3WsCGQQ88itifT6yz99HRcuTuS79T8z1J/FLGYxi381zBZSZvGk2LZ5EMs2WbS0+0nfs6SvnUYPyBAWHvePkfb8b0F9sko4NAXFDESajs78X9/pzyBzKbSCju6/bKxrakVUaSJsg2Lb33eMvwbHMogtE4RmaqzKomU9APS0fwj4EG1/cW+YGitz668eYNXJS1l8xBPZMmLCxWzC2CFMcg3T4MM/fSOVqTpLVs977PU5T08zdFUDtw9OcbJcORaAEpilGOfn87jpP9/yxLYsAx0FSJ3o1rVjcenbLmL1yUu5qfxT3CDLyN48BRHzumefytGnLmf+Yb1PaAdAKYVzTymhdQcRQkr2PyfPiisqnJ3bhiE0G+p9/OJMaP9Kgwtft5s5dpOVHWPc4c3lTyxibWYPH37u6/DjiN3T07zupq8Rp5JZefrcpOATmQbi3FHcwRzFHZqJtREXHLGKVnMvQzvHGB+ZoJoPGVq7HL+nndS+GkZoYobApIc9Cd2fH2Du4i60fj2DAz/gnmqG7538K75wYD/Z8eMhUKRck3MPewYbj55iV20/OgqZXJ0BT9Bz6zQImN7TyuoTUvCSA0ztcljXvgyQHPe0rax75OBkh4lLV+Hsnmbx6geJidnxUB8xDogwSRqyklXaQncrvzjwddae9fGZyYbCmg6BhCkCgNcksmHORJM1Hxrmrs914JOGOObIl25mz0MdjNk5yMZoNJVmml6vTNi0UNMBc+eWWLV0iJGwjWXZSXbqLPmFBr5hIRpBsjpcb3L8KZu58sG1RFIgDUmNHC3tPiISiCgGBRtufIj7fvcgYTHP9AkWn7/lIp561Dqu27KSif1zaDthnIcr+zBWaBw3RvUI2CxQAqwgKR5lu9ZRr3yMWv1aVj+lRmwmJrKZZyvUthzXv+YVnPXjn6EyYBgGwkmz68uSp523hdpIlt4fjaFdJ5FyWAFTW0E4dmJ2GWp02gQEW/Ysx8x7xOUGdKSSlXPANTWW6WEb07z3i/fRagbcODUf64YAlTMwIgthQm6BTyBNqlJTapqoWBH0WDDaxGlzIFBE7S7eYdC5JUJfVuGzJ1xHAHzkiksJoxa0NBh8+nzm3jBAfVEKe4OPLqaTCWQUM3hOnvSeJsZ0A0wTLTXakKiOHG3Pf4T4gw7xAY9e4TDmZVmx3CPd4jE6txU/MBJGlNYMekWaO/Jk4kpiXqpitGNhhMm9JDIp7r3taIqrduG2SJybNGxXdM4JSYsk3KSq4ILsJv5t+KkMRikKsslJ+QEsASelB7m33stbdl1E7Ysap5YFs5FMiKOIrFKUxjTMAT2uQSvabxpnaEHiC1Y5sY3WAw06bh1COSb1eWnMyEGjk9jvZojhSFSpihSga15iJlySxFGIIWQyAXdshCHR1TrCchCGCUJy9IGdDLTMww8skGCMlogzNnI6YSgRRqRHq5BNQ6OJyjhJQlbWglA+xmPWPSFRYGGUTOyzQqp3ulipEG1JRAS60cRtrbGr1svUAYPO8Tq6xUX4EmuXgxnEtP1pEKEEUyfOITcSY9YU2kq+20iBSluJr0fdT75XWhPmHaRhEhUtzGmJrDapLssT5xJ5XVW6WIamtd9HaEGl36HZA86oiTuhiFxBKGLa9kVMHdEO66dnWCCJP5EAZC3AsjNJEo4G3xL4hqB1bwM8jWhGaCHRIkQbJumhGsJx0a5NuKIXHDMp8IYhKgwQXkDQ145V8SCbSz6f10z8tooZtFbQbCYJQqaFNgxoNCHlIlOphMUYhkRSIgGtE2moSLmP+aNo16F57FzsUkBquIK1fQQpDfL7RlBK0cg65FJ5EALpR2BIHnA7KD53jI7xBgMHbJyqjwCqKdA9FjEKI2UxedYS2u7Yl6RFIWmmFCKUaKC8xKJzXQM5OEGzmbBhbdtGRAEYFlpp9m8fYu7SOYd8Ps5iFrP4R+C/S2TOf4c+/GMxW0iZxSExOlziO1++CSEFH/zsc8hkD+2l4doOl7+6zO7KJG9f9Z7/x73814I3USUGlNIgBbbx9xnB3nT7+5iYqNH5Vwow1227Aq/mkcr+7YyXvxWprANhhJYwOlY6aNvd129g//ZhLnz5GWRyhz72+tu2cuNP72Z8cOqQhRQ3F5FbOk0wVTzk/t3zOuie93iy0483fISPXvwz9p4zj7OXX8/mA8P87Is/BeADL76AZ69Zdch2rJSNbAQzPgMCjSbXmSeVdeioaaaFxKrEhGGZUy88io7eJ5ecBUoxckqOnltKaNch1hG4RdpXTdLABA195gS/PvZH3HTBYn76w1WcesYB7tm4EG+JYLKZZszIAuAYJivaO7C603iN5AF1bP9OBkfGUUZIS4fDwOI6KzsOMDCWYdmc1QwHMY2Kx4veeymFjgJXfPRKGo/sJdAWVi0CSxK3m+AFvHrenQAIYVNo+xLz/Vdy13AvD93cxeHlGto2Hi9a5FvQ1gDScBAqJrcrRNs2oLjp9yt4qBqz/xNNVB10XAbHon/xOI+MWNSaG6n6m2hLn4qTDuk6ocFgOsBoQOeycXSzmUR7mgY6ikAKfjnwDQCOOCxkyzoDJUDlHSIF5FOIgVGCjEPxW3Vq95m0n1yCT+UQKoDIoLnPYn7XFLkDMdtEF3FoIewY2ZRo10QpxbuWfYVXrvsw850xvj1wMlUvxQvM3eh0CtFoIMIQbRjkl9YZ35hFKU2U1igMJs42SG0IyE6aaKWZnm4QxRpixYKeIbbtm8fVe46juq2NnOFzcXE7Nw8sYmJxiuktHRjtPlYIUmvsuEytcTPZ9NncN/0gvR2Cecc1+dz+4+lJl7m6ejSrP7UYq5rmD5e+kPO/820MHLTSGH1Nrv78iQgF2VIdqUJUyka9JGB0rB3ZmktWyV8Qon6bAgTVUQNZq6BME6ljtGGhVIxlmbzia3ey+a5+rrnxCC49bwPry12kl2msnEINemAq+s8skxNN9qWyRO0zThUtkqg/hXkgotJhojWkd/jEQnLcgv0UjYiHmu289i038fkvPpvYAm2lGHjaQlruHkoKj45F1JqGlIPKWVhTJZAGqtYAKZnuzzP+2iKUBItHRjGaEeZuwQ+3LOXcaILnfWAn7yifgZ60cLMehWxAaX8ad1KjWnOIchXiRLqhpYEAIsske/ckteWtjG1VLNg5iQlMjZu0dEcYSC767vPouU6zonyAbxgrmRjN0/qHMovtKt2ux+f6buO9HzyLPesLiUYijmYkGJKwNU3qaoPMzVuSVB0kmTEv8SeRgLYRRhrlppG1JunRBqrFRigSbxvbRIQKw0+YMaoti1FtIit1QlciQwFOYnisCumkSFD1UVmDsE1y61cOR51kQhpkBGHdw4lnmF3pdOLzYVuQdpPijRaoWCGVgYohTCcSH+FGiMBEoqnszeAMlkntDRChTlLpVMzIyix+hwF5g0bJprh+hNLJ8xCmQBkCbQqkD8IHtCZwNamxRlIYihSyqRK/nWwalc0QWwKVc1GmgY4UQVcG6ZpYE3XioiIqSBppl3tG5zJ3fwUZGBwxtofjzpjmqrGViIqJO6kR9QgpNHacSmS0toXyA0QhjYw0jgd+VqPTFlYpJOwwqSx1UIWYti0N8AL0+BSx1YGIYgpbG0jbIvYDRBAimhEqZSUG20IjxcylTdmJGbOlET7JPRHrxEzXdSBMGD3opMBGw0OnU1BvoDPujPdQhFowB1HIwtA0UiVSw/HDizhIrFpAZtsY2A5xShK129i7pkiXm2iZpI6BRhsGnSN1lqyaZt8fC1gxCRPNkOTqkuk4RFoWsQLDNJle2Ul+KibK2tiBptSmISXQKZfWyiilMEQjKE/VKLRmOePiY7jlZ3eDYfCatR/j+sGDWZqzmMUsZvGvgNlCyiwOiUJLmnmLO0mlbFz3icagf47nL/vo/6Ne/Wuj4pr4ORfpxwg09t/hkQKJ6exfK6I8in9EEQUgm3OJMzYi0uzaOXrQtt/98HZKk1UOW7OII05aesj9Fx7eS3dfC0uPTBglkyMl8q1ZrBm50pmH7+aCznvZVPvbvF22lzdxQqtGiiSu+8BkKZl8SFjS+eT8mJRtUgUIwkQKFcV86Fmfo6OY4ZSroE1F7N/jIsJJLn/WF7nklWu54KVrD9lWEEdUzu+hckEP6XUxcZeNVRH4PTFNZSEEtFpNQgQnPnU/L7/iPG7ceDT65BpCBPgqxZq5bzmozUufuYYff/9eNDD1SDvvuPIbZPryVKciXrvmTo5vHeKWgcW0Gz3cO/ggdWHxy2/ewuRombjawEZj2DZ4TeKCkRjGxiaf3ngepy5OjjFQ7cY0LNL3+nT+coRSw0P2dhHNeN+09BcpL8oglKZQq7HoDQMceH8bmAaVWwTnnLWF7cUVOJ5HOt+k0Wxh4TGjrBgpceeBN9FiDDNV9rn83xwypqamHQ4oh3iXi+kYKK1BJ/HNtvO4nde7P3IkLzj/IYTQibSkGiBihTzcxdrWZOdgLyfLPPUD27BbwR9VSEMyPpWlsqOTZk2SO8ej7BhIpWnvLjI4WEejSdHCtWu/xXB9P6d9/udYFckPCoczd2pfsq7iOoDmyg8eT8dgGSPWTB6VI7Ya9F/o48230d8BBOTaMpi5NEYEhf/s5zufKfKj3xS5vTrB5S+4jqm85pUnDPGebz4Do8WAA2nMABCCua2T7C99h8PSZ/PbifmUJtspf7GTDc/pRTgaXTN4eNs+vvSzqzh+7QpO7RtjoJywQ7BAuybf/8WPyRiKq8a7+NlTjyN7nE96j8LLBMTaRB4don7pJ52dYdHEBYugO409WMFvd3nhM9YSuXdy181LqdZM9vS49HaNU7vrKHL1EYgUwtL8qbKEt7/wFtqsOuGMSbbsBuswi6lIYiIQOkBnHILBEvc+t8gFf3R5Sn6ctNBsP+8ufvWnkxCRxuuLOf6kCQ78rog2DbSEoCjouGmU9EATNMiWAlopoqN6EfUI7bQizWmEK5kesbnvCwvZdWGDTXEnViyIUhHNOMWcu+pUdszHLPvI6TraDxLvz6iJ6upFRSGikKM4ppi8Nkaf2AqZOngBV5x5Kn5Lir2vn096zOKSb11Fe7rJZq+Ty1Mb+MznVtH61ogWWWPfvS1sHl6Ao0YwhJ0UhfI5TFtjehFRyiZYvQDnwBQgUGPTZPdqoqzEmdBoaSFjhcqnMYMGUS1ERDGxCVYzMRGNW7IoA8ySD8pEWYKgw6J0eCeyHEEhRXpXBbuh0EoQFzPIgiYsSgwPnKZGK6ie2kdTCzIPjGDXkqQf5QUI18GIVGJ0apvE6RidAntuAx1B7CVuzs6uCk2dw8mnMcZJ2BRhlDBiSj6qMwMONNsgFzURfoy9u0GwMENlTS/ulMb0IMrbiamulEm0NiSeOTpp04xipK8wDkzj97dArMECc+8I1kSZuMUl7EoRG5rv7z2Rj/RfhRZw2qoDnLxsiOot8KfRJShLJpHG0iR/ICayJWa5niTtxDHCMAkcTeyARBPbOumPgDhno00PoWJ0GGBVFNaeaZTfRESJebH0QlRLOvk35xIv7cMamES5LsKME8qLFo+ZfQutiUWMKqYQtUZiJuzYKK+ZFFUajaSw4pGkndkmsVAIRxJ35bCHy+hsmpYpsNY9nMReOw4qZRHmBWFGYu8iKUpXa2CaVI/rJ9pXpjbmICqSaFqh/RA9UyMXQMsej9IyE0OJpDjWnqWS08RxkKx197oYATSKMW/43Av56PO+DLZFtZwUUt719Zdx668eACGIvOCvP6xnMYtZzOJ/IGbNZmdxSAghkkmKk+ih/zejHvrcPrqDSvh/Frv7t6JJ8nvp0ZjErGv/lT3++8GyLfAidDNg99aRg7Y95UWnsvrU5Sw+ct6T7A0bb9vKpj9u4o5r7uP+GzfytrM/xhWv/NZj25/TF3OkO82FrQf+al/CMOTqXx/NxzadyEeuOheAWx7eReQkEbQLO56cRVLoyif0a8MgFqBdCxlGeLUmA0GB+/wF6MhGaxgYqbNt3d4nbctXEVYqxjBj3KqP0RAYTdiqWrnlC/PYdu3RGN5ZhJ7BT/94AkJJwrwkTseoyKAvk+Oi+ace1OZLLz0FaYKQMTtvbeGhPos4rZmeXMrvrlzO1jt62LhlLmu7llKZrOLX6kyMlomDCBWEqGjGUyRWhK6guiBNs9Vl+p2ac+e+iXPnvokdpd+xwPE4fGGNQkuYJG54IaKZSCBaFrbidUKjW3D4RQe4+Ij1GPUAY7qO4Vj89ltHcOK/j7L4tSWyh0dgW3S3+6xaM85t4+2M+hm01ix1QxZZIW0ywBQx1399PhoDO5dm0bFLkFKy5qnHPvbZe1svQTsS7VqIUKN0woA67KgDHP2KDu59zuV8/JWvYtdDL2PypKX4rSZ+yqd8Xwq/FhEHMUvkOE62iciG7KuMEGZNwnaXO29LUid6MnNJDwhy+2NSO6zEj8OQScEhjKhuTWFWmohqk8LOCu1ug+OLe7n4aetQKQunmCKeKNEYmkSpmGAywwk9b+TclRuJbc12O8uG+lz+5C3AwSRKCSIXwjTEjsbxIh7ykkLqg3sXc8f0Uvb3d+AMC0TFxKpIlCOQUpArpolKFobfJOXXULdpnGaFNlPjCsHR+RKFOQV6nCqF5T6tl5Xh9BCjBJYBou4hgoA4CJCmS9iVp7amn6Xv28/PP/Nr+lrfjZurYHg++16cYe8bi7z0mbchlAQhcPISY79Ct0mm4jRTcQrV9AhOdVHCYfpUh9piB+UKYsenbd0QxQb86KrVOAIaccxphe3YY3Xq8yLued53+Prld/O9P17L5Vf/jM/+5Cd87SvfJTsZY51tI99soiyP0rwMWoK7R4GC6ePyBB027rYINe2xISwi1MyzSwtEJUT8KoDJOmiFbs0ljDPLQkkHWakiohgZRsi6T8f6GKKAOJ0YcwMIFSDGFWLZBF/YdC4fW/cUVjtDuKmIlq4qd3+km20Pzefan51JNS8Q9SYEYcKiUSFRxcfeNoLhaVTWRStN0N9KXEjTceMwbRt92h6YQk1NI8ank7QUZSUT+aaPmKpBtYkuN4hSRmIWKxWoGF3I0pxfACSqJZ2kjTkQ5ZN7NnYlRnuELsUIpRN2hyOI8zb1PpOgw0rMTqVEhhFKhcS+B02f0AGzHOL0N5jfOcGczhLaUihT0jjHJurXBHMKeIs7GH/6YkTaRaVN/KILsQAL4lU+sdJk95XJaIM4F6PNCCMAJxaISBPYELUmxtig0XFiBEyzia7PmCOHMUpFGOVaUpBwTLylLQRZ8OZExG0hkQ3X3bMU1WNw9+86uOk7PTx4Z56g1SboSKF0nJgMo5k6tQeZTtGzsIrRCBg5swfp+TjTEWZdY/oCEYnEksSSyTkXGpUywI9J7awQWALtWOiMi8qn0EISpyyCjER6Pvge1mQFwwvQnp9EwlsGcdpF51NErS7N9hTkc2AnvkMIEE7CMtNao/0wMYyVBmpGmmc0A2LXQOVTSMtG5HII00Q1ffTQGNaOMZpzivi2IHGyA6KI1ENDcHQflT9BfbNBYz1IU6JMkcQya43QisKmiSRVyQHixOTYdF2ktAimIxrditiFzv5WjEwKwzT5zKu+AyQLO0iNDhJm5723bMbzfA4Mjv/VZ/csZjGL/0s8aiz93+HvXxyzjJRZHBLTU3W2PjKEFALPC8g+ibTnfwM+cP/13DSymbVdK/jSyc/8hx0nVholFJbng4Cuuf8zE4qk7yO8kOGdBxdS7rp+A7se2s/KE5ey5uyVh9z3T1fdTXWiwvqbNrL8mIVMjZTY9dC+x7Yf2/FcauUt9FmH/dV+XPnbO8g/GLFjyyKWdCRMnT+N7ydKa8I0FDJPzspZvKqP7VuG0aYAP+Y7v3kzH3rOl4m78sw1NlBINXhQLgfDQGvF+Hj9Sduq+E20AcLQpEqKyhExQguCtgz3frKPo847gjmdx/P197cxenoPuAIdahrKJqwZdFrpJ7RpWSZOq8W4kqh8EevkSbzdiV5+X7mVb/z4XCq5GBtBc6KM0kAcopqJ0SFKJxMcpTBCjV8Q+C0OPRtjpGtCGPO7t/ioL/QTrwiZ/FA7fVfbTN+pWHniIgBaerKExybFxZMXXshQZRSRMdDKRscat9DDpvs0neMTCEsjpaKhXDpW1PjdV47l+q7VvKyzxCVrfwJATjj8/KuLOLBtDsI0iLTg5KcdzZGnLONF7774oM+/pK+VPdtGUWaysuwdHlMrSV772iXkbJdcq8t7Xn4On//6FjaFErRDfXIKWjLoKMa9JcC+OMYyQT41ROTLLF82wE/XZ3gKRwOQLisiU2AEmp9v/hRhFPG8kz8OaAzTRHgB2jQJz2giI4NgMoUyE0lSLudw4avP4f5tYzQyKbRrobViZd+V1Lv/jU2TPcztKGEJRSpbpxmkMQONEZkoAzbu7MW5e4DnLILBEY0oSqbWSvIbNKmpJEkktELe/rWL6Sr0sOvTN/Lb5n58KTm80M8juwfZ6lm02CG3jHZT+dgYD2xbwaLbJ7FuTqQm0VUC1R5Ba+KpJPwAK4jJPTQKGi74yE5u+N0i7lq3j/Et2cQI0xS882cbsKTinrY2JhsO3fOr7Fhms9dvZYE1zZ6pIsJ1Mfptwjtt9III35P4BZMPLbyfK3/TSjoTsvOm+Xwia3KD6OfUJTvoP3k/ZW8JtqEQCAwERSNky1gPf3jjfFQjYN+x/aiSi/2GCubmxFPEqTpY9ykqfW2Mn99B5uoB+kYk1hiEhQhREKg4Ysm/l4nTFpWjWkCAu7VGep9AhmAXTCLhItNx4qUSxyihWfiVMYTfQNspTCeiIHyWLt1Ffjk4UrGj3MJ0mGIqqnLai6apbpjix+87B9ko05oxMKIY1Sgl3hLFFFoaGM0oyYQ0JJFjJGkuWiMmpsk0QzAlUTaDKtWQjYA46yTRt0Ii/BCRT2MohTNUQzmSQEZYTR9BjBlYODvHaaxop7izgQ5jZJ+HTtnIMEZUQKoYtxbj2fKxaEqzFGGNNtBaImwTESapMRIDLBunFhO4kq4lJdCQdz0cO4cyBO6ASdijyW6vYDYinGotYc/kDHSbRDgKw4xITQeYYQzlBrGVRTuSOJVEfetYI2oBWd9HWzbx0j7kxj0IrTFKNYTUidzPU/idOdLr9kGsMIZLhF05vF4Tf3EL1jQ0W0Ly+Rpjv3CQjRIj2mJk3zyafTki10Q7Em+JS2q3B2XIjWm+cdfNtOVjKpHm+c9wafa24/gyKZZocMqKzlsmsO0Mwk3hLbWpdxRou38SrzNFfWkLYo+H7UkCV2J4TbQ0kFhUluVomaygUylkxYPIS3xfrDwiVsQS/HYHZRRJbRpCWia67iGkTLyK4hhcOymiNJpowGgECMPCGC1BGBN1mRjjJfRkCWwrMac1THQYkvrDbmqXrML9xcMzBjAS2fBJX/8Q40e0EN1Swx8XMEcjcmnUUAkjm0LNGPuaD40Rr+okFoCvEJaBMKF1EkpZheEYmJ2pZOEB2Ld16LFx+lWfeg7ffN9VaMPgE+/4GQ+9xoUAOn6n+eNP3kI2m3nS5+YsZjGLWfxPwCwjZRaHhG2bqGaEDmMM43/3bXLnngH8QLPuwOhff/P/BeI4RgYQZ1zijMuyIw5tXvrfHbLqQRBS/y9x2OMDk4wMTjGyZ5RYxfxx6JvcN37tQe8Z2zeGVopGuUHf0h6yxTSdf+Z5YrtnUWj9Btnip/9qP6LOEWQUYZY8Ln3mkXztq79mOKgTz0R//iW88LVno4ROEpQsyZ5N+xk7MMkWNc0VVz+dD173bIQxk66g4ZF7tz9pW44xRVeuTnu6QavpE3dFRF1RYi4qBIZpc9xpyxJquQtRWhNbiiA0iX0THT6xkAIwIhVhziDOmWQLIe6RU6SOnCISBuV+SW6gytVfuYGo0UQFAUoayEwmic00DHQcARojlSE9CcZgmEiZYo1Ou+zdMshTFtzAZi/PKe0DvPDtt3HDpo/zuW+9DIBiu03YsAgbNvPmrOL1x1/H77d9gee/4jQ+8o0X86Wvvpz6VwL2/SzLyA0Oq566k9HYZrdfoXhvTPEa2DN4HFqvZnrc4RMXnsqOXyxN/B3SDsqxuPKrf+S3P72XLQ/sPuizf/snb0hSO2KNNgVjy1swzGUcvfCFj72nuz3PMncx2tdoX6Migap7xBNTZIcbdLeU6CpW6TqmTt/SUe7ZvZjKkkce23/uvFamVmomj4Htk9O0d7agLTNJDNEzCS9+QHV1jpFqgbOPeD2//vBaZBAzNVZj6/p91Nsc6gvSNAuJqastevjaiV9hZEMbFxR2cGFhGwtWDWHVJNQlQaSQVYUlBTcNLGPD/kGcakRcctFK4iyaxAgVyhI4tSrf3vFZvvC+bzK1vS1hTUiT8XKATqeQ0mNaRZzbvQdpCoSMiTdZqFoT3YgS1uF4FeH5iKqHSLvEQSK9WLp6Dz8aOox+d4pb2zfRs7yONg0WrZqggUlTG7zgc5t5xtt3ccbHhqitKPDjTSfyoz3H86tfHIns0bT2NWnEWaiYxL0R6ZTg3h648MWDXP7VjZzQvY+7Ni3hg2f8hjevuI1PXHQ99tYpTrvqWQw0A0pxwHiUoXynzcDWDMIPiaousSloRlkQGqnBUGBakkzNJrdJU7+0l0kjxq3a5AYMihslHzvtpwgBccZCS0BAY6FL84QlNFyHnmU1pG3R2esRfMCnsaiVifPnUlrbj2k6CCExihpha5wdKZa3TLKgME1/toI2JVM6zf5mC8ccO4YsVRHlCs5YhWx7mDA6HRsdi4QJozXNoIkWmmh+J0YEyrGIZUSUtomyLsZEmcgxE9kRoDM2OuuiHGtGfpEwILAt7DoIpZAICCKcSkRq0EMGGgOJrWPiTOKFE5YMaiuyRBbIKEm/MmIgZdNc3A5KJUa1QYDVFIhCASJFFIdo32dp+xgv6r4fM45xvYgoC0HKIAxMZKSRocadrCXtOIoYgYoFmayH0WNC24xcq9kEHYPQWGUfGcaovAWmgNFJKnICrWOQAiMTIhxmYns11uBk4msDSM/H2jeBPd6gviDC643o/9wYXS8aQIzFEChAoctVrAMlgrxJkDOIXUnkJPIn1Z0jlY4BMAzBG79zD2YpRJkGMoiJUgKhYiwnjQxipB/gjjeJgKBg4c3NJEazpkGlYCCiAGf3OM6eMbQGsxpiBDPyIDTCdREaRBijoxiVSgys85vGMdyEpaQFiUdKGCT3T5RcF00S+SyGJzEmSwl7RIBoNsHzk4ErVgilEJaJcF2yGDj3HyDOJ7HsAGiwEKT7p8k8R2A/Mwu2gUbznWveiEInMc4IUqZNavM02d9vRuiICJX47iBo2w3uhODnf3wIiUo8X/4speeZL1kLloUUUAW0IdEZaHYJLj354wDc9OM7+d5HfolSTzSQ/3sx0Rik5s+mR85iFo95zf53+PsXx//uGfIsnhSNus/I4DSjgyVU/L/gm/AX0BhS+GMp5OQ/lsBlNCNmfiODhuWH9f1Dj/cPQ9qBfJbIPNhbp4nAD2L275tk4+QPiZpfZKL6Hqr+xGPvcWe8W6RlsnvDHqZGSmy58/HJrRAC016NNNr/ajdeesLFXPTpDC/5whxu/vJNXCF2QU5AXhG78V/ct7U9hzRNhCGxDEFrTxHimCifJlQWzUqK8190XKJ68zxs104m14fAnHQ3Vxw1zOePHYVxAyvbxMr5WJkY4dosXNZFT287cRRRm6+pz9VYTYU2BEIJXJxDthtnwW8Fv10wVcrQnaqQNz16bq5xprmeBflhHl63N4mzTaceMzHUYQhKI5BJvG3Tx9lbRrQqyKTQrglBgDIkB7YMsdgf4dyWR/jd1KKDjp830ljDFtaQRbkWPvb6i951MSect4ryeBkaAToKCAsG1gkxj/g9VKMKpZNNKodbrDjHoL//et7+rEspBV3obJrVJy1NzCqlQVRtEFQamIco5lYK48jNuxk/IYc7KZhk8glR7ZXBAKp1qDZQAehGE+KIyrBkYW6SOekyPfkSm25bgvn7DAM/elx29vxL12DM8ygsKfO5R/4EQNYUiIaPbAYJ3d6WMKlJZX1+e8tPENlkhR2l+f5nr0fes5ta3qPRbSKEoK9/HRectpeb3v5xXCNNFBlM72nDCME4tYRoDQkdsEuJp8CWsffTd+U0S784zOFDI1Sn8lSPjqgdGdBzUY0/nTbK7664jTt+syGJunVsfFVBKsU333QaRXMRb/3pc9Aaik4VmrVEIhFGj/9ZFqQcRDZN/xGLeOV7n8ZRz1jKWZ27WexMsKfUih86YEpME4o0cETA7zevYu0Lx6nns2BpvNE8W7YuIN5jsvSoCWKliUyX/BaT4p02bzn3JDQxjh0g0aR1k4Z2sXWM0Jq80Ky5exfd7x1hz+2d9AiXzMN5rv3ycQgnkTkqrYhyijAtWPvcm0BqlJmkVikBqf0+/Vdvp7nSIcwJvE5BLDWrO6b54I2/o6+/DAp0BH6XSaoiCI6fz8apuRi5BrlilXcddSNh3k68lFIGpEx0w6MZtFEPu2hWJS4RGkH1ZsVXv3kKVhyy1t1GKbJnpDwCHUXEvoHIOoiUi7ZFUqRQMakdoxhTAcqWBMTseVsvlW93Ii+fTKJ/lUJ0thPbJl5Wgp88GyI3+R5oy0BGMUY9hLSLKuQgUuQ2VtCuncg9JMQWeEEiz5XNEAKNCMHyNbIeI1SMCDRKg7cik6Q5CUGqGwqfbZB7bhmCCHd4CrtV8cbe+zgzP8hLeh7E25rBrguUEAQFTVhwiNMWfneSdtNsdTGnTGTJwEZRL6WJ+tsJe4u40iPOC6K8QCMQQZxEAgcxzZSksG4SmiFaSsK6jbLSSYpQysWwraSIZFloIZBBROMIm+6+afLdNWhvQbgplGMhl4L9bDVj3NpARwHKAmegjm4t4HcaxGnJ255xCiM+bG6myGRD3vXt64njBlqBVYkwPIXhqST+vRlh1Jp0XrefkTP68eZlKOzxcCqK1GQVGUu0Y9MsphAavLku2GbS50wqKWakU2jHTooOYSKR0gIIY4QfIDIpMGQSm2zb6ChG2ibCSFiEVqwQYyVUvYEIQlS9CbkMIpNGpFyUlEQph9AQaKXIDTWQjZminpSPTXDUDrCyCtImyhbEtuCVZ34C4SVMQxFptGsisy7CtSj+cTvmNeuTayUE2kjMsa+6aSP5ziI6VgT64LE66soTPmrGXhMQSYq3TqEQePUmn33lN/nJJ6/mp1f85qD9/n3L23jKzW/gCw9/4pDPv/+KN33j89y692Ku3/kUyt7jxZRyYxvV+oa/qY1ZzGIWs/h7MVtImcUhUas2aVSbVMsNAj/86zv8C0OMxaRGJLnS32f++vciagRoSyQGezKJM/2fCMOxUCpKzEL/DC3FFDKM2Hzndq7/6AGMUJOWIbZ8vOCiYo20LYQU3PqLu1Fek8Z07f+sH0aGt5/2Tl5w/KtYvXblQZVxM/PXh74bNnyUGx/6ONdv+Dgbbt6EX/MwVIw7rnHHNM1ShDYNhGGQKmQoTVQP2Y408pw09xucNPfr/PbuT5BSKaTUqMggzKZZe+nxyRuLaQoLSrTOn8Twm4i0RjoRafvQ94HT5RG5EKfg9LbNTDRTpGWFY07ZxxvfeRdv/cLd7Ng0iGhpAQRmMYMKwpmVXfWY95FIOwgzIus0GX7GPHSsUMUsKgz4xtt/wH++5XA+9+Ez2PHIwVKzguUiQomIBF7liWNEpi2LaVnIFpf6ym7OOm4vPUaJY9ydXP6yKs971X6ef8TJMx/GBMdC2ZKznnFMUkeRIHwfEQYs/C8JTlEcY3p5UIrI0oRpQdToeEIfLnndORAHKD9ZrZW5DEY+j9HvsjqzjxW5YeY5k2SHHF74rj/yxst/z+7xQQAuPOZw1i7YyvuW30R7Z5Jm9IM73zNz7hJZRJyWhKGBtyvLum3tnHDKQ0nBamZlX8Qa6cVU5hy82moaDsfMuZl3/OdljEy2IxDo+1oxl9UQ9RAzrrOqbxMtYpBmfwuNZe30VcYw6y7HHbWNw1btZWFnGZnLIVLJpAsh0FrT/bwxnJURS19o0p05mWtesYPfnvIc1j3rcwRumsStVSY+DWGYrHTrhH01WZ7injv/g+0PPMTVJyzixt2LeGn/rdREFroclj2rTMbU7Knl2bcgyw17n82d3ymSv36U9IjAHZG4BwJubjmMsek8RhNUShJnBGvmzuUF8x/mmu/P5ysfWsWDf2hFxvCeb78QOzBxpckVN9yDlpLf/aibjRvbuO6n85KVfJXIG9JOnX8740Zec8qt3P7gMegzJ1l82D6Ik2wtY6qC/YCisNPDriqsJizq3sWEznKE6/GNf78Br1VTnyMQgSIixm916HhzldaFU6gs3PidI3EqMamRkPy6KbQwCHqLxGmLZpdmd28/3//1Gez+VAvxf7jEruBNDz2L1+16Li+99fmJLDyK0VFEM8wAFv5hCoSFDiOIFe5kA1MrghaLqCeN3RbwtmU303d4k8LVPq+/9wF6vriH11x1HS/71u8w7WFUEOKM18EPoO6hEcSWAc0wuaaTJUSpiizVaRYN4rSFsizUqIVRbWLUQ8xJD1sJooyBUVfISCBMieFpzFQ8E3utqb/NZcc1K9g+sozRxRmIIChXeKjRwq5mivtH+rCqKUxPkxrTmBWNVQ8xagF0tDJxyUpqZ3WT7a+S9kOqd7aRvZHkS20YaEeSTjUwumO0JZJibz0iSDk0j+pGGiakbIIWmwPP7SfGhFwWkXERWQupmYmTTorDHasaLGkfZ3HHGM6Uj+5qJy6kUXVB3OqgFtqERRsZgQwhdcBDkzA/tNRMjrbxhlVn8PD1XXSYZfpSJea9a4Dw5GlCE2SsEFKAkIkXStpFAD2/342wDSZXpvFaJH5XBnuwBIakfmQbQ6sFUUeaoafNR0chulpHBUFSyNYagUAYoLMOlZUtyWeSBiKTRvd24p2wiHB5L8KZeUaqmYeYUhDNsD8sC9sLaXYVUBmXsDWLdi1wTRor2gnnteG3F4EZpkoUJSwWKWGXxeRnFM7+EYzNg9jlECPSGI0QOV0lKjpJsaQZYvR1gVLkDYk2FDotUI5EVBV+TrLlzG58V6ID/6CxbsWFy1CGoD43Rf6BCDllYBfSWOmkOCpUDCrG+C8phZsqQ7x5wR/Iy4MLLP8VWmsa/lZ2jw4l8dUC/Ch5Hv1x3wt5ZOh8Tv/lzzjhu+/+i+3MYhazmMX/CWYLKbM4JPbtGCZu+ES1JtVS45/dnX8qUgcU6WFNYfofa7rbOSePSHwTZ/7zPxOFtgxSGkgp+fWPbn/s9f7+NmQcURmvUBvK0+9+lCO7foBjFR57z5rzjkQIwYLD+znpomPBtHCeJCr5b4U0JPMPn4ssz6QgSMXGF73+72qj6QWApiUYxNaaYrvGbwZJe4bkuW85j2J77kn3F8JGiOTH8NGtffglm9g3GDvLZsEM80jadd6/8jY+eNifCE+NwQAVS9L2oYfp1cUG6RFwJ+C+LSs5uXsfEw/PofvIBm0pwbxigAx9qFXRjQax5SA6W8G0EKkZlosUCGliT8YU2qr0tUyiezLErVlURwvl8QrxoCS6VdK85WCGUco0k4QYA+rhwYUUpRS16XGEthC+SWbLFMObCix1p0hLePbij/PihZfyyNAZPLD/HLQUSQzpRInPv+IbrD11EW969wVYpqR/cRfZ/MHyJtMwWPusE0GAVBozAFlsecI5mrdsDqpRRdcbEEXk8ynSxQxLLjySLrPKyamdrEnv5xUv2MMZrbs4Ir+fWvjz5NRIyYu7tnBqZpSXdz8IQC6X4x1fuCwpPAApw8XalMYas7Btzc49XXgL2lC+j2g0EYYk7aXJzxRhDwyewMjAAjYOXozSVSylMH0wAhABeBss5ocDRK/T+D1pSl6O3AumMJ9Wp+UYD9ePYLfLSsbYdd18VN5BmImkgyjGMhT+UYKBy4pcx1zM7FtxCu/DdE5j+9h2rFIAhplIvZRKElJm/kXH+Nph+4Y2vD1p2haWeVAdxmH5KiJSWFGMk4fvTRzBn5qLeOdRL+elJ76BwZ8U6brNp7AtpHVryKrzR3jBazYyvDOFW9JkhmKcccX60j4+d8d5RIZgZDALpklME2UKpDawpcY2wHRg4+15PvDhc1i/bgFxNkW0qI9wfjdHNEY5Ib+fM9t30bKoxKLKKLt3dXHua29CCgj72mis7MFf3IYMIRYhr3j5/dyyYTGxhkjD0nv30XF/gzk3TiHqAZ0XDzN37yhjN5nsvjbFvntacFWTVDUm43iIOMYYbzBxfBfhGzJcct7dzDlimAm3ALk0SpvYDkxVHXTWwnMeLVbqpHAiBGFGMX3M3MR/ZIb1EVdruENNjEqEc5vHen8RC51xHNNASs3Le9dhiYhTU6N8/4e/ReoqIlTocgWNRrkGQgq0ZSAiBSkXYUh65pX4xr/9gre88joWf2ojc740jC8DgtY0UdoBErNrtxxj1hVmLUYZmuyuZlLosG1qQzn8NkmQ0oSXZhk4rcjST9W5r7aQLw+fwo1756NRxDIid8d+eq88QGzGaFMS2RJtCAprSizuG6F45DRzfz5MbmsFpuuYVZ90l8XyRSPM3XKAeLJEaZmB12+jOhxsT6G68ujWLKPnz0FlHUbO7iSYniKaLOH7YRKJrhRYSXpRxzKPVrNGyojANAm6bZpH5KgUiwyN9yNflaW8dgFBV4owDcKUuFsmcAbrOGPeTNx6zHXv7OURbw4b631smezhw8+9BhHGibGvKdHoRBJnmZimQ3ZPjbZf7sJ8uI5Vj3GrGlnzkF5I9oBP2yMB2tQEEkrLckRpSWwbCes0iNCOiTIMZAziUZKkYSBcF5VyEI6L9MJkcSWK0Sp+vJgiZxxppQTLwghC8JoY5TraMhFBSH5PmSAbYdkW2rKSccs0EwN1P4BYEWwwmSgXMCbLyPXb0OUqse8T97ciXQeVTaGFgL2DCWM2jGi9cSfCC5GRThh0TYH0JI3je2npOngc/szbLqHenUifWh+u49QMtCFQaZsH/7iZlp4i/ct6WHvZSQftd1guxpYx3faT+48BjE59isr4uXz+Zb/ghocO47rfP4vOXCKLrgQjfHn7WrLfcsh9yeDciz72F9uaxSz+VSBIUs/+6X//7BPx/wCzhZRZHBK//dm9yQq+Umxct/Of3Z1/GpRKVmuteozl/mOlPVf+5h0YSiEjzW9uf9c/9Fj/SDz/9WcnP/q04tufvu6x1xuVBlEzICxXOeLYeSyd9yxaUscftO9rrngBv5n+Ll+986O89COX8eNdX+aqkW//X/VnZM8Yv/ry9az96RSrP76Lp/2iiW3+fYlIF7/qbHoWdLGyPo///MiZXHP5C+nqzier+YZk3vI5T5CVPBk+dNxTEFail68fXkzSDYCuaJxTM0OclB6hr+ahVWJ0mJWH9khZeGA50tc4NY2/t8AffrGGYF+a17/kDZjCIWXm+e6Gj7DosF5e99kXELWkkwmz70PThzhGC1BpB9IOXauG2Lcwhb08prJW01gJqZYstnSJiTlp0ZEHHT/l2DMGpIqa/1/iLadfyfK5r6D3zCpYPsoW7K924aKxCYnjgLtH3shwZLK3MUq+O5s8+Zs+Kop55O7t3P2bBwm9APkkkqlPvPXpYJqExYigqCilDx2xadlJEgmmRd/CDPlCimVdC7mpupz5VoO5hs+Jc3+HRzsRaeYVj3ts35b0Gv6ztJgHG49PDk59ytGgY6RlEjcCivY0QgoOtOcY3dWHtCz83jyGJYn6JPFTyvRYGeJoFFeN4gBW/DCWNYf3XtLg0WAZIRTFkRxTCzo4fPEoc7IVRtzzuXj1/TzzhLvpPnyKj71tEZlH1pAdvYD+E2pYq02O+NQ0haURRBGZNslgkKfiO+wup/nj/gmEuZw7ht/NbVOvwB6tJ/1b3IFAIywL7fkQRRDEqCgmigSN+Rq7EKO0QBqQ6/QShlQ6YGlmmNW5/Vw79GWuuGc9hA1EFJMaaZIZ8HjdGdvpT09jhS71TgOvXRL7Hu+/6k4emOijfflkMiH0mnRevQf3D/t557lHUq2YbF1XJPIUIpXBnKijXIvYlIgZX5PqBpeRMMOOZpHPz3szY2/36L70AH+YPJLDL7oX1SZozGsjxmS6MyB/WJU/DXRyyepNTMWaa6ZbeMdnriWzr4YRK6RlIkuK3VvaUNqASOONmYSBhTKhkjdRfoDZ9On53W6esmITZ7Rv5ykLHia4w0RoKN7pUinbxKGNDmH/R5aiZ9KAtOeh6nW0ngMm1Ofk0Q0PrWKe947becsVv2a5uZPOdSaZzTUakcW4n6LDrPOLkeUoTAIRc8tUC/nPTNMoxIhQIacqiIaPqHvImoeuVBLfpzDkTR+/jwXdY0THeqwsTPH6uXfxvB8/wPHP24teYSW+TM0YO4jwOiRel4GyJJWODF+/8RpWn7kXebsEU7P4qdt512m/xF/Qwiktw7y6awOXdWwh/5YG3b/YTtdPtiINgZQSe38FfB+dSkxSjygOcVTLAMuyM95iQQhehLIs9IMB2X1N5G1VzLFpKot0kupkQ3pLmbiQQmVdDCO5DpRijDBCaFCEyc0ABK1pyqfPRaRi9jTaSAkfZQqmT7CYujDH1AVdRMJE3ZUns36cMKWIMoLQgZSnE8lTuYlQIG0HKW2u/3IvP9x1LK9ccgelMI1tWIk57IyviQiipKAVBsn4PODTPljDHvcwJzxiqakf3oWUFtkxcHaXmfunSVIHKpjSxnRTiSxGRUTFFLIRkto4Qm7DBCKVQmRS6KaHMTqFmKxiTlQQppUU4Gbkso+m+xHH6EoV7fvIgXFULk3Y1ULYlkvMX5VGBCCqdYSfLAYIx6Z5WHdyf5oSHYaoSWa2OWAayGoDZUoiV6INUNUy0o8SfxYr8XtqufphlCUQrsS+ZTsdP9tAfuMU08OTxPHj0tlM2iW/eZLcrgrmgSnym5tgWcRlj49c9kUmh8s42Qyd/QfLdV+74sd0597ISXN+cMhx/VH4zZ+RFpKi1Jx51ADff9vjzJMz+7/FCe2TONWQZtam5sP4eOkvtjeLWcxiFn8PZgspszgkMjkXYRoIy6Kl9clX2v/Vsbc0TpgWKFOQWfyPd5i/8cGPctPGj5JOH3ry/D8BT33WCZB44BE3EuO50YFpepb1EHhNpgcn+Nknf/Wk+1v24wWrjr42HMd60vf+Leic286plxzP2stOJuWaTA9PEzQPPen+S21896Er+NyN72Nl69G0ul0sXzUXlEL4Idf/8r6/ua35LW3oKRNds9De47KdXE/EHV6Re7082TtjnHSA7YZ0Pgkjp78rkUbEpsD2wZ1WREc2MJy1lIzlTBmHk28p8vU7P8LTX3sutfl56kvb8Oa3oVWSGkJ/B81VEJ6j6AwmefPxfyB31iTPv+QWnvaG+zjh2YfhOA5MNRncMXnQ8Q0pSbVaGBmDnrb/MkaoCQQxubAJAUk0q3DISIElNNPlr3FGrsZp6TppI+b9n3ke2jZgXhfzlvfykg9eio5jpClZsPJgWc+fI5tz0aYiatH0Hzv3kO+JokR+JYCWjjqmFLTF7Tyt7+X4CJoIPBUzp+1K3v6ry3jWt+98bCLwiJfnhMwAS939lJvJhNBxk1QibUi01rgLQqIFPrLfI847eB0O/sIOTrpmgPdc+yDfPfVnnHRGnenqLxlRMXvjmJGgHSEE5x37Gd58wYlkgdz1OxF+QKPkcPv+hdx5YAENXcOxI4pOjV2yg1MXXIw/cQeth/2Qo1duomsCRu7rYcqSLL5qklO+t5Ew0ASRSSwFr77pWvbWHub7e0psqMzDOEzg25rG4jZwXVQ6BUIj6n7i/+BFkLXoO6JG2XNY/ctH+PIX1lAt52k2HUrbu4gR+Mrkth2d/GDzRkLThpSBWWlglBr09tbJiRjlSJpdmkYvZJsRINAK7jtvKTqMk1QTKWk/4DE1kueVx6+llnsxMpNBOjYySIpDRqwwphoYjRAR+OwbzvCrDx3NG970Q1KFPNEzGnS/pcxJToNr338lH7r8WvxLqrAg4vzFW3lGz34WpGJ6TcmJ2Wn6O2KijE2ccVApi7Z8BUNIhGmgWxxiI4W2BBGS7uZowhbIpMC2MAYkU/Uc22/uwYojMvkKfktM5u48lhFiGBomLWpfzqPmddF6qstZ39uLqJsYdY1/2JzEeBboXONjFGNWXbQfY7TEnZ89mm3Pm4P1qoAPnXEmu54/hy+/cA1Cab43eAIvnLuBf//Dzbzrhj8hpED4YSLRQBM4LnHGQVsW997UQdg0KNZ8LBkDmtOyI7zhglt4xUtvAy2IwwjhBShTEFuJJVHDNWjJhLzwA5uQfkzrHYOsLA7xH4OnYwSCfeMtxFoyXsombAitkel04m9iGISuJHI0XkajbMhEARYx6X0hODakXcxyhShjEiuLvmKFlsPBm9+C5RvYFYGhTWSssSZ9lG0SZQ20hGaHSdjXSemkTkYuWwiPmqFGEeVlcPfWw9i0bgHbptpAKNxhA2cAzAlBenuEazfBiOj45VbcbWPUFthEKkCGAm+OyejTu5M4dcdm4qp2nFf73P66+XzpjRchohnfoyiCpo+ue+hqbSalRiQSP8ciaskgbRvd00HU6iYeNSlB+1aBLuRJmUXUjD0JRuKjggCjGZLdOo05UkLX6+imD6aJVhrTj9GmmYzVSiUx7K6dePA8KtFUCmGaWFpglD2c/eMwUUrGvryLbOkk9ptJf00Looj0WED1sG6ilhTe0iLVrsJB0lehNbH2idKSMCMwB6cSKeCMfBCvmTyz1o3R85Mt5CYTuZIxXSNoK/LZ137noDHYnKxgDk1hCEFmMsSseDBRRhbyyEKePY8MUJ46WBorpcVh7W+kJb36Scd/AMO6mEmlGIoNJqKDF2by7gLetOYatClodlmkxjyeff4Vf7G9WcxiFrP4ezBbSJnFIfHhL7+Qzu48i5d2ccpZR/yzu/NPg2uV8FZGVA+H55w555/dnf8xEHEifVCW5MDeIV5y1qf41oev5lHzC6/x9xUy/m9gmAaLjpxHZ38bKlJEQfTXd3qSdv6cdXLcGctnmB2KG7/7J6LoLxvY/jkEBqJhIiw478YPAFArZJgI0wwFWQ4U8kShgYoNulOHLuCJGZsTGYKQGu/UKqe33kndf4RbSmP8YXofZe8BYIZZZUqCooNz4kJuCn7M93auY+1z9rLmuQ8jl3rUUj08vf+7OP11claTYrrBzRP3UR2aRgQRO7cMPKEPX7zoqfzbaadw4WHLD97Q+m3Koo8D99joRoy7e5INNy+goWICrdk88XM0Cgl02Mfw3R/cjk47RC15XvOd13D6M49nYPswUinmrTg4vWrVSz7Pypd8np/+4X5+PfItFvYWMFMwb27xkOdp/mFzEkp7HHLECwa56BVrOfaslZzW2s/6Rg8lZbLZ7+DLt99FfJNJ7Y+CL37/JgCOLJ5Jm+HTbjYxZYVrdlzPcVe/i55vTibXPoh4/1kXcfKKXbx2+a2c/pqbufTyq1CmoCcf0utUyBoBZ/U32Dz5dboMg3ZpcPqCxyVvrzjvBJ7hZzh+YU+S6tKQBL5JHJqsG9nNCmeUtKkoGhrXMkilFbYVkTI9hBezSLYy+Jo5rCocoN+dYkl2EmIgTNI+7t1X4557FnPHusN45uUvJjxuCa5voF0HnbJR2TQ6n0Hn0glTJDA48IcCZ39vDHd3kfL3BF5DokOYHihwXKUdFX+Q0UYHwoxRKQPv0rbEb8KA/Rqu3HUcrXOmUYUYu73GovkjYCvQEEw5VJcmzAitFDqKESkHYRqsPfH1HPXUARaunsA4xic0IGrNELZn0WHEYMcCfvyxcxkb7kEi8Q7vR7R1EMk8td+nKTdS7JjoxK9bmAXFvWP93DExj5fefR4KzYjvcPPoXFbO2430Ysyyj/xNCJtBpDPotEQut0CamNWI3vZKkjITx/Bqxff2n8rH1p9P9dcpopTJWKFAs90hdiHc5sKeHDIX4/RovG7JRV/fQt/RIae9eB12GJOaDtFzNcK0WP+tbsbvSnHfl1rIXQRhf4FKros4yCFkDhFqTFHk2585gYW5EkudMotswfLuOpe9c1PCNohASwN/aSdRW5bgsHn8+puH8eLjz+btH342P3/lifzHD0/j/t/MJVYGu+IiMlYgFPHIOLIUYdXBrIK2NBUluGF6Pq4dYMU2D146hzX5XUgkP//lSTzvK8/nh284AoRAGAYqDFG2xXifwfRpPVSObCPsyZB6uMaKwgBtVo0eOYGYLCFKFYTSNNMh8TyLbrPMsZdNMHl6Nyo2iB1B5IrEzDUSqM4GqWXDdPxpKy3rh9FteZqdOaKUQ2V+Fq0UxmSDsM1ENCXYMPpIEblzCHe7T/EOjahFdNWHsHcNgamxx5sU7p+gdnSGsYs7Ka00GFubobIyx/jZbUT5FHHKQFU8avtcooyFUDOJOwKwLfSBIfTQaFJUUHHCHvF9go40lcPbMKYb2NUYI9QYniJudYmyBlHeYvAp3UwsM9Fh4m+im01U0EQHAXgz/0qBKmYIO7PE2TTCMtG2hZrxNMI08VodRCaTFFxcBy0EUWs2GT/SDs7wNKJUx9wzCpv3YFSbPGogIqSB8Hzye0o0V3cz8cxezFria/Kov4kGZDNGKI0xMI3MZJIiThgSu07ChFEx5ngFWSxgFPKPeTSJmket/Lgcx2+GnP+yU9GNJioKOXf1/JlClH7MM8dMu2y+a9sTxu6oeQPNqZcQBwcvVHxr20u4eucqvn3nM3j7q1Ls8ToYabazrnxoP7Hf3PlecnvrmLWEsfTJD//ikO+bxSz+ZfDPTur5X5Ta84/VKszifywc1+aHv/u3f3Y3/unoyS7iu8+FSMecMue4v77DLABIt6RoTNQRhuRVF34xoSJnM4hSFYRgyTGL/mob/39hYPsgH3/uF5Lkkt52Uln3b5bh/CVksukZR9SkraHdY8xd2vM37futS87lVbdfgyEV5SBZiUv5y9hzxySNms1kVwoxE618RMuhY7CdlJXQuyVoJWFnkXBVOyF5BsIikZYM+GMU03D/lr3IUGOEivkLugDYE7bw7Bc9yIU/fAkXn7yFeT3jfOz3H2f7wAL2FvtRuzS57w0hY9CGxG97ohzqtPkLOG3+gie8HkWjVMP19C5cySOjHagwZGgqS04cgyU2o5xB9jY1g3Eez5D09RR5aNMBtBTcdvMmVq+aix8qpOtwxKkrHmt35Us/hzRAmPCpH9/Oc846juKcImJ6P1unDh17+fV7P8ln33U2Z64d5/e/6iOc3sw5zz2JuwZfycapDtanehnxity1ex89dbArDX7/83W8/eXns6jlAsbNT2MIi4y9hIfrb+ItK8r8dnQZl//qd3z71c/mlKPOoLj/BWjg+JzNXaUe+pYNMC+c4rrxpSxIl8H4Dl1WjAImwl76jceTmIQQvOlLL6FR9XjKh76CWZIQamqRRZSL+dzeszm9fSsfXHklhmEwel+BY98yyiN3tTE93KTQ0sVSayfXDqxicWGcYS9D20MhlW4HZ0TwrStvwV2ehbzLBU8/gp986k9gCOTiNtSIh+GFaCNK/HI0pE2LIy4Yodcusb61iRYzkoZ6xN7deTasP4E1572f5x8xn5/uW82eF3aS0gXiwxVhRtAXS76z7E5e/sYzaVsO0UabiTNs5GCFBTckZqbOR2D4+oW49w0T9BXI33WAjpzLXevfzas+9BAGmqunFnDlZ+aidQqtFRnfp9lq0iwYtKyrolTiVzGxppPCngZ/vKaT/tc9i+/s34KQGseO2F5vZ/umbt606iY2Nl0+fvVFDIx0Ex9VZuGDiYn1xP1d9PePMVhySIVw8TH3sfr5A7zjNS/hkXI7BCGyWGC314lVNYimTCpDI9j1KtakoNHi4EYWLZsF1tv3Y0iIlKb8WogQmGgWHz/Ebb820X1Vhl/Tw5xXjHH3t7uQZi/CMolv1+jFBrqQQg9WHrs39EjEw7cto3pvRO1nD1AyAr4xdARjJ+cRV1qAmWjhoxhzuAwpizDrIIII3ZSo0GbgV7183+zli3ecThQJWvaPJylAQmPdu5fg7MXEaaAJz9pwGcW4jtgRYhMjIotr7z6J8MgIsyRoNh28F7USFV2iXyu8xV0QadJDIVHKInCt5HezjLi1uRzPs5ibmiDOppDNAJHPYtU1oXQY3ZGjMZJCSgPRhCADZk0hXJswZePsqBNP+FgjAaFZwppu0rMtIthQQ5a95HNbFtaERMQQtEDr5/YCBqIZoltd2u4dJ9hfIYg0cp4ibkkT513MqkbbJlrFCCERQlJb4NL2uxEwJSKTRgkLs+6jRPK8iG0bHAu7kJ2JGhZJ5Hgqg28KJtckXl+RVaSnpcSenjYwBB0PVpCRJnIN0mOK9u0haIVSCmugNiOri2aKChLd2YoYL2M2GsSBQmkjkTVl0uiGR31Oiuk1rWSuHUbkcwQ5m+biDioLoe2PMVYpnPE80gjbJjXtoY1EboVpJMWOMElEEpGHzgniLgNhmmitKZ1/GPmbt6JsgV+UtN00ig4CRCqFQhPP70IcGEuMd1ty4CWSHwSIQg7RmuOeO3Zy7POuQGlBr4DGcMC8px3Pd3/waoYPjHHrrzeAUDPnPynAtHU/0eMq8reyq7abFv175rStAWCqth/BdmwZ85N3HYWMQz5475mke3xsbKJ4gl2TXyZu/oJM5qXMa3snqVSK489awU3b9lCdn+Zbci8//Ojn2PL+tx/ymTGLWcxiFn8rZhkps5jFX4AQJifOeTen9r4bIWbrjn8rvvrLNyf0cwRh2SM2BNqU/Gz46/xk79f42l0f/X/WF9O2cNIOqVyKV3zsMt74xZeQ/r80sH0Uha4kOQat/uYiCsA5846ks22KjpYyoTb51vrb+fef/Rsff/WvmHvyU1h5sk1ctolqNi3OoWVekU5WSrUAiSY75XHx6lFSVhcZ2aTbqlAOk8nilXdvQpuCOGVwymlLAehzj2GPbiG33mbP1h6GplvZt6mdM0/czKiXY6LTQk3px34k033oGOZDwTBXIEQ77f2Jwa+WEr/TxTanQPq0m4qyNpFELC88jXPPOQJZizAqAQ+s38e2B3YzeWCcoNagOpkUmv7jmtsxpEaGyUr8hScln8OrQFg3GB5tHrovhsGCV89jvNPkzp/38sBNm7jpx3fRKid4+pxH+PnDx3D7nkX4oY014RGZEI88brDdkXs2rdlnAHBp6z5OTU+ytm0v7R0BVz6QmBeWgwIOgh31LgbKDpc9/U4uv/sptOcqdFkllqfqLHI8docOG/xn0wh2EMbTjx3DzTi0dhe5aM2xrDxpmJ+ceB1vPew+inbAxp0LuPHAqViWC4Bfdbn+66tYd20/zZLH4CMHCN6Vo/jvPrnvnsWWwfnUV0V03Kso7FfI8Qat1+wid/8UH9z9WrQpEUrT0t2GiGN0w0MojUQitKbuhWy+t5utfi+Hv2+K1OUSXQ0QtSZjO21G9RaELnN61y6iugVtEr8dKossqoske4fT/OCrCxmvdOJvyBHHDrVrTRZeHSYxrkHM0msqnHzUJvxj+ol683jPPJz0WUv51TeqOEJjCdg+1I1oOBj1GBFBeUEabWi0LWj2pIhaU4SuAYbJVLeBjocZ01/kjJ7tFFMNXr/0T1haIeKAYmudh/1WPvSsm3jjSx7i0ydd+6jNBtq2eMY7d9G9tIyODX7zn0t5+OpelFA0SllQmqjWoOePZbrumMaejpH1JjKOEX5EejTEHQ0wKzGNj7QwUs4wXsmRcUJSNAkUzLGqRA7Y11aI7mxnYG0P2DbCdZIV+WYMUYycbiAcE5FyEa6LYRhoEZNtmnzgqZdyYFeW/VErVyy4l0JrYtSrHImzbRij7iFHpzEnysh6k3QsCHrSDB/bwtQxrQRtDsUtZayxGvZkA0yTrLCQjQjm8NiJAADfeUlEQVRpCpxI0vLKOvJNNtKw0AqQkswBjeMpsrsgjE3esuIurjvrSk5Zup38AUVuWKMcE92IMb0wifNtc1mT38f5rY+w0+tl8CkdqLZ8IqeKJR25IToXV+g7fhqzLhA+uNMaU0FsSYyah4g00VyYvLiViUs7qSxxElKIF2MGib9PbUkbIhCgBPZASCa2CNsyOHuGcEaHsYbLEAm0MtGGiVGPMCpN5n53hK6rDtD2h1GMkkQ0DDKP1JCxxgjjJErctnDRYAmUJUHFCKXwO3KJyXgUJYatpRp+Xw53MsYuxcw7YwSvlkHMMEDMchOjHmBWfEwvRtlGIotq+JBLJ4WT9lZksYBsKaDCxGleaIFybFAxca0B9To4FqUL5xG05th96Vy8rjRiuoa1dwy/I8XEKV3EC7uJe1ofK/JrrRFx0ne8JrrZRFszkdiBCTWBMZmM80JA8aFxov5uGkWT2AHt+ehIob0mIpvGDgRCSJRroGyDqeUZJk5op3nyMlQ2lSwsmAb2SA0hNMOxQtRDJgaT8a6nv5MXf+Ap+EKjazV0tUZY9Vi46vHo+UexsdHgu5OdfGn/xsfklg9PXM7GSg+33roQJQVaQ2NfmrFakTiss2X4aXjeT2mVVer1/3isrU9e8QJaFuQJOhVIgbO9ydEv+Pjf/EybxSxmMYtDYbaQMotZzOL/d/TO70ADUa2O0CKJ66w3aGnJ09bzxJWnfyQ657Zz/kvXcuGrz+H4pxzF0qOfyKD4P8XP13+S3w9/jd8Pfe3v3vdVPc+i6qXxqmm+vuuGx15/6wkvptNZhBaAEvRmC4fc30g5iCDGqmtkE+7/9/fytOW34sgMeTPAkIqUSCjW2x4ZoNYrqfYKLj09MY2t+zdzcqrGhZc8wI4He7jhK8dxzHkbeWBkPvGeFPauLBqZJDbEitc+446/+bMZhklrx01s3tWVUMltm6DVQuIiERhCkpOKhfIHHN72DI5Y1Y+2Bdo1GR+rsujIeSxcvYB5K+dy5NrDAfjqT+9BBolszBDwsZdfCECu6SBKNl7pyaVVz17yfc444pcsWNFLa1eBEy84EowsEg1DNnoghVGX6DaX5sIC3rICwYwETKk6W4afx46xt2Gbi9AYHOceIGed9Vj7Jy/ezNb4x7TLES6atwftSxb0eqywqixJK1qNGFAoLZgrfszO0aezY+TCx/YvjVXYev8u3vvS88lkQhpNBzuI2FEXxGnFllIapZIJlk7Z/OmBwyjlQKfTVEKN9iPGH8iyZEkvx83Zz9L+CbLr95F+aBCz6iUThx3jfGzeHcggRiMojdd539cuA8tEBSE6CCGKEP0Nhs5po4cqvS0VxPEpZOQnK/WeYsQrsqHZxsNeGzR9lr5/L8IKqS+MCVoi3nfRYq75RhEjUJiVABHGvPmXc/nD9iu4ZfOnuPmRT7P1ni42fbZ9JkEFmiJi/+Zhtq2fw2c+cDqfe+dxTOw3SE0GONMBhq/oO3eUJRfupqc4hd1u0+yxCQsmQgrMdJrtzzmRz912AZf0ruPbx/yE4wr7uPucX/K983/EaneYOUaZQc9kvgG//eGJaNdCZ9NgGnz2Paew15iPUQgRpmZQW5huSK4/QBgCrWNSQ1XcgQrZbRW010AFEfgBRjOmY8EocdrEzqXIB03mt07j2j4bx3o43Jkg64eEGYGrYd4v9tK/rob2A1Qco5XimV98iLMX38Oxz9xCtjdESY02ZvwoHDfxpGj6vOfF52HpEAN42sWbCVtcItNEzSSlCCkgkyJcNAcLg8yIR8u2Ckpoun60GSPtIN00OpOhpQssT+OWY4yaxqrp5B7TGoIw8YxJOdARsaRtgOedfRv5VIUpw+JHk8s466x9BCmIXYhtyA4HtG6q07K9Tq69ysm5PRybOUBfME2QtzhwRjtBi4VMh0yXUozvznP9FUdiVn2cBigzYdaJSj0pVIUR2fda1PtyNNNFxi5qI+gtErVnCYsZdNpl7KQCUU4TZTWp/RF6Xg9SRcg4oNsdQPoRQimkNFCZFKIZYlR8aDSxSiHp3TVkI6TjN3twRhLWhXRTiZTLMGjG4NsxQmtkI0TWPKyxErJQQGQzMwk7DvlHJmjdUKHtninG/l3TCJxE6hbPFEWUAkOSXjeJlxMoHREzY3argCgitgxizwcDaC0gOtugmIaWHIgktSc2ksK5YwIpK2HzRArZCNC2wO/PIJVEOC4q5xCtSqMWPboAJB/zVGFyGioVKNURDUGYNsEQ6GKO2LYxBRiOg6o3EbYNpgloqHkoSxB0t+Ct6MdPS1KbhkitG6A836S0Mk80Ncmcw4e56Uc/4Nef/iaiXmFEjeGZgl/cdz+/3b6Z577sXIKl7QlTRmu0FMm9+1+w1xPsrrbycKmbLeUNAKzpeBlznTIPfPsYiCGyBIvmtGA5itAIuKmcYTKew2icIvyzMRrgpi+9GUJBajik/d4ybfdN87TjP/DXH2izmMX/NGj93+fvXxyzS+yzmMUs/iGQWqEy7ozGPKEZ/zMwtn+CB27cmJh7vuQMih2HLkz8v8bLV5/MF3ZcD4aikKsdtG1bdRwKATQlqSc5b7oni+GBsA+OmJNSEmlBjGCi+QgAwZYaTo/ErCty6YTZ8MeJHry2IU48cQ8ffNGvWfL+z/CDB8/GHpLEHTHffP4SPvC1h5IVRq05c9nzn9CHQPkMNA4wNz0fUx78OBEoKruy4AoMKQkK4Lb8jPrEakJi7r97Ls9+ymEAMyvvyYQjVGA7Fl//M9bS5OQkOiVQgAhh4388TskuxA7WlCAWB0cw/1ek3cV8446PPPb/YelljFY+z8cu3MCHNqwgl21yin0sNz64B6Sg7vnYtsn+qa8RBncSAnPbf04xfRTzxBOvycULTuWRAwUMNUHQMc2Vx3yGOLiWWvntSAS/G7uAVbn7sIwaWtURukEcBxiGzbff91MObBviZR95NmsPezHvvi9LNKfKSL0rkW4BD40Ps7qrF+lYGH0lrA4NQqKCAGTC0lm80uC0w3bTjEukb+vgVSfNI44ihGmCZRGi0a6GCPw4ZuWaHqQriJsabRjgmoQDLupDBt96y8kw4FBuZJifHYWRxNeo3jvIHd9uo+/0KZa8ZycCwZzCBMNxB3OaYxBrtGtCDFoIZBizJLvmoHN1w5bP8vUvX8s3H96JoSAWBl7WwSw12HxrG1K1Eq4zMYqJqagxFdG+ssy0StOzdpC7H07Re5tLQyuE1vgZgasFjYbDfeV+1rQdwFMWvWKSTgsMNCEWbZGiv3g0+7dNIEMP4SVRxTKfJhpvomqKtmdPcPcx/ZSW1GkfKCNuNjFyhSSR3jTI3D+MlqnEM0KYxPkUr3/XdXz4tS/CCBXyux0ceE0NhOanYyfw+2tWkx3MkrMbiIKBCgyCjhxyURctxgCXfvI+sq0hZx61hR4zYu/TH+TNL76EOC4kRa5GgBUnk26dMdGfOIrPv8hnzZl7MK5sYtQCUIpIQthi43enSA1WEbEA28BuaNYu+SOPiI6ZyXIW4Yc0MwaiAUz6FCoaESi0adAxb4y+UwI2XttP7GuMA5pPvOAGWsyYuT1T/HbqMI4S+7kr0w87JoiWd4CvCfMGcUWgtKZn7jQ3Ti2mz51mX65Ie71EuDFGSAcjcshkfVa1jHDXjm5aBup4RyfR0ChoLixi7a4mfiCGRe/3SoQtdbz2AD3qERez2JUasSGIO0xMXxOlNYX1U0jHobmohcO7BuleGTAw1IVVaiQR1P2teHVF2GqTK1UQgwFKCtyRJvaIR1S0wbGTFK2JaVS9TLRkPlFXiroZ0bYetGujc2nilgKy2kSMTUMYIcwUYqpG3JpBtSfSyVCAoR4tiAm0AZYGezQgKqaYPqJIy8MVrOkmeE2kaRCrCO2HYNnoXCoZE/0YutqgVMUwDFofClCuQVTyMRohMuWiiwVEHVIPTSP2jYPnEx9hE704B14bqQ+NJ3IvFSfFHcNICiqlGtJrR0RGYgBdriKyKbCStKTi7QfQYYgwJNqwMGwbZRpYhiDIWIRGhG2axMUUzqBP4badyEgxuKMNA0FBxpjPCGj5YQ/D8w0++6E/ErQK3n/eL7AKKcKiizVUSozpowjTPNjjZFXrJbzv3p+hlOC+nimOaAU3cxbXvfluJBFSK3QkOPvUI/n2zluoxA6/fvMJXD+t0ELwixueKN3Z8+F/Y/l7Pw1xRCA0J318HR+67nzSzQt45zPf9BefIbOYxSxm8V8xW0iZxSxm8Q/Bd25/Dy896v3ExRxYcPOOz/5T+tE5t52nvuocTMuk0J7/p/ThydDXWkEhkDIkiiJMMxmSx/wyomJB9smLA3G3TZgx8XskIgI/iHBmEo9GgzyBNrGMxG8hJCa/J0b/GQfxV5vO4Fe5EmPDebYsgbaNCr/VwJmKmV5qcmLvs1h41Dp2r9+DdCzS805g2ttDS+pxRs+1Bz6N37yZRzLP4Kl9bz2of4qQ+acNs/PGXkIZ8vGzrmV66od86duXcs8tbWjgZc9+XC6kDSOZLKKIo5jyZJXWriIAa9/wHUyZeE4o++CVS6fNROyARkr9zec9DveQ839BLn0KR/V/n0uXJ+wTS5qsuP5BOtpytBQSk9+ss5pK49G264g/K6LU6j/Ba95EMf9uhGhnc8NmoWsyFBaxLBtDnotbPxwpcxzVeif1sMaPD6zh7UvW45q9SBEBNuMDkwztHqM8UeWyZx7Pb/ZvZbO/jnSqRsNL7tkjOxOz64Ih2W5lCURP4tEQJswIDcxbobBliuFggiFzjC/vfDtvXHENQoM9J8Agg2NAM1Cg4Bm3f5LU3Czs9NGOOUPvTzwjdN2kZtk4IxY17ZDVHgCb3tSBLgu2fqUt8XSII1bcs5djzjnA0L8rSsJBz+1GRDEIaHRJVrad8oRr8Lo3Xcx/vPRzj9r8JKvofglTgdaa807bTJTJc8vdh5ENfU5p2cEev4N80OCe7Hxq5RKOnaU6R6KkILSTu+7pHZfwgP8DHEKGpMF4kEMLSa8s8Yb1z2fMr3OiZVAyTZoFC6EFsSWgbqJaXGr9sLdSpCcXEK9opfIMTcudduLJ0gww4wgdRoh0kroStGcYCXIsXL6PfQ/3YTRiqlsddKcJyqB8tMYYdHj+Sbdz0qt28f6LT0N3tXLh+b/hlMvGyYiYoT1tzFlaRyLJ5BXf+tXV/Hqqk1s+ehzBPTORt6aBiGN6UhZ37VuKXhoCOomxNk1EdwfG8CjOWJM46yJElMiiYk19oB1pNgiCEGlaxLYkGEmuT85roKtJQQrb4vzPDTC3pUR1XpodX5pDtqtBwYC0BKlMcnHAn0YXMz89iXd4DssUCEcQYFNxLcS0x95cK/dfuxi7pYmdaeIORRTGNSooUVvVw3S1wFc/vRaLEkYEqQN1wq4sALKsoNkEw0BlLNIHajQVGHuqmOUIERvoeoAhBcXtIJWm3ilxvQh8RbCim436WO7fGpG2J6mt7icqOERKIjNZgi6b2GtiuiHKN/AW5JG+wtkzhYoihGOhwhkmS7lBeLGNmjZgq4k2JOH8DiikMEsOzmQZmUmjfT/5DoYucUdxxp9Ekt1YI3ZttNLIUjO5TkIivRhruI62LXRKIcoawggZa8KggWgayXcilUH6IWiJymQQlsSIBOZUhFuPkKUGIowxqk2M6YCuLTUkBqrhY2wOUHdKhE7N+MmYqEdjiWdS24ymRsYSI0i2axS64SPyBtoxie2ExaJ1EpustEbZAmXYpB4eIkobSENihAqlBNK00Dqk2FrhEd/lwVo74e9TWCr5khuAEcJkIOkUguaSDqzxGsqUXPWbB3jes04+aIxYnF1I6FmgBCOVZNw/64yPo4MIoQVCaXLtLi02SAIcM4Q3hkSfymNEmmee9wV+fv3r6ezsPKjdrR9/F+f99K2UT28jbz/ED753OkJ4/PD6z2IYkg3fetvf/CyZxSz+e+K/i9Prf4c+/GMxW0iZxSxm8Q/BnL5Orh/5OoM7hpm7vO+f1g8pJee88PR/2vH/Eo5p6eWh2j4cEfLLvb/kuNYCC1vOI9gjcHwTXXjyIbq9xaW8THHJSRvRwHitQl9rKwAZI8LVAX6cFFL8okmoNNJ4vAjxwjU3sGF6DssXDwEfJE4J0hOKyBH0rioD8M37P8nuTfvY4G7gLeu+SD20+ckpHyNrJ8dZIG4EZxgj/hVwcCHFFDa3zD8F9XIHIQULivcR65DikmGiezuJbfFY4QhmLCtUko7x9BVvwh+u8Oz3XcKisxZhOIIYMAPNjZ992UHHmduv8QdDTDsijAMs4y8zn7SOaNR/gYp2oaNdVOq3Ucie8dj25zz12IPPc+5c4BP40Tg596SDtgXhQ6h4lCjayyOlL/PjoSM5ruUA903P5TnLQRp5jNx7GS9/k5yYJm1pnjXvflLpl9KWfy5CJv43vUvnMD1eo9iRFE1+dNkL0Pr5fG/jT1jRMZ8Tex8/bktHliAdsTfXyjxdRkiSFWY03X1n8OD+7/FIWMCSmuGRfYT/KfBCF1O4ZKxj6Jszxs5dHQB8Yt6NfK/lDA7kOgmiGJFPodGIMCI9r8qr+27li63n0HnUGPUr8jN+OWkgKaowY2r59ld+lnmH9XDxK98IbgyZxINIxzHnvvNgNspB10JGxKEBjqDWonBbsqjxKssPH+Nl730YgN3DHUwOZTjNHGWNM8bnd51Ia1uD4DWC3Psr5Hdrfn3PyVTr36Kl8DF8JfBHP4NAYRJzZKpCScfUQ4cx5SLTEWNBgBXDW99wE5/93oWIeghph2C3Qn/BJP0Kxb6+DqyMT//tfhJTqxRxGCCFRAuJbiTnIOgyqU+dxJveei3veN5LwDFZ8HsYfH3C4AmaJlpqrr+8j9/Zi8DS2GNlOo4NmWeFWFKTmzdNKYwwTIXSSaTxFF0UKj7jlkOcTiOCgNgy2PbgHqYWtnF/dT4q7UA9hIaHVBpl2gi/gQ4jFl6yjz2/XIBhCPb9oYcz3zXOHz4xQnzEQmS5jogV5z99B2vOH+PDrzoZgSbOu9w1vgDPHmBrrRcDDQXBR7eexlI5zY/3HEHKCWjc28keaxHhMTXkZhcZJzHKWgpy25tM7yySmdY0212KPx1AYjC5upPc+ilkoHEHGhhCojuKCCEYW6To3lVHoGkG/x975x0mWVHu/0/VSZ27pyfvzuacd1lYcs4IEowg6sUMgteIERQjRswoyFUEFEVQJOcclrzL5hxmZ2Ynd+6Tqn5/nHG5XMCA3h/q7c/zzPPACVV1Tvep7XrP+36/LiIejzRINBAqrF6P8rIWpF3HKWtkTVJpihEfVWgNKtSEQUDvm7tIDQhqvktpfgzbN0ntHiE4K2BWvs6zf1hIrLeKORKiPUFghGBJvCab3L0VKCtozyNF5ClmWBVUtQURV4SGjkSaMQhNSZiywTAiu+K4DV5AWCzgbFK4k/NIBfGqh0rH0AqsQBN6LkaxRBDTJNYWMFta0ckYZNNQrRMmBWHCxNg9BK1NyIwTldwBjBZQlolRzlLLmnj5OAk3QWJXGSU1UimyeZ/SzkgDRFbBua5CMCOHRoMfggojfRfTgjDE8MEaVkhMhGkgagFGtYZKJQgtTXlpB4ntpUgQ1vXBlKgwxBooYYxUkcUAd0KeoDUZlfGZBpgGA9UcH37bLLQXkkxajOYlndetR6rIGajpD0EUhFWa+pKJWCLGzy69/yWBFMs0MTyL0NcMl6PnKfQCJBA6AoHkN9f/Jzu9biZmhpEiZMRM4CkIYwYozZtPuZSuiTmuvvY/X9T2HZsv4cB9Po1NQGa7YnSWQXZrDafocezRX+aOuz73ivNWgwYNGvyJhkZKgwYN/tcwDOM1DaL8s/POyW8dKzswuHTd45zyldVc99xv0DWB0hCvvPIUvaA9yfgZg5w4YS2vn7gW2x7es8/TFmXlUA5cgiDAGQ0xXGjyXkid3ua2cVDbZkw7emMwOkNS7jIpTjY5bel4PK+P/l2ziedPYHnhCeZkd7OkpZv+WmlPGw+PNvPFHUdzTf9LrcFNNHXbRCtBaGgyqR/RlL2QHrk/hRkOlWkvDhJpKyohUlZIfbgOSnP3wyv48I/vjDJVhCQUgpbm5hedd9QE2HvGFg6buZZ60Ptn7/evNr6Dh7bN4Y+7bqSoFSNhSN/IpwBYfvtzfObkb/Lsfatfct62ahNnLN/Jyfd+8kXbs+mPkc1+lljsCDBn4dgut+yeR004FOqjAJxw32/5/oYaaSnJGgbNZsj2wi/Y0HsYy7fOQus6CjDjNsXRF0RuVw7fygb+wPLCpS/qM56wSa2o4QdO9K4nHoeYA5k0PaM/49HqCCO+RVv8vfz0cZdtu1sZLKbZ1zyMUK1nv323IF2FUVc8dsccJk0eQtc9rDDALrk4JY9KUrNtWzvX7lrIXpNLOAs9dK0O1RpGTGLla2hDIAKPEy/dQGpGnfNOuATZ1EQwoQ0Vj95ma+ALR57IK3HWiUV0TCAUSNNiaFacsFalf4eN50lc16BUjVMb0Hzn4wfxy08vYbU7ntPbHufSedfxmUtupLPVZWj0o3jeU6x55jxu+u4aihvjxKRmsiHIaJNiIcmDW84ECWHFQoXRd/7QQ4fQUiHiBgwMwXABv99FrUzCjiT8XgEKQVRGNXnvAXQYRK4oQuyxAX5i+f40Cx/DAVX16Zln4W63YItD7DkHLwUYEsIApQKMSp1rztwXT2kCDRtXNvGeo17PxV+cyWhg87vCZNrNYXzi4DhIQ2C2mJQnZxisVIj/cQtl34LPDBLGSuAFUQDMsRGGge0p4gMKrQVeNkaYcugfGB8J5NZ8BBJiMTIdAVIqwvY0QVsWlYjz7A9mcOkPj6F+T5YgbjO6M8VTGydx3dr5TBw/jL8xSbLbJ705QFcFGkEYA3vQJf9EP4miov2uPlru2U7Tk6PIfB7yOaSC2ux2wlo9KsmQEkwTZQnqM1IYA6MY/QWkADwP7QfYO4YiHZqyS9PyIvHAotQEKJBTxuP0ezijAcnV/ZjCwOpzsbuLZB8fpvmBUQ543U7mf2qAKWaNJB5IcNtjhIaPrlSQFRctQ4xAQzqBSCURpSrE4rTM9iHnoHssVG+calZEmitSIHwVZURAFIAINBqBHYCo1BAaxt07hOUJDE8hPYVGI5MJvCYDM53DdCMLexSQTaNbcvidWbyJTXh5C+oeouxGWSKeR+hE3zmj6mNXQox0N+GYPbP0QvDh7C8/hB6N5h1iMYRtY2zvB9d/4XurNXguaIVRC7DLIaYniKKFEso19LZutBFSnZ1i9MDIPl7GHEQshhwtY5Tr6KTDrvfNpO+0dlKrBnCe3RZl20gDe7TG7qYQwxDIqk9udx3pBTBWriUsE0KF1JrYMzuiDK9XkLiyqxKzLOjpL/Htr/8RbUqUpxCu4oLPnUwsFuO5kYcwBBhC0GRV2PejkS6UCDVCw87uAiee8I2XtP3Ik1/jvEMeYNzrOtGBwukrI3uGCLaMsPqZja84bzVo0KDBn2gEUho0aNDgNWJmfjIDtTj9lTTTcv3Ed2l+dc8Q3mQXr8ujZay85OVImDHeuPgJtgUJVg4005p8wVLaUw5eaFLyFdfe9SwIMMshXbNfSHE+b+Yl3DeykD5vXwDCSR7FRT71eTXOmfM+egs/xqJOjApndWWpBBncYCJTsy+4K6yqtrB3djta+pRqNYq1FwIBtbAf3Qx+R0jQEjJn8mEkUmdyyVvP5icfn8h9XzntRddj2mOL71BgnD2J+hETmHjaVLQlUZYApVl+xXkvuQ+zs4dx9vQR3jnZIGlHzklB9Q+4w+9D+WtedGw92IwhFMoMKIaSGhJb76Req/HMPavYsmIbqx/b8JI+rt+5mjdNeprTJi7n1h3X7NluGC3EnAMQwmRJy7mc1N5JU6xK0nTZXf4DD+5cSyLms9FvZkVxEk9VxvFUtYuEWUehyRgVtPscqUycerlOPPVCqdPvd/wW5YU8NvziDJtVtz1LevUgU67oBa0QSiFMEymg10viY+Iqi7ydwUlsZHK5SkutzFcPO5ZY8n0cccgmtGEQph2ee2oC8w9yEYFCuMGYGKUmXyzz7WN/x8V77cXbp4d4OAgpEfE4YU2y/l3L2P7hxeif5PH2WUB7bBLdG/ogDBHJWLTgDBT/8eFXzkYB+OBJH0OIAAIVLSodh8HXz2K0V/Duo47nTRecyYCbRVmCDTsnsPL5acy2Orlt5UK67IB5M4p8+JLbkGI8IFn78ELuvPIR1t7ZSd6AkjYpe4IrPn8uHz/6ixAY4AtqnSa1dpPzLj+WpadtYHRGMhIEFdHiXpAgv9Kj6+FKJJStFMeeso4v/fC56BgpEKZBfG5IYnSUZdPKrPZynHnxTfg5C5WXkDZRnoUhJEZvPxgm2jQRjgOuh2W6bO6Ps7VkksrW+e5td5A3Unzi0JNJuCXG2aPUy2M2tkHACW97jt9f+jOmnzdAZa8mRqpJBupZEgtGQUpU3EGl4qiYhZKCdU/OI0xZeCkbryXOE0+bHHnGAeggGLPBVfz+dwv56WX74yYMvKyNnzUwhj3s0bESOiEwFgWYnT5hVpK06yTKIVYpwCr4JDcZBA6IAGIDdexCgAxCzKoG0ya2q0yYsAmTNvEaqHQMWuLU7bGgKZqew1qhGEIqAak4IhmLRHalQMsoy0VLieEJXCskvctDJ2LYXmQdLt2Q5KYBdLFE6+3bsYohQZuN15mmWJYsf3wmo4Nx5Gj0ndOmpBo30L6HnzNxdlZperSfMG3h52y0YyNjDoUtcTL3DNH0YA9mRePnbMJQYfaMYtRD5GCJMGZCPhsJwmqFDgKU4yB8jRtqam1xyuMc6i0m2o9Ebsm3RUK0XW0ox0LHTJRjISp17JE61kgVa7gOwwVkrYZqTqNbsnjTWqEti47bmHWFsUGS3DaEDkN0GNK+ssra58cxcUEhKtNCR3ooro8IIsFcrfQezSu0wjwmiZGykHWF8iJbZkyJ4TjEllbpdLpJ7qqAaYBlIGwLc6iKrtWotpt4eRuVMFAoDCHHnIIkslJnwl2bCFZuQu/qxX5uSxTki8fQ9Wo0rrGSIXTkgpVvSb38JOGC0JrhwTK33LQCocCIWXznh2dy5NELAdi/eS4d9iizE320OiWsaQNcc837I5FeA6SnqAxWOfrgL75sF7/59PtZdeX5mGFAML4Ff3KeD3zsN3927mrQ4J8a9U/0929OI5DSoEGDBq8hJ3Wu4rD29SzM7sKaV6HQpLDzdaxml0P/jMNQ0sqzs7+FvnqSX169H0K8ULYTagcfg7o2uX7V83gZA7fZ5C1veKFspS05iZ8s+zmfX/QdAL57+EIMI2RaLkBKSUvydDZ7STZ4SSZlT+Abe13D1/f64YvG4OBx/67prBtuZs2mGez91e9z+NcjS+D/fPA72NkKOqVJtBX3nHPLnWv48VdHGR148Q/nfC4WvaUWJucf9wbuv+nL3PTkIIEBIQLTFMRisZfcB8tIc2DzaSyOmehgCwAqWI1WA+hg24uOreq3c+fIXO4d3I9mcwLtUtBkGDzVtwTbkti2QTr3Urvpd049han2EItjfYT+d1/28xBCcMqEszmj4wne3PokTU4TV23/GeV6HGGEbNdfQZjNbHfTe8SBa6GBcJZw928epWdjLzf++M497S1JrOCY/FoyorZnm+/7qHod7XnIQh2CEG1ZaDRmzAbpsjS5nf3SW+mMz6Bwbzvle1sI7mjFtm0SmbOZsmAjYcpESyh6Uzjh+BvRcQtScZRQaBES1izcULCucBn12t2MVp0obd/3EcBxxz9JYnKR3ngTb4g/zgWffSfacfDHyrL1mFr/O95x0sveqz9hmHne+8HlgEbLSEhYWILdb5qP3Zml3pRHS4FKJyPnFd/nU+NOo+epCZRcSV1qOpprpNhE2lxEKncynhLsrlr0BwYr682sfraD0Z4R6jWPzWdFGUUqaaBtwWNmG89NayJMKrAsZCaDtB0y2z1iPS5hSxq/q4XQDzjn42t58t4WRDJB67IiB5+5jW/8/hHGP7ae5tS3qao0ZC0WLNqA8EAUJH5bQHmyxzsvfJpjL1vP7uMnUm6zULaFNgT5tirxhI+YVKFquZzwyZVMWrSOQ1sqzHYC6mUbrTVa+Zxw/GqyhuCtZ2wh1d3Cfq1bOL7reZy3JKg2B4iRUpRNYZqY6RhaKUQ1QDkGBCCSNvfcvxlzw05wPYLApzYcsm1dCs+MshpEENJ/pk1q7SjJdUN4ZojqM7AKIc16FLscECQUTk8Ju7dIeoOL8jR+TJPZXsdQktCx0JZEOxLlGChLoE2B0Abp9YOYRQ+dkei+fvoPbkVozeTlPtWJGeqdabyOJILI6a06y0TaNtK2o4W/kUaMVqBao95s4CclkbmZBMdGxhPYSY/S5Dy1ZJIHV+zHcKGJNatmEioRqQYokJYJ0sCwHLQZBcbqnTZaRtpBhCFhPUT5GnvTIPV2CJ1IQNscE/fVcYugLQN1D+oebs5GeW6UrWGAOzXDwN6aWpcksEHWfaRj4+Vs5EgZFTMhZgECo+qiM0mKc5pxc7EoGBaPQSoRuaYpjdNXRhRqiNEy5trtZFZXIDAQpol2LEIhuOXaxZx/9Tq+8dQj5MfVkUJjOqCkQIUq6lNIRCxGkM8yWhiP3aewCz7RPx8iynDzfI7fayUznt+FM+whhIz2VapIKwoGxgYD7MEQWRXIqheVFzp29LzWagjLJK40VtmPAixBCH6Al0ngL5iOm09H51gWWkj6R8qsWbvzJXNEEChQgoGwn8AZ09EyBHvtPR2tNesHPsNI+QqWZC0yRoWu2Ai9IwZdXW3c/9hFUdaeiOalOnDoIV+iVCq9pB+A29d9iyBpIQIwQ8H7PvCzlz2uQYMGDf5EQyOlQYMGDV5DWi2fuOkREz5vOO4+frrxaAytqXqSDx/wUpHOP2EYBitu2ZfSVpfvfOvUF+1TpBlwDaT22FIqEIwzQMDhi2e9Ynuvn34Sr5/+wsI3mZjDkgn3ABrD7HzZc57ua2NJVx9+XfH2q85DNyt2JCT7XPkFjpjbx0UTnmeb20KbVd9zzje/HwUL3vWfV3L/Hz6xZ/vee03hprueBwG/ueFJHtq8FRuFckHZkmcue6kDw58I/dVoNYgKd2JYs7FSZ6P89Uh76YuOO3fuB4APAFApF9k1NJe6Fpj4HPyGfamWaux97KKXtD87P4vB0Tobq038ufcPCWcyJ4y/mKq3nKb463jit2uRZZPhcS38x5cP5cq1v+Ko3B1sdJt43dRnMAyH/tIgw73DyBAev3PFnrZW1zoZZxfp93P4/nZGRj7OU/dmEZYNWhMKEAkb4XmgFZfd8TGM9HOsrhuApCMxlaBiIE4eoS1fYMX2HSyaNBEA6SvMWoCLwHGiRb12bMTAKFpG2gXH2yVCW7O66hNeGQdczDG9hmLR4pAFa7jpsYX8MLuE1dc0o6mj23NIQGlN14T8K96n/84bp3yZ4kcu5vrvT0c5CWRNo0PYNb2VqZ0en/nUFdiEfPHskxnxkzy/YgfNFbjm5n344Jsei7Q9UGi1ioHebipVn23bWlleLVMotvPwJdOouz4P3HwK/V3tJDPNJDfbmDVFh+UQswMuO+9nfG/T3owMxNFKIypVRCgxdCSVp7IJTjnw9eAFiJjkqO/tYklqgEJoURsSdG/J8sutB/PTo65n2Ye6ObBnA1e/eRnUXQhCqh/R/Nw6Dj1f4+Y7kX/oZvsbJ1NR9yDRbHezTLeHKSmbt39nM742CEPwQwtT+yg7juMYDAUhz1VasTbv5j9bn6Y5XuXZdW1cEU5n1GzHCEJQijDU4AWYlon0FVoogphBxRHsf1aJZ5c7oBQq6WDvGkKOSrov7MKv2QgrJNWyhcquJnwNrRMLOC0eYdng9Amr+MYTXaSPrLD3CT08cUcXg6IJe1OZUAoMBFJpRC0KNvhtKaRlIDUgBNZwHZ2oomsuouIi6yHWhiq+BX7SIsgKdACFJoNMGCNMG4S2wvA0OhYjjJvIjmaqec3gYgcZajouX4cUEtHcRGCBV62Tv3c71UkpRg+cHC2iJaxd3U5s9W68cRlUykGkUmAaVGbGqHd2kXt8EHOgCtLCMxUiaWMUaxieINYvUAkD3T0KtoVQRNomRTeypgZE1kK3NUE+h91XRQAjTpzQcEkMlBGJJL4joVaPMjJiNnKgEgViPA9sk+Tzw/hNcUjGqbXGSAYGquaiRopRZhQCUaoizUj/I9I9UQgNIm7hbx3mI68/msPP7uW4d2/h9p9PYeZhAzx2aTPakOiuNvzBUQytCOZOQAaRILFR9cbK1CQ68EGH7JPezog7A6FBmWZkjQ7o2phoro6yhQxX47ankMog7o1ZcI9lvWilEMDUpcMM9ySoFkzsug3rtuHNGE+4e4RsLsFgPgmB4ux3XsaySa2cc9FpTJnbRRCGqERUNhWLlZBuHEyJbUUlql44wPbiH0H4HOUYmLGQ72+dw7bV4+HoaH554OHPc/iyz6MdE6kF2hKceNL3+N53TmfxXtNeNBcJITji8Dk8fPd6hqaYLA+HXyQC36BBgwb/k0ZGSoMGDRq8hrQ7e5E16hhCkbIlRqyGYfuk03XSzkszMP47d3zrfB69/gIOnDL/RdsDlWN+opv3dDyDG4QoWxCaAin/tinfMDteMYgC8OYJB1P2bKRjEIYhrXOGmTe7m4KrsHTILj9JzKxwcucX9pwTjsm01MWL1dxPf9N+CCUQWrBmez+/fnw9vhYoKdH/49j/SSz9EZz0R5BWVKYkZB7D2f9FDjv/k2QqQ2vuSyRljsVNH+epO1ey+tENPH3vo6zs+ySl2ovfjlbEMXxp3QlcuOI4rtl4yyu22136ERuLV7Fl+AM0LS/R9MwIRm+Ug7JlxbF8d8uR3LxlCdu23QrA139zF34mBmh06oXrvGNgIVf1LqMjvoDdwxdg+E+w4KC70F4FHQS0LpzEDc9+hTu6f8Adu35Ex7giLbG9WdD2A5Z2/govDHDnVclYdXY8OInz7nlBayWuQJZdGKmys3sI4fp4owVCpUBHNtQff+8hZKUgZmrO/ehdBE1OJFrp2Oz4TpLdxTQGBm4YcsDR3ZFehAARKoQBV93w4T/7mf2JzvRc3jnt3bz3E/fT3rYLAghtSCeLvOmY35M1XOKGzwEnrUOHmltuv4dUt+Km5/fikb42hj1FoARlN2T7yLUE2scsCnrdPCtLedyCCTWfbZtMnqfAj/e/jeltO/DjHpVUlWuXfo2RskGxJYaO2YiYQ1AqYZVqUKpGZRGFGgQKsmmcJslB6RFG3ST3r5qIlbK5Jdib/BSXsmuS1oonftlC5fgoiDL83Ty3LVpKYAeENvgtmtqMZmYcupnbds9jq9fCdduXUdAOPSpG0tZsr5icdut/sOOzFn0X23hKkc39jofuu5S7Dp9Ovm2IfGzMRemeDKVuBzwXZRqodAoyGUTdQ2iNHCqjDAPhQji5mfysMhKQOoSajxgtIipVZuudxJoqjJ/Yx7gFdbBMkkhyhTotsTKduogeNUjZir0O7eWY6RvY66BeZF3jtyTwJ2apT86iR0vRIt80kWZUlhfGTEStQui7hGmHcFwT2jTIP9JLplRmYFIcUfEwSiFWyUd0taPqdexdFsILUWGAHzcxh6swXET4CtDIYh0tQzAMQqXQdRdroAahAhUgaiA8QEPhcZPEyt0kVvQiewoEXh3P8ZC5GmFWUZuSIWhykKkE7gEzCaa2RRojSuD0uTijGtnZhFAKsxJgjHpI04qcbLTGHKxgDVWQwyXMzbuQW3sQniYf+pTaHQqzk1RnZJGGAc0ZpD/2vIzpwVCp4VQhtruGWfGIby9GIsJ9Q8hiFS0lKhZp4Gg/iEp6hEZrTRgz0LEA7AreYMAtV8xmzRPNnP2rZ1hy5gg971hEuGgaqr2J0aOm4u47HZ2OXKdkxQNFpJ+CIJg6HtURZyZlmhMuGCCEBs9HuR7a86JSHyT2qMKsKNzJWaQPulRBV2pI20aHCmlb6DBk1pHDZJurpFvq0XdSSESpglGpU941jLFqK3L9TuI9BZ5/bBMfOOYbfOPcn2MaBlPSNtlEidLmFNqM5qYfXvoOAByzjbXdnQzU0hgEGGi8NTniwy/+9+K+Jy5iyeIutAnUQ/ACPnzOVVz1i3teMh9d9Pk3otMmdiGg+dkyRxz3tb9qHmvQoMH/TRqBlAYNGjR4DTl83KdxlUmgDCy7ihQQaoHG+MsnvwKz0/NJGgoDTWhAkFaoxD/ehu4jS0/h6wu+yxfn/JjlF76HIyZsYP+Orbi1JNetW8D9gzOY6fWTTUbBmHs3LWdomqDULhj4H4kfE7taCOKCMCaoSTArCqOm0aZmzaWvnI0CoNGUyldQGP0YWtf/7LH/nabsWUybuJJY7mzm7DuDqQsmkj3ii9jedTzVezz3brx4z7HtqdOR92bJ/y7Jdy5ZQRi+fD9/usvPPQx+NkaYkLRvdQHYr2ka3lVNfHLufWCfS3HoI2xXu+g4tQ4JA6RD3d0GwO2Hf4nLln2an+57NoNhksFQ0B9I9n3PAEjBrx+4kHQ2Ko9S3vOEI+cRjn6Yir8dgUWoq+x18FYG7+tg5oxdpEvenjGmDQm2CaHmknXvpjInhxrfQn1GPrLTlZLm9jLdfkjvYIzbvr4UlXJw54xn8MgpeLUO4nGHo/ZfxfvnPM+HvvgcvtIIw0QJgfnKrt0vSyUMyRll3vWuhzloaQsJ6fL9837FwqYetrhNdNez3P7tmRiVGrueKVKteSTXVvjk6hN5rDKN7W6aHcoi64DCxPdinJ7bim8R6ZlIwQ3XLmBOuo8piWHOufBRDvrgZiptJrlcjl9W9mFHSyu6WEYXy9ilABF46GwS6h6m0lFwoO5x7seWM87y2Ttdxn1iBq2TZ1L2HUbLcRYmy6RMwac/voLZp41g5UN+c9i1/HLJb5k3cyfCNVEB4BbZt2MbN66Zz6fvfCNPbJ7Kb3+xmPmxEm2mS5sIMXMu88b38LG5d3DWdXcxMnIq13zlHrBjvOWqtWz1HFAmm9dl0NrGCBTaNAkTFmEYQrFGS3oT02ZsQgnoX2RQzUvuvvZA/IRBmEkTxuWYSaai/54pnCTWcmj/VjY8MhViDlpLOtoKNDtldlRzXHbzYWza3cL9q+byh5ULuG/VHKSUOPXIThcjKpMhDKHmghOjHrpoqTB2F9AC5HAZah6jB0/GrLoYVZfvn3kMWmiUVGgTtKUxbAtrpL4n46I43mDXoZLh/bM4NUXrjetpv3ktxaVNHPXZLchaHbNQxayDtiR9p3ZiC8CLNFy8yTmq05pwWx3MokuQMKgsjpGyCmS6CyS3VJGeonCgoDgValNz+DmDIBtDCgPDjQLRnoj0PYQxpgky9tRLy0IYJtINAIFuMkj2mAw4aSrT0ghXYNYUlicQyTgiVFCMMldIxBEq0oUhblOZ2YzXlqKYstDjWtHteYxsBplLI8IArUKUVijHJpjYStjeRC2boHBChqA9j6jUeeaONr7wvhP5zgePRPSXEbaNCCKLZe0rLM+npXMkCkRJomsC8DysaV00GzGOPqmf/N4VhFGOglOwJ9tECLBHfGI7qzjbRzGCEG1bCMdCN2UQiRjKNBFSkJsZ0LawTtNCD+FEbkeyUkf4IfghRs3HrvuRnXsscnm79/qn2LhlJ/GuLYybNEw9biIDjZKCmdNfEDe/98K9+eVPjuQ7X5vDrx+Zy+OPzcTxXyiH/BOX/PAsfvT9t43NB9Hn9rPLH+GwQ778kmPv/OPHiO32KMzMUOmMs2Vb/982oTVo0OD/DI1ASoMGDRq8hiRjrTjSI224uJjMau4hY1c4rn3+Xz75FXjzhNPp8Ryeqo4j0QPOgKC5939nuu9MZ2lPZWjJtuHICShtY7VW8KrNbB9oZb8OC2FEdrtt8Q7crpDRufDGg1+aLeIlJV7CIGiKtAyMQHPusXv9FaPQKF0lCPsjfY5XwZIj5nP+Lz5InUiTpa4kz9Vvp1CMMkcWtx5CogeEFtj9Fr6/6mXbmdP2Wxa2/YRf/moWdiFAGAbveHNkX7zfgZMIbfNP1QYofw0LlwwxYeYosfGKA163nc3FBwCImzbT0pE48Iz8F1lTz7PFbWZ0ZZxJs17skqT851HhZgLvcdYMfJ8Hu88j7XTwublvYfyMUd687FHedvhDFOqDAHR05VEGMLfOIwMzEK5ExQ1QBoMzc9RbUjy8ewHbB2Ncee4J7Hq+ncRwyOisNPUWSWF+hh2flLx+x2o6rCrFqkC256JFpFIcd/KSv+nej0/MZLxdp9P2+Mq5B5NL2lGGCyaX/egYLj73jWhlIG0Hs+Ij0MQsh/HpErbv8ZFnD8TTkrAWgGNTHRHERIgpzGhRbxhoX7OpFpUetbbX2N6axQtMnt2xCV9ZLDy1OxLUdOzINaZURRQqUK+jlYocR1yPbVuSUYmDhr1et5ozr7iS9+3zECObWvDHdDgK/RY/POhK5i8dwZAhQgRMCkaYN+d5hCepLbRYktjOm9ufQhsgtOTRHQvoG4qj0KRTNc7teogDmjazzW3l+dpEfjowHxVKdCLOwLYEw8R52k3ij2Vd6TCE0WEwDEwvgFqN4dVp+u7IkDQKxEcEwhEIXyMDDQbYpYD6vIm441qpNxnc+dA+3PH7RUjfxzHrJJwylu0jDNBFyaa+JsIAtvU28bt796ZnNIfQUQaRWfQxiz4UK+hqDR0GDLUKylPiWIUA3ZRDt+bxprQRjs+hEwYBAVRcvvTRX1HuiFHstCi2W2Se3AlKIZH4toEOQ2L9HlXXoTi1GVEIcAbAT+aoTu/gSn0EKhdHASJQyKpPbCMQJVihFQR5h5Hjuijs00Z5YTNmNaTlpiLOPSHNtxRJrS1gVHxijxapTAkZPFrgj2vCthKkVvUjFfgJAeOaCU0jcspBEzZn0ImxjLJKJRKdnTSOtqYCs4/bhJ8EPw12VWFVFCKMZFi0IcYmAY22TZQUY9lg4M7uoLpsErVlLRiGhcikkVUPY7AYBW9amhHSwJAmMhZHxUykYVKc2ozOpfAntRBObKWazeK1Z+h8roBVqGMVPXxb0T/L5sDjV3P44U8TcwcJapXI+Ssei9yfTElPTxNP3pujtlbjt4yjetBc+vedAIkYyvNQdRftBVi9JczROtLzkZYF0hh7diPXnLbxJRbNHuRtn91E5wkq0nyxTNRY4EZIgcilov4tM3pWhUT7Aeed+XkWjd/K4nE7WDx7C9qSvP/9h79o7jj48KXoX4/y3C2TuPeGBcR2hKTLVV6OBQuncP3vz46+FFKC0IRoDjrkSy86zjRNzLYkQUKgTMEbv3zV3zSfNWjQ4P8OjUBKgwYNGrzGZI1xjIQJcqJK2g6Z0lTg2we85VW3Z9s2b5v0Q2rVN+AUIdGrmTgu+w8c8ctzwaIf8h+Tr+CNs57jwPkbOKRzM4IAMSbdPn/8JP545jv49VtP4+tHfvgl55eneRQPqVGd5GMB0hacc+rhLznuf2IYLVT9Hsr+Bmq1O/6uazig7SGeqOTZ5CZJiDq1wvv37IsnTIwATB/q7rxXGItNLnE4zlu2sNf3VuOd6HLG+48CoCmV40sXHYKrD6HJWkSq5XrOWXAOM4+czz6f6WPhBwbZ7a58SZvJWCv3vOdAbn7DdDYsz3HBNee8aL+w5hPSQl0bLHAKTLQG6C89Tt17msvPOpvdxQ5GqjNI200ATJnfhZ+OYR4Be+c3ELyrCF6IlAYiYVOZnUBKyaU3fAiJQo+p4/qJSDwyPhCQbWphc9d4BoIs161cgDQthG1iIPjohaf+z0v4s6TsLha1/4iFbZeQtKZw2/c+zvlffwsf/fbprA7bWX9YCiMl0XUXLAvhK2TFJf2TLPcUJvG5RQ8z0anRPGcAxxsila7wi755jIYmo7NzjMzJUV0U46im9TxZyfJQuZP1O8ajh+N8+Z7LGGePUlYJmt9XQ/nB2Nt3jSs10jAQYUgYs1CmYPPOJENKsCUwyLT1MM52OWh8H9oKOeG6t3H2247l4ad/SDwZx4rvT8UX7PZS6E5FvilAT/Z420kbGF/0mZ5zufSAX/Gro68gM7fOl9//Bj513snEpeL10zeQ01VGVYLttVbWVCcR7GtSb0/xu7fOYLBiU1cS/8sGSNCmwcmXrKFtcl/0nTCMaFGvBfYjfQR2CHVNuVlirtqGtXobulhAOCZmUxIvEy34g4xBYAveeMljvP17D9NXSLDDbaImbCxDYNlgKYXUkOr1CcMAo66QbpRZIDWQjBGaAt2ZoLZorDQvGYNcBsMNEb4iTDrUu9L4zWmK+zRDiwFZAx0zqE3OozJJKrNbsLwoIyU16DHj5h4m3rwbw7QxXYgP+DQ9PsyMSdsQmRRiTMMHIcivKUTPo9aEOQ1aIIounT9ai9vRhKyHyKofCaCm45TmZ/GyBoYVEuu2sLstkr1Re9ZoHT8t0XGHIG3jZyRaCpASPxeLggAalGUQpGxCx2D+e8pUB22an/BRYYgxWMYsupiFGmzro5iQaNsBx0Yg0EGArtdx8w6hpaOgTSjwtY8sVKOMEaUhnUSUy1GA0PdRpiRMSGSoST4SRwmwij6mp4kVFfGyRmcS6EqVUChmLOrDSAT0Tze535qJdj20UOhaHV2pokyTYFeNb7x3PEUTOroqTDyrj5b8IFPsAsK0kAhEEGLWAlQ2hjVaR5cq0ZhMI9Ju8XyQguH+FKu3tLG9lmFLqR0RcxCGxKqOPWd+CJk0OpdB1V1wXUTgQTKGVfCYagwwxRlgv3GbwNWccfqBL5o73v+xEwmnjyMc14xZLiNrAelamTB0X3auaW1t4f5HLoxEnI0o6GMozWH7X4TnvZC1d+8fz8cqh6R6PBLd3su21aDBPy1jgu//FH//5jQCKQ0aNGjwGnPKtD/Q4+Z4pj6FQElSxp/XRvlrmNk0GzG8N6XJksoEyT57v7ID0D+SJwav4vYd89kw0k6nLUnac5FGes/+BR3j2G/CtJc99+Fz30/qYYfU8zZOxuCZn3/8r+7XMfJILEwj83eNP51Oc9acJzk82c3x6W6q6D1ZLtd99z0k0w5TpraQzfz5z+iAmZuYl+thwT47X+SotLDjBOZM/DW5jlsxzRQtsQkcb3+Inl9OZ/UNs9m/7QMv296mtS69PSlwbCbO7HrRPsNeSKz1BuzsN5BGJx3pM3my/4NsKt3MttKH+fCBN/HB/a5Byqhc7IAjZkOo2L6ynZ27J+PsNEn0FAkrdQwPtAu56Wl+8+WzueLus1FCowTEthVpvauX/IM78IpDiDGDIzHNgEIZqi5zF098Vfc9Gz8YreMM905juGc2N/zsLIYH0tQmAqGg2J5G5VOobILQCyBuM37GBM6b9SlmxYokJRx8/CAHfH0Hi761i7Kl2C+/mWS5Rry/jhvYnHXD+0iZMM4p8Z5lDyKrgiOXLWdOejenta3gsQmLMGM2wrJAg6lCcGxwHGQIMlDstc9uCqEigcKoQN4IaTUUx9lPEz6ZYeOUufxg1Ro8z+Pzl3+QVkvyw779GA5SZI0ap01bzSfmrGDeeM2sfDez8gN0Jkq8/8AH6JhSZndpHKOhSUkZDMksXihJS5+3N6/mLf+xEhUTEARcvXQWT45OQlgxznxkBT9dcTv1veLMvWgXk3+zGy8JWkikbSFknMm/3U7zmn5iI35kj2saGLUAc9VOrFXbsUpRwMIuS+qtCZQFWkDxdzHW3DeVui1J7HQJB0pY8ej77KYk8SFNfHMRMVJEVF2EYYJpMu+grdRbFFpLRD1Elj1kEGIN1rFGPHTKorjXOIKWTOSc89+oTrIp7DuBMJdAOjpafAPS9ZHFaiT46lgINyC7tp/6eQY6BFJxtOuig4Digjy+DdVWl3DUJ/N0kanX95MwDMbdsD4qzTEMZE+C3td1Yg3UMcohxUSO/JqQ5pUhSoeoIEAJgeofIHQDVL1OPSHRJgRJE5mK40kQpokRi2NggoDfDe/DCnMiowsNuh4IMEt1ZKGK8HxEGECrHZWY2HZk46w0OgwRWmEoiVA6KnEaKKBHChAGkYBrJoX2wygIUaygK1W0kIRKkRyuYe0cxPB8pBsgax7KkmhDIoRA5TQDPWnSmwPWXtXEzgeyuKERWUxrBVJGWS/d/ezeZvH+j2/hm9es5IzXb+QzX7+BT192Ox2XjIAdacNoKTF3V6LsDs8H24quxzLBkKggINQ2Nz02l8ue2o9yLU4YsyJ3NiIrZiGjsYlt3UgvgCAApRGeTz4b0GIUGWeOsLbUQceEl74MSKdjhI6BsiV110a6LoYRg7+gq/XAY58nl4tck0TFBQHHHvwVuncOvNB2XWNUA5yix/U3PPGq5rUGDRr8e9MIpDRo0KDBa4wQgsXpN9JfT9BlG/xsv0v/8kl/Bff3bAEByoBT9nn1pUJ/LUqF1LzrOXbSKg4et5HTp78TO38lQjh/1fmtrS08c/X5PHPtJ1j+44/+TX23t/2OSR134sSOeDVDfwlzJmwjl/oQ41pv2BMIaW3OcMfVH+KXl5z1ouDIy5Gzj6O70skZM18+MPLfef6R9exeY7Lj9inE7Rkv2b/87pUoN3JC6Zrx8uK/wuhASRvfmkY8djAOipgIgeAlx+69zzSoeyR6fLY+NZHqIwkwTJJ1RXpXlaev/yw3//hD0XWk50K5jtCKlhVFRLUOnk/7RQNc3zOdCzceyOPL04hQI/yQ7/3ifX/xev87ob8F343cijYNnIctFJaoY0nJo9d/ApQEJSm3SBCRpoc0JZ6j+PF3385e4xZiW6fha9juJxmc20S81WNqop/OWIEJMwYjx45aQKwXNtVaqSuL9V4H2a2SpFlFS4NAGAhHsv3LszDiJjIeQwQhgR8QoCJ7WMOkNl3TagYkDY8r3jsXExMbg2UDu9H5GEFKIAPBRU9ej+/dgSng85mHOTv3APsnt9KZGo70QDR0JHyqysTTklvXzEWcUmX/JT1cN7KAq4f3IiM8Wt3dLEn2MDM+zNR0kaA5QTzvIwyDzYPj6Ix53DcwD1cJCjrJcZm1NFlVdnx4KsJmzxtBoSB+YRH8IHJmcn20UpHLTujTtLJEdpNPansVApNffXAh//WppbhPG+Qf9CmVE4w/fQeiPYX2QVoC7URCrOk1u5F9w+jhIlgWSsHhby5w9/u/C0pR6DIQQYBRqCLCACkE8WcGyd+0gVhvkfzzZWSvC8N1iuNcRmfEEEqB1kx6/xDKD1BBlOEgpUCVyijPh5iNCiEgRIYKutpQS2bD3OlkTtjNvNNWM/WAnXRtGSa9pj/KvpACP21BSwbRFFnwGt0e1rCHUQuI1Vz8tCJIKkLlRot9rWl9ZhQd86i3JohVNNV2B7fJikpU0k7knAORJTmaqpuIXshKSSggDKLSMLSiNikLtUpUdiSjcrD61BYqC8ZRWtSO3D4UWR2HoB0LpERpDWGIrI9lR5gGwrGw+wok1g0S31nEfnITYvcgXlMCvzUFSYdQgZd0qE3OsP2dCXYtTNBS3krsrgrZeyt4aHQQIGKxyIrYFKAUmCbLi80MhAZ3D0yCSD0Je7Jg6145VMrB8SxMKx4FUAwjymqpu2jXA8tEhCGqVsPcXsb4UYHw9yMEmQRBMo527CiTJxGPvqNKgWOBaYJpgdK07V8kMA2qOAx8w2D4nlUcm3/3S+aQXD6JMg2qpThWPEEmtjeG/MsvI/544yfZ/4DJ6EQsCgxZFmee8VMeuG81ABd9/GTMkous+Fz1vbv+pnmtQYPXlNc6C6WRkdKgQYMGDf5/8ubp7+AX+13NxXtf8Q9rs397CbdV47Zopre1/sPafSXc2r2MM/uZnehnYbKbaS3HI2T6L5/4D0CIOMLo+KuPD/y1+O4jr7jfMAyyzecTiy17VeN595yPceHS6zm065UtrP/EzCWTyKVtJk9/4TMaHBlh6ZXf4UdP38/nT7sEAO16XPSb816xHdffRBgM44fbmZ47lw5nHw7o+t1LjpNjb4GtiqL5qSI53wArjPQLtOaM170gsnvR+36KqNfAD8AUEAboMGB7pY1Dx29iUdt2Ct9ujtw8wpcGbQBGCvfxhSeP45InDufd935kz3alitSHzsAdeTeu9yxO7GR2+pItfgypRxDebVx56skIHTL/7J0EliS0JASKZhwsywJgfMcPmNS1i6p4E3mzRs4sszDWzVxnF9nJZbSKFqTJIcFDw4fyze1H8+TwJMoTAjK6Tps5SvdABiyfarzC9vd1IGIO2rTw544jmNZGbXwOXSyxrLOCiUHBsygUm7nr1+Pp3pLi6UcnUh8nqLWDl9ScOHGYShjQGzZx18MHkLSiLJD29DC/L7Ty9Vv2YijUtEmNVJLHYxM5d+5qzn77I8yXA5yeW8k+yV7+uPEQOjNzeGK4nZ4UxA4e5pQ7tiFMn7MmruX0lnVMSxb47chsjoxvxiW6R59a/AfmXteLuf8o2NBzVoJgVGN096KUD6YZZaYAmHGsLf2k1gxiln3S1RF0NQXbU4hMCqu3gmn7ZGoeSAhNjXIV5uZhHCmiIAYgPR9vappwSpYffXY/tJJ8tOUGRufHo0wDrdEKwlCR3jyIs7uA2NGLKNZo3VAjmRnAnl6CaRXqTQb1vMGK4hy6c0QJDJooOyz0o//2xtq0Q5Rjoh0bPVJEao3cbjFspLBiEr1qGLNvFAZHx8S3LdwWm9ACjcnke0YoLGohSBhQrjPUrinFSsi6RgQBIgyRgUDP8EjvKGNWy4T1Yez1fdhb+zH7C3hZmyDuELakEDWP3IN1UqsFTc8pAuXjtSQgUOiaS6wiyO5UQBgFSjJxrJE6ygywt5dJPt9D9s5tiBDCbByd+VPqF9FzaEikbY+Jw/oorTC29CKqLr4l8YtVRN8w2g8wBEgpGJmscVbG0dti6H3Aa7EoLElTnddGfVFX9PzW64hyHYFESMEPj1jKu488kjuv2Jefb9+bK3v3pntdG/ZiTeB7KKXA9dF1l7C9ObKCrlTRtVqUdSIi4eF6aBJIA5UwcAYKmKVqJLZbd9HVKvQMgGGAGBMrlgItBKOFDEUVpxQ4jCxqij5z0+To5vdy7bdvZNXj6wG47rfncehBs9jvgOkIYeCrvy5wD/D1i8/kIx86HGlIRKDQaC684HoADjloNqLuYQwWqO0a+qvbbNCgwf8dGoGUBg0aNPgXx60/glt/8CXbvd0eyZ2C1NY/nz3xj8Jy9iKUMC/eQ8b8+0ps/jdRqkpp8BRKQ2fi1259rYeD74VUS1U2r9hO4Aes7+5hn9/8iKVznmGd/inBfAV+gLRNuqa0v2I7n3p2Mx9bPYne+mQmN7+bpV0/wTCslz3WStg4wyEygPO/dCRnfOmByFLV9dm9cZAjZ3yUI+d+mkdufj6yW9UCIQ1IOGCalC/Lcf1j+3DT1xaBG4Bt8dGvv/ll+/rB5m9wbG4HRkxwwYzruWPjnyybbJSIoTGQJFnYcQFzJ25k4YSVqJEPoIoXcnDXdpZ2tZG0a+hUBemGIAXF8ktdk14/7cvk7Hn88LuvZ4ffxK4gR22yJoxJtC2xKvDz/T7MO8Yn+PHC+bz/pNtZmBhlsTPMnTunIk0FWlCdbjIcCyPh0qKHcAMkEiyL9mwFTwseqjex8KReLv/6fM4/c38O/fR6jACMAObN2YoRfI+3PvwYJa+ESA8R800my1F2+xl6wiQHHbaSJkNQ05q3PfpmlCco2xUGMnW29Wb5+NOv5733voVj567gjZMvYWpukKmxXk7cfzmT4kX2vqrOoz0TKAYWcxI7SYkCNQNWVFuQd9s8aExjt2gl9YkETddU8Rcm6K3nsMs+hq+Yc8AuIrcZMyoTqbqRG4vvUZhgU5ttomNWFFwTgkV6F+tvmEbu6SK1VkXHkyPkhw2kG+C35ug+Ic3oL+Ic/sPlfOrK3/OR2+/n7IPeyG8/v5TzT70VbVkoy0DYJtUWGxGPIWIOMp3C0BrZM0TidzXG3z6AjgtMV2NXNLVyjgu/8wzVcQkYCwBK02JMRhYMScL0MXYMYG4fwCjW0ErjPWJT9B0G60nqXQlEzAHTwBypYA/XCbImKmUhZJT54sdNRvfvpHjAeETcYfxDHqojjzbMPSKonqxRmmhBKMk/WSDRXUS4PspUKFnETYdIL8TsHSK9vpfcRp/YmkHivQXM0RoqKyKL7VQcHXfwUzGo1BBeiOEpEhtGIkehpgRmJknbA71Y67qhpz8SpTXN6BmNR8KsWkbOQaGpMLREmCbhlGb8dA3fLRGWy8j+ErJ/mKaHBrFHQzLdguOPWEfPOycxcnAeQ9pY9TEnG8PA8EOEbUXlU56P1e9j7xjl2eMM7l6+kC3kyd0yhOmHyEp9rOxPo7JOFOQKgkjLpT6mUSIkgWMSe6+FPNJBSYmqjwVcVBhdj1boeAw8L8raUQoRj9Eyq85ImKaoE6hUiKrX0EEIo0Wu+Nx1fPSor3Jy5/uxLJMvfvENLFo6hbpQPP7UOt75qR/91fPvKW85iJ/98t2oqCoLaVu87YzofKPqRqVn7ssHiRs0aPB/m0YgpUGDBg3+hfHcLewafCu7Bs+gVntxHbfywaxqjL/RjvbVYprNTMp+jxHezKnT7/7/0+mrwkZoHwio1R79u1sLwjr3bH8rT/V++lWdP9wzwtCuEXo29bFr825O+uk1GDIkbgQYUmF2GGCaXHDNh16xjbJXYEHL0xw1bjXXbLvxL/Z55wMX8Pmvv4lr//gRjl6yDxPnvR6EQFrG2KLKRNoGwjCQiUT0tlmDcGJowyD7aC8dn+km93jk0CJRJPfvfUk/WlU4Mmtj6oBJsQKm0HRYFQCkjJFsvZVE651Y9kwAhDABC4wuEAkwJ3Htf7yJA82dGKtKBFkHkXAIUhZPbb1+Tz8jpVu5d/0iZltPU3fT3D00jzuGFmIaGi3ArGkMD2J2C++Y9TXmd76LjaMTKWuTkdDmyJkbxqyrNSDo/0wnxp/sbRVIJVCT8zhCkBAGzYbHjIOGEIaJVzM5eMowh0/dxOTWfj555N0M+yk2L2+jrGPU3ICwajPBcZlu9zLN3s06L8/KgTa2uml2D2bpGF/nwu3Hsnx4JnOnFrlo8Z189ZAbODxvIoQgZ+aZFAs4um0r42JVjp+9ET9dRslR9sn0sSRd5rjpGxhZ/kWevmkeASYaga8MpGHw+ukrOHvvR8B0wLZZc18LtfkTI0ciQWRbWyih/YB6ymK0K0u52SWMSQJL0n39RNycg2pOMP6xwUhHxlfsXFph1+cdgjekSTshM2MjzLPLCOCAn63l+N9u4B3jt6PKRXTfAIFbQZggpIHZJZEzQYcBQkqsQGPfClM/tJt6FmqtgtCUXHHu/ljlIMpWUAoQaD9AWDayOU+9kADbjARgCZGDI6SeLtC3pZX+rc2oVBzp2Mh0Gi0lVjYEOwAVYo5UMGo+E+8aoDKvmdq0JpQJffNiCE9Fgr22hY47NN2gCRIOtRgIN8qK8WMmlYPB3FXHHB5hKGNEmiGmQTxZhSlZZN8o1vAwbROLSOUjyzUwZSRkrEBUPYRloh2LMO2gs1mq0/Mox0TYFsKyEJaBsGx0Mg6pBEoIRCayGTaLLsK2kdk0QWuK8tI2ynu1I7NpVNbGm9eF6mpBW4IwDulsiKwZ2H0h6ZXDJNb0RYGMUEU23zpy3QmUj/Z9so9uI+0rJl6wAeupBLXJnXhduSj7xjSRCMJcKhIYVmM2yRqGDukCKanvEox8X1G+RSEqdYQflaZhmiAlUkhkEEYZLABKoas1tt7dhO8L+ocy6OU1qHtQLEXzhGmi/YDqSJnrLrkJgKRloAtVrAGPvqvWUq+9vODsyzF96niOPGo+mBJRqrFrUz+nve7rtHTlx8qNDPp7Rv7q9ho0eE3R/0R//+Y0AikNGjRo8C+MIsRAYaAJ9OCL9oUGhI7gH6Bd+1czvfl4jp30rf9/Hb4KpDSp0IqvJAXv+b+7vUd7PsxMuYK28A9U69v+5vOtuIkWUTr7d370W7xmhVxv8cDzk3n6BzHC21yMuM1BJ76yFXRP8SGWxnezJN7LPs25v6rfQ4+YR1tHdOyR0z7L57/3VrQeW88EIWHdQ2XiaMfklh3fZfa+E1GmhEwKkc9gZOPRwVqy7kPtnHn9BqrVF6xHlVLs7jmYGbFnmdt+BlJ8iXsL82ltesFOVMoEhvFiEUkpJUbz9RitDyGtGWitOXLcLox0jFpeUs/bnHXWY0w0Pkp56N2EqszK3Z/EMwOE6OEzH/kdR6bXsiDRjdPk4jeHyEDjZgW+/8KbZVk4nk+vPYGPPnUyXU6Btitr0Y66RIcm6z7Vjujux/Q10vPRyqQ/NNjmCYqBydOXt0dWsJ7HvevztM/oZfLs9WzWCba4CcgLFser9HTn2bErR1xIttebebo8iSSKRCpgoJrHigW4SqMR3DM4kRlOmdnxACnS7NV6AKWeKeTkduqBImNE4292KhwV7yWpJfVQk5JlHtkyieMOP5vCgQErb1xIpSJwpKJaa+Ww5GZmJkdQbh08DwHEnttKrT0DpokOFbgeqlgif/8QJ57+EH67ZOvpMUwqIEFqge6v4jo5iknJ5v902OfMYdwgiVuy2F2Os62cYqWb457KHIptWUZDCw286dSVgMYcLNO+bi2zP7KJtR+fxK6P5Cn+yKTQHKKVjko8tMYsKIwaGDVwDQhbUtFvcsuKAj9CICwTYRiYlgU1F+F6LPjAds6870E++eDtGMMGxqhFfHcd7flRtoRt07KfhxIgyj5uh4Goe2SPGcF6foTA0Uhf4M3MIf2A3FwVCcTWXUxPYY24JDcPRy47nkeYTuDOjaF1iCwEBO0B4dRxqAkdEJgkvBqGFJjAJKcfJ6jAUAGGy5HDTRhCGGU/1ae1YPiaxM4RYjtH0YFGZNOIpkz0+dRqiFodUfcQno8eLaCDkMA0oVZDez6h6xHEHIb3aSdwLIjZSAQ6m0THDBJWnamJYYKYj99MFBCLx1BxG2EaSEPiTtUMvSeNsAAiW2akQNbq2L6J6mhm9PAu/IxFkLIRponSCuJR1pAOAjAEoQFuRxqlNAqBtkCHfnQtZvTZiZgTBW7HygrRoGt1MAxGNjfzi+sO5o+b51E+PA/JePR8aoWu16PjpeDyz/6WIAhwcgmUbaAyMUgl6Nv9twU+LvzCaTiOgTIkwgsZ2T5C93AZESoEgp9/+7a/qb0GDRr8+9MIpDRo0KDBvzAxZwb53DfJZj5DKn78i/ZllcDwFYdN+//j2POvRCr7KVxzCfnsx/7utjqTh+MiqSsD22z+m89ffNg8hOMgHIeb0z7ChSBvUXrCIHa1C0HITx664M+2EXoP0GT4pIRkWX7xq7qOQ45YxC3PXgShQpomyw6fzV2rL+bOrd/Ftm2+9+v/5K713+Rr13by2/uv55r7bmH2/Bgj32rD2W2T3Gaxz1d/glKKsH4PA73zGVUjjCiN0D4nzTiBd8y7jXHZ/f/iWIQQewR9TTNFe/t93Lz63ex98Fre95kbWTR/J4bUbOt5grW9B5OwOpFjr79aM82Mt4pctXpf7umfTe7wAUanW9TzBpc8+EIJ3PmHHc3eLOHESSuYlXknGdcm9phCSw12gMhqCuMVCgiFwEsaXPSJc1k4ZROH5N7LxtrUSFjT91mbbKctN0xLVvOZu97Itx88gTZVpqwcnrltLl7VZFQHnJob5LSmNUx3iuRNl5npYZZO34EtAjxP8PaJVXLmJLaHM9lv/C8ZKf0UA0hK2OkbPLypi2fum8m3f34SHbEiCR2ww00ikGQsjWEqfnbOVdRbYO3yWazZkGf1HXk+u+p1kabGCU5UEjImBGjkU7z1088ghEATucHI0RpPv76D9PI62Xs9dk1pIhQas1RHtyYxAoFutqM1ttK0tgyjfZtib4673n8od9y5YM89vveZ2Zz+1CHcODSF9HSFrQL8PgPWCMykz2mTV3LL4uv53c23AW4kogo4PRWsosYua6pTslTntKHDMMqYEBItJfsePosJ4zO0jM+CaaBNk+d/NIkt308wwwqIUceoanypUPUaoSkQiTiDN5nEN9YwRl1i2zVIycgtaXIqjlkHIwRlwdazmhnpVVGWhVIINMr3CGwTJQXKNpCOjb41h4glkUYCS9XpWWYjfUUoDOpVJ9LoQdK3xUD59SibRoCoRfbLVKsIrUms6COxahc6m4SmNPXpTfjZGDqVAKVRY6V31Fy060alMKUy9eYYasxtKtvrMfUXm5nxg7Uknt2CdENEqJHFOoEFmWyZGhKZkUhHQyaJSCUiLZhUCoCB4ydSNzop750du98CpIGeNZlap8LPQG1CSLXLojIthTYNXBukKRCmCQiEYRDmYlQWtBI4Bt55OdyjkgjHQcZjEARo30dpUIaMtGLGBIZFLIY2QKVj6IKJ2GAgug1keyvMnISOOWjbRguiMqJQccy4s7njsRUYUiLrAdRc/rBqxV+cZ65e825+vnI/7toQ2c7fcfunQGgMDcRjCMdCxW10PMY9Nz/H1T9viM42aNDgBczXegANGjRo0ODvI50642W3/+YrZ7FyYy9H7Tvr//OI/vnJJk8lmzz1H9LWjPxbULk3ABIp//b3E38STa3pSOsAT6OBqZdvBiGw4zaTZk34s21kxQoSlk9FhbSnjnoVVxHhOA53bfjzGUVW7gdIobFtxXdveC91r52lT/6Eyvgwej1T/Rm6/ANyMsp8GAwNYpnPv+oxAVjmRDDhDaeswabAqJL0bJrAZbccwq7+HA/89Ch812HQe4zpLe/h/Ae/xvzOtewo5BnVCjX2Mvv3Q/dyPkcwUi9zzE2XoTX8YuFlHDRuKr+aeTbhqnFs3gtMERJ6NkPv7+KKxSfyzm/dTnWqRY8ViWxOHf9BhPF1/H18qNbpym4nYbhMTIzSOmmIcekCK++cw1uveDPpSp3nn2tj/v69OInZvO0Hy0iFLleefRUWmnyyRjrVA1rzuilfxTAMuswuRsq3EwQe27RFUUlCZXD5RSfy2Z8cRbX1v7h87QEYG0zW7O3w+ck3YksBAkIlMGsCJ1UFYZBb2ktxfTOpZU188oev55qP/YbuGy2McXkO/rHLLaWFqFkB4hk15sAiES15gs4UqYpGjYthlHwQGlkLCFIGzs4q4rkEj6lOZowfRNxrYQ1JKqbB8jsXYK+pUEtI3FqayUs2s3lRB/GRItVtoOqw9r4cdNVYtqiPuJDEDDj83t3k7jiI3391OdaG3YhcEg2EbRlMD1Q+hXQ1IhEHpXioZ5ALPnYMT7c8zV1vKQEaYVncf8d+3H9djRN+ez+3de9P7A+jWJaHX62DaaE9iVEOkEC8UIZYEukHxDaOIhyHYGFT9GUJU3QvnUjrilHiw5FbTn5DBdfyEYbgT+8is0Lgx2NYto+bS4Kw0JO7CftShE1NGDokqPoMj5hoGSI625CBRoQeoQ4xrOinuJQS2lso5+JY2Xiki2KZyHKdUOsoSCEFvvKxxoKMWimyK7pRuSRIA2OkjNIeMp0BYUYaLLaB2V/G2enSetgIOdPFrmu80EaLehS0SMbQfQW075PcaGNVQ+pd4ygvStHzrjjW7QUmrgeztYzcVadllYu/IIuXiINp4s1IEO6uIJRApOPgheiETXJIklV1wqYYiWqV0Xgzqhhpvgil8Pdpxag4BC0WiTXFKEPH91GGhd+eQqUFgedAwkKXd0FrE5W9xpHYVkVlE8hNu9ACjKrHo3dvZHRphnqsxozrqzy3ajuc+MpzyuHfv4Se3fNQ5my+8MZ38sv7fsI7Dt+X+x+7iKP2vQBRD1CmgRitQM0Fy+S/LrmfK75/Pzc+8EkymX9eHbAG/8f5Z3HM+WcYw/8yjYyUBg0aNPg3ZVxrjuMOmINpNKb6/22kNF9VEOVPNHVkEf/RilhQQMYUVqoIpoW0TW4e/cVfPD9GEY0mLiWGYbzqcfwlzvj6ldzUO5vRULG2mMCwppJMJgmyiti0Aqlpo3zt2cdBmAhjOsI+mqntjyOl/Q/pPxvbj7o2EUKzNpaiR8dRWnLoOffRlJ3PjNb3IoRgTsuTnD7hSc6dfR9HTd5K07JeYkuG6Ggb4hNPncp/PvIBlA5RKDaXugFw0mncpMGEPxiEvgFmAIbmQ1vvwW+T+E2RDfKiS74HwLy9pmBUQwxpc+/FM7GUpLecZlF2O54nQcGgkeGwnz3JI3fNYOK439DRcjkzDtzGlKO38evBOdxamMpgkMZEE2iJ6UxCmF08/tzB6NFzCKVDGp9xpsvyrbN48P7P8eNnb6YlX2IwZbF+cB5GoPnmtuN584/fw929E1neP57pi7Ywbtpu/OUZqnWHHxx2O3u1XcJRXW/g57/5LT/dcjEbvpBjRG8CQH8hRvullbG6dgVotBAICVqMbVIhVtVDD9YoLczSslvSclcTo5dPxas0sfc719L1wRksO+d2KqMZhrJpap2CP959MAcfvYVTz14H2kHG4ohewZSv9vOdfaayvu6wvJzGlAFnvKfCtavv5LaHb+XO7/yI9uwIwjIITRCVsSwMz4dyFbm6h6+dcynXfmkDVsyLLJN9D6vowvTxPHPhIbytsoJ99+khFldMmFRB6hAhJUJGZUR6rKKEMERWXSau9BCBRoQCARjJOIW9W1CZBCodR4Yap6+OsGywLdw2Se4T22n+WY1dJ05Bx7MIBf3ZSeDHMYarYBkgwK1aBLUUOgwiPRchEPEYYd0lrLtgRCUuibKHv3uI4RkOYdxCS4nMphGJGFg2JOMIDVgWIh4HNKYbgueRfkec8rxmwr5hVN8A1kgNZ9TDqPiY9ZCD5qwhb/rMSvRCjTHbZhGJSKsQvIDkjjrOsE9ik4dMJomvkXjHNrPzkAQnhk8yeesuJpd6mNyxDaMWIAxJrL+OVfIQQYgOFWEmzrh7B5AjBWKuS/anA+RWFaBcjrJwbBsRiyGn+BSWZajsb0EY4r9boy6x6H99C6UJJjoEayQgvrUKpRp6oIA/IYtOxZCmhcpnIk0aNPGV20GCkUyy4d0TuJ9uvv/pX7/iXDL6nCLRo7FGJTuqTVz8hxdc3O5e/iXuXfFl7n/6S4QC6vO68LvyCAN0qDj1mEs4ar8v/EPmtAYNGvzr0vh13aBBgwYNGrzG/GbDd9kXC9YmEK6i/RMbEFrx2x0/3lPi8ufINF+FaR1IMvuzv6o/rdWrGueqgUF+99NDOe4H5xLU/7Bn+83nvoPQNSj1p7l87XRkyx2YLbfS2noZMacDpQqRXerfyd6dP+JnK4/lkeHp/OaZZZSa48TTZazhkAPf/I09x01PjpLAJ2vVOah1MV/d7xQWT9QsSdcZFy+yrb+d1kyR1nSR0yZFFtfTphyOMCInlOQ9o5HIkC/ZUazz+0+cjj0isQqCoDu6jre//UC0Uqi4T2n/NBs3vYNDm7cT6yuz9t6ZaAPGzd+K6yRo//ou4s5idpR3EEu6JG2PX9+9Pz944lA6gyLDfpz9cpMpl8vse9zXmNvaQ8aQdFmMLbo1718caT58cNHprNk0gfUrJ3HdRe/lur1cfjr9fr70pj/wq2cO4rGNM/DKkt33TGDm/tv4/v7XMnXcZr6y5nxuWT+V8x8/jrc+911MC9xQ0OmM0JUaZfyM3Xz4sZV85OGnCatFjF0j6EDj9AUUJtsseP16Mp/djTcfXEfjxaMMmIvefQP5To8nr92Pt+63mnPm7OKUN9wBAoxR0Epw5feP5jtfPjayQfZcQCAcm/ZpigfL4+hWFk1GFUtMwsQACUJqDli6AyVAl33EaCnS16hU0LUahhsivQTpNTUwY2BZaGmgDQO5vR8qLo8/cQSP3jeVQinG+On7kmvLRWKmbhiV2Ax6KOUSz3oc8eknEUKTvW43ZkVjFwVSgLAt3JgCFeKZAQRjei6mQWJRkWViMx+a/hAfft8fMGohzfftJPVMD7NP3BC5XRkmIpYgdB2EbSOqLkhBaBkESRuVdBCteURzFtAYW3rIrh9g4tXrAB259NTqUKqgQp8w40ROMmMB08J+rSip0Ibg4PxqZnX047Uno8CI0JFtcMJm5Jg0x03cQlooTp74NGbBQ/hhVCpU89CmAXEHe6SONVTBrAWYGlpXKibeAJOv2syzF7cy/9whOs6oE9tXI12F0AJ7oBYJ5wYhjBQJLMC2MPvKFAsTqG0z8baIKOhhWUjTRCuFeX2BJnMXqf4SYZdH+EeL0t1xZh69FTUyhFaC+vgUYVMcbUmMag2jHOALFempNGUiEWIhEJ5P0+9WEQoNhkD4aX6yaBV3rF73knnkCz+7NRqrVlgVzX/99kS0hCM//eOXHJvYZxLDc+MMLs0SBBqZdDDqAbIeUi7V/u45rUGDBv+6NEp7GjRo0KBBg9eYC27/KpPe/iyXtZ/DIRNPQnzub7OsNu0ZpFuv/auOvXbzWeyo78IWMT48949/Uz+nndTKb2/qJ5+XHLD4hZKx2W3tBKMOie1R5smGQZjdHr2rGR39MmHlMjCm0NzxwN/U38tRenQO1zARZ0ijpwlEAIkTd9M0vsg7rz2Pn73hazxRzOHrOAuySzhx2pcBOKzzQFbuPA7UTjaOa+WuNUtIxwPSsUiN+V1nHcrvbn8Oq6JoqTdTVhphK6SpOeH+X2D7KZJ9HmYdjjvvR9z+gw8SpmyKb6tQsFvYVuvjsxOvZ+/ct2m6+0kSZw2wXXbQbJWJd0bWWQ/1/Z5PT7yHXW6aVWIJ7hPwQO9iHvz4iZz27tv5r/KPEbGATXWbWXGP7orJ1qEke08YYaC+i4fXz6YrXuLqwxRp0UHcPA4veJS0HXLsxGYWtV5FXETuR5dOPoyDW7rpskO2eCmylkJIybDOohV4JZN7ts0mZWmePvViJv34Yq4Nwegx4Wt1pn9ukKoDqUGPsA0ee2Y+qcsHaO/eArbDxb9ZzoIFVQoVE2MkRCnNitt7WXZqyHFLerh68yh+LIe0ILupSKq7irasaAGLRvs+nJ+g09lCxvS5d6CNxOxT+OR5m3nL264hNAU3PLAQqSD19A5EMgFCoLRAS4EwJFqAVYEg7kAMdKGICAKklKiDDPrWQFxZCGHw2KObkGGkiSJDEI6N1BpRD6j6Fr+77xg+9Yff8evevRi9qI+BibPRCQfdWyTRXWTHGZNp+eM6hBuCqkIxRF2TYsdjcbzvSWbkRrCc3Ti9RQQh+y7cwuBjLYysLiNc0JaJ8H3cWIDd3YdIJpBaQ71GYUoKQovsqt0ILehYVuTky7fiKYOfnLg/sTDSavEtjRgsRQ4+6SS4HrWZWXIry0jD5L57pzNrdh87kjEuumMDRmITX/nccVSrJmY+yh4Z6bd57NtZmvt2EsbbEHEbVSpE+jjpNMLzozQkzySMazQaEaooY6Ym6OvMU2mPMU30sbE0BaF15LYlZRSM0ZogqNG2n8vQc+14CYtctsbS169i+1N5LCEivZuYjajW8e5LYtp1Dv3UAFvXN7Hj22Dv69Lyx60UL8pjD2sSm2tIDCAKXA4vi5Go5UivGUY1ZZD9w6AU8ZE62bu2Ul42BW9SiALOvfL3TPnBKj70w//gxHcfDcDtd63CVCBDxdvPXMoPH16B8KG/+tLAyKyjprPt2Shz63WfPoxbv3kfum8EgeAN0z7MTx76LFNmTfy757UGDf5hNEp7/r/RyEhp0KBBg38DwrCEFwy81sNo8Cr4ya13kMs/yL6tm3l0x3/9VRkofw876/1YUhHyt79N/cKh72DNtz7Ow5/66Ev2nTFxEb6lCW3Nx6+9Zc/2kfKvgJAg3Pr3DHsPCdcgv0GRGNGcMnM8wUCCGhYLszvZ/HSOL934Vt6cf4bDMxvoc1/8Q85WW8jJgHdO2spv33wg95z+3j37MpkEbzhmIQqNUoorF5+CcBTpTJl8vsgFb12GNaIg1BS3RTashuczXM8SliyqAwne8dYbOfnYFh67dxK7l+epeibbai1sq+RRpZ9wbPJX5E2f+ckC+x+kKe3vImdXOP6km6gOBFglH2FKnq3nub7YwnfvOJBDJhZxhGCcFRKXGqUsomKGYfyghLAPJBSdWMkPMXPSLoRMkDUEH5/+IEdM+y5pQzDLdunz4oSY7BXfwtFtG3jPkuU8ccwnufOQTzD5y99ADhsgNapZI8yQsMMmKBQYPS+AAyrEdrmE47JRWYaUXHDu3uzwPD7xs72pD9vYtZDHrp7GbaOz+K9d8zj/pFuRYhTla8xtg8hiFV13UZ6LrtXpOXUazz+5kKzpRWK7Ks3bv3o5N9erfPL9h3HBW5dCLURoRb0jGelnjGXnVM+OoZVCK412nGg7IJRCoxnefxpqY5pwQgaZyaCmdGH4KgriKIXSjNn9KpTrgR8gzBTfPv9tjHxqMmFhPM3Plmi/fzdtD/cQ5ByCtEVtQgvCtqCpCRIJnPE+/X6OuKFxNzfRdI/AGK5hFhW/eeN0Pvim+zni5EGMuE0yo9A1F6e/jvACqNaRGrwmB6tYJbFjKHLwQTP9QwUkGscIkN+FmV/bid2ZoDotj7IMdC6FEBrSCVJPDiDicTSC0ckOGa9M7CshLTN8mjo9sjPK1NpNprT1st23GTIgdCQqo5GGRBiSeLNFJM+konInAbguZqnGsG2yc4HH+k/PZuN757Dl4k7YaPLQVxdh9weoMCSMicjGWim0aeJPSfLE4nbM3mGsDT2M7Mzw0H9NQSqFMA1IxBDZDCSTWBu7WfyOQU7aezXveNNTeE0xBs4DYVpYdUliQCDiCbT+k8ORxlJZcrevgR190JRBjWXnCCFofmaQrsuewmwpUd+cJndPDzrUfO/cKzlp3AcYGRkFD2QAaMEHTzsKqwKmB1pofnfPsy+aM845+UDEWNDlnhVbuP/Zr0UaL5aNSKf5wAnf56hDv8KhB32RN73hO/+QOa5Bgwb/GjQyUho0aNDgX5wgrHPQLV+lGhj8dL8T2H/cAa/1kBr8DVx+25M0L1hAeZ7D1nX7wcH/u/2dMu58bur9FtNSy/6h7X7y0BTvnf9Ttoym+dANZ+7Zvt2zaDUMSjpP+z+gn0P2msqVo88Bmr6bhll+wydZ+r3PcdsPD8cuSO5fcwjvWvY8rorxhonnvehcET+F3bV7mJj/JJ2Zg17StucptCkQdZ8LP3oD4j0hOiEYGk5y4egdtJkxrKIiSBp8+Wu/x9SatomjmHbA0K40Pd0aiBxdxi8c5j/GPYGnHTKJC9DlSzFwCRBI2cmH5i3Fsn9Kwq1wp7U/ComwIAxgavA17n3uWb5x6hVYQmBgUFUBvhbYwsVEojGR9v6YicP57wo06cwn8EpfR8lJmGaaXPwgnt42zK03HMlNSmJXXW75yOUgfa6873RufmABqUk2lXoOUTcgNkI2EeAf7JG9OY75FRdSNkFaEH9yJwQhJE163pTihO9+kF+c9188e+QWEl2w4uFxnNG8hlmOzbP1Li4/M8vlu5+l9IMQt2YiAC9uMPS2vSAWBT+u2b4vnZlRHnx+Njl7DRMPDhmY1Exwq0v70z1I20FrTdsPqqz6/VR0W4r6tBKTT9xCZaekujsJdR8tBd0njydVzuK4IQiBUfYigRfDjMqKAG2axMdXcMttkSbMSAHtOIQbBzE7smBaGK5CO6CrNWQ8hjrWx5hZJnX1aOR+5LpoKTD6bI66uIdJWmBNHuLgw7t5bMvYT2sNzz6Qp7m1itAhmdYKtWIkUKqUQhoGYcymsqwNZ9swwq8hW0H11rieBTS5z1H0bbYP5snMn8zcq3bw/EPjmPWsD5nIjUdrn9SOMlgm2BaxOxVPXD0VLU0u3z0PSoLepzTVcQG990/l07UUR56xnuL5OXpWdTHrFz4Emukz+nkqmSGxDSjXEIbE9CWhEWJPSuJl0gSAmqAoP6lxPqFIxEq4+QTDy8YjK1V0pYqKWbhLm3Dnhsy6uhfqNtqQqOEag04LhiihpREFsUZLaN9HhIpNt+dYc2A763rbkQMl0Jp4h8lnDrmfR++bzrrnbQgj56LqwhipVQJ7fB1/IB0FUxyHQCuMUIFhYAjBlPdsAsdBZFJoKfGXzsRP2Jx24vfR4xOgwB6Tk/rg0cv4yc1PYCC4+Mp7eOORS/Y8T1M6mkn01UkMB7jpKLNsn2Pm8/Sj21BhgNAKuW03emIr/TtHOeyAi/jqt97IAQfMe/WTXIMGfw+NjJT/bzQCKQ0aNGjwL46vAt406QkWpnbxm219jUDKvxB1r4+pJ2xm89WzuenRJp76xaf+1/uc0XQoH2069B/ebl/5EvJWwMzmETrM/j3bxzuT2OVqpqcP+4f0c87bDuX63zwFoeIDFx4JwNP/+WUOOvMroCUIqFpv5oDxn8cyUi86d1bbN16uyT285a37csvdz6OFgRSCjqsd+t4hUB6YSYjvZzK0UVCdqvj14Ho++q6DucK4HULIyBpgo6XEPrPIUCbD8vpk2u2Z7N3xTgZ2XsyQUuSdebS03cajz25gxdVTqZccgriFDEOcQPDoHz4JwOGLDqFeKKMqP0NhkO/cxMnmX/7Zls28DzLv2/P/TvZifrXiLkI9iga80GRYBOQMxeuWPs/79luP1rDvb9/BsbMW8+UjjueMxz+I8fqQuW9aTbUfjpjRy3BdEo9B2vD44nVvYPzsPk6ddCMPDE3mSb+DD8mHCQ6UbFqbZf7sKgdO/Ap27ADqzOOxc5p5/BuTQIATKjKZ3Yz4UVjNG61z/RP7EaZDvnnS9Zz3k7cy9YidcKjNtlssxt9YJX1llSlNBSr59awtzoTVCXR7gN5lEcZsdCJGsqNGZjSNcsB3wEw5xAZ9Tv/JXVTXJ7ntYzOJwlya+N6K+nWjRIqyEmEa5GoCthZQUiANiQ6IFvzVKuMq0OsEhBNM9ICPqNcRGsJhiz98fiHzv1mia/wI3UPNaEbHBFBh5mll1t2foNwsaF/iwMZI20MaEu16mEqRedxFBiAxUEmbo/9rEzc5bTzV3cKj/QtpbioihKY7bMUaTbPt3XOYfMW6SAzYNEErdBgizBgn/nQFD9ozGKoluXPNDFTNIJ/X5J8ZpD6zhUGvmWseOxAMjShZCHcIhGTjuiZiYgizmATTRNXq0NkCcYMwbmKEglBqdAixURcvJVGGxuodIl20sIZriDBEa4P8hyvEP11H9kEQBqAF9VQKWrPowMcaKoFpgBk9Y9owqD4Z8osT56EBS1dJfirDhONL/G7FVLJliWXZaNNAJQzGJ4apPDaId6iC2xRCSLRtYcUd1OBwZNccBHs0ZHShhNAhOBb6mU2YgSK52UK3NfHZS98JwPvfeDCX3bg8cr2SL80ITJUUouxjeJE+0levPo/l9z/Phe+8AmVaaMvA3DWM6mgisA0++YkbkFzPA49c+Bef1wYNGvzr0gikNGjQoMG/OHErRcm3uGjT8cxKNMp7/pWwzVYOn9jN9I8N8YEpn3ith/N3kXBOZXNxDdVQ8Lr5q/dsn9ZxJZO9pzBj/5jgjWmaPHDrJ1+y/eGrP8s3fng7++41hbmdr2z57YcjeEEPCXvuS8qoJk9qo7kjweCWEZRtYyhwtnjYizXVusO2qTXMmMGJhz5N1qyxtP1I5tfuIUTwmUcPRfgmsWRA9iAPZRg8Xx3Pe+dcAcDWUCERlOrb2frcJj560Y0cd8QWZs3s5WfXHsm7Xncob3/Li+9RLPtZdPo8EOlXXfIlzQn8+F3v4uDP/YDBIR+EhMAkZwm0CVprhBDc8ZZT6OxcDMCCVDfjrWFaYmUm50v8emAJx7Stp0nU2B40cdSstZy3z3OUlaQc7mLtQAeb/VZ8LfncV17H72/9NHYsQ191A896E7HfAOJbISoUzDy2xCnveSq6PlHHVTanTdjIjPwowyqG2SoZKGfoypdQB+ZIddeYkBnhjW2beHvrWi66LsGqkZnsruUJSgkKi9OUWsF2s2S2BxjlgDBmUDN8zHKBTBpmH9nHbUzbE2gbXtGKYZVQoQClCRM2whxzB/IUvhcwvNShdkCKGRd6DNxmsqx3B8OpJoanJ0hsHooW6lpDzyAf+8FRVN4VkG4rkW3OQ8zG68rxrUtmM5wtknd7WbciS9rxkGH0uWjPQ/kBZs1Fxh2EkKjNdZ65uoPzv3svPSWDabFuHvcXs2soxsizU7BKYHoi0izRGkwzCqIAc9/UTce0Ej3dWVqdAr1BOzohSCYCah0WWmkc7SA2eoi4g5aCwAgwXEEwvo71hIeIxUH5YAjstEvJShIaGjcewIAmsbpKKByCGTnM/irOaA2j4O0JHKElp9kr2H5igidv7aTktpCwY4i0g6EEpBKEhTJkExiFauSgFI8hhEBLoFJHWCbGFAthaAxbU54Qwu0FdK2OnNVKoncE1R/ADRZQQwsJjgVKIVIptAqjQIqUUcaRUmhpYj38PMJxEIkYMghRPUN85ZyrOGrFV6PnJCYJXI1Qmk/89Ea++f6T9zxDH//4ifzw4ps58PA5e7bte9gClhw+i6cf2QJBiLRs1HAJ2ZYGM6qPOvSQL/HAgxe8que2QYMG//wIrf8P5N38AygWi2SzWQqFQsM7vkGDBv90HHv3uRzZsoE+N8N39v3daz2cBv9Hue+Zg0mn+/nm84dz5Yk/IGZb//A+zrvuUmR2DV857Euk7NzffP4zO/dGqWHash9lYu7cP3tsb/ckIOS4p95GtWoxoaWINyz55YE30WpoVrqzyYrdKGBa580knU5Kxas46+n7cRxQoeTXB18OwNPbZ+PrGlLDBz9zLnhw5bd+gGWG1ILXsXDKZX/7zXgVPLF2E5//+RVc+aFr2O3H2eV6NNmKltgh5HNfYnPv0cRMl1tGZ7A02U1NmaQthYsiT510/D9IGRVS3g34WrCpLrng2SPQX0sjdAzlmFx/8yYqwUe5d/g9dOsOANrpw9GXctxkl0f7zkWgKboWhnA4KF3g3XccR9112L0qS9NhI/QOpUj2wrJ9xtPc9nvO6VxB3NSECg6/+l1M+co20Iq+N07Bz2cBgT2qEX7I4sVPMfh0O/sfuZ1nNk7BOHqE0bdYiBC0EOz/2R7u37SQWUPr6FucpfLLNnAsQgE6E0MJAyUUcmsPTG4muX4QtCCIGQRnmhyYXseTV8wFL0QnY2x5SzN7rdxA94Q06ikfp5jGn9UEShNUB8g+2AMaygvSpNfUI00WKaIg1lhABEAHAcY4WHJDhRHPYctxacimcQ2fvmUJ8r0OOu6QfWQLaI1OxNndHOIfNpkHP/sT/EDzWDVH9zMxvhOeCgiMIcWEb22jPrsDsim0hpFZHkFe0jG7H/2tFmJTuzGvHcXIt4IA0VGiolqwa5JaWxLtmBiDZeLdIwzuPY5wcor0XZtJlEEIGY2lWALHwvisReypMuWNJv0zJtK8UWP0DhE2JfCkwCxWsTIZ2LkbqaNYh4jZkV5NEELdxZ/RinWsQ/iApjIjRnyTwly1leTxEj05Se2HxTGdEhMtJSgFqQTCD9ExB8OxCXt3R5kvY1o4CIEwjMhOesw2WmlNfUoL9zz8eVZv6eFd37gehCCw4PmfvFQH6uU4ZurH0JZE1F0INbWMjWzOIfwQGShUwuIXv3wvkyd3/G88zg3+Rv6d13N/urZPnvl9HDv+Wg8H16vx9as/9G95r/9EQ2y2QYMGDf4NWJyrs1eqm8ObNr7WQ2nwf5gh5yKuH5nLtK5+KkHhH97+kd/7HJ0zbmTppCf44vIP79k+WHmKK9YdzC83HPEXbZY9VUahKLor/2J/LW33EDfauGLGH2nPldgxlGPUNshLsAVMi2vmdN3IvK4oiAKQTJ7EF6eu4+zOdfxyvxfsVF3dxvLKZJ6sTkCFPtqEgTBNqCzuKFVf3Q15FSybM53bvvE1krlnyZouU+MhWUMx5D7Mit1H8sWtB5ExAg5M7qTkCbQQLGz6Fvu0/5rFE1Yzu+Mixrd8kwdKS7hk10K+uPNYTp/xGGE6g0raBEmbE+9v5uTHfsXza7uYZvSybSjJFY8cw6/Xr6YldQiOakVXXdJGlTnxYVxd4btH38BHjnqSdZd+iiXGMuKhJsxr7ts4wBLj8ygk16+cwMGXf4js74ejshYEieeG0CEENtTbBS2Ld9Dz9SYW5jYxZ9Ymjjn1UUZOBu1G5R6hEXKlnMNHz7uTc77xPMuO6mGvSzaz8PwnQAoMaeCnJGHawl00mToh2DbEHYKET/6QYfqengy5NMQsRN0lff921s6czBFnbeboS7vZ/4p1GD4gBH4yC1ognTjZzQoCP7LZNsw95SdozcAyg8q8JIMnN/NA73Qe/VEzVGuoQokgn2DSHSNk1gyQfnon2rYhEWdoTpryG2cigVIoiZmCvudbuPl9cyAAUJgDYJcDEuv6UQjs3hITf7yRpb9+FvvbeazmELtDIx0b5VdpX9rP/K/0Y+8aIl7o4dhzHsfQQFMSaduorMQc8QkTJtTqqGIpcuEJQ4SG4BKT0dVp1C6J/f/au+8wK6rzgePfMzO3bu8FlqX3IogioMEG9t67scVEI2gsiT0m9th770aNNZaIgIIKFkDpSF3q7rIs2+/eMuX8/phlI1F/QaOC7Pt5nvu4O3PuuefOnrsy757zvvWKRMDGQ4PrEV5RRaCyFre2HiMUQkUjkBZBh8OQngbxBEopVGUjvBDD2GRjVRoox0VlA90U2YckSW1e1hMIQDLpr85pbMLTDtpJ4WqNUVQAGel+kmJP+7ltHAeVSuF5rh/E8jysliTDj7qO3x5zN05E4QTB/Y570OrYV7Qka7c49t7K29BJ20947GnCzTa9++SA62GnhdAaTj/pYWzb/rE/ykKIbUy29gghxA7gmiG38q/Vl9E5c+dtPRTRgR3ZfwyBVZVkBDPIi+Z/axvPS6K9Wkyr0/fu31AxRkTWsYkgJLq1H/94w510CmxCAZWxL+icMfw7++iecws18ffolfvfl9wHgj0JZF5JZ3c8luuQnR4nEDC4btVw/twnk4LsGzEt/326rstna3Yhw3DoU/o+wcCW77+OkXxav5GEEyS3rJ6GNYXcuGYf8kIx1sVyuWTw974c39vqxg2cMv0WuqXXMKHTZ/SIaFxDscnRGHgYSuG2/dNwbSyDcDSBoz285j+TX/Y5AMvr3uOjypt5Y3kP6tIzCSiXFiOXjD6baKwoRDsehXM0PQ5eybuTd6F7t/XMWlPEJiOLgLOIZxbsTshqpFuGjdYmr1X14HddFgFg2Bv5YtU+3H/0FHa7t5qqcAPE4PLpk9gpayRzW4ZgKkhf24p2HFCKTZ3yyVmW4vgJ/2KPntW8s6gnszrnsHDXzvRRa5ma6O3fnAcstGNjxOKMfmgexYdUk2ZaDE5Uc88xJeBFCTgrsEcOQFkWroJElolXkE9lr1yirywmaIRZ2ZxO8eI0sFxA4WgonJugpms9lgEOikDYQ7lgeZAoC6I8jQqFwLYxQmE8gFQKHMfPC3JBitTIXtQ3RvzgygZN739sAMMgo2s9awf1Jbqk1u/HdtCpJNqyMEKFaA8cI8ih95xJJD1OzgMNaJopv3kjiT65ROfW4KaSxCNhgjEXc+1GVCxBw0KLYz98BzdkMPHLwdhd0yBq8NmxhQQnJylqqiFhKV5NDSF7Qg3eH4PoVIrMFS2EGhyaS6JQ5/nBlZYYKiMNHQxgWCZmVQKVdDFbUygjhFucjQ6GMddsBNNEGeAaJpanIRhA0VZ1KRqF5hYCTQ5OWoRkaRbRmiQkkliZJl0PiRPVLqt3HUDLhDqiv27BMAw/obDrl7Im6aBjCVLpEQK2C1lZ6EQcbAdQ/uslU+hIGBUOY9Y0kk8ALxghtdEmHtWENrYyNvsMVMDiuhd/y257D2WXy2+nNQkYDnNvmoBl/jvF8ytz/8xRQ67BsAwIBlj9QQWHnjuC199a5ueBMTz2HfkXrABM+eS6n/6DLoT4WciKFCGE2AFEAzkc1fNhRhT/ZlsPRXRghjI5rNvJ7N3psO9s01R7ErHaw4i1vvO9+3/oqI8oj9YzJFTHLWOvaT8+pvQ6Njj51HmllKYN+3/7KMw8lIFF9xIK/LuG0NrKM/lqdS9WrL2Wpxf9ihkVR7avbIlEDqfONXlqpzcwDOiUVktGWjOPbQjRmFhEdctkAJ5YvC/dAnUUmE0s3XTHN173vtsMFm0sYl1jNk19Fdf8bl9cN5flzXkknW//u9bEVbdz0WcnUll72/e+Vv9Ja80by3/HiUWLWVBfRpUTwNOQ1BAwj+GdupE8vXZXDit0KS6awQlDZrNzMMEuoQbSrVB7PxV1F7Nb5gpO6vcldQvDdE/fwNDQGo46axrJrADJgiCrG0uJmCnG7TyTKc2DqUuUohzNeYM+oU+0nvWxYkqtFCWBOPnhJmrcAOudIAkjiPJWsGxlGdeMewo8RXZ6MyWZzXy1sDe0gmt5hBwLIzMDLItT9l3M8G4LOLB3NeVhmxN3WsgdL8+AXJPKrM5kGIqNI9L9kscoLn1mHo9OXUHPqEm9A15xq7+awHFQpknws8Xw1Xq/DHAUMMEzDWLHDaC+KEJ8kwVNCYzmOKo1ic5PA09T8NJGPtk3Dc/xWHRZBpHPVtC6dh1lTy0Ey0S3tKCTybZKNS7KNMFQ1D5YSLejNa42KS5qoHNBDb3OW4L2f2j071mP7pbA7t8FvLYS0G35WYwNzXS/ZTbZn26i+NEKcu6pxUulSHXOJVTXSsZXTVgbGzA8iFS14KExw2EwTXQoyHt3DKcoLUmPsgpCmXG6ZVVyTc/3iVTa/nYY2+OkspkcUT6XyPpaPAtCcYWBX4778bff565nJmMYYBgGbl4aGjBa/MBFaH0LzV0NrIY4ZkMTyvNQKLyMKE7nXOxUHFzPD4qlbIjF0PgVR1TKxQuADhpgKJJ1QdY8XM6XN/XDrEtS05RN4olcMNtyoRh+/htcF+W6WMkU2rb9ssxWwE9CGwpihIJgGKhwCJwUKiOK2ZrAamwl55P1pH1URXqFv91KKYOrj7mfS39zO01JDx3TOMrEf8F/y8rK4q/Pn+1vG3L8/DXvPvgZPftkoA2FSnh4GSG0GWCvEdfguu7//HkW4rvp7eixY5MVKUIIIYT42Xg6gdIaz/v+21kyIvvRFHua9MigLY7nhLtzWp8Pf/CYWu0PKAlo7q/+iNMKV+CynJoNB1BcMhHDMOhbtpLZFV25ouQtZhtdGBFZTUOqipnVH9E92MzSqizKg3G+jGeTYdoMzj1vi/7HHHcbMSeHQ7rMYO/sFUyt68HBuwyle32Y6+f/g8O6DPzGmLTWPFm1CFNFuOyrhTzzzWrNW822V2OaxRyQ38iGRCM90urIs7LJ7vTvpMBXd/7m8zp3mo6OPYAKH9R+bElzKenZSRY2F2G3RHhkxPPcNe8cRufPINnbwW0K0XXEahbVZpGb4zA8uIpjR86ntimTL5sLMZRmXnUphxUuBBSn9votp781i517rOTVz4by1qHP8mE8mwGRjVw/7O88WHkgoQwbtz5AzQYTssGIKjwbkp2DPJfXh8+Of5JNrolGoTVkBW0Gl1aSYSUoy65jr7vm4ppBdKtDVzMBgKUMPAzKgi77XrmCT+8qoWWjX9cnfVUtG/tmQyINFbbaghrAwBIij64jrc8GWmZH8UJB1MpqP+GraUADzBoRAS8FAU3ukph/0ZQCzwHD9Nu6Hto0yHgxQEU8i9pUPT2zNlCbSMc6ogbwV7BYZpzqiijRxkYCtZV4KQ1a+UGOYJDMigaMcIiMBWsgPYpOJCAShswckpEEAUf51XRCIUzXJW3qQtzenTFyc/A8h4aKXB44cxy1vSMUz61grZVO14ZGCno2Y6enofAoijaSHUnR+9w8ljzfGTcaxAsH2Hu/BWQXxMguUOR3jlGzEEz8xMVEI2jHJbShkcK5UYyWVgzTwMjPQ3seOhyCjU2YeXnQ2AKexgtaqGgUlfB/PjpoEam38QImpmXiZIVpmmuBoQnUxthtUi17XLKO6Y/1Y92dpZizl6BM5f+cQkH8yJmCRBIdCvlbhxwHN57wAykAZgA8BUqjNJCbQc7GBnR8E3bIQsVtAo7DJ59X0qU+RfU+hZheBMv8d2Bxs91GDeCyu07k1gnPAQbag7WTKoiW59NqggkYDX6OpP2HXMWkBTf88A+0EGK7IIEUIYQQQvxssvKfJumsIhIc+r2fW5B7PQW51//oY1IqCsTYnF5lTTJI3FjFgoqB7NttAYZhMKjTUsKqNytb4jy4ejQtySCPDJnCJjtEUME6O5cBkUo2shdZ0dL2vmvqm2nKVoDFgEgNnQLNjMypB6B/Tj+e+9V3l0jdXIk1/wcm7U02P0Oq+Roc7UD4ALqX3E43r47RvcdsVRUgwyqGrD9vcez8oe8y+PobSaQ89t3bv6EcP9hPlHv+8ZeTFXiJJjfIo1/tzu6dPqVXpIHe4TgqcxMfberE1a8dzHvnnENe+lJsZzUZacfy6gmn8vrS1xg+7iNalUWBlcLCZo/OrdzzephUnyDWR5q0bopRe3/BuLFf4SzRLOvRhadn+VvETG3zQlUveodirLTTePP9IYwYVcFuOQcyK/ESpvL/OnpAwSw2phRNHhSYNo1emBFH15J/iI1tp3FG/hqqWwJcuDqNDa2ZVM7II9ISRKU0TsggtW8ZK55tpKCpBdAYmZmAhpSD1toPmqD8VRXKTyiLabDLEZXM+mchOunnnQn3cDm3y0r+uKqQhTUl5EQSZBxR5VdVQtOSE0UXFZBasJ58vRactsCAaaCCQVRaFK+5BW2aGIEAiXgcqzAH4jaqZhNGcS7uplqUZfnBFNsBNNbqGj9g4WrMuItu8sitb0XZNo6t+Ns5A2g4J4uMcByCBiksqqvTCcccnEgAJ9FCpN5m2VdRlDZw0MSHBWC+v9rGCIT8wJPrgmEQN1LkRGKUHRWjZmMOsfcieAGDcHZG23UCNtSikgmUZeFZpl8Fx9VoT+PZSXBdPKUxahsxlYHWHrXv56AvrSSUmSKWbCa7tJjmVIJwPI6lFRjKTz6rFMTj/ngCAX81T2scIiE/1mIB2kQFTFTKxX+qQdAKQCRKaypFWk0SlKLwgxreXHDTt35WKtfV0HNIOX2P6cmcsvnEHkknPZKJ3RTHSA+Tsh0CSvkrbFDU1TaTm5/xfT7OQojtjARShBBCCPGzMc1combuz/668fhElIoQDv/qG+d6d1lAa+IrLissZ9GG14hxHSYajd1eHjgcDrNJHUGfyLs80TSKlDL4YFMJgzJriWHQP9xCMDqefYsu3KLvmoaW9q+ffngMfS+LsXvhb//reJVS3D3kZtYlqhmSM+AHvWcndjehthv7lFeLGfxxErHMu+JP33r8rME3MPDvBvFUkBt2eosWHIKuR5UdJag8FA6B9BSd8/OBfCKMan/u4b2PAI4Abievvp71TTuRrzQTz/k7+004hVi+iaegZ2gDPbKbmB/OY/6KEg7pOZXVCZObK/ZAOyGG9z6Ll5e9TLh/kvcmDabfEZuImC7DI2uYGy9GeyZZlsKJedSoDCIBm7gLAROaUg6fLs/iw0gBu5WuptBq5oaGsVwy8J/c/vCRYELaOg17D4DXF4AyMJWC3By8DTV+0lOtt9g4n1Ec508fLKBZm1R8kc7GpWmgNd5ql8qaXM5Pm8uz03ahamMnoieXo20/mpf5yjycnoUo2wPaKvu4HoSCqGDQz/mhNdg2nmFgoskI1JBozSOQdHEXrvKDCQH/eSpgtQc4tG0DGmXbxHrnEq5oJD6sN9r1aLUMakIO8d+EUZma2/55HAAqZFPkrcfclIKEy5p16cS8KAnPoGp9HhnmBrRW6KQfdMB1wbJwS7IgVkuqxsA8NIL9UYBgZR3k5/rbcRyPpAWhVrst2INfvrixBQpyMZetRSuwepZQXRamdH6N378K8I8Xd6VhYwG6PIZaV016IoVdkElN0KOwMuZv9UnZfu4Vz0U7bVujsjPxmmOY0TAOAVRaGC9lo52U/6PzNAQtSCSJNrXghUPoeIJUfgaNtTGiXaIA2LbN6OPvILqkDituk8oKUzNeUXR3PsGCNFJAwPZwEjbBYADXUn6cLZnimHE3o5TBK+9fQlaWBFTEj8hre2xr28MYfmKSI0UIIYQQO7RUcg41dRPYsOlcbLuSlXVLv9EmGu6LZUUY3OlEspWHo8EkuMXKjT273YXjBZjQ5S2cpMmjVcO4bvUINsbhmukjmF6T841+B3Yr4Zz9R3D4biW8fcfVDC25m4xwv60ad14k7wcHUQCstHNJEQJrINm5z/zgfr6PS4es5PU9n6bKSGNQtJ65yVIWNqWDE2dI+gYOLPrmtb9hygMcfO1NtLQ0AxDJ8FjrZvFhKkJjII0/XPUG1pAEux80m27hGm6dPQZlQnyVyVvz9uWEp87hV+krqXiuE6O7jSZWUcam2QUYnZN8WDeHOXU53Lt2NPYUk2mLS9lgw0VTx5KyDKbWdmaDzqbZDXBIpJohvTbSJ3MTK+KFLEsUkfQMHFeT7N1MuF4Tz4Lmck31uT25Ye5Uzr1lNiSSGAUFNOxeQGxgKQQDDDuoitM/X8ET0+dhxzO54Laz+GK/PWgZlElzvyKaO3fi2vm7MaW5jL+d9jzmuCbGnfE+dWObSe3bwPBnl+Fmp/slfE0TjLYKP5blr3KxLP9r0wTPw9QQqwwBbTWFXRcc1w+2KIV2HT+5re2gHQedtPG0QgdMvMwIpmFiGAbh9Q0U/d0hOjmCOSf93z8kHaBy32KspgRerJXYbIenl3bhkXf7kP5pPWiPZLqFkxYilh2iaURXGvfuSWR+NckaxbqGQpZ8Vk7VvgXU7tGZjQMy2dg9TNXYYhLdMlGRsJ+Q11CoYJANh/SiepdMvIywvwLHCpLbotCm6W/L0RpeNYiHDLKmrcFragHXJRCzSQtFMMJhtOthZGVibA4+6bacDZ6H0mAHFMbGBpSjIRrESE/D7lJA0tDo2jp0TR06LeJXDbJtQtX1nDToUsZFT+Evp9zL+GtfIrqmCRWwUIAyTVK2iRcJYecESOUHieWYGMvWYtQ30mtQqT+GgIVh+4GdQ/a8iV2OuJHfXfr0T/ipFEL8FJTWm3+riP/Pjlx3XAghhNiRuW4NVRsPQxHisvm9KAzXc0TJLIJmiIBRyLDyj7Zor7WN9lowzG8GRgA+WLEfLy4PUh0q4NryiRjKZMGmHJ7ZuBt1sTSqa9IgYPK7ocu5bOdnaYm9RbxxAq4qorhk+s/xln9WSXsDy9b/ipQ2iGY8yMpNfyDPbKSOEGnmeBa0LmBM9C080yUdg021kxg+rAcAcaeZ/QZfTbQuAUpx+z8vpGwwvL/uWDQwLPsY0hNP0JTwWIHCUIrrpxzM8aM+p741SH64heyAzcc1ZVQ05XFk6HecPHZXDp14Aa3a4/iyz+kTbOLyFePIvdXk0af+AYAROYX1sZdIw+YPZ47BWQNPTvyIYNBjTcri6dU7wdwE2bu1kqlTvPfXTlQO6kl9fjrn7zOFsOXQRVXRI5TigJuOQXcuQhekUPWtjP60kZvvexWbDDoFHGyt+MPMvhw+cAWZZpJkwmTnTJtWx8VWAT5rzOOq2YeSntvITjlr2T93HivcItbNTWPB5eWYtc1+DhZloONxP4gSCkHKxrBMPMcFO4UKBf3VGho/oazj+PlAykuwQyZ20CCwaA0B20V7HgpoSg8RKinAy45iOGAtW4vR3AoZaZCfw/p+IeiRA66f67bs6fkoZYBtc8ucz1iQKODpUb3aV8egFA3DOuPmp5P/wXJA4dk2blqQZO9SGgdGsVoDBKuaSFu6ESdqElhXh+mZKNNAhUK4BlQe2Q+lPIpe+woz7pAYUg6uIlCxASMa9vPTGAbG6vUoFCoaRZv+OjK3oYlAOIQKh/3kvoaB57h+wl9DoVwPSvLRhoVav8FPWJubiRsK4ZZmE5izHKWVvwKoLfktnvaDWlrjGQZW9xTBA3Jp/iAPsyHuVyGKtZDYLUCqKR0vJ5O0ygRgYH6+CDMQ8BfqdCsFR6MiQbQyaOgWJa3Gxs2yKM6M8Mpz47fJZ7ij2JHv5za/t8tOvItQ8DtqeP+Mkqk4Nz8/foe81pvJihQhhBBC7NBMs5DOxZ/QqXgqndOqOLp4MdoI0yXQQom5kpRduUV7pQLfGUQB2KvHRB7c701yAoqgocixHEYVbuTg4nk4nklZQRO4Ad6s9Esge423EdJJ0ry1P+n73FbWNH1AvpWkNBDn9XUPcmCfWXTPvoQh0XJGdD6Q3w24C6e+L3mGJuDCX257vP25ISONUEPKzw2iDGZOWURGqBfDC65iWN54uuRdTmbxTDqXL+LVj6/g4qdOIunCB409SQs75AcNLnz/WP65bBcK8lt5+p2ZANw/cn/O7fkxOaEkCW1yZOkevDbpNjyjFEdFiKYfT37mpfxp6Tj6/rEZ68EgV/1uONM+zufdJ8s4r2wm3fZqZO+cNbzTOAjnvBxy7qmkd5f11Nsh7v9yT25afDD3TRuJ7pePlTIIVIVJe7uBg8d9hkLxdm0J167owl8qexLpFCBsOCg0TUYEE3/3SFA5jEpfTzCrgcHh1WQEbVZ5pRyUtZIz9lhIw16d/Twnhh8kwLJQpol2bJT2/NUp2vOP2W5bFRwXnUy1V7DRK9aiDYWlLLxenYn3KfVXqQAZDa0El60jEdV4q2swYgl/xUYiBfEk+RtNsqfXo9sKzaRyI+hEEjzNRecdys5ptaj0NLTlbx3SrkfW7LV+DpJECi+Z8rcWORDvGiH8YTXKUBiJFGZDDNXYiokFySS6NU5jWYQNx/UGEzQas7EVEkmCa+tp7h7Fsl28hA2OjdHYisrMhPxcf0VIygZlEAiH8eIJNkYSbOibiRNrpSlsokpLMEwL3a0zhCPg2ehQ0A+Y1DYQ0Irw+gaUx79Xom3Oe6O1v+UIjWGaeOvTaH3KQSVShErS2f/InTCSNpEZKbLn1BJodnCDJm66QWp0X1TQrxxkVlRDaxNaAbaNcj2cdJNQVYyGBRuYP6/i5/vgih3Utq7U03Gq9kggRQghhBAdxsWDnuCptQNA26S0SVxbmEbBD+rridGP4Wl/y4WL4tkVIzih00wSjWFe2+cVHhg8HwCPtThonP+e3/UXqWfucUxp7sob9d04rvxMALzkTQT1AupqTwJgyLC3mTmviL3f/zVzx2Ty8Hx/ZY5hGFz65K/RnkswZHDaHw8DoFPmkXTJPh0Ay8zGskLcd9qv+cPxe3DwrvPYM2sFY9PXsziRwaVj3mV0r8U02WES7iYAOmceyLiCybh6GH0LXuPCwcdgGAZFpZ9T0mkZkdAQSrN/w4q6YvYrWEvvQBUzjunHI4UjmNylH1pDZ6uRZzcOY9GqUpZPLGP9ab3wGpK8tmgY1nsZJP9RwpTpQwgUx3DKkngFDXRa7rDvwbUc8sWx/G3BGFLRDMKWR47ZxOzmzqxK5hGtdmhOwRsLevNRcyZrdJibB/yTEZ1WM+mewURVEkt5hA2HSJpNbEw/vEQCUilAg+GS+EcW/e+vxLNtP+/J5gXmIRNc/4ZfGYZf4QcNNbUYK9cTWLiKQF2M5M7d2/K5KPA02Z9UYJTlkd651c9dApCIQziIUZBB3uJmlOfQcGgGyWFdiY3pjbs0wXM39fOXqnhtOVs8F2ybjPcWUnNQP3RRDro4F9OxyX9vNdGAhac0rK7FSDqEW9oiNH4NZVp274y2FaqhmbKXl0F+LkZWBiojDTDYcGAPArZDoCWF4br+ShGlwfPQhgLaEt6YBnlVSZL9s6g5pCcRx6ZyaLa/SmfZKnQqCbEU6ms3fMpxoG2LkHacf28FAj+YEgj440ymoLkVirIx8rJIpOC9V2cBClx/BUtg6mIM2yFSHSfc5OJC+woXszGFWl2NClhkVcRQKQe0QqeFuODsJ3/Uz6YQ4qcjyWaFEEII0WHkZxTw0N4v/Wj9Dey6GtveiGnm8slRflBlbPdbKXViuMSYuWEmfYOj8ZLvYwZG/ZfefpmUUpzUb+oWx1yVj+dtIBga0X7sybtGkToqCMDtCyZxzqDRAIwdN5yxG4a3t9uc4PfbnNhvH2Af6purmF/5K4pCLZjKwyHIH4qmcVFWt/a2mVn5HJv1xP879n27WFy8+ED2KuvEvoPepXukDjcfOnX5hDcWXElLawNmUJG9wuSim19iVMYGfvvJIWxKZNOaqbDTg9irFWS5eEpxxF+/pN5I4hkBQNHN2MCiVCmlgXoSSc2fJh5JWmUY5UBrqU3p8jXcfMA7ZAdSfFqVScGMTTx84Z5U/2YelavzCaM5+NCZrBiaz2dfdcNIuAz+/SaOyTyZP7z2PoHdm8hKGGjHxQua4EHAdjj96td484+9aFiZDigCG5r8bS4oVG0jqqyA5C69Cc2pQAHacQmOqaL1hWyMoIe2THA1Xsjw85aYQVp7ebTWZ5Fnryf4ZQrzwHTen1JEWn0jKhhEZ6Sh4gm0ZRFIuhixOFUHdSNtQQ3ZtY2QSBCqSFL4VaW/7cY020pDawgEQXuUPjYPohF/JY2n8Qqy8Ipy0KkUlqNxQiaV+/cg5+PVRFpdVCCA9iCVmY4VT2Bg+IGkVAoCQbJXpGgpDdGUHSLeP4I9GQIE8XQK0/Nwi3PY/W9rmX5Khn8NW+Oo9DTQoAyFFwlib6wjgF99Ccf1k9gaCmNFNayoxh7YBSMaxW1JYsSTeIbG7N8N3WqD62FWbsQJWn6CYEOh7RTKNWDpaoy0NNI3KOxOWWgHdNDkykue5a+3nryVnz4h/sPmpNfb2vYwhp+YBFKEEEIIIf4HgcCWK1pS7Mekljepc4I89tWLNKc6c9vQxzi079htNMKfX2np52jtoNS//6l55DGHsTjrfRw3QGZa8zee43kek1fthKcdhhX+hcKMo76z/5yMElbEb2Bq0xsYhkGB1cBV0w6npZu51WNcXLUEc0Mukw46ktz0ztwy9zXmtnTBUi7HPPII5+4zn64qzOz1zSQiufQIteAC9+zyFif8Y4IfbIgC2S5YDphBBu1TS0hByoGMjGZuW7APwanNJAcNB8NAFyWxlcZWFuEmh9pgMaatKYvC2Kw1THf6U/rpUk64cT45Q11enDSc48bNwtOKPz96IGHt0vR5mIvnvYcVVLidM1gdaaTTXIURtyFo4Xjw+NVHgOlisRYc299G0va+dTgIjiZrTA2teymMm5IoYHhoFUtCebRoP/9KojiMYyewXIt1uyuwHQK9U6yMdkYH/S0vqpeJuSFG7kMryTEVRjSKl0qBZZI/Yz3VPfJw0kPttbwdPALK8HO0mKafEDYUAs/DSyUh4Oc+8WwbA4VZWcvyc3uTXp2FAbgKTGXQMKYLocnrUApivXNIhQJEPl5KqCneVoIavLQwVTtByb/WUHlSGd1eqibQ6ifgNdZtYvhbmpEli1jUUoCOhGh1bcKeh2qrHqRtG5pbCJgmYLTngEEDVsBfsQOYc5az/rDuqBFlqESKYblzOOHMz3nl8UGsfToDTAPTUOhAwA/ymCbK85PO6mQSrTXmKptU5xwMFWH6jBU4joNlyW2aENsz2dojhBBCCPEj2qV4MDfO25P7FuxO19wGlAkTZs6i29M3MWnVV9t6eD+brwdRAE45ay/2XFrBLkUVjK1dzpo1NVucTzo1OLh4KKpa3vyv/f96p2MYlNVIVX06ITvEyWMKefHwXdBaU1P3Jyprf4Pnxb/1uR+vuYO/zbqB44bezupNe+C6cTqlH0jXtDrKoo107beEIWl17JxXzQW7fEpaymD8lUfj2EEsQzFi/Gd4ngPNgAtGCNSmENlGivV2gJd2/jvd/9pEr6sSlE026XnFUs4d/S5/HfZPPjz2QTJCMchzsZsiLGwqRWmoWZzNNZPf5tFFMygp8cgMmwzKWY6NwtGKHiW1XHvuJG479U06FzehAeWCyo/S2DOIYZgo169IA6BQ2D1L2rb34Ff+UQpdnk3321aQ2s/DHp2BcXsU+w9ZlB+boLnGT1KrtSZUk2DjsRZpv63CiyiKs1sJhfx+lGdgJCzMmIHTI8DRH2/i5OnL0arBz8+SslGeR/Hjs2nuV82mPToRO66U+H6FbUlbQXt+OWfPcfAAIxKBgOHnhDEtP1eL61E+pYW8iiRau4weupTnrnyaM46YTvUenajfo4x4XpBksoXasT2pPn1Xag7vB8EAKpag970rSF9n0+v25ZjVm/ytOUqBYfLpc+W8urw/r92+M0ZTjHBrEkwTx3VwbAftuhAO+xWQtAee9v/Ibhm4nt0+lwzHo+yN1XR+eiElE1ez4ckc7hy7B2f+bja61ERbba8ZDqEN01/VEg63lXvWflUirQlVNmCv3UCiNMrux92G67pb9TkTQmwbEkgRQgghhPiR/W23AvbqsoRUa9D/a7RngFZcM2vS9+7LcWM4TsOPP8htIL1qb6r/1IUZ9w7i9KPv3+JcJFhMUWQUmYFO9Mt7cKv6u2jQa7yxz6HcsPvDnNT7cnrlHoDj1tDY+gqNrROZX/nstz5vXesqssN+kMVfZOByfNfzURRiqGzqFo3izZoeTFw7kIpPMjFTGp3I4aabr6JLl/XcOvx2HvjTPD6/YQxLj19Ijm5GWza/X3YQHzX04OGqYcRrFK7joG0bnZfJS5/tTK9gnJCpePPgJ+jdey277zWToQXr2OBpojvFsAKaOlexIeXQHIfn7x7I7w88gMvP2I9lX8VwtcJxDVoTIdCQDLmEaiyOPPp9jL9U0pSTQnkew/dewv6nfUEo18QpK2zbQuNiZEHXkhUU2k2UBTZREKynsXuU5Gh4/NLuQBAsE+V5qIDBHn2X4KVpgrU2Sdcj2eSCSqGVxjM0eBDOTnBIwVJ2y1jPLtc3QyiA9jx/RYfWlD2vSC9WjDpjBaMmrKO1PBMCBih/W5hqK/GsHT9ZLlqjIiG8tAjkZhNYs5HA0rUUTVqN/WU1eeEkO/WqJr2imYyP15A9s4b0VIjsVUnCNQlUTZLa/XuiN+eO0R6mNrBtFxWw/K05WmO+uJ51p6XIeGcJaE2ifxl2NIJhmJimouncXiT7Z/tbFFwXZRioQADlaSyt0Ibhl6YOBv0AjZ3CaLYhmYTGZmrdEJ3PttGxVrSpUEGLeE6U9acOYr0Z8wNKbeuEVNvqk6itCXy8HGtjij3H3cjf7njrh3zMREe2rfPLdpxcs1L+eGvtyOWyhBBCCOF7Z8Fr3P30Uq494wB26zv4f+6vrq6OkRNvI+VEQRu8fcDp9C8o4bApf2Sj3crpnffmnIGHf+fzm1tns2zjEQB0L3iV7Ojw72z7S7H3iGtRKRfCFqm8EB+99ccftX/HWcOURWdjB6t5c8UQHjro6W+08TyPk5+/kvS09Zw0+BDG9Dj6O/vb75jLsVdZYGmGXZXB8UMPZXhJX8DP52JvGIFBM5d9uBeBbqtwMVkdzwataTguEyIhcGxOeHc2hlaUBOuxcehvJLE9RSrsbyWZFy+hMNhEVDlEvCSXjjoEUg4qFPTziaBxsyG+UwGtDQa5F1RStaGULsWVpJsJ7u/9AZaCdxtKCCxOEgg6vPLcbqxbXowZS1Bwwxoazg4BQfQwl37Xt6DsGHxmMufGIoIpF6U1nusnjP31C59xfevJuHGTVF0Sr6KJSLKcnOWNhBZXE0g6VKdrGs/vyZWHTKEo1MKjB/UmVhPEiCf8i2cYxLsUwOBMhl+whJNKPyHd8Djn04NJxdNJu7weFQpjPuzCnXXUzc8io8nGDIf8gIqn/eCH40IkiELhadCug2pp9bfKpKfjlOcRz43ipBs4UQMdDIJ2iM5cS8baFr/S0CGg33RR2gArgIqE0E3NfjAkLQ0SfsUiAgGwUyQtRehyE25QfhnpZNJfBeRpCAX9FSuOu3lCQSiIYRhordHaZZd/tjDnsV4kJjZgRcMYKRswWLdPJ8iJ4pkeJTkhbj1gT/54+H1opTCaYxANEw8FoGshaPBsj/TCdHr2LOLeO0/9sT4mHdKOfD/XXv74+Du2n/LHL1y4Q17rzSSQspV25A+eEEIIIXxDJlzPPid8iesY3Dbi+Z8kT8HGeCNHTbsa19AEDRcLk0n73v2tbavq76Sq6TYAOuX8laLM03708fzcPvx4AVb0FIrz4vz10TE8/peH/uc+Yy1/Z21rCZfNe51U0uSPJTN4vaYbi5Z1YcL+R3JAr11/cN8jD7uJ2p5Jivapo/EfnbB22cT1e0+iU6iZ0pzHyI2E0fYC7qtwmVc3g1bTJp608KwgmSEXT8OmY9Lo+chqzu83D8NQLG0O8a/GQbS6mmOKFoGh6KljVFsmmYbD83N60atbjL2z1zE71oXDuk0mHA6zaNUgplflMPnFbCpe74R5gMP4i75k/oc5zJ28E0Unr+C4Pku57x+7s3BTD5yMJnruso6qpiKKc5oJnB7HrU+B9ijYs5khNzcyuKKGO0/xkwJryy+jbAQttO1QnePQcv5g3KhmcPfVLNxUQO/YOtwJCTAtfzWJ5yeGxXX9XCjBILTG/ZLWbVtiKE5nv+tXcew+i1GGJohBVVMa91d1ZVBhkgWTw1Tc1sf/K7Zto+MJCAcBhWH6240wFEqDDgZQtoNnGtDYDJZFbEAxzUUGOj2D6IJaEjsVUfLGEpRSuG0rY4jH/ZUfpuFvq4lE/DE7DkZGOl6s1R9rW9AK7fnjaat+lOxVil6/gXDMaXvf2l9FE/Lfr4qE0Z4LSvk5TjoV46aFMNdtxM6OEqiqJ9a/iCat8EaWQgC8IKiUR+G76wk2xjHaXhLDIBVSuOXFEFAEk/5cvPCKg8nPT+eyS57D1CbvT7vyf/vgdDA78v2cBFJ+fpLFSAghhBCiTe6QBgZmVQKwNraUbln9f/TXKIhkYZgaQ2mKIy0oZXLc9DN5cfRj32hblHUBLcnPQIV3iCAKwB6jB7CxMoYFnPLrGSRTLYSC6T+4v/pNf8BKvsKf5+9HVoZNPBjgD1+MJTwvQJMRxraWAz8skNLaGqdhUArVFUARaYH87s30jjTTgMuKxtOoagzwyNpBDMqtZo8il2kbehAKpWgmiFJgKtA6xcqzS1gxZS0LagvomrORXmm1VCajPLR+JEcWzeG8d0/GaDUxy5Ps13kub9cVscEJsHeoguUbBjGwfBmVjGLvzu/Q+2SD3wR3JVQZ4q7fdSGSUJiuZvVNQ+n75BcsiPXCC3sU7ZrggE6reGBuJ/bKX8T7h/fCfDwJGmo/yGDybplM1p0wAv771a6HaksM26A1TWcOxWw0CDRrlmWVYKoU1cFiCryKtrsI7T9HqbaV/Eb76gw8DU5btZuY5tMXcjl2H38zS7oRJD9ik1oQ5YEF+xKtdMhKb8BqiaMDFtimX73JcdBK0eWSFay9vQeeMvyqN4bpB0Fcl2CveiIpj9CSIpy0RtaclgstLZS2JZ1VpgGO449P639vOTAUtCYhHMZrifmrSwwFZsBPLGsAHv6ATZNQZT3J/j1Iuh7Kc7C+WIHhaX8rT0YayjDQ2oJkEisUwm5qJtDYDGkRDBRYBukrG9h4Xk8GzJhC7JNsCIfAtkmlR3CKcglU1ftbooBAUhGY+RV2JIQe2B2loVePYn5/0dOoJDhRxZh9b2Da5Mt/0NwWOyjt+Y9tbXsYw09McqQIIYQQQrR585hrmL+xB+uad6JrZr+f7HWmjr0b7YGpNGWBak4r+Jg3l/ZhVeXkLdoZhkGv4r/Tq+j/L+P7S6KUYn4yj8WpAIZOsr71gx/c16K1u9HS+jIAh+YsZtWmPFZUF7BRBzn1+BCXnqc5qPyYH9z/qCvvxzYi6HlhTCdFa78mampDpNBoIKQ1hWYKpTRxN4irDU7odjrpqh9x2yPlGsQ2GWgziKMsHvr9KE4tn8/QUDUfbiijWNVjJ1wanSg66Phld02NFXE4onAxhh3hxM/O4DcL9+eYD0/m3E9zOeDuCzjztd9jxqKYWmE6/15c7gGu6bbnKAjoFF3NZvYqWcxj8/ZgZUEBrZ3a/lptGuBplBVAJ5N+tR3PRds2nm0zcHKKfff/HF3egvJgcOEynFUWu5kLIaq56d2lEHTbk7Eq0wTP36aEMvwSxCiIRkApGj9J58wBu1NZa7LedqnHoXVAGkbCpLVbCDcrikah7AQXvDGFF+b8i/0/WI56Gjbu0oXLPp5K9PkUt3z6FqqpBdUcQ4WC5B1hMPSRdTzx7jM888ozTD78PnrGFtJYaOG6Lro1TsLwcKIBtOFX41GWhWGamIUFGIaB8ry2ZDnaX03jOJBqSzKr/OCQk5fub+2xDD+RckbUzycTCGDYLiRt/8aqLYATSNrYQQvXALOhFZIpvJBFcJPHgffU+jlWEklIpgjWNdGc3sq6o0r9JLumiYpGIDeXQEsc87OFlKda6dO7mBOOHeEnsLVdVKvDnrtf94PntxDih5NAihBCCCFEm/T0dO7Y4xmu3uV+lFL//Qn/g2nj7iGLHAZnVJEE6t0oM5sm8O6KE3/S190e7N5pKq0U02r0o2vmQT+oD601aVQSNFyq3QyOGvp31qzKYVNzFGOjyaM3u5zc61LMzcstvodkYi61a7vwwfj7MBwg06L6kx4suO6vzDr5DvqXL6d/0bv0LHyd9W4a53aaS8zpS07wj4zrMpZ7R1/FO3s9youjHsG8PB8FtPYqJO+QFAEMukRCvDToY84r3cCjgyZyTO5y3j7oad44+S56O2vIdVuxlObDpm4YQU1hRhxHBzENC1Mb/sIPC1xT0ZofIIlNS1BjnruBD+Ol7D32fTIKFnFX+WTG5tRwfZ+ZdJnXzHU9v2DEnzzGPl5Fek4CpbSfkNUw/IfjgGlw4Zw5nF40l+JgE/27rif88JfUHZyi94Y5BPpqDn97CR+v7Y4+sSfJcMCvZqMUBENgpxg/exaXLJlL3/cT/HrqfJQJyvUgvYCcXPCUR8SEs7t8iU7zE8zSGCN0ywaum/Ev+nROUudoXl85kK6RjTQlQ6xPZVAXD2Ogyd2/ERWJoB2XqhvSmHl+EZtr3ASUyaEnVWKfH8LKaWbQPxuxBxSi4jZYQbAsf1tOaxydSOLFE/7WJK1RgYCfBDYYAtNAWSY6lYS8HOysdFTShYSLVgbN3XLQO/VBD+pJYqfuxPt2wnMcVDDoJ821AoS1QdDxV8U0uDb1exRTNHAjAVex68NtJcCVQoVC5C5oJjptHTHTQcda/O1NDY0QDOIETdYuqmRs9BRemvAUu4wox3Q0nvZQjsteu17zgz5DQogfTrb2CCGEEEJsI/eNuosNTcv4YsNBpHDoG64jak5l2dpd6FU2c1sP7ycTCoUY223K/9SHUopGYygt7lJKcu8jGOjEojMnsM8Rd6I8TbzQZNry3myIZTKpqSeB5sO5/8Djt6rv1qb7iJomV60ZzP77fsK0j4fx21Ef47rnYZphANLCvQHYuXwhAP27fntf/5hzI/uVXQAezFjdk5WtIQakOVjKoKk5SJO3C074S5bHFX3TFOfs+gmZQZvhhbMJJffh4nX7sykWpDjajKcdUsUJjMoQyTSbeHqC0aNX0TdzDY9NGcfpvVZREGxiRuVYWkJRGs25aJpxUDx58QyK2cQRejmPbCjjgg8r2OQE+SRWykG5HjuH5pPSHjOaA+wUdlBAU0Yl8XrFlTNXcMOXexMpSFAeqaLGLaJ38VzqqvqhD+lJiw4T+nIpQ3OryO/VQkGoBYAeGXWMzKvks1sLWTyhAEMZxBpDpGUlcbTBG5U9cVIpJlw0mbGXraLJtbisxyhAQSRM2KlnJSEso4Wn7a5ErFquGjCAovEx4rPqaG0OgGFgr8jgxqMGc+0rCzCUg4Mi+H4cpybE5OXdcS4JkZOVS6zSoHq6QZdn6ll2dA+MtDSCjTG6vLYCPFCGiTJNtG2TMjyaR3Uid3oVrK8isqEWwN965HkEczKhi5/7wVAGOhQkOaQ7ytZ4zTHCq6pxFWCYeI3NZJsW0Tnz6H+Rw+C0jbT0ycY1MjA9D+3YoAxyVqb8YFZGur81KjOD5u4FEDQxWmKENsRwa+qYe+9EQhlh4n3KwQGlXPba5Ro+mPnnH/RZEjsQ7QcFt7ntYQw/MQmkCCGEEEJsQ0WZvdgrOI83V+5C1ISYDpKuNpBMLiAUGrith/ez+Xj1Kk55/WUwoOL3F2/VcwZ3emOL76PRKOfdOpRb3vyC4oFrKLIcZhkZnFc2m2uXZgFbF0hJz7mFTdWDaNVRRnVeQb+jazk5ezW0r3v4fiauvZszD7wFb0oTp3Q5hdKyem4Zdji79Cyn0OoOwCBgbuWp5HizaPQi1NT3Zff8Qqbss4D9HxvJupAm1L2e5w5/nF4Rl93fPpWBXWo4vf8Q3pt4ENaSr3i9ZADhYTbluVUsbu3MuXP2Y0K3T9mp4GKi6gIyVYAWbTIou5JWbbJTuJpfZazF0wpFENCYRoRaN0ZYaT5dk8/h3SowlCYt5RLxEsxryGZQVi0zavvx1E0v80VdPv94sC9XvjiZAfkGnucxPRElYLhUNOfQlB2k384bSA1MsGxFDy7d70CIt/rVcYC8jNXY4xX+fiSFEY2CYaCtFDrhQCCI8jy0GUJ7kPiqlNW/cQEFmWG/oo7tsnpeLr/usyfsqqk+p4jSiZtAGRTcuJGqe8qIuSH2HLCYFxfuTe3fA7DIQzdqdlq7mE3BDD8ZrWn4JZvPbiJ6UJjVp60mWNJI0/ostGmirICfM8V28JSJozxM08RLuhiGAgO0obAywjhDeuJ6np9DJWIQak3SWh1m5ZsWb+5v8f6fywikJ1ENTWjXA0ODYUHA9HO0OA4qkSDjs01QkE1LtyIah2QSqs8lFlEQgewPl0EK6N8dAgZ7jbiGDz6TYIoQPwep2rOVduQsz0IIIYTYPny1qgzTVISVJj/3ASLRg7f1kH42ezz5EGubm1EaPNNm9Xl/+sF91Te9R2X9udha8XFzMb1Dzfx53hFMP3rrbzIdt4VdXr6BXTutYnV9Lq/ufQrRtBE/eExby3VbqKjsBUBYKSKGwSL7VO6qqGZg5lrOyFnKGi+Dk188gbePv5+kEcZCMah8GR+vGoCtXTqZCaZtzGVcURO5pqLZhQYdx9QWx/39FE7d71MaWsOcU/YFYdNAA1MbOpETSmDqBuYlyhiXtZo05bE6FuXVjQMZlfkVnhGgf/oJYDzDrOWd2b/3GmpSIR59fBAXnD2FkqC/Wue1ugKGBGuw3CBpaUninomtXSqa07hg0knkPbuK/NVttyCWiW6N022veqpmRUm1RIlfHuXsQ+dw9T8OQUdT/GHcVEZkVnH7IX3YVJHhb50xlB/UcNsSwngaZVlo7YEBRttYPNfB3UPR58omZi/uSkNTNuEeMXQKzLurKZij/l2tJ5n0/3tfFp2aktx79GRS2mON4/J5SxHTV/Vl/YcmLGmGNQHUfEWtZeHu2ossB7AURsIDpTEMA08pzJYUTtREpVyUMvAMFxpbsVqTmJEILF/rbycyTUD5eVkMw0/gqwE72RZgsXBNhdutFKUUgTXVaK1IlOdgmGGMRStQZYVYBblM+vjqn3ye/hLtyPdz7VV7jv0bocB2ULXHjnPzSxfvkNd6M1mRIoQQQgixnejbdS2p1EpcdwXh8JhtPZyf1R1j9+eYV15CK0Bp7po3gfGD7/xBfeVkjiMncyWe59HfbSYYyGJc7+/Xh2mkUW4EmDGvH88eeSTRtAE/aCzfl2mmkx7anVTqSwK04mnon38QL5ePBiCZTDLmgdsgz6VVhejSVj2nKfYxCa3RGCRRvBcbylg9DVB4aCwgheLU/aYQ9iyG5VcxrbmAPTM28vL6ATxZ1Y/r+0/mwo9O5vm9H2ejnYYOpHijoh87ldcSMIP0i2xCcz+dsl6n18iePPL+Baxcqfnr797Hw+Sx6jKKIk3ctXhPzGcyOeqQzznnwC/JMTVNCThl2aEQNOhyJzS2RhhXuohx2SvJVynspMlTl3fj88mdaUwvoU+kDh2yyO9bwyH5ywGXP/5zEZfsNJSCPk0MvKOKftkxgoEkb182gGVT8ttSACse/vAzzt9/GKl4CBwH66MAy8emke7VkOVV40dgtB+M8bRfutjVKMP0599v6ljvORz5h139pLD90vjNi9MZ3KWKxbsPIX3fTMrSa7mm9CMmTizllfNd7FeKIKRouT5IRiqKozWG47XNZwUoFArDCGHkBHDyFQ2FIcKJJtIqm8G1IRREKQOt/XeC11Y2KBhAmSaWoTBXVvrnggGMUJDIpgSe6VKzczl5qRCJplb+evnzXHnDjp9rSYhtSQIpQgghhBDbkWCwO9B9Ww/jZze8Uzm79Erji401eHhEA5v+5z4NwyBoZP2g5yqleP2YbbNNorjwHwA4jgOksKxo+7lQKETFBL/k7cI1U2nRq1BATmgwfbLPp7r5XhrdAAdnRbh+7XAOzV3OLlkXUprTm4zIrtRZ91Ibf4QmJ500Wrln1S78eZcXyMq6i5hzApP2f4gmN5NzZh/Ghb0m8srK3VhtLuLUrnPR+GWO3ZYryez0Ly44+Ck21FyCY/uLOt76+xAqnc6kV3hYHnSJtfg3/QrSwpriyEYavGzqWy2ygq2EVRLXM8DU5EUNLr69glnJagaqD7lw/n5YTisNlWk0Dg6RbSVo9gyunfcFXc04C1JhlrQU08lq4A8PLCLpGSz/OEh2V4/iTg4vzZ3F4b1GgWWhlQFKYwYslO2gDeVXG0K1VesB5blox4Gg5a8KsYJoz0V5Gv1VK9fcfjqdnQXkndCMo/1bKE/B4H3qWP9mNp8/GoVTQF0RpSWe5ITOM3lzSVecO7qjkim0slCGQrcVB1IKTED160q8iwsbaomsqgHTQpkGuq1ks9Yaw3XBNNGJpJ8UNxhAJW00BioYxspKo7iylbpij0xtMuWFz3n5pensmpPJwzNvBCDRmiAcDf9MM1iIHZ9s7dlKO/JSMCGEEEKI7cXcmudYG5vOAV3uwDRD/3N/X228kpi9kL75d5AW7Pq/D3AHUtnwL5bVXYPSpeze7VUMw+A3H9zFrE2VxBOaeCqLaFaMgkgT5/UspKf1DAXKJj/SDYM467z+VDa9ixGweKlmIB9uGEDo4RywDLStiVa28MAbT1IU8AMWH2wo48Bif0XI3AQkXZOMeIoFdibdrSQ9c2J8Gstm9/RGLAw8QxPAoMX1mFbThZ3zVxPCoR4TF0XcDoFh0KUtuW2TnUn3kM2cuCLLStDiBrjzqCFsWJLZHr1QnvYDKakUWBYpXA54sIqZfy2iabW/cgTT9CMdblsgJWCy7tQhdHl2AZ7jgGlz2eQvaDXSuWvcThiOB6kUKhxGJ5NgwMDr6uh9YCOPLt2Dvw54jT98dgTJl11K14RQIQs3EiSeHyJUn8K0NVbCIZ4Xoj5cT/mUWnBsdG42unojgZxsCATQiQRea9wPAinlVxkKBlHlJbjxJDgu2nNQpoVqTWDYDm48Tt2ALPiqljwn4I8zEmS/c/fmD7ecvg1n389vR76fa9/ac8yt28/Wnn9cskNe680kkLKVduQPnhBCCCHEjmrG2pF4Xitdci6hS+bJ23o425XVa7sTVpoldhhP5bJn1+lbnD/9oxOJewGKaeSoXufwq9IDWVh9GnXx6eQaDmGladEGuYZDretxzsIT8apdsh7MRKVs7AyLqj0jXHnUy3y4uB8fxst4YdS7lGfU8UVLmO7hZu5ePYJ/ztiNUJVBsM7DyTIZ3vsLLt73M4ozEgQMxb0VfVhe24Orhn3I0pZscqMbSDdtcoBPYjkMT28gpRXpKkS+5bIsZZNj2qS0Yp93z+L1fR5n9uo8nj9goB+AAH/1SSTir7EJBGgqDLLpkgIiaUmCK+uIPNHMsAPXMv1ffbGHl5Pz9gpCKRftOmitcQ7NxPzEwUwFwE7hJVP+ViHbAdPECATQKZviLjX87p6veLKyNys696Dii86MK/2SS0bO4qRfn4FSJmbcQQUM3GiA+n5h3IQm491FZIWyUavWodLTMIJBvETSD9Qo5edV8TQ4rr9TCfytQFqDZWDk56GbYhANoxMJlGHQaGlS+/RDzV9Dfk2qvezzwF/14o7XLv25p9/Pbke+n5NAys9PtvYIIYQQQogdVvecq2hKLqA0/ahtPZTtTkIbBJVLwjUJWt9c/fPkHs/Tkmwh6SXJi+QB0JxaiItBizZINxOEPQtHW7jaJt6SoKRTnNjoAGpZDvEMA43i7kV7YhgeGRkpjnnjaN4+7l5mJ3vwbG0+v+v0Me/l9sLZlIttOuj1SaZbg/h99FOqPIvX1g1ghFFJjaF4dUkB/1jTj0jE4Lgu82luLiSWpWhiPUPMDXzpmHQJhlhtmwxLq2WjHSaS6ZBrwYa8fNY91o0uaavJ/0sLm5ZFyC6qZX7f7iSGluHmJskpaKUlHiXcR3PfxHcoCJioP6zhwosHUNuahTaM9qqu1rsJPNNABdsqvXqen/jWUOB5aMAw4Y73F/FpPMSeXTYye+kg7JIU79QPYuKDu5K9WzX7uJ/y8ayxqGCIhh4W2gMC0HpoX9Kmf07T7r0wZ68nu74BDMMvbtS21QcNWCZKgzYAR/nloO0UesNGgqbpVyOKRiEQJDOZgo/XoONJGiIGqZ6FqA2NLJqymLEZp4Fpcewl+3P2Fcf8LPNPiF8yCaQIIYQQQogdVnH6gRSnH/j/tvlg/XM0xv7G8kQhE/q/hWUFfqbRbVs9Sr9i6cZHKc9ook/Rt69ISA+lk056+/d9Cu5ifcO9ZCsPw6vAJo2EV8yA4sdZcJz/l/DGkf9g1yfm4TRlQI9mmluzWHr2VXy2agyZuy5jdmshuUaMVruMl+p2omt5DV/pDOwSjWoNoqwELy4cRs/8jcxr7MTZA5sxC2rpFWlkTM9a/lnfhdvmjMNcFcEptHl1/7loU9MtkKRnsIWBEQONxbp4gHirwZJ4gANCFawqi7IyUUD3h1JcVjCXDak0fjN5GFR5YINqywKTbbWSaWkyDIPliRCX37yE/L/anDpsr7YAhl8lqKTzGsZcV8PUnP4MzGliyqpOrFjQjYNSH7P01hy09gMqllLMbS2j1QthmWCtz0Q5YDd2Zkrz4ZgZHsrVJKM2yjMJNQORJPE+Iwh7YB+ay7q+MW455HHuGTsS3aRIdssm++RmVn1VRsG7NWjXJfdRm8gyk+qr/JUmdb8pJ/eeZeiWFoi1+kGelhaUUmQ1AtWNeGhs00+ya1sG9704jbs/XUje0lb+fM8JjBk39KefiOLHozVsDxtOtocx/MQkkCKEEEIIITq0+fWvMiwthqM3sbD+HYYUHPaj9Ou6Lme9OYGq2gCjO3XjqgN+/6P0+2OxLIv+Jed+r+fkREaTExn9/7bJyjqGJROOYeRzN1DTEuD+/fsCMKLrNAD6tbX7zVa83jnxd/BSM5kZnwo0EFAeGhPQuGlJCHg0p8KURTfxSTKPfKORDMPj2pf3Izg8zhPDJrE43ot7GstYWVdAy1eZ9CqPM80qJZGweGzfd2h2LIpbq3h/TQGhzhandK6k2YN1nke2ivF5PJf9sur4x5IPOPe3A+kSbWb8Lav4PJ7B8nghO+tGjs77ikMyV2AMmIQbVgTO8tj/5fM4enYGN3f+F3umf8V7Ri9adaAtua2/I8fyQBmKYEJTuMRAtcQwWx3skInXmfZkvbo2jXVmCbrJAK0JraijYIhD7ftAdgbW2g2sqOjKbqWrwHPBMHFSIcjLhYZGMA0IhbADJlZdI6otAa8RDoPWGJaFlbSJLK6Flc0Ynsdfj7yTv6JRjovenCHXdUEZ+FWPDJRhgKG44rnzGXPYrt9rLgnxSyaBFCGEEEII0aEdXnYLz1SMp87J4PCe+31nuzsXHY/t1RPE4Xd93iLwX3IR3DT9rxzYbzpNTphXZyXZ+YRbmf33S37wOGOJTfxz9aE0ORZ9Q+sY3XUhlrX9VmL55KTL/+c+zMiBmJED2T90CY8vP5y1yTC68VAqzjyLlpYWDnjnfn7z2VGMK0lw697XEQr5W5Rq+t7OrC83srpPhCYsGlMhmhoCvH7kEJa5S8jJ6E+PwEWccMffuf3YF0nL1nTStexVqDENg7mtezOs8A4OfOJ+ph77AI7WfNmYjTO+kCPKF4PSRFWCMAnW2nm4WlFTG6JzYQJHg+cZdL7nK2r+Vsyxk3+P2WAS3qjpMr+Z9VULcA/phjLCGKYimPLw2v6Abyh/S1BL9whF3RZRu74vqYBB9m5rCRmptlLKkOyWzcqHUtiJGJG6JlK5Ebplr2PNE2mgmkiUpVMaqcFJJNqupALXIxgIkuzfleCiVRAMgethRMJ4qVRbM4XSui3Y46E3513RGmUYfl4VwwOzbUuR1uDB9Sfcw/Ua9v/1GC66/6z/+ecuxPZOAilCCCGEEKJD65LVgyt2euu/tmt1mghbLo12mMb4B+QH/v8tQ71yBpD03qK6NZPeReupdLr8T+N8v/JyBoSrsLXJ2mQajfGJ5GX8OKtntneRcJTzBr63xbH09HQ+OvbbtyS9fMBFcMC399U91R/HS5Ab7sunN/8Z8MtcDwA8zyPltHB40E+QGciBZ5YO5ejuC+gRqefCLh9x++wh/HnXL+gXstnUsIHemZs4Y8E4ohc0kOE2cvrf1vD8db2J1Lqc3nUm99fsxdDeHzG08yb+taE/v+60jLMLJtHgekys68YzS4aSloKGv3s4RiEh2wMgU0XofNIiRhStpK5F8eKonv7qkXAI96Qwa+Nl9M1bzLJYDnv2W8HOJbX0vq+Otzb2ompDjMTvNY4ZBBd/S5JpgHbbdl34dZiNaARlmZhGCDceB63Rnoc2DLyMCDqexEzaYBrotv/6W5s8tFKgDD+24wHa472nPpRAyrYkW3t+Nh0qkHL//fdz6623UlVVxYABA7jzzjvZY489tvWwhBBCCCHEL8ChxefzXs0djEmvIjd9///a/thBR+N5R3LCLXfy1ZwEF5zzv2192KVgPDOrZ7HJidDdqiUn7ZD/qb+OKjPY7TvPGYZBOPjvKiNfnu6vqnlk4Zs8seYDmlsVTRsyOPLO3Yj3T6CCGjOpMXUr6ZeYjOxSw+3H7Yz2DFoODJLjNUJ6M+voxBcf7QkKnpjcl5cK67n7sJfYObqSKRllpHQWf77uQ85begQ3Dn2L0mAj4584HuPtDDaMSmdM3lzC5RESayx0wCJ6Wz3pqpEzpsxjQ73Ds9Uj+Gh2XwKfFBCNeWhTEdy/nhvGP0NWloGJ5qJxQ1lX0AO7bwirMINOn8TxbBvTULiOgxEMQSAI0Qg6I0o84BIIFuCh8QwDU2vq+mRy7Sn7cMQBOwP+ipSzR1zGmvmVYJoM2bP/T/qzE2J70WHKH7/44ouccsop3H///YwePZqHHnqIRx99lEWLFtGly3//68COXC5LCCGEEEIIIXZkO/L9XHv54yNvJhTY9tv9knaCm1+9bIe81psZ23oAP5fbb7+dM888k7POOot+/fpx5513UlZWxgMPPLCthyaEEEIIIYQQQohfiA4RSEmlUsyePZtx48ZtcXzcuHHMmDHjW5+TTCZpamra4iGEEEIIIYQQQoiOrUMEUmpra3Fdl6Kioi2OFxUVUV1d/a3PufHGG8nKymp/lJWV/RxDFUIIIYQQQgghvr/NyWa3h8cOrkMEUjZTbeXCNtNaf+PYZn/6059obGxsf6xdu/bnGKIQQgghhBBCCCG2Yx2iak9+fj6maX5j9UlNTc03VqlsFgqF2uvQCyGEEEIIIYQQQkAHWZESDAbZeeedmTRp0hbHJ02axKhRo7bRqIQQQgghhBBCiB/Jtt7O04G29nSIFSkAF110EaeccgrDhw9n5MiRPPzww6xZs4Zzzz13Ww9NCCGEEEIIIYQQvxAdJpBy3HHHsWnTJq677jqqqqoYOHAg77zzDuXl5dt6aEIIIYQQQgghhPiF6DCBFIDf/e53/O53v9vWwxBCCCGEEEIIIX5UWmv0drCtZnsYw0+tQ+RIEUIIIYQQQgghhPgxdKgVKUIIIYQQQgghxA5pe0n0uj2M4ScmK1KEEEIIIYQQQgghtpIEUoQQQgghhBBCCCG2kmztEUIIIYQQQgghfulka8/PRlakCCGEEEIIIYQQQmwlCaQIIYQQQgghhBBCbCXZ2iOEEEIIIYQQQvzSydaen42sSBFCCCGEEEIIIYTYShJIEUIIIYQQQgghhNhKsrVHCCGEEEIIIYT4hdOeRnvbflvN9jCGn5qsSBFCCCGEEEIIIYTYSrIiRQghhBBCCCGE+MXTbY9tbXsYw09LVqQIIYQQQgghhBBCbCUJpAghhBBCCCGEEEJsJdnaI4QQQgghhBBC/NJ52n9sa9vDGH5isiJFCCGEEEIIIYQQYitJIEUIIYQQQgghhBBiK8nWHiGEEEIIIYQQ4hdPqvb8XGRFihBCCCGEEEIIIcRWkhUpW0lrP6rW1NS0jUcihBBCCCGEEOL72Hwft/m+bkeUdJLbegjA9jOOn5IEUrZSc3MzAGVlZdt4JEIIIYQQQgghfojm5maysrK29TB+VMFgkOLiYu6efNO2Hkq74uJigsHgth7GT0bpHTkk9yPyPI/KykoyMjJQSm3r4XwvTU1NlJWVsXbtWjIzM7f1cMR2QOaE+DqZD+I/yZwQ/0nmhPhPMifE1/0S5oPWmubmZkpLSzGMHS/DRSKRIJVKbethtAsGg4TD4W09jJ+MrEjZSoZh0Llz5209jP9JZmbmdvuLTWwbMifE18l8EP9J5oT4TzInxH+SOSG+bnufDzvaSpSvC4fDO3TgYnuz44XihBBCCCGEEEIIIX4iEkgRQgghhBBCCCGE2EoSSOkAQqEQ11xzDaFQaFsPRWwnZE6Ir5P5IP6TzAnxn2ROiP8kc0J8ncwH0dFIslkhhBBCCCGEEEKIrSQrUoQQQgghhBBCCCG2kgRShBBCCCGEEEIIIbaSBFKEEEIIIYQQQgghtpIEUnYQDzzwAIMHD26v3T5y5Ej+9a9/tZ/XWnPttddSWlpKJBJhzz33ZOHChdtwxOLndOONN6KUYsKECe3HZE50PNdeey1KqS0excXF7edlTnQ869ev5+STTyYvL49oNMpOO+3E7Nmz28/LnOhYunbt+o3fEUopzjvvPEDmQ0fkOA5XXnkl3bp1IxKJ0L17d6677jo8z2tvI/OiY2lubmbChAmUl5cTiUQYNWoUM2fObD8v80F0FBJI2UF07tyZm266iVmzZjFr1iz23ntvDjvssPZfXLfccgu333479957LzNnzqS4uJixY8fS3Ny8jUcufmozZ87k4YcfZvDgwVsclznRMQ0YMICqqqr2x/z589vPyZzoWOrr6xk9ejSBQIB//etfLFq0iNtuu43s7Oz2NjInOpaZM2du8fth0qRJABxzzDGAzIeO6Oabb+bBBx/k3nvvZfHixdxyyy3ceuut3HPPPe1tZF50LGeddRaTJk3imWeeYf78+YwbN459992X9evXAzIfRAeixQ4rJydHP/roo9rzPF1cXKxvuumm9nOJREJnZWXpBx98cBuOUPzUmpubda9evfSkSZP0mDFj9Pjx47XWWuZEB3XNNdfoIUOGfOs5mRMdz2WXXaZ333337zwvc0KMHz9e9+jRQ3ueJ/OhgzrooIP0GWecscWxI488Up988slaa/k90dG0trZq0zT1W2+9tcXxIUOG6CuuuELmg+hQZEXKDsh1XV544QVisRgjR46koqKC6upqxo0b194mFAoxZswYZsyYsQ1HKn5q5513HgcddBD77rvvFsdlTnRcy5Yto7S0lG7dunH88cezcuVKQOZER/TPf/6T4cOHc8wxx1BYWMjQoUN55JFH2s/LnOjYUqkUzz77LGeccQZKKZkPHdTuu+/OlClTWLp0KQBz587l448/5sADDwTk90RH4zgOrusSDoe3OB6JRPj4449lPogORQIpO5D58+eTnp5OKBTi3HPP5bXXXqN///5UV1cDUFRUtEX7oqKi9nNix/PCCy/wxRdfcOONN37jnMyJjmnEiBE8/fTTTJw4kUceeYTq6mpGjRrFpk2bZE50QCtXruSBBx6gV69eTJw4kXPPPZcLLriAp59+GpDfEx3d66+/TkNDA6effjog86GjuuyyyzjhhBPo27cvgUCAoUOHMmHCBE444QRA5kVHk5GRwciRI/nLX/5CZWUlruvy7LPP8tlnn1FVVSXzQXQo1rYegPjx9OnThzlz5tDQ0MArr7zCaaedxrRp09rPK6W2aK+1/sYxsWNYu3Yt48eP57333vvGXw2+TuZEx3LAAQe0fz1o0CBGjhxJjx49eOqpp9htt90AmRMdied5DB8+nBtuuAGAoUOHsnDhQh544AFOPfXU9nYyJzqmxx57jAMOOIDS0tItjst86FhefPFFnn32WZ5//nkGDBjAnDlzmDBhAqWlpZx22mnt7WRedBzPPPMMZ5xxBp06dcI0TYYNG8aJJ57IF1980d5G5oPoCGRFyg4kGAzSs2dPhg8fzo033siQIUO466672qty/GckuKam5hsRY7FjmD17NjU1Ney8885YloVlWUybNo27774by7Laf+4yJzq2tLQ0Bg0axLJly+T3RAdUUlJC//79tzjWr18/1qxZAyBzogNbvXo1kydP5qyzzmo/JvOhY7rkkkv44x//yPHHH8+gQYM45ZRTuPDCC9tXu8q86Hh69OjBtGnTaGlpYe3atXz++efYtk23bt1kPogORQIpOzCtNclksv0X2+bs++DvfZ42bRqjRo3ahiMUP5V99tmH+fPnM2fOnPbH8OHDOemkk5gzZw7du3eXOSFIJpMsXryYkpIS+T3RAY0ePZolS5ZscWzp0qWUl5cDyJzowJ544gkKCws56KCD2o/JfOiYWltbMYwtbxdM02wvfyzzouNKS0ujpKSE+vp6Jk6cyGGHHSbzQXQs2y7Prfgx/elPf9Iffvihrqio0PPmzdOXX365NgxDv/fee1prrW+66SadlZWlX331VT1//nx9wgkn6JKSEt3U1LSNRy5+Ll+v2qO1zImO6A9/+IOeOnWqXrlypf7000/1wQcfrDMyMvSqVau01jInOprPP/9cW5alr7/+er1s2TL93HPP6Wg0qp999tn2NjInOh7XdXWXLl30ZZdd9o1zMh86ntNOO0136tRJv/XWW7qiokK/+uqrOj8/X1966aXtbWRedCzvvvuu/te//qVXrlyp33vvPT1kyBC966676lQqpbWW+SA6Dgmk7CDOOOMMXV5eroPBoC4oKND77LNPexBFa7883TXXXKOLi4t1KBTSv/rVr/T8+fO34YjFz+0/AykyJzqe4447TpeUlOhAIKBLS0v1kUceqRcuXNh+XuZEx/Pmm2/qgQMH6lAopPv27asffvjhLc7LnOh4Jk6cqAG9ZMmSb5yT+dDxNDU16fHjx+suXbrocDisu3fvrq+44gqdTCbb28i86FhefPFF3b17dx0MBnVxcbE+77zzdENDQ/t5mQ+io1Baa72tV8UIIYQQQgghhBBC/BJIjhQhhBBCCCGEEEKIrSSBFCGEEEIIIYQQQoitJIEUIYQQQgghhBBCiK0kgRQhhBBCCCGEEEKIrSSBFCGEEEIIIYQQQoitJIEUIYQQQgghhBBCiK0kgRQhhBBCCCGEEEKIrSSBFCGEEEIIIYQQQoitJIEUIYQQQnyra6+9lp122un/bbNq1SqUUsyZM+d79//+++/Tt29fPM/7n8bwS3HxxRdzwQUXbOthCCGEEOJ/JIEUIYQQvwgzZszANE3233//bT2Un8TWBiT23HNPlFLf+Zg2bdqPNqaLL76YKVOmtH9/+umnc/jhh/9o/V966aVcccUVGEbH+OfIpZdeyhNPPEFFRcW2HooQQggh/gcd418uQgghfvEef/xxfv/73/Pxxx+zZs2abT2cdqlU6md9vVdffZWqqqotHqtXr2bgwIEMHz6cESNG/GivlZ6eTl5e3o/W39fNmDGDZcuWccwxx/wk/X8fWmscx/nJX6ewsJBx48bx4IMP/uSvJYQQQoifjgRShBBCbPdisRgvvfQSv/3tbzn44IN58skntzg/depUlFK8/fbbDBkyhHA4zIgRI5g/f357myeffJLs7Gxef/11evfuTTgcZuzYsaxdu7a9zYoVKzjssMMoKioiPT2dXXbZhcmTJ2/xWl27duWvf/0rp59+OllZWZx99tmAHxj41a9+RSQSoaysjAsuuIBYLLbF82644QbOOOMMMjIy6NKlCw8//HD7+W7dugEwdOhQlFLsueee33otcnNzKS4u3uLxl7/8hY0bN/Laa68RDocBPzhwyy230L17dyKRCEOGDOHll1/+xjWbMmUKw4cPJxqNMmrUKJYsWdLe5uvbaq699lqeeuop3njjjfbVL1OnTm1vu3LlSvbaay+i0ShDhgzhk08++a4fJwAvvPAC48aNax/vZjfddBNFRUVkZGRw5plnkkgkvvHcJ554gn79+hEOh+nbty/333//FudnzJjBTjvtRDgcZvjw4bz++utbrPbZ/N4nTpzI8OHDCYVCfPTRR//1mgEsWrSIAw88kPT0dIqKijjllFOora1tP//yyy8zaNAgIpEIeXl57LvvvlvMg0MPPZS///3v/++1EUIIIcR2TgshhBDbuccee0wPHz5ca631m2++qbt27ao9z2s//8EHH2hA9+vXT7/33nt63rx5+uCDD9Zdu3bVqVRKa631E088oQOBgB4+fLieMWOGnjVrlt511131qFGj2vuZM2eOfvDBB/W8efP00qVL9RVXXKHD4bBevXp1e5vy8nKdmZmpb731Vr1s2TK9bNkyPW/ePJ2enq7vuOMOvXTpUj19+nQ9dOhQffrpp2/xvNzcXH3ffffpZcuW6RtvvFEbhqEXL16stdb6888/14CePHmyrqqq0ps2bdqqa3PffffpYDCop0+fvsXxyy+/XPft21e/++67esWKFfqJJ57QoVBIT506dYtrNmLECD116lS9cOFCvccee2xxPa655ho9ZMgQrbXWzc3N+thjj9X777+/rqqq0lVVVTqZTOqKigoN6L59++q33npLL1myRB999NG6vLxc27b9neMeMmSIvummm7Y49uKLL+pgMKgfeeQR/dVXX+krrrhCZ2RktI9Ba60ffvhhXVJSol955RW9cuVK/corr+jc3Fz95JNPaq21bmpq0rm5ufrkk0/WCxcu1O+8847u3bu3BvSXX365xXsfPHiwfu+99/Ty5ct1bW3tf71mlZWVOj8/X//pT3/Sixcv1l988YUeO3as3muvvdrPW5alb7/9dl1RUaHnzZun77vvPt3c3Nw+/kWLFmlAr1q1aqt+vkIIIYTY/kggRQghxHZv1KhR+s4779Raa23bts7Pz9eTJk1qP7/5xviFF15oP7Zp0yYdiUT0iy++qLX2AymA/vTTT9vbLF68WAP6s88++87X7t+/v77nnnvavy8vL9eHH374Fm1OOeUUfc4552xx7KOPPtKGYeh4PN7+vJNPPrn9vOd5urCwUD/wwANaa90ekNh8s781pk2bpgOBgH7kkUe2ON7S0qLD4bCeMWPGFsfPPPNMfcIJJ2it/33NJk+e3H7+7bff1kD7mL8eSNFa69NOO00fdthhW/S5edyPPvpo+7GFCxdqoD1I9G2ysrL0008/vcWxkSNH6nPPPXeLYyNGjNhiDGVlZfr555/fos1f/vIXPXLkSK211g888IDOy8trfw9aa/3II498ayDl9ddfb2+zNdfsqquu0uPGjdvi/Nq1azWglyxZomfPnv1fgySNjY0aaA/OCCGEEOKXx/p5178IIYQQ38+SJUv4/PPPefXVVwGwLIvjjjuOxx9/nH333XeLtiNHjmz/Ojc3lz59+rB48eL2Y5ZlMXz48Pbv+/btS3Z2NosXL2bXXXclFovx5z//mbfeeovKykocxyEej38jJ8vX+wCYPXs2y5cv57nnnms/prXG8zwqKiro168fAIMHD24/r5SiuLiYmpqaH3Rd1qxZw9FHH80555zDWWedtcW5RYsWkUgkGDt27BbHU6kUQ4cO3eLY18dUUlICQE1NDV26dPle4/mufvr27fut7ePx+De29SxevJhzzz13i2MjR47kgw8+AGDjxo2sXbuWM888s31LFYDjOGRlZQH+fBk8ePAWfe+6667fOoav/xy35prNnj2bDz74gPT09G/0tWLFCsaNG8c+++zDoEGD2G+//Rg3bhxHH300OTk57e0ikQgAra2t3zomIYQQQmz/JJAihBBiu/bYY4/hOA6dOnVqP6a1JhAIUF9fv8VN6rdRSv2/33/92CWXXMLEiRP529/+Rs+ePYlEIhx99NHfSCiblpa2xfee5/Gb3/zmW0vbfj0gEQgEvvG6/1/p3+8Sj8c54ogjGDBgAHfeeec3zm/u8+23397iugGEQqEtvv/6mDZfhx8ypu/bT35+PvX19d/rNTb398gjj3wjqa5pmoA/N/7zZ6y1/tb+vv5z3Jpr5nkehxxyCDfffPM3+iopKcE0TSZNmsSMGTN47733uOeee7jiiiv47LPP2nPg1NXVAVBQULB1b1oIIYQQ2x0JpAghhNhuOY7D008/zW233ca4ceO2OHfUUUfx3HPPcf7557cf+/TTT9sDF/X19SxdunSLFRGO4zBr1qz2FQpLliyhoaGhvc1HH33E6aefzhFHHAFAS0sLq1at+q/jHDZsGAsXLqRnz54/+L0Gg0EAXNf9r23POuss6urqmDhxIpb1zf+V9+/fn1AoxJo1axgzZswPHtO3jXFrxrc1hg4dyqJFi7Y41q9fPz799FNOPfXU9mOffvpp+9dFRUV06tSJlStXctJJJ31rv3379uW5554jmUy2B0BmzZr1X8ezNdds2LBhvPLKK3Tt2vVbrzv4QaTRo0czevRorr76asrLy3nttde46KKLAFiwYAGBQIABAwb81zEJIYQQYvskgRQhhBDbrbfeeov6+nrOPPPM9q0bmx199NE89thjWwRSrrvuOvLy8igqKuKKK64gPz+fww8/vP18IBDg97//PXfffTeBQIDzzz+f3XbbrT2w0rNnT1599VUOOeQQlFJcddVVW7U647LLLmO33XbjvPPO4+yzzyYtLY3FixczadIk7rnnnq16r4WFhUQiEd599106d+5MOBz+xnsGuPXWW/nHP/7Bm2++ieM4VFdXb3E+KyuLjIwMLr74Yi688EI8z2P33XenqamJGTNmkJ6ezmmnnbZVY/pPXbt2ZeLEiSxZsoS8vLxvHd/W2m+//Xjqqae2ODZ+/HhOO+00hg8fzu67785zzz3HwoUL6d69e3uba6+9lgsuuIDMzEwOOOAAkskks2bNor6+nosuuogTTzyRK664gnPOOYc//vGPrFmzhr/97W/At69G2mxrrtl5553HI488wgknnMAll1xCfn4+y5cv54UXXuCRRx5h1qxZTJkyhXHjxlFYWMhnn33Gxo0b27d2gR+s22OPPdq3+AghhBDil0fKHwshhNhuPfbYY+y7777fesN+1FFHMWfOHL744ov2YzfddBPjx49n5513pqqqin/+85/tKz0AotEol112GSeeeCIjR44kEonwwgsvtJ+/4447yMnJYdSoURxyyCHst99+DBs27L+Oc/DgwUybNo1ly5axxx57MHToUK666qr2XCFbw7Is7r77bh566CFKS0s57LDDvrXd/fffj23b7L///pSUlHzj8eKLLwLwl7/8hauvvpobb7yRfv36sd9++/Hmm2+2bzH5Ic4++2z69OnD8OHDKSgoYPr06T+4r5NPPplFixZtUW75uOOO4+qrr+ayyy5j5513ZvXq1fz2t7/d4nlnnXUWjz76KE8++SSDBg1izJgxPPnkk+3vKzMzkzfffJM5c+aw0047ccUVV3D11VcDfCMny3/6b9estLSU6dOn47ou++23HwMHDmT8+PFkZWVhGAaZmZl8+OGHHHjggfTu3Zsrr7yS2267jQMOOKD9Nf7+979vkd9FCCGEEL88Sn/XxmEhhBDiF2Lq1Knstdde1NfXk52d/a1tnnzySSZMmEBDQ8PPOjbx3S699FIaGxt56KGHftLXee655/j1r39NY2PjNl0J8vbbb3PJJZcwb96879waJIQQQojtn6xIEUIIIcQ2ccUVV1BeXv6j5V3Z7Omnn+bjjz+moqKC119/ncsuu4xjjz12m2+nicViPPHEExJEEUIIIX7h5P/kQgghhNgmsrKyuPzyy3/0fqurq7n66quprq6mpKSEY445huuvv/5Hf53v69hjj93WQxBCCCHEj0C29gghhBBCCCGEEEJsJdnaI4QQQgghhBBCCLGVJJAihBBCCCGEEEIIsZUkkCKEEEIIIYQQQgixlSSQIoQQQgghhBBCCLGVJJAihBBCCCGEEEIIsZUkkCKEEEIIIYQQQgixlSSQIoQQQgghhBBCCLGVJJAihBBCCCGEEEIIsZUkkCKEEEIIIYQQQgixlf4PfJf5LY3rv9MAAAAASUVORK5CYII=", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot with continuous color mapping\n", "plt.figure(figsize=(12, 8))\n", "scatter = plt.scatter(\n", " filtered_df['apparent_zenith'],\n", " filtered_df['SR15D1Dn_Irr'],\n", " c=filtered_df['LWP_Corrected'],\n", " cmap='viridis', # Or any other color map\n", " s=0.5, # Size of the points\n", " alpha=0.7\n", ")\n", "\n", "# Add color bar\n", "cbar = plt.colorbar(scatter)\n", "cbar.set_label('LWP Corrected (g/m²)')\n", "\n", "# Labels and title\n", "plt.xlabel('Apparent Zenith (degrees)')\n", "plt.ylabel('Radiation (SR15D1Dn_Irr)')\n", "plt.title('Radiation vs Apparent Zenith with Continuous LWP Color Mapping')\n", "\n", "# Show plot\n", "plt.tight_layout()\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 5, "id": "ca619aba", "metadata": { "scrolled": false }, "outputs": [ { "data": { "text/plain": [ "\"\\n# Plot shortwave down radiation vs solar zenith angle\\n\\nplt.figure(figsize=(10, 6))\\n\\n# Adjust marker size (s), color (c), and transparency (alpha)\\nplt.scatter(all_solar_angles_df['apparent_zenith'], all_data_combined_swr['SR15D1Dn_Irr'], marker='o', s=0.1, c='black', alpha=0.1)\\n\\nplt.xlabel('Apparent Solar Zenith Angle (degrees)')\\nplt.ylabel('Shortwave Down Radiation (W/m^2)')\\nplt.title('Shortwave Down Radiation vs Solar Zenith Angle in Alkmaar, Netherlands (April-May 2024)')\\n#plt.grid(True) # Enable grid for better visualization of data density\\nplt.show()\\n\"" ] }, "execution_count": 5, "metadata": {}, "output_type": "execute_result" } ], "source": [ "'''\n", "# Plot shortwave down radiation vs solar zenith angle\n", "\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Adjust marker size (s), color (c), and transparency (alpha)\n", "plt.scatter(all_solar_angles_df['apparent_zenith'], all_data_combined_swr['SR15D1Dn_Irr'], marker='o', s=0.1, c='black', alpha=0.1)\n", "\n", "plt.xlabel('Apparent Solar Zenith Angle (degrees)')\n", "plt.ylabel('Shortwave Down Radiation (W/m^2)')\n", "plt.title('Shortwave Down Radiation vs Solar Zenith Angle in Alkmaar, Netherlands (April-May 2024)')\n", "#plt.grid(True) # Enable grid for better visualization of data density\n", "plt.show()\n", "'''" ] }, { "cell_type": "code", "execution_count": 25, "id": "c0337aee", "metadata": {}, "outputs": [], "source": [ "def calculate_clear_sky_sw_down_again(latitude, longitude, timestamps):\n", " # Create location object\n", " location = pvlib.location.Location(latitude, longitude)\n", " \n", " # Calculate clear-sky GHI using Ineichen model\n", " clearsky = location.get_clearsky(timestamps, model='ineichen')\n", " \n", " # Extract timestamps and clear-sky GHI values\n", " clear_sky_sw_down = clearsky['ghi']\n", " timestamps = clear_sky_sw_down.index\n", " \n", " # Create DataFrame with columns 'TIMESTAMP' and 'ghi'\n", " clear_sky_df = pd.DataFrame({\n", " 'TIMESTAMP': timestamps,\n", " 'ghi': clear_sky_sw_down.values\n", " })\n", " \n", " return clear_sky_df\n" ] }, { "cell_type": "code", "execution_count": 26, "id": "d6325025", "metadata": {}, "outputs": [], "source": [ "latitude = 52.6324 # Latitude of Alkmaar, Netherlands\n", "longitude = 4.7534 # Longitude of Alkmaar, Netherlands\n", "\n", "# Assuming `all_data_combined_swr` is a DataFrame containing your data\n", "timestamps = pd.to_datetime(all_data_combined_swr['TIMESTAMP'], errors='coerce')\n", "timestamps = pd.DatetimeIndex(timestamps)\n", "\n", "clear_sky_df = calculate_clear_sky_sw_down_again(latitude, longitude, timestamps)" ] }, { "cell_type": "code", "execution_count": 35, "id": "a2c4ab74", "metadata": {}, "outputs": [ { "ename": "KeyboardInterrupt", "evalue": "", "output_type": "error", "traceback": [ "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", "Cell \u001b[1;32mIn[35], line 2\u001b[0m\n\u001b[0;32m 1\u001b[0m location \u001b[38;5;241m=\u001b[39m pvlib\u001b[38;5;241m.\u001b[39mlocation\u001b[38;5;241m.\u001b[39mLocation(latitude, longitude)\n\u001b[1;32m----> 2\u001b[0m solar_position \u001b[38;5;241m=\u001b[39m location\u001b[38;5;241m.\u001b[39mget_solarposition(timestamps)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\location.py:193\u001b[0m, in \u001b[0;36mLocation.get_solarposition\u001b[1;34m(self, times, pressure, temperature, **kwargs)\u001b[0m\n\u001b[0;32m 190\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m pressure \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[0;32m 191\u001b[0m pressure \u001b[38;5;241m=\u001b[39m atmosphere\u001b[38;5;241m.\u001b[39malt2pres(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maltitude)\n\u001b[1;32m--> 193\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m solarposition\u001b[38;5;241m.\u001b[39mget_solarposition(times, latitude\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlatitude,\n\u001b[0;32m 194\u001b[0m longitude\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlongitude,\n\u001b[0;32m 195\u001b[0m altitude\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39maltitude,\n\u001b[0;32m 196\u001b[0m pressure\u001b[38;5;241m=\u001b[39mpressure,\n\u001b[0;32m 197\u001b[0m temperature\u001b[38;5;241m=\u001b[39mtemperature,\n\u001b[0;32m 198\u001b[0m \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\solarposition.py:112\u001b[0m, in \u001b[0;36mget_solarposition\u001b[1;34m(time, latitude, longitude, altitude, pressure, method, temperature, **kwargs)\u001b[0m\n\u001b[0;32m 108\u001b[0m ephem_df \u001b[38;5;241m=\u001b[39m spa_python(time, latitude, longitude, altitude,\n\u001b[0;32m 109\u001b[0m pressure, temperature,\n\u001b[0;32m 110\u001b[0m how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumba\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 111\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnrel_numpy\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m--> 112\u001b[0m ephem_df \u001b[38;5;241m=\u001b[39m spa_python(time, latitude, longitude, altitude,\n\u001b[0;32m 113\u001b[0m pressure, temperature,\n\u001b[0;32m 114\u001b[0m how\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mnumpy\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n\u001b[0;32m 115\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m method \u001b[38;5;241m==\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mpyephem\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[0;32m 116\u001b[0m ephem_df \u001b[38;5;241m=\u001b[39m pyephem(time, latitude, longitude,\n\u001b[0;32m 117\u001b[0m altitude\u001b[38;5;241m=\u001b[39maltitude,\n\u001b[0;32m 118\u001b[0m pressure\u001b[38;5;241m=\u001b[39mpressure,\n\u001b[0;32m 119\u001b[0m temperature\u001b[38;5;241m=\u001b[39mtemperature, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\solarposition.py:385\u001b[0m, in \u001b[0;36mspa_python\u001b[1;34m(time, latitude, longitude, altitude, pressure, temperature, delta_t, atmos_refract, how, numthreads)\u001b[0m\n\u001b[0;32m 380\u001b[0m spa \u001b[38;5;241m=\u001b[39m _spa_python_import(how)\n\u001b[0;32m 382\u001b[0m delta_t \u001b[38;5;241m=\u001b[39m delta_t \u001b[38;5;129;01mor\u001b[39;00m spa\u001b[38;5;241m.\u001b[39mcalculate_deltat(time\u001b[38;5;241m.\u001b[39myear, time\u001b[38;5;241m.\u001b[39mmonth)\n\u001b[0;32m 384\u001b[0m app_zenith, zenith, app_elevation, elevation, azimuth, eot \u001b[38;5;241m=\u001b[39m \\\n\u001b[1;32m--> 385\u001b[0m spa\u001b[38;5;241m.\u001b[39msolar_position(unixtime, lat, lon, elev, pressure, temperature,\n\u001b[0;32m 386\u001b[0m delta_t, atmos_refract, numthreads)\n\u001b[0;32m 388\u001b[0m result \u001b[38;5;241m=\u001b[39m pd\u001b[38;5;241m.\u001b[39mDataFrame({\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapparent_zenith\u001b[39m\u001b[38;5;124m'\u001b[39m: app_zenith, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzenith\u001b[39m\u001b[38;5;124m'\u001b[39m: zenith,\n\u001b[0;32m 389\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mapparent_elevation\u001b[39m\u001b[38;5;124m'\u001b[39m: app_elevation,\n\u001b[0;32m 390\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124melevation\u001b[39m\u001b[38;5;124m'\u001b[39m: elevation, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mazimuth\u001b[39m\u001b[38;5;124m'\u001b[39m: azimuth,\n\u001b[0;32m 391\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mequation_of_time\u001b[39m\u001b[38;5;124m'\u001b[39m: eot},\n\u001b[0;32m 392\u001b[0m index\u001b[38;5;241m=\u001b[39mtime)\n\u001b[0;32m 394\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m result\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\spa.py:1088\u001b[0m, in \u001b[0;36msolar_position\u001b[1;34m(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)\u001b[0m\n\u001b[0;32m 1085\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m 1086\u001b[0m do_calc \u001b[38;5;241m=\u001b[39m solar_position_numpy\n\u001b[1;32m-> 1088\u001b[0m result \u001b[38;5;241m=\u001b[39m do_calc(unixtime, lat, lon, elev, pressure,\n\u001b[0;32m 1089\u001b[0m temp, delta_t, atmos_refract, numthreads,\n\u001b[0;32m 1090\u001b[0m sst, esd)\n\u001b[0;32m 1092\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(result, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[0;32m 1093\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\spa.py:987\u001b[0m, in \u001b[0;36msolar_position_numpy\u001b[1;34m(unixtime, lat, lon, elev, pressure, temp, delta_t, atmos_refract, numthreads, sst, esd)\u001b[0m\n\u001b[0;32m 985\u001b[0m x4 \u001b[38;5;241m=\u001b[39m moon_ascending_longitude(jce)\n\u001b[0;32m 986\u001b[0m l_o_nutation \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mempty((\u001b[38;5;241m2\u001b[39m, \u001b[38;5;28mlen\u001b[39m(x0)))\n\u001b[1;32m--> 987\u001b[0m longitude_obliquity_nutation(jce, x0, x1, x2, x3, x4, l_o_nutation)\n\u001b[0;32m 988\u001b[0m delta_psi \u001b[38;5;241m=\u001b[39m l_o_nutation[\u001b[38;5;241m0\u001b[39m]\n\u001b[0;32m 989\u001b[0m delta_epsilon \u001b[38;5;241m=\u001b[39m l_o_nutation[\u001b[38;5;241m1\u001b[39m]\n", "File \u001b[1;32m~\\anaconda3\\Lib\\site-packages\\pvlib\\spa.py:574\u001b[0m, in \u001b[0;36mlongitude_obliquity_nutation\u001b[1;34m(julian_ephemeris_century, x0, x1, x2, x3, x4, out)\u001b[0m\n\u001b[0;32m 566\u001b[0m d \u001b[38;5;241m=\u001b[39m NUTATION_ABCD_ARRAY[row, \u001b[38;5;241m3\u001b[39m]\n\u001b[0;32m 567\u001b[0m arg \u001b[38;5;241m=\u001b[39m np\u001b[38;5;241m.\u001b[39mradians(\n\u001b[0;32m 568\u001b[0m NUTATION_YTERM_ARRAY[row, \u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m*\u001b[39mx0 \u001b[38;5;241m+\u001b[39m\n\u001b[0;32m 569\u001b[0m NUTATION_YTERM_ARRAY[row, \u001b[38;5;241m1\u001b[39m]\u001b[38;5;241m*\u001b[39mx1 \u001b[38;5;241m+\u001b[39m\n\u001b[1;32m (...)\u001b[0m\n\u001b[0;32m 572\u001b[0m NUTATION_YTERM_ARRAY[row, \u001b[38;5;241m4\u001b[39m]\u001b[38;5;241m*\u001b[39mx4\n\u001b[0;32m 573\u001b[0m )\n\u001b[1;32m--> 574\u001b[0m delta_psi_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (a \u001b[38;5;241m+\u001b[39m b \u001b[38;5;241m*\u001b[39m julian_ephemeris_century) \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39msin(arg)\n\u001b[0;32m 575\u001b[0m delta_eps_sum \u001b[38;5;241m+\u001b[39m\u001b[38;5;241m=\u001b[39m (c \u001b[38;5;241m+\u001b[39m d \u001b[38;5;241m*\u001b[39m julian_ephemeris_century) \u001b[38;5;241m*\u001b[39m np\u001b[38;5;241m.\u001b[39mcos(arg)\n\u001b[0;32m 576\u001b[0m delta_psi \u001b[38;5;241m=\u001b[39m delta_psi_sum\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m1.0\u001b[39m\u001b[38;5;241m/\u001b[39m\u001b[38;5;241m36000000\u001b[39m\n", "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], "source": [ "location = pvlib.location.Location(latitude, longitude)\n", "solar_position = location.get_solarposition(timestamps)\n", "#solar_position=pvlib.solarposition.get_solarposition(timestamps, latitude, longitude)" ] }, { "cell_type": "code", "execution_count": 28, "id": "0a37ec16", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "plt.figure(figsize=(10, 6))\n", "plt.plot(solar_position['zenith'], clear_sky_df['ghi'], 'o', markersize=1)\n", "plt.xlabel('Solar Zenith Angle (degrees)')\n", "plt.ylabel('Clear-Sky GHI (W/m^2)')\n", "plt.title('Clear-Sky GHI vs. Solar Zenith Angle')\n", "plt.grid(True)\n", "plt.show()" ] }, { "cell_type": "code", "execution_count": 22, "id": "2ea9f651", "metadata": {}, "outputs": [], "source": [ "# Filter out zero values in measured shortwave down radiation\n", "\n", "# Calculate solar position for filtered data\n", "solar_position = pvlib.solarposition.get_solarposition(filtered_data['TIMESTAMP'], latitude, longitude)\n", "\n" ] }, { "cell_type": "code", "execution_count": 31, "id": "3cd909aa", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " apparent_zenith zenith apparent_elevation \\\n", "TIMESTAMP \n", "2024-03-13 10:00:05 59.927867 59.956806 30.072133 \n", "2024-03-13 10:00:12 59.918398 59.947326 30.081602 \n", "2024-03-13 10:00:19 59.908938 59.937855 30.091062 \n", "2024-03-13 10:00:26 59.899485 59.928391 30.100515 \n", "2024-03-13 10:00:33 59.890041 59.918936 30.109959 \n", "... ... ... ... \n", "2024-06-30 23:59:56 104.212059 104.212059 -14.212059 \n", "2024-06-30 23:59:57 104.211902 104.211902 -14.211902 \n", "2024-06-30 23:59:58 104.211745 104.211745 -14.211745 \n", "2024-06-30 23:59:59 104.211588 104.211588 -14.211588 \n", "2024-07-01 00:00:00 104.211430 104.211430 -14.211430 \n", "\n", " elevation azimuth equation_of_time \n", "TIMESTAMP \n", "2024-03-13 10:00:05 30.043194 147.728023 -9.336757 \n", "2024-03-13 10:00:12 30.052674 147.759858 -9.336735 \n", "2024-03-13 10:00:19 30.062145 147.791700 -9.336712 \n", "2024-03-13 10:00:26 30.071609 147.823547 -9.336690 \n", "2024-03-13 10:00:33 30.081064 147.855401 -9.336668 \n", "... ... ... ... \n", "2024-06-30 23:59:56 -14.212059 3.572163 -3.885184 \n", "2024-06-30 23:59:57 -14.211902 3.576113 -3.885186 \n", "2024-06-30 23:59:58 -14.211745 3.580063 -3.885188 \n", "2024-06-30 23:59:59 -14.211588 3.584013 -3.885191 \n", "2024-07-01 00:00:00 -14.211430 3.587963 -3.885193 \n", "\n", "[9401642 rows x 6 columns]\n" ] } ], "source": [ "print(solar_position)" ] }, { "cell_type": "code", "execution_count": 33, "id": "5b6502a6", "metadata": { "scrolled": true }, "outputs": [ { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "# Plot Clear-Sky GHI vs. Solar Zenith Angle\n", "plt.figure(figsize=(10, 6))\n", "\n", "# Plot Measured SW_dn with black small dots and reduced opacity\n", "#plt.plot(filtered_angles['apparent_zenith'], filtered_data['SR15D1Dn_Irr'], 'ko', markersize=0.1, alpha=0.1, label='Measured SW_dn')\n", "plt.plot(merged_df['apparent_zenith'], merged_df['SR15D1Dn_Irr'], 'ko', markersize=0.1, alpha=0.1, label='Measured SW_dn')\n", "\n", "# Plot Clear-Sky GHI with a red line on top\n", "plt.plot(merged_df['apparent_zenith'], clear_sky_df.loc[merged_df.index, 'ghi'], 'ro',markersize=0.1, alpha=0.5,label='Clear-Sky GHI', zorder=5)\n", "plt.xlabel('Solar Zenith Angle (degrees)')\n", "plt.ylabel('Shortwave Down Radiation (W/m^2)')\n", "plt.title('Shortwave Down Radiation vs Solar Zenith Angle in Alkmaar, Netherlands (March-July 2024)')\n", "#plt.legend()\n", "plt.grid(True)\n", "# Save the plot as a PDF file\n", "#output_file = 'shortwave_radiation_vs_zenith_angle_vs_csmodel.png'\n", "# Save the plot as a PNG file\n", "#plt.savefig(output_file, format='png', dpi=300) # Save as PNG with 300 dpi for high quality\n", "plt.show()\n" ] }, { "cell_type": "code", "execution_count": 36, "id": "8f7db910", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TIMESTAMP SR15D1Dn_Irr ghi CSI\n", "120 2024-03-13 10:14:04 199.7 493.224322 0.404887\n", "121 2024-03-13 10:14:08 201.0 493.308733 0.407453\n", "122 2024-03-13 10:14:12 200.5 493.393089 0.406370\n", "123 2024-03-13 10:14:16 199.6 493.477392 0.404476\n", "124 2024-03-13 10:14:20 195.4 493.561639 0.395898\n" ] } ], "source": [ "\n", "# Ensure `ghi` from clear_sky_df is aligned with the timestamps in `merged_df`\n", "merged_df['ghi'] = clear_sky_df.loc[merged_df.index, 'ghi'] # Add 'ghi' values from clear_sky_df to merged_df\n", "merged_df['CSI'] = merged_df['SR15D1Dn_Irr'] / merged_df['ghi'] # Calculate CSI\n", "\n", "# Check the first few rows to confirm the new CSI column\n", "print(merged_df[['TIMESTAMP', 'SR15D1Dn_Irr', 'ghi', 'CSI']].head())" ] }, { "cell_type": "code", "execution_count": 37, "id": "a939eb35", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " TIMESTAMP SR15D1Dn_Irr_x apparent_zenith LWP_Corrected \\\n", "0 2024-03-13 10:17:13 125.6 58.628550 49.804138 \n", "1 2024-03-13 10:17:14 124.9 58.627378 47.703613 \n", "2 2024-03-13 10:17:15 124.9 58.626206 49.981384 \n", "3 2024-03-13 10:17:26 130.3 58.613327 48.218933 \n", "4 2024-03-13 10:17:30 144.4 58.608648 49.688721 \n", "\n", " LWP_Range SR15D1Dn_Irr_y ghi CSI \n", "0 45-50 125.6 497.153315 0.252638 \n", "1 45-50 124.9 497.173780 0.251220 \n", "2 45-50 124.9 497.194241 0.251210 \n", "3 45-50 130.3 497.419089 0.261952 \n", "4 45-50 144.4 497.500750 0.290251 \n" ] } ], "source": [ "# Merge on TIMESTAMP with inner join to keep only overlapping times\n", "final_merged_df = pd.merge(filtered_df, merged_df[['TIMESTAMP', 'SR15D1Dn_Irr', 'ghi', 'CSI']], \n", " on='TIMESTAMP', how='inner')\n", "\n", "# Check the resulting DataFrame\n", "print(final_merged_df.head())" ] }, { "cell_type": "code", "execution_count": null, "id": "c430475b", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.11.7" } }, "nbformat": 4, "nbformat_minor": 5 }