PriceForecast/聚烯烃预测值绘图调试.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "7fadc60c-d710-4b8c-89cd-1d889ece1eaf",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "WARNING:tensorflow:From C:\\Users\\EDY\\AppData\\Roaming\\Python\\Python311\\site-packages\\keras\\src\\losses.py:2976: The name tf.losses.sparse_softmax_cross_entropy is deprecated. Please use tf.compat.v1.losses.sparse_softmax_cross_entropy instead.\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "读取本地数据：juxitingdataset\\PP指标数据.xlsx\n",
      "读取本地数据：juxitingdataset\\PP指标数据.xlsx\n",
      "getdata接收：juxitingdataset\\PP指标数据.xlsx date \n",
      "getdata接收：juxitingdataset\\PP指标数据.xlsx date \n",
      "删除两月不更新特征前数据量：(2516, 354)\n",
      "删除两月不更新特征前数据量：(2516, 354)\n",
      "删除两月不更新特征后数据量：(2516, 314)\n",
      "删除两月不更新特征后数据量：(2516, 314)\n",
      "删除预测列为空值的行后数据量：(772, 314)\n",
      "删除预测列为空值的行后数据量：(772, 314)\n",
      "删除全为空值的列后数据量：(772, 314)\n",
      "删除全为空值的列后数据量：(772, 314)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "d:\\code\\PriceForecast\\lib\\dataread.py:226: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  values.dropna(inplace=True,axis=0)\n",
      "                  日度(218)         周度(94)                 84天(1)\n",
      "0    PP主力收盘价拟合残差/丙烷 CP M1      PE注塑开工率/周  中国：华东地区：市场平均价：BOPP厚光膜\n",
      "1         华南聚丙烯基差（折盘面收盘价）  PP：看跌比例：中国（周）                       \n",
      "2         华北聚丙烯基差（折盘面收盘价）  PP：看平比例：中国（周）                       \n",
      "3         华东聚丙烯基差（折盘面收盘价）  PP：看升比例：中国（周）                       \n",
      "4                 煤制聚丙烯利润     PP看空情绪指数环差                       \n",
      "..                    ...            ...                    ...\n",
      "213               PE期货收盘价                                      \n",
      "214               PP连续-1月                                      \n",
      "215               PP连续-5月                                      \n",
      "216               PP连续-9月                                      \n",
      "217                     y                                      \n",
      "\n",
      "[218 rows x 3 columns]\n",
      "                  日度(218)         周度(94)                 84天(1)\n",
      "0    PP主力收盘价拟合残差/丙烷 CP M1      PE注塑开工率/周  中国：华东地区：市场平均价：BOPP厚光膜\n",
      "1         华南聚丙烯基差（折盘面收盘价）  PP：看跌比例：中国（周）                       \n",
      "2         华北聚丙烯基差（折盘面收盘价）  PP：看平比例：中国（周）                       \n",
      "3         华东聚丙烯基差（折盘面收盘价）  PP：看升比例：中国（周）                       \n",
      "4                 煤制聚丙烯利润     PP看空情绪指数环差                       \n",
      "..                    ...            ...                    ...\n",
      "213               PE期货收盘价                                      \n",
      "214               PP连续-1月                                      \n",
      "215               PP连续-5月                                      \n",
      "216               PP连续-9月                                      \n",
      "217                     y                                      \n",
      "\n",
      "[218 rows x 3 columns]\n",
      "特征信息：总共有311个,日度(218),周度(94),84天(1), 详看 附1、特征列表\n",
      "        数据特征工程：\n",
      "    1. 数据日期排序，新日期在最后\n",
      "    2. 删除空列，特征数据列没有值，就删除\n",
      "    3. 删除近两月不再更新值的指标\n",
      "    4. 非日度数据填充为日度数据，填充规则：\n",
      "    -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据\n",
      "    -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值\n",
      "        数据特征相关性分析：\n",
      "    \n",
      "特征信息：总共有311个,日度(218),周度(94),84天(1), 详看 附1、特征列表\n",
      "        数据特征工程：\n",
      "    1. 数据日期排序，新日期在最后\n",
      "    2. 删除空列，特征数据列没有值，就删除\n",
      "    3. 删除近两月不再更新值的指标\n",
      "    4. 非日度数据填充为日度数据，填充规则：\n",
      "    -- 向后填充，举例：假设周五出现一个周度指标数据，那么在这之前的数据用上周五的数据\n",
      "    -- 向前填充，举例：采集数据开始日期为2018年1月1日，那么周度数据可能是2018年1月3日，那么3日的数据向前填充，使1日2日都有数值\n",
      "        数据特征相关性分析：\n",
      "    \n",
      "********************************************************************************************************************************************************************************************************\n",
      "********************************************************************************************************************************************************************************************************\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<Figure size 1000x1000 with 0 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "# 读取配置\n",
    "from lib.dataread import *\n",
    "from lib.tools import *\n",
    "from models.nerulforcastmodels import ex_Model,model_losss,brent_export_pdf,tansuanli_export_pdf,pp_export_pdf,model_losss_juxiting\n",
    "\n",
    "import glob\n",
    "import torch\n",
    "torch.set_float32_matmul_precision(\"high\")\n",
    "\n",
    "sqlitedb = SQLiteHandler(db_name) \n",
    "sqlitedb.connect()\n",
    "\n",
    "signature = BinanceAPI(APPID, SECRET)\n",
    "etadata = EtaReader(signature=signature,\n",
    "                        classifylisturl = classifylisturl,\n",
    "                        classifyidlisturl=classifyidlisturl,\n",
    "                        edbcodedataurl=edbcodedataurl,\n",
    "                        edbcodelist=edbcodelist,\n",
    "                        edbdatapushurl=edbdatapushurl,\n",
    "                        edbdeleteurl=edbdeleteurl,\n",
    "                        edbbusinessurl=edbbusinessurl\n",
    "                        )\n",
    "# 获取数据\n",
    "if is_eta:\n",
    "    # eta数据\n",
    "    logger.info('从eta获取数据...')\n",
    "    signature = BinanceAPI(APPID, SECRET)\n",
    "    etadata = EtaReader(signature=signature,\n",
    "                                classifylisturl = classifylisturl,\n",
    "                                classifyidlisturl=classifyidlisturl,\n",
    "                                edbcodedataurl=edbcodedataurl,\n",
    "                                edbcodelist=edbcodelist,\n",
    "                                edbdatapushurl=edbdatapushurl,\n",
    "                                edbdeleteurl=edbdeleteurl,\n",
    "                                edbbusinessurl=edbbusinessurl,\n",
    "                                )\n",
    "    df_zhibiaoshuju,df_zhibiaoliebiao =  etadata.get_eta_api_pp_data(data_set=data_set,dataset=dataset)  # 原始数据，未处理\n",
    "\n",
    "    # 数据处理\n",
    "    df = datachuli_juxiting(df_zhibiaoshuju,df_zhibiaoliebiao,y = y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time) \n",
    "\n",
    "else:\n",
    "    logger.info('读取本地数据：'+os.path.join(dataset,data_set))\n",
    "    df = getdata_juxiting(filename=os.path.join(dataset,data_set),y=y,dataset=dataset,add_kdj=add_kdj,is_timefurture=is_timefurture,end_time=end_time)  # 原始数据，未处理\n",
    "\n",
    "# 更改预测列名称\n",
    "df.rename(columns={y:'y'},inplace=True)\n",
    "    \n",
    "if is_edbnamelist:\n",
    "    df = df[edbnamelist]     \n",
    "df.to_csv(os.path.join(dataset,'指标数据.csv'), index=False)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "ae059224-976c-4839-b455-f81da7f25179",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 保存最新日期的y值到数据库\n",
    "# 取第一行数据存储到数据库中\n",
    "first_row = df[['ds','y']].tail(1)\n",
    "# 将最新真实值保存到数据库\n",
    "# if not sqlitedb.check_table_exists('trueandpredict'):\n",
    "#     first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)\n",
    "# else:\n",
    "#     for row in first_row.itertuples(index=False):\n",
    "#         row_dict = row._asdict()\n",
    "#         row_dict['ds'] = row_dict['ds'].strftime('%Y-%m-%d %H:%M:%S')\n",
    "#         check_query = sqlitedb.select_data('trueandpredict',where_condition = f\"ds = '{row.ds}'\")\n",
    "#         if len(check_query) > 0:\n",
    "#             set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
    "#             sqlitedb.update_data('trueandpredict',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
    "#             continue\n",
    "#         sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=row_dict.keys())\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "abb597fc-c5f3-4d76-8099-5eff358cb634",
   "metadata": {},
   "outputs": [],
   "source": [
    "import datetime\n",
    "# 判断当前日期是不是周一\n",
    "is_weekday = datetime.datetime.now().weekday() == 1\n",
    "if is_weekday:\n",
    "    logger.info('今天是周一，更新预测模型')\n",
    "    # 计算最近20天预测残差最低的模型名称\n",
    "\n",
    "    model_results = sqlitedb.select_data('trueandpredict',order_by = \"ds DESC\",limit = \"20\")\n",
    "    # 删除空值率为40%以上的列\n",
    "    print(model_results.shape)\n",
    "    model_results = model_results.dropna(thresh=len(model_results)*0.6,axis=1)\n",
    "    model_results = model_results.dropna()\n",
    "    print(model_results.shape)\n",
    "    modelnames  = model_results.columns.to_list()[2:] \n",
    "    for col in model_results[modelnames].select_dtypes(include=['object']).columns:\n",
    "        model_results[col] = model_results[col].astype(np.float32)\n",
    "    # 计算每个预测值与真实值之间的偏差率\n",
    "    for model in modelnames:\n",
    "        model_results[f'{model}_abs_error_rate'] = abs(model_results['y'] - model_results[model]) / model_results['y']\n",
    "\n",
    "    # 获取每行对应的最小偏差率值\n",
    "    min_abs_error_rate_values = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].min(), axis=1)\n",
    "    # 获取每行对应的最小偏差率值对应的列名\n",
    "    min_abs_error_rate_column_name = model_results.apply(lambda row: row[[f'{model}_abs_error_rate' for model in modelnames]].idxmin(), axis=1)\n",
    "    print(min_abs_error_rate_column_name)\n",
    "    # 将列名索引转换为列名\n",
    "    min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])\n",
    "    # 取出现次数最多的模型名称\n",
    "    most_common_model = min_abs_error_rate_column_name.value_counts().idxmax()\n",
    "    logger.info(f\"最近20天预测残差最低的模型名称：{most_common_model}\")\n",
    "\n",
    "    # 保存结果到数据库\n",
    "    \n",
    "    if not sqlitedb.check_table_exists('most_model'):\n",
    "        sqlitedb.create_table('most_model',columns=\"ds datetime, most_common_model TEXT\")\n",
    "    sqlitedb.insert_data('most_model',(datetime.datetime.now().strftime('%Y-%m-%d %H:%M:%S'),most_common_model,),columns=('ds','most_common_model',))\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "ade7026e-8cf2-405f-a2da-9e90f364adab",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "开始训练模型...\n",
      "开始训练模型...\n"
     ]
    }
   ],
   "source": [
    "if is_corr:\n",
    "    df = corr_feature(df=df)\n",
    "\n",
    "df1 = df.copy()  # 备份一下，后面特征筛选完之后加入ds y 列用\n",
    "logger.info(f\"开始训练模型...\")\n",
    "row,col = df.shape\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "dfef57d8-36da-423b-bbe7-05a13e15f71b",
   "metadata": {},
   "outputs": [],
   "source": [
    "now = datetime.datetime.now().strftime('%Y%m%d%H%M%S')\n",
    "# ex_Model(df,\n",
    "#         horizon=horizon,\n",
    "#         input_size=input_size,\n",
    "#         train_steps=train_steps,\n",
    "#         val_check_steps=val_check_steps,\n",
    "#         early_stop_patience_steps=early_stop_patience_steps,\n",
    "#         is_debug=is_debug,\n",
    "#         dataset=dataset,\n",
    "#         is_train=is_train,\n",
    "#         is_fivemodels=is_fivemodels,\n",
    "#         val_size=val_size,\n",
    "#         test_size=test_size,\n",
    "#         settings=settings,\n",
    "#         now=now,\n",
    "#         etadata = etadata,\n",
    "#         modelsindex = modelsindex,\n",
    "#         data = data,\n",
    "#         is_eta=is_eta,\n",
    "#         )\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e5b6f30-b7ca-4718-97a3-48b54156e07f",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "模型训练完成\n",
      "模型训练完成\n",
      "训练数据绘图ing\n",
      "训练数据绘图ing\n",
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19344\\741737176.py:111: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  names_df['columns'] = names_df.apply(add_rote_column, axis=1)\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "NLinear-NHITS-误差比例            float32\n",
      "RNN-NHITS-误差比例                float32\n",
      "TiDE-NHITS-误差比例               float32\n",
      "TSMixer-NHITS-误差比例            float32\n",
      "PatchTST-NHITS-误差比例           float32\n",
      "NHITS-NHITS-误差比例              float32\n",
      "DilatedRNN-NHITS-误差比例         float32\n",
      "DLinear-NHITS-误差比例            float32\n",
      "GRU-NHITS-误差比例                float32\n",
      "LSTM-NHITS-误差比例               float32\n",
      "Informer-NHITS-误差比例           float32\n",
      "DeepNPTS-NHITS-误差比例           float32\n",
      "MLP-NHITS-误差比例                float32\n",
      "TFT-NHITS-误差比例                float32\n",
      "TSMixerx-NHITS-误差比例           float32\n",
      "BiTCN-NHITS-误差比例              float32\n",
      "TCN-NHITS-误差比例                float32\n",
      "iTransformer-NHITS-误差比例       float32\n",
      "StemGNN-NHITS-误差比例            float32\n",
      "MLPMultivariate-NHITS-误差比例    float32\n",
      "columns                        object\n",
      "dtype: object\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'TCN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'StemGNN']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'StemGNN']\n",
      "<class 'list'>\n",
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      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['PatchTST', 'NHITS', 'DilatedRNN', 'GRU', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'TCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'iTransformer', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "['NLinear', 'RNN', 'TiDE', 'TSMixer', 'PatchTST', 'NHITS', 'DilatedRNN', 'DLinear', 'GRU', 'LSTM', 'Informer', 'DeepNPTS', 'MLP', 'TFT', 'TSMixerx', 'BiTCN', 'StemGNN', 'MLPMultivariate']\n",
      "<class 'list'>\n",
      "            ds            y        NHITS  upper_bound  lower_bound\n",
      "0   2024-01-30  7315.000000  7314.250977  7255.317383  7406.355469\n",
      "5   2024-01-31  7302.500000  7313.850586  7260.271484  7409.821777\n",
      "10  2024-02-01  7275.000000  7296.995117  7250.980469  7385.459473\n",
      "15  2024-02-02  7242.500000  7275.514648  7233.059082  7349.825684\n",
      "20  2024-02-04  7240.000000  7236.788574  7210.264160  7308.604004\n",
      "..         ...          ...          ...          ...          ...\n",
      "975 2024-11-13  7323.333496  7332.291016  7241.626953  7422.700195\n",
      "976 2024-11-14  7353.333496  7344.439941  7244.206543  7417.808594\n",
      "977 2024-11-15  7393.333496  7319.736328  7230.743652  7418.842285\n",
      "978 2024-11-18  7363.333496  7317.362305  7208.802734  7411.817871\n",
      "979 2024-11-19  7370.000000  7307.738770  7272.761230  7399.986328\n",
      "\n",
      "[200 rows x 5 columns]\n"
     ]
    },
    {
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UiTVe+RCLGwqEYwqEGw5Wt9ssyrJbtyVebMpyWOW0WTOrbVg8qr7vXyR7qFr+bqO1ZexVLV4qktdXofyByvKsVk7pfNX2P7bV4QUjDf/dAAAAAKQOiRYAAAAAMgxD8RZ0mIrGDEVjMflCMUmZ1SqsTs9Ff1fOli8Uc+Rq/ZH3ybA5W7Wet/ehyvKsVt7Gj1OUaKF1GAAAaCMOh/SnP23/GchQJFoAAAAAKBLLoD5fKZSz8RN1/+peSdLGQ+5QOH9Aq9f09j5UXZc9kbI5LeFoXPG4sdM2bwAAAC3mdEp33ml2FIDprGYHAAAAAMB8LR16n8lsgQr1/eASWWSocuivVDP4xJSs6+11oAyLTVme1XLUbkjJmsEo7cMAAACAtkKiBQAAAICiLekblsmMuPp+eJkcgXIFuwzRpgOnp2zpuDNf/u5jJEm5Gz9OyZq0DwMAAG0iHpfWrElsvJ5EBiPRAgAAAEBRWoc1S7dvHlLehg8Vt7m07sj7ZdjdKV3f2/tQSalLtISoaAEAAG0hEJAGDkxsgYDZ0QCmIdECAAAAQFFahzWZu2yRihcmepFvOnCGQl2GpvwayUTLpk+keOuTJFS0AAAAAG2HRAsAAAAAWoc1kTVUo37vXSSLEVX1oCmqGvrLNrmOv8cYxRy5soeq5d66pNXrBSNUtAAAAABthUQLAAAAAFqHNYVhqM/HV8rpXa9QXj9tPOQOyWJpm2tZHfKVHCgpNe3DojFD0RjJNAAAAKAtkGgBAAAAQOuwJiha/pQK1sxV3OrQ+iPvV9yZ16bXq+0zQZKUu/GTlKwXjJJoAQAAANoCiRYAAAAAitE6bJdcW5epZP7NkqQtY69WoPvebX7Nujkt2VsWyhJt/XBZ2ocBAAAAbYNECwAAAABFaB22U5aIX33fu1DWWEievkeqYuT57XLdcP5AhXN7yxoPK6f081avR6IFAAAAaBskWgAAAAAoRuuwner12Y1y1axQJLunNhx2d9vNZfkpi0XeXomqlryNH7V6uWCEqiUAAJBidrv0+98nNrvd7GgA0/DbDwAAAGS4WNyQQZ6lUYUrXlLRD7NlWKxaf8S9irmK2vX63t6HqOiHZ5W78eNWrxWKUtECAABSLCtLuv9+s6MATEdFCwAAAJDhIjEqHRrjrFmtXp9eK0kq2+cS+UrGt3sM3t6HyJBFrqrvZfdvadVa8bgUjvJvDQAAAKQaiRYAAAAgw9E2rCFLLKR+710oW8Qnb/F4lY252JQ4Yq4iBbqNlCTlbvyk1esFqWoBAACpZBhSeXlio0QaGYxECwAAAJDhojHeFP9U8YKZcm9doqirSOuP+IdktZkWi7d3Yk5LbkrmtJBoAQAAKeT3Sz16JDa/3+xoANOQaAEAAAAyXDROO6kd5a19W92+e1SStGHCXxXNKTY1nu2Jlk9a/U3RUIR/awAAACDVSLQAAAAAGS5K67Akh3eT+nz0R0lS+cjfqrbfUSZHJPl77q+4zSVHoFxZVd+3ai0qWgAAAIDUI9ECAAAAZDgSLdvEo+r7/h9kD9XI331vbRl7ldkRSZIMW5Z8JeMlSXkbP27VWqFoXAb90wEAAICUItECAAAAZLgYM1okST0X/U05WxYq5sjTuiPuk2Fzmh1S0vb2Ya1LtBhGItkCAAAAIHVItAAAAAAZLsKMFuVs/ETdv7pPkrTxkNsVye9vckT11W5LtOSUzpclGmzVWrQPAwAAAFKLRAsAAACQ4WIZ3jrM7i9X3w8ukUWGKof+SjWDTzQ7pAZCXYYq4u4uayyo7LIvW7VWMEJiDQAAAEglEi0AAABAhovEMviDdyOuPh9eJkegXMEuQ7TpwOlmR9Q4iyVl7cNCUSpaAABAitjt0m9+k9jsdrOjAUxDogUAAADIYIZhKJM7h3X75iHlbfxIcZtL6468X4bdbXZIO5WqRAsVLQAAIGWysqTHHktsWVlmRwOYhkQLAAAAkMGiGdw2zF22SMUL75QkbTpwhkJdhpoc0a55ex8iSXJXLJEtWNnidcLRuOIZ/O8OAAAApBqJFgAAACCDRWOZ+YG7NVStfu/9QRYjqupBU1Q19Jdmh7Rb0eyeCnYZKosM5W78pFVrBWkfBgAAUsEwJJ8vsRmZ+boSkEi0AAAAABktmol9wwxDfT6+Sk7vBoXy+mnjIXdIFovZUTXJ9vZhrUy00D4MAACkgt8v5eYmNr/f7GgA0zChCAAAAMhgGVPREo8pZ/MC2QNlcpd9pYI1cxW3OrT+yPsVd+aZHV2T1faeoG5L/qXcTR8nvjXawgRRMEJFCwAAAJAqJFoAAACADJYJM1ryV89VyfzpcvpK6+2vGXySAt33NimqlvGVHKC41Smnd6OcntUKFwxq0TokWgAAAIDUoXUYAAAAkME6e+uw/NVz1e/daXL8JMliSCr88QXlr55rTmAtZNjd8vfcX5KUt+GjFq9D6zAAAAAgdUi0AAAAABmsU7cOi8dUMn+6JEM/bbBVd7tk/nQp3rGqO7y9D5Ek5W78uMVrxOKGojGSLQAAAEAqkGgBAAAAMlhnbh2Ws3mBnL7SBkmWOhYZcvpKlbN5QbvG1Vq1fSZIknJKP5PikRavE4ySaAEAAABSgUQLAAAAkMFinbh1mD1QltL7pYtg15GKZnWRLeJVdtlXLV+HOS0AAABASpBoAQAAADJYZ65oibp7pPR+acNilbfXwZJa1z6MRAsAAGg1m0065ZTEZrOZHQ1gGhItAAAAQAbrzDNafMXjFM0q3OlxQxaFc0rkKx7XfkGliLf3oZJam2jpvNVMAACgnbhc0nPPJTaXy+xoANOQaAEAAAAyVCxuyOi8eRbZg5WyxBIzTH76MI1tk1tKx0+XrB3v25d1iZbs8q9kDXtatAYVLQAAAEBqkGgBAAAAMlQk1okrGgxDveZdL1vUp1BuH0Wyi+sdjuQUa91Rs+QZOMmkAFsnktdHoYJBshgx5W76rEVrGIYUipJsAQAAAFrLbnYAAAAAAMwR68TzWQpWvaaCNXNlWOxad8w/FewyTDmbF8geKFPU3SPRLqwDVrLsyNv7UGXVrFLuxo/lGXBci9YIRuLKsnfs5wEAAJjI55NycxM/e71STo658QAmIdECAAAAZKjOOp/FFqhQr3k3SJLKxvxBwa4jJEm+XgeaGVbK1fY+VF2XPt6qOS2hSExyO1IYFQAAAJB5aB0GAAAAZKhovBO2DjMM9f70OtlDVQoUDVf5mD+YHVGb8ZWMl2GxKcuzWo7a9S1aIxjphL8DAAAAQDsj0QIAAABkqM7YOqxg9evJlmEbDvurDJvT7JDaTNyZL3/3MZLU4qoWZrQAAAAArUeiBQAAAMhQkU6WaKnfMuzCZMuwzszbZ4Kk1iRa4jKMzvV7AAAAALQ3Ei0AAABAhop1shktveddL3uwUoGivVQ+5iKzw2kX3t6HSpJyN30qxZtfnWIYiWQLAAAAgJYj0QIAAABkqEgnmtGSv+p1Fax+I9EybELnbhm2I3/3MYo58mQPVctd+V2L1ghGaB8GAAAAtAaJFgAAACBDdZYZLbZAhXrPu17StpZh3UaaHFE7strl7XWgJCmvhe3DgpHOk3ADAADtzGaTjj8+sdlsZkcDmMZudgAAAAAAzBGJdY4P2HvNuyHRMqzLsIxpGbYjb+9DVbD2bRWufkO98/3KKbUr0OtAydq0DzuoaAEAAC3mcklz5pgdBWA6Ei0AAABABjIMQ52hc1j+qtdVuHqODItNGw7LnJZh9WwrTHJXLdP+VcuktQ8qnFOi0vHT5Rk4abenB6MkWgAAAIDWoHUYAAAAkIGinaBtmC2wdYeWYX9QsNsokyNqf/mr56rXZzfqp/+aDt9m9Xt3mvJXz93tGpGo0WnayAEAAABmINECAAAAZKBorON/sN5r3vUZ3TJM8ZhK5k+XZMjyk0OWbamXkvnTpfjuK1ZCVLUAAICW8PmknJzE5vOZHQ1gGhItAAAAQAaKdvC+YbQMk3I2L5DTV9ogyVLHIkNOX6lyNi/Y7VrBSMf+fQAAACby+xMbkMFItAAAAAAZqCNXtCRaht0gSSrf+8KMbBkmSfZAWcruF4xQ0QIAAAC0FIkWAAAAIAN15BktvebdIHtwqwJdhqlsn4vNDsc0UXePlN2PRAsAAADQciRaAAAAgAzUUVuH5a+eo8LVr29rGXZXRrYMq+MrHqdwTkkjE1oSDFkUzimRr3jcbteidRgAAADQciRaAAAAgAzUEVuH2QJb1fvT6yXVtQwbbXJEJrPaVDp+uiQ1SLbU3S4dP12y2na7VCxuKBIj2QIAAAC0BIkWAAAAIAPFOmDrsF6f3Sh7cKuCXYaqbJ+LzA4nLXgGTtK6o2YpklNcb38kp1jrjpolz8BJTV6L9mEAAABAy9jNDgAAAABA++sQrcPiMeVsXiB7oEzOmlUqXPWaDItN6w/7qwxbltnRpQ3PwEny9D9W+T++qP4f/1Fxq0Pfn/qxZG9eW7VgJK48VxsFCQAAOierVTrssO0/AxmKRAsAAACQgaJpXtGSv3quSuZPl9NXWm+/p/+xtAxrjNWm6kEnqven18geDyvLu06hwj2atQQVLQAAoNncbumDD8yOAjAdaUYAAAAgA6XzjJb81XPV791pcvwkyWJIyl/zpvJXzzUnsHRntanW1UeS5Kpc1uzTQ1ESLQAAAEBLkGgBAAAAMkwsbshI1zxLPKaS+dPVcLy7krdL5k+X4iQFGlPj7idJclUub/a5wUgHaCcHAAAApCESLQAAAECGicTS9wP1nM0L5PSVNkiy1LHIkNNXqpzNC9o1ro7C4+4rqWUVLYZBVQsAAGgmn0/q3j2x+XxmRwOYhhktAAAAQIaJpfF8FnugLKX3yzStSbRIiaqWLLstlSEBAIDOrqLC7AgA01HRAgAAAGSYaBonWqLuHim9X6apcSUSLU7vRllDNc0+PxShogUAAABoLhItAAAAQIaJpnHrMF/xOIVzShqZ0JJgyKJwTol8xePaObKOIWrPUTinlyTJVcWcFgAAAKA9kGgBAAAAMkw6tw6T1abS8dMlNYyxLvlSOn66ZKW91c4EioZJktxbm98+LMiMFgAAAKDZSLQAAAAAGSaSzokWSZ6Bk1Qx4rwG+yM5xVp31Cx5Bk4yIaqOI9hlL0ktm9MSjsZlGOn9+wEAAACkG7vZAQAAAABoX7FY+n+Qbov4JEnVAyfLM+A4Rd09Eu3CqGTZrbqKFldl81uHGUaifZjbyfMMAAAANBWJFgAAACDDROLpP4cjp3SeJKl6yCmq7XukydF0LMEu2xItVculeKzZyalQNEaiBQAANI3VKu2///afgQxFogUAAADIMGk9o0WSo3a9smrXybDY5OvJ0PvmCuUPUNyWJWs0IGftOoULBjbr/GAk/RNxAAAgTbjd0hdfmB0FYDrSjAAAAECGicTS+4P03NLPJEmB7nsr7sw1OZoOyGpTsMtQSS2b0xKMxFIdEQAAANCpkWgBAAAAMohhGEr3zmE5mxJtw7wlB5kcSccVLNpLUgsTLVESLQAAAEBzkGgBAAAAMkg0zduGyTCUW5do6UWipaWCRdvmtLQg0RKJGmnfXg4AAKQJv18aMCCx+f1mRwOYhhktAAAAQAaJxtL7A3SnZ7Uc/s2KW53y99zf7HA6rGDRcEmSuwWJFinRPiwni7eLAABgNwxDWrt2+89AhqKiBQAAAMgg0TTvG1ZXzeLvsa8Mu8vkaDquuooWZ+16WcO1zT+fOS0AAABAk5FoAQAAADJIule01M1n8fU60ORIOraYq4si2cWSJFfl8mafH4ymd0IOAAAASCckWgAAAIAMktYzWgxDuaWfSWI+SyoEuibah7Uo0UJFCwAAANBkJFoAAACADJLOQ86zqn6QPbhVcZtLge77mB1Oh1fXPszVgjktgXAsrX9XAAAAgHRCogUAAADIIJFY+raEyt30qSTJVzxWhs1pcjQdX7BoL0ktS7QYhuQNRVMdEgAAANAp2c0OAAAAAED7SecqhZxtbcN8tA1LiWSipWq5ZMQlS/O+Z1cbjKjA7WiL0AAAQGdhsUjDh2//GchQJFoAAACADBKNp2lFSzy2fT5LycEmB9M5hAoGKW51yhbxyVG7XpH8/s06vzZIRQsAANiN7Gzpu+/MjgIwHa3DAAAAgAwSTdOKFlfld7KFPYo5chXoNtLscDoHq12hLkMkSe4WtA+LxgwFwrFURwUAAAB0OiRaAAAAgAwSjaVnoiV307a2YcUHSFYK71Ml0Io5LVKifRgAAACAXSPRAgAAAGSIWNyQkZ55FuVumieJ+SyplpzTUrm8Red7aB8GAAB2xe+XRoxIbH6/2dEApuGrYgAAAECGSN/5LBFlb1kgSfKSaEmpYNEwSS2vaAmEY4rG4rLb+I4eAABohGFIS5du/xnIULxaBgAAADJEurYNyy7/RraIT9GswmQFBlKj7vl0etbKGvG1aA1viKoWAAAAYFdItAAAAAAZIhpPz0RLTl3bsJIDJQtvUVIp5u6qSHYPWWQoq+r7Fq1RS/swAAAAYJd4FwMAAABkiGgsPVuH5ZYmEi20DWsb2+e0tKx9GIkWAAAAYNdItAAAAAAZIpaGFS2WaFDZWxZKknwlJFraQrBLYk6Le2vLEi2xuCEf7cMAAACAnSLRAgAAAGSISBomWrLLFssaCyni7q5Q4R5mh9MpBbsOl9TyihaJOS0AAADArpBoAQAAANrRY489JovF0mB77LHHkve5+uqrNWXKlHrnLVmyRGPHjlWXLl10xRVXyDCMna41c+bMRteKxRKJlh+XL9WvTjhC+w7sptF9C5Pbb391kt5/a44mHby39hnQVadPOUqrfmzaXI8/nPOLBms1Rc62tmG+XgdJFkuTzkHzBJKtw5ZLRsuSbbXBSCpDAgAAnYXFIvXvn9h4LYcMRqIFu8Q31wAAAFLr9NNPV1VVVXJbv369unXrpgkTJkhKJFQeeOAB/f3vf0+eEwqFNGXKFO23335auHChli5dqscee0ynn366Fi1apIKCAj3yyCNatGiRnE6nXnjhhUbXisTjCodCuujcX2r46DHKzy/UvuMO1DW3/EWfLFmjK26aqRv+eKEuvXq6/vfFMvXq01fTr7y4SY9r2Tdf64V35umTJWv0yZI1uuffzzTpvNxN2+az0DaszYQLBiludcgWqZXDu6FFawTCcUXSdMYPAAAwUXa2tGZNYsvONjsawDQkWrBLvlCUZAsAAEAKOZ1OFRYWJrcnnnhCU6dO1aBBg2QYhi644AJdeumlGjx4cPKcuXPnqqamRnfffbcGDx6smTNn6t///recTqc2btyoO+64Q+ecc47mzp2rY489VkuWLGl0rVjc0CfvvyOvx6Mzf3ehrHabrrnlL3rz1ReVX1Co0g3rdfFVN+q4KT9T1+49dNqZ5+q7bxbv9jFtLt0oQ4b2HDZc+QWFyi8oVHZ2zm7Ps0T8cpd/JUny9TqwZU8odsuwORUq3FNS69qH1QZ5XwAAAAA0hkQLdskwpM01QbPDAAAA6JSCwaDuueceXXPNNZKkf/7zn/rqq680cOBAvf7664pEEu2avv76a40fP17Z274lOHr0aC1dulSSNHnyZE2bNi251vjx47XHHns0ulY0Zuj7ZUs0et+xWrF8qWKxmH5/1mla/MV8Xfn7c7XP/uN12pnnJuNbs3KF+g0YtNvHseSrLxWLxXT02OEaN6SXrvz9ufJUV+/2vJwtC2WNRxTO7a1wXv/mPn1ohmDRMEnb2oe1EO3DAAAAgMaRaMFuBcIx1QR4UwUAAJBqzzzzjMaPH68BAwbI6/Xq+uuv15577qkNGzbo7rvv1oQJExQMBuXxeDRw4MDkeRaLRTabTVVVVfXWGjdunB599FGdd955ja7l9wfkq61V7779tXbVSg0fNUYPPZ2oZlm3ZpXu+fPNyfUi4bAef+he/eLM83b7OOrWmvXUC3p2zvvauGFdvbV2JmfTp5IkX8mB9PRuY8HknJaWV7R4Q1EZLZzxAgAAOqlAQBo7NrEFAmZHA5iGRAuaZIsnyJsqAACAFJs1a5amTZsmSXrxxRfl8/n03nvv6YYbbtDbb7+t6upqPfHEE7Lb7crKyqp3rsvlkt/vr7dWVlaWcnNzVVhY2GCtqupqvfbCs7LZ7XI6nTrvwsv04JPPa4+he8mdnaPzLrxU/3vjleR69955q7JzcvTzM87e7ePYca1Bew7VZddMr7fWzuSWfiZJ8vZiPktbq0u0uFuRaInHJV84lqqQAABAZxCPSwsXJrY489yQuUi0oElCkbiq/FS1AAAApMqKFSu0YsUKHX300ZKkDRs26IADDlBRUZEkyW63a/To0Vq9erWKiopUXl5e7/za2lo5nc7kWsuXL9dbb72lZ555Rps3b26w1siRo7Rx/VoVFHZRVWVFvbX8vloVdClSVeVWhUMhffbR+3ruqUd1x73/ksPhaPZjyysoSK61M9awR+6KbyRJvhISLW2tLtHirFktS7Tl3zalfRgAAADQEIkWNFlZLVUtAAAAqTJ79mxNnjw5mcjo27evAj9pt7B27Vr1799fY8eO1fz585P716xZo1AolEykzJo1S+FwWLNmzdLw4cN3ulZJ774asfc++mbRQl1+wVn6etEX2rh+rcKhsNauXqVuPXqqbHOprr74t7p+5l81eMiwJj2WurXqLPl6sbr16CnnT6pwdpSzeYEsRlyh/AGK5PZq0nXQctHs7oq4uskiQ66qH1q8Tm0wmsKoAAAAgM6BRAuaLBI1VOENmx0GAABAp/Dmm2/qiCOOSN4+4YQTtGzZMs2aNUsbNmzQP/7xD3311VeaOHGiJkyYoJqaGj3xxBOSpDvuuENHH320bDabAoGAHnjgAR1wwAE66aST5PV6ddhhhyXXWrZsmf72t7/p22++1sGHH6X9DjhYtbUexeNx3TnjWt1583Xac9hwPXj37fr5L8/SH875hY487gQdcezx8vu88vu8yS/beGs9ikQaVjQM2WuE7pxxrb5ZvFAf/u9N3X/Xbbud7ZK7aV5iTdqGtZtg121zWrYubfEaoUhcoSjtwwAAAIAd2c0OAB1LeW1IRTlO2awMKwUAAGipQCCgzz//XA8//HByX1FRkd5880398Y9/1OWXX67i4mI9++yzGjBggCTp4Ycf1umnn64rrrhCsVhMH374ocprQ/rTnx9SIBDQRx99pLy8vOR6Z17/D91x78P6v//7vXILi3Ty5X/RuxsMacMGTTjnWs35xzUyDCkWCSmva7FGHTVV/vw+WvXj91r14/d64ZnHk2vNnfe1evftr1OOPVhX3nS7jpw4ud7jOe/Cy1W6Yb0uOP1nKuraTaedea7O+8Plu3wOcrYlWmgb1n6CRXspb+PHcrViTouUqGrJyrWlKCoAAACg4zMl0fLYY4/pnHPOabD/0Ucf1dlnny1Juvrqq/Xdd9/ptddeSx5fsmSJzjnnHK1YsULnn3++/vKXv8hisez2GFInFjdU4Q2pZ77L7FAAAAA6LLfbrVAj80vGjx+vTz/9tNFzTj75ZP34449auHChDjroIHXr1k1n/OtzzYvuqf5Xvd7g/h9FJE25Rf2nJG7Pj0vzv9yQuGEbop7nP6Tw5hXK6r2XbNkF2ihpY0T63RMLdeHhgxt9Lf3mZ982GpvD4dCMu+7TjLvua9LjtwWr5K5MVFX4Sg5s0jlovWBRohWcq3J5q9apDUbVLXfnbeEAAACATGNKouX000/XySefnLzt9Xq1zz77aMKECZISSZMHHnhAixcvTt4nFAppypQpOu644/Tss8/q4osvTiZsdnUMqVdeG1LXHKfsNjrPAQAAtKfevXurd+/ekqQXF23QvJVblWW36lfj+ml33zHyBqMKReM77OklaXTyVjga11vfbdZb323WoG45On5USeofwDY5pZ9JkoJdhiia3b3NroP6gkWJ1mHuyqWSYWi3vzQ74QtFFY8bslLlDgAAJKlbN7MjAExnSqLF6XTK6XQmbz/wwAOaOnWqBg0aJMMwdMEFF+jSSy/V4MGDk/eZO3euampqdPfddys7O1szZ87UhRdeqHPOOWeXx5B6hiGV1YbUq9BtdigAAAAZqcoX1q1zEu2fLjl6T/3+8D12e86aCt9uB5kXF7j02Lw1evjjVepblK1RvQtSEu9PJeez0DasXYUK95BhscsW9sjhK1Ukt1eL1jEMyRuOKt/lSHGEAACgw8nJkcrLzY4CMJ3pM1qCwaDuueceff7555Kkf/7zn/rqq690/vnn6/XXX9dxxx0nh8Ohr7/+WuPHj1d2drYkafTo0Vq6NNFuYFfHGhMKheq1avB4PJKkSCTS6HDPTBaNRhWPNXxDXuGJqiDLIqed3szoHOr+2+dvAICOgL9ZuG3OUlX6whrSI1dnj+/bpN+FUDiseCy+y/ucPLqnVpXX6qMft+qOuct0189HtknL2Lr5LLXFBzT6WhMtU/dc7vw5tSlYMEju6h/krFiikLtHi69V7Q3KzVsBAGmA10UAGsPfBqRCc35/TE+0PPPMMxo/frwGDBggr9er66+/Xnvuuac2bNigJ598Urfddpvef/99eTweDRw4MHmexWKRzWZTVVXVLo916dKlwTVvv/12zZgxo8H+t99+O5mswe6tNjsAoA288847ZocAAE3G36zMtKJGen5p4mX88T2q9c5bb6Z0/SldpNU5Nq33RTX9pcW6dGRMWSn8QD0rUq29a1bIkEXLyg1Fqj5J3eKQJK37dv5OjxVZuqqvpOB3b2nNVudO77c7ayQt3t2dAKAd8boIQGP424DW8Pv9Tb6v6YmWWbNmJZMeL774onw+n9577z0VFRXpmmuu0ahRo/TEE0/IbrcrK6v+wEWXyyW/37/LY40lWq655hpdfvnlydsej0d9+/bVscceq/z8/DZ4lB3XFk9IW70NB7XWGdw9R1kOvsqGji8Sieidd97RMcccI4eDNhgA0ht/szJXKBrXPfd/JsmnX47towtPHN7kc5eVemQYTbvv9KEh/fH5Jdrkj+jlih668tg9ZWnhPI+fKlz1qiQpUDRcvfefmJI1kRCPRbXu2/nqN2q8rLbG3+rZbN9JCz9TiSugyJhDWnU93gsASAe8LgJMFgjINmWKJCn22muSOz1GDfC3AalQ1wmrKUxNtKxYsUIrVqzQ0UcfLUnasGGDDjjgABUVFSWCs9s1evRorV69WkVFRVqyZEm982tra+V0Ond5rDFZWVkNEjOS5HA4+A/vJ+z2mKy22E6PV/hjGtAt9e0kALPwdwBAR8LfrMzz4Ec/alWFT91ys3TNpOFN/vePxQ1ZrHY1NVXSo8Cua47fS9e99K3mrarUc4tL9cux/Voe+A7yNieqLXy9D95pMgCtY7XZd/rchrqOkCS5q5a3+vn3x6TcbP4GAUgPvC4CTBIOSx99JEmy2mxSmv13yN8GtEZzfnesbRjHbs2ePVuTJ09OBty3b18FAoF691m7dq369++vsWPHav787SXwa9asUSgUUlFR0S6PoW3VBqPyheirDQAA0NZWlXt13/srJEk3ThmugmZ8wB2N73o2S2OGl+Tr/w4fLEl6+vN1mr9qa7PXaEzdfBZfr4NSsh6aJ1g0TJKUVbNKlmiwVWt5g7wPAAAAACSTEy1vvvmmjjjiiOTtE044QcuWLdOsWbO0YcMG/eMf/9BXX32liRMnasKECaqpqdETTzwhSbrjjjt09NFHy2az7fIY2t5mT+veoAEAAGDXDMPQ9S8vUTga14Qh3TVldEmzzo/Gmtgz7CeOHV6syduudfc7P2jtVl+L1qnjqN2grNp1Miw2+XqOa9VaaJlodk9FXUWyGHFlVf/QqrX84Zhi8Zb9bgEAAACdiWmJlkAgoM8//1wHHnhgcl9RUZHefPNNPfnkkxoyZIj+/ve/69lnn9WAAQNkt9v18MMPa9q0aerZs6eef/553XHHHZK0y2Noe/5QTJ5gxOwwAAAAOq2XFm/UvJVblWW36taTRjZ7Xkq0FR+Gn3fwQI3uXaBAJKZb5yyTJ9Dy1305pZ9JkgLd91bcmdviddAKFkuyqsVdubxVSxkGVS0AAACAZOKMFrfbrVCo4ZD18ePH69NPP230nJNPPlk//vijFi5cqIMOOkjdu3dv0jG0vS01QeW76HcIAACQalW+sG6ds0ySdMnRe6pf1+xmrxGNNb91WB27zaqrJg7T5c99pc2eoP7y1nLNOHGkbNbmJXskKXdT4nW+t+TA3dwTbSlYtJdyN82Tq3JZq9fyBCPNamMHAAAAdEamtg5rid69e+ukk05qNJGyq2NoW8FIXNX+sNlhAAAAdDq3z12mSl9YQ3vm6beHDmrRGq1t75Tvduj644fL5bDq6w01euTT1c1fxDCSFS1e5rOYKlA0XJLk2tr6RIuXeY0AAABAx0u0IH1t8YRkGPRoBgAASJX5q7Zq9sINkqSZU0fKYWvZy/dICuZoDOiWo8uPHiJJevXrTfrf0i3NOt/pWSOnr1Rxq1P+nvu3Oh60XF3rMFflskT/r1aIxgz5wyRbAADIaNnZiQ3IYCRakDLhaFxbfVS1AAAApEIoGtN1L30rSTr9gH7ar39Ri9eKxXb/Yfors5/W6L6FDbZXZj+t22+4QqP7FuqCw/fQ2j9P1saHfqv7P1ih5aUevfbCszr2gJE6YGhv/fZXJ2nj+rXJNevOG923UMNG7qM9/lErf499ZNjdu4xl4Wef6KQjxmnC6EF64uH7Wvy40bhQ4Z4yLDbZQ1Wy+5uXMGsMc1oAAMhgOTmSz5fYcnLMjgYwDYkWpFR5bUjxFHxjEgAAINPN+mCVVpb71C03S1cdN6xVa0Xju5/RcvzJp+qTJWuS29sLvlOXoq7a74CDtfTbr3X/47P1yZI1+ujbNTppxpOKxg3d9PS7uufPt+jv/3pKL783X71699UNl/8+ueaO56154FQtviBXvt20DavcWqGLzztdk076uZ585R3Nefk5LZj3UaseP+oz7C6FChJt6FyVy1u9nodECwAAADIciRakVDRmqMIbMjsMAACADm1VuVf3v79CknTjlOGtHjYebcIXYRxOp/ILCpPba88/q6MmTVFx7z5a8f0y7XfAQcovKFRhYaGunLyPBnTNVvma76Uee2rwXqNU0ruvTjrtDK1dtTJxzWh0+3n5BepV86Xysiy7nc8y56XZ6tajpy645Er1HzhY0y65Ui89+1SrHj8aChbtJUlyVS5t9VqBcEzR2O6TeQAAAEBnRaIFKVfuDfFGCwAAoIUMw9B1Ly1ROBbXYUO6a8roklavGW1C67AdhYJBPf3ILJ134WX6YdkSGYahU487VGP3KNa0X/9c1eWbdN0Jw1XYe6AqflikGY/NkaemWs8+/i+Nn3C4JNU/b89iTfnXWq2pdSjQfcwur/3D0iUad9AEWSwWSdLIMftp2ZKvW/KwsQt1c1rcKahokaRaqloAAMhMwaB0wgmJLRg0OxrANCRakHLxeCLZAgAAgOZ7cdFGfbZqq1wOq249eWQy4dBShmEo1szWrm+8/JxG77O/evftr9UrftDgIcP05/v+rZfemy+7w66br75MxfkuTT/zOOUMPUivzzhLh4wcoG+/Wqg/Xn+rJNU774OHrpDDZtFv37LKsGXt8tpeb6169+ufvJ2Tl6eyzaXNf+DYpe0VLctSsh6JFgAAMlQsJr3xRmKLxcyOBjCN3ewA0Dlt9YbVNSdLTju5PAAAgKaq9IV165xEK6dLjhqivkXZrV6zKW3Dfuq5px7V//3xGknSCT87TSf87LTksWtvuVPHHzxG3lqPjLIfZVm/SMVn/lVZ3ftpVPn7uvCsU/XM6+/VO6/fOw/qvkkuDbq3Qt5aj3Lz8nd6bbvNLqfTmbydleVSMOBv9mPArgW6DpckZVWvlCUW2m0CbHdqQxEZhtHqxCAAAADQEfEpONqEYUhltZQLAgAANMftbyxTlT+ioT3zdP6hA1OyZnOrWdatXqV1a1Zp/CGHN3o8L79A8Xhc5WVb9NarL+qkqadqytETJIdbK/tP1rq1a/T90m+3nxCPKWfzfBW6LIrHDZWXbdnl9fMLu6hqa0Xyts9bK7vDuYsz0BLR7GJFswpkMaLKql7R6vXicckf5lusAAAAyEwkWtBmqv0RBSO82QIAAGiKz1Zu1XNfbpAkzZw6Sg5bal6qR5o5O++t11/ShKOPk8PhkCTdOeNavfXaS8nj3329WFarVcW9eisWi6qyolzTDhusvUry5fV65fV55QuGk+e5KpfKHqrR/C3O5Hm7MnLvffTN4oXJ298v/VY9i1s/pwY/YbEo2IX2YQAAAEAqkGhBmzEMaYuHqhYAAIDdCUVjuu7lRBXIGQf00379u6Rs7eZWtHz6wf807sBDk7eHjhile/9yixbO/1Sff/qhbr/xSp106ulyu7M1Zv/xenfua3r2kQe1f/Q7Vb9ym6zZhXp1vUN7Dk+ct+Ttp/Xe6qgufjOUPE+SvLUeRSKRBtc//NjjtXjBfC2Y95Gi0agef+g+HXTYka17EtCoYNdtiZatqUq0NPz3BAAAADIBM1rQpjyBqPzhqLKd/KoBAADszKwPVmlVuU/dcrN05cRhKV07Emt6oiUYCOjbr77UTX++J7nvxFN+pdUrftAl55+unJw8HTnxBF181Y2SpIknTtWaVT/qqX8/qPKyLeo7aKiiP79eizbUasA+43X08SfqnBn3K98e1bGHH6zf3vzn5LqnHHuwrrzpdh05cXK9GLoUddUfb7hV0379c+Xl5cudna0Zd97bymcBjQkWpbaiJRiJKxyNM6cRAAAAGYdPv9HmNtcENah7rtlhAAAApKVV5V7d/35iRsZNU4arwO1I6frNqWhxud36cmVZg/2XXH2TLrn6pgb7LRaL/u+yq/V/l12d3PfhD+W66+3v9eLijTpo1FRVXPNf2aM+/fizPyuYnZO835uffdtgvTq//M35OmjCkVq14nvtP/5g5eblN/kxtKWyzaXatGGd9hw2XDm5eWaH02rbEy3LU7ZmbTCirrlZKVsPAAAA6Aj4qhHanC8Uo40AAABAIwzD0HUvLVE4FtdhQ7pr8ujUzyJp7oyWV2Y/rdF9Cxtsr8x+WrffcEW9fSccsk+Dcy46ak+t/fNkrf3zZC1/7UHZoz4FHQUKFu2lv98+XX845xdNiqPfwEE6/JhJys3L15W/P1e333BFo/dr7rE/nPOLeo/ht786KXls4Wef6KQjxmnC6EF64uH76p33+EP36mdHj9ct11ymY8aN0MLPPpGU+Dd85MF7NPnQfTVh9CDddt2f5Pf7mrTmruJ8/unHdOR+Q7XvwG664PSfqXzL5iY9huYIdhkiw2KVI1ghu79hgq0lmNMCAECGyclJzA8wjMTPQIaiogXtYosnqDxXar+dCQAA0NG9uGijPlu1VS6HVbeePFIWiyXl12jujJbjTz5VRxx3QvK23+/TLyZO0H4HHKznn3lc9z8+W3vvN06SZLPZdnrOKcceqlOGJb7X9X5wiB576m3NeeJfmj3342bF8+kH7+rzeR/ptQ8XpuTYsm++1gvvzFPPkl6SJLsj8Rq1cmuFLj7vdJ31uws16aRTdOWF52rYyNEad9AErVm1Qo89dK9efvdzde9ZrIf+/hfd/9eZevT5N/Tis0/qmUce0t8eflK5+fm69uLf6dZrLtPMex7e5Zp15n34rhbM+0iv7hDnogWf6b67btMd//inBuyxp67+w/n666036I57/7nLx9Bcht2tUP5AuWpWylW5XN7sHi1aZ0feUFSGYbTJ7zIAAACQrki0oF0EwnGtr/QrN8sut9OmLLuVN18AACCjVfrCunXOUknSJUcNUd+i7Da5TjTevIoWh9Mph9OZvP3fJ/6toyZNUXHvPlrx/TLtd8BBys7J3eU506+4SJ7qSl1399O6TpL0rqR31WWvA3Xfvfdo7uzHkvft23+g5nyyWK/Mflo3/PHCBvF06dpNl159k+6/6zb957F/Jvf37ttfFotFl159k/ILCuudEwwEdNt1f9SlV9+kH5Yu0S3XXq6qrRX6xVnnyZChPYcNb3CdOS/NVrcePXXBJVfKYrFo2iVX6qVnn9K4gyYoFo3qpj/fo+49iyVJQ4aP1NtzXpYkvfb8f3T2tIs0ap/9JEm//+M1uvLC83a7Zl2cd9zwJ103/ZZ6j2HNqh91/cy/avyhh0uSTj7tDP37gb9LkjaXbtzpY2iJYNGwbYmWZfL2mbD7E3bDMBLJFr5kBQAAgExC6zC0m2p/RBuqAvpxi1ffbfJoZblXm6oDqvaHFYzEzA4PAACgXd3+xjJV+SMa2jNP5x86sM2uE21mRcuOQsGgnn5kls678DL9sGyJDMPQqccdqrF7FGvar3+u0o3rGz1n0YLP9Pwb72nrNV1VdVWezjrvfEmS0WOo3vnf/3Tq1f/QR9+s0SdL1mj2mx9JSlTFfLJkTXJ7e8F3crncssgim92uzz/9WPc99t/k8aMmTVYoGNx27EMZxvbH+dA9dyoUDCoYDOr3vzlNE0+cqidfeUevvzRboWBQR48drnFDeunK358rT3W1JOmHpUs07qAJyS8DjRyzn5Yt+VqSNHjIMB1+zCRJkt/n1X8ee1hHTZoiSaquqlRJrz7Ja1utNllt1t2uKUlPPPhXhYJB5bid9R7D1F+epaMnnZi835pVK9RvwCBJ0pKvvlQsFmv0MbREsCiRsHFVLmvxGj9F+zAAADJIMCidempiCwbNjgYwDYkWmMIwJH8opq3esNZX1iVfarSy3KvSmkTyJRQl+QIAADqnz1Zu1XNfbpAkzZw6Sg5b270sj8Zanmh54+XnNHqf/dW7b3+tXvGDBg8Zpj/f92+99N582R123Xz1ZY2fs+9Y7dMtrCJnREZ2Nz33n2eUX9hFffPtilZt0iuP36ub3/xBPmUlh8o7nE7lFxQmt2ceeUjhcEgDBu+hTevXac3KH/TcU48oL79APm+tnn38X+o7YKA2b9ygu2+7UZf99tcyDEOlG9fryX/dr74DBurj995SPBbT8u++Vb8Bg7TP/uPlcrs166kX9Oyc97Vxwzrd8+ebJUleb6169+uffBw5eXkq21xa77F9/N7bOnK/YaooK9NvL/qTJGno8FF67+05yfu8MvtpHXTokbtds7p8k/75wL3aY489tGXTxnqPYUfVVZV67ulH9YuzElUya1et1PBRYxp9DC0RLBomiUQLAABooVhMev75xBbjszxkLhItSBvxeCL5UlGbSL78sDmRfFlV7tXmmqBq/BGSLwAAoMMLRWO67qVvJUlnHNBP+/Xv0mbXisUNGU3Js8Rjytn0mQpWvqKcTZ9J8cRrrueeelSnnnmuJOmEn52mp1/9n0aO2Vd9+g3Qtbfcqc8+ek/eWk+9perOydk0T5L05JqeCvj9+tezr+i6ab+WM8ul8JZV+t/VkzT1xMma/cGiBsmFUDCo/z7xL3Xr3lMPP/OyJhx9nLJcbn383jvaf3APnf+LE1XYpUgPP/OyfnfJFXpk9utaOP9TffbR+3rluWfUtVt3PfzMy+rarYdOmHqavtx27LJrZygvv0B7DN1Lg/Ycqsuuma7/vfGKJMlus8u5Q/uzrCyXggF/vbgOnHCkHnjiOdntNv3tthslSRdfdYOWfLVIv5k6Sacce7DefO1F/fLs3+5yTYfdondfma2ePXvqnXfe0Y033KBnXn4j+Rh2dNu1f9SY/Q7QhKOOkySdd+FlevDJ5xt9DC0RLNorEVv1Clli4Ravs6NwNE7FOgAAADIKM1qQ1uJxyReKyRfa/kbNZrXI7bTJ7bAl/9dpJ2cIAAA6hgc/WKlVFT51z8vSlROHtem1mjKfJX/1XJXMny6nb3v1RjinRJ/1maZ1a1Zp/CGHN3peXn6B4vG4ysu2KDcvX5K0bvUqrVuzSkcNdqng89ckSXN/CKiwS5GGjRitOS/N1tC9hsuRla0KWxeVb96ku268Qj9efa/+cMSeKnAn5nq88fJzKuzSVQccPEHOrCytXvGD9hi6l/w+ryaddIqef+YxSZIzK0uSlJObp34DB2vj+rXaUrpJBxx8mJxZWfJ6azV637HJY2PGHlCvSiWvoEBVlVsVDoWUX9hFVVsrksd83lrZHduTJJJkt9u177gDddWMP+vic3+pK6ffrt59++vl9z7X6hU/6O6ZN6prtx7a74CDEs9tI2s6HE71L8rRpk0bddRRRylr22Po2bVLMs46Lz37pL5cME/PvfnxTv/9dnwMdrttp/fbmUhub8Wc+bKFPXLWrFKoKDW/k7XBqFyO5scDAAAAdER8Oo0OJxY35A1GVV4b0rqtfn2/uVbLSj1aU+HTFk9QnmBEkVjzhr4CAAC0h5XlXj3w/kpJ0k1ThicTC21ld23D8lfPVb93p8nhq98iy+HbrE/+dbWOHj9KDkcixjtnXKu3XnspeZ/vvl4sq9Wq4l69k/s+fPLPmjI4rqFvnyFXTeJxfr/0O3XNc0naXhUTCfn16+Mn6HdX36bgmsWat2yDLvrPIi1cWykpURWz3wEHKbitz/cJPztNT778tmprajR81Bgdc/xJKttcmqymicfjKivdpJLefVVc0jt5nt1ml8NuTx677tJpCvh9yXiXfL1Y3Xr0lDMrSyP33kffLF6YPPb90m/Vs7hEkjTnpef0+EP3Jo/Z7XZZrduTCBaLRTm5efr84w91yTU3Jfc3tmav3r3kdtrUt29fBQKB5DG3w5qMU5K+Xfyl/jLjWv3lvn+ra/ceyftdfsFZ+nrRF40+hhaxWJLtw9xbl7ZsjUZ4Q7QPAwAAQOYg0YJOIRozVBuMqswT0toKv5aXJpIva7f6VLYt+RIl+QIAAExkGIaue+lbhWNxHT60u04YVdLm14zGd5FoicdUMn+6JEOWnxyyyNBbK6KamP9jso3Y0BGjdO9fbtHC+Z/q808/1O03XqmTTj1dbne2pETSZv5bz+vIPtvbT62ojKvSH1XFlk169e+Xa3PpRj39yCx9v/RbHXrE0TrtoKGSEVc3i0dV/ohmvLZUf/7vB1q3ZpXO+f2l+vB/b+qdN17R5tKNuueOGQqHQ9pn7AGadNLPJUmvPPefBseOnfKz5Hl2h1Nvvf5S8tiAQXsoHjf0zeKF+vB/b+r+u27TL85MzD45/NjjtXjBfC2Y95Gi0agef+g+HXRYYtbKwD321IN/+7PenfuaNq5fqwf+eruOnXxyvefs4X/cpWMmn6Tho8Yk9/10zf/8+wFNmjhRknTaaafptdde0wsvvKANGzbotuk3JOPcWl6mi879pc75v0s0fPQY+X1e+X1eSdKQvUbozhnXNvoYWqqufVgq57T4QlHFd/X7BwAAAHQitA5DpxWNGfIEovIEtn+bzmG31Gs5lu20y2b96UcLAAAAqffCoo2av6pSLodVt5w0UhZL278G2dUXTXI2L6jXLmxHgYihzzfG9HCPWg1+Zn9FcnvrorzuKh+dr8vO/rlysl06dsIBuvzCU+WsWaOoq0iFH9+YOGeKK7nO7O8iOnGoQ/831qk/Pv+Mbvr7Eyoo7KK/3Pdv9e7bX/M//kCSdMpAQ6VdS/TaN6Wa8+qLyho0VpYufXXnA4/ovjtv1Y/Ll6pbj2Ld8+9nlJObJ6/HI4vFopeefUJ/n3mT+vQfmDw2MDcved6K75fL7nDoyr89ofnr/Qr32Euu7GxdcPrPVNS1m04781yd94fLJUldirrqjzfcqmm//rny8vLlzs7WjDsTVSzDR43R9TP/qjtvuU61nhodc/xJ+tONtyUf57rVqzT3lef14v/m1Xsed1wzPz9febm5eurxRyVJQ4cO1X//+19df/31Wr58uQYPHqxZTzyrnNw8vfjsk6qsKNd9d96q++68NbneN+urdd6Fl6t0w/pGH0NLBbZVtLiqlrdqnR0ZhlQbirZ51RYAAACQDizGTydPZiCPx6OCggLV1NQoPz/f7HDSyuaaoMprQ2aH0aacduv25Mu2BAzJl8wTiUT0xhtv6Pjjj0+2SAGAdMXfrI6n0hfWUX/9QFX+iK6eNEzTDhvcLtct8wS1xdP4a7mCla+o3/sXten1Jzzq0zljHDpnn8Ssk7v0W937+Euafue9isWimnn9FRqz3zjNuOs+SdKidVW68PQpcg0/SgVjjtUZ4/pp6r59NOfFZ/XwPXfWO2/v/cbpspvvVkVtSOu2VKgmbFFlIK5yb0jltSGVe0PaWlGhjQ+eox6nTFdW3xEqf/FW2Qt6at9fXq5JI0t0+NDuynbW/+7ZutWrtGrF99p//MHJ2TOttXnDavm2rNORRxy+y/cbpTUBVdS2fCB9PBbVmq8+0YAxh8hqa/p36txli7XHqycp4u6u5Wd82eLr/1SXHIf6dMlO2XqGYSgWN2S30ZgBwHa8LgJM5vNJubmJn71eKSfH3Hi24W8DUqE5eQMqWpDxwtG4wtG4agKR5L4sRyL54nLYlL0t+WIl+QIAAFpo5hvLVOWPaFhxns47ZGC7XXdXrcOi7h47PbajjQfPVCS7p+yBcjkC5bL7y2UP7LD5y2WL+hucl6yK2aHC5bQjRmt5RVyXnH+6cnLydOTEE3TxVTcmjw/v7lJ40w86/NzrtMRv6In5a7VwbZWO3OsI9drnC037zS9lzXKrcNjB+qbvz3TWIwskSRsePFdFR/1W2UMOrBeDLbtA3Y46X2XP3SiHO1f2LJe6nXCp1mz168EPV+qxeWt0+NDumjSyWAO7JT4g6DdwkPoNHNSk56YprFZpwv6jlGUfs9v7JpI+LU+0tFSoyxAZssgRKJctUKGYu1tK1q0Ntn5Oi2EY8oaiqvZH5AlGVOBObfIGAAC0UnZ2IsFS9zOQoUi0AI0IReIKReKSGiZf3M5E8sVlJ/kCAAB277OVW/X8lxtksUi3/WyUHO34bfxobOeJFl/xOIVzSuTwbZZFDe9nyKJITrEqh/5K2mHwe2Ny17+vgW/9pt4+t8Oi0PX1v/UVdffQJVffpEuuvkmNcbnd+nJVmQzD0Pvfl2nWh6u0tNSjpaUeachUlQyZmrxvTJJFUpccp4ZOf07d87ISW26Wum373+55Wcp3Haz1a85PVqnIka33vt+iN77drI3VAc1dsllzl2zWsOI8TRpZokP26CanPTX/RhaL1K8oW1n2XT9/dXKzzHl7FnfkKJzfX1meNXJVLpev9yEpWTcaMxSMxORyNO3x78gXiqo6EFGNP6LYDglDTyAqo9Bol9Z7AACgCSyWtKliAcxEogVoorrkS7U/kXyxWKQsuzXZbiwny96iN5EAAKDzCkVjuu6lbyVJZxzQT/v179Ku14/Gdz6jRVabSsdPV793p8lQImlRx9h2q3T89N0mWSTJ23tCk5I2vuJxTYrbYrHoyGE9NbxXgZ6av1a1wWi9RErdz11znE1KXP20SuXEvXtryuhe+nZjjd5YslnzV23V8s21Wr65Vv/6eJWO2qunJo0sVq9Cd5Pi3Zme+S7luZreqsJmtcjttCoQ3sW/WxsJFu2V8kSLJHmCkSa/Rg6EY6oJRFQdCCsSbTxJGIsnKlya87wCAAAAbY1EC9BChiEFI3EFI3FVbat86VmQpR55rt2cCQAAMsWDH6zUqgqfuudl6YrjhrX79XfVOkySPAMnad1Rs9Tr0+vkCFYk90dyilU6fro8Ayc17UL1kjaWesmW5iZtdlSc79Kfjh3arHOaymKxaHSfQo3uU6gqX1hvL9uit77brPLakF7+aqNe/mqjxvQt1KSRxTpgYNdmz/ArzHaoe15Ws+PKdtoVCLd/+7Bg0V4qWDNX7sqlKV23NhhVj7ydHw9FY6rxR1QdiGyrKN+9mkCERAsAAOkiFJIuuCDx80MPSVnNf/0DdAZMEQRSaEtNSOsr/Yrv5kMNAADQ+a0s9+qB91dKkm6aMlwF7vb/YHhXrcPqeAZO0vrD/yZJCmcXa9Xx/9X3v5jX9CTLDuusO2qWIjnF9fZHcoq17qhZzV6vPXXJceoX+/fVP8/cXzecMFz79+8ii6Sv1lfr9rnLde7jX+iZz9eqwhuSJL0y+2mN7lvYYHtl9tO6/YYrNLpvofp1zZHFYtEee+zR4HpXX321pkyZUm/fRRddJIvFot5dsjW6b6FOOGSfJsW+8LNPdNIR4zRh9CA9+a8HWvwcBIv2kiS5Kpe3eI3GBMIxRWP1EyjhaFzltSGtKKvVD5u92uIJNTnJIm1rH2bwehsAgLQQjUqPP57Yoq2fzwZ0VFS0AClW7Y8oFI2pf9ecdu3BDgAA0odhGLrupW8VjsV1+NDuOmFUiSkxxJr45Q97qFqSFC4YKF+vA3d9513wDJwkT/9jlbN5geyBMkXdPRLtwppZybIjp92qSCyu9vhc3Wa1aNzAIo0bWKTNnqDe/m6z3lm6RZW+sP7zxXr9d+F6jRtYpGP2O1offbtGdUUufr9Pv5g4QfsdcLBeeOZxvfLqa5pwaKL9ls1W/7EvWbJEDzzwgBYvXlxv/5dffqk5c+bogPEHanmpp8F5jancWqGLzztdZ/3uQk066RRd+ftzVJhl0YAxzW/9FeiaSLRkVf0gxaOSNTVvFQ1D8oaiys2yb2sLFpE/FGvVmrQPAwAAQLoh0QK0gUA4rhVlXvXvmq1sJ/+ZAQCQaV5YtFHzV1XK5bDqlpNGmjK4e3dtw3ZkDyTahkVdRa2/sNXWqmTNTxXnuxSIxFReG0rZmk297lkHDtCvxvXTZyu3au6SUi3Z5NH8VZWav6pSJQUuTRxRrKP26qnXnvi3jpo0RSV9+mjlD8t05BGHKzc3t8GahmHoggsu0KWXXqrBgwcn90ejUS1ZskQTJkxQbm6uekTsCjahwmPOS7PVrUdPXXDJlbJYLPrdxX/Sy0//W1PO/L9mP95Ibh/FHLmyRbzKqlmpUJfUtWwrrQkqFjdSmiyjfRgAAADSCV+3B9pINGZoVblPVb7277ENAADMU+kL67Y5iTkXlx49RH2Lsk2Jo6nVLJJkD1YmznF1batwWiTLYVVBtkM98rLksLd/skqSHDarJgzprtunjtb9p++ryaNLlO20qbQmqEfnrdFZ//xE/5x1nyac+ltVrf9RhmFozJgxcrvdmjhxotatW5dc65///Ke++uorDRw4UK+//roikcScv2+++abeeb87Y6pKN67fbWw/LF2icQdNSCbyRo7ZVytXrmzZA7VYFdyWXEl1+7BoLLVJFon2YQAAAEgvJFqANmQY0oaqgDZVB3gjCABAhrhtzjJV+SMaVpyn8w4ZaFockVjTZ17YglslSVF3eiVauucmhqlarRaVFLhNjkbqV5StCyYM1uPnjNNFR+6hPbrnyrPkfVl6DNFfP6vSJQ/NUc/+e+hfjz2ppUuXyuFw6IJtw2G9Xq+uv/567bnnntqwYYPuvvtuTZgwQcFgUMuWLdOIESP0n//8R0uXLlWW06mbr75st/F4vbXq3a9/8nZubp4qKytb/Pi2z2lZ1uI12ktd+zAAAAAgHdDTCGgHW71hhaJx9SvKls1qzrcxAQBA25u3skIvLNogi0WaOXWUqfPamlXREtiWaEmjihan3arC7O2toQrcDuW67PIGzf9w3eWw6djhxTp2eLGmPnKJDjj5XK10WOXpPV7qPV4Xvl2jk/bJ1SU33K7jDtxbHo9HL7/8snw+n9577z0VFRXpmmuu0ahRo/TEE0/od7/7nc4444zk+vffd5+G7LmHvLUe5ebl7zQOu80up9OZvO3McikUanmLteC2OS3uremfaJFoHwYAAID0QaIFaCfeYDQ5t8XlaPlAWAAAkJ6CkZiuf2mJJOnXB/TXvv26mBpPJNac1mHpV9HSPS+rwWybXoUu/bjFm/I2VC21bvUqlW9aq29vnaZQzKIXFm3Q05+v1cpyn575fJ2eCvkUj8f12DuLVb12nQ444AAVFSXm4Njtdo0ePVqrV69usG63rkWKx+MqL9uyy0RLfmEXVW2tSN72eb2y21v+Fq8jVbRI29qHFRqmzEACAAAAdkTrMKAdhaNxrSz3yhOMmB0KAABIsQc/WKlVFT51z8vSFRNTN0i8pVpS0ZIuM1ocdou6ZDesVMiy29Q9L8uEiBr39pyXdMIJk+XKcqog26ElL96r3/XZov/8drxOGF2i6OYfJYtVd82r0L++8mjFpkqtrvAlz1+7dq369++vyy+/XLNnz07u/+KLL2S1WlXcq/curz9y7330zeKFydvfL/1WXbu2/N8w2GWYJMnh3yxbsKrF67QX2ocBAJAGsrOlsrLElm3ObEIgHZBoAdpZPC6trfCrrDZodigAACBFVpR59eAHiSHk06eMUH4atDNqzoyWZEWLq6itwmmWbrkNq1nqdM/NksOeHhUMX3zyvo4+6sjk7TFjxuj6669XZON3+nlxtXIXPakDJ05V3+5dZPTZVxvWrND+Z1ypqX95TdOuvkVfffWVJk6cqDFjxui6667TRx99pPfee08XXXSRTv/1WXK7Ex9WeGs9ikQaflHn8GOP1+IF87Vg3keKRqN64p/3a8yYMS1+PHFnrkJ5/SRJrsrlLV6nPdUE+AITAACmslik7t0TG1WmyGC0DgNMsqUmpFAkrt6FblmZ2wIAQIdlGIaue+lbhWNxHTG0u44fVWx2SJKaXtFiiYVlC3skSVF3t7YMqUnsNouKsp07PW61WlRS4Na6rf52jKqhfEdcX36xQI/865/JfWeddZaWLVumk046SXl5efrZz36mmTNnyuXO1gffl+lv+f/Q/x69Sy+/92/Zcgo14OfX6MUfw/rVSac2OO/mW27VOk8iWXbKsQfryptu15ETJ9eLoUtRV/3xhls17dc/V15evlzubN1+2y2telzBor2UVbtOrsql8vU6sFVrtQfahwEAACAdkGgBTFTtjygUjal/1xxTh+UCAICWe/7LDfp8daVcDqtuPmlk2nzgG21iosUWrJQkGRarYlmFbRhR03TLzdrtl1AK3A7lueyqDZrTNqrA7VC/rtmNDp6//fbbdfvttzfYf9RePXXULedq/WW/1H8WrNPshetV4Q3rH+/+qEc/Wa1X/3h9g/NcgVoFI3G9+dm3O43ll785XwdNOFKrVnyvfceOV8XKr1v12IJFw1Sw9q0OU9FS1z4sLw2qyAAAyEihkHT55Ymf775bykqfNq9Ae+KTXcBkgXBcK8q88ofpLw0AQEdT6Qtr5huJweGXHT1EfYvSpy91NN601mH2bYmWWFYXyWLu2wOb1aKuOTuvZtlRSaHLlO4ULodVfbq4W3x+36JsXTlxmOZdfZTu/dU+2qNHrmpDUT3z+doG983Jatr34voNHKTDj5mk3Ly8FsdVJ1g0XJLkqlzW6rXaC+3DAAAwUTQqPfBAYovy2RYyF4kWIA1EY4ZWlftU5QubHQoAAGiG2+YsU5U/omHFeTr3kIFmh1NPNNbUipZt81ncLR+inird8pxNbqmaZbepe177fmPSZrWof9eclLR9ddqtmrJ3L101MTGA/qXFGxvM1WlqoiWVgkV7SUokWgp+fFE5mz6T4rF2j6M5PIGoDKNpv+8AAABAWyDRAqQJw5A2VAVUWhPgjSIAAB3AvJUVemHRBlks0sypo9KqDWgsbqipLyfsgW2JFpe5iRarVeqa07zESffcLDnt7fO8WyxSv67ZKb/e4UO7q1tuliq8Yb2/vKzesRynLaXXagpX5VIZssgaj6jfh5dq0Bu/0ND/HqT81XPbPZamqmsfBgAAAJglfd4NApAkVdSGVVoTNDsMAACwC8FITNe/tESS9OsD+mvffl1Mjqi+prYNkyR7XUWLq6itwmmSbrlZsjWzUsRqtaik0NVGEdVXUuBSbhtUmDhsVk3dt7ck6bkvN9Q7ZrdZ5XK031u2/NVz1e/d/5NUP0vn8G1Wv3enpXWyhfZhAAAAMBOJFiANVfrCCkeb/gEJAABoXw9+sFKrKnzqnpelKyYONTucBpraNkzaYUaLu1tbhbNbiWqWps1m+al8l0N5rrZtsdUlx6GuuW3XpuzU/fpIkt5bXqby2lC9Y+3WPiweU8n86ZIM/TTdZdmWeCmZPz1t24jRPgwAAABmItECpCHDkMpqqWoBACAdrSjz6sEPVkqSpk8ZoXyXw+SIGorGm5FoCVQkzjGxoqVrTpbsrWi91qvQLUvrx6Y0KjvLpt6F7rZZfJs9e+ZpTN9CxeKGXl68sd6xHGf7JFpyNi+Q01faIMlSxyJDTl+pcjYvaJd4mov2YQAAADATiRYgTVX7IwpF0/MbgwAAZCrDMHTdS98qHIvriKHddfyoYrNDalSsGYkW27aKlqjLnIoWi0XqltuyapY6TrtVPfJSX3HisFvUvyhblrbK4uzg1P0TVS2zF66vV5mRk9U+c1rsgbLd36kZ9zMD7cMAAABgFhItQJoyDKnME9r9HQEAQLt57ssN+nx1pdwOm24+aWS7fADfEtFYM2a01FW0uM2paCnKcbaqmqVO97yslA6qt1ik/kU5KYmtKabs3UtZdqt+LPPq6w01yf3tNacl6u6R0vuZgfZhAACYwO2WVq9ObO62rQIG0hmJFiCNVfsjCkaoagEAIB1s9YY0841lkqTLjtlTfYuyTY5o55rVOqxuRoura1uFs1MWSyJBkpq1LCopdKVkLUnq08Utt7N9qkmkxKyZSSMTFVLPLVxf71h2O8xp8RWPUzinpJEJLQmGLArnlMhXPK7NY2kp2ocBAGACq1UaMCCxWfmoGZmL334gzW3xMKsFAIB0cNsby1Ttj2ivknydc/BAs8PZpWisOYmWrYlzTEi0dMlxypHCipF8l0P57tYnJbrnZakwu3XtzFri1P37SpJe/XpTvS/b5LbHnBarTaXjp0tSg2RL3e3S8dMla/sln1qC9mEAAAAwA4kWIM15AlH5w3wzDwAAM81bUaEXF22UxSLN/NnIlCYH2kI03rTWYZZYWLawJ3GOu30TLRaL1D039XNVSgrcak1HtzyXXcUFqauMaY4DB3VV70K3aoNRvfXd5uT+9prT4hk4SeuOmqVITv3ZQ5GcYq07apY8Aye1SxytQfswAADaWTgsXXFFYguHzY4GME16v0MEIEnawqwWAABME4zEdN3LSyRJZ47vr336dTE5ot1rausw27a2YYbFqlhWYRtG1FCB25HSmSp1nHarerSwHVmWw2pqSzir1aJT9usjSXpu4YbkfrvNqqx2mNMiJZIt3/9intYe+YAkyZBVP059p0MkWSTahwEA0O4iEemuuxJbhMpSZC4SLUAH4A1G5eMNIwAApnjgg5VaXeFTj7ws/em4oWaH0yRNbR1WN58lmlUkWdrvrYHFIvXIT301S53ueVnNTuJYrVL/rtmyWVtRDpMCdYmWT1dWaEOVP7k/px3mtCRZbfIMmqxwbm9ZFJd765L2u3YK0D4MAAAA7Y1EC9BBbGZWCwAA7W5FmVcPfrBCkjT9xBHKdzlMjmj3DMNQrIkVLfZAhSQp5i5qy5AaKHA7lGVvu3ZYFotFvQqb3v7LYpH6FWW3aUxN1bcoWwcN7irDkF74cmNyf7vMafkJf4/9JEnZW75s92u3Bu3DAAAA0N5ItAAdhD8UU22Qb+cBANBeDMPQtS99q0jM0JHDemjSyOLdn5QGmto2TNreOizqat/5LN1b2NqrOfJcDuW7m5ac6JnvUl4aJdFO3T9R1fL8ovWKb/v3bK85LTvy99yWaCnrWIkW2ocBAACgvZFoATqQLVS1AADQbp77coMWrK6U22HTjBNHyNKaCevtqKnVLJJkD26V1L6JlgK3Qy5H+yQNSgrc2t0/W2G2o10SP80xcUSJ8rLsWl8Z0PzViX+j9pzTUsffY19J2xItRrxdr91atA8DAABAeyLRAnQggXCcN40AALSDrd6QZr6xTJJ02TF7mjogvbkisaZ/IG4PJD7Ej7nbL9HSnkkNp926y1kwbqdNvQvd7RZPU7mdNk3eu5ck6fmFG5L723VOi6RA1+GK21yyh2qUVb2yXa/dWrQPAwAAQHsi0QJ0MGVUtQAA0OZue2OZqv0R7VWSr3MOHmh2OM3SsoqW9pnRkueyy+1s3xZY3XOzGq0Esdss6t81W1ZrelYq1bUPe2NJqTzb2se2+5wWq0P+7mMk0T4MAAAA2BUSLUAHE4zEVe0Pmx0GAACd1rwVFXpx0UZZLNLMn42Uw9axXjJHYi2Z0dKtrcKpZ1fVJW3FYrGopMD1k31S/67Zaf1vu0/fQu3RI1fBSFxzvimVJGWbOadly6J2v3ZrUQkOAEA7cLulJUsSmzv9KoWB9pK+7ywA7NQWT4hWCAAAtIFgJKbrXl4iSTpzfH/t06+LyRE1X7MqWgIVkqSou+0rWnJddmW3d0XGNnkuhwrc24fd9y50mxZLU1ksFp26X6Kq5bmF6yVJDjPmtNQlWsoWtut1U4H2YQAAtAOrVRoxIrFZ+agZmYvffqADCkfjqvLzDT0AAFLtgfdXaHWFTz3ysvSn44aaHU6LROPNmNHSjhUtPUweOF9S6JLFInXNdapLjtPUWJrqZ/v2ls1q0aJ11VpRViup/ee0+HvsK0lyVa+QLVjdrtduLdqHAQAAoL2QaAE6qLLaIN/QAwAghVaU1erBDxMDv6efOEL5LsduzkhP0Wa0Dqub0RJr4xkt2Vm2dk8Q/JTDZlX/rtkN2oilsx55Lh0xtLsk6bkvN0hq/zktMVeRggWDJUnZ5bQPAwAAPxEOS9OnJ7Ywre6RuUi0AB1UJGpoq4//AwMAIBXicUPXvrhEkZihI4f10KSRxWaH1GLRJrYOs8TCsoU9iXPcXdsyJNOrWerkuRyyWCxmh9Esp+zXV5L04qKNisbiJs9p+bLdr91atA8DAKCNRSLSjBmJLcIXHJC5SLSgcfGYtPpjuZa/qJxNnyVuI+2U14YUb0YfdgAA0Ljnv9ygBWsq5XbYdPNJIzrch/E7amrrMNu2tmGGxapYVmGbxeN22pTXQauD0sGRw3qoa45T5bUhffhDuTlzWra1D+uIiRbahwEAAKA9kGhBQ0tflf4+Unp8sgrn/p8GvfELDf3vQcpfPdfsyPAT0ZihCl/I7DAAAOjQKrwh3fbGMknS5ccMUZ8u2SZH1DpNbR1W1zYsmlUkWdrubUGP/PSoZumonHarTt6ntyTpuYWJ9mHtPqelrqKlfLEU73hJC9qHAQAAoK2RaEF9S1+VZp8leTbV2+3wbVa/d6eRbElDFbVhxahqAQCgxWbOWaaaQER7leTrnIMHmB1Oq8TihpraJcke2Dafxd1281ncTmuHnXWTTk7dv48k6X/LtmirN6QcZ/u2DwsV7qmYM1/WaECuyuXteu1UoH0YAAAA2hqJFmwXj0lvXiWp4ZsQy7Z9JfOn00YszcTihiq8VLUAANASn66o0IuLN8pikW6fOkp2W8d+edzUtmHS9tZhUVe3tgpH3XM7zuD5dDasOF+j+xQoGjf08leb2r2iRRbr9vZhZbQPAwAAAH6qY7+TRGqtndegkmVHFhly+kqVs3lBOwaFpqjwhhSNNf2DFQAAIAUjMV330reSpLPG99eYvoXmBpQCzalytQcrJElRV9tUtGQ5rCrIppolVU7dL1HV8tzC9bJbLe0/p2Vb+7CcDjinRaJ9GAAAANoWiRZs593SpLvZA2VtHAiaKx6XyqlqAQCgWR54f4XWbPWrZ36W/njcULPDSYlIE+ezSJI9kKhoibm7tkksPfKYzZJKJ+7dW067Vcs312rJRo+y27l9mK/HtjktHTTRQvswAAAAtCUSLdgut2eT7hZ192jjQNASW71hRahqAQCgSVaU1erBD1dKkqZPGdFp5og0r6IlMaMl6kp9osVqlQrcneM5TRcF2Q4dN6JYkvTcl+uV287twwLdx8iwWOX0rpfdt7ldr50KtA8DAKCNuFzSggWJzUXbWGQuEi3Yrv9BUn4vSZZGDxuyKJxTIl/xuPaNC01iGFJZLVUtAADsTjxu6NoXlygSM3TUsB6aOLLY7JBSpjmtRG1tmGjJdtplsTT+mhItV9c+7OXFG2W3tu/zG3fmKthlmCQpu2xRu147VWgfBgBAG7DZpLFjE5utfStugXRCogXbWW3SxD9vu1H/jVvddyNLx09P3A9pqcoXVjhKVQsAALvy/JcbtGBNpdwOm2acNKJTJQSizaloCWxLtLRB67CcLF4vtoWD9+imXgUueYJRvf99uZx2c+a00D4MAAAAqI9EC+obfqJ02hNSfkm93YYtS+uOmiXPwEkmBYamMAxpiydodhgAAKStCm9It72xTJJ0+TFD1KdLtskRpVa0OTNagokZLW1R0ZLjbN+2VpnCZrXo59uqWp77ckO7J7T82+a05JR1zEQL7cMAAGgD4bB0552JLRw2OxrANCRa0NDwE6VLl0i/eV2eg66RJBkWhzz9jzU5MDRFTSCiYCRmdhgAAKSlmXOWqSYQ0V4l+Trn4AFmh5Ny0XjTK1vrZrTEXEUpjcFiUbsPas8kp2xLtHz8Y7m8wfZNGvi2VbS4KpbIEu2YX+6hfRgAACkWiUhXXpnYIvz/LDIXiRY0zmqTBh4q/9iLFHPkyRb1ylX5ndlRoQkMQyrzMKsFAICf+uTHCr24eKMsFun2qaNkt3W+l8JNbR1miYVlC3sS57i7pTSGbKetU7VjSzf9u+bogIFFMgzpre/adyh9JK+fIu7ussbDcm9d0q7XThXahwEAAKAtdL53l0gtq02+4rGSpNxN800OBk1FVQsAAPUFIzFd//K3kqSzxvfXmL6F5gbURpraOsy2rW2YYbEpllWQ0hhysmgb1tZO3b+vJOnFxRvlsLVjUstiSbYP66hzWmgfBgAAgLZAogW75Ss5UJKUs5lES0fCrBYAALa7//0VWrPVr575WfrTcUPNDqdNGIahWBMrWurahkWzukiW1L4lINHS9o4fVawcp01rt/q1otzbrtf29+zYiRaJ9mEAAABIPRIt2C1fyXhJUs7mBVKcKomOwhOIyh/m23oAAPy4pVazPlwpSZpx4gjluRwmR9Q2mto2TJLsgW3zWdxdUxqDxSJlO5jP0taynXZNHt1LkvT2d1va9dr+HvsmYij7MtGztgOifRgAAABSjUQLdivQdYRijlzZwh65KpeaHQ6aYQuzWgAAGS4eN3TtS98qEjN09F49dNyIYrNDajNNrWaRdqhocaU20eJ22mS1Mp+lOR577DFZLJYG22OPPaaLLrqo3r499tgjeV6Pym+0cdZ5uveM/fXLyUdq1Y/fS5LeffddjRnQTaP7FtbbXpn99G5j+cM5v6h3zm9/dVKD+wS6jVLc6pAjUC5H7brUPRHtiPZhAAAASDUSLdg9q12+4nGSpJzSz00OBs3hDVLVAgDIbM99uV5frKlSttOmGSeN7NRD2iOxeJPva0smWopSGkOOk7ZhzXX66aerqqoqua1fv17dunXThAkT9OWXX2rOnDnJY4sXL5YkrVy5UrddfbGGTrlAfS58XM6Cnpp+5cWSpAkTJuijr1fqkyVr9MmSNXp7wXfqUtRV+x1w8G5jWfbN13rhnXnJc+/59zMN7mPYXQp2GyVJyqF9GAAAACCJRAuaqK59WG7pZyZHguaq9vMmEgCQmSq8Ic18Y7kk6fJjhqh3odvkiNpWsypaApWSpKi7W0pjyMmibVhzOZ1OFRYWJrcnnnhCU6dOVb9+/bRkyRJNmDAheSwvL0+StGzZMs2cOVO/P+9M2XK6yL33RH33TSIJ43A4lF9QoPyCQuUXFOq155/VUZOmqE//AbuMY3PpRhkytOew4clzs7NzGr2vr8e2OS1lHTfRQvswAABSxOWS3n8/sblcZkcDmIZEC5rEV3KgJCln8+fMaelg+LYeACBT3TZnmWoCEQ0vydfZBw0wO5w216wZLcEKSVIshRUtFktidghaLhgM6p577tE111yjb775RoZhaMyYMXK73Zo4caLWrUu06po8ebKmTZumn+/bR1aL9P3336uk74AG64WCQT39yCydd+Flu732kq++VCwW09Fjh2vckF668vfnylNd3eh9/T3rEi2LWvxYzUb7MAAAUsRmkw4/PLHZ+NINMheJFjRJ/Tkty8wOB80QjfEmEgCQeT75sUIvLd4oi0W6feoo2W2d/2VvNNb0RIstWFfRkroZLS6HVTbms7TKM888o/Hjx2vAgAFatmyZRowYof/85z9aunSpHA6HLrjggnr375nv0iGDCuVZ8JL2PGxqg/XeePk5jd5nf/Xu23+31167aqWGjxqjWU+9oGfnvK+NG9bpnj/f3Oh9/T32lSS5KpfJGva24JGmB76QBAAAgFTp/O84kRpWu/w9x0qScjbPNzkYNBdvIgEAmSQYien6l7+VJP3mwAHau2+huQG1k2i86TNa7IG6GS2pS7TkZFHN0lqzZs3StGnTJElnnHGG5s+fr7Fjx2rgwIG677779Pbbb8vj8dQ7xz/vaVmcLlX2ndCgfdxzTz2qU888t0nXPu/Cy/Tgk89rj6F7adCeQ3XZNdP1vzdeafS+0ZxihXP7ymLE5S7/qvkPNE3QPgwAgBSIRKT7709sET5/QuYi0YIm826b05JTSqKlo6nxR3gTCQDIGPe/v0JrtvrVMz9Lfzx2iNnhtJvmVLTYg6lPtNA2rHVWrFihFStW6Oijj270eGFhoeLxuEpLS5P73nnnHb39wlMadOo1qgzG9dX66uSxdatXad2aVRp/yOEtiievoEBVlVsVDoUaPV7XPiynA89poX0YAAApEA5Lf/hDYguHzY4GMA2JFjSZry7RsvlzyWj6NyZhPt5EAgAyxY9bajXrw5WSpBknjlCey2FyRO2neTNaEq3DYilsHZZLRUurzJ49W5MnT5bDkfidvfzyyzV79uzk8S+++EJWq1V9+/aVJK1atUpnnHGGHnzwQf3y2IMkSe9+X568/1uvv6QJRx+XXG93Lr/gLH296Ivk7SVfL1a3Hj3lzMr6f/buOzyO+lr4+He2r1Zdsmy5yN0GTDHFxqaY5tCDgVByIfQkEFoCJNyQakoguZDk5QKXkpAAAUINgYQWTC8GjME27pZlFauX1fY6M+8fK8mWra5dbTuf59kHa3d256yRd2fm/M45fW7f3T4spzl9Ey0gld9CCCGEECI+JNEihixQuj+q2YEp5MLWsTnZ4YhhkpNIIYQQmU7TdH720tdEVJ2l+5Zx0rwJyQ5pTA21dZiihjGGY+2n4lXRIvNZRu+NN97guOOO6/l5/vz5/PznP+eDDz7gnXfe4brrruPSSy8lJyeHQCDA6aefzplnnsmyZcs4bd9CtHCAT6s68HUd8n383goWLj56r/14PW4ifbT1mLPvPO6+9Wes++oL3l/xBg/c8xvOv+iKfuP1dVW05LR8mdaLsKR9mBBCCCGEiAdJtIihM5h3zWlpXJnkYMRwyUmkEEKITPf86jpWVTvJsRi5ddn+KEp2XfgfauswY1c1i64YUa0Fcdm3zGcZnUAgwGeffcbixYt77rv44os555xzWLZsGZdeeiknnngi//u//wvAm2++yaZNm/jTn/5EXl4eC+dMpu6P5xLqbGZ1m0IwGODrNas56NCFe+3rnBOP5MO339zr/iuuuZGZs+dy5QVn8T/Lb+G8iy7nimtv7DfmYPG+aCY7xrAba+e2OPwtJIdUfgshhBBCiHiQMyIxLN7yReTtfA9H46e079//CjeRelRNxxOKkp9FLVSEEEJkjzZviDtfi1Xc3viNOUwqtCc5orGlaTpDXU+xaz5LMSjxWXflkPkso2K32wn1MQvlrrvu4q677trr/jPPPHOvBTSPfbyD5f/ayKctOhfb7Kze3tLnvt5Y+XWf95vNZm69535uvef+oQVtMOEfdzC5jZ+Q0/wloaK5Q3teCnIFIlnVZlAIIYQQQsSfVLSIYZE5LenN5Zf2YUIIITLTHf/eiCsQYb/yfC49YlqywxlzkSG2DQMwBWKJFtVWHLf951iNcXstMTLL5k/CaFCo9ys0uoJjsk9/T/uw9J7TIpXfQgghhBBitCTRIoYlUHoAqikHU6gTW8eWZIcjhskdjMhJpBBCJFGTK0izO0i7N4Q7GCEQVomomnw2j9KH21r555oGDArcdfYBmIzZd4irakP/HdpV0RKf+SxWswFzFv6dp5oih4VDpsRawX1Z2zkm++xJtDSnd6JF2ocJIYQQQojRkhp/MTwGM/4JC8jb+T6OxpUES/ZNdkRiGDQNaR8mhBBJomo6rZ69WwMBKAoYDQpmo4LJYMBkVLAYDZiMsT+bu+6Ti9l7C0ZUfvHP9QBcvHgaB00pTG5ASRIZ4nwWAGN3osUen0RLjkWqWVLFMXPGsaqmk9W1nXxz/uSE788/7hAAbK7tGIMdca2SGmvSPkwIIYQYIasV/v3vXX8WIktJokUMm2/C4V2Jlk9p3//yZIcjhsnlj0iiRQghksAf7n+1tK7HBpnHhpn33wKqOyFj0FUAGl1BbBY1qxMy979TSU27nwn5Nm46cU6yw0maYVW0BOJb0ZJrlVOKVHHMnFLueWsbXze4CUc1LKbEfhaotkKCBbOwuSrJafkST8XShO4vkdyBKHqhjqIoyQ5FCCGESC8mE5x2WrKjECLp5KxIDJuvfDGw25yWOA1RFWPDHYygaToGg5xECiHEWAqE1VG/RndCRlNjyRinL4whuHdiZleFjCFWJWM0YDYovRIy3fens63NHh7+YDsAy8+Yl9Wr0aPqMGa0dFW0qHGraJFTilQxd3wuBRYdV1hjfb2LQ6YWJXyf/vGHxhItzavTOtHS3T4smz9HhBBCCCHEyMlZkRg2/7gD0Ux2TCEnVudWQsX7JDskMQzd7cMK7HISKYQQY8kfh0TLUO2qkFEJDLCdooCpq13ZngkZc1dljMmQmgkZTdP5+UtfE1F1lu47npPmjU92SEkVHUZFizHYEXtOHNo8WUyGhFdNiKFTFIX9CnVWtiisrnWOWaKleOuzaT+nBaR9mBBCCDEikQg89VTszxdeCGb5LhXZSRItYvgMZnzjF5BX/wG5jSsl0ZKGXP6IJFqEEGKMjWWiZah0HSJRnQgjSMjs0apsrBMyz31Rx6pqJzkWI7cum5f17X6iw5jREs/WYTKfJfXsW6izsgVW1zj53tGJ35+/7FAAclrXgBYBQ/oeY0r7MCGEEGIEwmG47LLYn889VxItImtJokWMiK98EXn1H8TmtMy7LNnhiGGS9mFCCDG2wlFtWDM0Us1IEjKWrjZle86OiUdCptUT4s7XNgFw4zfmMKnQPqrXywRRbfitw6JxaB3mkPksKWdugY7RoFDfGaDRFaC8ILH/PkKFM4laCzCFXNg6NhEsPTCh+0skaR8mhBBCCCFGSs6MxIj4yhcBMqclXek6eIJRCnLkJFIIIcZCPOazpIPeCZn+33N3QsZsNPQkYfZMyJiNBoz9LAj4zasbcQejzJuYz6VHTEvQu0kvw0nkmbpah6lxqGhxWKWiJdXYTDB/cgGraztZXePk9AMTnIhUDPjLDiW/7h0czavTOtEC0j5MCCGEEEKMjCRaxIgESrvmtAQ7sDq3ESqem+yQxDC5AhFJtAghxBjxR6LJDiGl9CRkoioMkpAxd1XGdCdhVtd08M81DRgUWH6GtAzrFhli6zBFDWEMu4HRtw4zGRWsJkm0pKJj55TulmiZmPD9dSdacppXp321u7QPE0IIIYQQIyGJFjEiutGCr+xQ8ho+6prTIomWdCPtw4QQYuyk4nyWdKDrsbZr4SiASiiqcudrmwE47YBybCYjGxvcfSZkzMZd82RMBmXACpl0p+v6kCtajEFn7DmKEdVaMKr95krbsJR17Nxx/H5FJevqXYSjGhZTYqvP/eO75rS0rE7ofsaCqun4wqr8fgshhBBCiGGRo0cxYr7yxeQ1fISj6VPa512a7HDEMOl6LNlSmGNJdihCCJHRdF3PmtZhifbcFztpdAUpcVj4zqKpPffvmZDpz54JGbMpNk8m3RMy0WG1Deuaz2IrHnXr1xyLVLOkqrnjcynLs9LiCbG+3sUhU4sSur/AuIPQFQMWbz0mXyNRR3lC95donf6wJFqEEEIIIcSwyNGjGLGeOS2Nn8aucEh5fdpxBSTRIoQQiRaMaOhDvw4u+lHT7uMfX+4E4MolM8ixDP8wdrgJGXNXZYzJuHdCxmI0pExV6LDmswRiiRbVVjzq/TrkQnTKUhSFo2eX8uKX9ayudSY80aKZHQSL98Pevp6c5tW4Z5ye0P0lmrQPE0IIIYQQwyVnR2LEAuMOQjPaYnNaOrcSKpL2YenGE4yianrardwVQoh04g/LfJbR0nSd/3tvO1FN5/DpxSyaMfoh7gMZakLGYOiqkDHsnZDZvXIm0QmZEVW02Ef3d2g0KNjMUtGSyk7YtyyWaKlx8r2jE78///hDsbevx9GS/okWaR8mhBBCDIPVCs89t+vPQmQpOXIUI6YbLfjHH0puw8c4Gj+TREsa0nVwByIUOaSqRQghEiUQkbZho/XWxmY2NrqxmQ18f8mMlFllrmkQ0jRCwHATMmajoWeezGgTMlFVG/K2xkB367DRJVocVkmypLqjZo/DaFCo7wzQ5AoyocCW0P35yg6lZOPj5DSn/5wWkPZhQgghxJCZTHDuucmOQoikkyNHMSre8sVdiZaVdOx3cbLDESPgkkSLEEIklMxnGR2nP8xfP9kBwIWHT6UsL7EXixNhpAmZ3atiBkrIjGxGy2gTLXIakerybWYOmlzAl7WdrK7p4LQDJyZ0f/7xhwJga9+AEg2im9Lv3+rupH2YEEIIIYQYDjlDEqPSPaclV+a0pC1vSNqHCSFEomiaTjAy9GoDsbdHP9qBL6Qyc5yDbyb4QnGyDTch052UCUWH/jvWnWhRR9k6zDGCGTli7B07t4wvazv5osaZ8ERLJHcykZwyzP4W7G3r8E9YmND9JZq0DxNCCCGGKBqFl16K/fmss2IVLkJkIUOyAxDpLTanxYop2I61c1uywxEjoOuxqhYhhBDx55e2YaPyZa2T97e2YlDg2uNmy6KALpoGoYiGNxil0x8ZVtWUqad1WPGI928wgN0ircPSwdJ9xwOwrt5FeBgJuRFRFPxlsaqWTGkfJsfIQgghxBCEQnDeebFbKJTsaIRIGkm0iFHRjdaeNgGOxs+SHI0YKTmJFEKIxPDHpqmLEQhFVR58bzsApx84kVlluUmOKDMYgx0ARG2lI34NqWZJH/uW51GaayEc1Vjf4Er4/noSLS0ZkmjxR9D1obfmE0IIIYQQ2UsSLWLUfOWLAXA0rkxyJGKkfKHosAbpCiGEGBqZzzJyz66qo8kdpMRh4cLDK5IdTsbomdFiH3lFi8xnSR+KonDkrFhSbXWNM+H761mA1bw6Vjad5rrbhwkhhBBCCDEYSbSIUfN2zWlxdM9pEWlH18EdlFXXQggRb365QDciNe0+/vFVPQBXHjOTHKmgiJvu1mGqbeQzWhxWaRuWTk7YpwwYm0RLoHR/NIMFU7Adi7sm4fsbC1L5LYQQQgghhkISLWLUAuPmoxmtmINtWF3bkx2OGKFOfzjZIQghREaJqBpRVRYgDJem6zzw3nZUTefw6cUsnjG6oe1iF0UNYYx4gJG3DlMUsJsl0ZJOjplbhkGB+s4ATe5gQvelG60ESg8AIKfli4Tua6xI+zAhhBBCCDEUkmgRo6YbrT39mKV9WPryh1Ui0j5MCCHiRqpZRuatjc1sanRjMxu4csnMZIeTUYzBWEWDrhhRrfkjeg2H1YSiKPEMSyRYgd3MAZMKgLFqH3YYADnNXyZ8X2NB2ocJIYQQQoihkESLiAvf7u3DRFrSdXBLawQhhIgbmc8yfE5/mL9+sgOA7xw+lXF51iRHlFlMgTYAorZiUEZ2GuCwSDVLOjpmzjgAVtd0JHxf3XNaclpWJ3xfY0XahwkhhBBCiMFIokXEhU/mtGSETjmJFEKIuPGHZfbVcD360Q58IZWZ4xycfuDEZIeTcUzB2EX26Kjms8i8nHT0jf0mALBup4twNLEVzP6yQwCwdWzGEPYkdF9jRdqHCSGEEAOwWOCvf43dLJZkRyNE0kiiRcSFv3tOS6AVi6sq2eGIEfKHpH2YEELESyAiFS3D8WWtk/e3tmJQ4NrjZmM0SHuqeOuuaFHtxSN6vqJAjlS0pKX9J+VT4rAQimpsaHAldF/RnDJCeRUo6OS0fJXQfY0VaR8mhBBCDMBshksvjd3M5mRHI0TSSKJFxIVusvWsXsuVOS1pTVojCCHE6AUjKprkrYcsFFV58L3tAJx+4ERmleUmOaLMZOypaCkd0fNzLEaZz5KmFEVh8cxYJdOYzGkpk/ZhQgghhBAiu0iiRcSNzGnJDJ1+OYkUQojRkvksw/Psqjqa3EFKHBYuPLwi2eFkLFOwHeia0TIC0jYsvR07t2tOS+0YJFq657Q0Z1CiRdqHCSGEEH2LRuHVV2O3qLRPFtlLzpZE3PgmdCVamrrmtMiKx7QUCKuEoxoWk+RhhRBipPzSNmzIatp9/OOregCuPGYmORY5PE0UUyCWaFHtI5vRIomW9LZ0n/EYFNjpDNDsDjI+35awffUkWlq+Ak0FQ/q3nOtuH5Yr/w6EEEKI3kIhOP302J+9XjDJd6XITnIlVcSNv+zg2JwWfwsW945khyNGQVojCCHE6ATCspJrKDRd54F3K1E1ncOnF7N4xsiHtIvB7apoGf7fs6JAjjn9L5Zns0KHhXkTC4DEtw8LFs1FNTswRjxYO7cldF9jSY6RhRBCCCFEfyTRIuJGN9nwjzsYAIfMaUlrchIphBAjp2k6wYgMaBmK/2xoZlOTB7vZyJVLZiY7nIy3a0bL8BMtNrMRg0GqldPd0bNj83kSPqfFYCIwbj6QYXNapH2YEEIIIYTohyRaRFz5yg8HIFfmtKS1QFglFJW2N0IIMRKBiIpchxuc0xfmsZWxCtjvLKpgXJ41yRFlPlOwDYCoffgzWqRdUmY4Yd8yANbVdxJRE5sQ9o0/DABHBs1p6W4fJoQQQgghxJ4k0SLiyle+GABHY9ecFpG2pKpFCCFGxi8X4Ybkzx/twBdSmTUul9MOmJjscLKCKdBd0VI67OfmWKVtWCY4pKKIYoeFYERjQ4M7ofvyl3XNacmgRAvIMbIQQgghhOhbUhItjz32GIqi7HV77LHHuO6663rdN2vWrJ7nrV+/ngULFlBUVMRPfvKTXmXbAz0mxo6/7BA0gwWzvxmLuzrZ4YhRcMtJpBBCjEgwIomWwXxZ4+SDba0YFLjmuFkYpSVVwilqCGPEA4A6zNZhigIOi1S0ZAJFUTh8eqyiaXVNR0L35S+bD4DVvQNjoD2h+xpL0j5MCCGEEEL0JSmJlgsuuACn09lzq6uro7S0lCVLlrB69WpeffXVnse++uorAEKhEN/85jc59NBD+eKLL9i4cSOPPfbYoI+JsaWbbPjLZE5LJgiENWkfJoQQIyAVLQMLRlT+7/1KAE4/cCKzynKTHFF26J7PoitGVGv+sJ5rMxskGZZBjpkzDkj8nBbNWkiwcDaQWXNapH2YEEIIIYToS1ISLRaLhcLCwp7bE088wdlnn01FRQXr169nyZIlPY/l5eUB8Prrr+NyufjDH/7AzJkzufPOO3n00UcHfUyMPV/5IgAcjZ8lORIxWi6/VLUIIcRwRFWNcDSxcw/S3XNf1NHsDlGaa+HCwyuSHU7WMHVVFERtxaAM7xQgR6pZMsrx+5RhUKDOGaDZHUzovvzjY+3DCra/QsH2l3E0rAQt/ZMU0j5MCCGE2I3FAvffH7tZLMmORoikSfpZUzAY5N577+Wzzz5j3bp16LrO/Pnzqa+v55hjjuGRRx6hoqKCtWvXsmjRInJycgA48MAD2bhxI8CAj/UlFAoRCoV6fna7Y/2JI5EIkYgcNO8uGo2iqdFhPcdbtoDxxCpatGgk1m9CpKUOj0aRPTt6snf/25fPACHEaHhCkWF/b45E9z7GYl/xVNPu5x9f1QPw/aOmYTOm33tIVwZ/KxBLtAz379xqNMv3Y5oYyvFMkd3IvhPy2NDo4YsdbZyy/4SExaMq5tg+q16hqOoVAMI5E2g4/Fe4pp2csP0mWodHZVyOEUXOc4RICXIuJ0QK+P73d/05Rf4tymeDiIfh/P4kPdHy9NNPs2jRIqZNm8bHH3/MvHnzuO+++ygtLeX666/nyiuv5PXXX8ftdjN9+vSe5ymKgtFoxOl0DvhYUVHRXvu86667uPXWW/e6/z//+U9PskaMnFELMU0xYfE30fr5P/BZxyc7JDEKW5IdwBh76623kh2CEEIMWe3XnyY7hCHTdPjfDUZUTeGAIo3xro1Ur0l2VNljcscnAHgjRqrXfDSs51YnIB6RWIMdz0wzKWzAyEfrq9g3WpmQGMo7V1G642973W/2NzH13atZNf06GgsXJGTfY6Eq2QEIIfYi53JCiL7IZ4MYDb/fP+Rtk55oeeihh3qSHhdeeCEXXnhhz2P3338/M2bMwO12YzKZsFqtvZ5rs9nw+/0DPtZXouWWW27hxhtv7PnZ7XYzZcoUTjzxRPLzh9ezOtM1u0O0e0ODb7iHQNN8cpu/YG5RlI45RyUgMjFWSvOslOVZB98wzUUiEd566y2+8Y1vYDabkx2OECJN1bT78YXGpqKl9utPqThgEQZj0g/nhuTNjc3s8OzAZjbww9MPZlxu5n+3pJLSDVugBkzjZjBt/tCPzawmAzNljk7aGOrxTElVO6/+dTWVHhOTDjgMszHOHaU1lX2fv7nPhxRAR2F+ywtYl1wPhvSsni5yWCgvsCU7DCEEci4nRNKpKspHsYU8+lFHgTE1vtvls0HEQ3cnrKFI6pl5ZWUllZWVLF26tM/HCwsL0TSNxsZGiouLWb9+fa/HPR4PFotlwMf6YrVa90rMAJjNZvmHtweTScVgHH4fZX/5EeQ2f0Fu8+d07nvh4E8QKcsX0bPq34V8DgghRiOsKWOa+DAYTWmRaHH6wjz+aS0AFy2ayvgCR5Ijyj7mUGzwuWovHdbvTJ7DIt+LaWiw45mFM8dRlGPG6Y+wqdnP/CmFcd2/o3kVFn9Tv48r6Fh8jeS1folv4uK47nuseMM6JpNJ2ocJkULkXE6IJAmH4RvfiP3Z6wVbai1EkM8GMRrD+d2J89Kl4Xnuuec4/fTTewK+8cYbee6553oeX7VqFQaDgSlTprBgwQI+/XRXe4zq6mpCoRDFxcUDPiaSw1u+CABH46eg60mORoxGKKIRjKT/0FIhhEi0UFRF1eQ7ry9//mgHvpDKrHG5nHbAxGSHk5VMgQ4AovbSYT0v15L6iTwxfBaTkQXTYudKq2s64v76pkBLXLdLRaqm4wvLMbIQQgghhIhJaqLljTfe4Ljjjuv5ef78+fz85z/ngw8+4J133uG6667j0ksvJScnhyVLluByuXjiiScA+O1vf8vSpUsxGo0DPiaSwz/+UDSDGYuvEYunJtnhiFFyBWRwmBBCDCYgF9z69GWNkw+2tWJQ4JrjZmE0yOrvZDAF2wFQbcNbiJRjlePpTHX07FjSbXWNM+6vHbWXxXW7VCXHyEIIIYQQolvSlqgFAgE+++wzHnnkkZ77Lr74YjZt2sSyZcvIy8vjrLPO4s4774wFajLxyCOPcMEFF/CTn/wEVVV5//33B31MJIdushMYdxCO5i9wNH5GOH9askMSo9DpjzA+P7VKP4UQItX4JdGyl2BE5f/ejw3aPv3AicySWR9J051oidpKhvwci8kQ/9kdImUcO7cMg7KBOmeAFneQsjge6/kmLCTsKMfsa0Jh70o/nViSxTdhYdz2mQwuf4SJBTZpHyaEEEIIIZJX0WK32wmFQuyzzz697r/rrrtwOp3U1tZy77334nDs6uF95plnsm3bNh555BE2bdrEvHnzhvSYSA5feazfsqNxZZIjEaMVjmqyUlsIIQYhiZa9PbuqjmZ3iNJcCxceXpHscLKasTvRYh96osUh1SwZbWKhnX0m5AOwujbOVS0GI42LlgOxwfe70wEFMER85DZ8HN/9jjFpHyaEEEIIIbql3RK1SZMmsWzZMsaNGzesx8TYkzktmUVaIwghRP90XZd5VnuobvPx0pp6AK46ZiY5MusjqXpmtAyjosUh/88ymtGgcPiM7jkt8W8f5p5+CrUnPETEMaHX/dGcMkJ5UzFGfUx782LGrX0grc8V5BhZCCGEEEJAEluHicznLzsUXTFh8TVg9tYRyZOVrOmsMxBmQoG0DxNCiL4EImo6XyeMO03XeeC9SlRNZ/GMEg6fPvSL+yL+FDWEMeIBQB1OosUqpwqZ7ti54/jrx9Ws3dlJRNXi3irOPf0U3FNPxNH0OaZAS0+7MEWLMPGTX1G89RkmrPod9pa17Dzm92iWvLjufyxI+zAhhBBCCAFpWNEi0oduzsE/7iAAchs/TXI0YrQiUV1W7AkhRD+kvWJvb25oYnOTB7vZyPeXzEh2OFnPGIxVs+iKCdWaP6TnmE0KFpOcKmS6g6cUUZhjJhjR2NjgTsxODEZ8ExfjmrkM38TFYDCim2zUL/kfdh71WzSDhYKaN5j58hlYnVsTE0MCSfswIYQQWc9shv/5n9jNbE52NEIkjZw9iYSSOS2ZpdUTTHYIQgiRkmQ+yy5OX5jHP6kG4DuLplKaa01uQAJToGs+i60IlKEd/kvbsOyQZzNxaEURAF8koH3YYJz7XEDV6S8QdpRjc21n5ivLyN/x6pjHMVqyGEkIIURWs1jgJz+J3SyWZEcjRNJIokUklG/3OS0i7QXCmpxICiFEHwIyn6XHnz+qwhdWmTUul9MOKE92OAIwBbsSLfbSIT9H2oZlB0VROGJW7Pdide3YJ1oAAmXzqTzzVbzlR2CM+Jj69g+Y8NlvQIsmJZ6RcPkj6NI/UgghhBAiq0miRSSUb/xh6BixeOspXv8XHA0rQZOLUelMqlqEEKI3VdMJRbRkh5ESVtc4+WBbGwYFrjluFkaDzCxIBd0VLaqteMjPcViNiQpHpJhj5ozDoEBdh5+WJB3nqfZSdpzyJK0HXAnAuK8fZvob38HY9bub6qR9mBBCiKymqrBqVeymyvehyF6SaBEJlbfzfXRD7Nds0qfLmfHa+cx99gjyd7ye5MjESAXCGu6gVLUIIUQ3fzh9Vl0nUjCi8uD7lQB888CJzCrLTXJEolv3jJaorWRI25uMClaTJFqyxaRCO3PHx4bQr05C+7AeBhNNh/+cmuP/D9WUQ27DJ8z652nYW9fGHtdUHA0rKdj+ckou3pKqbyGEEFkrGISFC2O3oCzOFdlLegKIhMnf8ToVb18F9C6jN/uaqHj7KmpPeAj39FOSE5wYlRZ3iHybDDgTQgiAgKxiBuDZVXU0u0OU5lq58PCpyQ5H7MYUbAMgah9aokXms2QXu8XIYdOK2dTkYXWNk1P2T27LP/eM09leNJupK67E6qpixr++Rcec88mvW4HF19izXdhRTuOi5SlzPuHyR5hYYENRpJJPCCGEECIbSUWLSAxNpfzT5YDOnqcaSlfipfzT5Sm3Ek0MTSCsSlWLEEJ08Uuiheo2Hy+tqQfgqmNmYLdINUQqMQWGV9EibcOyz5LZ4wBYt9NFRE1+K8RQ0Vwql72Ca+qJGLQwpZv/hnm3JAvsWryVKpXy0j5MCCGEECK7SaJFJISj6XMsvsa9kizdFHQsvkYcTZ+PaVwiflrcoWSHIIQQKSEQye4La5qu88B7laiazuIZJRw+fWgX88XY6a5oGeqMFodVKlqyzfyKAgrtZgIRlY2N7mSHA4Bmyaf2+AdRzXnokBaLt6R9mBBCCCFE9pJEi0gIU6AlrtuJ1BMIq3ikqkUIkeXCUY2oqg++YQZ7c0MTm5s82M1Gvr9kRrLDEX0wdc9osZcOuq3RoGAzS0VLtsm3mzlkahGQ5Dkte3A0f4Ex4kmbxVsufwRdz+7vBCGEEEKIbCWJFpEQUXvZkLYrXfcwBdtfRonKsKx01CxVLUKILJft81mcvjCPf1INwHcWTaU015rcgESfjMF2AKJDqGiRtmHp67HHHsNisXDmmWdisVhQFAVFUXjsscd6bXfyySf33PfYY4+hKAo2s4m/XraQmt+dzv/79sG8/NxTvPzcUxw4pXCv28vPPTVoLNdedn6v53zvv5aN6D2l2+ItaR8mhBBCCJG9JNEiEsI3YSFhR3kfE1piutd55bSvp+Ld69j36cOY+MkvsbV9PXZBilGTqhYhRLbzR6LJDiGp/vRRFb6wyqxxuZx2QHIHaIv+mQLdiZbB27pJ27D0dcEFF9DS0sKTTz5JS0sLdXV1lJaWsmTJkp5tnnrqKd58881ez3E6nTidTlZurGbK1Y9hsOczbf/DOPXMc/lofXXP7T+fb6CouIRDDz9y0Fg2rVvLi2990vPcex99ekTvaaiLt4a63ViQ9mFCCCGEENlJzqREYhiMNC5aTsXbV6Gj9PRQBnqSLzuPvAOzv4Wirc9j8TVQsvFxSjY+TqBkHs4559E580xUW1Gy3oEYohZPiDybOdlhCCFEUvizeOXyFzUdfLitDYMC1xw3C6Ohv+Y+IpkUNYQx4gUgah880WKXtmFpy2KxUFhYSG5uLoWFhdx9992cffbZzJgRa+nX0dHBTTfdxNy5c3s9x2KxALCPJQdb9UfkzDmCRq2AgywWzF2PATz7xKOccMo3mTx12oBxNDXWo6Mze5/9Rv2euhdvmX1Nvc4nuukoRBwT8E1YOOp9xYs7EGFigQ1Fkc9EIYQQWcJshl//etefhchSUtEiEsY9/RRqT3iIiGNCr/sjjgnUnvAQzn0vouXQm9hy/sfsOPlJOmd8E81gwd6+gYkrf80+Ty9gyjvXkLvzg5QZcCn25g+peEPZvaJbCJGddF3P2tZhwYjKg+9tB+CMgyYyqyw3yRGJ/hi75rPoignNUjDo9majnB5kgmAwyL333sstt9zSc99NN93EWWedxaJFi/p8jkmP0vDxPyhYfC6razt6PRYKBnnqLw9xxTU3DLrv9WtWo6oqSxfsx8I5E7n56stxd3aO7I10Ld4C9qqU7067NC5aDobUSRBGVWkfJoQQIstYLLB8eey22yINIbLNqM6kPB5PvOIQcdLdZ3nP21B6M/f3nLt++ZNePZZPO+rgIcXyxcqPOO7yXzP+rlZ+3nk+tcfdR9Wpz7Ll/E9wTz9l14YGI97JS6g7/gE2X7CKhsW3EijeD4MWprDqX0x/4zvMffZIylb/HrOnNg5/SyLemt0yY0cIkX1CUY1snXn8zKo6WjwhSnOtXLBwarLDEQMwBdoAiNqLYZAV9ooCZqOsws8EzzzzDIsWLWLatGkAvPvuu7z99tv87ne/6/c5zz37DPsffBimgvGsrXMRUbWex1775/McePBhTJoy+L/3mqrt7HfAfB568kWeefVd6nfWcu/vbhvxe+lv8ZZusFB7wkO9zytShLQPE0IIIYTIPkNOtGiattd9J554IjU1NXENSIzO7n2WnU7nsHsz9/WcjV+v5YHHn+vpsfzcGx8MGkdHexvXX3EBpyz7Fn97+S1eeudz3mouwjdx8YArzlRbEe3zLqPy7DfYduartO93CaolH4uvgfFf3cs+zx7F9Ne+TUHlP1GicnE/VUhVixAiG2Vr27DqNh//XFMPwA+OmYHdkjorycXeTF0VLUOZz2IyKtLuKEM88sgjXHXVVUCsuuXKK6/kwQcfJD8/v9/nPPTQQ1xz9VUU2s0EIiqbGt09jz3/5F8596LLh7TvK665gQf/9gKz5u7LjNlzueGW5ax47eVRvR/39FPYcv4nVJ36LPWLYq1JFC2Kr3zxqF43UdyBCHq2ZuKFEEJkH02DDRtitz6uHwuRLYacaFm5ciVHHHEEr7/+OgBtbW10dnaybNky7HY7OTk52Gw2TCYZ+5JM3b2Zu29PPPHEkHoz9/ecaDRK5ZZNHHr4EeQXFJJfUIgjN2/QOF596TlKy8Zz5Q9vZur0mVz1w5t56Zknh/VegqUH0HDE7Wy64Atqj7sfz8Sj0FHIbfiEiveuZ9+nD2Pixz/H3rqWrF1SnEJapKpFCJFl/OHsSzBrus7971aiajqLZ5SwcPrgF+9FcpkC7QCotuJBt5W2YZmhsbGR7du3s3TpUgBuv/12FixYwGmnndbvcyorK6msrOTUk0/ikIrYjMTVNU4AandUUVtdxaKjjh1RPHkFBTg72gmHQiN6fg+DEd/ExXTsfwXBorkoaOTtfHd0r5kg0j5MCJHK3MEIoah8Rok4CgRg//1jt0Ag2dEIkTTDPpu66667+P73v8/DDz/M9ddfj6ZpbN68mY0bNzJt2jS2b9+eiDjFCIykN/Oez9m84Wt0Xefck45mwawJXPWdb9FYXzfovrduXM/CI5b0rIrcf/6hbFq/dkTvQzfZcM08g+pTn2bL+R/RfMgNhHMnYwy7Kdn0N2a9/E1mvXQyJesf7elDLsaeL6Tik6oWIUQWycb5LG9uaGJLswe72ciVS2YkOxwxBKZgLNEStZcOuq1FEi0Z4aOPPuLUU0/F3DWM9umnn+bll1/uWVT19NNPc/XVV3P11Vf3POe5557j9NNPp8hh59BpvRMtb/77JZYsPann9QZz45UXs/bLVT0/r1/7FaVl47FYrfF6i7grYkmkvJq34vaa8Sbtw4QQqUjVdOo6/Gxt8lLb7s/KhUNCCJEowzqbmjVrFh988AE5OTn83//9H9/97nfRdZ2pU6cybdo0LBYLU6dKn+5U8fTTTw+7N/Oez6mq3MrMOfvwu/sf5aV3PsVkNnHbTwcfgun1ephUset3wZGXR0tT46jeD0Akbwoth9zAlvM/ouqUp+icuQzNaMXesYmJn97KPk8vZMrbPyC37j3Qsu8CWLLJrBYhRLbQNJ1QNLvK4jt8YR7/pBqAixZNpSQ3fhdNReIYuxMtUtGSNb766iuOPfbYnp8//PBD1q9fz5o1a1izZg1nnHEGt912G7fdtmtuyhtvvMFxxx2HwaBwxMwSDArUdPhp9YT4+L0VLFx89F778XrcRCJ7JxPm7DuPu2/9Geu++oL3V7zBA/f8hvMvuiKu79E99RsA5O18H0UNx/W148UtiRYhRApq84Z6Oju5AhG2t/jY3urFHZTPLCGEGK0h9fl6+OGHiUZjWe6NGzfyzjvvMHnyZCorK6WPcwp76KGHuPXWW4Hh9Wbufg7AxRd9h8Unntnz889uv5tTj5yP1+MmN6//1zEZTVgslp6frVYbwYB/FO9mD4oB36Sj8U06GkOok8LtL1O85Vns7esp3PEqhTteJewop3P2OTjnnEs4f1r89i361V3V4rBKC0EhRGbzR9Ss61r554+q8IVVZpXlcuoB5ckORwyRKTD0GS1moxzXp7tAIMDWrVs5/PDDe+6bPHlyr21yc3MpLS2ltLS05zmfffYZjzzyCAATC+3MGZ/H5iYPn25t5Os1q/n17+7da1/nnHgkN//6Lo4/+fRe919xzY007qzjygvOoriklPMuupwrrr0xvu9z3HwitlLMwTZymj7HN+mouL5+PERVHW8oSq4cFwshUkRU1Wjz7t3G0R9SqQn5sZoNlOZaKcoxy7U+IYQYgSEd9VVXV/PnP/8Zi8XCqlWreOaZZzCZTD2roLr/29zczO23384tt9wis1qSrLvP8kh6M3c/B8BqMqAou0ag5OUXoGkarS3NAyZa8guLcLa39fzs83owmS39bj8amrWQjv0uoWO/S7C1b6Boy7MUbn8Ji6+RsjX3UbbmPrzli3HOOQ/X9FPRTfaExCFiWjwhpssJpRAiw2Vb27Avajr4cFsbBgWuPW4WRoOcfKcLUzB2PBa1DyHRYpKKlnRnt9t54YUX2Gefffrd5rHHHtvrOaHd5qfkWk0cOrWIzU0ePqx28+GmBnIsex/bvbHy6z5f32w2c+s993PrPfeP7E0MhWLAU3E8xVufI7/2rZRMtEBstbgkWoQQqaJ1t2qWvoQiGvXOAM3uICW5FkocVjnmE0KIYRjS2dRdd91FbW0t119/PR0dHVRWVjJv3jxqa2s5+uijaWpqoqWlhW9961tUV1ejZ9sSzxTU3Wd5JL2Zd++/fNNNN/H2q//s+XnD2q8wGAxMmDhpwP3vf9DBrPvqi56ft2z8mvETEr/6NVgyj8YjbmPzf62i9vgH8Exago5CbuNKprx/A/s+dRgTP7oFe8sasm4p8hjxBqPS51UIkfGyKdESjKg8+F5sBt8ZB01k5rjcJEckhsPUNb9OHUJFi8xoEQA5FiNHzSrFaFDY1Ojmh8+sYVOjO9lh7cVTEWsfll+7ImWP66V9mBAiVURUjXbv0FotRlWdZleIzU1uGl0BwlnWLlcIIUZqyMtr7HY7P/7xj4lEIphMJjo6OjjggAO45JJLWLhwYSJjFCPwxhtvcNlll/X8/OGHH/a0fwP48Y9/zKJFi7j00kv7fQ7A/Pnz+fWtt5FfPA5VjXLXr25m2bkXYLfnALHezFabfa/hmMeeeCp3/uInfP7JBxyy8Agef/h+jjjm+AS8077pJhuuGd/ENeObmL31FG17gaItz2Hx1lGy+SlKNj9FsGguHXPOp3PWWahDWOUphq7ZHWJ6qazeE0JkLn8kexLKz6yqo8UTojTXygULZRZfujEGuipahjCjRRItAkBRFPYtz+eOZfvzhxVbaXIH+ek/1nHuYVP49mFTMKXI74ln0tFoRisWTx3Wzq2EiuYmO6S9SPswIUSqaPWEhp2T1jRo84Rp94YpsJsZl2fFZjYmJkAhhMgAwzpKNhgMPPbYYxx77LEsXryY0047TZIsKai7z/LixYt77ps8eTLTpk3rufXXm3n353xa1c4H+r4csfRUfvjdC/jljdeweMlx/Pdtv+vZ5pwTj+TDt9/cK4ai4hJu+uUdXPWdb3HCoXOp3LKR71//kwS+6/5FcifRcvAP2XL+h1Sd+necM89CM1qxObcw8bPb2OfvC6lYcSV5de+Alj0XzhJJqlqEEJksompEoqm5ejreqtt8/HNNPQA/OGYGdoucXKeb7oqWqL10wO0MBjBIexDR5V8vPM0Fi6byxS++Qc3vTmfHb0/nf845iO/89+/Y6dw1d/Gq73yLl597aq/n/7+7lnPtZef3/Pzyc09x4JTCvW59PXdP1152fq/nfO+/lgGgm3PwlR8BQH7NW6N9ywnjkqoWIUSSRVSNDt/Qqln6ouvQ6Y+wrdnLjjYfnqB8rok9mM3w4x/HbnssxBYimwxpaU1FRQU2mw2A2tpaFixYgNfr5aabbuKmm27q2U7XdaLRKJ9++injx49PTMRiUHv2We7LYL2ZAZ5dVceKTS2cfsylfPzL2/t8nf56MwN8+5LvcsSS46mq3MJhi44ccKbLmFAM+CYeiW/ikTSEbqNw+ysUbX2WnLZ1FFS/TkH160RyxuOcfS7OOecRLpiW3HjTXIs7xDSpahFCZCB/lrQN03Sd+9+tRNV0Fs8oYeF0qf5MN0o0iDHiBQavaLHKfBaxm0su+g6HHH1iz8/vbajl15d9k87C2fzw2TVcceR0tG0f8Mn7b3PKGWf3eu62zRt59olHee71D3ruO/XMcznupF2zIv1+H+efvIRDDz9y0Fg2rVvLi299wvjyiQCYdruA4566lLyd75JX+zat868d8ftNJHcgwqRCmREphEielhFUs/THG4ziDUaxWwyU5lopsJtRFFmokfUsFrj77mRHIUTSDekq6FtvvYXNZiMajbJo0SJOOOEEXnjhBY466ih+9KMfUVBQAOxKtIwbNy6hQYuxcfmR03npq3re3NDEOYdMpiTXOuzXqJg+g4rpMxIQ3eho1gI69ruIjv0uwta+iaKtz1JY+RJmfzNla++nbO39eCccjnPOebimn4Zuzkl2yGnH01XV0tfwVCGESGfZMp/ljfVNbGn2YDcbuXJJ6n2Xi8F1V7PoignNUjDgtuYUaQclUkO+w05RURGqFrsy17z6UU45/QyMB+zDmrpO7n9jDa2P3ULF9Fm9nqfrOrffcgPfueIqpkyb3nO/2WLBbLH0/PzsE49ywinfZPLUaQPG0dRYj47O7H326/Nxd8VSJn38c3JavsQYaEMdpHIrGaR9mBAimcJRDecoqln6Ewhr1HUEaDIFKXZYKM6xpExryUylajquQIQOXxib2cDkIrlOJUSqGdKn4Ny5c5k6dSrl5eWYzWYefPBBqqurmTVrFmeeeSZvv/02U6dOZdq0acyaNQuDQT5cM8EBkwtYMK2IiKrz+oamZIeTMMGSfWlcvJzNF3xOzQkP4pl8HLpiILfpM6Z8cBP7Pn0Ykz78b+wtX/Y/aFNTcTSspGD7yzgaVoKWHRfhBtPiHriySggh0lE2tEbs8IV5fGU1ABctmjqixRYi+YzBdgCi9mIYZLWpJFrEnvJsscRAKBjkqb88xDU/+jG3njGP7x41Hdd7j2KecTie/Olsb/P2POfFpx9n84avmTRlKu+veINIZO/2Mt2vd8U1Nwwaw/o1q1FVlaUL9mPhnIncfPXluDs7ex6POsoJlOyPgk5+3Tujf9MJIu3DhBDJ0uIJxq2apS+RqE6zK8TmJg87nX6CEbkWEm/eUJS6Dj+bGt3UOwMEwipOXyS1zkk0DaqrYzdNS3Y0QiTNsM6ocnJy2LBhA9XV1TgcDn72s5/x3nvvMWfOnETFJ5LsiqNiK9Fe/7qRUDSzvzB1oxX39NOoPvlxNn97JU2H/oRQXgXGiJfiLX9n1itnMvvFpZSue6RnsCxA/o7XmfvsEcx47Xwq3r2OGa+dz9xnjyB/x+tJfDepwROMZs3KbyFE9ghkwQnknz6swh9WmV2Wy6kHlCc7HDFCPfNZbIO3fZNEi9iTo6sC47V/Ps+BBx/GpClTMSgK5f7tWFs2ctDZVxNWNf61tpH739lGu9PFfXffwdTpM2hubOCJPz3AZeecSigY7PW6u7/eYGqqtrPfAfN56MkXeebVd6nfWcu9v7ut1zbuiqUA5NWsiNM7jz+3JFqEEEkQiqp0+sfm80fXwemLzXGpavXiljkuoxKOarS4g2xp8rCj1UenP7JXwqyhM5Cc4PoSCMD06bFbIIXiEmKMDfuM6vPPP+fwww8nHI6VHlZUVHDUUUfFPTCRGr6x3wQmF9lxB6O8t6U12eGMmaijnNaDr2PreR9QdeqzOGedjWa0YevcRvnnd7Dv0wupeOv7jF/1Oyrevgqzr7HX882+JirevkqSLUCzOzj4RkIIkSaCETXjF2l9Ud3BR5VtGBS49rhZGGVAetoydS0MGUqixSKJFrEHh9UIwPNP/pVzL7ociFWj3P7TG1j+2z/yvxcfydTiWNuSNzc2893bHsDv8/HnZ/7FlT+6mYefegmP28W/Xnym1+vu/nqDueKaG3jwby8wa+6+zJg9lxtuWc6K117utY17aizRklv/AUo0NY87u9uHCSHEWGpxx282y3D4Qio1bX62NHlo84Z62lCKgem6jssfYUebjy1NHprdIcLR/k88AmGNjgS0hRNCjNywzqhcLhc/+MEPuOWWW/oddhWQzGVGMRoULj1iGgAvr21AT8a3dDIpBnwTF7Pz2P/Hpgu/YOdRd+EfNx9Fj1JQ8wZlax8AdPb816AQ+3sq/3R51rcRk6oWIUQmyfTPs2BE5cH3twNwxkGTmDEuN8kRidHormhR7UOoaDFJQk30ZjUZ2VlTRW11FYuOOhaAh++9m3kHHcKSE07CbDQwqyyXbx0yidJcK+0tTShls3l1q5uoqmEymZizzzzq62p6XrN2R+/XG668ggKcHe2EQ7va0wZLDiCSMx5j1I+jceVo3nJCSfswIcRYCkbGrpqlP+GoRmNnkM1Nbho6AxnfJWWkAmGV+s4Amxo91Hb48QaHnphvcgWJqhm+CkyINDLkRIvP5+OMM85g2bJlHHDAAVitVoxGY6+bwWAgN1dOyDPNeQumkGMxUtfhZ01dZ7LDSRrNko9znwvZvuwVtp79Fp3TTgPYK8nSTUHH4mvE0fT52AWZolo8qbm6UAghhsuf4W3DnllVS4snxLg8KxcsrEh2OGKUema02IoH3VZah4m+vPPayyxZehJmsxmA115+nnf/8xpHzqvgyHkVvPbyCzxxz6+o2PQ082ZPR4uG+PuqOv77H+to6AzQUF9H+aQpPa/35r9f6vV6g7nxyotZ++Wqnp/Xr/2K0rLxWKy7zY1SlJ72Yfm10j5MCCEgtealahq0e8NsbfJS3ebDI23FiKoabd4QlS0eKlu8dHjDI6r8UTWdZk/q/L8WItuZBtvA5/Px4Ycf8tOf/pTzzz+fW265hWg0itvt3usAWdM0QiH5B55p8m1mzj10Co+vrObltQ0cXFGU7JCSLlQ8F/f0kymsfnXQbU2BljGIKLW5A1GCERWb2ZjsUIQQYlQCqTR0Ms52tPl46at6AK5aMhO7RT6z050p0J1oKR1wO0WRRIvo20fvreDUs/+r5+fHXnwdNbrrc/D3d/ySAw9ewLLzLsCgGDjxmT8Q+voNNroP46I3/o5z/Tp+d/+jPdt//N4Kzjzvwr324/W4sdrse51fztl3Hnff+jNuXn4XzvY2HrjnN5x/0RV7Pd9TsZSSzU+RV7sCjrgj9kudYrrbh+VaBz0FF0KIUQlG1JStovMEo3iCUWxmAyW5VgrtZgxZ1KbWHYzQ6YvgDu49c2WkOrxhinMscuwuRAoY9IzqhRde4NRTT+Xggw/mlltuAcBkMpGbm4vVau11s9vtFBYWJjpmkQSXHzUNBVhd46TO6U92OCkhai+L63aZTma1CCHSnabpBCOZWZqv6ToPvFuJpsMRM0tYOH3wCgiR+kzdFS32gf9/SpJF9CUQCPDlF6s46NCFPfdNKJ/EpClTe245OQ6KiospKi6hoKiIR57+ByWNn9H45ytxfvZPik7/CX9d56PTHyYYCPD1mtW9Xq/bOSceyYdvv7nX/VdccyMzZ8/lygvO4n+W38J5F13OFdfeuNd23olHopnsWHyN2No3xPcvIo5S9cKnECKzpMO5dzCiUe8MsLnJQ5MrOOAsknQXiqo0uWIt1Gra/LgC8UuydKvvlDEOQqSCQZfTXHLJJcyePZtf/vKXfOMb3+Dxxx/HYDDw8MMPM2nSJMrKyqioqGD//ffHZJLVOZlqaomDI2aV8HFlO/9a28DVx85KdkhJ55uwkLCjHLOvqWcmy+50FCKOCfgm7H0ymY2kqkUIke6CUTUpA0XHwhvrm9jS7MFuNvL9o2ckOxwRJz0zWmwDz2gxG7NnJakYOrvdjj8QZGODu99t7vjjg71+PuiQBfz9lbfQdJ2X19TzxMoaPq/u4Lq/f8WtZ8xj9fa+K73fWPl1n/ebzWZuved+br3n/gFj1U02PJOOpqDmP+TXriBYuv8g7y453IEIkwrtyQ5DCJHBAmEVdyB9KrBVTafVE6LNG6LYYaG8wNbvTOh0omk6rkCEDn8YfyjxrYcDYZUOX5hihyXh+xJC9G9Iy9eOOOII3n77bc455xwWL17Mpk2b8Hq9rF+/nn/+859cc801TJ48mT/+8Y+Jjlck0QULpwLwzuYW6akJYDDSuGg5EEuq7E2PPW6QxEK3VOoTK4QQw+UPZ+Z8lg5fmMdXVgNw8eKplORaB36CSBvGQBsA0UETLVLRIvpmNChYzcP//TAoCmcdPJk/nDefqcU5dAYi/KOrNWGieLrmtOSl8JyW7vZhQgiRKOlQzdIXXY/Ncals8RJM45mIvlCUnU4/Gxvd7HQGxiTJ0q3JFRzRnJe4MJng6qtjN1mEL7LYsH77r7zySgoKCrjyyiv58MMPGT9+fM9j69at4/zzz2f16tU8+eSTcQ9UJN8RM4uZUeqgqs3HmxuaOefQyckOKenc00+h9oSHKP90ORZfY8/9OqAAhqi0WdudKxCRqhYhRNoKZGii5U8fVuEPq8wuy+WU/cuTHY6Io+6Klqh94ESLxSSJFtE/u9lIaIRtE6eXOvjOoqn85rVN1DsT29bEM+UEdBRy2tZh8jURdUxI6P5GyhWIyJwWIURC+MOx+SfpLBjRqGzxMqHARmmaLP6JqBpOf5hOf2TE35fxoGo6Te5gcionrVZ44IGx368QKWbQs6oVK1Zw991343bHSsa//e1v893vfhePx9NruwMPPJB33nmHhQulTVKmsltMnHHQRAD+va6BqJq5PTSHwz39FLac/wlVpz5L7XH3UXXqs7TMvx6ASR//DKtzS5IjTC1S1SKESFeZWNHyRXUHH1W2YVDg2uNmYcyiYaSZTokGMUa8gFS0iNHJGeVw3YldF3zqOwPoCey/GM0ZR2DcfADy6t5O2H5Gyy1zWoQQCdKcIefaug6NnUF2tPmIpOh1J13XcfkjVLf52NLkodkVSmqSpVuHN5yxi8OESAeDLqUJhUL861//4je/+Q2nnnoqNpsNgDvvvHOvbTVNIxgMcv3118c/UpF0VpOBY+aO47GV1bT7wnyyvZ0lc8YlO6zUYDDim7i450ffhIXktH5FXv2HVKy4iu3L/oVmyU1igKlDqlqEEOkoqmoZN6QzGFF58P3tAJxx0CRmjJPvqUzSXc2iGcxolvwBt5UZLWIgOZbRVV+UF9hQgEBEpTMQoSgncf3j3RVLyWn9ivyaFTj3uTBh+xmN7vZhUtUihIgnXyiKN82rWfbkDUbZ1uxlUpGdArs52eEAsePnDl+seiVpbboGUd8ZYFbZGB/X6zq0xVrWUloKGTBnR4iRGPTo7rTTTuO0005jy5Yt3HPPPTz22GMsWLCAa6+9FqOx94XSYDBIOBxOWLAiuRRFIddq4tT9y3n681peWdsgiZb+GIzUHfu/zH7pFGyu7Uz66KfUHXeffNl0aXYHKc219vx1KF0zbvb86xno8e4/GhQFg6zAFkIkmD+Ne0X355lVtbR4QozLs3LBwopkhyPizBhsB0C1FQ16/CEVLWIgNrMBRYldQxkJs9HAuDwrLZ4QDZ2BxCZapn6DCavvJrfhI5RoAN2UmoPnpX2YECLemtJ0NstgVE2ntt1PkcPMxAJ7Us79VU2n0x/G6Q8TCKf+wqtAOJYMKnYk7vt2L34/lJXF/uz1gsMxdvsWIoUM+ehu7ty5/OlPf+L666/noosu4umnn+bf//53ImMTKchmNnLK/hN47os6tjR72NzoZp/ygVdJZivVXkLt8Q8w49XzKax6Bd+EhXTsd3Gyw0oJ7kAUdyB+q22sZgM5FiMOi4kcqxGrSaplhBDxlWkl+DvavLzUNZj6qiUzsY+yNZBIPaZALNEStZUOuq1FEi1iAIqiYLcYRzXQd1KhvSfRMm9iQRyj6y1UNJdw7mQs3p3k1n+IZ+qJCdvXaLgDkeT00BdCZCRPMDKmQ9eTwemL4AupTCm2j7rScqg8wQid/giuQGTEiw2SpckVpMBulrbAQoyxYZ9VHXDAAaxcuZLf/e53iYhHpDir2UBhjoVj58YqWV5e25DkiFKbf8ICmhbeAkD5p7dib12b5IgyUyii4fRF2OkMsLXJy6ZGN7Xtftq8oYy7OCqESI5M+ixRNZ0H3t2OpsMRM0tYOL042SGJBDAFuxMtA///NRqkMlQMLl5zWho6E7ziWlFwVywFIL9mRWL3NQrd7cOEECIeMmU2y2DCUY2qVh8t7mDCZn6FoirN7iCbm9xUt/np9KdfkgVix/uZWuUkRCob0fI1u93OvHnz4h2LSAPdczXOOGgSAJ9sb6PFIx/eA2nb/7u4pp6MQYtQ8fYPMAY7kx1SxouqOq5AhMbOIJUtXjY0uKhu89HiCeILRRM6iFUIkZn8GZRoeWNDE1uaPdjNRr5/9IxkhyMSpHtGi2ovGXA7i0mSLGJwOebRrR6eWBib81nfGYhHOAPydCVa8ureBj11W7y4ApFkhyCEyADuYCSjFgQNRtdjiaWqNh+haHzet9bVGqyq1cvWJi8t7hCRaPpfM3D6wln1uyFEKpA+AWJYbF0tmaaXOjhwcgGaDq+ua0xyVClOUdh5zD2E8qdi8e5k8vs/SumTvkykaeAJRml2hahq9bGhwc32Vi9NriDuYOoOsRNCpIZQVM2Yz4l2b4gnVlYDcMniqZTkWpMbkEgYYyA2kDRqGzjRIvNZxFDkWONT0dLoSnyixVe+CNWciznQir11XcL3N1JuSbQIIeKgJUurFvwhlcoWL07fyOdE+8NRdjr9bGpyU9cRwJdh7dd0HRrG4HtXCLGLnFmJYbGYDBi6fmuWdVW1vLmxSbLkg9As+dSe8BCa0Up+3TuMW/dgskPKaroeOzBr9YSoafOzscHN9hYvAK5AmHBUEmFCiF0y6TvuTx/twB9WmTM+l5P3L092OCKBuitaJNEi4sFsNGAyjrz6aWLBrtZhWoIri3WjBe/kJQDk176V0H2NhrQPE0KMlisQSYvh7ImiabDTGaC23T/kRVFRVaPVE2Jrs4ftLT6cvghaBv8V+kPqqJJRQojhkTMrMWzd7cMOm1ZEeYENX0jlnc3NSY4q9QVL5tGw+DYAxn9xN46GlUmOSOwu1JVcqXcG2dLkYXOTm7oOP+3eEMFI5lxkFUIMj67reIKZcSFsVXUHH1e2YVDgmmNnyXDMDNczo8U+8IwWi0lOB8TQOEYxfHh8vg2jQSGsarR7E3/Bx13xDQDyalN3TgtI+zAhxOhkazXLnlyBCNtaPP0mr3U91lq8pt3H5iYPTa4goUgGZ1f20OgKZkx1vhCpTs6sxLB1J1oMisIZB00E4JW1DQlfnZYJnHO/jXP2OSi6xpR3r8Xkb0l2SKIfkahOpz9CQ2eQbc1eNja4qW7z0eoJ4Q/LnBchMp0/HKW+M8CmRg+d/vS/EBaMqDz0/nYAls2fxIxxuUmOSCSaKRBLtKi20gG3k4oWMVR2y8jbhxkNChPyY3NaGsZiTsuU49EVA/aOTZg9OxO+v5GS9mFCiJFy+SMEsyhZMJhIVGdHq49GV6DnXD0YUWl0Bdjc5KG23Y87EE3LwfajpWo6zYlOyplMcMklsZtpdHPdhEhncmYlhs2228rHE/YZj8NipMEVZHWNM4lRpQlFof7I3xAsmos50MqUd68DLTNWSmc6VYutam9yBdneEpvzUtXqpdkdxCNzXoTICOGoRosn2NNKoMMbzph/23//vJYWT4hxeVYuWFiR7HDEGDB2V7TYBqlokUSLGKKcUSRaACYWdiVaxqBfvGorwl92KAD5KVzVIu3DhBAjoes6zR6pZulLmydMZYuXyhYv25q9tHnCRNXMOJ4fjQ5fOLGdOqxWeOyx2M0qMyBF9pIzKzFs3RUtEFvZduK8CQC8vKY+WSGlFd1kp+aEh1DNDnIbVzJ+9e+THZIYAV0HX0ilxR2ius3PpkY3lS0eGjoDuPwRIqqsLhLZIapqVLf5CEXTs8Wepuk4fWGqWr1safLQ7AplXCuBHW1e/tn1HX3Vkpm9vsdF5trVOmywihZpISeGxm42oozi16V7Tku9c2wG87qnSvswIURm6vRHMu54NZ6CES2jZizGg65D/RhUlAqR7STRIoZtzws0px9QjkGBtTtdVLf5khRVegkXzqT+6P8BoGztA+TVvp3kiMRo6ToEwrG+47UdfjY3etjS5KGuw5/41SNCJJE/ouIJRtnW7KXFHUybtnqeYIS6Dj8bG93sdAbwhTLz36iq6Tzw7nY0HY6cWcLC6QNXN4jMoESDGCOxY7KBKloUBUxS0SKGyGBQsJlH/vsyqSiWaBmLihYAT8VSAByNKzGEPWOyz5GQ9mFCiOHQdZ0WTyjZYYg05A+pOH0JmpOm6+DzxW5pcj4oRCLImZUYNqNBwWzatZytLN/G4pmx1ZKvrGtIVlhpxzXjm7TtdykAk9//EWZPXXIDEnEXjmp0+iPUOwNsa/ayqdFNTbuPNm+IQFhNmwvSQgwk2LVaTNeh2R1iW4s3ZdugBCMqTa4gm5vcVLf56fRHMv484I0NTWxp9mA3G/ne0TOSHY4YI6ZgBwCawYxmye93O4tJTgXE8NgtI++73l3R0tA5Nu1uQgUzCeVPw6BFyK3/YEz2ORLSPkwIMRxOf4RwVKpZxMg0uYOJaY3s90Nubuzm98f/9YVIE3J2JUbEZupd1bLsoIkAvLelhU5/gjLkGajp8F/gHzcfU8hFxTtXo6iyMiWTRVUddyBKY2eQyhYvGxrc7Gjz0eIO4g1F0TJkFoTILv49yvJDEY0drT7qOvxEU6CFXlTVaPOGqGzxsK3ZS6snRCSaHf/W2r0hnlhZDcAli6dSkiv9krOFMdgGgGorZqBeT2apZhHDlDOK1oMTC2OJloRd5NmTouCuiLUPS+U5LSDtw4QQQxOrZpHZLGLkoqpOs1t+h4RIFDm7EiOyZ/uwfSbkMbssl4iq88aGpiRFlX50o4Xa4/+PqLWAnNa1lH96G46GlRRsfxlHw0rQMrOVjYjRdfAGozS7Q+xo9bGx0U1li5dGVwBXIJISF6mFGMyeiZZunf4IW5o9tHvHPoGs6zouf4TqNh+bmzw0dgYJhLPv39OfPtqBP6wyZ3wuJ+9fnuxwxBgyBWIVLVFbyYDbyXwWMVx2y8gTLSW5FixGA6o2dhcKu9uH5dW+k9LH1dI+TAgxFB2+cNYsGBKJI63NhUgcSbSIEdmzP7OiKCybPwmAV79ulEHgwxDJm8zOY+8FoGTT35jx2vlUvHsdM147n7nPHkH+jteTHKEYK7E5LyptnjC17X42NXrY2uxhp9OP0xdO22HjInOFo9qAq5I1LdYiprLFOyYDKf3hKPWdATY2uqnt8OMJRjO+NVh/VlV38HFlGwYFrjl2FkaDXFDPJqZgOzDwfBYAi1S0iGGymY0YRvhrY1AUygtswNgN5PVNOAzVko8p5CSnZfWY7HMkpH2YEGIwmiazWUR86PrYfQ8LkW3k7EqMyJ4VLRAbslvssNDpj/DhttYkRJW+lGiIvq4Fmn1NVLx9lSRbslgoouH0RdjpDLC1KTbnpbbdT5s3JKtQRNINNXkSCKtsb/XS0BmIe7uYcFSjxR1kS5OH7S0+OrxhtCzP9QcjKg+9vx2AZfMnMWNcbpIjEmPNFIglWlR76YDbSeswMRI5o5nTUji2c1owmPFMOR6Q9mFCiPTW5A4SVbN0BZGIO39Ilbb/QiSAnF2JEbGaDHu1/DYZDZx+QKw1yctrG2TQ91BpKuWfLu/zIaUr/VL+6fKUbncgxk5U1XEFIjR2BtnW7GVDg4vqNh8tniC+UFT+3Ykx5Y8MffWtrkO7N8zWZg8u/+guJqmaTocvTFWrly1NHprdIRkKupunP6+lxRNiXJ6VCxZWJDsckQTGIVa0mE1yKiCGL2cU7cN2JVrGbiWtu6d9WGonWqR9mBCiP23eEO1euSgu4qvRNUYz04TIInJ2JUZEURSsfZycnzRvAhaTgapWH+sb3EmILP04mj7H4mukv6YuCjoWXyOOps/HNC6RHjQNPMEoza4QVa0+NjS42d7qpckVxB2MyIGTSKj+5rMMJKrq1Hb42dHmG1Y7PF3X8QQj1HX42dTopt4ZwBeSBPSeqlq9vLymHoAfHDOzzwpUkflMQZnRIhJnNHNaJhXGWoeNZaLFM/kYdMWErbMSi6t6zPY7XNI+TAjRl+5FdkLEW1Qdu5lpQmQLSbSIfj322GMoirLX7bHHHgN2tQ+76jvf4uXnngIg327m+LllALyytr7XYwDvvvkqpxx5EAdPK+GCb55A1bYtQ4rl2svO58AphT237/3Xsji+0+QyBVriup3IbroeKwNu9YSoafOzscHNtmYP9Z0B3EFZKSniazRzV7zBKNuavbS4gwNWYgUjKo2uAJubPFS3+en0R7J27spgVE3ngfcq0XQ4clYpC6YNXM0gMpcp0AZA1D5wokVmtIiRyBlFAre7omUse8Nr1gJ85QuB1K9qkfZhQojd+cNR6jr8yQ5DZLB2bzg+LcmNRjjnnNjNKAu9RPaSsyvRrwsuuACn09lzq6uro7S0lCVLlgBgNRt49aXn+OT9t3s974yDJgLw9r9e7PVYXfUOfnnTNfzop8tZsWoTEydPYfnN1w8plk3r1vLiW5/w0fpqPlpfzb2PPh2nd5l8UXtZXLcTYk/BiEaHN0xtu1/muoi4CUbUUSc8dB2a3SG2tXh7reKNqBqtnhCVLR62NXtp84SlJ/UQvLG+ka3NXnIsRr531PRkhyOSqLuiRR2gosVkjC2gEWK4TEYDlhG2netOtLR5Q0TUsWv52N0+LNXntEj7MCFEt3BUo6bdLwuMRELpOrR6QqN/IZsNnn8+drPZRv96QqQpSbSIflksFgoLC3tuTzzxBGeffTYzZswAwO9xcc/tv2DazNm9njelOIf9S4x0vPsoheXTeu6vqtzC9f/9K0765lmUjCvjvIsuZ8O6rwaNo6mxHh2d2fvsR35BIfkFheTkOOL6XpPJN2EhYUc5ej/Nw3Qg7CjHN2Hh2AYmMk7soraUBov4GE01y55CEY0drT5q2/1Ut/nY0uShyRUkEJa5K0PV7g3x+MoaAC5ePI2SXGuSIxLJ1DOjZYCKFrNUs4hRGOmclkK7GbvZiKZDk2vsjkk8XYkWR9NnGEKdY7bf4ZL2YUIIiFUpV7f7ZKGRGBOd/ogsyBQiTuQMSwxJMBjk3nvv5ZZbbum579e3/DfHn3Q6Bx582F7bu957lJzZi9HGzSLUNaD4mKUnc95Fl/dsU729koppMwbd9/o1q1FVlaUL9mPhnIncfPXluDs7R/+mUoXBSOOi5QB7JVt0QAE8U44Dg5RfitFzB6L45ARexIE/AQfjrkAETzAqK/dG4E8fVhGIqMwdn8fJ8yYkOxyRZKbuRMsAFS3SNkyMxkjntCiKwqQktA8L508jWDgbRVfJ2/n+mO13JKR9mBDZTdd1atp9hCKy4EiMnRZ3HKpahBCSaBFD8/TTT7No0SKmTZsGwLvvvsu7777DTb9Yvte2n3/yAZVrVrL/mT9A1XQ2NLj22iYSDvP4w/dx/kVXDLrvmqrt7HfAfB568kWeefVd6nfWcu/vbhvtW0op7umnUHvCQ0QcvS+OqZZ8AIoqX8LiqkpGaCIDNUlVi4iDQFgSdqni8x0dfLy9HYMC1xw3E6NB2kFlMyUaxBjxARC19T+nx2yS3xMxciOtaAGYWBhrKdIwhokWSJ/2YU5fmA5fONlhCCGSZKczgC8k1QVibLkCo6xq8flAUWI3ny9+gQmRZiTRIobkoYce4qqrrgJi1S1XXnklDz74IKXFRb22CwWD3P7TG/jFnX/g7MPnALCmrhNV6708+b677yDH4eBbF1466L6vuOYGHvzbC8yauy8zZs/lhluWs+K1l+PzxlKIe/opbDn/E6pOfZba4+6j6tRn2XThV3gnHokhGmDKez8ETVa4idHzh1RZLSlGRdd1grLKLiUEIyoPfbAdgDPnT2J6aW6SIxLJ1l3NohnMaF0LNvoircPEaNjNRkY64qe8q6JlrBMtnqnfACCv7t2UPqbWdah3BqTdqxBZqNkdpNOfup9PIrPJ944QoydnWGJQlZWVVFZWsnRpbBXY7bffzoIFCzjttNOwmXuvZnv43ruZd9AhLDnhJI6dOw6zUcEdjPL5jvaebVZ+8C7PP/lXfnvfnzGbzcOOJ6+gAGdHO+FQBpY2Goz4Ji7GNXMZvomLwWim7pg/oFryyWldS9lX/5vsCEWGaHYH0aU/kxihQESV9l4p4u+rdtLqCVGWZ+W/FlYkOxyRArrns6i2Yga6Ei6JFjEaiqLsdR4wVN2twxrGcEYLgH/cwURtxRjDbhxNq8Z03yPR4g6x0+mX4zWRcvZcRCniw+kLS/smkVTuQDSucziFyEamZAcgUt9zzz3H6aef3pMUefrpp2ltbaWwsBAd8Pv9/Off/+TrNV/y0Xtv4Wxv58h5sYs9Pp8ffcOH/MZVzb+feYydNdX89Prv8Ys7f8/MOfsMaf83Xnkxl1x5HQcdsgCA9Wu/orRsPBZrdgz6jTrKqT/yTirevZayNffhnXws/vGHJjsskeZCEQ2nP0Kxw5LsUEQa8ssBeErY6YNXvm4E4AfHzBzxRU+RWUyBDmDg+SwgM1rE6DmsxhFdkJlYMPYzWgAwGPFMOZ6ibS+QX7sC38Qjxnb/I+D0RYioOlOLczBIW0iRImrafYzPt+GwyuWkePGGomP/mShEH1o8QaaWOJIdhhBpS74ZxaDeeOMNLrvssp6fP/zwQ6LRWG9+fyjKD2+8iQMPXsCy8y7gu9fdiBrd1bf/ruU/Z320DG3eCWyobeXnl53P8SedxnEnnorf5wXAnuNAURS8HjdWm32vKpc5+87j7lt/xs3L78LZ3sYD9/xmSLNdMolr5hk4696mqPIlJr//IyrPegPNLF9+YnSa3UEK7WY5cRfDJiudkk/VdJ6rMqLpcOSsUg6b1v8sDpFdTME2AKL2QRItJkm0iNHJMZuA4c8S6Z7R0uELEwir2Ecx72W43BXfoGjbC+TVvkXj4b8csOorVXiDUaravEwtcUglmki6Nm8IX0ilxRNiuiRa4iIYUalp90m1uEgJ3VUtY/ndLEQmkSM1MaBAIMBnn33G4sWLe+6bPHky06ZNY9q0acydPZOcHAdFxcUUFZcwoXwSk6ZM7bkV5uexz9RJGHMKeOTvL1G1bQsvPv04i/aZ3HNr2FkLwDknHsmHb7+5VwxXXHMjM2fP5coLzuJ/lt/CeRddzhXX3jhmfwepouGI2wnnTsLqrqH801uTHY7IAFFVp80n5eli+AKjGZQo4uKNjc3UeBVyLEa+d9T0ZIcjUkh3RYs6QEWLwQBGSbKLURrpRZg8m5k8W+wCbaNrbFdweycvQTNYsLprsHZWjum+RyMQ1tje6h3doGIhRikYUWnqavnnDUbxhaKDPEMMJqJqVLf70GT0oUghMqtFiJGTJQhiQHa7ndAAs1CMBoXf3fcQkWjfyy/u+OODbGv2cOPza6nNm8eD71ayeGYJhj5Wj72x8us+X8NsNnPrPfdz6z33j+xNZAjNks/OY/7I9FfPp3jLM3imnIB72knJDkukuVZPiOIcCyZZISmGSNV0QhE5G0ymdm+Iv31aB8BFh0+hJDc7WmmKoeme0RK19V/lJG3DRDxYTAZMRoWoOvxl2JMK7Wxu8tDgCjJjXG4CouubZnbgm7iYvJ3vk1e7glDR7DHb92hFojrbW71MK3FIyyYx5nRdZ6cz0KvqQqpaRkfTdGra/f1eSxEiWTzBKP5wlByL/PsWYrjkLEuMms008Gq22ePzOHByAVFN57dvbObqp77krY1NRFS5UDdcvvJFtB14JQCTPvpvTP6WJEck0p2mQatXqlrE0Ek1S/L96cMqAhGVqbk6J+03PtnhiBRj6k602Ev73UbaD4l4yRlhVUvS5rQQax8GkF/71pjve7Q0DXa0+XD5I8kORWSZVk9or9axUtUyOnVOv7TjFSmr2T3MawRGI5x6auxmlLZjInvJWZYYtaEM373llH0577ApOKxG6jsD/O87lXz3iS946aud+MNycDYczYfeRKB4P0zBDiZ/8BOkmasYrXZvmHBUEp9iaOQzO7k+39HBx9vbMShw/gxV2j+JvZgCg1e0mGU+i4iTkbYPm1gUS7Q0JCHR4qk4AYCcli8xBjvGfP+jpetQ2+Gn1SMLZcTYCIRjM1n60t/9YmANnQHcATmmFqlr2IlUmw1efTV2s9kSF5gQKU7OssSo2cyD/xrlWk1ctGgqf7lkAZcdMY1ih4UOX5i/fFzN5Y+v4m+f1tDpH/4wzWykG63UHXcvmtFK3s53Kd70ZLJDEmlO16UPqxg6WXmXPIGwykMfbAdg2UHlTHIkOSCRkkxdF44HrmiRBJ2Ij5G2FZlYELsI05iEREskdxKB4v1QdI28unfGfP/x0uQKJiVRJbJLrGWYv9+1fVLVMnxt3hDtXrn2IVKfXCMQYvgk0SJGbSgVLd1yLCbOPmQyf774MK47fhaTCu34QirPfVHHFY9/wUPvb6dJPswHFSqaS9OCnwJQ/tntWDq3Jzkike46/REZsCqGxC+JlqR5+vNaWj0hyvKsfPuwyckOR6QoU7ANAFVmtIgxkDOM84DdTSpMXuswAPfUrvZhNenXPmx37d4wNe0+NE0q3EViNLtDBAeZzScXY4fOFYjQ2Cl/XyI9+EIqXkmkCjEscpYlRs1qMtDHbPsBmY0GTtxvAg9ccAi3nLIPs8tyCasar37dyJV/+4J7/rOFHW2+xAScIdrnXYZn4lEY1CBT3vshaNKrWYxOk0sO+sXAIqo2oqHHYvS2t3p5ZW09AD84duawFjmI7NLdCilqK+l3G5nRIuLFYFCGVN2+p/KuGS3uYBRvcOwv4ngqlgKQW/8BiprerY/cgSg72n1EZf6liDNfKDqkFnW+kCpVLUPgD0ep6/AnOwwhhmXIiVSfDxyO2M0n1/JE9pKzLDFqiqJgHWGvb6NB4YiZpfz+3IO448z9OXhKIZoO729t5fpnvmL5vzawvt6FLnNI9qYY2HnMH4haC8hpW8f4L+9NdkQizXmCUVmxIgYk1SzJoWo6D7xbiabDUbNKOWxq/5UKIrsp0SDGSOzkNmqXRIsYGyOZ02K3GCnOsQDQ4Br7qpZA6QFE7OMwRnw4Gj8b8/3Hmz+kUtXmIxSV72kRH5qms9M59H+bUtUysHBUo6a9/xZsQqQq/3CqWvz+2E2ILCZnWSIuRruyVlEUDppcyG3L9ueP583n6NmlGBRYXePklpe+5icvrOPTqnY0OTLpJeqYQP2RdwEwbu395DR/keSIRLqTqhYxEJnPkhyvr29kW4uXHIuR7x09I9nhiBRmCrYDoBnMaOa8PrdRFJnRIuJrxHNaCmNzWpIyZ0Qx9FS15Nemd/uwbqGIxvYWH/6wLJoRo9foDhKODr1KSqpa+qdqOtXtPqkKF2lLEqlCDJ0kWkRcWEfQMqA/s8pyufmkfXjwwkM5ed4EzEaFLc0efvPaJq59+ktWbGomIqXxPdwzTsc562wUXWPyez/CEPYmOySRxgJhFZdf2tCJvgVkjs+Ya/eGeGJlDQCXLJ5GscOS5IhEKjN2JVpUWzH99XU1GRWU4fZ8FWIAOSOoaAGYmOw5LV2JlrzaFWTKMnNV06lq9eEOyrGcGDlPMELHCIa1y8XYvem6Tk27j9Agc26ESGX+kIpHvleEGBJJtIi4SESv+ImFdq45bhaPXryAcw6ZTI7FSJ0zwL1vb+P7f/uCl9fUy+rqLg1H3EY4dzJWTy3ln96a7HBEmmv2BKVdn+iTrJIde498WEUgojJ3fB4n7z8h2eGIFGcKxBItUXtpv9tI2zARbzazEcMIfq0mdSVaGpI0GNo76Sg0oxWLtx5bx+akxJAIug617X7avek9e0YkhzrMlmG7k6qWve10BvCF5JqFSH/NbvlOEWIo5ExLxIXNlLihvEUOC5ccMY2/XLKAS4+YRlGOmTZvmD9/tIPLH1/FU5/V4Apkd3Zds+RTd+wf0VEo3vos+TteT3ZIIo2FIhodvuGvYhOZLRRV0WQx3pj6fEc7n2xvx6DANcfNwiBVCGIQ3a3Dorb+5/hYJNEiEsA+gkVX5T2JluRUtOgmO95JRwOQlyHtw7rpeiyBNZRB5kLsrqEzMKoWV1LVskuzO0indAoQGSIQVqVaUoghkDMtERcWk2FEK9mGw2E18a1DJvPnixdwzbGzKC+w4Q1FeWZVHZc/voqHP9hOSxYf2PknHE7rgVcBMOmjn2LyNuBoWEnB9pdxNKwETVbSiKFr8YTQNKlqEbtIBeHYCoRVHvqgCoCzDp7E9FJHkiMS6cAU6ABAtZX0u41UtIhEGMmclp6KFlcgaZW07p45LSuSsv9Ea/EEiUrLZTFELn9k1IkBqWqJcfrCtEgFgMgw2Xy9TYihGtnkQiH6YDMb8Y9BWazFZODk/Sfwjf3Gs7KqnRdW17G91ce/1zXy2teNHDNnHN86ZDJTS7LvolTLoTeRV/8B9vYNzH3+GAzqroO7sKOcxkXLcU8/JYkRinQRVXXavCHK8m3JDkWkCL8kWsbU05/X0uoJUZZn5dsLKpIdjkgTpmAbAFH7QIkWqYwS8WcfwZyWCfk2FGLfL65AhMKcsZ9B5ak4AYCc1jWY/M1Ec8aPeQyJpGnQ5g0zoUCO58TAIqoWt3lJze4gM8blxuW10pE3FE3a7CkhEikQ1nAFIhTYzXs/aDDAMcfs+rMQWUp++0XcJGJOy0CMBoWjZpXyx/Pmc/uy/TlocgGaDu9uaeXav3/Fbf/ewIYG15jGlGy60YJz9jno0CvJAmD2NVHx9lXSVkwMWas3JKsgRQ9JtIyd7a1eXllbD8APjp055t+vIn0Zg7GKluhAFS0mOfwX8ZczgkSLxWRgXJ4VIGkXJaM54/GPOwiAvNp3khJDorXJ8ZwYgobOAGqcqtmzuaolGFGpafch4y5Fpmr19FPVYrfDe+/Fbnb7WIYkREqRMy0RN7YknbgrisL8KYXcceYB/OHcgzhyZgkKsKrayU//8TU3v7iOz3d0oGXD0Y6mUvr1I30+pBB7/+WfLpc2YmJINC3WQkwIXdcJRuRzYyyoms4D71ai6XDUrFIOm9r/rA0h9mQKdM9o6T/RIjNaRCKYjQbMpuFXS03sah/W2Jm8diSeDG8fpuuxxTNC9MfpC+MOxDcxko2zWiKqRnW7T2YaiowWCGu4ZPaQEP2SMy0RN6mw4nb2+Dx+esq+PHjhoZy033hMBoVNjW5uf3Uj1/39K97Z3JLRK7ocTZ9j8TXS32mugo7F14ij6fMxjUukrw5fmHA0c//NiKEJRjRZmTdGXl/fyLYWLzkWI987ekaywxFpxhTsSrQM2DpMDv9FYuSYRz6nJZltdrrntOTWf4gSzcyLw+3eMJEUOQfyBCO0S+InZYSjGg2u+P/784VUvFlU1aJpOjXtPiJROWAWma+lv6oWIYQkWkT8pEKipdukIjvXHj+bP198GGcfPAm72Uhth58/rtjK959czStrGzJydbYp0BLX7YTQ9exckSZ684ez50Q5mdq9IZ5YWQPAJYunUewY+3kFIr11J1pUW9+VUAZDrPWqEImQYx3+ucDEwtjskERc6B2qYPF+hB0TMahBchs+SlociaTr0JoCVcq6rtPkCtLiCaHFqU2VGJ2dTn/CKjCyaXB2ndNPIJwayUwhEi0Y6aOqxeeDceNiN58vOYEJkQIk0SLixmhQRtQyIJFKcq1cduR0/nLpAi5eNJVCu5lWT4g/fVjFWTf8hgOnFO51e/m5p3q9xlXf+Vav+95981VOOfIgDp5WwgXfPIGqbVuG9NhArr3s/F4xfO+/lo3o/UbtZXHdTgiATn+EgMznyGqBDExMp6KHP6giEFGZOz6Pk/efkOxwRBraNaOltM/HpW2YSKSRzGmZWBCraGlI5uBoRelpH5aXoe3DIDWqlNt9YYIRjaiq0+ZLfuIn27V5Q/hCiTvGy5aqlobOQNxbrwmR6pr7qmppa4vdhMhicrYl4spmSp2qlt3lWk2ce9gU/nzJYVx97Ewm5Nswzj6aKT98hpk3Pcf3//Q+z7zzFUXFJRx6+JE9z3v1pef45P23e36uq97BL2+6hh/9dDkrVm1i4uQpLL/5+kEfG8ymdWt58a1P+Gh9NR+tr+beR58e0fv0TVhI2FGO3k/zMB2FsKMc34SFI3p9kb2asmhFmtibJNriQFNxNKykYPvLOBpW7jUr67Md7aysasdoULj2uFkYlNRauCBSnxINYozEVhBG7X1XtEjbMJFIdrOR4X50dc9oaXAFkzpP0b37nBY9M1elJ3tWS1TVaHHv2n+rJxS34eti+IIRlSZX4o/vM72qpc0bot0bTnYYQoy5UESj0y+/+0LsafiNdIUYgM1sxBNM3dUcVpORU/Yv58T9JvBxZRsvfrmTqjYfb1Z6eP7JRxl3wBK0vFi1h8vp5J7bf8G0mbN7nl9VuYXr//tXnPTNswA476LL+cFF5wz62ECaGuvR0Zm9z36jf4MGI42LllPx9lXoKCjsOnnp/lPjouVgSM2EmEhd3mAUbyhKrlW+NrKNpumEZE7PqOTveJ3yT5dj8TX23Bd2lNO4aDnu6acQCKs89H4VAGfOn8S0UkeyQhVprLttmGYwo5nz+tzGbJJEi0gcRVGwmY3DSs6Pz7dhNCiEoxrt3jDj8qwJjLB/vomLUc0OzP4W7G1fExh3UFLiSDSnL8y4XCuWJHwWNO+RWNG0WLJlQoFtzGPJdrqus9MZGJP5e91VLZl4DuEKRGjszOxEkhADafGEKLCbUWSBmBA95GxLxJXNnB6/UkaDwpI54/h/58/n1m/OY78yG65Vr+Db93SuefpL7nh1I7/82c0cf9LpHHjwYT3PO2bpyZx30eU9P1dvr6Ri2oxBHxvI+jWrUVWVpQv2Y+Gcidx89eW4OztH/N7c00+h9oSHiDj2bjtTf+SduKefMuLXFtmtKYn900XyBCLqmJyIp6qXn3tqlG0miznvv75DZfXOnsfWt6gc+f8qmX/if3H/T77DU59V0+YNUZZn5dsLpvQbyxcrP2LZcQtZcuAM/vbn/4v/mxVpzdgzn6WE/soKpHWYSLThtg8zGhQm5Cd/TotutOKdtATI7PZhup6cIcbBiIrTt/fK5zZviIgqiznGWqsnNKbVyplY1eIPR6nr8Cc7DCGSKlbVEhl8QyGyiJxtibiymdOrUkJRFA6ZWsQibROHHLaAow/eDwV4/733+PD99+g84FzavWH6usYYCYd5/OH7OP+iK4b12J5qqraz3wHzeejJF3nm1Xep31nLvb+7bVTvyz39FLac/wlVpz5L7XH3ESiehwJYPLWjel2R3QLhPobeiYznz/K2YaeeeW5PW8eP1lfzn883DL3N5H//ih0/m87UAoXv/St2kSEU1fnm3/0cWm5g1fdyqfnqXf7+5OMA/ODYmf1+j3a0t3H9FRdwyrJv8beX3+K1f77A119/ncB3LtKNKRBLtETtJf1uI4kWkWgjmdNS3lXRkNQ5LezRPiyDdfojhKJj+93e0Nl39YSuQ3MGXoRPZYGwSotnbFvIZdqslnBUo6bdn9ULkYTo1uIJocs/BiF6yNmWiCuryTDs3syp4Pkn/8pl3/0+Pzt1X/54zn4E3n2IcSdfzdYOlTU7O3nqsxre29LSq9z9vrvvIMfh4FsXXrrX6w302J6uuOYGHvzbC8yauy8zZs/lhluWs+K1l0f/pgxGfBMX45q5jOZDbwCgeMvfUaJSlSBGrskdlAOpLJPt81nMFgv5BYU9t3+98AwnnPJNJk+dBgzcZvLswyYyxdDKDw6z8EVD7O/x9coorqDOH06yMavYwO+OBce6Zzl7eoTDKgr7jePVl56jtGw8V/7wZqZOn8n3r/8xK1Zk9sVAMTzdrcOitr7nswCYTWl4kCbSin0EiZZJ3XNakpxo8Uw5Hh0Fe/sGzN6GpMaSSLpOr1kpieYKRAYcuJ6MxE+2irUMS06CIFOqWlRNp7rdR1SV8yEhIJZ4dMpiTCF6ZF6jTJFUiqJgMRkIRdKnBLx2RxW11VUsOupYAF59/AGOWnw4P77tal5e08BfX1do84b5/Vtb+dunNZx98CRy2zby/JN/5cmX38JsNvd6vZUfvNvvY0ORV1CAs6OdcCiExRqfPtWeKScQzpuCxVNHYeVLOPe5IC6vK7JPOKrR4QtTkpucHupi7AUicvGjWygY5Km/PMRTr+xKcNxz+885/qTTCQV3XSA8ZunJAJi2x5LmW9o1ZhXH1rasbdJYNNlEjjl2wfvA8QZcbc38ofEStMftBIvmECzah2DRXEJFcwgWzyVqL2PrxvUsPGJJTw/k/ecfwu9v+9mYvG+RHnoqWmyl/W5jlooWkWBWkxGjQRnWkPOJPYmW5F6IVe0l+MsOwdGymrzat+nY76KkxpNIrkCEcRE14d0IdF0fdOC6rkOzK0RFSU5CYxHQ7A4RTNJ5eibMatF1nZp2X1pd6xBiLLR4ghTlm1AO62q7b5DjTZG90vdbTqQsu9mYVgcfb/77JZYsPaknKfLay8/jbG/nw3diq5MDgQBs/gi9pRJOuIr7XvmE5r/dxOnf+xnjK2b1eq2dNdX89Prv8Ys7f8/MOfsMaf83Xnkxl1x5HQcdsgCA9Wu/orRsfNySLAAYjLTtdxkTP7uN0g1/wTn3v/rt3y7EYFo8IYpyLBgM8juU6aKqRjiaPp/nifbaP5/nwIMPY9KUqQB8/skHfPbxB/xjxSf89lf/vdf2UXsZYVXnnk9C3LAo9pnuDulML9z1b0dRFAwGI+1hMyUEyGldS07r2t6vYy1C3xZg33mzKd70JMGiueSZy+no6EjguxXppruiRbX3XdGiKJJoEWMjx2LEExx6m6Duipb6JFe0ALinfgNHy2rya9/K6ESLrsfmdEwpTmxyo9UbGtJxhCsQIRBWR1QRJYam1ROidYxbhu2p2R0kd1xuUmMYjZ3OwIDVWUJkq0hUp0MzUrJqVbJDESLpJNEi4s5qTq+T+I/fW8GZ513Y8/NjL76OGt11cvj7O37JgQcv4OSzzufTWi93Xnk19tmL+NIwi0v/9AEn7Duecw+fSa5J59rLzuf4k07juBNPxe/zAmDPcaAoCl6PG6vNvleVy5x953H3rT/j5uV34Wxv44F7fjOk2S7D5ZxzLuNX34PNuRVHw8f4Jh0V932I7BBVdZrcwZ4VqCJz+aWapZfnn/wrP7jpFiBW3XL7T2/gF3f+nty8/D63901YyC0fmsi1KHz/0Nhnv8kA1q72TZoObYZSTAW5fHXmm0zJCWNzbsbWsQWbM3azuKsxhZzYIn6K279g0sexuSzTNJ1w0M/0/1xGqHgfgsVdVTCFs9BNtjH42xCpxhiMJd6itr5ntEiSRYyV4SZaygtjn1nN7iCqpmNM4kIOT8VSylf9FkfDJxgiPjSzI2mxJFqnP8K4vMRVtURUbVgtyprcQaaXZu7fd7Jomk59ZyAlBlb707iqpdkdTIm/QyFSVas3RLHD0lN9L0S2Sr9vOJHyEl2CHk/BQICv16zm17+7t+e+CeWTem2Tk+OgqLiYCePLyP/qc4KttdBai3ftmwBsBV79wV+YY2qnatsWqrZt4cWnH+95/uufrGXSlKmcc+KR3Pzruzj+5NN7vf4V19xI4846rrzgLIpLSjnvosu54tob4/5eNWsBnXPOpWTj45Ru+KskWsSotHvD6MDEApscTGWwYJbPZ9ndnm0mH773buYddAhLTjip3+es/OgD/vSZh08vt2Myxv6dFNsV1rdqdHfVqVnwS/x/+CFmq51w4RTChTNxTz+t5zWUaBBrZyWWL39BjSWEZ3IZVucW/O31WIyQX/8+1L/fs72uGAjlT4+1HSuaS7B4LsGifQjnTwWDHPZlMlOgDRgo0SKf1WJsDLcqoTTXisVoIKxqtHiClBckbyFHqHA2obwKrJ5acus/xD3t5KTFMhZa3Ilr2dXkCg5rFog3GE3bi/CpKhzVqO3wEQinTnVyOla1OH3hMZ1rJEQ6ikR1aTEuBJJoEQlgM6VPosVmt7N6e8uA29zxxwd7/nz8yaezrq4TiPVoXV3j5IUvd7Khwc02ytj3l69z5ZKZHDd33F4Xn99Y+XWfr282m7n1nvu59Z77R/dmhqBtv0sp2fg4ebUrsLirCedPS/g+Rebq8IaJqhpTinKkjViG8kuipUd/bSaPnFcBxNpM/uff/+TrNV/yizt/z86aam659jL+71QL+40zErUWYgp1smCSkT9/FaGJEv4z5UdMKDqEcChMQWFRn/vVTTaCpfsze8m3eP2VF6k+OZbIX/XuaxSXXE/d4l+R49qGtWMLNudmTKFObK7t2FzbKah+ved1NKOVUOGsWPKlaC6hrgRMxFEurSQzhKm7osUuFS0iuXIswzvFNCgK5QU2ajr8NHQmN9GCouCZ+g2s6x8lr+atjE+0uAIRggmY1eIPR0e0+r/JFWRWWXpdhE9V3lCU2nb/sOYljYV0q2rxhqIp0dZQiFSnBPzkLT4Q1QDa+g2Y8+SzXGSn9Ph2E2nFYjJgMICWOgtnEkJRFA6bVsxh04rZ3Ojm0Y93sLnJwx9XbGVVdQdXHzuTPJt58BcaQ+HCmXgmH0fezncp2fg4jYt+neyQRJpzB6JUqT6mleRgkot4GUcSLbsMtc3ksvMuIBgIcO2l53HmPmbOnKuyc+LJ1B99N6XuDdR8uY66F27n6PXf4plLv8Pdv/oxhx99DEZj7CJXf20mjz3xVO78xU/4/JMPOGThETz22F858JCFdOxzAZ3GrsM5XccUaMHWsQWrc2usDZlzCzbnVgzRAPb2DdjbN/R6XdWcR7BoDsHifQh1JWGCxXNRbX3P+RCpq2dGSz8VLRaTfEaLsWE0KFjNhmHNbJxYaKemw099Z4BDp/adeB4r7oqllK5/lPy6d6jXVDCkzyKykWh2B5laEt+WXQ2dwRE9LxBWcQUiFNhT6xwq3bR6QjS7h1dRNJbSpaolGFGpafel7N+jEClF17HsrAVgU6ObnCAU2M0U2M1ynUBkFUm0iISwmowEsugC3T7l+fz27AN5YXUdf19Vx0eVbWxqdPOjpXOYP6Uw2eH10jbvMvJ2vkvRludoPuQmNEvqH+SK1BYIq1S1+ZhakoM1jSraxMDCUS3lVkEmy3DaTBYVl/DOG/+mqnIrVZXwl5UAzwHP8fC/V/Lb2v3JP+lH7Hz195z8weNoqspfnn+153X6azNZVFzCTb+8g6u+8y3y8vKx2XO46ze39w5UUYjmjMebMx7v5CW77tc1zJ66WNKlZ/7LZqydVRgjHhwtq3G0rO71UhH7uF6VL8GiuYSKZmf0vIJ0ZwzEEi0yo0WkArvZOOxEC0BjCqwc901YiGrJxxRsJ6d1Df7xhyY7pIRyB6JxHUTv9IVHdR7Y4g5KomWEUmkey0DSoaolompUt/syfvGoEIniC6n4QiqNriAOq4lCu5l8uzmpc9iEGAup+80m0prNbMiqRMvLzz3FL2+6ptd9NcANn/6IC79zMRcvnobFZOCq73yLU844m2W7rYrudHbw7dOO5dFn/8WkKVN77n/3zVf5n9t+RlP9TvY9YD53/OH/mDF77qCxXHvZ+Xyw4s2enw8/6hj+9PeXe372Tl5CsGAmNtd2irY9T/u8y0bxzoWICUU0qlp9TCtxxO1EXSRXNn2GD2a4bSZPPWwq1y8vQtFVak54sGfmyi3/WIemw0mnfZNLbrmQjeu+4qDDDqe4pLTnuf21mQT49iXf5Yglx1NVuYVDFiyibfvaob0BxUAkfyqR/Kl4pp646241jMW1I1b50pWAsTq3YPXUYg60Yg60ktfwUa+XCuVV7Fb5EkvAhAumoxstQ4tFJIQSDWKM+gGI2vuuRpIZLWIs5ViMw7rYO7HQBkCDK/mJFgxmPJOPpbDqFfJq38r4RAvEKgymxWEQvabpNLlHVs3SLRjRcPrCFDnke2U4UnEey0BSuapF03Rq2n1EorLgSIjR0vWuGVzBKEpngFyricIcM/k2s7QfFxlJEi0iIWJ9flN7JU08nXrmuRx30q7hxU6Xm3NPXoJ1yv68vLaBNXWdHBpdzyfvv80pZ5y9a7uOdq677Ns01NX2er266h388qZr+OVdf+SwRUdy169uZvnN1/PES28ymE3r1vLiW58wvnwiAKY92s+gGGifdxmTPvkFJRseo32/S0CRVa5i9KKqzvZWL1NLclKubZ4YPn8kOvhGYi+KGmby+zeh6Cqd00/vSbJ4g1E2NLgBuHTxNMrybT2f08NRMX0GFdNnoKlR2kYZq260ECqOVa24Zu663xDxYXVu21X50pWEMQdasXpqsXpqya99a9frKCZChTN75r8Ei2P/jeRNke+XMdLdNkwzWNDMeX1uIxUtYiwNd07LpK6KllSZheCuWEph1Svk166gecFPkx1OwnmCUfzh6LD/v+2pxRMiqo7+4nSzJ0hhjnmvmZeib6k6j2UgqVzVUuf0p03CSoh0ouux7xtPMIqiBMi3xVqL5dlMknQRGSP1vtVERrDHeaBiqjNbLJgtu1ZdPfvEo5x+xpmc8p3j+d+3t1FV38zHj/6KkknT2f2Q7eZrLufkM85m3Zerer1eVeUWrv/vX3HSN88C4LyLLucHF50zaBxNjfXo6MzeZ78Bt+uc/S0mfPE/WN07yNv5Hp4pxw/9zQoxAF2HmnY/kwrtshIxzUlFy8iMW3MfdudmorZiGo7Y1drr6/pOdGBykZ2yfFvyAhwCzewgUDafQNn8Xvcbgx29Kl9sHZuxObdijHi6kjJber+OyR6b/9LTemwOweK5RO1lIBfP4soUiKXdVFtxv3+3Fkm0iDFkMxtQFIY822BiQSzR0uoJEVG1pCcGPVOORVeM2JxbMbtriORPHfxJaa7ZHWJ66cgvD4SiKm3eUFxiiUR12n1hSnOtcXm9TJbq81gGkopVLQ2dAdwBWWwkRKLpOrgCEVyBCAYDsaRLjpk8q2nYSXZd11E1nagW+6+q66hq7OdwJLYAvMMXJteuYDUZZGaMSChJtIiEsGVZomV3oWCQp/7yEE+9soJJU4q5/4JDuOiSS7DPXkwwGuYfX+7kyFOClOXZ+PVv72Xy1Gn8z/Jber3GMUtP7vVz9fZKKqbNGHTf69esRlVVli7YD7erk2OXnswv7vwD+YWFvbbTzA465pzPuPV/omT9XyTRIuJK12GnM0BE1VL+grLon18SLcNma/uasjX3A1B/xB2o9l2zMtbtdAFw0OTCZIQWF6qtGN/ExfgmLt51p65j9jX2qnyxObdg7azEEA2Q07qWnNbeLc6i1qLd5r903+agWQvG+B1lDmOwA4Cove/5LEaDIisFxZhSFAW7xYg/NLTvksIcM3azkUBEpckVZEpxToIjHJhmLcQ3YSG5jSvJr11B+/5XJDWeseANRvGFojhGWGHQ5Irvxf4Wd4jiHIt8dvUjXeaxDMQfUvEEIylTCd/mDdHuDSc7DCGyjqZBpz9Cpz+C0aCQbzeRbzejQO8Eiqbv9XNU0wacpaSpscRpkyuIwRv7s9GgYDMbsJqNWE0GbF3/TfYiD5EZJNEiEsJoUDAZlbiUjqeb1/75PAcefFjPvJUtX60kWLOW/77vZe6+9afsdAa4/u9fcdUxMzl27rRBXy8SDvP4w/dx0XevHnTbmqrt7HfAfG76xe0YDAZ+edM13Pu72/jlXX/Ya9v2eZdQuuFR8uo/wOrcRqho9rDfqxADaXaHiGg6Ewts0vohzQQjalqujEwmRQ0z+YMfd7UMOw33jN7D7NfWxxItB07OsGSCohDJnUgkd2LvpL0WxeKu6TX/xebcgsVdjSnkJLfpU3KbPu31UmHHxK75L3N65r+ECmehmyRhO5ju1mFRW9+JFotJPoPF2MsZRqJFURQmFtrY3uqjwRVIeqIFwFOxNKsSLRCrMJgxggoDbyga9yoAVdNp84Zk0U4f0m0ey0AaXUFsZmPSL3C6AhEaO0c3X0iIrKYoBOfs0/PnkVI1HacvgtOXuCSyqun4Qiq+PY5RjAYFq9nQK/liNRmxmCQBI4ZOEi0iYWxmI141+8pun3/yr/zgpliFSigY5Paf3sAv7vwDSxbO4v1pxTTm2PCGVX7/1lZWVTv5wTEzB3y9++6+gxyHg29deOmg+77imhu44poben6+4Zbl3HTVJX0mWiJ5FbgrvkFBzZuUbHyMhiN/M7w3KsQQdHjDRFWNKUU5siIxjUjbsOEbt+Y+7B2b9moZBrFS9boOPwpwwKQMS7T0x2AiXDiTcOHMnjk1EBvabu2sjCVeulqPWZ1bsPgaem55O9/t2V5XDITyp8fajvXMf9mHcP5UMMhhbDdTYOBES7IvYInsFJv3MfTV4ZMK7Wxv9VHvDMD0xMU1VO6KpZR/djuOxs8whN1olvxkh5RwvhHMzdB1ncYEzdZp9YYodlikzctu0nEey0BCEY2qVh/TSx1Ju5jpD0ep6/AnZd9CZArdnsO2tz8dfMMUpmo6/pDatUhkV6LHYACrabfqF7MBmyRgRD/kDFUkjM1swJtli0Jqd1RRW13FoqOOBeDhe+9m3kGHsOSEk4DYyr7zDptCcHoFz6yq5YNtrWxsdPX7eis/eJfnn/wrT778FuY9h9oPQV5BAc6OdsKhEBbr3j2O2+ddTkHNmxRte4Gmw36CZi0c9j6EGIw7EKVK9TGtJEdOlNOEPyKJluGwta2nbM0DADQccTuqvbTX4+t2dgIwY5wjZdpjJItushEs3Z9g6f697jeEXNicW3e1HuvYgs25GVOoE5trOzbXdgqqX+/ZXjNaCRXO6mk9FupKwEQc5Vk5/6W7okXtp3WYJFpEMuRYhtdKuLwwNqelwZUaJxDhgukEC2Zhc1WSV/cerplnJDukMTHcuRkdvjDBSGIqKzQtlmwp75rhk+3SeR7LQMJRjao2L9NLHVhNY9uCPBzVqGn3Z9zfqRAifjQtthAxthhxVwJGUWLXPa2mruTLblUwIntJokUkjC0LP1ze/PdLLFl6Uk9S5LWXn8fZ3s6R8yoACAQC/Off/+SMcy/gf65fzu/f2kJj18nkc1/s5NqJU3ouhuysqean13+PX9z5e2Z2l2AO4sYrL+aSK6/joEMWALB+7VeUlo3vM8kC4CtfRKB4X+wdmyje8ixtB145qvcvRH8CYZWqNh9TS3LkwCMNBMLZV404UrGWYTeh6FFc00/FNeObe23TPZ/lwDSez5JomrUA/4QF+Ccs2HWnrmMKtGDr2ILVuTXWhsy5BZtzK4ZoAHv7BuztG3q9jmrO62k9Fuqe/1I8NzYkPoMZeypa+n6fkmgRyWA2GjCbFCLRoV3BnNh1Mb0hQdURI+GpOAHb15Xk1b6dNYmW4czNUDWdZncoofG0e8OUOKxZvXI4E+axDCYS1XsqW8Zq3quq6VS3+7Ky3bkQYvR0HQJhba82jr0SMKZds2CsJoO0VM8CkmgRCTNWB0ip5OP3VnDmeRf2/PzYi6+jRnddsPz9Hb/kwIMXsOy8CygqzuPe8w/m0Y938Ajwn41NVAXXcNM35jLBYeDay87n+JNO47gTT8Xv8wJgz3GgKApejxurzb5Xlcucfedx960/4+bld+Fsb+OBe37D+RcN0FNaUWifdxmTP7yZko2P07b/FdKKRSRMd2uAaSUO7MNc5SrGjq7rCVuZmonGrbk/1jLMWkT9EXf0uc3aroqWgyTRMjyKQjRnPN6c8XgnL9l1v65h9tR1tR/rnv+yGWtnFcaIB0fLahwtq3u9VMQ+rlflS7BoLqGi2Whmxxi/qcQYdEaLJFpEkuSYTbiiQ7s4PKkw9RIt7qlLGff1w+TtfAe0aNYcJze7Q0NKtDS7gwlvYaXr0OIJMrko+XN7kiGT5rEMJqruSrYk+lxB13Vq2n2E5JhXiLhQAn5mnR6b11j573fQ7dn5mQ0DJ2C6K152r4SRBExmyY4jRZEUNrMBRSFrynCDgQBfr1nNr393b899E8on9domJ8dBUXExRcWxCyF2i5Frj5vFI0Ce1UR1u58bnlvDQmU7Vdu2ULVtCy8+/XjP81//ZC2TpkzlnBOP5OZf38XxJ/cetnzFNTfSuLOOKy84i+KSUs676HKuuPbGAePunHkmEz6/C4t3J/m1K3BPO3mUfxNC9C+q6mxv9TK1JCftWijpuo6q6Wg6aF1/VnUdXQO162ddPJ3EewABAABJREFU13v+rGmx7XSgJNdCfpq830BEzZrP7dGytW+gbM39QN8twwCa3EFaPCGMBoX9yjO/v/+YUAxE8qcSyZ+KZ+qJu+5Ww1hcO2KVL7slYCyeOsyBVsyBVvIaPur1UqG8it0qX2IJmHDBdHSjZazf1aiYgh0ARPtrHWaSkzeRHHaLEVdgaImWiYWxoeftvjDBiJoSi7b8ZYcStRZhCjlxNH+Br3xRskMaE4GwijsYGfDYJRhR6fANfQbPaHT6I5TmpsbvRKJFVI1ARCUYVglEYjNztCzKBaiaTlWbl2klDhzDmBU0XDudgb0GYQshRkHXsW3d3PNnsTddh2BEIxjRcO22pkRRwNJV8dLdfqz7v5KAST+SaBEJoygKFpMha1aJ2Ox2Vm9vGXCbO/74YJ/3r6vrxOkPc98721hV7eQTZrD85fWcfcjkPrd/Y+XXfd5vNpu59Z77ufWe+4cct26y0bHPhZStvZ+S9X+RRItIOF2HmnY/kwrtFDkSfzFT1boTJHpPgqQ7CaLqOlpXwkTTY60ZehIouo6q0fPc0RwveoNR8mwmJhTYUv4igT8sJ51DoahhJr/f1TJs2il9tgwDWFvXCcCc8XlSyZVgutFCqDhWteKauet+Q8SH1bltV+VLVxLGHGjF6qnF6qklv/atXa+jmAgVzuyZ/xIsjv03kjcFlNSsDDF1tQ5TpaJFpJjhzGnJs5nJs5nwBKM0ugJMLx36nJCEMZjwTDmOosp/kFe7ImsSLQAt7tCAiZZG19jNCtH1WPXM1JLMqELsFo52JVUisd7/gYgqbayIzUPY0eZjWqmD3AQkW5rdwYxuwyaESC+6Huv+EYpouAO7OuIoSqwN6+5tyLoTMAaDJGBSlSRaRELZTMasSbSMVlGOhV+eth8vflnP4yureWZVHcfNLRuTC9Ht+13EuHUPktv0Kbb2DQRL5iV8nyK76XpsJVlE1SjLt+31eHdyY1eCpOu+nqRILFnSnSjpa9tYhUkS3lw/PMEo3pCXYoeF8fk2jCl6cBSQRMuQjFv7APaOjUStRTQccUe/A9i757McNLlgLMMTu9HMDgJl8wmUze91vzHQjs25FZtzC9aeKpitGCOerqTMlt6vY7LH5r/0tB6bQ7B4LlF7Wb///8eKcYDWYYoCJkm0iCSxm43DqnCfWGBnS9BDQ2cwNRItgLtiKUWV/yC/dgVNh/8i2eGMmUBYxRWIUGDfO9niDkbwBsd2nps7EMUfjpJjSc9LGN1Jle6ESiCsJrztWjrTdahu81FRkhPXqnCnL0xLgucKCSFEPOh67LsjHNWA3t+5ZpOCrav1mN1spMBuluqXFJGeRykibdjMhl4lcWJgiqJw9iGT+GR7G9tavDz5WQ3XHT874fuNOspxTT+Vwqp/UbLhr9QvuSfh+xQCYj3AXYEIOruSK5ncHkHXY0NdO/0RxudbKXZYUu6AKBCRRMtgbO0bKfvqPiDWMiyaM67P7XRdZ119JwAHynyWlKPaS/DZF+ObuHjXnbqO2dcQS770tB/bgrWzEkM0QE7rWnJa1/Z6nai1aLf5L923OWjWsUmuKdEAxqg/FksfrcPMkmQRSWQwKNjMhiHPl5hUaGdLs4f6FJrT4p18DJrBjNVVhaVzO+HCmYM/KUO0eoJ7JVp0XaexM5iUeJpcQWaMS40E3EBCUZVguCuxIkmVEdN1qG33M6U4p8+E33B5Q9GU+mwRQoiRikR1ItEonq6vY6ctQkVxTsou5swmkmgRCWVN8RY5qcigKHzv6Bnc/OI63trYzGkHlI/JCUX7vMsprPoXhdtfpmnBLaj99HkXIt6ycfC6quk0dAbp8IUpL7QnpC3CSKiaLlWIg9EiTP5g8JZhALUdfjr9ESwmA/tMyBvDIMWIKQqR3ElEcifhmXL8rvu1KBZ3zR7zX7ZgcVdjCjnJbfqU3KZPe71U2DGxa/7LnJ75L6HCWeimvav4RqO7bZhmsKCZ9z5esJgk0SKSy24xEQgPbZZH95yWhhS6GKpZ8vCVLyKv/kPya1fQlkWJlkBYw+WPUJCz6yJ3mzfctbp27PlCKp5gJKXm/AW7W3/tVq2SyYuGxpquQ12HH73ITmHOyDs9BCMqNe2+lKp2F0KIePEGoz2zcK0muQ6bTKlxZUdkLJtZTu5HYt/yfJbMLuWDbW386cMq7jzrgISveveXHYJ/3EHktK6lePPTtB58XUL3J4SIJZl2tPrIt8fmtyT7oEiqWQZXtuYB7O0bBm0ZBrvahu1Xni9VBenOYCJcOJNw4Uzc00/ruVuJBrF2VsYSLx2bsTm3YnVuweJr6Lnl7Xy3Z3tdMRDKnx5rO9Yz/2UfwvlTwTCCw3JNJW/ne7E/mh2ga6D0/hwxG2Vlm0iuHLORjiFuO7HQDqRWogXAU7GUvPoPyatdQduBVyY7nDHV4gn2JFoiqkaLJznVLN2a3cGkJVp2T6r4w7E/S1Il8WLJlgCaDsUjaKsdUTWq233y/0oIkdFCEY3KFi//n73zDo+i6uLwuzV903snlRB6770q1YLyiQ0LgoKAoCgdFBSkKgiIIr0KSO+9SicEQklCei+bns3ufn8sWYgpBEhIgHmfZx/YuXPvnJndzM6955zfcbeunPpWAuVDuPIClYqB9PF0mQUe8F5zD86EphAUo+RMaDLNvWwq94AiEcm1PsT4yHCsb6wgse5gEFefaDEBgRcZZU4BGbmZWJvKsTOruvot2fnPVm/9eUMnGTYfKFsyrJArUWkA1BHqs7ywaKWG5NoEkmsTWGS7OC9dX//lgQzZTaR5aRim38Uw/S7m4bv1+2skBuRZeOulx/LuO2BUJo6lOvMUYbtxPDMJeVYsANK8VPzWtyC22SSUnt31+8kFJ59AFWMkL38Qgd7Rkl61i/n/RenWCafTEzGJ/xdJbipqQ8uqNumZkat6kNUSr8yt8sXqkrJsKhqtVktegeZBPZX72SrCnLZqiU7NQaPVYmNqUO4+Go2We8lZqAqED09AoFIRich3cdX/X6Bq0Gh09a0czQ2xfox7pUDFIThaBCodQ5lEKK78BNgpDOlb35n15yP542Q4jTysKj0iOt3zFRzOTkOWHY952C7SvXpX6vEEBAQeoNVCUkZh/RbDJ4rYe1qEe/V/0KgxiTuHNCeBAkMrHM/+oJMMc+9WpmQY6GTYgqJ1GS11hfosLx0aA3OyHRqT7dD4wUatFmlOAoYpIRik3tLJkKWGYJh6C3FBDkbJ1zFKvl5kHLXMTC89lldY/8XKD5PYs7gdHAwUXTiSZcXhdnAwER1/0ztbhGwqgarGUCZBLKZcC/RO5jpHS3qOisy8gmoTkakycyXH0h+j1JuYRR4mzadfVZv0TInPyEUmFZGapapqUwCdPQojaYVk/Bc6VbIfKlKfqxKcKtWV2LRcNFotdmblk+GMTM0ud40oAQGBJ0drZEzI6WtVbYYAunWFmLRccgs0OJkbVruasC861ePJVeCFRlcAU1i8exJea+DC/uB44pS5bL8SQ78GLpV6PK1ETkrNgdhfnI3N9T+rv6Pl4UVQIzuyHJqAWNCjFHi+KVBriU7NISUrD0dzI0ye4SKTIB32gP9mCxSilhoT07JsyTCAu4mZZOWrMZFL8HoOCvcKPANEIgqM7ck0tifTpc2D7VoNsozI+/JjhfVfbmKQFopElYFJwgVMEi4UGUqLGNDy32+hCC1aRDiemYTSvQuIJciEGi0C1QBjuZTM3EdnTRrJJVgZy0nJzicmLQdf++pT3yrDrZPO0RJx4KVztOSpNIQnZVe1GXryVBpSs1WPHZSi1WrJVRUtUi84VZ4/4tPz0GjAwbxsZ0tMWg7KHCFbW0BA4OUkJTOfPJUaNytjpELg1TNDcLQIVDqGMglQPaKfnjeM5BLebe7O3IO3WfdvJO397bB8iiKA5SHF/3/YXv4F44SLGCVcJseuXqUe70kpaRE038SxmGSKgMDzSk6+htDELMyNZDiYG1Z6QWuVWiPIKtxHEba7xGwBAHFBNsbxFx55nymszxLobF5lUnACzwkiMSqFOyqFOxnuXR5sVucjTw/TZb485ICRZ0QiovToXBFa5FmxmMSdI8upuVCjRaBaYCKXlMvRAuBoYVgtHS1K987YXfkFs6gjiNT5aCXPPvO0KlFrqtczQkJGLhZGMsSl/MZqNFpyCx4UqNfVV9EITpUXhMSMPDRarV5u8L8kZeaRnJn/jK0SEBAQqF5k5am5m5iFu7Xx/bVZgcpGcGkJVDrCH/PT0d7fDm9bU3JUalafjaj04xUY25Lu1QsAm+t/VPrxnoTCRVDZfyLNCyVTFGG7S+kpIPD8kZ6j4lZ8xn1d9MpbHcgWMg91aNQ4nplESdkCOnTZAmjKvl5X9fVZLCrSOoGXCK1ETp6VH+levYlvPIZ7XZYR0v8kka1nlau/NCcBEGq0CFQP7BSG1LA1wdxI9kjpdufCOi1pOc/AsvKTY1sXlZEtElUmxnFnq9qclx5VgZbkLN1CukajJTu/gKTMPCJTsrkdn0FwrJK7CVnEpOWSmqUiJ19wsrxoJGfmE5VaPNMqPUdFbFr1qvMkIPCiI8rJweuV9ni90h5RTvX6/X7ZyS/QcDcxk4xcIQD+WSDMvAQqHUNBsuKpEItEfNTaE4D9wXGEJWVW+jGTan0AgHnoDqRZcZV+vMeijEVQ0f3o8/IsggoIPE9otZCgzONuYmalRZTmCrJhAJjEnUOeFVuKk6VotkBpqNQarscqAajrYl4JVgq8zKjMXMu1X4GRHVKJSNBlFqg2mBhIcbM2xs/BDDuFAdJSsq0KI9Rj0qvZQqlITIZrRwAU9/ZXsTECoMtqedipEpuWS1q2SshceYlIzVIRmZKN9v4HnpNfQGRK9ZG5ExB4adBqML56CeOrl0Ar1EWqbmg0cC85m8SMvKo25YVHWAEXqHSkEnGpEymB8lHLyZxW3jZotLD0eJj+QbKyyLWpTZZ9Y0TaAqxvrKrUYz0uFbEIKiDwvJKr0nAvOatS7gFCRgvIMqOxvLW+XPsWZguUREhcBvkFGiyMZLhZGVeUeQICAGQ5NCHfxLHUnCstIvJNHMlyaIJMyGYRqIbIJGLsFYb4O5jhZmWMsUHR7PdCR0t0NctoAVC6dwJAEXEQYSW/6tFoEJwqAqRlq4hO1TlmI1JzhO+DgICAQAlotRCXnktUanalrym+zAizL4FngiAf9vS838IDmUTEteh0zoSlVPrxkgI/BMDq5mpEBWVHFKalpnD5/FlSU5Ir3a6yFjefZD8BgeeNrDw1kSkVv/iUnf9yFguVK8OxubIIr2298F/XHMs7f5erX4GRXaltD2TDzIVsAoGKRywhttkkoHhuZ+H72GaTQCwRZMMEqjUikQhzYxletqb42JtiaaKTFXO6X+A6Ni2n2i0EZDq1QiMxQJ4ZiUHqrao2R0BA4D7K+5I4anX1umcICAgIVDdSs1SEJmVRoBYyjyoDYfYl8EwwlAlftafFXmFIn3rOAPx5MgxVJd8Ule5dyTdxQpqbzL6F31DH1aLYa9uG1ezetplXWtfnh3Ff0bVpILu3bdaPsXX9Kvp2bE7LWm6MGTqoiCNm+vjRRcZ6pVX9ctl18kYcNX/NxOanDGafLj3t0TjuX8T5lS+zJiBQFaTnqCo00jevQI3mJXrOMki9je2l+Xhv6Y7fhjY4/jsd48TLaBGRad8EtVxBadP0h7MFSuNKVDog1GcRqDyUnt2J6PgbKhOHIttVJg5EdPwNpWd3AGRSwdEn8HxgKJPgYmlMTUcF9d0tEAFZ+WrSc6qXnrhWZkymU0sAFBGCfJiAgICAgIDA80d2npo7iZmCfHglUCWr38uXL0ckEhV7LV++vMh+3bp1K7ItKCiIxo0bY2lpyejRo4tEOJXVJlD1GEqFjJaK4PWGLlgay4hNz2X7lZjKPZhYSnLAuwAMcrzBiWvhnAjSvfadu46llTU+NWsxY8IYlm/azYY9xxk/Yw5zp08E4MzxI8yY+A1jJv7Axr0nyMrM4MuP3tEPH3ztCr/+tUE/5oY9xx5pUkpyEp98NZm3AmWcHmTM6msqDocVjcIv/Mu3ubEC/3XNcDj7PdKs2Aq5JAIC1YmUzHwSlBWjX5/zosuGabUYJt/A7sLP+GzqiO/mjjhcmIVR8nW0IgkZTq2Ibvk9Nwf8S1jPTUS1nolOiLDsbIGSyFWpCYnPAKCu4GgRqESUnt0J6X+K0B7riWi/gNAe6wnpf0rvZAEE6TCB5w6JWISzhbFePiytmjlaADLcCuXDDlSxJQICAgICAgICT4aqQMvdxEx9RqBAxVAls68BAwaQmpqqf0VGRmJjY0ObNm30+6xevZq9e/fq3+fl5dGzZ08aNmzI+fPnCQ4O1jthymoTqB4I0mEVg7FcyrvNPABYfz6StOz8Sj1eqt8ANBJDzNNv4BH1N65JR3HMusH2jWvo2L0nVtY2jJk0HR//AAD8AgJRpusiuf/ZvJZ+bw2keZv2OLm4MfK7KVz69zRpqSkUFBRwJ+QGDZu2QGFugcLcAhNTs0fas3f1Lzgb5TOhjRxvawnj2xiy7NKDHwXdIqiIJP93yDX3RpKvxPbaYvzXtcTl8HAMk4Iq5ToJCFQV8co8UrKe/j7wQtZn0WoxSryC/b8z8N3YFp8tXbG/NA/DtNtoxDKUrh2Iaj2TG/+7SHiPNaTUHEiBsU4OrLzZAiVxPUaJWqPFzswAe4VBpZ6igABiCVlOzUn36k2WU/NiDkDB0SLwvFLD1gSAHJUaPwczbM0MkIirR4aW8r6jxSjhEtLsxCq2RkBAQEBAoPJ5Urn4J+2XEBfL5fNnycrMqLC26kR1sVOjgXtJ2SRkVEwApwBIq+KgcrkcuVyuf79w4UL69etHjRo1AEhJSWHUqFH4+fnp99m9ezfp6enMnj0bY2NjfvjhB4YOHcoHH3xQZltJ5OXlkZf3QHJIqVQCoFKpUKkET15lIEGLVlMgFKarANr5WrHjqgl3k7JYdSacIW1rVNqxNDJTsuwaYRZ7AuczkwDILdCyflE2m5fOwszege69+qFRF6BSqVj+23w6dH0FjbqA1ORk/GoGolHrMk5E93NNREDI9StotRre6NqahLhYGjZtwfjps3F0dinVFrnyHtGHltHBQ0S2XUOSag6kfsH3fHswXL+PysSBmCbjSffoBloNZlGHsQtahmncGSzvbsHy7hYyHJuTGPgxGc5t4aHaCYV2Fv4rIPC8EJWcgVZjhMJQ9sRjZObkoVG/AM4WrQbjhEuY39uDxb09yDOj9U0aiZwM57akeXRH6dIBjYHiQb8S/u7T3DqT5tIBk/h/keUkoDKyI8u+sW4hu4z7xJVIXQ2tOs4KtBp1qRJkT4twzxIoD2KNGuHR9uWicC7zvM9p3Cx1GS2hCRmItGqsjSVYGhqizFWRmJFf6RK6ZZFvaEO2dSDGyUGY3ttPiu+bVWaLgICADuG5SEDg6di2cS0TR39RbPvkmQswMDDg+3GjcXJx417oHSb9NI9uvfoBsHXDGlb+vpCE2Bh6yOXMNTZBqy5Aoy5gzz9/l9qvLFYs/ZWlC2bj4ORMbHQkc5euolGzlk/U1qBxU+DBvUGlUvH2qx34etJ0GjdvhVarZfniBfy9biUZ6el0ebUPI8ZOxMjYpIhNX3/+EZbWNnwzeYZ+2/kzJ/n+u69ITUlm0NAvGfjREIBHjlnaOSya8yOL580sdj2Wrt1K4+atGDZoAMcO7tNvb9qyDYtXl6++6KOITSkgOycfJwtDocZoCTzOc3WVOFoeJjc3l3nz5nH27Fn9tlGjRtG3b19ych7oz1+5coVmzZphbGwMQJ06dQgODn5kW0lMnz6dyZMnF9u+b98+/RgCAtWZV+xhfpKUfcHx1JXG4Gzy6D5PgmPav5jGniiybc01Fc2cRbS8OZF/85KItWhMWFgY48ePRyqV8ssvvxB++QQO1ubs3bqeVg0DEYlErFixAh8fH5JDr3Lu6FFcnJ35+OOPUSgULF26lO+++JAJEyaUaIdhfgqtbk8jMzsHbw9rjjp8jCrDhNzAaURkfcB598/IlVmQbOoHaWK4XGizATgOwdy8O94Ju3FKPYdZ7GnMYk+jNHTmrl03oiyboxE/cPxGXDtTORdTQKASCatqA6oSrQbrzFs4pZ3DMf0CRqpUfVOBWE68oh4xFo1IUNSlQGIEGcCNq495ECtQFkD86Ufuef62BBDhWBBH+OXKly0U7lkCZRFe1QYIVBn79z/f9UOyE0SAhNNBd9mlul3V5hTDQOaDP0FIgzYSnu1U1eYICAjcR3guEhB4MgI8HFi1apX+fW5uLiNHjsRUnM/kcd8xbepU3N3dOXz4MD9P/Q5/NzuuXLnCjOnT+eabb3BycmLx4sV0yM5m+q1LZGZm8sO4r0rsVxbR0dH88esc5s2dg5WVFevXr2fO1G/5/vvvn7gNHtwbNm7cyJ2QG8TduUa4ke55ad26dXz99deYmJgwZ84cvvnsXUaMGKG36dKlS5w5fphff/2V8PtrTenp6Qz77DN69+5N69atmTVrFhYGImrXrl3mmGXZ2bFlY1o3fvAZJCUlMXHiRIw1WYRfPsG1i/8yb948rK2tAZBKpXp7KoorFTrai0N2dna5961yR8uaNWto1qwZHh4eABw+fJiDBw8SFBTEsGHD9PsplUo8PT3170UiERKJhNTU1DLbLC0tix1z7NixjBw5ssjYrq6udOnSBYVCUWx/gYohI09FdGrOS1VwubLwAC7k3uLk3RT2JFsxpUXNivc6a9TU3Dim2ObfzuczuZ0BIKJewiYM2gzDvW5Lfvepy+wfJrJsxRrmLFnBMI9aDBv0P8aOm4DcwJCrF/9l2uyFeNRrhUe9Vrw3/Dv9mK61mvBqm4bYeNXF1KyohJgkNwXvXW9hmJ+EWG5Cdr1BODfqCoBarSZPpUbWYTQyoHTxsVYkMxBlZjQ2wX9hfWsditxo6kcsIzBhK0k13yPRpz9ht0Nwq90MsaTKb40CAo+NWAye1iYYPKZUY26+mtCkrEqy6jHQqEvOHilxXxWmsWewCN+DImIfstwHqehqmSlK146keXQjw7kNWqkRcqD0fLmKIyO3gKjT5wHo0KIxVibyR/R4cjTqAiKunRHuWQKlIhJBTUfhufZlQ6VSsX//fjp37oxM9uSZjlWN8a1EtoRfIk+moEePFkXaCtQabsVnVpFlOsQuFrB9C3ZZwXgGNkYrFaQiBQSqEuG5SECgYvn91zl0fqUPtZp34uspxrTt/QYALQ0s+X3ZMjzqteL3FWt4bcB79Hn/cwCsPWvxWueWWLgHYJiTw9dTfiyxX1moTUKYPOsXGnToBkDTxAzOX7qCR71WT9TmVruZ/t4QGXGPf7bvwMnFDQfv2njUa8XpH2bw4dCRdHnzQwA0xlZ888XHejtzc3NYNuxLRnw3hcBW3fR2rlr2G3YOToz5YT4ikYgvpKbs3/UPPQd+VuaYZZ3Df5kydgTvfvIFtVp2JT42BolURtve/3uCT7P8WJnIcTA3rNRjPI8UKmGVhyr/Bfrtt9/02SW5ubl8+umnLFq0qJjDQyqVYmBQ9AHW0NCQ7OzsMttKcrQYGBgU2x9AJpM91xOS6o6VTIaxgQH3krPJLxC8LU/L+y1rcC48lavRSs5HKmnqaV2h45vE/4s8O67ItjspGu6kaOhUQ4oILfKsWMwSL5Ll1JyAug2YNmcRXZvVJjMjE0trW1Zu3UdEWCh/LVlAhjKdV/r1RywpvnBqbmmFRqMhOTkJhcWDv1lxfgae+z/EMP0O+SaOSPzrkpKVr394zszMRCqTl/thWm3uTnzzCSQ2/BKrm+uwvv4H8qwYHC/Nxv7qQswsW5Dr6UyBlc9TXDkBgaojMj0fL1tT5NLy12XIyNdW+YRUEbYbxzOTkGc9yADJN3EkttkkfT0UkToP0+gTKMJ3o7i3D2lemn7fAgNzlG5dUHr2INO5FVqJ7jdedP/1rAiOS0MLuFoaYaN4NhmyYom0yj8/geqJgUwsPNe+xDzv8xpve3MA7qXkIJFIET9Un0UmA1NjFdl5VSd5mWdXF5WxA7LsOBQJZ8lw7VBltggICDxAeC56TtGoMYk7hzQngQIjO7IcmpQecCVQ6eTl5rLmzyWs/ucATq7uOLm6A7pgjhW/L6Rjt56IJVLSUlOpGVhX/zcnlemCzGRyA6xs7ErtVxY+/rXw8a8FQHZWJutWLKNjd12/J20D3b1h2ndfMWjoCE4c3o9YItGfg5OL24NzkMr1bQBLF8whLzcXqUzOv2dO0qRFG0QiEbdvBtOkZVskUt2zVp0GTVgw8/tHjvkoOwtJiIvl8N5d7D51BbFEyvVrl1FrNHRpVgdlehrtOnVj3A+zUVhYPMlHXCoSqfS5fn6sLB7nmlRphcw7d+5w584dOnXSFRScOnUqjRs35pVXXim2r5WVFYmJRYsNZmRkIJfLy2wTqF4YyiR425liaig8/DwtDgpD+tRzBmDZibAK16qW5iQU27bhuopXfWXIJLrJ7qGwAn6cPV/fLrn/4yB6aDJs6+DAgd3bGf71BCT3nSwzJ3/L3u1b9Ptcv3IJsViMg5OzfpuoIBf3fYMwTrpKgaEV4d1XE9CoNVcvndfvExJ8DXsHx8c+N41cQVKdTwjpf5yI9gvIsQ5ErM7FM+kQ/n93wm3/xxjHnUMoKiTwvFGg1hKenEXBY9wPsvOrVstaEbYbt4ODkWUVldmSZcXhdnAw9ud+wOXwcGquaoDHvg+wurUBaV4aBYbWJPsPIKzbKm787yLRbX8mw62j3slSFVyJSgegjotFldkgIFCITFKlj/kCAk+Fi6URUrGIHJWa+BIKtJobVfEigEiE0k03hzW7d6BqbREQEBB4jlGE7cZvfQtq7OqP2+EvqLGrP37rW6AI213Vpr207Nq6kTr1G+F831ECurWX9vV9OH3sMGMmTQfAL6A2R/bvRqvVIsrJ4ciAPjQyMUUhk5fZrzwcP7SPDg39SUpI4OMvvnrqtq0b1pCZoeTdTz4vsr9fQG0O7dupf79tw2patNYFT8RGR7Ly919x9fAkLjqK2d9PYMTH76DVasnMzMDZ7cH1MTEzIyEu9pFjluccADau+oPufV7H2MQUgHuhdwmoXY/fVm1m3c7DREdFMO/HKY++kALPnCqdgW3YsIFXX31V7xlas2YN27Ztw8LCAgsLC9asWcOQIUMYMmQIjRs35syZB3qb4eHh5OXlYWVlVWabQPVDIhbhaWOCjZngCHtaXm/ogoWxjNj0XHZerdhaAAVGxbUz99wpoL3Hg8gSfxsxa7YfYdPq5cTFRDF3xiSat+mAmcJcv8/aP5fg6eVDh26v6rf51arNgp+mcv7MSc6ePMr0CWPo/cYAjIx0EeCZ6Sk47v0E07gzqGVmhHVbSZ6FN+269ODSuTOcO3WMgoIC/lr8Cy3aPkUEoVhGuldv7vTZyZ1ua4hT1EOEFvN7e/Ha8Tpe//RGEboDNEJRRYHnhzyVhvDkbDSa8jkKc/KrLiIYjRrHM5MAbbHMExFaQIvd1d+wvLsFiSoDlbEdSQHvE9pjPTcGnCem1QwyXdqAuHpE3VyNSgOgrot52TsKCDwDCoMiBASeR2QSMa5WuufCsBLkLRWGVX/fz3DXOVoUEQeE4BwBAQGBJ+BRAVeCs6Vq2LjqT94Y+GGRbb41A1m6bhtevv5MGDUUgPc//QKVSkX/Hm15p/+rzImJZlhWJmg1ZfYrD83bdGDhio1IpRLmfD/hqdrS09NZ8NM0Js9cgFRaNOh72NfjCbp8kff6def1Li3Zs/1v3nr/YwC2bVyDtY0tS9Zs5ZPho/ljww7OnznJ6WOHkUqkRYL7DQwMyc3JfuSY5TkHtVrN5rUrePOdB5/BoKEjWLRyE95+Nanh48eIsZM4sGtbua+nwLOjSh0te/bsoX379vr3x48fJygoiMuXL3P58mV69erFlClTmDJlCm3atCE9PZ0VK1YAMGPGDDp16oREIimzTaD64mhuhKuVERVdWuRFJy01hcvnz5KakoyxXMq7zXRe9HX/RpCeo6qw42Q5NCHfxFG//Jmj0nI2Wk1z1wd/V05mYla978XqJXPp27E5uTk5/DBvsb5dmZ7Gn4vm8dX474uM3abHa3Ts3ovhHw1g/MihNG/Tnq+n/Khr1Kjp3642xw/vRyMxILzLH+Ta1AbA0sqaUeOnMfid1+jY0I87IcF8Mmz005+sSESWYzPOeo3kZt99pPi9jUZigHHiZdwPDcFvQ1usg5Yhzq9aPXABgfKSk68mIiUb7SMWfjQaLXlVKOVoEncOeVZsqfJehdvTPXpwt+ff3Hz7HLEtppDl1LzayQmkZOUTmZqDCAh0FhwtAlWPXMhoEXjO8bQxAUp2tMilYozkVfs7kOnYAo3UCFl2HIbJQVVqi4CAgMBzxyMDrtC1a6owKOwlJCIslIjwUJq1aldku0gkomZgXabNXsjhfbtQpqVhbmnJii17mbVwOb6+NfEHBvxnvJL6lQepVEqDJs35evKPbNu4+qnali1bRp/+/8O/Vp1ix3F2dWfrobNM/HEuDs4uNG/dnoZNdbXh4mNjaNqyLfL7pSdMTM1w8/QiOvIeCgtLUpOT9ONkZWbopdPKGrM85/DvqeNYWFpTw8ev1OtjZm5Oakoy+Xl5ZV5HgWdPlek35eTkcPbsWZYsWaLf5uJStFStqakpNjY22NjYALBkyRIGDBjA6NGjUavVHD16FNB9QUtrE6jeWBjLMZBKuJeShapAiAQDXVrh+BI8/VN//hW5gSHTvhuJs4sb4XfvMHnWL3Tp2Y8d12K5eng73Re+jzonkzoNGjHpp/k4u7qXOV7vN8sopCWW0GOrCQdPpes3dfSU4G8juf8opEUjMaS3bQQ935ORWP8rEut8hlbywKuvMLfg+LWwIsMeCUlgzoFbePv04ciVCUWlTbRanE6NJ2KoBK3IgHudFpPt2LRI/7fe+4gWbToQeieERs1aYmpWsYV+8yy8iW79I3GNRmMdvALrGyuQZ0bidGYy9hfnkOz/P5JrfUCBiUOFHldAoKLJyC0gKjVHHxFcEjkqdZUG4ZYkUVgS6Z7dybZvVMnWPB2F2SxetqaYVYNIawGBx6nVJCBQHfGw1jlawktwtAAojKRVmpWplRqS4dwG83t7UUQc0AcGCQgICAg8msKAq9IorAlrEndOF2Ql8EzYu2MLbTp11SsPnT15lBOHDzBq3FSgdLn4g/t3swwoDIEoT7+S2LllI0kJcbz36ReAbr1XfD/A7knbjh07xoWLF9mwchkA2VlZfP7+W3wyTFezRSQSYWJqxtnjR/lryx69LQ6Ozty9HaJ/r9FoSIiNwdHZFalUyu5/Nuvb/itrX9qYZdn58GfQsVvRkhojP32X9z79groNGgMQdOUSNnb2eieQQPWhyhwtRkZG5D3C87Z8+fIi7/v06cPt27c5f/48LVq0wNbWtlxtAtUbI7kEb1tT7qVkV2lRy+pCjz5v0L7rg5tqdnYW/bu1wadmLT575zWWb9qNj38A2zevY+70iXTv/Ro9PSQcO7YS+9e+Y2r/FuxYPp/xI4fwx8adpY7XsGnLR9py9U4Me5f/RL17S5BnxyO7v2ajMnEgttkksu3q4XziWxSRB7G/8DOKsF1Et/6JHNu6JY53+m4Scw7cQqOFW/GZrDh9j0GtPPXt9ud/xPrmKrSIiGw3t9TCom6eNXDzrFGey/nEqI1sSGg4ksS6Q7C8vQmboN8xSA/F7uoibK8tJc2rF0m1PyHXOqBS7RAQeBrSslXIJLk4mBuW2J5dlbJhlCxR+DT7VSVX9fVZhGwWgeqBUKNF4HnH07b0jBbQ1WmJT6/aSM4Mt06Y39uLWcQBEhqMqFJbBAQEBJ4nyhtwVd79BCqGk0cO0OehgFxPL1++/Ogd3D29aNW+EwtmTitRLt6jhjd9UpLL3S8zQ4mBoVGxIuOe3j5MHTsCFzcP/APrsPDn6XR5tc9TtS1evBiXgMaI76sejRk6iHcGfUbLdh31x10yfxadX+1NQO16+m1devblrR7t2L9rG7XrN2Ltn0vIz8+jfuOm5Ofn88O40Zw7dYwGTVqUKGtf0phl2fngMzjItDkLi2zzrVmLmZO/Zcyk6aQmJ/HrrO/pP3BQKZ+iQFXy3FUkd3Z2xtnZ+bHbBKo3UomYGjYmxKTnkpKZX9XmVCkyuRzZQ1qP61cso2P3nlhZ2zBm0nR8/HUL+34BgSjTdQt7mqQwnHxrI7b35p+7+fR5YwBjhnxY5ngu7h5l2hEXG40WLY4dPyFeMwiTuHNIcxKIN7Ijy6GJXrbnXpc/MA/9B6fTEzFKuYHXP71Jqv0J8Q1GopU+WNy9eC+Vn/aGoNFCLScF12OUbL0cTUN3S+q5WmBzZSF2V3Q/JtGtppPu1evpL2YFoJUaklLzHVL8B2AWcRCba0swjTuL5Z2/sbzzNxlOrUiq/QmZLm0RdPAEqiOJGXlIJSJsTItHu+SqqtbRIleGo4VSpcO0iFCZOOjuOdUYrVbLlfsZLXVcLKrUFgGBQgRHi8Dzjqd12Y4WA6kEQ5mYXFXVSWBmuHZAiwjjpGtIs+KEjGcBAQGBcvIiBVy9KOTm5HDt8gUm/jhPv83OwZFZi5Yzc8q3/DxtPC3adihRLv63pavgjVfK3e/1Li0ZM3F6kVq+AAG16zHuh5+ZOfU7MpTpdO7Rm68mfP/EbUduJbI00olXrUT0b+yKVCLGwMAAGzs7FOYWgE4ubfe2Tfx94FQRWzy9fJi58A9+mTmNsDu3cXH3ZN6yNZiYmmECell7MzMFRsbGTJ65QN+3tDHLOgeAyPAwEuNjCazboEi/QUNHEhsVyacD+mJlbcObAz9k0OcjH/mZCjx7RNpHCbi/BCiVSszNzUlPT0ehqFgZIoHHJzkzj9j0XKGmJJCXm0vX5rVZ/c8BnF3d9dtVKhUTR3+OWCRm2pxF3L11k/de64Gi7yRE5vY4XluJvaUp38/5rVzjlcSB3f8wdexIZDI5yvQ02nXqxrgfZqOwsChxf0lOMk5nJmFxV1eQK0/hSVTrn8h2bEpQdDoTt18nv0BDS28bRnfxY/Gxu+wOisPKWM6GRjfwPjcegNgm35JUZ/BTXLUnQ6MuIPzyCTzqtUIsKdsHbZR4BZtrSzEP24lIq1uozrX0Jan2J6R59UYrEdI3BaofrlZGWBjLi2wLicsgvypqtGg12P/7I3ZXF+neAiDS6zHrtuncLxEdf0Pp2f3Z2/gYxKXn8vHK80jEItZ93AxDWeXXDXice5bAy4dIpAtqEAkBAC8dKpWKXbt20aNHj2JRos8b0Wk5tJxxCJlExI0p3ZCW4DyMV+aSoKzarBavf/pgnHCR6JY/kFLznSq1RUDgZUV4LnoO0ajxW98CWSn1GgsDrkL6n6p2tRkFiiPKziLQTxf4HhQSjdbYpIot0gXDfbLyPHH3nxN87U0Z1dkPJwujCjtGRFhopcnaVwXWpvIKvT4vCo/jNxBC3QSqHdamBnjamCB5hHbjy8CurRupU79REadISPA12tf34fSxw4yZNB0AL19/ur7am6g/hxE5tz9XLp5n+Ngp5RqvNO6F3iWgdj1+W7WZdTsPEx0Vwbwfi49ZiNrImsj2CwjvvAyVsT0GyjC8dr6B8YExzNpxgfwCDY3cLRnV0QtF3Bm+crxGT8VdWuceosa5CQAk1B1aJU6WxyXHti6RHX4h5M3jJAV+hFpmgmHqLVyOfYXfuhbYXlqAJDe1qs0UEChCVGoOGbkq/fsCtaZKnCyighzcDn6md7LE1x9ORMffUP0nClhl4vBcOFkAfTaLn73ZM3GyCAg8CqlEJDhZBJ57HBWGGEjFqNRaYtJyS9zH3KjqnUlKt04AmEUcqGJLBAQEBJ4jxBIS6n9ZYlNh6FVss0mCk+U5QmNkjMao9Pqgz5pb8ZnEKfOQibWYyCXcis9k2LpL7AmKo6JyDtw8a9Cuc/cXwskiUDEIjhaBaomJgRRvO1OM5C/3V3Tjqj95Y+CHRbb51gxk6bptePn6M+F+kfsrF//l6P49LPt7H4Fjt2Lg34Z3+/ct9uNR0nilMWjoCBat3IS3X01q+PgxYuwkDuza9sh+Ge6dufXaAVL83gbAK3wd/4hH8b7tbWYEhBO4qRU1dvXH6/hwFuSPZ65sIWK0XLJ/jfhGY8plW3VBZeZCbLMJ3HzrDLFNvkVl7IAsJxGHCzPxX9cMp1PjkSvDq9pMAQEAtFq4l5ytLxycUwWyYdLsBGrs7I95+G40YhmRbeeQ0HAUSs8ehPQ/RWiP9US0X0Boj/WE9D/1XDhZAK7ed7TUFeqzCFQTBNkwgRcBsViEu7VuwSYsuWT5MEOZBLm0ar/vSrfOAJjGnESkyq5SWwQEBJ5P0lJTuHz+LKkP1biozH4JcbFcPn+WrMyMMtv+O35Z/Z7ELrOoI4gAjVjnNI/J0HAqsoDMPC3JAe8WmQuU1+bHsfNZU13tqgi0xiZcvxXD9Vsx1SKbBeDILV19nzpWWub1r0MdZ3PyCjT8euQO03beIC375S5bIFA5CDmVAtUWuVRMDRtTotNySMtWPbrDC0ZEWCgR4aE0a9WuyHaRSETNwLpMm72Qrs1qo0xLY+8/f9OtVz8aN27Ce8ZxLFAPJHrBAC5cvESjhg3KHK+8mJmbk5qSTH5eHnKDsqWxNAbmnAucxOYQH8ZrFuEmTmRSxkS0h4vvKxLpFoCXxbjSIy0HV8vqEwFRXjQG5iTVGUxS4CAsQndgc20pRslBWAf/hVXwCpQe3Uiq/THZ9o2q2lSBlxytVqd172Vnone4PCsMUm7ise8D5JnRFBhYcK/TUrIdmz7YQSwhy6n5M7WpItBqtVyN0tXLEuqzCFQX5IKjReAFwdPGhFvxmYQlZtLW17bEfRRGUpIyqm6xJM/Sl3xTV+SZkZjGnCDDvUuV2SIgIFA92bZhNePvB0k+zNSff0VuYMi070bi7OJG+N07TJ71C917vwbA9s3rWPDTNNLTUqnToBGTfpqvV6fYvW1zqf3K4q/FC1iyYBYOjs5EhIeSl1s8Y9DewYm0tFQkEjFuHjUIv3uHjt1f5dihffp+JiZm5ORkF7FLpVLRo2U9MpRp+n7tu77C9SsXUaan0bVnP0Z8NxljYxMU4XswD9+NViSl58l6xMfFcjs0HBdLQ6ISUpjQdQO/fruH1JRk6jZswsV/T+Pg6ExsdCTder3OuZNHUaan4V7Dm9DbITg4ORMbHUWPPq+z+5/N9/eNYv6yNdRt1JTOjWuSkpxU7FxNzcx4pW9/oiLDOHn4oH5701ZtsbS0xtLamrFTZ+q3nz99gqnfjiQ1OYmPPh/Ju598DujmBH/+Np+/1/xV7Fz/e90L7WrUvBULZ0/ntzk/FrNr2YbtNG7ems8/6M+xA3uL2LV07aMDYF92CtQajt/Wfd6NbLTYmhowtU8g2y5Hs+L0Pc6Fp/DF2kt80cGHJp5WVWytwIuEMAsTqNaIxSJcrYyxN3/5al7s3bGFNp266vW1z548ys/TxuvbJfe1Z0ViEWp1AclJiQB0qmmPm5kIdX4uu67FlDreoxj56btcufiv/n3QlUvY2Nk/0skCOr3scduusT/Xn8FmvxDj956+4HWJ+qsi+Fa8ktl7g1Gpq66g6VMjlpHm3Zc7fXYS2mMtStcOiNBiHr4br+398PqnD4qwnaCp2gLkAi83ao2W8KRsMvIKntkxTSOP4LW9H/LMaPLMa3C31z9FnSzPMREp2aTlqJBLxfg5mFW1OQICgJDRIvDi4GGjW6AKTy49U6TK5cNEIpRuHQFQ3BPkwwQEBIrTo88bnAgK17/2nbuOpZU1PjVrMWPCGJZv2s2GPccZP2MOc6dPBHRFsRf8NI25v69i66EzODm7Mn7kEEBXgLy0fmURHnqH5YsXsPXgWTbvP8UHg4dTt2ETTgSFs2bHYSysrDG3sGTeH2sQicCjhjcb9hzns5HfsHvbZrYePMvcpauRyeTYOzoWs+u3OT8SHxvN15N/ZMOe43Tr/Rp7t29h+vyl/LVlL0GXLzBt7AjE+UqcTo0DYIO6CyfOXSQsOoEtRy6y/lgwnzSz4Ovt8fRr4c2MX37nxJEDTPxxHpv3n6JR89Zs27Ca6fOX8sP8JVy/cpGmLduwef8per0xgL/XrdSf33uffM6vP//An4vmkZKcxPw/1nIiKJyvJ83AysYWM4U5S9ZuI+jyBc6fOsnm/af0n9Hb73/C2VPHGPrVd/rrl5KcxLBBA+je+zVWbtvPzq0bOXfqGAB/r1vJmj8WFzvXkq57oV0Ag4aMKPLd2LTvBJbWNvjXqgPAjatXitg1b9maivhKvvBcjkojPUeFuaEUPwud0otYJKJvfRdmv1kPdytj0nJUTN0ZzK+H75BbBWoPAi8mwixM4LnAzswQdxtjxC/RN/bkkQM0ad5a/97Ty5dNq5ezafVy4mKimDtjEs3bdMBMYU69Rs04uHs7K5f+yp5tm0jZ+j0SEwvOK025d19q4b/jFZKZoUSlKp4x5FuzFjMnf8vVS+c5emAPv876nv4DBz3S7pSsfMZvCyIpMx8XSyO+7dOIXK8eJTpYChEDTqJkbFIusurMvUceo9ojEpHl1JJ7XZfrZNR830IjlmOccBH3g5/hu7Et1tf/RKwqWQZDQKCyyS/QkJ33bB4mrYJX4LHvfSSqTDIdm3O351byzT2eybGfBVfuZ7PUclQIi9sC1QaZRKjPIvBiUOO+oyU0qfRnJmO5FJm0ar/zSnedfJhZ5EHQPsdBQwICApWCTC5HYW6hf23ftI6O3XtiZW3DmEnT8fEPAMAvIBBluu7Z8sb1q9Rp0IiA2vVwdHal95v/417oXQCyszJL7VcW6oICJv44D1t7XW3EmrXrkp2VicLcAiMjI1q160TnV3pjZW3DG+98oF8ncPOogUxugK29AzeuX8XbryYFBQVF7LoXdpc1yxdjYWWNi5su6+bm9atIpVJq12+Ip5cPQ0aN5fC+3Ticm4EsO4E0I3e++us0Az/6jEkzF2Br74BWakiUoT8SMfwQEIKDrRUfDh7O4b27AIgIu4uFpRW16zfE0cmFjz4fyenjRwBw96yBnb2D/vx8AwJJSoznryULcHJ1w8TUFIW5Bft3bcPFzYMPPhtOYN0GvP3BJ+Tn5+HjH4DC3AK53IBZU77jy28mojC30F+/nVs2YGNnz6fDx+Du6cXg4WPYsm4VANs3reX9wV8UO9eSrrtvQCDK9DQADAwNi3w31i5fysCPhmCmMCcuNhotWr1dCnMLfYZMdUOUm4v7e2/i/t6biErIknrWHA3RBSK38rbmv4/FnjYmzH6zHn3qOQGw53ocw9dd4lb8iyfpJvDsEVYEBJ4bFIYyvO1MMZC9+F/b3Jwcrl2+QN2GTfTb7BwcmbVoOauWLaJvx+bk5uTww7zFAHTr1Y8Phgxn1bJFjB81BE1eFh0//wmtWMrvJ8LIyckuNh7otFN7tW3E7q2bitkwaOhIvHz8+HRAX36aNJY3B37IoM9Hlml3eo6KcduCiE3PxV5hwLTegZgbyZDmJJTrvO1I4+9L0VyJTCvX/s8DeZa+RLf5iZC3ThNffzgFBpYYZETgdHoifuuaYf/vDKRZcVVtpoBAxaNR43hmMs6nxiHSakj1eZ3wbitRG1pUtWUVSmF9FkE2TKA6IavimhUCAhWFh/X9jJYyHC2gmydUJdkOTVHLzJDlJGKUeKVKbRF4eXjS+hwCVUtebi6r//iNQUNH4ODkwit93wRApVKxfPECOnZ7FQAvHz/OnTzGjaArZCjTWffX7zRr0w6gzH5l4eXrT7uOXTCJOY302no2LJ6p7+fi5sGpY4cYNHQECnMLbt8MpmP3nqhUKg7u3UHXV/sA4OziStD9tYWH7ZryzZd8/PkovH399XZF3QunbsPG+uOLxRIkIi3WN3XOiW9vBpCXm4ejsytGxsZotVqyszI5eCmMQEdD5LlJNFGd4a33P+ZG0BWyszKJjYqkQZPm+vOp27ApYomY7KxMDu/bRa83BgA6Z9Ta5UtQqVQMGjICJ2dXvR1JiQncvXWDAR98AkBEuM6B1alxAE18nXirR1tycrKRSKWcPXlUX/v2VnAQTVq0QSTSrdwH1mvIjSDdPT8tNQVHJ5ci5yq+H4Tl5etPu87di9jVsXvPYp9PQlwsh/bs0NsVdPkCarVab9eYIR+iTE3GJOY05ne3YRJzuvqoZWjUKA7tQ3FoX5XblJOv5nSo7r7Y1semxH3kUjGDWtVgWu9ArE3kxKTnMnrTFdaei0Ct0ZbYR0CgPAizMIHnCgOpBC9bU8wMX+zyQoZGRly4m4Cnt2+R7S3bdWTrobOcvhHJz7/9hZW17kdDJBLx2Yhv2HsmiIuhiWzYfYxRA7qRHXSAbZ+3oqmvE6r8fHq3b0IdVwu2bVjN7m2beaV1fWzs7Jn27Uh2b9usP872zet4pVV99mzfQmC9BixZu5XBX36NVCrl9s1g3n6lPS0D3fl52nj9Q0dWXgET/wkiMiUbaxM50/rUxtpUJzNWYGTH0fACav6aic1PGcw+nVfieXt41ABg9oFbKHNerLo8Bca2JDQcxc23zxDd4nvyFJ5I89Kxu7IQv/UtcTk6EoOUm1VtpoBAhSBWZeG+/yNsgpYBENdoDFFtfkYrkVexZRWLWqMlKFoXPVjXxbyKrREQeIBQo0XgRcHTVudoiUrNJr+g9EwRRRXLh2klcjJc2uhsiRDkwwQqhm0bVlPH1aLY6+G53A/jvqJr08Bic7kuTQNp6ufMx2/3JjrygWJAaXO5R3H+9Al6t29Cmzo1WLHklwo/15eJXVs3Uqd+I32tFYCQ4Gu0r+/D6WOHGTNpOqBbnO/8Sm/6d29Ly1ruXLt8nlHjphUZq6R+ZaEI243f+hbcnNePJn0+JT38CrPst6II2623K/R2CB0a+pOUkEDbTt2KjH/80D4G9e+FsYkZm1Yv19sVEFiXzAylvl5JZHgY7ev7kJ+fh7Wtnf7429avoLOXbi3nqvWrLFu7FVcPT+Kio5j9/QQG9ulCh4b+ZGdn07lbDwBsryzEzEBETFQEHRr6I5FKkcof3PO3bViNj2+A3uaPv/iK44f20aGhP6G3b6Ewt9DbVYhcJsfOwQljE1MAjuzbjZWNHb+t2syCP9YSdvc2IpFIb9eIj99Bq9WSmZmBs9uDz83EzIyEuFgA/AJqc2jfziJ2tWjdochxC+0qtPO/bFz1B937vK63617oXQJq1+O3VZtZt/MwcXev8tcnDaixqz9uh7+gxq7++K1vgSJs9yM/+5eJs2HJ5BVocDQ3xNfetMx967pa8MvbDWjtY4NGC2vORfD15qvEpuc8I2sFXjSEWZjAc4dELMLDxgQ7xctXt+VxcDQ34qvPPsTnq424Dl+H6/B1dJi8CTMLqyfWgs3Py+OLD98ioE491u04QujtELZtWE2uSs3k7de5m5iFuZGMqX0CcVAY6m2JlNWg1/pc3gqUcXqQMauvqTgc9qA+hBYR+SaOtO7YC2cLI1Ky8vnl8J1yP/g/T2ilRqQEDOTWG4cJ7/w7WfaNEWtUWN7ehO/fXfDY/Q6mUcd0VcsFBKo7GnWxiCppViw1dryOIvIgGokB9zosJLHe5yB68aSM7iZmkpWvxsRAQg3bsh/iBQSeJYKMncCLgq2pASZyCRqtriZWaZjIJUjEVfs7k+F2Xz7s3v4qtUPgxaGi63qUNpd7FGXVpRB4fDau+pM3Bn5YZJtvzUCWrtuGl68/E0YNBeDKxX85un8Pq/85yJmbUXTv9TpD332jyBy5pH6loQjbjdvBwciyYuniJWX3/4yRiuG7reG4HRzM38vm8sbAD2nepgMLV2xEKpWwY/P6IuM3b9OBkeOmkJ2VQbde/ThzM4r2nXswa9p4Jv00H6lU50Rxcfdg6bpt1Aysy7GDe3mvX3de79KSPTu2MbxePiojW5bctcfaxpYla7byyfDR/LFhB2F3bjFk5FjEYjEHg+LJNfdGmpeG8+1VqPLzWbhiI3YOjhw/uO/BmNv/ZshX3+ptnvP9BJq36cCMBUtJTkqghref3i4AtVpNcnIiqvx8/Ri3blxn1qI/8faryaXzZ7CytqFApdLbdf7MSU4fO4xUIkUufxA4ZmBgSG6O7rdp2NfjCbp8sYhdb73/cZHP4OFrO+f7CUXa1Go1m9eu4M13Hnw3Bg0dwaKVm/D2q0k9aShzGsey5aqySD9ZVhxuBwcLzpaHOHJLJxvW1tdWn31UFqaGUkZ38WNUZ1+M5RJC4jMYtu4S+4LjXsg1KYHKRZiFCTy32CsMcbMyfhHX7iqM7nXd+OOTdvRt6ouBiRkXD/6D1r0JKy+l8v6oSY+tBXvi8H4ylUq+mvA9rh6eDPt6PJvXrWTazmBuxGVgYiBhSq9auFoaF7Fj57bN2Ng5ML6NAd7WUia0MWDZJV3GivZ+9ZbYZpMwNJAzuqsfUrGI06HJ7AuOf1aX6tkjEpPh3oXQnpu50+sf0jxfRSsSYxZ9DM897+D9d1csbm1EpM6vaksFBEqkMCLu4Ygq/zWN8dncBaPk66gMbQh9ZT3KGo+WMXheuXJfNqy2s3mVL/AJCBQiFiN8HwVeGEQikT6rJawM+TCRSITCqGoz3jNc26MViTFKvYksI6pKbRF4Majouh4lzeW2rF/1SDvKqksh8HhEhIUSER5Ks1btimwXiUTUDKzLtNkLObxvF8q0NPb+8zfdevWjdv2GGJuY8sWYcURFhBMSfK3MfiWiUeN4ZhKFs2+pWEQrNynzuxmy/HI+d1LURNwLo1mL1kilUho0ac7Xk3/kn01rioyfnZlJ+J3bdOzek5NHDmBsYkpyUiJSibTIgnahXTMX/kFOdjajxk3B2daSTl4yWrtLiWkxhbjEFJq2bIvcQBdAa2JqhnsNb4yMjWnYrCVXL54nvqFOutzgwlLkcjkNmjRn/PQ5aLUaJv44FwdnF5q3bk+TFq31Nm/buBqpVMqef/6me6/XOH5ob5FL8e+p41jb2LHzxKUiYzRs2gKA+NgY6jRoTFpqCvl5eZiYmuHm6UV05D0UFpakJifpx8rKzEAq0zlenF3d2XrobIljFvLwtd22saiT899Tx7GwtKaGj1+pn5+FISRla8kreLD4L0L3f8czk6pcsqs6kJadz6WIVADa+do9Yu8HiEQi2vnZseCt+gQ6KchVaVhw6A4/7L5B+gumtiJQuQiOFoHnGnNjXd2Wqi6AWZ0xN5LxUesaLHgjkLzLO7Bo/gZBShlblJ7M2hdCRKKy3FqwITeCqNOgMUZGOkdKDd8AgoODuRKVjqFMzKSetUqM6r4VHESjtt2J7LQYlYkDTZwlXIzVPQSoTByI6PgbSk+dZqmXrSkDm+nScZceDyUqtfToxReFHLt6RHZcSMibx0iq9SFqqTFGqTdxPTYKv/UtsL38C5LctKo2U0BAz8MRcQ8jzU1Cmp9OvrEjd3tvI8euQRVZ+Gy4GqVb1KjjbFG1hggIPIQgGybwolHuOi1VLB+mNrQky15Xi0CQDxOoaCqirsd/53K+NQO5e+vR0sVl1aUQeDz27thCm05dkcl096uzJ4/y87Tx+naJROcwFolFqNUFJCcl6tuyMjPIyclGo9aU2a8kTOLOIc+KZe01FT+feiDjLb0fnLHxuopatiI2zR+nt2vjqj8QiyUAHN63G632gV0ZynR92+5tm8jLy+X917rT1M+JC2dP8fn7b7Hs1zl6u0xNTTh9+hQ/dpSjdOuM0qMHDo7O5N4vmr5zy0aW/zafhNgYHJ1d8fbxR6NRo/TswfIwO8bvTcbRQqeYIZVKEYslmJiacerIIbz8/PXnc/bkMVQq3aL4rq0bObB7OxnKDFrWcuPSv2f4/P23mP/jFDp2ewWRSISJqRlnjx9FrVFz5eK/ADg4OpMYH4eNnT1yAwM0Go3ersC69bl66bz+eCHB17B3cNS/f3jM4WMn6rfv3LKRvxYveHDd75/Df78bHbu9UmTbyE/f5crFf/Wf3/kYNQ6mIgz+s/4lQos8KxaTuHMlfv4vE8dvJ6HRgo+dKc6WRo/d305hyLQ+tfmghQdSsYgzoSl8vvYi58NTKsFagRcRYSYm8NxjKJPgbWuKiYHk0Tu/xJw78A9NmzZl6ZAetPaxQQvsP3GWXi0COHjgAB+PngyUrQWblZGh15JVa7TMOXgbtVaEJD+LCa8E4O+gKPHYhVqmSs/uhPQ/RXKvlUTlGBDaYz0h/U/pnSyF9KnvTF0Xc/IKNPy87xYqdema3C8SKjM3YptP4ubbZ4ltPBaVsT2y7AQczv+E/7qmOJ6agEx579EDCQhUJv+JiHsYEdyPqdKiMnF6xoY9W1RqDcGxutT9OkJ9FoFqhCAbJvCiUcPmfkZLctmOFjMDKeIq/vpnuHXS2RIhyIcJVCwVUdfj4bkc6BaFJRJJ6VkQ9ymrLoXA43HyyAGaNG+tf+/p5cum1cvZtHo5cTFRzJ0xieZtOmCmMKdeo2Yc3L2dlUt/Zeff6xk5sCe2FqbUs8jAs4ZXqf0AMjOUqPLzMUi9jXXQHzicnQqAv42YSUfz2HJDRXiaholH8ngjQMqeOwV09JQwf+kqDu7ejpGRMXu3b8HbryZxMVGcPHIAsUTMuZPHcPP04szxI3h4+bBzy0bqNmiMnb0j63cfZenabYhEIrr3eo02Hbvq7do8+yveqCnB086M8CYTQSSiS8++HD2wh/27tqGwsGD+T1PJzMzAwcmJyxfOodFoOXf6BJatP2LJBRV+plnE37rAwp+n0+XVPiyZP4vmbdrx99qVHNy9nejIe5w8coAClYqDu7ezfPMuagbWoUef19iw5zgBdeoz6af5JMTH0biF7jNYMn8WnV/tTaNmLZk5+VuuXjqPpbUNQVcv0ahpS+Jio5k3YzL5+XnUb9yUdl16cOncGc6dOkZBQQF/Lf6FFm2L1mEpHDOgdr0Hn7O3D4vm/Ki3s/Acin43DurtKsS3Zi1mTRhF+Lbp7LilYvzhPIY0Kr3mpTQn4XG/ki8cR+/LhrXzs33iMSRiEf0auPDzG3VxszImLVvF5B3BLDp6l1yVkDUkUDYvdkVxgZcGqUSMp40Jsem5JGcKUkslsXHVn3w2aizOlkaM6erPaw0yWXHagjPa70k9vIy3Br7Hx1MW4i+O02vBevn68fsvcxj67hus2XEIiVSKXKtFo9Xy6+E7HL+dhEgqZ2hrV2q7WJR67CJapmIJBR5tyMlTkeXUvMT9xSIRIzr58sXaS9xJzGT12Qjeb+FR8RelmqIxMCep7mckBw7CPHQ7NteWYpQSjE3wcqxvrEDp3pWk2p+Qbd+wqk0VeAkpjKgqDREgz47DJO5cqX/jLwI34zLIL9BgYSzDzcr40R0EBJ4RMqngaBF4sfAodLQklu1oEYlEKAxlpGVXncSH0q0Tjue+xyT2DOL8DDRysyqzReDFonAu9zCF9Tl+njaeCaOGMvf31UXqepQ2l3sYuYEhOTnZKCwsSj12WXUpBMpPbk4O1y5fYOKP8/Tb7BwcmbVoOTOnfMvP08bTom0Hfpi3GIBuvfoRHnqbNYvnkJCURKCtmG29jPDb9z88TRxZOGkokxcvKtJPmh2PafRJXnlzGLN7mPG6Z9H7ZgNHCYteMWTkvlzScrW8XlPG9x0McJqdyZKehijaD2Hm1O/IUKbTrHU7oiMj6NuxOS3aduCbKT8yc+p3KNPT8AuoTXxsNONGDsHEwYPJc/7A3dMbPHXOvtPHD7N3xxZatO3AsM8+4pP3BhD0mQl1lubzlftVOnRzw9PLh5kL/+CXmdMIu3MbK2tbNBo1773Wnc49etO+Sw8Gv/MaZmYKLIxlBMWreL1XDzr26k//dz/i/de68/eBU5w/c1Jvc+ceven5xjuMGzuGgpxMevTqwzdTfsTIyBiD+9kpyYnxBNZtQERYKLu3beLvA6ewtrUnNiqSTwf0xcrahh59XuduyE16tm6Ii7sn85atwcTUDBNg1PhperuMjI2ZPPNBpsrDYz5MQO16jPvh5yJ2fjXhe317ZHgYifGxBNa9rwag1WISc4IpvjcZffAGfWapsDMR8VkjOWNbl+5oKTAqv1TWi0hMWg4h8RmIRdDa+8kdLYXUsDVl9pt1WXH6Hv9ciWHXtViuRKYxqrMvPvbC77tAyYi0QmUflEol5ubmpKeno1CUHJEv8PyQkpVPTFqOUEv8ISLCQhnQswOHL93WpykXcj0mncW7znFk8pu4DF9L1ul1+NgrmD93NsZyKVqtljZ1arB03TZOHT3E7ZvBeL7+NduvxiIWQdyCt9l98hJW1jalHn/q2JFYWlnx+WhdKrIyPY1OjQM4dyum2L5pqSmE372New1vbqZq+WH3TUTAtD6B1CnDmfO0aNQFhF8+gUe9Vogl1cwHrdViEnMS22tLMIs6ot+cZdeQpNqfoHTvAmIho0vg2WB+dxtuh7945H4R7ReQ7tX7GVhUNaw+e491/0bSxseW0V1L0FKuZKr1PUugSrE3N8DOzLCqzRCoIlQqFbt27aJHjx7FnvmeVy5GpNJv4SkczQ05PbZjmfum56iISK7aBWDfje0wSA/lXsdFKD1feXQHAYFHUNZcDiAuJoquzWpz/GoYv839EbFYrF/E/e9c7k5IMD/MW6Lv27KWG9uPXaywuVxV8aI+FxXK9f43k7zwXWS7uWjkCkxjTmAafRzD1FtF+mskBmTZNybTqQU2QX8gzU3W1/SgyHigMnEkpP+pcs8rU7Pz+XTlBXJUauzMDJj1el0sTf7jBNBqcd/3IYrIg2TZNSS052YQlT8gJCIslNA7IbRxl1H32IdoxDJuvXEElZlrifsXqDVM2RHMpcg0AFp62zC6i1+F164rtKtRs5aYmlXcGqJYlYXF7b+xDv4Tw7Q7+u0ZTq0xSg5CkpdWyucnQmXi8Fif34vI2nMRrDkXQQM3Cyb3CgQq7t5wKSKVuQdvk5KVj0Qs4u0mbrzewOWFq4tobSrHyeLxJddedB7HbyCEvAm8cFiZyKlha4JU8mLd8J6GsrRgazmZM+7VQEQi8LQ2oaCggOt3I/h4xXm2Xo4mNS1drwVbq259Tp4+w/arumj2dwKM0BSoMLewLPP4D2uZbtuwmlaBHuTmZFPH1UL/2rZhNbu3beaV1vX5YdxXdG0aSFrQUboG2JNx7QDvNPcosn9hH4Dp40cX2f5Kq/rlui7nT5+gd/smtKlTg5W/L3yia/tMEInIcm5FeLcV3HptPym+/dGI5ZgkXMD94Kf4bmqH9fXliFRCZJlA5VPeSKkXPaKqsD5LXVdBNkygeiHUaBF40SiUDotNzyUnv2zJDjMDKaIqngIo78uHKe4J8mECFUNF1fWoVbc+Vy8+qC8RHXmP/Lz8x5rLQfG6FAKVRJlyvVpAi+uR4Xjs+wCboGUYpt5Ci4gc60AS6nxGaPfVBA+8RniPNSTV+5yYlvedb/8ZTYsuI11lbIdIU351kLXnIsi5L6OUkJHH1J3BxWSVzMN2oIg8iEYsI7r1j4/lZAFw86xBu87dEft2IsOpFWKNCruLc0vcV6vVsvhYKJci05BLxUjFIk7eSWL2/hDUmoqNwi20q6KcLPL0cBzPTMZ/bVOcT32HYdod1DITkgPe49brhwjvsZroVjOAkj8/gNhmk15qJ4tWq+VIiE46ra1vxc9D67tZ8svb9WnpZY1ao2XVmXuM3XKNOGVuhR9L4PlGmIkJvJAYy6V425liJH95f2ge5lFasPN+nEzzNh1Y8H4rXu/RkZzbp4k6von5S/6kZ+/eGJtbU8MvgHCZG0plOplBBxnc1oub+1bRtHVbJBLddc7MUOqLzz3Mw1qmXV7tS4s2HXjt7Xc5ERTOvnPXsbSyxqdmLWZMGMPyTbvZsOc442fMYe70iXzUugY+zbvhOnwdr83dx/FrD/o0bNoSgOBrV/j1rw2cCArnRFA4G/Yce+Q1SUlOYtigAXTv/Rort+1n19ZNXLt2rYKueOWRZ+lHdJuZhLx1ioR6X1BgYIGB8h5Opyfgv64p9v/+hDQ7vnhHjRqTmNOY392GScxp0AjaogJPRpZDE/JNHEuY8unQIiLfxJEshybP2LJnR06+mpD4DIBKzbQTEHgShBotAi8aFsZyLIx1C8zhj6jTIhaLMDOs2mh2fZ2WyMPC89YTkJaawuXzZ0lNSa7yMZ+0X0JcLJfPnyUrM6NC2p64rseWjQz/6H9Y29jiU7MWDZu2JCNDyT+b1gLwx8K5jz2XKygoYMn8WfjUrFXiOQhUHIVyvaX5jkX3XypDW1L83uZeh4XceOcSd/ruIr7JWLKcW6OVPshwVXp2J6Ljb6hMHIqMozawRCOWYpJ4hRq73kaS++ii35Gp2ey9HgfAFx28MTOQcjshkzkHbqG5Ly0iyU3D8ZSuIHxivc/Js/R97GvwMPGNxgBgeWczBg9lexSy9XI0e67HIQJGd/FjbHd/pGIRx24nMefArQp3tjw1Wg2mUUdx3/s+vhvbYhO0DEm+kjyFJzHNJnHz7XPEtJhKnoU3UPrnJwIS6wwuVvf2ZeN2QiYx6bnIpWKa1bCqlGOYGcr4ups/Izr5YCSTcCNWybC1lzhwIx5BLEqgEGEmJvDCIpOIqWFjop+YvawUasHWbfhg0bNQC3bVskX07dic3Jwcfpi3GLFIxJefvM8nn49AG7SL5F1zycvOxLD7GD5be5lV56Kx7vo56fsXMfPdNuzfuY0vv5mkH/f1Li05fnBvMRssraz1WqbdmtcmPPQ2n48eh8Lcgu2b1tGxe0+srG0YM2k6Pv4BAPgFBKJMT8dQJmFMj0Dkxmacj83nXEyuvo+LuwcFBQXcCblBw6YtUJhboDC3wMT00XqZO7dswMbOnk+Hj8Hd04tPhn3FgQMHnv6CPyMKjO2IbzSam2+dIbrFVPIU7kjz0rG78gt+61rifOwrDFJCAF3Kud/6FtTY1R+3w19QY1d//Na3QBG2u4rPQuC5RCzRRUxBscT1QufLix5RFRyrRK3RYmdmgINCkGgSqF4IjhaBFxHPwjotSWU7WgDMjar22T/LvhEFBuZI81IxTrhQpbZUR7ZtWF0sS7207Pbd2zY/sg/AjAljKnzM0vo9ir8WL6Bvp2ZMHTuCzk1qcf70iadqe3gup1Kp6NepBffC7ujmcr8v4tXWDTmwaztBly/w/Xdf0aZTVz4YMpxVyxYxftQQMpTpuNfwZszoUdxKzGbijLlMGzuSVoEebF7zF5fOnWbFkl8A3Vzu2MG9/LFoHq+2bkCbOjX4/ruvMDA01M/lWtZy49zJY9y9dbOInQtnTy/xev57+jgAn3/Qv8j2j99+ceVlK4ryFjaPbT6B6NY/oqzxKmrDsheYlZ7dCel/itAe64lov4DQHuu58b+LhPVYR4GBOcYJF/H6py9yZXiZ4/x1KhyNFpp6WtElwIHvXqmJVCzi1N1kVpzW9XU49z2y3CRyLbxJrDukXOdSFjl29VC6dUak1WB3YXaRttOhyfx5UnfcD1t50qyGNU08rRnTzR+JWMTRW4nMO1g9nC3i/Aysr/+J76YOeO4ZiCLyECK0ZLi0J6zrCm69cZjkwA9LrPH1388vzbMnAIZpt5/1aRRDlJuL2+D3cBv8HqLcZ5/lcfSWLpOvmac1xvLKC7gQiUR08Ldnwdv1CXBUkKNSM+/gbabvvkl6TtXViBOoPgg1WhBqtLwMJGbkEa/MFeq2PCb5BRp2BcWy8XwkytwCAPo3cqWzu4zgq5eo26hpmXq+/+W/WqZ5ubl0bV6b1f8cwNnVXb+fSqVi4ujPEYvETJuzCIDNF6NYfiocuaiAxCUfsXbHQZxd3Qm+dpkP33gVaxtbEuJiadisJRN/nIujc8m6rYWMHzkEA0Mjxv3wMwDxsVF80K8bO05cfj51fTVqFBH7sbm2BJP4B6n9OdYBGCYHA5So6xvR8beXPvpF4MmwubwQx/MzimzLN3EkttmkF/479cfJMLZciqZzgD3DOvhUiQ0vqha5wNMhEkGgsyBn9zLzItZoARi5/jJ/X4pmdFc/hrb3LnNftUbLjVhllT73uxwejuXdLSTW/pS4pt9VnSHVEFV+PjkPFVLPzs6if7c2LFq1mc/eeY3f12/Hxz+A7ZvX8cvMaew9E1Rqn9X/HESr1fLWK+0YPeEH6jduyuwfJhAfG0NsVOQjx+zSNJA5S1dia+9ATnY2H77xCmu2H0JhYUHPNg1L7FcW4aF3+OD1HmzYfQxbewcWz/2JMyeO8OemXU/c9jBL5s/il5nTWLZhO42bt2bz2hUsmj2DOUtWYqpQ8O2wT/D09tHXYFGpNSxatZnl00fj+NFvyI3N+OO9xiTcu8XA3p15+4NP6Pf2u4wZ+iGjxk2lSYs2ZY556thhRn/2Pqv/OYiHl3cRO/Nyc8nLe7C4GhcTxcdv92HHsQuYKczp2NCf31b/jb2jEwBSmQxjY5On/j4V8iI+F5nEnKbGrv6P3C+0x3qynJo/9fEMUm/jsfc95JlRFBhaE951OTm2dYvtdy06nW+3XEMsgl8GNMDV0hiAwyEJzN6vqxHzY4NU+gcPBeDuq5vJdmj81PYBGCbfwHtLN0Roud13N7nWtbgdn8E3W66RX6Che6ADn7X1QvSQhuTJO0n8tPcmGi109LdjWEcfxFWgMSlPD8X6+nIsb29CosoEQC0zJdX3DZID3iPfvMZjj2mQdgffTR3QisTcfOsMBf/JdnmWiLKzCPRzBiAoJBptBf59Pwq1Rsv7f54jLUfFhFcDaOzxwOFYmfcGtUbL35eiWHM2ggKNFktjGV929KWBe9lyjNUZoUZLyQg1WgQE/oOtmQHu1sYvXKGqykYuFdOnnjNL323EBy08GNLOi/81dcPe0Yn2XV95LCcLFNcy3bV1I3XqNyriZAkJvkb7+j6cPnaYMZOm67f3re9MHRdzUq4cQuroh52TzpESducWXr7+/PjLMrYcOoNUJmXKNyMeaUtmZgbObg+Oa2pqRkrKo9Okqy1iCUqPboT2/Js7vbaS7tkDLSKMkoP1aeUPU1hEz/HMJEHWQuCJEGt1ETuZ9k30EXEh/U+98E4WgKtRaQDUERa0BaoZQn06gReVx8lokYhFmBhUtXxYRwDMIg5WqR3VEZlcrs9CL092e1l9XNw9CL0TwvBvJtKn//9wr+HN/z4czM3r1x45ZnJyIiKxiHqNmuLs6s7hfbvo1KMXLu4eZGdlltqvLNQFBUz8cR629rrFTt+AQJTpaU/VVsi9sLv8tWQBTq5u+m3bN63l/cFfULt+Qzy9fBgyaiyH9+0mO7+Avy9G8eHvJ1k+azzmbd5FbGhKgUbLhYhUzp06hoOzC1+OnYS7pxeDh49hy7pVZY4JYO/gyPdzf8PDy7uYnQaGhkU+o7XLlzLwoyGYKcyJi41GixYf/wB9e0U6WV5UshyaUGBoVULpcx0VLdebZ+nD3V5byLEORJqbTI2dbxa7h2m0Wv44GQZA11oOeicLQHs/O95u7IoB+TS9PgWA5JrvVpiTBSDXuibpXr0AsD8/k4SMXKbuDCa/QEMDN0s+bVPUyQLQ0tuG0V39EYvg4M0Efjl0Ry9vVuloNZhFHsJjz0D8NrbDJng5ElUmuebeRLeYys23zxHbfPITOVkA8iy8ybJvhEirwfL2pgo2/vnhSmQaaTkqFIZS6rtaPLPjSsQi3mjoyszX6+JqaURqtoqJ26+z+Ohd8gqENZaXFcHRIvDSYGYow8vOBEOZ8LV/XIzlUvo1cKF7oGOxB5enYeOqP3lj4IdFtvnWDGTpum14+fozYdRQ/XaxSMSITr5kX9mDuGZn1p6LAOCVvm+y+p8DBNZrgIubB99OncnpY4fIzFCWeWypRIpcLte/lxsYkpeXV2HnVpXk2DUgouNvRLabV+Z+IrTIs2IxiTv3jCwTeJEwjdLVQkrz6Uu6V29dJN0LLBdWSEauitBE3UJfXaE+i0A1Q5ANE3geSU5O5tSpUyQlJZW6j8d9R0t4ORwtUPXyYRmu7dCKpBim30GeHlaltlRn8nJzWf3HbwwaOgIHJxde6fsmoMvMWr54AR27vVpmH4C2nbrx5kPziTs3byCCR44ZdPkCarWaTo0DaOzjyOK5P9F/4CCActvyX7x8/WnXWRdwkp2VydrlS+jYvedTtRUy5ZsvGTRkBE4PZe2npabg6OSif5+t0lCghQ+X/8ufp8IJO7ACkTqftv4ONJRGodVquXAvlVvBQTRp0UY/rwus15AbQVdKHFMsliC+/9tSHjtBV2vm0J4dDPjgk2LXuomvE2OGfIgyLe2R1/NlR5qTCGoVIp6dXG+BsT2hr2wgw7kN4oIc3Pd/hOXNNfr247eTuJOQiZFMwttN3Ir1f7uJGz/b7cFDFE+c1orz3sMqzLZC4huMRCuSoIg8xOZtf5OarcLD2pivu/mVGljbytuGr7r4IRbB/hvxLDxcuc4Wcb4S66Df8d3YDo+972MWdRQtIpSuHQnrtorbrx8kJeA9NHLTpz5Wqt9bAFjeWs/LKuFy5JZOZq+1jy3SKngW9rYzZU7/erxaxxGAHddiGbH+MncSMp+5Lf+lMuqeCZSNMBsTeKkwkErwsjVFYfRipBM/z0SEhRIRHkqzVu2KbBeJRNQMrMu02Qs5vG9XkYfw7MRoRBnxGHrUY9OFKK5FpfFfzBTmaDQaEhNKKAj/EAoLS1KTH0zoszIzkUpfsO9FOZ1i5dX/FRAoRJyvxDjhIgCZzm2q2Jpny7XodLSAq5UxlibyR+4vIPAskQuOFoFqyPLlyxGJRMVey5cvZ926dXh7ezN06FDc3NxYt25diX161XPm3o+vcn7/FgCCgoJo3LgxlpaWjB49ulgRWoWhtMTHoPOnT9C7fRPa1Kmhr0tRGWjkCrIcm+psiXh+agA+ax4nu72sPoWo8vNZPH8mNXz8HjnmvdC7BNSux2+rNvPxFyMxMDJiw6o/i4z3KFtK4/ihfXRo6E9SQgIff/HVU7dtXb+KzAwl737yeZH9/QJqc2jfTuLSc1l45A6Tf16IxLUeWflqbEVKsi9sI7CmH06SLC5t/oXELd9zMSKFjIyimf0mZmYkxMUWGbOQbRtW06J1h3KfA8DGVX/Qvc/rGJvoFpEfvtbrdh4mOiqCeT9OKff1fCnRqHA7NASpKoM8UxdUxkUloVQmDpUmAa2RmxLe9U9SfN5ApFXjcuIb7C78TL5Kra+/8lpDFyyNiz8HG6Xc4JWMjQCMU33A+D33SM3Or1D78s09SfF5HYB3slZgaSxj/KsBj6zL0drHlhGdfBGLYG9wPL8dvVvhBcwNUm/jdHIc/mua4HRmCgbKcNRyBYmBH3PrzaPc6/onmS5tyj1PLw/pnq+glplioLyHSeyZChv3eSFXpeZ0qM6J0M7XtsrsMJBK+LSNF5N71sLSWEZkag6jN11h44XISq0NVBl1zx71jFUaR48epWbNmtjY2DB79uxHd3hBEWZjAi8dYrEId2sT7BUGVW3KS83eHVto06mrXj/87Mmj/DxtvL5dcl8/U/RQVMreHVvo0KUbXWo7owVmH7jF9xO+Ye/2Lfp9rl+5hFgsxsHJuczjB9atz9VLD2qZhARfw9rauiJOrdpQYGRXofsJCBRiGnMKkVZNnnkNVGZl10N60bgapZMOqSvIhglUQ+RS4dFeoPoxYMAAUlNT9a/IyEhsbGyoU6cOX3zxBcePH+fSpUssXryYr7/+usQ+N++EITZSoLLzJzE9k549e9KwYUPOnz9PcHAwy5cvL3JMqUSMsbxolHdKchLDBg2ge+/XWLltPzu3buTcqWOVdt5Kt04AmAmOllJ5nOz2svoUsmDmNHKysvhizPgi20sac9DQESxauQlvv5oc2bebQUO+5MCubY9lS2k0b9OBhSs2IpVKmPP9hKdqS0lOYt6PU5g8c0GxoLA+H4/i6InT9OzWgWUjXyfzxjFqdXqDb3vUpHH+NWzt7Fi6ZiufDB/Nys27yI8MIjnkPNkF2iKZ/QYGhuTer4Ez7OvxBF2+yHv9uvN6l5bs2f43b73/cbnPQa1Ws3ntCt5858Fn9PC1ruHjx4ixk4pda4GiOJ6bjkn8edQyM8K7ryHkrdNFCthXulyvWEZ0m1nE1x8OgP2leYi3DyUlIwtrEzm96zoV76NR43x8DCKtmiS37lw3a0lCRh7f77xRoTJKWq2W2fl9ydNKaSEJZn4TJXZmhuXq287PjuEdfREBu4PiWHws9OmdLRo1Zvf24bFrAL6bO2J9YwWSgmxyLXyIbvk9N98+S1yz8eQrPJ7uOKUdXmZCeg1dZpnlrXWVcozqzNmwFHJVGhwUhvg5mFW1OTRwt2TB2w1oXsOaAo2WFafv8d3Wa8Qrcx/d+Qno0ecNTgSF61/7zl3H0soan5q1mDFhDMs37WbDnuOMnzGHudMnltmnYdOW5OXlPfIZqyQSExPp1asXb7/9NqdPn2b16tUcPny4Us65uiPMxgReWuwUhrhZG1dkMIHAY3DyyAGaNG+tf+/p5cum1cvZtHo5cTFRzJ0xieZtOmCmMC/W5+NWNXAyNyQpM5972LHgp6mcP3OSsyePMn3CGHq/MQAjI51ebGaGEpVKVez47br04NK5M5w7dYyCggJWLP2VevXqVfp5P0uyHJqQb+KoTy3/LxWt6yvw8mAadRSAjJcsmwXgSmF9lmeo/ysgUF4E6TCB6ohcLsfCwkL/WrFiBf369cPW1pa5c+cSGBgIQN26dUlNTS2xz+b1a7Cu1RqZhQOrN24jPT2d2bNn4+XlxQ8//MCyZcuKHVfxH/mwnVs2YGNnz6fDxxSrS1EZFDpaTOLOIc5Lq7TjPK88SXZ7aX0ATh87zPoVy5DKZLRs1+mxx2zaqi2pKcnkPyQlXFa/spBKpTRo0pyvJ//Ito2rn6rtp0lj6dt/IP616uj3C03MZOI/QfxwPBmLd3/BuusXWNs7E9ikNb+PGUjzGtYkxMXQtGVb5Aa64EKFQoGFoxsF6fHkiIz+k9mfgVSmc7w4u7qz9dBZJv44FwdnF5q3bk/Dpi3KfQ7/njqOhaU1NXz8Sr0+Zubmxa61wAMUoTuwCfodgMh2c8g39wCxhCyn5s9WrlckIqHhKKJazUArklA/ZRd/yGbyQSMbDGXFj299/U+Mk67qMjhaTWXiq7UwM5ASEp/BnAO3K0yq658rMay+qWGtWlcLq+HdXx5LMquDvx3DOvogAnZei2Xp8Sdztojz0rC5ugS/jW3w2P8RZjEn0IrEpLt3JbTHWm6/doCUmgPRyCq/HlGK39sAmIftQpz36HpSLxJHQnTqHG19bStU5v5pMDeSMba7P8M7+mAkk3A9RskXay9x6GZ8hWdRVXTds8MH9pbrGeu/rF69GkdHR8aPH4+Pjw8TJkwoV78XEWE2JvBSY24kw9vOVIgAfcbk5uRw7fIF6jZ8sMBv5+DIrEXLWbVsEX07Nic3J4cf5i0usY+RXMKoLjoN1li7JtjXbcvwQQMYP3Iozdu05+spP+r7vd6lJccP7i1mg6WVNaPGT2PwO6/RsaEfd0Ju8uabb1buiT9rxBKdbi8Uc7YU/rxXtK6vwEuAVovZ/fosmS5tq9iYZ0tyZh5RqTmIRVDbSchoEah+yCTVY4IpIFAaubm5zJs3j7Fjx+Lq6sr//vc/QFcHY9asWfTr16/UPo376CLkz164SLNmzTA21gXV1KlTh+Dg4GL9FIZFHS1l1aWoDFQKd3ItfRFp1ZhFHqm04zyvPGl2+8N9Com6F843wz6mdYfOtOvSvVxjjvz0Xa5c/Fc/5s3r17Cxs0duYFAuW0pi55aN/LV4gf69VCpFfP85+0nbdm3dyNrlS2hRy42m/q5cOHeGH778gMMbliIWQVtfO6a92YjU2xcYN2mq/vvt4OhMbu6DCGqNRkOBMgmpwo48S49imf32Do769yKRCBNTM84eP8rwsRPLdX6F7N2xhY7dXimyrfBaFxJ05ZL+WgsURZ52F5fjowFIrDOYDPcuVWwRpPoPYLHTNLK1BrSRXOPDO0ORZseDRo1JzGnM727D/O5WHM7/BEBsk+8oMLbD2dKIb3vURCoWcfJOEqvO3HtqW86EJrPshK7uVVL9z9FIDDFOvIRZxP7HGqdTTXu+6OANwParsSw7EVbuBXCDlBCcTnxDzbVNcTw3DXlGJAUG5iTW/pSQN48T0XkpWU4tK1Qe7FHk2NYl19IPsToPi7svT7ZYeo6KixG6AI22flUnG1YSIpGITjXtmf9WfWo6KshRqZlz4DY/7g0hI7d4IHBFUBF1z4KDrpXrGeu/XLlyhQ4dOuh/g5o0acLFixcr6tSeK16wggQCAo+PoUyCt50pESnZZOYWVLU5LwWGRkZcuFu8LkjLdh1p2e5sufr42pvxbjN3/jwVToL/a3g3fJv3mrvTzs8O8UMPNXtOXyvVjrfe+4gWbToQeieEBo2bkXS38ibbVYXSszsRHX/D8cwk5FmxDxpEEiLaL6jclHOBFxK5Mgx5ZiQasZxMx+ZVbc4z5Vq0Lgqohq0ppobCI5RA9UPIaBGo7qxZs4ZmzZrh4eGh33blyhXat2+PXC7n5s2bpfbx9/Pm+vlI4hJTCfD01LeLRCIkEgmpqalYWlrqt8ulYozkEnLydZI1mZkZ1PD117c/XJeislC6dcIw9RaKiAOke/ep1GM9b5w8coA+b/5P/97Ty5cvP3oHd08vWrXvxIKZ00rMbn+4D+iCsT7/oD8dur7C3Vs3ebXvm2RnZWJkbIKnly/DB/0PFzcP2nbqWmRM35q1mDn5W/Jyc2nUrCW/zvqe/gMHlcuWzAwlBoZGxRw+nt4+TB07Ahc3D/wD67Dw5+l0ebXPU7VtP36Zs6HJ7L4eR5wyl8RtP2HVpA89enTnrZb+OJgbMnXsSDq/2puA2vX0tnTp2Ze3erRj/65t1K7fiLV/LgF1AQbONclUqwjdtoBzp47RoEkL/lr8Cy3aFq3DsmT+rGJjlmXng8/oINPmLCyyrfBaj5k0ndTkpCLXWuABIlU27gc/RaLKItOhGXGNxjzROGmpKYTfvY17DW8srZ5eGjs2PYdZYR7s0o5jvelsjJOv4/13NxCJkeUkFtk3x8JXX5wdINDZnC86eDPnwG02XojC0dyQzgEO/z1EubiTkMmsfSFogW61HOjSxIsk8YfYXVmI/YWfyXDrREJ8PDFREfj4B2BiWraMVOcABzRa+OXwHbZdiUEsFvFBC4+SsyI0BSgiDmB9fTmmsacenK9VTZID3ifNuw9aqdETnVeFIBKR4tcfpzNTsApZR0rAu8/cBK2RMUEh0fr/PwtO3E5EowVvW1NcLZ/NMR8XB3NDpvetzeaLUaw5F8HJO0nciFXyZUcf6rtZPnqAx6C0umeD3uyJTC5n2+Fzj+yTkZGBZzmesf6LUqkkICBA/16hUBAdHV0Rp/XcIczGBAQAiViEh7UxNmZCYePnib71nfmmmz/2CgNSsvKZc+A2ozZeIThWWe4x3Dxr0K5zd0zNql7Ps7JQenYnpP8pQnusJ7LNbNQyBSJtxenkCrxcmN2XDct2aIRWVj0faCuLQtmwui5CNotA9UQuOFoEqjm//fYbgwcPLrKtTp06HDx4kFq1avHBBx+U2sfTVie/kpGvweA/kfCGhoZkZ2cX66sweuAUl0qkpdalqCwy3DoDYBZ1BDSVE8H6PPK02e0Pc+roQUJvh7B5zV9cPn+Wad+Nopm/CzFREdg5OGJoZMjvv/xcbMxBQ0fi4elFSPA1Du7ZzpsDP2TQ5yMBsLF3YMavf7Di94X06dCMjIwsRv8wn3hlLjFpOfTp1IJNW7dxOz6Dm7FKgqLTuRKZRo7CnbdHTGbahLH069IaTG1o+MZw9gTFEaa1o/un45g87hv6dm5FtoElzt0+ZfmpME6lKWgx8GsmfPs1vTu1IkmkQNr8PWbsvsHEwwmsDM4hSWSOuZ0T9pZmjH2tGV/2qIeDuSERYaHs3raJYf+pS+Pp5cPMhX+wdP4serZuyLGD+5j/xxp8XeyQGJvT8+MxD2X2B/PJsNH6vqWNGVC7HuN++JmZU7/jze5tcHRx5asJ3+vbI8PDSIyPJbBugyL9Bg0diZePH58O6MtPk8YWudYC99FqcT4xFsPUW6iM7Yjs8AuISw7qeZLi1w8zd/okPv+gf4ljl9S24vQ9CjRaJC4Niei7DZWRHbLcZKQPOVne2pTN57tyMEy7hSJ8T5H+e+aPxfqqTqbx1yN3uRKZpm8bM+RDpo8fXWT/zz/oX+S8Pn67N4kZeUzdEUx66BWSlw9l/fAurFz6K0m1B6OWmWGUcoP104fSt1Mzpo4dQecmtTh/+sT9S6vlj0XzeLV1A9rUqcH3331FdnYWAF1rOdDNKpnopYOZ/0Fbhn03rUhmiyQ3FZsrC5naqwazRg7ENPYUWpGEdI/ufJ//Pm7TwvHs/gWfvDuAxPi4Ms+hsknz7odGLMcoOQjDpKBKP14xRCK0xiZojU2eWTbPkVu672B1y2b5LxKxiDcbuTLztTo4WxiRkpXPhH+us/R4aIXWL6qIumdSibTcz1gPI5UW7VeePi8qIm1FC8Q9hyiVSszNzUlPT0ehUFS1OQJVTGpWPtFpOY8j8ylQxeQXaNh+NYb1/0aSo9L9ULXytuH9Fh7YK8pXGE+jLiD88gk86rVCLHmxI9XtLvyM/aV5ZNk1JLTXlqo2R+A5w33vBygiDxLb+BuS6g6panOeiCeJ9NNqtQxacZ7EjDwm96xFA/eKjUB6XF6me5ZA+ZCIRQQ4Cc+xLzsqlYpdu3bRo0ePYtH2Vc2dO3do0qQJ8fHxJdoWGRmJu7s7ycnJ+qjJh/scDElm8KoLGN/YQUNFJitXrtT3tbCw4Pbt29jaFl1syStQcysuE4CpY0diaWXF56PHAaBMT6NT4wDO3YqprFMGjZqaaxohzU0mtMdanZyMQIWg0WrZHxzP7YRM1BoNBRotao2WAvX9fzXaotsf/lf9YHvRNg1qjRZNNZsHWt0vPt4t0AFj+dP95q86c4/15yNp7WPDW75yQu+E0KhZS0zNnu/fj+f9ucgqeCXOp75DK5IQ+so6sh2alrqvKj+fnIecxNnZWfTv1oZFqzbz2Tuv8fv67fj4B7B98zp+mTmNvWceLLrfvhnMu327smH3MVw9PIuMW1LbzTglozddRQTMe6s+nlaG+K9rhjQ7Xi9MvfdOAQO35HDrC1PMDcWoTBwI6X8KxBJOHjnIt19+yj9HzrPkbDzHbidiIpcw8/W6RFw9zbdffsr2o+dRmFvo7ejY0J/fVv+NvaOT7ny1IibtvsvdyFhil3zCB4M/p/drbzJm6IeMGjeVV40ukb7vJ9r8lceag5exdXRh8dyfOHPiCH9u2sXmtStYNHsGc5asxFSh4Nthn+Dp7cMP85aQkpzEq60b0KzXuwQb1ybpn5/oO/gbpvQJwCb4LyzubmF/SBYDt+QQPMoFTf13SKk5kLPB9xj56bvMmL8UD28fvvn8IxycXJixYGmJ5yCVyTA2rvxaLa4Hh2ARtoPkgPeIaTG10o9XlcSl5/LxyvOIRfDn+02wMik5aLq63RtyVWr+PBXOrmu6jFo3K2O+6uKLp43pU40bERbKgJ4dOHzpdonPWHExUXRtVpvjV8NQWFiU2mfd7wuIuBtSrmesh/nss8+wsbFh6lTd9y4tLQ1nZ2eysrKe6ryqC4/jN6j6b5mAQDXD0kSOgUzMveRsCtTV7ClboETkUjGvNXChg78dq89GsO96HCfuJHE2LJk+9Zx5vaHLU09KXiRSag7E9soiTBIuYJRwiRy7+lVtksBzgkidj0nsaaB612fZtmE140uI2Jn686/IDQyZ9t1InF3cCL97h8mzfqF779eK7Dd3+iTu3LrBL3+u128b/80ozq/5A4D3fwRXd092nrj0SFvOnz7B1G9HkpqcxEefj+TdTz5/yrMTECgZuVSozyJQvdmwYQOvvvqqfjJ/6NAhdu/ezcyZMwFdNCSAWCwusU+N+xktORYenDn1u36f8PBw8vLysLKyKnZMA6kEQ5mYXJWGwLr12f3Pg+ju/9alqBTEEpSuHbC6vRFFxAHB0VKBbL8Sw+/3azU8CyRiERKxCGmRf8VF30tK2f7Qv7r/i4u2SUrvY2Esp4mnVYVJQzZ0t2T9+UguRaQxqnNT3DxrPN4AGjUmceeQ5iRQYGRHlkMTod7jU2KUcBnHM5MAiGs8tkwnC+gKWcseys5bv2LZI4tfgy5oaOrYEbwzaHAxJ0tJbVqtlj9OhgPQsaYdnjYmmMScRpYdr++Xo9IyZFcOMzoZYGEoArTIs2IxiTtHsmU9vv9uFF9+MxFzCwuGd1SQmJnHjVglE7dcJGbZSL78ZmIRJ0tcbDRatPpzUGu0TNsZTHhyNprbx3BycuSLUd8gEokYPHwMW9atotms2agPLWbpK6n4Kk+S6tgf34BA9u3cCsD2TWt5f/AX1K7fEIAho8YyZqhOum7nlg3Y2Nnz8/Sp7LgSyfGUBqRt/gZfVPrz+2yPmjFDBhL34U9opbogzvDQA4z74WeatW4HQJ83/8eyhXNLPIdnSarfW1iE7cDizhZim3ynt/dZIMrLw/mbLwGInjEXbSXXYDpySycrX8fFolQnS3XEUCbhs7ZeNHa3ZN6h20SkZDNywxXeaeZOn3rOSB5RC6w0Sqp7duLwAUaN0zk+ylv3rG6DhmxY/Zf+fVnPWA/TuHFj1q5dq39/+fJlnJ2dn+hcnneElUcBgRIwlkvxtjPlXnK2XtdZoPpjaSzn8/bevFLbgd+Ph3E1Op2NF6I4cCOed5t50KFm0fotLysFxnake/XC8vYmbIKW6VLTBQTKgXH8eSQF2aiMbMm1qlnV5pRKjz5v0L7rg0KshZF+PjVr8dk7r7F80259pN/c6ROLOFpu3wxm/YplbNh9rMiYVy5dxO71idRt2ISJPWshkTx6USElOYlhgwbw7idD6d77dcYM/RD/wDo0adGm4k5WQOA+Qn0WgerOnj17ikiD+fv706dPH3x8fOjevTvjxo2jS5cumJubl9jHzcoYkQg09jVJS0tnxYoVvPvuu8yYMYNOnTrp78tKpRIjowf1M8yNZOSq8mjXpQc/jBtdZl2KyiDDvTNWtzdidm8/sU0nPNMCyS8qYUmZLD8VDkDnAHsczQ1Ld3BIxCU6PIo4OySlbH/o3xLrNjyH+NqbYWogJTOvgFvxGdR0LBqZW1bWryJsd7G6j/kmjsQ2m0SERdMnqguSEBdbak2NJ22rTjzKTkluKm6HPkOsUZHu0Z2k2h8/1viFhaxX/3PgkcWvN6/5i5vXr9HvrYEcPbCHFm076u+TJbWdj0jnRqwSuVTMO0119RukOUXrrE49lkeOCqRiOBRWQHsPCSKRCGlOAotXzCQvNxeJVMrZk0dp0qIN3/WoyehNVwj+ZzF56ZloRRJ9m0gkIujyBdRqNZ0aB6BMT8Olbitym36IkamCGrIUbFu11f8tBtZryLwfp6CRm2Lb+UvqnJtG/qW5xDh2Zu3yJXTs3hPQfacdnVz0NovFEsT3n5luBQfRtGkT7K78ylc3VvKBezQdj2ZToFUQbNGORVcNyZQcIc+5OWfOntXb2e+tojVQwkPv4Oahc1r+9xzaderGuB9m67MIKpNM51bkm7ogz4zCPHw3ad59K/2YetQFWG7SLbRHfz8LqDxHi1ar5UiITjasnW/FyIY9aW2jJ+3nZpjHp34FHIg34EJMHstPhXM+PIURnXwhO/Wx730VVfesWYtWpKeX/xmrkF69ejF06FAOHz5M69atmTZtGnXq1CEjIwOzF1imvySEGZmAQCnIJGK8bE2wNKlesgsCj8bTxpRpfQL5rkdNHM0NSc1WMe/QbUZsuExQdPqjB3gJSArURfGYh+1EmlW5hWAFXhxM79dnyXRuDaLq+wghk8tRmFvoX9s3rXuqSL+CggJiwm5h4BpII19XFOYW5ZrUF0bJfTp8DO6eXvrIOwGBykBwtAhUZ3Jycjh79izNmzfXb3NycmLjxo3MnTuXWrVqkZ2dXUSq4r99DGUSnMyNEIklDJv0E4MHD8be3p5NmzYxY8YMfb86deqwc+dO/XuFke5Z3tLKmlHjp5Val6KyiDKuxYkoERnx4Rik3a70473o5BWombnvFgUaLU08rPiivTdvNHSlb30XetV1okdtR7rWcqBjTXva+dnRytuG5jWsaexhRQM3S+q4WFDLyRw/BzO87UzxsDHB1dIYR3Mj7MwMsTKRY24kw8RAiqFMglQifmGcLAA7Nq3h+pRu3PvxVfo3cSt3fQ9F2G7cDg5m/LYweq59IFsly4rj1LwPebVF7TLrgpTEX4sXlFhT42naClGpVPTr1IJ/Tx8Hyq7T8TAl1Qw5f/oEvds3oU2dGqxY8iBA7VFjlmbnwtnT9de9lo8nBqNvIJqs5G95PxCJHqu+R2nFr9vX9+H0scOMmTQdgOysTBbMnIa7Zw3iY2NYsfRXPni9B3m5uSW2vf9ad/44eguAvvWcsTbVLZoXGNnpjxORrmH26Xy8rcREpGsZvT+Xfhty0Gq1RKTDyt9/xdXDk7joKGZ/P4ERH7+DwlDK4IYKMv7dCgoHtpy4zOxpujatVsu90LsE1K7Hb6s288ns9UTcu0fa0b8Y2ckXcUEuzm4PztPEzIyEON08NjngXVTG9hy4FE6HRjVJSkjg4y++AsAvoDaH9j34Tdi2YTUtWnfAMOkq6vCTNEjaisP5n5BnxWJsbk1EppSWefPpcbsXS1f/Xewc/lt9IS01hY2r/6T/u7r59cPnsG7nYaKjIpj345RSP8MKRSQm1VfnbLMMWf+InZ9f7iZmEZ2Wg1wqprlX+Zwb2zaurfDaRuXpVxKF94Y5k0azb1w/etikYCgTExSjZMDwcfRs3/Sx7n0P1zArvPfdC7ujq3v2+yJebd2QA7u2E3T5gv4+9d+6Z2OGfMj340az/0Yi02bNZ/DgwVhZWfH7779z/PhxZs+eDeiesXbs2MFPP/2Ej48PNjY2DB06FCMjI37++We6du2KhYUFhw4d4vr167i4uHD0qG4NYdKkSYhEomKvI0eOANCzZ88i2zt16lSu61ndEGZkAgJlIBKJcLE0xtHCUAg+e84QiUQ0q2HNrwMa8GFLD4zlEkITsxi75RrTd98gLj23qk2sUnKta5Hp2ByRVo319eVVbY7Ac4JZtC7LozrLhv2Xwki/QUNHlDvSz9nVnaMH9qBS6WQDbgUHodZoif1zGLMGNGbwO68RGx35yGPfCg7SR76BLvLuRtCVSjhLAQHB0SJQvTEyMiIvLw9/f/8i27t27UpwcDBKpZKNGzcW0f8uqU+hfNiySFvcBy/Fu99IBv68mYsZpvwbnkJ6jorw8HD69Omj72MokyCX6v4+3nrvI7YePMvkWb+wad9JrG0fLBo+KY8qTN29QyuG7tPiNieTgyt/Lta/pMWbw3t30r1lXep7WDOgZ0dCb4eUy5aqKMD8rPnzZDiRKdlYGMsY1tHnhXKCPAt69HmDH/7+F9fh62gzdTv7zl3H0soan5q1mDFhDMs37WbDnuOMnzGHudMn6jpp1DiemURQQgGL/s1nbtcHckTpuRqG7c7lyCe2bNh1pGi/MggPvcPyxQvYevAsm/ef4r1PPufXn394qraH+XPRPO6EBOvf/71uJWv+WMz0+Uv5a8tegi5fYNrYEUX6nDxykLOnjjH0q+/02wqzk7v3fo2V2/azc+tGzp069sgxy7Jz0JARnAgKJ2T116R+bcalIVZYWVriV68ZADeuXmHz/lOcCArnRFA485atKfU6lrf49YHd28nJzub3ddv59MsxLF69hQxlOts3ryuxLS4xhdundmFhJKNfgwfyP1kOTcg3cUSLiOWXVdibitg/0JhxbQw48p4JR8PV7IoxZ+3RG1jb2LJkzVY+GT6aPzbs4PyZk5w+dphz+7ZgZWuH89vfk+bXiy5jFunbBg0dwaKVm0iR27P9ngjLdu+jCT1DS28bpBIp8ock0wwMDMm9X6tGKzUkof4wunhJ2fGuNXJNDgvH/A+TmNMMG/0dQZcv8l6/7rzepSV7tv/NSJ9wfLa+inF2NIbiArJt6hDZdjbh/ztFXoGGbs0bkHntIBiZ03X0L8XO4WG+/3YU9Ro2pU3HrrrP9/45ePvVpIaPHyPGTuLArm2lfoYVTarvG2gRYRp7Crky/Jkd91lyOESXWdXU06rcEvE9er+m/5s6ERRevnvffQoVD76e+CCoQ5me9sh+JVH83vAF5zb9xvy36uMiSiPp9Cas3vuFpl/9wdsfDinXvc/QyIgLdxPw9PYtcu9r2a4jAz8ZioWVNb+v+4eV2/br71MP9zl8cD/Hjx3hpnMPpu68QZhpAGfOnCE3N5dRo0bx77//snr1ag4fPkx4eDjJycnMnz+f1atXc/LkSc6dO8fgwYMZMmQIO3fuRCaTcfPmTW7cuMFXX33FhAkTAPjmm29ITU3Vv65cuYKtrS316+uk7C9cuMC1a9f07du2Pbu/m4pEmJEJCJQDG1MDPGxMnlgvUaDqkEnE9K3vwpKBjege6IBYBKfuJvPZ6gssPxVOdn5BVZtYZRRmtViFrEGkyn7E3gIvO9LsRIySrwOQ4dy6iq0pPxUR6ffv5WtIrd1w6vs1mw+cQSqTMuWbEaUdUk9mZkapkXcCAhWNXHC0CLwEDGrlia+9KRKxiBy5BdHmgWy5kcn4rUG88dtp6k7eR4vpB/ngz3PM2H2TLZeiCI5RYiR7IPfo5lmDdp27V1jx7x593njk4s2u379n8auGTP99a5G+JS3eRIaHMX7UUL78ZhIH/r2Bk4srk8YMK5ctj7NA+zxyLiyFnfcLCI/o6Iu5kaA88LjI5HJa1nJHbGjKvUwRG9aufmTWr0ncOWSZMQzekcuXzeR4WT34vcnI0zK3qyH1zVIwiTtXLFu4NNQFBUz8cR629g4A+AYEokxPe6q2Qu6F3eWvJQtwcnXTb3u4Toenlw9DRo3l8L7d+vbcnBx9PZGHa4aUlZ1c1phl2WlgaIhTxhV8bv2GhaGIn+4F8s4nX2CmMC9S36MwM7u0IuoRYaFEhIfSrFW7IttFIhE1A+sybfZCDu/bhTItjfjYGGrXb4i5pSWgq4nl61+L6Mh7xdry1JBv7kJBehwDmroVXcgWS4htNgmASKWGjp5SDO7XiDM1EONjLeaKeVfi42Jp2rIt8vt1OkxMzXDz9NIfr3WbdnzRWSdDvPV6ChYObkRH3gPgbmImM/fdRKOFlgFuZCtTyc/LQ2FhSWpykt6UrMwMpLIHjpdU3/5ojW1o55DJby3j2bR9HzV29afDqbc4umgk895viDdhdK4hoYviLlqRFBP7GoQ4v8Hd3ttJ83mdzFwVUpmc1xu6UMMoF0P3uqw5H8vfF6OKnEMhW9at5MK5U0yZVboUt5m5OakpyeTn5ZW6T0WiMnUm00UnU2wZsuGZHPNZotZoOX778WXDKlrxIDsrs8x+pdpfyr3B0dyIER29eG3YFORmVhy/ncShRCMSklLK7Pcwj3vvS89R8dexEL4aMQzDFu+QppZhZSLHy9aUQ4cO4ebmxowZM/Dx8WHChAksW7YMgBUrVjB69GiaNGmCn58fkydP1jtFnJ2dWbFiBb6+vgDUrVuX1NRUAAwNDbGwsNC/fvnlF0aMGIG5uTlRUVFotVoCAwP17SYmJd/7qjtCjRYBgXJialBYtyWLXJWmqs0ReEzMjWQMaefNK7Ud+f1EGJcj09h8MYqDN+J5p5k7HXzLr6f5opDh2pE8hTsGyntY3t5MSsDAqjZJoBpjGq2TXsixDkRtZFPF1pSfjav+5LNRY4tsK4z0+3naeCaMGsrc31cXieYzt7Rk0Ocjea1zC7ZvXodl3c44vutJAzcLPDw8+XbqTHq0rEdmhrLMhbqyIu8EBCoamVQIBhF48WnnZ0c7PzvyCtTcTcgiJF7JzbgMQu6/YtNzibn/Onxfvx1AKhbhaGGEh7Ux7tYmun+tTLBTGDx1/b7yFKbOyLSgroOEtKwsJDnJqI2sS128Cb0TwrCvJ9C1p05b/82BH/LZwNcfaUdVFmB+FqRm5TP/kE56rVddJxq4W1axRc8vlsZyvGxNuBObyro/F7Nx16Eys36lOQksvajicpyaj+rL2HFLRVcvKTKJCFdzMf+ro3O8aDNiWL54X5Fs4dLw8vXHy1eXrZadlVmkpsaTthUy5ZsvdVkjh/frt5VVpwNg8bzi9UREIlGJ2cmFMlBljVmWnbLMaFwPDyMlW81uTSt2nTzL3p91TtHHqe/xOMWvHZycycstqugQEx1J4xatMTI2LtK26UIUOSnxuPg0oEuAQ7HjKj27E9HxN5xPf05IzIOF5Twje+7libGs2x0HLnL3oUw8jUZDQmwMjs6upCQlcvd2CB1r2hOTnsv6f+8RHR1NnoElnw96hzj3zuRZeVHP1YJamXc4ZmeP3MCAwLr12f3PA2mmkOBr2Ds4ArBzy0Yybx5hqm0yoKsZUxgkK8uKxeXEGMRKDYduZ3P0Mxfi639ISs3/4WGyTzfm/c/34TFb1PEjN/si2cCfp8IBrf4cAK5dusBPk79lwR9ri2RHjvz0Xd779AvqNmis+0yvXMLm/jk8K1L83sIs6iiWtzcQ33AkiF+cpd+rUWmkZqswM5RS3+3JfgcqorbRo/qVRln3Bl//moz3r8mt+Ax+2n6Zqye3YOBSn2UnQhnYzLfC7n0isZglx+6yLzieuIN/olHlYWFiSEeLJL4e1I8atmZ88McVOnTooL/3NWnShLFjdXPqpKQk3NweOHMkEom+hktAQAABAbrnkMzMTBYsWEC/fv2KXYeYmBi2bNlCWFgYAOfOnUOtVuPi4kJqaio9e/Zk0aJFWFo+f7/1QuibgMBjIJeK8bI1FaKnnmPcrU2Y0qsW418JwNnCiLQcFb8cvsPITde4nf6SLVKJJSTX0qWa21xfBlrBgShQOqbROm3VDJfnp5B7RUX6XY3STSLruFgAYKYwR6PRkJgQX+bxHxV5JyBQkQjSYQIvEwZSCQFOCvrWd2Fs95os/6AJp8d25MqELmwc3JypfQJ5p5kbjT0sMTOUUqDREpmSzfHbSaw6c49pO2/w8crzvLXkDF9tvMKCQ7f550oMV6PSSM9RPbFdpclVZhvY8uMFI/rVlGEW+X/2zjs8qqKLw+/dlmQ32fTeCCEhQOi9d5AmNsAC+gkqKIgCioJUaSqgIEhRQUBAehGpgvTeIfSQRiAhpCebtu37Y2EhpgJp4H2fJw/snTszZ+7m3tyZM+f8/gEKTlfZusML9H4kFVDEzYciy4Xx6AJto0APRn7Un9Tk5CceS0XCaDQya88NUjK1VHJU8k7TSuVt0jNPfV8HNJf3YVepesFRv+OmoI7YgdWxWYz5J5sABwnRqUa+P5pDqyUZZOkealWcj9VT/5VPckULF4eD/+yiXf2gXJoaT1O2ac1K0tNSefuDIbnOL0inAyDm9q189USMRmOh0cmFtfngcy1vO5oEeXHs4D4W/PAttbzt2D/lZdadjqPK3Ew+XXqa9LQ09u/eCeTW9+ja8zUO79+TS9/jxtXLvNGtLc2DfVm9bBENmz6MMvfzD2TdiiWsW7GE2DvRzPpmgln82kZtS8i50zSu6sFPM6awYvECrl2+SPM27WnVrjPhoddY8/tiLt8IZ9mv88mJC+P9N15GKhFIT0s1P58ekOrXhXpfbuXPmzIWSN7mcP25fBTRmWy9QN2GjenU42X2797B39s2Extzm9nfTCQnJztPWQcfKdYX1mDUa9l814ZIoxMRW+dhlx5JC0Uk87+fRp9+piwMbTp15eyJY5w4cgCdTsfShXNp1tp0vf0q+zP715VsvKIlItnA+H3Z9Kp+39F0/2fSQS1d2zZFOeIMcfWHo1O6Ftpmpx4vE3H2IHX119ClxvP91AmkZ2ZSt2FjEu7F8XH/13n3w0+oXqsOGZp0MjTpAARWq8H0iaO5cPYU+3fv4KcZU8xjKCvSfDqis3RAnhGHTfS+Mu27tNl33bSJokUVpyd+9y2JjAeF1SsOhT3f7l46SsiM11Hq07Bt2odN5+4wYu05wuM1BT/7Vi8v8tkXmaDhuzkLMHrWYsuFGDSJsaSf2kQlv8p09pVzcOUPDH73LYxGI6mpqfj5PdwEolaruX37NgB16tRh06ZN5rLffvuNTp065b7G27bh7u5ObGwsX331Ff9mwYIFvPnmm1hbWwNw/fp16tevz86dOzl16hQRERGMHj262NezIiHOyEREHhOJRMDHUYmrbdntSBApWQRBoJGfA3PeqMv7Lf1QWUiJSMhg7mUpU3dc405yZnmbWGYkBfRCL7fBIiXsuXsJEylBjAaso00RLemez44+S347/WZOHmsuL85OP1cPL3b/Nh3NlYPUvu9ouXT+LBKJBDcPTwojuHZdLpw9Zf786C45EZGSRBBER4uICICtUk7DSg70a+LL5JdqsnZQMy6M78SRL9sxo1ct/tesEm2qOuPnpEImEcjU6rl2N41dl+/yy8EwvtoUQt9Fx+m3+DhjN4fw68Ewdl++y427aWRp9UX2X9jizd83Mpn1giXqqN3FWrwB0ObksHThnGIt0pWrAHMps+VCDGeiklBIJXzWqapZc0fkyanva0/aue1Iq3VCb3joMAmsFsyvK9YQ5GnHd2/WwXf3B2w/dgWN1sied1SMbW3Brn5KkrOMLDtvWnw3IhBU2ZOf/9iSSxekODRt1Y55y9Yik0n5Ycq4pypLSUlhzneTmTh9DjJZ7h38Q78Ym0en4/X/vQ/A5rUrC9QTKSw6ubA2wZRWcP/5m/y0bC0BQdV5+fV+ONpY0lB5m4+3Z/PbitUIEglDPv/KrO3wQN/DaDSy86+NfDV5hlnfIyc7m4/7v071WnVYun4H9+7GEB8Xa+7Pxc3dJH69aD4vt29KVmYmU2cvJDEhntGfDOSlPn2pVDmAn3+cwaKffuC7uYvw9PbF1t6eeb+v568Nq3nrhSYkndhE/f9NoGvTWgC81qk5B/fszPPd+QUE8d38pcxes5/2vT7gwD+7mb1oJSprG/z8A5g+bzG//DiDHi3rc2DPrnzLXmzVgKywUzR8bypZWGCs/RLWrpW4seQLfpwyht79+jNgyHAA7B0cGTF2MoP6vkr7+lUJvXaZD4Z+DkBD52zmd7NgxK4s6i5Mx9dWwvePaAmFJhr442I2wz7+EKP04fdZWJsP7LyyfQl3fx1I5s2T2PQYzd6wNLZtXkdi/D3mTp9MkyAv84/pOxyOf0BVBr75Mt9NGJVrDGWFUaogqcqrpjFeW1WmfZcmWVo9R2+aopbaVH1yjbWS0DYqrF5xKPr5tg4XWyWBkZuxs5ITkZDB8DXnuKsO5Kela3LVS0yIZ/a3Xxf47Dt96hTt2rTmlY7NCT/xN9Z1u1Hby5ZGORdxdXVl9catDPrU9Ow7dvgQf//9NzKZDItHorAsLS3JyDA9+6ZOncqJEydo2bIltWvXZvXq1QwZktvB06lTJ7Zv345MJmPkyJG5yvR6Pb/88guDBg0yH/vyyy/Zvn07NWrUoFq1anz77besW7eu2NezIiEYjUZj0ac936SmpmJra0tKSgpqdcnk6hX5b5CapeVWYgYGMRDgmSY1U8vK4xFsD4nFgIBMItCjtgd9Gnijsnh+wmwLwu34ZJwv/kyaRwsiuj5fubxFSgbL+BACNnVFL1dxpe/5XBOUisz/Xu3CS73f4qU+fQGIi42hZ9tGjBgziRZtOzBn+mQS7t1jwfL1pCQl0bVFbT75cgKtOnRmz/Yt/DB1PLPW7mXswrWkHV3NvAU/YzTomTrmc+rUb8TE+/mY09NSsbC0Mjt0HpCUmECnRjX4adka6jVqxqfvvYWntw+jJk1/6rEZ9Doizh2iUp0WSKTP/3NKpHAUMglV3WzK2wyRCoBWq2Xbtm107do1zzPpv056to7wexrzZ53ewJ2ULCITNEQkZBCZoCEyIYPY1Kx86wuAu60lvo4qfB2VVLr/r7utlTlFzZvd2/HhiFG0bNvRXM9oNHL10gVmjRuGa9oF1vV15lur0Uwa8wV/H7+Erb09Op2OVzs2o997H/HaW/8z1/1+yjiOHviHlX/tfezv89TRQ4wY9A77z9/Mtzw5KZGImzfwrVwFe4eKm0I3Il7D8LXn0OqNDGxVme61PMrbpOeC8LCbvNy5NV5DljOjdz2C3NVIM+NxvLwUx8vLiIlLwHdWOnfHevLDzSrsvxzDoZ6mSF4BI33WZVDZTsLUDlYARLVfQKpfF2LvRNO5SU0OXgjPN91VQZw+foSh/V/n8KWoJyo7eCGMoW+/QkDN+nzypclp0b9XNz4c/qU56sNoNBIeep3vp45Dm53DwpUbAZj4xSfodTq+nvmTud03e7Tn5T59uXrpIvYODgz5fAxgEsDu0LA6J67fKbTN/Owc9MaLvFNTYEwrC9bbDkBVtS3fTRzNN3N+5p1XunD0yi1zm++88gKNm7eiQ9cX6dW5JadC73Jo79+M+2wIf5+8jJWVkmuXLzJ1zOcs3bCj0Pv591/nsXb5b2zeewJBENi7cyu7tm5m2o8/5zrv5r10hq0+hxH4vldtAlzL7r0iJVPLF+svkKjJYVLP4ELfaaLCwwgLvUaDJs3NKXxtb27GZ+/HRfYT1XYOKf49i9XmoxiNRpYdjWTdmWgAPmztT9eaFXvzlEXSdQLXd8AoSLn6xnF0yid3TBQLoxFposkJondwNKdjK0kO3rjHdzuv4WJjwa9vNzCntSqMf8+ZosLDeLNHO/aevZHv39VHn2Grf1/EsUP7WLR6i7n88w/fxcu3kvk5k1+9kn72bT0Zypx/QjkRYdJrqeVpSzu7RMYN6cfhS1F8+fH7eHj5MPQL04bC/r26MWjYlwgeNVh3OppLd1LQJUSTtG8xtgqBub+vJ9DVJt9n3zsvdWDg++9x7tw5nJycmDTJlIowOTkZT09PNBrTe5TRaOTq1auMHDmS7Oxsdu3ale/4Dh48SI8ePUh+JMJ29+7dDBs2jIsXLxZ4Xc6fP0+dOnXIysrK5fApLx7HbyDOzEVEngK1pRx/Z2uiEjPIFnVbnlnUVnI+aOlHLeltdiY6cuZWChvP3jbrt3Sq7maeQD+PJFR/B6eQX7G5cwiLxGtkO1Qtb5NEKhg299OGadybPjNOlqzMTC6eO834b2ebjz3Y6Tf969HMnDyWZq3bMXX2QgDzbr6Zk8Yw4+uvcHRx4bu5i4gx2GAd3B4XfTzD3n8LlcqGdi90Y+gXD3cevdapOSPHT6Pdv/LyPrpLzsZGjZVSycTpc8rmAoj8p5BLn9+/USIiJYW1hQypRDDv3pdJJfg4KPFxUNIy4OF5mTl6ohIziEjQmJ0vEQkaUrN0Zv2Xo2EJ5vMVUgneDlbY6xIJvRmKhU9tEtKzcVApEATBnK5y4pwldG5ai9TUdBJvHS8wXeUDjh7Yy9rlv7F8899P5DQ7dfwwSYkJ1PK2y3V80syfUFhYMvmr4Xh6+RBxM5SJM+bSpadp9/O0sZ/zx5JfzOd7+/qx9dBZNq9Zwdh8dutOmvkTPXu/VagtQ97tw4HdD3fEN27Rml/+2FzkGLJ1embsuoZWb6SBrz3dKvjCZoXFoEcVewJZZhw6Kxc0bo3YvXUTvnVaYpDKiLhxHtnmnzm8bzczOph+1ww2HiDc4Oaru7Het5/0q4uIaj8R92MTUGhiiEw20raShF0xNmxIrcOQ97oAuaOFC2PrxrXEx8XyzkDT4rhMJkMikT5V2YEDBzh95gxrfjcJNmdoNAz53+t8MPQzBgwehiAIqKxtOH5wP0s37jDb4ubuWaCeiEwmK1AXBCiwzX/bKU2JJEerY1RLayxaf0r7Bp8x8YtPaNPxhVzaDsMHvo13pcpmTYgNf/yOo7ML2zevM99/jQMfOhstrazYvnl9se7n2j72ePv6sWT9diZ/NSLPswHAseundH3ljUKdLE96PxeGrZWcuW/UJUdvQKkofJnSx68yPn650ynqrIrnRCjovPzafBRBEHi7qS8Go5ENZ28zf/9NJILAC8F5NWwqCtn2gWhc6qOKO43djfXE1/6wdDsUBPSOpavjue++9lqbqi7FcrLkR0lpGxVWrzCe5Plmp1RQTxtCZmIo113acOF2ChfP3kRvNEV3btu0FpW1DauX/YLRaHr2fdCvF+qmfbBt0gu5VEKLYB/WLb/IrI07CLx/f+f37Iu5cxtfX1/kcjl//PGHuezcuXN4ej7M5iAIAmq1mt27d3P48GHz8ZUrVxITE8OIESPMY3ig3/KANWvW8PLLL+c69tprr/HZZ5/RpEkTAE6ePImbm1uFcLI8LqKjRUTkKbGUS83OlvQsXXmbI/IUuClhfLNqnIlOZdGhcKKTMpm37yZbL8QwoIXfE4utVXS0Nt6kVnoB2/BtOIUs4nar78rbJJEKhnX0AQDSvJ6dtGGWVlacvhmX53jzNu1p3uZ4vnVq12vIso0PJ456g5EPV5wG4O2ho+n2S/5Okh1HC96N8/o779GsVbtCd8mJiDwtYtowEZHiobaSkaQpXIPFSiGlqptNrh3VRqOR5Eyt2enyIAomKjGDHJ2Bm/c0pBzdjNS3PpO2XwdAuBOC5PY5uvT/HF9HFWpjNgASAfylsfxdwOINQHRkBF8OfZ8xU2eaxW+L4t8CzA6Ozjg4OfPnvpMAZGRo6PNCKwKq1eDDvq+yZN12AoKqs2X9KmZNG29emL188Tw/LV1D7fqNAMwLJF1f6kXbzt3M/T1or37j5kXaduXCedb/fQRXd9MCsayYjqMlRyKITMzAzkrOJ+0Dnnhh7b+MOny72TnygByVO8d3CHRu0YZX5DPpeP00dzINvHMyk0rentR/9WOmrTpC01beqBzdadWuM9+MG8mvh2Jo1W4th1b9yLnYX3mhihVRL/7Mip59cKuzxBwt/EAXBAqO+vWrEsCkUcPw8qlEUHAt5s2cRqfuLz1V2cKFC/Gq3hDJ/d/ZkYMH0HfAhzRv097c788/zqBj955Ur1nHfKxTj5d5vWsb/t62mZp1G/DHbz+b9URycnKYOuZzThw5QL1GzXJpeBTW5qN2Vq9ahWmjh+FjK+BYvRUR9YYBsO/v7WRmZGClVLJ57wlTPf8AFs+fjae3L8cP72fXX5twcHKh/Qs9uHT+LNk52YwYM8l8/+l0OqaN/bzQ+7l2/UY0a9Oet94diFRqEsLO0Gg4FBJhtvfY9duMfKsLKp9g3m76MPVhfjzp/VwUMqkE2RO+z2jcGpGjckeuiUUgb9IeIwJalRsat0ZPbJ8gCPyvWSX0BiObz9/hp32hSCTQqXrFdbYkVe2DKu40DtdXE19rUKlEmZQVKZlaTkclAdAm0PmJ2zm8bzcvPbI5wM8/kE/f64uvn3+eZ9iDZ9+a3xebMx480DaSyxUF1oPSePYFMmnUcEZMqcreeCWn9v6OzL8JM3ZdY/2+M0glAoduxLPrcixXV05G3eBF7Ks2ontdT16s7cG8KaPoVIxnX3Z2Ds2bNyc7O5vBgwezd+9eWrZsyYwZM+jcuXOusUyePJlevXpRr14987GgoCAGDhxI5cqVqVu3LuPHj6dXr1656u3YsYOlS5fmOlarVi2GDRvGrFmzuHfvHmPHjuWjjz56ou+4vBEdLSIiJYBUIuDjoORabFquHLcizyYNfB2o42XHjkuxrDweRWRiBuP+vESjSg70b+6Hp71VeZtY4sTXGIBt+Dbsbm4ktuEX6K0qbgoJkbJFotWgvGvSGXmW9FlKgj1X7xKTkoWtlZx2T5EHuKhdciIiT4NUIvwn0lyKiJQEtlbyIh0t+SEIAvZKBfZKBXUe2QWuNxi5m2pKPzb5zxA8GnVBamdFTEomOTbu3Dk0kT/kTlhVrk/ywd/xqFQFW8s4OhDKsKvxTJk5m84vdOXy0d3mxZuszEyGvNuHdp270bZTV7PAspVShSAIBS7ePBBgHjlhGkkJ8SyY9S1vvPM+aluTvauXLaJ9lx44ODoxcsI0AoKqA1C1ejCpKSkA6HQ6Qq9doX7jZihV1rnalysUyB/RqnjQnpdvpUKvXWzMbYwYzf0Vl1MRifx1weQc+KRDAHbKZyOitiKhDt+Oz55B8K+FZ13yHc5dSmNpy1sE3XdKKII6MntGAyb/uIy7f40pMur3t/dqU8kuHJkhosBoYSg46rd6zTqMmTqT6ZO+Ii01hY5de/LZuClPXBaXls15rRtxSXJslRaoreQYJDKk1nYolCanaVR4GNs3r2PD7iO5bHmgxTF3+mTCQ2/g5etn1hNRQaHRyQW1+aidmoQYFOj46TV3brWdAxIptyLCSUqI57d12/hp5lTGjRjMrF9X4OXrB0Yj8XF3uXDmFAOGDGfHnxvYsWUDVioVcoUCta2d+f7b9/d23hsyvND7uUmLNqjVtuZngV6vJzsr8+Fng5E5CyehDGzGK23q46q2pCCe9H4udSRSYppMwGfPIIwIuZwtRkzOhZgmE0AiLaCB4iEIAgNa+GEE/jx/h7n/hCIRBDpUc32qdkuLFL/uuB+dgEVKGMq7J8hwa1xqfQnZ2bh/bRI9jxk3BWMJRyIcDo1HbzBS2VmFt4PyidooqYwHD3TYyuPZN3fGRNJSUqjasD3Jdd5k//V7hNxWoNUbSM3SgcweucKCLo2q8eEbbbG2kD3Ws++3FWuwsbHBxsaGmTNn0rlzZ2xtbVGpVCxatMhcNzQ0lJUrVxISEpKrzXr16jF//nyGDx9OcnIyr732Gt9//725/ObNm9y5c4eGDRvmqjdq1CgiIyPp2LEjLi4ufPjhh4waNerxvuAKgqjRgqjRIlJyJKRncyc5/7zOIhWbgvQO0rK0rDp5i60XY9AbjEglAt1quvNGQx+sLZ+jhS2jEf8/X0R57zx3648gru4n5W2RSAXBJmo3lXb1J8fGm2u9Dz3TO6EeB63ewMDlp7mXls2AFn68VKdw4fuyRtRo+e+ikElQKqSoLGQoFVIs5U+3aCDyfCFqtBSO0WjkckxqiesrZmVm0jzYl3U7D+FXJZBsnZ5biZns3LmDtT9NITXhLtb+9XFo158Q+8+xEnJoGvYBZ//ZSs7dMKTWdgS+OJj6rTqTFXqUtd98mqeP7UfO4+ntywtNa+a7eKPVapk8ahi7tm7GwdGJHq+9zntDRiCTycjOyqJz05qs+HO3eYHoQZ3xnw9BIkiY/MN8Ll88R/9e3XF0ciYuNob6TZoz/ttZuHt65+qroPbyY/f2P5k0ajhyuYLUlGTadHiBMVO/LzSHfVJGDkP/OEtyppYetdz5oJV/0V+CSG4MeqquboZcE0NBb25G4C9ZJ2ZpOvJi+za0Cyr+YrHL6Zm4np1Nkv/LRLedXXSFUkRvMDJizTluxmsKPEchk6C2lKO2kpn+tbz/r9X9/1vJc5XbWMpRyB5GVxSl4VEQjiGLyNg2nka/aDiybwdaz7wL3cXVhLBR2xJ67TLjv/3RfP/1fqElWw6cwcHRqcD7WRAEsjIzadyiNeO/nYXK2iaXzsxfZyMZ83pr/N+bxdKPuxc6x32S+7ksKSiCK6bJBFL9upRYP0ajkZ8PhPHXRdP99WmHgMe6f8oSzwMjcbi+iqSAV4lu/UOp9SNkaAiuapovhVy7jVGpKtH2R66/wJWYVAY09+OlusWflz3Pc6ZrsWnM/PsaMSmmNUg3tSWv1POkXZALFrInmx84WivwsHu4sTg0NJQrV67QunXr//R6uajRIiJSTjioFCRqcsgS9VqeG2ws5bzfsjJdgt1YfDickxFJ/Hn+DnuvxvFWYx9eCHZ/PvRbBIH4GgPw2TcUh8vLuFdrEEbps5cPU6TkMacN82z9n3GyAOwIieVeminHfpcKnH9Z5PnHSiFBqZChUshQWkjFNGEiIk+BIAioLeUkZzx+VEth/DtdpYVMShUXa6r0e43B/V4zH0/N1JLw90a84vbxZX0t8+stIDIhg0ytHg1w4MY9oAp1v97F9Fdr4ZLPzvKC0lXK5XImzpjLxBlz85Rt27SWWnUb5HKKXLt8kQG9eyBXKMxpi8JDr+MfGMSor7/DzsGBb8Z/wddfDmP+7+uKbK8gIsNuUr1mHUaMmYREImHsiMHM/vZrxk77Pt/zjUYjP+65QXKmFl8HJf9r5ldkHyJ5UcWeyLXYnB8CEO3VjZtXnTkdmfRYC8Wa+zvjrWOOgtFYru+I20NiuBmvwUpqpF4lR9Ky9aRmaknL0pGSqUVnMJKjMxCfnk18enax27WSS7HJ5YSpzI1z8aitUh46ah4pt7GU5Up9pYw9ifvxKUy7pKVd8/pmJ8uTakL4VvZnwx/LzPcfQE52DrZ29oXez01btuHE0YPI5DK+/nIY/T/6xKwzk5mjZ8GipVh4VOXtjg2L3Ej4uPdzWZPq14VU3055NImeNpLl3wiCwAetKqM3GtkeEsus3TeQCAJtniICvrRIqtoHh+ursA3byp2mEzEonr3F8tjULK7EpCIALQNKVwfmWaKqmw2z+9Tlr4t3cFNb0szfqcTXpqpUqUKVKlVKtM3nHdHRIiJSggiCgJutJRHxGeVtikgJ42WvZFz3GpyJSmLRoXCiEjNYcCCMrSGxDGjuR33fZ1+/JaVyN7QnpiDPuItt2BaSA14rupLIc49N9H4A0p8hfZanJUurZ83pWwC83tD7iXcEiYg8LoJArmgVpUL2fDjzRUQqEGqrkne0PE7fBHaBuH10lJ2hcs+JGI1G7qVlE5GQQWSChj1X47idnMm4Py/x3au1THWekrXLf+PDEblTcARWC+aXVZuZOXmsOW1Rt5d70+3l3uZzRk+aTtfmdUhPS821iz+/9gpiwOBhDBg8zPx52KgJjBj0ToELs1svxnAqMgm5VOCzTlVzRRWIFB9ZZl6duvyobWda1D8blWyO3i8OGa71MUjkyDNiUaRFkqOu9KSmPhWJmhx+PxYJQHcfA307BebatW40GsnSGkjN0pKaqSU1S5f7/5nafMsMRsjU6snU6olLK75zRqWQoraS463QMF/zKYJRx7pwa7y6vcjOS7GoreTorN1Zs/w3XDwr0a5jJ+bNnFIsTQhXd0/S0lL5ec4MRk+eweJ5s2jcsjVSqZTAasHMXrSSeTOn5bmfkxITWLpwLl/PnMuooR9gNBrNOjObzt0m9sQW/Dq+S9ea7kWO73Hv53JBIkXj0bTUuxEEgUGt/TEYYeelWH7YfR2JINDqKfRDSprkpETORWmxkPnhpQvH7uafJFbrW6x6ETdv4Fu5CvYOxU8pfgeIAKTp6Sj/FdESFxvDnegoAoKqo7K2KXYZwP7r9wCo5WWLo3X5bQYtys7ywEohpVd976JPFCkzREeLiEgJY3N/N0talq68TREpBer52FP7dTt2Xopl+fFIbiVmMGHLJer72jOgud8T5wutEEjkJFR/B7dT3+EUspjkKq/+pyIYRPIiT4vCIjUcoyAj3aNZeZtTZmy9GENyhhZXtUWFzbks8nwglQgmh4qFFGsLGVZyqSj2LCJSythYyBAE0yb88iDNxyTMrbx3HlnGXXRKV1zUlrioLWnk50Cbqi6MXH+e28mZfP3XZSa/FPxUKQKjwsOIigijSYs2uY4LgkC14NpM/n4enZvUJDU5OU/6Hxu1LQaDgXtxd82Olvzae5xFORtbW5ISE8jJzkbxrxz+kQkaFh8OB+B/zfyo5FSyqWf+S+isirez3sXdF5WFQFq2jht30whyL95ud6PMikzn2qjunkIVc7zcHC2LD4eTkaMnwEVFM9eUPOWCIGClkGKlkBaqPfIoRqMRTY7+ESdMbudMWlbe42lZOoyAJkdPZo6WGfJvcJQmcDHbnXNRN7mb5cyFvaHmPmy6fs6s2bOYMXkMNv71qfrKMEauO4/aSk7bobNYvGQ230wYjdrBiffHzSJNZo+QrmXgp1/yzdjPGDPsQ4wGA4vXbjWP86tPB/L+x5/x9Zef5rqf7R0cGTF2MqM/HYTBYCD06iWm/DCfJE0Oq/acQJcUw+C+Lz1RxGxh9/N/AYkg8FEbfwxGI39fvsvMv68hkQi0qFI2UReb16xg7IjBeY5PmvkTCgtLJn81HE8vHz4Ivcpv3WW86LSKxGp9mTb2c/5Y8ov5fG9fP7YeOgvA9s3rzfUiboYyccZcuvR8tUhbli5ewK+ANxDeugE/Lv6DBk1bmMoWzuHnOTNwc/ck5nY0Py5aWawyMN2Pey/f4c6iwXT7YhJQE6PRyG8LfmTDyqWkpiTTuccrDPtqYh7nzsiP+mPn4MDrLz9MtXnq6CEmjR5OUkI87w0ZztsfDDH3U1ibBdk57/tpLPjh2zzXY9GaLTRs2pIh7/bhwO6d5uONW7Tmlz82F3k9RZ5dREeLiEgp4G5nSfrd9HKbwImULlKJQNea7rQKdGb1ySj+uhDD6cgkzkYl0fW+fktJ7D4sDxKD3sLl7I9YJYSgij2Oxr1JeZskUo48iGbJcK2HQVExdu2UNppsHetPRwPwZiMfMU2TSIkilwmmFGD3o1ZEfRURkbJHIjGlD0vJLJ+oFp3ShQznOijvncMmag9JQW/mKne2sWDii8F8sf4C1+6m8d3Oq3zVtfoTR7ft/GsjrTp0Nmv2FJa2aPrE0dSq15DOPV4G4NL5s0gkEk4dO0TPNrmFa+tXds6zmPfvRbn8FvP6Dx6Gk4srh/ft5ruvRxN7O5pqNeswfvpc5p7NRKs3Ut/Xnh618t9dLy5aFQ+9wjqPKPijGBHQqtzIdG9MHe8bHA6N51RUUrEdLQAatyb3HS1HSarap6RMLzbnbyWz//o9JAJ82KoykttnS6RdQRCwtpBhbSHDA6uiK2DSidFkmxwvvudmUv3mJXIkVuyrM5NPVnjmddpUa4RV5frm+nE6iItNu//JBfnLU3igQvFXKvy1/gIAqUcv4Bjckhqte1CvQSMOXQxl5fJljPl6KjuOXuTe3VjTGP51P7/+znsolSrGjviIVdv24+jswty9oSSFHMA1uBmtg4qOZgEYPvBt3hn4MbXrmZ4HIefP4uTi+p90sjxAIggMaVsFg8HInqtxTN95FYEgmpeBs6XrS71o27mb+XNGhoY+L7QioFoNPuz7KkvWbScgqDpb//iVL6aM5PXgC1gmXObyxfP8tHQNtes3AkAqNb2PpqYk8824keZ6W9avYta08UU6WiLCQvlt0QIuA+7AkP4D+WnmVH5bt42IsFCWLJzDpj3HcXZ1Y+Gs74pV9oCweA2XdyxHGx9JDQ/T82nDqt9ZuXghP/z8O9ZqNaOHfsDkUcOYOvtnc73D+/Zw/MgBNv9znMRwU8rNxIR4hg54k7c/GEyXnq8xcnB/goJr0ahZq0LbLMzOAR8No++AD839xt6J5v03XiKoRi0Arlw4z/q/j+Dq7gGATNTPe+4RHS0iIqWAhUyKo7WC+LSc8jZFpBSxtpAxoEVlugS7s/hwOMfDE/nrQgz7rt3jjUbedA12z5Wn91lAb2lPUsCrOF5dgWPIItHR8h/HOvogcF+f5T/C5nO3ScvW4WVvRevAipdnWeTZwlIuQWkhQ3U/DZiYAkdEpGKgtpKVm6MFINWnA8p751BH7c7jaAHwcVAytnt1xm4K4WREEj/tDeXjdlWeKOLt8L7dvNT7LfNnP/9APn2vL75+/rRo24E50yeb0xZVrVGTOd9NwtHZBb1ex7RxI+nZ601e6vUWnbubnC8fvd2Ljl1fZPG8WbkW89w9vdi9fUuuRbnLF8/T7eVeRNwMZeiX40lNTuLbCV/yQo9XGDtiMGOn/UCDJs2ZNm4kQwcPQvHyFGyt5HzSPqDAsYqLVkVjGX8Rv+1vIWC872bJ7XAxYrq2MU0mgERKAx97DofGczoyib6Ni9bdeYDGvQmcn4sq9niJ2l8ctHoD8/ffBKBrTXf8nVVE3C5zM8xIJQJqKzme9/ZT6abJuRjbejpt/FvQpoA6Or2BtGydOSImb/RM7vRmKZlaYsNOY12zA5HW1Ym8mo4uzcCdlUs4GCcjsH5LYv9ZSpW6zQhLNeITUD3X/bxo3g+81PstnFxciUzQ8PflWDLDTvP6u+/mud/S01KxsLQyO2gfEFitBtMnjmbkhGkkJcTz04wp9Ok3oBSu6LOFRBD4uF0ABqORvdfuMX3XNSQCNPUvXWeLXKFArlCYP69etoj2XXrg4OjEyAnTCAiqDkBAnSYkZZveQW0uryT02hXqN26GUmWdq70MTXquelWrB5OakjdS7N/odTomTPoO9w/fNdULqsGuXdvMZeO/nY2zq0nzMrB6MLu2biqy7AEb950i9cQGVI7u5g1KW9b9wf8GfUzNuiZn5UcjRjFy8MPfw6zMTKZ8NYJPvxyP2taWxPvHt25cg5OLKwM/GWlK/fbJSDauWk6jZq0KbbMwOy0sLbGwfBgt98PU8fR77yNs1LbExtzGiNF8PUX+G4iOFhGRUsLFxpIkjRa9QQxred7xsLNiTLfqnL+VzK+HwohIyOCXg+FsuxjLgBZ+NPC1f6ZSwSTU6I/j1RWoI3ehSI0ot1QAIuWMQYv1ncMApHu1KmdjyobUTC2bzt0B4K3GvqI2hshjIQimPMkPROtVor6KiEiFxcZSjiBklmP6sI64nZ6B9e1DCLpMjLK8u+aru6sZ+UJVpm67wt9X7mKvUtCvSfEXwcG02HTx3GnGfzvbfMzFzZ0Z85cw/evRzJw8lmat2zF19kIAXnztDcJDr/PJe2+iUtnQ7oVuDP1inHkxLyszkysh56lVt0GexbwXmtak74APzYtyOp2O0GtXmLtkNd9PHsvwD/rh4OhE7379CawWTCX/AHPkTJ1Ovdm1/W18gU/aB2CvVOQZCyAuWhUDq7iz+O3ohzQnlQznusRXfxu3U9+h0MSYz9Gq3IhpMoFUvy4A1LuvNRkal05yRg52BVz/f6NxbYBRkKFIv4087RZam7LTCdhw9ja3kzOxV8ofyzlUmshTI/He9ykA8dX/R4r/i4WeL5NKsFcqCvx9/zeZmRm0+PYGw7+dRY61OxEJGiITVBheHkX87l84tPtXrPzq4dDpI8ZsCgEqkeXVkIFv98FCaU3dlh15dcgYsrR6lhyJQJeTjTb2Ol3a5X3Pf61Tc0aOn0a7F7rnOj5g8HBiom8x8M2XzffzgCHDi2X/845UIvBJ+0CMRth3/R7f7rzGqC4Cjf2Kr3HyNGRnZbFi8QJW/LkbNw8vs96WVqtlycI5dGrXCjhF1OG1GI0GenVuSVxsDPWbNGf8t7Nw9/TOt177f/0O5Id/YBBVvEz3fzqwcvli2nfpYS7zDwwCTI6cP5b8XKwyMEWKbZgzHnWTXrikXDEfT05KxN3Dy/xZIpEieWSD68LZ08nOykIqk3HiyAGcrUzv49cvh9CoWSvz2kxwnfrM/vbrItssys4HxMXG8M+Ov9h+5DwAIedOo9fr6dCwOqkpybTp8AJjpn6fJ02nyPOF6GgRESklpBIBV7UFd5KzytsUkTKitrcds/rU5e/Ld1l+PNKcW7uutx0DWvjh6/hs5JnOtg8gzasNNtH7cLy0hJimE8rbJJFyQBl3Fqk2DZ2FPZmOweVtTpmw/kw0mVo9lZ1UNPMvm0mRyLOLREIup4qVXIpEdKyIiDwTSCWm1EDlpamY5RBEjrUnivTbWN8+RJpvx3zPa+znyEdtqjB3byhrTt3CXimney2PYvdjaWXF6Zt5RdGbt2lP8zb5RyF88uV4PvlyvPmz3mBk7albnIxMoqGvPZuOXadfp4Z5FvO2HDjD+M+HmBflrl8JwWg08mb3dnkW8x4lOSOHFbuOIrfzoHtNdxpWcihwPOKiVeEoY09Saec7SLXpaFwbENF5KQaFDSn+L6GKPYEsMw6dlQsat0YgeZi60kGloLKTirB4DWeikmkXVLyIXqNcSYZzbVRxp1HFHCO5jBwtsalZrDl5C4ABLSqjspBh0JevPqqgy8J3z4cmB5dLPWIbjynxPqyslJwOy3s/63rV5k7Ke0QmaIhMyLjvgMkgNjULq+b9sGreD4Ao4Ku/biBwAyMgt7Bk29koPOzyOnp3HL2Yrw1yuZyJM+Yyccbckhzac4NUIvBph0AMRiMHbsTzzfarjOpSjUZ+BT/XSoptm9ZSq24DPL0fOh6vXb7IgN49kCsUbN5zjJwdXblxIZKqPh58/t0v2Dk48M34L/j6y2HM/31d/vX2nihW/0ZLKxbOmMvwcSPxSIjn/Y8/y1V+8J9dfP5Rfzy8fIpdNm/hL+RkpOPbsheWf081H69avSb/7NpqdnZsXrOCZi3bARBz+xa///oTwbXrEXs7mhWL5qNWWbFgVQvS09OofN9hAqCysSEuNqbINoszBoC1yxfT5aXXzJFCkWE3qV6zDiPGTEIikTB2xGBmf/s1Y6d9X6xrKvJsIuYvEBEpRRxUCizl4m32X0IqEXgh2I2Ffevzaj1PZBKBs7eSGbrqLPP33yzXNBWPQ3zwewDYX1+NJCe1nK0RKQ9sog8AkO7ZMtdk/HklUZPDXxdNL9p9m/gieYai0ETKBplUwNZKjoedJQGu1tTwsKWSkwoXG0tUFjLRySIi8oxRrnp6gkCaTweTHVG7Cz21cw033mzkA8DPB8I4HBpf6uY9ICVTy8Qtl1h2LJIrMaksOxbJ2199j8wtkHNJcpIzTGmSr12+SNu6ARw9sJeRE6YBEB56Hf/AIL6du4iN/xxDJpfx9ZfDcrVvNBqZtesydw+tw6fFS/yveaVC7XmwaLVg+XpWbd3L7ego827k/zqqmGNU2tEPqTaddPemRLzw+0N9PYkUjUdTUvx7ovFomu97Xf37US2nI5Meq1+Ne2Nz/2WB0Whk4f6b5OgN1PaypVVA2YiOF4XH0fFYJYSgs3Qgqt08jNLiRamUBDKpBB8HJS0DnOnbxJcx3arzy9sNWPNBU2a8VpshbavQo5Y7tbxssbWSm5PIda/pnq+TReTpkEoEhnesSvMqTugMRqZtv8KpyMSiKz4la5f/Rq9+/XMdC6wWzC+rNuMfGMS4zz8mKbAXb9WSc+DTQILr1MPLpxKjJ03n6IF/SE9Lzb/eiMHFM0AiodGrrzPv9/XIZDJ+mDIuV3HTVu2Yt2wtMpm0WGWJCfEsn/sNjl0+oUWgK49OzYZ+MZaQc2d455UuvNapOTu2bOD1/70PwOa1K3F0cubnlZv44JPP+XXVZi5dusTRg/uQSWUoHkm1ZmFhSVZmRpFtFmcMer2e9X8so3ffh9/BgMEmB1aVqtWoHFCVYaMmsHubqCn2vCOuAIuIlCKCIOAuvrz8J1FZyPhfMz/mvVWPppUdMRhh28UYBv5+ik1nb6PVG8rbxEJJ92xJll0AUq0Gh2ury9sckXLA+vZ+ANK8/hv6LGtP3SJHZyDIzYYG9xcbRP7bWMgl2KvkeNlbEehmTTV3NT6OShytLUQRexGR5wC1pYzy9Kmn+piiWGxu7QFj4e+Frzf0pkuwG0Zgxq5rXIxOLnX7Lt1J4ZNVZzl7KxmFTEKv+l7U8rIl7dx2COrIggNhvPPbCcb/GUK04Myc3zfkWpTr9nJvVvy5u9DFvG0hsez+/UckCku++2o4FrLCn63iolX+qG4fpNKOt5HqMkjzaEFE5yUY5I8XSf/A0XI2KumxUl9r3Ex6jtZl5Gg5Fp7IqcgkZBKBQa39K0R6Zrvra3G49gdGBG61+RGtdfGjzkoTK4WUqm42dK7hxget/JnyUk2WD2jMsv6N+L5Xbd5t7lfeJj63SCUCn3UMpJm/IzqDkanbrnDmMZ2Yj0NUeBhREWE0adEm13FBEKgWXJvJ389j765tRLh2xoiA9Z3DyNOiALBR22IwGLgXd7fAeqnJycWyQyaTUa9RU76Y+C2b1654qrJp479EWbMjCtfKtKnqnOt8T29fNv1znPHfzsLN04umLdtSv3EzAO7G3KFx89YoLCwAUFnb4O7uzp1bkajt7ElKeLhZQZOehkyuKLLN4ozh5JGD2Nk7UjmgaoHXx8bWlqTEBHKys4u8liLPLqKjRUSklLG2kKG2ErP0/Vdxt7VidNdqTH0pmMpOKjQ5ehYdDmfwyjMcD0/AWF7JwYtCEIgPNom/OV76DQzlG44vUrZIsxKxuncBuB/R8pwTl5rFjkuxgCmapSJM2kXKlgf6Kk42CnwclVRztyHQ1QYveyX2KkWRi38iIiLPHjKpBKWi/O5tjXtj9HIV8ow4rOLzT9PzAEEQGNjKn6aVTYt2k7ddITw+vVTsMhqNbDgTzeiNF0nQ5OBlb8X3vWrzdtNKDKytwiLjHh+99RIBLtYYjHAmKplZe0KZfCwL9x7D2btrG/EJeXdv/3sxLyoxgzm/ryft3HYGjp9FgLvdY9sqLlqB9a19VNrVH4k+izSvtkR2Wpyv5k9RBLmpUSmkpGXruBGXVux6Ga4NMApSFOm3kKeXrhp9Zo6enw+EAfBKPS+87JWl2l9xsEy4jOfh0QDE1R/+TOga2isVBLjaiDpypYxMKuHzTlVpUtkBrd7IlG1XOHcruVT62vnXRlp16IxcborUPH54PzMnjzWXS6Wm9Si92puPDzuw5pIWh2trALh0/iwSiQQ3D88C6wlF/K5s3biWZfNm4TZ5LG6TxyIzGpHcj5zbunEtSxfOMZ8rk8mKVbZz8zoST/5J9Ow+vN+xJmdPHmPI/15n0U8/mGwSBFTWNhw/uJ9PRj1Md+nm7klW1sP0/QaDgYSEBNw9vQmuXZcLZ0+Zy65dvoirm7v5c0FtFmbnA3b+tZH2L3TLdWz4wLc5f+ak+XPI+bM4ubianUAizyeio0VEpAxws7Us1x1zIuVPTS87vu9dh4/bVcFOKScmJYvJW68wdnMI4fGa8jYvX5KrvILOwh5FejTqyF3lbY5IGWJ9+xACRjLtg9Cp3MrbnFJn1clb6AxGannZUtvLrrzNESkDBAGsLWW4qi3wc1ZR3V1NFRdr3G2tsLWSI5OKr8giIv8FipM+LDkpkXOnjpOUmFCifRulFqR7mhZlbYpIHwb3d0h3qkoNDzUZOXom/HmZu6klqwWZnqVjyrYr/HYkAoMRGjjD/yrn4GRhirjZ+ddGWnfozCsNKvF97zp8GJCJ25W1eNpZkaM3cOpWKkYjfLjiDG++9xELlyw3R0c8upin1RuY9MdeYjdPp/4bnzOgR/EWp8VFq9zYRP6N79/vIdFnk+rTkciOP2OUWT5RW1KJQB1vO+Dx0ocZFNZkOtUESj992OpTUcSnZ+OqtqB3A6+iK5QykuwUfPYMRKLPJs2rLXF1Pi5vk0QqGDKphJGdg2js50CO3sCkvy5zvhQiEg/v202jpg83x/n5B7JuxRLWrVhC7J1oZn0zgaat2mGjtsW/YSe++iebkB1LOH5wL9PGjaRnrzexslIWWg8gPS0VrTZvKnS/KgHMmz2dQwvnoFk4h3mzvqVT95fMZfN/+JY927dw+1Yk82ZOK1ZZt8nr8eg/l/4z1rB2x0Gq16rLhO9+pFffd839/vzjDDp270n1mnXMxzr1eJn9u3fw97bNxMbc5sdvJ6HVaqnToBFtOnXl7IljnDhyAJ1Ox9KFc2nWOrcOS35tFmbnw+9gDw2b5d6gGFitBtMnjubC2VPs372Dn2ZMoU+/AcX5SkWeYcRZpIhIGWAhk+JoXXZ5WkUqJlKJQKfqJv2WXvW9kEsFzken8Onqs8zdG2rOc11RMMosSazWFwCnkEXlbI1IWWJ9+74+y38gbdjtpEz2XDXtru3XxLeIs0WeVR7oq7jbWVLFxZoaHmr8nFS4qC2xFvVVRET+s9haydm8ZgW1vO3y/Gxes4Ltm9fTrWVdpo75jM6Ng9m+eX2+7Qzq+yqb16zIc3zWtAkMebdPgf2n+prSh/1bp2XkR/2ZNvbzPOcrZBKyd32P9uCvJGbkMP7PS7n0//KrN+TdPrnG9f4bPc1lp44eomfbRrSqVZmZM2fyyeqzHA9PRCYRCE44wK6v3+K7cSPo2KgGp44e4vC+3TRs2pLF82fTvWU9po54n9PbV9Iy5yRjWrugvrgGpWcgNxd9yqV/NjB/6mh6TVjEuAWrmDj6M168v5jXu3cvTs4aiG1QMyYN/R9ZGRpWLl5I23qB1PNzYkDvHsTcvpVnDLu3/Um/nh15o3vb//yilTp8O767ByIx5JBSqQtR7edjlD6dw+mJdVrcSl+nJTJBw6ZzdwAY2Mq//CNNjUa8DnyGRWokOdZe3GozGwRxeU0kL3KphC9eCKKBrz05egNf/3WZi7dTSqz9rMxMLp47Te36jczHXNzcmTF/CcsXzefl9k3Jysxk6uyFAHQc+DWv1LTm1aXRjB/2Pk1bteWLr78tsh7Aa52ac3DPzjw2VK9ZhzETpjEcqAt4eHjx2bgpD8umzmT6pK/o3aUV7l7eRZalZWm5qrFEZutKz5Z18fT2xcLCAicXF9S2doApXdr2zesYOnJsLlv8/AOYPm8xv/w4gx4t63Nw79+MHj0albUN9g6OjBg7mUF9X6V9/aqEXrvMB0Mf/s0sqM3CxgBwKyKce3djCK5dL1e9AYOH4x9QlYFvvsx3E0bRu19/BgwZXpyvVeQZRjBW2Lw1ZUdqaiq2trakpKSgVqvL2xyR5xS9wci12LTHynkrUnYY9Doizh2iUp0WSKRlk+otNjWLJUcizKKmSoWUPg286VHbA3kF2U0t08RSdXVzJAYtoT23kOlcu7xNerYw6FHFnkCWGYfOygWNW6OKLyxvNBL0RyPkGXcJ67ICzXOeOmz6zmscuHGPhpXsGde9RnmbU2zK45n1LKGQmVICWVvIUFpIy39BRkSkjNBqtWzbto2uXbuaU4iIFM7l6ESSUh6mSsrI0NDnhVbMX76eD/u+yq+rtxAQVJ0t61cxd/pkdh4LyVV/68Y1jBr6AZNm/kTP3m+Zj9+4epm3X+7Mmu0H8K6UvxaCNCuRaivqIRgNXH39KFprTw7v28PoTweyZf8p84LSAx6ULdt2hAm7IolPz6aqqw2TXwrm9OF9+dZrXz+IBSs24Opu0o2QyeUolSoSE+Lp3rIe/d4fjLJaK+aMHYJ9mwH41mzI29UsGD/wNdZsP4CzqxsLZ33HkQN7CDl/lg+Gfs7a5b/xw8+/Y61WM/Td10mIv4fRaKRBk+acOnqQLm8ORBbQgrUT+2PIyUJiaYMysAlVu3+AiyaC7T9+gTEnI8/1mDp7IQ2atqB7i3oE16nPkvXbco3B0cmZ6V9/xd5d23B0cqbHa6/z3pARyGT/rb+Dtjf/xHvfJwhGPcn+PbnV+geQPP01SEjP5n9LTiIAvw9ojG0xIr4AbG79Q6Wd/yNb7cv13gef2o5/YzQaGbXxIpfupNK0siOju1bLc05Zvxc5XViA+4mpGCQKwnqsF+dIIkWi1RuYsu0KpyOTsJBJmPhiDWp42JaLLe7HJuIUssjkpO2wsOgKxUDI0BBc1ROAkGu3MSofTyfqUf48f5tfDoZT2UnF7NfrPpVd+T0bosLDCAu9RoMmzbG2EdeAH8XRWoGHqDOdh8fxG/y33khERMoRqUTAzdaS20mZ5W2KSAXBTW3Jly8EcelOCr8eDCf0Xjq/HYlge0gs/ZtXokllx3LXitCp3Eip3B370I04hiwmuu3scrXnWUIdvh33YxNQaGLMx3JU7sQ0mUCqX5dytKxwLJKuI8+4i0FqSYZrw/I2p1SJiNdw8MY9APo2FqNZnlUEASzlEpQKGSqFybFSUZzVIiIiFR9nWxV6HjpjVy9bRPsuPXBwdGLkhGkEBFUHoGr1YFJTcu9CTklKYsakMVTyD8h13Gg0MmnUMPoOGFSgkwVAb+lAhks9VHdPYRO1hzt+vZjy1Qg+/XJ8HidLVmamuczXw4WvX7Tmi/UXuHY3jal/nufU93nrxcbcxojRPIZH2bpxDY7OLqRVe5FtoQnYNnsDy7B9zJr8AXcjQxn/7WycXU3pQwOrB7Nr6yZO34zjf6924X+DPqZm3foAjJwwjZGDB3D0yi1+/3UekeE3GTt2LIIg0NBuLmvXriXw9dEcC0vgblom5xZPwaH9e/R+6x0+bO0PwIZVy1Db2tGhy4sAjJk6k0XzZuU7hmk//lzg9fwvYHdjPV4HRiAYDSQFvEp0yxkltonH0doCPycV4fEazkQl0baqS7HqaVwbYhQkWKRGItPEoFO5F13pMfjnahyX7qRiIZPwXsvyF3BXxRzD7eQ3AMQ0nSA6WUSKhVwqYXSXakzeepmzt5KZsOUSE18Mprp72S/0Jwa+jlPIItSRfyPNjEdv5VTmNhSEVm9g41lT9NoLwaWTwtrHrzI+fpVLpW0REXEWKiJShjioFFjKxdtOJDc1PGyZ2bs2n7YPwEGpIDY1i6nbrzJ640Vu3isdodPHIT7YlJLBLmwLMk1sOVvzbKAO347PnkHIH3GyAMg1sfjsGYQ6fHs5WVY0NtH7AdC4N3niPN/PCsuPR2IEmldxorKzdXmbI1JMBAFUFlJc1BZUclLe11exwcPOClulXHSyiIiIPBZqq4d7D7OzslixeAEDBg/DzcOLbi/3BkyRQksWzqH9C91z1Z0x6Svade5OrboNch1fv3IpVy9dxNPbl/27d+Sb0/4BqT4P04ctnD2d7KwspDIZxw/v59HkE/8u87K3Ylz36ihkEv5ZNZ+E1HSk0tz1Qs6dRq/X06FhdRoFejDyo/6kJicDcObsObKcq3MwNAGpROB/L3ZAmhiJtYUM/8Ag2nQ0bQrJ0KTzx5Kfad+lB2DSrHH3eKiPIZFIkdx/7l6/HEKjZq3MG4Xq1GtAXMRVPutUleUDGlMt5m9kRh0BbnYEE2W285XX3zY7WQAiwkLxqVS5yDE81xj0qO4cxfbmZlR3joJBj/211XjtH45gNJBY9XWiW80s8Ujp+j6Pnz7MoLAh0zEYAOsSTh+WlqVl8eFwAN5o5IOLTfm+m8oy7uL9z2CTo6vKKyQGvVV0JRGR+yhkEr7qVo063nZkaQ1M+PMSV2NSy9yObIeqZDjXQTDqcD39fa7nTHmz//o94tOzsVfKaR/kWt7miIg8NuJMVESkjHEXw/BE8kEiCLSv5sqCvvXp08AbhVRCyJ1Uhq0+x4//3CBJk1e/pbTEWf9NllMtNG6NEIw6HC8vK9W+ngsMetyPTQCM/DseScC0oOB+bEKFeJHND+vbJkdLmlfxhGmfVa7fTeN4eCISAd5q7FPe5ogUglQioLaS4WZrib+Lihoeaio7W+OqtsTGUi7qq4iIiDwVFjKpeSPUtk1rqVW3AZ7eD6Mcr12+SNu6ARw9sJeRE6aZj584coDjhw8wbPSEXO1laNKZM30yvn6VuRtzh2W//MS7r3UlOyt/4fo0nw4AJFw+yO+//IR3JT9ib0fz/ZRxDHu/L0ajkZjbt/j917xlVd1seK+ODWknN2GwdmX78ZBc9SLDblK9Zh0WLF/Pqq17uR0dxexvv2b3lbscuxZNtpUjTtYKpr1ck1ebBhB3N/cGkYP/7KJd/SDi4+J4/+PPAKhavSb/7NpqPmfzmhU0a2kSE05PT8PT5+G1U9nYEBdrajMp7g77NvxG9aoBNHDUM/fbCWY7HyU5KZG1K36jz9umjT4FjeF5Rh2+naqrm1F5Wx989n5M5W19qLa8Nl4HP0fASEK1ftxu8U2paII80Gk5E5X0WCmvNe5NgJLXaVl2NJLULB3eDkp61vYo0bYfG4MWn38GI8+8R6Z9ELdbTDPt/hAReQwsZFK+6lqNWl62ZGr1jPvzEtdi04quWMJk3HeOOl5dbn7OVF3drFw3BOoNRtadjgbgpTqeKGTikrXIs4f4WysiUsZYW8hy7ZwTEXkUK4WUvk18mf9WPTzjjhHxbXd+fachrYNcSkScde/OrXRpXpu6lRx5s0d7wm5cK5ZdPX9PRJiYiusrU/MIqYrkRhV7AoUmJo+T5QECRhSaGFSxJ8rUruIg6DLNdqV7ti5na0qX349FAtCmqgve9spytkbkURQyCXZKOZ72VgS4WlPdQ42vowpnGwuUClm5p1QUERF5/nigRbF2+W/06tc/V1lgtWB+WbUZ/8Agxo0YDJgiXyZ9OYwxU2fmye++e/sWMjMy+HXVFgZ+OpKFKzaSlprClvWr8u07264K2Wpflp3NwNnehp9XbuKDTz5n8Zq/OHXsMEcP7GXz2pU4OjnnWxZ5bBu2js649plMdKUu9Br7q7lswOBhzP99HVWqVqNyQFUGjxzHls0bmL3nBkZBio+Tmll96lLNXY2FhSVZmbl1U5q2ase8ZWuRyaT8MGUcAEO/GEvIuTO880oXXuvUnB1bNvD6/94HQCaVoVAozPUfbbOwMTzKlNEjqFO/Ma3adwbIM4Zhoyawe9vm4n+5zxgFRUXLcky73lO923Gn2eRSE14PcrNBqZCSlqUjNK74kfUat8YAqGKPl5gt12LT2HnJFE3/UWt/ZOUcsep28jtUsSfQy62J6rAAo0zcQCnyZFjKpYztVp2ang+cLSFcv1t2zhZ1+HYcr67g367U8s6+cCwsgdvJmVhbyEotbZiISGkjOlpERMoBN1tLcfOLSKG4qC2ZM2Yoi/dcpM2ULXh/sgrPD5cgU9qisfbkm3EjWbJuO2t2HGTsNz8wa9r4PG1s3biGI/v3mD/fighn7IjBfPrlBHafvIKHlzcTRg4tlj0XQm9zZoQ/SV/YELJxOrMXrSyxsT5PSHLSsL++uljnyjLjStmax0cVewKJPpsclQfZdlXK25xS4+LtFM7dSkYmEXijkRjNUt5YyiU4WCvwcVAS5G5DVTcbvB2U99NtiiL2IiIipY/aSk5UeBhREWE0adEmV5kgCFQLrs3k7+exd9c2UpOTWTh7OjVq1zM7Ax7lbswdatatj629KTJAJpMRGFSD27ci8+9cEEjz6Uh0qoHWVe1RWFgAoLK2wcfPn9u3Irkbc4fGzVsXWNamTVv6tTD93V52+i727j55+otOyuCX43fJSktG0GsJ8nWjsYfc7GTSpKchkyty1ZHJZNRr1JQvJn7L5rWmjTue3r5s+uc447+dhZunF01btqV+42am62hnT1JCvLn+o20WNoYHbFz1O6dPHOHrGXPzv1aAja0tSYkJ5GRnF3jOM0shUdEARsAy8QoYDaVmgkwqoY63HQCnIxOLXU/j1ggjAhYpYcgy7j61HXqDkXn7QzEC7YJcCPYsH9HwB6jDt+N80SQaHt1qJjm2or6DyNPxwNlSw0NNRo7J2fI4zs0nppSyLxgtrbi++yjXdx/FaPn4Tkij0cja07cA6F7LHaVC3Jws8mwiOlpERMoBC5kUJ2uL8jZDpIIjVyhoEOjNrH4t+KxHPYzX92MZ0JTfzyXh3fVDBAfTAnFxxVnDQq8x9ItxdO7xMo7OLvTu159LF84WaYdJhBRcO3yInaWAf/hKnJPPV6hcruWNPC0K92MTCfqjMfahG4pVR2dVPIHRsuSBPku6V6vnNhWC0Whk+f1olo7VXXFTP986NBUNQQClhRRnGwt8nZRU91AT4GqDp6ivIiIiUo5YyqXs2b6JVh06I5ebHA/HD+9n5uSx5nOkUtOijyAR2LZ5LXt3baN5DR+a1/Bh2+Z1TPnqMyaPHoGbh2eeNGF3bt/C3dO7wP5TfTrgrZagT75jfq8yGAzExdzB3dMbN3dPsh5pM7+y3vW96FbTHYPRQHT0bbIs7Bk+8G3OnznJwRv3GL7mPOFXLyCzdmDSq3Xp2aEVF8+eMrd57fJFXN1MIuZbN65l6cI55jKZTIbkES0QQRBQWdtw/OB+Phn1cLNPcO26XCigzcLGAHDx7Gm+mzia7+YuwtH54TvSgzE8IOT8WZxcXM0Om2cegx5ZRhyW8RdwPj+viKhoyiQq+kH6sNNRj6HTYmFLlmMNAFQxTx/VsvViDGH3NFhbyHi3WaWnbu9pUKSE43XAlDrvXs0PSPXrUq72iDw/WCmkjOtenWruajTZesZuDil1jdZSy74gkZBdtRrZVauB5PHf589GJXPzngZLuYQetco5TaCIyFMgughFRMoJZxsLkjJy0OmLn/tW5L+JRBBo5qsm69xf9J20lD23IUXtzPA152hTxYHbW2YXKM6anZVpPta6wwu5zom4+VBotDAeiJDWe3cGaQmp9Ai8yPzYPthbmV7PclTuxDSZYJ50JCclEnHzBr6Vq2Dv4Pi0w6+4GI0o757AKWQx6sidCPd3F2ap/ZFl3UOak2beFZSrGqBVuaNxa1TGBheN9X1HS5rn86vPciYqmcsxqSikEvo0KHjRS6RkkEhApZChtJCa/lVIxdRfIiIiFZIj+/bQ7dU3zJ/9/AP59L2++Pr506JtB+ZMn0zTVu2wUduyZP129Dqd+dyZk8dSq25DevZ+E4kg4ZtxI1nz+2JadejMnu1buHb5Is3btAcgPS0VC0srs0MHQOPWkFfr2PHtT3dpuWo2gW378MdvP5OTk03dho3x9PHl9a5t+HvbZmrWbZBv2e7tf/Ji7frs/X0NRr2WTTE21PMO4LMRwxGa/g99RgqaIyt4u//71Payw6dTV6aO+ZwTRw5Qr1Ezli6cS7PWJq0VvyoBTBo1DC+fSgQF12LezGl06v5Sruv1848z6Ni9J9Vr1jEfa1NIm516vFzgGBLuxfFx/9d598NPqF6rDhka00KjUmVNYLUaTJ84mpETppGUEM9PM6bQp9+AEv3uSwWjEUlOCvKMu8g1d5Fl3DX9P+MuMk3s/eOxyDLvIRgfb9NSaUdF1/cxOVpu3E0nJVNrjnoqCo17YzJuXeDS/o2o7Js/8TwgUZNj3hTzdlNf7JSKImqUHoIuE5/dA5Fq09C4NiS24RflZovI84lSIWNCj+qM+uMIl69cZNTKFL59qzl+Ttal0l9xnx9lnX1hzf1ols7V3VBbSFDdOYosMw6dlYtp3iwRo9xFng1ER4uISDkhlQi4qi25nZRZ9Mki/3m2bVpLrXoN+KhHM3qlZbPsaAS7Dh1n2ezXEaRyPl2whWydHguZ1CzOumH3Eb4Zl/9kQJuTw9KFc+j33kdF9v1AhHT8u53wPD6RHiszcPju0RyyqcAbTB81CINnQyZ/NRxPLx8iboYyccZcuvR8NU+bg/q+SpcXX6Fn77cAuHH1MuNGDCYqMoxXXn+b4V99XazF2FNHDzFp9HCSEuJ5b8hw3v5gSJF1nhZBn4Nt+FYcQ35FGX/RfDzNsxXxwe+R7tUKdcROfPYMwoiQx9kiACl+3Srcy6I8/Q6WyTcwChLSPVuUtzmlwqPRLF1ruuEoRhaWOHKZYHaoqCxkYuovERGRZ4LMzEzOnj7J6Kk/mI+5uLkzY/4Spn89mpmTx9KsdTumzjalDnJz98xVX6lUYe/gYF5Ynvf7emZOGsOMr7/C0cWF7+YuwtPbJBL/WqfmjBw/jXaPbpKRyPGo14nVr61j1E9zuD52Gj5eHsz+ZTkqaxv8rG2YPm8xc6dPJjz0Bl6+fsxetDLfMk/fSjT+YBp3JBYctWlJutUFMlaPwdbeiXffG8gHH48AwN7BkRFjJzOo76vY2KixUiqZON0UxVK9Zh3GTJ3J9ElfkZaaQseuPfls3BSzuVHhYWzfvI4Nu4/kug6FtennH1DgGDas+p3E+HvMnT6ZudMnm9u7cCuZAYOHExN9i4FvvoyDoxO9+/VnwJDhYNCjij1RLgtxgi4LeUYsMk1e54nJoRKLXHMXiT6r6MYAoyBBZ+WEXqbCMjW8yPNLOyra0dqCSo5KIhIyOBuVRJuqD/vbvGYFY+9rFT3KpJk/YZtmYNycdHwdd3Bt2o4nngfoG7xJplZPoKs1nWsUrNPw6DxgwOBPadmgVgmM/hGMRjwPj8Yq6SpaK2ei2v0EkuI5nURE/k1h947CwpJT3w0HG2fi7t5icPgw5o35mEpOqlznlsQcWmflwv4IHYO2ZnFPY2R0SwXDm+adEz3uc0bIycF57kwA7g0ZgVFRfAfp5ZhULt1JRSYReN8phMDVb6B4RKfq3xs7RUQqMoLRaPzPb6dPTU3F1taWlJQU1Gp10RVEREqQ0Lg0MnNKL8+uSPEw6HVEnDtEpTotkEgrng/6ze7t+HDEKFq27Wg+djUmle9X7eTc+jkICiU13pnEW/Xd+eGjnowcP5VW7TszZtiHNGzawvwy9oDvp4zj6IF/WPnX3lw7KgvEoKfq6mbINTHsuamlz/pMbg61ASA9x0jdhRr+es+HLsvT+XX1FgKCqrNl/SrmTp/MzmMhuZraunENo4Z+wKSZP9Gz91vkZGfzYtuGNG/dnv8NHMo347+gY9cXealP30JNSkyIp3vLerz9wWC69HyNkYP7M2LMJBo1K51oDGlWEg5XV+B4eSny+7mnDVILkqu8Qnxwf7Ltq+Y6Xx2+HfdjE3K9JOplSqS6DAwSORGdl6KpQA4N+2ur8Do4kgyXetx8cVN5m1MqHLkZz7TtV7GSS/nl7QbF3qFZEakozyxLuQSlhQyVQopSIUMhE1N/iYiUJ1qtlm3bttG1a9fi/X0XycXV2FS0uvKZHrsdm4hzyKJcx550cSk9W8eoDReISMjAxkLG8I6BNKjkkO+5UeFhhIVeo0GT5ljblMxcuDTafJT83rFKZCHOoEOWGY/c7DSJfSQa5aEjRZadUnRb99FZ2KJTuqFVuqJVuqJT3f+/yg3dg2NWTiCRPfK+HVtAVLSAVuXGtT5HSt2ptORIBOvPRNMm0JkRnR6+42pzcsjMzDB/zsjQ0OeFVsxfvp4P33qF/a/nUNNFwrfKscz+cfZjzwNGjxxOtF0t1LU7MrNXHaq45L+rP8884KN3ebNPL3r0+7DE3ovsr67A69AojIKE8C5/oPFoWiLtivw3KfTe6fsqv67egrtfIO+N/Z5rW3+hxvDlTHkpGF9Hk7OlxObQ9+7yYrNqDG8i581gGa+vz2RGR0va+pnumwfZFx73OSNkaAiuatqEEHLtNkalqogaD5m45RKnIpP40vc6A+9O5N/6MQ8+RbVf8NjP+IoyZ3pWcLRW4GH3+Bo7zzuP4zcQf8tERMoZN1srwu9pytsMkQpMQeKsQe5qFn76GpsbVmXcm62JvZfA+K9/QWXvh1twswLbO3pgL2uX/8byzX8XexHmQS5XAGeVhMRMsJKBhUxg3skcXgmS4SFJYOwnHxIQVB0ovnbMob1/k56aymfjpmBlpWToF2OZOubzIl8St25cg5OLKwM/GYkgCAz6ZCQbVy0vcUeLRdINHC8twv7GBvPORK3ShYRq75BY7S30lvkvXKT6dSHVt1Pu3ZYu9fHe/wl24Vvx3f0BYd3WkOUUXKL2PinPe9owvcHIiuNRALxY2+OZdrKUF4Jg0jFQWZiiVZRyKTJRU0VEROQ5Qm0pJyE9p+z7Dd+OU8hijJBrcUmuicVnz6DHXlyytpAx+aWa7L9+j6aVHXG2KTiC08evMj5+JSvsXRptPkAdvh2fPYPgX46IQq+V0Yg0Oymv0+TRKBRNLLLM+HwdHPlhkFqive80eeA8eeA40apc0SpNjhSj7DG04CRSYppMyDcq+sFCY0yTCWUSuVPf1571Z6I5E5WEwWhEcn+XvFyhQP7ITvXVyxbRvksPHBydGDnxG6oYF0PiZeo45zz2PECmsISGb5C+fhav9327QCcL5J0HfDD0MzatWESPfh+WyPit7p3H44hJfyi2wUjRySLy1BR670yYZp5Df/lmZ97dMIuUTC1jNoUw9eWaqIXskptDb16Pk4sbY1ulIwgC41pZsOislrZ+MvPfoCyH6mUWIRh2L51TkUnIBAP/S13Av50sYNKNMSLgfmwCqb6dKlxmCBGRRxEdLSIi5Yy1hQxbKzkpmdryNkWkgrLzr415xFkP7d3NiDGTEASB5gEuCECfBt7MXrKftIwUejUPQiYRMOqy2fXXJi6eO8OYqTOJjozgy6HvM2bqTPwDg4rV//CBbzO4W20eTJdP3tHjZi1gIRPI0hmZfTyH4++p8LaV8GrrYFIw7ahdsnBOsbRjrl0JoVa9hlhZKQEIrBbMzetXi7Tr+uUQGjVrZQ6PDq5Tn9nffl2sMRWJ0Yj17QM4hSzCJnqf+XCmYzDxwQNIqdwDo7QY4dASaZ6JWXSbWciykrCOOUKlne9ws8cGtGrfkrH7STHosb5zCIB0r9bla0spcfDGPaISM1BZSHmprmfRFUSQSEx5o1UKKcr7jhWJRNRXEREReX6xtSoHR4tBj/uxCZT04pKtlZwXaz9ngsJFXivwOvgZSTFHkWXeux+ZEocs4y4SQ/G+V6MgRat0ue80cUOncn0YjXI/EkWrdMWgUJt2IJQwqX5diGq/IE/EjlblVqapc6q52aBUSEnN0hEal06gq02ec7KzslixeAEr/tyNm4cX3V7ujeboJWT3LrH4t2WPPQ9YfTKKJEt3dIlR9G1c+Ltx3nlAPWZ+PboERg7SrGR89nyIxJBDim8n4muVjPNGROQB+d07YJpDr1kyj05de5DlpCIsXsNXmy7idGZRic6hG7Tuwq0OHXA/NoFGnrcZtScbAL2FHbLsZNS39uAY8isJwe+Vwuhzs+5MNAD9vWKwvBdb4HkCRhSaGFSxJ0THp0iFRnS0iIhUAFxtLUjN0iIm8hPJj8P7dvPSI6m/ChJnfadNDRpv2sGaU5EcvZkAQMq+xTRq1Jj3hg4iKzOTIe/2oV3nbrTt1NUsNGqlVCEIQr7irACB1Wrw9fy1uDfWcS/DyNi92XzUwORkWHlRSxMvKZXsTLvadVYuXLt8kQG9eyBXKNi894S5nYK0YzRpaea85QCCICCVSklNTkZtZ1fgdUlPT6PyI84ilY0NcbExBZ5fHARdFnah63EKWYxl8g3ANJVP9e1MfPAAMtwaPfWk2ii1ILLjz1T+qzdWiZfx29GPmz02oLdyeqp2nwar+AvIslPQK9RkONcuNztKC53ewMoTpmiWV+p6YW0hvv7kh0wq5BKut5RLROF6ERGR/xQqCxkyqYBOX3Yv5Y9GDeeHuLj0kKKvFUhz0nC6vCTfcp2lgznS5EHkyaPOE53SFZ2lY7nvls43KrqMxaBlUgm1vew4GpbA6cikfB0t2zatpVbdBrne40+mu/PWjDQU8pusP7rTfLyoeUBsShZrTkUjCAJWCjn6zHSwsCvQvn/PA6ytbUhMTHzKUQNGA177P0WRHk222pfoVjNLxaEm8t8mv3snzxxaoeKrTRe5euYYl/bt5Y/th1n2/Xjz+U87h37wnBHCDxC98E3Cuq5G49YIp5BfcD8xFfdjk9CqPEvVuXsnOZPDofEAdPYV4F7RdWSZcaVmj4hISSDmexARqQBYyKQ4iaLMIvmQlZnJxXOnqV2/kfnYA3HW5Yvm83L7pmRlZprFWYOq+DHu9Tb8+F4nagYFYJRZcj7ewKjtESz8YwNhN66xfuVSmgR5mX/uRJsWoF/r1JyDe3bmsWHA4OFUql6fjsuz+HRHNh82UDCqpcnRsuBUDoPqyzECOUo3NG6NCKwWzC+rNuMfGMS4+4J/2VlZTPpyGGOmzsyTq1sqk6H4l1iewsIyVw7b/JBJc9ezsLAkq4g6BbaVcRfXU9MJ+qMxXodGYZl8A71cRXyN/lzrfYCojj+T4d64xCZaBoWaiBeWkmPtjUVqBJV2/g+JtvxSCNrcTxuW7tnClCP8OWPP1ThiUrKws5LTo9Zztrv3KbCQS7BXyfGytyLQzZpq7mp8HJU4WVtgpZCKThYREZH/JDaWZft3sLiLRuLiUvGvQap3e+40GU9k+/nc7LGBq32OEPLuDa70PUfoKzuIeGEpt1t9R1z9ESRW60uaTweynGqiU7qUu5PFzP2o6BT/niYHWznYVd/XHoDTkUn5lq9d/hu9+vXPdcynRS/2vK2ihpOR8Z++DxQ9D5ArFCw8cJMcvYHaXraolFaPPQ9QWFiSnZ392GP8N87nfkJ96x8MUgui2i/AYGH71G2KiPyb/O6df8+h1VZyxnYJIHX3POw6fsSU3RFk5OjN55fIHFoiRVepFZnZWvNzJr7mQBKqvY2AEe99Q1HePV0yg86H9WeiMRihYSV77F28i1VHZ+VSavaIiJQEz99qiojIM4qzjQVJGTlluoNOpOJjaWXF6Zt5J5XN27SneZvjBdYLcLXh21dqcqjWApYciSAuLZvd+PLmL8cY2bmqOc/yo+w4ejHftuRyORNn/sQPQ7rfz4lt2l0ZmmggNNFA+8oyBMCgsEaiz8IgV1EtuDaTv59H5yY1SU1OZsnCOdSoXY9W7Tvnad/Wzp7Qa5dzHcvQpOXKYZsfajt7khLizZ816WnI5MVI5/UIlvEXcQr5Fduwv5AYTOn7cqy9ia/xLklVe5vSQpQSOqUr4V1+x3/LKyjjL+CzZxCRHRcVLyVZCfM867Pk6AysOnnfmVjfCytFBVlAKWMe1Vd5kA5M1FcRERERyYutlZwkTdml9C3uopG4uFT8axBf84P/fPRPSfDA0XL9bhopmdpc+nYFaUgarBypVr06S5RX8J29u1jzgKOnzhLrmIRMIjCotT97v0p/gnlAOjLZ0y1vqW4fxPX0DADuNJ9ClmONp2pPRCQ/Crp3BEHIM4detXAOzZs0Iqtha24lZhB1K4na91POl9ocWhC403QCcs0d1FG78d3Vn5svbiLH1u/pBv4v4tOz+eeqaZ2jdz0vLBNW59EpexQjAlqVaWOniEhFRnS0iIhUEKQSAVe1JbeTMos+WUSkGAiCQMsAZxr5OfDnuTusPBHF4dB4Vtlb8WYReY/z4985o9dc0tI9UI5g7Yxeq+HI2ats3tqM938+ilGuRCo1/YkRJALbNq8lKSGB5jV8AMjMzDRrx3Ts9iIb/lhm7uf2rUhysnOwtbMv1J7g2nXZ/ud68+drly/i6uZe9EAMetRRf+MYsgjr2IfOKo1rQ+KD3yPVt2OZRXXk2FYmotNvVN72OjbR+/E8+DnRrX8AoewWwCXZKSjvnQOeT32WHZdiiU/PwVGloEtwMX4/nhMEAZQKk2i9StRXERERESk21hYyJBIwGMqmP41bI3JU7sg1sfkKsRsBrcpdXFzC9K6mlymR6vLfsS0uxJUsTtYWVHJUEpGQwdmoJNpUfejoKkxDUuPeBFnUFcBY5DygdafuzF/4M2713ufVel4I6feeeB7g6Oj4xGOVaWLw2fsxAkYSA18nKbD3E7clIlIYhd07QL5zaOn+3WRq9ehzsvk+5ADXLp6l58uvlN4cWiIjqu1cKm/tjTL+wn1d0Y3orZ78Hvs3G8/eRmcwUstdRbvw73C88jvA/b+CQq6/hw/+F9NkQsWJOhQRKQBxK6OISAXCQaXASiHeliIli4VMSq8G3gxuUwWAP07e4sjN+CJq5U+qXxeu9TlCWNfVbI73JfjFIVx94yThXVcR4GbDbwci+WdsR+5GhTLrmwk0bdUOG7UtS9ZvZ8PuI6zZcZA1Ow7SpmMXPhoxmsGfjaZ+4+akpaXy57o/AFg8bxaNW7ZGKjW9RKWnpaLV5t1Z2qZTV86eOMaJIwfQ6XQsXTiXZq3bFWi7JCcNx5BFBK5tje/uD7COPY5RkJHk/zKhPbcQ1mO9KQdtGafOynSpS2T7BRgFGfahG3E7Ma1M+7e+cxjBqCfLtgpa6+dLJD5Lq2ftqVsA9GnojUL2/D5fpRIBtZUMN1tL/F1U1PBQU9nZGle15f1FQ9HJIiIiIlIcBEFAbSkv+sSSQiI1LR6Rn8S7ibh6w8TFJcDp0mKkugyMkMcl9eDaiQtxJYs5fVhU7vRhh/ftplHTlubPfv6BrFuxhHUrlnBdEsCXe7JpX9W2yHnANYkX2iwN0psH6NXA64nnAct++Yk6deo80RgFfQ6+ez5ElpVIpmMwd5p9/UTtiIgUh8Lundg70fnOodfuPMiKv/bjUL0pts3fIrpyD3yq1S/VObRRriSy028PU13/PQBBl1XguIwWloRu+YfQLf9gtLAs9BqkZGrZeSkWGzKYL/kGxyu/Y0QgptEYotovQKtyy1MntuGXpaoXIyJSUjy/Kw4iIs8o7rZW5W2CyHNKh+quvFjbpE/xw+7rRMQ/oSaIREqCfR3OXo0gqP0bIJGS6VKXnN4rWPWGAwt2XuGV9k3I0qSbtWPc3D3x9PY1/yiVKuwdHDh/T893u27wwahvmDxqOG3qBvD31s18+uUEc3cFacfYOzgyYuxkBvV9lfb1qxJ67TIfDP08z3nytCjcj31N0B9N8Dg2EYu0KHQWdsTVHszV148Q3XY2meUsAJ/u3ZboVt8B4HxxIY4Xfy2zvq1vHzDZ4PX8pQ3bcuEOyZla3NSWdKzmWt7mlCjy+2m/3O0sCXC1prqHGl9HFc42FigVMlFfRUREROQpUFuVoaOFh1HD/15cMgqm9Kx2N9aDoezSmVVE1OHbcTsxBYCkwF5oVbl3YGtVbkS1XyAuxJUw9X0dADgblYzBaHJvFaUh2fF/o8jQGlnZXY80K6nAeUCURsqWi3dx7DyE6C0/0rlh0FPMA67Su/eTRaG4nZiCMu4MeoWayPbzMcoKXyQWEXlSHld/9dF7p3rVKjSs4oGjoyNJBkvG/nWFz76eWWpzaACd0pmIF5ais7BFGXcG732fgEGf77lIpWTWqUdmnXogLdzZveXCHZz1d/lT+TWeCUcxyKyI6vAz8bU+INWvq3ljZ1TbOaR5tEAA1JG7wCim2Rep+AhGo/ibmpqaiq2tLSkpKajVpZePX0SkuEQnZZRpbmgRMOh1RJw7RKU6LZBIn9+sinqDkfF/hnA+OgUXGwu+710nV77lp0UZe4JKO95GqssgzasNkR1+LnCysufKXWbtuQGATCLQvYoFvoa71GvYGAdHp2L3GRUeRljoNRo0af5QYNNoRHn3JE4hi1BH7kQwmvJ/ZNlWISG4P0kBr2KUVTynptP5ebif/AaAqLZzSPHvWbodGo1UXd0cRXo04Z2Xku7dtnT7K0PSs3W8v+wU6dk6hnUIpF3Qs53b3kohua+tIkNpIQWDnm3bttG1a1dz6gERERERrVYrPhtKAIPByOWY1LJf0zHoUcWeQJYZh87KBa2VI1X+fAmpNp17we8T22RsGRtUMbCKO0flrb2R6LNIqP4Od5p+DUZDrmulcWskRrKUAjq9gbcWHScjR8/MXrUJdLUpVr2Ade2xTL5BZIdfSK2UW5tFqzew4ngkG87cxgg0rezIgLq2XL5wltoNnmweUK9hE+Jvnn/suZztzT/x2TsEgIiOi0jz7VjsuiIi5UFCejZfbrhIbGoWbzT0pr2P/KnunVxz6AJQxhzHb/tbSAw5xAe/R0yTcU9sf0aOjh+WrmQO3+EspKJVuhDRaTFZTrXyPV+miaXq2tZIdJlPND/+r6zzlBSO1go87CreOkl58zh+AzGiRUSkAuJlr8THUYlcJu5IFilZpBKBkZ2DcFNbEpeWzXc7rqLTl1wS8gy3RkR0XopBZoVN9D589gxC0GfnOe9waDw//mNysnjZW6EzGNl0PYvdGd5o5daP1aePX2XadOyCtY0aQZ+DXehG/Df3wP+v17CN2I5gNJDm2Yrwzsu48dpuEqv1rZBOFoD4Wh8SX6M/AF77h2MdfaBU+1OkhKFIj8YgUaBxb1KqfZU1m8/dJj1bh7eDktaBzuVtzmMhCKCykOKitqCSk5LqHmqquNjgYWeFrVJujmYRERERESkdJJIyTh9m7liKxqMpKf490Xg0Jcc+kOjW3wPgHPILtmFbyt6mckaedgvfXf2R6LNI9W7HnSbjTX8o/3WtRCdL6SCTSqjtZQfA6cikwk9+hAfvlaqYo7mORyZoGLH2POvvO1k6VndlWIdAXN09aNu522MtFMOj84DiOYAexSLpBp4HRwIQV3uw6GQReSZwtLbg7aYmvdXtl2JxcHF7ynun6M3mGe6NzX+LnEJ+xTFkUZ5zhJwcnBb8iNOCHxFycgpsK+rgCpYwEWchlUyH6oT23FKgkwVAp3IjrrbJGep2YiqCNn+NLhGRioI4UxcRqaDYWskJdLHB2cYCMQOMSEmitpIzpls1rORSLtxOYdGh8BJtP8O9MRGdlmCQWqK+9Q8+ez5E0D982ToVkcj0XdcwGE2Tq5/erMcn7QKwkku5dCeVj/84yz9X43icgEtpVhLO5+ZSdXVzvPd9gjL+AgapBYlV3+D6q38T0WU56d5tylRk/okQBGKajCO5cg8kBi0+ewZiGX+x1Lqzid4PQIZbwwrrfHoSUjK1bD53B4C3GvkgreD6JFKJgI1l/voqNpbyCm+/iIiIyPOI2qpi7HxNrfQCcbU/AsDzwOdYJF0rZ4vKDkl2CpV2/g95VjyZjjW41e6nMtfS+6+QnJTIuVPHSUpMyFMWZA9Z0Vc4eqn4cwaNW2MAVLHHATAYjWw+d5tha84RHq9BbSljdNdqDG0XgJWi7J1kEq0Gnz0DkeoySHdvxt36I8rcBhGRJ6VpZUccVQqSM7QcDn0y7dXHJcX/RWIajgLA/djXqCN25D5Bp8V9yjjcp4wDXT7ZWYxGHM7MoVf4WCwFLWH2LQjrsR7dv9JA5kd8zffJsfZCoYnB+cL8khiOiEipUcFXnERE/ttIJAJutpZUcbE2pYoRESkhfB1VDO8YCMBfF2PYeSm2RNvXeDQlotNiDFIL1FG78f5nMBi0XIhOZtr2q+gNRloFODG4TRUkgkCH6q78+HpdqrnZkKnV88Pu63y78xppWYWn0LNIuoHHoVEE/dEYt1PfIc+4i1bpQmz9z7n6+jFut/yWbPuqJTq2UkeQEN36e9I9miPVaqi04x0UyTdR3TmK7c3NqO4cLTg37mPyQJ8lzat1ibRXUVh/JppMrZ7Kziqa+juWtzl5kMsE7JRyPB7RV6nkJOqriIiIiFQkbCzlFWaz0936n5neC3QZ+O4eiCQnrbxNKn0MWnz3DMIy+QZapRsRnX7DIFeVt1XPLJvXrKCWt12en81rVrB983q6tazL1DGf0blxMNs3rzfX2755PTPee4HEv+ez/+vebFi7xlx24+pl3ujWlubBvsycPDbXJqkHES2WCZdJSYxj/J+X+PVQOFq9EZ/sCFJ+/5gvXmrIsp/nlt1FeIDRiOfBL7BMDkWrdOVW2zmiA0/kmUImldClpslBseXCnTLrN77WIBKq9UPAiPfej7GKO1OseoI+B68DI/A8Mx2AlZLupLy4tNjPdKPMkpjGXwHgfGEB8rToJxuAiEgZIDpaRESeASzlUvydrfGytxJ3NouUGE0qO/JWYx8AFuy/yaU7KSXavsazBZEdF2GQWmAbuRP7bYOYtvUCOXoDjf0cGNYhMNfvs5utJdNeqUXfJr5IJQKHQ+MZ8sdZzt1Kzt2w0Yh19AEq7XibwPXtcby6Aok+i0zHYG61/oFrfY5wr+7H6K0q3gJ7cTFKLYjs8DOZjjWQZ8UTuL4Dlbf1wWfvx1Te1oeqq5uhDt/+VH0I+mys76dzSPd8fhwtCenZbL0QA0C/xr5IKsoqGaZMJ4Fu1gS5qfF2UOJobYGlXHSii4iIiFREpBIBa4sKsvgqkRHVdi45Kg8sUsLw2j/i+RYFNhrxPPQV1ncOo5cpiej8GzqVW3lb9UzT9aVeHAqJMP/sOnEJewdHAqrV4JtxI1mybjtrdhxk7Dc/MGvaeABSU5L5ZtxIlq7fTrPPfsWh82BmfzMBgJzsbD7u/zrVa9Vh1V/7CLtxjc1rVpj70yldyLL1R8DIqvVrOHcrGYVMQt869pxdNIruL7/G75v/ZuumtZw4Urqpcv+N4+Wl2IX9iVGQEdVuHjrls5ViVkQE4IUabsilAtfvpnMttoyc74LAnaYTSfVuj0SfTaVd/VGkRBRaRZqVTKXtfbG/sQ4dEsZo3yWywRhkj6kjl1qpK+luTZDos3E7Oe0pBiEiUrqIjhYRkWcIe5WCQFdr7FWiuKlIydCngTfN/R3RGYx8s/0q99Ly6qk8DelerYjs8DN6iRyv2L/5hrnU87JhZOcgZPnoTEglAn0aeDP91Vp42lmRqMlh7OYQfjkYRk6WBvurKwlY3wG/HX2xid6HEYEU387c7LaW0Je2khzwKkapokTHUF4YFDYkVH8HIyAYc0ewyDWx+OwZ9FTOFuXdU0h0mWiVLmQ5BD2ltRWHNaejydEbqOZmQ31f+/I2JxdO1hZYyETHioiIiMizgtqq4rxz660ciWq/AINEgW3kDpye4/Qpzhfm4XB9FUZBwq12P5HlWKO8TXrmkSsUqG3tzD9b1q2ifZceODg6MXLCNAKCqgNQtXowqSmmzVcZmnRzWX1fexQufqSnJgNwaO/fpKem8tm4KXhX8mPoF2PZuHq5uT9Nto5jetP7ZW19CFWcrZnVpw45V/fj5OLGwE9G4uvnz6BPRrJx1XLKCqu4M7gdnwRATKPRZLg1LLO+RURKElsrOS0DTE7Cv8owqgWJjKh2P5HhVBNZViKVdr6NNCsxV8YFVcwJMOhRpITj/2dPrGOPkSNVMSDnc/5SdKVjddfH71cQiGk6HqMgwS5sC8r7aQlFRCoaoqNFROQZQyaV4GWvxN9FhZVCvIVFng5BEPi0QyCVHJUkZ2qZsu0yWdqSSUv1gCvWjfnUMIIco5Tu0mP8YvMLComh0DoBrjbM6lOHLsFuOJNElUuz8F/eGK9DX2KZfAO9XEV8jf5c632AqI6/kOHemAqT36OkMOhxOfNDvkUCpl2s7scmPH4aMYMe1Z2jOF38FYB0j5bPzbW7m5rFrvtp8Po18a1QKbgEAZysnw8noIiIiMh/BbWlrEL9icx0qcOdZhMBcDv1Harbh8rZopLH9uafuJ38FoA7TSeS5tO+nC16/sjOymLF4gUMGDwMNw8vur3cGwCtVsuShXNo/0J3gFxltT2sST2xAeuqzTEYjVy7EkKteg2xslICEFgtmJvXrwJw6U4KQ1edZUOiHwBdrEOZ/lotvO2VXL8cQqNmrczvaMF16nMl5HyZjFuamYDPng+RGLSk+HUlIXhAmfQrIlJa9KjlAcCh0HiSNAUL0Jc0RrmSyE6/kWPthUVqBJX/fIXAde3M5X4736baygZU2dgFi9RwclSevCefyn5DbV6s4/HEG8+yHGuQWPUNADyOTiyxdNoiIiWJuEorIvKMolTI8He2xt3OEol4J4s8BZZyKWO6VUdtKePmPQ1z/rnxWEL0hRGbmsXYTSFsyarF18ovMAgyHMP/xOvACNDlFKo7YpdymanM5ZjVJwyVbcKOVG4ZndnpOZRLfY4R03QCWrVvidhZEVHFnkChiaGg9R0BIwpNDG4npmJ17zyCLqvINtXh26m6uhmVt/VBfWsPADa39jx1GrKKwqqTUegMRmp72VLTy668zcmFo7Ui3yguEREREZGKi0wqQVkOQt2FkVT1TRIDeyMYDfjsHYI8vQx3MpcyyrunTO+IQHzwABKrv1POFj2fbNu0llp1G+Dp/fA9+trli7StG8DRA3sZOSF3Wp5rly/ycfeGZEWcw7rNAG7GpaNJS8tVXxAEJFIpC3ddYNSGi8SlZXNTVRsAz+xQFPp0ANLT0/D0eVhPZWNDXGxMaQ7XhEGP976hKDQxZNtWJrrl9Odmo5HIf5cqLtZUc7NBZzCyo4Q1V4tCp3QhovNS9DIrLFPDkGfczVUuzUpAqssgW12J1XWWcCDZGZVCStdg96fq9279z9Ar1FglhGB/fU3RFUREyhhxxi8i8gwjCAJO1hYEutpgW4FSG4g8e7iqLfmySzWkEoEDN+JZf+b2U7eZkJ7NmE0XSdDk4O2g5IVXBnCr/TyMggz70I3UWF4zr+5I2FbUETvx+6sXAZu6YR+6AalRR4pzfWbajaFN9vcMvNmE0dsiiUsr2rHwLCPLjCvWec4hv1Blcw9qLA0iYF07vP8ZgtP5eVhH70eW8bANdfh2fPYMQq7JPZmVZqc8dRqyikB0Ugb/XDWNt1+TSuVrzL8wRbNYlLcZIiIiIiJPQIV7xxYE7jSbTKZjMLKsRHz2DELQl2zq1/JAkRqB79/vIdFnk+LbiZhGY8rbpOeWtct/o1e//rmOBVYL5pdVm/EPDGLciMH5ljl6VSZ+2yxORyUhlclQKB5G6kYlZpCpl7DpdBhGoEM1F8a90Z5stR+C0YAq9iQAMmnuehYWlmRlZpTeYO/jcnYWNrcPYpBZEdl+IQaFTan3KSJSFvSobYpq2R4Sg1ZfeNaIkibbtjJGmdKU6vpfZQKYjuuyWH7B5GjtWtMd1VNqn+mtHLlb71PAFNkpyUl9qvZEREoa0dEiIvIcIJdK8HFUUslJiUIm3tYij09yUiL6mKu8VcukabHsaAQnIxKfvL2MHMZsDuFuajbutpZM7hmMrZWc1EovcC+4P0ZAosvMVUeuicHnnw/x3f0+1rHHMQoykv1fIrTnFqJ6bqTjq+/zUbuqWMolhNxJZegfZ9l3rXjOiGcRnZVLsc7LcKqJztIRwWjAMjkUu7A/cT/5DX47+lFtZQOCVtSn0ra38DowHDDm8xL8FGnIKhArT0RhMEKjSg5UdatYk2cHlQK5GM0iIiIi8kxSkXRaHmCUWRLZYSE6CzuU987hfnRCeZv0VEizkvHd+S6yrEQynGpyq82PIKlYkUTPC1HhYURFhNGkRZtcxwVBoFpwbSZ/P4+9u7aRmpycp+z9MTPIvHGcY1eisLWzJykxHqPRyJbzdxi2+hw5WRnYWFkyqksQn7QPRKmQoXFvDIAq5hgAajt7khLizW1r0tOQyUs3tar1rb24np0NwO0W35DtULVU+xMRKUuaVnbEQaUgKUPL4dD4oiuUIKrYE8iyEkzzSxnwjtL0c9+XIgCKjFjsE06jkEl48b5T6GlJqP4OWbb+yLIScDn7Y4m0KSJSUoizfhGR5wgbSzkBLta4qC3ESGiRXGxes4Ja3nZ5fjavWcH2zevp1rIuU8d8xvR321Ml7TxGYMaua6xZv4EuzWtTt5Ijb/ZoT9iNa+Y2p439PFdb3VrUBSA9S8f4Py8RnZSJk7UFk3sG46C6P4Ey6Lnw92qq/5SO03dpfH/04Q5MgQc7XwTian3I1dePcKvtj2Q6m9IOCIJAp+puzO5Tl6quNmhy9Mz8+zrTd14lPUtXRley7NC4NSJH5Z6Pa8SEEYEclTs3X/yTK2+d4cqbJwnvvJTYhl+QXLkHWbb+GBGQZ97D5s5BpFpNkWnIVLEnSm9ApUh4fDoHb5gmFn2b+JSzNbkRBHC2EaNZRERERJ5V5FIJVhUsfRiA1sabW23nYETA8eqKZzaFiqDPwWf3B1im3CRH5UFkp8UY5cryNuu5ZedfG2nVoTNyucmBePzwfmZOHmsul0pNK6SCRMhTVsfHEYCb9zRUrlaLs6dOMmHLJX4+GEZGYgyCQcvcd1vRzN/JXEfj1gQAVazJ0RJcuy4Xzp4yl1+7fBFXt6dLJVQY8rRbeO/7BICEav1IrvJyqfUlIlIeyKQSuga7AfDXhTJIw/do349mYJAIUAg1xHIAAIOmSURBVElm+pHknnW6kEzn6q7YKUvIqSqRE9NkHACOl35DkRJWMu2KiJQAoqNFROQ5QyIRcFVbEuBqjbXl04Vlijw/dH2pF4dCIsw/u05cwt7BkYBqNfhm3EiWrNvOmh0HGfvND1zaPJ8aHmpS7kYzbdQnDBoxlt0nr+Dh5c2EkUPNbV6+eJ6flq4xt7lmxwEycnRM2HKJsHgNdko5k3sG46K2NNfJurKLV5fc4o1gOUcHKFlxUcve8NxOEgEj6V5t0Knc8h2Lh50V375aizcb+SAR4MCNeD5edYbz0cmlcu3KDYmUmCYTgLxxKA8+xzSZYNrxKQjolK6ke7flXu3B3Gr3Ezd67eXSO1cIfXEzCVX7FKvL4qYrq2isOB4FQMsAJ/ycrMvZmtyI0SwiIiIizz4VLn3YfdK9WnO3vknTxOPwV1jGXyxnix4ToxHPQ19gHXsMvdyaiM5L0Cldy9uq55rD+3bTqGlL82c//0DWrVjCuhVLiL0TzaxvJtC0VTts1LZ5ypbNmYp9YAOwUHFO505sfCIHtq1HIZXgGbmTFq3a4mJrcpKlp6Wi1WrRuJscLVbxIUhy0mjTqStnTxzjxJED6HQ6li6cS7PW7fK19WkR9Nn47PkQWXYyGc61zQuzIiIVneSkRM6dOk5SYkKxzu9cww2ZROByxG3+3LW32PUeEBcbw7lTx9Gkpz1W2e10KUdu6UjLLlzfNV6w56W6no9lU1Gke7cl1bsdEoMW92OTCj03MTGRc6dP5DsGEZGSRpz5i4g8p1jIpPg5qbBTVsyJoUjZIlcoUNvamX+2rFtF+y49cHB0YuSEaQQEVQegavVg0lJS+PKFIJQZsdi27McZWTXsHJ3p3a8/ly6cBUCn0xF67Qr1GzcztymzVDJl6xWu3U3DxkLGpBeD8bS3ymXHX1u24G4tYWwrBQGOUsa1smDRWW0ee4ta8JdKBN5o5MN3r9bG3daS+PQcxmwKYdGhMHJ0ZZubtjRJ9etCVPsFaP/ldNKq3Ihqv4BUvy6F1jfKlWS61CXF/5Vi9VfcdGUViWuxaRwPT0QiwBuNxGgWEREREZGSR21VcTcv3aszhFTv9kj02fjuHoQ0K6m8TSoYgx7VnaPY3tyM6s5RXM78gP2N9RgFKVHt55PtEFTeFj7XZGVmcvHcaWrXb2Q+5uLmzoz5S1i+aD4vt29KVmYmU2cvLLCs92ffALDnWgIOnYeQtGse9xa8zfVju/l01ARzu691as7BPTvRWnuQbeODYNSjvHsKewdHRoydzKC+r9K+flVCr13mg6Gfl8p43Y9ORBl/AZ2FHVHt5mOUiu9kIhWD4mab6Nw4mO2b15vr7d25Nd9sE3ZKBV6JZ7mz8H2+n/hFnnqFsXThHF7u0IRJo4bRsVENTh09VOyyzm8OZuA2HV4/pLE/TAsnckw/epPjJVtvpNr8TFL0SlxsLDEajSyeP5vuLevRqlZlpnz1GRkZmjw2jfyoP9PG5n4unDp6iJ5tG9GqVmWW/TwXgJjGYzEgZcGqbfRoWi3fNpf98hMff/wxU776LNcY5n0/Ld/v4OTRgwAMebdPruPvv9GzWNdTREQwGo2Fux7/A6SmpmJra0tKSgpqtbq8zRERKVFydAau301DvNMLx6DXEXHuEJXqtEAirbiT6ZIgOyuLzk1rsuLP3Xh6+5qPa7Vaxn8+BIkgYfIP87l5L52R6y+QozPwcl1PrMP28seSn9m45xiXL56jf6/uODo5ExcbQ73GzbDrNJjLaRZYyaVMfimYQNe8OhkTB/XC5e4+5nUzOWBi0gy0X5bB5cG5oxDCuq5G49G0WOPJ0upZdCicHZdiAajkqGREx6pUclI96SWqeBj0phy4mXHorFzQuDV6vNzlBj1VVzdDrok1a7I8ihEBrcqNa32OPHM50cduDuHcrWTaB7nwaYfA8jYnFw7WCjztrIo+8QnQarVs27aNrl27mtN/iIiIiIjPhtIjNC6NzJyKuZlDkp1Clc3dsUiNJM2rNRGdllS4v+fq8O24H5uAQpM3tU10i2kkBb1VDlaJPC6X7qTw5YaLCMBr9b1o7yPjesh5ajdojIOjU751PA98hsP1NcTV+pC7jUYBJq2YsNBrNGjSHGubp1+D+fdczu7Gerz3D8OIQETnpaR7t3nqPkRESgptTg6ZmRnmzxkZGvq80Ir5y9fzYd9X+XX1FgKCqrNl/SrmTp/MzmMh3IoI543ubRk77QcaNGnOtHEjiYuNYdnGnaSmJNO1RT1Ur3yN0tWPPnbh/PbjN+w8FlKoHRFhobz7WlfWbD+As6sbC2d9x7FD+/ht3bZil/lnnGXpuP+x56aO/bfu632OssGokDDlQBZj92bzzaJ1dO3UgfV/LGP+99/ww8+/Y61WM3roB/hVCWDq7J/NNh3et4fRnw5ky/5TqG3tAEhMiKd7y3q8/cFguvR8jZGD+zNizCQaNWvF7im9mLliN3/0DySp5xJGf/qRuc2IsFDefbUr07/7hnrtXuSXOd+bx5CdlUV2dpa539g70bz/xkv8deA0Nmpb2tcPYsGKDbi6m3RlZHI5SuVztL5QAI7WCjxKaf76LPM4fgMxokVE5DlHIZM81McQEQG2bVpLrboNcjlZrl2+SNu6ARw9sJeRE6YB4O9szaftAwDYcCqCBXNn0affAADCQ6/jHxjEt3MXsX73MSKTs9m7aCoKmYTxParn62QBSNZb4O1iZ059pbYQuJ32cNHige6Ixq1RvvXzw1IuZXDbKoztVg1bKzkRCRkMW3OOTWdvY3hePIwSKRqPpqT49zQ5oB538eRx0pA9Q1yMTubcrWRk9yOcKhKCAM7W4s5JERERkecFtWXFdVwZLGyJav8zBqklNtH7cTk7K0/0CAZ9udmnDt+Oz55ByPNxshgBvYVD2Rsl8kTU8LDl805VmdGrNm83rYSnpxdtO3cr0MkCmNOHWcceNx/z8atMm45dSsTJgkGPKuYYnolHUcUcwyLhEp6HTA6duLqfiE4WkQrH42SbSE1JASAs9BpDvxhH5x4v4+jskivbRIYmnVFff0OtmjXRGYzclbmZ6xWGXqdj/LezcXY1ZU8IrB5MakryY5Wl+nXBo9MQEnNyzyNPJquZdFRA5ehujvDfsu4P/jfoY2rWrY+ffwAfjRjF3l3bzXWyMjOZ8tUIPv1yvNnJArB14xqcXFwZ+MlIfP38GfTJSDauWg7AitMpDG9tT0u7GBpkH8nVpl6nY9w33+Pg4JBnDBaWlrm+gz+W/EK/9z7CRm1LbMxtjBgJCKpuLv8vOFlESgbR0SIi8h/A2cYCoSAVbJH/HGuX/0avfv1zHQusFswvqzbjHxjEuBGDzcdbBjjTq74XyQd+J1UnpVbHVwHo9nJvVvy5m+q167I+NAeh2QCyIs4yvJUXNTxsC+xbJpOTXaUrYFrgt5RBxv3MYU+74N/Iz5E5b9SlYSV7dAYjiw6HM3ZzCPfSsh+7reeRp01DVtEwGo38fiwSgE413HB9RAuoImCnlKOQia9ZIiIiIs8L6gqq0/KALMdq3G5hSuvkenY21VbUo/K2Pvjs/ZjK2/pQdXUz1OHbi2ilFDDocT82gbxbPR4gmMrL0REk8ni0CnQucFNVfmjc7uu03DuPRJs3TdDToA7fTtXVzaiy400aRM6nyo43CdjUHYk+izTPVsTV/aRE+xMRKWmys7JYsXgBAwYPw83Di24v9wZMEapLFs6h/QvdAWjd4QV6PzKHj7gZik+lygDmej1qeWDU69iwbD5tO3cvsm//wCDadDTNATM06fyx5Gfad+nx2GWLtp+m5Wsfmds90+JXWm+0Q9n4dSpVeri5MzkpEXcPL/NniUSK5BEty4Wzp5OdlYVUJuP44f08SMB0/XIIjZq1Qri/qBVcpz5XQs4DkJScgk190xqF65kfkOkzzG36BwbRusML+Y7hUeJiY/hnx1+8+e4HAIScO41er6dDw+o0CvRg5Ef9SU1OLvJ6ioiA6GgREflPIJdKcBJ3VotgCtOPigijSYs2uY4LgkC14NpM/n4ee3dty/Ui4Z8TRsaFHTj2+Jxvd4WSqMkBTAvdPx8I45+rccgsVWA04GFRuFNDbWdPDM7mBf+0HFDc96mUxIK/vVLB/9u77/CoqvQP4N/bprf0TghdOiJdUQQREFfEtbv2upa1l7WviLhWfnZdFQs2cBFdQcXeQRCBCNJLgAQIJJlkZjL1/v4YMhDTk6mZ7+d55iEz995zzx1mztx733Pec/dJfXH1cT2glUWs3lmFa9/5Fd9u2NfuMjsTe9FkrD/zR2yZ8i52jHsKW6a8i/Vn/phwQRYAWLGjAuvKqqGRRJwxNL/lDaJIEIBMc3wFfoiIqGN0igStEt+Xz5U9p8OePw4AILnrz9WiOMrQ5Ysrox5sMZYtg8ZR2kSQBRCgQuMohbFsWVTrRdHjNefDYyo4OE/LirCV29RIKUH1QwVQ2f2UhButTcmntdkmDuf1ePDaC0+Fsk3USXeXYtczf0PVphU45vybW12H7778DMcP7YPyvXtx2bU3t2/ZVdeHXr9t3koE3E4ce9pF0CuHvoO9+w7Al599HHq+8L25GH3M8QCA0l0leOM/z6CgaxHKdu3E4w/egxsuOw+qqqKmphp5XQ69P0azGXvLSkNlflRshyu1LySPHYtfmhEqs87y5csxYXi/Ro8BAOa9+QomT/srDMZgOvPtWzaj74DBeP7N9/HOx19h184dmP3wv1r9flJyi+8zRSIKmwyzFiK/8Unv0/8twNgJJ4byti/94Rs8NuPu0HLp4Pw0ghi8HN65fRv++Y/LcdfMx9C9Z28ccHgwc9E6zLrvDvzz8Rfx8ZrghfPUHCdEUUR2bl6z++8/aAhWr1weuuG/qPAuZGVlhfWGvyAImNQ/G7PPHIJeWSY43H488tl6PPrZetS4fR0uP+F1NA1ZHFBVFW8eHM0yZUAO0uIskMzRLEREnVM8pw8DAAT80B1YBxVoENiom6Mt2qNHZNfesK5HicmRMwIAYCz9OTwFtjhSCsha8ShHSlHca0u2iTpPPTIDBqMRp517Yb3Xj+g/ABc88B9o0rvgsTtbP5pr1Njj8ezr8yDLEp548J52LXv8kRkAgH0Avn7veaRN/gfOGtG13vrX3XY3in/7FRdMn4y/ThyDTz76L8668DIAwMJ5byEtPQMvvvUBLv/HLXjlvf9h+c8/4Kdvv4IsydBoDqXD12p1qD04x811t92N4lUrMfaVagx6vgYf/LQJ50+fWG+/Q4YMwTNz3m30GPx+P95/+3Wccd6h/4NLrr4Bz70xHz16H4FuPXvjhjvuw+eLFrb6/aTkxrsARElCEgXOF0D44evPMXzUMaHnRd17Yf7cOZg/dw7Kdu/Ek7Puw6ixx8NssaLW5cI1F52J4088CZOmnIybxhVCL3jwR5kdy6ut+PS1J1FbUoyJtnIseukhnHL6OdDrDQCAmmo7vF5vg/0fN3EKVi77Gct+/Ba+gIoX3v8SIyf8JSI3/PNS9Hh4+kCcNawAogB8s2Efrn17JdbsrAzrfij6fty8H5v3OaBXJPw1Dkez1OUhJiKizsUa5+nDjGXLoHGWxdXoEZ8+M6zrUWKqm6fFWPpTWMpreaQUOFKK4l57sk389O1XmPfmq5j11H9CnScP3+6Ck8cha+qN2Fv8A37dWNKqesiyjCOHj8Jt9z+MhfPmtm/Zf98DAFwPwNr/eAwaPLhBSvG8gkJ88OVS3Pvwk8jOy8eoY8Zh6IjRAIA9pbsxYsyx0GiD11FGkxldirpjV8l2WGwpqNhfHirHUVMNWdHUK/OuJ15BTnY2Tugm4S+ehcBhc7VKkoQhw0Y2egy//PgdbClp6Nazd5Pvj9lqRcWB/fC4mZKcWsZAC1ESSTdpIYmcrCVZ1bpcWPPbCgwaemii+czsHDz63By8+fJzOHX8KNS6XJg5+wUAwI/ffIEtG9fj/bdew8g++Th1RA/8MWs6Ava9cBcdDUPvMahaOBMfPnUXRo0dh9v+9XCo3L9OHIPvvvi0QR1SUtNw090zcOV5p2H80N7YtH4tLr/ulogdsyyJOHdEIR4+bSByrDqU17hx5wfFePWHrfD6AxHbL0WOP6Bi7tLgaJa/DM6Nu5teVr0CrZx4o4SIiKhleo0ERY7fc+l4HD3iyB4OjzEHahPLVQjwGHPgyB7exBrUGdQcDLToy1dD8Lk6XF48ftaJ2qo92SZuv+6yYLaJXn1C6x2+XYpBgxHdMwAAn61t/vP/8YJ5eO2Fp0LPZVmGeLDzY5uXHZwX5S0AFb8txjd3nYQx/bpg5S8/45oLz8LLzzwRPBZBgNFkxtLvvsE/7rg3VEZ2Th5qa2tDzwOBAPaW7kZOXkEoK0ad9WvXICs7J/S8rsyv/9iPmRPNMJX+CMu2T/Dxgnl4/aVnGj2GOp/+bwHGTzqp3ms3XnE+Vv36S+h58aqVSM/MCgWBiJojx7oCRBQ9oigg06JFaWVtyytTp6PT67Fic8OTrTHHjceY45Y2eP34SVOxuqSyweuL1pTitZ+24fzb78XpQ//T6L4++WlNk/U464JLMXrs8diyaT2OGjkGJrOl9QfRTn2yLZh95hD85/st+GztHvx35S6sLKnETSf0QmGaMeL7p/D5ZsM+lFS4YNLKmDa4+VR10cbRLEREnZ9Fp2B/jSfW1WhUXI4eESXsGXoL8r+9scGiuqRPpSPvS8hUptR6XlMBPMZcaBy7YdizAo68oztUXlx+1ona6IevP8e0M84NPS/q3gvXX3oeCou64+hxE/DUIzMazTYxbuIUOB01AAC9wdhguz1fvApd0RAs3VWLCqcHir8WWp2+wQiYoh498cAdNyC/S1f06T8Qzz72ECZOnda+ZVNOwWtdh+G0lRuxe8Aw3H3KAAgCcOvVl+C8S67CmOPGh/b74v89ihOmnoK+AwaHXpt48qk4a8pxWLJoIQYMOQpvv/oiPB43hgwbAY/Hg5l33YJlP36LI4ePxmsvPI3Rx9afhyVY5jQUTOoFrJyNnKUz0K3PE3jgjuuhufZaKGld6h3Dof+DLzDjiWfrvdbriH545P5/4tb7HkLF/nI88+iDDebDIWoKR7QQJZk0oyaue+JR/JsyIAdvXToSpw8taHcZXYq64bgTJkclyFJHr5Fw7fE9ceeUI2DRydha7sAN7/2Ghb/tQkBtqp8lxROfP4C3l+0AAEwfkgeTNr76i1j1CnQKbxQREXVm8TaS8nCHRo80fq4fq9EjusoNEAAEhPq/215jNnaMfz4sc/RRnBOE0DwtpjDM0+LIHg6vLo0jpShhdTTbRN1j984dDbbTwIuRF90LX0DFp7+XNZltou+Awbhr5mN45IE7ccbkscjJL8DN9zzYrmVX3v4vPCr3wPKBJ+C8SSOQ36UQeQWF0Gq1SM/MhMVqAxBMl7Z44Xxcd+vd9epS1L0nHnn2Fbz0f4/i5GOG4tsvPsPsl9+C0WRuMSvG4WXuG3gVvIZsaGpKMBbLcOeMR/DKK6/grJPG1TsGACjZthX79pSi/6Aj69XlkqtvRPeevXHFOafi3/fdgTP+djEuuaZhZwGixgiqyrtLdrsdVqsVVVVVsFiid9OPKFYOODzYVdHxIdudScDvw7bfvkfXwUdDlOLr5i2FX4XDg//7ciOWb68AAAwusOH68T3jblL1tqisOIBtmzeisFsPpKSmxbo6EfFJcRme+XoTbHoFL51/VNwFNXpmmaJWJ6/Xi0WLFmHKlCkNeqcRUfJi2xAd60rt8Pnj8zLasnUxunxxJYDgnCx11IOvRDuwITtK0fu9sRD9bmw74WUEFBNk11749JnBm+AcyZI0Uta/g/zvboUjezi2TJ3fobL0+1ah6OMzIPlcUIF6ocW6QCODeJTMvtmwD49+th6pBg1evuAoyFLk+tl7/QHc++HvWLOrCnk2PZ4558iIpKzfsXVLq7Ji2DYtQMHX/4Bf0mPH2Eexf0sxbEccA1cE5oXtbNJMGuTa9LGuRtxpS9yAI1qIklCKQYFW4defkleKUYN7pvbFVcd2h0YW8VtJJa55eyW+31Te8sYxsvC9uRhYYGvwWPjeXCxe+D5OOmYIZt51M04c0R+LF75fb9vKigOYNHogdpVsr/f6Q3ffUq+sk44e0qq6LP/pe5wybjjGDuyG1198OmzH2ByPL4B3lwdHs5x+VH7cBVk4moWIKHlY4nhUi71oMnaMfx5eY3a91wUA+/ueH/Ubz1m/PgnR74Yjaxiqu0yAI3cUqrqfAgdveCUdR/bBeVr2/gbB1/5U1vp9q1C0+FxIPhdqrd3hM9T/rHOkFBEwunsaUgwKDjg9+HHz/ojtR1VVPP3VJqzbsR9nr/0Sz3hWQfL7IrKv1mbFqOw+DbWWbpD8LhR9dTWO2v4cenxyDnq/OxqWrYsjUjeiOuy2TZSEBEFAplmLkgMc1ULJSxAETBmQg4H5Vjz22QZs2leDhz/5A8t6Z+CKsd1hjLO0VFOmnY5xJx6aqM/pdODMSWPR84h+uOq80zBn/mL07NMXH73/Dp586F5MPuU0AEDFgf249qKzsLtkR4My165ZhWdeey80ZF2SWr7hcWB/Oa675Bycf/nVmHzKX3Hr1RejT/+BGD56bJiOtHGLi0tRXuNBukmDSf1yWt4gyjItiTsaioiI2saik3EgTudpAYLBFnvhRBjLlkF27YWxdCnS/ngT1m2LUTbsDqiKISr10FRuRsqG9wAAZcNuD05mRknLYymE15AFxbkHhr2/wpE7us1l6MpXB4MsHjscWUdh24mvIyDrod/9EyrXfcde60QHKZKIyf1z8NayHfho9W6M7ZURkf28t7wEX/6xF8aADw999DjwEVB81plQYziq1rLtE2jtWxq8rjjK0OWLKxmIpYhil3aiJGUzaKDjqBYi5KcY8MhfB+LMowogCsBX6/fhundW4vfdVbGuWj2KRgOL1RZ6fDT/HYyffDJS09Jx630PoWefvgCA3n37w151qO63Xn0xJv1leoPyfD4fNq1fh6EjRofKNJrMLdbj4wXvIT0zC1f841YUFnXHlf+4FQveeTN8B9oIl8eP+St2AgDOGtYFGjm+2i6OZiEiSi4mrQwxvn6KGhKl0OiR0lH3wmMugOLci4zVz0etCtkrHoGg+mHvMgHO7GFR2y/FKUGAI2cUAMDYjnladOVrDgVZMocGgywaU/CznjMSu1JHwZEzkkEWooMm9cuGLAr4o6waG/dUh738bzbsw5tLg535Lj2mKOzlt0vAj5yf72t0UV06zZyf7wMC/ujVKZICfhh3/wTr5oUw7v6p8xxXAouv7rpEFFVZVh22lztjXQ2imJMlEeeNLMTQwhQ8tmQ99tjduOO/a3Dakfk4Z0QXKBHMadse7tpazH3lecz98HNk5+bjpFPPABDMzT/nhacwftLU0Lr3zpqN/MKu+Pd9d9QrY8O6YqiqitNPPAZ7y0oxdOQY3Pvwk8jJK2h23xvWFmP46LEQDvZK7T94KGY//K8wH2F9/1u9G5UuL3KsOozvkxnRfbUHR7MQESUXQRBg0SmodHpjXZVWUSUtSofdgcIv/46MNS/gQJ9z4PtTarFw0+9bBevWRVAhoOyo2yK6L0ocNTkjYNv8QZsDLbryYhQtPgeyuyoYZJl0MMhCRE1KMWpwdM90fL1+H/63uhQ3nNByp7rWWldqx+wvNgAApg3OxQl9s8JWdkcYy5ZB4yhtcrkAFRpHKXrNHwevIQsBxYSAxgy/YoJfY0ZAY0ZAMcGvMSGgmOHXmEN/Bw6uo0rauBihadm6GDk/31fveD3GHJSOvI8jdmKIgRaiJGbRKdBrJLg8jHoTAcARORb831lD8NJ3W/D5ur2Y/+tO/FpSgZtO6I0uqdFJs9Eaiz6Yh4FDjkJeQWHotfVr1+CSM06GotFg4VfLQq/nF3ZttIytmzage68+uONf/4YtNRWz7r0N/7r9Bjz3RvOTk9bUVKNbrz6h50azGXvLmj6Z7agatw/vrwyOZjl7eJeITuTYHha9zNEsRERJyKJPnEALANiLToIjcyiMe1cga/kj2HXsYxHdX9YvDwMAKntOhzu1d0T3RYmjbkSLYd9KCL5aqLKuxW10+38PBVmcmUceDLKE74YxUWd28sBcfL1+H77duA8XjekKm0HT4TJLq1yY8fFaeP0qRhSl4sLRRUBtfHTglV17W7We1r4NWvu2du0jICoHAzQm+BXzYf8GAzb1nocCNqZQoKYugNORgI1l62J0+eJK4OAonTpMjxZ7DLQQJblsqw5b9zliXY12EQSgMM0Ar1/FAYeHASMKC4NGxj/G98Kwrql4+qtN2LLPgRve/Q0Xju6KqQNzUFVZgW2bN6KwWw+kpKbFpI7z3nwVV91Uf4RKryP646V3FuKxGXfjnpuuxpP/mdtsGSedekZoJAwA/POBRzBlzGDUVNvh8/maPEZZkqHRHDpB12p1qHU1fWJdWXGgQ+/XByt3weH2oyDVgLE9I5NbuCMyzS3fICAios7HrJUhCICqtrxuXBAElI68Bz0+PAUpG+djf78LUZs+ICK7Mu76Dubd3yMgarDnyBsjsg9KTB5LEbz6DCiufdDvWwVnzohm19ftX4uiRWdDdlfCmTEEWxlkIWqTXllm9M4yY/2eanz6exnOHNalQ+XV1Ppw/0drYa/1oUeGCTdP7A1JjP3ojjo+feuyH5QO+yc85nxI3hqInhpI3mpInmqInhqI3mpI3pqDz6shemsgeWogemsgQIUY8EJ0VwDuig7VVRXkeiNnggEa02EBG/NhgZyDzzVmBGQ9cn+8C4CKP7/zAlSoEJDz832wF05kKsUYYKCFKMmZtDJMOhk1tb5YV6XNcqw6mHXBSdZSjRrUev044PCg0umFP5AoV70Ubxa+Nxd333R1g9cfn3I9PsiyYd37TyC/oAu2bd6E+x99OjTpPBAMKpx10nF4+d2P6o022fjHWtxz09XYsX0Lpp91Pm6881+h1FvNWf7T93jgnzeiYn85Lr3mRpx/+TXYsXULdmzbgpFHH1dvXUEQcET/QZjx+LM4ceQA2CsrYbHZ2nSMAPD+22/gxf/7N/LyGz9GrV6PZx+bieNOmIy8gkLMe/MV+Hw+DCyov69/PfYMtFodZtx5Y5NlVVYcwPTxo6AzGFBjrwodY937tX3bFmj6ToBp7IU4b0SXFk/iG3u/Ismsk6HX8OSViCgZiWIwfViVK3FGtbgyh6Cy+zTYNn+AnKUzsHXKO+FPf6KqyP5lFgDgwBHnwWtuPiUpJRlBgCNnJGxbPoKp9KdmAy31gyyDsXXyGwhoLFGsLFHnMHVgDtYvqcai4jKcdmR+uzMEeP0BPLR4HXZVupBu0uKuk46Iu5H9juzh8BhzoDjKQnOyHE6FAK8xG+UDLmt7EEINQPQ6DgZeDgvAeOzNBmxETw0kj71BwEZQfZDdlYC7MizHXqcuPZqxbBkcuaPCWja1jIEWIkKWRZtwgZYUo4I0U/15EXSKhFybHjlWHewuHw44PQl3XBR7U6adjnEnnhR67nTUYNoJx8CQ3Q0r3r4L3S74N64/awL2//Y5nnzo3lDgoOLAflx70VnYXbKjXnketxvXXnwWxhw7Hv9+5hXMuvc2LHxvLqadeV6z9TiwvxzXXXIOzr/8akw+5a+49eqL0af/QKxa8QvGTjgRihIMMi794Rt8/9XnuOmuBwAAkhT8aReaCUpMmXY6in/7Ff0GHYnjJ50Ep9OB6eNHwVFTjZeffgxz5i9Gzz598dH77zQ4xl+X/ohquz1UVmG37sgvKIQv4MeIMWMx/czzcclZJ2N3yQ68+/p/mi3rqr+dhvJ9e3DepVfhzPMvxa1XX4zuvY/AA3fcgDHHjsdRF9+P9/7vX8jc9h1GdTu6Xe/X8NFjm92uI7IsHM1CRJTMrPrECrQAQNmw22DZthim0p9g3rEE1YUTw1q+ZdsiGMrXwK8YsXdwZDs8UGKqC7QYy5qep0W3f93BIEsFnBmDsHUSgyxE7TWmRzpe+WErDjg8+GnLfhzTjiwBqqri2a83YfWuKugVCfdM7dvgfkxcECWUjrwPXb64EiqEesGWuvEfpSPva99ID0EMzuGiMcNnzGl/HdUARK8zGIipN2LmUJBG8h7811NdP2DjrYbs3BsM0LSgtWnUKLwYaCEiGDQyLHoZdldiBCX0Ggl5Nn2TywVBgNWgwGpQ4Pb5Uen04oDDA5+fo1yoZYpGA+Ww1Fjvvv4yppx8CqaeOQoP114DpzUfDy3+A0OMZlRVVYbWu/XqizHpL9Ox+tdf6pX3/VdLUGO34+Z7HoReb8B1t92NmXfd0mKg5eMF7yE9MwtX/ONWCIKAK/9xKxa88yZKd5Vg2hnnhtYr6t4L1196HgqLuuPocRPw1CMzMGrs8TBbrM0e44Ajj8KLsx9BfmFXfDj/bQiCgIlTp+G4EyajZ5++qKm2o3vPPrBXVdU7xlPPOg9P/XsGVq1YhqycPMx9+QUU9eiFVb/+gn/OeBRv/uc5HHfCZHz35We49b6H0LNPXwBA7779G5SVnZOHtat/wzkXXYn8LoW48h+34j9PPY4aux0X33gPrpu/DrZjz4dv+WsQhH+26/2KVKCFo1mIiMisS7D0YQC8pjyU978UmaueQc7SB1GTfxxUqeM5+wEAAR+ylz8CACjvfxn8+vTwlEudiiN7JADAsOdXCH53cJ6Cw2gP/IGixQeDLOkDsXXSmwhomz6vJaLmKZKIyf1z8NayHfhodWm7Ai3zV+zE5+v2QhSAWyf1RlG6sd5yVaPF9ufmhP6OJXvRZOwY/3yDieK9xuz4mCheEBHQBOdyaU/Axrj7J3RbdGaL67U2jRqFV3zNKEtEMZMoPbMlUUCXVEOr0i4BgFaWkGXRoU+2GYXpBlj0ctgzJFDn5a6txdxXnsclV9+AI/v2xJszb8bpQ/MBvw9L3v0P9D1H4ffdweDBvbNm47xLrmpQxvp1xRh45DDo9QYAwblUNm/4o8V9b1hbjOGjx4Y+6/0HD8Xvq1dizW8rMGjo8NB6mdk5ePS5OXjz5edw6vhRqHW5MHP2C82W/fGaUvwo9kP+keNw7cVn48N5b2HciSfh/kefDs3bctoJo/HoA3dh/KSpoe3unTUbl117MwDgzhuuwvihvbFp/Vp079UHA48cBlEQMfeV53HjnQ+gZPvWUFlerxdzXniqQVl1waC672T/wUOxZeMfGHjkMCz8fT88/gAGDhyEvTs2t+v9Wle8qsXt2ivTEoc9uIiIKKpEUYBJm3h9F/cN+ju8unRo7VuRuu7NsJWbsuE9aKu2wKdLRfmAy8NWLnUublsPeHXpEP210O+rf66mPbAe3RadBbn2AJzpA7B1MoMsROEwqV82ZFHAulI7Nu2tadO2323ch9d/3g4AuHxsdxxVmNpwJVmGfeo02KdOA+TY/y7aiyZj/Zk/YtOkt7C88CpsmvQW1p/5Y+yDLGFQlx6t4Qwth6gQIdUeiGKtqE7sP/1EFBd0igSbQUGlM37THwgCUJhmgEZue4xYEIJ5tC06BV5/ABVODyocXnh8gQjUlDqLRR/Mw8AhR4XmW1EkESOs1XjuxQvgDohIveQ5/HPBGpx2ZD7OHt74xIKO6up687UIggBJkpqdQwUAamqq0a1Xn9Bzo9mM8r17sGJzwyHAY44bjzHHLW32WFaXVAbLdfvw6g9b4fYFgJ6nQucywlCyAiMvvAtuKDAAWL92DRw1Ndi6eQOe/M+hGzD5hV1Df7849wM4HNU4auQYPP/Ew8grKAy9X/ldCkPHWLq7BJeccTIUjQYLv1pWr6yamup6dTSazaiprkZKVh4+W7sHAHD+qK5Y1s73a29ZaZPrd4RJJ8Og4SkUEREF04dVJ1iq2oDGjD1H3YT87+9A5sonUNljOvw6W4fKFHy1yPr1CQDA3sHXIqAxhaGm1CkJAhw5I2Db+jFSNsyD4iiFT58Jn86KbovOhVx7AK60/tg6eS4CWlusa0vUKpUVB7Bt80YUduuBlNS0WFengRSjBkf3SMfXG/bho9W7ccOEXq3a7o9SO574fAMA4C+DcnHSgIYjMPaWlWL3zh3o2acvjCZzWOvdIaIER85I7Nrjg5IzEmJnmRi+2fRoQQICKPzyKlRun4bdo/7V4d94aj2OaCGikEyLNq5He2RbdTCGodegIonINOvQO9uMogwjbAYlro+bYmfem6/i9L9dXO+1Xkf0xyvvfojBA/pD+O45BFRg3oqduHX+apRUOBuUIckyNJr6KTk0Wh1crobrHk6W6m+n1epQ28I2rfH1+r1w+wLINGsxMN+K6t8WQ+hzAl74dgsufHUZ7llYjBJk4Kk3/ovuvfrgnpuubrSc3IICHHfCZJjMltAxHv5+1R1jryP646V3FjZalizV/z5rtTr4fF5sOeCGP6BicIENA/KsMX2/GpNp5mgWIiIKqksflmgqep2J2pTekN1VyPzt/zpcXtrvr0Jx7oHHlI8DRzSfHpXIrwnejE3d8C66fHUtui06Ez3/OwVy7f6DQZa3GGShuLLwvbkYWGBr8Fj43lwsXvg+TjpmCGbedTNOHNEfixe+H9pu4x9rcfZJ4zCmfyEem3E31EZyTT750H245qKWU0H92a1/vxgP3X1Lq5dNHZgLAJj375tx3x031Vt2zUVn1juuy84+BWVVtZixaB2qt67GgdeuxhtXT8DrLz5db7vXXngKp04YiQduvwETh/TC2sdnAT4fVFXFK8/NxtRjjsTYgd3w4J03w+l0hLZb/tP3OGXccIwd2K1Bmc0dw/y5c3D80N44sigdV5xzKvbtKWv2GDqruvRoXmN2vde9xhyUjHsaewddA1UQYdv8AXq+Px7mHZ/HqKbJh4EWIgrRyhJSjGHK0RxmNoOC9AhMtmbSyihINaBXVvBkX6ewWaSgHVu3YMe2LRh59HH1XhcEAUf0H4SZTz6HkpXf4toxOTBpZWzaV4Pr3/0NQP1c7VZbCioOlNcrw+morjcPTGMsthRU7D+0naOmGrLSse+nqqpYVBw8GZ02OA9XDDJC69yHv587DT0zTQiowMqSSsz+chMe+NmFnJNvxFefLUL5/uaHHVttKdi5Y2u996vuGOverxmPP4uvPlsEe2VlvWM8nKOmGqIkYduuYB3/NrKwXlnNicT71RijVgpLwJeIiDoHWRJhSMQ5u0QZpSPuBgCkrn0Nmqqt7S/KXYnMVc8AAPYMvbHBnBtEh7NsXYzU9e80eF1AACqA8n4Xs/d1EtJrJGRZ47ftmDLtdHxfvC30+GzZ70hJTUPPI/ph1j23Ys78xXjvk+9w96wn8ORD9wIAPG43rr34LPQdOBjv/O9rbNm4Hgvfm1uv3I1/rMW7r7+M2+6d1ab6/PD1F1j647e4+uY7W72sd7YZqRVr4dy+CkWT6ncmXLd6Fd5f8mPo+GY++zru/9/vOLC/HOULZuCss87GGwuX4OMP5mHZj98CALZt2YQ5LzyFD75Yiv9+uAS31Lrw7BOzIHjc+O87b+CtV17AQ//3El5b8CmKf1uBGXfcAAA4sL8c111yDiafclqDMps7hl+X/YSnH30QM598AYt+XAW3uxaPzbi7yWOY/fJbbXpPE01derQtU97FjnFPYcuUd7H+zB9R1f0v2DPsVmw++QPUWntAce1D188uRt43N0F0V7VcMHUI7ygSUT2Z5vgb1aLXiMiz6SO6D0kMHnS3DBN6ZJqQatJAZAuZ1D793wKMnXAiFEUBACz94Zt6J3LSwdEYo7qn4+mzh2BwgS2Uim72lxtR4fAAAPoNGoLVvy4PbberZDs8bg+sfwoy/Fn/QUOweuWh7davXYOs7LZPlne4taV2lBxwQiuLOL5PJj793wIcO+FETD+qKx4/YzCu6ulC9rp5yLPp4fWrWF5ih6oCV879FbO/2IDfSirhDzTshdVv0BD8uuzn0Pu1q2Q7al0uvPLsk6F16t4vQTzUwPQfNKReOevXroHGlAL37j8woigVvbLMMX2/GpOZIPNZERFR9Fj1Sqyr0C41+WNRnT8OYsCL7GUPtbucjNUvQPLYUZvSG5XdTw1jDanTCfiR8/N9zawgIGvFI0DAH60aUZzItemQadbBqI3PwLWi0cBitYUeH81/B+Mnn4zUtHTcet9D6NmnLwCgd9/+sFcFb2Z//9US1NjtuPmeB1HQtQjX3XY3Frx7KC2zqqp44I4bcN4lV6Kga1Gr61LrcuHBO2/C9bffC4vV1qZlOz56CinHXoCvtjjg8wevXctKd0GFip59+sJitcFgsmD2Nzuws8IFbPoOuTk5uObG21BY1B1X/uNWLHgneAx+nw/3PjwbGVnBURWDAFQc3NdH89/GhVdeiwFDhqKoe0/8/aY78NVniwEAHy94D+mZWbjiH7c2KLO5Y9i2ZSPumvkYRh5zHLJz8jDtjHPx++qVjR6DxWqDwWBs9XuasEQJjtxRqOp+Chy5o4DD0qO5Mgdj06mLsG/AFVAhIHXjPPT870SYdn4Twwp3fryNSET1KJKINFP8jGqRRAFdUo0QxehFf/QaCXk2PY7ItiA/RQ9DnJ7sUWT98PXnGD7qmNDzou69MH/uHMyfOwdlu3fiyVn3YdTY42G2WJFm0uL+v/TDZcd0AwAU76rCNW//ip+27MfQEWNQXW3Hh/PfBgC88uyTGHHMsZCk4OeqptoOr7fh3EjHTZyClct+xrIfv4XP58NrLzyN0cce36FjWnxwNMvYXhkwauUGx3jUoP4o/vK/GI/VuOvYTFjWvAdLj6Fwizp8vm4v7l5YjAvnBOdZ2bLPERr6PnTEGDhqqqEcHEHyyrNP4siRo/H+W69h/tw52LzhDzw+857Q+3X4MQLAqhXL4PP58Nz/PQmpaBgCbidy9i6L+fv1ZwatlJCTHhMRUWRZEjTQAgClI/4JVRBh3f4JjKU/t3l72VGG9OKXAQBlR91a7yYP0Z8Zy5ZB4yhtcgpnASo0jlIYy5Y1sQZ1RjaDEpr/sCDVEOoEGa/ctbWY+8rzuOTqG5Cdm4+TTj0DAOD1ejHnhacwftJUAMD6dcUYeOQw6PUGAMEU1Js3/BEq5/23XsMfv69BXkEhvvn8k0avcRrzwuxH4K6thSTLWPrDN/XSkbW0TPB7YdBpsHPtL/hxczAbQPFvK+D3+zFhWF8M75WLv555Jn7dtAt6RUIv5QBGHX0shIO9cfsPHop1xasAAN179cFxJwQnl3c6HHgKwPSD+6qsOICc3PzQvkVRgigFb0FvWFuM4aPHNlpmc8cw/azzMWHyX0LrbduyCV26dmv0GG79+8X1MikkK1XWoWzEndgydT7clq7QOEpR9MnfkPv97RA9NbGuXqfEQAsRNZBh0sbFaA5BAApS9dDIsamMKApIMWrQPcOEnlkmpJs1cX/SR+FR63JhzW8rMGjo8NBrmdk5ePS5OXjz5edw6vhRqHW5MHP2C6HloiDgL4OCeW8LUgyw1/owc9E6PPvtVtwx43HMuONGHDekJ5Z8vBDX335faLu/ThyD7774tEEdUlLTcNPdM3Dleadh/NDe2LR+LS6/rvEcvK1R5fLih03Bk+nJ/bKbPca5rzyP688Yj3yzhA/mvY2HTh2ASf2yYdbKqHQGLwBmLl6Hy99YgTd+3o7lW/dBVYH/vf8ujh7YA4s/+gCnXHEnrvrX0/jP809j+oRRWL9hE8659d/4raQy9NjuCN6M+ef1V+HYIb3w+9rfYRtzNo656J94YeYdMX2/GpPF0SxERNQIRRITtmOOO6U3DvQ+BwCQ8/O/ADXQpu0zV/4fRH8tHJlDUd1lQiSqSJ2I7Nob1vUo8QlC/XNsRRKRlxLZbBYdteiDeRg45CjkFRSGXlu/dg3GDemJn779CrfeFxwh6KiurreOIAiQJAn2yko4HTV46pEZKCzqhj2lu/H6S8/gor9Ogbu2ttl9l+4qwRv/eQYFXYtQtmsnHn/wHtxw2XlQVbVVy7p0LUJXrQuVX72CB66/BKqqYvuWzeg7YDCef/N9XPTvt7Fzxw5Uffsabj2xNwSvC3ldDh2D0WzG3rLSenX67svPMO7oISgDUJfoq3ffAfjys49D6yx8by5GHxPsBFdTU91kmc0dw+EqKw5g3txXceb5lwBAvWN45+OvsGvnDsx++F/NvpfJxJk9DBunf4ryfhcBANL+eAs9/zsRxt0/Hlop4Iem5AdgzXxg63ccWdhOgtrYTExJxm63w2q1oqqqChaLJdbVIYoLe+y12Gt3x7QOWVYtMs3RubHp9XqxaNEiTJkyJZQqqjGqqsJe60OFw4PqWl9U6kaJx+sPYO7SHfjvrzuhAhjTPQ0XDbZi7eqVGHTUCKSmpbe6rB1bt2DLpvU4auQYmMzt/416/9edmPPjNvTINOGJMwa3qwyvP4DfSirxzYZ9+HnLfrh99W/G+KrL4SnbBG3eEZAM1iZKaaTcit3w7i+BrmAAZJ0Bz5xzJJTaypi+X39m0EronmEKW3kd1do2i4iSC9uG2NlX7UZZVfM3yOKV5CpH7/fGQvLWoOTYJ1DZ87RWbaep2ope84+HoPqxeeo8OLNHRLimlOiMu39Ct0UtT/q9Zcq7wTQ4rRTw+7Dtt+/RdfDRECWOPk4kWRZto6l5d1Y4UeFo3QiPaDtn6vG46qY7cMy4E0KvqaqKP35fjcdm3A2TyYwn/zMXT8y8Fz6vF7fcOzO03gnD++HNhUuw9IdvMOOfN2HJ0t9hTUmBz+fDaSeMxt8u/Tv+eu6FTe77+ScfxoJ33sBH36yARquFo6YaJ44cgH8/8wpWr/ylVctqfAIufPEb7Hj2Itz92As47S8nAQB+2FSOWZ/8gdoda1D98b/x8+9bcctVF2HwsBE49+IrAQB+vx/DemTh162H5sb0+XxY8+N3eOLcU3EcgEvW78LO/eX4+/mnw5aSBkeNHRvW/Y5X5y/C0BGjmy2zueM7PFvBLVddBKfTgWdee6/R92n5T9/jpisvwFe/rmfb8CfG3T8h/9uboakpAQCU970Qzswjkf3LQ9A4DguiWXKBSQ8Dff/SREnJoy1xA37KiKhR6SYt9td4Gp2PIRqseiVqQZa2EAQBVr0Cq16BxxdApdODA04PvL6kj1nTYRRJxIWju2JwgQ33LCzGD5v34+zhXTDuxJPaXFaXom7oUtStQ/UJqCo+OZg2bHL/7HaXo0gihnVNxbCuqaj1+rF06wH8sKkcFU4PVBUIZJqgdu8KVVURUNGg51GT0noAPXoAAMb1zkR+igGAAVk5uW2uYzjer8ZkmuN3ck4iIoo9q15J2ECLX5+OvYOvQc4vs5D9y8OoKpoCVW65R3nWikchqH7YC45nkCWJCALQ3u66juzh8BhzoDjKIKBhISoEeI3ZcGQPb2Rr6mwUWUC6qfFz7FyrHk6PH25v20bZRdqOrVuwY9sWjDz6uHqvC4KAI/oPwozHn8WJIwfAXlkJqy0Fm9avrbee01ENRaPBntLdGDBkKKwpwXkoZVlGrz79sKtke7P731O6GyPGHAuNNvi+GU1mdCnqjl0l21u9LFULjO3XBe+k5OLzZWtw2l9Owvqyajy+ZAMAYNyArnjn7Qp43G5YbCmo2H8oqOKoqYas1E81L8syjjxqOP4PwMkALgGQV1CID75ciq2bNuDxmfcgLT0TQ0eMBoBmy2zuGOoseOcNrFj2I+Z98l2T75PZakXFgf3wuGPbeTgeOXJHYeNpnyF76YNI++NNpK+dA3XtnIYr2kuB984HznidwZY2YKCFiBoliQLSzRrsqYr+D5NOEZEf58OFAUAji8i06JBp0aG61osKhxf2Wm+7Lzyo8xlcYMPIbmn4act+zP91J246oXdM6vHbjkqU2Wth1EgY2zMjLGXqFAnH9srASQNzUJTe9ESDXn8Abl8Abq8ftQf/dfsC8PkT54ui10gw69gznIiImqaRReg1Ilye+Lop2Fr7+12MtHVvQlOzExlrXsTeIf9odn1d+RrYtnwEFQL2HHVblGpJsWbRy8i16bFhTzUC7fmoixJKR96HLl9cCRVCvWCLenDmltKR93GunySRbdE1ORerKAooSDFg876auLq+/vR/CzB2womhUaNLf/gG33/1OW666wEAgHRw1IQgCug3aAj++/broW13lWyHx+2B1ZaC7Ny8BmnCdu8qwbDRx6A52Tl52Lxxfeh5IBDA3tLdyMkrwIHyfa1eNqV/FuZWl2NLrR6XX3AOyosmwpveA8O6pqBn1WakZ2ZBo9Wi/6AhWPzh+6Ht1q9dg6zsHADAxwvmoXxvGS644loAwRvMh39zBUGA0WTG0u++wWsLPgm93lyZzR0fAKxZuQL/vv+feOqVt5GWkRla78YrzscFV1yLQUcOAwAUr1oZOgZqKKAYsfvombAXTkTXzy6E0GjaUBWAAHxyO9DnJLbLrRQHszAQUbxKN2ohS9Gdk0QUgS5phiZPuOKVWaegS5oBfbLNyLbqoFXYvFLQ6UODkwB+u2Efyuyx6em6+PfgEOBxfTKhU8J7gqRvoTxFEmHSykgzaZFn06NbhglH5FjQN9eC7plG5KXokW7WwKyToZFFyJIAWRKgyAI0sgitIkKnBG9eBR8SDFoJRq0Ek06GWSfDog8+rHoFNkPwkWJUkGrSIO3gI92sQYZZi0yLFlkWLbKsWmRbdci26pBj0yHXpkNeih75KXoUpAYfXVIN6JJmQEFq/Ad+iYgo9iwJHJRXZR3Kht0OAMhY9SxkR1mz62cv/zcAoLLHNNSmHRHx+lFsiSKQn6JHYZoRiiR26LNuL5qMHeOfh9dYf5S115iNHeOfh71ockerSwlAr5FgM2haXCfe5kj84evPMXzUoWBIUfdemD93DubPnYOy3Tvx5Kz7MGrs8TBbrBg6Ygyqq+34cP7bAIBXnn0SI445FpIkYezxJ2LrpvV4741XUFa6C3NfeR7r167BmOPGAwBqqu3wehumTpt48qn45vNPsGTRQpSV7sLsWffD43FjyLARbVr2yauPQwz4IOccgfXeFOxY/BzSnNsxSt6O5x6fiTP/Fpz75LiJU7By2c9Y9uO38Pl8eO2Fp0MpvIp69MRzTzyMLxZ/hJ17ynBbrz6YPOpoqIeNeHnx/x7FCVNPQd8Bg0OvNVdmc8ewf99eXHvxWbjoqn+g78DBcDpq4HQEJ3TvdUQ/PHL/P7F65XJ88/kneObRB0PHQE1TJW0TQZbQGoB9F7D9x2bWocNxRAsRNUkUBWSatdhdGb2bwwWpBmjlxI2Uy5KIDLMWGWYtHG4fDjg8qHJxlEsy65llxpACG1aWVOK/v+7E34/rEdX9l9e4sWzrAQDApH7tTxvWFF07g4qSKMCgkdHC9RUREVHCsOgV7InxHIcdUdXtZDh/fwWGvb8ia8Vj2DX2kUbXM+7+Eead3yAgKthz5I1RriVFm1ErIT/FAI186JzPoldQ6Wz//Bn2osmwF06EsWwZZNde+PSZwXRh7DGdNHJtrQugZJi1qHH7UBMH86PWulxY89sK3Pvw7NBrmdk5ePS5OXjkX//EYzPuxuhjj8fM2S8ACKbUunfWk7j92svw+IP3IOD345V5wQnirSkpePaN9/HYA3fh0X/dibTMTPz76ZeRVxCcJP6vE8fg1nsfwvGTptarQ1H3nnjk2Vfw9CMzsHXTRuQXFmH2y2/BaDKjyGRu07KrHnwBC8sMMI84HaKjHH+8chueTM/AGX+7GJdcE2zbU1LTcNPdM3DleafBbLZAbzDg/keeAgD0HTAYd818DI88cCeq7VU4YcopuO7+WcDB0T47tm7B4oXz8d/P69+kb67M5o7vv++8gQPl+/D0IzPw9CMzQuWtLqnEJVffiNKdJbjinFORmpZe7xioabJrb+tWrNkT2Yp0IoLa6gTqnVdbJrUhSjaqqmLDnhp4fJFPg9DURHjREMnJY/0BFVUuLw44PHB5/GEtmxLDml1V+OeCNVAkAf85fxhSjdGLLry9bAfeWrYD/XItmDV9YNjL75llCvsoGWoZJ7wmosawbYi9DXuq425OgbYw7FmB7h+dChUCNp26CLVp/eqvoKro/uEpMOz7DeV9L0Tp6H/FpqIUcYIAZFl0yGhknrpAQMXaUnvcdCYL+H2c8DqB2AwKClINrV7f6w9g456amM0f21F7Sndj7eqVGHTUCKSmpce6OiFefwBXzV0Bu8uHh6YPQPcMU5Pr7ti6BVs2rcdRI8fAZA7PfdNIlPlnbBuaZ9z9E7otOrPlFS/4H1DUfFq7zqwtcQN+yoioWYIQHNWys8IV0f2YdXLMgiyRJokCUo0apBo1qPX6ccDhQYXT0768xpSQ+uda0CfbjD/KqrHwt124aExRVPbrD6j49Pdg6o/J/XPCXr4gAFqZafKIiIjqWPUK9noTd1SLM2soKrudDNuWj5Cz9AFsnfx28Af/IMv2T2HY9xv8sgH7Bl8bw5pSJOk1IvJTDE12phFFAWadDLsr9qMMKLEIApBtbdt1vyKJyE/VY3u5M0K1iqysnFxk5eTGuhoNKJKI2WcOgS+gwqpvvnNGl6Ju6FLUrekVfD6Yv/kCAFB97HhAbvl2c4tlUsQ5sofDY8yB4iirN2fWIQJgyQUKR0e9bomKd0eIqEUpRk1E5xzRKmKberQkMp0iIdemxxHZFhSk6mHUciRAMhAEAacPDU7gt7i4LGpD35dtO4D9Dg8sOhmju6eFvXydIkEQEms+JSIiokhK5Hla6pQNux0BSQvT7h9hLvni0IKAD1m/BOdmKR9wGXyGjBjVkCJFEIKpmrpntDxiuTN81in6Ms1aKFLb7y1YdArSTMmbc1gQgnPWZFq06JZhRGqY3gujVm4xyNIagseNrheeia4XngnBk7idDZKOKKF05H0AABV/vq4/+HzSLKZ1bAMGWoioVbLMkRltIghAl1QDJDG5btaKogCbQYNuGSb0yjYhw6yFLCXXe5BshnVNQdc0A1xeP/63ZndU9vlJcSkA4IS+We26oGlJe+dnISIi6qz0GgmKnNjndF5zAcr7XQwAyP55Boy7voN180Jk//Jv6Ko2wadNQfmAy2NcSwo3jSyiKN2IbKuuVR1pLHoF7G9DbaHIAtJNDVPRtVaOVZdU1x9aRUSaSYMuaQYckWNBj0wTsiw6GLUybGEIjhABwTmzdox/Hl7jn+ZzteQCZ7wO9P1LbCqWoOIyddj+/fuxfv169OrVC+np8ZO/kCiZWQ0KesgmuLz+4MPjR63X3+G8vAXNDElPFlpZQrZVQpZFC3utDxUOD2rcvrjJeUzhUTeq5ZHP1uPD33bjlEF50Gsi99kvrXLh1x2VAIBJ/cKfNgwA9En+3SUiImqMVa+gvNoT62p0yL7BVyNt3RvQ2beg2+Jz6y2zd5mAgMYco5pRJKSaNMix6CC2ofObJAowauW4mKScEkN2Gz9jfyYIAgpSDdi0t6ZTXivLkgCTVoZJK8OolaFpJkWzUStDkQV4fZ3wjaCosxdNhr1wInKqfkU6KgFTVjBdGEeytFlMQsFz5syBIAgNHnPmzME777yDHj164Oqrr0aXLl3wzjvvhLYrLi7GsGHDkJKSgltuuQXqYS1rc8uIKDz0GgmpRg3ybHr0yDShf54VPbNMKEjVI92sgUknt2lkSoZZC6uBPTHqCIIAq15B13QjemebkWXVNntyRYlnTI905Fh1qHb78Onasojuq25uliO72NqcB7m1kj1ISkRE1JjOkFLJtOsHiN6aBq+rAFI2zodl6+LoV4rCTpYEFKYbkGfTt+sGuEUXl313KQ4ZtBJsho6nu9IpEnIidG0TbYIQnKs226pDzywTjsixoCDVgBSjplX3AWz65E2lRhEgSvAUjAEG/DU48T2DLO0Skzt455xzDioqKkKPkpISpKenY+DAgbj22mvx3XffYeXKlXjhhRdw2223AQDcbjdOPvlkDB06FMuXL8fatWsxZ86cFpcRUWTplOAJU45Vj6J0I/rmWtA724wuaQZkWbSw6OVG0yeYDp5QUOMUSUSmWYfe2WYUZRhhM3BofmcgiQJOOzIfAPDByl3w+gMR2Y/XH8CStXsAAJP6R2Y0C8ARLURERI0xauXETgkb8CPn5/saXVR3VDk/3wcE/NGqEUWAVa+gZ6apQ4FBC9MXUSvlWvVhKyvNpIU5AYN8f55npV+uBV3Tjcgwa9vVgS0cc6sQUXjFJNCi0Whgs9lCj9dffx3Tp09HRkYGnnzySfTv3x8AMGjQIFRUVAAAFi9ejKqqKjz++OPo3r07Zs6ciZdffrnFZUQUfRpZhFWvINOiQ2GaEX2yLTgiJxgwyLbqkGJUUJASvhOtzs6klVGQakCfbDNybMmVl7YzOr5PJlKNGux3ePDlH3sjso8fNpXDXutDmlGD4V1TI7IPjSx2aOg/ERFRZ5bIN6CNZcugcZQ2mBa3jgAVGkcpjGXLolovCg9RBApS9eiSZoDcwTn8FEmMaCpc6hxsBiXsn5P8FH1CBLS1iojUJuZZac1cSM3RayRoeW+AKK7EPARcW1uL2bNnY+nSpSgoKMC55wbzv3q9Xjz66KOYPn06AGDVqlUYOXIkDAYDAGDgwIFYu3Zti8sa43a74Xa7Q8/tdnton16vN/wHSUQAAK0IaHUiABFqwA9vHPWCq/vux3sbYNWKsGp1cHl8qHB6Ya/1IhCZQREUIRKAaYNy8MqP2zF/RQmO75XWppR7rbG4uBQAMPGITAiqPyIdTjWKEvffl84sUdosIooutg3xwyCrKPcn5twVkqO01esFEvQYk5VBIyHXpodGFsLWThgUwOGK7eeg7nPIz2P8EQQgzaCLyO9StlnGjv2usJfbEaIowKyVYdRKMGjqz7MS8PvCfl1mUgS44mCeJPGw757q98XNd5FtQ9v4fSLPIRvRlvck5oGWt956CyNHjkTXrl1Dr61atQrjxo2DRqPBH3/8ASAYDCkqKgqtIwgCJElCRUVFs8tSUlIa7POhhx7C/fff3+D1zz77LBSsIaLktGTJklhXgZJAHz9gkCWU2d1Y+OWPODI9fPOKlTqBtaUyRKjoE9iGbb9tC1vZf7Y6YiVTa7HNIqLGsG2gjkir3oPCVqxXsmsP9tu/j3h9KLya7pKa+Has+TnWVaBGbI11BSjiBJ8P6uWXAwC2rf0Fqhzz2831sG1onW2xrkCccjqdrV435p/8559/vkHQY+DAgfjiiy9w880346KLLsKCBQsgyzK0Wm299XQ6HZxOZ7PLGgu03HHHHbjxxhtDz+12OwoKCjBx4kRYLJYwHh0RJQqv14slS5bghBNOgKIkZqoHt9ePSpcXFU4vAoHw3binyDhF3Ym3f9mJbw6Ycer4AR0eOl7ns++2AtiD4UWpGDyid1jKbExBmh5mbWJ+VzqDztBmEVH4sW2IL7sqXKhyJWDP0MAoeHa/CsW5BwIanlOqEOA1ZsM85mKYOVluQkg3a5Fp1ra8Yjtt3lsDty92w+wDfh92rPkZXQaMhCjF/DYXHSRLAnpkmCKablhVVWwtd6DWG53PnyAAWlmCSXdw1Ioihe06rr227KuJ2vE3Rz3qOABoVaA+Wtg2tE2qUcO5lBtRlwmrNWL6Kdu0aRM2bdqECRMm1HtdEAQMGTIEc+bMQWFhISoqKpCamori4uJ661VXV0Oj0TS7rDFarbZBYAYAFEXhBQlRkkvkdkBRFJgMOuSlqrC7fDjg9KAmDoYRU+NOHpSHD34rxbb9Tvy6sxrDwjCXSq3Xj682lAMAJg/IjejJpFmvg9LBvN7UcYncZhFR5LBtiA+pFqDak4CdXyQZpaPuR5cvroQKoV6wRT04c0vpyPsgKpG7cU/ho9eIyE0xRvRmcIpZj712d8srRpgoybyZGkfyUw3QRqFjVmGGhE17a6BGqLnVyCJMOhkmbfAR7rTPHZVi1mNPVey/f/GMbUPrSLLM88dGtOU9iekdkvfeew9Tp04NVfjLL7/ELbfcElouHxxqJooihg0bhp9/PjTUa9u2bXC73UhNTW12GRFRshEEAVaDgqJ0I3pnm5Fp0UKR4+tkkACzTsGk/tkAgHnLS6CG4crg24374PT4kWPVYXCBrcPlNUUSBQZZiIiIWmDWyohxR+d2sxdNxo7xz8NrzK73uteYjR3jn4e9aHKMakZtIQhAfooh4j3uLTremKP6DFoJVkN0Phc6JTj3ULhIogCbQUFeih69s83onW1Gnk0Pq16JuyALANj0jXcyjyq/H8afvoPxp+8Af/zMxUsUbTEN533yySe46KKLQs/79OmDadOmoWfPnpg8eTLuuusuTJw4EVarFWPHjkVVVRVef/11nH/++Zg1axYmTJgASZKaXUZElMw0sogsiw5ZFh2qnF7sOND63JIUedMG5+GjVbuxrqwav++2o3+etUPlLV5TBgCY1C8bYgQvqPUa/r4SERG1RBAEWHRKYqYPQzDYYi+cCGPZMsiuvfDpM+HIHg4wXVjCyLbqoFMi//+l10hQZAFeXwKO4KKIyLWGL/DRGqlGDWpqfe1qbwUBMGoPjVhJtGsdjSzCoJXgdMcuwCG4a9HtjJMBAMXrd0E1GGNWF6JYill3VJfLhaVLl2LUqFGh13JzczFv3jw8+eST6NevH5xOJ9544w0AwdEtL774Iq688kpkZWVh/vz5mDVrVovLiIgoyGpQkGqKg94uFJJq1OCEvlkAgHkrSjpU1sY91di0rwayKGD8EVnhqF6TdApHsxAREbWGVZ/gPf1FCY7cUajqfgocuaMYZEkgJp2MdFP00rsl/GedwsZmUGISrMhL0bc6k4NeIyHDrEVRhhH9ci0oSjciw6xNuCBLHX7/iOJDzEa06PV6uN0NcwieeOKJWLt2baPbTJs2DRs3bsTy5csxevRoZGRktGoZEREFZZm1qHJ64Q+wt1m8mD4kH5/+XoZfd1Ri094a9Mg0taucxb8HR7OM6ZEe8RNtfRR6RhIREXUGZl0wfVik5g4gaowkCshPie6IAotOQXm1J6r7pPgjCIjZZNqSKKAgxYAt+xwNlsX7PCsdZdUrKKuq5W8NUYwl3ExAeXl5yMvLa/MyIiICZElEtlWHXRWuWFeFDsq26jC2Zwa+3rAP81aU4I7JR7S5jBq3D99s2AcAmNw/u4W1Oy4aKSiIiIg6A1EUYNbJsLt8sa4KJZE8mz7q8+kZD968Zoeu5JZp1sZ0LkejVkamRYv9NZ5gUOVgcEUjd+4R+YokwqCR4Ihh+jAiimHqMCIiio1UoyZhh0R3Vn8dmg8A+GnzfpRUtH0ena/+2AuPL4AuqQb0zbGEu3r1CAKg7eQXKkREROHEicIpmmwGJWqTkP+ZRZ9wfXkpjBRZiGq6uqZkWXTom2tBlzQDUo2aTh9kqWMzME04UawlR2tDRET15Nn0iOBc6dRGhWlGjChKhQrg/RU727StqqqhtGGT+2dDiPB/rE6RIr4PIiKizsSiV3jeRVGhyAJybdFNGXY4C+eJiJhEaENyLHqInSwlVyKx8reGKOYYaCEiSkJ6jYRUI3u8xJPThxYAAL7esA97q2tbvd3vu+0oOeCEVhYxrndmpKoXolN46kBERNQWkijAqGVPf4q8/BRDTOeeMGtliDxVDBtBAEw6GQWpevTNsaBPjhld0gxINwczFMTTTXWDVorZSCoKkkQBJv7WEMUUv4FEREkqy6JDlcsLn595lONB72wzBuZbsXpnFRas3IUrxnZv1XaLi4OjWY7tlRGVmzh6zs9CRETUZhadjJpaztNCkZNu1sT8JqsgCLDoFFQ6vTGtR6LTa6RgCji9Um++ExECrHoR1oMjhwIBFU6vH063Dw6PH06PD4FAbOqca43dSCo6xGZQUB2L3xpZQemd/wr9TZSsGGghIkpSkigg26LDzgpXrKtCB50xtACrd1bhs9/34MyjClrMs1vp9ODHzeUAgMn9c6JRRegYaCEiImozi17B7srWj1glagudIiLboot1NQCAgZZ20sgiUg7Or6OVW3e+LR4cwXB4gK3W64fD7YPT44fD44PXF/lOdTaDwjlA44RFp0AQXFCj3JdS1WhQfuV10d0pURxioIWIKImlGDU44PTA6fbHuioEYGC+Fb2yTNiwpwYfrtqN80d1bXb9L/7YC19ARc9ME3pkmqJSR45oISIiajtFEmHQSjznorATBKAg1RA3c+iZdTIEAVG/0ZuIZEmAVa/AZlBg0ITn9pxOkaBTJKQdfO7xBeA6GHRxenyo9QbC+n8jCEC2NT6CfBQMvll0CqpcDHYSxQIDLURESS7PpsemvTW8GIoDgiDg9KEFeHDROny8phSnHZnfZDqwgKrik4Npwyb3z45K/TSyyAkuiYiI2smiUxhoobDLsujiasRx3SiLmKQvSgCCgFBwxaSVIx4g08giNLIYmj/FH1Dh8voRUFWoAUCFClUNXluoCAbI1MP/hopA3WvqodeC/wbTIh6e3oxiz2qIQaDF74d+zSoAgGvAIECKnzaJKJoYaCEiSnI6RUKaSYPyak+sq0IAhheloiDVgJIDTixaU4rTjypodL3fdlSizF4Lo0bCMT0zolI3jmYhIiJqP6teQVkV04dR+Bi1EjLM2lhXowGrPkbzRMQpQQBMWhk2gwKLTolpxyVOmN75WXQyRBFRna9HcNeix8nHAwCK1++CajBGb+dEcYRhZyIiQqZZB1niSIVwW/jeXAwssDV4LHxvLr769GNMHjMIQ7qm4ZyTx2PLxvUAAFEQ0PXAcux89iI8eMYwXHLmX7CrZHuozI1/rMXZJ43DZRMHouKrVzCud0arejEu/+l7nDJuOMYO7IbXX3y6XcejU3jaQERE1F4aWYRew99SCg9RBPJTDLGuRqPq0oclO4NWQq5Nhz7ZZnRNN8Jm0HB0OEWcIATThxFR9PEsj4iIIIkCcphbN+ymTDsd3xdvCz0+W/Y7UlLTcOTw0bj7pqtx/e334fNf1iE3vwD33RqcPLBk21Z8/sZs9P7b/ci59FkEjOm4+8a/AwA8bjeuvfgsdDtiALL+9ji85Tsgb/62xXoc2F+O6y45B5NPOQ1vLFyCjz+Yh2U/trzdn+k4ySUREVGHWPS8+UXhkWvVQyPH5y0dWRJhSNLzRq0iIsuiRa9sE7pnmJBm0kJmai2KMpuBvzVEscDWnoiIAAA2gwZGbXJeEEWKotHAYrWFHh/NfwfjJ5+MLZvW47rb7sGJJ5+KtIxMnPG3i/H76pUAgHW/r8bAI4fh/KnjIFsyUVN4DLZt2QwA+P6rJaix29HrL3+HZMvB0L/+Hd/+770W6/HxgveQnpmFK/5xKwqLuuPKf9yKBe+82ebjYeowIiKijmEvYwoHq15BilET62o0K5mCirIkIN2sQY9ME3plmZFp0UEr87yZYseklSFx9BRR1DHQQkREIbk2PYf5R4i7thZzX3kel1x9A46dMAln/O3i0LJtmzehS9duAIDuPXtj2Q/fIj+wB2bRg5IfFqDrwJEAgPXrijFgyFH4crMdAHDmxKOxecMfLe57w9piDB89NjTZZv/BQ7GueFWb6i+JAie6JCIi6iCdIkHLVJzUAbIkINcW/yPRO3tQURSDowaKMow4IseCHKse+iQdxUPxRxAEWDmqhSjqOAMWERGF6BQJ6SYt9lW7Y12VTmfRB/MwcMhRyCsorPe61+PBay88hb9dGkwP1r1XH5xw0ik4b+pxAADZmgVh+rPwB1Q4qqsh27JwwOGBVa9gdI90SJIEe2UlLDZbk/uuqalGt159Qs+NZjP2lpW2qf68cCQiIgoPq17BXi/Ptah98lP0CZGKKjgnkQSXxx/rqoSNIATnn7HpNTDrZM63QnHNpldwoMYT62oQJZX4/3UmIqKoyjRroci8aAi3eW++itMPG8VS56lHZsBgNOK0cy8EAKz69Rd8s+QTzP3wC3y5ehtsA47Dqpdvx0+byyHJMnZW+QAAE47IgiKJ0Gh1cLmcze5blmRoNIfSS2i1OtS2sM2f6dj7loiIKCw6e09/ipxUkwbmBPr8WPSdo2+vQSshL0WPI3IsKEwzwmpQGGShuGfUyryuJ4qyzvGrR0REYSOKAnIseuw40LYb8dS0HVu3YMe2LRh59HH1Xv/p268w781X8ebCJVCU4EXzpx/+F5P+Mh0DhgwFAFz0jzvw2N+OwasffY0BWhPK9u5FBoBJ/bIBAE5HNRRN8zm6LbYUVOwvDz131FRDVtqW15vzsxAREYWHXiNBI4vw+AKxrgolEK0iIscS/ynDDmfRKdhTlZijt3SKCKtBgU2vgUZmhyNKTFa9gvLqKIxqkRXsueG20N9EyYqBFiIiasBqUGByyqip9cW6Kp3Cp/9bgLETTgwFUwBg5/ZtuP26y3DXzMfQ/bC0Xn6/DxUHqkLPx3e34FFvLUoOOBAwpMO9+w8M6ZKCbKsOu0q2w+P2wGpLaXb//QcNweIP3w89X792DbKyc9p0DDoGWoiIiMLGopejc/OLOgVBAApSDAk3iqJuTiK3NzGCiooswKbXwGZQeO5LnYJNr4nKb42q0WDvjXdEfD9E8Y5heSIialSuTQchsa7l4tYPX3+O4aOOCT2vdblwzUVn4vgTT8K4iVPgdNTA6aiBqqoYfNRIfLH4I7zx0jP4eME83H3NBTClpEGT0RX7TN0QcDth2/kjAOCVZ5/EiGOOhSQFLwRrqu3wer0N9n/cxClYuexnLPvxW/h8Prz2wtMYfezxra6/IABa9uQjIiIKG6YPo7bINGsTdr68eP+siyKQYgxOat8n24Jsq45BFuo06kZQElF0cEQLERE1SitLyDBrsdeemMP940Wty4U1v63AvQ/PDr324zdfYMvG9diycT3ef+u10OuLf1yFSX+Zjm1bNuLNl5/Dvr170KP3EXjk+dfxyAovfAEVRadcj7efuAcLX/o3An4/Xpn3cWj7v04cg1vvfQjHT5parw4pqWm46e4ZuPK802A2W6A3GHD/I0+1+hh0igiBUTciIqKwMWplyJIAn1+NdVUozuk1wXPyRGXVK9hXHV/XE4IQDABZDQosOpnnudSp2QxK5K/pAwFoN64HALh79g5GMImSEAMtRETUpAyTFhVOD7w+3gRoL51ejxWb99Z77fhJU7G6pLLJba664XZcdcPt9V6bWLsZi9aU4twzT8cxN5yJtatXYtBRI5Calh5a55Of1jRZ5lkXXIrRY4/Hlk3rcdTIMTCZLa0/BvbqIyIiCjuLXsGBGqYPo6YJAlCQqk/oQIBeI0GRhbi4njBqJdgMGlj1CqQES8NG1F5WfeQDLUKtC70mjAIAFK/fBdVgjOj+iOIVAy1ERNQkURSQY9Vjx35nrKuS9C47ughH90hHv1wLREFAVk5um8voUtQNXYq6tXk7PQMtREREYWdloIWaoZFFZJq10MqJfx5m0SnYH6PPul4jwnpw3hVFYi97Sj46RYJOEVGbIHMlESUyBlqIiKhZVr0Cs05Gda0v1lVJarIkYkCeNSb75ogWIiKi8DNqJEiiAH8g9j39KXZEse5GqASdLEKvkaCTpYSb+L45Fn10Ay2c1J6oPqtBQW1VfKXwI+qMGGghIqIW5dh0qNlTA5X3AZISR7QQERGFnyAIMOtkVDq9sa4KRYEgBEep6ORg73LdwYBKMkxUHa2gYopRgzSzHkYtb3URHc6qV7CHgRaiiOOvDxERtUgrS8g0a7En0pPoUdzRyGKn6lFJREQUT6wGhYGWTkgShWAw5eBIFb0iQZvE51SCIMCil1HhiMxnXa8JdgrKseqgKLzNRfRnWlmCXiPB5fHHuipEnRp/gYiIqFUyzFpUOL3w+JjbNZlwNAsREVHkmLUyRBEI8PQqIQkCoJWDARXtwcCKXpE4F0gjLHolIoEWi15GtknBurCXTNS52AwKAy1EEcZACxERtYogCMix6bC93BnrqlAU6RTeKCAiIooUQRBg0SXXqJZEnZdGloTQpNK6g73DtbIIQUjOUSptZdbKEASENRVxqkmDPJseXm/yfH+I2suqV1BaWRvrahB1agy0EBFRq1l0Cix6GXaXL9ZVoSjRaTiihYiIKJKSKdCSYdYi26qDy+NHtduLmlofnB5/XM0DKAjBjiZaWQoFVvSKBJmjVDqkLqhY5QrPZz3LokWmRReWsoiSgSKJMGolONwRGNUiK9h3xbWhv4mSFQMtRETUJjlWPaprq+Pqgpgih6nDiIiIIsusC39P/3hk0cvItgZvjOs1wREhmWYgEFDh8PhQ4/ahptaHWm/08qgpsnBwcnopNKcKR6lEjkUvdzjQIghArk2PVKMmTLUiSh5WvRKRQIuq0aDsrgfCXi5RomGghYiI2kQji8i16bGrwhXrqlCESaLAHONEREQRJooCzLrOPWJYp4goSDE0uix4/ArMOgWwAj5/ADVuH6prg8EXn7/jEajgKBWpwQT1UpJOTh8rZp0CQXC1O6goCEBBqgFWPXvME7WHVa+gtKq20wf2iWKFgRYiImqzVKMGsiSg5ICTk7d2YnqmDSMiIooKi07ptIEWSRRQmGaE2MqghiyJsBk0sBmCIxZqvf7QaJcat6/FG4QaWTwUUJEl6DTBNGAUe5IowKSVUV3b9s+6KAJd04wwankbi6i9ZEls93ewWYEAlF0lAABvXkHwC0sJRyvz/62j+AtFRETtYtEp6JFpwvb9TrijmOKBoken8ESLiIgoGix6BUJl+3v6xytBAArTDNB04OZN3QiUdJMWqqrC6fGHRryERqrIh0aqcJRKfLPolTbf5FVkAV3TjNAxpS1Rh1nb8R1siVDrQp/RgwAAxet3QTUYw1o+RYdBwzBBR/EdJCKidtPKErpnmFBywBn+XjEUc5yfhYiIKDokUYBRK6Omk51P5dr0YR2BIAjB98molZFlCVuxFEUWnYxdbVhfq4jommbsULCOiA7prIF96phgxwW2sx3Fd5CIiDpEEgV0TTci06KNdVUozNhrkIiIKHosuo4FJOpukqQYFeTadOiSaojp6I40k4YTllMDsiTCoG3dOaZBG+zUxSALUfhIB+cFIzqcQSNBEDgitKP4zSIiorDIsuigkyWUVDjZO6YTEATmaCUiIoomi17B7sraVq1b9zutUyToNRIMmuB8JH+eB0WnEbGt3AmPL7ppXk06GTlWXVT3SYnDqlfgdPubXcesk9El1dDquX2IqPVsek2nnReM2odpw8KD7yIREYWN1aBAqwTnbYn2BT2Fl04R2aOFiIgoipSDPf0buwGtVUToDwZV9Erw0Zob0ME0r0ZsP+Bs8cZ2uGgVEV1SDTyPoCZZdApK0XRQMcWoIM+m52eIKELMOhmiCAR4yU4H6TXMZhEODLQQEVFY6ZTgBf2OA044onRBT+HHtGFERETRZ9Ur8AfUdgVVmiJLIorSjNhZ4UKVyxvG2jYkikBhWmxTllH808gi9BoRLk/Du7yZFi2yLBwNRRRJoijAolNQ6YzsbwIlDiMDLWHBQAsREYWdLIkoSjeitKoW+2s8sa4OtQMDLURERNGXbtIi3RT+ee9EUUCXNANKq1wor47MuZkgAF1SDdDKPIeglll0Clwed73Xcm06pEXg809EDVkNDLRQkEYWIUtMGx4ODLQQEVFECIKAXJseekXCrkoX521JMHoGWoiIiDqdHKseGklEaVVt2M/Nsq06mHVKeAulTsuiV7DHHgy0CAJQkGKA1cDPD1G0mLUyJFGAPxCGHwNJxv7zLw39TYnFwNEsYcNPPxERRVSKUQOtImL7fid8fkZbEgVHtBAREXVOaSYtFFnEjv3OsAVbUk2aiIzEoc5Lp0jQKiK8/gAK04wwaXl7iiiaBEGARS+jwtHxUS2qVovdDz4ahlpRLDDQEj4cF0RERBFn0MjokWniBGsJQiOLzK1ORETUiVl0CrpnmCBLHf+9N2ol5Fo5pwa1XapRg+4ZJgZZiGLEZtDEugoUBwwatsHhwkALERFFhSKJ6J5hRIqRKQHiHdOGERERdX56jYTuGSbolPbfFtDIIrqkGiAI7KBBbZdu0nIUNVEMmbQytB34DQhRVUj7yyHtLwdzhicWQUCHzgOoPr6TREQUNYIgID/FgBybDrwej1880SIiIkoOGllEtwwTjNq23+wWRaAwzcAJdImIEliuTd/hMgSXE30H90DfwT0guJxhqBVFi0EjsbNEGPGMiIiIoi7dpEXXdCPTU8UpHVO8ERERJQ1JFFCUboStjRORF6QaOBqBiCjBmbQys04kMaYNCy8GWoiIKCZM2uC8LWadHJb84BQ+Opk3TYiIiJKJIAgoSDUgy9K6Ce2zrTpYdLwxR0TUGeRY9bwmT1KGdoxopaYxbEVERDGjkUV0TTcCAHz+AFxeP2q9AdR6/XD7gn8zxWt0SaIAjcx+GERERMko06KDRhaxs8LV5DmYzaAgw9y6gAwREcU/SRSQa9VjxwGm/Uo2Bo5MDSsGWoiIKC7IkgizJMKsO/Saqqpw+4KBl1pvXSDGD5+f0ZdI4fwsREREyc1m0ECWRGzf70AgUH+ZXiMhP6Xj+fyJiCi+WA0KLC4Zdpcv1lWhKNHIIudZCzMGWoiIKG4JggCdIjXI/+3zB1B7MADj8nD0SzjpOT8LERFR0jNpZXTPMGHbfge8vuAJliILKEwzcNJcIqJOKtemR427ukGQnTonA6/9w46BFiIiSjiyJMIkiTBpD/2M1Y1+cR828qXW5w/dHKDW0XPoMBEREQHQKRK6Z5iwfb8TtV4/ClONUNjzlYio01IkEdkWHXZX1sa6KhQFDLSEHwMtRETUKRw++sWKQ5Oz+gNqcORLXfDl4BwwHP3SuD+PHiIiIqLkpUgiuqUb4fL6OeqViCgJpJm0qHR54XT7W7+RJKPir2eH/qbEYNTy/yrc+I4SEVGnJokCjFq5wUlErdcPtzeAWp8/FIhJ9tEvggBoZfZUJSIiokPEg+dSRESUHPJsemzaW9PqzomqVoudTzwX2UpRWIkir/0jgWdLRESUlJob/RJMOxaAy+NPqtEvOkVk3nUiIiIiIqIkplMkZJq12GN3x7oqFCF6ReK1fwQw0EJERHRQU6Nf3L5gyjF3KAVZAB5f55shkGnDiIiIiIiIKMMcTCHm9rbiuldVIbicwT/1hmCqBIprHKkaGXxXiYiIWqCVJWhlCdAfGv0SCKio9fmDo158gdBImEACx18YaCEiIiIiIiJBEJBn02PLPkfL67qc6N87DwBQvH4XVIMx0tWjDuK8a5HBQAsREVE7iKIAg0aGQVP/p9TjC8Dl9cN9cOSL0+tLmLlf9Ay0EBEREREREYKjHlJNGhyo8cS6KhRmBl77RwQDLURERGGkkUVoZDE0+iUQULFpX03rhlzHGEe0EBERERERUZ1siw7Vtd6E6TxILdMqImRJjHU1OiW+q0RERBEkigIKUgxxn6ZWI4uQxDivJBEREREREUWNJArIsepjXQ0KI2ayiBwGWoiIiCJMr5GQZdHFuhrN0ik8JSAiIiIiIqL6rHoF1sPmK6XEZtQywVWk8K4KERFRFGSYtTDp4veEhr1aiIiIiIiIqDE5Nh1E3kXuFAwaXvtHCr8iREREUZKfoo/b9Fw6nmwRERERERFRIxRJZAqxTkAUOTdrJMVv11oiIqJORpFE5Kfqsb3cGeuqNKCTebJFREREREREjUs1alDp9MDh9tdfIEqoOumU0N8UvwwahgIiie8uERFRFFl0ClJNGhyo8cS6KiGSKEAjc5ArERERERERNS0vRY+Ne2qgqodeU3U67Hj+tdhVilqNacMii3dViIiIoizHoouryefjqS5EREREREQUn7SyhEyLNtbVoHZioCWyeGeFiIgoykRRQEGqAUKcTNei58kWERERERERtUKGScvOegmKqcMii98KIiKiGNApErKtulhXAwDnZyEiIiIiIqLWEQQBeSn6Q8+dDgwosGFAgQ2C0xHDmlFztIoISYyT3p6dFAMtREREMZJu0sKsi32PEo5oISIiIiIiotYyaGSkmTSxrga1AdOGRR4DLURERDGUn6KHLMWuV4kgAFqZpwNERERERETUetkWHRSZIyQSBdOGRR7vrBAREcWQLInIP2zYdbTpFBFCvEwWQ0RERERERAlBFAXk2cJzLSsIwWtTm0FBhlkbN/OZdiYc0RJ5DGURERHFmFmnIN2sQXm1J+r71ik82SIiIiIiIqK2M+sU2PRKm7aRJQE6RYJOEaFXJOgUCVq5fgdAo1bC9v1OqGq4a5ycRJHX/tHAQAsREVEcyLbo4HD74PIEorpfnmwRERERERFRe2VbdY2+XjdKRStL9QIrstRygiWzTkFRuhHb9jsQiO4lcqfEtGHRwXeZiIgoDgiCgPwUAzbtrYlqrx09Ay1ERERERETUTocHTjJMWmht+kZHqbSVUSujW7oJW8sd8Ac4tKUjmDYsOhhoISIiihM6RUKuTY9dFa6o7pOIiIiIiIioXSQJmDIFAJCVagR0mrAVrddI6JZhxNZyB3x+Blvai4GW6GCghYiIKI6kGjWorvXC7vJFfF8aWYQkcpZBIiIiIiIiaiedDvj448gVrwSDLdvKnfD4mEesPZg6LDpaTopHREREUZVn00ORIx8A0Sk8DSAiIiIiIqL4ppWDwRYtr2HbTKewg2W08NNJREQUZ2RJRH6KIeL74fwsRERERERElAgUSUS3dCP0Gt7Obgs904ZFDT+ZREREcciklZFh1kZ0HzqecBEREREREVFHOByA0Rh8OBwR3ZUsiShKN8Gg5bVsazFtWPQw0EJERBSnsizaiPY+0ck8OSUiIiIiIqIOcjqDjyiQRAFFaUaYdAwgtIaBHSyjhoEWIiKiOCUIAgpS9RAj8GstiQI0Mk8DiIiIiIiIKLGIooCuaQZY9Ay2NEcUAR1ThkcN77AQERHFMa0sIdeqD3u5Ok4iSERERERERAlKEAR0STXAZlBiXZW4xbRh0cW7LERERHEuxagJ+8kjJ8QjIiIiIiKiRBbMAmFAmkkT66rEJSOv+6OKgRYiIqIEkGvThzXVF+dnISIiIiIios4g16ZHhlkb62rEHXawjC6OHyIiIkoAkiigV5YJqhp8fvAfqAdfUA9b99A6ar3nh9NI7GtBREREREREnUO2VQdRBPZUuWNdlbjB1GHRxXebiIgoQQiCAEFo8GosqkJEREREREQUnHH92GMP/R1DmWYdJEHA7sramNYjHugUEZLI+wXRxEALEREREREREREREbWdXg98/XWsaxGSZtJCEgXsKK+OdVViimnDoo95Q4iIiIiIiIiIiIioU7AZNMhL0ce6GjFlZNqwqGOghYiIiIiIiIiIiIg6DYtOAQAYtckZcOCIluhjoIWIiIiIiIiIiIiI2s7hADIygg+HI9a1aaAwzYBuGUYYtckTeJBEAToleY43XiRnSI+IiIiIiIiIiIiIOq68PNY1aJZRK6Nbhgk1bh/22GvhdPtjXaWIMnA0S0ww0EJEREREREREREREnZpJK8OUYUJ1rRd77LVweQKxrlJEMNASGwy0EBEREREREREREVFSMOsUmHUKqlxe7LXXotbbuQIuhiSdlybW+K4TERERERERERERUVKx6hVY9QqqnF7sqa6Fu5MEXPScnyUmGGghIiIiIiIiIiIioqRkNSiwGhRUOj3YY3fD40vcgItOESGJQqyrkZQYaCEiIiIiIiIiIiKipGYzaGDVK6hwerG3uhZenxrrKrUZ04bFDt95IiIiIiIiIiIiImo7UQSOOurQ3wlOEASkGjVIMSg44PBgb7UbPn/iBFwMTBsWMwy0EBEREREREREREVHb6fXAL7/EuhZhJwgC0kxapBg0OOD0YF+CBFwMWgZaYoWBFiIiIiIiIiIiIiKiPxFFAekmLVINGpQ73Civ9sAfiM+AiyQK0MoMtMQKAy1ERERERERERERERE0QRQGZZh3SjFrsr3FjX40bgUCsa1WfQcMgSywlfuI8IiIiIiIiIiIiIoo+pxPo2jX4cDpjXZuIk0QBmRYd+mRbkGnRxtW0NEwbFlsc0UJEREREREREREREbaeqwPbth/5OEpIoIMuiQ5pRg/IaD8pr3DE/fIOGt/pjKY5ibkREREREREREREREiUGWRGRbdeidbUaaSQNBiE09BAEwKBzREksMtBARERERERERERERtZMiici16dE724zUGARcdIoIUYxRlIcAMNBCRERERERERERERNRhiiQiz6ZHrywzUoxK1AIueqYNizkGWoiIiIiIiIiIiIiIwkQji8hPMaBnlgk2gxLx/Rk1TBsWawy0EBERERERERERERGFmVaWUJAaDLhY9ZELuOgZaIk5jikiIiIiIiIiIiIiorYTBKBv30N/U6N0ioQuaQbUev3YY6+F3eULW9mSKEArM9ASawy0EBEREREREREREVHbGQzA77/HuhYJQ6dIKEwzwuXxo8xei5rajgdcjFoGWeIBU4cREREREREREREREUWJXiOhKN2IbhnGDgdKmDYsPjDQQkREREREREREREQUZUatjG4ZJhRlGGFoZ8DFoGHSqnjAQAsRERERERERERERtZ3TCfTrF3w4nbGuTcIyaWV0zzCha7qhTSNUBAEwKBzREg8Y7iIiIiIiIiIiIiKitlNVYO3aQ39Th5h1Csw6BVUuL/baa1HrDTS7vk4RIYpClGpHzWGghYiIiIiIiIiIiIgoTlj1Cqx6BVVOL/ZU18LdRMBFz7RhcYP/E0REREREREREREREccZqUGA1KKh0erDH7obHVz/gYmxDmjGKLAZaiIiIiIiIiIiIiIjilM2ggVWvoPLgCBevL5imrS3zuVBkMdBCRERERERERERERBTHBEFAilEDm0HBAYcHFU4PtDIDLfGCgRYiIiIiIiIiIiIiogQgCALSTFqkmbSxrgodhoEWIiIiIiIiIiIiImo7QQAKCw/9TZSkGGghIiIiIiIiIiIiorYzGIBt22JdC6KYE2NdASIiIiIiIiIiIiIiokTFQAsREREREREREREREVE7MdBCRERERERERERERG3ncgHDhgUfLlesa0MUM5yjhYiIiIiIiIiIiIjaLhAAli8/9DdRkuKIFiIiIiIiIiIiIiIionaKSaBlzpw5EAShwWPOnDlYuHAhunXrBlmWMWLECKxbty60XXFxMYYNG4aUlBTccsstUFW1VcuIiIiIiIiIiIiIiIgiISaBlnPOOQcVFRWhR0lJCdLT03HMMcfgoosuwqxZs7Br1y4UFhbi0ksvBQC43W6cfPLJGDp0KJYvX461a9dizpw5LS4jIiIiIiIiIiIiIiKKlJgEWjQaDWw2W+jx+uuvY/r06Vi3bh1mzpyJM844A1lZWbjqqquw/GCOv8WLF6OqqgqPP/44unfvjpkzZ+Lll19ucRkREREREREREREREVGkyLGuQG1tLWbPno2lS5eia9eu9ZatX78ePXr0AACsWrUKI0eOhMFgAAAMHDgQa9eubXFZY9xuN9xud+i53W4HAHi9Xni93rAdGxEljrrvPtsAIkoEbLOIqDFsG4goGbHtI4oxrxdK6E8vECffRbYNFA5t+fzEPNDy1ltvYeTIkQ2CLB6PB48++ihuuOEGAMFgSFFRUWi5IAiQJAkVFRXNLktJSWmwz4ceegj3339/g9c/++yzULCGiJLTkiVLYl0FIqJWY5tFRI1h20BEyYhtH1FsSLW1OMFiAQAs+fRT+HW6GNeoPrYN1BFOp7PV68Y80PL88883GvS46667YDKZcPnllwMAZFmGVqutt45Op4PT6Wx2WWOBljvuuAM33nhj6LndbkdBQQEmTpwIy8GGgYiSi9frxZIlS3DCCSdAUZSWNyAiiiG2WUTUGLYNRJSM2PYRxYHp0wEAJ8a4Godj20DhUJcJqzViGmjZtGkTNm3ahAkTJtR7fcmSJXj++efx888/h74IqampKC4urrdedXU1NBpNs8sao9VqGwRmAEBRFH7xiJIc2wEiSiRss4ioMWwbiCgZse0josawbaCOaMtnR4xgPVr03nvvYerUqfUqvGXLFpx77rl47rnn0Ldv39Drw4YNw88//xx6vm3bNrjdbqSmpja7jIiIiIiIiIiIiIiIKFJiGmj55JNPMG7cuNBzl8uFqVOnYtq0aTjllFNQU1ODmpoaqKqKsWPHoqqqCq+//joAYNasWZgwYQIkSWp2GRERERERERERERFFgMsFHHdc8OFyxbo2RDETs9RhLpcLS5cuxYsvvhh67dNPP8W6deuwbt06vPTSS6HXt27diq5du+LFF1/EOeecg1tuuQV+vx/ffPMNgOD8LU0tIyIiIiIiIiIiIqIICASAuvuwgUBs60IUQzELtOj1erjd7nqvTZs2DaqqNrnNtGnTsHHjRixfvhyjR49GRkZGq5YRERERERERERERERFFQswCLe2Vl5eHvLy8Ni8jIiIiIiIiIiIiIiIKt5jO0UJERERERERERERERJTIGGghIiIiIiIiIiIiIiJqJwZaiIiIiIiIiIiIiIiI2inh5mghIiIiIiIiIiIiojhhMMS6BkQxx0ALEREREREREREREbWd0Qg4HLGuBVHMMXUYERERERERERERERFROzHQQkRERERERERERERE1E4MtBARERERERERERFR29XWAiedFHzU1sa6NkQxwzlaiIiIiIiIiIiIiKjt/H5g0aJDfxMlKY5oISIiIiIiIiIiIiIiaicGWoiIiIiIiIiIiIiIiNqJgRYiIiIiIiIiIiIiIqJ2YqCFiIiIiIiIiIiIiIionRhoISIiIiIiIiIiIiIiaic51hWIB6qqAgDsdnuMa0JEseL1euF0OmG326EoSqyrQ0TULLZZRNQYtg1ElIzY9hHFmMNx6G+7HfD7Y1eXw7BtoHCoixfUxQ+aw0ALgOrqagBAQUFBjGtCRERERERERERElIByc2NdA6KIqK6uhtVqbXYdQW1NOKaTCwQC2L17N8xmMwRBiHV1iCgG7HY7CgoKUFJSAovFEuvqEBE1i20WETWGbQMRJSO2fUTUGLYNFA6qqqK6uhq5ubkQxeZnYeGIFgCiKCI/Pz/W1SCiOGCxWPgDTEQJg20WETWGbQMRJSO2fUTUGLYN1FEtjWSp03wYhoiIiIiIiIiIiIiIiJrEQAsREREREREREREREVE7MdBCRARAq9Xi3nvvhVarjXVViIhaxDaLiBrDtoGIkhHbPiJqDNsGijZBVVU11pUgIiIiIiIiIiIiIiJKRBzRQkRERERERERERERE1E4MtBAREREREREREREREbUTAy1ERERERERERERERETtxEALERERERERERERERFROzHQQkRxYeHChejWrRtkWcaIESOwbt06AEBxcTGGDRuGlJQU3HLLLVBVtcVt/mzSpEmYM2dOq+rR3P5effVV9O/fHzabDWeffTbKy8tbfXz79+9HUVERtm3bVu/1a6+9FoIghB49evRodZlEFBvx0l4BTbctzdWlOS1t19T+iKhztw3NlclzGSJKhPavpWVN4bkRUft15rahue14bpS8GGghopjbvHkzLrroIsyaNQu7du1CYWEhLr30Urjdbpx88skYOnQoli9fjrVr14Z+SJva5s/mzp2LTz/9tFX1aG5/n3/+Oa677jo88cQTWLVqFex2O0499dRWlVteXo6pU6c2+qO9YsUKfPzxx6ioqEBFRQVWrlzZqjKJKDbipb0Cmm5bmqtLc1rarrm2jCjZdea2obkyAZ7LECW7RGj/WlrWFJ4bEbVfZ24bWtqO50ZJTCUiirGPPvpIfe6550LPv/zyS1Wj0agLFixQU1JSVIfDoaqqqv7222/qmDFjmt3mcPv371ezsrLU3r17q6+++mqL9Whuf3/729/U66+/PrTu77//rgJQy8vLWyx3/Pjx6pNPPqkCULdu3Rp63ev1qmazWa2urm6xDCKKD/HSXqlq021Lc3VpTkvbNbU/IurcbUNzZfJchogSof1raVlTeG5E1H6duW1objueGyU3OTbhHSKiQ6ZOnVrv+fr169GjRw+sWrUKI0eOhMFgAAAMHDgQa9eubXabw91000049dRT4XK5WlWP5vZXXl6OIUOGhNaVJAkAIMstN6MvvvgiunXrhuuvv77e66tXr4aqqhg8eDB27dqFY489Fi+++CK6dOnSqvoSUfTFS3sFNN22NFeX5rS0XVP7I6LO3TY0VybPZYgoEdq/lpY1hedGRO3XmduG5rbjuVFyY+owIoorHo8Hjz76KP7+97/DbrejqKgotEwQBEiShIqKiia3qfPVV1/hiy++wMMPP1xv3WnTpsFmszV4PP30083ub/Dgwfjwww9DuUNfffVVDB8+HFartdkyAaBbt26NHuu6devQr18/vP3221i7di0URcEVV1zRsTeQiKImlu0V0HTb0lxd2tsGNrc/Iqqvs7UNzZXJcxkiOly8tn/NLeO5EVHkdba2obnteG6U3DiihYjiyl133QWTyYTLL78cd911F7Rabb3lOp0OTqcTKSkpjW4DALW1tbjiiivw3HPPwWKx1Nv+hRdeaLTnQ2pqKh588MEm93fzzTeH8ojqdDr89NNPeP3111sssznnnnsuzj333NDzp59+Gt26dYPdbm9QbyKKP7Fsr5ojy3KTdWlvG3j4MRBR8zpb29AcnssQ0eHitf1rDs+NiCKvs7UNzeG5UXJjoIWI4saSJUvw/PPP4+eff4aiKEhNTUVxcXG9daqrq6HRaJrcBgAeeOABDBs2DCeddFKDfWRlZTW5/+b2l5qaih9++AGbNm3Co48+ioqKCpxzzjktltkWNpsNgUAApaWl/AEminOxbq+a01xdMjIy2rUdEbVOZ2wb2oLnMkTJK57bv+a09/qQiFqnM7YNbcFzoyQT2yliiIiCNm/erGZkZKhvvvlm6LUvvvhC7dGjR+j51q1bVZ1Op/p8via3UVVV7dq1q2o0GlWr1aparVZVURRVr9erV111VbN1aGl/qqqqDodDzcjIUBcsWNDmY8SfJkm74YYb1HfffTf0fMmSJaooiqFJ4YgoPsVDe3W4P7ctrWnLGtPa7f68PyIK6qxtQ3Nl8lyGiFQ1/tu/1i77M54bEXVMZ20bmtuO50bJjSNaiCjmXC4Xpk6dimnTpuGUU05BTU0NAOCYY45BVVUVXn/9dZx//vmYNWsWJkyYAEmSmtzGaDTiu+++g8/nC5V/8803Y+TIkbjwwgubrcfYsWOb3F+dp556Cn369MG0adM6fNyDBw/GnXfeiezsbPh8Plx77bW48MILQ5PCEVH8iZf2qjmtacvCuR0Rde62oTk8lyGiRGj/2ovnRkTt15nbhubw3CjJxTrSQ0S0YMECFUCDx9atW9UFCxaoer1ezczMVNPS0tTi4uIWt/mzCy64QH311VdbXZfG9qeqqlpRUaGmpqaqy5Yta9dxNla/22+/XbXZbGpBQYF63XXXqTU1Ne0qm4iiI57aqzqNldVcW9bS8bW0XVN1J0pmnb1taK5MnssQJbdEaf9as6yp4+O5EVHbdfa2obnteG6UvARVVdWwRGyIiCJk165dWL58OUaPHh22HOLxtD8i6jziqf1ob13i6RiIOot4+l7FU12IqPPrDG1OZzgGonjD7xV1Rgy0EBERERERERERERERtZMY6woQERERERERERERERElKgZaiIiIiIiIiIiIiIiI2omBFiIiIiIiIiIiIiIionZioIWIiIiIiIiIiIiIiKidGGghIiIiIiIiIiIiIiJqJwZaiIiIiIiIiIiIiIiI2omBFiIiIiIi6vQWLlwIQRAgy3KjD1EUcckll4TWLykpgSRJoecXX3wxbrnlFgBAjx49UFRUhP79+yM/Px/HH3981I+HiIiIiIjiBwMtRERERETU6cmyjMLCQvh8vkYf559/PmRZBgB4PB54vV5otdrQ9jqdDpIkobq6GlqtFi+99BKKi4sxY8aMeusREREREVHykWNdASIiIiIiokg7fHRKU+oCLevXr8f06dPhdrvRv39/AEBpaSlEUURBQUGDslpTNhERERERdV4c0UJERERERJ2eIAgtrlMXMBkwYAB++eUX6PV6FBcXo7i4GGeffTauvvpqXH311a0qi4iIiIiIkgdHtBARERERUafXmuDIn9dxuVz1RrRce+21ofUuu+wyGI1GVFZWYvDgwWGvLxERERERJQ4GWoiIiIiIqNMTBAElJSVIT09vdHlNTQ2uvPLKeq/VjWgBgGuuuSb0ut/vx0svvYQJEyZgzpw5mD9/fuQqTkREREREcY+BFiIiIiIi6vQEQUBBQQG2bdvW6PILL7ywwWtNjWjxeDyRqiYRERERESUgBlqIiIiIiKjTU1W11et4PB74fL4GI1pUVYXP54PdbsdVV10Fo9GIAwcOYODAgRGtOxERERERxTcGWoiIiIiIqNNrS6Dlqaeewpw5c9CjR4/Q/CuqqsLj8cBoNGLPnj1YunQpunTpwtRhREREREQEMdYVICIiIiIiirRAINDiOn6/HwBw00034ZtvvkG/fv3wxRdfYMmSJRg+fDhWrlyJnj17Ij09HQUFBZGuMhERERERJQiOaCEiIiIiok7P7/ejpKQE6enpjS6vqanBBRdcEHp+1VVXIS0tDWlpaVBVFRUVFbjsssuwfft2nHHGGRAEIVpVJyIiIiKiOMcRLURERERE1On5/X4UFBSgvLy80cdZZ50Fn88HAFiwYAHKysowe/ZsAIAgCPjPf/4Dg8GAX3/9FbfeeisAYOnSpXjppZeQlpYWs+MiIiIiIqLYE9TWJCsmIiIiIiJKIj6fD7JcPwHAjh07sHr1akydOhUAUFpaiqeffhrXXHMNcnJyYlFNIiIiIiKKAwy0EBERERERERERERERtRNThxEREREREREREREREbUTAy1ERERERERERERERETtxEALERERERERERERERFROzHQQkRERERERERERERE1E4MtBAREREREREREREREbUTAy1ERERERERERERERETtxEALERERERERERERERFRO/0//8I0YQOIKZMAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 2000x1000 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "C:\\Users\\EDY\\AppData\\Local\\Temp\\ipykernel_19344\\741737176.py:303: SettingWithCopyWarning: \n",
      "A value is trying to be set on a copy of a slice from a DataFrame.\n",
      "Try using .loc[row_indexer,col_indexer] = value instead\n",
      "\n",
      "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
      "  first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
      "更新数据sqlUPDATE trueandpredict SET ds = '2024-11-20 00:00:00', NHITS = '7337.594', Informer = '7316.5264', LSTM = '7313.051', iTransformer = '7342.468', TSMixer = '7344.392', TSMixerx = '7306.8916', PatchTST = '7350.6377', RNN = '7378.303', GRU = '7405.318', TCN = '7418.5396', BiTCN = '7321.0723', DilatedRNN = '7320.571', MLP = '7338.035', DLinear = '7387.8057', NLinear = '7361.405', TFT = '7362.294', StemGNN = '7351.2534', MLPMultivariate = '7331.5254', TiDE = '7354.707', DeepNPTS = '7376.7446' WHERE ds = '2024-11-20 00:00:00'\n",
      "更新数据sqlUPDATE trueandpredict SET ds = '2024-11-20 00:00:00', NHITS = '7337.594', Informer = '7316.5264', LSTM = '7313.051', iTransformer = '7342.468', TSMixer = '7344.392', TSMixerx = '7306.8916', PatchTST = '7350.6377', RNN = '7378.303', GRU = '7405.318', TCN = '7418.5396', BiTCN = '7321.0723', DilatedRNN = '7320.571', MLP = '7338.035', DLinear = '7387.8057', NLinear = '7361.405', TFT = '7362.294', StemGNN = '7351.2534', MLPMultivariate = '7331.5254', TiDE = '7354.707', DeepNPTS = '7376.7446' WHERE ds = '2024-11-20 00:00:00'\n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Column 'ds' already exists in table 'trueandpredict'.\n",
      "Column 'NHITS' already exists in table 'trueandpredict'.\n",
      "Column 'Informer' already exists in table 'trueandpredict'.\n",
      "Column 'LSTM' already exists in table 'trueandpredict'.\n",
      "Column 'iTransformer' already exists in table 'trueandpredict'.\n",
      "Column 'TSMixer' already exists in table 'trueandpredict'.\n",
      "Column 'TSMixerx' already exists in table 'trueandpredict'.\n",
      "Column 'PatchTST' already exists in table 'trueandpredict'.\n",
      "Column 'RNN' already exists in table 'trueandpredict'.\n",
      "Column 'GRU' already exists in table 'trueandpredict'.\n",
      "Column 'TCN' already exists in table 'trueandpredict'.\n",
      "Column 'BiTCN' already exists in table 'trueandpredict'.\n",
      "Column 'DilatedRNN' already exists in table 'trueandpredict'.\n",
      "Column 'MLP' already exists in table 'trueandpredict'.\n",
      "Column 'DLinear' already exists in table 'trueandpredict'.\n",
      "Column 'NLinear' already exists in table 'trueandpredict'.\n",
      "Column 'TFT' already exists in table 'trueandpredict'.\n",
      "Column 'StemGNN' already exists in table 'trueandpredict'.\n",
      "Column 'MLPMultivariate' already exists in table 'trueandpredict'.\n",
      "Column 'TiDE' already exists in table 'trueandpredict'.\n",
      "Column 'DeepNPTS' already exists in table 'trueandpredict'.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "训练数据绘图end\n",
      "训练数据绘图end\n"
     ]
    }
   ],
   "source": [
    "logger.info('模型训练完成')\n",
    "# # 模型评估\n",
    "\n",
    "pd.set_option('display.max_columns', 100)\n",
    "\n",
    "# 计算预测评估指数\n",
    "def model_losss_juxiting(sqlitedb):\n",
    "    global dataset\n",
    "    most_model = [sqlitedb.select_data('most_model',columns=['most_common_model'],order_by='ds desc',limit=1).values[0][0]]\n",
    "    most_model_name = most_model[0]\n",
    "\n",
    "    # 预测数据处理 predict\n",
    "    df_combined = loadcsv(os.path.join(dataset,\"cross_validation.csv\"))  \n",
    "    df_combined = dateConvert(df_combined)\n",
    "    # 删除空列\n",
    "    df_combined.dropna(axis=1,inplace=True)\n",
    "     # 删除缺失值,预测过程不能有缺失值\n",
    "    df_combined.dropna(inplace=True) \n",
    "    # 其他列转为数值类型\n",
    "    df_combined = df_combined.astype({col: 'float32' for col in df_combined.columns if col not in ['cutoff','ds'] })\n",
    "    # 使用 groupby 和 transform 结合 lambda 函数来获取每个分组中 cutoff 的最小值，并创建一个新的列来存储这个最大值\n",
    "    df_combined['max_cutoff'] = df_combined.groupby('ds')['cutoff'].transform('max')\n",
    "\n",
    "    # 然后筛选出那些 cutoff 等于 max_cutoff 的行，这样就得到了每个分组中 cutoff 最大的行，并保留了其他列\n",
    "    df_combined = df_combined[df_combined['cutoff'] == df_combined['max_cutoff']]\n",
    "    # 删除模型生成的cutoff列\n",
    "    df_combined.drop(columns=['cutoff', 'max_cutoff'], inplace=True)\n",
    "    # 获取模型名称\n",
    "    modelnames  = df_combined.columns.to_list()[1:] \n",
    "    if 'y' in modelnames:\n",
    "        modelnames.remove('y')\n",
    "    df_combined3 = df_combined.copy()  # 备份df_combined,后面画图需要\n",
    "\n",
    "\n",
    "    # 空的列表存储每个模型的MSE、RMSE、MAE、MAPE、SMAPE\n",
    "    cellText = []\n",
    "\n",
    "    # 遍历模型名称，计算模型评估指标  \n",
    "    for model in modelnames:\n",
    "        modelmse = mse(df_combined['y'], df_combined[model])\n",
    "        modelrmse = rmse(df_combined['y'], df_combined[model])\n",
    "        modelmae = mae(df_combined['y'], df_combined[model])\n",
    "        # modelmape = mape(df_combined['y'], df_combined[model])\n",
    "        # modelsmape = smape(df_combined['y'], df_combined[model])\n",
    "        # modelr2 = r2_score(df_combined['y'], df_combined[model])\n",
    "        cellText.append([model,round(modelmse, 3), round(modelrmse, 3), round(modelmae, 3)])\n",
    "        \n",
    "    model_results3 = pd.DataFrame(cellText,columns=['模型(Model)','平均平方误差(MSE)', '均方根误差(RMSE)',  '平均绝对误差(MAE)'])\n",
    "    # 按MSE降序排列\n",
    "    model_results3 = model_results3.sort_values(by='平均平方误差(MSE)', ascending=True)\n",
    "    model_results3.to_csv(os.path.join(dataset,\"model_evaluation.csv\"),index=False)\n",
    "    modelnames = model_results3['模型(Model)'].tolist()\n",
    "    allmodelnames = modelnames.copy()\n",
    "    # 保存5个最佳模型的名称\n",
    "    if len(modelnames) > 5:\n",
    "        modelnames = modelnames[0:5]\n",
    "    with open(os.path.join(dataset,\"best_modelnames.txt\"), 'w') as f:\n",
    "        f.write(','.join(modelnames) + '\\n')\n",
    "\n",
    "    def plot_predict_vs_true():\n",
    "        # 预测值与真实值对比图\n",
    "        plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "        plt.figure(figsize=(15, 10))\n",
    "        for n,model in enumerate(modelnames[:5]):\n",
    "            plt.subplot(3, 2, n+1)\n",
    "            plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值')\n",
    "            plt.plot(df_combined3['ds'], df_combined3[model], label=model)\n",
    "            plt.legend()\n",
    "            plt.xlabel('日期')\n",
    "            plt.ylabel('价格')\n",
    "            plt.title(model+'拟合')\n",
    "        plt.subplots_adjust(hspace=0.5)\n",
    "        plt.savefig(os.path.join(dataset,'预测值与真实值对比图.png'), bbox_inches='tight')\n",
    "        plt.close()\n",
    "    \n",
    "    plot_predict_vs_true()\n",
    "    \n",
    "    \n",
    "    '''\n",
    "    # # 根据最佳模型的绝对误差的平均比例，最佳模型乘平均比例的百分比，确定最大最小值\n",
    "    # 计算最佳模型对应的绝对误差的平均比例\n",
    "    most_model_mae = model_results3[model_results3['模型(Model)']==most_model_name]['平均绝对误差(MAE)'].values[0]\n",
    "    \n",
    "    def mae_upper_lower(row):\n",
    "        # 计算上边界值\n",
    "        upper_bound = row[most_model_name] * (1 + most_model_mae/row[most_model_name])\n",
    "        # 计算下边界值\n",
    "        lower_bound = row[most_model_name] * (1 - most_model_mae/row[most_model_name])\n",
    "        return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound'])\n",
    "    \n",
    "    df_combined3[['upper_bound', 'lower_bound']] = df_combined3.apply(mae_upper_lower, axis=1)\n",
    "    '''\n",
    "    \n",
    "\n",
    "    # 计算每个模型与最佳模型的绝对误差比例\n",
    "    names = []\n",
    "    for col in allmodelnames:\n",
    "        df_combined3[f'{col}-{most_model_name}-误差比例'] = abs(df_combined3[col] - df_combined3[most_model_name]) / df_combined3[most_model_name] * 100\n",
    "        names.append(f'{col}-{most_model_name}-误差比例')\n",
    "\n",
    "    # 设置阈值 rote\n",
    "    rote = 1.5\n",
    "    names_df = df_combined3[names]\n",
    "    # names_df['rote'] = rote\n",
    "    def add_rote_column(row):\n",
    "        columns = []\n",
    "        for r in names_df.columns:\n",
    "            if row[r] <= rote:\n",
    "                columns.append(r.split('-')[0])\n",
    "        return pd.Series([columns], index=['columns'])\n",
    "    names_df['columns'] = names_df.apply(add_rote_column, axis=1)\n",
    "    \n",
    "    def add_upper_lower_bound(row):\n",
    "        print(row['columns'])\n",
    "        print(type(row['columns']))\n",
    "        # 计算上边界值\n",
    "        upper_bound = df_combined3.loc[row.name,row['columns']].max()\n",
    "        # 计算下边界值\n",
    "        lower_bound = df_combined3.loc[row.name,row['columns']].min()\n",
    "        return pd.Series([lower_bound, upper_bound], index=['lower_bound', 'upper_bound'])\n",
    "    df_combined3[['upper_bound','lower_bound']] = names_df.apply(add_upper_lower_bound, axis=1)\n",
    "    \n",
    "    print(df_combined3[['ds','y',most_model_name,'upper_bound','lower_bound']])\n",
    "    \n",
    "    '''\n",
    "    # 计算波动率,取近60日波动率的10%和90%分位数确定通道上下界\n",
    "    df_combined3['volatility'] = df_combined3['y'].pct_change().round(4)\n",
    "    # 计算近60日的波动率 10% 90%分位数\n",
    "    df_combined3['quantile_10'] = df_combined3['volatility'].rolling(60).quantile(0.1)\n",
    "    df_combined3['quantile_90'] = df_combined3['volatility'].rolling(60).quantile(0.9)\n",
    "    df_combined3 = df_combined3.round(4)\n",
    "    # 计算分位数对应的价格\n",
    "    df_combined3['quantile_10_price'] = df_combined3['y'] * (1 + df_combined3['quantile_10'])\n",
    "    df_combined3['quantile_90_price'] = df_combined3['y'] * (1 + df_combined3['quantile_90'])\n",
    "\n",
    "    # 遍历行\n",
    "    def find_min_max_within_quantile(row):\n",
    "        # 获取分位数10%和90%的值\n",
    "        q10 = row['quantile_10_price']\n",
    "        q90 = row['quantile_90_price']\n",
    "        \n",
    "        # 判断flot值是否为空值\n",
    "        if pd.isna(q10) or pd.isna(q90):\n",
    "            return pd.Series([None, None, None, None], index=['min_within_quantile','max_within_quantile','min_model','max_model'])\n",
    "        \n",
    "        # 初始化最小和最大值为None\n",
    "        min_value = None\n",
    "        max_value = None\n",
    "        min_value_model = ''\n",
    "        max_value_model = ''\n",
    "\n",
    "        # 遍历指定列，找出在分位数范围内的最大最小值\n",
    "        for model in modelnames:\n",
    "            value = row[model]\n",
    "            if value >= q10 and value <= q90:\n",
    "                if min_value is None or value < min_value:\n",
    "                    min_value = value\n",
    "                    min_value_model = model\n",
    "\n",
    "                if max_value is None or value > max_value:\n",
    "                    max_value = value\n",
    "                    max_value_model = model\n",
    "        \n",
    "        # 返回最大最小值\n",
    "        return pd.Series([min_value, max_value,min_value_model,max_value_model], index=['min_within_quantile', 'max_within_quantile','min_model','max_model'])\n",
    "\n",
    "    # 应用函数到每一行\n",
    "    df_combined3[['min_within_quantile', 'max_within_quantile','min_model','max_model']] = df_combined3.apply(find_min_max_within_quantile, axis=1)\n",
    "\n",
    "    \n",
    "    '''\n",
    "    \n",
    "    # 去除有空值的行\n",
    "    df_combined3.dropna(inplace=True)\n",
    "    # 保存到数据库\n",
    "    # df_combined3.to_sql('testandpredict_groupby', sqlitedb.connection, if_exists='replace', index=False)\n",
    "    df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
    "\n",
    "    \n",
    "    def _plt_predict_ture(df):\n",
    "        df = df[-50:] # 取50个数据点画图\n",
    "        # 历史价格\n",
    "        plt.figure(figsize=(20, 10))\n",
    "        plt.plot(df['ds'], df['y'], label='真实值')\n",
    "        # 颜色填充\n",
    "        # plt.fill_between(df['ds'], df['min_within_quantile'], df['max_within_quantile'], alpha=0.2)\n",
    "        plt.fill_between(df['ds'], df['upper_bound'], df['lower_bound'], alpha=0.2)\n",
    "        # plt.plot(df_combined3['ds'], df_combined3['min_abs_error_rate_prediction'], label='最小绝对误差', linestyle='--', color='orange')\n",
    "        # 网格\n",
    "        plt.grid(True)\n",
    "        # 显示历史值\n",
    "        for i, j in zip(df['ds'], df['y']):\n",
    "            plt.text(i, j, str(j), ha='center', va='bottom')\n",
    "\n",
    "        for model in most_model:\n",
    "            plt.plot(df['ds'], df[model], label=model,marker='o')\n",
    "        # 当前日期画竖虚线\n",
    "        plt.axvline(x=df['ds'].iloc[-horizon], color='r', linestyle='--')\n",
    "        plt.legend()\n",
    "        plt.xlabel('日期')\n",
    "        plt.ylabel('价格')\n",
    "        \n",
    "        plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
    "        plt.show()\n",
    "        plt.close()\n",
    "\n",
    "    _plt_predict_ture(df_combined3)\n",
    "\n",
    "    '''\n",
    "   # 去掉方差最大的模型，其余模型预测最大最小值确定通道边界\n",
    "    \n",
    "    \n",
    "    # 历史数据+预测数据\n",
    "    # 拼接未来时间预测\n",
    "    df_predict  = loadcsv(os.path.join(dataset,'predict.csv'))\n",
    "    df_predict.drop('unique_id',inplace=True,axis=1)\n",
    "    df_predict.dropna(axis=1,inplace=True)\n",
    "    df_predict2 = df_predict.copy()\n",
    "    try:\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')\n",
    "    except ValueError :\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')\n",
    "\n",
    "    # 取第一行数据存储到数据库中\n",
    "    first_row = df_predict.head(1)\n",
    "    first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
    "\n",
    "    # # 将预测结果保存到数据库\n",
    "    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)\n",
    "    # # 判断 df 的数值列转为float\n",
    "    for col in df_combined3.columns:\n",
    "        try:\n",
    "            if col != 'ds':\n",
    "                df_combined3[col] = df_combined3[col].astype(float)\n",
    "                df_combined3[col] = df_combined3[col].round(2)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    df_combined3.to_csv(os.path.join(dataset,\"testandpredict_groupby.csv\"),index=False)\n",
    "    df_combined3['ds'] = df_combined3['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
    "    # # 判断表存在\n",
    "    if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
    "        df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
    "    else:\n",
    "        for row in df_combined3.itertuples(index=False):\n",
    "            row_dict = row._asdict()\n",
    "            check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f\"ds = '{row.ds}'\")\n",
    "            if len(check_query) > 0:\n",
    "                set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
    "                sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
    "                continue\n",
    "            sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
    "\n",
    "    ten_models = allmodelnames\n",
    "    # 计算每个模型的方差\n",
    "    variances = df_combined3[ten_models].var()\n",
    "    # 找到方差最大的模型\n",
    "    max_variance_model = variances.idxmax()\n",
    "    # 打印方差最大的模型\n",
    "    print(\"方差最大的模型是：\", max_variance_model)\n",
    "    # 去掉方差最大的模型\n",
    "    df_combined3 = df_combined3.drop(columns=[max_variance_model])\n",
    "    if max_variance_model in allmodelnames:\n",
    "        allmodelnames.remove(max_variance_model)\n",
    "    df_combined3['min'] = df_combined3[allmodelnames].min(axis=1)\n",
    "    df_combined3['max'] = df_combined3[allmodelnames].max(axis=1)\n",
    "    print(df_combined3[['min','max']])\n",
    "    # 历史价格+预测价格\n",
    "    df_combined3 = df_combined3[-50:] # 取50个数据点画图\n",
    "    plt.figure(figsize=(20, 10))\n",
    "    plt.plot(df_combined3['ds'], df_combined3['y'], label='真实值',marker='o')\n",
    "    plt.plot(df_combined3['ds'], df_combined3[most_model], label=most_model_name)\n",
    "    plt.fill_between(df_combined3['ds'], df_combined3['min'], df_combined3['max'], alpha=0.2)\n",
    "    plt.grid(True)\n",
    "    # # 显示历史值\n",
    "    for i, j in zip(df_combined3['ds'][:-5], df_combined3['y'][:-5]):\n",
    "        plt.text(i, j, str(j), ha='center', va='bottom')\n",
    "    # 当前日期画竖虚线\n",
    "    plt.axvline(x=df_combined3['ds'].iloc[-horizon], color='r', linestyle='--')\n",
    "    plt.legend()\n",
    "    plt.xlabel('日期')\n",
    "    plt.ylabel('价格')\n",
    "\n",
    "    plt.savefig(os.path.join(dataset,'历史价格-预测值.png'), bbox_inches='tight')\n",
    "    plt.close()\n",
    "    '''\n",
    "\n",
    "    # # 历史数据+预测数据\n",
    "    # # 拼接未来时间预测\n",
    "    df_predict  = pd.read_csv(os.path.join(dataset,'predict.csv'))\n",
    "    df_predict.drop('unique_id',inplace=True,axis=1)\n",
    "    df_predict.dropna(axis=1,inplace=True)\n",
    "\n",
    "    try:\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y-%m-%d')\n",
    "    except ValueError :\n",
    "        df_predict['ds'] = pd.to_datetime(df_predict['ds'],format=r'%Y/%m/%d')\n",
    "\n",
    "    def first_row_to_database(df):\n",
    "        # # 取第一行数据存储到数据库中\n",
    "        first_row = df.head(1)\n",
    "        first_row['ds'] = first_row['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
    "        # 将预测结果保存到数据库\n",
    "        if not sqlitedb.check_table_exists('trueandpredict'):\n",
    "            first_row.to_sql('trueandpredict',sqlitedb.connection,index=False)\n",
    "        else:\n",
    "            for col in first_row.columns:\n",
    "                    sqlitedb.add_column_if_not_exists('trueandpredict',col,'TEXT')\n",
    "            for row in first_row.itertuples(index=False):\n",
    "                row_dict = row._asdict()\n",
    "                columns=row_dict.keys()\n",
    "                check_query = sqlitedb.select_data('trueandpredict',where_condition = f\"ds = '{row.ds}'\")\n",
    "                if len(check_query) > 0:\n",
    "                    set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
    "                    sqlitedb.update_data('trueandpredict',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
    "                    continue\n",
    "                sqlitedb.insert_data('trueandpredict',tuple(row_dict.values()),columns=columns)\n",
    "\n",
    "    first_row_to_database(df_predict)\n",
    "    def find_most_common_model():\n",
    "        # 最多频率的模型名称\n",
    "        min_model_max_frequency_model = df_combined3['min_model'].tail(20).value_counts().idxmax()\n",
    "        max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().idxmax()\n",
    "        if min_model_max_frequency_model == max_model_max_frequency_model:\n",
    "            # 取20天第二多的模型\n",
    "            max_model_max_frequency_model = df_combined3['max_model'].tail(20).value_counts().nlargest(2).index[1]\n",
    "\n",
    "        df_predict['min_model'] = min_model_max_frequency_model\n",
    "        df_predict['max_model'] = max_model_max_frequency_model\n",
    "        df_predict['min_within_quantile'] = df_predict[min_model_max_frequency_model]\n",
    "        df_predict['max_within_quantile'] = df_predict[max_model_max_frequency_model]\n",
    "\n",
    "\n",
    "    # find_most_common_model()\n",
    "\n",
    "    df_predict2 = df_predict.copy()\n",
    "    df_predict2['ds'] = pd.to_datetime(df_predict2['ds'])\n",
    "    df_predict2['ds'] = df_predict2['ds'].dt.strftime('%Y-%m-%d 00:00:00')\n",
    "\n",
    "\n",
    "    def _add_abs_error_rate():\n",
    "        # 计算每个预测值与真实值之间的偏差率\n",
    "        for model in allmodelnames:\n",
    "            df_combined3[f'{model}_abs_error_rate'] = abs(df_combined3['y'] - df_combined3[model]) / df_combined3['y']\n",
    "\n",
    "        # 获取每行对应的最小偏差率值\n",
    "        min_abs_error_rate_values = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].min(), axis=1)\n",
    "        # 获取每行对应的最小偏差率值对应的列名\n",
    "        min_abs_error_rate_column_name = df_combined3.apply(lambda row: row[[f'{model}_abs_error_rate' for model in allmodelnames]].idxmin(), axis=1) \n",
    "        # 将列名索引转换为列名\n",
    "        min_abs_error_rate_column_name = min_abs_error_rate_column_name.map(lambda x: x.split('_')[0])\n",
    "        # 获取最小偏差率对应的模型的预测值\n",
    "        min_abs_error_rate_predictions = df_combined3.apply(lambda row: row[min_abs_error_rate_column_name[row.name]], axis=1)\n",
    "        # 将最小偏差率对应的模型的预测值添加到DataFrame中\n",
    "        df_combined3['min_abs_error_rate_prediction'] = min_abs_error_rate_predictions\n",
    "        df_combined3['min_abs_error_rate_column_name'] = min_abs_error_rate_column_name\n",
    "\n",
    "    _add_abs_error_rate()\n",
    "\n",
    "    df_combined3 = pd.concat([df_combined3, df_predict]).reset_index(drop=True)\n",
    "    # 判断 df 的数值列转为float\n",
    "    for col in df_combined3.columns:\n",
    "        try:\n",
    "            if col != 'ds':\n",
    "                df_combined3[col] = df_combined3[col].astype(float)\n",
    "                df_combined3[col] = df_combined3[col].round(2)\n",
    "        except ValueError:\n",
    "            pass\n",
    "    df_combined3.to_csv(os.path.join(dataset,\"df_combined3.csv\"),index=False) \n",
    "    \n",
    "     # 历史价格+预测价格\n",
    "        # 将预测结果保存到数据库\n",
    "    # 判断表存在\n",
    "    # if not sqlitedb.check_table_exists('testandpredict_groupby'):\n",
    "    #     df_combined3.to_sql('testandpredict_groupby',sqlitedb.connection,index=False)\n",
    "    # else:\n",
    "    #     for row in df_combined3.itertuples(index=False):\n",
    "    #         row_dict = row._asdict()\n",
    "    #         check_query = sqlitedb.select_data('testandpredict_groupby',where_condition = f\"ds = '{row.ds}'\")\n",
    "    #         if len(check_query) > 0:\n",
    "    #             set_clause = \", \".join([f\"{key} = '{value}'\" for key, value in row_dict.items()])\n",
    "    #             sqlitedb.update_data('testandpredict_groupby',set_clause,where_condition = f\"ds = '{row.ds}'\")\n",
    "    #             continue\n",
    "    #         sqlitedb.insert_data('testandpredict_groupby',tuple(row_dict.values()),columns=row_dict.keys())\n",
    "    \n",
    "    \n",
    "\n",
    "    def _plt_predict_table(df): \n",
    "        # 预测值表格\n",
    "        fig, ax = plt.subplots(figsize=(20, 6))\n",
    "        ax.axis('off')  # 关闭坐标轴\n",
    "        # 数值保留2位小数\n",
    "        df = df.round(2)\n",
    "        df = df[-horizon:]\n",
    "        df['Day'] = [f'Day_{i}' for i in range(1,horizon+1)]\n",
    "        # Day列放到最前面\n",
    "        df = df[['Day'] + list(df.columns[:-1])]\n",
    "        table = ax.table(cellText=df.values, colLabels=df.columns, loc='center')\n",
    "        #加宽表格\n",
    "        table.auto_set_font_size(False)\n",
    "        table.set_fontsize(10)\n",
    "\n",
    "        # 设置表格样式，列数据最小的用绿色标识\n",
    "        plt.savefig(os.path.join(dataset,'预测值表格.png'), bbox_inches='tight')\n",
    "        plt.close()\n",
    "       \n",
    "    def _plt_model_results3():\n",
    "        # 可视化评估结果\n",
    "        plt.rcParams['font.sans-serif'] = ['SimHei']\n",
    "        fig, ax = plt.subplots(figsize=(20, 10))\n",
    "        ax.axis('off')  # 关闭坐标轴\n",
    "        table = ax.table(cellText=model_results3.values, colLabels=model_results3.columns, loc='center')\n",
    "        # 加宽表格\n",
    "        table.auto_set_font_size(False)\n",
    "        table.set_fontsize(10)\n",
    "\n",
    "        # 设置表格样式，列数据最小的用绿色标识\n",
    "        plt.savefig(os.path.join(dataset,'模型评估.png'), bbox_inches='tight')\n",
    "        plt.close()\n",
    "\n",
    "\n",
    "    _plt_predict_table(df_combined3)\n",
    "    _plt_model_results3()\n",
    "\n",
    "    return model_results3\n",
    "\n",
    "\n",
    "\n",
    "logger.info('训练数据绘图ing')\n",
    "model_results3 = model_losss_juxiting(sqlitedb)\n",
    "\n",
    "logger.info('训练数据绘图end')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "85b557de-8235-4e27-b5b8-58b36dfe6724",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 模型报告\n",
    "\n",
    "logger.info('制作报告ing')\n",
    "title = f'{settings}--{now}-预测报告' # 报告标题\n",
    "\n",
    "pp_export_pdf(dataset=dataset,num_models = 5 if is_fivemodels else 22,time=end_time,\n",
    "        reportname=reportname,sqlitedb=sqlitedb),\n",
    "\n",
    "logger.info('制作报告end')\n",
    "logger.info('模型训练完成')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d4129e71-ee2c-4af1-81ed-fadf14efa206",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 发送邮件\n",
    "m = SendMail(\n",
    "    username=username,\n",
    "    passwd=passwd,\n",
    "    recv=recv,\n",
    "    title=title,\n",
    "    content=content,\n",
    "    file=max(glob.glob(os.path.join(dataset,'*.pdf')), key=os.path.getctime),\n",
    "    ssl=ssl,\n",
    ")\n",
    "# m.send_mail()   \n"
   ]
  }
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