TreesEnsemble.ipynb

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  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
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  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "id": "yILWQ2A4zKq-"
      },
      "outputs": [],
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn.tree import DecisionTreeClassifier\n",
        "from sklearn.ensemble import RandomForestClassifier\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import accuracy_score\n",
        "!pip install xgboost --quiet\n",
        "from xgboost import XGBClassifier\n",
        "import matplotlib.pyplot as plt\n",
        "# plt.style.use('./deeplearning.mplstyle')\n",
        "\n",
        "RANDOM_STATE = 55 ## You will pass it to every sklearn call so we ensure reproducibility"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Load the dataset using pandas\n",
        "# there we go so we loaded the data using pandas\n",
        "df = pd.read_csv(\"/content/heart.csv\")"
      ],
      "metadata": {
        "id": "NKnV-LRLzSdn",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 339
        },
        "outputId": "b6820c6d-0c01-4176-d16b-cc9c9fb71185"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "FileNotFoundError",
          "evalue": "[Errno 2] No such file or directory: '/content/heart.csv'",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mFileNotFoundError\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-3-d99d380c1e61>\u001b[0m in \u001b[0;36m<cell line: 3>\u001b[0;34m()\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;31m# Load the dataset using pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# there we go so we loaded the data using pandas\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mdf\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mread_csv\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"/content/heart.csv\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36mread_csv\u001b[0;34m(filepath_or_buffer, sep, delimiter, header, names, index_col, usecols, dtype, engine, converters, true_values, false_values, skipinitialspace, skiprows, skipfooter, nrows, na_values, keep_default_na, na_filter, verbose, skip_blank_lines, parse_dates, infer_datetime_format, keep_date_col, date_parser, date_format, dayfirst, cache_dates, iterator, chunksize, compression, thousands, decimal, lineterminator, quotechar, quoting, doublequote, escapechar, comment, encoding, encoding_errors, dialect, on_bad_lines, delim_whitespace, low_memory, memory_map, float_precision, storage_options, dtype_backend)\u001b[0m\n\u001b[1;32m    910\u001b[0m     \u001b[0mkwds\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mupdate\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mkwds_defaults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    911\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 912\u001b[0;31m     \u001b[0;32mreturn\u001b[0m \u001b[0m_read\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    913\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    914\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_read\u001b[0;34m(filepath_or_buffer, kwds)\u001b[0m\n\u001b[1;32m    575\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    576\u001b[0m     \u001b[0;31m# Create the parser.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 577\u001b[0;31m     \u001b[0mparser\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mTextFileReader\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mfilepath_or_buffer\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    578\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    579\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mchunksize\u001b[0m \u001b[0;32mor\u001b[0m \u001b[0miterator\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m__init__\u001b[0;34m(self, f, engine, **kwds)\u001b[0m\n\u001b[1;32m   1405\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1406\u001b[0m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhandles\u001b[0m\u001b[0;34m:\u001b[0m \u001b[0mIOHandles\u001b[0m \u001b[0;34m|\u001b[0m \u001b[0;32mNone\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1407\u001b[0;31m         \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_engine\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_make_engine\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mengine\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1408\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1409\u001b[0m     \u001b[0;32mdef\u001b[0m \u001b[0mclose\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m)\u001b[0m \u001b[0;34m->\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/parsers/readers.py\u001b[0m in \u001b[0;36m_make_engine\u001b[0;34m(self, f, engine)\u001b[0m\n\u001b[1;32m   1659\u001b[0m                 \u001b[0;32mif\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1660\u001b[0m                     \u001b[0mmode\u001b[0m \u001b[0;34m+=\u001b[0m \u001b[0;34m\"b\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1661\u001b[0;31m             self.handles = get_handle(\n\u001b[0m\u001b[1;32m   1662\u001b[0m                 \u001b[0mf\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1663\u001b[0m                 \u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/pandas/io/common.py\u001b[0m in \u001b[0;36mget_handle\u001b[0;34m(path_or_buf, mode, encoding, compression, memory_map, is_text, errors, storage_options)\u001b[0m\n\u001b[1;32m    857\u001b[0m         \u001b[0;32mif\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mencoding\u001b[0m \u001b[0;32mand\u001b[0m \u001b[0;34m\"b\"\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32min\u001b[0m \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    858\u001b[0m             \u001b[0;31m# Encoding\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 859\u001b[0;31m             handle = open(\n\u001b[0m\u001b[1;32m    860\u001b[0m                 \u001b[0mhandle\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    861\u001b[0m                 \u001b[0mioargs\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmode\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mFileNotFoundError\u001b[0m: [Errno 2] No such file or directory: '/content/heart.csv'"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "id": "A_ZL5KcA0q0X"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "cat_variables = ['Sex',\n",
        "'ChestPainType',\n",
        "'RestingECG',\n",
        "'ExerciseAngina',\n",
        "'ST_Slope'\n",
        "]"
      ],
      "metadata": {
        "id": "C9a3Q5v00s-6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.get_dummies(data = df,\n",
        "                         prefix = cat_variables,\n",
        "                         columns = cat_variables)"
      ],
      "metadata": {
        "id": "3xDemCI70_Gw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 255
        },
        "id": "7VpCt4LM1QRi",
        "outputId": "0f152fd7-66c7-4bf7-8592-1ddfac81bc80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "   Age  RestingBP  Cholesterol  FastingBS  MaxHR  Oldpeak  HeartDisease  \\\n",
              "0   40        140          289          0    172      0.0             0   \n",
              "1   49        160          180          0    156      1.0             1   \n",
              "2   37        130          283          0     98      0.0             0   \n",
              "3   48        138          214          0    108      1.5             1   \n",
              "4   54        150          195          0    122      0.0             0   \n",
              "\n",
              "   Sex_F  Sex_M  ChestPainType_ASY  ...  ChestPainType_NAP  ChestPainType_TA  \\\n",
              "0  False   True              False  ...              False             False   \n",
              "1   True  False              False  ...               True             False   \n",
              "2  False   True              False  ...              False             False   \n",
              "3   True  False               True  ...              False             False   \n",
              "4  False   True              False  ...               True             False   \n",
              "\n",
              "   RestingECG_LVH  RestingECG_Normal  RestingECG_ST  ExerciseAngina_N  \\\n",
              "0           False               True          False              True   \n",
              "1           False               True          False              True   \n",
              "2           False              False           True              True   \n",
              "3           False               True          False             False   \n",
              "4           False               True          False              True   \n",
              "\n",
              "   ExerciseAngina_Y  ST_Slope_Down  ST_Slope_Flat  ST_Slope_Up  \n",
              "0             False          False          False         True  \n",
              "1             False          False           True        False  \n",
              "2             False          False          False         True  \n",
              "3              True          False           True        False  \n",
              "4             False          False          False         True  \n",
              "\n",
              "[5 rows x 21 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Age</th>\n",
              "      <th>RestingBP</th>\n",
              "      <th>Cholesterol</th>\n",
              "      <th>FastingBS</th>\n",
              "      <th>MaxHR</th>\n",
              "      <th>Oldpeak</th>\n",
              "      <th>HeartDisease</th>\n",
              "      <th>Sex_F</th>\n",
              "      <th>Sex_M</th>\n",
              "      <th>ChestPainType_ASY</th>\n",
              "      <th>...</th>\n",
              "      <th>ChestPainType_NAP</th>\n",
              "      <th>ChestPainType_TA</th>\n",
              "      <th>RestingECG_LVH</th>\n",
              "      <th>RestingECG_Normal</th>\n",
              "      <th>RestingECG_ST</th>\n",
              "      <th>ExerciseAngina_N</th>\n",
              "      <th>ExerciseAngina_Y</th>\n",
              "      <th>ST_Slope_Down</th>\n",
              "      <th>ST_Slope_Flat</th>\n",
              "      <th>ST_Slope_Up</th>\n",
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              "      <td>156</td>\n",
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              "      <td>98</td>\n",
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              "      <td>True</td>\n",
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              "      <td>48</td>\n",
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              "      <td>1.5</td>\n",
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              "<p>5 rows × 21 columns</p>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "df"
            }
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [],
      "metadata": {
        "id": "uEwdyKZAFQtX"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "var = [x for x in df.columns if x not in 'HeartDisease']"
      ],
      "metadata": {
        "id": "5E6elPe01TAX"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "wzsRwYKbFRB1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 141
        },
        "outputId": "1c3a604e-4eaa-4ee6-b42c-0cebb62c92d6"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "error",
          "ename": "NameError",
          "evalue": "name 'var' is not defined",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-1-84ddba356ca3>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m \u001b[0mvar\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m",
            "\u001b[0;31mNameError\u001b[0m: name 'var' is not defined"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(len(var))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "UQDdK4mx1bCw",
        "outputId": "8989c5e9-6b36-426c-9c86-a055589b09c4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "help(train_test_split)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "sS1EoXDY1dJF",
        "outputId": "9f820639-e241-405a-b3cc-120901ee71a1"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Help on function train_test_split in module sklearn.model_selection._split:\n",
            "\n",
            "train_test_split(*arrays, test_size=None, train_size=None, random_state=None, shuffle=True, stratify=None)\n",
            "    Split arrays or matrices into random train and test subsets.\n",
            "    \n",
            "    Quick utility that wraps input validation,\n",
            "    ``next(ShuffleSplit().split(X, y))``, and application to input data\n",
            "    into a single call for splitting (and optionally subsampling) data into a\n",
            "    one-liner.\n",
            "    \n",
            "    Read more in the :ref:`User Guide <cross_validation>`.\n",
            "    \n",
            "    Parameters\n",
            "    ----------\n",
            "    *arrays : sequence of indexables with same length / shape[0]\n",
            "        Allowed inputs are lists, numpy arrays, scipy-sparse\n",
            "        matrices or pandas dataframes.\n",
            "    \n",
            "    test_size : float or int, default=None\n",
            "        If float, should be between 0.0 and 1.0 and represent the proportion\n",
            "        of the dataset to include in the test split. If int, represents the\n",
            "        absolute number of test samples. If None, the value is set to the\n",
            "        complement of the train size. If ``train_size`` is also None, it will\n",
            "        be set to 0.25.\n",
            "    \n",
            "    train_size : float or int, default=None\n",
            "        If float, should be between 0.0 and 1.0 and represent the\n",
            "        proportion of the dataset to include in the train split. If\n",
            "        int, represents the absolute number of train samples. If None,\n",
            "        the value is automatically set to the complement of the test size.\n",
            "    \n",
            "    random_state : int, RandomState instance or None, default=None\n",
            "        Controls the shuffling applied to the data before applying the split.\n",
            "        Pass an int for reproducible output across multiple function calls.\n",
            "        See :term:`Glossary <random_state>`.\n",
            "    \n",
            "    shuffle : bool, default=True\n",
            "        Whether or not to shuffle the data before splitting. If shuffle=False\n",
            "        then stratify must be None.\n",
            "    \n",
            "    stratify : array-like, default=None\n",
            "        If not None, data is split in a stratified fashion, using this as\n",
            "        the class labels.\n",
            "        Read more in the :ref:`User Guide <stratification>`.\n",
            "    \n",
            "    Returns\n",
            "    -------\n",
            "    splitting : list, length=2 * len(arrays)\n",
            "        List containing train-test split of inputs.\n",
            "    \n",
            "        .. versionadded:: 0.16\n",
            "            If the input is sparse, the output will be a\n",
            "            ``scipy.sparse.csr_matrix``. Else, output type is the same as the\n",
            "            input type.\n",
            "    \n",
            "    Examples\n",
            "    --------\n",
            "    >>> import numpy as np\n",
            "    >>> from sklearn.model_selection import train_test_split\n",
            "    >>> X, y = np.arange(10).reshape((5, 2)), range(5)\n",
            "    >>> X\n",
            "    array([[0, 1],\n",
            "           [2, 3],\n",
            "           [4, 5],\n",
            "           [6, 7],\n",
            "           [8, 9]])\n",
            "    >>> list(y)\n",
            "    [0, 1, 2, 3, 4]\n",
            "    \n",
            "    >>> X_train, X_test, y_train, y_test = train_test_split(\n",
            "    ...     X, y, test_size=0.33, random_state=42)\n",
            "    ...\n",
            "    >>> X_train\n",
            "    array([[4, 5],\n",
            "           [0, 1],\n",
            "           [6, 7]])\n",
            "    >>> y_train\n",
            "    [2, 0, 3]\n",
            "    >>> X_test\n",
            "    array([[2, 3],\n",
            "           [8, 9]])\n",
            "    >>> y_test\n",
            "    [1, 4]\n",
            "    \n",
            "    >>> train_test_split(y, shuffle=False)\n",
            "    [[0, 1, 2], [3, 4]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(df[var], df['HeartDisease'], train_size = 0.8, random_state = RANDOM_STATE)\n"
      ],
      "metadata": {
        "id": "mtjWSCfV1fgW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f'train samples: {len(X_train)}\\ntest samples: {len(X_test)}')\n",
        "print(f'target proportion: {sum(y_train)/len(y_train):.4f}')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pGGKYzQ-1rvh",
        "outputId": "1c086c18-1ad3-4549-fdde-e1ee91369e1f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "train samples: 734\n",
            "test samples: 184\n",
            "target proportion: 0.5518\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700] ## If the number is an integer, then it is the actual quantity of samples,\n",
        "max_depth_list = [1,2, 3, 4, 8, 16, 32, 64, None] # None means that there is no depth limit."
      ],
      "metadata": {
        "id": "tMDRETzd1v_I"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_list_train = []\n",
        "accuracy_list_test = []\n",
        "for min_samples_split in min_samples_split_list:\n",
        "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
        "    # so here we learn a new function named as DecisionTreeClassifier(min_samples_split = ,random_state=RANDOM_STATE)\n",
        "\n",
        "    model = DecisionTreeClassifier(min_samples_split = min_samples_split,\n",
        "                                   random_state = RANDOM_STATE).fit(X_train,y_train)\n",
        "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
        "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
        "    #\n",
        "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
        "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
        "    accuracy_list_train.append(accuracy_train)\n",
        "    accuracy_list_test.append(accuracy_test)\n",
        "\n",
        "plt.title('Train x Test metrics')\n",
        "plt.xlabel('min_samples_split')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\n",
        "plt.plot(accuracy_list_train)\n",
        "plt.plot(accuracy_list_test)\n",
        "plt.legend(['Train','Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "i_lDIAfC13Oi",
        "outputId": "83b814c0-a085-48fe-dcf8-aef6ed7aa19f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7df0c6978550>"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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iYiL+/v526/z9/UlNTeXs2bOcOHGCvLy8AtucS7mJiYm4uLjg4+NzyTYFmTx5Mt7e3rYlKCioeDr1D86ODtzfrjYAL3+/g/iTGcV+DBERKRsMwyAjO9eUpTgvLo0dO5bXX3+duLg4WrZsyZkzZ+jZsyfR0dFs2rSJHj16EBkZSXx8/GX38+KLL9KnTx+2bNlCz5496d+/P6dOnSq2Oi+kiSILady4cURFRdlep6amlkhIerhTXZbHJbHhwCnGLIjlq6HhODroUpuISEVzNiePphN/vHLDErDjpQg8XIonIrz00kt0797d9rpq1aqEhobaXr/88sssWrSIb7/9lhEjRlxyP4MGDaJfv34AvPbaa7zzzjts2LCBHj16FEudFypTZ5ACAgIuutssKSkJLy8v3N3dqV69Oo6OjgW2CQgIsO0jOzv7omfd/LNNQVxdXfHy8rJbSoKjg4U37wulkosjfxw8zUe/7C+R44iIiFwPbdu2tXt95swZnn76aZo0aYKPjw+enp7ExcVd8QxSy5YtbX+vVKkSXl5edjdgFbcydQYpPDycpUuX2q1bvnw54eHhALi4uNCmTRuio6Ntg72tVivR0dG2VNqmTRucnZ2Jjo6md+/eAOzatYv4+HjbfswWVNWDiZFNefabrbz5025uauRL44CSCWQiIlI6uTs7suOlCNOOXVwqVapk9/rpp59m+fLl/Oc//6F+/fq4u7tz7733kp2dfdn9ODs72722WCxYrdZiq/NCpgakM2fOsHfvXtvrAwcOEBsbS9WqValduzbjxo3jyJEjfP755wAMGzaM6dOn88wzz/Dwww+zcuVK5s+fz5IlS2z7iIqKYuDAgbRt25b27dszbdo00tPTGTx4MADe3t4MGTKEqKgoqlatipeXFyNHjiQ8PLzQd7BdD33aBrF8RxIr4o4xet5m/je8Iy5OZeqEn4iIXAOLxVJsl7lKk19//ZVBgwZxzz33APlZ4ODBg+YWVQBTP/k///yTm2++2fb63BifgQMH8umnn5KQkGB3yq1u3bosWbKE0aNH8/bbb1OrVi0++ugjIiLOJ+y+ffty/PhxJk6cSGJiIq1atWLZsmV2A7ffeustHBwc6N27N1lZWURERDBz5szr0OPCs1gsTO7Vko3TfiYuIZVpK3bzTI/GZpclIiJyTRo0aMDChQuJjIzEYrEwYcKEEj0TVFSmBqSuXbtedqR8QbNkd+3alU2bNl12vyNGjLjsQC83NzdmzJhR6AkqzeJb2ZVX727O419s5P01+7i1iR9t6lQ1uywREZEimzp1Kg8//DAdOnSgevXqPPvssyUydc61KjUTRZY1JTVRZEGi5sWycNMR6lTzYOmTnankWv5OuYqIVGRldaLI0qrCTRRZUU26sxk1vN04dDKDyT/EmV2OiIhIuaeAVAZ4uzvzn/vy54yY83s8a3YfN7kiERGR8k0BqYzoWL86gzoEA/CvBZtJzrj87ZAiIiJSdApIZcizPRpTz7cSx9KymPC/7WaXIyIiUm4pIJUh7i6OTO3TCkcHC99tPsq3m4+aXZKIiEi5pIBUxrQK8mH4zfUBmLB4G0mpmSZXJCIiUv4oIJVBI2+pT4ua3qSczeFfX28p1qcui4iIiAJSmeTs6MBbfUNxcXLg593H+WL95R/wJyIiIldHAamMqu9XmWf/fvTIq0viOHgi3eSKREREyg8FpDJscIdgbqxXlbM5eUTNjyXPqkttIiIixUEBqQxzcLDwn/tCqezqxMb4ZD74eZ/ZJYmISAVisVguu7zwwgvXtO/FixcXW61XSwGpjKtVxYNJdzYD4K3lu9l+NMXkikREpKJISEiwLdOmTcPLy8tu3dNPP212iUWmgFQO9L6hJrc19ScnzyBq3maycvPMLklERCqAgIAA2+Lt7Y3FYrFb99VXX9GkSRPc3Nxo3LgxM2fOtL03OzubESNGUKNGDdzc3KhTpw6TJ08GIDg4GIB77rkHi8Vie3096bHw5YDFYuG1Xi2IOXSaXUlpTF2+m3G3NzG7LBERuRaGATkZ5hzb2QMslmvaxRdffMHEiROZPn06rVu3ZtOmTTz66KNUqlSJgQMH8s477/Dtt98yf/58ateuzeHDhzl8+DAAf/zxB35+fsyaNYsePXrg6OhYHL26KgpI5UR1T1cm92rB0Nkx/Pfn/XRr4k+74KpmlyUiIkWVkwGvBZpz7OeOgkula9rFpEmTePPNN+nVqxcAdevWZceOHXzwwQcMHDiQ+Ph4GjRoQKdOnbBYLNSpU8f2Xl9fXwB8fHwICAi4pjqKSpfYypHbmgVwb5taGAZEzY/lTFau2SWJiEgFlJ6ezr59+xgyZAienp625ZVXXmHfvvwbigYNGkRsbCyNGjXiySef5KeffjK5ans6g1TOTIpsyrp9Jzl86iyvLtnB5F4tzS5JRESKwtkj/0yOWce+BmfOnAHgww8/JCwszG7buctlN9xwAwcOHOCHH35gxYoV9OnTh27duvH1119f07GLiwJSOVPZzZn/3BdKvw9/58sNh+ne1J9bGvubXZaIiFwti+WaL3OZxd/fn8DAQPbv30///v0v2c7Ly4u+ffvSt29f7r33Xnr06MGpU6eoWrUqzs7O5OWZd9ORAlI5FB5SjSGd6vLx2gM88/VWfhpdhaqVXMwuS0REKpAXX3yRJ598Em9vb3r06EFWVhZ//vknp0+fJioqiqlTp1KjRg1at26Ng4MDCxYsICAgAB8fHyD/Trbo6Gg6duyIq6srVapUua71awxSOfWviEbU9/PkxJksxi/eqgfaiojIdfXII4/w0UcfMWvWLFq0aMFNN93Ep59+St26dQGoXLkyU6ZMoW3btrRr146DBw+ydOlSHBzyo8mbb77J8uXLCQoKonXr1te9fouh35xFkpqaire3NykpKXh5eZldToG2/pXCPTN/Jddq8Pb9rbirVU2zSxIRkQJkZmZy4MAB6tati5ubm9nllHmX+zwL+/tbZ5DKsRa1vHny1gYATFi8jYSUsyZXJCIiUjYoIJVzT3QNITTIh9TMXP61YAtWPdBWRETkihSQyjknRwem9gnF1cmBtXtPMGf9IbNLEhERKfUUkCqAEF9Pxt3eGIDXlsax//gZkysSEREp3RSQKogB4cF0rF+NzBwrUfM3k5tnNbskERG5gO6bKh7F8TkqIFUQDg4W3rg3lMpuTsQeTua91fvMLklERP7m7OwMQEaGSQ+nLWfOfY7nPtei0ESRFUigjzsv3dWM0fM283b0Hm5u7Efzmt5mlyUiUuE5Ojri4+PDsWPHAPDw8MBisZhcVdljGAYZGRkcO3YMHx8f22NNikIBqYK5u1VNftqexA/bEhk9L5bvRnbCzbno/4BERKR4nHtq/bmQJEXn4+Nj+zyLSgGpgrFYLLxyd3P+OHiaPcfO8OZPu3j+jqZmlyUiUuFZLBZq1KiBn58fOTk5ZpdTZjk7O1/TmaNzFJAqoGqervy7dwuGfPYnH609wK1N/LmxXjWzyxIREfIvtxXHL3i5NqYP0p4xYwbBwcG4ubkRFhbGhg0bLtk2JyeHl156iZCQENzc3AgNDWXZsmV2bYKDg7FYLBctw4cPt7Xp2rXrRduHDRtWYn0sjW5t4s/97YIwDBgzfzNpmfrfioiIyDmmBqR58+YRFRXFpEmT2LhxI6GhoURERFzy+uv48eP54IMPePfdd9mxYwfDhg3jnnvuYdOmTbY2f/zxBwkJCbZl+fLlANx33312+3r00Uft2k2ZMqXkOlpKjf+/ptSq4s6R5LO8/P0Os8sREREpNUx9WG1YWBjt2rVj+vTpAFitVoKCghg5ciRjx469qH1gYCDPP/+83dmg3r174+7uzpw5cwo8xqhRo/j+++/Zs2eP7Y6Arl270qpVK6ZNm1bk2svCw2oLY/3+k9z/4e8YBnw4oC3dm/qbXZKIiEiJKfUPq83OziYmJoZu3bqdL8bBgW7durFu3boC35OVlXXRU3nd3d1Zu3btJY8xZ84cHn744Ytul/ziiy+oXr06zZs3Z9y4cVeceyIrK4vU1FS7pTwIq1eNRzvXA2Dcwi2cPJNlckUiIiLmMy0gnThxgry8PPz97c9Y+Pv7k5iYWOB7IiIimDp1Knv27MFqtbJ8+XIWLlxIQkJCge0XL15McnIygwYNslv/wAMPMGfOHFatWsW4ceOYPXs2Dz744GXrnTx5Mt7e3rYlKCio8J0t5aK6N6SRf2VOnMnmuUVbNZOriIhUeKYP0r4ab7/9Ng0aNKBx48a4uLgwYsQIBg8ejINDwd34+OOPuf322wkMDLRbP3ToUCIiImjRogX9+/fn888/Z9GiRezbd+nZpceNG0dKSoptOXz4cLH2zUxuzo5M7RuKs6OFH7cnsXDjEbNLEhERMZVpAal69eo4OjqSlJRktz4pKemSkzv5+vqyePFi0tPTOXToEDt37sTT05N69epd1PbQoUOsWLGCRx555Iq1hIWFAbB3795LtnF1dcXLy8tuKU+aBXozqltDAF74djtHks+aXJGIiIh5TAtILi4utGnThujoaNs6q9VKdHQ04eHhl32vm5sbNWvWJDc3l2+++Ya77rrrojazZs3Cz8+PO+6444q1xMbGAlCjRo2r60Q581iXerSu7UNaVi7/WrAZq1WX2kREpGIy9RJbVFQUH374IZ999hlxcXE8/vjjpKenM3jwYAAGDBjAuHHjbO3Xr1/PwoUL2b9/P7/88gs9evTAarXyzDPP2O3XarUya9YsBg4ciJOT/VyY+/bt4+WXXyYmJoaDBw/y7bffMmDAALp06ULLli1LvtOlmJOjA1P7tMLd2ZHf9p3ks3UHzS5JRETEFKbOpN23b1+OHz/OxIkTSUxMpFWrVixbtsw2cDs+Pt5ufFFmZibjx49n//79eHp60rNnT2bPno2Pj4/dflesWEF8fDwPP/zwRcd0cXFhxYoVTJs2jfT0dIKCgujduzfjx48v0b6WFXWrV+K5no2Z8L/tvP7DTjo3qE59v8pmlyUiInJdmToPUllWXuZBKohhGAyc9Qc/7z5Oy1refPN4B5wdy9R4fhERkQKV+nmQpPSyWCxM6d0SLzcntvyVwoxVlx68LiIiUh4pIEmBArzdePnu5gC8u3IvW/5KNrcgERGR60gBSS7pztBA7mhZgzyrweh5sWTm5JldkoiIyHWhgCSXZLFYeOWu5vhWdmXf8XT+vWyn2SWJiIhcFwpIcllVKrkw5d786Q9m/XqQ3/aeMLkiERGRkqeAJFd0cyM/HgirDcDTCzaTmpljckUiIiIlSwFJCuX5nk2oXdWDoymZvPjtDrPLERERKVEKSFIolVydmNonFIsFvtn4F8u2JZpdkoiISIlRQJJCaxtclce6hADw3KKtHE/LMrkiERGRkqGAJFdldPcGNA6ozKn0bMYt3IImYhcRkfJIAUmuiquTI2/1bYWzo4UVccdYEPOX2SWJiIgUOwUkuWpNangR1b0RAC99t4PDpzJMrkhERKR4KSBJkQztUo82dapwJiuXpxdsxmrVpTYRESk/FJCkSBwdLEztE4qHiyPrD5zik18PmF2SiIhIsVFAkiKrU60Sz9/RBIApP+5id1KayRWJiIgUDwUkuSYPtK9N10a+ZOdaGT0vluxcq9kliYiIXDMFJLkmFouFKb1b4uPhzPajqUxfucfskkRERK6ZApJcMz8vN165uzkAM1bvY1P8aZMrEhERuTYKSFIs/q9lIHeGBpJnNYiav5mz2XlmlyQiIlJkCkhSbF66qxn+Xq4cOJHO6z/EmV2OiIhIkSkgSbHx8XDhjXtDAfhs3SF+2XPc5IpERESKRgFJilWXhr48dGMdAP61YAspGTkmVyQiInL1FJCk2I3r2Zjgah4kpmYy6dttZpcjIiJy1RSQpNh5uDjxZp9WOFhgcexRlmxJMLskERGRq6KAJCWiTZ0qPN41BIDxi7dyLDXT5IpEREQKTwFJSsxTtzakaQ0vTmfkMHbhVgxDD7QVEZGyQQFJSoyLkwNv9W2Fi6MDK3ceY94fh80uSUREpFAUkKRENQqozNMRDQF4+fsdxJ/MMLkiERGRK1NAkhI3pFM92gdXJT07jzELYsmz6lKbiIiUbgpIUuIcHSy82SeUSi6O/HHwNB/9st/skkRERC5LAUmui6CqHkyMbArAmz/tZmdiqskViYiIXJrpAWnGjBkEBwfj5uZGWFgYGzZsuGTbnJwcXnrpJUJCQnBzcyM0NJRly5bZtXnhhRewWCx2S+PGje3aZGZmMnz4cKpVq4anpye9e/cmKSmpRPon5/VpG8Stjf3IzrMyet5msnOtZpckIiJSIFMD0rx584iKimLSpEls3LiR0NBQIiIiOHbsWIHtx48fzwcffMC7777Ljh07GDZsGPfccw+bNm2ya9esWTMSEhJsy9q1a+22jx49mu+++44FCxawZs0ajh49Sq9evUqsn5LPYrEwuXcLqng4E5eQyrQVu80uSUREpEAWw8TJacLCwmjXrh3Tp08HwGq1EhQUxMiRIxk7duxF7QMDA3n++ecZPny4bV3v3r1xd3dnzpw5QP4ZpMWLFxMbG1vgMVNSUvD19WXu3Lnce++9AOzcuZMmTZqwbt06brzxxkLVnpqaire3NykpKXh5eV1Ntyu8H7Ym8PgXG3GwwIJh4bSpU9XskkREpIIo7O9v084gZWdnExMTQ7du3c4X4+BAt27dWLduXYHvycrKws3NzW6du7v7RWeI9uzZQ2BgIPXq1aN///7Ex8fbtsXExJCTk2N33MaNG1O7du1LHleK1+0tanBP65pYDYiav5n0rFyzSxIREbFjWkA6ceIEeXl5+Pv726339/cnMTGxwPdEREQwdepU9uzZg9VqZfny5SxcuJCEhPPP+goLC+PTTz9l2bJlvPfeexw4cIDOnTuTlpYGQGJiIi4uLvj4+BT6uJAfzlJTU+0WKboX7mxGDW83Dp3MYPIPcWaXIyIiYsf0QdpX4+2336ZBgwY0btwYFxcXRowYweDBg3FwON+N22+/nfvuu4+WLVsSERHB0qVLSU5OZv78+dd07MmTJ+Pt7W1bgoKCrrU7FZq3uzNv3BsKwJzf41mz+7jJFYmIiJxnWkCqXr06jo6OF909lpSUREBAQIHv8fX1ZfHixaSnp3Po0CF27tyJp6cn9erVu+RxfHx8aNiwIXv37gUgICCA7OxskpOTC31cgHHjxpGSkmJbDh/WYzOuVacG1RnUIRiAfy3YTHJGtrkFiYiI/M20gOTi4kKbNm2Ijo62rbNarURHRxMeHn7Z97q5uVGzZk1yc3P55ptvuOuuuy7Z9syZM+zbt48aNWoA0KZNG5ydne2Ou2vXLuLj4y97XFdXV7y8vOwWuXbP9mhMveqVOJaWxYT/bTe7HBEREcDkS2xRUVF8+OGHfPbZZ8TFxfH444+Tnp7O4MGDARgwYADjxo2ztV+/fj0LFy5k//79/PLLL/To0QOr1cozzzxja/P000+zZs0aDh48yG+//cY999yDo6Mj/fr1A8Db25shQ4YQFRXFqlWriImJYfDgwYSHhxf6DjYpPu4ujkzt2wpHBwvfbT7Kt5uPml2SiIgITmYevG/fvhw/fpyJEyeSmJhIq1atWLZsmW3gdnx8vN34oszMTMaPH8/+/fvx9PSkZ8+ezJ49227A9V9//UW/fv04efIkvr6+dOrUid9//x1fX19bm7feegsHBwd69+5NVlYWERERzJw587r1W+y1CvJh+M31eSd6DxMWbyOsblX8vdyu/EYREZESYuo8SGWZ5kEqXjl5Vu6Z+SvbjqTSpaEvnw1uh8ViMbssEREpZ0r9PEgi/+Ts6MBbfVrh4uTAz7uPM2d9/JXfJCIiUkIUkKTUaOBfmWciGgHw2pI4Dp5IN7kiERGpqBSQpFR5uGNdbqxXlbM5eUTNjyXPqivAIiJy/SkgSani4GDhP/eF4unqxMb4ZD74eZ/ZJYmISAWkgCSlTq0qHkyKbArAW8t3s/1oiskViYhIRaOAJKXSvW1q0b2pPzl5BlHzNpOVm2d2SSIiUoEoIEmpZLFYmNyrBdUqubArKY2py3ebXZKIiFQgCkhSalX3dOW1Xi0A+O/P+/nj4CmTKxIRkYpCAUlKtYhmAdzbphaGAVHzYzmTlWt2SSIiUgEoIEmpNzGyKTV93Dl86iyvLtlhdjkiIlIBKCBJqefl5swb97UE4MsNh1m5M8nkikREpLxTQJIyoUNIdR7uWBeAZ77eyqn0bJMrEhGR8kwBScqMZ3o0or6fJyfOZDF+8Vb0nGURESkpCkhSZrg5O/JWn1Y4OVhYujWRbzcfNbskEREppxSQpExpUcubkbc0AGDC4m0kpJw1uSIRESmPFJCkzHni5hBCa3mTmpnLvxZswaoH2oqISDFTQJIyx9nRgTf7tMLVyYG1e08wZ/0hs0sSEZFyRgFJyqT6fp6Mu70xAK8tjWP/8TMmVyQiIuWJApKUWQPCg+lYvxqZOVai5m8mN89qdkkiIlJOKCBJmeXgYOGNe0Op7OZE7OFk3lu9z+ySRESknFBAkjIt0MedF+9sBsDb0XvYdiTF5IpERKQ8UECSMu+e1jXp0SyAXKvB6HmxZObkmV2SiIiUcQpIUuZZLBZevac51T1d2XPsDG/+tMvskkREpIxTQJJyoZqnK6/3agHAR2sP8Pv+kyZXJCIiZZkCkpQb3Zr607dtEIYBY+ZvJi0zx+ySRESkjFJAknJl/P81oVYVd44kn+Xl73eYXY6IiJRRCkhSrlR2c+bN+0KxWGD+n3+xfEeS2SWJiEgZpIAk5U5YvWo82rkeAOMWbuHkmSyTKxIRkbJGAUnKpajuDWno78mJM9k8t2grhqEH2oqISOEpIEm55ObsyNQ+rXBysPDj9iQWbjxidkkiIlKGKCBJudW8pjejujUA4IVvt3Mk+azJFYmISFmhgCTl2rCbQmhd24e0rFz+tWAzVqsutYmIyJWZHpBmzJhBcHAwbm5uhIWFsWHDhku2zcnJ4aWXXiIkJAQ3NzdCQ0NZtmyZXZvJkyfTrl07KleujJ+fH3fffTe7dtnPrNy1a1csFovdMmzYsBLpn5jLydGBqX1a4e7syG/7TvLZuoNmlyQiImWAqQFp3rx5REVFMWnSJDZu3EhoaCgREREcO3aswPbjx4/ngw8+4N1332XHjh0MGzaMe+65h02bNtnarFmzhuHDh/P777+zfPlycnJyuO2220hPT7fb16OPPkpCQoJtmTJlSon2VcxTt3olnuvZGIDXf9jJ3mNnTK5IRERKO4th4u09YWFhtGvXjunTpwNgtVoJCgpi5MiRjB079qL2gYGBPP/88wwfPty2rnfv3ri7uzNnzpwCj3H8+HH8/PxYs2YNXbp0AfLPILVq1Ypp06YVufbU1FS8vb1JSUnBy8uryPuR68MwDAZ8soFf9pygZS1vvnm8A86Opp9AFRGR66ywv79N+w2RnZ1NTEwM3bp1O1+MgwPdunVj3bp1Bb4nKysLNzc3u3Xu7u6sXbv2ksdJSUkBoGrVqnbrv/jiC6pXr07z5s0ZN24cGRkZRe2KlAEWi4U37g3Fy82JLX+lMGPVXrNLEhGRUsy0gHTixAny8vLw9/e3W+/v709iYmKB74mIiGDq1Kns2bMHq9XK8uXLWbhwIQkJCQW2t1qtjBo1io4dO9K8eXPb+gceeIA5c+awatUqxo0bx+zZs3nwwQcvW29WVhapqal2i5QtAd5uvHx3/r+Dd1fuZctfyeYWJCIipVaZusbw9ttv06BBAxo3boyLiwsjRoxg8ODBODgU3I3hw4ezbds2vvrqK7v1Q4cOJSIighYtWtC/f38+//xzFi1axL59+y557MmTJ+Pt7W1bgoKCirVvcn3cGRrIHS1rkGc1GD0vlsycPLNLEhGRUsi0gFS9enUcHR1JSrJ/VlZSUhIBAQEFvsfX15fFixeTnp7OoUOH2LlzJ56entSrV++itiNGjOD7779n1apV1KpV67K1hIWFAbB376Uvu4wbN46UlBTbcvjw4St1UUohi8XCK3c1x7eyK/uOpzNl2a4rv0lERCoc0wKSi4sLbdq0ITo62rbOarUSHR1NeHj4Zd/r5uZGzZo1yc3N5ZtvvuGuu+6ybTMMgxEjRrBo0SJWrlxJ3bp1r1hLbGwsADVq1LhkG1dXV7y8vOwWKZuqVHJhSu+WAHzy6wF+23vC5IpERKS0MfUSW1RUFB9++CGfffYZcXFxPP7446SnpzN48GAABgwYwLhx42zt169fz8KFC9m/fz+//PILPXr0wGq18swzz9jaDB8+nDlz5jB37lwqV65MYmIiiYmJnD2bP4vyvn37ePnll4mJieHgwYN8++23DBgwgC5dutCyZcvr+wGIaW5u7Ee/9rUBeHrBZlIzc0yuSEREShMnMw/et29fjh8/zsSJE0lMTKRVq1YsW7bMNnA7Pj7ebnxRZmYm48ePZ//+/Xh6etKzZ09mz56Nj4+Prc17770H5N/K/0+zZs1i0KBBuLi4sGLFCqZNm0Z6ejpBQUH07t2b8ePHl3h/pXQZf0cTft17gvhTGbz47Q7e7BNqdkkiIlJKmDoPUlmmeZDKhz8PnuK+D9ZhGPD+g23o0bzg8W8iIlI+lPp5kERKg7bBVXmsSwgAzy/ayokzWSZXJCIipYECklR4o7s3oHFAZU6mZzP2m63opKqIiCggSYXn6uTIW31b4exoYUVcEgti/jK7JBERMZkCkgjQpIYXUd0bAfDSdzs4fEqPnhERqciKFJBWrVpV3HWImG5ol3q0qVOFM1m5PL1gM1arLrWJiFRURQpIPXr0ICQkhFdeeUUzSku54ehgYWqfUDxcHFl/4BSf/HrA7JJERMQkRQpIR44cYcSIEXz99dfUq1ePiIgI5s+fT3Z2dnHXJ3Jd1alWiefvaALAlB93sTspzeSKRETEDEUKSNWrV2f06NHExsayfv16GjZsyBNPPEFgYCBPPvkkmzdvLu46Ra6bB9rXpmsjX7JzrYyeF0t2rtXskkRE5Dq75kHaN9xwA+PGjWPEiBGcOXOGTz75hDZt2tC5c2e2b99eHDWKXFcWi4UpvVvi4+HM9qOpTF+5x+ySRETkOityQMrJyeHrr7+mZ8+e1KlThx9//JHp06eTlJTE3r17qVOnDvfdd19x1ipy3fh5ufHK3c0BmLF6H5viT5tckYiIXE9FetTIyJEj+fLLLzEMg4ceeohHHnmE5s2b27VJTEwkMDAQq7V8Xp7Qo0Yqhie/3MS3m49Sr3olljzZGXcXR7NLEhGRa1CijxrZsWMH7777LkePHmXatGkXhSPIH6ek6QCkrHvprmb4e7my/0Q6r/8QZ3Y5IiJynehhtUWkM0gVx8+7jzPgkw0AzB7Sns4NfE2uSEREiqpEzyBNnjyZTz755KL1n3zyCf/+97+LskuRUqtLQ18eurEOAP9asIWUjByTKxIRkZJWpID0wQcf0Lhx44vWN2vWjPfff/+aixIpbcb1bExwNQ8SUzN54TvdnSkiUt4VKSAlJiZSo0aNi9b7+vqSkJBwzUWJlDYeLk682acVDhZYtOkIS7fq37mISHlWpIAUFBTEr7/+etH6X3/9lcDAwGsuSqQ0alOnCo93DQHg+UVbOZaaaXJFIiJSUooUkB599FFGjRrFrFmzOHToEIcOHeKTTz5h9OjRPProo8Vdo0ip8dStDWlaw4vTGTmMXbgV3eMgIlI+ORXlTf/61784efIkTzzxhO35a25ubjz77LOMGzeuWAsUKU1cnBx4q28rIt9dy8qdx5j3x2Hub1/b7LJERKSYXdNt/mfOnCEuLg53d3caNGiAq6trcdZWquk2/4rtvz/v47WlO6nk4sgPT3WhdjUPs0sSEZFCKNHb/M/x9PSkXbt2NG/evEKFI5EhnerRPrgq6dl5PL1gM3lWXWoTESlPinSJDeDPP/9k/vz5xMfH2y6znbNw4cJrLkykNHN0sPBmn1B6TPuZDQdP8dEv+3nsphCzyxIRkWJSpDNIX331FR06dCAuLo5FixaRk5PD9u3bWblyJd7e3sVdo0ipFFTVg4mRTQF486fd7ExMNbkiEREpLkUKSK+99hpvvfUW3333HS4uLrz99tvs3LmTPn36ULu2BqxKxdGnbRC3NvYjO8/K6Hmbyc4tnw9nFhGpaIoUkPbt28cdd9wBgIuLC+np6VgsFkaPHs1///vfYi1QpDSzWCxM7t2CKh7OxCWk8nb0brNLEhGRYlCkgFSlShXS0tIAqFmzJtu2bQMgOTmZjIyM4qtOpAzwq+zGa/e0AOC91fuIOXTa5IpERORaFSkgdenSheXLlwNw33338dRTT/Hoo4/Sr18/br311mItUKQsuL1FDXq1ronVgDHzY8nIzjW7JBERuQZFmgfp1KlTZGZmEhgYiNVqZcqUKfz22280aNCA8ePHU6VKlZKotVTRPEhyoZSzOfSY9jMJKZk8eGNtXrm7hdkliYjIBQr7+/uqA1Jubi5z584lIiICf3//ay60rFJAkoKs3XOCBz9eD8BnD7fnpoa+JlckIiL/VGITRTo5OTFs2DAyM/WgTpELdWpQnUEdggF45uvNJGdkX/4NIiJSKhVpDFL79u2JjY0t5lJEyodnezSmnm8lklKzmPi/7WaXIyIiRVCkmbSfeOIJoqKiOHz4MG3atKFSpUp221u2bFksxYmURe4ujkzt04re7/3Gt5uP0r2pP5GhgWaXJSIiV6FIZ5Duv/9+Dhw4wJNPPknHjh1p1aoVrVu3tv15NWbMmEFwcDBubm6EhYWxYcOGS7bNycnhpZdeIiQkBDc3N0JDQ1m2bNlV7zMzM5Phw4dTrVo1PD096d27N0lJSVdVt8jltAryYfjN9QEYv3gbSam6JC0iUpYUKSAdOHDgomX//v22Pwtr3rx5REVFMWnSJDZu3EhoaCgREREcO3aswPbjx4/ngw8+4N1332XHjh0MGzaMe+65h02bNl3VPkePHs13333HggULWLNmDUePHqVXr15F+ShELmnkLfVpXtOLlLM5PPP1Fopww6iIiJikSLf5F5ewsDDatWvH9OnTAbBarQQFBTFy5EjGjh17UfvAwECef/55hg8fblvXu3dv3N3dmTNnTqH2mZKSgq+vL3PnzuXee+8FYOfOnTRp0oR169Zx4403Fqp23cUmhbEnKY073l1Ldq6VV+9pTv+wOmaXJCJSoRX293eRxiB9/vnnl90+YMCAK+4jOzubmJgYxo0bZ1vn4OBAt27dWLduXYHvycrKws3NzW6du7s7a9euLfQ+Y2JiyMnJoVu3brY2jRs3pnbt2lcVkEQKo4F/ZZ7t0ZiXv9/BK9/H0TGkOsHVK135jSIiYqoiBaSnnnrK7nVOTg4ZGRm4uLjg4eFRqIB04sQJ8vLyLppLyd/fn507dxb4noiICKZOnUqXLl0ICQkhOjqahQsXkpeXV+h9JiYm4uLigo+Pz0VtEhMTL1lvVlYWWVlZttepqXpyuxTO4A7BLN+RyO/7TzFmwWbmPxaOo4PF7LJEROQyijQG6fTp03bLmTNn2LVrF506deLLL78s7hpt3n77bRo0aEDjxo1xcXFhxIgRDB48GAeHInXjqkyePBlvb2/bEhQUVOLHlPLBwcHCf+4LxdPViZhDp/ng531mlyQiIldQbMmiQYMGvP766xedXbqU6tWr4+joeNHdY0lJSQQEBBT4Hl9fXxYvXkx6ejqHDh1i586deHp6Uq9evULvMyAggOzsbJKTkwt9XIBx48aRkpJiWw4fPlyofooA1KriwaTIpgC8tXw3O47qDKSISGlWrKdenJycOHr0aKHauri40KZNG6Kjo23rrFYr0dHRhIeHX/a9bm5u1KxZk9zcXL755hvuuuuuQu+zTZs2ODs727XZtWsX8fHxlz2uq6srXl5edovI1bi3TS26N/UnJ88gan4sWbl5ZpckIiKXUKQxSN9++63da8MwSEhIYPr06XTs2LHQ+4mKimLgwIG0bduW9u3bM23aNNLT0xk8eDCQP9i7Zs2aTJ48GYD169dz5MgRWrVqxZEjR3jhhRewWq0888wzhd6nt7c3Q4YMISoqiqpVq+Ll5cXIkSMJDw/XAG0pURaLhcm9WrDx0Gl2Jqbx1vI9jL29sdlliYhIAYoUkO6++2671xaLBV9fX2655RbefPPNQu+nb9++HD9+nIkTJ5KYmEirVq1YtmyZbZB1fHy83fiizMxMxo8fz/79+/H09KRnz57Mnj3bbsD1lfYJ8NZbb+Hg4EDv3r3JysoiIiKCmTNnFuWjELkq1T1dmdyrBUNnx/DBz/u4tYkf7YKrml2WiIhcwNR5kMoyzYMk1+LpBZv5OuYvgqq688NTXfB0LdL/VURE5CoV9vd3yd/+JSIXmRjZlJo+7hw+dZZXl8SZXY6IiFygSAGpd+/e/Pvf/75o/ZQpU7jvvvuuuSiR8s7LzZk37st/qPOXG+JZtbPgx+uIiIg5ihSQfv75Z3r27HnR+ttvv52ff/75mosSqQg6hFRnSKe6ADzzzRZOp2ebXJGIiJxTpIB05swZXFxcLlrv7OysGaZFrsK/IhpR38+T42lZjF+8TQ+0FREpJYoUkFq0aMG8efMuWv/VV1/RtGnTay5KpKJwc3bkrT6tcHKwsGRrAt9uLtw8YiIiUrKKdOvMhAkT6NWrF/v27eOWW24BIDo6mi+//JIFCxYUa4Ei5V2LWt6MvKUBb63YzYTF22hftyo1vN3NLktEpEIr0hmkyMhIFi9ezN69e3niiScYM2YMf/31FytWrLhojiQRubInbg4htJY3qZm5PPP1Fl1qExExmeZBKiLNgyTFbd/xM/R8+xeycq28fFczHgoPNrskEZFyp0TnQfrjjz9Yv379RevXr1/Pn3/+WZRdilR4Ib6ejPv70SMvL4nj9R926s42ERGTFCkgDR8+vMCn2R85coThw4dfc1EiFdWA8GAimvmTnWvl/TX76PTvlbz50y5SMnLMLk1EpEIp0iU2T09PtmzZQr169ezWHzhwgJYtW5KWllZsBZZWusQmJcUwDKLjjjF1+W52JORPm1HZ1YkhnevycKe6eLk5m1yhiEjZVaKX2FxdXUlKSrpofUJCAk5OeqaUyLWwWCx0a+rP9yM78f6DN9DIvzJpWblMW7GHzv9exYxVezmTlWt2mSIi5VqRziD169ePhIQE/ve//+Ht7Q1AcnIyd999N35+fsyfP7/YCy1tdAZJrher1WDptgSmrdjD3mNnAKji4cxjN4UwILwOHi76T4mISGEV9vd3kQLSkSNH6NKlCydPnqR169YAxMbG4u/vz/LlywkKCip65WWEApJcb3lWg++3HGXaij0cOJEOQHVPF4bdFMKDN9bBzdnR5ApFREq/Eg1IAOnp6XzxxRds3rwZd3d3WrZsSb9+/XB2rhjjIxSQxCy5eVYWxx7lneg9xJ/KAMC3sivDu4Zwf/vaCkoiIpdR4gEJYMeOHcTHx5OdbX8r8p133lnUXZYZCkhitpw8Kws3/sU70Xs5knwWgAAvN4bfUp8+bWvh6qSgJCJyoRINSPv37+eee+5h69atWCwWDMPAYrHYtufl5RWt6jJEAUlKi+xcKwtiDjN95V4SUjIBqOnjzohb6nNvm1o4OxbpXgwRkXKpRO9ie+qpp6hbty7Hjh3Dw8ODbdu2sWbNGtq2bcvq1auLWrOIFIGLkwP9w+qw6umuvHhnM/wqu3Ik+SzjFm7lljdXs+DPw+TmWc0uU0SkTCnSGaTq1auzcuVKWrZsibe3Nxs2bKBRo0asXLmSMWPGsGnTppKotVTRGSQprTJz8vhifTzvrd7LiTP5l7+Dq3nwVLcG3BlaE0cHyxX2ICJSfpXoGaS8vDwqV64M5Ielo0ePAlCnTh127dpVlF2KSDFxc3ZkSKe6/PzMzTzXszFVK7lw8GQGo+dt5ra31vDd5qNYrXoEo4jI5RQpIDVv3pzNmzcDEBYWxpQpU/j111956aWXLppdW0TM4eHixNAuIfzyzM38K6IR3u7O7DuezsgvN9Hj7Z/5YWuCgpKIyCUU6RLbjz/+SHp6Or169WLv3r383//9H7t376ZatWrMmzePW265pSRqLVV0iU3KmrTMHGb9epAPf9lPWmb+TNxNangxulsDujf1t7vRQkSkvLout/n/06lTp6hSpUqF+SGrgCRlVcrZHD5ee4BP1h6wPbKkRU1voro3pGsj3wrzPSwiFdN1D0gVjQKSlHWn07P58Jf9fPrbQTKy86fmaBXkQ1T3hnRuUF1BSUTKJQWkEqaAJOXFyTNZ/Pfn/Xy27iCZOfnTAbStU4Wo7g3pUL+6ydWJiBQvBaQSpoAk5c2xtEzeX72fOesPkZ2bH5RurFeVqO6NaF+3qsnViYgUDwWkEqaAJOVVUmomM1ft5csNh8n+e4LJTvWrM7p7Q9rUqWJydSIi10YBqYQpIEl5dzT5LDNW7WX+n4fJycv/MXFTQ19Gd29IqyAfc4sTESkiBaQSpoAkFcXhUxlMX7mXrzf+Rd7f8yZ1a+LHqG4NaV7T2+TqRESujgJSCVNAkorm0Ml03oney6JNf3FufsmIZv6M6taQJjX0PSAiZYMCUglTQJKKav/xM7wdvYdvNx/l3E+PO1rU4KluDWjoX9nc4kRErkABqYQpIElFtycpjWnRe1iyJQEAiwUiWwbyVLcGhPh6mlydiEjBSvRhtcVpxowZBAcH4+bmRlhYGBs2bLhs+2nTptGoUSPc3d0JCgpi9OjRZGZm2rYHBwdjsVguWoYPH25r07Vr14u2Dxs2rMT6KFIeNfCvzIwHbmDZqM70aBaAYcC3m4/SfeoaoubHcvBEutkliogUmalnkObNm8eAAQN4//33CQsLY9q0aSxYsIBdu3bh5+d3Ufu5c+fy8MMP88knn9ChQwd2797NoEGDuP/++5k6dSoAx48fJy8vz/aebdu20b17d1atWkXXrl2B/IDUsGFDXnrpJVs7Dw+PqzoTpDNIIva2HUlh2oo9rIhLAsDRwcK9N9RixC31CarqYXJ1IiL5ysQltrCwMNq1a8f06dMBsFqtBAUFMXLkSMaOHXtR+xEjRhAXF0d0dLRt3ZgxY1i/fj1r164t8BijRo3i+++/Z8+ePbZHJ3Tt2pVWrVoxbdq0IteugCRSsM2Hk3lrxW5W7zoOgJODhT7tghhxc30CfdxNrk5EKrpSf4ktOzubmJgYunXrdr4YBwe6devGunXrCnxPhw4diImJsV2G279/P0uXLqVnz56XPMacOXN4+OGHL3qu1BdffEH16tVp3rw548aNIyMj47L1ZmVlkZqaareIyMVCg3z4dHB7vnm8A50bVCfXajB3fTxd31jNxP9tIzEl88o7ERExmZNZBz5x4gR5eXn4+/vbrff392fnzp0FvueBBx7gxIkTdOrUCcMwyM3NZdiwYTz33HMFtl+8eDHJyckMGjToov3UqVOHwMBAtmzZwrPPPsuuXbtYuHDhJeudPHkyL7744tV1UqQCa1OnCrOHhLHhwCmmLt/F7/tP8fm6Q3z1x2H6h9Xm8a4h+FV2M7tMEZECmXaJ7ejRo9SsWZPffvuN8PBw2/pnnnmGNWvWsH79+oves3r1au6//35eeeUVwsLC2Lt3L0899RSPPvooEyZMuKh9REQELi4ufPfdd5etZeXKldx6663s3buXkJCQAttkZWWRlZVle52amkpQUJAusYkU0m/7TjD1p938eeg0AG7ODgwID+axLvWo5ulqcnUiUlEU9hKbaWeQqlevjqOjI0lJSXbrk5KSCAgIKPA9EyZM4KGHHuKRRx4BoEWLFqSnpzN06FCef/55HBzOXzE8dOgQK1asuOxZoXPCwsIALhuQXF1dcXXVD3GRouoQUp3wYdVYu/cEb/60m9jDyfz35/3M+f0QAzsEM7RzPapUcjG7TBERwMQxSC4uLrRp08ZuwLXVaiU6OtrujNI/ZWRk2IUgAEdHRwAuPBE2a9Ys/Pz8uOOOO65YS2xsLAA1atS4mi6IyFWyWCx0buDLoic6MGtwO1rW8iYjO4/3Vu+j85RVTP1pFykZOWaXKSJi3hkkgKioKAYOHEjbtm1p374906ZNIz09ncGDBwMwYMAAatasyeTJkwGIjIxk6tSptG7d2naJbcKECURGRtqCEuQHrVmzZjFw4ECcnOy7uG/fPubOnUvPnj2pVq0aW7ZsYfTo0XTp0oWWLVtev86LVGAWi4WbG/nRtaEvK+KOMXX5buISUnln5V5m/XaQRzrVY0jnuni6mvojSkQqMFN/+vTt25fjx48zceJEEhMTadWqFcuWLbMN3I6Pj7c7YzR+/HgsFgvjx4/nyJEj+Pr6EhkZyauvvmq33xUrVhAfH8/DDz980TFdXFxYsWKFLYwFBQXRu3dvxo8fX7KdFZGLWCwWujf159bGfvy0I5G3lu9hV1Iab63YzZz1h3i2R2N6ta6Jg4PlyjsTESlGetRIEWkeJJHiZ7UaLN2WwH9+3MXBk/lTb4QG+fBCZFNa165icnUiUh6UiYkiyzIFJJGSk5Wbx6xfD/Ju9B7Ss/Nnxu91Q03G9miMn5emBhCRolNAKmEKSCIl71haJlOW7eLrmL8AqOTiyPBb6jOkU11cnRyv8G4RkYspIJUwBSSR6yf2cDIvfLud2MPJANSp5sH4O5rSrYnfRbPki4hcjgJSCVNAErm+rFaDRZuO8PqynRxPy5+0tXOD6kyKbEp9v8omVyciZYUCUglTQBIxx5msXGas2svHvxwgO8+Ko4OFAeF1GNWtId7uzmaXJyKlnAJSCVNAEjHXwRPpvLIkjhVx+bPxV63kwtO3NaJvuyAcNS2AiFyCAlIJU0ASKR1+3n2cl77fwd5jZwBoWsOLF+5sRvu6VU2uTERKIwWkEqaAJFJ65ORZmb3uEG+t2E1aZi4A/9eyBs/1bEKgj7vJ1YlIaaKAVMIUkERKn5Nnsnhz+W6+3BCPYYCbswOP31Sfx26qh5uzpgUQEQWkEqeAJFJ6bT+awovf7mDDwVMA1PRx57meTejZIkDTAohUcApIJUwBSaR0MwyD77ckMHlpHEdTMgG4sV5VJkU2o0kNfc+KVFQKSCVMAUmkbDibncd7a/bxwZp9ZOVacbDAA2G1iereiKqVXMwuT0SuMwWkEqaAJFK2/HU6g8lLd7JkawIA3u7OjO7WgAdvrIOTo4PJ1YnI9aKAVMIUkETKpnX7TvLid9vZmZgGQEN/TyZFNqNj/eomVyYi14MCUglTQBIpu3LzrHz1x2He/GkXpzNyAIho5s/zPZtSu5qHydWJSElSQCphCkgiZV9yRjbTVuxh9u+HyLMauDg58GjnujzRtT6VXJ3MLk9ESoACUglTQBIpP3YnpfHSdztYu/cEAP5eroy7vQl3tQrUtAAi5YwCUglTQBIpXwzD4KcdSbyyZAeHT50F4IbaPrxwZzNa1vIxtzgRKTYKSCVMAUmkfMrMyePjtQeYsWovGdl5WCxwX5ta/CuiMb6VXc0uT0SukQJSCVNAEinfElMy+feynSzadAQAT1cnnry1PoM61MXFSdMCiJRVCkglTAFJpGKIOXSaF7/bzpa/UgCoV70SE/6vKTc39jO5MhEpCgWkEqaAJFJxWK0GX2/8iynLdnHiTBYANzfyZcL/NaWer6fJ1YnI1VBAKmEKSCIVT1pmDu+u3MusXw+Qk2fg7GhhUIdgRt7aAC83Z7PLE5FCUEAqYQpIIhXX/uNneGVJHCt3HgOguqcLz0Q05t42tXBw0LQAIqWZAlIJU0ASkVW7jvHydzvYfyIdgBY1vXnhzqa0qVPV5MpE5FIUkEqYApKIAGTnWvnst4O8E72HtKxcAO5uFcjY25sQ4O1mcnUiciEFpBKmgCQi/3Q8LYv//LiL+TGHMQzwcHFk+M31GdKpLm7OjmaXJyJ/U0AqYQpIIlKQLX8l8+J3O4g5dBqAoKruPN+zKRHN/PXYEpFSQAGphCkgicilGIbB/2KPMvmHOJJS86cF6Fi/GpMim9HQv7LJ1YlUbApIJUwBSUSuJD0rl/dW7+O/v+wnO9eKo4OFB8NqM7p7Q3w8XMwuT6RCUkAqYQpIIlJY8SczeG1pHMu2JwJQxcOZqNsa8UD72jhqWgCR60oBqYQpIInI1fp17wle/G47u5POANA4oDKTIpsRHlLN5MpEKo7C/v42/YmLM2bMIDg4GDc3N8LCwtiwYcNl20+bNo1GjRrh7u5OUFAQo0ePJjMz07b9hRdewGKx2C2NGze220dmZibDhw+nWrVqeHp60rt3b5KSkkqkfyIi53SsX52lT3bmxTub4e3uzM7ENPp9+DtPfBHDX6czzC5PRP7B1IA0b948oqKimDRpEhs3biQ0NJSIiAiOHTtWYPu5c+cyduxYJk2aRFxcHB9//DHz5s3jueees2vXrFkzEhISbMvatWvtto8ePZrvvvuOBQsWsGbNGo4ePUqvXr1KrJ8iIuc4OTowsEMwq5/uyoM31sbBAku3JnLrm2uYunw3Z7PzzC5RRDD5EltYWBjt2rVj+vTpAFitVoKCghg5ciRjx469qP2IESOIi4sjOjratm7MmDGsX7/eFoJeeOEFFi9eTGxsbIHHTElJwdfXl7lz53LvvfcCsHPnTpo0acK6deu48cYbC1W7LrGJSHGIS0jlxe+28/v+UwDU8HZjXM8mRLasoWkBREpAqb/Elp2dTUxMDN26dTtfjIMD3bp1Y926dQW+p0OHDsTExNguw+3fv5+lS5fSs2dPu3Z79uwhMDCQevXq0b9/f+Lj423bYmJiyMnJsTtu48aNqV279iWPC5CVlUVqaqrdIiJyrZrU8OLLR29kZv8bqOnjTkJKJk9+uYkHP17PqfRss8sTqbBMC0gnTpwgLy8Pf39/u/X+/v4kJiYW+J4HHniAl156iU6dOuHs7ExISAhdu3a1u8QWFhbGp59+yrJly3jvvfc4cOAAnTt3Ji0tDYDExERcXFzw8fEp9HEBJk+ejLe3t20JCgoqYs9FROxZLBZ6tqhB9JibiOreEDdnB37de5K7Zqxld1Ka2eWJVEimD9K+GqtXr+a1115j5syZbNy4kYULF7JkyRJefvllW5vbb7+d++67j5YtWxIREcHSpUtJTk5m/vz513TscePGkZKSYlsOHz58rd0REbHj5uzIk7c24PuRnahd1YPDp87Sa+ZvrNpV8LhMESk5pgWk6tWr4+joeNHdY0lJSQQEBBT4ngkTJvDQQw/xyCOP0KJFC+655x5ee+01Jk+ejNVqLfA9Pj4+NGzYkL179wIQEBBAdnY2ycnJhT4ugKurK15eXnaLiEhJqO9Xmf8N70hY3aqcycplyKd/8NEv+9GsLCLXj5NZB3ZxcaFNmzZER0dz9913A/mDtKOjoxkxYkSB78nIyMDBwT7TOTrmPwTyUj84zpw5w759+3jooYcAaNOmDc7OzkRHR9O7d28Adu3aRXx8POHh4cXRNZGis+bBD8/AtoVABf5laHGEmm2gQXdocBtUqWN2RdddlUouzB4SxsT/beOrPw7zypI49h0/w4t3NsfFqUyd/Bcpk0wLSABRUVEMHDiQtm3b0r59e6ZNm0Z6ejqDBw8GYMCAAdSsWZPJkycDEBkZydSpU2ndujVhYWHs3buXCRMmEBkZaQtKTz/9NJGRkdSpU4ejR48yadIkHB0d6devHwDe3t4MGTKEqKgoqlatipeXFyNHjiQ8PLzQd7CJlJifJsAfH5ldRemw58f8BcC38fmwFHQjOFWMx3S4ODkwuVcL6vt58trSOL7ccJj9x9N5/8E2VKlUMT4DEbOYGpD69u3L8ePHmThxIomJibRq1Yply5bZBm7Hx8fbnTEaP348FouF8ePHc+TIEXx9fYmMjOTVV1+1tfnrr7/o168fJ0+exNfXl06dOvH777/j6+tra/PWW2/h4OBA7969ycrKIiIigpkzZ16/josUZMOH8PuM/L9HvgO1K/AZzew02L8G9iyHw+vh+M785bd3waUyhHSFBhFQvxt41TC72hJlsVh4pHM96vlW4skvY1l/4BR3z/yVjwe2pb6fHnwrUlL0qJEi0jxIUqx2LYOv+oFhhVsnQucxZldUepw9DftWwZ6f8gNTxgn77QEt888sNbgNarUFB0dz6rwOdielMeSzPzh86iyVXZ2Y3v8Gbmroe+U3ioiNnsVWwhSQpNgcjYVZPSEnHVo/BHe+C5ogsGBWKyRsyg9Ke36CIxuxG6vlXiX/rFKD2yDkVqhU/p5xdvJMFsPmxPDHwdM4WGDi/zVlYIdgTSopUkgKSCVMAUmKRcpf8OGtcCYR6nWF/l+Do7PZVZUdZ47D3hX5YWlfNGSm/GOjJf+M0rmzSwEtwaF8DG7Oys1j/KJtLIj5C4D+YbV54c5mODuWj/6JlCQFpBKmgCTXLDMVPukBx7aDbxMY8iO4eZtdVdmVlwt//XH+UlzSVvvtnv5Qv3v+YO+Qm8v8Z20YBh/+sp/JP+zEMKBDSDVm9r8BHw8N3ha5HAWkEqaAJNckLwfm9s0/6+HpD4+sAJ/aZldVvqQcgb3L88PSvlX5lzDPcXDKHwR/7s4438Zl9rLmih1JPPXVJtKz86hbvRIfDWxLiK+n2WWJlFoKSCVMAUmKzDDg+9EQMwucPWDQEqh5g9lVlW+5WRC/Lj8s7f4RTu6x3+4ddD4s1e0CLpXMqbOIdiamMuTTPzmSfBYvNydm9L+Bzg00eFukIApIJUwBSYrs17dh+UTAAvd/AY3vMLuiiufUftjz99ilg79Abub5bY6uENzp77FL3aFaiHl1XoUTZ7J4bHYMMYdO4+hg4YXIpjwUHmx2WSKljgJSCVNAkiLZvhgWDMz/e4/X4cbHTS1HgOyM/JC05yfY/ROkxNtvr1b/fFiq0xGcXM2psxAyc/J4buFWFm46AsCA8DpM/L+mOGnwtoiNAlIJU0CSq3b4D/js//LPVrR/DHpOMbsiuZBhwIndf4elH/Mvy1lzz293rgT1bjp/Oc67lnm1XoJhGLy/Zj9TfswfvN2pfnVmPHAD3h66O1IEFJBKnAKSXJVTB+CjbvmTHDa8Pf/SWjme0LDcyEyF/avP3xl3JtF+u1+zfzwCpX2pmqLhx+2JjJ4XS0Z2HvV8K/HxwHbUrV62xlaJlAQFpBKmgCSFlnEKPr4tf2BwQEsY/AO46i6jMscwIHHL+bD01x/5M5+f4+oN9W/JD0v1u4Gnn3m1/m370RQe/exPjqZk4u3uzHv9b6BD/epmlyViKgWkEqaAJIWSmwWze8GhteBVK/92/nL+7LAKI+MU7Ft5PjCdPWW/PbD1+UkqA28wbZLKY2mZDP08htjDyTg5WHjxrmb0D6tjSi0ipYECUglTQJIrMgxYNAy2fJX/gNUhP4J/M7OrkpJgzct/7Mmen/KXhFj77R7V/34ESncIuQU8ql7X8jJz8hj7zRYWxx4FYFCHYMbf0USDt6VCUkAqYQpIckWrX4fVk8HiCP0XQP1bza5Irpe0pL8fgfJj/iSVWannt1kcoFb7/LDUMAL8m1+XSSoNw2DGqr3856fdAHRp6Mv0B1rj5VZ6xk2JXA8KSCVMAUkua/NXsOix/L9Hvg1tBplajpgoLwcOrz8/jcDxOPvtlWucH+hdryu4Vi7Rcn7YmkDU/M2czcmjvp8nHw9sS51qGrwtFYcCUglTQJJLOvALzL4HrDnQcRR0f9HsiqQ0SY7PH7O0ZzkcWAM5Gee3OThDnQ7nxy5Vb1AiZ5e2HUnhkc/+JDE1Ex8PZ95/sA031qtW7McRKY0UkEqYApIU6Phu+Lhb/lPlm94N984qN0+QlxKQkwmHfj0/79LpA/bbferkB6WGEfmzezu7F9uhj6Vm8ujnf7L5rxScHCy8cndz7m+v5wFK+aeAVMIUkOQiZ47DR7dC8qH8MSYDvy3WX2hSAZzcdz4sHfoV8rLPb3Nyy39O3LlZvasEX/PhMnPyeHrBZr7fkgDAkE51ea5nExwdyuaDe0UKQwGphCkgiZ2cs/BZZP7cOFWC4ZFoqKT5ZuQaZJ2BAz+fn0Yg9S/77dUbnr8UVzscnFyKdBjDMHgnei9vrcgfvH1zI1/e6deayhq8LeWUAlIJU0ASG6sVvh4EO/4Hbj75cx1Vb2B2VVKeGAYcizs/jUD872Dknd/u4pk/wLthBNTvXqS5tpZsSWDMglgyc6w09PfkowHtqF3No/j6IFJKKCCVMAUksVk+EX59O3+A7YDF+WNFRErS2WTYv+r8YO/0Y/bbA1qcP7tUsy04OhVqt1v+SubRz/8kKTWLqpVceP/BNrSve33nbBIpaQpIJUwBSQD4cxZ8Pyr/7/f8F0L7mlqOVEBWa/7ElHuW559dOhID/OPHupvP35NU3pY/F9cVLv0mpuQP3t56JAVnRwuv3tOCPm2DSrIHIteVAlIJU0AS9qyAuX3yL3V0fQ66Pmt2RSKQfgL2RudPUrk3GjKT/7HRAjXbnB/oXaNVgXdZns3OY8yCWJZuzX8479Au9Xi2R2MN3pZyQQGphCkgVXCJ2+CTHpCdBqEPwN0zr8tsyCJXJS8Xjvx5fuxS4lb77ZX8/p6ksjvUuxncfWybrFaDadF7eCd6DwC3Nvbj7X6t8XQt3OU6kdJKAamEKSBVYKkJ+bfzpx6B4M7w4MIi30Ekcl2lHj1/KW7/asg+c36bxRFq33h+7JJfE7BY+HbzUZ5esJnsXCuN/Cvz0cC2BFXV4G0puxSQSpgCUgWVdQZm3Q6JW/Jvsx7yE7hXMbsqkauXmw3x686fXTqx2367Vy3bI1C2uIQy5Ms4jqdlUa2SCx881Ia2wRq8LWWTAlIJU0CqgKx58GW//LEdHtXzb+evWtfsqkSKx6kDfz9g96f8+ZdyM89vc3Qhq1YHPj3egC9PN+aoQ00m92pB7za1zKtXpIgUkEqYAlIFYxjwwzOw4b/5MxoPWgK12ppdlUjJyDmb/0zBPT/l/4cgOd5u8wGrP6utrfBo3pP7evXFwUUzxkvZoYBUwhSQKph1M+HHcYAF+nwGTe8yuyKR68Mw4MQeW1gyDq3DYs2xbc6yuOEY0hWnRn+PXfLRlABSuikglTAFpAok7nuY9yBgQPeXoeOTZlckYp7MVDiwhoO/L8b9YDT+ltP22/2a2sYuERQGjnpkiZQuCkglTAGpgjgSA7PugNyz0PZhuGOqbucX+VvMwVP8Z/Y3tM7cwG3OWwi17MZiWM83cPWCkJv/nqSyO1T2N69Ykb8pIJUwBaQK4PQh+Khb/mMc6neDfvMK/cgGkYriSPJZHvnsT+ISUvF1ymBmWDLtcv7IH/CdcdK+cY1W/3gEyg3g4GhKzVKxKSCVMAWkcu5sMnwSAcd3gn9zGPwDuOnrLFKQ9KxcRs2LZfmOJACG3xzCmFvr45AYe34agaOb7N/kXjX/Px4NIyDkFvDQtAFyfRT29/fFc8xfZzNmzCA4OBg3NzfCwsLYsGHDZdtPmzaNRo0a4e7uTlBQEKNHjyYz8/ztqJMnT6Zdu3ZUrlwZPz8/7r77bnbt2mW3j65du2KxWOyWYcOGlUj/pAzKy4H5A/LDUeUa8MB8hSORy6jk6sQHD7bh8a4hAMxYtY8nvtxMhl8ruPk5GLoant4Dd82EpneDqzecPQVb58M3Q+CNEPj4Nvj5DUjYnD8wXMRkpp5BmjdvHgMGDOD9998nLCyMadOmsWDBAnbt2oWfn99F7efOncvDDz/MJ598QocOHdi9ezeDBg3i/vvvZ+rUqQD06NGD+++/n3bt2pGbm8tzzz3Htm3b2LFjB5UqVQLyA1LDhg156aWXbPv28PC4qjNBOoNUThkGfDsCNs0B50rw8A9QI9TsqkTKjG9i/mLcwq1k51lpFujFRwPbUsP7gmkA8nLg8IbzZ5eO7bDf7hkAdbuAS6XrV7iUTmHDwK9xse6yTFxiCwsLo127dkyfPh0Aq9VKUFAQI0eOZOzYsRe1HzFiBHFxcURHR9vWjRkzhvXr17N27doCj3H8+HH8/PxYs2YNXbp0AfIDUqtWrZg2bVqRa1dAKqd+/g+sfBksDvljjhreZnZFImXOnwdP8djsGE6mZ+Nb2ZUPB7SlVZDPpd+QfBj2Ls9/DMr+1ZCTcb1KldLuwYVQ/9Zi3WVhf3+bNuI0OzubmJgYxo0bZ1vn4OBAt27dWLduXYHv6dChA3PmzGHDhg20b9+e/fv3s3TpUh566KFLHiclJQWAqlXtr29/8cUXzJkzh4CAACIjI5kwYQIeHpd+vlBWVhZZWVm216mpqYXqp5QhW7/OD0cAt09ROBIporbBVVk8vCOPfPYnu5LS6PvBOt64L5Q7QwMLfoNPUP5dom0fhpxMiP8NjmyEf94RJxWTiU8rMC0gnThxgry8PPz97W/79Pf3Z+fOnQW+54EHHuDEiRN06tQJwzDIzc1l2LBhPPfccwW2t1qtjBo1io4dO9K8eXO7/dSpU4fAwEC2bNnCs88+y65du1i4cOEl6508eTIvvvhiEXoqZcKhdbD48fy/h4+A9o+aW49IGRdU1YNvnujAU19uInrnMZ78chN7j51h1K0NcHC4zFQZzm75g7ZDbrl+xYoUwPRB2ldj9erVvPbaa8ycOZONGzeycOFClixZwssvv1xg++HDh7Nt2za++uoru/VDhw4lIiKCFi1a0L9/fz7//HMWLVrEvn37LnnscePGkZKSYlsOHz5crH0TE53cB1/1g7xsaPx/+ZNBisg183R14r8D2vJYl3oAvBO9h5FfbuJsdp7JlYlcmWlnkKpXr46joyNJSUl265OSkggICCjwPRMmTOChhx7ikUceAaBFixakp6czdOhQnn/+eRwczue9ESNG8P333/Pzzz9Tq9blH6gYFhYGwN69ewkJCSmwjaurK66uroXun5QRGafgi3vh7GkIvAF6fQgOZer/DSKlmqODhXE9mxDi58nzi7ayZGsC8acy+HBAWwK83cwuT+SSTPtN4OLiQps2bewGXFutVqKjowkPDy/wPRkZGXYhCMDRMX+isXNjzQ3DYMSIESxatIiVK1dSt+6Vr1/GxsYCUKNGjaJ0RcqqnEz46gE4tR+8a8MD88Dl0uPQRKTo+rQN4otHbqSKhzNbj6Rw5/S1bPkr2eyyRC7J1P8qR0VF8eGHH/LZZ58RFxfH448/Tnp6OoMHDwZgwIABdoO4IyMjee+99/jqq684cOAAy5cvZ8KECURGRtqC0vDhw5kzZw5z586lcuXKJCYmkpiYyNmzZwHYt28fL7/8MjExMRw8eJBvv/2WAQMG0KVLF1q2bHn9PwQxh9UK/xsO8evy52TpvwA8L55aQkSKT/u6Vfnf8E408PPkWFoWfT5Yx5ItCWaXJVIgU5+b0LdvX44fP87EiRNJTEykVatWLFu2zDZwOz4+3u6M0fjx47FYLIwfP54jR47g6+tLZGQkr776qq3Ne++9B+Tfyv9Ps2bNYtCgQbi4uLBixQqmTZtGeno6QUFB9O7dm/Hjx5d8h6X0WPUqbPsaHJyg7+fFPs+GiBSsdjUPFj7RgSe/3MSqXccZPncje4815Mlb62PRcw6lFNGjRopI8yCVYRtn508GCfkz+7bub249IhVQntXgtaVxfLz2AACRoYG8cW9L3Jz1fDYpWWXmUSMi19W+VfD9qPy/d/mXwpGISRwdLEz4v6a83qsFTg4Wvtt8lL4frONYauaV3yxyHSggScVxLC7/GWvWXGhxH9z8vNkViVR497evzewhYfh4OLP5rxTunP4r246kmF2WiAKSVBBpSfBFH8hKhdrhcNcM0HgHkVIhPKQai5/oSIhvJRJTM7nv/XUs26bB22IujUEqIo1BKkOyM+DTO+DoRqgaAo+sAI+qV36fiFxXqZk5jJi7iZ93HwcgqKo7rYKq0DrIh9a1fWga6IWrk8YoybUpEw+rLcsUkMoIa17+ZbWd34N71fxwVK3gyUBFxHy5eVYm/7CTWb8ewHrBbycXRwea1fSiVZAPrWvnB6daVdx195tcFQWkEqaAVEYsew5+nwGOrjDwW6h9o9kViUghpGXmsOWvFDbFn2ZTfDKbDidzKj37onbVPV1pXdvn79DkQ8taPni6mjqDjZRyCkglTAGpDNjwISx9Ov/v934CzXubW4+IFJlhGBw+dZZNh/8OTPGn2X40ldwLTjM5WKChf2Va1/ahdVAVWtf2IcTX8/IPyJUKRQGphCkglXK7luU/gNawwq0TofMYsysSkWKWmZPH9qMptjNMsfHJHEk+e1G7yq5OtPrHWaZWQVWoWsnFhIqlNFBAKmEKSKXY0ViY1RNy0qH1Q3Dnu7pjTaSCOJaayabDybazTFv+SuFsTt5F7YKredC6dhVbaGoc4IWLk27srggUkEqYAlIplfIXfHgrnEmEel2h/9fg6Gx2VSJiktw8K7uS0tgUn0zs4fzQtO94+kXtXJ0caFHT23aGqXVtH2p4u2kAeDmkgFTCFJBKocxUmHU7JG0D3yYw5Edw8za7KhEpZVIycoj9K/+S3LkxTSlncy5q5+/lahvH1CrIhxa1vPFw0QDwsk4BqYQpIJUyebnwZV/YuwI8/fNv5/epbXZVIlIGGIbBgRPp588yHT5NXEIaeRcMAHd0sNA4oLLdWaa61SppAHgZo4BUwhSQShHDgO9HQ8wscPaAQUug5g1mVyUiZdjZ7Dy2HsmfZiD2cDIb40+TlJp1UTtvd2fbOKbWtavQqpYP3h66rF+aKSCVMAWkUuTXt2H5RMAC938Bje8wuyIRKYcSUs7aBn/HHk5my18pZOVaL2pXz7eS3aW5xgGVcXLUAPDSQgGphCkglRLbF8OCgfl/7/E63Pi4qeWISMWRk2dlZ0KabRxT7OFkDpy4eAC4u7MjLWp5283N5O/lZkLFAgpIJU4BqRQ4/Ad89n+QmwntH4OeU8yuSEQquFPp2Wz++265TYfzQ1NaZu5F7QK93fIfl1I7//Jcs0Bv3Jz1nLnrQQGphCkgmezUAfioG2ScgIa3519ac9APFxEpXaxWg/0nzrAx/vzcTLuT0i56zpyTg4WmgV5/P5g3f34mPy9Xc4ouRVwcHYr98qQCUglTQDLR2dPw8W1wYjcEtITBP4Crp9lViYgUSnpWbv5z5myPTUnmxJmLB4ALfP5we7o09C3WfRb297cmdCht1n+Q/4u/aghUq5//5Hmf2prs8JzcbJj3UP5n5FULHpivcCQiZUolVyfCQ6oRHlINyJ9m4EjyWVtY2nT4NNuPpJKdd/EAcLl+FJBKm53fw4Gf7ddZHKFKnb9DU8jff9bL/9OndsW5tGQY8O1IOPgLuFSG/vPBq4bZVYmIXBOLxUKtKh7UquJBZGggkD8DeE6eLvCY+fgXBaTSpu0QqNkWTu2Dk/vg1H7Iycj/89R+2Lvcvr2DM1QJzg9O1epD1XrnQ5RXTXAoR7eWrvk3bPkqPzD2+Qz8m5ldkYhIiXBydMCpgvzft7RSQCptmt2dv5xjGJCW8HdY+kdoOvdnXhac3JO/XMjJDarU/Tsw/SM4VQuByjXK1gNcN38Fqyfn//3/pkL9W82tR0REyjUFpNLOYgGvwPylbmf7bVYrpB75OzjthZP7z4eo0wfzb38/Hpe/XMjZw/5S3T/DUyXf0hWeDvwC/xuR//eOo6DNIDOrERGRCkABqSxzcACfoPylXlf7bXm5kHL478C0/x8hah8kx+dftkvamr9cyKVywcGpWn3wqHpdumZzfDfM6w/WHGh6N9w66foeX0REKiQFpPLK0Qmq1s1f6l+wLS8HTh/6xyW7f/yZfBiy0yBhc/5yITcf+9D0z7NQ7j7F24f0EzD3PshMgVrt4Z73y9eYKhERKbUUkCoiR2eoXj9/uVBOZv7luYvC0/78y3mZyXAkJn+5kEe1i4PTuYHjrpWvrsacs/Dl/fm1VAmGfl+Cs/vV91VERKQIFJDEnrMb+DXOXy6UnQGnD/wjOP1j3NOZJMg4mb/8teHi93r6FzzmqWo9cPGwb2u1wqLH4K8/8s9Y9f8aKlUvke6KiIgURAFJCs/FI//W+oJur89K+8fddfvsB4xnnMgPUGeSIP63i99bOdD+TruT+2DH//KnMLj/C6jeoOT7JiIi8g8KSFI8XCtDjdD85UJnky8OTef+zEyGtKP5y8Ff7N931wwI7nQ9qhcREbGjgCQlz90HarbJXy6Ucco+MJ3cm3+XXYv7ILTvdS9VREQEFJDEbB5V85egdmZXIiIiYqN7pkVEREQuYHpAmjFjBsHBwbi5uREWFsaGDQXcAfUP06ZNo1GjRri7uxMUFMTo0aPJzMy8qn1mZmYyfPhwqlWrhqenJ7179yYpKanY+yYiIiJlk6kBad68eURFRTFp0iQ2btxIaGgoERERHDt2rMD2c+fOZezYsUyaNIm4uDg+/vhj5s2bx3PPPXdV+xw9ejTfffcdCxYsYM2aNRw9epRevXqVeH9FRESkbLAYhmGYdfCwsDDatWvH9OnTAbBarQQFBTFy5EjGjh17UfsRI0YQFxdHdHS0bd2YMWNYv349a9euLdQ+U1JS8PX1Ze7cudx7770A7Ny5kyZNmrBu3TpuvPHGQtWempqKt7c3KSkpeHl5XdPnICIiItdHYX9/m3YGKTs7m5iYGLp163a+GAcHunXrxrp16wp8T4cOHYiJibFdMtu/fz9Lly6lZ8+ehd5nTEwMOTk5dm0aN25M7dq1L3lcgKysLFJTU+0WERERKZ9Mu4vtxIkT5OXl4e/vb7fe39+fnTt3FvieBx54gBMnTtCpUycMwyA3N5dhw4bZLrEVZp+JiYm4uLjg4+NzUZvExMRL1jt58mRefPHFq+2miIiIlEGmD9K+GqtXr+a1115j5syZbNy4kYULF7JkyRJefvnlEj/2uHHjSElJsS2HDx8u8WOKiIiIOUw7g1S9enUcHR0vunssKSmJgICAAt8zYcIEHnroIR555BEAWrRoQXp6OkOHDuX5558v1D4DAgLIzs4mOTnZ7izS5Y4L4Orqiqura1G6KiIiImWMaWeQXFxcaNOmjd2Aa6vVSnR0NOHh4QW+JyMjAwcH+5IdHR0BMAyjUPts06YNzs7Odm127dpFfHz8JY8rIiIiFYupM2lHRUUxcOBA2rZtS/v27Zk2bRrp6ekMHjwYgAEDBlCzZk0mT54MQGRkJFOnTqV169aEhYWxd+9eJkyYQGRkpC0oXWmf3t7eDBkyhKioKKpWrYqXlxcjR44kPDy80HewiYiISPlmakDq27cvx48fZ+LEiSQmJtKqVSuWLVtmG2QdHx9vd8Zo/PjxWCwWxo8fz5EjR/D19SUyMpJXX3210PsEeOutt3BwcKB3795kZWURERHBzJkzr1/HRUREpFQzdR6kskzzIImIiJQ9pX4eJBEREZHSSgFJRERE5AKmjkEqy85dmdSM2iIiImXHud/bVxphpIBURGlpaQAEBQWZXImIiIhcrbS0NLy9vS+5XYO0i8hqtXL06FEqV66MxWIptv2mpqYSFBTE4cOHK+zg74r+GVT0/oM+A/W/Yvcf9BmUZP8NwyAtLY3AwMCL5lb8J51BKiIHBwdq1apVYvv38vKqkN8U/1TRP4OK3n/QZ6D+V+z+gz6Dkur/5c4cnaNB2iIiIiIXUEASERERuYACUinj6urKpEmTKvSDcSv6Z1DR+w/6DNT/it1/0GdQGvqvQdoiIiIiF9AZJBEREZELKCCJiIiIXEABSUREROQCCkgiIiIiF1BAKiUmT55Mu3btqFy5Mn5+ftx9993s2rXL7LJKzM8//0xkZCSBgYFYLBYWL15st90wDCZOnEiNGjVwd3enW7du7Nmzx5xiS8B7771Hy5YtbZOghYeH88MPP9i2Z2ZmMnz4cKpVq4anpye9e/cmKSnJxIqL3wsvvIDFYrFbGjdubNte3j6D4vg3f+rUKfr374+Xlxc+Pj4MGTKEM2fOXMdeFF1hfsYV5mseHx/PHXfcgYeHB35+fvzrX/8iNzf3enalyIrj+74s9z84OPii73mLxcLw4cOB0td/BaRSYs2aNQwfPpzff/+d5cuXk5OTw2233UZ6errZpZWI9PR0QkNDmTFjRoHbp0yZwjvvvMP777/P+vXrqVSpEhEREWRmZl7nSktGrVq1eP3114mJieHPP//klltu4a677mL79u0AjB49mu+++44FCxawZs0ajh49Sq9evUyuuvg1a9aMhIQE27J27VrbtvL2GRTHv/n+/fuzfft2li9fzvfff8/PP//M0KFDr1cXrklhfsZd6Wuel5fHHXfcQXZ2Nr/99hufffYZn376KRMnTjSjS1ftWr/vy3r///jjD7vv9+XLlwNw3333AaWw/4aUSseOHTMAY82aNWaXUuIAY9GiRbbXVqvVCAgIMN544w3buuTkZMPV1dX48ssvTajw+qhSpYrx0UcfGcnJyYazs7OxYMEC27a4uDgDMNatW2dihcVr0qRJRmhoaIHbyvtnUJR/8zt27DAA448//rC1+eGHHwyLxWIcOXLkutVeXC78GVeYr/nSpUsNBwcHIzEx0dbmvffeM7y8vIysrKzr24FicjXf9+Wt/0899ZQREhJiWK3WUtl/nUEqpVJSUgCoWrWqyZVcfwcOHCAxMZFu3brZ1nl7exMWFsa6detMrKxk5OXl8dVXX5Genk54eDgxMTHk5OTY9b9x48bUrl273PV/z549BAYGUq9ePfr37098fDxAhfoMoHD/5tetW4ePjw9t27a1tenWrRsODg6sX7/+utd8rS78GVeYr/m6deto0aIF/v7+tjYRERGkpqbazsKUFUX5vi9P/c/OzmbOnDk8/PDDWCyWUtl/Pay2FLJarYwaNYqOHTvSvHlzs8u57hITEwHsvgnOvT63rTzYunUr4eHhZGZm4unpyaJFi2jatCmxsbG4uLjg4+Nj17689T8sLIxPP/2URo0akZCQwIsvvkjnzp3Ztm0biYmJFeIzOKcw/+YTExPx8/Oz2+7k5ETVqlXL3GdS0M+4wnzNExMTC/yMzm0rC67l+7489P+cxYsXk5yczKBBg4DS+fVXQCqFhg8fzrZt2+zGY0j506hRI2JjY0lJSeHrr79m4MCBrFmzxuyyrpvbb7/d9veWLVsSFhZGnTp1mD9/Pu7u7iZWJiWtIv+Mq+jf9+d8/PHH3H777QQGBppdyiXpElspM2LECL7//ntWrVpFrVq1zC7HFAEBAQAX3b2QlJRk21YeuLi4UL9+fdq0acPkyZMJDQ3l7bffJiAggOzsbJKTk+3al7f+X8jHx4eGDRuyd+/eCvcZFObffEBAAMeOHbPbnpuby6lTp8rUZ3Kpn3GF+ZoHBAQU+Bmd21YWXMv3fXnoP8ChQ4dYsWIFjzzyiG1daey/AlIpYRgGI0aMYNGiRaxcuZK6deuaXZJp6tatS0BAANHR0bZ1qamprF+/nvDwcBMrK1lWq5WsrCzatGmDs7OzXf937dpFfHx8ue7/mTNn2LdvHzVq1Khwn0Fh/s2Hh4eTnJxMTEyMrc3KlSuxWq2EhYVd95qv1pV+xhXmax4eHs7WrVvtguLy5cvx8vKiadOm16cjxexqvu/LS/9nzZqFn58fd9xxh21dqex/sQ/7liJ5/PHHDW9vb2P16tVGQkKCbcnIyDC7tBKRlpZmbNq0ydi0aZMBGFOnTjU2bdpkHDp0yDAMw3j99dcNHx8f43//+5+xZcsW46677jLq1q1rnD171uTKi8fYsWONNWvWGAcOHDC2bNlijB071rBYLMZPP/1kGIZhDBs2zKhdu7axcuVK488//zTCw8ON8PBwk6suXmPGjDFWr15tHDhwwPj111+Nbt26GdWrVzeOHTtmGEb5+wyK4998jx49jNatWxvr16831q5dazRo0MDo16+fWV26KoX5GXelr3lubq7RvHlz47bbbjNiY2ONZcuWGb6+vsa4cePM6NJVu9bv+7Lef8MwjLy8PKN27drGs88+e9G20tZ/BaRSAihwmTVrltmllYhVq1YV2N+BAwcahpF/2/OECRMMf39/w9XV1bj11luNXbt2mVt0MXr44YeNOnXqGC4uLoavr69x66232n5IGoZhnD171njiiSeMKlWqGB4eHsY999xjJCQkmFhx8evbt69Ro0YNw8XFxahZs6bRt29fY+/evbbt5e0zKI5/8ydPnjT69etneHp6Gl5eXsbgwYONtLQ0E3pz9QrzM64wX/ODBw8at99+u+Hu7m5Ur17dGDNmjJGTk3Ode1M0xfF9X5b7bxiG8eOPPxpAgT/PS1v/LYZhGMV/XkpERESk7NIYJBEREZELKCCJiIiIXEABSUREROQCCkgiIiIiF1BAEhEREbmAApKIiIjIBRSQRERERC6ggCQixWr16tVYLJaLnqlU3rzwwgu0atXK7DIKxWKxsHjxYgAOHjyIxWIhNjbW1JpESjsFJBEpVh06dCAhIQFvb2+zS5ECBAUFkZCQQPPmzYGKE2hFrpaT2QWISPni4uJSpp4sXtE4Ojrq6yNSCDqDJCKX1bVrV0aOHMmoUaOoUqUK/v7+fPjhh6SnpzN48GAqV65M/fr1+eGHH4CLz0h8+umn+Pj48OOPP9KkSRM8PT3p0aMHCQkJhTr+6tWrad++PZUqVcLHx4eOHTty6NAhAPbt28ddd92Fv78/np6etGvXjhUrVti9Pzg4mFdeeYUBAwbg6elJnTp1+Pbbbzl+/Dh33XUXnp6etGzZkj///NP2nnM1L168mAYNGuDm5kZERASHDx++bK0fffQRTZo0wc3NjcaNGzNz5kzbtuzsbEaMGEGNGjVwc3OjTp06TJ48+Yr9NwyDF154gdq1a+Pq6kpgYCBPPvmkXf9efvll+vXrR6VKlahZsyYzZsy45P7+eYnt4MGD3HzzzQBUqVIFi8XCoEGDrliTSEWggCQiV/TZZ59RvXp1NmzYwMiRI3n88ce577776NChAxs3buS2227joYceIiMjo8D3Z2Rk8J///IfZs2fz888/Ex8fz9NPP33F4+bm5nL33Xdz0003sWXLFtatW8fQoUOxWCwAnDlzhp49exIdHc2mTZvo0aMHkZGRxMfH2+3nrbfeomPHjmzatIk77riDhx56iAEDBvDggw+yceNGQkJCGDBgAP98NGVGRgavvvoqn3/+Ob/++ivJycncf//9l6z1iy++YOLEibz66qvExcXx2muvMWHCBD777DMA3nnnHb799lvmz5/Prl27+OKLLwgODr7iZ/DNN9/w1ltv8cEHH7Bnzx4WL15MixYt7Nq88cYbhIaGsmnTJsaOHctTTz3F8uXLr7jvoKAgvvnmGwB27dpFQkICb7/99hXfJ1IhlMgjcEWk3LjpppuMTp062V7n5uYalSpVMh566CHbuoSEBAMw1q1bZ3tq/enTpw3DMIxZs2YZgLF3715b+xkzZhj+/v5XPPbJkycNwFi9enWh623WrJnx7rvv2l7XqVPHePDBBy+qdcKECbZ169atMwDbk8PP1fz777/b2sTFxRmAsX79esMwDGPSpElGaGiobXtISIgxd+5cu1pefvllIzw83DAMwxg5cqRxyy23GFartdB9MQzDePPNN42GDRsa2dnZBW6vU6eO0aNHD7t1ffv2NW6//Xbba8BYtGiRYRiGceDAAQMwNm3aZBiGcdHXS0Ty6QySiFxRy5YtbX93dHSkWrVqdmcx/P39ATh27FiB7/fw8CAkJMT2ukaNGpds+09Vq1Zl0KBBREREEBkZydtvv213ae7MmTM8/fTTNGnSBB8fHzw9PYmLi7voDNI/6z9X65Xqd3Jyol27drbXjRs3xsfHh7i4uIvqTE9PZ9++fQwZMgRPT0/b8sorr7Bv3z4ABg0aRGxsLI0aNeLJJ5/kp59+umL/Ae677z7Onj1LvXr1ePTRR1m0aBG5ubl2bcLDwy96XVCdIlJ4CkgickXOzs52ry0Wi926c5e8rFZrod9v/ONy1uXMmjWLdevW0aFDB+bNm0fDhg35/fffAXj66adZtGgRr732Gr/88guxsbG0aNGC7OzsSx7/XK1XU/+VnDlzBoAPP/yQ2NhY27Jt2zZbrTfccAMHDhzg5Zdf5uzZs/Tp04d77733ivsOCgpi165dzJw5E3d3d5544gm6dOlCTk5OkWoVkcJRQBKRUq9169aMGzeO3377jebNmzN37lwAfv31VwYNGsQ999xDixYtCAgI4ODBg8VyzNzcXLuB27t27SI5OZkmTZpc1Nbf35/AwED2799P/fr17Za6deva2nl5edG3b18+/PBD5s2bxzfffMOpU6euWIu7uzuRkZG88847rF69mnXr1rF161bb9nMh7J+vC6qzIC4uLgDk5eUVqr1IRaHb/EWk1Dpw4AD//e9/ufPOOwkMDGTXrl3s2bOHAQMGANCgQQMWLlxIZGQkFouFCRMmFPks0IWcnZ0ZOXIk77zzDk5OTowYMYIbb7yR9u3bF9j+xRdf5Mknn8Tb25sePXqQlZXFn3/+yenTp4mKimLq1KnUqFGD1q1b4+DgwIIFCwgICMDHx+eydXz66afk5eURFhaGh4cHc+bMwd3dnTp16tja/Prrr0yZMoW7776b5cuXs2DBApYsWVKoftapUweLxcL3339Pz549cXd3x9PTs9Cfk0h5pTNIIlJqeXh4sHPnTnr37k3Dhg0ZOnQow4cP57HHHgNg6tSpVKlShQ4dOhAZGUlERAQ33HBDsR372Wef5YEHHqBjx454enoyb968S7Z/5JFH+Oijj5g1axYtWrTgpptu4tNPP7WdQapcuTJTpkyhbdu2tGvXjoMHD7J06VIcHC7/Y9jHx4cPP/yQjh070rJlS1asWMF3331HtWrVbG3GjBnDn3/+SevWrXnllVeYOnUqERERhepnzZo1efHFFxk7diz+/v6MGDGiUO8TKe8sRmEHAoiIVBCffvopo0aNKhOzSwcHBzNq1ChGjRpldiki5YrOIImIiIhcQAFJREz1z9viL1x++eUXs8srcV988cUl+9+sWTOzyxOpsHSJTURMtXfv3ktuq1mzJu7u7texmusvLS2NpKSkArc5OzvbDcYWketHAUlERETkArrEJiIiInIBBSQRERGRCyggiYiIiFxAAUlERETkAgpIIiIiIhdQQBIRERG5gAKSiIiIyAUUkEREREQu8P9o8s0pSrj0SgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_list_train = []\n",
        "accuracy_list_test = []\n",
        "for max_depth in max_depth_list:\n",
        "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
        "    model = DecisionTreeClassifier(max_depth = max_depth,\n",
        "                                   random_state = RANDOM_STATE).fit(X_train,y_train)\n",
        "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
        "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
        "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
        "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
        "    accuracy_list_train.append(accuracy_train)\n",
        "    accuracy_list_test.append(accuracy_test)\n",
        "\n",
        "plt.title('Train x Test metrics')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\n",
        "plt.plot(accuracy_list_train)\n",
        "plt.plot(accuracy_list_test)\n",
        "plt.legend(['Train','Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "ANQ9u8yl-WuF",
        "outputId": "770985ad-281f-4218-bb09-59ad1076d693"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7df0ca36a230>"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "decision_tree_model = DecisionTreeClassifier(min_samples_split = 50,\n",
        "                                             max_depth = 3,\n",
        "                                             random_state = RANDOM_STATE).fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "yIfQNlE3Bzv9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(decision_tree_model.predict(X_test),y_test):.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nQ8GR6GiB8TN",
        "outputId": "2aa54b58-9d75-4329-e961-a113ed3e47fc"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics train:\n",
            "\tAccuracy score: 0.8583\n",
            "Metrics test:\n",
            "\tAccuracy score: 0.8641\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "min_samples_split_list = [2,10, 30, 50, 100, 200, 300, 700]  ## If the number is an integer, then it is the actual quantity of samples,\n",
        "                                             ## If it is a float, then it is the percentage of the dataset\n",
        "max_depth_list = [2, 4, 8, 16, 32, 64, None]\n",
        "n_estimators_list = [10,50,100,500]"
      ],
      "metadata": {
        "id": "3-kbh4ukCA4L"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_list_train = []\n",
        "accuracy_list_test = []\n",
        "for min_samples_split in min_samples_split_list:\n",
        "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
        "    model = RandomForestClassifier(min_samples_split = min_samples_split,\n",
        "                                   random_state = RANDOM_STATE).fit(X_train,y_train)\n",
        "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
        "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
        "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
        "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
        "    accuracy_list_train.append(accuracy_train)\n",
        "    accuracy_list_test.append(accuracy_test)\n",
        "\n",
        "plt.title('Train x Test metrics')\n",
        "plt.xlabel('min_samples_split')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xticks(ticks = range(len(min_samples_split_list )),labels=min_samples_split_list)\n",
        "plt.plot(accuracy_list_train)\n",
        "plt.plot(accuracy_list_test)\n",
        "plt.legend(['Train','Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "yRfYtvKXCFKB",
        "outputId": "8f840593-1285-4378-f03b-1d6a4da18446"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7df0c6847760>"
            ]
          },
          "metadata": {},
          "execution_count": 19
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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xm5sboqKiDI/ff/99rF69GmvXrsWkSZPu+zpjxozByJEjAQCzZs3C/PnzsX//fgwYMMAkdVbErP7q+vj4ID093Whfeno6VCoV7O0r7ueiVCqhUqmMtrriqLTBwqfa4o3+TSEIwI97k/DMN/twI6+ozt6TiIioLrRv397ocV5eHt544w1ERETAxcUFTk5OOHPmzAPv3ERGRhq+dnR0hEqlQkZGRp3UXMas7txER0djw4YNRvtiY2MRHR0tUUXlCYKASX2aIMJXhSm/HsX+y5l4+Itd+OrZ9mgVoJa6PCIiqmP2tnKcfk+aQS72tnKTvZajo6PR4zfeeAOxsbH49NNP0bhxY9jb2+Pxxx+HRqOp9HXuXUZBEATodDqT1VkRScNNXl4eLly4YHicmJiIo0ePws3NDUFBQZg2bRquXbuG77//HgDw0ksvYcGCBfj3v/+N5557Dlu3bsVvv/2G9evXS3UJ99U3whurJ3bF+O8P4tKNfDy+eA/mPB6Joa39pS6NiIjqkCAIJmsaakh2796NMWPGYNiwYQD0f8MvX74sbVH3IWmz1MGDB9GmTRu0adMGADB16lS0adMGM2bMAKAfqnb37a7Q0FCsX78esbGxiIqKwty5c/HNN980iGHgFWns5YTVE7uidzNPFJXo8OqvRzFrwxlodabr8EVERFQfmjRpglWrVuHo0aM4duwYnnrqqTq/A1NTkkbLXr16Vdqzu6LZh3v16oUjR47UYVWmpba3xTejO2BebAIWbruIr3dewpnUHHwxsg1cHBRSl0dERFQl8+bNw3PPPYcuXbrAw8MDb775Zp1Nr1JbgmjKcWNmICcnB2q1GtnZ2XXaubgi646n4P9WHsftYi2C3R2wZFR7NPV2rtcaiIjItAoLC5GYmIjQ0FDY2dlJXY5Zq+yzrM7fb7MaLWXu/hXphz9e7oIAV3tcuVmAYQt34+9TaVKXRUREZFEYbupZcz8V1k7qhuhG7sjXaPHiD4fwv9hz0LEfDhERkUkw3EjAzVGB75/viLFdQwAAn8edx0s/HkJeUYm0hREREVkAhhuJ2MplmDmkBT55PBIKGxk2n07HsIW7cflGvtSlERERmTWGG4k90T4Qv70YDW+VEucz8vDwgl3Yce661GURERGZLYabBqB1oAv+mtQNbYNckFNYgrHL9uOrHRdNugAaERGRtWC4aSC8VHb4ZXxnjGgfCJ0IzN54Fq/+ehS3NVqpSyMiIjIrDDcNiNJGjo8ea4X3h7aAjUzA2mMpeHzxHlzLui11aURERGaD4aaBEQQBz0aH4McXOsHdUYFTKTl4+Itd2HfpptSlERERmQWGmwaqcyN3rJ3cDS38VLiZr8HT3+zDD/GX2Q+HiIjoARhuGjB/F3v8/lIXPBzlhxKdiOl/nsK0VSdQVMJ+OEREVHOCIFS6vfvuu7V67TVr1pis1pqwvDXZLYy9Qo7Pn2yNFn4qfLTpLH49kIxz6blY/Ew7eKm4hgkREVVfamqq4esVK1ZgxowZSEhIMOxzcnKSoiyT4Z0bMyAIAl7sGYZlYzpAZWeDw0lZGLJgF44mZ0ldGhERmSEfHx/DplarIQiC0b5ff/0VERERsLOzQ3h4OBYtWmR4rkajwaRJk+Dr6ws7OzsEBwdj9uzZAICQkBAAwLBhwyAIguFxfeOdGzPSq5kX/pzUDeO/P4jzGXkY/lU8Zg1rhcfbBUhdGhERlRFFoLhAmve2dQAEoVYv8dNPP2HGjBlYsGAB2rRpgyNHjmDcuHFwdHTE6NGjMX/+fKxduxa//fYbgoKCkJycjOTkZADAgQMH4OXlhWXLlmHAgAGQy+WmuKpqY7gxM6Eejlg9sSteW3EUsafT8cbKYziVko23B0XARs4bcUREkisuAGb5SfPe/0kBFI61eomZM2di7ty5ePTRRwEAoaGhOH36NL766iuMHj0aSUlJaNKkCbp16wZBEBAcHGx4rqenJwDAxcUFPj4+taqjNvjX0Aw5KW3w1TPt8GrfJgCAZbsvY9TS/cjM10hcGRERmbP8/HxcvHgRzz//PJycnAzbBx98gIsXLwIAxowZg6NHj6JZs2Z45ZVXsHnzZomrLo93bsyUTCbgtYeaIsJXham/HcWeizfx8IJdWDKqPSJ8VVKXR0RkvWwd9HdQpHrvWsjLywMALFmyBJ06dTI6VtbE1LZtWyQmJmLjxo3YsmULhg8fjn79+uH333+v1XubEsONmRvQ0gehHl0x7vuDSMoswKOL9uDTJ6IwONJX6tKIiKyTINS6aUgq3t7e8PPzw6VLl/D000/f9zyVSoURI0ZgxIgRePzxxzFgwABkZmbCzc0Ntra20GqlnbKE4cYCNPNxxtpJXTH5lyP45/wNTPz5ME6nhuH1h5pBJqtdxzIiIrIu//3vf/HKK69ArVZjwIABKCoqwsGDB3Hr1i1MnToV8+bNg6+vL9q0aQOZTIaVK1fCx8cHLi4uAPQjpuLi4tC1a1colUq4urrW+zWwz42FcHFQYNmYDhjfoxEAYOG2i3jh+4PIKSyWuDIiIjInL7zwAr755hssW7YMrVq1Qs+ePbF8+XKEhoYCAJydnTFnzhy0b98eHTp0wOXLl7FhwwbIZPpIMXfuXMTGxiIwMBBt2rSR5BoE0crm88/JyYFarUZ2djZUKsvsm7LmyDW8+cdxFJXo0MjTEUtGtUeYp3lPyERE1FAVFhYiMTERoaGhsLPj5Kq1UdlnWZ2/37xzY4EeaeOP31/qAl+1HS5dz8cjC3Zj69l0qcsiIiKqFww3FqpVgBprJ3VDhxBX5BaV4PnvDmLhtgtceJOIiCwew40F83RW4qcXOuOZzkEQReCTvxMw6ecjKNCUSF0aERFRnWG4sXAKGxk+eKQVZj/aCrZyAetPpOLRRXuQnCnR1OBERER1jOHGSozsGIRfxnWGh5MSZ9Ny8fCCXdhz4YbUZRERWQw2+9eeqT5Dhhsr0j7EDX9N7orIADVuFRTj2aX7sXRXIn8giYhqwdbWFgBQUMA74rWl0eiXEartgpucxM/K+Krt8duL0fjPqhNYdeQa3lt3GqdScvDhsJaws5Vm9VYiInMml8vh4uKCjIwMAICDgwOEWq7MbY10Oh2uX78OBwcH2NjULp4w3FghO1s55g6PQgt/NWZtOIM/Dl/Fhet5+OqZdvBRc44GIqLqKlsBuyzgUM3IZDIEBQXVOhxyEj8rt/uCfrmGrIJieDorsfiZtmgX7CZ1WUREZkmr1aK4mDPD15RCoTDMdHyv6vz9ZrghJN0swPgfDuJsWi5s5QLeH9oST3YMkrosIiIiA85QTNUS5O6AP17ugoEtfVCsFfHWqhOYvuYkirU6qUsjIiKqNoYbAgA4Km2w6Om2eKN/UwgC8MPeK3j6m324kVckdWlERETVwnBDBoIgYFKfJljybHs4KW2wPzETD3+xCyevZUtdGhERUZUx3FA5/Zp7Y83Ermjk4YiU7EI8vngP/jx6TeqyiIiIqoThhirU2MsJqyd2Re9mnigs1uHVX49i9oYz0Oqsqv85ERGZIYYbui+1vS2+Gd0BE3qFAQC+2nkJY5cfQHYBhzkSEVHDxXBDlZLLBPx7QDgWPNUG9rZy7Dx3HUMX7sK59FypSyMiIqoQww1Vyb8i/fDHy13g72KPyzcLMGzhbmw+lSZ1WUREROUw3FCVNfdT4a/J3RDdyB35Gi3G/3AIn205Bx374RARUQPCcEPV4uaowPfPd8SYLiEAgM+2nMdLPx5CXlGJtIURERGVYriharOVy/Duwy0w5/FIKOQybD6djkcX7cblG/lSl0ZERMRwQzU3vH0gfn2xM7yclTiXnoeHF+zCznPXpS6LiIisHMMN1UrbIFesm9wNbYJckFNYglFL92Pc9wdxNDlL6tKIiMhKMdxQrXmp7PDr+M54ulMQBAGIPZ2ORxbuxtPf7MXuCzdgZQvPExGRxATRyv7yVGfJdKq+Cxl5WLzjItYcuYaS0lFUUYEumNgrDP0ivCGTCRJXSERE5qg6f78ZbqhOXMu6jSU7L+GX/UkoKtEBAJp6O2FCr8b4V6QvbOS8aUhERFXHcFMJhpv6dSOvCEt3JeKH+CvILR0uHuhmjxd7hOHxdgGws5VLXCEREZkDhptKMNxII6ewGD/EX8HSXYm4ma8BAHg6K/FCt1A83TkYTkobiSskIqKGjOGmEgw30rqt0WLFgSR8vfMSUrILAegX6BzdJQRju4TA1VEhcYVERNQQMdxUguGmYdCU6PDn0Wv4csdFXLqun/zPQSHHUx2D8EL3RvBR20lcIRERNSQMN5VguGlYtDoRf59Kw8JtF3AqJQcAoJDL8Fg7f7zYIwwhHo4SV0hERA0Bw00lGG4aJlEUsfP8DSzcdgH7EzMBADIBGBzphwm9whDhy+8VEZE1Y7ipBMNNw3fgciYWbbuAbQl3lnLoG+6FCb0bo12wq4SVERGRVBhuKsFwYz5OpWTjy+0Xsf5EKsr+l3Zu5IYJvRqjexMPCAInBCQishYMN5VguDE/l67n4asdl7DqyFUUa/X/XVv5qzGxdxj6N/fhrMdERFaA4aYSDDfmKzX7NpbsTMTP+6+gsFg/63GYpyNe7tUYQ1v7wZazHhMRWSyGm0ow3Ji/m3lFWL7nMr7bcxk5hfpZj/1d7PFiz0YY3j6Qsx4TEVkghptKMNxYjtzCYvy0Lwnf/JOIG3lFAAAPJwWe6xaKZzoHQ2VnK3GFRERkKgw3lWC4sTyFxVqsPJiMxTsu4VrWbQCAs50NRkeHYGzXELg7KSWukIiIaovhphIMN5arWKvDX8dSsGj7RVzIyAMA2NnK8GSHIIzv0Qh+LvYSV0hERDXFcFMJhhvLp9OJ2Hw6HYu2X8Dxq9kAAFu5gGFt/PFSzzA08nSSuEIiIqouhptKMNxYD1EUsfvCTSzcdgHxl24CAAQBGNTSFy/3CkNLf7XEFRIRUVVV5++35GNnFy5ciJCQENjZ2aFTp07Yv3//fc8tLi7Ge++9h7CwMNjZ2SEqKgqbNm2qx2rJnAiCgG5NPPDL+M744+Uu6BfhBVEE1p9Ixb++2IUxy/YblnogIiLLIWm4WbFiBaZOnYqZM2fi8OHDiIqKQkxMDDIyMio8/5133sFXX32FL774AqdPn8ZLL72EYcOG4ciRI/VcOZmbdsGu+GZ0B2ya0h1DW/tBJgDbE65j+FfxeGLxHmxLyICV3cQkIrJYkjZLderUCR06dMCCBQsAADqdDoGBgZg8eTLeeuutcuf7+fnh7bffxsSJEw37HnvsMdjb2+PHH3+s0nuyWYoA4MrNfCzecQl/HLoKjVY/IWBzXxUm9A7DwJa+kHPWYyKiBsUsmqU0Gg0OHTqEfv363SlGJkO/fv0QHx9f4XOKiopgZ2dntM/e3h67du267/sUFRUhJyfHaCMKdnfE7Edb4Z83e2Nc91A4KOQ4nZqDST8fQb95O/DbgWRoSnRSl0lERDUgWbi5ceMGtFotvL29jfZ7e3sjLS2twufExMRg3rx5OH/+PHQ6HWJjY7Fq1Sqkpqbe931mz54NtVpt2AIDA016HWTevFV2eHtwc+x+sw9e7dsEantbJN7Ix7//OI6en2zD0l2JKNCUSF0mERFVg+Qdiqvj888/R5MmTRAeHg6FQoFJkyZh7NixkMnufxnTpk1Ddna2YUtOTq7HislcuDoq8NpDTbH7rT54e1AEvJyVSM0uxHvrTqPbx9uwYOt5ZN8ulrpMIiKqAsnCjYeHB+RyOdLT0432p6enw8fHp8LneHp6Ys2aNcjPz8eVK1dw9uxZODk5oVGjRvd9H6VSCZVKZbQR3Y+T0gbjejTCzn/3xofDWiLIzQGZ+Rp8uvkcun60FR9tPIvruUVSl0lERJWQLNwoFAq0a9cOcXFxhn06nQ5xcXGIjo6u9Ll2dnbw9/dHSUkJ/vjjDwwdOrSuyyUrY2crx9OdgrH19Z74/MnWaObtjLyiEizecRHdPt6KGX+exNVbBVKXSUREFZB0tNSKFSswevRofPXVV+jYsSM+++wz/Pbbbzh79iy8vb0xatQo+Pv7Y/bs2QCAffv24dq1a2jdujWuXbuGd999F4mJiTh8+DBcXFyq9J4cLUU1odOJiDubgYXbLuBochYAwEYmYGhrf7zcqxEaezlLWyARkYWrzt9vm3qqqUIjRozA9evXMWPGDKSlpaF169bYtGmToZNxUlKSUX+awsJCvPPOO7h06RKcnJwwaNAg/PDDD1UONnUq/TSwdrLUVUhP6QQEdgZCuwMBHQAby1i0UiYT8FBzb/SL8EL8pZtYtO0idl24gT8OX8WqI1cR09wHE3qHITLARepSiYisHpdfMJXkA8C3/R58njWxsQMCOwIh3fWbfzvARiF1VSZzLDkLi7ZfwN+n7vQb697EA92beMBHbQ8/tR181HbwVtnBVm5WffeJiBocri1ViToLNwWZQNJe072eucpNBS7vAi7/A+RfNz5m6wAEdgJCugGhPQC/NoDcVpo6Tehcei4Wb7+IP4+lQKsr/+MkCICnkxK+LvbwVekDj5+LHQMQEVE1MNxUgn1u6okoAtcT9CHn8j/6wFNw0/gchRMQ1FkfdkJ6AL5RgFzSltJaSc4swMqDybiSWYDU7EKkZt9GWnYhirUP/hEzBCC1HXzV9vBR2+m/drEv3ccARETWjeGmEgw3EtHpgOtn9CEncSdwZTdw+5bxOUoVEBSt768T0g3wiQRkcmnqNRGdTsTNfA3SsguRUhp2yoJPTQKQh5PScLfHV21vFIB8Su8KMQARkSViuKkEw00DodMB6SfvNGFd3g0UZRufY6cGgrvq++uEdge8WgCVTNhornQ6EZkFGqRm3R167nydVrqVrYFVmfsFIH1TmD18VPo7QAoby/sciciyMdxUguGmgdJpgbTjpXd2/gGu7AE0ucbn2Lvqw05oD33g8Qy3yLBTkXsDUFpOIVKyCpGWfRspNQxAZc1ddwegsq8ZgIiooWG4qQTDjZnQlgCpx+702bkSDxTnG5/j4F7aX6e7PvB4NNX/5bZSonhXE1iWcQAquxtUkwDkoyq963NPGGIAIqL6xHBTCYYbM6UtBlKO6INO4j/6kWklt43PcfQqHYnVXd9B2T3MqsNORSoKQKnZhUjNqn4AAkqbwFz0Aais/0+wmwNaBajh72IPgZ8/EZkIw00lGG4sRIkGuHaotM/OTiB5P1BSaHyOs+9dd3a6A66hDDtVIIoiMvM1Ffb9uTsQaUoqD0DujgpEBqgRGeCCqEA1Wvm7wNPZMiZ1JKL6x3BTCYYbC1VcCFw7eKfPztX9gFZjfI4q4K47O90B12BparUA9wagsr4/qVm3cT4jDwlpuSipYM4fP7UdIgNcEBmoRqS/C1oFqKG2N/+5joio7jHcVILhxkoU39bfzSkbjXX1IKArNj7HJUjffFUWeNQB0tRqgQqLtTidmoPjyVk4fi0bx69m4+L1PFT02ybUwxGRAWq08lcjKtAFLfxUcFCY73xHRFQ3GG4qwXBjpTT5QPK+O3d2Ug4DuhLjc1xD79zVCekOqHylqbUhK9EARTlAYTZwOwsozNJ/ffdmpwaaDQQ8mxk9NbewGCev5eDEtSwcu5qN41ezkJx5u9xbyASgqbczWvmrERnogqgANcJ9VOy8TGTlGG4qwXBDAICiPH2n5LLRWClHAPGePiTuje/01wnpDjh5SVOrKem0dwWRrPLBxBBa7t1fuq+4oOrv5d4EiPgXED4E8G9bYX+nzHwNTlzLxvHkO4EnI7eo3HkKuQwRvs5oVdaHJ8AFjb2cIJexDxWRtWC4qQTDDVWoMAdIitfPnnx5l34YOu750fBodtednW6Ao0f916nT6ef/uW8IuU9oKTv33rmDakqp0t+hsXMp/bdsUwE3LwKXths3Azr7AeGD9WEnuGula4ql5xTiWHIWjl/NxrGrWThxLRtZBcXlzrO3laOlv0rfhydAjagAFwS7O3CEFpGFYripBMMNVcntLP1EgmV3dtJOolzY8Wp+585OcFfAwe3BryuK+iayB4WQcsfLvs4pX0dN2DreCST2LvcElIpCy13nKVUPXhajMAc4vxk4uw44Hwto8u4cs3PRN1uF/wsI6wMoHCp9KVEUkZx5G8euZuH4Vf0dnpPXslGg0ZY7V2VnYwg7Zf/6qu0YeIgsAMNNJRhuqEYKMvXrYSWWLgKaceqeEwTAuyUQUnpXorLQIpb/o1xtNnaVB5EKQ4tL6aaq39XYiwuBxB3Amb+AhI1AwY27rsMeaNwXiBgCNI3Rz0JdBVqdiEvX8wxNWcevZuN0ak6Fw9M9nJSIKgs7gWpE+qvh7sQh6UTmhuGmEgw3ZBL5N+5aF2sXcP1s9Z4vszEOJZXePbnnPKUKsLUz+SXVC51W39fp7DrgzDogO+nOMZmNvrkv/F/6JiyVX7VeWlOiw7n0XH1T1tVsHLuajXPpudBWMCTd38UeUYGlgcdfjZYBaqjsOCSdqCFjuKkEww3VibyMO0POBdmdQHK/0GJrzwkFRVG/ntiZdfqwk3Ha+Lh/+9IOyf8CPJrU6C1ua7Q4naofil7Wh+fS9fwKz23k6YioAJfSIelqtPBTw87WvFelJ7IkDDeVYLghaqBuXrxzR+fqfuNjHs3uBB2/NrUKhjmFxTh5rSzwZOFYcjauZZUfki6XCWjq7YyoADValXZYbubjDFs5h6QTSYHhphIMN0RmIDcNOLteH3YSdxrPSaQKuDPyKqgLIK/9hH8384r0kw0mZxs6Ld/Iq2BIuo0MzX1Vd5aVCFCjkSeHpBPVB4abSjDcEJmZ21n6EVdn1gIXthjPtWPvdtfIq9765j4TEEURaTmFOJZ8p8Py8atZyCksKXeuo0KOFv5qQ6flqAAXBLpx0VAiU2O4qQTDDZEZK76tn0PnzDogYQNwO/POMVvHOyOvmvTX93cyIVEUceVmQemQ9GycuJqNE9eycbu4/Og3H5UdosPcEd3IHdFh7gh0q3y4OxE9GMNNJRhuiCyEtkQ/8WJZP52cq3eOyWz0cxCV9dNx9qmbEnQiLmTkGUZoHb+ahdOpOSjWGv9a9XexNwo7fi6mucNEZE0YbirBcENkgUQRSD16Z+TVvUPzAzroQ07EEMA9rE5Lua3R4nDSLcRfvIn4SzdxLDmr3Arpwe4OiG7kjs6lYcdbZaZD+4nqEcNNJRhuiKzAjQvA2b/0YefaQeNjnhF37uj4RtX5kPz8ohIcvHIn7Jy8ll1u7p1GHo7oXHpnp3Mjd3g6c5JBonsx3FSC4YbIyuSk3Bl5dXmX8cgrddBdI6+iH7yshAnkFhbjwOVMxF+8ib2XMnEyJRv3/hZu4uVkaMbq1Mgdbo6KOq+LqKFjuKkEww2RFbt9Czj3t34piAtxQMld89s4uJeOvBoCNOpVb7NAZ98uxv7ETMOdnTOpOeXOCfdxvhN2Qt2hduBsymR9GG4qwXBDRAAATQFwcav+jk7CRv3ipGUUTkDjfqUjrx7SzyxdT27la7Av8aYh7JxLzzM6LghACz+VoXNyhxA3OHPpCLICDDeVYLghonK0xfpV4MtGXuWm3DkmswVCe+ibrpoNBpy967W0G3lF2HvpTti5d/kImQC08lcb+ux0CHGDo7L2ExsSNTQMN5VguCGiSokikHL4zsirG+fuOigAgR1LR179C3BrVO/lpecUGoWdKzcLjI7byAREBqhLm7E80C7YFfYKrpFF5o/hphIMN0RULdfP3Rl5lXLY+JhXizsjr3xaSbIYakrWbUPQib94s9w6WQq5DK0DXQx3dtoEuXBBUDJLDDeVYLghohrLvgqc3aAPO5d3A+JdsxO7BOk7IzcbCLgE6vvpKFX1MgLrbsmZBYi/dBN7SwNPanah0XGFjQztglz1d3bC3BEV4AKFDRcDpYaP4aYSDDdEZBIFmcC5Tfo7OhfjgJLCis9TqgA7F33YuXuzr2DfvecpnWt1N6hsyYj4u5qxrucaLwhqbytH+xBXdC6dYycyQM2Vz6lBYripBMMNEZmcJl8/tLxsLp2CTONh5jUlyCoIQHeHIJfKw5Ktg1E4EkURF6/nG+7s7L10EzfzNUZv6aiQo32Im2HoeUt/NVc9pwaB4aYSDDdEVC9KioDCHP0Q88Lsu/7N1q90Xva1YbvnuK649jXIbCu9YyQq1cgotsPZLBmO3RBxIE2HlEIFckRHZMMRGtjCWWmDjqH6sNO5kTua+6ogY9ghCTDcVILhhogaPFHUN3NVGISyjIPQ3dvd54rlVyuvrkLYIkd0RI7ogBw4IFt0xG25ExxU7nB184Cvtw88PL0hs7/nrpJNw1w+QhRFaLQ6aLQiNMU6FJVooSnRoUirg6bkrk2rQ1FJ2XERmvudV6I/T6PVlj7WnyvYqzBrZFd4OXPNMFOqzt9vToZARNTQCAJga6/farKiuSjqm8ruvSNUYRDKquDuUQ4AEXYohp2QBS8hy/j1c0q3y7W6ynonAFCWbnWpOFOO1XHfYvgjw+r4neh+GG6IiCyNIABKJ/2m9q/+83U6QJNbLghpb2chNT0NqWnpyLyZgds5mXAU86ES8qFCAZyFArgI+VDgzvpdYrkvGhihwi/vPBaMjwr3nFDuOTotbAUtnBJ+B8BwIxWGGyIiMia7qyPzXeQAAko3ANCU6HD8apZhJNahK7dQVKKr8tvYygUobeRQ2shgZ6v/V2Ejg7L0a/0mh9JWZnRO2XP0++8611YOu0qeX/a1nY0ctnIBQh3MS3TryFq4/vksogricTO3EO5smpIE+9wQEZFJFJVocSw5G3lFxXeFjjuB4u6QobCRWeYorOLbKPwwGHYowpYef6Bfn35SV2Qx2OeGiIjqndJGjo6hblKXIS1be1x17YTGt3bi9ql1AMONJDhTExERkQnZthgMAAi9sRPF2qo305HpMNwQERGZUEDHYdBBQEvhIo6fPiN1OVaJ4YaIiMiE5CpvJNlHAADSD66RthgrxXBDRERkYoWNYgAAblfjJK7EOjHcEBERmVhA58cAAG1KjiMp7brE1VgfhhsiIiITcwpoiXS5L5RCMc7Hr5W6HKvDcENERGRqgoAM394AAJsLf0tcjPVhuCEiIqoD7u2GAgBa5sUj/3aRxNVYF4YbIiKiOuDbqg9y4QB3IQcn97NjcX1iuCEiIqoDgo0Cl127AgAKTqyTuBrrwnBDRERUR2yb62crDr6xAzqdVS3lKCmGGyIiojoSGj0UxaIcjXAV588cl7ocq8FwQ0REVEeUTm646BAJAEg/uFriaqwHww0REVEdKgjtDwBwvbpF4kqsB8MNERFRHQrq/CgAIEJzCjcy0iSuxjow3BAREdUhj6BwXJYHw0bQ4cIeNk3VB4YbIiKiOpbu2wcAYHNhk8SVWIcahZtt27aZug4iIiKL5dZGP1txs9x90BQVSlyN5atRuBkwYADCwsLwwQcfIDk52dQ1ERERWZSw1j1wAy5wFm7j3H6uNVXXahRurl27hkmTJuH3339Ho0aNEBMTg99++w0ajcbU9REREZk9mVyOiy5lsxX/JXE1lq9G4cbDwwOvvfYajh49in379qFp06aYMGEC/Pz88Morr+DYsWOmrpOIiMis2UToZysOur4DEDlbcV2qdYfitm3bYtq0aZg0aRLy8vKwdOlStGvXDt27d8epU6dMUSMREZHZa9rlXygUbeEjZuDq2QNSl2PRahxuiouL8fvvv2PQoEEIDg7G33//jQULFiA9PR0XLlxAcHAwnnjiCVPWSkREZLacndU4Zd8OAJB2gEPC65JNTZ40efJk/PLLLxBFEc8++yzmzJmDli1bGo47Ojri008/hZ+fn8kKJSIiMncFof2BM3vhkhwndSkWrUZ3bk6fPo0vvvgCKSkp+Oyzz4yCTRkPD48qDRlfuHAhQkJCYGdnh06dOmH//v2Vnv/ZZ5+hWbNmsLe3R2BgIF577TUUFnJYHRERNXzBpbMVNy5OQN4NjjauKzUKN3FxcRg5ciSUSuV9z7GxsUHPnj0rfZ0VK1Zg6tSpmDlzJg4fPoyoqCjExMQgIyOjwvN//vlnvPXWW5g5cybOnDmDb7/9FitWrMB//vOfmlwGERFRvQoKDsUZWRMAQOKeVRJXY7lqFG5mz56NpUuXltu/dOlSfPzxx1V+nXnz5mHcuHEYO3YsmjdvjsWLF8PBwaHC1waAPXv2oGvXrnjqqacQEhKC/v37Y+TIkQ+820NERNRQpPnoZyuWn+NsxXWlRuHmq6++Qnh4eLn9LVq0wOLFi6v0GhqNBocOHUK/fv3uFCOToV+/foiPj6/wOV26dMGhQ4cMYebSpUvYsGEDBg0adN/3KSoqQk5OjtFGREQkFdfS2Yob5R2ErjBP4mosU43CTVpaGnx9fcvt9/T0RGpqapVe48aNG9BqtfD29jba7+3tjbS0ildNfeqpp/Dee++hW7dusLW1RVhYGHr16lVps9Ts2bOhVqsNW2BgYJXqIyIiqgvNW3fGVdETdtDgysENUpdjkWoUbgIDA7F79+5y+3fv3l2nI6S2b9+OWbNmYdGiRTh8+DBWrVqF9evX4/3337/vc6ZNm4bs7GzDxuUiiIhISgpbOc65dAcA5B1fJ3E1lqlGQ8HHjRuHKVOmoLi4GH366NsO4+Li8O9//xuvv/56lV7Dw8MDcrkc6enpRvvT09Ph4+NT4XOmT5+OZ599Fi+88AIAoFWrVsjPz8f48ePx9ttvQyYrn9WUSmWlHZ+JiIjqmzxiELB3FQKu7wB0OqCCv19UczUKN//3f/+HmzdvYsKECYb1pOzs7PDmm29i2rRpVXoNhUKBdu3aIS4uDo888ggAQKfTIS4uDpMmTarwOQUFBeUCjFwuBwCInMqaiIjMRPPOA5ATbw9XZCHz3B64hXeTuiSLUqNwIwgCPv74Y0yfPh1nzpyBvb09mjRpUu07JFOnTsXo0aPRvn17dOzYEZ999hny8/MxduxYAMCoUaPg7++P2bNnAwCGDBmCefPmoU2bNujUqRMuXLiA6dOnY8iQIYaQQ0RE1NB5ujjjH7sO6F60E2kHVjPcmFiNwk0ZJycndOjQocbPHzFiBK5fv44ZM2YgLS0NrVu3xqZNmwydjJOSkozu1LzzzjsQBAHvvPMOrl27Bk9PTwwZMgQffvhhbS6DiIio3uUH9wfO7YQ6aYvUpVgcQaxhe87Bgwfx22+/ISkpydA0VWbVqoY7MVFOTg7UajWys7OhUqmkLoeIiKzU6UtX0PS71rARdCiaeBhKzzCpS2rQqvP3u0Y9mH799Vd06dIFZ86cwerVq1FcXIxTp05h69atUKvVNSqaiIjImkSEBuGorDkAIDm+4d4UMEc1CjezZs3C//73P/z1119QKBT4/PPPcfbsWQwfPhxBQUGmrpGIiMjiCIKAVJ/eAADZ+Y0SV2NZahRuLl68iMGDBwPQj3rKz8+HIAh47bXX8PXXX5u0QCIiIkulbv0wACAo9wjEglsSV2M5ahRuXF1dkZubCwDw9/fHyZMnAQBZWVkoKCgwXXVEREQWrF3rtjgvBsAGOqQfWS91ORajRuGmR48eiI2NBQA88cQTePXVVzFu3DiMHDkSffv2NWmBRERElspRaYOzqq4AgLxjayWuxnLUaCj4ggULUFhYCAB4++23YWtriz179uCxxx7DO++8Y9ICiYiILJkQPgg4sAK+13cBJRrARiF1SWav2uGmpKQE69atQ0xMDAD9St5vvfWWyQsjIiKyBq069sH1/Sp4Igd55/+BUwRbQGqr2s1SNjY2eOmllwx3boiIiKjmgj1VOKjoCADIOLha4mosQ4363HTs2BFHjx41cSlERETWKS+kPwDoZyvmWom1VqM+NxMmTMDUqVORnJyMdu3awdHR0eh4ZGSkSYojIiKyBkHtBqHo3NtwL06FNv005D4tpC7JrNVo+YV7V+YG9JMRiaIIQRCg1WpNUlxd4PILRETU0BRrddjzfl/0xGFca/d/8B/CwTn3qs7f7xrduUlMTKxRYURERFSerVyGa169gIzDEBI2Agw3tVKjcBMcHGzqOoiIiKyaS+shwOZ58Ms7CeSmA87eUpdktmoUbr7//vtKj48aNapGxRAREVmrTpEtcGxTI0TJLiHr+Dq4dH1e6pLMVo3Czauvvmr0uLi4GAUFBVAoFHBwcGC4ISIiqiZ3JyU2OXdFVP4l5B1by3BTCzUaCn7r1i2jLS8vDwkJCejWrRt++eUXU9dIRERkFWTNBgIAPK/HAxqu1VhTNQo3FWnSpAk++uijcnd1iIiIqGoi23XFVdEDSrEImvPbpC7HbJks3AD62YtTUlJM+ZJERERWo7mfGvHyDgCAG4fXSFuMGatRn5u1a41XLhVFEampqViwYAG6du1qksKIiIisjSAIyAnuDyRuhPOVLYBOB1QwtxxVrkbh5pFHHjF6LAgCPD090adPH8ydO9cUdREREVmlkLYPIfeSPZxLMiGmHIYQ0F7qksxOjcKNTqczdR1EREQEILqZL7aLURgk7EXm4T/hznBTbbzXRURE1IA4KGyQ7NlL/yBhg6S1mKsahZvHHnsMH3/8cbn9c+bMwRNPPFHrooiIiKyZOnIQSkQZ3PMvALcuS12O2alRuNm5cycGDRpUbv/AgQOxc+fOWhdFRERkzbq2aoKDYjMAQMHJdRJXY35qFG7y8vKgUCjK7be1tUVOTk6tiyIiIrJmgW4OOOYQDQDIP/6XxNWYnxqFm1atWmHFihXl9v/6669o3rx5rYsiIiKydmKzAQAAt+sHgMJsiasxLzUaLTV9+nQ8+uijuHjxIvr06QMAiIuLwy+//IKVK1eatEAiIiJr1LZ1B5w/4o8msmvQnd8CWavHpC7JbNTozs2QIUOwZs0aXLhwARMmTMDrr7+Oq1evYsuWLeXmwCEiIqLqaxvkgn9k+mHgt478KXE15qVGd24AYPDgwRg8eLApayEiIqJSNnIZsgP7Acl/wvHKVkBbDMhtpS7LLNTozs2BAwewb9++cvv37duHgwcP1rooIiIiAkLb9MJN0Rl22lwgKV7qcsxGjcLNxIkTkZycXG7/tWvXMHHixFoXRUREREDPZj7YpmsDAMjjqKkqq1G4OX36NNq2bVtuf5s2bXD69OlaF0VERESAq6MCl9x6AADEsxsAUZS4IvNQo3CjVCqRnp5ebn9qaipsbGrcjYeIiIju4dIqBkWiLZxvXwWuJ0hdjlmoUbjp378/pk2bhuzsO+Pus7Ky8J///AcPPfSQyYojIiKydj1ahmC3rgUAoPj0eomrMQ81CjeffvopkpOTERwcjN69e6N3794IDQ1FWloa5s6da+oaiYiIrFYzb2ccVHYCABScYL+bqqhRuPH398fx48cxZ84cNG/eHO3atcPnn3+OEydOIDAw0NQ1EhERWS1BEICmMQAA55tHgbwMaQsyAzXuIOPo6Ihu3bohKCgIGo0GALBx40YAwMMPP2ya6oiIiAjtI1vi+MlQRMoSIZ7bBKHtKKlLatBqFG4uXbqEYcOG4cSJExAEAaIo6pNlKa1Wa7ICiYiIrF2XMA98jfaIRCLyjv8FZ4abStWoWerVV19FaGgoMjIy4ODggJMnT2LHjh1o3749tm/fbuISiYiIrJudrRw3/fvqv07aCRTflriihq1G4SY+Ph7vvfcePDw8IJPJIJfL0a1bN8yePRuvvPKKqWskIiKyek0io3FNdIetrhBI3Cl1OQ1ajcKNVquFs7MzAMDDwwMpKSkAgODgYCQkcAw+ERGRqfWO8MYWrX4C3aKT6ySupmGrUZ+bli1b4tixYwgNDUWnTp0wZ84cKBQKfP3112jUqJGpayQiIrJ6/i72OKfuBuTHQpewEdDpAFmN7lFYvBp9Ku+88w50Oh0A4L333kNiYiK6d++ODRs2YP78+SYtkIiIiPTcWvRBnmgH+6LrQOoRqctpsGp05yYmJsbwdePGjXH27FlkZmbC1dXVaNQUERERmU6vFgHYER+JwfL90J3ZAJl/O6lLapBMdj/Lzc2NwYaIiKgOtQ50RbxNRwDA7VPsd3M/bKwjIiIyE3KZAF3jh6AVBTjeOgtkJUldUoPEcENERGRGOrdsioNiM/2DhE3SFtNAMdwQERGZkZ5NPLFVp+9rU3iSC2lWhOGGiIjIjKgdbJHm2xsAYHt1D1CYLXFFDQ/DDRERkZmJaNkWF3W+kIslwIU4qctpcBhuiIiIzEzfcC/EljZNlZxZL3E1DQ/DDRERkZlp7OWE4w7RAADxfCygLZG4ooaF4YaIiMjMCIIAr+bdkSk6wVaTDSTvlbqkBoXhhoiIyAz1ivDFVp1+IU3x7AaJq2lYGG6IiIjMUOdG7tiJ9gAAzel1gChKXFHDwXBDRERkhuxs5dA26o0i0QbKnCvAjXNSl9RgMNwQERGZqa7NQxCva6F/kMCmqTIMN0RERGaqT7gXtpT2uyk+w3BThuGGiIjITPmo7XDFoycAwObaASD/hsQVNQwMN0RERGasdYvmOKELgQAROPe31OU0CAw3REREZqx3uBfiSpumdBwSDoDhhoiIyKxFBbjggKIzAEC8EAcUF0pckfQYboiIiMyYXCbAu1lHpIpukGtvA4k7pS5Jcgw3REREZq5vhA+2aPVNUzi3UdpiGoAGEW4WLlyIkJAQ2NnZoVOnTti/f/99z+3VqxcEQSi3DR48uB4rJiIiaji6N/XANrFslfANVj9bseThZsWKFZg6dSpmzpyJw4cPIyoqCjExMcjIyKjw/FWrViE1NdWwnTx5EnK5HE888UQ9V05ERNQwqOxsoQnqijzRDjb5aUDqUalLkpTk4WbevHkYN24cxo4di+bNm2Px4sVwcHDA0qVLKzzfzc0NPj4+hi02NhYODg4MN0REZNV6RgTiH10r/YME626akjTcaDQaHDp0CP369TPsk8lk6NevH+Lj46v0Gt9++y2efPJJODo6Vni8qKgIOTk5RhsREZGl6R3uhS1afdOU1sqHhEsabm7cuAGtVgtvb2+j/d7e3khLS3vg8/fv34+TJ0/ihRdeuO85s2fPhlqtNmyBgYG1rpuIiKihCfN0xHmXaGhFAfL0E0BWstQlSUbyZqna+Pbbb9GqVSt07NjxvudMmzYN2dnZhi052Xq/2UREZLkEQUDb8CY4JDbV7zi3SdqCJCRpuPHw8IBcLkd6errR/vT0dPj4+FT63Pz8fPz66694/vnnKz1PqVRCpVIZbURERJaob4SXYUi4aMX9biQNNwqFAu3atUNcXJxhn06nQ1xcHKKjoyt97sqVK1FUVIRnnnmmrsskIiIyCx1D3bBLrm/NEBN3AoXW2c9U8mapqVOnYsmSJfjuu+9w5swZvPzyy8jPz8fYsWMBAKNGjcK0adPKPe/bb7/FI488And39/oumYiIqEFS2sgR2CQSF3W+kOmKgYtbpS5JEjZSFzBixAhcv34dM2bMQFpaGlq3bo1NmzYZOhknJSVBJjPOYAkJCdi1axc2b94sRclEREQNVp9wL8QltEWYbL1+SHiLR6Quqd4Jomhd0xjm5ORArVYjOzub/W+IiMjiZOQUYtLsBfhN+T50dq6Q/d8FQC75vYxaq87fb8mbpYiIiMh0vFR2KPLtgFuiE2SFt4DkfVKXVO8YboiIiCxMzwhfbNW11j+wwoU0GW6IiIgsTN+7ZisWzzLcEBERkZlr5a/GKfsOKBJtIGReAG6cl7qkesVwQ0REZGFkMgGdwoOwTxeh35FgXWtNMdwQERFZoD7hXojV6ZumrG2VcIYbIiIiC9StiQd2oLTfTfI+IP+mxBXVH4YbIiIiC+RsZ4vA0KY4pQuGIOqA89Yz8S3DDRERkYXqE+6NLYamKevpd8NwQ0REZKH6hHshtmyV8AtxQHGhxBXVD4YbIiIiCxXq4Yh8t5ZIE10hFOcDl3dJXVK9YLghIiKyYH0ivBFXevfGWpqmGG6IiIgsmH5IeGnTVMJGwArWy2a4ISIismAdQtxw3DYKBaISQm4KkHpM6pLqHMMNERGRBVPYyNCpiR926iL1O85tkragesBwQ0REZOH6hHthi856+t0w3BAREVm4Xs28sFXbBjpR0DdLZV+TuqQ6xXBDRERk4TydlQgMDMJhsYl+xznLXmuK4YaIiMgK9GnmhS2GIeEMN0RERGTm+kbcWSVcTNwJFOVKXFHdYbghIiKyAi38VMh1DEWizhuCVgNc3CZ1SXWG4YaIiMgKCIKA3kYLaVpu0xTDDRERkZXoE+GFLdrSpqlzmwCdVuKK6gbDDRERkZXo1tgDx2XhyBIdIdzOBJL3S11SnWC4ISIishKOShu0b+SJbbrW+h0WOqEfww0REZEV6RN+p2nKUvvdMNwQERFZkT7hXtipi4RGlAM3zwM3Lkhdkskx3BAREVmRYHdHeHl6Yq+uuX6HBc5WzHBDRERkZfpGeN+1kCbDDREREZm53s28EFe6FIOYFA8UZEpckWkx3BAREVmZ9iGuyLHzxRldEARRB5zfLHVJJsVwQ0REZGVs5TL0aOqJWEPTlGUNCWe4ISIiskL6VcJLh4Rf2AqUFElbkAkx3BAREVmhXs08cRKhSBddAE0ucHmX1CWZDMMNERGRFXJ3UiIq0M3QsdiSRk0x3BAREVmpvuFeiL17lXBRlLYgE2G4ISIislK9w72wR9cCt0UFkHMVSDshdUkmwXBDRERkpZr7quCqUuEfXSv9DgtpmmK4ISIislKCIKC3UdOUZQwJZ7ghIiKyYn3DvbBN2wY6CEDqUSAnReqSao3hhoiIyIp1aeyOHBtXHNE11u84t0nagkyA4YaIiMiKOShsEN3I/c6EfhbQ74bhhoiIyMr1jfC6s0r4pR1AUZ60BdUSww0REZGV693MC+dFf1wRvQBtEXBpm9Ql1QrDDRERkZULdHNAU29ni2maYrghIiIi9A6/q2nq3N+ATittQbXAcENEREToG+6NA7pmyIEjUHADuHpQ6pJqjOGGiIiI0DbIBY729tiqjdLvMOMJ/RhuiIiICDZyGXo29bSIfjcMN0RERAQA6BPuhR26KJRADtxIAG5elLqkGmG4ISIiIgBAz6aeyBccsFcbrt9hpndvGG6IiIgIAODqqEDbIFdsKVtI00yXYmC4ISIiIgOjIeFX9gAFmdIWVAMMN0RERGTQN8ILV0UvJIhBgKgFLmyRuqRqY7ghIiIig2bezvBT22GztvTujRkOCWe4ISIiIgNBENAnwgtxZeHm/BagRCNtUdXEcENERERG+oR74ZjYCDfhAmhygSu7pC6pWhhuiIiIyEiXMA8obW2wuaSNfoeZDQlnuCEiIiIjdrZydAnzuDNqKmETIIrSFlUNDDdERERUTp9wL+zWtUQRlEB2EpB+SuqSqozhhoiIiMrpHe6FQiixU9tSv8OMmqYYboiIiKgcfxd7hPs4I1ZnfkPCGW6IiIioQn3CvbBNW9qpOOUwkJMqbUFVJHm4WbhwIUJCQmBnZ4dOnTph//79lZ6flZWFiRMnwtfXF0qlEk2bNsWGDeaTJomIiMxFn3AvXIcLjqOJfoeZrDUlabhZsWIFpk6dipkzZ+Lw4cOIiopCTEwMMjIyKjxfo9HgoYcewuXLl/H7778jISEBS5Ysgb+/fz1XTkREZPnaBLnCxcEWm4pL794w3DzYvHnzMG7cOIwdOxbNmzfH4sWL4eDggKVLl1Z4/tKlS5GZmYk1a9aga9euCAkJQc+ePREVFVXPlRMREVk+uUxAr6aed1YJv7Qd0ORLWlNVSBZuNBoNDh06hH79+t0pRiZDv379EB8fX+Fz1q5di+joaEycOBHe3t5o2bIlZs2aBa1WW19lExERWZU+Ed44JwYgVfAGSgr1AaeBkyzc3LhxA1qtFt7e3kb7vb29kZaWVuFzLl26hN9//x1arRYbNmzA9OnTMXfuXHzwwQf3fZ+ioiLk5OQYbURERFQ1PZt4Qi6TYWNZ05QZjJqSvENxdeh0Onh5eeHrr79Gu3btMGLECLz99ttYvHjxfZ8ze/ZsqNVqwxYYGFiPFRMREZk3tYMt2gW7Gs9WrGvYLSaShRsPDw/I5XKkp6cb7U9PT4ePj0+Fz/H19UXTpk0hl8sN+yIiIpCWlgaNpuIVS6dNm4bs7GzDlpycbLqLICIisgJ9wr2wXxeOfMERKLgBXDskdUmVkizcKBQKtGvXDnFxcYZ9Op0OcXFxiI6OrvA5Xbt2xYULF6DT6Qz7zp07B19fXygUigqfo1QqoVKpjDYiIiKqur7hXiiBDbZpSwfwNPDZiiVtlpo6dSqWLFmC7777DmfOnMHLL7+M/Px8jB07FgAwatQoTJs2zXD+yy+/jMzMTLz66qs4d+4c1q9fj1mzZmHixIlSXQIREZHFa+zlhABXe2wuKWuaatjhxkbKNx8xYgSuX7+OGTNmIC0tDa1bt8amTZsMnYyTkpIgk93JX4GBgfj777/x2muvITIyEv7+/nj11Vfx5ptvSnUJREREFk8QBPQJ98Ka+EhoIYf8+hkg8xLg1kjq0iokiKIZrWFuAjk5OVCr1cjOzmYTFRERURVtT8jAmGUHsNJ+NjqIJ4CY2UD0hHp7/+r8/Tar0VJEREQkjc6N3GFvK8dGTWv9jgY8JJzhhoiIiB7IzlaOro097gwJv7IHuH1L2qLug+GGiIiIqqRPuBeSRG8kyYMBUQuc3yJ1SRViuCEiIqIq6RPuBQD4q6h0SPi5hjlqiuGGiIiIqsRHbYfmvips0ZYupHl+C1BS8SS6UmK4ISIioirrG+GFo2IYcuSuQFE2kLRH6pLKYbghIiKiKusd7gURMsSWlC2k2fCaphhuiIiIqMqiAlzg7qjApuLW+h0JG4AGNmUeww0RERFVmVwmoGczT+zStUSxoACykoCM01KXZYThhoiIiKqlT7gXbsMOB2UNcyFNhhsiIiKqlu5NPGEjE/BnIcMNERERWQC1vS3ah7giTlvaqfjaQSA3Xdqi7sJwQ0RERNXWN9wb1+GKi4pm+h3nNklb0F0YboiIiKjaepfOVrz2dsNrmmK4ISIiomoL83REsLsD/i4pXUjz0jZAUyBtUaUYboiIiKjaBEFA72ZeOCsGItPWBygpBBJ3SF0WAIYbIiIiqqG+EV4ABGzWlt69SdggaT1lGG6IiIioRjqGusFBIcdfhiHhmwCdTtqiwHBDRERENaS0kaNbYw/s10WgSO4I5GcAKYelLovhhoiIiGqub4QXimGD/fJ2+h0NoGmK4YaIiIhqrHcz/ZDw3/Na6nc0gCHhDDdERERUY14qO7TyV2O7rjV0gly/iOaty5LWxHBDREREtdI73AvZcMJ5u1b6HQnSzlbMcENERES10rd0tuI1BZH6HRL3u2G4ISIiolpp5a+Gh5MS6zVtUKx0BVyCAFGUrB6GGyIiIqoVmUxA72aeSBK98VGLv4ChCwBBkK4eyd6ZiIiILEaf0qapreduSlwJww0RERGZQLcmHrCVC0i8kY9L1/MkrYXhhoiIiGrN2c4WHUPdAABbz2ZIWgvDDREREZlE2YR+2xIYboiIiMgC9I3whouDLfzU9hAlHC1lI9k7ExERkUUJ9XDEoXceglwm3UgpgHduiIiIyISkDjYAww0RERFZGIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUVhuCEiIiKLwnBDREREFoXhhoiIiCwKww0RERFZFIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUWxkbqA+iaKIgAgJydH4kqIiIioqsr+bpf9Ha+M1YWb3NxcAEBgYKDElRAREVF15ebmQq1WV3qOIFYlAlkQnU6HlJQUODs7QxAEk752Tk4OAgMDkZycDJVKZdLXNgfWfv0APwNev3VfP8DPwNqvH6i7z0AUReTm5sLPzw8yWeW9aqzuzo1MJkNAQECdvodKpbLa/9QArx/gZ8Drt+7rB/gZWPv1A3XzGTzojk0ZdigmIiIii8JwQ0RERBaF4caElEolZs6cCaVSKXUpkrD26wf4GfD6rfv6AX4G1n79QMP4DKyuQzERERFZNt65ISIiIovCcENEREQWheGGiIiILArDDREREVkUhptamj17Njp06ABnZ2d4eXnhkUceQUJCgtRl1amdO3diyJAh8PPzgyAIWLNmjdFxURQxY8YM+Pr6wt7eHv369cP58+elKbYOfPnll4iMjDRMUBUdHY2NGzcajhcWFmLixIlwd3eHk5MTHnvsMaSnp0tYsWm9++67EATBaAsPDzcct8TrN8X/+czMTDz99NNQqVRwcXHB888/j7y8vHq8ipqryu+5qnzfk5KSMHjwYDg4OMDLywv/93//h5KSkvq8lBoxxc+8uV57mZCQkHI/94IgYOLEiQAa3mfAcFNLO3bswMSJE7F3717ExsaiuLgY/fv3R35+vtSl1Zn8/HxERUVh4cKFFR6fM2cO5s+fj8WLF2Pfvn1wdHRETEwMCgsL67nSuhEQEICPPvoIhw4dwsGDB9GnTx8MHToUp06dAgC89tpr+Ouvv7By5Urs2LEDKSkpePTRRyWu2rRatGiB1NRUw7Zr1y7DMUu8flP8n3/66adx6tQpxMbGYt26ddi5cyfGjx9fX5dQK1X5Pfeg77tWq8XgwYOh0WiwZ88efPfdd1i+fDlmzJghxSVVS21/5s352sscOHDA6Gc+NjYWAPDEE08AaICfgUgmlZGRIQIQd+zYIXUp9QKAuHr1asNjnU4n+vj4iJ988olhX1ZWlqhUKsVffvlFggrrh6urq/jNN9+IWVlZoq2trbhy5UrDsTNnzogAxPj4eAkrNJ2ZM2eKUVFRFR6zhuuvyf/506dPiwDEAwcOGM7ZuHGjKAiCeO3atXqr3VTu/T1Xle/7hg0bRJlMJqalpRnO+fLLL0WVSiUWFRXV7wWYQHV+5i3t2kVRFF999VUxLCxM1Ol0DfIz4J0bE8vOzgYAuLm5SVyJNBITE5GWloZ+/foZ9qnVanTq1Anx8fESVlY3tFotfv31V+Tn5yM6OhqHDh1CcXGx0fWHh4cjKCjIoq7//Pnz8PPzQ6NGjfD0008jKSkJAKzm+u9Wlf/z8fHxcHFxQfv27Q3n9OvXDzKZDPv27av3mmvr3t9zVfm+x8fHo1WrVvD29jacExMTg5ycHMMdEHNQk595S7n2MhqNBj/++COee+45CILQID8Dq1s4sy7pdDpMmTIFXbt2RcuWLaUuRxJpaWkAYPQfuOxx2TFLcOLECURHR6OwsBBOTk5YvXo1mjdvjqNHj0KhUMDFxcXofEu6/k6dOmH58uVo1qwZUlNT8d///hfdu3fHyZMnkZaWZvHXf6+q/J9PS0uDl5eX0XEbGxu4ubmZ3edS0e+5qnzf09LSKvyMyo41dLX5mTf3a7/XmjVrkJWVhTFjxgBomN9/hhsTmjhxIk6ePGnU/4AsU7NmzXD06FFkZ2fj999/x+jRo7Fjxw6py6oXAwcONHwdGRmJTp06ITg4GL/99hvs7e0lrIzqg7X+nrPmn/l7ffvttxg4cCD8/PykLuW+2CxlIpMmTcK6deuwbds2BAQESF2OZHx8fACgXC/59PR0wzFLoFAo0LhxY7Rr1w6zZ89GVFQUPv/8c/j4+ECj0SArK8vofEu7/ru5uLigadOmuHDhglVef1X+z/v4+CAjI8PoeElJCTIzM83qc7nf77mqfN99fHwq/IzKjjV0tfmZN/drv9uVK1ewZcsWvPDCC4Z9DfEzYLipJVEUMWnSJKxevRpbt25FaGio1CVJKjQ0FD4+PoiLizPsy8nJwb59+xAdHS1hZXVLp9OhqKgI7dq1g62trdH1JyQkICkpyWKvPy8vDxcvXoSvr69VXn9V/s9HR0cjKysLhw4dMpyzdetW6HQ6dOrUqd5rrq4H/Z6ryvc9OjoaJ06cMAp5sbGxUKlUaN68ef1ciAlV52fekq592bJl8PLywuDBgw37GuRnYPIuylbm5ZdfFtVqtbh9+3YxNTXVsBUUFEhdWp3Jzc0Vjxw5Ih45ckQEIM6bN088cuSIeOXKFVEURfGjjz4SXVxcxD///FM8fvy4OHToUDE0NFS8ffu2xJWbxltvvSXu2LFDTExMFI8fPy6+9dZboiAI4ubNm0VRFMWXXnpJDAoKErdu3SoePHhQjI6OFqOjoyWu2nRef/11cfv27WJiYqK4e/dusV+/fqKHh4eYkZEhiqJlXr8p/s8PGDBAbNOmjbhv3z5x165dYpMmTcSRI0dKdUnVUpXfcw/6vpeUlIgtW7YU+/fvLx49elTctGmT6OnpKU6bNk2KS6qW2v7Mm/O1302r1YpBQUHim2++We5YQ/sMGG5qCUCF27Jly6Qurc5s27atwmsePXq0KIr6obHTp08Xvb29RaVSKfbt21dMSEiQtmgTeu6558Tg4GBRoVCInp6eYt++fQ2/5ERRFG/fvi1OmDBBdHV1FR0cHMRhw4aJqampElZsWiNGjBB9fX1FhUIh+vv7iyNGjBAvXLhgOG6J12+K//M3b94UR44cKTo5OYkqlUocO3asmJubK8HVVF9Vfs9V5ft++fJlceDAgaK9vb3o4eEhvv7662JxcXE9X031meJn3lyv/W5///23CKDC3+cN7TMQRFEUTX8/iIiIiEga7HNDREREFoXhhoiIiCwKww0RERFZFIYbIiIisigMN0RERGRRGG6IiIjIojDcEBERkUVhuCEiAMD27dshCEK59WEszbvvvovWrVtLXUaVCIKANWvWAAAuX74MQRBw9OhRSWsiMgcMN0QEAOjSpQtSU1OhVqulLoUqEBgYiNTUVLRs2RKA9YRRopqwkboAImoYFAqF2a1QbE3kcjm/P0RVxDs3RBaqV69emDx5MqZMmQJXV1d4e3tjyZIlyM/Px9ixY+Hs7IzGjRtj48aNAMrfCVi+fDlcXFzw999/IyIiAk5OThgwYABSU1Or9P7bt29Hx44d4ejoCBcXF3Tt2hVXrlwBAFy8eBFDhw6Ft7c3nJyc0KFDB2zZssXo+SEhIfjggw8watQoODk5ITg4GGvXrsX169cxdOhQODk5ITIyEgcPHjQ8p6zmNWvWoEmTJrCzs0NMTAySk5MrrfWbb75BREQE7OzsEB4ejkWLFhmOaTQaTJo0Cb6+vrCzs0NwcDBmz579wOsXRRHvvvsugoKCoFQq4efnh1deecXo+t5//32MHDkSjo6O8Pf3x8KFC+/7enc3S12+fBm9e/cGALi6ukIQBIwZM+aBNRFZC4YbIgv23XffwcPDA/v378fkyZPx8ssv44knnkCXLl1w+PBh9O/fH88++ywKCgoqfH5BQQE+/fRT/PDDD9i5cyeSkpLwxhtvPPB9S0pK8Mgjj6Bnz544fvw44uPjMX78eAiCAADIy8vDoEGDEBcXhyNHjmDAgAEYMmQIkpKSjF7nf//7H7p27YojR45g8ODBePbZZzFq1Cg888wzOHz4MMLCwjBq1CjcvUReQUEBPvzwQ3z//ffYvXs3srKy8OSTT9631p9++gkzZszAhx9+iDNnzmDWrFmYPn06vvvuOwDA/PnzsXbtWvz2229ISEjATz/9hJCQkAd+Bn/88Qf+97//4auvvsL58+exZs0atGrVyuicTz75BFFRUThy5AjeeustvPrqq4iNjX3gawcGBuKPP/4AACQkJCA1NRWff/75A59HZDXqZDlOIpJcz549xW7duhkel5SUiI6OjuKzzz5r2JeamioCEOPj4w0rX9+6dUsURVFctmyZCMBoxe+FCxeK3t7eD3zvmzdvigDE7du3V7neFi1aiF988YXhcXBwsPjMM8+Uq3X69OmGffHx8SIAw+rDZTXv3bvXcM6ZM2dEAOK+fftEURTFmTNnilFRUYbjYWFh4s8//2xUy/vvvy9GR0eLoiiKkydPFvv06SPqdLoqX4soiuLcuXPFpk2bihqNpsLjwcHB4oABA4z2jRgxQhw4cKDhMQBx9erVoiiKYmJioghAPHLkiCiKYrnvFxHdwTs3RBYsMjLS8LVcLoe7u7vR3QNvb28AQEZGRoXPd3BwQFhYmOGxr6/vfc+9m5ubG8aMGYOYmBgMGTIEn3/+uVFzVl5eHt544w1ERETAxcUFTk5OOHPmTLk7N3fXX1brg+q3sbFBhw4dDI/Dw8Ph4uKCM2fOlKszPz8fFy9exPPPPw8nJyfD9sEHH+DixYsAgDFjxuDo0aNo1qwZXnnlFWzevPmB1w8ATzzxBG7fvo1GjRph3LhxWL16NUpKSozOiY6OLve4ojqJqHoYbogsmK2trdFjQRCM9pU1E+l0uio/X7yrCagyy5YtQ3x8PLp06YIVK1agadOm2Lt3LwDgjTfewOrVqzFr1iz8888/OHr0KFq1agWNRnPf9y+rtTr1P0heXh4AYMmSJTh69KhhO3nypKHWtm3bIjExEe+//z5u376N4cOH4/HHH3/gawcGBiIhIQGLFi2Cvb09JkyYgB49eqC4uLhGtRJR1THcEFGdadOmDaZNm4Y9e/agZcuW+PnnnwEAu3fvxpgxYzBs2DC0atUKPj4+uHz5sknes6SkxKiTcUJCArKyshAREVHuXG9vb/j5+eHSpUto3Lix0RYaGmo4T6VSYcSIEViyZAlWrFiBP/74A5mZmQ+sxd7eHkOGDMH8+fOxfft2xMfH48SJE4bjZQHq7scV1VkRhUIBANBqtVU6n8iacCg4EZlcYmIivv76azz88MPw8/NDQkICzp8/j1GjRgEAmjRpglWrVmHIkCEQBAHTp0+v8d2Xe9na2mLy5MmYP38+bGxsMGnSJHTu3BkdO3as8Pz//ve/eOWVV6BWqzFgwAAUFRXh4MGDuHXrFqZOnYp58+bB19cXbdq0gUwmw8qVK+Hj4wMXF5dK61i+fDm0Wi06deoEBwcH/Pjjj7C3t0dwcLDhnN27d2POnDl45JFHEBsbi5UrV2L9+vVVus7g4GAIgoB169Zh0KBBsLe3h5OTU5U/JyJLxjs3RGRyDg4OOHv2LB577DE0bdoU48ePx8SJE/Hiiy8CAObNmwdXV1d06dIFQ4YMQUxMDNq2bWuy937zzTfx1FNPoWvXrnBycsKKFSvue/4LL7yAb775BsuWLUOrVq3Qs2dPLF++3HDnxtnZGXPmzEH79u3RoUMHXL58GRs2bIBMVvmvTxcXFyxZsgRdu3ZFZGQktmzZgr/++gvu7u6Gc15//XUcPHgQbdq0wQcffIB58+YhJiamStfp7++P//73v3jrrbfg7e2NSZMmVel5RNZAEKvagE5E1MAtX74cU6ZMMYtZe0NCQjBlyhRMmTJF6lKILA7v3BAREZFFYbghohq5e+j0vds///wjdXl17qeffrrv9bdo0ULq8oisGpuliKhGLly4cN9j/v7+sLe3r8dq6l9ubi7S09MrPGZra2vUcZiI6hfDDREREVkUNksRERGRRWG4ISIiIovCcENEREQWheGGiIiILArDDREREVkUhhsiIiKyKAw3REREZFEYboiIiMii/D8F6bPkGA6W0gAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_list_train = []\n",
        "accuracy_list_test = []\n",
        "for max_depth in max_depth_list:\n",
        "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
        "    model = RandomForestClassifier(max_depth = max_depth,\n",
        "                                   random_state = RANDOM_STATE).fit(X_train,y_train)\n",
        "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
        "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
        "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
        "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
        "    accuracy_list_train.append(accuracy_train)\n",
        "    accuracy_list_test.append(accuracy_test)\n",
        "\n",
        "plt.title('Train x Test metrics')\n",
        "plt.xlabel('max_depth')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xticks(ticks = range(len(max_depth_list )),labels=max_depth_list)\n",
        "plt.plot(accuracy_list_train)\n",
        "plt.plot(accuracy_list_test)\n",
        "plt.legend(['Train','Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "hkeIGJAiCHtQ",
        "outputId": "1b097a36-5d95-42bf-8870-d7596c2f6f18"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7df0ca53fb50>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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5OtoX6aeoxo4dy5tvvknt2rWpVKkSJ0+epFu3brz66qs4Ozvz/vvv0717dw4cOECNGjXy3M/LL7/M66+/zhtvvMGMGTPo27cvJ06coHLlykWWVeR6JVxKo9+CKE4nplLZzQlfd2ezI4mUa9UqmjvqX6oK0M0ybtw4xowZY3uelJREYGBggbe/nJFFg4nr81+xiO2dHEoFp6L7I5w8eTJ33nmn7XnlypVp2rSp7fkrr7zC6tWr+eyzzxgxYkSe+xkwYACPPPIIAFOmTOGdd94hKiqKu+++u8iyilyPpNQMwhZGcSwhmYCKrnw8rC3VvFzNjiUiJipVBahq1arExcXlWBYXF4enpyeurq7Y29tjb2+f6zp/PwX0T87Ozjg761+DrVq1yvH80qVLTJo0ibVr13L69GkyMzO5fPky0dHR19xPkyZNbF+7ubnh6elZ7i9CF/OkZmTx+JLt7DmVhLebE0sHtVH5EZHSVYDatm3Ll19+mWPZhg0baNu2LQBOTk60bNmSyMhI28XUVquVyMjIa45Y3ChXR3v2Tg69afu/1usWJTc3txzP/+///o8NGzbw5ptvUqdOHVxdXXnwwQdJT0+/5n4cHR1zPLdYLFit1iLNKlIQmVlWRizfRdSxc7g7O7BkYBtq+7qbHUtESgBTC9ClS5c4fPiw7fmxY8fYvXs3lStXpkaNGowbN46YmBjef/99AIYOHcrMmTN57rnnGDhwIN9++y0fffQRa9eute1jzJgxhIWF0apVK9q0acP06dNJTk4mPDz8ph2HxWIp0lNRJcWPP/7IgAED6NmzJ5D953X8+HFzQ4kUkNVq8Pwnv/HNvjicHOx4L6wVjQK8zI4lIiWEqb+1t2/fzm233WZ7fuU6nLCwMBYvXszp06dznG6pVasWa9eu5emnn+btt9+mevXqvPfee4SG/jX60qdPH86cOcPEiROJjY2lWbNmrFu37qoLoyV/devWZdWqVXTv3h2LxcKECRM0kiOlgmEYvPrlPj7Z+Qf2dhZmPdqCf9XW5MUi8hdTC1CXLl241m2IcrvLc5cuXdi1a9c19ztixIibesqrvJg2bRoDBw6kXbt2+Pj48Pzzz5fJWwRI2TPru8Ms2HwMgNd7NeHOBvoHkIjkVGJuhFiSlMUbIZZUej+lqC37+QTj12TfLHXCfQ0Y1KGWyYlEpLhcz40QNRmqiJQZn/9yigmfZpefEbfVUfkRkTypAIlImbDp4BnGfLQbw4C+ITV45q5bzI4kIiWYCpCIlHo7Tpxn6NIdZGQZ3NekGpN7NCrSu6SLSNmjAiQipdqB2IsMXLyNyxlZdKzrw7TezbC3U/kRkWtTARKRUuvkuRT6LdhK4uUMmteoyNx+LXFy0I81EcmfflKISKkUfzGVxxZsJf5iGvX8PFg0oHWZvCGpiNwcKkAiUuokXs4gbOE2TpxNoXolV94f1IaKFZzMjiUipYgKkIiUKpfTs3h8yTb2nU7Cx92ZZYNC8PPUPaRE5PqoAIlIqZGRZWX48p1sO34eDxcH3h/YhiAft/w3FBH5BxUgESkVrFaDZ//3C9/uj8fZwY4FYa1p4H/tO72KiORFBaicsFgs13xMmjTphva9Zs2aIssq8k+GYTD5i72s2X0KezsLsx9rQZtalc2OJSKlmD4yUU6cPn3a9vXKlSuZOHEiBw4csC1zd3c3I5ZIgbwTeZjFPx0H4K2HmnJ7fU1uKiI3RiNA5UTVqlVtDy8vLywWS45lK1as4NZbb8XFxYX69evz7rvv2rZNT09nxIgRVKtWDRcXF2rWrElERAQAQUFBAPTs2ROLxWJ7LlJUlvx0nP9+cxCASd0bcH/zAJMTiUhZoBGgomAYkJFS/K/rWAGK4Hb/H3zwARMnTmTmzJk0b96cXbt2MXjwYNzc3AgLC+Odd97hs88+46OPPqJGjRqcPHmSkydPArBt2zaqVKnCokWLuPvuu7G3t7/hPCJXfLo7hpc+2wPAU3fUZUB7TW4qIkVDBagoZKTAFP/if90XToHTjX8C5qWXXuKtt97igQceAKBWrVrs3buXuXPnEhYWRnR0NHXr1qVDhw5YLBZq1qxp29bX1xeAihUrUrVq1RvOInLFd/vjeeajXwAIa1uT0V3rmpxIRMoSFaByLjk5mSNHjjBo0CAGDx5sW56ZmYmXlxcAAwYM4M4776RevXrcfffd3Hfffdx1111mRZZyYPvxcwz7YAeZVoN/N/Xnpe4NNbmpiBQpFaCi4FghezTGjNe9QZcuXQJg/vz5hISE5PjeldNZLVq04NixY3z11Vd888039O7dm65du/Lxxx/f8OuL/NO+00kMXLyN1AwrXer58lbvpthpclMRKWIqQEXBYimSU1Fm8PPzw9/fn6NHj9K3b9881/P09KRPnz706dOHBx98kLvvvptz585RuXJlHB0dycrKKsbUUladOJtM/4VRJKVm0qpmJWb3bYmjvT6rISJFTwVIePnllxk1ahReXl7cfffdpKWlsX37ds6fP8+YMWOYNm0a1apVo3nz5tjZ2fG///2PqlWrUrFiRSD7k2CRkZG0b98eZ2dnKlWqZO4BSakUn5RKvwVRnLmYRv2qHiwIa42rky6qF5GbQ/+0Eh5//HHee+89Fi1aROPGjencuTOLFy+mVq3sT9x4eHjw+uuv06pVK1q3bs3x48f58ssvsbPL/uvz1ltvsWHDBgIDA2nevLmZhyKlVGJKBv0WRBF9LoUalSvw/sA2eFVwNDuWiJRhFsMwDLNDlDRJSUl4eXmRmJiIp2fOW+2npqZy7NgxatWqhYuLJmC8UXo/JSU9k34Lothx4jy+Hs58MrQdNbxv/Po2ESl/rvX7+580AiQipknPtDJs2U52nDiPp4sDSwe1UfkRkWKhAiQiprBaDZ753y9sOngGF0c7FoW3pn5VTW4qIsVDBUhEip1hGLz02R4+/+UUDnYW5jzWkpY1NbmpiBQfFSARKXb//eYQS38+gcUCb/VuSpd6VcyOJCLljApQIena8aKh97H8WfTjMd6JPATA5H83pEczTW4qIsVPBeg6OTpmfzQ3JcWEyU/LoCvv45X3Vcq2VTv/4OXP9wIw5s5b6Nc2yNxAIlJu6UaI18ne3p6KFSsSHx8PQIUKFTRHUSEYhkFKSgrx8fFUrFhRs8iXA9/sjePZj38FILx9ECNvr2NyIhEpz1SACuHKrOdXSpAUnmaRLx+2Hj3L8OU7ybIa9GwewIR7G+gfDiJiKhWgQrBYLFSrVo0qVaqQkZFhdpxSy9HRUSM/5cCeU4k8vmQ7aZlW7qhfhdcfbKLJTUXEdKYXoFmzZvHGG28QGxtL06ZNmTFjBm3atMl13YyMDCIiIliyZAkxMTHUq1ePqVOncvfdd9vWycrKYtKkSSxbtozY2Fj8/f0ZMGAA48ePL/J/cdrb2+sXuMg1HEtIJmxhFBfTMmkTVJlZfVtoclMRKRFM/Um0cuVKxowZw0svvcTOnTtp2rQpoaGheZ5aGj9+PHPnzmXGjBns3buXoUOH0rNnT3bt2mVbZ+rUqcyePZuZM2eyb98+pk6dyuuvv86MGTOK67BEBIhNTOWx97aScCmdW6t58t6AVrg46h8MIlIymDoXWEhICK1bt2bmzJkAWK1WAgMDGTlyJGPHjr1qfX9/f1588UWGDx9uW9arVy9cXV1ZtmwZAPfddx9+fn4sWLAgz3Xycz1ziYjI1S6kpNN77hYOxl0iyLsC/xvaDl8PZ7NjiUgZVyrmAktPT2fHjh107dr1rzB2dnTt2pUtW7bkuk1aWtpVE2a6urqyefNm2/N27doRGRnJwYMHAfjll1/YvHkz99xzT55Z0tLSSEpKyvEQkcJJTstkwKJtHIy7hJ+nM0sHhaj8iEiJY9o1QAkJCWRlZeHn55djuZ+fH/v37891m9DQUKZNm0anTp0IDg4mMjKSVatWkZWVZVtn7NixJCUlUb9+fezt7cnKyuLVV1+lb9++eWaJiIjg5ZdfLpoDEynH0jKzGLpsB7tPXsDL1ZGlg0IIrKzJTUWk5ClVVyO+/fbb1K1bl/r16+Pk5MSIESMIDw/Hzu6vw/joo4/44IMPWL58OTt37mTJkiW8+eabLFmyJM/9jhs3jsTERNvj5MmTxXE4ImVKltVgzMpf+OFQAhWc7FkU3ppb/DzMjiUikivTRoB8fHywt7cnLi4ux/K4uLg87wvj6+vLmjVrSE1N5ezZs/j7+zN27Fhq165tW+fZZ59l7NixPPzwwwA0btyYEydOEBERQVhYWK77dXZ2xtlZQ/QihWUYBhM+/Z21v53G0T57ctMWNSqZHUtEJE+mjQA5OTnRsmVLIiMjbcusViuRkZG0bdv2mtu6uLgQEBBAZmYmn3zyCT169LB9LyUlJceIEGR/XN1qtRbtAYiIzZtfH2D51mgsFvhvn2Z0usXX7EgiItdk6n2AxowZQ1hYGK1ataJNmzZMnz6d5ORkwsPDAejfvz8BAQFEREQAsHXrVmJiYmjWrBkxMTFMmjQJq9XKc889Z9tn9+7defXVV6lRowYNGzZk165dTJs2jYEDB5pyjCJl3Xs/HGXWd0cA+M/9jbivib/JiURE8mdqAerTpw9nzpxh4sSJxMbG0qxZM9atW2e7MDo6OjrHaE5qairjx4/n6NGjuLu7061bN5YuXUrFihVt68yYMYMJEybw5JNPEh8fj7+/P0888QQTJ04s7sMTKfM+3vEH/1m7D4BnQ+vRN6SmyYlERArG1PsAlVS6D5BI/r7eE8uwD7Ln93q8Qy1evPdWze8lIqYqFfcBEpHSa8uRs4z4cBdZVoMHW1ZX+RGRUkcFSESuy29/JDL4/e2kZ1q5s4Efrz3QWOVHREodFSARKbAjZy4RtiiKS2mZhNSqzIxHmuOgyU1FpBTSTy4RKZBTFy7Tf0EU55LTaRTgyXthmtxUREovFSARyde55HT6LdhKzIXL1PZxY3F4GzxcHM2OJSJSaCpAInJNl9IyCV8UxZEzyVTzcmHp4yH4uOvO6SJSuqkAiUie0jKzeGLpdn75I5FKFRxZOqgNARVdzY4lInLDVIBEJFdZVoOnPtzNj4fPUsHJnsXhbahTRZObikjZoAIkIlcxDIMXVv3Guj2xONnbMb9/K5oGVjQ7lohIkVEBEpGrTF13gJXbT2JngXceaUb7Oj5mRxIRKVIqQCKSw9xNR5izKXty0yk9G3N3o2omJxIRKXoqQCJis3JbNBFf7Qdg7D31ebhNDZMTiYjcHCpAIgLAut9PM27VbwA80bk2QzsHm5xIROTmUQESEX48nMCoD3djNaBPq0DG3l3f7EgiIjeVCpBIOffLyQsMeX876VlW7m5YlVd7NtLkpiJS5qkAiZRjh+MvMmBRFMnpWbQL9mb6w800uamIlAv6SSdSTsVcuEy/BVGcT8mgSXUv5vXX5KYiUn6oAImUQ2cvpdFvwVZOJ6YS7Js9uam7s4PZsUREio0KkEg5czE1gwGLtnH0TDL+Xi4sHRRCZTcns2OJiBQrFSCRciQ1I4sh7+/gt5hEKrs5sfTxEPw1uamIlEMqQCLlRGaWlZEf7mLL0bO4OzuwJLwNwb7uZscSETGFCpBIOWAYBmNX/caGvXE4OWRPbtq4upfZsURETKMCJFLGGYbBlC/38fGOP7CzwIxHmtM22NvsWCIiplIBEinjZm86wvwfjgHwWq8mhDasanIiERHzqQCJlGHLt0bz+roDALzY7VZ6two0OZGISMmgAiRSRn3522leXJM9uemTXYIZ3Km2yYlEREoOFSCRMuiHQ2d4asUuDAMeaVODZ0PrmR1JRKREUQESKWN2RZ/niaU7yMgyuLdxNf5zvyY3FRH5JxUgkTLkYNxFwhdvIyU9i451fZjWpyn2dio/IiL/pAIkUkacPJdCvwVbuZCSQbPAisx5rCXODprcVEQkNypAImXAmYvZk5vGJaVRt4o7iwa0xk2Tm4qI5EkFSKSUy7IaDFqyjeNnUwio6MrSQSFU0uSmIiLXZHoBmjVrFkFBQbi4uBASEkJUVFSe62ZkZDB58mSCg4NxcXGhadOmrFu37qr1YmJieOyxx/D29sbV1ZXGjRuzffv2m3kYIqb56vfT/PpHIp4uDix7PISqXi5mRxIRKfFMLUArV65kzJgxvPTSS+zcuZOmTZsSGhpKfHx8ruuPHz+euXPnMmPGDPbu3cvQoUPp2bMnu3btsq1z/vx52rdvj6OjI1999RV79+7lrbfeolKlSsV1WCLFxjAM3v3uCAADO9Silo+byYlEREoHi2EYhlkvHhISQuvWrZk5cyYAVquVwMBARo4cydixY69a39/fnxdffJHhw4fblvXq1QtXV1eWLVsGwNixY/nxxx/54YcfCp0rKSkJLy8vEhMT8fT0LPR+RG62TQfPELYwigpO9vz4/O069SUi5dr1/P42bQQoPT2dHTt20LVr17/C2NnRtWtXtmzZkus2aWlpuLjkHN53dXVl8+bNtuefffYZrVq14qGHHqJKlSo0b96c+fPnXzNLWloaSUlJOR4ipcG73x0Gsm92qPIjIlJwphWghIQEsrKy8PPzy7Hcz8+P2NjYXLcJDQ1l2rRpHDp0CKvVyoYNG1i1ahWnT5+2rXP06FFmz55N3bp1Wb9+PcOGDWPUqFEsWbIkzywRERF4eXnZHoGBmi9JSr4dJ86z9dg5HO0tPN6xltlxRERKFdMvgr4eb7/9NnXr1qV+/fo4OTkxYsQIwsPDsbP76zCsVistWrRgypQpNG/enCFDhjB48GDmzJmT537HjRtHYmKi7XHy5MniOByRGzJ7Y/a1Pz2bB1DNy9XkNCIipYtpBcjHxwd7e3vi4uJyLI+Li6Nq1aq5buPr68uaNWtITk7mxIkT7N+/H3d3d2rX/muSx2rVqtGgQYMc2916661ER0fnmcXZ2RlPT88cD5GS7GDcRb7ZF4fFAk90DjY7johIqWNaAXJycqJly5ZERkballmtViIjI2nbtu01t3VxcSEgIIDMzEw++eQTevToYfte+/btOXDgQI71Dx48SM2aNYv2AERMNGdT9ujP3Q2rEuzrbnIaEZHSx9RbxY4ZM4awsDBatWpFmzZtmD59OsnJyYSHhwPQv39/AgICiIiIAGDr1q3ExMTQrFkzYmJimDRpElarleeee862z6effpp27doxZcoUevfuTVRUFPPmzWPevHmmHKNIUfvjfAqf7T4FwLAuGv0RESkMUwtQnz59OHPmDBMnTiQ2NpZmzZqxbt0624XR0dHROa7vSU1NZfz48Rw9ehR3d3e6devG0qVLqVixom2d1q1bs3r1asaNG8fkyZOpVasW06dPp2/fvsV9eCI3xXs/HCPTatChjg9Nqlc0O46ISKlk6n2ASirdB0hKqrOX0mg/9VtSM6x88HgI7ev4mB1JRKTEKBX3ARKR67f4p+OkZlhpWt2LdsHeZscRESm1VIBESomLqRks+ek4kH3tj8ViMTeQiEgppgIkUkp8GBVNUmomtX3duKtB7reKEBGRglEBEikF0jKzeO+HYwAM7RyMnZ1Gf0REboQKkEgpsGpnDPEX06jm5cL9zQLMjiMiUuqpAImUcFlWg7l/3vjw8Y61cXLQ/7YiIjdKP0lFSrivfj/N8bMpVKzgyMOtNVGviEhRUAESKcEMw7BNejqgXRBuzqbeu1REpMxQARIpwb4/lMCeU0m4OtoT1jbI7DgiImWGCpBICTZ742EAHmlTg0puTianEREpO1SAREqondHn+fnoORztLQzuVMvsOCIiZYoKkEgJdeXan/ubBVDNy9XkNCIiZYsKkEgJdCjuIhv2xmGxwBOdg82OIyJS5qgAiZRAs/+8709og6rUqeJuchoRkbJHBUikhPnjfAqf7T4FZE96KiIiRU8FSKSEee+HY2RaDdrX8aZpYEWz44iIlEkqQCIlyNlLaazYFg3AsM51TE4jIlJ2qQCJlCCLfzpOaoaVJtW9aF/H2+w4IiJllgqQSAlxKS2TJT8dB2BY52AsFou5gUREyjAVIJES4sOt0SSlZlLb143QhlXNjiMiUqapAImUAGmZWby3+SgAQzsFY2en0R8RkZtJBUikBFi9M4a4pDSqerpwf/MAs+OIiJR5KkAiJsuyGsz9Pnv05/GOtXBy0P+WIiI3m37Siphs3e+xHEtIpmIFRx5pU8PsOCIi5YIKkIiJDMPg3Y2HAQhrG4Sbs4PJiUREygcVIBET/XAogT2nknB1tGdAuyCz44iIlBsqQCImmr0xe9LTR9rUoJKbk8lpRETKDxUgEZPsij7PlqNncbS38HjHWmbHEREpV1SARExyZfTn/mYB+Fd0NTmNiEj5ogIkYoJDcRf5em8cFgs80TnY7DgiIuWOCpCICeZsyr7vT2iDqtSp4m5yGhGR8kcFSKSYxVy4zKe7YwAY2kWjPyIiZigRBWjWrFkEBQXh4uJCSEgIUVFRea6bkZHB5MmTCQ4OxsXFhaZNm7Ju3bo813/ttdewWCyMHj36JiQXuX7zvz9KptWgXbA3zQIrmh1HRKRcMr0ArVy5kjFjxvDSSy+xc+dOmjZtSmhoKPHx8bmuP378eObOncuMGTPYu3cvQ4cOpWfPnuzateuqdbdt28bcuXNp0qTJzT4MkQI5l5zOim3RADzZpY7JaUREyi/TC9C0adMYPHgw4eHhNGjQgDlz5lChQgUWLlyY6/pLly7lhRdeoFu3btSuXZthw4bRrVs33nrrrRzrXbp0ib59+zJ//nwqVapUHIcikq/FPx4jNcNK4wAv2tfxNjuOiEi5ZWoBSk9PZ8eOHXTt2tW2zM7Ojq5du7Jly5Zct0lLS8PFxSXHMldXVzZv3pxj2fDhw7n33ntz7DsvaWlpJCUl5XiIFLVLaZks2XICgCe7BGOxWExOJCJSfplagBISEsjKysLPzy/Hcj8/P2JjY3PdJjQ0lGnTpnHo0CGsVisbNmxg1apVnD592rbOihUr2LlzJxEREQXKERERgZeXl+0RGBhY+IMSycOHW6NJvJxBbR837mpY1ew4IiLlmumnwK7X22+/Td26dalfvz5OTk6MGDGC8PBw7OyyD+XkyZM89dRTfPDBB1eNFOVl3LhxJCYm2h4nT568mYcg5VBaZhbvbc7+6PvQzsHY22n0R0TETKYWIB8fH+zt7YmLi8uxPC4ujqpVc/8Xsq+vL2vWrCE5OZkTJ06wf/9+3N3dqV27NgA7duwgPj6eFi1a4ODggIODA5s2beKdd97BwcGBrKysq/bp7OyMp6dnjodIUVq9M4a4pDSqerrQo7m/2XFERMo9UwuQk5MTLVu2JDIy0rbMarUSGRlJ27Ztr7mti4sLAQEBZGZm8sknn9CjRw8A7rjjDn777Td2795te7Rq1Yq+ffuye/du7O3tb+oxifxTltVg7vfZoz+Pd6yFs4P+DoqImM3B7ABjxowhLCyMVq1a0aZNG6ZPn05ycjLh4eEA9O/fn4CAANv1PFu3biUmJoZmzZoRExPDpEmTsFqtPPfccwB4eHjQqFGjHK/h5uaGt7f3VctFisO632M5lpCMl6sjj7SpYXYcERGhBBSgPn36cObMGSZOnEhsbCzNmjVj3bp1tgujo6Ojbdf3AKSmpjJ+/HiOHj2Ku7s73bp1Y+nSpVSsWNGkIxDJm2EYzN50GICwdkG4OZv+v5yIiAAWwzAMs0OUNElJSXh5eZGYmKjrgeSG/HDoDP0WROHqaM+PY2+nspuT2ZFERMqs6/n9Xeo+BSZSmrz73REAHm4TqPIjIlKCqACJ3CS7os+z5ehZHOwsDO5Y2+w4IiLyNypAIjfJ7I3Zoz/3Nw/Av6KryWlEROTvVIBEboLD8Rf5em8cFgsM7azRHxGRkqZQBei7774r6hwiZcrsjdn3/bmrgR91qniYnEZERP6pUAXo7rvvJjg4mP/85z+aNkLkH2IuXObT3TEADOtSx+Q0IiKSm0IVoJiYGEaMGMHHH39M7dq1CQ0N5aOPPiI9Pb2o84mUOu/9cJRMq0G7YG+aBVY0O46IiOSiUAXIx8eHp59+mt27d7N161ZuueUWnnzySfz9/Rk1ahS//PJLUecUKRXOJaezIip7VHRYl2CT04iISF5u+CLoFi1aMG7cOEaMGMGlS5dYuHAhLVu2pGPHjuzZs6coMoqUGot/Os7ljCwaB3jRoY6P2XFERCQPhS5AGRkZfPzxx3Tr1o2aNWuyfv16Zs6cSVxcHIcPH6ZmzZo89NBDRZlVpES7lJbJkp+OA9mjPxaLxdxAIiKSp0JNTDRy5Eg+/PBDDMOgX79+vP766zkmGnVzc+PNN9/E39+/yIKKlHQroqJJvJxBbR83QhtWNTuOiIhcQ6EK0N69e5kxYwYPPPAAzs7Oua7j4+Ojj8tLuZGWmcX8H7I/+v5E59rY22n0R0SkJCtUAYqMjMx/xw4OdO7cuTC7Fyl11uyKIS4pDT9PZ+5vHmB2HBERyUehrgGKiIhg4cKFVy1fuHAhU6dOveFQIqVJltVg7qbs0Z/BHWvj7GBvciIREclPoQrQ3LlzqV+//lXLGzZsyJw5c244lEhpsn5PLEcTkvFydeThNjXMjiMiIgVQqAIUGxtLtWrVrlru6+vL6dOnbziUSGlhGIZt0tOwdkG4OxfqrLKIiBSzQhWgwMBAfvzxx6uW//jjj/rkl5Qrmw8n8FtMIq6O9gxoF2R2HBERKaBC/XN18ODBjB49moyMDG6//XYg+8Lo5557jmeeeaZIA4qUZFdGfx5uE0hlNyeT04iISEEVqgA9++yznD17lieffNI2/5eLiwvPP/8848aNK9KAIiXV7pMX+OnIWRzsLDzesbbZcURE5DoUqgBZLBamTp3KhAkT2LdvH66urtStWzfPewKJlEWzNx4GoEezAAIqupqcRkRErscNXbHp7u5O69atiyqLSKlxOP4i6/fEATCsi0Z/RERKm0IXoO3bt/PRRx8RHR1tOw12xapVq244mEhJNufP+/7c1cCPOlU8TE4jIiLXq1CfAluxYgXt2rVj3759rF69moyMDPbs2cO3336Ll5dXUWcUKVFOXbjMml0xQPakpyIiUvoUqgBNmTKF//73v3z++ec4OTnx9ttvs3//fnr37k2NGroRnJRt8384SqbVoG1tb5rXqGR2HBERKYRCFaAjR45w7733AuDk5ERycjIWi4Wnn36aefPmFWlAkZLkXHI6K6JOAvDkbRr9EREprQpVgCpVqsTFixcBCAgI4PfffwfgwoULpKSkFF06kRJm8U/HuZyRRaMATzrU8TE7joiIFFKhLoLu1KkTGzZsoHHjxjz00EM89dRTfPvtt2zYsIE77rijqDOKlAjJaZks+ek4AMM618FisZgbSERECq1QBWjmzJmkpqYC8OKLL+Lo6MhPP/1Er169GD9+fJEGFCkpPoyKJvFyBrV83Li7UVWz44iIyA247gKUmZnJF198QWhoKAB2dnaMHTu2yIOJlCRpmVnM/yH7o+9PdKqNvZ1Gf0RESrPrvgbIwcGBoUOH2kaARMqDT3edIi4pDT9PZ3q2CDA7joiI3KBCXQTdpk0bdu/eXcRRREqmLKvBnE3Zk54+3qE2zg72JicSEZEbVagC9OSTTzJmzBhmzpzJli1b+PXXX3M8rtesWbMICgrCxcWFkJAQoqKi8lw3IyODyZMnExwcjIuLC02bNmXdunU51omIiKB169Z4eHhQpUoV7r//fg4cOHDduUQAvt4Ty9GEZLxcHXkkRPe5EhEpCwp1EfTDDz8MwKhRo2zLLBYLhmFgsVjIysoq8L5WrlzJmDFjmDNnDiEhIUyfPp3Q0FAOHDhAlSpVrlp//PjxLFu2jPnz51O/fn3Wr19Pz549+emnn2jevDkAmzZtYvjw4bRu3ZrMzExeeOEF7rrrLvbu3Yubm1thDlnKKcMweHdj9uhPWNuauDvf0PR5IiJSQlgMwzCud6MTJ05c8/s1a9Ys8L5CQkJo3bo1M2fOBMBqtRIYGMjIkSNzvbja39+fF198keHDh9uW9erVC1dXV5YtW5bra5w5c4YqVaqwadMmOnXqlG+mpKQkvLy8SExMxNPTs8DHImXP5kMJPLZgKy6Odvw09g4quzmZHUlERPJwPb+/C/XP2espONeSnp7Ojh07GDdunG2ZnZ0dXbt2ZcuWLbluk5aWhouLS45lrq6ubN68Oc/XSUxMBKBy5cp57jMtLc32PCkpqcDHIGXbuxsPA/Bw6xoqPyIiZUihCtD7779/ze/379+/QPtJSEggKysLPz+/HMv9/PzYv39/rtuEhoYybdo0OnXqRHBwMJGRkaxatSrP025Wq5XRo0fTvn17GjVqlOs6ERERvPzyywXKLOXH7pMX+OnIWRzsLAzuVNvsOCIiUoQKVYCeeuqpHM8zMjJISUnBycmJChUqFLgAFcbbb7/N4MGDqV+/PhaLheDgYMLDw1m4cGGu6w8fPpzff//9miNE48aNY8yYMbbnSUlJBAYGFnl2KV3m/HntT49mAQRUdDU5jYiIFKVCfQrs/PnzOR6XLl3iwIEDdOjQgQ8//LDA+/Hx8cHe3p64uLgcy+Pi4qhaNfc77fr6+rJmzRqSk5M5ceIE+/fvx93dndq1r/4X+ogRI/jiiy/47rvvqF69ep45nJ2d8fT0zPGQ8u1w/CXW740FYGhnjf6IiJQ1hSpAualbty6vvfbaVaND1+Lk5ETLli2JjIy0LbNarURGRtK2bdtrbuvi4kJAQACZmZl88skn9OjRw/Y9wzAYMWIEq1ev5ttvv6VWrVrXf0BSrs3ddATDgLsa+FHXz8PsOCIiUsSK9DO9Dg4OnDp16rq2GTNmDGFhYbRq1Yo2bdowffp0kpOTCQ8PB7KvJwoICCAiIgKArVu3EhMTQ7NmzYiJiWHSpElYrVaee+452z6HDx/O8uXL+fTTT/Hw8CA2Nvtf8l5eXri66lSGXNupC5dZvSsGgGFdgk1OIyIiN0OhCtBnn32W47lhGJw+fZqZM2fSvn3769pXnz59OHPmDBMnTiQ2NpZmzZqxbt0624XR0dHR2Nn9NVCVmprK+PHjOXr0KO7u7nTr1o2lS5dSsWJF2zqzZ88GoEuXLjlea9GiRQwYMOC68kn5894Px8i0GrSt7U3zGpXMjiMiIjdBoe4D9PdCAtk3QfT19eX222/nrbfeolq1akUW0Ay6D1D5dS45nfavfcvljCzeH9iGTrf4mh1JREQK6KbfB8hqtRYqmEhJt+Sn41zOyKKhvycd6/qYHUdERG6SIrsIWqS0S07LZPFPxwF4sksdLBaLuYFEROSmKVQB6tWrF1OnTr1q+euvv85DDz10w6FEzPBhVDSJlzOo5ePG3Y1yvw2DiIiUDYUqQN9//z3dunW7avk999zD999/f8OhRIpbeqaV9344BsATnWpjb6fRHxGRsqxQBejSpUs4OV09L5Kjo6Pm0ZJSac2uGGKTUvHzdKZniwCz44iIyE1WqALUuHFjVq5cedXyFStW0KBBgxsOJVKcsqwGc77Pnvbi8Q61cXawNzmRiIjcbIX6FNiECRN44IEHOHLkCLfffjsAkZGRfPjhh/zvf/8r0oAiN9vXe2I5eiYZTxcHHgmpYXYcEREpBoUqQN27d2fNmjVMmTKFjz/+GFdXV5o0acI333xD586dizqjyE1jGAazN2WP/oS1C8LduUhvji4iIiVUoX/a33vvvdx7771FmUWk2P14+Cy//pGIi6MdA9oFmR1HRESKSaGuAdq2bRtbt269avnWrVvZvn37DYcSKS6zNx0G4OHWNfB2dzY5jYiIFJdCFaDhw4dz8uTJq5bHxMQwfPjwGw4lUhx+OXmBHw+fxcHOwuMda5kdR0REilGhCtDevXtp0aLFVcubN2/O3r17bziUSHGYvTH72p9/N/OneqUKJqcREZHiVKgC5OzsTFxc3FXLT58+jYODLiKVku9w/CXW740FYFjnYJPTiIhIcStUAbrrrrsYN24ciYmJtmUXLlzghRde4M477yyycCI3y9xNRzAMuLOBH3X9PMyOIyIixaxQwzVvvvkmnTp1ombNmjRv3hyA3bt34+fnx9KlS4s0oEhRO3XhMmt2xwAwrItGf0REyqNCFaCAgAB+/fVXPvjgA3755RdcXV0JDw/nkUcewdHRsagzihSp9344RkaWwb9qV6ZFjUpmxxERERMU+oIdNzc3OnToQI0aNUhPTwfgq6++AuDf//530aQTKWLnk9P5MCoagGFd6picRkREzFKoAnT06FF69uzJb7/9hsViwTAMLJa/Zs/OysoqsoAiRWnJluNczsiiob8nner6mB1HRERMUqiLoJ966ilq1apFfHw8FSpU4Pfff2fTpk20atWKjRs3FnFEkaKRnJbJ4p+OA9nX/vy9tIuISPlSqBGgLVu28O233+Lj44OdnR329vZ06NCBiIgIRo0axa5du4o6p8gNW7HtJBdSMgjyrsA9jaqZHUdERExUqBGgrKwsPDyyPzrs4+PDqVOnAKhZsyYHDhwounQiRSQ908p7PxwF4InOwdjbafRHRKQ8K9QIUKNGjfjll1+oVasWISEhvP766zg5OTFv3jxq165d1BlFbtia3TGcTkylioczD7QIMDuOiIiYrFAFaPz48SQnJwMwefJk7rvvPjp27Ii3tzcrV64s0oAiNyrLajBnU/a0F493rIWzg73JiURExGyFKkChoaG2r+vUqcP+/fs5d+4clSpV0oWlUuJs2BvL0TPJeLo48GhITbPjiIhICVBkE3dVrly5qHYlUmQMw7BNehrWLgh3Z81VJyIihbwIWqS0+OnIWX75IxEXRzsGtAsyO46IiJQQKkBSpl0Z/Xm4dQ283Z1NTiMiIiWFCpCUWb/+cYHNhxNwsLPweMdaZscREZESRAVIyqwroz//buZP9UoVTE4jIiIliQqQlEmH4y+xbk8sAEM7B5ucRkREShoVICmT5n1/BMOArrf6cYufh9lxRESkhFEBkjLndOJlVu+KAeDJ2zT6IyIiVysRBWjWrFkEBQXh4uJCSEgIUVFRea6bkZHB5MmTCQ4OxsXFhaZNm7Ju3bob2qeULe/9cIyMLIOQWpVpUaOS2XFERKQEMr0ArVy5kjFjxvDSSy+xc+dOmjZtSmhoKPHx8bmuP378eObOncuMGTPYu3cvQ4cOpWfPnjlmoL/efUrZcT45nQ+jogF48rY6JqcREZGSymIYhmFmgJCQEFq3bs3MmTMBsFqtBAYGMnLkSMaOHXvV+v7+/rz44osMHz7ctqxXr164urqybNmyQu3zn5KSkvDy8iIxMRFPT8+iOEwpJtO/Ocj0bw7R0N+TL0Z20NQsIiLlyPX8/jZ1BCg9PZ0dO3bQtWtX2zI7Ozu6du3Kli1bct0mLS0NFxeXHMtcXV3ZvHnzDe0zKSkpx0NKn5T0TBb/dByAYV2CVX5ERCRPphaghIQEsrKy8PPzy7Hcz8+P2NjYXLcJDQ1l2rRpHDp0CKvVyoYNG1i1ahWnT58u9D4jIiLw8vKyPQIDA4vg6KS4fRh1kgspGdT0rsA9jaqZHUdEREow068Bul5vv/02devWpX79+jg5OTFixAjCw8Oxsyv8oYwbN47ExETb4+TJk0WYWIpDeqaV9344CsATnYKxt9Poj4iI5M3UAuTj44O9vT1xcXE5lsfFxVG1atVct/H19WXNmjUkJydz4sQJ9u/fj7u7O7Vr1y70Pp2dnfH09MzxkNJlze4YTiemUsXDmV4tA8yOIyIiJZypBcjJyYmWLVsSGRlpW2a1WomMjKRt27bX3NbFxYWAgAAyMzP55JNP6NGjxw3vU0onq9VgzqbsaS8GdaiFs4O9yYlERKSkczA7wJgxYwgLC6NVq1a0adOG6dOnk5ycTHh4OAD9+/cnICCAiIgIALZu3UpMTAzNmjUjJiaGSZMmYbVaee655wq8Tylbvt4bx9EzyXi6OPBoSA2z44iISClgegHq06cPZ86cYeLEicTGxtKsWTPWrVtnu4g5Ojo6x/U9qampjB8/nqNHj+Lu7k63bt1YunQpFStWLPA+pewwDIPZGw8D0L9tEB4ujiYnEhGR0sD0+wCVRLoPUOnx0+EEHn1vKy6Odvz4/O14uzubHUlERExSau4DJHKj3t2Yfe1Pn1aBKj8iIlJgKkBSav36xwU2H07A3s7C4E61zY4jIiKliAqQlFqz/xz96dHUn+qVKpicRkREShMVICmVjpy5xLo92Xf2Htol2OQ0IiJS2qgASak0b9NRDAO63urHLX4eZscREZFSRgVISp3TiZdZtesPIHvSUxERkeulAiSlzoIfjpGRZRBSqzIta1YyO46IiJRCKkBSqpxPTmd5VDSg0R8RESk8FSApVd7fcoKU9CwaVPOk8y2+ZscREZFSSgVISo2U9EwW/3QMyB79sVgsJicSEZHSyvS5wEQKakXUSc6nZFDTuwL3NKpqdhwpTbIy4cIJSDgEZw9B8hmzE4lI1SbQ+EHTXl4FSEqF9Ewr8384CsATnYJxsNfgpeTi8nlIOJxdchIOZheehENw7ihYM8xOJyJ/1+hBFSCR/Hy6O4bTian4ejjzQIsAs+OImaxZf43mJPxZdM4ezv7vtUZ2HFzAuy741AEPf9ApVBFzVWtq6surAEmJZ7UazNmUPe3F4x1q4eJob3IiKRaXL/xZbK6UnEPZozvnjkBWet7befhnlxzvuuBzS/bXPreAZ3Ww08ihiGRTAZIS7+u9cRw5k4yniwOPhtQwO44UJWsWXIj+69qchIPZJSfhICTH572dgwt418l++NwCPnWzH951wFl3BheR/KkASYlmGAaz/xz96d82CA8XR5MTSaGkJv1ZcP5x2ursEchKy3s796p/lRufW/4c1akLXoEazRGRG6ICJCXaliNn+eXkBZwd7BjQPsjsOHIt1ixIPJn7tTmX4vLezt75z5GcOjlLjncdcPEsvvwiUq6oAEmJdmX0p0/rQHzcnU1OIwCkXfyr5Pz9tNXZw/mM5vj9WXD+cdrKKxDsdF2XiBQvFSApsX77I5EfDiVgb2dhcMfaZscpX6zWv0Zz/v6R8rOH4eLpvLezd7r62pwrn7xy8Sq+/CIi+VABkhJr9qbDAPy7qT+BlSuYnKaMSrv4t09a/f3anMOQmZr3dm5Vcn7C6sppq4o1NJojIqWCCpCUSN8fPMNXv8cCMLSzJj29IVYrJP2R+2mri6fy3s7eCSoH/+Mj5X9em+Nasdjii4jcDCpAUuLsjD7PE0t3YBjwYMvq1KuqjzUXSNqlv0Zz/nltTublvLdz883j2pwaYK8fESJSNumnm5QoB+MuEr5oG5czsuhY14cpPRubHalksVohKSb3j5QnxeS9nZ0jVK6dy0fK64BrpeLLLyJSQqgASYlx8lwK/RZsJfFyBs1rVGRuv5Y4OZTTe72kJ+dybc6h7PvmZKTkvV0Fnzyuzamp0RwRkb/RT0QpEc5cTKPfgq3EJaVRz8+DRQNaU8GpjP/1NIzsUZvcrs1J+iPv7ewc/hzN+cdpK+86UKFy8eUXESnFyvhvGCkNklIzCFsYxfGzKVSv5Mr7g9pQsYKT2bGKTnrKXzcEzDG31RHISM57uwrefys5fzttVakm2OuO2CIiN0IFSEx1OT2LxxdvZ+/pJHzcnVk2KAQ/TxezY10/w4CkU7lfm5N4Mu/t7BygUq3cT1tpNEdE5KZRARLTZGRZGbF8J1HHz+Hh4sD7A9sQ5ONmdqxrS0/Jno38n6etzh6B9Et5b+da+W8Tdv7tI+WVgjSaIyJiAhUgMYXVavDcx78SuT8eZwc7FoS1poF/CZn3yTCy73b8z/msEg5DYnTe21nsoXKtXK7NqQtu3sWXX0RE8qUCJMXOMAwmf7GX1btisLezMPuxFrSpZcLpnozL2SM3uZ22utZojkvFP8vNP05bVQoChzJ07ZKISBmmAiTFbsa3h1n803EA3nqoKbfX97t5L2YYcDE25yesrnyk/MJJwMh9O4t9dqHJ9docb7BYbl5mERG56UwvQLNmzeKNN94gNjaWpk2bMmPGDNq0aZPn+tOnT2f27NlER0fj4+PDgw8+SEREBC4u2RfOZmVlMWnSJJYtW0ZsbCz+/v4MGDCA8ePHY9EvLdMt3XKcaRsOAvBS9wbc3zygaHackZr7tTkJhyH9Yt7buXj9NZrz99NWlWppNEdEpAwztQCtXLmSMWPGMGfOHEJCQpg+fTqhoaEcOHCAKlWqXLX+8uXLGTt2LAsXLqRdu3YcPHiQAQMGYLFYmDZtGgBTp05l9uzZLFmyhIYNG7J9+3bCw8Px8vJi1KhRxX2I8jef7o5h4md7ABh1R13C29e6vh0YBlyK+8e1OX9+fSGavEdz7LJHc66M4Pz9I+VuPhrNEREphyyGYeTxW+PmCwkJoXXr1sycORMAq9VKYGAgI0eOZOzYsVetP2LECPbt20dkZKRt2TPPPMPWrVvZvHkzAPfddx9+fn4sWLDAtk6vXr1wdXVl2bJlBcqVlJSEl5cXiYmJeHqWkAtzS7nvDsQzeMl2Mq0GYW1rMunfDfMfkTuxBU5s/ttpq8OQlpT3+s5ef5Wbv5+2qlwLHJyL9oBERKTEuZ7f36aNAKWnp7Njxw7GjRtnW2ZnZ0fXrl3ZsmVLrtu0a9eOZcuWERUVRZs2bTh69Chffvkl/fr1y7HOvHnzOHjwILfccgu//PILmzdvto0QSfHbceIcw5btINNq8O+m/rzUvQDlZ9t7sPaZq5db7LKndbCN4vzttJWbr0ZzRESkQEwrQAkJCWRlZeHnl/MCWD8/P/bv35/rNo8++igJCQl06NABwzDIzMxk6NChvPDCC7Z1xo4dS1JSEvXr18fe3p6srCxeffVV+vbtm2eWtLQ00tLSbM+Tkq4xyiDXZd/pJMIXbSM1w0qXer681bspdnb5lJTfPoa1/5f99S33QPVWfxWeyrU1miMiIjesVM00uXHjRqZMmcK7777Lzp07WbVqFWvXruWVV16xrfPRRx/xwQcfsHz5cnbu3MmSJUt48803WbJkSZ77jYiIwMvLy/YIDAwsjsMp86LPptB/YRRJqZm0qlmJ2X1b4mifz1+5g1/D6icAA1oPhkc+hE7/Bw16QJVbVX5ERKRImHYNUHp6OhUqVODjjz/m/vvvty0PCwvjwoULfPrpp1dt07FjR/71r3/xxhtv2JYtW7aMIUOGcOnSJezs7AgMDGTs2LEMHz7cts5//vMfli1blufIUm4jQIGBgboG6AbEJ6Xy4JwtRJ9LoX5VD1YOaYtXhXzueHxiCyztCZmXodGD8MB8sCtVHV1EREx0PdcAmfbbxcnJiZYtW+a4oNlqtRIZGUnbtm1z3SYlJQW7f/xCtLe3B7JvrnetdaxWa55ZnJ2d8fT0zPGQwktMyaD/wiiiz6VQo3IF3h/YJv/yE/sbLO+TXX7q3gU956j8iIjITWPqx+DHjBlDWFgYrVq1ok2bNkyfPp3k5GTCw8MB6N+/PwEBAURERADQvXt3pk2bRvPmzQkJCeHw4cNMmDCB7t2724pQ9+7defXVV6lRowYNGzZk165dTJs2jYEDB5p2nOXJ5fQsBi3Zxv7Yi/h6ZE9uWiW/yU3PHoGlD0BaItRoCw8t0fxYIiJyU5lagPr06cOZM2eYOHEisbGxNGvWjHXr1tkujI6Ojs4xmnPlZobjx48nJiYGX19fW+G5YsaMGUyYMIEnn3yS+Ph4/P39eeKJJ5g4cWKxH195k5FlZdgHO9h+4jyeLg4sHdSGGt4Vrr1R0mlYej8kx4NfY3hkBTjls42IiMgNMvU+QCWV7gN0/axWg6c/2s2nu0/h4mjHB4+H0LJmPvN7pZyDRd3gzL7sOy8PXA8eN3FaDBERKdNKxTVAUnYYhsGkz/fw6e5TONhZmPNYy/zLT9ol+OCh7PLjUQ36r1H5ERGRYqMCJDds+jeHeH/LCSwWeKt3U7rUu3oakxwy02DlYxCzHVwrQb/V2VNViIiIFBMVILkhi348xtuRhwCY/O+G9GiWz+Sm1ixYNQSOfgeObtD34+z7+4iIiBQjFSAptDW7Ynj5870AjLnzFvq1Dbr2BoYBXzwNe9eAnSM8vCz7Ls8iIiLFTAVICuXb/XE8879fABjQLoiRt9fJf6PIl2Hnkuz5vHq9B8G33+SUIiIiuVMBkusWdewcw5btJMtq0LN5ABPva5D/5KY/vg2b/5v99X3ToeH9NzumiIhInlSA5LrsPZXEoCXbSMu0ckf9Krz+YJP8Jzfd+T5s+PM+TF1fhpZhNz+oiIjINagASYEdT0im/8IoLqZm0iaoMrP6tsh/ctO9n8HnT2V/3f4p6DD6pucUERHJjwqQFEhcUiqPLdhKwqU0bq3myXsDWuHiaH/tjY58B58MAsMKLfpnj/6IiIiUACpAkq8LKen0W7CVP85fJsg7e3JTT5d85ur6Yzus6AtZ6XDrv7Ov+8nvOiEREZFiogIk15SSnkn44m0cjLuEn6czSweF4OvhfO2N4vfBBw9CRjLU7pL9iS+7fEaLREREipEKkOQpPdPKE0t3sCv6Al6ujrw/MITAyvlMVHr+BCztCZfPQ0Ar6PMBOORTmERERIqZCpDkKstqMOaj3fxwKAFXR3sWhbemXlWPa290KT57ZveLp8G3PvT9Hzi7F0teERGR66ECJFcxDIOJn/7OF7+extHewtx+LWlRo9K1N7p8AZY9AOeOQsUa2fN7VchnQlQRERGTqADJVd76+iAfbI3GYoH/9mlGp1t8r71Begp8+DDE/gZuvtBvDXj6F0tWERGRwlABkhze++EoM787DMB/7m/EfU3yKTJZGfC/ARC9BZy94LFV4B1884OKiIjcABUgsflkxx/8Z+0+AJ4NrUffkJrX3sBqhTXD4NB6cHCFR1dCtSbFkFREROTGqAAJABv2xvHcJ78C8HiHWjzZJZ9RHMOAdc/Db/8DOwfo/T7UbFsMSUVERG6cCpDw89GzDF+ePblprxbVeaHbrflPbrrxNYiaB1jg/jlwy13FklVERKQoqACVc7/HJPL4ku2kZ1rpeqsfU3s1zn9y05/nwKbXsr/u9gY0eejmBxURESlCKkDl2NEzlwhbGMWltExCalVm5qPNcchvctNfVmSf+gK47UVoM/jmBxURESliKkDl1OnEy/RbEMXZ5HQaBXjyXlgBJjc98BWseTL765Bh0OnZmx9URETkJlABKofOJ6fTb0EUMRcuU9vHjcXhbfDIb3LT45uzP+5uZEGThyF0iiY3FRGRUksFqJy5lJbJgMXbOBx/iaqeLrw/qA0+7vnM1XVqNyx/GDJT4ZZ7oMdMsNNfHRERKb30W6wcScvMYujSHfxy8gIVKziydFAbqlfKZ3LThMOwrBekX4SaHeChRWCfz2iRiIhICacCVE5kWQ2eXrmbzYcTqOBkz+LwNtT1y2dy08Q/sic3TUmAak3hkQ/B0bVY8oqIiNxMKkDlgGEYjF/zG1/+FouTvR3z+rWiWWDFa2+UfBaW9oTEk+BdB/p+Ai6exZJXRETkZlMBKgdeX3+AD6NOYmeBtx9uRoe6PtfeIO0ifNALEg6CZ0D25Kbu+UyIKiIiUoqoAJVx874/wuyNRwCY0rMx9zSudu0NMlLhw0fg1C5wrQz9VkPFwGJIKiIiUnxUgMqwj7afZMqX+wF4/u76PNymxrU3yMqETwbB8R/AyR0e+wR86xVDUhERkeKlAlRGrfs9lrF/Tm76RKfaDCvI5KafPwX7vwB75+wLngNaFENSERGR4qcCVAb9dCSBUR/uwmpA71bVGXtP/WtvYBjw9XjYvQwsdvDgQqjVqXjCioiImMD0AjRr1iyCgoJwcXEhJCSEqKioa64/ffp06tWrh6urK4GBgTz99NOkpqbmWCcmJobHHnsMb29vXF1dady4Mdu3b7+Zh1Fi/PrHBQYv2U56lpXQhn5M6dk4/5ndN0+DLTOzv/73TLj1vpsfVERExEQOZr74ypUrGTNmDHPmzCEkJITp06cTGhrKgQMHqFKlylXrL1++nLFjx7Jw4ULatWvHwYMHGTBgABaLhWnTpgFw/vx52rdvz2233cZXX32Fr68vhw4dolKlSsV9eMXucPwlBizaRnJ6Fu2CvXn74QJMbrptAUROzv46dAo073vzg4qIiJjMYhiGYdaLh4SE0Lp1a2bOzB59sFqtBAYGMnLkSMaOHXvV+iNGjGDfvn1ERkbalj3zzDNs3bqVzZs3AzB27Fh+/PFHfvjhh0LnSkpKwsvLi8TERDw9S8e9b2IuXOah2T9xKjGVJtW9WD74X7g759Nvf/8EPh4EGNDx/+COCcWSVURE5Ga4nt/fpp0CS09PZ8eOHXTt2vWvMHZ2dO3alS1btuS6Tbt27dixY4ftNNnRo0f58ssv6datm22dzz77jFatWvHQQw9RpUoVmjdvzvz586+ZJS0tjaSkpByP0uTspTT6LdjKqcRUavtmT26ab/k5/A2segIwoNVAuH18sWQVEREpCUwrQAkJCWRlZeHn55djuZ+fH7Gxsblu8+ijjzJ58mQ6dOiAo6MjwcHBdOnShRdeeMG2ztGjR5k9ezZ169Zl/fr1DBs2jFGjRrFkyZI8s0RERODl5WV7BAaWnvveXErLJHzxNo6eScbfy4Vlg0Ko7OZ07Y2it8LKfmDNgIYPQLc3NbO7iIiUK6ZfBH09Nm7cyJQpU3j33XfZuXMnq1atYu3atbzyyiu2daxWKy1atGDKlCk0b96cIUOGMHjwYObMmZPnfseNG0diYqLtcfLkyeI4nBuWmpHF4CXb+fWPRCq7OfH+oBD8K+YzV1fs77D8IchIgTpdoedcsLMvnsAiIiIlhGkXQfv4+GBvb09cXFyO5XFxcVStWjXXbSZMmEC/fv14/PHHAWjcuDHJyckMGTKEF198ETs7O6pVq0aDBg1ybHfrrbfyySef5JnF2dkZZ2fnGzyi4pWZZWXUh7vYcvQsbk72LA5vTZ0q7tfe6NxRWPYApCZCYAj0fh8c8hktEhERKYNMGwFycnKiZcuWOS5otlqtREZG0rZt21y3SUlJwc4uZ2R7++zRiyvXcrdv354DBw7kWOfgwYPUrFmzKOObyjAMXlj9G1/vjcPJ3o75Ya1oUr3itTe6GJs9uemlOKjSEB5dCU5uxZJXRESkpDH1Y/BjxowhLCyMVq1a0aZNG6ZPn05ycjLh4eEA9O/fn4CAACIiIgDo3r0706ZNo3nz5oSEhHD48GEmTJhA9+7dbUXo6aefpl27dkyZMoXevXsTFRXFvHnzmDdvnmnHWdRe+2o/H23/AzsLzHi0Oe2C85nc9PL57PJz/jhUCoJ+q8C17N8WQEREJC+mFqA+ffpw5swZJk6cSGxsLM2aNWPdunW2C6Ojo6NzjPiMHz8ei8XC+PHjiYmJwdfXl+7du/Pqq6/a1mndujWrV69m3LhxTJ48mVq1ajF9+nT69i0b97eZvfEIc78/CsBrvZoQ2jD304U26cnwQW+I3wvuVbNndvfIZxsREZEyztT7AJVUJfU+QB9GRTNu1W8AvNjtVgZ3qn3tDTLT4cM+cORbcPGC8K/Ar2ExJBURESl+peI+QHJ9vvrtNC+uzi4/w7oE519+rFmwekh2+XGsAH0/VvkRERH5kwpQKbD5UAJPrdiN1YBH2gTyXGi9a29gGLD2GdizGuwcoc8yCGxTPGFFRERKARWgEm73yQsMWZo9uWm3xlX5z/0FmNz021dgxyLAAg/Mgzp3FEtWERGR0kIFqAQ7FHeRAYuiSEnPokMdH/7bpxn2dvmUn59mwA9vZX9933+h0QM3P6iIiEgpowJUQv1xPoV+C6K4kJJB08CKzO3XEmeHfO7YvGsZfP3nnF53vAStwm9+UBERkVJIBagESriURr8FUcQmpVKnijuLB7TGLb/JTfd9Dp+NzP663Ujo8PTNDyoiIlJKqQCVMBdTMwhbGMWxhGQCKrqydFAbKuU3uenRTfDxQDCs0PwxuPMVTW4qIiJyDSpAJUhqRhaPL9nOnlNJeLs5sXRQG6p55TO5acwOWPEoZKVD/fvgvrdVfkRERPKhAlRCZGZZGbF8F1uPncPD2YElA9tQ2zefyU3PHIBlD0L6JajVCXotAHtTb+4tIiJSKqgAlQBWq8Hzn/zGN/vicHbInty0UYDXtTe6EA3v3w+Xz4F/C3h4OTi6FEteERGR0k4FyGSGYfDql/v4ZOcf2NtZmPloC/5V2/vaG106k11+Lp4Cn3rw2Cfg7FEseUVERMoCFSCTvbvxCAs2HwPg9V5NuLOB37U3SE2EZQ/AuSPgFQj9VkOFysWQVEREpOxQATLRsp9P8Mb6AwBMuK8BvVpWv/YGGZfhw0cg9leo4JM9s7tXwM0PKiIiUsaoAJnki19PMeHT3wEYcVsdBnWode0NsjLgfwPgxI/g7An9VoFPnZsfVEREpAxSATLBpoNneHrlbgwD+obU4Jm7brn2BlYrfDocDq4DBxd4ZAVUa1o8YUVERMogFaBitjP6PEOX7iAjy+C+JtWY3KPRtSc3NQxYPw5+XQkWe3hoCQS1L77AIiIiZZAKUDE6EHuR8EXbuJyRRadbfJnWuwCTm256HbbOyf665xyod/fNDyoiIlLGqQAVox8PJ5B4OYPmNSoy57EWODnk8/ZvnQcbp2R/fc/r0KT3zQ8pIiJSDui2wcVoYIdaVHZzoks9Xyo45fPW//oRfPVs9tedx0LIEzc/oIiISDmhAlTM7m9egI+tH1wPq4dmf93mCegy9uaGEhERKWd0CqykOfETfNQfjCxo0gfufk2Tm4qIiBQxFaCS5PSvsLwPZKbCLXdDj1lgpz8iERGRoqbfriXF2SPZU1ykJUGNdvDQYrB3NDuViIhImaQCVBIkxmRPbpp8Bqo2hkdXgKOr2alERETKLBUgs6Wcyx75SYyGysHw2Gpw8TI7lYiISJmmAmSmtIvwwYNwZj94+EP/NeDua3YqERGRMk8FyCyZabCiL8TsANdK0G81VKxhdioREZFyQQXIDFmZ8MkgOLYJHN2g7ydQpb7ZqURERMoNFaDiZhjwxWjY9znYO8Ejy6F6S7NTiYiIlCsqQMVtw0TYtRQsdvDgQqjdxexEIiIi5Y4KUHHa8i789E72193fgVu7m5tHRESknNJcYMUp+DbwqAb/ehJa9DM7jYiISLlVIkaAZs2aRVBQEC4uLoSEhBAVFXXN9adPn069evVwdXUlMDCQp59+mtTU1FzXfe2117BYLIwePfomJL9OVW6FJ7dA+1FmJxERESnXTC9AK1euZMyYMbz00kvs3LmTpk2bEhoaSnx8fK7rL1++nLFjx/LSSy+xb98+FixYwMqVK3nhhReuWnfbtm3MnTuXJk2a3OzDKDjXSmYnEBERKfdML0DTpk1j8ODBhIeH06BBA+bMmUOFChVYuHBhruv/9NNPtG/fnkcffZSgoCDuuusuHnnkkatGjS5dukTfvn2ZP38+lSqpdIiIiMhfTC1A6enp7Nixg65du9qW2dnZ0bVrV7Zs2ZLrNu3atWPHjh22wnP06FG+/PJLunXrlmO94cOHc++99+bYd17S0tJISkrK8RAREZGyy9SLoBMSEsjKysLPzy/Hcj8/P/bv35/rNo8++igJCQl06NABwzDIzMxk6NChOU6BrVixgp07d7Jt27YC5YiIiODll18u/IGIiIhIqWL6KbDrtXHjRqZMmcK7777Lzp07WbVqFWvXruWVV14B4OTJkzz11FN88MEHuLi4FGif48aNIzEx0fY4efLkzTwEERERMZmpI0A+Pj7Y29sTFxeXY3lcXBxVq1bNdZsJEybQr18/Hn/8cQAaN25McnIyQ4YM4cUXX2THjh3Ex8fTokUL2zZZWVl8//33zJw5k7S0NOzt7XPs09nZGWdn5yI+OhERESmpTB0BcnJyomXLlkRGRtqWWa1WIiMjadu2ba7bpKSkYGeXM/aVQmMYBnfccQe//fYbu3fvtj1atWpF37592b1791XlR0RERMof02+EOGbMGMLCwmjVqhVt2rRh+vTpJCcnEx4eDkD//v0JCAggIiICgO7duzNt2jSaN29OSEgIhw8fZsKECXTv3h17e3s8PDxo1KhRjtdwc3PD29v7quUiIiJSPplegPr06cOZM2eYOHEisbGxNGvWjHXr1tkujI6Ojs4x4jN+/HgsFgvjx48nJiYGX19funfvzquvvmrWIYiIiEgpYzEMwzA7REmTlJSEl5cXiYmJeHp6mh1HRERECuB6fn+Xuk+BiYiIiNwoFSAREREpd1SAREREpNxRARIREZFyx/RPgZVEV64L15xgIiIipceV39sF+XyXClAuLl68CEBgYKDJSUREROR6Xbx4ES8vr2uuo4/B58JqtXLq1Ck8PDywWCxFuu+kpCQCAwM5efKkPmKfD71XBaf3quD0XhWc3quC03t1fW7W+2UYBhcvXsTf3/+qWSP+SSNAubCzs6N69eo39TU8PT31P0kB6b0qOL1XBaf3quD0XhWc3qvrczPer/xGfq7QRdAiIiJS7qgAiYiISLmjAlTMnJ2deemll3B2djY7Somn96rg9F4VnN6rgtN7VXB6r65PSXi/dBG0iIiIlDsaARIREZFyRwVIREREyh0VIBERESl3VIBERESk3FEBKgYRERG0bt0aDw8PqlSpwv3338+BAwfMjlUqvPbaa1gsFkaPHm12lBIpKyuLCRMmUKtWLVxdXQkODuaVV14p0Dw4Zd33339P9+7d8ff3x2KxsGbNmqvW2bdvH//+97/x8vLCzc2N1q1bEx0dXfxhTTZ79myaNGliuyld27Zt+eqrrwA4d+4cI0eOpF69eri6ulKjRg1GjRpFYmKiyanNExMTw2OPPYa3tzeurq40btyY7du357ru0KFDsVgsTJ8+vXhDSr50J+hisGnTJoYPH07r1q3JzMzkhRde4K677mLv3r24ubmZHa/E2rZtG3PnzqVJkyZmRymxpk6dyuzZs1myZAkNGzZk+/bthIeH4+XlxahRo8yOZ6rk5GSaNm3KwIEDeeCBB676/pEjR+jQoQODBg3i5ZdfxtPTkz179uDi4mJCWnNVr16d1157jbp162IYBkuWLKFHjx7s2rULwzA4deoUb775Jg0aNODEiRMMHTqUU6dO8fHHH5sdvdidP3+e9u3bc9ttt/HVV1/h6+vLoUOHqFSp0lXrrl69mp9//hl/f38Tkkq+DCl28fHxBmBs2rTJ7Cgl1sWLF426desaGzZsMDp37mw89dRTZkcqke69915j4MCBOZY98MADRt++fU1KVDIBxurVq3Ms69Onj/HYY4+ZE6gUqFSpkvHee+/l+r2PPvrIcHJyMjIyMoo5lfmef/55o0OHDvmu98cffxgBAQHG77//btSsWdP473//e/PDyXXRKTATXBk6rly5sslJSq7hw4dz77330rVrV7OjlGjt2rUjMjKSgwcPAvDLL7+wefNm7rnnHpOTlWxWq5W1a9dyyy23EBoaSpUqVQgJCcn1NFl5k5WVxYoVK0hOTqZt27a5rpOYmIinpycODuXvJMJnn31Gq1ateOihh6hSpQrNmzdn/vz5OdaxWq3069ePZ599loYNG5qUVPKjAlTMrFYro0ePpn379jRq1MjsOCXSihUr2LlzJxEREWZHKfHGjh3Lww8/TP369XF0dKR58+aMHj2avn37mh2tRIuPj+fSpUu89tpr3H333Xz99df07NmTBx54gE2bNpkdzxS//fYb7u7uODs7M3ToUFavXk2DBg2uWi8hIYFXXnmFIUOGmJDSfEePHmX27NnUrVuX9evXM2zYMEaNGsWSJUts60ydOhUHB4dyfxq6pCt/9d1kw4cP5/fff2fz5s1mRymRTp48yVNPPcWGDRvK5bUY1+ujjz7igw8+YPny5TRs2JDdu3czevRo/P39CQsLMzteiWW1WgHo0aMHTz/9NADNmjXjp59+Ys6cOXTu3NnMeKaoV68eu3fvJjExkY8//piwsDA2bdqUowQlJSVx77330qBBAyZNmmReWBNZrVZatWrFlClTAGjevDm///47c+bMISwsjB07dvD222+zc+dOLBaLyWnlWjQCVIxGjBjBF198wXfffUf16tXNjlMi7dixg/j4eFq0aIGDgwMODg5s2rSJd955BwcHB7KyssyOWKI8++yztlGgxo0b069fP55++mmNnuXDx8cHBweHq0Y4br311nL5KTAAJycn6tSpQ8uWLYmIiKBp06a8/fbbtu9fvHiRu+++Gw8PD1avXo2jo6OJac1TrVq1a/69+eGHH4iPj6dGjRq2n2EnTpzgmWeeISgoyITEkheNABUDwzAYOXIkq1evZuPGjdSqVcvsSCXWHXfcwW+//ZZjWXh4OPXr1+f555/H3t7epGQlU0pKCnZ2Of8dY29vbxvhkNw5OTnRunXrq25HcfDgQWrWrGlSqpLFarWSlpYGZI/8hIaG4uzszGeffVauR2fbt29/zb83/fr1u+raxdDQUPr160d4eHix5ZT8qQAVg+HDh7N8+XI+/fRTPDw8iI2NBcDLywtXV1eT05UsHh4eV10b5ebmhre3t66ZykX37t159dVXqVGjBg0bNmTXrl1MmzaNgQMHmh3NdJcuXeLw4cO258eOHWP37t1UrlyZGjVq8Oyzz9KnTx86derEbbfdxrp16/j888/ZuHGjeaFNMm7cOO655x5q1KjBxYsXWb58ORs3bmT9+vUkJSVx1113kZKSwrJly0hKSiIpKQkAX1/fcvePkqeffpp27doxZcoUevfuTVRUFPPmzWPevHkAeHt74+3tnWMbR0dHqlatSr169cyILHkx+2No5QGQ62PRokVmRysV9DH4vCUlJRlPPfWUUaNGDcPFxcWoXbu28eKLLxppaWlmRzPdd999l+v/d2FhYbZ1FixYYNSpU8dwcXExmjZtaqxZs8a8wCYaOHCgUbNmTcPJycnw9fU17rjjDuPrr782DCPv9xEwjh07Zm5wk3z++edGo0aNDGdnZ6N+/frGvHnzrrm+PgZfMlkMQ7eMFRERkfJFF0GLiIhIuaMCJCIiIuWOCpCIiIiUOypAIiIiUu6oAImIiEi5owIkIiIi5Y4KkIiIiJQ7KkAiUq4tXryYihUrFstrDRgwgPvvv79YXktErk0FSESkiB0/fhyLxcLu3bvNjiIieVABEhERkXJHBUhEbpouXbowcuRIRo8eTaVKlfDz82P+/PkkJycTHh6Oh4cHderU4auvvgIgKyuLQYMGUatWLVxdXalXrx5vv/22bX+pqak0bNiQIUOG2JYdOXIEDw8PFi5cWKBMixcvpkaNGlSoUIGePXty9uzZq9b59NNPadGiBS4uLtSuXZuXX36ZzMxM2/ctFguzZ8/mnnvuwdXVldq1a/Pxxx/bvl+rVi0AmjdvjsVioUuXLjn2/+abb1KtWjW8vb0ZPnw4GRkZBcouIkXI7MnIRKTs6ty5s+Hh4WG88sorxsGDB41XXnnFsLe3N+655x5j3rx5xsGDB41hw4YZ3t7eRnJyspGenm5MnDjR2LZtm3H06FFj2bJlRoUKFYyVK1fa9rlr1y7DycnJWLNmjZGZmWn861//Mnr27FmgPD///LNhZ2dnTJ061Thw4IDx9ttvGxUrVjS8vLxs63z//feGp6ensXjxYuPIkSPG119/bQQFBRmTJk2yrQMY3t7exvz5840DBw4Y48ePN+zt7Y29e/cahmEYUVFRBmB88803xunTp42zZ88ahmEYYWFhhqenpzF06FBj3759xueff25UqFAh38k0RaToqQCJyE3TuXNno0OHDrbnmZmZhpubm9GvXz/bstOnTxuAsWXLllz3MXz4cKNXr145lr3++uuGj4+PMWLECKNatWpGQkJCgfI88sgjRrdu3XIs69OnT44CdMcddxhTpkzJsc7SpUuNatWq2Z4DxtChQ3OsExISYgwbNswwDMM4duyYARi7du3KsU5YWJhRs2ZNIzMz07bsoYceMvr06VOg/CJSdHQKTERuqiZNmti+tre3x9vbm8aNG9uW+fn5ARAfHw/ArFmzaNmyJb6+vri7uzNv3jyio6Nz7POZZ57hlltuYebMmSxcuBBvb+8CZdm3bx8hISE5lrVt2zbH819++YXJkyfj7u5uewwePJjTp0+TkpKS53Zt27Zl3759+WZo2LAh9vb2tufVqlWzHbuIFB8HswOISNnm6OiY47nFYsmxzGKxAGC1WlmxYgX/93//x1tvvUXbtm3x8PDgjTfeYOvWrTn2ER8fz8GDB7G3t+fQoUPcfffdRZb30qVLvPzyyzzwwANXfc/FxeWG95/b+2G1Wm94vyJyfVSARKTE+PHHH2nXrh1PPvmkbdmRI0euWm/gwIE0btyYQYMGMXjwYLp27cqtt96a7/5vvfXWq8rUzz//nON5ixYtOHDgAHXq1Lnmvn7++Wf69++f43nz5s0BcHJyArIv6haRkkkFSERKjLp16/L++++zfv16atWqxdKlS9m2bZvtU1WQfYpsy5Yt/PrrrwQGBrJ27Vr69u3Lzz//bCseeRk1ahTt27fnzTffpEePHqxfv55169blWGfixIncd9991KhRgwcffBA7Ozt++eUXfv/9d/7zn//Y1vvf//5Hq1at6NChAx988AFRUVEsWLAAgCpVquDq6sq6deuoXr06Li4ueHl5FeE7JSI3StcAiUiJ8cQTT/DAAw/Qp08fQkJCOHv2bI7RoP379/Pss8/y7rvvEhgYCMC7775LQkICEyZMyHf///rXv5g/fz5vv/02TZs25euvv2b8+PE51gkNDeWLL77g66+/pnXr1vzrX//iv//9LzVr1syx3ssvv8yKFSto0qQJ77//Ph9++CENGjQAwMHBgXfeeYe5c+fi7+9Pjx49bvStEZEiZjEMwzA7hIhIaWKxWFi9erWmtRApxTQCJCIiIuWOCpCIlBn33HNPjo+v//0xZcoUs+OJSAmiU2AiUmbExMRw+fLlXL9XuXJlKleuXMyJRKSkUgESERGRckenwERERKTcUQESERGRckcFSERERModFSAREREpd1SAREREpNxRARIREZFyRwVIREREyh0VIBERESl3/h8+/KUvgfCEPQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "accuracy_list_train = []\n",
        "accuracy_list_test = []\n",
        "for n_estimators in n_estimators_list:\n",
        "    # You can fit the model at the same time you define it, because the fit function returns the fitted estimator.\n",
        "    model = RandomForestClassifier(n_estimators = n_estimators,\n",
        "                                   random_state = RANDOM_STATE).fit(X_train,y_train)\n",
        "    predictions_train = model.predict(X_train) ## The predicted values for the train dataset\n",
        "    predictions_test = model.predict(X_test) ## The predicted values for the test dataset\n",
        "    accuracy_train = accuracy_score(predictions_train,y_train)\n",
        "    accuracy_test = accuracy_score(predictions_test,y_test)\n",
        "    accuracy_list_train.append(accuracy_train)\n",
        "    accuracy_list_test.append(accuracy_test)\n",
        "\n",
        "plt.title('Train x Test metrics')\n",
        "plt.xlabel('n_estimators')\n",
        "plt.ylabel('accuracy')\n",
        "plt.xticks(ticks = range(len(n_estimators_list )),labels=n_estimators_list)\n",
        "plt.plot(accuracy_list_train)\n",
        "plt.plot(accuracy_list_test)\n",
        "plt.legend(['Train','Test'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "71e9G6cBCiDT",
        "outputId": "7b441860-a122-4d78-f00b-5460a5df84ed"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.legend.Legend at 0x7df0ca6a0f40>"
            ]
          },
          "metadata": {},
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "random_forest_model = RandomForestClassifier(n_estimators = 100,\n",
        "                                             max_depth = 8,\n",
        "                                             min_samples_split = 10).fit(X_train,y_train)"
      ],
      "metadata": {
        "id": "8I6-8AjbCpwu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_test),y_test):.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NHLTpIfdCz5z",
        "outputId": "d2680da8-e184-4274-c01e-ddb4445382e3"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics train:\n",
            "\tAccuracy score: 0.9237\n",
            "Metrics test:\n",
            "\tAccuracy score: 0.8804\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "n = int(len(X_train)*0.8) ## Let's use 80% to train and 20% to eval"
      ],
      "metadata": {
        "id": "9tW17UIUC2NN"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]"
      ],
      "metadata": {
        "id": "QwSv3JgIC5bw"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train_fit, X_train_eval, y_train_fit, y_train_eval = X_train[:n], X_train[n:], y_train[:n], y_train[n:]"
      ],
      "metadata": {
        "id": "VrDYZQJAC8Ng"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "X_train.shape"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PR1VARJVC9Me",
        "outputId": "0ac7c028-cd9a-4da8-cef0-7841e08cf9f7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(734, 20)"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# X_train[ go from the start to :.8]\n",
        "random_forest_model = RandomForestClassifier(n_estimators = 100,\n",
        "                                             max_depth = 8,\n",
        "                                             min_samples_split = 10).fit(X_train,y_train)\n",
        "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(random_forest_model.predict(X_test),y_test):.4f}\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FzLZQHUoDoYj",
        "outputId": "198c00cb-4965-4d4c-8123-cde240a92c11"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics train:\n",
            "\tAccuracy score: 0.9223\n",
            "Metrics test:\n",
            "\tAccuracy score: 0.8804\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# xgbboost would do everything itself.\n",
        "\n",
        "xgb_model = XGBClassifier(n_estimators = 100, learning_rate = 0.1,verbosity = 1, random_state = RANDOM_STATE)\n",
        "# early stopping rounds what the name indicates is -> stop after this much\n",
        "xgb_model.fit(X_train_fit,y_train_fit, eval_set = [(X_train_eval,y_train_eval)], early_stopping_rounds = 50)\n",
        "# Here we must pass a list to the eval_set, because you can have several different tuples ov eval sets. The parameter\n",
        "# early_stopping_rounds is the number of iterations that it will wait to check if the cost function decreased or not.\n",
        "# If not, it will stop and get the iteration that returned the lowest metric on the eval set."
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "EvMK8FQ_EGpC",
        "outputId": "a3dcede0-8888-461c-de8a-a25c08f3ce7f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/xgboost/sklearn.py:889: UserWarning: `early_stopping_rounds` in `fit` method is deprecated for better compatibility with scikit-learn, use `early_stopping_rounds` in constructor or`set_params` instead.\n",
            "  warnings.warn(\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[0]\tvalidation_0-logloss:0.63302\n",
            "[1]\tvalidation_0-logloss:0.59554\n",
            "[2]\tvalidation_0-logloss:0.56614\n",
            "[3]\tvalidation_0-logloss:0.54187\n",
            "[4]\tvalidation_0-logloss:0.52349\n",
            "[5]\tvalidation_0-logloss:0.50611\n",
            "[6]\tvalidation_0-logloss:0.49373\n",
            "[7]\tvalidation_0-logloss:0.48366\n",
            "[8]\tvalidation_0-logloss:0.47323\n",
            "[9]\tvalidation_0-logloss:0.46538\n",
            "[10]\tvalidation_0-logloss:0.46000\n",
            "[11]\tvalidation_0-logloss:0.45621\n",
            "[12]\tvalidation_0-logloss:0.45483\n",
            "[13]\tvalidation_0-logloss:0.44975\n",
            "[14]\tvalidation_0-logloss:0.44495\n",
            "[15]\tvalidation_0-logloss:0.44073\n",
            "[16]\tvalidation_0-logloss:0.44078\n",
            "[17]\tvalidation_0-logloss:0.43936\n",
            "[18]\tvalidation_0-logloss:0.44206\n",
            "[19]\tvalidation_0-logloss:0.44536\n",
            "[20]\tvalidation_0-logloss:0.44322\n",
            "[21]\tvalidation_0-logloss:0.44310\n",
            "[22]\tvalidation_0-logloss:0.44419\n",
            "[23]\tvalidation_0-logloss:0.44797\n",
            "[24]\tvalidation_0-logloss:0.44843\n",
            "[25]\tvalidation_0-logloss:0.45337\n",
            "[26]\tvalidation_0-logloss:0.45206\n",
            "[27]\tvalidation_0-logloss:0.45435\n",
            "[28]\tvalidation_0-logloss:0.45316\n",
            "[29]\tvalidation_0-logloss:0.45524\n",
            "[30]\tvalidation_0-logloss:0.45401\n",
            "[31]\tvalidation_0-logloss:0.45349\n",
            "[32]\tvalidation_0-logloss:0.45437\n",
            "[33]\tvalidation_0-logloss:0.45625\n",
            "[34]\tvalidation_0-logloss:0.45458\n",
            "[35]\tvalidation_0-logloss:0.45467\n",
            "[36]\tvalidation_0-logloss:0.45771\n",
            "[37]\tvalidation_0-logloss:0.45972\n",
            "[38]\tvalidation_0-logloss:0.46170\n",
            "[39]\tvalidation_0-logloss:0.46459\n",
            "[40]\tvalidation_0-logloss:0.46673\n",
            "[41]\tvalidation_0-logloss:0.46745\n",
            "[42]\tvalidation_0-logloss:0.46765\n",
            "[43]\tvalidation_0-logloss:0.46790\n",
            "[44]\tvalidation_0-logloss:0.47139\n",
            "[45]\tvalidation_0-logloss:0.47243\n",
            "[46]\tvalidation_0-logloss:0.47138\n",
            "[47]\tvalidation_0-logloss:0.47377\n",
            "[48]\tvalidation_0-logloss:0.47644\n",
            "[49]\tvalidation_0-logloss:0.47711\n",
            "[50]\tvalidation_0-logloss:0.47813\n",
            "[51]\tvalidation_0-logloss:0.47792\n",
            "[52]\tvalidation_0-logloss:0.48056\n",
            "[53]\tvalidation_0-logloss:0.48048\n",
            "[54]\tvalidation_0-logloss:0.48240\n",
            "[55]\tvalidation_0-logloss:0.48509\n",
            "[56]\tvalidation_0-logloss:0.48523\n",
            "[57]\tvalidation_0-logloss:0.48638\n",
            "[58]\tvalidation_0-logloss:0.48457\n",
            "[59]\tvalidation_0-logloss:0.48534\n",
            "[60]\tvalidation_0-logloss:0.48637\n",
            "[61]\tvalidation_0-logloss:0.48847\n",
            "[62]\tvalidation_0-logloss:0.49046\n",
            "[63]\tvalidation_0-logloss:0.49263\n",
            "[64]\tvalidation_0-logloss:0.49466\n",
            "[65]\tvalidation_0-logloss:0.49539\n",
            "[66]\tvalidation_0-logloss:0.49581\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=55, ...)"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=55, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">XGBClassifier</label><div class=\"sk-toggleable__content\"><pre>XGBClassifier(base_score=None, booster=None, callbacks=None,\n",
              "              colsample_bylevel=None, colsample_bynode=None,\n",
              "              colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
              "              enable_categorical=False, eval_metric=None, feature_types=None,\n",
              "              gamma=None, grow_policy=None, importance_type=None,\n",
              "              interaction_constraints=None, learning_rate=0.1, max_bin=None,\n",
              "              max_cat_threshold=None, max_cat_to_onehot=None,\n",
              "              max_delta_step=None, max_depth=None, max_leaves=None,\n",
              "              min_child_weight=None, missing=nan, monotone_constraints=None,\n",
              "              multi_strategy=None, n_estimators=100, n_jobs=None,\n",
              "              num_parallel_tree=None, random_state=55, ...)</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "xgb_model.best_iteration"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Wc1RXf7KEWaw",
        "outputId": "b96d47a8-3577-4354-a6d4-bd1284f53c80"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "17"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print(f\"Metrics train:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_train),y_train):.4f}\\nMetrics test:\\n\\tAccuracy score: {accuracy_score(xgb_model.predict(X_test),y_test):.4f}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l_SeGDYqEfgn",
        "outputId": "da158a9c-dd9a-45e6-ac7a-25e2f26a182a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metrics train:\n",
            "\tAccuracy score: 0.9319\n",
            "Metrics test:\n",
            "\tAccuracy score: 0.8533\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "a4dhFsssEmye"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}