ML_Final_Project_Model.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "import datetime\n",
    "\n",
    "import IPython\n",
    "import IPython.display\n",
    "import matplotlib as mpl\n",
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import seaborn as sns\n",
    "import tensorflow as tf"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>country_id</th>\n",
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       "      <th>0</th>\n",
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       "      <td>-0.183319</td>\n",
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       "      <td>-0.215762</td>\n",
       "      <td>-0.505902</td>\n",
       "      <td>-0.266474</td>\n",
       "      <td>-0.356543</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.828113</td>\n",
       "      <td>0.413319</td>\n",
       "      <td>0.384665</td>\n",
       "      <td>-0.415652</td>\n",
       "      <td>1.671988</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-0.085792</td>\n",
       "      <td>0.676853</td>\n",
       "      <td>0.529280</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>3</th>\n",
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       "      <td>-0.182832</td>\n",
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       "      <td>-0.085792</td>\n",
       "      <td>0.676853</td>\n",
       "      <td>0.529280</td>\n",
       "      <td>1</td>\n",
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       "    <tr>\n",
       "      <th>4</th>\n",
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       "      <td>-0.314638</td>\n",
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       "      <td>-0.206470</td>\n",
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       "      <td>-0.620648</td>\n",
       "      <td>-1.666104</td>\n",
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       "      <td>1605484800</td>\n",
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       "      <td>-0.182717</td>\n",
       "      <td>-0.178499</td>\n",
       "      <td>-0.247697</td>\n",
       "      <td>-0.200794</td>\n",
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       "      <td>-0.419488</td>\n",
       "      <td>-0.270310</td>\n",
       "      <td>-0.366532</td>\n",
       "      <td>...</td>\n",
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       "      <td>-0.176204</td>\n",
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       "      <td>-0.174598</td>\n",
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       "      <td>-0.418978</td>\n",
       "      <td>-0.301336</td>\n",
       "      <td>-0.366623</td>\n",
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       "      <td>213</td>\n",
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      "text/plain": [
       "             date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0      1585008000    -0.166838  -0.180584           -0.184410     -0.269410   \n",
       "1      1585094400    -0.166786  -0.178258           -0.183865     -0.269324   \n",
       "2      1585180800    -0.166735  -0.178258           -0.183319     -0.269324   \n",
       "3      1585267200    -0.166672  -0.177288           -0.182832     -0.269238   \n",
       "4      1585353600    -0.166645  -0.180390           -0.182487     -0.268981   \n",
       "...           ...          ...        ...                 ...           ...   \n",
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       "35228  1605484800    -0.147345  -0.182717           -0.178499     -0.247697   \n",
       "35229  1605571200    -0.147096  -0.161196           -0.176175     -0.247697   \n",
       "35230  1605657600    -0.146988  -0.173411           -0.176204     -0.247439   \n",
       "35231  1605744000    -0.146907  -0.175737           -0.176806     -0.247353   \n",
       "\n",
       "       new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0       -0.183330            -0.215762                -0.508423   \n",
       "1       -0.192062            -0.214388                -0.507163   \n",
       "2       -0.200794            -0.215762                -0.505902   \n",
       "3       -0.192062            -0.214388                -0.504368   \n",
       "4       -0.174598            -0.210238                -0.503710   \n",
       "...           ...                  ...                      ...   \n",
       "35227   -0.200794            -0.211622                -0.420666   \n",
       "35228   -0.200794            -0.214388                -0.420666   \n",
       "35229   -0.200794            -0.215762                -0.419488   \n",
       "35230   -0.174598            -0.213005                -0.418978   \n",
       "35231   -0.192062            -0.211622                -0.418596   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                  -0.297002                       -0.373851  ...   \n",
       "1                  -0.266474                       -0.365192  ...   \n",
       "2                  -0.266474                       -0.356543  ...   \n",
       "3                  -0.253750                       -0.348801  ...   \n",
       "4                  -0.294454                       -0.343343  ...   \n",
       "...                      ...                             ...  ...   \n",
       "35227              -0.314638                       -0.370769  ...   \n",
       "35228              -0.324983                       -0.373677  ...   \n",
       "35229              -0.270310                       -0.366532  ...   \n",
       "35230              -0.301336                       -0.366623  ...   \n",
       "35231              -0.307252                       -0.368476  ...   \n",
       "\n",
       "       extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0            -0.828113               0.413319             0.384665   \n",
       "1            -0.828113               0.413319             0.384665   \n",
       "2            -0.828113               0.413319             0.384665   \n",
       "3            -0.828113               0.413319             0.384665   \n",
       "4            -0.828113               0.413319             0.384665   \n",
       "...                ...                    ...                  ...   \n",
       "35227         0.499589               0.446225            -1.472583   \n",
       "35228         0.499589               0.446225            -1.472583   \n",
       "35229         0.499589               0.446225            -1.472583   \n",
       "35230         0.499589               0.446225            -1.472583   \n",
       "35231         0.499589               0.446225            -1.472583   \n",
       "\n",
       "       female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0           -0.415652      1.671988               -0.071486   \n",
       "1           -0.415652      1.671988               -0.071486   \n",
       "2           -0.415652      1.671988               -0.071486   \n",
       "3           -0.415652      1.671988               -0.071486   \n",
       "4           -0.415652      1.671988               -0.071486   \n",
       "...               ...           ...                     ...   \n",
       "35227       -1.028791     -0.206470               -0.726538   \n",
       "35228       -1.028791     -0.206470               -0.726538   \n",
       "35229       -1.028791     -0.206470               -0.726538   \n",
       "35230       -1.028791     -0.206470               -0.726538   \n",
       "35231       -1.028791     -0.206470               -0.726538   \n",
       "\n",
       "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                       -0.085792         0.676853                 0.529280   \n",
       "1                       -0.085792         0.676853                 0.529280   \n",
       "2                       -0.085792         0.676853                 0.529280   \n",
       "3                       -0.085792         0.676853                 0.529280   \n",
       "4                       -0.085792         0.676853                 0.529280   \n",
       "...                           ...              ...                      ...   \n",
       "35227                   -0.620648        -1.666104                -1.209113   \n",
       "35228                   -0.620648        -1.666104                -1.209113   \n",
       "35229                   -0.620648        -1.666104                -1.209113   \n",
       "35230                   -0.620648        -1.666104                -1.209113   \n",
       "35231                   -0.620648        -1.666104                -1.209113   \n",
       "\n",
       "       country_id  \n",
       "0               1  \n",
       "1               1  \n",
       "2               1  \n",
       "3               1  \n",
       "4               1  \n",
       "...           ...  \n",
       "35227         213  \n",
       "35228         213  \n",
       "35229         213  \n",
       "35230         213  \n",
       "35231         213  \n",
       "\n",
       "[35232 rows x 47 columns]"
      ]
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       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
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       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
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       "      <th>life_expectancy</th>\n",
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       "      <th>0</th>\n",
       "      <td>1585008000</td>\n",
       "      <td>-0.166973</td>\n",
       "      <td>-0.181553</td>\n",
       "      <td>-0.185271</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.192062</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.513742</td>\n",
       "      <td>-0.323856</td>\n",
       "      <td>-0.395517</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>3.052576</td>\n",
       "      <td>0.274489</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.680666</td>\n",
       "      <td>-1.159998</td>\n",
       "      <td>-1.207938</td>\n",
       "      <td>-1.466395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1585094400</td>\n",
       "      <td>-0.166968</td>\n",
       "      <td>-0.182329</td>\n",
       "      <td>-0.185242</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.513734</td>\n",
       "      <td>-0.324610</td>\n",
       "      <td>-0.395490</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>3.052576</td>\n",
       "      <td>0.274489</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.680666</td>\n",
       "      <td>-1.159998</td>\n",
       "      <td>-1.207938</td>\n",
       "      <td>-1.466395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1585180800</td>\n",
       "      <td>-0.166894</td>\n",
       "      <td>-0.176319</td>\n",
       "      <td>-0.184295</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.513600</td>\n",
       "      <td>-0.318775</td>\n",
       "      <td>-0.394380</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>3.052576</td>\n",
       "      <td>0.274489</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.680666</td>\n",
       "      <td>-1.159998</td>\n",
       "      <td>-1.207938</td>\n",
       "      <td>-1.466395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1585267200</td>\n",
       "      <td>-0.166894</td>\n",
       "      <td>-0.182717</td>\n",
       "      <td>-0.184295</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.513600</td>\n",
       "      <td>-0.324983</td>\n",
       "      <td>-0.394380</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>3.052576</td>\n",
       "      <td>0.274489</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.680666</td>\n",
       "      <td>-1.159998</td>\n",
       "      <td>-1.207938</td>\n",
       "      <td>-1.466395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585353600</td>\n",
       "      <td>-0.166858</td>\n",
       "      <td>-0.179615</td>\n",
       "      <td>-0.183893</td>\n",
       "      <td>-0.269581</td>\n",
       "      <td>-0.192062</td>\n",
       "      <td>-0.217145</td>\n",
       "      <td>-0.513536</td>\n",
       "      <td>-0.321974</td>\n",
       "      <td>-0.393903</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>3.052576</td>\n",
       "      <td>0.274489</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.680666</td>\n",
       "      <td>-1.159998</td>\n",
       "      <td>-1.207938</td>\n",
       "      <td>-1.466395</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10176</th>\n",
       "      <td>1605398400</td>\n",
       "      <td>0.049370</td>\n",
       "      <td>-0.124360</td>\n",
       "      <td>-0.124528</td>\n",
       "      <td>-0.197318</td>\n",
       "      <td>-0.174598</td>\n",
       "      <td>-0.188127</td>\n",
       "      <td>0.021033</td>\n",
       "      <td>-0.247490</td>\n",
       "      <td>-0.297728</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.482058</td>\n",
       "      <td>-0.427038</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-1.025160</td>\n",
       "      <td>-0.216159</td>\n",
       "      <td>0.362395</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10177</th>\n",
       "      <td>1605484800</td>\n",
       "      <td>0.051414</td>\n",
       "      <td>-0.006096</td>\n",
       "      <td>-0.114973</td>\n",
       "      <td>-0.196717</td>\n",
       "      <td>-0.139669</td>\n",
       "      <td>-0.185370</td>\n",
       "      <td>0.026086</td>\n",
       "      <td>-0.090440</td>\n",
       "      <td>-0.282382</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.482058</td>\n",
       "      <td>-0.427038</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-1.025160</td>\n",
       "      <td>-0.216159</td>\n",
       "      <td>0.362395</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10178</th>\n",
       "      <td>1605571200</td>\n",
       "      <td>0.052282</td>\n",
       "      <td>-0.107687</td>\n",
       "      <td>-0.103869</td>\n",
       "      <td>-0.196374</td>\n",
       "      <td>-0.165866</td>\n",
       "      <td>-0.179838</td>\n",
       "      <td>0.028233</td>\n",
       "      <td>-0.225344</td>\n",
       "      <td>-0.264550</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.482058</td>\n",
       "      <td>-0.427038</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-1.025160</td>\n",
       "      <td>-0.216159</td>\n",
       "      <td>0.362395</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10179</th>\n",
       "      <td>1605657600</td>\n",
       "      <td>0.052980</td>\n",
       "      <td>-0.122421</td>\n",
       "      <td>-0.111071</td>\n",
       "      <td>-0.196117</td>\n",
       "      <td>-0.174598</td>\n",
       "      <td>-0.186744</td>\n",
       "      <td>0.029958</td>\n",
       "      <td>-0.244913</td>\n",
       "      <td>-0.276117</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.482058</td>\n",
       "      <td>-0.427038</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-1.025160</td>\n",
       "      <td>-0.216159</td>\n",
       "      <td>0.362395</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10180</th>\n",
       "      <td>1605744000</td>\n",
       "      <td>0.053654</td>\n",
       "      <td>-0.124554</td>\n",
       "      <td>-0.102463</td>\n",
       "      <td>-0.195945</td>\n",
       "      <td>-0.183330</td>\n",
       "      <td>-0.183987</td>\n",
       "      <td>0.031622</td>\n",
       "      <td>-0.247746</td>\n",
       "      <td>-0.262294</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>-0.482058</td>\n",
       "      <td>-0.427038</td>\n",
       "      <td>-0.040519</td>\n",
       "      <td>-0.032366</td>\n",
       "      <td>-0.071486</td>\n",
       "      <td>-1.025160</td>\n",
       "      <td>-0.216159</td>\n",
       "      <td>0.362395</td>\n",
       "      <td>207</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>10181 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "             date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0      1585008000    -0.166973  -0.181553           -0.185271     -0.269667   \n",
       "1      1585094400    -0.166968  -0.182329           -0.185242     -0.269667   \n",
       "2      1585180800    -0.166894  -0.176319           -0.184295     -0.269667   \n",
       "3      1585267200    -0.166894  -0.182717           -0.184295     -0.269667   \n",
       "4      1585353600    -0.166858  -0.179615           -0.183893     -0.269581   \n",
       "...           ...          ...        ...                 ...           ...   \n",
       "10176  1605398400     0.049370  -0.124360           -0.124528     -0.197318   \n",
       "10177  1605484800     0.051414  -0.006096           -0.114973     -0.196717   \n",
       "10178  1605571200     0.052282  -0.107687           -0.103869     -0.196374   \n",
       "10179  1605657600     0.052980  -0.122421           -0.111071     -0.196117   \n",
       "10180  1605744000     0.053654  -0.124554           -0.102463     -0.195945   \n",
       "\n",
       "       new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0       -0.192062            -0.218528                -0.513742   \n",
       "1       -0.200794            -0.218528                -0.513734   \n",
       "2       -0.200794            -0.218528                -0.513600   \n",
       "3       -0.200794            -0.218528                -0.513600   \n",
       "4       -0.192062            -0.217145                -0.513536   \n",
       "...           ...                  ...                      ...   \n",
       "10176   -0.174598            -0.188127                 0.021033   \n",
       "10177   -0.139669            -0.185370                 0.026086   \n",
       "10178   -0.165866            -0.179838                 0.028233   \n",
       "10179   -0.174598            -0.186744                 0.029958   \n",
       "10180   -0.183330            -0.183987                 0.031622   \n",
       "\n",
       "       new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                  -0.323856                       -0.395517  ...   \n",
       "1                  -0.324610                       -0.395490  ...   \n",
       "2                  -0.318775                       -0.394380  ...   \n",
       "3                  -0.324983                       -0.394380  ...   \n",
       "4                  -0.321974                       -0.393903  ...   \n",
       "...                      ...                             ...  ...   \n",
       "10176              -0.247490                       -0.297728  ...   \n",
       "10177              -0.090440                       -0.282382  ...   \n",
       "10178              -0.225344                       -0.264550  ...   \n",
       "10179              -0.244913                       -0.276117  ...   \n",
       "10180              -0.247746                       -0.262294  ...   \n",
       "\n",
       "       extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0            -0.001038               3.052576             0.274489   \n",
       "1            -0.001038               3.052576             0.274489   \n",
       "2            -0.001038               3.052576             0.274489   \n",
       "3            -0.001038               3.052576             0.274489   \n",
       "4            -0.001038               3.052576             0.274489   \n",
       "...                ...                    ...                  ...   \n",
       "10176        -0.001038              -0.482058            -0.427038   \n",
       "10177        -0.001038              -0.482058            -0.427038   \n",
       "10178        -0.001038              -0.482058            -0.427038   \n",
       "10179        -0.001038              -0.482058            -0.427038   \n",
       "10180        -0.001038              -0.482058            -0.427038   \n",
       "\n",
       "       female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0           -0.040519     -0.032366               -0.680666   \n",
       "1           -0.040519     -0.032366               -0.680666   \n",
       "2           -0.040519     -0.032366               -0.680666   \n",
       "3           -0.040519     -0.032366               -0.680666   \n",
       "4           -0.040519     -0.032366               -0.680666   \n",
       "...               ...           ...                     ...   \n",
       "10176       -0.040519     -0.032366               -0.071486   \n",
       "10177       -0.040519     -0.032366               -0.071486   \n",
       "10178       -0.040519     -0.032366               -0.071486   \n",
       "10179       -0.040519     -0.032366               -0.071486   \n",
       "10180       -0.040519     -0.032366               -0.071486   \n",
       "\n",
       "       hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                       -1.159998        -1.207938                -1.466395   \n",
       "1                       -1.159998        -1.207938                -1.466395   \n",
       "2                       -1.159998        -1.207938                -1.466395   \n",
       "3                       -1.159998        -1.207938                -1.466395   \n",
       "4                       -1.159998        -1.207938                -1.466395   \n",
       "...                           ...              ...                      ...   \n",
       "10176                   -1.025160        -0.216159                 0.362395   \n",
       "10177                   -1.025160        -0.216159                 0.362395   \n",
       "10178                   -1.025160        -0.216159                 0.362395   \n",
       "10179                   -1.025160        -0.216159                 0.362395   \n",
       "10180                   -1.025160        -0.216159                 0.362395   \n",
       "\n",
       "       country_id  \n",
       "0               0  \n",
       "1               0  \n",
       "2               0  \n",
       "3               0  \n",
       "4               0  \n",
       "...           ...  \n",
       "10176         207  \n",
       "10177         207  \n",
       "10178         207  \n",
       "10179         207  \n",
       "10180         207  \n",
       "\n",
       "[10181 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
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       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>date</th>\n",
       "      <th>total_cases</th>\n",
       "      <th>new_cases</th>\n",
       "      <th>new_cases_smoothed</th>\n",
       "      <th>total_deaths</th>\n",
       "      <th>new_deaths</th>\n",
       "      <th>new_deaths_smoothed</th>\n",
       "      <th>total_cases_per_million</th>\n",
       "      <th>new_cases_per_million</th>\n",
       "      <th>new_cases_smoothed_per_million</th>\n",
       "      <th>...</th>\n",
       "      <th>extreme_poverty</th>\n",
       "      <th>cardiovasc_death_rate</th>\n",
       "      <th>diabetes_prevalence</th>\n",
       "      <th>female_smokers</th>\n",
       "      <th>male_smokers</th>\n",
       "      <th>handwashing_facilities</th>\n",
       "      <th>hospital_beds_per_thousand</th>\n",
       "      <th>life_expectancy</th>\n",
       "      <th>human_development_index</th>\n",
       "      <th>country_id</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1585008000</td>\n",
       "      <td>-0.166901</td>\n",
       "      <td>-0.181360</td>\n",
       "      <td>-0.184295</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.512784</td>\n",
       "      <td>-0.319932</td>\n",
       "      <td>-0.389307</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>2.717146</td>\n",
       "      <td>-0.283135</td>\n",
       "      <td>-1.173715</td>\n",
       "      <td>0.874789</td>\n",
       "      <td>1.504596</td>\n",
       "      <td>0.727728</td>\n",
       "      <td>-0.087214</td>\n",
       "      <td>0.334580</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>1585094400</td>\n",
       "      <td>-0.166867</td>\n",
       "      <td>-0.179809</td>\n",
       "      <td>-0.184123</td>\n",
       "      <td>-0.269667</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.512551</td>\n",
       "      <td>-0.314155</td>\n",
       "      <td>-0.388537</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>2.717146</td>\n",
       "      <td>-0.283135</td>\n",
       "      <td>-1.173715</td>\n",
       "      <td>0.874789</td>\n",
       "      <td>1.504596</td>\n",
       "      <td>0.727728</td>\n",
       "      <td>-0.087214</td>\n",
       "      <td>0.334580</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>1585180800</td>\n",
       "      <td>-0.166854</td>\n",
       "      <td>-0.181553</td>\n",
       "      <td>-0.184123</td>\n",
       "      <td>-0.269581</td>\n",
       "      <td>-0.192062</td>\n",
       "      <td>-0.217145</td>\n",
       "      <td>-0.512458</td>\n",
       "      <td>-0.320649</td>\n",
       "      <td>-0.388537</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>2.717146</td>\n",
       "      <td>-0.283135</td>\n",
       "      <td>-1.173715</td>\n",
       "      <td>0.874789</td>\n",
       "      <td>1.504596</td>\n",
       "      <td>0.727728</td>\n",
       "      <td>-0.087214</td>\n",
       "      <td>0.334580</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>1585267200</td>\n",
       "      <td>-0.166789</td>\n",
       "      <td>-0.177094</td>\n",
       "      <td>-0.183578</td>\n",
       "      <td>-0.269496</td>\n",
       "      <td>-0.192062</td>\n",
       "      <td>-0.215762</td>\n",
       "      <td>-0.512006</td>\n",
       "      <td>-0.304045</td>\n",
       "      <td>-0.386078</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>2.717146</td>\n",
       "      <td>-0.283135</td>\n",
       "      <td>-1.173715</td>\n",
       "      <td>0.874789</td>\n",
       "      <td>1.504596</td>\n",
       "      <td>0.727728</td>\n",
       "      <td>-0.087214</td>\n",
       "      <td>0.334580</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>1585353600</td>\n",
       "      <td>-0.166692</td>\n",
       "      <td>-0.174380</td>\n",
       "      <td>-0.182344</td>\n",
       "      <td>-0.269496</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.217145</td>\n",
       "      <td>-0.511337</td>\n",
       "      <td>-0.293935</td>\n",
       "      <td>-0.380520</td>\n",
       "      <td>...</td>\n",
       "      <td>-0.001038</td>\n",
       "      <td>2.717146</td>\n",
       "      <td>-0.283135</td>\n",
       "      <td>-1.173715</td>\n",
       "      <td>0.874789</td>\n",
       "      <td>1.504596</td>\n",
       "      <td>0.727728</td>\n",
       "      <td>-0.087214</td>\n",
       "      <td>0.334580</td>\n",
       "      <td>12</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>...</th>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "      <td>...</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4835</th>\n",
       "      <td>1605398400</td>\n",
       "      <td>-0.128693</td>\n",
       "      <td>-0.181941</td>\n",
       "      <td>-0.180393</td>\n",
       "      <td>-0.239715</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.218528</td>\n",
       "      <td>-0.367218</td>\n",
       "      <td>-0.323387</td>\n",
       "      <td>-0.382685</td>\n",
       "      <td>...</td>\n",
       "      <td>2.860677</td>\n",
       "      <td>-0.214837</td>\n",
       "      <td>-0.995904</td>\n",
       "      <td>-0.861571</td>\n",
       "      <td>-0.756262</td>\n",
       "      <td>-1.824236</td>\n",
       "      <td>-0.485810</td>\n",
       "      <td>-1.336883</td>\n",
       "      <td>-0.840573</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4836</th>\n",
       "      <td>1605484800</td>\n",
       "      <td>-0.128635</td>\n",
       "      <td>-0.177676</td>\n",
       "      <td>-0.180967</td>\n",
       "      <td>-0.239457</td>\n",
       "      <td>-0.174598</td>\n",
       "      <td>-0.214388</td>\n",
       "      <td>-0.366995</td>\n",
       "      <td>-0.314631</td>\n",
       "      <td>-0.384116</td>\n",
       "      <td>...</td>\n",
       "      <td>2.860677</td>\n",
       "      <td>-0.214837</td>\n",
       "      <td>-0.995904</td>\n",
       "      <td>-0.861571</td>\n",
       "      <td>-0.756262</td>\n",
       "      <td>-1.824236</td>\n",
       "      <td>-0.485810</td>\n",
       "      <td>-1.336883</td>\n",
       "      <td>-0.840573</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4837</th>\n",
       "      <td>1605571200</td>\n",
       "      <td>-0.128491</td>\n",
       "      <td>-0.170309</td>\n",
       "      <td>-0.179618</td>\n",
       "      <td>-0.239457</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.214388</td>\n",
       "      <td>-0.366446</td>\n",
       "      <td>-0.299499</td>\n",
       "      <td>-0.380767</td>\n",
       "      <td>...</td>\n",
       "      <td>2.860677</td>\n",
       "      <td>-0.214837</td>\n",
       "      <td>-0.995904</td>\n",
       "      <td>-0.861571</td>\n",
       "      <td>-0.756262</td>\n",
       "      <td>-1.824236</td>\n",
       "      <td>-0.485810</td>\n",
       "      <td>-1.336883</td>\n",
       "      <td>-0.840573</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4838</th>\n",
       "      <td>1605657600</td>\n",
       "      <td>-0.128366</td>\n",
       "      <td>-0.171860</td>\n",
       "      <td>-0.178757</td>\n",
       "      <td>-0.239457</td>\n",
       "      <td>-0.200794</td>\n",
       "      <td>-0.215762</td>\n",
       "      <td>-0.365965</td>\n",
       "      <td>-0.302683</td>\n",
       "      <td>-0.378621</td>\n",
       "      <td>...</td>\n",
       "      <td>2.860677</td>\n",
       "      <td>-0.214837</td>\n",
       "      <td>-0.995904</td>\n",
       "      <td>-0.861571</td>\n",
       "      <td>-0.756262</td>\n",
       "      <td>-1.824236</td>\n",
       "      <td>-0.485810</td>\n",
       "      <td>-1.336883</td>\n",
       "      <td>-0.840573</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4839</th>\n",
       "      <td>1605744000</td>\n",
       "      <td>-0.128283</td>\n",
       "      <td>-0.175543</td>\n",
       "      <td>-0.178815</td>\n",
       "      <td>-0.239200</td>\n",
       "      <td>-0.174598</td>\n",
       "      <td>-0.211622</td>\n",
       "      <td>-0.365648</td>\n",
       "      <td>-0.310246</td>\n",
       "      <td>-0.378768</td>\n",
       "      <td>...</td>\n",
       "      <td>2.860677</td>\n",
       "      <td>-0.214837</td>\n",
       "      <td>-0.995904</td>\n",
       "      <td>-0.861571</td>\n",
       "      <td>-0.756262</td>\n",
       "      <td>-1.824236</td>\n",
       "      <td>-0.485810</td>\n",
       "      <td>-1.336883</td>\n",
       "      <td>-0.840573</td>\n",
       "      <td>212</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>4840 rows × 47 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "            date  total_cases  new_cases  new_cases_smoothed  total_deaths  \\\n",
       "0     1585008000    -0.166901  -0.181360           -0.184295     -0.269667   \n",
       "1     1585094400    -0.166867  -0.179809           -0.184123     -0.269667   \n",
       "2     1585180800    -0.166854  -0.181553           -0.184123     -0.269581   \n",
       "3     1585267200    -0.166789  -0.177094           -0.183578     -0.269496   \n",
       "4     1585353600    -0.166692  -0.174380           -0.182344     -0.269496   \n",
       "...          ...          ...        ...                 ...           ...   \n",
       "4835  1605398400    -0.128693  -0.181941           -0.180393     -0.239715   \n",
       "4836  1605484800    -0.128635  -0.177676           -0.180967     -0.239457   \n",
       "4837  1605571200    -0.128491  -0.170309           -0.179618     -0.239457   \n",
       "4838  1605657600    -0.128366  -0.171860           -0.178757     -0.239457   \n",
       "4839  1605744000    -0.128283  -0.175543           -0.178815     -0.239200   \n",
       "\n",
       "      new_deaths  new_deaths_smoothed  total_cases_per_million  \\\n",
       "0      -0.200794            -0.218528                -0.512784   \n",
       "1      -0.200794            -0.218528                -0.512551   \n",
       "2      -0.192062            -0.217145                -0.512458   \n",
       "3      -0.192062            -0.215762                -0.512006   \n",
       "4      -0.200794            -0.217145                -0.511337   \n",
       "...          ...                  ...                      ...   \n",
       "4835   -0.200794            -0.218528                -0.367218   \n",
       "4836   -0.174598            -0.214388                -0.366995   \n",
       "4837   -0.200794            -0.214388                -0.366446   \n",
       "4838   -0.200794            -0.215762                -0.365965   \n",
       "4839   -0.174598            -0.211622                -0.365648   \n",
       "\n",
       "      new_cases_per_million  new_cases_smoothed_per_million  ...  \\\n",
       "0                 -0.319932                       -0.389307  ...   \n",
       "1                 -0.314155                       -0.388537  ...   \n",
       "2                 -0.320649                       -0.388537  ...   \n",
       "3                 -0.304045                       -0.386078  ...   \n",
       "4                 -0.293935                       -0.380520  ...   \n",
       "...                     ...                             ...  ...   \n",
       "4835              -0.323387                       -0.382685  ...   \n",
       "4836              -0.314631                       -0.384116  ...   \n",
       "4837              -0.299499                       -0.380767  ...   \n",
       "4838              -0.302683                       -0.378621  ...   \n",
       "4839              -0.310246                       -0.378768  ...   \n",
       "\n",
       "      extreme_poverty  cardiovasc_death_rate  diabetes_prevalence  \\\n",
       "0           -0.001038               2.717146            -0.283135   \n",
       "1           -0.001038               2.717146            -0.283135   \n",
       "2           -0.001038               2.717146            -0.283135   \n",
       "3           -0.001038               2.717146            -0.283135   \n",
       "4           -0.001038               2.717146            -0.283135   \n",
       "...               ...                    ...                  ...   \n",
       "4835         2.860677              -0.214837            -0.995904   \n",
       "4836         2.860677              -0.214837            -0.995904   \n",
       "4837         2.860677              -0.214837            -0.995904   \n",
       "4838         2.860677              -0.214837            -0.995904   \n",
       "4839         2.860677              -0.214837            -0.995904   \n",
       "\n",
       "      female_smokers  male_smokers  handwashing_facilities  \\\n",
       "0          -1.173715      0.874789                1.504596   \n",
       "1          -1.173715      0.874789                1.504596   \n",
       "2          -1.173715      0.874789                1.504596   \n",
       "3          -1.173715      0.874789                1.504596   \n",
       "4          -1.173715      0.874789                1.504596   \n",
       "...              ...           ...                     ...   \n",
       "4835       -0.861571     -0.756262               -1.824236   \n",
       "4836       -0.861571     -0.756262               -1.824236   \n",
       "4837       -0.861571     -0.756262               -1.824236   \n",
       "4838       -0.861571     -0.756262               -1.824236   \n",
       "4839       -0.861571     -0.756262               -1.824236   \n",
       "\n",
       "      hospital_beds_per_thousand  life_expectancy  human_development_index  \\\n",
       "0                       0.727728        -0.087214                 0.334580   \n",
       "1                       0.727728        -0.087214                 0.334580   \n",
       "2                       0.727728        -0.087214                 0.334580   \n",
       "3                       0.727728        -0.087214                 0.334580   \n",
       "4                       0.727728        -0.087214                 0.334580   \n",
       "...                          ...              ...                      ...   \n",
       "4835                   -0.485810        -1.336883                -0.840573   \n",
       "4836                   -0.485810        -1.336883                -0.840573   \n",
       "4837                   -0.485810        -1.336883                -0.840573   \n",
       "4838                   -0.485810        -1.336883                -0.840573   \n",
       "4839                   -0.485810        -1.336883                -0.840573   \n",
       "\n",
       "      country_id  \n",
       "0             12  \n",
       "1             12  \n",
       "2             12  \n",
       "3             12  \n",
       "4             12  \n",
       "...          ...  \n",
       "4835         212  \n",
       "4836         212  \n",
       "4837         212  \n",
       "4838         212  \n",
       "4839         212  \n",
       "\n",
       "[4840 rows x 47 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## downloading cvs of created data\n",
    "import pandas as pd\n",
    "\n",
    "train_df = pd.read_csv('train.csv', sep=',')\n",
    "val_df = pd.read_csv('dev.csv', sep=',')\n",
    "test_df = pd.read_csv('test.csv', sep=',')\n",
    "\n",
    "display(train_df)\n",
    "display(val_df)\n",
    "display(test_df)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Setting up Window Generator"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "class WindowGenerator():\n",
    "    def __init__(self, input_width, label_width, shift,\n",
    "               train_df=train_df, val_df=val_df, test_df=test_df,\n",
    "               label_columns=None):\n",
    "        # Store the raw data.\n",
    "        self.train_df = train_df\n",
    "        self.val_df = val_df\n",
    "        self.test_df = test_df\n",
    "\n",
    "        # Work out the label column indices.\n",
    "        self.label_columns = label_columns\n",
    "        if label_columns is not None:\n",
    "            self.label_columns_indices = {name: i for i, name in\n",
    "                                        enumerate(label_columns)}\n",
    "        self.column_indices = {name: i for i, name in\n",
    "                               enumerate(train_df.columns)}\n",
    "\n",
    "        # Work out the window parameters.\n",
    "        self.input_width = input_width\n",
    "        self.label_width = label_width\n",
    "        self.shift = shift\n",
    "\n",
    "        self.total_window_size = input_width + shift\n",
    "\n",
    "        self.input_slice = slice(0, input_width)\n",
    "        self.input_indices = np.arange(self.total_window_size)[self.input_slice]\n",
    "\n",
    "        self.label_start = self.total_window_size - self.label_width\n",
    "        self.labels_slice = slice(self.label_start, None)\n",
    "        self.label_indices = np.arange(self.total_window_size)[self.labels_slice]\n",
    "\n",
    "    def __repr__(self):\n",
    "        return '\\n'.join([\n",
    "            f'Total window size: {self.total_window_size}',\n",
    "            f'Input indices: {self.input_indices}',\n",
    "            f'Label indices: {self.label_indices}',\n",
    "            f'Label column name(s): {self.label_columns}'])\n",
    "    \n",
    "    def split_window(self, features):\n",
    "        inputs = features[:, self.input_slice, :]\n",
    "        labels = features[:, self.labels_slice, :]\n",
    "        if self.label_columns is not None:\n",
    "            labels = tf.stack(\n",
    "                [labels[:, :, self.column_indices[name]] for name in self.label_columns],\n",
    "                axis=-1)\n",
    "\n",
    "        # Slicing doesn't preserve static shape information, so set the shapes\n",
    "        # manually. This way the `tf.data.Datasets` are easier to inspect.\n",
    "        inputs.set_shape([None, self.input_width, None])\n",
    "        labels.set_shape([None, self.label_width, None])\n",
    "\n",
    "        return inputs, labels\n",
    "    #WindowGenerator.split_window = split_window\n",
    "    \n",
    "    def plot(self, model=None, plot_col='new_cases', max_subplots=3):\n",
    "        inputs, labels = self.example\n",
    "        plt.figure(figsize=(12, 8))\n",
    "        plot_col_index = self.column_indices[plot_col]\n",
    "        max_n = min(max_subplots, len(inputs))\n",
    "        for n in range(max_n):\n",
    "            plt.subplot(3, 1, n+1)\n",
    "            plt.ylabel(f'{plot_col} [normed]')\n",
    "            plt.plot(self.input_indices, inputs[n, :, plot_col_index],\n",
    "                     label='Inputs', marker='.', zorder=-10)\n",
    "\n",
    "            if self.label_columns:\n",
    "                label_col_index = self.label_columns_indices.get(plot_col, None)\n",
    "            else:\n",
    "                label_col_index = plot_col_index\n",
    "\n",
    "            if label_col_index is None:\n",
    "                continue\n",
    "\n",
    "            plt.scatter(self.label_indices, labels[n, :, label_col_index],\n",
    "                        edgecolors='k', label='Labels', c='#2ca02c', s=64)\n",
    "            if model is not None:\n",
    "                predictions = model(inputs)\n",
    "                plt.scatter(self.label_indices, predictions[n, :, label_col_index],\n",
    "                          marker='X', edgecolors='k', label='Predictions',\n",
    "                          c='#ff7f0e', s=64)\n",
    "\n",
    "            if n == 0:\n",
    "                plt.legend()\n",
    "\n",
    "        plt.xlabel('Time [h]')\n",
    "        \n",
    "    def make_dataset(self, data):\n",
    "        data = np.array(data, dtype=np.float32)\n",
    "        ds = tf.keras.preprocessing.timeseries_dataset_from_array(\n",
    "            data=data,\n",
    "            targets=None,\n",
    "            sequence_length=self.total_window_size,\n",
    "            sequence_stride=1,\n",
    "            shuffle=True,\n",
    "            batch_size=32,)\n",
    "\n",
    "        ds = ds.map(self.split_window)\n",
    "\n",
    "        return ds\n",
    "    \n",
    "    @property\n",
    "    def train(self):\n",
    "        ds = []\n",
    "        for df in self.train_df.groupby('country_id'):\n",
    "            ds.append(self.make_dataset(self.train_df))\n",
    "        for i in range(len(ds)):\n",
    "            if i != 0:\n",
    "                ds[0] = ds[0].concatenate(ds[i])\n",
    "        return ds[0]\n",
    "\n",
    "    @property\n",
    "    def val(self):\n",
    "        ds = []\n",
    "        for df in self.val_df.groupby('country_id'):\n",
    "            ds.append(self.make_dataset(self.val_df))\n",
    "        for i in range(len(ds)):\n",
    "            if i != 0:\n",
    "                ds[0] = ds[0].concatenate(ds[i])\n",
    "        return ds[0]\n",
    "\n",
    "    @property\n",
    "    def test(self):\n",
    "        ds = []\n",
    "        for df in self.test_df.groupby('country_id'):\n",
    "            ds.append(self.make_dataset(self.test_df))\n",
    "        for i in range(len(ds)):\n",
    "            if i != 0:\n",
    "                ds[0] = ds[0].concatenate(ds[i])\n",
    "        return ds[0]\n",
    "\n",
    "    @property\n",
    "    def example(self):\n",
    "        \"\"\"Get and cache an example batch of `inputs, labels` for plotting.\"\"\"\n",
    "        result = getattr(self, '_example', None)\n",
    "        if result is None:\n",
    "            # No example batch was found, so get one from the `.train` dataset\n",
    "            result = next(iter(self.train))\n",
    "            # And cache it for next time\n",
    "            self._example = result\n",
    "        return result"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<ConcatenateDataset shapes: ((None, 6, 47), (None, 1, 1)), types: (tf.float32, tf.float32)>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+9Fm3fS/nPjWZWDPeu75vic2ZnZO2nWvGppCZncvTl3fnmFbF/xWeSGHl5Tn/+HQBz3+3guPbJPLEZd3K5Hxid2f++p1MWrSJbxZtInX1NnLznGoVyuXPtW9Th+NaJ+obthKSk5vHjNXbf/6gNX99fm/yulXL//xBq2/L2hp9jwIq5n+p2KfZHKABUAXYGnpeObRMJOrszMxm8Ojp7NmXy9vDjy7Rk986JVXP73QzejqDRk/jb+d24OIe6pYkwdublcstb8zk83kbuapPE+45s12ZHYU2M9o3qEb7BtW4/viW7NibzfdLQqP2izfx0Zz1AHRsWI3+bRJDo/Y1IqbDT2mQvjOTSYvzP0x9t2QTOzNziI0xujepwe2nteH4NnVoW0+j71K2hFvMPwDMDI3QAxwHjAxznyIlLisnj+GvpLJsUwZjr+5Jm3olP9WlYfUKvDW8D9e/OoM/vfMTq7bs4Q+ntFGnGwlM+q5MhoxNYc7aHdxzZjsG922qIukA1SqU44xO9TmjU33y8vaP2uePEj85cSlPfL2U6hXLcWyrRI5vk0i/1onUrqxR+8ORk5vHrDXbfz6HYV7oyqB1qpTntA716N+mDn1b1qZaBY2+S9kVdjcbM6sH9Ao9/dHdN4SdKkpomk3p4O784a05vDMjjYcv7MwF3ZN+e6NilJ2bxz3j5zFu2mrO6FSfRy7sTEI5dbqRkrV44y4Gj57O1t1Z/PuSLpzSvqDLgsihbN+TxXdL8jvkfLN4E5sz9mG2f9Q+fwpI56TqGrUvwKZd+/hmcf7c9+8WHzD63rhG/pV82yTSrn5VfbAsRTTN5pdKbJqN5f9LOglo7u73m1ljM+vp7tPC2a9ISfr3hCW8MyONW05qFXghD1AuNoa/n9uBZrUr8vdPFrJ++16evyqZWhrRkxLy/ZLNDH8llYT4WN4c1oeOSdHfsaakVa8Yz1mdG3BW5wbk5Tnz1u38eTrOf75ewuMTllCjYjn6tc4vTvu1Siyz/8Zz85xZa7b93Bp07tr80ffEKuU5tX3+6PsxrTT6LsWrcuXKZGRkFGrdkSNHUrlyZf7whz8Uy/4PV7jTbJ4C8oATgPuBXcA7QI8w9ytSIt5KWcNjXy3hgu5J3Hxiq6Dj/MzMGNqvBY1qVOSWN2Zx7lNTGDWoBy3rVA46mpRyr09bzZ/fn0uLxMqMGtyDhtVLf8ea4hYTY3RMqkbHpGrceGIrtu/J4tvQXPtvFm1i/Kx1mOWfO9O/dX7P804Nq5XqKXabM/bxzaJNTFqcP/d9+55sYgy6Na7BH09tw3Gt80ffS/OfgRyZjIwMHn7kYZ5+/mk2rd9EYv1Ehg8Zzh9+/wcqVy6bvyPD7WYzw927mdlMd+8aWjbb3TsXWcIIpmk20e37JZsZNHoavZvXYvTgHhF7GfeZq7cx5KUUsnLyePbKZPq0qBV0JCmF8vKch75YxNOTlnFsq9o8dXk3dQEpAXl5ztx1O34elZ61ZjvuULNSPP1a1eb4tnU4tlUiNSvFBx01LLl5zuy07UxamP/txJy0HQDUrlye41oncnzbRI5tmUi1ivo7VxYVdppNRkYGRx93NBvLb6TKaVVISEogMy2TnZ/upF5WPaZ8M+WIC/qCRs4//PBD/vrXv5KVlUWtWrV49dVXqVu3LiNHjmTZsmWsXbuWNWvWcPvttzNkyBAAHnroId5880327dvHueeey3333fc/+1+/fj0XX3wxO3fuJCcnh6effppjjz22UH8mxdXNJtvMYgEPHSSR/JF6kYi2cMNOhr+SSss6lXnqim4RW8gDdG1cg/dG9GXwmOlcNepH/nFep4iYDiSlR2Z2Lr9/czYf/7Sey3o15r6z20f0v4nSJCbG6JRUnU5J1bnpxFZs253Ft0s2/TzX/v3QqH3npOo/t1vsGCWj9lsy9vHtkk1MXJg/+r4tNPretXENfn9ya/q3qUP7Bhp9l8J7+JGH2Vh+I4lDE38+Z6JCkwokDEtgw7MbePiRhxl578giO94xxxzD1KlTMTNeeOEFHnzwQR555BEA5syZw9SpU9m9ezddu3bljDPOYO7cuSxZsoRp06bh7px99tl8++239OvX7+d9vvbaa5x66qncdddd5ObmsmfPnrBzhlvMPw68B9Qxs78BFwB/DjuVSDHasCOTwaOnU7F8LKMG9aBqFIw+NqpZkXeGH83wV1L5w1uzWb1lN7ee3FongEnYNmfsY8hLKcxas53/O70tQ45trr9XAapRKZ4BXRoyoEtD8vKcn9bu+LmTy2MTFvPoV4upVSme41rnX4m2X6tEakTIqH1unjMnbTuTQlddnbN2B+5Qu3I8x7etQ/82dejXqjbVK0ZGXok+Tz//NFWG/rL1qJlR9XdVeeb5Z4q0mE9LS+Piiy9m/fr1ZGVl0axZs59fGzBgABUqVKBChQocf/zxTJs2je+//54vvviCrl27AvnfJCxZsuR/ivkePXpw9dVXk52dzTnnnEOXLl3CzhlWMe/ur5pZKnAiYMA57r4g7FQixSRjXw6Dx0xn595s3ryuDw2iaD5wtQrlGDO4J39+/yce/3opq7bu4cELOlE+Tp1u5MgsTd/F4DHTSd+5j6cu68bvOtYPOpIcICbG6NyoOp0bVeeWk1qzJWNfqENOOhMXpfPuzLXEGHRpVP3nDjkdGpTsqP3W3Vl8uzi/eP9mcf7ouxl0bVSdW09qHUgmKb02rd9EYlJiga8lNExgxfoVRXq8G2+8kdtuu42zzz6bSZMmMXLkyJ9fK+gDhbtz5513MmzYsEPus1+/fnz77bd8/PHHXHnllfzxj3/kqquuCitnUVzCbyPwXWhfFcysm7vPKIL9ihSp7Nw8Rrw6g8UbdzFqUA/aN4i+Dh3xcTH88/xONKlViYc+X8S67Xt59srkqJ9PKyVvyrLNXPdyKvFxMbw+tDddG9cIOpL8hlqVy3NO14ac07XhL0bBH/1qMf/6cjG1K8fTr3Uix7epQ79WRT8H/eBvC2an5c/xr1UpnuPb1Im4bwukdEmsn0hmWiYVmvxyIC5zbSaJ9Qsu9I/Ujh07aNiwIQBjx479n9fGjx/PnXfeye7du5k0aRIPPPAAFSpU4O677+byyy+ncuXKrF27lnLlylGnTp2ft1u1ahUNGzZkyJAh7N69mxkzZgRbzJvZX4BBwDJC8+ZD9yeElUqkiLk7d78/l28Xb+Kf53fkuNZF+w++JJkZ1x/fksY1K/L7t2Zz3lOTGT24J81qVwo6mkSJt1LWcOe7P9GsdiVGDepBo5oVg44khyk2xujauAZdG9fg1pNb/8/89K8XpvPujLU/z08/vk0i/dvUOeLuMAfO4/928Sa27M76eR7/zSe24vg2daJmHr9Et+FDhvPUp0+RMCzhf0bG3Z2dn+5kxLUjjnjfe/bsISnpv+ej3XbbbYwcOZILL7yQhg0b0rt3b1as+O/If8+ePTnjjDNYvXo1d999Nw0aNKBBgwYsWLCAPn36APknvb7yyiv/U8xPmjSJhx56iHLlylG5cmVeeumlI868X7jdbBYBHd09K+wkUUjdbKLHkxOX8tDni7jh+Jb84dQ2QccpMqmrtjLkpVTy3HnuymR6NqsZdCSJYO7Ov75czBNfL6Vvy1o8dXl39e4uhfL7tm/nm0W/7BzTP3TRpV/rHHNwh53Za7aTd0CHnf5t6tCvdfR32JHIcbjdbDbEb6Dq76qS0DCBzLVF080m0hxON5twi/l3gOHunn7EO4liKuajw/sz1+b3au/akH9d1LnUndy3astuBo+ZTtrWvTx4QSfO6dow6EgSgTKzc/nTO3MYP2sdFyc34q/ndlDHmjJi0659+fPaF+ePrO/Ym01sjNGtcf5c+zpVyrNqyx7KxcWwastuvl28ic0ZWf/T+75/m0Q66Yq1UkwO5wqw+/vMP/PCMz/3mb/u2utKXZ/5kizmk4HxwFxg3/7l7n72Ee80iqiYj3w/LNvCVaN+pHuTGrx0dS/i40pn8bJ9TxbDXk7lxxVbufWk1tx0YstS96FFjtzW3VkMezmF6Su3cftpbRh+XAv9/SijcnLz8vu9H3S11f0ql4/lxKPqlvmr0krJOpxivqwoyT7zY4F/Aj+h/vISYZZs3MWwl1NoUqsSz16RXGoLeci/dPzL1/Tijnfn8OhXi1m1dTcPnNepVP/MUjjLN2Vw9ZjprNuRyX8u68qZnRoEHUkCFBcbQ/cmNenepCa/P6UND362kKcnLcOBGIPrjmvBDSdEztWwReS3hVvMb3b3x4skiUgRSt+VyaDR0ylfLpYxg3uUiasKxsfF8MiFnWlaqxL/+nIx67bv5Zkruquncxn24/ItDHsllRgzxg3pRfcmOqdC/teJR9Vl1OQVZOfkUS4uhj4tagcdScood9c3hiGHO2sm3GI+1cz+AXzA/06zUWtKCczufTlcPWY6W3dn8eawPiTVKDudOsyMm05sReOaFbn97Tmc9/QURg/qQZNa6nRT1rw3M43b355Do5oV9XdADql7kxq8em1vpi7fQu/mtejeRC1KpeQlJCSwZcsWatWqVeYLendny5YtJCQkFHqbcOfMTyw4h5eJ1pSaMx95cnLzGPpyKpMWpfPCwGROaFs36EiBOXBU9vmrumtUtoxwd/49YQmPfbWE3s1r8uwVyWXimykRiV7Z2dmkpaWRmZkZdJSIkJCQQFJSEuXK/e//3UV+AqyZxQI3ufujR7SDUkDFfGRxd+4eP5dXpq7mr+d04IreTYKOFLgD50s/cmFnzuqs+dKl2b6cXO585yfenbmW87sl8Y/zOuq8CRGRUuJQxfwR/y/v7rlAmehaI9HhuW+X88rU1Qw7rrkK+ZDmiZV5d0RfOidV48ZxM3ly4tLDnosn0WH7niyuenEa785cy20nt+bhC3UCtIhIWRDu//RTzOw/ZnasmXXbfws3lJnVNLMvzWxJ6L7ASXxmNsrM0s1s7kHLu5jZVDObZWYpZtYztPxkM0s1s59C9yeEllc0s4/NbKGZzTOzB8L9GaRkfTh7Hf/4dCFndqrPn05tG3SciFKzUjyvXNuLAV0a8NDni/jTO3PIzlXzqdJk1ZbdnPfUFGau3s5jF3fhphNblfl5pyIiZUVEzpk3sweBre7+gJndAdRw9z8VsF4/IAN4yd07HLD8C+BRd//UzE4Hbnf3/mbWFdjo7uvMrAPwubs3NLOKQC93n2hm8cAE4O/u/umv5dQ0m8gwfeVWLn/+Rzo3qsbL1/QioVxs0JEikrvz6FdLeHzCEl39sxRJWbmVoS/rKsAiIqVdsfSZd/fjw9n+VwwA+ocejwUmAb8o5t39WzNrWlA0oGrocTVgXWj9mQesMw9IMLPy7r4HmBhaJ8vMZgBJYf8UUuyWbcpgyEspJNWowHNXJquQ/xVmxm0nt6ZxzYrc+e4czg91umlUs+x0+yltPpy9jt+/NZsG1RIYPbgnzWqrY42ISFkT1jQbM6tmZv8KTWVJMbNHzKxaEeSq6+7rAUL3dQ5z+1uAh8xsDfAwcGcB65wPzHT3fQcuNLPqwFnkj85LBNucsY9Bo6cRa8aYwT2pUUn91Avjgu5JjL26J+k7Mzn3qcnMXL0t6EhymNydJycu5cZxM+mcVI33RvRVIS8iUkaFO2d+FLALuCh02wmMLsyGZvaVmc0t4DYgzEwAw4Fb3b0RcCvw4kHHbk/+lWuHHbQ8DhgHPO7uyw+Re+j+Dy+bNm0qgqhyJPZm5XLN2BQ27drHi4N60LiWRpcPx9EtavPuiL5UiI/lkuem8ulP64OOJIWUlZPH7W/P4aHPF3FOlwa8cm0vfZAVESnDwp0zP8vdu/zWsiPY7yKgv7uvN7P6wCR3b3OIdZsCHx00Z34HUN3d3fLPAtvh7lVDryUBXwOD3X3yQfsaBWS4+02Fyak588HIzXOGv5LKlws28uwV3Tmlfb2gI0WtLRn7GPJSCjPXbOeO09oytF9znTgZwXbszWb4K6lMWbaFm05sxa0n6URXEZGyoshbU4bsNbNjDjhIX2BvmPuE/CvKDgw9HgiMP8zt1wHHhR6fACwJ5asOfAzcWUAh/1fy59ffckSJpcT85aP5fDF/I/ee2U6FfJhqVS7Pa0N6c3rH+vzj04X833tz1ekmQq3Zuofzn57C9JVbeeTCztx2cmsV8iIiEt4JsMB1wEuhefIGbAUGhRsKeAB408yuAVYDFwKYWQPgBXc/PfR8HPknytY2szTgXnd/ERgC/Ds0bSYTGBra7w1AS+BuM7s7tOwUIB64C1gIzAj9gvyPu79QBD+LFKEXv1/BmCkrueaYZgzq2yzoOKVCQrlYnrikK01qVuSpSctI27aHJy/vRtUEdbqJFDNXb+PasSlk5+bx0tW96NOiVtCRREQkQoQ1zebnnZhVBXD3nWHvLIpomk3J+vSn9Yx4bQanta/Hk5d1IyZGo5JF7Y3pq7nrvbm0SKzMqME9aFi9QtCRyrxPf1rPLW/Mom7VBEYN6kHLOpWDjiQiIgEoltaUZlae/K4wTYG4/V/5uvv94exX5GCpq7Zxyxuz6NqoOo9e3EWFfDG5uEdjGlavyPBXUjnnycm8ODCZTknVg45VJrk7z327nH98upBujavz/FXJ1KpcPuhYIiISYcKdMz+e/J7wOcDuA24iRWbl5t0MeSmFetUSeP4q9ZIvbse0qs07I44mPjaGi579gS/mbQg6UpmTnZvH/7039+erGr82pLcKeRERKVC4c+aT3P20IkkiUoCtu7MYNHoa7s6YwT1V0JSQ1nWr8P71fbn2pRSGvZLKn89ox9V9m+qEyxKwMzOb61+dwXdLNnP98S34/clt9E2UiIgcUrgj81PMrGORJBE5SGZ2LkNeSmHdjkxeGJisi+KUsMQq5Xl9SG9ObVePv3w0n3s/mEeOOt0Uq7Rte7jg6Sn8sGwLD57fiT+e2laFvIiI/KpwR+aPAQaZ2QpgH/kdbdzdO4WdTMq0vDzntjdnMWP1Np68rBvdm9QMOlKZVCE+lqcu78YDny3kuW+Xs2brHp64rBuVy4f7X4ccbE7adq4Zm0Jmdi5jr+5J35a1g44kIiJRINzfyL8rkhQiB/nHpwv45KcN/PmMozi9Y/2g45RpMTHG/51+FI1rVuTeD+Zx4TM/MGpQMvWrqdNNUfl83gZufn0mtSuX57Vre9GqbpWgI4mISJQIa5qNu68q6FZU4aRseumHlTz/3QoG9mnCNceol3ykuKJ3E0YN6sGarXs458nJzF27I+hIUc/deeG75Vz3Sipt6lXlvRF9VciLiMhhOaJi3sxmFMU6Igf7cv5GRn4wj5OOqss9Z7XXCZcR5rjWibw9vA+xZlz07A9MWLAx6EhRKyc3j3vGz+OvHy/gtPb1eH1IbxKr6ARvERE5PEd00Sgz2wss+bVVgGru3vhIg0UDXTSqaM1es52Ln/uBNnWr8PrQPlSIVwvKSJW+M5NrxqYwb90O7j2rPQOPbhp0pKiSsS+HG16bwaRFmxjWrzl/Ok0nuoqIyK8r6otGtS3EOrlHuG8pg9Zs3cM1Y6eTWKU8LwzsoUI+wtWpmsAbw3pz07hZ3PvBPFZu2c2fz2hHrArS37R+x14Gj57OkvQM/nZuBy7v1SToSCIiEsWOqJjXvHgpStv3ZDFw9DSyc53XB/XUVIMoUTE+jmev7M7fPl7AqMkrWLN1D/++pCuV1OnmkOau3cE1Y6eze18uowb14LjWiUFHEhGRKBdun3mRsOzLyWXoy6mkbd3L81cl07JO5aAjyWGIjTHuOasd953dnq8XpnPxcz+wcWdm0LEi0oQFG7no2R+INePt4X1UyIuISJFQMS+Byctz/vDWHKat2MrDF3WmZzP1ko9WA49uygsDk1m+aTfnPDmZBet3Bh0pooyZvIIhL6XQIrEy71/fl7b1qgYdSURESomwinkzq2RmMaHHrc3sbDMrVzTRpLR76ItFfDh7HX86rS1nd24QdBwJ0wlt6/LWdX3Ic+eCp6cwaVF60JECl5vnjPxgHiM/nM+JR9XljWG9qVM1IehYIiJSioQ7Mv8tkGBmDYEJwGBgTLihpPR77cfVPD1pGZf1asx1xzUPOo4UkfYNqvH+9X1pXKsS14xN4ZWpZff0mt37chj2cgpjpqzkmmOa8cwV3akYr/MJRESkaIVbzJu77wHOA55w93OBduHHktJs4sJ07h4/l+PbJHL/2eolX9rUr1aBt67rQ79Wtfnz+3P528fzycs7/Ba40Wzjzkwufu4Hvl6Yzv0D2nP3mer0IyIixSPsYt7M+gCXAx+HlmnoSQ5p7todXP/aDI6qX4X/XNaNuFidtlEaVS4fx/NXJXNVnyY8/90Khr+ayt6sstGtdsH6nZzz5GRWbNrNiwN7cFWfpkFHEhGRUizcSuoW4E7gPXefZ2bNgYlhp5JSKW3bHgaPmU6NivGMGthDLQxLubjYGO47uz33nNmOL+Zv5JLnfiB9V+nudDNpUToXPD0Fd3jzuj4c37ZO0JFERKSUO6IrwP5iJ2aV3H13EeSJKroCbOHt2JvNBU9PYcPOTN4ZfjSt61YJOpKUoC/mbeDm12dRs1I8owf3KJXv/8tTVzHyg3m0qVuFUYN6UK+aTnQVEZGic6grwIbbzaaPmc0HFoSedzazp8LZp5Q+WTl5XPdyKiu37ObZK7uXykJOft0p7evx5rA+ZOXmcf5TU/h+yeagIxWZ3Dznrx/N5+7353Jc60TevK6PCnkRESkx4U6zeQw4FdgC4O6zgX5h7lNKEXfnjnfm8MPyLfzz/E4c3aJ20JEkIB2T8jvdNKhegUGjp/H6tNVBRwrb3qxcRryaygvfr2BgnyY8d2V3Kmv6mIiIlKCwzz509zUHLQr7LDczq2lmX5rZktB9jUOsN8rM0s1s7kHLu5jZVDObZWYpZtYztPxkM0s1s59C9ycUsM8PDt6fHLlHv1zMuzPX8vuTW3Net6Sg40jAGlavwNvD+9CnRS3uePcn/vnZwqjtdJO+K5NLnvuBL+Zv5N6z2nHfgA46oVtEREpcuL951pjZ0YCbWbyZ/YHQlJsw3QFMcPdW5Pevv+MQ640BTitg+YPAfe7eBbgn9BxgM3CWu3cEBgIvH7iRmZ0HZIQbXvK9OX0Nj3+9lIuTG3HDCS2DjiMRokpCOUYN6sFlvRrz9KRl3DhuJpnZ0dXpZtGGXZz75BQWb8zguSuTGdy3WdCRRESkjAq3mL8OuB5oCKQBXULPwzUAGBt6PBY4p6CV3P1bYGtBLwH7r5deDVgXWn+mu68LLZ9H/gWvygOYWWXgNuCvRZC/zPt28Sb+772fOLZVbf56bgf1kpf/US42hr+d04H/O70tn8xdz6XPT2Vzxr6gYxXKd0s2ccHTU8jOzePNYX04uV3doCOJiEgZFtbkTnffTH6P+aJW193Xh46x3swOt7/bLcDnZvYw+R9Yji5gnfOBme6+v4L4C/AIsOfIIst+89ftZMSrM2hZpzJPXd6Ncpp6IAUwM4b2a0GjGhW55Y1ZnPvUZEYP6kHLOpF7gvS4aav58/tzaVWnMqMG9aBB9QpBRxIRkTIu3G42D5pZVTMrZ2YTzGyzmV1RyG2/MrO5BdwGhJMpZDhwq7s3Am4FXjzo2O2BfwLDQs+7AC3d/b1C5B4amoefsmnTpiKIWrqs37GXq8dMp3L5OEYP7kGVhHJBR5II97uO9Xl9aG/2ZuVy3lNTmLIs8jrd5OU5D3y6kDvf/Ym+LWvz1nV9VMiLiEhECHfI9BR33wmcSf40m9bAHwuzobuf5O4dCriNBzaaWX2A0H36YeYaCLwbevwW0HP/C2aWBLwHXOXuy0KL+wDdzWwl8D3Q2swmHSL3c+6e7O7JiYmJhxmrdNuVmc3g0dPJ2JfD6ME9qF9NxY4UTtfGNXhvRF/qVk3gqhen8VbKwefVByczO5cbx83kmW+WcVmvxowamKwPqSIiEjHCLeb3/0Y7HRjn7gXNXz8SH5BfkBO6H3+Y268Djgs9PgFYAmBm1YGPgTvdffL+ld39aXdv4O5NgWOAxe7e/0jDl0XZuXmMeHUGS9MzePqKbhxVv+pvbyRygEY1K/L28KPp1bwmf3x7Do98sYiiuKhdODZn7OPS56fyydz13HX6UfztHHWsERGRyBLub6UPzWwhkAxMMLNEoCiu1/4AcLKZLQFODj3HzBqY2Sf7VzKzccAPQBszSzOza0IvDQEeMbPZwN+BoaHlNwAtgbtDbStnHcF8fDmIu3PXez/x3ZLN/P28jhzbSt9YyJGpVqEcYwb35OLkRjzx9VJufn1WYJ1ulqbv4tynJrNg/U6evrw7Q/o114ncIiIScSzcka9QD/id7p5rZhWBqu6+oUjSRbjk5GRPSUkJOkbgHp+whH99uZibTmzFbSe3DjqOlALuztPfLOPBzxaR3KQGz12VTM1K8SV2/ClLNzPslVTKx8XwwsAedGlUvcSOLSIiUhAzS3X35IOXF8X3xQ2B883sKuAC4JQi2KdEiXdnpPGvLxdzXreG3HpSq6DjSClhZozo35L/XNaVOWt3cO5Tk1m+qWQuAfFmyhquGjWNelUTeG9EXxXyIiIS0cLtZnMv8ETodjz5F2c6uwhySRSYsnQzf3pnDke3qMUD53XSFAQpcmd2asC4Ib3YlZnDeU9P4cflW4rtWO7Ow58v4va359C7eS3eHn40jWpWLLbjiYiIFIVwR+YvAE4ENrj7YKAzUD7sVBLxFm/cxbBXUmlWuxJPX9Gd+DidFCjFo3uTmrw34mhqVornihd/5L2ZaUV+jMzsXG5+fRb/mZh/xeLRg3tQrYI61oiISOQLtwLb6+55QI6ZVSW/hWTz8GNJJEvfmcng0dOpUC6W0YN7quiRYtekViXeG96X7k1qcOsbs3nsq8VF1ulm6+4srnjhRz6YvY4/ndaWB87vqAudiYhI1Aj3N1ZKqN3j80AqMAOYFm4oiVy79+UweMx0tu3JYtSgHjTUhXOkhFSrWI6Xru7F+d2SeOyrJdz25mz25YTX6Wb5pgzOfWoyc9bu4MnLujG8fwtNFxMRkagSF87G7j4i9PAZM/uM/E42c8KPJZEoJzePG16bwcINu3hhYDIdGlYLOpKUMfFxMTx8YSea1KrIv75czNrte3nuyu5Ur3j4nW5+XL6FoS+nEhdjjBvSm+5NahRDYhERkeIV7gmw55pZNQB3XwmsNrNziiCXRBh3554P5jFx0Sb+MqADx7dRe34Jhplx04mteOziLsxavZ3znprCys27D2sf781M44oXf6R25XjeG9FXhbyIiEStcKfZ3OvuO/Y/cfftwL1h7lMi0DPfLOe1H1czon8LLuvVOOg4IpzTtSGvXNuLrXuyOO/pKaSs/O0LULs7j321mFvfmE1yk5q8O7wvjWupY42IiESvcIv5grYPa+qORJ7xs9byz88WcnbnBvzhlDZBxxH5Wc9mNXlvRF+qJsRxWegk1kPZl5PLbW/O5rGvlnBB9yTGXt2TahV18raIiES3ojgB9l9m1sLMmpvZo+SfCCulxI/Lt/DHt+bQq1lNHrqwEzExOjlQIkuz2pV4b0RfOidV46ZxM/nP10t+0elm+54srnxxGu/NXMsfTmnNQxd0UjtVEREplIyMDEbeN5K6SXWJiY2hblJdRt43koyMkrmY4W+xcNq7mVkl4G7gpNCiL4C/ufvhTWCNUsnJyZ6SkhJ0jGKzND2D85+eQu3K8bw7vK9GMSWi7cvJ5fa35zB+1jou6J7E38/tSHxcDCs37+bqMdNJ27aXhy7sxIAuDYOOKiIiUSIjI4OjjzuajeU3UuW0KiQkJZCZlsnOT3dSL6seU76ZQuXKlUski5mlunvywcvD7WazG7jjVw76hLvfGM4xJBibdu1j0OhplIs1xgzWdASJfOXjYnns4i40qVWJxycsYeH6nbSpV4Uv5m0gLjaGV4f0okfTmkHHFBGRKPLwIw+zsfxGEocm/ty6uEKTCiQMS2DDsxt4+JGHGXnvyEAzhjUy/5s7N5vh7t2K7QABK60j83uycrj0uaks3pjBG8N60ympetCRRA7LI18s4omvlwJgwBOXdeXMTg2CDSUiIlGnblJdqgytQoUmv7yuzt5Ve8l4PoMNazaUSJZDjcxr0qj8j9w856ZxM/lp7Q6euLSrCnmJSgnlYtl/ekeMwaote4INJCIiUWnT+k0kJCUU+FpCwwQ2rd9Uwol+ScW8/Mzduf/DeXy1IJ2RZ7fnpHZ1g44kckR6N69FfFwMsQbl4mLo3bxW0JFERCQKJdZPJDMts8DXMtdmklg/sYQT/VJxF/NqfRJFXvx+BWN/WMXQfs25qk/ToOOIHLHuTWrw6rW9ue2UNrx6ra7uKiIiR2b4kOHs/HTnL7qkuTs7P93JdddeF1Cy/wq3m01zd1/+K68PcvcxR3yACFea5sx/8tN6Rrw6gzM61ueJS7uqBaWIiIiUefu72WyI30DV31UloWECmWsjq5tNuCPzY8xsmZm9bmYjzKzjgS+W5kK+NEldtZVb3phF9yY1eOSizirkRURERIDKlSsz5ZspjDhtBBnPZ7Bg2AIyns9gxGkjSrSQ/zVhd7Mxs3igB9AfGAZUdvcy0f+tNIzMr9i8m/Oemkz1ivG8M/xoalaKDzqSiIiIiBykWPrMm9kxwLGhW3XgI+C7cPYpJWdLRn4veTNjzOAeKuRFREREokxYxTzwDZAC/AP4xN2zwo8kJSEzO5drX0phw45Mxg3tTZNalYKOJCIiIiKHKdw587WA+4E+wGdm9pWZ/SXcUGZW08y+NLMlofsCW1GY2SgzSzezuQct72JmU81slpmlmFnP0PKTzSzVzH4K3Z9wwDbxZvacmS02s4Vmdn64P0ekys1zbnl9FrPWbOffl3SlW2N1+hARERGJRmEV8+6+HVgOrADWAy2AfuHH4g5ggru3AiaEnhdkDHBaAcsfBO5z9y7APaHnAJuBs9y9IzAQePmAbe4C0t29NdCO/G8dSqW/f7KAz+Zt4M9ntOO0DvWCjiMiIiIiRyjcOfPLgEXA98AzwOAimmozgPwTagHGApOAPx28krt/a2ZNC9jegaqhx9WAdaH1Zx6wzjwgwczKu/s+4GqgbWi9PPIL/1Jn9OQVvPj9Cgb3bco1xzQLOo6IiIiIhCHcOfOtQoVvUavr7usB3H29mdU5zO1vAT43s4fJ//bh6ALWOR+Y6e77zKx6aNlfzKw/sAy4wd03HkH2iPX5vA3c/9F8Tm1flz+f0S7oOCIiIiISpnDnzLc0swn756ybWScz+3NhNgzNr59bwG1AmJkAhgO3unsj4FbgxYOO3R74J/mtNCH/Q00SMNnduwE/AA8fIvfQ0Dz8lE2bNhVB1JIxa812bn59Jp2TqvPYxV2JVS95ERERkagX7hVgvwH+CDzr7l1Dy+a6e4ewQpktAvqHRuXrA5Pcvc0h1m0KfHTgMc1sB1Dd3d3MDNjh7lVDryUBX5M/JWhyaJkBGUAVd88zs0bAZ+7e/tdyRkuf+dVb9nDuU5OpVD6Od0ccTe3K5YOOJCIiIiKHobiuAFvR3acdtCwnzH0CfED+CaqE7scf5vbrgONCj08AlgCEptN8DNy5v5AH8PxPNB/y33n6JwLzjyB3xNm2O4tBo6eR686YwT1UyIuIiIiUIuEW85vNrAX5J5xiZheQ39UmXA8AJ5vZEuDk0HPMrIGZfbJ/JTMbR/6UmDZmlmZm14ReGgI8Ymazgb8DQ0PLbwBaAneH2lbOOmA+/p+AkWY2B7gS+H0R/ByByszOZejLKaRt38vzVyXTPDH4Sw6LiIiISNEJd5pNc+A58k8w3UZ+i8or3H1lkaSLcJE8zSYvz7nx9Zl8PGc9T17WjTM61Q86koiIiIgcoUNNswmrm427LwdOMrNKQIy77wpnf1J0/vn5Qj6es547f9dWhbyIiIhIKRVun/ny5Ld4bArE5Z9HCu5+f9jJ5Ii9PHUVz36znCt7N2Fov+ZBxxERERGRYhJun/nxwA4gFdgXfhwJ14QFG7l3/FxObFuHe89qx/4PWCIiIiJS+oRbzCe5+2lFkkTC9lPaDm54bSbtG1Tjicu6Ehcb7vnNIiIiIhLJwq32pphZxyJJImFZs3UPV4+dTs1K8bw4KJmK8eF+ThMRERGRSBduxXcMMMjMVpA/zcbIb9veKexkUmg79mQzeMx09mXnMm5IL+pUSQg6koiIiIiUgHCL+d8VSQo5Yvtychn2Sgqrt+zhpWt60rJOlaAjiYiIiEgJCbc15aqiCiKHz93509tzmLp8K/++pAu9m9cKOpKIiIiIlCCdIRnFHvliMe/PWscfT23DgC4Ng44jIiIiIiVMxXyUen3aav4zcSmX9mzEiP4tgo4jIiIiIgFQy5Mok7pqG+OmreLdGWs5rnUifxnQQb3kRURERMooFfNRJHXVNi59fipZOXkYMKRfc/WSFxERESnDVAlGkanLt5CTmwdAjMHsNduDDSQiIiIigVIxH0V6N69FfFwMsQbl4mLUvUZERESkjNM0myjSvUkNXr22N1OXb6F381p0b1Ij6EgiIiIiEiAV81Gme5MaKuJFREREBNA0GxERERGRqKViXkREREQkSpm7B50hapnZJmBVAIeuDWwO4Ljy6/S+RB69J5FJ70vk0XsSmfS+RJ4g35Mm7p548EIV81HIzFLcPTnoHPK/9L5EHr0nkUnvS+TRexKZ9L5Enkh8TzTNRkREREQkSqmYFxERERGJUirmo9NzQQeQAul9iTx6TyKT3pfIo/ckMul9iTwR955ozryIiIiISJTSyLyIiIiISJRSMR9lzOw0M1tkZkvN7I6g8wiY2SgzSzezuUFnkXxm1sjMJprZAjObZ2Y3B52prDOzBDObZmazQ+/JfUFnknxmFmtmM83so6CzSD4zW2lmP5nZLDNLCTqP5DOz6mb2tpktDP1+6RN0JtA0m6hiZrHAYuBkIA2YDlzq7vMDDVbGmVk/IAN4yd07BJ1HwMzqA/XdfYaZVQFSgXP0byU4ZmZAJXfPMLNywPfAze4+NeBoZZ6Z3QYkA1Xd/cyg80h+MQ8ku7t6zEcQMxsLfOfuL5hZPFDR3bcHHEsj81GmJ7DU3Ze7exbwOjAg4Exlnrt/C2wNOof8l7uvd/cZoce7gAVAw2BTlW2eLyP0tFzoptGkgJlZEnAG8ELQWUQimZlVBfoBLwK4e1YkFPKgYj7aNATWHPA8DRUoIr/KzJoCXYEfA45S5oWmc8wC0oEv3V3vSfAeA24H8gLOIf/LgS/MLNXMhgYdRgBoDmwCRoempb1gZpWCDgUq5qONFbBMI1sih2BmlYF3gFvcfWfQeco6d8919y5AEtDTzDQtLUBmdiaQ7u6pQWeRX+jr7t2A3wHXh6ZzSrDigG7A0+7eFdgNRMS5iyrmo0sa0OiA50nAuoCyiES00Lzsd4BX3f3doPPIf4W+mp4EnBZskjKvL3B2aH7268AJZvZKsJEEwN3Xhe7TgffIn2YrwUoD0g74RvFt8ov7wKmYjy7TgVZm1ix04sUlwAcBZxKJOKGTLV8EFrj7v4LOI2BmiWZWPfS4AnASsDDQUGWcu9/p7knu3pT83ydfu/sVAccq88ysUujEfULTOE4B1C0tYO6+AVhjZm1Ci04EIqKpQlzQAaTw3D3HzG4APgdigVHuPi/gWGWemY0D+gO1zSwNuNfdXww2VZnXF7gS+Ck0Rxvg/9z9k+AilXn1gbGhrlwxwJvurlaIIr9UF3gvf0yCOOA1d/8s2EgSciPwamhAdTkwOOA8gFpTioiIiIhELU2zERERERGJUirmRURERESilIp5EREREZEopWJeRERERCRKqZgXEREREYlSKuZFRERERKKUinkRERERkSilYl5EREREJEqpmBcRERERiVJxQQeIZrVr1/amTZsGHUNERERESrnU1NTN7p548HIV82Fo2rQpKSkpQccQERERkVLOzFYVtFzTbEREREREopSKeRERERGRKKViXkRKpdRV23hy4lJSV20LOoqIiEix0Zx5ESl1Uldt47Lnp5KVk0f5cjG8em1vujepEXQsERGRIqeReREpdSYuTGdfTh4O7MvOY+ryLUFHEhERKRYq5kWkVMnMzmXCgo0/P3dgc8a+4AKJiIgUI02zEZFSIy/P+cNbs1mwYRe3ndyaGIPvlmxmzJSV9GpWk9M61A86ooiISJHSyLyIlBr//HwhH81Zz59Oa8tNJ7bihhNaMfbqnnRpVJ2bX5+lk2FFRKTUUTEvIqXCK1NX8ew3y7m8V2OuO675z8sTysXywlXJ1KuWwJCXUli5eXeAKUVERIqWinkRiXoTFmzknvFzOaFtHe47uz1m9j+v16pcnjGDe+LuDB4zna27swJKKiIiUrRUzItIVPspbQc3vDaTdg2q8sSlXYmLLfi/tWa1K/HCwGTWbt/LkJdSyMzOLeGkIiIiRU/FvIhErbRte7h67HRqVopn1MAeVCr/6+f0d29Sk8cu7sKM1dv4/ZuzycvzEkoqIiJSPH71N5+ZPV6Ifex09z8XUR4RkULZsSebQaOnk5mdy6vX9qJO1YRCbXd6x/r83++O4m+fLKBhjQr83+lHFXNSERGR4vNbrSkHAPf8xjp3ACrmRaTE7MvJZdgrKazaspuxV/ekdd0qh7X9tcc2I23bHp77djlJNSpwVZ+mxRNURESkmP1WMf+ou4/9tRXMTNdIF5ES4+786e05TF2+lUcv7szRLWof9j7MjHvOas/a7ZmM/GAe9atV4OR2dYshrYiISPH61Tnz7v7Yb+2gMOuIiBSVR75YzPuz1vH7k1tzbtekI95PbIzx+KVd6NCwGjeOm8HsNduLLqSIiEgJCWvOvLvfVLRxREQO7fVpq/nPxKVcnNyIG05oGfb+KsbH8eLAHpz71GSuGTud90b0pVHNikWQVEREpGT8Vjeb1NAtAegGLAndugBh9XUzs5pm9qWZLQndFzhdx8xGmVm6mc09aHkXM5tqZrPMLMXMeoaWn2xmqWb2U+j+hAO2mWRmi0LbzDKzOqHlg8xs0wHLrw3nZxORovfN4k3c9f5c+rVO5K/ndvhFL/kjlVglvwd9dq4zaPQ0tu9RD3oREYkevzXNZmxoznwr4Hh3f8LdnwBOJL+gD8cdwAR3bwVMCD0vyBjgtAKWPwjc5+5dyD9J98HQ8s3AWe7eERgIvHzQdpe7e5fQLf2A5W8csPyFI/qJRKRYzFu3gxGvpNK6bhWevKwr5Q7RS/5ItaxTmeeu7M6arXsZ+nIq+3LUg15ERKJDYX8jNgAObBdRObQsHAOA/SfXjgXOKWgld/8W2FrQS0DV0ONqwLrQ+jPdfV1o+TwgwczKh5lVRAKybvterh4znaoVyjF6UA+qJJQrluP0al6Lhy7sxLQVW/njW3PUg15ERKLCb3Wz2e8BYKaZTQw9Pw4YGeax67r7egB3X79/ysthuAX43MweJv9DydEFrHM+MNPd9x2wbLSZ5QLvAH919/2/sc83s37AYuBWd19zmHlEpIjtzMzm6jHT2b0vl7eu60O9aoXrJX+kBnRpyNrte3nws0Uk1ajA7ae1LdbjiYiIhKtQxby7jzazT4FeoUV3uPuG39rOzL4C6hXw0l2Fj3hIw8kvut8xs4uAF4GTDjh2e+CfwCkHbHO5u681syrkF/NXAi8BHwLj3H2fmV1H/jcFJ1AAMxsKDAVo3LhxEfwYIlKQ7Nw8Rrwyg6XpGYwe3IOj6lf97Y2KwPDjWrBm616emrSMpBoVuayX/p2LiEjksv8OTP/KSvlnml0ONHf3+82sMVDP3acd8YHNFgH9Q6Py9YFJ7t7mEOs2BT5y9w4HLNsBVHd3D+Xb4e5VQ68lAV8Dg9198iH2OQhIdvcbDloeC2x192q/9TMkJyd7SkpKIX5aETkc7s4f357D26lpPHRBJy5MblSix8/JzWPISyl8u2QzLwxM5vg2h/vFoYiISNEys1R3Tz54eWHnzD8F9AEuDT3fBTwZZqYPyD9BldD9+MPcfh35030gfxR9CYCZVQc+Bu48sJA3szgzqx16XA44E5gbel7/gP2eDSw4zCwiUoQen7CUt1PTuOnEViVeyAPExcbwn8u60bZeFa5/dQZz1+4o8QwiIiKFUdhivpe7Xw9kArj7NiA+zGM/AJxsZkuAk0PPMbMGZvbJ/pXMbBzwA9DGzNLM7JrQS0OAR8xsNvB3QlNfgBuAlsDdB7WgLE/+HPs5wCxgLfB8aJubzGxeaF83AYPC/NlE5Ai9nZrGo18t5rxuDbn1pFaB5ahUPo5Rg3pQo2I8g8dMZ+32vYFlEREROZTCTrP5kfwTTKe7ezczSwS+cPeuxR0wkmmajUjRmrx0MwNHTaNns5qMGdyT+LiibUF5JBZv3MX5T0+hfrUE3rruaKpVKJ5uOiIiIr8m3Gk2jwPvAXXM7G/A9+SPhouIFIlFG3Zx3cupNE+sxNNXdI+IQh6gdd0qPHtFd1Zs3s3wV1LJyskLOpKIiMjPCvXb0t1fBW4H/gGsB85x97eKM5iIlB0bd2YyePQ0KsTHMnpwz4gb/T66ZW3+eX4npizbwh3vzKEw32iKiIiUhML2mQfYCHwX2qaCmXVz9xnFE0tEyoqMfTkMHj2dHXuzeWNYHxpWrxB0pAKd1y2JtG17+deXi0mqWZHbTm4ddCQREZHCFfNm9hfyTwpdRv6VVwndF9iLXUSkMLJz87j+1Rks2riLFwYm06Hhb3aEDdSNJ7QkbdseHp+whKTqFbioR8l32hERETlQYUfmLwJauHtWcYYRkbLD3bln/Fy+WbyJf5zXMSp6uZsZfzu3I+t3ZPJ/7/1E/eoJHNsqMehYIiJShhX2DLO5QPVizCEiZcxTk5YxbtoaRvRvwaU9o+cqq+ViY3jq8m60rFOZ4a/MYMH6nUFHEhGRMqywxfw/gJlm9rmZfbD/VpzBRKT0Gj9rLQ99vogBXRrwh1MKvPBzRKuSUI7Rg3tQuXwcg0dPZ/0O9aAXEZFgFLaYHwv8k/wLOz1ywE1E5LBMXb6FP741h17NavLgBZ2IibGgIx2R+tUqMGpQj59P4N2VmR10JBERKYMKW8xvdvfH3X2iu3+z/1asyUSk1FmavouhL6XQqGYFnrsymfJxsUFHCku7BlV56vJuLEnPYMSrM8jOVQ96EREpWYUt5lPN7B9m1sfMuu2/FWsyESlV0ndlMnDUdOLjYhkzuCfVKkZWL/kj1a91Iv84tyPfLdnMXe/9pB70IiJSogrbzaZr6L73AcvUmlJECmVPVg7XjElh6+4s3hjWm0Y1KwYdqUhd1KNRfsvKr5fSqEZFbjyxVdCRRESkjPjNYt7MYoEP3P3REsgjIqVMTm4eN742k3nrdvDclcl0SqoedKRicevJrUnbtpdHvlxMUs0KnNs1KehIIiJSBvzmNBt3zwXOLoEsIlLKuDv3fTifCQvTue/s9pzUrm7QkYqNmfHA+Z3o07wWt789hylLNwcdSUREyoDCzpmfYmb/MbNjNWdeRArr+e+W8/LUVQzt15wr+zQNOk6xi4+L4Zkru9OsdiWGvZLK4o27go4kIiKlXGGL+aOB9sD9/Lct5cPFFUpEot9Hc9bx908WckbH+txxWtug45SYahXKMWpQDxLKxTJ49HTSd2YGHUlEREqxQhXz7n58ATed/CoiBUpZuZXb3pxNcpMaPHJR56jtJX+kkmpUZPSgHmzbk8XVY6eze19O0JFERKSUKlQxb2bVzOxfZpYSuj1iZtWKO5yIRJ/lmzK49qUUGlavwPNXJZNQLrp7yR+pDg2r8eRl3Viwfhc3vDaDHPWgFxGRYlDYaTajgF3ARaHbTmB0cYUSkei0OWMfg0ZPJ9aMMYN7UKNSfNCRAnV82zrcP6A9Exdt4p4P5qkHvYiIFLnC9plv4e7nH/D8PjObVQx5RCRK7c3K5dqxKWzcmcm4ob1pUqtS0JEiwuW9mpC2bS9PT1pGoxoVGd6/RdCRRESkFCnsyPxeMztm/xMz6wvsLZ5IIhJtcvOcW96Yyey07fz7kq50a1wj6EgR5Y+ntOGszg3452cL+WD2uqDjiIhIKVLYkfnrgJdC8+QN2AoMKq5QIhJd/vrxfD6ft5F7zmzHaR3qBR0n4sTEGA9f2ImNOzP5w5uzqVulPL2a1wo6loiIlAKF7WYz2907A52Aju7e1d1nh3NgM6tpZl+a2ZLQfYFDeWY2yszSzWzuQcu7mNlUM5sVOim3Z2j5yWaWamY/he5POGCbeDN7zswWm9lCMzs/tLy8mb1hZkvN7EczaxrOzyZSloz6fgWjJ69kcN+mXH1Ms6DjRKzycbE8d2V3kmpWYOjLqSxNzwg6koiIlAKF7WZT3swuA24AbjGze8zsnjCPfQcwwd1bARNCzwsyBjitgOUPAve5exfgntBzgM3AWe7eERgIvHzANncB6e7eGmgHfBNafg2wzd1bAo8C/zzCn0mkTPls7gb+8vF8Tm1flz+f0S7oOBGvesV4xg7uSblYY/CYaWzatS/oSCIiEuUKO2d+PDAAyAF2H3ALxwBgbOjxWOCcglZy92/Jn9bzi5eAqqHH1YB1ofVnuvv+SanzgAQzKx96fjXwj9B6ee6+/3rrB2Z5GzjRzMpWY2yRwzRj9TZufn0mnZOq89jFXYktY73kj1SjmhV5cWAPNu3ax7Vjp7MnSz3oRUTkyBV2znySuxc0Oh6Ouu6+HsDd15tZncPc/hbgczN7mPwPJUcXsM75wEx332dm1UPL/mJm/YFlwA3uvhFoCKwJZckxsx1ALfJH+UXkIKu27ObasSnUrZrACwOTqRBfNnvJH6nOjarzxKXdGPZyCjeNm8WzV3bXhyERETkihR2Zn2JmHQ9352b2lZnNLeA24HD3VYDhwK3u3gi4FXjxoGO3J3+6zLDQojggCZjs7t2AH4CH969ewP4LbAhtZkP3Xzxr06ZN4f8UIlFm6+4sBo2eTp47Ywb3oHbl8r+9kfzCye3qcu9Z7flqwUbu/1A96EVE5MgUdmT+GGCQma0A9pFf/Lq7d/q1jdz9pEO9ZmYbzax+aFS+PpBe2NAhA4GbQ4/fAl44YN9JwHvAVe6+LLR4C7AntHz/NteEHqcBjYA0M4sjf9pOQVN7cPfngOcAkpOT9dtXypTM7FyGvpTC2u17ee3aXjRPrBx0pKg28OimpG3bw/PfraBRzYpce2zzoCOJiEiUKWwx/7tiOPYH5BfkD4Tuxx/m9uuA44BJwAnAEoDQdJqPgTvdffL+ld3dzexDoD/wNXAiMP+gLD8AFwBfu4bJRP5HXp7z+zdnk7JqG09e1o3kpjWDjlQq3Pm7o1i7fS9/+2QBDapX4PSO9YOOJCIiUaRQxby7ryqGYz8AvGlm1wCrgQsBzKwB8IK7nx56Po78Ary2maUB97r7i8AQ4N+hkfRMYGhovzcALYG7zezu0LJT3D0d+BPwspk9BmwCBodefzG0fCn5I/KXFMPPKxLVHvhsIR//tJ7/O70tZ3RSwVlUYmKMf13UhQ07pnLLG7OoW7U83Zvog5KIiBSO/doAtJnNCM0vD2ud0io5OdlTUlKCjiFS7F7+YSV3j5/Hlb2bcP+A9qjZU9HbujuL856azI692bw7oi/NalcKOpKIiEQQM0t19+SDl//WCbBHmdmcX7n9BNQunsgiEgm+mr+Rez+Yx0lH1eHes9qpkC8mNSvFM2ZwT8yMwaOnsSVDPehFROS3/dY0m7aF2EduUQQRkcgzJ207N46bSYeG1Xj80q7ExRa2AZYciaa1K/H8Vclc9vxUrn0phXFDepNQTm0/RUTk0H71N7O7ryrELa2kwopIyVmzdQ9Xj0mhZqV4XhiYTMX4wp4vL+Ho3qQGj13chVlrtnPrG7PIy9O5+CIicmgaZhORX9ixJ5tBo6eRlZPL2Kt7UKdKQtCRypTfdazPXacfxadzN/D3TxYEHUdERCKYhtpE5H/sy8ll6MsprNm6l5eu6UnLOlWCjlQmXXNMM9K27eWF71eQVKMCg/o2CzqSiIhEoEKNzJtZJTOLCT1ubWZnm1m54o0mIiUtL8+5/e05/LhiKw9d2InezWsFHanMMjPuPrMdp7Sry30fzeeLeRuCjiQiIhGosNNsvgUSzKwhMIH8/uxjiiuUiATjkS8XMX7WOv54ahsGdGkYdJwyLzbG+PclXemUVJ2bXp/JrDXbg44kIiIRprDFvLn7HuA84Al3PxdoV3yxRKSkvfbjap6cuIxLezZiRP8WQceRkArxsbw4MJnEKuW5Zsx0Vm/ZE3QkERGJIIUu5s2sD3A58HFomebbi5QSExelc/f4uRzXOpG/DOigXvIRpnbl8owZ3JNcdwaNmca23VlBRxIRkQhR2GL+FuBO4D13n2dmzYGJxZZKRErM3LU7uP7VGbStV4UnL++mXvIRqkViZZ67Mpm0rXsZ+nIKmdm6xIeIiBSymHf3b9z9bOA/oefL3f2mYk0mIsVu7fa9XD1mOtUrlGPUoB5ULq8v3CJZz2Y1eeSizkxfuY0/vDVbPehFRKTQ3Wz6mNl8YEHoeWcze6pYk4lIsdqxN5vBo6exNyuX0YN7UreqeslHg7M6N+CO37XloznrefDzRUHHERGRgBV2GO4x4FTgAwB3n21m/YorlIgUr6ycPIa/ksryTbsZe3VP2tRTL/loMqxfc9Zs3cMz3ywjqUYFrujdJOhIIiISkEJ/p+7uaw46KU4TNkWikLtzx7tzmLJsCw9f2Jm+LWsHHUkOk5lx39ntWb8jk3vGz6VB9QROaFs36FgiIhKAwp7ptsbMjgbczOLN7A+EptyISHR57KslvDtjLbee1JoLuicFHUeOUFxsDE9c2pV2Dapyw2sz+SltR9CRREQkAIUt5q8DrgcaAmlAl9BzEYkib6as4d8TlnBB9yRuOrFl0HEkTJXKxzFqUA9qVIzn6rHTSdumHvQiImVNYbvZbHb3y929rrvXcfcr3H1LcYcTkaLz/ZLN/N+7P3FMy9r847yO6iVfStSpksCYwT3IzM5l8Ojp7NibHXQkEREpQYXtZvOgmVU1s3JmNsHMNpvZFcUdTkSKxoL1O7nulVRa1qnMU1d0o5x6yZcqrepW4dkru7Nyy26GvZzCvhyd0iQiUlYU9jf6Ke6+EziT/Gk2rYE/FlsqESkyG3ZkMnj0dCqVj2X04B5UTSgXdCQpBke3qM1DF3Rm6vKt3PHOT7irB72ISFlQ2G42+3/7nw6Mc/et+opeJPLtysxm8JjpZOzL4c1hfahfrULQkaQYndO1IWnb9vDwF4tJqlGB35/SJuhIIiJSzApbzH9oZguBvcAIM0sEMosvloiEKzs3j+tfm8nijbsYNagH7RpUDTqSlIDrj29J2ra9PPH1UpJqVODiHo2DjiQiIsWosCfA3gH0AZLdPRvYDQwI58BmVtPMvjSzJaH7GodYb5SZpZvZ3IOWdzGzqWY2y8xSzKxnaPnJZpZqZj+F7k84YJt4M3vOzBab2UIzOz+0fJCZbQrta5aZXRvOzyYSNHfnz+/N5dvFm/j7uR04rnVi0JGkhJgZfzmnA/1aJ/J/783lm8Wbgo4kIiLF6HDOgmsInG9mVwEXAKeEeew7gAnu3gqYEHpekDHAaQUsfxC4z927APeEngNsBs5y947AQODlA7a5C0h399ZAO+CbA157w927hG4vHNmPJBIZnpy4lDdS1nDjCS01MlsGlYuN4anLu9G6bhVGvJLKvHXqQS8iUloVtpvNvcATodvx5BfOZ4d57AHA2NDjscA5Ba3k7t8CWwt6Cdg/b6AasC60/kx3XxdaPg9IMLPyoedXA/8IrZfn7pvD/BlEIs57M9N4+IvFnNu1Ibed3DroOBKQyuXjGD2oB1UrlOPqMdNZt31v0JFERKQYFHZk/gLgRGCDuw8GOgPlf32T31TX3dcDhO7rHOb2twAPmdka4GHgzgLWOR+Y6e77zKx6aNlfzGyGmb1lZgde//x8M5tjZm+bWaPDzCISEaYs28ztb8+hd/Oa/PP8TuolX8bVq5bA6ME92LMvl6vHTGdnpnrQi4iUNoUt5ve6ex6QY2ZVgXSg+W9tZGZfmdncAm5hzbcPGQ7c6u6NgFuBFw86dnvgn8Cw0KI4IAmY7O7dgB/I/xAA8CHQ1N07AV/x328MCvqZhobm6Kds2qS5qBI5Fm/cxbCXU2laqxLPXpFMfJx6yQu0rVeVp6/oztL0DEa8MoPs3LygI4mISBEq7G/7lNDI9vNAKjADmPZbG7n7Se7eoYDbeGCjmdUHCN2nH2b2gcC7ocdvAT33v2BmScB7wFXuviy0eAuwJ7R8/zbdQjm3uPu+0PLnge6/8jM95+7J7p6cmKiTCiUypO/M7yWfUC6/l3y1iuolL/91TKv8q/5+v3Qzd76rHvQiIqVJYbvZjHD37e7+DHAyMDA03SYcH5BfkBO6H3+Y268Djgs9PgFYAhD60PExcKe7T96/suf/9voQ6B9adCIwP7RN/QP2ezaw4DCziARm974crh47nW17shg1sAdJNSoGHUki0IXJjbj5xFa8nZrG4xOWBh1HRESKSGFPgD3XzKoBuPtKYLWZnRPmsR8ATjazJeR/QHggdKwGZvbJAcceR/6UmDZmlmZm14ReGgI8Ymazgb8DQ0PLbwBaAncf0Gpy/3z8PwEjzWwOcCXw+9Dym8xsXmhfNwGDwvzZREpETm4eN46byfx1O3nysm50TKoWdCSJYLec1IrzuyXx6FeLeTs1Leg4IiJSBKwwX7ea2axQC8gDl810967FFSwaJCcne0pKStAxpIxyd/78/lxe/XE1fz2nA1f0bhJ0JIkCWTl5DB4zjR+Xb2Xs1T3p27J20JFERKQQzCzV3ZMPXl7YOfMFrVfYq8eKSDF49tvlvPrjaq47roUKeSm0+LgYnr6iOy0SK3Pdy6ks2rAr6EgiIhKGwzkB9l9m1sLMmpvZo+SfCCsiAfhw9joe+HQhZ3VuwO2ntgk6jkSZqgnlGD24BxXiYxk8ehobd2YGHUlERI5QYYv5G4Es4A3gTWAvcH1xhRKRQ5u2Yiu/f3M2PZrW4KELOhETo17ycvgaVK/AqEE92LE3m8Gjp5OxLyfoSCIicgQK281mt7vfsb8lo7v/n7vv3v+6mT1RfBFFZL9lmzIY8lIKSTUr8PxVySSUiw06kkSxDg2r8eTl3Vi0cRfXvzqDHPWgFxGJOkV1VZm+RbQfETmETbv2MWj0NOJijDGDelK9YnzQkaQU6N+mDn89pwPfLN7E3ePnqge9iEiU0UmsIlFgb1Yu176UwqZd+3h9aB8a11IveSk6l/ZsTNq2PTw5cRlJNSpy/fEtg44kIiKFpGJeJMLl5jk3vT6TOWnbefaK7nRpVD3oSFIK/eGUNqRt28tDny8iqUYFBnRpGHQkEREphKIq5nUGnkgxcHf+8tF8vpy/kZFnteOU9vWCjiSllJnx4AWd2LAjkz++NYe6VRPo3bxW0LFEROQ3FPYKsM1/Y5V/F0EWETnIi9+vYMyUlVx7TDMG9W0WdBwp5crHxfLclck0rlWRoS+lsDRdPehFRCJdYU+AHWNmy8zsdTMbYWYdD3zR3ccUfTSRsu3Tn9bzt08W8LsO9fi/048KOo6UEdUqlmP0oB7Ex8UyaPR00nepB72ISCQrbGvKfsBRwBNADeBjM9tanMFEyrLUVdu45Y1ZdG1UnUcv7qJe8lKiGtWsyKhByWzJyOKaMSnsyVIPehGRSFXYaTbHAL8H7gLOAD5CF40SKRYrN+9myEsp1K+WoF7yEphOSdX5z2VdmbduBzeNm0lunlpWiohEosJOs/kGOAd4Dujv7iPcfVyxpRIpo7buzmLQ6Gm4O2MG96RW5fJBR5Iy7MSj6nLf2e35akE6Iz+Ypx70IiIRqLDdbGqRf2GofsBNZpYH/ODudxdbMpEyJjM7l2vHTmfdjkzGDelF09qVgo4kwpV9mpK2bS/PfrucRjUrMLRfi6AjiYjIAQpVzLv7djNbDjQCkoCjgXLFGUykLMnLc259YxYz12znqcu60b1JzaAjifzsT6e1JW37Xv7+yUIaVq/IGZ3qBx1JRERCClXMm9kyYBHwPfAMMNjds4ozmEhZ8o9PF/Dp3A38+Yyj+F1HFUoSWWJijEcu7MzGHZnc+uYs6lYtT3JTfeAUEYkEhZ0z38rdT3f3v7v7dyrkRYrO2Ckref67FQw6uinXHKNe8hKZEsrF8vxVyTSsXoFrX0ph+aaMoCOJiAiFL+ZbmtkEM5sLYGadzOzPxZhLpEz4Yt4G7vtwHie3q8vdZ7bDTC0oJXLVqBTPmME9iDVj0OjpbM7YF3QkEZEyr7DF/PPAnUA2gLvPAS4prlAiZcGsNdu56fWZdGxYjccv6UqseslLFGhSqxLPD0xm485Mrh2bwt6s3KAjiYiUaYUt5iu6+7SDlukqIiJHaM3WPVw7djqJVcrzwsAeVIhXL3mJHt0a1+Dfl3Rldtp2bnlDPehFRIJU2GJ+s5m1ABzAzC4A1hdbKpFSbPueLAaOnkZ2bn4v+cQq6iUv0ee0DvW4+4x2fD5vI3/7eEHQcUREyqzCFvPXA88Cbc1sLXALMDycA5tZTTP70syWhO5rHGK9UWaWvn++/gHLu5jZVDObZWYpZtYztPxkM0s1s59C9yeEllcJrbv/ttnMHgu9Vt7M3jCzpWb2o5k1DednEzmUzOxchr6UStrWvTx/VTItEisHHUnkiF19TDOu7tuMUZNXMOr7FUHHEREpkwpVzLv7cnc/CUgE2rr7Me6+Msxj3wFMcPdWwITQ84KMAU4rYPmDwH3u3gW4J/QcYDNwlrt3BAYCL4d+hl3u3mX/DVgFvBva5hpgm7u3BB4F/hnejybyS3l5zh/fnsO0lVt5+KLO9Gym1n4S/e464yhObV+Xv3w8n8/mbgg6johImVOoYj40cn0ZcDNwq5ndY2b3hHnsAcDY0OOxwDkFreTu3wJbC3oJqBp6XA1YF1p/pruvCy2fBySY2f/MYzCzVkAd4LsCsrwNnGhqKyJF7KEvFvHh7HX86bS2nN25QdBxRIpEbIzx2MVd6ZxUnZtfn8mM1duCjiQiUqYUdprNePIL3hxg9wG3cNR19/UAofs6h7n9LcBDZrYGeJj8bjsHOx+Y6e4H90+7FHjD3feftdUQWBPKkgPsAGodZh6RQ3r1x1U8PWkZl/dqzHXHNQ86jkiRqhAfy4sDk6lXLYFrx6awaku4vx5ERKSwCnUFWCDJ3Qua6vKrzOwroF4BL911uPsqwHDgVnd/x8wuAl4ETjrg2O3Jny5zSgHbXgJceWDUAtYpsD2DmQ0FhgI0btz4yJJLmTJxYTp3vz+XE9rW4b6z26uXvJRKtSqXZ/SgHpz39BQGjZ7Ou8OPpkal+KBjiYiUeoUdmZ9iZh0Pd+fufpK7dyjgNh7YaGb1AUL36Ye5+4H8d877W0DP/S+YWRLwHnCVuy87cCMz6wzEuXvqAYvTgEah1+PIn7ZT0NQe3P05d0929+TExMTDjCxlzdy1O7j+tRm0a1CVJy7tSlxsYf/JiUSf5omVeeGqZNZu38uQl1LIzFYPehGR4lbYyuIYINXMFpnZnFCnmDlhHvsD8gtyQvfjD3P7dcBxoccnAEsAzKw68DFwp7tPLmC7S4Fxv5LlAuDrA6bgiByRtG17GDxmOjUqxjNqYA8qlS/sF2Ei0Su5aU0evagLKau28fs3Z5OnHvQiIsWqsNXF74rh2A8Ab5rZNcBq4EIAM2sAvODup4eejwP6A7XNLA24191fBIYA/w6NpGcSmvoC3AC0BO42s7tDy05x9/0j/xcBpx+U5UXgZTNbSv6IvK5uK2HZsTebwaOnk5mdy6vX9qJO1YSgI4mUmDM61Wft9rb8/ZOFJNWowJ2nHxV0JBGRUss0AH3kkpOTPSUlJegYEmGycvIYOGoaKau2MvbqnhzdonbQkURKnLtzz/h5vDx1FX8Z0J4r+zQNOpKISFQzs1R3Tz54uSbwihQhd+eOd+bww/ItPHhBJxXyUmaZGfee1Y6TjqrDvR/M46v5G4OOJCJyRDIyMhh530jqJtUlJjaGukl1GXnfSDIyMoKOBmhkPiwamZeD/euLRTz+9VJ+f3JrbjyxVdBxRAK3JyuHS56bypKNGbwxrDedkqoHHUlEpNAyMjI4+rij2Vh+I1VOq0JCUgKZaZns/HQn9bLqMeWbKVSuXDJXc9fIfCmRumobT05cSuoqXZglkqSu2sZ1L6fy+NdLuTi5ETec0DLoSCIRoWJ8HC8MTKZW5XiuHpPCpz+t1/9hEUa/VyKT3pfI8PAjD7Ox/EYShyZSoUkFLNao0KQCdYbVYUP8Bh5+5OGgI2pkPhwlPTKfumoblz0/lezcPOJiYrjrjKNoXbdKiR1fCrZ44y7++vF8snOdGIPXh/amZzNdc0zkQEvTd3H2fyazNysXM/R/WIRYvHEXf/t4ATl5+r0SSQ58X+LjYnj12t50b1Ij6FhlUt2kulQZWoUKTSr84rW9q/aS8XwGG9ZsKJEshxqZV6+8KDJ1+RaycvJwICs3j3s/mBd0JDmIAdNXblMxL3KQlnWqcHbnBrw+fQ3u+j8sEuk9iUzZOXlMXb5FxXxANq3fRGJSwdcVSmiYwIr1K0o40S+pmI8ivZvXIj4uRiPzEWb/CEpuXh7l4mLo3VyFvEhBLkxuxHsz1+r/sAiikfnIdOD7ot8rwUqsn0hmWmaBI/OZazNJrB/8BURVzEeR7k1q8NqQ3kxdvoXezWvpU3qE6NOiFh0aVtP7IvIb9H9Y5NH/X5FJ70vkGD5kOE99+hQJwxIws5+Xuzs7P93JiGtHBJgun+bMh0HdbERERERKr/3dbDbEb6Dq76qS0DCBzLXqZiMiIiIiEvEqV67MlG+mMOK0EWQ8n8GCYQvIeD6DEaeNKNFC/tdoZD4MGpkXERERkZJwqJF5FfNhMLNNwKoADl0b2BzAceXX6X2JPHpPIpPel8ij9yQy6X2JPEG+J03c/Rdn3KqYj0JmllLQJzMJlt6XyKP3JDLpfYk8ek8ik96XyBOJ74nmzIuIiIiIRCkV8yIiIiIiUUrFfHR6LugAUiC9L5FH70lk0vsSefSeRCa9L5En4t4TzZkXEREREYlSGpkXEREREYlSKuZFRERERKKUivkoY2anmdkiM1tqZncEnUfAzEaZWbqZzQ06i+Qzs0ZmNtHMFpjZPDO7OehMZZ2ZJZjZNDObHXpP7gs6k+Qzs1gzm2lmHwWdRfKZ2Uoz+8nMZpmZrk4ZIcysupm9bWYLQ79f+gSdCTRnPqqYWSywGDgZSAOmA5e6+/xAg5VxZtYPyABecvcOQecRMLP6QH13n2FmVYBU4Bz9WwmOmRlQyd0zzKwc8D1ws7tPDThamWdmtwHJQFV3PzPoPJJfzAPJ7q4LRkUQMxsLfOfuL5hZPFDR3bcHHEsj81GmJ7DU3Ze7exbwOjAg4Exlnrt/C2wNOof8l7uvd/cZoce7gAVAw2BTlW2eLyP0tFzoptGkgJlZEnAG8ELQWUQimZlVBfoBLwK4e1YkFPKgYj7aNATWHPA8DRUoIr/KzJoCXYEfA45S5oWmc8wC0oEv3V3vSfAeA24H8gLOIf/LgS/MLNXMhgYdRgBoDmwCRoempb1gZpWCDgUq5qONFbBMI1sih2BmlYF3gFvcfWfQeco6d8919y5AEtDTzDQtLUBmdiaQ7u6pQWeRX+jr7t2A3wHXh6ZzSrDigG7A0+7eFdgNRMS5iyrmo0sa0OiA50nAuoCyiES00Lzsd4BX3f3doPPIf4W+mp4EnBZskjKvL3B2aH7268AJZvZKsJEEwN3Xhe7TgffIn2YrwUoD0g74RvFt8ov7wKmYjy7TgVZm1ix04sUlwAcBZxKJOKGTLV8EFrj7v4LOI2BmiWZWPfS4AnASsDDQUGWcu9/p7knu3pT83ydfu/sVAccq88ysUujEfULTOE4B1C0tYO6+AVhjZm1Ci04EIqKpQlzQAaTw3D3HzG4APgdigVHuPi/gWGWemY0D+gO1zSwNuNfdXww2VZnXF7gS+Ck0Rxvg/9z9k+AilXn1gbGhrlwxwJvurlaIIr9UF3gvf0yCOOA1d/8s2EgSciPwamhAdTkwOOA8gFpTioiIiIhELU2zERERERGJUirmRURERESilIp5EREREZEopWJeRERERCRKqZgXEREREYlSKuZFRERERKKUinkRERERkSilYl5EREREJEqpmBcRERERiVJxQQc4HGZWE3gDaAqsBC5y920FrDcKOBNId/cOByx/A2gTelod2O7uXcysHPAC0I38P5OX3P0fv5Wndu3a3rRp0zB+IhERERGR35aamrrZ3RMPXh5VxTxwBzDB3R8wsztCz/9UwHpjgP8ALx240N0v3v/YzB4BdoSeXgiUd/eOZlYRmG9m49x95a+Fadq0KSkpKUf6s4iIiIiIFIqZrSpoebRNsxkAjA09HgucU9BK7v4tsPVQOzEzAy4Cxu3fBKhkZnFABSAL2Fk0kUVEREREike0FfN13X09QOi+zhHu51hgo7svCT1/G9gNrAdWAw+7+yE/DIiIiIiIRIKIm2ZjZl8B9Qp46a4iPMyl/HdUHqAnkAs0AGoA35nZV+6+vIB8Q4GhAI0bNy7CSCIipV/Kyq38uGIrvZvXonuTGkHHERGJehFXzLv7SYd6zcw2mll9d19vZvWB9MPdf2gqzXlA9wMWXwZ85u7ZQLqZTQaSgV8U8+7+HPAcQHJysh/u8UVEyqoxk1cw8sP5AJSPi+G1Ib1V0IuIhCnaptl8AAwMPR4IjD+CfZwELHT3tAOWrQZOsHyVgN7AwrCSiogIAO7OM98s475QIQ+wLyePBz9byJ6snACTiYhEv2gr5h8ATjazJcDJoeeYWQMz+2T/SmY2DvgBaGNmaWZ2zQH7uIT/nWID8CRQGZgLTAdGu/uc4vsxRETKhl2Z2Qx/ZQYPfLqQPs1rkRAXQ6xBrBk/rtjKSY98w2dz1+OuLzpFRI6E6T/QI5ecnOxqTSkiUrCl6bsY9nIqK7fs4Y7T2nLtsc2YsXo7U5dvoXfzWrg7f35/Lgs37KJf60TuO7s9zWpXCjq2iEhEMrNUd0/+xXIV80dOxbyISME++Wk9f3xrNhXiY3ni0m70aVGrwPVycvN4eeoq/vXFYvbl5DHsuOaM6N+SCvGxJZxYRCSyHaqYj7ZpNiIiEsFycvP4xycLGPHqDFrXq8KHNx5zyEIeIC42hsF9mzHh98dxRqf6PPH1Uk5+9Bu+nL9RU29ERApBxbyIiBSJzRn7uPLFaTz77XKu7N2E14f2pn61CoXatk7VBB69uAuvD+1NxfhYhryUwjVjU1i9ZU8xpxYRiW6aZhMGTbMREck3c/U2Rrw6g627s/j7uR05v3vSEe8rOzePMZNX8thXi8nOc0b0b8F1x7UgoZym3ohI2aVpNiIiUuTcnVd/XMXFz04lLtZ4Z/jRYRXyAOViYxjSrzkTft+fU9vX47GvlnDKo98yceFhX1pERKTUK/KReTN7vBCr7XT3PxfpgQOgkXkRKcsys3O5+/25vJWaxnGtE/n3JV2oXjG+yI8zeelm7hk/l2WbdnNyu7rcc2Y7GtWsWOTHERGJZCXWzcbMVgH3/MZqd7j7UUV64AComBeRsmrN1j0MfzWVuWt3ctOJrbj5xFbExlixHS8rJ48Xv1/B4xOW4Dg3HN+SIf2aUz5OU29EpGw4VDEfVwzHetTdx/5GGF2/W0QkSn2zeBM3vz6T3DznxYHJnHhU3WI/ZnxcDMP7t2BAlwb89eP5PPzFYt6ZsZb7zm5Pv9aJxX58EZFIpRNgw6CReREpS/LynKcmLeWRLxfTpm4VnrmiO00DusjTN4s3MfKDeazYvJvfdajH3We2o0H1wnXOERGJRiU5zeZX58y7+01FesAAqZgXkbJix95sfv/mbL5asJFzujTgH+d1CvzCTvtycnnhuxU88fUSDOOmE1txzTHNiI9TbwcRKX1KsptNauiWAHQDloRuXYDcYjieiIgUo0UbdjHgP98zaVE6I89qx6MXdwm8kAcoHxfL9ce35Mtbj+PYVrX552cL+d2/v2XK0s1BRxMRKTHFNs3GzCYCp7h7duh5OeALdz++WA4YAI3Mi0hpN37WWu545ycqJ8Tx1OXd6NG0ZtCRDunrhRsZ+cF8Vm/dw1mdG3DX6UdRr1pC0LFERIpESZ4Au18DoAqwNfS8cmiZiIhEuOzcPP7+yQJGT15Jj6Y1ePKybtSpGtmF8Qlt63J0i9o8880ynpq0jK8XbOSWk1ozqG9TysVq6o2IlE7FWcw/AMwMjdADHAeMLMbjiYhIEUjflckNr85k2sqtDO7blP87/aioKYYTysVyy0mtOa9rEiM/nMffPlnAW6lruH9AB3o3rxV0PBGRIles3WzMrB7QK/T0R3ffUGwHC4Cm2YhIaZOycisjXp3BrswcHji/IwO6NAw60hFzd75akM7ID+axdvtezu3akDtPb0udKpH9DYOISEFK8gTY/Qc04CSgs7uPB+LNrGdxHU9ERI6cuzNm8goueW4qFeNjee/6o6O6kAcwM05uV5evbjuOG09oycdz1nPiw98w6vsV5OTmBR1PRKRIFOcJsE8DecAJ7n5U6EJRX7h7j2I5YAA0Mi8ipcHerFzufHcO789ax0lH1eGRi7pQrUK5oGMVuRWbd3PvB/P4dvEm2tarwl/P6UByBJ/QKyJyoBIfmQd6ufv1QCaAu28D4ovxeCIicphWbdnNuU9NZvzsdfzhlNY8d2VyqSzkAZrVrsTYwT145opu7NybzQXP/MDv35zN5ox9QUcTETlixXkCbLaZxQIOYGaJ5I/Ui4hIBJiwYCO3vDGL2BhjzOCeHNc6MehIxc7MOK1Dffq1TuSJr5fywnfL+XL+Bv54ahsu69WE2BgLOqKIyGEpzpH5x4H3gDpm9jfge+DvxXg8EREphLw8519fLuaasSk0rlmRD284pkwU8geqGB/Hn05ry6c396NDw2rcPX4eA578nhmrtwUdTUTksBR3N5u2wImAARPcfUGxHSwAmjMvItFm+54sbnljFpMWbeKC7kn89ZwOJJQL/mquQXJ3Pv5pPX/5aD4bd+7jkh6NuP20ttSspJmhIhI5DjVnvriL+RpAIw6YzuPuM4rtgCVMxbyIRJN563Zw3SupbNiRyb1ntefyXo3JbzwmABn7cnh8whJGfb+Cyglx3H5qWy7u0UhTb0QkIpR4MW9mfwEGAcsIzZsH3N1PKJYDBkDFvIhEi3dS0/i/936iRsV4nr6iG10b1wg6UsRavHEXd78/lx9XbKVzUjX+ck4HOiVVDzqWiJRxQRTzi4CO7p5VhPusCbwBNAVWAheFuuQcvN4o4Ewg3d07HLD8DaBN6Gl1YLu7dzGzeOBZIJn8k3RvdvdJv5VHxbyIRLqsnDz+8tF8Xp66it7Na/Kfy7pRu3L5oGNFPHdn/Kx1/O2TBWzO2MdlPRvzx1PbUL2ipt6ISDCCaE05l/yCuSjdQf7c+1bAhNDzgowBTjt4obtf7O5d3L0L8A7wbuilIaHXOwInA4+YWXRcu1xE5BA27Mjk4ud+4OWpqxjWrzmvXNNLhXwhmRnndG3IhN8fx+Cjm/H69DWc8Mg3vDl9DXl5xTc9VUTkcBVnwfoPYKaZfW5mH+y/hbnPAcDY0OOxwDkFreTu3wJbD7WT0NVpLwLGhRa1I//DAe6eDmwnf5ReRCQqTV2+hTOf+I7FG3bx1OXduPP0o4iL1RjF4aqaUI57zmrHhzccQ/Palbj9nTlc8MwU5q7dEXQ0ERGgePvMjwX+CfxE0fWXr+vu6wHcfb2Z1TnC/RwLbHT3JaHns4EBZvY6+Sfsdg/dTzt4QzMbCgwFaNy48REeXkSkeLg7L36/gn98upAmtSry+tDetKxTJehYUa9dg6q8dV0f3pmxln98soCz//M9V/Zuwm2ntCm1F9kSkehQnMX8Znd//HA3MrOvgHoFvHRX+JF+din/HZUHGAUcBaQAq4ApQE5BG7r7c8BzkD9nvggziYiEZfe+HG5/Zw4fz1nPae3r8dCFnaiSoEKzqJgZF3RP4uR2dXnki0W8PHUVH/+0njt/dxTndWuozkAiEojiPAH2X8A+4IPQPRBea8rQSbX9Q6Py9YFJ7t7mEOs2BT468ATY0PI4YC3Q3d3TDrHtFOBad5//a3l0AqyIRIplmzK47uVUlm3K4PbT2jKsX3MVl8Vs7tod3D1+LjNXb6dH0xr85ZwOtK1XNehYIlJKHeoE2OIcme8auu99wDIHwmlN+QEwEHggdD/+CPZxErDwwELezCqS/8Fmt5mdDOT8ViEvIhIpPpu7gT+8NZv4uBhevqYXfVvWDjpSmdChYTXeue5o3kpdwwOfLuSMx79nYJ+m3HpyK30jIiIlpliKeTOLBT5w90eLeNcPAG+a2TXAauDC0PEaAC+4++mh5+OA/kBtM0sD7nX3F0P7uIT/nWIDUAf43MzyyB+1v7KIc4uIFLncPOfhLxbx9KRldE6qxlNXdKdh9QpBxypTYmKMi3s05tT29Xjw80WMnrKCD+es489nHMXZnRvo2xERKXbFOc1morsfXyw7jxCaZiMiQdm6O4ubxs3k+6WbubRnY0ae3Y7ycbFBxyrzZq/Zzt3j5zInbQe9m9fkLwM60KquTkAWkfAFcdGovwHVyL/I0+79y8OZMx9pVMyLSBDmpG1n+Csz2JSxj78O6MBFPRoFHUkOkJvnvD59NQ9+tojd+3K4+phm3HRiKyqXL86ZrSJS2gVRzE8sYLG7ezhz5iOKinkRKWlvTF/N3ePnkVi5PM9c0Z2OSdWCjiSHsHV3Fv/8dCFvpKyhXtUE/nzmUZzRsb6m3ojIESnxYr4sUDEvIiUlMzuXkR/M4/Xpazi2VW3+fUlXalaKDzqWFMKM1du4+/25zFu3k2Na1mbk2e1pWady0LFEJMoEMTJfDbgX6Bda9A1wv7uXmsvmqZgXkZKwdvtehr+Sypy0HVx/fAtuO7kNsTEa3Y0muXnOqz+u4qHPF5GZncu1xzbnxhNaUjFeU29EpHAOVcwX57W9RwG7gItCt53A6GI8nohIqfP9ks2c+fh3rNi0m+eu7M4fT22rQj4KxcYYV/Vpyte/78/ZnRvy9KRlnPTIN3w2dz36hlxEwlGcI/Oz3L3Lby2LZhqZF5Hi4u48/c0yHv58ES3rVOaZK7rTPFFTM0qL6Su3cvf7c1m4YRfHtU5k5NntaVa7UtCxRCSCBTEyv9fMjjkgQF9gbzEeT0SkVNiVmc11r6Ty4GeLOL1jfd4b0VeFfCnTo2lNPrrxGO45sx2pq7Zx6qPf8sgXi9iblRt0NBGJMsU5Mt8ZeIn89pQGbAUGufvsYjlgADQyLyJFbcnGXQx7OZVVW/fwf6cfxdV9m6r7SSmXvjOTv3+ygPdnrSOpRgXuPas9J7erG3QsEYkwgXWzMbOqAO6+s1gPFAAV8yJSlD6es54/vj2bivFxPHlZV3o1rxV0JClBPyzbwj3j57IkPYMT29bh3rPa07hWxaBjiUiECKKbTXngfKAp8PPp+u5+f7EcMAAq5kWkKOTk5vHPzxby/Hcr6Na4Ok9d3p161RKCjiUByM7NY8zklTz21WJy8pwR/Vsy7LjmJJTT1X1Fyrog5syPBwYAOeRfAXb/TUREQjbt2sflL/zI89+t4Ko+TXh9aB8V8mVYudgYhvRrzoTf9+fkdnV59KvFnPrYt0xclB50NBGJUMU5Mj/X3TsUy84jhEbmRSQcM1ZvY8QrM9i+N4u/n9uR87olBR1JIsz3SzZzzwdzWb5pN6e0q8s9Z7UjqYam3oiURUGMzE8xs47FuH8Rkajk7rw8dRUXP/sD8XExvDu8rwp5KdAxrWrz2c39+NNpbfluyWZO+tc3PDlxKfty1PVGRPIV58j8fKAlsALYR35HG3f3TsVywABoZF5EDldmdi53vTeXd2akcXybRB67uCvVKpYLOpZEgbXb9/LXj+bz6dwNNKtdifvObk+/1olBxxKREhLECbBNClru7quK5YABUDEvIodjzdY9DHs5lQUbdnLzia246YRWxOhqrnKYJi1KZ+QH81i5ZQ+nd6zH3We2o361CkHHEpFiFlhrytJMxbyIFNakRenc/Pos3J1/X9KV49vWCTqSRLF9Obk8981y/jNxKbExxk0ntuLqvs2IjyvO2bMiEqQSmzNvZjOKYh0RkdIgL895fMISBo+ZToPqFfjwxmNUyEvYysfFcuOJrfjqtuM4ukVtHvh0Iac//h1Tlm4OOpqIlLAiH5k3s73Akl9bBajm7o2L9MAB0Mi8iPyaHXuzue2NWUxYmM65XRvy93M7UiFe/cKl6E1YsJGRH85jzda9nNW5AXedfpRanIqUMocamY8raOUwtS3EOjoN/whNX7mVKUs3c0yrRLo3qRF0HBE5hAXrd3LdK6ms3baX+we058reTTDT/HgpHiceVZe+LWvz9KRlPP3NMr5esJFbT27NwKObUi5WU29ESjPNmQ9DSY/Mp67axqXPTSUrN49YM+4+8yiu6tNUJ9CJRJj3Z67ljnfnUDWhHE9f0Y3uTWoGHUnKkFVbdjPyg3lMXLSJNnWrcP+A9vRqXivoWCISpiD6zEsRm7p8Czl5eQDkujPyw/n0/efX/OPTBSzasCvgdCKSlZPHyA/mccsbs+iUVJ2PbjpGhbyUuCa1KjFqUA+eu7I7GftyuPi5qdz6xizSd2UGHU1EikFxTLORYtK7eS3i42LIzsmjXGwMI45vyew123nxuxU8+81yjqpflfO6NmRAlwbUqaq5kiIlKX1nJiNenUHKqm1cc0wz7vhdW01vkMCYGae0r8exrRJ5cuJSnvt2OV/N38htp7Tmyt5NiNPfTZFSozj7zFcC9rp7npm1Jn8u/afunh3GPmsCbwBNgZXARe6+rYD1RgFnAunu3uGA5V2AZ4AEIAcY4e7TQq/dCVxD/nz+m9z989/KE8QJsKmrtjF1+RZ6N6/185z5LRn7+HD2Ot6buZbZaTuIMejbsjbndm3Iqe3rUam8PrOJFKdpK7Zy/WszyMjM4Z8XdOLszg2CjiTyP5ZvyuDeD+bx3ZLNHFW/Kn8Z0J7kpvrWSCSaBHHRqFTgWKAGMBVIAfa4++Vh7PNBYKu7P2BmdwA13P1PBazXD8gAXjqomP8CeNTdPzWz04Hb3b2/mbUDxgE9gQbAV0Brd//VE3UjsZvNsk0ZvD9zLe/NXEvatr1UKBfLaR3qcU7XhvRtUUujMSJFyN0ZPXklf/9kAY1qVuTZK7vTum6VoGOJFMjd+WzuBu7/aD7rd2RyQfck7vhdW2pXLh90NBEphJLsZvPzMd19j5ldAzzh7g+a2cww9zkA6B96PBaYBPyimHf3b82saQHbO1A19LgasO6A/b7u7vuAFWa2lPzC/ocw8/5/e/ceXXV95nv8/eRGAiEkhECSHS4Cish1A7V4x7YyoKiEsU6v69hzOlbt9OLU9vQys7Q9nTWOtTPT5Uxb6+W0PdrbWIKKRaytVq2jVtiBcBFFFMxOIBBIQiD3/Zw/9gYDJIDC5ped/XmtxUqys/fv+7h+y+wn3zzf5znjJpXk85WFU7j1I+ewZsc+lq+N8sT6+K59yfAhXDOrnMpwiGnlBeqsIXIKDnZ2843lNTxaXccV543h+9fPoiA3O+iwRPplZiyeUcal55Rwzx+3cv/z23hq407+5gNjGZGXzQWTRqlLmkgKSubOfAS4Bfg34H+5+0Yzq3H3GadwzSZ3L+z19T537/MnTyKZX3nUzvxUYDXxXvcZwIXuvt3M/gN4yd0fSjzvAeIlQY8cL56BuDPfl47uHp55rYHla6M8s6WBrh7nnDH5LA2HWDo7RHmhxoCLvBdv7TnATf9vDa837Oe2hVO4+bJJ6iolKWdrw35u/XU1NdEWALIyjHs/PZcPTx0TcGQi0pcgdua/DHwDqEok8hOBZ070IjN7Gijt41vfOg0x3Qzc6u6/NbPrgQeAjxBP7o/W5285ZnYjcCPAuHGpMfdqSFYmi6aXsWh6GU0HO1m5vp6qSJS7ntzC91ZvYf5ZxVSGQyyeUcpw7SyKHNfTm3Zx62+qycwwfvaZ87n0nJKgQxJ5XyaPHs6i6aVsiLbgQHfM+ezPX+Wyc0qoDIdYeF6phpyJpICk95k3s2HufuA0XWsLsMDd682sDHjW3af089wJHLsz3wwUurtbvMak2d0LEodfcfd/TjxvNXCHux+3zCZVdub7s73xACsidVRFanm78SBDsjK44rwxVIZDXHpOiTpxiPTSE3P+/enXueePW5kRGsEPPzmHsSOHBh2WyClZs30fn7z/Jbq6Y2RlZnDljDJe3tZIXXM7w3LiG0HL5oSYP7GYTP31SSRQQRyAvYD4zne+u48zs1nA59z9llO45veAxl4HYEe6+9f6ee4Ejk3mNwM3u/uzZvZh4C53n2tm04Bf8O4B2D8AZ6fiAdj3w92pfqeJqkiUx9fVse9gF8XDcrh6VjlLwyFmVYxQfb2ktaaDnXzxV9U89/purp9XwXeunU5utnYsZXA4uktaLOa8/NZeVkSi/K6mnv0d3ZQW5HLt7Ph7wtSyghNfVEROuyCS+ZeB64DH3D2ceGxD7+T6fVyzGPgNMA7YAXzU3feaWTlwv7tfmXjeL4kflB0F7AJud/cHzOxi4AfEy4vaibemXJN4zbeA/0m8ZeWX3X3VieIZLMl8b53dMZ57fTdVkSi/37yLzu4YE0cNozIcYmk4pJ1ISTsbos3c9NAaGlo6+Pa10/jYB8bql1tJG+1dPTy9eRcrIlGe3bKb7phzbulwls0Jcc2sEKUjNNNE5EwJJJl39w+aWaRXMr/O3WclZcEADMZkvreW9i5W1dSzfG2Ul9/aC8AHJhSxNBxiyYxyRgxVfb0Mbv/16jv8w4oNjByWw48+NZfZYwuDDkkkMI2tHYfPXFW/04QZXDQpMdNkein5mmkiklRBJPOPAP8K/AcwH/giMM/dP5aUBQMw2JP53qJNbYf7129taCUnM4MPnTuapeEQl59bwpAslRzI4NHR3cN3Ht/Ewy/v4MJJxdzz8TDF6sUtcti2QzNNqqO8szc+02ThtPiZq4snj9JME5EkCCKZH0W8pOVQt5ingC+5e2NSFgxAOiXzh7g7G+taWL42ymPr6tjT2sGIvGyWzCyjMhxi7vgilSBISqtvbuPmh9ZS/U4Tn7tsIl9dOEWJiUg/3J012/exPBLlifX1NLd1MSo/PtNk2RzNNBE5nc54Mp8O0jGZ7627J8YLW/dQFYmyeuNO2rtijBs5lKXhEJXhEGeNGhZ0iCLvyYtv7uELv4jQ3tXD3R+dxeIZZUGHJJIy4jNNdrMiEuWPrzXQ2RNj8uj8w2euQpppInJKgtiZvwv4LtAGPAnMIn6w9KGkLBiAdE/me2vt6Gb1hp1URaL8+c09uMPssYUsmxNiycxyRg7LCTpEkX65O/c9v407V73GWaOGce+n5zF5dH7QYYmkrKaDnTxRU8+KSJS/vL0PgA+eNZJlc0IsnlGmacki70MQyXy1u882s0pgKXAr8IwOwA5+O5vbeWxdlOVro7y2cz9ZGcaCKSVUhiv48NTRauknA0prRzdfe2Qdv6vZyZUzSrnrulk6yCdyGu1oPMiK6viZq7f2HCAnK4Mrpr470yQnS2VsIicjiGR+o7tPM7P7gN+6+5PqZpN+Nte3sCISZUV1lF0tHQwfksWVM8qonBPi/AkjydAQEgnQ1oZWbnpoDdt2t/L1xefyt5dMVH2vSJK4O+tqm6laW8vj6+vZe6CToqHZXD2rnMpwiNljC/X/n8hxBJHM30l8R76N+DCmQuJDnD6YlAUDoGT+5PXEnP9+s5GqSJQnN9RzoLOHUGEe186OH5KaPHp40CFKmnlyQz23/dd6hmRlcM8nwlw4aVTQIYmkja6e+EyT5ZEoT2/aRUd3jLNGDWPp7PiZq3HFmmkicrRADsCaWRHQ4u49ZjYUKHD3nUlb8AxTMv/+HOzs5vebdlEVifL8G3voiTnTQwVUhiu4ZlY5JcPVAlCSp7snxvee2sK9f9rGrLGF/OiTcyjXwTyRwLS0d/FkzU6WR2p5aVt8psnc8UVUhkMsmVlG4VCduRKB4JL56cB5wOERce7+86QteIYpmT91u/d38Pi6OqoiUWqizWRmGBdPHsWyOSGuOG8MQ3NUuyynT2NrB1/4ZYQX32zkEx8cx+1Xn6cZCSIDSLSpjUero1StjfJGQyvZmcblU0azbE6Iy88drf9fJa0FUWZzO7CAeDL/O2Ax8IK7X5eUBQOgZP702tqwn6pIlBWROqJNbQzLyeSvppeyLFzBBZOKyVR9vZyC6neauPmhNTQe6OS7S6dz/byxQYckIv04NNOkKhLl0er4TJOC3CyumhkvzZynmSaShoJI5muIt6OMuPssMxsD3O/uVydlwQAomU+OWMz5y9t7qYpEeaKmnv3t3YwpGMK1iVrKqWUFQYcoKeaXr+zg9kc3UjJ8CPd+ei7TQyOCDklETlJ3T4w/v9lI1dpaVm/cRVtXDxVFeVQmZppMLFEbWUkPQSTzr7j7+Wa2Brgc2A9scPdpSVkwAErmk6+9q4c/bG6gKhLl2S0NdMecc0uHUxkOce3sEKUjck98EUlLa7bv44U3drO+tpk/vNbApeeU8IO/mU2RZh6IpKwDHd2s3piYabJ1DzGHWWMLqZxdztWzyinO15krGbyCSOZ/CHwT+BjwFaAVqHb3zyRlwQAomT+z9h7o5In1dSyPRInsaMIMLpxUTGW4gkXTS9UbXGjv6iHa1MazWxq4c9VrdPXEf75dNzfEv/z1LJVqiQwiu1raeaw6/p6wub6FzAzjsnNKqAzHz1xppokMNoEcgO21+ATinWzWJ32xM0jJfHDe2nOAFZH4EJIdew+Sm53BwvNKqZwT4pLJo8jK1BCSwcbdaWnrprbpINF9bUSb2qhrin889PWe1s5jXpdh8JWFU/j85ZMDiFpEzoTXdibq6yN17GxpJ39IFounx98T5p9VrJkmMigEsTNfCfzR3ZsTXxcCC9x9RVIWDICS+eC5O2t37KMqEmXl+nqaDnYxKj+Hq2eVsyxcwfRQgQ5JpYhYzNnd2kFt70R935HJemtH9xGvGZKVQagwj1BRXvxj4vMDHd1894nNdPfEyM7K4OHPzmfu+KKA/stE5EzpiTkvb2tkeSTKqpr4TJOyEblcOzvEsjkhzhmjmSaSuoJI5qvdffZRj0XcPZyUBQOgZH5g6eyO8cyWBlZEovxhcwOdPTEmj85P1NeXU1GkISRB6uyOUd/cK0FvOvLz+qZ2OntiR7xmRF425YkkvaIo78jEvSiP4mE5/f6ytmb7Pl7a1sj8icVK5EXSUFtnD7/fvIuqtbU8l5hpcl5ZAcvmhLhmVjmjC3TmSlJLEMn8enefedRjNe4+IykLBkDJ/MDVfLCLJ2rqWRGJ8srb8SEkHzxrJJXhEItnlDEiLzvgCAef1o5uovviO+q1R+yqHyTa1EbD/g6O/nEzeviQI5LzisPJ+lDKC3MZnqv7JCKnbvf+Dlauj880WV/bTIbBxWeXUBku56+mlWqmiaSEIJL5B4Em4D8BB74AFLn7DUlZMABK5lPDO3sPHq6v37bnADlZGVwxdQxLwyEuO6eEnCzV15+Iu9N4oPOI0pfao0pgmtu6jnhNdqZRXphH+Yi8PhP20hG5GgAjImfc1obWw+8J0aY2huZksmhaKUvDIS6aPEoH5WXACiKZHwb8I/CRxENPAf/k7geSsmAAlMynFndnfW0zVZEoj6+ro/FAJ0VDs1kys5zKOSHCYwvTtr6+uyfGrv0dicT84DEJe11TG+1dR5bADMvJPCJJDxUOPfx1RVEeJflDdOhMRAasWMx5dfs+qiK1rFwfn2kyevgQrpkVf084r0xnrmRgCbSbTV/M7B53/0Igi58mSuZTV1dPjOff2E1VpI6nNu6kozvGWaOGsXR2iKXhcsYXDws6xNPqUMvGow+UHvq4s6WdntiRPwuKh+Ucc7D03d31oRTkZemNTkQGhfauHp55rYHliZkmXT3OlDHDWRqOvyeUjcgLOkSRAZnMr3X3OYEsfpoomR8c9rd3sWrDTqrWRnnprUbcYe74IpaGQyyZUTbghwz11bLx6EOmjQeObNmYmWGUFuQek6T3/qgezSKSjvYd6GRlTT1Va2tZm5hpcsHEYpaGQyyeXqqzPBIYJfNJoGR+8KlrauPR6jqqIrW8vquV7Ezj8imjqQyH+NDU0YHUeB/dsrF3KUxdU3ufLRtzszP66QITL4UZM3yIevGLiJzA23sOsKI6Xl+/vfEgQ7IyWDitlMpwOZecXUK2fo7KGaRkPgmUzA9e7s6m+haq1kZ5dF0du/d3UJCbxVUzy6kMh5g3vui01YP3btnYuwtM3XFaNhYOzT7iYOnRbRtHHqdlo4iIvDfxmSZNrIhEeXx9HU0HuygeFp9pUhkOMbNihH7mStINxGT+PfecN7ORwK+BCcDbwPXuvq+P5z0ILAEa3H16r8dnAz8GcoFu4BZ3f8XMioFHgA8AP3X3vzuZeJTMp4eemPPnrXtYEYny5MadHOzsoaIoj8pwiKXhEJNK8o/7+kMtGw/tptc2JXbU+2nZaJZo2ViYR6ho6DFdYMoL88gfojZqIiJB6OyO8afXd1MVqeXpzQ10dseYWDKMZeEQ184OMXakZppIcgTRzWaiu287zvdvcPefvsdr3gXsdfc7zezrxFtd/u8+nncp0Ar8/Khk/ing39x9lZldCXzN3RckOu+EgenAdCXz0p8DHd08tWkny9dG+fPWPcQcZlWMYO74Iva0xrvjmNlxWzbmZGZQVpjb78HS0hG5apcpIpICmtu6WFVTz/JIlFfeis80OX/CSJaGQ1w1o4wRQ1VfL6dPEMn8c0AI+AvwHPC8u9ec4jW3AAvcvd7MyoBn3X1KP8+dAKw8KplfDTzo7r82s48DV7v7J3p9/wZgnpJ5ORkNLe08tq6Oh17aztuNBw8/npedybiRQ/s8WFpRmMcotWwUERl03tl7kMfW1bF8bS1v7j5ATmYGH54aP3O1YMpobdLIKQukzMbMcoiXriwAPgfku/vIU7hek7sX9vp6n7v3Oae9n2R+KrAaMCADuNDdt/f6/g2cIJk3sxuBGwHGjRs3d/v27f09VdLEfz6zle8/tYWYQ6bB3y+cwucvnxx0WCIiEgB3pyb67kyTPa2dFA7NZsnMMirDIeaMK1J9vbwv/SXzSSu8NbOLgUsS/wqBlcDzJ/G6p4HSPr71rdMQ1s3Are7+WzO7HniAd4danRR3/wnwE4jvzJ+GmCTFzZ9YTE5WBl3dMbKzMpg/sTjokEREJCBmxsyKQmZWFPLNK6fywht7qIpEeWRNLQ+9tIPxxUNZOjtEZTjEhFGDa6aJBCOZZTY9wKvAPwO/c/fOE7zkZK55qmU2zUChu7vFfy1udveCXt+/AZXZyPuwZvs+XtrWyPyJxcwd3+cfi0REJI3tb+9i9cZdVEVqefHN+EyT8LhCloVDXDWznJEDfKaJBO+M78wDxcBFwKXAF80sBvy3u//jKVzzMeB/AHcmPj76Hl9fB1wGPAt8CHjjFGIROWzu+CIl8SIi0q/hudlcN7eC6+ZWUN+cmGmyNso/PrqRbz++iQVTRrNsTogPnTtaQ/vkPUl2zfxU4snzJcCFwA53v+wUrlcM/AYYB+wAPurue82sHLjf3a9MPO+XxOv0RwG7gNvd/YFE6c8PiP8S0068NeWaxGveBgqAHKAJWOjum44Xj3bmRURE5FRsqmthRXWUFZEoDfs7GJ6bxVUzyphWXkBzWxcXTBqlzSIBgulm8yawBXiBeK38y6ej1GYgUTIvIiIip0NPzHnxzXh9/RPr6+nojmHAkOwMHv7sfCX0EkiZzdnuHjvx00RERETSW2aGccnZJVxydgnjRg7lB0+/gQNd3TFe2taoZF76lcymp5PN7A9mtgHAzGaa2T8kcT0RERGRlHfJ2SUMyc4g01CXNDmhZO7M3wd8FbgXwN3Xm9kvgO8mcU0RERGRlDZ3fBEPf3a+uqTJSUlmMj/U3V85ajBCdxLXExERERkU1CVNTlYyy2z2mNkkwAHM7DqgPonriYiIiIiklWTuzH+e+KTUc80sCrwFfCqJ64mIiIiIpJWkJfPuvg34iJkNAzLcfX+y1hIRERERSUdJS+bNbAjw18AEIOtQ7by7fydZa4qIiIiIpJNkltk8CjQDa4COJK4jIiIiIpKWkpnMV7j7oiReX0REREQkrSWzm82LZjYjidcXEREREUlrydyZvxi4wczeIl5mY4C7+8wkrikiIiIikjaSmcwvTuK1RURERETSXjJbU25P1rVFRERERCS5NfMiIiIiIimttbWVO759B2MqxpCRmcGYijHc8e07aG1tDTo0AMzdg44hZc2bN89fffXVoMMQERERkSRobW3lwssuZNeQXQxfNJzcilzaa9tpWdVCaWcpL/7pRfLz889ILGa2xt3nHf24duZFRERERPpw9/fvZteQXZTcWELe+Dws08gbn8foz41mZ85O7v7+3UGHqGReRERERKQvP7rvRwxfNBwzO+JxM6NgcQE/vv/HAUX2LiXzIiIiIiJ92F2/m9yK3D6/lxvKZXf97jMc0bGUzIuIiIiI9KGkrIT22vY+v9cebaekrOQMR3QsJfMiIiIiIn24+W9vpmVVC0c3jHF3Wla1cNNnbwoosncpmRcRERER6cNtX7mN0s5SGu5toG17G97ttG1vo+HeBko7S7ntK7cFHaKSeRERERGRvuTn5/Pin17klkW30HpfK5s/t5nW+1q5ZdEtZ7Qt5fGoz/wpUJ95ERERETkT+uszr2T+FJjZbmB7AEuPAvYEsK4cn+7LwKN7MjDpvgw8uicDk+7LwBPkPRnv7secuFUyn4LM7NW+fjOTYOm+DDy6JwOT7svAo3syMOm+DDwD8Z6oZl5EREREJEUpmRcRERERSVFK5lPTT4IOQPqk+zLw6J4MTLovA4/uycCk+zLwDLh7opp5EREREZEUpZ15EREREZEUpWQ+xZjZIjPbYmZbzezrQccjYGYPmlmDmW0IOhaJM7OxZvaMmW02s41m9qWgY0p3ZpZrZq+Y2brEPfl20DFJnJllmlnEzFYGHYvEmdnbZlZjZtVmpoE2A4SZFZrZI2b2WuL95YKgYwKV2aQUM8sEXgeuAGqBvwAfd/dNgQaW5szsUqAV+Lm7Tw86HgEzKwPK3H2tmQ0H1gBL9f9KcMzMgGHu3mpm2cALwJfc/aWAQ0t7Zvb3wDygwN2XBB2PxJN5YJ67q8f8AGJmPwOed/f7zSwHGOruTQGHpZ35FHM+sNXdt7l7J/Ar4NqAY0p77v4csDfoOORd7l7v7msTn+8HNgOhYKNKbx7XmvgyO/FPu0kBM7MK4Crg/qBjERnIzKwAuBR4AMDdOwdCIg9K5lNNCHin19e1KEEROS4zmwCEgZcDDiXtJco5qoEG4PfurnsSvH8HvgbEAo5DjuTAU2a2xsxuDDoYAWAisBv4v4mytPvNbFjQQYGS+VRjfTymnS2RfphZPvBb4Mvu3hJ0POnO3XvcfTZQAZxvZipLC5CZLQEa3H1N0LHIMS5y9znAYuDziXJOCVYWMAf4kbuHgQPAgDi7qGQ+tdQCY3t9XQHUBRSLyICWqMv+LfCwuy8POh55V+JP088Ci4KNJO1dBFyTqM/+FfAhM3so2JAEwN3rEh8bgCriZbYSrFqgttdfFB8hntwHTsl8avkLcLaZnZU4ePEx4LGAYxIZcBKHLR8ANrv7vwYdj4CZlZhZYeLzPOAjwGuBBpXm3P0b7l7h7hOIv5/80d0/FXBYac/MhiUO7pMo41gIqFtawNx9J/COmU1JPPRhYEA0VcgKOgA5ee7ebWZ/B6wGMoEH3X1jwGGlPTP7JbAAGGVmtcDt7v5AsFGlvYuATwM1iRptgG+6+++CCyntlQE/S3TlygB+4+5qhShyrDFAVXxPgizgF+7+ZLAhScIXgIcTG6rbgM8EHA+g1pQiIiIiIilLZTYiIiIiIilKybyIiIiISIpSMi8iIiIikqKUzIuIiIiIpCgl8yIiIiIiKUrJvIiIiIhIilIyLyIi/TKzYjOrTvzbaWbRxOetZvbDJKz3UzN7y8xu6vX1dX08b9KhOE53DCIiqURDo0REpF/u3gjMBjCzO4BWd787yct+1d0fOUFcbwKzlcyLSLrTzryIiLxnZrbAzFYmPr/DzH5mZk+Z2dtmtszM7jKzGjN70syyE8+ba2Z/MrM1ZrbazMpOcrlLzexFM9vW1y69iEg6UzIvIiKnwyTgKuBa4CHgGXefAbQBVyUS+nuA69x9LvAg8E8nee0y4GJgCXDn6Q5cRCSVqcxGREROh1Xu3mVmNUAm8GTi8RpgAjAFmA783sxIPKf+JK+9wt1jwCYzG3NaoxYRSXFK5kVE5HToAHD3mJl1ubsnHo8Rf68xYKO7X/B+r51gpxamiMjgojIbERE5E7YAJWZ2AYCZZZvZtIBjEhFJeUrmRUQk6dy9E7gO+BczWwdUAxcGGpSIyCBg7/4lVEREJFhm9lNg5YlaU/Z6fqu75yc3KhGRgUs78yIiMpA0A//n0NCo/hwaGgXsOiNRiYgMUNqZFxERERFJUdqZFxERERFJUUrmRURERERSlJJ5EREREZEUpWReRERERCRFKZkXEREREUlR/x8tTgqBJ4ZJ+wAAAABJRU5ErkJggg==\n",
      "text/plain": [
       "<Figure size 864x576 with 3 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "w1 = WindowGenerator(input_width=6, label_width=1, shift=1, label_columns=['new_cases'])\n",
    "w1.plot()\n",
    "display(w1.train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 4
}