Completo_COVID_19_Mercosul_Brasil.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Completo-COVID-19-Mercosul-Brasil.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPO9jxxUpbB99siLxelq/is",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/nascimento-luciano/Artificial-Intelligence/blob/master/Completo_COVID_19_Mercosul_Brasil.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4_shQlW-OyqP"
      },
      "source": [
        "#---------------------------------Etapa 1--------------------------------------\n",
        "#Importanto bibliotecas\n",
        "\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "#import geopandas as gpd -----> Verificar porque não carrega a biblioteca\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go"
      ],
      "execution_count": 349,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "50HAQscEO-HT",
        "outputId": "0964dfd6-9aec-4206-879d-9920d7d3a499"
      },
      "source": [
        "#Importação da biblioteca geopandas via instalação no Colab\n",
        "!pip install geopandas"
      ],
      "execution_count": 350,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: geopandas in /usr/local/lib/python3.7/dist-packages (0.10.2)\n",
            "Requirement already satisfied: fiona>=1.8 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.20)\n",
            "Requirement already satisfied: shapely>=1.6 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.8.0)\n",
            "Requirement already satisfied: pyproj>=2.2.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (3.2.1)\n",
            "Requirement already satisfied: pandas>=0.25.0 in /usr/local/lib/python3.7/dist-packages (from geopandas) (1.1.5)\n",
            "Requirement already satisfied: six>=1.7 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.15.0)\n",
            "Requirement already satisfied: attrs>=17 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (21.2.0)\n",
            "Requirement already satisfied: certifi in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2021.10.8)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (57.4.0)\n",
            "Requirement already satisfied: munch in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (2.5.0)\n",
            "Requirement already satisfied: click-plugins>=1.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (1.1.1)\n",
            "Requirement already satisfied: click>=4.0 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (7.1.2)\n",
            "Requirement already satisfied: cligj>=0.5 in /usr/local/lib/python3.7/dist-packages (from fiona>=1.8->geopandas) (0.7.2)\n",
            "Requirement already satisfied: numpy>=1.15.4 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (1.19.5)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.25.0->geopandas) (2018.9)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Nc0h3H3rPDEq"
      },
      "source": [
        "#---------------------------------Etapa 2--------------------------------------\n",
        "#Importando Arquivo CSV\n",
        "#Dados não \"Oficiais\"\n",
        "covid_cases = pd.read_csv('worldometer_coronavirus_daily_data.csv')\n",
        "covid_vaxxs = pd.read_csv('country_vaccinations.csv')\n",
        "df = pd.read_csv('covid_19_data.csv')\n",
        "train=pd.read_csv('train.csv')\n",
        "test=pd.read_csv('test.csv')\n",
        "sub=pd.read_csv('submission.csv')\n",
        "submission=pd.read_csv('submission.csv')\n",
        "confirmed_df = pd.read_csv('time_series_covid_19_confirmed.csv')\n",
        "\n",
        "#train['Date'] = pd.to_datetime(train['Date'])\n",
        "#test['Date'] = pd.to_datetime(test['Date'])\n"
      ],
      "execution_count": 351,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ewu71qOlQQXk"
      },
      "source": [
        "#---------------------------------Etapa 3--------------------------------------\n",
        "#Vamos marcar o datase e usar como amostra os países do Mercosul: Brazil, Argentina, Paraguay e Uruguay\n",
        "\n",
        "def fix_country_name(country):\n",
        "    name_dict = {\n",
        "        'BRA': 'Brazil',\n",
        "        'ARG': 'Argentina',\n",
        "        'PRY': 'Paraguay',\n",
        "        'URY': 'Uruguay'\n",
        "     }\n",
        "    try:\n",
        "      return name_dict[country]\n",
        "    except KeyError:\n",
        "      return country\n",
        "\n",
        "covid_cases.country = covid_cases.country.apply(fix_country_name)"
      ],
      "execution_count": 352,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "OJLmTUSDQSS7"
      },
      "source": [
        "#---------------------------------Etapa 4--------------------------------------\n",
        "#Vamos observar esses 4 países\n",
        "countries_of_interest = [\"Brazil\", \"Argentina\", \"Paraguay\", \"Uruguay\"]\n",
        "\n",
        "#Vamos começar a limpar os dados filtrando-os para o subconjunto de países que desejamos.\n",
        "covid_cases_subset = covid_cases[covid_cases.country.isin (countries_of_interest)]\n",
        "covid_vaxxs_subset = covid_vaxxs[covid_vaxxs.country.isin (countries_of_interest)]"
      ],
      "execution_count": 353,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Y33OiuZBQT3y"
      },
      "source": [
        "#---------------------------------Etapa 5--------------------------------------\n",
        "#Unindo os databases usando as datas e países\n",
        "   \n",
        "merged_data = pd.merge_ordered(covid_cases_subset, covid_vaxxs_subset, \\\n",
        "                               left_on=['date', 'country'], \\\n",
        "                               right_on=['date', 'country'])\n",
        "\n",
        "#Remove as colunas desnecessárias\n",
        "merged_data.drop(columns=['iso_code', 'source_name', 'source_website', 'vaccines',\\\n",
        "                           'daily_vaccinations_per_million', 'daily_vaccinations_raw'], inplace=True)"
      ],
      "execution_count": 354,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pyw-C9mpQVWa"
      },
      "source": [
        "#---------------------------------Etapa 6--------------------------------------\n",
        "#Vamos lidar com os NaNs\n",
        "merged_data.fillna({'total_vaccinations':0, 'people_vaccinated':0, 'people_fully_vaccinated':0,\\\n",
        "                 'daily_vaccinations':0,'total_vaccinations_per_hundred':0,\\\n",
        "                 'people_vaccinated_per_hundred':0 ,'people_fully_vaccinated_per_hundred':0}\\\n",
        "                 ,inplace=True)\n",
        "\n",
        "covid_data = merged_data.dropna(how='any', subset=['cumulative_total_cases'])"
      ],
      "execution_count": 355,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R1NLUjqmQWsC"
      },
      "source": [
        "#---------------------------------Etapa 7--------------------------------------\n",
        "#Vamos também definir o índice para a data\n",
        "covid_data.set_index('date', inplace=True)"
      ],
      "execution_count": 356,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bWBOmmGuQX_7"
      },
      "source": [
        "#Vamos mudar o índice para um objeto datetime agora\n",
        "covid_data.index = pd.to_datetime(covid_data.index, errors='coerce', infer_datetime_format=True)"
      ],
      "execution_count": 357,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "VesqVGugQZZC",
        "outputId": "2af80829-5d65-4174-d9b8-916a192755bc"
      },
      "source": [
        "#---------------------------------Etapa 8--------------------------------------\n",
        "#Taxas de infecção nos países do Mercosul\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data.groupby('country').daily_new_cases.plot(legend=True,rot=45)\n",
        "\n",
        "plt.ylabel('Novos casos diários')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Novos casos diários por países');"
      ],
      "execution_count": 358,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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x5513NDy+/fd/G67rMm3aNHbYYSeWLHmJefO2R+vHufzyS+jrW4frurz44gujnwxBEARBECYMptCf+CmMDAngx4CFe204Sz5WlEolbrvtFlpaWrnlFpstL5fL/PGPfwCgo6OzoX6MMey7736cdNK3q9qee24xra1t0XvXdfE8r+ExZl1bKpU46aSvct55F6LUbrz8ci+HHfaOhvsUBEEQBGHiY/wgXpiiG1E2CylinWTcccffmDdvB6699o9cddUfuOqqP3DOOedx8803Vp37qle9hrvvvou1a9cCcMstlXP22ef13HPPP3n22WeiY48//u8Nfn5X1zQGBjZ+VV0sFvA8j6222hqAa675/Ub3IQiCIAjCBCcM4MfASGMqIRn4ScZNN93A29+ezFy/4hX/ge/7PPjgIpTaPTq+664L+OAHj+G44z5KZ2cXr33t6+jqmgbAvHnbc/LJ3+G73/0OhUKBcrnEXnu9kt1337Pu5++88y5sv/0OHH30Eeyww46ceuqZDY27q2saxx77KT7xiWPo7p7B/vu/bSPvXBAEQRCECU8UwEsGfjQ4Y2ElOInZEVi8alU/vl+Zt+XLn2ebbXYY98Hk8y7l8uj+AAwODtDZ2QXARRddwJIlL3Hyyd9pxvDGnHrzPnv2dHp714/ziDYfZH7qI/NTH5mf2sjcVCNzUp+pNj/l5x9k6P9+SPtbP0HLgoV1z51qcxPHdR1mzZoGsBPwXLpdMvBTnJ/+9DweeeQhyuUSc+duy1e+8r+bekiCIAiCIExSjJEMfDOQAH6K8+Uvf3VTD0EQBEEQhKlCIKExEsCPCiliFQRBEARBEMYHKWJtChLAC4IgCIIgCONDaB8pGfhRIQG8IAiCIAiCMC4Yv2xfiA/8qJAAXhAEQRAEQRgfxEayKUgALwiCIAiCIIwPEsA3BXGhmaQcfvihtLa20traRrFY4JWvfDVf/vLXyOdH/5X/4x9/46GHHuSzn/0Cixbdx/nnn8tFF13ehFELgiAIgjCpEQ18U5AAfhJz6qnfY/78XfA8j89+9hP87W9/5m1ve3vU7vs+juPgOM5G9fvGN76FN77xLc0eriAIgiAIkxwT2kiKBn5USAA/BSgWixSLBaZP7+aiiy5g8eJnGRjoZ8WK5fzsZ5fwy19ezIMPLqJUKtHT08OJJ57MNtvM4Q9/uI6rr74SAGMMzzzzFJdccgVPPaW56647OPXUMzfxnQmCIAiCsFkRFrFKBn5USAA/BpSevJOS/vuY9N2i3rzBrYdDvvGNr9La2saSJS+xzz77ss8+r+eRRx7iscce5eKLf01PTw8ARx31ET73uS8C8Ic/XMdPf/ojTjnlDA499DAOPfQwAC644Hy22247dt55F556So/JvQmCIAiCMMkRDXxTkAB+EhNKaAqFAt/4xle48sorANhvv4VR8A5w9913cs01v2doaBDP86r6ufHG61i06D5+9KOf4rpS9ywIgiAIwggxooFvBhLAjwEtCxY2nCUfD9ra2njDG97EXXfdwW677UFHR2fUtnz5Mn7847O58MJfMnfutjzyyEOccso3ovZ//eturrjics4//xe0tbVviuELgiAIgjBZCDPwooEfFRLATwF83+fBB+9n3rztq9oGBgbI51uYNWsWvu9z3XVXR23PPPM03//+6Zx11o+ZOXPmeA5ZEARBEIRJiBEJTVOQAH4SE2rgy+USO+20Mx/5yCf4/e9/kzhn5513Yf/9D+Soo45gxowe9ttvIQ899AAAv/vdrxkaGuTkk0+Mzv/2t08f13sQBEEQBGESEbrQSAA/KhxjzKYew+bEjsDiVav68f3KvC1f/jzbbLPDuA8mn3cpl6fuH4B68z579nR6e9eP84g2H2R+6iPzUx+Zn9rI3FQjc1KfqTY/w3dcRunxv9Cy10G073dk3XOn2tzEcV2HWbOmAewEPFfVPt4DEgRBEARBEKYoIqFpChLAC4IgCIIgCONCpIH3q13vhMaRAF4QBEEQBEEYH4xk4JuBBPCCIAiCIAjC+CASmqYgAbwgCIIgCIIwPoQuNL6YqIwGCeAFQRAEQRCEcUF84JuD+MBPUg4//FBaW1tpaWnF9z0+/OFjOfDAgzb1sARBEARBmMpIAN8UJICfxJx66veYP38XnnzyCY477lhe+9p96enp2eB15XKZfF5+NQRBEARBaDISwDeFcYnSlFI/AP4buxHSXlrrR4PjC4DLgFnAKuAYrfVTm6JtMrNgwW50dnbyzW+eyMDAAKVSiZ6eHk488WS22WYOy5Yt5eMfP5p3vONQFi26l3e/+7/YbrvtufDCn1IsFvA8j2OO+ViUwV+8+FlOP/0UhoeH2HVXxUsvvciHP3wsCxe+icMPP5QzzzyH+fN3AUi8P++8H/Lgg4uqPv+ss77HnDlz+OAHjwHgySef4Jvf/DpXXHE1juNssnkTBEEQBKHJSADfFMYrzXodcC5wR+r4z4Dztda/UkodBVwAHLCJ2prGPcvu55/L7m12twDsN+d17Dtn7426ZtGi+ygWi5xyyhlRBv4Pf7iOn/70R5xyyhkArFu3jt1334PPfe6LAPT19fGTn/yCXC7H6tWrOPbYo9lnn/3o7u7mO985mfe//4McdNA7eeKJx/jkJz/S0DiOOuojUf/xz//v/z6Cr371/3HkkUfjOA5XX30l//Vf75PgXRAEQRAmGSYM3H0J4EfDuATwWut/ACilomNKqa2A1wD/GRz6DXCeUmo24Ixnm9a6t6k3PEH4xje+SmtrG11dXZx22ve4++47ueaa3zM0NIjnJTdQaG1t44AD/jN6v3btGs4449u89NIL5HJ5+vrW8cILz7PTTjuxePEz/Od/HgzAbrvtwc4779LQeGp9/o477sTcudty9913seeee3HnnX/n+OO/1IQZEARBEARhQiEZ+KawKYXO84AlWmsPQGvtKaWWBsedcW5ragC/75y9NzpLPhaEGniA5cuX8a1v/S8XXvhL5s7dlkceeYhTTvlGdG5HR3si433WWd9l4cI3c/rp38dxHD7wgfdSLBai9lrZ8Vwuhx+zhioWi9Hn//jHZ9f8/MMP/wDXXnsVzz23mDe/eX+mTZvWnEkQBEEQBGHi4JeBWCZeGBFSqTgCZs1KBpcrV7rk85vGkbPe5+ZylXENDw/S0tLCVlvNxnXhhhuuia7P5VzASfTV37+e7bbblpaWHPfcczdLlrxILucyY0Y3O+20M3/+85846KB38MQTj/Pss8+Qy9nr582bx5NPPs5uuynuvfceVq9eRS7n1v18gDe96U2cd945PPXUE5x99o8bmk/XdZk9e3rN9nptgszPhpD5qY/MT21kbqqROanPVJqfYRd8oDVf/9/wkKk0NxvDpgzgXwS2VUrlgmx4DpgbHHfGuW2jWLWqP5Fl9n2fcnn8V5L5vFv3cz2vMq4dd9yZt771QI488nBmzOhhv/0WAosol308zwdMoq/jjvscZ531PX7+85+x++57sPPOu0b9feMb3+KMM77NZZddzPz5uzB//s50dHRRLvsce+xxnHbat7jyyt+y996vZeutt8HzfObP36Xm54ccfPAh3H33Xey00y4Nzafv+/T2rs9smz17es02QeZnQ8j81EfmpzYyN9XInNRnqs1PuVgCoFgobvC+p9rcxHFdpyphHGeTBfBa65VKqQeBI4FfBT8fCPXo49022bjqqj9UHfviF0/gi188IXp/7LGfAmDOnLncdNPtiXNf97rX89vfXpvZ9zbbzOXnP78Mx3FYvPhZjj/+U+y0084A7L77nvzqV7+Pzo1r2Wt9fsgDD9zP+953ZKO3KAiCIAjC5oZo4JvCeNlI/gh4L7ANcJtSapXWek/gOOAypdTJwBrgmNhl490mNMijjz7M+eefC9inEF/96v/S3d094v6eeOIxTj75RBYsULz1rU03BRIEQRAEYZwxfpnBa06hbZ/3kd/+PyoNEsA3hfFyofk88PmM408A+9a4ZlzbhMbZZ5/Xs88+r29af7vttgdXXnl90/oTBEEQpiamXMSUhnE7Rp5UEppEcRh/9Yt4q1+oEcCb7OuEhtg0lZeCIAiCIAhNpvjQHxm89pRNPQwBMGGg7pWTDcFxcaEZHRLAC4IgCIIwKfD7VmIG123qYQgQ2UXilRKHo8BeNnIaFRLAC4IgCIIwKTCFAfDLGAkONz1hpr2cDOAxooFvBhLAC4IgCIIwKTCFAfsilfUVxh9TIwNf0cAnd4QXNg4J4AVBEARBmBwM99ufEsBvesIMfEwDb4ypBPC+FLGOBgngJyFvfONrGRwcTBw75JC3sWzZ0k00IkEQBEEYe8IMvCkXN/FIhChQjy+m4rIZkdCMCgngpzjlcnnDJwmCIAjCBMcYH1OQDPxoKS99HG/lM6PvyMuQ0PgV2Yy40IyOTbYTq7DpOPzwQ3nb297OokX3Mn/+Lrzyla/mrrvu4NRTzwTgj3/8Q/S+VCpx9tln8sAD9zNz5kx23XUBq1ev4tRTz+Siiy5gaGiIz33uiwCJ9/fd9y8uvPCnFIsFPM/jmGM+xoEHHsTjj/+b008/hcsvvzIaz4c/fCQnnPA19trrlZtkPgRBEIRJQGk48hY3nmTgR0rhnt/jtHXS+c4TNnxyHUwkockO4CUDPzokgB8D+u66k3X/+PuY9D3jjW+m+w0LR93PwMAAF174S8AG7LW4/vqrWbFiOb/61ZV4nsfxx3+KrbbaaoP9L1iwGz/5yS/I5XKsXr2KY489mn322Y/dd9+Tjo5OHnjgfl796r156KEHcF1HgndBEARhVJjhgcqbtPOJ0DjlIsZtgkAjS0IjAXzTkAB+CuE4TvT64IMPaeiaRYvu5+CD30k+nyefz3PggQfx8MMPbPC6tWvXcMYZ3+all14gl8vT17eOF154nle8Yi8OP/wDXHvtVbz61XtzzTVX8t73HjHiexIEQRAEoCKfIZX1FTYK45VwmjF9gQtN/LtIyGbE6nNUSAA/BnS/YWFTsuQjpadnJn196+js7ASszr2/v5+enpnROZ2dHdHrXC6HH6sGLxYLDX1OLpdL/GEsFiuPLM8667ssXPhmTj/9+ziOwwc+8N6o3wMOOJALLjiPJ598gkWL7ufEE785shsVBEEQhIDIQhJEAz8avFJz9OmeZODHEilinYS87nX7cv3110Tvb7jhWvbccy/a29szz99223k888xTFItFSqUSf/nLn6O2V796b/70p5spl8sUCgX+/Odbo7bttpuH1k/g+z6DgwPcddcdUdv69euZM2cOjuNw7713s2TJi1FbPp/nkEPezde+9mXe/vaDa45LEARBEBrFDMcy8OJCM3K8EpQbS+TVI9MHPjzm5iWAHyWSgZ+EfP7zX+bcc3/Ahz/8ARzHZauttuakk75d8/xXvGIvXvvafTj66CPYcsvZ7LLLrqxa9TIAhx323zz99JMcddQR9PT0sMMOO0bXveUtB3D77bfyoQ8dztZbb4NSu0dtn/705zjrrO9x0UU/Z/fd92DnnXdNfOahhx7GJZdcyGGHHd7cmxcEQRCmJHEJDRLAjxjjlaJi4FGRtRNrmIHPtYiEZpRIAD8JmTlzJt/61mk126+6qrpo9X/+5+uZ57a0tPCFL3yZzs4uisUiX/val9h//wOjtjPO+EHmda973ev57W+vrTmGRYvu4/WvfwPz5m1f71YEQRAEoSFEQtMkvBL4Hsb4OM4ohBrRhk2xjZyCoN3J5SOXGmFkSAAvbJAvfOEzlEolisUCr33tPrzjHe8aVX9f+tLnWLLkJb773bObNEJBEARhqhN3oZEi1pFhjF8JvMtFaBm5xDWS0NTKwMt3NCokgBc2yIUXXtbU/s4++7ym9icIgiBMfsov/Ru3eyvc7tmZ7aYwAG1dUBgYkY2kv24Fhbt/S/tbP47T1jXa4W6eeLFseamAM4oAnno+8PkWKIiEZjRIEasgCIIgCBOeodt/QvGB2vuWmEI/bmePfT2CjZyKj/+V8vMPUPz3bSMe42ZPPNgebSFrlg+8sccct0WKWEeJBPBNwjSj4ENoGJlvQRCEqYPxylAYwO9bUfucoT6crpmAMyJ5hts5AwBv2ZMjHeZmT8KzvVQ7gPcH11J89EgA5A0AACAASURBVNb6/xaH2XzjV3ZljWfgffl3fDRIAN8E8vlWBgb6JKgcJ4wxDAz0kc+3buqhCIIgCONA6DDj963E+GVMcaj6nKE+nI4ZkG+paSNpykW8NUuz24I+vRVP2wXDVCS+8CkN1zyt9PhfKdz1a8z6l2uekyhSDeczOObkJAM/WkQD3wRmzpzNmjW99PevHdfPdV0Xf4raMOXzrcycma2DFARBECYXYYGqGVjD4A1n4K98humfvLTSbgxmaB1OR7ctkMzQwHsrnmbw+lMB6Drqh5HcJuqjOGhflAsM//1i2vY9ouqcyU4iA19HQuOvsnu7+H0rEzUJ3oqn8VY8Ret/vCO5aZNXgpa2ZBErBmNMYpd4oXEkgG8CuVyeLbecM+6fO3v2dHp714/75wqCIAjCeBL3ePdXPlN9QmkIvDJuZzdOvjVTQlP89+2x/gYgI4B3OnvI7/AqSo//Fbd7K9r2Pqx5N7E5UG5MQuOtegEAf31v4vjgDaeBMbTs8bakfaRXwoFYAB+En8YHJ9eUoU81REIjCIIgCMKEJr7Lamb7YB+AldDkWjKLWP2Xn6u8yZLIFAZx2qfT/qaP2ABzKm4G1YCExhQHMUHgbvpWJhtbOwFsrUI6A09FVuPkWoLOpqaKoBlIAC8IgiAIwoTGDFc/bTYxCak/tA4Ap6MbJ9daJaExxSH8tctxt5ofXFAdwJviIE6bDUBx81NSBx+/51oSGi+Qz4CV0MRxZ2xtj69ZluwrXBgkJDTIbqyjQAJ4QRAEQRAmNJGEJhczL/Bjco+hWAY+31K1kZOVfBhyW+9qz88Izk1xCCfIIDu5lswgf9KTyMBnB/Ch/t3dYl6VhMbtDgL4tUszM/BIBr5pSAAvCIIgCGNM8d+3Ubjv2k09jM0WM9wPuVbcmXMrB+N67cF4Br56l0+/9zkAclvvbA9kBvCD0Nph37i5bJnNJKeRIlZ/7TJo7SC3zQLrChR34HOttt1fkwrgyzUy8BLAjxgJ4AVBEARhjCm/8DDl5xdt6mFstpjhAZz2abTtdyT5Xd9gj8WDzaF14Dg47dOtBj6lX/defg6naybutFn2QJaEpjAYZeCtjn7jveQ3exI+8DVsJP0STr4Nt3srKA7ZnW8rjfb/a5dh/AwJTRiwB0WsRgL4ESMBvCAIgiCMNb4net/RUOjHae8iP0eR33ZPe8xLSmic9uk4rhu40CQDeFPox+nsiTLEaQmNMT4UhyINvJPLT1EJTeyea3np+z64OZzAPjKhgw9+x/11y5JPQYJ+w4DdEQ38qJEAXhAEQRDGGr+clBQIG4UZ7sdpm2bf5G3wZ8qpAL7D7qSa6QPv++C4FfvCdHBeGgZMJQPv5qe2hMbN18nAe+C4uB3d9pqYxSehnMYr4/evqu43DNjdwDpSMvAjRgJ4QRAEQRhjjO+JXGAUmEI/TrsN4KPsbSzD64ebOEFmEav1G3di16Yy8AW7iVNFQpNPSECmDMG8Oe3TahaxYnxwXbsxEympTfx3vDAQPfGghoRGAviRIwG8IAiCIIw1vicZ+FGQyMAHQbhJS2iCAD6riBXj4zhulPlNB+fRLqyhhGaqZuDLlQC+5k6sxsdxcjgt7fZ9PNCPBeS2piAoCk4F8I5k4EeNBPCCIAiCMNb4ZQlWRogxfiIDT0YGnnKxElDmWquKWDEmkNBkXIu1kIRkBn4iB/DG+AzefDbllx5tbseBNafTNq2OhCaQIwXznczAVxxpTHGwck4UwAftYWY+7mAjbBQSwAuCIAjCWONJBr5RSs/8i/JzD1QOFIfAmAwJTSVIN75nZR2Ak6/OwJtA9hFlfr3Ud1EloWmZ2BKa4hDeiw/jrXi6uf2WS+C4NnNeIwMfzrWTGcDHFqleCSeQ2dSU0MifiREjAbwgCIIgjDHG9yTb2CDFB26g+Mj/Re+jbHo+CAYzilhtVjgIznOt4HuJnVqtBr6SgTd+OgMfBPCRhGZi+8BHQXMNpxh/3fLaEph6/XolO0ctbZi6GviczaI7uaAAOGgK2wKclo5Kv+G1UJEyyVOpESMBvCAIgiCMNX7ZBvHCBjFDfZi4DWQY5DmO/ZElgzGpDDzgLX2sEsSHEpqoqDL5XUQBfCwDX6Wjn0CEkp8sr3rjlRi4+mSKj9628R17JZxcS3YdQfQBdjHkOA60tldJaKI5hKjQNXQFimwkXSliHS0SwAuCIAjCWON7Eqw0gDE+Znh9MrMcBn1OELJkFLHiexV5TPBz6I8/wFuug/bAhcZ1bSCfltgEEpr4TqwTesEVBPBVdpmAWf8ylIv258bile0TDset7dHue9F34eTbqiU0YS0CwWLLzVfm209LaOTPxEiRAF4QBEEQxhpxoWkIM9wPxiTlMaH0KBXAJ4LwsLAS7G6sIWFwGbrQgPU4T+vbvUD7HQT/dTPQE4AwaM7KwPt9K2zb8PqN79crBdIYt/aCM7SRBJzW9pQLjbHHQtxcsKttOWoHYjaSIisbKRLAC4IgCMIYY/yyZBsbwAwFQWdcvx1t/hMEjakA3soyTJR5z++8D+1vOTbWRkUDD9kOM/H28JwJvOAypSAD71Vr4P2+XnvOCAJ4vJKVIDlubX26H9O559MSGh8nnwzgE0XFYiPZNPKNnKSUmg0Maa37lVI54BjABy7XWsvsC4IgCEI9fA+MhzHGaoeFTMxwn/0Zk9BEgWQYYKeLWP1ku+PmcbfcMdkWzxrn8lU7sZp0AO/mM7PbE4VIA58hofH7Vtq2oRFm4HMtdq5qBNfGeDiO1bY7VRr4YJ7zrVYG5eaTc2l8wKnMtQTwI6ahAB64ETgOeAA4DTgUKAGvAv7faAehlHoX8B3ACf47RWt9jVJqAXAZMAtYBRyjtX4quKbpbYIgCIIwJoTZXGOiYkwhSenZe2Pa7qwi1iDoc3J2DqOgMJjbmPtJGKyHEo1EgO7mK5KO6DNM5RqCIH8Cu9BQDILmTAlNGMD3bXy/XjmQKDm15S1xuVK+Lfk5xgRZ9zZMuWgz7fHFQFhMHFwvLjQjp1EJzQLgweD1UcA7gAOAD4x2AEopB7gcOFpr/SrgaOAypZQL/Aw4X2u9ADgfuCB26Vi0CYIgCEJTMcbEAviJK8vYlPjrVjB82/kM//3i4EA55iCTyrA7TqCrTmbgnbh9YZTh9SrnxCU0aQ18mBkOybXYMWQEseUXH8EfWDOi+2wWUdY7y4UmDOALA0krzUYIXGjqZeAxfmWuW9oTdpPRQil0n8kFVpNxKZMby8CLrGzENBrAe0CrUmovYJ3W+gVgLTCtSePwgRnB6x5gGbAl8BrgN8Hx3wCvUUrNVkpt1ey2Jt2HIAiCICSJB0ISsESYwgB+/yr7OkuuEuq70y40kLR59DMy8OkAMZaBz8yuxyQ2ib6Cvk1xiMKiGzDlIkO3/JDiw7c0fqNjQKiBT+84a3zfauBb2gGDKfRvXL+BhMapV8Qaz8C3tCd84O08OxUdvJuzC67ge4gCfJHQjJpGA/ibgSuBnwK/DY7tASwZ7QC01gY4ArheKfU8cB1WYz8PWKK19oLzPGBpcHws2gRBEASh+cSzvZKBjyjcezVDt/zQvsnYdCgKTqMi1kqG3Mm3popYSWnYUwFiXLpUQ0ITXyBEhbLBd1d88CaK911D8cGbbC3DSCwam0koNUrbYQ6uAb9MbvZO9n2GjMZm5mvIg8pBBt5xwa8hoYl57tsMfNIH3mbgW+37tIQmDP7T34+w0TSqgf848GGs7v3y4NiWwLdGOwClVB44EXiP1vpOpdRC7GLh6NH2PVbMmtWsBw+jZ/bs6Rs+aYoic1MfmZ/6yPzUR+anNum58YddwjzorJmd5Dqn3txl/b4s9wbwy0PMnj2doYEcg6n2LbpbaOmZznChnUFgRs80OoN+hlrbaMvbfsvrSwwA07s76Q7ay20FBoBpXa10z57OoGPo6Ghj9uzpFNvacPMmMabethyDuVx0bN2MLgrArJ52cp3TWdM9jSLgvvw0ALnC2qb+GdjYvla4ZUqAi5e4driwxN73Dop1Sx9nRluZjli7MT4vnPtFZuz3Hnr2fXdVv8OOR2tXBy1d7RSNnzmuYRdag7lc09PNGq/ElrM6cdwcBRfybS34uS6Gga5pnQy2tJBvcZg9ezovt+fxcjlmbjGdIWDo5rOZ+ab3MfNNRzRtbqYKDQXwWusC8PNAl761UmqF1vqvTRrDq4C5Wus7g8+6Uyk1AAwD2yqlclprL3C/mQu8iBWqNbutYVat6sevtTIdR2bPnk5v7whsoqYAMjf1kfmpj8xPfWR+apM1N37Mzu/ll/twO8Z7VJuWWr8vhcFB/HKZ3t71lHurNeWrVq4mV+qgvMYuf9b1DTMQ9OOTY3hgkN7e9fj9NsvcP1CiELYP2OXA+r5BCr3r8T2P4YJnP8t3YKiQGNPwYAHfEB0rDtonJS/3rsXthBJ2d9Hhl+zGUMW1vU37MzCSP0/D6+35XmE4cW1p6TIACu1bAbBm+Ur6uyrt/lAf3sBa1i9fRql3PeXnH6Sw6Ho633MSjutSLhQwJYfSUAkwrFzZV+WaVC6VMUWf3t71FIs2k967tBenrYtyuYxf8jHGSpAGhz3KHnjDRXp71zM8WMAYh7VrgycIxmf9i89QrnH/U/nvGtd16iaMG5LQKKW6lVKXAUNY2cyQUuoypdSMDVzaCC8B2ymlVPBZuwNbA09hC2ePDM47EnhAa92rtV7Z7LYm3IcgCIIgVBP3E5/A3uLjTrkYSShMXEIT7uRZwyYSSBWxZrnQpHzG4y4zuZYqCUnaRtIJNxoKPyPaOdReZ4b6qvTnzcT4HsWHbq79GTU2cjKD6wBwt9jOvh9OSmjMwGr7IujXW/UCfu/iyn16JXvv9SQuvleZq6BYNZLR+FZC44RFrJGExlT6c5xEvcFEtuucyDSqgf8RtmB1L6Aj+NkZHB8VWuvlwKeBq5RSD2E19h/TWq/GWlcer5R6Ejg+eB8yFm2CIAiC0FziemvR/EaYcrFi8xgL4HOztk8ey9C4J3ZKTW30BHEXmnjxZKiBz224iDUK4MuV9vT4w2B4DPCWP0Xhnt/hLX08s91EdpvpAH4tOA5uzxzAqfKCN/32SUc0tyl3JFvEmq9fZGp8HDdWxAoVJ5qoiDUM4IO+os9JFbHChN7xdiLTqAb+YGC+1jqUqD2plPoo8EwzBqG1/jXw64zjTwD71rim6W2CIAiC0HQkA59NuVApQA0yuF1HnIE/uJahG79X8YIPz4ln2PMtUfBqwsJgJ+5CEwTrfqp4Ehv8VxexVm/kZA+Xk/3E8PtX487YpsGb3TjMkM2k18rAV3ZiLSU2BzND63Dap+PkWnDauqp2Y/VTGfhEcSnY3083X5m/LC9440dzHQbwkRNNykbScXPWcjJhI5kK4DM2oxI2TKMZ+GEgbbW4JVBdNi4IgiAIQoRJBPCSgQ8xpULFXjDI4DrTZ+O0Wr25KadtJJM+7Wkf+EwbyHjgGG3klKt2YfFrSWgCyUwsE+10W325CSwwN4bCPVdSeODGDZ4XucfUyE5HGXhMwuXIH1yH09ljx9kxvcqFxgxkZ+BNTGrkuG7VE4xEHwkJTZiBDyQ9gQtNlIHPBZtuJZ6EJAN4442dFGky02gG/hfArUqps4HngR2wO7D+fKwGJgiCIAiTglgAb8RGskJMA0+5CE4OJ5evBH8bkNCY1E6sjlPtAx/NdzyAj8tvIkzVAgHIlNDktppPua838rBvFL9/NcWH/ghA26vfVffcMPBO68P9wXWUnvgbFAas1MUr23sJxmuG+nA6bXmi0z59gxn4aHEZl7gQ22gpUwNfkRs5qQA+0ri3xCQ0bi5Zz+C4SU9/kdCMiEYD+NOwfukfxLq2LAXOBC4eo3EJgiAIwuRAMvCZmHQRaxj05a2HeCXDHsg4Ehr1DWTgMzZyinTbuVy1lCmVga+S0MQCWbd7K5zOGRudgS89/pdE3/WIMucpCU35uUUU77sGAKe9GzOwGlMuVp5aDK7Dnbmtbe/oxl+zNNlvlIEPPfbjgTvVEpes31fjR084MiU0biUD77g5cNzYQsokfeABIxKaEdGojaTBBusSsAuCIAjCxiAbOVVhjLHBaRjIlYYj5xInCOCjLPEGi1izXGjCDHLc/SSU0LRUO5/UdKEJA/iKFtzp2gKnswc/cHxplNJTd9nr27pqnmOKg9bhpoaEJtTGgw3QzcDqxIZWZmgdbt0MfGDXmaGBT8xz9ATDJ2kiCRivkkFPu9AYA7ixDLwN4OPfg+M4ycWSZOBHRM0AXil1tNb68uD1x2qdp7WWoF4QBEEQaiAa+CRe7+JAp20qLjSlAuRTGfg6Ehpy8SLWjAA/koB4drEQZn7BSk9SGfi0jWSVC03wvbW++lBa5r+O8tN3V7LODWKKg4mfWRQfuJHiE3/DnbG1PTftMhPTtDutHcEtBvNQGADfw+kIAviO6Zjhfoxvnz4YYyLnnIoGPlygeMl5rmsjmSwItscqmXzH3TgXmrG045zM1MvAH0ll19Vau6KGmXlBEARBELIQF5oIv6+XwWtPIbfdK+yBmISmUvgYBIXpLHE8wM5vwAc+PN/3K9nfKOjMV2d94z7xEJPQhDr7IIB/5Ttt4NzShhlY2/iNh58B1jmmXKw8aYjh96+CwgD+qpeCc5PBbTKAt7KZaCETPBGoaOC7AYMp9ON0dENxsDKnkQa+Ulzq+NUZ+LQLjV0MVSQ00ZzFpErpIla7eIj58ackNJKBHxk1A3it9TsBlFIOcCzwgta6XOt8QRAEQRAyiEloTFZGc4pgCgMUH7rJvh6sBL/G+FAuViQ0jgP51gwXmvoSGicdwLuuvTZaAIQ+8DYDH7dfjIovo/7TLjTJRYLT0oFfXr5xE+D79ulCuYgpDmUG8Ga4P/jcIMDOyMDn5uxG697vgXKR8nP3VyQ0UQBfcaGx16yHju4o+Hc6uiu+7VERq4/NyRIE8LF5SQwgVY8Qe9IRtadsJK2EJq6xr5bQJL4LoSE2aCMZ6N8fAabu3zqCIAiCMFIkA4+3/Cn6L/sspcf/Wt1ofKuhDnXTYDO4UaFlRpFqrsVe53uV4NFJhTROkPk1qetTu6qGY6iS6ECVhCbK4re0QXHjJDQYH6dtmn0d2UCmTkntnJrOwPtD63A6usnP3T2SHIULnVDv7rRPC35OT/QZZtud1k67iDAmFsB70T06jlPbRjI91zGtfPQz2EjKmT4bt2eu/R78eHvKhQYkCz8CGvWBfwBYMJYDEQRBEITJiJGdWCP7wpbd3owzfXalmBJs4BiX0IB1mamS0MQz5GGAXYqCw9oSmnDOg+DbTenbgzEkgsqgr4oLTVKGQ0t7YvfYhjAGp90WsNbSwUcZ+PB9uoh1MGYTma/Mgb2H5JOIKAMfFrKG89DaARh7nR/LnGcUsVZt5BQtZHKVc+PHgwDd7ZrJtCO/j9u9lZ3LtB9/OtsuAfxG06iN5F+BW5RSlwIvEj1nkSJWQRAEQaiLFLFGAVrrq96FP7AW78WHK23GJItYCZxo6rjQRL7n5WJMA5/KSYaBYxCEOm4QNOYqFpFRGFkloanhAx+c47S0QcnuJBsG/l7vYtxZ21dLeWKfEWbgzfB6vDVLyAWWj/Y+jZW7xIlJaEy5CKUhq2cHyLVWjifGGATw7fa8SDcf+uWH1o9xH37jJeUx6Z1sY/cARJacVcWu8WLhkHgRa1gAmzrHeKVqtxuhLo0G8AuBxcBbUseliFUQBEEQ6hEP4KeojWSk5c61VIovo8akBh6AfFvMhSaV/YaYDMarClxDnFB7nV4AZGXgjUnZUKZkNkGA70QBfDsQWGG2tOMPrmXw2lPIz9+HjgM/U33/xgAmspAsPnor3pLHmXbMjwCbKac0bD/PcRIFr1EfMQ27vd1kBt6kpEKhlCZaFEQSmsC9plyMbeQUt5GsaNSrajb8pISmSmrj+1XZdceN2Uhi7DXpIF+caDaaRn3g9x/rgQiCIAjCpCS+E+sUz8A7uRactuoA3pSGqzPwaZeZeBFrGGz75Zh0JJ35dar9zcnweIdEJh2oZOnjGfh4e2wHUqelPQpAy8/+C2OOq9Z4h5nrIKj2VjwDxktIaUKpizt7J/yVz+J0bZGwWAwDeDfMwAcSmsqOtCm3HTcHbV3VEpp4Bj6YO2N8nAZsJNOLhOj86Pfar773qgy8LVJu2fNtkGul9PDN1b78wgZpNAMfEbjSRMsrrfUU/dtIEARBEDaMkY2cKgFavjoDb3wPvFIqA99a7QOfsHkMA/jaGfjI/SRVgFqziNVNLRAcp5LdTu3UGun1024ugLf0CfLb7pGagEDGExaxlmwRayJAD/Tvra98J05bF8X7r6+RgZ8R3Edot5m0uoxnwN326dF1YfAdSmhMuZAsYo0vAGoWsWbMdej2A3bX3KwAPqWBdxyH9oVHU37uAUoP35yQCgmNUbOIVSnlxl5vq5S6Vim1CigDpdh/giAIgiDUQjTwlUDUbaneiTRwZHGqNPDJAD67yNSryEAyNPCmjoSmqrg4HXi6sQ2f0u2tlQw8JKUm5bi+P95/eF28n0QAH2TYu2YGLjPJHWP9YBfWSEIT2lBGTyoyNrTq6K5k4MMnFYGEJqmBT9lt1rKRzJprJ+7zXi2hIeYDb1ILpaqnCELD1HOh+atSauvg9c+AIvA2YCmwN3ADcNzYDk8QBEEQNnPERtIGma7d1IeUhMYE2WiqMvB1NnKKZ+DrutDEMvShNrxGBr5K+pHLx+Qp6Qx8MoBPBLpZwWhsERJ/ApGVgQ/tH51ca7KINaWBjwp5vTp++e3TK842YYa9JdTAFypPh/zKkwonbvNYw4UmMVeBhCbU+WcXsaZcaELSm3YJDVMvgD8f+JNSah/gDcDHtNYPAn7w81jgy+MwRkEQJhH+UB/Dd/82KSsQhEmMSRSxTs0MvCmXomCtSkKTkYEnF3Oh8auLWJ2EhKa2D3zcHjEKOqPAN24jWR14OvEMvJ+S2LSkJTTxAD5jkRZfhIQZcKg8ZaBSbBoG8DYDHwvwC4OQa40y71bmk4tJaDKy47l8zAozLGINJTTFWGDtJce4IQlNouA3l3SxqXqSEbeRNKmFUOopgtAwNQN4rfXvgP2BVqxsJvxNH1JKdQODwLY1LhcEQcjEW/IYpYdvwV+zdFMPRRDGh0QR69TNwIeuKdUSmiCLHd/IKZdLylcgKc3IyMCn7RudaCfWVGAZL4ANyZJ+xHd7TWfoIx15dQY+DJiN8TGFgeCzKvcQL+KN77RqhtfbsQV9J3abDT8jLROKyWwqC53YPMSz36GEpiUmoYkVsSaeVKT93cMhZCyWnNRCqcqFxnEri4u0S036KYLQMHU3ctJar9Za/wP4F3BIcPgW4GrgWuC+sR2eIAiTjvAfZXlkKkwV0oHiFMR49TLw1oklkYF3c5XFTmYRa+jlvoEMfFxiE1pApuwXgWiH0AS5fE0XmshLvZghoQnGXbjnSvov+yymNIyhsohI3H88Az+8Hqd9emRVSa41IbHJ0uk7qUVG/D6B6k2UoKLfLxcSgbXJyMBX20hmudA4yQ2zqtyAXPuEw3aQlPiEGXgpYt1oGnWhOZpKsP9V4ARgBnDOWAxKEIRJTFjMJAG8MFUQDby1bMyHGfgGNPDpAlKoqYGvuxOrqbaRzC5irSGhiQfHbnUAHzrlJOxBgwVb6ZH/q9x7rMC0ngY+ks+Ana94YJveLRayd6x1U9nxMMsePqmIZeATi6T4k4oaNpIVS886Epp0btiNZ+BT85yTItaR0qgP/NrY6wJw2piNSBCESU30D8bGbkMuCJspooEnKaFJb+QUBKBRZpxADpPQbjvJzG2GD3y1C41bLQ2BpPwmJNM9pZK9rsrQB4uN7CLWcNymcm1CAx/PwMcC+NBTPrzHILtujLFZ+SwJTa4lY7fY+EInZeFIfPFRTMprMjZyqipijXa1zXChiXZpTc1jPJtfc8dbCeA3lpoBvFLqf7XWpwWvv13rPK31yWMxMEEQJilhNkgy8MIEofj4Xyk98ic6Dvkf3K6Zzf8A37MZS+NVaYpDyksfp/TYX2h/04erNeKTgLiEhrSEJgxAM5xNgEA3nVEYCY35wFdl4IPz4kF3VnDsuomdSpOBcd5+XinlVU9GnUPKCadlwULc7tkU77umWiITf4qQawGMXaTkWrIlNPHsdpbdppOr0sBHTzpiGviqeapZxJohVwq/q6wFRPx9KNOJt4c2kvLvwUZTLwO/Xez1vLEeiCAIU4TwL3n5C1uYAJSeu5/CHZcC4PetHJsAPpSPlLxKEWCK8tP/pPzsvxjsX0Xnu0+08o3JRDwD77rWyjCUzoSZ9vRGTSawJjQ+pLO6MR94u0ByK9rxkKrA0ql8PlRl4Kt3EK3jnuI40Npekf/Uk0nF5CkODvm5u5Gboyjed21SA+971V74YLPTNQL4ZICetdBxK79zUYY8V9koK75ASdhIOpXjiVvJcqEJi1hrudDEFgPphZJk4EdMzb8htNafjr3+6PgMRxCESU/o0CASGmEC4K9dVnkzVkGE7+HkW63cokYGPgyM/JXP4K96kdzsncZmLGOIt/JZhm7/KZ2HnYQbepUHGK+UDE7bOivBb5SBT+mqwWrcMzdZirvQeNXZc4JANR48V7nQpDbYygo8Y/7l6QWCk2/DpDPwuTz45Yq0Juw7LGINvegdJ+l1H56X2OTIBvCmXMRp7QzmIWOTpFqbTdlJSHqwB8ecfFttF5p4Bp4aNpJVLjRerNYg7UKTelqSvtbNS0JnBNST0MxvpAOt9bPNG44gCJOdMFAxJfkLW5gAxIO4MXLCML4XBWO1ilijzXYAE+y4ublRfuFBzPpe/FUv4m63Z6qxBG3TordO+3TM4FoboAcBvJPhMmODvqwC03SAn5LPgA0ksyQ0Tix7H5IpT4lZWfpeVZGs09IOaQ18rhXjlfHXwP24QAAAIABJREFUr0r2TYaXfb41ZRPpJdvT2elaUqJau5yG9xCOLb5Ta7B4iLLzvhdsxETKRjK9kVNYb5BhVbkhCY3xs+8hteOs0Bj1ntE9jf2Nc4h+8yDjfcafGkEQhBpIEaswkYg5kRh/7DLwuLlKQJmBKfTjTJ+NWd8bbeizueH1LgbA7385o7GEk68Uqba/+aN4yzSFf15RkdDEgvBEkeoIM/A4bpUDTNW1AZlZ/piDS+YYWtqjTHvk8JJvtcFwbA4SsiknmWFPPIn0/aSXfejQUs52won6iwfodTLwUYbczVVl4Ks18E7ymuhm6hTKblBCY3dqrXqSkWsRG8kRUE9CE30DSqmPAgcC3wKeB3YATgZuH+PxCYIw2ZAiVmEiMQ4ZePwyjpvDOLmaGXiG+3F75uCt78XfDDPwxhi8lfaBvFlfHcAniliB3JY72I2LoJJhdlNBIUEAmWWfGPeB9/2KTCNxTi7IMieDTqfhItZc9PdUVoDvtLRVF7HmWsAv4/fHMvDxLHbcgSUMoqMxeKkAP5WBz9DpJ54SZC50qotccRyb9U5r4OPtG7CRrOdCUyXzCV1oQk/+qgx8q2TgR0CjVTLfAXbVWgeCNZ5SSn0KeBK4dCwGJgjCJEWKWIUJRFxGMWZBRJiBD20Ns8ZRGCA3bQu8fNtmmYE363sh2HXUX99bfUKsiLVCkOUNn4K42Rr4WsG1bS/bALVWBr6ujWR9H/hEdtuYanlKSzt+IVishAWi+VbwyslFjPHDW83IwMdsJFPBrZMLNzkKzvEzpEKJMdYoxKWyEIruP7SfjOvj4xn0mi40GY4/qXmuXmzFFkw1N6OSfw82lozf+Jrn7Zg6tgMinxEEYWORDLwwkYgHcWMUwBvfq9gOZmTgjTGYwgBO2zScju7NUgMfZt+d9un4DWTggUpAHM57jY2aqjLTJDXwtvizhgbez5B2hNn9eEFxRoFodXY7lVluacOEO7GGfeVbMb6HP9QXv/lqGQ9BsJ8oYk3p7NObHNVw40kUsVZJbGJuMkEm3nFcnFyQgSf0qo/bcbpREG4GVjN44/fwg6cl0Zyld3vNmuf0GEyGjWRwn6bG0y9vcH3yaYYQ0WgG/hzgz0qpS4AXsbaSH0F2YhUEIUV56ROUX3iQ9td/ILNdNnISJhSeB/k2+/s4Dhn4TBea4qDNnraHAXxf9TkTHK93MeRayM3bC2/JY9UnlDMC+DBIDItYExs1VYpYTXr3Tki51FQH+LaPXLW7SuraiMzsdcrBpWoMLVW7xTq5Fvt3nJfcQTWS76Qz8MXBxBgSu6hGNpK1ZTxpp5ya85QK0Mm1wHDsSU/CRtKJgm7v5efxlj6Ov2Yp7hxVkePEFhpOmIHPCu7j9xzKdNKLjFgxrzGG0uN/oWXBQpx8G8+f8xEApn/yUoQkDWXgtdbfBz4KbA28G9gG+JjW+swxHJsgCJsh5RcepPTwLdWbmYSEjgkSwAsTAON7lZ0pYwWtTSXQwDvhlvPpMQTSE6d9Gu5mGsD7K5/F3XIH3BlbYwbXJqUhxlQVsQIxp5PaEhoTFbHW84GvlYFPBpZR8aSTEcDXWiRELi0Z+vNcerdYbDDqlxPF0UkZT1wDX52BT2j58+ki1loyn+CpZoa+vOJ578c83IMMfNzqspaNZPjZca180EflQ2xxtiG1UApJLCIyHIWCHWcB/DVLKPzjl5RfeDj5hESoouGdIrTWtwC3jOFYBEGYDAT/cJniIE779Or2KAMvEhphAuCXbdDluGMqoXHybcmMbrw9sJB02rpsBn7lM6nry5iBNbjTZ4/J+EaL8ct4Lz9Py+5vxZ22pT3WvxqnZxt7gl8GTFUGPgqovayNnFJZ2w240FRlz6HiIpMuYnWcpMOMvYmMRYJbCSJrjCFMVIQ/wwx8vJ7CGJ9Kz2kNfNxGMpWBT9tI1rC6TIwxy6UmbIvr1/MtlQJcqNh1BvddKTwNvpvUQsVJe/aXS5Vi3Ro2ktHTkPQ851owxdSmXl4Jf9ULCLVpVAMvCILQGOE/xkFWsQrRwAsTCd+zmfHcGHpRRxKaXGYRqymEAXwgoRleX9kvwfgMXv1NBn7zP5l/ZowxlF94iOE7LktmVMcRf/US8IrktpqP020XGfFC1jBIrSpiTQeJNTZyygpMndDqMGrPcqFxbX1Buog17D/yT7f2hpm7nIbZ7cxC2nzl77sw+A2KWNMSmoTHeth9vjUpJUwXqeYqGznZFzVkPnGXmVpFrOGTiuAam4EfqpwXnyecSj/h/cX98MPPjY0hqaGvXghF95flpBNsfhWdg9X9e8ueiA0v5UcvSAAvCEJzCf8xNjUC+ChbJAG8MBHwPcjlx9aLOpDQVGV9A6IMfPs0nI4ZNpgKgvrSo7fhr1liz+tfXXVtefF9DN1yDqXH/4K37MmxGf8GCP3fc7N3wp02CyBReBhlmTdUxBoPblMBfGaGPSwyrecDn6WBD641Kf169SZIsScmWfKUXL5aAx9IaIxXqmxGVWsM+baU1Ch1H2kbyfROrdF91B5jwg4yHmCHLjThZ8efVLhu1XcTPWnImivHtQuYGvOY3MiphlQpvVus71FeWgngE8XmAiABvCAIzSYM4IdrZOCNZOCFiYPxy4GtXn4cilhju2LGxxAudoMiViByovFWPF3pJmODJL9vRebr8cTvXQxtXTjdW+F09oDjYAYqi43wz3qtDLwNJJ1kkJ7yec8qUsXNW7lKzZ1YsyU09tpcVfCdmb2u67FuNfDGmMp5ObuRE+UStLRVrs3ITldr4JNPEhqR0KQ3m8rM0Id9B9c7YQAfp4YGPnoqVaWBj9tIJt1+nFRoGcltaklonFy02VW0qPJK+OuWV86JyX3sUxXJyI8ogFdKdSil2po9GEEQJgGRBr6+hEZcaIQJQWjxOIYSmtBG0mZ062TgWztjAfz68GIry4BsO70woMq14PetbP7gG8AM9eF2zcRxHFus29mDH3ta4IcZ+BpFrMYr19G419iJNTzHL1fbL0bd13ZHcdK7rMbGU+nfrbRlBvBhhj22SIgKT4dt3QMk9eXxPnJBtj4aR0rLHxXqVuRU2YW24RgzCkTjBaR+xa2najGV8nGPxpEuYg3lOgnHoFxyDqqsLkMrS49MqVLaSYfgdyJeRxCTh/Vf+FEKd/6KqU5DAbxS6gdKqX2C14cAq4E1SqlDx3JwgiBsfoROHjUz8JEGXgJ4YQLgBQ4x+Zaxy8CXCjbb6tTIwA/3Q2unHUdQ+B0G9RjfFoY6DiYrgA/6c2dsg79u02TgTUqD7nRtkZmBr2UjiV+uLbvwa3iHQ8Wn3fiVQDXdh/Eh8DqvCo5NMqvsZDjdxGU2TpYGHhJZ/sj6sThsd2oNrs1aJITnmlJso6ZEZjsWfIf9VHnVpxYi6eA5kq+YpI4//V2kNPLh59TKwCfmsp5UKXYfJqtYOWxPZ/iDAN5tn2avTdV3lB67nalOoxn4DwGPBq9PBo7C2kmePhaDEgRhM8bfQAZedmIVJhKBBn6sMvDG9zFD63A6ZwSuJskM/PDdv6P01F04QaDi5JK7hBrfh1wLTudM/PVZGXgPHMfaN26iDDwpCYvbNTOh169VxBoFgV6pKoOe1MBX74JqPyimga/pQuMnA9PYtZXvoo57Sp0MfDRGr1zRoYeFp6Uhu78AwXeYtUgIn6yUClYSkv4MJ565JltKFFsUGt+rlhJFBaQpKVJVAF9Z6CRsJFMa+EqWPbUTa7xQt6aMJ6MAlthCLN6/X8Z4ZdwO++eCIICvtZPxVKTRAL5Taz2olJoFzNdaX621vg27G6sgCEKFMMNeIwMf/UPgexX3CUHYRJhQfpEq6mta/8N9YEygDa/OwJefX4TT3kXba95jD8T1wuFP18WdNivKwPuD62LOJFaS4HZvhb++d9N4Z6eKK51ps/AHVkcBnfFqZOCjHVEzAvB0druGhKauD3ytjZyCtkSBarqdVGCZFTznYg4vsY2cACgXG8/Al4uZgbHjONUFnhnyk0RwXdOFJhhD0H+VJ39aapQK4BMLytR9WLefOhn4qCC2XLs9+n0P/v3wSuCVyHUGkrJQAy8BfESjPvBPKqU+BOwC3AqglNoSGKp7VYMopdqxu7oeCAwD/9Raf1IptQC4DJgFrAKO0Vo/FVzT9DZBEEZPmMWs5UKT0ACXS9Da8HYUgtB8/DJOoIEfCwmNGVwLgNPVk70Tq++T22YBLQsW2vfxoNa+AMfBmT4Lb8UzGGMY+NUXyO/wajoO+kKUdXVmbG0XxQOrcMbbLz604gxwu7awT9gKA9A+rY6NZOgDX6roxaNOkhs51XOhMcarlrdAlEHPck6Ja+BNTelH5fsyWZsoZUhowqy6fV0J4DM/I8zQlwrg52qMIVdJdGTJeJx4gJ/xpCLlQuPUyMAb4yXHWEtCE+3EWk9Cky3jCe+jrlQp9JL37GZYuY5AUlYOJDSyuVNEoxn4zwCfBQ4ATgqOHQT8qUnjOBMbuC/QWu8V+4yfAedrrRcA5wMXxK4ZizZBEEZL+I9irQA+lkERHbywyYll4MdEQhME8G5nT7JwMjohlTWNS0eCdsfJ4U7b0spSioMAlJ9/oHK9azPwAH5fxX99Y/AH147cRz51D860mbbPQAdvNlDESt0i1jDDXkcDX1NCk3RHSc5ztQVklkUjxgvkLV6G/rwioYkkMrF7jPTwiSJWp6rdlAqVDH7WGDaQgY9r5Gu60JikhKZqMeX7yXlIZ82rCn6rJTSZ80xcKlUjA59YTAU/SwXA4AYBPJKBr6Kh1JfW+l7gDaljvwZ+PdoBKKWmAccA22mtTdD3CvX/2XvzOEuuuz70e2q5a9/ume5prZZkeVFZXhTjBYRjIhsjljx4BBzHBmIn70HyDBgISdhewoMAecDjBcISdgjGNmBjEghmsQjGGMuWbCNjW5JdsvZtRtPd09vdb1Wd/HH2U+fUvdMzPeoZ1e/zmc/0vaeWU8ut+p7v+f6+vyS5DMDLANzKF/1dAL+QJMk6AHK+29I0PdhTr4466jBjwUJOAGodfB1PewgbSXJIAL4YMjtI0jkGBCGK7Scwuu3n0PrSt3LPcIs91m3/xP9BALK0CtAc+dZj7PtmVxwAk9Acu5ItvvkIcPULz7qfo/f9JMJnvQStV33jWa9Li9wAhEF3lX3fPwOsXTs/idVRiIlYzLFPIiMAviuJlQiHFhczbEhPPNpt2QfqlumEtsyHKFYeAOKWWt81SNA08AiFPtyhYa+Q8ZASwHckuYp1dZ/5BW0klXTGSjLVByJCAuOV0Jh5Hc5joNYAgReZCjucgReDyxrAy1iUgUeSJK9JkuQ3kyR5P///teepD88Fk7L8UJIkn0iS5INJkrwawDUAnkjTNAcA/v+T/PvDaKujjjrOR8yR0OhJfDUDX8fTHodsI0kHXELTXgFIADraQ/bwXchPP8AXMBNADds/2R4wJxqAea4DIM0l2X8ShAg6xxCsXaOY+bOMor8Fun9AHst2oRHFnCwG3iuhsf8GDAbe50IjfOAZO+6X0PiSWI08A1cfdBcYF7ttyXxAQvM8GDaSjj6EkVzfWSAJJrilLokM0Rh4X5IrP0b9PJYZeDVLYNhI6u3if+Elb/RBAfiSRGYeA+/IR6BTBuADbqsqGfhaQiNjIQY+SZJvAXOc+XUAdwK4FsDvJknyg2ma/to59iEE8BwAn0zT9HuSJPkiAH8M4A3nuN1Di7W1pae7CzLW13tPdxeObNTnpjoO6/yMSIECAJkNnfuYhgQCth9bitA6YD9mu6dBgghRb/Xgna2I+v6pjkvl/AxpgXanBUopRhv5eTkufRsbdICss4zLrjiOk62GTBxbvfwEmus9DFCg3W3hBF+nmEboA+h2Yhxb7+GJkCBoxFi75ll4HEDcfxITAI2lZayv97DRDDEMQ6yv9xDeeDN2bv8DrHYKhN2VhftL8wz72RRh7v7NzotJAESthlyXrnXwEIA2Rlhd72HvMcbAr60fQ7Sstp8PCohhfhjHxr6z5hQDAEudCPshQdCMS32bNhoIIiAjQKPdLLWf6bYwpQWWujEmAE6sL0tNtVh3fb2HWTzEAMDycgc9bRs7vTbOADix2sGIAK2OuY/BmR7GAI4vN9FvhZgFAVaOs+8AoLPSwy6ApaUGgriJMYDVtR4aa2wb42mP3Q9FgbXjbQwA9JY7WNb2MYwitBrs+o4CipZ1nNtLHUwpxYkTXTwZAkGzYbQP+12MAKwsN7HfCDGOIqyv9zDcXzaSGBuNEO1uAxMAa+vLCFtd7GsuPJ1WiNX1HrbaEWZBaOxjs9NCHxQrvRZGAI4dXzKe65OihyGApU6IMYDects4xjNLbUxpgRMnltA/1cAYQESnyAGE3IWm3aBYXe8hHxTgBqs4cWKpPFh4BsWi2WPfC+DWNE0/Jb5IkuTdAP4AwLkC+EcBZGByFqRpemeSJJtgCbJXJ0kSpmmaJ0kSArgKwGNgUpjz3bZwbG31URRPfxWw9fUeNjb2n+5uHMmoz011HOb5yWeMbctHfZw+vVd6wM6mUza1PBtje3MHUePs+0GLDP1f/1aQ7iqWvumnz0u/9ajvn+q4lM5Pkc0wnlGAUhTTifO4aFFg/KHfROOmr0K4enXl9uxzM9raAFrL2NjYx3Sm3hvb20OE4T6KLMdonMt1hNykvz/EbGMfs+kMJGhgZ8LkFsPHmedCFrawsbGP8XCCAgQbG/vIL3sxQH8fp+76GzRecMvC50B4zk/3dw90XbPZDMWMmusGIYb9IfKNfTT4zMaZ3SnIRC1Dx0P5d1HAWL8YMRi8vzdg5yAsSn3LCgDjCYrZDHRabp+MGePb32P72TozBGno606xsbGPYpett9+fYqxtYzpk629u7KLIc4wnubGPrM+Oa/vMHmaDCSgJsNdXssDRlPBjGILEDAhvbw8RFGwb+S63RixybG3yPgxmmOjngRKMhmN2fbNyHyYj1oeN07uYzTKQ0LwO2R6jS3a2+5iNJsgpO89Z33RcmowmyPf5edoagDS4HIffssP+CPnGPsaDMSgJjH2MJzmKPMfuDhuO7eyOEWrP9XyHDRX2dwfOY5yMMnkMGb9WsyFblkQNIGpisLOLfGMfxXBPrrdxcks5/VyCEQSkkjBeVEKzBuBe67sUwDlTX2mabgL4K3BdOneJuQzAfQD+DsA38EW/AYyl30jT9PT5bjvX46ijjjp4yKSnDMWZx8slr4scpNFhfx9QQpPdfycAGMVi6qjjIEGNJFa3jSQd7iC778PIn7Rfg/OjGO4w/Ttg6rR127zAknYAmq6Zy0OaXSBsyGJNpME08FSTjwRr14IsX4bsvtvPqo+UJ8bS8QEHZUVZe607i8gkVo+NpFheD9MHfo6NpE8jLyu9zozP8u+SBt4+BqUfpw59uZTQ5Fq1WE0DT+KGXN8poZHSklz2paTlD6LqJFX9fvFcB70PhEtqSKjxt8Ktp7ByAbR9UUtCY4RYHx4pkjyP2Zz2XN0z/J4kUcxAukNCI5Z5psaiAP7DAH46SZIOACRJ0gXwUwA+cp768VYA/3eSJJ8B8HsA3pym6Q7//juSJLkPwHfwz/o657utjjrqOMegeSbt0YZ/8IPIT37OXKAoQBpttuz0YK4X00//OQAgOH7VwTtaRx0AAxXzKrFKz/Wz19/SIS/iBJiAtfCAMrvoDW8nhHB3Fwts6s4ihKBx42uRn7oP+dajpb7M7rsd+eYj5T5yvTEmg1KhqYWOkeZlAB2EcjCvklitSX/LS9zZJiut+m0kS4nArm2U9qcNMOYkX0qv+ZIPvJbEWuTsOurHqNlIenX44OfP0wcyN4nVOk8+BxjLhcYYTIWxO9mXOO5XlyOQcPvx+enLgVRmfFbHYBXtgkpaJWEMxC1lI6m7mE1HyB79NIZ//OOl+gezBz+Gycd+H5dyLCqheSuAdwPYTZLkDBjz/hEoJvucIk3TBwG8xvH95wB8kWed895WRx11nIcoMkTXvwJ0vI/88btBB9tGM6U5gu4JYPuJAzF+dDpEcYap3mRxjzrqOGgUOfeBjwCeTGgDDJlsfZYJdJQWoMNdBB1mq6gnq0rQaLmbEFFER/fFFgx7dxU5Z+ANWz9t/Tj5Ekw+8d8wu/evEH7JP9P2RzH+m99C/LybEd7yzWY/NSaTjvtqwKFFMdwFiowVlMpnmNz5HjRf9rWsgqwTWCqbRppnACFldtkYuDgGAIAqQORMUg01ht7lQmMzv3YS6xwbSd0lp6oSq0hiDQLjGGUSq+2xXlpfMfClgZDh8+4Cz1rhL2chJ92FRq2vJ7EKN6TSeXABeFp2/FFuP/MqsfpsJLVjEPsRAN5m4PVB9HSI7PHPID+ZMnvVlpKbZA/fheyRv0PzC49sOuU5x0IMfJqmJ9M0/QcArgfwNQCuT9P0ljRNnzzU3tVRRx0XXxQZgt4JtF7zLQBQ9pYucpD2MkAI6GjPsYE5m985BQAg3dWD+1bXUQe4q4deiRVwVmMVDLJdxr0Y72P4vp9EsXvKvYNsxkCikIxZDDwV9oKVlSk10MWlOPJ7vh2dfSatJQQnriv1iY72gHwm9e5G21SlM7raAWD8wV/D6C9/CQBj8md3/wUmd/2R1kcHANcBfGDJZ4BqBl4vkuRivwHpA89kUH4GXkqjDOvD0BwE2f2BBbBdTjeij7km83HYSNJCs5E0rCwVwy8ZZIcPvDFT4JGfKCmRZxaBFjAq3lYy8EG5L3ohJ6eERh8AeGQ8juvgPAZ901EMErWcNpJ0OgTd32R/z6y6otkUmI2Ne/tSi4UAfJIk60mSLKVp+jiAvwVwa5Ikb0mSZGEbyjrqqOPSDyqmQMMIJPbIZIoCJIxAWj3Q0dkz8MUO4w3C9eslS1NHHQcKqlhPyUi6ZDSSgTflJcXpB5A/+VmMPuCpB0iFrtnxqjQYz7LuWYE2TeO+tGquzxZwA08LCNH+FvvfBdANAF8eVNN8hvxkqrYx6ct+ir64ChDJY8hnQOjXqJf+hjhnhM+KOKqg6vsoChM429t0Mb9alVWvf7nOwBe0fIyhspGkRRnAywRLqhdycsmlCnUvunzgD2ATWd6HBfBtAK8VYhIzUPpMlLyWzj4E0L3uiQ0tSwOpKgbeAvBhDMRNOdtKDQ38CAW3PrXJHDno5oXULsVYFIC/D8Dz+d//EcC/BfCvAfynw+hUHXXUcZGGeFEGMS9SQoCszMCDhCDtZdDRrtGUbz+J/ju+E8Xeaf8utp8EghDB2jWMffMkHtZRx9zIBYCPlCe3A8BLCY2dkB3yQjwbD5U0uKzBBOh0MjTbpGzCnRQolxOAqntc9Ym3U/57MkIbABTDHQz+6MeQn7qPLe+QrdkSGjvy0w8y9n60x8A0Pw7S5DMLLgmLJaEp+Y4DZUbcjkCXyDjsAmW7xwdeMr85AGI6YmnnSFZR9SaIehJp7XZLQmNo4F0yHVtjD8dgb5FKrKIPLnCtF6PSfeCNirGx0uH7vPk9kq1SH+xjZAfF2zNneylhWW8LY5C4pd4j1AbwnIG3iSIO4IsawOMGMPcWAPinAL4KwJcCeNNhdKqOOuq4SEMkKYUhe1nGTQcDzyQLpL2CwgIT2SN3gY72MP3Un3p3UeycRLByOYioRFmz8HUcNArtfnUw8HTcx+z+OzT9rZXgqYGJ/tu/DdPP3GY227IJvbiZxso6k/p0wCQBvIuBd8kmlIY+e/ATKJ66H5M7fo8t7iiwZkpoygA/P5mqfU6HcnnhJmVIM4xjEEmsmZMhZ3p/R8KkdhxeaQigBgmeSqyS+S0yj0ypWkJjSDvgmukQMxBaISdtpoFEZRcab8Kyh4HXz6NTh+/RuKt2rRiVfp5KEhqHnMvQwAt3MZcO3y9VAuBwofEPhKj1GyMRT2J1aODp/oZ6/ltSGcnYWzlYl1IsCuBzAI0kSV4CYDdN00cB7AA4OhWN6qjjGRbS2eEIhWS0+IuNcL93Yxlum0favZIGPuitAwCyx+/27iPfOYng2FUyQaykfayjjgVD3a9KA68z8LMH7sD4A7+MgktHSkms4nPcBmZj5FuWw4sEhoKB72tNftBmyjuUdCPQGHhDI++SNPD1g94Js0uTfmm2gE6H8jfrBvDKSYqO9xVjL11YPNpryXBnZQcava9ieTtkcqTDPhEauLWtOOUCHEjmWUmXrQ+SpNWtL0E0dzPH8pyJQQQh5kAljNVsSkUSKy0Kvwbekvo4GXoA/mqxFsCX51uT+oQxzyWoAvBKA1/aR5XbD9Qgg+budqOPtoSGJ7G6NPC601JZA88AfDGoGfg/A/AeAL8EZvMIAC8E8MRhdKqOOuqojmK4i/7bvw2ZYMaOSgjwEyoAX05iZVOwpLVcTmIVsoD9TRS7T5Us7Wiege6dRnDsSqDBE8RqBr6Og4Y+4BSSgkyT0PD7WbJ4to0kB+Cdr/5ekN66A+CbkgKD/dbBSoV+XPfdDo5fjei5N7NkVq297Aqi5CuAJfuhlDl26DEdsRmtRtspoSl2nwJpM2eaYrSvGPtKWYXpA09cGnXWWfN/13F4kliVDzwtD4Kgs9MuBt6RxAob5PN1XD7yYhsAA/iC/Q4tYCwBvMNrXjL8mRf8IggVK12hcafSB96v49c18oQQxcJLDbwN4FVfDblRaZZAnGfPebIkNKXqqWKAqyfCyqbYJIK031ixqQF4m4GXGviagf8WAH8C4DcA/Dj/7gSAHz6EPtVRRx1zgo73gDyTSWVHJmQxEv4Sa7TdLjQB08BjNlb6YsB4OA/e/X2YfvrPjFXpcIexTL0T7KEOKHlDHXWcbQhA4UliFawoHe7yz9aAUmdVHYmjtmyCjk0JjTEDoIct75C65Qbar3srG8AKQOh1sRFJh2Vfdxuk0+kQpNHmg2pHYnmegfAEWjraUwx8FStru9B4GXgG5vwa+Mx9jKJdzET6fOLF/h2Jk9QG8B4GnnqkH7IYkiZfWsNgAAAgAElEQVShMTXwDMDTwm0jKYGwXujJ4+bDHIscybwWA+9rV8m+WrsO4F02lB4bSb+ERgxC5rnQVOj4HS40CBvKbUiX0Og5VC4XGsCbxJo9cS/6v/e9R3Ime9FYyAc+TdMJgF+1vvvgYXSojjrqWCAE0D1A0ZXDDKmBDP0SGpFwFrSX2cfRPkhPS/YC87KePfS3KHaestblgCiMmWwBNQNfR3XM7r8D+cZDaN78pjLzJwBHGGkSGi0pWiSCCqBgA3QtSdUouGO3c8ASPutFyB/7tGrzup+oxEWns0gQKuBRFGr2QFtfSjK0QTHprYPub3CZzBXyezodAY0OCCHuJNciQ9g9zpJ1x/syidUs7uPSbmsuNAdg4AXD7jwHvF2x4xUuN14GXsmUnH2wGPiqJFeZB2BLU4KAbb+qkJPmA+/Kh6CaQ4wz3wGAqhZr30v8nqcF7GRfEsagYCC5EDkZjhkCcYyyr2cpoTFyERztKonVLaExNfLWb5DL1+xcK/H7KDwa+Mmd7wHdO43izOMIL3uOc5mjHosWckKSJP87gFvAmHd5hdM0fcsh9KuOOuqoCp3ZOkohXQb4oyVqgg7OyGYqppIFAw/G6IHrdMXDufHyf4T8zOMllxr9BSEs2i5ln986zj3Gt78DmAwQrl+P+Hk3G206A+60kRS/M87AV0pkiJ+BFwCkfeu3I994GKM//nEGZlyJjeCAhs5hPIXcjOYgpGm2G4mPalARnrgO2f6Gn4GPGij2NlCKIpce9DoDT4uiwsteq8SaZ4qttsPlOa63VSSxGkWTqiqx5plbGz7HB95g8J3tkWqnlG3T0MBH2jE4Bgku5tlV7EpvL+nLy044rmMQA0bjPIhrEkTOdkICJcBagIH3DnS06+A6BhBrFsFoik2XGv4bbL3u25A/9XmQRgfTz9xmJmLTAsirGXjCiz756h5cDLGQhCZJkh8C8Ct8+TcA2ALwFWCJrHXUUceFDq2Iy5EKzYUGAEjD0sDrjKUO4D3t9sOX6u1CQpPVEpo6/BGsXA4AmHzkXWU2Th9wOpJYJUgW92GVREY4phjNJugiUVP2x7SRdEhodAa9yqXGyX4H2jOC/R/d8GrEL76V9WtigZbpiAH47ioKl2a4yNjvrdG2JDSZBjz9On7mA+8G8BLsOSupcpa8wgfe+bf8TmN+S9aFkRrcSBtJW5ttaeC94FjJV6R/PQAEHHwaSayadSZhy+o2ks7zSP0AXtlEujXw8vzSci4B0+iHRrKwsX9tWV1uREqWobbUyD6P/LPHRlJdp8Ji2AnbtsNmknRW0HrVN6H5iq8DabRNCU2mZmXoYEclKetbbvXYPg9QDfyoxKIa+P8TwK1pmn43gCn//2sAPPuwOlZHHXX4w0hwO0JBdR94OCQ0muuGE8BTxbAHnRXFfNrtQaCqHNo2lXXUoQWdDBGsXgOaTTH+y18yQbbM2dArsZYZePmdz0aShB4G3uGLbQMqu51vz9BnV7jMOJ1HiA7w2f/NV3wdwrVr2SoWaKHTEUijA7J0HJgMlGWfiDxXiefDHaU7N4CpvxIrc4HxMPDi2OfYRM4D8AKQGaEzvx4GnmoFiLzSDg9zzGww+XEWWh5AGCpZFSHaeSLlQQIfrFGfI5EcxLhna1QCqEPDrvdZ2kxaGn1d/rWIBr5wOP5IgO5zoSEACdTguEKKZLzTwpg7+2hSI5ebT6NlMvCc1CG9NTZosJO2oTPwlz6AP5amqfB1myZJEqdp+jEwSU0dddRxoUMWcTliEhpLA4+4ZQJsrViJAPCFAeCVLzZpr4CO92BY3mkFVwQDX2vgn7lRDHfm/gboeB/hFTegefMbkZ+6D8XWY6pNDjh9SaweRt1uDwKTNVcLsP8d1oHVoE1j0H3WgVXVOXX2W+9D3AKCyCmhQaONgPvM697Z7JgpEEQg7R6KnZNqxYoCRKRUyGmeBt6dxCqkRk6JjK43588TY9PzNPAAQKk6R97kTA/wBIBQ9VENRtSMDubNIhCtmqyzD1oxK1cfLBmOz2aSUm5VaUhouEafS7JoYfXRUciJ8joeRhfnSWREP+cludpJrFLiU5bQGAMh2+2MDzCF7aqr9oF8f9hOaBdRLArgH0iS5EX877sBfGuSJG8GcOn689RRx1GOI8rASwkNf+CSuA3k0/KMQRCyIidhZLIj2kuMdI6xl+vYxdCHymO5BvDPmCiGO8ieuBcAAySDd/4rjD/wK97laZEDkyFIawnB6rPYd7p8RLORFFVFDRcWn6Zdrq9AlVFwx2o3QJUDjFR6qHtcZqjGwLsLOQkJjTZIIQSktWQAeFpkQDZlDDwHPIWWtyLXD0MErR6rhCzbcvM3WToGoYGfn8TqBujVlVYNDTy3uTTX1xl4t3WhIQOqsonUt2csEymNu9TzawNCEih23PKil9vUzqMtT5Fyqap7BVAM+zyHF219lmTLZ4/k+nolVp2B5+cgm8oKxOU+eCQ0Ylu+RF3XrJToH7Tr7JESkbhtMfAMwBMJ4MsMvExQv4grtS4K4P89gDX+9/cD+E4APwXg3xxGp+qoo445Yb+cj0hIRlM8eHmiqQDZtm0eiS2bSU1CQzrshazLaPRiJ6zSq8Nnvo5LNmb3fgCjP/8Z9oHfS9mDH/cuz5g3CtLqMZ0sYCY9axIa0uyCtBwMsx4+hj0ITVmL3e6QJTBds09yoMs7HMwtUQw8pbkbPJf03Rwot5ZM2QCXy5C45WTgjXPUXgY0X/m52u2zsJH0athtcGwfp9hMp8zAG+4nPnmMp8iScUyFJzlTbCfPDHcWojPw8lr6ZUDV51Fbn3XC3L/uAOO8V3SNvDUQEgmiQeCW4Lg08NlUVZi1lpPLeGca5klotGsBOBl4OWulX/uSBp7f0x0/Ay/vTVumeRHFojaSf6r9/TEAzzu0HtVRRx3zQ76cjxgDb7vQSJnLhBWKsV+UcavsHsDbA86oGU40pQFADeCfSUGnYyCfMWBrg2XX8hyoktYSSIMz7PqMjyX5Co5fhXxHZ5jnMPBGEmto1jSADmgcyZaaZZ5T9mA4k9jgU2fYywy8TP4ENB0+/820V4xkXmX9GjoZeKr9poP164HP/bVx/F7QpttIZhmCAxVyCufIMjQQ1+z6t51nQGQ79VjAF/Cy9F4XGoDdO7aEJozUM1DYSHoGIYTw5GdvQvO8JNY5XvU8UVYMVIiTgeeSLbuPLhvJbAp4APx8CY3PhUYNag2Zmi5DgjVg1LcRtw2pJpUSGu6c5NDAK4epS5yBT5LkLUmS3GR99/e4jKaOOuq4wCELzBw1AG+70Ng69cKW2FhJroaEpszA2y8xp898HZdu6NaIWkLp5ON/gNH//MXS4kIqojPw0AeMFmgKjl2JYuekdK0o/b58n4VThpehd7Cahq7ZAdqqdM96kqo3yVVolrVZAgDB2jUoth9XgE/2MQKJGiDNJScDjyBE/IJb0P7y70L84i8HGh1rkOGSfnAJTVHBwAd+AI8g8jubQD1HWHMFwC9yR/JoOTGybOdpA1OXBCZS25ASmkhq/okEx4Vn/RDSw53t1NHuAa7658o+Bm6JTSiSWNWAkZRccnhoCd0lBl4OhiquFQn8ffRo4IkF4H12nMztzMHAixklBwMv9vNMkND8KIDHrO8eA/Bj57c7ddRRx0IhwMtR84HPTQaeNES1VKsMto9B115iAsAXDgAvX9w1A//MCpFMqAMaANnDdyF77NMluzjJwLd7fDaIWBIa834Njl3FXFhEYpvPdUZ+1O5XEpSXd4Au3bnEW4lVB32AA+AHqq3IPYmLPDlTT7QF84JHnimpkD2o7h5H0dc08DKvJQIhBNGzvwCtV30jA3EV4FcCWwA082vg5Xo+DXzmsXCUxwk501feOGd287KNpNLAV4FjoYGvSGIVswSFcgMyXI2IBsArJTRVcqoKF5rA6qNLiiRZ/sK4l6Jnvwzx819l3m/GYNORxJpN1bFp2wfEgNfhtCP74M4lMAo56b8hMQhyAXwtn4A02sB0rAbepSRWFwPP+zIZmNaxF1EsCuCXAdipursAjp3f7tRRRx0LxVGtxFqYkgS7Wiq1Nb9x0wngCQlAoiabGnVJaMSL0vaZr+OSDill0MEvgGL7CVaN0Sr8JRJSSavHgE+jZU6n6zaSYBIaABq4tQC7t1BTUMnAO4sIVWiv7cRFYic/Es1znpqgjG3foSnm+wjWrmNNm4/yZSyJTfc46NDNwDv7UCH90G0k57nQEAfwJEEowVUVw+6UzwAKSFZp4BdweKE+H3iwgU3JgjHUklgDTb7i1IYHRh9K50HkM3j99s0+es8T5Sy/tn78vJvRfMXX+xl6SwNPKXVq4A0XGtf+RT+9BbEcgyloDLxdrAowkn1J3AZAVU0QkcTaWmLbnvo18MDFq4NfFMDfC+D11ndfB+Cz57c7ddRRx0IhXV2OGAMvARFnTkS1VDG96dCwlyQ0+oO5s2JqFB0Mfi2heQaFZOAL5+C12H3K+CwZeA7wSKNjMvC2Bv7YlWw7QgdfYtRtRl7TuGuylVK7S9dsgF+XBKYquTLUGHiXhMby1SZEgqxg5QogaiDffJh10ZYRdVdB+w4NvA3AF3FH4aCvqpBTlQYeJJjPfsMP4A13FB9opJp8xefwMkeH77aR5MdLQlWB2iOhqbwXgpCt62XoF+ijIeNxJ5gC1EjELW2ryPn7hpY18LqExuW0I7blLfTE19cHtYBHA+84D1aCuqxjEDXZb77ChQa4eHXwCyWxAvg+AH+aJMkbATwAlsT6OgD/8LA6VkcddVSE9IE/Wgy8fNmKqU8xtT11S2hQcqExrdaC3gmjtHuJwY+a5aIzdVyyIRh4BphcAP4UcGWilh/3gbglGUM21a5e5iVXpO4qEDZQ7J3mG7QBuVXRUUovCE82tBj4oqzXlfvTwIpTFlFkfl205nhDae5OcgXYOSpMlxoSBAhWr0Gx5WHge2ug432WeB43S7I4s495+Tcp96NZQLrW149F239pHxUAXhIFPgAvgaEjgVRP/nToqvXP4r4rVSAFWNGmgidf8vbGi75Mgdw54FkkJCs5lkNOpfXBN0sgnHKcswRicOmT8eiFmAwbSUtCI5jtShcaDy+sDXB9MiAj7wPwuNCUB1sq12oE4LjqZ9wEml23Bl6fweufQXi5u9tHORZi4NM0/TCAFwP4OIAugI8BeHGaprcfYt/qqKMOT0jgcR418JRSTD7+Byj2Nw++DftlLx6sYmpTMvSaBEZ3D7BecnZSoc32Mfswk4Eff+R3MHr/zyJ79O/Ouv/Zw5/E7PMfwezMSYz+8pellrKOIxKFktAYcpUwZpprBwMvKi4CDgbenjEihL30+XWfm8TK71dWLbLMwCvLO5euOS8PaEUIWz9PO7FtIl2yC95G7eqbAMLVZyk/d1tG1FtnX4vngNVu9kHro2+QwjXsfgkNca/Pt1FVREnKRuYBeGf/BPDVZzosi0Z5HgU4diXS6hIa1h7f8PcRP+eVaj/iOnjYbwO4lhh4ft5856HklONJYqVMX+712wfKEhj5NwForp6Htg+8LqFxbV/fh+sYdIBe5CqHyvaB9yWxCqKIkznyfRM1QJodjwtNAbLE3NFp3/3OO+oJrosy8EjT9FEAP3GIfamjjjoWDS2B7SCR7W6AzojyaQdAd09h+sk/Rvbop9F9/X84WL9KEhkxtTk224kmgclY8hEhpCShCY5dCWQT0ME2yNKqBC1EW99wHwAwS/8GmI1QDLYRXfvSyu7SIsfs7tsQv/B1IFEDo9t+FgAwav4LZA/cgeIlX47wsucc7FzUcf5Dk9BQjYEPVp8FzMZlCc2kD9LqqS8abUuSpYocqY1ZDi/GBm2GXbHfRHeGsZf3OIv42OuFqm9qhZz8jGZm9FFG3FLA2JbQLDMAT/dPA6tXq0FMSUJjard9gwgJpuzER3msVQx8pCVnOgA8L8pFmkulttI63gJIZ+Pw4gG/uSWh0TfhSxDV+6XPVJQKOc3R4Zeccjx99BV60vdhS43kTGeDPYf5s7bsQqNLldwSGkICVUHAlajLOsAAetQAplmZgffZaerXEmADcBKyxOtmt1R5WBwraS6BToco9rdK7fmZJzB8779D5x/9P0f2HbCoBr6OOuo4SiEt4s4ewFNa4PHf/F5M/+59ZgNnPYzKp2fdrwzgbhUA1Eubv3yo/bKPW+yBLZgdSw6gNMlWUqFYhk/xUz0XgJ+TYl9Jb+yY3v0XGN32c8hP3YfJHe9G/qSZzpOP+nO3Ucf8KPY3MPnEfy+5wxw09CRWmeDZOYb4+a9CsHIFk9Doy4/7FgNvVWx0JWAKTTMwN4mV6gmkui5dhE8fTngl1UqG3c8MS82yYMB9wJPvw1ndU8qRTIkLEQy8kK65Bjl8eV2TXHbC4eBL/LZdAB3QBkAuYKkNilztfHAWnLjOuWl9m+Xqn67ESM9AqEqHH0ZyoOSU2CygPzf9zT0Wi/MSQBdxofEVxJIDlZl5zsS1EYBdEDFVPvBVSaz28vYx8PtJSt6qNPCO6sbK9Uh51bNZN48GPggRLJ1A4WDg6T6T0Uk53RGMGsDXUcfFGOdQiZX2z6AY7jnAKbfgmo7KKy26baviInsBEFP6AGgMveUTT6nxAikDePf60HXwIhFrMnA+uIvBNiYfeReyh++Sfte2VCYfskFMDeDPLbKHP4npXX9Ucoc5cOigk98LzVe/GY0X3wqycjmKvadMkJ1NmJsRD9LoGD7wpcJj4m9fkrjtMqMn/QVBeUBdBQypB4yIz4YLTVliI/tDc/cAAID0y7e3z4sPUT05UvymWsxyU977FsDXdmLNErj7IHJUiIeBr7aR1EFfmdmNrn8F2l/9fYhvfI1z2yabXOU97inktAC77XShsfajwLPHy14mG4dev3pfNVjxudKFRk8gdQJ8MQjI3UCbg2HxPCWReS1Fn2mRu/evbwvwziJQMUsgZoadPvCO31SJgZ/I2WXS7AKeJFYShCBLa6D9MgMvdPMu9v6oRA3g66jjYowFKrFO7nwPspNp6XuhfRUWe3KT4gF/Lq4ueSb1xDJ0Fs1KYlIAXEty1Rmg9grQaEsAX0rqswYArPR8zpw2ACMBVsT0rj+SfxfbT7A/LACf7TLWhZ5DPkAdUKDhfOUSCJZRA7+C9Qy6q2w2RktYo4UJbgUbp3IqcgAEZnVKxU4bgJ0QlBJndVkECd0AX7TpIRM8PQBfbKuqkBOgzu88Wz4Hey77b/vAE2Imj1vtsgvCmrDKRhJQv20fAy818J4kVrmYS55CEF11o9t3HFiI9aUVdp5qJmOePIUnwroGIWSOjWQQKOBaoU+nmWcWYEGnnLlVUgHQYuaU0BAJ4Pngt1TVdr6EpjofwZTISMZfzArNsfwkFoAvMfCTQXkWUDDwvTWnhEYAd+FkdRTjQAA+SZLnJEny7PPclzrqqGPBkI4FHgBPiwzTT/0psofvKrUJi7ySVOZ8ONq4Ki6GqqBLyVavYTPwJgNECGGJrLucgbckOCWNPd9+cIwDeA7AdYa92FEyi3zrsVI7AMzOnDTWr+NgIV1jzhOAN3Tb9r3Q4WXTdY17npnMcaPN1hXbyTPmIqKHNuCkNJcWdaS94pTISPAQBCWA77MnJLZ+3FGoidIqh5c5umjpClKUrQEBdU6ERl4ct2jurcvBK80rALpxDJ5Kpwtr4P3g1VjubGIR0OhxNgH4oIEQBZ6dfYxA89x9nsV+hFyqgqG3B5tGHwB/LkDJpcZdBVUy9M5Bhi6BIeXvBaDmz+nSbMpZS2g8NpJiwMkHCJLpN5JcHY5BUkOvNPBShtPssHvUIqZEcnewdAKYjUpONZKBn1zkDHySJL+bJMmr+N//B4B7ANyTJMk3H2bn6qijDk9QoYF3S2jktJ+DTZcA3mLgzweAZxUPXS96IX3wAHDJoJdfcsHyZRobaDP4nAnKzCRZwcDT/Q0UOyfR/69vRS7Y9iKXLyRppZdbAH6bgfxaQnOOoSeVnZftOQo5SQDPKvcaRVmKnDHqPIj0i2ZT6rrjhQytgiiKAuHlz0fn634Y4WXPLTPsVIE2IoCa0d8KBt1grx3gsshRaUMJqCqllYxm+RjlOckzJ4NOeuso9k9ziY3fB55pvyt0/NAkNAewkTTW8bmbVEWFbGOhQk4Anw2ZUyRJ+MA7ZwnmaeC1wZxv+6jQ4dtOOT6GvWIWwTgXDtkRCU0JjU8DX0qCdSzDNuseKInBFIkaQNhQ7kKGzeQiGviJ6iPfRklOyQdMpMecaApLRnMpSWheB+AT/O9/DeDLAHwhgO8/jE7VUUcdc0K8NHM36BbTfpL90iIXEprxvjmtaIOPA/WrzMAbiWgWYFESGlHoqfwSI2HDLOADqJeWkNBYLjek3QPiNor9Dcai00IWp6FFzuQWUGxtiSEWzPH+Jka3/TyyJz93FiehDhn6lPYBI998WHn96xp4u8KoB8Dr4JU0OmwZ8TJ3sZ7W/UqCEOH6s83vedBCSwokZQa+ykXGSFy0wS8JTAmNh3VV8pZqCY2v3VdJNVheB7Ipe454bCQh+2gOqkt9ECSCz0ayIonVZOA9EpyqMPpcwfpWAfiqCqLggyGxDR+7XRTsWest5FTIe817DD6G3XbKcfYhVIM9T5IrwAcJPhcaqOds2YVG9CH3SmiMY/MNlKRbT4TO1/0g4hd+qdln37WSM0o6A89ZfPGbt3XwRQYiGHgA1JLRKAnNxQ/gG2maTpMkuRrAapqmt6dpeg+Ai9D6vo46LoGwAbEVioE3ATyllOnJBevjKmrDY3bf7Rh/6L+eXb/yDCSwpledAN4DwF0vQX19S1JgJ8HqjhrB8gkU+5tlu7wil/6/MlxOGWEMFDmyh/8Ws8/+1fxjr6MU9Bw18DSbYviHP4bZZz/IPuvXssTAMwmN7t1MC3NGSDDwMpGVv8T1INqMkZEgSoKyjlZvD0KAUjOJVmjcvfITt0RGJrlWtUPXRdvHIMBpIZMjzfWFt7iS0Bil6TnowWzsLeSkki8XtZE8QCXWOUms84K4wKjdP6odg88lpgociyTWwq9xr2LgiUgwLQr3MdoAvcReE3ZvVlasDdRv0ecTD6BcyMnSwAuixfKBJ/qAcpEkVm8fc3kewtVrFMFj53RY2zDud/DnhPCQtwftIgQDv8TInGJw6TLwf5ckyQ8A+EEAfwIAHMyfg99cHXXUceCQPvAeCQ2Xx1Bb9zfeByYDNK+43ljO3hYtcow/+GuYfe6vz7JbbgmN0sCLpD0hoWma/fR4WsupUVchJ0BLglWgLuhdBrq3oRXlUfILErekthlQDLEELgCCtWvV3zbgr2OxkBr4g1XLpbMxUGQKlBs2koIZ1gZzcctk4HOLYZcv85HaToWEhuozQg6NuzFjpDO6ertYVwsi2EbhMuME8NTbrgCTT1ahMZKOQbEsquTRwOs+8pVJqhU6fcXAT8w+WaFcaKqTWA8moVFgtHKWghYAiDsZNggqq8Ei4AC/QiJDqyQ00qXGo4EX9r5VLjPaLIHXyrLqGKQLjW0jyc+HtJH0+MBLDbrnGO39emYixKDWOagG/NfKdqHRBuZKZmmRWQLAc5tZOrY18H3j/6MYi/4ivhnASwC0wUA8AHwxgHcdRqfqqKOO6lAAw8fAs7G1DZyEnV/jcgbgCz3DXtuWzjr4dPbOKHKHVjYygRfgkNDoLjTWC4hb3rHOWK4ekTUA0MAG6RxjwK/EwLNBRtBeUfsQy4j+AIiuuQnhNS/hu515Dzl/6n4pS6rDinN1oRH3r3CO0e8DB/NL2itWoabc0FGXNPB5WfJVmjHSGHiXBl4CQwl6C6NdrmvsQ9M9W8egLz9X9+xLrpSaZE9ypACFPoAeagOASg18hQxI7EMmPs7TwFdos+EBrvNC32cpEViAPl48yLN93TPfDcAjpYH3Smhy+F1quMTGm+QqGPiKRFoSau1uq0o1i+AC+Hydkgae/x3aLjQeCQ3gdwQKq/MZiO685Jst8Z1HbcDJjkNzXpIkkUUi8PNNgogN/H1JrEfYhWahSqxpmj4A4But794L4L2H0ak66qhjTsypxOqV0PDv47Wr2eeRNommAY/8qfvV99Mx0DIrHebbT6LYegTx877Y3HHukyRYAw4Bqko+8EXpBWRq6E3HC2K72Gh6XRI3gXyqQJCunw5ClvTIC/+4NNrB8jqaL/9a9N/xXZUAdPSBX0F44jq0b32bd5lnbJyjBl6sRyeD0gyR8u7WtNudFTlIFZaiBsAXbJzU1M9Kjhok0Ao5UQUEiJBCGMenBpy2lR3rg08DH4Jm09KMkt7O+ueRbgjfbV/xHpnEWpRBmd7u0cDLY8lz4zdV2kaV1WVoSWi8Sax+G0miD7IPAOBJ1ERw/GpmF1t1DqhHvsL3S4uptw8k1KrFOq0wlYTGp/OvYuDVvTBHIlM1yCBErj8/12ARCY3Hhca3fwDh2rXIH/1U5TGA5u6BjHat3K5K2mAMMAatsg6EPQuozb4xe1kLwIt3aDYF1VxtjlIsBOAB6T7zZgBXA3gCwDvSND1LgWwdddRxXkJnkx3hl9Cwh1JDAHgPA58/9mm1znSkTTP2gaiB2Wfej9nnP1oG8EUORGUNvC2hkS+GIATCRqULjWBEZdEZvdhJ2GAvHKmB11imMGYASIA1i1U1wIEAmNo5EFUeETW8AJQWGWh/E3SlTgdyhQQV+QEZeH7t6HSowCzAQRf/W2f/OseQC2chcS0N5k8wz3xgkc+ctqcyObzQGHYhe9FDZ10tJwy2fgVA1xJAvRr2eQy8lE342GUGTkuDanHMPoCu2Uyq35RdyCk0BgDeSqyykFM1A++yNwyvuKG03NlGdN0XYLr9ROlZWLKR9G0/CA8GfuVXWnJmlXTEq6Hn91VVIq3mlOOT6VQXo/IkmJaSWEfMHcaXSOvbP8BcnNRC7j76/PDtqrlVgzHxv51nZV9/XfMq9I8AACAASURBVGbT6hoSGlpkwGwM0l0FHZxhFZ25Vv4oxUK/iCRJ/h2Y48zvAfhO/v/38u/rqKOOCx060+AICcwt4CmmBeO1q9hnjYHXpTLZY59R32vJP/3ffhuGf/SjzNkln5b279PA24WnjKnxRkuV6K5KuKNlPS8hBIhaTgmNlNeIqVBdAy8YeNFvcZ40gCYGLSSMFZCygvbPMCb4fNkkXmpxzgw8B/CToSljMtxPtHupo0loXAWI5IteDCyyMpuoDzj1+5GUK60abKDUpZeTWEuAQzjW+AA+EaDNA8qkRKa6XQJsj4TGq4EPhe5ab/cksXq97Bdl4LUBkhVB70R5ubOM6LqXAgDyx+9x7tcry9CXs6R/ZifnWF1Ktx63TIcYhZw8+nUAVTaQ85xy2L1bkYirF2ZyFL8iugbeJmjsfTokPAAQXPYctbjPjUcUaLMHtDxR1wfgS7Nf2jaIlFla70KdpW90TUMH7lgTcGLmqOrgF2XgvwXAa9I0fUR8kSTJ+wF8CMB/PIyO1VFHHRUxpxKrtJG0GXj+IAp7q0Dc9jLwdHBGajvtbRRbj4Fwn3XMxtJnV2xjkUqsxosq1gB4hb5RMmU2m9hoOZNY5bSvePjaDLwA8FGzrJMHQJpcNhTFXgAq/OlL+so6WJwnDTydDMoMvKDgDQZ+BZiN2fVwAE+DeQacEhrp6w2Y91vg0sD7GHgNQIM4GXJaacE4R/dc0sB7wDPlvtrEOkYNoDs18HoSbJ6xYyj1IVCSBkcfJaiazXOhIe5jFM29E6yo1AFcaAAFHEMNQLIGITOaI6GZx7DHTa3ZL6FxXgeAz+yIgZafHfdWYuXfVUtogkqZD9Fyf5zJpgIET0dKkuLoIwv3dQzay87v1UbEgNCTjyClRhUMvfZelH2KYgBE1QoRoQP4ZlcVC4SaqQ5WLkf+5GePrBPNokPaLgC7oskWWFJrHXXUcYFDamdzj4RGAPN8ZiTV0XEfCGMEcROk3TNdaCx5QPzC17I/bPstAJQXvXBNSzqT2aT+vMw4kqip9IkOnajBrjj0jyQuM/AkCCVTJJOTZDEppn0MT1wP0l5BcPwqxfQ6GfiGl4GXhZ4O6LJyqYcqrHKOGvjp0LgGvuTLQFRjHe26k/ZKEhpX3YLIn8TqkNBIRyWXC01l4mIOZ9I2oL7zSmTmsLK6JtjJwGszEa6Bjt1uV6vl26CeZGL981wbSSlRcsOR6Jqb2HasJMNFg5AA3W/6GbS/6rvN743nil9CQxyMtNGuOVd5wbVwHPK43FAp46nQwItiUr6KtRVJrmYibvUgxCWHERVR6czHwOuOMAebKSFigKzL1vSokhrZ8jUN5BMSMBmkK4lVLNPsGj7x4l6TBQEvcgD/5wDelbBoJ0nyAgBvB/D+w+taHXXU4Q2NaSh5U8N64OjgcjKQ1e1IqyfdasS29IhveDXblnAe0IO/LKR/u9ivlhgkw5AkiJeIOV2vy1vKYESfznc8vOOWfDjrSbLEltDkJiiLnvUiLL35Z0Hay4rJFLZ9UUM50kQNrwsN3TvN/q8lNO4QoOGgA5yZ5kJjS2hcwFG3iXRq4C0JTTFzzxjl5fuR6FIw2Q+NNXVo4J0JdwAQRAz8epxLJOvqldBYrKyLHRf9cxVHMyQ0DoZdc/Vw/qbFMoaExjOImFOJtdJGEkDz5jei8YVvQHTdFzjbF4mge9wE2vr+RJ6A1/7QIcHSQ7OjrZTQwMMsk5A9mzwMvFxnLgNfodPXB5+uY9AYeNNG0tTAYzqSVVmdfWQbKLfz6Lz+R9F89T9zNxLtfvLp+KmnXb/fUZZykrjpTGKV92Sza8pkJoKB5wB+uO09pqczFpXQvA3ALwD4NF9nBuA9YHr48xZJkvwQgB8G8JI0Te9OkuRmAL8CxvQ/DOCfpml6mi973tvqqOOiCcmqU/5AUw8rSilj4JtdYDIAnY2Vfd64r5jl1hLoQC96o8lHVi6XhXGcAF7EzGrLPRp4O4lVZ+BFsikYA15KdjNetOWXHIndEhqZeCWSkzRW1dDgR01QkWRJc0TP+2Ks3/yV6MtE2RjwWIkJBv6gPueXfIhZj3PUwEPc03K7OWQeHLHuJYANMF0aeBIAIJq0Z+bQwEfagFNLACUBUFiD5aJQA4RA01TLA/Domjnb6LR4BNR3Phcay5nElm4QPemvcFT4lAMEj0Zelxq5ftPg51VzofGyokK6MC+J1ceAR000X/q/udc9lzA08FVJrOfIwIv6ARUMvBpEuO4FZfnp30d1kqqZN+Q4Bt1hxelCozH0LjcWXxKsFeHaNQjXrnE3BqGUGnkHtR4JDSEB67f2uzX6FDUNBt52qCLNLpBn0m1GMvDHrkRw/CrM7v0rxC/6svLv6GmOhRj4NE330jR9CxjovRJAJ03Tt6RpujNn1YUjSZKXAbgZwCP8cwDgnQC+PU3TG8D09j9xWG111HFRheF0of7OTqbo//bbgCKXJaJ1K0k6GUhtN2kumawDf0F0vv6H0f36H9E8sxlIp7b+Fy4JTc5Ki2th2kDmALE0wWGsmMZ5CUr2gxlCQqMV5gE8GvjMaS1oaNyLAsHSKtrXvVhtP2p4NdxCA18nsbpDgtkDa+DVeqblqZJuGC9VDsZpPnO6pxBCjAElLdxJrHJmy6i0GpQYeEMjr4NmuYCvuuY8tlGAyzlJrBLgW/uQ+u7COeiFVsjJNYggVpKrE7hIGZBDQw9tFkEkD85LYr3Q4CgU2uipdyYEgEGOOMGvzsA7HWI4++1h+UkQsWerVwM/Jx9CLFNVLdaYRWDtv/jfP4N33Jay7+ZIaHTQ7rRT1AfRDo38QmFUYvVLaLyOQdZ7xiBpbAaemvesmJWWs7XinRm30Hjl61HsnkJ2/0cPdlyHGAsB+CRJ/jBJkjcAiNM0PZ2maflNfg6RJEkTwH8B8K3a1y8HME7T9MP88y8D+CeH2FZHHRdP6FpcTds9+fBvA4I9WOVWkZkO4PtKQtPsmrpSDoiCpRPsgRfG7AEnNPAOy0oXgHdJaCRgymdA4EioyzUJi/1w1sEGdUgSNAmNwbpKAK8x8A6wQUINoLu2H8VeCY3UwOczQz9fB49z1cDrg0+9wirVrqUBHvi9lc28wBJhpIBvNivN+BCRxEqtJFkhW9Gvsw4mJOjWf5tuVpUECvy6JQ8WA+9xePEmJmpyHuoa9OpJqpUa+ZzLEVwSmggA9bubSAnNmEnaKnzWnesfchBCZAJ9tY1ktTyENDvuZeV32kyFb7DmISfk+sAcCY0m75rDwIv2B0/u4WP3PoWioOx+EL8DpwZeA+32gNdaJ+gdsGq1ZNj9s1KVAJ9Y7lElowQNwFvyOxvAy/dIGCG67mWIky8xnXqOSCz6i/lrAN8D4HSSJG9PkuQrOJt9vuJHALwzTdOHte+uBWfjASBN000AQZIkq4fUVkcdF02UdLYAsvs/imL7CbRu/Q50/vGPIX7+q1i7BrJtCQ1mYwVmLMBDCGEFLqQ8xZEwa7vcuKbbdY17npWSoPQk1yqPX+p5yZG4qfnAlxl42eazxOMad1buvKxTNQC+fqzZhA2WhAuPB6TmT92P2f13ONsu+ZBA+RwlNIBleZqzwRxg3g8aAy+cZtyuSGLQ62Dgw4jdh3a+hqxWqQN0SyMvvpMd9eiauVzAy/yWWFe7uJndbv3m9IRampcBvpTIeBh220bSJX8Rfch8gwi+jWwCElWoda0B0oUM6WBFqXumBC4Jlr0NBeAr9efehOYQciDkY5ahS2g8MpyKPtqgnFKKvcEUg3GGR55i0jQStRzbEi408xh41SfSWy+3LxDzZiqMnIuK3xQbYFNz5iRqmjJHaXbAf7t8EFbKl+IDz9Yt34z4Oa880HEdZixaifVnAPxMkiTPB6vI+p8BHE+S5D1pmp6TDj5Jki8G8Aown/mLItbWluYvdIFifb33dHfhyMalfG5OxgRCfb52rI2o18OZe3cxBnDlK28BIQTjJ+7DCMBKJ0BnvQdKKfYnA3RX2Xi1t7aGLQCrSwTRUg/b7QgTACcuP4aAP6RH7S6aZIb19R7yQQEhuAmXjiPvb6PboFjRznOf5ugsdbCmfXe608IYBdbXe9iIgSKKjWvzVLeNyW6O9fUeJiEQtZtG+2BrCWMAx1da2GkEmMaR0b61soy92Rjr6z30NxsYA1hdWwaCELp/TrsZ4PhqG30AS8sdHOPbOLO8hJ1sivW1LvoAuj32MBf72Op1sVfMSvdTtjtGH0Dj2GWYPvUQ1lYaCLvle+7UB9+P2amHcNUX3+q/oBdhLPL7GpMCBYCI5Af6PW5GFGLuo1EMIYYB3VYEBAGmANYvPyZB1jQ4hiGA5W6IaKWJIYCV40voaPseRjFajZDdL0WGTs+8X7d7HUwBrK002L3Sa+PYeg87vQ7OADix1kHAJQf6/TrcXcIIbHAhjnWjGWEYRqVj3+y20EeBViPEMCq3D7bZPd9qENaXtR7iVbXMJOthCKDdZO3HjnfR1raRj4ABgKVujB1QtDvmbyprZ6y9E2HSCDC0fpP5ELJ9HAPTOC71UZwP0Yf1y1cM4JV3CgwAYDYBaXe913+r08QugNW1HhoX+Jk9bnXQCDKQKMLYcR0A4GSzIZ+1Jy5bkddeBC068rnYW+mgZ21je6mFKYCAFGi2GqV9bPfamIL9RsJmuT1rzTAAECJHAeDEieXSc2baaEDA095KB2//6wfxpi9PsLrMQPlGpwWRQXJsdQl5r40sZzNMj2wM8IU3XY1Rs4Vs0ken05S/h+0l1rcTz7oaT7SXUIz6aC25r6U4B8euvgZLB7iOTzYbQJEjpxSdbgur2jbW13sYxzEacQAQYOr6zUQR2o0Aa6sd+Rw/zpc51e0i29mX6+RD8PdAFyvrPUyyy/AEgOVmge56D9vtUD1bfLkbRyDOqmdpmn4ewH9IkuQPAfwUgG/HuSey3gLgRgAPJUkCAM8Cc7f5OQDXiYWSJDkBoEjT9EySJI+e77az6fDWVh+Fncz0NMT6eg8bG+7kumd6XOrnZjpWjObWxg6CcYTxHrOI3Nxkj9K8zxibna0dDFb2pTPHKGeM42DGQM/mk6cQHg8x2Wdwd3NrBBKw10ERNDHa38PGxj6KAZMwRDe8GvFzvxCjP/tp7G/vYqqdZ5pnGI1z49xPpgXyLMPGxj5GgxEKEprtMyCfTrGxsY9smqGYmutnfQbhtjd3MR1NUBTEWp+VpT/91C6yHXbsZ3bHJbeE0WCE/DRL2xkMM8z4NiYTlgi8cYo9BgajGY4Dch+TKdPx2vdTvnGKnaP2cQAPYfPUFoLlMjM03jqNYjS4pO7HRX9f2ZTdp7PR6EDHP+r3GbtHKUbbm/L7QX8oWcWNzYGUZxT7bH972/sgOdMm7/ZnGGj7LhBgNGD9odkUowk1+jYdMfZt8ylxP7B7ZTpk9+Hm6V2pe86mGYpZwe7dfSHjKuT2RsMxCorSsY/HOYo8x2g0RkFJqV1sa9Rnv8kz2yMEuVom32WzSsN9xhju7E3Q13+HPG+lvzdEkWUYTwpjH3TM1t/fGyAfjlHA7INav8/aaVDq43SYGX3Y3LRK0U8E7KUgYey9/uMx2872zggBLuxvJA8aGPf7IHEbeVG+TgAwzdS7fnNrABL6Z5P2+1OMrW1MRuz48tkME+vZBgATfh5nkwmKNi21FyN2HrMpuye2zgxBhuZzZparPn7m85v4s48+jstWmrjlpUxGOZ6o2dOd3Qk2RwryfPyeU3jNTVei4M/L4ThDIZ59/J4/s58j/oKvxeQj78LwiQcrf8t99DA6wG99lgN0xqRvw3GGnG9DPGtySjAZjQESoHBcK4oAo+EIG6fZe2o4miHjy0yLELn2DCp4sbf+MMN0Yx8FZ3p2NzYxXN2X78KNraFf+nUBIghIJWG8sAwmSZLnJkny75MkuQfAXwD4PBj4PqdI0/Qn0jS9Kk3TZ6dp+mwAjwP4CrABQjtJklfzRd8K4Pf53397CG111HHxBLWm8QE2na7JAeSUqJCQ8GROmcTKpTRS9yc1lNp0aKPNqu8BUg4RXfUChM96CQNQujxHSAJKLjKRt48AjEqnTi2qZfdWdqHhjFg21qZGo5JbAvVUnZTJrvxYSprlKGZTs5aESLiikCWm+fTpvGl/i0mVnokaeZn7cMAk1tkEpM0KbtGRpoHXEkCNF6yQ0GRTvwaeS2hkBU5XEisAiD6L+0Hcd9p1NHIyHDaSPhcaqbOfp3v2SmRMjbyvEJS38I3ucuPKWxESmiKf75STzdzyFyPPxM8VzrORPMwgMa8CXWUjKfolcoIqN+hJ9gXYtfRUYgXAz6PHfUW0A36HFh5nOOGxsaPJG3U5SRBgb8Du7eO9Jk6d4eiVvy8MG0luSEDCCPELX4vwygSNm76yfIx6Xw4oocGcYlNS414hoWHJwOX6D0atEaAsF3Vp4IPwaQXvi8RCDHySJB8HcAOA/wHg3wL4izRN3RVkzlOkaVokSfJmAL+SJEkL3PLxsNrqqONiClMDL/TlVlXJWJSQ5h7pwk6xJZJYOYAfa5VKrYcWabRR7HPmU3P1UAlgmo1kBWDy9hEwkwpdgMdIyHPoeQUAz2ZuDbzePwnww/L6YqBiDxAEk5/NgIZ6ZAoAL91+HFaSdDZRLjiziekZ/UyIc6zESrMpSHsFdLhrJLHSImcukva1ijQbSQFu7ZyJkA8oRbvHtlQOyKxCQ5QW0sHSSKhzFXLyJtwFkEVrqnTPPh94ron3t9tJqB6NO/eBL1dZFQA/n6uBp/nUr0nmQVzFf2Sjdf4uYJC4hWK8CUKX/fvn1zO6/uV+kC836Ehy1QdTFdca+ax6oFRlI6l9t81noTZ2tGezpZEXAP6K1Q4eOslySyQRom0reu7NIM0lqfPvfM0PlPdtd6V1MBkUCULtfnbp/LkLDSH+81holYH190TcdCaxyuvZaAMgSgPvq31wxGLRHv4UgD9O07TCEPr8BGfhxd8fAfASz3Lnva2OOi6asBPpwF/m2otSPJBlldESA8+TL42HlvUCabQlSKcWs2FUQNX64U4a1G37LNePMIaoMugs5KS50DhtJg3vb20QYRccyasZeDmb4GLgwcGkBsBFFVvSEwx8GcAXvGItAMOP/5kSqhLrAX3yswlLNGy0TRtJWjAivJScyQdjxr3gmhHKFNvnSmIF1KBD2kh6bCLF78HJwFdUlaxKYpWsrTtxUc4SzUtiFRVAfS4zkoG3Bjkk4IMMAfCrGPhpNfAEy5nxhlUI64KGcKGZjU0rRS3yx+9mi97wJf7tCIZ3zrV0MrrSEnRWub53sAYY52677wDwVhLr3pAtc/nxNj77yDYKSpXLitbHoLOC4Ia/X95fRRyYtSaBu3qybNZ+Mz67TsHQW9tgNpJTNvgWy2nLEBIAzY6qxuqo0HwUY9Ek1vckSXKcW0leDeAJAO87W+14HXXUcfCg4z5oPkPQPa7kLoB6ydvsdtgAQJSERlRE5FX3JAM/MRl4PUijrZhp4erBH2ykwaef7X64LOtEVn+eORl45MKj3eECU/KBtwG+APCZMYggQSC3LdanDgAvQZ8A8D4G3rKSpON9gAQIeMErm2We3f9Rw6aTTkdAtwLIXIpxzi40U5BWj1meCjtTMSAM4AGmhNl6igFnWF6GFrkCRLaky2LgjUJOAKBXPi40gK4XBpLtFZ7V4KDMaTM5j4GvBnVGYRtaZvlZe6h8tZ0AnM2M0SJ3ensbffQBKu4sEi1XyCqeTga+0WbytnEfpHvMuUx4ZYL8ZIrwqhv924mZy4mzEJOsD1DtMgPPvVC2DK1m4Lf2hYRmpDVrYLbVw95gBwTA+jFGKIwnuZOBv6ARhFphsgoXGnFfldqjilnWFgDKZlHjplGxW4Qu5fQOWo9YLHSluFPMA2Ca8ZsA/F8A7uff11FHHRcgxnf8Lka3/Rz7UBSKYZYsp6WBJ8ScOuQsqHwZxy2mK9QqlZYqOjY6wHTEwLVdGMdi4OVD0SVJoKwwDnVo4OVnzrD7pvOp8M32Wt7NUNI/aiw81R7uTg28GKiUfOAFq2uCUDreZ3kEfEBkWB5ORxh/4FcxueP31Ap21dpnQhT64KladTn+yO9g9sCd5pezCUjUQLCkOf3GLfgKDBFCWGGwOT7wyBUDX5bQWAy8sIl0SWQMH3gfQz/HJnIBBr50z9s+8V7/8Qp/8TBUeSGedq9PvNg+74NXWsLZ2Gi5whucL/N0ACZRBI7Vx3AnC7a/8rvRfcvPu2dSRPBnQKUHu0dCoxMUzn2UZmOqNfBbXEIzGGcYjE3QH1z+PARLq9gbztBtx+i22bN3PM3Us/KAfvykt474xefgtBWEc+9nKt8R7vvR+4yPhczSrBdiW9CKATHNLy0JzX8G8G1pmsq3UZIkbwRzijl65ph11HEJBh3tgQo9Os1Zyed8qoBzPislburJO1LTy5chhIC0upUMPOI2AyHZVE1vCgY+blsSGs/0p/hMc9ZH4Zsu+mh5Uvv1vJlTI69LaGyGnUQNxdwa7IzpAw9AzlSUXg56cSAtmKf+spLgaCxzvvUoAKpeSNAGCM+QYIO+XMkLrBwCPYrdU5jdfRto/2WIn/tFahvZBIiaIL0WcJJVjSRRE7QoQOBhtyPOpMmiXq5CTbk2XT9PA29JaIwE8kKBaTnQ1JNcfdP9CyaAzmHgFWM5J+lvDmNZkr2Bn7ciYwDdBZh0GY8P9An9+PIJ5O4ltAHQ08D8xi02+zfak0n9dpC4JWctfUHiFihQPRiz/3a2O2ZjCIFZqMmhs++syL+39qdYP7aMjZ0xNnfG6F4Ro9g5CQDyt7U3mGKl20C7ya77cJKhI55zB2Tgl77hpw60nowgmC8Tkvezxwuf+kgaTlrNxkB72cnSmxXDHXkjRzAWvVI3AHiP9d17ATzv/Hanjjrq8EY2ZTKagldXFMCxKkFUr0AnJAF6UY7mkkxidZZU55ptOh2WGHgSt1hipogqDbxozzNHuyq+4wI8etVIp05fL95jP7z1qX+u5zXaoQYAcjDiKuQElGQgdLwP0u7JfeguNMXWo7DjmQbgBeCQRVIqZDSzz7My5UXfUmVmU5C4iUB3thBJqJ6KjWIq3CmXApg0RNfA2wmWUtvN720p8SgXcqJ6oSadaRXhSrrW9uHVPc+RyKj1qxhLzdXDeZ4irwZeriMYemcSqzbQmQN2ohW/hIb9/sjTApgkMM9n0onknLbjkxKJmJPsi4ZnoKDJuFwa8+DYlfLvaQ684Fom1RMyGiEPingxor3BFMvdBtoNtt3xJNfIn6fHeYWQUHPa8cjKxIySF+AXagCtn2t7lpQ6ljEkNPmR9n8XsSiA/zyAN1nfvQFMVlNHHXVcgGAAiDLAXWgPXD2J1dbzxk2VhCoAlJasRZpdzTrLAeClReNEYzT5MpYLjZ3kKrehO2I4KrEaVSGrLO88bKIziVWWAFf7ki42dh+ljeSo3Ka32xp4ztqpJFg1mMk3H2FT8mEM0uXyjwMAeDoZYPbgx896vSMRImeCDwIH7/wuZE/cayxCsymmn7kNs/RD7PPABPB0NgGiBgJNQy2S0NhgzvEKE1PhVQx7kSv2OnBUBoa63nIAKQCrnn+igQnBgptJrJ7qmxqDXuVC409iNRl4t6wiYlIifX/GPhjD7nSpAbg7lMfFBtrv2qOB1yNaPuFve/6r0Lr1bU6d/WGHzqz7GPiFQjwnrWcEAOvcV1dR9Q4irDyLUvOKAvAFJXjBdQzAf/LzmygKiuYX/RN03/gTMl9nbzhFrxOjxRn40VTLTdLv3wsZgkEHqgG6b1ZLDjg9NpKAfEa7NPCsYvilycD/KwC/kCTJHUmSvDtJkjsB/CLOvYhTHXXUsWjwlzEd7zEdoHBHkVZ9ZQaeNLuAyKzPJgwI6A+t5jwJjbKilOWlQ8XAGxKa3M94ApwtzGeScZd91DXwLnZlrgbeAvCBZoWpS4rmudBMfUmsQkJjM/B9ZpnmZOAfQbD+bDRf+Xo0/t4/ZO3aYGd2/x0odk5hXkzv/QDG//O/oNg7PXfZIxfivtRKzc8+9yH5N6UU4w+/HZOP/g7obILgsucwmZheEyCfgkRN01taSj88HuuCSXMls0G96CV7PY+Bt8GTLqGh2v3oYeCdAF2AfY914FyXGTvJ1SlxCaolNkHIzoGXgVcSGqceWJ8lmMfAVwD4oL2M+PqXV65/aKEx3j4N/CIhAKJhVSgb5zDwuke7F8ArBt7ZrDHwFARXn+jiy195DT56zyn8tw89CBI1EaxcIZeRDLwA8JNMPisfeGwDt3/mJJPAXcjQ7yGPjaTygZ+vgSeWjSSgXR/XeyCIVD2Si8RGciEAz60XnwvgF8CKIf08gOfx7+uoo44LENIOcrjHp7UXYOCbXelXTrMp0xPrPu+tJTOJ1QY7kWZFaUtoGi2jkNNiEppZeWpSFt+ZufW6gcXQl2wmNQmOBUZISUJT4QMvALbPRlJLYqVFAToZMIcU4XYjrk+eodh+EuGJ69C46SsRv/BL2feaY8/4A7+MwXu+H/OiOP0gO/TNR+YuS7Opyf4+zSH6YrCcmlY3f+xTyO67HY2XfS16//wX0bjxtWy9wTZbQLDHURNBTwOA8kVeTroGwJNYp+p+Ld1v3BXJZti17QPQ2GsboOsaeM01STLwOsD3TfdrDHpFu1dCU2LgPb7ZuaXjNzahklRdGvd57ep36ZlF0BdtHk37VOPePAcJjUxi1Z+HcsMaw15VyAmQfuvlZax70G5fUknCBQiWuw286XXPR3LNMXz2kW1j2aKgGE9zdFuxlNCMJpmUCj7w6BZ+408+i/d+8AILLIxiU1WSLp8GnjH0XhtJADTj18eeTQaUNA+4tGwkASBN020A7zzEvtRRRx1VkdsMvAngnVVOWz0lkcmmpeJGuk2kUwMvX0yTcuGbuMVlMZz599j2lSQ0Pt/tYuZ0GJhnI0n0aEJqngAAIABJREFUJFP7GPTj9TgUoFSJ1aeBV9PjbNaCqqIlUVMNsPY3gSJHcOwq3v+AtZ+lCw2lFPlp9hItNh8BnlPtF9D/zX+J8Kob0fnq7zur/RxaiPtFAyViMAlAFggTAxwhNSr6ZxAsXybPJ4kbBvBXyWqhG9BEMUtM9GrguZuFTMq2Z4TmuNBwgMBsT3MHwFeDKEqLcl4KtEFD5mGvDZcZUtY920muPkYyr5DQCMBSwcAzG0kPAy9sM636CBdVxKrf5yKhabzwS5Hd92G31aTBLB+QgbfzMErNarsFCJa4u8xV613ccc9ToJTKe2gyY/dnMw41Bj4HemydGOx3cdd9G3jDay9gmqNVbMoOQlQSq3dWyyOTlESOIFGcdsKRxtBfHDaSlQA+SZK/AlA1j0LTNH3d+e1SHXXU4QoJEEd77CElwKnuAx/ZAH4JdNIHpQVn4G0A32E+yLRwJwXKqeGxs5CTaCNh7NYVasv7GHjThcbBrugDAOpICgw1KZHlqCHBU9SUntbGNvVlPBIaVelVY+B54q946TO3H9YumXqd3Wu0pQberKLrsM0UbYMzsnhR7kiKdUX+5GcXWu6CBL9fomtvQvzC12Jyx7tV1V+gVFdAWEVKHfxM2Z6aiYBcC0vcCaJSQuMrCsMlOFQUD7MBtmTgJ8Zn5efNr5+QGFg2kmUfeL/u2eehPrfdKu7j1tEHatDplNCwZF5nYjjAbST9EhtTA3/0wY4rzpcGPrzsOej9y9/y7EQHph7mWDT7AHyVTaUVjUaEKGTLXbHawWiSYW84w0qXPcfGU3Z/thohmo0QBJyBP8YBPMmxfqyF09sjTKY5mo0Lc23JvIGO4ULj1sB7bSRbPYCEGP/Nb7GBq3hH6LMjQSSfCb7aB0ct5t0N7wTwLse/D4L5wdc+8HXUcaFCaOBH+0ChGHhZ7dLJwC8xoDEZsqqWJQDPC1zMJk5NsZnEagJ0yazKQk/VNpKibLvPB56BkXIhJ7Psu6uQk8Zm2slH/CFMGm2LgY9K61OP3MBIkhUhHH3E+Ykair2R/uJ6VdyWcqHR/NBphbY95/KZ4NhVTlcbPahmV3lUgmqSq+iqG0HaPYOBp5bEhSwpBh7QBkzWi1Qmq+kOMHqIJFZuSVqaUQm4+0o2zwdeSGxMhl1qgy0nC7EfuxJrNcM+cw/gdHDsaCeinPwcb/C5DHyFBp7IXIPM7cghcwX8NpKdf/yj6L7pHO0FDzHOm4Smah/zbCSNJFa3hEYkn1ZJleLkHwAAllrqGX/lKtveqS1VUG4qGPhGiIAQtJohRtNM/s5ikuPG646DAnh8QxtwH3ZosyFeH3hZvdgjsdFsJM3iVUvovP5HABIgP5m63wNBqPK4fIPaIxaVDHyapr+hf06SZA3ADwD4FwDeDeBHDq9rddTxzIv8qftBsymiq19ofE+LQgI/OtpjD6rQspF0JbFyiQcd96UG3ggOwoVNZNkDXUv+KUzGVNoD6i42cAAi8VkA3BIDLyQwFuMpwpDQlFnXkg+8I0GVNNqsny7tIwkBkJJkQp0DRyEnq4oniZoScCrgqV2LRltJaDSwnZ95DMExlVymR/7U/Qz83vAqTD/2XhTDXQS6lEQLmYh8lMJKaibNJTNx186piJosqVow8LLwGDv/3W/8T0A+w/jDv81kGxVJrDSb+aUfgZhq92jkbQberhQqGXjhmBEa69mFnLzsOG+vtB7Ms9KsmbGNyiRWpYH3a9zdBbHkNislNuI76mXgw9Vr3H0/KiGSWIOo/Gw8XzFHGmKcW88gQlaJrQDwzX/wz/GrT70USwN1/10hAPyZIRJuLSkZ+Jjtt92MGAPPn1cNZHjBtcfxoU+dxGOn+3ju1e5nzvkO0u5pH/z3I62wkTQYeGuZcPVqRuTkmfs9oBVyYhXDLxENfJIkywC+B8DbALwPwMvSNK0tJOuo4zzH+PZ3gE4GWPqG/99s0MBjMdrjDLyy/RJJfbajhnBWoJO+WwMfC5/3EZ82tNuFTGaiZn8FKOIvGwHg59lISo25RwNPZ24AbZaFd0hObB94lwY+brOBj2t6lRBT/+hzudE18CUA31DXyMXA67kGGoAvzjyG4sSzkT1xDxo3vkZtv8iR3X8Hwme9COGJ69myO09WAPiB8/unNewBX8tk4IW+W7/ewdIqiv4WgPI5DkSinkhW8wHPKHbfCyJCywfeHvRKZnlOISeerEoCYrQbSawesGH0uypB1DUjJTcSAvAz8AgCdU97CjlRMbPmGuiEEbtnKfUMhOa4q1wEIZ5vpLXk9Fc/PzupltDoAzxvEitn4KvOMyEB9scFum11rVZXWoijAKfODOV3E42BB4B2I2I+8HzfYxrjhst7aDcjPHr6whEDQXtZ/u1O9hUzbxWyNCGzBPy/fVe9EEBK6wDwmdyLHMAnSdIGs5D8N2CymVenaXrPBehXHXU844KO+yg2HwVAmcOJxsaY+mvOwAt2XHfUcElowJIHaTYFaVlVUEXy2XTktuWLeJGVbAIq2kLBqJoA3jktCahtehh4aStpM57GMpHX8o4EIXug8wezMXWqMfC+BCfZJyGLKQ0giHI2EZFb8ou4qQH0MjAkcZu5BwGGhGZ2/52Y3vU/AADR9S9HwGdMskc/BTraReMFr1HXsAKkS225I2HyaQubYW8tsXwLMZDkBYIMV6TOMan79xZa0pLZ5tlIulg0CQQ8vxklqbLYbdtGsrAYeFsjL5adYx04T1bhBW2LFAiSx+AG6JgMKnzeo3IegL19sewBq3c+3UHCGAjCc7KQnLsPXVs/p5CTL3Ey6DD2fF4xuP5ohrUVJQsKCMHlx9s4taUAvGDgJYBvRhhOMoRXvBgPX/PVeO+nu/h/OzGuWe/isdP7uFAhDQEA/3mSv3sfwPcYFchlRO0DVy6UZiOZXxw+8POGGA+D6eT/PwCfAHB5kiSX6wukafqBw+laHXVcmjG7/w7QbILGC24xvs+e/CxEzni+9Sgi3dFAB/DDXYBSwz/dBRoBU0KDbAoSHjfbG4qBF5pho50EXN891qoN+gC8Y1oSkOBBWnh5XD+k60jFw9tXxMPQPTsYeBK3oNtIlgcBc8CKzrBDY9E1Bp4Od9l3mQN4NlolCU30/Fchf/xutcy4DwgAf9/tIO0VhNfeBCqqk1a8vMU1OIgbyPhv3o5g5XI0bvrKs163KqiVE0E4w0YnA3a+HP7iJGyogZJ0PSoz5KIolz1jBEBVVPQVYxH3Ul4tobF94OV9KXJO7GqOfCBiJym75QBWUq4dZAFwLLdB3IwlCdW59AAeWpGkiiBU91VclpcYFp4XAdjxRtwqERvnM4LjV+mfygssMPiREhqXTaUW/dEM3bb5e7n8eAdPahp4wcALCU2rGWIwmoEQgod6L8eQPoRuK8Jzr17BbR9/DHvDKZY7HhnXeQyiMfBeqRGl8NZW0OtDwDMYCmNlRwyYvwsxMwf4Z6WOWMy7c0YA+gC+FcBvOP79+qH2ro46LsGY3n0bJne+p+TZnT9xjwSEjIlXIfTXpHMMdLjDvtRtJDMfgBcMfJ8BVBvwCAA/G5UcXOQ24qaqxBooxlRM9y7KwAsf9DLjaRVKqnIg8MoiYrcGXuQJNDpMk+3ygRfrV8gNSBgbEpoSe2sAT1cSa1lCE137UnTf+JNo3fLN7HuNYS+G2wjWrgEJQpVrMFUsGgAUg21MP/WnLKlSrHsAAJ898kk+eDzPYSeparNBor3EkEfqPKpBqeN+on43CjFbQnO/hAaUahIZaxAhB5xiRsbDwIv/xYCWEDU7IMI73T8HoOvreACevIcrAL5yqfHIdPislg8QSbclLdnTdQyLgNCjGqTRPlwGPmpKcCrlVnosMPgh3eNzlykKiuE4w1LLfL72OjEGI/XsGk/Z71KX0Iwm7J7tj2doN0OEQYCbX3QF8oLiznufmrvv8xEGgK+Y8aE+1yMSeF1oZAiQ7qpZIn4PAH82Hf1B6bwk1mdfoH7UUcczJuhgG5gMUJx+EOEVz5ff56fuQ3jVjSi2HkW+ZRXuEWCiu6oAfBDyh87/Yu89AyTJzirRc8Olz8ry1d53jdfMaGY0RmaEzAohtHIIJMRbEEjLogX2Abssj9Ui9j1YnliEfcgAwq6QEELeIKQR443Gz/TMVHdP2+ou79Jn2PfjmrgRcSOz7XR1d54/3VU3IzIyKjLi3HPPdz4nJI1xgm5mmZJWZyk0sUQPUcTaSlcsjQxraZ+JkCliWJS4cg98SgqNIBpcPUopcu3m1+VLn2mEKNJ9U3p/bWwHtLGd0AoVAEF650o9JCvqbHErpYiVfRb+/pCJZ/i3IFYOcFqUbEt5+cTKQRvcRLeTC1E9qSCZ1yl0ogS++U+/gaBVhbHthpBopXho0xAEASXUPZbmzwixh2RkNQhQXm/EsEJinWZxYQo8Sclqjl4L6ekpgdNOWHjkccTVa5EyE7PQxOwuQpnnr1Eq7N3tL4QQ+t112r0tNGnjUmfJ1FSPrpNiPZykqK6rXj7+iwSZm991SgT5bKBVNsBrVXvbpdK25x74Lmh2XASAyIDnyGdNNNquyILvcAtNrIgVABotBwU2AdgyVsS28RIeeGYWb7gpLEb+6gNH4Lg+3vHqnT2P6bQgP5tU9/geyUx0Yu93t9BwBV5Rr0V0IwyKuEQU+D766OMcIvA9QcDd409Hx9p1aIUhaMNbkwo8U3/lIkbaIIhaR1ItNISAZIrCA5/IgefKmt2ixEN10+NEQkW4MnkaUQmEN86UWD5RxKrFLTS8E2usdX1kH5xsJBs98c8duE5iFcGY2IvC2/57mKOcYpOh/ke+AtDlxs+QKGKVbv4iflC20JjZUPUVHUDZttyK1JY87m4YCUo0DZBjKAF4C4eFV9yvL4VWhxQPvP3C3WqVna289PLWngkSfQNiCnzgeclrRZoopV3T4WpMSpycYdIHudtRqmhCYbfbYf1FfP+Q6k749RQvYmX/JjPqYyk0aX5d8YOa/IoamFSFPVZcmxjXepAZo6tHPtJPQbWyc4ko8ObuW2FsmDyv78En6YitugIxFTgF5BQIPFfZ5SJW/rPnB8I6Iyw0wgPPYiQBNNpuxIJz0xWjODpXQ6MdKvhfvOcQvvbAEUH6zxUiE+luNReek07wU/zt8j5Ex+74a9jKXMAK5C+GSenF+63ro491Dm/xKJwXH478jvvXAQWBZwRbH9oMf3UmzJsGwkg9OYWE6NQiIpNCVdfHbCn0wMe9rGYWAGExkim52iaLSPSSFhuSKZyCB54TIkae40WJKZ0vE/vgSTtpFpdTaDqTIGVi/xJZ6ZYtzpGw0JhikqUsYpWiKIPYRCdRS8D3IZ0nOcUGAJz994v/UwJPVe2I+svHq/Po3POXaH3rDxJjQYuR6dPsEntKEDGScQU+tNAkPfCSVSk1JUaTIkVTrgWwCWMXBR5OK9H4TD5e8f5pMZJciY8R2cCLEvie0YEpCqywdaSm0ES996njafvQuiQvsXGxuYLAR9JTLgKycyGhVTYAAPw1hR3lVDzwaVGiEuqMwMcVeK6oN9v0/ty2PWiEiGZPuYwB2/Hhej4aLQfFbPh3rxQzkX3LeHQqvYfF2SK10ypAn5+9bJZAqr1O9D7g24jtpY7gF0mMZJ/A99HHeYC/OoPmP/0G2t/9eOT3PONaG90Bf/GYUKWDIKCKoZmhJD3wANnzzFVdWYnhCrxrp6uVoMqn31ylZCKuwBNCCyxt7oFXkF8jE3ZijWe4SwQ+iBG28DjZPkWn0xSFXhSxdlELgyDVAx9mf3chI04HIEQRRRlr6JHYv6RWQlHEqhv0xg+w15Ho55SjKOP2GysPgERTZrxopj+x8pHrIWisgAxMAIQgqC9JtpSkwmc//S36sUrDiTGhhp8XCw0vQo0VPfNj9dyk+mhYTEXzU4tYRSfVtIYuEQKffi2kEXx6bZDE9SiuGTZJEnF1cSKb6MSqOkbpe5hCfkVTn1SLDPfep2wfb1KTOAYjFAZ6pcyYvRT48xTBeIlAG94KIJomFg6y86iqMzgNcJU8XsSaz9DrgJNw3l2VK945i463bS9RBMv/z8k/QD31APDgs1JPh3ONnpPelOtVsiimP0fccPId68QKILTY9C00ffRxeaL94N8rf+83VgAAxrYbAATwlo6zAZfefAwrqVRCIrc5hQLvOerkE/6ybFEkmaiUHGLlRRGrktDEilgjY5kCAjuqwKfmwLu8iDW2D5bD3q2IlC99po4bag+8vD09BjvdGxkesHpcUcQa9cBTn2ng2oBuRuMR+Xn3bOkBw5RpTaONniQPfCBZaADQcYnA+601aMUhkPxgxEIDP6rAB64NZ+pedozJvz1X4KmFKoiMte/+NJwDDyS2OWXEO/dqOm3UxCcNvisiSQX4MXaxhUW92ykqGwDYLXVRti4R+LTYTU1P9cDHc+DjiTKRFJqUbrFaZYPoo5CWMkN6jfP9phL8Hgo59wzz/8f3L39Pre5FrGkdRC9lfPobz+OL9xyK/G652k58jwBAn9iLzB3vQ+a29yR3xK1YZ0ngUxX4GAlvO56wzwA0hQYA2h03YaHhBbHRIlh6fe8/vib89OcM3DbWw3amLrqWilyln6OvkYpYiR617Yg0NHVH7vWIPoHvo4/zgKC2EP5fIlVcgTe23wAA8BeP0AGHd53MhAS+JTe94R54hQLvOWHHRZUCnxuQkmsUcXBmjhK4NPLLi1g9RWV+Jh96t7tlrCNMoVGSJk3KWU9L7ehyYyW8YC9tFUHYdDoptopeaqUZeroBqtJoevggkSI9EbO/yOOB64QrFXJBsGxFApQKvKySB801kNwAtOIwgppE4IPoA9VfOUmPRzeUOfJikhj40ZhM34czdQ/a3/sUnIMPJs/HKUAV00hXc9j7KCaM3NISeKEtLPH3IJooVlOpz6SnAh8WVafVDEA3ktczJwiiE2s0hYYfW6SRU8pyP9E0GJuvCT+PAqGFJoVIxNNx0sZTXtNToZcJk6qIVTdASqPQRrbDevnb1cdwCePZQ0t48eSa+LnatPGrn3gQ338haS0hhMC6+vWRZkVijJ1b62Vv7vp+hff+Hgo/9tHU8XqLft8KsRSaArPEcIXedjxRwAqECn2z46LRdiLbcz99nW3rej4c18eezQPwgyDy+c8FutZ99Kq54Nd7NwIu0soUK7WiY3jKSvE6RJ/A99HHeYDflG5schfVxgqgm9AGN4PkyvAYgRfk1bBES2k/osDzGEm5iDX0wKuSTzi00ki4jVKBz6XmwANMgXfaasKVKYbKsErVACKEiR6jYpIhLeenKif8HChTYszURk/yPtMU+IiFJi3lJt6JVSbYurT8GiPfQEhMESGmErHNFkMrUuDTiUAKgQ+CgBL4/ABIcZgq8MJCE1Xg/WW6wqNPTCZiKIHYNSbbaPjfAoD9zLcT250SYisNAMKVEkDtM+XXp2vTcxlbyaD708NEopRrAQhTZhKQLTRpPldVkalQ4JnCKvKmY0Ws8iQq5TsFAMaW6wAAHvsbxRFaaFLsKUKBTytiPQXLQbdxfm6Irv7OEg3F9/wuCu/4CLTznOKy3uB6PtbqtlCjAWCtbsPzAxyZPb3mR8TKYeevfwHWtW/s+jqtOAytPJY63mg5IADy2eg1zQl5Q/LAZyIKPH39UrWNIIgq+PmYf54Xrl6zcxgEwIHp80Tgu1mNAPU9OlHn1M1Co2oIGAtbuAhiJPsEvo8+zjEC1wbsprC7RLqo1pdBCkMghEAb2Q5/kcVFcouMmVFaaKAg8CCSAq9KPuEvk73PqmIoicArl9rNLAK3w1SLOIHP0+6avqu2RAASeWafUUWa5E6naW20e6bEJHPgw2MIFXilQn8qCnysiDVC0rnqy60fiax7VsTqhm28SVyB5ysZXLmOFbEKD7zTAjwbWn4AWmkYQWNZEPggpsB7y9OAbkEf3QHYzUSRa+QakxV+TuCtPPzFo2Gx4+lAGdUmxW2qrieJwAds5SAB5jNP+1uLv0svBd5OJ/CRayRuoREKvKJYTtOi/R2ClNQkAPrmq+nmaRnk/PeydUuCdc0bYey+DdaNb035DN2jKiMrI13OE7FyyUn5ZY61uo0AiFhIOLnlXU+bbRfzq+ehtiQFHceDZenQYn+rfEyB79ieaOIEhAr84iolrgVpAiDUe2ah4Z9xqJTBlrEi9h9fPaefQR9l0ZTKAtReK0ZsG9cGUpqbyRaaZC+QKIG/GBT49X+EffRxkYGTIlIeRdBai3ZRbawItUof2QZ7+llahCor8CoPPLfIyB54jSlj7UbXIlat2FuB96tz6QkuRoZ+BtdJ+tctnqDSVCr0gEQOuijw0M2eKTQ85UUdM2moO7GK/fdS4CWynJpeEPPARwi8lFyiVOBDD3ygUuAzBfjVhXAfQMxCQz3wQRDQgmSwa8HIMEIZS0Zh8JePQxvaRDtNBgG1akmJIrJNK6rA07+FsfkauIcegbdwGMbGK5LnpQvCombpfOtSnr7qbyUmOvx6S4l5ZB0ZexWIqj3w7Fpw2tBSPfB8u5AIiOs44YGXvbmqIlY1+dXyFeTf8RFo5XHlOO8OmlZgbO69A+beO9THD0SLIlWT3h6TVjF+Bs3BLnUs1+i9jDdFAqgFBQBmlhpwXA//8Q/uAQB8+r/+wHk7Dt8PoLEVmrg1hiNr6dA1EvHADxSsyDgALK4xAi8p8IauIWPpQr3nDZ9yGQN7Nldw7zMnYTseLMX7ngkyt70HxpZroY8lM+b5c5H+0M0Dn3KPB8IwAuW9Rwo6kH9ex+gr8H30cY4RMPsMt67Q6EAXjS98GN7cAZDiEB0fmAACnyqoruSBNyzqO2/FFHhNjxJootHXSrYMpQdestAoPfBWjmW5B+lFrAC1YMSXHXkL8k6DpYp0UeC5zz/FQhOmfqSoL90IvlTEqlbY+SSi09tCo5zERAl8XB0Wn8lzWBxougc+3qEU4B74erhvaRsAQCZPHzqeI64vkq9AK4arK9rojkQKjb88Te1aVjKqEohNEhUKvL7pKvqx5g7ET0lvKDrzEkOOiUxaaEKrkYPAV6xkAOHfx01p6CKf+26rMTGbkvI9VMv2Pk+hSSZZUAVeHk9JTWLQR7arM9YR2gnONOJTH9kW7quHAt+tLoSoClgvQwRBgCVGcper9PvRVijwC6ttfO6ug5HtzgdWah38zEe/h/ufmQEAdBwflqFuCpbPGoKEd+wo0c9xBX6NXmfJGEpDqPd8wpKzdNx0xShsx8fnv/di5PVP7F/Ap7/xfNfPvdaw8WufeggnFuqR3xPdgLHteuU2EZtWN4982n0BfAXQVfdESfQr6Vto+ujjskNI4EfpL1wHQbsOf+k49K3XI3MDXfIWXVCddkhOGcEm2WIshcZO+ts1nSqaUg68OkayJLZV5gmbuR5dSBmB7zSSMZIyMfQVjXn4cUIiIkoLTY8UGqKnFzVCsmakdt+Uoip7WWjSll5lBT6uDgsLjSu825H9yyk0Ih4xSuDRYRYXV6XA8465TYnAD0Af2wVj+8uRf/tHoA9vixB4v7mGoFWFPryZTgDY9jKCdk1Ek0aIIvtbaIVBaJWN8OYO4rThu3SSKV9Tuikp8IqotpgCr7yehRKeVrDci8BLirms6qneI+5vByQFXmGhIXpogVIR/NNAVz/wKUAb2SH9oDhPcmFql0nt6Xb3vVTx6NQC/vPHH8DzR5axUqPfD94UCaApLgDgBwHuevxE+PtzndTC8Lm76KT6yQOLANIVeID64LkNpuNEPfCcwC8oLDThtvSz8VWGXNbA5NZBvOGmLfju49OYng+J+J997Tnc9/QMjs1FybmM6YU65pab2Hdk5ZQ/b8I+Gh/vVecEsGZNPi1gj4tZiYaCfQW+jz4uO/itKIGnZIQ+hM2dN0GrTNAXMmUrsFuhOs2IHsmVEx74OPkmmhZ6v7sReELCDPAUCw0QsH0qHvRsKT7oNJKNnHjBbWMlPYOdL98LC82ZeeBFEWuKxSVISxcAJHXGVisrp6vAx4tMe1howoeDLXUolQl8EUBA04BUjaCYSisTeC1fAckWkXvjz0Mf3U7JpuRxD+pL9HXlMWWzKAAIWtWwMC6iwIcTSm1sR1ircRoIPEXnXtkDrxqPxW0qLTQ90lUi34FeHUZzagIvtlO8l6gzUEUwSgq8MmbyNCAI/BmCNw+iO0te0zwJC0j53vPfnWW84aWC+RU6+X3khXksV+m9zPUCuB79OzdjnUlf9/LNANRNkNJwYqGOY3O9i2Cn5+t45HmadsPtKx033cpSyBpoChU96oG3DA0aIUKBj+fIRxR4bqFh2fG3Xk3tX4vsfADAplF63T7QJSe+1qT3l+mFdJIfh0y4iaqwm1+vnpPaGyFMRGsmn4Vxq2dfge+jj8sPQiHl1pWYx51DLJ3b7bCINaLAy7ng0pIfX8onOmvkxIpYNV29VA6AMKuF0gMvEwVljCTbRlEUqA1tAQwL3skX0j3whIS+ZULUflzd7J5CI1toeirw6Qp7cCoWmjRSyJsHASKaMTIOSkqVRaxGmG8OVbxiViLYnMArr5UW9cDrBmsAJSGeQS6RcFnBlxG06yCMwKtSaIiZgTYwgaC5GhZ3nSqUCrtkofE9RQqNHLfZw0IDKK83rTgsrjFlkkREgU/G+tHjVCnwvIg1StBTU2h4vvfZKvBniF5FrMTKhw2GPIXKL4pY+wo8AOjsHB6eqWK5FhZ1c4W91fHErXnreBFX76BWSRWB/9bDx/A7//vxxO8//BeP4CN/+f3UY3jq4CI+9dV9eO4IjSMeKFiCDNu2h4ypvtbyWRP1Nu1T0Yml0BBCkMvo4nMkFPicKew3fJLCk2u4et+SJi++T8Wgh5+bhReryeGoNeg5iVtoeoHwGE4VQZfDElLIN9HY/aXTSHQlD+NnuZWzr8D30cdlh6C1BpIphg8+SYGPqAisu2HgtEKgbK/IAAAgAElEQVSyZXICX0LQqoY7de3QH2wwRUzThQc+lewwiChJlQe+IGfLpyvwAJIWGt2AvmES3snn0j3w8n61ZCxgYr9pOfBslaBri2wvJUlHqDO2epLBzx0hKcfHvdmu+FdloaFNtexkEWukE6sLGrcpNdqRu5Sq6hmssKAxaNEM+MRxkqgCL+9HRBJ2pGZQjRXA7UAf2gQAsJ/6Bup/959oTCW3MxkZaANUZfOrp9k6XVWPYFjhCoMyhYZdnylF0wASinccxMpBH9+VfC2HtE8tTYHn5FzV4IufY5FCI5EheRKlyok/DZwtgY+8d8okIvvaD0Ib3Ah9bFdyUFho+kWsANDscNLZwMxSuJLFfeGtjotC1sSbbtmKd71mF0pMya41QwL/9QeP4Pkjy3h8/wL2H1+F7ajtNWmq/V9/6wU8tG8Odz1xAiMDWWybKIn9d1w/XYHPUQXe9Xz4QRBp5ASERDyXMcRERWyr8MDnWfOnHNtPWyLwtaYDQyeoNp3USM0qm3ScWGzAP40aATHh7poD73Sx0DCS3zkFBb4fI9lHH5cfeEY3pKY0wlcn3zSELaIlKfDMQpMtxVJonNAfz5UDogllGE4nvSkN2HK6bii7/Wl5uThIQeAjCn2SUBmbroK/OgO/vpjuG+Q3x9TYvu4KeDTWr0uKTJCWQtMrJlJhmZDfX7bIAIkJkzxO/1axhwP7mRc0Jx4OYjLX7m6h6TQRtKrRNCL+Gi2agCJUVcMMFXiJwHuz1EOrb7iC1lI0V2nDL88JC4rNjEhJ8dfmlOcmDYGbbGiVsNDEr4e41UipwMc85wrorEmSMsHlFCw05t7bYWy/Edk7fyZ8LdEAEHGOhVVGnkgRLaHQn6mFRjXZPl3k3/5hGHvuSI2q1Ic2o/Ajvx0phhbgRax9Cw2AMEfd8wPMLDWRYyS2IxR4F/mMgXf/wG5cs3MYxTy9duutcHXjC3cfwu9+9kkcZTaZhZSYyYMp+epbx+n1Or/SwuTWCkp5U5DhbmkwhQz1sXOVPe6VzzJLTDGXvD9zD3wQBGh1POgagaHTazonNYHiqDZt3DRJV/UOHFd/Dj7psB0/9RyoQPKMwHfr1No1hUZS4OP36IswRrJP4Pvo4xzDb7EmO3qYaa1W4Jm33GlFFE+AEnhqvWG/dyWCzvfBilgBZo3oQuDNK1+L/Nt/U22hYak4gNoLKzcPUS0r6htpUom/eLTL0iUjA6mdLyWy2i05BFDfvE+ncLHb/tPIlpTzDqBHEasiRlLTKbnjCnxceeaTMqejzPSXPfBwOonlX/EZ5SVrblXRLTZZJAjsUDn0ZvdThX14a1Rlla5XGJb4+/vV0yPw8JJJD7TWQVbgu3jgXXUOfMTDnvL34l1OvZkpxfa9LTTWdT+I3Bt/IeyWyqFpEI2cuAIfm1zyDrTKlJrTwLnIXtdHtiP32g+knqeu788/V99CA4Dmuo9Wsvih27ZhYiiPG/bQGqc2U9GbHRfZTHgtcAW+zhVySW13XHptxHPiuX3lwAl1vrqc8b53SwXlPLXQBEFAi1gVKTQAVeBbHVco+5mYAs8V9XgXV7qtCdfzYbs+Wh0XuYwhrk3T0KBrREwMOrYH2/GxabSAscFcak58rWlDZz726flkh+g0dFXgT6GIVdw73E4yhSZmoenHSPbRx2UI3uZeqK4pHnjoJr3R2CyFhmih75QpgzxKkqqZTJ032b+EhK3nO41kdKEEYljCKpEYk0mM6saXKYQPccWyoja8JZx49OpsmabAyyp/jzbaygKlXkWovTKveyrwkkUGSEYcygp9SnqKsI+oOpDyqE63o1bguQrqdNKVaaIBgRfGt8kWGqLRhl0xBV4f30WJmpwN73aisaZWjhZVn6YCT61EsYckjz0FmN0pTYF3AD8lB17+DqU8qLWR7SDZEjI3vSM5qPdW4FNBpEZNCoWdmFn4XPVXpdRcTLiMYyR9P8C/PnEiQrAbbQfFnIl3vmYXfvuDt+LVL9sIIPTAt5kCz5HLGNAIQY2R5obCFjO/EiXwzD6OJw8s4qHnZhNRjNxrbugEV20bQilvwfWoMt5xfFiW+vuwdbyEAMDDz9Hv8GApKgBwT3u8gBUIG0E12y5atitWHgDunzeEAs/9+OW8hb1bKjgwvaq0yFSbNrZPlGCZGr503yFRFMyx7/Cy0j8vvq+qepxIUEF3IQZAMoUmZqHpx0j20cdlhqDTQFBfhDYwHlMTuQIvFSYSAmLSLqi8SJUrG3xJ268tiH2IbXmso++Fk4Tm2hkXmyWK8OLjhAgftLpIVYM2uCl1+8jvUxR4kpWW+HsQeKXFppeC39NCw7Y/DQU+NQfeS1pHxGu4NSTF+x24HbUHnr+/73RvcAQIhVgcKzsWmjVP1a7AbsFfPgZ9fA8dixF4GiNJxPtqAxNKD7wzdS9a//yHSquK8jzwYmDfowq20kJDaF1HykRIq2xMfuYYiKah+H/8sbrR0akUsaZB0yUPvKJI1czB55Ok4CwtNADyb/8NFN79P894+7MC98Cbl5cH3vcDfOLLz+Jv/nkK//LIcfH7ZttFXlKouY+cW2iaHU9YSgB63yzmTaF6y174csFCLmNEJghBEAiP+cxSE5/6ynM4PBP1kLc6Lq7fPYLf//lXYnggi3KB++xtpsCrvw+7N1PL3b88Og1dI9izqRIZ58cdz4AHgCL7zI2Wg1bbFQk08nngHng+WSkVLOzdXEGj7eLkYlJhrzUdjFRy+IV3XoeF1Ra+dO9hMXZioY7f+9yTuOfJk4ntlF3KOUQjp24eeOnYFauDQN9C00cfly3cE88BQQB909VSokaKBx4ArCzLge9EFAFtgEZNct9x4LQlD3yoxnIi5zdXzq7oTbblKMBtFGkKu1D30wgVU5i1tNztiM++lwe+V/OeHoWPXbpSpqouchEqkIw4lAm+lyxipcfIupAqc+K5haatzvSX3j89nYUXWXqRY+XHQqy8SKHx1+aAIIA2spWNRS00gWsDZkZMKEl5TOmBd/bfB/foE2h96/dDy4i0n7gCL46bE/74RIaQMFJUFccJhJNJ4IzU7YgFR2VF6roxUeTASxMCKwe/wxX4s0uhAQB9dEc0DvIlhLhuzkUx7UWE544s49EpKpzw4k36fzeS0MJtKHIRq0zgAWqj4RaaOtvXjg1lvPLaDRgbzGFBUuAd10cQAO98zU589D/cBl0jeGwqOmludqgCzq0u5Tz9fq01bNiuDyslhaactzA+lEer42LnxnLCQsOPO55AI/+u0XbQsj2h1nPkM4bo0FptUKGqlDexYwO918sZ8Ry1po1SzsRV24ewdayEJUmB5wk/Dz2XvN/wLHi/WU2OSQp8z1omJBX4MEe+nwPfRx+XJbzpfYCZhT62gxIFolMiFYuJ5CBmDnBaiHeGI8UhQDfgr9Es3aBdF+qDzqLfYGVDQtRpnFXcm4jn6kHggZS28IM081i2aMjIvuankX39h5B7039Sv38vBf5UFfT4//kuT1WhTyFbYnuW4R6kKfBOhyrgacfAOrEm4g1FPUSKhYYQiDbg8QhL8Rq2T94J1IvatkgmL1JoOJHnxY3ytRO4Np0cStejVh6jRa6xpkJ8O292v4hPjewn7jPlk1rWNEoV80gMiz6EFRMduo0hztcZLXOfxdI4IWGhcKBQ2InFVtR8X+2Rv4igjW5H5lU/KQqCLxc8cXARlqlh+0RJNGwCgGbbiSnw9DsoF7HG1elizkxYaN7/5ivwrjt3YaySi1houBUnaxkYGcjhyu2DeHRqPmKjiU8SSozAcwKc1sgJAPYwFf7KbYOJMZ4mo/LA889cb7kJmxBA7Tfc2lOVLDRjgzkQALPL0WeC4/podTyUCvTYBwqWIP5AOAk4ML0mOt9y6OO7AQDGhr3JDyinRKWuBPdW4CE6hq//722fwPfRxzmEe2IfjI1XhiqfYcY88DHllT3w4UYLEwnRoJUn4K/OUkLntAXJtW5+B3I/+EswJvZGLTlnoZRxb2EaIeKZ9n4zpbiKKfB8whGHPrYL5s6bU48xkpJxJhYaSXExtt+o2H+hu82nB4GX88kBJNVhuUEIEClAlfcRuCzyU4uns/DJnq0sYqXvYTKFP81CE1PgRefasKMmt7pwKw0n4NZNb4d1y7vo6/mKkVxwzZeu442gJC9qEL82vGTzMbFSYXdZpjasMAc+pa5DqNJnom6fDaG2cmGWvp8k6GIlw22fdRHrhQYhGqwr70yva7kEEQQBnjywiGt2DGNsMCcIfBAEaMYUeN4MqW3TupOW7SKXjV5bsoWG/8ttKmODOSxV28LrzYthuTXnpskxLKy2cZwp2DwFJkrg6b4WGdFNS6EBgMkt1DZz1fahxFg3C00hxz3wTqJQF+AKPCXwfLWhlDdhGjqGB7KYi/n8Q588W0UoWFiTCXwz/P8jL0RVeH1wE4r/7v+DsfeVyQ/YK6gAiHng4zGS8RSaPoHvo4/LBn5zDUFtAfrGK8TvhJrIKuMTRXsmtdCo1EptYBzB2qxo6MQJPNEMGFuuoy86RwReY7GEcYVVjBfoTZ9390yMD1EFHrZage8FWYFXFSDJCrGya2RlHDAyyP2bX4SWU/uajW2086RYIpW354Q6bQIje9yDgCrxkeQbTkzZ51cRbN0Km02p1B3TSo2RFMfgdbHQsEkML7LkHnKRZmJY4rOHCjw9r/rgJhibr2XbdVjfAZnA079P0IkuhwdOR0y+4gQ+cG31Z5DeX03gTVo34rmpzVS4xUycq9PBWSyNk1w5LCxvrgKERFeP5GjYs42R7OMlx9G5GlZqHVy/ewSDpQxW6h3a/Mjx4PmBKOgEAMvUQEAJPCXxSLHQ0HsqJ7e8UHR4IAvPD7Bao+PcR85V9N2b6D2Ze8hth2a451UKPOuimmahAYBXXDWOX3jXdUKJlyEsNCkxkgC1EKlWGbIZHS07VOAtQxOfYXwoj7mYAs9rAbj9Z6Bgod5yREfbasOGZWrYsaEsim5lkExBndDUI6gAiFlAFZ3NQTSpE+v6n7j27yx99HGKCAIf7vGnE8kAYrxNfXmkIC1RMt8zJeiKJkpcgXc6CXuNVtkAv7qAoMU6u2aTWc4Ra0jm7C00yuIghIqnVhpN2Z49FM4wM7qXAm/ufSWtKwBA8pXEuF7ZiOJPfUKQdBWMbdcDALwTzycH+Y09tYiVqTPcwgLEoi81gOhhMafK+mFYohOrSkEnZpZarWLKeXgMZnqKjXzsXBmOxTgSMxN2u2VWmsjESGqiFLgdwExODoN2rCDNbYPwRk+NmAIfmwQAkLr6cguNqqkWncgg8Hoq8GkrPt1wJpGK4n1zZfF99GtLIPnBqKdejvu8yBX4yxH3PjUDQ9fwst3DGCxm4Lg+Gm0XTZYBL1tMCCHIWDo6jifsL3ECTxV4F34QoN5ykMvoIkN9gBHYGsuJ5zGTXOHmSTHcE86TXuT3MA0NuYyBhdXeFhpD13D97hEl+c12sdBkLR26RlBr2mi23YRKn5M88LWmg1I+FA3GB3OYW2lGnplcYeeTjzKz0nBiX23YKOctvOKqcRybq2N6PvlMuuvx6UQX115hDACitkdVnwXNuKhiJC/4EU5OTg4D+FsAuwDYAA4A+PdTU1MLk5OTtwL4JIAcgCMA3jc1Ras6zsdYH310gzf9LFrf/Bhyb/11GBN7EuNCKZfIKNGZAq8b6gx2MwfYLQRGJkHAtYFxIPDgLRyhr1UVgMpE8CwUeGPvHXBeuFt4DOPQSiPIv+3DwuseByEE+X/735QNhk4FvTzwxMoh/0P/GYGfrsr2ys3WRncCCIl8BDxxI61rptyJVZUSw/bBawBUf2voJiX4uqF+eBgZqmh7MeWcgafYJNR/8QGjHviE1YbtH2AEk5DohIsnGjmdxIRSEPiEhaYDfXgr/PlDCJqrcI8/DX1iEsTMpDdygmyhUZxvwxRFrml9A8y9d8B+6psw99yuHD8V6BMKH20PkFwZwfyLAEDTpmINkETDLLsdFoGeYSfWPl5aNNsO7n92Bq+4agylvIXBMv1urNY6vAd0wv+dsXS0bVeQ6/h4MWfBDwKawd52IgS5FCOuwgNvhl1RcxkDK1X6nW0pCDxArSiLQoE/s2uNk3JOpmUQQpDPGphbaSFQvCZnUQtNEASoNm0U8+E4LZz1UGs6YjvZZgNQBR6gxH2wlEGVvfbmK8bwue8ewJfufhHvfNUOMfGxHQ9/9+39uOOaCfz0W64KD0TVNTn+WSTrovIerOvhvaevwJ8SAgAfnZqampyamroWwIsAfmdyclID8HcAPjQ1NbUXwD0AfgcAzsdYH330gl+j9pEgRfULrS4S0Tas0AOvumFYWdrIyVUo8Mwm4M0dpPtVdFOMeODPoojVmNiL0gf/KiyQVUAf29U1tUMf3w2trFboe0I+9i7ew7O5qRJNQ/En/xTZ134wfb8p6mw0JpJ1HYwpNEQ3QwtRqgLPizMVyrORCXPglSk2Zqjwq2IqSdQDH49hpAo8bfoSdJqAlY9MEsS1JK5X6dpKsdDA6YBYBZBcCe6xp9D65sfQeeQf6JjKA88nCeI8pSjwfKKQQuC14jBKP/XxrtdrNxTe+3vIvfmXT3s7kisjaNcQ+D782qKoDRHj3ANvN8Mi4otAyesDeHDfHGzHx+tfvgUAMFgMFfAmS5CJp7RkLQNt2xPkOpsoYqU/15sO6i0nol5zAsuLNjt21AMPAEOlDJZrdLKbRuAHihksrVGSn9bIqReu2TmEn3vbNdg+oU4JK2RNzCzR72SCwGd0eH4Ax/UTCv34IL2vy4Wsca8/399ag34GrsAPljJ41cs24J8fOopPfmWf2J6vSBw4ES2apyuzLDXrTGIkERMM+h743piamlqempr6V+lXDwHYBuDlANpTU1P3sd9/AsC72f/Px1gffXQF9/j6tUX1eMyrDki2CVdBZsAe+K5NZ/3xhBreAXPhUGK/AgqSdTEisvx5DjpQpr6PlVfbT/iNu4cCL1tolAo895YrPfCsC2mKt5uYGdGJNXV7vn/tFBV4megbFoCATkLsRrJmItJ4LEWBj1loArcNYmZA8gPwF4+I1wS+x7z+Z1jE2oPAny204rBagesBkisDQYCgXUXQWEko8DBDD3xQpVGEWozk97E+MbfSRNbSsY2R2EqJXrur9Q4azEKTj1lMsqaOtu0Ji41KgQdoPnqj5aCYD7fnHvC4Ai9HPA6WM4KwtlJU/krREs2SzlSB1zUNN10xlrqKWcgZIjGnnI+eAz6haLHzIE8wJobo90H2wduMwPNjHRAEnk5kqg1b5Nv/uzddgVddvwkvSmSdJ+7Mr7Qixa9EN0L76ilZaBSrpBEf/fqfeF9wAi+DKeT/AcBXAGwFcJSPTU1NLQLQJicnh87TWB99dEXQXAFACXzrO38K5+BD0fEOt9BIxIgVDqqKVAGIJilBa010WBVjuTJgWPBXZujPKoIuK6yXSsvzC+EZ7uGBj3Ri5Qq8KiWmqweeFaH6agUepqTApzWC4jGdKm84O2+BUODtCIGWm0UFnWbyeuFNlNxOIhUJRoY+0CQFPvBdei7MbKQuQSsOKRuXic8AhB541UTGsEIr0jpTr7lFzF88BgR+qgIf2C3qzycEJKVupI/1hUbLjVhcKsUMCICVWkfkwccV+Iylo2N7WK132DbR652r7PWmg1ozqsBnLeqH555wnicfV+BXEh74KDkdKITf024e+LNBIWvCY21iS/m4As8IfIcWucoTjOGBLAgQyXnnBN5kqwVlyULjBwHz0YfdxkcqOTGBAhDp2npwOqrC88nykfkmPvqZxxMFtKRbjCQgpZGRs6qVeamwvu6OwB8DqAP4EwBvv8DHkorh4fWjdI6OnmYr8MsI5/rczDh1OADI0iE4yzPIZHSM3vYGMb5EOnDMLMY2hKrcbD4H166BwAXJ5xPHVBseBOu1iny5hOHYeGdwAs7CMRAri7GJ5DzT7xjgmujwxjGYA6f+mdfbtcNLlUZGB6DnXtrvmK0NoAnAtExxXuTz49sm6gAKWQ25sokGgPJgCUXpNe1MBl5tGQGAwZEBZGPnd7FYRN13oMFENp9LnP+5QgF2awWWEcC2MonxmVwWdpWmMpQrJZRi4/XFItoAhgZysEZLOKkHCLJZsZ/q4AA6AIbKBuaDDrRiKfEeDTODnAW4no1cqYgRabyVKyJDbLHN8ICFOoBipQzHGUWNNa3MZQ1UKhk2VsKAtA8bFTQBZHUXNoDKUAm52DHMF/KoM/tJebAcOccXGq3mOGYAZBsn0AIwuGkz8vJ1MkCvjYLlo7O8DH9gDGMTydztyxHr7X4ThxsEGChFv3cDpQzarg+NdTjdunlQpMgAQLmYwWqtjZbrQyPAnp0jwqsNAB4jgcTQ0ey4GB0qRPZfKWXgMGJsMPvN5o0VoU5vHi/jnqdmUBnMR8ZHKmHjtU3j4f4mxssYHTn3jbeGpPfbuW0oQuInRukTKJu30Oq4GBnMJz5jy/HF7wzLgKETbJgI66XyWQO2D2TzGfhBgI3j4b2plDfhuD5KAzlkLQMdNwAhtCj3iRcXcdv1mzDA7E7zoxtRn92PmZU2Xqiv4o/+6Rl86tdeL96H38cBYGhsCNZw9JpsWxZ9xuvmur9egXVE4CcnJ/8XgD0AfnhqasqfnJw8Bmql4eMjAPypqanl8zF2Ose6tFSH76uTSF5KjI6WsLCgTg253HE+zk1nlVpnnGWqiLfmT0Teo7WyAmQKkd/Zngav0wbxAFLIJI5JitFGyyaJcT83BOAYYBWUn4dHBgLASh0g9ql95vV87SwtN0Gsl/b75TOVy/WAhYVa4vzw81yv1tFepKpPreGiJb3GCzT4TDlerTnQY+e34wC+00Ggmeg4QeL8d3wdXrsFv9FEAD15rXgaPGZhqTU9tOPjTAVcXqpCJzXYrRaIEV5zTpue06W5ZTiNGrTKxuQ1YFhoVuvw7Q7aTvR6DKw8Wmur4vwsztKakEYHCLSQNDSrVbhzbKzlw5b24VcpMW+uVdl5slGPnwc3XMavxs7xhYbXYb7loy/Qf/08GvI5CgKAaKivrMKdnwYpja3b79lLifV8v+FYWWsjY2iR4xwoWDgxX4MWBDB0gnqthWY9vGlrCFBr2JieqWKgmMHKctRiZjPVfHp2Dc22Cz2Ifu8LWQMLTCVeWmlC1whWVxrCypLR6b8HDi9hnsVJNuttLDihIm2Q8F7ZqLWwEO+IfA7AiaKuETTrbbQbYRSv3abf6WMn1mC7PgLfT5zDmYW6+N3KWgumEb2/lfIW5hbrOHyMUjHND88TV+iPHl/BUDmLY7NVDBQsXLtzGPc+PYPjszX85vtvocdi0kmBxo51ZrGB/YcWRaJP5HlZc6H50WvSC9gqJtHWxfWqaaSrYLwu1ggmJyd/G9Sf/rapqSl+ZTwGIDc5OckT+38WwOfP41gffXRFPOfaX5uLxGMF7VrC5hLmwCeLVAFEWtirCkD575QJNGDFOoTl155hhOO6w4VYuuzViVXTwbvqiuzxuA9dtjOlJQZ5LmvkpC5ihcNiJFM88GGEZZdOrPwBHu8Wy68/h1loVLGjhkW7pPouECtYJpliNIWGzT6JmRUtzgHWTZY3vErrdmh3scjIdR3rzELDewz4C4fpz/EUGkKgZfII7Cb8tTlo5fGX/Bj7ODM02k5EXQeAkXIWS2ttLFXbGCplocU84kPlLJaqbcyvtjA8kLz/UpsMwewSvd6LMf94KW+KIta27SFj6hEf+mCZfgdXah00Oy4IiVpsAAj1GThzD3wv8PNSzJuJc8AtNNzaEvfo8zx9DtvxE3n1A3kTa3UbVZ4RLxXK8lQb3ghrudrGcDmLn3rzlXjdjZsxtxzGVPLnZYk0cf0eZqeZqYp90fs4K3RVWWgYzjiM4SXGBSfwk5OTVwP4NQAbATwwOTn55OTk5BenpqZ8AD8B4OOTk5MHALwGwH8FgPMx1kcf3RD4LoJWNepndTsiExqgHvhEUowup9AoPPCMEOgbJmHsvi0xrrFC1q4FqoZFizPPY/HnS4oL4IEX3uxuyQO6Ec2BT0Qk8gcXiRBaAf56FiWZHKce+NROq/IEoWuMZNiJNfI6M+qBD8w8PvznD+P+Z2bC/RqWaFQUn3CSTEEUagMQkZS0iFWyiThtmrYDJJuliKjK9CLWCCk+T0WsZ4xMHiA6vRcUhpSTci2Th782BzhtGgXbx0WBRstBMeZxHx6gBH1pra0k6JtHC3C9AIdOVjFcTo4TQlDMmTgyS79T8deU85ZUxJrscjpYoq9frrZFE6X4fb4ikd1ujZzOBryBVTmffIZxAr+UQuArpQxWaxKBdz1kjOjnLBcsVJu2mMyUFcW+DUbgl6odEfE5UsnCdn1RH8CfzxWtget2DkMjBIdnQwIPIBReFM9jf+kYACBzy48kT8I6xAWXN6ampvaBZ/8kxx4AcO1LNdZHH2kImvQmoI/vhltbACkOI6gvUZWNFfAF7Uay0ZFhUSUURDnj14a2IP9v/xu00R1KAs73143AE92MxjBepDB23wr34EPpSTDnEz1iJAF2nj1XFLEmU2hY7nd+QF2cyQtKA1/dwMjM0iJXtyMmdon3j71X9DOwTqxMgQ/ijZx4EWunCXg2Vjs6Tiw28P0X5nHHtbQxEoyMaEiWeMBliggWRQZASMLNLIzRHbBufCvcY0+zzsIsqSdFgRfFvorzpG+QOhmvMwJPiCZiOs29dyhfo2VycFjvhj6BvzgQBAEabTehwA+Xs7AdH9MLDdx8xVhiu82j9L7s+YGSwAM0iYY3HRqVvOQAJ/A02rXDFHgZQ8z6sVLvUAKfSX5fKuw1hk6gn6fVy2I2PSeek/uFVfqdzsUmQYPFDBptF7bjwTJ1psBHP2cxb6HeWkUt1uQJCPPy622aNb9SbeP63XSSz60xK7UOCllTFLFWtCbcwRw2j13ZeqkAACAASURBVBZw+GSMwOs64BPlvcd62Zvhr86Enc7XOS64At9HHxcDuH3G2HgloOkwr3g1gGgnyFQLjcc6W6osNIRAH9+dmltLuIVGkQEvYFhn1YV1vSB758+g8L4/vDDV/z0sNACogu5JFhpVDjwAUkgJtZLJrDJGkqnT7XpI9uPvL443PYVGKPAsjpI3WRExkQ2apjRTp8vO+4+vwmPRk10V+GwhmgPvtsXriJlF5qZ3gORKVJnnHV8VUZtASOCJnrzuNTnbfZ0ReBnmlXcqf69l8uI88V4OfaxvtG0Pnh8kOpFy1b3jeEoFfsNwQVhKhsvqWNJS3hSNoEZi+ygVTNiuj7ZNu7nGc+S5BafedFIJfD5jwNA1WMb5Ez4KOa7AJ7+P/P151rvKQgNA2Ghsx0MmtlJQzJmotxxUmw6I9H6AlOTToln6tutjiE2WZAIPQKwEznoVVIoZbN9QxsGTVXz1/sNwPXaP003AyCgFs8wr3o3cv/nFUz0tFxx9At9HH6cAn0VIaiPbUHjP/4J1/VsATUewRlNBAt8D7GaSaHPS5TldPXdp0EojtHNnGikEaNfOs+jCul5ANAOaynrykrw39UaSbgReYxYa7v02Yg90Rk61ovpvFVXQ1RYagHU7VcVQnrKFhivwDjxi4Ff+9H78xdefR8AmBTwO9diKBwJKXo7OMmJuWAia1BYWXwUgmULY5AlA4DCbjOSVJ0aGEnuhwMcmAUSjn91JV+AjE7h1SODN694EY+ctyQx4Bo1NpsnAeD9C8iKByHGPW2gkVX1IQdBNQ8M4yzpXEXwg7HI6ULQSynOJ5cSv1TtoO17C384tOLUWbwSlqn0hqBStSH78uQbPv49HSMrvz33+KgsNAGGj6TAlXkYpZyIIgPmVJvJZI7KSUJIsNDz3vcJ8/7zZliDwmoYndn8Qn6i9DgNFCzddMQrL0PDFew/jAI+c1NRd0WXYjoeDsUZR6xF9At9HH6cArlqS/AC0wiCIpkMrjcI9sQ9+Y0UU98WLFyME5kwaxxgZ5N/+EVhXvy71NeaOl8PYdv1p77uPGDSzexdYrsB3kg276Pb0wSWaicQhKejqRk6MADjt9CJW6f9La2186d5DobKkxRR4z8Zay0er4+GBZ2fxr8/QwFKfXctHljzcfCW1BUwdY9e3zpo9ASCxiYho5sS7qEpFrAJmFoHdDruQqvLqI3n5ahense0GdjwX3OWZQPbWH0Pu9T+XOh74lAyaO26+dOpSLnGEOe9qBR6gBa0qcBtNqoWGKchx+wwA0bBotd5RWmgAasGpN6k6rbKwAGxycIZdWE8FPP8+7f0rxQxsl96H4qsEQzGV3Hb8xLHyczS71ExMEixTh2VqqLecxEQrPjkAgGl/BJ5ZQNYycM2OYfzqe28EQCdJAOh9tMez+LuPTeO3//YxPLF/oevrLjT6BL6PPk4B/tJxIFOIqJLmy34Q/soJtL7xu8ourAAixYxnosADgD60OdpUJ4bMLT8C6+rXp473cYrQ9e4WGt1E4Dm0G6kq9YcRt1QFXl6d6aLAA4h2UJXeX4zrBr77+DS+cv8RPPDsLP8l/TfwqGXGc7HY8EEAbBwp4MnD1BrDJ6NV18DLJ8ewZayI7z4+TdUt6TrTCnECT4+fX+sBs8lEOrayZlSikZPCCkR0K+xmm9LtMPu6n0X29R8SRdwXEzqzNKHG2HnzBT6SPuI4eGIN/3TPocTveYFkXOEuZA2hbKcp7Ds2lGHoWup4iSnwowMqAs+6vdY6yiJWgFpI6m0HtYatVMABYNt4CeND589GOVzOYvtECXs3V5TjcgOr+CpGJaaS225SgeerFLPLzURSDx9vtBxRrMpVfkPXUMqbkZSb1bodSebhx7Za5/ek3go8P9Z/vPtFYS9cj+gT+D76OAV4cwehj+2KWCysK14D68a3wV85iaBGZ+pxAh8pyDtDAt/HSwOid1fgRQxkpwGSKSTUVeEdT7E76RuvEGMiZlF+f9mKolDx40WsT79Is9a/ev8ROK4vCnAD3xcEeb7qYtNoAbs3DeDYEn0ocQW+5VvYOFLA+998JepNB3/1jefDa9TMRSJOgfDaDtpsIiCKWGUCnwOctrDZKBV4+XdptR9GBuZFSoBH3/IhGHtuj3r5+1gXeGjfLL72wBHh1+bgnT7jCjwhBMNl2k10KEVhf93LN+N//PQtCf86Byeno5Xk9ryL6nK1jWabpsyotl9lMZIqDzoA/Pgb9uIX33X+Ci8tU8d//8mbsXuz2uLICTMhSFh5chkDWUuXFHgvkZbDz5Ht+mLCI6OQpR75VozAA9RGsyIp8Gv1DgalCUUuY8A0NKzx7HrdiNQjeb6Pz373QOSa4FadmaUmnjywpPzM6wF9At9HHzH4zVXU//5X0Hnk8wh8D0GnAX/lBPTx3YnXaoMbAQDukcfozzHFUMuVw3b2fQK/vmHlu650EJ1baBqAouaAE9u4ci22JxqyP/Dv6WsqyeJGWck2Nl+T3IFEfJcaHk4uNnDdrmEsVdv4fz/zOOotZp0JPInAO9i9uYJNIwXU2x4C3RKTzVqQw1glh20TJdx+7QbqEWXHoBUVEwi2+sQnKnA6VPWXVXQzA/heWKSqKtzuVQtwkaOw5ybkXvvBvn1mHYLnjD9xIGqNqHMLjYI8DpezKBetSIdVGaahYaKL+t3LQkNArSONtiuKMuPbL63RyXIpxcJCCLmg1xtXuXOWkciJp+MZrDJS3FGk0MikvZSmwLddYaGRbTqDsZjKtZgCTwjBQMHCGlPgoZvwiClsg0dma/j294/jsal5sc1ytY29mweQy+h45tDiqZ2EC4BL7+7ZRx9nCffQowhqi7Cf/DpItghtcBMAKAm8XqHxe86hRwErpyxa0yf2wDuxL2yw08e6RO4N/7F73r5uUtWZKfBxBC0aVxb3jsswNkyi+FOfiPrGOWT7yrBoGI2O7eHep0/ijnz40Hv2KH2v97xuD26/ZgKf/Mo+PPyCj9sAwPdFUk7T1XD1pgGU2QPW10zojg0XBvLlMkzmRR2r5NDsuHAC+h6qVQRe38FjJgOnDZjRNAf+ufi5UHvg5TSeCxAZ2sdlixojkU8cWMQPviL8jnELTSGbpERvvnVrxKJxuhgfpOR+y1jy3qJrGkoFCweOUzKpKpQt5cIUG1UO+3oAt8nE7TPhuCU86KoceNk2o7IJFXImpufrQoGPE/hDUrOm1UYHA7GJTqWYwSp7/8yNb8XXHzqOL3/mCbztlTvEZGK5Gv6NF6ttXLtjGKW8hWcOLSMIgnU5Ie8T+D76iME9/H1ogxsRtOvwV+eomkgI9LGdideS8iglIXYT+oZJ5ZfcvPI18E7sU2Z797F+oA9t7jpODAuB3UIQ+CC55FKysfcOOE9/S93ESd5PSsdc2WJFNA33PzODJw8uYrnaweGZKrbd4YLr9ofmmqgULYwP5TE+lMeX7zuME0vM0uJ7woPuBjqu3jEEn3UqdIkJHUAdeUwMh5OQsUGqDjZcDQXQVYTvPHocE8N5XLODpq3QyQ0JFXi3k/gsImu+XQWIpva4c1JP9O6pP330cY5RY0T9xek1lupCr8Vm24VpaMpOppNbU4rSTxE7NpTxex+6Q6muA7QR08HjNKaYN26SUZTU6fVO4FUxlwD1+h+ZrSEIAmUn1oypw9A1uJ4f+bwcPGay2aF/J1Mqgh0sZVBrOrAdD0FAi2TjxbYDRQsnF2nQhLHlOtz/tTpMw8aX7juMMbYywjvJOq6PtbqN4YEsBksZPLZ/AScWG6JYeT2hf/fsow8JfqsKb3Y/jB03sdbxdfirsyClMSXxIpoh2qXLqqkMc+ctKLznd9W2iD4uGpDCIPzmKs1pVyjwmVe8G8X3fyq1MLPn/tkEL3P7j6PatPGZ7+zHY1MLOMzUpdUWW8EhBHMrnciy/dbxEk6wGDcEoQI/NFRCuWBhoGChkDXQCVjbcy+HicFwe768X2OrzE5mAJ/5zgF87HNP4VsP0+6ERNNZN1buge8kLUdCga+lWsY0ntTEGiJd6nj+6AqWq208e3gJj00lUy2OzFYxz5rg9HF+8Kmv7MO3v38c1YaN4XIGAYC5ldDz3Gg7qerxuUAaeQdokkrbpt8FlQIfUacL6y9WFQgtNPEIyXA8g7W6TWt1gMREiRAirDMqC00ha6DRdtBsO4n32MCEiJNLjbARVGwSUClkRBHrWsPG4lobb751G3IZXXz3uI9+pUaJ/FA5g2t3DoMA+PYjx0/hLLz06CvwffQhwZt+FggCGNtvhHfyhTAeMldK3UarbIC/ehL6SHrRWqJDax8XHbTiECvQdJRWG0K0065zePbwEg6drOItt22HZuVR/MCnQYiGL3znANq2h5972zVYqrbxlfsPY7nJCLxmYm61hRv2hNfU1rEiXnjeBQZpT4LV1RosANs2DrJjI9g4UkCrraEMYMXNRVIreIOZageYALDkUkK/ebSAz3/voHjNlbmysMcETjsRxyYsNO1aatF25rb3wD36xGmdp4sVrY6L3/376Gd97+v34PU3bRE//4+/ehQA8MlfuRO1pp1aLNnHmeOZQ0tYa9hotBzs2TyCpWoHK9UOQEuYUGs6otvoSw3Z7jFYVFlowvH1qsAP9FDgBwoWOo6HKiPYqsjLYs7ESq2DYi75GQcKFsuJbyXeYzOzJk3PN7BplJL5uA1noGih1aHdYA+dpPnuV20fRMf28K1HjiGX0bHMCDyvNxgpUwX+zbdtw9cfPIprdg7hlivXV2flPoHvow8J3uwBwMxBG9oKkinAry8CAUSLZhU05oNPU+D7uDRACqxxT+Cdk8ZZ9ZaDT355HxptFwurLbz/zVeCEA2+H+DBfbO4+Yox3MTatz/y/ByW6qyxiG6g1nQwPhgWxW0ZL4IGRgIIfBw9uYI9ALZtCpsNbRopoHFYA3Rg1c9jj0TgcxkDpbwJLgSfbNIH4P/57uvx+//wJP6Bkfg/vKII0pYtNHEFnlloWtXUJkxaeQyFn/gjBPXl0z5nFxt4mgUAvO7GzVhYa+Fzdx3Eq67biIyl07hPhg//+cNYrnXwWx94hbLgsY9TR8f28NzRZdywZxS+H6DZdqmFA8C2iRKeOLAoCBsALK61U2Mgzzc4+S3mTKWFh1tKDF1LNHpaLyhkaTfWtFWMAabQL6zQG0y3z6lS4LlFZ2apmfg7jVVysAwN0wt1kasf3wd//7WGjUMnq9A1gm3jJYxVclhrdFDKW/j294+j43hYYl54/j5vf9VOOK6vzOi/0OhbaProQ4I3dxD6+C7aFCdTRNBuUDUxm67AG7tvhXnlnSKRpo9LE3K++6kS+L/79hS+qMidBoB/+N5BtDoeXnHVOO5/ZhYnmEfz4Anqz71xb6iwjw/msVCly+w+oQ/JMZnAj5XgcQLve1hbo/saGgxXCjaOFNDy6EOo6uexcST6GcYqOUy3C4BuYaqax1glh8FSBv/lvTfiXXfuAgB09HyowHcaIFY0fYNIFppusalargx9dHvq+KWCKiPwv/yj1+PH37gXP3DjJnh+gCOz9BzyojwAmF9tIQgCfPm+wxfkWC8lPLhvFn/8hWdwcrGBRttBgPBcTwzlYRma8DwHQYCF1dYFmzRx+8lQis2Gk9GBgrkuCykBusL3hps24+V71SvNfJLC7SpxDzwQfk5VjCQn8GsNO6HAaxrBhpECphfqqLGUoTiBF9vXbRyZqWLTaAGWqWOgmMEHfvhqbB2n98mVWgdL7Lrg9QiaRvBjr9uDl+1OF/EuFPoEvo8+GAK7BX95WqTNkGwBQbtOPc9d0kn0oc3IvuonQfqJGpcUgiDAiyfWRAGonMzyzHQbv/O/H8cDz86kbn/oZBV3PX4CX33gCJ49FM0S/t7j07jv6Rm86RVb8e7X0uvtGZbr/tTBRegaEcWjACXrSw1KQFzQ62xc8rAPFCwUC0yZ8n3U65TA65JCvmmkIDzwtlWONF8BqA9+X20Axfd/Es8vUKUSoMrYdbvosTSCrChidapLWPFiWfH8/QIPgW4KT+rlCk7geVHdzo20wPnQSUrgV5gv9403b8HPv/NavOGmLXjw2VlBLgHgwPQqPvMv+3H3kyciin0f6eAk7OhsDfVWtOfCQMHCYCnMDq+1HLRtTxQzvtTgWfBp1qmCUKbXp32G40deuxs3pBF4dv0vrNK/SzyFBpAVeIWFRm4UpbDpbBktYnqhIRH4mIWGN8yqd7Bc6yQma5ysr1TbWK62US5YkULZ9Yr1f4R99HGO0H7oc2h+82Op4978iwCCkMBnCoBnA77bVYHv4+KF7Xj4yn2HI0oox1MHl/Bbf/sY7n+GknSSr4hOrXftW8XcShN//rXn8emvP4+OExZkrtU7+KU/uQ9/9I9PoZA1sGE4j8/ddVCM+36Af/jei7h6+yDe8eqdGCxlsGWsiKc4gX9xCXu3VCLL0eODeRHx6DACH38I7djIuiQGHhoN5oWRbCwbR4twmGuyODSaUPNGKzksVdtYrdMir+0TpcgYAFS9DC3sbjeguy08csxFEASYW27i4196Fg03fDAvNXx8+C8eQcf2sFLr4COffgTPHbm0bDOu5+PbjxzDx7/0LL72wJHEhGUtRuCLORPjgzkcPEHtUDxa7/rdI7hhzyhu3DuKAMDx+brYxzcePIrvPDaNv/7WFP71yZMvwae6+MHJ+dG5Ghqt6He7lLcwVM6K13Bbx4VW4NMKXTOmDsvUEskqFxMqcQVeYQXavWkAuzaVlep8JdaYKY7NowVUGzZOLjZg6CRhNRqQFPxqw06cS148vFzrYK1hJ8SN9Yo+ge/jsoG/cAjeiecQ+Or0C2+eWh30MWoX4K3jgWSH1T4uDTw6NY8v3XcYj+9PpoN84+GjAID7n2YEXtMoiQdga1l89Gdvx1tu3477n5nBn3zhaaHUP3t4WSQevPWOHbj1qnGcWGyIScJStY2O4+HmK8ehaZREX7drGAen17BS6+DkYgOTW6Ity8eGcoK4256GStFKdDzctZkWrDaaNloNmrAhN00q500ELIN9eCLZSGrTaAFBQLtVAqECD1ASUSlaWLbp/laOHgAAnGhaODC9hl//s4fx/Rfm8eJ86CtecXOoNmzc/eQJ/NlX9+HYfB37LjECf2B6DZ+96yD2T6/in+45lLBLVRs2CInaAnZuHMChk1UEQSCay3CFcXyIksg5qSvkYrWN63eP4JqdQ/j77xyIkPs+1ODk/NhcUoEv5U0MljJYZmkjC4xUjg5eIAW+B4EH6AReXnG72FDIGtA1Is61SoG/9eoJ/PpP3KSOYjZ0kdGv8tlvYoWszx9dRjGXtBqV8iY0QrBcbaPRdhPFwLx4eLnaxmq9IyYc6x19At/HZYOguQb4Lvy1WeW4v3ICpDQiWsiTbOgR7pZC08fFiyf20y57R2drkd8/9OwMDk6vYcNwHvun1zDPIud4k6ZipQLT0PCOV+/E+964F/uOrOCux6YBAC8cW0ExZ+JjP/9KvOHmLdgyTq+d6QVKvGZY3KMcA3kNy2q/+8kTAIBNsczh8cE8XKbAd3yifJjv3UqPbW6phoK3igAEpBBOBAghMHN0uw1bkvUaW9lx3ssmLDKBB6hHfq5FH56z+58HAKz6BfzRP4aTl4VaODm+vzMJAPjsXQfxwrFVGDrB/PKlFZfIEyt+7cdvxKbRQqRoFaCKXylniokaAOzaVMZaw8ZytYNV1t69IhUyFrIGZpkqHAQBFlfbGK3k8DNvuQqFnIGPf+lZtO2oqrxa7+DbjxwTMX2XO0IFvo5aK/ybEEItKbR7pw3fD4QqPHqBiliHy1m8701X4Nar0hNO/st7b8A7X5PsQ3KxgBCCgaIlFbGePvXsljW/hd0vl6odpQVHIwTlgonj7B5cjnnkLVNHMWdiqdrBar2vwPfRx7qDz4rv/OVpBJ2GyMoW46snoVVCYhNV4PsE/lLCI8/P4Q8+/xSeOUxtK0ckAv/4/gX8z7/+PraOFfHz77wOhADfZFnoGvPBDw2Hfvg7b9iEq7YP4qsPHIEfBHjh6Comt1RES/FtjBgfm6MPj1mmrm4YDkn49okyNEKEXYfHoXEUcybMDH2AtVwSKWDl2DxWhh8A03NVTOhrcLJDorESx/LoTfi7+h3YvinZaXWskoNlaphdbmJkIItCLFZvbDCPmQZ9ZLROUkvQwNg4Wh0X73vjXmQtHQtroQL/eHUQV24bxBVbK/iFd12Ha3YMY1bK3n76xSX83Mfuxme/e0DEy11s4CruYCmLQsYQrd456HJ99G/AG8LMLDWwWrORMXVBSgghGB/KCwW+3nLQcTyMDGRRzlv4mbdchdnlJh5+bk7sb2G1hd/6m0fx2bsO4qHn1OLE5YQgCLBS6yCX0dHquGJyns8YKOUtaIRgqJyFHwT45sNH8dzhZVSKljIZ5aUAIQQ/+oZJjHSx8BSy6oSaiwkDhQyabBXyTD5Lt6z5csESpFyVYgNQG800W71SkfzRSg5zy03UGnZfge+jj/WEwLUBmz4U7Se/gfpffwj1T/8s3JkpOu778FdnIkkyctJIn8BfWvjOo9N4+sUl2I6PTaMFHJurwfN9BEGAL957CJvHivjVH78RE0N5vOGmLbj7yZN4/ugKnNww3EDD+HhYYEoIwR3XbkCt6eDRF+axVG1jcmuofFeKFoo5E8fmKJGYXWqgkDUiD5GMpWPjSAFL1Q4MXVMW1A1WKPFru5qSwGsaQUA0VOstbNBXARZvKuO2267D1a/7IWW3Q00jglxun0he76ODOcw26MPTXD2GIAB+4m234jd+6mb8wI2bMVrJYX61heydH0Dr9f8XgoDg9msm8F/eeyOu3z2CiaE85pZbQq3fd3gZtuPjXx49jl/9xIMRUnqxYLnaQTlvwjQ05LMmGnEC37QxEGu+w1deZpaaWGt0IgV6AF1t4U2GFnkmdYWqw1dtG0QxZ4oiWAC456mTWK3bGCpn8OCzs6g27ctaiW91PHQcD1ezIvDnj65A1wi2jheFdYLbVb5w9yHsn15blxGBlxpGK+EKhyoHvhe4jz2tWRRftUwr9q0ULGFtVNUTjA/mcHi2ikB6r/WOPoHv47IA7x4JAP7SUZooEnjw5l6k4/VFwHOhywp8tu+BvxTRaDt48eQabrpiDG+9YzvedMtW2K6PmaUm9h9fxYmFBt726l1CFX07KzT9zqPHcXL0Nvxp7Q3YPF6O7PPancMgBPj771Bv+NU7pMhJQsnDsfnQQsO7B8rYuZGS5o3D+YjlgmNkkI670FL9sJquwyQeRrUqrJEtifENwwW8+mXpcadbmZc0bp8B6ANu0S8hMDIoucto6QUMlPPCejNWyWFhtQVz7x2YDSh5kgsDx4dycD1fJKwcn69h20QJ//dPvwJbx4r41Ff24bGp+dRjW49YrrUxyNJDClkDzU50VU9VMFfKU5vMzHKTLddHycL4UA7L1Q46jhcS+AFm6yMEOzaURXdeAJier2NiOI/XvGwjXji2il/+k/vx1QfCKMrF1RZ+86++j6ljK+fug69D1Jo2HNfDCisMvmo7rQmZWWqikDXwrjt3472v3wMAIkL1tqsnsHWsiNuvTU52+zi3uGp7eE88MwWeWWhSsua3sHuXKoYSiJJylUo/WsnBdujEt3KRFAz3CXwflwWCJmuCw/zt1vU/BJItIahS1c9fod7jqALPSLumA2a/scqlgn2HlxEENLrvba/aiZ0bKRk/PFPF3U+eRCFr4FU3bBKvz5g6dm0s48RiA8dWgRfdcdH9j6OYM7Fr4wDWGjZuu3oiQdC3jpVwYqGOZtvFzHITE8NJAr59Az2OjaPqjPmxoTzcQIMT6EoFHgCIpuOaoTZ0EiA7miTwvcAfgtsnyomx8cE8POhYLe8FALiZgcj4aCWHxbU2fJarDUSz6rnyPLdM886Pz9exZayIjSMF/NKPXo/RwRzuTklZOT5fF4kt6wkr1Y7I744r8EEQKAk8IQQTQ3nMLjWwVu8k/Lb8PM2vtLDIzuOI5M/esaGEE4sN4YOfXmhg82gRt10zAcvUEATAiydCgv/CsVUcna3h9z//FB7aN3vJRlH+P3/zKP7yGy9glfnfNwzlhdJeyJnYubGMK7ZRUj9WyeHjv/QafOCHr8JH3n8Lfvj27RfqsC8bXCOJGpkz8MAPdLHQAJC6sKoJvPw9Uynw8r2q0qWgeD2hT+D7uCwQtCiBN/fcDq2yAebeV4IMjMOvUsXPW6HeY022HRgWoBsg2dK6baDRx+nhX584gS/fdxiFrIEdG6hyPD6URyFr4OD0Gp47uoLrdg0ja0UfEptGi1hYaWHq+CoGSxllS/ObrxxDMWfiR167KzF2y1Vj8LwAf/WtF1Bt2NgwlCTwOxhp3jSiJvA8StJFOoGHpmM0oL5+bXCT+jVdcPOV4/ih27ZFLEAcE8N5EABPdejEQM7FB+gSueP6/z977x0myVXd/X8qdA6Tc96Z2dq8K2mDVjmhgBAIIQEiI15jsDE25n0xNuFFjvBifsaACMYYAxYIkAAFhAISymlz3to4OafOqbrq90d1987sBK2WXc2O5n6eR89quqq6b5++Vffcc8/9HkLRNEPjCZwOeUqZ+Krcdx4YizMeSRFLGoUJg8uhoDUU29UyT3Iwn93Vx5d++ArfvX/fa/4+ZwLTsuicoV1gR+BLJ0XgU+ksRtaO4iXTWdKGOaOzUF3mpX/UjsAXnZQjn3fge4ejjISS+NzqlI17LTVBLMveeB1PGoyGk9RX+Cgv8vCNT17KRauqC0XBAPpGbWm9unI///Hgfv7zof2Y5hvLiY8mMgxPJHn5wCCHuicAO02mNjdR9s0QlT1ZxUlwdpmsc68qr931rCnzIkmzq/XUv0oKTT4Cr8jSjJOAyc/UIhGBFwjOHcxcBN659s343vkvSA4XcrASM5SPwPcgeYun5r1LEpLLIUY5CwAAIABJREFUL9JnFihD43Ge2NZTSB0wLYt7njxMPGlw4+ZmFNl+/MmSRHt9MVv1IcKxNO0N053XunIfFvbGy9a6omnHAa65oJ6v/fnFM26Aaq4OcunaWrYeHCLodcxY8KShys9tV7Zy8SzL+ZWlHlKoWKpr2gQjjyTJdqVUSZo6GT1F/B4H77i8dcYB1uVQKCty83hfERlLxlU6VYoyny4zPJEoVLacPPEt8tnSlwNj8YIUYr4CItgrENFEpqDsAras238/cpCAx4HePVHYhPZ6kTGyfOc3e7nzv7fwRE5lKE8iZZBIZQsa0nl5u2iumEx+Y+5Mk73qUi+hWJpUJjvFBmBHEn1ulT3HxhgOJaZtbmypya8YRegdse2Rd16cDoW6nCZ2XpO+byRGdamXz73/At52SQsv7hvkl08d4UwTT2Z4emcvT+344wpO7e8YK6zg5LEsi6d29HL344f49m/2cvdjh6bIafblJiyWBY9t6QbslIua3GTY7545Kit4fblAs597pxMQW9lcylc/ftGsev1N1QHefVUb65dVzng8nxYT8M5c0Ta/70hi5gj9uYhw4AWLgnz5d8lzIjVALqrCio1hGWmyA4cL+u+TkVw+sYH1HCSezLzqRr17njjC3Y8f4is/3cGPHznIWDhJOmPy1ktauH5T45RztcZiEilbArG9fgYHPrc8mzUt2mZx4CVJmrN63zuvbOW2K1v5+49smiIhmUeWJG7Y1DSrAkJViZcfRy9D922c9TPIVQOWPEVI6pkfhGrKfESzDr4Zu5HijW+bciyvoz04HrfThE7K05ckidoyH73D0cJ+gPpJcpn5jbOTFYFeOTCEZcEnb12LQ5V5ckdv4ZiRNfniD17mnicOY2RNsuaZ37j51M4+tunDVJZ4uO/pY4yETjiW+Vz+0lwVx3yUN5qTLcw7oaUzRAzzKVY1ZV4uXDlVPlCRZda0lrH76AjHesNT1IrAdi6K/E56R6L0DNuO62Q75vtq3qntH41RW+5DliXedkkLG5dX8uyu/sJKAdiqTM/t7n/VNCUja/LC3n5+9vvDU643TYtv3LeHHz2i8+NH9Sm/YZ5oIsMDzx9nLJzkV88cmybdCvDk9h7+9Z6dfPEHr0zZD9E7EuPHj+o8v6ef7qEoz+7u48t3byukEeVXHK5YV4uqSLTUBHA6FGpzdp5p07bg9efjb1vFdz59+WldK+XUg2ZDliSu3dg462+dj8DPNKEG+75yORQCXsdprRDMBzOHcQSCNxhWPAQu35TCNnLQHjizfQewIsMoq6+ddp3zvLcgOeZHH3gxMzKR4J4njxCJp7lkTQ2XrqnFyJpsOTBEdZmXr92zEwuLWy5r5eoL6qddn0gZ7D0+ymVra3E7FR7b0l3YOFU9Q/pJPmXE73EUlt0nU1niQVVkjKxJa930/PBTwet2cMOmptO6Nt+2EVcDDVUzlysHCpVi8wWnzjQ1ZV72HBtFLm9C8U6d2JYXuXGoMh0DEYbG42ycIRLWUOlj+6ERPC6VqhLPlNSQ+go/iixxfCBciKK9tH+AlpogS2qDXKBVsOXAIO9701JkWaJn2HZge4ZjPLGth+oyL1/4wPrT2iBnZE0OdI6zvKlkyuB9uHuC8iI3f/2udXz2uy+yTR/muo325G8sl2udj8DnC81EExlcXlspRgKaZthP0FITpMjn5D3XLC2sBE1mbVs5L+4bRAKu39g47XhNqZeBsThO1ZagzLcBoK7cduZ7hmM01wQZmUhy0aoTqzEbllXyyoEhjvaG0BpLGA0lC+lJQZ+TP7lpBcubSgoyqJN58PkOHnyhA4DVraWsyim9PLalm0PdE9xwYSO/e6mLvpFYYaUgz6OvdPHbFzt58PkOsqbFRDTFHW9eDuSrEx/hsS3drGktYyyc4udPHuH8pXbFYL3LTou5846NVBR7ONQ9wZfv3s42fZiLV9fQNxzD7VR4/3UaH7h+2Qk75e7l03Hg08NDqEXFyM6FEY1dCMiyhEuen9SlfA58YJbouiRJVBR7mEE/4JxlYUwzBILTxDJN4g9+mcz+J5A9UyOnctB2EjIHnwFAqdGmXe9o24zadN7Zb6igwMv7B/m/P3yF/R1jjIVT3Pf0MbKmyfZDw3z/of38w4+2IssSdRV+fvnUkWmVFieiKV7YO4CRtbh4dTVvv3QJkgQv5WQKq2dQgGmsDOBxqbTXF824vKrIMjVlXlRFLui6zwd/897zueWyOQq65CPw3plXCf5Y8uodjVXTnVJFlqmv8LHtoB01P1nLHuy9BNFEhv0d49NSkRyqTH2ln45+OzI7OB6nazDKplyBm3Vt5cSSBsdyCix5KcWbLmrm4tXV9A7HuPfpozO2eySU4Nu/2cs//GjrlGJLHQNhfvtiB9+8bw//9otd3PnDLYWNowBH+8K01hVRUeTGqcpMTIpQ5xVi8hF4by5NI59Cc6wvTHWZd8bKkSUBF//2F5dMUSuazKqWMpyqzOZV1QWVn8nYm2DjdA5GaKz0T+mzxX4nXpdK70iMgdE4Fid+N7DVQBRZYtcRe69EXt70A9dpOFWZr92zk3/80dZpKxqWZfHy/kHa64tQZIkDHSdUbV7cN0B7fRG3XLYERZboG41NuTZjZHlmVx8tNQEaqwIU+530T8rT33VkhMe2dHPleXV84pbVXLO+npFQslA74VBu70l+M297fRGVJZ5C3YTekSi15b5p925tuQ8Jpkl1vhqWYdD5pS8y8fvHXtN1gnOXfFrMyUWcJvPmCxsLE/SFgHDgBW8IzNAAmWNbMKOjpHY8RM/3P41lmZihAbL9B+1zYlNl1OQi2zEwOneCy4dcOj2SKzh7JFLGtNf2dYzxvQf2UVvu4847NvLuq9sJx9Ic6BinczCCIkusayvnE7es5n1vWko6Y/Kd3+zlrl/tIWNkMS2LO3+4hbsfP0SR30lrXREup0JduZ9wLI3TIc9YZU+WJf7y1jW866q2Wdu7cXkll6ypmdfl1epS79zRxHxev+fsOPD5iGbjDDKTYKvYhHMObO0Mm3HzFRNTmeyMewkaKv305qol5h355TnlkBXNpUgS7DlqO55He8MEfU5uvrSFD92wnCvOq+OJbT0zFoV68PkOdhwa5nh/mB2Hhguv3/vUUe57+hh7j41y7YYGhicSPPRiB2CnyIxHUiypDSJJEsV+V0FHGuBYX4iA1zFjBN6yLI71hQsKR68Vr1vlzjs28oHrpgcVwN4QHEvaRYpOlvyUJCk3EQoXHOnJq0oel8qyxmJ2Hc1VIR6MIEmweVU1d96xkduuaKVjIMKWg1MlPftGYgxNJLhwZTWttUEOdNrP01gyQ89QlJUtpSiyTFWpl/6R+JRrn9/dTySe4ZbLWvnCB9dz/tIK+kZjhVx5vXsChypz+zXtqIrMuvZyJMkuqmZZFoe67eJoeQddkuwaAwe7JgjH0vSNxgvpMpMJeJ185j3nzSmdOhOZkRGsVJJUb++rnyxYEKiKzJLaYEHtayYuXFnN5lXVsx4/1xAOvGBBYvTuJ/nSPQBY6QSxez9P8vd3Ebv3C6S33kd6qANzYgBzpKNwjWPJ+invIbl8OFa9CSRQG1YjSeJ2OFP0jcR4+KXOaXnqpmWRSmfZ1zHGX3z9WX78yMEpkb5dR0ZwqDKfuf08Koo9rGktw+tSeXHfAN2DUeoqfHzy1jUsbSimsSrA8qYSDnSOs+3QMK8cGGJgNE4olua89nI+eN2yQhpAXnGmusQ76waqpQ3FVM6irw5w4+bmWR2qc4ecg3OWIvBNVQHWaxVsnmWjbUOlbWdFlmbM858svznTXoL6ch/heIZwPE3vSBRZOvE+eanO3TkH/lh/mNaccw1w+dpaLOuEg5/HtCx2Hx3l/KUVlAXd7DlmH88YWQ73hLhkTQ1f+dhm3n11OxeurOKlfYPEk5lChL+11m5nsd9ZkCgEONIToq3uxIpNPgIfi6cZDiWJJjIsqT3936Gq1DtrOlDeJlnTmlGzf1ljMZ2DEXYcGsbtVAoKQHnaG4rpH42TSmfpGoxSXeotVIS9blMjNWVeHnmpa8pm1O2HbYd/XVs5y5pK6ByMEEtmONwdwgK03ObvmjIv/SdF4LfuHyToc7I8p81eW+4jkcoynrPnkd4QLTXBwuQ46HWiNRSz4/AwQ+MJQrE0S09SRsrvVdnfMUY4lp5xxQdAayyZkqp1KqSH7NW6zMjwq5wpWEh8/gPredP61y6ve64iPBbBgiRz4Ckyux/BSkbJjvVA1sC58TYkT7Cw6dQcOkp2pBMUJ/4PfRvXpR+c9j7ui96L/wPfwn3ZHa/3V3jD8syuPr70wy3c+9RRfvbE4SnH7n/2OH/x78/wvfv34XYqPLWzr1D8COBg5zhtdUU4VNtxcagyG5ZXsu3QMB0DERorpzorH71pBZ97/wXUlHl5cnsvR/tstaFbr2hlXXt54byWXCT0ZEfmDUeu2vDZcuCdDoU/e/tqGmZJI8rLQub3DJyM3+Og2O/E7VRmlMvMa+D3DcfoGYpRVeqZsjF4/bJKOgcjPPDccQbH4lMi3I1Vfor8Tnad5MB3DkQIxdKsaytn9ZJS9neOY2RNjvSGyRgm57dXFJRerjyvnrRh8vyeAY70hlAVuaASUxxwFVJowvE0g+OJKZOQfAQ+ksiwMxflXzJHtO+PYXIdgZlSupY3lWBZsFUfZvWSsmm/Rd72/WMxuociU9J0ZEniTesb6BqKFlJYAPYcG6WlJkBJwFV4/0NdE+jd46iKVPgtasp8DE8kC5N3y7LYc3SEZY3FhQl1PlreNxIjncnSORCZNqFb1lRC73CMfR1jwPQJX32urzyzy64dMFP14NMlM2yvPggHXnAuIzaxChYk5mgXANmRTsxcMSZH24U411wHWYP4zz5NdvAoZqgfuawByTm74yY5RZGmM8VjW7q554nDrGwuobLUyx+299JaG+Ti1TVEExke29qN3+Mgkc7ymdvP4+X9gzy2pZuBsTh+j4Oe4Ri3XDZVlWPzymqe3tlHOmMWHMQ8RX4XRX4XV51fz92PHyJrmnhd6jRHPa+xPlNU+I2ElbQdLukspdC8GvnfZzYte7Dzu42sOWO12fwGzN6RGL0j0WkFpa6+oI7th4b5zXPH8bhUzp8kxylJEmtby9hycAgjaxac1l1HRpCAVUtKceUmjUd6QhzoHEOWpCma903VARor/WzVh4gnDdrriwrvU+x3sSs6imVZHO2xJ4pt9SfsrCoyTofMrsPDHOwYY2VL6TSJyDNFeZEbRZZQFXnGPt1aV4RTlUkbJudNmsjmyac3HeoOMRpOcdVJ99W69nJ+/KjO3uOjNFUHSGWyHO8Lc+0GO3rZUhNEkSWO9IbQuyZYUhMsTLpry7yYlsWn73qe265spb2+mLFwkmWbT2zgrp2klONQZVvdqX5qn22qCmABz+7qx+mQp6XIBLxOiv1ODnZNIEkzVw8+XTJDufogoRBmKoXsWhiFfQSLC+HACxYcViZV0G/PjnRgRYbB6UXyldrL2bKKq7ad1OBhzOgojvaL5rnFiwMja/LQCx2sbCnlL29dgyRBf07+raHSzzZ9mFQ6y+fedwG1FT5kSaKh0k/nQITBsTj7w3Z0M18tMU9bfRFlQTej4eSsDtElq2t46MUOugajrFpSOk1Bo6HSz9Xn17Nx+cwawW8YLDvqebZUaF4Nj0vl+o2N037Dydxx4/JZjxX7nfjcKsf6wgxPJKdp4iuyzJ/dvIrn9/Zz8aqaaXrNa1rLeWZXf0FhBWBXTrs/4HWyvKkERZbYc3yUg50TtNQEpqVXrG0r56EXOrCAS9ac+Pxiv4tUJksynUXvnkBVpGlRX5/bwd6joxT5nHz0phVnrQBcPtfc51ZnnAipikx7QzEHO8dZ3Vo27XhliQdFlng2F70+2fkt9ruor/Cz7/gYN25u5lhfmKxpFSY7TodCY1WAnUdGGBiN87ZLWgrX5idx0USGHYdGyOaKRk3uE0GvE7/HQe9IjExOjvLkCHu+TZ2DEdrri2b8nvWVfiaiY9SV+2atjXA65CPwAJnREVy1r70o2rmAZVmiCOEbGJFCI1hwmOM9gD0omCOdZMd6UMoapjyoXHVLMcd7IZNEKW+en4a+QckYJulMlqO9Ib7+y10kUgbxZIbth4aJJjJcu6EBVZFRZJk/fdsq/B4H37hvNw+/1MnG5ZXUV/oLDraqyPzNe8/nq392cS5aVzTNKZIliYtXV6Mq8rQIfB6XU+Htl9rqLK0z5B3LssR7r11KXcXiKMoln6UUmlPhnVe1sWYGp/FUkCSJ2nIf23MpKPUz/F5Bn5MbNjXNWGxlWWMxkkRhg+V4JEXnQIS1bXZ78kpDL+0b5Hh/eEqaVZ61beXkM78nf4/85ufRcJKX9w+yeklZIeqcJ59ic836+lkrQp4pPvzmZbzv2tn3ZNx8aQsfuF7DN0MRI0WWqS7z0jsSw+dWWTpD8bJVS0o53BMimTY41D2BBLTVnTivra6I/pzKzdq2E3asq/DzufdfwIZllRztC7H/+BilQRdVJ8m3NlX5Od4f5khPiJqy6Zuzi/2uwm98siRlnvzzYLbjp0t6aBC1xJ5wzJRGE3ruGVJ9fWf0M88kZiZDx//9PIc/egeDd/94yjEjFKLzH+9k7NHfYZkmljFdTGAylmVhptNT/rbOQs0FwWtHROAFC47siJ0+I5c3kx0+jpUI49AunXKOf/lFRI7sQq5Ygrpkw3w08w1JNJHhy3dvx8iaOFSZ3uEYj77SxaOvdJM2spQEXKxsPiGNV+Rz8vGbV/GVu7cT9DnndDhu2NQ0q076Wy5qZtOKqsJGwZm4ZHUNsWSGC1csHBWBs8V8pdCcCeor/BzOpag0zjJhmw2v20FzdYD9nePcfCmFDatrW084mKuWlHHwKVtucsMMWvXNNQGKfE4c6tT0lHyBred29xOKpadE5/Pk93y+VtWT02GmierJx+c6p67cR+9wjPOWVsy4X2F1SymPvNzFjkMj6F3jNFT5p0hittYFeXyrLdF48spYa10Ry4eibDk4xI7DI1x7YdO0SPCyphLue/oYw6HkjDUDwE6j2XNsdNb89ryq0emq/QBkxseRXU4Urw8zlSK6czvGyAiBjRcSfuE5MiMj084f/O//wrdmLXWf/NRpf+4fy1ypPdHt20j39uCsbyD0zNOU3XgTanEJlmHQ9+1vkuo4Tqqzg4k/PIEkSTT83RdQA9NtmDh6hP7/+A7G2Bgl191A+dvfQf/3vo2ZSFD3qf+NNEMNA8Hrh3DgBecMlpGes3qkZZrEH/gnzLEecHpQm88nvfVXAMhlU3eWOysa8N70t2e1vW9ETMvi188c42DnOJUlXi5bW4MkSSxtKCZjZPnWfbsZGo8DEkbWxOVUeOD5DhRZYmVLKeu1ymlL3W11RfzNe88n4HWcdkVEVZEL1StnQ5alP6pQ0hsJybFwc3Zv3NxEQ5WfxspAYXPpa2F5UymPvtJFPJnhpX0DlAVdUxRKVi8p496njtJUFZhRdUiWJD5wnYYsS1P11XMVVR/b0k3Q52T1kumrDB+/eRWKQz3r0fczQT4PfjbnWWsqob7Cx8+eOEw0keHmSWkycCLlZW1r2YxpGnmnOmtaXLp2egrK8qZS4BipdHbW6sZN1X72HBudNb99RUsp69rKp6wAnCpmMoHkdNH9L/+Iu6WF2o9/gpFf3cvEE48D4G5tI7L1FTIDA1Oui+/dDUBs7x6MUAi1aHrbLcNg7He/JRuNUvHu97zmNJb04CCRl1+k9M1vQVKnu2mp7m66/ulOiq+6hvLb3jXt/UNP/wFHRSW1H/8EHZ//LONP/J6Kd9xGZPtWkkePUPn+DxF+/lnMVIrM0CB93/oGNR/9GI6ycqI7tpHq7cW3chX9991DJp0kuGET4488jBosIrZ7F5Zh0P8f3wUsfKtWE9x8MZIyPwWaFjPCgRecE2SHjhF/4J/x3vRZJE+Q7NAx5LIGlJITD36jYyvm0FEkbzFK7TIc7Zsxx/vANFAb185j689NTif/8aEXOvjti5201gXZqg/x4j578PrkO9bwwt5+DvWE+NjbVuJ0KBzpsXWwf/7kES5dUzOlAuLJzDZACwQnUxp0c8W60885Xt5cwsMvdfJ3//ES4XiGd13VNuU+qK/wsXpJGZtWzL4f4ryl06vdFk1K2cmniZ3MhmWVVFQEGB6OnHb7Xy8uXFFFMp0tSDuejCxJvO2SJdz16z3Ulvu44cKpBW5Kg24+dMOygk7/ydRV+HA6ZDxOlRVLyhgbjU453lTtx+NSSKSy0zaw5rliXR0Bj3PWzedBr5NP3rrm1b4qYD8PE/pBjIlxZI+Xvm9/k+Irr8YYGyUWjZDsOM7EH57Af8F61NIyAudfQOiZp5h48vcY4+PUfPzPkWSZ2N49yB4PZiLBxFNPUvbWmwv9yzIMer/5dRKHD2Hl0k48S5cSuODEKrCZSSNJ8oyOOdgTi75v/Tvp/j5kt5uSa68/8R2yWbLRKOGXX8QyDMYfe4RsNErVBz+MpCgYoRBDP/0JiUM6xg1X8OuJF6hqLcX63W+RZJnQvt3Egi7utB6n6poKLq+/npW9JoM//AEdn/9bvCtWEtu1E4DBB3+NmrV4Ya2PFTdtoKqni9GH7scyDNIBD9Gtr6AEg0S3biG6ayc1H/04suNEgMYyTRGhP8soX/rSl+a7DQuJYuCvEok0k+Rx5w2fz0V8hqIlC5H0jocwh45ixsZIbbkP4+hLGF27cKy8BkmSsYwUyad+AE4Pvnf/PxxLNtg67kvW42jdiORwT3m/N5JtXgvRRIZnd/ezr2OM796/j8HxOGuWnIiQZYwsj23p5rcvdHDvU0fZeXiYoNcJksQv/3CUR1/p5qJV1fzlrWu4aFUNy5qK6R6K8uT2HvpG4rzrqjYuX1dHdamXFc2l1JZ5iScN3npJyxndRDbfLNz+IyEX16A2rTurn3Iu26cs6MI0LVRF5qaLm7nivKmTAUmS2LyyuqBZf6o4VJn7nzsOwF/dtnbGTZVwbttmMj6Pg5Ut0zd8T6amzIvX7eCmi5sp8k1f1WmqDsyYYw/2BCBtmKxrK2N1e8U0m8iSxLG+MPFkhluvaJ0x2OBxqbRO0tqfSIU4NH4UVVbxOuzVGcs0Cf3hCSSXm2Pbn2L44G7Kliyf8n6WadL/3bsYue+XRLdvI7p9K2SzJI/lKvdms0R37kCSFer/+v/gXLuaISNExYaLkBUH4WefxnI56Q1mMe57CP/6DSg+P+HnnyVx+BD+deeDqrD/0V9gPvMiY8vr6djUTHAiRXrXHpwVlVhGhpHf3MfAD77PyGO/o/fYbpSeQRwlpQSryojH04zc/2v6vvXvZCNhspVlRHftxDDSeGrrkV0uBr7/XQb+50eM9xzDbKyl8pIrmXjicVJdnfjXnkf3975FbP9eDqwu45d1w/QnhphoqcAKh/G/sh9rIsT21UGaV1/EeGqC5/peptObZNONH0YOR4lt38ahNj8PXBJkVVcG2YRD16/imaGtbPQuJasfxpTg7psq2Nbu4tiFzRQXV+F6YSdYFt7lK+h65Df03vUNxh+8H2tZG8kXXkQNBFACp6cStFDup7OBJEl47dW8fwcmph23zgVPdOHQDBwfHY1imvNvt4US6QGw0nHM8T7kihYkeepSm2WaxO7+FFYiDFggyTjX30J6y724r/woKCqpV+7DCg/ivvrjOFo3vernLSTbnC4ZwyxoZIdjabYcHOKRlzsZzam52AVV4ixtKKalJsDgWIKxcJKuIbsgUlWJl6O9oUJpeQm4flMjb79syZTo4uGeCb7967287dKWPyoyupBYDP3nj2Gx2mfH4WGqSrwzVpnNs1htMxez2WQsbBe8qq/00RcdoNRdUnDMT6YvOsDXtt1FMpvCq3r45HkfpcRdDPsP0/fNr4PDARm7CrDS3Ej55VfjqKrG2dpK+PHHGLn3F8SuuIA6TxXhZ56m6NLLGX/kYbyrVpM8chgzmSR8w0U8WDvOaGIMC4tL6zbzrqU3c/wb/0pmzz4iPplgzGTX29cxVhPgsl43ykNPopSW0llsUtI9Ttgn8+vrK/E7/Tj6h3nHi2mcIbuwlSHD0VY/cjJD7XAGb9pCQmLlnV8k4S3h2Gf+mkh9Gc8vUxlSk7z16QmKoyZubRklV11N/3fuwsJ+Vj+xIUB642qu7fWj3P84ksOBlU7z9MYi4utXsLl2AxdUrkWSJO7a+QPi+/ayoh/WfuivaKxowbIsXurfyi8P348qqzQFG+jo2U97/WqubrqChpQbY3ycdEst//LK1wkMRnjnIyMYDdUs/+K/8PLANp7ueZ7uSB+3b5epPDaK+6LNJJ56mu4qB+XjBmrWwpEFtbqa5i/cWcjZN2Ixuo0RGgP1KPLcqTeL+X6SZYmyMj9AC9Bx8nHhwL82mhEO/GvG6N1P8snvYiXCSL5SXBtvRW3bXIiQGH0HSDz0FZxr30x618M4VlyF6+L3Ef/F32GG7BQOuaga16UfRK2dXYJuMgvFNqfDgY4x/uvhg4yFk7xpQwNvWt/AV3+2g6GJBDVlXj50wzIqij0U+Zw8tbOPh17oIBxLU1XqJWNkeeeVbVx/SSvDwxHSmSx7j48xPJHg/KUVVMySc7zY5MjeyP3nTCDsMzvCNtOZyyYjiTF+uO+ndIRtcYLlpUvRStpoL1lCc9BO2wmlwnxt210YpsHtzW/h/l33MeBKISkKH33GwhgbY8ILkVIP/aUKF+yNEIzY6iqHG120dqc4Wu/i4UuC1PpreN+yW6nxVDL207vpWVZJcPdRop3H+a8rnSwpWcLy0nZ6on3sHtnPZzf8JT/e8RPat/bS3p3i92td9LcU4VbcRDMxPqxsIPK7R3HF0/hdAWo+/BGKtFUA/PTgfbzU+zLLRh0QjlK0ei1m0E+5p5RkNsWL+h949+OMCHN2AAAgAElEQVQhgkVldDb4aXzpKD+9qZLSxjbe1HQF4VSE7Q/+N1e/YttuuFhl4CKN87ePMvDBN/Pz3kfImAbLRhS0fpNuJcqad3yEDTXnT7FxwkjQFe6lrbhlmsM8GB/mB3v/h95oP9c1XcVbW6/nZA6MHeKuHf/JHc9bLLn0eoqvvLpw7LfHHuP5PY/wgd+OI5sWB1q9rP3YZ7B2HyDzk59zqNHF0q4Unk0bqf/Ix+i/7x4ijz7G/iVuYvVluFpbaWpdx4aq85AkiYSR4Pm+VyhxFeNVPaxtbicdWTxjz2SEA39maUY48LNiWSYF/TWJXOpLmtjPP4ukOnGsvYHMgacwh4/jWHY5zvPfipWIkHjiO5CO47v9q2SHO1AqW5BUF0bPPoxjL6M0rkNtXPea8unONducKtFEhgOd43QNRojE03QNRlnXXs66tnL6R+P0DEd5YlsPJQEXTVUBXtpv6+E7VZlPvXMtSxuKpznapmlhZM0pZdkXqn1eL062TyabQZEVZEnkdMIbt/8MxYcpc5e+alRwLk7VNmY6jew8kVcf3b2L8AvPUfWBD6F4Z4/wz+dkujfaT0+kj/VV62a0UTqbRh8/Qk+kj001F1DqtvPjKyoCHOvtZ9vgLvpiA4wkRllRpnF5/cX827bvMJQY5i0t1xHJRNl19CWCXaNEfDIbrDraDk7w2Plu9OIUf5G6APPBxzATCSxFJuWScccNXt5UQfFVV7Oh+nw6w9083/syDUk3JS8fpPHAEGG/ypZ3X8CmJRfzy0P3E07bv49X9RA3EmBZSBZct+Qa3tJyLZIkEUpF+NKLXyZjGlhYfGLd/2JpcSsv9W9lRZmGJEn888v/RsyI43f4+LO1d9AUnCqmYFomv+t4gie6nuZdS9/OppoLphw/MnGcZx/+ARc/2QNAvKWalZ/9BxzKibSkB478jsy995NRJcyrL+Fda99dsH00E6Mz3MODxx4hko5yXdOVXFb/2uuepLMZjk4cRyttm/UZF05HCDj80/pe1sxy7+EH6dn1An5/KVdf9C7aiu0Nz0Y4zL5UN7vu+S4X7oqQKQngGI8wUOagctxAzvlRPZUO9l3ciFRXw1BsGAaHSbgknBkLSoK8TXsrzcFGgs4ACSPBg8ceJWkkkSQZv8PLBVXraCtumbHtlmVhYS3IZ7dw4GdA07SlwI+AMmAU+ICu64fnvgoQDvw0LMsi9cLdZI68CKnYiQOShNp6IcgKxqHn8Lzls6i1y7BMk/TWX5He+dCJcxUV741/g1LdfsbaNZ+2SWWyyJKt0pI1LfweB6ZlsV0fZnA8jmnZG+JkSWJoIsF57eXEEhnufeooXUP2Ri9ZkvC4FCpLPBzvP/E9JMkuQ/6pd66lNOhm99ERugajLG8ueVVZucmcC33nXKakzMuBrg4M00CWZO7a9QMqvRV8cMW76In04VKceB1eanxV9Eb7iaZjNATq8Dt9WJaFPn6EB44+wvKypZS5S+iN9rO8dCkry5YVBkDTMhlLjhNOR/CoHqq8FbazYFm4FCeSJLFvVGfLwA6agvUsK22n3FOGQ1YZjA3REe6m2lfJSGKM4cQoWkkbLUV2xDJpJBmMD9MQqCNppOiMdFPlrSg4VHlMy0RCwsLKDYgSHvXEKoxlWWRMA0WSpzhsp9N/ZnI845kEcSPORCpMQ6AOCQnTMnGrLizL4ni4iwOjOsvLNGRJIp5JoJW0kTHt6Oq9hx8glA7jUdzIkozX4eHapispcgaZSIXwOry4FCfxTII/9DzHc70vcUPz1VxcuwlJkrj38AN0hLu5uuFS+mND/K7j97QXL+FDK2/Hpbg4PH6U8VSIUncxQ9Eh1latptxjq8+EUhG2De1kWUk7qWyaQ+NHaCtewoXtqwu2GU2MsX1gJ94DnRyJ97CtJIrf5WfDkJeVj+7Hcc0VHFTHiKgGy3+v44qlGahyk3nXjVy+6gZUWSVrZnny6BMcPryVYALW/6GDwEUXU3Pb7Uiqg53DezkycYwLa9bjd/jYNbyPWn81S0tap9g6aaSYSIWYSIUIOP3U+my51e5oLyOJMfqi/cSNJCWuIpqDjTgVB2XuUn5x6Dd0Rnpwyg4G4kOYlkmlp5x6pRSXP4jf6edYqIP4QB/FwzFCbgvZBI8nQPuKzaQVi6gZYW/nbuq6orgVJ1URCd/gBDG/k4MNKtc1XEl76RKiu3YReuYpyGYL7TYUkE2QWpqQjnXiWbacwMZNZAYHSYTGOOZPsuot76PcP33jsWUYjD36O7zr1uGqrUOWZCLpKFsHd5I0kgwlRlhWYkfbs1aWW9vfOsXROzJxnD0j+2kI1LG+avq+kiMTx+kKd7O5dsOU++ZkTMuc1YGMpWPseOiHVCmlNF/8JhzlFdOu/c2Rhyl1l3B5/UXn7EroXBPLfSMHefGX36axLwl11ax8z8eocZdjjI8R2bmd4d8+gBRPMlQfxBdJ4Q+lCtdGg04eutDHSLFKVrXf3yk7KPOUYgHjyXFS2TQBp59iZxCX6mIiGSKSiZI1sxhWFgmJhkAdt7S9hfYSu17ISGKM4fgIS4qbcSnnpmqUcOBnQNO0J4H/0nX9fzRNex9wh67rV53Cpc28wR14y7JsMWMJrPAwVtZAcrqRXH7MyBBWZATLSGNlkigVLWT7dVIv3I3afAFyaT1Isn1tPExGfwayGdS2zXiu+tMpn5Pp2G6n1Lh8yCV1KCVnVjf5dB0My7Jvmlgyg2WBqtjlyiciKToGIgyOx9l1ZJT+0RipTBbLsgvLLGssJpow6BuJMRpOFt5TwtaVtizoGJi7PWVBF1edX8+S2iDt9cWFTXL9ozG6h6IU+1201RfNuensVDkXHPiMaeCQZ9/4OtOAYDu4JruG9+FUnHSGuwmnI1xUa6s8+FQvvbEBhuMjHJk4TnekF0VWuKbxcjbXrCeWSfBk9zN0hnvwO70sL9XoDHcTy8RJZVN4HV7S2TTHwp2kjBODiFtxkcymCs5unqAzUIjmuRQn51Ws4fDEUUaT4/hULzEjDoAiKWStLG3FLfRGB6j2VjCWHCeUPvEbqJKCYdmOi0d1E3QGGIwPFz47T0OgjsHYEGkzM81mfocPh+wgmomRMTP4HF7imQQWFk7ZQWOwnq5wDxZQ6S1nKD6MmYtQmZaJIimcV7maWl81LtXFsz0vMhAfwqt6WF2+gqyVxefwsqKmlXgsw5GJ40TSUZyKg85wN6vKljOSHKPWV02Fp4wjE8dJGAlkWWH/6EEcsoMlRc3U+2s4MnGcQxNHC20vcgZImwaZbJolRc1EMzH6YnYK3WS7y5KMZVmUuUsYTY7TEKglYSQxLYtQOoyVzeLOSsRUe3LiUBxIiSS+uEmRv5TR5DjtAyaZIh8dngS1cQeu8Sj+uEmJtxRzZJSEW2KkxIlBlvrBDEt6UrgyFnqbj4brb2ZJJsgLL/8Ga2QUf8JkpFjFHzdxZ6CmrgWltJKJ8QFGhroom8gQiNuFbzJBLxN1xZQc6seQwZ2ZOo50nFdH464+ZNMi45AxWxvpdsSoPTSCN2WfG/XI+BMmWUWit7mIfk+apv4MFhZhn4I3aeLKWGQ9TiaqAxjVZZhdPciJNE7DIu6WiXpkAs4AitvNHm+IyjGDpoEMGbdKyGVREjFwpS266zwkXDJVta0QjVERMqmISaS6ugiMxYn6VDpqHBSnVeq6I0gnDYsph8RwqQPcTipHDZzR3LNRUchWliINjyIbk4oCyTLBiy6h+MqriO/by9GeffyuNcW7O0vh5R0UXXY5lbe/7w2nbnIuPI/PNgfHDhM3EpxXsXp6FD8eY+zBB4jt24taXIz/gg1YmTSSLDP20AMY4TCWQyW2vAkrnSbYNYrictvFtpwOIh6JQ+1BhsqcxElRHZYoMZxIHje4XMihCC86+hmzYpxfuYbuSG/h2aJKCpXeCs6vXEtzUQN90QG6I32EUiHiRiIXPFnK6vIVc45XZwPhwJ+EpmmVwCGgTNf1rKZpCnYUvl3X9ekl16bSzDw48FYmRbb/gO1YW+QGMouigIdQOIFlmvSMRDFzJamxLBJWmn4zxImcFgsJkIwUUsbCMRpFNg2wLGQjgZKJY8oqamocOZvBtBTUdApLlexNM7l+YskSkmUhpcF0SiBDVApwzKHh97nIZrN4TBMUCbCwJDBlh+3J5ltiWSRSWbxuFUWSyGRNLAv6RmOUBlyUBt3EEhnSRjaX9mE71lnTwsxaZE3Ldu5kCZdDzn9lsrn3kWQJ1SGRMVPIyKiygpG1SGczZLIGhmlgkkVVwEg6sLIqpgVpwwTT1hzPl/eWJAt7VMrZ0ZIJel0U+V2FjZ6xRJpQLI1TlfF5HPg9as7iJlkry0QkTSZj0VQdpLzEiQRkDRksGbdTYWgigarIlAfdFIKcljXplzvxWv6vqcdO9MV0NkPcSOBWXITTUcYSY2StLEGnnzJ3KQ5ZRZZl3G4H8XgKM+e42U5c7l/LwsTEtMzC8iNA0BHAxCRhJEln0/aysySRyqbxql4CLj8yEhOpEDEjgU/1TnK8TnyfjJlhPDHBWHKcSm85QWfQ/j0kFbfqJpqJEknHGEuOE3QGCDoDJI0UyWySUCp80t0hoUpywfGdTNDpp9pbRTgTYSA2hCRJhUlBlbeCcCpM3EjiUdx4HR5UWSWZTSEj0VhcS1ApQpEVQqkQTcFG+mIDjCfHaStuwbLs5euuSA9l7hKqfZXsGTnAQGyQGl81TcF6WoqaOT7RgSzJtBQ1sXtkH3tHDlDvryWSieFTPdQFavA5fKSMFGPJcdyqGwn7vZPZFAGHn7UVq0gaSfpjg8QyMbojfbhVF2srVhHPxAk4A/hUL0dDxwmnI2TNLA7FQam7hP7YEEGnnXOrjx0hko5S469CMi3i0QkC7gCS6kCWJFySg2g6Rme4uzA5CDj9NAcbmUhOMJQYxSErJLNpjFwEXJUVPIqbTCZNsTPAaHwUj+wiYaawJHDJTtyyk6yRodJVCpLEUHKURDaJR/XQ6K/FI7twyCpd4R5UWcVnqkQSIVI+J43uKsplP73Dx3GlTVxZiVQ6gQUMKQnq3ZUUmU6y8ThmPIYRi0HKnuxki/yQTiMn0pzSlFdRwDSRS0rIREIoGbtPSR431splqA4HmRe3FN7LkMEqLQa/F7V/BLmkmF45iieSIhDPknDJ4PNSVteKf9MmHJJK5OmnSfV2ozY3oV+pUXZ4kMaWVcg9AyDLlN14E5mxUQ79/tcc6dpN8/EIDgNobaJu85UY4TDh9Us5vv8V0rv20KiPoGZMHK1LiFlp5LEQzuISog4TMxTCN2w71aYiY3pcSE4nSiQGmenVN5XiYsxEEiuVxCoOkpVBHTv5fgO1tBRndQ2e9qWkuruI79+H7PEQ2LSZwPqNZKMRJFXFSMSJ7dxBZnAQ2UhjOd2UvfVm1KIi1PJyZIcTIxQi3d9nb8A0DFyNTSieqZHs/D1rZtLIjnMzUvrHshgc+NOlSDXofu4V4vv3E925HcXrw7N0KZZpkg2HMdNp0n29mHE7WIIknaioNgnJ5WK4ykPSzFA2kcGVMpE8Hqx4nKRHZdxtEvHJZFQJn+nAayo4spBJxkkrFs5rruKyK25/Xb+7cOBPQtO0C4Af67q+ctJr+4H36bq+/VUubwaOn8Xmzcj4c/cy/vTPXtM191YE2Fo085LelVsirDmcOBNNEwgEgjOK7HSCLGMm7Wit7Haj+v04An5kt9suGGOapMfsCpqq34/q96H6fCg++/9lVSV65Kh9XUkxiseDq7wcM53CTGcoWrWCRG8/mUgET10tntoaHMXFgC3dZmWzpIaHsbIm7uqqQpGa6PEOtu54il4pQsWKVVyrXTml7bF0nKNjncQzCRyKg/NqVp527m3WzDIUHqLIHcTrnjknPhOJkE0kcFfOrGmfCYVI9Pbhb2st5Ntb2SxmTqM8PTFB5KCOv3UJnoYGLMPAMk0Ul53ClBoaxkwlSQ4Oofr9+Ja0oJxU/dMyTZCkcza1Q/DGJ5tMMr51G/GeXizDwFNXi6uyAiMcxYjHcBQVMb5lK+GDOpIs466pwVlaihGN4ggGyIRCRAb6SA+PIGWyOLxeZLcbxe1GcjpJJKI03HoLNRe+9v0FZwjhwMOZceBf9wi8mcUc7cr9JeWi2RIlJT7GJ+JYlsXQeBIj3yZJImMZjGTG7fNz11hIoLogGccxMoIl2xtlTKcHkPPvDoDD6SBYFiARiuFwKDhdDsAiEUuRNi2KK0vIhiKEoymCPieKqkLuQY7LmVstMCFrFmbDFuT+X0KWZeIpewXAqcokUlkCPieYFqF4Go9Txek8kXM7ZWg4hYGirNhPOmZhWFkyWQNFllElFYfiQJUVZGQsLKKZGKlsGisX5bej/Sc2vciSlPvXto9hZsmYGfJaXlLOvhJ25H9ya1VJwaGoZE0zF+U2ceZy7ZLZVCGKKeV+H2nSt5SYPCBKU762dOIqe+Cc9KpDVvE5fCSMJB7VzgtGsqNY6Wwaw8qSNbOUlPgIh5L2d0NClhQUSUbKbT4+GcuyiGXiOBQVl+I6+ReBnC0BfA4vEq/utMz0M5qWdXopQmfYeSgt9TM2Fnv1E0+FM9q0M/RmsoTi9th93cil4kiy3edO4SPKyvyMFgrzSHZKQ+4/SZbtFSQzF8FWVPv1fKVG017ZAcuWlLU73bTPsNJpJIdjwaVLiGjqdIRN5kbYZ3YWs21eLQL/xqm8cup0A3WapimTUmhqc6+fk0iyglLRMu11V0UARbE7du0MlaRbp790Zqk/2x9w+pzqTe9+1TMWJjOtvUz+rhUVAdTX+FB8NVtNL/OycPFUBHA6F+egcSo4SwKoxmkOH7J8StMQyfVG6lECgUBwZllYoY0zgK7rQ8BOIJ/MdDuw4xTy3wUCgUAgEAgEgnlnMUbgAT4G/EjTtC8C48AH5rk9AoFAIBAIBALBKbEoHXhd1w8Cm+a7HQKBQCAQCAQCwWtl0aXQCAQCgUAgEAgECxnhwAsEAoFAIBAIBAsI4cALBAKBQCAQCAQLCOHACwQCgUAgEAgECwjhwAsEAoFAIBAIBAsI4cALBAKBQCAQCAQLCOHACwQCgUAgEAgEC4hFqQP/R6AAyPKpFAJ/fTiX2nKuIWwzN8I+cyPsMzfCPrMjbDMdYZO5EfaZncVqm0nfW5npuGRZ1uvXmoXPJcCz890IgUAgEAgEAsGi4FLguZNfFA78a8MFbAD6gew8t0UgEAgEAoFA8MZEAWqALUDq5IPCgRcIBAKBQCAQCBYQYhOrQCAQCAQCgUCwgBAOvEAgEAgEAoFAsIAQDrxAIBAIBAKBQLCAEA68QCAQCAQCgUCwgBAOvEAgEAgEAoFAsIAQDrxAIBAIBAKBQLCAEA68QCAQCAQCgUCwgBAO/AJA07TFWUdYcFpomrZkvtuwUBD3lkBw5hD3k+C1IMaqPw7hwJ+DaJq2XtO0N2madjWAruui2lYOTdPerGnarfPdjnMVTdOuAR7WNO1N892WcxFN09Zqmnahpmmrwb63NE0Tz8EceVtomqbOd1vONYRtpiPGqrkR49XsiLHqj0cMXOcYmqZdD/wAuA34W03Tbpx0bFFHN3I3+v8HjM13W85FNE27Dvj33J/nz2dbzkU0TXsb8BPg08Dfa5r23wC6rpvCiS/Y50lN0wK6rhuapinz3aZzBWGb6Yixam7EeDU7Yqw6Myz6QetcIvdA/FfgT3Rd/yhwDJA1TSuFxR0t1DTtzcD3gLfruv6kpmkBTdPKNU0rme+2nQtomnYDdt95B/A24C81Tdswv606d9A0rQn4W+A2XddvA/4TuF3TtHuh4MQvWqdD07TLgO8APuDpnKOaFY6qsM1MiLFqbsR4NTtirDpzLNob7BylAvi0ruuvaJpWBVwP/Dlwl6ZpPwDb0ZjPBs4j64B64KCmaU7gHuCnwHc1TfvkvLZsntE0rRi4EviErusHgV7gYWBF7viidTQm4c79O5z79znsaHy7pmlfh0W//O8Cvqrr+gZgP/DsJEd1saeMCNtMR4xVcyPGqxkQY9WZRbKsxTxmnZvkOvGPgL3AV4CV2BGgL+i6/tQ8Nm1e0TTtH4A/A/qxl99+D1yNPXj8la7rPfPYvHlF0zS3rutJTdOkXPTrfwMfB9bouh6b7/bNN5qmeYDvAqPYy/7vAYqBh4D3A3foup6cvxa+/uT7yqS/A7quRzRNcwD/BawGLtN1Paxp2hpd13fPW2PnGU3TinVdnxC2mYoYq2ZHjFczI8aqM8dijR6cM2iath6ownYsXtZ13cpFdj6i63oqd9peTdN6gcXmYORtMwa8pOv6F3LLsmld17+fO+cXwO2AY/5aOj/k7FONHVV+JfeyE0jpuv6vmqZdDHwY+NbJztpiIGefSmBU1/WXNU37LvAnwFeBMV3X36dpmhc7tcbPIru/sJ//GU3TVF3XDV3XIwC6rmc0Tftf2GlGj2iadg/wdk3TbtV1fXQ+G/x6ManvjGM/eyZydlq0thFj1dyI8Wp2xFh1dhAO/DyiadrbgX8BdmF3ZremaTfmlh7NSefdArQCi2bGfpJtXIArZ5vP5ZYk81wLeIHoPDRz3pij76Ry9jGAbdjLld9abA/Ek+zj1jTNAm7Rdf1FTdPKOLGx7GZAAtLz09L5Ibcp805N096q63qXpmmKruvZ/PGcQ/Z+TdP2AF8ErnqjO6h55ri3DE3THIvRNmKsmhsxXs2OGKvOHiKFZp7QNM0H/Ab4XC6PsBJ7ib8ZWJ/bVOcCPoi9DPdeXdf3zVuDX0dmsc33gCZytsmddwfwF8D7dV3fO28Nfp05lb6TO68ZeBS4FBheLA/GWezzH0AjsCHvqGqa9n+ADwG3L6YUiFy06+eAjp2n+6aZnHhN094H/DNwva7r++enta8vr+HeWjS2EWPV3IjxanbEWHV2EZtY5w8ZKMGejaPr+pCu67cAx4Ff584px94stKgeiMxsm7czyTaaptUD7Syih+Ek5uw7Wk5NRdf1DmBd7vhieiDOZJ+bse3zK+2E2swB4F2LyXnPYQBf03X9auxB80lN0xpn2JQ5Drz5je6gnsQp3VvACIvHNmKsmhsxXs2OGKvOIsKBnydy+aY/A/5Um1qN7FOAqWnaCl3Xe4H/t9geiKdomx7gzkX2MARe3T7AqknnJl7n5s07p2CffBGnhxZp/3kZO0KIruufxN7I+4ecE29odnEeSdf13y42+7yGvvPIYrGNGKvmRoxXsyPGqrOLcODnl8eBIeDP851b1/Uu7A0u1bm/M/PXvHnlVGyz6DZKTWIu+5TNZ8POEeayT+l8NuxcQNf1+KQ/P4XtxD+sadrfAV/HjqYuVkTfmY4Yq+ZGjFezI8aqs4TIgZ9nNLuc8Fuw83O/h92h/xZ4i67rnfPZtvlG2GZuhH3mRtjntaFp2gvAUuxNmYstrWgKou9MR9hkboR9ZkfY5uwgIvDzgKZp8qRc0+ewO/RTwGeAd2PnyS3KTi1sMzfCPnMj7DM3OfvIuf8PTHr9PdiD6+WLwXnP595qJ1ULXcx9R9hkboR9ZkfYZn4QEfizjKZpVwBXAUeAA7qub8mrPWiadiXwSeAjuq6PaZrmBkxd1xeFpJ2wzdwI+8yNsM/cvIp9Lgc+DXxQ1/VxTdMuBMKLZFMmmqbV5fK20TRNzimp5AvLLMq+I2wyN8I+syNsMz8IB/4somnaW4BvYpdQ9gNvBj6v6/rPNU1bA3wf+Iqu67+ax2bOC8I2cyPsMzfCPnNzivb5sq7rv9YWWeEUzdbA/zV2tdB/yr2WdzpWYhdp+upi6jvCJnMj7DM7wjbzh3DgzxK5JaWvAU/run5/binpZuwy3B8CngGW6rr+wiIcQIVt5kDYZ26EfeZG2Gd2NE1bDjwA/AR4P/BDXdf/edLxNYBX1/WXFotthE3mRthndoRt5hfhwJ9FNE37CZDUdf1PJr12G/D3wK2LUXIrj7DN3Aj7zI2wz9wI+8yMZhcceoeu6z/VNO1S7BWK70x2OnLnLRpnQ9hkboR9ZkfYZn75/9u7/1A76zqA4+8ruwXRVMKUmq5UPJ8SQ5BMIywqzNgauIzQiEl/1DCDIBCDqMwyg8xaRkaxftpviqEYVP9EKov948wSPrVKsqImrC0t1OlufzyPdbjnPp97d7edc56d9wsO5+45zwPf++bAvvc53+c8XsR6bN0MnBIRVw1tuwu4h+bbHmaZbWr2qdmnZp8lZOaTNN9LTWbeA1wFXBPN12cSEZdGxMtmabJhk5p9utlmstYsv4tWqr0w7DLgr8D9wE6aj6vf1K4J+3ZmPhERC0BMcKhjZ5uafWr2qdmn26I2uzPzvnb7XGbeGxFXAl+P5qLf04GNExvsmNikZp9utpkeLqE5SiJiA/Bp4Ps0b9p54H00txG+Ang98Diwi+aK7A2Z+bvJjHa8bFOzT80+Nft0W6LNGuCDmbl30X63AFuAN2bmg2Mf6BjZpGafbraZLi6hOQoi4jyaN/XWzLwRuB04Hzgzm1sofw34ALCP5gYGm2foP1DbFOxTs0/NPt2KNi9ctN8baL6X+rifbNikZp9utplCCwsLPlb5GAwGc+3z+sFg8PZF23YMBoPNkx6jbabzYR/72GdibS5f4pj1kx63TewzjQ/bTO/DM/BHZg1AZv4Z+Em77YTFr0fEmyPilWMe26TZpmafmn1q9um2XJt5+F+bi4b2PZ7ZpGafbraZUl7EukoRcRnw7oj4A/BwZt7evjQPPAMcBB6JiI3Ah2muzp4JtqnZp2afmn262WaUTWr26Wab6eZFrKvQrvHaDnwceAq4BfhRZl47tM/ngHNp7oL4nsz8zSTGOm62qdmnZp+afbrZZpRNavbpZpvp5xKa1TkLuC0zv5qZd9BcyLEhIr4wtM/pwMXAlhl7U9umZp+afWr26WabUTap2aebbaacE/jVOYHmtsEAZOY/aN7Eb9treqsAAAP+SURBVI2Ire3m24ALMnPPBMY3Sbap2admn5p9utlmlE1q9ulmmynnEpoViojX0PwF+i/gZ/z/Y6X3Z+ahdp+twCmZeVPM0K2DbVOzT80+Nft0s80om9Ts0802/eIZ+BVoL9D4InAOsAn4LLADOAnYNrTrqcAgImamq21q9qnZp2afbrYZZZOafbrZpn88A7+MiLgQ+BZwZWbujohXA9fT3OlwHXAdcDbwc5q7Hm7KzIcmNd5xsk3NPjX71OzTzTajbFKzTzfb9JN/QS3vKWBbZu4GyMydwIuA9cCuzHwbzd3JHqC5RfksvaltU7NPzT41+3SzzSib1OzTzTY95PfALyMzH4iIPwJExHxmHgT2Awczc6FdM3Z3Zh6Y6EAnwDY1+9TsU7NPN9uMsknNPt1s00+egV+BzHys/fHZ9UZPAnsj4grg8zRrxGaSbWr2qdmnZp9uthllk5p9utmmf1wDvwoR8Q3gecCZwNWZ+dsJD2lq2KZmn5p9avbpZptRNqnZp5ttpp9LaA5DRMy1P55D86a+JP3+U8A2y7FPzT41+3SzzSib1OzTzTb94Rn4VYiIzcDv0zuPjbBNzT41+9Ts0802o2xSs08320w/J/CSJElSj3gRqyRJktQjTuAlSZKkHnECL0mSJPWIE3hJkiSpR5zAS5IkST3iBF6SJEnqEW/kJElasYh4GDgNeBp4BngI+Cbw5cw8tMyxLwX+BMxn5tPHdKCSdBzzDLwk6XBtysy1wEuATwHXA9snOyRJmh2egZckrUpmHgDujIi/A7+KiM/QTOo/AZwNHAC2Z+YN7SG/bJ/3RwTApcBe4CvA+cAC8FPg2szcP67fQ5L6xjPwkqQjkpm7gL8AlwD/BrYAJwMbgWsi4vJ219e2zydn5vMzcycwB9wMvBh4OXAGcMP4Ri9J/eMZeEnS0fA34AWZ+Yuhbb+OiO8CrwN2LHVQZu4B9rT/fDQibgU+eiwHKkl95wReknQ0rAP2RcRFNOvizwOeAzwX+GHXQRFxGrCN5uz9WppPhv95zEcrST3mEhpJ0hGJiAtpJvD3At8B7gTOyMyTgC/RLJOBZo37Yp9st78iM08E3jm0vyRpCU7gJUmrEhEnRsRbgO8Bd2TmgzRn0fdl5hMR8SrgHUOHPAocAs4a2rYWeBw4EBHrgOvGM3pJ6i8n8JKkw3VXRDwGPAJ8CLgVeFf72nuBG9vXPwL84NmDMvM/wE3AfRGxPyIuBj4GXEDzjTV3Az8e228hST01t7Cw1CeakiRJkqaRZ+AlSZKkHnECL0mSJPWIE3hJkiSpR5zAS5IkST3iBF6SJEnqESfwkiRJUo84gZckSZJ6xAm8JEmS1CNO4CVJkqQe+S81dqe/ox8bdQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 444
        },
        "id": "NxQOMjlDQbi6",
        "outputId": "b973b91c-4c67-4e55-a8d7-8421b956301d"
      },
      "source": [
        "#---------------------------------Etapa 9--------------------------------------\n",
        "#Agora vamos ampliar o gráfico e observar o padrão. [17/01/2021 - Refere-se a primeira pessoa vacinada no Brasil]\n",
        "week_intervals = ['17/01/2021','24/01/2021','31/01/2021','07/02/2021']\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Brazil'] \\\n",
        "  .loc['12/1/2020':'1/1/2021'].daily_new_cases.plot()\n",
        "\n",
        "{plt.axvline(d, color='k', linestyle='dashed', linewidth=1) for d in week_intervals}\n",
        "\n",
        "plt.ylabel('Número de casos')\n",
        "plt.xlabel('Date')\n",
        "plt.title('Total de casos de COVID no Brasil');"
      ],
      "execution_count": 359,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 445
        },
        "id": "8GtjdXp-Qe_Z",
        "outputId": "5b3e24bc-e268-463d-9a8b-7559a7366c41"
      },
      "source": [
        "#---------------------------------Etapa 10--------------------------------------\n",
        "#A época do ano é importante para infecções?\n",
        "BRA_infections = covid_data[covid_data.country == 'Brazil']\n",
        "ARG_infections = covid_data[covid_data.country == 'Argentina']\n",
        "PRY_infections = covid_data[covid_data.country == 'Paraguay']\n",
        "URY_infections = covid_data[covid_data.country == 'Uruguay']\n",
        "\n",
        "plt.figure(figsize=(12, 6))\n",
        "BRA_infections['daily_new_cases']\\\n",
        "  .groupby(BRA_infections.index.to_period('M'))\\\n",
        "  .sum().plot(kind='bar', legend=False, color='g')\n",
        "\n",
        "ARG_infections['daily_new_cases']\\\n",
        " .groupby(ARG_infections.index.to_period('M'))\\\n",
        " .sum().plot(kind='bar', legend=False, color='b')\n",
        "\n",
        "PRY_infections['daily_new_cases']\\\n",
        " .groupby(ARG_infections.index.to_period('M'))\\\n",
        " .sum().plot(kind='bar', legend=False, color='r')\n",
        "\n",
        "URY_infections['daily_new_cases']\\\n",
        " .groupby(ARG_infections.index.to_period('M'))\\\n",
        " .sum().plot(kind='bar', legend=False, color='c')\n",
        "\n",
        "plt.legend(['Brasil', 'Argentina', 'Paraguay', 'Uruguay'])\n",
        "plt.ylabel('Número de casos')\n",
        "plt.xlabel('Meses')\n",
        "plt.title('Casos de COVID em meses');"
      ],
      "execution_count": 360,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "dqVmQ4fAQg_h",
        "outputId": "07cebf5e-1af8-4f6d-e1cf-8395f5af3d1f"
      },
      "source": [
        "#---------------------------------Etapa 11--------------------------------------\n",
        "#Vemos que o padrão muda de país para país\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Brazil'].daily_new_cases.plot(legend=False,rot=45)\n",
        "covid_data[covid_data.country == 'Brazil'].daily_vaccinations.plot(legend=False,rot=45)\n",
        "\n",
        "#Marque o início do lançamento da vacina  -----> Mônica Calazans (Primeiro Vacinado)\n",
        "plt.axvline('17/01/2021', color='k', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.legend(['Casos diários', 'Vacinações diárias'])\n",
        "plt.ylabel('Número de pessoas')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Taxas de COVID e vacinas no Brasil');\n",
        "\n",
        "#É possível conferir se os gráficos abaixo estão certo,\n",
        "#checando nos sites da Wikipedia e Our World Data\n",
        "#https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Brazil\n",
        "#https://ourworldindata.org/coronavirus/country/brazil"
      ],
      "execution_count": 361,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "XnAdc8Zf-pzX",
        "outputId": "1f393576-da16-469e-a18c-db7439ee2442"
      },
      "source": [
        "#---------------------------------Etapa 12--------------------------------------\n",
        "#Vemos que o padrão muda de país para país\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Argentina'].daily_new_cases.plot(legend=False,rot=45)\n",
        "covid_data[covid_data.country == 'Argentina'].daily_vaccinations.plot(legend=False,rot=45)\n",
        "\n",
        "#Marque o início do lançamento da vacina -----> Flavia Loiacono (Primeiro Vacinado)\n",
        "plt.axvline('29/12/2020', color='k', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.legend(['Casos diários', 'Vacinações diárias'])\n",
        "plt.ylabel('Número de pessoas')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Taxas de COVID e vacinas no Argentina');\n",
        "\n",
        "#É possível conferir se os gráficos abaixo estão certo,\n",
        "#checando nos sites da Wikipedia e Our World Data\n",
        "#https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Argentina\n",
        "#https://ourworldindata.org/coronavirus/country/argentina"
      ],
      "execution_count": 362,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "kFKjSa4o-qeN",
        "outputId": "897b85e4-b975-413b-ae00-d6a9b5940799"
      },
      "source": [
        "#---------------------------------Etapa 13--------------------------------------\n",
        "#Vemos que o padrão muda de país para país\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Paraguay'].daily_new_cases.plot(legend=False,rot=45)\n",
        "covid_data[covid_data.country == 'Paraguay'].daily_vaccinations.plot(legend=False,rot=45)\n",
        "\n",
        "#Marque o início do lançamento da vacina\n",
        "plt.axvline('17/01/2021', color='k', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.legend(['Casos diários', 'Vacinações diárias'])\n",
        "plt.ylabel('Número de pessoas')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Taxas de COVID e vacinas no Paraguay');\n",
        "\n",
        "#É possível conferir se os gráficos abaixo estão certo,\n",
        "#checando nos sites da Wikipedia e Our World Data\n",
        "#https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Paraguay\n",
        "#https://ourworldindata.org/coronavirus/country/paraguay"
      ],
      "execution_count": 363,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 407
        },
        "id": "WFCyu1Aq-rJf",
        "outputId": "8aa20158-fc7e-4df9-97a1-87d35ee48c46"
      },
      "source": [
        "#---------------------------------Etapa 14--------------------------------------\n",
        "#Vemos que o padrão muda de país para país\n",
        "plt.figure(figsize=(12, 6))\n",
        "covid_data[covid_data.country == 'Uruguay'].daily_new_cases.plot(legend=False,rot=45)\n",
        "covid_data[covid_data.country == 'Uruguay'].daily_vaccinations.plot(legend=False,rot=45)\n",
        "\n",
        "#Marque o início do lançamento da vacina\n",
        "plt.axvline('17/01/2021', color='k', linestyle='dashed', linewidth=1)\n",
        "\n",
        "plt.legend(['Casos diários', 'Vacinações diárias'])\n",
        "plt.ylabel('Número de pessoas')\n",
        "plt.xlabel('Data')\n",
        "plt.title('Taxas de COVID e vacinas no Uruguay');\n",
        "\n",
        "#É possível conferir se os gráficos abaixo estão certo,\n",
        "#checando nos sites da Wikipedia e Our World Data\n",
        "#https://en.wikipedia.org/wiki/COVID-19_pandemic_in_Uruguay\n",
        "#https://ourworldindata.org/coronavirus/country/uruguay"
      ],
      "execution_count": 364,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lA9cU4t5tUS7"
      },
      "source": [
        "--------------------------Fim do Código---------------------------------------"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9UyFrkAgALmT"
      },
      "source": [
        "2ª Parte - Plotagem do Modelo de Predição da Covid no Brasil"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wXa5zMZbMbM2"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import datetime\n",
        "import requests\n",
        "import warnings\n",
        "\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib\n",
        "import matplotlib.dates as mdates\n",
        "import seaborn as sns\n",
        "#import squarify\n",
        "import plotly.offline as py\n",
        "#import plotly_express as px\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "from lightgbm import LGBMRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.metrics import mean_squared_error, mean_absolute_error\n",
        "from sklearn.preprocessing import OrdinalEncoder\n",
        "from sklearn.svm import SVR\n",
        "from sklearn.model_selection import train_test_split\n",
        "from statsmodels.tsa.arima_model import ARIMA\n",
        "from fbprophet import Prophet\n",
        "from fbprophet.plot import plot_plotly, add_changepoints_to_plot\n",
        "\n",
        "from IPython.display import Image\n",
        "warnings.filterwarnings('ignore')\n",
        "%matplotlib inline"
      ],
      "execution_count": 365,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8nbyDLNRM9en",
        "outputId": "06730993-5a81-4cb1-c0b1-2978d5acb691"
      },
      "source": [
        "#Importação da biblioteca squarify via instalação no Colab\n",
        "!pip install squarify\n",
        "#!pip install plotly.offline\n",
        "!pip install plotly_express"
      ],
      "execution_count": 366,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: squarify in /usr/local/lib/python3.7/dist-packages (0.4.3)\n",
            "Requirement already satisfied: plotly_express in /usr/local/lib/python3.7/dist-packages (0.4.1)\n",
            "Requirement already satisfied: patsy>=0.5 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (0.5.2)\n",
            "Requirement already satisfied: statsmodels>=0.9.0 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (0.10.2)\n",
            "Requirement already satisfied: plotly>=4.1.0 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (4.4.1)\n",
            "Requirement already satisfied: numpy>=1.11 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (1.19.5)\n",
            "Requirement already satisfied: pandas>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (1.1.5)\n",
            "Requirement already satisfied: scipy>=0.18 in /usr/local/lib/python3.7/dist-packages (from plotly_express) (1.4.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.0->plotly_express) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.20.0->plotly_express) (2.8.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from patsy>=0.5->plotly_express) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly>=4.1.0->plotly_express) (1.3.3)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "C9lo7hukMjm9"
      },
      "source": [
        "train['Date'] = pd.to_datetime(train['Date'])\n",
        "test['Date'] = pd.to_datetime(test['Date'])"
      ],
      "execution_count": 367,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 476
        },
        "id": "fCZ-CClzMmOO",
        "outputId": "b7b39bab-f4d0-4b01-eef3-7787c4d7f8f2"
      },
      "source": [
        "#Predição usando Prophet Model \n",
        "dates = list(confirmed_df.columns[4:])\n",
        "dates = list(pd.to_datetime(dates))\n",
        "\n",
        "df1 = confirmed_df.groupby('Country/Region').sum().reset_index()\n",
        "k = df1[df1['Country/Region']=='Brazil'].loc[:,'1/22/20':]\n",
        "brazil_confirmed = k.values.tolist()[0] \n",
        "data = pd.DataFrame(columns = ['ds','y'])\n",
        "data['ds'] = dates\n",
        "data['y'] = brazil_confirmed\n",
        "\n",
        "prop=Prophet()\n",
        "prop.fit(data)\n",
        "future=prop.make_future_dataframe(periods=60)\n",
        "prop_forecast=prop.predict(future)\n",
        "forecast = prop_forecast[['ds','yhat']].tail(60)\n",
        "\n",
        "fig = plot_plotly(prop, prop_forecast)\n",
        "fig = prop.plot(prop_forecast,xlabel='Data',ylabel='Casos confirmados')"
      ],
      "execution_count": 368,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "INFO:fbprophet:Disabling yearly seasonality. Run prophet with yearly_seasonality=True to override this.\n",
            "INFO:fbprophet:Disabling daily seasonality. Run prophet with daily_seasonality=True to override this.\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "OfEpUyefPGcH",
        "outputId": "ad281026-0960-4f23-f0ec-d93019480de8"
      },
      "source": [
        "arima = ARIMA(data['y'], order=(5, 1, 0))\n",
        "arima = arima.fit(trend='c', full_output=True, disp=True)\n",
        "forecast = arima.forecast(steps= 60)\n",
        "pred = list(forecast[0])\n",
        "\n",
        "start_date = data['ds'].max()\n",
        "prediction_dates = []\n",
        "for i in range(60):\n",
        "    date = start_date + datetime.timedelta(days=1)\n",
        "    prediction_dates.append(date)\n",
        "    start_date = date\n",
        "plt.figure(figsize= (12,6))\n",
        "plt.xlabel(\"Datas\",fontsize = 12)\n",
        "plt.ylabel('Total de casos',fontsize = 12)\n",
        "plt.title(\"Predição de valores para os próximos 60 dias\" , fontsize = 12)\n",
        "\n",
        "plt.plot_date(y= pred,x= prediction_dates,linestyle ='dashed',color = '#cc0000',label = 'Predição');\n",
        "plt.plot_date(y=data['y'],x=data['ds'],linestyle = '-',color = 'green',label = 'Atual');\n",
        "plt.legend();\n"
      ],
      "execution_count": 369,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "p7XbglMiryGA"
      },
      "source": [
        "train=pd.read_csv('train.csv')\n",
        "test=pd.read_csv('test.csv')\n"
      ],
      "execution_count": 370,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HbnM3FRqfGBH"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot as plt\n",
        "from sklearn.linear_model import LinearRegression\n",
        "from sklearn.metrics import mean_squared_error, r2_score\n",
        "from sklearn.preprocessing import PolynomialFeatures\n",
        "from sklearn.tree import DecisionTreeRegressor\n",
        "from sklearn.ensemble import RandomForestRegressor"
      ],
      "execution_count": 371,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gSPvODLrfIu3",
        "outputId": "5b39a356-51c2-45f9-8ea8-fb45ed28045a"
      },
      "source": [
        "train.info()"
      ],
      "execution_count": 372,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 35995 entries, 0 to 35994\n",
            "Data columns (total 6 columns):\n",
            " #   Column          Non-Null Count  Dtype  \n",
            "---  ------          --------------  -----  \n",
            " 0   Id              35995 non-null  int64  \n",
            " 1   Province_State  15295 non-null  object \n",
            " 2   Country_Region  35995 non-null  object \n",
            " 3   Date            35995 non-null  object \n",
            " 4   ConfirmedCases  35995 non-null  float64\n",
            " 5   Fatalities      35995 non-null  float64\n",
            "dtypes: float64(2), int64(1), object(3)\n",
            "memory usage: 1.6+ MB\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "avxiMylofJyU",
        "outputId": "6fb9968b-dc4a-4bc0-d92a-a0d30f10b6d4"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 373,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id Province_State Country_Region        Date  ConfirmedCases  Fatalities\n",
              "0   1            NaN    Afghanistan  2020-01-22             0.0         0.0\n",
              "1   2            NaN    Afghanistan  2020-01-23             0.0         0.0\n",
              "2   3            NaN    Afghanistan  2020-01-24             0.0         0.0\n",
              "3   4            NaN    Afghanistan  2020-01-25             0.0         0.0\n",
              "4   5            NaN    Afghanistan  2020-01-26             0.0         0.0"
            ]
          },
          "metadata": {},
          "execution_count": 373
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vY_jKSecfK5f",
        "outputId": "b62d1419-9289-496b-a09b-535dc842b440"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 374,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Province_State', 'Country_Region', 'Date', 'ConfirmedCases',\n",
              "       'Fatalities'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 374
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "aZBz4XKdfMWX",
        "outputId": "fc3bd764-55d7-4fb4-b90b-ca69d69f3e19"
      },
      "source": [
        "# Deaths bigger than 10.000 and ConfirmedCases higher than 100.000 \n",
        "train[(train['Fatalities']>10000) & (train['ConfirmedCases']>100000) ]"
      ],
      "execution_count": 375,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3557</th>\n",
              "      <td>3558</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-08</td>\n",
              "      <td>146894.0</td>\n",
              "      <td>10017.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3558</th>\n",
              "      <td>3559</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-09</td>\n",
              "      <td>156061.0</td>\n",
              "      <td>10656.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3559</th>\n",
              "      <td>3560</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-10</td>\n",
              "      <td>162699.0</td>\n",
              "      <td>11123.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3560</th>\n",
              "      <td>3561</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>169594.0</td>\n",
              "      <td>11653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3561</th>\n",
              "      <td>3562</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-12</td>\n",
              "      <td>178214.0</td>\n",
              "      <td>12461.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35070</th>\n",
              "      <td>35071</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>223060.0</td>\n",
              "      <td>32065.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35071</th>\n",
              "      <td>35072</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2020-05-12</td>\n",
              "      <td>226463.0</td>\n",
              "      <td>32692.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35072</th>\n",
              "      <td>35073</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>229705.0</td>\n",
              "      <td>33186.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35073</th>\n",
              "      <td>35074</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2020-05-14</td>\n",
              "      <td>233151.0</td>\n",
              "      <td>33614.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>35074</th>\n",
              "      <td>35075</td>\n",
              "      <td>NaN</td>\n",
              "      <td>United Kingdom</td>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>236711.0</td>\n",
              "      <td>33998.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>197 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "          Id Province_State  ... ConfirmedCases Fatalities\n",
              "3557    3558            NaN  ...       146894.0    10017.0\n",
              "3558    3559            NaN  ...       156061.0    10656.0\n",
              "3559    3560            NaN  ...       162699.0    11123.0\n",
              "3560    3561            NaN  ...       169594.0    11653.0\n",
              "3561    3562            NaN  ...       178214.0    12461.0\n",
              "...      ...            ...  ...            ...        ...\n",
              "35070  35071            NaN  ...       223060.0    32065.0\n",
              "35071  35072            NaN  ...       226463.0    32692.0\n",
              "35072  35073            NaN  ...       229705.0    33186.0\n",
              "35073  35074            NaN  ...       233151.0    33614.0\n",
              "35074  35075            NaN  ...       236711.0    33998.0\n",
              "\n",
              "[197 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 375
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 320
        },
        "id": "HTMjhP30fSF3",
        "outputId": "2f802178-a4dd-4e7c-e0da-46400b291767"
      },
      "source": [
        "confirmed_value = train.ConfirmedCases.values.reshape(-1,1)\n",
        "dead_value = train.Fatalities.values.reshape(-1,1)\n",
        "\n",
        "regression = LinearRegression()\n",
        "regression.fit(confirmed_value,dead_value)\n",
        "\n",
        "print(regression.predict([[4000000]]).reshape(-1,1))\n",
        "\n",
        "print(regression.predict([[3511229]]).reshape(-1,1))\n",
        "plt.scatter(train.ConfirmedCases,train.Fatalities)\n",
        "\n",
        "x = np.arange(min(train.ConfirmedCases),max(train.ConfirmedCases)).reshape(-1,1)\n",
        "\n",
        "plt.plot(x,regression.predict(x),color=\"r\")\n",
        "\n",
        "plt.xlabel('Confirmed')\n",
        "plt.ylabel('Deaths')\n",
        "plt.show()"
      ],
      "execution_count": 376,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[343676.76555961]]\n",
            "[[301673.03748261]]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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e3rUb6nlk9RaaWrspG1WA3+cxXWMUo3xUQcoOnms31PPIC5refmOXjtfr4ZZLj2b+LON3NCv3d+2Gev7fyk2G40UFXm6/8njT8zhBvr8PPQ0N7Hj4jzS/UUthIMC0H95KzamnGMaJrNpr5fc5eM6qkqx1d83l/c2pICmlfglMAxZqrUMAWuv66N/t0XjP7dHddwKnxR0+CajPcMwyzc2dps3KklFTU562jzgfiL3ZJd7eRLdLx95+fJ7I2pDO7oHBDq9GXHb6tJSf/aFVdSkfXrEEhhmTKg3PY+X+mmXV+Txw9dnTc/J7yuf3Ibh3Ly3Pr/qqUd43vknVWWfjKSqiqbkr6TF27DX7fcYo9Hu5cO6UrNwHO/Z6vZ6MXuQhh4KklPoFMAs4L+qSQylVBfRorbujLruLgY3RQ14A7lFKTYvGg24ClmU4Jgg5Ze2Geh5aVUdze29SwQmGI9URfnPbqVx358spz5UqgcHMreNUtpVZVp3XJNliOBAOBtnz2jqan36KYEcHFSfPIfBN643yzLDjpnP7Qle75CrtewbwU2Ab8IZSCuAT4L+A+6Op3wXAG0RcdmitO5RS3wZWKaV8wLvAbZmMCUIuqa1r2MflYjT7aW7vpbauIeW5jBa/Wgl2O5l5ZbZmKhQe3q3Iu+o+oHHZ43GN8v6e4smTMzpnYmHU3v6QqRt3qKVzWyVXWXZ1gFGlwCNTHPc08LSTY4KQK1as227J5RKoKDJ90But4zHLpnMygcFs/QsM30Z7fV/sijTK2/R+pFHed2+m7Fh7jfKSYbcw6lBa5JoOUqlBEBzGjsslJhgPPGvcm6iowJP0AWQmEE6+RccenKkYjo32gp2dND/zFG1rX8FbVET1xZdSefqZeAsKMjpvOnXohuusKB4RJEFwEKulekLhfR8wqR5OAwNhausa9nkQmQmE0wskzVqdFxX4uPpsNWweluGBAdpe/gvNq54m1N3N6FPnE/jGN203yosRE6CW9l5GWXTLxTPUF7xaRQRJEBzEigstmctl0byp/OH5LUkfUsFw5LyxY8xKAjm9zsispYTZ2qihxFeN8h6n/8svGTXja5FGeQcelPY50+lXFM9wWPBqFREkQXCQTFxo4RQ537HzWikJ5GSMwSyrLlBRNGzEqLd+J7uXLqF76xYKxx/AhFt/SOnMIzOOE9ktEeXzQEmxf0jWossUESRBcBCj6gtmLpcV67aTyoMTSxawUhLISTH6/Srj2BYwLN7cB/a00bRyBe3rX8M7ahQ1V1xJ5anzbTfKiyedGBGMjDhRKkSQBCEDEtuOHzk1wOubGvYRDSOXi9WHVqHfy5FTA/zo3tdzVhLIykws01bn+SbWKK/5uVWEB/qpPOMsAudfgK+0NK3zpStCMPyz56wigiQIaZKs7fjrmxqYM3M8729vprm9l5qqEi6cO2W/B01tXYNhzCgerwfmzBy/n8gl28+pB5qVthUwdFtKhMNhOt/6G41PLmOguZnSo4+JNMobl/69s9tKZCS75VIhgiQIaZKq7XjMPWdUemXJS9tMxSj21pxuokQ6rN1QbzozgqHbUqL7449pXPoYPds/ijbKuz6jRnkxrMaJPMAYESBDRJAEIU0yaTtu1rYh5v7LVUmgGI+s3mI6E5t39ATe397MdXe+PGTe7vtbmml68gk6/lqLr6KCcVdfS8Vc4wKoVrDrogtUFPHQP589pGox5hoRJEFIk1QJDKkwKxP04E8W5LwkUIym1m7DsUK/dz/3YXN77+B6KDeKUqinJ9Io788vQCjEmHPPZ8y55+EtLrF1HiuxwlSMpNTtTBBBEoQ0WTRv6n6iYfbgMctci9Wsy2VJoHiqq0poTCJKsRiVkZsyfp2UGwiHQrTXvk7TiicJ7mmj/PgTqL4ovUZ5yWKFVtq2xxgqs0g3IIIkCBZJfEteNG/q4EM6flsmbcU9Ho9p1e9sPuCuPudw7l62cR/R8fs8FBV4U5Y3Srf7bTbYu03T+Phj9O78lOIphzDhuzdTcmj6vYLSaTUvIpQeIkiCYIFkb8kPr97K4nOmW3abWXmwWYktZbOEzPxZE2nv6BkU2bISP909A6bVBdxQVLVv926anlhK5zsb8FeNYfwN36b8hJMyihOBPbEdKSV+soUIkiBYIFNXlZVK2WbkKg4xe8b4wc/0o3tfNxXJfMdHgnv30vLcM7T95aV9GuV5i6yJZLKZb/zv1Gqr+Xzfh+GACJIgWCCTjDqzQqhmnWIh9y4gqxlk+XRNhYNBvlj9Ap/+8XGCXZ1UzJ5D9aKL8Fdab5RnNPOFr5I0jGKF8evNxEXnDCJIgmBA/EPZSDSsuKpSuepiD7Z1G3cZnj/XLqB8ZfjZoeuDTZFGebs+p+QwRc23Lqf44MmG+xvNgqzMfGN/W40VCukjgiQISUh8KCcTC6sumlSzjFgadSbnd4L49ggekxlbPl1TvbsijfL2fvA+BTU1TP/JjwhOPSJlAdRUsyCrM994N6aQPUSQBCEJRrOaZL2MzDCKQXg98NbW3YbXyVVts8QHdtikyGs+ZgfBjg6anlnJnnXRRnmXfIvKBWcQmDDGdKFpqllQumvJhOwggiQISTB6cw6FIwtX7ZAsBhE7l1HCQCicu4WmZs33YuTDTRdplPcSzaueiTTKm3cagW9ciL/ceqO8VLOgGxceYXstmZA9RJAEIQlOvjnHhMVKwdJMrpMOtXUNpll0kPuHdDgcpmvjOzQuX0b/7lijvMspOvBA2+dK9buU+JC7yIkgKaUCwKPAVKAP+BD4jta6USl1EnA/UALsAK7UWu+OHuf4mCAYER/4Li324fd59imAmulD2aoY5fLhv2LddsOxdNyTTtCz81Malz0eaZR3wAQOvO12Smcemfb5zCpqSHzIPeRqhhQG/ktrvRZAKXUXcKdS6kbgj8A1Wuv1Sqk7gDuB65RSXqfHcvRZhSFIsjbTPg+UlWTeIsAs7TuebD38a+saeOxFPbjAtazEz+VnHJYy4eL683PbmnygrY2mlU/S/vp6vKWljL3iSkZn2CgPJEtuKJETQdJatwBr4za9CXwXmAX0aK3XR7ffR2RGc12WxgQhKckC38EwFBX4+M1tpzp+7mRkK0ZTW9fAg6s279ORtrN7gD88v2VQcBPJZfO9UF8frS+uoeX5VYQHBqg64yzGZNAoLxkyCxoapF1TQyl1iFJqchrHeYmI0TPAJODT2JjWugnwKqXGZGlMEJKSycLXVKzdUJ/3Vf5G7dEHgmHC4TCF/n0fA4V+b06a74XDYdr/9iY77vgpzU89yagjZjD5335Ozbcud1SMhKGD5RmSUmoJcLfW+g2l1LXAvUBIKXWr1vr3Nq55N9AJ3AN805a1OSQQKEv72JqacgctyT5iL9QYVLmuqSpJ+3prN9Tzq8ffNd3P6/Vwy6VHM3/WxLSuY0ZLCkHc2xPk9iuO5ZHVW2hq7aa6qoSrzzk8a7bE6NDb+OT3D9GhNaVTpqB+eAuVR85M61xWfj9rN9Tn/DMaIf/fjLHjsjsdWBz99+3AGUAbsBKwJEhKqV8C04CFWuuQUmoncHDceDUQ0lq3ZGPMxmelubmTkNUodBxGHULditgb4cK5U5IGvi+cO8Xy9RKrAXR29xG08B0KhcLMmFTp2OdKtKPUwC0Hke6lMyZV8p/fmQ18dX+z9Z3ob26macVyOv76Jr7Roxl3zXVUnDyXfq83rWta+T4kxgcbW7u5e9lG2jt6cu7GG87/37xeT0Yv8mBPkAq11n1KqQOBMVrr1wGUUuOsHKyU+gWR+M55WuvYK9sGoEQpNTca87kJWJ7FMUHYh8TMusKC9JIYklUDsIqTKd7J7PB5wOPZf8Gr3+fJWTZfpFHec7SueQGAMectZMw559pulGeFREHu7Q8OiR5Ogj1B2qiU+imR2cdzAFFxajc7UCk1A/gpsA14QykF8InW+ptKqauA+5VSxURTtAGiMyhHxwQhnmSZdYV+LzcutJ9dlk7PHHA+dmSUnBFr/JeYZZftB3I4FKL9jddpeiraKO+EEyON8gLVWbmenRcDN/VwEiLYEaTrgf8F9AM/im6bDfzJ7ECtdR2QtNiU1voNIKnzOBtjghDDye6ndh5usdlKpqnkydKYjezo6gnarjCRKXv1VhqXLok0yjvkECZ87/uUTD00q9e082Ig5YHch2VB0lpvB65I2PYE8ITTRglCLnAys85qzxy/z8O15x6e0cwkVbFQN9Rm6/vyS5qeWEbnuxvwjxnD+BtvovyEE1MWQM0Eq60y4pHyQO7E1jokpdRZwNHAPpErrfXPnDRKELKFUy0lEjlyaoBX3t2VdMzpigepZnZmVQmySXBvFy2rnqX1Ly/i8fsJXLgo0iivsDBr11y7od60VQZEXJbFhX5ZGOty7KR93wNcCrwC7M2aRYKQJZxsKRF/TrO383QKsqYi1cwuH1UJwsEge15dS/PTKyON8ubMpfrCi/BXVjp+rURXZd9AyFSMYuuqRIDcj50Z0hXAUVrr+mwZIwjZxMmWEmCtkR044y6zM7PLZVWCrg/ejzbK20WJmh5plDfpYPMD0yCdTEaZDQ0t7AhSE5F1R4LgepIF/Z1sKQHWA+iZusuyMbPLlN5dn0cb5W2ioGYsE26+hdKjj81anAjsJyzks6OtkB4pBUkpdUjcj/8b+JNS6j+AL+P301p/nAXbBCEtjIL+RnXb7M5g7ATRz519cMYJDEZtK/JRjTvSKO8p9qxbi7eoiJpLL6NywRkZF0BNRuJLhdWkBUlYGLqYfYs+IlKpO/615/yEfcKAz0mjBCETjIL+BX4PhX5vRgF/q246rydSLfuC+dPSXpkfu5ZRsQenY1OpCA8M0PqXF2lZ9Qyh3l5Gz5tP9QXfxFeenbIydtxzkrAwfEgpSFrrtIuvCkK+SLUW58aFR2QU8LfqNnKi46vZtXKRyh0Oh+l89x2ali+lv3E3o742k5pLL6Nogv1GeXawep+LCnySsDCMsJNl9xut9a1Jtv9Ka/0DZ80ShPQx6xCaycPLqtsoE7Gw4hLMhVuqZ+enNC5dQrfeSuGECRz4g9sp/Vr6jfLskOqzx36/gYoirjl/BjMmOZ/NJ+QHO47fa4D9BAm4ChBBElyDU2txkmW2GWW4xZOJWFhxCXo9sPic6VmbFQy0tdH01JO0vxFtlPd3V0Ua5fmc8cwbVZmIJ9VLRXyywlArViqkxlSQlFKxxnb+uH/HOIRI9p0g5JXEh9ycmeN5f3tz2q45o8y2ZGLk93koKvDS1RPMOIZh5qoq9HuzJkahvj7qlz1B/RMrIo3yzvw6Y85fiG+Uc72JUlWZiP9M+VzgK+QPKzOkq6J/F8b9GyLJDF/yVUsKQcgLyR5yr29qyOjBbSYM2cpwM3NVZSNgHw6H6fjbmzQ9uZyBlhbKjplF9cWXUjjOUiF/W1itHyhtx0cmpoKktT4NQCn171rrO7JvkiDYw8kiqTHMYkVOZrhZXfSajXU13ds/onHpY/R8/DFFkw5m+u230Tc+OwtbwV79QGk7PvKwU1x1UIyUUh7iUsG11vbr7guCQ2Sj/XhpsW+wVUMynMpwy9ei1/7mZpqeXE7H32KN8q6n4uQ5jB43OqsxGTcUfxXci50suwnAb4FTgcS0FlmHJOSNbDzkUlUccFIgnC5nZEaop4eW1c/R+udoo7zzFzLm7PPwFhc7cn4zJDYkpMJOlt39RIqqng6sIyJM/wI877xZgmBMYgLDkVMDvL6pwdGHnFHLb8g8wy0fBVkjjfLWRxvl7aH8xJOoXnQJBYGAY9ewgsSGhFTYEaSTgUla6y6lVFhr/Z5S6nrgDeCB7JgnCPuS2G4glsCQaVYdWI/lONnLyAgnXVh7t26JNMqr30nxIVOZcPOtlBySvxmJxIYEI+wIUhCIvTa2KaVqiLQvz+6SbUGI45HVW5ImMLy/vTmjoH+uYjlWKhA45cLq+/JLGp9YSte77+AfE2D8t2+i/PjsNcoThEyxI0h/Bc4FngLWAEuBbuDtLNglCElpau1Ouj2TBAawVqbHiXbjZjjhwgru7aLl2WdoffklPP4CAt+8iKozv57VRnmC4AR2BOkqIFbb7gfA3wPlwK+cNkoQjKiuKqExiSil6+KyIjxxP94AACAASURBVBaZpFxb7WjqRFp3OBhkz7pXaHpmJaGuLirmnEL1NxfhHy2ldYShgZ2077a4f3cD/27nQkqpXwIXAZOBmVrrD6LbdwA90T8AP9Zar4mOnUQkmaIE2AFcqbXencmYMLS5+pzDuXvZRkcSGKzGczKZfSVzMSbihIuua1O0Ud4XuyiZfjg1l16WtUZ5gpAt7KR9FwE/Ay4HAlrr0Uqps4DDtNb3WDjFSuDXwGtJxi6OCVTc9bzAH4FrtNbrlVJ3AHcC16U7ZvWzCu4ifhZTU1XiSAIDwJKXtlmqKJ1JgoGRizH+3Jm46Ho//5zGZUvYW/cBBWPHMeHmWyk9+pi8xYms1KkTBCPsuOz+D5EEhr8DVke31UW3mwqS1no9gFLK6vVmAT2x44D7iMx2rstgTBhiJM5iGlu7eb2j15HU61Sp3THS6ZcU/0AuG1VAx97+/fbL1EU30NFO89Mr2bPuFbwlJdRcejmVC07PSqM8qyQr4fTAs5t54NnNIk6CJex8e78JHBpN+w4BaK0/V0o5kWX3p2j1h/XAP0bdg5OAT2M7aK2blFJepdSYdMe01i0O2CrkECfLAiWmdRuR7qLUZA9kn9eD3+dhIPhV2l4mLrpQfz9tL7802Civ8rQFBBZemLVGeXZIlRhiVERVEOKxI0h9iftHU7+bM7ThFK11fdQl+Csis60rMzxnxgQCZWkfW1OT/4eDHdxsb4tB/KalvdeW3Ws31PPIC5re/kg5oFQtJH54+bHMnzXRlp0AK9fX7vdADobClI8qoLjIT1NrN9VVJVx9zuG2zx8Oh2l586/sfOhRehoaqJp1LJOvXcyoiQfZttOMdL8PRr+rGH0DIVau/4QL5k9L6/xGuPn7mwyx1xg7grQceFgp9UMApdQBRATk8UwM0FrXR//uVUrdCzwTHdoJDEZllVLVQEhr3aKUSmvMjl3NzZ2EzBrfJGGo9Wdxu71jDMoCjakosmX3Q6vqBsUoFaXFPmZMqkzrniTL/gPo2NvPr289Zd99bZy/59MdkUZ52zSFEw7kwB/+A6UzvkYX0OXw7y6T74PR7yqextZuR79vbv/+JjKc7fV6PRm9yMNXadxW+EfgY2ATkVp2HwJfAP+a7sWVUqVKqdHRf3uAy4CN0eENQIlSam7055uIiGImY8IQoLaugR/d+zrX3fkyPX0D+H37+tfScXlZyZQr9Hu54kzLMc79MEp+SDcpYqCtlYYHf8fOf/9X+nbtYuyVV3PwP/8bpTO+lraN2WTRvKkU+lM/UqSIqpAKOzOkQwEN/IJIMdWVWutNVg9WSv0GWASMB15SSjUDC4EnlVK+6Dk3A9+DSAVxpdRVwP1KqWKi6duZjAnuJzEO09UTxOeBshI/nd0D1FSVcOHcKZbiEFZKAcVIt4mfWU29ogKfbfEM9fbS+ucXaHnh+UijvLO+zpjznG2Ul4p0M+US69QlIkVUBTM84XBqt1R05vJ7Io34PgN2Ecm2mwA8Clyntbbv23Ivk4FPxGWXH2799atJs99iWWlW7bW6xij+3HZIdv5Cv3e/lPRrzp/BjEnWFqaGQ6Foo7wnGGhtoezYWVRf/C0Kx461ZVsm1O1sS7rOK52sxlykgLvt+2vGcLY3zmU3hchEwDZWZkjfBuYDJ2mt34ptVEodDywBvkMktVoQMiJVKrbdxalWasZB+m/tRtl/iTX1rP6H7v7oQxqXLqHnk0ijvPE3fJtRarptuzLFqFZgOlmNUkRVsIvVFua3xosRgNb6LaXUD4CfIoIkOMCKddsNx+zGHsxKAWX61u5UU8D+5iaanlhGx1t/wze6knHXXk/F7Dl4vHbCu86RrVqBgmAFK4J0BJH+R8lYR8RtJwgZUVvXkPKhZ3cWk6ppXzoLUvdb8BqNaSU7vxVCPd20PB9tlOf1Mub8Cxhz9rlZaZRnx3XmdK1AQbCDFUHyaa2T+hy01h3RUj2CkBa1dQ0seWlbyqoJpcU+20kMpcU+xxakJl3w6iGt84dDIdrXv0bTyicJtrdTfuJsqi+6mIIx2WmUl8z2VAtUnawVKAh2sSJIBUqp0wCjte35q1UiDGmsJB5YTcU2y87LxD2XLF4UDENpgZfRpX7L7r+9WzbTuGwJvfX1FE89lAnf/wElhxxi255MbU8VE5o/ayLtHT1Sj07IC1bEZDfwoMm4INjGSuKB1ewuI9EoKvDxm9tOzchOI1diV0+Qu38wz/T4vi8baFy+lK6N7+IPBDjg29+l7PgTclIANZ1YlyQjCPnCVJC01pNzYIcwAjELlNtpF+5UkkEMq+3MUxHs6uLjp5/gi+dX4/EXUL3oYirPPAtvQe4a5aWKpQmC2xB3m5A3jB6WYC1u4YRoGJ03k3bm4YEB2ta9QvMzKwnt3UvF3FOovtD5RnlWkhUWzZuadL2UxIQENyKCJOQUKx1aS4t9XHGmSjk7ylQ0UmHkSjSrAh4Oh+na9B5Ny5bS1/AFJdMPR910A3vLnE9YsJqskFg9QWJCgpsRQRJyhlkSQ1mJn8vPOCztmBGk3zoiHiOxDIXhwZ8sSDrW+/lnNC5dwt7NdRSMG8eE799G6VFHUzq2gr1ZWJlvJ1lBYkLCUEEEScgZZkkMRQXW0rshPdEwIpM1RgPt7TQ/vYI9r66LNMr71uVUnpb9RnlOx8wEwQ2IIAk5w+xhaedh6lSwPt01RqH+ftpeepGW55+NNso7ncAFF+Iry6z8fqJtRq42SVYQhiMiSELWsDrziGH2MK2ta2Dl+loaW7sdW/hqd41ROBym8523aXpiGf2NjZQeeRQ1l3yLwgMm2LquGWYxIjckK+SieKowshBBErKC1ZlHDLOHabYWvtpZY9SzYweNy6KN8g48aLBRXjYwixHlO1nBbgUIQbCCCJKQFazMPOwkIDix8DXZG70V19dAWytNK56gvfYNfGVljL1qMaPnnorH57N03XTsshIjymeygt0KEIJgBREkIStkWt3A6vmsxp2M3ujnzBy/X1O92GxtsFHe6ucgFKLq6+cw5tzz8Y0aZdt+u3ZlWrw120hShZANRJCErJBq0euP7n3dsnspNntIdR0rpOpftPic6fvOUE6dwoz2j9lxR7RR3qzjqL74UgprnG+UZ2RXgd9Dod+blxiRldiQJFUI2UAESXAMs2rbMazGG8zWLdl5QKd6o493fXV/+CG7lz5Aw45PKDp4MuNv/A6jDjMv7moFO665rp4gNy48IucxorUb6i3FhtyQVCEMP0SQBEcwSzpIxEq8IdW6JbsPaLM3+v6mRhqfWE7n23/DV1nJuGtvoGL2yY41ykvHNZePGJHVjrH5TqoQhiciSIIjGCUd7O0xTvPOZF1SqiZ7yWYiRm/0F510II1PLqftxTWRRnkLvxFplFfkrOvJja65ZNjpGCsVIASnyYkgKaV+CVwETAZmaq0/iG4/DHgYCADNwNVa6w+zNSZkj1SVE4wwizekE6cwmoksPmf6PrGi6vICLgu0MvrR/6a1o53yk2ZTvegSCsaMSWmT0TXNZgpuc80ZIR1jhXySqxnSSuDXwGsJ2+8Dfqu1/qNS6krgfmBBFseELJEqiSEZyWYBiQ/2I6cG9suAKyrwpZw9pEpHvut7c5g9Yzx7t2xm99Il9L1bT8Gh05hwS/qN8h5ds5VX3t01+HNzey8PPLuZx17UXHGm4oL55UBqcc3HTMNIRKVjrJBPciJIWuv1AEp9FRxWSo0FjgXOjG5aAtyjlKoh0p3W0TGtdWN2Pp0AyYPcRiSbBSSb2by+qYE5M8fz/vbmwQfnNefPYMYk4zYOqZIX+hoaaFz+OF3vbcRfXc0B3/keZccdn3ajvNq6hn3EKJ6uniAPr95KRXkxMyZVuioJINWi1gvmT5OOsULeyGcMaSLwudY6CKC1DiqldkW3e7IwJoKUIalcU4lB7lT9iZLFf1KlZcfvX1NTTmOK6tnJZiLFwV5O76xjxz//CW+Bc43ylry0LeV430CIR1Zv4T+/M9tVSQCpZpEXzJ8msSEhb0hSgwGBQPpFMmtqyh20JPtYsXfthnoeeUHT2x8EIm/Vj7ygqSgvZv6siQBcML+cC+ZPG9z/nuXvDe4PEXfbNefPSHq9FoOZTUt77377p7L3mvNnDF7XGw5xzB7N3Nb3KQ71Me6sM5h0xWUUVmbeKO//PrExZV2+GE2t3YP2xt+ffJLqXsPw/P66CbHXmHwKUj1woFLKF53J+IAJ0e2eLIzZorm5k1CqiLwBZm/wbsOqvQ+tqttHXAB6+4M8tKouqQttxqRKrj5b7TcjmDGpMun1xhjEWMZUFO2zf8xeo9najEmVXP31w3j72Vc4buebBPrb6Zt0KJOvXUzRxIns6Qcy+P1E3F1b6Buw9t2orirJ6/ch2X1Kda+BYfn9dQvD2V6v15PRizzkUZC01ruVUhuBy4E/Rv9+NxbrycaYkD7plIpJ5fqxksBgFGNJFQM5dnQ/E9c8TmB7HQXjxlNz6XWUHnlU2nGiRFvtUOj3cvU5h6d93UxJp1ySIOSTXKV9/wZYBIwHXlJKNWutZwA3AQ8rpX4GtAJXxx2WjTHBgEfXbGXdxl2EwpGuq/OOnsBVX58+OG4lBdtqOwKrCQxGxyeLgfh7utj96EN82qzxloyi5rIrqJy/IONGebV1DTy4ajNJCk6kJGb//FkT8/ZGbKtckiQuCC4gV1l2twK3Jtm+FTjR4BjHx4Tk3H73a7R19Q/+HAozmD0WEyWzLDE77QisJjAYES+MvlCQ4/Zs4eSWTfjDA1SefgaBhd9wrFHew6u32BKjGxce4ZoHu9VySYLgFiSpYYTzvf98aR8ximfdxl2DgmSWJWanHUGmlaIDFUU07+lBdX3KaU3vUDnQyUejDuKdybO54/LzLJ0jnmQzO4DHXtSWY0UApx0zIS8PeaOZqRRAFYYaIkgjmEfXbKV+d5fheCgcqcwd/6AzmsHYEZlM3X+XqCI6Vz7NQd272V1YyZIJZ/BFxUEsjnMxWiXZzO7BVZvxeJMXhjXitGP2dXHmilQzUzetfRIEK4ggjVBSLeqMJyYcZhW6U4mM3QQGo4est2MPh330BpVr11ExqozXJs3ljYLJjBldwuI0YyBGNfis+umKCnxcfbZyZaO82MuDxIqEoYII0gjlkRe07WOSueBSZaAV+r0cOTVgO4Eh8SFbEOrn+N2bKf/9ozT5PFSdfS5jzlvI9JISrrf9KfYl3YZyZSV+Lj/jsLw/3M1mphIrEoYSIkgjkNq6hv3WFFkl/gGYql9RTGTSSWAYvEY4zIyOj5nX/C4Vwb1sKTuYK//7x3R4rXdsNcv8s1uDD/Ljnquta9hnYTJEZmdu7ywrCHYQQRqB/OH5Lab7WHnQGfUrii8P9MCzm5Oev7m9N2UwvuTLnZze9DYH9DbzRVGAZ8afQve4SdwwbhwdFtOozTL/ausa6Ozus3QuyN+sqLaugd+t2kw4wYvY2x+kt5/9GiFKnEgYqoggjTAeXbPVNFh/2jETOPSgStM071TuolibcqMZSGmxL3mcaE8LV3e8SdHnm2j3jeLZsXOoKz+EwgIfi20+ZI1mZ4+9qHnsRU1Xj/VZYmmxj9/cdqqt6ztBbV2DoajHKCrwMrrUL3EiYcgjgjSCsJLIUFTg45V3d/H+9mbDOE9s5pEKs6oAHo+HvoGvBKEw1Mfshk1U/GEL/gI/XSedwZPdB/FlV8jSQ9Zue3A7FPq9XHGmM23M7ZDY2sKIrp4gd/9gXg4sEoTsIoI0grCSyBBfPPX1TQ0sPme6pYWtyUhVFSD21u8Jhziy/SNObdlIabCHTeWHcMHPbqGgqoqjLX4uu+3B7eD1kPQeZBurYgQSLxKGDyJII4R0EhnsLmxNhlFVgBXrtlPW8AmnN73N2L426ovHsvyABfSPPZCLqqoMz7d2Qz0PrarbR9zstAe3Q6Hfm3UxSp4S/4XlBblej0fiRcKwQQRphJBOmjfYW9iajGRv730NX7C49TUKd22lzV/GU+PnoUsnmcaJEjPNYjMhI8Hp6glSVuK3JUgeD4TDyZsIOk2ymZ3VWVGM688/XOJFwrBBBGkE8Oiaraazo6ICX9J9kgnKonlTLRUcja1DilV7mDAqzCXe7ZRs+ivFBQV0zD2HFZ3j2d0ZNOwiGz97iGSV7WujmdhYddnlI4POquvTCDfVzRMEJxBBGuZYiUVYyaqDfQXCqJlDrFNsfEWGgf4BjtujmfPxexSF+umZeQJHXPt3+CsqOMbgPMlmD9kgn1lp6X6mfFeHEIRsIYI0jLFaHsgsqy52rniBMJochcLw4E8WAPCj365n0p5POa1pA4H+dj4pOYCXq48jVDCeuyoqktpr1gLdSfI9wzCalRqRr3p5gpArRJCGMUte2mZ531RZdWDdvRRz8fXW13Pm1lVM7m6guaCC5QcsYPuoAyNBmiQzg0TBy7YYlRb7si5GtXUNrFxfS2Nr934Cb8WNGo+IkTASEEEapjy6ZqvtlGejrDqw7l7qbmnj6R/fyfQWzXhvIS9WH8+7oxUhj3dwn5ho5XpGFE+21xWlqhLx0WdttpIXRIyEkYII0jDEzhqWRJIJT21dg+H+MSHxhYIcv2czs1s+wB8e4J2qwwmfeiab9B5CSeJSuZ4RxZOLvkVGqeiJ9ehSIbEiYaQhgjTMyESMIHlW3Yp12w33v/68w9n49IvMqv8blQOdfDjqIF6uPo7WwgoC9XsNW2X/6N7XM8owSwevB64/PzdxI6MZpVUxklmRMBIRQRpGZCpGibXqjNpKxBjf08SBqx4k8MmH7C6sYsmEM/l01AGD44nHNrf38sCzm01rs2UDJxe5mlUQh/SqiIPMioSRjQjSMMFqRp0RibXq/vD8FsMirOUDXcxrfpevdXxMf3kFr046hdqCgwnHxYkgsrYn1cLVXOFk+R+zCuIxFs2bmvIeJkNmRcJIxxWCpJTaAfRE/wD8WGu9Ril1EnA/UALsAK7UWu+OHpPW2HDFTkZdIvHtImLnSvYgLQj1c2JrHSe21eEhzIaaI5lyySJmFBXzdpI1TH39QcslcLKF0+V/UnVoTbxG2EZgTMRIEMBrvkvOuFhrfXT0zxqllBf4I3Cz1vow4FXgToB0x4Yrkb4+6RURLSrw7VcLbb9zhcN8rX073/50JXNb3+ej0oN4YNKFvDj6aB56eQcAc2aOxxtdLev1QPXooryLUVmJ3/FadGYdWmvrGvjRva/zwLPmlSxiiBgJQgRXzJAMmAX0aK3XR3++j8hs57oMxoYlmcyOevuD/G7VZh5evYW+gfB+SQ0HdX852ChvV1GAlePn8XnJ2MHxvoEQS17aRl9/aDBTLhSGXc3daduUDqXFPjweD53dA1mtvmAUGwpUFNmO4bmlDboguAU3CdKflFIeYD3wj8Ak4NPYoNa6SSnlVUqNSXdMa92Sqw+TKzKZHcUIhxmczcQetqP7Ozit6R2md31Ku38Uz46bS13ZlMjC1gQyvX66ZDNrzihxYdG8qUlLLI2tKpG1RYKQIW4RpFO01vVKqSLgV8A9wFP5NCgQKEv72JqacgctMWbthnp+/5x5O3I7FAX7mN26iePathDyeHltzFH8tXIGQa9/sBK2Gygq8PH9S45i/qyJjp977Yb6/aqKP/KCpqK8mAvmT6OivJhHVm+hqbWbslEF9PUH2fJpm6Vzl48q4NsXzsyK3U6Rq++vU4i92SWX9rpCkLTW9dG/e5VS9wLPAL8GDo7to5SqBkJa6xal1M50xuzY1NzcSSiN1Zo1NeU0NnbYPs4usWyvdGxMhicc4qj2Dzml5b1oo7yprAscQ6d/1Fc75VGMfB4oKfbT1T3AmOiMZcakyqzc64dW1e23Xqi3P8hDq+qYMamSGZMquXDuFB57UdOxt9/SORPdiLn4jqRDrr6/TiH2Zhc79nq9noxe5MEFgqSUKgX8Wus9UZfdZcBGYANQopSaG40H3QQsjx6W7tiwoLaugd+v2uxYdYPJe3exIK5R3rIDTufL4oAzJ3eA+Ie5U/+hU60lSpW4EGulYYd8F3EVhKFC3gUJGAc8qZTyAT5gM/A9rXVIKXUVcL9Sqpho+jZAumPDgdgaISfEaEzfHhY0vc2hez/fp1FesjhRPshWx1aztUSpFrXaFaOjDg2IGAmCRfIuSFrrjyF5Wxyt9RvATCfHhjpGa4TsUBzs4ZSW9zhmzzb6vH5eDhzLxsrD6fP4HLIyc5xczJqI2VqiZIkL6bKreW/G5xCEkULeBUmwRyYZbd5wkFl7NHNa3qcw1M/GimmsH3MUB089gPLW7qw1wUuHUJiMxChdlxxErvvRZ22s27gr45loU2tu098FYSgjgjREqK1r4MHn0qwBFw4zraue05o3MKa/g4+jjfKaiqoALGeI5ZJkRV6tkolL7ro7X077usmoripx9HyCMJwRQRoC3PFAbdoLTcf2trCg6W0mdzfQVDCaZQcs4ONYozyXktg63S5WXHJ268ylQ6Hfy9XnHJ7VawjCcEIEyeXcfvdrtHVZSy2Op3Sgm1Na3uWo9o/o8Rby5+oT2Dj6sH0a5bkRJ6oXpHLJ3fKrdQwEQ1kXo9jnmD9r4pBK8xWEfCKC5FJq6xp4cJX1emgxfKEgx7dtZnbrJvzhIG+NPpzXxxxJry99F1gusFPux6z9Q1mJ3zDW1tVjvW14Okg5IEFIHxEkF1Jb12C/Z1A4zPTOHZzW/A6jB7rYVjqRVwKzaC2syI6RDlHo97D4nMMtP8DXbqjfLz70h+e38NiLOutikwwRIEFwDhEkl5GOGB3Q08TpTW9xUE8jXxZW8fyEk/dplOdm+gbCSfsJGfHI6i37xYcGgmEGgrkRIxEgQcgeIkguwm616PL+LuY1v8PXOj+h01fM82Nns6l86n6N8txOLOEASOqKs9K9Nttka5GukH2sdPgV3IEn7JZqme5hMvBJrmvZ3bXkHcvp1wWhfk5qreOEaKO8v1UewZtVM+nzFti+rpso9Hvz3l02nlgx2UweYsO5dlm2sSIkdTvbeGhVneE+iUsAIL8vF266v1ZIs5bdFCIVcmwjMyQXYDmtOxxmZsd2Tm1+l/JgN5vLJrMucCx7CjIraOgW3CJG2WxrMRKwIiRm+1hpFV9b17BfVfbEfex0+BXyjwhSnrlryTuWxGhi95ec3vQW43tb2FVUvV+jPMEZRqJrLl4caqpKuHDuFNsCEr+fFSEx28eKkKxYt32/quyJ+zhVk1DIDSJIeeTRNVtN3XSV/R2c1rQB1bWTdv8onhk3l80GjfIE++Sq02w+sDpTiReHxtbutAQkhlUhMdvHipBY2SdVh1/BfYgg5QmzBIaiYB8nt77PrLathDxeXh1zNH+rPIIBr/zK0iHWT8ntwmNnJpKpywucE5AYuRQSK/sYdfjNpBKIkD3k6ZYHUomRJxzi6PYPOaV5IyWhXjaVT+XVxEZ5gimlxT6KC/05y6zKVdzE6n5WRcQpAYmRSyFZNG/qPjGkZPvE3w/JsnM/Ikg5JpUYTen6nAXNG6jpa2Nn8Tj+Un2cqxrlDRUK/V6uOFMBXz2IYmnliQ/2TEUkto+RQFwwv9x0H7si4pTLC5wTkBhWhcRsHytCMnvGeCrKi1Nm2cX2EwEaGogg5RAjMQr0tbGgaQNT935Oq7+MFePnsc1FjfKyjc/rIRQKO9IhvbTYNyhGqQTAqeA7pBaIC+ZPM93Hrog4GTtxSkBiWBUSs31i+5kJyfxZE5kxqTLlPsLQQQQpRyRL7S4J9jB3n0Z5s9hQOZ2gixrl5YKgU73Ygf6ByLnMBMDJ2EmuA/BOxk4SxSFZlp1dt5cVIZFZi5AMEaQsk2xW5A0HmdWmmdP6VaO81wJH0+0rzpOVucPrwZH265C8iGpMMMwEwMnYSa4D8E65vOL3jW03WggpAiLkAhGkLPCdu16mP1lptcRGeaMm8HLgOJqKRo7LwcHJkGFF79gDOJUAOBk7yWXcxO5+IiLCUEIEyWEW/v3TSbeP7W3h9Ka3OXiwUd7pfFx6YI6tGz7ERMFIMMwEwMnYSa7jJnb2E4ShxLAVJKXUYcDDQABoBq7WWn+YzWsma39dOrCXU5s3cmTHR3R7i4ZMozw3Ey8KRoJhJgBOikhsX4mbCEJmDFtBAu4Dfqu1/qNS6krgfmBBti6WKEb+0EC0Ud4H+MIh3qo8gterZrq+UV4+8XnA4/Xs08210O9lzszxvL+92TDonkp0UgmAiIgguIthKUhKqbHAscCZ0U1LgHuUUjVa60anr7ePGIXDHN65g/lDrFFeLonVi4P9xSTZtnhBSAy6i2AIwvBhWAoSMBH4XGsdBNBaB5VSu6LbHRekGGN7Wzir8a+DjfKemzCHnaOG58PS54Vg1FMW37Qu2UJSMHedxSMCIwgjk+EqSBkT7ethi7kt71HZ3zlkG+UBnDv7YABe+OtOQqEwXq+HA6tH8XnT3sGfzz5xEt+9+Oikx18wv3xwMei+2/ffli41NeWOnSsXiL3ZRezNLrm0d7gKUj1woFLKF50d+YAJ0e2WSKdB39PjTgUPQ2phqzdaJSFx5nKxSfHJfDUZG84NztyA2JtdhrO9cQ360mZYCpLWerdSaiNwOfDH6N/vZiN+FE/Q604hSpUdNtT+gwiCMHwZloIU5SbgYaXUz4BW4OpsXejBnyxImvKdbeJjN4IgCEOdYStIWuutwIm5ut6DP4lklMuMQxAEIT2GXtRdEARBGJaIIAmCIAiuQARJEARBcAUiSIIgCIIrGLZJDRngg0hOfbpkcmw+EHuzi9ibXcTe7GLV3rj90l7/4gmHHWxQMzyYC7yWbyMEQRCGKKcA69M5UARpf4qA44EvgGRt9gRBEIT98QEHAG8BydstmyCCJAiCILgCSWoQBEEQXIEIkiAIguAKRJAEQRAEVyCCJAiCILgCESRBEATBFYggCYIgCK5ABEkQBEFwBVI6yCGUzc9FKgAABydJREFUUocBDwMBoBm4Wmv9YQ6uuwPoif4B+LHWeo1S6iTgfqAE2AFcqbXeHT3G8bEU9v0SuAiYDMzUWn8Q3W54v3I9ZtHepPc5W/fT6r1WSgWAR4GpQB/wIfAdrXVjru2yYrOJvWFgExCK7n6V1npT9LiFwF1EnlkbgGu11nuzNZZg80pgStSuTuAWrfVGF3+HjezdgQu/w/HIDMk57gN+q7U+DPgtkV9ErrhYa3109M8apZSXSOv2m6P2vArcCZCNMRNWAqcCnyZsT3W/cj1mxV5IuM+Qnftp816Hgf/SWiut9UxgO3Bnru2yYXNSe+PGT467xzExKgMeABZqrQ8FOoB/yNZYEhZrrY/SWh8D/BJ4MLrdrd9hI3vBnd/hQUSQHEApNRY4FlgS3bQEOFYpVZMnk2YBPVrrWD2p+4BLszhmiNZ6vda6Pn5bqvuV6zEr9pqQ13uttW7RWq+N2/QmcHAe7LJkcwp7U3EO8HbcbOA+4FtZHEu0eU/cj6OBkMu/w/vZm+xzxeGa54UIkjNMBD7XWgcBon/vim7PBX9SSr2vlLpXKVUJTCLuDV9r3QR4lVJjsjRml1T3K9djdki8z+Ciex19K/0u8Ewe7LJtc4K9MdYqpTYqpf5DKVUU3bbPuYGdfPW7y8ZYMlt/p5TaCfwcWIzLv8NJ7I3h6u+wCNLQ5xSt9VFECsJ6gHvybM9wZSjc57uJxAzcaFsyEu2dpLU+jojL9Ajgn/JlWCJa6xu01pOAfyQSd3I1Bva6/jssguQM9cCBSikfQPTvCdHtWSXmXtJa9wL3AnOIvO0NukGUUtVASGvdkqUxu6S6X7kes4TBfQaX3GsVScaYBnxLax3Kg122bE5ib/w9bgd+h8E9JvL2XZ/FMUO01o8CpwGfMQS+wzF7lVIBt3+HQQTJEXQkc2QjcHl00+XAu1rrxmxeVylVqpQaHf23B7gsascGoEQpNTe6603A8ui/szFmi1T3K9djVuxNcZ/BBfdaKfULIj77C6MPm3zYZdnmZPYqpaqUUiXRf/uBi/nqHr8AHK+UmhZ37mVZHIu3tUwpNTHu54VAC+DK73AKe3vc/B2OIe0nHEIpNZ1ISmYV0EokJVNn+ZqHAE8S6UPiAzYDt2qtv1BKnUwkC6eYr1Iuv4we5/hYCht/AywCxgNNQLPWekaq+5XrMTN7gYVG9zlb99PqvVZKzQA+ALYB3dHNn2itv5lru6zYbGQv8F/RY8NAAfAG8AOtdWf0uG9E9/EB7wLXaK27sjUWZ+844GmglEh/tBbgH7TW77jxO2xkL9CGS7/D8YggCYIgCK5AXHaCIAiCKxBBEgRBEFyBCJIgCILgCkSQBEEQBFcggiQIgiC4Aqn2LQguI7oeZxmRigV/JlLwdbHW+qw82bMDuEFr/VI+ri+MHESQBCEDlFJXALcD04lUjN4I/Fx/VVQyHS4GxgEBrfVAdNufMjJUEIYA4rIThDRRSt0O/Ar4BREBmUSkJMs3Mjz1wcC2ODFKZYO8VArDBlkYKwhpEC3D8jmRpm77lURRkUrV/8lXJfeXEWmI1quUmk+kV8z/AX5MZEX9P2qt/6CU+lfgp0SKX/YCt0XHb9Baz42eOwx8H/gBES/HtdHz/YbIqvwgkSrafUQEsxr4pdb6F9HjvcD/AG4EKoG/ADfF6owppa4C/h0oA/47up+47ISsIzMkQUiP2URKojxlMP4/gZOAo4GjgBOAO+LGxxPpVXMgcD3wW6VUldb6n4nMuJZqrcu01r83OP+FwIlEqmLHzlccPd/PiDSfu5JIzbhTgH9SSk2J7ntL9Ph5RAp0thJp+IZS6gjg/wJXRccCwEHmt0MQMkcESRDSIwA0pXCr/R3wb1rr3dECmP9K5CEfoz863q+1fp5IGwZl4/r/oSPN7mL14PqJxK76gceJzIp+rbXu0FrXEalddlR035uA/6m1/ixa3PRfgIvjipqu0lq/Gh37J8wbvAmCI4ggCUJ6NAPVKWI4E9i3Adyn0W2DxyeI2V4iLjKrJLYdaNbR5m18VbQ0vpBld9z5DwaeUkq1KaXagC1E3HzjSGhpEC022mzDLkFIGwmICkJ61BKJ8VwIPJFkfBeRB39d9OdJ0W1OkUnwtx64Tmv9euKAUuoL4PC4n0cRmQ0KQtYRQRKENNBa71FK/YxI7GeAyHqhfuAMIg3clgB3KKXeIiIePyOSeOAG7gN+rpRarLX+VClVA5ystX6aiLj+NdrH5m/AvyGeFCFHyBdNENJEa/2/iaxBugNoJDLz+D6Rhaz/DrwNvA9sAt6JbnMDvwaeAf6slOoA3iSSIEE03nQz8BjwBZGEh8/yZKcwwpC0b0EQBMEVyAxJEARBcAUiSIIgCIIrEEESBEEQXIEIkiAIguAKRJAEQRAEVyCCJAiCILgCESRBEATBFYggCYIgCK5ABEkQBEFwBf8fLN1t0Y/UuDIAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lM03s2Y9keQw",
        "outputId": "450d185c-c0da-4786-daa6-72be7228fc99"
      },
      "source": [
        "print(r2_score(dead_value,regression.predict(confirmed_value)))\n",
        "# 0.80 very good accuracy i think"
      ],
      "execution_count": 377,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7924415719935288\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "VBBPjAgIkiWp",
        "outputId": "ff7a5cce-6c86-46fa-aaff-e6557d822603"
      },
      "source": [
        "train.head()"
      ],
      "execution_count": 378,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>1</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>3</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5</td>\n",
              "      <td>NaN</td>\n",
              "      <td>Afghanistan</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   Id Province_State Country_Region        Date  ConfirmedCases  Fatalities\n",
              "0   1            NaN    Afghanistan  2020-01-22             0.0         0.0\n",
              "1   2            NaN    Afghanistan  2020-01-23             0.0         0.0\n",
              "2   3            NaN    Afghanistan  2020-01-24             0.0         0.0\n",
              "3   4            NaN    Afghanistan  2020-01-25             0.0         0.0\n",
              "4   5            NaN    Afghanistan  2020-01-26             0.0         0.0"
            ]
          },
          "metadata": {},
          "execution_count": 378
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1FESRP9yknJi",
        "outputId": "517e4ec9-b0da-44fe-9f87-688fc6481efd"
      },
      "source": [
        "train.columns"
      ],
      "execution_count": 379,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['Id', 'Province_State', 'Country_Region', 'Date', 'ConfirmedCases',\n",
              "       'Fatalities'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 379
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FPUGizzako-Z"
      },
      "source": [
        "Deaths_data = train.iloc[:,5].values.reshape(-1,1)\n",
        "\n",
        "Confirmed_data = train.iloc[:,4].values.reshape(-1,1)"
      ],
      "execution_count": 380,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RJAgN-Axkooo"
      },
      "source": [
        "Poly_regression = PolynomialFeatures(degree = 4)\n",
        "\n",
        "Deaths_data_poly = Poly_regression.fit_transform(Deaths_data)"
      ],
      "execution_count": 381,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3JzKO2VdkslZ",
        "outputId": "0dc6a785-476d-4073-a357-329922a1f72f"
      },
      "source": [
        "regression2 = LinearRegression() \n",
        "regression2.fit(Deaths_data_poly,Confirmed_data)"
      ],
      "execution_count": 382,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LinearRegression()"
            ]
          },
          "metadata": {},
          "execution_count": 382
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "6pyrk0wOkuUf",
        "outputId": "e5a1cf54-a5d5-4ddf-c053-00642194d29c"
      },
      "source": [
        "plt.scatter(Deaths_data,Confirmed_data,color='red')\n",
        "plt.plot(Deaths_data,regression2.predict(Deaths_data_poly))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 383,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 268
        },
        "id": "-QcfcKKLk6-i",
        "outputId": "793ba4ce-c85f-4207-e15a-4c10ded71be3"
      },
      "source": [
        "plt.plot(Deaths_data,regression2.predict(Deaths_data_poly))\n",
        "\n",
        "plt.show()"
      ],
      "execution_count": 384,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4DDlRZG2qslQ"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "from plotly.offline import iplot, init_notebook_mode\n",
        "import plotly.express as px\n",
        "import bokeh \n",
        "from bokeh.io import output_notebook\n",
        "from bokeh.resources import INLINE\n",
        "import tensorflow as tf\n",
        "import sklearn\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "output_notebook(resources=INLINE)\n",
        "%matplotlib inline"
      ],
      "execution_count": 385,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WjMGqwQ5tJwJ"
      },
      "source": [
        "#!pip install MinMaxScaler"
      ],
      "execution_count": 386,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "49rsEskQqunZ"
      },
      "source": [
        "train = pd.DataFrame(pd.read_csv('train.csv'))"
      ],
      "execution_count": 387,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "JZr0HI-Xq1Oy",
        "outputId": "f8fd7081-eb55-40aa-9931-af459e88d5fd"
      },
      "source": [
        "print(\"The shape of training data is = {}\".format(train.shape))"
      ],
      "execution_count": 388,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "The shape of training data is = (35995, 6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UEpXqWQuq30x"
      },
      "source": [
        "train.Province_State.fillna(\"\", inplace = True)\n",
        "train.ConfirmedCases.fillna(\"\", inplace = True)\n",
        "train.Fatalities.fillna(\"\", inplace = True)"
      ],
      "execution_count": 389,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cxL8Uz9Kq5yo",
        "outputId": "373200a1-1bbd-40a4-c78b-0400903d75ec"
      },
      "source": [
        "train[\"Country_Region\"].unique()"
      ],
      "execution_count": 390,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Afghanistan', 'Albania', 'Algeria', 'Andorra', 'Angola',\n",
              "       'Antigua and Barbuda', 'Argentina', 'Armenia', 'Australia',\n",
              "       'Austria', 'Azerbaijan', 'Bahamas', 'Bahrain', 'Bangladesh',\n",
              "       'Barbados', 'Belarus', 'Belgium', 'Belize', 'Benin', 'Bhutan',\n",
              "       'Bolivia', 'Bosnia and Herzegovina', 'Botswana', 'Brazil',\n",
              "       'Brunei', 'Bulgaria', 'Burkina Faso', 'Burma', 'Burundi',\n",
              "       'Cabo Verde', 'Cambodia', 'Cameroon', 'Canada',\n",
              "       'Central African Republic', 'Chad', 'Chile', 'China', 'Colombia',\n",
              "       'Congo (Brazzaville)', 'Congo (Kinshasa)', 'Costa Rica',\n",
              "       \"Cote d'Ivoire\", 'Croatia', 'Cuba', 'Cyprus', 'Czechia', 'Denmark',\n",
              "       'Diamond Princess', 'Djibouti', 'Dominica', 'Dominican Republic',\n",
              "       'Ecuador', 'Egypt', 'El Salvador', 'Equatorial Guinea', 'Eritrea',\n",
              "       'Estonia', 'Eswatini', 'Ethiopia', 'Fiji', 'Finland', 'France',\n",
              "       'Gabon', 'Gambia', 'Georgia', 'Germany', 'Ghana', 'Greece',\n",
              "       'Grenada', 'Guatemala', 'Guinea', 'Guinea-Bissau', 'Guyana',\n",
              "       'Haiti', 'Holy See', 'Honduras', 'Hungary', 'Iceland', 'India',\n",
              "       'Indonesia', 'Iran', 'Iraq', 'Ireland', 'Israel', 'Italy',\n",
              "       'Jamaica', 'Japan', 'Jordan', 'Kazakhstan', 'Kenya',\n",
              "       'Korea, South', 'Kosovo', 'Kuwait', 'Kyrgyzstan', 'Laos', 'Latvia',\n",
              "       'Lebanon', 'Liberia', 'Libya', 'Liechtenstein', 'Lithuania',\n",
              "       'Luxembourg', 'MS Zaandam', 'Madagascar', 'Malawi', 'Malaysia',\n",
              "       'Maldives', 'Mali', 'Malta', 'Mauritania', 'Mauritius', 'Mexico',\n",
              "       'Moldova', 'Monaco', 'Mongolia', 'Montenegro', 'Morocco',\n",
              "       'Mozambique', 'Namibia', 'Nepal', 'Netherlands', 'New Zealand',\n",
              "       'Nicaragua', 'Niger', 'Nigeria', 'North Macedonia', 'Norway',\n",
              "       'Oman', 'Pakistan', 'Panama', 'Papua New Guinea', 'Paraguay',\n",
              "       'Peru', 'Philippines', 'Poland', 'Portugal', 'Qatar', 'Romania',\n",
              "       'Russia', 'Rwanda', 'Saint Kitts and Nevis', 'Saint Lucia',\n",
              "       'Saint Vincent and the Grenadines', 'San Marino',\n",
              "       'Sao Tome and Principe', 'Saudi Arabia', 'Senegal', 'Serbia',\n",
              "       'Seychelles', 'Sierra Leone', 'Singapore', 'Slovakia', 'Slovenia',\n",
              "       'Somalia', 'South Africa', 'South Sudan', 'Spain', 'Sri Lanka',\n",
              "       'Sudan', 'Suriname', 'Sweden', 'Switzerland', 'Syria', 'Taiwan*',\n",
              "       'Tanzania', 'Thailand', 'Timor-Leste', 'Togo',\n",
              "       'Trinidad and Tobago', 'Tunisia', 'Turkey', 'US', 'Uganda',\n",
              "       'Ukraine', 'United Arab Emirates', 'United Kingdom', 'Uruguay',\n",
              "       'Uzbekistan', 'Venezuela', 'Vietnam', 'West Bank and Gaza',\n",
              "       'Western Sahara', 'Zambia', 'Zimbabwe'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 390
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bn-NBB9cq82L"
      },
      "source": [
        "confirmed_cases_brazil = train[train[\"Country_Region\"] == \"Brazil\"].groupby([\"Date\"]).ConfirmedCases.sum()\n",
        "fatal_cases_brazil = train[train[\"Country_Region\"] == \"Brazil\"].groupby([\"Date\"]).Fatalities.sum()"
      ],
      "execution_count": 391,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "P3RqPhJ6r0LJ",
        "outputId": "6901de92-2d38-46de-f476-f2d7c8dc33b2"
      },
      "source": [
        "train_brazil = train[train[\"Country_Region\"] == \"Brazil\"]\n",
        "train_brazil"
      ],
      "execution_count": 392,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Id</th>\n",
              "      <th>Province_State</th>\n",
              "      <th>Country_Region</th>\n",
              "      <th>Date</th>\n",
              "      <th>ConfirmedCases</th>\n",
              "      <th>Fatalities</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3450</th>\n",
              "      <td>3451</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-22</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3451</th>\n",
              "      <td>3452</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-23</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3452</th>\n",
              "      <td>3453</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-24</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3453</th>\n",
              "      <td>3454</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-25</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3454</th>\n",
              "      <td>3455</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-01-26</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</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",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3560</th>\n",
              "      <td>3561</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-11</td>\n",
              "      <td>169594.0</td>\n",
              "      <td>11653.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3561</th>\n",
              "      <td>3562</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-12</td>\n",
              "      <td>178214.0</td>\n",
              "      <td>12461.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3562</th>\n",
              "      <td>3563</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-13</td>\n",
              "      <td>190137.0</td>\n",
              "      <td>13240.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3563</th>\n",
              "      <td>3564</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-14</td>\n",
              "      <td>203165.0</td>\n",
              "      <td>13999.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3564</th>\n",
              "      <td>3565</td>\n",
              "      <td></td>\n",
              "      <td>Brazil</td>\n",
              "      <td>2020-05-15</td>\n",
              "      <td>220291.0</td>\n",
              "      <td>14962.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 6 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "        Id Province_State  ... ConfirmedCases Fatalities\n",
              "3450  3451                 ...            0.0        0.0\n",
              "3451  3452                 ...            0.0        0.0\n",
              "3452  3453                 ...            0.0        0.0\n",
              "3453  3454                 ...            0.0        0.0\n",
              "3454  3455                 ...            0.0        0.0\n",
              "...    ...            ...  ...            ...        ...\n",
              "3560  3561                 ...       169594.0    11653.0\n",
              "3561  3562                 ...       178214.0    12461.0\n",
              "3562  3563                 ...       190137.0    13240.0\n",
              "3563  3564                 ...       203165.0    13999.0\n",
              "3564  3565                 ...       220291.0    14962.0\n",
              "\n",
              "[115 rows x 6 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 392
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2ewbM62_ruMA"
      },
      "source": [
        "train_brazil[\"ConfirmedCases\"] = train_brazil[\"ConfirmedCases\"].astype(int)\n",
        "train_brazil[\"Fatalities\"] = train_brazil[\"Fatalities\"].astype(int)"
      ],
      "execution_count": 393,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Of9mgEJSrvjm",
        "outputId": "49f0d9fd-a8e7-4248-f183-d06da4c8f860"
      },
      "source": [
        "train_brazil_size = int(len(train_brazil) * 0.75)\n",
        "val_brazil_size = len(train_brazil) - train_brazil_size\n",
        "print(\"Training size = {}\".format(train_brazil_size))\n",
        "print(\"Validation size = {}\".format(val_brazil_size))"
      ],
      "execution_count": 394,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training size = 86\n",
            "Validation size = 29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "K37HPRSDrh4B"
      },
      "source": [
        "train_brazil_confirmed_cases = train_brazil[[\"ConfirmedCases\"]]\n",
        "train_brazil_fatal_cases = train_brazil[[\"Fatalities\"]]"
      ],
      "execution_count": 395,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AR4tHvVB0D0s",
        "outputId": "db105010-a02a-4259-dae7-5522993cce80"
      },
      "source": [
        "print(train_brazil_confirmed_cases, train_brazil_fatal_cases)"
      ],
      "execution_count": 396,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "      ConfirmedCases\n",
            "3450               0\n",
            "3451               0\n",
            "3452               0\n",
            "3453               0\n",
            "3454               0\n",
            "...              ...\n",
            "3560          169594\n",
            "3561          178214\n",
            "3562          190137\n",
            "3563          203165\n",
            "3564          220291\n",
            "\n",
            "[115 rows x 1 columns]       Fatalities\n",
            "3450           0\n",
            "3451           0\n",
            "3452           0\n",
            "3453           0\n",
            "3454           0\n",
            "...          ...\n",
            "3560       11653\n",
            "3561       12461\n",
            "3562       13240\n",
            "3563       13999\n",
            "3564       14962\n",
            "\n",
            "[115 rows x 1 columns]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 502
        },
        "id": "wWmVaIlczU7y",
        "outputId": "c88cd663-cf46-4f0c-a674-37aa905a30d1"
      },
      "source": [
        "plt.figure(figsize = (8, 8))\n",
        "x = np.arange(1, 116, 1)\n",
        "y1 = train_brazil_confirmed_cases \n",
        "y2 = train_brazil_fatal_cases \n",
        "plt.plot(x, y1, color = \"m\", label = \"Casos Confirmados no Brasil de Janeiro a Maio (2020)\")\n",
        "plt.plot(x, y2, color = \"r\", label = \"Casos Fatais no Brasil de Janeiro a Maio (2020)\")\n",
        "plt.grid(True)\n",
        "plt.legend()"
      ],
      "execution_count": 421,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efea40e8550>"
            ]
          },
          "metadata": {},
          "execution_count": 421
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ZXhBl4d2ztda",
        "outputId": "a7a17c19-345a-458f-f393-94f2de5ef8c3"
      },
      "source": [
        "len(train_brazil_confirmed_cases)"
      ],
      "execution_count": 398,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "115"
            ]
          },
          "metadata": {},
          "execution_count": 398
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 424
        },
        "id": "YwQMFuKnzvpb",
        "outputId": "d7a62835-675f-475c-98fa-86411c677e30"
      },
      "source": [
        "train_brazil_confirmed_cases"
      ],
      "execution_count": 399,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>ConfirmedCases</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>3450</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3451</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3452</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3453</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3454</th>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3560</th>\n",
              "      <td>169594</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3561</th>\n",
              "      <td>178214</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3562</th>\n",
              "      <td>190137</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3563</th>\n",
              "      <td>203165</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3564</th>\n",
              "      <td>220291</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>115 rows × 1 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "      ConfirmedCases\n",
              "3450               0\n",
              "3451               0\n",
              "3452               0\n",
              "3453               0\n",
              "3454               0\n",
              "...              ...\n",
              "3560          169594\n",
              "3561          178214\n",
              "3562          190137\n",
              "3563          203165\n",
              "3564          220291\n",
              "\n",
              "[115 rows x 1 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 399
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xdVndkPAsjjC"
      },
      "source": [
        "train_brazil_confirmed_cases_data = train_brazil_confirmed_cases.iloc[0:train_brazil_size]\n",
        "val_brazil_confirmed_cases_data = train_brazil_confirmed_cases.iloc[train_brazil_size : len(train_brazil_confirmed_cases)]"
      ],
      "execution_count": 400,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j7lQ98xIsup5",
        "outputId": "64e9c849-6c5a-4fda-eac2-af09ba4c0c87"
      },
      "source": [
        "print(len(train_brazil_confirmed_cases_data))\n",
        "print(len(val_brazil_confirmed_cases_data))"
      ],
      "execution_count": 401,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "86\n",
            "29\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Drt7kV-mtEzL"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range = (0,1))\n",
        "def createDataset(train) : \n",
        "    train_scaled = scaler.fit_transform(train)\n",
        "    x_train = [] \n",
        "    y_train = []\n",
        "    time_step = 2\n",
        "    for i in range(time_step, train_scaled.shape[0]):\n",
        "        x_train.append(train_scaled[i-time_step : i , 0])\n",
        "        y_train.append(train_scaled[i, 0])\n",
        "    \n",
        "    x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1 ))\n",
        "    y_train = np.reshape(y_train, (y_train.shape[0], 1 ))\n",
        "    return x_train, y_train     "
      ],
      "execution_count": 402,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LwR6PESes1tS"
      },
      "source": [
        "def createDataset(train) : \n",
        "    train_scaled = scaler.fit_transform(train)\n",
        "    x_train = [] \n",
        "    y_train = []\n",
        "    time_step = 2\n",
        "    for i in range(time_step, train_scaled.shape[0]):\n",
        "        x_train.append(train_scaled[i-time_step : i , 0])\n",
        "        y_train.append(train_scaled[i, 0])\n",
        "    \n",
        "    x_train, y_train = np.array(x_train), np.array(y_train)\n",
        "    x_train = np.reshape(x_train, (x_train.shape[0], x_train.shape[1], 1 ))\n",
        "    y_train = np.reshape(y_train, (y_train.shape[0], 1 ))\n",
        "    return x_train, y_train   "
      ],
      "execution_count": 403,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rvrDrCJRs4sZ"
      },
      "source": [
        "x_train, y_train = createDataset(train_brazil_confirmed_cases_data)\n",
        "x_val, y_val = createDataset(val_brazil_confirmed_cases_data)"
      ],
      "execution_count": 404,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dtw5ySo1thPZ",
        "outputId": "475fdf3c-18df-445d-c766-5ef2648d7b5c"
      },
      "source": [
        "print(x_train.shape, y_train.shape)\n",
        "print(x_val.shape, y_val.shape)"
      ],
      "execution_count": 405,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(84, 2, 1) (84, 1)\n",
            "(27, 2, 1) (27, 1)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uqWGqOKnrTZI"
      },
      "source": [
        "model = tf.keras.Sequential()\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True, input_shape = (x_train.shape[1], 1)))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = True))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.LSTM(units = 50, return_sequences = False))\n",
        "model.add(tf.keras.layers.Dropout(0.2))\n",
        "model.add(tf.keras.layers.Dense(units = 1))"
      ],
      "execution_count": 406,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HnqtBKzFtkVo"
      },
      "source": [
        "model.compile(tf.keras.optimizers.Adam(lr = 0.001), loss = \"mean_squared_error\")"
      ],
      "execution_count": 407,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "20cJDKv5tmOO",
        "outputId": "ec28b2b5-aa40-4e29-9840-bfc33613f247"
      },
      "source": [
        "model.summary()"
      ],
      "execution_count": 408,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_6\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_18 (LSTM)              (None, 2, 50)             10400     \n",
            "                                                                 \n",
            " dropout_18 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_19 (LSTM)              (None, 2, 50)             20200     \n",
            "                                                                 \n",
            " dropout_19 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_20 (LSTM)              (None, 2, 50)             20200     \n",
            "                                                                 \n",
            " dropout_20 (Dropout)        (None, 2, 50)             0         \n",
            "                                                                 \n",
            " lstm_21 (LSTM)              (None, 50)                20200     \n",
            "                                                                 \n",
            " dropout_21 (Dropout)        (None, 50)                0         \n",
            "                                                                 \n",
            " dense_9 (Dense)             (None, 1)                 51        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 71,051\n",
            "Trainable params: 71,051\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dNshwY0ItvD5"
      },
      "source": [
        "EPOCHS = 200\n",
        "BATCH_SIZE = 1"
      ],
      "execution_count": 409,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5_ZDolDutwb6",
        "outputId": "9dc436e1-7ac5-498a-efc9-2686f2659ca8"
      },
      "source": [
        "with tf.device(\"/device:GPU:0\"):\n",
        "  history = model.fit(x_train, y_train,epochs = EPOCHS, verbose = 1,\n",
        "                     batch_size = BATCH_SIZE, validation_data = (x_val, y_val))"
      ],
      "execution_count": 410,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/200\n",
            "84/84 [==============================] - 8s 29ms/step - loss: 0.0624 - val_loss: 0.1352\n",
            "Epoch 2/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0400 - val_loss: 0.0029\n",
            "Epoch 3/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0083 - val_loss: 0.0032\n",
            "Epoch 4/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0036 - val_loss: 0.0109\n",
            "Epoch 5/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0023 - val_loss: 0.0038\n",
            "Epoch 6/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0028 - val_loss: 0.0046\n",
            "Epoch 7/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0030 - val_loss: 0.0058\n",
            "Epoch 8/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0032 - val_loss: 0.0117\n",
            "Epoch 9/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0035 - val_loss: 0.0134\n",
            "Epoch 10/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0034 - val_loss: 0.0052\n",
            "Epoch 11/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 0.0024\n",
            "Epoch 12/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0020 - val_loss: 3.6693e-04\n",
            "Epoch 13/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0028 - val_loss: 4.9622e-04\n",
            "Epoch 14/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0017 - val_loss: 4.4469e-04\n",
            "Epoch 15/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - val_loss: 0.0049\n",
            "Epoch 16/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0035 - val_loss: 0.0023\n",
            "Epoch 17/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - val_loss: 4.7146e-04\n",
            "Epoch 18/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0034 - val_loss: 0.0045\n",
            "Epoch 19/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 9.9605e-04\n",
            "Epoch 20/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - val_loss: 0.0011\n",
            "Epoch 21/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0026 - val_loss: 0.0088\n",
            "Epoch 22/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0011 - val_loss: 7.1024e-04\n",
            "Epoch 23/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0012\n",
            "Epoch 24/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0023 - val_loss: 5.5024e-04\n",
            "Epoch 25/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0038 - val_loss: 0.0053\n",
            "Epoch 26/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0017 - val_loss: 0.0029\n",
            "Epoch 27/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0011 - val_loss: 0.0014\n",
            "Epoch 28/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0033 - val_loss: 0.0128\n",
            "Epoch 29/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0033 - val_loss: 8.8346e-04\n",
            "Epoch 30/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0026 - val_loss: 0.0093\n",
            "Epoch 31/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 8.7376e-04 - val_loss: 7.9483e-04\n",
            "Epoch 32/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0020 - val_loss: 9.5079e-04\n",
            "Epoch 33/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 7.1503e-04 - val_loss: 0.0043\n",
            "Epoch 34/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0024 - val_loss: 0.0062\n",
            "Epoch 35/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0051 - val_loss: 0.0280\n",
            "Epoch 36/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0020 - val_loss: 0.0019\n",
            "Epoch 37/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - val_loss: 7.6280e-04\n",
            "Epoch 38/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 0.0046\n",
            "Epoch 39/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0019 - val_loss: 8.7976e-04\n",
            "Epoch 40/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0024 - val_loss: 9.1936e-04\n",
            "Epoch 41/200\n",
            "84/84 [==============================] - 1s 10ms/step - loss: 0.0015 - val_loss: 7.3536e-04\n",
            "Epoch 42/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 8.4673e-04 - val_loss: 9.9548e-04\n",
            "Epoch 43/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0024 - val_loss: 5.3012e-04\n",
            "Epoch 44/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0012 - val_loss: 0.0012\n",
            "Epoch 45/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0025 - val_loss: 0.0031\n",
            "Epoch 46/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - val_loss: 0.0040\n",
            "Epoch 47/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0056 - val_loss: 0.0021\n",
            "Epoch 48/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0028 - val_loss: 0.0018\n",
            "Epoch 49/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0019 - val_loss: 0.0015\n",
            "Epoch 50/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0037 - val_loss: 0.0122\n",
            "Epoch 51/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0046 - val_loss: 8.1760e-04\n",
            "Epoch 52/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - val_loss: 0.0033\n",
            "Epoch 53/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0024 - val_loss: 5.7923e-04\n",
            "Epoch 54/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0034 - val_loss: 0.0113\n",
            "Epoch 55/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0045 - val_loss: 0.0020\n",
            "Epoch 56/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0035\n",
            "Epoch 57/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 7.2150e-04 - val_loss: 0.0012\n",
            "Epoch 58/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0013 - val_loss: 6.1380e-04\n",
            "Epoch 59/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0012 - val_loss: 0.0026\n",
            "Epoch 60/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0030 - val_loss: 0.0063\n",
            "Epoch 61/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0025 - val_loss: 0.0055\n",
            "Epoch 62/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0022\n",
            "Epoch 63/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - val_loss: 0.0016\n",
            "Epoch 64/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0024 - val_loss: 0.0024\n",
            "Epoch 65/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 9.3133e-04 - val_loss: 0.0017\n",
            "Epoch 66/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0026 - val_loss: 0.0012\n",
            "Epoch 67/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 9.2240e-04 - val_loss: 7.0472e-04\n",
            "Epoch 68/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0025 - val_loss: 0.0040\n",
            "Epoch 69/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 8.3755e-04\n",
            "Epoch 70/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 8.0663e-04 - val_loss: 6.5848e-04\n",
            "Epoch 71/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0045\n",
            "Epoch 72/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0031 - val_loss: 0.0014\n",
            "Epoch 73/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - val_loss: 0.0032\n",
            "Epoch 74/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0029 - val_loss: 0.0056\n",
            "Epoch 75/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 0.0013\n",
            "Epoch 76/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0019\n",
            "Epoch 77/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0027 - val_loss: 0.0030\n",
            "Epoch 78/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0015\n",
            "Epoch 79/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0018\n",
            "Epoch 80/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0021 - val_loss: 0.0023\n",
            "Epoch 81/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - val_loss: 5.1710e-04\n",
            "Epoch 82/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0013\n",
            "Epoch 83/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0015 - val_loss: 0.0027\n",
            "Epoch 84/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0014 - val_loss: 0.0035\n",
            "Epoch 85/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0012 - val_loss: 0.0019\n",
            "Epoch 86/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0025\n",
            "Epoch 87/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 0.0021\n",
            "Epoch 88/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 9.5803e-04 - val_loss: 6.5632e-04\n",
            "Epoch 89/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0044\n",
            "Epoch 90/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0037 - val_loss: 0.0030\n",
            "Epoch 91/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 8.3966e-04\n",
            "Epoch 92/200\n",
            "84/84 [==============================] - 1s 10ms/step - loss: 0.0014 - val_loss: 8.2608e-04\n",
            "Epoch 93/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0012\n",
            "Epoch 94/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0037 - val_loss: 0.0075\n",
            "Epoch 95/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0035 - val_loss: 0.0016\n",
            "Epoch 96/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0019 - val_loss: 0.0032\n",
            "Epoch 97/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0014 - val_loss: 7.5972e-04\n",
            "Epoch 98/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0013 - val_loss: 0.0010\n",
            "Epoch 99/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0041 - val_loss: 0.0062\n",
            "Epoch 100/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0027 - val_loss: 0.0055\n",
            "Epoch 101/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0024 - val_loss: 0.0013\n",
            "Epoch 102/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0018 - val_loss: 6.8214e-04\n",
            "Epoch 103/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - val_loss: 0.0027\n",
            "Epoch 104/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0026 - val_loss: 0.0049\n",
            "Epoch 105/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0040 - val_loss: 0.0185\n",
            "Epoch 106/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0032 - val_loss: 0.0152\n",
            "Epoch 107/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0021 - val_loss: 0.0015\n",
            "Epoch 108/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 7.3870e-04 - val_loss: 0.0019\n",
            "Epoch 109/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0015\n",
            "Epoch 110/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0011 - val_loss: 0.0037\n",
            "Epoch 111/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 8.9791e-04 - val_loss: 0.0011\n",
            "Epoch 112/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0023 - val_loss: 9.0888e-04\n",
            "Epoch 113/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0018 - val_loss: 0.0013\n",
            "Epoch 114/200\n",
            "84/84 [==============================] - 1s 11ms/step - loss: 0.0011 - val_loss: 0.0014\n",
            "Epoch 115/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0012 - val_loss: 9.9949e-04\n",
            "Epoch 116/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 8.7340e-04\n",
            "Epoch 117/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0016\n",
            "Epoch 118/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0030 - val_loss: 0.0066\n",
            "Epoch 119/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0024 - val_loss: 0.0019\n",
            "Epoch 120/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0022 - val_loss: 0.0030\n",
            "Epoch 121/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - val_loss: 0.0014\n",
            "Epoch 122/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - val_loss: 8.3373e-04\n",
            "Epoch 123/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0010 - val_loss: 0.0012\n",
            "Epoch 124/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0014 - val_loss: 6.6744e-04\n",
            "Epoch 125/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 7.1739e-04 - val_loss: 0.0013\n",
            "Epoch 126/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - val_loss: 0.0012\n",
            "Epoch 127/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 7.7932e-04 - val_loss: 0.0036\n",
            "Epoch 128/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0017 - val_loss: 0.0010\n",
            "Epoch 129/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0012 - val_loss: 0.0063\n",
            "Epoch 130/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0017 - val_loss: 0.0053\n",
            "Epoch 131/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 9.0821e-04 - val_loss: 6.4939e-04\n",
            "Epoch 132/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0027 - val_loss: 0.0017\n",
            "Epoch 133/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - val_loss: 0.0011\n",
            "Epoch 134/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0012 - val_loss: 0.0017\n",
            "Epoch 135/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 9.3423e-04 - val_loss: 0.0046\n",
            "Epoch 136/200\n",
            "84/84 [==============================] - 1s 10ms/step - loss: 0.0024 - val_loss: 0.0017\n",
            "Epoch 137/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0023 - val_loss: 0.0014\n",
            "Epoch 138/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0016\n",
            "Epoch 139/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0010 - val_loss: 0.0022\n",
            "Epoch 140/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0019 - val_loss: 8.9705e-04\n",
            "Epoch 141/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - val_loss: 6.6447e-04\n",
            "Epoch 142/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 5.8834e-04\n",
            "Epoch 143/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - val_loss: 0.0013\n",
            "Epoch 144/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0016 - val_loss: 0.0014\n",
            "Epoch 145/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 7.4739e-04 - val_loss: 0.0012\n",
            "Epoch 146/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0017 - val_loss: 0.0067\n",
            "Epoch 147/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0010 - val_loss: 8.7895e-04\n",
            "Epoch 148/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0012 - val_loss: 0.0013\n",
            "Epoch 149/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0025 - val_loss: 0.0022\n",
            "Epoch 150/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0026 - val_loss: 0.0066\n",
            "Epoch 151/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0029 - val_loss: 8.9362e-04\n",
            "Epoch 152/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0023\n",
            "Epoch 153/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - val_loss: 0.0034\n",
            "Epoch 154/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0021 - val_loss: 5.3041e-04\n",
            "Epoch 155/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0048 - val_loss: 0.0020\n",
            "Epoch 156/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0012 - val_loss: 0.0024\n",
            "Epoch 157/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0040 - val_loss: 0.0043\n",
            "Epoch 158/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0023 - val_loss: 0.0048\n",
            "Epoch 159/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0012\n",
            "Epoch 160/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0016 - val_loss: 0.0019\n",
            "Epoch 161/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0013 - val_loss: 0.0014\n",
            "Epoch 162/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0012\n",
            "Epoch 163/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0025 - val_loss: 0.0023\n",
            "Epoch 164/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0018 - val_loss: 6.6931e-04\n",
            "Epoch 165/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0017 - val_loss: 6.3021e-04\n",
            "Epoch 166/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0038\n",
            "Epoch 167/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0010 - val_loss: 7.9096e-04\n",
            "Epoch 168/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - val_loss: 0.0057\n",
            "Epoch 169/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0020 - val_loss: 0.0018\n",
            "Epoch 170/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0013 - val_loss: 0.0021\n",
            "Epoch 171/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0028 - val_loss: 3.9455e-04\n",
            "Epoch 172/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0016 - val_loss: 0.0015\n",
            "Epoch 173/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0010 - val_loss: 0.0027\n",
            "Epoch 174/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - val_loss: 0.0015\n",
            "Epoch 175/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0015\n",
            "Epoch 176/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0011 - val_loss: 0.0013\n",
            "Epoch 177/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0021 - val_loss: 0.0025\n",
            "Epoch 178/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0034 - val_loss: 0.0018\n",
            "Epoch 179/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0020 - val_loss: 0.0045\n",
            "Epoch 180/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0028 - val_loss: 0.0018\n",
            "Epoch 181/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0015\n",
            "Epoch 182/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0013 - val_loss: 0.0010\n",
            "Epoch 183/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0015 - val_loss: 0.0011\n",
            "Epoch 184/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0010 - val_loss: 0.0043\n",
            "Epoch 185/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0022 - val_loss: 9.4074e-04\n",
            "Epoch 186/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0021 - val_loss: 0.0013\n",
            "Epoch 187/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 7.0235e-04 - val_loss: 0.0013\n",
            "Epoch 188/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0019 - val_loss: 0.0028\n",
            "Epoch 189/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0018 - val_loss: 0.0023\n",
            "Epoch 190/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0018 - val_loss: 0.0012\n",
            "Epoch 191/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0068\n",
            "Epoch 192/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 8.8442e-04 - val_loss: 7.6887e-04\n",
            "Epoch 193/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0010 - val_loss: 0.0010\n",
            "Epoch 194/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0026 - val_loss: 0.0012\n",
            "Epoch 195/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0014 - val_loss: 8.9196e-04\n",
            "Epoch 196/200\n",
            "84/84 [==============================] - 1s 9ms/step - loss: 0.0016 - val_loss: 8.6864e-04\n",
            "Epoch 197/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0022 - val_loss: 5.9112e-04\n",
            "Epoch 198/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 8.1874e-04 - val_loss: 0.0020\n",
            "Epoch 199/200\n",
            "84/84 [==============================] - 1s 7ms/step - loss: 0.0051 - val_loss: 0.0012\n",
            "Epoch 200/200\n",
            "84/84 [==============================] - 1s 8ms/step - loss: 0.0014 - val_loss: 0.0016\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 737
        },
        "id": "hdBBOaZnuNSp",
        "outputId": "3f812b26-63f7-4150-b91a-327be6049338"
      },
      "source": [
        "x = np.arange(0, EPOCHS, 1)                                                                  \n",
        "plt.figure(1, figsize = (22, 12))                                  \n",
        "plt.subplot(121)\n",
        "plt.xlabel(\"Epochs\")\n",
        "plt.ylabel(\"Loss\")\n",
        "plt.plot(x, history.history[\"loss\"], label = \"Treinamento do Loss\")\n",
        "plt.plot(x, history.history[\"val_loss\"], label = \"Validação Loss\")\n",
        "plt.grid(True)\n",
        "plt.legend()  "
      ],
      "execution_count": 418,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7efe9e376590>"
            ]
          },
          "metadata": {},
          "execution_count": 418
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1584x864 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 410
        },
        "id": "DznK2mxPunrC",
        "outputId": "aeeb456c-9ec9-4490-9428-2ee8212f6798"
      },
      "source": [
        "predicted_cases = model.predict(x_val)\n",
        "predicted_cases = scaler.inverse_transform(predicted_cases)\n",
        "real_cases = scaler.inverse_transform(y_val)\n",
        "plt.figure(figsize= (10, 6))\n",
        "plt.subplot(1,1,1)\n",
        "plt.plot(real_cases, color = \"green\", label = \"Número Real de Casos\")\n",
        "plt.plot(predicted_cases, color = \"blue\", label = \"Número de Predição de Casos (Validation set)\")\n",
        "plt.title(\"Casos de Covid-19\")\n",
        "plt.xlabel(\"Tempo\")\n",
        "plt.ylabel(\"Contagem de Casos\")\n",
        "plt.legend()\n",
        "plt.grid(\"both\")\n",
        "plt.show()"
      ],
      "execution_count": 417,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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naFa8uUO9dIlJqaUv8ucvSJUq1Vi1asUDx00m0wPL2ERFPbiAtYeHR9x58d8bjUZiYqIBW0L02mtVGDZs9CP3vXjxAt7ePk8dd+wzgCEhwXzySUfWr19D48YfYrVC374DqVDh1UeuGTlyCD16fMYbb9TAYrFQq1b1Rz5XUlit0KDBe7Rv3+Wx5xUsWJioqCgCAy+RP/+j666tWrWCe/fuMm/eYjw9PZkwYSxRUZEA9OrVl3Pn/uHo0cMMGzaQZs1aUKtWnUTvlTdvPpYvX82RI4c5cOBX5s2bzZIl3+Lp6fnUn1MIIUTquhVxk04725DZI7NLVxBJtWVghGu0b9+Zdeu+IywsLO5Y3rwvcubMKQCOHDkU12uXFJUqVebgwf2cP38u7tjp06cSPDd//gKYzWaOHTsCwNGjh4mOjk4wYYove/Yc9O7djyVLFhAZGUH16m+watUKIiMjANsQ8cWLFwAIDQ3lhRfyALB16yaHkr/y5V/lxx93ER4eTkxMDNu2/W9zmmrVXmfbti3cuGF7/jAmJoYzZ04/UoePjw9NmzZn4sSx3Lp1E8A+CeRnrl69wr1798iePQeenp4EBd1g375f4q4NDLxIkSJFadr0Y+rUqcfp03/h4+NL0aLF+P77LYAtmT537m9KlizNjRvXMRpNvPFGDXr16svt27eeqldSCCGEa1itVvr/8hlB4TeYXWueS2ORHsAMLleu3NStW59vv/3fM4mdOnVl7NiRrFmzmgoVKpI79/NJrvfFF/MzfPgYxo8fQ2RkJNHRZkqXfiXBiR3u7u6MHTvxgUkg/v4THvv8X6yqVatToEAhNm5cR8uWbVm4cC4dO7bGaDQCBtq370TBgoXo1asPgwf3I1OmTLz2WlWyZMnyxLqrVXudkyf/oG3bj+MmgQQFBQFQtmx5unbtzsCBfYiJsRAdbeatt2pRvHiJR+rp0qU7q1atoGdPW2+h1WqlTJlylCtXgQ8//Ihhwz6nVaum5MyZ+4Hey4CAWVy5EojJ5Iafnx+DBg0HYMQIf778chyrV3+DyWRi6NDRZM2alf37f+Wrr2wb6FgsMbRs2ZYcOXI+8XMKIYRIG777+1s2nVvP0MojKZOzrEtjMVgTm20gElIQuPDwTiD//XeJ559PuW2XZPeDtEHawfWkDZ5OSv8/Kb3tfpARSRukDclph8C7l6ixqiqlcpRm/XtbMRkdXx7tacXbCaQQcPGBMqffXQghhBDiGRZjiaH7btukyVk156ZK8vckMgQshBBCCOFEs45P4+C1/cyqOZf8mVOudz45pAdQCCGEEMJJfr9xnAmHx/JekcZ8WOwjV4cTRxJAIYQQQggnCDOH0W1XR3J652Lim1NcuuzLw2QIWAghhBDCCUbtH8o/t8+ypuEmsnplc3U4D5AeQCGEEEKIFLbr0g6+PrmArq/04I18NVwdziMkAXzGrV27ilu3brk6DCGEECLDCA4PpveP3SmRrSSDXxvu6nASJAlgBtSkSQNatWqKxWJ54Nj58/88cN7GjesIDg4ma9asqR2iw8aOHcnatauSdM21a//y5puv0bZtc9q0+YguXdpx9qxO0VgWLPiK3bt3PvZ8i8XC0KGf061be4YNG5jgTiJJtXDhXGbNmpbk68LC7jN16kSaNWtE27bNad++JUuXLkp2PMkxZEh/Tp8+xe7dO+P2XI5v4cK5jBkzItHrjx07QocOrQAIDg6KW4g7IdWrV3xgN5yEXLv2Lxs3rnvgWL9+veL2v3aV1au/idtlBmD9+jUubzshROKsVit9fu7JncjbBNRegJebl6tDSpAkgBlUeHg4O3ZsS7TcYrFgMBjo3PkTp8cSExPj9Hs8zM/Pj8WLv2HJkm+pWbMOX3zx6J7F0dHRT11/x45dqVkz8X17wbZ3sr//BAICFjFmzPgEdxFJDVarlX79emO1Wlm+/DsWL/6GgICF+Pg8/X7NyXXq1EnCw8MpUaIkr79egytXLnPp0sUHYt6+fSsNGrznUH05cuRk5sy5yYrp2rV/2bRp/QPHJk2aQd68+ZJVb3KtXr3ygQSwQYNGbNmykfv3Q10YlRAiMStOL2X7ha0MrTKSl7M/ujtWWiGTQDKo9u07s2jRfGrVqvvIlmtNmjRg4sSpNGz4/gPvCxcuSpMmDahTpx5Hjx4mKOgGXbv25Pbtm/zww3bu3r3LoEHDKVu2PAD79+9j6dJFREZG4e7uTs+efShVqjTHjh1h+vRJKFWCv//WdOrUjWzZsj2wFdynn/ZLcNu4oKAb+PuPICQkmOeff8G+5ZvN/fuhzJw5lXPnzhIVFUW5chXp2fMzTKbHL6j56quvMW/e7LjPWrNmHY4dO0zhwkXp128Q8+bN4cSJo0RFmSlatCh9+w7Cx8eHGzduMGrUsARjGTt2JMWLl+CDD5oRFRXF3LmzOHToAEajiRdfzI+//wSOHDnE/PkBREVFEhMTQ+vW7alVqy4AV65c5ssvx3H79i1MJhOdO3encuWqj8QeGhrK+PGjOX/+HNmyZSd37txkzZodALPZnGjs8R05coj//rvGjBlf4eZm+5X39PSkSZOP4soTi3PRonns2rUDDw9PDAaYMWMumTJl4sCB35g7dxYWi4XnnstK//6DyZfvRQIDLzJ27CgiIiKwWGKoV68BzZu3euRzbdq0jtq1/w8ADw8Pateuy9atm/jkk16ArXfPZHKjbNnyDBs2mMDAS5jNUeTN+yKDBg0nc+bMD9R37dq/dOzYiq1bdwPwyy8/MnfubDw8PKlR4+0Hzh01amiC9U2ZMpFr167Stm1z8uXLh7//xAd+Nx7XZtWrV6Rz50/Ys+dn7ty5Q/fuvahRo+Yjn3vv3p+ZPz8Ao9FETEw0n302gPLlKxIcHMy0aRO5fv0/IiMjqVWrLq1bt2fJkoUEBwcxdOjneHh4MmKEP4UKFaZSpcrs3v1D3O+wECJtOH/7H4bu+5zX89Wgcxnnd7AkhySATrBqlRsrVz55n9vEGAwGEtui7+OPzTRr9uSeq+LFS6BUcdavX0PTph8n6f5ms5m5c7/m9OlT9OzZhW7dejF//lJ27/6BuXNnExCwkKtXr7B48UKmTJmJr68f58+fo1+/XqxbtxWACxfO07//YEqVKoPZbKZZs0YMHjyCihUrcfjwQYYMGcCqVRseSU6nTfuSV14pR/v2nbl69Qpt2zbntdeqADBz5lTKli3PwIHDsFgsjBo1lK1bNz3xL8GfftrFSy+puPf3799n/vylACxevABfX9+493PmzGDZsq/p0qU7U6ZMTDSW+JYt+5pr166xaNEK3N3duX37NgDFihVnzpwFmEwmbt4MoUOHVlSqVIXMmTMzatRQ3nvvfd59txEXLpynR49OLF++5pHh+K+/no+Pjy/ffLOW27dv0759C95+uzYAK1YsSTT2+P7++wxKFY9L/h6WWJxgZfXqb9i4cTuenl6Ehd3Hw8OTW7du4u8/nJkz51GoUGG2bNnAqFFDmT9/CevWraF69Tdo1aodAHfv3k3wnsePH6V589Zx7995pyH9+39Kly7dMZlMbNu2iXfeaQBA7979eO655wCYN28OK1YsoVu3ngnWC3DzZggTJozlq68Wkj9/QVasWPJAeWL19ekzgNmzp7Nw4bIE631Sm/n6+rJgwVL++OMEw4cPSjABXLBgLgMGDKFUqTLExMQQEREOgL//cNq27UjZsuUxm8307t2NEiVepk2bDmzevAF//wkULlw0rp6SJUtz4MCvkgAKkYaYY8x8sqsTHiYPZr4dgNGQtgdZJQHMwDp37kbPnl15913HhtFi1axpSzCKFStORERE3FBn8eIl4p6HOnhwP1evXqF7985x18XExHDzZggA+fK9SKlSZQAIDLyEu7s7FStWAmw9cu7u7gQGXqJIkf/9pQZw7NhRPv20PwB58+ajYsVX48r27dvD6dOn+PbbFQBERESQK1fuBD9DaGgobds2x2q1kidPXoYMGRlX9n//907c619/3cP9+/f5+ecfATCboyha9CV7LEfo3btfgrHE99tv++jZ87O4ZDY2ubh9+xZffDGaK1cCMZncuHv3DoGBlyhcuDD//PM39es3BKBQocIULao4depPqld/44G6jx8/Evd9PPfcc7z55v96sx4Xe1IkFmeJEi+TN++LjBkzgkqVKlO16uv4+Phy6tRJihQpRqFChQGoX78hkydPICzsPmXLlmPOnBlERERQvnxFypevmOA9g4JukDXr/5ZEKFasONmyZePgwf288kpZ9u7dQ7dutt7A7du3sHPndqKjzYSHR/Dii/kf+3n++uskxYop8ucvCEDDho0JCJgZV57U+sD2DOWT2qxmTVuvacmSpQkODiIyMhJPT88H6qlQoSIzZkyhRo23qVy5KoULFyU8PJzjx4/G/cMh9n4XL17k1VcrJxhP9uw5uHHjxhPjFkKknqlHv+TYjaPMr7OYPH55XR3OE0kC6ATNmkU71EuXGDc3I9HRlief+AT58xekSpVqrFq14oHjJpMJi+V/PYxRUVEPlHt4eMSdF/+90WgkJsb2uaxWK6+9VoVhwx59tu7ixQt4ezvj+TIr48ZNcuiZrNhnABPi4+P9vxqt0LfvQCpUSDi5c0RiC3tOnjyeatXeYNy4LzEYDHz0UWOioiKf+j4PczT2YsWKs27dd0RHRyfYC5hYnCaTiblzv+bPP3+3T7hoyeTJMxO4w//UqFGTUqXKcOjQAZYvX8zWrZsYPnzMI+d5eno+8ufunXcasm3bJoKDg3jllbLkyJGTEyeOsWHDWgICFpE1a1Z27tzOpk3rHqnPUb//fjxF64vv4d+bhJ597dWrL+fO/cPRo4cZNmwgzZq1oFatOhgMBhYsWJpoL+3DoqIeTS6FEK5z+L+DTD36JR8W+4j3ijZ2dTgOSdv9kyLZ2rfvzLp13z0wAzJv3hc5c+YUYHv+K7bXLikqVarMwYP7OX/+XNyx06dPJXhu/vwFMJvNHDt2BICjRw8THR1N/vyP7odYoUJFtm7dBMC//17lyJHDcWXVqr3B8uVL4v5ivX37Nv/+ezXJscdXvfobrFq1gsjICCC25+WCPZZXE40lvqpVq7N69UrMZnNcXAD37t3jhRdewGAwcPjwAa5evQyAj48vRYsW4/vvtwC2hPncub8pWbL0I3WXL/8q27ZtBuDOndvs2fOTQ7HHV7FiJXLlysWsWdPiYoyKioqb0ZxYnGFh97l9+zblylWgQ4cuFC5chPPnz1GyZGnOnfs7btLG999v4aWXFD4+vly5cpls2bJTv34D2rXrxF9/JfxnonDhogQGXnzgWJ069Th06CBr167inXcaxsXm6+tHlixZiIqKimuPxylZsjRnz2ouXw4EYPPmDXFlj6vP19cv0YkVSWmzxwkMvEiRIkVp2vRj6tSpx+nTf+Hj48srr5Rj+fLFceddv/4fISHB9rh8CQ19MK6LFy9QtGixJN1bCOEcoVH36L6rM3n88vLF61+6OhyHSQ9gBpcrV27q1q3Pt98ujzvWqVNXxo4dyZo1q6lQoSK5cz+f5HpffDE/w4ePYfz4MURGRhIdbaZ06VcSnNjh7u7O2LETH5gE4u8/4ZHn/8D2fJa//wh27drBCy/koVy5CvHK+jJnzgzatv0Yg8GAu7sHvXr1JU+ep+9qb9myLQsXzqVjx9b2SR4G2rfvRMGChfjss/6MGjUswVgermPu3Fm0atWMqKhISpUqw+jRX9CtWw8mT57AwoXzKFHiZYoU+d/w7IgR/nz55ThWr/4Gk8nE0KGjE1yOp23bjnzxxSiaN/+AbNmyU7ZsOYdij89gMDBp0gy++mo2LVo0wcvLtiRB7dr1ABKNMzQ0lCFDBhAVFYnFYqFYseK8+eZbeHp6MnToaEaNGkJMTAzPPZc1rpfvxx9/YOfO7bi7u2EwGOjdu2+C39mbb77FoUMHHhgizpw5C5UrV+XYscNUr/4mAFWqVOX777fx8ceNyZLlOcqWLZdoUhkra9ZsDBgwhM8//wxPT88Hhs0rV67Kzp3fJ1hfkSJFyZ+/AK1aNaVAgYL4+098oF5H2+xxAgJmxQ21+/n5MWiQbX2w4cPHMGPGFFq3bgbYEs5Bg4aTPXsOmpnu2mMAACAASURBVDT5iHHjRuPl5RU3CeTQoQOpMoNfCPFkw34dxKW7F9nY6Hsye2ZxdTgOMyQ22UAkqCBwISQk9IEh1P/+u8Tzzz/am/W0UmoIWCTP07TDoEH94oZSReLu3w/lk086Mm/eYjw9E18jS34XHnXp0kW+/HIcs2bNS/SclP5/Us6cmQgKupdi9YmkkzZIGx5uh63nN9Nuewt6l+/LkMqJr1vqKkajgezZ/QAKARcfKHNFQEJkNNeu/Uvbts25fv3aAwtwi4T5+vrRo8en/Pvvv64OJd25ceM/+vYd6OowhHjmXb//H31/7kmZnGXp/+ogV4eTZDIELEQKeOGFPIlOOhEJS2yGq3g8+d6EcD2r1UqvH7sRHh1OQK0FeJg8XB1SkkkCKIQQQgiRBItOzuOny7sZ/8ZkXsqaPidkyRBwCpFnKYUQaYHVagHkGVQhnEXfPMOo34ZRM39t2pXs6OpwnpokgCnAzc2D+/fvShIohHAZq9VKdLSZ27eD8fBIm5vPC5HeRcVE0W1XR3zdfZn29px0PeFPhoBTQNasObl1K4jQ0NtPPtkBRqNRJhKkAdIOridtkDRGowlvbz/8/NLPUhRCpCfDfxrOyeA/WFJvJbl9Et6JKr2QBDAFmExu5MjxQorVJ9P90wZpB9eTNhBCpBW/Xd3HxF8n0rJEG+oVeufJF6RxMgQshBBCCPEYV+9doduujhTOWpjR1b9wdTgpQhJAIYQQQohEhISH0HRzI0LNoaxpugY/d79k1RcaClOmeNClixcJbBmealJlCFgplR1YBhQBooCzQBetdZBSqjIwF/DGtkp1S631Dft1qVomhBBCCBErNOoeH29pzOV7gaxqsJ6yz5d96sdSIiJgyRJ3pk/3IDjYSJMmZowu7IZLrVtbgYlaa6W1Lg2cA8YrpYzAcqC71roYsAcYD5DaZUIIIYQQsSKiI2jzfXP+DP6D+XWXUCVPtaeqx2yGZcvcqVzZl2HDvHj5ZQvff3+fOXMicOUk4lRJALXWN7XWP8c7dAAoAFQAIrTW++zHvwKa2l+ndpkQQgghBNGWaLr+0IG9V39h+ttzqFuwXpLrsFhg3To3qlf3pW9fL154wcratWGsWRNOhQquX90g1Tsf7b1w3YBNQH7gUmyZ1joYMCqlsrmgTAghhBDPOKvVSv9fPmXbhc34VxtPU/VxEq+H7dtNvPWWD127euPtbWX58jC2bQvj9ddd+NDfQ1yxDMxMIBSYBbzvgvsnW/bsyXsA1BE5c2Zy+j3Ek0k7uJ60Qdog7eB60gap4/MfPmfF6aUMe2MYQ976/JHyx7XD7t0weDAcOgQvvQQrV0LTpiaMRh9nhvxUUjUBVEpNAl4CGmitLUqpQGxDwbHlOQCL1vpmapcl5XOEhIRisThv1w9Z+yxtkHZwPWmDtEHawfWkDVLHzOPTmLh/Iu1KdaRHyX6PfOeJtcORI0a++MKTvXvdyJvXwtSpUTRrZsbNDUJCUiv6RxmNhkQ7rVJtCFgpNQ7bM3iNtNaR9sNHAW+lVHX7+67Ady4qE0IIIcQzavlfSxizfzjvF/2AL16f5NA2b6dOGWnVypv69X05fdqIv38E+/ffp0ULW/KXlqXWMjAlgUHA38BvSimAC1rr95VSrYC5Sikv7MuyANh7CFOtTAghhBDPpi3nNtHvl968nb8WM2vOxWh4fP/YuXMGJk70ZP16dzJntjJ4cCQdO0bh5/wnxFKMwWp13lBmBlQQuCBDwM8GaQfXkzZIG6QdXE/awHn2XPmZ5lua8EqucqxusAFfd99Ez42IyMTgwVGsXOmOpyd06hRF9+5RPPdcKgacBPGGgAth6/SKk8Y7KIUQQgghnOP49aO0+b45RZ4ryor6qxNN/q5cMRAQ4MGSJQDutG9vplevKHLnTr+daJIACiGEEOKZ8/dNzcdbPyC7dw5WN9jAc15ZHznnzBkjs2Z5sG6dG1YrtG4NPXrc58UX02/iF0sSQCGEEEI8U67cu0zTzY0wGdz4rsEGcvs+/0D5oUNGZs70ZMcON3x8rLRrZ6Zr1yjKl/cjKCj9J38gCaAQQgghniHB4cF8uPk9Qs2hbGz0PYWyFAZsCzjv2mVixgwPDh50I2tWK/36RdKhg5ns2TNG0hefJIBCCCGEeCbci7rLR1sa82/oVVY12EDJHKUwm2HDBjdmzfLg9GkTefNa8PePoEULM76JzwdJ9yQBFEIIIUSGFxEdQettH/NXyEmW1ltJmSxVWLDAnTlzPLhyxUjx4jHMnBlO48bRuLu7OlrnkwRQCCGEEBlatCWazj+049d/9/Llq8s48V0Dei50JyTESKVK0XzxRQS1a8dgTLXtMVxPEkAhhBBCZFgWq4U+P/dk+4nfqRZ4mBETKxAWZqBOnWh69IigcuUYV4foEpIACiGEECJDslqt9Fo1i9ULa2E8uYQDGGncOJoePaIoUcLi6vBcShJAIYQQQmQoViv8+quJzycGcvbAEEweUbRrF023buYMsYZfSpAEUAghhBAZwq1bsHq1O0uWuPPPPybwzknxxt+x1r8uOXMYXB1emiIJoBBCCCHSLasVjh0zsnixBxs3uhERYeDFEv9Co4HUrn+PxQ0W4m6S5O9hkgAKIYQQIt0JDYW1a229fSdPmvD1tdKsmRnfysuZc70D9Qs1YF6dr3E3PQNrujyFZ2jCsxBCCCHSu1OnjAwY4EmZMn707++FxQITJ0bw55+hFGk5mTnXO/Bu4feYX2cxHiYPV4ebZkkPoBBCCCHStIgI2LTJjcWLPThyxISnp5X33oumTZsoKla0YDDA7OMzGLV/KA2LvE9ArQXS8/cEkgAKIYQQIk06f97AkiUefPutO7duGShc2MKoURE0a2YmW7b/nTfz+DTG7B9Oo6KNmVNrAW5GSW+eRL4hIYQQQqQZZjNs3+7G4sXu7N3rhpublXr1omnTxszrr8dgeGg+x/Sjkxl7cBSNX2rCrJrzJPlzkHxLQgghhHC5q1cNLFvmzvLl7ty4YSRvXgsDB0bSooWZ3LkTXrtv6pEv+eLQGD54qSkza34lyV8SyDclhBBCCJewWOCnn0wsXuzBDz+YsFqhZs0Y2raNoGbNGEymxK+ddHg8Ew+P48NiHzHj7QBMxsecLB4hCaAQQgghUlVQkIGVK91ZutSdwEAjOXJY6NkzilatzOTP/+SdOiYeGsekI+Npppoz7a3Zkvw9BUkAhRBCCOF0ViscOGBiyRJ3Nm92w2w2ULVqNEOHRlK/fjQeDqzYYrVamXB4LFOOTOTj4i2ZUmOmJH9PSRJAIYQQQjjN3bvw3Xe2BZvPnDGRObOVtm3NtGljplgxi8P1WK1Wxh8aw9Sjk2hRojWTa8zAaJDljJ+WJIBCCCGESHF//GFk8WJ31q1zJyzMQLlyMUybFk6jRtH4+CStLqvVyriDo5l+bDKtXm7Ll29Ok+QvmSQBFEIIIUSKCAuDjRttCzYfP27C29tK48Zm2rY188orjvf2xWe1WhlzYASzjk+jTckOTHhjsiR/KUASQCGEEEIky9mzRpYscWfVKnfu3DGgVAzjxkXw4YdmsmR5+nqtViuj9g9jzokZtCvVkfGvT8bw8EKA4qlIAiiEEEKIp7Jnj4lp0zzYt88Nd3crDRrYFmyuXPnRBZuTymq1Mvy3wcz9fTYdSndmXPUvJflLQZIACiGEECJJzp0zMHKkFzt2uJE3r4WhQyP5+GMzOXM+eQkXR1itVob9OpB5fwTQuUw3xlQbL8lfCpMEUAghhBAOuXULJk/2ZNEid7y8YNiwSDp1isLLK+XuYbVaGbJvAAv+nEuXV7ozuuo4Sf6cQBJAIYQQQjyW2QyLF7szaZInd+5AixZmPv88ily5UqbHL5bVamXg3r58fXIB3V7pyciq/pL8OYkkgEIIIYRIkNUKu3aZGDHCk3/+MfHGG9GMHh3Jyy8/3Yzex4mKiWLQ3n4s+2sxPcp9yrDKoyT5cyJJAIUQQgjxiNOnjQwf7skvv7hRpIiF5cvDqF07+ZM7EnL21t90/aEDfwb/zmcV+jGw0jBJ/pws1RJApdQk4AOgIFBaa31SKVUQ2BDvtOeAzFrrbPZrLgIR9h+Az7XWO+xllYG5gDdwEWiptb6RnDIhhBDiWRcUZGDCBA+WL3cnc2bw94+gbVuzQ1u1JZXVamX56SUM3fc53m7eLKm3knqF3kn5G4lHpGYP4AZgOrA39oDW+iJQNva9UmpaAjE10VqfjH9AKWUElgNttdb7lFJDgfFA+6ctS9mPKoQQQqQvkZEwb54H06Z5EBYG7dub6dcvkmzZnHO/mxEh9PmpF9subOaNfG8xq+ZXPO/7gnNuJh6Ragmg1nofgFIqwXKllAfQAqjrQHUVgIjYOoGvsPXmtU9GmRBCCPHMsVphyxY3Ro/25NIlI7VrRzNyZCQvvZTyz/nF2nvlF3rs7kJweBAjqvjTrWwP2d0jlaWlb7shcFVrfeyh4yuUUn8opeYopZ6zH8sPXIo9QWsdDBiVUtmSUSaEEEI8U37/3UijRt506OCNj4+V1avDWLEi3GnJX1RMFGP2j6DJpob4uvvy/Qe76V6ulyR/LpCWJoG0BxY9dOx1rfVlpZQnMA2YBbRM9cgekj27n9PvkTNnJqffQzyZtIPrSRukDdIOrpeSbfDvvzB4MCxdCtmzQ0AAdOxows3NJ8Xu8bC/Q/6m+frmHL12lE7lOzG17lR8PXyddj9nySi/C2kiAVRK5QXeBFrFP661vmz/b6RSag6wyV4UCBSId30OwKK1vqmUeqqypMQbEhKKxZKyax/FlzNnJoKC7jmtfuEYaQfXkzZIG6QdXC+l2iAmBmbN8mDqVA+io+GTT8x89lkkmTPbFnl2BqvVysozyxm8dwCeJg++/r8VvFO4AWF3LISRvv5cpbffBaPRkGinVVrpc20DbNVah8QeUEr5KqWy2F8bgI+AE/bio4C3Uqq6/X1X4LtklgkhhBAZVkiIgY8+8mbsWE9q1Ihm7977jBhhS/6c5XbELTrubMOnP3WnfO4K/NTsN94p3MB5NxQOS81lYGYAjYHngV1KqRCtdUl7cVug10OX5AbWKqVMgAn4C/gEQGttUUq1AuYqpbywL+eSnDIhhBAiozp+3EiHDt4EBRmYMiWCli3NTr/nb1f38cmuTtwIv87QyqPoXrYXJqPJ6fcVjjFYrc4bysyACgIXZAj42SDt4HrSBmmDtIPrPW0bWK2wfLk7gwZ5kiuXlUWLwilb1nmzewHMMWa+PPwF049NplCWwgTUWkC53BWces/Ukt5+F+INARfC1ukVJ008AyiEEEKIlBUeDoMGefLNNx7UqBFNQEAE2bM7t9Pn/J1zfPJDR47dOErz4q3wf30Cfu7Onzgpkk4SQCGEECKDuXTJQPv23vz5p4k+fSLp3z8KkxNHX61WK6v0Nwza2x83oxsL6iyhYdH3nXdDkWySAAohhBAZyO7dJrp188ZigeXLw6hTJ8ap97sTeZv+v3zKhn/WUSVPNebUnE/eTPmcek+RfJIACiGEEBmAxQKTJ3swaZIHL79sYdGicAoVcu6Q74+Bu+j3c2+u3f+Xwa8Np2e5z2SiRzohCaAQQgiRzt26Bd27e7NrlxtNm5qZODECH+et6cw/t84y/NdB7ArcSeEsRdjSeCcVcr/qvBuKFCcJoBBCCJGO/fmnkXbtvLl2zcDEiRG0aWPGYHDOvW5H3GLykQksPDkPbzcfRlTxp2OZLniaPJ1zQ+E0kgAKIYQQ6dTKlW58/rkX2bJZ2bQpjAoVnLPES7QlmqV/fc3EQ2O5FXGLli+3ZWCloeT0yemU+wnnkwRQCCGESGciI2HwYE+WLfPg9dej+eqrCHLmdM7zfj9f/pHhvw7izM3TVMvzOmOqj6dUjtJOuZdIPZIACiGEEOnIlSsGOnTw5vhxE716RTJwYBRuTvjb/Nzts4z8bSg7Ln5PgcwF+fr/VlC/0LsYnDW+LFKVJIBCCCFEOvHLLya6dPHCbDaweHE49etHp/g97kTeZvKRiSz8cy6eJi+GVRlN5zLd5Dm/DEYSQCGEECKNs1hg2jQPxo/3oFgxC19/HUaRIik75BttiWb5X0uYcMifmxE3aV6iFQNfG0Zun9wpeh+RNjxVAqiUKgxYtNYXUzYcIYQQQsR35w507AibNnnSuLGZyZMj8PVN2XvsvfILQ/cN5PTNU1TJUw3/auMpnfOVlL2JSFOMjpyklFqplKpqf90OOAWcUkp1cGZwQgghxLPs9Gkjder4sm0bjB0bQUBAyiZ/5++co833zflgUwPum0NZWHcZG97bJsnfM8DRHsCaQBv76z5ALeA2sAFY6IS4hBBCiGfahg1ufPqpF35+Vn76CZQyp1jddyPvMPXoJOb9MQd3owdDXhtBl1e64+XmlWL3EGmbowmgh9Y6SimVF8imtf4VQCklDwYIIYQQKchshtGjPZk714NKlaJZuDCCUqX8CApKft3RlmhWnlnOFwfHEBIezEfFWzD4teHk9n0++ZWLdMXRBPCEUmoQUADYCmBPBu86KzAhhBDiWXPjhoHOnb347Tc3OnaMYuTISDw8kl+v1Wpld+BORu8fzpmbp6n0fGVWvrOGV3KVS37lIl1yNAHsAIwBzMAA+7EqwApnBCWEEEI8a44cMdKhgze3bxuYPTucDz9MmSVe/gg6wajfhrH36i8UylKYhXWX8W7hhrKe3zPOoQRQa30OaP7QsTXAGmcEJYQQQjwrrFZYutSdwYM9eeEFK1u2hFG6dPK3dLty7zLjDo5mzd+ryOaVjXHVJ9K6ZHs8TCnQpSjSPYeXgbHP/m0F5AWuAsu01l87KzAhhBAio4uIgIEDPfnmGw/efjuagIBwsmZNXp13Im8z/dgU5v8RgAEDvcr1oVf5z8jsmSVlghYZgkMJoFJqCNAamAxcwvYs4AClVB6t9VgnxieEEEJkSJcvG2jf3pvffzfRp08k/ftHYTI9fX1RMVEsObWQyUcmcCviFk2KNWPQa8PIl+nFlAtaZBiO9gB2BGporS/FHlBK7QD2AJIACiGEEEnw888muna1bem2bFkYdevGPHVdVquVLec34n9gJBfunOf1fDUYWWWMrOUnHsvRBNAXeHgCegjgnbLhCCGEEBmX1QozZ3owbpxtS7fFi8MoXPjpt3Q7dO0gI38bwpHrhyiR7WVWvrOGt/PXlgke4okcTQC3AyuUUgOBQGxDwGOBHc4KTAghhMhI7t2DXr282LrVnUaNzEyZEoGf39PVdf72P/gfGMWW8xvJ7fM8U2vM4qPiLTAZkzGGLJ4pjiaAPYBZwB/2a6KB1UBPJ8UlhBBCZBhnzxpp29aL8+eNjBoVQdeuZp6mky4kPITJR8az+NRCPIyeDHh1MN3K9sTXPYU3BxYZnqPLwNwFWiul2gI5gGCtdfLnqAshhBAZ3JYtbvTs6YW3t5U1a8KpVi3pz/uFm8OZcWwq049NJsx8nxYl2tC/0iBy+8iGXOLpODoL+GUgRGt9XSkVBoxQSlmAL7XWYU6NUAghhEiHoqPhiy88mDnTkwoVYli4MJw8eZL+vN+uSzv4fHkfLt+9TN2C9RhWeTTFsiknRCyeJUYHz1sJPGd/PQl4A6gMzHVGUEIIIUR6FhJioFkzb2bO9KR16yg2bAhLcvIXbYnGf/9Imm/9kOe8nmP9e1tZVn+VJH8iRTj6DGBBrbVWShmAxsDLQDhwwWmRCSGEEOnQ+fMGmjb14fp1A9OmhdO8edK3dLt+/z+6/NCe3/7dR6uX2zHv/Tncu2V2QrTiWeVoAhihlMqELfEL1FoHK6XcAC/nhSaEEEKkLydPGmna1LZC2qZNYZQrl/TH5fdd3UOXne25bw5lds15fKg+wsvNi3tIAihSjqMJ4DfAj0AmbLOBAcojPYBCCCEEAAcPmmjRwptMmax8910YRYsmbcjXYrUw/ehkJhweS5EsRVn73maKZyvhpGjFs87RWcCfKaXqAGat9U/2wxbgM6dFJoQQQqQTP/5ool07b/LksSV/+fIlLfm7GRFC912d2R34A41f+pBJNabj5/6UiwQK4QBHewDRWu986P2RlA9HCCGESF82b3aja1cvlLKwalU4OXMmLfk78t8hOu1sS1DYDSa+MZU2JdvLTh7C6RxdBsYN+AR4E9s6gHF/MrXWbzhYxyTgA6AgUFprfdJ+/CIQYf8B+FxrvcNeFjvT2Bu4CLTUWt9wVpkQQgiRFCtWuNO3rycVK8awYkU4WbI4fq3VamX+HwGM3D+UPH752Nr4B17JVc55wQoRj6PLwEwFugB7gArAWiAXtucCHbUB2/IxlxIoa6K1Lmv/iU3+jMByoLvWupj93uOdVSaEEEIkxZw57nz2mRc1asSwenXSkr+7kXfosKM1Q38dSK38ddjV5BdJ/kSqcjQBbAzU01pPB6Lt/20EvOXojbTW+7TWl5MQWwUgQmu9z/7+K6CpE8uEEEKIJ7JabQs8jxzpRcOGZpYuDcfHx/HrTwb/Se01b/L9hS0MrzKGJfVW8pxXVucFLEQCHE0AfYDY5C1cKeWjtT4DpNQ/V1Yopf5QSs1RSsUuOJ2feL2FWutgwKiUyuakMiGEEOKxLBYYNMiTqVM9adkyirlzI/DwcOxaq9XKir+WUn9tTcKjw1nfaBs9yvWW5/2ESzg6CeQ08CpwCDgCjFRK3QWupkAMr2utLyulPIFp2JaZaZkC9TpN9uzOn5mVM2cmp99DPJm0g+tJG6QN0g5gNkP79rB8OfTvDxMmeGAwOJb9hZnD+GTrJyz5fQm1CtdiReMV5PLNlaT7SxukDRmlHRxNAHsDsbtX9wECsK0J2Dm5AcQOC2utI5VSc4BN9qJAoEDseUqpHIBFa31TKZXiZUmJOSQkFIsl6fs5OipnzkwEBd1zWv3CMdIOridtkDZIO0BEBHTq5M2OHW4MGRJJr15RBAc7du3ZW3/TcUdrztw8Tb+KA+lb8XMMYSaCwhz/TqUN0ob01g5GoyHRTitH1wE8HO/1WaBWSgSmlPIF3LTWd+zbzH0EnLAXHwW8lVLV7c/sdQW+c2KZEEII8YjQUGjVyptff3Vj/PgI2rd3fEeO9WfX0OfnXniZPPn23XW8lb+mEyMVwnGPfQZQKVVNKTUhkbLx9iVVHKKUmqGUugLkA3YppU4BuYGflVJ/ACeBYtiWm0FrbQFaAQFKqbPYlqAZ6KwyIYQQ4mEhIQYaN/bhwAETc+aEO5z8RcZE8vmePnT5oT0vZy/J7qb7JPkTacqTegAHA3MSKfsFGAI0cORGWuteQK8EihKdSKK1/g0onVplQgghRKxr1ww0berNxYtGFi8Op27dmCdfBASFBdFqW1OO3ThKt1d6MrTySNxN7k6OVoikeVICWBbYnkjZD8DClA1HCCGEcL0LFwx8+KEPISEGvv02nGrVHEv+Lt8LpOnmRvwbepVFdZfzbpGGTo5UiKfzpAQwM+ABhCdQ5o5tIogQQgiRYfz1l5GmTb2Jjob168MoW9bi0HV/39Q03dyIUHMoqxpsoPILVZwcqRBP70nrAJ4B6iRSVsdeLoQQQmQIR48aadTIB6MRNm4Mdzj5O3b9CA031MVsMbOx0feS/Ik070k9gFOBuUopE7BBa22xb6fWCJiNbUkYIYQQIt3bs8dE69be5Mpl5bvvwihQwLHlvvZc+ZnW2z4mh09OvmuwgUJZCjs5UiGS77E9gFrrb4CJwBIgQin1LxBhf/+l1nql80MUQgghnGvtWjeaN/emQAELmzc7nvxtObeJ5luaUCBzAba8v0OSP5FuPHErOK31FCAvttm+/ez/zWs/LoQQQqRbkZEwYIAn3bp5U758DBs2hJE7t2PJ3/K/ltBxZ2teyVWOjY2+53nfF5wcrRApx9GFoO8CO5wcixBCCJFqLl0y0KmTNydOmOjePYrBgyNxd3C1lpnHpzFm/3Dezl+LhXWX4evu69xghUhhjm4FJ4QQQmQYO3aY6NHDG6sVliwJp169aIeus1qtjN4/nNknptP4pSbMePsrPEyO7QcsRFryxCFgIYQQIqOIjobRoz1o1cqHAgUs7Np13+HkL9oSzWc/9WD2iem0K9WRObUWSPIn0i3pARRCCPFM+O8/A507e3HggBtt2kQxZkwkXl6OXRsRHUHXHzqw7cJm+lb8nAGvDsZgMDg3YCGcyOEEUCmVHagPvKC1nqiUygMYtdZXnBadEEIIkQL27jXRpYsXYWEGAgLC+eADx3r9AEKj7tHm++bsvfoLY6tPoFOZbk6MVIjU4dAQsFLqTUADLYBh9sMvAQFOiksIIYRINosFpkzx4MMPvcmWzcqOHWFJSv6Cw4NpvPFdfvt3H7NrzpPkT2QYjj4DOA1oprX+PyD2N+cgUMkpUQkhhBDJFBJioHlzb8aP9+T996PZvj0MpRzb2QPg6r0rNFxflzM3T7Ok3jd8qD5yYrRCpC5Hh4ALaq1321/HLpAUlYTrhRBCiFRz5IiRTp28CQr6//buPM7G8v/j+OucM3Nmxs7YSna5ioikRFqUNomvtH1LpCR90+JbKvkVLUhEkp0SiiSklfYQyW5wha9lpLIzwyxn+/1xjppKmWHOnDNz3s/HYx7m3Nc59/0Zl3t6d133fd0OXnopkzvv9JCXS/Y2HfiRm+e153D2YWa0nc1Fp7cIX7EiEZDbEcD1xpir/7TtSmBtPtcjIiJy0gIBGDcunhtuKIbLBR9+eJTOnfMW/lbtXsENs68my5fFnPYfKfxJkZTbEbz/Ah8YYz4EkowxYwk+EaRd2CoTERHJg8OH4eGHE/ngg3iuucbDiBGZlCmTt30s/OkbOn10K8mJybxzwxxqla4dnmJFIixXI4DW2iXAuUAKMAnYClxgrV0WxtpERERyZd06J61bF+fjj+N45plMJk/Oe/j78H/zuHVeB6qWrMoHHeYr/EmRlutrmNQRaAAAIABJREFU+Ky1PwGDw1iLiIhInr31VhxPPJFImTIBZs/OoFkzX54+HwgEmLh2LH0XPUHjik14q81MyiaWC1O1ItHhbwOgMWYKv9/w8bestXfma0UiIiK5cPQoPPFEItOnx9OypZcxYzKpUOGE/9n6g/TsNHp91ZM5m9/jqurXMPaq1/VcX4kJ/zQFvBnYEvo6BLQHXMDO0OfaAQfDXaCIiMifrV3r5NprizFjRhz//W8W77yTkefwt35fCq3fvZT3t8yhb7N+vHnddIU/iRl/OwJore1/7HtjzKdAG2vttzm2Xczvi0KLiIiEXXY2DBvm5pVX3CQnB5g+PYPLL8/blC/A9I3TePybXpR0l+K9Gz6geZWLw1CtSPTK7TWAzYAlf9q2FLgof8sRERE5vnXrnPTsmUhKiouOHT288EImZcvmbR9HPUfp8+1jvLVxCi2rXMro1hOpWKxieAoWiWK5XQdwJTDAGJMEEPrzBWBVuAoTEREB8Hhg6FA3V11VjN27HUyenMGoUXkPf1sObuLaWVfw1sYp9GryGO+0naPwJzErtyOAXYC3gEPGmANAWeAH4N9hqktERIT16508+GAia9a46NDBw4ABmZQ7iRt03988m4e/fAC3K57p18+iVbXW+V+sSCGSqwBord0GNDfGVAVOB3621u4IZ2EiIhK7vF549VU3Q4a4KV06wOuvZ9CmjffEH/yTbF82/RY/xYS1Y2lSqSkTrppMlZJnhKFikcIlT8/ytdamAqlhqkVERISNG4OjfqtWuWjf3sPAgVkkJ+ftDl+A1LQddPu0Myt2L6f7uf/h/5r1x+1yh6FikcInTwFQREQkXLxeGDXKzeDBbkqWDDBxYgZt2+Z91A9gwbZP+M/n9+IL+Jl49RTa1taTS0VyUgAUEZGI+/HH4KjfihUu2rb1MGhQVp7X9QPw+r28+P0LvLJiKPWTGzDxmjf1SDeR41AAFBGRiPH5YPToeF58MYHixQOMH59Bu3YnN+r365Ff6L6gK4t3LaRTvS48f/GLJMUl5XPFIkVDrgOgMaYYUAcokXO7tXZxfhclIiJF3+bNDnr2TGL5chfXXedh8OAsKlbM+6gfwMKfvqH7/K4c8aQz8oqx3Gxuy+dqRYqWXAVAY8ydwEggG8jI0RQAqoWhLhERKaJ8Phg7Np5BgxJISoIxYzL417+8OBx535c/4OeV5UN5cdkL1C5dh1nt5nFWubPzv2iRIia3I4CDgRuttQtO9kDGmCHAjUANoIG1dp0xJhmYAtQmGC43Ad2ttXtCnwkAawF/aDedrLVrQ21tgZdCP8Ny4C5r7dFTaRMRkfDassXBgw8msWyZi2uu8fDSS1lUqnRyo377Mvbxn8+78cWOz+hwZkeGXDaCEvElTvxBEcn1k0Cyga9O8VhzgEuA7Tm2BYDB1lpjrW0AbAEG/elzza21jUJfx8JfCWA80NZaWwdIAx49lTYREQmfI0fg5ZfdtGpVnE2bnLz2WgaTJ2eedPjbdOBHWs+8hIU7v2HwJcMYfeVEhT+RPMhtAPw/4GVjTPmTPZC1dmFoHcGc2/Zba7/KsWkJUD0Xu7sW+MFauyn0egxwyym2iYhIPvN64c0342nWrDiDBiVw+eVevvnmCDfddHJTvgCrdq/ghtlXk+nLZN6/PqXLOXfjONmdicSo3E4B/wg8C9xvjDm2zQEErLWu/CjEGOMEegDv/6npK2NMHPAx0M9am0XwusOcI4k7gKqh70+2TURE8kkgAB99FMcLL7jZvNlF06Y+xo8/SrNmvlPa78KfvqHTR7dSLrEcM9vOoVaZOvlUsUhsyW0AnAK8CczgjzeB5KdXgXSCN5scU81am2qMKRWq4f+AvmE6fq4lJ4d/mqFChZJhP4acmPoh8tQH0SEv/bBwIfTuDd99B2edBbNnQ7t2LhyOYqdUw9yNc7ntg1uoXa428++YT5VSVU5pf4WNzoXoUFT6IbcBMBl42lp7chdrnEDoBpEzCV6bd+yGj2OPnsNae9gYMwHoFWraAVyeYxfV+P0RdSfblmv79qXj94flrwII/uPasyctbPuX3FE/RJ76IDrkth+sdfLCC24++SSeSpX8DB2azW23eYiLg717T62G6Run8ciXD3BuhUa8df27uLNKxdS/DZ0L0aGw9YPT6fjbQavcXgP4OtAp3yrKwRgzAGgCtA9N7x7bXtYYkxT6Pg7oCKwKNX8CNDXGnBl6fR/wzim2iYjISfj5ZwePPJLApZcWY9GiOPr0yWLJkiN06hQMf6dqzOqRPPhFD5pXacm77eZRLjH51HcqEuNye2peADxgjHkK+DVng7X2ktzswBgzAugAVAY+M8bsA24GniR4jeHi0PWFW621/wLOAsaGloKJBxYTnALGWptmjLkX+MAY4wJWAg+dSpuIiOTNoUMwcqSbcePceL3QrZuHhx/OJjk5f2ZIAoEAg75/jmHLh9Cm1g2MaT2RBFdCvuxbJNY5AoETn6jGmM5/12atnZyvFUW3GsBWTQHHBvVD5KkPosOf+yErC15/PZ5hwxI4cMBBhw4ennwyi+rV8+/3oj/g54lv/ssbKRO5/ew7GXLpK7ic+XLPYaGkcyE6FLZ+yDEFXBPYlrMtVyOAMRbyRETkOPx+mDUrjkGDEkhNdXLppV6efjqLBg38J/5wHmT7sun5eXdmb57FA40f5v+a9dcyLyL5LLePgnMA9wC3AeWttQ2NMZcAla21uoZORKQICwTgiy9cPPdcAikpLho08DF06FEuu+zUlnQ5nqOeo9z9aSc+37GAvs368+B5j+T7MUQk9zeBPAvcDYzj92f/7gQeD0dRIiISHVavdtK6Ndx6azHS0hyMHp3BggXhCX+Hsg5y87z2fJn6OUMvG6HwJxJGub0JpAvQ2Fq71xgzOrRtK1ArLFWJiEhErV3rZMgQNx9/HE9yMjz/fCadO3tICNM9GL8e/ZVb5v2LTQcs4696g7a124fnQCIC5D4Auggu0gzB5/cClMixTUREioB164LB76OP4ilVKkDv3ln06ZNAdrYnbMfcfngbN73fjt1HdzOtzUwuq9oqbMcSkaDcBsCPCD4L+BH47ZrA54B54SpMREQKTkpKMPh9+GEw+D32WBb33ptN6dJQunQCe/aE57gb9q3n5nntyfJl8u4Nczm/8gXhOZCI/EFuA2AvYDJwiOCafOnAfODOMNUlIiIFICXFydChbj74IJ6SJQM8+mgW3bsHg1+4/fDL9/z7w44kuBKZ2/4Tzk6uF/6DigiQ+2VgDgP/MsZUIvT4NGvtL2GtTEREwmb9+mDwmzcvnhIlAvTqlcV992VTpkzBHP+r1C/o8vHtVCxWkZk3zKV6qRoFc2ARAXK/DMyxu4X3hL4wxjhzPrdXRESi34YNweD3/vu/B7/u3bMpW7bgapi3ZQ73LbibM8saZrSdTaVilQru4CIC5H4K2MvvN3/8xhjjBXYB7wHPWGt1U4iISBTauPFY8IujWDF45JHgiF9BBj+AKevf4LGvH+b8Shcwrc07lE4ooCFHEfmD3AbAnkB7YBCQSnAauDfwIWCBZ4DhBBeLFhGRKGFtMPjNnRsMfg89lM1992VTrlzB1nEgcz8vLRvIhLVjuaJaayZePYVi8cUKtggR+U1ebgI5z1p7KPT6R2PMD8Bya21tY8xaYHlYKhQRkTyz1snLL7uZMycY/B58MJsePQo++Hl8Ht5ImcBLywZyOPswdze4l/7NB+B2uQu2EBH5g9wGwFJAMYJ3AR9TDDh2n9gvQFI+1iUiIidh82YHQ4YkMHt2HElJ0LNnNj16eEhO/stVPGEVCAT4bPunPLP4KTYf3MSlZ1xO/xYDqJdcv0DrEJHjy20AfBNYYIx5heAU8BnAQwSXhgG4iuBUsIiIREBqqoOhQ91Mnx5PYiI88EA2999f8MEPgmv7Pb3oSb7e+SW1y9Rh2nXvcGX1q3E4HAVei4gcX24D4GPAJuBW4HTgZ+A1YHyo/Uvgq/wuTkRE/tnu3Q5eecXN5MnxAHTr5uHBB7OpUKHgg9/ejL28+P0LTFn/OqXcpXi+xSDuOqcb8a74Aq9FRP5ZbtcB9ANjQl/Ha8/Mz6JEROSfHToEo0a5GTvWTVYW3Habh169sjnjjIIPflm+LMavGcOw5S+R4T3K3efcy6NNn6BsYgFfcCgiuZbbEUBCi0BfAJQHfhvHt9ZOCkNdIiJyHEeOwMSJbl591c2hQw7at/fQu3cWdeoUfPALBAJ8+L959P+uL9sPb6N19avp1/wFzixbt8BrEZG8ye1C0O2BqQSngesDKcA5wEJAAVBEJMyys2HKlHiGDXOze7eTK6/08uSTWTRoEJn1+NfsWcXTi/qweNdCzip3NjOun83l1a6ISC0ikne5HQF8HrjLWjvTGHPAWtvYGHMXwTAoIiJh4vPBzJlxDBmSwI4dTpo18zJhQibNmvkiUs+vR35hwNJnmb5xGuUSyzH4kmHcUa8zcc5cTyiJSBTI7RlbzVo780/bJhNc/uXR/C1JREQCAfjwwzgGDXLz448uGjb0MXjwUS6/3EckbqbN8GYwZtVIXlnxMh5/Nj0a9eSRJo/qSR4ihVRuA+BuY0wla+2vwDZjzEXAXsAVvtJERGJPIABffeViwIAEVq92ceaZPiZOzOD6670RCX6BQIC3175N7/mPszM9letqtuXp5s9Sq3Ttgi9GRPJNbgPgeOBiYBYwjOCyL37g5TDVJSISc77/3smAAQksXhzHGWf4GTEig44dvcRFaHb1h1++5+lFffjh1+85p3xDXr1iDC2qtIxMMSKSr3K7DMyLOb5/0xjzFVDcWrshXIWJiMSKlBQnAwcmMH9+HOXL+xkwIJNOnTwkJESmntS0HbywpB/vbXqXisUqMaHtBNpUuRGXU5M+IkVFbu8CnmutbXfstbV2R2j7e9baDuEqTkSkKPP5YPhwN0OGuClRAp56Kot77smmePHI1JOencYrK15mzOqROHDQq8ljPND4YWpWOZ09e9IiU5SIhEVuJxYu/5vtl+VTHSIiMWXnTgf335/IkiVxdOjgYdCgTMpE6H4Kn9/H2xunMnDpc+zJ2M2NZ97MU82e4YySVSNTkIiE3T8GQGPMs6Fv3Tm+P6YWsD0sVYmIFGHz5sXRq1ciXi+MHJnBzTd7I1bLNzu/4ulFfVi/bx1NK1/Im9e9TZNKTSNWj4gUjBONAB773z9nju8BAkAq0C8MNYmIFElHjsDTTycwZYqbxo19jB6dQa1aBf8ED4DNBzbRb/FTzN/+CdVKVmfCVZNpW7s9jkjcaiwiBe4fA6C19i4AY8xia+34gilJRKToWbfOSffuiWze7OTBB7Po3Tsbt7vg69ifuY8hywbxRspEEl1J9G3Wn3sb9iAxLrHgixGRiMntXcDjjTGlAQOU+FPbF+EoTESkKAgEYPz4eJ59NoGyZQPMnJnBJZcU/FM8sn3ZTFo3jqE/DCYt+zCd6t1F76Z9qFCsQoHXIiKRl9u7gLsArwHpwNEcTQGC1wKKiMif7Nnj4KGHEvnssziuvtrL8OGZJCcX7JRvIBDg460f0v+7vmw99D8uq9qK/s0HcHZyvQKtQ0SiS27vAn4B6Git/TicxYiIFBVffunigQcSOXzYwcCBmXTt6inwJ3ms3bOapxf1YdGub6lb1vB2m3dpVa21rvMTkVwHwDhgfjgLEREpCrKzYcCABEaNcnPWWT5mzsygXj1/gdbwy5GfGbj0OaZvnEa5xHIMumQod9a7izhnhB4pIiJRJ7e/DV4E+hpjnrPW5vk3mTFmCHAjUANoYK1dF9peF5gMJAP7gDuttZsi0SYicqq2bHHQvXsSa9a46NIlm/79s0hKKrjj2/0bmbphMlNS3sDjz6ZHo5480uRRSidEaIFBEYlazly+7xGgL5BmjNmR8yuXn58DXMJf1w0cA7xmra1L8BrDsRFsExE5KYEATJ8exxVXFCc11cnkyRkMHlww4e+I5whvb5hKm/da03L6BUxaO45ral7LwtuW0a/58wp/InJcuR0BvONUDmKtXQhgjPltmzGmInAe0Dq06W1gpDGmAuAoyDZr7Z5T+flEJHYdOgS9eycye3Y8F1/s5bXXMjnttPDf6LF690qmrJ/Me5tmku5Jo06ZM+nX/AVuqnur7uwVkRPK7TIwX4fh2FWBn6y1vtAxfMaYXaHtjgJuUwAUkTz7/nsnPXoksWuXg6eeyuKBB7JxucJ3vENZB5m1aSbT1r/J2r2rSYpLom3t9txRrwsXVm6mmztEJNdyuwxMPMEp4E7A6cAuYArwgrU2O3zlRafk5BInftMpqlChZNiPISemfoi8aOwDnw8GDID+/aFaNVi0CC68MAFIyPdjBQIBFu5YyISVE5iZMpMMbwaNKjfitete498N/k2ZxIKZ4o3Gfog16oPoUFT6IbdTwIOBC4D7CF7HVx34P6AUwesDT0YqUMUY4wqNxrkIhstUgqN1BdmWJ/v2peP3h2+Kp0KFkuzZkxa2/UvuqB8iLxr7YNcuB/ffn8jixXHceKOHwYMzKVkS9uTzPMLejL3M2PgW0zZMZvPBTZSIL8nN5t/ccfadnFuxMQCeNNiTFv6/n2jsh1ijPogOha0fnE7H3w5a5TYA3gSca63dF3ptjTErgNWcZAC01u42xqwCbgOmhv5ceex6vIJuExE5kU8/dfHgg0lkZcHIkRncfLM3X/fvD/j5OvVLpm6YzCdbP8Tj99C08oWMaDWatrXbUzy+eL4eT0RiV24D4N9dWJKrC06MMSOADkBl4DNjzD5rbX2CI4qTjTFPAweAO3N8rKDbRESOKysLnnsugXHj3DRo4GPcuAxq186/WYBd6T/x9sapvLVhCqlpOyiXWI6uDe7ljrM7Y8qdlW/HERE5xhEInPiXmDFmOMEp4P7ADoJTwH2BH6y1D4e1wuhSA9iqKeDYoH6IvGjogy1bHNx7bxJr17ro3j2bvn2zSMiHS/38AT9f7viMySmTmL/9E/wBP5eccTl3nH0n19a6ngRX/l9PeLKioR9infogOhS2fsgxBVwT2JazLbcjgL0JBr7XCF439xMwHXg+36oUEYkyM2bE8fjjiSQmBpg69ShXXeU75X3uPrqb6Run8mbK6+xI2075pAo80Ohh7qjXmRqla+ZD1SIiJ5bbZWCygadDXyIiRVp6enBtv3ffjad5cy+jR5/a2n6BQIDFuxYyOWUiH/5vHh6/h4urXML/XdSfa2tej9vlzsfqRURO7B8DoDGmBXCDtfbx47QNAuZYa5eEqzgRkYK2Zo2Tbt2S2L7dQe/eWTzyyMmv7Xcw8wAz7FtMTpnE5oObKJ1Qhq7ndOPO+l05s2zd/C1cRCQPTjQC2AcY9TdtXwNPAW3ztSIRkQgIBGDcuHiefTaBChUCzJmTQbNmeZ/yDQQCrNj9A5NTJjFn0ywyfZk0qdSUEa1G065OB5LiCvDhwCIif+NEAbAR8MnftC0AJuZvOSIiBW/fPgcPPZTI/PlxXHONh+HDMylXLm/7SM9OY9ammUxOmcS6vWsoHl+CW866nTvr30WD8g3DU7iIyEk6UQAsBbiBjOO0xQNFYzlsEYlZixa56NEjkf37HQwcmEnXrh7y8kS1lL3rmJwykXd/fId0Txr1ks9h8CXD6Fj3Zkq49StSRKLTiQLgRuAqYO5x2q4KtYuIFDpeLwwZ4mbYMDe1a/uZNi2DBg38ufpshjeD9zfPZnLKJH749XsSXYm0q9OBzvW70qRSUz2TV0Si3okC4DBgbOixaXOstX5jjBNoT3BJmF7hLlBEJL/99JOD++5LZOnSOG691cOAAZmUyMUjvrcd2srklEm8teFNDmQdoHaZOjzbYgC3mH9TNjGPc8YiIhH0jwHQWvuWMaYyMBlIMMbsBcoDWcAz1tq3C6BGEZF889FHcTz8cCIeD4walUHHjv/8ODef38eXqZ8xae14Pt+xAKfDybU1r+euc+7h4iqXaLRPRAqlE64DaK192RgzAbgISAb2Ad9Zaw+HuzgRkfySmQn9+iUwaZKbc8/1MXZsBrVq/f3afvsz9/HWhqm8kTKRHYe3UbFYJXqd35s7693FaSVOL8DKRUTyX24Xgj4MfBrmWkREwmLtWic9eyayfr2L++4LPs7N/TdrL6/8dTmT1o1nzuZZZPmyuOj0FvS98Bmuq9VWCzaLSJGR20fBiYgUOpmZ8PLLbl591U1ycoC33jrKlVf+dW2/DG8Gcze/x6S141i1ZyXF4opz21l30OWce6iXXD8ClYuIhJcCoIgUScuWOXn44UQ2bXJx660enn02kzJl/viebYe28kbKRN7eMIUDWQeoW9YwsOVL3Gxuo6S7VGQKFxEpAAqAIlKkHDkCAwcmMH58PFWqBJg+/SitWv0+6ufz+/hixwJeXzfhDzd1dG3QjRant9RNHSISExQARaTI+OYbF716JbJjh5OuXYPX+h1b3kU3dYiI/E4BUEQKvcOHoX//BKZMcVOrlp+5c49y0UXBUT+v38u4NaMZ/P0AjnqP6KYOEREUAEWkkJs/38VjjyXy668O/vOfbHr3ziIpKdi2/NdlPPrVw6TsW8vVNa7lyQuf1k0dIiIoAIpIIbVvn4OnnkrgvffiOftsH2+8kUHjxsFHuR3OOsQLS/vzxrqJVC5+Gq9fM43ral6v6/tEREIUAEWkUAkEYO7cOPr0SeDQIQePPZbFQw9l43ZDIBBg3pY59FnYm70Ze+jW8D6euKAvJdwlI122iEhUUQAUkULjl18c9O6dwCefxNO4sY9ZszI4++zgqN/2w9t44pv/8vmOBTSs0Iip182gUcXzIlyxiEh0UgAUkagXCMDbb8fx9NOJZGfDM89k0r27h7g48Pg8jF49kqE/DMLpcPF8i0F0bXAvcU79ehMR+Tv6DSkiUW3HDgf//W8iX38dR7NmXoYPz/ztGb7f/7yUx75+iA3713NdzbYMaDmY00tUiXDFIiLRTwFQRKKS3w8jR8LjjxfH4YAXX8ykc2cPTicczDzAc0v6MWX961QpcQZvXjuda2peF+mSRUQKDQVAEYkqgQB88YWLgQMTWLMGWrXyMWRIJmecESAQCPDepnfpu/AJ9mfu475zH6D3BX0oEV8i0mWLiBQqCoAiEjWWLHHxwgtuli6No1o1P1OnQuvWGTgcsPXQ/+j99SN8vfNLGlc8jxnXv0eDCudGumQRkUJJAVBEIm71aicDBybwxRdxVKrk58UXM7n9dg9VqpTkp1+yeW3lKwxb/hJxzngGtnyJLvXvweV0RbpsEZFCSwFQRCLGWicvvujmgw/iKVs2wDPPZHLXXR6KFQu2f7v9W+6Z240fD1huqP0vnr94EJWLnxbZokVEigAFQBEpcNu3OxgyJIGZM+MoVgwefTSLHj2yKRlar3nH4e0M+v553v1xBlVLVmPade/QusY1kS1aRKQIUQAUkQLz668OXn7ZzdSp8bhccN99Hnr2zCY5Obisy96MvQxf/hKvr5uAy+HiiRZP0L3eQxSPLx7hykVEihYFQBEJu/374dVXE5g0KR6PB26/3UOvXtmcdlow+KV70hmzaiSjVr3KUe8R/n1WJx5t+gTn1jyLPXvSIly9iEjRowAoImGTlgZjxrgZPdrNkSPQsaOXxx7LokaNYPDL9mUzZf3rDP1hMHsz9tCm1g30ufBpzixbN8KVi4gUbQqAIpLvMjLg9dfjGTHCzf79Ttq08fD449mcdVbwub3+gJ85m2cxcOlzbD+8jeanX8yb177N+ZUviHDlIiKxIeIB0BhTA5iTY1MZoJS1tpwxZhuQGfoCeNxa+2noc82AsUASsA24w1q7+1TaROTUeDwwbVo8L7/s5pdfnFx2mZcnn8ygceNg8AsEAnyZ+jkvLOnP2r2rqZ/cgLfbvEuraq1xOBwRrl5EJHZEPABaa7cBjY69NsYM5491dbTWrsv5GWOME5gKdLHWLjTG9AUGAV1Pti18P6FI0Xf4MEyfHs+4cW527HBywQVexozJpHlz32/vWfHrDzy/pB8Lf/qGaqVqMOrK8XQ48yacDmcEKxcRiU0RD4A5GWPcwO3A1Sd4axMg01q7MPR6DMHRvK6n0CYiebRli4MJE9xMnx7PkSMOmjb1MWjQUa64wsexAb0tBzcxYOlzzNsyh/JJ5Rlw8WDurN8Vt8sd2eJFRGJYVAVA4AbgJ2vtihzbphljHMBCoI+19iBQDdh+7A3W2r3GGKcxptzJtllr9+e2yOTk8D93tEKFkmE/hpyY+uGv/H6YPx9GjICPPwa3G269FXr2hPPPdwHBVZx3pe2i/1f9mbhyIknxSfS7tB+9LupFyYS8/Z2qD6KD+iHy1AfRoaj0Q7QFwK7ApByvW1prU40xCcBwYCRwR0Qqy2HfvnT8/kDY9l+hQkktfREF1A9/lJ4OM2bEM3FiPJs3u6hY0U/v3h7uvNNDxYrB82HPHjiUdZBXVwxn/NrReP1e7jrnHh5p0psKxSqQeRgyyf3fqfogOqgfIk99EB0KWz84nY6/HbSKmgBojKkCXAp0OrbNWpsa+jPLGDMKeD/UtAOonuOz5QG/tXa/Meak2sL3k4kUblu3Opg0yc1bb8WTluagcWMfo0ZlcMMNXtw5ZnGPeI7w+roJjFgxlENZh+hw5k08fsFT1ChdM3LFi4jIcUVNAAQ6Ax9aa/cBGGOKA3HW2kOhKeBbgVWh9y4HkowxF4eu57sPmHmKbSISEgjAt9+6GD/ezfz5LlwuuOEGL926ZdOkif8P7z2UdZCJa8cxbs0o9mfup1W1K3mqWT8alG8YoepFROREoikAdgEezPG6EjDLGOMCXMB64H4Aa63fGNMJGGuMSSS0nMuptIkIHDkC774bz4QJ8Vjronx5P488kk2XLh4qV/7jZQ97M/YybvUoJq4bR1r2YVpXv5qHmzxK08oXRqh6ERHJLUcgEL5r2YocLEEOAAAUAElEQVSgGsBWXQMYG2KpH1JTg9O806bFc/CggwYNfHTrlk379l4SE//43p/TdzFq1QimrH+DDG8GbWu356Em/w3LiF8s9UE0Uz9EnvogOhS2fshxDWBNgoNev4mmEUARKWDLlzsZOdLNxx/H4XBAmzZe7rnHw4UX/r6MyzHbDm3l1ZXDmbFxGr6Aj451b+HB83rpsW0iIoWQAqBIDNqwwcnAgW4++SSesmUDPPBAcJr3jDP+OrJt92/klRVDmb3pXVwOF7ed3YkHGj9E9VI1Cr5wERHJFwqAIjFk2zYHgwcnMGtWHCVKwBNPZHHvvdmUOM4qAWv3rGbY8iF8+L/3SYpLolvDHtzfqCeVi59W8IWLiEi+UgAUiQG//upg6FA3U6fGExcH//lPNj17ZlO27F/fu/TnJQxf/hKf71hAKXdpHmnyKN0a3k9yUnLBFy4iImGhAChShB04ACNHupkwwY3HA3fc4aFXr+y/3NEbCAT4eueXDF8+hMW7FpKcmEyfC5+m6zndKJVQOkLVi4hIuCgAihRB6ekwfryb115zk5YGHTp46d07i5o1/xj8/AE/87d9wvDlL7Fi93IqFz+N51oM5I56XSgeXzxC1YuISLgpAIoUIVlZ8Oab8Qwb5mbvXifXXOPhiSeyqVfvj4s3bzm4iXd/fId3f5zB9sPbqFaqBkMufYVbzvo3Ca6ECFUvIiIFRQFQpAjw+WDmzDheeimB1FQnLVp4mTw5g6ZNfw9+ezP2MnfzLGba6azYvRynw0nLKpfyxAV9aVenA3FO/ToQEYkV+o0vUogFAvDBB3G8+KKbH3900aiRj6FDj3LppcF1/DK8GXy69SPe/XEGX6R+htfv5ZzyDenX/AU6nNlRd/SKiMQoBUCRQigQgK+/djFgQAKrVrmoW9fHpEkZtGnjxR/wsfCnhcz8cTofbHmfdE8apxevQo9ze9Kx7i2cnVwv0uWLiEiEKQCKFDLLljkZMCCBRYviqFrVz4gRGdx0kxd7MIXnlsxg1o/v8PORXZSIL0nb2u24ydxK89MvxulwRrp0ERGJEgqAIoVAIACLFrkYPtzNN9/EUb68nwEDMrmqQyrztr/Dle/OIGXfWuKccbSqeiXPthjAVTWuJSkuKdKli4hIFFIAFIlifj8sWOBi+PAEli93UbGinyeeOkRyy5nM2/kWT03/igABmlQ6n4EtX6JdnRspn1Q+0mWLiEiUUwAUiUJeL8ydG8eIEW42bHBR5QwPN/93EQfMywz/9VMyl2RSvVQNep3fm451b6Z2mTMjXbKIiBQiCoAiUSQrC2bMiOfVV91s3+6kYvW9nNl1NJvPeI53nB6qHD6DO+p1pn2djjStfAEOhyPSJYuISCGkACgSBdLTYcqUeF59zcne3W6Sqq+DW/qy27xPcvmzeaTWI1xX83oalD9XoU9ERE6ZAqBIBO3d5+f5EXuYNeU0stIToebncOdAzrnoCG1qt+Wamv2oVbp2pMsUEZEiRgFQpIBlejOZu+o7Ro12sfHTSwlk18Fx1jya3vgZt7auw9U1xlKxWMVIlykiIkWYAqBIATiUdZAF2z9l1tJlfD3jfLzLO4E/nqoXLaZrj/10vrwpJdyXRbpMERGJEQqAImESCAT4btcixq4ZxfxlO/B98yisew2nM8AVN+ykf+8y1K3dKNJliohIDFIAFMlnWb4s5myaxdjVo1m3oiTxS5/Et/46EpO8dOnu4/4eHipX1lp9IiISOQqAIvlkz9E9TE6ZyKTVr7N3ZQsSv58M28+lRFk/dz+axT33ZFOuXKSrFBERUQAUOWXr96UwbvUo3l33Idk/3E7iD8th72mcVtNHj8GZ3Hyzh2LFIl2liIjI7xQARU6CP+Dns+2fMnbNaL7dsJG4Zb1wLh8NR4rT8AIv9w/J4Oqrvbhcka5URETkrxQARfIg3ZPOjI1vMX7NaP73YyJJP/TFufIm/H4n17bx0qPHEc4/3x/pMkVERP6RAqBILuxMS2XC2rFMSZlMmm1CqR8mw/oWkBTgrs4e7r03g5o1A5EuU0REJFcUAEX+RiAQ4PuflzJuzSg+2PQRgXU3U2r5Cthei4QKfp58MovOnXVjh4iIFD4KgCJ/csRzhI+3fsAbc8fz/ZaNJK5+kGLfTyR9X1kq1fXRb1gmN97oITEx0pWKiIicHAVAEYKh7/Pt85m7ZTafb5/P0X3lKLu6HwlL7iDzaAIXX+zl/vuP0qqVD6cz0tWKiIicGgVAiVlHPEf4bPunvL9lDp9t/5SMQ8UosbkLpTf9QOb6szjsgHbtgjd2nHuubuwQEZGiQwFQYspfQt/hREps6UzZzcvIWnc26T4nlWr7efjhbB58MIFixTIjXbKIiEi+i4oAaIzZBmSGvgAet9Z+aoxpBowFkoBtwB3W2t2hz+R7mxRN6Z50PtsWDH2f75hPRpqbkls7kbxpKT+vrU+610lydT89H/DQrp2X+vX9OBxQoUICe/ZEunoREZH8FxUBMKSjtXbdsRfGGCcwFehirV1ojOkLDAK6hqOtIH9QCb+/hL70OEptu4Pym7/j59UNSPM4KVPVz33dvbRv76Fhw2DoExERiQXRFAD/rAmQaa1dGHo9huCIXdcwtUkhdyz0zd0ymy92LCDjiJNS22+n4uaF7Fp1LoezXZQ43c89d3tp187Deecp9ImISGyKpgA4zRjjABYCfYBqwPZjjdbavcYYpzGmXDjarLX7w/0DSv7L8mXx+fYFzNr0Dgu2fUJmhoNSO/5NpS3fsmtlIw5nukiq5KdLZy833JBF06a6i1dERCRaAmBLa22qMSYBGA6MBGZHuKa/lZxcIuzHqFChZNiPUVj5A36+3f4t09ZOY+b6mRzcG0/JXe2puvNbdv7QmMNHXSRWhHu6wi23QIsWTlwuN+DO87HUD5GnPogO6ofIUx9Eh6LSD1ERAK21qaE/s4wxo4D3gVeA6sfeY4wpD/ittfuNMTvyuy0v9e7bl47fH77HflWoUJI9e9LCtv/CKmXvOmZteodZaz7l55Q6xG2/mmI7+0JqNdKA+HJ+Ot7opX37LJo39+FyBT+3/yTHdtUPkac+iA7qh8hTH0SHwtYPTqfjbwetIh4AjTHFgThr7aHQFPCtwCpgOZBkjLk4dM3efcDM0MfC0SZRKDVtB++sm8Pb8//HjtW1YettsGsIBFzEJQZofKGPlp2zaNnSS4MGfuIi/i9aREQk+kXDfy4rAbOMMS7ABawH7rfW+o0xnYCxxphEQku2AISjTaLH7rT9jP5oKXMXHGTnmjMh9VHwJeB0+WjU2MNlt3i55JIsmjTxkZAQ6WpFREQKH0cgEL6pzCKoBrBVU8D5y++HFWs8vD5vC199DXs2ngXZJcHhp1KtX7jysniuv7I4F17oo0T4L7/8Taz1QzRSH0QH9UPkqQ+iQ2HrhxxTwDUJDnr9JhpGACXG+P2wcaOT75bA3AUHWbG0FNnp5YByuCpspn6rVXS8ujy3XnMGycnHLrb1RbJkERGRIkUBUMIuPR1WrnSxaImfLxdlsn51CbKOJAYbS+0nru77NGuWRte2dWjb+HxczkqRLVhERKSIUwCUfPfTTw6WLnXy5eIMvlsaIHVTWQJ+Fzj8UGErnLWIcnU30bSph5uaNeWqGteQGJcY6bJFRERihgKgnBKvF9avd7LoOz+fLTzC6hVJHN5TOtgYH4AqS4m75HvqNNzDJRcm0KJOQ5pUak3FYrdHtnAREZEYpgAoeXL4MCxb5uSLRUf59jsPm1PK4c08Np17EKp+RdkWlkbnH+WKCypy4elNqZd8L/Gu+MgWLiIiIr9RAJS/5fXChg1Ovl2awddLj7BuVRJ7dpSHgBMciVBpNa5GcznznF9o0czFZeecyfmVL6BisTaRLl1ERET+gQKgABAIQGoqfLUkjS++S2PNqgR2bToNf3YCUBySgCrfU/qqjTQ4L50rm5emRc3G1Eu+RaN7IiIihYwCYIw6eDDA/MV7+fy7Q6xe6WbnxtPIPlwOKAmuTDhtJaWbfUXdBge58Px4Lm1YjYYVGlI2sWWkSxcREZFTpAAYAzKzfMxfuosFiw+wcmUcqRsqk/FLdaBU8A3lN1L6rIU0qL+fC893cuWFp9PotPqUiD8nonWLiIhIeCgAFjH7MvaRsmc9n3+3n0XflOB/K2qSvr0ueOsF31D8V0rXspjLVnDB+Q6uvqgCTWvWJTHu8sgWLiIiIgVGAbCQSvek8+P+jWzcv4EN+1JYvfUXUpaeTtr65rDlKshIBoefEtU30vDaxTRp4ufqi8vTsn4N4l2NI12+iIiIRJACYJTz+DxsPriJjfvXs3H/ejbsW8+G/evZfmAnpDaHzdfg2HIvgZ/PBaBE2XSaXLGfNldl07Z1CZKTqwJVI/tDiIiISFRRAIwyP6XtZPzG2SxPXcmGfevZfHATHr8HAOehmpTfdQeuLf+HO6UR2RmJuOICXNDUxxV3Z3H55V7q1w/gdJaN8E8hIiIi0UwBMMpMXDeOkSuHU7VkNc4s2ZC6/p6kbWjBlmW12f6/YuwGzjjDz603eWnVKoOWLb2ULBnpqkVERKQwUQCMMg+Y/lTbPIhPP3KxeLGLjAwHCQkBmjf3cc9dmbRq5aNOHT8OR6QrFRERkcJKATDKDBuWyNixburU8dGpk4dWrbw0a+ajWLFIVyYiIiJFhQJglOnTJ4unnnKTmHg00qWIiIhIEeWMdAHyR0lJUFU37YqIiEgYKQCKiIiIxBgFQBEREZEYowAoIiIiEmMUAEVERERijAKgiIiISIxRABQRERGJMQqAIiIiIjFGAVBEREQkxigAioiIiMQYBUARERGRGKMAKCIiIhJj4iJdQCHjAnA6HWE/UEEcQ05M/RB56oPooH6IPPVBdChM/ZCjVtef2xyBQKBgqyncLga+jXQRIiIiInnQEliYc4MCYN4kAE2BnwFfhGsRERER+Scu4DRgGZCVs0EBUERERCTG6CYQERERkRijACgiIiISYxQARURERGKMAqCIiIhIjFEAFBEREYkxCoAiIiIiMUYBUERERCTG6FFwUcQYUxeYDCQD+4A7rbWbIltV7DHGbAMyQ18Aj1trP41YQTHAGDMEuBGoATSw1q4Lbdc5UYD+oR+2oXOiQBhjkoEpQG0gG9gEdLfW7jHGNAPGAknANuAOa+3uSNValJ2gHwLAWsAfensna+3ayFR68jQCGF3GAK9Za+sCrxE80SUyOlprG4W+9B+68JsDXAJs/9N2nRMF6+/6AXROFJQAMNhaa6y1DYAtwCBjjBOYCvwndD58AwyKYJ1F3XH7IUd78xznQ6ELf6AAGDWMMRWB84C3Q5veBs4zxlSIXFUiBcNau9Bam5pzm86Jgne8fpCCZa3db639KsemJUB1oAmQaa099jzXMcDNBVxezPiHfigyFACjR1XgJ2utDyD0567Qdil404wxa4wxo4wxZSJdTIzSORFddE4UsNCoXw/gfaAaOUZmrbV7AacxplyEyosZf+qHY74yxqwyxgw0xiREqLRTogAo8lctrbXnAk0BBzAywvWIRJrOich4FUhHf9+R9ud+qGatPZ/g5RL1gP+LVGGnQgEweqQCVYwxLoDQn6eHtksBOjYFZq3NAkYBLSJbUczSOREldE4UvNANOWcCt1hr/cAOckxBGmPKA35r7f4IlRgTjtMPOc+Hw8AECun5oAAYJUJ3cq0Cbgttug1Yaa3dE7mqYo8xprgxpnToewdwK8F+kQKmcyI66JwoeMaYAQSv+WsfCt0Ay4EkY8zFodf3ATMjUV+sOF4/GGPKGmOSQt/HAR0ppOeDIxAIRLoGCTHGnEVwyYuywAGCS17YyFYVW4wxtYBZgCv0tR540Fr7c0QLK+KMMSOADkBlYC+wz1pbX+dEwTpePwBt0TlRYIwx9YF1wI9ARmjzVmvtv4wxzQneCZ/I78vA/BqRQou4v+sHYDDBPggA8cBi4GFrbXok6jwVCoAiIiIiMUZTwCIiIiIxRgFQREREJMYoAIqIiIjEGAVAERERkRijACgiIiISYxQARURERGJMXKQLEBEpjIwxOdf9KgZkAb7Q6+7W2mkFX5WISO5oHUARkVNkjNkG3GOt/SzCpYiI5IpGAEVE8pExxgn0BroBZYDPgfustfuNMTUIPk2gK/AsUAJ4kuBjviYC1YCp1toHQvvqEtrPSqAT8DPwH2vt56H204ExwMXAfuBFa+34AvlBRaRQ0zWAIiL5qyfQHrgUOJ3gI+xe+9N7LiT0gHlgOPAUcCVQH7jZGHPpn967BSgPPAO8Z4wpF2qbDuwMHacjMMAY0yoMP5OIFDEKgCIi+es+4Clr7c7QA+T7AR1DD44/5jlrbaa1dj5wBHjbWrvbWvsT8C3QOMd7dwPDrbUea+0MwAJtjDFVgRbA46F9rQImAHeG/ScUkUJPU8AiIvmrOjDbGOPPsc0HVMrx+tcc32cc53WJHK9/stbmvFh7O8ERv9OB/dbatD+1nX8KtYtIjFAAFBHJX6lAV2vtoj83hK4BzKsqxhhHjhBYDXgf2AWUM8aUzBECqwE/ncQxRCTGaApYRCR/jQFeMMZUBzDGVDDGtDuF/VUEHjTGxBtjbgLOBj6y1qYCi4GBxphEY0xD4G5g6inWLyIxQCOAIiL56xXAAcwP3aW7G5gBzD3J/S0leMPIXoJTxR2ttftCbbcRDJy7CN5s8oyWohGR3NA6gCIiUSq0DMw91tqLI12LiBQtmgIWERERiTEKgCIiIiIxRlPAIiIiIjFGI4AiIiIiMUYBUERERCTGKACKiIiIxBgFQBEREZEYowAoIiIiEmMUAEVERERizP8DhQBH/kxguTgAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 720x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}