Open IIT Data Analytics 2020-21.ipynb

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  "metadata": {
    "colab": {
      "name": "Open IIT Data Analytics 2020-21",
      "provenance": [],
      "collapsed_sections": [],
      "mount_file_id": "14XKY4_WnGWafR5He1rTnckyH2sY-Py45",
      "authorship_tag": "ABX9TyPVAa/KZP8lU3E7PXwWygTE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/Jeevesh28/Song-Popularity-Predictor/blob/main/Open_IIT_Data_Analytics_2020_21.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "edpD8iHNBrgE"
      },
      "source": [
        "### **Importing Libraries and Data:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nSDO0Ex6qWNg"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "from pandas.core.common import SettingWithCopyWarning\n",
        "warnings.simplefilter(action=\"ignore\", category=SettingWithCopyWarning)\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kvoO0sxvqaku",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "068d5807-e0e9-469e-a192-2c7cc63ab3f5"
      },
      "source": [
        "train_df = pd.read_csv(\"/content/drive/MyDrive/Data Analytics (Open IIT) 2020-21/openiit_train_data.csv\")\n",
        "train_df.head()"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
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              "    }\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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>release_date</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2015</td>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>No</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5</td>\n",
              "      <td>0.513</td>\n",
              "      <td>-27.398</td>\n",
              "      <td>Major</td>\n",
              "      <td>01-01-1947</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1947</td>\n",
              "      <td>3.0</td>\n",
              "      <td>very low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>15901</td>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4</td>\n",
              "      <td>0.151</td>\n",
              "      <td>-10.046</td>\n",
              "      <td>Major</td>\n",
              "      <td>13-11-2020</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>2020</td>\n",
              "      <td>2.4</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>9002</td>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>-8.009</td>\n",
              "      <td>Minor</td>\n",
              "      <td>01-01-1950</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1950</td>\n",
              "      <td>2.6</td>\n",
              "      <td>very low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>6734</td>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>No</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7</td>\n",
              "      <td>0.218</td>\n",
              "      <td>-15.917</td>\n",
              "      <td>Major</td>\n",
              "      <td>30-04-1974</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>1974</td>\n",
              "      <td>2.4</td>\n",
              "      <td>low</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>15563</td>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.322</td>\n",
              "      <td>-12.344</td>\n",
              "      <td>Major</td>\n",
              "      <td>01-01-1973</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>1973</td>\n",
              "      <td>3.4</td>\n",
              "      <td>average</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  acousticness  danceability  ...  year duration-min  popularity\n",
              "0   2015         0.949         0.235  ...  1947          3.0    very low\n",
              "1  15901         0.855         0.456  ...  2020          2.4         low\n",
              "2   9002         0.827         0.495  ...  1950          2.6    very low\n",
              "3   6734         0.654         0.643  ...  1974          2.4         low\n",
              "4  15563         0.738         0.705  ...  1973          3.4     average\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1RlcZw1UqcQL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "64a0d84e-c068-4813-c35c-215850753584"
      },
      "source": [
        "test_df = pd.read_csv(\"/content/drive/MyDrive/Data Analytics (Open IIT) 2020-21/openiit_test_data.csv\")\n",
        "test_df.head()"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
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              "\n",
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              "    }\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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>release_date</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>4943</td>\n",
              "      <td>0.03360</td>\n",
              "      <td>0.523</td>\n",
              "      <td>0.861</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>-3.447</td>\n",
              "      <td>Major</td>\n",
              "      <td>01-01-2014</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>127.049</td>\n",
              "      <td>0.346</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>16165</td>\n",
              "      <td>0.76500</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.453</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0897</td>\n",
              "      <td>-11.649</td>\n",
              "      <td>Major</td>\n",
              "      <td>01-04-1972</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>129.987</td>\n",
              "      <td>0.830</td>\n",
              "      <td>1972</td>\n",
              "      <td>3.8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>6774</td>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.640</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0948</td>\n",
              "      <td>-5.862</td>\n",
              "      <td>Major</td>\n",
              "      <td>02-06-1998</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>142.483</td>\n",
              "      <td>0.289</td>\n",
              "      <td>1998</td>\n",
              "      <td>4.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>9959</td>\n",
              "      <td>0.00463</td>\n",
              "      <td>0.890</td>\n",
              "      <td>0.617</td>\n",
              "      <td>No</td>\n",
              "      <td>0.003850</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0291</td>\n",
              "      <td>-12.096</td>\n",
              "      <td>Minor</td>\n",
              "      <td>08-09-1980</td>\n",
              "      <td>0.0418</td>\n",
              "      <td>121.126</td>\n",
              "      <td>0.970</td>\n",
              "      <td>1980</td>\n",
              "      <td>3.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>13818</td>\n",
              "      <td>0.00187</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.943</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>-2.492</td>\n",
              "      <td>Major</td>\n",
              "      <td>17-04-2007</td>\n",
              "      <td>0.0891</td>\n",
              "      <td>165.026</td>\n",
              "      <td>0.882</td>\n",
              "      <td>2007</td>\n",
              "      <td>3.2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      id  acousticness  danceability  ...  valence  year  duration-min\n",
              "0   4943       0.03360         0.523  ...    0.346  2014           3.8\n",
              "1  16165       0.76500         0.687  ...    0.830  1972           3.8\n",
              "2   6774       0.36300         0.536  ...    0.289  1998           4.4\n",
              "3   9959       0.00463         0.890  ...    0.970  1980           3.9\n",
              "4  13818       0.00187         0.555  ...    0.882  2007           3.2\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wGCDh-pXBjRB"
      },
      "source": [
        "### **EDA and Statistics Checking:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_iiiNTEGrrpc",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d04bc14e-94d0-4446-b38a-33a256b9e370"
      },
      "source": [
        "print('Train data contains: ' + str(train_df.shape[0]) + ' rows and ' + str(train_df.shape[1]) + ' columns')\n",
        "print('Column Names are:\\n', train_df.columns)"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Train data contains: 12227 rows and 17 columns\n",
            "Column Names are:\n",
            " Index(['id', 'acousticness', 'danceability', 'energy', 'explicit',\n",
            "       'instrumentalness', 'key', 'liveness', 'loudness', 'mode',\n",
            "       'release_date', 'speechiness', 'tempo', 'valence', 'year',\n",
            "       'duration-min', 'popularity'],\n",
            "      dtype='object')\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "YIoOQZPIqfnu",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0b83ccd9-de95-49ae-c198-0c7b0cc3b536"
      },
      "source": [
        "train_df['popularity'].value_counts()"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "very low     3222\n",
              "low          3118\n",
              "average      2912\n",
              "high         2606\n",
              "very high     369\n",
              "Name: popularity, dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XIOGa0kErtkp",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "00bc87f3-6757-4215-b9c6-c3096dfc192f"
      },
      "source": [
        "train_df.apply(lambda x : len(x.unique()))"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "id                  12227\n",
              "acousticness         2714\n",
              "danceability          898\n",
              "energy               1396\n",
              "explicit                2\n",
              "instrumentalness     3658\n",
              "key                    12\n",
              "liveness             1477\n",
              "loudness             8718\n",
              "mode                    2\n",
              "release_date         3859\n",
              "speechiness          1275\n",
              "tempo               11264\n",
              "valence              1256\n",
              "year                  102\n",
              "duration-min          172\n",
              "popularity              5\n",
              "dtype: int64"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "20ul2Jkjrpu-",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "8f65653f-8d27-4e9e-ce79-dd94ca966a0c"
      },
      "source": [
        "train_df.info()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 12227 entries, 0 to 12226\n",
            "Data columns (total 17 columns):\n",
            " #   Column            Non-Null Count  Dtype  \n",
            "---  ------            --------------  -----  \n",
            " 0   id                12227 non-null  int64  \n",
            " 1   acousticness      12227 non-null  float64\n",
            " 2   danceability      12227 non-null  float64\n",
            " 3   energy            12227 non-null  float64\n",
            " 4   explicit          12227 non-null  object \n",
            " 5   instrumentalness  12227 non-null  float64\n",
            " 6   key               12227 non-null  int64  \n",
            " 7   liveness          12227 non-null  float64\n",
            " 8   loudness          12227 non-null  float64\n",
            " 9   mode              12227 non-null  object \n",
            " 10  release_date      12227 non-null  object \n",
            " 11  speechiness       12227 non-null  float64\n",
            " 12  tempo             12227 non-null  float64\n",
            " 13  valence           12227 non-null  float64\n",
            " 14  year              12227 non-null  int64  \n",
            " 15  duration-min      12227 non-null  float64\n",
            " 16  popularity        12227 non-null  object \n",
            "dtypes: float64(10), int64(3), object(4)\n",
            "memory usage: 1.6+ MB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JUPs3t-U3ytX"
      },
      "source": [
        "train_df.drop('id', axis = 1, inplace = True)\n",
        "test_df.drop('id', axis = 1, inplace = True)"
      ],
      "execution_count": 8,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wyTWYoYorvb0",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "outputId": "aee98fc0-eefc-4aca-b6a4-01168ac0bf4b"
      },
      "source": [
        "train_df.describe()"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "      <td>12227.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.430578</td>\n",
              "      <td>0.556353</td>\n",
              "      <td>0.522129</td>\n",
              "      <td>0.149321</td>\n",
              "      <td>5.205202</td>\n",
              "      <td>0.201365</td>\n",
              "      <td>-10.668687</td>\n",
              "      <td>0.097680</td>\n",
              "      <td>118.167495</td>\n",
              "      <td>0.525300</td>\n",
              "      <td>1984.517298</td>\n",
              "      <td>3.888133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.366893</td>\n",
              "      <td>0.175373</td>\n",
              "      <td>0.262482</td>\n",
              "      <td>0.297954</td>\n",
              "      <td>3.526954</td>\n",
              "      <td>0.173987</td>\n",
              "      <td>5.506888</td>\n",
              "      <td>0.155895</td>\n",
              "      <td>30.200064</td>\n",
              "      <td>0.258205</td>\n",
              "      <td>25.911998</td>\n",
              "      <td>2.383133</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.000001</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000020</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.014700</td>\n",
              "      <td>-43.738000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>1920.000000</td>\n",
              "      <td>0.200000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.058950</td>\n",
              "      <td>0.438000</td>\n",
              "      <td>0.303000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>0.096200</td>\n",
              "      <td>-13.656000</td>\n",
              "      <td>0.034700</td>\n",
              "      <td>95.050500</td>\n",
              "      <td>0.321000</td>\n",
              "      <td>1966.000000</td>\n",
              "      <td>2.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.354000</td>\n",
              "      <td>0.569000</td>\n",
              "      <td>0.534000</td>\n",
              "      <td>0.000115</td>\n",
              "      <td>5.000000</td>\n",
              "      <td>0.132000</td>\n",
              "      <td>-9.584000</td>\n",
              "      <td>0.045600</td>\n",
              "      <td>116.915000</td>\n",
              "      <td>0.532000</td>\n",
              "      <td>1987.000000</td>\n",
              "      <td>3.600000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.805000</td>\n",
              "      <td>0.685000</td>\n",
              "      <td>0.739000</td>\n",
              "      <td>0.055650</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>0.252000</td>\n",
              "      <td>-6.571500</td>\n",
              "      <td>0.078900</td>\n",
              "      <td>136.108500</td>\n",
              "      <td>0.737000</td>\n",
              "      <td>2008.000000</td>\n",
              "      <td>4.400000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>0.996000</td>\n",
              "      <td>0.980000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>11.000000</td>\n",
              "      <td>0.997000</td>\n",
              "      <td>1.006000</td>\n",
              "      <td>0.968000</td>\n",
              "      <td>216.843000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>2021.000000</td>\n",
              "      <td>72.800000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       acousticness  danceability  ...          year  duration-min\n",
              "count  12227.000000  12227.000000  ...  12227.000000  12227.000000\n",
              "mean       0.430578      0.556353  ...   1984.517298      3.888133\n",
              "std        0.366893      0.175373  ...     25.911998      2.383133\n",
              "min        0.000001      0.000000  ...   1920.000000      0.200000\n",
              "25%        0.058950      0.438000  ...   1966.000000      2.900000\n",
              "50%        0.354000      0.569000  ...   1987.000000      3.600000\n",
              "75%        0.805000      0.685000  ...   2008.000000      4.400000\n",
              "max        0.996000      0.980000  ...   2021.000000     72.800000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0eMjlcoUrxAS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 607
        },
        "outputId": "ab593393-dbcc-4049-b73f-2ea19cc81162"
      },
      "source": [
        "train_df.hist(figsize = (10,10));"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 720x720 with 12 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TbDcueyNryz6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 672
        },
        "outputId": "760e8e56-50df-477f-fa05-90b9588c6190"
      },
      "source": [
        "plt.figure(figsize=(15, 10))\n",
        "sns.heatmap(train_df.corr().round(2), annot = True, linewidths=0.2, cmap = \"YlGnBu\");"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BoGHLVN_3l2n"
      },
      "source": [
        "stats = train_df.copy()"
      ],
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1NhL1TVOruAc"
      },
      "source": [
        "stats.drop(['release_date'], axis = 1, inplace = True)"
      ],
      "execution_count": 13,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WzAegm70qkXq"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "stats['mode'] = LabelEncoder().fit_transform(stats['mode'])\n",
        "stats['explicit'] = LabelEncoder().fit_transform(stats['explicit'])\n",
        "stats['popularity'] = LabelEncoder().fit_transform(stats['popularity'])"
      ],
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6ZUSVOog_f-_"
      },
      "source": [
        "stats = abs(stats)"
      ],
      "execution_count": 15,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "4fCVFB7L4Uvb",
        "outputId": "9d141811-09b4-413e-90e2-67fbce1e1bc9"
      },
      "source": [
        "stats.head()"
      ],
      "execution_count": 16,
      "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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>popularity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.513</td>\n",
              "      <td>27.398</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1947.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4.0</td>\n",
              "      <td>0.151</td>\n",
              "      <td>10.046</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>2020.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>8.009</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1950.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7.0</td>\n",
              "      <td>0.218</td>\n",
              "      <td>15.917</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>1974.0</td>\n",
              "      <td>2.4</td>\n",
              "      <td>2.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5.0</td>\n",
              "      <td>0.322</td>\n",
              "      <td>12.344</td>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>1973.0</td>\n",
              "      <td>3.4</td>\n",
              "      <td>0.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ...    year  duration-min  popularity\n",
              "0         0.949         0.235  0.0276  ...  1947.0           3.0         4.0\n",
              "1         0.855         0.456  0.4850  ...  2020.0           2.4         2.0\n",
              "2         0.827         0.495  0.4990  ...  1950.0           2.6         4.0\n",
              "3         0.654         0.643  0.4690  ...  1974.0           2.4         2.0\n",
              "4         0.738         0.705  0.3110  ...  1973.0           3.4         0.0\n",
              "\n",
              "[5 rows x 15 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N08DUaTGraGf"
      },
      "source": [
        "X_data = stats.iloc[:, :-1]"
      ],
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "zS4sc35drXHS"
      },
      "source": [
        "Y_data = stats.iloc[:, -1]"
      ],
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HPx-IEthrb-6",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b4ba73bf-d00d-441b-831d-04d2c35407f6"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
        "data = train_df\n",
        "X = X_data\n",
        "y = Y_data  \n",
        "bestfeatures = SelectKBest(score_func=chi2, k= 'all')\n",
        "fit = bestfeatures.fit(X,y)\n",
        "dfscores = pd.DataFrame(fit.scores_)\n",
        "dfcolumns = pd.DataFrame(X.columns)\n",
        "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "featureScores.columns = ['Specs','Score']  \n",
        "print(featureScores.nlargest(10,'Score'))  "
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Specs        Score\n",
            "7           loudness  5980.503233\n",
            "12              year  1675.518511\n",
            "3           explicit   979.745149\n",
            "4   instrumentalness   805.665282\n",
            "0       acousticness   717.234276\n",
            "10             tempo   666.133891\n",
            "2             energy   196.116615\n",
            "9        speechiness   147.213313\n",
            "13      duration-min    75.233076\n",
            "8               mode    39.986979\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP6kIILerdpU",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "a0dda7a3-25dc-4eed-bdef-345263d24609"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
        "data = train_df\n",
        "X = X_data \n",
        "y = Y_data   \n",
        "bestfeatures = SelectKBest(score_func=f_classif, k= 'all')\n",
        "fit = bestfeatures.fit(X,y)\n",
        "dfscores = pd.DataFrame(fit.scores_)\n",
        "dfcolumns = pd.DataFrame(X.columns)\n",
        "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "featureScores.columns = ['Specs','Score']  \n",
        "print(featureScores.nlargest(10,'Score')) "
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Specs        Score\n",
            "12              year  2080.300532\n",
            "0       acousticness   705.813139\n",
            "7           loudness   635.212415\n",
            "2             energy   422.843907\n",
            "4   instrumentalness   380.883475\n",
            "3           explicit   301.585175\n",
            "1       danceability   173.956579\n",
            "9        speechiness   155.392881\n",
            "6           liveness    35.511399\n",
            "11           valence    22.753811\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6_dVwSqHrfEg",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e2b8b26c-9507-4f7e-aec1-0f121ad02d91"
      },
      "source": [
        "from sklearn.feature_selection import SelectKBest\n",
        "from sklearn.feature_selection import chi2, f_classif, mutual_info_classif\n",
        "data = train_df\n",
        "X = X_data\n",
        "y = Y_data   \n",
        "bestfeatures = SelectKBest(score_func=mutual_info_classif, k= 'all')\n",
        "fit = bestfeatures.fit(X,y)\n",
        "dfscores = pd.DataFrame(fit.scores_)\n",
        "dfcolumns = pd.DataFrame(X.columns)\n",
        "featureScores = pd.concat([dfcolumns,dfscores],axis=1)\n",
        "featureScores.columns = ['Specs','Score']  \n",
        "print(featureScores.nlargest(10,'Score')) "
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "               Specs     Score\n",
            "12              year  0.563831\n",
            "0       acousticness  0.137062\n",
            "7           loudness  0.115340\n",
            "4   instrumentalness  0.087938\n",
            "2             energy  0.081075\n",
            "13      duration-min  0.072789\n",
            "10             tempo  0.068400\n",
            "9        speechiness  0.053183\n",
            "1       danceability  0.037766\n",
            "3           explicit  0.032554\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jf43qjw0rhff",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "2d11f8bb-e35e-4df1-e28e-6cc882967b46"
      },
      "source": [
        "X = X_data \n",
        "y = Y_data    \n",
        "from xgboost import XGBClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "model = XGBClassifier()\n",
        "model.fit(X,y)\n",
        "print(model.feature_importances_) \n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(10).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.03116641 0.02962268 0.03700556 0.08252462 0.12498511 0.01167935\n",
            " 0.03369593 0.03213377 0.01083734 0.02565999 0.02273731 0.02951304\n",
            " 0.48735103 0.04108782]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RplHhM92riQ7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 315
        },
        "outputId": "1eb064e7-dc74-447d-95f2-7cb80faa1485"
      },
      "source": [
        "X = X_data  \n",
        "y = Y_data    \n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "model = RandomForestClassifier()\n",
        "model.fit(X,y)\n",
        "print(model.feature_importances_) \n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(10).plot(kind='barh')\n",
        "plt.show()"
      ],
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.09245612 0.06248663 0.07217951 0.0101033  0.06239967 0.03617937\n",
            " 0.06017999 0.08228524 0.00952812 0.06823558 0.05885625 0.06163711\n",
            " 0.26384284 0.05963027]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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vB9tPAg9JOrGUSdI+pX4bsLpsn9ajr+MkjZH0CuBwYEE/5/JUiRsA289QW5F+nV4OYUZExNBqiWRnexFwNbAU+DHPJ5Z/Au4B7gIe6Ec/N1I7Z9YpaQm183NQS5Tvk7QUuJ/aeT2AC6kd3lwIPNqju2XAbcB84FO2H+7ndL4LnFcuetmtlF0FbADm9rOPiIgYRLJzemiolb/Za7P9T/2pP3rCJE847ZIhjqo6cgeViACQtNB2R6N9rXKBSmVJuh7YjdqVnf2y98Q2OvMLPCJi0CTZDTHbJzQ7hoiIka4lztlFREQMpSS7iIiovCS7iIiovCS7iIiovCS7iIiovCS7iIiovCS7iIiovCS7iIiovCS7iIiovNxBpQV1rV5D+4w5zQ6jEnLfzIiArOwiImIEGLHJTtIqSeMalL9D0oxmxBQREUMjhzF7sD2b2jPxIiKiIlpiZSfpZZLmSFoq6T5JJ5WV1+ckdUm6V9Lupe54SddKWlBeh9T1cXmpu1jScaV8lKTPl36XSTqrbuizJC0qY0wu9U+X9JWyfYWkL0n6maSVkqbVxXxeGX+ZpIt6m0cpnylpean7+WH5UCMi4s9aZWV3DPCw7WMBJLUBnwXW2N5b0qnAJcDbgC8CX7B9p6RdgJuAPYDzgVttnyFpe+BeSbcApwLtwBTbz0naoW7cR23vJ+mD1J5q/ncNYpsAHApMprbiu0bS0cAk4ABAwGxJhwHje85D0iuAE4DJtl1iexFJ04HpAKO2G7/JH2BERPSuJVZ2QBfwJkmflfR622tK+X/W/TyobB8FfEXSEmrJZztJY4GjgRml/HZgDLBLqX+p7ecAbD9eN+515edCagmxkRtsb7C9HNiplB1dXouBRdQS4aRe5rEGeAb4pqR3AmsbDWJ7lu0O2x2jtmnb2GcVERGbqCVWdrZ/IWk/4K3Av0ia172rvlr5uRVwoO1n6vuQJOBdtlf0KN/Y0OvKz/X0/lmsq9tW3c/P2L60Z+We87D9SUkHAEcC04APswlPLY+IiIFriZWdpJ2BtbavBC4G9iu7Tqr7eXfZngucVdd2Stm8ido5OJXyfUv5zcDfS9q6lNcfxtxcNwFnlBUlkiZK2rHRPEqdNts/Aj4G7DMI40dExCZoiZUdsDdwsaQNwLPAB4BrgJdLWkZtdfXeUvds4KulfGvgDuBM4FPUzustk7QV8BC1c3yXAa8p5c8C3wC+MpBgbc+VtAdwd8mtTwOnALs3mMe2wA8kjaG2IjxnIGNHRMSmk+2+azWBpFVAh+1Hmx3LcOvo6HBnZ2ezw4iI2KJIWmi7o9G+ljiMGRERMZRa5TDmi9hub3YMERFRDVnZRURE5SXZRURE5SXZRURE5SXZRURE5SXZRURE5SXZRURE5SXZRURE5SXZRURE5bXsH5WPZF2r19A+Y06zw6ikVTOPbXYIEdEEWdlFRETlJdlFRETlJdlFRETlJdkNgu4Hw0ZERGsakclO0imS7pW0RNKlkkZJelrSpyUtlTRf0k6l7nhJ10paUF6HlPILJX1H0l3Ad0q9myXdL+kySb+WNE7SJyV9tG7sT0v6SJOmHhExIo24ZFeeMH4ScIjtKcB64GTgZcB82/tQe/r5+0uTLwJfsL0/8C5qTz7vtidwlO33Av8M3Gp7L2pPWd+l1LkcOLWMvRXwHuDKBnFNl9QpqXP92jWDOeWIiBFvJB5+OxKYCiyQBPBS4BHgT8APS52FwJvK9lHAnqUuwHaSxpbt2bb/WLYPBU4AsH2jpN+X7VWSHpO0L7ATsNj2Yz2Dsj0LmAUwesKk1nx8fETEFmokJjsB37b9jy8olM613Z1k1vP8Z7MVcKDtZ3rUB/hDP8e8DDgd+EtqK72IiBhGI+4wJjAPmCZpRwBJO0jadSP15wJndb+RNKWXencB7y51jgZeXrfveuAYYH/gps0PPSIiNseIS3a2lwMXAHMlLQNuBiZspMnZQIekZZKWA2f2Uu8i4GhJ9wEnAr8Fnipj/gm4Dfie7fWDM5OIiOgvPX/kLgZC0mhgve3nJB0EfL1cANN9Ycoi4ETbD/bVV0dHhzs7O4c24IiIipG00HZHo30j8ZzdUNkF+F5JbH+iXM0paU9qF75c359EFxERgy/JbpCURLZvg/LlwKuHP6KIiOg24s7ZRUTEyJNkFxERlZdkFxERlZdkFxERlZdkFxERlZdkFxERlZdkFxERlZdkFxERlZc/Km9BXavX0D5jTrPDiGLVzGObHUJEDFBWdhERUXmVSXaSnh6kfg6X9MO+a0ZExJaiMskuIiKiN5VLdqq5WNJ9kroknVTKX7Bik/QVSaeX7WMkPSBpEfDOujoXSrpc0u2SVko6u27fKZLulbRE0qWSRpXXFXVjf6zUPVvS8vJMvO8O12cRERE1VbxA5Z3AFGAfYBywQNIdvVWWNAb4BvBG4JfA1T2qTAaOALYFVkj6OrA7cBJwiO1nJX0NOBm4H5ho+7Wl7+1LHzOAV9leV1fWM47pwHSAUduN3+RJR0RE7yq3sgMOBf7T9nrb/wv8BNh/I/UnAw/ZftC1J9le2WP/HNvrbD8KPALsBBwJTKWWSJeU968GVgKvlvRlSccAT5Y+lgFXSToFeK5RELZn2e6w3TFqm7bNmXdERPSiismuN8/xwvmO6We7dXXb66mthgV82/aU8vpr2xfa/j21FeXtwJnAZaXdscBXgf2oJcgqrqgjIlpWFZPdT4GTyvmz8cBhwL3Ar4E9JY0uhxKPLPUfANol7Vbev7cfY8wDpknaEUDSDpJ2lTQO2Mr2tcAFwH7lyeWvtH0b8HGgDRg7OFONiIj+qOIK43rgIGApYOAfbP8WQNL3gPuAh4DFALafKefL5khaSy1ZbruxAWwvl3QBMLcks2eBDwF/BL5VygD+ERgFXCmpjdqK8Eu2nxjMCUdExMapdpoqWsnoCZM84bRLmh1GFLmDSsSWQdJC2x2N9lVxZbfF23tiG535BRsRMWiqeM4uIiLiBZLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8pLsIiKi8nK7sBbUtXoN7TPmNDuM2Ey5l2ZE68nKLiIiKm9EJTtJH5W0Td37H5Vn20VERIWNqGQHfBT4c7Kz/dY8Wy4iovqamuwk3SBpoaT7ywNUkXSMpEWSlkqaV8p2KHWXSZov6XWl/EJJ59b1d5+kdkkvkzSn9HGfpJMknQ3sDNwm6bZSf1V5ujiSTi39L5X0nVJ2haQvSfqZpJWSptWNdZ6kBaXNRaXsReOW8pmSlpe6nx+OzzYiIp7X7AtUzrD9uKSXAgsk/QD4BnCY7Yck7VDqXQQstn28pDcC/wFM2Ui/xwAP2z4WQFKb7TWSzgGOsP1ofWVJewEXAAfbfrRuXIAJwKHAZGA2cI2ko4FJwAHUnj4+W9JhwPie40p6BXACMNm2eztsWpL9dIBR243v+5OLiIh+a/ZhzLMlLQXmA6+k9sv+DtsPAdh+vNQ7FPhOKbsVeIWk7TbSbxfwJkmflfR622v6iOONwPe7k2DduAA32N5gezmwUyk7urwWA4uoJcJJvYy7BngG+KakdwJrGwVge5btDtsdo7Zp6yPciIjYFE1LdpIOB44CDrK9D7XEsWQTu3mOF85hDIDtXwD7UUs+/yLpEwMIdV3dtup+fsb2lPLa3fY3G41r+zlqK8BrgLcBNw4gloiI2AzNXNm1Ab+3vVbSZOBAasnqMEmvgtq5ulL3p8DJpexw4FHbTwKrqCUXJO0HdLfbGVhr+0rg4u46wFPAtg1iuRU4sRxypMdhzEZuAs6QNLbUnyhpx0bjljpttn8EfAzYp5+fT0REDJJmnrO7EThT0s+BFdQOZf6O2qHM6yRtBTwCvAm4ELhc0jJqhwFPK31cC5wq6X7gHuAXpXxv4GJJG4BngQ+U8lnAjZIetn1EdyC275f0aeAnktZTW2We3lvgtudK2gO4WxLA08ApwO4Nxt0W+IGkMdRWhOdsxmcVEREDINvNjiF6GD1hkiecdkmzw4jNlDuoRDSHpIW2Oxrta/bVmNHA3hPb6MwvzIiIQdPsqzEjIiKGXJJdRERUXpJdRERUXpJdRERUXpJdRERUXpJdRERUXpJdRERUXpJdRERUXpJdRERUXu6g0oK6Vq+hfcacZocRETGshvJWe1nZRURE5SXZRURE5SXZRURE5SXZNYGkUc2OISJiJEmy64OkT0r6aN37T0v6iKTzJC2QtEzSRXX7b5C0UNL9kqbXlT8t6d8kLQUOGuZpRESMaEl2fbscOBWgPD39PcBvgUnAAcAUYKqkw0r9M2xPBTqAsyW9opS/DLjH9j627+w5iKTpkjolda5fu2ZoZxQRMcLkTw/6YHuVpMck7QvsBCwG9geOLtsAY6klvzuoJbgTSvkrS/ljwHrg2o2MMwuYBbUnlQ/BVCIiRqwku/65DDgd+EtqK70jgc/YvrS+kqTDgaOAg2yvlXQ7MKbsfsb2+uEKOCIinpfDmP1zPXAMtRXdTeV1hqSxAJImStoRaAN+XxLdZODAZgUcERHPy8quH2z/SdJtwBNldTZX0h7A3ZIAngZOAW4EzpT0c2AFML9ZMUdExPNk5/RQX8qFKYuAE20/ONTjdXR0uLOzc6iHiYioFEkLbXc02pfDmH2QtCfwS2DecCS6iIgYfDmM2Qfby4FXNzuOiIjYfFnZRURE5SXZRURE5SXZRURE5eVqzBYk6Slqf7owkowDHm12EMMo862+kTbnVpjvrrbHN9qRC1Ra04reLp+tKkmdI2nOmW/1jbQ5t/p8cxgzIiIqL8kuIiIqL8muNc1qdgBNMNLmnPlW30ibc0vPNxeoRERE5WVlFxERlZdkFxERlZdkN8wkHSNphaRfSprRYP9oSVeX/fdIaq/b94+lfIWkNw9n3Jtrc+crqV3SHyUtKa9/H+7YN0c/5nuYpEWSnpM0rce+0yQ9WF6nDV/UAzPAOa+v+45nD1/Um68f8z1H0nJJyyTNk7Rr3b6qfscbm3NrfMe28xqmFzAK+BW1G0u/BFgK7NmjzgeBfy/b7wGuLtt7lvqjgVeVfkY1e05DON924L5mz2EI5tsOvA74D2BaXfkOwMry8+Vl++XNntNQzrnse7rZcxiC+R4BbFO2P1D3b7rK33HDObfSd5yV3fA6APil7ZW2/wR8FziuR53jgG+X7WuAI1V7QuxxwHdtr7P9ELXHDh0wTHFvroHMd0vU53xtr7K9DNjQo+2bgZttP27798DNwDHDEfQADWTOW6L+zPc222vL2/nAX5XtKn/Hvc25ZSTZDa+JwH/Xvf+fUtawju3ngDXAK/rZttUMZL4Ar5K0WNJPJL1+qIMdBAP5jrbE7xcGHvcYSZ2S5ks6fnBDGxKbOt/3AT/ezLatYiBzhhb5jnO7sGhVvwF2sf2YpKnADZL2sv1kswOLQbWr7dWSXg3cKqnL9q+aHdRgkHQK0AG8odmxDJde5twS33FWdsNrNfDKuvd/Vcoa1pG0NdAGPNbPtq1ms+dbDtc+BmB7IbVzBq8Z8ogHZiDf0Zb4/cIA47a9uvxcCdwO7DuYwQ2Bfs1X0lHA+cA7bK/blLYtaCBzbp3vuNknDUfSi9pKeiW1C0y6T/Tu1aPOh3jhBRvfK9t78cILVFbS+heoDGS+47vnR+3E+Gpgh2bPaaDzrat7BS++QOUhahcuvLxst/R8B2HOLwdGl+1xwIP0uPCh1V79/De9L7X/nE3qUV7Z73gjc26Z77jpH+RIewFvBX5R/mGcX8o+Se1/QwBjgO9TuwDlXuDVdW3PL+1WAG9p9lyGcr7Au4D7gSXAIuDtzZ7LIM13f2rnPP5AbcV+f13bM8rn8Evgb5s9l6GeM3Aw0FV+eXYB72v2XAZpvrcA/1v+7S4BZo+A77jhnFvpO87twiIiovJyzi4iIiovyS4iIiovyS4iIiovyS4iIiovyS4iIiovyS4iIiovyS4iIirv/wPg9WsgJa4GvgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2SebyRXDrlng",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 282
        },
        "outputId": "c9041dd9-f02f-4d81-911e-0956c3d32129"
      },
      "source": [
        "X = X_data\n",
        "y = Y_data   \n",
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "model = AdaBoostClassifier()\n",
        "model.fit(X,y)\n",
        "print(model.feature_importances_) \n",
        "feat_importances = pd.Series(model.feature_importances_, index=X.columns)\n",
        "feat_importances.nlargest(10).plot(kind='barh')\n",
        "plt.show();"
      ],
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0.06 0.06 0.1  0.   0.02 0.   0.06 0.1  0.02 0.02 0.08 0.04 0.4  0.04]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XsZGlHktrEz_",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 513
        },
        "outputId": "9e00fc80-9a77-4108-feb6-d664e0733c74"
      },
      "source": [
        "fig = plt.figure(figsize=(15, 8))\n",
        "columns = [\"acousticness\",\"danceability\",\"energy\",\"speechiness\",\"liveness\",\"valence\"]\n",
        "for col in columns:\n",
        "    x = train_df.groupby(\"year\")[col].mean()\n",
        "    ax= sns.lineplot(x=x.index,y=x,label=col)\n",
        "ax.set_title('Audio characteristics over year')\n",
        "ax.patch.set_alpha(0.3)\n",
        "ax.set_ylabel('Measure')\n",
        "ax.set_xlabel('Year')\n",
        "ax.yaxis.grid(True)"
      ],
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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Eousg/nmms2c3kAHeGgCLGWPvAZgAwMI5rx/A/hBCCCGEEELa+O2PRnYZiPWHzz//XLFx40blzp07a9RqNZ84cWJGSUmJ58iRI5Jo6xCLxTwYDJ6eU+fxeBgAjBgxwldaWlr9ySefqJ566qnE9evXu1588cWu1g3plEwm45HXnPPTPx955JHmhx56yNq+fEft7tmzp2bdunWKDz74QL106dK4LVu2dJjw6on+3CbhXQDbAOQxxmoZY3cxxu5hjN0TLrIOwAkAFQBeB7Cov/pCCCGEEEIIuTC0trYKcXFxAbVazcvKyiR79+6Vu91utn37dsXRo0fFQGjuHQBMnDjRvXz5ci0QCgzj4+MD8fHxwUGDBvlLS0tlALBt2zZZTU2NBACqq6tFKpWK/+xnP7M98sgjLfv27ZMBgFqtDloslrNio6uuusr1r3/9S93U1CS0bbczV199tfPtt9/WWa1WBgBVVVWiU6dOiTpq12q1spaWFuH66693vvrqq6ZDhw7FJFPabxk8zvnN3ZznAP67v9onhBBCCCGEXHjmz5/vfO2113RDhw7NHjp0qHf06NHu5OTkwF//+tem6667Lo1zzvR6fWDz5s11v/3tb80LFy5Myc/PNygUiuBbb73VCAC33HKLfeXKlZphw4YZxowZ4x40aJAPAEpLS2WPP/64XhAEiMVivnTp0iYAuOOOOyyzZs3KSE1N9W/duvV0Fq2kpMT76KOPNk+ePDlTJBKhqKjI8/777zd21vd58+Y5Dx48KB0/fnwWAKhUquCqVasajxw5ImnfrsViEa655pp0j8fDOOdYsmSJMRbPj0XSiReKxMTEY2azmebgEUIIIYQQ0g/279//RlFRUaeLeJBza//+/bqioqK72h5jjD3DOR/bUfkLcvO+HxKv/z9jhwkhhBBCCCGkKxTgnccO1llURf/zxZv3vbN3+kD3hRBCCCGEEHL+owDvPPabTw/P8viDCUeb7HkD3RdCCCGEEELI+Y8CvPOUye4Rl1a3XAMArU5fykD3hxBCCCGEEHL+owDvPPX0vw5O9fiDCTKxYLa7fakD3R9CCCGEEELI+Y8CvPNQIMjx7VHjdVq5uDIvVbPJ6QukuLwB+qwIIYQQQgghXaKg4Tz0/KeHRts8fsOUYUkfp2jlDZxDvPW4KWGg+0UIIYQQQsi59thjjyU899xzcedb+9XV1aK5c+emAqFN1q+44op0AHj//fdVTz/9dDwAvPvuu6p9+/ZJz2V/+22jc9J7a8vqr5OJhebfzB/x7dJNxwsBYG91S+rl+Smmge4bIYQQQgghBDAYDIG1a9c2tD++YMECBwAHAHzyySdqv9/vKC4u9p6rflEG7zzzxncnBhttnlFjsuPXxKuk/lFZugYAOGF00EIrF6h6i0saCPKB7gYhhBBCyAXjySefjB88eHD2uHHjMo8ePSoFgFdeeUU7atSorIKCAsOcOXPS7HY7A4AFCxak3H333Uljx47NNBgMOStWrFBH6nnmmWfihw8fbigoKDDcf//9iQBQXl4umTZtWnpRUVHW+PHjM8vKyiQAsHr1alVJSUlWYWFh1uTJkzPq6upEkXrKyspkY8aMyRw0aFD2K6+8ogWAY8eOifPy8gzt+7506VLNXXfdlbRhwwb5F198oXriiSf0hYWFhvLycsmIESOyIuUOHjx4xvtYoQzeeWbFtqprRQJzPTW34AsAmJKbZGJAsMHqpoVWLkAnjHbFzJc3/33CoMR/rrx7wicD3R9CCCGEkB759wN6NB6UxbTOlEIPrnml05FpW7dulX344YeasrKyap/Px0pKSrJGjx7tvvnmm+0PPPCAFQAefvjhxFdffVX7y1/+0gIAjY2Noh07dtTu379feu2116YtXLjQ/tFHHyk//fRT1e7du2vUajU3Go0CANx9993Jr732WlNhYaFv8+bNsnvvvTd5y5YtdTNmzHDdcMMNNYIg4M9//rP2+eefj/+///s/EwAcOnRItmvXrhq73c5KSkoM1113naO725wxY4Z75syZjrlz5zoWLlxoBwCtVhvcsWOHdMKECd7XX39du3DhQmssHmlbFOCdR9YfbtRXmZ2TC9K1a/PTtA4A0CokAblEZGxxeCmDdwF6dWPFGF+Aa3ZXNV/j8gbWKKSi4ED3iRBCCCHkfLZp0ybF3Llz7Wq1mgPgs2bNcgBAaWmp7Kmnnkq0Wq2Cw+EQZsyYcTrImj9/vkMkEqG4uNhrMpnEAPDVV18pFy5caA3Xg6SkpKDVamW7d++W33jjjWmRaz0eDwOAqqoq8fXXX69vbGwUe71eZjAYfJEys2fPtqtUKq5Sqfill17q2rJli3zs2LGent7bnXfeaf373/+uHTNmjOnjjz9W79q1q6bXD6oTFOCdR17++tg1ANj9l+euaXtcLRc3Wt1+yuBdgHZVtlwCgLt9waQXPztc8uz8EXsGuk+EEEIIIVHrItN2rt19990pH3744anx48d7ly5dqvnmm2+UkXMymez0fBjOO58aEwgEmFarDR48eLC6/bnFixcnPfjgg60LFixwfP7554pnn3329CKHjLEzyrZ/H61bbrnFvmTJkoQPPvjANWrUKE9ycnLM//hPc/DOE0cabMpD9dZZWQnK72YWphrbntMpJA12z/kT4H1cWpc29aWNvzDZPfQHgi6Y7B5xXatr3CC9aqNUJLR+dbhx1kD3iRBCCCHkfDd9+nTXp59+qnY4HKy1tZV98cUXKgBwOBxCZmZmwOv14r333tN2V89VV13lXLFihTYyV89oNArx8fFBg8Hge/vtt9UAEAwGsXPnTikAWK1WISsryw8Ay5cv17Sta926dWqn08mampqErVu3KiZNmuSO5l7UanXQarWejrmUSiWfPn2684EHHki+/fbbYz48E6AA77zxm7WHrgoEueLWCYaz5mnp1dIGrz8YV93sjO345156c8vJq6rMzimfltVnDnRfzmd/3VAxKhDkism5+u/y0zRfn2p1j9tY3pQ40P0ihBBCCDmfTZw40fOjH/3IVlRUZLjyyisziouL3QDw5JNPmidMmJA1fvz4rNzc3G5Xpbzuuuucs2fPdoQXTjG88MIL8QDwzjvvNPzjH//QFhQUGPLy8gwfffSRGgCeeuqp5ptuuimtqKgoKzEx8YzMWkFBgWfy5MkZEyZMyHrssceaDQZDIJp7ueWWW2wvv/xyXGFhYVZ5ebkEAG677TabIAh83rx5zp4+m2iwrlKY56PExMRjZrP54YHuRyxZXT7R+CVfv66Wiet3//rKJ9uff3j1vss+2lv32PPXjlj8k0uyz0onn2ujnv3yZYvLN/i/pw954tGZww8MdH/OV1f+7zf/fcLkmLLt8Rk/2VnZnLD4ndLXx2bHv/PBvZPeG+i+EUIIIYR0Zv/+/W8UFRVZBrofF6vnnnsuzmKxCH/84x+boym/f/9+XVFR0V1tjzHGnuGcj+2oPGXwzgNPfXLgMrcvqJ9dlPZxR+dzkzWNAHC43tqjYZr3rNhz5bs7q2O69OrOk806i8s3GABaHD5VLOu+mLi8AaHK7JyQEafYk6yV++aOTG9M0cpKD5yyXGX3+Ol/d4QQQgghP0BXX3112rvvvqt97LHHWvurDfqiOcACQY6NR5uuU8vENb+eU9DhAhwThyQ2AEBNszPqAK/S5JB/frBh8crtVVfFqq8A8M7O6lGR160un7qrsj9ky745nucNBOPGZMdvixybNiz5c7cvqH9x3eExA9k3QgghhBAyMD777LP6w4cPV6ekpPTbyuoU4A2w339RPtLq8g++bKj+E6lY6HC8bFGGziYWmNNo90Qd4H26v94AgHn9QXnMOgugrLa1RGDwAIDNTQFeZ9YfbpzIGPyLpg/ZHTn25Jz8nTKx0LK+vIkWWyGEEEIIIf2CArwBtqncOEksMOdz8ws3dVZGJDAopaLGVqcv6r3wvq9pzQEAbyCo6HsvQwJBjroWV0lGnGI3AO7w+GmIZgcCQY7jRsfEFI38+9xkjStyXKuQBPLTtF/VW9xjvjrUqB/IPhJCCCGEkIsTLXM/wFpd3jS1TFyXrJX7uiqnkUsaLC5fRrT11rQ4cwDAF8MM3vu7qg0efzChIF27t97iLnZ6A5TB68CKbZU5Ll8gZXKu/p/tz/186uAv712598fLvjl+1ZUFKe8MRP/627Emm+KT0rohTVaPttnp1VmcPp3d49c5vQGdyxvQegNBtVwiWDQyiSlOKTEmaWSmzHilMS9VbZo4WG/MiFd0uyoWIYQQQgjpGAV4A8zm9qfq1bLj3ZWLV0ka6i2uMYEgh0jofmNFo82TAwC+AI9ZgPfZgYYSAPjR6Mx9m4+a7G4fBXgd+XdZ/UQA/M7LBu1sf+7qEWlNKVr53gN1lqvsHv97apm438ZfD5SFb+x8tMHiPmNVJ7GI2WUiwSKTiKwKqajZ7Q3oWpzOQRXGYFz76zPjFVu/++WMF89djwkhhBBCLh4U4A0glzcguLyBpDilZGt3ZZM18sYD3CrddsIcf9lQfUtXZQNBDovLlw0AvhgO0TzWaC9WyUR1MwtTjVKxYHf7gxdUgLdye5Xh+tGZtQqpqF+DqmONtomJKunhSwYndrg60ozhSZ+/u7PmySWfHh635EdFO/qzLwPB7vanJmlkZYunD/17boq6dUS6zqZVSDrcK8Zk94i3nzAnHq63JtU0u/Sl1S0Talpcl352oD756hFpTee674QQQgghFzqagzeAvqswJXBAnKKVN3RXNjtR2QAAuyubu11oZUuFKcEX4BoA8Ad5TAI8k90jbrK5iwwJylIAkIoFu/cCCvBe3Vgx7NefHPjrs/8+OL4/21lbdirF6vbnFKRrt3VW5sk5BbtkYqF5w0W62IrHH9AlqKS1P52UUzlpiL61s+AOAPRqmX/uyPTGR2cOP/Dnm0s2/Wp2/nIAWL6lcuo56zAhhBBCLno+X5ezoS4qFOANoH01LakAkJOo6jbAG5GhawCA4032bgO8jUeacgBAKRU1+IOxmYP3+rcnCoIc0rHZCaUAIL/AArx/7au7AgCqzM6o5zH2xns7ayYCwM3jDds7K6OWiYOF6dovG6zu0Z8dqE/uz/6ca3aPX/AFuEYjE/dqb5fZRWmNCSrp4YOnrFMDwQ4XlSWEEELID8Drr7+uKS4uziosLDTcdtttyX6/HyqVashDDz2UWFBQYBg9enRmXV2dCADq6+tFc+bMSRs1alTWqFGjstavXy8HgMceeyzhhhtuSBk3blzmj3/849T6+nrRlClTMoYNG2a49dZbkzMyMnIaGxuFX/ziFwlLliw5PW3koYceSvztb3971jSSCwUN0RxAJ4yOFAAoytQ1dld26rCkJgC83uLudiXNIw22bABI1sjKa1tc4/rcUQDbjptLGBC449KcAwAgl4js3oDnggjwmqxuyQmjYzIAtDi9/bp65eF66yVaufjk7KK0Lj/Te6cN/fJnb+++8W/fnLjq6hFpK/uzT+dSWW2rFgB0Somlt3WUGOI2rT/cdO9bWysH3XnZoJOx6x0hhBBCeurZrc/qj7YelcWyzmFxwzzPTHrG1Nn577//XrJ69Wr1zp07a6RSKe64446kv//97xqn08kmTpzo/tOf/mRevHhx4quvvqpdsmRJy6JFi/QPPfRQyxVXXOE+fvy4eObMmRkVFRVVAHDkyBHp9u3ba1UqFb/rrruSpk6d6vzNb37T8vHHHyvfeecdLQD8/Oc/t1533XVpTzzxRGsgEMBHH32k3rVrV00s7/lcogBvADVa3SkAgtOGJRm7K6tXy/xyiWA2O7zdZvBOtbpyZGLBHKeUGqvMTnm0C7N0pcrsLElUSw8PTlK7AEAuETl8gaA6FnX3t99/cWS8P8hVDPBb3b5+C/C2nzDHmR3e/LHZ8e92V/bKghRTmk6+51C99Uqry/duV8MYLyTHGu1xAJCokvY6wLtvRu53Gw43/ddHpbXTKMAjhBBCfni++OILZVlZmbykpMQAAC6XiyUlJQUkEgm/4YYbHAAwduxYz1dffaUEgM2bNyuPHv1PEGq32wWr1coAYPbs2Q6VSsUBYPv27YqPP/74FABcd911Tp1OFwSA3Nxcf0JCQnD79u2y+nHeizcAACAASURBVPp6UVFRkSc5OfmCXQiPArwB1OzwpiokIlO0X+7VMnGD1eXrNsBrdnhz4pXSSplYcHNA1OL0ivVqmb+3/dxd2ay1uHyDxw9KOJ1pUslEds4hbrK5pWm66Je133SkKeFXH+3/79cWjvnTyMw4e2/71BNbKkwzQgGvpNrhCST1Vzv/+O7keADsmlHpnQ7PbOvy4cmfr9xR/dQLnx4e/7sbRnY6Z+9CUtvi0gFAskbe6wCvOCvOlqaT7znWaJ/q8gbe6u9FcQghhBDSua4ybf2Fc46bbrrJ+sorr5jbHn/11VfjBSE0w0wkEsHv97NwebZr164apVJ51vwOlUoV1feIO++80/LGG29oGhsbxXfccYc1BrcxYGgO3gCyuv0papm42/l3EVqFpNHu8Xc5RNPq8olsHn9WslZWKZeIXABQ2+Lq00IrK3dUFwPA9Lzk0sgxtUxsB4BjjfYeDdNc8/2pgnqLe9zK7VUj+9KnaG0/YY5rsLjHDE/VbNTKJU0ubyCxv9oqq7VMVEpFDT+5JLsymvKPz87fI5cIpk1HL57FVppsbh0AGBKVvZqDF3HJkMRNHn8w4eX1R0fEpmeEEEIIuVDMnDnTtWbNGvWpU6dEAGA0GoWKiopOE1NTp051vPTSS7rI+x07dkg7KjdhwgTXqlWrNACwZs0apcViOR0L3Xzzzfb169er9u3bJ58/f74zdndz7lGAN4AcHn+qViHudv5dRKJK2uDxBxObrG5JZ2XWlp3K4BzinERVlVIqcgNAo9Xdp4VWympbS8QiZr99Us7p/fq0CokdAGpbnD0K8FqdPi0AHDc6cvrSp2i9urFiKgeEWy/JXh+nlJi8gaCuq+fXW8eabIpGm3vUYL1qe7RDVtUycTA/Tbu+0eopPlhnUcW6TwOhxeGNA4DhqZpeZ/AA4JEr83aKBOb6+lDjtJh0jBBCCCEXjOLiYu8zzzxjvvLKKzPy8/MNM2bMyKirq+s0wFu2bJlxz5498vz8fENubq5h6dKluo7KPf/8883r169X5uXlGVavXq1OSkoKaLVaDgAymQyTJ092XnvttTax+MIe5Hhh9/4CVtfiknr8wfhEtSzqAC9dp2gAWrDpqDH5xrFZdR2V2VXZkg0AY7LjK8tqWzMBwGz39DqDFwhy1LW4StK08n1th8rFK6V2AGi0enoUmNjcoQCv3uIe1Ns+9cT3Na0z4pWSozeOzarbdMRoAoBtJ8z6+cUZ9bFsZ+nG42M5h/jy/JQeDbWcODjx+9Lq1gWrd9cMfzZDtyeWfRoIVrdfx4DA8FStoy/1ZMQrvNkJyq1VZuekeotrWU+GARNCCCHkwnf77bfbb7/99jOm8zgcjtPJhoULF9oXLlxoB4CUlJTgmjVrzhoV9/vf/7657fv4+Pjgxo0b6yQSCTZt2iQvLS2VKxQKDgCBQAC7d+9WrF69OqbfEQcCZfAGyOZjxhQASNN1vwdexJBkdQMAHKyzdDoP76TJkcOAwLxR6bVqmdgFAC1OX68zeKt312R5/MGEgnRtadvjerXMAQBme89W0rR7AhoAaHF4c3rbp2gt33Iyx+r2DyoxxG8AgMx4hREAyuttMV9oZU9Vy0SpSGi9Z+qQIz257qbxhmMMCJbVWvJj3aeBYHf746RiwSIVC33e4+Dy/JRN/iBX/vHLozFZCZYQQgghP2wnTpwQFxcXZxUUFBgeeOCBpNdee60RAPbt2ycdNGhQzpQpU5yFhYUX/IZ5lMEbIIfrrSkAkJusjjqDN35QQiMAVDc7Ow3wGq3uHLVcXBuvkvo1cokbACyu3gd4n+2vLwGAH43O3Nf2eHqc3A4ArS5fjwI8l9evAQCXL5ByrMmmyE3WuHrbt+78c0/t5Qzw33957rcAkJeqMQFATYszpgFek9UtqWt1jRmsV23u6YIghgSlR6eQHK9pcV4UAZ7T69fJxEKfhmdGPHzlsP1vb6ts3nrcPA3AlljUSQghhJAfrsLCQt/BgwfP2v6guLjYW11dXTkAXeoXlMEbIJEgbWxOQtQB3richFaBwdNk83Qa4LU6fdkJKmkVAOiUEhcA2N3+Xg/RPNpoL1HJRHUzC1PP2MohO1FlBwCb29+zAM8X1ERef1pWn9PbfnXH7vELxxrtUzPiFTuLs+JsAHDJoEQTABhtnpgGeH/dWDEqEOSKS4fqo1o9s720OEV5s8M7zOryiWLZr4Hg8gV0cokoJgGeQioKDk1Wf1vf6hpTVtt6Qey5SAghhBAy0CjAGyBGmydFYPCMy0mIerVBkcCgkoobW5wdb5VwpMGmdPkCyWk6eSUA6NUyFwA4PP5eZfBMdo+4yeYuyopXlrY/l5eicQCA3dOzAM/jC2g1MnE1AOyvs+T0pl/R+P3n5aO9gWDclNykjZFjGfEKr0TEbLHe7HxvdUuRwOBdPGPo9725viBNczjIIXt3Z/U5mZfYnzz+oE4pFfVpBc22ri3J2MgB8V/WV1wWqzp7yuUNCNe9uuUny7eczBmoPhBCCCGERIsCvAHS4vSlqqTixp5uEq6Rixtsbl+HWyV8dqA+GwCGpWgqASBJI3MDgNMb6FUG7/VvTxQEOaRjs+PPCvAUUlFQLDCn0xvoWYDnD2oT1dITYoE5apqd/RbQbCxvmiERMeujM/POWLhEIRGZbG5/TAM8pyeglYoFS2/3Gpw7Mv0wAGw9br7gh2l6/EGdSiaOSQYPAO68dNAJtUxcu7e6ZWqs6uype1bumVNa03rjyh3VcweqD4QQQggh0aIAb4DY3b4UjTz6PfAi4pTSBqc3kBIInr2GRSQjNjk3qRIA0nVyNwC4fIFeZfC2HzcXMyBw52WDDnR0XiIS7G5fzwI8XyCoUUrF1jil5KTJ3j8LrRyss6hqW12XDE3WfBOvkp4RdKlkYpPD44/pZuduf0AtFQm9XjVy+vBks1wiGE+aHBd0gFfX4pIGglyhkUtilsETCQwFadpNZoe38IuDDf22SX1n1padSvn2mPE2AKhvdRWe6/YJIYQQQnqKArwBEAhyOLyBFJ1SGvX8u4gkjawhEOSK0uoWbftztc2ubLHAHDOGJ5sAIE2n8ACAu5cBXqXZWZKolh4enKTucCEUiYg5ehLgWV0+kT/IlSqZyJakkVVanL4crz/YsxRmFF7++thkziG+riRjQ/tzWrnE6PQFYprB8/qDKqlYsHdfsnPJGvnhRqs7v6PA/UJx4JRFBwBxCok1lvXeNin7GwB4c8vJc5rFCwQ5nv33ocWMsWB+mvbfDm8gY+txU9y57AMhhBBCzo3MzMycxsbGs2Kj999/X/X000/HD0SfeosCvAHwfW2rJhDkimSNrMcZPEOCshEAdpxsPmsentHuGaRTSKoiwz4VUlFQYPC4/T0form7sllrcfmGDNar93VWRioW7F5/MOp98A6csmgAQCOX2AwJypMBzuWfH2zocLhpX+ypbpmhkYmr77ps0PH25+JVEpM/wNXVzU5ZrNrz+oNqqVjUp33fhiSpDnv8wcQN5U0x38LhXDlhdMQBgF4tjVkGDwDmjkxvTFBJDx+ss047lwHwf6/ae6XR5hk1Iy/5zWtGpW0CgI/21lEWjxBCCPkBWbBggeO5555rGeh+9AQFeANg+wlzKgBkxSt6HOAVpGsbAOBYk/2MAC8Q5LC6fAa9RlbZ9rhYEFxef7DHAd7KHdXFADBteNJZ8+8iZGKR3esPRp3BqzQ5tAAQp5RYi7PiKwFgyzFTTOfhfbi3Nr3Z4R1elKlb39H8xiS1zAwAO0+aYxZIeQNBlbyPGbxJQ/WHAeDT/fUF0V7j8gaEjeVNiX1pN5bqLS4dAKTFKWI2By+iOCvuG5vHb1ixrTIn1nV3ZPNRY8LXhxvv1KulB169dfQXP5mQfULEmPtAnYUCPEIIIaSf2Ww2dvnll6cXFBQY8vLyDMuXL1dnZmbmLF68OHH48OGG4uLirMOHD0sAoL6+XjRnzpy0UaNGZY0aNSpr/fr18kgdN998c3JxcXFWYWFh1nvvvacCAL/fj0WLFunz8vIM+fn5ht/97ne6SLt/+MMf4kaMGJE1fPhwQ1lZmQQAli5dqrnrrruSAGDBggUpd999d9LYsWMzDQZDzooVK05/D3722WfjRo0alZWfn2949NFHEzq7DwC4//77E3Nzcw35+fmGRYsWxfyP+7QP3gCoaLKnAMDwNG2Ph2hOyU1qBIBTra4zArwN5U16f5CrsuIVlW2Pi0XM7fUHezxEs6y2tUQsYvY7Jp2dBYuQSwS7yR59gHeq1a0BAL1aZp03Kr3qd5+XByuM9hwA23rav86s3F41HUDwnqlDvunofFaC0ggARxrsegB1sWjTH+AquaRvGbybxxsqX1xX7i6vtw4HsDmaa+5ZuWf25qPGO/9yS8m9c0em9/h3KdaMNo8OAAbpVTEP8BbPGPrtxvKmn324t2767ZcOejPW9bcVCHI8/lHZvUHOJU/NLfiLVCxwqVgIJKql5adaXSP6s21CCCHkfFP/9NN6z5GjMRv5BACyvGGetOeeM3V2/l//+pcyLS3Nv379+lMA0NzcLPz617+GTqcLlpeXVy9btkxz3333JX399denFi1apH/ooYdarrjiCvfx48fFM2fOzKioqKh66qmnEmbMmOF69913m8xmszBu3LisOXPmOP/2t79pq6urxQcOHKiWSCQwGo2nE156vT5w4MCBmj/84Q+63/3ud/GrVq1qat+3xsZG0Y4dO2r3798vvfbaa9MWLlxoX7NmjbKiokJaWlpawznHzJkz07/88kt5U1OTqP19NDU1CWvXrlVXVFRUCYIAs9kc84QbZfAGQCQ4mzIsqcdfyjPiFV6ZWGg22z1nDG3cfNQ4CABGZOiq2h4XC8ztDfAeZ/AaLO7CVK28rKuNuxVSkd0XiD7AM9o8WgBI1cptGfEKr0omqm+wuGOWwfP6g+xwvXVGila2b8qwpOaOygxP05oAoDZGm527vAHBH+RKhbRvAZ5aJg4mqKVH6y3uqDN4+2paL+eA+PXNJ2b2pe1YaXX64gBgRIYupkM0AWC0Id6WqpPvPdpom+LyBvr1/7d+8c/vJ59qdU+YNES/cn5xRn3keE6i6oDV7c+mPfkIIYSQ/lVcXOzdtGmT8r777kv86quv5AkJCUEAuO2222wAcOedd9r27NkjB4DNmzcrH3jggeTCwkLDvHnz0u12u2C1WtmGDRuUf/jDH+ILCwsNU6ZMyfR4POzEiRPiDRs2KH/+859bJBIJACApKen0d92bbrrJDgDjx493V1dXSzrq2/z58x0ikQjFxcVek8kkBoDPP/9cuXHjRmVRUZFh5MiRhoqKCsmRI0ekHd1HfHx8UC6X81tvvTV51apVKrVa3el37d6iDN4AMNu9KVKR0GpIUHp6c71KJm60uM7cC+9Yky0bAK4ekXZmgCcSXL5AzzN4Hn9Qp1NIjF2VUUrFjiCH1GT3iKPZIqDF6dUAQHai0gYACUrpSbPDO7SnfevMn746OsLtCybNGqF/q7Myk4aENjs32WOz2fmxJpsSANQycZ+GaAJAVrzi0N7q1hsrTQ55jl7l7qrsh3tr0y0u3xCBwXu43nZli8P7TvsVQ881m8enFRg8vf297s4lgxM3fVxaN+7P648V/vLq4fv7o4291S2atWWnfh6vlBx9/baxa9qeGz8o4eDOymb2/q6agpGZcTv7o31CCCHkfNNVpq2/jBgxwldaWlr9ySefqJ566qnE9evXuwCAsf9Mv4m85pyzXbt21SiVyjMm6nPO8cEHH9QXFRX5om1XLpdzABCJRPD7O/5aJZPJTrfDOT/985FHHml+6KGHzlporv19vPjii8179uypWbduneKDDz5QL126NG7Lli0xGVUWQRm8AdDq8qWqZKJeD6nTysUNdo//jADvVKs7RyERNeWlapxtj0tEzO3vYYDn9QdZIMgVcomow9UzIyJBzdFGW1QZDavbrwGA4alaKwCkxSkqnd5A6rEmW6/26Wvvq0ON08QCc/5yVt72zsro1TK/VCS0tjp9MQnwqpudKgBQy8R9yuABwKisuHIAwjs7q4d1V/a9ndVTAPCZhanLvIGg7jefHrqkr+33lcMTiJOJY7fJeXsPXTlsJ2Pwf1thHNtvbby/77/8Aa585Kq8P7fPXt82MfsoY/B/X9tK8/AIIYSQflRdXS1SqVT8Zz/7me2RRx5p2bdvnwwAVq5cqQGAt956SzNmzBgXAEydOtXx0ksvnZ5Ht2PHDikAXH755c6XX345LhgM/ed8+/btssjx1157TefzheK+tkM0e+vqq692vv322zqr1coAoKqqSnTq1ClRR/dhtVpZS0uLcP311ztfffVV06FDh2I6/BWgDN6AsLv9qcla2ZHeXp+oljVWmp1TWxxecSRr0+Lw5sQrJZXty0pEgsvpCfRoIY6qZoccAJTdDDvUKiR2AKhpdqoxBN1+sXd4/FqBwZsRr/ACwLBk9cmdJ5uxrqw++4ErNOU96WNHGm3uYUka2cE0Xaj+ziikIrM1Rpud11vcKgDQKSR9DvBuHJt15M0tlby0uiUfQFln5QJBjsP1til6tfTgKzeVrN/07JcLvj1qmgXgu2jbKqttVT/9r4PXOjx+rS8QlPkCXO4PBmX+AJf5g1wWCHLZ2Jz4z5bfMX5ttHU6vX6dXCLEfP5dhCFB6dGrZYeqzM7RAGI+D++pTw6MqzI7p47Njn/nJ5dkV7c/n6yV+xKU0iO1LTQPjxBCCOlPpaWlsscff1wvCALEYjFfunRp04IFC9JaWlqE/Px8g1Qq5e+9914DACxbtsz4s5/9LDk/P9/g9/sxadIk14QJE4wvvPBC87333ptUUFBgCAaDMBgM/q+//vrUfffdZzl69KikoKDAIJFIcPvtt1see+yxPn1/mTdvnvPgwYPS8ePHZwGASqUKrlq1qvHIkSOS9vdhsViEa665Jt3j8TDOOZYsWdLliLneoADvHLN7/ILbF0hKUEo7XAQkGqlaeQMAYfMxY9L84ox6k90jtnv8GUOS1WdlriQiwe0P8h5l8KrNoayUUipydlUuXhkK8Bos7qgyeA6vXysVC6dT11Pzkk+u3FGNsjrLIAB9CvBMdo/Y7vZnDklS7+iurEomNtlcvrS+tBdhtHnUABCv6nuAl5+mdWjk4urqZmeXG56v2FaZY/f4My8ZnLxGKhb4qKy4z7efMP909e6ajBvHZkWV4n/w/X13nTA6pkvFglXEmEcsYm6xwDwigXkUUlFrs9079ECdZRKAqAM8ty8Yp5SKzNGW743cZPXercfNt2860pQwLS+5w3mWnTlYZ1FVtzhVAmMcAATGuMBCP20ev2T17ppFGrm46vXbxn7QWR2GBOXBfTWtN5ww2hWd7Q9JCCGEkL6ZP3++c/78+Wf9sfXXv/51y1/+8pczvmukpKQE16xZc9bK9CqVir/99ttnLZIikUiwbNmys4ad1tbWVkZeX3rppZ5t27bVAcCiRYtsAGwA8P77758xAs/hcJxejPBXv/pV669+9aszEh7Dhw/3dXQf+/btq2l/LJYowDvHNh81JnFASNHKez1Ec3CSqgEAymotKfOLM+rXfn8qkwOiwUmqyvZlpWLB7Q/2bJuERqs7Mq+syy+werXMDgBmhzeqAM/tDWikIsEWeT9jeLJJLDBHTbMzpyf960hXz6A9rVxsbLK6i/raJgC0OkP3nqyR93kOHgCk6eSHTxgdU7z+IJOKhQ43ffuotG4qA4KLpg/dAgAPXpG7/ua/mW99a2vlrBvHZr3RXRsrtlVmnzA6ZhRl6D75932XdZgJm/bSxocbrZ4uA8323L6ATq+WdrrqaixcVZCyd+tx8+3v76opmZaXvD7a67afMMfd8vr2vwc5pF0UCz54xbAlXc1lHJ0df6C0pvXGVTuq856aW9DpHpGEEEIIIQOFArxz7Pva1hQAGKRX9XgPvIgx2fENAFBldqQCwN7q1hwAGJeTUNW+rEwsuAI9zOCZ7F4FAGgVki4zeKm6UFDT6vRFF+D5gxqZRHQ6gycSGOKUkpMmuzenJ/3ryJ6qlkFAx8+gvQSV1OQPcuWxJpsiN1nTpyyM1eVXAUB6nCImAd6wFM3ho432We/vqjYsnJhz1r0EghzHGu2Tk7Xy0tGGeBsAXDI4sTUrQbntSKPt8nqLa0V3Q1SXbjp+m1hgzheuG/HPzsrEKaXGKrNzsssbELpaSbVtv7z+oE4lE581uTiWFk7MqfztZ+UtB09ZRwOIOsB749uTlwQ5pJcN1f9DIxfbADDOwTg4Qj/BCtO11fdOG3Ksq3punWA48o/vTgb3VrWMAEABHiGEEHKOtM2wka5RgHeOVZocKQAwMkvX6wzepCH6FoHB12j1pALASZMjhzH45xSlnWpfViYW3EEOidXlE2kVkkA09bc6vUoAiFdKuxx2mBWvdACAze1TRVOvxx/QJqpkJ9se06tllcea7Jd3lbGKRqXZmc0Y/NeMSu92iGKyVm4CgN2VLfrcZE2fUuR2TyjAy9Er+zxEEwCuKkw9vLasHpuPmvI7CvBe3ViR5/IFki/PT17V9vicorTP/++b45Nf+PTwpX+9ZfTGzup/6YvyEfUW97hJQxKXj8yM6zQo1aulRg6Idpw0x0UzFPJoo03FAbFOIem3RVaA0B8F0uMUpbUtznHRBp8A8H1t6ySVVHTqrTvHfyISWPcXdGJwktoVp5RUVDc7aaEVQgghhJyXaBXNc6zR6klhQGByblKvl5yVigWukIqaWpzeVABosnmyNXJxdUcBnFwicgNAXasr6iye1e1XAoBeLe0yuzU8VWMHALvHH1UGz+sPahVtMngAYEhQVgaCXPHloYbkaPvXkSabO0crl1SrZeJuv/BnxStMAHCs0dbnhVYcoXsPZid0va1BtOYUpTVIRUJrhdHe4fDIzw7UTxUYfA9ckXvGfMtfzMzbr5KK6rYeN8/qrO5AkOOdHdU/lUsE0x9vHNXl3Lr0uNAzOlBnSYqm34fqrToAiFdK+22RlYiRmbq9vgDXvLn15JBoypfVtqqNNk/RkGT1tr4EdxGZ8cqDzU5vXpPV3eH+OIQQQgghA4kCvHOs2eFNVUhFxmgCka5oZJIGmzu0VUKr05uTqJJ1ODRREd7qoMHijjrAs7l9SgBI0cq7HKKpVUgCIsbcTm+g2wDP6w8yX4CrlDKRre3xYkPcSQD47pipTxuetzp9OYkq6cnuSwIF6aHNzutaXX0O8Fy+gEoiYo6+ZB/bEgkMSRrZ4Uar+6wAz+UNCMeNjsvS4xS72g8tFQkMo7PjP292ePPf2lqZ01Hdj39YNqnF6cubnpe8qrthnLnJaiMAnDQ5ogrwqs1OHQAkaWT9msEDgIWXZO8DwDeWN42OpvzfNp8YxwHRrBGpW2PR/qhM3QHOIX57W1W321kQQgghhJxrFOCdYza3L0UtE/d6/l1EnFLS4PD4U8tqW9UefzAxPU5e2VE5VXihFKPdE3WA5/QGlACQGa/sMsADAImY2V2+7gO88garCoCglUvOyODNHZleDYBXNNlzou1fe3urWzQefzAhPU7R7fw7ALh0iN4MgJvs3qiCl664fQGVRCTEZHhmRI5eWe70BlK3HjfFtT3+p6+PjvD6g3GXDE78tqPrHp2Zt15g8L2zs/qsLJ7V5ROt219/m0Ymrv7fG4s7HcIZMX5QohEAGqzuqJ5Ro9WtA4CMOEW/Z/DG5iRYdQrJ8eNGR1QB3t7q1klyiWD6+ZSu59dF6+bxhkMA+M6TzTRMkxBCCCHnHQrwzjGHJ5CqU0h6Pf8uIkkja/AHufKdHdUjACAvRVPZUTm1TOwGgBaHN+qVNCMB3iC9qtsFSCQiwe72BbsN8I412bUAoFNIzsjgGRKUHpVUdKre4u51Bu+rg405ADA8reNn0J5WIQnIxEJLq9Pbo/0BO+LxB2Me4E0YlHgIAP5VeuqMLN6G8qYpIoG5Hrlq2K6OrhuZGWfPTlR9V9Fkn15pcpwR0D+0et9VDm8g/dqSjLeimbeWl6pxigXmbHZEFwSb7d44ABiarO73AA8ABulVpS0Ob96RBpuyq3KVJoe8weIqGaSPzfBMACjM0Dm0cnFVpdlB++ERQggh/USlUg0BQpuez507N3Wg+3MhoQDvHKo0OeTeQFCXqJb1OYOXGa9oAICdJ5snAMC0vOQOs1caeSiDZ3H5os7guX0BpUhgrmiGHcrEgt3rD3S7yEpdi0sDAAkqqa39uQSVtLLF2fuVNA/VW3MA4Ir8lMpor1FKRSab29/nDJ7HH1RLxbEN8G6ZYDguMPgO1luGR461OLziSrNjUla8YntXwyuvLU7/LBDkiiXrDk+JHKs0OeTfHjPdnKiSHvyfeYUdBocdUUhEJosrumfU6vLqAKAoQ9evq2hGXDo0sZQDwptbTo7sqtxfN1aMCXJIZwxP2hbL9tPjFAfMdu9wq8snimW9hBBCCDmTwWAIrF27ts/fnX9IKMA7h749ZkwBgIy43u+BFzE8VdsIADUtzvESEbNdOlTf4UqHOoXEDQA2tz/qDJ7bF1CKBdbt8EwAkIpFDo+/+wye0ebRAkCyVnZWAJCmU5x0egNpJ4z2Hu3XF1Hb4syRigTLJYMTo57/pZKJTU5voM9z8Lz+oEomFmKyRUKEXi3zxymlFXUtroLIsT9+eaTEH+DqKcOSOhyeGbF4Rm65Ri6u3HGy+fQwzUc/+P5arz8Yd8elOct7ksVSycRGh8cf1TOyuf06sYjZu9pDLpbuvmxwuUhgrr3VLV0O09x5snmiVCRYFk/PPRTL9kdk6A4GOJev3FE1OJb1EkIIIeRMx44dE+fl5RkAYPTo0ZmlpaWn97SdOHFixpYtW2Q2m43dfPPNycXFxVmFhYVZ7733ngoAli5dqpk9e3batGnT0gcNGpS9ePHi06O31qxZoxwzZkzmhvu+OQAAIABJREFUiBEjsq655ppUq9XKAOD+++9PzM3NNeTn5xsWLVqkB4C3335bnZeXZygoKDBMmDAh89w+gZ6jbRLOofIGWwoADE3W9DnAm5yrbwQAX4Br9Grp/s6+uMerQith2jzRB3hef1ApEQlRBXhyiWBvdgS7Xc2w2eHVAIAhQXlWgJeboq7cWdmMtWX1hvsvzz0SbT8jzHZvjk4pqezJNVq5xFTf6h4dCHL0ZeieLxBUy8WimGbwACAzXnFof61lfpPVLUnWyn3fVpimSETM9tAVw7rce00kMIzLSfh8Q3nTPa99c3xoiSHeuLe69brMeMXWxTN69my1CrHR7PDkRlPW6Q3EycTCORmeCQDxKqk/RSsrq2l2lXT2GTZZ3ZK6Vte4wXrV5mi3U4jWj0ZnHPxgTy22VJhGLJo2NCZz+wghhJDz0caV5XpznV0WyzoTM9Se6T8Z3uMV5a+//nr7O++8oy4pKWmuqakRNTY2ii+99FLPgw8+mDhjxgzXu+++22Q2m4Vx48ZlzZkzxwkABw4ckO3bt69aLpfz3Nzc7IcfftiiUqmCL7zwQsKmTZvqNBoNf+aZZ+KXLFkS//DDD7euXbtWXVFRUSUIAsxmswAAS5YsSfjyyy/rsrOzA5Fj57PzvoMXk5pmZyoATByS2Oc08+AktUsqCn2hTtJ0vMAKACSpZW4AcHr8UQ/R9AaCSokougyeQiKy+/y82wyexeXTAkBusuasIZpThiWdBICy2tYez8NzeQOC1e3LTtbIKntyXYJKagpwLi+vt0a1h19nfIGgSi6NbQYPAEak68o5IF61o3poXYtLWtvsmpCTqNoaTYbsl7OGbxIYPP/cXXv10/86sCAY5LIHr8hd0dM+xCulRl+Aa+taXNLuyjq9fp1cLDpnAR4ADE/Vlrp8gZRP9tWld3T+rxsrRgWCXHFZrj6mwzMBYNIQfatKKqo7YaR5eIT8P3t3GhhXVbcB/LnrzNw7W/Y9Tbqn6U432lIQSoGCLBYRCgiioCCICPUVRXhRq6IsArIJr8oOCpQWDKVAC6Ub3dt0S5ckTbPPZJn13rnr+yGkpmmWyTKZpDm/L8Lce8+cxJTOk///nEMQBDFQbrjhhsDKlSvtAPDaa685rrjiiiAArF27Vnj00UcTCgsLcxcsWJAdiUSo0tJSFgAWLFgQTkxMNARBMMeOHauUlpay69evtx45coSfM2dOTmFhYe4bb7zhqKioYBMSEgyr1Wpef/31qa+//rpot9sNAJg9e7Z04403pj/55JNOXY/qWOm4IhW8AeQJKmkMTUmTslynhZzeEC1MnRI2XLmJtvLO7kl3WWUAkFS9RxU8no2ugifwTFA3TWt3B6kHI5qDAvTRqfbTxl1YkOZhaSp8olHKi3aOrYr21aQbJvgRSWJ5T55Lc1q8ALCzoim5MMvVqwpcU0hhDRMWgWP7vYL3relZB9/YWoFt5Y0FlU3hJN00rQsL0tZH8+y4dEd4ZIr9i1JP8DzTBDMmzb7m6rNyuj0Avr00p9UDAJtLvSndPS+rhsstcJU9fY++uHJa1s61h+rxwZ7q6UumZ1e3v77xqHcuS1Phn5w/Zm8s3j/Dbdtf7g3N6+rAdd0wcdur2y9JdVgaH7584tb+Ok6DIAiCIAZKbyptsTJixAg9ISHB2L59O//uu+/an3/++XoAME0T77zzTs2kSZPUtvdv3LjRarFYTv7dyzAMNE2jTNOkFixYEH7//fdPK7rs2LHjRFFRke2dd96xP/vss+6NGzdWvfzyy57169dbVq1aJU6fPj13586dFampqf3aHdSfSAVvADWHlXSRZ2r7azc/h5WrBYApOe7yzu7JcttkAJBVPeoKnqqbAh9li6bdwgYB4FBtoMsqXiiiOTiWDnT0tTM0BZeNK/cEIz2u4G051pAHAFO7+B50ZESS6AGAo/XBXq/DO1IfFAFAtPR/i+aMvES/yDPV5Q2hgi2ljQssLN34kwvG7I/2+Wtm5HxkmOApCtqD3yx8szdzyElsOez8UG2g241WIpruFviBreBdPiWzVuCZmpLawLT214IRja5oDM/OTrBtjdW6wIJ0x37NMMW3t1XkdnRdN0xc9ezGmz47WH/7m1tP/Oqs337y9N1v7VoQjGjkv7sEQRAE0UtXX3114A9/+EOC3++nZ8yYoQDABRdcEP7LX/7iNoyWzLVly5YuW0rPOeccedu2bdaDBw9yABAIBKh9+/Zxfr+fampqopcsWRJ+5plnvAcOHLAAwKFDh7gFCxZEHn300cakpCS9rKxsUBfJyAeNARSUtTSHte9HJLRKdVgqKUD7+iy5DjltnE5TUCOa0YOAF30Fz2HlQgBwoincZaujpOpOnqE73WExxWEp80lqnqIZPUq/xzzBPADGN6dknOjJc5OyXF4AqG6O7py3jrR+zU4r1+8tmgCQ5rIerA9ECqt90oxRKfYNPVlHdtuCkcfyk8W1545N+ef80clNvXn/wkyXBwCqmro+EF5SdFrVTYfDyg1owAOAnERhV51fnuwNRk75D+3Ta49MVHXTMWdkUr8cbt6Ry6Zk7gOAzw97OmzTXPrilm/vrfQtGZfmKLpyWtajALByd/V9s5Z/+twPX91+YVNIGdR/ORAEQRDEYLR06dLgypUrHVddddXJz1/Lly9vVFWVmjBhQu7YsWNzH3jggS6PwsrIyNBffPHFumuvvTa9oKAgd9asWTn79+/nfT4fvXjx4syCgoLcuXPnZv/+97/3AMDPfvaz5PHjx+eOGzcud9asWdLMmTM73dF8MCAfMAaIbpgIKVraiCRhZ3+NufyqSau+OOzZlpsoRLq6j6EpOaIZUbdoaoYpWDkmqoDnFlrCTa1P7rKCJ6u608LSnbam5iQK5YdqA7Y1B2pTL5ucGXUIrvNH8uwWtrqrowM6MjM/sQmA4Q1Gel3Bq/PLIgC4BK7fK3gAMDrFfrDUE7oAAC6dnBFVe2Zb6+477y99ef+zRyY1ADDrA5EuQ3Bxlc8BgHLZuKh3Me0vM0Yk7CypDSx+8cvSCfdfUnCyFfOLEs/ZNAXlrvPH9Nuft/YuKkz3WDnac6w+WAjgw7bXbv7H1su+Kmu8MT9ZXPfBXfNf4FnaVDTjy4dW7Zv1UXHtNR/vr7vri8OfXTc7P+m93181aU1WQs9+fgmCIAjiTBcKhY4BwJgxY7SSkpKTxYysrCxd07Sjbe8VRdF85ZVX6tuPcccddwQAnPz8+emnn55c0rF48WJp8eLFpxUIdu/efdprRUVFNb3+QuKAVPAGyLbyRrdhwpLisPRbBW9cuiN824KRx7q7j6VpSYmygqdoBqUbpi3agJck8kEA8AYjXQa8iGY4rBzTacCbmuMuA4CNR7150bxvq6awkpco8uU9eQYA7BbWsHJ0o09Se33YeUNQEQEgUeRjEvC+MT71IADYOKbuR+eOOhyL9+hKgshrXx8I32XAO1wXcAFAosgPeAXv+/PziylA33ys4WSbpqIZVJk3dHam27Y91sEpw2XbV+ePFOrGf5fW3fH6zgs+L/HcluW2bf7grvlPtq6741na/MO3Jn+149cX3nvLvLyHRJ6t++Kw57aFj3/xTPuD6QmCIAiCIHqLBLwBsrWsMQ0AchOFAT+okaUpWdWjC3hl3pANAAQ+uoCX7rIGAaAprHYZ8BTNcNh4ptMWzW9OyTwOwDxSF4x6HV6pJ2gLK3p6urPzXUS7IvCsNxDp/WHnTWHFDgCpDktMAt6S6dmVAs/UTM1xr+6vdZs9JfCMx9/NgfAnmsJuAEhzWgY84I1MsUtJdv5guTd08jy85784NjaiGYln5Sb0++6Z7Y1Ns+9TdMO9YldlFgAs+/eeuUXFNXelOiy7V9457892C3taWy1DU3jwm4W7dvz6wvsvm5zxhKTqaf/cVF4Y67kSBEEQBDE8kIA3QI55gmkAMCHT2W8VvGixDCWpenQtmpVNYQGIPuDlJApBAPBLnQc83TChaIZT4Duv4OUmChGRZ2pq/XJeNO8LAB/urckFWs7Ri/aZtuwW1huO8iDvjgQkzQ4A2QlCTNbg8SxtFv/vRT987Qez343F+NGwW1hPuJsQXO+PuAAgN1EY8BZNABidat/pl7X8Tce8bgD45EDdXArQfnTeqO2xfu9Fhen7AWDN/rrChz/YP/2dHZX3JYp8yTu3z12ebLd0u7nL/ZcUbKQAbWdFEzlugSAIgiCIfkEC3gCpaZbTAWDB2JTT+oNjjWVoWdXNqCp4dX5ZAACHlYsq4I1NcwQBIBjROg14xxtCNhNg7Ra20woeACSIfHljSIm6gldc5csDgPljksujfaYtp431SKqe3La9rieCEU0EgFEp9pgEPKCl2hOv6h0AuASu2+9RY0hxAcC4dOeAV/AA4IKCtJ0A8MZXFdN0w8QxT/DsNKd1T0GGMyaV1baunJpVzbN081dljZe8vKn8l04bV/HmrXMe7m5dbKusBJuSKPKHKxrDk2M9V4IgCIIghgcS8AZIQyiSZmHpxp5uBtIfOIaWtSgreN6gYgMAp5WNKuAl2y0aTUEJK1qnu2iW1AUcAOCycV2e/5fhspaFFT092vVIJxrDeQxNSYsmpPcqNCcKfINhgt9T2ezozfNhRRMpQEt3Wc/YDTKSRIvHMMHvqmhydnaPX1LdFGBMyHDGLOh25aaz88p4hvYVV/mmv7q5PC+s6OlTclwx2z2zLYamkOaw7PNJ6iiBZ+v/76YZD45Ld0T1Z6fViCRhb3NYHVVSGxBiNU+CIAiCIIYPEvAGiE9S00ULO+DtmQDAM5SkGdFV8JrDigAAboGP+kMqx9BBSdE7reCdaJQcAJAo8l1W8MakOsoBUB/urR4Rzft6ApE8l40r722FK91l9QLA7ormXrVpSqpu5xg6FM8KW6xlulsOO99Z0dxpm2Ywork4lvbH6xBvnqXNTLd1V3WzNG3l7ur5AIzvzx+5daDe/8IJaZ+k2C17/7p02gMz8hK7/BnvyOz8pGIA9OtfHZ8Qg+kRBEEQBDHMkIA3QEIRPc1l4wZ8gxUA4Fla0qJs0fRJqggAyXZeinp8hg7KqtFpwKv1SU4ASHFYuqzgnTM2uQwA9pzwddumqRsmfJKal2zv+Q6arfKSWw47L/X07rBzWTVEjqHiUrUaKCNT7K3fo04DXkjR3RaWjsv6u1aTsly7VN10Flf5Lk228wdm5ScOWLvog98s3LXtgYUPnDcutbE3z3937ogSioK2s6JpUn/PjSAIgiCGA1EUR8V7DoMJCXgDoCmksJKqJyeKfFwqeBaWkTUjuhbNYESzAUCa0xp1BY9n6WBE67yC1xBSHACQ6bZ1Wd1YNCG9nqEpqaIpnNfde35eUp+sGaaYkyiURzvP9iZnu1sOO/f17rDziKaLPEvHfJ1XPE3Lafke1fjkTkOwrOouK8fEZf1dq+vnjNgFAJphihOzBqY9s79kuGxKksgfOtEokXV4BEEQBEH0GQl4A2D9EU8KADrdaY1LBc/C0pJhwiIperf/f4cVXQCA3CQh6uBiYemgonVewfNJqhMARqfauwx4DE0hQeCOVTaGpyia0WXf4xeHPXkAMDHTVR7tPNublZfYTAF6Qy8PO1c048wPeLkJfpqC0hDq/LBzWdVdAh/fgDdnZFKz08aWAsCNc0ZsiedceiMvSdzrk9SR+6t8na5lJQiCIIjh4ic/+UnSn//8Z1frv//85z9P/OUvf5kwf/78rIkTJ+aMHz8+96233urw78yHH37YPWXKlJyCgoLcZcuWJQLAkSNH2NGjR4+44YYbUseOHZt77rnnZoZCIQoADh48yJ1zzjlZEyZMyJ04cWLOoUOHuM7GGSrYeE9gOCiu8qUBQH6yGJcKnpVjZACo8UmWkSn2LlsvWwPeiERRjnZ8C8cEm8JqXmfXA7LmAGAWpHe/q+G8Uckfr9xTfe9vPzww47dXTtzW2X2H6wJ5ALB4UsbxaOfZHs/SppVjGnyS2tuAZ3dYOU9v338oYGgKNo7x+MJqpwFP0QyXyLNxbdEEgHPHpKw86gmOuaAgzRvvufTUnJFJxduPNy19Y2tF4fKrJg3Y+kGCIAiC6M6avz2d7K0ot/TnmMm5eZFFt93V6d/XS5cuDf70pz9NXrZsmQ8AVqxYYf/444+rly1b5ktISDDq6uro2bNn51xzzTUhmv5v/WLVqlXC0aNH+V27dp0wTRMXXXRR5po1a6z5+flaeXk59/rrr9fMnj1bufzyy9PfeOMN+6233hpYunRp+rJlyxqXLl0aCofDlGEYnY6zaNGiqD8fxxOp4A2A495wOgCcNSIhLhU829cBr7JJ6rZNU1Z1gaEpqScbZlg5JqTqnVfwQhHNyTJUyMYzpx363N5vr5y4wcrR3o/21VzV1X01PjnPxjH1Pd2xsD2BZ7zBXp6Fp+qmaOXoM3oNHgCIFtYT7OQsvHq/zGmGKTisbFwreADw9NLp6z66e8Hf4j2P3rh5Xl4JTUHZfaKZrMMjCIIghr05c+ZEvF4vW1FRwWzdupV3u91Gdna2dt999yUVFBTknn/++dm1tbVsdXU10/a51atXC+vWrRMmTZqUO3ny5NyjR49yJSUlPADk5OSos2fPVgBg2rRpkbKyMra5uZmqra1lli5dGgIAQRBMu91udjXOUEAqeAOgLiCnURS0eaOTe7UJQ18JPCMBgCcY6XajFVnVBZamehSaBJ4JaoYpSIpOdxTiworu4Bm6yw1WWjltnD4zL3Hll0e833/286Nj7jhv9JGO7msMKnkJAlfek3l2xG5lvZ5AZGxPn9MNE6ph2K0cc0a3aAKAw8p6Kpukszq6VlzlcwGAW+DiXsEbypLtFi3Jbjl0ogfn4e2saHL8eXXJohdvmrHCbmG7/eUJQRAEQfRGV5W2WLryyisDb7zxhr22tpZdsmRJ4KWXXnJ4PB5mz549FTzPIzs7O0+SpFOW9JimiXvvvbfxnnvuOWVZ0JEjR1ie508WLxiGMSVJ6rTQ1dk4QwWp4A2AppCSJnBMXby2kRctrAwAjSGl24CnaIbAMT1bV2a3sEEAKKkLdNgLLau608LSUf8BefjywjUsTYXe/KqiwyqeNxhhgxEtO9VpLe/JPDvisrUc5N3dmr/26gMyb5pgBZ494yt4boH3RjQjoSmknPYLoWOekAsAku2WuFfwhrr8JHGvX9byd5+I7lzGB1fuu3pzacNNf1p9aHqs50YQBEEQA+36668PvvPOO45Vq1bZr7/++qDP52NSUlJ0nufx0Ucf2aqqqk77XHLJJZeEX3nlFZff76cA4Pjx40z7Kl9bbrfbzMjI0N58800RACRJooLBINXTcQYbEvAGQEDW0uzW+JyBBwB2KysBgC+sdtuiqeiGwDFU1EckAIDD2hJyjjeEOmzTjGiGw8oxUQe8kSl2aUKmc/WJJmnuh3ur09pfX7W7OscE6JEpYnlP5tmRJJH3mibYnRVNru7v/q8jdUE7ANgtZ34FL9Vh8QCgNpc2JLW/Vt0suQEgw2UlAa+Pzh6VVAwAb22tKOzu3nq/zJXUBhYCwMaj3rNjPTeCIAiCGGjTpk1TgsEgnZ6eruXk5Oi33HKLf/fu3Zbx48fnvvzyy45Ro0ap7Z+5/PLLw9dcc01g1qxZOePHj8/91re+leH3+7vMO6+++mrdX//6V3dBQUHurFmzsquqqpjejDOYxLRFk6KoiwE8CYAB8JJpmn9sdz0XwMsA3F/f8wvTNItiOad4CClaeprT3mGr4UBw2TgZAAKyGlUFj2fpHrVoum1cEACqm+VOAp7uTBT5Hm2Gct+icR/c9PetV/x17dErLpucecq6ql0VTXkAMDs/sbwnY3ak9bDzPSeak+eMTIq6zbCqWRIBwGHlzviAl50geABgf7UvZfGkjFN+UeEJRFwAkJcskhbNPrp5bt6Rp9ceieypbJ4MoMudQH/74YH5qm46BJ6pqWgMzw5GtGdImyZBEARxpjl06FBF6z+npaUZO3bsqOzovlAodKz1n++///7m+++//7TPJSUlJSfHevDBB09eLywsVDdu3FjV/v7OxhkKYpZEKYpiADwD4BIAEwBcR1HUhHa3PQDgX6ZpTgNwLYBnYzWfeDlSH7CpuulIssfnDDwASBBaAl7rGXddUXVT4JmeBbwkuyUIAN5gpMMWTVUzHQLPRLUGr9WCsSmN+SniF4frAhe2b1krbwjn0RTUSydnVvdkzI7kJ9u9LWOGTqtOdaXeL4sAkCBwZ3yL5vgMhwcAKhrCp2200hRWXAAwIcM5JHvUB5MEkdeS7ZaDlU1StxutbDzWcInIM1WXTsp4WdVN57Prjrb/bytBEARBEMNULEuNswAcNU2z1DRNBcBbAK5od48JwPn1P7sA9PkD+2Cz50RzIgCkOCwN8ZpDkt0iAUBY0but4Km6IVhYpkcBL81pCQFAU1g5rYJX75c53TStdgvb4wBwy7z8FYYJy+//c/CSU8YMyHkOK3eiPyoWU/97kHePDjtvDCkiACTZLWd8BW9OfpIXAOoCp5+FF5A1N01BGZEk9qitl+hYfrJYHJC1EdvLG52d3fPPjWV5jSFl/PQRCavvumDMDpqCsq7EQ9o0CYIgCIIAENuAlwXgRJt/r/z6tbb+F8ANFEVVAigCcFcM5xMXxxvCLgBIdcRvjVKa0yoD0QU8zTAFC9ezCl52ghAEAL+knhbwDtT4HQDgtHI9quABwA1zRlSkO607dp9ovqzeL3OtrzeH1bwkkS/r6XgdOWtEgo+moDYElR4dldAsqSLw33B7JstKsCkcQ/mbw6d/j0IRzcmztI+he7RHDdGJeaOT9wLA29tOTOzsnje2VlxCU1CWXTTus9xEIZLusu0q8wbP1o247OFEEARBEMQgE+9jEq4D8E/TNB+jKOpsAK9SFDXRNM1TKjMURd0G4DYASExMzC0qKno8DnPtlUCd2dLKFyy9taioTInHHFTDpADAJnuWFBUVnd/ZfbppQjdgy6T95xQVFXW70UOriN4yPheq/05RUdGitteOB0weADKNum+1vxaNb+Watmf3wf3ntz977htZlN+nmExEQ0KhGJrSXz8Hbh6gZN+ioqKiqLeop32mCwCUir0/L6or1vtjHoNZisW0IBJcUFRUNLrt66xmZibxYIbSn8nBLM8wwdMwKysrby8qqrq6/XVJM6lyD0ZOTUawcu+m31TuBRYkm463mpH87NtFz4xyUZF4zJsgCII4s6Snpyf4fD4nRZFf4MabaZpQVbWjz1qdFo9iGfCqAOS0+ffsr19r6/sALgYA0zQ3UxRlBZAMoL7tTaZp/g3A3wAgKSnpyOLFi38Wq0n3txUvb78YqLtj4rRZDy4YmxKXc/B0w8RPNxStqDXdaxcvnvdqZ/eV1AYEfLn+LdmWvGLx4tkre/Ie9276z7tVhuvzxYvnv9z29Uc+OjQJOLZcSB/19OJLxhf3dO4XGSZe/+0nTxRVGtbl31t072OflEwCSn+Xlpv/1OLFE3b3dLyOPLjzk+VVMpjFiy/8RbTP/POFzdcAjTcsunDhvcl2i9Yf8xjMlu9d+8saSc1cvPiiU/7s3b9tzeM2nmlevPiC38Rrbmea5Xs//d/iZj2l/fcaAH7w8raLFaP+jvOnjf3N4vPHlABAdmWz/e2/bnzts0b3lruu6/zPN0EQBEFE6+DBgw9EIpGc5OTkEE0Pmc0jzziGYcDr9Yo2m+3E4sWLf9fu8kOdPRfLgLcNwBiKovLREuyuBbC03T0VAC4A8E+KogoAWAF4YjinAdcstWxCMSnLFbdNKBiaAkNTckTTu9xkpbIpLACAyLM9atEEAI6hg5Kin9ai6Q1GHACQ6bb26utnaArnj099b8WuqmUPf7B/5onGcDoALCxIK+/NeB2xW1hvnT8SdcUSACRFF2kKynAIdwDgtHGeWr88RTdMtG3HjGi6O8nesx1Sia6NTLYXby5tuGlLaYO77c6uumFia1njJU4bW3r7eaNLWl+fnO0OJjssxUfrg2cDIAGPIAiC6LPs7OynKysr76qrqxsBcqxaPBksyx7Pzs5+uicPxSzgmaapURR1J4CP0XIEwt9N09xPUdRvAGw3TXMVgHsBvEhR1D1o2XDlZtM0z6iFJMGI5mJpKpQg8nENAixDSYpmdLkGr84vCwBgt/Yq4IVk9fSA1xRWnAAwIkns8Rq8Vr+9cuLG1ftqb1q9r/ZbboGr5hm6uSdHGnTHLfDe4w3hJEnRaRvPRLVxi6TqIsfQZ/wOmq0SRc6rG6btcF1ALMhwhoCWwKFohstuYYfkFsKD1Tljk/duLm3Av7afmDhnZNKG1tefWXd0nF/W8i8sSHu2/ZrHSVmuzWsP1f/o7W0V2d+ZmdvhFtIEQRAEES2Hw+ErKChoXzEihoiYJnLTNItM0xxrmuYo0zSXf/3ag1+HO5imecA0zXmmaU4xTXOqaZprYjmfeAhFdJeFpeP+AZilabm7gOcJRAQAcPYi4PEsHZQ147SA55c1JwBMyOz9Nvp2C2vMyk9c2RBSJlQ0hme7Ba68t2N1JMnOe02A2Vbe6I72GVnV7RxDn/EbrLRKd1o9ALC9vPHkRiul3qDNMME5rRw55LwffffsvGMMTUn7qnynrAldsavqEoampF9dWvD56c+M2AIA7+6smjtA0yQIgiAIYpAiJdcYk1TdZeGYuJ8RxjKUpOpmly2azZIqAIBb4HsV8JQOAl4oojkYmpL62sr48OWFn7A0FVJ105HisPRrS2Cmy+YFgOIqX9Q7aUY0Q+TZ4VPBy08WPQBwpD548qiEA9V+NwAkCCTg9Se7hTVSHJb9VW3Ow9t9otlxvCF0zuhU+7q8ZFFu/8x541IbEwSupKQ2MGdgZ0sQBEEQxGBDAl6MyarusnFM3Ct4HE1Lqt51Bc//dcBLcVh6HPCsnQS8sKI7eIbudXtmq7xkUZ6Y5SoCgBHOVSNFAAAgAElEQVRJYnlfx2trVEpLeDneEIo64CktAW/YVPAmZ7s9AFDVLJ0MeOXekAsAkh0WEvD62agUsTik6FnrD3sSAeCxNSXnGya4G2bnftTZMwUZzs0+SR398f7aHp3pSBAEQRDEmYUEvBiLaIZL4Jm4fwDmGErWjK4reMGIZgOAdKe15wGPY4KqfnrAk1TdybN9D3gA8OA3J7yfnyyuvXlu3vb+GK/V1NyEBgCo9clRBzxVN0TLMAp4c0YmNVGA5m1z2HmtX3YBQKbbFvdfYJxpzh2buhcA3t1ZOVHRDGrn8aZLEkX+4I1n53Vavf72jJzNAPDGVxWkikcQBEEQwxgJeDGkaAalaobTbmXjH/BYWta6qeCFFV0AgOxEW48DnsAzQc0wBUUzTtn9IaLqDks/BbzpuQmBdfed95dZ+Yn9+v2clOUK0BSUxlD0h52rumG3csywCXg8S5tWjmloltSTAa8h2LJD7OgUe9x/vs80N84ZUcbSVGh/tX/yo2tKJoUUPXP+6OROq3cAcNW0rBqHlS3fX+07e6DmSRAEQRDE4EMCXgwdqPHbTYAeDJtQ8AwtdVfBCyu6CMAckXj6Gp/uiBY2BIA6UhcQ2r4e0QynjY//GsSuMDQFG8d4fJIaVcDTDROqbooCxwybNXgAIFoYT1DWTn6PmiXVDQCTsl1x//k+09h4xkh1WPZXN0uTioprFnMMFXjg0oKN3T03Ns2xxRtUJmwta3QNxDwJgiAIghh8SMCLoZJavwsAEkQ+7gGHZ2lZM8wuK3iyqttYmpJ4lu7xURUOKxcEgLKG0CltmopmOG2DYJOZ7ggW1huMaFGtXTreELIBoAXL8KngAYDdwnrCin7yexSQW44AGS5nAQ60Uan24rCiZ1Q2SXPGpzs/SXVa1e6e+ebkjE0A6P/bUDprAKZIEARBEMQgRAJeDJ1olFwAkOqwxH2NkoWlJd0wre1bKNuSVV1gGarH7ZkA4La1BLzqZvlkwAtGNFozTFG0sP3SohlLDivrDSt6VBW8Mm9IBAC7hR1WFTy3wHtkVU+WFJ0GgFBEc1lYmlTvYuS8cSnFX/8j/YNz8j+O5pkbz84rt3FM3Z4TPnJcAkEQBEEMUyTgxVDd15tQZLltca9gWTlGBkDV+mW+s3sUzRBYmu5VwEsU+SAAeIMRsfW1QzV+OwA4rIM/4CUIvCeiGYl+SWW6u7eySbIDgNPKDasKXord4jUBektpQwIASKrutnDx30DoTHXjnLwyjqH8aU7LriumZtVE8wxDUxiVKm6qC8hTSmpPbZcmCIIgCGJ4IAEvhhrDLZtQjEmzx72C93XAQ3Wz1Ok6vIhmiBxDSb0ZP81pCQJAU0g5WcEr9YYcAOAW4t+i2p1Uh6UeALXhqLfbKp4n0BJi3QI/rAJeprvlsPO9lc0pwOA5AuRMxbO0+dA3C3/5xHemPtGT5xYWpG0xTbDPfXFsRqzmRhAEQRDE4EUCXgz5JdUFABMzXXGvYAk8IwFAfSDS6To8VTdsvT3bLdNtCwGAT1JPBryqJskJAEmDYA1id3IShXoA2Ffl63YdXlNYEQEgxWEZVi2aY9IcLecFNoaTASCiGi7RQip4sXTDnBEVc0cl9yhE33He6EMWlm7aUd5IjksgCIIgiGGIBLwYCsiai2OogNPG6fGei8izMgA0BrsKeKbAM71r0RyVYg8CQDCinQx4nkDECQBpTmvcA253CjKcHgCoaAx3G/B8kioCQLrLOqwqeLNHJnoAoNYnp0iKTiu64bRbWFLBG2R4ljZHJAlfVTfLM2p8Uqct2QRBEARBnJnYeE/gTBZWdBfP0oOiemW3shIANEtqpy2aqm4IFpbpVYtmTqIgU4AeahPwmsKKo+WabdAHvHmjkrwAUOeXU7u7NyC3fI25icKwCnhjUh0SS1OhxpCSsr/aZwdAO23coPj5Jk51zpiUTYfrghf/de3Rqd+fn19cUhdwnGiUHLU+ydkQUhw+SXVGVMMyOcd15Pvz8g9Gs0MnQRAEQRBDAwl4MRRWNJeVHRxrlJzWlgqeX1I7reBphilYuN5V8BiaAsfQQUnVTwY8n6Q6AKAg3TnoQ0Cq06paWLqpKaR2G/BCEU0EgNGp9mEV8ADAxjNev6ymHKkPugAgUeQHxc83cao7vzF638ubykOvf1XxwOtfVXR63+bSBry4vlRJslsOjkwW9y4Ym7Lnprl5R+0W1hjA6RIEQRAE0Y9IwIuhiGq4XAJXGe95AIBb4GUACEa0Dit4kqLTumHarBzTq4AHABxDBWXVaHtMgpOioOUkCj0+OD0eBJ6p98tqty2akqqLDE1Jw/FDsGhhPcGIlnKiMewCgDSHlazBG4QSRF5bMj37qYO1/rF2C+t32Th/osgHUhyWQKbb5h+davdbGNp4a9uJCTsrmiafaAxP+aqs8cavyhpvfOKTw+FUh2Xft2fkvHvPhWMPxvtrIQiCIAiiZ0jAi6GIZrgEnjkQ73kAQJKdlwAgFNE7rOBVNIatACDwvQ94PEsH5TYVvHBEd/IM7WfoTo/eG1TsFtbTGFLyu7tPUnU7x1DDrnoHAE4r62kIRsbV+WU3AOQk2kjAG6QeuXryZgCbu7rnt1mubQC2AcD28kbnm1srJhdX+SaXe8NzXvqy9Cf3XDj29oGYK0EQBEEQ/YdsshIjX29C4Rgsm1CkOqwyAEhqxwGvsiksAIDIs30IeExQ0YyT5+BJqu4YLGsQo+EWeI+k6im6YXZ5X0Q1RI6hh9UOmq0SRd6j6qajullOA4CxaY5B8fNN9N2MvET/Y9dM3bDmnnOfnZGXsCKk6Fmbjnnd8Z4XQRAEQRA9QwJejAy2TSiy3DYJACRF77BFs8YnCwBgt/Y+4Fk5OqTo/23RlFXdaWHpQb/BSqtkO19vmOC3lTd2+aE2oul2nundcRJDXZqj5Sy8yqbwKABGYaZrWAbdM938Mcn7AOC9nVWF8Z4LQRAEQRA9QwJejAy2TSjSXVYFgCFrHVfwGoIRAQCcfQp4TFBtE/AimuGwcsyQCXiZbpsHAHZVNHe5Dk/RDNHCDc8KXk6i4AUAb1AZwzO038Yzw24d4nBww+wRpQxFyfuqfBPjPReCIAiCIHqGBLwYad2EIsVhGRQVPIamwNKUrGhGhwGvWVIFAEgU+V4HPIFjgqpuiq0tjopmOGwcMyi+/miMSbXXA0CpJ9hlwFN1U7SwzLCs4BVmtZwXKKl6moWjyfq7M5TTxulJdv5QVbNEKngEQRAEMcSQgBcj9YGICwByEoRBUcEDAIam5IhmdNii6f864CXZLb0OeKKFDQKgS71Bm26YUHXDKVrYIRPwZo9M8gBAja/rs/BU3RCt3PAMePNGJTcAMAHAyjEk4J3B8pLFfQFZy9tb2Wzv/m6CIAiCIAYLEvBipDGkuABgdKp90AQclqYkpZOA13p8QrrT2uuAZ7ewIQAo9YTsh+sCognQDis7ZFo0x6Y5QgxNSQ2hSKcBT1J0WjNMUeCHZ8Bz2jjdwtKNAGDjBscZj0RszM5P3AcAb209Qap4BEEQBDGEkIAXIz5JdQEwJ2a5Bk/AY2hZ1Ttu0QxFdBEAshNtvQ54LhsXBICqJsl+pD7oAACnlRsyAY+hKQg8U++TtE5bNEs9QRsAiDwzLNfgAS1n4bX879BpvyV67sY5I47QFNS9lc0k4BEEQRDEEELOwYuRYERzcQwVHEyHYXMMJal6xxU8SdUFAOaIRLHXh5IninwQADyBiF3WdPbr14ZMwAMAu4WtD0Y6P+z8eGPYDgB2KzcsK3gAYLew3saQAoeFIxW8M1iq06ominxJZRNZh0cQBEEQQwmp4MVIOKK5LOzgWqPUUsEzO6zgyapuY2lK4lm660PgupDisAQBoDGk2Ov9EScApDkHxyYz0XJaOU9Y0Ttt0azxSSIAuGzssK3guWycBwBcAjeofr6J/pebKOxrltRRR+oDHf5iiCAIgiCIwYcEvBiRVN1l5ehBVeHgGVrWjI4reLKqCyxD9bo9EwCyEmxBAPBJqr0h2BLwshKEIRXwEkXeo+mmvbUVs736QMQOAAkCP2wreEl23gMAyfbBcQQIETtnjUjcD4B+46uKgnjPhSAIgiCI6JCAFyOyarisg+yIAI6hJK2TCl5EM0SWpvsU8EamiEEACERUu09SHQAwKkUcUi2a6S5rPQBsKW3osE2zOaSKAJBstwzbgDciUagBgLwksSHecyFi68azRxyiAH1XBVmHRxAEQRBDBVmDFyMRTXeJvK043vNoi2cZWTc6DniKZgg828eAl2yXKMAIRXSRomACMManO4dUEMpPFusBoKQ2kAKgov11v9wS8FqrlcPRry6dsJNnmV//4JyRR+M9FyK2chOFiFvgjlY0hsmB5wRBEAQxRJAKXgxIik6ruumwWwfXGXAWlpZ0w7S1HkTelqobNq6PLZoMTYFlqJCk6PZQRHNyDBXsy5q+eJic7fIAQFWz1GEFLxjRRADISxKHVHDtTzxLm7+6tGAPQ1PxngoxAHIShX1NIWVMVZPEx3suBEEQBEF0jwS8GCiu8jkAUC7b4Npl0MrRsgnQDcEI1/6aqpsCz/StggcAHEMHZU23S4ru4Fl6SLVnAsDcUclNFKB5Ah2fhReMaHYAZn6yKA3w1AgiLqbluPeZAPvaluPj4j0XgiAIgiC6RwJeDByuC7gAIEHgBlUFz8oxMgBUNkuntWmquiFaWKbPoYVn6aCsGqKsGU4LO7jWIEaDZ2nTxjPeZkntMOBJii6yNBUeapVJguitpbNHHARgbi1vJG2aBEEQBDEEkIAXA1VNkgsA0pzWQVXBs3EtAa7OL5+2Q6RmmDYrR/e57dDC0kFF0+0RVXdYOXrIBTwAEHjGE5A7PuxcVnWRY+hhu/6OGH7GpTvCLhtXVtEQJhutEARBEMQQQAJeDNQFZBcAZCfYBlXAES2sDAANQeWUCp6k6LRumDYrx/S5RdPCMiFFM+wRzXDaOGbItWgCgNPK1YeVjgNeRDPsPNv3IEwQQ0l2gq24IRQZ3xRSyMZcBEEQBDHIkYAXA00hxQUAo1Mdg+ogaPvXAa85rJxSwatoDFsBwMb3vUXTytFBVTftqm44BJ4dkgEvQeQ9smok+iWVaX9N0QyRJxU8YpiZlOXab5jgX95cPibecyEIgiAIomsk4MWAX9ZcAMxJWa5BFXBcNi4MAD5JO6WCV9EYFgBA5Nk+V/BsHBNUdMNpmOBFy+DaRTRaKXaLBwC94ag3uf01hVTwiGHompk5+wFgS2kDadMkCIIgiEGOBLwYCMiqi2OogI1njHjPpS2XjZMBIBhRTwl4dX5ZAACHle1zcBEtbBAA1fJ+QzPg5SQK9QCwv9p3WpumohuilSMVPGJ4mZ6bEHBY2IoyL1mHRxAEQRCDHQl4MRBWdJeFZQbVBisAkGTnZQAIRfRTWjQbghEBAJxfV/j6wt4S8AAACQI/qCqY0ZqQ6awHgOMN4dMCnqaboo1jSAWPGHYy3bZ93mBkQjCikb83CIIgCGIQI39Rx4Ck6K7BuINkisMiAUBYObVFs1lSBQBIELg+r8Fz2biTAS/ZbhmSAW/eqCQvANT55VOOSvBLKqObptXGk4BHDD8TMp37dcO0vb7l+Mh4z4UgCIIgiM6RgBcDEc1w2bjBV8HLdNlkAAgrp1bwfF8HvCS7pc8VvASRPxnwMt3WIRnwUp1WlWfp5qbQqWfhHa4LiAAgWvreykoQQ81V07L2A8CGo17SpkkQBEEQgxgJeDEQ0XSXMAg3GMl02yIAENGMUwJeUNYEAMhwWfscXFIclpMBb2SKfdB9D6Il8ky9X1ZPadE80SSJAOC0cmQNHjHsLBib0ijwTM0xT7DLA88/O1iX/O7OysyBmhdBEARBEKciZxr1s2BEo1XddDgs7KCr4PEsbTIUJUdU/ZQWzbCiCwCQmyj0uUUzy207GRInZDiHZAUPAOwWtr4xpOS3fa3WJ9sBwGXjSAWPGJYyXNZ9xxvDZyuaQfEsbba/fu+/ds9/f3f1XQAgKfqyG+aMqBj4WRIEQRDE8EYqeP1sb2WzEwCcNi7m1SvviePClvfeHteTZxiGkiOacepB56ouADCzEwS5r3PKTxaDAMDSVNhp4/S+jhcvLoHzSKqeohv//QzbEIqIAJDYpg2VIIaT8enO/Zpu2t/eVpHb9nVvMMJe/Jf1t727s+rnTitbwdKUtPw/B3+9tazRFa+5EgRBnMlWHl2ZpegKFe95EIMTCXj97Fh90AUAiSIf80POP3zyTzdt/Ndrf/B76vlon2FpSlL0U1s0ZVW3sTQlnfIbednHwNB6PKdRKfYwAJNj6CFbvQOAZLvFY5jgdxxvOvkBtSmk2AEgxWEhFTxiWLpsSsY+AFh7qP5km+bH+2tTFj7+xSOHagOXFWY6V66997z7f7pw7PKIpif86LUdv/QGI6RThCCIYenWNbdeddmKy366qXpTYn+O+/Supwsf2PjAc9/96Ls39ee4xJmDBLx+dqJJcgFAmtMS04AXaPDyTdWVC2CabOnObRnRPsfSlKzqp1bwZFUXWYY6dYOVv0x6HH+/+PqezotnaZOlqbBlEO4i2hNZbls9AOw43nRyoxW/rIlfXyMBjxiWFk1Ir7dytPdofbAQAB5cuW/Gj9/Y+WRQ1rKumZH9x//85Jz/SxB57fbzRh25alrWXxpDSsG3n998Z9tKOEEQxHDQLDez22u3X3Pcf/z8Oz6949k7P7vzkv6quL17+N3rAOBAw4Er3zr01oj+GJM4s5CA1888gYgLALIThJgGvC/f+OccQ9dFAKgrO5oV7XMsQ8uqbp5SwYtohsDR9H8DXunnCZB9+QjU5PVmbhxDBy0sM6QD3qgUuwcASj3BkxuthCItAW9Uqp20aBLDEkNTSHda99UFIhOv+OuG776y+fiDAs/UP3bNlHv+dPWUTW3vfeyaqRtm5iW8UeYNnX/DS18tidecCYIg4uG5Pc9N0UxNnJ81/2+J1sTDX1R+cfvCfy985O2St3O7f7pzT+18amKD3DB5SsqUt1maDT67+9kfk1ZNoj0S8PpZU1hxAcC4dEdMN1mp2L/3AobjGwHAV1cbdQWPYyhJa1fBUzRD4Ng2Ae/AyjEtF8Lu3sxtRl7Ch/NGJ33Wm2cHi9n5ifUAUOuXTwa8sKKLFKBnfb0bKUEMR2PTHPsUzXDvqfRdPSbV/vHHP13w8yumZtV0dO9bt539Vm6isH5zacN3739v7+yBnitBEES8bK7ZPJel2NDv5v1u9SdXf/LgxXkXPxFUg5nLtyx/cul/lt5QF6qLenlNW+8dee9anuabHjv3sX+fn3v+35siTeOXrV92YX/PnxjaSMDrZz5JdQEwJma6YlblKd21LSnU1Dg1LX/UGobjGkPNTVFX8LiWCt4pAU/VDRvXtkWztngsAECTexXwXv3+7JWPXzN1Q2+eHSzGZzhDDE1J3qByskUzrGh2lqFCDE1+UUYMX9+dm7ctQeBKvjk544lPfnbuMxkum9LZvQxN4e0fznnKLXBH/7Wt8t6/byjL7+xegiCI3vrxZz9ePO/NeU8Ue4sd8Z4LAPgVP3MicGJOliNra5ItSWNoBn8+98/r/nHxP24f4RzxebG3+JrL37/86cd3PD65J+M+vevpwga5YfKM9BnvpolpyiPnPLI2yZq0b33l+pt31O0gm1oRJ5GA189CEc3FM7S/oy3E+8u2Ve99AwA17eLL1loEsVoKBqIOeDxDS5px6iYrqm6KPNOmguc70RrwEnqz0cqZgKEpCBzj8Un/PexcVg2RY2iy/o4Y1uaPTm7a9eCiZU8vnb4umvszXDblr0un/45lqNCfPj70603HvL36xRFBEERHvrf6e99eX7n+R37FP+qpnU9d0Jsx+rvF8fk9z0/SDM0xK33WxravT0mZEvjgqg+evLnw5gdMmPjHvn/8btkXy74R7bjvHXnvOp7mm34z9zerAYChGdw9/e5ndEO3PrTpoVv6Y+7rK9cn/Xrjr+f2x1hE/JCA189CEd1lYWO3wYih66grPbJQcLn3jZ93bq3N4ahSwuGoWzR5lpZ147QKnmDhmJaAZ2hAqGEMAAOmwaOxzNbROMOBaGXrg5H/HnYe0QyRZ2my/o4gemj+6OSmn1887reKZjh+/PrOX9X7ZS7ecyIIYmjTDR1L/7P0xu1122/MdeR+nmBJOLjXs7fHG5n8cesfp894bcaK2a/Pfm7xe4vvu3XNrVc+tfOpiceaj/X688+mqk3zGIqRbp9y+66Ort874969H1z5wV1J1qTiNcfX/DiajVKe3vV0oVfynqzetb5+1ZirqianTH73uP/4N3paEezIw5sfvvP9o+//4u51dy/q61hE/JCA189kVXdZOSZm6+++WvGv8aosZ2aNL/wMAER3YrWuqW7P8TIxmud5lpY149RNVjTDtFlb1+AVv5sBQxUhphwEAFTtHLa/bXdZuXpJ0U8GPEXT7Typ4BFEr3x//sjSJdOzn2gKq+N+vXLfefGeD0EQQ5eiK9Q1H15zW7G3+NujXKNWr7hixRNzM+f+J6yFM57c+eSUnoxVVFr0HZ7hm10WV2V9uH7Clpott7xY/OLvr1x55dutoe+5Pc+Nj3a8kBqiKwIVZ2faM7elCClqZ/eliWnKnxb86c8sxYae2PHEL7oLlO2rd209du5j/7axtpq3D719e4PU0OujaVYcWZFVH64/i6XY0LqKdT/6296/9eisZWLwIAGvn8mq7rLxsdtBsmTz+gsomo7Mv/bGTQDgTs+oBoCy3Tsyo3neyjKSaYJtCiksAEiKTuuGabO2VvCOftLSnpk6YSsAoPHYsA14CSLvUXXTUe4NWQFA0Q27hVTwCKLX/rhk8iaepZtLagOT4j0XgiCGJkmT6KtXXX3X4abDlxUkFrz/7uXvPsszvLls5rJNHM35Pj3+6SXRjvXcnufGN0WaCmamz3xnzdVrlm+/Yfstf7/o799dOn7pw9NTp7/usrgqa0O1014qfukXnrAnqs6DF/a+MFE1VOfM9Jkbu7t3Vsas5psn3vwnSZMy7vzszrt0Q+/wvs6qd63SxDRlyZglz4e1cNY9n99zdTTz7MgrB165nAat/u/c/73Pylq9L+x94f4t1VsSejseET8k4PWziG64BJ6JyREJfk8931RTfY47LWNDYma2BABpI0dXA4C3ojyqNk0bz0gAUN0sWQCgvCFka/s66g+NBcVEMGbh3pY3rRq2f7DTXVYPAGw65k0FWtYqWjmGVPAIopcYmkKaw7Kv1i9PJGfjEQTRU37Fz3xr5bfuLfOXLZyaMvXNNy998+8MzQAAkmxJ2riEcZ/WhGpmb6jaENXB4iuOrPgWS7OBX83+1Setr81Mn9l8/+z7d7x8yctvr7l6zfLrC65/RNGVxIc3P7wwmjG/rPxyLk3RkR9O/uGOaO6/a9pd+2emz3y1Mlg5/861d17W0T1dVe9a/c+s/9mVbc/+ck/9nm9/eOzDqJfutCr2FjtKfaUX5LnyPr9i9BVVPzvrZ8s1QxN//uXPf9EsN/e6KkjEBwl4/cgvqYymm3aHlY1JwNvw1itzDF0Xxsyee/IIgtEz5tQAMH2euqg2WrFxjAwAtX7ZBgCVTZINAOwWtiW4BKrHQEg49to7ey874EsBQp5hW8HLTxLqAeBwXctZeJpuiDaeIRU8guiDMWmOfbJqJH+8vzYt3nMhCGLo8IQ93JJVS+6vDFaeMydjzj9eXfzqm63hrtVNhTetNmFSL+x54aLuxlt1bFVmTahmdmFSYVG2I7vT449+Ov2ne90Wd8lXNV8t8St+prP7gJbqYoW/Ym6GmLEt057Z6Q7D7T238Ln30sX0rRurNt7Svi2yu+pdWw+d/dBLFEWpj+94/PbOqoGdeWL7ExcZpsFfN/66VQBw7fhrj1828rInm+Smgu99/L0f9GgwIu5IwOtHe6t8TgBw2riYBLyKfXsWshZL3dyrl+5vfU1wuTXOYqkP+5qjatEULC2VOm8wYgWAOr8sAIDdwoYh+xhITaPgzDrsqamfWxpMAqTmYRvwpuS46wGgsimcUu+XOcMEJ/CkgkcQffGNcSn7AeDDvTUT4z0XgiCGBk/Yw33nw+/8ujZUO+vc7HOfe3HRiys6uu/i/IvrUoXUnQcbD17UXRj7e/Hfr6RBa3dPv/s/Xd3H0AwW5S36l6zLqb/Z/Jtzu7r3peKXChRDcZ+Vdtam7r+q/+IZ3nzqG089YWEtDS/uffF/2h738HX1rrmr6l2rOZlzmuZlzXvFI3mmPrDxgQXRvr9f8TN7vXsvS7Gl7L52/LXHW19fPn/5hsKkwnePNh9dfM+6e8hZe0MICXj96GhdwAUASSLf7wHv6PYtyaHmpimpeSPXMhx3Sm8TL4jVcjAYVcCzW1gZABpCig0AGoIRAQCcNi6Mna+OgGlwauK4o4Zh2MK6RYPsG7YBb3Z+UjMF6J5AJPWoJygCgNha6SQIole+MzO3gmOoQEmtvzDecyEIYvCTNIm+oeiGez2SZ+qiEYue/OsFf/2oq/vPzT73o4geSXx8++MzO7tnW+02d5mv7IJ8V/5nM9Nndrsx3i9n/XKbg3eUbaja8G1Jkzr97Pz5ic/n0RSt/HDyD7d3N2Z7BUkFoR9P+fEfI3rE/dN1P/2ZoitUT6p3rR4999HVLovryMflH98a7dl4v9/y+3kRPZK4cMTCle2v/ePif7yaYkvZ9VnFZ7e/uPfFsT39uoj4IAGvH1U1yy4ASHVY+z3g7fjw/fMBUGctvmJt+2s2h7NKkcJZht59Od5hbQl4Pkm1AUBTWBUBIEHgJBzfMBYAvM6ZtQAQ1jkDSnDYrsGz8Yxh5Rhvs6SmnmgM2wHAYeVIwCOIPuBZ2kxxWA/U+GQS8AiC6JJu6Ljuw+turw5Vz52XOe/Fx8577LPunlk2c9l2K2P1rqaQPqMAACAASURBVK9cv7ize57c+eSlBgz2lkm3vB/NPBiawTdyvvHvkBrKWr5l+dkd3aPoCnXcf3xuupC+I9eZK0czbns3T7z52Lk55/6tPlx/1m2f3Pbt1urdQ2c/1GWobcvG2oy7pt31pG7owi++/EWnG7e00g0dG6o2XCFyYtV9M+7b2dF4T5//9J+trLXhhb0v/HJrzdbTfvGvGzq21mx1P7r90SmPbn+0R7uYxt2BVal4ovBX+OdlS7D//TNm6QAJeP2oPtAS8HKThH49JsHQddSVHb1AcLmLx86ZX9f+uiMxqdrQdaH68KFuf1PjsvESAARlzQoAfrkl6KU4LCE0HBsDhgvUI1MGAEnjKKjSsK3gAYBoYeoDspZS54+0BmGyBo8g+mhUirgvrOgZn5fUR7URAkEQw9ONH914wzHfsYsmJU/61/MXPv9BNM/YWJtRmFy42iN5pna02UhloNKyv2H/4gwx46vLR11eHe1cHpjzwCaRE6vWnlh7TUeh6f/2/d+4iB5JnJY6rdvdM7vyl/P+sjrXkfv5jrod17dW73qyng8AvjPuOxVzMue8XBuqnfWzL37W5Xl2L+x9ocCn+MbMSJuximf4Dne/KkwuDN4z/Z7lqqGK962/7/7n9jw3/idrf3LRklVLbjvv7fOWz3x95mvfX/P9V17e//JvX97/8m8/KvsovSfzjRstQqHo3p8E/NUzUP7lTfj3TS/ikbzH8MoVV6JkdXK8p9cXJOD1o+aw6gKAsWmOfj0mYfO7bxWospyRPWFih7+5cmdkVQPA8eLd3bZpJoqcDADBSEvAC8qaAABpTmsYgdqxEFIO+xsbnAAQ0RlmuAc8h5XzhBUtpTGk2AEgSeRJBY8g+uicMSn7AOD93dVkHR5BEB364Sc/vLzYW3zNKNeo1a9e8uprPXn2jil3rKFA6a8eePXi9teWf7X8Qs3QHFeNueq9noxpY23GvMx57wSUQP4ftv7htPbPdRXr5lGgtFsn37qtJ+O2x9AMnlv43LN2zn6CZ/jGnlTv2nr6/Kc/SLYl7/n8xOe3drWr5qpjq65gaTb4i1m/OK1DrK3rCq4rX5y/+Kkmuang2d3P/mndiXU/PtZ8bKFu6nyOI2fL/Kz5f7tq9FV/BIC3S96Oev1fXP37pot2qL7J8/Ky6bfPu3M58hf8EwCF0s9vwZvf+Tv+NPIRvLbkMhxbO+R+GUm2Pe1Hfkl1UYAxIcPZr1Wew1s2LKRoWp5/7Xc7XLSbNbagatdHq9BQeTwLwMGuxkq2WyQACCu67ev/FQBgJFNvIuLPRWrBprCv2QkAuknRqqK6OUMD6OH5o5IgcPVl3lCiNxhxAkCq00oqeATRRzfOGVH2yOpD0oFq30QA6+M9H4IgBpefr//5eZuqN/0gU8zc9OZlbz7ffrfM7szKmNWcac/cfLj58MK6UN3rrevXQmqI3l63/YoES8LB26fcfqin83po7kOfr69cf93H5R9fc/+s+7e1zks3dJT5yualiWk7R7lHST0dt71cZ6785qVvLqsP11t6Wr1rxTO8+fDch/9y97q7n35k2yP3LshZ8D9O3nlK6XFN+Zq0qmDVnIlJE1d0tZNoqz+c84cv08X0Jr/it81On11xXs559e2rfl9UfnGgpLFkAYB/9WbevdbTz6oHP0zBkTXfeys9vcEEklaGysZ+56YPXgXwHvb+OwM7/jEfdfvm4+int+HE1otx/4k7Yzb3GCAVvH4UjGgujqX9PEv32wFPvvpaS1NN9Xx3esbGhPTMDnu686ed5QFFaQGvt9sKXobLJgOArOpWAJBUXQBgZpW9lwWAQsaUw1IgcHL3JkmjLGgss/XPVzP0pDqsHgD08YbwCADITrCRCh5B9JGNZ4wUu+VAdTNZh0cQxKl+/9Xvz1pdtvruJGvS3tcWv/aYjbUZvRlnUd6i1ZqhOR7d/ui81teWf7V8rqzJaRfkXtCj6l0rJ+/UZ2XMeq850jzuyV1PTm59/R/7/zFG1uXkqSlT+9Se2VaeK0+alTGrT0t+FmQvaLg0/9JnmiPNY3/86Y+vaX/9peKXLqVAmbdPvf3DaMe8e/rd+34959fbFuUtquuopXNi8sT1QTWY+9aht0b0Ze49suaBKVie8Qr+c2+nG+ucwtCAonvvUgB8ZuWtAHDcf/ysk9cnf7sG3yv6N35RcTeueOZ2zLr1+ZjMO4ZIwOtHYUV3WVi6XzdY2fDmK2ebhm4bO3v+p53dw9sEg7daa8P+7o9KyEo4GfBsACApusDSVJip3j4GADBxyZFIKOhsvV/SOaB697Bt08xOtHkAoD4g5wPA6BQ7CXgE0Q/yk8V9wYj2/+ydd3gUVdvG79nes9n03hskhFBC74gQEAQEQYoiooIoKiggTUBBEOETBSkivfcWmrQAoYQkhPTey6bsJtv7fH/EICWdhOK7Py+vS2fOnPPsZndn7vM0l3vZkkZVeTNjxsx/n40PNgYcTD04n8/g52x7c9uPNhwbfXPnmhUy6yGXzi28U3wnDKj2skXkR4zi0DiF87vMv9fceb/v9v0lBoUhPZVx6pFg+jv37x4ECMOHgR82e97W4oeeP9xyE7hdjSuLe/fxHns5VTnsNGnaIGe+863ezr0rWmq9j4I+ukmAMJ3KPPViwjRNBiB27yQYdULc/2s+zs3t0OA1h95/A/KS9sc8O13Rk0auJdMyWaaTeUaVRD37vBsysRADFie0humtiVngtSBqvdGCRae2qMDLT4ofQGeySrqNHpdU3zgml1eoVSobFHg8Js1EIaDT6E0sANAajBwalVBDmuMDOkcM164ynUb9yIOnMdIBScb/rMALcBCUAoBUpfegENDZCljNvtk0B4VUQm9MdVQzZl43unlZJQDAkeh8sxfPjBkzOJ5+3Gnrw62LWVRW+fr+679/3lBHKoWKYJvgc5XaSr89SXs81seuD6rSVXl3cehyoq5iIo3BhmOj72DX4XiFpqLd5rjNfkaTEVlVWT1sODZxAVYBr+Qm8Lq+6zYzqczyP+P//DpPlscCgJ/u/TTQSBo5o3xGPdMa4XkIsQ2R2XBsHmRUZvRuarP1ZnFpSXuoJb5w77UTTEEe7m1ZgIsL667kmXbRCmnnp4Jn+3APh86hETTl+IDx2wHgcNrh9q1v8IvBLPBaEK3eaMFuQYFXmp3JVVZK21m7uV97uvfd07D5giKdWuVo1OuJhualUgiNzlAj8EwcOoWigrLUFzzbdADQazSPefBoQFXh/6zA6+5lVQ4AOoNJSKdSXugPd+z5M+5bZ3644/APC995keuaMfMimNTVLZNCQBdfWGUutGLGjBnsStr1lgkm2o89f1zc0a5jizxLfdnhy8sUgqI7knZkyJmsM6MYFEbloq6L6i0m0hgWdV10nkahyQ+nHR67J3mPp9qgtmtn067FwjNbGh9LH9X7bd9fqzao7T+/8vlHaoOaElMa85Yl0zL5o6CP0lt6vRDbkAi1QW23LWGbX8Ojn5OHB8eCxqrAmO0nMenYIjD5hbi9YREuLW73zFiTATg9ayZIkioduOSPAkVhVxeBS+TUwKmpdApdllieGNLq9r4gzAKvBdEaTBZcZssJvOSb17wBEI4+/vV67wCAb2VTRJIkIzsu2qqhsTQKRa0zmtgAoDOYOI5UqRZ6tS0s3dMAQK/T8mkMRgUAqA10QFnaYr3wynKzuec2ruv5unilHCzYOgaVUgkAL1LgJd246nB9z7ZlJoOBX1Va0vo/kGbMvGAsuQyDFY+ZXChVmz14Zsz8j2M0GVGgKOhoy7F9MNBtYGlLzRtgFaB047tFZMuy+5eqSjsG2QSdeZ6wzxpcBa6adtbtTolV4s67k3ZPIECYPgz88G5L2NxazAyZmRRoHXg0qypr0Pgz46erDWr7Pi59WtR7V8P04Om3KaDoL+ZcbN0wzSvL20JZFgj3nkfBtTHAqaMc7x1aCAavBJG/L8KV5U/eX45MHQB5UUd49d/5qyrDxUga2b2cet1gUBmkA9chtkRZEqIz6hp0lLwOmAVeCyFV6mgGE8nlMWktJvDE2ZneAODXvXdmQ2NFTs5FAFCYktRgmCaN+q8HT280cToTKdUfZufQNAAw6nQCBptTBAAaI52EurJFPHhpd27aHVgyd3XS9cvf3jq054Xu2sdfuej8+5R3f71/5rhnU6/lMKllAECnEi9E4GVG37O+tOX3H0CSFBaPn65RyF1exLpmzLxo3K04iTKNwT2xsIr7sm0xY8bMy+NU5iknjUFj5y/yj27WBOXpbJz+skttp0Z4jzhnIk0MCkHRzu40u1ktB2pjXui8M1SCqharxJ2t2dYPg6yD5C01d2vxx4A/9gkYgqzMqsw3WVRW6bzQeXdaYx0voZfanmsflV2V3UupV7ae1ojdMxZURiWG/d/FR8dcu8owfv9C0DlluLFuCa6tDAAAZF4RIeXsR+DaJGLc3rN3i+/2plPoVTPaz3gIAG2s2sToTDrh/pT9Hq1m7wvELPBaiLiC6tYCQjajxQSerEzsRWMyxQ7evg3+aLi0CSoEAElRgVNDY2kUQqP/x4OnN5Kc9kQaA4AJIRMyAcCg1wnoLJaUQqUqlSaWHhrZcwu820cP+IX/tmaNQae1AoCynCz3552zsVQU5LGv7dy6QKtSemTev9ux4SuehM+klQIAk0Zt9RYJBcmJFuHrf15uMhq4Pce/v9jS0Slar9HYq6oq/zf7VJj5TxPqIUoAQOy/l9fmZdtixoyZl8f5nPOdAOBt77ebJ/COfPgeorcvQOR6r6dPTQ2amu7Idbzd3qb9kZYUYQFWAcoAq4CzABBoHfjKhmc+jpAlNMzuNHsNjUKTd3PsdphL5zarQmljCHUIjdCZdMI/4v4IapUFbqz1gbwkBK5dT0Do8mQrCfeelRi3ZwHo7ApE/Pw9In72w8nPZ4A00vHmyvUF6jJGkbKos5vA7VbNezDGd0wsAFzPv95wkZbXALPAayGyypQWACDitpzAU1VV+nAEwozGjHULai8hKBStoqLhVgl0KkVtMJGPPHgBZAYHTEEeLN21AGDUG/h0JktOpdFlKiNTD53iuQRe+O+/9Lp9eO8KCo2mGvTJF19TaDS5rLzM9XnmbCxGvZ44tnLJVzqN2oFCparkFeVN3pmx4NCrBR69dUM0y3KzuSd+XrZUr9PahI4Ys6zTsJFZQlv7AgCU9LuRDf5dzZh53ZjY1S2NIGB4UFBpzsMzY+Yplt5e2mXBzQU9X7YdL4J0aXpHLp2bP8B1QFmTLy6K5UGc+CYAIPFEj9qGXHjnwsqdQ3YefD4rn+X7bt8fbWfd7tCcTnOut/TcrcUon1EF19+9Pnl9//UXWnOdme1n3qcSVHVEQUTrhGlGbXsXVLocQ9fW7pX17CvFmJ0LQGNV4uqKFZAVhMKz7260G1O84cGGUBNpYvZz6XezZnioQ2ilgCHIyqrKMgs8M/9SWKm2AAB7C9Zz9SypoSQrnafXau0sbGwbJfAoVCoYLHaRSi5rlMDTG6tDNA0mE9vDmCcA3z4dAHRqFcVkNPAYbLaMSqfLVUaGCXpVswSeyWjEgSVzxybfuPoNmy9IG7NoxZy2fQYUsbi8XFVVpXtz5mwqR35c9I6svKyre3CHv7iWoni1vKrJ61pxmWUAwGpFD15VaQnz8A8LF2tVKtf2bw5b0ePdiUkAYO/lkw8AxRmp5jBNM/85HCzYOhGHkVogVZsFnhkzTxGeFf5heHb4jErNfzuCI0+WxypXlwe68l2b5707P38oSCMLDF4hylJ6wGRoYQvrxk/kp9w7dO8eV4FrrX2KX1WebnjeGthx7XTOfOc7+bL87hXqikZ9hgvkBcxGTX7nDw/ICkLh1OkUrH3qrrbqM1CC0X8tAJ1dDq5NAsbtPQ0A0eLoXkwqUzKt3bQnaly48l1jJRpJQE5Vzmvf/9ks8FqAG/t3BurvhXcFABcRW9YSc6beivACAFsPrwbz72pg8XiFWqWiwRBNBo2iMRhJtlpnpDiRYjYHagasfNIAoCwvhwcATDZHTmMwZGojnYBe3eQiK6qqStqO2dO/KkxJnCi0d7z6/poNi2pCTTkWwlyNUunW2oVWruzY0qEgOWGi0N7h+si5S07xRdbZOrXaUV5RzmjKPE7C6l54bAa1VTx4qqpK2v7F336nllX5te0zYE3/Dz6OqTnn06VHIQCyUlxsFnhm/pO4ijiJVSq9V1aZ4rW/oZox01JEFkWKVAaVg8Fk4G14sOE/U7q9NnYm7QwiQdK6OHS53+SLK/MZKIx+CwKnKHj2PQ6d0gFRf7q3vJVmmkMPxx4RBtLA/f3B7w2mx0wMn/je0GND9214sCGgwYnv/DEGFJoKYWsabtDuN7gcXyfPwOfRi0DnmNKl6RyxUtzRw8LjJpvGfiJENdQhNIYESd2VtKt1wkpfIGaB1wKk34scwM280xcAfO34LRKiWVNgxb9nn0Z58ACAYyEs0mu1dhqlglrfOAaVojaaSFZOhZIdTPyjH917pAOAtKiQDwAsHl9GZzLlGgOVCoO2SR684ow0/s45ny2XFhf1c/Jvu3fK2j/WcSyEj7bULGxsc0mTkZ0VG2XdlHmbQmrkDbu4i+FzmBxu7piFP/5GoVIhcnLOAUBJvnnNrSlzedvySgGAw2h5D55OraLsXfD1HKVUEuLTpfvvg6d/Gfn4eb6VtY7OZJYqpBKzwDPzn6Sju2UCCVD23Mn1f9m2mDHzqnA683RbACBAmCKLIl9Mw+iXRIw4piOVoGo+aPtBgxXDn+Hs7IEw6gRoP+Eoun9+B4AJ8Ue7t7yVZprDZyGfPaBT6PI7RXfq/QwvvrW4e1xZ3DgSJGV7wvZv48ri+HUOjtnljMrcHnBofwb2gY3beGdZGMGyMALAlodbuphgor/h9saNp4dNaTslmUpQNbGlsa99mKZZ4LUAHIGFmGbQcGgmg9HfXtAiXp6qUrE3nckU23v6NFpU8K1tikCS1KyYKNv6xjFoFI3BZGIXSNXsYEomDKAaETIxFwCqykoFAMCxsJDRmWyZ1kihgzQyIcliNdaOqz/N/E0jrwwI6Nl3zbilqw5SqE/qTRt3r1wAyH34oElCq7HIykoZl7b+/h0AvDFt5o8CG1sdALgGBmcDQElGmntT5hvYxq6Ix6TlBTlbNNqb2lhO/9+qMFlZaXf34A5/Dv/6u79rG8Pk8vI1cnMlTTP/TSZ2cUshAFNM3v9eHt75jet6bPpk0rLC1CRBw6PN/C+RIkkJpBJUtbvA/UqhorBrkaKoSZEnrws17RFsODZxVmyrpsVWauUU5NwYCY5VCvrOTYJrVxl4tgkoS641D8/Mi0fAEBjdBG43i5RFXWoarD/N4dTDrqcyT30pZApTZ4bMnKcz6iy+vvb113W2K7j1f2NAUHV488dTzbEptjS2F4vKKp0aODX16XNCltBgw7aJL1QUmgWeGYAnshYDgBWUCgaNUm9D8saillV6sxtZYKUGaxf3QgAoTk+pN0yTRadoTCQY+RIVP5iSiSq6XUnNzoaqSioAAK6llZzBZsuNJtAMJgIojGm0F69KBUs/YaUq7PM5EbWd9+3SPQ8AKgryWlzgmYxGHP5hwedaldI9eFDYGr/uvcT/rtujhKBQNJXi4iYVWnFlaYwJ1gvS51tHtkh+5eNICgsC6UxWyejvltX5Q8WxEObr1ConvVZr/r6a+c/hbs3VCDn09DyJ6n9G4JmMRhz8ft6YxOuX5yorpe0j9u4Ie9k2mXm1KFYWB1qxrZL6uva9ZiSNrA0PNoS+bJtag+dqj3D2657Qq+zgP+woKP+keDmGREIrd0H0DvOm6CtCf9f+ESbSxNj4YOMzn+FUSSp3zf01C6gEVb2y18qVH7f7OLWXc68/S1WlHaf/PX3kM5PFH7FHRVYf2Aeeg2vXJqdExZfH80tVpSFeQq8bVErtwW6+It9otUFtfzbrrENT53+VMD8wtgCW9o5iALA2VrVIkm1TC6zU4BbUvggAKkuK6y20wqJT1QBQWFZhHUjkoJLtkldzTi2TVbd7sHOQMblcGQBojHSgIrNRAs+Qd5+nNtIJAUXOgUpSa1KtjZuHksZglMsryltc4B1ftfStypLiPs4BgXsfz2UDACqdTrK4vFxlpdS9SZPe2eCHivQBuL9tcEvaCgBqWZUnW1C/Z9DCxq6AJEl65v079XpmzZh5XXG25CRKlTqfQqn6P+mleJzq/OQZswqSEyYJ7R2ucy1FD8RZ6WHmVihmaogWR1so9UoXN4FbwvTg6QkMKkNyv+R+r5dtV2twLvtc89ojmAxA2oXRYPALEPbzvUfHu864DYBE3AGzF+8V4ZN2nyQxqczyaHF0n8ePqw1qymeXP5utNqhtpwZN/amnU08JAKzvtz7cied0K6okavLGBxufzMeL+Hk0CMKIgd+faI4t2+K3dSVBUod4DHkmPLOGMI+wWAAIzw4Pac4arwpmgdcCOPj6iQHA0ijTt8R8NQVW7Dy9myTw7L185BQqTaGQVDQk8DQAIBTf9WYTOsgEPlk15zRKBR8ArF3d5CweXwYAaiMNqMpvVKEVScw5HxIEuDQdBfGHnOu0gcfPVcurWlTg3Tywu21OXMxUgbXNnXcWLD9c2xiOhTBbo5B7NKnAS86t6mRbaW6LftnLcrO5eq3GXmBtk1XfOBs3j3wAKEhJNO9ImvlPEuIqTCAB2p47uX4v25bWpDgjjb/zm5nLpMWFA5z82+ybsnbTL/7dex0z6vXCK9s3/ycf4M00nZMZJ9sCQBf7LglsGtvkaeF5s0RZ0ildms552bY1hNFkRFPszKjM6Mij8/Ka3B7h4sIQaKo84NXvGGjMfyOnPPtKwbVJgjjRnIf3isCgMkgvC68bYpU4JLE8kVdzfNrFaePFKnGn3s69t8xoPyO55jiVQsVv/X/7jUVjlf6V8Nc38eXx1fl4qeetUZY6ADb+l+DVX9IcW+LK4npzaJyiiQET63zuGuo5tJhNY5ekSdKa3Df5VcIs8FoAlzbtpEZQYKmvbJHwTHFWhjcA+PXo3aScLwqVCgaHXaiWy+oN0eQwqj14dvJETwCQOXRPqzmn06j5BEHoLWzsNFyh5T8Cjw4oyxrlwZMXZHgAAJemA/Lu1hkKyRVa5ulUKhedWtUin0GFVEKPDj/xJZ3FLh41f+k6Kp1e69/Cws4+22Q0cptU4KUis1rg6RROSD3fYoVhUiIjPADAxs2jXoHn1TE0HwCkRYV1CmYzZl5nxoe6JgMg7+VI/rNhmonXLzseXv7dGpWsyr86P3n1AQqVit4TPnzAYHPycx7GjmjtysJmXg8SKxIDKQRFOz5gfAYAvOH2xg0TTPRNcZu6vWzb6sNoMuKtE2/NGXtm7NZocbRFQ+Ofqz1CwrHRoLEkGLb22jPnHNpFQitzR9x+c//YV4QhHkMiSJC0LfFbugPAksgl3eLK4t71tPC89Gu/X5/pY+dj6aOaETxjlc6oE3519asvdVo5BZcWjwNAoN+Co82xIaokSliuLg/ysfSpMzyzBieeU0yZuizodW5RYhZ4LQCdyTQpaDxSoJfV/4lpJFVlpU0usFIDi8sv0qpU9f6o8Zg0DQA4ajOdKkkuSK/+uTXn9Gq1gEKjyylUKvgiazkAqI10EmppowSeurLcGQC4dIMRksw6BZ6FnX0uSZL0tLuRLRLjfPbX1SMNWq1d+0Fhf1g5u9bZE8XOwzsHAHLiYhqXhycvoUNV7g++fSwA4MHeFitXLc7K8AIAny7d6xXyth5eSiqdXqmQVJg9eGb+kwQ4CJQWbHp2XoWqbV1jZGWljANL5o7Nio2yepG2tQQ3D+wKvLj5tzUmg4HbbfT4BY/nJ1OoVLi0DTqtVSo8bx/Z3+Zl2mnm1aBYWRxoxbJKrulVNjVwaiqbxhbHlsa+0tU0v7z25eB8eX5vg8nAX3F3xbsNja9pjxDqENo0gXfrV28oxO3g2vUkuDbPFmbp/PFtAEDsHrMX7xVhUptJmRwapyiuNK73kbQjLiczTn4pZArT/hz056a6xNYHgR9k9nTquU2sEnfeuq3zZpSnDoJdm3MIGNY0b+8/7Ejc0R0AZYTXiDrDM2tob9s+1kgaWdsTtzfcsuEVxSzwWoByhZZWRRMQbJ28cQ0aG0Atq/TmWAjTm3MtV2hZZNBpberr9cZjVQs8V2OeVZzJC65W/EeCSK/TCmh0ugwALB2dZACgMrG10MoaJfA0CoU9ALDY7HzIS+oUUQ7efjkAUJiS+GSY5oO9Tjj3bUeUJHAbsx4MWqLkyJJOxSnx4z0FVerepb8MrW+4X7eeOQBQkZ/XOIF3b4sfSBMdfmFnQWNKUBLfYgJPVib2pNLpEpc2QQ0Wb2FyuPlqucws8Mz8Z3ESshMqlFr/coX2mR3Tstxs7p7vvlpemJI4MfLQ3uEvw77mcub/VvW7e+LwMhqDIR3y2ezZ3ce8l/z0mP4ffHKFQqUpkm5cfa1em5mWJ7E8kSfXyd1c+C6JNceoFCq8hd4R5ery4MZ4xl4Ge5P3ukfkR0yz5dhGe1p4XkyXpg8+l33Ovr5rmt0eIWrbaFBoSgxZfb7W836Dy8GxSkVJgjkP7xWBSqHC19I3okJTEbQ6avViKkHVruy1coUNx6bu1KbydPbvmQm2A5QqbOXQ7M759j6EaVe3NteGhPKEXjw6L2+M35i8hsa+3+b9hwQI473ie69tNc1WFXgEQQwmCCKVIIgMgiDm1TFmLEEQSQRBJBIEsa817Wkt4guqLGR0Phg6xXPHxxdnpPH1Wq2dwMauWSX5BTa2RQCQFX2vzh9WCzZdzYYGTmQx+yHpCTcr7iOBZ9DpBFQ6QwYAtu6ecgBQmthaaBWNysHTaHRWAMAVWmRBLfWAqfaqx37dehUAICVFha5PnDj/3Rzc3bwEm3rsw0qX9djY7VMc/ag3UsL/DY1UJKwnWQAAIABJREFUSWg4Py8EG7tOx08u22MunFsMkNS+jgVVqMrvhlu/etdln5Wzq5rOZIrlFeXujXk9yLkZBMCETlMTYeEcB1lhexi0tZfubSLKykqvhgqs1MARWORrVUpncwiXmf8q7ZwtEkwkGHvu5Po8fjw/KV548Pt5K9RymQ+NwSivFJcEvywbm8rRFYuHp96+8RXXQpg4bumqbx+v6vs4Ahtbna27xwVZmbhr2t1IczGl/2GOph9tA4DoZN8p4fHjw72GR5AgKX8l/PVyRYteRcFfg9/F3U3uNYfyZHms9THr59KpdPmaPmvWzQ2du48gCNNvsb9NrGuaZrdHiDvogMrc7nAIDoeNX53ROrAPugVNpRcSjtk1em4zrcpIn5ERAAiNQWM9JXDKo6Iqz2AyACc/645NPTdSiuNGLobVZSaNXbrYVNL/evEdqzJVGd1oatqzUERBhJVEI2nra+nboPcOANwt3NUilig5T5732hZaabXYUoIgqAA2AHgDQAGAKIIgTpEkmfTYGB8A8wH0IElSShDEa3ljyyhTWMhoAlCNOnZVaQnTwtZe29y5UiP/KbDi4VVdYGXPqLfQbvx9tBtT3Jjrbd09C5NvXEVJVoZTMFDrLoUlh6FpS+SAChKJpJeGSvlXrxj1Oj6LJ8gFADZfYCAoVLXKyNJDX9EoD55WZ7SgUUgjy9o1A5Wp/ZEdIaotGZZvZa2js1jFCkmF+6OD0hwmNJUeEHleB98+H5LsNihP64fSpDDEHwbo7DKwhHlQlgXAZOCAoGpySK/0ZJmtyNnL/bjlzHUH8Ef3HYjeMQI9Zv1Sl41sviBbLatqnAdPkhUEtmUW7AOVcGj/ABWZ/RD1pwe6fVZv3lxDyMpKGTq1ytnK2fV2Y8bzrawLyvNzuflJ8ZZuQe2lz7O2GTOvImM7uyQeiMrHnayKtgCSASDtbqTt+Y1rlxt0OlHHsBHLSjLTfQtTEicUZ6TxHbx95S/Z5DoxGY04tGz+uMKUpPcENnaRE1euW8PmC+p9iO06evzZE6uXjbx7/OBQ3y7dt78oWxuLUa8nKgryOOUFeQK1rIrdtu/AXBaXZ95xamESyhMCKaDox/mNS3v8+Dj/cbm/xvyal1Ce0BtA+EsyD7i6MhB5tyegKCYMQvfZRp83yqf/PX26yqBymBY0bWGIbYgMANpatT0RXx4/dmfizmPvt33/mfvlo/YIDv5Ny6e6uXYkCIoBbyw7Xe+4jlNuI+vah4je0R2Bo443aQ0zrcIon1EFh1IPHfOw8MicGTKzdq/tw8MOuLToY8iLO4JlkY3ec34S9f4m9dOE7d7rotetnnll5l81QwkQBgpB0VEJqo5CUPRUClXLorIqOXSOhEfnSS2YFhIRSySx49pJ75fcbwcAo31HN0rgAYCnhWdMlDhq8r3ie8JQh9AWb5PV2rRm8mAogAySJLMAgCCIAwBGAHj8jzoNwAaSJKUAQJJkaSva02oUV6otZLTqIj85D2PtggcOadD9Wxel2ZlewD8FVvLuCJBxeRrKUtuh3ZgfG3O9Z4fOxdd3b0OVuO5WCdY8pjqUkgIASKB4P9GY3ajX8+ks1qPeIlQ6TaY0MgC9umGBp5LQNHqwmTQo4RicjYxLQNp5j7qqHbF5glyNXPZviGbMLh8AFHgNuI6ha+4DqG5kGrPLHZlX26AivQ1UFa4Qed6CR+87xm5fPDz7zbwVVLpWMuiL7/bDxlED2zYXURI/DGkXd8B3UEVt6/KsrLNl5WWhsrJSRk0T9FqRl9ChqvCDfbszAIAOkx8g4SiQdr798wq85JvX3AFQrJxdGzWPlbNrfvaDaOQ+jHUxCzwz/0U6uFrK+UxaXna5MhDAkYeXz7tc2b5lOWkyMru9897CbqPHpd4+sl9TmJI4Me5SeJCDt2/ky7a5NkxGI/YtnD1FnJUx0tLR6fLEFet+Y7A5poau8+oYWm5hax9ZnpczSFpStN/S3rFF2u40h/Dff+lVkJzQ26DTCYw6ncBo0PONBgMPj0X93Dywq9zRN+B038kfXbRx81DWM52ZJlCkKGprybJMrS10zU/kFxEtjp54Ne+qdT/XfuUvwz6knusHClUNk5GF458s/K7TW+fy5Hn9QmxD9n3R4YtHXsel3ZceG3tm7JCdiTs/eL/t+4ufnqZZ7RGyI4QoTxsAa7/LcO9Z/wN327fFOGOZgZL47gDMAu8V4cCwAztqPSHJYuHYx++gMLpawHsP3IIxO8LB5JsAYErglAydUTc/pjTGX2/U0/UmPcNgMtANpIFuMBnoBpOBoTfpWWqDWliuLvcpVBSKTKTpibQpAUOQOdxreFFjbe3j0icmShw1+XDa4ZBQh9Crz/O6XwatKfCcAOQ/9v8FALo8NcYXAAiCuAWACuB7kiRrj6l+hSmTa4U1Aq8sN9sOdXjOGkN1gRVWib2njwI3TvlWHywIRdxBBwS/26AXT+TorKbS6VJlpbROgWcnYGrCqPcQY/JGFcXq0Y3ZqNcTRoOBz2CxHu2M0+gMmdpIF8CgbVjgpV+yURoYBJ1BlaHNyBxErAHESe4Aav0B54pEubLy0i4KqYTOsxTpkX+vukR6u3dTHw1i8k3o9lnWP4LqzOPXn127coBGIffx79Hnl0cPQ72/OY1Dk97CtZXD4DtoZ+3vkUtOUWoyJSUywi10xDt15zre3eQP0kSH8z+hMp59pWAKclGW1h7AsQbfj3ooTk/xBACPkE6NCtF0DWqff//McZTn5zkDePg8a5sx86riIGQlZpYp+94+dsj3zpF9SwgKYej3/sfz2r85NBcAQoa8lX776H51SWZ6MIBXTuDptVrK3u+++rSiIG+wtav7mYkr1m2tq6JvbbTtO/Bk5KE9Pa/u2Np/1LwlL8VLc+Ln5YMz79+dQWcyxQw2R8wQCLJpTJacwWLJGWyOnMXlyUAQyE98ODAvIW7K7nmzxlu7uF3q8vbYU3WFoL4KRJVECUUskdZL6FV3WN9LJrMyk12lq/IKtgk+VNv5Mb5jbkSLoyfuS9nXq59rvxcvWirzGZBmdYfI6ya8+kVmxmxbdLng2nRrtnX85jc2P2Gzj6WPKtQ+9FBkUeTUn6N+bv9N528ePH6+0e0RTAagIpONslQ+rv/0NkgTFb1mN+612wXeQs6N95ESbg3/sJcjiM3Uj15FwbFP+iP9wiQYtJYQeV7H0F+21+YY+CT4k1QAqbXM8gxGkxE5shx2QnmCKKsqSyRWii27O3VvUuux8f7js9fHrK9MqkjqAMAs8Jqxvg+AvgCcAUQQBBFEkuQTOzMEQXwM4GMAEIlEruHh4WtftKH1UkUK5TQBAKC4QvppeHj4+OZOpVIq3Fk29prw8PC1zmViXrvqw0T+jX2r4wv5jaocRLcQsVQ6Xa/w8PBawxBZynx6f0oOlusnwJJtsK15Pw0aNQUAxcAT9g8PD+8AABQu31GlUjNAGmkXTx9ZZ6By6nxYsZOmcJQGBghLoWV4TN7yfnSRoapSPTomPLz2nAEbRx7SUigXTp1cx7ax03WWyhy4DBv9tfiypYiv/9nGqNVSsh5Eu7GsbDTwajMiPDx8RPUZGjoKQ1QicfLIq6ePtK/NXq2FFR0A0jIy5paHh8uePl+Df166yAME/jZ2maD/52/aVtDZwqX8uuul08fXGanMZrfFKJNW2lLoDFN6uXReRnjDz3EkSYJCo5skcvn48PDwgc1d14yZV5kQPilQ5RSwI4+c+5nGYhld33iroMhIzCp67DvCtnMkZVLJwPDwcJ96pnrhkEYjCiMu2skL8viWAe2k1p17+F+4dKnOUPFa52DxwbS00hakp049e/bsQIJokXTfRlOR+MCi9P5dG46Ds8qlf5iCQqNxAHAAPJPT7eYTCHWZOL8i8YGwLC/7rTO/rnrr6pH9Sqs27aUcByfNi7a9PsoMZbT1letdDaSB4kZ3U/kz/JWBjEClNc268blfL4AEbQIHACVEG9IrPDy809PnCRBwoDpoc0pz3gsPD3/hfROdy67x2pmM7CjRyDYFpgDPLU7XSK5JQf1ZauVy9eLVNU+PDyPDiARKguFi6sUFAaUB+RSi2gGsMWkIiVri2ZnVuSo8PHwtSBJCZQbTVhrNESozWXSDkkozKql0o4pCN6ioBP51gIuFHRXRBdxvUNDwfZPHH07vjRtIvXlidWYWXrsQu1cS0oQ2uTutjFQWWWTVQy7nuDa7/7N1ZRw7IH+PNV9dwKzieGiSvCflSwUBTkjVLERqy+xv0UGH3z//IBUIb+K8vjRfaqYis+eZs2ecaj6/rxhVdZ1oTYFXCODxqn/O/xx7nAIAd0mS1APIJggiDdWCL+rxQSRJbgGwBQCsrKzSw8LCvm41q5vB5t9vTlZTybcJCsUESemtsLCwvxq+6lmKM9L4ySrlXksPr6NhYWFHsWPDOwAmQ+h206XiRmeX0csWwSG4wVCY7X+fnlklLgmt833aHjYGwKRzxi4wUliJYWEDlgJA0o2rDunAZhGbuTssLOwqAGy7cPxrrdYYAsBikDtWISispM6F9+0Iu2Ogfyqk0yPDwsJ+RSpnAVud6liXHfFXaM7Ft65sZFWWHQmbNOkaYj7YCYHzvbCwsHUNvcY987/8wKjVePZ4Z/w3nYcOfXJXhhPvj2uxqwfJDl3ChEPPfJuNej3x66mDB4wlBffCZs/bUuciP3+xAmxhxRvDx85+dOzctY4ou7zkTXrUTgxe8aDOaxtgw9Hdv7C53JyhQ4cubOw1Ocf3/GwoF2vDwsIafY0ZM68T+eu3DLIqOTvTwOJWjJ71zRz34A7P7OIeib09Ire4YKqfnfUKr46hL2RX/vaR/f4qWSXPztOn2DOkk5hjIXxCGCikEvq+hbO/lZeXebu0Cdo5dsmKZvVpAoDwrOTeyTevzWGJ8/cO+PDTpvcHayYnVi8bXBp9bwbfyjpq4tKfVj79GusjJy5GFHlob5g4O3NIXmGeM9/a5u5H6//8kUJtkc5Bjzieftyps33ncme+c6Pz3NUGNeXNo2+uNMKo8RP5XcyT53XKVmY7n1Oes+HRebkeFh73+jj3uTe57eR0No3dYChta7L7zO73CRAjxr8xfpYjz7HW9IGTl06OiCyKnGrwM6xpSrhZi7B2ziLQWLTOY2fP+PHMuzMKCLXXaj07qlPJ351hbbEd7/z1jJfj5vWb/c7nnP/qluDW8ZW9Vt4AgOV3lnc2VhgXDaRWngnLXmoDaU4nGDRWAEgwBXmgc6RgchSgW8lB58rB5CnAspCDLVLY9Z0XE8a3b7yoSP9hvZ/kstrvoy1zW+6N+B/myIf9UHrxKwDwKj4lAlOQC7u2t9D+vZvoMLmgUXM8POyAy0unoCq/K+jsMrR7d4fF2xtvdKO8bJ/Ts1yLuNY3ITvh63K38g0fBH7QrOKHrcySuk605rsZBcCHIAgPVAu7cQDee2rMCQDjAWwnCMIa1SGbz5Xb9DJQaA0WDDpVRmeylBqFvNmFYh4VWPH0rg4bVJTZg8qoQveZhxD+TU9cWDAYH5xp8MGBK7QskhTmC8vzcznWLm6qZwaIE3s8MHmbimBNcaVRHoWryMpKBQDA5gseebXoTJZcbgALAFCRKQRQp8DTV5Y4akwCULmi6jF8x2xUFXSGrJABgdMzNyufLt2LL275zSAVF7sh9bwNDFpLWHk36H5PvH7ZUZydOVzk6Px35+Gjn3W5956dgrub0pB7cwQM2nOgPelpo9LpJIvLy1VVVbrXuYiskAFVhT/s2z2ZyN11RgLubTUg91YIgGYJPI1SQdUqFe427p5nGh79L2y+oEBWVvraVnQyY6YhDA+vDVTQeOSDthOj59Yi7gDAq2OXuNyHsUi6caWdV8fQK61tU3FGGj/yyL4VIMma+6WJxmCWMzmcYhaPX8y1tCyuyM/roKyUBnuEdN70vKGV/T74ODLtbqQk/V7k8LoEXlluNvfcxnXjJQX5vfu9P21h8KCwZqcFAMDx1cuGZEXfm863so6auPL/miTuAMA9uIPEPbjDHllZ6aHT61aOL8lMH33zwK52vSdMabFw8qt5V62XRC75XcgSph0ffnxBYysvfvb3Z6OkGmnAEI8ha1f3Xn0NwF9nMs84nMo8FZomTQtNKE8YHV8eP+bP+D9Lf+z544JB7oNeWphpoaKwrZApTK9L3AHApDaTbkQWRX54JO1I7+Feww+8MOPy7gggK+oIx5Dj829/3yuzKvPNIOugw0MG/LEPv3VahsTjn8PapwR95z/RBmRZj2XXbxXdGnk57/IkWU5EvCByQzeVPOEdNgMYmXBxIig0NQSOsXDsEIXQadEN5tY1Fds2t5B3+z1kXqm14JuZJiDJYiHl7PtgW6Zj5JYVuLelG4of9EDe7fHIu/0eLizIg22bm/DsEwe9mg61lAutnAOdnAOdigu9igOt3BKS7B6gUPRw67Ebo7eerO358FXhXb93Y89ln8O1gmsdX1GBVyet5m8kSdIAYCaAC6iuiHaIJMlEgiCWEQRR0+vnAoAKgiCSUB3f+g1JkrUWxniVCXYRxoa4Wp5msNmlWpWq3r4v9VFTYCWgR5/qD5Gm0g4MXglCP84Bzy4OBVHDoKlqcEtUaO9QCADZsfefzcOL2+8ITaXnJYSqAIBBozwSgMpKCR8AeJaiRzl4DDZHZjSRTCNJALKCevPwFFUyZwBgCyyqi4DY+OYAoCD+iGtt41lcnpHJ5hQopRI3pJyuzr/z6NmgwLt1cPdUgqDoB0ydvqvWARQa4DvoBHRKB5yf17m2IRyhZbZGIfeos+3A3c3+IE00uIQ++YBi6a4FxzoZ0uxm98NLuXndmSRJusjRuUmbGTxLq3yjXi8qz8997nYcZsy8auTGP7DUyGX+hUKf0nwNzb2uce0GvJlLpdGqynKyXki7hNtH9vcCSdICevZd49e99zpH34BDPJEoyWg0sKQlRT3y4uOmKCulQb7dev5fS+TNsfkCg4O3b7hSKgl5ePn8E70vjXo9ceqXFW/s/e6rTWU5WW8ZjQZezPnTI55nvcfE3b3miLvHEdjY6kZ8s2gflU6vTL55beTz2PU0Wx5uGUqCJKQaacDkc5O/aEyZ9B0JO7zui+9PcOY531zZc+W1muPDvIYVbxm05eS1d68tODjs4IQwj7BfDCYDd+ntpfOLFEV19pBtTQrkBcxKbaWPE98psb5xPZ16SqxYVolp0rTeTS0V/1zc+KUXQFI2OfuIz2Wfm2nJskzeOmjrXrAsjJhwaCXo7DLcWPsdkk49scnN1qkp02n2d9QGtf3pY+/tINPOT79PI0X+JL2E0f2LhZiTPgFfxv+EsTsbLpzSHNq/dwsAgTuburb43K8r9/50wxrfH3Dmq9AmXXfs49EwaEToMn0rfAdVYOKRM/gmYz4mHZ8Cn0GbQWPKkH9nPK6vWoXI9T8gdvd8JJ2YhYzL05B3+z2UJAyGrCgI1r5XMOnEJ5gSfvhVFncA0NGuY5WAIcjKrsp+7TbXW9UfSpJkOJ4q50uS5OLH/psE8PU//762rB3b/iYA7Pxmt42qqsqvufNUlZX60JmsElsPr+owTK3cDjy76lLJAcNPImrrYoTP6YFRWyPqm8fO07s4/vIFlOVmOwJ40sMVs7snAFwkelQB4LHolEchnypZlQAALGztHnnwmByOHAA0Rhq4yrJ6BZ5KobYHAI6FZfWPtEffbMTuAQqiPJ6x4x/YAkGuQiJpA3GWHwiKDh3ez6lvjSs7tnSQV5R3dg0M3u4aGFz3zSBsTSQST5Yj+cwIDFt37+nTFrZ2ORX5uUOyYqOsvTt1fTbMK/d2IAATOn/0bClfa58HyL01CTk3hc25IeUnx3sBgGtgcJN2g0SOzvn5iQ+REXXHxdrFrVGJxmbMvC7EhJ/sBgBqx4D4KoW+j1Spo1lyGc+IDSqdTvJE1vGy8vJgk9GIlg4DfJri9JQBTA43O+zzObX+7pZmZ3L1Oi3Vya9Nnfm8TaXn+MnnDy6ZOzYm/NRb7QYM3ggAd08c9rl/+tinGoXchy2wSO7y9pgl8VcuDpYUFfQvyUrfbu/po2jqOsdXLQ3Lion69B9x99PziLsaeJYivYO335mC5ISJDy6cdaspkPM8FCmKGCnSlEFOPKfb9lz77Ghx9MQpF6YU7Rqyq04PVpGiiLHp4abZDCqjck2fNRuplNo/JwFWAcpVvVddFzKFin0p+xZPuzjt81Nvn/qlrvGtxYHUA/4kSGqwTXBCQ2Pb2bSLuJp/dcb+lP0eE9tMzH4R9pEF9/v9aWUr2VB2+1MBQ5DzU6+fVnHp3OqQVscQBcJ+XoZTX6zByc8WwTbgW6Sdc0TcwQEoT+s90agT/O1gb/hdZGlitx31W0nJ1dkBLr2Pof/y1i8Y1mFyAS58l4ei2B54me0lXhWOfNgPicc/A2liIGaXNzz6zELbtxv2WiefsUFh9EhYekSg79yUJ8559ZfAq/9ZAGeRdc0SKWc9wRJowLVRgu+ogqWbEiJPdU1FzNeNN9zeOECAaHbNhZfFK5kx+LrCEQjFJqOB11wPi0pW6cWxEFYLIa2cAr3aFhxR9RfvzR+jweAVIv3SiLqah9fg1bFLMQCyqqz0WQ+eOKEnOFbJ5VRbJQCw6dRHIZpapZIPAFbOro88eKx/wjXVRjqgktYt8EwGqLUGawAQ2NhWe/DaDC8BhaqGJLvOnnM8kXWuQae10UiKA8ARZYBlUeeWpEapoCZevTSNzmIVDftybv09cJh8E1xCT0NZGoTbGzyfPm3v6ZMNADkPYmq3TZrVDmzLzFobqfoMqg7NjN7Rrl4b6kBaXORJUCha/x59mpQ/4eTfpgAASnOynJuzrhkzrzLirIxuDBa70N7bL8ZEgn4kpqBWzz8AWLu6xxn1OlFSxBWn1rTp4eXzLhqF3Mfe27fOUFBbDy9lS4o7AHDyayOzdHS+Likq6B9/5aLz9q8+/eLm/p2/6LUaK/8efX75dNOuuR2Hvp0V/EZYOGkyMW7s29nkwkv1ibvJ5yaPe/PIm/NzqnLYTZ3XaDIiYMzwKwSFook5d6pFvHirolb1NZgM/GGew05vG7TtkJvA7Upsaex782/Mr7PQyOdXPv9AqVc6j/Mf939trds2KH7nd5kf3cmu0548eV6fGZdnPJdXtDk8KH0QSIAwjfMbl9zQ2Gntpt0iQBjPZJ3p/SJsU8TscFnChe96AUvkxHO6c3DYwbndHbs/Ge7YfkIhuk7/CVq5CzZ2/QsXF61DadJg8B0eEp2nLesTOus7BQHGT+V3JgLASO+R91+E7QAAm4BIKEvbIudmo/r5PkPmFRHy7gha2KoXi7KMho1dpyPh6FfgWKVhwOI5AEic+WpOYyLDcHHBFADA4J921DvOs68UYT9Ho/+iRHT5NAdthpfCIVj5uoo7APi++/d3lnRfcvdl29FUzAKvBeFb24gBIC/+gV1Try3OSOMbtFo7gY1ttcDLvGoNkBTwHaoFHo1JwqPXSailPri+OqC+uXiWIj2NySxVVUmffPh5sNcJmip3OLa/SaNSNADAplMfhWhqVUoBAJOVs+sjrx5HYCEDAJWJrYK2yrLORfPuCFV6GgMArJxdpI9sZglzoRC713WZlZNLLgBUVOk8IXCu1yt1defWbjqN2sm/R5/tDTUNBgC8ueIiCKoG97c9c7P2694rFwDK83OfFXiV+QwoK3xh6V777mKXjzNBoStQ9KBZLnulVOLJ4vJy6Exmk37wvDt3ExMEoZeViet88DVj5nWkOCONr6yUBlk5u97qH2CXDgB3syp86xrv371XHACk3Y1s1TDNh5cv9AdBGLuOGnetNdepjZA3h50iTSbGxc3rN0iKC/vaefkcnbz6t+lDv/jmeo3XMmTwsBy2wCKpOC0lzKjXN7ps5c0DuwJrxN2kVeufEHeHUw+7xpbGjitSFnWbdG7S8uSKZG5j540sihT1Odhn7cT7n/6Z76KvkpQU9n1472qj74c39u8M3Ldg9sTHX4vRZMS94ntvCRiCrOnB05OoFCp2Dt75u4glSgzPCv9yc9zmZ6Jmfrr3U4c0adowf5H/yTmd5sQ1dv0/B/152JHrGHm76PaUtdFrG7WBtz95v/uEsxMmNkcMP06+PL+tgCnIdLdwb7CNQ5B1kNyWYxubWZnZW2fUtWq50tjSWMGkh79+f5zPQxcLn5On3j61qs4iN4N+iEObEb+BI8qE35CN+PjaJHz5cDWGrrn/YciMFCee0y21QW3Ho/PyXmgfv3ZjIgFQcPv3podpRm1zxb6xv2HnsD9w/rtmp2Y0C5Ohuh7A85ISbo3fO69CafIQOIYcxRexC9Frdhrajf0daokfdr09sd7rr/zYBtKcnnAOPQK/weZ2E68JZoHXglg5uZQCQHlBXpMLrTxWYKVa4BXFVN8URZ7/FjUZ8vMVUOlyxO1vcHeRyeEWaxSKJz14sXuq2xV0mXGLTiXUAMBl0h4JPL1Gw6fS6LLHQ574VtYyAFCSXCW0irp3v3Ij7VRGOgDA0cf/37BFnm021FKPuryOrkHtqwWehk2DfWC9Ai8/Ia4fjcGo6Pf+x1H1jXuEfaAStgF/oyKrNzKviB4/JXJ0VtOZrBKFpNz9mevubfEHSBpcQmsPlaFzTBA4xqEqv31D3tSnMer1hFou9+JaWjY5WZfOZJoYbE6hsqrS7MEz858i6tTRUAAUn649bg8JdBDTqYQ8u1xZZxsE3649S2gMZmlFfm6jBd79M8c9JUUFjX4I16lVlPK8nH58kVW0c0DbOktRtxbt3xyaK3J0vsS3trn35qezPp+4Yt1OkaPzMw//Hu07ntFrNfbXdm/r0Jh5TUYj4i6d+4DGYJSPX75m9dObZZsfbv6ARtDUA90Grq/SVnl+dPGjH2NLYxv0XuxP3u8+68qsNQq9wtlL6HU+xkvCJEFS1h9bumH0qdEf70i8tagWAAAgAElEQVTY4VVfzlhhapIg+szxecUZqWPP/vbzgJrj62PXByn0CrcuDl1O14RNWrGtDOv7r1/BpDHLNz/cvPBizkW7stxs7q1DewPiyuL4h1MPz+LReXkbB2ysPU+7DqgUKja/sflXLp1bsCdpz9zLeZdt6hqr1CspH57/8J2V91aufVj+cOzUi1O/VeqVzXqmKlOV0Ss1lX5OXKcGwzNrCLENuaExamy2PNxS74bv83Ag5YDbxxc/XpsHvc1SuT73z7ePbWNQGXWGqpmMRiTaTU5O6PTHHxh/4PzTVb9ntJ+xmwBhdBe4N+4e3lJ0+jAHTEEO0v+ejBtrG99eJeGYHS7MXwaCogeNLcGdDUuxPWwsDNrW7wGiktCwLmgJ1rY5gqWiQ/jRYStWeazB2jaL8Hvnz7G1/2TsHjkC5+eFIDui7mez8/NCcHjyr9DKndFh8gp8fG3nI2/a23/cgo3/eRTFjK5TvBq0BO5tmQYauxyj/3yu/r9mXixmgdeCOLcJEgOArKy0yYVWxNmZ3sBjBVYqMqrncAz5NzZa6KKDfbvzqMzrioRj9e6KsvmCQp1a5fhEEZGShJ7gWifBZ6CE/o8Hj896TOBptQIqnS5/fB6hvYMcAJQmjhZ6Vd0/ImWpDkoDA1QqRfVEHoelezZMBi5Sz9d6o/Tq0LmMSoG+XMsB2oxMqW0MABQkJ1rIK8o7Wru4XWuS56vnV6cAkoIrP4Y9fYrNF2SrZLJnPXh5t9sBhAmdpj6bf1eDXeADGDRWeLCvSWIr7e4te9JkZAvtHJtVjYnF4xdoFQqXhkeaMfP6UJKR2p3GYJZ2DBuRSaUQEHGZaaVybZ0PYhQqFQJrmziFVBKk12obvI8lXr/seH33trUnVi/7tLE23Ty4p71Rrxe5tetwubHXtDRT1m367eMN238M7Dvw6RZDj+g7+aM7VDpdmhF1e2hj5ry0dUM3jULu6xHSaR/fyvqJAgdr7q8JFqvEnTradzy0ru+6vycETFiu0CucZ/w9Y8Wdojt1RnCsurcqZFXUqlUkSMqcTnPmHh9xfNP5qdc+IGz58b55XGpuaeabv0T/sq7ngZ6/T7s4bWRthUzO/f7Lp0ajkcPkcHMy79+bUpiaJACAc9nn3qJT6LL5ofOfyIEMtgmWL+iyYBlJkrQlt5YsOrRi4cI7R/evWnF07rd6k54/M2TmGhuOTZN7dLlbuKsXdFnwI0mSlMW3Fi+ozdaTGSedBh8dvCpKHDXZked4t6tD179KVaUdJ5ydML05hU/2p+z3M8FED7QJbLTAm9VhViSNoCnPZp1t1N+9qSy7vSz0p3s/raaYDKxdRWKMsu1y4ukxsrJSxs0Du9seWjp/9JYZHyxcP3n0nvMb1226uHn9r8UZafynxw/3Gl70fffvP/+5z88vrvonUF14bcSGH0ClK3H1xx9wZXnbBq/JumaJk58th8nEwJsrF+PTG3Mg8oxA7q2J+K3DdyhJaLRnu8kYtAS29v8S8qKOsAs6Dbu2F8B3SAKNoYROaYXKvI4ojHkbmVem4s4fS7HzrV1YbrsLv/gtxZa+7+PYtN6I3uGCvwaPw50/vgeNLcGI37/C8N/uPLPWhCN/ginIQ9TWr2sVikc/GgBNpRfavLUDQpdXuiCKmScxC7wWxM7DS05QqGpVVWWTPXiyslJvOpNV/KjAirzEDoAJ3gOedIf3X3gWIEy4ufat+ubjWYqKTEYjtygtxQIAELvHCVqZOxyqC8IwqNXtEQRs+iOBZ9BpBTQ6/YlcEhtXj2qBZ2TpYdDULfBkhfZKAwNUOkP6xHHHDtUJ4JmXa811o9LppCVLrynVCvTwGVhnCeO7Jw71AkAJ7D/omT479RL0TgksXO6iJG4IKvOfuFHzrKyz9Rq1o6ys9MkbuCQ7CGzLjFrz72poN7Y6Dy/pZJNCNnIexnoBgJN/m2YJPK6lZb5eq7GTV5S/lEpvZsy0NBUFeWy5pCJE5Oh8uyZ6wEnISpdrDK55EhWzruvsPL3jTEYD78HFs3Xm+NZw78ThdwBQpMVFfR9cOOvWGLuyYu4NoFBp8l7jJ79Yb0MTYfMFBjtP7/MKSUXHlFvX691c1KlVlLTbNyYz2Jz8wdO/fCKvUG1QU46lH5vKorHEK3uuPAMAc0Pnxn4Y+OH3aoPadtbVWT9dzbtq/fScX1z5YvDe5L1LOHROydq+a2fXFP1g09imvoPe3UY1EtQvigYe7ufSbyOdQlfdKb4zZeqFqV8+LoTOb1zXo6pU3NM5IHBfrwkfrDGZjJwLf/w65WLORbtiZXGov8j/fG1ibYT3iMJJbSetdEujOGsqq9qSAGmdog0OdQjdPSFgQk7z3tHqKpvv+L7zi0wn85h2cdrMGlt1Rh0x/e/pwxZHLv5VoVc4DfMc9vPZkWdXbR209USQddChzKrMN6ddnPZOU9e7L74fCIAc6zu27k3Fp3DmO2t9LX0vFioKe0QURFg1dc26yJPlscafHT/5cNrhBTw6r+CQkhHVVm/Qod93kUC1l+7g0vnvbJg6/petn089ePf4wZX5SfHva5VKJ4GtXZRzm6BdpMnEuHVwd//a5h/lM6qgKX0MW4w2w0sxdvdc0NgVuLF2ab3hlkWxPByctAwGrRD95n+PzlPzYOmuxcyoX+D9xhZUFXbCX4PW4t6fjfotaTLbBn0AaXZvuPfaiek3t+LTG9vwRcw6zE5dgnm5X2Kh+AMsLh+FqX9PQK/Z38F7wFYIXaOh11ig6MEIPDw0B6dnbUDe7fcg8rqGT2/MQfD42nP+hS46vLFsNUwGDo5N+/oJ72RZKhtp5yeDY5WMt/+ot7ifmVcPs8BrQShUKhgsllijkDc5B++fAiv/PvSrKuxA55Q9U3TEq78EIs8bKE16A6VJdRZzsXRwKgKAvIS46jDN2D09AZDoMj0SAJi0ag+eJYfxr8DT6/g0BuMJDx7fylpHUChapZFpgl5Tdw6essxeYWAZaEzWkyIt6J1cACRKk+t8CLNlyGjl2vrr0pRkpPZncXmZwQOHNL3fU/C7J2HU83HumyduOFZOzjkAiORb1//9kZbmMKGq8IGle3y9c7Z9WwwGtxilyU3Kw5MU5HuCIIxtevVrVt8qoZ1DPgAi/V7kswV0zJh5Dbl74nAnkCTNs2NoZM2xNg6CdACUw/fzveq6LrDfGw8BIDv2fr1hmml3I20lxYX9LB0cr1KoVNW9k4cnN2RTWW42V1Yq7ipycr7eEpUlW5tuo8dfAEGYok4fG1LfuHMb1g3QadROft177WKwOU9EQiy4uaCfXCd3H+g6cNfjYmpWh1kJM9rPWKQz6gTfRny76lz2OXugWuy8e+bdKVfzr86w5djG7AvbN6+3c+8n2hx1GjYyiyu0jJOkZQ1e1fmHvyPGRXzb2b7zzgJFQc9vI74dAFSHZqbcuj6dxeOnj/x20bHggUPy7L18jkmLCwccO7t5MgECnwZ/eq6u1zSgwr+yU6olmWerQqJHFeFRzMEs7nvPeiqayIKuC+53tOu4N0+e1/ezy5+99Xfu37aDjgz64WbhzY9t2DZxfwz447OVvVbeqAkb3T1k9x5Xvuv1KHHU5HkR85pU/CRfnt9WwBBk+4n8lA2P/pdp7aadJUFiU9ymZyJUmorOqCO+jfi276hTozYllCe842Hh8fexQX8tcivPDIXQ7U7NhufdE4f9CpLiJwOAvZfPseBBYcvHL18z4fOdh6d/uG7zr+8uWXmEzRekFKelDK6zDdHLwmegBBMOzQeTX4S7fyyutU2AJIuF3aOWQKdwQrfPfkCv2f/P3lmHRZW+b/yebhgaJAURA0RRFBEVFRW7u7vXXHvNtdtV1167OxELCxQUKVG6e6hJJs/vj1kQJFV0d78/PtfFdemc877zzjDMOff7PM/9RJccI1OB0VfuoOPi5dComLi/eDuuTaldo5vTA/siI2QATJrexdgblfc9JlMBS1cRuqyKwOhrtzHn7R4sTZqHxfFD0XPbL2g6cBdcxm3A7MBd0LOpWlC3mpCMhj2OQJTZHGcHfzZGuj5tKNQKPtx/OYp/YRPyOqqmTuDVMnQ2O0suk36VwCtnsAIARYWmYHArtq5tNfEmNGoWfJZ3rWzOeg0bpQGAICVJKwSyPniAYxRZHCVj0ChFAGDI/Szw1EqlDpXBLOcGR6HShFIVHSDUTOQnVryjXlRgKlEzNDQms2wET9+2CHROJoRpFQu8xJd8Y0YBS66m0FI/ftCt6BStk524QVVOdlXiuewDWHqxSHjRr/TulLVTiwQAyIyN+ry2wCON/66/KyPwgm5dbSATCct+w/GtQiDOdKyRA9XfiPMEtgwWO/lbbxpN7RqmAkBmXExdmmYd/xjp0Z94tXXzlh4V2ZZCoxW07je4pAa3ZzOzGAAISSmoNE3TytG5gM5iJ+elp1Yp8AIunx0IgPAcN+WUuUOTK6JcgeurS2errFt6delMe4IgaE3ad/rhjdRrAxtnlzxdI+MAQXJS13IZCX8jyhXQE0PfjWTxdKK8Js0s4wiXKkplPEt9NobP4Ef93u73F1+OndpsatS8lvNWqAk1c+WrlZtPfjhp2/dG3yWRuZED7PXs797sf/P3ysxBHNw7XFMrFfqPjh7oCAD7u+y/bsA0CH+c/Hjanbg7ZsWpmR4jxu4pFp195i29SGUwsk3eiT0smeYBXwrHYqSFBdQX508upFKoYlF7kxuxdvJ8EomsCr5xtVZcMA93PXzZjGP22j/df9KiZ4v+KJAXNPCy9tr7YNCD9W713Mpc6yhkCk71OLXHgGkQfi/h3ry9wXsda/IcBUUF1LyivMZmHLMq+99VhJe1V3Y9Tr03n/I+eX9P/75j4cfsu1zusvV+wv0FTCozd3bz2b/e6n/rD+OAPx2hVvLQwMuv+NyYQP/2IJFUw9Zs/m3Uhp2nvSbNDKrXsFGZjWErR+f7iiKZ+atLZ2r0HvxUrN0LMfbmcjB1E/D2r+W4Ps2j5Jgok4bj3isgy7dHizFb0K2SNg6dln/EyEvzwNaLQ9ilRTjoMRly0fffT1+f5oG4x5PBt/LH5EdHvklUsfVVaD01EUP+eoq+e9/UeI5hpx6Ab/0SCS/G4MWOhgi/YoqM0H4waPAYHvNivn4hdfzT1Am8WobJ4WYri4qMv+bmJ8r/mR0AmNrZfxZ4crEJmPyKBZ777HhwjCKQ8rpvZV8qti6u2SCRVCJBjjmCT1lALrRGvRYvi48zqNoUTTNdlhTQpl2oVSoenckUfTkXhUYTyVQU7fOkBVecpikXmUpVVAqDxc4vd4xtkABpbsUCL+JqQ0OGVmPGBwdWmO4Q/ti3EwBNmwHDvi1FgEwFGnjdhEJsjisTS+zEG7i6ZZPIZFlBVqZNybnJrx0Bkgauk0usqv1OHWn+/OxfOy+tXjK1zLzmrUKgUbPgv69GvQ81ajVkIqEdh//1BivF2LdxTwOgKczKrBN4dfwjBN68Yn/+t0Vn/pw6eofvob1uX+Pe+CWiXAFdKMhpxTetF1C6ttbdzrCASSMLknKlVRoi6BqbhEkLC5pKCwsqvItJDA3WF6QmdzMwt3xk28I113vm/NsUGi0v9OG98VV9R6dHfexMZ7GTW/bqX2H/zn8jjdp53tWoVVy/00crjCj4HNjVS6VQGDTz8j75Ze/A5S+XD1CoFfojG488Vln/t3FNx8Uvab1kGQiQt7/dvjtNnNbWvZ770cu9Lx8q6YdWAR1HT3zPYHMSE0ODB2jUarCoLM26dut2kkgk1dkrO9YUp2aWzs7QMTJWZLbmBupIaKSuH8wrbXFwfcvaUXKppH5Tz65/HB96/vjjiS/H69ezeJqbkuxV2Ybh10Cn0InDXQ/v0qHrxOkz9aN2eO6Yvctz16PK3iMDloHqT68/N7Jp7Iy/Iv5acS3mWrU12uejzjfQEBp6U8OmNa6/K00v2163lRolb0vQFs+vHRuYEcjvc73P3N3Bu3dIlVKTbtbd9jwZ8mTRNOdp2s2W2MeeoNCF6PLbe0BrEpaXlurBMzB8Z2hpLa1s3o5jJr0iU6jiqIAX3t/ymr6FxNBg/Tu7t3Sq0fdRvRZiTHywEhzDjwi9sAiXxnaBXETGUa9fIc5yRtMBe9B3b9W2+Lae+ZgTvAKmTreQGd4XJ/uM+a4X8GiNE8IuLQDH6AMmPdwBGvvnthUgU4Hh5/aDxszF8+2/wnflNICkQs9tp3/qOuqoNeoEXi3D4etlERoNKzMuplyBcWVkJcRrDVY8PLU3/gUpdKjlfHCMKm8+2aj3TShlRri/uG1Fh+kstoZGZ+TIxEIThJzTpme2nVWSAtWpkXGolT77hYu1til5QVYGCwRBpbPY5SJ4VDpdWKQmay0yc2PLC7yCFLpCodRXaUgUBodbvvG3jnkiFBJT5MUzyx3LCHUwZEjVAJCTnFjO/l+tVJIEKYmeXAPD73Oy67XjJbgmYfh0dxbuLnQFtPV/TA43UVqQ/1l85ic4ga0fA0N7GQDIREJq+COfGQAgSEvpHvro/uc1tpkeBkCDeL8a1eElhYfoq1UqXV1jk/hvfRlcPX0ljcHMkhTk1Tlp1vGP8PGlX2cSiaRUK5Xc8Ce+yw9MGbXvzu4tnYok4q/uDP362oUWhEbDtGnWIuDLY4ZcRrRAXLnRCgCY2TuEEhoNPfj+rUYVHX9x7uQAEAS57aARVwGtcLBp1uKCTFjY+OnJw60qGhPh98hcJhI2MrWzf/yjm6gDgFAhrJUncR8y8gOdzU5Kjgjr/aV4zUlK4KR+/DCEq2/wzmP42DJC4nX6a73QnNBB5lzzVzOcZ1RqdAUAw+z6p5yWUELdpTLszhZoDkW+aUjx29i0KjdhMoUCm+YtrytkUqunJ4+0BIAOFh1y++l1Pe4YwTQr4pHyByz+rYw7n1qjxlP9KOcMU6VYE53VOfLFU7Mv53118UyTzLiYgfrmlr5dp8wKBLSCzLXvoGsEQdCenz1eZZ16TbHRtZG9HPFy4ZOhT1Z1seqSU935jQ0aS35v9/taMoms3PRm05rAjMAqe68FZQQ5AsBA+4HfJPBmNZ8VwaPzEgIzAvvW1OBFrVFjiu+U/lMfTj2UJEzq2NSg6dVLfS5N2+G543GJS2Z2JBuFKW1gaP+8uFTk9bWLjdVKhb5Fo6bloryl4RkYKgwsrZ4UZme514bQro7E0GD9Wzs3booKeDH/xfkTNXPWNXKQYcrT1eCZhiLy5lzsbrYFhSluaOB1GEP+qlmtP1NXjekvj8K48X2kvx+Em7Pcv+kFBB62gf8fK0DnpGP01d/BM/1qc6BawdRRgvYLt0EpM4IooyWs3C7DrnOl3gh1/LupE3i1THEvvLRPH2qcpllssGJkXV+bfx/7SFsor1Mvs9JB3huDQOdkIOr+QEhyKty9prNYWXKJxOTv9MwPsPUsia5NaW8b/3xxp21cBlUDAILkRB0AYHC4FQo8uZLQirPC1PIXq9hHplKVNjuExdMpL/CMGiUAICH8SvkIXWGKA5vLSaBQqUJhTrbNl4dfXTrjpFIoDK2aNvs6c5UvYeqqMf7uBrD48Xh7fAmebmwMABy+XkKRRGyjUau1uffSvIal6+/u7NnaWyGXmzVLzgIFUAZcOT+p5ObJ1FECln4M8uJqVIcX9+6NthWGXcNvjuABAJPLTZWJRXURvDp+OkUSMSU/PdVDx9jkzfRDp2Y0bt9pG4lEUkcFvJh/aPq4Q9c2r+35NQZAKR/C3MkUqtht0PByNa8WeuwYqUJtFpZawK1sfPNuvSIAaJI/hJXrWZYWFamTk5zQQ8/M3M/BvX3JZpn3rAUPaUxmxscXfmMr2u0Pe3i/EwBN6/5D/Gr6Or6VzYGbXdpfaH/xVtyt766pJVMosGzseFcuEdu+uXG5TFaB76E/BmnUKo5r30Envxy3KXDTaIIgKHNd5pY7Vga5iIw/3X9pkhnV8RBM7nTh2d1FYWpLPN++CVts9uHM4N6VOQt6TZ75nEqnC6JfvxxY/JjFg1wXupJC+DRP4++PPFTG1XBX8C5niVJiSfV0OEcik5XPzxyfUVq05qYms97evT6fxmBm9Vu04ljpsY6eXmk6RsYBWfGxvb6mLUZt4mXtlT27xex1Co1Cd4HfglWpotRKzYKSRElNuTRusrORc7nsmZpAIVPgZuZ2S6wUW+0O3l2j3n3z/OZ5v854PdGQZRix0WPjrAu9L5y049uVTbF9urEdCA0NjoP8ih+Kffvag0QiKdwGDa/WeKhF994+IAiq/+WzXao793tI/fhB9/auTb+rFAo+mUKRxAQG1DxqyLdUYPrL9dC1eANZngOs2p7D6Kt3vnoR424fAUs/CqHn5+L9GfPqB5Ti0z1D+P62BmSqDAOPrvmytcRPp8OvUWjY/RC4piEYfLycc2od/x3qBF4tY2hpnQUAuanJNRZ40sKCBmUMVrIitGMNG1YewaOxNbDvfgGyfHvsa70Zn+6VczdjcrlZqiKpGeRCK9RzeVnRNMUUZmfxAIDF0yl3kaExmEKVitBeKCXZ5Y1WMsNNJCptgI+jp18+RdOus9ZJMz24bJqmUkqGNNeepFsvisHhJsmEheUEYGzQ605kCkXqMXxsYFXrrxGG9jKMuLAGNHYOXuxYhcDDNrrGJokatZoT9y7QCIFHGwEEpbj+Lik8RC/lQ/gIPp0Za5Evhi2JES7Jz2tRvAsNADCwC4E0174mlsmC5EQ7AERjj46J3/My2Lr8FIVMZq6QSev+fuv4qfhfPuesVql0rZ2aP6ez2Jqesxe+mHn03C/O3XqupzEYeQnvg6Yf+2XK0Vs7NlZaH1yMTCSkFmZntdY1NnnD5HDLhR6aWejGAMCN92mVRvGMrOtLmFxebEFmRrlde79TR/sRGg2tdb/Bl0s/zuRw1fat3U/LpRIbnwO7OpY+ppTLyTnJCZ25+gbvrZ2al/8uq2VepL7w1BAa+tnIs91rY76OYyf7kSkUaeTzx72LH0sMDdbPSojtyzc1e+bSo29i6fPPfzxvE18Y79VIv9HdHvV7VL6hKM2j4s92i5Ab2wVWbc9i2rPDmP7yKH55Pw5N+u0BmVaE2IdTcajDCexznfOluyCTw1XXc2hyS1pY4BR062qDYtdMs0aNziv0aOlnP55dEC4I5+HOAlfscf717YeLk5kkqmhx12UPrJu5nJIU5Df3ObCrJPX01o6NU1RyuZFr30E7K+oN2Myrx1WNWs15fPxgrbyv38IExwmxQxoO2VqoKLTtc6PP8Q4XOmzpc73P3PE+44cuf7nc40TECbsPgg/cXFluE1OO6TdF74pZ1nrZcxqZVuiT6NMXAPDuhCUOtp+EXU1X4E/3qTjZdwCuT/PAs60Oj17vbPw89fkkI5bR+3sD7/3e2653RoWTJr/xBJ2bjnZzowHt30Z+Rlo7nqHx24re8y9x6twtla2j+yEzNrr796RxV0VGbDTvxtZ165VyuVGb/kPXGlnX9xXmZLslhgbrVz/6bzhGKsx8vQndN87D+Dvf1r6BY6TCgEObQaIocH/pCghiaraxEO1rgOtT10KjYqDbhjX/mibiIy/ex6KoVf9YJLGOWqHuBrGWsXJ0zgYAoSCnRgIvIzaap1LIjXWNTT4XseYnacdau1cu8ABgyF9P0WLMJsiFlrg8dg98lpaJJLF0+FkqlZqn1JAJuM/2r2waABDl5eoAAFdPv1wEj85iidRqNUdDAJDml4/g5SeaFUfw+MYm5W+KGvXMBpkqQX5iWYH3/pwlNGoWjBpHsXX5SUUSsVXpXdrC7ExGQVaGO9+03qsv+zV9M1ZuQgw4tApkahF8V661NiIXAkBiaHB9pLx2AkhqtJ76EQAeH/tzLEFoaC403lsAaKKhJNKYzPTI548nlaSj2XYMAUDGm4PV7pyKBDm2NCYrvSYXx6rQMTJJAUFQY9+++Wq31jrq+B4SQ951IFMoknZDR78rfoxMocBr0syg6YdOL24zYOhyBpudFhPoP+fhkX1tqprrzfWLjhq1mmPZtFm59EwAGNDCPA4AItKEVaZp8k3NQmUiYcP8zPSSFPDshDhOVnxcb11j01eOnbqW6yHXbeqcVwwONz723ZtRpc2T/IszBpya//Ded0KFkJIuTncFgJiCmC4FRRXXEX4Neqb1igwsrB4XZGW2S4kM5wOA36mjwwmCILcfMe7sl+cfCT8ygUqmSla3XX2x0klFmTQc9FiGgiQP2HY6hok+F0uMG/iWCgw99RhLEhah24Z5MLR/ity49rj/6w48XlfGYKPz+KkPyBSK9N29m2OKXTOHLll3aYbzjO0KtUJ3re+M5cS7v5Yli9PbRBIy63F5uTz9XU4n+tMvO+uykRPz+sW07NhPXN9De93y0lO9zOwdrrQdPKLClNI2/YfEsHX1wtI+RfYvqc88P8Ibt+a4VZVOWtusdFsZNNxh+O9WPKsAMomszpBkNH+X9W707bjbi3e827Fr+N3h59SEmtXYoPF3CTwjtpHSSa+hb6Yks3X8jgY7cXvufmSG90JRoRlyojoh4dkEhF5YrHq6Ydvx0INbuCol42x8lAV9l9NaHGg7HWcG9sG9X1si7LIZ5CIyPt0zhCTbCaZOfsW/64Cr55uqlUo9iyaOVW4Wl8a6WYv7SnmR2csLp2oUWdSo1aiof15F5CQlcK5tXLVOIZOat+zVf327YaMjXXr28wFAfn3tole1E5SGwdOg7az473KKbNgtF21nbYVCVA9nBv1S7efs0RonXBy1C0qpIdrN3YDWk5O+/cnrqKM8dQKvNjg/sjsOdZgIAIaW1lIyhSqWCQtr1Auv2GDFxLbB5wieOMsEJIocVm7l0x2/pN++APTbNx9UVh5e/7kGx7oPL3aK5OkbZANAHrleLOp3qHIumbCQBwA8Q6NyETwGmyMEgCKwxJALyws8cZapWM1SAoChlU15gUemAiy9RIizywq8+HGtecYAACAASURBVKfaNKKG3lG6xibJhEbDinsXWNIQ/eWF026ERsOyb+1eu052jXvnoMeWVSA01KYpf04EgNzUZBvkJzYDWz8G+rZFAVcvOORnpHUxtbW/wRdJaABAVqqojT06HVfIZBb3/tiuTQNxmxUFEqUIaW+rrcOTCoW2bF3d70rPBABjG9tUAEj7FFmXplnHT0OUK6AXZGW05ZvWe1WRCyyZQoHH8LERY7b+sYrJ5cWEP/FdEP7Et9Ja0cTQ9+4kMrmo7eAR7ys63thMR8JhUNJSC6o2WrFo1DQMBEEJvnerJM3vyYlDfQiNmtWyV/9LFY2h0GhE045dTqrkcpP7+3eWRHli377pTKZQJO1HjKvaYKEWOBp+tKmKUHHsdO0eKDVKne1vt1cpiGuKS89+90AQVP9LZ7tF+D0yz01N7mZkZePT0M2jzIbh5sDNLjmynBatTVtfaGrYtGIjk/xEBg51WAVhWis49DyAsTduVvrE7rPjMevNAYy/MwV0ThZe7fkNr/Y0KD5sYGElM7KxvS/Jz2tR2jVzvOP4uK68BneilIVNL/D1RcuatnsEElntbdb2CPg2gWRZrm0fk2AjtUrFe71lwqlPzx7OZ3K4cQOWrK4y2tLYo8MVtVKh/+jYAU9cn+aBqHszEXxqObbZb8fDVTUSHLXBCrcVb2/2v7nPb5jf8rej3064P/D+kBVtVswZaD9wU2vT1icdDRyvzm4+u8IMlUdHD7ie/HX2tLMrFoy5uHbZ4Gub1/S6s3tLJ99De92enTneLPj+LRvNm6NW+LPd1G2hT3pRCQJnmSQz2Hr+hYk+47EsdTZW5Y7AzIDh6LVz9tJGrf3CmQzMIHQCzFhGH6GWcyCI7ojYx1MQeHg1rk0+hE2WV3F53E4AgOtkv+K1xAcHeZDIZLn74BE17gvpOXayP5lKFUW/8a9R2uTpJb9MP7diwZkjsyYuC7hyvsK6WgDIS09lXVq/fE2RVGLdvHvvjR1HTwwDgCbtO2Vw+HqhWfGx3ZVy+c+/v/VaEw5bz5MoSGqHM4MGVHiORgWc7DsAL3etB4UhRu/dC9Fl1XcJ/DrqqIga/QGQSKSGJBLpMYlEivj7/81IJNLKH7u0/xD58bbIjizJM6czmVlFEnGNoivlDFYAQJZnCjo7q8a7Sc4j0jH9xSIY2Pkh5fVI7HFehbR3PHO21pMkg9awWie4IomYBwD69SzKRfCYHK622TnBFUIhLi/wZHmmQg1XDIAwtbMvNx4AwDVOgKzApkwTTUG0Ayg0ERwHZBjb2CUBQHJESElqT2pkeCcqg5HVdtDwGjd/rTEtx6eg45J1dELG16ErVFJBugOkefbQswlXK5Wk4Hs3plFo9Lyevyy6pBEK9QGAUCoZXSZOD+Tw9cKSwkJGZifEccDWV4FnGo6C5CoFXnGkVsfQuKzByt2Frthquw2hF8uZCFRGA1e3FADIz0irE3h1/DReXTztSmg0LLtWbZ5VdR5XT1/ZY/aCjWQyRf705OGVOUkJ5dKXlXI5uSAzw03H0PgtV0+/0jQgIy4jOlesaKjWEJU+n0vPfh9JJJIyPfqjM6C9+cuIie7LMzR608K7d2Jl4zqOnviercsPTw4PHZafmc7MTU1mFWZnttOrZ/G8qjXVFv5p/m3IICt3eO44zqQwc/zT/bvVxryOnl5pHD39kMy46B4Bl8+NJZHJik7jp5YRuhKlhHwz9uZEFpWVsaX9lnsVTpQdycaRLusgznKC46DdGHHep0YLsHYvxOATv4FCF+LJ+rV4d6Lke8pj2JjbFBqtwNqp+ckS18xoX4Ntka88WhUplFv4XM4HYWInC57FqwbDLmp7e63ImGIy/uhEWzNGcIzQgKrRqFjdujQ9z+LpVBki6TBqYgiDw41PDn03lAi7Ogss/Sg07rsXSpk+Xu35HTsc1sJ/ny2grS2Nfv3yp2REWPAs5MMbDU9a23LRm2MwjTufmaOud3thZzxZ3xQpQSXRK1GugB7+1HeeICWpe2ZczMDUyPCxCe/fTosKeDE//Inv8re3r/3+9MThvXGXNu5DdqS3Mbfe20Z0/ZCrPA45ru9OH1i5fb4WGzeRHmOC4SvP6mDFs/IbPen1JswN2Y4liQuxMmsEJj4YDc+lS9Ck3x6Yu1wDzzQSZs2vw2lwJgAoZFJyQUaau46RcaCusWmNm5SzdfkqQ0vrR0JBtltyRGiVZjO3dm70EqQk9eTw9ULF+XlO/pfPbj0weeTWBwf3ti0t1gqzMxkXVy9ZVSQS2Tt17ral84Rp70rPY9O85X2VQm70/Oxxl5qus1YZffU6+NavEO83Do/WOJU5lhPFwt4WS5HwbAL41gGY+nQhXMam/iPrrON/nprucBwBsAyAEgAIgggDMPxHLeo/B9swG2olrzjvms5mZylq2AuvIDO9KZ3FTikxWAEAucgEDJ2q0zO/RM9GjlmBu+DQ8wDEWc440Wt3/dy77QEgWVO/2rxuuVSiA4Aos46/YevyhQAgITgyKKTla/CKRKZCNVtBodIKv2ye+3l99RNAqJmIvGVa8pgowwEc408gU9HQrV0SAOSmJFsD2roRcX5ecyOr+n4UGq3yu7vvocOvUWgzdaMxQ0ghhBkuAEGGVdvwO3u2ehWJxQ0atnE/rmdar0gtEWvrG1VKBplCQZsBw45q1CrOg4N7tH8DhvYRUEjMkORfqVtY9OuX9QHA2Ma2rMCLfdwR0lwH3JqzBYGHbWqybENLaymFRssT5+XWOWnW8dNI+RDWkUKj5bUdNKLafl22LVxzXfsO2qyUy02ubVm74MsanDfXLzVWq5R880ZNqkwdtzbgxMhVGr3n0TkGlZ3DMzBUsHT4HwuzMp0B4NHRAz00ahXXuWvPCqN3xZApFLj06HtSrVLyH/y5p++rS2faERoNvZF7hx/e+06tUSNJmNTGiG0UYse3kzU2aPwwR5bT/FHSoxplflSHXcvWd1UKhYFQkN3WrIHDDcsmTmUyONb4r+koVoqtutt0P8lnVtCTM+0dD3/1/B3S3IZoPmorBh//OpMre6889P3jN5DIKtxfsh4fbpgA2n59M4+cHT94xXptJDAzgoOrk9aQNSrO/IYj1pPIZLmaULMGNBhwq8x8jXoKOizeu4Wry/vYuV6yxD7p0PgKXZlLQaZQYOfS6qq8SG4aI+TT4b15J4adfoS5IdNg2+kYpLkN4Ltid+p61zXHZ4/74/auzUceHNzzbS6INUUpJePxOkccaDsdW21PImDfOqQGDUXUvZl4vn0TjnmdxXqjM9jWYGPktiGrNSoVr2dzss8C93S/X5wjEqbbv1FNsgvCmPrBGGYdrmRRVeq3kiaJmOg7DnNDtns5jTutJtSs7W+3lzE2SRen0w+HHV5Ap9DzdnruPFT2jaJqSxc8l33E0FOPMeXJacwL34xpz/4qPsX/8rlmapVK17JJsxqnZxbj0qPvAxAEJeDK+UrrcoNuXW0QGxgwg8PXC5m09+iaibsPTqjfotUhlUKuF/HUd9mfU0b9eX3L2p4ZsdG887/9+ptUWNi4SYfO27tNnVMu0u45dvIbCo1WEPf2zU9r0VAGMhUYfXUvGLw0BOxfjCgf7f3DuxOWONJ5JwpS3GDb6Rh+Cd5S7NZdRx0/gpoKPDZBEF+mEPy8RPZ/O7rmWjGW8NwIAJhcXlZNeuHJREKqpCDfUcfIOLTkQY0KUEhMwNL7OoEHaL9YRpz3gdeaxQAIniSuDZWk0RSK1TrVDVUWyXTIFKq4dC+qYrj6BkIAEGs4RVDJyu7CqeQkKCUmYhVTQ6XTK08DtXBNBAAk+GnTNDMjOJCLrKBnHQVoRQuVTheI8gTWABB0+2pHAGTnrj2+zz2zOrw3v6fqWfoXKBgUhYaiFlj0Tkp4HzSOpaMb6T1z/nMAIKQyAwAglCoGALTw7p1oYGH5MDspoXeE3yNzmLfU1k9GXKs0lSw7Ic4OABq6eZRN0ZRk24OlHw0SSQ2f5RvxYkfDmiybyeGmyETCugheHT+FnKQEjjBX0MrA3OpFRd8RFdFu2OhIWxfXw+JcgevFtctGlj4W/z6oLYlEUrYZMPRdZeMBoKW1XgwAPPiQWWWapn498zC5VFI/9u1rw/Soj/05evrv2/QfUm1z3jYDhkbrGBoHpEd/GpQcEdabzmSllW64/qO4FHXJpkhdZNzUsOkbAJjQdMIjADjx4cTX1Q5VgufYKUFUOiObQqUKu02bc730MYlSQn6e+ny4Dl0nfk3bNWUFdkEKHaf69cdx7wMoKrSG6+QN6H+gShFeKU6DM9F902/QqOm4MWM94v30AG0LH+1Ccqg4PWA55CJzuM3c0KzTmpDJTpN/dzVxPTWl2ZToL6fTr2chm3b4/JJmPYdshEJUD2cHz6iuzqmbzis9Pk2G53mN8zSOg7VmIjxTJcbeuCkbdX+GT2HbyEsRxi5kuciCz1QLPzx7tCDo9jW7b3q9laFRAc+2OuCgx2RssTmOFzs2IueTF3TqhcNl7EYsiByMERcnwm3Gath1PgZ92wBCQ1Ai00lNjRhiNJL59SYJU1xoDGYex8rxJr9Vv23GQzfPtFgfMYRTr8H9jAJSvTyKmQoAJjlNitFj6H0Kzgruo1ArSjZV5jyZM16qkpqPaDRit4O+w1e7NCaEvPMgkcky9yEjq/x7rYimHbukc/h6YZnxMd0qMltJi4rU8b98djmFRi/ou3DFNhqDodE1NpUPXLrm7owj56Y19fTaTKHRRPHBQdPPrVhwWlKQ7+Tg3mFPj1kLKhSbTA5XbWRd/6EoV9Aq7l1gOfO5n4KhvQzemzeCUNNxY8ZSXJ3cEXcW7IBawUH7BSsx9sbN76r3q6OOGlBTgScgkUh2AAgAIJFIgwFU7Lz0/xGDBtreONkfjQGAw9fLJgiCnvoxosqUhHd3bzoQGg2jXsNGnwVe2nseNCoWuMZfL/CKaTc3FuPvzieZNLlHZ9AFRWJhtdFEZZFch0KjVZheyTc10wo8NVsBVVHZ1xT/TB+EhiZRUilUBqNy17lmQ5IAaJATbQMACD2vvWEzb1lyM8Xk8pJkQqE1AGTFxXZi8nhRTTt2Sa9u7d8Lzb6DH0BCMtfj7r2T5warVSqu24Chh8gUCgi1GpqiIq3AU6lK7N+7TJp5lkQiK/wvnZmAFqNjAWiQGVbpTagwJ9uOSqcL6jVs9LnGMe0dDwqJGUyaBmDgkSWg0MR4suH3cmkdFcDS0U2RS6WW1W0i1FFHbRBw9XxbEAS1UbsOVaZnfkm/hSvu65tbPsyI+TTswcG9bQGtkUJ+eqo7V9/gfXWGQ0NaWSSQAPWnTFGVAs+6WYtQAHh4eN8CtUrJd+rUrXLTkC9oM3DoGUKjZsglYltj2wY/pfedT6JPGwDEqEajAgGgk1UngTHbOPhT3qeuEqWkRtflAyEHGrudc9t/OvK07ZfHaAyGpv2IsRvaDRuzxsDCqsx7vNp/tadUJTXrZtPtfEnDblEmDWcG98a+lkcQ7zcRbP0EdF27GL22v/2uF+o6KRmdlq+GWq6Li2PWI+2dNgVRJSfhqNd8SLKd4DR4D7qtDwOAWc1nfTzuffxKlXN6rQmHpdsF5MZ1wpWJlVvwv/3LipLgN87JVJpYKCVMXl06U2L68ubGZfuj67Zt/JBObWJgavRydGuJ73DLIC6HIqcEXDy1OiHkXY1dGJVyOTni6UOtNb5cRMa7E5a4OrkDDnWcgO0O67DB9CyebtiGrA89wDGORrNh2zDn3WjMDd2Kvn+8ho65Ag7eAnhvfo8x129i1pv9z81XnM2Ts8nGjm2PkCbeH4OV2eOw8NNaTH16EgMPv4DL2FQweJoGrm1fEARBD7hyvnXxejwsPG5JVVKzrUFbWwHAlsAtLaLzo3s30m90c2GrhWE1fV3FFEnElILMjLa6xiZvvtXszNrZxUcll5s8O/tXGSM4hUxKvr1z069qpVLXfeiojWWuj9B+jr1nzPOfcfjMIrdBI5bwTcxeOrh32N177uIqN35d+wz0BUAKvHG5VtKev4kWo9PgPGIPZHkNEX55IVj8RAw/P6+u3q6On0VNBd4sAIcANCKRSGkA5gGY/sNW9V/Dso1WjBUkGwOArrGJthde1McqhVXyh1BnAJq/ezlpSXqlTWHUtazcsrommLcUYWbAQTKHnySXSatN+1Ep5DwqjVZhHx4jKxttDZ6aqYFGzUJByuc+VymvzQCgSEVi0JmsygWejrkCdF46ROnaCF7aOwcABJqPLNll5/D1kuQyqWXQ7Wt2cqnEpp59ox8bvfsbm2YtEgHAX2BNyklK6GVgaf3ApWe/BACQx8RwoNEwAIBQq0tet2UTpwKLxk0viXIFrZ/dfeoAhk4KClIqjb5JhQW2LJ0vDFbCL2sNCMxdYtCkbzaGn10KOicbr/aswZ0FrlWtWcfQKJXQqFlJ4SE1t4P+X+fO/NY47j3sn17G/yLpUR870pjM9Ja9+ldbz1saMoWCYas3/cni6UR9ePZofqjvPau3d67bqxQKQzP7RhW6Z5bGTJel0GHRktILZFUKPJcefWJIZIpMWljgyNblR7QbNrrGdbvNunin6NezeAxA49p34E/5zokriGvDZ/CjW5u1Lsl68DD38JWr5Qa73+2utnYoV5ZLPRV5ao5EKbE8HHZ4RuloTTEuPfsluPYdVOb39Xf0bpgOXSd+ZZuVbyDNo+L8CG/scT6E2IdTwdBJQ4fFS7EwahXcf/luQygAQPsFMXD/5XcoxGY4M3g1BDEsHOs6AfmJ7VG/418YdPSrNg0AAGOvXwLXJAwfb89A0DGrcseleVQ8WrMAZKrUcfza9RQarSDy+ZNBolwB/cyyeeNfnj+5TaNWcZp377123J4TW7nzXu3jeExdM8DqUxHUcr7vnvWbCrMzK+1dV0x69CfeyTmjtz44uOfPoAWtjmKz1SXcnrsf4ZcXITOsN9RyDvRsA9B04C7MeDUG8yM2YuDhF9C3Lapq3ij/5/0oVGqhx5RffWDtXljZeW4Dhn6i0umCtE8fSlpILG29NIBBYeQ+SX7SJ1wQzrsUfWkul8ZN3td53+nqXk9F+F8+56xRq3hWjs2/Oj2zGM8xk15TqLSC2KCAHqUfv7R+xRhJQb6zfZt2B1z7DKz080amUNBu6KiPk/Ye2V6duAOAhm4eWVx9g+CshLhu/2g7oX77/dHA6zAsXC9h9tvlsPeqaxpex0+j2g8+iUSiAJhJEIQXACMAjQiC8CAIos7StRhr9wKQyAqIs0wAwMi6fhYA5KenVinwCjLSnZk8XkyZurecT9oxpo7fHsErBZPDzVIWFVUbwVMpFTwqnV6hwOMZGMlJJJKiuNcd0t59rsPLjTUhCEChJDh0Fqtq10+OQQKkuVqBV5CkFUXGTaTFh/kmZkkgCGrw/VsjQSKp3AaNeFGDl/jd2LVsk00iU2Q5SQl9yBSqtPv0uWeKj8lCQz/X/qhUZS74PecsukVlMLLCn/hO0nBNoyHNaVhRylB+ZjpTIZOZ8wyMyl7A0kMaAiDgPFx7E2bXOQ9jbywDUzcRb48vx7Wp7Stbs4GldQoAJIeH1KVpFhN1rzeSA0YgO5L9Ty/lf4mk8BA9SUF+MyOr+s+/JbrF1uWres5ZtIlMocienTm+IvL5k64ANK79BtXIqdKYx4jOkyjsFSpNpb206Cy2hqunFwEAjT08q6y9q4hBy9cd6jRuynzbFq65Xzv2a3me+tygUFHYwF7P/nXpx+e7zA+ik+kFz1KfVdu7beGzhQMlSomFjY7N4wJ5gcOSF0vKR7L899rh/HBvXBjVFZfGdsGViZ2OXR44TaaSmU0hGwRTLozoiV1N/0TUvZmgcwRw/2UlFn5ajs4rat/UymtNOFqO3wJZfgMc6rAXGaH9YeJ0G2OuXa9+cAXQ2BoMOrodZJoUD1cvLVePd2bQKBQV2KLF6D/Yjt1yzOwb3RLn5bb8a/60/VnxsQP1zS0fjdq4a1aXidM/pxx6bwwxHrV3nrd1SqZEqjS7/9uknWqZuNJ7pIjTv7e5tW7+cbFQ0tCEKda8SDMxjiBc3sFx8E702jkbSxKHYkniQswO3Ichfz0tfa2rio8v/cxEuYJWJrb296sz+6HQaIS+ueULUV5ui+yEOA4A6NB11M0Mm93NkeU0n/149gqlWqkzs/nMnSYck2+KviWGvmtPplCk7YaOCv6W8QDA4umojKxtHosEOa7F0VGfA7vaZcXFDDK0tL7XZ/7SWm9LUr9FKx+1UqH/7MzxKjdLfzijr97B5EdnwNavK2uq46dSrcAjCEINwOPvf0sIgqhQBPy/hkwFaOwcyPKNAcDaqXk2AIhyBZUKq9zUZJZMJHTQM60XWuZA4d+i0LZTrQg8ti4/W6NWc4q//CtDrVTqUBmMClM0yRQKKDSaSKqiau/uBNGf0zSF6WZyDVVDEASVyeVV3RhY1yIBSpkxMiM4kOQ4gGdaptbFzN4hCQDEuQJXHQPDt2YNGv6UzxqFRiOYXG4iANg4tzhT+nkV8Qn6AECi0USEWl1G4HH19JUObdv/JZdKbF5mWdCgVvLw8U653/mnl89sAJAMLa3LGqwUpNiDzksrc+E3bynCxAcrwTH8iLCLi3B+ZIU3ezbNWqQAgCA1+X9G4IlyBfSrG1f1vbNna6evHqxRAdK8+gDIeHO42hTXOmrO29vX2gMgOXXu9vWRlr+xcXbJa9N/6CalQm6Um5rszeHrhZna2ldszf8FtkacGJWG4NwOS6/SabaRh+cNE9sG1zqMmhDytevTMTJWFEftfzQXoy62AYDetr3LCFw+k6+y17N/nCnJdH2d/rq8mdXf3Im7Y/Y+6/0wC67Fyxv9buzRY+p9fJbybPzH3I/a73hJDhWHO42D7287EXV/Jj7dmYPIm3NVEVfn+0iTejSWKzDu47PBiH4wHVSGCG2mrcWi6MXotj7sh9YF9d4VCKfBu6CUGoNv5Y/Jvse+6/nqdyiA2/TtUIjMy9TjPVnfFOnBA2Ho4IveuwIBoPP4qffJFKqYIEBq2av/qgk7/9xnaGldXnA17Z/VcOn92W0sRLFpBSTLp0t6HkBOVNmm1dG+Bm+Xdtz46O6rFWRCRe/XXOXbZ+nvExgcXpxvFKfFO1afZLhOSgZT95vy54NuXe0NEkndbtjoit1Nv8ChbfuXIAjq6+sX2xY/Nr/V/AdkElmRV5TXpJVJq3NjmoyJr2qOypAWFlALszLddI1NX1fUGuVraNV7oA8A8pvrF7uFPfaxjHzxdC6LpxM1dPWmo98zb2V4jpkURKHR8+KDg/4Zs5U66viHqWno+j2JRLpFIpHGkEikgcU/P3Rl/zUY3GzIhX+naJrKKVRagVRYWKnAe+9zxxEA2aKJU1mBJ8kxAYUmqi13JR1DbS1f8oewKqN4apVKh8ZgVtziAACFShPKlH9fjQtTPws8icBUDJ08AGDr6FYt8IybaG+gnm1pC7WSB8OGZQReQzePVAAaALBu1uKHO9mVxsi6fgBHTz+k97ylZazAlRkZhgBA5nDSvxR4ANBt6hx/to7uh7exSg+fdHtEPbra4cu6uIzYaFsAsGvV+nMET6MCJDkNwTMpZyYAIwcZpjxdA169YETdnYUrkzy/PMWisWMBmUKRiHIF/3mBp5BJybd2bvQ6Pm/qocTQ4Mmxgf6Tvrq2MPGlHtQKrYtpaqDzD1jmfw5hTja9NnpBZcbFdGByuHEVNQz/GtoOHvHJrmWbQwBgZt+oxtH5trYGMQDwPCqnyjTNDiPHh4/etPvEz6ih+x4+5n5sw6ay0/vZ9Stnjz6y8ciHBAjy4bDDFdaWqTVq7Hi3YyaJRFKubrv6CIVMwVSnqQeVGiV3xasVoxF42AZ/tNyJ9OBBMGrki+HnJ2Lk5QkYenry7636n0ih0dDeov1BUs9tv6D37tn4NXYBemx999MMHwYdfYYhJ6di1putoFXiuPw1dF0XBku38yX1eDlRLATsnw86JxMjL5QIByPr+pJBy9fOnLDzz5meYydXvQHAt1S03fxwQX1j0sfQLJ16YZuHHETwKQukBPFUf3aY9HjnsmPPEniOBhwit9+MabPrL/Xdp9vYI7/3/KXrKFSq+MX5U6vi3wdV6vpaFYKUJLYgJcmLb2L2wsrRufo+uABa9R4QQ2MwM9OjP5WkaToZOolaGLe4ZM41f7Xfa/+1b1kLAPhfOddCo1ZzrJu1+O5sGgf39lkcPf33WfGx3fxOHVtOJlOKesxeuKm6dhffCp3F1pjUt/MV5+W6RL/xr7JMJTE0WP/qxlV9atpkvY46/gvU9OLPBJALoDOAPn//9P5Ri/pPwuRnQSEp+RKhMZnZcom40i+V9JhPziQyWeHSo++nMgeKCkxB535f/V0pDC2tsgAgNzXZtPTjmRs2unxyaXlIlZNDE+Zk0wmNhs5gsSsVeFQ6XSRXQStwJDmfd5eLCk2FBL8AALj6BlVfkOy7awVe4kvtjpptpzICj2dgqKAxWRlkClXsMXzs9xX3fyVDVv5+Y/rBU6u+dAhU5eYaAABZVzcDpWrwiiFTKPCetWCbgbnlo2iREe745485MHnknhtb13nnpaeyAKAwK8OOQqUKbZq5fE7/inlkCLWcD33bip3++JYKzHi1ASy9WET7DCvTP/Dv52WwOSkyYeF/VuBp1Go8PLK/9aEZ4/fGvPH/hcpgCAwsrHzUKpVO9OuXptXPUIqoe1qjCQpNhPyqexL+f+HMsnnrD88cvy0/M71KO/mq+PjSz6xILGpobNvgm6N3pen/68oH3afPndFn/tKHNR0zuJVlMpkERUy2uEqB918gJj+GLZAJnGx0bd6UGJyUoq9d33QDpkFERG5E14rq6pa/XN5RIBM4tzNvd8qtnls+AIxuMjqhIb+BT2x+TM+Pj1fuhLKID9fJ6zHrzX406ilAw265wgaeufcKP3rr0HXiZnof27uHlAAAIABJREFUuofWUxPRakLyP+Lk17R/Vq2Iu2JK1+Od6rcYSpkh2s3b+WWdm5Wjc4GOkXGN0hTJNAZ6bj6/nK/DiHucaqGXcnHVLvHhPkevvCH6heSbkc2tTH2H7bs2ycRjcErxGGun5vkdR09aq1GrWff2bl+dm5rMquo5KuLZ6WNehEbDaublfav6s/9eK4UCQ0vr55L8vGapHz+UtOo54X3iks8gny0sKuub3+uksBAPMoUqdh8yMrT6s6unfvNWPiqFwlBZJDNr1WfglvrNW/7QmrTW/Yf4AiDe3rpaqdmK36kjzW9sXbcnMTR4yoXViw/e3L6ha0Vun3XU8V+jRgKPIIgJFfxM/NGL+0/BMcyGWqGL/EQGADA4nCyFTFZp1KwwK7M5W5f/oVyOvVxkAqZuraRnAoCVo3MWAAgF2WXWIo+JtiGkUjPp27f6OcmJOn+vudKUSCqdIVQqNdraJln+5wieQmxaqNGRAADf1KzqCJ6dZx4oNBFkeQ1BpsjQYmTKl6fYurhesm/jfuR700FqC3VhoQGJRhOSmUwxodGUE3gAUL95y7xxO/48MM0pJqajRVYGSCDi3gXOPLFw5okTC2fOFOUJmjJ5OvFlIgufbmsNWazalo/gFcPWV8Guyy0oxOZ4tLZcVIrF00mRSyT/yV54/pfPNT44feyWsEf3VxIEQXHs1G3TjENnfm3m5X0PAOKDg2rULqKErEibLBkXwcqW4VCILBDt+0076P8rRL9+aSITCRsXiUX2l9evWPitkbzQh/c7ACBa9upXa/Wwjp26pn1NlI3LoGr4bHqcrDDHAdsabMTDVc1qay0/m78+/OVCgKB2sOjwurJzWpu19pWpZGb7Q/Y7ln78g+AD92HSw8l8Bj9qe8ftnzMNPtwwORoTXl9PrSb9ZmwiU465Nge9dgSVHrvGf42nTCUz9a7vfa4iYfmfpnQ9niijJSxaX0LHxd/d6oLJ4ar7LN+6ksZkZt5MaUw9Fd+Ckl7EVzb28Nw+fNvRfRX1fG3evVeSS48+m+QyqeWVDb8t/hqDD6VcTk779KEPS0f3Y1WGIxXRuH2nlwDIQbev1lofP3F+Hq0wO8uNb2IaUFtRto5jJgZyDQyD7Fu77/cYPqbafprfi13L1gKegeHb7KT4bkUScZkPvkImJZ9buWjUu7s311Jo9MJmXt4bmGxOcmxQwJyD08duq65dhlqpJPmdOtL8yOyJS3eN7Hf+3h/bO1R1fk0R5mTTX1445XhxzdKhh2aMW31gyqjNJxfNmn5j23rv19cuOnzPhl0d/7+o0fYdiUT6C3+3SChNncgrBc8sG4C2F56eTSqLq5NVmJ3VVimXk7+MCiVHhPIVMqmViW2DsmmISikZCqkRDPW/re9QBRhZ15eQKRSJtLCgjMAj5AoGAChS03QKCYUKAJhcnUoFHo3BEIpUSh1QaCIUFWoFXk4UC2qFTqGGJ9c+l23VETwyFWDpJ0Cc1Qxsw5iKdnFr4pD1M9GIxQYkFjOXRKMqUEGKZmkYeqafWikju7msfzgsyPehXeSzxz3y0lM7ExoN3cDcquzvNOuDPUBSwWVM1XU/PTa/xMdbk/DxZi94byyTWsQ1MEzJS0/tmhwRyq9pOs8/jbSwgHp2xYJFwpxsdwqNlm/Xym2/98x5j5gcrhoAHD29kv1OHZULUpIcADyv8cSFKbbPBXaKFAnd1dmBBEroOWc07PZT03z/TYQ/fegKAMb17W5kJ8T1v7B68bgxm/f8Vd240mjUauQkxnuydfkffob5SFWY6TKjh+Yc7g1NDgUhZ0ei67qvtnv/LhKe82HmLPrquiq5iIz9rVeCxhZi1OWDIdkhbjQyrXCi48RKBcjClgv9HyY+nPYw6WH3+S3nhxc/vuLlinEqjYo33Xn6bywqS4OiQgpuzOyM6PuT9UAiBtRr6XtMmdFtWdKNltttPEo++0KFkPIy7eUwXbpu7PLWy4Mqftb/OPU7FMBzye+IvNUOo6/UuEVGdRjXt5N0njxn9YM/92ynUChSz9HjNrj06JtY1RjPsZNDhDnZB2IC/eecW7lo+titfxyoyYaG36kjrkq53KRh2/Zf9XcKAM5deyS+vHAqJTM2uj2A+187viL8L591ITRqVv0WLWttc4fJ4aqnHTixvrbmqwl2rdr4hDy429rv1NE23jPm+QPalMz7+3cukhYWOOrXs3g4aPm6QzpGxoouE2e8ub9vh2dMUMDE52eO7/zw7LGP98x5p0vXCydHhPIDrl7wyoqL7q6Uy00oVKqQxmDmfHzpt1ClUDD6Llxe4+wEQNsH8MOzx42z4mObCAXZTYrEYjsQBBUA6Cx2MoVGE+elp3oKUpLYcW/f4NXF0wSNycxk8XQTdAyN4rpMmnG3wnrSOv7fU9P8jDul/s0EMADAD+9P9p/CwF4r8LIiTACkcvT0skEQ1OSIUH27lq0FpU8Nf+LrDAC2Lq5lawHi/PQBggqeWa2laAIAjcnKKhKXTRcllFqBp8rK0hExSSoA4PD1Kk3RpLFYQo1KxdFQmKlkhVgr8OKemAJAoZKlBqlIZWxdv3rTBK6JVuDpWn6q9tx/ARqp1IDMYueSaHQFNBq6RqEgken0cpsdAADjRtHICu9Djrhk1ab/1Jg2/YfE5CQlHAu8cdnVuVuv92XOFabZg8VPAMeo6p1RjpEKZs4PkBo0BJG3jNGkb3bxIdsWrcKSw0MQ4nuvpZWjc627kP0IAq5ecBbmZLub2Nlf7TNvyQVdY1N56eN0FlvD4unEiHIFDl8zr0YssE2XmlMIgqBkyPXFFhlhzgD+3wq8nMT4NnQWO3nM5j3HTy2eQ81OiBtwY9v6jP6//uZT/Wgt731u2yqKZOaWjs43fuRaa0JvdmT+KPIjioLCLqBLBE3gv9eu1mz8qyP8iimuTd0Lnmk45oWt/6q0xouj+0KY3goAlIc62GeaGxpa8KxeVZU2Z8IxUdjybZ/G5Md4hwvCeU6GTqL9IfsbxxXGdW+ua39nVORTa/huG4zC1JbQKDngGoeh957dM+29cq9e7mL9NPnp+Jj8mNf2evZSAFjtv7qTTCUz7evQd/3/XPSuNO0XRqP9wsozIr6RJu07Zegam8zg6RsW1TTFs+/C5Q/PrlhgmhkbPeTqxlUZQ37bUK1baGzQ675UOiOn8/iplUZ3K4NMocDI2uZF2qfIEQkh7/SrS33UqNV4dHS/m765ZXaL7r0TKDRauWtackRoezKVKnIbNCK8ojn+K3QYNTE44umjnMSQd94A/P1OHWke8uDuQo1Gw3Ro235X73lLSjaVyRQKes1d7CdISQq8v2/HiOzE+D7nf/vVw7aF6wldY5OcmMAAb6Eguw0IgsLW1Qtr0Nr9lOeYSQEqhYJ8/rdFy2MC/edc3bSaMWjZ2jtVLAmAtr/g9c1rh6VHfxwKgEwikZRMnk6Mqa39DdMG9pFNPb0+FQtLjVqN2LdvjJPCguvnpibXF+UKbGSiwvrCnCz3a5tWO0z+49jv//ba4zp+PjVN0bxa6ucsgKEAWv3Ypf3HsHTV3njnJxkDAN/ENBMAMmKiyqVpZiXENidTqKIW3XuXjd6kBmnrjvTr11qKJgAw2OzML9NFCYWSAQDq/HyeVFjIAwCeoWGlETwGiy0CQJaRuCIopNoavKwIMwAQK+kUKo1WUNFFohz6tokAADPn706h+RkQRUX6ZC43l0SnywFALRDQKj25QVftzUX8s5JaISPr+pJecxf7WTRu+rmXkUpOgjTPHjyziuvvvqT9Ih8ABF7uLNNDqIV3n3gqnS7IiotpXcnIfx25qcnWANBtyuwrX4q7YniGRlFFErGttLCgZnfS+YmMHCFRT6XR3r3GyS0FEKY2r6hlxf8HcpISOBLtznQgAAxfu+UoV9/gbdzbN9P9Th2tcX3ix5d+HUAiqdoMGFprGQXfhEaFcbm724rBwnaTbbdApsjw7kTfn/LcKjkJPkt/AaGmQ5jmWmVj7S/5cMMECS9GQcciEO3mrgykEvpKQs0aKCcq3UgrZrD9YF8CBHX/+32e4qAj1tdDjywzVhPKQ6FPeiL88kII05pB3yYALSdswLyI39Cop4BOoROTHCcdVGgUustfLh8JaKN3r9JeDdOl68Ysa73sfzN69xMwd2girKm4K2b4mi1ndE1MXyRHhE64uX1D16qMo9773LGRFhY41WvY6E5FqZ81wbFTtxcASMH3bnpUd+71LWv7hD/xXf7s9LHdf4wfevLY3CkL7uzZ2iklMpwPaNMEhTnZrfVM670qzq74r0JjMDQmtg0eSArym59YNGtGcUqm1+SZC0qLu9IYWlpLx2zZe6zzhOnzGGxOSmxQwJx3d2+sE+fnOhnb2N7ynjl/+ozDp1f2nL3wBVuXr9IxMlaM2bL3dx1D44DEkHdTL65dNriqNUX4PTI/9suUrenRH4fzTes9cx8yavH0Q6eHzTxydumojTtPdZk4423pqCGZQkHDNu7ZXafMfjN87dYLU/Yd3/zLySvTbJxdjohyBa1vbFvfq7bftzr++3yrw5o9gGqbZ/+/wrpdPkhkFcRZxgBgZG2bDQAFmell3ieNWg2RIMeZq28QVk4Q5cVpRZhZ81oVeEwuL1spLzIpfYEhlH8LvMICHZlIpAMA+vUsKr3xYHJ5QgCQEDwpVDJtBK8gxQQAJAoSnUpnVF1/V0znlf6wbncGnZa9r/7kfxZVbi6VUCj4FF3dEoGnEggqT9N0GpQBMk2C3NiqzSDCLppDo2LBsGHNBJ6DtwB8q9fIiugGYVpJHSCZQgHf1CxQlCdwEefnVS48/0WI8wTWVDo917i+naSyc4yt60eBIKhhjx9UWQNRQsRV6zSZLgkAKDRaQaJYlwyVXA/Bp8s3QP5/QNDtay4gCIqti2sgoI2KDvltwzYGm5P83uf20rDHPtUa86iVSlJuanIHnr5B8M9qV1Ipt35xZ8vSHfZoBsufii1NYNT4MXLj2yPer9JWArXGxTE9IMlxRKPeB8AxisCnu1MQ5WNY7TiNCvBZOgMkaNBrx0F0XRe237Z5AJ0giKFRLwbioMdkSPMq3cAYQTUSWhFUQXrSiwnnX677Iwtq/hKhLJdt7noJXVYvxLLUcZj9di/67H4DKqPkOjLecXxcA34Dn6i8qN4XPl2wXv1qdWeZSmbSo36P8//T0bt/IRQajRi+dututi4/PDYoYM7BaWO2vrl+qcLa4pAHd/qQyGR5x9ETfb/1+Rw9vdIYHE58ZnxMpf1TASDo9jW7xLD3E7gGhkEObdvv4hkahooEOS5R/s/nX1q77NS+icN2X1y7bAGh0TBtXVy/ubn5vwm3gcMeAdDkpiT10K9n8Wjctn0LmnXxLucB8CUtvHsnTj94aqlTl+4bmnTovHXKvuPjx2ze81fTjl3KZbCxdfmqcdv3beGbmvmlRoaPPbdi4egvRb1Grcb1LWv/j73zDo+i6sL4OzvbW7ak90ISIIGEHjrSmzQFpAuIYKHohyBWLCgWFAEVwUIXBAFBeu+dBBIgpPfsJtlks73OfH8soaVtIKG5v+fJ88Ds3Dt3ky3z3nPOe/rv/2XJDxaj0TuqW8+Fk39Y8X37F0clP4jvwNC5H+8Uyt0vZCVcmnR5z47guo538WzjlMAjCEJLEISm4gfATgBzG3ZpTxlMDg0W73YvvJDYVo5eeKWqeyJnN04e9bVZLO4eQcGVXak0hV4AKDTqUVyfSxNIpEqaoth5N5Jum6PQt5p2UzqdyGLUiwHAIyik2hs5nthNCwBaSmCCzeyYR1fkA5KltVisYiaH41wNmHu4ERN3/1VrauITgOGSoyErUy5XMTgcRwSvrKxKoxUAjhpDgTwVWkXNBiHphx2Phz3nfDpR1NDdsFtF2PvePV/egdGx52iK4pzb9tdTYTxh1GgCOQJhTk3nRMR1SgGA/ORrzhmt5F0KyTeIwWazVFIf39OlOtrLThNA8s7/ZLuEwtTktiSTWd76+WG3X18yX39j3zfe+pRgMMxHVq/86G63vao48/fGpjaLxd2/SbTzdZANgTqXjWtbJ4PrlrmHM+CGUmsKR4cZOwGaxJEv+tU+wUOQtNULaQdfhsg7HiNW70e/r38ATTPw78zptUaHt03rAm1hS4Q+txaRfUvslB2pxqIYucD7It+r2T9QJA7CstYLkHpQdnuMVsHCtqmdsKjxx1j/wu8TVUr3TBbJXCqT0CFczwu9Z6W/ilcObEDnt1PvFnX383nHz9cyGUz9z1d+fu1kwckRbmy31HfbvvtIXYldOBBKZdZXlv72YXi7DkssJqPXyY1rvv1txpTZqefPeFSck3/zurisML+b3C/gUE0bX87gGRx2wqTVRqacPVmlwZsqL4d3ZvOGOUwWSz3o7fcWD5w198jkH1Yumr5q87guYye95d8kei3JZBk0JUXtmGx2SdywkUkPs54nheCYlqXh7Tosa9q1x9cTv1++tC7RWAZJover08/1e+Ptk7U1nmfz+NT4r5ctlvsH7CtMuzli3XtvvVIh8rITE6Qr35z0ccblC9MEbpKkoXM/ml5RE/igMEgSA2fO+YHBZGpPbVo7p7xIUaNPgIv/Fs6maIpomhbf9RNB0/TfDb24pw62SAmToxce301iI1nsUqP23l54KedOxQBAZPvOlQWeQeUFFk8FvqxexY+bp5cSAPJv3ri9ltsCT28QW4xGEYMkDTWlYohkcg0A6CihBZSNB3UuG6Yyb7CFCpvFImFzeU+FyUddMKekyAGA6e2tIrgcCwDYytQ1f4C6BaTAVB4MdW71QrAkJRwMphHNRzjfV6zHR1fBEeUi89iAu28u2w0ZnkgwGMbca1fbOT3XY8JqNjPMBn2AQCLNrum84JiWpUw2u6RMUeBUHR5dmhmSZ3Cj+TL3q54hjZIoiuYobB4lKEr+z7VLMOl1pKa4qLWbp/eF+82dGrWOK+kwfMzndqtVumPRgg+0qpJ7XqPF2ZmCkxvXRm1dOH/A1YN7JhAMhrnjyLHn8Dj5e/KLsBo90HbKCm+pMEVrtAXnBw5SQex/AQXx/aBVNEzk2mYmsGfuDBAEjQHfLwWDCUQPUyK85ypoFS2waXyfasfmXxLh+j9TwJOlYOTa3QDwZ/KfISa7ySPKvdlZTDvxG2JGfQ2jOgQbR/2AzROfw0/tp+H76NW4snEOjOog+LfZ3LHrpzNIgjQxCNL0bufPfnR26VHuUbrO/p1Xl5pKm5psJlf07jHD4nCoQW+/d3DCtz9O9YlovLG8SBG387svlq9//+1xqrwc3okNq/vSNM1qNWDIzoe9Vmzv/icBIGH/7kppmpTdju1ff/aG1Wzyav38C9/cHZknWSy6zfPD0kfOX7j5tRXr3nv52x9Hj/r0m+kPmi76JDLo7fcO9nv9rQaPSLI4HGr818t+9AwO/ac4K2PQ6nfefHPvz4s7bF04f5leXdYspEWb5VN+/GN+cEzLemkR4RfZVNOi7/PfWYxGv60LP5lSH3O6eDZw1kWzI4AEmqb1BEGMBdASwA80Tdd4o/afg+tWhLKs2/VQbB5Padbr7xF4JTlZMUw2pyiyfefCSuMdPfDqNT0TADyCHDV9ZQV5XgBuAgBttXIBgDIaRVazyU4yWTXWhbh5ejkieDYuBQaA/EtSmDTelMArxW61hnH4fOdSNJ8irHn5cgBghwSrTEaDQxBrtdULNwDwik5F3gUGEjaEotvcqo1kNAUR4MnS6tQLisEEAjvsQuq+aTj1Q0SFmQDfTWITyT3i1UpFW8pud8qt7XGRduGMF03TbDdPrxojeADAd5PeNKjLnBJ4qhJ1pMEuJ3z8g65Hd+uZdP3YIaSZAlR+uoRomMrJOjsfPklkHpfAPUIPkXeNO8cVnP9nS1PKbhf4NYmqUpi1HfxiallhwaKkI/vn/fnh7DlCmTxLV6oKNmq1ITaL+XZUgUEytQFRzTZUVyf5SLi23Qt5F16ANOQ4un94rak6UZiQq2ZsuZQbOjN62A6cXvI59szpghFr6t9g6K/xfaEvaobGA5ehcf87Jlkj1u7B4mbtkbp3Eq7viL/b9Og226ZOhN0qxHPzPqx4j+/P3t8OAD26yWhHHdzQ5ScR1CEL+96bh2tb3wLBsEAafAaR/Q+h+/tXweJTPgAGmDKXiNgiQwffDnW6GfymyzcHem3p1Y0AAVf07slA6u1rGv3ZtxvSL53ff3TNr+MUaSnD186d2YsGTQqlssvRz/VyfsOvGiLiOim5ItHNoqz0zgDu2YT/d/FXvdTKwi7+TaLXdRwx5kZN88j9A40Pu5b/MgySxJgvvv9t48dzTYWpySNL83N7c4Wi1K5jJ31XH3/n++k6dtLVvBtJWxRpKcP3/PR9wqMQsi6efJytwfsZgIEgiBgA/wOQDmBNg63qaUUgL4LdLKmok+LwBfeYm1jNZoauVBUj9vC8UuWNuEXnBZ6kXh00ASC4eUslAGhVJXeaR9+y/KdMJrHNbBaRLFaNdTbugcGOCJ7tVgCr6LoMVqOnluFeBoDBFYqeOYFnKy6WAwCveXMVg8c3AwCl09UcwYsa4kiLyz1XdXqhvpgJU3kI3Pzq7vbWZ8ERMJhGJKy/p6Dap1HEObvVIru0+x/natYAlBcpOHt++r5TTYX/9U3u9cRAAPAKDa9+Y2hpq5n4PnqexNs7xWo2e9WWSgibmSgsowMBILRl6+sBTZup2TxeXpbOjQnKzsOZH+vWT+9JovCKAOuG/YhfunzkrGFM9pXLbQmCsLYbMjyhunP6TJtxJiCq+SqtqqRtYerNF80Gg7dQKrsR0LTZ6hZ9n/9k2LxPXp659u8xwz+o3fmvQdn/wSSAsKP/t78DQP/mPikA6O0JBX3t3T+6Co44GxlHB9W7mc71HZ5IPTARIu8EjFh9b00Uk0NjwHdLAAC7Z8+AzUwAgJ269T468FFzlKT2hF/LbWj7albFsHR1epyUI01u493mTqZDy/F5mHr8f2g3bT6mnpiAGfGL0GdBwt0bPws6LTj5btt3L9f1KbBJNr33hb0f7Bi640NX9O7JIqxV25LJP6z4vvPol//H5vMLKJtNFNmhS7051XqHhp8w6/WhSUcP+lUcu3JwT2DaxbNTBRLplWHzPtlSX9dyUT0MksToz79dH9aq7U8BTZutnrxk5ZyGEHcVvPDepxt4IvHN5JNH30g5d9rlkeHC6TYJNpqmaYIgBgNYRtP0bwRBTG7IhT2ViHwdu7kZRz0QOyafJxIXqRUFcpNeR3IFQnvCvn9DKbtd4BUSVkX9XT4bNrMMfPd6j+CJPTwtJIulNmjKb7/pK1I0abNZbLNabEw2p7z6GQCJl48RBGHT25gE2ADyLoYDNKMU7jpAB76b5JkTeHZ1mQwMhoXbpIlOe+CgBQAofS0CL7RbGZjcEpRmVC0s4teHgKaY8GzqnMHK3biHG+EReQhFN/oi+/TvCOpQDgAt+w++ePPMCSrt/Jl2zjbI3bXk2xcLU5NHimTuZY+i4SwAlObnBQFARFzHqovb/32rLVRpPQBQgWHe+3ISryD51LEI/yZR1bv/XdvunW8QslgkTNHdeuUCgEjukVRamNeV8gbNyDgai+feq3G3+ollx4wRsFtF0CljsGt2Ozy/uMZ0ScpuR1lhQTuBVHbFzdPbjEOfRoMvN6D9Gxn3nzvioy+2Zl25fNQ9MFhbW13JY2Hf+7Eoz22P4E5rEN6zFAA6NXIvaxko2XQ5R/3SK2vjU/8I7boDN3ZOx+EF0ej5cf3UClE2h3BzpGYuqbIlQpOBxYjo9xuS/33TvnFsvzES0jO5NLmvB1eWPLEwK6wXV6j0GLl2Y8XpR3KOuGssmtC23m1XVZpLFmpCv6/qLOCcgcfkUTzwGmJqF/VA28EvprYeOPTdrKuX5fXZZ7Jl/8Ens65cnpx0ZH/n6G49N2qKi9jH1/0xh8EgDf2nz150f+q2i4ZlyJyPnG5N8zBwBUJ7z1de/3bXkm9+OLhy2TuB0c3ffdodUF08HM5G8LQEQcwDMBbALoIgGACeCte+R4o8zCHOFImeACCUyZQAGNlX4t0BIDP+YgwANOvRp3KT3rRDDvEl9nNa4Cn1SvbezL1VFlPfD4vLVZr1ujs1eLcieLTFIrJZrSImm1NjiiaDJEEymRqDheH4u6vSGgNAqV1qBgCRzP2Zq8Gza7TuBJerIkgSDMGtCJ7BWHOKJgAIPVOgL6raSTP7tON444EP1q+p3bTdoCkmjizoVXHIN6Kxlid2S1bl5zjVLkGrKmEXZab1A4DM+ItxD7SOB0BXpgpkcjhKqbevqdKDJak8JGyYBpKlBcBozr3BB0AVZaXXnKaZeSwk3+AGsZsgrSIq7hkcmkTZKV4h5ZsLVfpTU4dn0uvIjPgL8vP/bAk/vuSDXgnXiwYdVrfIKrB5KZH416SaXBcBIOnogQCr2eTtFdroPOLX+eHk95/gwEcLkLS1ys+I4JiWpU+kuDOVk7i0+lWwBYUYvuqeyMbGV9v/6evGPX/0ZvGUJfzpxSBZWiRurr+WCZvG9YVO2RzhfX6/JzXzfkas3m8U+cTP0cS/ek11bZicJ79hNagiv5QI3Lr7yDy77Bn12ZT9U4buydzjvTllczsAGBQ26PHWM7p44mCQJOpT3AFASGyrUr6b5FpxdlZnym7H1oUfT7EYDYGxffp/Fxgd88x9T7u4Q0RcJ2Xjjl2XGbWayK1ffjzqca/HxePFWYE3EoAZwGSaphUA/AF802CrelrxbelwvyzL9gIAiZdPEQAoM9O8AKC0IC+GwxdkBTRtVvlDVpHouAlzj3A6RfODUx/0f/fEu0v0Vn2tf0cOX6C8pxdehcCzWsV2q03M4nJrtUInWSytyQpHBEunbAIAKqvYDgBSX79nLoJH6fVyBo+nAgBSJHYIPKOxdpcqSVAqLHofFMQLKz1Wmh4Okq1GRO/qbx5rouXngQRaAAAgAElEQVT4PAi9riDvUn+Ytbf/7h6BwefMen1o2sWztVq4H/7jl652m03MZLNLygrz4h5VmqZRqwniCUVV199tnjgWNpMcHd9aAAZp5BVfbsYVCDM1xUU1Cjx1TmpTtZUHkU/wpYpjTbt0TwKANFNgGQyqSBTffGLDGPtXLG3346SXvl88ZuiaHye9tHXbwk/+OLFh1aILpxKmH1KEkfGFwuBt2Y05NpPBB1sm1djrKPnksbYAENu950Uc+GgmCNJRP7fr7bkNZkbSEPw1YSAsWn/EjPr1frddNpNBr57cdpGAwyxYelY1WyVvfQzlue2qE7F14sa/HkjdNxFC7wSMXLOvplOvld4UPu/rzt8v4DFe11kVBwJeXH0kK5u7Rs8818KzxZ92ys45W3h24pzjc1acKjg1UcAS5A9uNLjB0rNcuLgb70YRJyxGQ8DGj+eMVeXl9vEOi9jSbfyUatO2XTw79Hvj7ZMyX/8Dhak3hx/fsKrZ416Pi8eHsy6aCpqmv6Np+sSt/+fQNO2qwbuf0C6lAGGHTukBAN5h4UoAUCsKvbSqErahXB3l5uldOT0TuC0KEdjO6Qie2qx2t9N2XkZ5hqC2c3kisdJqMXtYzWYGcCeCR9EUi6bsPDaXV2vzXSaLrbFZLCKQLC1sJjkIhq3c5IjoeYeFP3M7g5TRKGcIBCoAYIhFjhRNk6n2CJ5fS0f65dVNlaN4OmUEBB6pVaZ+OUtEn12wGd2x7/02FYeadH7uPAAkHtpXYxSPstuRk3RlMEcgyAhq3nKT1Wz2unpwb9CDL8Y5THodaTEa/QQSaWWBd+K7cCgTB8Kz6R50f/86hD6JUOfECmXymwaNJqLiNVsVuQWapgDg37z17ah4cEzLUhaXW5ilFbEBmoHzK6Ib5Ek5S9JWL5RlVdoY+PvLjwcmHtr3HkVRLIm3z1nfyCYbw1q3+ymuXfjKMcHx9MQ4/a6W/Qd/bLJQkrOapvnIOvESci+IqrtMSW52O65QmBZ845v2MKgao+mglWgxbjGMZY2wdujTkVKfcVSKrBOjIfa9iAGLqkzNDfcUGecParqApsEcXTS+GQ2CwsnvBz7UdSkbsOt/0wEA/b9ZVtP781DOIY/J+yd/VWTRhM1g+u55rbjQm7H/wy8IkqVv8fyKJWv6rdl4atSpWYu6LprSwbfDr3Ku/Hp73/Yu12kXj4w2z79wGgBVmHpzBE8kTn7h/U/XP+41uXh0DJs3fwWbyyuI37NzllGreYibDRdPM866aMYBWAqgCQA2ABKAjqbpmg0Q/muw+BRYvGIYSr0AILBZbAkASlem8rq8Z0djmqZZvpFNqt5F0ym8QTAsCO7kdCTMaDMKASBPmydo5t6sxgicUCpTgqbJrCuX5eFt2xfDbucQLFa5habcAIAjENQawWOyOVqTTusPJlcNu1UEFl9p0hgkBINhlnj5PFOuW7TdDtpslpFikQoAmFKZGQBos7n2CF7MS2k49QONgoQIAHcauhff5MGs9YfXQ/YW6/PFeVzdXIKUvQMBnAMcTW4P//FLflFmelsAu6sbenz977EWoyEwon2nxTE9+11Ov3j29eTTx+Ni+wyomyNu0lYvRA9zejMi5exJX9A0U+Llc+91TOUkTi1+E0xuGV78zbFp5NU0Hqn72/p6cPeU5Np5108c9o/p2a/KyJ+ynPYjCZqK6dXvnjozkdwjqVRR0J72ZliI3HMxAKqv42tIim/ysPWVJeCI8/Hq0XmQBpvtViux4cPZE4sy04eI3T3Ojpz/1bf39GVaFPkJBGY9Xlq87jmfGH3q+dPnLiiJ2BZCBkuwc8YYvH5m+f2Xybtxzc2o1USGhPntRPpfEyD2vYhhK46AwQQK4rehMGEotr56DcNWnHikz/9+9MVM/DV+GHRFPrCZhLCZHT92ixB2qxC0nQMQNvT46NeapnmxVUD+1bzyb9ecoT88K2hZHFd0pTdRfHMDPCJr/xwyaxlI/tcbOecCoErzh7YwAPqSYJjUoYjs91OVzpi3WHt9bejiS4s/pmiKPaX5lA+nxLx2DYubu0OT3wbRL36HgDa3P0d7B/dW9g7uvQPAjrr8ily4eFj8m0SVC2XyeEO5unGvV6d/46rF+m/h5ultjnqu18r4PTvmH/rt584DZ8098rjX5OLR42yK5jIAowCkAuABeAXATw21qKcajqgIZkcvPK5AaGeyOSVGrcYz70ZSDAjC3rL/oKoNLQylXmAJiuoS2THbzQIAUOgVlVMB70Pi5aMEAEV6ipddr2eAppkEn1diZTrqlrhCUa0Cj8XhaOxWqxjMWz3vOGKFxWiQMFks9ZNsz/8gGBMTRaAoFimROlI0ZVIL4KTA82xqAFuUD3XuvRG8K382AkDAJ6buBit3wxFR8G+1GzplDC6v8a84LPXxPa9TlzVX5eVUm5J488zJwSSLpe4xcdrxwOgYNU8kTlblZtetDm/3O62wZeJK7P/Q6ebq+TdvBAGAd6OIe4XapnGDYCoPQbPhv8CzqQEAEDUkAQCaCHK4AJB9Nb5qw5rcC6ICPZ8nFRLK+29gPIJCkii7XVhIBGaiLPvx1eEd/bI9KDsPxrJGWDXwbV2Jgv3H29PeKcpMH+IeGPzvy98tX3iPuNv3fiy0ihYI7rQJPjF6AOj00vjfKRrkgdIW+Si60RcX/wi8/zKX9+xoDYCIY59uDIJhw8Affrz9WTJhxxrw3W8gcct0xK/zu3/sI2Xt0JeRfXosyvNjYNZ6AaDBdSuEJCAe3tF7ENRhHbq88wFiRhXUNtWng6MvtguRrVtoGOxJUDYe9r3Xo8oTKRtw8JNoeknLt/Cl/zJ8GbAF26Ytx6U/3kfWiQkoz4sFk6NFUId1GL662tTMr85/1WLRxUVfMgiG7f249+dMbzH9GhhMYPRf36H1pM8x7JejD/x7ceGinnn+rXnfDZg5d2Z42/bFj3stLh493cZNvszhC7Iyr1wa9ijdsl08OTgr8EDTdBoAkqZpO03TfwDo23DLeorhiotg0d12q6zohadWFMbyROKbVRpMAIBZ6wWuqE4tEix2ixAAVCZVrSmaXqGNHL3wCgu8bAoFBwBIgbDYQjpeAgI3Sa0pmmwuT2O3WcU0S+CIMvKlhVaTScpkc565+jtTYpKjybmnhyOC5+HhiOBZLLWnaAKAyCsF+uKIeyzc8y85BF/zEQ8n8ACg+4f7QTBsOLe8f8WhkNjW50HTzAs7t7aoakji4f3+ulJVK++wiF18N4kNANwDg8+Z9LowZ2r3bpNxtAMAIGmr0+YW6sKCQABURFynvNsHr233QtaJMXDzP4fnF5+5fbz5yHwweSW+1uRgBknqS/Nzq6zD01zY0rjELIBU7lap32Djjl0SASDFGFAOsyYQGUelzq61Xsk50xUsvhJh3X8zlha23zJn4vLyImWnwGYxf4xb+MOKexztbGYC8eteBotXhBdW7qo43LTzc4WewaE701Vsf6VZbMaxrybdfxlleko7Lgs6HzonAhF9f0dE7zvGDVw3O4b8/DUYpAV7582DOte513B9s2NGOygSB8G72Q58oJiEd3Nm4J209/BW0hd48+ISTD3+Oybu+Qvd37/u7JQbpsRtLpU0O3WJCoch8/yLsBrufKcVXefnrhw1umxBxBqc/O6LcpXiuXTal4Jvi38QNXQxenz8P7xxfiQ+UL6M2SkfYuKev8Dk0FVdZ/ax2d3X31j/sYAlKPyh+w/vvBjx4h0nWO9oPQZ+f/6h0q5duKhnfCMaayPadag2Gu3i2YZBkgiOabnNYjAEHV3za6vHvR4Xjx5nBZ6BIAg2gASCIL4mCOKtOoz9b8F3L4LNLIO+mAkAHIGgyGzQB5p02jCpj2/V9XeUDbDovMGT1qlFgtVuFQCA2qSuNYIX1LxFCQBKX1bqZSsu5gAAQywuttyK4All8toFHp+vBcAwMEQ6xyAvpdVslrI43GdO4FkyM90BgO0f4IjgSSQ2ABRtsdQewQMAWVgK7GYJUvbfEU5lWRFg8RXwa1VrtLRWAuM0kIWcQPHNHhX1XW0Hv3CDQTK1BTdvVFmHd2nX9kEEQVg7vTT+tm1zVNceZwHg6sG97Zy6LmUD1NmtAMIGTV4bXNvulLmFrkwVyOLyCkVyd8vtefa++xoIwo7+i3655+aYwQQkAfEMbWEMX+yWoistrVLgZV+/0ZYGAffw5mfvf6xR67gSJoejzCoXcgEAl9fGOPX86pPM4xJoFTHwijqWEv72mbXZbXVqA+HeKYq7d/gHC7ZVinr/80YXmNShaDxw7f3mIv3eeHsTyWRq9hQ1L6c1hS2xZ87tL2ytqoStLS1pGSFUcAmR15VKvdsAIKK3Cm1f/RZmTQDWDnmt2t5xpnISW1/tjK/DvsKixvNx5U/fevhNOHrLJWyYBZ40FeN3rKqXOQGQDAJrJ7f74W9G72K+XSO7unF+302rfxxwekGfZcYfu/4ZkL/7pSyrVPIxNaWsB/1zQT/DJ5LCkbv/xPBVh9H57VRnUjpP5p+U7c/aP13Okyet779+Xl2bjrtw4cLF46DH5NdOMNnskptnTg593Gtx8ehxVqSNu3XumwD0AAIAvNBQi3qqETlSIZF22AMA+GI3JWW3CwAwAqNjq66/UyQKQdn4EHjUSeBZKEcET2vVOmOyYmOy2aVGbbmXvayMAwBMmbTYeusmU+rjV6vA4wqEGgDQ0mJHFFIaXGizWqRsHu+ZE3hWpVIOAJwmjVUAQJAkQJIW2mp1LvoR1MERpUv+9056oa44AkKvB2uPUBVNBh0EZePh+LctAYDN41NiD8+L5cXKNhaj4Z73dmFaiqi0IK+71Nf/qH+TqNs9D6O69ihg8/i5RZnpzqVpnl0eCptZhqAOfwIEjVOL+zkzzKTV3uuguW1aF2gLWyK0+1pE9q3sKOrXMh6UVeAlIUrMBn1gmaKAe/8pimJtYwI0ogZNqLKPmEjmnlSmMYXQDJYWhQmPXuCd+qETRYNxmW6fu3vZd9/qbWxicHhhajvqYC8c+Oje9FatgoXkf8eBK8nA4B8r1Wi6BwQZgmNbrVfp4J1sCChF/IbJMJWTAHB++1/NaYpmNxKV0uj3dfUGIX0WJCCg3Uao0npg88u97nlMkSTAmsFD8W3kSlzd9A5sJjfoSyKx/fWlWP38sLsdW+uMqZzEzpnvADSBAd99Db6sXjuTB7sLTJ0HTfxQQcvosLRV00Zmvje1heVy4EmyjWqx54It+S/unPLJp99OGNE+8keLjZJ8uD2pe13m/ynhpwE0aMbcNnOXBrsFP1O1xi5cuHh24YnENt/IpjsM5WXNL+z4u9HjXo+LR4uzLprZAAgAPjRNf0LT9Nu3UjZd3I8sxJHvrrjqBQBCqVwJAASDYWw1YHDVN/dZJxxREElgnVI0bZRNAAA6i67WCB4AsHl8hUmv97Kryx0pmu4eKgvT8RLwCAqpNarEE7tpAEBDuZkBwOLeTEHZbCKOQPDIHDRtKtUjyYOyl5bKAdD8Vq1ui1eCwTA7LfBajMkECBuKrjvSMjOPS2AzukMW8vDpmRV0/t81kCwtsk+1rzjkF9nkPGWziS7s2Nr47lOPr/+jD01R7BZ9BlYyfJD5+p/Tq8uaFWWm17pRgOR/2wKg0f2D/ZAEnoYiqXdtKX+6slKWxWT0EchkDoOVgnghrv8zBTxZCkauqdoQpvUrVwDQYfwiJgBG0pEDlb6cijXwlvMsBjdPb3NVU7gHBidRNptIQQSnojwvttqo1UNit1qJCzu3hu356ftOf30y74VV/3vtjeVTx322Yq9u4uLkTjhyMP5/BMGw9pj82pyQ6es/BEeYjzM/zUPC+jv1cNum9YfV6ImW4/6oLk1wwIw5+zl8QdZhRSjDZtL54+9X+gJA2ZUDQ1gMG/yiWqxG1JCaN4nGb98EoXcCkndNxbnlwbi23QvLO72CFV1+R8bRieCKCtDmlc8wJ+M1jN3yOsR+F5F5/GUsbvYtzq8IfqBf0OpB42EsjUTzEUvrYsxTF/q1DC9Q+PbcrCbl6svug3brxu8f3+uj3ZNmvf7mmoHNfZUAMLtPZKKUz0o5na4apjPbnPruy9PmcW6U3ujrK/A90zekb4Os3YULFy4aiu4vv7qPQZL6Kwf2DHvca3HxaHHqS44giOcBJADYe+v/sQRBuJzBqsK3peMmoCzLAwCkPo7G5QKJ9Fq1TlZFN7wBAJ5Nnb6B0Fv1DDtt5wGA0Was/cYcjl54VpPRy67VOASeSGSwsFlWBg27Mw2PhVKZFgDyhW0z0OaVz/K5MToA4ApFj0Tg5c+Z2zm1U+e/VKtWhzb0tezl5XKCzVaTYvGdvxlJWmib1bkUTYGHDTxJJsrzHRG8a9sdQs+/Tf1F8DgiCtLgc1DntKlogt1m0AuXQRC2zISLt1MujVoNszD15kCBRJpQlVtmozZxZwEwLuz4u839j1Wi+GZr8GQpCOpQjubDd4GyCrFvXteahqScOeEPgCH19nVE8La+OhF2qwjd5i4Di09VOSigjRY8aXooM8MHAApTb96TpmlQZHCLjDyOu5jMq3I8gMi4TkkAcNMYqIPNJMeVjQ1iMLLt608HHV/3+/fXjx2ak3s9cYJaURgHm0nsxytjNfEjroS367Bk+IcLZjXv0TcXnk0NGLTsMxCEDbvf+Qi5F0QovCJA1smREHnHo/fnVadxA2BxOFTznn1/NVkoyRlNEwUyjoy2n/89sESlb+YvNGrZYzfsrHWxLD6F4X8sAsnWYd8HX2DzhF+gSBoASfBZ9F4wC7NTP8CARRfA5NAI7VaGmQlfInb0V7Do3bF7zvf4tdfoivRzp9g1uzUKE4bCs+luDPn5lNPjHoDYqSvX+X10Y3zLN9cu9whrUekziWQQ6BPlvcVgsXt/uD2pozNzLjy/sLuNsokGNRr0T/2v2IULFy4aFrl/oNEzOHRveZGiw83TJx6+X6iLpwZn027mA2gLQA0ANE0nAAhpoDU93YR2VQEEBZ3SCwACopoVAKA8AoOrTCMDAJTned0a67TAS1On3RZ1Fe0SaoMndlPaLBaZSa3mAwBDwDeb2Swbi6adCm24eXppAECvN/MxYNGFktxsKQAIJNIGT9EsXrq0iWbnzrdA00xLRrpHQ1+P0utlxK0m5xUQJGmhrTbnBB4AiH1TYFQ1gtXAgCIxHCAoxI7OqH1gHQjpegaUTYDjXzcDHB/mAok0sawg/3Yd3pFVKzrYrRZZeLuOVd6kth44NJVksUoLUm/WXIeXfdoNxtJweEReBAB0m3cNHHEWMk8MqCk6VpiWEggAvpFNcnDwk2iUpPSCb+w2tJuWVeP1ZKHxAosynM3lFJYrC+8ReKn//NLRTjPg6S2r2pUWQHi7Dkomm12SWc7nAwCuba/SfOZhKSvMb8Jkc4q6T5w2Y+L3y0fOWr993LSO2jMD/G7S/abNWDzo7fcO+kY0vhMhjxqiRJd3PofV6I6No97DP2++BMoqQMdZq2q7VpcxE6+K3T3OXlS6S3UmQlC0df4ivY1NiMNa/F1d5K8SQR3K0WnWV2Bxy+DXaivGbJmMGZe/R4c3K782GUxgyM+nMGnvG5CFHkfe+ZewpOVinFhUtbPp3aTsl+PyqrfAlWRg3LbfnFpbA/Pp4OhzAg6Zf+iG8kU7VfOvy2K3EBcUFwa5sd1SpzWfduMRLdGFCxcu6pWOI8ftBEFQZ7dtGvK41+Li0eGswLPSNF1+3zHnbib+A2xN3eq/NH5pFABHVIXFK4ZB5QkAgdEx6h6TXps5cNbcanuTQV/sBZKlvW0T7wR52rzboq6iXUJtiGRyJQCiRFXsBQAModBsZTIplp2qOopyH3L/QC0AmPQ6MQBoipVSABC7ezaowCvf+a+PasXKDwgm0wAAlN5QbRuA+oIyGOQMPv8egQeSNNO2Ogg894hUUHYerv7lh/LcCHDFOZAGV5lO+MB0eScBDNKI9CO30zQ9g0PPW0xGv6SjB/0oux2ZCZcGs7m8/G7jJle5yUCyWLTE2/ecVlXcSldWyqr2WudXtAJAoPFAR085BhMI6fIvTOpQHPu6SXXD1IrCIBCELbJJSAnO/zIdLL4CI9b+WetzC+mSANCkXEiU6MvVkXdbPSsy0loDQEibjtVGhRgkCaFMnlSu1jeimXwFim80SB2eUVMeJpBIUlr0HZgl8/U3grIBisSuEHhcu8fN8m66zrmJZsMXQ18cBcXVwZCHHUXca5nOXK/TqAl/UDTIg2WxhRlaCQcA3XTYtAN1WnS3eTcwL+8NTDm8BuE9azcN8WulxYzL36PNlE9ht/Bx6NNv8G345/hzdJ8qm6+btQz889o7oCkm+i38GiLvWjMEHgVsJoPuEu7xt8ZkC1mw63qNgv+bC9+0MtgMfp39O/9DMp6tNjAuXLj47xAc07JU5uN3RJWX06sgJbny57WLZxJnBd41giBGAyAJgggnCGIpgNMNuK6nit+Tfh++5vqaWbcPsAXFMGluh8Jj+wzIZvOqSUUDAKPaC2xhneo77u59Z7abnYrgVaSLqnXlHgBAisRmC5ME2+ZcjxS5f6AeAGUxGEQAoC8rkwKAzC+gwQSeMSlJqJg//yMAkL/yyhcAQBkMlQw36hvKaHQnhcL7I3hm2O3OW8yHdXekY6YfjoChJBxC7/pLz6xA5G2FW8AllGbGVVjEN+ve5zwA3Dh5pO3ZrZuamHTacP+oZjtIFqvaTZnA6JhzNEVxz23bVL0Iyr/cBkxuKeKm3Yn09FlwDAyWHlc3DaxumF5dGsjm8gq4u6aNgEXvg3ZTl0ISYKnu/Nu0f+MGCNIcJCgj7FarNCP+wm1HUlWpvpGMbaDdu02ssRbYPSAoyW6zSpTM0FRoFc0fyiykCoqzMwVWs9lL5O5553dy9qcwWHR+CGh3tMbBw1acQHDn1SA5avT8dJ2z12zSqVuhV0jYjnQVx/eyOkjPF4tv3BMhbEgGfHsRU4+/gYB2G2ExyHFz1xv4rddaLGr8MTa/3B1F1x3R0jWDx0Bf0hRRw350pqfdo+SzIdFHOUyGaseVghdrOu9gzsHBHJJTMq/dvAZNLXXhwoWLhqb188O20RTFPr7+9wGPey1PGgUpyaK1786cmJN0RfK411KfOHuzMx1AFAAzgD8BaADMqnHEfwgek6e12q13dkW4bkpYdM6nEZq13uCI6yTwSk2lAgBgEAyzxW5xKoLnExGpBACd0eAOAKTEzWwhCQbbZnfqdcAgSZBMlsZiMooBwKjVSADAJzyyQWrwbCoVM3fqtPcog8FL/uqUz8WDns8EANpsalCBZ1Uo2LDZhKREcq/AYzLNtM3mvMBrPjIfDKYRuee6wG4VQd6o/gxW7iYw7jTsZglOLo4EgPC27Ys5fEFmSXZW22vHDg1mkExd95dfPVzTFHFDR1wlGKQx99rVqtM0TeUkNPktIAm6eI9LozTYDK+mB1CW3QHph2VVDtVpg9wEDBUK4ofAo/E+9Jyf6NTzEnjYIPRKCmHleAJA2vkzkQBgNZsZJTrCw0tg0VRbw3eLRm3aJwHADUOAAZSNh9NLq2y58KAknz4eAgAeQSF3BF7ilq4AYUOX2bVvgr3879+YlzsBTQbWqRlx39ff+otkMjUWGy1wDwo5V9d1PxQekUZM3v8n3s1+DX0XzoRvi20wqQNwbdss/NxxHRZFfoL8S8PhHrkfL/xayRG0Jjbf3Bw4bve4UeN2jxs1fs/4l8bvGf/ShD0TRr689+URE/dOHD71wNTBqWWp/IdZvruQY2sbItteorM0W3Y4tcrXw/ob64NLjCUxMR4xu8RssatLsAsXLp5qmnXvnSeSu19QpKUM1BQXPZ5eqE8oh377eURRZvrQ3csWvW23WonHvZ76wlkXTQNN0+/TNN2GpunWt/5ddcPu/yA8Jk9np+18jUXjyOPhy4tgM8krjC9qxGYmYDV4gi+rk4Om2uzofcdn8pUVDc9rI6Bp8zIQhM1gNcsAgJRKzTaCYLItVqdNE0gWS2M1m8UAYDbopQyS1Dtj0FJXaLsd2WPGTLerVNHigQMWe0yffoPl7XBLpEzmBhV4hgsX5QBAyuX3ptcxmRbabnc+RZPJocGXp0KrcKSChXSu/wgeAHSefQkEw4abezpUHJL5B5wzaMqbaIqVcZ7BIfuqc5qsgO8msYk9PC6qFYp2VrO58ufCye+bgrLxERh3odJjHWbsBmgGji7se/9DZYoCrtVs9gphpIeAyVXjhd9W1em5eTZO8GIWexIEYSvOyYoAgMRDewKtFMnwlDIrGcbcT5NO3QpIFqssq4wrAoNpxMnF87D3vdg6raEGlBlpYQAQ3ra9Q+BZDQwUJ3eBm98l+LbQOTWJs7Vzd+EeEGQIbdVuFQjC1uy53mdqH9EAMJhA3GuZePXIaszLewU9PpoNr+hdMJYFgSvJwNi/V9R1ypWJK8clFCeMSihOGBVfFD86vih+9OWiy2MuKS+Nvai8OO50wenJbx19a+rDLv2TQVH7mSSh+/N8bpXtfjYmbxzMIBjm2a1n763qcRcuXLh42ojq2vNvu80mPvTH8h6Pey1PCnk3rrkV52T2Y3E4Sn1Zaey2rz8d9LjXVF/UKPAIgthR08+jWuSTjpAl1AJAuvqWzbzIuwgAgfTD7jWNAwBkHJOBppgQedcpgqe1OHrfCVlCZUW7hNpgcTgUi8MpNtpsEgCgJRKrjQCLbbWRtuLi6muv7oLJYmltFrMIACxGg4TJYjdI9C57/PiXLFnZz/FatVrv9803xwGAweNRYDAstNncoDV45rQ0OQCwfH0qmaygLgIPAMR+jqgdwbAgdnROLWc/GO7hRoh84qFKi6swO2nUOu4Cbr2/2w8f/a8z0/iGNz5nt1klF3durWygkX6kNQiGDZ1mVXZ5bPaiAmK/iyhI6Hv/pkbqudMBAODNUkkQO/oneEfr6/TcmgyKJ3vOsaYAACAASURBVAkabnyiWKsqaQwABZeOtgOAgAD3q7UNv12HV6ZpTPX56h0wORqc/fFTrOg2wakNmFrQFCtDSRarNKBpM8f74OjCaNjMMgR3Pvawc9fGoLfnHZr43c9jGnfsWqfNoQaBwQQ6/y8F0078hvcKXsbslLecSsO9Cztlh8qoigwSBx1JGJcw6NLYS4PPjzk/5NSoU0NPjDwx7OiIo8Oayptuy9Zkd9t0c1Pgwyw31ENobObntitfbYzbdCHH/+7Hzheel2RrsruGScIONpE3qdvr1YULFy6eUNq/OOo6VyS6mZt0dUiVG7n/QY6t/W0wTVGs516e+onY3eNsdmLChEu7tje4U/ujoLY/cHsA/gBOAPgWwKL7flwAELPFWgDI0eQ40jRloUUAAMVVz1oH551ztEiQBtdJ4OmtegEAiDlipZWyCu2Uc1lEbB5faaLsYgAoMxtZAMC222FKdq7wlsnmaGxWixgArCaTlMnh1Hv9Xf7s2V2Nly6PZgcHHw5as3rT3Y8RJGmkLZa6iaw6Yi3IlwEAJzT0XoHHYpnrFMEDAK8oR9SOJ00H163hUr38Wp2B1eCFc7+EAkCr/oPTWByuQurjdzS0RZuqjT7uo+2Q4RdBELb0S+cqNz1XpbWF0DMRstCqI/dNnt8Fu1mCvXM73H24/NrxWABwc5ddxMDvz9f1aaHluBwwuaV+fC1l0mrCTHodWV6Y20LINMOzaZtqWwrcjdw/MMlutchuWhtZ8drpt+HRZC8K4l/A0lYLkbT1oWyj9eXqUJ7YLf32gZt7uoJBGtHjw7o/1wdA5uv/5DXeZjAfKCp5IPuAl4WySILFwckkgwSbZNM8Jo8Ss8V2CVdik/Pktg/iPthCEqTxt8Tfxj7sMt/v32Qng4BlxfGMe/pDLY1f2p8GTU6KmuTaxHThwsUzA4Mk0ah1+61Ws8nn0O8/V/6e/4+hyEgVKjPTBrh5ep9q1r133sC35i0lmSzN6b/Wv1NepGjQ+8xHQW0CzxvAewCiAfwAoBeAEpqmj9E03eA71E8LEq5EB9xlfOIT6xB4pZm1CzxVuuMG06dFnXbhDTaDkABhk3AkKho0U2lQOpVTzRUIFSaaEoEgbGpVsRAA2DYKlqxssTPjmRyO1m511BtaLRYJi8utV4FX/MOSpppdu2eS7vLEoI1/LiPI+9zrmEwTbbU0aATPVlzsDgC8Fi0q1eDVyWQFAJoMcgg8sW/D1N9V0P6N8wAoXNvWHnA4Y45esGjWS598tczZKdwDggwCiTSxtCAv7m7HSiRu8YZF5wev6MrpmRX0+jQebGEB0g7dMVuxGhhE/sUBJEFBNuK7JQ/wrBxiwc0/PohTIKNpmp14eH+wWmMK8eeXg2g+vNYUTQBo1DouEQBSz52OhiTAgjfO/oTYMQth1vph65Ql2Dqly4MsTasqYVsMhgCRzN2RnqlVsFCa0QHSkDMQ+9UpeuUCOJp7NBIA4nzikqs7p5l7M22sZ+zWQn1h3MqrK2tv1VADrYNlmggv0f6MYn23QzeU7gCg1CvZ11TX+nsLvC8MDBtY+DDzu3DhwsWTRveJU8+xuNzC9Ivnht3zPf8f5MgfKwbRFMVrPXDoJgDwaRShbdnv+e8sJqPvtq8+nfy41/ew1CjwaJq20zS9l6bpCQDiAKQBOEoQxJuPZHVPCe48dy0AlBhLHFGwsOdKAFDQKmoXeJoCLwA0GnWvk8mCyWYSMBlMvYAl0AFARnmGU3V4fLGb0kaAbWMxzZqSYhEAsGx22JQKpwQem8vT2G02MWW3w261SNlcXr2maJZt3jya4HDKApYv/5IpkVRqrkYwmUbaYm3QGjy7ulwOkjRywsLuiY4QbJaZpqi6CbyI3ipEDvgRXeY0bDQgME4Dgec1FN+83S7BPSDIwHer/DusCa/QRmetJpPvtWOH7qStXfmzNQAgZtTFagcyOTQC2+2CQdUYp5eEAQA2je9fboSMz2MWMRt1efDXiW+LBD9uGQ8Akk8d62KyEjxvoaW82mjifUR17ZFHMlnqktys6NsHh/x0GsNXzQDXLQtX/5qNZa1noDSjTq+r6yeOBAFgyP0DHQLv8OetQdkECO9ztC7zuHCQqk5tTBKkaWj40BqF+/z283eyGKzyDckbxj/sNWf2DN8OgPHdgZTBAPDl+S+7WimruH9If1djcxcuXDxzsDgcKjCq+TaTThuxZu6M17Z/83mfE3+ujs66cln2XxJ8JbnZfEV6yvNid4+zsX0G3P7O6TJm4lXvsPCtqrycvvt/WfJURzlrzcElCIJDEMQwAOsAvAFgCYBtDb2wpwkfgY8OANRmtUPgcd3sYPJKYVTVnv5VmtEMHHEOBB51uhE32UxCFoOlE7FFegAo1BU6JfBE7h5KADByOTZDeZkYcKRo2kpUzgk8Pk8Lmmbm37wuoex2PkcgrNcIHm02uTFlsjRedHSVBhUEk2mmrdYGjeBRWq2cweVWSmskWCwL7HYOXdcPwVEb9qHpoKL6Wl+1+MScgVkTiCt/+j7oFLG9B5wDgOsnjtz5YFMmtQFblIdmLyoyEy7J1syZPmXJhBeXXz9xxOeewb0/PwSCNOHymgG4vsMT6YfHF5nFVlLsVW0zcqdo9fIVEdMMNhOm4uysvgDgI2Om1zasAgZJQiCVXdOqVNF3f4EZfDuVnfL/YO1+Tftrmy7xem5+Z9Iqymxw2kGrMDU5DABCYls6BF7msa4g2Wo8N6/W2kAXlVHqlZESjiRVwBLU6Iwa7BZsbOvd9q8SY0nzRRcXNX+Ya/aL9ikKkvOPJxdq+yTkqoTnCs8NFrPFGTNazHDO6dWFCxcunjJ6TH79sFAqu1yal9Mj/eLZN85v3/zF3198tGrx2KGblk16afGvM16ZvWXBh4OfZbfNQ7/9PICy2wWxfZ/fdP9jw+Z9sp4rFKZdO3Z4ekb8BfnjWF99UJvJyhoAZwC0BPDJLRfNz2iazn8kq3tKCBYHawFAa9HeEVkcoRImTc2tEjKOSqEvjoJn0zr3WbJQFgGbZOslHEd6aJGxyCmjFZmvfxEA6Dlsu1GjcQg8GwV7udqpGjwOX6gBgMz4i4EAwBOJ61fgWW0CgsOutuE7wWQa69Rs/AGossk5AILFtgAg7Gr1Q5tzNAhtXjkLAIhf376WM6slJLZVKVcoSi3JzXK0SyjN4EJX1EzFDU9aPfuNaVsXzv+1OCerv9Vs9jy37a97HQg9mxrgEXkYqvSu2PX2WyaKpA02Jksk93g4c5ngTmqC55bpw9dbaMrO4zKs8PbzvF6XKeT+AUk2i9njwMof22+a/+7w5VPHfbp82viNZ//Z+kViPrNJsdVNl6MT8nM3fdTZ2TnVisJQBknqG7Vpr0TRdT7K89rAPfIEOKIaBYqLyij1SrbGogn1EfrcdOb8BZ0W7OGS3JLtadvHO1t/XB2TO4X8badp7py9q17RWXWBHXw71GtjcztV53JEFy5cuGgwRHJ3y9Tla+bPXLt1+OB3PpjUeuDQD0NatFnuHhB0kMXhlBnUZZHZV+Mn//H2tF/+/eHr556l1gGAw927IDV5iFAmv9Tm+WGVNot5IrGt2/gp39I0xdr385K3nlZDmtpuVMcC0AOYCWAGQdz+GxMAaJqmnYr6POuESkINAGi9VX9XLzxxETQF0dWPAnD2p/YACMSOqrvAs1uELAZLL+PK9ABQZipzKoLn1zhKCQAGDgsmvc6Romm3w67ROvW35IvdNABQkpMVCABCqaxeUzRpq5VPcLjVCzwWy0SZTA3ajJIyGuUsb+9KURiCwzYDgK24mMOUy+sUcX0kRPYtAU+aCuW19gD+ftBp3AOCzubdSBqXdeWyjHVxeZtrBaHMpHJBLxo5tNwv4GCH4aP/Pr3lz2GqvJxeOUlX1gVGx9x5DbR9dTf+ndUf+uKobEGP7YBliMzP/+HdQ6UhCQGcksHZEMCXrwHDq2NG7YPuENqiTVJm/EUkHdk/DwA4fEGWe2DwXp9GkVdje/e/pkq57L7r1z+WFiRd6h8EONW7Ta8uC+UKRRkMkgSOLmwPmmIheqirNvkB2JK6JYwGTTaRNam2/u5u5Dy5rZN/pw0Hsw/O+OzsZ3HzO8w/W9P5qWWp/Lkn5o6jaZrhK/TNbipvmtk7qHd2uDTcMK59cPZPR9Mv5FlPduVyOeXvt3v/xMM8FztFY8O57KAD15UxqUW6mGKtOaqxj2jfv9M7//Ew87pw4cJFfUKyWHSj1nEljVrHlQC4x7Ts1KZ1TRP275p88/Txt7Kvxg+M7TPw144jxtx4TEutVw7++mM/ymYTNe/Zb2N150R17VGQfun8L6nnTs3ctnD+0BEff/nA91SPixoFHk3TT6VqfdTwmDyKyWDqDTbDHZHFkxVBlS6HqZys1j2xIL4DOKJctHo5t67XtNgtAiFLqPQWeOsAoNxc7pTA841oXM6gacrAZhIWo0FMEISVJBg2Sq93KoInkEi1AKApKQ4EADdPr3qL4FEWCwG7ncfgcqu3JmexTLTN1mA1eJTRyKDNZilDLK4cwWNzLABgK1Fx4Nj4ePLwijqDrJPjkbJfjojeTrln3k+Tzt3O5t1IGrfrh68/NOu1oQzCC+4BQfs6vjRhS1irtiUAYLNat+9ZtqjvyY1rB4z+PGb97cGtJ+bg+DcXQNk4yVRsPnAegdGxTpmh1Ehwp3iftLVDAcCPrwEi+tZJ4DXv2Tc7OzF+GUcg1DXr3jvJL7Kp5u7H5f6B+sOrfzUXl5oioFWwIPKusbejxWhgmPW6YI+g0D0AgJyz3cAWFKLjzIbpdfiME18U3xgABoQOcCqCBwCfd/z88JmCMy/sy9o3dm7bued5TF6VkdNjucfc3z3x7sd6q96fJEhTmjpNcDzvOJZfWQ4uyS1247hlScM8dDpjGmkv62Gm7XXvo747sdBre3x+TLJCG6MoNzW32Ck3AOCzyUIemyy+XqAZcDVPvbm5v8S53oguXLhw8RjpOHLs9bhhI2fv+en7bukXz44/+/efX904cfhklzETV0fEdaqT6/uThFZVws5Pvj5UIJFeaf/CSzV+3wycOefQ77NebZV7I2ns+X+2XG07+MWGNcurZ1wCrp5gMVhak810RyQ5euExkHGs6vzd7NNu0BVFw6NJnaN3AGCjbAIuk6v3F/nrAEBn1TmVoskgSfBslNXIYpJWk0nEYLK0DDZbQxuNTgk8sYenBgAMmvJAAJD7B9ZbBM+Slc0DQDD4vBojeLDbG0zgGeMT3EDTJFMqrSzwOBwzANjL1U9uXnrsaEfT6wsrH7g4OLpbr1w2j59jNhoCYuVK88TYwvPjv/lxeYW4A4CmnZ8rFLt7nlVmpPWvZCc86+pnmJX4YXlJcRDBYBjDWrapk4FQlbR//bqfQGdtJ89BlLRUh7BupXUZziBJDJ79wf6+r806fb+4q3hcKhFdzzWImdTBT9vUNt+NU8f8aZpmS339MpBxVAqdsjk8o46B8WRm7z7p5GpyG/OYPEUrr1blzo4RsARUz8Ce63RWXeBHpz7qWtU5666vC/nfsf99a7QZPSZETfj44tiLo5Z1XzZpVONRn7TxbrPak+95XW/Ve+YZr3VhgGXRKDt4jlp5dpKza7BTNIb8eGrc6+svr9x/XfmmUmOK8pVwL/dv5vPDyvGtJ13/tO/U//WKWETRYH+1N7mXs/O6cOHCxeOGZLHogTPnHHl50U/TfCObbtCUFLfZufirn9e999aE4uxMp+45nzQOrFja2261SqKf61Wp9u5+GCSJQW+/9yOTxSo7t+2vmU9bqqrrbqSeYJNsncluuhNFk4Y4djgK4j2rNNg4vbQ9AAaaDz9d12vZKTuslFXIZXJ1jSSN9ACgt+qdiuABAM9mtxtYTCbDYhYzWSwNwWZbKZPJqRRNmV+AFgDMel0QANq7UYTTN2S1Yc3P4wMAgy+oNjrG4LCNdAMKPNP163IAYHp5VRJ4DB73lsArf3L7o8SOyceeuTkoTGgPYNeDTMEgSQybN/89XuqOQNnFL79A2NAqX6PRz/Xadnrz+vaHfv+lx7B3P959ZwImwGDSerU6kMMX5JIs1sMXIYn9LKTI41onZMdC6JXeEEJKFBZzrKD4TAtF4rn+vkNR4/sy71piKAAERDVLx4nvugEg0GqCKz3zAbBTdpQYSxp7C7yd6mt4Nx+1/+j0kdwj6Ufzjo5Wm9QnJNw7rrELzy9suTF541wWg6V/t+27c19q/FI2AHQN6FrSNaBrCYBLFeeqTWqmyqRizVxTMCqpQDPkzQ2XU5eNbnmk5nXTeOHn02MTctXDwzwEB6d2Dfv7hZb++STj3nuAlzuGZC07kpZ0OVs9wGix/8Njk64aTRcuXDw1uHl6m0d9+vXGzIRL+4+sWjFOmZ76wtp3Z/X3DA7Z23HkuH9CYlvVacP1cWEoVzNzrye+wBe7Xev00vgkZ8Z4hoTp2w0d+RWHLzDXy73MI8QVwasnOCRHa7Fb7kTBfGJu9cLLqNpJM/9yB7CF+Wg96f/snXd4FOXaxu/puzu7m01vpBFCQu9VuiiKWBF7QfxEPXaPnqOiHns5iseGBXvD3hBQFAGR3juEkJDe6/aZnfL9EYKUbHaTbAiR93dd/sHsO/M+G7Obued+Sn5r96pwV/A6dNbEmlwiJ2oMxXi8ijfopykmWdG8DCUosmxlON5OGwx2XZKCEnhRSSkuAJqmqiLDsnaDaA5ZX12lvKJR4JnNfoc3U7zghaoaNFnukCcpcmFhJADwycknCjyDUQYAzek8dR08AIjOWg9nVV8UbfLvyu76Jg6fXzXF35rEzN72iMqVfQDoGD57S3NrRl165X6jxbq/eO+ui5orQpZczmSTNaz96ZlNRGVuAwBY4g6F7JpH0XvC2TsAoLTK2xel21p8YFJbWpJOUZTca1DfchRtuBBizC4MuoY0n2oDK4tXRsuaHJ5iTQk6PbMJnuH1C9Iv+NireGMfWv3QlKbjd6246+wF+xY8KnJi2SuTXrmvSdz5w2awKem2dM8XN4/6MNos7Fyyq+y2d/7M697SOTPeWnvV9qL6yzJizL/8fNe41y4bmnSCuGvizKzYnzw+NeaJRXsCusNH88bKgxlfbirsFnglgUAgdCxpA4fUznr57Vcmzpx9Z1h0zKaKvIMXfvfcY+9+cM8td+xe8VtiZ8cXiF/nv36mIsuRvcZOCOjeHc3ISy7PHnTOtPwOCqvDIAIvRAiM4DxG4KVPqgagw1F24iy8ok0WOCv6IzprTVuciKaZdyLX6HRxNOfyKJ6gHTyT7KNUimJktzue5XkHZTA4tCAFHicIGs2yLgBgeSGkHTSVmmoRABir1a+DRwmCFwCUsrIOcdGUyspIADD07n2iwBNNEgBoLvep6+ABQO+L1gI6jXWvDT/htbIdIt4eNwvf/t8byF58Gz6Y8i7enXw1ynef+ICgKnsojBEHkDLar0ubPnTk9z7JG/f7+28ekxJakr3Xqio+myWqnR00j6bPxVsA6Ijt02ohEAzdBw2rMRi4yiK3lcYfz49paa2zrqa7IJrz+WUPjofijUC/GV93REynA78X/p4JAMPjhgfVYOV47ht637YIQ8TuDWUbLi+0FxquWnzVNcsLl98ebYre9tnUzx4cnTA66KfLZoHV3rxm8H95lrbP/TV7ztbCumYfgFz65torthbWX9Ejxvzr4jvHvsmzdItPdh85v/cGA0dXLdtbeX6wsazNrbbN/fXAU88s2X9fsOcQCARCRzP43Avyb3z1nRfPv+eBm6OSUpbWlZWMX/rWK2+8c9sND63/7svMzo6vObwuJ1Owa9ulBrPlwLirZ23v7HhOBkTghQgDa3DImvyXyDJFKGANtXDXnijw1rw8EtBp9L2k1emZwF8z7yy8xQkAHM05JVUK3sHzyjQAqIrPxhkMdtpodOiyHFQNHgCwLGcHAJbnQ9pBU62rNwEAEx7utwaPNhq8AOCrqOiQNE2lrjYCFKUZBw08QdTQJrHRwXO5Tm2BN/KWPHDGShRvHn3kmLeBwafTp+HdM+ejbMeFiMpYgXH/egDWbltQvOlyzJ/wLj6YOuPIsO+CtWHw1PZEdKb/4eYAJt1w8wbOYCjL3bzhkqNnzOVt3ZQMAFFJyaFz8IbeUIiL3vgHpr3cps9NMJij4zcXuW26WrBpor81mqrC63R0N9tsh5C7YjoMtlyc/cRp8QejI8ipy8miKVq+pOcl+W05n6EZXJF1xceyJttm/DTjlV3Vuy5LD0tfuvCihU+mhqX6zQbwx9DUCPs9k3s+Iyua7eZPtvzLKSnH/J2c8dbayzcX1F2VHi0uW3Ln2HmBxB3QKBwHJYUvqXJK/T9Zl58STByP/LD7OlXTjQ0eX3fi4hEIhFONniPHVFz/wutvX/7487Pie2Z96ayr67vmy09eeOuW656oLipofbeqDuTXt1+boEhSbObosV/STOjG4JzKEIEXIkysyaFoillWj0od5M2V8DacKPBKtpwBXizDiJtb1QmwiaaZd2FCWKPAYzinrMrB1+B55SO2IW8wOGhRtENVTUqQ890YrlHgcQZjSB081W4XAYCNjvJfg2c0NTp4VVUdIvA0uz2S4vla2nhiRz7abG508LyeUztFk2aByIx1cJQNRHWOEYvuGY6Xer+Og8tmwxiRh3Ofvwu3b3oNk+bsxd07/4spz9wNc8xeFKy5FvNGvINPLr7wcI0ohaxpm1raihMELalP/x+8TkfPdd9+0avpeFXBoRQASB0wJHQOHtBYY8gKHZYHH5vec6dPY6iKWqkXdn/XbHr1wU3rYjVVFZPFOkB2JiDrvK9Jc5W2U+4qz7IJthwrb21zuvetA27dH2eK2+RW3PHDYod9/O0F384LNDC9JW4en35wSp+4N6oc0oAr56+/run45W+vm7Epv+7qtChx+aI7xr4ejLhr4t/nZv1KU5A/XlcwLdDaN1fmZuRWuSZ3jxJ/B6B9sbFoQtveCYFAIHQsiZm97Vc9+eKCmS+9MSu538D3XfV1/b55+pEHvC5nq5RUdVGB6df5r41o7Xn+qC0tNv70v+fOfHP2tU/lbFhzp0E0506aeXOL9zR/J4jACxEiJzoBULn1uX89tTBYKyE7jxV4pdvMcJQPQFTm2rbeFDbNvIs0RLoAgGd4l6zJQTl4mixTJq/MNf2bN5ocjMXS2Dhl//6gRCIrCHYAEEymkDp4mstlAgA2Lt6/gyeaPACg1tZ1jMBzuqJoo7HZ8QJMmFUCAN3jPbUdPADoec466BqH+eNfxub3HwYoHcNuegL37n0EI27JP2btqNvycO/eJzFxzv0whucjd/mNyF7yD7CGWoy8JeBDiDNvuOV3mmUde/5YdknTMXtVZQrNMK7kvgO6RPF1EwPOmroLAArdNmDj/AnNrTm0fWt3AMjQdvQFby7BeS+2OION4J8qdxXXIDV0jxfj25SeeTRvTH5j7r1D7r33/XPe/yYUg8rfvGbI75mxliW7Shouufer7WOunL9++oZDtdemRppWLLlz7KutbZYyMMnm6B5tXplb5Zy4s7je73etrGjU/FW5s3mWrn/7uiHzY63Cjv3ljvFkYDqBQDiVCY9L8M54+KkfMoaPmueqqx34+cP33XZ0Zk9LlB7Yb/niP/9+ZtfvS+e8c9sNry3/cP7gtsQge9z08g/nD37vrpv++eE///HJgfWr7/JJnujErN6fX3DfnMdPF/cOIAIvZFh5qwMA8u35f6U6GiMq4PNEQ3L89XNe/fIIQGfQ56I2jUcAALvc6HTFmmKdAGBgDE6f6gtKnCkVlTynaWAAGQAMZoudsdnsACAXFARXh8cLDgAQRHNIHTzN3Sjw+KRu/gWeSfQCgNpQbwzl3n/F4I6kRdGPwAtrTNGUvKe2gwcAY+/ZD9ZYDVU2I2PKW7h3zx0478XNLT5UGP+vbNx34BGMuWcOLHHbkTR8YTAPIazRMXJc94zFjuqq4btXLksEGsdoCKK5oKt9mcb36OkQRDEvzx3rQvmuCdBOnGdfU1zYHYAWS5cnIn3St+BMpCtiGzk84JzNishqd11lRniG+4a+NxwMRVxNfDF75LsRIr/v+60l967Lq7k+JdL0x893jXulrZ0wrx2ZskjTwT/3s/+RCfd9vWN8nduXOblX7EcZMRbPsNSIlR6fGvva8pxe/s4hEAiEU4UL7n1oWXzPrC9qS4snf/XEQ5cHWl+4e4ft22cefVZ2u7p1693vY13X6W0/L3zs7Vuvf7TpnqIlVJ+P2vDD1xmfPHDXrDduuvqDbT8vfMxeVTUkMjFp2ejLrrn/9ve/vPmKx//7RVLvfiE1JU51iMALEU3pkqXO0uNm4ek0Dv0ZceRY8cYzwJkqMOq2Nt+IOGSHGQCSrEmNAo81uHyaLygHT6mqFABAoBk7ABgtVjsTGWEHAF9pWXACz2CwA4DJGhZagefxmEBRGtetm9ffGtpq8QKA5nB2iIumeb2RtMXSvMCLjGx08CT51Bd4nEnDdT/cjVvX/R+u/moJDGHBp79NfmwX/pn9KK7/6btgTxl71czFFEX5Nv/03YWaqkJyOVNM1rDQpmeeJMJi4nZUuI0Gn9ediLWvZxz/uqO2urtNkH0cz9fg/FdWdkKIfxu2VWzLBIBzUs/pkMY57SVc5JVXrxz0nIFjatKixBWL7xz7v/aMObh+dGp+lJnftbWwbtrxtX0AkF/tMizdUz7TZuJyXr584HIAuPesnutpCvLPu8qbnfVHIBAIpxpXPPb8gvD4hBUl+/dcvejl5/3WtOdt2xT5wwtPPeuTvLFDz7/kicv/8+w3N73+/u3J/Qa+726o7730rVde/+SBu2YdP3dP9rjpPz//qO/H/7pj9uuzLn9v9ecfza3Mz5smhtmy+048+9mb3/zwuutfnPfWqOlXZHe1B82hghSOhIhIY6QDAKo91UfNwkttHJVQ9kNFogAAIABJREFUuiUWWVOrUb5bhL1sIOIH/NSemh23zy0CQFpYmhsAjKzRqeqqyaN4aCN7Yu3Y0ai1tQIAGBi23q2pUeaISDvHmX0AoNRUB9VohTeaHABgDo8I6dMQ3eMVwTBuqoUPIxNma3TwXM6QO3jSoUNGqKqRsYU1K/DYqCgZAHRJOvVTNAEgeeQJA707im69+jREJCYtry0pOnP70kU/a6oqWqNjQtdg5SSS0LPXzspDuReXeMKU1F1fT8SYu3OOfl2y12Umm+oEpIz+FKaIEy0+QtAUOgqzDIyhcmTCyJA+LAolY3pE1e34z9mzW1Nv1xKTe8X+9MWmooeeWrR3+HPT+x+T3nv3l9tnSIoWccekHs827dc92uzpFm5an1ftHGv3+N6xGrmQjaYhEAiEjoBmGFz55IuvfXTfbZHZ61ffaf0spmbc1TfsPHrNgQ1rY36ZN/dpxadYR15yxaOjZ1y1DwCMFqsy4+Gnfijet2fFsndfv6byUO6Fnz10z8TUgUM/tUZFVxfs2j66oaJshKooVoqiZEtk1Nb4nr0+GX7B9I0xael+ezicbhAHL0TEmeIcAFAv1f8lkuL6Ngq8mrzGOrzVLw0HdBa9L2hzeiYAeBSPmaEYb1NTAhNrcgHAwfqDAV08tb5eAAAjy9cCQFh0jINPbhxertbVB+XgGczmBgCwxcaH9KZMlyQTxXEtfjiZ8EaBp7ncIa/B82zdFgEAbFRU8wIvOrpR4Pl8XUPgnWSGnn/Jj7qus+u/+/JWAIhOSeuSAm/QOdP2gqLUg1JKOaoPjIW34cgTh6K9u2w+n2qNMnglnDd3aWfG+Xeg2lOdGW2Kbnf9XUcTKnEHAA9P673RwNFVy/ZVHDMyYdHO0tgdxfUXp0WJK26flHGMozm+Z/QfPlW3vLA0u011KQQCgXCyMVqsyvSHnniWNxhLtiz+4cEdy35Obnpt758r4n9+/cXnVEUVx1xx7cNN4u5ouvXq0zBz7pvzJlz3f/cIJrE4d/P627b98tN/6spKx1iiorf1nXjWcze++s7VN8374Jlpd/1rBRF3x0IcvBCRbE12AoBdtv/l4HWfWAUAR2bhFW44A6yxGmfcdaA9e3kVr8jSrLPp303jEoodxWK/qH6Ols5V7XYBACLN1oJynycroWevWkFtNP00hyMoB2/U9CvX+STJ0O/MKSFNwdNk2URxrN/6OwBgo6O9AKB7PSEXeFJebiQAcAkJzQo8imEAmpZ1WSYCrxn6TphcsvbrzzY6qqtGAED3wcO6ZIpmREI3j9FsOXDIyYQhQg7D8qcGYuoLWwCgZNnHIwDAEpv4JyK6+00lJgRmVfGqSEmVolKsKae8wAslZoHVBieHL16bWzPzo7X5qdePTs0HgGeX7L+RBqU8fkGfj44/5/4pmVu/2FTo+ONA1QQAp00XOAKB0LWJTklznXv7vY8tevn5F1d+9M5jYdGx99mrK8XlH7z9FHSdGX/trIcGn3tBfkvXGHLeRXmDzjn/wZWfvDdY1zRq1PQrdpjCbCR7JgDEwQsRGeEZTgBwys6ja/B8YIVauGtiUJVthKN0MKIy2jTc/GgkVRI5+i+ny8JbXABQ7ioP2GilqXatX3L6jrs/++Eaa3SMzMXFyaBpSXM6g3LwolPSXNMffHxRqPOadVkWKY5v8QkMF5/gAQDN4w25wPOVlEYBgNCjR7MCDwDAMJLu8536NXidRJ/xk78DAIbl6hMze5+0FNFQY4tP2GF3KXFeGBzI+e1I/YB0aNO5ABB97j1fdF50fw9+zf81CwCGxA45JevvOpIHDo9M+HR9wXkA8PTifQNK6j0jh6WFfzWuZ/QJnWetRk7tHmX+s7jOPSKvKvTp6QQCgdBR9Bg6snrsldc/oSqK+aeXn3t8+ftvPQtAnzTrlgcDibsmaIbBpJmzt54565YtRNwFBxF4IcLKW1WGYjxuxX2syOLNVfA2xGLVC8OgayyyprUrPRMAJFUy8wx/xMGLMEQ4AaDGWxMwRbNpSDdrtkhHH6d43qF53EEPO+8IdJ/PRHFci4OJ2dgYGYCuS1LIb3LUmppIADANHepX4FE0LemKQgSeH0ZNv2KfKcy2W7TZurQrk9S7304AdI7aKxv1BSNRlW3Enh9i7Q5vmonX3JGDp1R2doxdnQN1BzJp0L5LMy491NmxnGz6d7M506PNK3KrnBPX59XYPt9YeJOJZ8pfvWLQj/7OObdf3B+aDv6l3w6MPJmxEggEQnsZct5FeQPPPu952e1OomhaPvvmOx/sf+Y5RZ0d198ZkqIZQjiac3gUz7EiSbBWwFnRE4XrzwBrqMWYu9v9tNqn+UQTZ6pu+neUsXEweL3X/2ylJlS3ywAAtOUEgWfXPN6gHLyOQvf5REoQWkzro3leB8NIuiSF3MFTGxoiwbJOLiFB9reGYhiZ1OD5h2YYXPv8q/+h6dDVLHUGQ6ZeuH/Tj9/I+12J3n7WbTxWPDMa1QcyKr0iJVgjTqgVILSecld5VpgQdtBmOD2fxl43KmXRIz/umTL7482POyUl+arhyU/HWA0+f+vvmJSx7+0/8io25ddOALDi5EVKIBAI7WfSDTdvsURF/zMuPaPmdBtZ0BkQBy+E8AzvlBTpWIFniqiEzxMNe8kQRPZYC1Zo942vrMpmgRGOOHgJ5gQnADh8joAOXtOQ7qah3U3QgmDXJalzBZ6imGhBaLEGDwAohvFqshx6gedwRNJGg//0TABgGFlXFCLwWsAcHuHr6ikUpjCbYgqz7S2v8yWBM5Ujb+U0qTxncr3PCFN0IhF47aTGU8M2SA3poRhw3lW5dlRqQZSZ32X3KmkxFmH7kxf13dDSeoamkBVn+aPCLg1Ym1ttO1lxEggEQqgYdv4luUTcnRyIwAshPMM7JFU61kUzx1YCOgNd45E5dXUo9lE0RTQwhiO1aqnWVBcAOGVn4Bo872GBFx5+rINnMNh1Seq0FE1dVQFFMVEGQ+AuSCzr0X2+kAs8ze2OoI2mFgUexbISVJWkaJ4GRCR02yG73Sn11n7r4K1Pr/QaOQCITknN6+zYujrf5XzXXYPGZUZknnb1d0dz4cDErzmGctw9OeNdhqYCrr9ieNJKAPRbK3PHdXhwBAKBQOiyEIEXQgRGcMiafKxIsiU31uqwQh3G3tPup9UexUMruiIaWeMRIZRoTvRSoFSP4gns4EmNAo+Njj7WwTMaHZosd5qDp1RU8NB1hjYaAzt4LOPVZTnkNXi61xtJm82BHDyJOHinBykDBu8EgK3e/tUAUKSmHgKAHsNG5XZmXH8HtlRsyQSAs1PPPm0dPAB4ZFrv7TlPT736qhEpQXWcvXxYcnGYkcvbWdxAhp4TCAQCwS9E4IUQI2t0+lTfsS5aXL8KAEBE+jpwphaHkAdDfkO+EQBMnOlIiiZDM2Bp1uVRPAEdPF1qbPHPxsUdK/BE0QFFEVWXq1N+J+T8AhEAaFMQAo9hvVCUkDp4qt3O6JIUzlitAR08nTh4pwWDppyXSzOMq7DclYwhNzydo/QoYTiuPqXfwFN2KHdXocBekGVgDNVjEsec0DGS0DL9EsNW1nt8Gd9sKUrs7FgIBAKBcGpCBF4IMbJGh0/zWVRN/etgvxmlSBj8Lcbd/30o9ihyFJkBwMyZj0ll5GjOKalSYAdPlgVQlMZYLMfUSNEWix0AJWVnBxSJHYGvrMwEALTZHDBFk+I4rx5igefesiUcAMVERgYQeIwMlTh4pwO80aSJ4RG77VVVA3D+yxscdneS0WIl7l0IqPZUZ0UaI09r966tzB7XfRUAfcGGQuLiEQgEAqFZOlTgURR1DkVR2RRFHaQo6oEW1k2nKEqnKGpoR8bT0Yic6NShM0WOor/SB1lBx+wVH6HvJRWh2KPMVSYCgFWwOo8+zjGc64T6v2bQfLIAmpao42bYMWFWOwDIh/I7pQ5Pqa4SAYCxWAM7eBzn0ZXQ1uBJ+7Mbh5zHxgZy8GRd1YiDd5oQ2S15h0/yxu1euSxR8riTzBGRpP6unawtXRvhVb3RKdaU07r+rq2M6xldG20Rdu4vd0xQtS7drJZAIBAIHUSHCTyKohgA8wCcC6A3gCspiurdzDoLgLsAtNhBrCtg5s0OAMhtyO0wF6zGW2MGgHAh/Bini6d5p6zKQTh4PoFiGOn442xEpB0AfKUlnVKHp9bXmwCACbcF4eCxkq6oIRV4cnFRJADwqSkt1+CxnARVJQ7eaUKPISN2AsCWxT9Mg64zkYlJROC1k6X5SzMBYEjsEOLgtZGhKeEr3bIa9+bKg5mdHQuBQCAQTj060sEbDuCgrut5uq7LAL4AcGEz654E8DwAbwfGclKw8TYHAJQ6SzvMBWuadRdtjD7GwRMYwSWrcmBh6fMJaE7gxcY4AECpqu4cgdfQ0CjwIiODcPB4T6hr8JTKqggAMPTt27KDx3GSTgTeaUO/M6cUMixXX1NceBYApPQfRAReO8muzc6kQCnTM6aTdNc2cvfknutoCr5FO8tImiaBQCAQTqAjBV4igKOn1BcfPnYEiqIGA0jSdX1xB8Zx0ogwRjgBoNJd2WEOnkNunHUXJ8YdI/AMrMGpaEpgB09RmnXw+ORkOwCo9fWdkqKpOZwiAHAxMYEFnsB7dVUNaRdNta42ChSlGAcMsLe4N8fJ0EiK5ukCzTAwR0bu1DWNp2jG03PEGeWdHVNXp8xVlhUmhOVGGiO79KzEziQzzuJOtBk35lY5x9k9PibwGQQCgUA4nWA7a2OKomgALwGYGcTa2QBmA0BERETykiVLXurY6NqG4BN4AHAXum9fUrnEGWh9W6BclA0AanfWPrxk95IjXTljpJjIPC3Ptnjx4pcoyv88pTijIYHWdeb4nyHl9VKpAAopXL1zyZKpHRF7S4SxjC0CwJq6ujnakiUtdhsNj4mNsO3ewy9ZtOgl0KF5RhHDMLGCxYKfly5t8XcrPDo63KbrzJKFC18C22kfH8JJxJDSw9pQUQ4hIgpLf/ttbmfH05VRdAV2rz19iGFIw6n6Pd5VODtOF9+rg3XOR7++c24yRQYHEwgEwulHg78XOvIOtQRA0lH/7nb4WBMWAH0BrDwsSOIALKQo6gJd1zcffSFd1+cDmA8AkZGROVOnTr23A+NuM6tLVkdgGT6sslV9N3XS1F86Yo/PFn92Ddy49KKpF93N0H89uP126bfTNY92/aDxgx5IMCfI/s4/8PQzz4CCPnXq1DlHH9dVFfsffuTb+JLSlaOnTv2oI2JvifxPPr3KA1xx9rRp99JGY4sCr/Cbby5yAbPO7NVrjpCe7gnF/jkvvPiwBj060O/Wkb379AnZ3oRTm2ybJXbRxtXvWMzib1OnTn23s+PpqlS4Kvhrf772PgVKj/TU9Femjpi6pbNj6spM0XT8/Pzyhxflewf365319Oxx3UnKK4FAIJxe/MffCx2ZorkJQAZFUWkURfEArgCwsOlFXdcbdF2P0nU9Vdf1VADrAZwg7roSGbYMJwA4fc6AqZJtxaN6RJZmXUeLOwAQOdEFAHkNeS2mhzamaLInpGhSDAOK4xya290pNXia12sEw3gCiTsAoAxGLwAoFZUhq8PTZdlEcXzgBi+CIAGAUlND6vBOEzJHj61IHzL8jeEXTF/U2bF0VXZV77LM+GnGk2WushFjEsfMf3DEg0TctROGpvDGNUNe4Vm64eVlB/6VV+UMado6gUAgELouHSbwdF1XANwOYCmAfQC+0nV9D0VRT1AUdUFH7duZxIqxMk3Rstvn7rA6NkmRzBzNnZD+aeEtTgAoc5a1XP+nqgLFnSjwAIDiebvm8XSKwNO9XpFi2YACCwBok9EDAEp1VegEnk8WKZ4Pov5PkAFAra0jAu804qJ/PfpL1hnjSf1dG/i98Pfom3696fkGqaHHRT0uev7NyW8SoRwiBibZHDeN7f6iW1ZjZ3246ZbOjodAIBAIpwYdOgdP1/Uluq731HU9Xdf1pw8fe1TX9YXNrJ3Qld27Jjiac3oUT8cJPFUSefpEpylMCGts8OKpbNE91FVVoFiueYEnCHZdktot8Ipuu31K2X8eG96acxodNC6gwAIA2mRqdPBq60L2xFr3KSaaD+zg0YJBAgDVbieNVgiEAHy699O0f6361wuSIoX/X///e/TJM55c29kx/d24b0rmnqEp4V/k17gn3r5g68TOjodAIBAInU+HCrzTEY7mHF7F22FdNGVVNnMMd4IQiTREugCgzlsXhIPXvMCjDYJDl6R2i1PXunWXOFeuPLc15+iyLAYt8MxmLwBoDQ0hdPB8IiUIgR08o1ECAM1uJw4egdACczfP7f/i5hefo0BpD4146N93DLpjT2fH9HflkxtHfBVl5nf/vKv81m+3Fid0djwEAoFA6FyIwAsxPMM7JLX9IskfsiaLAiOckKIZa4p1AkCD1NByDZ6qGvwKPKPJrsty+1M0fT5za51ATZZNVBAOGgAwFqsXAFSnMyQCT1dVQFVNtNEY2MEzGWUA0Fwu4uARCH54YNUD4z7a89FjRtZY+dKEl+6fkTmjsLNj+jtj5BnthUsHzKVp+B7/ac+/qp0SafFLIBAIpzFE4IUYgRGcQQ0cbyM+1WcWGOEEIZJkTQrY4OWwkBEo3o/AM5kcus9n1mTZ/5yFAOiqCl1RRK21qZ6KYgqmBg4AGJvNAwCaKzQCz1dcbICu01QwAk8UGx08t5sIPALhKFRNxfyd8zMv+fGSWxYfWnxfhCFi34fnfPjAuG7jajo7ttOBiVkxNZcPTXrZ7lG6X/vexhs6Ox4CgUAgdB7kKV+IMbAGR623NqOjrq/oimhkjSc4eGlhaW4AcPlcfsWlWlvLAaAoXmhe4FnMdgC0fPCgydC7d1Bu2vE0iaXWOoGHUySDc/DCw5tEVkhq8OT8fBEAaFEMKDBp0dzo4HncJEWTcNqjaio+2ftJ+i/5v4zNrc8d41W9MRQopXtY91/fn/L+W2SY+cnlqYv7bdpaWL9wb5n9gjnf79rx9MX9NnZ2TAQCgUA4+RCBF2KMrNHp03wdkqJZ5a7iNF3jjeyJTpOVt6oMxXi8itevg+erqBAAgBIEb3OvM9YwBwBIBw9a2yrwmsQSVNWo1NSwbGRwN3i6ophoQQhqrhwbE+0BAN3jDYnI8pWXiwDAmM0B3zNjsTSKyxDtTSB0Rb7M/jL5x4M/jj9Yf3CMR/HEU6DUGFPMtgkxEz6b3X/2hozwjKDceELo+fjG4R+eOfePvl9tLrprcq/YOydmxRAHlUAgEE4zSIpmiBFZ0aHpGl/hqgh5Ct+hhkMiAIiceIKDBwAszbo8ise/g3d4dhstNO/gMeHhdgDwlZS2uQ7PV1p2RGB69+0P6jpKfT0LTeODSZEEAC4hobHJitcbEgdPqa42AQBttQa8KWVsNgkAdEkiKZqE05IPd3+Y/vT6p1/dXb17upkzV0xOnvza5+d9fu2yGcueeGH8CyuIuOtcosyC8uj5vf+raeAe/mH3zZ0dD4FAIBBOPsTBCzFm3uwEgNyGXHOsGFsbymuXOEtEALDwlmaFEE/zTkmV/Dp4Sl19o4NnNDQr8NjYGDsAKJWVbRZ4SmXlEYEp5+dbMeaMgD8DOS+vUWCZTEHdGLLR0TIATZe8IanBU+vqRQBgw8MDO3jhtsYUTYk4eITTk4V5CydTFKXMO3Pe7DGJY0L6HUcIDdMHdyt9789Dv+4vt59TafdyMVaDr7NjIhAIBMLJgzh4IcbKWx0AUOQoCnmjlUp3o3iy8tZmhQjHcK6WGrxo9oZGB+9wq/8Tzk9MdACAUlfX5hRTpa7uiMBUysuCEoq+srJGgSeKQTl4FMMADCNpkhwagWe3mwCAiYoKKDDZ6JjDDp5MHDzCaYddtjP5Dflj4sX4jUTcndoMTQ3frung3119qFdnx0IgEAiEkwsReCEm3BDuAIBKd2XI6/BqvbVmAIg0RjaboskzvFPWZL8OnupwHBZ4pmYFnpDR0w4Amr2hzQ6eZrcfEZhKdU1wKZqVlY0Cy2IJOrWLYlmPLodG4GnOxs6jXHxcQIHJxsbIAKDLMnHwCKcd87bNG+jTfGGjEkb90dmxEFpm1hlpuylAWZ9XM6izYyEQCATCyYUIvBATY4pxAkCtpzbkDl6D1CACQLQxulmBJzCCy6f6/Dt4h+fG0WaxeYGX3t0NilJVp6vN4lR1/jWmQa2vP0Hg1Xvr2fv/uH+irP41ikGtrW1schIWuAauCYphvLrPF5IaPM3tMgEAn5oWeEwCz+ugKEX3+YjAI5x2rC5ZPY6lWNftA2/f0tmxEFomNUr0Rpr5/QU17oGdHUtXxykptEdWyf0SgUDoMpAvrBCTYE5wAEC9XB9yB8/hc5gBoJulW7NC5HAHT78OnuZqbO1Pm83NCjyKYUBxnENzu9scu+Zy/SXwHI4TrjN3y9zhv+T/cs+87fP6HlnX0NDo4EVEBN+5k2W9us8XGgfP4zGBojQuIb7Zn8sJMIyk+3wkRZNwWlHqLOVLnCWjUqwpq8n4g65B9yjz9gaPr/vWwran3Z/uqJqO0c/9/tJ5r/55R2fHQiAQCMFCBF6ISQ9Lbxw4LjtD/gfV7XOLANA9rHuzDp6BMbhUXTV5FE+z/181j0cAAMYa5lfIUDxv1z2eNqdo6h6PCIbxgmVdmst1wnVKHCUJAJBTl5PcdEy1OxqbnMTEtCZFM2QCT/d4zWBZF8Uwwe3dKPCIg0c4rXht22sjVF01TEyeSNIzuwjjM6O3A6A+XV8woLNj6ar8sL0kwe5RuudVu858+4/cHp0dD4FAIAQDEXghJsmS5KFAqS0NHG8rbsVtpila9vf0vGl8Ql59Y1fK49G9jZ0fm1r9NwclCHbN6217DZ7XK1Is66J43q653Sdcp9pbHQ8AZa6ypCPnuN0mAOASE4N28CiO80BRQuPgSZKJYtngW7szjKQrCnHwCKcVmys2jxcYoebWAbfu6exYCMEx64y0gyxNuXYWNZA6vDby47aSoQDA0pTrnT/zblA1vbNDIhAIhIAQgRdiGJoBR3MOj+IJuYPnVbwiS7PNuncAYOYbB3UXOgqbFZeaJAkAwEZH+RV4tCA4dElqu4MnSSLFcS6a5+16M0KxQWqIB4A6b90RB69J4PGpqUENOgcAimO9eogEXlPMQe/N0DKIg0c4jdhVvctS4aoYkm5LX8UzPLnD7SIYeUaLCzPsKK53DyTCpG3sL3cMNQts0diMqE+rnXK/J37aM6SzYyIQCIRAEIHXAXAM52xp4HhbkVTJzNH+hYiVtzoBoMJV0Wwdnt4k8GJi/Dt4RqNDl+W21+BJkpnieSdlMNg16USB5/K54gHALtuPOHi6xyOCpmXWZgu6rofiOK+uKCFpsqLLsoniuFY0eGFlXVWJg0c4bXhn5ztn6NCZad2nkfTMLkZWnGW716dFf7+tOLGzY+lq5Fe7DFVOqW9alLj5pcsGLjXxTNl320pmkoYrBALhVId8SXUAPM07JFUKuYMnqZLI07xfBy/c0Diou9pb3ay4bGrtf3hQeLPQJpNd9/msuqq2KUZdls0Uz7too8GuS/IxAq/CVcFLqhTJ0Zzdp/nCtlVuswKA5vW2LkUSAMXxXqhqaBw8n0+keD74Bi+NKZrEwSN0KTyKh15VvCryzR1vZj20+qExD/754Di7bA+q8HRH1Y7xIicWXZV1VV5Hx0kILRcMTNwOAIt3lpFumq1k/qq8AboOdkyPqM3hIq9M7Rf/scOrpNz71faJnR0bgUAgtATb2QH8HREYwelW3JGhvq5P9YkG1lDv7/Wm8QlN4xSOR5dlATQttdRMhDabHdB1Rs7PNwrp6UGnTB7Zw+cTaUEooE0muy4fK/BWFa+KA4BEc+KmfHv+mX8W/5k8KGbQ7tamSAIAJQhePXQCz0QLfGsavEjtSWMlEE4Gc1bPGbOtcttIp88Z7fF5oiVVitChH/NQr8BekLLgvAWftHSdFYUromq9tX2GxA75lKGDa0REOHW4YEBC+b+/2VmRXeEYCGBxZ8fTldiUXzuEoSnP7HHd9wHA89P7r/l9X8WB5fsrrylr8PwZH2b0+7CUQCAQOhPi4HUAAis4ZFUOeYqmrMlmgRH8OnjxYrwLAOyy3a+DRzFMi6MAmDCrHQCkAwfaJGB0n0+kDAYXLZrt0DTBV1p6JJVxb+3eeAAYHDt4IwAcqDuQdPgcY2tSJAGA4nkvNI1T7cE5EC3GrCgiJRhaUYPHyLpKmqwQTl1cPhe9JG/JbeWu8gE0aCVWjN3VN6rvNxOTJr5xZdaVj88ZMeeOtLC033ZV77r01a2v9m3pWp/u+3Q8AFyReQVJz+yidAs3bquwS/3tHh9R6EGiajqK6txD48MM28JFXgEAhqZw9YiUDyRFi7znyx3nd3aMBAKB4A/i4HUARtbo8Gm+kKdo+jSfKLCCXyGSFpbmBACXz9W8g6f4BAQSeLZwOwDIxSVWABWtiU+TZQqKYqKNBhdjbRSK3uxsK5eQUA0AxY7ieAC4rOdlu348+KOnqZOmLsutdvBog8ELAL7SUgNjtbbq3KPRVRVQFCNtDF7ggeMkXVFJiibhlOW9Xe/1VnRFvKTHJc8+Pvrxdc2tGZ0w+p0ZP83o88neT+6dkjrljsyIzGY/A/tq9423Cbbsc9LOadX3AeHUYWCSbXtOpfOc99cc6nn35J77OjuersCCDQUpXp8W1Tch7POjj983JXPPd1uLN27Or52xtbDu18HJ4Y7OipFAIBD8QRy8DsDEmpyqrhqDrW8JBlmVKUVTRBNr8uvgJZoTvRQoze1zN+/g+ZSADh4XE2MHAKWystUC1VdUZABA0yaTi4k4LBQLCo84gdWe6jiWZp19ovo4LbylqMZTk9wYl89E8cGnSAIAZTR4AECpqmqX0JILC40AaMpoak2KpgxNIw4e4ZRlTcmaYRQo5YY+N2z3tybZmuyrEwwGAAAgAElEQVSd3X/2XEmVwu9Zec+tqnZi3e2X2V8mO2RHar+ofis7Ml5Cx3LtqJSdAPQ/c6pJHV6QLNlVPhQArhqRvOX41+44M+MjRdMNj/yw+7KTHxmBQCAEhgi8DsDMmx0AkFOXE7I0zSJHo3gyska/ThNDM2Bp1ulRPH4cPEUA27LAYxMSHACg1ta2OkVTzi8QAYAWzS42OrpRKFaUH7lOg9SQIHJiGQCEC+GFDtnRJPDE1go82mjyAoBSXd2uTppyfv7hmE2tmcEn6Spx8AinLgWOguFRxqhdqWEtjx65sd+NOUNihywochSNe3D1gxOOf/3Hgz+Op0BpN/a7cXWHBUvocPp3szltJu7goWoXEXhBklPpGBJm5PLG9YyuPf61K4cnF/WIMS/bW2o/b9HO0tjOiI9AIBBaggi8DqBpXEGhvfl5dG0h355vBgAzZ25RiHA055JUqXkHT1EEimG9LZ0vdE9rFHgNDa138MrKzABAWy0uLiGxUeBV1xwReE6fM87KW8sAIFaMLZI12barepdFVxQTZWhFiiQA2tQo8NS6unY1WlHKK0wAwJgtwTt4HCeDCDzCKcqi3EXxLp8rMSsia2Mw69+Y/Ma3EYaIPb/m/3rrr/m/HrlZlVWZyqnLGR9tit42JHZIQ8dFTDgZpEaK2+pccmZ2ucPU2bGc6uwpaRBrnHLv7lHiZn9rHp3WewFFQfvvL9nXnszYCAQCIRiIwOsAbILNAQDl7vKQ1eGVu8pFALAKVr8pmkDjDD5/Ag+KIlAs26KDJ2RkuABomtPRagdPqa4WAYAND3fy6d3tAKA2NFgBoN5bz3oUT0yEIaIMANLC0goB4M9DK5KgqkbaYGiVg8dYzI0Cz+5on8CraYy5NXV8FMdJ0HVW83jI54dwyrEwd+FwALgk45JNwaw3skbt0VGPvgRAf2rDU/e6fC4aAN7d9W6WV/XGDI4ZvKoDwyWcJEanR27XAfqjtfn9OjuWU5331xwapAP0hMxovwJvXM/o2gFJtu8La93j3v4jt8dJDI9AIBACQm5QO4AoY5QTAGo8NSETeDWeGjMAhAlhLQoRnuZdsio3n6KpqgEFHm00ahTHuTSXu9Wxq7W1ZgBgIiOdhsxMFwBdczQKxdUlq6MB0HFiXBkADI8bXgQAhUW70wGANgVfAwcAtNXqAQDN0T6Bp9bVNzp44eHBO3g8LwGAUlnZZevwfjz4Y+KUb6Y8UOwoJk7k34wDdQeGmTlzweSUyZXBnnNm8plV56ad+0adt67XP5b9YwYALCtYNp6maPm2gbet77hoCSeLWWPS9tMUpK2FdSRNMwDbCuuHcAzlmD0u/UBL6/57af/vOIayv/Nn3g1OSWnV/VRZg4ef9eGmqaOe/f2R9Xk1tvZFTCAQCMdCBF4HkGBurGNrkBpClqJZ560Tgb9m3flDYASnvxENuqoK4LgWBR4AUDxv1zyeVjt4qt0uAgAXF+c6LBSdmstlBYDdNbvjASAtLK0MAMZ1G1fFUIy3oaY0DWi9wGNs4V4A0FyudtXgNcXMRkcF7+AJvAwASk1NlxV4X2R/cWapq3T0gv0LenV2LITQkV2bLdZ6a/ukhaUFlZ55NM+OfXZVsiV55bbKbVfO2z6vV749f0yCmLAhUB0foWsQZRaUGKthV1Gtmwi8FpAVjSqu8wxJsBm3GXlGa2ltRozFMzYjekG1U+435Mnf3pv+5torV2ZXRrR0Tk6lw3jl/PXTx/935bvL91feUtbgHfbGioPjWhun3eNjZn24aeqK/ZUhn7lLIBC6PkTgdQCp1lQHANhle8gcvKbZdrGm2BaFiMAITkVTmnXwoCiGQA4ecFjgeb2tFnia09ko8JKSXEeu43ZbAaDQXhgPAMNih5UBAM/wupkzF3kd9d0AgLa0XFt4PGzEYYHndrfLwdOcThMAcAmJQe9PCwYJANS6+i4r8IrsRQMAYG/N3p6dHQshdHy096NBOnRmbLexQaVnHs/LE19+S2CE6nd2vvOYT/NZRyWMIrPv/kb0jDFvd8lq4tI95dEduY9HVrvsvcUHaw6ly6pmG9DN5jc982jeuW7okqtHJD9lM/H5Wwrqrrjhg03vn/Hc8oee+GnPIFnRqKZ1m/NrrRfPW3PNuS//+f66vJrrw0Xu0G0T0x+yGNiCvWX2ka2N8/Gf9oxavr/ylps+3vz6PV9uH9va8wkEQnDsK7OLXfE7jczB6wDSbeluALrL5wqZg+f0Oc0AkGRJatHBM7JGl0/zmVVNBUMfO6VBV1WBCsbBMxjsmtPZ6s5gmtstAgCfluYGAEoQjgjFKk9VPEMx3iGxQ+qb1tsMtiLFXTIUAJiwsFY5eGxMTKPA83rbJ/CaYk5NaYWDJ8gAoNbXd8n0xn01+8QGuaEHAJQ6S0ntyN+IHZU7hnM0Z5/ZZ2aLqWX+yAjPcM/sO3PuWzveepalWeedg+7cGuoYCZ3HOX3jtq/KqcZ3W4sHTukT91sor61qOh5buGfYj9tLrnFKSmK0RdjTI8a8/axesduuHZWaz9BUi+c7JYVeuqc8TmBpdVr/hE6bufjb3oqhAPTrRqUE9bvP0BSevrjfxqcvxsZFO0tj31mVN2VfmeOs99fkj/xiU1F5/262pQ0eX0R2uX2KpoPrFm5cd92o1K9nj+ueCwBrc2vWbyusn7G1sM7Smpl6Gw/VjuYZukEUmPLvt5Xcv7WwbsQbVw1+s09iy2UcBAKhdcz6cNO9blmN2PGfs+/p7FhaAxF4HQDP8DpLs0630vo6Nn+4fW4RgH5YPPrFyBmdOnS2wl3BJ5gT5GNe1DShqX6sJWiDwaHW1rb6xl/zeEQwjIcRRQ04LPAOC8V6b328iTOVHy06Y02xRT7p0CQAYMLDW/VHiUtM9ACA3l6B5/GYQFEqGxsrB17dCG087OA5HF1S4H2R/UV/AJSRNVbUelv//5lwauLyuehSV+nQbuZuG4ysscXUspa4beBt+w7WH3ydpVjVZrApoYyR0LlcPiy58PGf9tbuK3MMAhAygffi0uw+CzYWXlfrknuZeKYsI9ayrKTO03fNwZob1hysueGZJfvrE2yG7X0SwrZfNChxZ5XDa9paWJ+UX+1KqnRIyXVuuZtTUrrpOliagmQW2JsnZMacMJ6gJVRNx66SBsvAJFu7Bo/nVjmHhpu4A0NTI+ytPXda/4SKaf0TPq52SgueWbJv5KoDVeeuz6u5ngLUtGhx5S3j07+5bGhSydHnnNs3fv22wvrL3/vz0LDBV4cvD2afkjoPX1LvGZYRY/n923+Mnj/rg02XbsqvvXL6W2t7Xzcq9eWHpvba2dx5qqbj/TWHui/cXjquoMY1aFr/hAXPXNJvQ2vfJ4FwOuHwKvE2E1fY2XG0FiLwOgie5p1exRs6gae4zSzFunmG11ta1zRGIa8hz5xgTjjyB1K12xnoOhOUwDOZHLrPZ9FVFRQT/Kx23es1Uxx3RKjRRqNdra3NAACHzxEfxocd8wFJDUstLJEa+zew0dGtrMGzKaAoTZPaJ/B0j0ekWNbdmvdJG00SAGguZ5dM0dxVtWsAQzGeflH9ft5YvnHmxrKNtuHxw+sDn0k4lflg9wdZiqaYB8YMbFN65tH8b8L/loUiJsKpBUNTSLAZtxfXuYfJikbxLN3i35NAvLc6r/v8VYeurbB7hwgsXXtWr9g35l424DerkVMBYGV2ZcTXm4sH7iltGFha7x2YX+OesHhX2dGX0E08UxFm5IrSosQtUWahakV25f89tXjfVRMyY15vTSwXvr561t4y+3lzzut1/41juue15f1sPFQbVuf2ZQxLDV/QlvObiDILykuXDVwNYPXCHaVxNiMnNzdPDwBuHJOW+9Jv2dXbi+pHAghK4L36e84QTYcwISt6rVlgta9uGfXV23/kbn11ec4/56/Ke2rNweofP5g57OMYq8EHAJ9vLEz6enPR2OxyxziXrCZQgMqzdP3nmwr/LQrs43PO67WjPe83GGRFo+78fNvklQcqr5yUFfvZG1cP/r2j9yQQ2otHVmmXrMSlRYmtrmvvbIjA6yB4hnf4HVfQBiRVElmGbTE9EwDMvNkJAGXOMjOAI39QfOUVAgDQghBY4JnNdmga7ystE/ikbgHXN6FLkkhx3JEYaZPJrsuy1S05abfPHZdqTT3mAzIsdlhhnfQFAICLjWuVwKMYBmAYjy7J7WqyokmSCJZt7Qw+GQA0l6tLOnglzpIBUcaoPYNjB+/bWL4Rvxb8mjE8fni7RQGhc1ldsno4BUqZ2Wfmts6OhXDq0i8xbPuhatekD9fmd29KE2wt324tTnhlWc41hbXuMRxDOUanR34w97IBi+PDjMdkQkzIjKmdkBmzHMByWdGoBRsKUv44UNXHauQcA7rZis7tF1dy/Dnnvfpn/J5S+/lfbir84fJhycXBxLNgQ0HynlL7BTpA/++3nPvO6RN/d2K4MeisjCY+WZc/GAB1Vu+4oOrvguGCAQnlLb3O0BRSI8UNByocZ5XUefhg4l5/qOYMjqHsd0zK2N107Obx6Qen9U+468aPNs3cU2q/cNLcPwZlxlnWHKhwjHJ4lVQAWrRF2DUmI+rb2yb2WAcAV7+74Zn3Vx+aE2ZkH7l9UkZ2e9+rP95ffSjt9RUHb611yVkA9C0FteMAEIFHOOVZfbA6QtfBxoUZygKvPrXockWDXYXDAi9kDp6kSiJP8wGFiE2wOQGg0lN5TKMVtbpaAAAqCIHHWCx2AJAOHGhV/Josicc4eGazHbrObtq1NF6HzsaYYo75gIxPGl9l9lIq0LoauCYohvHqstw+B0+WTRTHtW5Eg8Xc6OC5PV3Owfuj6I8ot+JOTLel77gw/cI8AFp2bXZGZ8dFaD/59vzhUcao3em2dNL1kuCXK4YnbQeA5fsr29RN850/87rf9/WO10vqPEMHJdu+XHTH2JsW3DTy++OF2vHwLK3PPCMt/4Mbhi9+5YpBq2aNSTvU3DlPXtT3K5amvK/8fvD6YOJRNR3/W5ZzE0NTnumDE//rlJTEWR9umtWW97ajuGEIz9L1M0entskBbCtjM6LXazr4eSsODgq0ttLu5YrrPMNSI8V1ZoE9JhU7Mdwo/3L3uPmzzkj9j0/VzFsK6q7kGNozMTP67QU3jZi5ac7kR96+duhv/bvZnP272ZxvXzvkUYGj617+Pec/n6zLTwn1+8oud5jOfWXV7CcW7f2f06vEn9cv/uWsOMviKofUp84lE4OBcMqztaCusQN8lEgEHqERA2PwO66gLciqbOZoLqCDF2GIcAFAnbfumL2VutpGB89gCCzwIsLtACAXFrSqk6Yuy2ZKEI7EyIRZ7QCQm7MhHQBSw1KP+YAYWaMW5WUdKg20pgauCYplvbrP116BJ1I81zoHz2yRAUD3eLqcg7cwd+EAAJjQbcKObpZukpkzF5W5ykgdXgeSU5dj8iieDv2uXZy3ON7lc3XLjMjscmkkhJPL6PSoeouBzc+tcgYUE8fjlBT69eUH7+AY2vnRjcNv/v4fZ3yWGWdp1QOyQAxODncMSQn/trTeM+LlZQcCjnGZ8/2ukVUOacDYjKhP5142cHW/xLDvsyscU+d8v2t4a/Z1Sgpd2uAZ3C3cuKW9qaut5Y5JPXazNOXacKhmRKC1Ly/LGaxqunFcz+g1/tY8en6fbb/dO/7mBTeNuG7rI2f9+4Mbhi8enR51Qhr+6PSo+rmXDXiEoSj5qcX7nli4ozSuve8FaBTdd36+bcL5r69+a1+Z47zMOMsv3/9j9C3zrh68fFCybYemg39/zSHSwZlwypNX7YwDgEHJthad+FMRIvA6CANrcPg0X8gcPFmVRYERAgqROFOcEzhxBp/a0NDo4BkDCzw2MsoBAEplZSsFnk+khb9iZMIbi9TrK4qSAWBA9IATnoBEeViXh4femhq4vwJl2i/wfD4TxfGtq/+zWhtTNCVvlxN4+2v3D+Bpvn56z+kFABBljDpY563roWpqZ4f2t+TBPx8ce+lPl3581eKrZnfkPgtzFw4DgAvTLySptoSApESYtlc7pd4lda3LQrj10y3nN3h86RcMSHh7TI+ouo6K74UZAxYKLF378bqCmarmX2uVNXj4H7eX3mgxsAWvXTX4ZwD44IZhn4YZubyvNhfdufpgdXiwe76zKi9TUXXz4OTwkKVnBovVyKndwo2bCmvdIwINTF+bWz2aYyjHXWdm7GppXXKESWpO1B3PuX3jKx+7oM8jmq6z//5m55OB5vj5Y09Jg/jGyoMZd32+bfyIZ5Y9s3BH6b0iz1Q+NLXXvUvvHvdWU3fPa0ak7Aagr8ut6d+WfQiEk0lZgzeBApQJPWOqOzuW1kIEXgdhYk1ORVPEUD2592k+czACL9HSOM/N6XMek6KpNdgNwF8NQlqCS0ywA4BSXdM6gacoImUwHImRi4mxA4C3viaBAqWMThhdc/w5ER5GdhpA5dbntrqWjmI5j660W+CJtEEI6IweDRMRLgGALkldKkVT1VSUu8oHxIqxO5ua9SRbk3NkTbatKl4V1dnx/Z3wKB76ykVXXr8ob9H9FCg1tz73nJ8P/RySp+PNcaDuwDAzZy48J+2cTmsvT+g6DE+L2KbrYB/5cffkYM/5eXdZzJqD1dfEhxk2PT+9v1/3KBQkR5ikcT2jP691yb0e+WG3X1frrs+3X+TxqbHXjEiZ35SuGGUWlIemZr2oarrhn19tv/voWXQt8ceBqqEUoN00tnun1LAOTY3Y4FN1y1src/26ltVOiS2u84xIjjCtb2pkEwquHJ5cdN/Zmf+RVc16+4JtT2wvqm/24bSq6fh5d1nMv7/ZOeryt9fNmPTiyrsGP/nb8xlzlnx63murP//vL9lzf9xR+s8Gjy/lrN6x8zY8NPn+4+s8+ySGucKMXO6hateAUMVPIHQUdS45zsQzFUaeaXNn6s6CCLwOQuREBwAqrz7PFIrrKZoiGlhDQCGSbkt3AcDxM/g0l7MxRVMUAwo8PrVxULtqbwjagdRkmYKimGij8S+B1y3RDgCq0xlr4kwVzbVuD/NQikcAlhcuTwp2ryYaUzSV9gk8RTFRgqF1M/giI5sEXoc6eC6fi75z+Z3nfLH/i5DURnyX812yrMnhmeGZRzqmDYoZlAMAK4tXkjq8EJFdmy1O+37aI7trdk/vYevxy9wJc2+nKEqZt33eFR2xX05djqnGU9M31ZpK3DtCUNw/JWtHrFXYtmJ/5U3P/rwvoJPSOONu760UKP2JC/u+GWimXSj43+UDfxMFpmThjtLrmnO1ft9XEbW5oPaypHDjmn+fm3WMm3X5sOTiSVkx71bYpUE3fbx5WjD75VU5h0aa+b2hTjkNln9MSN9KUVBWZFf6HXr+6u85AxVNN43NiF4b6v1vHp9+8OZx3Z90y0r8zA82PpZX5TSu2F8Z+eB3O0dcPG/NNSOeWfZY1iM/f3brp1vf/XJz0YMbDtVeW1LvGURTUFMjxXWj0yM/uGp48tPPXtLvtnUPnnn9O9cNXeov1TU5wrSz1iVnFta6u1wWDOH0wu5V4i0GrsulZwKki2aHYeEtTgDIt+db+kT18SvMNi0+lLV/XdkF/q9EaZnjo35QddVgZI0BHTwrb1UZivF6Fe+xDp6r8YuUNpsDCjyhVy8HAGgOZ9ACT87LMwGgaJPpSIxCjx52AKDcnggzF9ls0brZC5SYgH21+5IAtGo4M8VxXjidbXZFjojSo1zHYGCjoxtTNGVfhzl4q0tWR8xZPee+Wm9t39Ulqx0mznT/BekXlLbnmr8X/j4AAKalT9vedOzC9AvzX936qppTl9MDwLp2hn3a813Od92e3/j8wx7FEzsxaeIbr0569RcAyArPWry3du+F3+d8//XFGReXBLpOa/hwz4eDdejMmG5jSP0dISiMPKN9PGvE89PfXPvf9/489GBGjPm+S4ck+f29vO/rHWMr7N4h43tGv3NW79iTkqpkFljt/P4JH32xqeihf361/cy3rx16zNy+x3/aewMAPDyt9/vNnf/2tUN/Gfv88qGrcqpu+Ght/q7rR6fm+9trZXZlhN2rpI1Oj/wwlO+hNXSPNntirYbth6pdI1RNf685Eb36YPUZLE257jozo0PGGvzrnKzdDq/y3KfrCx6a/NIfn2g6eACgAM1iYAtSIsT1KVGmg0NSwg+e0yeuuHu0uU0NnYalhu/YVdJwyUdr83s9Mq339sBnEAgnH1XT4ZSU+ASbYW9nx9IWiIPXQdiExmGrZa6yFhutHNhYMcVe4x3pcfpSmvvPUeMZtfuPkhkAIHJiUKmELM06PYrnWAfvcEMQxmoJ3GRFFDWwrEtzuYJO0ZQLC0UAoM1/xcj36OEGRWmM1xcWbgj/f/bOMzyqauvj65Q50yeTSTLphRRCICSE3lvoIEWsFDtWsF9UVBQQC6JSvMLFqyCIIL2qdIEQAiGBFEjvPdP7zKnvhxBMSJs0kffO7wvPc8re+0yGmfnvtdZ/NetAxHOwPDuBcBXmiiBn56oH4fHsHN3xCB5VViYAAAQVCdtbg8cAgjAcSXbL7uOnVz4d8OrZVzcaHIbwYb7DfkAAYVYlrVqRVJnkdD1JcxQYCmKFuLAqPiheVX/MS+RFSQlpSbWl2hXB6ySrklYNWnl55Vc0S4teiH1hWb24AwB4d8i7+zEEI7ekb5nX1fOmqdIG4Shueib6mW6zOXfx/49IH6l1zUMxq1AEoZcfvrm8pbS89HK95Hh61SK5kJe3ecGA43/nGlfP6ZukEBPZf+ao5jesF1x7IqdPqdY6Ki7Iff/kPj6q5u7FUAQ2PB63gcBQ09oTOW9XGRrXGzIsB5v+LIiY+W3Cky/9nPoFAMCMGN+/vf6uITH+bklWkvH55UpJk6wNnYXESzXWoYEK0RV3MUF31xpWzY5OXjgseHWYl+Tc+F7KzW9P6vmvxPfGP5L+8eTXTr81ZuMPTw76/eWx4XkdFXcAAE+N6HELAWCSi7X3fZomw3Kw5UJhWGu1oi7uT66X6mQMywmV0vuvRQKAK4LXbXgK64xK1DZ1q1Ewh41WCsW8vGfWjnqnufO/rEh6RVdjGYsxPJAQEqciTTyUZ3EwjsYRPPttgefubndmDJQgDKzFInfmWgAAurpaDACAyWR/tUkgCI7j4WaxjZZ5Cj2bD3HTtJAlcIvGpml/iiZB2DmG6bDAI4tL6kSpSNzuFg2AoiRHdW0Ez0gasedPPr/wpubmg1JCWvzOoHe+mBU+q2Jb5rbMdanrPnvz/Jsf/zTlp/ci3CPanUJkJI2Yyqrq28Otx/m7z3kJvXJLjCWjGJYBDO2A2c3/OAzLwLMnn30kpSZlvoyQFX468tPVYwLHNIpyxCnjjL09eh/NUGc8vDt7957Hej1W0hVz22gbWmmuHOgn9rvWXAq0CxetMa2vb01ejWn1+jN5q5/7Kfm9P14fvdxTwm8kHt78Ne1pimFlr0/otfzvrkPBUATmDwnatvFs/udv7b0xc/fzw/bZSAbdfrn4eQEPVW14LG5/a/cPDFEY5w8JWvfjpeIVz2xLfurYklFbNp8v6Pl7ZtWIglrLCBvFKBEARikT3JgR47t13pDg0r/r2ZrjqREhV0/equGOpFUOXTgspNFnxIazeTE0y4lHhHl0a/0jAMDKWdHXAKDbxG6QQuRQiImcUq31vjdaee9A+vA918rfrdTbVnw8s0/KvV6Pi64jqVDjCwAQpBDdlymargheN+EjrnOz1Nv1rUbwKDutJIR4i8YIwX08LgGL8AP1UeBGuDkVwSMwokmLBs5eVy+GKxRONS5HBAIda7M5L/DU6jqBJ5c3EkuUALdJrQBB0qBmd0A4ihKjBGE0OAxORfBUVhWv3vER4fNt0AmBR9XUiVJU2v6aCwTDHBxFdVkE70zpGa/pB6Z/flNz88FwefjvB2cefHtW+KwKAICnop8qmBc171MzaQ5adHLR+xqbpt0bMzuzdkYwHCOM9oxukg4T7BacT3O0+I/iP3y74ln+13j5zMuzUmpSFgRJg84fmHngnbvFXT3vD3n/IIZg1q2ZW7ssirc1c2skxVLSfsp+rvo7Fx3itQk9sx+I9VuvNpPRj2y+/ErDSMSXJ7Kj81XmiX0D3A4+NaJH8b1Y31uTIm/5uQmuXivWPXSjTC9dsit1ktFO93ggxu8HZ5qCL3+gz/U+frLDWVWmGb2X/7H1yxM5a29WGB9wE/FKp/X1XX948YiFV5bFr/jy4dgur2trL8PDPPUKMZGTW2NuUod3MVc9AkMR26vxEf8vUhpDPMXpeisVllVlFLd99T+XC3nqCQAAf+bUjr7Xa3HRteTWmH0BAPoGuHWqPOZe4RJ43USANMAMAGAkjS1G8Eg7jdIk6yWQ8GpbumbQjB7pHMZYwzT9wF3g7lSkicAIC8mSjSN4tw1BcC8vpwQeKhLqObvdaYHH6Or67mEeno3WaBWitNTGQR/PPk0EHscwAAwjInhCrZ2xK0uNpa2Kte/Tv+8Zvzd+7+Cdg7fH743/KEFS1Qs4Dr+Q9bt3R2z+GY1GBNA46ug0GOYAumsE3orEFUPe/vPtDSbSFDQzbOaag7MObvIWN+4LuHTQ0htTe0xdp7Fr+s7/bf6bJEO2y+UgqTIpFgC4RyIfaWKtPdB7YD4AQEJFgqsfXjthWAZu1N6YphAobh2ZfeTru/9uDenj2ccc4xVzqNJSOWxb5rawrpj/YvnFwQggzFN9nkrtivFc/G+y/rG4CwOC3XcVqi3x8/97ZS5AXUPtbYnFr4gIrHrT/AG77uX6Xh4Xvp2uc8V8+nyuaqGnhMj4fG6M04Js61ODtnvLBCkKMVE4I8b3m99eG7Uw6b34ld/N738mJkDeLhfl7qaXjzTJYKPCzmTV3HE2NtoorERrGRrgLryqlAmoe7m+rmJoqCIdANCfk0r63Ou1dJTEArW8xmDvjyJAlbDiEpwAACAASURBVOlsQ2uNdt69XpOLrqNSb/MBAG58L+V96U7tEnjdRIR7hBkAwEy1bFRSnq3zAABU7MZv8c1DCHDW4WXIC9FFgwendKoZOB/jmymGahzBI28LPKVzY6AisZ51OJyu+WKMdbtwPD/fRl+WRhHCyawAo/1HNxGxVHm5ADgOFRMSFQDAmdIzAa3NsSd3z8MYiln8JH4pNsrmkSnSRAMALD3/r+8H7Ry0I35v/MfvXXxvlNNr1unroo4K945F8Gi6Uyma2zK3hU3ZP+WdfXn73hfzxFWfjfrstdUjVye0dP0Xo784P9R36I8V5oqRjx9/fFF7RG2xsThWRsgK+3r2Nd19blb4rBIUUKpQX+gSeO1kc/rmKCtt9R3kM+ikM+mtHw798AiO4uafs36e3xXzFxuLB3kIPW52JG3XhYuG7Hlh2K5gD9GFpELNk0v3pQ1/aWfqwxYH4z9vSNB3zkTKupMFQ4NLw7zEZwtUlgkUw4lfi4/Y0h4nT6VMQF1ZFr/i8nvxq76d1/9clG8HNvX+JuYOCEgCAPg5qeROe4iNZ/OjKYaTDg/zaPH74X7j6RE9slEEyOul+vs2TfO7cwVjOQB0VITXVoblhN+czu1/r9fkouvQWEhfAQ/V3J22fr/gEnjdxG03S9vdZicNqSk2egMAyLwELUbwAAB0gcUlPJYPyA2vUGfmFmACC8VSjSJ4nIPkA4pSqNC5Oh1UKtUDTYtpjXPpgKy5ri0DERTU6ItTK+EwNyvCygXyJv9ByNJSEQCAQqCoAgC4pbnVYh3egbwDAdWW6iHRHtHHjs45uj5xXuKri5DRmwAAJokG7AiQBlw1OAzBJ4tPOi18GJNRBACAK5UdieCRHEW3O4LHsAx8nfJ1zPg941d+lfLVN9WW6rhYr9jdh2cfXjq1x9Q287y/n/T9od6K3gdzdbkzFp1aNNeZOctN5XydXdcrUBrYbGqPjJAxMr6ssNrqMlppLyeKTkzEEMz2ev/XnaqLiXCPsMYp4/bXWGsG/iftP5EdmdNG29CTxSe9P0j4YISZMgf1dO/pcs900WkwFIG9LwxbrxAT2ftSyt9MLdE91MNTfO6D6f8Ml8MPpvfeiaOItY+f7Ojd9Wn/n5jbP6BSwsfLMyuNd9I0z+fWjsAQxP5qfMQ96dHXHXhK+LSnhH+rTHd/1uExLAdp5fp4dxEv99/z+//GwxBTYoHG6Q1mF/98DDbKV8rn3ZfpmQAugdet8FCeyUbbWozgGWpt3gAAyiBZq+HfKt9svQ03g7UAjXNmXiEuNDMcI2rYZJ2jKD6gqFPpmQAAJne+HQDAlp7uVJoma7WKAYAjQkMbRRLUYoYvsXEoxzQVXXRNjQgAwE/sW4UAQpeZylqsw9t2c9scFEHJtwe9faz+mEhUV5P4puiBy0dmH9k4zG/YryRLyk+VnPJ2as3mumbwPF/fdgs8BMNIjmGcjuCRDIl8lPjRsDF7xny1NXPrJwaHIXiwz+Btex/Y+8zP037+xUPo4fQO0Y5pO7YFSYP+TK5OfvKtP9+Kb+v6n7N+7sMBh/f37t+itbZSpMwzOAxhDd8zLlqn1FgqKDWVjgyWBScESAOc/r/18bCPj/NQnmF3zu4FbV27N2dv0Bvn3pj42LHHnpy0b9KyYb8M+3bIziH73jr/1veHCw6/gwBCz42Ye6VzT+LCRR1KmYDavGDAaj6O6XAMsX35UMwP93pN9YzrpdQcf3XUM0cWj/zHrKm7CPMSJ6lNjuibFQaxjWTQYrV1mL+7MNnX7d5GUruaUC9xuslOh1wt0rrd67W0lx8vFYWa7HRw/2D3MxI+zoZ4iC+Va22DG7q9uri/MdtpHzfR/dkDD8Al8LoVAiPMdtreYgTPYnAoAYALjFK02lfIylrExYp01qqlBpp19jY/PES8ul50DZuscxTFRzDMqR+hFsqC/lue8ggAgCO/wKk0TdZmEwOOW1GCaOQVXCOixSgH4MjLa1JITavqjFn4MnezmCeu0Ng0zQq8hIoERbGheFy4PPxkrFfsnRRDVCRyAADQOr0QAGCE34gsAIAzpWd6O7Vmi1UEAECEhLQ/RRPHHcAwbUbwGJaBpReWjh396+jvDuQdeI9kSMm4wHH//mPuH8/9MPmHAx1JrSMwgts5becGL6HXjVMlp5asvba2Vavp6zXXYxFA6AVRC1rs5dLDrUc+wzHC34t+92vvev4pWCgLWmmu/Nu+XNelrhvBcIxgSo8pp9q++i+CZEH2gd4D96lt6tgNqRuim7tmZ9bOkEn7Ji1bmbTy29Olp5fc0tyaZSSN/lJCWtnbo/fhCUETNr4Q88K7O6fvfHJSyKT7sj7AxT+TwT0Uhl3PD33zu/kDXhsYojDe6/U0JNJHav07mqzfayb18UniALAtFwoHbjyb15tkWLchPRTd7p75dzMqwisdAODX5NK+93ot7eVAakU8ggD9WnzERQCA8VHKBIbjBOvP5A6812tz0XkKVWYhybByTwn/vmyRAOASeN0KgREmkiFbjODZzZQS5yFaNjdD1NI1AAB2xi4u9bxp5VgQXD1WNKCteeubrJeaSu+Iy/YIvC3pW/rUiGgpAABdWelUBI+z28UIjjeKhGVpssRaEcsHAHDk5TfpqcfodHUmJ+5yi5wvLzOQhmZTNL+9/u1MAEBfiHnhcMPjqLSuDw9rMvIBAGaFzyrDEMyar8/v5cyaWZtNDAhC1zcubxcYRnJ06ymaGpsGn3N4zqu/F/3+JoZgjplhM9ecf/T8SxvGbzjhJfLqVKG8XCCnf5z842cSnqR0562d7x7IO9Bi/WK5uTxWIVBk+Un8WnzOIT5D8gEALldebjNNk9br8Yq33h7Lku0zeukujKQRe+PcGxPH/jr2+9mHZ3+rsqr+lkL3lJqUiWKeuOL5vs9nt/feVSNW/U5ghHZ/3v75DVOKd2fvDp68b/K7n1/9fIPKquobp4z7Zc3oNc9fnnf5oaR5Sa+cfOjkp7tn7P7pm3HfnFoct/hWczWVLlx0ln6BctPf1dDcRVMWjQrN4+OoNrVUN/RMdu1IFAFySXzE/zsL/ieHh+TjKGLNqDDcV2maOguJ59eaxgTIhVfqTXqWjI/IJDBUf7lAM/Jer89F5/kzR+UDABDoLnQJPBdN4WN8E8m2LPAcVtpbYNewpc88+01r49hpu0SlKDGgGGKsyNGPaGteKSG1AADUWGruRM04muYD7pzAu1hxcXh9cwezusrDmXtYh0OMELxGAi+pKsnXdFu6UuXlTQXebWMWXOFh9RJ5ldpom/fdEZgcbY44W5s9NUAakHB3pAKTudkBAFiTWQgAIMSFrIfQI7vGUhPlzJpvi1IrgrW/99vtCF6L0aKUmhS3B488+EmRsWhCP69+u889cu6N1SNXJ7S3VxlVWUmwtubTJkPcQmyrRq5ahSIo9fnVz5enqdKavNeu116XGUljaIhbSIvpmQAAM8JmlGMIZi80FLYp8Ko/XjHaePz4m5otW5wS0t2Fjbah71x4Z8zEvRO/O116egmO4jYbbfP58NKHU7p77iMFR/y0dm3vPh59TnWkd6C32Jsc6jt0j9au7fN1ytf99ubsDZqyf8rS1VdWb6yx1vTr59Vv9+4Zu5/bPnX77qk9plaLeWJXjzsXLv5HIHCUC/YQXa3U2wcUqS3D/eTCa0EKkdNp4PcLEj7OKqX8zAqd7b4SeGtOZA+gGE42NlJ5tv6YhI+zPbzEiRV62+BSrbXLWii5uDdkVxt9AQB6+UpdKZoumiLEhU360TWEctBKoV3DcXa7F63Xt2hm4mAcYhzHzHKlKNGktQ1uK01TIVCYAQDUdvVfc9M0H8HwNpuckwyJlBhKhlskuAkAoNJY7pSxC0eSYoRHNBJ4ubpcX5OwLshD19Y2FXjmOvmH+3hbg6RBZQCA3O2kuSZ5zRSGY4SP93r8wN33Y/I6gcdYLHfaKwRIArJMlCk4R5vTZm8d1uEQA4/XITc1hMdztFSDtytrV8iLp178Su/Qhz8Q9sCaHdN2/EJgjVNXnYElSaRg2vTNZS++NKela+KD4lUvxr74iYNxKF49+2qTHnl7c/fGAACM8BvRqlGCEBeybny3glprbZsCz5GVFQsAQFVUKpx7kq6FYRlYfmn58PF7xm/4rei3tzAUczzc8+FPEh5LWOwp9ExPrk5+uNxU3q1fsLuyd01AAGEX9V10rqNjrBy+8qQAE6h3Ze/618qklRurLdUDYjxj9uyctvO5HdN2/BKpiPzHOv25cOGiexkR7pnEcJyApFn5oJD/f+mZ9YQrJekWkvFr2Bbin86FXHU8gaP6f02ObNSeZkKUMoHlgFh3OndQV8yTV2sSHkmr9GnYm/J+xkYy6MBPTn064evzi9VmR7v7+XZm3snrLry48Icrs5y9p1xn8wEAGN3T676N4P1tL/D/IkJcaKJZWsqwDNy9y++wUhhNsp5Cm0oNAGDPyJBJRo3SNjcOyZISPsY3BUd7JGirLFOuHisaMH5h1OWW5vUU1PWiMzgMjSJ4CI63uQP4fcb3vUiWdB8aMHSrlZ/wtMmqb7V1wZ3xSVKCSiQ1AAA/vXfpA0KIq6sGV2lNwrrztE7bROBx1ts1cEFBllg6tuxwwWHIUGcEAUAhQF1T8xu1N2Z6Cb1uLOy9sPDu+zEPD/vtce4IvBivmKzU2lTkUP6hnu8MfqdVxzHO4RAhPLxD9vIIjpMcyzYRESsurxhyMO/gWziKW1/v//q7T0c/nd+R8QEAjL/97svZ7Z5UZWWP1q5bFLMot9hYvO5IwZGlT/7x5JLDsw5/U/9+u6W+FYshmHVe1Lw21+Et8s7P0eVMNZJGTEbImrUi5RgGqOrqWAAAWqNxuo2Gs3x46cPhN2pvDCIwwibABFYBLrAJcaFVxBPZJDyJ1Ubb+BfKL8w1ksZQMU9cPjNs5pqPhn10qV5APxL5yM/f3fhuzfLE5TN+nPzj/q5eH0BdrV+eLm+8t8g7ZajfUF1Hx/EQetATgids+6P4jxf7evbd996Q9w65Ui5duHABAPDKuPD07ZdLbBzH4UvGh1+71+vpLuKjvNMv5KnhQGpFTHyU99m277i3pJbqpJV628A+frJjMiGv0ffk4nERt/57sUh7tUg7EgA63dLi6a3Jr5XrbMP/tTdN6+smSOvt55Y+J84/7X5Nn/7wcOYwtZmMVpvJ6EnfXAjcsnDA6u6u82VYDmZ+m/BSXq15cpnWWgUAh9u8CQBUJocvgaGGCKXU1p3r605cAq8bEfPEZg44tNhYLAyThzV6k5Rl6TwBABVaanAAALKoyA1aEHgUQ4llhKxq4LSQzLSzZcaKHN1IAGhR4PlJ/OqbrP9Vg8cwfEQgaPM/0p9lfw5HAaXeH/L+H0WiaU/SdouXM8/KUZQEFQgKSDuNWvSOOLPegVi0jkRSwtcBWN0Zg7GJwGOtNhEAcLzAQPtE1qNyVdIqptxUfqcOb/WV1WNJlnSfHjr9q+bm5Hl719Xg2e13BN7DPR/O/enmT2yGOiMKAFoXeBTVJOroLAjBa2SywrAMLDq56KHkmuSFbny3/DWj16we7je82b+ns1gSE8MAAFhL23+D1SNXJ1SYK/xSalIWPHvy2YptU7btAQCotFT2U4qUGc6k+IXJw/KytFmzjhYcDZwfNb+4uWsMBw8GcA6HAgCA0eu7VOC9evbVKefKzr3MQ3kmjuNQmqOF0EyWgRAX1kwJmfLNyhErzzdMedXvP+D/YL5RsD/U+9r12utzc7Q5f3RHFGx96vo4B+NQjPAfsbmzY3026rMLn4367EJXrMuFCxf/f/CU8Ol+gfJDNMPyQr0k9+2PzLZ4fHBQySfHbxlvVRljAOAfL/A2nMkbwwHgDw8MbLJWIYGxoV7ixJxq06RClVnYmb9bXq1JWKm3DVJK+TcEPMxUqbf3L9ZYx/2WUQUiAqvylwvTYwLc0j57MOYSgaP/+BAfw3Jw+lbNgyICq5rcx+fnwzcqXlv4w9Wvlj/Qe+Xjg4PKumveOd9dejKv1jxZTGAVFpLxTy3VSfsHube5kaq3Ub5iPnbfpmcCuARetyIhJCYAgCJDkfRugacqNSoBAISmKgEAAFVR2aJNMMVSYj7GtxACnJUrRYm6GstYk8ZOSD0EzZpmBMuCLQAAFsryV5oiTQvaiuAxLAPFhuIRSrEyNcQtxJYjxK2ojXTOZIWixIhAYM5LrvHnOOABAChLInvV9qioBMQmZc3mpgLPbr/jvCkHghbxRJVqmzoIoC5V9HLl5QdlhKzg9f6vpzc3J+7n5wAA4Bx2Yf2xIFmQXUpIiyrMFW06aXIUKcKksg71OEF4BAksS3AMA2qHlvfUH08tKTWVjg2UBl7YOnnrBm+xd6ftrMnCgjqBZ7U6lbryw6Qf9sw8NNM/pSZlwfsJ71eO8h+VZ6Nt3gO8Bzi1YzXCb0TescJjkFydHNGSwDOeOlXn2ImiFGsydZnAW3xm8dTz5edf8hZ5J/8649fPPIQeNMMyUGOtIcpN5aJaa61QbVOLbIyNNz9qfl5zEUb1f/7zGF1dHbtw15sfr722dt0nSZ/M2jFtxy9dtcZ6LpRfmMhDeYbX+7/+/3ZX3YULF/ee/S8N33Wv19DdEDjK+cgE6VUGWwzDctBdLqnPb782KchDVNvZvo7XS/XjZUK88MnhIcXNnZ/cx+diVpVpxvozeYPWPxbX4c27f58rGMRywJs3JGjX6xN6ZjEsB7uulgadulUTk1drji1SW0bm1ZonV+ht23c/P2xfhx/ob+KrkznRehsVMTHK+7tvHu13McJbUr3+dN4HHx7K/DKvxvTF8gf6dHmPx8e3JM1NLzfMjfSR/jYhSpnw73MFnx66XhHZP8i9ze9us5328ZbxW3Qevx9w1eB1I3JCbgIAqDBXNKnDM6hsdQLPXC0CAKA16mYFHsMyQLO0RIgLzQAAwdEeCRwLguTjRS1a8QZKA20IIKyVsjaO4PF4rQq8rTe3RtgZu2c/r36JAACcgK8TWhl+W/VMjMWCAsMIUaHQUpVvCAEAwHmoOqi6r6ec516FEISRtVqbGIBwDrsIwfE7wlfOl5cZHHVOmp9e+XSIlbb6jw0cu78lEwtcLqcBQWjW7hA0PO4j9snS2XU9LZSl1fc3R9FihM/vWIrm7ddy06W1/R449MCGUlPp2IHeA3ccnX10bVeIOwAAuqY2DACAczg8WjJaaQiGYrB96vaN7gL3rGOFx17fnLZ5LgDAxOCJTn2hTe0xtQpHcEuJsaTFOjxHbl4/RCisQaXSEtZq7RKB98qZV6adLz//ko/I5464q38eP4kfOdh3sH5G2Iyqp6KfKngp9qXsltJHWYvFgyNJtwUhDxX7S/wTM9QZs5oznukMaao0aaW5cnC4PPycXCB3unehCxcuXLhonkgfabqdYj2PpFX6dsf4FTobcTqr5oUdl0terzXaO+yy/HNSSZDBRoXHBbqfaemal8eGZ/NxVJNcrO1U0/NrxdrhfBzVvjw2PBsAAEMRWDA0uPSnZwYfS3x3/OqMjyfP95EJUlJKdHOK1RZBW+Pdaw6kVswlMNSwcnafswAAL48Nz/tufv+3RARW8+Ol4o+e3np1elfOt2j7tcmXCzVPBnuIzh9dPPI/jw4KykMA2IwKQ2Rb96rNDtxGMV4e93GLBACXwOtWFMLbZic2dROBZ9GT3gDA8R16AABgdPpmI2VVlio+BxwmxIUWAICB00MyUQwxVuTqWnTTxFAMcBS32GjbXzV4Tgi80yWnRyCA0M9EP3MVAIAvlFS7WQGOFh5ttQaMLCoSAQCgIpFFX2MNAQDGL1bys8ThjvasHYIiBGHkbLamNXgOUozw/mqt4Cn0LLXSVh+VVcU7U3pmrhAXVi8bsiyxtbkBw+wcSTb6cOvp3jOL4RjBvtx9Ia3dylGUCOHzO5TCZxJwCADA9ls7VrIciy+IWvDR1ilb93bEUbHZtTEM0DpdGKAoCRyHWq9edUpMeQg96PXj1q/mY3xdgaFgCh/ja2eFzSp35l4MxUAukOerrKrw5s4zFgtKq1TRPD+/NFQo1LE2W6cF3itnXpl2ofzCiz5in6u7Z+z+rD0N3++Gs9vdAQCxXb8hf67vczsZjhF8duWzuZ1dY0M23dg0lgMOn9tz7umuHNeFCxcu/leZ2tc3HQDgt/SqVnu6dpT/JhRGsxzwHDSreGd/+qSOjvNrclk8AsAsGR9+vqVrCBzlwpWShCqDfUBOtanVFlgtUaq18iv1toE9PMWXW0q/FBIYu2Bo0G6K4aTvH8qY2pF5/i52XC4OrjbaB/QLlB/1dRPe2QCPj/JWH1k88h0/uSD5XI7qhSnrLrxgdtCd1iVv7rkx8tStmpd93QTJRxePXEfgKBekEDlkQl5xuc7Wpvv3nzm13gCA+LkJ7usUTZfA60aUIqUJAEBn1zWJItjNpBJHWQPK1QUjGJOpiQACACg0FEoAAMQ8sQUAoD5N06S1DzZpWnbT5KE8s4Nx/CUsWbZVgcewDBQaCod7ibzSojyiLAAACpFHqcQOcKv8es/WnpMqKxMDAKASicWsd/QghHiZaXB+sYVnAP+SPiEIn29k7famKZokKUJ4xJ0IWpAsqBQA0I8SP5qkd+gjB/kMOtRW7RiC400E3qTgSVkAAFeqrjRql5B5viKIoVnk9tzI7ahjuyJ4JEMir597feI20fVHAQAGE5FHj84++kpbhi7txXTmjBJoWoJ7e18HALDfvOW0w1icMs64dNDSlTiCW/wl/intEZ0+Ip98I2kMuduNEwBAt3NnGNC0WNCnTxoqFutuC6oO8/Lpl6dfKL/woq/Y98reGXs/74y4AwBg7XYFAIAjL1f+UM+HyoKkQX9mabNmJFUmdUmkkWEZSFelT3Tju+U9GvloaVeM6cKFCxf/68zu518p4KHqnBpTt7RLuFKo7Y8iQCrERHZigebhjkTxzA4aza0xjfWVC661ZQwyNdongeMA33g2b0hH1rvxTN4AlgNifC9lq+6pi8dH5Cil/BvJRbo5FTpbq+7q95IfLxXPQRFwLJse9fvd50I8xfYzb479rK+/24HsatP08Wv//PhIWqVPR+dacfRm/4OpFW96iIlb+14c/kVDIxwfmSBbayF72kimVe2TUW7wAQAIV0pcETwXzRMoDaw3O2ki8Bw2WslHyDsfEqzZ3FIETwwAICNk5vpjt9M0+a2lad4WeGIAANZmQ4FleQhBtCjwfsn+JdRG23xiPGPuRMwUMu8qAABzRXGrfeWo6moxAAAmk1nsFipEKOUV5ZqzlVneicDTS3pYZAEk53A0jeBRlAhp0KYgxjOmDAAgsTLxCR7KMywfurzNKAmCYzaOohoJvHFB49R8jK8uNv617uunSsPO78r59rdNGdMBAMjCQhEAIKhQ6HQEb3f27uAJeyd8fqb0zBI+T1gLALDK88ljXZWS2RDzhQthAADCmL5JAABkSYlTZjf1PNTzobItk7a89O/4f29pz30R7hF5HHD4ofxDTaK2loRLsQAA7o88nI5JJTqOJN2cSR1tjpdOvzTjYsXFF3zFvkl7Zuz5orPpjo6iIiEwjBAAgCorVwAALI5bvIvjOPzLa18+3Jmx69lxa0eYiTKFxCnjTnXFeC5cuHDhoi790NdNmF5tsPclb2/CdiUlWssApVSQ8ejAwB0djeKtPZET56BZ91HhXm0awbw0NjxHwENVKSW6DjU9v1KkHUFgqGHJ+Ig2a8AeHRS4m2RY+bKDGZM7Mld3cy671qNYbRkT6SM72S9Q3qy5iZDA2KNLRm6bGu2zQW129H5t1/VNk9ddeDGxQO2UB0Q9607nRv2UWLxMJuSV/rJo6Cp/d2Gj32YR3pIchuWE+1PLW3WHL9ZYfQEAhoV5ugSei+YJcwszAwCYSXOTFE3KzvgIWHOduMAwG2uzNVuDp7KqJAAAbny3O0Jk4PSQTBRHDK2laRIYYanvwUfX1hIAAAif36LA+6Poj+EIIOxT0U8l1R/DvZQ6AKhLFWwFRqORAADYxN4sQ7GeUoWguNxc7pulvAwACFfiMVzBkWRzAq9RDdzE4IkVAMAyHCPs69nXOeGE4XaOooR3H/YSemWprKo7Rit5yTWjAQAqcnUP2s0UTpWX16WVisVtRvBIhkTmHZ+38NMrn64zU+aAySGT171MTPwZAIAxGLul3xqZlx8GCMK6z5+fAgBA1da2S+ABAAzyGaQPkAa0qznumMAxeQAAqbWpTerwyKKiWFQqLRYNHGjA5O46AEBtaWnNRp5b48VTLz6QUJHwvJ/Y7/KeGXvWdEUtmy0t/U6Ujlap3AEApvaYWh3qFnoqT5c35UzpmXa/fndzpODIRBRBycX9Fl/s7FguXLhw4eIvevvK0kmGdfs1uTSoK8f9LaPK2+Jg/CN9pClvT47M8JQQmR2J4p3Nro3nYYjpX1Mik9u6FkMRiFBKE6oN9ribFYY2e/I2pMpgIyp0tkHBHqLLQgJr0/36rUmRtzwlRGZSoWZuZ+oLu4uvT+XOBADk1fjwNs3eNi0YcHr7s0MWRXhLTuZWm6Ys+O+V7+d8d2l+Xq2pyW+8emwkg357Ni9y7qbEed+ezV8u5GGq758Y+FGkj7TJb7txvZQ5AAAX81St1uHVmhy+GIrYBgS7G5x4xH8sLoHXjXiLvUkUQUkLbWkUwbObKZymWIWQ0jsAADCZrISz25sVeDqHTgwA4Cn0vBPBIwQ4K/cSXW4tTZPACDPFUnUCT6XiAwCgLQg8hmWgwFAwwkPokRHrFXtnh4Xn56sHAEBNVmWpsbTFIl5arxcDAFSDrxsAgIefuFhtVfuSQqtJ6iFIqhZFBNAMJ2kS7aEoEUL8laLpIfSgRbioCkVQx9JBS4+3NF9DEB5u52i6icgKcQvJsjN2z/Nl5z0ZmkV0VZZRGA/VMBTreW5n9liquqYurVQqaTOCL5D1jAAAIABJREFUt/TC0gkZ6oyHg2RB53+a+tOLa8esPYsJRSQAAGs2d4vAo6qrw1CJpFQ8eLAeMMzG6HRd2gQ2cX9+zObF57YXpakajTs2YKyKh/KMZcayRnV4tErFozWaKF5AQBoAAObpoQMAcOTmtiv9ce21tbGXKi8t8hP7Je6dubdLxB0AAFlUdKfpOqPX3VnTa/1f+xUAYEPqhseau49hGVifuj564r6JHwz9Zeimx4499tSOWztCGbaxj4vKquIVGYrGBEgCEl0NyF24cOGia5kd558OAHA6q7ZL0zQPpJbHAQA8EOt7HUMReHhA4K72RvFuVhjE5TrrkDAvyXlPCd+p76wZMb4XOQD823P5Q9uz3o1n8uMYjhOMifRq3X+gAQ/2D9jtoFnFewczJrRnru4mp9okulVlnBLkIUqYGu1b68w9I8M9dSffGLPp60f7vRTgLkq+Xqp/dNr6i/9d+MOVWfUCNqlQI1/8S+q40WvOvR2z4sSOtSdzv0wp0T0iF/GK1j0W9+HgHopmhdnsfv6VPAwxFdRaWq3D01tJHzGBVXeXo+vfhUvgdTM8lGey0bZGEbyybK0HAKBiu4oFFHWgUmkN24LAMzrqetkpRcpGPyqD+7aepinABGaKocQAALRWywcAQATNC7wDeQeCLJTFv49Hn0YfKPzQUD0AgNwCaGtGK6yhbo1aSuYJABAS61VkIA2+Yp64MnyA8jcGIYhar/6oIy+v0U4WR9NiVCBo9FyjA0bvmRg8cVMfzz5mcAIE59k5umkEb4jPkCwAgBMlJ3qlnijpSVOsZ49Yz+18EV5Ydks7l1Rp69JK3dxajeBdrboqP192/hkPgUfm4VmH19U3okbFYgcAAGu1dHneu8PiQGs5ZWR61LPEplfO/VwSOs3OGo1dKvDyU2unMTQnTz1ZOrHhcQzFwF3gnqe2qRsJPO1PP0UByxKiuLg0AACer68OAIAsK3da4JEMiRzMO/i0ABPU/jL9l7UtuWF2BKqy8i+BZzDeWdO4oHHqnu49fy8yFMUfKzh2x6FNb9fjSy8sHTv619Hr/pvx3081Nk2kCBepbmluzVqTvGbd8F3DNy38beG8A3kHAgAAvk75eijN0eLxQeNd5iouXLhw0cVM7O2tFhFYVV5t19bh3aoyDRDysJo5cQEVAAAdieKtO503iuWANyfO3+k+fc+NCs0XEVh1aqmuXW6aiQXq4TwMMb06PiLD2XvemdIrXSEmsi7lqx/WWch/TPuzFUdvTmFYTvjEsOAD7b13Tpx/1YWl49Ysmxb1hruIKLiYp3529JfnNseuOPnNY1uSth9Lr3qj2mCPCXAXJc+J8//y0CsjFl77YOKy1prAYygCHhJ+To3R3moEz2in/GRC3n2dngngEnjdDoESJjttbxTBU5WavAEAhJYqFOHz9ahYrOdIslmBZ6bMYgCAIFlQI8EzcNqdNM1mc7wFuMBCsZSEYRlgdDoBAAAqEDYr8I4WHh0OANwTvZ9o1DxdGBurBwBwswBkqDKadVYEAGAtdWs02glfFEcMfhFyvZk0+8oIWfXQ2WHpfMShrfAbBY6CgjvpfLRGgwPH4XebnHw55stza8esdfpDFOHx7EAzTaKLD/Z8sBhFUEeONqd34XXVKAQBatD0Hkk9Yj33Ug7GP6tCMgAAAFN4tBqN+Sjxo+dZjiXeGPDGvxualaBiUZ3As1i7JILH0CySfLyo1+5VV5/fuvTS9hu9X5SohaFeKIqQpd6jJJTV3ukUw3o0FWaRWWsfBACgLjNNoO4qOPYV++aZKFNQw/YYlqvJsYAgjPvCBTcBAPhh4XXpu7U1Tgu8jxI/GmkkjaHjgsb93FlDlbthNBp3AACEz9eyFkujNS0dtHQvgiD0prRN87I0WeJnTjwzd8K+Cf/9vej3NxmO4cUHxW/8/cHfnz37yNnlO6fvfGJ80PhvRTyR5obqxiMfJX703fBdwzecKzs3T4AJapfELXH6S9eFCxcuXDiPv1yYVm2wx4398tybj225/NCyAxlDDt+o8G3LFKMldBYSrzHaYwIVwtT6aEzDKN7S/elt1q1dLdK6JRaoZ0v4eOlzo0LznZ27Lk1TklBrdPRLLW1qtNccarMDL9fZhgQpRFcamoM4M9esfn6/2inW890D6eOcva87UZsdeEqJbqZSyr/x7MjQwo6O8/zo0IKr709YvmhU6IcSPl6DoQg5uIfi5/enR72euWLyk+feHrvum0f7XWypvu9uAt2FOSYHHdiSw6mNZFAryXgrxMR9L/D+MUr//ysERjR2s4S/euCJjRUEKhDoMYnECAwjpFUqHu7lRTW81kJZJAAAoW6hjYTInabnVZb45ONFvQZN75Hd8LyIJzJzwOE11hqCMJrqUjSFzQu8PF3eCIVAcWuw72B9w+O4lxcFOG7xMgOWYiprWeBZreK6f7hAgZhXpHNocTtj91QIFFUoioCv2JBUzIVOK7hZ3mvAHKgAACCLiupSJEWiTqW7ITyenaPpJhE8GSFj3PnuOTXm2l66GqtC7M5PVfiKbaMfj7xckKqqKLIoR3nXPWOL83+S9MmgcnP5yAHeA36eFT6rouE5TCqrE3j2zjlXVebp3S7ty5utrbKMpknWC0GAcsNM+QGZu2VhD43+uMB7vCDzPPthjTDCt1Wnm3aQfLx4GMcBzzNQclhdZp515XBhzMiHI+70yuvp3jM/TZWGHik4Evpyv5ezAACo0tJYTC7P5ffoYQMAEMb01QEAMDq9UwJPb9fjZ0rPLJQS0uJPRnzSosV0R2EMegWgKIlKpeV39+cb7DtY31vR+2imJvPBx48/PoThGIGX0OvGxOCJG98a+FYqgRF3bKj7evY1rR+3/iQAnLxadVW+7ea2EZmazNE6uy5kkM+gnxpe68KFCxcuuo75Q4J+23y+0KvaaI8u1ljHJhVq4ZerpYAiQEoEeLm7iChbNCp074KhwU65GH9/sbAXw3LCgcGK1IbH354cmbE3pSzzcoHmoVqj/YRSJqCauz+pUCN/7qdrn9goxvPFMWEftzdlb2Y/v4S0csNDm/8sGLbliYEn27p+45m8WJrlRCPDPVt1z2yOD6b3Tj2YWpF3MU/9sNlBn5Hw8Tbr9zpCocosfGFHytMB7sKS9Y/F/dGSEP3wUOYYB80qHoj1W9cV874/PSrt/elRaZ0dJzZQnpNcrEP2pZRHNDfehTyVB8cB7i27v1skALgieN0OH+ObSIZstHtjNTiUAMDyTdUiRCDQY25uegAAW2ZmkyiejbaJMQSzNdcuYMRD4b/iBKZOPl684urRwka//+vbKhQbiyX1dWL1UaeGHM4/7G+mzMG9PXo3+4GC8vl6PxNm1dg1LQs8m13M4TwraaODxDJ+cUJFgjcAoD5inyoAgOgw6iLKkJBfIxlTfw9VWVlnciKRdKjReD0IQdg4hmk2ihYgDcgSaTxCGYpV+IXLLwIA8AiMDeytOGDjhN5a9yjg+fk2O3+xoVh4KP/QSxKepGTd2HVN0gtQmZS8/ewdjuCZNHbit03pH9eWmObwRbzS8IHKbx5bPmTBCO2e60r1DU45Z1re8LnhKQRrs1QqB4tplapLCqirCvRjeHysasbi2O0ohpiK0lSNahEmBE/IBwBIV6WHAwDYs7PFjF4fTgQH3/kw5Pn5kYBhVsZodErgvX/p/Uk22ubzQOgD3SKSGJNZgfD5WlQk0rHNtG9YNnTZARkhKwqUBl56d/C7r5595Ozy94a8l9LaWgb7DtZ/N+G74xcevfDOnhl7Ht88YXO700xcuHDhwoVzPDWiR3HSsvgV2aumPnPqzdGP/mty5NtTo33W9/aTHRfxcF25zjb461O5i50dLyFfPQABYJ4b1SO94XFnongJ+Wr353669qmVpL1fHBP28dIpvTLb/TzDexSKCazyarF2sjNRyIR89QgcRSxL4iPaLWQwFIEZMb67rSTj8+7+9DFt39F+Ukt10ge/S/wkr9Y85VyO6oVhn53597v704cybOOvUZJmkYt56gdlArxo2bSoGy0Md094eEBgLgBwN8p0zdbhpZbqfAEAQr3E930EzyXwuhkBLjBTLNVI4NnMlDdOoBrEbpWjYrEOU9T1VCGLipoIPDttl/BQXrNRpqDeHtopL0QvwwlUe+334hVJhwqi689JCakZAKDCVCFhLZbbAk/SROAdyj80HADg8V6PX777HAAAIhDoPE0obaEsAcWG4madjDi7XWyV+ds5Dgg3pbA4U51Z9x9EHloFAKDo6V/jXZMMGkoWrS6vS+eka1X1vfM6JfBQPt8ODCPkmKabSH08+mSHqfuhHMLRA6f3uON8NW5+r3M8zmEtCZ4E/B49mn1t3zr/1kIH4/B4Ovrpb5szAsHc5CQAAOdwdEjgsSwHh9ddX+Kw0qHRo/1XP/X5iBWTn4s+p/AV26iqqjBULK4ggoPtPAJjfXiqGzr3XpBz+FrvtkdunfIcndxqIGM8/MXnxW58SuEr/tOosQ+tKf6rlcdwv+FaPsbXZmuzh9poG6r7ZVc0AKCiIUMafUmiAoGONZvbFHjFhmJhUmXSYx4Cj8ylg5amdPYZmoO1WhSoUKjDJBId53C43/1+6OvZ13Tp8UuvH51zdP38qPnF7R0/yiPK4oreuXDhwsXfQ4RSantlXHjupgUDzhxbMmpr0rL4FSPDPX/WWsheWy4UtursXU+x2tLfQ0LcCvWS2O4+V1+LdzuK12jzNCFf7f7Cjmuf2kjG8+Wx4R0SdwB1omt8L+WveisV8cy25DmtXWu0UViJ1jo0UCG66qyRy92smBWdLBPiheeyax/paFprS5zJqvF84oerXxjtVPCCIUGrnhwWvAJBEHp3ctmywatPf/7vc/l3+iWvOnZroNlBB46LVB74pxmVRPpIrRI+XlamszVbh1eksvgAAPQPcncJPBcAVhOJ512r8W7unBAXmurdLOshbbQ3j4/VciQpwyQSPc/HRw8AQFVVNxF4DsYh4aG8Fg1Hgnp7aKe9FPMeTmCq1BMlHyceyI8BAHAj6toqqGwqMWurqxNDpdImAi9XlztczpfnjA4YrWlufFQo1LtZWAwAkGOFx5o1WmEdDolJFswAAPiEuRWVm8t9AAAG+wyuAgAQRPUyBlReBA5Q/PLB/HEAALROKwIAwOTyzkXw+Hw7cBzKaLVNoluzQubkhGr7gdm9ttLdW3TnA14g4dGB9lt5enlPuH7VHHr3fd+nf98zV5c7vad7z+PPxzyf09y8uKeHAwCAIx0dStE8uuHGHIPKNsYvQr5zzLzGtsuMVhuGeXreyfXv5We+gLA0ZN1yTOnIXA25fqJkFACgUSP8LgAARI3wOwUc4FePFI5teN1Q36G/auyavvOOz3vRlpYWCyhKKp5Y2CgNGBEIdHenQzbH8sTls0iWlM+Pmr+tPU3X2wNnsytQkUiDurnpgGX5ZHHzmxEuXLi4t5B22vW7w0WHWDYt6gyGIPbdV0tntHXthVyVwmine0QopanNnW8pivdnTq3ihe3XPrVTrGLx+PCP3p4cebMza173WNy5QHfhpaRCzfwfE4paNKtbfyavL81wkmGhHk67Z94NhiIwpY/PHgvJ+L93MKNDPfiaY8+1Mv9XfkldY6cYxctjwz/6ZE7f5BWzolOSlsW/OrG3979NDtrvyxM5a0evOfev3zKqvH/LqHpQwEPVq2ZHJ3TVGroSbxk/R2N2RN4deQQAqDbafREAenSEV7O/ie8nXB+0XcC+z6+9fW5H9srmzol4IhPLsUSNpeaOECDtjFKAM1oAQDE3Nz0vMNAIAECr1U0EHsmQYh7WssADAAiIdNfPeCV2GU+AVd04Vbr80r68fh5CDzMAgNaulXC30wgxd7m94X1/FP3hbSSNYb0UvVr8QEHFYj1hq6txy1BnNOmNBgDAkaTYJAlCAICNGORdVmut9cMQzNbPq58BoC6dT2qtIMW0TldVYJjOshywRqMIAAD3UHSuBk8gsAMAUFVVTYxWDIl4DxElg1zv5Ca7YT20ibU4ZeFuJVQ2aoJtJI3Y1syti/kYX7tm9JodLc2Le3nVCTwHyd/7WfKTW147vznlj+JmX5+7ubgnt395tu5JmacgYdYbcXsanrNeu+bGORyehL9/Qf0xZUxIsZf6BqitogFmXfNtMZyltsQ0hi/CC3uP8CsHAIgZF1DMF+N51UXGiWyDD7tv47/9Pdozel++Pn+KqqZoFO7peRP3aGyMgopEOq6ZdMiGpNSkuKWr0uf4if0SF8Usyu3M2luDtdsVqESiwxUKHQCALSOzXe0bXLhw0f3UlhglP7x1ceeJ/2b+I4wgXNxfRPpIreHekrPFGsvoa8XaVnuw7k4uiwMAmNzHu8WskbujeOeyaz1e3pn6mZ1mFUvGh3/8xsSeWZ1dM4YisHFe/+8IHDV+cyr3rZacOy/kqoZjKGJbEh9+vTPzfTK772UpHy89c6vmka5oGv+f8wXh7x/M+IJlAX9vWq9lDQWvhI+z3z8x8MQfr416YUCw++5ynXXIKztTN2ksZJ/BIYpD7TGK+TsJ85LkUAwnPZJW6Xv3Oa2F9BXxsRpnehD+03EJvC7AzUuYQzkY38o8fROBJuFJzAAAefo8CUBdDzyGYj2EYDUDAGAeCr2gd5QeAIDR6+V330+ypJjAiDZFkF+E3DBjcez7PAFemXam7EP+Df8QAACDwyBh7XUCD3dXNIrg7c/bPxwA4OGeD7co8DCZVI/QjEjCEppyU3mzaREcRYrNIl8eIcDKRVKC1jv0vmKeuKphtAYhCGOg8UYlZWf8kw4VxDAmkxgAAFd6Ox3BY1kO7n6NUaHABgBA19Y2EXjFGZqRDEozGbJL3iRDNvqgw+0mfoAqyWzWOQZn/FkeXH/8tbOvPWiiTCEzw2ZuCpOHNUnrqAeTy2kAYHPRPr1qS0xzaYrxSDpc+MVv36VPYZvZFaon50q1b+afFf8ihHjprNfj1qN3pS+YTp4KBQAQRPe5I/DEQ4eo/SsuAgO4IGFvfrssl++e226henoFSRuZnPiGyU+RNjrkxqnSRgL156k/b4+j/RJketItvSfR5O+EisU61uFoVUh9euXTR1mO5b/c7+UWxXJnIUtKBMAwQszNTYv7eGsBGvfFc3F/UFWgl+1aeeWlH96++MW+z68tvHwwv6/V9M+x/HbReW6cLhvIMpy4IkfXpnuhCxfN8dTwkOMsB7y1J3Na7WOXWWHoz8dR3cJhIcUtXdMwivfc9mvzXvkl9VMHzcpfi49Y/vqEzou7evoFyk3zhgRtMDnooKe3JT9x93mzg0ZLNNZh/nJhsq+bkOzMXASOchN6e/9qctBBHxzKGNaZsb74PbvvmhM5q3EUtX/xUMw7LblhhnpJbPtfGv7Lj08Nej7US3zOTcjL/3hmn1Odmbs7GRXhmQMAcDarpkkdntFO+UgF93+LBACXwOsSfMPcsgEAcq9WN3mzuPHdTAAA5aZyKQBAWZbWEwAQMaO3AwDwvH30REiIDRCEZk2mJjtSFENJBJjAqZ5wvmFy48zX+r1PCPEyYyL/pSBdbzBTZnF9nRjm6dlI4GVrs4fLCFnBpJBJNS2NibnJdQAAPRzSUo1d00IEj5JY+J5CoZQoUllVPJ1dF+HGdytveA1CEEZ/VZINxRBTfkrtNNZsEQEA8AL82xSvmecrgvZ9cW3+f9+4sPngV6k7Tv5wc2z9OVRYZxxDazSNUvJIO43qa63DKYUp345axQfzDwY2XjMpDlJfViEoYks7U/YQAMCxgmO+qTWpj/lL/C8tH7b8amtrQjAM9O4RdJ5gwECRG3Fj9hv9nxO7EelF6eqXd6248lpzkTZtlUV4flfOB4AAO/6JXp/IPJu6mtqzs8MAAGTTphXVH8O9vCh3a4lBQBstFTm6qW29Xi1x80LFGADgYuMDLzQ8PnRW6AUEATInqbpJT7yVt6KuAQD8FFE1bFXSqkGNzru56YBhhGRJSSNxra+1CjQVZtEfRX945+nypoS6hZ6624W0K7GlpbkDAGAKhZYICtIDANDV1U02S+4FFMmgv66++mx96rSLppB2Gj32bdq0Q19f/4+20jKJ4zi8ptg4N/VE6eptSxN2b3s34ePD667PTj9XHtLa5omLfz41RYbBAAA2E9W7KE3Vpb09Xfxv8PjgoDIvKT/tRpl+mtnRfLqvjWTQKoOtn7/7X+0RWqI+ipdebphL0qzbGxMilr8aH9FsaUZn+OiBPqm9fKTHb1YaZ336W1aj74N/n83vQzKs25Aeig6nZzbk0zl9L4n5WMXxjKqFzrZouJt396cP3XyhYIWIh6k2Lei/dE6cf5uiZ2ykUnvmrbEb0z6a9GZzdY//FB4aEFiGoYgtt9bcqA6PYTmwOBhfdxHvvnfQBHAJvC6h90i/AkCAVpWamgg8d4G7GQCg1lorBQBQlZqUAAASew0FAMALDtYjGAYIQehZi6XJj1KKpcQCXOB0GqN3iMw087V+HxBCrGRyzrNgzcHHpLLFfQAAvq76ue9X176K2ZK+JXJD6oZovUMfGeke2aodL+bpqQcAiLS41Vgoi1+BvqBJbRMJhJjExAKJgl/8yZVPxlEsJZsYPPFEw2tQPt+I2i1SjwDJKZPGPjQRxkwqCp4CiVfYfjcvVATZzVSjnfqsy1X+B75Meez7189/e35Xzrc1RcaHeXxMxRfhhQUptS+U3NQoAABQsdgGAMDo9Y1ERvKxohiW5tyUoZJzAACXKi41MijhSFLERymzZ4DkD4PKNionucp77bW1r6AISn487OMtbb3Oxelqz4yoZ3kEa7XNeCX2C78IuWH+ymErfULddutrrPG7V11dk59Se6cuk6FZ5OiGG29Rdsa/38Sgz8LilLXNjUtVVoQhIlGVoFevRn9zVCRS+WtTtHYL1fPG6dImdYNtwbIcqCvMY4QyIjOkr2ej3HIPf4lV5iW8pKuxjjZpGgtT5lZuX+DhZl2AW8G+3H3v/CftP3c+EDF397p0yMzMO+9bValJ/OsnVzce+ub6Rxuvb1yAIAj79qC3d7V3ve3BUVgXreP5+mgFUVF1/flu98XrKu5+fzrLsY1pD6nLzLNuXqxY5BInTbl6tDDqp/cSvynJ1LwoEPPyx86PfPW5r0a/9fhHQx6PHu2/SuEvOUnaGa/ybN0zF3/N3bDl1fM/JR0u6HOv1+2i/djNFG7SOvqLZEQ6AED6ufIOZyO4+N9mYpT3MTvFeq46emtIc+d/vFQUTjGctF+gvE1TLwxF4JmRPbbKhby8Nyf1/HDx+K4Xd/X88OSgrWICq9h+ufj1rCqjuP74uZza4SgC5JL4iC4xIRMSGDt/SPAmG8l4PfHD1S/OZNU4vZnCsBw8sy152u7ksnflQl7BzkVD3h0bqdR2xbr+KQgJjFWIidxqQ+OG5yklOjeG5YRKqaDyXq2tK3EJvC5A4i4gBSJegUlrbyLwlEKlCaCuFg4AwKC2eQMASC11AS5B7956AABEwDewNlujCJ6FsqAMx4gEmPMCDwBAGSwzz3lzwAcmoYaKyZoUmQWWoTQKsKv4wPvbbm77ZOP1jV9+n/H9pwAAsyNmt7pjxPPz1QMA9DaIdQCAHC883ihNkzEaMbPAWwAA4O4nKr5SdWWOjJAVvBr3aqOG0IhQaGQdDtmIueGHpArBFRMq9yrq8QBkJ6uX/vlLzrc/vH1x35bXzm/6adml979/48L6sz9lbaoqMDyO8VBjcF+PzbPfjHvq6TUjPxz9WM8vOODwczuyl7AsB6hUagcAYE2mRgKvJFMzEkER28xZo04TKKEvNBQ2+ttwFCVG+Xzz0NmhhwEB9uDxs69r7JqY0YGjtw71G6pr7TUx6+zE6W23lrEYD+JqD6V7BUktAHUtGOYuHfBL9Bj/VZSD8T71481vLvyaOwAA4NDXqY+bdY7BwdEe3w+bHdaiIxet0YbhCkXB3ccxsVgdUHEeEBQctxIqp7W2vuZIP1sWStkZf59QWbM96MIHKE9yLCdKPJA/ov4YxzBAVVXF8pTe6esmbFgpwASazemblx/MO+gPAMBTKnUAAGRhoTtAnYj8bXP66zTJetvNVJSxxjGmt0fvIyP9Rzr95WA1kXju1WrfvGs13oU3VF7FGWqP0lsaRUWuTl5daJDqa61NUnHpqip3AAAiOFgn6NPHBAhCM//H3lnHR3FtcfzMrPtudpONu7tD0OAQvLQUKFCjtKVeSgUKfaVeXt0oVKFQihQpEtyDxN09m2TdfWfm/ZGGhhDZCLXH9/Ppp2Tmzsyd2ZF77jnnd7TaYQvRPL61ZOJ3L17cuevN6ysHYqTlZjaEtFZrlpCpqMxmxvyuH6qL7n+r/w+ay1WCnzZceS77SMN7uAPnhKd5vHv/u6PXR431agYAcPFgmccvCcte9GrqlpUfj39i5hOxDwYnuX2MoOAoOX/HWP4nknu8IYrACWZAnOgQnUWpkjfpb4uU+x3+/ayfFZnNoJBkpyukPYqtnK2QJQIAsTzN36lyA6vSg6sLXpu6elV6cPWwdrQbXgKG7cmJIR9a7bjLYz/lPgbQUVKgXmEc5cln5Pi63FrKarCszYgoWpUe/JrFjrk8sTPvvX15LZ79bSNRm6mTPzz/7JkK2WMePHruwSfHvBrrzXcqguyfhjefUakz2wOaVKYbSuhX65TuAAB+Qua/woN3J79hmGC70CqULYYZFoOdTGdTbohReLG99AAASouSDwBg1HTUwGNqmyk2FLXTggJNAAAoja4lLJabPHg7yneEAAB4c7xvCnd0BpE32zh7wahnLu2o/UiIPm1C0LcZzyc9v1Zv09ONdiPN5DDR+DS+YU7QnD5nKmhBwWoAgDA1wwQAUKIoCQKAGwaKta6OaWR7AQDAJfZhnlFh9JoVOGtTd7VElMHQETYb1ytUoFn+9qh3amfOetLcKkt2bNq7UdYEjluyAAAgAElEQVSo99EpzD4mrc3HYrL7kMio3ifS5Zu4ST6X/KKENxkHoanubVXXpT80ligfPb6lZEoqj1sLAIDpDTcG/laTnaSVm9O4Qvp1npBpEzFE5TKT7KY6gYTdzkSoVJNvpFCld2uvZkpdo/yCgos2jdvUZ9w4jhNw4MP8p6wmR1BM/W4VDyS3CLiMXxyW7RnMf+7czspXis+2bGgsVl7UKczjBO7MkxmrYo/0tm9LWRmLMJvdKZ6ex7uvQzkcBUlaE8t3Y17QyEzjlRLDd0Iv50tMVF2TpgMCjpSMgB49tqmzA0uLzrS0SqrUUwDgLACA7sgRD8JiEdFCQ/YkiZO0r496fcO6S+s2vX3t7dfFLPGL4b6+agAA++8G1uHPC+cYVNYRbv7cve0NmrsjFCNtq+9dts/ZPgIA7H0n50W9yjKyjyZY2Ej3DyY/EHlDncuhUAgBABgxMUqERAKERtPgBv2QQzQ1MhP9yOeFj2lk5okkCqpQthhmHfmiqHX2U3GHndk291jjC2Qqqpz5RNwLv31a8EVVtnTmyD6M+/8HcJyAI58Xzm6uUC8lCIIiDuDunvpw1J6ewpW74h8jUvrHiM5kbilBavNkz1z4uTI1/b7wPsOo7/D3oqVcnYogYEua7l+gU1rcm8tUK8qvtHlFpHnctvDt4cJqspMULQYOQQDgGI4SBACBEShBEEAQgDA4FKtHEF/3V/fz/wUGlYQn+PKPZtUqH/jpaqNv98LntXJDkoBJqYr34ev/qj72xuPpQdVnKqS7shvUS577peC6rwtTYXXgghR/lwEXN++PF6aFlfKYlLWbMitff2Vf8Xsak+213vLoDhW2ur+6v3itzuLwS/EX7NyxYuQvVDL6r51Ji/biVeY3a9Dd2c3BncIx1VK9JwBArDfvX5GDd8fAGyZE3uwKRbNhbtnl1oDEaX43ZoFGe41WUlGqplZTGw0AmZaOGngKMOh5CJWqQUgdhhDKZGjter1X131eklxKAABiScSSQRWKjB3t19Jaqt9cmwfPNPnOMD8Y/WBN/1vdDCM2RgsAwNPYaDQSTdFiaLkpD8/e3MLWs7yABA7LCf1vU+kkuuzVka/e8qJCWSwdOBwsTKcjkbhcjLBamRQUN0VP9q0DgB5fOL0x47GYo9vWZqXVFylWBCQJ3wQAwE3GGwbe9cP1cThGcLzDBRcBAHy5vhWtba1p2e3Z/BT3FA0AAOFwsBA6zbj82PJFtZ5NEYulr8Li+jWthBVBgQm9Kj91KW/wkzivfBTBYvWoahmSIm53D+StOfhJ/hNamXkCnU2pmLc68avuoipd0R09GgQAQI+IuNWD5+IiBwxjhkdQz1xpN025sr92wqwn43o1Frtit2Goqt04ji2g5XZ6G7uDogi4+XFOSqo091dea/cIG+HepjuWGQcAwJk2vRAAYHrAdGm7sf31j/M+fvv5c8+/kUB45T8NABc0uaOrfvrIy6U89j5EYCvNG7WvAdN4QpR8tD2YG+K0EVpXIHfVqywjeK6Mc66+nAKCIBACB5TACRJOECiBAyqt106qzpE+GjvBu8DNj2sAAMC0GgGgqJ0WFmYEAEDpdBVuNA3Jg1d0ptn/6qG6l+wWzNMjiLdz5hOxe355M/ulplLligu7qtrGLQrtM5Tm6JdFj9itmDg5w3+td5hAI/Rmn5Q36uc1FCuE3UNk/5/IPlwf2VSmeoTFp+WOuitoS2iq+4A+pBOXh59tKlUurMmVLRm3OOx6X8/THf4+4DgBGpkplcWnFXKEdFv8ZJ9LzWWqh8sutY6LSPO4rSHcQ0WnMNN+eev6f21mzK+PZnjaXUEvJE71G/A39g6D48Xp4Sfv+vLykm1XGmYtHen3ZefygmYNR22yhyT5CX75K/vXF98/mLp77Htnkn4rbF3lwaPnowjYn5oYnHM7jvXI2MA6Fxb1pXX7i99852jF2xqTfePqqWFlXdusP1CS8vP1pucBAeKBUf4b/zMn6rbUq/07MTfes3L71UbIa1KHAUApAECr1uIOAER6mFuvuhT/JO6EaA4TQYluFQAAkkr1TaGAVBKV8GB75LUZ2xLMDjNqMzvEFDpZiptMfIRG03S2Q5ksLWG13uR1qNfWJ/FovJoYUcygZ6Gmrog6LTLVyep9pjKyj9TfEkLaH2RXVzuQyUZMp+e70F1qlGblTSGaDqmUZWR7AYmsV6usqsgkcdJBFoV1i7wsicPRAQBYKio4AB0iJwil5wLu/UEio8S4xaGfAgBcLWXfRwACuMl8IzewqVQ1FkERU+qcwDwAgGRxchkAwJG6I+EAALjZjAKGMS7wpBH5svwlLm6c0zw35hlNq2X6d2su/fjzxmuPXTtUF4l1kxi+8EtVUkuF+oHO8gYIiWQFh6PXsgUcId225D8jP0qY4vvqnKfjX2dyqH0WLzWXlgUBAHBmTL/FwKO4uckBAALwKj2NRa5uq9HMcDZMLedIfRRmx128QgU9hmd2kjTD/wwA4EVnW6YAAFirquIROl3Bmz3rhpf3gegHapdGLn3bgTsYWVjVTAwBqLUoxlKzvZcZqCr0u+DXog7XH36h0a1YT7bRWNd/cz4sMe944zQAIEbfHbxt2iPRZ6avjDk947GYkxmrYjNnPRF3dPZTcYdTZwd+gjsIzqnvyx7o3A7TG1wQGk3VOVmCMBhq3GweVA4ejhNw5IvCGZf2VH+AOwhG4jTfV+9ak7SLxqRg855P+IDGJDeUXJC82FV5tTunfywfpW43TREHcPeMmBNYBgCQONXvGAAgucca/q/VA2vzZBMRFDEvWJP0zkCNOwAAKp2M+0YJd1tNjsCLu6pS+t/iDn8Hyi+1+jpsuNjVl3MdoKN+K5NLLVa2GMb93cNtM78uuc9mxvw8Q/g/+ceKvgyIFX0ZECf6IjDe9bPABNfPAhNcP0VJiKHwVPMDf/dz+TcR78PXB4hY52tkhgmlEu2NfLZtVxriAACdEObaY/27wXBlf01M4ZnmXmvYDRQ2jYxvmB31IU4Q5Ga1eYw7j553O0VJFiR6t36xJPFFOoWk+uJszcYNB0uSATrEaO768vKS7Vcb17NoZOmnixKe+38w7gAAkv1ddEwqqa1RabqRh6c0WN3pFFQx2ELzfzfuGHjDhH+MSEmmoAqNzBTRfV2MKCbXgTs428u2h9ismBuNQZbhFgsfZTD+MPA4HC3gOM3W3EIDAChXlrM0Vk2IP9d/SA8biiIQ03qggW7TOvKON76gaDGw+t+q2z5oNA1uNPI92Z41JofJq6vQilWpYhtYHiCn1VHJCNm4dsTaHkMcSfyO8BVbQ2OngcdEKJRBv9CCEtxk/jHC74wmiJR4jgHCYqEDdCTy6+TmkTwR/WqnQXV36N21KKD2cmV5BACAqqaUDQBQwdKGRrhEHNg3Z9+nC19O+SxyjOdbbAGtSN1mnJxztOHdrc9d+Hb329kP5p9sCiq/0uZVel7yAo1Jru8sb4CQyTbC4aD13suO6z9qQXBRb56zrtglkiCETpcz4+NvMegpvj4KAABrba2rV6jgmM2C+V7/rc4psYn6QkU6giLmEbMD+wxp84lwUbMFtBxli2GSRWcm2WWyGIqHR2Gn4dTJC8kvFOYszXkob3nBfJRKVfnhTyo5NheMN9r+6T0xd/1nVuCsTfdMyliHoIi5rkAx0Zk+Wk12kqJZP5UtoOUExLkqemsXO8G7wdWPc0Ddbpp67VBdJAAAbjS6oHT6jVBelMVSd58scQZFi4H10/orLzcUKx9n8mhFc56JfyZtfvCNXFKuiGGd/GDkGyiKmLN+rVkvqVLfcoyGIoWo6nr7U3QWpWr2U3G7OpcHJ7lJ2QJajrxJP/3/VfrfoLZQNTLzGJ4rI4sjpA9aCnzi8vCzFBqprTpHuuTOgPqfQVWONBUAIHq8V3bnMnEg94LdinkVnWkesGjUn0Xe8cYQebN+josHK3P+6sTdM1fFZmasis3MeDz2+IzHYk7OeDTm5IxHY055hwt2mXS22Kx9NfF/dZ//DdgsDrQ6Ryq+vLc6/siXRdN3v5394La1Wa988/yFj058W3ojd3PJCN8jOAG0dzMrJncuK2jWJJFJiOHhMYFDzqfDHDiy972c+/KON711ZX/t+uF8d89P8GqbFC7+FgAgxc/lthcEnxQhVvzwYMrLHDqlafuVxnUrfsyenv7fsxvymjSLAl1ZpzOfHftiRozHv8Jz5SxuHFqlwmAN7yx4rjXbPf8tJRIA7hh4wwqTRy03aW23eMmWRizNBwD8UkNWCmbHXRhsipSwWvkok3nDwCPxeFoAAEt5GQ8AYEf5jngAQEd5jhpS0UsAAIpNT45u2itx2HDhsc1FTwx0UITQ6WrcbOaHu4TXAAAcrjt8w4snVxBeOIkGjcwKlwhhxFFfrq+lp32QREIdAIC9tZUL0CFygtD6r+/XF9MfjTnO4lLya4Lmg9rGFAEAXPutLgHHCZZPpPBiZzshQ+jg0Xk1bca2iFZDK/XpS6tfBAAIpnhm/Tzz5++oJCpBY1KwCUvDry17c9T7S14fuTQ0VfxfBptSL2/Sz8naV/PRmW3lnwEKjonLI966kS9EJlsJDOvTwBsImFLZo8AKAAAjOloOAGBvaxONuTvkAkpCjNXZsn5LJpj0NrJWZh7FE9GvODOo9osWnsQcuODqJ8dmgt3OoUdG9pqkTkJJ0OQ3HdfS/YQ+EcLvH1+45NTLqS/nvTP2nYt3x81r4LsxLmnl5lHdlTl74tKemhGYg+AHxLke66/t9EeifyZTUVnRmeZVFoOdTJjNLiiLecPAI3E4asJm42FGo9Pvt5LzEt997+d8rFdaRniHC75f9mbaRs8QvrZ7O/8YkTJ1TsCbmB3nZ24pWWfUWm8UrbXbMPTMTxXPEQRBGntvyAc05s1FXoMSXA9jDoJ/eU/1KGf79W/i6sG6VAInmAGxorND2U8XL17QHS/ePwNVqzGVxiTX+Eb+kVOdPMM/CxBwVF2X/i3FViwGOzk3s/EpEgXVTF0R9UNfbSc9EJlJpqLSiittD3SP/LiD81zaXZWw5Znzm7c+e+HXE9+Ubi041byxoUixStGsn2Ux2b0dNkzYWKy4p3MM8/CYwDoXFrU8t1GdYXPgCIYTIFGbEz15jPyhFqpWS02MnzZcWSut193L5FILMTsuOvNjeZ+19wbK5mVJmS9OD1u96Z64C/23HjrJ/i66vY+nrROyqeWnymWrZHpr7JRI8Rcnnxv/yVDr7/0TCRCxKq0OXHCmQiYCADBaMXc+446Bd4ce4IuZ5Q47LmooUtwkSRslijIIaIJKhUyTCgAIk0uREzYbH2Wzbxh4ZKFQCwBga2ziAQAUK4oTyQjZeH/U/UOW7CUcDprA2qL1CuX/pFNYxhzfWjKl/63+AGUwNITFIpjmP60GAKBEURLcuU5lpPsCAMg4rdjTCU/3Kj5BcffQAQA4FHLu731iIlSa0/lZPfYLRSB9acRnCIFDIW3saMyBI83lqjEoCTGkzgq4yTDxZHmWaaya4EVHFr2h0ytjAAAyWKkXuovBAADw3ZiWKQ9FXbj/ndFv3PNK8rKAeNHnHAE9O3mG/1uB8a7yznYIiWSDYTLwrPX1DNxo9CR7ePRo4DFTU9WAIDimULhyhHSbiwfrtE5pHtWTF6kr1w7WJeE4wfKLFvYZntlJ2vygHBIZ0Uia7VMBAHjz5xf11jY3syGkzmOyUKirMs56Mu5Q9/UB8a5nCZxgXD1Y25doCgAANJUqZ5CpqCztrqB+JzS4IoY1PM3ja5sF8z22pXgebrUKUBb7hvIpic9XAwBizi9w2ot3/XDdKgInaKmzA16e+2zCflIfyeWJU/1qwtM8PrQY7GH7/5v3TOdg48gXRfPMOltMYLzr1z2FH6bdFVxAoZFam8pUPSq//duRVKonkCioMmV2wJCFZiYuDz9LppLa/9+9eIoWA+vP9gg3FClE+zblLm6p7Pvd00lrtYZnMdjDXDxYN0UQuPlxDWw+LV/VZhz7dzSKjm4uWmAzO/wj0jy+6E/QismhOgLjXbdbTY7AM9vK75R/GARKiYFZcqH1GQAAcQB3b2CC66dJ0/3Wznwi9sFHP0u/e+XH45/wiRTutFkw35JzLf6d26WHuf5msmEebx4pS9p+pcHf6sAF0V68IYVnVudIxXvfyd5kUFlTfCNdtt7/7uj1DA6lpKVCvbCnGreDhYQisCo9uPrPFDMJceOYf3tqzH9S/AU7X5wW/tLW5cnH+6sV+G8lLUhUAQCQWdIWXi3TM2wYzhOxaf8KBU2AOyIrw4pXqKCyqVQFNXmycP9Y0U0u90B+YG67VL8UAIBHNuqAIFASl/uHgecu1gIAONraeBiOgcQgSXBjuhX2lM82UAjMQUepTNXsp+N/3bY2K76+UL6y7HJreeRoT6fUOVEWS2Nvb+cnuCXo6CS6XGKQ/GHg2Sk+CIoB3YN6qa/yAlR/Px0AAKbW3DDwUPrAyj/0hH+sSBFSf8BSEbJY9NtnBXfpFJaRfDfGpa5KpgAAEcKIilJl6QKNRRPyABH/M0DuEhKP1+/x3fy4hozHYk8AwInu6xAK2Upg2LC87HWHD/sDAEIPC+vRwEMZDByh0VSYVisCAIid6H3szLaKOdcP10+e/7xgb2/7balQjSeREU3qnECn5KJpTAom9GKfljVgC2wCTwl7zOgeSxzIm/SsnKMNL1FwizWqcpsNRR+7pU3qrIDSojPNckmVZgIA9DpDWXG1zdOks8V5hvC3U5ycdR2/OCy7sUSZ1VatWeRH5lGZPN6NfpJdXdUAANaqSkFv/e/KtUN1kWa9PdI3SrglZWaAUxMqE5dHZGkV5m2tVZrlBz7IawmId82WVKmXcoT0y1NXRJ/uaRsSGSXcg3hHm8tUKwpONQXGdwgM/V/QVqvhGtTWRDc/zgFnf+O+oNLJuF+0yy+1efJnLu6qShm/JCy7/63+PSglBubxrSUPq9tNUwAAEBQxk8iIjkwhaUkUVEehkbQUGkmHkhAbgRNkHCfIf/wfyAROkAEB3DtMkDX23tDrfU1odKJXWqgnvitdIK3TLiAIoJ7bUeG+dGPaR/1tV3S2JRkAkMBE11tCxD1D+OerrktTco42RHTmq/4dKLvc6t1Wq72XK6JfdPbemrg84mJjifKuugL5MpPeltVfzvUdbubk92VLMQcuSJ0d9EJXobquJGf4X24oUjxacbV9QuxEn+8BAP4zO+rKkaI21fFS6cxAEasEAGDJCN9BG3hZv9bEFp5ufgkAkPjJPv8ZfXdIAQBA5GjPnbmZjW+f2V4xfc7T8bdMaP7ZFJ9r8bt6oHaN0JtzYfbTcXsH8l714DFsex4btav/lv9uFqX4NLx7rNxW0a4Pu1AlbwUA8HFh3PHg3eFWosZ61iEI2JQSQ1j3dene6bkca4ewn8DeZgcAILm43DDwqH7+HQaeUsHbX7Pfx4pZRWEuYUMOzwQAAAdGQ8hkK4mMEpMeiPgIQRDr5b01a7qGl/UFicvRgMPBcsjllN+FVm4YeEog+TFMMliQct+evvZBDw/XAwDgOh0XMxpRwDA6wqAPyYPXiacyRyewNCsklZr7CZxg+EbeGs9+f+T9Rd5s70vLIpdtnGEOqQUAILsIh2RgImSKFYbJwDMXFXcIrEye3KOBB9BR7BwzGEQAABGjPCUMLrVYWq+beeqHsjFttRpu9/aqNiNDr7Sk8t1Zl6h0stMv/+h071OAoFAadj91/wd5C3/7tGD2sa+Lp5z6oWzMhZ8rk68eqI0++lXRsw477hJnunCJbNLwcLP5lncJiYwSQi/2WaPGmtBcrupV9KToTMs0AMBSZgX0WaKiO+MWhW4FIIjK0EWAClxuGHIUL8+O8g0tEqeEVsqz2u5ByYg2fUnYLUZ8X8x9NmEv341xpq1Wu+TawdrXSGRUM31l9Bd9KTuOuiv49O+1DGcO5Fj/dHKONo4DAFLEKM8hhWd2ZcLS8HP/j168C7uqkva8m/OFut00SejNPuwZwv9J6MU6weLRykkUxOCwYQKD2hKraNbPlNbrFsqb9LOVEuNUdbtpnE5uSTGoLTEmnS3UoLIklF5sXfft6otfHfmicEZvodQ4TsCJb0vH7/jP1c3ttdrFHCH9msCdeVIrM6eXXpD49tdfWYMulURBFbETfG6Z0EiZFXAdQcBWVyAfNxzXZjjAHDhyZX/tUyiKmCfdH7nF2e1IZJQIT/P4wWHDxad/KJt+O/v4byPvRGOwssWQIfRiH+nNuAMAEPtz9WwBLVfVZhxnt2EoAACXQcFivXmZUp0lqahFM4VLJ9ePCRb1Wcu2J3CcgMOfF87MP9G0kURBNZPuj1jdadwBAIycF1TC5FKLWqvUd+sU5mFLzRgMdQVy18v7al63WzH3thrN0u3rst6qL5Q7Xcz8n8xwvuu5DAomYFJrWjXm8Io2vTsAQLg7919j4N3x4A0jNCYFo7MpVQaV9RahlSURS+qu7K+14AhO4+nqQQMAFLHbDQOPHhGuBQDAtFreycaTiQAAc4PnDosKFIFhNIRCtgJ0qJdFjfH8uPi8ZMPhzwofuPfV1K39bU/i8dUAAObCQr4n27Om1diaVq2uZnKpXIcNEXC45gbi7vA1zX3tg+zqagcSyYwZDVxbfT0TAABlMIbFwEPJZEtM2yHppeAn6QBApMwKuCWs0J/nbz624Nj7AACSXS+kAwCQ3VyHZuBRKDYCx4flRW9vaQ5CqFQ1a1Rarx8mlMWSO5TKG2UqIkZ5/Fxwomld5dX2FyuvtgOVQW7giuiF7oG8oph075Lsw/VpBAHU4EQ3p8IzOwngqTVusjyQu8YL1dWapb2184l0+ca9VIcZCWKypbSUw0xOviVnLWqs51lpvW5h/onGcT4RLge7rzdqrRSlxDCZK6Jf8Q4TaLqv7wv/GJHSj6s53kBEzqmi8zx9fl9OCwpSAwA45PJ+DbyCU02BRo01ySuU/+NAhT9QFIH5a5I+37XxmrtZb49InObzamfpht4QebONAjHrrLrdOFHaoPtB7M/9S+o0VVxt88w+XL80JFl89M+ozSdt0E6gMcl10eO9mvpv7Rw0JgW74cX7pSpl/OJ/txdP3qRnnfi2dIVGappEpZOaUmcFvd3XYLg/bBYHemFX1aiGYsX8hmLl401lV5a6+nKOps0POuIV2vEs5h5rCC043fyIxWAPozHJNYlTfTelzg4slzboOPvezx2de7xxSdQ4r3d7O4ZRa6UYNJYEgTvrbE8TH3w3poUjpF/TSE1jrCb71u55q38FxzYXZ1gM9ojgJLePesrD7YtRC4ILqrOlhS0V6kWqNuNpFw/WbVNG/Kuw2zD02FdFsyNGe14NSRYPWZTDbsPQvMzGVSQyqp3yUORP/bX3ChOcq7zanppzpD6qUwTruSmhmfdtvbbQaMM8Y715A6q9CtAh8rVvU+5j6jbTNLaAdn320/Ef9PTbRY313JF9pOG9sz9VZMx9NmH/QI8zHEgbdJxTP5RtJDCCNvbe0NVttdqAmlzZ45lfl3wWkir+omuN2H8L0gYd5/LemhmyBu0snivz6uLXRnzZ/1bO4clnVJa0amc3KI2+AADpYa53QjSdAUGQ6QDwCQCQAOAbgiDe7bb+eQBYAQAOAJADwEMEQTTezj7dbjgu9EpZo36eQW2hsgV/DBipJCrhYfXXGKkasV3RzgUAoPj43hjQUry9LYCiNlyv59Vomv1YFJZkku8keU/HGDAYRkPIlBuFhMctDstprdEeVrQYZtfkyg4FJ/Vd84Ps6qoBALDW1fHD48NrcqQ5cLjucFBTe2tAMDoDWNbrTn3EECpVR5hMXHuLhAUAgLJYQw7RBABAyGQL3awgpcwMeN1mdtD6GyTgBgMTAIDi6TkkAxOhdnjwCAyD7kqTA8WhUAaRBIJevXcAACQeV2GXSNI6j5c2L6gkabrffcVnW4KbylRxGqkpVtlimKFoNswtOS/BERSxkKmoNHG634DyOA3nL3hHl30L/EWL3hK8sDZfr7LQDGoLw6SzMSxGO91qcjAoNJI1aYZ/Zft60SgAAEt5haAnAy9ilKfk8r6aalmjfgIA3GLgXd5bMxrHCE5QolvmQPrYSRzkVir1IVCN+k6PazHsF3mzjYyEhA4DT63u18ArOttyD4oixrH3hvYr7tITTA7VcfdLya81FCk8Yif6NDizTfR4ryMXdlVNv3qgdvJfMUjQKy3Ui79UvWIzY365mY1j6gsVmVMejvpR5M0eluexO+VZrV5WoyPEJ0Lw7XDve8LS8HONJap7q7Oli8feG5r9b62Ld25HRWrFlfZVmAPniwO4v2Ssiv1lqCGAVDoZn/xA5CUcJy5lH66PrLjaNl9ar1t44KP8BQIx8yyOE2StzDyBREbVQYlun0x5KPJMZyin2J+rdw/gHmir1S7JO9EY3Fv9t7zjjTEEDnSvUEGvCr4+ES4XSi+2jr1+uD5u7MLQYZO270TVZmRc2l09tb1eO5VKJ8vC09z3ps4OLO3pXqkvlIuaylTLWTxq/pSHowbsbUZRBOIm+/x45dfaD89uL5+/4MXknUPpe6e34u90X5/+oXxCc7n6YVmTfpxftHDNQKJDeiLz65IMq8kRHDbS/f3+ch0BAEbMDrxedV1qri9UpHcaeKOCRBo/IfNSg9KUPiZYNGDl8d8+K1ysbjNNEwdwd89fnbijt5Dl1NmB5WWXWvNbqzULNDLTMb4bs0dRua4c/bJoOkEQyMwn4gb1jemKTmGmHf68cIPDirklTvfbEJPu3RiT7t0YECcqv/Bz1QuVV9tflNbrkjJWxW4RiJl/6uSCRmai1xXIveWNeh8AgCkPR/U4qTMQanJl4pxjDXNVrYYpBA40MhWVqtqM00ovSn6LGuvVp1PBWSI8OJXFEu38inb9KCoJ1dzOchV/NrfNwEMQhAQAXwDAFABoAcvYmRoAACAASURBVIBsBEEOEQTRNc4+HwCSCYIwIQjyOAC8DwD33q4+/Rm4+nLKZY36BWWXWoNSZweWd10nsIgxKa0FqdXUhrgDAD087IaBh5BIgFCpWpvRIFCYFdEh/JDjw9UnAsNoCPUPAw8AIGm636ET35bOKr/cmhqc5PZbX9uTPT01AAD21jb+9PnzKn4q/wlKFCWh8gbTzGAA4FtVTnleECpVh5stXIdczgAAQDmcYfHgAZlsIRwOenKGv1OGDG4ysQAAqAEBQ/TgUW0AgGIaDZksFA56sGVvb6fiBoMvLSjwWl/tSEKhHHCcYs7P53UaU1Q6GU+a4V+VNMO/CgD2GLVWStGZ5nBJlSZOpzBHeQTzTw/0JWuprPQCAGDEx0vobIqDzqY4eivzQPHw6AiHbG4WAEBDT23E/ryzTaXKlcXnWvxi0r1vmsBpKVfNoNBIkpHzgnoVc+kLXCkThFXmQE7yy5xT35UuW7RhxGayUOgAMtmA63R9GnjlWa1eeqVllDiAu8eZgUVvcEUMq7PGHQBATLp3Y/aR+pL2Om2G3YYdHI6ctIFw6NOCx2xmzDditMc7imZDuLxJP3fvuzmpYSPEmycsi7g63Mcrvdg6AQDwxOn+A/IkO8O/3YsnbdBxTn5b+ohWbk6nMsgNI+cFbhzu3E0URWDEnMCyEXMCyyqutnnmn2iaq24zTgIARBzA3T3pgch9PQ0W05eGH/zlzeuzi840L02c6vefnvYtqVCnIihYk2b49fp8p84JzC3PajM2lSjHwUIYNgOvrkDumnOkYbZCYphG4ASDzqaUmw224Nxjje+UXmwtD0502zt20R+TAjhOwPmfq54AIJCxi0L7DLXui8SpfjWlFyQXpQ26+S2V6qMDjUzoRCMz0X/dlPsagQNlzjPxrzlTaud2o1OYaQ3FimUkMqKxGh0hmV8Xz57zTMItE3fO0lSmdGkuVy1j8aj5E5dHOOV54gjpNp4rI0sjM482aq2bWTyaHQDg+alhO7dcqFWvmhA8oFzOorMt/tJ63QK+G+PM3S8l9+tBjB7vvfPaobpN536qmDnv+cQ+vYWHPi2Y01ymWgEAxPXD9Q2pswLK+2rfF1aTnbT/g7wXLQZ7SMRoj/dGzg0q7VwXkiyW+ka6vPzbZ4WLpPW6e/a8nR2ZNMPvv0nT/YdcKqInSi9KfJpKVeE6pdnXrLf5WE0OH4cNd+3ahkRB7YP1JuafbAoqOd9yl05hGQ0I4Hw35vn4ST77XbxYmv0f5G/NO964OGqs1/vDcS4ZMR4Vp69JYKQS8S9yIfc4UfVP5XZ68FIBoIYgiDoAAARBdgHAXAC48fARBNF1huwqAPQaDvZPIWyke0XpxVZordFGAMBNDzPVymTq+SpCapaFuiMIRgsPvymcC6HRNFqb1gsncGqiOHG4wjMBcJyGUKg3GXghKeL2czsqmhQthhEA0KeBRwsM7PCIKOSCONc4PZ1ElxXICuaEGFIFAABch9ypDxhKp+sIq5XrUCpZAADOiJw4A0KhmHGj0bX/lh3gZjMTUNQ+FKMMAAChUm0AAA65nDaUfWkPHfIDgkBpISF9evAoYncFAIC5oNC1J28ZAACLR7P/PqtZ3NN6Z7BLJN6AIA7OxAn9ht9QAwI6DDyZrFdjKjnD70JTqfLh8ittE2LSvX/oXF58rsXPbLBH+EQIvh3sYArTaAVcY4tD6MU6ppQYZ+UeaziTNMO/CqXTVbjR2KeBl3e86W4EAfvou4P/9IR5v2jhkYor7S9l7atJ+jONkswtxZM0UtNkcQD3l4nLIq4AwJX8k00Xco81PFV2uW1tc4U6K/2+sC1d5eyHAubAEaXEMIHFp+UPdqDbHx1ePOWinrx4OE5AdbbUozZXFqdqN0YwOdS2oCS3azHp3vXD5RW5vK86riKrfRlfzMgLH+lxKWrc8IShNhQpRMe/KXnHYcNFHkG8nRmPx+7tLh413ISP9GgNH+nxlbxJvw3HCbSvEGIXD5bZM4S/t6VC/eC1Q3WR3UVScJwArdycyubT8zsH4T3B5FAdvw/YxxjUli+7Rr4MhtzMhpCyS63zdQrLKAAAnivjYuRYz4OJU/1qDGoL9dyOysmSKvVdJRck66uy2xv9okV70+8Lu3huR+VYo8aa5BvpsjUowU02lD6MmBP408nvykZd/KVq0eINIzYPdHuT3kbe/0HeWrPeHgEI4Ac/yd84//nE9UOZiBoOTv1QNg+z4y4pswJeKr/cuqClQr2sOkc66FDN8zsrVwBBkEffHfLVQJ5H/xjh+YJTzZOuHaxLmbg8IgsAYE6cZ/ucOM/vB3J8uw1Drx+uewolI4bJD0U5FWGQnOFfWXJBktNWq71L1WY82lsYbuaW4knNZaoVHBf6VbPBFlRwqunpqLGeT/f1LPQGjhOw973cJwxqa4p/rOjL39/dN0FjUrC7X0rece1QXX7ByaYXrh6oe7+5XL19zjPxvw6nB/j4NyUTanJkzwAAiiBgozLILSwerZTFp7Xw3RhN4kBeS9avNc/V5EhXxqR7Fw4kDaE6Ryq+tLv6KZPOFougiMnNj7M/dU7gb35Rf3yP3Pw4h6T1unsLzzTviZvoUz/U8/HQ4LRFBhrBJBCEboJhU0j9O3A7RVa8AKCrC7Xl92W98TAADNmF/VfjEcTXUWikNq385oLnJr2NjNtBQDAcCotZ74VQqVqUSr0pDACl07VWm9kVBdS+PHL5sOTFOORyKgAAQqNZu68TeLCumnS2aFmjjt3XPhjxcRoAAEyj5QMACBnCGhtuE7ibAsxkhxlnIianDTzcauViGg0TAIAscBmWjxVCoVgJh4PRf8sOcIuFhZDJQzYuO68pplIP6aVgzs8PBgBgp0/oc/aIGuAvBwCwNTbc1mRqTKn0QpnMdhKX228+DCM2Tg0AgKl6F1HxCOLr2AJarqrVmN6ZGA8AUHpRMh1BwD5ibtCZwfYV1+tdEBpNNXVF9E8kCqrOO974tKLFwELodA1uMvXap7oCuatGakoXerEzPYL4usEef7CMuSfkKomCquoL5YMSWzFqrZS97+Ys+/rpc1vP/1zpVC24kvMS37p8+eNMLrVo7nMJP3cuT5jiW7vszbTVXmH8Hw0qS/KRz4u+OPpV0bThkK7PPlwf5bDhrl6h/GETV+kOjUnBfKOEv1hNjuCLv1Sl1BfKRZlbSibt2HDl2S3PnP/u1PdlX9cXKVbpVZbEtlrt4ku7qz/Z+uz5b3a9eX1l1r6aWKvJPuj4arXUxCg+J3nGZnZ4tdfpFp7bWfn51mfPf/7rf3Pvrbja5jnY/TaXqwQnvi19E3MQnBFzAl++a03Srttt3HXF1ZdjdGZgNun+iCMkMqoqvShZ1l38oPhcS4DDjotc/Ti9hmd24hcjvEDgBOP6b/XJA+0rjhNQcl7ie/jzwpnfvXDxvasH6j4wqKwJbn6c/TNXxa5Y+kbaB50hpGwB3TbrybijD7w7+tHgZLePgACkOlu6+vsXL31dmydbSWdRKqc/FnNkoH3oTmiqe5vQi5WpajVOq7zW7jGQbe02DN33Xs7zJq0tPijR9bPosV7vWI2OwAMf5f9HLTU5/Z0bbprLVYL2Ou0CjpCelToroHz8krCvAEGwS3uqB1xbFwDg4u6qRJ3CMsY9iP9LSIp4QHlPqXMCi0hkVN1SoR5SDcVjXxXPthodISFJ4i0DMURiJ3rvxDGCc25H5eye1p/ZXp5Wmyd/ismjFtz9SvL7UWO9PrNbMK+jXxUvHkw/f92Uu1QjNU12D+Ttmrkqts90hhFzAsvuWpP0FEdIvyqpVD+w+83rj3X97g6FI18UzqjJkT3H5FJLpq6IeuTRz9LvWfHhuGeXvpH24fzVibsnLIu4GjnasyV1VuBnmIPgnPq+7CFn961XWqjndlSusxjtQd7hgu8XrU998J5XUn7satwBAKTfF34AJSHGgpNNS4Z6PnUFctdzP1W8RUYQrJmEgZ8R3P9qAZ3hBCGI26M+hiDI3QAwnSCIFb//vQwARhAE8WQPbZcCwJMAMJ4giFsMEQRBVgLASgAAFxeXmO3btw9bov7tQJZDEZvlKNN3urUe+X2IZNMjFMlZml97RL4h7MS37EijyNb+4ss3nYfrtm1iW10x591nA82P8x+XDEdfUL0e9XvtP4GqWTPl2okTb/L6WJQIre0yzUcYa5dy/bE+X25+a9cFGuNi9Yp775UfNx4XnDGdET5Y+rrDTaIkhRFn9fKlS/ud8RTu2SNi5+ZxVXPnykV79ohb1qxptHu4D3g2qzuuO3a4MSorWU0bNzo1m+P27Xfu1LY2Wsur64aU78nJusIR7d0rbnnpxUa7WDzo83D9aYcbo7yc3fTmG3WA9D6mvvFbzsyQaydNGlDy/0DwfvsdX7tIaJeuXOmUmpTfy68EGhITdcqF9yh6a2NoRtnyfKq7ONUmYbrjZtwOSNMJWgDDDTeKU+yDTtT3+OxzL8RuR1qff67F1I4ypNkULxqfMMfUfOtgNjfQmzes7/E3lheQXQ3NJJ73RFsDhUX8JXLmqjKyi7aG7OI1wdpI5RBO3z9mGUpXFJHFDhNKIdEIB2YFMj8UU/LDHOrebh/cDojkPNUXdyCo13hrE5kBPRrvNj1CURRQ3KxqlEFm4TamGDeyPDADzYWw9nFr9oosl+Jmakc5vtOsdSgZbpvUJYEDNJ+m+WMWIAHR0VOUQmA0F9zMEOFmhituonAIO2YFkqmNxDK2oyyLEmUCjiAImcAZrriR7Y3pWR74gCadFAVkkb6JxHcfZW+hsHC7QUJiG1tJHJsGpQMAUDi4leWBGzh+Dl1v17w7DguQ2i5TvTALQhGPsEsYIrzfPJ+/Em0Niacqo7i6pdhau16/jvub5OIzxVrf37kTOEDTCVoAjY+b3Ufa+xzsEwSATYdQLHKUaVaiDKsKZeB2hAQAQGbido4fpuH6YzqU0v/9RhAAxlaUpa0hC+x6hOYx1tZM4xHDUvDZYQZSyxmaP0OEG8Uj+j6nrv1R5FNcDS0kHj/MrhCEYRoAAEMLypLnUzxofMLsnmZrvZ3PUm/IciluxlaU65Vuu/G+6vztnRlDdAV3ANJyluaHIAThNcHWhJIGfj6KQrJI30Ti+U6z1pOoMOAwd7sBIUvOUf1oLrjZPc3eOtD3W/sViodFjTJ8Jlsbuh7f2I4yZdkUTyqXsHiMtkk6f6vO6+cx2tZMd7l1nNsbmmoST11OcWV7YzpRgl3mbD8JAkBVQhbq6skChhgzuCXbpYO5zp2oK0l8TSVFRBdhRvEIe3t/+1KWkIW6OrKg+3uht77KcyliYyvKcUu1t7Lc+26vKicLtNVkocdoazNd6Py17IrDBKS2yzRvzA6k+gCrNqsNESwx0MAlyi7jBWF/+qTvYJk5c6aWIIgeJ8ZuZ4imBAB8uvzt/fuym0AQZDIArINejDsAAIIgtgDAFgAAoVBYnZGR8fzwd3f4ONpQNL2+VbEq1C3p/c6Zqct7q+Ml0LzR3y3ge56ReMpEsrR2P4/C7zc/TjXhM9yF4l0Z0zKGRXxBf/qMqAXgOz+rbZd3RsZNMvSYA0e2Xr/wvU3Cqc1YNapXJTQAgMr1G75ykbTWp2ZkvE9uIIsb85sW0A38dI6uEBFRradTMjL6DY1o/PnnhSabbalfa+thI8DDo2Ki17NSU4cctlX3zTcPW82Wqc7eF9Xvvf8ajmHcjIyM1UM5buulS6O0AC8n0xmbBBkZg550qHz99Y9QNrsuY+bMDX21IzAMKl7fuNezoeHSaCeu92DAjEa0avULezkeHplJGRk/OrNN5avrNwslktoRGRmbemtj1Fop2wqztlkaeFV3P5T20bHNxVMITP5UdHLwGykZARWD7W/lhg1fkHj85oyMjHcBAE5C6biq69LVtdxZ7XG6d2kzpk17vrsAjqRKzT/4W/43AjHz5Nx7Jn022GMPlZYgNf/gx/nfWWuEBfPWpfYbHqRqMzKOby15QNVqnEGmotL4KV6fx6b7lB/4KP9pTZV5HK7ilM57LuHT7mqgOE7AzteurnaYzAGJ03zXdwoT9AZ+DwEnvyudIKnSTNbV2aJ0dWQBiYyquSJ6tmcI/3rSdP8CZxRHDWoLddvhK9t4rowzs+ZM+qT/KzI0LpqqEusLFVP4Yma5f4yoKGqsZ2Nf9d10CjMtN7MxvrVaM1InM6eY2kieYSPd33c2byT3WENofVPdJqEX68iC5SO+7rquoVghLDrTMkbepB+jqbKH6Wop9MB4161THo4601e4lKxRxz74ccFbmMUBCdP81qX9CQqnQ8VisJN/XHv5K20p07TgwbHPd57fN6cvfEBnI/I5CyatcWY/uwqur1RJDNNGJoxa1z3sTd6kZxWcakqS1utSDWpLLOYg+AAAJAqq4Aho2SJvdlFIirh4sKGVOE6AWW+jDCZ8ri/21eYuaa/TLnInhX7ljOLp3vdylhpadAvd/Ll773kueVvXdad+KBtTebX9BfklrvGeV5I3DjWUdSAUn2vxq5dUfSLyYR+cd+8f7yrMgSM/vnz5XXUp4jN2cvyaTvXV/tj7Xs5SzKwLSpzmty5tdt/vo97IpzQFZTXWfERq9z76e61ap8FxAn585fJGALt40sK4J/1jRb1OTvZGIb054NLu6k9sVeLrC15M2gkAkH2kPrwhp/4NKp3cMO+JhFe65k3Ko/Wsfe/nfqnMZTmWv5W2xhnF2JPflY5Tl0tXs11o1xa/MOKdAQvazAQ4+HH+/JYK9YPyi4zaeasT3x6o+AqOE7D/v7lLNHW6RVwR/eLCtSkfOtN342grZcdrVz9VFdIpU2anru1LkOboV0XTjK2KJzyCeDvveiip3/p86iQTY9fGa1t1JRzFXe+NeW0g5wPQUZf1t88K38FsuGPE7IBXBELUfOBAyTsog6Qx1JLg3ifGPf93Ejbqh17P/3aGaGYDQAiCIAEIglABYBEA3JTrgiBIAgB8DQBzCIIYUsz73wnvCJcKAIC6QvmNME2t3CwGAEiPHJMvMCJ4Kxe7JdSihmfhUh0A01kppd3XDRZMo6EBACB0+i3GM4mMEjxXxjWD2pLYX008hE5Xd4a8TfWfKn3f66v9BA50tr6ZhDKZToU7ohyuDgDAoVCIAQBoQxQ5ubFfKs0MGEYnMOcUtgmbjYVSqUM+Nkpn2AAAML1u0C59h0ZDxnV6P7K7e5/5dwAdQjwona7AtDqn8w0HiuH0aTEQBJni5dni7DYIg6HqKxwSoCM3kC9mXtQpzKM0MhNdUqWeQWWQG5Jm+A/auAMAwC1WF5TNvhHCMeWhqAt+0cItaoqHR2XIIqqlrv6W5yxrX81cggBy0gz/XgvE/xl4hwk0XCEjSykxTNv5+tUnjm8tmVidLXXvKdzp/M7KlN1vZ3+pajVOc/XhHFi8YcSToxeEFHKEdNt9G0f+1yuU/6NOYR77y1vX32soUtwUwpu5uXi6Vm4e7xnC39mfcQfQIboxbUX02YfeH7PunleSl4akiD9g8aklGpl5TOnF1le3v5q188dXLq8rv9LWV8g9XD1Yl0rgBDMgVnTbwjO7MnZhaN7yt0a9N+fp+EOxE7wb+ivezRUxrBOWhl+77/WRn9z/7ugH6GxKedX19meKzrb493csq8lOyjvR9CSJgqqmroje1n29f4xIOeeZ+IMPfzB2zYRl4atoTHJjTa7s2W1rs17r/vt0opQYmIc+LXjdZnH4xE70efOfYNwBANDZFId/jOhnq8kRfG5HRRpARzif1egIcfFg9Rue2UlQgut5ggBK9uH6kQAAldfaPQ5+nD/vuzWX3t79dvaOquvSF/RqSxxbQC8MjHf9bOrDUStXfjL+oaUb0z6avjLm9FDy5lAUgeE27gAAJt4fsR8lI9prh+o27n47+8HafJlbb20PfZI/V1qvWyjwYB5f8GLSLffU5AciLwUnu31i0tli9r6Xs9bZOrbDQW5mw4MoCTFPWBr+S9flJDJKjJwf9BmOE/Qz2ypWOLOvssut3tIG3V18N8YZZ95HvRE3yaeWQidJ2mt16QPd9uS3pRNMWlu8b6TLj4Mx7gAA4ib61HOE9Cxpg26urFHHLjrT7J9zpOE1MoWkyng8ZkN3URxXX44xPM39S5vZEXDky+IFfe0bxwnY/2He3VXXpS8wOJTSBWuSNg1WrXTuswn7g5PcPjLpbDH73st5q6eauX31Y8872Q+31+kWCdyZJxdtGPGBs6VMWDyaPX6y76cOG+56fEtJr/oa+SebguqLFI+y+NS8uc8l/NJbu64IxEyzZwh/r1FrS7h6sDbK2fMB6JgsOvJF0UaHFRMnTvV9I2mGf9XiVN/m6rczlgZGCvfZLJjvlV9r4geyz78rt83AIwjCAR1hl8ehQ2xkN0EQpQiCbEQQZM7vzTYBABsA9iAIUoAgyJ8udnA7iBjt0YSgiFnVagzvXGbS2twAAPMJ5av4RgJp5FoFRrvxputfyTW4AgBM1HgOm3sY02lpAAAog9Gjd9Q7XHCNwIGee6wxtq/9oAyGhrBa+Z1/t1Sq/QEA2EYJoExWn7W/OiEJOnKdMI3GAxDEQXZ1HZYP6u+5cIhDKnUqF46w25kIlTrk/D+U2WHg4UbToA087YGDvkAQZFpQUL8GHgAAwmQqcKPxtuXgmfMLvAEA6KFhTht4KIOhISyWfksSBCW6niVwoGVuKVluNTmCPYJ5mUOZJbO3t1PB4WCRuNybagfOejLuiBe59UqbRxoc+b7u4a7r5E16lrzZkMEV0S+HjXD/ywuajpwXuJ3FoxVrpeYxNbmyZ098W7ply9Pntm1bl/XyoU/y5149UBu9/dWsNSUXJOtJZMQwcl7gmoXrUr7jiv54nlEUgXnPJ+6LHu/1ps2CeWZuKf7w+uH6CICOOn/1xYqVLD41b84z8XsG2j83P65h6sNR55e9OWrTQ++PWRo/xWe9ixf7hElnizy7veKDczsqUnvbVlKpnkCioMqU2QF/e0OFyaE6pj4c9S6KosYr+2vW9ZeXfGxz8Tyb2eEfPtJ9c3+1ziJHe7bc/87oV3yjhFtMOmv00c3FXxzdXDS1qyGvkZnoBz7Kf81qdARGj/V6Z8w9fxRY/icw8f6IcxQ6qaU2V3af3YahxedbUgAAgpPcnDbwkmb4V5KpqLSxRLloy7Pnvzr1fdnXLRXqhzAHxhEHcPeOmBO4ZuXH45cvfSPtgxmPxZwMSRG3/91n2QVipnn0XcHr2QJavrxJPzfz65It29ZmvXLlQG1019//+NaSic3l6oc5Qvrle15J6VVwZNqK6LOBCa6fGTW2xL3v5rxsMdhvey3jS7urEowaW6JXKH9XT3U+I0d7tngE8X7RKczj+ssHriuQu17ZX/sMiiKWCcsihhSFgqIIiLzZ58x6W/RAiny3Vmt4dQXyFXQ2pXzGYzFD0n1InOq3k8AJ+olvSx/J+rVmI0JCzJMfjHy1N09m+n3h17gi+sW2Ws2i0gsS357aWAx28s+vX3umtUqznOfKuHDvq6mvDdVbO+2R6LMx473etJkdvr99WvheXxMNndhtGLpr47UnFc2GuSJv9m/3vpr6+UAVn1NnBZQLvVhHFS2G2dlH6sO6r5c16tjXf6t7mUxGNdNXxnzQ36RcVyYuizhKIqOqskutS53NAVVLTYxDnxT8x2p2+MZO8H6rew3YsYtCL5LIiKYmV9ZjbuU/jdvpwQOCII4SBBFKEEQQQRBv/b5sA0EQh37/92SCIMQEQcT//t+cvvf4z4BCJeEMDqXSoLbeMPAsRruYTCXJ8cY6BgkHRMkmqD+W/njjhjc7zGgN2+ADAIA1S3jD1RfcYOgw8FjMHg28pBn+RQiKmCWV6hF97QdlsTR4FwNP3WYKACAIlrENUI5z9bPIIlfd731yHw6Rkxt9o9PNAAD29na6M+0Ju52F0GhD9+Cx2FYAANxkGrTIijknJwgAgDV6tFMGHonDkeMm020z8KwNDd4AAKxxY53OAUXZbDXuhIGXPDOgnExFpcoWwywEBcuou4KH5Nkx5+cLAABIQpdbFB9H+rYd8ZJcAKmKPPXgx/nzO5df3F09k8AJRtxEnwEbO7eDkGSx9P53Rr+x8tPxS8YvDnsyIFb0JduFnm/W24Kay9UP52Y2vq1TWtI8gnk7lr0x6rm+ZK/HLw7LTr8vbDVKQs3ZR+rf+u2zglnXDtW/TCKj2oF+PHuCzqY4Ri8IKVz0auqW6Sujn6EySJLSi62v7ns/Z0l3QZbWag3PoLYmCT1Z5/7sMhCDxSfCRZ2c4f+Ow44Lj3xZ9KLN4ujx+1h1vd2jtVqzmCOkZ6XfF95naZNOSGSUmP1U3OEpD0U9yWBTqusLFE/++MrljbX5Mje90kL99b956y0Ge1h4msem8Uv+eaUeKFQSHpTgusNmwXzPbq8YJ2/Up5KpqCxqnJfTec4oioCbH/ek3Ya5UmgkuW+UcMv0R6NXPPLR+Kfufin5p+QM/8qh3sN/BbETfRqWvTnq/ZmrYle4+XN/NWqt0XmZjW9/u/rip8c2F0859UPZmJpc2dNMHrXg7peTP+jveZnxaMwp/1jRlwa1NWX329lrmst7F7jqieJzLX57381Z9u3qi5sOfpw/r7f7HKBjkF9+pe0hMpXUPvmhqF7FZzIei91HZZAay7PaHle1GW+JmqgrkLv+/Pq1Vcc2F2+xGO1BwUlumwdaRL4nYsZ7nwcAKDzT7LTYyukfyx/BcYIxcm7g50O9n6LHezVxRfRLWpl5AkEAOn5x6PrAeNc+6xdPfjDyaxRFzFcP1j3VXfxE2qDj7Hz92kaN1DTJI4i3c8nrI/87XJ7lcYvDclJmBbyKOXD+ye/K3i8+1+LXUzubxYFKG3ScX964/ry63TRVHMDdfc/alK2DvVbTVsZsI1NQZcHJpqdMetuNCQnMgSPHNhc/57DjwuSZ/u+4B/KcR+i34QAAIABJREFUzuEE6CiX4R0u2GPW26Oc8bjplRbq/v/mrbcY7SFRYzzfG7MwNL97GyaH6nD14x4zqK0pAxVH+jty22d//l/hChkV7XXahao2I8PFg2W2mh1uVDpJZikv5wMA6JgIcVlyOWlV/KpyAIDtZdtDlEycAQBgb28fRgPP+LuBx+rRwGNyqA6OCy1XKzePwBz4V709xCQuVw0OB8shl1PIrq52o8bqTyXhchJudyNxnSt3QPH06DDwTCYxwmAMTxF3AEAYDAsAgEMmZwBAv95PwuFgInTa0D147N8NPPPgPXimgoLxCI2m4Eyb6lQSPonHk9vq610woxElsVjDPnB2yGReCIWiZURHO+WVBfj93sAwpr21lUrx9Ox1prFjxpVztr1Ou4gvZp7vyfPR/NjjGZhGw/ff9XO/BYKttbUuAAAUN/EtBh7N318d+t1rYPKNrWipgAePby3Rps0Puiyt185lC2jZA6lb92dAIqNE9HivpujxXk0AkAnQEeZWkysL9QzhNznrbYwc7dni6sN5/sgXhS82lapWAgCWOjvglYF+PPsjIM5VsXgD9+UDH+Y/3l6nW7T91SvBM1fFftAZlpSb2TAWANCIUZ5/SnjmcJGc4V8pb9J/VVcgf+rAR/nLF76S8kPX9ThOwOW9NasAQRzp94VtGej+Q5LF0sB41/WZW0qmNZYoHzy+teRzKp3cYjU5gkJSxB9Our9D8v2fSPp94Vn1hYq6+kL5fZgdF7h4sU8M1MM297mE3XaLY5+zYWD/JPxjRQr/WNE2g9qy6+Lu6vEtFerZdQXypwDgf+ydZ3xU1dbG16lTMzWTOulAAoQQeijSL1KkSRFFVLAXLAh2rxXsiBU7eFWsNKlSRHroLZQkpPeZyfR+2vshhBchZSaZBND9/+Lvzpxz9s5cMjnPedZ6FohlVN7kJ3suCnR4/biHMjav++gEWXq69r7fPzjen5aQxcoIyVF9J/XRzFHxZy6/Tv7hmsicnRWDTWWOIX4vFw8APC0mysvPWeb877l9Q3qPTfyoofmK2745M8Lv4RLS+ke/2dTexHKK7Tkq4aPstYXvbPkq544ZL9b1pBYeN+oOrC2cZq52jQQA0MbKNmdNSvktsVt4bRAfXaN07BNZvevnvHPGUucQAGhyJh0AwM4fc/vYTZ7B0SnKFaEalN1vQvL3+1adl2WOjP9f5wExzT4YjU5R2Tv0ivg872DN/E1LT16cI5h7oDp654rclxg/F9GxT+R7o+7uGvK5oX3GJZ2TyOmn9/yW/+qeX/PfPLK55BzH8jKe5aUcJ8h4jpcJPFx8UB7bSfVtc7P+mkMdKfV0Hhjzyam/yl/atPTU9Pp+xd8/OD7VafH1SUjXftbSeX3D7+z8x3fP7Ztybn/17f1v7nC8se+bnJ0V8QfWFT7idTKpnfpGvtfUg7l+E5I3rV1ybNrxraU3pfaL+rIl+7pWQAKvjYhMDDtbXWjDz+6r7DRwSscTjJeLUISLj/hLSuuaw+XykmJ7cS8A+B4AYE/Fnh52KQgAgHFmc+gE3gXxgcvDGk0aikxSHsg/VDPo2JbSTo0NCydUKisAgOfECVXYyJFGj5NJlBNeAwBEEBp1QAKPTkqqE1+CQGIUGbJ5Prj0gsAz1zbr4HEuFw4cJ8HFklY7eIRS4QMAELzeFjl4tcu/TeZMpgxpVtayy0dmNLpmuNYEALg7+4AmbMTwFvUONAVnsejxsLCAyzMBAAiN2gIA4DlxQk3FxDSZiNnzxvgt2/93tlfmyPgGh+O6Dx26iff51ALHrbg8HOVymIrKOoGXmHCFwBN36WzBQIAs3x/7dmpnec8fMTxaU2zvy3NCWJdBMdeEe9cccZ01lrjOmoAcokvRxYe5bnsl65UNn568Wa4SGfqMa3mITVPIlCLm1pf6fbhp6cn84pzae1e/d3Rx/8kpi7oN1ZfUFNuHiaRk4QXBel0x5oFuW3989UCKscRx87ZlZwpGzu6yu/69rV+fHu62+7snZoR/2tJZgQSJC+MeythcdMJ4eOeK3Lkum79Hcg/dh21xQ9eeECQudOoT+f2pnRX/BQCIS1MHXJ5ZD45j8E8Ud5ciV4v9Y+7vtpXnha0Hfy9MLztn6Tl4RqfVTYVQNMT4ud3Xn/qr/FTBMUNvS5W7h7HUMcFY4rj5+PZSn1QhOqWNlR0FATBDiWOw18WkAgBI5NTZhG6qz3qOStgblaK07fju7MD8QzX37/3t/OK8gzVrR9+XvqK+/NtqcItLcmpvF8ups8NmpTX74KHXmMS880cM60zlzvHblp85bSxxZFwUdjGyP0Ip7C4lMlHxV0lO7QOn/ipP6DZU36hjbK5ySc7uq3qQFhOlYx/MCFn/dae+UVWd+ka9HMw5I+7qsqsy3zq4/JxlVt7B6oPmSpf22NbSZwFA6DMu6fnWDERvjvQhsaVSJb1g54+5c1mGUxAE7qZklJkgcTdJ4y6SJtwUTbi1elnxwCkdT4RizcEzOh0pPV37V3WRbWrOzoo9dpNHVZlvnanUSXaNfShjY0uvKw2j2fiu2h8Ljxvn7v45r8/lM2VN5U7Z9uVnbjWVO2/CCcyV2i/q3Uu/zxtCn6q2KnWS3bWVrpHmKtf3zZXgX8sggddGpA2IyTvxZ7lQU2RPc9l8ZziW14jllIGtqVEBAGg0sSft/rwJB6sOqvpG97UW24p7UiplAYC5A2ezhVDg1c30IFSqRv949PhP/OH8QzV80UlTv8YEHhkebgUA8BUWqpgql5P1c1FhlC0fAIDU6QISS+K0tIsuAka1PuSkHlwm9wIA8FZbswLPX1QkBQDApZJWC0xCqfQDAAheX4sEnvXHHycCQXgin3k64AQwOibWCADgPXtG1xYCj3c4Yqn4+KBEBamLsAAA+AoL1QDQpMBL6q4z3fOersH0Uk9Ojpx3ufQAAI4tWyIVY8Y0eS3WZFIDAIi7dLlS4HXu7AQMYzGbRTlpYc83Vr59+HVHrXeAJIzOaSvBcy1Bi0l+8ryebR4ig+MYjHu4+6ZDG4qKjmwqeWb3L3nvnj9i+MXnYjvGddYENDj4WmTyvJ5frXglOzHvYPVjEQlhZRnD44qrCqyKwuPGuyVy6uzo+9L/aO0aSd11poRu4S9VF9iUoShXuxYYdEunw/lHDGd9bjah1+jEa7738mqC4xhkTUrJyQJo8efUbai+5IKoWWk1uMUntpd1qzpv62E3eXqWnjb3BgAQScnC2FT18oxh+t2Xlw+OuLPL3oxhcce3Ljt9l7HUMfnHVw8M6Doo5tNB0zsd2778zM0cy6t7D09cFKgTe+N96d///PrB/rnZ1U8BBqw2WrYla1LKby0NMgmEXmMS9pTk1N53bn/V0G5D9Q0mP1tq3JKNn558kGN4be8xyW+150zJhsBxDIbenvbphk9OfvrXitz/Mj4ukhIR1cNuT3ulpUPjgyE5U2dMztQ1mdodakbO7vLl6veO9sxeWzCP8XNaSkxU3DS3+8et7aMdfkfan2VnzVPzD9XMHDSt42GCxAWO5bFty84MKzphvItjBaUmRrZ5+B2dvw901mGXQTHr9q8qGLb31/yR4x/NXNeqDV5FkMBrI8L1chctJsrsJm9a2VmzDgBArhbXsPm1KgCAjJQBB1YZ8yb8nPtzrzA6LNvis3Tqruv+CxA2Ped0hEzgCR6vCACAVKsadfB08WEuqYI+Zal29QOAK9K7AACo2FgLAABTUakuP2ogAABUrMEOAEDFxAQWsqJQcECSLmBZGR6CkJP/v26YBwCAczqaFXhMeYUMAACXyVrv4KnVdSErPl/QJZrOnTu1/tLSwaLU1A3itLSA90J36GACAPCXlOqgLrwoZHhycuQCwyipyIigHDw6Tl/3b6OyzlFrKbbVay72pDp3705pTuBxVqsWMIyVdOt2xZc2RhCAiUQW3uFQqyOlnpse7v7ytmVn7k0fErumNXtENEyfcUnnopKVT2z9+vQzlfnWWQDA9xydcN06UmI5xY66J/3N9R+feH//moLnI5OV87Z/e/Zunhck/ULQu1MPjmPwTxF3AHU/z3/mdHnLXOlSXe2b6H8bqgip94KDcQgA4PwRQyTH8nhz5d26+DDXbS9lfZK9tuCvkzvKHznxZ/krRSdNexy13r6KcPGexh76NraH3mMT3yw8burfZ2zixrYUdvVEp6jsMpXoqLnSNYRj+f9d+rvpqPXSf353dlxlvnUqzwlhkUmKn4P5edqShK5ac1L38K8LjxnnShX0ifGPZr4Zrg8sz+B6JCpZ6ejQK+KLvIM18zEc89wwveOiYF3rhhBJKS4xI/zH/EM18/76Ibe/NlZWfWRzyQNeJ5MmllO5WaMTXm6o/Lgpeo5KOH9sS+nZqgLbTYyf23C99JFfDhJ4bYhMLTprq/EMMpY6ogAAVBGSGt5m6wkA/IQh959etHqF+UztmV4rzq3wAAA+IGbAMYw+M4x3uUPn4Hm9YgAAQqttchhkeFzYgdLTtfflHqiObugPAp2SbAUAYGtNqtwD1QMBgNN6ilwAAHRi4OMOMJq2Cywrw0Shc/AIpbLOwXM6mxV4rKGmzsELC2u1wCQjIupKNP3+oAWe8eNPbgJBwMIfeCCop0PSPr1NAACs0RDyoBXXrt2xAAB0QmLAASsAAKK0NAsAAGeqVTV3bFN4T+ekAQAPGCb4CgqSAaDJsiDe4VBjIpGlsVJO/JLRHlHJSsftr/Vf3Jr9IZomrrPGctvLWc+v++j4TAzHWH1qYDOxrlX0qWprn3GJi7LXFr71+5Jjb/i9XHxUsvKnUPXu/FOJ76I1t7R8FRE6OvSKCMoJypqYcrrbUP2jmz/PmVZdZJsKANB/coeAZqFeSq/Rifkt7alqKbGdVH/lHayZf2h9UdesSSk5boef3L787OiKXMs0juXVMhV9tNtQ/Q/tva/muPHe9K3Ht5YWd70hpvCfXpoMUFea6rT4IiMSw/IC6VcMlGGz0nYVnzJNyztY/QjPCTKCxGwdekUsGTm7y46WPoxLSNeuy82ufmrfb+d7XY/BVwBI4LUpmmj5OUuV+8bKPGsvAICIRIWBczhVGE3bKZmcj5XHHimzlw04bjjuIzHSdWfXO3MrRN9ZBY8ndA7eBfFBRUY2KfC63hBzoPR07X3n9ldlpfaLumLIuiQjwwoAUOSM7GpjPcOikhU/SQ/VkF4M46iY6CavfSk4Tds5tzsao0Uhq2uuLz/lXe4r0rsuh601ywAASJWq1QKT1OnqSjSDFHj+khKx9+zZ0ZRev08x+sag/giLkpI8QJIuzmINucDz5p7TAwBIevYMysGTdOtmBwCes1mDSnO7HKaiMg1XKIpBEHC2uialueN5l0uDSySN3khiEomFdzojW7MnRHCI5RQ77dk+Qd8UXqv0Gp2Ybyx1fFJw1PgYJSYqxjzY7ZervScEoq2QKUXMlKd6rTiXXfWX0+wLC1YkXi36TUg+kH+4xltwzDjCXO2KKTttnsEyfLgkjMrpekP8W/0mJJ+52ntsCBzHoOeNCdeU6GxLcByDyU/2DHkPPEUTfGrfqOU5uyueDdfL142c3WWFNlbeqof4N0zvuP/8EYOp8LhxAhJ4iCtI6KY9V3DUAJZq1w2AARubqrYUu90qTCSyAgB013U/UmQr+k+JvWRItCz6oIyS8ZhYbOfdbm2o9iD4fSIAEAiNpsmo3eRMnVEkJQtrK5z9AOAKgUdqtSwjkrvOUT3702KidMyDGb9U7/bei5Gks7kwjEvBxGI7AAAuEYfMwSPDdXUCz+1uPmTFapUCABAaTavXxwgCAMf9AuMPqgeveuGikcCyMtWUKQ0GjTQHLpEYOXvoh50zlVV6wDBWPnxYUH/UcYmEx2jaxtkdLS7R5D0enDWbO4lSUnYIDCNiKip6BXCOhlAoGn0KiMtkFs5kSmvsfQQiEEbf1237tuVnfHGdNQWBphwiENczaVnRlVd7D8GgCJf4FOGS/dYa9whrjXuEWE7lpg+N/aD/5A4nrvVZiYjQMOS21EMDpnaYGqpySpGU4iKTFBsr86x3nN5VEd918PUXGNamc/D+7aT2i6rACczBsYKKogkjRRO84PGocLHYCgBwR5c7jmOA8QIIZKom9RgAAC6V/m2geGsR/IwICMIXiAhTR8sOeBxM58p8a4MO4vmO04DBReKeoxM+kIbRLO/1yjCKCkoo4ZI6gYdJpCHrwSOjo7wAAILP26zA4x2OOgdPFxGa9QnCJzBswAKP93hw96FDEwi1+mz4gw+0qBcArxt2HnKBx5pMelwmq2zJ+AWsrhyyxf9urb+tjAeOk4g6djxH6fUFgt+vcu3b36QjyHu9Glwub9TBI8LCLALDKDi7PfAnEAhEA4y8q8ueQEdVIBCI9idjqH61XC060G1I7Kuz3x60YOCUjkjc/csIda/cgJs7/IFh4D+5o/ymUF63vUACrw3BcQykCvocAAAlIgwAALzPp8KkUisAQEd1R7darD4DADCxw8SjAAC4TGYX/H6FwIWmHFtgGBGG4wGVUKb01B0AAOzEn2V9Ln9v38rzGVXhvWV682FTfR274PfLMDq4XjpcKq1z8GTSkDl4VFRU3Tw6ry+AMQlOKQAAFRsTkvUxHPcLDBNwiWb1woX9BI8nSj58WIvcOwAAQqEwCh5PyEs0ebs9tilHrClwicTCezwtLtF07duXBgAQNnLEWUl6eiEAgGPbtkbLNFmjkQKWlRNKhaWxYwi12gIAmOfEiZCVPCMQCATi2iNjeFzxnW8MXDj41tTDSNghQkFkosKhipT+Zal2DTeU2OVXez/BggReG6MIl5wDABBJSYPAcSD4fGpCJrsYPjA8fvjaDqoOG0fEjzACABBhYVYQBNKXny8LxfoCU+fgBXJsxrC4QpLGjYZie79LX7ca3OJTO8vnihmbp2PRmouDrAW/Xx60gyeT2wEACHnrQ04uXlMi4QHH/YLP12wPnuB2ywAA6OTkkKyPEYRPYNmABZ5z+5+TMImkJuqFF7JbuiahUpkEhlEwlZUtGs/QEJzdTvBudzQRHh5U/109uFRqEbzeFpdo+osKO2M0bQ0bNapGMWF8EQCANy8vubHj3UeOagAACI2m0blKZHh43fiG/PxW9QYiEAgEAoH499FtqH6dIAC9b9X5G6/2XoIFCbw2JipFeRYAQBJG1/hLSyXA8zSuCLso8F7q/9KB1RNXf1b/vwmVyg4A4MvLU4RifYFlRBhJBhRFi+MYKCOkB1xWXw9H7f8P7/7ji5xZrJ+P7OLcfRTzOC+W4Ql+vwwTiQIakVAPoVDUOXghSLG8FIwgPIGEnfBujwxw3E+qVKHppSGIgB0809LPUjmLpbO0d++1uETS4lICUqczAQC4Dh4KmYvn+HNHJAgCQcW0LNkKV4SZBb9fxfv9LXp0yhiMaYRWm4sRBIiSkjy4VFrJVlY26uD5Cs6rAQDIiMhGHTxKH2sGAGDKK5DAQyAQCAQCERTdhupLpAr6pLHEMYznQzIdp91AAq+NyRimz5VrRAeSMsMPe3NOqwAACFXj8eH1A8X9paUh6cMTGDZgBw8AIL6zJlsQgD6yubgHAMDB9UWdTeXOm7Sx8vUxtKkQOE7KVFfTdddmZJhIFJSDR+p0VgAAMiIioIGTgV+Y9AqMv1kHj/d6pRhJhkxcYgThA44LyEmzrlw5EUjSFfXC89tasyYVF2cEAPDl54VM4HmOHdMDAIg7p7XIwSOUKisIAu7NyQkL9lz30aMKwe2OphPiL871I7TaAtZiadTBYyoqNAAAdEJCoz14opQOFgAA1mBAAg+BQCAQCETQDJrW8eMpT/decL2V/iKB18bIlCLmzkUDF2aOiC/0l5YoAf5f5DQEGR1lAwBgDYbQOHgcK8JIMmCB12tMwmmcwFwVedZ+TouXPr619FGSxo2j70//H6FSWQEAPCdPqgAABJaVB5uGqZv/5BHlxIlva+fMDmk0MEYSXsHPNNuDJ/h8QQfDNAlJ+gS2+ZAV++Y/Ipny8gHitLTNdEJCq4Z7itNSjQAATHl5yIJW/CUlsQAA8iFDWuTgkVqtBQDAe+5c0GLKtvb3NAAAaY+e5+pfo2JiCgWPJ9KTk9Ng3TtrqtUAAIi7dG5U4Ekyu9fNbrS2bnwDAoFAIBCIfycd+0RWa6JlIRvt1V4ggdeOsNXVKgAAKja2UYEnSk6uE3i15tAkabKcOBiBJ5JSXJhGfNhu8vTduPTUTMbHxaYPif1IFSH1kjqdBQDAX1SkYo1GCniewiXBhaUQMhkf89abe4IZrRAIGEl5BYZpvgfP75eGUuBhBOEXOK7ZEk3T55/fBBgm6OY+sr61a0r79q0FAGBNppAJPNZg0GM0bRWnpbXos6FiouvKIUtKgxZT3jNn0gDDONUt08/Xvybu0rkAAMCxeXODLh5nsWgAw1hxenqjTjCp1bJAUQ7ebm9xbyACgUAgEAjE9QYSeO0Ia6pVAQCIOnRoVOCJ09NtAAC83RaS5D+BDc7BAwCISlEe4DlBYSx1TFZHSbcMnNLxBAAAFRNjBQBgKqtUvoICGQAALpcF1YPXVmAk6Qkk7ERgGBlGUaEr0aTIZks0vefOyXx5eaOo+Pjd8iFDGg0FCRRSq2UxmrZwVlvISjQ5q1WPh4W1qDwTAIBOTKr7t9GCckimsjKNUCoLqaioiwE+YTfeWAAA4Dl9psE+PN7pUGMikRWn6SaL4nGRyMK7XCEbO4JAIBAIBAJxrYMEXjvC2WwqAABJRjdbY8eQWi0LBOHmHM7QCDyOC1rg9bwx4QhgwBIUbr7x3vRv6l+nU5LrSt5qTSqmovKCwAsLXbljK8AoyiuwbPMOHuOXYqLgRjs0uS5JNuvg1bz19ijgOIl6xi1rQrUuLpWaeIcjZA4e73DoCbW6xQJPktHNAgDAWcxBCTzObic4i6UTGRV17tLXpZmZDkwsNjEVFQ06eLzTpcXF4mbFMi6RWHi3G5VoIhAIBAKB+NdAXu0N/JvgHQ4VRlEOQqFocsgdJhLZeLc7NLO7OE4EFBWUwNNEyzyp/aIWa2Jk1dpY+UW3S5KRYQUA4CxWNWswyAEACKXymhB4UCfwmu/BY1gZRotCl+BJUj5oQuDxHg/uOXZsPBGuPaWdPbswVMviMpmRs1njQnEt9/HjYQLDhFERES3qvwMAoBMSvEAQHs5mD0pMWX/7LRF4nhanpp69/D1CrS7gzLUNCzyvR02EKZodPI1JpRbOYOgczJ4QCAQCgUAgrmeQg9eO8G63ChOJGo11rwcXiWyCxxM6By9IgQcAMPKuLnt6jko4f+lrpFbLAkk6eYdDxZrNMgAAUqO5Nko0KcoLHNe8wGNZKS4OLhimmXV9QhMlmpYfViQLXm+4LCtrS6jWBADAFQoj7/GGC1yTzwoCwrVnjx4AgE5KarGDBwCAiURW3ukMSuC5DhyoG3A+atS5y9+joqIKeadL7y8rv0JAC16fBpfLGw1YqYeQyyyCz6cOxeeEQCAQCAQCcT2ABF47wns8akwsbrT/rh5MLLbxXm+rBZ7AcQA8L8Lo4AVeY+AikZV3u1SczVYn8CJ014SDh4toj9CMwONcLhw4ToxJxCHswaP8wPONOnjOnTt7AACoZ8w4Hqo1AQBItdoEHCfx5ebKWnstX15+LACApGePFjt4AAC4WGzhPcGVQ/qLijtjIlGtfOgQ0+XviTp1KgAAzL5uXeKlr7O1taTAMGG4QtG8wFMqLcDztK+gUBrMvhAIBAKBQCCuV5DAa0d4r1eFSyTNCjxcKrUJPl+rBR7ncJAgCDhOi0Im8DCJ2MK7PWre4ZABAFAxsdeEwMNokQ84TtzUoG1/fTBMkMmfTa9L+0AQCM7lavB3yVdUmIkrwoqkvXs32nfZEsjISBMAgPvosVb34TGVFXrAMDZs+HBDa66DS6VmweMNKrGSNRpTSV34uYZSVeVDhxQCAHhOnPhb0Irn2LG6IecaTfMCT6OpG99wOgf14SEQCAQCgfhXgAReOyL4fCpcJmte4MllVoFhlE2JlUBgq6tFAACYiA6dgyeRWgWvV8W7XDIAADo56doQeCKRBwCArapq1E1jKiqkAAC4XBYyBw+nKT8AAGswXLGuv6xcxNWaO9Nx8SF17wAA6MQEIwCAv7Cw1UmabK05FpfJKnGJhG/NdXCZzMr7fAEnVrr27VcLHk8kHZ9wRXkmAIB88GATRlF2f1nZ3wSeLy9fAwBARkQ0W+5MRUXVjfYoLkECD4FAIBAIxL8CJPDaCaa6mgaOkxAKRbMCjwgLs4Mg4L68vFaV37FG4wWBJw6dwLtwE8+73XLAMJbU6fzNn9X24BKxFwCAqalptEyTqTHIAAAIhSJkfYMYLaoTeBc+60sxf/ttOggCKe3b51io1qtH3DXdBADAVFW22sHj7XY9oVS2qv8OAIBQKCzAsjKmurrZwe8AALaNG+oGnPfp06DAwwgCCJWqkDWZ/ibw/JUVGgAAOiGh2RRNKj7BAgDAVFUhgYdAIBAIBOJfARJ47YTn5EkVAAChUjUv8NRqKwCALzevVWWanMUiAgDAxaETeIRCYQGOk/JOhwajKGeoB5a3FFwi9QIAsEZjowKPM9dKAQAIpTJ0PXjiuvJXzmq9QtS4jxzJBBxnNHfeeSZU69Uj7dXTChjGsbXmVjl4nN1O8G53FBGubb3A06jNAACe4ycCElO+s+fSAMNY1fRpBY0dQ0ZFFfAORzxrtV5M/GWNRg0AgLhzWrMOnrhL57rxDeZaJPAQCAQCgUD8K0ACr53wFxWpAADI8PBmBR4ZHm4HAGDKy1sn8Gz2OgdPEkKBd0GgclZbLEZR10R5JgAAJpV4AQA4s6VxgWetC4YhNJqQ7Ru/4I5yVtsVDh5TVtaD1GpPXzrAO2TrSiQ8JhabeFvrhp07tm6LAkEg6Fh9qwJWAABIXYQVAMBfWBhQmSZTVZVcPbJTAAAgAElEQVRGqFTnSa2WbewYUXJyAQgCaVuzNr7+Nc5qVQOGceKMDHtza4g7d3YChrGc1YYEHgKBQCAQiH8FSOC1E0xlVZ3Ai45uNmyDiom1AgAwhpqA+5kagnc46hw8iTRkAq9eoHJOZyxGUdfEiAQAAEIm9wAAcDZro8POOYdDCgBARkSEzsGTSHwAALzT8TcHz7lnr4Z3OuPplJSQl2fWg0skJs7pbFWJpufECT0AgCgttdUOHqXXXyiHrGxWTLFWK8lZrR2omOgr5t9dirR//7qglaNHL87D4+0OLUbTFpymhebWwQgCMJHIEuz4BgQCgUAgENcPnN1+bZSUXSOgQeftBFtrUgEAiJKTmy0ro5OS7AAAnNmiaOo4w3vvdXNs2z48adXKjxoKyOBczjqBJ5OFTOBR+ti6/bOsDKPpa8bBwxVhXgAA3uFsfOj4hWAYKjZ0yZ+4VOK/cO2/CTzrb79lAgCEDR8W8oCVi2vL5SbWaExrzTX8JSV6AAD50GGtdvDEnTpZAABYo6nZJE3rzz8nA89TorS03KaOU4wdU1X1/PMef3FxCgBsAwDgXS41LpE0m6BZDy4Wm3l3cOMbEAgEAoFAXB+UzJ4z2Z2dfQcmEllIjeY8GRNzXpyaWhB246gCWd++zVbO/RNBAq+d4Kw2FQCAJKNbsw6eOL1rncCz25p08Gzr1k9iq6v71Lz51v7oV14+ePn7vMstAgAg5PKQCTxRSsrFXxRMJLpmBB6hUNaVaLqcjTp4vNstBQAQJSeHLkVTKq0TeE7X34Sl9+zZTIyibKoZM4pDtdblEEqlkSkrG8j7/VggblZDsAZDLEbTFnGnjq3+TCQ9Mm0AwHNWa7POs/vQoTQAAMWYsQ0GrNSD07RAKJVFrNH4/w6ex6PB5fKaQPeFSSRW3umMbOoY05dfdXQfPJga/+UX6wO9LgKBQCAQiKtLyew5k937988mIyOPYCKRi6utTWEqK7M8hw+D5YcfABOJagmN5jyt1+dJ+/bJUd92W35TrSH/FJDAayd4h0MFJOkidTqmuWNJlYoFknTxDmejPXj+khIxazBkAgA4tm+f0JDAEzyeOoGnqhM/oUDcvftFgXpNCTyNus7Bc7kb7cETPG4Z4LifUCi4UK2Ly8PqSjQvfNYAdQPm2aqqTDI6+kRLhVcgUNHRld5Tp8jazz9P082d22SpY2NwVqseV4S1ujwT4EJfIE3bOYejWbfMX1yShonFBvmggc06cWRERIEvP/8/vMeD4xIJL3i9GiIqKuDgGlwms3AmU6NOp8BxUPvllw/zdnuy5cefTqpvnVEa6LURCAQCgUBcHUrnzJnk3r9/NhUXtytpzerFhEzGAwD4CgoktnXrk72nTnXwl5encCZTB/ehQ/3chw6BaelnDKFW59JxcTmSXj1z1DNuzaXj9CEzQq4VUA9eO8G7XCpcJArYJsZFIivvdjcq8Eyff94TeJ4io6IOcSZThvn7HxKvWNNbL/BUoevBU6lYoCgHAAAukVwzAo/U6bwAAILX06jA470+GUaSId0zEXalwLP8+FOi4PerxF06t1n/HQBAxIIFuzGatlp+/mVmS84XOA44h0NPqNStLs+sBxOLLLzL1WyJJms0ppE6XZPuXT10YkIhcJzYvnFTNGu1kgLDKHCFotlS53oIhcIsMIyysfp8w7vvZvJ2ezIAgGXFirGBXheBQCAQCMTVoXTO3RNd+/bPoeLidl8q7gAARCkpnojHHzsd//VXazv8sXlx6pHDDyX+8vNM1fTpC8VpaRsElhV5jh+fbv76m9cLRo36Ka//gLcqFjw1mPd4/jG66B/zg1zr8B6PCgtC4GEikY33ehoVeO4jR7MwinLELn7vA8Bxv2XFivFXrOn1igEACI0mpE8mcBFtBQDAJZJrJmSFio7xAADwHm/jDp7PJw118iehVPgvXPtiD55j+/YeAACqKVPaVODRcXqftG+f3ziTKcOweHF6sOd7TpxQAMvKqajIkDh4AAC4RGrmPZ4mSzQdO3aECz5fOJ2UFJDAk/buUwAA4MrOTvEcOVoXVqTRBNyDV5/86jlxosHfJ9u69VMxkchMxcXt8hUWDvMVFDRa5otAIBAIBOLqUnr3PRNd+/bdTen1e5LWrH7vUnHXGJKMDEf0q68cSFq18pvUA9lPJm9Yf6t61qyXxd26rRIYv9y+bt38/EE3fFwxf/4QzuW67vXRdf8DXC8IXq8al0oCd/DEYpvg9TV4Q8rZ7QRTUdGH0usPSnv2tItSUrb7i4uHuo8e/Vsoi+DziQAASF1ESAUeJhbXCTyZ7Npx8CIj6oVWozfngt8vC7nAuyCeLxV4/oKCTFwuK5PfcEPAIqSlxLzxxiZMJDJbf1t5u8AFV3nq2rs3FgCATkwKmYOHS6VWwettskTT8ceWNAAAWd++AQk85YTxZYDjjL+wMMWXl6cFACAjIgL+bEmdzgwA4MvPv2JftV993YEzmTIkPXqsVU2fvhY4TlLz9tvDA702AoFAIBCI9qP0nnsmuPbuvZvSx+5NWrUyIHHXEKKUFE/U888dTfrl5+867tv3iPLmyW8CjrP29RuePH/D4E/Ln5g37HoWetftxq83eJ9PhUtlgTt4UqlN8DUs8EyffZYOLCuT9umTDQCgufOOdcDzlPH990dfepzg89cJvAviJ1TgEqkVAIBQKK4ZBw+naQEIwiv4fI07eH6/FKPpkAWsAACQWm1dyMoFMc0ajRRrMnWl9HFt6t5dXF+nY2T9+//Cmc1dDG+/kxnMub7cPD0AgLR379A5eHK5RfD71bzfjzV2jDc3NxVw3K+aPq0okGsSCgWHh4WVsAZDMlNZoQYAoOLiAi7RpGJj6sY3lJVfUTpq+enHKUCSrqgXX9gcfu89+YRKle85cmRssGIZgUAgEAhE21J67303ufbsvYeKjd2XtGrVu6HKVMBpWohZtGhfx717HlNOnfIGkITPsWnTE/k3DF5a/vjjI65HoXfdbfh6hK2tJYFlZXhYWMACj5DLbALDhDVUD+zaszcLcNwf/sD9xwAAVFOnlpMREUc9J0+NZa3Wi8E5AuMXAY77Qx30gctkFgAAQqm8Zhw8AACMILy839+4wGOYkI92ICMuOIdMnZiuXbasC/A8Le3Zs83GI1xO9MLXt2Biscm2du3MYIQJU1WlBxxn5EMGG0O1F0KlMoMgEN4zZ+SNHcNWVaURanVeMF/MZHh4AWuxpLAGowYAQNylc8AOniilw4XxDca/lY5a16yNYcorBoi7dNkoSknxAABIs7I28E5XnHHJkm6BXh+BQCAQCETbIXBcXc/d7t33UbGx+5JWr3onlIF59eA0LcS8/vr+jnv2PK6aPm0hRpIex+Y/HisYMfKd6+3BLxJ47YA3J0cJ8P+9QIGAK5Q2AMC9Z8/+7UZZ4Djwl5RkUVFRR6mYmIvOXNioUesEn09Ts3DhgIvH+hkREETIk4EIhaLOwdNqrimBByTpERimcYHHslJMJAqpg4dLJDxgGCv4GREAgPvgoUzAMFZz5x05oVynKUitlpUNGvgTZ7Wm1ixc1DvQ89jaWj0uk1U0NEOxFXuxAgD4zp5rsEyTNRopzmZLoWJimpx/dzl0fFwhsKzcX1ycChjGSzIzmx03Uo8ks7sVAIC1WP7m4NV++eVkwHE2Yv6T6+pfi3zm6T1AUQ77xk3jgtkfAoFAIBCI0OM+elRxfuR/nnft23d3W4q7S8FpWoh+9dUDnfbve1x1yy2vS7OyNmDE9TVHHQm8dsBXUFAXDHHh5jcQCJXKBgDgO1/wtzJN87LlHQSfTyvOyMi+9PWIpxYcxWWyCtfuPRPrnzIIDCPCcDzkAo8MD68FAKDj4wO+yW4PMILwCX5/4z14DCPDxW0w2oEgfALD0AAA/tLSHoRGc45OSAjZaIpAiFm0aDsmkVTbNqwP2MXj7fZYQqkIWf8dAAAZFWUBAPCXllxRDuk6eFBVfMuMBSAIpCQjIygBLMnMLAAAYCore2M0bQnGlSa1WhYoysFfMr7BtW+/2l9YOEKUkrLt0iGoVFSUX5zaaStTWZnl3LlTG8weEQgEAoFAhI7q1xf2Kr1r9sdsdXUv2cCBXydv2vhWW4u7S8EIAqJfefmgfsn7f7bXmqECCbx2gKmoVAEAUNHRAQs8KkJnAwBgysv+JvAcW7dmAYbx2jmzD136Ok7TgrRPn3Wc1dqx9vMvUgEABJYVYSQZcoEX+fxzu9UzZ76iGDMm4GHT7QFGkh5g2QYdPM5uJ4DnRZhYElIHD+CCsGQY2n30qIK325NFyUntVp5ZD6FQcPIhQ37ibfaU6pdfzmrueNZqJXmPJ4oMDw9Z/x0AgCg5yQwAwNYYLoopgeOgYsFTg0vvvucTprq6lzSr3/LI5549Esx1lePHFwMALzBMGCYWBx1eg4tEFt7luliiaVi8eAIIAq594P7Vlx+rvefeTSAImGnpZzcGu87lmJZ+lpo/ZOhLlc89N6D5oxEIBAJxOfYtWyLKn5g3rOCm8Q8VTpx0X+n994+reeutTMf2P8Ob6vdGXL8wlZV04YSJ91u+//4lTCSyRr74whPxX3+1ti1nC//TQIPO2wHWVNf7QyclBSzwyJgYGwAAa/h735CvoCCL0GpzJBkZjsvPiXz2mT+de/bMsq5aNTH8oQffFli2TUo0Sa2WjXrxhaBu0NsDjKK8nM2WUDLrjmlEeLiJjo2ppTt0NMn69jExBoMIAACXSkPv4OG4X2BZkeXHnzIBAGQ33NAuASuXE73w9b9cu3dPs2/ePDPyxRcPNPVF6Ny2LQoEAadi9SF18MTdutWVQ5rNKoA6167q2eceYCoqBhAqVV7E/CeXqKZODVpUUjExflwuL+edznhcKg1e4EkkFt7t1gAAePPypd6zZ8dQcXF7lePGVV9+rGL0jTU1UVGHvWfOjGat1l9IlYoNdj3nnr2amtdeu9NfUjIMAMD55w4FAOwL9joIBALxb0LgOLBv3BTt2LIl3Zefl85U16QLXq8OAABI0gWCgPtycyWunbvAvGw5AI77cLmsklAoK+j4uLzYjz76vaWpiohrg9ply5JNH38yn3e59OIuXdbEff7Zd6ROx1ztfV1vIIHXDnAWqwoAQJLRLeDkP1GHDra6cy0XRx9Y16yN4Z3OeFmPHpsbOodOSPCKU1O3eM+cmejYsSNcYBhxWzh41yp0Sspxz+HDHdyHDs264k2CcAMA4DJZ6B08kvABy9LenJxMIEmnZtasglCvEQiETMbLhw//0b5u3fzqF/87MOatN/c0dJyvoEBSu2z5RAAAcefOIXXw6Ph4D+C4j7PbNBVPPX2DY9OmBwSOE0uz+i3Xf/LJmtb84SW12gK/0xlfH/ITDJhUauEMhs4AADULF44BjpOqZ85c2djxYSNHbrR8//1LhjfezGrsc2wI1mikKuY9Ocl99Og04HlS3D3jFwwneM+xYzPsW7ZEKEaNMgS7dwQCgfin4z58WFnz9jsTfLm5IwSfTwMAgFGUjYyMyKGTU1bLBw/OUU2fVoIRhOA+cFDtyt4f68s/r2eqq2M5s1nP1tZ2YMrLBxVPmZKU9PvvS5DT03649u1X29av6+w9c7YLU1XVWfB4dIRaXUDFxOSJ09NzlRMn5EnS05tMXec9Htx9+LCq9suvhrkPHZqJ0bRNc/ecFyIXLDjZXj/HPw0k8NoBzuFQAUF4Lg1FaQ5x1652AADObr/o4NnWrOkHAKC+ZfqBxs4Lf/CB9eWPzJ1o+nTpWOA4EUaSntbs/XoiYdk3qwFgNVNdTbsPHtT68vPDmYrKcNZk0nJWazjv9cpUkyedCPW6GEH6BZYVsSZTJyoq8kQoQ0uCJXrh67udO3ZMd2zbdhvv8ey7dC+8x4NXLnhqhHPXrlmC36+ik5O3qu8IrRjFCAIwsdjsy8270Xfm7ERCpcrTzZv3gXr6tLLWXpuKjS30l5QMI5SKoB08Qi63MGVlatZopDzHjk0kdbrj2rvuLGzs+IinFhy1rlxZ7dy9exwANCvwBI6D6lde6Wdbt/5uweOJomJisiMWzP9aMWZMjX3zH5EVx47NsKz4caBi1KgrSkIRCATiesGx/c/wmjfemCMbNGhH9MsvHWr+jGaut2NHuPGDDyf78vJuBJ6nqJiYA6LU1KNhI4bnKCdPLm8o2EI2oL9FNqC/BQD+1stdMmvWdPehw7cXT5nqTVqzemmgoRi8x4NbV66KDxs5opKKigrpWKl/IvYtWyJsa9b29BcWdGZqDF0EjycSAABw3E+o1XlkTMxRttaU4jl2rKfn2DHM8t13gEullaROl0slJORjGMazZrOOs9l0vNMZzrvd4YLPpwVBIAAAKH3s3tglSz5pThQimgYJvHaAd7nUmEgUcHkmQJ0bg1GUg3c6Lzp4vtzcLFyhKAwbObLRWPuwkSONVGxstvfs2dEYTTuIC0PJ/01QUVF+5YQJVQBQ1S4LEoSPs1iSBb9fLUpN+7ld1mwEnKaFsBtHrbCtXPVM1fMvDIld/N4OAADD4sXplp9+upe3O5IItfqsZu7c18LvvSe/LfZAyGQ1rMejk/br963+009Wh6pcRpzetcC1bx8QanXwAk+psADP0xULnrpJ8PtVyokTfmvqeJymBUlm5ib3/v2zzStWJGhuu62ksWOtK1fFGpcseYA1Grvjcnmpes7sFyOfeurigwTF6BtrqpTKfN+5c4MAAAk8BAJxXWJYvDjdvGz5MwLDKKw//TSAM5k+0X/80daWXMu2bn20aenSqf6iomEAgNGJiTu099//m2rSxMqW7i9++fJfimfcKvGeOjWl+JZbPIk//7y8OZHn2rdfXbFgwQKutja9ZtEihtRqz9BJScflgwefUM+8rbChB7b+khKxdfWaFM/x452Y8rJOnMMZKene/c+YNxZtJrXaoEv6rydql3+bbHj77beB52mMpq2kTneGTkhYL83qd0Y1fXrRpS0NvqIiiW3Vqg6eEydT/WVlqUxVVWZ92wJgGIuJxSZcKjVRkZGncZXSSGq0JlGnTuW6R+fmXG+JldciSOC1A7zbrcKDFHgAAJiItvJud10vU3a2irNY0iS9ev7Y3HnKCRN+Ny1dOkBgWTkWHt4mN/GI/wcjSZ/g96sBAJTjx7d7wMrlRL300n7H1m2Fjr/+mmFdvTrP9NHHdzCVlf0xsdionDjx7ehFC/e05ZdnzFtvvc/7vETYsGGmUF5XM2fOOdeevWs0t99+MNhzCa3WDADgPnToFlypzNc98USzZR8RT87bWjz9lpmWFT+O09x226eXv8/Z7UT5w4/c7D58+FYgCK9syODPYxcv3tSQoBV3Ttvrzj5wFyrTRASD4f0l6YRa7W7KbUYg2hqB46DsoYfGuXbtvheXSit18574r3n5t7Mc27bNLZk1Sx2/fPkvgf5NMa9YkWBetnwaU1Y2CHCcFXXqtFn36KOrwkYMb/XfC4wgIPGnH78tmnyzxJtz+uaSO+70JP7wfaMPXWveeSfD8t338wWWlUqz+i3n7Q6Vv6ws033w4J3ugwfBsGSJk4qMPCnq1Ok4RhCcr6golTUYOvEORzxcCCnEJJJqjKadrl277js/YuQk+dChK2LeWPTX1azkaSu8Z87IjB988CxG0/aoF198UTlpYkVT/7+LkpI8EU8+eQoATgHU/Tty7dmjxUQiTtKzpw2V0bYtSOC1A4LXqyIUwcfRYyKxjfd6FQAA5u+/7wsAmGLM2OxmToPwRx4+Y17xQwFvs6f8m3rwrhYYQfgBADCptEox+sarniyK07SgGDv2B+tPP71Y9exzS4EgvNI+vb+PeeedNe1RfnKhdCbkkCoVm7Rq5TctOZeKjKx7wMJx0rCRI1YGcjMiSU930gkJu/xFRUO9efnLxZ06XuzfrF3+bbLp008f5e32ZEqv3xO9aOEXl45buBz1jFv3urMP3GX58SdUpokICPfRo4rar776L4bjPllWvwfEaWnX1txRxL8C1mikSu6480F/UdFIMjr6QPxXXy4WpaR4VFOmvF48bfqj7kOHby+6eYoq8ddfvmzqht21b7+6+rXXZvmLikYAQXjF3bqtjlgwf21T35stASMISPzt18+LJkwUe44cmVl6zz2e+K+++v3SY3iPBy+9+57pnqNHb8XlsvLI5597QX3LLaUX93rwoMr6668Z3pzTmUxVVSazY0ddCjJJOkmNJk/UoUO2uHPnPMVN4/KkPXvaBY4Dw9vvZFrXrLnDsXnz4/l79tysGDP6+6iXX87+pzhRvN+PlT308DzB69XqHn/sGdWUm4O/pyUIkA8ZUtsW+0NcCRJ47YDg86lwmex0sOfhErGNs1gTAAC8p3KyMImkWn3rjOLmzsMIAmQDBv7u2LTpCYyikMBray58xnRs7FVJz2yIqBdfOOTav/8vjCCYyOee+0E+aGDQZY3/JOjERDMAAC6VVka98EKzD0nqUU2busHw9jsjDe++Ozz+i8/Xs0YjVfbwIzO8p05NwSjKppo2dVH0a681e72LZZpnz6IyTURA1Cx6YzJwnEjgOFHls8/NSF696uurvSfEvwvn7t2aygVPPcdZrZ0kPXr8GP/t8p/qRRyhUHBJ635fUnLrrVbv6TOTC8eOUyX+/NPiy0sUmepqunLBgknuo8emAs+T4q5d10S/9uov4i5d2uyBBU7TQuLK3z4smjBR7Nqz957yR+Z66ktJXQcPqiqfnD+PNRoz6cSEHXGff7708rm1sr59rbK+fXcBwC6B48C+YUM0AIBi3LiqhgQbRhAQ+ewzxyOeWnC86sUXBzj+2DLL+utvzzm2bstTTZ3yv4j586/7oJCyu++ZylZX95EPGfJZ+P33513t/SCaBwm8NoZzuXCBYcJwuTzop1S4RGpjawxKX1GRhDUYMsWdO68P9GlQ1AvP73bt2TOZjosrCnrTiKDASNIPACDJ7H7VyzPrwQgCOvyxefHV3se1gmzAAAMmEtXKR478IZjSGe2cOQW1X3yZ6zlyZKzx448LzMuWz+VdLj2dlLQ99r13vwrmJgWVaSICxX34sNJ75sxNVELCToyivL5z58Zbfvl1SyjCihCIQDB+/HHn2i++fFbgOJFq+rSF0a++ekW4G07TQtLKlctKZs+xuvfvn104cZIi4dvlC0UpKR6B46DymWcHO7ZuvVPwenVUTMx+3RNPLFeOv6ldeuMJmYxPXPnbu0UTJz3v2LbtkYoFT/momBizefny+QLDyMP+85+PYpe8v7W5eyqMIOBCT3+zYAQBMYsW7eOefz676plnRzh37bq19quvX7et33Ao6qX/Lg1V24Lpy6862latHOsvLRtIKBTF4m7ddmnnzN4jy8pq9j7TvmlTpPXX37J8RUXpku4Z2bHvvbe9uc+g5u23u7sPHZpJxcfv1H/6ycZQ/AyItgcThOurBFar1ebX1tbOu9r7CATvmTOyiifn3+0vKhoZNnr0Ev2S9/8M5vziW2+71XPs2K2K8ePfta9bNz/8wQef0T326Jm22i+iZRROvvlu39mz45PWrJ6JyqiuXQSOg5aUy1TMe3KYfePGJwAAMLHYqJ4+/ePI554N2q21b/4jsuLxx7+U9u+/7ELiKwLRIEVTps72nj49MfqNRQ/TSUn2kttnfU6q1QUd/trx4j+l5AtxbeLcuVNr+nTpGM/JkzdjEokx8umnFl5avtgY5U/MG+bYvPkxPCysWDl+/Hf2jRtncBZLKq5QFKpn3PJVxLx5Oc1doy1gKivpomnTX+bM5i4gCIDLZFW6J554S3P7zOK2Xps1GqmKp56+yX3gwG2AYbx82NCv9R98sKUlv8Os0UhVL1x0gzs7exxntXYEgvDQcfpsttacxDsciQDAkzrdKUn37ru0D9y/vz6BUuA4MP/vf8mOP7Zk+QoKsi4cCxhNWwW/X0XFxu6LfX/xJw3NVgao+/dQ/sjcDzCRyJq0evWTdJweVYVdQ2AY9pIgCL0bfA8JvNAjcBxUvfDiQPuGDfcLDKMQp6evivvyixXBDkwuu/+Bsc6dOx8gwsNP8jZbYqeDB+74JzbuXu/Y1q2Pdh86GN/QE07E9Q9rNFKFEye9Rup0hbFL3v9OlJTU4tEjuf2y3sMwTOiUvX9+KPeI+OfgPnxYWXLHnV/TcXF7U/7Y/D7A//8tUE6d8kbM66/vv9p7RPyz4P1+zLhkSYZj8x9jmaqqfiAIGKWP3af/8MOPg6lSqH59YS/LihXPAM+LMJq2yIcN/V/Mm2/uuNr3Lb6CAknp7DnP4DKpWf/pp1+05ju8Jdg3bYqsXrhoLmcyZZA63YnIF1/4KNAqDvvmPyJrv/pyjC837z911WCyMmmv3hsinlqwQ5SS4gEAsPzya5xt1aobvHl5QwS3OxowjCUjI48RCoXRX1raV/B6wwGAJ7TaM+LOadmqqdMOyIcMNpY9+OAk94GDt2M0bVfPvO39S9OfAQBYq5UsHDN2EWe3J0S/+uq8lvTdIdoWJPDaEefOndqql195kK2q6osrled1Dz/0keaOO1pUJln57HMDbatXPw0AQCcnb03ZuOGj0O4WgUC0JyV33XWzO/vAXbEffnAPKtNENETRlCmzvafPTIx+882H6iPjOZcLPz902BKBYaQpGzc8FMxMVcQ/E/N33yfaN24cADjO4XKZkwgLcxJKlZPUap1kZKSTio1xiTp2dDYV2+89c0ZmeG/xSM/x46N5lysWoyiHKDV1i/buOZsVY8a0KDCs9ptvUlx79qZHvfTfPy7vbfs3w/v9WMVjj9/o3LVrNgBg8kGDlsd+9OGmy4NpOLudMH/7bSfX/uzu/uLi7pzZ3AUwTKBiYrIVY8ds0D3++KnGHECB48D87bcp9g0bB/vOn79BYBgFGRl5TNI9I1tz110HpZmZV7h0tcuWJZs+/mQ+73LpxV26rIn7/LPvSJ2OAQAonPh6h1oAABscSURBVDT5Xt+5c+OVkye/FfPGor1t8bkgWgcSeO0A7/djFY8+Nsa5e/edIAiELCvru9iPPlzXmhlghsWL02u/+HIRAIBqxozXQjFUFIFAXD1QmSaiKVzZ2arSOXd/RcfH7U3ZXOfe1WN4f0l67eefL5L07LkiccUPP7XlPswrViS492d34l0uKe/xyHivVyJ4vTLB75cKfr9M4HlCNW3aDxGPPxZ0eBii5TDV1XTNW28PdGdnj+EsljQAEAAAa/IkHGcwknRhJOnGaNqFUZQLo2k3AABTVdUbeJ4m1OpzsgEDNkY+8/Te+pt7RNvg2P5nePUrr8xlDYYehFabE7Fgwce83SZy/vVXd19BYXfWZOoKHCcGAAFXKgtEycmHwh+4f0uw6ZO8348JXi9OKBRcc8cylZV02QMPzvbl5Y3Dw8KKI+bNe9d97FiC/fffF4i6dF6bvAoFPF2rIIHXxlh++TXO+P77j3AWS2dSpzse+dyzn7T06dfl163+738/ARz3dfhz+8z2iLhHIBBtS26/rMUYhvGoTBNxOYU333y37+y58TFvvflgQ8EO5/8zaj5TUZEVu+T9h0LtAHN2O1Gz6I3+zl27xnFmc9e/vUkQHowk3RhFuTCKcvMeT7jAMDLdY4++EH7ffWjWahtj+/33aPPyb8d48/NHAMOE4TJZhSQzc7Puice3U3q913/+vMxfViZnDQYZa6qVc1arnHc45LzLJeM9Hinv9coEn+//BTrDSAWWFVN6/VH19GmbWlplhGgZAsdBxRPzRjr+/PNuYFlZ/eu4TFZBxcaeEGd0O6GeMeNUfR9de1H96mu9rb/99qhQtyeeUKkKUzZvei4QkYi4OjQl8FCKZgio/eKLWzmnUx82Zsz7se++syNUTfCiDik2AAAqOvoIEncIxD8DcVraXveBA3eiNE3Epbiys1W+c7lj6MSEHY2l9kU+9+zy8ocf6Wd46+05ilGj3gzVusaPPhrtzTk9WvD5NJhEUiMb0P8b1bRp++mkJCedkOC5vIfKuWevpnzu3DeNH370Ci6XP6u57baSUOwFUQfncuG2tWvj3PuzO3mOH7+BNRozAcM4KjZ2v2L0jZt1Tzxx8tL7DLJ3b5u0d2/bVdwyIggwggD9hx9sc+7efdT06dLRZFRUlermySfkN9xwVccZRf33xcOKsWPmVix46hHOYkmOeevNt5C4u35BDl4IcB08qMJwXAj1F6zAcVA4fsJcxbixW3UPP3wulNdGIBBXB/umTZEVT8wLukzTX1Iitm/YmOA5dTLZX1aexJlMSQLDyFS3TP/88ub4QPDm5UvJCJ0/2PAnRNtQOPnmu33nzo2PefvtB5uKki+ZNWu6+9Dh2zV3z3khcsGCFs3X8hUVSWxrf0+2b9w4mikrGwiCQJKRkUfCRozYELFg/tFAQjHsm/+IrHz66TdBEIjo1197OtAo+X8CbG0taV25Mslz9FhHf0lJR85hjyC14SWiTp3OhY0YkRv2n5E1gT7oFTgO7Bs3RTt37eroKzjfka2u6chZrSnA8yKAuuReSbf0P8IffGirbEB/S5v+YAjEBVqaOo1oX1CJJgKBQFxDBFKmKXAcVMybN8J7+nRv1mxJFtzuKKjvtyFJF6FUFgler5p3u6NlgwZ9FffZ0oDmZDKVlXTZ/Q/M8eXnj71wLScuEtkwsdiCS8Q2XCqz4mFymywr64Ru7tyzofmJEU3h2rdfXXrPPV/SCQm7UzZt/KCpY1mjkTo/6sZPMILwd/hz+2MNPWEXOA6cf+0Mdx88qPeXlUWxRmMkZ7FEcg5HpOB2RwkMEwYAAAThFnXsuE1z110b6wNdgsG6clVs9csvvwUE4Yt9772nw0YMD8mcr7aG9/sxz/HjSm9OjtZfXBLO1tRoOYdDgZEkg9G0HxOJ/LhIVPdfsdiPScR+prQswldc3JE1GjrydkcCCAIJAIBRlB2XyWo4uz3+oiijaSuh1ebScXHnJD165BJqtZspKdEyBoOGM5u1nM2m5V0uDe92a3mPR3exTA/H/YRSWUhGRuaLUlLyZYMG5ivGjau8PIgDgUAgAJDAQyAQiGuKkjvvmuI+cOBO/ccf3R02cqTx8vfZ2lqy5PZZD/uLikZgEkkNqdEUkFGRxaIOHQvlNwwqkg8bZsQIAnxFRZLSOXfPY6uq+tHJyVsTVvywtClHrnb5t8mmjz6az7tcelHHjhtxhcLCO51K3uVS8R6PSvB6Vbzfr4QLAkCc3nVV3Fdfff9PcPkErk4HXYtPpQsnTb7Hl5t7U8y77zygHDeuurnjq156ua/1559fkA0a+FXc55//7ti6LdK5e1eKL/98Cltd3YGzWFIEhlFcPAHDWFwiMeByeQ2uUNSQWm0NpY+tCr/vvqOtTTqsXf5tsuHddxfiNG3Tf7b0GVnfvs0OW27wOsuWJZu/WTabs9vjJBkZG6JefWV9qOLsmepquvzhh2cyVdWpvMejFbxebb1ACwqSdJFqdT4ZFXVe1Knj+bBhw/Lrfxc5lwu3/vpbovtAdqq/qDiVNRjSeLc7poGrCBhNWzGJpBaXSs2EXG6iEuILZX375isnTSpFJXEIBCJQkMBDIBCIa4j6Mk3ZgP7fxH/zzZpL33MfPx5W/tDDz3Fmc1dJjx4/Jnz/3Y9NiRLe78dKZ90x03PixHRCozmj//CDNy4vF+f9fuz/2rv36Kiqew/g39/MZDIx7wcJIRCCVIw8BVEoPtHqhaI8rmjVFr1Ai5Zyq3KVh1BA0arISyWoWEEF0RaKFQRluQQVxACCgAEJBAoh5P2cvB8zv/vHHOoYk8CgwyTD97PWWXNm7zNn7QO/tTO/2efsferBh0ZWpaaOaW7NI3d1p7ICsyZOHFd79OhQU3j40fYznpj/c9yCV5+ba8179tnrLxkwIONCPbflsNvNWQ8/Mrxq1657zKGhWeHDh6+KnTrlm58r0av4/PPo2qMZkVFj/yfjfM5Zsf3LqFMTJrxuTUr67FyXwlGHAxk33zK7oaCgF8zm2jMJOUQcptCQTEtMuwxrYuKxwOTkk0F9eucFDxxY7M21yApSUpILU5bONQUH5ySuWP6EJ5NDlH+6JSZ/3rwxdSdPDpaAALs5IuLfDQUFfRAQUH5J377vt39yzsafkuiVb90akz1t2gxnmf1Sc3T0QXNISIEpLKzIEhVVaImLK7ImdS609exZFNSrl91RVmZpKCy0OkrLrI6yUqvTXm51VFQEOisrrYGXdikOGzYsx5P/46p9+0LtGz7s5qytsVo7dSqyJScXBfXtW8okjoh+DkzwiIhamaZu0yz5x5pOec8+O0tra6PCbr99ccK857ed6/myp067rmzDhkfEai2Lnfzo02dmxqvYti0q54kZjzYUFPQJ6NDhq4TFi5YE9e79o/WQmpLzl78MLH3/X3+GqiX01ltfTVgwf8v5JkZl69fH5859erqzvDwJACzt2u0LGzr0/XNNttThQOX27dFB/fuXnOvyMwUpKcnFb7410VlenmSJjf3GYbcnaE1NrDkq6ruI0XeujJ08Oc3T66jauzesdM3aXjVpab3rs7N7OysrEwDAEhv7TbvJk1/z9FbH4yNGTqg9cmRowsIFD3ky+3LZhg/j8+fNm2gKC8uzJiZmBPXrdyxi5IgTvprmPm/evD7FK96cbQ4PP9b53dWzzpaU1Rw5eknOjBl31Rw8OBwAbD16fBD/zDNrbd0uqypctuyykndW39uQl9dfAgLKg/r2Xdf+yTmbPE30CpYsuaJo2evT1eEIjLz77vntZ8/iUkNE5DeY4BERtTKNb9PMfeav/UpWr54iZnNtzJ8mPhPz4INHPD1n0fLlXQtefGmG1teHho8csQgmk5Z9sH4SHA5r6C23LEtYvOgTTxO08q1bY3Jm/mWyo6ioZ0Bi4uedXlm6NLBrV4++aGfPnPnLsvf/9TBMJkf4Hben1Ged7lC9f//tWlsbZQoJyQweNOhf7WfP+qzxosxV+/aFlr77Xp/qb7/tW5+d3VdramLEai21/qLr5+HDR2yNGvO7401dT3VaWkj2tOn312VkDBGbrTB8+PDX4p96cmdDaaklZ/r0Wyt3fPUbra2NMsfEHIi8955VzU1i1VBUZCn/dEt89YH9HWsOHepen5XV22kv7wIAMJurLe3apQV27XoA6pTKnbvugdNpDerde13CooX/ONti5M66Oil86aVeRctXzPZk9K41y5k1e0DpmjXTJSgozxIdnWGOiMi3tGuXZ+3UKdfWo3te8LXXFkhAgJ5+/PEhVTu+ulfr68OsSZ23xj4+ZWVTz+8VvvZat5LV797bkJd3lQQElAf167cubvq0j23JyZVna8upSZNuq/h0y0MSFFTQ/onpcyNGj87yzlUTEfkGEzwiolbGfTZNsVjqK7dt+70pLOxEh+efezp08ODznqyiMjU14vSjk58wFkKGKTz8aNz06QvOZxKNM5zV1abMP0wYXb1nz31isxXETJgwP+aPD6Wf7XMOu92cOXbs/TUHD40yR0QcjX967nNnnjlsKC215D751PWV27ePcpaXJ4nVWmrr1WtjUO9e6VVf7+ldl5l5pbOsrCsAgcVSGRAXt9/aOfFw3YmTV9Tn5FwNVYspJCTT1qPH1uhxYz8LufHGInU4kP34lMHln3wyThsaQm3dr/ggYeHCdxs/Z1afm2vNmTlzSNWu3aO1ri7CEhe3J+SG6zc3FBRG1efmdnCUlCQ47PYEra6OBWACAJhMdZbo6O+sSUkHggcNOhB5/5ij7iOJlampEblz5oytO3FysNhs+eHDh7/efvasnY0T0LINH8aXvPPO4Jr09MFaXR0nAQH2DgvmT/aXJTNyZs0eULF16zBHZWWcVle3a/Ssm8JsroHDEWSOiTkQPX7c8uixY4+f7ZyFr7x6ecl7793TkJd3FUym+oD4+N3B1w7a2u7hh/c0/lHAYbebT/5uzO9rjxwZZomN3dvp1VdesHXvftaEkIiorWGCR0TUCqUPGLDIWV7REU5nYECHDqmJK5Yv/KmTXgCukafMP/zhAVOgrarjK0vX/FyTpBSkpCQXvf63x7SmJsYcE5NmS758V/ioUbuamhikYvuXUdlTpkxxFBd3D+zWbWPiiuVvNP4yDrhuvcxfsKCPfeOmkQ15eVcBAEQc5sjIdGuXLt+EXHfdvsbJVHVaWkhhytLrqvftu9lIZNUcE/MtVE2OoqKe5sjI9JiJE1OixvzuREvXU3cqKzBn5szbq/bu/W98P7NkjSk4ONscHp5liYnJDuiYkGW7PDk7fMTwk+dy+2P+4hd7lKxa9UdnRUWipX373XFTpyyzdulSUfDykuuq9+692VFScgUApyU2dv8lA675NPbRR1PPNtrXVjmrq02VqalR1fsPxNWdOBHXkJ8f67Dbo4Kvvnp33MwZuz0dTS5avrxr2foNN9cdP3691tVFwGKptCYlbQv91a8+j5n4x0M1aWmhWX/+81RHYVEvW88e6zqvXPm2N589JCLyJSZ4REStUOa48SMqd+wYb+vVa23nd1atbAvTodccPhycO3vOqNpjxwY6KyoSAcAUEpJp7dJld8iNN+yKHj8+vTAlpWfx2ysfU4fDFvbrXy9JeGHeF+dy7tK1azvWHE6Pi/ztfYfO9Xmrsg0fxpesXn1TzeHDg7W+PiTkhhveTli8aLMn/5a1x44F2TdtSgq68sq84EGDin/qBCwOu918+pFH7qjcueve/4xgOZ0BppCQTFvPHluix4//zNeLGrdljspKU8HiF6+s+OKLm+pPnfolnM5AsdkKAIjW1YWHDR36csKC+Z/5up1ERN7EBI+IqBVyVleb7B9/HB8xatRpX7flfNg/+iiu9J/rrqlNT7+mobCwJ1TNEhBg1/r6EFNI8OnYKVOfi7z7rlMXoi2tcRmEim3bovLnvfAb17OHd2w535k2qXl1J0/aCl5+eUDV7q9vclZXR8ZMmLAk+vfjM3zdLiIib2OCR0REXlVz+HBw8Yo3+1bv23e12GyVHV968e2f43ZTIiIi+jEmeERERERERH6ipQTPdKEbQ0RERERERN7BBI+IiIiIiMhPMMEjIiIiIiLyE0zwiIiIiIiI/AQTPCIiIiIiIj/BBI+IiIiIiMhPMMEjIiIiIiLyE0zwiIiIiIiI/AQTPCIiIiIiIj/BBI+IiIiIiMhPMMEjIiIiIiLyE0zwiIiIiIiI/AQTPCIiIiIiIj/h1QRPRIaISLqIZIjItCbqA0Xk70b9ThFJ8mZ7iIiIiIiI/JnXEjwRMQNIATAUQHcA94pI90aHjQdQoqq/ALAIwPPeag8REREREZG/8+YI3jUAMlT1uKrWAXgPwIhGx4wA8JaxvxbALSIiXmwTERERERGR3/JmgpcA4JTb+yyjrMljVLUBQBmAaC+2iYiIiIiIyG9ZfN2AcyEiEwBMAICoqKjETZs2LfRxk4iIiIiIiHylrLkKbyZ4pwF0cnvf0Shr6pgsEbEACAdQ1PhEqroMwDIAEJGCYcOGNXtBPhQDoNDXjSC/xNgib2BckbcwtshbGFvkDW01rjo3V+HNBG83gMtEpAtcidw9AO5rdMx6AA8A+ArAaABbVFVbOqmqtvNCW38yEflaVfv7uh3kfxhb5A2MK/IWxhZ5C2OLvMEf48prCZ6qNojIJACbAZgBLFfVgyLyFICvVXU9gDcArBSRDADFcCWBREREREREdB68+gyeqm4CsKlR2Sy3/RoAd3mzDURERERERBcLry50fpFZ5usGkN9ibJE3MK7IWxhb5C2MLfIGv4srOcsjb0RERERERNRGcASPiIiIiIjITzDBa4GILBeRfBFJcyvrIyJfici3IrJBRMKM8ltFZI9RvkdEbnb7zFVGeYaIvCQi4ovrodbBk7hyq08UkQoRecytbIiIpBtxNe1CXgO1Tp7Gloj0NuoOGvU2o5x9Fv2Ah38PA0TkLaP8OxGZ7vYZ9lv0HyLSSUS2isghox962CiPEpFPROSo8RpplIvRJ2WIyAER6ed2rgeM44+KyAO+uiZqHc4jtn5rxNS3IrJDRPq4navt9Vuqyq2ZDcANAPoBSHMr2w3gRmN/HIC5xn5fAB2M/Z4ATrt9ZheAgQAEwEcAhvr62ri1jbhyq18LYA2Ax4z3ZgDHAFwKwApgP4Duvr42br7dPOyzLAAOAOhjvI8GYDb22Wdx+ymxdR+A94z9SwCcAJDEfotb4w1APIB+xn4ogCMAugOYB2CaUT4NwPPG/q+NPkmMPmqnUR4F4LjxGmnsR/r6+ri1qdgadCZmAAx1i6022W9xBK8FqvoFXMs3uOsG4Atj/xMAdxrHfqOq2Ub5QQBBIhIoIvEAwlQ1VV2R8jaAkd5vPbVWnsQVAIjISAD/hiuuzrgGQIaqHlfVOgDvARjhtUZTm+BhbN0G4ICq7jc+W6SqDvZZ1BQPY0sBBIuIBUAQgDoAdrDfokZUNUdV9xr75QC+A5AAV1y8ZRz2Fr7vg0YAeFtdUgFEGH3WfwH4RFWLVbUErngccgEvhVoZT2NLVXcYsQMAqQA6Gvttst9igue5g/j+P/YuAJ2aOOZOAHtVtRauYMpyq8syyojcNRlXIhICYCqAJxsdnwDglNt7xhU1p7k+qxsAFZHNIrJXRKYY5eyz6Fw1F1trAVQCyAGQCWC+qhaD/Ra1QESS4LobaieAOFXNMapyAcQZ+83FEGOLmnWOseVuPFwjxUAbjS0meJ4bB2CiiOyBa8i3zr1SRHoAeB7Agz5oG7VdzcXVHACLVLXCVw2jNq+52LIAuA7Ab43XUSJyi2+aSG1Uc7F1DQAHgA4AugD4PxG51DdNpLbA+DHznwAeUVW7e51xJwGnfKfz4mlsichguBK8qReskV7g1YXO/ZGqHobr1iaISDcAw87UiUhHAO8DuF9VjxnFp/H9MC+M/dMXprXUVrQQVwMAjBaReQAiADhFpAbAHvxw9JhxRU1qIbayAHyhqoVG3Sa4nrFaBfZZdA5aiK37AHysqvUA8kXkSwD94foVnP0W/YCIBMD1BfwdVV1nFOeJSLyq5hi3YOYb5afRdAydBnBTo/LPvNluav08jC2ISG8Af4PrufMio7i5mGvVOILnIRGJNV5NAGYCeNV4HwFgI1wPbn555nhjGNguIgONmejuB/DBBW84tWrNxZWqXq+qSaqaBGAxgL+q6hK4Jje4TES6iIgVwD0A1vuk8dSqNRdbADYD6CUilxjPSt0I4BD7LDpXLcRWJoCbjbpguCbDOAz2W9SI0ce8AeA7VV3oVrUewJmZMB/A933QegD3G7NpDgRQZvRZmwHcJiKRxqyItxlldJHyNLZEJBHAOgBjVPWI2/Ftst/iCF4LRORduH4RihGRLACzAYSIyJ+MQ9YBWGHsTwLwCwCzRGSWUXabquYDmAjgTbgeNv8I39/XSxchD+OqSaraICKT4PoDZgawXFUPtvQZ8n+exJaqlojIQrj+eCmATaq60TiOfRb9gIf9VgqAFSJyEK7ZDleo6gHjPOy3yN21AMYA+FZE9hllTwB4DsA/RGQ8gJMA7jbqNsE1k2YGgCoAYwFAVYtFZC5c/RkAPGU890kXL09jaxZcs0kvdeWGaFDV/m31+5YYU4ASERERERFRG8dbNImIiIiIiPwEEzwiIiIiIiI/wQSPiIiIiIjITzDBIyIiIiIi8hNM8IiIiIiIiPwEEzwiIiK41k0Ske0iMtSt7C4R+diX7SIiIvIEl0kgIiIyiEhPAGsA9IVrrdhvAAxR1WPncS6Lqjb8zE0kIiJqERM8IiIiNyIyD0AlgGDjtTOAngACAMxR1Q9EJAnASuMYAJikqjtE5CYAcwGUAEhW1W4XtvVERHSxY4JHRETkRkSCAewFUAfgQwAHVXWViEQA2AXX6J4CcKpqjYhcBuBdVe1vJHgbAfRU1X/75gqIiOhiZvF1A4iIiFoTVa0Ukb8DqABwN4A7ROQxo9oGIBFANoAlInIlAAcA95G6XUzuiIjIV5jgERER/ZjT2ATAnaqa7l4pInMA5AHoA9eEZTVu1ZUXqI1EREQ/wlk0iYiImrcZwP+KiACAiPQ1ysMB5KiqE8AYAGYftY+IiOgHmOARERE1by5ck6scEJGDxnsAWArgARHZDyAZHLUjIqJWgpOsEBERERER+QmO4BEREREREfkJJnhERERERER+ggkeERERERGRn2CCR0RERERE5CeY4BEREREREfkJJnhERERERER+ggkeERERERGRn2CCR0RERERE5Cf+HzhIsFXWoD26AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sKqFByIkBIWE"
      },
      "source": [
        "### **Data Preparation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "u502W0meqqeU"
      },
      "source": [
        "train_df['season'] = train_df['release_date'].str[3:5]\n",
        "train_df['season'] = train_df['season'].replace('01', 0)\n",
        "train_df['season'] = train_df['season'].replace('02', 0)\n",
        "train_df['season'] = train_df['season'].replace('03', 1)\n",
        "train_df['season'] = train_df['season'].replace('04', 1)\n",
        "train_df['season'] = train_df['season'].replace('05', 1)\n",
        "train_df['season'] = train_df['season'].replace('06', 2)\n",
        "train_df['season'] = train_df['season'].replace('07', 2)\n",
        "train_df['season'] = train_df['season'].replace('08', 2)\n",
        "train_df['season'] = train_df['season'].replace('09', 3)\n",
        "train_df['season'] = train_df['season'].replace('10', 3)\n",
        "train_df['season'] = train_df['season'].replace('11', 3)\n",
        "train_df['season'] = train_df['season'].replace('12', 0)"
      ],
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "a0AXjxq3qtS3"
      },
      "source": [
        "test_df['season'] = test_df['release_date'].str[3:5]\n",
        "test_df['season'] = test_df['season'].replace('01', 0)\n",
        "test_df['season'] = test_df['season'].replace('02', 0)\n",
        "test_df['season'] = test_df['season'].replace('03', 1)\n",
        "test_df['season'] = test_df['season'].replace('04', 1)\n",
        "test_df['season'] = test_df['season'].replace('05', 1)\n",
        "test_df['season'] = test_df['season'].replace('06', 2)\n",
        "test_df['season'] = test_df['season'].replace('07', 2)\n",
        "test_df['season'] = test_df['season'].replace('08', 2)\n",
        "test_df['season'] = test_df['season'].replace('09', 3)\n",
        "test_df['season'] = test_df['season'].replace('10', 3)\n",
        "test_df['season'] = test_df['season'].replace('11', 3)\n",
        "test_df['season'] = test_df['season'].replace('12', 0)"
      ],
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "8bfsMpnsqvoW"
      },
      "source": [
        "from datetime import date\n",
        "import holidays"
      ],
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dBjl5W2WqxFz"
      },
      "source": [
        "us_holidays = holidays.US()"
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xP9oPxrQqy33"
      },
      "source": [
        "for date, name in sorted(holidays.US().items()):\n",
        "    print(date, name)"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "1VMWTAC0q0XP",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "02dd19f2-3674-4052-9548-d3ef614a8569"
      },
      "source": [
        "print('01-01-2018' in us_holidays)"
      ],
      "execution_count": 31,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "True\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aH82faBQq21Q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "21dd5cb9-6655-4543-ad35-93b3a138e163"
      },
      "source": [
        "print(us_holidays.get('02-01-2018'))"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "None\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Gkebzr7vq4p5"
      },
      "source": [
        "train_df['release_date'] = pd.to_datetime(train_df['release_date'])\n",
        "test_df['release_date'] = pd.to_datetime(test_df['release_date'])"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAR8RSEJq8mi"
      },
      "source": [
        "train_df['release_date'] = train_df['release_date'].dt.date.tolist()\n",
        "test_df['release_date'] = test_df['release_date'].dt.date.tolist()"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HuUrkyNLq-F5"
      },
      "source": [
        "train_df['holiday_or_not'] = 0\n",
        "test_df['holiday_or_not'] = 0"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AIr72FSXrAJp"
      },
      "source": [
        "for i in range(len(train_df)):\n",
        "  if (train_df['release_date'][i] in us_holidays) == True:\n",
        "    train_df['holiday_or_not'][i] = 1"
      ],
      "execution_count": 36,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-wxvgN-G957R"
      },
      "source": [
        "for i in range(len(test_df)):\n",
        "  if (test_df['release_date'][i] in us_holidays) == True:\n",
        "    test_df['holiday_or_not'][i] = 1"
      ],
      "execution_count": 37,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_kGGgYYNsDTI",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "0b2d47c7-928f-43f5-9e26-76772b9e0d97"
      },
      "source": [
        "columns_drop = ['release_date']\n",
        "train_df.drop(columns_drop, axis = 1, inplace = True)\n",
        "train_df.head()"
      ],
      "execution_count": 38,
      "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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>popularity</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>No</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5</td>\n",
              "      <td>0.513</td>\n",
              "      <td>-27.398</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1947</td>\n",
              "      <td>3.0</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4</td>\n",
              "      <td>0.151</td>\n",
              "      <td>-10.046</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>2020</td>\n",
              "      <td>2.4</td>\n",
              "      <td>low</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>-8.009</td>\n",
              "      <td>Minor</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1950</td>\n",
              "      <td>2.6</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>No</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7</td>\n",
              "      <td>0.218</td>\n",
              "      <td>-15.917</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>1974</td>\n",
              "      <td>2.4</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.322</td>\n",
              "      <td>-12.344</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>1973</td>\n",
              "      <td>3.4</td>\n",
              "      <td>average</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ... popularity  season  holiday_or_not\n",
              "0         0.949         0.235  0.0276  ...   very low       0               1\n",
              "1         0.855         0.456  0.4850  ...        low       3               0\n",
              "2         0.827         0.495  0.4990  ...   very low       0               1\n",
              "3         0.654         0.643  0.4690  ...        low       1               0\n",
              "4         0.738         0.705  0.3110  ...    average       0               1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "id": "DR5T6mqt-Gf5",
        "outputId": "2eb23754-bb8b-4f29-db4b-8c4beb464955"
      },
      "source": [
        "columns_drop = ['release_date']\n",
        "test_df.drop(columns_drop, axis = 1, inplace = True)\n",
        "test_df.head()"
      ],
      "execution_count": 39,
      "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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.03360</td>\n",
              "      <td>0.523</td>\n",
              "      <td>0.861</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>-3.447</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>127.049</td>\n",
              "      <td>0.346</td>\n",
              "      <td>2014</td>\n",
              "      <td>3.8</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.76500</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.453</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0897</td>\n",
              "      <td>-11.649</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>129.987</td>\n",
              "      <td>0.830</td>\n",
              "      <td>1972</td>\n",
              "      <td>3.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.640</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0948</td>\n",
              "      <td>-5.862</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>142.483</td>\n",
              "      <td>0.289</td>\n",
              "      <td>1998</td>\n",
              "      <td>4.4</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00463</td>\n",
              "      <td>0.890</td>\n",
              "      <td>0.617</td>\n",
              "      <td>No</td>\n",
              "      <td>0.003850</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0291</td>\n",
              "      <td>-12.096</td>\n",
              "      <td>Minor</td>\n",
              "      <td>0.0418</td>\n",
              "      <td>121.126</td>\n",
              "      <td>0.970</td>\n",
              "      <td>1980</td>\n",
              "      <td>3.9</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00187</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.943</td>\n",
              "      <td>No</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>-2.492</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0891</td>\n",
              "      <td>165.026</td>\n",
              "      <td>0.882</td>\n",
              "      <td>2007</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ... duration-min  season  holiday_or_not\n",
              "0       0.03360         0.523   0.861  ...          3.8       0               1\n",
              "1       0.76500         0.687   0.453  ...          3.8       1               0\n",
              "2       0.36300         0.536   0.640  ...          4.4       2               0\n",
              "3       0.00463         0.890   0.617  ...          3.9       3               0\n",
              "4       0.00187         0.555   0.943  ...          3.2       1               0\n",
              "\n",
              "[5 rows x 16 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bSgc8OD6sID1"
      },
      "source": [
        "train_df['year'] = pd.qcut(train_df['year'], q=16)\n",
        "train_df['duration-min'] = pd.qcut(train_df['duration-min'], q=4)"
      ],
      "execution_count": 40,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "L7G9tgCq-K5k"
      },
      "source": [
        "test_df['year'] = pd.qcut(test_df['year'], q=16)\n",
        "test_df['duration-min'] = pd.qcut(test_df['duration-min'], q=4)"
      ],
      "execution_count": 41,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BhNRrX1YsK6Z"
      },
      "source": [
        "cat_col= ['explicit', 'mode']"
      ],
      "execution_count": 42,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pVsHHf3bsMhH"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "train_df['year'] = LabelEncoder().fit_transform(train_df['year'])\n",
        "train_df['duration-min'] = LabelEncoder().fit_transform(train_df['duration-min'])"
      ],
      "execution_count": 43,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "c6OKTDn--UGM"
      },
      "source": [
        "from sklearn.preprocessing import LabelEncoder\n",
        "test_df['year'] = LabelEncoder().fit_transform(test_df['year'])\n",
        "test_df['duration-min'] = LabelEncoder().fit_transform(test_df['duration-min'])"
      ],
      "execution_count": 44,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VYeTcN4Q9D2Y"
      },
      "source": [
        "replace_label = {'popularity':{'very low': 1, 'low': 2, 'average': 3, 'high':4, 'very high': 5}}\n",
        "tarin_df = train_df.replace(replace_label)"
      ],
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gJX85_absP41",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 203
        },
        "outputId": "5e63c411-023c-416f-ac80-55f1b9899d16"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 46,
      "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",
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              "\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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>explicit</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>mode</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>popularity</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>No</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5</td>\n",
              "      <td>0.513</td>\n",
              "      <td>-27.398</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4</td>\n",
              "      <td>0.151</td>\n",
              "      <td>-10.046</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>-8.009</td>\n",
              "      <td>Minor</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>No</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7</td>\n",
              "      <td>0.218</td>\n",
              "      <td>-15.917</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>No</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.322</td>\n",
              "      <td>-12.344</td>\n",
              "      <td>Major</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>average</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ... popularity  season  holiday_or_not\n",
              "0         0.949         0.235  0.0276  ...   very low       0               1\n",
              "1         0.855         0.456  0.4850  ...        low       3               0\n",
              "2         0.827         0.495  0.4990  ...   very low       0               1\n",
              "3         0.654         0.643  0.4690  ...        low       1               0\n",
              "4         0.738         0.705  0.3110  ...    average       0               1\n",
              "\n",
              "[5 rows x 17 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 46
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6AS7CUqysRhr"
      },
      "source": [
        "train_df = pd.get_dummies(data=train_df,columns=cat_col)"
      ],
      "execution_count": 47,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hEIe1WDRsSxc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "c17255b7-b4c9-4258-b9c5-48c9b8dc738e"
      },
      "source": [
        "train_df.head()"
      ],
      "execution_count": 48,
      "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",
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              "\n",
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              "        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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>popularity</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "      <th>explicit_No</th>\n",
              "      <th>explicit_Yes</th>\n",
              "      <th>mode_Major</th>\n",
              "      <th>mode_Minor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5</td>\n",
              "      <td>0.513</td>\n",
              "      <td>-27.398</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4</td>\n",
              "      <td>0.151</td>\n",
              "      <td>-10.046</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>-8.009</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>very low</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7</td>\n",
              "      <td>0.218</td>\n",
              "      <td>-15.917</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>low</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.322</td>\n",
              "      <td>-12.344</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>average</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ...  explicit_Yes  mode_Major  mode_Minor\n",
              "0         0.949         0.235  0.0276  ...             0           1           0\n",
              "1         0.855         0.456  0.4850  ...             0           1           0\n",
              "2         0.827         0.495  0.4990  ...             0           0           1\n",
              "3         0.654         0.643  0.4690  ...             0           1           0\n",
              "4         0.738         0.705  0.3110  ...             0           1           0\n",
              "\n",
              "[5 rows x 19 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 48
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tCV6pHaN-7D8"
      },
      "source": [
        "test_df = pd.get_dummies(data=test_df,columns=cat_col)"
      ],
      "execution_count": 49,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "id": "gLd1Dzo9-9i3",
        "outputId": "588315ee-5af2-45f7-a1fa-9a518bc770b1"
      },
      "source": [
        "test_df.head()"
      ],
      "execution_count": 50,
      "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",
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              "\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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "      <th>explicit_No</th>\n",
              "      <th>explicit_Yes</th>\n",
              "      <th>mode_Major</th>\n",
              "      <th>mode_Minor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.03360</td>\n",
              "      <td>0.523</td>\n",
              "      <td>0.861</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>-3.447</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>127.049</td>\n",
              "      <td>0.346</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.76500</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.453</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0897</td>\n",
              "      <td>-11.649</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>129.987</td>\n",
              "      <td>0.830</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.640</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0948</td>\n",
              "      <td>-5.862</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>142.483</td>\n",
              "      <td>0.289</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00463</td>\n",
              "      <td>0.890</td>\n",
              "      <td>0.617</td>\n",
              "      <td>0.003850</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0291</td>\n",
              "      <td>-12.096</td>\n",
              "      <td>0.0418</td>\n",
              "      <td>121.126</td>\n",
              "      <td>0.970</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00187</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.943</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>-2.492</td>\n",
              "      <td>0.0891</td>\n",
              "      <td>165.026</td>\n",
              "      <td>0.882</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ...  explicit_Yes  mode_Major  mode_Minor\n",
              "0       0.03360         0.523   0.861  ...             0           1           0\n",
              "1       0.76500         0.687   0.453  ...             0           1           0\n",
              "2       0.36300         0.536   0.640  ...             0           1           0\n",
              "3       0.00463         0.890   0.617  ...             0           0           1\n",
              "4       0.00187         0.555   0.943  ...             0           1           0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 50
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NgpfbnKksUoH"
      },
      "source": [
        "y = train_df.loc[:, 'popularity']\n",
        "train_df.drop('popularity', inplace = True, axis = 1)"
      ],
      "execution_count": 51,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "9TBqCjPQBQ5M"
      },
      "source": [
        "### **Final Dataframes:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6TUY71MNsiV2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "ff2cd228-3079-447e-e5bc-d5ec6e8aa45d"
      },
      "source": [
        "x = train_df.iloc[:, :]\n",
        "x.head()"
      ],
      "execution_count": 52,
      "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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "      <th>explicit_No</th>\n",
              "      <th>explicit_Yes</th>\n",
              "      <th>mode_Major</th>\n",
              "      <th>mode_Minor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.949</td>\n",
              "      <td>0.235</td>\n",
              "      <td>0.0276</td>\n",
              "      <td>0.9270</td>\n",
              "      <td>5</td>\n",
              "      <td>0.513</td>\n",
              "      <td>-27.398</td>\n",
              "      <td>0.0381</td>\n",
              "      <td>110.838</td>\n",
              "      <td>0.0398</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.855</td>\n",
              "      <td>0.456</td>\n",
              "      <td>0.4850</td>\n",
              "      <td>0.0884</td>\n",
              "      <td>4</td>\n",
              "      <td>0.151</td>\n",
              "      <td>-10.046</td>\n",
              "      <td>0.0437</td>\n",
              "      <td>152.066</td>\n",
              "      <td>0.8590</td>\n",
              "      <td>15</td>\n",
              "      <td>0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.827</td>\n",
              "      <td>0.495</td>\n",
              "      <td>0.4990</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.401</td>\n",
              "      <td>-8.009</td>\n",
              "      <td>0.0474</td>\n",
              "      <td>108.004</td>\n",
              "      <td>0.7090</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.654</td>\n",
              "      <td>0.643</td>\n",
              "      <td>0.4690</td>\n",
              "      <td>0.1080</td>\n",
              "      <td>7</td>\n",
              "      <td>0.218</td>\n",
              "      <td>-15.917</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>83.636</td>\n",
              "      <td>0.9640</td>\n",
              "      <td>5</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.738</td>\n",
              "      <td>0.705</td>\n",
              "      <td>0.3110</td>\n",
              "      <td>0.0000</td>\n",
              "      <td>5</td>\n",
              "      <td>0.322</td>\n",
              "      <td>-12.344</td>\n",
              "      <td>0.0488</td>\n",
              "      <td>117.260</td>\n",
              "      <td>0.7850</td>\n",
              "      <td>5</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ...  explicit_Yes  mode_Major  mode_Minor\n",
              "0         0.949         0.235  0.0276  ...             0           1           0\n",
              "1         0.855         0.456  0.4850  ...             0           1           0\n",
              "2         0.827         0.495  0.4990  ...             0           0           1\n",
              "3         0.654         0.643  0.4690  ...             0           1           0\n",
              "4         0.738         0.705  0.3110  ...             0           1           0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 52
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QjO6RbR1sj9I",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 240
        },
        "outputId": "70e4f0a1-7e16-451c-a12d-6b830cc7afd8"
      },
      "source": [
        "x_test = test_df.iloc[:, :]\n",
        "x_test.head()"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
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              "\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>acousticness</th>\n",
              "      <th>danceability</th>\n",
              "      <th>energy</th>\n",
              "      <th>instrumentalness</th>\n",
              "      <th>key</th>\n",
              "      <th>liveness</th>\n",
              "      <th>loudness</th>\n",
              "      <th>speechiness</th>\n",
              "      <th>tempo</th>\n",
              "      <th>valence</th>\n",
              "      <th>year</th>\n",
              "      <th>duration-min</th>\n",
              "      <th>season</th>\n",
              "      <th>holiday_or_not</th>\n",
              "      <th>explicit_No</th>\n",
              "      <th>explicit_Yes</th>\n",
              "      <th>mode_Major</th>\n",
              "      <th>mode_Minor</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.03360</td>\n",
              "      <td>0.523</td>\n",
              "      <td>0.861</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.1440</td>\n",
              "      <td>-3.447</td>\n",
              "      <td>0.0644</td>\n",
              "      <td>127.049</td>\n",
              "      <td>0.346</td>\n",
              "      <td>13</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.76500</td>\n",
              "      <td>0.687</td>\n",
              "      <td>0.453</td>\n",
              "      <td>0.000008</td>\n",
              "      <td>7</td>\n",
              "      <td>0.0897</td>\n",
              "      <td>-11.649</td>\n",
              "      <td>0.0335</td>\n",
              "      <td>129.987</td>\n",
              "      <td>0.830</td>\n",
              "      <td>5</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.36300</td>\n",
              "      <td>0.536</td>\n",
              "      <td>0.640</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>0</td>\n",
              "      <td>0.0948</td>\n",
              "      <td>-5.862</td>\n",
              "      <td>0.0368</td>\n",
              "      <td>142.483</td>\n",
              "      <td>0.289</td>\n",
              "      <td>10</td>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.00463</td>\n",
              "      <td>0.890</td>\n",
              "      <td>0.617</td>\n",
              "      <td>0.003850</td>\n",
              "      <td>2</td>\n",
              "      <td>0.0291</td>\n",
              "      <td>-12.096</td>\n",
              "      <td>0.0418</td>\n",
              "      <td>121.126</td>\n",
              "      <td>0.970</td>\n",
              "      <td>6</td>\n",
              "      <td>2</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.00187</td>\n",
              "      <td>0.555</td>\n",
              "      <td>0.943</td>\n",
              "      <td>0.000042</td>\n",
              "      <td>2</td>\n",
              "      <td>0.1510</td>\n",
              "      <td>-2.492</td>\n",
              "      <td>0.0891</td>\n",
              "      <td>165.026</td>\n",
              "      <td>0.882</td>\n",
              "      <td>11</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   acousticness  danceability  energy  ...  explicit_Yes  mode_Major  mode_Minor\n",
              "0       0.03360         0.523   0.861  ...             0           1           0\n",
              "1       0.76500         0.687   0.453  ...             0           1           0\n",
              "2       0.36300         0.536   0.640  ...             0           1           0\n",
              "3       0.00463         0.890   0.617  ...             0           0           1\n",
              "4       0.00187         0.555   0.943  ...             0           1           0\n",
              "\n",
              "[5 rows x 18 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BordnaH2A52X"
      },
      "source": [
        "### **Model Testing:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DiCMbDcHsliH"
      },
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, random_state = 42, test_size = 0.25)"
      ],
      "execution_count": 54,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "V67F27fdsnTK"
      },
      "source": [
        "from sklearn.preprocessing import StandardScaler\n",
        "std_x = StandardScaler()\n",
        "x_train_std = std_x.fit_transform(x_train)\n",
        "x_test_std = std_x.transform(x_test)"
      ],
      "execution_count": 55,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2roh3ffwsooq"
      },
      "source": [
        "from sklearn.metrics import accuracy_score\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.metrics import confusion_matrix "
      ],
      "execution_count": 56,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pxMnDFQ_sqFT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "40d79494-5221-42a3-8e72-530b85cb5ae4"
      },
      "source": [
        "from sklearn.neighbors import KNeighborsClassifier\n",
        "knn = KNeighborsClassifier()\n",
        "knn.fit(x_train_std, y_train)\n",
        "knn_pred = knn.predict(x_test_std)\n",
        "knn_accu = accuracy_score(y_test, knn_pred)\n",
        "knn_report = classification_report(y_test, knn_pred)\n",
        "knn_cm = confusion_matrix(y_test, knn_pred)\n",
        "print('[0]K-Nearest Neighbors Performance:\\n')\n",
        "print('Accuracy:', knn_accu)\n",
        "print('Classification Report:', knn_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(knn_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[0]K-Nearest Neighbors Performance:\n",
            "\n",
            "Accuracy: 0.5024533856722276\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.37      0.46      0.41       723\n",
            "        high       0.49      0.53      0.51       678\n",
            "         low       0.44      0.42      0.43       770\n",
            "   very high       0.24      0.10      0.14        92\n",
            "    very low       0.79      0.65      0.71       794\n",
            "\n",
            "    accuracy                           0.50      3057\n",
            "   macro avg       0.47      0.43      0.44      3057\n",
            "weighted avg       0.52      0.50      0.51      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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2LfjnB1/x1txlAISjjtW5ecy46XwaZ6YH+TRqXOumIS78aYOK2y0ah3jj4xKmzyll1MBURg5MI+pgwapyXp1ZEmCkwbj64s4MHdiYnflhsq9fBED3zhlccUEnUlNCRCKOB5/8hqWrigKOtPa1bJbC77M70aRRCuB46/3t/GfqNi76ZTuGDWhEecSxKbeUex9fx+6iSNDh+k+VYnwJhZI4Y/wEOnfvQ0nxbm6/5lf0OXQon7z/Or37D+Gk0y7gv69M4u1XnuQX514BQI8+A7nsxgcDjrx2pCYn8fhFY8lMS6E8EuG8R17l6F6dAJhw0pEc36/7PvufN2Ig540YCMCMxWt45qOvEz4hAmzJi/LnyQVAbIb5nb9txNzlZfTsmMyhPVK4Y3IB4Qg0zKz7bwA/xrsfbmfK1Fyu/X9dK9ouPqsDT7+yiS++zmfIoY24+KwOXHPHsgCjDEYk4sh5biMr1haTkR7ib7f25MuFBXy5sIBJL20kGoULx7XlzFNb8cSLm4IO138JUCnW/WewlybNWtK5ex8A0jMa0LZDV3Zu38rXn8/giFFjADhi1Bjmfv5+kGEGxszITEsBIByJEo5GgQN7Y3/76+WcdGiPGowuPvXunMy2nVF25DtGDEjlnc9KCXsf8AuKXLDBBWT+kkIKCvetcpxzZGYkAdAgM4ntO8uDCC1wO3aFWbG2GIDikijrNpbSomkKXy4oIBqN7bN4ZREtmqYEGKVU5kcnRTM7389A/LYtdyPfrF5K156HkL9zO02atQSgcdMW5O/cXrHfqqXzuO2qcUz8v0vY+M3KoMKtNZFolHEPvsAxdzzJsIM60r9TawAeevczTp/4PH994yPKwvu+4RWXlfPxsm847pBuQYQcqEG9U/hicRkArZolcVCHZK49O4urzsyic5ukgKOLH/94ej3ZZ3Xg2Qf7kf2rDjzxwoagQwpc6xapdO+cwZKV+3YjnzC8GV/MLwgoqhpmVr0lDlSnUrz1+zaYWbaZzTaz2Tk5OdU4xI9TUlzEI3dfwy8vuIaMzKzvxoZ5L36nbr2589G3+OP9L3LsyWfy8F+uqvVYa1tSKMSLl/+Sd68fz4L1W1i+eTuXnzCMKRPO4l+XnMGuolImzfxyn/vMXLKGAZ3b1Iuu070lhaB/9xS+XBqrepIMMtONu58t5JUZxVw0JjPgCOPHqce15B/PrOPsy+fzj2fWc/XFnYMOKVDpaSFuvqwLjzy7gaKSaEX7WWNaE4k6pn+SF2B0NSgUqt4SByqNwszmfc8yH2j9ffdzzuU45wY55wZlZ2f7HnRlwuFyHvnrNQwdcRKHDRsNQKMmzdm5YysAO3dspWHjZgBkZGaRnhF7Y+t3+HAi4TAF+Qn6x/odjTLSGNytPZ8s+4aWjRpgZqQmJzF2UG8WrMvdZ9+3v15RL7tO+3ZL5pvcSEU3aV5hlLnLYgly7eYIDsjKiI9Pt0H7yfDmfPTFTgA++CyPXt0bVHGPxJWUBDdf1oXpn+Tx8ZxdFe3HH92MIQMa8ZdH1gYYXQ2zUPWWOFBVFK2Bc4Ex+1m2V3K/QDjneOrvt9K2fVeO/+k5Fe2HDh7JpzNeB+DTGa9z6JBRAOzK24ZzsTe81csXEHWOrIZNaj3u2rKjsJj84lIASsrDzFqxni4tm7I1fzcQe/3eX7iag9o0q7hPQUkpc1ZvZNTBXff7mIlscO9UZi/+dmzs6+Xl9OwUm5vWqmmIpJBRWFw/xxW/a3teGf37xHplBvZtyIbN9W9W7h4TLuzEuo2lvPLO1oq2Qf0acsbJrbjlgVWUlulvJp5VNfv0DSDLOTf3uxvMbEaNRFQNK5bMZdbMN2nfuQe3TfglAD8/+1JOPO18cu65jo+n/YdmLdvym6vvBmDOp+8x852XSAolkZKaTvaEOyu6VhPRtoLd3PTSdKIuStTBT/p1Z2SfLlz02BTydhfjgF5tm3Pzz0ZV3Gf6wtUc0aMjman1a2JAagr07pLMs+9+Ox70yfwyzjkpk5vPa0g46njqv/XvlAOAP1zSlf59GtK4YTL/eqgfT728kfseX8vvzu1IUsgoK3c88Pg3QYcZiL49GnDcUc1Yta6Yh2/rBcCTL2/kd7/uQEqycefvDwJgycrdPDh5fZCh1owEOCXD9lRKNcjNXFg/3zyqMrJvJiWvTAw6jLiUflrslJnf/nVnwJHEp3/8vgnHnz0n6DDi0tRnD+eE8f/zOV4870weUGOZq2TK36qVUNLHXhp4Vk2o8xRFRCRACdDTpqQoIiL+iJMZpNVR95+BiIiIT1QpioiIP9R9KiIi4omTcw2rQ0lRRET8kQBjikqKIiLijwToPq37aV1ERMQnqhRFRMQfGlMUERHxJED3qZKiiIj4IwEm2tT9ZyAiIuITVYoiIuILp+5TERERjybaiIiIeJQURUREYhKh+7Tup3URERGfqFIUERF/qPtURETEkwDdp0qKIiLijwQ4eV9JUUREfKGJNiIiIglElaKIiPhDE21ERERinJKiiIiIR2OKIiIiiUOVooiI+ELdpyIiInskQPepOedq+hg1fgARETlgNZa5Cma/Xa33+4aDTgw8q9ZKpbhpydzaOEyd07b3AC57ID/oMOLSQ1c2AqDow5cCjiQ+ZQ4/g6PHzAw6jLj00esjufrh3UGHEbfu/V2DGntsnbwvIiJSS8ws3cw+N7OvzWyhmd3qtXc1s8/MbIWZvWBmqV57mnd7hbe9S1XHUFIUERF/WKh6S9VKgWOdc4cCA4ATzWwY8BfgfufcQUAecKG3/4VAntd+v7dfpZQURUTEFw6r1lLl48cUejdTvMUBxwIve+2TgZ9562O923jbR5tV3serpCgiIr5wFqrWYmbZZjZ7ryX7u8cwsyQzmwvkAlOBlcBO51zY22U90N5bbw+sA/C27wKaV/YcdEqGiIj4o5rnKTrncoCcKvaJAAPMrAnwKtC7Wgf9DlWKIiJS5zjndgLvA0cATcxsT5HXAdjgrW8AOgJ42xsD2yt7XCVFERHxhTOr1lIVM2vpVYiYWQZwPLCYWHI83dttPDDFW3/Nu423fbqr4uR8dZ+KiIgvauEyb22ByWaWRKyoe9E594aZLQKeN7Pbga+AJ7z9nwCeNrMVwA7gzKoOoKQoIiL+qOGT951z84CB+2lfBQzZT3sJcMYPOYa6T0VERDyqFEVExBf6lgwRERHPgZyAH++UFEVExBeqFEVERPbQt2SIiIgkDlWKIiLiC5cAdZaSooiI+CIRvmRYSVFERHyhiTYiIiKeRDglo+6ndREREZ+oUhQREV+o+1RERMSjiTYiIiIejSmKiIgkEFWKIiLiC40pioiIeBKh+zShkmLu1m38+YG/k7dzF2bGqSeM5vQxJ3Pr3Q/wzcaNABTuLiKrQSZPPHA3m7bkMv7SCXRs3w6Ag3v24OrfXRzkU6hVxwxM5YhDUnAONm2P8sy7xRx5SAqjBqbRskmI6x8pYHeJCzrMWlFaXs6Ff3mcsnCESDTKcYf35bdjR/OHx15k0ZqNJCeFOKRrB248ZywpyUkV91u4ej3j78zhzuxxHD/okACfQXCGHtaUKy4+iFDIeGPqJp55eV3QIQVqeP9khvZJwQxmLSrnw3lhMtLg3J+k07ShkVfgeOrdEopLg47Uf6oU40xSUhK/u+AcenbvRlFRMdlX38CgQ/vzp2uvrNjn4UlP0SAzs+J2uzateeKBu4MIN1CNGxgjB6Ryx1OFlEfg/JMzOLxXCqs2RliwejeXn94g6BBrVWpyMjnXXEBmehrl4QgX/OUxjjqkJycNPZQ7LjoDgBsee5FXP5zNuGOGAhCJRpn473cYdvBBQYYeqFAIJvy/Hlx18zxyt5fy+H2H8dFn21mzrijo0ALRppkxtE8KE/9dTCQCF5+azqI1EY7om8zy9RGmf1XOsQNTOHZgCm/OKg86XN8lQqVY99P6Xpo3a0rP7t0AyMzMoHOH9mzbsaNiu3OO9z+axegRRwUVYlwJhSAlGUIGqcmwqzDK+q1RduTXj+pwb2ZGZnoaAOFIhHAkghkM798LM8PMOKRLB3Lz8ivu8/y0WYw+rC/NGtWvDxB769OjEes3FbNxSwnhsOO9D3I5emjzoMMKTKumIb7JjVAehqiDlRsj9O+WTN8uyXyxNAzAF0vDHNI1oeqRhFJlUjSz3mY22syyvtN+Ys2FVX2btuSyfNVq+vT89lP8vEWLadqkMR3ata1o27xlKxddeR1X/OEW5i1cHESogdi12zFtThm3XdiQ2y/OorgMlnwTCTqsQEWiUX55698YPeEuhh18EP26dazYVh6O8OasuRx5SA8AcvPymf7VIs4YNSSocONCy+ap5G77th9w6/ZSWjZPCzCiYG3eEaVb2yQy02IfOPt0TqJJltEw0ygoin3YLChyNMys+xXV/jgLVWuJB5V+XDGzy4FLgMXAE2Z2hXNuirf5z8DbNRzfj1JUXMKf/nIfl140fp+u0mkffMLoEUdW3G7erCkvPP53GjdqyNIVq7jpz/fwz7/ds899ElVGGvTvnswtTxZSVOq48JQMBvVOYfaSxOvSOVBJoRAv/OlSCoqKmfD3f7FiwxYOat8agDuffY3DenbhsJ5dAPjr829yxS9OIBSKj39kiQ+5eY7pX5WTPSadsjBs3BYlup+OF5egnTGJ0H1aVQ1/MXC4c67QzLoAL5tZF+fcRPj+Z29m2UA2wKOPPsqYEbX3aTocDvOnu+7luJFHM+KIod+2RyJ8+OnnPHrfnRVtqSkppKakANDroG60a9uadRs20btH91qLNyi9OiWzfVeUwuLYf+fXK8J0a5tUr5PiHg0zMxjUuyufLFjOQe1b8+hr08krKOKmc8ZW7LNo7Qauz3kBgJ2FRXw0fxnJSSGOGXhwUGEHYuv2Mlq1+LYybNk8ja3bE3AGyQ/w+eIwny+OdZWeNDSFXYWuojrc83PP/12iqQ9XtAk55woBnHNrzGwUscTYmUqSonMuB8jZc3PTkrl+xFol5xx3P/QInTq2Z9zYU/fZNufr+XTq0I5WLb4d79i5K5+GWVkkJYXYuHkLGzZuol2b1rUSa9DyChxd2iaRkgzlYejZMYlvttTf7tMdBbtJSQrRMDODkrJyPlu0kvNOHM4rH8zmk4XLefTqC/apCt+865qK9T9O+jfD+/eqdwkRYMnyfDq2y6Bt63S2bi/luBGtuPWe+jMMsT9ZGVBYDE2yjP7dkpn472KaNTIG90pm+lflDO6VzMI14aDDrBHOJX5S3GJmA5xzcwG8ivFUYBLQr8aj+4HmL17KuzM+pFvnTlx45bUAXPzrsxg2aCDTP/yEY4fvO8Hm64WLefJfL5KUnETIjAm/vZhGDbP299AJZ+3mCHOXh7nuVw2IRGH91gifLChn5IBURh+eSqMGxg2/bsDCNWGee68k6HBr3LadBfxx0r+JRqNEneP4wYcw4tDeDMr+I22bN2b8nY8CcOxhB/ObMccGHG38iEThvkdWcN+t/QiFjDff28zqb+rnzNM9xp+QTma6EY06XvmglJIymP5lOeeekM6QPskVp2RIfDJXSee2mXUAws65zfvZdpRz7uMDOEatVYp1TdveA7jsgfyqd6yHHrqyEQBFH74UcCTxKXP4GRw9ZmbQYcSlj14fydUP7w46jLh17+8a1Fg5t3zl2mr1C/fo3jnwUrPSStE5t76SbQeSEEVEpJ6oDxNtREREDkgiJEXNJxcREfGoUhQREV8kQqWopCgiIr5QUhQREfHUh/MURUREDkgiVIqaaCMiIuJRpSgiIr5IhEpRSVFERHyhpCgiIuJJhIk2GlMUERHxqFIUERFfRNV9KiIiEqMxRREREU8ijCkqKYqIiC8SoVLURBsRERGPKkUREfGFuk9FREQ8idB9qqQoIiK+UKUoIiLiiQYdgA800UZERMSjSlFERHyh7lMRERGPJtqIiIh4EqFS1JiiiIiIR5WiiIj4Qt2nIiIinqgLOoLqM+dq/FkkwMskIpIwaqycm7mwqFrv9yP7ZlYam5l1BJ4CWhPLLTnOuYlm1gx4AegCrAHGOefyzMyAicDJQBFwnnPuy8qOUSuV4ugzP6+Nw9Q5054fwkV3bAs6jLj0+I0tADh6zMyAI4lPH3yrnw0AABQISURBVL0+klGnfxp0GHFpxstH6O+mEh+9PrLGHrsWJtqEgaudc1+aWUNgjplNBc4Dpjnn7jKz64HrgeuAk4Ae3jIU+If383tpoo2IiNQJzrlNeyo951wBsBhoD4wFJnu7TQZ+5q2PBZ5yMbOAJmbWtrJjaExRRER8UfOjcd8ysy7AQOAzoLVzbpO3aTOx7lWIJcx1e91tvde2ie+hSlFERHwRxaq1mFm2mc3ea8ne33HMLAv4N3Clcy5/720uNlHmR6dnVYoiIuKL6o4pOudygJzK9jGzFGIJ8Vnn3Cte8xYza+uc2+R1j+Z67RuAjnvdvYPX9r1UKYqIiC+cq95SFW826RPAYufcfXtteg0Y762PB6bs1X6uxQwDdu3VzbpfqhRFRKSuOAo4B5hvZnO9tj8AdwEvmtmFwFpgnLftLWKnY6wgdkrG+VUdQElRRER8UdNXtHHOfcT3n2c5ej/7O+CSH3IMJUUREfFFIlzRRklRRER8oW/JEBERSSCqFEVExBe1efJ+TVFSFBERX0T11VEiIiIxqhRFREQ8mmgjIiKSQFQpioiIL3SeooiIiEdjiiIiIp6avsxbbVBSFBERXyRC96km2oiIiHhUKYqIiC80pigiIuJRUhQREfFEdfK+iIhI4lClKCIivlD3qYiIiEdJUURExJMI5ykmdFK85jddGXZYE3bml3PR7xcAcN649hx1eFOizrEzP8zd/1jF9rzygCMNRkaaMf6ULNq3TALgyTcKOX5IBq2bx25nphlFpY7bHt8ZZJiBu+Hynhw5uDl5u8o599LZQYcTd35xchtOPa41GLz53hZefnNz0CHFhVYt0rjpqt40bZICwGtvb+Kl1zcEHFXNSoRvyUjopPjOzG1MeWcL113SraLtxdc38c8XY3+YPz+xNeec1p4HnlgTUITBOusnDVi4qoxHXiklKQSpKcajrxZUbB83ugFFpdEAI4wPb03bwr/f3MhNV/UOOpS407VjBqce15r/d/18wuEod9/Uh0/n7GTD5pKgQwtcJOL426SVLFtZSEZGEpPuP4wv5uaxZl1R0KFJJaqcfWpmQ8xssLd+sJlNMLOTaz606pu/pID83eF92oqKv32TT08L4UiAev9HyEgzenRK4cO5pQBEolBcuu9rMejgVD5fWBpEeHHl64W7yC+on70JVenUIYNFywspLYsSicLcRfkMH9os6LDiwva8MpatLASguDjCmnVFtGieFnBUNcu56i3xoNJK0cz+BJwEJJvZVGAo8D5wvZkNdM7dUQsx+u6CX3bg+BHN2V0U4erblgQdTiBaNAlRWBTl/FOz6Ng6mbWbwzz3biFl3nt/j47J5O+OkpunSlG+3+pvirnorE40ykqmtCzKsIFNWeolAvlWm1Zp9OyexaKl+UGHUqPqw5ji6cAAIA3YDHRwzuWb2T3AZ8B+k6KZZQPZAI8++qj3EPFj0gvrmfTCes4a25afndCayS8ndj///oRCRqc2yfzrnd2s3hjmzOMbcNKRmUyZGevaGdo3jc8XlgUcpcS7bzYU89x/NvLXm/tQUhplxZrdRPU5ah8Z6SHuuKEvEx9bSVFxJOhwalS8VHvVUVX3adg5F3HOFQErnXP5AM65YuB7//SdcznOuUHOuUHZ2dk+huuvaR9tZ/jQpkGHEYi8/Ah5+VFWb4x1L89ZUkrnNrHPSCGDw3ql8cUidZ1K1d6anstvrpvPFX9cSMHuMOs2FQcdUtxISjJuv6Ev787I5YNPtwUdTo1LhO7TqpJimZlleuuH72k0s8ZUkhTjWfs23/bpHzmoKes21s8JAfm7HTvyo7RuFptp2qdLKhu3xhJkn64pbNoeIa+gTv6KpZY1aRT7MNWqRSojhjZn2oeJ/+Z/oG64vCdr1xXxwpT1QYciB6iq7tMRzrlSAOfc3u+QKcD4GovKJzde1p1DD25I44bJPP/3AUx+eT1DBjShY7t0XBS2bCvlgcfXBB1mYJ57t5CLf5ZFcsjYujPCk2/ExoKGHJzG56oSK9xyTR8G9GtMk0YpvPLkMJ741xrenKrTDva47fe9aJSVTDjieODxVRQWJXYX4YHqf3AjTjy2DStWF/LkxFhN8ehTq5k1Z0fAkdWchB9T3JMQ99O+DYj7j4N3PLTyf9r++37ch11r1m2JcPukXf/Tvic5Sswt9ywOOoS4dvnNC4MOIS7NW5TP0WNmBh1GrYqXLtDqSOjzFEVEpPYkwiQrfUuGiIiIR5WiiIj4Qt2nIiIiHiVFERERT8LPPhURETlQrtqlYvDfsqGJNiIiIh5ViiIi4guNKYqIiHgS4TxFJUUREfGFKkURERFPIsw+1UQbERERjypFERHxhbpPRUREPK7a/afBn6eopCgiIr7QmKKIiEgCUaUoIiK+0JiiiIiIJ5oA/adKiiIi4gtViiIiIp5ESIqaaCMiIuJRpSgiIr6IJkCpqKQoIiK+cPqWDBERkRinSlFERCQmEb5PURNtREREPEqKIiLiC+dctZaqmNkkM8s1swV7tTUzs6lmttz72dRrNzN70MxWmNk8MzvsQJ6D1UIfcN3vZBYRSRw19lUUN/2zrFrv97efl1ppbGY2AigEnnLOHeK13Q3scM7dZWbXA02dc9eZ2cnAZcDJwFBgonNuaFUx1MqY4qX37aqNw9Q5f5vQmAtuzQ06jLg06U+tAJj4uj5T7c8VY4wRP/8o6DDi0gevHs2bKb2CDiNunVK+tMYeu/pfHVXF4zv3gZl1+U7zWGCUtz4ZmAFc57U/5WKV3ywza2JmbZ1zmyo7hrpPRUQkLphZtpnN3mvJPoC7td4r0W0GWnvr7YF1e+233murlGafioiIL6o7GuecywFyqnF/Z2bVikJJUUREfBHQt2Rs2dMtamZtgT1jUhuAjnvt18Frq5S6T0VExBc1Pfv0e7wGjPfWxwNT9mo/15uFOgzYVdV4IqhSFBERn9T0Zd7M7Dlik2pamNl64E/AXcCLZnYhsBYY5+3+FrGZpyuAIuD8AzmGkqKIiNQJzrmzvmfT6P3s64BLfugxlBRFRMQX+pYMERERjy4ILiIi4glo9qmvNPtURETEo0pRRER8kQC9p0qKIiLij5q+9mltUFIUERFfaPapiIiIJxEqRU20ERER8ahSFBERXyRCpaikKCIivkiAnKikKCIi/lClKCIi4kmEy7xpoo2IiIhHlaKIiPgiEa59qqQoIiK+SITuUyVFERHxRSJMtNGYooiIiEeVooiI+CIRKkUlRRER8YUuCC4iIuJRpRjHWjUNccEpmRW3mzcO8eYnJSxfF+bM4zJISzW274oy+b9FlJQFGGiAMtKM83/akPatknEOnnwtn/490hjQKw3nHPm7HZP+k8/OwmjQoda46S/8gbWLZpCR1Zwzf/86AJ+9PZHVC6dhFiIjqxmjf3knDRq3ZvWCaXz2zkTMQoRCSRw99g+07Xp4wM8gOFmZSVx7SQ+6dor9v931t+UsXFoQcFS165jl0wgX7sZForhwhI+H/YI2vziRnjdfSlaf7nx85BnsmrMAgMaD+9HvH/8HgJmx7LaH2DLlvSDD941mn8ax3Lwodz1TCIAZ3JHdkK9XlHPRmExe/aCEFesjDOubwuhBabz5SWnA0QbjVydmMX9FGQ+/lE9SCFJTjA25Rbz6/m4AjhuSwZiRDXj6zcR/g+s96Of0O+pspj13fUXbwFEXMvTEKwCY9+FTfDH1YUadfisdegyjS99jMTO2bVzKu09fya+u+29QoQfu8ou68dlXefzxr0tITjbSU+vn/L1Zx42nfHtexe3ChcuYM+4y+j186z77FSxYzsdDf4GLREhr05Lhc6aQ+8b7uEiktkOW/fjBf71m9lRNBFKTenVKZuvOKHkFjlZNk1ixPvbHt2RtmAE9UgKOLhgZaUbPzql8+FUJAJEoFJc6Ssq+/aSXmmpA3f/kdyDadR9MWmbjfdpS07Mq1svLijEzAFLSGlSsh8uKYp+66qkGmUkcenBj3nxvCwDhsKOwSG/uAIVLVrF72er/aY8Wl1QkwFB6GiRAdbVHNOqqtcSDSitFM3vtu03AMWbWBMA599OaCsxPh/dKYc7ScgA2bY/Qv3sy81aGOaxnCk0b1s9PtS2aJFFQFOWCsQ3p2DqZtZvC/OvtAsrK4bRjG3Bk/3SKSh1/nZxX9YMlsFn/vZ+ls6eQlt6Qsb+dXNG+av5UZr11H8WFOzjlwkcCjDBYbVulszO/nBsu60H3Lg1YtrKQB59YRUlp4ne578PB0P8+Ac6x9rEXWPf4i5Xu3mRIf/rn/JmMzu2Ye961CVMlJsKYYlUZoQOQD9wH3OstBXut75eZZZvZbDObnZOT41esP0pSCPp1T+arZbGk+Ow7xQw/NJVrz84iLdWIROr+L/HHSApB57bJzJhdzK05eZSWO045ugEAr0zfzTUPbGfW/BKOHZJZxSMltmEnXcX4m2fQ47BTmf/xMxXt3fodz6+u+y8nnfc3Pn/nwQAjDFZSktGjWxb/eXsTF109l5LSKGef1iHosGrdJ6PO4qMhp/H5qRfT5bdn0+zoQZXuv/PzeXww4FQ+PuJ0DrruN4TSUmsp0prlnKvWEg+qSoqDgDnAjcAu59wMoNg5N9M5N/P77uScy3HODXLODcrOzvYv2h/h4K7JrNsSoaAo9oJvyYvy91eKuPvZQuYsKWfrrnr2idazIz9KXn6UVRvCAMxeVEqnNvt2HMyaV8LhfdKCCC/u9DxsDKvmTf2f9nbdB5O/fR3Fu+tnRb11eylbt5eyeHls/H7GJ9vo2S2rinslntKNuQCUbd3B5v9Mpcng/gd0v8IlqwgXFtHwkJ41GV6tcdFotZZ4UGlSdM5FnXP3A+cDN5rZ36hjk3MG7dV1CpCVERv/MeCEYWl89HX9nHqavzvKjl0R2jRPAuDgrqls3BamVbOkin0G9k5j87ZwUCEGbufWNRXrqxdOo0mrrgDs2ra24lPt1vULiYTLSM9sEkSIgduxs5zcbaV0bJcBwOH9m7BmfVHAUdWupMwMkrIaVKy3PP4oChYu/979M7p0wJJi/2cZndqR1asbRWs21EqsUrUDSnDOufXAGWZ2CrHu1DohNRl6d07mufeKK9oG9U5hxIBYV8Xc5eXMWlj+fXdPeM/+t5Ds0xqRlGRszYswaUo+541pSJsWsVM0tu+M8FQ9mHkK8O4zE9i48gtKducx+f9GMvgnl/HNkpnszF0DIaNhk3aMPD02i3DlvHdZOmcKoaRkklPS+Mk591dMvKmPJj62ipuv6klKcoiNW0q486FlQYdUq1JbN2fQy38HwJKS2Pj8G2x990Najz2Ovg/cTGrLZgye8ij5Xy/m81MuotlRh9P99xcTDYchGmXBZbfsM2u1LouXyTLVYbXQj+suvW9XTR+jTvrbhMZccGtu0GHEpUl/agXAxNfr/j9ZTbhijDHi5x8FHUZc+uDVo3kzpVfQYcStU8qX1tgnuHFXr6nWP+yL93YJ/NNlneoKFRGR+FUfZp+KiIjUG6oURUTEF4lQKSopioiIL6IuPk6rqA4lRRER8YUqRREREU8iJEVNtBEREfGoUhQREV/Ey/VLq0NJUUREfBGNk+uXVoeSooiI+CIRxhSVFEVExBcuAU7J0EQbERERjypFERHxhbpPRUREPEqKIiIinkS4zJvGFEVERDyqFEVExBfqPhUREfE4nbwvIiISo0pRRETEo5P3RUREEogqRRER8UVU3aciIiIxmmgjIiLiSYSJNhpTFBERXzgXrdZyIMzsRDNbamYrzOx6v5+DkqKIiNQJZpYE/B04CTgYOMvMDvbzGOo+FRERX9RC9+kQYIVzbhWAmT0PjAUW+XUAc67Gn0Td72QWEUkcVlMPfPSYmdV6v//o9ZGVxmZmpwMnOucu8m6fAwx1zl1anePurTYqxRr7BfwYZpbtnMsJOo54pNemcnp9vp9em8rVl9enqqRWFTPLBrL3asqp7detPo4pZle9S72l16Zyen2+n16byun1OQDOuRzn3KC9lu8mxA1Ax71ud/DafFMfk6KIiNRNXwA9zKyrmaUCZwKv+XkATbQREZE6wTkXNrNLgXeAJGCSc26hn8eoj0kx4fv1q0GvTeX0+nw/vTaV0+vjE+fcW8BbNfX4tTH7VEREpE7QmKKIiIin3iTFmr40UF1mZpPMLNfMFgQdS7wxs45m9r6ZLTKzhWZ2RdAxxRMzSzezz83sa+/1uTXomOKNmSWZ2Vdm9kbQsUjV6kVSrI1LA9Vx/wRODDqIOBUGrnbOHQwMAy7R384+SoFjnXOHAgOAE81sWMAxxZsrgMVBByEHpl4kRfa6NJBzrgzYc2kgAZxzHwA7go4jHjnnNjnnvvTWC4i9ubUPNqr44WIKvZsp3qKJCh4z6wCcAjwedCxyYOpLUmwPrNvr9nr0xiY/kJl1AQYCnwUbSXzxugfnArnAVOecXp9vPQBcC9T9LxqsJ+pLUhSpFjPLAv4NXOmcyw86nnjinIs45wYQu7rIEDM7JOiY4oGZnQrkOufmBB2LHLj6khRr/NJAkrjMLIVYQnzWOfdK0PHEK+fcTuB9ND69x1HAT81sDbEhm2PN7JlgQ5Kq1JekWOOXBpLEZGYGPAEsds7dF3Q88cbMWppZE289AzgeWBJsVPHBOXeDc66Dc64Lsfec6c65XwccllShXiRF51wY2HNpoMXAi35fGqguM7PngE+BXma23swuDDqmOHIUcA6xT/lzveXkoIOKI22B981sHrEPn1Odczr1QOosXdFGRETEUy8qRRERkQOhpCgiIuJRUhQREfEoKYqIiHiUFEVERDxKiiIiIh4lRREREY+SooiIiOf/A3YUMuz8E0wAAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "I94R0544sroJ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "c3b0c225-46d9-4e34-dfb5-f4f9c100b9cb"
      },
      "source": [
        "from sklearn.tree import DecisionTreeClassifier \n",
        "dt = DecisionTreeClassifier()\n",
        "dt.fit(x_train_std, y_train)\n",
        "dt_pred = dt.predict(x_test_std)\n",
        "dt_accu = accuracy_score(y_test, dt_pred)\n",
        "dt_report = classification_report(y_test, dt_pred)\n",
        "dt_cm = confusion_matrix(y_test, dt_pred)\n",
        "print('[1]Decision Tree Classifier Performance:\\n')\n",
        "print('Accuracy:', dt_accu)\n",
        "print('Classification Report:', dt_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(dt_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[1]Decision Tree Classifier Performance:\n",
            "\n",
            "Accuracy: 0.5116126921818777\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.39      0.39      0.39       723\n",
            "        high       0.45      0.45      0.45       678\n",
            "         low       0.49      0.50      0.50       770\n",
            "   very high       0.31      0.41      0.36        92\n",
            "    very low       0.73      0.71      0.72       794\n",
            "\n",
            "    accuracy                           0.51      3057\n",
            "   macro avg       0.47      0.49      0.48      3057\n",
            "weighted avg       0.51      0.51      0.51      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QbZi_PMfstFr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "3f71ee03-b6a5-4ff5-96f0-49c5de70d70a"
      },
      "source": [
        "from sklearn.svm import SVC \n",
        "svc = SVC()\n",
        "svc.fit(x_train_std, y_train)\n",
        "svc_pred = dt.predict(x_test_std)\n",
        "svc_accu = accuracy_score(y_test, svc_pred)\n",
        "svc_report = classification_report(y_test, svc_pred)\n",
        "svc_cm = confusion_matrix(y_test, svc_pred)\n",
        "print('[2]SVM Classifier Performance:\\n')\n",
        "print('Accuracy:', svc_accu)\n",
        "print('Classification Report:', svc_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(svc_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 59,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[2]SVM Classifier Performance:\n",
            "\n",
            "Accuracy: 0.5116126921818777\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.39      0.39      0.39       723\n",
            "        high       0.45      0.45      0.45       678\n",
            "         low       0.49      0.50      0.50       770\n",
            "   very high       0.31      0.41      0.36        92\n",
            "    very low       0.73      0.71      0.72       794\n",
            "\n",
            "    accuracy                           0.51      3057\n",
            "   macro avg       0.47      0.49      0.48      3057\n",
            "weighted avg       0.51      0.51      0.51      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "B3nPvhsjsuxc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "5531ef4e-ed51-423b-9752-f066178199bb"
      },
      "source": [
        "from sklearn.naive_bayes import GaussianNB \n",
        "gb = GaussianNB()\n",
        "gb.fit(x_train_std, y_train)\n",
        "gb_pred = gb.predict(x_test_std)\n",
        "gb_accu = accuracy_score(y_test, gb_pred)\n",
        "gb_report = classification_report(y_test, gb_pred)\n",
        "gb_cm = confusion_matrix(y_test, gb_pred)\n",
        "print('[3]Gaussian Naive Bayes Classifier Performance:\\n')\n",
        "print('Accuracy:', gb_accu)\n",
        "print('Classification Report:', gb_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(gb_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 60,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[3]Gaussian Naive Bayes Classifier Performance:\n",
            "\n",
            "Accuracy: 0.4870788354596009\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.42      0.30      0.35       723\n",
            "        high       0.46      0.32      0.38       678\n",
            "         low       0.47      0.68      0.56       770\n",
            "   very high       0.20      0.64      0.31        92\n",
            "    very low       0.69      0.60      0.64       794\n",
            "\n",
            "    accuracy                           0.49      3057\n",
            "   macro avg       0.45      0.51      0.45      3057\n",
            "weighted avg       0.51      0.49      0.48      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hZ4e157nswkr",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "ac8434c4-5dc1-4302-b008-e9a2e9e435c2"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier \n",
        "rf = RandomForestClassifier(n_estimators= 900)\n",
        "rf.fit(x_train_std, y_train)\n",
        "rf_pred = rf.predict(x_test_std)\n",
        "rf_accu = accuracy_score(y_test, rf_pred)\n",
        "rf_report = classification_report(y_test, rf_pred)\n",
        "rf_cm = confusion_matrix(y_test, rf_pred)\n",
        "print('[4]Random Forest Classifier Performance:\\n')\n",
        "print('Accuracy:', rf_accu)\n",
        "print('Classification Report:', rf_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(rf_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 61,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[4]Random Forest Classifier Performance:\n",
            "\n",
            "Accuracy: 0.6241413150147204\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.51      0.46      0.49       723\n",
            "        high       0.61      0.57      0.59       678\n",
            "         low       0.55      0.71      0.62       770\n",
            "   very high       0.56      0.16      0.25        92\n",
            "    very low       0.83      0.79      0.81       794\n",
            "\n",
            "    accuracy                           0.62      3057\n",
            "   macro avg       0.61      0.54      0.55      3057\n",
            "weighted avg       0.63      0.62      0.62      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "i4LGDntusyUN",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "a05cdc2f-2098-479f-8a9d-35281645412e"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "xg = XGBClassifier(learning_rate= 0.04, n_estimators=450)\n",
        "xg.fit(x_train_std, y_train)\n",
        "xg_pred = xg.predict(x_test_std)\n",
        "xg_accu = accuracy_score(y_test, xg_pred)\n",
        "xg_report = classification_report(y_test, xg_pred)\n",
        "xg_cm = confusion_matrix(y_test, xg_pred)\n",
        "print('[5]xgBoost Classifier Performance:\\n')\n",
        "print('Accuracy:', xg_accu)\n",
        "print('Classification Report:', xg_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(xg_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 62,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[5]xgBoost Classifier Performance:\n",
            "\n",
            "Accuracy: 0.6149820085050703\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.52      0.49      0.50       723\n",
            "        high       0.62      0.52      0.57       678\n",
            "         low       0.54      0.70      0.61       770\n",
            "   very high       0.46      0.25      0.32        92\n",
            "    very low       0.80      0.77      0.79       794\n",
            "\n",
            "    accuracy                           0.61      3057\n",
            "   macro avg       0.59      0.55      0.56      3057\n",
            "weighted avg       0.62      0.61      0.61      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KoPY9MFCsz9W",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "ecf944ba-fabe-4a07-a245-b471c2f27558"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ab = AdaBoostClassifier(learning_rate= 0.4, n_estimators= 200)\n",
        "ab.fit(x_train_std, y_train)\n",
        "ab_pred = ab.predict(x_test_std)\n",
        "ab_accu = accuracy_score(y_test, ab_pred)\n",
        "ab_report = classification_report(y_test, ab_pred)\n",
        "ab_cm = confusion_matrix(y_test, ab_pred)\n",
        "print('[6]AdaBoost Classifier Performance:\\n')\n",
        "print('Accuracy:', ab_accu)\n",
        "print('Classification Report:', ab_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(ab_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 63,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[6]AdaBoost Classifier Performance:\n",
            "\n",
            "Accuracy: 0.5731108930323847\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.46      0.41      0.43       723\n",
            "        high       0.57      0.50      0.53       678\n",
            "         low       0.51      0.62      0.56       770\n",
            "   very high       0.28      0.29      0.28        92\n",
            "    very low       0.78      0.77      0.78       794\n",
            "\n",
            "    accuracy                           0.57      3057\n",
            "   macro avg       0.52      0.52      0.52      3057\n",
            "weighted avg       0.58      0.57      0.57      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fzkWPAZTs1jS",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 626
        },
        "outputId": "afaf7e02-bdab-4590-d5e7-1fb10ff9b070"
      },
      "source": [
        "from sklearn.ensemble import GradientBoostingClassifier\n",
        "gbc = GradientBoostingClassifier()\n",
        "gbc.fit(x_train_std, y_train)\n",
        "gbc_pred = gbc.predict(x_test_std)\n",
        "gbc_accu = accuracy_score(y_test, gbc_pred)\n",
        "gbc_report = classification_report(y_test, gbc_pred)\n",
        "gbc_cm = confusion_matrix(y_test, gbc_pred)\n",
        "print('[7]Gradient Boost Classifier Performance:\\n')\n",
        "print('Accuracy:', gbc_accu)\n",
        "print('Classification Report:', gbc_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(gbc_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": 64,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[7]Gradient Boost Classifier Performance:\n",
            "\n",
            "Accuracy: 0.6175989532221132\n",
            "Classification Report:               precision    recall  f1-score   support\n",
            "\n",
            "     average       0.52      0.49      0.50       723\n",
            "        high       0.62      0.53      0.57       678\n",
            "         low       0.55      0.70      0.61       770\n",
            "   very high       0.36      0.22      0.27        92\n",
            "    very low       0.81      0.78      0.79       794\n",
            "\n",
            "    accuracy                           0.62      3057\n",
            "   macro avg       0.57      0.54      0.55      3057\n",
            "weighted avg       0.62      0.62      0.61      3057\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x432 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wuo7u5jbApA9"
      },
      "source": [
        "### **Stacked Model and Result Preparation:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "PMgSJ9Vds4tH"
      },
      "source": [
        "from sklearn.ensemble import RandomForestClassifier \n",
        "rf = RandomForestClassifier()\n",
        "rf.fit(x_train_std, y_train)\n",
        "rf_pred = rf.predict(x_test_std)"
      ],
      "execution_count": 65,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SuiN4tUGs65u"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "xg = XGBClassifier(learning_rate= 0.04, n_estimators=450)\n",
        "xg.fit(x_train_std, y_train)\n",
        "xg_pred = xg.predict(x_test_std)"
      ],
      "execution_count": 66,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weSgAE4ns8R4"
      },
      "source": [
        "from sklearn.ensemble import AdaBoostClassifier\n",
        "ab = AdaBoostClassifier(learning_rate= 0.4, n_estimators= 200)\n",
        "ab.fit(x_train_std, y_train)\n",
        "ab_pred = ab.predict(x_test_std)"
      ],
      "execution_count": 67,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Cj3sxE56s9kL"
      },
      "source": [
        "x1 = []\n",
        "\n",
        "for i in range(len(rf_pred)):\n",
        "\n",
        "  if(rf_pred[i] == 'very low'):\n",
        "    x1.append(1)\n",
        "\n",
        "  if(rf_pred[i] == 'low'):\n",
        "    x1.append(2)\n",
        "\n",
        "  if(rf_pred[i] == 'average'):\n",
        "    x1.append(3)\n",
        "\n",
        "  if(rf_pred[i] == 'high'):\n",
        "    x1.append(4)\n",
        "\n",
        "  if(rf_pred[i] == 'very high'):\n",
        "    x1.append(5)"
      ],
      "execution_count": 68,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pJbJuL3rs_Ft"
      },
      "source": [
        "x2 = []\n",
        "\n",
        "for i in range(len(xg_pred)):\n",
        "\n",
        "  if(xg_pred[i] == 'very low'):\n",
        "    x2.append(1)\n",
        "\n",
        "  if(xg_pred[i] == 'low'):\n",
        "    x2.append(2)\n",
        "\n",
        "  if(xg_pred[i] == 'average'):\n",
        "    x2.append(3)\n",
        "\n",
        "  if(xg_pred[i] == 'high'):\n",
        "    x2.append(4)\n",
        "\n",
        "  if(xg_pred[i] == 'very high'):\n",
        "    x2.append(5)"
      ],
      "execution_count": 69,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "sl8DJVaQtAPL"
      },
      "source": [
        "x3 = []\n",
        "\n",
        "for i in range(len(ab_pred)):\n",
        "\n",
        "  if(ab_pred[i] == 'very low'):\n",
        "    x3.append(1)\n",
        "\n",
        "  if(ab_pred[i] == 'low'):\n",
        "    x3.append(2)\n",
        "\n",
        "  if(ab_pred[i] == 'average'):\n",
        "    x3.append(3)\n",
        "\n",
        "  if(ab_pred[i] == 'high'):\n",
        "    x3.append(4)\n",
        "\n",
        "  if(ab_pred[i] == 'very high'):\n",
        "    x3.append(5)"
      ],
      "execution_count": 70,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HRGoRwKPtBkN"
      },
      "source": [
        "x = []\n",
        "\n",
        "for i in range(len(x_test_std)):\n",
        "  x.append(np.ceil(((x1[i] + x2[i] + x3[i]) / 3 )))"
      ],
      "execution_count": 71,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WnyhlPiLtDo_"
      },
      "source": [
        "pop = []\n",
        "for i in x:\n",
        "  if i == 1:\n",
        "    pop.append('very low')\n",
        "  if i == 2:\n",
        "    pop.append('low')\n",
        "  if i == 3:\n",
        "    pop.append('average')\n",
        "  if i == 4:\n",
        "    pop.append('high')\n",
        "  if i == 5:\n",
        "    pop.append('very high')"
      ],
      "execution_count": 72,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3P8hM45QtGfj"
      },
      "source": [
        "y_test = y_test.values"
      ],
      "execution_count": 73,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7HXttmvQAeFF"
      },
      "source": [
        "### **Final Result:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WTnaignbtLB8",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "e557b636-8f5a-4c67-e04b-d45c964a1d72"
      },
      "source": [
        "r = 0\n",
        "b = 0\n",
        "\n",
        "\n",
        "for i in range(len(x_test)):\n",
        "  if(y_test[i] == 'very low'):\n",
        "    y = 1\n",
        "\n",
        "  if(y_test[i] == 'low'):\n",
        "    y = 2\n",
        "\n",
        "  if(y_test[i] == 'average'):\n",
        "    y = 3\n",
        "\n",
        "  if(y_test[i] == 'high'):\n",
        "    y = 4\n",
        "\n",
        "  if(y_test[i] == 'very high'):\n",
        "    y = 5\n",
        "  \n",
        "  if(x[i] >= y):\n",
        "    b += x[i]\n",
        "    r += 2 * y\n",
        "\n",
        "print('Revenue Earned:', r)\n",
        "print('Amount Bid:', b)\n",
        "print('Maximum amount that we can bid:', len(y_test) * 2.5)"
      ],
      "execution_count": 74,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Revenue Earned: 11136\n",
            "Amount Bid: 6469.0\n",
            "Maximum amount that we can bid: 7642.5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Wgqcl3AoASlG"
      },
      "source": [
        "### **GridSearch to Optimise Parameters:**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UBur9B0htM0p"
      },
      "source": [
        "param_xg = {'learning_rate': [.03, 0.05, .07],\n",
        "              'max_depth': [5, 6, 7],\n",
        "              'min_child_weight': [4, 5, 6],\n",
        "              'subsample': [0.7, 0.8, 0.9],\n",
        "              'colsample_bytree': [0.7, 0.8, 0.9],}"
      ],
      "execution_count": 75,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "dr9ltK99tZbP"
      },
      "source": [
        "from xgboost import XGBClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "xg = XGBClassifier()\n",
        "xg_gs= GridSearchCV(xg, param_xg)\n",
        "xg_gs.fit(x_train_std, y_train)\n",
        "xg_gs_pred = xg_gs.predict(x_test_std)\n",
        "xg_gs_accu = accuracy_score(y_test, xg_gs_pred)\n",
        "xg_gs_report = classification_report(y_test, xg_gs_pred)\n",
        "xg_gs_cm = confusion_matrix(y_test, xg_gs_pred)\n",
        "print('xgBoost Classifier Performance:\\n')\n",
        "print('Accuracy:', xg_gs_accu)\n",
        "print('Classification Report:', xg_gs_report)\n",
        "plt.figure(figsize=(8,6))\n",
        "sns.heatmap(xg_gs_cm, annot = True, cmap='coolwarm', linewidths=0.2, fmt = \"d\");"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o94LoZsutd_V"
      },
      "source": [
        "xg_gs.best_params_"
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}