Regression_Health_Costs_Calculator/predict_health_costs_with_regression.ipynb

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      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1rRo8oNqZ-Rj"
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      "outputs": [],
      "source": [
        "# Import libraries.\n",
        "import os\n",
        "import requests\n",
        "\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib\n",
        "import matplotlib.pyplot as plt\n",
        "import plotly.express as px\n",
        "import seaborn as sns\n",
        "%matplotlib inline\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {},
      "outputs": [],
      "source": [
        "sns.set_style('darkgrid')\n",
        "matplotlib.rcParams['font.size'] = 14\n",
        "matplotlib.rcParams['figure.figsize'] = (10, 6)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "CiX2FI4gZtTt"
      },
      "outputs": [
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              "<div>\n",
              "<style scoped>\n",
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              "\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>expenses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>female</td>\n",
              "      <td>27.9</td>\n",
              "      <td>0</td>\n",
              "      <td>yes</td>\n",
              "      <td>southwest</td>\n",
              "      <td>16884.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>male</td>\n",
              "      <td>33.8</td>\n",
              "      <td>1</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>1725.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>male</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>no</td>\n",
              "      <td>southeast</td>\n",
              "      <td>4449.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>male</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>21984.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>male</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0</td>\n",
              "      <td>no</td>\n",
              "      <td>northwest</td>\n",
              "      <td>3866.86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age     sex   bmi  children smoker     region  expenses\n",
              "0   19  female  27.9         0    yes  southwest  16884.92\n",
              "1   18    male  33.8         1     no  southeast   1725.55\n",
              "2   28    male  33.0         3     no  southeast   4449.46\n",
              "3   33    male  22.7         0     no  northwest  21984.47\n",
              "4   32    male  28.9         0     no  northwest   3866.86"
            ]
          },
          "execution_count": 3,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "if not os.path.exists('insurance.csv'):\n",
        "    url = 'https://cdn.freecodecamp.org/project-data/health-costs/insurance.csv'\n",
        "    response = requests.get(url)\n",
        "    with open('insurance.csv', 'wb') as file:\n",
        "        file.write(response.content)\n",
        "\n",
        "dataset = pd.read_csv('insurance.csv')\n",
        "dataset.head()"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Explanatory Analysis"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "id": "LcopvQh3X-kX"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 1338 entries, 0 to 1337\n",
            "Data columns (total 7 columns):\n",
            " #   Column    Non-Null Count  Dtype  \n",
            "---  ------    --------------  -----  \n",
            " 0   age       1338 non-null   int64  \n",
            " 1   sex       1338 non-null   object \n",
            " 2   bmi       1338 non-null   float64\n",
            " 3   children  1338 non-null   int64  \n",
            " 4   smoker    1338 non-null   object \n",
            " 5   region    1338 non-null   object \n",
            " 6   expenses  1338 non-null   float64\n",
            "dtypes: float64(2), int64(2), object(3)\n",
            "memory usage: 73.3+ KB\n"
          ]
        }
      ],
      "source": [
        "# We have 4 numerical features and 3 categorical features.\n",
        "dataset.info()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
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        "# The minimum age in the dataset is 18 and the maximum age is 64. \n",
        "# Thus, we can visualize the distribution of age using a histogram with 47 bins (one for each year)\n",
        "fig = px.histogram(dataset, \n",
        "                   x='age', \n",
        "                   marginal='box', \n",
        "                   nbins=47, \n",
        "                   title='Distribution of Age')\n",
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                    17.4,
                    36.3,
                    35.6,
                    26.3,
                    28.6,
                    28.3,
                    36.4,
                    20.4,
                    33,
                    20.8,
                    36.7,
                    39.9,
                    26.6,
                    36.6,
                    21.8,
                    30.8,
                    37.1,
                    37.3,
                    38.7,
                    34.8,
                    24.5,
                    35.2,
                    35.6,
                    33.6,
                    28,
                    34.4,
                    28.7,
                    37,
                    31.8,
                    31.7,
                    22.9,
                    37.3,
                    27.4,
                    33.7,
                    24.7,
                    25.9,
                    22.4,
                    28.9,
                    39.1,
                    26.3,
                    36.2,
                    24,
                    24.8,
                    28.5,
                    28.1,
                    32,
                    27.4,
                    34,
                    29.6,
                    35.5,
                    39.8,
                    33,
                    26.9,
                    38.3,
                    37.6,
                    41.2,
                    34.8,
                    22.9,
                    31.2,
                    27.2,
                    27.7,
                    27,
                    39.5,
                    24.8,
                    29.8,
                    34.8,
                    31.3,
                    37.6,
                    30.8,
                    38.3,
                    20,
                    19.3,
                    31.6,
                    25.5,
                    30.1,
                    29.9,
                    27.5,
                    28,
                    28.4,
                    30.9,
                    27.9,
                    35.1,
                    33.6,
                    29.7,
                    30.8,
                    35.7,
                    32.2,
                    28.6,
                    49.1,
                    27.9,
                    27.2,
                    23.4,
                    37.1,
                    23.8,
                    29,
                    31.4,
                    33.9,
                    28.8,
                    28.3,
                    37.4,
                    17.8,
                    34.7,
                    26.5,
                    22,
                    35.9,
                    25.6,
                    28.8,
                    28.1,
                    34.1,
                    25.2,
                    31.9,
                    36,
                    22.4,
                    32.5,
                    25.3,
                    29.7,
                    28.7,
                    38.8,
                    30.5,
                    37.7,
                    37.4,
                    28.4,
                    24.1,
                    29.7,
                    37.1,
                    23.4,
                    25.5,
                    39.5,
                    24.4,
                    25.2,
                    35.5,
                    27.8,
                    26.6,
                    36.9,
                    39.6,
                    29.8,
                    29.6,
                    28.2,
                    37,
                    33.2,
                    31.8,
                    18.9,
                    41.5,
                    30.3,
                    16,
                    34.8,
                    33.3,
                    37.7,
                    27.8,
                    29.2,
                    28.9,
                    33.2,
                    28.6,
                    38.3,
                    20,
                    26.4,
                    30.7,
                    41.9,
                    29.9,
                    30.9,
                    32.2,
                    32.1,
                    31.6,
                    26.2,
                    25.7,
                    26.6,
                    34.4,
                    30.6,
                    32.8,
                    28.6,
                    18.1,
                    39.3,
                    32.1,
                    32.2,
                    24,
                    36.1,
                    22.3,
                    28.9,
                    26.4,
                    27.7,
                    31.8,
                    41.2,
                    33,
                    30.9,
                    28.5,
                    26.7,
                    30.9,
                    37.1,
                    26.6,
                    23.1,
                    29.9,
                    23.2,
                    33.7,
                    33.3,
                    30.8,
                    34.8,
                    24.6,
                    33.9,
                    38.1,
                    41.9,
                    31.6,
                    25.5,
                    36.2,
                    27.8,
                    17.8,
                    27.5,
                    24.5,
                    22.2,
                    26.7,
                    38.4,
                    29.1,
                    38.1,
                    36.7,
                    22.1,
                    26.8,
                    35.3,
                    27.7,
                    30,
                    38.1,
                    35.9,
                    20.9,
                    29,
                    17.3,
                    32.2,
                    34.2,
                    30.3,
                    31.8,
                    25.4,
                    33.6,
                    40.2,
                    24.4,
                    31.9,
                    25.2,
                    26.8,
                    24.3,
                    37,
                    38.1,
                    42.4,
                    19.8,
                    32.4,
                    30.2,
                    25.8,
                    29.4,
                    34.2,
                    37.1,
                    27.5,
                    27.6,
                    26.6,
                    20.6,
                    24.3,
                    31.8,
                    21.6,
                    28.1,
                    40.6,
                    27.6,
                    32.4,
                    31.2,
                    26.6,
                    48.1,
                    26.2,
                    36.8,
                    26.4,
                    33.4,
                    29.6,
                    45.5,
                    28.8,
                    26.8,
                    23,
                    27.7,
                    25.4,
                    34.4,
                    28.9,
                    27.6,
                    22.6,
                    37.5,
                    33,
                    38,
                    33.3,
                    27.5,
                    33.3,
                    34.9,
                    33.1,
                    26.6,
                    24.7,
                    36,
                    35.9,
                    31.4,
                    33.3,
                    32.2,
                    32.8,
                    27.6,
                    37.3,
                    25.3,
                    29.6,
                    30.8,
                    40.9,
                    27.2,
                    34.1,
                    23.2,
                    36.5,
                    33.8,
                    36.7,
                    36.4,
                    27.4,
                    31.2,
                    28.8,
                    35.7,
                    34.5,
                    25.7,
                    27.6,
                    32.3,
                    27.7,
                    27.6,
                    30,
                    27.6,
                    36.8,
                    41.5,
                    29.3,
                    35.8,
                    33.3,
                    29.9,
                    27.8,
                    23.2,
                    25.6,
                    27.7,
                    35.2,
                    38.3,
                    27.6,
                    43.9,
                    29.8,
                    41.9,
                    20.8,
                    32.3,
                    30.5,
                    21.7,
                    26.4,
                    21.9,
                    30.8,
                    32.3,
                    25,
                    32,
                    30.4,
                    21.1,
                    22.2,
                    33.2,
                    32.9,
                    33.3,
                    28.3,
                    24.9,
                    40.2,
                    30.1,
                    31.5,
                    18,
                    30.7,
                    33,
                    43.3,
                    22.1,
                    34.4,
                    39.1,
                    25.4,
                    22.6,
                    30.2,
                    35.6,
                    37.4,
                    31.4,
                    31.4,
                    32.3,
                    19.9,
                    34.4,
                    31,
                    25.6,
                    38.2,
                    20.6,
                    47.5,
                    33,
                    32.3,
                    20.4,
                    38.4,
                    24.3,
                    23.6,
                    21.1,
                    30,
                    17.5,
                    20.2,
                    17.2,
                    23.9,
                    35.2,
                    35.6,
                    34.1,
                    22.6,
                    39.2,
                    27,
                    33.9,
                    35.9,
                    32.8,
                    30.6,
                    30.2,
                    24.3,
                    27.3,
                    29.2,
                    16.8,
                    30.4,
                    33.1,
                    20.2,
                    26.9,
                    30.5,
                    28.6,
                    33.1,
                    31.7,
                    28.9,
                    46.8,
                    29.5,
                    32.7,
                    33.5,
                    43,
                    36.5,
                    26.7,
                    33.1,
                    29.6,
                    25.7,
                    29.6,
                    38.6,
                    29.6,
                    24.1,
                    23.4,
                    29.7,
                    46.5,
                    37.4,
                    30.1,
                    30.5,
                    39.6,
                    33,
                    36.6,
                    30,
                    38.1,
                    25.9,
                    25.2,
                    28.4,
                    28.7,
                    33.8,
                    24.3,
                    24.1,
                    32.7,
                    30.1,
                    29.8,
                    33.3,
                    25.1,
                    28.3,
                    28.5,
                    35.6,
                    36.9,
                    32.6,
                    41.3,
                    37.5,
                    31.4,
                    39.5,
                    34.3,
                    31.1,
                    21.5,
                    28.7,
                    38.1,
                    31.2,
                    32.9,
                    25.1,
                    25.1,
                    43.4,
                    25.7,
                    27.9,
                    23.6,
                    28.7,
                    24,
                    39.2,
                    34.4,
                    26,
                    23.2,
                    30.3,
                    28.9,
                    30.9,
                    31.4,
                    23.8,
                    25.3,
                    28.7,
                    32.1,
                    33.7,
                    22.4,
                    30.4,
                    28.3,
                    35.7,
                    35.3,
                    30.5,
                    31,
                    30.9,
                    27.4,
                    44.2,
                    33.9,
                    37.7,
                    26.1,
                    33.9,
                    30.6,
                    25.8,
                    39.4,
                    25.5,
                    42.1,
                    31.7,
                    29.7,
                    36.2,
                    40.5,
                    28,
                    38.9,
                    30.2,
                    28.1,
                    31.4,
                    38,
                    31.8,
                    36.3,
                    47.4,
                    30.2,
                    25.8,
                    35.4,
                    46.7,
                    28.6,
                    46.2,
                    30.8,
                    28.9,
                    21.4,
                    31.7,
                    41.3,
                    23.8,
                    33.4,
                    34.2,
                    34.1,
                    35.5,
                    20,
                    32.7,
                    30.5,
                    44.8,
                    32.1,
                    30.5,
                    40.6,
                    30.6,
                    31.9,
                    40.6,
                    29.1,
                    37.3,
                    43.1,
                    36.9,
                    34.3,
                    27.2,
                    26.8,
                    38.1,
                    30.2,
                    23.5,
                    25.5,
                    30.6,
                    45.4,
                    23.7,
                    20.7,
                    28.3,
                    20.2,
                    30.2,
                    35.9,
                    30.7,
                    29,
                    19.6,
                    31.1,
                    21.9,
                    40.3,
                    33.7,
                    29.5,
                    33.3,
                    32.6,
                    37.5,
                    39.2,
                    31.6,
                    25.3,
                    39.1,
                    28.3,
                    34.1,
                    25.2,
                    23.7,
                    27,
                    37.8,
                    29.4,
                    34.8,
                    33.2,
                    19,
                    33,
                    36.6,
                    28.6,
                    25.6,
                    33.1,
                    37.1,
                    31.4,
                    34.1,
                    21.3,
                    33.5,
                    28.8,
                    26,
                    28.9,
                    42.5,
                    38,
                    39,
                    36.1,
                    29.3,
                    35.5,
                    22.7,
                    39.7,
                    38.2,
                    24.5,
                    38.1,
                    26.4,
                    33.7,
                    42.4,
                    28.3,
                    33.9,
                    35,
                    35.3,
                    30.8,
                    26.2,
                    23.4,
                    28.5,
                    33,
                    42.7,
                    39.6,
                    31.1,
                    36.3,
                    35.2,
                    25.3,
                    42.4,
                    33.2,
                    35.9,
                    28.8,
                    46.5,
                    24,
                    31.5,
                    33.7,
                    23,
                    38.1,
                    28.7,
                    32.8,
                    32,
                    29.8,
                    31.6,
                    31.2,
                    29.7,
                    31,
                    43.9,
                    21.4,
                    40.8,
                    31.4,
                    36.1,
                    23.2,
                    17.4,
                    20.3,
                    35.3,
                    24.3,
                    18.5,
                    26.4,
                    26.1,
                    41.7,
                    24.1,
                    31.1,
                    27.4,
                    36.2,
                    32.4,
                    23.7,
                    34.8,
                    40.2,
                    32.3,
                    35.8,
                    33.7,
                    39.3,
                    34.9,
                    44.7,
                    41.5,
                    26.4,
                    29.5,
                    32.9,
                    38.1,
                    28.7,
                    30.5,
                    27.7,
                    35.2,
                    23.5,
                    30.7,
                    40.5,
                    22.6,
                    28.9,
                    22.6,
                    24.3,
                    36.7,
                    33.4,
                    40.7,
                    36.6,
                    37.4,
                    35.4,
                    27.1,
                    39.1,
                    28.4,
                    21.8,
                    40.3,
                    36.1,
                    24.4,
                    21.4,
                    30.1,
                    27.3,
                    32.1,
                    34.8,
                    38.4,
                    23.7,
                    31.7,
                    35.5,
                    24,
                    29.2,
                    34.1,
                    26.6,
                    26.4,
                    30.1,
                    27,
                    21.8,
                    36,
                    30.9,
                    26.4,
                    29,
                    37.9,
                    22.8,
                    33.6,
                    27.6,
                    22.8,
                    27.8,
                    37.4,
                    38.2,
                    34.6,
                    35.2,
                    27.1,
                    26,
                    25.2,
                    31.8,
                    32.3,
                    29,
                    39.7,
                    19.5,
                    36.1,
                    26.7,
                    36.5,
                    28.9,
                    34.2,
                    33.3,
                    32.3,
                    39.8,
                    34.3,
                    28.9,
                    24.4,
                    41.1,
                    36,
                    27.6,
                    29.3,
                    27.7,
                    37,
                    36.9,
                    22.5,
                    29.9,
                    41.8,
                    27.6,
                    23.2,
                    20.9,
                    31.9,
                    28.5,
                    44.2,
                    22.9,
                    33.1,
                    24.8,
                    26.2,
                    36,
                    22.3,
                    42.2,
                    26.5,
                    35.8,
                    41.4,
                    36.6,
                    30.1,
                    25.8,
                    30.8,
                    42.9,
                    21,
                    22.5,
                    34.4,
                    31.5,
                    24.2,
                    37.1,
                    26.1,
                    35.5,
                    33.7,
                    17.7,
                    31.1,
                    29.8,
                    24.3,
                    31.8,
                    31.8,
                    28,
                    30.8,
                    21.9,
                    33.1,
                    25.8,
                    23.8,
                    34.4,
                    33.8,
                    36,
                    31.5,
                    28.3,
                    23.5,
                    31.4,
                    31.1,
                    24.7,
                    32.8,
                    29.8,
                    30.5,
                    32.5,
                    34.2,
                    50.4,
                    24.1,
                    32.8,
                    30.8,
                    32.3,
                    35.5,
                    23.8,
                    23.8,
                    29.6,
                    33.1,
                    24.1,
                    32.2,
                    28.1,
                    47.6,
                    28,
                    33.5,
                    19.9,
                    25.4,
                    29.9,
                    37.3,
                    43.7,
                    23.7,
                    24.3,
                    36.2,
                    29.5,
                    24.9,
                    30.1,
                    21.9,
                    28.1,
                    27.1,
                    33.4,
                    28.8,
                    29.5,
                    34.8,
                    27.4,
                    22.1,
                    37.1,
                    26.7,
                    28.9,
                    29,
                    30,
                    39.5,
                    33.6,
                    26.9,
                    29,
                    24,
                    38.9,
                    32.1,
                    44,
                    20,
                    25.6,
                    40.3,
                    22.5,
                    22.5,
                    40.9,
                    27.3,
                    36.9,
                    35.1,
                    29.4,
                    32.6,
                    32.3,
                    39.8,
                    24.6,
                    28.3,
                    31.7,
                    26.7,
                    27.5,
                    24.6,
                    34,
                    26.9,
                    22.9,
                    28.2,
                    34.2,
                    25,
                    33.2,
                    31,
                    35.8,
                    23.2,
                    32.1,
                    23.4,
                    20.1,
                    39.2,
                    34.2,
                    46.5,
                    32.5,
                    25.8,
                    35.3,
                    37.2,
                    27.5,
                    29.7,
                    24.2,
                    26.2,
                    29.5,
                    23.2,
                    46.1,
                    40.2,
                    22.6,
                    39.9,
                    35.8,
                    35.8,
                    34.2,
                    31.3,
                    29.7,
                    18.3,
                    42.9,
                    28.4,
                    30.2,
                    27.8,
                    39.5,
                    30.8,
                    26.8,
                    35,
                    36.7,
                    39.6,
                    25.9,
                    35.2,
                    24.8,
                    36.8,
                    27.1,
                    24.8,
                    25.4,
                    25.7,
                    34.3,
                    28.2,
                    23.6,
                    20.2,
                    40.5,
                    35.4,
                    22.9,
                    40.2,
                    29.2,
                    40,
                    29.9,
                    25.5,
                    21.4,
                    25.9,
                    30.6,
                    30.1,
                    25.8,
                    30.1,
                    27.6,
                    34.7,
                    20.5,
                    19.8,
                    27.8,
                    31.6,
                    28.3,
                    20,
                    23.3,
                    34.1,
                    36.9,
                    36.3,
                    26.9,
                    23,
                    32.7,
                    25.8,
                    29.6,
                    19.2,
                    31.7,
                    29.3,
                    28.2,
                    25,
                    27.7,
                    22.8,
                    20.1,
                    33.3,
                    32.3,
                    27.6,
                    25.5,
                    24.6,
                    34.2,
                    35.8,
                    32.7,
                    37,
                    31,
                    36.1,
                    23.3,
                    45.3,
                    34.6,
                    26,
                    18.7,
                    31.6,
                    17.3,
                    23.7,
                    35.2,
                    27.9,
                    21.6,
                    38.4,
                    23,
                    37.1,
                    30.5,
                    28.9,
                    27.3,
                    28,
                    23.1,
                    30.7,
                    25.8,
                    35.2,
                    24.7,
                    25.1,
                    52.6,
                    22.5,
                    30.9,
                    37,
                    26.4,
                    29.8,
                    29.8,
                    21.5,
                    27.6,
                    28.9,
                    31.8,
                    39.5,
                    33.8,
                    32,
                    27.9,
                    41.1,
                    28.6,
                    25.6,
                    25.3,
                    37.3,
                    42.7,
                    21.7,
                    31.9,
                    37.1,
                    31.4,
                    31.3,
                    28.9,
                    18.3,
                    29.6,
                    32,
                    26,
                    31.7,
                    33.7,
                    21.8,
                    27.8,
                    20,
                    31.5,
                    30.5,
                    18.3,
                    29,
                    31.5,
                    47.7,
                    22.1,
                    36.2,
                    29.8,
                    32.7,
                    30.4,
                    33.7,
                    31.4,
                    35,
                    33.8,
                    30.9,
                    34,
                    19.1,
                    28.6,
                    38.9,
                    36.1,
                    29.8,
                    31.2,
                    29.9,
                    26.2,
                    30,
                    20.4,
                    32.3,
                    38.4,
                    25.9,
                    26.3,
                    24.5,
                    32.7,
                    29.6,
                    33.3,
                    35.8,
                    20,
                    31.4,
                    38.2,
                    36.9,
                    32.4,
                    42.8,
                    25.1,
                    29.9,
                    35.9,
                    32.8,
                    18.6,
                    23.9,
                    45.9,
                    40.3,
                    18.3,
                    33.8,
                    28.1,
                    25,
                    22.2,
                    30.3,
                    32.5,
                    37.1,
                    32.6,
                    24.9,
                    32.3,
                    32.3,
                    32.8,
                    32.8,
                    31.9,
                    21.5,
                    34.1,
                    30.3,
                    36.5,
                    32.6,
                    35.8,
                    27.9,
                    22.1,
                    44.9,
                    23.2,
                    30.6,
                    41.1,
                    34.6,
                    42.1,
                    38.8,
                    28.2,
                    28.3,
                    26.1,
                    40.4,
                    24.6,
                    35.2,
                    34.1,
                    27.4,
                    26.7,
                    41.9,
                    29.3,
                    32.1,
                    27.1,
                    24.1,
                    27.4,
                    34.9,
                    29.8,
                    41.3,
                    29.9,
                    30.3,
                    27.4,
                    28.5,
                    23.6,
                    35.6,
                    32.7,
                    25.3,
                    28,
                    32.8,
                    21.8,
                    32.4,
                    36.6,
                    21.8,
                    27.9,
                    30,
                    33.6,
                    29.4,
                    25.8,
                    24.3,
                    40.4,
                    32.1,
                    32.3,
                    27.3,
                    17.9,
                    34.8,
                    33.4,
                    25.6,
                    37.1,
                    30.9,
                    34.1,
                    21.5,
                    33.3,
                    31.3,
                    39.1,
                    25.1,
                    37.3,
                    34.6,
                    30.2,
                    21.9,
                    25,
                    25.3,
                    24.4,
                    23.9,
                    39.8,
                    16.8,
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          "metadata": {},
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      ],
      "source": [
        "fig = px.histogram(dataset, \n",
        "                   x='bmi', \n",
        "                   marginal='box', \n",
        "                   color_discrete_sequence=['red'], \n",
        "                   title='Distribution of BMI (Body Mass Index)')\n",
        "fig.update_layout(bargap=0.1)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
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                "title": {
                  "text": "Annual Medical Charges"
                },
                "xaxis": {
                  "anchor": "y",
                  "domain": [
                    0,
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                    "text": "expenses"
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                    "text": "count"
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              }
            }
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "fig = px.histogram(dataset, \n",
        "                   x='expenses', \n",
        "                   marginal='box', \n",
        "                   color='smoker', \n",
        "                   color_discrete_sequence=['green', 'grey'], \n",
        "                   title='Annual Medical Charges')\n",
        "fig.update_layout(bargap=0.1)\n",
        "fig.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/plain": [
              "smoker\n",
              "no     1064\n",
              "yes     274\n",
              "Name: count, dtype: int64"
            ]
          },
          "execution_count": 18,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# An imbalance in the number of smokers and non-smokers in the dataset is observed.\n",
        "# We will examine if any actions need to be taken to address this.\n",
        "dataset.smoker.value_counts()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "c:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
            "  if pd.api.types.is_categorical_dtype(vector):\n",
            "c:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
            "  if pd.api.types.is_categorical_dtype(vector):\n",
            "c:\\Users\\User\\anaconda3\\lib\\site-packages\\seaborn\\_oldcore.py:1498: FutureWarning: is_categorical_dtype is deprecated and will be removed in a future version. Use isinstance(dtype, CategoricalDtype) instead\n",
            "  if pd.api.types.is_categorical_dtype(vector):\n"
          ]
        },
        {
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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "sns.barplot(data=dataset, x='region', y='expenses');"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preprocessing\n",
        "* Convert categorical columns into numeric, via One-hot encoding\n",
        "* Apply the min-max scaler to numerical values to be in range of [0 1]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get a list of categorical variables\n",
        "obj_cols = dataset.select_dtypes('object').columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>expenses</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>27</td>\n",
              "      <td>0</td>\n",
              "      <td>16884</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>1725</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>33</td>\n",
              "      <td>3</td>\n",
              "      <td>4449</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>22</td>\n",
              "      <td>0</td>\n",
              "      <td>21984</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>28</td>\n",
              "      <td>0</td>\n",
              "      <td>3866</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  bmi  children  expenses  sex_female  sex_male  smoker_no  smoker_yes  \\\n",
              "0   19   27         0     16884           1         0          0           1   \n",
              "1   18   33         1      1725           0         1          1           0   \n",
              "2   28   33         3      4449           0         1          1           0   \n",
              "3   33   22         0     21984           0         1          1           0   \n",
              "4   32   28         0      3866           0         1          1           0   \n",
              "\n",
              "   region_northeast  region_northwest  region_southeast  region_southwest  \n",
              "0                 0                 0                 0                 1  \n",
              "1                 0                 0                 1                 0  \n",
              "2                 0                 0                 1                 0  \n",
              "3                 0                 1                 0                 0  \n",
              "4                 0                 1                 0                 0  "
            ]
          },
          "execution_count": 5,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Apply one-hot encoding to each column with categorical data\n",
        "df = dataset.copy()\n",
        "df = pd.get_dummies(df, columns=obj_cols).astype('int')\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get a list of numerical features, omitting the target\n",
        "num_cols = dataset.select_dtypes(include=np.number).columns.tolist()\n",
        "num_cols.remove('expenses')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>expenses</th>\n",
              "      <th>sex_female</th>\n",
              "      <th>sex_male</th>\n",
              "      <th>smoker_no</th>\n",
              "      <th>smoker_yes</th>\n",
              "      <th>region_northeast</th>\n",
              "      <th>region_northwest</th>\n",
              "      <th>region_southeast</th>\n",
              "      <th>region_southwest</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0.021739</td>\n",
              "      <td>0.297297</td>\n",
              "      <td>0.0</td>\n",
              "      <td>16884</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>0.000000</td>\n",
              "      <td>0.459459</td>\n",
              "      <td>0.2</td>\n",
              "      <td>1725</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>0.217391</td>\n",
              "      <td>0.459459</td>\n",
              "      <td>0.6</td>\n",
              "      <td>4449</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>0.326087</td>\n",
              "      <td>0.162162</td>\n",
              "      <td>0.0</td>\n",
              "      <td>21984</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>0.304348</td>\n",
              "      <td>0.324324</td>\n",
              "      <td>0.0</td>\n",
              "      <td>3866</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "        age       bmi  children  expenses  sex_female  sex_male  smoker_no  \\\n",
              "0  0.021739  0.297297       0.0     16884           1         0          0   \n",
              "1  0.000000  0.459459       0.2      1725           0         1          1   \n",
              "2  0.217391  0.459459       0.6      4449           0         1          1   \n",
              "3  0.326087  0.162162       0.0     21984           0         1          1   \n",
              "4  0.304348  0.324324       0.0      3866           0         1          1   \n",
              "\n",
              "   smoker_yes  region_northeast  region_northwest  region_southeast  \\\n",
              "0           1                 0                 0                 0   \n",
              "1           0                 0                 0                 1   \n",
              "2           0                 0                 0                 1   \n",
              "3           0                 0                 1                 0   \n",
              "4           0                 0                 1                 0   \n",
              "\n",
              "   region_southwest  \n",
              "0                 1  \n",
              "1                 0  \n",
              "2                 0  \n",
              "3                 0  \n",
              "4                 0  "
            ]
          },
          "execution_count": 7,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Scale the numerical features \n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "scaler = MinMaxScaler()\n",
        "df[num_cols] = scaler.fit_transform(df[num_cols])\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {},
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_dataset, test_dataset = train_test_split(df, test_size=0.2, random_state=0)\n",
        "\n",
        "train_features = train_dataset.copy()\n",
        "test_features = test_dataset.copy()\n",
        "\n",
        "# Remove the target from features and return it at the assighned variable\n",
        "train_labels = train_features.pop('expenses')\n",
        "test_labels = test_features.pop('expenses')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Dense NN implementation"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Epoch 1/100\n",
            "27/27 - 1s - loss: 12872.4844 - mae: 12872.4844 - mse: 303991552.0000 - val_loss: 14506.4590 - val_mae: 14506.4590 - val_mse: 371957984.0000 - 558ms/epoch - 21ms/step\n",
            "Epoch 2/100\n",
            "27/27 - 0s - loss: 12869.4131 - mae: 12869.4131 - mse: 303907968.0000 - val_loss: 14501.7188 - val_mae: 14501.7188 - val_mse: 371813824.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 3/100\n",
            "27/27 - 0s - loss: 12861.9775 - mae: 12861.9775 - mse: 303711168.0000 - val_loss: 14490.4150 - val_mae: 14490.4150 - val_mse: 371473984.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 4/100\n",
            "27/27 - 0s - loss: 12845.5371 - mae: 12845.5371 - mse: 303263520.0000 - val_loss: 14467.2822 - val_mae: 14467.2822 - val_mse: 370782944.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 5/100\n",
            "27/27 - 0s - loss: 12814.6680 - mae: 12814.6680 - mse: 302450144.0000 - val_loss: 14426.6387 - val_mae: 14426.6387 - val_mse: 369574624.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 6/100\n",
            "27/27 - 0s - loss: 12763.5254 - mae: 12763.5254 - mse: 301135072.0000 - val_loss: 14362.5176 - val_mae: 14362.5176 - val_mse: 367681408.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 7/100\n",
            "27/27 - 0s - loss: 12686.4707 - mae: 12686.4707 - mse: 299092736.0000 - val_loss: 14269.8945 - val_mae: 14269.8945 - val_mse: 364966016.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 8/100\n",
            "27/27 - 0s - loss: 12578.5791 - mae: 12578.5791 - mse: 296308672.0000 - val_loss: 14143.4893 - val_mae: 14143.4893 - val_mse: 361288032.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 9/100\n",
            "27/27 - 0s - loss: 12434.6250 - mae: 12434.6250 - mse: 292606080.0000 - val_loss: 13977.7656 - val_mae: 13977.7656 - val_mse: 356521632.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 10/100\n",
            "27/27 - 0s - loss: 12248.6777 - mae: 12248.6777 - mse: 287833376.0000 - val_loss: 13768.0410 - val_mae: 13768.0410 - val_mse: 350573568.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 11/100\n",
            "27/27 - 0s - loss: 12016.6963 - mae: 12016.6963 - mse: 282168448.0000 - val_loss: 13508.5615 - val_mae: 13508.5615 - val_mse: 343326112.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 12/100\n",
            "27/27 - 0s - loss: 11733.2930 - mae: 11733.2930 - mse: 275227456.0000 - val_loss: 13196.1748 - val_mae: 13196.1748 - val_mse: 334790528.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 13/100\n",
            "27/27 - 0s - loss: 11401.2637 - mae: 11401.2637 - mse: 267366896.0000 - val_loss: 12835.9297 - val_mae: 12835.9297 - val_mse: 325036096.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 14/100\n",
            "27/27 - 0s - loss: 11024.1289 - mae: 11024.1289 - mse: 258250096.0000 - val_loss: 12436.8818 - val_mae: 12436.8818 - val_mse: 314226656.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 15/100\n",
            "27/27 - 0s - loss: 10629.4102 - mae: 10629.4102 - mse: 248344656.0000 - val_loss: 12024.4707 - val_mae: 12024.4707 - val_mse: 302792896.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 16/100\n",
            "27/27 - 0s - loss: 10236.0732 - mae: 10236.0732 - mse: 238492272.0000 - val_loss: 11614.8770 - val_mae: 11614.8770 - val_mse: 291191840.0000 - 37ms/epoch - 1ms/step\n",
            "Epoch 17/100\n",
            "27/27 - 0s - loss: 9852.5137 - mae: 9852.5137 - mse: 228694496.0000 - val_loss: 11208.4346 - val_mae: 11208.4346 - val_mse: 279079136.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 18/100\n",
            "27/27 - 0s - loss: 9478.0596 - mae: 9478.0596 - mse: 218198784.0000 - val_loss: 10809.7656 - val_mae: 10809.7656 - val_mse: 266774896.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 19/100\n",
            "27/27 - 0s - loss: 9125.1445 - mae: 9125.1445 - mse: 207895232.0000 - val_loss: 10428.1602 - val_mae: 10428.1602 - val_mse: 254966864.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 20/100\n",
            "27/27 - 0s - loss: 8822.2188 - mae: 8822.2188 - mse: 198372384.0000 - val_loss: 10084.6025 - val_mae: 10084.6025 - val_mse: 244306496.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 21/100\n",
            "27/27 - 0s - loss: 8569.2344 - mae: 8569.2344 - mse: 189982304.0000 - val_loss: 9781.5410 - val_mae: 9781.5410 - val_mse: 234307616.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 22/100\n",
            "27/27 - 0s - loss: 8353.6084 - mae: 8353.6084 - mse: 181894576.0000 - val_loss: 9531.6885 - val_mae: 9531.6885 - val_mse: 225485552.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 23/100\n",
            "27/27 - 0s - loss: 8175.1611 - mae: 8175.1611 - mse: 175263680.0000 - val_loss: 9319.7676 - val_mae: 9319.7676 - val_mse: 216991120.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 24/100\n",
            "27/27 - 0s - loss: 8023.0435 - mae: 8023.0435 - mse: 168313632.0000 - val_loss: 9149.3848 - val_mae: 9149.3848 - val_mse: 208961536.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 25/100\n",
            "27/27 - 0s - loss: 7889.8901 - mae: 7889.8901 - mse: 162699856.0000 - val_loss: 9033.7803 - val_mae: 9033.7803 - val_mse: 202812400.0000 - 37ms/epoch - 1ms/step\n",
            "Epoch 26/100\n",
            "27/27 - 0s - loss: 7792.2686 - mae: 7792.2686 - mse: 157794976.0000 - val_loss: 8927.2314 - val_mae: 8927.2314 - val_mse: 196620592.0000 - 35ms/epoch - 1ms/step\n",
            "Epoch 27/100\n",
            "27/27 - 0s - loss: 7708.6553 - mae: 7708.6553 - mse: 153532448.0000 - val_loss: 8849.6797 - val_mae: 8849.6797 - val_mse: 191814640.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 28/100\n",
            "27/27 - 0s - loss: 7647.5645 - mae: 7647.5645 - mse: 150154416.0000 - val_loss: 8783.2900 - val_mae: 8783.2900 - val_mse: 187728448.0000 - 37ms/epoch - 1ms/step\n",
            "Epoch 29/100\n",
            "27/27 - 0s - loss: 7597.2671 - mae: 7597.2671 - mse: 147299952.0000 - val_loss: 8726.0225 - val_mae: 8726.0225 - val_mse: 184223296.0000 - 38ms/epoch - 1ms/step\n",
            "Epoch 30/100\n",
            "27/27 - 0s - loss: 7554.3682 - mae: 7554.3682 - mse: 144821248.0000 - val_loss: 8676.3389 - val_mae: 8676.3389 - val_mse: 181415904.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 31/100\n",
            "27/27 - 0s - loss: 7516.7607 - mae: 7516.7607 - mse: 142679712.0000 - val_loss: 8628.5254 - val_mae: 8628.5254 - val_mse: 178659856.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 32/100\n",
            "27/27 - 0s - loss: 7479.7549 - mae: 7479.7549 - mse: 140801376.0000 - val_loss: 8587.1387 - val_mae: 8587.1387 - val_mse: 176525056.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 33/100\n",
            "27/27 - 0s - loss: 7445.4458 - mae: 7445.4458 - mse: 138944432.0000 - val_loss: 8545.3652 - val_mae: 8545.3652 - val_mse: 174379136.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 34/100\n",
            "27/27 - 0s - loss: 7411.1724 - mae: 7411.1724 - mse: 137635904.0000 - val_loss: 8506.8711 - val_mae: 8506.8711 - val_mse: 172853616.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 35/100\n",
            "27/27 - 0s - loss: 7376.6284 - mae: 7376.6284 - mse: 136208752.0000 - val_loss: 8467.0225 - val_mae: 8467.0225 - val_mse: 171216304.0000 - 44ms/epoch - 2ms/step\n",
            "Epoch 36/100\n",
            "27/27 - 0s - loss: 7342.0322 - mae: 7342.0322 - mse: 134916176.0000 - val_loss: 8428.1992 - val_mae: 8428.1992 - val_mse: 169693648.0000 - 41ms/epoch - 2ms/step\n",
            "Epoch 37/100\n",
            "27/27 - 0s - loss: 7307.2681 - mae: 7307.2681 - mse: 133698696.0000 - val_loss: 8388.3135 - val_mae: 8388.3135 - val_mse: 168142096.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 38/100\n",
            "27/27 - 0s - loss: 7272.2979 - mae: 7272.2979 - mse: 132423960.0000 - val_loss: 8348.3740 - val_mae: 8348.3740 - val_mse: 166446032.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 39/100\n",
            "27/27 - 0s - loss: 7237.8740 - mae: 7237.8740 - mse: 131178744.0000 - val_loss: 8309.1504 - val_mae: 8309.1504 - val_mse: 165052048.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 40/100\n",
            "27/27 - 0s - loss: 7202.1064 - mae: 7202.1064 - mse: 129700968.0000 - val_loss: 8264.7539 - val_mae: 8264.7539 - val_mse: 162965216.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 41/100\n",
            "27/27 - 0s - loss: 7165.5156 - mae: 7165.5156 - mse: 128385008.0000 - val_loss: 8225.3086 - val_mae: 8225.3086 - val_mse: 161673728.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 42/100\n",
            "27/27 - 0s - loss: 7129.4819 - mae: 7129.4819 - mse: 127511272.0000 - val_loss: 8188.0762 - val_mae: 8188.0762 - val_mse: 160699472.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 43/100\n",
            "27/27 - 0s - loss: 7092.0737 - mae: 7092.0737 - mse: 126067240.0000 - val_loss: 8142.8755 - val_mae: 8142.8755 - val_mse: 158758496.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 44/100\n",
            "27/27 - 0s - loss: 7055.2202 - mae: 7055.2202 - mse: 125171656.0000 - val_loss: 8103.3911 - val_mae: 8103.3911 - val_mse: 157660976.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 45/100\n",
            "27/27 - 0s - loss: 7015.2715 - mae: 7015.2715 - mse: 123621672.0000 - val_loss: 8054.1006 - val_mae: 8054.1006 - val_mse: 155528432.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 46/100\n",
            "27/27 - 0s - loss: 6976.6421 - mae: 6976.6421 - mse: 122118128.0000 - val_loss: 8006.2954 - val_mae: 8006.2954 - val_mse: 153724272.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 47/100\n",
            "27/27 - 0s - loss: 6934.6548 - mae: 6934.6548 - mse: 121177880.0000 - val_loss: 7968.5537 - val_mae: 7968.5537 - val_mse: 152964320.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 48/100\n",
            "27/27 - 0s - loss: 6894.5854 - mae: 6894.5854 - mse: 119868496.0000 - val_loss: 7917.1982 - val_mae: 7917.1982 - val_mse: 150989968.0000 - 61ms/epoch - 2ms/step\n",
            "Epoch 49/100\n",
            "27/27 - 0s - loss: 6851.3521 - mae: 6851.3521 - mse: 118647224.0000 - val_loss: 7872.4292 - val_mae: 7872.4292 - val_mse: 149774256.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 50/100\n",
            "27/27 - 0s - loss: 6807.8833 - mae: 6807.8833 - mse: 117358160.0000 - val_loss: 7821.7744 - val_mae: 7821.7744 - val_mse: 147894912.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 51/100\n",
            "27/27 - 0s - loss: 6762.4546 - mae: 6762.4546 - mse: 115860792.0000 - val_loss: 7771.7549 - val_mae: 7771.7549 - val_mse: 146366352.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 52/100\n",
            "27/27 - 0s - loss: 6716.3799 - mae: 6716.3799 - mse: 114609144.0000 - val_loss: 7719.0830 - val_mae: 7719.0830 - val_mse: 144625120.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 53/100\n",
            "27/27 - 0s - loss: 6670.4043 - mae: 6670.4043 - mse: 113334256.0000 - val_loss: 7668.4233 - val_mae: 7668.4233 - val_mse: 143235712.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 54/100\n",
            "27/27 - 0s - loss: 6618.7241 - mae: 6618.7241 - mse: 111645008.0000 - val_loss: 7605.4019 - val_mae: 7605.4019 - val_mse: 140666544.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 55/100\n",
            "27/27 - 0s - loss: 6569.3281 - mae: 6569.3281 - mse: 109842616.0000 - val_loss: 7545.9453 - val_mae: 7545.9453 - val_mse: 138748912.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 56/100\n",
            "27/27 - 0s - loss: 6517.2422 - mae: 6517.2422 - mse: 108342904.0000 - val_loss: 7485.6895 - val_mae: 7485.6895 - val_mse: 136859728.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 57/100\n",
            "27/27 - 0s - loss: 6463.9312 - mae: 6463.9312 - mse: 106669008.0000 - val_loss: 7424.9404 - val_mae: 7424.9404 - val_mse: 135079632.0000 - 37ms/epoch - 1ms/step\n",
            "Epoch 58/100\n",
            "27/27 - 0s - loss: 6407.6982 - mae: 6407.6982 - mse: 105570704.0000 - val_loss: 7363.0732 - val_mae: 7363.0732 - val_mse: 133364816.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 59/100\n",
            "27/27 - 0s - loss: 6349.9111 - mae: 6349.9111 - mse: 104070936.0000 - val_loss: 7299.4258 - val_mae: 7299.4258 - val_mse: 131560416.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 60/100\n",
            "27/27 - 0s - loss: 6289.9692 - mae: 6289.9692 - mse: 102227152.0000 - val_loss: 7229.0122 - val_mae: 7229.0122 - val_mse: 129407512.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 61/100\n",
            "27/27 - 0s - loss: 6228.4551 - mae: 6228.4551 - mse: 100770952.0000 - val_loss: 7160.4253 - val_mae: 7160.4253 - val_mse: 127587296.0000 - 47ms/epoch - 2ms/step\n",
            "Epoch 62/100\n",
            "27/27 - 0s - loss: 6164.9824 - mae: 6164.9824 - mse: 98936064.0000 - val_loss: 7085.3130 - val_mae: 7085.3130 - val_mse: 125338384.0000 - 39ms/epoch - 1ms/step\n",
            "Epoch 63/100\n",
            "27/27 - 0s - loss: 6098.7178 - mae: 6098.7178 - mse: 97303080.0000 - val_loss: 7010.5488 - val_mae: 7010.5488 - val_mse: 123414560.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 64/100\n",
            "27/27 - 0s - loss: 6030.5269 - mae: 6030.5269 - mse: 95995304.0000 - val_loss: 6936.0479 - val_mae: 6936.0479 - val_mse: 121782952.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 65/100\n",
            "27/27 - 0s - loss: 5957.3560 - mae: 5957.3560 - mse: 94085696.0000 - val_loss: 6851.6694 - val_mae: 6851.6694 - val_mse: 119413160.0000 - 44ms/epoch - 2ms/step\n",
            "Epoch 66/100\n",
            "27/27 - 0s - loss: 5883.3892 - mae: 5883.3892 - mse: 92376416.0000 - val_loss: 6767.3423 - val_mae: 6767.3423 - val_mse: 117304072.0000 - 37ms/epoch - 1ms/step\n",
            "Epoch 67/100\n",
            "27/27 - 0s - loss: 5806.0410 - mae: 5806.0410 - mse: 90549392.0000 - val_loss: 6685.2051 - val_mae: 6685.2051 - val_mse: 115470640.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 68/100\n",
            "27/27 - 0s - loss: 5728.0239 - mae: 5728.0239 - mse: 88929320.0000 - val_loss: 6589.5210 - val_mae: 6589.5210 - val_mse: 112843112.0000 - 41ms/epoch - 2ms/step\n",
            "Epoch 69/100\n",
            "27/27 - 0s - loss: 5643.3394 - mae: 5643.3394 - mse: 86711232.0000 - val_loss: 6492.7798 - val_mae: 6492.7798 - val_mse: 110260304.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 70/100\n",
            "27/27 - 0s - loss: 5556.1763 - mae: 5556.1763 - mse: 84818944.0000 - val_loss: 6401.2109 - val_mae: 6401.2109 - val_mse: 108598088.0000 - 48ms/epoch - 2ms/step\n",
            "Epoch 71/100\n",
            "27/27 - 0s - loss: 5466.6758 - mae: 5466.6758 - mse: 82980104.0000 - val_loss: 6300.0732 - val_mae: 6300.0732 - val_mse: 106186520.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 72/100\n",
            "27/27 - 0s - loss: 5373.8999 - mae: 5373.8999 - mse: 81290904.0000 - val_loss: 6200.3999 - val_mae: 6200.3999 - val_mse: 104106928.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 73/100\n",
            "27/27 - 0s - loss: 5280.9048 - mae: 5280.9048 - mse: 79236496.0000 - val_loss: 6094.2734 - val_mae: 6094.2734 - val_mse: 101548976.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 74/100\n",
            "27/27 - 0s - loss: 5181.4932 - mae: 5181.4932 - mse: 77373408.0000 - val_loss: 5988.2607 - val_mae: 5988.2607 - val_mse: 99422632.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 75/100\n",
            "27/27 - 0s - loss: 5080.3804 - mae: 5080.3804 - mse: 75439352.0000 - val_loss: 5876.9126 - val_mae: 5876.9126 - val_mse: 97077728.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 76/100\n",
            "27/27 - 0s - loss: 4979.3960 - mae: 4979.3960 - mse: 73578288.0000 - val_loss: 5764.7856 - val_mae: 5764.7856 - val_mse: 94901120.0000 - 40ms/epoch - 1ms/step\n",
            "Epoch 77/100\n",
            "27/27 - 0s - loss: 4878.3765 - mae: 4878.3765 - mse: 72100480.0000 - val_loss: 5653.3936 - val_mae: 5653.3940 - val_mse: 93073400.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 78/100\n",
            "27/27 - 0s - loss: 4770.9087 - mae: 4770.9087 - mse: 70244832.0000 - val_loss: 5536.9795 - val_mae: 5536.9795 - val_mse: 90849392.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 79/100\n",
            "27/27 - 0s - loss: 4665.4961 - mae: 4665.4961 - mse: 68742592.0000 - val_loss: 5424.2568 - val_mae: 5424.2568 - val_mse: 89208456.0000 - 55ms/epoch - 2ms/step\n",
            "Epoch 80/100\n",
            "27/27 - 0s - loss: 4558.5376 - mae: 4558.5376 - mse: 66963900.0000 - val_loss: 5306.0020 - val_mae: 5306.0020 - val_mse: 86725664.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 81/100\n",
            "27/27 - 0s - loss: 4454.5205 - mae: 4454.5205 - mse: 65030996.0000 - val_loss: 5192.8633 - val_mae: 5192.8633 - val_mse: 84743928.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 82/100\n",
            "27/27 - 0s - loss: 4349.2549 - mae: 4349.2549 - mse: 63642852.0000 - val_loss: 5086.1465 - val_mae: 5086.1465 - val_mse: 83337360.0000 - 44ms/epoch - 2ms/step\n",
            "Epoch 83/100\n",
            "27/27 - 0s - loss: 4251.5317 - mae: 4251.5317 - mse: 62254908.0000 - val_loss: 4974.6128 - val_mae: 4974.6128 - val_mse: 81864904.0000 - 40ms/epoch - 1ms/step\n",
            "Epoch 84/100\n",
            "27/27 - 0s - loss: 4155.7163 - mae: 4155.7163 - mse: 61510568.0000 - val_loss: 4875.9609 - val_mae: 4875.9609 - val_mse: 80649600.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 85/100\n",
            "27/27 - 0s - loss: 4062.8125 - mae: 4062.8125 - mse: 59565932.0000 - val_loss: 4777.0483 - val_mae: 4777.0483 - val_mse: 78571648.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 86/100\n",
            "27/27 - 0s - loss: 3978.4619 - mae: 3978.4619 - mse: 58396056.0000 - val_loss: 4690.1880 - val_mae: 4690.1880 - val_mse: 77445608.0000 - 44ms/epoch - 2ms/step\n",
            "Epoch 87/100\n",
            "27/27 - 0s - loss: 3903.3257 - mae: 3903.3257 - mse: 57770200.0000 - val_loss: 4612.7319 - val_mae: 4612.7319 - val_mse: 76552552.0000 - 41ms/epoch - 2ms/step\n",
            "Epoch 88/100\n",
            "27/27 - 0s - loss: 3835.8882 - mae: 3835.8882 - mse: 56779956.0000 - val_loss: 4536.6538 - val_mae: 4536.6538 - val_mse: 74933976.0000 - 43ms/epoch - 2ms/step\n",
            "Epoch 89/100\n",
            "27/27 - 0s - loss: 3771.5354 - mae: 3771.5354 - mse: 55650164.0000 - val_loss: 4478.3574 - val_mae: 4478.3574 - val_mse: 73776264.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 90/100\n",
            "27/27 - 0s - loss: 3715.8347 - mae: 3715.8347 - mse: 54579448.0000 - val_loss: 4429.0015 - val_mae: 4429.0015 - val_mse: 72451848.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 91/100\n",
            "27/27 - 0s - loss: 3667.9302 - mae: 3667.9302 - mse: 53781124.0000 - val_loss: 4380.8223 - val_mae: 4380.8223 - val_mse: 71532448.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 92/100\n",
            "27/27 - 0s - loss: 3624.6682 - mae: 3624.6682 - mse: 52828164.0000 - val_loss: 4341.8950 - val_mae: 4341.8950 - val_mse: 70323920.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 93/100\n",
            "27/27 - 0s - loss: 3587.1914 - mae: 3587.1914 - mse: 52256140.0000 - val_loss: 4315.3770 - val_mae: 4315.3770 - val_mse: 69253528.0000 - 42ms/epoch - 2ms/step\n",
            "Epoch 94/100\n",
            "27/27 - 0s - loss: 3558.8767 - mae: 3558.8767 - mse: 51170588.0000 - val_loss: 4299.7495 - val_mae: 4299.7495 - val_mse: 68693960.0000 - 45ms/epoch - 2ms/step\n",
            "Epoch 95/100\n",
            "27/27 - 0s - loss: 3535.8533 - mae: 3535.8533 - mse: 50491180.0000 - val_loss: 4286.9429 - val_mae: 4286.9429 - val_mse: 67533184.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 96/100\n",
            "27/27 - 0s - loss: 3518.4280 - mae: 3518.4280 - mse: 49938312.0000 - val_loss: 4274.8315 - val_mae: 4274.8315 - val_mse: 66755432.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 97/100\n",
            "27/27 - 0s - loss: 3504.6736 - mae: 3504.6736 - mse: 49353476.0000 - val_loss: 4266.2651 - val_mae: 4266.2651 - val_mse: 66141564.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 98/100\n",
            "27/27 - 0s - loss: 3496.4661 - mae: 3496.4661 - mse: 49139636.0000 - val_loss: 4263.0913 - val_mae: 4263.0913 - val_mse: 65878552.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 99/100\n",
            "27/27 - 0s - loss: 3486.2183 - mae: 3486.2183 - mse: 48423580.0000 - val_loss: 4255.3135 - val_mae: 4255.3135 - val_mse: 64959128.0000 - 36ms/epoch - 1ms/step\n",
            "Epoch 100/100\n",
            "27/27 - 0s - loss: 3479.2827 - mae: 3479.2827 - mse: 48269504.0000 - val_loss: 4252.0391 - val_mae: 4252.0391 - val_mse: 65044416.0000 - 36ms/epoch - 1ms/step\n"
          ]
        }
      ],
      "source": [
        "model = keras.Sequential()\n",
        "model.add(layers.Input(shape=(11,)))\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer='adam', loss='mae', metrics=['mae', 'mse'])\n",
        "history = model.fit(train_features, train_labels, validation_split=0.2, epochs=100, verbose=2)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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++z3VHG7laZfJkyezaNEi9u/fz5AhQ2jRogW5ubkcPXoUp9NJr1692LBhA4mJibhcLgwGAwaDgZdeeonJkyezZs0arrjiClq3bk1OTg5Hjx7F5XLRvn17nn76aY/fo4h4R/4Q0U8//ZRPP/20yDL5I1bWrl3LmjVrGDRoEG+//bb78xEjRvDkk0/y2WefMXToUK644gqvxy3epz63aOVpl9tuu40VK1awadMmRo0aRZMmTQgJCeH48eOkp6cTFhZWoO+MiYnh2Wef5amnnuL555/njTfeoHHjxsTFxZGcnAzAQw89RL9+/Tx+fyL5lECL1BDDhg0jNjaWVq1a0aJFi1KdExMTw6JFi3j33Xf56aefOHDgAMHBwfTt25dbbrmlyH0W27RpwxdffMHrr7/OL7/8wv79+2nZsiV33303dru9yG0z2rdvz6JFi3jvvffc1zEajVx22WVcf/31TJgwwWuLgZS1XZo2bcpXX33FK6+8wrZt2zh48CDBwcH07NmTW265hREjRjBgwABOnz7N1q1b3UPtGjZsyFdffcWcOXNYunQpR44cwel00qpVK6655hruvPNO98IwIlL15c8PnTBhQrHb8OT/3Mp/czZ69OhCZW655Ra++OIL1q1bpwS6BlGfW7SytovFYuHtt9/m/fffZ9myZRw/fpz4+Hjq1avH9ddfz7Rp0wqN3Bo9ejTt2rXj3XffZePGjezZs4egoCD69evHhAkTGDp0qFfuTSSfwXXhZqYiIiIil5hXX32V1157jblz59KrVy8AduzYwS233MKECRP429/+VqC80+lk6dKlNG7cmMsvv5yPPvqIf/zjH7z00ksMHz68QNmtW7dy6623cvvtt/Pkk09W2j2JiIh3aA60iIiIyB9cfvnl7pElf5xrumDBAh588EG+/PJLIG/hJpPJxMcff1xggUGATz75BPh98SMREaneNIRbRERE5A9MJhP/+Mc/mDZtGqNHj2bcuHE0adKEnTt38uWXX9KkSRPuvfdeAJo1a8a9997Lq6++ys0338wNN9yA0Whk+fLlbNy4kZEjRzJo0CAf35GIiHiCEmgRERGRIvTt25fPPvuMN954gy+//JL09HTq16/P+PHjmTZtGnXq1HGXnT59Oq1atWLOnDm8/PLLOBwOWrRowVNPPcX48eN9eBciIuJJmgMtIiIiIiIiUgqaAy0iIiIiIiJSCkqgRUREREREREpBCbSIiIiIiIhIKWgRsSokOTmd8s5INxigdu3QCtVRk6l9Sqb2KZnap2Rqn+JVpG3yzxXPU3/rPWqfkql9Sqb2KZnap3jlbZvy9LVKoKsQl4sK/2PwRB01mdqnZGqfkql9Sqb2KZ7apmpRf+t9ap+SqX1KpvYpmdqneJXRNhrCLSIiIiIiIlIKSqBFRERERERESkEJtIiIiIiIiEgpKIEWERERERERKQUl0CIiIiIiIiKloARaREREREREpBS0jZWISBGcTgcOhwPI2yMwJycHm82qbSOKoPYp3oVtYzAYMZnU7YqIXMjhsON0OgH1Jxej9ileZfa36slFRC7gcrk4dy6F7OxM4PfeKSXF6O7gpTC1T/EubBuz2Y/g4DACA4N9HJWIiG9lZ2eSmXkOu91a4Lj6k5KpfYpXWf2tEmgRkQtkZ2eSnZ1BSEgE/v4BgAEAk8mAw6HHvcVR+xQvr22cOBwOsrIySEtLAlASLSKXrOzsTNLSkvDzCyQiog4mkwn1t6Wj9ileZfW3SqBFRM5zuVxkZJwlICCYkJDwAp+ZzUbsdj3xLY7ap3j5bWOxgL9/IKmpZ8jMPKcEWkQuWZmZ5/DzCyQysg4Gg6HAZ+pPSqb2KV5l9bdaRExE5Dyn04nT6SAgIMjXoUgNZTAYCAoKxm634nDYfR2OiEilczjs2O1WgoJCCiXPIp7izf5WCbSIyHlOZ96iYUajyceRSE2Wv7CJ5rCJyKUo/2df3rBtEe/xVn+rBFpE5A/0RFy8S99fIiL6WSje553vMSXQIiIiIiIiIqWgBFpERERERESkFJRAi4iIiIiIiJSCtrESERHee+8tPvjgnVKVveOOu/jTn6ZV6Hpbt25mxoy7PVKXiIhIdaH+tvpTAi0iIgwaNIRGjRoXOPbqq//l7NmzPPXUswWOt2zZusLXa9asOU899axH6hIREaku1N9Wf0qgawjL8Z9hwT+IcLpw+YXisgTjDKyNI7QRztBGOMKaYK/TAZd/mK9DFZEqqFWr1rRqVbBzfeedN4CzXH31cI9fr1at2l6pV8Tb/A59DwteJMJpwOUXitMvBFdgFI6wxue/mmKPuhwsgb4OVUSqIPW31Z8S6BrCfGYXnNlT4l+oy2DEXqcDtoZ9sTa5AlvDvqDtekRERErNnLgdzuwtub81+mGL7oatYT+sTYdgr9uxssITEREvUwJdQ2R3vZfgjteSFn8SrBkYrBkYs89gOncSU/oJTGcPYzp3HMvpHVhO7yBo2xvYI9uQ3fkuctrcCOYAX9+CSJXmcrnItjl8HUaxAszGStu/etasp1m1agXPPvs8//nPC6SmpjJ48BCeeurv2O12PvtsPj/9tJxjx45is1mpVas2vXr1YerUe4mMrAUUPSdrzJjraNGiJePG3cZ7773F/v17MZlMdO3ag3vuuZ/GjZtUyv2JlCSrz6MEdRtDWmIc5KZjtKZjzDqN8dzxvD439SCmrET8Tv2K36lfCd74H2zRPcjqPA1r86vAoPVbRUqi/vZ36m+rJiXQNYXBANGdsFla4HIVXcSYHofl1Dr8Tq3F79D3mFP3E7pyJsHr/0lm9xnkdJisjl2kCC6Xizs+2c6OU+d8HUqxOjUI451xnSqtU7darfzf/z3J2LHjCQ0NpUGDBgA89dRjrFnzM9deO5LrrhuF1Wpl/fp1fPvt1yQkxPPSS/8rsd6DBw/wl788yNVXD+fqq4ezf/8+vvnmSw4e3M/8+QsxmUyVcXsixTMYoWFXbP6ti+5vXS5MaUewnFyL38nV+B35EUv8JsLjN2EPb05m70exthpZ6WGLVAfqbwtTf1v1KIG+hDhDG5AbM4bcmDEY+j9DwO75BMa+hykjjtDVf8P/6I+kD/0vzuD6vg5VpMrRZIeCHA4Ho0aNdj/NNpuN7Nmzl9WrVzFmzFgefHCmu+zNN4/jrrtuZ9OmDZw7l0ZYWHix9Z4+ncgzzzzP0KHD3MfsdhuLF3/D1q2b6NGjt7duScQzDAYcES1wRLQg5/KJGDMTCYz9gIDfPsKcdoTwpXeTc3QMGQP/gcsvxNfRilQ56m8LUn9b9SiBvkS5/MPI7jKN7I53ErD7E0LW/QO/E78Q+ekw0gf/G2uLa3wdokiVYTAYeH9CFzJy7L4OpViVOaQsX79+gwr8uXXrNixb9jOGP4xkSU1NISQkFICsrOwSO3R/f3+uuGJIgWMxMe1ZvPgbkpOTPRS5SOVxBtcjs89jZHa7n6CtrxG09X8E7PsCS/wmzg2bjb1+N1+HKFJlqL8tmvrbqkUJ9KXOZCGnw2RsDfsRuvx+LEm7CP9+Chl9niC7672+jk6kyjAYDARaNJzpQrVr1y50zGLx48cfl7J58wbi4k4RF3eK5ORk9y8bLpezxDrDwyMKDRvz8/MDwOks+VyRKs0vmKzej2JrcgWhy2dgOneMiIWjSR82m9zWN/g6OpEqQ/1tYepvqxZNeBUAHLVac3bMIrI65w0PCfn1OQK3l26TdxG5NP2x483MzOCee/7Ec889Q0JCAm3btuOOO+7i3Xc/4qqrSjeqxWhUtyQ1m61BL1LHLSOn5UgMLgehy2fgd3Cxr8MSkSpM/W3VojfQ8juTH5n9nsJlCSZ4038JWfsMLqOZnI53+DoyEakGPv/8U/bu3c2f//w4o0bdVOAzDQcT+Z3LP5z0q1+Hn4II2PsZYcunc85owdrial+HJiLVgPpb39KjBykkq8dDZHa7H4DQ1U8RsOtjH0ckItVBWloaAK1atS5wfNeuWLZv3wrkLYYiIoDBSPrgf5PT5kYMTjthS+/G7+gKX0clItWA+lvf0htoKcxgIKvXXzA4rARtf4uQX57EXjsGe3R3X0cmIlVY//4D+eKLT3nmmae48cYxhISEsHfvbn744TtMJhN2u52MjHRfhylSdRhNpA99CZwOAg4uImzp3aTe8gOOyJa+jkxEqjD1t76lN9BSNIOBzL5/Jaf1KAwuB2HL78eQW3X35BMR3+vWrQdPP/0coaEhfPDB27z55mvs3bubKVPu4R//+BcA69ev83GUIlWM0Uz6la9gbdgXgz2b0GX3gSPX11GJSBWm/ta3DC6Xy+XrICRPUlI65f3bMBggKiq0QnUUWW/uOSI/uwbTuePktLmR9GGveq7ySuSt9qkp1D55bDYrycnx1K4djcXiV+Azs9mI3a5VKYuj9ineH9umpO+zC+X/uxTPq4r9rTEjnsgFV2HMSSWr01Qy+//Nc5VXIvUnJVP7XPxnoPqTkql9ilee/rY8fa3eQEuJXP5hnBv2Ki6DiYD9X+G/70tfhyQiIlLjOEOiSR/yXwCCdryN5dhKH0ckIiJFUQItF2Wv342sHg8BEPLzkxjTjvk4IhERkZrH2nwY2R0mARC24iEMmad9HJGIiPxRlU6gd+zYQbt27diwYUOJ5bKysrjqqqsYMmRIkZ8fOHCAe++9lz59+tClSxcmTZrEli1biiy7detWJk+eTI8ePejevTt33303Bw8eLLLsypUrGTduHF27dqVXr178+c9/JiEhoWw3WU1kdbsfa3QvjLYMQlY/5etwREREaqSMvn/FXqstxuwkQtb9w9fhiIjIH1TZBPro0aPcd999OJ0XH+P/j3/8g2PHin4reujQIcaPH09sbCwTJ07kwQcfJCEhgUmTJrFx48YCZTdu3Mjtt99OfHw806ZNY+rUqezatYuxY8dy6NChAmW//fZb7rnnHmw2Gw888AC33XYbK1euZOzYsTVz/zWjiYwh/8ZlNON/7CcsJ9f6OiIREZGaxxyYtzI3ELB/IebTO3wckIiIXKhKJtDLly/nlltu4cyZMxctu2zZMhYuXIjFYiny8+effx6r1cr8+fO59957mTRpEp9++imRkZE888wz5K+h5nK5ePbZZ4mIiGDBggVMmTKFqVOnMm/ePOx2Oy+88IK7zqysLGbNmkXbtm2ZN28ekyZN4v777+edd94hMTGRV1+tngttXYwjogU5l90GQPCvz4FLCxiIiIh4mr1uR3La3gRA8NpnuWRXmxIRqYKqXAI9depUpk+fTp06dRg5cmSJZRMTE3nqqacYP348devWLfR5UlISq1ev5sorr6Rx48bu45GRkdx8880cPHiQ2NhYAHbu3MmBAwe46aabiIiIcJdt0qQJ11xzDWvWrOH06by5SKtWrSI1NZUJEybg7+/vLtu1a1e6d+/Ot99+i9VqrUgzVFmZ3R/EaQnBcnoH/ge/9XU4IiIiNVJmr0dxmfzxi9uA35Glvg5HRETOq3IJ9OHDh3n44Yf56quvaNasWbHlXC4Xjz32GLVq1eIvf/lLkWV27Mgb9tS5c+dCn3Xq1KlAme3btxc4fqGOHTvidDrZuXNngXOKK5uRkcHhw4eLjb06cwVFkd31HgCC1/9Te1WKiIh4gTO0AVmdpwIQvG4WOGrmg3kRkerG7OsA/mjJkiX4+RW/L2a+Dz74gE2bNjF//nwCAgKKLJO/oFd0dHShz+rVqwfAyZMnC5Rt0KBBobL169cvV9mYmJiL3seFDIYyFS/y3IrUUVrZne8iYOdcTOeOE7jrI3I6T/H+RSuoMtunOlL75LnU718ql8FQ8vecvh8lu+t9BO6ejzntCIG7PiK70598HZKIyCWvyiXQpUme9+7dy0svvcS9995Lhw4dii2XkZEBQHBwcKHPAgMDAcjOzi5QNigoyKNly6J27bJt4u2tOi4uFIY+Cd/OIGTLK4T0vxMCwivhuhVXOe1TfV3q7ZOTk0NKihGTyYDZXHiATlHH5Hdqn+Jd2DZOpwGj0UhkZHCxD4BFAFx+IWT2/DOhPz9G0KaXyGl3Cy6/S/vntIiIr1W5BPpicnNzeeSRR7jsssuYNm1aiWUvXCCsuM+MRqNXyppMpovfzB8kJ6eXe50QgyEv+alIHWXS+HoiIl7BfPYQGWver/JvoSu9faoZtU8em82K0+nE4XBhtxdcJM9sNhY6Jr9T+xTvj23jcLhwOp2kpmZisdiKPS//36Vc2nLajyNwxzuYzx4iYM8CsjtV7f5WRKSmq3avC/71r39x/PhxHn/8cdLS0khJSSElJQWn04nT6SQlJYX09HTg9zfPRb0NzsnJASA0NPSiZfOP/bFsfh1FlQ0JCSnzvblcFfvyRB2l/jKYye50FwCBsR/gcjgq79rVoX2q4Zfa5/d2EKkM+n6UUjGaye58vr/d8R447T4OSETk0lbtEuiVK1ditVq55ZZb6NOnj/srPj6e+Ph4+vTpw7333gtAo0aNgN/nLF/oj/Oj88smJiYWKpt/7I9lS1NvTZbTdjRO/3BM547hd+wnX4cjIiJSI+W0vQlnQC1M6SfwO/yDr8MREbmkVbsh3P/+97/JzS288vPMmTPdn4eFhQHQoUMHjEYjsbGxTJgwoUD5/JW0u3TpAuStnp1/fNCgQYXKGgwG96rbF5Zt1apVgbKxsbGEhobSsmXLCt1ntWAJIqf9rQRte5PA2PewNh/m64hERKQKOnHiBC+//DKbNm3i3LlztG3blkmTJjF8+PCLnrt58+ZCfXi+nj178tFHH3k63KrHHEj25bcTvPllgra/jbVVydt8ioiI91S7N9DdunWjb9++hb78/f3x9/enb9++XH755QBERUXRt29fli5dyokTJ9x1pKam8vnnnxMTE0P79u2BvKS4efPmfP7555w9e9Zd9vjx4yxdupTBgwcTGRkJwBVXXEF4eDhz584tkMxv3bqVLVu2MGrUKPd86Zou+/LJuAxG/E6uwZS819fhiEg5PfLIDPr3787WrZtLLLd162b69+/O888/W6p6lyz5lv79u7Nkye/7xvfv353p06de9Nz8a7333lululZRkpKSCkzNmTXrafr37058fFy565SyiY+PZ+zYsfzyyy+MGTOGmTNnYjabeeihh3j33Xcvev6+ffsAePjhh/nXv/5V4Ovuu+/2dvhVRnaHSbhM/lgSt2KOL/nfqYhUXepvq79q9wa6rB599FHGjh3LrbfeyuTJk/Hz8+OTTz4hLS2Nl19+uUDZv/3tb9x1112MHTuWCRMmYLVamTNnDgEBAe433JC30vZjjz3G448/zvjx4xkzZgzJycl88MEHNGzYkHvuuaeS79J3nGGNsDa/Gv/D3xMY+wEZg//p65BEpBxGjryeDRvWsWzZ93Tt2r3Ycj/88N358jeU+1pPPfUstWrVKvf5pfXDD9/x4ov/ZO7cT907JNxww2i6d+9JRESk168veV5++WVSUlJYsGCBeyTXuHHjGDNmDK+99hpjx451rzFSlH379mEwGJg4cWKRu19cKlxBdchpcyOBez4laMfbnIsu/t+piFRd6m+rvxr/mrRNmzbMmzePdu3a8frrrzN79myio6P56KOP6N694Ddt3759ee+994iKiuLFF1/k3XffpVOnTsybN48WLVoUKDt69GheeeUVXC4Xzz33HJ9++ilDhw7lk08+oXbt2pV5iz6Xvy9lwP4vMeSk+jgaESmP/v0HERERwapVP2G1Wossk5OTw6pVP9GsWXM6dOhU7mtdffVwevToXe7zS2vLlk1kZ2cVOHb55R25+urh7g5evM9gMDBo0CB38gx5O1X07t2b7Oxsjhw5UuL5+/bto1GjRpd08pwvf/FOv8M/YEw75uNoRKQ81N9Wf1X6DfT999/P/fffX6qyP/1U/CJW7dq145133ilVPb1796Z379J9o11zzTVcc801pSpbk9mie2GLugxL0m8E7J5Hdtf7fB2SiJSRxWLhqquu5bPP5vPrr2sYNGhIoTK//LKSrKxMRowo/9NwufS88MILRR7fvXs3RqOR+vXrF3uuy+Vi//797n7Z4XBgtVpr5C9kpeGo3RZrkyvwO76KwNj3yRzwjK9DEpEyUn9b/VXpBFqqCYOB7I53YvnpEQJ/m0d2l3vzNjAVqUlcLrBlXbycr5gDK/zvbuTIG/jss/ksW/Z9kR36998vxmw2c801w8nKymTevI9YvXoVp06dxOFwUKdOXQYMuII//WlaiW8L+/fvTufOXXnttbfdxw4c2Mc777xJbOx2wEW/fgPp23dAkef//PNKvv76C/bv30tGRgYhISFcdllH7rxzKjEx7QAYM+Y6EhLiAbj55uvd15s162m+/34xn3++iOjoBkBeUvbVV5+zePEijh8/htlsJiamHbfeOpE+ffq5r7tkybc899wzvPrqW6xbt4YVK5aRmppCdHQDRo0awy233Fq2Br8EZWRkcOTIET7++GPWr1/PpEmTqFu3brHljx07RlZWFllZWdx2221s374dm81Gy5YtmT59eqkWIStKRf6p5J/rq24uu9Of8Du+ioD9C8nq9ySY/HwTSDF83T5VndqnFPeu/lb9rYf7W4Oh+L+y8vxVKoEWj8htORLXL09iOncM8+kd2Ot19nVIIp7jchH6+agqvXCPLboHZ29cWKFOvUWLVrRr155ff11Lenp6gXmpZ86cZsuWTQwceAWhoWHcc8+dHDx4gBtuGM2YMePIycnixx+Xs2DBJ2RlZfHoo0+W+rr79u1l+vS7sFj8uPnmcYSGhrJ06fesXv1zobKffTaf2bNfpEuXbtxxx12YzRb27dvD998vZteuWL744luCgoKYMeMRFiz4hB07tjFjxsM0b96iiCuD0+nkySdnsmbNL3Tt2p27755OVlYm33+/mJkzH2D69AcZN+62Auc899yzBAYGcPPNt2I2m/nqq8+ZPftFgoODGTHi+lLf96XoscceY/ny5QB07tz5oouA5S8gFhsby+TJk7nzzjuJj49nzpw5PPTQQ6Smpha7QndJatcufs51ZdZRLrVGwMr6GDMSiEpdDzEjfBPHRfisfaqJS7l9cnJySEkxYjIZMJv/MJvU5SL08xuqdH9rj+5B+s1fV6i/bdOmDe3bX8avv64lOzuzQH97+nRefzto0GAiIyOYOvUODhzYz6hRN3HLLbeSlZXJihV5/W1OThaPP/4UAEajwf3fC9vVYPj9z3v37uHee6dgsfgxduythIaG8sMPS9z97YXnLlgwj5de+g9du3bnT3+ahsViZs+ePSxZ8i27dsXy9dffERQUxEMP/Zn58z9m+/ZtPPjgIzRv3hKz2YjhfPuYTEbMZiNOp5PHH/8Lq1f/TLdu3bnvvvvJzMxiyZJFzJz5ADNmPMz48bcVuJfnn3+WwMBAxo4dj9lsZuHCvP42LCyk2LnhF96702nAaDQSGRlMQEBAuf++Cl3DYzXJpc0vmNzmVxNw4Bv8D3yjBFpqoEvjdcGIEdfzn/+8wM8/r2DkyFHu40uXLsHpdDJixA2sX7+WPXt2F0guzWYjN954C2PGXMeKFcvKlEC/9tpLWK1W3n77Q3eiO2rUGB588N7zT8jzOBwO5s59jzZt2vLyy69jMpncn4WGhjJv3kds2rSeQYOGMHDgFaxevYodO7YxYMAV7qfff7Rs2fesWfML1147kiee+D93h3/LLbdy112TeOONVxkw4AoaNmzkPic4OJh33vkQi8UCwMCBVzBmzHUsXvy1EuiLuOmmmxg1ahS7du1izpw5jBo1ik8++YTGjRsXWb5p06ZMnz6dQYMGubeQBLjhhhsYMWIE//nPf7juuuvc21eWVnJyOi5X+e7BYMhLfipSR0UFtb6BoG1vkbtxLulRA30TRDGqQvtUZWofsNmsOJ1OHA4Xdruz4IcuF1W9v3VBXtwVfAs9fPh1/Oc/L7BixfIC/e2SJYtxOp0MH349a9asZvfu34rtb5cvX8bMmXn9rdPpcv/3wnZ1uX7/8yuv/LdQf3v99Te5+9v8cx0OBx988C5t2rTlpZf+5+5vr7sOQkJCmDfvI379dR2DBg2hX79BrFq1ku3bt9Gv3yCioxtgtztxnf8Gdzic2O1OfvjhO1av/rlQfztmzFjuumsS//vfK/TrN5CGDRu57yUoqGB/27//IMaMuY5vvvmKa665rlCbms3GAvfucLhwOp2kpmZisdiK/HvI/zdZFkqgxWNyW99wPoFeRGbfv4LRdPGTRKoDg4H0m7/GnpPp60iK54EhZQBXXnkNr776EsuW/VCgQ//hhyXUrVuPXr36YDQaWbJkBQEBBeehJicnERYWRnJyUqmvl5Z2lh07ttGnT78Cb4n9/PwYO3ZCgQTaZDLx1Vffk52dXSB5zvtzXneWlVW2YX8//fQjAHfddY+7M4e8Tvv22+/k73//G6tWrWDChEnuz4YMudLdmQPUq1efWrVqkZycXKZrX4oGDx4MwJVXXknHjh255557eP3113n++eeLLB8TE0NMTEyh4yEhIYwePZrXX3+dLVu2uOstLZeLCicvnqijvHLajiFo21v4HV0B2am4AqreKre+bJ/q4FJunxLvW/2t+lsv9Lee/vemBFo8xtrkCpz+4ZiyErHErcfWqN/FTxKpLgwGsNT8VYBDQkK44oqhLF/+A2fOnKZOnbrs2fMbR48eZvLkKe497s1mC99++zWxsduJiztFXNwp0tLOYjAY3E+dSyM+Pg6n00mjRoXfQBY17NpisRAbu52fflrOyZMniYs7RWJivPuaZbk2QFzcSYKDg6lbt16hz1q0aOmO8UK1ahXeacFi8cPpdBY6LsUbMmQIISEh7Nq1q1znR0VFAZCZWYV/0fYSR+127sU7/Q8sIqfDpIufJFJdqL9Vf3teVe1va/w2VlKJTH7ktsxb0MX/wDc+DkZEymvEiOtxOp0sX74UyNvf0WAwMHx43nCpM2dOM2nSrbz66n9JSztLx46dmT59Bh999BkdO3Yu1zWL6oiLOvbvfz/HAw/cw44d26lfvz433XQzL730Px5++NFyX9dQzJsEhyOvg7ZYCi7SlP9LjVxcSkoKV199NQ8++GChz6xWK7m5ufj7+xd7/jPPPMOQIUM4depUoc8OHjwIQJMmTTwWb3WS23YMAAH7vvBxJCJSXupv81S3/lZvoMWjcluPInD3fPwPfUfGwH9UudVBReTiunTpRsOGjVixYhljx45nxYrldOvWgwYNGgLw3ntvER9/ihdffJVevfoAv887KstwMoAGDRpiNBo5erTwXsAnThwv8OcdO7bzzTcLGTbsGv72t78X6Ih37Yot620C0LBhI44dO8rp04mFnoofOXIIgHr1Cj8tl9KpVasWFouFH3/8kf3799OmTRv3Z++//z42m41hw4YVe350dDSnTp3iww8/5IknnnAfP3ToEAsXLqR169Z06NDBq/dQVeW0voHgdf/AkrgNU+ohHJEtfR2SiJSR+ts81a2/rZppvVRbtga9cQTVw5ibht/xwivoikjVl//0e9++Pfzww3ecPZtaYLXLtLSzALRs2brAeatWreDkyRMA2O32Ul0rLCyc7t17sWnTBnbt2uk+7nA4WLDgkwJl86/bokXLAp352bNnWbx4kfu8fPlPrksa6nXFFUMBeOedNwo8gc/OzuaTTz7EZDIxcGDZ5tdKQU8//TRGo5Hbb7+d2bNnM2/ePGbMmMFLL71Et27dmDx5MgAnTpzgm2++Ydu2be5zJ06cSLt27fjwww+ZMWMG8+fP58UXX+SWW27BbDbzwgsvFPtGo6ZzBdfF2mQQAP77vvRxNCJSHupvq2d/qzfQ4llGE7mtryNox7v4H/gaa/Pi3yyISNU1fPh1vP/+2/zvf68QFhZeoFPr338Qq1f/zCOP3M91192A2WwhNnYbP/64DH9/f3Jzc8nIyCAiIqJU13rwwT9z99138tBD93LTTWOJiorip59+LPREvGPHzoSHhzN37vtkZWXRsGFDTp06xZIli8jIyAAgPT3dXT5/7tT8+R/Rs2cfBg68otC1r756OCtX/sj33y8mMTGB/v0HkZOTzZIlizl58jj33HO/+02AlE/37t1ZsGABs2fP5qOPPiI7O5vGjRvzwAMPMGXKFPz88kYqbdq0iccff5wbb7yRLl26ABAYGMjHH3/MG2+8wffff8+KFSsICwtj0KBB3H///TRv3tyXt+ZzuW3H4H/sJwL2fUlWrz+DQe9FRKob9bfVr79VAi0el9v6hrwE+sgy0m1Zl8RCECI1TZ06denZsze//rqWMWPGuZMcyJuzlZuby8KFn/H6668SFBREo0aN+MtfnsDhcPLvfz/Hhg3ruPrq4aW6VpMmTXn77Tm8/fbrLFr0FTable7dezFlyt3MmPH7PsERERH897//4623XuObbxZis1mpU6cuV1wxlHHjbmP8+JvYsOFXxo+fCMCoUTexbdsWvvtuEZs3byqyQzeZTDz//It89tl8fvhhMW+++Sr+/gG0a9eeBx/8M717961YQwoA7dq144033iixzOjRoxk9enSh4yEhIcycOZOZM2d6K7xqK7f5MJx+oZgyTmGJ24CtYR9fhyQiZaT+tvr1twZXWZdQE69JSqrYvpRRUaEVqsNjXC5qfdwf07ljnLvqdXJb+35f1CrVPlWQ2iePzWYlOTme2rWjCy1k8ce9BaUgtU/x/tg2JX2fXSj/36V4Xo3pb4HQFQ8RsPdzsjpNIbP/074Op8q1T1Wj9rn4z0D1JyVT+xSvPP1tefpajfURzzMYyG15LQB+x37ycTAiIiI1V27zqwHwP7z00t1YWESkEimBFq+wNh0CgN/xleDSUzIRERFvsDYeiMvkjyn9BKbkPb4OR0SkxlMCLV5hq98Dp18oxuxkzKd3+DocERGRmskShLXx+dW4jyz1cTAiIjWfEmjxDpMFW+OBgIZxi4iIeFNui7xh3H6HlUCLiHibEmjxmtz8YdxKoEVERLzG2uxKXAYjlqRdGM+d9HU4IiI1mhJo8Rprk7x97Cynd2DIOuPjaERKT5sTiHfp+0s8yxVYG1t0D0DDuKU60c9C8TbvfI8pgRavcQXXxVanIwB+x1f5NhiRUjAaTQA4nQ4fRyI1mcNhB8BoVBcsnmNtfg0AfkqgpYrL/9nncKivFe/yVn+r3lu8yto07y20hnFLdWA0GjEaTeTkZPk6FKmhXC4XWVmZmM1+mExmX4cjNUhu86sAsMRtwJCT6uNoRIpnMpkxm/3IysrQiC/xGm/2t+q9xausTYcSvPkV/I7/DE47GPUtJ1WXwWAgJCSCc+eSyciw4OcXgMFgAMDpNOBwqKMvjtqneHlt48ThsJOVlYnVmk14eJSvw5IaxhneFHvtGMzJe/E7toLctmN8HZJIsYKDw0hLSyI19QxBQcHnExz1t6Wh9ileZfW3ymbEq+x1O+EMqIUxJwVLwmZsDXr7OiSREgUGBmOz5ZKRkQacdR83Go04ndrTvDhqn+Jd2DZmsx/h4VEEBgb7OCqpiXKbX405eS/+h5cqgZYqLf9nYGbmOc6eTSrwmfqTkql9ildZ/a0SaPEuowlrkysI2L8Qv2M/KYGWKs9gMBAeXpvQ0Aj3/CyDASIjg0lNzUSjzQpT+xTvwrYxGIwati1eZW1+9e+jvhxWMPn5OiSRYgUGBhMYGIzDYXcnPepPSqb2KV5l9rfqycXrrE2H5CXQR1eQ2ecJX4cjUipGo8m9qJjBAAEBAVgsNnVYRVD7FE9tI5XJXudynIG1MWYnY0ncqofWUi2YTGZMed2tfmZehNqneJXZNlpETLzO2mQQLgyYU/ZpOysRERFvMRixNhoAgOX4Lz4ORkSkZlICLV7nCojEUbsdAH6n1vs4GhERkZrL2mQQAH4nlECLiHiDEmipFNZGfQGwxP3q40hERERqLluj/gCYT+/QdlYiIl6gBFoqha1BHwAsp9b5OBIREZGayxkSjb1WWwy4sJxc6+twRERqHCXQUilsDXrlzYNOPYgh87SvwxEREamxrI0HAuB34mcfRyIiUvMogZZK4QqIwB51GQB+GsYtIiLiNe4E+vgvaKleERHPUgItlcbWMH8YtxJoERERb7E16I3L6Icp4xSmtCO+DkdEpEZRAi2VRgm0iIhIJbAEYovukfe/xzWMW0TEk5RAS6Vxz4M+ewhjZoKvwxEREamxrE3y50FrOysREU9SAi2VxuUfjr3O5QBYtB+0iIiI19ga5+0HbTm1DhxWH0cjIlJzKIGWSqXtrERERLzPHtUeZ2BtjLZMLIlbfR2OiEiNoQS6Bjl1NptTZ7NJyrSSnmPH7qx6K2/aGvUFNA9aRETEqwxGrI36A2A5sdrHwYiI1BxmXwcgnvHJ5pO8tOpwgWMGICzATESghVpBFprWCqJ1nRBa1wmmdZ1gQvwr/6/fFt0Dl8GIOe0Ixox4nCHRlR6DiIjIpcDWsC8BB77BEqdpUyIinqIEuoZoEhlIvTB/zmbZsNqduAAXkJZjJy3HzrHUbLadOucubzIa6NU0gmFt63BFq6hKS6Zd/uHYoy7HciYWy6lfyW07ulKuKyIicqlx736RuB3sOWAO8G1AIiI1gBLoGmJAy9rc2KsZSUnpOJ0ubA4X6bl2UrNtnM2ykZRp5XByJgfO5H0lpuey7kgq646k8pzpANfE1OWe/s2oE+Lv9VhtDfvkJdBxG5RAi4iIeIkjvDmOoLqYsk5jSdzmTqhFRKT8lEDXQAaDAT+zgdpmP2oH+xVZ5mhKFsv3nWH53jMcScni298SWb7vDLf3bMzE7o0IsJi8Fp8tujtsfwtLwmavXUNEROSSZzBga9Ab08FFWOLWK4EWEfEALSJ2iWpWK4i7+jRlweRuvDuuEx2iw8ixO3l73TFuen8T206mee3atvrdATCl7MeQ673riIiIXOpsDXsD2j5SRMRTlEBf4gwGA50ahvPerZ2YNSKG6DB/TmdYue+LWJbsTvTKNV1BdXCENcWAC3PiNq9cQ0RERMDW4HwCnbhF+0GLiHiAEmgB8hLpq2Lq8tnk7gxuHYXN4eL/vt/Hm2uP4nJ5fjssW3TeW2hLvIZxi4iIeIsjsjXOwNoY7DmYT+/wdTgiItWeEmgpIMBi4oXr2nF7j8YAvLf+OP/3/T6cHk6i84dxWxK2eLReERERuYDBgK1BLwD8NIxbRKTClEBLIUaDgfsHNuepq9tgMhr4fs9pXl9z1KPXsNXvBoA5cSs47R6tW0RERH5nzR/Grf2gRUQqTAm0FOv6y+vzf9e0AeDDjSdY/FuCx+p21GqL0xKC0ZaJKXmfx+oVERGRgtzzoOM3gcPm42hERKo3JdBSomvb1ePOXnnDuWctO8B2T63ObTRhr98VQNtZiYiIeJGjdgxO/wgM9izMZ3b6OhwRkWpNCbRc1LR+zRjaJgq708XMRbs5eTbbI/X+Pg9aCbSIiIjXGIzuedAaxi0iUjFKoOWijAYDT1/Tlnb1QjibbePZHzyzqJh7JW4tJCYiIuJVNs2DFhHxCCXQUioBFhP/vL49gRYj206dY9HOis+HttfrggsDpnPHMWZ6Z89pERERAVvDC+ZBOx0+jkZEpPpSAi2lFh0WwN39mgEw+5cjJGdaK1Sfyy8UR+0YAMx6Cy0iIuI19trtcfqFYrSmY0re6+twRESqLSXQUia3dGlITN0Q0nPt/HfloQrX554HHa950CIiIl5z4eKd8Rt9HIyISPWlBFrKxGw08ORVrTEaYNm+M6w7klKh+mzReftBayExERER77LV7wGcH8YtIiLlogRayiymXijjujYE4J8/HiDHVv65VPlvoM1ndoI9xyPxiYiISGG26PwEeiN4YDFQEZFLkRJoKZdpfZtRN8SPuHO5fF2BBcWcYU1xBtbB4LRhPrPLgxGKiIjIhWz1uuAymjFlJmBMP+XrcEREqiUl0FIuQX4m7uzdBICPNp3AaneWryKDAVv+nKzErZ4KT0RERP7IEoQ96rK8/03QMG4RkfJQAi3ldt1l9akb4sfpDCuLfyv/W2hbvS6A9oMWERHxNlt0T0DzoEVEyksJtJSbn9nIxB6NAfhw4wnsjvK9hc5fFdSsN9AiIiJeZYvO3/1CCbSISHkogZYKGdWhPrWCLMSdy2XJntPlqsNWpxMugxFTRjzGjDgPRygiIiL58lfiNiXvxZCb5uNoRESqHyXQUiEBFhO3dW8EwJwNx7E7y7Gqp18w9trtADAnbvNkeCIiInIBV3Bd7OHNMODCnKCRXyIiZaUEWirspk4NCA8wc+JsDsv3le8ttL3e+YXE1JmLiIh4lT1a+0GLiJSXEmipsCA/E+O75b2F/njTSVzl2FtSK3GLiIhUDvd+0FqJW0SkzJRAi0fc1CkaP5OB/Wcy2Xs6o8zn57+BNp+OBYfV0+GJiIh4hMvl4mhSJidSs0lMz+Vslg1bORfR9JX8edCWxG3qc0VEysjs6wCkZggPtDC4dRRL957hm50JtKsXWqbzHREtcPqHY8xNw5y8B3vdTl6KVEREKtuJEyd4+eWX2bRpE+fOnaNt27ZMmjSJ4cOHl+r8r776ig8//JCjR48SEhLCsGHDePDBBwkPD/dy5IW9ufYY760/Xuh4oMVIeICFyCALjSMCaRkVTIvaQcTUC6F+WEClx1kSR2QrnAGRGHNSMZ/Z5d4NQ0RELk4JtHjMDR3qs3TvGX7Yc5oHBrUg0GIq/ckGA7Z6XfE/vhJzwhYl0CIiNUR8fDxjx47FZrMxceJEateuzZIlS3jooYeIi4tjypQpJZ7/1ltv8d///pc+ffowc+ZMTp48yUcffcTWrVtZsGABAQGVm5y2igqmXpg/GTl2cu1O9+KZ2TYn2bZcEtJz2ZOYAfvOFDhnYMtaDGwVRft6IRgMhkqNuRCDAVv9HvgfXYYlYbMSaBGRMqjSCfSOHTsYN24cc+bMoVevXgU+27x5M2+++SY7duwgOzubhg0bMnLkSKZNm4afn1+BsgcOHOCll15i27Zt5OTk0LFjR2bMmEG3bt0KXXPr1q3Mnj2b3377DZfLRffu3fnzn/9Mq1atCpVduXIlb731Fvv378disTBgwAD+/Oc/U79+fc82RDXRrXEEDcMDOJWWw4r9Zxh5WdnawV4/L4G2JGwlp+OdXopSREQq08svv0xKSgoLFiygU6e8h6Pjxo1jzJgxvPbaa4wdO5bQ0KJHLSUkJPDqq68ycOBA3nrrLYzGvJlnl112GY888ggfffQRd911V6XdC8CwmDrc2r8FSUnpuFzgcLrItNpJy7aTlmMjOdPG0ZQsDidncigpi4NnMjiYlMnBpEze33CCVlHB/Kl3E4a0icLow0TaFt09L4GO30h256k+i0NEpLqpsnOgjx49yn333YfTWXhe0caNG7n99tvZt28fkydP5oknnqB169a89tprTJs2rcA5hw4dYvz48cTGxjJx4kQefPBBEhISmDRpEhs3biyy3vj4eKZNm8bUqVPZtWsXY8eO5dChQwXKfvvtt9xzzz3YbDYeeOABbrvtNlauXMnYsWNJTk72TqNUcUaDgRs65CXN3+xMKPP5tvp5DzQs2spKRKTGMBgMDBo0yJ08A5hMJnr37k12djZHjhwp9txvv/0Wm83G5MmT3ckzwMiRI2nYsCELFy70auylYTIaCAuw0DgykMujwxjUqjaTejbmmWtj+HhiV364pw/PXNuWK9tEEWgxcjApk8cX72Hch1tYtvd0uRbe9AT3QmLxW8BHMYiIVEdV8g308uXLefLJJ0lLSyvy86eeeoqQkBAWLlxInTp1ABg/fjzPP/88c+bMYenSpVx77bUAPP/881itVhYuXEjjxo0BuP7667n++ut55plnWLx4MQaDAZfLxbPPPktERAQLFiwgIiICgGuuuYbrrruOF154gXfeeQeArKwsZs2aRdu2bZk3bx7+/v4A9OvXj/Hjx/Pqq6/y9NNPe7GFqq6Rl9XjzbVH2X7qHEeTs2hWO6jU59rrdsaFAdO5YxiyknAFRXkxUhERqQwvvPBCkcd3796N0WgscdTWjh07AAok3/k6dOjADz/8QHp6erFvsKuCiEALw9vXY3j7epzLsbFgaxzzt57iSHIWT363lxX7k3jq6jaE+Ffur2T2Oh1wGf0wZp/BeO4YzvBmlXp9EZHqqsq9gZ46dSrTp0+nTp06jBw5stDn8fHxHD16lCuvvNKdPOcbNWoUAJs25W3LkJSUxOrVq7nyyivdyTNAZGQkN998MwcPHiQ2NhaAnTt3cuDAAW666SZ38gzQpEkTrrnmGtasWcPp03l7HK9atYrU1FQmTJjgTp4BunbtSvfu3fn222+xWi/NVS3rhPjTr3ktAL7ZVba30C7/MByRrQFtZyUiUhNlZGSwc+dOHn30UdavX8/EiROpW7duseUTEhIICwsjJCSk0Gf5ifepU6fKHIfBULGv8tYRHmhhar+mfDu1J1P7NsVsNPDTgSQmfbKNQ0mZFY6rTF+WAOx1OwDgl7DZo3V7oo1r8pfaR+2j9qlabVNWVe4N9OHDh3n44Ye54447eOuttwp9XqdOHZYuXVogcc2XlJQE4B7mlf/kunPnzoXK5j/N3rFjB506dWL79u0Fjl+oY8eOfP311+zcuZOhQ4eW+ES8Y8eObNq0icOHDxMTE1OKO655bugQzerDKXz3WyL39m+GxVT65zS2+l0wp+7HkrAVa/OrvBiliIhUtscee4zly5cDeX3z3XffXWL59PR0goKKHsmUv3hYVlZWmeOoXbvib6wrUkcU8ETDSK7t3JD7PtnK8dRsJs/bxgujOzKqS8MKx1ZqLfpCwhZCU7cTGnWHR6v2RBvXZGqfkql9Sqb2KV5ltE2VS6CXLFlSaBGwC5nNZpo1a1bkZ++//z4AvXv3BvKeXANER0cXKluvXj0ATp48WaBsgwYNCpXNf8pd1rJlTaDL8wTkj+dWpA5P6d+yFlHBfiRlWll7JIXBrUs/FNtevxvsWYA5cYtH76UqtU9VpPYpmdqnZGqf4lWkbWpie950002MGjWKXbt2MWfOHEaNGsUnn3xSYJTYH11sjrDJVIYdH85LTk4v97RfgyHvF7SK1JGvcZCZubd14W/f7WXd0VQeWrCdtHPZjLisXsUqLiW/iE6EAfYjv3I2Kd0jdXqyfWoitU/J1D4lU/sUr7xtk39eWVS5BLqk5Lkkr7/+OuvWreOyyy5jyJAhQN5QMYDg4OBC5QMDAwHIzs4uULaoJ90VKVsWvn4i7kmjujTk3TVHWHvsLDf3aV76E2MGwErwOx1LVGQgmDz7LVpV2qeqUvuUTO1TMrVP8dQ2eQYPHgzAlVdeSceOHbnnnnt4/fXXef7554ssHxwcTGpqapGf5fezRQ3vvhiXq+LrZnmiDoDwAAsvjb6cF386xGfb43jmh30EmI0MaVPn4idXkLVe3uKdppT9kJOGy99z+2p7qn1qKrVPydQ+JVP7FK8y2qbKJdDl8frrr/PKK68QFRXFK6+84h7Cnf/Uuqin1/nHvFW2Oj8R94TejcN4F/hxTyLxiWmlH8ZtbEgtv1CM1nRS92/GUecyj8RT1dqnqlH7lEztUzK1T/Eq0jbleSpenQwZMoSQkBB27dpVbJlGjRrx22+/kZWVVeihdUJCAkaj0T2irDozGgw8MqQlOXYHi3Yl8uR3e/mP2US/FrW8el1XUB3s4c0wpx3FkrAFa9MhXr2eiEhNUOUWESsLu93O3/72N1555RXq1avH3LlzCwwDy3/zXNTb4JycHAD3yp0llc0/9sey+XUUVbYiT8TL++WJOjz11SE6jNrBfmTkOth0/Gzpz8WIvW5nAMwJWz0aU1Vqn6r4pfZR+6h9ql7bVHcpKSlcffXVPPjgg4U+s1qt5ObmFrmmSb6OHTsCuBf8vNDOnTtp3bp1ufrbqshoMPDEsDZc1bYOdqeLR7/dzfaTaV6/rv38dlbm+M1ev5aISE1QbRPozMxM7r77bhYsWEDr1q359NNPadmyZYEyjRo1An6fs3yhP86Pzi+bmJhYqGz+sT+WLU29lyqjwcAVrWoDsPJAUpnOtdXvCoAlYYvH4xIRkcpTq1YtLBYLP/74I/v37y/w2fvvv4/NZmPYsGHFnn/ttddisVh49913uXDU1+LFi4mLi2P06NFei90XTEYDz1zbloEta5Nrd/Lkd3tIy7Z59Zq2+nnDuC0JSqBFREqjWibQWVlZTJkyhdWrV9O7d2/mz59f5IJeHTp0wGg0FvnkOn8l7S5dugC/P+XOP/7HsgaDwb3qdkllY2NjCQ0NLZTMX4oGt8pbPOzng8k4nKV/lWI/35mblUCLiFR7Tz/9NEajkdtvv53Zs2czb948ZsyYwUsvvUS3bt2YPHkyACdOnOCbb75h27Zt7nMbNmzI3XffzerVq7nzzjv57LPP+Oc//8ljjz1Ghw4dGDdunI/uynvMJiN/Hx5Dk8hATmdYmbX8ABdbSK0ibPXz3kBbEreBw7vJuohITVAtE+innnqKrVu3MnjwYN555x330Oo/ioqKom/fvixdupQTJ064j6empvL5558TExND+/btgbykuHnz5nz++eecPXvWXfb48eMsXbqUwYMHExkZCcAVV1xBeHg4c+fOJTc3111269atbNmyhVGjRrnnS1/KujUOJ9TfTEqWjdi4c6U+z1Yv76GGOe0IhpxUb4UnIiKVoHv37ixYsIAuXbrw0Ucf8dxzz3HgwAEeeOAB5syZ4x7CvWnTJv7yl7+wYMGCAudPnz6dp59+mtOnT/Pss8/y/fffM3bsWN577z33VlY1TZCfiX+MiMFsNLDyQBLf7Cw84s1THLVa4/QPx2DPxpy822vXERGpKardImLbtm1j8eLFWCwWBg4cyPfff1+oTJMmTdxvlh999FHGjh3LrbfeyuTJk/Hz8+OTTz4hLS2Nl19+ucB5f/vb37jrrrsYO3YsEyZMwGq1MmfOHAICApg5c6a7XGBgII899hiPP/4448ePZ8yYMSQnJ/PBBx/QsGFD7rnnHq+2QXVhNhkZ0LIWS3afZuWBJLo0Ci/Vea6ASOwRLTGfPZS3H3SzoV6OVEREvKldu3a88cYbJZYZPXp0sUOyb731Vm699VZvhFZltasXyr39mzH7lyO8uPIQnRuG06x20XtiV4jBiK1eV/yPr8QSvwl73U6ev4aISA1S7RLotWvXAmCz2XjmmWeKLHPjjTe6E+g2bdowb948/vvf//L6669jNBq5/PLL+ec//0nnzp0LnNe3b1/ee+89Xn31VV588UUCAwPp1q0bDz/8MC1atChQdvTo0QQFBfH222/z3HPPER4eztChQ3n44YepXbu252+8mhrcKsqdQD90RQsMpdzY1F6/G+azhzAnbFECLSIil6QJ3Rvx69FUNh0/y1+X7GXO+M6YS7urRRnYo3vgf3xl3tSpTlM8Xr+ISE1icHlzYo2USVJSxbaxiooKrVAd3pBjczDs9V/JsTuZe1sX2tUr3ZYsAbs+JvTnx7A26k/aDZ9WOI6q2j5VhdqnZGqfkql9ileRtsk/VzyvOvW3ZzJyufXDLaTl2PnL0Fbc3Lnwmi8VZTm1joivb8ERXJ+USZvybrKc9POgZGqfkql9Sqb2KV5526Y8fa0m6opXBVhM9G2et4/lqjKsxp2/Erc5cRs4HV6JTUREpKqrE+LPtH7NAHh73TEycu0ev4atbmdcBhOmzASM6ac8Xr+ISE2iBFq8bnDrvNW4Vx5ILvU5jlptcVqCMdoyMaXs81ZoIiIiVd6NHerTNDKQs9k25mw8cfETysoShL3O5Xn/m6gdMERESqIEWryuX/NamAxwJCWL+HM5pTvJaMJetzMAlsSt3gtORESkijObjMwYlLcWy/wtJ0vfl5aBPX8HjAT1uSIiJVECLV4XGmDmsugwADYcLf22VLbz+0Fb1JmLiMglbkCLWnRvHI7V4eJ/q494vH5bvbypU3poLSJSMiXQUil6N83bQ3vDsdIn0Pb8edAJGk4mIiKXNoPBwAODWmAAlu49w2/x5zxaf/5Da/OZ38CR69G6RURqEiXQUil6No0AYOPxszicpVsaL/9puPnsIQw5pU+8RUREaqKYeqEMb18XgNfXHPVo3c6wJjgDa2NwWjGf2eXRukVEahIl0FIpLosOI9jPxLkcO3sT00t1jiuwFvbw5gBYErd5MzwREZFqYVq/ZpgMeQ+kdyeUrj8tFYPh92HcmjolIlIsJdBSKcxGAz2aRACwXsO4RUREyiU6LICr2+W9hf7Qwyty2/NHfmketIhIsZRAS6Xp3ez8POgyLSTWHdDTcBERkXy392gMwMoDSRxNyfJYvbb6egMtInIxSqCl0vQ6v5BYbHw6mVZ7qc5xL2qSuA2cDq/FJiIiUl20jApmYMvauICPN530WL32up1wGYyYMk5hzEzwWL0iIjWJEmipNI0iAmkUEYDD6WLLibRSneOo1RanJRijLQNT6n4vRygiIlI9TOqZ9xb6u92JJKZ7ZtVsl18IjlptAe0HLSJSHCXQUqny30KXehi30YS9bmcALJoHLSIiAkDHBmF0aRSO3eli3hbPvYXWftAiIiVTAi2VKj+BLstCYvnDuDUnS0RE5HeTz7+F/io2nrRsm0fqdE+dStDuFyIiRVECLZWqe+MITAY4nppNXFpOqc6xuztzvYEWERHJ16dZJK3rBJNtc/Ld7kSP1Jm/+4XlzA5weCYpFxGpSZRAS6UKDTBzWXQYABtK+RY6f1VQ89lDGHJK/+ZaRESkJjMYDIzuGA3At7sScblcFa7TEdECp384BnsO5uQ9Fa5PRKSmUQItla7n+f2gt5w4W6ryroBI7BEtAA3jFhERudDVMXXxNxs5mJTJnsSMildoMGKv1xnQftAiIkVRAi2VrmvjcAC2nUwr9dNyDeMWEREpLDTAzBWtagOwaJdntp6y1dPaIyIixVECLZWuQ3QYJqOB0xlW4s+VbuuN3ztzJdAiIiIXuv7y+gAs3XuaHJujwvXlT51SnysiUpgSaKl0ARYT7euFAHlvoUvDPQ/69HZwVvyXAxERkZqie5MIosP8ych1sOpgcoXry98+0nTuGIbslArXJyJSkyiBFp/o3PD3Ydyl4ajVFqclBKMtE1PKPm+GJiIiUq0YDQauuyzvLfQ3HhjG7QqIwB7REgBLorazEhG5kBJo8Ykujc4n0KdKl0BjNGGv1wXQkDIREZE/Gnl5PQzA5uNnOZWWXeH68vtcsxJoEZEClECLT3RqGIaBvP2gkzKtpTpHc7JERESKFh0WQI/zu1ws3lXxPaFt+Q+tlUCLiBSgBFp8IizAQqs6wQDsKOVbaK3ELSIiUrz8xcS+213xPaHtF6494nJWNDQRkRpDCbT4TFnnQdvqne/M045gyK74IikiIiI1yaBWtQm0GIk/l8uu+PQK1WWvFYPL5I8xNw3T2SMeilBEpPpTAi0+454HXcoE2hUQgT2yDaBh3CIiIn8UYDExsGXentA/7j9TscpMFux1OwJgTtR+0CIi+ZRAi890aRgGwIEzmaTn2Et1ji06fz/ozV6LS0REpLoa1rYOAD/uO4OzgsO480d+aR60iMjvlECLz0SF+NM4IgAXEBt3rlTn2Op3B8AcrzfQIiIif9S7WS2C/UyczrASe6p0fWtx8hcSMyfoDbSISD4l0OJT+cO4t5ZyGLc9ugcAltPbwVG61btFREQuFf5mI4NaeWYYt3srq+Q9YKv41lgiIjWBEmjxqfyFxLaXciVuR3hznAGRGBy5mM/s8mZoIiIi1ZJ7GPf+JBzO8g/jdoY0wBFUD4PLgfnMTk+FJyJSrSmBFp/KfwO9OyGdHJvj4icYDO5h3FpITEREpLBeTSMJ9TeTnGkt9QPqIhkM2Ot1BsCihcRERAAl0OJjDcMDqBPih93pYndi6bbcsNXXQmIiIiLFsZiMXHF+GPfyfRUbxm2rr4XEREQupARafMpgMNCxQd5q3DtKudiJPTp/IbHNUMEVRkVERGqiYTF5w7h/2p+EvQLDuN3zoJVAi4gASqClCshPoEu9EnedTriMZkxZiRjTT3ozNBERkWqpR+MIwgPMpGbb2HLibLnrsdXphMtgxJQRhzEzwXMBiohUU0qgxec6nV9ILDbuXOn2rLQEYo+6PO9/NYxbRESkELPJyBWtowD4+WBy+SvyC8ZRq21endrOSkRECbT4Xts6wQSYjZzLsXM0JatU59ii8xcSUwItIiJSlAEt8uZBrz2cjKsCU55s9fLnQSuBFhFRAi0+ZzYZuSw6FIDYUs6Dzl+J2xyvlbhFRESK0rNpBBaTgbhzuRxNKf8+zr8v3qkEWkRECbRUCe6FxEo5D9p+flVQc/JuDNYMr8UlIiJSXQVaTHRrFAHAmsPlH8btXrzz9A5wWD0RmohItaUEWqqETg1+nwddGs6QBjhCGmJwOTEnbvdiZCIiItVXvxa1AFh3JKXcdTjCm+MMiMTgyMV8ZpenQhMRqZaUQEuV0KFB3hDu46nZpGSV7un27/OgN3ktLhERkeqs//kEetupc2Tk2stXicHw+zBuzYMWkUucEmipEsICLLSoHQTAztJuZxXdAwBL3EavxSUiIlKdNYoIpGlkIA6niw3HUstdz+9rj2jxThG5tCmBliqjU8Pz86BLu5BYdE8ALAlbwFnOp+oiIiI1XP4w7rWHyz+MO3/tEUuiFu8UkUubEmipMsq6kJijdgxO/3AM9izMSb95MzQREZFqq1/z8wn0kRSc5dzOyla3My6DCVNGPMb0OE+GJyJSrSiBliojfyGxPYnp5NqdFz/BYHQPKdMwbhERkaJ1aRROsJ+JlCwbexPLuXOFJQh71GV5/5ugYdwiculSAi1VRqOIAGoFWbA5XOxNTC/VOe550PEbvBmaiIhItWUxGenZNBLIewtdXu4tJBM0jFtELl1KoKXKMBgM7mHcpd3OytagFwCW+E1QzmFpIiIiNV2/5ucT6ArMg3aP+tIbaBG5hCmBliqlU8O8YdylXUjMXrcjLpM/xuxkTGcPeTM0ERGRait/HvTuhHRSS7ld5B/lb2VlTvoN7Nkei01EpDpRAi1VyoVvoF2leaNs8sdWrzMAlnjNgxYRESlKVIg/resE4wI2HT9brjqcoY1wBNXD4LRjOR3r0fhERKoLJdBSpcTUDcHPZCA128bx1NI93bZFnx/GrYXEREREitWzSd4w7o3HzpavAoMBe/T5t9Aaxi0ilygl0FKl+JmNtK8fCpR+O6vfFxJTAi0iIlKcnk0jANhwLLV0o7yKYKuXl0BbErZ6KiwRkWpFCbRUOR3Pb2cVW9p50NHdcRmMmM4dx5gR783QREREqq2ujcKxmAwkpOeWepTXH9miL1hITIt3isglSAm0VDmdGubNg94Rl1aq8i6/UOy12wN6Cy0iIlKcAIuJTufXGtlQzmHc9jqXa/FOEbmkKYGWKid/IbGjKdmczbaV6hxbg56AEmgRkapo3759zJgxg969e3P55ZczZMgQZs2aRXp6+kXP3bx5M23bti3ya+LEiZUQfc2Svx/0puOp5avA5O9ejdty6ldPhSUiUm2YfR2AyB9FBFpoViuQoynZxMadY2DL2hc9xxbdE2Lf10JiIiJVzOHDhxk3bhwmk4kJEyYQHR3N9u3b+fjjj1m/fj0LFiwgKCio2PP37dsHwMMPP0z9+vULfBYVFeXV2Guink0jeX3NUTYdP4vd6cJsNJS5DlvDPvidWofl1K/kXK6HGCJyaVECLVVSpwbhHE3JZsepMiTQgCl5L4acVFwBkd4OUURESmHWrFnYbDYWLFhAmzZtABg3bhzt27dn1qxZzJs3jylTphR7/r59+zAYDEycOLHERFtKJ6ZuCGEBZs7l2NmdkO4e9VUWtoZ9APA79WvePGhD2ZNwEZHqSkO4pUrq2DB/P+hSzoMOros9shUGXBpSJiJSRVitVjZv3ky3bt3cyXO+UaNGAbBp06YS69i3bx+NGjVS8uwhJqOBHk0igLzVuMvDVrfz+XnQZzQPWkQuOUqgpUrKX+Rkd0I6VruzVOfYGvUHwO/kWq/FJSIipWc2m1m8eDF///vfC32WlJQEgNFY/K8iLpeL/fv307p1awAcDgfZ2eVbPVp+554HXc4EGnOA5kGLyCVLQ7ilSmoSGUhkoIXUbBt7T2eUaoiZtVF/AnfOwXJydSVEKCIiF2M0GmncuHGRn73//vsA9OrVq9jzjx07RlZWFllZWdx2221s374dm81Gy5YtmT59OsOHDy9XXBUZcZx/bnUetdzr/H7QsfHpZNnsBPuV/dfB/HnQfnHrye3w+zzomtA+3qT2KZnap2Rqn+KVt23K05ZKoKVKMhgMdGwQxs+HktlxKq1UCbStYR9cBiPms4cxpsfhDG1QCZGKiEhZff3113z++edER0dz8803F1sufwGx2NhYJk+ezJ133kl8fDxz5szhoYceIjU1lQkTJpT5+rVrh5Y7dk/W4StRUaE0qRXE8ZQsDqZZGdquHOuGtB8KG1/EP349/rVDCv0WWp3bpzKofUqm9imZ2qd4ldE2SqClyurUMC+Bjo07V6ryLv9w7HU6Yjm9HcvJNeS2u8XLEYqISFl99dVXPPnkkwQFBTF79myCg4OLLdu0aVOmT5/OoEGD6Nixo/v4DTfcwIgRI/jPf/7DddddR1hY2RbCSk5Ox+UqX/wGQ94vaBWpoyro3jic4ylZLN8ZR6c65ZhfHtCG2iZ/DBmJpB7cgSOyJVBz2sdb1D4lU/uUTO1TvPK2Tf55ZaEEWqqs/LfOO06dw+VyYSjFGAtr4wFYTm/H7+RqJdAiIlXM//73P2bPnk1oaChvvvlmgaS4KDExMcTExBQ6HhISwujRo3n99dfZsmULgwcPLlMcLhcV/uXTE3X4Uq8mESzcEc+Go2fLdx+mvHnQfqfWYT75K/aIlgU+ru7t421qn5KpfUqm9ileZbSNFhGTKqtdvVD8TAZSs22cOJtTqnPyFxKznFyrnywiIlWEzWbj8ccfZ/bs2dSrV4+PP/6Y7t27V6jO/D2gMzMzPRHiJad7kwiMBjiSkkViem656sjfzsoSp4XEROTSoQRaqiw/s5H29fOGVGw/VbrtrGz1u+Ey+WPKOo0pZb83wxMRkVJwOBw88sgjLFy4kLZt2/L5558X+Va5KM888wxDhgzh1KlThT47ePAgAE2aNPFovJeKsAAL7erl9bEby7udVX4Cnb8ftIjIJUAJtFRpnRqGA7CjlAk05gBsDfJWdPU7ucZbYYmISCm98sorLF26lI4dO/LJJ59Qr169Up8bHR3NqVOn+PDDDwscP3ToEAsXLqR169Z06NDB0yFfMvJX467oftCmrNOYzh72YGQiIlWX5kBLldalYTgfcoLtp0q3kBiAtVE//E78guXkGrI7/cmL0YmISEni4uJ47733MBgMDBs2jJ9++qlQmaioKPr168eJEyfYunUrTZo0oUuXLgBMnDiRJUuW8OGHH5KQkECfPn2Ii4tj3rx5mM1mXnjhhVKtjyFF69k0kvc3nGDT8bM4XS6MZW1L8+/zoC2nfnUvJCYiUpMpgZYqrUODUAzA8dRskjOt1A72u+g5tkYDgOfzhpQ57WDUt7mIiC9s3LgRu90OwIsvvlhkmZ49e9KvXz82bdrE448/zo033uhOoAMDA/n444954403+P7771mxYgVhYWEMGjSI+++/n+bNm1favdREHRuEEWgxkpJl48CZTNrWDSlzHfn7QVtOrSXn8tu8EKWISNVSpTOLHTt2MG7cOObMmUOvXr0KfBYXF8fLL7/MunXryMjIoE2bNkybNo2hQ4cWqufAgQO89NJLbNu2jZycHDp27MiMGTPo1q1bobJbt25l9uzZ/Pbbb7hcLrp3786f//xnWrVqVajsypUreeutt9i/fz8Wi4UBAwbw5z//mfr163uuES5xYQEWWkYFczApkx2n0hjSps5Fz7FHXYbTPxxjbhrm0zuw1y/89ywiIt43atQoRo0aVaqyo0ePZvTo0YWOh4SEMHPmTGbOnOnh6MRiMtK1UQRrj6Sw8VhquRJoa6P+BG98Eb+Ta8HlBINmB4pIzVZlf8odPXqU++67D6fTWeizM2fOcNttt/Hjjz9y00038Ze//AW73c69997Lt99+W6DsoUOHGD9+PLGxsUycOJEHH3yQhIQEJk2axMaNGwuU3bhxI7fffjvx8fFMmzaNqVOnsmvXLsaOHcuhQ4cKlP3222+55557sNlsPPDAA9x2222sXLmSsWPHkpyc7PkGuYR1bpi3nVWph3EbTdga9QM0D1pERKQkPSs4D9petzNOSzDGnBRMSXs8GJmISNVUJRPo5cuXc8stt3DmzJkiP3/ttdeIi4vj3Xff5aGHHmL8+PHMnz+fmJgYZs2aRVZWlrvs888/j9VqZf78+dx7771MmjSJTz/9lMjISJ555hlc51eNdLlcPPvss0RERLBgwQKmTJnC1KlTmTdvHna7nRdeeMFdZ1ZWFrNmzaJt27bMmzePSZMmcf/99/POO++QmJjIq6++6t0GusR0Pr+QWGlX4gawNhoAgN+xlV6JSUREpCbo3SwSyHtInWsv/NLiokwW92rcfid+8WRoIiJVUpVLoKdOncr06dOpU6cOI0eOLPS5w+Fg0aJFdO7cma5du7qP+/v7c/vtt5OamsqqVasASEpKYvXq1Vx55ZU0btzYXTYyMpKbb76ZgwcPEhsbC8DOnTs5cOAAN910ExEREe6yTZo04ZprrmHNmjWcPn0agFWrVpGamsqECRPw9/d3l+3atSvdu3fn22+/xWq1erJZLmmdG+Ul0PtOZ5BptZfqHGvTIQCYE7diyE7xWmwiIiLVWfNaQdQJ8SPX7izTg+oL2Rr1BzTqS0QuDVUugT58+DAPP/wwX331Fc2aNSv0+YEDB8jKyqJz586FPuvUqROQN3f6wv+Wpuz27dsLHL9Qx44dcTqd7Ny5s8A5xZXNyMjg8GFt5+Ap9UL9iQ7zx+mCXXHppTrHGdoQe+32GFxO/I4XXvVVREREwGAw0LNp3lvo8u4HnT/qyxK/Aew5HotNRKQqqnKLiC1ZsgQ/v+JXWk5MTATy9ob8o/zFu06ePAlAQkJCsWXz96H8Y9kGDRqUut6LlY2JiSn2PopSkZ048s+tqbt5dG4UTvzu0+yIS6N388hSnWNtfiXm5N34H/0RW7sxQM1tn4qq6d8/FaX2KZnap3gVaRu1p1SWXk0j+O63RDYcO8v95TjfUasNjqC6mLJOY0nYAvWv8XiMIiJVRZVLoEtKngHS0/PeQAYFBRX6LCAgAIDs7GwAMjIyAAgODi5UNjAwsMiyRdVbkbJlUbt2aJnP8UYdVVH/tnX5fvdpdiVmEhVVynvsfANsno3/8Z/xD88bal9T28dT1D4lU/uUTO1TPLWNVGU9m+Q9mN53OoOULCu1gi6+ZWQBBgO2Rv0x7V+I5cQa6KwEWkRqriqXQF9M/qJfJX1mNBoL/Lmoc7xd1mQyXexWCklOTqeE2yuRwZD3C1pF6qjKWp9PgLceTyUhMQ2zqRSzD/xbUyswCmN2Emm7VhDe+doa2z4VVdO/fypK7VMytU/xKtI2+eeKeFvtYD9a1wnmwJlMNh07y9Xt6pa5DmvjAQTsX4jl5GovRCgiUnVUuwQ6/21yUW94c3Ly5t2EhoZ6tGz+sT+WzcnJISQkpMiyfzxeGi4XFf7l0xN1VEVNawURHmAmLcfOnsQMLo8OK8VZRnKbDiVw7wL8jiyHztfW2PbxFLVPydQ+JVP7FE9tI1Vdr6aRHDiTyYZjqeVKoPMXEjOfjoXsVKrhr5giIqVS5RYRu5hGjRoBv89DvlD+sfx5yKUpmz8/Or9s/hzrC/1x3nVZ6hXPMBoMdHJvZ1XK/aABa7OhAPgd+VG/vYqIiBSj1wX7QZc02q84zpBo7JGtMbiccFSrcYtIzVXtEugWLVoQGhrq3n7qQvmrY+dvb9WhQweMRmOJZbt06QLkrZ594fE/ljUYDO5Vt0sqGxsbS2hoKC1btizzvUnJOjfMe+u8/WTpt9mwNR6Iy+iH6dwxSNrvrdBERESqtc4Nw/EzGTidYeVoStnXcQGwnn8LzeFVngtMRKSKqXYJtNlsZvjw4WzevJmtW7e6j+fm5jJ37lyioqIYOHAgAFFRUfTt25elS5dy4sQJd9nU1FQ+//xzYmJiaN++PZCXFDdv3pzPP/+cs2fPusseP36cpUuXMnjwYCIj8xbZuOKKKwgPD2fu3Lnk5ua6y27dupUtW7YwatQo93xp8ZzO7jfQaThL+XTc5ReCrWGfvD/s/8FboYmIiFRrARaTu59dX87trGyN87az4tBKT4UlIlLlVMss7/777ycqKoq77rqL1157jfnz5zN+/Hj279/Pk08+ib+/v7vso48+isFg4NZbb+Xdd99l7ty5jBs3jrS0NJ566qkC9f7tb38jJSWFsWPHMnfuXN59913Gjx9PQEAAM2fOdJcLDAzkscceY+/evYwfP5758+fz2muvcdddd9GwYUPuueeeSmuLS0lMvRD8zUbScuwcTckq9Xm5za7M+599SqBFRESK07tZxfaDtjXojctggpRDGM+d9GRoIiJVRrVMoOvUqcP8+fMZMGAAc+fO5V//+hcWi4W33nqL4cOHFyjbpk0b5s2bR7t27Xj99deZPXs20dHRfPTRR3Tv3r1A2b59+/Lee+8RFRXFiy++yLvvvkunTp2YN28eLVq0KFB29OjRvPLKK7hcLp577jk+/fRThg4dyieffELt2rW93gaXIovJSIcGecO4t5VhGLc1P4E+sR5Ddvl+KRAREanpejbNS6C3nDiLzeEs8/ku/zDs9bsB4HdshUdjExGpKgyu8qwUIV6RlFSxbayiokIrVEd18M66Y7z96zGujqnDP0a0K/V5kZ9eiTl5L+mD/0VO+/FejLB6ulS+f8pL7VMytU/xKtI2+eeK56m/LZrT5eLaN9eTkmXjzVs60q1xRJnrCNr2OsHrnsPa5ArSrvvY80FWczX5+8cT1D4lU/sUr7xtU56+tlq+gZZLV5dGefOztp1MK9MqobltRgEQsO9Lb4QlIiJS7RkNBno0iQDyVuMuD2uzYQBYTq7DYM3wVGgiIlWGEmipVi6PDsVszFsl9FRaTqnPy20zGgBL3AbNyxIRESlG/jzoDcfOlut8R2QriGyOwWnFcuIXD0YmIlI1KIGWaiXAYqJ9/bxhFmWZB+0MbQDN8lYHDdj/lVdiExERqe56NslLoPckpJOWbSt7BQYDtL0WAP+jP3oyNBGRKkEJtFQ7Fw7jLpOOYwHw3/8lmjgiIiJSWN1Qf1rUDsIFbDp+tnyVnE+g/Y6tAKfDY7GJiFQFSqCl2nEn0KfKmEC3vwGXyR9z6kHMZ2K9EJmIiEj116tp/jDucu5c0aQPTr8wjNnJmE9v91xgIiJVgBJoqXY6NQjDaICTZ3M4nZ5b+hMDwrC2uBoAfy0mJiIiUqReFdwPGpMFW9PBAPgfWeapsEREqgQl0FLthPibaVMnBIDtZXwLndP2JgACDnwDjnLM7RIREanhujQMx2SAuHO5xJVhwc4LWZtdCYCf5kGLSA2jBFqqpfxh3FvLOA/a1nggzsDaGLOT8dPqoCIiIoUE+f2+YOeWE2fLVYe16WBcBhPmlH0Yzx33YHQiIr6lBFqqpXIvJGaykNP6BkDDuEVERIrTrXEEUP4E2hUQga1BTwD8jyz3UFQiIr6nBFqqpc4NwwA4nJzF2ayyDcXObTsGAP/D32PIOuPx2ERERKq77ucT6M0n0nCVc+cKa7NhAPgdVQItIjWHEmipliKD/GheKwgo+zxoe92O2Op1weC0EfjbJ94IT0REpFrr1DAMs9FAYnoup8o5Dzq3+VUAWOLWY8g568HoRER8Rwm0VFv5w7i3lHUYN5Dd8U4AAnZ9BA6rR+MSERGp7gIsJi6PzpsHvbmc+0E7w5thr9UWg9Oetye0iEgNoARaqq3uTSKA8s3Pym05AkdQXUxZifgf/t6zgYmIiNQA3dzDuM+Wu47cFtcA4H9kqQciEhHxPSXQUm11a5z3BvrAmUxSs8r4FtnkR85ltwEQGPuBp0MTERGp9rq7FxKrwDzo5lcD4HdsFdizPRSZiIjveDWBzsnJIS4uzpuXkEtYrSA/WkblzYPecqIcw7gvuw2X0YIlYTPm07GeDk9ERKRa69AgDD+TgaRMK8dTy5f82ut0wBHSAIM9C7+Taz0coYhI5StTAn3jjTfyyy8F98612WysWLGC1NTUQuWXLVvG0KFDKxahSAm6V2B4mSu4LrmtRgIQuFNvoUXk0jR9+nQ2b95c4JjT6WTv3r1kZxdOmhYtWkS7du0qKzzxIX+zkQ4N8na9KO92VhgM5Oa/hT78g4ciExHxnTIl0Hv27CEtreCbvoyMDKZPn87evXs9GphIafQ4Pw+6vAuc5C8m5r//GwxZSR6KSkSk+vjxxx+Jj48vcCwtLY0bb7yR7du3+yYoqTK6XbCdVXlZ8+dBH10OTocnwhIR8RmPDOEu77wYkYrq2igCowGOpWZzOj23zOfb63XBVrczBqeVoB3veiFCEZHqSX27wIXzoM+W+3vCFt0Tp384xuxkLAmbL36CiEgVpkXEpFoLDTDTtm4IUP5VQrO63Q9AYOx7GDMTPRWaiIhItXdZ/VD8zUZSsmwcSckqXyUmC9ZmVwLgd1ircYtI9aYEWqq9C5+Ol4e1+VXY6nXFYM8maPNszwUmIiJSzfmZjXQ8Pw+6vNOlAPc8aP8jS0GjG0SkGlMCLdVe9wrOg8ZgILPP4wAE7P4E49kjnglMRESkBnCvN1KRedBNrsBl8sd07him5D0eikxEpPIpgZZqr3PDcExGA3HncjmVVr5tNmwN+5DbZDAGp53gjf/xcIQiIiLVV34CveXEWZzlfXtsCcLaeBAA/oe/91BkIiKVTwm0VHtBfiYurx8KVGx4WWbvxwAIOPANpjO/eSI0ERGRai+mXijBfibO5dg5cDqz3PXkthoOgP/B7zwVmohIpTOX9YTPPvuMdevWuf9stVoxGAy89957LFq0qEDZkydPVjxCkVLo1iSCHXHn2HwijRs6RJerDkedy8hpfQMBB74h5NfnSLvuYzAYPBypiEjVs3z5co4dO+b+c05ODgaDgW+++YYtW7YUKLtv377KDk98zGw00KVROGsOp7DpxFna1gspVz3WZlfhMvphTt2PKWU/jlptPBypiIj3lTmB3rRpE5s2bSp0fM2aNUWWNygBkUrQo3EE768/zubjedtslPf7LrPnn/E/9B1+J34mMPY9sjtN8XCkIiJVz7Jly1i2bFmh419//XWR5dW3X3p6NInIS6CPp3Jb90blqsPlH4a1yUD8j/6I/8HFZPV82MNRioh4X5kS6Llz53orDpEK6dAgDH+zkaRMKweTMmldp3xPx50Rzcns+1dC1jxN8Lp/YK/TAVuDXh6OVkSk6nj++ed9HYJUA/k7Xmw7mYbd4cRsKt8swNyWI/IS6EPfKYEWkWqpTAl0z549y3yBtLTyr9goUlr+ZqP76fiawynlTqABsjv+CXPiNgIOfEPo0ns4e8v3OIPreTBaEZGq48Ybb/R1CFINtKoTTHiAmbQcO78lpNOpYXi56rE2vwqX0YI5ZR+mlAM4arX2cKQiIt7ltUXEtm3bxqOPPsqgQYO8dQmRAga0qAXA6kMpFavIYCB98L+x12qLKes0YUvvBofNAxGKiFRP6enpWK1WX4chPmQ0GH7fNvLE2XLX4/IPx9p4AAD+hxZ7IDIRkcrl0QQ6MzOT+fPnc8MNNzB+/Hi++eYbnE6nJy8hUqz+LWoDsCv+HClZFfxFzxLEuWvfwekXiiV+EyFr/g/Ku3WHiEg1sGTJEp5++ukCx3bv3s2oUaPo2bMnnTt35r777iMxMdE3AYrP5Q/jrsiOFwC5LUcC4H9Iq3GLSPXjkQR67969/N///R8DBw7k2WefZd++fURGRnLfffexcuVKT1xC5KLqhvrTtm4ILmDdkQq+hQYcES1Iv3I2LgwE7ppLYOx7FQ9SRKQKeuCBB3j44YdZsGABdrsdgDNnzjBp0iT27dtHy5YtufLKK1m/fj0TJkwgM7P8WxlJ9ZX/Bjo27hw5Nke568kbxm3GnLwXU+ohD0UnIlI5yp1AW61WvvrqK8aOHcuNN97IggUL3B3q1KlTWbVqFffffz+1a9f2WLAiF+OxYdznWZsPI7PvXwEIXvssfkd/9Ei9IiJVxaJFi1i6dCkjRozgq6++wmzOWx7lzTffJD09ne7du/PNN98we/ZsvvzyS5KTk/nggw98HLX4QtPIQOqE+GF1uIiNO1fuelwBEdga9Qc0jFtEqp8yJ9BHjhzh+eefZ8CAATzxxBPs2LGDBg0aMHXqVN544w1cLhedOnXCz8/PG/GKlKh/y7wHNuuPpmK1e2b6QHbnqWS3H4/B5SRs6b2YknZ7pF4Rkargm2++oWPHjrz44ovExMS4jy9duhSDwcDdd9+NyWQCoFmzZlx33XUsX77cV+GKDxkMht+HcVdgHjRcMIz74LeaIiUi1UqZEuhJkyYxfPhwPvzwQwICArjtttv49NNPWbFiBQ8//DBt2rTxVpwipdKuXgi1g/3IsjnYdtJDK8AbDGQMnIW1UX8M9izCF9+OKfWgZ+oWEfGxPXv2cOWVVxY4dujQIZKSkggICKBXr4Jb+V122WWcOHGiMkOUKsS9kFhF50G3uBqXyR9z8l7MCVsqHpiISCUpUwK9YcMGgoKCePDBB1m6dClPPvkknTt39lJoImVnNBjo3/z8MO7DyZ6r2GTh3DVvYY9sjSkzgYgvb8Bycq3n6hcR8ZFz584Vmm61efNmALp27eoe0p3PYDDg0hvDS1aP8wn07oR00nPs5a7HFRBJTptRAFpjRESqlTIl0DfccANOp5NXXnmFnj17cuedd/L5559rr2epUga0zE+gUzz6S57LP5yzN36BrX53jLlphH87Af89CzxWv4iIL0RERJCcXPCB4/r16zEYDIXePgMcOHCAWrVqVVZ4UsVEhwXQJDIQhws2VXAYd3bHPwHgf2gJxvQ4D0QnIuJ9ZUqg//nPf7JmzRqeeeYZ2rdvz7p16/jb3/5Gv379uPvuu/nhhx8wGAzeilWkVHo2jcTPZCAuLYfDyVkerdsVWJuzN3xKTusbMDjthP30CMFrngWH9kcVkeqpc+fOLF261P3nc+fOsWrVKgAGDx5coGxGRgZLlizR6LNLXJ9mkQD8WsEdLxxR7bE26I3B5SBw11xPhCYi4nVlXkQsODiYW265hU8//ZQlS5Zwxx13EBERwapVq/jPf/4DwJdffslvv/3m8WBFSiPQYnLP0Vp9yIPDuPOZA0gf9hqZ3R8AIGjH20QsHI0x7ZjnryUi4mUTJkzgt99+Y+rUqXzyySfcddddZGdn07dvX1q3bu0ud+LECe677z5SUlK4+eabfRix+FqfZnkjENYfTa3wSK/sTnlvoQN2fwL27ArHJiLibRXaB7pFixb85S9/4eeff+b1119nyJAhmEwmfvrpJ8aMGcMtt9zC119/7aFQRUpv4PnVuH86kOSdCxgMZPWaSdq17+D0D8dyejuRn12D/4FvvHM9EREv6dOnDzNnzmTdunX8/e9/Z8eOHbRu3Zrnn3/eXebf//43V111FRs2bGDixIn07t3bhxGLr3VtHI7FZCAhPZdjKRVLeq3NrsIR2ghjTioB+7/yUIQiIt5jvniRizOZTAwZMoQhQ4aQkpLCN998w8KFC4mNjWXnzp2MGjXKE5cRKbUhraP490+H2JOYwZHkLFpEBXnlOtYW15JapyNhy6djid9E2LL7yD20hIwBz+IMrueVa4qIeNqf/vQnRo4cyY4dOwgJCaFXr17urasA6tevT9++fbnpppsYPnx4mevft28f//vf/9i4cSMZGRnUrVuXoUOHMmPGDEJDQy96/ldffcWHH37I0aNHCQkJYdiwYTz44IOEh4eXORapuECLiS4Nw9l4/CzrjqbQrHYF+lijiewOkwlZ9w8Cd7xHTrtbQdMBRaQKq9Ab6KLUqlWLO+64g2+//ZbPPvuMsWPHevoSIhcVGeTnnqP1w55Er17LGdqQs6M+J7P7g7gMJvwPfUfkvMEE7PoYXJ7Zi1pExNvq1avHVVddRd++fQskzwATJ07kvffeK1fyfPjwYcaNG8e6desYO3Ysf/3rX+nZsycff/wx48ePJyur5LUq3nrrLR577DEiIiKYOXMm1113HZ9//jm33347OTk5ZY5HPKNP89+HcVdUTrtxuMyBmFP2YTm1rsL1iYh4U5neQN9+++3eikPE465tV5c1h1P4Yc9p7unfzLsXM5rJ6vVnclsOJ3TlTCyndxD682ME7P2MjH5PYY/u4d3ri4iUU3n6doPBwIcffliqsrNmzcJms7FgwQLatGkDwLhx42jfvj2zZs1i3rx5TJkypchzExISePXVVxk4cCBvvfUWRmPec//LLruMRx55hI8++oi77rqrzPFLxfVuFskrP8PWk2nk2BwEWEwXP6kYroAIcmJuJnDXXAJj38fWqJ8HIxUR8awyJdAbN250r7Jd2kUjtCq3+MrAlrUJspiIO5fLjrhzXFknzOvXdES15+xNiwjcOYegDf/CkriVyIU3ktviGjJ7P44jsqXXYxARKQtv9u1Wq5XNmzfTrVs3d/Kcb9SoUcyaNYtNmzYVm0B/++232Gw2Jk+e7E6eAUaOHMl///tfFi5cqATaR1rWDqJuiB+nM6xsP5VG72YV29osu8NkAnfNxe/ocoznjuMMa+KhSEVEPKtMCXSDBg2Ii4sjKiqKa665hhEjRtClSxdvxSZSIQEWE4PbRPHdb4l8v/s0V3ZqVDkXNprI7vQncluNJGjjiwTs+RT/wz/gd2Q5Oe3GkdXjQZwh0ZUTi4jIRXizbzebzSxevLjIxDwpKW+RxwsT4z/asWMHAJ06dSr0WYcOHfjhhx9IT08v1Txq8SyDwUDvZpEs2pXIr0dTK5xAO2q1wdp4IH4nfiFw54dk9nvKQ5GKiHhWmRLon376iR07drBkyRJ++OEHPvnkE6KjoxkxYgQjRowgJibGW3GKlMu17ery3W+J/LjvDFZ75c5HdgbXI2Pwv8juNIXgX5/H/+hyAnd/QsC+L8jueAdZXe/DFRBZqTGJiPyRN/t2o9FI48aNi/zs/fffB6BXr17Fnp+QkEBYWBghISGFPqtfvz4Ap06dKnOMFRkcl3+uBthB3+a1WLQrkfVHUwu1S3naJ7vjnfid+IWAPZ+S1esRsHhnAVBf0vdPydQ+JVP7FK+8bVOetjS4KrCB36ZNm/juu+9Yvnw5KSkpNG3alJEjRzJ8+HBatGhR3movWUlJ6ZT3b8NggKio0ArVURM5nC5Gvr2BpEwr79zenS51g3zWPpa4DQSvfwFL/CYAnJYQctqPJ7vTFJyhDXwT1Hn6/imZ2qdkap/iVaRt8s+tbJXRt3/99dc8+uijREdH89133xEcHFxkuauvvpqcnBx+/vnnQp+99NJLvPnmm8yfP5+uXbt6JC4pm7QsG13+vgynC9Y9NoQGEYEVq9DphFe7QuoRGPkSdL/TM4GKiHhQhRLofE6nk19//ZUlS5bw448/cu7cOWJiYhgxYgTDhw+nQQPfJgfVhRJo73hp1SHmbTnFiA7RPHN1a9+2j8uF37Gf8uZHJ/2Wd8hoJrfV9WR1uRtHVHufhKXvn5KpfUqm9iledUyg83mrb//qq6948skn8ff358MPP6Rjx47Flr366qvJzs7ml19+KfRZfgL92WefFTnEuyTJyRXrb2vXDq1QHTXJnfO2Ext3jr9e1ZpRHaMr3D4B298lZM3T2Gu14eytK2rcqzZ9/5RM7VMytU/xyts2+eeVhUe2sTIajfTr149Zs2axdu1a3nzzTerVq8eLL77IlVde6YlLiJTb8HZ5+zEv35NIRq7dt8EYDFibDeXsLT+QNnIu1oZ9MTjtBOxfSK0FVxH+7QQsJ9ein4oi4mve6Nv/97//8dhjjxEUFMQ777xTYvIMEBwcXOxWVdnZ2QBFDu++GJerYl+eqKOmfPU+v2Xk6kMpHmmfnJhbcFqCMafsx3xirc/vzxtf+v5R+6h9qlbblJVH94FOSUnhyy+/5IMPPmD16tW4XC7q1avnyUuIlFmbusG0qB2E1e7k+92nfR1OHoMBa9MhpI36jNSbl5DT6jpcBiN+x38m4puxRHw+Av+9X4Aj19eRisglzhN9u81m4/HHH2f27NnUq1ePjz/+mO7du1/0vEaNGpGWllbkXtEJCQkYjUb9nuFjg1rWBmD9sVSybY4K1+fyDyM35mYAAmPfr3B9IiKeVqZFxIqSkpLCsmXL+OGHH9i8eTN2u526desyfvx4hg8frlW6xecMBgOjO0Xzn58O8dm2OG7qFF2ltlez1+1I+tVvkJl2jKAdbxOwZwGWM7FYVjyIc93fyW4/gZzLJ2rlbhGpNJ7s2x0OB4888ghLly6lbdu2vPPOO6VOejt27MjSpUuJjY2ld+/eBT7buXMnrVu3LtcbaPGc1nWCaRAeQFxaDr8eTWVom6gK15nd4Q4Cd87B7+hyTMn7cNRu64FIRUQ8o1wJdFEda1RUFGPHjuXaa6+lW7duVSpBERl5WT3eWHOUIylZbD5xlh5Nqt7q187wpmQMnEVmj4cJ/G0eAb/NxZQRT/CW2QRte53cliPJ7jQFe73Ovg5VRGogb/Xtr7zyCkuXLqVjx468//77Zdpy6tprr+Xll1/m3XffpVevXu7rL168mLi4OB5//PEyxyOeZTAYuKJVbeZtOcWqA0keSaAdkS3JbXEt/oe/J/jX5zg38kMPRCoi4hllSqDnz5/v7lgdDge1atXipptuYvjw4fTs2VNJs1RZIf5mbuzakI/XH+fz7fFVMoHO5wqsTVb3+8nqeg9+R5YSGPs+fnEbCDjwNQEHvsYW3YPsDneS2+IaMFl8Ha6IVHPe7Nvj4uJ47733MBgMDBs2jJ9++qlQmaioKPr168eJEyfYunUrTZo0cb/hbtiwIXfffTevvvoqd955J9deey1Hjhzho48+okOHDowbN67csYnnDG4Vxbwtp1hzOAWbwzNbRmb2eRy/o8vxP7YCy4k12Br390i9IiIVVaYE+plnnsFgMNCkSROuvfZaevfujclkAmDz5s3FntejR4+KRSniAbf3acbH64/z88EkEs7lUD8swNchlcxoxtpyBNaWIzCf2UngjvfwP/ANlvhNWOI34QiuT87lt5N92QRcgbV9Ha2IVFPe7Ns3btyI3Z63eOOLL75YZJmePXvSr18/Nm3axOOPP86NN95YYIj49OnTqV27Nh9//DHPPvus+634jBkzCAio4j/HLxEdGoRRK8hCSpaNLSfOMrJeeIXrdES0IPuyiQTt/IDgdX/n7C3fg8GjS/eIiJRLmbaxiomJ+f3EMjyR3rNnT9miukRpGyvvyW+fm/63hi0n0rijV2Pu7d/c12GVmTEzkYBdcwn87ROM2UkAuEz+5LQZRXanKThqtytXvfr+KZnap2Rqn+JVh22sLsW+Xf2t5z23fD9fxSZwU6doXry1q0fax5CdQq2P+2G0pnNu6MvkxozxTLA+pO+fkql9Sqb2KV5526Y8fW2Z3kBPnz69TJWLVDW3dGnAlhNpfB2bwJTeTfEzV6+n2c7gemT1mklW9xn4H/yWwNgPsJzeQeCeBQTuWYC10QCyO03B2nSwntSLSKmobxdPuKJVFF/FJvDzwWScTs/8Zu8KrEVWt+mE/Po8wRv+SW6rEWAO9EjdIiLlpQRaLimDWkVRN8SP0xlWftx/huHtq+n2JyZ/ctuOIbfNTZgTNhO04138Dn+P38nV+J1cjT2iJdmd/kRO2zFgCfJ1tCJShalvF0/o0SSCYD8TSZlWtp04S9PgCm/0AkB2xzsJ3DkXU8Ypgra/Q1b3GR6pV0SkvPSKSi4pZmPellYAn249RRlmMFRNBgP26B6cu+YtUm5bS1bnaTj9QjGfPUToz09Q+8MeBK+bhTH9lK8jFRGRGsxiMtK/RS0Alv2W4LmKzYFk9n4UgKDNr2A6e9hzdYuIlIMSaLnk3NgxmgCzkT2JGaw5nOLrcDzGGdaYzH5PkTJpExn9n8ER1hRjbhpB296g1kd9CfthGuaELb4OU0REaqjBrfO2sPrhtwSPPqDObXMj1kYDMDhyCVk5E1yeWelbRKQ8lEDLJadWkB+3dGkAwNvrjlX/t9B/4PILIbvTn0iZ8Atpw9/H2rAfBpcD/0PfEfnlDYR/cyuWuPW+DlNERGqYPs1q4WcycCw5i4NJmZ6r2GAgffA/cZkD87Z1/O1jz9UtIlJGSqDlkjSxe2OCLCb2ns7gl0PJvg7HO4wmrM2vIm3UAlLGLSc7Ziwuoxm/k6uJ+GoM4V/dhN/RH/UkX0REPCLIz0Sf5nnDuH/an+TRup1hTcjs/RiApiaJiE8pgZZLUkSQhbFd895Cv7XuGM4a9hb6jxy125Ex9EVSJqwm+7KJuIwW/OI2EP7dZCI/GUjAjvcgN93XYYqISDU3tE3eMO4VHk6gAbI7TMZWvxtGWyYhqx5D+/iIiC8ogZZL1oRujQj2M3HgTCarDni+o6+KnGGNybjieVImriWry904/cMxpx0lZPX/wYvtCP75SUzJe30dpoiIVFP/z959h0dVpQ8c/96ZySST3iAEQg2EHkKXIogC0qsQOggIiKjYEHvfdX+rqKusBZUO0qX3oii9JdSQ0JIQEhLS60xm7u+PmCgLiQQymZT38zzzuN577j3vvDvxzDv33nO6+Hthp1W4fCuTy7dK8DZuAI2WtG6foGr02EfulVu5hRA2IQW0qLTcDHaMbFUDqBxXof/K4lydjI5vcmvcEdK6fESuuz8Y0zCcXojnT91xXzsE/ZUd8uu+EEKIYnG21/FwgyqAda5Cmz0bkPHQrLy+9r+DLv50ifchhBBFkQJaVGqjWvvhYq/j8q1Mtp2/aetwSp/eiezm40kevQ/GrSfHvw+qosXuxhHctkzEfWVv9Je3SyEthBDinvVpnrdcZEk/B50vK2gKOXV6oFiMuG6bhpKTapV+hBDibqSAFpWai4OOsW39APhq/xUyjLk2jshGFAXqPUJa7+9IHHeIzFbPYLFzwi7hDG5bJ+Gx4nHsL6wGc46tIxVCCFHG9Wjsg1ajEJGQwdXEzJLvQNGQ9tgczC5+aFOv4bLnJfmhVwhRasp1AX3lyhWef/552rdvT7NmzejduzcLFizAYrl9VuGYmBhmzZpF586dCQoKYvjw4ezevfuu5wwPD2f69Ol06NCBli1bMn78eI4fv/vauSdOnGDChAm0bduWNm3aMG3aNCIiIkr8fQrrGt3aj5ruDsSnG/n+YKStw7E5i7MvGR1eI3HsQTJbzcBi54Tu1jlcd8/Ea+FDOB75FCWrgs5cLoQQ4oG5OdrRvpY7AHutNMeI6uBB6uPfoGrssL+8FUPI91bpRwgh/le5LaCjo6MZMWIEe/fuZciQIbz++utUq1aNf/7zn7z33nsF7eLj4xkzZgy7du1i6NChzJo1i9zcXKZPn87GjRtvO+elS5cYNWoUoaGhjB07lpkzZxIbG8v48eM5cuTIbW2PHDnCuHHjuHHjBlOnTmXKlCmcOXOG4OBgLl26VCo5ECVDr9PwUrf6ACw/cZ0rt6zwa3k5pBo8yegwm8Rxh0h/aDZmp2posuJxOvoZnos74nj0czBJroQQQtzp0YZ5s3HvCou3Wh+5PkGkd3obAKff38c+fIPV+hJCiHyKqpbPe14++OADlixZwmeffUafPn0Kto8fP55Dhw6xZcsW/P39eeedd1ixYgXLli2jVatWAOTk5DB8+HDi4uLYs2cPjo6OAEyePJmjR4+yadMmatasCUBSUhIDBgzA1dWVTZs2oSgKqqrSv39/kpOT2bRpE+7u7gBERkbSv39/2rVrx7x584r9nhIS0u77DiRFAW9vlwc6R0V2L/l56eez/HrpFm1ruTP3ieYoilK6QdrQPX1+zCbsL2/BcPIb7P6YtMXsWJXMdi+S3XgEaHSlF3Apk7+vokl+Cvcguck/VpQ8GW+tJz8/EZGJPP71QcwqrJ3YlpoeBut0qKo4//I6hrOLUTU6UnvNw1i3h3X6KgHy+Sma5Kdokp/C3W9u7mesLbdXoK9evQrAI488ctv27t27A3DhwgXMZjMbNmwgKCiooHgGsLe3Z9y4cSQlJbFv3z4AEhIS2L9/P927dy8ongE8PDwYNmwYERERhIaGAnD69GnCw8MZOnRoQfEMUKtWLXr16sVvv/3GzZuVcEKqcu6FR+qh1yocjUxmTyVZ1qpYtHbkNBhI8rDNpPaci9m1FtrMm7jsm43Hsm55v/yrlr8/jxBCiArP3dGONn/cxm3VMVVRSO/6EdkBQ1Asubhun4Zd1G/W608IUemV2wK6bt26AHc8c3zlyhUAfHx8CA8PJzMzk6CgoDuOb9GiBQAhISG3/fNe2p46deq27X8VGBiIxWLh9GlZVqG88XM3MK5t3o8nc/ZeIj2nkk4o9ncUDTkNBpI4ai/pnd/F4uCJLuUKrjum476yT97yVxazraMUQghhY48G5C1ntf3CTax6w+Mfk4rl1H0cxZyD25aJ6GLvPn+NEEI8qHJbQE+ZMoW6desye/ZsDh48SHR0NEuWLGHFihV06NCB1q1bExcXB4Cvr+8dx1erVg3Ie5YaIDY2ttC2Pj4+d21bvXr1vz1vcSjKg71K4hwV+XUv+ZnQviY13By4mW5kzr5LNo+5rOXntpfOnuygySSNO0BGu5ew2Dnnzdq9ZSKeSzrjeGIumuxEm78vm+Wnkr0kP9bJjRDlWfcAb+x1GsLjMzgbm2bdzjQ6Uh//L8aaXVFyM3HbOAbdzVDr9imEqJTK7UOLVatWZebMmbz++utMmDChYHvLli2ZO3cuiqKQlpb3H+v8Z5z/ysHBAYCsrCwA0tPTAXBycrqjrcFguGvbu533f9sWh5fXgz/rVhLnqMjuJT+fjWhJ8HcH2XgmjoGtatK9iU8pRFY23N/nxwWqvw1dn4EDX8DxhWjTonA6+E+cjsyBtpOh6ytg8CjxeEub/H0VTfJTOMmNqIxcHezoHuDN5nM3WRtyg2a+rtbtUGtPSu/vcds0Bn3MYdw2jCJ50ErM3k2s268QolIptwX0d999x6effkrt2rV55ZVX8Pb25tixYyxdupTx48fz448/Fnm7UP4+jUZz27/f7ZgHaVsct2492KQmXl4uD3SOiqw4+annYseY1n4sPhbNq6tDWPFkG9wNdqUTqI2UzOdHDy1fgeYzsL+4AYfTC7GLD4VDc7GcWpY32VjTMaAtf7mUv6+iSX4K9yC5yT9WiPJscKAvm8/dZEdYPC884o+Lg5W/etoZSO27ELcNo7CLO4H7hpEkD16D2aO+dfsVQlQa5bKATk9PZ+7cuVStWpVVq1bh5uYGQI8ePWjSpAmzZs3i66+/pm3btsDdrwZnZ2cD4OKS9+Uk/8rzg7bN35bftjhUlQf+8lkS56jI7jU/UzvV4bfLiVxJzORfuyL4R7/G1g+uDCiRz4/WQHbjYLIbB2MXuQ/n395Hl3QR51/fwiF0AZltZ5JTfwBotCUSc2mSv6+iSX4KJ7kRlVVgdVfqeTly+VYmW8/fZHjLOx9/K2mq3pmU/otxWz8Cu/jTuK8bRkqf78mt1trqfQshKr5y+Qz0lStXyM7OpkePHgXFc77+/fvj6OjIwYMH8fPzA/58Zvmv8rflP7N8L23zn4/Ob5v/jPVfFfXctSg/7HUa3u3dEK0CO8Pi2XLuzv+vxd8z1XqEpBE7SOv6j7zJxpIv4brzWTyWd8M+bDVYZKI2IYSoyBRFYUhg3neidaE3rDuZ2F+o9m6kDFhGrlcTNFnxuK97Aodzy0qlbyFExVYuC2i9Xg+A2XznTL+qqmKxWFBVlXr16uHi4lKw/NRf5c+onb+8VfPmzdFoNEW2bdmyJZA30/Zft/9vW0VR7jpDtyhfmlRzYeJDtQD4YPtFDl5NtHFE5ZRGR3azcSSO/Z2M9rOw2LujS76M666ZePzUA/3V3XJpTgghKrDeTapir9MQkZDB6RtWnkzsL1QHD5KGrCPHvw+KxYTL3lk473sNzMZSi0EIUfGUywK6QYMG1KhRg23btt1xFXjVqlVkZ2fTqVMndDodffr04dixY5w4caKgTU5ODosWLcLb25suXboA4O3tTceOHdm+fTtRUVEFbZOSkli1ahWNGjWiSZO8SSgCAwOpW7cuq1atIjk5uaBtZGQk27dvp1u3bnh4lP8JkwRMeqg2PRtWIdeiMmv9OUKup9g6pHJL1buQ2eY5EscdIr3Da1gcPNAlheO2eTxuG0aiTThn6xCFEEJYgauDHd0b5i1ptTb0Rul2rnci9fFvyWj/KioKhrOLcV8fjJJxs3TjEEJUGIpaWvfSlLDff/+dqVOn4u7uzogRI/D29ubkyZOsX7+eevXq8dNPP+Hq6kp8fDyDBw8mKyuLJ598Ei8vL1avXs3Zs2eZM2cOffr0KTjnxYsXCQ4OxsnJiQkTJqDX61m6dCkxMTHMnz+fNm3aFLQ9cOAATz31FH5+fowePRqj0ciCBQswmUwsX76cevXqFfs9JSQ82CRi3t4uD3SOiuxB8mMyW3h5/VkOXEnCxV7Ht8GBNKjibJ1AbcQWnx8lJwXH419iCPkRxWJERSGn0TAy2r+Mxdn6z8gVh/x9FU3yU7gHyU3+saLkyXhrPYXlJzQmlUnLT2Gv07BlantcHUp/Qkn91d247HwWjTEVs1M1Unt/T65PUKnGIJ+fokl+iib5Kdz95uZ+xtpyW0ADnD17lv/+978cO3aMjIwMfHx86NmzJ9OnT79tEq+oqCg+/fRTDhw4gMlkomHDhjz99NN07dr1jnOeP3+eOXPmcPz4cTQaDc2aNWPmzJkEBQXd0fbQoUN8+eWXnDlzBoPBQOvWrXnxxRfx9/e/r/cjA7r1PGh+sk1mZqw+TUhMKp6OdswdFkh97zuXPCuvbPn50aRG4nTwYxwiNgCg6hzIbPEUWa2mo+rLRvEgf19Fk/wUTgrosknGW+spLD+qqjJq0QkiEjJ45VF/hresYZP4tMmXcd0yCV1SOKrWnrSu/ySn0bBSW3hdPj9Fk/wUTfJTOCmgKykZ0K2nJPKTlp3L1JUhhMdn4Oqg47PBzQisbuU1LUtJWfj86GJP4HzgQ+xuHAHAYvAm46HZZDceDoptnzYpC/kpyyQ/hZMCumyS8dZ6isrPTyeu8+neSzSq6szisa1sEyCgGNNw2fk89ld3AGCs2YX0Tu9g9mpo/b7l81MkyU/RJD+FK80Culw+Ay2ELbg46Ph6WCDNfV1Jzc7lmVWhMrFYCcqt1orkwWtI6f09uW510WQl4LL3ZdxX90cXe9zW4QkhhHhAvRpXxU6rcOFmOmE3020Wh6p3IbXP92S0fwVVo0cf9SseK3ri/OsbKNlJNotLCFE+SAEtRDG4GeyYO6w5Hep4kJ1r4cV1Z9lwJrbUluWo8BQFY71eJI3cTXrHN7HYOWN3MwSPNQNx2fEMmtRoW0cohBDiPrkb7Ojq7wXAxjN3LhtaqhQNmW2eJ3HUHnLq9UJRzRhOL8RzaRcczv0EqsW28QkhyiwpoIUoJoOdlk8HNS2YnfuD7Rd5Zf05bmXIshglRqsnq+U0EsfsJ6txMCoKDuHr8VzWFaeD/0DJSbV1hEIIIe5D/2bVANh2/ibGXNsXqRa3OqT2/p7kgSvI9WqEJjsp7+6ndU+gvXXB1uEJIcogKaCFuA92Wg0f9G3E053qoNMo/HLpFsELjrEzLF6uRpcg1bEK6Y9+SvLwrRhrdEQx5+B44r94Lu2at360EEKIcqV9bQ+qOutJyc7l10u3bB1OAZNfJ5KGbSW941uoOkfsbhzBY2UvnPa/g5KdbOvwhBBliBTQQtwnjaIw8aFaLBrTkoAqTqRk5/L6pvM8s/o0YXG2e7arIsqt0oyUgStI6buAXHd/NFnxuG0ej/O+2WDMsHV4Qggh7pFWo9CvqQ8AG2x9G/f/0tqR1XIqiaP25d3WbcnFMfQHPJc+jMPphWDJtXWEQogyQApoIR5QgyrOLBjdkqc61MJOq3A0MpmxS07w7tYLxKZm2zq8ikNRMNbpTlLwdjJbTAHAcHYJHisfR3fjmI2DE0IIca/6Nc27jfvQ1aQyOU5aXKrn3dY9YBm5ng3zbuv+9Q08VjyOXdRvtg5PCGFjUkALUQLstBqmdKzD6ifb8nijKqjA5nM3GfzDUT7cfpHIpCxbh1hx6BzI6Pw2yQNXYHauji7lKu5rB+P027tgyrR1dEIIIf5GTQ8DrfzcUIEt527aOpxCmWp2ISl4O2ldPsLi4IEuMQz3DSNw3foUmtRIW4cnhLARKaCFKEHV3Rz4sG9jFoxuSeuabuRaVNafieWJH4/y2sbznI5JlWekS4jJrxNJI3aS3WgYCiqOId/j+VMP7KJ/t3VoQggh/saAPyYTW38mFrOlDI+LGh3ZzceTOPpXMps/iaposb+8Fc9l3XA8+jmYZQJRISobKaCFsIKm1Vz4ZngLvh/Rgs71PFGBXRfjmbj8FGOXnOTn0Btkmcy2DrPcU+3dSHvsM5L7LcbsXB1t6jXc1wfj/Mvr8my0EEKUYY8GeOPmoCMmJZs94Qm2DudvqQ4eZHT5gKTg7RhrdEIx5+B05BM8VvZGF3vc1uEJIUqRFNBCWFGLGm58NrgZy8a1ol9TH+x1GsJupvPRznD6fHuIT/ZEcPmWFHoPylS7G0kjd5PVdCwAhjOL8Pypu1yNFkKIMspgpyW4ZQ0A5h+OLDd3Z5m9GpEy8CdSe3yFxeCVd1v3mkE4//IGSlbZmVVcCGE9UkALUQoaVHHmnV4N2TSlPc93rYefuwPpOWZWnIwheMFxpq4IYev5OLLlqvR9U/UupD/yT5IH/ITZxQ9tWlTe1eh9r6EYZVZ0IYQoa4a3rI7BTkN4fAYHribZOpx7pyjkBAwicdQ+shsNR0HFcGYhnos74nj43yg5qbaOUAhhRVJAC1GK3A12jGnjx5qJbflyaDMeqe+FRoET0Sm8vSWMXt8c4qMdFwmVZ6Xvm6lmZ5JG7CKr2TgADGcX4yHPRgshRJnjZrBjcKAvAAsPl79JuVQHD9Iem0PywBWYqgSiMWXgdOwLPBd3wOHsEpBxXIgKSQpoIWxAoyg8VMeTfw9syoan2jOlQ22qu9qTYTTz8+lYJi0/xfAFx1h4JIr49Bxbh1vuqHpn0rv+I2+m7r9ejf71DZmpWwghypDRrf2w0yqcvJ7KqegUW4dzX0x+nUgetpmUXt+R69EATU4KLvtm47L7BTDJKhxCVDRSQAthYz4u9jzVsTbrJrfjm+GB9G1SFQedhquJWXy1/wr9vjvMzLVn2HMxHpPZYutwy5W8mbp3kdV0DACG0wvx/KkHuhtHbRyZEEIIgKou9vRt4gPAgiNRNo7mASgKRv8+JI3YRXqHN1AVDQ5hq/FYMxBNyjVbRyeEKEFSQAtRRmgUhdY13Xm3dyO2TnuIN3s2oEV1Vywq/H4lkVc3nqf3N3kTj128Kc/03itV70z6Ix+TPGDZnzN1rx2C04GPIDfb1uEJIUSlN65tTTRK3lhX7sc3jZasVk+TMmB53iRjt87hsaoPhhNfy3wcQlQQUkALUQY52+sY2NyX70cGserJNoxvV5MqznpSsnNZcTKG0YtPMHbxCVaejCEly2TrcMsFU80uJI3Y9ee60Se/xmNVX7QJ52wdmhBCVGo1PQw8FlAFgO8OVIyrtSa/TiQN24qpahCanBScD36E56KHMBz9HLKSbR2eEOIBSAEtRBlXx9ORGQ/XZcNT7fl8cDMebeCNTqNw4WY6/94TQZ9vD/HGpvMcvpqERSYsKZJq70raY5+R0vsHLAZvdIlheKzqh+Hkt6DK7fFCCGErT3WojUaBXy7dIuR6+XwW+n9ZXKqTPGQdqY/OIdetLpqcZJwOfwKfNsRl+zPor+4GS66twxRCFJMU0EKUEzqNQqd6nvxrQBO2Tn2IF7v506CKE0azyo6weGasOc3AeUf47sBVbqTKrclFMdZ7nMQRu8ip0xPFYsT5wAe4rR+BJj3G1qEJIUSlVNfLkQHNqgHwxS9XKs5KFFo7choPJ2nUPlJ7fEWuVyPIzcY+fD1um8fjtaAt9udXyIzdQpQjUkALUQ65O9oxslUNlo5txeIxLXmihS8u9jpi03KYdzCSgfOO8MyqUHZcuElOrlxZvRvV0ZvUPj+Q9si/UHUG9NcP4LGiF3ZRv9k6NCGEqJSmdKyNvU7D6Rup7Iu4ZetwSpZGS07AIJJH7IQp+8gKnIjFwRNNVjyue17CZecMFGOaraMUQtwDKaCFKMcURaGRjwuvdm/Alqnt+aBPI9rWckcFjkQm88bmC/T+5hD/2hXO+bi0ivOLfklRFLKbjiYpeDsm72ZoshNx2zgKw/Gv5JZuIYQoZVWc7RnVugYAc/dfIddSAccsRYHqLcno8j63Jhwno/2rqIoWh/D1eKzohS72hK0jFEL8DSmghaggHOy09Gpclf8OC+TnyW2Z9FAtfFzsScvJZXXIDcYtOcnoxSdYceI6qdky8dhfmd3rkTx0HVmNglFUC86HPsZ1yySU7GRbhyaEEJXKuLY1cXPQcS0piw2nb9g6HOvS2pHZ5lmSh6zF7OKHNvUaHmsG4PZzMPqITWCWsVqIskgKaCEqoBpuBqZ1qsP6ye34amhzHm9UBb1WITw+g0/2XqLPt4d5e8sFTkWnyFXpfDoD6Y99Slq3/0PV2mN/dSceK3ujuxli68iEEKLScLbXMalDbQC+OxhJhrHiT7KVW601ScHbyQ4Ygqpo0F//Hbft0/Bc9BAO536ydXhCiP8hBbQQFZhWo9C+jgcf9m3M1mkP8cqj/tT3diIn18LW8zd5akUIwQuP89OJ67Ic1h+ym4wiecg6zK610KZF4b5mMA6hC2SCFyGEKCVDA32p4ebArQwj3/5eMZa1+juqvRtpPf5D4tiDZLR+DovBG21mHC57X8Z532wwG20dohDiD1JAC1FJuDrYMbxlDZaNa8X8UUEMaOaDg07DlVuZfLLnEu3/sYt3t4YRcl2uSudWDSRp+FZy6j6eN0v3r2/CmklgyrJ1aEIIUeHpdRpmPVYfgBUnr3MhrvJMrmVxqUHmQ7O4Nf4IGe1noaJgOLsE95+Ho8mIs3V4QgikgBai0lEUhWa+rrz1eMM/rkrXp763E9kmC5vOxjH5pxBGLDzOihPXSc+p+LfOFUa1dyO19/ekd34XVaODM2twW/cEmvQK/kyeEEKUAR3retKzYRUsKvxjZzjmijihWFG0ejLbPEdq3wVY9K7YxR7DfWUf7KL22zoyISo9KaCFqMSc7XUMb1md5eNbsXZ6R/o388Fep+HyrUw+2XuJ3t8c4h87LxJ2M93WodqGopDVYjIpA5eDwRO7myG4r+qHLu6UrSMTQogK74Vu/jjbazkfl86qUzG2DscmjHUeI3nYJnI9AtBmxuG+YSTOv7wOxgxbhyZEpSUFtBACRVFoVcuDd3o1ZNsfV6XreTmSnWthXWgsYxafYOKyk2w6G0u2yWzrcEtdbo0O8NQecj0b5n2BWTcU+4s/2zosIYSo0Lyd9Dz7cF0Avvn9KnFpOTaOyDbM7vVIGraJrOYTADCcWYTnip7oL20BU6ZtgxOiEpICWghxm/yr0j+Nb823wYH0aFgFnUbh9I003tt2kb7fHebzfZeJScm2daily7MuKU/8TE6d7ijmHFx3zsDx0L9kvWghiikkJITGjRtz+PDhe2p/7NgxGjZseNfX2LFjrRytsLVBgb4093Ulw2jmox0XsVTWOTrsHEnv8iHJA37C7FwDbeo13LZNwfv7ZrhtGIUh5HsUYyW9W0yIUqazdQBCiLJJURRa+bnTys+dWxlGNpyJZV3oDW6k5rD0eDTLT0TTrYE3I1vVILC6K4qi2Dpkq1P1LqT2/gGnQx/jePJrnI5/iS7xImndv0DVO9s6PCHKvKtXr/LMM89gsdz7D09hYWEAvPjii1SrVu22fd7e3iUanyh7NIrC6z0bMGHpSQ5eTWLegWtM7VTH1mHZjKlmZ5JG7sLx6OfYX9qCNi0KfdSv6KN+xeHMIlJ7fo25SlNbhylEhSYFtBDib3k56XmyfS3Gta3JgSuJLD9xnaORyey+mMDuiwk09nFmeMvq9GhYFXtdBb+xRaMlo+Mb5Ho1xGXPLOyvbEe7dhApfX7E4lrL1tEJUWbt3LmTN954g5SUlGIdFxYWhqIojB07FkdHRytFJ8qy+t5OvN6jAe9sDeP7Q5E0qebCw/5etg7LZlS9Cxmd3iKj45toky+hv7YXQ8h36JIv47FmAOmd3yG76VioBD9sC2ELFfybrhCiJGk1Cg/7e/HfYYEsH9eaAc180GsVzsel8962i/T77jBf/36VxMyKv15lTsMnSB68CrNjVXS3LuCxqh921w/aOiwhyqQpU6YwY8YMqlSpQr9+/Yp1bFhYGH5+flI8V3J9mvgwPKg6AG9vvUBUkiwriKJg9qhPVtBTJAXvIKdODxRzDi6/vI7rlono4s/YOkIhKiQpoIUQ96V+FSfeerwhm6a0Z3rnOvi42JOcZeLHQ5EMmHeEj3eFE51csb/g5FZrTfKwTZiqBKLJTsRtw0gczi6xdVhClDmXL1/mxRdfZN26ddSpU+eej1NVlYsXL9KgQQMAzGYzWVkV+78ronAzH6lHYHVX0nPMvLLhLJnGyjepZWFUBw9S+/z4x9KLdthf3YnHyl64rR+BXeQ+qKzPjgthBXILtxDigXg45t3ePbZtTX6JSGDx0WjOxqaxJuQG60Jv8Eh9b0a1rrjPSVucq5M8eA0ue1/GIXw9Lvtmo02+QkbHN+X2OSH+sGXLFvR6fbGPu3btGpmZmWRmZjJmzBhOnTqFyWTC39+fGTNm0KdPn/uK50H+NPOPlT/vu7NmfvQ6Df8a0Jgxi09yKSGTV9af5bMhzcrVo0NW/fwoCtlBkzH5dcJw/CvsIzahj/4NffRvGGs+TPpjc7A4+1qh45Ijf19Fk/wU7n5zcz+5lAJaCFEidBqFxwKq8GgDb05Ep7DoaBQHriSxJzyBPeEJNKnmwujWNXg0IG9W7wrFzkBaj68wezbC6fC/cDz1LUpOMumP/As08p9ZIe6neIY/JxALDQ1lwoQJTJw4kRs3brBgwQJeeOEFkpKSGD16dLHP6+Xlcl/xlPQ5KjJr5cfb24UfJ7Rl1LxDHIlM5r0d4fx3dCt02vJTRIOVPz/e7aDRIki6Boe/gWPz0Uftx3NFT+j/BTQZaL2+S4j8fRVN8lO40siNoqpyT0dZkZCQdt932ChK3qDyIOeoyCQ/RbNWfiISMvjp+HW2no/DaM47sZ+7A+Pa1qRvEx/05eSqQXHyY39+JS57X0ZRLeTU601qz69Aa186gdqI/H0V7kFyk39sRfPll1/y1VdfsWjRItq3b19k2wsXLrBz5066du1KYGBgwfb09HT69u1Lamoqv/zyC66ursWK4datBxtvvbxcHugcFVlp5edYZDLPrTmN0azSv6kPb/UKQFMOLsvZ4vOjTYrAZcez6OJPA5DdOJiMjm+iGjxKJ4BikL+vokl+Cne/uck/rjjKx7dXIUS5VN/biTcfD2DjlPZM6VAbNwcd0cnZ/GNnOIN+OMKy49FkmSrWM2w5jYeT2us7VI0e+8tbcds0AYwZtg5LiHKpUaNGPPvss7cVzwDOzs4MGTKEzMxMjh8/XuzzquqDvUriHBX5VRr5aV3TnX/0a4xWgY1n4/h0zyUsFtXm772s5Oevr1z3+iQNXU9mqxmoKDicX4HHks7Yh/yImmuyeT5snZ/y9pL8lHxuiksKaCGE1Xk66nmqY202PNWeFx6pRxVnPfHpRj7bd5kB847w46FI0rJzbR1miTHW60VKv0WoOkf00ftx3zACJTvJ1mEJUaHkrwGdkSE/UFVWXet789bjDQFYcTKGD3dcJNdyH9+GKwOtnowOs0kevIZcr8ZoclJw2f82Hit6Yhe139bRCVGuSAEthCg1jnoto1r78fOkdrzWowE13BxIzjLx9e9X6T/vMN/+fpXUbJOtwywRppqdSR60Aou9O3ZxJ3Ff9wSajFhbhyVEufLee+/x6KOPcv369Tv2RUREAFCrlqy/Xpn1berDO70C0Ciw4Uwcr286jzHXYuuwyqzc6u1IGr6NtK4fY3HwRJcUjvuGkTjveQklp3hrtAtRWUkBLYQodXqdhiGBvqye2Jb3+zSkrpcjGUYz3x+KZOD3R/juwNUKcUU616clyYPXYHbyQZcYhvvaIWhSrto6LCHKDV9fX65fv87ChQtv237p0iXWrl1LgwYNaN68uY2iE2VFv6bV+Ff/JthpFfaGJ/DCujOyxFVRNFqym40hccx+sppPQEXBcH4FHssfRX9lh62jE6LMkwJaCGEzOo1C78Y+/DS+NR/3b4y/tyPpOWbmHYxkwPeH+f7gNdJzynchbfZqSPKQdZhda6NNjcRjzWC08WdtHZYQZU5UVBTr16/n5MmTBdvGjh1L48aNWbhwIc899xzLly/n008/Zfjw4eh0Oj7++OMKuTyeKL5HGnjz+eBmGOw0HIlMZuLyk0Qny5rhRVHt3Ujv8mHebd3u9dBmxOG2ZSJuG0ajiztl6/CEKLOkgBZC2JxGyVsCa9m41vyzX2PqeeUV0t8euMag748w/3Bkub6aYHGtRdKQdeR6NUGTFY/7uqHYRf9u67CEKFOOHj3KrFmzWLFiRcE2g8HAkiVLmDx5MmfOnOHDDz9k9erVdO3aldWrV9OsWTMbRizKmna1Pfh6eAu8nfRcSshk/NKTHLqaaOuwyrzc6u1ICt5OZsunUTU69FG/4LG6H65bJ6NNDLd1eEKUObKMVRkiy1hZj+SnaGUtPxZVZVdYPPMOXuNqYt4VBHeDHePa+vFEUHUMdtpSjaek8qPkpOK6ZSL6mEOoGj2pPf6DsX6/kgvURsra56cskWWsyiYZb62nLOQnPj2HWRvOceZGGhoFpneuy9i2fmVimauykJ+iaFKu4XT0M+wvrkVRLahae1L6/IipVtdS6b+s58fWJD+Fu9/c3M9YK1eghRBljkZR6NmoKj+Nb8P7fRpSy8NAcpaJ//x6hUHf5y1/ZTKXv0liVHtXUvovIce/D4rFiOv2p7EPW2PrsIQQokKp4mzPt8NbMKCZDxYVvtp/hWdXnyY+PcfWoZV5FrfapHX/nKQRuzD6dUYx5+C2+Un0V3baOjQhygwpoIUQZZb2j2ekV0xowzu9Aqjh5kBiponP9l0meMExfom4Rbm7iUbnQGrPr8lqMhoFFZfdL6K/vNXWUQkhRIWi12l4s2cAr/VogL0u77nokQuPsyc8wdahlQtmzwBS+i0ip17vvB98tz2FPmKTrcMSokyQAloIUebpNAr9mlZj9ZNteKNHA7yc9EQlZ/Py+rM8s/o04fHptg6xeDRa0h/5J9mNhqOoZly3P4Nd5C+2jkoIISoURVEYEujLkrGtaOzjTEp2Lq9uOMf728LIMJbvCSpLhVZP6uNfk91gEIolF9cd03E4vRC5d1hUdlJACyHKDZ1Ww6BAX9ZMbMOEdjXRaxWORiYzetEJ3t0WRmxqtq1DvHeKhrRu/0eOf18UixG3rZPQxRyxdVRCCFHh1PF05IeRQUxoVxMF2Hg2jlGLTnAqWtY9/lsaHWndvyCrcTCKasHl1zdw3fYUSnaSrSMTwmakgBZClDtOeh3PPFyXVU+2pUfDKqjA5rNxDP3xKF/+ern8LH2l0ZHa40tyanVDyc3GbfN4dPGnbR2VEEJUOHZaDc88XJdvg1tQ3dWemJRspq4M4ctfr5SfMcNWNFrSu/2b9E5vo2rssL+8DY+feshqEqLSkgJaCFFuVXdz4B/9GrNgdEta13TDaFZZdDSaIT8cZV3oDcyWcnCbmVZPau/vMFZ/CI0xDbcNo9EmRdg6KiGEqJBa+rmxdFxr+jXNm2Bs0dEoBn5/hHkHr5GWLYV0oRQNWUFTSH5iI7nu/mgzYnFbPwKng/8Es9HW0QlRqqSAFkKUe02rufD1sEA+G9yUOp4GkrJM/GNnOGOXnOB4VLKtw/t7OgOpfedjqhKIJjsRt/Uj0KRG2ToqIYSokJztdbzTqyH/HtCE2h4GUrNz+e7ANfrPO8y8A9fk+egi5FZpRtLwrWQ1GYmCiuOJubivGYg26ZKtQxOi1EgBLYSoEBRFoXM9L5aPa82L3fxxsdcRHp/BtJWhvL7pfJl/PlrVu5DSfwm5Hg3yftnfMBIl46atwxJCiArrkQberJjQho/6NqKulyMZRjPfHbzG4O+PsuLE9XK5XGKpsHMkvdu/Sen1HRZ7d+ziT+OxshcOZxbLBGOiUpACWghRoei0Gka2qsHaiW0Z2sIXjQI7w+IZNv8YPx6KxJhbdr8QqQZPUgYsw+xSE13KVdzXj0DJlCVXhBDCWrQahZ6NqvLT+NZ81LcRNd0dSMoy8cneSzwx/xgbTseSK4X0XRn9+5A0YmfeetG5Wbj88hpuG0ahSY22dWhCWJUU0EKICsnd0Y7Z3RuwaEwrgmq4kp1r4evfrzJy0XGORpbd2UMtzr4kD1yO2akauqSLuK8PRsm6ZeuwhBCiQtMoeYX0ygltmN29Pp6OdsSkZPPBjosM+fEoq0/FkFOGf4C1FYuzLykDlpHe+V1UnQP66P14/NQdh7NL5Gq0qLCkgBZCVGgNqzrzXXALPujTCC8nPZFJWUxfdZq3t1wgMbNsTnxicatDyqCVmJ180CWG/VFEJ9o6LCGEqPB0Wg1DW1Tn58nteL5rPTwd7biRmsO/dkcw6PsjLDgcKZON/S9FQ1aLySQF78Dk2xaNKR2XfbNxWx+MJvmKraMTosRJAS2EqPAURaFX46qsfrINw4KqowBbz99k2PxjbDgTi1oGfyU3u9cjZdAqzI4+6G5dyLudO0fWLBVCiNJgsNMypo0f6ye345VH/anqrCchw8jc367S77vDfLbvEnFpObYOs0wxu9cjedDqvOWudA7orx/Ac0UPDCe/AYv86CAqDimghRCVhrO9jlmP1Wf+qCACqjiRmp3LB9svMn31aaKSsmwd3h3yiugVWAxV0N06h+uWSZBbtidDE0KIisTBTsvwljX4eXI73u3VEH9vRzJNZpYdv86g74/w4Y6LZXL8sBmNlqygKSSO2IWxRieU3GycD3yYN1N34kVbRydEiZACWghR6TT1dWXhmFY816Uu9joNxyKTGbnoOIuPRpW5taPNHvVJHrAUi94FfcwhXHc9DxazrcMSQohKxU6roW9TH5aPa80XQ5rRys+NXIvK+tOxPDH/KG9uPk94fLqtwywzLG51SBn4E2ndPsFi74bdzRA8Vvb+42q0jGGifJMCWghRKek0CmPb1uSn8a1pW8udnFwL//n1CpOWn+LyrQxbh3cbs3cTUnt/j6qxw/7SZpx/e0cmZxFCCBtQFIWOdT35NrgF349oQae6nlhU2H4hnlGLTvD82tOciE4uk48GlTpFIbvJCJJG7CKnVjcUc07e1eifn0CTGmnr6IS4b1JACyEqNT93A3OfaM6bPRvgpNdyNjaNMYtPMP9wJLll6Gq0ya8Tad2/AMBwegGGE3NtHJEQQlRuLWq48fmQZiwZ04oeDaugUeDAlSSmrghl2spQzsWm2TrEMsHi7Etqv0Wkdfs3Fjsn7G4cxWNVX+yif7d1aELcFymghRCVnqIoDGzuy4oJbehU1xOTWeW/v11l8vJTXL2VaevwCuQ0GEB653cBcD70MQ7nfrJtQEIIIWjo48w/+jVmzcS2DG3hi16rcCI6hfFLT/LWlgvcSJW5K/KuRo8kacQuTFVboMlOwm3DKAwh38sdVaLckQJaCCH+4ONiz2eDm/Jur4Y42/9xNXrJCZYdj8ZSRq5GZ7WYTGar6QA475uF/soOG0ckhBAC8u5omt29AWsmtqVPk6oAbDt/kyd+PMoneyK4KbN2Y3GtSfLg1WQ3HIqimnH+7V1cdr8AxrL16JQQRZECWggh/kJRFPo29eGn8W14qI4HObkW5uy9zMh5h7iRUjauImQ89BrZjYajqBZctz+NLuaIrUMSQgjxh2quDrzXuxGLx7SkdU03jGaVFSdjGPTDEf61K5yoxLJzZ5NN6AykPfY56Z3fRVU0OIStxnNFTxnLRLkhBbQQQtyFj4s9/xnSjNd6NMBgp+HwlURGLDzO1vNxtp8cRlFI6/Z/5NTpjmLOwW3Lk7I8iBBClDGNfFz4elggc59oTssarpjMKqtO3eDh/9vL+CUnWXw0iujkSroElqKQ1WIyKQN/wuxcA23qNdzXDcXx9w/BVDZ+rBaiMFJACyFEIRRFYUigL8vHt6ZlLXcyjGbe3hLGG5svkJptsm1wGh2pPb/GVK0NmpwU3DaNR8mMt21MQgghbqMoCu1qe/DdiCC+GR5I+9ruKAqcjU3jP79eYfAPR3ll/VmuVtKr0qYaHUkasZOsRsEoqDie/Abm90KTHmvr0IQolBTQQgjxN/zcDaya2oFpnWqjVWBnWDyjF50g5HqKbQOzM5DS50fMrrXRpkXhtmUS5FbSqxlCCFHGta7pztxhgRx+/TFmd69P21ruaBTYF3GLEQuO8c+d4SSkV77npFV7V9If+5SUPj9icfCAmJO4re6PNv6srUMT4q7KdQFtsVhYsmQJAwYMIDAwkK5duzJ79mzi4uJuaxcTE8OsWbPo3LkzQUFBDB8+nN27d9/1nOHh4UyfPp0OHTrQsmVLxo8fz/Hjx+/a9sSJE0yYMIG2bdvSpk0bpk2bRkRERIm/TyGE7em0GiZ3qM0PI4Pwc3cgNi2HqStC+PFQJGYbTjCmGjxJ6bcIi70bdnEncNn9IqgWm8UjhBCiaFVdHHgiqDr/HRbI8vGt6eLvhVmFtaE36DfvCM+vPc2ms7GkZefaOtRSZazbk+RhG8E7AG36DTzWDpaJMkWZVK4L6NmzZ/PBBx/g5+fH66+/Tq9evdi0aROjR48mNTUVgPj4eMaMGcOuXbsYOnQos2bNIjc3l+nTp7Nx48bbznfp0iVGjRpFaGgoY8eOZebMmcTGxjJ+/HiOHLl9YoMjR44wbtw4bty4wdSpU5kyZQpnzpwhODiYS5culVoOhBClq6mvK4vHtOLxRlUwq/D171eZseY0CRlGm8Vk9vAntfc8VI0dDhEbcTz8b5vFIoQQ4t7V83Li00FN+S64BYHVXTFbVA5cSeK9bRfp+fVBPtxxkcRM240vpc3iVgcm7cRY82GU3Exct0zC8dD/gdnGj00J8ReKavPZcO7Prl27eOaZZxg1ahTvvPNOwfZ169Yxe/ZsXnrpJaZMmcI777zDihUrWLZsGa1atQIgJyeH4cOHExcXx549e3B0dARg8uTJHD16lE2bNlGzZk0AkpKSGDBgAK6urmzatAlFUVBVlf79+5OcnMymTZtwd3cHIDIykv79+9OuXTvmzZtX7PeUkJB230vhKQp4e7s80DkqMslP0SQ/RbtbflRVZdPZOP5vdwTZuRY8He34sG8j2tbysFmc9hdW4br7BQBSH51DTuPhpdKvfH4K9yC5yT9WlDwZb61H8lO0v8vP1VuZ7LoYz86weC7fynsu2kmvZUrH2gwPqo5OW66vff2tgvzEJeK0/10MZxYCYPJpRWrPr7C41rJxhLYlf1+Fu9/c3M9YW27/CpcvX46TkxMvvfTSbdv79u3LlClTqFOnDmazmQ0bNhAUFFRQPAPY29szbtw4kpKS2LdvHwAJCQns37+f7t27FxTPAB4eHgwbNoyIiAhCQ0MBOH36NOHh4QwdOrSgeAaoVasWvXr14rfffuPmzZvWe/NCCJtTFIX+zaqxeEwr/L0dScw0MWP1aeYdvGazW7pzGg0jo/VzALjsexW76wdsEocQQoj7U8fLkckdarNiQhvmBbegsY8zGUYzn+27zMhFx/n9cqLtV4IoDVo70rt+RGrPr7HoXbGLO4HHisexv7AaqRyFrZXLAtpsNnP06FHatWuHs7MzANnZ2RiNRvR6PS+99BI9e/YkPDyczMxMgoKC7jhHixYtAAgJCbntn/fS9tSpU7dt/6vAwEAsFgunT59+oPcohCgf6ng5smBUSwY2q4ZFhe8OXOPFn8/Y7Nm1zPYvk11/AIrFhOvWp9AmySMlQghRHgX5ubFgdEve7NkAT0c7riZmMXPdGZ5dc5qI+Axbh1cqchr0Jyl4R96KE8Y0XHfPxG39CBnbhE2VywI6OjqanJwc/Pz82L59O/3796dFixYEBQUxadIkLl++DFAwmZivr+8d56hWrVrBuQBiY2MLbevj43PXttWrV//b8xaHojzYqyTOUZFfkh/Jj7XyY9BreatXAO/2boi9TsOBK0lMWHaSK7cySj9WjYb07p9i8mmVt7zV5nFoshNtmp/K/nqQ3AghKjeNojCwuS9rJrZlbBs/7LQKh68lM3rxcd7cfJ7frySSa67YE0daXP1IHryajPavomrt0V//HY+feuTN95Era0aL0qezdQD3IyUlBYDff/+dFStW8OSTT/L8889z4cIF5s2bx8iRI1m9ejVpaWkABc84/5WDgwMAWVl5S76kp6cD4OTkdEdbg8Fw17Z3O+//ti0OL68Hf9atJM5RkUl+iib5Kdrf5WdCVxfa1K/C1MXHiUzKYuLyEOYMb0HPptVKKcJ8LjB2JXz/GNrka3jtfgbGrgOtnVV7lc9P4SQ3QogH4Wyv47mu9RjSwpev9l9h98UEtl+IZ/uFeDwMdnRvWIXgltWp7Xnnd9MKQaMjs82zZDcYgPOvb2IfuRenY19gd/0QqX1+QHVwt3WEohIplwW00Zg3G+Hly5f58ssv6dmzJwDdu3enSZMmPP3003zxxRd07dq10HPkPz+i0Whu+/e7PVfyIG2L49atB5vUxMvL5YHOUZFJfoom+SlacfJTzV7DglEtmL3xPMejUpiy+DjTOtVm0kO1UEr1kqID2j7zcVs1AM3V/WT9/BIZXT+ySk/y+Sncg+Qm/1ghhMjn527g4/5NOBubxuazcewMiycpy8SqUzGsCYmhZ6OqTGxfi7peFbOQtrjVJrXfIvSXNuOy9xX0Nw7jvnYIKf2XYHG5885QIayhXN7CnX/l18fHp6B4zvfoo4/i6+vLgQMHCq4m3+1qcHZ23i0fLi55X05Kqm3+tvy2xaGqD/YqiXNU5JfkR/JTWvlxN+j5amhzhgflDebf/H6N2RvPk2k0l2rMuR4BpPX4EhUFw+mF2J9ZWibyU9leD5IbIYS4m6bVXJj1WH22Tm3PF0Oa0bmeJxYVtp2/SfCCY7y28Rzn49JsHaZ1KArG+v1IHrIWs5MPuqSLuK8ZgPbWeVtHJiqJcllA5z9n7O3tfdf93t7epKWl4efnB/z5zPJf5W/LP9e9tM1/Pjq/bf4z1n9V1HPXQojKQ6fV8Mpj9XmjRwN0GoXdFxOYtPwUN1JL93ktY90eZLZ/BQDnX99Ed+NoqfYvhBDCenRaDR3revLZ4GYsHtOSR+p7oQK7LiYwbslJnl4VyoErFXPmbrNXY5KHbiDXIwBtRizuawajv7LD1mGJSqBcFtCenp7UqlWLq1evkpOTc9s+i8VCdHQ0fn5+1KtXDxcXl4Llp/4qf0bt/OWtmjdvjkajKbJty5YtgbyZtv+6/X/bKopy1xm6hRCVz6BAX74ZHoinox3h8RlMWHqSMzdSSzWGzNbPku3fD8Viwm3rU2jSYkq1fyGEENbXyMeFfw9syrJxrejduCpaBY5FJvP82jOMWXyCPRfjsVSwQtriUoPkIWsxVn8IjSkdty0T8yYXUyv2xGrCtsplAQ0wdOhQMjIy+P7772/bvnLlSpKSkujbty86nY4+ffpw7NgxTpw4UdAmJyeHRYsW4e3tTZcuXYC8q9YdO3Zk+/btREVFFbRNSkpi1apVNGrUiCZNmgB5BXTdunVZtWoVycnJBW0jIyPZvn073bp1w8PDw4rvXghRnrSo4cbC0S0JqOJEYqaJaStD2RkWX3oBKAppj80h16sJmqwEXLdOhtziT3QohBCi7GtQxZn3+zTi58ntGNW6BgY7DRfjM3h143lGLDjOtvM3K1QhrTq4kzJgOZnNnwTA6dgXuG6egJKdbNvARIWlqOX0ng6j0ci4ceM4efIk/fr1o127dpw7d46VK1dSv359Vq5cicFgID4+nsGDB5OVlcWTTz6Jl5cXq1ev5uzZs8yZM4c+ffoUnPPixYsEBwfj5OTEhAkT0Ov1LF26lJiYGObPn0+bNm0K2h44cICnnnoKPz8/Ro8ejdFoZMGCBZhMJpYvX069evWK/Z4SEh5sEjFvb5cHOkdFJvkpmuSnaCWVn0yjmTc3n2f/5UQApneuw4R2NUttcjFNahQeq/qgyU4iO2AIad2/KJG1kuTzU7gHyU3+saLkyXhrPZKfotkiP8lZJn46cZ0VJ6+TnmMGoFFVZ2Y+Uo/WNd1LJ4h79KD5sQ9bjcveV1HMOZidq5P26BxMNTuXfKA2In9fhbvf3NzPWFtuC2jIm7Br3rx5bNy4kRs3buDl5UWPHj2YOXMmzs7OBe2ioqL49NNPOXDgACaTiYYNG/L000/fdZbu8+fPM2fOHI4fP45Go6FZs2bMnDmToKCgO9oeOnSIL7/8kjNnzmAwGGjdujUvvvgi/v7+9/V+ZEC3HslP0SQ/RSvJ/JgtKl/8cpnlJ64D0L+pD6/1aICdtnRuCLKL/h23DaNQVDPpnd4mK2jKA59TPj+FkwK6bJLx1nokP0WzZX7Sc3JZeTKGRUejyDDmFdJd/b2Y8XBd6pSRWbtLIj+6+DO4bJ+GLuUqAJmBk8joMBt0hpIL1Ebk76twUkBXUjKgW4/kp2iSn6JZIz+rTsXwyZ4ILCq0qeXO//VvgotD6awsaAj5Aeff3kFVNKT0X4KpZpcHOp98fgonBXTZJOOt9Uh+ilYW8pOUaeS7A9dYF3oDswoK0K2BN+Pb1aRJNdv+N6fE8mPKxPnAhxjOLAIg16M+KX0XYnGrXTKB2khZ+PyUVaVZQJfbZ6CFEKI8GxZUnTmDmuFop+VYZDKTlp8iJqV0ZujOCpxIdqPhKKoF1+1Po/njV3ohhBAVn4ejnle7N2DZ+NZ08c+btXtPeALjl57kmVWhHItMLv+zdts5kt71HyT3W4zZ0QddUgTuPw+T8U6UCCmghRDCRjrV8+S7ES2o6qznSmImTy47SUR8hvU7VhTSuv4Dk09LNDkpuG2ZhGJMt36/Qgghyox6Xk58Oqgpy8e3Lpi1+0hkMk+vCmXS8hB+u3yr3BfSptrdSB6+hVyP+mjTY3Bf9wTa5Mu2DkuUc1JACyGEDTWs6sz8US1p8McM3VNXhnA6phSWudI5kNp7Xt4v84lhuOx6Xpb9EEKISqi+txPv92nE2kntGBZUHb1W4fSNVF5Yd5bxS09yIS7N1iE+EIuTD8mDVhWsF+328zC0SZdsHZYox6SAFkIIG6vqYs+3w1sQWN2V1OxcnlkdyuFrSVbv1+JUjdTe81A1euyvbMfxyKdW71MIIUTZVN3NgVmP1Wf9U+0Z28YPRzst5+PSGb/0JJ/vu0yWyWzrEO+b6liF5EEryfVsiDYjLq+ITrxo67BEOSUFtBBClAEuDjq+eqI5D9X2IMtk4YV1Z9gXnmD1fnOrtSKt27+AvLUz9Ze3Wb1PIYQQZZe3k57nutZj7aS29GxYBYsKS49HM2LBsVL5cddaVEfvvCLaqzHazJu4/zwMbcI5W4clyiEpoIUQooww2Gn5dFBTHgvwxmRWmb3xHDsu3LR6vzmNhpEZOAkAl90vyPNhQggh8HLS81G/xnw+uBnVXOyJSc1hxurTfLwrnExj+bwarRq8SB60ElOVQDRZt3D/eTi6+NO2DkuUM1JACyFEGaLXafiwb2P6NqmKWYU3N19gw5lYq/eb0fFNTL7t0BjTcN36FJgyrd6nEEKIsq9TPU9WTGjDsKDqAKwJucGoRcc5GZ1i48juj+rgQcrA5Zh8WqHJScbt52C5+0oUixTQQghRxug0Cm/3asiQQF9U4IPtF1l1Ksa6nWrtSH38a8yOVfMmFds7C1lkUgghBICjXsusx+oz94nmVHOx53pKNlNWhPDWlgvEppbOEowlSbV3I2XAMoy+7dEYU3HbOhnXLZPQpFl5rBUVghTQQghRBmkUhdnd6zOyVQ0A/m93BMtPXLdqnxYnH9Ie/xpV0eIQ/jMOp+dbtT8hhBDlS7vaHiwf35qBzasBsO38TZ6Yf4yvf79a7m7rVvXOpAxYQkbrZ1E1OuyvbMdjeTccQufLD8iiSFJACyFEGaUoCi88Uo/x7WoCMGfvJZYei7Zqn6bq7cno+CYAzr+/j13MIav2J4QQonxxttfxZs8AFo1pSUs/N3JyLfx4KJJh84/yS4T1J78sUToDmQ+9StLw7Zh826IxZeCy/y2c978lSzuKQkkBLYQQZZiiKDzTuQ4TH6oFwOe/XGbx0Sir9pnVYjLZDQaiWHJx3TZNbmkTQghxh8Y+Lnw7PJD/G9CE6m4O3Ew38vL6c7yy/iw303JsHV6xmL0akjx4Dekd3wLAcHoBLjueAXP5eh+idEgBLYQQZZyiKDzdqQ5TOtQG4D+/XmHRESsW0YpCWrdPMHk3RZOVgOvWyZCbZb3+hBBClEuKotCtgTcrxrdmfLuaaDUK+yJuMWz+MT7de4mrieVoQkpFQ1bLqaT2nIuqscMhYiNuG8ehGNNsHZkoY6SAFkKIcuKpjrWZ2jGviP5y/xWWHbfi7dx2BlJ7f4/FwQO7+FBc9r0mz4QJIYS4Kwc7LTMersuSMa1o7utCpsnMTyeuM2z+MZ5eFcr+S7dQy8kYktNgICn9FmGxc0J//XfcVw9AmxRh67BEGSIFtBBClCOTO9QuuBL92b7LrDxpvdurLa41SX38m7xJxcJW43B6gdX6EkIIUf7Vr+LE9yOD+GJIM7r4e6FR4FhkMi/+fJbpq0IJi0u3dYj3xFTzYVIGrcLs5IMuKRz3Vf3QX9pi67BEGSEFtBBClDOTO9TiyfZ5E4v9e08Ea0NvWK0vk1+n2yYV08WesFpfQgghyj+NotCxriefDmrKz5PbMbaNH3qtwrGoFMYuOcH728K4lWG0dZh/K7dqIEnDtmKs/hAaUzpu26bgdOAjmVxMSAEthBDlTf4z0WPb+AHwz53hbDgTa7X+slpMJse/L4rFhOv2aSjZSVbrS1R8ISEhNG7cmMOHD9/zMevWrWPQoEEEBQXRuXNn3nvvPVJSUqwYpRCiJPi6OvBc13qsntiWxxtVQQU2no1j5MLj7Asv+zN2q05VSRn4E5lBUwFwPPk1LrtfAEuujSMTtiQFtBBClEOKovBsl7qM+GOd6A+3X2TLuThrdUbao5+Q61YXbXoMrjuflV/gxX25evUqzzzzDBbLvX9+vv32W2bPno27uzuvvPIK/fv3Z9WqVYwbN47s7GwrRiuEKCm+rg582LcxP44MokEVJ5KyTLyy4RzvbwsjPaeMF6MaHRmd3iK1x5d/PNK0Btcdz4C57F9FF9YhBbQQQpRTiqLw4iP1GNrCFxV4b1sYO8PirdKXqnchtfd3qDoH9JH7cDz2H6v0IyqunTt3Mnz4cOLj7/0zGhsby5dffkmXLl348ccfGT16NK+++ioff/wxFy5cYPHixVaMWAhR0ppXd2XBqJaMa+uHwp9Xo7edv4mljE8ylhMwmNRe36Fq9Nhf2ozrtqmQKz/iVUZSQAshRDmmKAqzHqvPwGbVsKjw1ubz7LXSbXFmr8akdf0nAI5HPkV/bY9V+hEVz5QpU5gxYwZVqlShX79+93zcxo0bMZlMTJgwAY3mz68s/fr1o0aNGqxdu9Ya4QohrEiv0/Bsl3p8ExxIdVd7YtNyeGvLBcYvOcnRyLL9iJCx3uOk9P0RVWuP/dWduG0cg5J1y9ZhiVImBbQQQpRzGkXhtR4N6NOkKmYV3th83mpfQnIaDSOr6RgUVFx2Pos2+bJV+hEVy+XLl3nxxRdZt24dderUuefjQkJCAGjRosUd+5o3b87ly5dJS5M1WoUoj1r5ubNiQhumd66Dk17LhZvpPL3yNE8vOU5ypsnW4RXKVOsRUvovzlvmKuYQHit7o7sZauuwRCmSAloIISoArUbhrccb0q2BNyazyss/n+NCnHUKi/SH38dUrQ2anBRct0xGMZaPZUmE7WzZsoWpU6ei1+uLdVxsbCyurq44Ozvfsa9atWoAXL9+vdjxKMqDvUriHBX5JfmR/Nzry6DXMvGhWqyb1JbhLauj1ShsPRNL8IJj/Hb5ls3jK+yV69eRlGEbyXWvhzY9Bve1g3EIW10qfcvnp+RzU1y64h8ihBCiLNJpFD7o04jn157meFQKz689w/cjgqjpYSjZjrR6Unt9i/uqPuiSLuK86wUYu7xk+xAVSnEL53xpaWk4OjredZ+DgwMAmZmZxT6vl5fLfcVT0ueoyCQ/RZP83M7bG/6vthfjOtfjhRWnCL+ZzgvrzjKibU1e79sYVwc7W4d4J+/WUGsfrJ2CcnEbLrtm4pJ9DR59GzTWvUYpn5/ClUZupIAWQogKxF6n4ZOBTZm6IoSL8RnMWHOaH0a0wNvZvkT7sTj5kNrrO9zXDcP+8lb45f+g2fQS7UMIAPVvJhbSarXFPuetW2nc73xFipL3Be1BzlGRSX6KJvkpmq+Dho3PduaD9adZduw6Px2NYufZWF7o5s/jjaqg3M/lQqvSQI/vcHT7FMejX8Bvn5F98yrpj30K2vv74bAo8vkp3P3mJv+44pBbuIUQooJxttfxxdDm1HBzICYlm+fWnrHKMiG51VqT3vWjvH/Z9w/04RtKvA9RuTk5ORW6VFVWVhbAXW/v/juq+mCvkjhHRX5JfiQ/D/JysNPywiP+fD08kFoeBm5lmnhz8wWmrzrNlYRMm8d3xwsNGe1eIfXROagaHQ4X1+G6YQxkp8jnp5Rf95ub4pICWgghKiBvJz1fPdEcT0c7wuMzmLXhHMbckl+7ObvJSLKCpgDgsusFdLHHS7wPUXn5+fmRkpJy19u0Y2Nj0Wg0+Pj42CAyIYS1ta7pzvJxrZnWqTb2Og1HI5MJXniMNzef5/KtDFuHd4ecxsNJ6bsQi50z+usHcF87BE3KNVuHJaxACmghhKig/NwNfD6kGY52Wo5GJvPetjCrrLOZ0fENaNgHxZyD25aJaFKjSrwPUTkFBgYCEBp65wy3p0+fpkGDBvd1BVoIUT7odRomPVSbn8a3pqu/FxYVtl+IZ8SC47y28Tw3UsvWOsymWl1JHrwGs5MPusQwPFb1xS76d1uHJUqYFNBCCFGBNfZx4f8GNEGrUdgRFs8Xv1hh2SmNFobMI9e7KZqsW7htnoBilKWFxIPr3bs3dnZ2fP/99/z1WehNmzYRExPDkCFDbBidEKK0+Lkb+GRQUxaPackj9b1QgV0X4xm16Dg7Lty0dXi3MVdpSvKwzZiqtkCTk4zbhlE4hM6/v3uFRZkkBbQQQlRw7et48PbjAQAsO36dJceiS74Te2dS+80v+NXdZedzoJb8LeOi4oqKimL9+vWcPHmyYFuNGjWYNm0a+/fvZ+LEiaxcuZJ//etfzJ49m+bNmzNixAgbRiyEKG2NfFz498CmLBvXiua+LqTnmHlj8wXe2xZGhrHk5/q4XxanaiQPXk12wBAU1YzL/rdw2TkDJTvZ1qGJEiAFtBBCVAJ9mvjwXJe6AHzxy2W2no8r8T4sztVJ7f09qtYe+6s7cTzyaYn3ISquo0ePMmvWLFasWHHb9hkzZvDuu+9y8+ZN3n//fbZu3UpwcDA//PBDwVJWQojKpUEVZ74bEcSkh2qhUWDT2TjGLD7BschkW4f2J52BtO5fkN7xTVRFi0P4ejx+6o5d5C+2jkw8IEX9u/UhRKlJSHiwZTW8vV0e6BwVmeSnaJKfolWU/Kiqyue/XGbZ8evoNAqfD25G+zoeD3ze/82P/YXVuO6eCUDK499grN/vgfsorx7ks5N/rCh5Mt5aj+SnaJKfot1Pfk5Gp/D2lgvEpuUAMLB5NZ7rUrdMrR2tiz2By+6Z6JLzHqPKajae9M5vg7Z4S0zK56dw95ub+xlr5Qq0EEJUEoqi8HzXevRsWIVci8qsDee4EFfyzyrnNHqCzBZ5M3O77n4BbcK5Eu9DCCGEAGjp58by8a0Z2sIXgPWnYxm+4Di/RCTYOLI/5VZrRdLw7WQ2fxIAw5mFuK8fgZJ1y8aRifshBbQQQlQiGkXhnV4NaVPLnUyTmefXniE6OavE+8no+DrGml1QcrNw2zweTdr1Eu9DCCGEAHC21zG7ewPmBbegtoeBWxlGXl5/jne3hZGeU0aejbYzkNHlA5L7Lcaid8XuxlE8VvVDe+uCrSMTxSQFtBBCVDJ6nYZ/D2hCQBUnEjNNPLfmNImZxpLtRKMjtedccj0aoE2/gduGUfJLuxBCCKsK8nNj6bjWjGtbEwXYfDaOkQuPczQyydahFTDV7kby0PWYXWujTYvCfc1A9BGbZJbuckQKaCGEqISc7XV8MaQZ1V3tiUrO5oV1Z8k0mku0D9XBg5T+SzE7V0eXfAm3TeNQjOkl2ocQQgjxV/Y6Dc92qct3wS2o4eZAbFoO01ed5v1tYSSV9I/F98ns2YCkYZsw1uiAxpSB2/ZpuG55Ek1qlK1DE/dACmghhKikvJ3t+WJoc9wcdJyLTeO1TefINZfs0lMWl+qkDFiGxcEDu5shuG6ZBOacEu1DCCGE+F9Bfm4sG/fns9Ebz8bxxPxjrA29gaUMXO3N/5E5o/VzqBo77K/uwnN5Nwwn5oKlZH/QFiVLCmghhKjE6ng68vmQZjjoNBy4ksQney9R0oszmD3qk9JvMarOEf3133Hd+Zx8ORBCCGF1jnots7s34IeRQTSo4kRqdi7/3BnO0ytDy8bVaK2ezIdmkRS8A2P1h1Bys3E++E9cd0yXH5vLMCmghRCikmvm68qHfRujAGtCbrDiZEyJ95HrE0RKnx9RNXrsL23G+dc35HkvIYQQpSKwuiuLxrTipW7+ONppORGdwoSlJ4lIyLB1aEDeLd0pg1aR1u3feVejL23GbfNEMGXaOjRxF1JACyGEoGt9L57tUheAz/Zd4vfLiSXeh6lmZ1J7/AcVBcPZJTge+aTE+xBCCCHuRqdRGNGqBgtGt6SGmwMxqTlMXn6K3y6XkQkuFYXsJiNJ6bcQVWdAH/UL7htGoWQn2zoy8T+kgBZCCAHAmDZ+DGxWDYsKb2w+T0R8yf8yb6zfj/Su/wDA6dgXGEK+L/E+hBBCiMLU9XJkweiWtPJzI8No5sV1Z/nuwFVyLWXjrihTzS4kD/wJi70bdrHHcF87BG1iuK3DEn8hBbQQQggAFEXh1e71aV0z70vFC+vOkJBR8s+IZTcbS0a7lwFw/u1dDKfmlXgfQgghRGHcDXZ89URzBjWvhgrMOxjJlJ9CiE7OsnVoAORWa03yoFWYHX3QJV3EY1Uf7C+ssnVY4g9SQAshhChgp9Xwr/5NqOVhIDYthxfXnSHbVPITfmW2eZ7MVtMBcP79PRyPfSHPRAshhCg1dloNb/QM4IM+jXDSazl9I5Uxi0+w5VxciU+meT/M3k1IGr4Vo19nlNwsXHe/gPOuFyA71dahVXpSQAshhLiNm8GOzwc3w81Bx/m4dN7acgFzSd/apihkPPRawZVop8P/xunQx1JECyGEKFW9Gldl2bjWBNVwJcNo5p2tYby26TzJmSZbh4bqVDVvqav2r6AqGhwurIIvWmA4+S3kZts6vEpLCmghhBB3qOlh4JOBTbHTKuyLuMWXv14p+U4Uhcy2M0nv+BYAjifm4rxvNpht/6VFCCFE5VHdzYFvhrfg6U510GoUdl9MYMSi41aZULPYNFoy2zxPysAV5Lr7Q1YiTr9/gOfSh3E4t1yWhbQBKaCFEELcVZCfG+883hCApcejWRNS8stbAWS1nEpa13/mzc59bilum8ej5MgtakIIIUqPVqMw8aFaLBgVRF0vR25lGJm57gzvbwsjJcv2P+yaanQgedRuGPAVZufqaNNv4LL3FTxW9ER/bY/cwVWKpIAWQghRqMcbV2Vap9oA/Ht3BIeuWufX+OxmY0nt88MfS3f8ivvawWhSo6zSlxBCCFGYRj4uLBrdklGtawCw8WwcwxccY/v5m7Z/Nlqjg1ZjSRrzK+md3sFi744uMQy3TeNw2zASXewJ28ZXSUgBLYQQokgT29eiT5OqmFWYvfE8l2+V/PJWAMa6PUkeshazkw+6xDA8VvdHF3vcKn0JIYQQhXGw0/LCI/58P6IFdb0cScw08eaWC7yw7iyJmSW/OkWx6RzICnqKxDG/kRk0FVWjRx/9Gx5rBuC2bij6q7tAtdg6ygpLCmghhBBFUhSFN3oEFEyw8sK6syRZ6QtEbpXmJD+xkVyvJmiyEnD/eTj2F9dZpS8hhBCiKC1quLFkTCumdKyNnVbh9yuJjFl8gpDrKbYODQDVwZ2MTm+ROHof2Y2GoWrs0Mccxm3zBDx+6oEu/rStQ6yQpIAWQgjxt/Q6Df8e0JQabg7EpGTz8vpz5ORa59dti3N1koasI6fu4yjmHFx3Povj4X/Lr+lCCCFKnV6n4akOtVk8phV1PA3EpxuZujKU5Seu2/6W7j9YXGuR9thnJI49QGbLaVj0LugSw3BfMwj78ytsHV6FIwW0EEKIe+LumLe8lbO9ltCYVN7degGLtb486J1I7T2PzJZPA+B07AtcN41Hk3bdOv0JIYQQRfD3dmLB6Jb0aFgFs0Vlzt5LvLrxPMllYIKxfBZnXzI6vkni2IPk1Ome9yP0npdw3vsqmHNsHV6FIQW0EEKIe1bHy5F/D2iKTqOw62ICX1ljeat8ioaMjm+Q+ugcVI0e+8i9eCx/FIfQ+XI1WgghRKlz0uv4qG8jXurmj1ajsDc8gZELj1ttgs37pTq4k9rnx7z1o/9Y4cJz2aMYQr6XVS5KgBTQQgghiqVNLXfeejwAgMXHoll9yjrLW+XLaTycpODtmHzbojFl4LL/LdzXDkYXd8qq/QohhBD/S1EURrSqwY8jg6jtYSAhw8iza87wyZ4Iqz3adF8UTd760f0XY3HwRJt6Deff3sVrQRuc972G7sYx+TH6PkkBLYQQotj6NPFhase85a3+b3cEu8/HWbU/s2cDkgevIa3LR1jsnLCLPY7H6n64bH8aTcpVq/YthBBC/K8m1VxYMrYVw4KqA7DiZAwTlp602koV98tU6xFujT1IWtePyfVsiJKbieHsYjzWDsJzUXucfnsPbfxZW4dZrkgBLYQQ4r5MeqgWA5r5YFHh2eUnuRCXbt0OFQ3ZzceTNGpv3myjKDhEbMRzWTecf30TJTPeuv0LIYQQf+Fgp2XWY/X5fEgzPB3tiEjIYNySk6wLvVFmJhgDQO9EdrMxJI3YRfKglWQHDMZi54w2/QaOIfPwXPk4zntfQclOsnWk5YIU0EIIIe6Loii81r0B7Wq7k2k088K6M8SlWX+SEotzddIe+4yk4O0Yaz2CYjFhOL0Ar8WdcDz8bxRjmtVjEEIIIfJ1quvJ0nGteai2Bzm5Fv6xM5zZZWyCMQAUBVONjqT1+JJbE0+R0vsHcvz7AmA4txzPpV1wOLdcbu3+G1JACyGEuG86rYb/G9CEBlWdiU838sK6M2QYc0ulb7N3E1L6LyF54ApMVVug5GbidOwLPBd3xPHwJ2gyrHtbuRBCCJHP20nPF0Ob8VyXumg1Cnv+mGDsYBmbYKyAzgFjvcdJ7fUtSYPXkuvVCE12Ei57X8FjWTccTi8EY9m6Hb2skAJaCCHEA3G21zH/ybZ4OdoRHp/B65vOk2suvV+vTX6dSH5iEym9viPXoz6a7CScjn2O56L2uOyYgV3MIbCYSy0eIYQQlZNGURjbtibzRwVRxzNvgrHn1pzh37sjyDaV3XEot3o7koZtJb3TO3lrSCdfwuXXN/Ba1A6nAx+iZNy0dYhlihTQQgghHpifhyNzBjfDXqfhwJUkPthx0XprRN+NomD070PSiF2k9vwak287FEsuDuE/477uCbx+bIHLjhnYh63Nm3RMbk8TQghhJY19XFg85s8JxlaeimHoj0fZdDa2dMfG4tDakRX0FInjj5L28PuYXWujyUnB8eQ3eC3ugNNv70kh/QdFLVNPuFduCQlp3O//G4oC3t4uD3SOikzyUzTJT9EkP0X7a35+jbjFK+vPYlYhuGV1Xurmj6IoNolLF38ah9AF2F/ZhiYn5bZ9qs5ArmcAZs8AzG51MbvV+eNVG9XercRieJDPTv6xouTJeGs9kp+iSX6KVhHzc+BKIh/vCudGat4cIQFVnJj5SD3a1vIo9rlKNT+qBf3V3Tge/xK7uBN5m7T2ZAcMJidgMKbqD4FGa+Ug7t395uZ+xlopoMsQGdCtR/JTNMlP0SQ/Rfvf/Gw5F8c7W8MAmNKxNk91qG3bAC256GJPYH9tN3ZR+9ElhqGYC5/szGLvhtm1NhbXmpidfLA4VsXiWBXV3g1V5wA6B1SdAYuDBxaDN9gZCj2XFNBlk4y31iP5KZrkp2gVNT85uRZWnrzOj4cjSc/Ju5V7aAtfnu9aD4PdvRehNsmPqmIX9StOR+dgF3u8YLPZsSo59fuR3Ww8Zg//UgqmcKVZQOuKGZsQQghRpD5NfEjNzuXTvZf47sA1XO11BLeqYbuANDpyq7cjt3o76PAaWHLRplxDm3gBXVIE2pSrea/kK2iyEtDkpKCJD4X40Hs6vapzxOLgjqoz5BXYdk5ktpiM0b+Pld+YEEKI8sBep2Fs25r0b1aN7w5cY9WpGNaE3OBoZDLv9W5IM19XW4dYOEXBVKsryTW7YBdzCPuL67C/tBlt5k0cQ3/EMfRHcur0JKvlVEy+7fIq0gpOCmghhBAlbkSrGqRmm5h3MJJP9l7CyV5Lv6bVbB1WHo0Os4c/Zg9/jP+7z5iBNi0SbUok2rQoNJnxf7ziUHLSUHKzwZyNYspEk52EYs5Byc1Em55522kcDF5SQAshhLiNu8GOWY/Vp1sDL97dGkZkUhaTl59idBs/nmxfC2f7MlyaKQqmGh0w1ehAepcP0Uftx+HsEuyv7sT+6g7sr+7AVLUF2U3HkN1gINg52jpiqynD/y8JIYQoz57qUJu0HDM/nbjOB9sv4min5dGAKrYOq2h6J8xejTF7Nf77tqqKYspAyUpAk52MYs6G3GwUswmTb1vrxyqEEKJcalvLg+XjW/N/uyPYfiGeRUej2XQ2jqmd6jCwWTW0mjJ+FVerx1jnMYx1HkObFIHh1Hc4hK3B7mYIdjdDcPr9fXIChmCs3Q1T1RaojmV87C8mKaCFEEJYhaIovPBIPTKNuWw4E8cbmy/wqZ2WjnU9bR1ayVAUVL0zqt4ZS8nNOyaEEKIScHWw48O+jenZqCpf/HKZyKQs/rkznFUnY3i9RwOaVy/Dt3X/hdmjPund/o+M9rNwuLASw9mlaFOvYTizEMOZhXltnGtgqt6erObjyfVpVe5v85ZlrIQQQliNRlF4vUcA3QOqkGtRmbXhHMejkm0dlhBCCFEmdPH34qfxrXmxmz+uDjoiEjKY/NMpvvz1Mjm55WfJRdXRm6xW00kcs5/kAcvIahRMrkcAKgra9Os4XFyLx5qBuK/uh/3FdVDEZJ5lXYUpoM1mM6NGjaJhw4Z37IuJiWHWrFl07tyZoKAghg8fzu7du+96nvDwcKZPn06HDh1o2bIl48eP5/jx43dte+LECSZMmEDbtm1p06YN06ZNIyIiokTflxBClHdajcL7fRrSuZ4nObkWXlh3hpDrKX9/oBBCCFEJ2Gk1jGxVgzUT29K7cVUsKiw6Gs3YxSc4eyPV1uEVj6LBVLML6Y99StKoPdx66hzJA1eQ1TgYVWuP3c0QXHc+i9cPgbhunYzDuWVoMmJtHXWxVJgC+ptvvrlroRsfH8+YMWPYtWsXQ4cOZdasWeTm5jJ9+nQ2btx4W9tLly4xatQoQkNDGTt2LDNnziQ2Npbx48dz5MiR29oeOXKEcePGcePGDaZOncqUKVM4c+YMwcHBXLp0yarvVQghyhs7rYaP+zehfW13skwWnl97pvx9KRBCCCGsyN1gx/t9GvHJwKZ4Oem5kpjJpOWnmH84Eks5XddL1btg8utE+qOfcmv8ETLav4LZqRoaUwb2l7fhsncWXgva4Lm4Ey67ZuJwdgm62ONoUqPL7FXqCrEOdGhoKCNHjkSj0WA0GgkLCyvY984777BixQqWLVtGq1atAMjJyWH48OHExcWxZ88eHB3zZombPHkyR48eZdOmTdSsWROApKQkBgwYgKurK5s2bUJRFFRVpX///iQnJ7Np0ybc3d0BiIyMpH///rRr14558+YV+33IupTWI/kpmuSnaJKfohUnP9kmMzPXneF4VArO9lq+HhZII5+Ku9axrANdNsl4az2Sn6JJfoom+flTSpaJf+2OYGdYPABtarnzQZ+GNK7jXf7zo1rQxZ9Bf203+qu70d0MQeHub8hi8MZYsws5DQZirPkwaPV3bVea60CX+yvQGRkZvPzyyzz88MMEBQXdts9sNrNhwwaCgoIKimcAe3t7xo0bR1JSEvv27QMgISGB/fv3071794LiGcDDw4Nhw4YRERFBaGjemqCnT58mPDycoUOHFhTPALVq1aJXr1789ttv3Lx502rvWQghyisHOy1zBjWjRXVX0nPMTF91mtAYuRIthBBC/JWbwY6P+jbirccDcNBpOBaZzIiFx1l5NIpcS3mungFFQ27VQDLbvkDysE3cmnyG5H6LyWjzPMYanTC7+KFq8gplTVYCDhfX4rZ5PF7zW+G6aTxuG0bjtm4o7muHoL+ys9TDL/cF9EcffURaWhoffvjhHfvCw8PJzMy8o7AGaNGiBQAhISG3/fNe2p46deq27X8VGBiIxWLh9OnTxX4vQghRGTjqtXw+pBmB1V1Jy8nlmVWhHLqaaOuwhBBCiDJFURQGNKvG4rGtaFjVmZSsXGatCWX4/GPsuHCz3N7W/b9UezdMtbuR2f4VUgatIHHcIRKmXSJh0mmSBq8lM3AiZseqaHKSsb+2G33UL+hjDmN34wj6K9tKPd5yvYzVjh07WLNmDXPnzsXb2/uO/XFxcQD4+vresa9atWoAREdHAxAbG1toWx8fn7u2rV69+t+etzgeZEb3/GPL+azwViP5KZrkp2iSn6LdT35cHHTMfaI5szac4+DVJF5Yd5YP+zaie8OKtVbkg3x25PMmhBACoI6nIz+ODGJVSAyLjkYTmZTFG5svsOBIFE93qkPnep4oFW3QUBRUBw9yq7cjt3o7Mjq9g13MIbQpV1C1Dqg6B9AZMPp1LPXQym0BHRcXx1tvvcUTTzxB9+7d79omLS0NoOAZ579ycHAAICsrC4D09HQAnJyc7mhrMBju2vZu5/3ftsXh5fXgz7qVxDkqMslP0SQ/RZP8FO1+8rNw8kO8sPIUm0Nv8Pqm8xg1GsZ1qFPywdmYfHaEEEI8CL1Ow5g2fkzu1oCvdlxg8dFowuMzePHnszT3dWV65zq0qeVu6zCtR6PF5NcJk18nW0dSPgtoVVV59dVXcXFx4fXXXy+y3d/t02g0t/373Y55kLbFcevWg01q4uXl8kDnqMgkP0WT/BRN8lO0B83P293ro0dlXWgsb68/S+jVRF5+1B+dttw/ZfRAuck/VgghhMjnbK9jcofaPNGiOouORrPi5HVO30jl6VWh1PE00KGOJx3qetCyhhsOdlpbh1shlcsCev78+Rw6dIi5c+eSk5NDTk7eFOcmkwmAxMREtFptwdXku10Nzs7OBsDFJe/LSUm1zd+W37Y4VJUH/nJeEueoyCQ/RZP8FE3yU7T7zY9GUXitewNquhv48tcrrA65wdWkLD7u1xg3g13JB2oD8tkRQghRktwMdjzbpS4jW1Vn/uEo1obe4GpiFlcTr7P8xHWc9FpeftSfvk18Kt7t3TZWLgvovXv3oqoq06dPv+v+Dh06UKNGDb755hvgz2eW/yp/W/4zy35+fn/bNv/56Py2cXFxNGrU6La2RT13LYQQ4u4URWFs25rU9nTkrc0XOBaZzPilJ/lX/yY09HG2dXhCCCFEmeTtbM8rj9VnaqfaHI1M5uDVJA5eSeRmupH3tl3k8LVkZnevj5O+XJZ9ZVK5zOSrr75Kauqdy558/PHHhIWFMX/+fOzt7alXrx4uLi4Fy0/9Vf6M2vnLWzVv3hyNRkNoaCijR4++a9uWLVsCeTNt52/v2rXrHW0VRbnrDN1CCCGK1sXfix9GBfHSz2e5npLNk8tPMrOrP8OCfOUXdCGEEKIQrg52PBZQhccCqmC2qCw8EsV3B66y7fxNztxI5Z3HGxLk52brMCuEcvmAWbNmzejYseMdLze3vA9Fx44dad26NTqdjj59+nDs2DFOnDhRcHxOTg6LFi3C29ubLl26AODt7U3Hjh3Zvn07UVFRBW2TkpJYtWoVjRo1okmTJkBeAV23bl1WrVpFcnJyQdvIyEi2b99Ot27d8PDwKIVMCCFExVPf24lFo1vS1d8Lk1nl33sieHXjedKyc20dmhBCCFHmaTUKEx+qxbfBLajmYk90cjZPrQhh4rJT7AqLL//rSNtYubwCXRzPPvsse/bs4amnnuLJJ5/Ey8uL1atXc/HiRebMmYO9vX1B21dffZXg4GBGjhzJhAkT0Ov1LF26lJSUFD7//PPbzvv222/z1FNPERwczOjRozEajSxYsAAHBwdeeeWVUn6XQghRsbgZ7Pj3wCb8dDKG//xymb3hCZyPTeP9Po1oKb+gCyGEEH+rRQ03lo5rxX9+ucKW83GcvpHKa5tSqeqsp2NdT4JquBHk50p1Vwe5y6sYFLWoqarLmbFjx3LkyBHCwsJu2x4VFcWnn37KgQMHMJlMNGzYkKeffvqO268Bzp8/z5w5czh+/DgajYZmzZoxc+ZMgoKC7mh76NAhvvzyS86cOYPBYKB169a8+OKL+Pv731f8CQkPNgu3t7fLA52jIpP8FE3yUzTJT9GsnZ9zsWm8sfk80cnZaBQY364mUzrULhezdD9IbvKPFSVPxlvrkfwUTfJTNMlP0R4kPwkZRtacimFNyA2Ssky37avh5kCfJlXp08QHP3dDCUZceu43N/cz1laoArq8kwHdeiQ/RZP8FE3yU7TSyE+GMZc5ey+x4UzeRI2NfZx56/EAGlQp2xOMSQFdNsl4az2Sn6JJfoom+SlaSeQnJ9fCoatJhFxP4dT1VM7Hpd12S3fLGq4MCvSle0AV9Lqy/0N1vtIsoCv8LdxCCCHKPye9jrceb0jHup78Y2c45+PSGbv4BCNa+TGlY20c9bLWpRBCCPF37HUautb3omt9LwCyTGZ+ibjF5rNxHL6WxMnrqZy8nsoXv1xmcKAvQwJ9qepi/zdnrVykgBZCCFFuPBZQhea+rszZd4ndFxNYejyanWE3mfmIP90DvOUZLiGEEKIYDHZaejWuSq/GVbmZlsPGs7GsDbnBzXQjPxyKZMHhSB7292JIC1/a1/ZAI+OsFNBCCCHKl6ou9nzcvwm/X0nk/3ZHEJOSzeubzrPYx5npnevQvraHFNJCCCFEMVV1sWfSQ7UZ37Ym+yJusfLkdU5eT2VfxC32RdyiupsDvRtX5WF/Lxr7OFfaYloKaCGEEOVSp7qerBjfmsVHo1lyLJrzcek8u+YMbWq68VTH2rSs4SaFtBBCCFFMOq2G7g2r0L1hFS4lZLAu9Aabz8URk5LND4ci+eFQJF5OejrX86Rnwyq0rumOVlN5xlspoIUQQpRbDnZanupYmyeCfJl/OIrVITEci0rh2IpQWlR35cn2tehYV65I21pSUhJfffUVe/bs4datW9SpU4dx48bxxBNP/O2xx44dY/To0Xfd165dOxYvXlzS4QohhPiDv7cTLz9anxkP12VPeAK/RNzi0NUkbmUYWX86lvWnY6nirKdnw6p0a+BFIx8X7MvR5GP3QwpoIYQQ5Z6Ho54Xu/kzsnUNFh6JYsOZWEJiUpm57gwNqjjxRFB1ejWqKpON2UBmZiaTJk3i4sWLjBo1inr16rFt2zbeeOMNEhISmDZtWpHH5y9N+eKLL1KtWrXb9nl7e1stbiGEEH9ysNPSp4kPfZr4YMy1cDI6hd3h8ey+mEB8upGlx6NZejwanUahQRUnmvu68lhD7wp5N5gU0EIIISoMX1cHZndvwKSHarH02HXWhsYQHp/BP3eG859fLtO3iQ99mlSlcTWXSvvsVmlbsmQJZ8+eZc6cOfTt2xeA4OBgnnrqKb766isGDhyIr69voceHhYWhKApjx47F0dGxtMIWQghRCL1OQ/s6HrSv48HL3epz8Goi287f5ER0ComZJs7HpXM+Lp2Vp2Ko6+XI0EBf+jTxwcWhYpSeFeNdCCGEEH9RxdmemY/U48n2Ndl0No41ITFEJWez8lQMK0/FUMVZTxd/L7r4e9HSzw2DnVyZtpaff/4ZHx+fguIZQFEUJk+ezP79+9m4cSNTpkwp9PiwsDD8/PykeBZCiDJIr9PQtb43Xet7o6oqManZnL2RxpFryWy/cJMrtzL5ZO8lPv/lMk2rudCmljttarrTzNcFh3I69koBLYQQosJyM9gxuo0fI1vX4Oi1ZNafieX3y4nEpxtZE3KDNSE30GkUmlZzoXUtdwKru9KwqjPeTnpbh14hpKWlcfnyZXr06HHHvhYtWgAQGhpa6PGqqnLx4kUeeughAMxmM0ajEYPBYJ2AhRBC3DdFUajhZqCGm4Gejaoy85F6bDl3k7WhMVxKyCQkJpWQmFR+OBSJVoF63k40qeZCk2ouNPd1oZ6XU7mYjEwKaCGEEBWeRlEKbjcz5lo4GpXMvvAEDl1NIjYtp2BQz+flpCegihN1vRyp7WGgtqcj1d0c8HTUV/jJUUpSXFwcqqre9RZtg8GAm5sb0dHRhR5/7do1MjMzyczMZMyYMZw6dQqTyYS/vz8zZsygT58+9xXXg9y9n3+sPAFwd5Kfokl+iib5KVp5y4+Lg47gVtUZ3tKX6ynZHItK5nhkCseikolPNxIen0F4fAbrT8cC4GinpamvC3U8HXE36PBw1OPlZEdzX1equtgX2df95uZ+cikFtBBCiEpFr9PQqa4nnep6oqoq11OyOR6VzPGoFM7HpXEtMYtbGUYOZhg5eDXpjuOd9Fq8nPRUddbj42KPj4s9VZztcTPY5Q34Bj31vB3lGWvyrkADhd5+7eDgQFZWVqHH508gFhoayoQJE5g4cSI3btxgwYIFvPDCCyQlJRU6Q3dRvLxcin2MNc5RkUl+iib5KZrkp2jlMT9VqrgSVL8qk//499iUbE5FJRMancypqGRCopLJMJo5GpnM0cjkO46vX9WZzvW9CarpjpvBDhcHHe6OevyrON02SVlp5EYKaCGEEJWWoij4uRvwczcwsHneVdIsk5nw+Awi4tO5lpTFtcQsriVlEpeWg8mskmE0k2HMIjKp8MJvQDMf3nq8YWm9jTJLVdXb/nm3/RpN4Vf0a9euzYwZM+jatSuBgYEF2wcOHEjfvn355JNP6N+/P66ursWK69atNAoJ6W8pSt4XtAc5R0Um+Sma5Kdokp+iVaT86IA21ZxoU80J2tTAbFG5ciuT0zdSiU3NISnLRHKmiRup2YTdTCfij9f/Gt26Bi9087/v3OQfV9zYhRBCCPEHg52WwOquBFa/vShTVZX0HDO3Mo3cyjASl5bDzbQc4tJySMgwkpJlIjk7l4ycXOp5Odko+rLFySkvD9nZ2Xfdn52dXeQM3I0aNaJRo0Z3bHd2dmbIkCH897//5fjx43Tr1q1YcakqD/zlsyTOUZFJfoom+Sma5KdoFTE/GkXB39sJf+87x8/UbBPHIpM5fC2Za0mZZOSYSTfmYsy1UNvT8bZclEZupIAWQggh7oGiKLg46HBx0FHHU2aEvhd+fn4oikJsbOwd+zIzM0lNTb1jbed7lb8GdEZGxgPFKIQQomxzdbDj0YAqPBpQxdahACAzoQghhBDCKpycnPD39+f06dN37AsJCQGgVatWhR7/3nvv8eijj3L9+vU79kVERABQq1atEopWCCGE+HtSQAshhBDCagYMGMD169fZvHlzwTZVVfnhhx/Q6/VFzqTt6+vL9evXWbhw4W3bL126xNq1a2nQoAHNmze3WuxCCCHE/5JbuIUQQghhNePHj2fDhg28+uqrnDlzhrp167J161YOHDjArFmzqFq1KgBRUVGcOHGCWrVq0bJlSwDGjh3Lli1bWLhwIbGxsXTo0IGYmBiWLVuGTqfj448/vm32VSGEEMLapIAWQgghhNU4ODiwePFi5syZw/r168nIyKBu3br861//YtCgQQXtjh49ymuvvcbgwYMLCmiDwcCSJUv4+uuv2bp1K7t378bV1ZWuXbvy7LPPUrduXRu9KyGEEJWVoha2toQodQkJD7ashre3ywOdoyKT/BRN8lM0yU/RJD+Fe5Dc5B8rSp6Mt9Yj+Sma5Kdokp+iSX4Kd7+5uZ+xVp6BFkIIIYQQQggh7oEU0EIIIYQQQgghxD2QAloIIYQQQgghhLgHUkALIYQQQgghhBD3QApoIYQQQgghhBDiHkgBLYQQQgghhBBC3AMpoIUQQgghhBBCiHsgBbQQQgghhBBCCHEPpIAWQgghhBBCCCHugRTQQgghhBBCCCHEPZACWgghhBBCCCGEuAdSQAshhBBCCCGEEPdACmghhBBCCCGEEOIeSAEthBBCCCGEEELcA52tAxB/UpQHP/ZBzlGRSX6KJvkpmuSnaJKfwj1IbiSf1iPjrfVIfoom+Sma5Kdokp/C3W9u7mt8VlVVLf5hQgghhBBCCCFE5SK3cAshhBBCCCGEEPdACmghhBBCCCGEEOIeSAEthBBCCCGEEELcAymghRBCCCGEEEKIeyAFtBBCCCGEEEIIcQ+kgBZCCCGEEEIIIe6BFNBCCCGEEEIIIcQ9kAJaCCGEEEIIIYS4B1JACyGEEEIIIYQQ90AK6AogKSmJDz74gG7duhEYGMiAAQNYvXq1rcMqdWFhYTz33HM89NBDNGvWjEcffZSPPvqItLS029rFxMQwa9YsOnfuTFBQEMOHD2f37t02irr0mc1mRo0aRcOGDe/YV5lzY7FYWLJkCQMGDCAwMJCuXbsye/Zs4uLibmtXGXN05coVnn/+edq3b0+zZs3o3bs3CxYswGKx3NauMuUmJCSExo0bc/jw4Tv2FScP4eHhTJ8+nQ4dOtCyZUvGjx/P8ePHrR2+uE8y3uaR8fbeyHh7dzLeFk7G2zuVxfFWUVVVva8jRZmQmZnJmDFjuHjxIqNGjaJevXps27aNgwcP8sILLzBt2jRbh1gqLl++zNChQ9FqtYwePRpfX19OnTrF+vXrqV+/PitWrMDR0ZH4+HiCg4NJTk5m7Nix+Pj4sHr1as6ePcsnn3xC//79bf1WrG7u3Ln85z//AfK+BOWr7LmZNWsW69ev57HHHqNLly5cuXKFpUuXUq1aNdauXYurq2ulzFF0dDRDhw4lKyuL0aNHU7NmTXbu3MmBAwcYMWIE7733HlC5Pj9Xr15lzJgxxMfHs2jRItq3b1+wrzh5uHTpEiNGjMDe3p5Ro0bh5OTEsmXLuH79Oj/++CPt2rWzxdsThZDxNo+Mt/dOxtu7k/H27mS8vVOZHW9VUa59++23akBAgLpp06aCbRaLRZ00aZLatGlTNSYmxobRlZ6JEyeqTZs2VcPCwm7bvnDhQjUgIECdN2+eqqqq+vbbb6sNGzZUjx8/XtAmOztbHTBggNq+fXs1IyOjVOMubSEhIWqTJk3UZs2aqQEBAbftq8y52blzpxoQEKC+++67t21fu3atGhAQoH777beqqlbOHL3//vtqQECAunnz5tu2jxs3Tg0ICFAjIiJUVa08udmxY4fatm1bNSAgQA0ICFAPHTp02/7i5GHSpElqYGCgGhkZWbAtMTFR7dy5s9qnTx/VYrFY/w2JeybjbR4Zb++NjLd3J+Nt4WS8vV1ZHm/lFu5y7ueff8bHx4e+ffsWbFMUhcmTJ2Mymdi4caMNoysdRqORY8eO0bp1awICAm7bN2jQIACOHj2K2Wxmw4YNBAUF0apVq4I29vb2jBs3jqSkJPbt21eKkZeujIwMXn75ZR5++GGCgoJu21fZc7N8+XKcnJx46aWXbtvet29fpkyZQp06dSptjq5evQrAI488ctv27t27A3DhwoVKk5spU6YwY8YMqlSpQr9+/e7YX5w8JCQksH//frp3707NmjUL2np4eDBs2DAiIiIIDQ21+nsS907GWxlv75WMt4WT8bZwMt7+qayPt1JAl2NpaWlcvnyZFi1a3LEvf1tl+AKm0+nYtGkTH3zwwR37EhISANBoNISHh5OZmXnHYAZ/5iskJMSqsdpS/vNpH3744R37KnNuzGYzR48epV27djg7OwOQnZ2N0WhEr9fz0ksv0bNnz0qbo7p16wIQERFx2/YrV64A4OPjU2lyc/nyZV588UXWrVtHnTp17thfnDzk/7Oi56yikPE2j4y390bG27uT8bZoMt7+qayPt1JAl2NxcXGoqoqvr+8d+wwGA25ubkRHR9sgstKl0WioWbMmtWrVumPfjz/+CED79u0LJqe4W76qVasGUGHztWPHDtasWcMHH3yAt7f3Hfsrc26io6PJycnBz8+P7du3079/f1q0aEFQUBCTJk3i8uXLQOXN0ZQpU6hbty6zZ8/m4MGDREdHs2TJElasWEGHDh1o3bp1pcnNli1bmDp1Knq9/q77i5OH2NjYQtv6+Pjc1lbYnoy3eWS8/Xsy3hZOxtuiyXj7p7I+3koBXY7lz3bp6Oh41/0ODg5kZWWVZkhlys8//8yqVavw9fVl2LBhRebLwcEBoELmKy4ujrfeeosnnnii4Dag/1VZcwOQkpICwO+//87LL79Mt27dmDt3LtOnT+fYsWOMHDmSqKioSpujqlWrMnPmTGJjY5kwYQKPPfYYH3zwAc2bN2fu3LkoilJpclPYQJ6vOHlIT08HwMnJ6Y62BoPhtrbC9mS8LZqMt3lkvC2ajLdFk/H2T2V9vNUVq7UoU9Q/JlBXC5lIXVVVNJrK+RvJunXreOONN3B0dOQ///kPTk5OheYJ/sxhRcuXqqq8+uqruLi48PrrrxfZ7u/2VbTc5DMajUDe7UJffvklPXv2BPKeOWrSpAlPP/00X3zxBV27di30HBU5R9999x2ffvoptWvX5pVXXsHb25tjx46xdOlSxo8fz48//lipPz9/VZw8FPXf78qUs/JCxtvCyXibR8bbvyfjbdFkvL13th5vpYAux/J/ScnOzr7r/uzs7LverlDR5S8b4eLiwjfffENgYCDwZ77u9itTfg5dXFxKL9BSMH/+fA4dOsTcuXPJyckhJycHAJPJBEBiYiJarbZS5iZf/q+XPj4+BYN5vkcffRRfX18OHDhAnz59gMqVo/T0dObOnUvVqlVZtWoVbm5uAPTo0YMmTZowa9Ysvv76a9q2bQtUrtzcTXH+jirz31x5JOPt3cl4+ycZb/+ejLeFk/G2eGw93koBXY75+fmhKErBvf1/lZmZSWpqasFzAJWByWTi7bffZu3atfj4+PDdd9/RqFGjgv1+fn4Ad81X/raKlq+9e/eiqirTp0+/6/4OHTpQo0YNvvnmG6By5SZf/vu627Nq+dvDwsIq5efnypUrZGdnM3To0ILBPF///v159913OXjwIIMHDwYqV27upjifkXtpWxkLsrJKxtvbyXh7Jxlv/56Mt4WT8bZ4bD3eSgFdjjk5OeHv78/p06fv2Jc/m9xfp3avyMxmMy+99BLbt2+nYcOGzJs3r2BigHz16tXDxcXlrjOlVtR8vfrqq6Smpt6x/eOPPyYsLIz58+djb29fKXOTz9PTk1q1anH16lVycnKwt7cv2GexWIiOjsbPz69S5ij/GSSz2XzHPlVVsVgsqKpaKXNzN8XJQ/PmzdFoNISGhjJ69Oi7tm3ZsqWVIxb3SsbbP8l4e3cy3v49GW8LJ+Nt8dh6vK34N8lXcAMGDOD69ets3ry5YJuqqvzwww/o9fqC22Aqui+++ILt27cTGBjI0qVL7xjMIW/5jT59+nDs2DFOnDhRsD0nJ4dFixbh7e1Nly5dSjNsq2vWrBkdO3a845X/62bHjh1p3bp1pczNXw0dOpSMjAy+//7727avXLmSpKQk+vbtWylz1KBBA2rUqMG2bdsKZrzMt2rVKrKzs+nUqVOlzM3dFCcP3t7edOzYke3btxMVFVXQNikpiVWrVtGoUSOaNGlS6u9BFE7G2zwy3t6djLf3Rsbbu5PxtnhsPd4qalFPYYsyL/92j2vXrjF27Fjq1q3L1q1bOXDgALNmzWLSpEm2DtHqYmJi6NGjB2azmRdffPGug7m3tzedOnUiPj6ewYMHk5WVxZNPPomXpSsw0gAAB0BJREFUlxerV6/m7NmzzJkzp9J8ARo7dixHjhwhLCysYFtlzo3RaGTcuHGcPHmSfv360a5dO86dO8fKlSupX78+K1euxGAwVMoc/f7770ydOhV3d3dGjBiBt7c3J0+eZP369dSrV4+ffvoJV1fXSpebL7/8kq+++opFixbRvn37gu3FycPFixcJDg7GycmJCRMmoNfrWbp0KTExMcyfP582bdrY4q2JQsh4K+Pt/ZDx9nYy3hZOxtu7K4vjrRTQFUBiYiJz5sxhz549ZGRkULduXSZMmMCgQYNsHVqp+Pnnn3n11VeLbNOuXTsWL14MQFRUFJ9++ikHDhzAZDLRsGFDnn766SJnfaxo7jagQ+XOTVZWFvPmzWPjxo3cuHEDLy8vevTowcyZM3F2di5oVxlzdPbsWf773/9y7NgxMjIyCiaAmT59+m0Tb1Sm3BQ2oEPx8nD+/HnmzJnD8ePH0Wg0NGvWjJkzZxIUFFRK70QUh4y3Mt4Wl4y3d5LxtnAy3t6pLI63UkALIYQQQgghhBD3QJ6BFkIIIYQQQggh7oEU0EIIIYQQQgghxD2QAloIIYQQQgghhLgHUkALIYQQQgghhBD3QApoIYQQQgghhBDiHkgBLYQQQgghhBBC3AMpoIUQQgghhBBCiHsgBbQQQgghhBBCCHEPpIAWQgghhBBCCCHugRTQQoh78uWXX9KwYcO/fX300Ue2DrUg1sOHD9s6FCGEEKJYZLwVomzT2ToAIUT5EhwcTOvWrQvd7+/vX4rRCCGEEBWTjLdClE1SQAshiiUoKIiBAwfaOgwhhBCiQpPxVoiySW7hFkIIIYQQQggh7oEU0EIIq3j00UeZMGECv/32G4MHDyYwMJBu3brxySefkJ2dfUf7DRs2EBwcTFBQEEFBQQQHB7N+/fo72qmqyvLlyxkyZAhBQUF07NiRadOmcfbs2TvaJiUl8fbbb9OxY0cCAwMZNGgQmzZtssr7FUIIIWxBxlshSpcU0EKIYsnMzCQxMbHQl6qqBW0vXbrEtGnTqF+/Pq+++iqBgYHMmzePSZMmYbFYCtp98MEHvPLKK5hMJmbMmMGMGTMwGo3MmjWLDz/88Lb+X331Vd59910MBgMzZ85kwoQJnD17ljFjxnDu3Lnb2r722mtcvHiRZ555hhkzZpCQkMBLL73E/v37rZskIYQQ4gHJeCtEGaUKIcQ9+M9//qMGBAT87SslJUVVVVXt1q2bGhAQoP73v/+97TwfffSRGhAQoK5du1ZVVVU9evSoGhAQoI4fP141Go0F7YxGozp27Fg1ICBAPXz4sKqqqnrw4EE1ICBAffHFF1WLxVLQNiIiQm3UqJH67LPP3hbrk08+qZrN5oJ2+cfPnj3bOkkSQgghHpCMt0KUbTKJmBCiWCZNmkTnzp0L3e/o6Fjwv11cXJg0adJt+6dNm8bChQvZvn07gwcPZuvWrQDMmDEDOzu7gnZ2dnY899xzjB49mi1bttCuXTt27doFwOTJk1EUpaCtv78/q1evpmrVqrf1NWjQIDSaP2+0CQoKAuDmzZvFfNdCCCFE6ZLxVoiySQpoIUSx1K9fn44dO95T29q1a6PX62/b5unpiZubG1FRUQBERkYC0KBBgzuODwgIACA6Ovq2f95t6Y6mTZvesa1KlSq3/buDgwMARqPxnuIXQgghbEXGWyHKJnkGWghhNf87mOczm81otVqA257hulu7v57HZDIVq/+//houhBBCVFQy3gpReuTTLoSwmsjIyDsG7Li4ONLT06lTpw4AtWrVAiA8PPz/27tflsiiMA7Av0XEYLAZHItgME1STJbBP8GxKpj8BiZNFg3CoBhkMGsQvKjRqMkPIIJNEEwGo3Fgtw0M7i7XRUd393nqfc/lnPTy45xz76vx9/f3SZKhoaEkyfDwcJLk4eHhVe3e3l62t7ffbe4A8LfQb6F7BGjgwzw/P7/6NcbBwUGSpF6vJ0nm5uaSJM1mM61Wq13XarXSbDY7aqanp5MkR0dHHe98fHzM4eFh+5gaAPxP9FvoHneggTe5ublpHwf7mf7+/nbj7e3tzcbGRm5vbzM6Oprr6+tcXl5mZmYms7OzSZLJycksLS2lKIosLi5mfn4+SXJxcZG7u7ssLy9nYmIiSTI1NZV6vZ7z8/M8PT2lVqvl5eUlx8fH6evry9ra2gevHgC6Q7+Fr0mABt6kKIoURfHL55VKpd3QBwcHs7m5mUajkdPT01Qqlayvr2dlZaVjzNbWVqrVak5OTrK/v5+enp6MjY1ld3c3CwsLHbU7OzupVqs5OztLo9HIwMBAxsfHs7q6mpGRkXdfLwB8Bv0WvqZv33/3RQGAP1Sr1ZIkV1dXnzwTAPh36bfQXe5AAwAAQAkCNAAAAJQgQAMAAEAJ7kADAABACXagAQAAoAQBGgAAAEoQoAEAAKAEARoAAABKEKABAACgBAEaAAAAShCgAQAAoAQBGgAAAEr4ARiKVsPwAFelAAAAAElFTkSuQmCC",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "def plot_loss(history, metric='mae'):\n",
        "    plt.plot(history.history[metric])\n",
        "    plt.plot(history.history[f'val_{metric}'])\n",
        "    plt.title(f'Model {metric.title()}')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(metric.upper())\n",
        "    plt.legend(['Train', 'Validation'], loc='upper right')\n",
        "    \n",
        "plt.subplot(1, 2, 1)\n",
        "plot_loss(history, 'mae')\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_loss(history, 'mse')\n",
        "\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {},
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "9/9 [==============================] - 0s 168us/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error (MAE): 3611.59\n",
            "Mean Squared Error (MSE): 49144026.66\n"
          ]
        }
      ],
      "source": [
        "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
        "# Make predictions on the test set\n",
        "predictions = model.predict(test_features)\n",
        "\n",
        "# Visualize actual vs predicted values\n",
        "plt.figure(figsize=(10, 6))\n",
        "plt.scatter(test_labels, predictions)\n",
        "plt.xlabel('True Values')\n",
        "plt.ylabel('Predictions')\n",
        "plt.title('Actual vs Predicted Values')\n",
        "plt.show()\n",
        "\n",
        "# Calculate and print evaluation metrics\n",
        "mae = mean_absolute_error(test_labels, predictions)\n",
        "mse = mean_squared_error(test_labels, predictions)\n",
        "print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "print(f'Mean Squared Error (MSE): {mse:.2f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "## Preprocessing (different approach)\n",
        "* Convert categorical columns into numeric, via Label encoder\n",
        "* Normalization layer"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {},
      "outputs": [],
      "source": [
        "# Get a list of categorical variables\n",
        "obj_cols = dataset.select_dtypes('object').columns.tolist()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>age</th>\n",
              "      <th>sex</th>\n",
              "      <th>bmi</th>\n",
              "      <th>children</th>\n",
              "      <th>smoker</th>\n",
              "      <th>region</th>\n",
              "      <th>expenses</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19</td>\n",
              "      <td>0</td>\n",
              "      <td>27.9</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3</td>\n",
              "      <td>16884.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>18</td>\n",
              "      <td>1</td>\n",
              "      <td>33.8</td>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>1725.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>28</td>\n",
              "      <td>1</td>\n",
              "      <td>33.0</td>\n",
              "      <td>3</td>\n",
              "      <td>0</td>\n",
              "      <td>2</td>\n",
              "      <td>4449.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>33</td>\n",
              "      <td>1</td>\n",
              "      <td>22.7</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>21984.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>32</td>\n",
              "      <td>1</td>\n",
              "      <td>28.9</td>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "      <td>1</td>\n",
              "      <td>3866.86</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   age  sex   bmi  children  smoker  region  expenses\n",
              "0   19    0  27.9         0       1       3  16884.92\n",
              "1   18    1  33.8         1       0       2   1725.55\n",
              "2   28    1  33.0         3       0       2   4449.46\n",
              "3   33    1  22.7         0       0       1  21984.47\n",
              "4   32    1  28.9         0       0       1   3866.86"
            ]
          },
          "execution_count": 12,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "'''\n",
        "If the categories have a natural order (e.g. cold, neutral, warm, hot), then they can be converted to \n",
        "numbers (e.g. 1, 2, 3, 4) preserving the order. These are called ordinals.\n",
        "# '''\n",
        "# from sklearn.preprocessing import OrdinalEncoder\n",
        "# df = dataset.copy()\n",
        "# oe = OrdinalEncoder()\n",
        "# df[obj_cols] = oe.fit_transform(df[obj_cols])\n",
        "\n",
        "# However, in this case, the categories do not have a natural order\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "df = dataset.copy()\n",
        "le = LabelEncoder()\n",
        "df['region'] = le.fit_transform(df['region'])\n",
        "df['sex'] = le.fit_transform(df['sex'])\n",
        "df['region'] = le.fit_transform(df['region'])\n",
        "df['smoker'] = le.fit_transform(df['smoker'])\n",
        "\n",
        "df.head()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "id": "pCFXxk8WbZl4"
      },
      "outputs": [],
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "train_dataset, test_dataset = train_test_split(df, test_size=0.2, random_state=0)\n",
        "\n",
        "train_features = train_dataset.copy()\n",
        "test_features = test_dataset.copy()\n",
        "\n",
        "# Removes the target from features and returns it at the assighned variable\n",
        "train_labels = train_features.pop('expenses')\n",
        "test_labels = test_features.pop('expenses')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### Baseline models using sklearn"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "id": "MxmQXsTKgewf"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error (MAE): 3930.22\n",
            "Mean Squared Error (MSE): 31841055.89\n"
          ]
        }
      ],
      "source": [
        "# Linear Regression\n",
        "from sklearn.linear_model import LinearRegression\n",
        "model = LinearRegression().fit(train_features, train_labels)\n",
        "pred_labels = model.predict(test_features)\n",
        "\n",
        "from sklearn import metrics\n",
        "mae = metrics.mean_absolute_error(test_labels, pred_labels)\n",
        "mse = metrics.mean_squared_error(test_labels, pred_labels)\n",
        "print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "print(f'Mean Squared Error (MSE): {mse:.2f}')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "id": "02cNQgOzkzgm"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Mean Absolute Error (MAE): 2474.73\n",
            "Mean Squared Error (MSE): 16569842.09\n"
          ]
        }
      ],
      "source": [
        "# Gradient Boosting\n",
        "from sklearn.ensemble import GradientBoostingRegressor\n",
        "model = GradientBoostingRegressor(random_state=0).fit(train_features, train_labels)\n",
        "pred_labels = model.predict(test_features)\n",
        "\n",
        "from sklearn import metrics\n",
        "mae = metrics.mean_absolute_error(test_labels, pred_labels)\n",
        "mse = metrics.mean_squared_error(test_labels, pred_labels)\n",
        "print(f'Mean Absolute Error (MAE): {mae:.2f}')\n",
        "print(f'Mean Squared Error (MSE): {mse:.2f}')"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {},
      "source": [
        "### NN using keras "
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "-oHyjBxJBs52"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "First 5 rows: [[37.   1.  34.1  4.   1.   3. ]\n",
            " [18.   1.  34.4  0.   0.   2. ]\n",
            " [23.   0.  36.7  2.   1.   0. ]\n",
            " [32.   1.  35.2  2.   0.   3. ]\n",
            " [58.   0.  32.4  1.   0.   0. ]]\n",
            "\n",
            "Normalized: [[-0.15  1.02  0.54  2.41  1.97  1.31]\n",
            " [-1.5   1.02  0.59 -0.89 -0.51  0.4 ]\n",
            " [-1.14 -0.99  0.97  0.76  1.97 -1.4 ]\n",
            " [-0.5   1.02  0.72  0.76 -0.51  1.31]\n",
            " [ 1.34 -0.99  0.27 -0.07 -0.51 -1.4 ]]\n"
          ]
        }
      ],
      "source": [
        "'''\n",
        "In a 2D array axis=1 or axis=-1 can be used interchangeably, since when -1 is selected\n",
        "the last axis of the input is assumed to be a feature dimension and is normalized per index,\n",
        "hence column-wise.\n",
        "'''\n",
        "normalizer = layers.experimental.preprocessing.Normalization(axis=-1)\n",
        "normalizer.adapt(np.array(train_features))\n",
        "head = np.array(train_features[:5])\n",
        "\n",
        "with np.printoptions(precision=2, suppress=True):\n",
        "  print('First 5 rows:', head)\n",
        "  print()\n",
        "  print('Normalized:', normalizer(head).numpy())"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "id": "x8pjtu4nOWBF"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(6,)"
            ]
          },
          "execution_count": 14,
          "metadata": {},
          "output_type": "execute_result"
        }
      ],
      "source": [
        "train_features.shape[1:]"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 16,
      "metadata": {
        "id": "59JaAF-9mSSJ"
      },
      "outputs": [],
      "source": [
        "# Neural Network\n",
        "'''\n",
        "It is good practice to normalize features that use different scales and ranges.\n",
        "One reason this is important is because the features are multiplied by the model weights.\n",
        "So, the scale of the outputs and the scale of the gradients are affected by the scale of the inputs.\n",
        "'''\n",
        "normalizer = layers.experimental.preprocessing.Normalization(input_shape=train_features.shape[1:] , axis=-1)\n",
        "# It can be passed either as a tf.data.Dataset, or as a numpy array.\n",
        "normalizer.adapt(np.array(train_features))\n",
        "\n",
        "model = keras.Sequential()\n",
        "model.add(normalizer)\n",
        "model.add(layers.Dense(64, activation='relu'))\n",
        "model.add(layers.Dense(32, activation='relu'))\n",
        "model.add(layers.Dense(1))\n",
        "\n",
        "model.compile(optimizer='adam', loss='mae', metrics=['mae', 'mse'])\n",
        "history = model.fit(train_features, train_labels, validation_split=0.2, epochs=100, verbose=0)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 17,
      "metadata": {},
      "outputs": [
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 1000x600 with 2 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "\n",
        "def plot_metric(history, metric='mae'):\n",
        "    \n",
        "    plt.plot(history.history[metric])\n",
        "    plt.plot(history.history[f'val_{metric}'])\n",
        "    plt.title(f'Model {metric.upper()}')\n",
        "    plt.xlabel('Epoch')\n",
        "    plt.ylabel(metric.upper())\n",
        "    plt.legend(['Train', 'Validation'], loc='upper right')\n",
        "\n",
        "\n",
        "plt.subplot(1, 2, 1)\n",
        "plot_metric(history, 'mae')\n",
        "plt.subplot(1, 2, 2)\n",
        "plot_metric(history, 'mse')\n",
        "plt.tight_layout()\n",
        "plt.show()"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 18,
      "metadata": {
        "id": "Xe7RXH3N3CWU"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "9/9 - 0s - loss: 3244.0825 - mae: 3244.0825 - mse: 42199252.0000 - 18ms/epoch - 2ms/step\n",
            "Testing set Mean Abs Error: 3244.08 expenses\n",
            "You passed the challenge. Great job!\n",
            "9/9 [==============================] - 0s 778us/step\n"
          ]
        },
        {
          "data": {
            "image/png": 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8eXB1dcWrr75aZ7v6HrOzs1P7u6bnGLutvb19rf2sTdOm0lpgZ+2ktqDR2nG8TY9jblwKReXM1E/HrkMmAxaO7IqnQn3M3S0yE8kEqpUrV+LgwYP473//i7KyMpSVlQEAKioqAAC3b9+Gvb29arZJ0wxQaWkpAMDVtfKHiFhtlceqty0tLYWLi4vGttWPa+P2bd7lZwoyWeUXTV4e74AyBY636XHMjU8h/LNm6p+ZqYUju6KvnxtycwvN3TWbYGcnvUkIyQSqvXv3QhAETJ8+XePjvXr1QqtWrbBkyRIA0FhAU3lMuY7Jx8en3rbK9VXKtjk5OejQoYNa2+rrtqqet127dnWeVxeCAP7wMyGOt2lxvE2PY24c1e/mezsqEE+F+rBMhQlJcZwlE6jmzZuHu3fv1jj+4Ycf4uzZs1i5ciUcHR3Rtm1buLq6qsodVKW8+05ZTqFLly6ws7NDRkYGRo8erbFtaGgogMq785TH+/XrV6OtTCZT3dVXtW31QJWRkQFXV1f4+/vrNgBERCR5LI1AtZHMXX5BQUHo3bt3jT9ubm4AgN69eyMsLAwNGjRAVFQUjh49irS0NNXzy8rKkJCQAC8vL/Tt2xcA4OXlhd69eyM5ORnZ2dmqtvn5+diwYQM6dOiATp06AagMSW3atMGGDRtQUFCgapuVlYXk5GRERETAw8MDANC/f3+4ubkhISFBdWkSqCzsmZqaiqFDh6rWWxERkXVgmKK6SLZSutLYsWNx+PBhtUrpt27dwlNPPYWSkhJMnDgRnp6e2LhxI06ePImFCxciKipK1fbcuXOIjY2Fs7MzJkyYAAcHB6xevRrXrl3DypUrER4ermp74MABTJ48GT4+Phg9ejTKy8uxatUqVFRUYO3atWjbtq2q7aZNmzB//nwEBQVh+PDhyMvLw8qVK+Hu7o7ExER4enrq/F5ZKd00WEXatDjepscxF19dYYrjbXpSrJRukYEKALKzs/HZZ5/hwIEDqKioQGBgIKZNm1bjch0AnD59GgsXLkRqairs7OwQFBSEWbNmaSz4efDgQSxevBgnTpyAk5MTwsLCEB8fr/ES3s6dO7F06VJkZmbCzc0NvXv3Rnx8vGoNl64YqEyDP/xMi+NtehxzcdU3M8XxNj0GKqoTA5Vp8IefaXG8TY9jLh5tLvNxvE1PioGKC32IiIg04Jop0gUDFRERUTUMU6QrBioiIqIqGKZIHwxURERE/2CYIn0xUBEREYFhigzDQEVERDaPYYoMxUBFREQ2jWGKxMBARURENothisTCQEVERDaJYYrExEBFREQ2h2GKxMZARURENoVhioyBgYqIiGwGwxQZCwMVERHZBIYpMiYGKiIisnoMU2RsDFRERGTVGKbIFBioiIjIajFMkakwUBERkVVimCJTYqAiIiKrwzBFpsZARUREVoVhisyBgYqIiKwGwxSZCwMVERFZBYYpMicGKiIisngMU2RuDFRERGTRGKZIChioiIjIYjFMkVQwUBERkUVimCIpYaAiIiKLwzBFUsNARUREFoVhiqSIgYqIiCwGwxRJFQMVERFZBIYpkjIGKiIikjyGKZI6BioiIpI0himyBAxUREQkWQxTZCkYqIiISJIYpsiSNND3iffv30dGRgaysrJw+/Zt2NnZoXnz5vDx8UGXLl0gk8nE7CcREdkQhinLJFcISL96B7lF5fBycUBIKzfY29lGHtA5UGVkZGDFihX47bffUFpaCkEQVI8pQ5S7uzsiIiIQFxeHoKAg8XpLRERWj2FK+jQFp98u5OGzlPO4WVSuatfcxQGzI9shsr2XGXtrGjKhaiKqw6VLl/D222/j0KFDaNSoER555BF06tQJfn5+aNKkCeRyOfLz83H16lWkpaUhPT0dxcXFeOSRR/Dyyy+jffv2xn4vFi8vrxAKhbl7Yf1kMsDLyxW5uYXQ7tNPhuB4m54lj7klhilLHm99pGTm1ghOTRo1wN3S+7U+56OYTqKGKjs7wNPTVbTziUGrGaply5bhq6++wkMPPYQvvvgC/fv3h6OjY53PKS8vxy+//IINGzZg2LBhmDFjBp5//nlROk1ERNbHEsOUrUnJzMW8badqHK8rTAHAwr0X0M/f06ov/2kVqH799VesWrUKoaGhWp/YwcEBTz75JJ588kkcOHAAn3zyCQMVERFpxDAlfXKFgM9Szuv13JzCMqRfvYMwX3dxOyUhWgWqxMREgxaZ9+7dG5s2bdL7+UREZL0YpixD+tU7apf5dJVrwHMtgVZlE8S4Y493/RERUXUMU5bD0EDk5eIgUk+kSe86VGfPnsXGjRtVf//xxx/Rq1cv9OnTB6tWrRKjb0REZMUYpiyLIYHI29URIa3cROyN9OgVqNLS0jBs2DB89913AIDTp0/jvffeg0KhgJOTEz766CPs2LFD1I4SEZH1YJiyPCGt3NBcz1AVH+Fv1QvSAT0D1dKlS+Hu7o6PP/4YALBt2zYAQEJCAnbu3Inw8HCsXr1avF4SEZHVYJiyTPZ2MsyObFdnG7dG6kuzvV0dRS+ZIFV6VUr/66+/8OyzzyI4OBgA8Pvvv6N169YIDAwEAAwePBgLFy4Ur5dERGQVGKYsW2R7L3wU06lGHSpvV0fER/ijn78nK6XrorS0FF5elWkzNzcXmZmZGDVqlOpxe3t7aFkvlIiIbATDlHWIbO9VZ3Cy5tIIddErUD3wwAO4dOkSAGDv3r2QyWTo06eP6vHDhw+jZcuW4vSQiIgsHsOUdbG3kyHM1121Bc3us7dUwQqAXrNUlr4PoF6Bqm/fvvjxxx9RXFyMHTt2oEmTJnjkkUdw8+ZNfPPNN/jll1/wwgsviN1XIiLSklwh4GhWgSS+nBimrJOmLWjcGjWAAPXK6drs56fpXJa2D6Begeqll15CVlYW1qxZA1dXV3zwwQdwdHTElStXsHbtWjzyyCN47rnnxO4rERFpIeVcLj7/7TCu3ylVHTPXlxPDlHWqbQuaOxq2oLlZVI55207Vuji9tnPV9zyp0XpzZE3y8/Ph7OwMB4fK2yjv3buHc+fO6bRFDf2LmyObhq1tZGpuHG/Tqu3LScmUX062EqZs7TMuVwiIWXZI56rpbo0aIHlaL7WZUm3O5e3qiK2Teqg9T4qbI+td2BMAPDw8IJfLcenSJZSUlMDJyYlhiojITLTZa23h3guQK4z/rW8rYcoW6bsFzZ3S+1hxKEvncyn3AZQ6vQNVdnY2pkyZgu7duyMqKgrp6ek4fPgwoqOjkZqaKmYfiYhIC1L5cmKYsm6GbEGzNvUKDl/OR/Lpm0jNLsCNu6X1P8nA1zQVvdZQXb9+HSNHjsS9e/fQrVs3HDlyBACgUChw6dIlTJo0CWvXrkWHDh1E7SwREdVO2y8dY345MUxZP0O2oCksk+OFjcdVf9f2NglL2AdQrxmqRYsWoaysDJs3b8YXX3yhqjnVu3dvbNy4EQ4ODvjmm29E7SgREdVN2y8dY305MUzZBkO2oKlOm4vPlrIPoF6Bav/+/XjmmWfg7+8PmUw9X3bo0AGjRo1Cenq6GP0jIiItafNFZ6wvJ4Yp26HNFjRispR9APUKVAUFBWjdunWtjz/wwAPIz8/Xu1NERKS7ql90tX39GOPLiWHK9ii3oKke4MX8ZHk4NbSYkgmAnmuoWrRogfPna7+TJD09Hc2bN9e7U0REpJ/I9l74OKYTPv/tolodKuVea2J/OTFMWR9tK5Zr2oImv6QC85NOi9KPl/q3tZgwBegZqAYOHIh169ZhyJAh8PX1BQDVpb8dO3Zg27ZtiIuLE6+XRESktcgALwx76EHsSr9i1ErpDFPWR9eK5cotaKqyi5HVOIc+mrs6GvR8U9OrsGdhYSFGjhyJ7Oxs+Pv749y5cwgLC8OdO3dw/vx5tGjRAj/99BOaNm1qjD5bLRb2NA1bK8Jnbhxv0zPFmDNM/ctaPuNiFoWtOsvVtHFDvLXzrE4BS1Mxz6qsprCnq6sr1q9fjxEjRuDGjRsQBAFHjx7F1atXER0djfXr1zNMERFZKYYp6yN2UVjlzNWgjs3RvbWHzovYLWUhelUGbT2jdPv2bSgUCjRt2hR2dgYVX7dpnKEyDWv5bdJScLxNz5hjzjBVkzV8xlOzCzA1MaPedktGBte4xKctTZcT7WRA1Yym7Vo/Kc5Q6bWGqrqmTZtCLpdj//79sLe3R+/evRmsiIisDMOU9TJFUVhNi9i7tGyC49fvGnWtn6noFagEQcDChQtx/vx5fPPNN5DL5Rg9ejSOHTsGAOjYsSMSEhLg4uIiameJiKyVtndWmQvDlHXTttjrxbx7SM0u0PvzqWkRu74zXlKjV6BatWoVli1bhh49egAAkpOTkZ6ejgEDBiAgIADLli3DkiVLMGfOHFE7S0RkjXS9s8rUGKasn7IobH0Lx1ccysaKQ9mS+nxKhV7X5bZu3YpevXrh+++/BwDs3r0bDg4O+Oijj/Cf//wHI0eOxK5du0TtKBGRNVLeWVX9i+xmUTnmbTuFlMxcM/WsEsOUbdC1+rlUPp9Solegunz5MgYPHqyqPXXw4EF07doVzs7OACq3n7l+/bp4vSQiskJi31klNoYp21Jb9fO6mPPzKTV6BSoHBwco/rkd7fTp07h9+zYeeugh1eN3796Fq6u0Vt8TEUlN+tU79V5iySksQ/rVOybq0b8YpmxTZHsvbJvcE0tGBuO5h3zrbW+uz6cU6RWo2rZti7179wIA1q9fD5lMhv79+wMAioqKsGnTJvj7+4vWSSIia2SKO6v0wTBl25QLx9s0ddaqvak/n1KlV6AaO3Ys9u/fj7CwMKxbtw5du3ZF586dcfz4cQwePBgXL17ExIkTxe4rEZFV0fbOKm3biYFhipSk+PmUMr3u8ouKikKDBg2wadMmtGjRAi+++CIAoFGjRnBxccH8+fMREREhakeJiKyNNndWebs6IqSVm0n6wzBFVUnt8yl1olRKJ3GwUrppWENVY0vC8a6bmPunKekz5gxT+rPmz7gxPp9ikGKldJYzJyIyo9rurPJ2dTTZlxXDFNVGCp9PS6HXJT+FQoGvv/4aGzZsQF5eHuRyeY02MpkMp07VnmqJiKiSpi05TFUpnWGK6mPOz6cl0StQffXVV/j666/h6uqKoKAgNGzYUOx+ERHZFE1bchgbwxRpyxyfT0ujV6DaunUrunbtilWrVsHJyUnsPhERkZExTFFtpL6vpFTpFahu3ryJ559/nmGKiMgCMUxRbaS+r6SU6bUovWXLlrhzh5VRiYgsDcMU1Ubq+0pKnV6B6umnn8b69etRVFQkdn+IiMhIGKaoNlLfV9IS6HXJz8/PDzKZDFFRUejfvz+aNWum2ihZSSaT4YUXXhClk0REZBiGKaqLLvtKcnG6ZnoFqvj4eNX/T0xM1NhG30CVnZ2NL774AkeOHMHdu3cRGBiI8ePHIyoqSq3dtWvX8MUXX+DAgQMoKipCQEAApkyZgkcffbTGOTMzM/H555/jr7/+QmlpKYKDg/Gf//wHYWFhNdqmpaVh0aJFOHnyJARBQHh4OObMmYN27drVaLt37158++23OHfuHBo2bIhHHnkEc+bMQYsWLXR+30RExsIwRfWR6r6SlkSvQJWQkCB2PwAA169fR2xsLCoqKjB27Fh4enpix44deOmll3Dt2jVMmjQJAHDr1i2MGTMGBQUFGDt2LLy9vbFx40ZMnz4dn376KaKjo1XnvHDhAuLi4uDo6IixY8fC2dkZa9aswfjx47FixQr06NFD1fbw4cN49tln0apVK0yZMgUKhQIJCQmIjY1FYmKi2obPSUlJePnll9G5c2fMnDkTd+/exapVq3DkyBFs2rQJnp6eRhkjIiJdMEyRNrhvn+EktfXMvHnzsHXrVqxfvx5du3YFAMjlcgwfPhyXLl3C/v374erqijfffBPr16/HmjVr0K1bNwBAWVkZRo4ciZycHKSkpKBx48YAgEmTJuHIkSPYvn07fH19AQD5+fmIiYlBkyZNsH37dshkMgiCgOjoaBQUFGD79u1wd3cHAGRlZSE6Oho9evTAsmXLAADFxcWIjIyEt7c3EhMT4ejoCKBydisuLg6jRo3CW2+9pfP759YzpmHN20RIEcfb9JRjfvPWXXy4i2HK2KzhMy5XCIhZdqjeffu2TuohiRIKVrf1zPHjx7Fw4ULMnj0b586dw5UrV5CcnAx9M5pMJkO/fv1UYQoA7O3t8dBDD6GkpASXLl2CXC7Htm3bEBISogpTAODo6Ihx48YhPz8f+/btAwDk5uZi//79GDBggCpMAYCHhwdGjBiB8+fPIyMjQ/VeMjMzMWzYMFWYAirXiw0ePBi///47bt68CQDYt28f8vPzMXr0aFWYAoBu3bohPDwcSUlJKC/ntCgRmY9CIeBDzkxZLLlCQGp2AZJP30RqdoHRF4Pb28kwO7Lm0paq4iP8JRGmpErvQPXBBx9g5MiRWLp0KXbs2IG8vDycOXMGM2fOxIwZM1BRUaHzOT/88EN8++23NY6fOnUKdnZ2aNGiBTIzM1FcXIyQkJAa7ZRB7NixY2r/q03b9PR0teNVBQcHQ6FQ4Pjx42rPqa1tUVERLl68WNdbJSIyGoUg4PWtJ7DpGMOUJUrJzEXMskOYmpiB13ecwdTEDMQsO2T0sgXct88weq2h2rx5M77//ns8+eSTiIqKwpQpUwBUBoyoqCj88ssvWL16NSZMmKB3x4qKinDp0iX8+OOPOHjwIMaPH4/mzZvj9OnTACprYVWnXAx+5coVAMCNGzdqbevt7a2x7QMPPKD1eetr26FDB63eKxGRWBSCgI/2nGeYslDKWlDVKWtBGTvYcN8+/ekVqH788Uf06NEDH330EfLz81XHmzVrhoULF+LOnTvYtGmTQYHqlVdewa5duwBUzjBNnToVAFBYWAgAqjVSVTVq1AgAUFJSAgCqOlnOzs412iqrvFdvq+m8hrTVhUxW+YeMSznGHGvT4HibjlqYkgH/93ggHmeYMjqxPuPa1oLq387TqAGngb0M4X7uRju/GKT480SvQHXhwgW10gnVDRgwAB9//LHenQKAYcOGYejQoThx4gRWrVqFoUOHYvXq1XWuz1I+Zmdnp/Z3Tc8xdlt7e/v63mINTZtKa4GdtZPagkZrx/E2LoWiymU+GbBwZFc8Fepj7m7ZFEM/439eyNOqFtTFwgr08ued5FKjV6Cyt7eHoo7b0e7evatXoKgqIiICQGU4Cw4OxrRp0/D1119j4MCBADTPAJWWlgIAXF0rP9TKmSlD2yqPVW9bWloKFxcXjW2rH9fG7du8y88UZLLKH3x5eZZ7R44lMcd4yxUC/rpyB7n3yuHl7IBQH+u+ZKEQ/lmA/s9lvv97PBBPhfrwM24iYn3GL1wr0LpdezfbLl9gZye9SQi9AlVQUBB++eUXjZf0ysrKsHnzZnTs2NHQvqlERkbCxcUFJ06cwMSJEwH8u46pKuUx5TomHx+fetsq11cp2+bk5NRY+5STk6Ox7Y0bN2oU/Kxr3VZ9BAH84WdCHG/TMtV417e5q1whWNX6EE11ppSX+fgZNy1Dxzs7X7ulIl7ODjb/7yrF96/XXX6TJk1CRkYGpk+fjt9//x0AcPXqVfz666945plncPnyZYwfP16nc96+fRuDBg3CrFmzajxWXl6OsrIyODo6om3btnB1dVWVO6hKefedspxCly5dYGdnV2fb0NBQAJV351U9Xr2tTCZT3dVXV9uMjAy4urqqFQElItOob3PXRb9dNMvdU8bCop3WIyUzF98euFxvO29XR4S0cjNBj0hXegWqRx55BK+//jr279+PuXPnAgAWLFiAmTNn4syZM/jPf/6DAQMG6HTOpk2bomHDhti9ezfOnTun9tiKFStQUVGBgQMHokGDBoiKisLRo0eRlpamalNWVoaEhAR4eXmhb9++AAAvLy/07t0bycnJyM7OVrXNz8/Hhg0b0KFDB3Tq1AlAZUhq06YNNmzYgIKCAlXbrKwsJCcnIyIiAh4eHgCA/v37w83NDQkJCSgrK1O1TUtLQ2pqKoYOHapab0VEpqHNgt4fjl6pNWxZWqhimLIe2nx2lVgLSroMqpSek5OD5ORk/P3335DL5fDx8cFjjz2G1q1b63W+o0eP4tlnn0Xjxo0RFxcHLy8vHDx4EMnJyQgLC8PKlSvh6OiIW7du4amnnkJJSQkmTpwIT09PbNy4ESdPnsTChQvV9v07d+4cYmNj4ezsjAkTJsDBwQGrV6/GtWvXsHLlSoSHh6vaHjhwAJMnT4aPjw9Gjx6N8vJyrFq1ChUVFVi7di3atm2rartp0ybMnz8fQUFBGD58OPLy8rBy5Uq4u7sjMTFRr61nWCndNKyhqrElMdV4p2YXYGpizdlobXk4NcRL/dui+T8zAFL+0qovTPEzblqGjre2n93ne7fGsz39rOqStb6kWCldUlvPAMDp06exaNEiHD16FCUlJfD19UV0dDQmTZoEB4d/F+FlZ2fjs88+w4EDB1BRUYHAwEBMmzYN/fr103jOhQsXIjU1FXZ2dggKCsKsWbM0Fvw8ePAgFi9ejBMnTsDJyQlhYWGIj4/XeAlv586dWLp0KTIzM+Hm5obevXsjPj5e782RGahMg182pmWq8U4+fROv7zgjyrmqrrmSGm1mpvgZNy1Dx1vbz+4z3Vphz7lbta4PtCVWF6i2bNmCX3/9FdnZ2bC3t0ebNm3wxBNP6Hy5jyoxUJkGv2xMy1JmqDSRWnVobS/z8TNuWqaaoaqL1D6rxibFQKXXXX5lZWWYNGkSjh49CkEQ4ObmBrlcjjNnzmDnzp0YOHAgvvzyS8ikWHmLiKxSSCs3NHdxqLeOjy4W7r2Afv7GLaKoLa6Zsl7afHbtZEBd2/mJ/Vm1trthTUGvldNfffUVjhw5gnHjxuHAgQM4dOgQjh49iv/9738YOXIkdu3ahe+//17svhIR1UqbzV11lVNYhvSrd0Q9pz4YpqybNp/d+vZGFvOzaq69BC2dXoHq559/xuOPP4758+ejadOmquPNmzfH22+/jYiICCQmJorWSSIibdS1uevYcP2qhueKOOOlD4Yp6yBXCEjNLkDy6ZtIzS6AvFpCquuz+0y3Vlq9hhif1fpKjzBU1U6vS355eXno0aNHrY8/8sgj+OOPP/TuFBGRvura3DXogSY1in7Wx8vFfBWpGaasQ33FZpVq++ymX72DtWlX630dQz+r2u4lKJXL4FKjV6Dy9/fHsWPH8Mwzz2h8/Pz58/Dz8zOoY0RE+rK3kyHM173G8apfWDcLy7Bw30UUlFTUeh5zFlFkmLIOyhmf6pQzPtUXk2v67GqzxkqMz2r61Tta7SWYfvWOxv++bJ1el/xmz56Nn3/+GUuXLkVFhfoPoy1btmDDhg145ZVXROkgEZGYlF9Yj3fyxvyB7etsa64iigxT1kHbGZ/ql/+q02aNlRifVW0vGZr7MrhU6TVDtXz5cnh4eODzzz/Hd999h9atW8PBwQGXL19GXl4eGjRogDfeeEPtOTKZDLt37xal00RE2qrrbiXlupXql2O8XR0RH+FvltvQGaash5gzPqb4rGp7ydCcl8GlTK9A9ffff6NBgwaqDYDz8vIAAA4ODqpj1ctbSax+KBHZAG3WrtS15srUGKasi9gzPsb+rJrq0qK10itQpaSkiN0PIiJR6bJ2pbY1V6bEMCU9htZiMsaMjzE/q8pLi5r+u1HiXoK10ytQXbt2DQ888ECdbXbs2KG2px4RkalY2t1KDFPSo+2deXWxxBkfKV4GtxR6LUofOnRoreuh7t27h7lz52L27NkGdYyISF+6rF0xN0sNU/XVVbJkYtViMtVicrFFtvfCtsk9sWRkMN6N6oAlI4OxdVIPhql66DVD1bhxY7z44osYPXo05s6dq9q0OC0tDXPnzsWVK1fw8MMPi9pRIiJtWcrdSpYapsSYvZEqsWc3LXXGRwqXwS2NXoFq27ZteP311/Hjjz8iLS0Nn3zyCbZv346lS5fCyckJ77zzDkaMGCF2X4mItJJdUKJVOxbt1J2udZUsjTFqMUW290KfNk2x8dg1XCkohY97Iwzv+gAcGuh1kYgkSq9A1aRJEyxatAgbNmzABx98gCFDhgAA+vTpg//7v/9T3elHRGRqcoWAzRnX623X/J9FxuZgqWHK0tam6cMYs5uaZvRWH71iFTN69C+D4rGbmxsaN24MQRAgCAKcnJzg6OgoVt+IiHSmzQwDAAwNbsnSCDqypLVp+hL7zjzujWc79ApUt2/fxsyZMzFz5kzY29tj6dKlmDhxInbt2oUhQ4bgl19+EbufRERauVlYplU7P3cnI/ekJksOU4DlrE0zhPLOvLpoe2eeWJXSyTLoFaiioqKQnJyMgQMHYtu2bejbty/mzZuHFStWoEGDBoiPj8fMmTPF7isRWTEx7hpLyczFwr0XtGpr6vVTlh6mANuopC3mnXm2MKNH/9JrDVVZWRneffddDB8+XO14r169kJSUhAULFuDXX38VpYNEZP3EuGustsXSmpi69o81hCnAMusq6UOXO/OUxT/LrtyFo0KhVvzTFmb06F96BaotW7agdevWGh9zc3PDokWLsGnTJoM6RkS2QYy7xrS5tFKVKWv/WEuYAmyrkrY227zU94uALczo0b/0uuRXNUxdunQJv//+O/Lz81FaWory8soP1tNPPy1OD4nIaom1xkTbhegeTg1Nelu/NYUpJeXsTfV1Rt6ujhZfMqE6ZS2mQR2bI8zXvUaYqm+xuZjrsUj69JqhAoD09HQsWLAA589X/jBcsWIFBEHAnDlzsGDBAjz++OOidZKIrJNYNX+0vWTyUv+2DFMikNKG0uagS/kIW5nRIz1nqDIzMzFx4kTk5uYiJiZGddzJyQlyuRxz5szBkSNHROskEVknbYPQb+fz6nxc20smzV1NU9bFmsOUUl2zN9ZOl18EbGlGz9bpNUO1ePFiNG7cGNu2bYNMJsPWrVsBAKGhodi2bRtiY2OxbNkydO/eXdTOEpF10TYI7Tx9EzP7ta31S1tKi6VtIUzZOl0Xm9v6jJ6t0GuG6vDhwxg1ahQ8PT0hk6l/ILy9vREbG4tTp7S724aIbFdIKze4N6r/97r8koo6by2Xyia0DFO2QZ/F5rY8o2cr9ApU9+7dg7d37T8k3NzccPfuXb07RUS2wd5Ohse1DBz1zQqY+9IKw5Tt4GJz0kSvS34+Pj44fvw4Ro4cqfHxgwcPolWrVgZ1jIhsQ792nlibdrXedtrMCpjr0grDlG2xpfIRpD29ZqiGDBmCzZs3qxXvlMlkUCgUWLp0KXbt2oVBgwaJ1kkisl5i/7Zv6ksrDFO2SZcZUTF2ASDpkwmCoPO/bHl5OSZOnIi0tDQ0adIEd+/ehZ+fHwoKCnDnzh106NABa9euhZOT6ffKsmR5eYVQKMzdC+snkwFeXq7IzS2E7p9+0pU2411flXPlF5SyKrVUFvZKNUzxM246qkrpdnY1KqUD4uwCQDXZ2QGenq7m7oYavQIVAFRUVOD777/Hjh07cOnSJcjlcvj4+GDQoEGYPHkyGjduLHZfrR4DlWnwy8a0tB1vTV88Vbf6kNoXk1TDFMDPuKnVNt7a/qJAurOqQEXiY6AyDX7ZmJYu413bDJTUvpikHKYAfsYNpetMqKbxlisExCw7VG8pj62TenCtlR6kGKi0WpS+b98+9O/f36AX2r17NwYMGGDQOYjIuinXP1WlS1VqU3wxST1MkWHEmgkVaxcAshxaLUp/88038eyzz+L48eM6v8CRI0cwZswYvPPOOzo/l4hIly8mY2OYsm7a7M+nLV2Lf5Ll0ypQ/fzzz2jRogVGjBiB6OhofP311zh37hwUGq5PyeVyZGRkYOnSpXj88ccxbtw4eHt7Y9u2baJ3noisn1S+mBimrJtYG3Ur6VP8kyybVpf8XFxc8P7772PYsGFYunQpFi9ejMWLF8PBwQE+Pj5wdXWFIAjIz8/HzZs3UVZWBkEQ8PDDD+P9999HaGiosd8HEVkpKXwxMUxZP7Ev0UlpOyQyDZ0Ke4aFheHbb7/FhQsXsHv3bhw6dAh///03rl27BplMhmbNmiEsLAx9+vRBZGQkWrdubax+E5GNMPcXE8OUbRB7JpTFP22PXpXS/f394e/vjylTpojdHyIiNeb8YmKYsh3GmAlVFv+sqxwIWQ+9AhURkSmZ44uJYcq2GGsm1FzbIZHpMVARkWg01e9pYC/OF4cpv5gYpmyPMWdCNZUDIevDQEVEoqitfs+cyHYY6SVOAT5TfDExTFmv+gp28hIdGYKBiogMVlsl85tF5Zi77RSaNGmE8BYuZuiZbhimrJe2BTt5iY70pVUdKiKi2mhTv+ftpFNa1+8xF4Yp66VrwU7lTOigjs0R5uvOMEVaYaAiIoNoU7/n+p1S/HXF+JXM9cUwZb3ELthJVBu9A1V5eTlOnfp3ij8jIwMzZ87E7NmzcezYMVE6R0TSp21dniNZ+Ug+fROp2QWS+vJimLJuUtq6iKybXmuorl+/jrFjx8LZ2Rlbt27FzZs3MX78eJSUlAAAdu3ahdWrV6NLly6idpaIpEfbujzLD2ar/r97owaYN7A9BgQ0M1a3tMIwZf2ksnURWT+9ZqgWL16MnJwcPP300wCALVu2oKSkBJ988gl27doFb29vLF26VNSOEpE0dWnZBO6NdPvdrKD0PuYnncai3y4aqVf1Y5iyDcYo2ClXCEjNLpDkjCuZj14zVAcOHMDo0aMxfvx4AMD//vc/eHl5ITo6GgAwYsQIrFy5UrxeEpHkyBUCVh7Kwtq0q7hbel+vc/xw9Ao6tXDFgEDTzlQxTNkOsQt21na34P8NDbKIO1nJePSaocrLy0P79u0BACUlJUhPT0ePHj1Ujzdt2hTFxcXi9JCIJCclMxePffMnvj1wWe8wpfTxnvMm/Q2fYcq2KAt21kXbgp113S047cc0pJzLreWZZAv0ClTNmjVDXl4eAODgwYO4f/8+evfurXo8MzMTzZqZd20EERmH8kulviDl7GCv1fnySypMtiCYYco2KQt2Nq92Wc/b1REfxXTSqmCnNncLfsa7BW2aXpf8goODsXbtWvj5+eGbb75BgwYNEBERgfv372PXrl3YsGEDoqKixO4rEZmZNl8qSvfK5Vqf1xQLghmmLE99lc11YWjBzvruFhTw792C3GbGNukVqGbPno1x48Zh1qxZAIDp06fD09MTf/75J1566SU88MADmDZtmpj9JCIJ0OYW9KoaN7RDcYWi3na6LAjWB8OU5dG2snlV9QUwQ7Yu4t2CVB+9ApWvry+2bduGAwcOoGXLlggODgYAtG/fHi+99BJGjBiBpk2bitpRIjI/Xb8sRof7YNmfWXW20WVBsD4YpixPXVsZzdt2SuNlOn0CmC6McbcgWRe99/JzdXXFoEGD1I55eXlhypQpBneKiKRJly8Lb1dHPPdQa5RWKPDD0Su1ttN2QbA+GKYsj7aVzfv5e6o+N/UFsA+iO8LDqaFBlw7ru1tQBqC5kX85IGnTO1AJgoCDBw/i1q1bUCg0T+kPHTpU39MTkQRpcwu6kjIozezfFr0CmuPVzcdRUFKhetzb1RHxEf6izB5owjBlmXSpbB7m665VAHtt+2lUXSuuz8yV8m5BTcFNabYRfzkg6dMrUF2+fBmTJ09Gdna22nFBECCTyVT/y0BFZF20+VJxa9QArz4WoPZlFRXcEmEtnPHXFXEWGNeHYcpy6bpWSZsAVv3Gu7ouHdZFebdg9UuL3q6OePvJzghv4QKBN/nZLL0C1aeffors7GwMGzYMXbp0gYMDrxkT2YravlSaNGqAUd1a4dmefhqDkiELgnXBMGXZdF2rZMgi8OqXDrWh6W7BUB83eDdvgtzcQr37QpZPr0B18OBBPPPMM3jjjTfE7g8RWQBDb0E3FoYpy6drZXNDFoHrW+ag+i8HMl7lI+hZ2PP+/fvo2LGj2H0hIgui/FIZ1LE5wnzdGaZIFLpWNlcGMH2xzAGJRa9A1blzZ5w8eVLsvhAR6YVhyrroUtlcmwBWF5Y5ILHodcnvP//5D6ZMmYJevXrhscceg4zznURkJgxT1kmXy8q1reuzk9VckF6VsWugkW3RK1CtWLECbm5umDVrFho1agQPD48aoUomk2H37t2idJKISBOGKeumy40MmgJYfnEF5m8/XetzjFkDjWyPXoHq3LlzsLOzQ8uWLVXHhGr3ilb/OxGRmBimqDpNAczOTqaxzIExa6CRbdIrUKWkpIjdDyKyApr2UmtgL/4MAMMUaUuqd6SS9dG7UrqSIAjIz8+Hg4MDXFxcxOgTEVmg2vZSmxPZDiO9XEV7HYYp0pWpaqCRbdM7UOXn5+OTTz7Br7/+inv37gEAXFxcMGjQIMTHx3NzZCIbUtdeanO3nUKTJo0Q3sLwX7gYpohIqvQqm3D37l2MGjUKmzZtgqenJyIjI9GvXz94eHhg48aNiI2NRVFRkdh9JSIJ0mYvtbeTTkFe1+1WWmCYIiIp02uG6ptvvkFWVhbeeecdjBgxQu2xjRs3YsGCBfj2228xe/ZsUTpJRNKlzV5q1++U4q8rulekVmKYIiKp02uGateuXXjyySdrhCkAGD58OIYOHYpff/3V4M4RkfRpvZntPf0qUjNMEZEl0CtQ3bhxAyEhIbU+3rVrV1y/fl3fPhGRBdF6M1tn3StSM0wRkaXQK1C5ubnh2rVrtT5+5coV3vFHZCO02UutpVsjhProVpGaYYqILIlegapnz55Ys2YNLl26VOOxCxcuYO3atejRo4fBnSMi85MrBKRmFyD59E2kZhfUWFyuzV5qb0Z30qnuD8MUEVkamaBHSfMLFy5g2LBhEAQBTz75JPz9/SGTyZCZmYlt27ZBJpMhMTERAQEBxuiz1crLK4RCYe5eWD+ZDPDyckVubiFY0L9utdWWmh3ZrkaVaU1tvV0dMTvCHyN7t9F6vBmmDCfVz7imwq/WUGBTquNtzezsAE9P8erbiUGvQAUAR48exauvvoqsrCy1476+vnj33XfRs2dPUTpoSxioTIM//LRTW20ppY9iOtUIVbVVStd2vBmmxCHFz7gu4dzSSHG8rZ1VBSqgskr6qVOnkJWVBUEQ4Ofnh06dOsHOTq8riTaPgco0+MOvfnKFgJhlh+osh+Dt6oitk3rUO8Og7XgzTIlHap9xfcK5JZHaeNsCKQYqg7aekclk6Ny5Mzp37ixWf4hIArSpLZVTWIb0q/rXlqqKYcp6aVP4deHeC+jn72kVl//IdmkVqObPn49Ro0aha9euqr/XRyaT4f333zesd0RkFlrXltKyXV0YpqybqcM5kbloFag2b96M3r17qwLV5s2b630OAxWR5dK6tpSW7WrDMGX9TBnOicxJq0C1Z88etc2O9+zZY7QOEZH5KWtL1beGKqSVbrWlqmKYsg2mCudE5qbV6vFWrVrByclJ9fdr167ByckJrVq10vhHJpPh8OHDenXo7Nmz+M9//oOHHnoIQUFBiIyMxHvvvYfCwkK1dteuXcPcuXPRp08fhISEYOTIkbUGvczMTEyfPh29evVCaGgoxo8fj9TUVI1t09LSMGHCBHTv3h3h4eGYOnUqzp/XfP1/7969GDVqFLp164aePXtizpw5uHHjhl7vm0hKtKktFR/hr/eaF4Yp26FN4VdDwzmRFOh1O964ceNw4MCBWh//448/8NZbb+l83osXL2LUqFE4cOAAYmNj8frrr6NHjx748ccfERcXh+LiYgDArVu3MGbMGOzevRvDhg3D3Llzcf/+fUyfPh1JSUlq57xw4QLi4uKQkZGBsWPHYtasWbhx4wbGjx9fI/QdPnwY48aNw/Xr1zFlyhQ8//zzOHHiBGJjY3HhwgW1tklJSZg2bRoqKiowc+ZMjBkzBnv37kVsbCzy8vJ0fu9EUhPZ3gsfxXSq8WXo7epo0F1ZDFO2xdjhnEgqtCqbkJWVhW+++Ub1982bN6N79+7w8fGp0VYQBBw+fBilpaV1hi5NnnvuORw6dAibNm1SKwqakJCA9957Dy+//DImTZqEN998E+vXr8eaNWvQrVs3AEBZWRlGjhyJnJwcpKSkoHHjxgCASZMm4ciRI9i+fTt8fX0BAPn5+YiJiUGTJk2wfft2yGQyCIKA6OhoFBQUYPv27XB3d1e99+joaPTo0QPLli0DABQXFyMyMhLe3t5ITEyEo6MjgMrZrbi4OIwaNUqvQMmyCabBW5x1Y2gxxqrjLVcwTJmCOT/jtX1eaiv8Gh/hb9ElEwD+TDEHiy2b4Ofnh8uXLyMtLQ1A5YLzI0eO4MiRIxrb29vb4+WXX9apI+Xl5Th69CjCwsJqVFgfOnQo3nvvPRw5cgQTJ07Etm3bEBISogpTAODo6Ihx48bh1Vdfxb59+xAVFYXc3Fzs378fQ4YMUYUpAPDw8MCIESPw3//+FxkZGejatSuOHz+OzMxMTJ06VRWmlO998ODB2LZtG27evInmzZtj3759yM/PR3x8vCpMAUC3bt0QHh6OpKQkvPrqq3Bw4JoAsnz2djKWRiCt1Fe8s5+/p1VWSicCdKhDtXTpUty5cweCIGDAgAF49dVX8eijj9ZoZ29vDw8PD7WgoVVHGjTA9u3boWnCLDc3FwBgZ2eHzMxMFBcXIyQkpEY75V2Ix44dQ1RUFI4dOwYA9bbt2rUr0tPT1Y5XFRwcjC1btuD48eN49NFHVeetre2RI0dw8eJFdOjQof43TmQDFAoBHzJMWbXainfeLCrHvG2nVJeJxQjn1rqFDVk2rQOVi4sLXFxcAAAffPABunfvjlatWonWETs7O7VZpKpWrFgBoHJT5pycHABAy5Yta7Rr0aIFAODKlSsAoFogrqmtt7e3xrYPPPCA1uetry0DFVHlzNTrW09g0zGGKWulTfHOz1LOi1K805q3sCHLptei9KeeegpFRUWYNWuW2gLsDz74ADNmzMDff/8tVv+wZcsWbNiwAS1btsSIESNUd/sp10hV1ahRIwBASUkJAKCoqAgA4OzsXKOt8q7F6m01ndeQtrqQyfjHVH843qb5I6DyMt+aQ1mQAXg7KhBPdPY2e79s4Y8pP+PaFO+8WVSOlYeyDHqdvf/MglV/LeUs2N7MXJsYb/75d8ylRK+tZ06dOoXRo0ejvLwcU6ZMgaenJ4DKdUz79+/HkSNHkJiYiNatWxvUuc2bN+O1115D48aNsWjRIjg7O2u8JKikfEy5l6Dy75qeY+y29vb29b29Gpo2ldYCO2sntQWN1kahqJyZ+unYdchkwMKRXfFUaM0bWch4xP6MyxUCDl+6jZuFpWju2gg92jSFvZ0MZVfuavX8bw9cRmhbTwwOqnnVQJvX/vy32svxyAB8/ttFDHvoQbNd/uPPFNumV6BavHgxXF1dsXLlSvj7+6uOx8fHY/jw4RgzZgwWLVqEzz77TO+O/fe//8WiRYvg6uqKJUuWIDg4GMC/s02aZoBKS0sBAK6urqK2VR6r3ra0tFR1GbR62+rHtXH7Nu/yMwWZrPIHX16edO/IkSsE/HXlDnLvlcPL2QGhPpa1RkQh/LNm6p/LfAtHdkVfPzfk5hbW+1wynDE+4ynncvGphkttcyLboUkj7b9K3tx6EqHNnXX+PB/NKsD1O6W1Pi4AuH6nFLvSryDcz12ncxvKEn6mWBs7O+lNQugVqDIyMjBhwgS1MKXk5+eHuLg4rFmzRq8OVVRU4I033sCmTZvg7e2NpUuXqq1FUpZq0FRAU3lMuY5Jm7bK9VXKtjk5OTXWPlVft1X1vO3aqddXqWvdVn0EAfyP0YSkOt6Wvkak+t18b0cF4qlQH95SbgZifcbrWnA+d9spfDCkY72V9ZVyCsvw1xXd9+3TZQsbc33OpPozxRpJcZz1WkNVXFxc5118rq6uuHPnjs7nlcvlmD17NjZt2oTAwEBs2LChRrhp27YtXF1dkZGRUeP5yrvvlOUUunTpAjs7uzrbhoaGAoBqBkx5vHpbmUymuquvrrYZGRlwdXXVGDaJ6pNSzxqRlMxcrc4jVwhIzS5A8umbSM0ugFxhmp8+LI1gfbRZcP7FbxfxUoT2P/P02bePW9iQ1OkVqNq2bYtdu3bV+nhKSgoefPBBnc/75ZdfIjk5GcHBwVi9erXqTryqGjRogKioKBw9elRVFwuoLOyZkJAALy8v9O3bFwDg5eWF3r17Izk5GdnZ2aq2+fn5qrDWqVMnAJUhqU2bNtiwYQMKCgpUbbOyspCcnIyIiAh4eHgAAPr37w83NzckJCSgrKxM1TYtLQ2pqakYOnSoar0Vkba0+eJauPdCveEoJTMXMcsOYWpiBl7fcQZTEzMQs+yQ1mFMXwxT1kmbBec5hWXwcGqI53trt25Wn9DDLWxI6vT61h8+fDiOHDmCuXPn4syZM6ioqMD9+/eRmZmJ119/HQcOHMDw4cN1Oue1a9ewfPlyyGQyDBw4ECkpKdi6davanz/++AMA8OKLL8LLywuTJ0/GV199hbVr1yIuLg7nzp3Da6+9pjZ7Nm/ePMhkMjzzzDP47rvvkJCQgFGjRuHOnTtYsGCBWh/eeOMN3L59G7GxsUhISMB3332HuLg4NGrUSK1QqZOTE1555RWcOXMGcXFxWLt2Lb766itMnjwZrVq1wrRp0/QZVrJx2n5xpV+tffZXrBkuXTFMWS9dLrU929PPaKGHW9iQ1Gm19Ywmc+fOxbZt2yCrdu+iIAh44okndF6QvmXLFsybN6/ONj169MAPP/wAAMjOzsZnn32GAwcOoKKiAoGBgZg2bRr69etX43mnT5/GwoULkZqaCjs7OwQFBWHWrFkaC34ePHgQixcvxokTJ+Dk5ISwsDDEx8drvIS3c+dOLF26FJmZmXBzc0Pv3r0RHx+vWsOlK249YxoymTS3iUg+fROv7zhTb7t3ozpgUMfmNY7LFQJilh2qM5R5uzpi66Qeon7p1BempDre1kzMMU/NLsDUxJrLJqpbMjIYYb7uta63UjJkH0hA8xpDc29hw8+46Ulx6xm9AxVQGT52796NK1eu4P79+/Dx8cGAAQPQp08fMftoMxioTEOqP/x0/eIS+/n60GZmSqrjbc3EHHN9grqxQ4/UKqXzM256UgxUet3lp/TQQw/hoYceEqsvRDZNuUakvi+u2i6X6HJpRgy8zGcblJfa6pp1qn6pzdj79om1vySRmLQKVNeuXUPTpk1VlcivXbum1ck1bc1CRJrp88VVlSnvgmKYsi2R7b3wUUwnnWadGHrI1mgVqB599FF8/PHHiI6OBgBERkbWWDtVnUwmw6lTtX8xEFFN+nxxKRk6w6UthinbZOxZJyJLp1WgGjp0KPz8/NT+Xl+gIiL96PvFZegMlzYYpmwbZ52IamfQonQSFxelm4a1LyA11oJgfcOUtY+3FHHMTYvjbXpWtyidiKTHGJdmODNFRFQ3rQLVuHHjdD6xTCbD999/r/PziMhwYl6aYZgiIqqfVoHq8OHDNY7JZDIIggCZTAYvLy8IgoC8vDwIggAnJye4ubH8P5GlY5giItKOVoHqzBn16s0nT57EuHHjMGrUKEyePBnu7u4AgKKiIixfvhzff/89Fi5cKHpnich0GKaIiLSn115+H3/8MR5++GG8/PLLqjAFAC4uLpg5cyYGDBiAjz/+WKw+EpGJMUwREelGr0B17Ngx9OrVq9bHQ0NDcfr0ab07RWQr5AoBqdkFSD59E6nZBZArzH+LEMMUEZHu9LrLz9XVFZmZmbU+fuzYMTRt2lTvThHZAk3lDZq7OGB2ZDuzbfLKMEVEpB+9ZqgiIiKQmJiIjRs3omoZq/v372P58uXYunUrHn/8cdE6SWRtUjJzMW/bqRpVzW8WlWPetlNIycw1eZ8YpoiI9KdXYc/8/HyMGjUKWVlZaNKkCVq1agUAyMrKQlFRETp37owff/wRTk5OonfYmrGwp2mYuwifXCEgZtmhereI2Tqph8m29TBmmDL3eNsijrlpcbxNT4qFPfWaofLw8MDmzZvxwgsvwNvbG5cuXcKlS5fg6+uL+Ph4rF27lmGKqBbpV+/UGaYAIKewDOlX75ikP5yZIiIynN6V0hs3bowZM2ZgxowZYvaHyOrl1hOmdG1nCIYpIiJxGLT1TE5ODvbt24erV69i2LBhaNy4MXJychAUFCRW/4isjpeLg6jt9MUwRUQkHr0DVUJCAj799FOUl5dDJpOhV69eKCsrw7Rp0zB69Gi8/vrrYvaTyGqEtHJDcxeHetdQhbQy3m4DDFNEROLSaw3V3r178f777yM0NBRvv/226k6/tm3bIjQ0FKtXr8amTZtE7SiRtbC3k2F2ZLs628RH+BttQTrDFBGR+PQKVMuXL0enTp2wYsUKPPbYY6rjfn5+SEhIQFBQENauXStaJ4msTWR7L3wU0wnNq13W83Z1xEcxnYxWh4phiojIOPS65Hfy5EnMmDED9vb2NU/YoAGefPJJfPnllwZ3jsiaRbb3Qj9/T6RfvYPconJ4uTggpJUbZ6aIiCyQ3muoHB0da32svLwc9+/f1/fURDbD3k6GMF93o78OwxQRkXHpdckvICAAe/fu1fiYQqHAjh070L59e4M6RkTiYJgiIjI+vQLVmDFj8Mcff+Ddd9/FhQsXAABlZWU4efIkpk+fjpMnT2LkyJGidpSIdMcwRURkGnpd8ouOjsaZM2ewfPlyrF69GgAwbdo0AIAgCBg+fDiGDx8uXi+JSGcMU0REpqP3GqqXX34ZgwYNws8//4y///4bcrkcPj4+GDRoEHr16iVmH4lIRwxTRESmpVeg+vzzz9G3b1+EhYUhODhY7D4RkQEYpoiITE+vNVQJCQlITU0Vuy9EZCCGKSIi89ArUDVu3FhjDSoiMh+GKSIi89ErUMXHx2Pp0qVYt24dcnJyoFAoxO4XEemAYYqIyLxkgnIjPh1ER0fjypUrKC0trf3EMhlOnTplUOdsTV5eIZhNjU8mA7y8XJGbWwjdP/3SI/UwZW3jbQk45qbF8TY9OzvA09PV3N1Qo9eidHd3d7i7u4vcFSLSldTDFBGRrdArUP3www9i94OIdMQwRUQkHTqvoSosLER+fr4x+kJEWmKYIiKSFq0D1R9//IGYmBj06NEDvXv3xsCBA7FlyxYjdo2INGGYIiKSHq0u+f3111+YMmUK5HI52rVrB3t7e1y8eBHz589HaWkpRo0aZex+EhEYpoiIpEqrGarly5ejSZMm+Omnn5CUlIQtW7YgOTkZHTt2xNdff23sPhIRGKaIiKRMq0B17NgxxMXFoVOnTqpjLVu2xMyZM3Hr1i1kZ2cbrYNEBFTIFZiz5aQqTL0xmGGKiEhKtApU+fn5aNWqVY3jHTp0gCAIuHnzpugdI6JKu8/dQuRXB7D/4m0AgADgm98vISUz17wdIyIiFa3WUN2/fx8NGtRs6uDgAAAoLy8Xt1dENkquEJB+9Q5yi8rRtHFDpF0pwHcHa84A3ywqx7xtp/BRTCdEtvcyQ0+JiKgqvepQEZH4UjJz8emeTNy6V6H1cxbuvYB+/p6wt5MZsWdERFQfvfbyIyJxpWTmYt62UzqFKQDIKSxD+tU7RuoVERFpS+sZqqNHj0Iul6sdu3fvHoDKGlU5OTk1njN06FDDekdkA8rvK/D2L2f0fn5uES+5ExGZm9aBKjExEYmJiRofW758udrfBUGATCZjoCKqx+6zt/Bu8lkUV+i/K7aXi4OIPSIiIn1oFahmzJhh7H4Q2ZxFv13ED0evGHQOb1dHhLRyE6lHRESkLwYqIjPYfe6WwWEKAOIj/LkgnYhIAniXH5GJyRUC3jFgzRQAeLs4Ij7SnyUTiIgkgoGKyMRSswtQfF/Q+/nP926NZ3v6cWaKiEhCGKiITCztSoFez/N2dUR8BGeliIikiIGKyMQEPSanXurfFrGhrTgrRUQkUSzsSWREcoWA1OwCJJ++idTsAsgVArr56HZXnrerI8MUEZHEcYaKyEhSMnPxWcp53KxSeNPV0R6t3J10Og/v5CMikj4GKiIjUG4lU11hmRxncoq0OgfXTNmmqhtke7k4IKSVGwM1kQVgoCISmVwh4O1fzmrVtomjPe6W/bulk3NDOwzp0gIR7bz4RWqDNM1qNndxwOzIdgzWRBLHQEUksuUHL6O4Ql5/QwBODRvgw+hOuF1cwdkIG1fbrObNonLM23YKH0R3hIdTQ85cEUkUAxWRiMrvK5BwOFvr9jlFZbCzk2FQx+ZG7BVJnVwh4LOU83W2eW37aSiq3CHKmSsiaWGgIjKQcs3Lb+fzsO3EDZTJdauLkFvl8g6ZllTWK6VfvaN2mU8TRbWPlXLm6qOYTgxVRBLAQEVkgJTMXHy6JxO37lXofQ4vFwcRe0TaktJ6JUNC9cK9F9DP35OX/4jMjHWoiPSkXPNiSJjycGqIkFa61aUiwyn/7arPCilnfVIyc03aH0NCdU5hGdKv3hGxN0SkDwYqIj3IFQLe+/WcwecZ3LE5ZxZMTJv1Sgv3XoC8+jU2Iwpp5YbmBoQqXjYmMj8GKiI9HMnKx93S+wafp187TxF6Q7rQZr2SqWd97O1kmB3ZTu/n87IxkfkxUBHpqLbb23XV/J9F0LZK07Y8xnyekrazOaae9Yls74WPYjrVmKmqbwLT29XRpj9HRFLBRelEOhArTAHA7Mh2Nnu5T98F4WIsJNd2Nsccsz6R7b3Qz99T7c7D/OIKzN9+utbncGsiImngDBWRlsrvK7BgxxmDz+PWqIFN3+qu74JwsRaSa7NeyZyzPvZ2MoT5umNQx+YI83XHgMBmGmeuvF0dbfpzRCQ1nKEi0sLuc7ew4OfTuK/Q7/ldH3BFmK87wn3d0c3X3WZnFLRdEF69DIC+z9NEuV6prplGqc36aJq5YqV0ImlhoCKqxxf7LmB16lWDzjGl94Po3tpDpB5ZLl0WhIf5uhv8vNoo1ytVv3wo5Q2plTNXRCRNDFREdfjiN8PDlLODPbrxixCA/gvCjbGQnLM+RCQmBiqiWuw+dwurjxoWpgDg9UEB/JL+h74Lwo21kJyzPkQkFgYqIvx7K37alQIIAhDq44YPdxleuHNsuA8GBDQToYfWQbkgvK7Ld5oWhOv7PCIiU2GgIpuXkpmL9389hztVC3UeyjbonB5ODTH30XYYEMgwVZW+C8ItcSE5EdkWBiqyaWLWlQrzdcNTXVpyLU499F0Qbu6F5HKFwPVWRFQrBiqyWdrciq+txg3t8d/hwfyC1ZK+C8LNtZBcjIKiRGTdGKjIZv11pf5b8bX15uOBDFNVVJ/NGdjUpUYbfReEm3oheW2zmMqCoiyuSUQAAxXZsNx7hocpGYD3h3TkF2oVmmZzWiafw0v92iLin3GylMtnYhYUJSLrxkBFNsvL2fC92t4d0oELz6uobTbnxp1SzP1nNgeAxVw+E7ugKBFZLwYqsklyhYANfxlWY2psuA8eC2wuUo8sX12zOcI//1vjbsp/iHn5TMzZL2MUFCUi68RARTYn5Vwu3tr5O4rL9duYTwbg3Sc64LEODFNVaTOboylMVWXo5TOxF48bq6AoEVkfO3N3gMiUUjJzMXfbKb3DFAC8H92RYUoDMWZplJfP9KG83Fg91Clnv1Iyc3U+p7KgaF1YUJSIAIkHqmPHjqFjx444dOhQjceuXbuGuXPnok+fPggJCcHIkSOxZ88ejefJzMzE9OnT0atXL4SGhmL8+PFITU3V2DYtLQ0TJkxA9+7dER4ejqlTp+L8ec2XMfbu3YtRo0ahW7du6NmzJ+bMmYMbN27o/4bJqMrvK/DuzjN6P9+tUQN8FNOJlc9rIdYsjT7BTNvF43KFUGeb6pQFRevCgqJEBEg4UP3999944YUXoFDUnEm4desWxowZg927d2PYsGGYO3cu7t+/j+nTpyMpKUmt7YULFxAXF4eMjAyMHTsWs2bNwo0bNzB+/HgcPnxYre3hw4cxbtw4XL9+HVOmTMHzzz+PEydOIDY2FhcuXFBrm5SUhGnTpqGiogIzZ87EmDFjsHfvXsTGxiIvL0/8ASGDpGTmIuKrP1Cox8yUg70Mi4cFIXlaL8ktmpYSbWZztKFPMNNl8biulAVFq783b1dHlkwgIhWZIAi6/cpmArt27cJrr72GO3cqf/glJCSgZ8+eqsfffPNNrF+/HmvWrEG3bt0AAGVlZRg5ciRycnKQkpKCxo0bAwAmTZqEI0eOYPv27fD19QUA5OfnIyYmBk2aNMH27dshk8kgCAKio6NRUFCA7du3w93dHQCQlZWF6Oho9OjRA8uWLQMAFBcXIzIyEt7e3khMTISjoyOAytmtuLg4jBo1Cm+99ZbO7zsvrxAa8iPpoerC5KyCEiw9cFnvcz3fuzUm92otYu+sV213+clQuTC9SaMGuFvHOipvV0dsndRD5xmf5NM38fqO+mcf343qgEEd9btcaymlHgBAJgO8vFyRm1sI6f2Etz4cb9OzswM8PV3N3Q01kpuhev755zFjxgw0a9YMQ4YMqfG4XC7Htm3bEBISogpTAODo6Ihx48YhPz8f+/btAwDk5uZi//79GDBggCpMAYCHhwdGjBiB8+fPIyMjAwBw/PhxZGZmYtiwYaowBQB+fn4YPHgwfv/9d9y8eRMAsG/fPuTn52P06NGqMAUA3bp1Q3h4OJKSklBezrt+zCUlMxcxyw5hamIGXt9xxqAw5exgj2d7+onYO+tW22xOC7dG+DimE157LKDO5+t7+cwUi8eVBUUHdWyOMF93yYYpIjIPyQWqixcvIj4+Hps3b8aDDz5Y4/HMzEwUFxcjJCSkxmNdu3YFULn2qur/atM2PT1d7XhVwcHBUCgUOH78uNpzamtbVFSEixcv1vEuyVhqW5isr5igFvzi1FFkey9sm9wTS0YG492oDlgyMhi/z4tEZICX0S6fcfE4EZmb5Mom7NixAw4Otf9gzMnJAQC0bNmyxmMtWrQAAFy5cgUAVAvENbX19vbW2PaBBx7Q+rz1te3QoUOt70MTmazyD+lHzL35lPq18zT5v4lcIeCvK3eQe68cXs4OCPWR7qWl2jSwlyHczx1A5Wfa3k6mGsdHA7zQv52nqO+xgb0McyLbYW4dG13PjvBHA3vLGkd9KceaP09Mg+NtelIca8kFqrrCFAAUFhYCgGqNVFWNGjUCAJSUlAAAioqKAADOzs412jo5OWlsq+m8hrTVRdOm0roebGn+vJAn2swUALR0a4SBIT4mDTM7T1zH20mncP1OqVo/3ozuhMFBNX8xsCTV1zsMbt5E1POP9HJFkyaNrHb89CG1NSbWjuNt2yQXqOpT1xp65WN2dnZqf9f0HGO3tbe3r++t1HD7NhelG2Jbarao53upX1vk3y4S9Zx1STmXq3GG5fqdUkz9MQ0fx3RCZIDl3VEmk1V+0eTlGX/BbngLF2x5rrvG2a/c3ELjvriEmHLMieNtDnZ20puEsLhApZxt0jQDVFpa+Vupq6urqG2Vx6q3LS0thYuLi8a21Y9rQxDA/xj1lJKZi7Vphm0lo+Tt6oj4CH9EtPcy2b+HXCHg03ouV3629wL6WvAmvKb6fNvJZDX21bPV/674M8W0ON6mI8VxtrhA5ePjAwAaC2gqjynXMWnTVrm+Stk2Jyenxtqn6uu2qp63XTv1on91rdsi4xBj7dTAwGbo5+9pttvhuQkvEZFlk9xdfvVp27YtXF1dVeUOqlLefacsp9ClSxfY2dnV2TY0NBRA5d15VY9XbyuTyVR39dXVNiMjA66urvD399f5vZF+tAkjdflqWBDeH9LRrLfDcxNeIiLLZnGBqkGDBoiKisLRo0eRlpamOl5WVoaEhAR4eXmhb9++AAAvLy/07t0bycnJyM7+d31Nfn4+NmzYgA4dOqBTp04AKkNSmzZtsGHDBhQUFKjaZmVlITk5GREREfDw8AAA9O/fH25ubkhISEBZWZmqbVpaGlJTUzF06FDVeisyPkNChodTQ4T7eYjYG/1wE14iIstmcZf8AODFF19ESkoKJk+ejIkTJ8LT0xMbN27EuXPnsHDhQrVim/PmzUNsbCyeeeYZTJgwAQ4ODli9ejXu3LmDL774Qu28b7zxBiZPnozY2FiMHj0a5eXlWLVqFRo1aoSXX35Z1c7JyQmvvPIK5s+fj7i4OAwfPhx5eXlYuXIlWrVqhWnTpplqKAiAR+OGej937oB2kliTpKyjVNdMG+soERFJl0VOozRr1gxr167FI488goSEBHz88cdo2LAhvv32W0RFRam1DQgIwJo1a9CxY0d8/fXXWLRoEVq2bIkffvgB4eHham179+6N5cuXw8vLC5999hm+++47dO3aFWvWrEHbtm3V2j799NP48ssvIQgC3n//faxbtw6PPvooVq9eDU9PT6OPAVVKyczFW7/ot+Hx2HAfyWx0zE14iYgsmyT38rNV3MtPO3KFgNTsAvx07DpSMnN1fr4MwOJRoXjIx1Vyd4qkZObis5TzajNVyrsOLXUTXu5zZnocc9PieJueFPfys8hLfmS7dp+9hXd/PYd75XK9zzGplx+GhDwgybpEke290M/f02I24SUiokoMVGQxFv12ET8cvWLQOZwd7DGpV2uRemQcyk14iYjIcjBQkeTJFQKWH7xscJgCgNcfC+BsDxERiY6BiiQtJTMXn+7JxK17FQafa2y4DwYESmMROhERWRcGKpKslMxczNOwt52uPJwaYu6AdpK5o4+IiKwPAxVJkhjbyQDAS/3bIja0FS/zERGRUVlkHSqyfoZuJwNUlhtgmCIiIlNgoCJJEmPPOhbCJCIiU2GgIkm6nF+s93NlAD4Y0tFiC2ESEZHl4RoqMhu5QqhRwBIAFvx8GrvO6V4BXen96I5cgE5ERCbFQEVmsfvsLXy05zwKSv4th9DYwR735QqUy/Xfu+EDhikiIjIDBioyudoqnhcbsJ0MADz3kC/DFBERmQXXUJFJ7T53S5SK59U1bmiPyb0eFP28RERE2mCgIpORKwR8tCvTKOd+8/FA3tFHRERmw0t+ZDLpV++goPS+qOf0dnFEfKQ/7+gjIiKzYqAikxGjtpTSI/5NMTrMByGt3DgzRUREZsdARSbj5eIg2rlGhbZCmK+7aOcjIiIyBNdQkcmEtHJDM+eGBp/HrVEDhikiIpIUBioymd8u5BlUY0rp1ccCeJmPiIgkhZf8yOjkCgHL/7yMZQezDDqPW6MGePWxAC5AJyIiyWGgIqNKyczF+7+ewx0D7u5zsJdhQk8/PNvTjzNTREQkSQxUZDQpmbmYt+2U3s8P92mCCT39EO7nwSBFRESSxkBFRiFXCPgs5bxB5xga/AB6PthUpB4REREZDwMViUKuEJB+9Q5yi8rh5eIAuULATQPrTolZZoGIiMiYGKjIYCmZufgs5bxagHJqaNgNpO6NGiCklZuhXSMiIjIJBioySG3rpEoqFAadd97A9lw3RUREFoOBivQmxjopTQYGNsOAgGain5f+Vf0SLbfwISIyDAMV6S396h2D10lV18TRHu9EdRD1nKRO0yXa5i4OmB3ZjjW+iIj0xErppLebhWWin/O1QYGcKTEi5SXa6kH4ZlE55m07hZTMXDP1zHbIFQJSswuQfPomUrMLIFcYvnsAEZkfZ6hIb4ezCkQ7V+OGdnjz8Q6cITEibS7RLtx7Af38PRlqjYSzg0TWizNUVKu6fpOWKwTsOXdLtNf69MnO/EIxMm0u0eYUliH96h0T9ci2cHaQyLpxhoo00vSbtHujBni8kzf6tfNEhVxh8J18St6ujujm6y7Kuah2uVqud9O2HWmPs4NE1o+BimqorRRCQel9rE27irVpV9FAxLnN+Ah/fomYgLaFUllQVXy6zA6G8ZcLIovES36kRttSCPdFmJxq7uKAj2I68VKfiYS0ckPzesKSt6sjC6oaAWcHiawfZ6gIwL91iQ5fzhe9FIImU3q3xsSefpyZMiF7OxlmR7arc8NqzhYaB2cHiawfAxVpXC9lTB9Ed2ThTjOJbO+Fj2I61fj39nZ1RHyEP2cLjUQ5O1jXf2OcHSSybAxUNq629VLG8sEQhilzi2zvhX7+njUqpQNAanYBq6cbAWcHiawfA5UNM9bWMZpwBkRa7O1kaoufWR/J+Dg7SGTdGKhsmDG2jtHkpf5tERvair99S1Rts5TK+ki8cUA8tc0O8r8NIsvHQGXDTHVHkWdjB35hSBTrI5le9dlBIrIOLJtgw0x1RxHvXJIuVk8nIhIHA5UN06YukaHcGzXgnUsSxvpIRETiYKCyYco7j4xp3sD2vFQkYayPREQkDgYqGyZXCHBr1ADPdGsFt0b2op9/bLgPSyRIHKunExGJg4vSbZQxi3m6N2qAeQPaY0Agw5TU6VsfSVlZn3eqERFVYqCyQbvP3cL8pNNGOTdLJBjO1GGltvpIbo0aYFS3Vujn76nW3hJqVjHwEZGpyQRBEMzdCaqUl1cIhQibDtdl99lbmL/dOGHK29URWyf1kPwXl0wGeHm5Ije3EFL79JszrMgVAlYcysK61Cu4WybX+Pr1VdbXVLPK1ONtCYHP2KT8GbdGHG/Ts7MDPD1dzd0NNVxDZUNSMnONFqYAbp1hKGVYqX4ZVllgMyUz16iv/9uFPCw9cFktTFV9/d1nb2lVs0quMN83irnHkIhsFwOVjZArBHy6J9Mo5/Z2dWQ1bQNpW2DTWGFFm9f/eE/9a+7MWbPK3GNIRLaNa6hsgFwhYP1fV3HrXoXo537uIV9M7vUgZ6YMpEuBTWNU2dbm9fNLtPv8mKtmlTnGkGu1iEiJgcrKGfNuPgDo7ufBLxARmLvAppjnNVfNKlOPIddqEVFVvORnxWpbTyIW1icSj7kLbGp7XvdGdf8OZs7PhCnHkGu1iKg6Biorpc16EkNxEbp4zF1gU9vXnzewfZ1tzPmZMNUYcq0WEWnCQGWltFlPoi87GfDBkI68rCEibbYBMmZY0fb1BwQ0w0cxnWoEFyncmGCqMeSG0kSkCddQWQhdF78ac2Hwe0M6cksZI6itwKa3qyPiI/yNHla0ff3I9l7o5+8pycXYphhDc693IyJpYqCyALosfpUrBKRlF+CnY9dE74epvthtmbnDiravb28nM8rdhmIw9hiae70bEUkTA5XE1VaZWrn4tepllpTMXLybfBaF1Qoz6is29AE84NYIHk4N0fyftSfG/GLnLeiVzB1WzP36YjDme1Cu1arrsh9v2CCyPQxUEqbt4tc+bZpi5eEsfPdnliiv28TRHq8NCjTpTBRvQSdLoe+G0kRk3biXn4RU38svNbsAUxMz6n1e44Z2KK4QZxPAyb388NxDrU36ZaDP/nCGqG3fLc6QGYe17nOm6ZcAqVwWt9YxlyqOt+lJcS8/zlBJmLaLWsUIU26NGuDVxwJM/kWg7SxcP39Po4YbzpCRrsy93o2IpIWBSsJMtah1Yg9fTHnYPNvHmHvLFUC3dWpEVVnDejMiEgfrUEmYNoUKxdDzQfNtH2PuW9BZpJGIiMTAQCVh2hQqNJR7owZmvRvJ3Legs0gjERGJgYFK4pSFCo01UzUi9AGzrvkw95Yr5p4hIyIi68BAZQEi23th2+Se+O/wLghu6SLquVt7NBb1fLoy95Yr5p4hIyIi68BAZSH2ns/Fa9tPI+N6kajnlUJQqG0WzhT7w5l7hoyIiKwD7/KzAIt+u4gfjl4R/bxSCgrmugWdRRqJiEgMDFQSt/vsLaOEKUB6QcFct6Cbe1NiIiKyfAxUEiZXCPhoT9239GtDBqDqTf8MCjWxSCMRERmCgUrC0q/eQUFJhcHneX9IR3g0bsigUA8WaSQiIn0xUEmYobfqe7s4Ij6SM1FERETGxkAlYfregdfQXoYvngpCmK87Z6KIiIhMgGUTJEzfrWfefaIjerQ233YyREREtoaBSsJ03XrGFHWbiIiIqCZe8pO4/u080bO1Bw5dzlc77uHUEHMi/eHp7MDF5kRERGbGQCVhCkHAR7vP49DlfMgATOjpC39PZ4YnIiIiiWGgkihlmNqUcR0yAG89HoioTt7m7hYRERFpwDVUBsrPz8c777yDiIgIBAcHIyYmBhs3bjTonAxTREREloUzVAYoLi7Gc889h3PnziEuLg5t27bFzp078dprryE3NxdTp07V+ZwMU0RERJaHgcoAP/74I06ePImFCxfiiSeeAADExsZi8uTJ+Oqrr/Dkk0+iZcuWWp+PYYqIiMgy8ZKfAbZs2QJvb29VmAIAmUyGSZMmoaKiAklJSTqdb+HeiwxTREREFoiBSk+FhYW4ePEiunbtWuMx5bGMjAydzpl0IodhioiIyAIxUOkpJycHgiBovKTn5OQENzc3XLlyRadzMkwRERFZJq6h0lNhYSEAoHHjxhofb9SoEUpKSnQ655uPt0dE+2YG943qJvunfJedHSAI5u2LLeB4mx7H3LQ43qYnk2AZRgYqPQn//Fcj1PJfjyAIsLPTbQJw+ENtDe4Xaa9pU1dzd8GmcLxNj2NuWhxv28ZLfnpydnYGAJSWlmp8vLS0FK6u/I+LiIjIFjBQ6cnHxwcymQw3btyo8VhxcTHu3r2LFi1amKFnREREZGoMVHpydnaGv78/jh8/XuOxY8eOAQC6detm6m4RERGRGTBQGSAmJgZXr17Fzz//rDomCAKWL18OBwcHREVFmbF3REREZCoyobZV1VSv0tJSDBs2DJcvX8bYsWPRpk0b/PLLLzhw4ADmzp2L5557ztxdJCIiIhNgoDLQ7du3sXDhQqSkpODevXto06YNJkyYgKFDh5q7a0RERGQiDFREREREBuIaKiIiIiIDMVARERERGYiByozy8/PxzjvvICIiAsHBwYiJicHGjRvN3S1JOXbsGDp27IhDhw7VeOzatWuYO3cu+vTpg5CQEIwcORJ79uzReJ7MzExMnz4dvXr1QmhoKMaPH4/U1FSNbdPS0jBhwgR0794d4eHhmDp1Ks6fP6+x7d69ezFq1Ch069YNPXv2xJw5czTWJpO6s2fP4j//+Q8eeughBAUFITIyEu+9955qiyUljrk4srOzMXv2bPTt2xchISGIjY3Fjh07arTjeItPLpcjLi4OgYGBNR7jeIvntddeQ2BgoMY/mzZtUrWzpjHnGiozKS4uxpgxY3Du3DnExcWhbdu22LlzJ/7880+89NJLmDp1qrm7aHZ///03xowZg1u3biEhIQE9e/ZUPXbr1i3ExsaioKAAY8eOhbe3NzZu3IiTJ0/i008/RXR0tKrthQsXMGrUKDg6OiIuLg7Ozs5Ys2YNrl69ihUrVqBHjx6qtocPH8azzz6LVq1aYcSIEVAoFEhISEBJSQkSExPh7++vapuUlISXX34ZnTt3RkxMDO7evYtVq1bBxcUFmzZtgqenp2kGykAXL17EsGHDYG9vj9GjR6Nly5ZIT0/H1q1b0a5dO6xfvx6NGzfmmIvk+vXrGDZsGCoqKjB27Fh4enpix44dOHr0KF5++WVMmjQJAD/jxvLf//4XixYtAlD5i4QSx1tcw4cPx+3btzFz5swaj3Xr1g2+vr7WN+YCmcW3334rBAQECNu3b1cdUygUwnPPPSd07txZuHbtmhl7Z36//vqr0L17dyEgIEAICAgQDh48qPb4G2+8IQQGBgqpqamqY6WlpUJMTIzQs2dP4d69e6rjzz33nBAcHCxkZWWpjt2+fVvo06ePEBUVJSgUCkEQKsf/iSeeEB5++GEhPz9f1fby5ctCcHCwMGnSJNWxe/fuCT179hRiYmKE0tJS1fHU1FQhMDBQePPNN8UaCqN79tlnhc6dOwtnz55VO/79998LAQEBwrJlywRB4JiLZe7cuUJgYKCQnp6uOnb//n1h6NChQteuXYW7d+8KgsDxNoZjx44JnTp1EoKCgoSAgAC1xzje4pHL5UJwcLAwc+bMOttZ25jzkp+ZbNmyBd7e3njiiSdUx2QyGSZNmoSKigokJSWZsXfm9fzzz2PGjBlo1qwZhgwZUuNxuVyObdu2ISQkRK0avaOjI8aNG4f8/Hzs27cPAJCbm4v9+/djwIAB8PX1VbX18PDAiBEjcP78eWRkZAAAjh8/jszMTAwbNgzu7u6qtn5+fhg8eDB+//133Lx5EwCwb98+5OfnY/To0XB0dFS17datG8LDw5GUlITy8nIxh8UoysvLcfToUYSFhSEgIEDtMWXpjyNHjnDMRSSTydCvXz907dpVdcze3h4PPfQQSkpKcOnSJY63Edy7dw9z5szBI488gpCQELXHON7i+vvvv1FaWor27dvX2sYax5yBygwKCwtx8eJFtR+oSspjyg+HLbp48SLi4+OxefNmPPjggzUez8zMRHFxcY0fisC/46fc/kf5v9q0TU9PVzteVXBwMBQKhWqrIeVzamtbVFSEixcv1vEupaFBgwbYvn073nnnnRqP5ebmAgDs7Ow45iL68MMP8e2339Y4furUKdjZ2aFFixYcbyNQrgl89913azzG8RbXmTNnAED1S1pJSQnkcrlaG2sccwYqM8jJyYEgCGjZsmWNx5ycnODm5oYrV66YoWfSsGPHDkyZMgUODg4aH8/JyQEAjeOn3JBaOX7KhYWa2np7e2ts+8ADD2h9Xm3aSpmdnR18fX3h5+dX47EVK1YAAHr27MkxN5KioiIcP34c8+bNw8GDBzF27Fg0b96c4y2yX3/9FT/99BPeeecdeHl51Xic4y0u5dq0/fv3IzIyEiEhIejatSumT5+O7OxsANY55g20bkmiUd451bhxY42PN2rUCCUlJabskqTUFqSU6hq/Ro0aAYBq/IqKigBUbmZdnZOTk8a2ms5rSFtLtGXLFmzYsAEtW7bEiBEjsHfvXgAcc7G98sor2LVrF4DK376VN6PwMy6enJwcLFiwAMOHD8eAAQM0tuF4i0sZqNLT0zFt2jR4eHggLS0NCQkJ+Ouvv7BhwwarHHMGKjMQ/rmxUqjlBktBEGBnx8nD2tQ2blUfU45fXWNt7Lb29vb1vRVJ2rx5M1577TU0btwYixYtgrOzM8fcSIYNG4ahQ4fixIkTWLVqFYYOHYrVq1dzvEUiCALmzZsHV1dXvPrqq3W2q+8xjrf2oqKi0KlTJzz//POqcDRgwACEhITgxRdfxOeff47+/fvX+nxLHXMGKjNQpuzS0lKNj5eWlmqc2qRKyvHT9JuDckxdXV1Fbas8Vr1taWkpXFxcNLatftwSKG8pd3V1xZIlSxAcHAyAY24sERERACq/bIKDgzFt2jR8/fXXGDhwIACOt6FWrlyJgwcP4r///S/KyspQVlYGAKioqABQuRervb09P98ii4mJ0Xj8scceQ8uWLfH777+rbsiypjHnNIgZ+Pj4QCaTaSwcVlxcjLt376qu31JNPj4+AKBx/JTHlOOnTVtleFW2VV7br6r69X5dzmsJKioqMH/+fCxatAje3t748ccfER4ernqcY258kZGRcHFxwYkTJzjeItm7dy8EQVAVg1T++euvvwAAvXr1wlNPPcXxNiFPT0/cu3fPKsecgcoMnJ2d4e/vr7rToCrlXQdVbyMldW3btoWrq6vGOyGrj1+XLl1gZ2dXZ9vQ0FAAUM3GKI9XbyuTyVR3g9TVNiMjA66urmrF46RMLpdj9uzZ2LRpEwIDA7FhwwZ06NBBrQ3HXBy3b9/GoEGDMGvWrBqPlZeXo6ysDI6OjhxvkcybNw8rV66s8UdZJX3lypX45JNPON4iun37NqKjozFjxowaj1VUVODy5cto3bq1VY45A5WZxMTE4OrVq/j5559VxwRBwPLly+Hg4ICoqCgz9k7aGjRogKioKBw9ehRpaWmq42VlZUhISICXlxf69u0LAPDy8kLv3r2RnJysursEqNz2RxkcOnXqBKDyP642bdpgw4YNKCgoULXNyspCcnIyIiIi4OHhAQDo378/3NzckJCQoLqMAFRud5CamoqhQ4dazDq4L7/8EsnJyQgODsbq1atVd81UxTEXR9OmTdGwYUPs3r0b586dU3tsxYoVqKiowMCBAzneIgkKCkLv3r1r/HFzcwMA9O7dG2FhYRxvETVt2hRyuRx79+7FiRMn1B779ttvUVhYiKeeesoqx5xbz5hJaWkphg0bhsuXL2Ps2LFo06YNfvnlFxw4cABz587Fc889Z+4uSsLixYvx1Vdfadx65qmnnkJJSQkmTpwIT09P1ZYFCxcuVAuk586dQ2xsLJydnTFhwgQ4ODhg9erVuHbtGlauXKl2aevAgQOYPHkyfHx8MHr0aJSXl2PVqlWoqKjA2rVr0bZtW1XbTZs2Yf78+QgKCsLw4cORl5eHlStXwt3dHYmJiRaxTcS1a9cwcOBAyOVyxMfHawxTXl5eePjhhznmIjl69CieffZZNG7cGHFxcfDy8sLBgweRnJyMsLAwrFy5Eo6OjhxvIxo7diwOHz5cY+sZjrc4Dh06hEmTJsHR0RGjR49G8+bNcejQISQnJ6NHjx5YsWIFGjZsaHVjzkBlRrdv38bChQuRkpKCe/fuoU2bNpgwYYKqQjXVHqiAyg1mP/vsMxw4cAAVFRUIDAzEtGnT0K9fvxrnOX36NBYuXIjU1FTY2dkhKCgIs2bN0lgo7uDBg1i8eDFOnDgBJycnhIWFIT4+XuPU786dO7F06VJkZmbCzc0NvXv3Rnx8vMWsgduyZQvmzZtXZ5sePXrghx9+AMAxF8vp06exaNEiHD16FCUlJfD19UV0dDQmTZqkVjaE420cmgIVwPEW08mTJ/HVV18hNTUVxcXF8PHxQUxMDJ577jm1quTWNOYMVEREREQGkv4FWSIiIiKJY6AiIiIiMhADFREREZGBGKiIiIiIDMRARURERGQgBioiIiIiAzFQERERERmIgYqIiIjIQAxURBKxePFiBAYGavVn8eLF5u6u0Sg3ad60aZO5u6KVY8eOITw8HDdv3jR3V6xOTk4OwsPD1fZ6I5KqBubuABFVGjhwIPz8/NSOffDBB8jPz8fHH3+sdjwwMNCUXaNalJeX49VXX8Wzzz6L5s2bm7s7Vsfb2xuTJk3Ca6+9hi1btqhtWUIkNQxURBLRoUMHdOjQQe3Yl19+ifz8fDz55JNm6hXVZcWKFcjNzcWECRPM3RWrNX78eKxatQrLli3DjBkzzN0dolrxkh8RkR5KS0vx/fffIyoqCo0bNzZ3d6yWk5MToqKi8MMPP6CkpMTc3SGqFQMVkYV65ZVXEBoait9++w0REREIDg7GnDlzcOXKFQQGBuKVV17R+JzAwEBcuXJF7XhSUhJGjhyJkJAQhIaGYvTo0UhJSam3D0OHDkVYWBjKyspqPDZ16lQEBQXh9u3bAICioiJ8+eWXiI6ORkhICIKCgjBgwAB8+OGHuHfvXp2vExgYiLFjx9Y4rlx3dujQIbXj+/fvx7hx49CtWzd07doVTz/9tMY1WX/++SfGjx+PXr16oUuXLnj88cfxxRdfoLS0tN73vnnzZty+fRuPP/54jcdycnKwYMEC9O3bF0FBQYiIiMC7776L/Px8VZvt27cjMDAQY8aMQdU96tPT09G5c2cMGzYMFRUVqn/Pr7/+GqtWrUJkZCSCg4MRHR2NxMTEGq+tUCjwww8/4Mknn0RwcDDCw8MxadIkpKamahy7Cxcu4PXXX8fDDz+MLl264Mknn0RSUpJaW7lcjq+++kr1bxceHo6xY8dq/IxcvHgR8fHx6NWrF4KCgvDYY49pHNPs7GzMmjULERERCAoKQr9+/TB//nxcu3atxjkff/xxFBQUWMy6OrJNvORHZMHKysrw0ksvYeLEiXB1dUWrVq10Pscnn3yC7777Dg8//DDi4+NRVlaGn3/+GdOmTcP8+fPrvJz19NNP47333sOePXsQFRWlOn779m3s378fjz76KJo2bYr79+9jwoQJOHPmDEaNGoVx48bh3r17+OWXX7By5Urcu3cP77zzjj5DUMPq1avxzjvvoEuXLpgxYwbs7OywZ88ezJ8/H6dPn8Zrr70GoDK4PP/88+jYsSOmTZsGR0dH/PHHH/jmm2/w999/44svvqjzdXbv3g13d3eEh4erHc/OzsYzzzyD8vJyxMbGolWrVjhz5gzWrVuH//3vf1i3bh2aNm2KIUOGYN++fUhKSsLq1asxZswYFBUVYfbs2WjUqBG++OILNGzYUHXejRs3Ii8vD2PGjEHz5s2xfft2LFiwAFevXsVLL72kajdnzhz8/PPPGDRoEEaOHIk7d+5g06ZNGDt2LBYuXIjBgwer9ff5559H8+bNMWXKFJSXl+P777/HnDlz0KxZMzz00EMAKtfyrV69GiNHjsS4ceNQWFiIdevWYfr06fj222/Rr18/AEBGRgYmTJgAFxcXjB49Gk2bNkV6ejqWLFmCP//8EwkJCXB0dMTdu3cxbtw4KBQKPPPMM/D09ERmZibWrFmDQ4cOYceOHWjUqJGqj2FhYfDw8MDu3bsxevRo3T8URKYgEJFkRURECAEBARofmzdvnhAQECB89NFHasezs7OFgIAAYd68ebU+Jzs7WxAEQTh27JgQEBAgvPXWW2rtysvLhbFjxwqdO3cWrl+/Xmv/8vPzhaCgIGHKlClqx1etWiUEBAQI+/btEwRBEHbv3i0EBAQIK1asqPE6ffr0EUJDQ1XHfvrpJyEgIED46aefVMcCAgKEMWPG1Hj9RYsWCQEBAcLBgwcFQRCE69evC507dxamTJkiKBQKVTuFQiG8/PLLQkBAgHDs2DFBEATh7bffFgICAoTc3Fy1c86aNUuIjY0VysrKan3f5eXlQteuXYXx48fXeGzy5MlCt27dhMuXL6sd/+OPP4SAgADhzTffVB0rLCwUIiIihJCQEOHKlSvC7NmzhYCAAGHHjh2qNsp/z8DAQOHIkSOq42VlZcLw4cOFjh07ql5rx44dQkBAgLBs2TK11y4qKhIGDx4s9OzZUyguLlYbu8mTJ6uN1ZEjR4SAgAAhPj5edSwkJESYNGmS2jmvX78uDBgwQFi8eLEgCJVj/MQTTwj9+vUT8vPz1domJiYKAQEBwtKlSwVBEIRffvlFCAgIEH7++We1ditXrhSGDBkiHD9+vMa4TpgwQQgODq7z34XInHjJj8jCRUZG6v3cn3/+GQAQFRWF27dvq/4UFhYiKioKFRUV2Lt3b63Pd3d3R2RkJPbv36+6tAcAW7duRfPmzdGnTx8AwKOPPopDhw7VmF3Izc2Fu7s7iouL9X4PVf3666+oqKjA448/jvz8fNX7yc/PxxNPPKFqAwAtWrQAALz99ttIS0uDXC4HAHz++edYt24dHBwcan2dnJwclJSUoHXr1mrH7969i/379yM8PBwuLi5qY9qhQwf4+vpi165dqvYuLi745JNPUFZWhmeffRZJSUmIjY3VeBnx4YcfVpsNc3BwwMSJEyGXy7Fnzx4A//57Dho0SO21y8rK8NhjjyE/Px9HjhxRO290dDRkMpnq78HBwQAq/22UWrRogcOHD2P58uWqy8UtWrTArl27VAvFz549i8zMTPTr1w8KhULt9SMiIuDo6Kh67y1btgQALFmyBCkpKap//wkTJiApKQlBQUE13r+fnx9KS0tZnoIki5f8iCxcs2bN9H7upUuXAABjxoyptc3Vq1frPMewYcOwc+dO/Pzzzxg7dizOnz+PkydPYsqUKbC3t1e1a9iwITZs2IDU1FRkZ2cjKysLBQUFkMlkamuIDKF8P3Pnzq33/YwdOxapqalITk5GcnIyXF1d0b17d0RGRmLIkCFwcnKq9RzK8NikSRO143///TcUCgX27duHXr161fr80tJS1SWtsLAwTJw4Ed999x1atmypuiRZXUBAQI1jbdu2BQBkZWUB+Pf9DxgwoNbXrv7v6eXlpfZ3ZZBUKBSqY++99x5mzZqFjz/+GB9//DH8/Pzw8MMP44knnkD37t3VXnvdunVYt25dna/dtWtXTJs2DUuXLsW0adPQsGFDdO3aFX379sXQoUPh7e1d47nKsb59+zZ8fHxqfX9E5sJARWThqoaW+ty/f1/t78pZma+//rrWAKGcTahNnz590LJlS2zduhVjx47F5s2bAVSur1LKyclBXFwccnJy0L17d4SFhSE2NhYhISF48803cfToUa3fgzbv55133qn1S7dp06YAKu8e+/bbb3H+/Hns3bsXBw8exJ9//omUlBQsW7YMiYmJcHd313gOO7vKyf2qoaPq3wcNGoRRo0bV2u8GDf790Xv//n3V+79x4waOHj2Khx9+uMZzNM2YKd+v8jMgl8vh7OyMr776qtbXbtOmjcb3Updu3bph9+7dOHjwIPbv349Dhw5h3bp1WLt2LSZOnIhXXnlF9d5Hjx5da6Cr+r5nzZqF0aNHY9++ffjjjz9w+PBhHD16FEuWLMHKlSsREhKi9lzlv7Uun3ciU2KgIrIyyi8tTXeqVb2MA0AVOpo3b44uXbqoPXb58mVcvHix3pIAdnZ2ePLJJ7FkyRJkZWXh559/Rnh4OB588EFVm0WLFuHKlSv47rvv8Mgjj6g9/9atW/W+J3t7e53eT5MmTdC7d2+1x27evImMjAz4+voCqJxRycvLQ3h4ONq1a4fJkyejrKwMH374IdasWYPt27fXOnOnnBWsetde1dcvKyur8frAvwvZqwaLr7/+Gunp6XjxxRexbt06zJs3D0lJSfDw8FB77uXLl2uc7+LFiwD+DUk+Pj64dOkSOnTooAqOSqdPn8bNmzfrnHnTpKysDGfPnoWbmxv69u2Lvn37AqhcfP/ss8/i+++/x4wZM9QCbPX3rlAokJycrBr7W7duITMzE+Hh4RgxYgRGjBgBQRCwbds2zJ07FytWrMCiRYvUzlFQUADAsBlZImPiGioiK+Ph4YGGDRvi9OnTqhkMoPILufoWHso7vhYvXqzWtqKiAvPnz8fUqVORk5NT72sOHz4cMpkMH3/8Ma5fv45hw4apPa4MHtUrvCcnJ6uCQvXZpqqaN2+OixcvqpVXKCgowL59+9TaPfbYY7Czs8OSJUtqBLAPP/wQL7zwAk6cOAGgchZrwoQJarfpOzo6qtbv1DUT4uXlhUaNGmm8fBYWFob//e9/NcoU/O9//8MLL7yApUuXqo6lpaVhyZIl6N69O1544QW89dZbuHXrFhYsWFDjNXfv3o2///5b9ffy8nIsX74cDg4OqhmhQYMGAUCNOxSLioowa9YsvPDCCxpLXNQlPz8fsbGxNe7C9PX1hbe3N2QyGezs7BAUFIRWrVph8+bNqkuQSuvXr8esWbPw008/AQB++uknTJw4Ebt371a1kclk6NatGwDNY3/16lU4OTnB09NTp/4TmQpnqIisjKOjI4YMGYLNmzfj+eefx6BBg5CTk4M1a9bAx8cHFy5cULXt1asXhg8fjo0bN2LkyJGIioqCg4MDtm3bhoyMDMTFxakWKdfF19cX3bt3x65du9C4ceMat+Y/+uij2LNnDyZNmoQRI0agYcOGOHLkiOr2+NLSUty9e7fGrIrSsGHD8NVXX2HChAl4+umnUVhYiPXr18PNzU1tlurBBx/Eiy++iC+//BJPPvkknnrqKTRp0gR79uzB77//joiICDz22GMAgGnTpuHw4cOIi4vDyJEj0axZM1y+fBlr1qxBy5Yt1cpAVGdvb49evXrh0KFDuH//vtqM05tvvokxY8ZgwoQJiI2NRfv27XHx4kWsW7cO7u7umDdvHoDKkPPyyy/DwcEB77//PmQyGQYMGIAhQ4Zg+/bt2LBhA0aMGKE6r52dHUaNGoUxY8bAxcUFW7ZswenTp/Hqq6+q1hw9/fTT2LlzJ9avX4+srCxERkbi/v372LBhA/7++2+8/PLLGtcn1aVFixYYMWIE1q9fj+eeew6RkZGQyWT4/fffceTIEYwZM0Y1i/nuu+9iypQpePrppzFq1Cj4+fnh+PHj+Omnn+Dn54fp06cDAEaMGIF169bhtddeQ3p6Otq3b4/8/HwkJiaiYcOGNWqO3b9/HxkZGXj44Yd5yY8ki4GKyAotWLAALi4uSE5OxqFDh9CmTRvMnTsXd+7cwQcffKDW9t1330VISAjWr1+PxYsXw97eHg8++CDeffddDB8+XOvXHDZsGA4fPqyxcviwYcNQWlqK1atX45NPPoGzszP8/Pzwf//3f1AoFHjjjTewf//+WrfYmTZtGgBgy5YteO+99/DAAw9g9OjR8PHxwYsvvqjWdvr06WjXrh0SEhKwdOlSKBQK+Pr6Yu7cuRg7dqzqC7l79+5YtWoVvv32W6xZswYFBQXw8vLCkCFD8MILL8DNza3O9/voo49i7969yMjIUM2sAFBt7Pz1119j586dWLduHZo1a4bBgwdj+vTpqjsD33nnHVy5cgWvv/662h6OCxYswKFDh/D++++jR48eqv5GRUWhffv2+P7773H37l106NAB//3vf9XWK9nb22PJkiX4/vvvsXXrVnz66adwcnKCv78/Fi9erAqTunrjjTfQtm1bbN68GQsXLoRcLkfbtm2xYMECxMXFqdr17t0biYmJ+Oabb/DTTz+hsLAQLVq0QFxcHKZMmaK6XOfp6YkffvgB33zzDXbv3o21a9eicePGCAsLw8KFC2uE+GPHjqGkpASPPvqoXv0nMgWZINbtNURENuTevXuIjIzEY489JlpRUk2uXLmCRx99FE899RQ+/PBDo72OlL322mtISUlBSkqKzmvAiEyFa6iIiPTg7OyMiRMnYvv27SgqKjJ3d6xWUVERduzYgYkTJzJMkaQxUBER6WncuHHw8vLCd999Z+6uWK1ly5bBy8urzlppRFLAQEVEpKfGjRvjww8/REJCglZ3Q5JucnJykJCQgA8++KDe8h1E5sY1VEREREQG4gwVERERkYEYqIiIiIgMxEBFREREZCAGKiIiIiIDMVARERERGYiBioiIiMhADFREREREBmKgIiIiIjIQAxURERGRgf4f2RIb+Jhj+CAAAAAASUVORK5CYII=",
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ]
          },
          "metadata": {},
          "output_type": "display_data"
        }
      ],
      "source": [
        "# RUN THIS CELL TO TEST YOUR MODEL. DO NOT MODIFY CONTENTS.\n",
        "# Test model by checking how well the model generalizes using the test set.\n",
        "\n",
        "# loss, mae, mse = linear_model.evaluate(test_features, test_labels, verbose=2)\n",
        "loss, mae, mse = model.evaluate(test_features, test_labels, verbose=2)\n",
        "\n",
        "print(\"Testing set Mean Abs Error: {:5.2f} expenses\".format(mae))\n",
        "\n",
        "if mae < 3500:\n",
        "  print(\"You passed the challenge. Great job!\")\n",
        "else:\n",
        "  print(\"The Mean Abs Error must be less than 3500. Keep trying.\")\n",
        "\n",
        "# Plot predictions.\n",
        "test_predictions = model.predict(test_features).flatten()\n",
        "\n",
        "a = plt.axes(aspect='equal')\n",
        "plt.scatter(test_labels, test_predictions)\n",
        "plt.xlabel('True values (expenses)')\n",
        "plt.ylabel('Predictions (expenses)')\n",
        "lims = [0, 50000]\n",
        "plt.xlim(lims)\n",
        "plt.ylim(lims)\n",
        "_ = plt.plot(lims,lims)\n"
      ]
    }
  ],
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