Copy_of_MiPrimeraApp.ipynb

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      "name": "Copy of MiPrimeraApp.ipynb",
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      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
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      "source": [
        "<a href=\"https://colab.research.google.com/github/cesarriat/tensorflow/blob/master/Copy_of_MiPrimeraApp.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "metadata": {
        "id": "b33fTF8bNhWI",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "from __future__ import print_function\n",
        "\n",
        "import math\n",
        "\n",
        "from IPython import display\n",
        "from matplotlib import cm\n",
        "from matplotlib import gridspec\n",
        "from matplotlib import pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "from sklearn import metrics\n",
        "import tensorflow as tf\n",
        "from tensorflow.python.data import Dataset"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "EelZVLzYN4FU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "tf.logging.set_verbosity(tf.logging.ERROR)\n",
        "pd.options.display.max_rows = 10\n",
        "pd.options.display.float_format = '{:.1f}'.format"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "ThkM6MJsOGrM",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe = pd.read_csv(\"https://storage.googleapis.com/mledu-datasets/california_housing_train.csv\", sep=\",\")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "grJ7co0rOL3M",
        "colab_type": "code",
        "outputId": "41c4b46d-ac62-4d00-9d39-6bd7220f06ca",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 411
        }
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe = california_housing_dataframe.reindex(\n",
        "    np.random.permutation(california_housing_dataframe.index))\n",
        "california_housing_dataframe[\"median_house_value\"] /= 1000.0\n",
        "california_housing_dataframe"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
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              "      <th>12860</th>\n",
              "      <td>-121.8</td>\n",
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              "      <td>1446.0</td>\n",
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              "      <td>1414.0</td>\n",
              "      <td>4.8</td>\n",
              "      <td>195.3</td>\n",
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              "    <tr>\n",
              "      <th>3095</th>\n",
              "      <td>-117.8</td>\n",
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              "      <td>2168.0</td>\n",
              "      <td>421.0</td>\n",
              "      <td>1050.0</td>\n",
              "      <td>397.0</td>\n",
              "      <td>4.6</td>\n",
              "      <td>238.3</td>\n",
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              "    <tr>\n",
              "      <th>10943</th>\n",
              "      <td>-120.9</td>\n",
              "      <td>37.6</td>\n",
              "      <td>32.0</td>\n",
              "      <td>4579.0</td>\n",
              "      <td>914.0</td>\n",
              "      <td>2742.0</td>\n",
              "      <td>856.0</td>\n",
              "      <td>2.7</td>\n",
              "      <td>86.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4162</th>\n",
              "      <td>-118.0</td>\n",
              "      <td>33.9</td>\n",
              "      <td>30.0</td>\n",
              "      <td>2395.0</td>\n",
              "      <td>565.0</td>\n",
              "      <td>1214.0</td>\n",
              "      <td>521.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>212.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14135</th>\n",
              "      <td>-122.1</td>\n",
              "      <td>37.9</td>\n",
              "      <td>25.0</td>\n",
              "      <td>7201.0</td>\n",
              "      <td>1521.0</td>\n",
              "      <td>3264.0</td>\n",
              "      <td>1433.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>252.1</td>\n",
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              "      <th>...</th>\n",
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              "      <th>4493</th>\n",
              "      <td>-118.0</td>\n",
              "      <td>34.0</td>\n",
              "      <td>36.0</td>\n",
              "      <td>2722.0</td>\n",
              "      <td>515.0</td>\n",
              "      <td>1390.0</td>\n",
              "      <td>486.0</td>\n",
              "      <td>3.8</td>\n",
              "      <td>178.5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6115</th>\n",
              "      <td>-118.2</td>\n",
              "      <td>34.1</td>\n",
              "      <td>45.0</td>\n",
              "      <td>1747.0</td>\n",
              "      <td>484.0</td>\n",
              "      <td>1680.0</td>\n",
              "      <td>441.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>155.5</td>\n",
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              "    <tr>\n",
              "      <th>3515</th>\n",
              "      <td>-117.9</td>\n",
              "      <td>33.8</td>\n",
              "      <td>26.0</td>\n",
              "      <td>2678.0</td>\n",
              "      <td>702.0</td>\n",
              "      <td>3262.0</td>\n",
              "      <td>685.0</td>\n",
              "      <td>3.7</td>\n",
              "      <td>176.8</td>\n",
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              "      <td>-121.9</td>\n",
              "      <td>37.4</td>\n",
              "      <td>37.0</td>\n",
              "      <td>1525.0</td>\n",
              "      <td>363.0</td>\n",
              "      <td>1075.0</td>\n",
              "      <td>374.0</td>\n",
              "      <td>2.9</td>\n",
              "      <td>186.1</td>\n",
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              "    <tr>\n",
              "      <th>14669</th>\n",
              "      <td>-122.2</td>\n",
              "      <td>37.8</td>\n",
              "      <td>46.0</td>\n",
              "      <td>2109.0</td>\n",
              "      <td>387.0</td>\n",
              "      <td>922.0</td>\n",
              "      <td>329.0</td>\n",
              "      <td>4.0</td>\n",
              "      <td>208.1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>17000 rows × 9 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "12860     -121.8      37.3                23.0       7609.0          1446.0   \n",
              "3095      -117.8      33.9                24.0       2168.0           421.0   \n",
              "10943     -120.9      37.6                32.0       4579.0           914.0   \n",
              "4162      -118.0      33.9                30.0       2395.0           565.0   \n",
              "14135     -122.1      37.9                25.0       7201.0          1521.0   \n",
              "...          ...       ...                 ...          ...             ...   \n",
              "4493      -118.0      34.0                36.0       2722.0           515.0   \n",
              "6115      -118.2      34.1                45.0       1747.0           484.0   \n",
              "3515      -117.9      33.8                26.0       2678.0           702.0   \n",
              "13153     -121.9      37.4                37.0       1525.0           363.0   \n",
              "14669     -122.2      37.8                46.0       2109.0           387.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "12860      6034.0      1414.0            4.8               195.3  \n",
              "3095       1050.0       397.0            4.6               238.3  \n",
              "10943      2742.0       856.0            2.7                86.2  \n",
              "4162       1214.0       521.0            3.7               212.3  \n",
              "14135      3264.0      1433.0            3.7               252.1  \n",
              "...           ...         ...            ...                 ...  \n",
              "4493       1390.0       486.0            3.8               178.5  \n",
              "6115       1680.0       441.0            2.6               155.5  \n",
              "3515       3262.0       685.0            3.7               176.8  \n",
              "13153      1075.0       374.0            2.9               186.1  \n",
              "14669       922.0       329.0            4.0               208.1  \n",
              "\n",
              "[17000 rows x 9 columns]"
            ]
          },
          "metadata": {
            "tags": []
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          "execution_count": 5
        }
      ]
    },
    {
      "metadata": {
        "id": "DBnRzXdNOahM",
        "colab_type": "code",
        "outputId": "7d592a2f-249f-4358-ac00-d56471f75ecc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "cell_type": "code",
      "source": [
        "california_housing_dataframe.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>longitude</th>\n",
              "      <th>latitude</th>\n",
              "      <th>housing_median_age</th>\n",
              "      <th>total_rooms</th>\n",
              "      <th>total_bedrooms</th>\n",
              "      <th>population</th>\n",
              "      <th>households</th>\n",
              "      <th>median_income</th>\n",
              "      <th>median_house_value</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>-119.6</td>\n",
              "      <td>35.6</td>\n",
              "      <td>28.6</td>\n",
              "      <td>2643.7</td>\n",
              "      <td>539.4</td>\n",
              "      <td>1429.6</td>\n",
              "      <td>501.2</td>\n",
              "      <td>3.9</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>2.0</td>\n",
              "      <td>2.1</td>\n",
              "      <td>12.6</td>\n",
              "      <td>2179.9</td>\n",
              "      <td>421.5</td>\n",
              "      <td>1147.9</td>\n",
              "      <td>384.5</td>\n",
              "      <td>1.9</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>-124.3</td>\n",
              "      <td>32.5</td>\n",
              "      <td>1.0</td>\n",
              "      <td>2.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.0</td>\n",
              "      <td>0.5</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>-121.8</td>\n",
              "      <td>33.9</td>\n",
              "      <td>18.0</td>\n",
              "      <td>1462.0</td>\n",
              "      <td>297.0</td>\n",
              "      <td>790.0</td>\n",
              "      <td>282.0</td>\n",
              "      <td>2.6</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>-118.5</td>\n",
              "      <td>34.2</td>\n",
              "      <td>29.0</td>\n",
              "      <td>2127.0</td>\n",
              "      <td>434.0</td>\n",
              "      <td>1167.0</td>\n",
              "      <td>409.0</td>\n",
              "      <td>3.5</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>-118.0</td>\n",
              "      <td>37.7</td>\n",
              "      <td>37.0</td>\n",
              "      <td>3151.2</td>\n",
              "      <td>648.2</td>\n",
              "      <td>1721.0</td>\n",
              "      <td>605.2</td>\n",
              "      <td>4.8</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>-114.3</td>\n",
              "      <td>42.0</td>\n",
              "      <td>52.0</td>\n",
              "      <td>37937.0</td>\n",
              "      <td>6445.0</td>\n",
              "      <td>35682.0</td>\n",
              "      <td>6082.0</td>\n",
              "      <td>15.0</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       longitude  latitude  housing_median_age  total_rooms  total_bedrooms  \\\n",
              "count    17000.0   17000.0             17000.0      17000.0         17000.0   \n",
              "mean      -119.6      35.6                28.6       2643.7           539.4   \n",
              "std          2.0       2.1                12.6       2179.9           421.5   \n",
              "min       -124.3      32.5                 1.0          2.0             1.0   \n",
              "25%       -121.8      33.9                18.0       1462.0           297.0   \n",
              "50%       -118.5      34.2                29.0       2127.0           434.0   \n",
              "75%       -118.0      37.7                37.0       3151.2           648.2   \n",
              "max       -114.3      42.0                52.0      37937.0          6445.0   \n",
              "\n",
              "       population  households  median_income  median_house_value  \n",
              "count     17000.0     17000.0        17000.0             17000.0  \n",
              "mean       1429.6       501.2            3.9               207.3  \n",
              "std        1147.9       384.5            1.9               116.0  \n",
              "min           3.0         1.0            0.5                15.0  \n",
              "25%         790.0       282.0            2.6               119.4  \n",
              "50%        1167.0       409.0            3.5               180.4  \n",
              "75%        1721.0       605.2            4.8               265.0  \n",
              "max       35682.0      6082.0           15.0               500.0  "
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 6
        }
      ]
    },
    {
      "metadata": {
        "id": "-tpyYw-UO2g7",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Primer Paso: Definir las características y configurar las denominadas columnas de características\n",
        "\n",
        "# Definir la característica de entrada: total_rooms.\n",
        "my_feature = california_housing_dataframe [[\"total_rooms\"]]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "abwLCSh2PBgc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Configurar una columna numérica de característica para total_rooms.\n",
        "feature_columns = [tf.feature_column.numeric_column(\"total_rooms\")]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Na_jAGnIPVkF",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Segundo Paso : Definir el Objetivo (Target)\n",
        "\n",
        "# Definir la etiqueta.\n",
        "targets = california_housing_dataframe[\"median_house_value\"]"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "-ZjlfUbbPoIc",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Tercer Paso: Configurar el LinearRegressor\n",
        "\n",
        "# Usar descenso de gradiente como el optimizador para entrenar el modelo.\n",
        "# Configurar una tasa de aprendizaje de 0.0000001 para Descenso de Gradiente.\n",
        "my_optimizer=tf.train.GradientDescentOptimizer(learning_rate=0.0000001)\n",
        "my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "CcF93AvlP2bU",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "# Configurar el modelo de regresión lineal con nuestras columnas característica y optimizador.\n",
        "linear_regressor = tf.estimator.LinearRegressor(\n",
        "    feature_columns=feature_columns,\n",
        "    optimizer=my_optimizer\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "mJaRbKwmYFW9",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Cuarto Paso: Definir la Función input**"
      ]
    },
    {
      "metadata": {
        "id": "BgFe1nYVVKZn",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def my_input_fn(features, targets, batch_size=1, shuffle=True, num_epochs=None):\n",
        "    \"\"\"Entrena un modelo de regresión lineal de una característica.\n",
        "  \n",
        "    Argumentos:\n",
        "      features:DataFrame pandas de característicass\n",
        "      targets: DataFrame pandas de objetivos\n",
        "      batch_size: Tamaño de lotes pasados al modelo\n",
        "      shuffle: True or False. Si se deben mezclar los datos.\n",
        "      num_epochs: Número de epochs por los que los datos se repetirán. None = repetir indefinidamente\n",
        "    Devuelve:\n",
        "      Tuple de (features, labels) para el siguiente lote de datos\n",
        "    \"\"\"\n",
        "  \n",
        "    # Convertir datos pandas en un dict de arrays np.\n",
        "    features = {key:np.array(value) for key,value in dict(features).items()}                                           \n",
        " \n",
        "    # Construir un dataset, y configure batching/repeating.\n",
        "    ds = Dataset.from_tensor_slices((features,targets)) # warning: 2GB limit\n",
        "    ds = ds.batch(batch_size).repeat(num_epochs)\n",
        "    \n",
        "    # Mezclar los datos, si se especifica.\n",
        "    if shuffle:\n",
        "      ds = ds.shuffle(buffer_size=10000)\n",
        "    \n",
        "    # Devolver el nuevo lote de datos.\n",
        "    features, labels = ds.make_one_shot_iterator().get_next()\n",
        "    return features, labels"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "Qs6iDSj2YUWP",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Paso cinco: Entrenar el Modelo**"
      ]
    },
    {
      "metadata": {
        "id": "lgOk_RknR4pj",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "_ = linear_regressor.train(\n",
        "    input_fn = lambda:my_input_fn(my_feature, targets),\n",
        "    steps=100\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "n97eXyZQt2J8",
        "colab_type": "code",
        "outputId": "3bfe8b0f-b0bb-46df-f4b8-c69bf2e29f32",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        }
      },
      "cell_type": "code",
      "source": [
        "_"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.estimator.canned.linear.LinearRegressor at 0x7f45673a09e8>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 14
        }
      ]
    },
    {
      "metadata": {
        "id": "kRgZSph4Yet1",
        "colab_type": "text"
      },
      "cell_type": "markdown",
      "source": [
        "**Paso seis: Evaluar el Modelo**"
      ]
    },
    {
      "metadata": {
        "id": "kNnSijs3Xl_z",
        "colab_type": "code",
        "outputId": "7b95f20d-1909-4a29-93b9-03db56664458",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "cell_type": "code",
      "source": [
        "\n",
        "# Crear una función input para predicciones.\n",
        "# Nota: Como vamos a hacer sólo una predicción para cada ejemplo, no tenemos \n",
        "# que repetir o mezclar los datos aquí.\n",
        "prediction_input_fn =lambda: my_input_fn(my_feature, targets, num_epochs=1, shuffle=False)\n",
        "\n",
        "\n",
        "# Llamar a predict() en el linear_regressor para hacer predicciones.\n",
        "predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "\n",
        "# Formateamos las predicciones como un array NumPy, para que podamos calcular las métricas de error.\n",
        "predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "\n",
        "# Imprimimos Error Cuadrático Medio y Raíz Error Cuadrático Medio.\n",
        "mean_squared_error = metrics.mean_squared_error(predictions, targets)\n",
        "root_mean_squared_error = math.sqrt(mean_squared_error)\n",
        "print(\"Error Cuadrático Medio (en datos entrenamiento): %0.3f\" % mean_squared_error)\n",
        "print(\"Raíz Error Cuadrático Medio (en datos entrenamiento): %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Error Cuadrático Medio (en datos entrenamiento): 56367.025\n",
            "Raíz Error Cuadrático Medio (en datos entrenamiento): 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "dmWeF5XUXsyL",
        "colab_type": "code",
        "outputId": "37ce7ad9-e65f-4b26-a42a-9307f983f936",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 88
        }
      },
      "cell_type": "code",
      "source": [
        "min_house_value = california_housing_dataframe[\"median_house_value\"].min()\n",
        "max_house_value = california_housing_dataframe[\"median_house_value\"].max()\n",
        "min_max_difference = max_house_value - min_house_value\n",
        "\n",
        "print(\"Min. Median House Value: %0.3f\" % min_house_value)\n",
        "print(\"Max. Median House Value: %0.3f\" % max_house_value)\n",
        "print(\"Diferencia entre Min. y Max.: %0.3f\" % min_max_difference)\n",
        "print(\"Raíz Error Cuadrático Medio: %0.3f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Min. Median House Value: 14.999\n",
            "Max. Median House Value: 500.001\n",
            "Diferencia entre Min. y Max.: 485.002\n",
            "Raíz Error Cuadrático Medio: 237.417\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "metadata": {
        "id": "A0Ixh65LX2kz",
        "colab_type": "code",
        "outputId": "b4445fa2-836c-41b1-face-7e530a7cd717",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 291
        }
      },
      "cell_type": "code",
      "source": [
        "calibration_data = pd.DataFrame()\n",
        "calibration_data[\"predicciones\"] = pd.Series(predictions)\n",
        "calibration_data[\"objetivos\"] = pd.Series(targets)\n",
        "calibration_data.describe()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>predicciones</th>\n",
              "      <th>objetivos</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.1</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.1</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>0.1</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.2</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1.9</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predicciones  objetivos\n",
              "count       17000.0    17000.0\n",
              "mean            0.1      207.3\n",
              "std             0.1      116.0\n",
              "min             0.0       15.0\n",
              "25%             0.1      119.4\n",
              "50%             0.1      180.4\n",
              "75%             0.2      265.0\n",
              "max             1.9      500.0"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "metadata": {
        "id": "kABRszebb0P0",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "sample = california_housing_dataframe.sample(n=300)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "FK_oApGpf5iz",
        "colab_type": "code",
        "outputId": "fc27e4e5-c82f-47d9-a36c-f96e606e05c9",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 361
        }
      },
      "cell_type": "code",
      "source": [
        "# Obtenemos los valores mínimo y máximo de total_rooms.\n",
        "x_0 = sample[\"total_rooms\"].min()\n",
        "x_1 = sample[\"total_rooms\"].max()\n",
        "\n",
        "# Recuperamos el peso y sesgo final generado durante el entrenamiento.\n",
        "weight = linear_regressor.get_variable_value('linear/linear_model/total_rooms/weights')[0]\n",
        "bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "# Obtener los median_house_values predichos para los valores min and max total_rooms.\n",
        "y_0 = weight * x_0 + bias \n",
        "y_1 = weight * x_1 + bias\n",
        "\n",
        "# Trazamos nuestra línea de regresión desde (x_0, y_0) to (x_1, y_1).\n",
        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
        "\n",
        "# Trazamos nuestra línea de regresión desde (x_0, y_0) to (x_1, y_1).\n",
        "plt.plot([x_0, x_1], [y_0, y_1], c='r')\n",
        "\n",
        "# Damos nombre a los ejes del gráfico.\n",
        "plt.ylabel(\"median_house_value\")\n",
        "plt.xlabel(\"total_rooms\")\n",
        "\n",
        "# Trazamos una gráfica de dispersión de nuestros datos sample.\n",
        "plt.scatter(sample[\"total_rooms\"], sample[\"median_house_value\"])\n",
        "\n",
        "# Mostrar gráfico.\n",
        "plt.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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W5k3mNqQD3jgRUaIw4KeARNatT6Y2pAPeOBFRojDgp4B4bYiT7G1IB7xxIqJEYdJeikiG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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4562b71668>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "cwAKA8FjgW6b",
        "colab_type": "code",
        "colab": {}
      },
      "cell_type": "code",
      "source": [
        "def train_model(learning_rate, steps, batch_size, input_feature=\"total_rooms\"):\n",
        "  \"\"\"Entrenar un modelo de regresión lineal de una característica.\n",
        "  \n",
        "  Args:\n",
        "    learning_rate: Un `float`, la tasa de aprendizaje.\n",
        "    steps: Un no-cero `int`, el número total de pasos de entrenamiento. Un paso de entrenamiento\n",
        "      consiste en un paso adelante y atrás usando un único lote.\n",
        "    batch_size: Un no-cero `int`, tamaño del lote.\n",
        "    input_feature: un `string` especificando una columna de `california_housing_dataframe`\n",
        "      para usar como característica de entrada.\n",
        "  \"\"\"\n",
        "  \n",
        "  periods = 10\n",
        "  steps_per_period = steps / periods\n",
        "\n",
        "  my_feature = input_feature\n",
        "  my_feature_data = california_housing_dataframe[[my_feature]]\n",
        "  my_label = \"median_house_value\"\n",
        "  targets = california_housing_dataframe[my_label]\n",
        "\n",
        "  # Crear columns característica.\n",
        "  feature_columns = [tf.feature_column.numeric_column(my_feature)]\n",
        "  \n",
        "  # Crear funciones input.\n",
        "  training_input_fn = lambda:my_input_fn(my_feature_data, targets, batch_size=batch_size)\n",
        "  prediction_input_fn = lambda: my_input_fn(my_feature_data, targets, num_epochs=1, shuffle=False)\n",
        "  \n",
        "  # Crear un objeto linear regressor.\n",
        "  my_optimizer = tf.train.GradientDescentOptimizer(learning_rate=learning_rate)\n",
        "  my_optimizer = tf.contrib.estimator.clip_gradients_by_norm(my_optimizer, 5.0)\n",
        "  linear_regressor = tf.estimator.LinearRegressor(\n",
        "      feature_columns=feature_columns,\n",
        "      optimizer=my_optimizer\n",
        "  )\n",
        "\n",
        "  # Configuración para trazar el estado de la línea de nuestro modelo cada período.\n",
        "  plt.figure(figsize=(15, 6))\n",
        "  plt.subplot(1, 2, 1)\n",
        "  plt.title(\"Línea aprendida por Período\")\n",
        "  plt.ylabel(my_label)\n",
        "  plt.xlabel(my_feature)\n",
        "  sample = california_housing_dataframe.sample(n=300)\n",
        "  plt.scatter(sample[my_feature], sample[my_label])\n",
        "  colors = [cm.coolwarm(x) for x in np.linspace(-1, 1, periods)]\n",
        "\n",
        "  # Entrena el modelo, pero haciéndolo dentro de un loop de modo que podamos periódicamente\n",
        "  # evaluar las métricas de pérdida.\n",
        "  print(\"Entrenamiento del modelo...\")\n",
        "  print(\"RMSE (en datos de entrenamiento):\")\n",
        "  root_mean_squared_errors = []\n",
        "  for period in range (0, periods):\n",
        "    # Entrenar el modelo, empezando desde el estado anterior.\n",
        "    linear_regressor.train(\n",
        "        input_fn=training_input_fn,\n",
        "        steps=steps_per_period\n",
        "    )\n",
        "    # Tómate un descanso y calcula las predicciones.\n",
        "    predictions = linear_regressor.predict(input_fn=prediction_input_fn)\n",
        "    predictions = np.array([item['predictions'][0] for item in predictions])\n",
        "    \n",
        "    # Calcular pérdida.\n",
        "    root_mean_squared_error = math.sqrt(\n",
        "        metrics.mean_squared_error(predictions, targets))\n",
        "    # Ocasionalmente imprimir la pérdida actual.\n",
        "    print(\"  período %02d : %0.2f\" % (period, root_mean_squared_error))\n",
        "    # Agregar las métricas de pérdida de este período a nuestra lista.\n",
        "    root_mean_squared_errors.append(root_mean_squared_error)\n",
        "    # Por último, rastrea los pesos y los sesgos a lo largo del tiempo.\n",
        "    # Aplica algo de math para asegurarte que los datos y la línea se representan claramente.\n",
        "    y_extents = np.array([0, sample[my_label].max()])\n",
        "    \n",
        "    weight = linear_regressor.get_variable_value('linear/linear_model/%s/weights' % input_feature)[0]\n",
        "    bias = linear_regressor.get_variable_value('linear/linear_model/bias_weights')\n",
        "\n",
        "    x_extents = (y_extents - bias) / weight\n",
        "    x_extents = np.maximum(np.minimum(x_extents,\n",
        "                                      sample[my_feature].max()),\n",
        "                           sample[my_feature].min())\n",
        "    y_extents = weight * x_extents + bias\n",
        "    plt.plot(x_extents, y_extents, color=colors[period]) \n",
        "  print(\"Entrenamiento del Modelo finalizado.\")\n",
        "\n",
        "  # Muestra un gráfico de métricas de pérdida por períodos.\n",
        "  plt.subplot(1, 2, 2)\n",
        "  plt.ylabel('RMSE')\n",
        "  plt.xlabel('Períodos')\n",
        "  plt.title(\"Raíz Error Cuádratico Medio vs. Períodos\")\n",
        "  plt.tight_layout()\n",
        "  plt.plot(root_mean_squared_errors)\n",
        "\n",
        "  # Muestra una tabla con los datos de calibración.\n",
        "  calibration_data = pd.DataFrame()\n",
        "  calibration_data[\"predictions\"] = pd.Series(predictions)\n",
        "  calibration_data[\"targets\"] = pd.Series(targets)\n",
        "  display.display(calibration_data.describe())\n",
        "\n",
        "  print(\"RMSE final(en datos de entrenamiento): %0.2f\" % root_mean_squared_error)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "metadata": {
        "id": "3-5vz5OBgcpI",
        "colab_type": "code",
        "outputId": "0caa1a00-bb9b-4a62-8324-2b866e93415e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 960
        }
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "    learning_rate=0.00001,\n",
        "    steps=100,\n",
        "    batch_size=1\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entrenamiento del modelo...\n",
            "RMSE (en datos de entrenamiento):\n",
            "  período 00 : 236.32\n",
            "  período 01 : 235.11\n",
            "  período 02 : 233.90\n",
            "  período 03 : 232.70\n",
            "  período 04 : 231.50\n",
            "  período 05 : 230.31\n",
            "  período 06 : 229.13\n",
            "  período 07 : 227.96\n",
            "  período 08 : 226.79\n",
            "  período 09 : 225.63\n",
            "Entrenamiento del Modelo finalizado.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.2</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>10.9</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>7.3</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>10.6</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>15.8</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>189.7</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean          13.2    207.3\n",
              "std           10.9    116.0\n",
              "min            0.0     15.0\n",
              "25%            7.3    119.4\n",
              "50%           10.6    180.4\n",
              "75%           15.8    265.0\n",
              "max          189.7    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE final(en datos de entrenamiento): 225.63\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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TSEpKAmD//v0MGTKEIUOG8P777xscdWF+Xu48cUcIEV0a8GfSWWbMj+anPSeM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\nRERERERETPH/UPmfSVwngAcAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f4562afa208>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "d4bf8VVhgp0p",
        "colab_type": "code",
        "outputId": "c61295af-9501-4702-c8d3-efe12bba9540",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        }
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=500,\n",
        "    batch_size=5\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entrenamiento del modelo...\n",
            "RMSE (en datos de entrenamiento):\n",
            "  período 00 : 225.63\n",
            "  período 01 : 214.42\n",
            "  período 02 : 204.04\n",
            "  período 03 : 195.33\n",
            "  período 04 : 187.55\n",
            "  período 05 : 180.80\n",
            "  período 06 : 175.00\n",
            "  período 07 : 171.07\n",
            "  período 08 : 168.50\n",
            "  período 09 : 166.90\n",
            "Entrenamiento del Modelo finalizado.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>120.0</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>99.0</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.1</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>66.4</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>96.6</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>143.1</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>1722.3</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         120.0    207.3\n",
              "std           99.0    116.0\n",
              "min            0.1     15.0\n",
              "25%           66.4    119.4\n",
              "50%           96.6    180.4\n",
              "75%          143.1    265.0\n",
              "max         1722.3    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE final(en datos de entrenamiento): 166.90\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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YunQpo0ePxmazsXTpUp544gkMBkOxZWdnu26McNvGAajUapLTzFhybQT6Gbmx\nQyjjI5q77ElH28YBmC02TJm5zn30aV+XJmF+pGdZr9pv+yaBhd5v0GvQqFXYi+nKEORn4MYO9Xh4\nVFtycu3O7YIDvOjdvvCxFBXLjR1CeXhUW8wWGwmXs7DZ/9mPQadmQJcwJgxq4fInP8XFcq1z36dz\nfZJTswudH61Gjd2uEOSi+itvbBVRq5bBpdd2dVGW4yrpvGcf/JMz055DFxRIq9VvovFxQdd/RUG7\n+xs0CaewN++OvVNEmTYv9bEpjrw5JGw54BUIPlWTeC1OQrqWE0l6dGroFGbGx1D4b4urrsWq/Bwd\nOmXjw805WG1we4SBiG56VFfsQz5jrtufJ6uMc6VSqbipawN+io7lwMlkmoX5ERLg7fL9iJJdr59x\nTyJ14H5SB+4ndVC0ktoPKqU04yXKITMzk6eeeor09HSsVivTp0+nTZs2PPPMM1gsFsLCwnjllVfQ\n6XRERUXxwQcfoFKpuPvuu7n11ltLLNvV3VSDg32Ji0/DlGnB38dQaU/CLVb7Vfso6ndFvR/AlGnB\ny6DFlJULioK/jwGzxXbVtvnbNWscRIap6GEOxe3XYrWTlGYm12pDr9MSXNur0nsGlHQOipLfVbmo\n8+Pq+itrbBUhXcv/ceV5t2dkcmTo3VjOxdHq87fw79/LJbFp/tqNdu+3OOqEY715KmjKNqKtVMem\nKJAeDxYTGHzBLxzc2LX7YkbepJZaNXQuIiEBrr8WK/NzpCgKP++3sumXXPQ6mDTMSJvGRdejfMZc\ntz9PVlnnKjjYl98PXeCVT/dh0GmYd+8N1PH3qpR9iaJdr59xTyJ14H5SB+4ndVC0ktoPlZaUqEyV\nkZS4Hi8cOS7PIsdVvNOPPc/lr7dS79HJNJj7uEviUiWeRffdR2DwJnfEI+Bd9uEUpTq2zEuQnQxa\nLwhoBKWcQLMyJGZo+OuSAa0aOoXl4GtwFPk+T7kWHQ6F9T/nsuuQFb9aKu6/1Uj94OKTHp5yXGV1\n5XEpisLv+03Ur2ckvJ6xUvbnySozKZGUlMGP+y+wattxGof68uzdXdG5YGJuUTrX62fck0gduJ/U\ngftJHRStpPaD+1rHQghRCslrt3D5663U6tKO+s886ppCs0zofl4DgLXf+HIlJErFnJqXkNDooXYD\ntyYkLmXmJSQ010hIeAqLVeGjLTnsOmQlNEjN43d4lZiQqCkyMm38660z/OutM3yzNdHd4dRI/TuH\ncWP7UM5dzGD19pPuDkcIIYSo9ip1SVAhhKiInDMxnPu/f6H2qUWz5S+j1rngT5bdiu6nL1DlZGG9\nYQRK3cYVL7MolgzISACVBvzJoEbnAAAgAElEQVQbgtp9f26TMjX8mfh3QqKe5yck0rMcfLgph9hL\nDlo00DBluBEvg0xo+eeJTF5bcZbLqVbat/bhrjFh7g6pRlKpVNw9tBUxlzL56UA8zcL86dvx2hM0\nCyGEEDWVJCWEENWSI9fKqUfm4Mg202zZQoyNwiteqKKg/X0L6stx2Jt2wdGqZ8XLLIrVDOlxgCqv\nh4RWXzn7KYWkrL8TEiroWC8HP6NnJyQSUxy8v9FMSrrCDW20jIswoNHU7ISE3a6wZkMCX25MABVM\nvK0eY0aEolHX7PPiTgadhmm3tefFj/byyf+O07CuDw3revawFyGEEKKyyPANIUS1FPfKW2QfPkad\nO24h6LZIl5SpPrkXzal9OALDsPW8pXImnLTn5q20oSjgVx907puBPzlLw58XDaj+Tkj4e3hC4nSc\nnTe/yiYlXWFoTz3jB0tCIjkllxlzD/LFhgSCAvUsfKYl426pJwmJaiAkwJsHbmmL1ebgrXWHycqx\nujskIYQQolqSpIQQotpJ27GLiys+w9i0IY1enu2SMlVJMWj/2IJi8Mba/07Q6lxSbiEOO6TFgsMG\nPnXBWElzVZTC5SwNRwsmJLw8OyERfdzKivVmLFa4c4iBm3teveRnTfP7/jRmzvuLA0dM9OpWm9fm\nt6ZNCx93hyUK6Ny8DiP7NCLZlMN7m/7E4XlziwshhBCVToZvCI9Xlct2isqXm5jMmRnzUel1NHt7\nEZpaLuhpkJ2B7qcvQHFgvWk8+NSueJlXUhx5PSTsFvAKBO8g1++jlFKyNRxJzEtIdKiXQ20PTkgo\nisL3e61s3Z2LUQ/3jDDSokHNvnVZrQ4+/uoCW7YnodOqeOrRFvTp5lPjkzTV1ei+TTkbn86h05fZ\n8us5brmxibtDEkIIIaqVmt2yEx7N7nCwZscp9p9IIiXdQqCfgS4tgxkf0RyNWjoBeSLF4eDM4y9g\nu5xKw5dmUatD64oXareh+/kLVOYMbN0iUeo1rXiZV1IUSE8AazYYfPN6SbhJSraaIxcNqIAOoTkE\neHBCwm5X+PpHC3uO2gjwzVvyMzSoZiceLyTksGTFWc7GmAmvZ+SpR5rQvUuILD1WjanVKh68tR0v\nfvQH63eepUmYH+2buC9pKYQQQlQ38s1NeKw1O06xfW8cl9MtKMDldAvb98axZscpd4cmyilh+SrS\nd/6O/+C+1J06wSVlavduRZ0Ug71xB+xt+rikzKtkJYHFBFqvvHkk3PTEOtWs5shFIwrQPtRCgLfn\nJiRychU+2JTDnqM2woPzlvysyQkJRVHYsesyT710jLMxZgb3C+LVF1rRKNzL3aGJUvD11vPo6A5o\nNCre3fgnySazu0MSQgghqg1JSlQDFqudS6nZWKx2d4fiMSxWO/tPJBX52v4TyXIuPVBm9BEuLH4b\nXd06NH19nku6oqtPRaM58TuOgLrYeo2unGSBORWyk0Gjy1tpQ+WeP6tpZjWHE4woCrSvayHQ23M/\nA6ZMB8vWmjkeY6dNYw2P3u6FX62ae7sym+0sfe8cb35wHrUaZj3cmGn3NMJoqLlJGk/UNMyPOwe3\nJNNs5e31R7DaPDdpKIQQQriSDN9wIxl+UH6mTAsp6ZYiX0vNyMGUaSEkwH2rHoiysaVncvrR51Ds\nDpq+8RK6oIAKl6lKjkO7ZxOK3gtr/4mgc/2ynJaMNMhIAJUG/BuC2j1/Uk1mNYf+Tki0C7UQVMtz\nExLxyXbe35iDKVOhdwctt/U31OiVJE6dzWLJinNcvGShZVNvnnyoCXWDDe4OS5TTgM5hnL5g4tcj\nF/l8+wkmR7pgiJoQQgjh4SQp4Ub5ww/y5Q8/AJg4uKW7wvII/j4GAv0MXC4iMRHga8TfRxrtnkJR\nFM49swhLzAXqPXYv/jf1qHih5kx0P30ODjvWmyaCb2DFy7yS1Ux68nlAlddDQuuea86U809Com2o\nhToenJA4ft7Gx9/mYLHCiBv1DOyqq7GTNzocCpu+u8Sna+Ox2RVuG1aXibeFodXWzPNxvVCpVEwa\n2oqYxEx+PBBPs/r+3NihnrvDEkIIIdxKHse7iQw/qBiDTkOXlsFFvtalZR1ZhcODJK/ZRMqG/1Gr\nWwfqP/VQxQt02NHtXIMqOx1750EoYS0qXuaV7Na8lTYcjrw5JHTu6ZWT/ndCwq5A27oWgj04IbHn\nqJX3N+Vgd8CkSAMR3Wrukp+mdCuL3jjNR2su4FNLw7xZzZk8rr4kJK4TBp2G6WPa42XQsmrbcWIS\nZZJSIYQQNZskJdykNMMPRMnGRzRncPdwgvyMqFUQ5GdkcPdwxkc0d3doopTMp85x/rnFaHxr0XzZ\nQtS6infe0uzbhjrxHPaGbbG37+eCKK/gsENaDDhs1AptCEY/1++jFDIsag4mGLE7/k5I+HhmQkJR\nFLbutvDl9xaMenjoNi86t9S5Oyy3OfRXBjPnHWPfoXQ6t/Pl9Rfb0Lmde64xUXlCArx5YGRbrDYH\ny785QnaO1d0hCSGEEG4jwzfcRIYfVJxGrWbi4Jbc3r8ZpkwL/j4G6SHhQRyWXE4/MgeHOYdmby/C\n0LB+hctUnzmA9thuHP7B2PqMcf3EloqS10PCbgGvQLyD6pHlhqUYMyxqDsbnJSTahFgI8dCEhM2u\n8OV2C/uO2wjyU/HAKC+CA2pmrtxuV/h8fTzrvk1ErYbJ48IYNbQu6ho8n8b1rnOLOozo3Ygtu8/z\n/ua/mH57B9Q1tHeQEEKImk2SEm6SP/yg4JwS+WT4QdkYdBqZ1NIDxb78JtlHTxB85yiCRt1c4fJU\nKfFof9uIojNgGzARdC5O7CkKpMeDNRsMvuBT17Xll1Lm3wkJmwNah+RS19czExLZOQofbcnh9AU7\njULV3DfSCx/vmvmF7FKyhdffPcexU1nUraPnyYea0LJZLXeHJarAbTc15WxCOgdOJfPt7vOM7NPY\n3SEJIYQQVU6SEm6UP8xg/4lkUjNyCPA10qVlHRl+IMrMYrV7VG+R1O92kvj+5xibN6bhgqcqXqAl\nG92Pn6OyW7H2uxvFr07Fy7xSVhJYTKD1yptHwg1PNDMtqr8TEipaBVsI9bVVeQyukJLu4P0NZhJT\nFTo003DXUCO6Gjpfwu69qSz7KIasbDt9ewTw8OSG1PKu/p9h4RpqtYoHb23Hiyv/4JudZ2gS5ke7\nxpUwMa8QQghRjUlSwo1k+IGoKE9cVjb3YhJnZ76IyqCn+duL0Hh7VaxAhx3dz1+iykrD1nEgjvBW\nrgm0IHMqZCeDRpe30oaq6s9tVq6Kg/FeWB0qWgZbqOfnmQmJ2EQ7H2zKISNboV9nHbf01dfIIQqW\nXAcrv4hj24/J6PUqpt3TkEE3BdXYyT1rMj9vPY/e1p5/fRrNig1HmX/vDQT6Gd0dlhBCCFFlque3\nlhomf/iBJCREWeUvK3s53YLCP8vKrtlxyt2hFUmx2zn92PPYUtJo+PwMvNtVfOlbzYHtqC+exh7e\nCnvHARUP8kqWTMhIAJUG/BuCuupzudm5Kg7EG7E6VLSoYyHMQxMSf561sfxrM5nZCqP76xnVz1Aj\nExIxF8w8veAY235MpnG4F/95oTWD+9WRhEQN1izMnzsHtyDTbGXZN0ew2hzuDkkIIYSoMpKUEMJD\neeKysgnLPiZj115q39yPkHvvqHB56nOH0R79BYdfELYbx7q+B4PVDOlxgCqvh4S26iegdSYk7Gqa\n17FQ398zExK7Dln5cHMOCnDPCCM3ddK7O6QqpygK//spmdkLjhFzIYfIgXX419xWNAirYG8hcV0Y\n2KU+vdvV5WxCOl98f9Ld4QghhBBVRoZvCOGhSrOsbHWaADRj7yHiXl2Brl4ITV57ocJPhVWpiWh3\nr0fR6rH1nwh6F3d3tlvzVtpQHOAXDrqqP5dma15CIteuplmQhXAPTEg4FIUtu3L5MdqKj5eKqbcY\naRha83qFZWXbWP5RDL/uTaOWt4aZDzSiV7fa7g5LVCMqlYrJka2JvZTJD/sv0Ky+H33a13N3WEII\nIUSlk54SQnio/GVli1LdlpW1mTI4/ehz4HDQ7M2X0AVW8MuYxYzup9WobLnYbhyDUjvENYHmc9gh\nLQYctrxVNox+ri2/FK5MSDSo7XkJCatN4ZOtOfwYbSU4QMXjd3jVyITE8dNZPDn/GL/uTaN181q8\n/mIbSUiIIhl0Gqbd1gEvg4ZVUceJvZTp7pCEEEKISidJCSE8VP6yskWpTsvKKorCuadfJjcugbAZ\nU/Hr072CBTrQ7lqLKiMFW/t+OBq2c02gzvKVvB4Sdgt4BYJ3kGvLL4Uca94qGxabmqaBuR6ZkMg0\nK7zzjZlDp+w0DVPz+Dhvgvxr1i3H4VD4estF5rxynKTLuYy7JZSFz7QkOKjmDV0RpVc30Jv7R7Ql\n1+Zg2brDZOdY3R2SEEIIUalk+IYQHswTlpWN/XAtKZu243NDJ+o/eX+Fy9Mc/AHNhRM4wppj7zTI\nBREWoCiQHg/WbDD45vWSqGI5trweEjk2NU0Cc2kY4HlfSJLTHLy3wUyySaFLKy0TBhnQ1rAlP1NN\nVv773jkO/plBYG0dTzzQmA5tfN0dlsdYvHgx+/btw2az8dBDD9GhQweeffZZbDYbWq2WV199leDg\nYDZu3MjHH3+MWq3mjjvuYNy4ce4O3SW6tAxmeK9GfPvbed7f/BfTb++AWiZCFUIIcZ2SpIQQHqy6\nLytrPnmWozMXovH3pdmyhai0FfuTo475E+3hH1F8ArD2HQeuXvY0KwksJtB6gV99qOIvARZbXg+J\nHJuaxgG5NPLAhMS5BDsfbDKTnQODuuuI7K2vcV+m9h9J57/vn8OUbqN7Jz8eu68xfr5yuy2t3377\njZMnT7JmzRpSU1O57bbb6NmzJ3fccQfDhw/ns88+Y+XKlUyfPp1ly5axdu1adDodY8eOZciQIdSu\nfX0MjbmtXxPOJqRz4FQyW387z4jejd0dkhBCCFEppJUkxHUgf1nZ6sSRY+HUI3NwmHNo/saLGMIr\nNmGbypSE9td1KBod1gETweDi4zWnQnYyaHR5K224eiWPa7D83UPCbFXTyEMTEodO2fhsWw4OB4yN\nMNC7vc7dIVUpq83B6nXxrI+6hFaj4r47wxk5OFiW+iyjG264gY4dOwLg5+eH2Wxm3rx5GAx58+QE\nBARw9OhRDh48SIcOHfD1zeuB0rVrV6Kjo4mIiHBb7K6kUat56NZ2vPjRH6z7+QxN6vnRtnGgu8MS\nQgghXE6SEkKIShGz4L+Y/zxJwwfGEziigsMscnPQ/rgaldWCte84lIBQ1wSZz5IJGQmg0oB/Q1BX\n7Z/GXBsc/Dsh0bB2Lo0DrFXdSaNCFEXh5/1WNv2Si16Xt+Rnm8Y16/Zy8ZKFJSvOcupsNvXqGpj1\ncBOaNapeiUJPodFo8PbOO3dr166lX79+zp/tdjurV69m2rRpJCcnExj4z5f0wMBAkpKKXia5oIAA\nb7TayulRFhzs2iE6wcEw594ePLvsF97d9CdLZw4gOECWkC2Jq+tAlJ3UgftJHbif1EHZ1KxWoxCi\nSqRG/cillV/i1bIpbf/zLClZFZioUXGg/XUd6vRkbG1vxNGko+sCBbCaIT0OUOX1kNBW7aoluTY4\nEO9FtlVNA/9cmgR6VkLC4VBY/3Muuw5Z8aul4v5bjdQPrj5DiKrCzt9SeHtVDOYcBwN6B/Lg3Q3w\n8qpZ56AybN++nbVr1/Lhhx8CeQmJp59+ml69etG7d282bdpU6P2KopSq3NTUbJfHCnkN0KSkDJeX\nG+StY3xECz777gQLP/yNZyZ2RaetWZPGllZl1YEoPakD95M6cD+pg6KVlKiRpIQQwqVy4xM5M2sB\nKqOBZm8vQuPtBVnl/8OsOfwzmti/cIQ2xd5liAsjBezWvJU2FAf4hYOuap9s59rhYEJeQiLc30rT\nIM9KSFisCp9F5XD0rJ3QIDX332okwLfmfFnKsdh5/7M4vv/lMkaDmhn3N2JAn6pfreV6tHPnTt55\n5x3ef/995/CMZ599lkaNGjF9+nQAQkJCSE5Odm5z6dIlOnfu7JZ4K1tE1/qcjjfx29FEvthxkkk3\nt3J3SEIIIYTL1JzWoxCi0il2O6cfex57qomG82bi3aZiq4CoL5xAc3AHSi1/rDfdAWoXPn122CEt\nBhy2vFU2jH6uK7sUrPa8IRtZuWrq+1lpFpTrUQmJ9CwHb39t5uhZOy0aaJg+1qtGJSTOxmTz1EvH\n+P6XyzRt5MWS+a0lIeEiGRkZLF68mBUrVjgnrdy4cSM6nY7HH3/c+b5OnTpx+PBh0tPTycrKIjo6\nmu7dK7jkcDWlUqmYMrQ19YNr8UP0BXYfuejukIQQQgiXkZ4SQgiXiX9jJRm7owkYNpCQybdXrLD0\ny2h3fgVqDdb+d4KxlmuChLylP02xYLeAVyB4V+2XyX8SEhrC/Kw0r+NZCYnEFAfvbzSTkq5wQxst\n4yIMaDQedAAVoCgKW3ck89GaOKw2hVuGhDBpbBg6Xc1JyFS2b7/9ltTUVJ544gnn7+Lj4/Hz82PS\npEkANGvWjPnz5zNr1iymTp2KSqVi2rRpzl4V1yODXsP02zrw0sd/8HHUMcJDfGgQ4uPusIQQQogK\nk6SEEMIlMn4/wIUl76KvV5cm/5lbsRUHrBZ0P61GZc3B2mcMSlB91wWqKJAeD9ZsMPjm9ZKoQlY7\nHEowkpmroZ6vlRYelpA4HWdn5RYzZgtE9tIz+AZdjVldIiPTxrKV59mz34Svj4anpzameyd/d4d1\n3Rk/fjzjx48v1XsjIyOJjIys5Iiqj7qB3kwd0Za31h1m2TeHeWFKd7yNNWuVGyGEENcfebQjhKgw\nW1o6p6fNBaDZ8oVoAyrwRU1R0O5ejzrtEvZWPXE06+KiKP+WlQQWE2i9wK8+VZkRsDnyEhIZFg2h\nvlZaBntWQmLfMSsr1pvJtcKdQwwM6aGvMQmJP09k8uT8v9iz30T71j68/mIbSUgIt+jaMphhvRpy\nKdXMB1v+wlHKCT6FEEKI6kp6SgghKkRRFM7OXkjuhYvUn/Ugvj0rlkTQ/LkLzfkjOEIaYes+zEVR\n/s2cCtnJoNHlrbShqrq8bMGERF0fK608KCGhKArf77WydXcuRn3ekp8tGtSM24fdobB280W+3JAA\nwMTb6jFmRCgatYdUnrgujenXlLPx6ew/mczW384zondjd4ckhBBClFvNaFVWcxarHVOmBX8fAwad\nLCMnPEvSp+tI3bID355dCJtxX4XKUsWfQrP/fyhevlj7jXftxJaWTMhIAJUG/BuCuur+/NkccDjB\nSHqOhhAfG61DPCchYbcrfP2jhT1HbQT45i35GRpUM/5OJafksvS9cxw9nkmdQB0zH2xC25Yyhl+4\nn0at5uFR7Xnxoz9Y9/MZmtTzo23jQHeHJYQQQpSLJCXcyO5wsGbHKfafSCIl3UKgn4EuLYMZH9Ec\njVpG1ojqL/v4ac7Pew1NbT+avrUAlbYCf1IyUtHt/BJU6ryJLb1cOGGd1QzpcYAqr4eE1uC6sq/B\n/ndCwpSjIdjHRusQi8ckJHIsCqu25nA8xk54sJqptxrxq1Uz/jb9ccDEmx+eIyPTTs+u/ky7pxG+\nPnLLFNWHXy09j4xuz78/i2bFxqPMu+cGAv2M7g5LCCGEKLOa0bqsptbsOMX2vXFcTregAJfTLWzf\nG8eaHafcHZoQ1+Qw53D64WdRciw0XfIChvqh5S/Mlps3sWWuGVuPkSjBDVwXqN2at9KG4sibQ0Ln\n7bqyr7VrBxy++HdCopaNNiEWPKXXf1qGg7e+NnM8xk6bxhoevd2rRiQkrFYHH6yOZdEbp8nJcfDQ\npAY8M62pJCREtdS8vj8TBrUgI9vK2+uPYLM73B2SEEIIUWbXfwuzmrJY7ew/kVTka/tPJGOx2qs4\nIiHKJualpZiPnyFkyjgChg0of0GKgva3DahTL2Jv0R1Hi+4uixGHHdJiwGHLW2XD6Oe6sq/B7oAj\nF42kmTXUqWWjTV3PSUjEXLTyxpdmEpId9Omg5d6RRgx6Dwm+Ai5czOH/Xj7O5u1JhNczsvj5VkQO\nDK4xk3kKzxTRtT692tbldHw6X3x/0t3hCCGEEGUmj37cxJRpISXdUuRrqRk5mDIthARU3RNdIcoi\n5dsdXPp4LV5tmtPwhRkVKktz7Dc0Zw/hqNMA2w0jXBQheUt/mmLBbgGvwLx/VcShwNGLBlLNGoK8\nbbT1oITE8fM2VkVdJseiMPJGPQO61owlP3/YdZl3P40lx+Jg8E1BTJ0YjtFQM+bOEJ5NpVIxJbI1\nsUmZ7Ii+QLP6/vRuV4Gea0IIIUQVk54SVchitXMpNRuL1Y6/j4FAv6LHtQf4GvH3qbox70KUhSXu\nImdnLUBtNND87UWovco/hlmVeBbNvigUow/W/hNA46I8qaJAejxYs0Hvm9dLooq+WDsUOHLRQIpZ\nS6C3jXahnpOQ2HPUyvsbc7DbFSZFGhjY7fpf8tNstrP0vXO88cF51GqY9XBjpt3bSBISwqMY9Bqm\n3dYBL4OGj7ceI+5SprtDEkIIIUpNekpUgeImtOzcog7f77tw1fu7tKzjllU4ZBUQcS2Kzcbp6XOx\nmzJovHgOXi2blr+wLBO6n9YA5CUkvF04tCIrCSwm0HqBf/0qTUgcvWggJVtLgJeNdh7SQ0JRFKJ+\ny2X7H1a8jfDk3UEEeBfdk+t6cvpcNkveOUvCJQstmnjz5ENNCA2RhLDwTKGB3tw3vC3LvjnMW98c\n5oUpN+BtlGaeEEKI6k/uVlUgf0LLfPkTWkZ0q8/g7uHsP5FMakYOAb5GurSsw/iI5lUan6wCIkrr\nwtIPyPz9AAEjBxF8123lL8huRffT56gsWVh7jEQJaeS6IM2pkJ0MGl3eShuqqrmGHQr8mWjgcraW\n2l522oda0HjAx8dmU1jzvYXo4zaC/FU8MMqLlo30JCVdv0kJRVHY9N0lPvkqHptd4bZhdZl4Wxha\nrQdkkIQoQbdWwQzr2ZCte2L4YMufTB/T4brv7SSEEMLzSVKikpU0oeXBk5dZ+EBPbu/fzK09FIpL\nmgBMHNyyyuMR1VP67n3EL/0Aff1Qmix+rvwNXUVBu2cz6ssXsDftgqNlD9cFacmEjARQacC/Iair\n5k+cQ4G/Eg0kZ2mpbbTTITTHIxIS2TkKH23J4fQFO41C1dw30gsf7+v7C4wp3cqbH55n36F0/P20\nPHF/Yzq3r7oJUIWobGP6N+VsQjr7TyazdU8Mw3u5MOkrhBBCVAIPaDa7X8G5IMqqNBNaGnQaQgK8\n3TZkQ1YBEddiTUnjzPQXQKWi2fKX0dYu/5c49ck/0JyOxhEYhq3nLa4bWmHNgfQ4QJXXQ0JbNd3w\nHQocu2QgKUuLv9FOh3qekZBISXfw1lfZnL5gp2MzDY+Muf4TEtGHUpk57xj7DqXTqZ0vr7/YRhIS\n4rqjUat5aFR7avvo+fqn0/x1LsXdIQkhhBAlkp4SJSjPsIYr52XIn9DychGJieowoaWsAiKuRVEU\nzs5aQG5CIvWffhjfGzqVuyzVpRi0f3yLYvDGOuBO0OpcE6TdCqYYUBzgFw66qrlmFUXh+CUDlzK1\n+HlQQiI20c4Hm3LIyFbo30XHyL561NdxF2+7XeGLDQl8veUiajVMHhfGqKF1UXvChB9ClIN/LT2P\nju7Av1dH887Go8y75wYC/co/KbEQQghRmSQpUYKyDGsoKYHRpWVwoXLyuWtCy4Kqe9JEuN+lj9eS\ntu0nfPt0I+yxe8tfUHY6up8/B0XB2m881KrtmgAddkiLAYctb5UNY9U8+VYU2HtGITFTi5/BTsd6\nOWg9ICHx51kbn2zNwWqD0f313NRJ7+6QKtWlZAuvv3uOY6eyqBdi5IkHGtGyWS13hyVEpWse7s/4\niOas3n6St9cf4Zm7uqL1hKypEEKIGkeSEsW41rCG2/s3K5RQKCmBkT9xpbsntCyKQaep1kkT4V7Z\nf50i5sXX0QT40+zNBag05bwe7DZ0P69BZc7E1i0SJbQCq3YUpChgigW7BbwC8/5VAUWBE0l6EjLA\n14MSErsOWfnmJwtaDdwzwkj7Ztf3LWD3vlSWrYwhK9tO3x4BzH2yLeZss7vDEqLKDOoWzun4dPb8\nmcia709x180yT5QQQojq5/pukVZAWYY1lCaBMXFwS7dPaFmc0Tc1xZxj41hMKqkZlmqVNBHuY8/O\n4dTDz6JYcmn6zivo64WUuyzt3q2ok2KwN+6AvU0f1wSoKJARD9Zs0Pvm9ZKogiEIigInkvUkZOgI\nqAVtg3PQVp+Pc5EcisKWXbn8GG3Fx0vF1FuMNAyt5kFXgCXXwUdr4oj6IRm9XsW0exoy6KYgfGpp\nMWe7Ozohqo5KpeKeyNbEXcrk++g4mtb3o3e7UHeHJYQQQhQiSYlilGVYQ2kTGPkTWlYXVw45CfDV\n06tdKBOHtMDb4KKx/h7qyrlBaqKY+UvIOXmWuveNJ2Bo/3KXk3vkNzQnfscRUBdb79GuSxxkJUGO\nCbRe4F+/yhISJ5P1JKTr8NHb6ddGiym10ndbIVabwur/5XDolJ3gABUP3OpFkL8HdOsop9gLZpas\nOMv5uBwahRuZ9VATGtT3cndYQriNQa9h2pgOvPTRH3wcdYwGIT6EB/u4OywhhBDCSZISxSjLsAZP\nnZfhyiEnKRm5/HrkIt5GbY1dCrQ8k5tej1I2bSfp02/wbtuSBnMfL3c5quQ4cr7/CkXvhbX/RNC6\naP4CcypkJ4NGl7fShqry60ZR4NRlPfHpOmrp7XQKy0Gv9a30/VZEpllh5WYz5xIcNA1Tc+9IL7yN\n1+fkjoqisH3nZd5fHUturkLkwDrcMz4cg77mfG6FKE5ooDdTR7Rh2TdHWLbuMM9PuQFvozQBhRBC\nVA/SWivB+IjmDO4eTpCfEbUKgvyMDO4eftWwhvwERlGq67wMNW0p0NIu65qfqLmcbkHhn7lB1uw4\nVTWBVgOWuATOzl6I2nTpEsMAACAASURBVMtIs7cXoTaWM6lmzkT30+dgd2C9aRz4umi+B0smZCSA\nSgP+DUFd+Q1rRYHTl/VcMOnw1jnoFJZDNfxYF5Kc5uDNL7M5l+CgSystD42+fhMSWdl2lrxzluUf\nxaDTqnl6WhMemtRQEhJCFNCtVQiRPRuSmGrmw2//QlEUd4ckhBBCANJTokQatbrUc0FU58ksi1JT\nlgItS8+Hsk5uej1SbDZOP/oc9vRMmvxnLl4tGpevIIcd3c41qLLTMfQdiSWshWsCtOZAehygyush\noa38XkiKAmdSdMT9nZDoHGZGX80vg7MJdj7cZCY7BwZ11xHZ+/pd8vP46SxeW3GWS8m5tG5ei5kP\nNiakTvXsnSaEu93evyln49OJPpFE1J4YhvVq5O6QhBBCCElKlEZp5oIoSwKjOvDUISdlVZZlXWtK\noqYkF157j8y9hwi8dQh17hxV7nI0+7ahTjyHvWFb9DcMguTMigdnt4IpBhQH+IWDrvLrQlHgbIqO\n2DQ9Xn/3kNBX87+aB0/aWP2/HBwOGBdhoFf763N+GIdDYX1UIqu/ic871pGhjB9VD43m+ky+COEK\nGrWah0e1Y/5Hf7D2p9M0rudHm0YB7g5LCCFEDSd9W10sP4FRnRMSUP2HnJR2uMW1yijLEJX8RE1R\nrqdETXHSd+0l/r8fom8QRuPFz6Eq55N19ZkDaI/txuEfjK3PmHKXU4jDDmkx4LDlrbJh9Kt4maVw\nLlVHzN8Jic5hORi01be7s6Io/Bidyydbc9CoYeotxus2IZFqsvLS66f4ZG08/r46XnyqBRPHhElC\nQohS8Pcx8Ojo9qhVKlZsOEJqRtHJeCGEEKKqVPNnfqIyVcchJ66caLKsPR/KMrnp9cZ6OY3Tjz0P\najXNl7+M1q98M7OrUuLR/rYBRWfANmAi6FyQyFEUMMWC3QJegXn/qsC5FB3nU/UYtXk9JKpzQsLh\nUFj/cy67Dlnxq6Xi/luN1A++Pq/XA0f+n737Do+qTPs4/p0+k957AqFLLyKoFGkKCgIKSLEhYF3L\nyq7rqru6ruuu7ruuvSIKKohGRUCaCCiogAJSRHpIL5M6SabPOe8fERYwCTPJTGYmeT7X5XWZkHPO\nMyWTc37nee7bxPOLTlFtcjKobwT33taByIi2Gb4Igq90TYtixqguLP/qGK+uPMCfZg9ErRL3qQRB\nEAT/EKFEOxaIS048WW5xIc1ZohKIQY2vybJM9oN/w1FsJO3P9xA2qE/zdmStQ7N1OQqXE8eImcgR\ncd4YHNQUgsMM2vD6WRKtUBshp1LDqV8Dif4pVvQBHEjYHDLvr7dyKNtFcqySedfqiQ5vexcXDqfE\nsk8LWbm+FLVKwdyZqUwal+CdmTiC0A6NvTiNE4XV7PqllBWbjzNnXPvsuiUIgiD4nwglBLdqZrQG\nbxeabM7Mh0AManytZPEKqr7cRsSwS0i+55bm7URyodn2MYq6Kpx9RyGldffO4OqMYK0GtQEiU1sl\nkMit1JBdoUX36wwJvSZwAwlTncTi1VbySiW6pau4+Wo9Bl3bu0gvLrXx3BvZHMs2k5ygY+GdmXTu\n6P/PLEEIZgqFglsn9CDfWMdXu/PpnBrB0J5J/h6WIAiC0A759Haa1Wpl7NixfPrppxQVFXHTTTcx\ne/Zs7r//fux2OwCrVq3i+uuvZ/r06Xz88ce+HI4Q4NxZbuEpd9u6ni9YaoO0VN3BI+T9/QXUMVF0\nevFvKDxcInOa6qdNKItP4ErrjqvvFd4ZnKUSzGWg0tR32lD4/u5/XpWakxVadKr6GRKGAA4kSiok\nXvzIQl6pxOCeauZf2zYDiW07K1j4t184lm3miktj+M/jPUQgIQheotequWdqb3RaFe+uPUxOcY2/\nhyQIgiC0Qz6dKfHaa68RGRkJwIsvvsjs2bOZMGECzz33HFlZWUyZMoVXXnmFrKwsNBoN06ZNY9y4\ncURFRflyWEKA8kVHkPY488FdLrOFE3c9gmx30OmFJ9AmNVz49EKUpw6g/nk7UkQszsuneSc8sNVC\nTREoVBCZAUrfT+rKr1ZzolyHViXRLzWwA4nj+U7e/cKKxQbjh2oZO1jT5pYxWG0u3l6Wz6Zt5eh1\nSu6b14FRl8f6e1iC0OYkx4Zy+6SevPzJAV7I2sdfbhlMdHjbLuwsCIIgBBaf3Xo8ceIEx48f54or\nrgBg586djBkzBoBRo0bx/fffs2/fPvr06UN4eDh6vZ6BAweyZ88eXw1JCHC+7AjSXmY+eCLnsX9j\nPZFD4oJZRI0Z1qx9KCqLUX/3GbJai3PkbNDqWz4whxVM+YCifoaE2vcnxwXVao6X1QcS/VOshARw\nILH7sIM3V1qxO2D2lTrGXaJtc4HEqTwzf3jyMJu2ldMpw8D/Pd5DBBKC4EMDusYz7YrOVNXaefGT\n/S3qfCUIgiAInvJZKPHMM8/w8MMPn/naYrGg1WoBiI2NxWg0UlZWRkzM/yrpx8TEYDQ2XFNAaB+a\nu9xC8Ez5yg2UfbiKkN7dSX/k3ubtxGZB8/VyFC4HzsuvQ45KaPnAXA6ozgVZgohU0Ph+mn6hSc2x\nMh0aVX0NiRBtYAYSsizz5S47yzba0Kjh9sl6BvVoW10nZFlm3WYjD/39CAVFNiaNS+Bfj3YnNckL\nYZcgCE0aPySDYX2SySmu4e01h5DkwPwsFARBENoen8yJXrlyJf379yc9Pb3Bf5cb+UPX2PfPFx0d\nglrt3Tve8fHhXt1foAjGx3X/rEFY7U4qTTaiI3Totb99mwbj43JHazwuc3Yeex7+J6rQEAaveIGw\nNM/vQMuShGXlMpw1FWgvGUfEoKFN/rw7j0tyOanKzsYlOQlNzCAkLtnjcXkqu1TmqFFGp4aRPVVE\nhnjeCrU1XjOnS2bJ6mq+3m0nLkrFwpuiSU3wbSDR2r9jphoH/3rpKN98X0ZkuJpHHujB5Zd4f3ZE\ne/jskGWZ/YeqSU40kBAnpuEL7lEoFNw8vjullWZ+PGLk823ZTB3Ryd/DEgRBENoBn4QSW7duJS8v\nj61bt1JcXIxWqyUkJASr1Yper6ekpISEhAQSEhIoKys7s11paSn9+/e/4P4rK81eHW98fDhGY9sr\n7hQeaeDEqfKgraOgBmqqLZz/yrTV16s1HpfkcPLLzAdwmmrJfP4JLNFxWJpxTNVPm1Cf+gUppQs1\nXYdR08Q+3HpcsgxVOeCwgCGGOimUOh8/F8UmNYeNWtRK6JNkwV4nY6zzbB+t8ZpZbTJL1lk5musi\nLUHJvEk6tAorRqPVZ8ds7d+xQ0dr+e+b2ZRVOOjVPYzf396R2Git18fQHj478ousLFqWx76fa7ji\n0hjuX9DRJ8cT2ia1Ssk91/XhqaU/svq7UyTHhjC0l+jIIQiCIPiWT0KJ559//sz/v/TSS6SmprJ3\n7142bNjA5MmT2bhxI8OHD6dfv3489thjmEwmVCoVe/bs4ZFHHvHFkNoVlySxYvNx9p8ox1hpISZC\nx4Bu8dwwuguqZnZXENqGgv97g7o9B4mdOp646dc0ax/K3EOoD3yNHBaNY9h0aOl7SpahphAcZtCG\nQ1iiz1t/FteozgQS/VOshOkCc5pyVY3EotVWisokenZUceN4PTpt26kf4ZJkPllTzIrPiwCYNSWZ\n6ycmoVK2ncfYWiwWFx+tLmL1l6W4XDCgdwSzpvp+tpHQ9oSHaLlvWj+efu9HFq89TFyUgS6pkf4e\nliAIgtCG+b6k/a/uvfde/vSnP7FixQpSUlKYMmUKGo2GhQsXMm/ePBQKBffccw/h4eIOTEut2Hyc\nTT/mn/m63GQ78/Xssd38NSzBz6q37aLo5XfRdUil478eblZxREW1EfV3nyKrNDiumA06L9R8qDOC\ntRrUeohM9XkgUVKj4nCpDrUS+qVYCdNJPj1ecxWWuVj0uZXqOpnL+miYMlLbpi7WyyvtPP/WKQ4e\nriUuRsPvb8+kZzfPl8+0d7Iss3FrCS8tOkFltYOEOC23zUrjkv6Rba4AqtB6UuNCuXNyb57/eB8v\nf7Kfx265mLhIg7+HJQiCILRRPg8l7r33f0X03nnnnd/8+/jx4xk/fryvh9Fu2Bwu9h5tuFjo3qNl\nXD+yc1Au5RBaxlFeycl7/4JCpaTzq/9AFd6Miz+7FfXWZSgcNhzDpiNHe2FKr6USzGWg1EBUhnfa\niTahtFbFL6U6VErom2wlPEADiSM5TpastWJzwMRhWq4Y0LZafv7wUzUvLT5FTa2LIQMiuWduB8LD\nWi0jbzOyc80sWpbPoaO1aDUKZk5JZsr4RHRaMSNOaLk+nWKZNaYryzYd48Ws/fz5xkEYdOL3VBAE\nQfA+8dclgNgcLqprbS2qAVFda6PCZGvw3yprrFTX2kiI9n1HAyFwyJLEyQeewFFaTvqj9xI2oHdz\ndoL6209Qmspw9rwcKbNvywdmq4WaIlCo6gMJpW8/joy1Kg6V6FAp6gOJCH1gBhI7f3aQtdmGUgk3\nT9DTr2vb+Zh2OCSWflzAmk1GNGoFt9+YzvhRcW0qcGkNtXVOln1WxIYtRiQZRlwax5ypiaKopeB1\nYwalUVRuZsveAt5afYjfXdcHZRuasSUIgiAEhrZzthvETteA2HvUSIXJRkyEjr5d4hg7KI2YCP0F\nA4qzw4zIMB0xETrKGwgmosP1RIaJk9b2puTtD6n+6lsiRgwh6a6bmrUP1YFvUOUfRkrqhGvAuJYP\nymEFUz6ggMh0UPv2fVlWd1YgkWIlMgADCVmWWb/DzqYfHITo4baJBjJT2s6spoJiK8+9ns3JXAup\nyToW3pFJZoYISD0hSTJfbS/n/axCTLVOUpN0zJ+dzrhRqW2ygKfgfwqFgllju1JSaean42VkbT3B\nDNGiWxAEQfAyEUoEgIZqQGzZU8CWPQXENlGksqEwY0C3ePp3jeOr3QW/Oc6AbnFi6UY7U7f/MHlP\nvYg6LoZOL/4NRTOKUioLjqLatxk5NArH8BmgbOF7yOWA6lyQJYhIA61vL0zL6lT8XKxDoYA+yYEZ\nSDidMiu+srHniJPYSAULJhuIj2o7U/C3fFvOm+/nYbVJjB0ey7zZaeh14rPIE0dP1vHWB3kczzaj\n1ym5eXoqE8fFo1G3nfeJEJjUKiV3TenNU0t3s35XLkmxIYzol+LvYQmCIAhtiAgl/KypGhDQdJHK\nxgpajh6UytiL09h/opyyKgvR4XoGdIvjBnF3o11x1Zk5fvcjyA4nnV54Am1CnOc7MZWj3vYxqFQ4\nRs4CfWjLBiW5oCoXJGd9lw19RMv2dwHl5nMDiShD4AUSZqvMu19YOVHgokOSktsmGggLaRvToy0W\nF2+8n8fX31dg0Ct58I6ODB8S4+9hBZUqk4MPPilk07ZyAEYMjeaW6anERGv9PDKhPQnVa3hgWl+e\nWvoj7204QmK0ge4Z0f4eliAIgtBGiFDCz5qqAXG284tUNhVm7DtWzlMLhnDH9f04caq8RTUqhOCV\n8+iz2E7mknTHjUSNuszzHThsaLYuQ+Gw4rj8euTYFt4Zk2WozgeXDQwx9f/5UIVZycHTgUSSlegA\nDCQqTBKLPrdQUinTt7OK2Vfp0ajbRiBxIsfMf17LpqjURtfMEB68I5OkBLF8zF0ul8z6LUaWfVaE\n2eKiY5qB+XPS6NVddKgS/CMxJoR7pvbhPyt+4uVPD/CXWy4WNaoEQRAErxChhJ81VQPibOcXqXSn\noGVaSlTAnzB4o7in8Ftln66j7KM1hPS9iLQ/3+P5DmQZ9fefoawuxdl9KFKn/i0bkCxDTSE46kAb\nXj9LwofFDSvNSg4W6wHonWQjOiTwAom8Ehdvr7ZSY5YZOUDDxGFalG2g4KMsy6z50sjSjwtwumSm\nTkhk1tRksczAAz8fqeGtD/LIybcSGqJiwZw0rroiHpUq+N8fQnDr0SGam67qzrvrDvNC1n4evWkQ\nIXqNv4clCIIgBDkRSviZTqNiQLf4c5ZhNOT8IpXBXtCysXoYDdXOEDxjPZXPqYf/hTI0hC6vPY1S\n6/kJo+rQdlQ5PyMldMB1sRda9tYZwVoNaj1Epvo0kKiyKDlQrEeW6wOJmBCXz47VXD+fdPL+eisO\nF0wdqWVYv7YxFb/a5OClxTns3m8iMkLN/fM7MqC3b5fotCXllXaWfFTAtp2VKBQwdkQsN16XQmSE\nuOgTAseIfikUltWx8Yc8Xvv8Zx6Y3lf83RYEQRBaRIQSAeCG0V1wSTI/HS2jsrbh2Q/nF6lsKswI\nhoKWjdXDgN/WzhDcJ9kdnLj7EaTaOjq99CT6zHSP96EoPI5q75fIIRE4RsxscWFLS2UpmMtAqalv\n/anw3clrlUXJ/qL6QKJXko3Y0MALJLbvs7PyGztqFdx6jZ7endrGx/CBX2r475unqKx20K9nOPcv\n6Eh0pLiYdofDKbF6Yykfry7GapPokhnCgjnpdOvUwhouguAjM0Z1objCzP4T5SzfdIwbr+zu7yEJ\ngiAIQaxtnA0HsdMzBvYfrw8kosK0hBo0WKxOqmptTRapPP29vUfLqKyxBk1By6bqYZxfO0PwTP4z\nr1L30yFip11N3PVXe76Dmko02z4ChbI+kDCEtWxAtlpqS3NBoaoPJJS++8iptio5cFYgERdggYQk\ny6zZbufrvQ7CDArmXasnIzH43+cul8yKz4vI+qIYpRJumpbClPGJKJViqYE79h40seiDPApLbESE\nqZk3K43Rw2LF8ycENKVSwR3X9uLp93ezeU8BybGhjBmU5u9hCYIgCEFKhBJ+dv6MgapaO1W1dkYN\nTOWqwelN1lpQKZXMHtuN60d2Dqq6DO7Uwwj0WhiBqHrrDopfew9dZjodn/6T5ztw2tF8vQyF3YJj\n6GTkeM9nWZzDYQVTfv1Sjch0UPtuSZHJqmR/oR6XDL0SAy+QcDhllm20sv+4i4RoBfOvNRAbGfzT\nnY3ldp57I5vDx+tIiNPy4B2ZdO8s7u67o8Ro450P89m5txqlAq4ZE8/MKcmEhYo/y0JwMOjU3H99\nX/6+9EeWbzpGUkwIvTJFdx1BEATBc+Lsx4+amjGw/3g5M0Z1cStk0GlUQXURH+z1MAKRw1jOyfsf\nR6FR0+W1p1GFeXhhKMuod3yOsrIYV9fBSF0vbtmAXA6ozgVZIiKtCya772ommKxK9hXVBxI9E23E\nhwVWIFFrllm8xkJOsUTnVCW3XmMgRB/8d8F37K7i5XdyqDO7uHxwFHfdkkFoiPiTciE2u8Rna4v5\nbF0JdodMz25hLJiTRsf04PkMF4TT4qIM3HtdX55dvodXVx7ksZsHkRwrgklBEATBM+IM0o/a64yB\nYK+HEWhkSeLk/U/gMJaT/vgDhPa9yON9qA7vQJW9HykuHefgZiz7OJvkgqpckJwQloguMhaMNS3b\nZyNqbPU1JFwSXJRgIyHAAgljVX3Lz7JqmQHd1cwco0Md5C0/bXaJd1fks35LGVqtgrtvzWDs8FgU\nbaBziC/JsszOPdW8syKf0jI7MVEabp2RyrAh0eK5E4Jal7RI5k64iLfWHOKFj/fz2C0XE2YQ9WQE\nQRAE94lQwo8CccZAa7XobKwexpThnSitNAfNUpRAUPzmMqq3fk/kqMtIWjDb4+0Vxdmodq9HNoTh\nGDkTVC34WJBlqM4Hlw0MMfX/+UitTcm+Qj1OCXok2EgMD6xAIrvIxeLVFsxWGDtYw/ih2qC/+Mwr\ntPCf17PJybeSkarnD3dmkp5q8PewAl5BkZVFy/L46eca1CoFUyckMn1iEgaD+IwT2oZLeydRWF7H\nF9/n8MqnB1g4sz9qVfAvURMEQRBahwgl/CiQZgy0dovO8+thhIVoWLktm8ff3ilahHqgdt8h8v/5\nMpr4WDq98AQKT5+rumo036wAqC9sGdKC9o2yDDWF4KgDbTiEJfqs9WetTcFPpwOJeDtJARZI7Dvm\nZNlGK5IE00frGNo7uO8ayrLMpm3lLFqWh90uc9UVccydmYZOK343m2KxuPhodRFrvjTidMn07xXO\n/NnppCbr/T20gPfss8+ye/dunE4nd9xxB1deeSVLly7lmWeeYdeuXYSG1i8RWLVqFUuWLEGpVDJj\nxgymT5/u55G3X1NHdKK4wszuI0be23CEWyf0CPogVhAEQWgdIpTws9bqoHGhGRD+atF5uh7Gsk1H\nRYtQD7lq6zhx1yPIDiedXvwbmjgPZyW4HGi+Xo7CVofjkonICR1aNqA6I1irQa2HyFSfBRJ1dgX7\nCg04JQXd420kRTh9cpzmkGWZrXsdrNluR6eBuRP19OgQ3B+zdWYXry/NZfuuSkJDVDwwP4NLL472\n97ACmizLbNtZybsrCqisdpAQp+W2mWlcMiBSXKS5YceOHRw7dowVK1ZQWVnJ1KlTMZvNlJeXk5CQ\ncObnzGYzr7zyCllZWWg0GqZNm8a4ceOIiory4+jbL6VCwfxrelJWtYdt+4tIjg1l/JAMfw9LEARB\nCALBfbbcBvi6g4Y7MyD83aLT38cPVqf+/C9sp/JJvucWIkcO9WxjWUa9cw3K8gJcnQcgdbukZYOx\nVIK5DJSa+tafCt/cQa+z18+QcEgKusXbSA6gQMIlyaz82s53BxxEhiqYf62elPjgft8ePVHHc29k\nU1Jmp0eXUH5/e0cS4kQh2qacyjPz1gf5HDpai1ajYObkZKZMSBSzSjwwePBg+vbtC0BERAQWi4Ux\nY8YQHh7O6tWrz/zcvn376NOnD+Hh4QAMHDiQPXv2MHr0aL+MWwCdVsV90/ry5JIf+HjLcZJiQujf\nNc7fwxIEQRACnAglAoSvOmi4MwPC3wU3/X38YFSW9QXln6wjdEAvUh+6y+PtlUd/QHViD1JsKs4h\nk1o2q8FWCzVFoFDVBxJK33ysmO0K9hXqcbiUdI2zkRJAgYTNIfP+OiuHTrlIjlMyf5KeqPDgvQiV\nJJnP1hXzwaeFSBJMm5jEzMnJqFTiLn9jauucLF9ZxPrNRiQZhgyM5LaZaSLEaQaVSkVISP1nflZW\nFiNGjDgTPJytrKyMmJj/zRCLiYnBaGw44D5bdHQIarVvAsP4+N+Os72Jjw/n8flDefiVb3lrzc88\n87vhZKZEturxBf8Sr4H/idfA/8Rr4BkRSrRhVrvTrRkI/i646e/jBxvryVxOPfwvlGGhdH71Hyg1\nnv0aK0pzUP+4FlkX8mthyxbUO3BYwZQPKCAyHdS+ea3MjvoZEnaXki6xNlIjAyeQMNVJvL3aSn6p\nRLd0FbdcrUevC96L96pqB/986QC79lYSHanhgds70vci8Ye1MZIks3l7Oe9lFWKqdZKSqGP+nHQG\n9G5BfRYBgE2bNpGVlcXixYvd+nlZlt36ucpKc0uG1aj4+HCMPuo0FGyi9GrmX3MRr648yN/e+p7H\nbhlMZKjvWkOfJl4D/xOvgf+J18D/xGvQsKaCmuC9lSdcUKXpwjMQ4H8FNxvSt3MM1bU2bA7fFRJs\n6viiRei5JLuD43c9gmS2kPnMn9F3SPNsB2YTmm8+BFnGMeIGCG3B2muXA6pzQZYgIhW0vpnNYnEo\n2FdQH0h0jrWRFhU4gURBqYMXP7KQXypxSU81868N7kDip4Mmfv/4L+zaW8mgvhH89289RCDRhKMn\n6/jTP47wyru52B0SN09P4fm/XyQCCS/Ytm0br7/+Om+99VaDsyQAEhISKCsrO/N1aWnpOTUnBP+6\nuEcCU4dnUm6y8fKn+3E4A6sgsSAIghA4xEyJNiw6wv0ZCOcX3IwK0xFq0LD/RDlb9xb6vBtGaxX8\nDHb5/3wZ84HDxM2YROzU8Z5t7HKi+eZDFJZanIMmICd1av5AJBdU5YLkrO+yoffNRZjl1xkSNpeS\nTjF20gMokDie72TJ2nLMVpnxQ7WMHawJ2iKGTqfMss8K+WxdCWqVgnvndeaKSyNQKoPz8fhatcnB\n+58UsmlbOQAjhkZz8/RUYqN9fye4PaipqeHZZ5/l3XffbbJoZb9+/XjssccwmUyoVCr27NnDI488\n0oojFS5k4mUdKSo3s+NQCe+sPcyCST2D9nNSEARB8B0RSgS5prpq6LXqRluOhujVqM9aH35+wc0N\nP+SxZU/BmX/3dTcMXxf8bAuqNn9L8RsfoO+UQYd//NHj7dU/rkVpzMPVsS+uiy5t/kBkGarzwWUD\nQ0z9fz5gddTXkLA5lWTG2MmIdvjkOM2x+7CDFZtsoIDZV+oY1CN4W34Wl9p47o1sjmWbSU7QsfDO\nTIYOThTTDhvgcsls2Gpk2WdF1JlddEjTs2BOOr26i9kk3rR27VoqKyt54IEHznxvyJAh7Ny5E6PR\nyIIFC+jfvz8PPfQQCxcuZN68eSgUCu65555GZ1UI/qFQKJh7dQ+MVRZ2HCohOTaESZdn+ntYgiAI\nQoARoYQfXKg9pzvc6aoB9TMQjuRWkVdae872eaW1rNh8/JyA4fS4DDo1+4+X0RBfd8PwVcHPYGcv\nKePk/U+g0Gro/NrTqEI9e46Ux3ajOvoDUnQSzksnN7+wpSxDTSE46kAbXj9Lwgd3vazO+hkSVqeS\njtF2OgRIICHLMpt+cLB+hx2DDh6YE0NcmN3fw2q27bsqeG1JLmaLxMhLY7jjxnQMBhEGNuTnIzW8\n9UEeOflWQgwq5s9OY/yoeFH80wduuOEGbrjhht98/3e/+91vvjd+/HjGj/dw1pjQqjRqFb+7vi9P\nLfmBz7ZlkxwbysU9xDIbQRAE4X9EKNGK3A0S3OFOVw0Ap0vGbG34gu50wKBWKc4ZV1SYjspa0Q0j\nUMiSxMn7/oqzvJKMJxcS2qeHR9sryvJR71qNrDXgGDkb1C2YYl5nBGs1qPUQmeqTQMLmrJ8hYXUq\n6RBtp2NMYAQSLpdM1hYbuw45iQ5XMP9aAxdl6jAagy+UsNkkFi3PY9M35eh1Su6b14FRl8f6e1gB\nqbzSztKPC/hmRyUAY4fHMuf6FKIignd2jCC0tshQLfdN68fT7+9m0ZpDxEbqyUwWtVcEQRCEeiKU\naAWnZyBcaEmEtSbS2wAAIABJREFUuzMobA6XW101wL12m+ePq7FAAkQ3DH8oenUppm27iBw7jMR5\nMz3b2FKL5uvlIEs4hs+A8OjmD8RSCeYyUGrqW38qvF9bxPbrDAmLQ0lGlJ2OATJDwmqTWbLOytFc\nF2kJSuZN0hMRGpx1gnPyLfzfa9nkF1nplGHgwTszSU3S+3tYAcfhlFjzZSkfrSrGapPokhnCgjnp\ndOsU6u+hCUJQSk8I445JvXjpk/289Ml+/nLLYKLDxfmEIAiCIEIJnzp7ZkS5yUZjNeP2HDHikmT2\nHy9zawaFO0HD6ZkMTbfb1LF2xym27y92+zGJbhitq3bPQQqefQ1NYhyd/vu4ZwXCJBeab1agMJtw\nDhiHnNKCoqG2WqgpAoWqPpBQev+jw+6Efb8GEulRdjJjHL6YiOGxqhqJRautFJVJ9Oyo4sbxenTa\nABiYh2RZZsPWMhYvz8fhlJk4Np6bp6ei0QRnuOJLew+aWPRBHoUlNiLC1MyblcboYbGi8KcgtFD/\nrnFMH9WFj7Yc58Ws/Tw8ZyA6rTinEARBaO9EKOFD5y+xkBppoV5RY/OoqGTTQcO5MxlOt9tsuNil\nhm/2uRdI6LUqLuuTJLphtCKnqZYTdz+K7JLo/NLf0cR6NstBtXsDytJTuDJ64eo1vPkDcVjBlA8o\nIDId1N6/s2V3wb4iA2aHkrRIB50CJJAoNLpYtMpKdZ3MZX00TBmpRRWEF6Y1tU5eeTeHnXuqCQ9T\n8cfbOjC4fwvawbZRJUYb73yYz8691SgVcPWYeGZNSSYsVPypFARvueqSdArL69i+v4hFaw5x19Te\nKAPhA18QBEHwG3Gm5SNNLbE4n1LRcGDRWFHJpoKGhmYyNNRus2+XWPYdc298AFa7C6VC4ZN2oMJv\nybLMqT89jS23gOT75hIxbLBH2ytP/oT68PdIkQk4L5va/NoPLgdU54IsQUQaaL1fS8Thqp8hUWdX\nkhrpoHOsPSACicM5TpautWJzwMRhWq4YEJwtPw8dreW/b2ZTVuGgV/cwfn97R9G68jw2u8TKdSV8\nurYYu0OmZ7cw5s9OIzND1M4RBG9TKBTcfFV3jJUWdh81snLbSa4b0dnfwxIEQRD8SIQSPtLUEovz\nNTaDoqmikg0FDQO6xTU4k6GhdpvVtTa2njU7wx2+7rwh/E/ZR2uo+HwjoYP6kLrwDo+2VVQUot7x\nObJGj/OK2aBp5swGyQVVuSA567ts6L1flOx/gYSKlAgHXQIkkNj5s4OszTaUSrh5gp5+XYPvo9Il\nyXz6RTEfriwCYOaUZKZNTArKmR6+Issyu/ZWs/jDfErL7MREabhlRirDh0QHZQAlCMFCrVJyz3V9\neGrJj6z5LofkmFAu7Z3k72EJgiAIfhJ8Z9pBoqklFkoFyEBMuJ6+nWPYf6LcraUYZ2soaLhQWHB2\nu82mxteYYOy84Y32q63NcvwUOY88gyo8lC6v/gOlxoNfU2sdmq3LweXCOWImckQzOyrIMlTng8sG\nhmgwxDRvP01wuGBfkZ5au4rkCAdd4/wfSMiyzPoddjb94CBED7dNNJCZEhzvm7NVVNr571unOHi4\nlthoDQ/ekUnPbmH+HlZAKSiy8vbyfPYeNKFWKZg6IZHpE5NES1RBaCVhBg33T+/LU0t38866X4iP\nMtAlLdLfwxIEQRD8QIQSPqJWKQjRaxq86B/ZP4WrLsk4c6G8bNNRt5dinO/soMETTS0B0WmU2BzS\nb74fTJ03vNl+tTVJNjsn7noEyWKl8+v/RJee4sHGLjTbPkJRV4Wz32iktO7NG4QsQ00hOOpAGwZh\nSV5v/el0wf4iPbU2FUnhDroFQCDhdMp8+JWNvUecxEYqWDDZQHxU4L5XGvPjvmpeejsHU62TIQMi\nuWduB8LDxEf9aRaLi4/XFLN6YylOl0z/XuHMm51OWrLoQCIIrS05NpS7pvTi+Y/289Kn+/nLzRcT\nF2Xw97AEQRCEVibOVH1kxebj5JXW/ub76QlhzB7X7ZwLY0+WYnhTY8eVZZmvdv92aUcwdd44v8jo\nhYqHBoq8f7yE+eejxM+eQuy14zzaVrV3E8rik7jSeuDqM7L5g6gzgrUa1HqITPN+ICHVBxI1NhWJ\n4Q66x/s/kDBbZd79wsKJAokOSUpum2ggLCS4pu87HBLvZRWy+stSNGoFC+akM2F0nFiG8CtZltm4\ntYSX3z5BRZWD+Fgt82alccmASPEcCYIf9c6MZdbYrnzw5VFe+GQ/j9w4CINOnJ4KgiC0J+JT3wea\nKnJptjpxumRUZ92Abc5SDG9o7LguSUKhULR6SHK+5i69aOr5D+S6GCVfbKFk0XL0XTqS8eRCj7ZV\nnjqA+tB2pIhYnJdfD4pm3uG3VIK5DJSa+tafzd1PI04HEiabioQwJz0CIJAor5ZYtMpCaaVM384q\nZl+lR6MOrovUwhIr/3k9m5M5FlKTdCy8M1MUaTzLqTwzb32Qz6GjtWg1Cm64NompVyeh0wbfTBhB\naIvGDEqjqLyOzXsKeGPVz9x3fV/RglcQBKEdEaGEDzRV5LKpugzNXYrRUucf118hyWktXXrR3Off\nn+zFRg7N/zMKnZYurz2NKsT96auKymLU332GrNbWF7bUNnMauq0WaopAoaoPJJTe/XhwSXCgSI/J\n+msgkWDzeyCRW+Li7VVWai0yIwdomDhMG3St6bZ+V84b7+VhtUmMGRbL/Dlp6HWBF7r5Q53ZyfLP\nili32Ygkw/ChscyZmkRifHAsQxOE9mTW2K6UVFrYf6Kcj7YcZ+aYrv4ekiAIgtBKRCjhA00VkQym\nugz+CklauvQi2J5/2eXixL1/wV5WSYen/khILw+Wl9gsaL5ejsLlwDFyFnJkQvMG4bCCKR9QQGQ6\nqL37HJ0OJKqtKuJD6wMJf98EO3jSyQfrrThcMHWklmH9gqtNpsXi4s3389j6fQUGvZIHb+/I8KHe\nL0gajCRJZvO35byXVYipxklKoo75c9K5clQqRmONv4cnCEIDVEold03uxT/e283GH/JIiQtlRD8P\n6ioJgiAIQUvMXfWB00UkGxJMdRn84UJLL2wO1wX3EWzPf9ErS6j59kcSJ40mYe4M9zeUJNTbP0ZR\nU4Gz90ikjJ7NG4DLAdW5IEsQkQpa7wZRLknmYLGeKquKuFAnFyX6P5DYvs/Ou19YAZh7jT7oAokT\nOWYWPnmYrd9X0CUzhP88cZEIJH51LLuOh/9xhFfeycVul7h5egrP//0iBvT2fktbQRC8K0Sv4b5p\nfQnVq3lvwxEO51T6e0iCIAhCKxAzJXzEX8Urg507Sy/S3NhPsDz/NT/uJ//fb6BJTqDvW09TLbl/\nta7avxlV4TGklK64+o1u3gAkF1TlguSEsETQe/fCzSXBd0dkKi0qYkOc9PRzICHJMmu22/l6r4Pw\nEAXzJulJTwyskKopsiyzZpORpR8X4HTKTBmfwOzrUtCoRb5cbXLw/qeFfLWtHFmG4UOiuWVGKrHR\nwRU4CUJ7lxgdwu+u68P/ffgTr3x2gMduuZjEAFtyKQiCIHiXCCV8xN91GYKVt5ZeBMPz76yu4cTd\nj4Ik0fmlJ9HGRoObU8uVuYdQH/gaOSwax7Dp0Jw2p7IM1fngsoEhGgzevdMuyfBziY4KM8SEOOmV\n5N9AwuGUWbbByv4TLhKjFcyfbCAmIngu5k01Tl5afIof95mICFdz//wODOwT6e9h+Z3LJbNhq5Fl\nnxVRZ3aRkapnwY3p9O4e7u+hCYLQTN0zorn5qu68s+4wL3y8n0dvHkSoXuPvYQmCIAg+IkIJH/NX\nXYbzNbeTRWs7vfTi7JoSpzVn6UWgPP/nk2WZUw/9A3t+ESkPzCfisovd3lZRXYr620+QVRocV8wG\nXTN6ussy1BSCow60YRCW5NXWn5IMPxfrqDCrSYqErjH+DSRqzTKL11jIKZbonKrk1msMhOiDp6Dl\nwcM1/PfNU1RUOejXM5z75nckJkqcoB86Wstb7+dxKt9CiEHF/NlpjB8Vj0oVPK+tIAgNG94vhaJy\nM+t35fLayoM8ML0falXwBMmCIAiC+0Qo0ca1tJOFPwTL0ouWMC77nIrVmwgb3I/UB+e7v6Hdinrr\nchROO47hM5Cjk5o3gDojWKtBrYfINK8HEodKdJSb1UQbXFzWXU1Fudd27zFjlcSizy2UVcsM7K7m\nhjE61EHS8tPlklmxqoisNcUoFHDj9SlMnZDY7lvlVVTaWfJxAd/sqF9vPmZYLDdOSyEqQgQ1gtCW\nTLuiM0Xldew7Uc7yTce48cpuKIKsQ5IgCIJwYSKUaONa2snCH4Jh6UVLWI5lk/uXf6OKDKfzK0+h\nULv5ayhLqL/9BKWpDGfPy5E69mnmAKrAXAZKTX3rT4X3wilJhl9KdJTVqYkyuOidZEWl9N80+uwi\nF4tXWzBbYexgDeOHaoPmhNZYbue/b2bzy7E6EuK0PHhHJt07h/p7WH7lcEqs+dLIR6uKsNokunQM\nYcGcdLq18+dFENoqpVLB7df24p/v72HL3gJS4kIZM8idylKCIAhCMBGhRJByZzmG1e5sspPF9SM7\nB/TFfqAuvWgJyWrj+F2PIFltdHnpSXRpyW5vqzrwNar8w0hJnXANGNe8Adhq65dtKFT1gYTSex8B\nkgyHS3UY69RE6l30SbLiz5m2+445WbbRiiTBjDE6hvQKnrvoO/dU8fI7OdTWubjs4ijuvjWD0JD2\n/XH900ETi5blUVBsIyJMzW2z0hgzLLbdzxoRhLbOoFNz37Q+PLXkR5ZtOkpitIHenWL9PSxBEATB\ni9r3WW4Q8mQ5RqXpwp0s2tpFf6DL/fsLWA4dI/6m64i5Zozb2ynzj6DatwU5NArH8BmgbEaY5LCC\nKR9QQGQ6qN0rGuoO+ddAorT210Ai2X+BhCzLbN3rYM12OzoN3DZRT/cOwfFRZ3dIvPNhPuu3lKHV\nKLjrlgzGjYgNmtkdvlBaZmPxh/ns3FONUgFXj4ln1pRkwkKD4zUVBKHl4iIN3Ht9X55ZtpfXPj/I\nIzddTGqcmCElCILQVoizuiDjyXKM6AjvdLIQvKNyw9eUvvMRhu6dyHj8Qbe3U5jKUW/PApUKx8hZ\noG/GiZjLAdW5IEsQkQZa74VR9YGEltJaNRG6+kDCXx0qXZLMyq/tfHfAQWSogvnX6kmJD9zZQGfL\nK7Tw3OunOJVvIT1Vzx/uzCQjtRlFTNsIm11i5boSPl1bjN0hc1HXUBbMSSczQwSpgtAedU6N5Lar\ne/Dm6kO8mLWPx26+mPAQ0fJXEAShLfAolDh69Ci5ubmMHTsWk8lERESEr8YlnOX0Ug2DTu3Rcgy9\nVu3VThYXGl9bq/3gTfbCEk4++CQKvY7Orz2NKkTv3oYOG+qtH6BwWHFcfj1ybIrnB5dcUJULkhPC\nEkHvvd9bWYYjRi0ltRrCdS76pvgvkLA5ZN5fZ+XQKRfJcUrmT9ITFR6YxVzPJssyX20rZ9GyfGx2\niauuiGPuzDR02sAfuy/Issyun6pZvDyf0jI70ZEa7rkhleFDotv1jBFBEGBoryQKy82s+e4Ur3x6\ngD/MGiA6cgiCILQBbocS7777LmvWrMFutzN27FheffVVIiIiuPvuu305vnbt/KUaUWE6Kms9W47h\ny04WwdjZwx9kl4sT9/4FV2U1Hf75MCE93HzuZRn1d5+hrDbi7DEUqVP/Zhxchup8cNnAEA2GGM/3\n0cSujxq1FNf8Gkj4cYaEqU7i7dVW8ksluqWruOVqPXpd4F/A1pldvL40l+27KgkNUXH//EwuvTja\n38Pym4IiK28vz2fvQRMqFUwZn8CMSckYDCLsFASh3pThmRSX1/HjESNL1x9h7tU9RGApCIIQ5NwO\nJdasWcNHH33ELbfcAsBDDz3EzJkzRSjhQ+cv1WgskIDGl2P4spNFMHb28IfCF9+h5vs9RE8YRcLN\n17u9nernbahyf0ZK6Ihr0HjPDyzL9UUtHXWgDYOwJK+1/pRlOFampahGQ5i2PpDw1ySZ4nKJRass\nVNbIXNJTzbRROlSqwD9BPXqyjudez6akzE6PLqH8/vaOJMS1zyVVFquLj1cXs3pjKU6XTL9e4cyf\nnU5aspszigRBaDeUCgXzJvakrHoP2w8UkRwXwoQhHfw9LEEQBKEF3A4lQkNDUZ5191upVJ7zteBd\nNoer0aUaDbnQcgxvd7JoanyB3tmjNZeb1Oz6iYL/vIk2JZHM/3vM7bs5isLjqH7ahBwSgWPEDc0r\nbFlnBGs1qPUQmebVQOJ4uZZCk4ZQrYt+Kf4LJI7nOXnnCytWO4wfqmXsYE3A3zGTJJnPN5TywacF\nSBJMm5jEzMnJQRGkeJssy2zfVcm7KwqoqHIQH6vltplpDBkYGfCvoyAI/qPTqLj3+r78fckPZG05\nQVJMCAO6xvt7WIIgCEIzuR1KZGRk8PLLL2Mymdi4cSNr166lc+fOvhxbu1Zd23jnDICoMC2mOrtX\nl2N4oqnxBWpnj9ZebuKsMnHinscA6PzKU6ijI93bsKYSzbaPQKGsL2xpCPP84JYqMJeBUlPf+lPh\nnccny3CiXEtBtYZQreTXQGL3YQcrNtW/B2dfqWNQj8Bv+VlV7eDFt3PYe9BEdKSaB27PpO9F4f4e\nll/k5Ft464M8fj5Si0at4IZrk5g6IQmdToTdgiBcWHS4jvum9eVf7+/hzVWH+PONA4mPb5+fp4Ig\nCMHO7VDir3/9K0uXLiUxMZFVq1YxaNAg5syZ48uxtWuRYY13zoiN0PPXWy/GYnP6rbhkU+ML1M4e\nrbncRJZlsv/4FPaCYlIX3k74kAHubei0o/l6GQq7BcfQKchxaZ4f3FZbv2xDoaoPJJTeabIjy3Cy\nXEN+tYYQjUS/ZAtaPwQSsiyz6QcH63fYMejg1mv0dEkL/EZCP/1s4oW3TlFlcjKwTwT3zutAVETg\nByneVmd2snxlEes2G5EkuGRAJHNvSCMpIfA+MwRBCGwdkyJYMKknr3x2kBc/2c9/M7xXN0kQBEFo\nPW6fyatUKubOncvcuXN9OR7hVzqNqsnOGeEhWr+2wrrQ+AJt6UZrLzcxvv8plV9sJnzoQFIemOfW\nNrIso/7+c5SVxbi6DkbqOsjzAzusYMoHFBCZDmrvXOjJMmRXaMir1mLQ1M+Q0PohB3C5ZLK22Nh1\nyEl0uIL51xpIig3sO+tOp8yyzwr5bF0JapWCW29IZdK4BJTK9rU8QZJkNn9bzntZhZhqnCQn6pg3\nK41Bfd2cQSQIgtCAQd0TuG5EJz795iSPvf4tD87oT2SoaBUqCIIQTNy+rOjZs+c5a3wVCgXh4eHs\n3LnTJwMTfNs5wxtaMr7WbiPamstNzEdOkPP4c6iiIuj00pMoVO49Pvver1Gd2o8Un45z8NWeH9jl\ngOpckCWISAWt95bPnKrUkFtVH0j0T7GiU8te27e7rDaZJWutHM1zkZagZN4kPRGhgR1IlBhtPPdG\nNkdPmklK0LHwjo50yQz197Ba3bHsOt56P49j2Wb0OiU3TUth0rgENJrAfv0EQQgO11zagVqLg40/\n5PHssj08NHugCCYEQRCCiNuhxOHDh8/8v91u5/vvv+fIkSM+GZRQz5edM7yhOePzVxvR1lpuIlms\nnLjzz8hWG51e+Qe61CS3tlMUn8T29SpkQxiOETNB5eE0BMkFVbkgOSEsAfTeu/t8qkJDTqUWvdp/\ngURVjcSiVVaKyiV6Zqq4cbwenSawZxp8u6uSV5fkYLZIjBgazR03ZRDSzlpbVpscvP9pIV9tK0eW\nYdgl0dwyI5W4GHGxIAiC9ygUCm4Y3QWDQcvn35wQwYQgCEKQadYEbK1Wy8iRI1m8eDG33367t8ck\nnMfbnTO8zZPx+auNaGstN8l98nksR06ScMt0oidc4d5GdVVovvkIFNQHEiERnh1UlqE6H1w2MESD\nIdbjcTcmp1LDqdOBRKp/AokCo4tFq6yY6mQu76thyghtQC99sNkk3l6ex5fflKPXKbl3XgdGXRbT\nrrpJuFwyG7aWseyzQurMLjJS9SyYk07vHqIInSAIvqFQKJh3bS8sFruYMSEIghBk3A4lsrKyzvm6\nuLiYkpISrw9IaLv83UbU18thKtZupnRJFoaLupDx1/vd28jlQPP1hyhsdehHT8OW4GGvdVmuL2rp\nqANtGIQlea31Z26lhuwKLbpfZ0jo/RBIHM5xsnStFZsDJg3TMnJAYLf8zMm38J/Xs8krtJKZYWDh\nHZmkJuv9PaxWdehoLW+9n8epfAshBhXzZqUxYXR8u2x5KghC6zo9YwIQwYQgCEIQcTuU2L179zlf\nh4WF8fzzz3t9QIJnWrs2Q0v4u42oL5fD2PKLyV74d5R6HV1eexqlwY0LUVlGvXM1yvICXJ0Houl3\nOZTVenbgOiNYq0Gth8g0rwUSeVVqTp4dSGhaP5DYcdDBJ1tsKJVw8wQ9/boGbocNWa6fGfDOh/nY\nHTLXjI3n5umpaNtRzYSKSjtLPi7gmx2VAIwZFsuN01LaZYcRQRD8RwQTgiAIwcfts/x//vOfvhyH\n4KEL1WawOVwUldXhcrgCJqwIlDai3l4OIzudnPjdY7iqa+j47CMYunVyazvl0V2oTuxFik3FOWSi\n5zMALFVgLgOlpr71p8I7F8D5VWpOlOvQquq7bBhaOZCQZZl139v56kcHIXq4bZKBzOTAeA83pLbO\nySvv5rJjdxVhoSoW3tmBSwZE+XtYrcbhlFjzpZGPVhVhtUl06RjCgjnpdOvc/gp6CoIQGEQwIQiC\nEFwuGEqMHDmyyYulrVu3enM8wgWcnhmxYVcuW/YWnvn+6doMsiyjUCjqw4oaGzHhrVNI0h06jYq+\nnWPPGfdpgdhG1F0Fz79N7a6fiJ44hvg5U93aRlGag/qHtci6UBwjZ4HKw7vJttr6ZRsKVX0gofTO\nLIKCajXHfw0k+qdYCWnlQMLplPnwKxt7jziJi1Qwf7KB+KjAnW2w/1A1jz97GGO5nZ7dwvj97R3b\nVRHHnw6aWLQsj4JiG+FhKm6blcGYYbEBXfOjvcjJtxATpSE8LHBnGAmCL4lgQhAEIXhc8Gxl2bJl\njf6byWRq9N8sFgsPP/ww5eXl2Gw27r77bnr06MFDDz2Ey+UiPj6ef//732i1WlatWsWSJUtQKpXM\nmDGD6dOnN+/RtCHnL8s4f2ZEYznRtweKsdpdZ75uqJCkP5Z8nB7//hPlACgVIMkQE65jYPf4gGlz\n6inT97spfP5ttKlJZD77qHuzHcwmNN98CIBjxA0Q6mGnDIcVTPmAAiLTQe2dGSYF1WqOlenQ/DpD\nIkTbuoGE2Srz7hcWThRIdEhSctskA2GGwLy4dUkyn35RzIrPi5BlmDklmWkTk1C1k4vx0jIbiz/M\nZ+eeapQKmDA6nllTksUFsJ+5JJlde6v4fH0pR07UccVlMdw/v6O/hyUIfiOCCUEQhOBwwTPI1NTU\nM/9//PhxKivr1wvb7Xaeeuop1q1b1+B2W7ZsoXfv3ixYsICCggJuu+02Bg4cyOzZs5kwYQLPPfcc\nWVlZTJkyhVdeeYWsrCw0Gg3Tpk1j3LhxREW1n+nPZ2tsWYYsy3y1u+DMz8mNXC+eHUicbe/RMqYM\nz2Tltuxz9t0jI5pZ47oRovPtxcT5XTekX8ffr2ucT7tu+JKjooqTv/srKBR0fvUfqKPc6JrhcqL5\n5kMUllqcF09ATsr07KAuB1TngixBRCpovbMMpdD0ayChlOmfYiW0lQOJ8mqJRasslFbK9O2iYvaV\nejTqwLzAr6i089+3TnHwcC0JcTrum5dBr+7to6uEzS6xcn0Jn35RjN0hc1HXUBbMSSczI3C7A7UH\nVquLdZuNrNpYSnFp/fK4wf0juf4a91oSC0JbJoIJQRCEwOf2lehTTz3Ft99+S1lZGRkZGeTl5XHb\nbbc1+vNXX331mf8vKioiMTGRnTt38re//Q2AUaNGsXjxYjIzM+nTpw/h4fUn9QMHDmTPnj2MHj26\nuY8pqDXWMlOvbdmMhsoaK8u+PMZ3B4vP2fe3B4vZfbSUYX1TfLbEo6muG/uPl2MbFTh1L9wlyzKn\n/vAU9qISUh+6k/DB/dzaTv3DWpTGPFwd++LqcalnB5VcUJULkhPCEkDv4QyLRhSZ1Bw1atEoZfql\nWFo9kMgtcfH2Kiu1FpkrBmq45nItygDtsPHjvmpeejsHU62TSwZE8vgfemG3Wf09LJ+TZZldP1Wz\neHk+pWV2oiM13D0jlRFDowO6G0pbV2VysPYrIxu3llFd40SjVjBuRCzXXpVIWjvr+iIITRHBhCAI\nQmBzO5Q4cOAA69at46abbuK9997j4MGDfPnllxfcbubMmRQXF/P6668zd+5ctNr6PwCxsbEYjUbK\nysqIiYk58/MxMTEYjQ1fwJ4WHR2CWu3di9j4eP/f6bTanWeWN/z23xqeAXE+g06Fxfbbn42LMnAs\nv6qRfUts+jGfEIOWBVP6uD9gNxWV1VFR03jXDZVWQ3ycZ0Xx/P16nXrtAyrXbyVm5CX0e/I+FKoL\nvx/tB77HeuwHlPEphE+6EYXmtydDjT0uWZaozjmCw2VDH5NIWFIHr1wM5hhljhhltGoYeZGCqNCw\nFu+zIY09rj2/WHnt0zocTpmbJ0YwdkhgFke0OyTeWHKSFZ8XoFEr+P0dXbjumpRfX4O22V3i9GuW\nW2DmhTePs3NPJSqVgtnXpXHrDR0ICQnOpRr+/uzwhtx8Mx+uzGf95voZKxHham69IYPrrkklJlpc\nZAlCQ0QwIQiCELjcPqs8HSY4HA5kWaZ3794888wzF9zuww8/5JdffuGPf/wj8llrDuRG1h809v2z\nVVaa3Ry1e+LjwzEaa7y6z+YorTRjrLR4tI1SUb+UIyZCz4BucUiyzOazlnmc1jU18pxZEg35dl8h\nEy5J9/qsBZfDRUx44103XHaHR8+/v18v8y/HOfTHf6GOjiTjuScoq7jw+1FhzEPzVRZoDVgun4ml\nygac+3wxefViAAAgAElEQVQ0+rhkub6opdUE2jCsqhisnrYObUBJjYpfSnWoldAnyYrDLGH07q8W\n0Pjj2r7Pzspv7GhUMPcaPb06SQHxe3i+whIr/3k9m5M5FlKTdCy8M5PMjBDKymr9/l70lfj4cHLz\nqshaU8yqDaU4XTL9eoYzf046acl66uos1NX5e5SeC+bXS5ZlfjlWx8r1JfzwUzUAifFarr0ykRlT\nOlBbY8bltGE0NhwAe1NbCHaE9kkEE4IgCIHJ7VAiMzOTDz74gIsvvpi5c+eSmZlJTU3jJ3cHDx4k\nNjaW5ORkLrroIlwuF6GhoVitVvR6PSUlJSQkJJCQkEBZWdmZ7UpLS+nfv3/LHlWQaqplpl6ranC2\nxMgBqVw1OP2cgphKhYK9R8uorLESHV4fVkwZ3onDuZUN7vu0yhor1bU2r7bLhPquGwO6xZ+zLOW0\nYOu64TJbOX7nn5FtdjLf+Bfa5IQLb2SprS9sKUs4hs+A8GjPDmouA2s1qPUQmUajVU49UFpbH0io\nlNA32Uq4TmrxPt0lyTJrttv5eq+D8BAF8ybpSU8MzPfA1u/LeWNpHlabxOhhsSyYk4ZeF5hj9RZZ\nltn0TSkvLTpOeaWD+Fgtc2emMnRglFiq4QcuSWbH7io+X1/Csez61LBbpxCmjE/kkoFRqJQKDHoV\ntcGZtQhCqxPBhCAIQuBxO5R48sknqaqqIiIigjVr1lBRUcEdd9zR6M//+OOPFBQU8Oijj1JWVobZ\nbGb48OFs2LCByZMns3HjRoYPH06/fv147LHHMJlMqFQq9uzZwyOPPOKVBxdsmrp4v6xPUoNhw/l1\nIFRKJbPHduP6kZ1RaTW47I4zF/2N7fu06HA9kWHe6eRwvtMnAA2NP5jkPvEfrMeySbztBqKvHHHh\nDSRXfWFLswnngHHIKR4+XksV1BlBqalv/aloec0PY62KQyX1gUS/ZCsR+tYLJBxOmWUbrOw/4SIx\nur7lZ0xE4LX8tFhdvPl+Hlu/q8CgV/Lg7R0ZPjTmwhsGuZx8C299kMfPR2rRqBXMuDaJ6yYkodMF\n3mvU1lltLjZvL2fVhlJKyuwoFDBkQCSTxyfSo0uoCIgEoQVEMCEIghBY3A4lZsyYweTJk7nmmmu4\n9tprL/jzM2fO5NFHH2X27NlYrVb++te/0rt3b/70pz+xYsUKUlJSmDJlChqNhoULFzJv3jwUCgX3\n3HPPmaKX7VFTF+8qpZLrR3Z2q52nTqMiPi70nKnKp/e9fX9Rg7MufDlr4eywpLXbkXpLxepNGN//\njJCe3Uh/7D63tlHtXo+yNAdXRi9cvYZ7dkB7LdQUIiuUVCgTCHMpaOm1YVndr4GEon6GRGsGErVm\nmcVrLOQUS3ROVXHrNXpC9IF3YXUyx8x/Xs+msMRGl44hPHhnJskJvgnrAkWd2cnylUWs22xEkmDY\nkFjmTE0iqY0/7kBUWV1fvHL9FiO1dS60GgVXXRHHpCsTSE0SxSsFwVtEMCEIghA4FLI7RRyA3bt3\ns27dOr766it69OjB5MmTGT169JlaE63J22uCA3Gdsc3huuDF+4V+prHHZbY5WPblMQ7nVFJVa2tw\n1oU7x/cXf7xetvwiDo6dhexw0mv9+xi6drzgNsqTP6H59hOkyAQcE24HTdMXeOc8LqcVufIUkkvi\n9a9N7Mk2n2kP29wuKWV1Kn4u1qH4NZCIMrROIBEfH86hY9W89bmF8mqZgd3V3DBGhzrAWn7Kssya\nTUaWflyA0ykzeXwCc65LQaNu/LkOxM8OT0iSzJZvK1iaVYCpxklyoo55s9IYPyYtqB9XYwL59cor\ntLBqQylbv6/A6ZSJCFMzYXQcE0bHExnRdDHV1n5cwV5TwlfPVSC/v9qL5rwGsiyzYvNxNv6QR3Js\niAgmWkj8HvifeA38T7wGDWvq/MHtmRKDBg1i0KBBPProo+zatYtVq1bxxBNPsGPHDq8MUjiXTqNq\ntLaDS5JYsfk4e48aqTD9P3vnGRhVnbbva/okmfRk0kNCEJDepUmVokhRFATFVQSxrOuq7+67r7v6\nV1dXXXdRd5XVBbEgIBgVUJSqVEHpRaSXNJJMkkmffs7/w0ikJJNJmMkk4Xd9SmZOeWYm5fzu8zz3\nbWvwYjVYp2HWrZ1qFR4uP3aEQUeP9jFMv+k6v8SFtgRkp5NTj/wZV3kl6f98xitBQlGch3rnSmSN\nHuew6fUKEpfgckBpFgpZ4r+bS9lzxh05eSEeFmD6Te0b9BqKAyRIABw/Z+dfy6uptsJNfTWM7a9t\ndq3n5RVO3nr/HLv2lxEWquZ3D7ShdzffRK42V06eqWL+4myOn65Gp1Vyz+REJow2otFcm7/ngUCW\nZX46XsnKNQXsPlAOQIJRx4QxRoYPjBZjMwJBEyA6JgQCgSDwNCjTrby8nA0bNrBmzRqys7OZOnWq\nv+oSeGDZtycv8YZo7GK1NuHj8mObK218tzeXkzllPHtfn2YtTPiruyN37nwqdx8kasIoYu6qf3QJ\naxWazUvA5cI55C7ksGjvTya5oDQLJCdfHaxm1y+CxMXsO17E5KEZXr/GkmoVhwvcgkTXJhYkDpxw\nsnR9JS4XTBmp44bOzS8+8/DRCl7/71lKSh10uz6Ux2enERXR/Or0FeUVTj7+LJcNW4uRZRjcL5Lf\nTEkiJkpcgDcVLpfMjj1mVq4p5ORZt3llx3YhTBobR58e4aiUzUu0EwhaO0KYEAgEgsDitSjxwAMP\ncOLECUaNGsVDDz1Er169/FmXoA5sDhf7jptqfa6hi9WGHDu7sJIl648zY0zHRh3bn1xt54gnyrfv\nJu/NhWhTEkn7+5/rv8MvudBsXY6iqgxn9xFIyR28PpcsS1CWAy4b1cpQvthde4RrQ1JSzNVKDufr\nUABd4q1ENpEgIcsym/Y6+Gq7Hb1Owf3jdHRo0yAN1O+4XDLLVp0n86t8FAq4Z3Iit90ch7KVLghd\nLpm1m4pYuiKPyioXqUl6Zt+dQpeOLbsVvyVhsbjYsK2YL9cVYip2m1f27x3BxDFGOrYzBLq8ZsXf\n//539uzZg9PpZM6cOXTt2pU//vGPuFwuYmNjee2119BqtaxatYoPP/wQpVLJlClTuPPOOwNduqCF\nIoQJgUAgCBxerxLuvfdeBg8ejEp15YJ3/vz5zJ4926eFCWqnrNJGSR2xnlcb6WkqtdR5bIB9J4qY\nMsLV7DwmfNU5cjmO4lJOPfYMKJW0m/cS6rD6Fw2qfetR5p/GldwRV9eh3p9MlqnIOwOOKtAaUAUn\nEBWWVWuEq7cpKWaLkkP5emQZuibYiApuGkHCJcms2Gzj+0NOwkMU/OE30QSpr+z4CCRFJXbmvnuG\nn09UERut5ck5aa16UXjkeCXzF2dzNttCcJCSB6Ylc/OIWFSq1inANDdKzHZWbzSxdlMRVdUutFoF\nY4fHMGG0kYQ4YV55OTt37uTEiRMsW7YMs9nMbbfdxoABA5g+fTo333wzc+fOJTMzk0mTJvH222+T\nmZmJRqPhjjvuYNSoUURERAT6JQhaKEKYEAgEgsDgtSgxdGjdC6ytW7cKUaKJCDfoiArTXdVi9XIu\ndBrsPVaIJ9fTskr7VYke/qC+zpHxA9Ow2JwNHumQZZkzTz6PI99E8v/9FkPvrvXuozx7CPWR7Uhh\nMTgHTW5YfGd1EbaqIlDrITwZnUJZZ4SrNykppRYlh867BYku8Taigq9MW/EHNrvMojVWfj7rIiFG\nyazxelITNJhMzUeU+GFfKW8tPEdllYuBfSJ45L5UQoKbVxeHrygx2/nw01y27DQDMGJwNDMmJxIR\n3nrHU5oTWbkWVq4pYMtOM06XTFiommmTEhg7PJaw0Nb5M+cL+vbtS7du3QAICwvDYrHwww8/8Pzz\nzwMwfPhwFi5cSHp6Ol27dq1J7OrVqxd79+5lxIgRAatd0PIRwoRAIBA0PT65KvIywEPgA3QaVaMX\nq3V5LlzeaVAXUWGNEz38iafOkeJyK88t3EVpZcNHOgoWLqN0/VbCBvcj4dF7691eYc5H/f0XyGot\nzmHTQNuAu5+WUqgyodTokMJTa8QMT/GwniizKDn4iyDROd5GdEjTCBLlVRLvrbKSY5Jon6riNzfr\n0euaz514u0Pig2W5fPOtCa1GwcP3pjJqaHSzM930BQ6nxOoNJpatPI/VJpHRJpjZ96TQISMk0KW1\nemRZ5tBRt3nl3kNu88qkeB0TxsQxdEAUOm3z9eVpLqhUKoKD3eJ3ZmYmQ4YMYdu2bTVpX9HR0ZhM\nJoqKioiKiqrZLyoqCpOpdpFaIGgIQpgQCASCpsUnokRrvKivj0BGZjZ0serJc8HpkuvsNLgcb+7Q\nNzWeOkfAbdQJDRvpqDp8jOy/vok6KoK2/34BRX0ihq0azaYlKFwOHEOnIYcbvX8B9kqoyAOFivA2\nHTCXO2ueUimVTL+pPZOHZnj9s1ZmdQsSkgyd42zENJEgkV/sYsEqK+YKmX6d1NwxXNesRgOy8yzM\nfecsZ3MspCTpeWpOOm2SgwJdll/Y/1M5C5Zkk3veRqhBxf13pTLyxmhhnuhnnE6Z73ebWbmmgNNZ\nFgA6tTcwaayR3t3CW61XiT/ZsGEDmZmZLFy4kNGjR9c8XteNEG9vkERGBqNW++d/WUuPS20N+PIz\n+O3UngQFaVm55RRzl+/npYcHERkqRq7qQ/weBB7xGQQe8Rk0DNE/2kB8aarYWGGjoYtVT54LN/VO\n9ugjARAd5t0d+kDgqXOkNuozA3VVWzj18NPIdgdt33wObVyM5wNKEpptmSgqzTi7DEVK7eR98U6r\n29gSBYSnoNYFAVdmGnuKh72Y8l8ECZcMneJsxBqaRpA4me3k/dVWrHa4eYCWkX00zUaolGWZjduK\nWbA4B5tdYvSwGGZOTW6VUYuFRTbeX5bLzj2lKBVw84hYpk1KINQg/sz7E4vFxbotRXy1vpCiEgdK\nBQzsE8HEMXG0F50pjWbr1q288847LFiwgNDQUIKDg7Farej1egoKCjAajRiNRoqKimr2KSwspEeP\nHvUe22yu9kvNIpc+8PjjM5gwIBWLxc66Xdn877+3io6JehC/B4FHfAaBR3wGteNJqBFXqw3EF6aK\nvhI2vFmsevZcMNG/cxyRoVpKKuxXPB8VquP3U7oTGxHU7DokLubizpHics/eBfWZgWY98w+sp84R\nN3saESMH13tu1YFvUeadwJV4Ha7uDZhjdjnc0Z+yBGFJoL06n44K2y+ChATXG20Ym0iQ2P2zg+Ub\n3aLW9NE6endsPl4FVdUu3vkoi20/mgkOUvGHR9IZ2Ccy0GX5HJtdYsWaAj5fnY/dIdOxXQgP3pNC\nemrz8X5pjRSb7aze4DavrLa40GmVjBsZy62jjMQbm9eYW0ujoqKCv//973zwwQc1ppUDBw5k7dq1\nTJw4kXXr1nHjjTfSvXt3/vKXv1BeXo5KpWLv3r08/fTTAa5e0NoQoxwCgUDgf3wiSqSlpfniMM0e\nX8Vx+iotwptOC8+eCzZe/HAPem3t+/bqEEtyrOdEgkCOsVzgQufI+IFpPLdwV83IRm14MgMtXrkO\n09KVBHfpQMrTj9V7XmXWEdSHNyOHRuEcfCd4KyhJLrcgITkhxAj6cO/2q4MKm5IDeXqcvwgScaH+\nFyRkWWbDLgdrdtoJ0sF94/S0S24+GueJM1X8850zFJjsdMgI4ck5aRhjWtdCUZZldu0vY+HSHAqK\n7ESGq3l4ShJD+0c1m06V1sjZ7GpWri1k6w8luFwQEabmtpsTGTMsRnSl+Iivv/4as9nM73//+5rH\nXnnlFf7yl7+wbNkyEhMTmTRpEhqNhqeeeooHHngAhULBo48+WmN6KRD4EiFMCAQCgX/x+goqNzeX\nV199FbPZzKJFi1i+fDn9+vUjLS2NF154wZ81Nht8EcfpC2GjIZ0W9XkuAFjt7kWsXqvC7nB5Zajo\nyzEWX2GxOSn1IEhA3b4Ytqxczv7xJZTBQWT8528odZ4vNBRlhai3f4as0uAYOg10XvoTyLJ7ZMNl\ng6BICI72br86qLQpagSJjrH2JhEkXC6ZT7+zseuIk6gwBbMmBBEX1TzGISRJZtW6Qj7+LBdJgsnj\n4rhrYiJqdetapOfmW3lvSQ77DpejUsHEMUamTEggOKj5djS1ZGRZ5sCRClauKWD/T+52zOQEPRPH\nGBkyIAqtpnn8/LcWpk6dytSpU694/P3337/isbFjxzJ27NimKEtwjSOECYFAIPAfXosSzzzzDHff\nfXfNRUF6ejrPPPMMixYt8ltxzQ1fxHH6QthoSKdFQzwXQvRqnr6nF7GRwfUKI77q9vAlnj4fBXBD\nJyOTbky/4jnJ4eTkI3/GVVFF+hvPEZTRxvOJ7FbUm5agcNpx3DgFOTLeuwJl2W1q6agCrQEM8XAV\nd7TdgkQQTklBh1gb8WHO+ne6Sqw2mQ+/tnI820WKUcnM8XrCQprHgqy0zMG/3jvHvsPlRIar+f3s\nNLp1Cgt0WT7FYnWR+VU+q9YW4nTJdO8UygPTk0lJbJ2mnYHG4ZTY/qOZlWsKOZvjNq/s0tHAxDFx\n9OoaJswrBYJrDCFMCAQCgX/wWpRwOByMHDmSDz74AHDniF9rXE0c5wWuVthoTKfFxZ4LJRVW6jIo\nLy63oVIp630dvhpjufyYVzsG4unz0WqU/HCkkBM5ZVd0dOT+412q9h4m+raxxNw5zvNJZAn19s9Q\nlhfj7DQYKa2r9wVWF4G1DNR6CE++KkGiyq7gwPkgHJKC9rE2EppAkCitkFiwysr5YolO6SruGatH\np2kei7L9P5Xz5vyzlJY76dU1jMceaENEWPPxt7haZFlm+y4zHyzLpdjsIDZay/1Tk+jfO0KMaviB\nqmoX638xryw2O1AqYXC/SCaOMdIuXZhXCgTXMkKYEAgEAt/ToAHY8vLymgvgEydOYLN5bpVvjTQ0\njvNyrlbYaEynxcVpHaZSC28s31+rsSXAht3ZzBjT0ec11IWvx0Au/3y0GhVWuwubQwKu7Ogo2/oj\n59/6AF2bJNJe+VO9CzzVoc2oco4ixbfF1fMm7wuzlEKVCZQaiEgFReO7C6rt7pENh0vBdTE2EptA\nkMg1uSM/y6tkBnXTMGmItlncJXY6ZZauyOOLbwpQKRXcNyWJ8aONzaI2X3Eux8KCJdkcPlqJRq3g\nzvHxTL4lvlUmiASaohI7X60vZN3mIixWCb1OyfhRRm4dFdvqPEkCzdmzZ68ZPypB60MIEwKBQOBb\nvBYlHn30UaZMmYLJZGL8+PGYzWZee+01f9bWLGloHGdt1CdseOoauJpOC51GRXKsge7XxfLd3txa\ntzl4qgSbw+XxNflijOUCvh4DuUSAMVfzZubBGs+Mi9l3vIiJXaI4/dgzKFRKMua9hCrUs6mnMucY\nqgPfIYdE4LhxCii9/Nztle6xDYXSLUgoG2+GV21XsD9Pj92lpF2MjaRw/wsSR885+ehrK3YHTBis\nZUjP5hH5WWCyMfe/Zzl+qop4o46n5qS1qrvYVdVOPllxnq+/NSFJ0LdHODPvShbJDn7g9LlqVq4t\nYPsuMy4XRIZrmDwunjHDYjCECPPKxnL//fdf4gMxb948HnnkEQCeffZZPvroo0CVJhBcNUKYEAgE\nAt/h9dVW//79WbFiBcePH0er1ZKeno5Od+1eHHsTx1kXdQkbLkliyYbjHrsGfDFCclPv5DpFCW86\nHXxRA/hnDOTiGrUaVd0dHeXVnP79czgKi0n5y+8w9Ozi8XiK8mLU2zJBpcIxbBrovVz8Oq1uY0sU\nEJ4C6sb/zlgcvwoSGdE2kptAkNh52MFn39lQKuHeW/R0a9c8Fmjbd5mZ98E5qi0SQ/pHMmdGaqsx\neZQkme+2l7Dos1zKyp0kGHU8MD2Z3t2uLqVFcCmyLLPvcDkr1xRy8Ge3eWVKkp5JY+K48YZINMK8\n8qpxOi/9G7Vz584aUUKua45QIGhBCGFCIBAIfIPXK4zDhw9jMpkYPnw4r7/+Ovv37+exxx6jT58+\n/qyvVXO5sOFt18DVjpBEhemJvspOh6utAXw7BlIbnjo6+v38A5YtOwkb2p/4h+7xfCCHDfWmxSgc\nVhyDJiNHJXpXgMvhjv6UJQhLAm3j7+JfLEi0jbaREuFfQUKWZb7ZYWfjbgchepg5Poi0hMAv+m02\nifeWZrN+SzE6rZLHZrZh+KDWE4F58kwV8xdnc/x0NTqtknsmJzJhtFEskH2Iwymx9QczK9cUkJVr\nBaDb9aFMHGukZ5ewVvOz1By4/L28WIgQ77OgtSCECYFAILh6vBYlXnzxRV555RV2797NoUOHeOaZ\nZ3jhhRdE+6WPaEjXwNWOkPii08EXYyy+HAOpjbpeZ0xhDt2/XYU6Joq2bz6HwpN3hSyj/v4LlGUm\nnB37I7Xt4d3JJZdbkJCcEGIEfePvclsdbg8Jm1NJepSdVD8LEk6nzCcbbew75iQmXMHsiUHERAR+\nUXwux8I/3zlDdp6VtJQgnnooneQEfaDL8gnlFU4+/iyXDVuLkWW3qeJvpiQREyUuan1FVbWTtZlZ\nLF+ZQ0mp27xySP9IJo6Jo22bxoufAu8RQoSgtSKECYFAILg6vBYldDodaWlpLFu2jClTptCuXTuU\njTAiFNROY7oGrmaExBedDldbg6/GQDxx+euM0Sm49bvlKFwu2v7rebTGGI/7q37aiirrJyRjGq7e\nY707qSy7RzZcNgiKhODoRtdvdbo7JKxOJWlRdtpEOhp9LG+otsq8/5WF03kSaQlK7r81CENQYBcS\nsiyzdlMR73+Sg90hM25kLPdOSULbCroHXJLMuk1FLPkij8oqFylJemZPT6Hr9aGBLq3VUFhk46v1\nJtZvKcJqc5tXThht5NZRRmKjxYLBn5SVlbFjx46a78vLy9m5cyeyLFNeXh7AygQC3yOECYFAIGg8\nXosSFouFb775hg0bNvDoo49SWloqLip8iL+7Bi6nvk4HX0R0eoOvxJG6uPx1lv2/VzEX5BP/0Awi\nhg3wuK8i7ySq/RuQg8NwDJnqnbGlLLtNLR1VoDWAIb7R0Z82p7tDwupU0ibSTpqfBYniMokFqywU\nmmW6tVMxfbQejTqwgkRllZN5H2SxY08phhAVTz7Uhht6RgS0Jl9x5HglC5ZkcybLQnCQkpnTkrl5\neCzqAL/nrYVTZ6tZsaaA73ebkSSIjtQwc3oaA3sbCAluHt4orZ2wsDDmzZtX831oaChvv/12zdcC\nQWtDCBMCgUDQOLy+MnvyySf56KOPeOKJJzAYDPz73//mvvvu82Np1xaeugY6pvpvEXZ5p4OvIzrr\nwxdjIN6g06hQfrcZc+ZqQrp3IvlPj3jeoaIEzdbloFDiGDoNgjwnc9RQXQTWMlDrITz5qgSJ/Xl6\nLA4lqRH+FySyCly8t8pKpUVmWC8N4wZpUQa41froyUrmvnsWU7GdTu0NPPFgWqsYZygpdfDRp7ls\n3lECwIjB0cyYnEhEuCbAlbV8JMltXrliTQGHj1YCkJYcxMSxRgb1iyQxIRyTqSLAVV47LFq0KNAl\nCARNjhAmBAKBoOF4LUr069ePfv36ASBJEo8++qjfirpWubxrQKtRATLbD+dzNMvsV3HgAr6O6PSW\nqxkD8Qbr2RzO/ukVlCHBZMx7CaXWwwLQaUezeSkKuwVH/0nIMcnencRSClUmUGrc0Z+Kxn1Odicc\n+EWQSImwkx7laKy24RWHTzn5eK0VpwtuH6ZjULfALo5dkswXXxewdEUeyHDXxATuGB+PStmyOwgc\nTonVG0wsW3keq00io00ws+9JoUNG64kxDRQOh8TmnSWsWltIdp7bvLJ751AmjYmje+dQ4WUQICor\nK8nMzKy5gfHJJ5+wdOlS2rRpw7PPPktMjOfxOYGgpSKECYFAIGgYXosSnTp1uuTCTqFQEBoayg8/\n/OCXwq5FLu4aWLT2GN8fzq95zpM44KtRC39GdAYSye7g1CNPI1VW0fbfL6BPT6l7Y1lGvWMlSnM+\nrvZ9ka7r7d1J7JXusQ2F0i1IKBvXHm53wf68IKodSpLDHbT1syCx9YCdlZvtaNRw/zg9ndsGtq29\nxGznjQXnOPRzBdGRGp54MI3OHVp+m/f+n8pZsCSb3PM2Qg0q7p+aysgh0S1eaAk0FZVO1m4q4uuN\nhZjLnKhUMGxAFBPGGElPFeaVgebZZ58lKSkJgDNnzjB37lzeeOMNsrKyeOmll3j99dcDXKFA4D+E\nMCEQCATe4/UK5OjRozVfOxwOvv/+e44dO+aXogRwLMtc6+MXiwO+HrXwd0RnoMj9+3+o2n+E6DvH\nETP5Fo/bqn7egersQaTYVJx9PG9bg9PqNrZEAeEpoG6c/4fNIXPgF0EiKdxBRrTdb4KEJMt8udXO\nlv0OQoMVPDBeT0pcYAWnPQfL+NeCc5RXOunbI5zfzmxDmKFlz/4XFtn4YFkuO/aUolTA2OExTL8t\nkdAW/roCTYHJxpfrC9m4tRirTSI4SMmksUbG3WRsFSM+rYXs7Gzmzp0LwNq1axk7diwDBw5k4MCB\nrF69OsDVCQT+RwgTAoFA4B2NujLWaDQMHTqUhQsX8uCDD/q6pmuSi7sdvBUHfD1q0dRmm01B2aad\nnJ/3Ebr0FNJe+qPHbRX5p1HtXYscZHAbW6q8+PVwOdzRn7IEYUmgbVwrvsMFW36WqbIrSQxz0M6P\ngoTDKbN4rZVDp1zERSqYNTGIqLDAJVk4nBIfZ+axal0harWCWdOTuWVkbItuubc7JFZ8U8BnX+dj\nt8t0bBfC7LtTRPTkVXLiTBUr1xSwY3cpkgwxURrumpTAqCExBAe1vC6u1k5w8K8/7z/++CN33HFH\nzfct+fdbIGgIQpgQCASC+vFalMjMzLzk+/z8fAoKCnxe0LVGbd0O3drFEBmqpaTCfsX2F8QBb0Yt\nGkpTRHQ2JQ5TMacf/38oNGra/edvqAweBIOqUjRbloFCgWPIXRAcVv8JJBeUZYHkhBAj6MMbV6fL\n7YHo+nQAACAASURBVCFRaYfEMAfXxfhPkKislln4lYVz+RIZSSruG6cnWB+4xcH5Aiv/fOcsp85V\nkxSv46mH0lt0270sy+zaX8bCT3IoMNmJDFfz8L1JDB0QJRZhjUSSZPYcLGPFmkKOHHebV6anBjFp\nbBwD+0SKtJJmjMvlori4mKqqKvbt21czrlFVVYXFYglwdQJB0yGECYFAIPCM16LEnj17LvneYDDw\nxhtv+Lyga43auh2+25tLitFQqyhxQRwoNFfX203hpT3jJfg7orOpkCWJ048/h8NUTOJfHqcqpQ1q\nh6t2YcXpQLNpKQpbNY5+45GNbbw4gewe2XDaICgSgqMbVafTBQfP66m0q0g3QqrBf4KEySwxf5WF\n4jKZ3h3UTBmpC+iCbvOOEt75KAurTWLEoChm3Z1CkL5lCV8Xk1dg5b0lOew9VI5KBRPHGJkyIUHc\nwW8kdofEpu9LWLW2gNx899+6nl3CmDTWSNfrhXllS2D27NnccsstWK1Wfvvb3xIeHo7VamX69OlM\nmTIl0OUJBE2KECYEAoGgbrwWJV5++WUASktLUSgUhIc37q6w4Fc8dTtUWx0M75nIwVMltYoDVzNq\n4ckY0+mSual3MuMHpmGxOf0W0elv8v+7hLJNO6jq1p035TRK3t1Zu+eGLKP+8UuUJXm4Mnohte9b\n/8FlGSrOg6MKtAYwxDcq+tMpuQWJCpuK+FAHvdO1FBU1+DBecSbPxcKvLFRb4aa+Gsb21wZsUWex\nupi/OJvvtpcQpFfyxINpDOkfFZBafIHV5iLzq3xWri3E6ZTpdn0os6Ynk5IUFOjSWiTllU7WfGvi\n629NlJU7UasUjBgUxYQxcbRJFu9pS2Lo0KFs27YNm82GweCOVdbr9fzhD39g8ODBAa5OIGh6hDAh\nEAgEteO1KLF3717++Mc/UlVVhSzLRERE8Nprr9G1a1d/1teq8ewdYWNMv1SmjLiuVgGhMaMWnowx\nAY/PtSQqDxwh5+W3cIaH82m/iVgrHEDtnhvK4z+iOrUPKToJ5w23eicuVBeBtRTUeghPvipBotym\nIs7goEOsHYXCP54d+487WLrehiTDlJE6bugcuMjPM1nV/OM/Z8grsJHRJpinHkojIU4fsHquBlmW\n2bi1kH/NP0mx2UFMlIaZdyXTv3eEuIvfCM4X2vhyXSEbtxVht8sEB6m4/ZY4xo2MJSpSXLC3RPLy\n8mq+Li8vr/m6bdu25OXlkZiYGIiyBIKAIoQJgUAguBKvRYl//vOfzJs3j/bt3Yu5I0eO8NJLL7F4\n8WK/FdeS8Sam0xCsRadVYrVLVzx3odtBp1HVmXjRkFELm8PlMWYU8KlpZqBwVVZx6uGnkR1Oto+b\njjXYcMU2Fzw39OYc1Lu+RtaF4Bg6DVReLNYtpVBlAqXGHf2paLhBpEuCQ+f1lFtVGA1OOhr9M7Ih\nyzLf7XWwersdnQZm3qKnQ5vApD7IsszqDSY+/DQXp1Nm4hgjd09ORKMOnMHm1XAux8KCJdkcPlqJ\nRq3gzlvjmTwuHp2uZb6eQHLslNu8cufeUmQZYqO1jB9l5KYbowkSoy8tmhEjRpCenk5sbCzg/jtw\nAYVCwUcffRSo0gSCgCKECYFAILgUr1coSqWyRpAA6NSpEyqVuGC8nIbEdK7YerpWQQK8M5ZUKZVM\nv6k9k4dm1CmAXFxPbaMeAHuPmepcFF8cQeot3ggy/uLs/72C7WwOYQ9M52hIeq3bmCusVJqKCN3+\nCQCOoVMhxItxJHslVOS5hYiIVFA2fIF/QZAos6qIDXHS0WjziyDhkmRWbLbx/SEn4SEKZk3UkxgT\nmN/X0jIHL//7NLv2lxEWquZ3D7Shd7eWOf5VVe1i2crzrN5YiCTBoH7R3H17PAnGlpdME0gkyW0I\numJNAUdPVgHQts2v5pUqleg0aQ28+uqrrFy5kqqqKsaNG8ett95KVFTLHdUSCHyJECYEAoHgVxok\nSqxbt46BAwcCsGXLFiFK1IK3MZ2e/CT0WhWTbqx9QV0bnropLq+nNswVtYsV7ud+jSCtj4YIMv6g\nKHM1xZ99Q0jPzqT936NEfbC7ViEmNlSDcf8KFNZKnH1uQY7z4r12Wt3GliggPAXUDV+EuiQ4lK+n\n1KoiJsTJ9XE2lH5Ye9nsMovWWPn5rIvEGCWzJugJNwTmDv7hYxX8a8FhTMV2ul0fyuOz04iKCNz4\nSGORJJlN35fwUWYuZeVOEow6HpiezNiRyZhMFYEur8Vgs0ts+r6YlWsLOV/g/t3s3S2MSWPj6NzB\nIMZeWhkTJ05k4sSJnD9/ni+++IK7776bpKQkJk6cyKhRo9DrW+bolkDgK2oTJp6c0oPocPG7IRAI\nri28FiWef/55/vrXv/LnP/8ZhUJBjx49eP755/1ZW4vDm5jOC50Dnvwk7A4XldUOgnVXt3iz2p11\n1nMxkaE6FAoaZZp5Md4KMv7AejqLs396BaUhhIx5L6EP1tXpufFg3BnUxTm40rvh6ti//oO7HFCa\nBbIEYUmg9RAtWtchJDicr6PUoiI62EknPwkS5VUS762ykmOS6JCq4t6b9eh1Tb/Qc7lkPv3yPJ9+\nmY9CAfdMTmTSzXGo/PGi/cyps9X8d3E2x09VodMquWdyIhNGG9FoxKiGt5SVO1jzXRFfbzRRXulE\nrVZw043RTBhtFIag1wAJCQk88sgjPPLII3z66ae8+OKLPP/88+zevTvQpQkEAeeCMKFUKljzQxZ/\n+3gPT9zZnWTjleOnAoFA0FrxWpRIS0vjvffe82ctLR7PxpWXdhxcTXqGt5jL667nYnp1cM/7NsQ0\n83IaIsj4Gsnu4OTDTyNVW8h4+0X0bdxhqLV5bkxJNtOx7ARSZDzO/hPrN6mUXFCWBZITQoygb/jY\ngSTDTwU6zBY10cFOOsf7R5DIL3axYJUVc4XMDZ3VTB6mC0gbfFGJndf/e5YjxyuJjdbywv92Ij6m\n5S3gyyucLP48j/VbipBlGNQ3gvumJhMTJVprvSU338qX6wr5bnsxdoeMIUTF5HFxjLvJSGR4y+uY\nETSO8vJyVq1axeeff47L5WLOnDnceuutgS5LIGg2KBQKpgxvR1iwluXfneTlxXt57PaudGwTGejS\nBAKBoEnwWpTYsWMHH330ERUVFZeYVQmjy19piNDQkPSMxno0RIbVXY+7BiU3dk+8xBjTG9PM2miI\nIONrcl5+i+pDR4mZOp7o28bWPH6550aUzUTwxnXI2iAcw6aDup7FpSy7RzacNgiKhODoBtcmyfBT\nvo6SajVRfhQkTmY7eX+1Fasdbh6gZWQfTUBa4X/YV8pbC89RWeViQO8IHr0/lbQ24S1qxMElyazb\nVMSSL/KorHKRkqRn9vQUul4fGujSWgw/n6hk5ZoCftxfhiyDMUbLhNFGRgyOJkgvxv6uFbZt28Zn\nn33G4cOHGT16NK+88sol3lQCgeBSxt6QSkSolve++pm5y/cze3xn+nY0BrosgUAg8DsNGt945JFH\niI+P92c9LZqGxnTWl55Rl0fDpBvbUllt9yhS2BwunOU2OreNYsv+87VuYwjSMHloRo3fQ32mmZ5o\nis6P2ij9djv57y5Gn9GGNi/+odZtdBoVRr2E9tvlIEs4bpwChnruPsgyVJwHRxVoDWCIb3D05wVB\norhaTWSQi85+GtnY/bOD5Rvd7/vdY3T06tD0d6DtDokPl+fy9UYTWo2Ch+5NYfTQmBbnEfDziUrm\nL87mTJaF4CAlM+9K5uYRsajVLet1BAKXJPPjvlJWrink2Cm3eWW79GAmjY2jf68IYV55DTJr1izS\n0tLo1asXJSUlvP/++5c8//LLLweoMoGg+dK/UzxhwVre+vwQ76w4TOlN1zGqT0qgyxIIBAK/4rUo\nkZSUxIQJE/xZS6vAm5jOizsfPAkBdXk0bDuYh80u1WokeYmQUWEjLLjuBaq5wnZFB4Mn00xPNFSQ\n8QX2giJOP/4cCq2GjHkvoQq5tO6a9zlYjWHLJyiqy3H2Go2c6EX3R3URWEtBrYfw5EYJEkcK3IJE\nRJCLLvFWVD6eYJBlmQ27HKzZaSdIB/ePCyIjuenvQuect/LPd85wNttCSqKepx5Kp01yy/IJKCl1\nsOjTXDbtKAFgxKAoZtyRRIQYMagXm03i2+3FrFpXSH6hWxzr2yOcSWPjuP66kBYnTAl8x4XIT7PZ\nTGTkpUJwTo5nA2aB4FqmU1oUf7q7F68vP8DSDScorbAxeVgGSvH3VCAQtFLqFSWys7MB6NOnD8uW\nLaNfv36o1b/ulpIi1NuL8RTT6Smd4nIhwJNHw4UY0dqMJC8XMsqqHHXW6usOBm8EGV8hSxKnf/cs\nzmIzqS88RUjXjjXPXf4+z449xVBtFs7Uzrg6Da7/4JZSqDKBUgPhqe4I0AYgyfBzgY6iKjURehdd\n/SBIuFwyn35nY9cRJ1FhCmZNCCIuqml9G2RZ5tttJcxfnI3NLjF6aAwz70pGp2s5/hFOp8zqDYUs\nW3Uei1WibZsgZt+dQsd2wmCsPkrLHHz9rYk135moqHShUSsYNSSaCWPiSE4QzvECd2rXE088gc1m\nIyoqinfffZc2bdrw8ccf89///pfbb7890CUKBM2W1LhQ/jyjN3OXH+CbH7IwV9qYecv1qH19QSEQ\nCATNgHpFid/85jcoFIoaH4l333235jmFQsHGjRv9V10ro67Oh2qrkxljOlzSTeDJo+FyLhhJur+u\nP23jAr7uYPAkyPia/P8sonzrj4TfNJi4B+665LmL3+fBQfkM1WaR7Qhhk60rU+q7y2CvhIo8txAR\nkQoqr5uJAPfUx9FCHaYqNeF6F10SfC9IWGwyH35t5US2ixSjkgcm6AkNbtqLlGqLi3c+ymLrD2aC\ng1T8z8PpDOrbsgy5DvxUzoIlOeSct2IIUfHQvSncNCSmRSaENCW5562sXFvApu9LcDjd5pV3jo/n\nlhGxorNEcAmvv/46H3zwARkZGWzcuJFnn30WSZIIDw/n008/DXR5AkGzJyYiiKdn9ObNTw+w86cC\nKqrsPHJbV4J0Dbs2EQgEguZOvX/Vvv3223oPsmLFCiZNmuSTglo6nnwg6hIMvj+cz7Esc03XhNMl\nY3e4PJpUXswFI0nAo5ARadBRVmXzawcDNH4ExFvMPxwg59V5aOJiaPv6/7ukPfziDpM0TQUPRB6j\nSlLzenEXnNYyJg531S2UOK1uY0sUEJ4C6oZ1kbgFCS2FlWrC9C66JlhR+1grMFe4Iz/PF0t0Tldx\n91g9Ok3TLqJPnKli7rtnyS+00SEjhCfnpGGM8Y9niD8oLLLxwbJcduwpRaGAscNjmHZbImEGcZFX\nF7Is8/OJKlasKWDX/jIA4o06Jow2MnxQFHqdMK8UXIlSqSQjwy2Yjxw5kpdffpn//d//ZdSoUQGu\nTCBoORiCNPzPtJ68u/In9p8s4tUle3nizu5+8+oSCASCQOCTq/DPP/9ciBK/UFc3hMXq9CgYXNju\nWFYp1VYHJeU2dFrvVrQXxjDsDhfhBi2llfYrtokO0/PsfX2w2Jx+7WDwN87ySg7NeBLZJZHx77+i\nib707vyFDhOD0s4TUYdQI/FGSRcKXMEoPaWAuBxQmgWyBGFJoA1pUF2yDMdMWgoqNYTqXHTzgyCR\na3JHfpZXyQzqpmHSEC3KJryrL0kyq9YV8vFnuUgSTB4Xx10TE1uMCaTdIbFyTQGZq/Ox22U6tgth\n1t0pZLTxn4DW0nG5ZHbuLWXlmgJOnKkGoH1GCJPGGunXM0J0lQg8crmfSEJCghAkBIJGoNOoePT2\nLixae5wtB/J4adEenpzag/go8f9LIBC0DnwiSlwcEXot48kH4miWmchQLSUVVwoGF5NdWFnz9QXv\nCL1Whd3hQqtRYbW7rtinx3XRfLb5FPuOm2oVJMA9qhEarCU0uJ4YzEbS2NjShiDLMmf/9DKWMzkk\n/O5+wgb3vWKbcIOOmDANszT7iFHb+LQ8nQM2d5RnnR4akgvKskByQogR9OENrAuOm7TkV7gFie5+\nECSOnnXy0TdW7A6YMFjLkJ5NG/lZWu7gXwvOse9wOZHhah6flUb3zmFNdv6rZdf+Ut5bmkOByU5E\nmJqH701i6IAoYcJYB9UWF6s3FPLlukIKiuwoFHBDz3Amjo3j+uuE34agcYjfN4Gg8aiUSn4ztgNR\noTpWbDvD3xbt4fE7u5GR2LBrFoFAIGiO+ESUEBcabjz5QJgrbPTvHM/3h/MbfNxgnZqnZ/QmKkzH\niq1nrjCSlGSZjbWkXoC7Q8LTqMblYkJDxQVP5p0XEkF8RdHyryhZsZaI/j1JempOrdvoNCoeiM+h\nS1Upuy0xrKxoU/NcrR4asuwe2XDaICgSgqMbVJMsw/EiLecrNBi0v3RI+FiT2XnYwWff2VAq4d5b\n9HRr17RjBgd+KufNBWcxlznp2SWM381qQ0RYy/AOyCuwsnBpDnsOlqNSwYTRRqZOTCA4qGV2Cvkb\nc5mD1RsKWbe5mIpKJ1qNgjHDYhg/2khSvDCvFDSMffv2MWzYsJrvi4uLGTZsGLIso1Ao2LRpU8Bq\nEwhaIgqFggmD04kI1fHhmqO8tnQfD0/sQvd2MYEuTSAQCK4KMUTtQ8INujp9ICJD9UwfdR3BejV7\nj7njOr2ltNKGVq0kWKe5wkgS4C/zd9a6X4RBy5/u7kl0+JXxjJeLCZGhWkKCtDWjI96KC3WNq8Cv\niSC+wHLyLOeefhVVaAg9F/2DKk3tP7rKMwfpWnWEUlUYy509UChcRNXloSHLUHEeHFWgNYAhvkHR\nn7IMJ4q0nC93CxLdE634sklEkmXW7LCzcbeDED3MHB9EWkLTLaadTpmlK/L44psClEq4b0oS40cb\nm3RkpLFYbS4yv8pn5dpCnE6ZbteHMmt6MilJLSuqtKnIzrWwal0hm3aU4HTKRIRpuGtiAmOHxxDe\nQgQoQfNjzZo1gS5BIGiVDOmeSFiIlndWHObfnx3i3rEdGNI9MdBlCQQCQaMRooQP0WlU9Gwfe8ki\n/QI928dcIip8vPYY273smrh87OBiI8lCc3Wd3RmllXb+tmgvvTteKS5cLiaUVNgvGS3xRlzwNK5y\nIRHEF6Mcks3OqYefRrJYyXjnZYLTkqkyVVyxncKcj3rHCmSNjuCb7+MvwVGeuz6qi8BaCmo9hCc3\nWJA4Wawlr1xDiFaim48FCadT5pMNNvYddxITrmD2xCBiIpouYaOwyMY/3z3L8VNVxMVqeeqhdK5L\nb5jPRiCQZZnvd5Xy/rIcis0OYqI03H9XMgN6R4iOrsuQZZmfjlWyYk0Bew6WA5AQ5zavnDKxDeXl\n1QGuUNDSSUpKCnQJAkGrpUe7GP4wrSdvZh7kg2+OUlphY/ygNPG/TiAQtEh8IkoYDGLGGNzdB5Is\no9cqL/GDGNQ1/pK79DqNivtu6UiQXn3JKEawXn2Jp8QFPEV3eurOADBXXikuVFTb2XPUu+hQT+KC\n53EVD6aSDST7pX9T/dNxYqdPInpCHSZptmo0m5agcDlwDJ6GHB6LDuo+v6UUqkyg1EB4qjsC1Etk\nGU4Va8kt0xCskeieaEHrQ0Gi2irz/lcWTudJpCUouf/WIAxBTXeRsX2XmXkfZFFtcTGkfyRzZqS2\niHGHrFwL8xdnc/hoJWq1gjtvjef2cXEiGeIyXC6Z73ebWbmmkFPn3MJDx3YhTBobR58e4aiUCnTi\nPRMIBIJmT0ZSOP93Ty9eX36AFdvOYK60cc/o9j4fnxUIBAJ/47UoYTKZ+PrrrykrK7vE2PLxxx9n\n3rx5fimupbHs25N8uyf3ksesdhcKheKKfxAqpfKKUQy1SvHLSMWlnhGeojs9dWdczL7jRUy6MZ0V\nW8+w+2hhnYaYl+NJXKhvXMUXcVXm9VspWLAUfbs0Ul94qvaNJAnNtkwUlWacXYcipXbyfFB7JVTk\nuYWIiFRQea/NyTKcLtGQ84sg0cPHgkRxmcT8VRZMZpnu7dRMG61D00TpFjabxMJPcli3uQidVslj\nM9swfFDzN4OsqnaxbOV5Vm8sRJKgT/cwZt6VTEKc8EC4GIvFxYatxXy5vhBTsdu8ckDvCCaMMdKx\nnRCWBQKBoCWSEB3C0zN688byA2zen0dZpZ05Ezu32JQ1gUBwbeL1amzOnDl06NBBtGPWQWNHGS4e\nxQCuECq8+acydUQ7jmWV1tplcQFzhZUl60802GjTk7hQ37jK1f5DtOebOPPE8yh0Wtr952+ogmv3\nA1Ad2Igy7wSupPa4uo3wfFCn1W1siQLCU0DtvXAiy3CmREN2qZYgjUT3RCtaHw5Ancqx86/lFiot\nMsN6aRg3SIuyiQSBczkW/vnuGbJzraQlB/HUw+kkJzTvRb0kyWzaUcJHn+ZSVu4k3qjjgWnJ9Oku\nnMgvpsRsZ/VGE2s3FVFV7UKrVXDziFjGj4oVwo1AIBC0AiIMOv737l68/cUh9p8s4h+f7OPxO7pj\nCBKeQAKBoGXg9ZIqODiYl19+2Z+1NEu8TaNo6CiDp+NeLlTUV1uQTk211eFx28hQHUfPldR7zMup\nT1y40MXRkO4Ob5BdLk499gzOklLavPgHgjvX7muhzPoJ9eEtyKFROAfdAZ5aFl0OKM0CWYKwJNA2\nzCPhnFlD1i+CRI9EKzq176JwD59ysnhdJQ4n3D5Mx6BuTXMhIcsy6zYXsXBpDnaHzC0jY/nNlCS0\nmubd+nnqbDX/XZzN8VNV6LRK7r49kQljjM2+7qbkXI6FVWsL2LLTjNMlExaqZtqkBMYOjyUsVNgJ\nCQQCQWsiSKfm93d2Z+Hqn9l5pIC/LdrDk1O6ExMhDJ4FAkHzx+sr0+7du3Pq1CkyMjL8WU+zoaFR\nl96OMlxNhOYFEcIQrGXF1tM1x4gw6DBXek7z6Jga6bFLIiJES2iIO33DXGHzWlyobQzFFy2D59/+\nkIrtu4kYPQTj/VNq3UZRWoh6++fIai2OodNB5+Efr+SCsiyQnBBiBH3D7qafNWs4a9aiV7s7JHwp\nSGzdb2flFjsajYKZt+rolN40C8bKKifzPshix55SDCEqnnyoDTf0jGiSczeW8koniz/PY/3mImQZ\nBvWN4L6pycREaQNdWrNAlmUO/VzBijWF7DvsNq9MitcxYUwcwwZGCdFGIBAIWjFqlZJZ4zsREapj\nzQ9ZvPTxHp64szupcaGBLk0gEAg84vXqZ+vWrXzwwQdERkaiVqtbfc54Q6MuvR1laEyE5uVChu4i\nI03AoyChVMDQHolMHtaOo1nmWkWTCIOW52f2IzRY63VnyOV4293hDRW7D5Lz2rtoEoykz322dk8D\nuxX15iUonHYcN05Bjoyr+4Cy7B7ZcNogKBKCoxtUzzmzhrMlbkGiR6IVvY8ECUmW+XKrnS37HYQG\nK/ife6MxaK0+OXZ9HD1Zydx3z2IqttOpvYEnHkxr1gt7lySzfnMRiz/Po7LKRUqinll3p9DtenGh\nBe60Frd5ZQGnsywAdGpvYNJYI727hbeIGFeBQCAQXD1KhYIpw9sRadDxycYTvLJ4L4/d3pXr06IC\nXZpAIBDUideixH/+858rHisvL/dpMc2FxvpD1DfK0NjjXi5kXCxI1IdWo0SlUqLTKOsUTfp0NBIa\n7F6Q+lJcaAzOsgpOPfJnkCQy3vormqgr79zLsoR6eybK8mKcnQcjpXWt+4CyDBXnwVEFWgMY4hsU\n/ZlVquZMiRbdLx0Seo1vBAmHU2bxWiuHTrmIi1Iya4Ke9CQNJpN/RQmXJPPF1wUsXZEHMkydEM+d\n4xNQqZrvovXnE5XMX5zNmSwLQXol99+VxC0jjKibyAC0OVNtcbF+SxFfrS+kqMSBUuHuHpkwJo72\nbZt/hKtAIBAI/MOovimEG7Qs+OoIc5cfYNatnbihk4cbOAKBQBBAvBYlkpKSOHnyJGazGQC73c6L\nL77IN99847fiAkVjoy7rG2Xw5rjhBt0l+3oSMmpDq1Zgd/66cLbapRohYtKN6VRbnRw9Z6a00vsR\njaZClmXO/vEl7DnnSfz9LMIG9K51O/vOdahyjiHFZ+DqcZPng1YXgbUU1HoIT26QIJFdquZ0sQ6d\nyt0hEeQjQaKyWmbhVxbO5Uu0S1Zx3zg9QTr/L7BLSh28Of8sB3+uIDpSw+8fTKNLh+bbaVBS6mDR\np7ls2uH2Qhk+KIoZdyQRGS6Mu4pK7KzeUMi6zUVUWyR0WiXjRsZy6ygj8carT70RCAQCQcun3/Vx\nhAZreevzg7y76idKK22M6Zca6LIEAoHgCrwWJV588UW2b99OUVERqampZGdnM3PmTH/WFjCuNuqy\nrm4DT8eNMOhYuyubgyeLLvGaGN4zqU4h43KUCi4RJC5my75cdh8tpKzSTlSYjgGd45k2qj3Busb5\nFzR2zMMTpiUrKflyA4a+3Ul6clat2yhzjmHbsQY5JALHkCmg9HBuSylUmUCpgfBUdwSol+SUqTlV\nrEOrcndI+EqQMJndkZ/FZTK9O6qZMtIdBetv9hws41/vnaO8wknfHuH89v42zdbs0OmUWb2xkGUr\nz2OxSrRNDWL2PSkithI4k1XNqrWFbP2xBJcLIsLU3HZzPGOGxRBqaJ6fp0AgEAgCx/VtIvnT3b15\nffl+ln17EnOFjSkj2jVZupdAIBB4g9dXsYcOHeKbb75hxowZLFq0iMOHD7N+/Xp/1hYw/BV1qdOo\n6JYRzXf78q54LiRIw3d7c2u+v+A14XJJdQoZlyN5WDfbXTL2SnvNsbcfzidIr67Vx8KT4HA1Rp2e\nsJw4Q9Yzr6EKDyXj7RdRqK/80VSUF6He9imoNDiGTQOdhzETeyVU5LmFiIhUUHm/YMstU3OyyC1I\n9Ei0Eqz1jSBxJs/Fwq8sVFthVD8NY27Q1u6X4UMcTomPM/NYta4QtVrBA9OSGXdTrN/P21gO/FTO\ngiU55Jy3YghR8dC9Kdw0JAbVNeyJIMsyB45UsHJNAft/qgAgOUHPxLFGhvaPQiPMKwUCgUDgHyX+\nGQAAIABJREFUgRSjgT/P6MPc5ftZtyubsio7M2+5Ho1a/P8QCATNA69Xalqt23PA4XAgyzJdunTh\n1Vdf9VthgcbXUZcXFvMHTxUD7q4GSYaoUB3d20XXPH45B0+V0K1dzCWCxQX0Wvd4hwLPgkRdXO5j\n4Y3g0BijzvqQrDZOPvw0ktVGu3+/gC454cqNHDbUm5agcNjQj70HW1Ri3Qd0Wt3GliggPAXU3rez\n55WrOVGkQ6OS6e5DQWL/cQdL19uQZJgyUscNnf0/gnC+wMrcd89y8mw1iXE6nnoonbZtAucX4glT\nsZ33l+WwY3cpCgWMGRbD9NsTCbuG7/47nBLbfzSzck0hZ3Pc5pVdOhqYNDaOnl3ChHmlQCAQCLwm\nOlzP/93Tm399dpAfjhRQXmXnt7d3JaiRHbMCgUDgS7z+S5Sens7ixYvp06cP999/P+np6VRUVHjc\n5+9//zt79uzB6XQyZ84cunbtyh//+EdcLhexsbG89tpraLVaVq1axYcffohSqWTKlCnceeedV/3C\nrhZfRV1e6DpYuyv7EmHhgojQ/boYxvRNYVMt3RPg9pq4qXcyKqXiCoFk0o3pnDtfwWuf7G/Ua7zc\nH6M+waGxRp31kfXXN7EcOUHsjNuJGjfyyg1kGfX3n6MsM+HsOABtpz5gquNnz+WA0iyQJQhLAq33\nZn/ny9UcN+nQKGW6J1gI8YEgIcsy3+11sHq7HZ0GZo7T0yHV/xcAW3aW8J8Ps7DaJEYMimLW3SkE\n6X0zZuNL7A6JlWsKyFydj90u0yEjhNn3pJDRTMWTpqCq2sW6zUWs3lBIsdmBUgmD+0UycYyRdunC\nvFIgEAgEjcMQpOF/pvbg3VU/se9EEa8s3svv7+xOZKjwIhIIBIHF69XR888/T1lZGWFhYaxevZri\n4mLmzJlT5/Y7d+7kxIkTLFu2DLPZzG233caAAQOYPn06N998M3PnziUzM5NJkybx9ttvk5mZiUaj\n4Y477mDUqFFERFyZuhAIGptGcXHXQXG5jbpuah48WcykwekePSyiwvR1CiRtk8KJ9nK8o7ZjX/DH\n8EZwaKwBqCfMazdT+P5ygjq0pc1zT9a6jeqnraiyjiDFpeHqPabug0kuKMsCyQkhRtCHe11HfoWa\nYyYtaqVM90QLBl3jBImLR1/UKiVfbLax45CTcIOC2RP0JMT4VxiwWF0sWJzNt9tL0OuU/H52GkMH\nNM8YsF37y3hvaTYFJjsRYWoempHE0AFR12wHgKnYzlfrC1m/pQiLVUKvUzJ+lJFbR8VijBEXjIL6\ncUkyx09VsWt/GQd+KmfowCgmjBZu+wKB4Fe0GhWP3taVxeuP892+XP62aA9PTu1OQrQQvQUCQeCo\nV5Q4cuQInTp1YufOnTWPxcTEEBMTw5kzZ4iPj691v759+9KtWzcAwsLCsFgs/PDDDzz//PMADB8+\nnIULF5Kenk7Xrl0JDXWnAPTq1Yu9e/cyYsSIq35xgeTyroO6xivMFVYsNqdXHha1CSSe/C/q4+Jj\ne5sMcjUGoJdjzyvg9JMvoNDryPjP31AG6a/YRpF3AtW+DcjBYThunFq3saUsQ3kOOG0QFAnB0V7X\nUVCh4mihFrUSuidaGyVIXD76EhkaRFjQdZRV6kmMcUd+hhv8O7t5Jquaf75zhtx8GxltgnnqoTQS\n4q58TwPN+QIr7y3NYc/BcpRKmDDayJQJCYQEN79Ojqbg9LlqVq4tYNuPZiQJIsM13HFrPKOHxmAI\nEW21As9YLC42bTexYUs+ew+WU17pBECrVaBt4X4jx48f55FHHuG+++7jnnvu4dSpUzz77LMoFArS\n0tJ47rnnUKvVzbLbUiBoziiVCu4Z3Z6IUB1fbDnN3xbt4fE7u9MuyfubOQKBQOBL6r3iXbFiBZ06\ndWLevHlXPKdQKBgwYECt+6lUKoKD3QvozMxMhgwZwrZt22q8KaKjozGZTBQVFREV9eud3KioKEwm\n7yMwmyMNifG8sJi/Gg+L2vYN1qvJLqysdXuVEob3Sr7k2N4IDr40AJVdLk499gwucxlpr/yJ4I61\nvM6KEjRbPwWlCsfQaRBUR/qCLEPFebBXgdYAhnivoz8LK1X8XKhD9YsgEaqTvH4NF3OxCKVAg8PR\nljKXnrAQK4/eEY1e6/3d/4Ymm8iyzNcbTXywPBenU2bCaCP33JHY7AysrDYXmV/ls3JtIU6nTNfr\nQ5k1PZnUpKBAl9bkyLLMvsPlrFxTyMGf3aNIqUl6Jo6N48YbIpvdZydoXpiK7ezaX8buA2UcOlqB\n85fUpchwDaOHxtCnezjdOoWi07bcn6Pq6mr++te/XnKN8Y9//IMHH3yQoUOH8vbbb/PNN98wcuTI\nZt1tKRA0VxQKBeMHphFh0PLhN8d4bek+HprYmZ7XxQa6NIFAcA1Sryjx9NNPA7Bo0aJGnWDDhg1k\nZmaycOFCRo8eXfO4LNd+N7quxy8mMjIYtdq3d1VjY0N9dqzzRVWUVHg3TjGoeyLJie6Lp8en9cZq\nd2IutxEZpkOv9f4u6cX7BuvVPPnG5lq3Uyph/tM3ERt5ZZveoO5JrNp62mONsyZ1RULB4VNFFJVa\niIkIon+XBGaO74xK5f0F8ImX3qZix17iJo2i05P3XZEGITtsVH2zDMluQT/6LsKvv/6S5y/+vKoK\nc6m2lqLWhxCR3hGFp5jQi8gplvm5QEatgqHXK4gyNK510Wp3XmRgGoRB1x6VUofNWUi1o4DY2BSv\nPkuXS2LF9rPsPHweU6mFWC/e27JyBy//6xjbfigmIkzDn5/owIA+3neJNAWyLHPomJW33jtFYZEN\nY4yOx2ZlMGxgTLNNAWkIDfnbYXdIbNhcyCcrcjh9rgqA3t0jmHZbCjf0imxW74cv/yY2J1ri65Ik\nmWMnK9j2YzHbfyzm5Jmqmueua2tgUN8oBt8QQ/sMQ6sZf9JqtcyfP5/58+fXPHbu3LmaDswbb7yR\nJUuWEBMT0yq7LQWCpuLGbomEh2iZt+Iwb31+iBljOjCsR1KgyxIIBNcY9a6UZsyY4fFC+aOPPqrz\nua1bt/LOO++wYMECQkNDCQ4Oxmq1otfrKSgowGg0YjQaKSoqqtmnsLCQHj16eKzJbK6ur+wGERsb\niqku48RG4HK4iAqt3+fBEKRmTN+kK86tBirKLNRXUW131NVATl4pJrOl9p1kKDRVgvPKjoDxA1Kp\nttiv6NYYPyCV/IKyy8YTtPTvHM/0UdcRrNNQUlJVy8lqp+LH/Rx/4S20iXEkvfQnioou6+iQZdTb\nPkVVlIerfT/K4jpfYmx5yedlKXVHfyo1OEOSKCr27mfDVKniSIHu/7N33vFRnOfavravpNWuepcQ\niN4kirDBxmBMcwHcYmwcJy5xP0nsJOdLzpf4xGkn7fjEST7nxMEFN9xtirFpBmNsbIMASfQiAepd\n2qLtM/P9sUio7KqAhCR4r7/47czOvLO7jOa93/u5H9QqmJTkRnLJ1Ib4yLqjptFJbaMLrTqSCMMo\n1CotLm8pbn8lbh8Una7vUdbGmi9PtxOFahpdrNtZjNPlDdrZ5OAxO8/+6zT1jT4mjYvkie8NIyZa\n36e/5QulpNzFK+9Wsq+wCa1Wxe03JXHbjYkYDZrO3/sQpKf3jmann43b69iwtZZGayC88poro1m2\nKLG1I8pg+jz6+p44WBhK1+XxyBQesbE738reAiuN1kBZhlarYspEM7k5FqZnW4iP1bdeV339xfkN\nXQxhR6vVou3QGnr06NHs2LGDm2++mZ07d1JXV3dJui0FgovN5Kw4/s9dU3n23QJe3XiMJruHZVcP\nH1RCuUAguLTpVpR47LHHgIDjQaVSceWVVyLLMrt27SIsLLTt2m6386c//YlVq1a12ihnzZrFpk2b\nWLZsGZs3b2b27NlkZ2fzi1/8ApvNhkajYd++fa3ujKFKT3MeHC4/f3h9P7+6f0avjt9d606LyUB0\npJ4Gu7fTe7vKfuiq48jqrcfbXU+D3cuug1WEG7W9agXqb7JR9PgvAMh67rdoozvXL2qOfIXm9AHk\n+Az8068PfTCvA+wVKCo1Dep4TLIKQw9MEnXNAUFCpYKxCU48Licezfl1V4FA6Uu0KQlZSgPA4SnC\nJwWcEz3N2vD4JL4+WBl0W+fWrQrvrqvk3fVVoIK7b03hlhsS0QyiFdJmp8Tb6yrZsLUGWYbp2Wbu\nvzNtUGZc9Cc1dR4+2lLLls/rcHtkwoxqli1K4Mb5CcTH6gd6eIJBRkOTj7yCQFlGwSEbXl/AOWg2\naZl3VQzTcyzkjDcTFnZ55q/89Kc/5emnn+aDDz5gxowZQZ2VA+W2bGEoOnEuNcR3cH7Ex0fyTKqF\nX/7rK9Z9eRqXT+bx27N75YJteyzBwCK+g4FHfAe9o1tRoqWe88UXX+SFF15ofX3hwoU8+uijId/3\n8ccf09jYyBNPPNH62h/+8Ad+8Ytf8Pbbb5OSksLNN9+MTqfjxz/+MQ888AAqlYrHH3+81YY5lGmb\n81Bvc4fcr7zWgd3pJTK85xOUrlp3Lp83kvd3FOH0SEHf25Psh46Bmn3VClRRFE79+2/xlleR+uOH\niLxiSqd9VJXFaPZtQgmLxHfNnaAJ8RP1u1GsZcgyPP+5lb3FFZ3EmbbjbxFZHF49h6oCgkRNxQnW\nbzwdVNjpKYqisGOfhCJnoOCn2XMCv3xuJbanWRtWh4fapuBWjbadTeoavPzlX6c5fNxBfKyeHz2c\nydiRIbI2BgBZVvjsqwZee7ecJpufpAQDTz48itHDL68JeNFpJ2s2VrMrLxBeGRutY/myZBZcE3fZ\nBnoKOqMoCqdLXezJt7KnwMrJU+ecXukpRqZnW5gxxcKoERGDSnQcKJKTk3n++eeBgBOzpqZmULgt\nWxhKTpxLFfEdXBg64KcrpvLsOwVs2V1CdX0zjy6biEHf879b4jsYeMR3MPCI7yA4XQk1PQ4tqKqq\n4tSpUwwfPhyAkpISSktLQ+6/fPlyli9f3un1l19+udNrixcvZvHixT0dSr9gd3opq3GQlmDqlUAQ\niraug71Ha3hhw5Gg+8kKlNU4GJFq6VG4YXcCgSTJbN9f0WmbUa/h6snJPQrO7EhftQKtff0DGjds\nI/LKqaQ88UDnHZqb0O18G1SqgCARHvyHK/m80FSCSpF54fMm8ooDok9bcWbF/NGdHCWjh6cwY9oU\nNGqoqTjJx18caz1mx/f2BL+k8N42D3uO+ImOVJGc0MDRMz4a7fQqqBQCbov4qDBqgpTdtLgtdu9v\n4u8vncHRLDFzWhSP3ZsxqLozFJ1xsvL1Uo4VNaPXq7j71hSWLkogNcVyWdyYZVlh3wEbazdVc/Bo\nwEafmRbGssUJXDUjeHhlb0NNBUMfn0/m4DFHQIjIb6KuwQeARgOTxkWSm21heo6F5ATRBrYjf/vb\n35g8eTJz587lgw8+YNmyZZek21IgGEgsEXr+z4op/O+agxQW1fPnt/bzg9snY+6DZ2OBQCAIRY9n\nNE888QT33nsvHo8HtVqNWq2+JP7we/1+fvjMdk5X2pAVUKsgNd7Ez78zFb32wid8Bp2GSVmxqFXB\n24KqVfDN0Wpe+vhIj1bsuxIIGuxuvjxYFXRbhFHLbXOyeuUCaKEvWoE6jxVx5pf/gybKzIi//xqV\npsMEzO9Du301Ko8T1/QbUSdkBD+QLGEtOQaynw0HnHxT3NmF0uLeeH9HUavQkJwQx/QpOUiyQm1l\nEXsOnAp6+J46P1wehVc+dnOiVCI9Qc0DS41Eho/A4xt2XpNMg07DlROTgwaNTs6K5bV3KtjwaS16\nnYqH70ln0dzBExJpc/h544MKtuyoQ1Fg1vQo7l2edtmUJ/h8Mju+bmDdphpKKwK/x5wJkSxbnEj2\n+Mig31N3JViCSwurzcfeAzby8q3sP2jD7Qlk+kSEa5h9RTS5ORamTjITET54RMaB5uDBg/zxj3+k\nvLwcrVbLpk2b+MlPfsJvfvMb/v73vzN9+nTmzp0LcEm6LQWCgSTMoOUHt09m1SdH2XWwit+/tpcf\nLc8hPury65YlEAguDj1+Apo/fz7z58+nqakJRVGIjo7uz3FdNH736r52rTNlBUprHPzu1X29znoI\nRWS4ntR4U9AWneFGLZ/nn8sS6G7FviuBQK9V4/EFb2nZaPf02NHQkQttBSq73BQ98h8obg8jnvsd\nhtSkdtslSaJ8zeuMclXyWXMyH2z3M6X8eOcJmqKArQzJ68SpjuSDPcEFmEa7m9pGZ6ujJCk+lmuv\nDnyX27/cg7vZRqPj/J0fjXaZF9a5qaqXmTBcw92LjRh0gYlnx9KX3nD/kgmdgkZHJsWwb5fE6dJa\n0lOM/PiR4QxLGxwPBZKssGVHHW98UIGjWSIt2ciDd6cxebx5oId2UbA7/Hyy/QzvrC2jyeZHo4G5\ns2JYujCB4Rld/wa6KsHqTUaLYHCiKAplle6zbggrx4uaW0XppAQDuTkWZuRYGDvShFY7OMTFwcbE\niRODdv167733Or02GNyWAsGlhlaj5oEbxxFlMvDx12f43Wt7efJb2QxLEqKfQCDoe3osSpSXl/PH\nP/6RxsZGXnvtNd59911yc3PJzMzsx+H1L3anl/La4Gnl55P10BU//85UfvfqPsprHa2OjJS4CJxu\nX9D9Q63Y9zREsyPRkYYeORpC0TYjo21njp6UJ5T8+llcx4pJ+O63iL5+bqftBR99zExXMUXeSFY1\njcKHt/METVHAXgneZvSmKDzaBGLMJSHdG6hUNNg8JMYFBAkV8NmuPCqra1GpIMqkp8nRuyBQgLIa\niRfXu7E1K1ydrWPZbH2fteDTaM6V/DTZ3eQfcPLym+V4vDIL58Rx/51pGAyDYxX96EkHK18vpbjE\nRZhRzX13pnLDvITLYoJVVePhoy01bN1Zj8crEx6m5ubFgfDKuJju7xd9ldEiGFz4/QpHTjjYUxAQ\nIqpqAvcmtQpGZ0UwY0qgW0ZasnHQuJwEAoGgK1QqFbfPzSI60sDqLcf5w+p9/Nstk5gwPKb7NwsE\nAkEv6LEo8dRTT3H33Xe3ZkJkZmby1FNPBV3JGCqU1TiCllTAuayHcZl9c+PVa7X86v4Z7bIrXB4/\n//H810H372rFPphAMDYjKmTpBsCoNMsFTXS66szRFQ0fb6PmlfcIGzeSjP/8Yaft/vJiZlj3YJV1\nPNswER/njtluguasA3cTaI2Y00bibXB26d6IjwojKyOR3GlTUavV7NiVR3lVDQAxkUYmZ8UEzd7o\nyvlx5LSf1z5x4/XB0tl6rsnR9cvkQvLD6vdq+PzrRsLDNPzk0eFclTs4nEmNVh+vvlvOZ7sagIAz\n4DvfSiXaohvgkfU/x4ubWbuxmq/3NiErEBej48FbMpk5NZLwXnRD6KuMFsHA0+z0s6/Qxp4CK/sO\n2Gh2BgKGjQY1M6dFkZtjYdpkC+ZIUZYhEAiGLtdNS8MSoedf6w/z7LsF3H/DOGZOTOr+jQKBQNBD\nevyk5PP5uO6661i1ahUAubm5/TWmi0ZagqnLrIe0hL7vahAZrm8VOvQ6zXllNbQIBEtmZbYKHBqN\nmr3Ha3B7g5dvHC9tYvXWICURvaQ35QmesipO/fg3qI0GRv7vf6EO69AO0mnD+OU7APytYQINUvvt\nrRM0oxeaa0GtA0tGax5FV+4Nh1fLlbnTABU7vtpLWWV163Fb9tFo1D12fnx10McH2z2o1fCdG4xM\nHtk/k4yTp5p55vnTVNV4GD0inB89PJzE+IEPvPP7FTZ8WsPbaytxuWVGZITx4LfTB1Xnj/5AlhX2\nFlpZs7GGw8cDrqrhGWHcvDiRWdOjSU429zrEsy8yWgQDR2WNh7x8K7vzmzhywoF0ttFRfKyea66M\nITfHwsQxJnS6weFqEggEgr5g+tgEIsN1/O39A6z86DBNzR4Wz8gQzi+BQNAn9GpmZbPZWm8+J06c\nwOMJvto3VOgq6yE1vm+6cHTH2IzooA6Hrlbsg4XkhRt1IQUJgAZ7oCTC6fZzz6Ix/W4PV/x+ir//\nFJLVTuaff07Y6BEdLsKPbsebqD3NvO8Zy1FvZydAdKSRKIMf7BWgUkNURrsWoaHcGza3msIKI2o1\nNFSfwuloQq1q3xGjp84PWVHY+JWXT/N8RBjh/iVhZCb3/WcnywpvfljK86+cQpIVbr0hkbtuThkU\n5RCFh22sfKOMsko3pggND9+TzoI5cZd0i0KvT+azXQ2s21RNeVXgPjdlopmbr09k0ljTBT2EXWhG\ni+DiIskKx4uaW/MhyirPheuOHB7OjJxAWUZmeph4OBcIBJc0YzKi+Y9vT+Uv7xTw7vYiGu0e7rxu\nFGpx7xMIBBdIj0WJxx9/nDvuuIPa2lqWLFlCY2Mjf/7zn/tzbBeFn39nKn9anR+0+0Z/0VZUqLd5\nMOrVgAqvT+pRVkOwkLxgq67B2HWwimMljf2e9F/+7IvYv9lPzJL5xK+4udN27Z4NqOvKkIZnY3NN\ngvryTvvMnRyLvrkCUIElHbTBV5DbujdsbjUFlUYkBcYnekjISuTG3LiQwkNXzg+/X+GtrR72H/cT\nF6XiwaVhxEX1/efVZPPxtxfOsP+gjSizlh8+mEnOhIEPi6yt97Lq7TJ25TWhUsGiuXGsuDUFs+nS\ntaLb7H42bq/l4221WG1+tBoV866KYemixD4NGL2QjBZB/+NyS+QfsrEn38reAhs2hx8AvV5Fbo6l\ntSwjJurSL1sSCASCtqTFm/j5PdP4yzsFbM0ro8nh5cGbxqHTCkFdIBCcPz2eXQwfPpxbbrkFn8/H\n0aNHmTNnDnv37mXmzJn9Ob5+R6/V8tcfX0vxmfrWUgi9TkOT3YPFpOqXVcuOokKLw2HWxKQuXQwe\nn0Rtk4t9x2ou6Pz9nfRv+3ofFc++iD4tmcw//bzT6qH6RB6aE3nI0Un4r1zKcrUWVKp2E7RZ42O5\nYawKZAnMqaCP6Pa8do+awkojkgzjEjwkmAK+6vPpiOF0K7z8kYviCpnMZDX33RSGKazvVwIKD9t4\nduVpGq1+ZkyN5tF70oga4HwGr09m7cZq3t9QjccrMyYrgge/nU7WsEs356Cy2s26zTVs+7Ier1ch\nPEzDrTckcuN18cRE971j6nwzWgT9R12Dt9UNceCoHb8/UNcXbdGy4JpYcnMsTB5nHjRhswKBQDBQ\nxJiN/OzbU/n7+wfIO1qDvdnL92+bRLhRCLUCgeD86LEo8eCDDzJhwgQSExMZOTKwmuf3+/ttYBeb\nyHA9ozOiOpVF9LWjoKvk/WMlTUH3b7C52bq3jMKTdTTYPITI5uw1/ZH072toovjxp0ClIuu536K1\ntG8dpaotRbv7IxR9GL65K0CrRwPtJ2gRWgyOUvB7ICIBjJZuz+vwqCmoMOKXYWyCl8RI6byvod4q\ns3Kdi9pGheyRWu5aaEDXx2UUfr/CW2sr+ODjatRq+O4dqTxwdxb19cG7wVws8gqsvLC6lOpaLxaz\nlofvSWfOzJg+6zAy2DhW1MyajdV8s68JRQnkAixZmMD8q2MJ60V45flyIS1kBReGLCsUnXG2ChGn\nS12t2zLTw1odEVnDwi/Z379AIBCcLxFGHT9ens2/1h9m77Fa/vDGPp68I4f4eNEyVCAQ9J4eixJR\nUVH8/ve/78+xDDjByiL62lFgdYQutWiwnUve71ji0R/0ddK/oiic/slv8VZWk/bTR4nMzW6/g8uO\nbseboMj4rlkOpvY5EgadhoSoMLCWBASJsGgIj+32vA6PqlWQGBPvJSny/MWykqpAy0+HS2HuVB03\nXqXv81rJmjoP//P8aY4VNZMYr+dHDw9n9IiIAZ34VFa7eeHNMvYV2gAFQ5SHmEyFSlc9CtHApTMp\nk2SFvHwrazZWc/RkMwBZw8K5+foEZk6LRqO5dK5V0B6PRzorQjSRV2Cj0RpoyazVqpgy0cz07IAQ\nER/b/3lCAoFAMNTRaTU8umwib249waf7yvjda3n8+qFZhA+CPCyBQDC06LEosWDBAtatW8eUKVPQ\naM6tIKakpPTLwC42XTkYzsdR4PFJQW3ZFpMBo14dNJTSoNcQZtBS0+hk0+6SoC0ruyPKpMfW7EWn\nVePxhQ6+hL5P+q955T0aN35G5KxpJP/bve03Sn50n7+NymXHP3URSnJW5wMoCtgrwdsMehOYkqAb\nQaDZq6KgIgyfrGJ0vIdk8/kLEgeL/Ly+yY1fgtvmGpg1ue9tiLvyGnnu5RKcLomrZ0Tz6HczetVO\nsq9xeyTe31DNmo3V+P0K2jAf4QkuNAaZJif9WuZzsfF4ZbZ/Wc+6zTVUVgeEvmmTzdy8OJEJYy4s\nvFIweGm0+sgrCLghCg/b8Zy990aaNFx7VQy52RZyJpgvijNGIBAILjXUahUrFowi2mzgvc+K+PHf\nPue7i8dw5XjRMlQgEPScHosSx44dY/369URFRbW+plKp+Oyzz/pjXBcdq8NDQwhHQm8cBcE6Y3Qu\nAQk++fH5JX69ag8NNk93c/GgxEQa+OV9uVgdHv76XiEeX9cOi75M+nceOUnJr/6CNtpC1t9/09q2\nswXt3o2oa84gDZuINP6qEAepA3cTaI1gTuuRIJFfYQwIEnEeUi5AkNiZ72Xt5150Orj/JiPjh/dt\nmKPHK/PSW2Vs/qwOg17N4/dlcN3VsQM2EVYUhV15Tax6u4y6Bh8x0Tr00Q7cGlenj70/ynwuJlab\nj0+21fLJtjpsDj9arYr5s2NZujCB9NS+C68UDA4UReFMmau1LOPEKWfrtsz0cKZMjCQ3x8LorIhL\nuoNMKGFcIBAI+hqVSsUNVw4jMTqMlz4+yr/WHaaozMby60ai1YgcHoFA0D09nnkVFBSwZ88e9PpL\n09ZqMRmIMRuClkr0xFHQ8gC4aU8p2/ed6yTRsQTE6vDg8QbPO5BkWs+vnEdwxNQx8USG63F5/CEF\nFoBok4FpY+N7lPTfkwdbyenm5CP/geLxMvz5P6BPTmi3XV20H82xb5CjEvDPvDm42OC1Ihd3AAAg\nAElEQVRqguZaUOvAkgHdZHg4vYGSDZ+kZlSchxTL+QkSsqyw/gsvn+f7iAxX8cBSI+kJffsAX1Lu\n4r//eYrScjeZaWH86JFM0lMGbjJcWu5i5eoyDhyxo9WquP2mJK6ZZebpVbuDymV9XeZzsSivcrN+\ncw3bv6zH61MwRWi4/aYkbrgunugBDhMV9C0+n8yhYw5251vJK7BSW+8FAreRiWNNgXyIbAuTJ8ZT\nW2sf4NH2Lz0TxgUCgaDvmTYmgUljEvnNi1/z6b4yTlfZePTmicSYjQM9NIFAMMjpsSgxceJEPB7P\nJStKGHQapoyOb5cp0UJXjoKO2Q+hFt5aVpu7Ej/OF6New1WTklpFhq7OEWXS8/T9uUSGd/099ubB\ntuTpZ3CfOEXiA3cSvfCadttU9eVov16Hojfim7MCdEHEHa8D7BWgUkNUBmi6/lk63Ar5FUa8kpqR\nsR5Sz1OQ8PoUVm92c6BIIjFGzfeWGokx991Du6IobN5Rx0tvluH1KVw/L557l6ei1w3MxKDZKfH2\nuko+/rQGSQqULjxwVxrJiUY8PumCRLnBgqIoHD3ZzNqN1ezOt6IokBgXCK+8bnYsRoNYMb5UsNn9\n7C20sqfAyv4DNtyeQFlGeJiG2VdEk5ttYcokM6aIS7eFbTAuRjaSQCAQhCI13sQv7pnOK5uO8vWh\nap5+eQ+PLJvA+MyYgR6aQCAYxPT4aa26upp58+aRlZXVLlPijTfe6JeBDQQtk/q2rSmnjI7r0lHQ\n8QFQDuFwaLvaHEr8OF/cXgmVStUqFnQlsEwfm9CtIAGhH2wlSeaeRWNbX29Yv5Xa1z8kfPxo0n/+\n/Q4Daw4EW8oS/qvvAnOQ0Eq/G6xlgAos6aDtevLr8qn45rCCV1KTFeshLer8BAm7U+al9W5KqmVG\npmm490YjYYa+s3I3O/08t6qEr/KaMEVo+NHDw7hialT3b+wHZFlhx1cNvPpuOU02P4nxeh64K53c\nnHNdTc5XlBssSLLC7n1NrNlUw/GiQHjlyOHh3Lw4kSunRonwyksARVEor/KwJ7+JPflWjp1sbr3f\nJiUYyD0bUjlulAntZRqy1tfZSAKBQHA+GPQaHrxpPKNSLazeeoJn3s7nltkjuGHmsD4PDxcIBJcG\nPRYlHnnkkf4cx6BAo1a3b03ZTS1uVw+AHWm72txR/IgyGXB6/LiDlHWoVaGFjrZ0fOBcPm8k4WF6\nviyo6LHA0pPr2pFfASoVK+aPwl9Rzal//y3qMCNZ//tfqI1tBAVZQrfzHVTNVvzZ1yGnBlmhk3zQ\nVAKKDOZU0Ed0OS63L5Ah4fHDiBgv6ecpSNQ2yqxc66LepjBtrJY7rjOg7cNJ69GTDv7n+dPU1nsZ\nP9rEkw9lEhczMA6jojNOVr5eyrGiZvR6FStuSWbZ4sSgbo3zEeUGGrdHYtsXDazfUkNVTcDlkZtj\n4ebFiYwbFSHCK4c4fr/C0ZNnyzLyrVSe/Y5VKhiTFdFalpGWYhTfNX2XjSQQCAQXikql4tqpaWQk\nRfKPDw/ywefFFFfYeOCmcUQYRQmlQCBoT49FiRkzZvTnOAYVBp2mRw9uXT0AdqTtanMw8eP9HUVB\nV6nnTEnl2impPPtOPg12b8jjd3zg1KjVPHjzJK6fkd5OYPH4JOqtzi4Fl66uS1Zg+75yNIpMznN/\nRrI5GP7MU4SNymy3n2b/FtRVxUjp45AmXRPkQFKg9afsh4gEMFo679MGt79FkFAzMV1FnN7X5f6h\nKK6QePkjF043LJihY9EV+j6bzMiywoefVLP6wwoUBe5YmsQdS5IHZJXe5vCz+oMKNu+oQ1Fg5vQo\n7lue1mWrw96KcgNJk9XHx9tq+WRbLY5mCZ1WxcI5cSxZmEBasqhdHco0O/3sO2Ajr8DK3kIbzc6A\nWGs0qJk5LYrpORamTTJjMYuH2o5caDaSQCAQ9DVZKRZ+eV8uK9cdIv9kHb9etYfHb5lERmLkQA9N\nIBAMIi6vYts+pqsHQLUKFCCmzWpzx9DItuJH21XqBpsbi0nPlFFxrJg/Co1azdQxCV2WfESZDEEf\nOFvOIckyq7ce71FGRE9yL7wvvo4jr5CYpQuIu3Np+2s/VYj28JfI5jj8s24NZEW0RVHAVgZ+D4RF\nQ3iQso42ePwq8suNuP1qMqO9jEs1Utszg0o79h/38dYWD7ICy+cbmDG+7yY1DU0+/rryNIVH7MRE\n6XjyoUwmjr34f3AlWWHLjjre+KACR7NEWrKRB+9OY/J4c4+P0VNRbiAoq3SzblM1n+1qwOcPhFd+\na0kSN8yLJ0qEVw5ZKms85OUH8iEOH7cjnTWNxcXomH1FNDOmRDFxjAndAOWxDBWGehmWQCC4NDGH\n63nyjhzWfHGKj3ad5nev7eXbC0cze3LKQA9NIBAMEoQocQF09QA4JyeFRTMysJgCpQHdhUZq1GqW\nzxuJJCvkH6+jyeGhsKgejeYky+eNbBUttu8rQ5I7jyUiTNflA2dvws8MOg3Zo+LYtrecYKSUnmTc\nF5vRpiaR+aeft3MaqBoq0X61BkVnwD93Beg7rForCtgrwdsMehOYkrps/ek565Bw+9UMi/YyLNoH\n9G4lXFEUtu/1sWGXF4MOHrjRyOiMvvvp7ztg5a8vnMFm9zM928z378/EHHnx/2sdPelg5eulFJe4\nCDOquXd5KjdelzDk6+sVReHwcQdrN9WwJ98KBDIEli5MYN5VsRgMYqI61JBkhRPFza1tO0sr3K3b\nRg4Pb82HyEwPE2UZvWQolmEJBIJLH7Vaxa3XjCArxczK9Yd5+eOjFJVbuXvBaHRaIZgKBJc7QpTo\nBcHaY3b1ANgiOKzeejxkaGSLcGHQaVi99USX7URvm5PFvrPCRkeaXV7Kah3ER4V1EifOJ/ws1DTA\n6Grmus1voahUZP6/36I1m9qcyIlux5uoJB++2StQLPGdD+CsA3cTaI1gTutWkCioMOLyqcmI8pIZ\n7etq96BIssKHOzx8dcCPxaTiwaVGkuP65o+fzy/zxvsVrN1Ug1ar4oG70rhxfvxFn0Q1Wn28+m45\nn+1qAGDurBjuuT2VmKih7RyQJIWv9zWxZmM1J085ARidFcHNixOYMSUKTahWN4JBicstkX/IRl6+\nlbxCGzZ7IBNGr1MxPdtMbk4U0yebiYm+NDs8XSyGUhmWQCC4/MgeGccv78vluQ8P8HlBJWeqHDx2\ny0TiowauVbpAIBh4hCjRA7prj9nVA2B3oZGf7a8gxmzAaNBSWdccdL8W4cDq8NAYoqSiwe7lly/u\nbje2FroLP6ttcqHXqtvlTuSfqOu8s6Iwd+s7RDTbqPvWncRckXNumyyj++JdVI5G/JPmIqeP6/x+\nVxM014JaB5YMUIde4fb6oaDCiNOnJt3iZXhM7wUJj1fhtY1ujpyWSIkLtPy0mPpmVb2yxsP//PMU\nJ087SU408JNHhjNi2MUtefD7FT7eVsNbaypxuWWGZ4Tx4N3pjBtl6v7NgxiXW2LbF/Ws31xDdZ0X\nlQqumBoIrxw7cmhf2+VGXYOXvAIru/dbOXDUjt8fSO2NtmiZf00sM3IsTB5nFm6XfmAwl2EJBILL\nm/ioMP7vt6fxxpbj7Cys5Ner9vDgkvFMzoob6KEJBIIBQogSPaAnpQ+hHgC7C41sOR6Ezm9oCbHs\nLutB6TC2H941Deg6I0Kv0/DsO/k02r2tgsa1U1KDjnli4S4yTx3BPnY8C595ot02TcGnqCtOIqWO\nRsq+tvPgvA6wVwTyJaIyQBP6p+eVoKAyDKdPTZrFx4jY3gsSVofMi+vdlNfKjB2m4Z7rjRj1fbOy\nvvPrBv731RJcbplrr4rhwbvTCTNe3JXIwiN2XnijlNIKN6YIDQ/fk86COXFD2j3Q0OTj409r2PRZ\nHY5mCb1OxaK5gfDK1CQRXjkUkGWF4jNO9hQEyjJOlbhat2WmhZGbY2F6joWRmeGoh/BvVSAQCAQX\nhl6n4b4bxpGVauH1zcd59t1ClszKZNnVw8XfB4HgMkSIEt1woX3fexIa2R3RkQbCDFqsDg+Ts2LZ\nvr+i2/fsP16H2xuwR2s1KsKNuqBjcHul1lakbctKOo45traCmTs/whMewRWv/hmt9txPR33mENqD\nnyNHxuC/+vbOwZZ+N1jLABVY0kEbOgHeJwUcEs1eNalmH1mx3l4LEpX1Ei+sddPkULhygpZb5xr6\npAOG2yOx8o0ytn1Rj9Gg5ocPDmPuzK5DOvuaugYvL79Vxq68JlQqWDg3jrtvSRmQDIu+orTcxdpN\nNez4ugG/X8Fs0nLnsmQWXxsnOiwMATxemcLDdvLOChGN1kBnHK1GRc6EyEBZRraZhDjR+SEYwcoC\nBQKB4HLhmuwUhiVG8tyHB1i/6zTFFVYeWjqByHBRyicQXE4M3ZnMRaIrp0NDD/q+dxWG2VM8Xolf\nr9rTWjqSnmCi2eWj0e5BCfGeepubiloHkXoNb287SWmNo9M+GjVBQzMLixqYPDKuNd9C6/Myf+Mb\naGSJ6kcfw5SW2LqvqqkG7a4PULR6/HNWgL5DTaDkg6YSUGQwp4I+IuR1nhMkNKSYfYyM670gcbzU\nzysb3Li9cMNMPfOm6/ok4+FUiZNn/nmK8ioPI4aF8eNHhpOSePFW730+mbWbanjvoyo8XpnRWRE8\ndHc6WZlD056tKAqHjjlYs7GavYU2AJITDSxblMDcWbEY9MLOP5hptPrYW2Bld76VgsM2vN7AnSjS\npGHurBhycyzkTDATHiYm2aHorixQIBAILheGJUUG2oauP0xhUT2/WrWHx26exIiUnncOEwgEQxsh\nSnRDV04HFbBpdwkrFozu8iGyJd/hi8LKVldCb3C4/TjcAddDvc1Dvc3DtVNTuTYnhb++VxjShfGz\n577gyglJFJwI7vQIJkhAoFxk/rQ0NGoV+4/XMenDd4lurKVp0fUsefJb53b0utF+thqV34vvmuUo\n0YntDyRLYC0B2Q8RCWC0hLxGnwSFlUYcXg3JkT5GnYcgkXfEx9ufelABdy8yMHXMha+yK4rCJ9tq\nefntcvx+hSULE7jntpSL2powr8DKS2+WUVnjwWLW8tA96cydGTMk7Y2SpLArr5G1G2soOhMIrxw3\nKoJlixPJzbYMyWu6HFAUhTNlrtZuGSfOBo8CpCYbmJETxfRsC2NGRgzpEqKLSW86IgkEAsGlToRR\nxw9un8yGr86w5vNifv/6XlbMH8XcKamiC5NAcBkgRIluMOg07VwDbZEV2L6/Ao1G3eVDpEat5rY5\nWew/XnteokQwCk/Wc9ucEYQZtITKo3B5pKDj7o7oSCMxZiMr5o9mnq2YkkN7CJswhun/fAp1i/ii\nyGi/eA+1vR7/hKuRh01sfxBFAVsZ+D1gjIbw0GUO/rOChN2jISnSx+j43gkSiqKwZbePTd94CTPA\nfTeFkZV64Su0Noef514+w+79VswmLT/43jCmTQ4trPQ1lTUeXnqzlLwCG2o1LFmYwPKlyUSED73V\nZ5dLYuvOetZvqaG2PvD9zpwWxbLFiYzJCu2eEQwcPr/MoWOOViGitt4LBPJpJ441kZtjITfbQvJF\ndAxdKlxoWaBAIBBciqhVKpbMymREspnn1x3itc3HOVlu5TuLxmLQi3uiQHApI0SJLmix14ZyGrTQ\nk4fIrspAzodGu5vVW05QVhu8Y0db1KpzoZptMeo1QUWSKaPjAl04Ssop/4/fow4PY+Q//wu14Vx9\nn6bwMzTlx5CTs5ByFrQ/gKKAvRK8zaA3QWRSyNaffvmcIJFo8jGml4KEX1J4b5uHPUf8xJhVfG9p\nGIkxF+5iOHTMzl/+dZr6Rh8Tx5p48sHMi9aq0O2ReH9DNWs2VuP3K0wca+LBu9PJSB167bIaGr28\nt6GYDz+uwOmS0OtVXD8vniULE0hOEBkDgw2bw8++QisFR0r4Zm8DLnfAThUepuHqGdHk5liYOsmM\nKUL86bgQuuuI1F1ZoEAgEFzKTBgew9P35fKPNQf56lA1JTUOHr9lEkkx4r4oEFyqiCfLLuhorw1F\no91NbaMTvU4TMqysLwIv2xIdaeDI6foe7RtMkACYNSkJtSpQotFodxMdaWTK6DiWzxuJ7PNz8rGf\nI9mbGf7s04RlDWt9n7r0KNrC7SgRUfhm39G5taezDtxNoDWCOa1LQeJApRGbR0OCyc/YhN4JEi6P\nwisfuzlRKpGeqOaBJUYiwy9MkJBkhffWV/HOukpQwYpbkrn1xqSLYklXlEBpw8tvlVHX4CM2Wsd9\ny9OYlRs15KyLZ8pcrNtUzedfN+KXFCxmLSsWJ7Po2njMJnHbGSwoikJ5leesG6KJYyebW+8XifF6\n5s+OYnqOhfGjTGi1Q+s3OJjp6u9BdKQRi0kIdgKB4PImxmzkZ3dP5a1PT7BtXzm/eWUP998wnmlj\n4gd6aAKBoB8Qs4MQdGWv7Yhep+Gv7xV2GVZ2PoGXsWYDHp+Ew+XvfE6thsqGngkcsWYDk7NiKSxq\n6CQ+tJSWdEx/L/3j/9K87yCxt15P3LdubD2WylaH9sv3UDQ6fHNXgCGgWrckyEfr3OictaDWgSWj\ns2BxFumsIGF1a4g3+Rmb4OmVINFol3lhnZuqepkJIzTcvciIQXdhk6a6Bi/PrjzNoWMO4mP1PPlQ\nJuNGmS7omD2ltNzFb/9azN6CJrRaFbfdmMhtNyZd9FajF4KiKBw4YmfNxhr2HwyEV6YmGfj27cOY\nOikc/UXM4RCERpIUjpx0sGe/lT0FViqrA/cRlQrGZEUwPdvConkpRBilISeGDRW6+nvQ4lQTCASC\nyx2tRs23F44hK9XCKxuP8tyHB1h8RQa3zRkhAoEFgksMIUqEoDflFsHaakLnsLLl80YiSTI78itC\nuhcADDo108ckoNWq2JFf2Wm7KUxLZYMzyDuD0+z2odGo+dUDM3A4vZ3cHAadpp1V2LpzN5X/bxWG\nzDQyf//TcxMTnwfN9jdQ+Tw4r7wVTUxyuwT5xAiFJxdF45VVaOLS0GiC/7zaCRIRfsYleOiNEeF0\nhY+/vePC1qwwO1vH0tn6Cw5I3L2/ib+/dAZHs8SV06J4/N6Mi2JRd7ok3l5byYZPa5AkmDrJzAMr\n0i5qZ48Lxe9X+HJPI2s3VXOqxAXA+NEmbl6cwLTJFhITzdTW2gd4lJc3zU6J/QcD2RD7DthwNAfu\nV0aDmiunRZGbbWHqZDNRZ1uwxsdHiO+sn2kJQA7mVBMIBALBOWZOSCI9wcRzHx5k4zclnKqw8ciy\nCcJVJhBcQghRIgRd2WvVqkBsQnSkAafHHzSXIVTOhEajRqtR4/WHaH0BeHwyXx6swhBiZdnp7uyc\n6Aq3V+5xqruvvpHi7z+FSqMm6x+/QxMZcApIkkTNmlfJdNfxiSONT7Y4mXLmOLKisG1vOalRWh67\nLgZZgf/ZXE9GmjbouSQZDlYZaXJriIvwMy6xd4LEkdN+Xt9Yj8ersGy2nmumXFjOg9cn8+o75Wz4\ntBadVsXD96SzaG5cv68Qy7LCjq8aePXdcppsfhLj9Tz5yChGZ+qHzOq00yWxZUcd67fUUN/oQ62C\nq3KjWLookdEjRHjlQFNV42FPgZW8fCuHjtuRzt6mYqN1rfkQE8dGCgfLAKFRBwKSgznVBAKBQNCe\ntHgT//nd6by04Qh7j9fy9Ko9PLpsIqPTowZ6aAKBoA8QokQIurLXzpmSyqLcdLx+mV++uDvo+zuG\nlXl8Eq98coSvD9f0eAweX3DhoiuXRVd0F8ipyDLFTzyNr6ae9F/8AFPOhNZth9evYbq7hMOeKN60\nZiERcIQY9WqiwtU8uTCacL2a5z9r4niVj3pn53NJMhyqMtDo0hAb7md8LwWJnQUe1n7uQ6uB79xg\nZPLI7n++LWUlwR74yyvdPPP8KU6VuEhLNvKTR4czLK3/wySLzzhZ+UYpR082o9erWHFLMssWJ5Ka\nYhkSq9N1DV42bK1h8446nC4Zg17NjfPjWbIggcR4sWoxUEiywoni5kA+RIGV0nJ367aRmeFMz7Ew\nI8dCZnrYkBG+Lgc6OtUEAoFAEJwwg5bHbpnIpt2lvPdZEX9avZ87rs1iQW66+LsmEAxxhCjRBV3Z\nazVqNR6fFNJNYYkwEGbQIskyr285xlcHKvH2zuDQ53SX6l794ltYP/0S85wrSXrk262v+0uOMtWW\nT71k4O8NE5Bos7IqKzyxIIYYk4b38ux8U+wOei5ZgUPVBhpcWmLC/UxI6rkg4XB7+ds7tdQ3WZAV\nH2rtGQ6eDmfCiJEhawrblpV0zPpQq1Rs39XAytdLcXtkFlwTywN3pWMw9O+Ksc3hZ/UHFWzeUYei\nBFpi3rs8lYS4oTGRP1XiZN2mGnbubkCSINqi5dYbklg4J45IEV45ILjcEgWH7AFHRIEVmz1wk9Hr\nVEzPNpObHcX0bPNF6xwjEAgEAkF/olKpWHxFBsOTI/nn2kO8te0kJ8ut3HfDOMIM4llEIBiqiP+9\nXdCdvbYrN0Wjw8OvXt6N1y8HDaocCLpKdW8uPErpb/+GNi6GEX99GlXLZN/egHHX+/hR82zDRGxy\nm7agKnh0XhQZsTq2H3XyceG59qQt5/L4JJrsHqrcUTS6tESH+ZnQQ4eEJMu8ufUkew4b0apjkGQ3\nDs8xZJeHrXkNQOhylI6dU1qyPnw+hYZSPZ9/3Uh4mJqfPDKcq2ZEdz+YC0CSFbZ+Xsfr71fgaJZI\nTTbw4Ip0sieY+/W8fYGiKBQcsrNmUzUFhwIujrRkI8sWJzDnyhh0wvp/0alr8JJXEMiHOHDEjs8f\nsE5FmbXMnx1Lbo6F7PHmfhfZBAKBQCAYKMZkRPPL+3L555qD5B2rpay2mcdvnURqnCgfFQiGIkKU\n6AFd2Wvbuinqbe522xrs3h4dX68NnjFh0KmDlnBYInRYm309OnZbQqW6S81OTj72f1F8fkb87Vfo\nE+ICG3xedDtWo/a5ecs9kWJf+0n0PbPMTEozUFDq5o2vbO225YyK5f0dReSfqGPC+IkMS9PhcdkY\nN0zV48TkN7YUkXfYhE5jxi/ZcXhOoHBO4AlVjhKqc4rfrWHDOjs+j5rRI8L50cPD+73c4OhJByvf\nKKX4jIswo5p770jlhvnx6LSDe8Lo88t88U0gvPJMWeB3PXGsiZsXJzJlovmCg0UFPUdRFIrPuNid\n30RevpXis2GiAJlpYUzPsZCbbWHk8HDxvQgEAoHgsiHKZOAnd03h/R1FbNpdym9fyeO714/hyvFJ\nAz00gUDQS4QocYG0uCmWzMrkly/tpsnRMyGiLQnRYZTVNnd6PT4q+OtTR8dTWFQftGykBaNejTnC\nQF2Tq9tU9zM//zOe4hKSHrmHqLkzAy8qCtqv16BurEYaPQOvfQw0nHMe3JQdwTVjwql3wjFrBNGR\n7nYlLi0BmLOvmMqwtGQqa+rY9sVu6quSuw3bBKis91FwLBqdxojXX0+ztxhoH6YRqhylY+cURQFP\nkwFXrRFQsejaGL531zC02v6bwDVafbz2Xjnbvww4OubOjOGeb6USE6Xrt3P2Bc1Oic076tiw9Wx4\npRpmXxHNskWJZGWKuveLhccrc+CIvTWosqEpIEJqNSqyJ0QyI8fC9GzLkCn9EQgEAoGgP9Bq1Cyf\nN4qsFAsvfXyEf607TFG5jeXzRqLVDO4FIIFAcA4hSvQRLo8fay8FCbUKrs5O5lBxQ8hjXjslhcKi\nhs6ZFpqTQctGWrh6cgoP35ZN0en6LlPd6z7YSN0764nIHk/azx5rfV1zZBea0weQ4zPwT7+e5arA\njX3/8TrGJsCt0yJxeCEqbSR3ZOpZNvtcoCTAUy98w9UzppCZnkJVbT3bv9iNJEndhm0ClFRJrFzr\nAYy4fRW4fMGvM1Q5StvOKbJfRXNVOH6nDpVGJnmEj/vuTO83QcLvV/hkWy1vra3A6ZIZnhHGg3en\nM26UqV/O11fU1nv5aEsNWz6vw+WWMRrULFmQwE0L4sXE9yLRZPUFyjIKrBQcsuPxBlxSkSYNc2fG\nkDvFQs4EM+FhokODQCAQCARtmT42gdT4CJ778CCf7i3jdJWNR5dNJMY8dFqsCwSXM0KU6CO6aiEa\nijk5KSyakcHO/Mqg2xvtHhbNyOCOeaM6ZVqcKxuppd7mQXW2TWlMpIGpYwKBjka9NmTZiccnUXe4\nmMqf/RfqiHCy/vE71PrAKr6qshjNvk0oYZH4rrkTNFo0BPIbbp+VhM5RhqJSY0oaDtpAxkTbEpfq\nBidjx4xjeEYqNXUNbNv5Df6z/Qi7C9s8UOTnjU1u/BKo1GW4fBUhP79Q5SgtWR+ffF5Fc2U4iqRG\nG+4jIsnJVbmp/dZ278AROytXl1Ja7sYUoeHhe9JZMCcOzSC21BefcbJ2UzVf7G5EliHaouP2mwLh\nlaYIcXvoTxRFoaTcHeiWkd/EiVNOlLNmoNRkA7nZFnJzohiTFYFGM3h/QwKBQCAQDAaSYyP4xXem\n8crGY3xzuJpfrdrDw0snMD4zZqCHJhAIukHMOvqIrkIvg3HdtFTuvG4UfkkJKWa0OAFCZVooioLD\n5Tv7bzBo1UwaGdvaHSQYLV0p8o9Ucs1Lz5LgcFL18GNMGZYa2MHRhG7n26BSBwSJ8Mhzb/a70TdX\nACqwpIO28wq6okCd18KIYXpq6hrYuvPrVkGi7TUF4/N8L+s+96LTwQNLjOw/qWZrXuf9wgwarpqU\nHLIcRZIUpKYIHGUmUCmEx7tIzlAxdUxqyPdcCHUNXla9XcaXe5pQqWDhnDjuvjUFc+Tg/O+lKAr7\nD9pYs7GGA0cC4ZUZqUaWLU5k9hXRgz7vYijj88scOuYg72zbzpq6gLtKrYbxo03k5ljIzbGQkihW\ndgQCgUAg6C1GvZaHloxnZKqFtz49wTNv53PrNSO4/sphqEXbUIFg0DI4Z01DlLahlw02NypVoBVm\nR2LNBm6fGxAONGpCihmhnAAQ6C7x6d7ydq95/DI79leg06i5bU4WlXXNSD6p3XInFHsAACAASURB\nVDFaulJc8eXHJFSXcmzsNLYbMnFsO8mKucPR7XgTlceJ74olKAkZ5w4u+aCpBBQZzKmg75xurChw\nrFZPbbMOr7uZT3d+g98vtdsn2DXJssK6L7zszPcRGa7ie0uNpCVoGJ3RsSWrgbEZ0Xz/zik4HcEd\nKTV1Hv7yr9McPdlMYrye7z8wjPh4TZclLOeLzyezdlMN731UhccrMzorgofuTh+02Qs+n8zOs+GV\nJeWB8MrJ4yK5+fpEciZEih7f/YTN4WffASt79lvZf9CGyx0oywgPU3P1jGhycyxMmWgWbVUFAoFA\nIOgDVCoV101LIzMpkn+sOcj7O4opKrfxvZvGEW4c3NleAsHlingK7oDH1z4bIVgr0FC0bSFaXG7l\nv9/KD7pfo93TroShrZjRMTsi1BiDdZdoYWdBBfuO1dDo8BIVYSBndBwr5gdcGfuP15J25jhT9n6G\n1RLLF3OXnT13LXcbClE3VCCNnIY8KvfcAWUJrCUg+yEiAYyWTudUFDhep6fKrsNkkJiUIVNXmdTt\nNXl9Cqs3uzlQJJEYo+Z7S43EmNWdPs+230NEmD6oKPFVXiPPrSqh2Slx9YxoHvlOBhHh/VOqsbfQ\nyoury6is8WAxa3no2+nMnRUzKLsfOJr9bPqsjg1ba2m0BsIrr7kyEF45YtjgFFCGOuWVbvacbdt5\n9ISjVZxMjNNz3dUBN8S40SbhShEIBAKBoJ/ISrXwy/tyeX7tIfJP1vGrVXt4/JZJZCRGdv9mgUBw\nURGixFlayhr2H6+lwebBoNcACm6vTKzZwJTR8SHLItoKGQadBoNOw4hUS7dlGS2EmnwHOzZ07i7R\neTwyHl/AFt7o8LB9Xzkny6w8smwCrqo6btzyFpJaw5br78anD9jEp0lF6E+fQIpJoWLUPCx+OXA+\nRQFbGfg9YIyG8NhO51MUOFGnp9Kmw6SXyE52o9OEvqYW7E6Zl9a7KamWGZmm4d4bjYQZOk/qg5Wv\ntBOPFBUvvVXG5s/q0OtVPH5vBtfNju2Xlf/KGg8vv1XGnnwrajXcND+eO29OJiJ88P1XqqnzsH5z\nDVt31uP2yIQZ1SxblMBNCxKIi9EP9PAuKSRJ4cjJQFnG7nwrldWB/58qFYweEdFalpGeYhSOFIFA\nIBAILhLmcD0/Xp7Dmi+K+WjXGX732l7uWTiGqycnD/TQBAJBGwbfTGqAaClraMHtPVd2UG/ztG5r\n286yo5AR00a86CpjoquAxpbJd1fHtpgMREUaaLT3PFSztMbBpq9PsXDbu4Q7Hey6+ibqEtIAGKNv\n4tuWk7hURn5fMoriA3lnzxfHnbkm1N5m0JsgMikwy2qDosDJej0VNh0ReonsFDdtLy1UHkZNo8wL\na13U2xSmjdVyx3UGtD0I85NkmZVrDvBlQTkNNg8ROiPWsnBsVpnMtDB+9Egm6SlhPf5ceorHI/P+\nhirWbKzG51eYONbE91akMyyt7891oZw81czaTTXs2tOIrEBstI7ly5JZcE1cvzlHLkeanRKFO2v4\n9PMq9h2w4WgO3DOMBjVXTLWQmx3FtGwzUWZhFRUIBAKBYKBQq1Xcek0WI5ItrPzoMC99fIST5Vbu\nXjAKnVY8FwkEgwEhSgBur7/LcogWOraz7ChkdBQveluW0ZZQx5YVBbVKhcvj69U1Anje+pCk4qOU\nDBtD4ZSrAYhWe/hhzEEA/rtmLEVedev5jL4m1B4JtEYwpwUVJIrq9ZRbdYTr5E6CRCiKyyVe3uDC\n6YYFM3QsukLf49Xjls9FUcBr1dNQawBFJmuUjv/6yRj0ur61wyuKwld7m1j1djm19V5io3XcuzyV\nq3KjB2TFO5hzBgK5HPsO2Fi7qZqDRx0AZKaHsWxxAlflivDKvqK61nO2W4aVQ8fttGS4xkbruHpG\nNNOzLUwaF9nnv0OBQCAQCAQXRs6oOH55Xy7/+OAAnxdUcKbKzmO3TCQ+avAtMAkElxtClAAabV2X\nQ7Tu16adZVe5Dm3Fi+5KGILR1bF3Hahq5+Joi1GvRpYUvFLndM346lImb/8In9nCrhvuBJUaLTJP\nxh3EovHxjnMMR73RrfvPzDJy67RIGpplIlJSMXQoW1EUKG7QUXZWkMhJcaHvgSCx/7iPNzd7UIDl\n8w3MGN/zVeSWz0WWVDirw/A59KjUMuFJTlQWDQpBUkUvgNJyFy+sLqPwiB2tRsVtNyZy241JhBkv\nvqoeyjlzy+wRfPlNE2s31VBWGQivzJkQybLFiWSPF+GVF4osK5w45WRPfhN78q2tAaEAWcPCmXtV\nPONHhTE8I0x81gKBQCAQDHISosL4v/dM4/XNx/niQCW/XrWHB5dMYHJW5/JkgUBw8RCiBBBtNoTM\nf2i3X5ssiK5yHdqKFxC6hCEUXR07lCBhidDx6weuYM0Xp9i+r31XDp3XzfyNq1HLMh/Pu4MmXaBz\nxnejjpOls7HLlcTaxnO1dWOT9dx3tYVmj8xfNtXz/eUjSGjTxVNR4FSDjtImPWFnHRL6bn5JiqKw\nfa+PDbu8GPXw3RuMjM7o3c/P6vBQU+3HUWlC9mvQhvmJSGpGrVNotPvafeYXgtMl8c66Sj7aWoMk\nwdRJZu6/K43UpIFr09jROVPb6GX9plrWfdCMx62g0cDcWTEsW5RAZroIr7wQ3B6JgkN2dudb2Vto\nxWrzA6DTqpg22UxujoXp2RZio/XEx0dSW2sf4BELBAKBQCDoKXqdhvtvHMfINAuvbz7OX98tYMlV\nmSy9avigDCwXCC4HhChBoKdxqPyHtrTNgrCYQgsZHYMsIbTtPhhdHTsUdqcPl8fPivmjOFlmpbTG\n0bpt9vY1WKz1HJhxLeUZowC4NryCeRGVnPKaWNk4GgjchFOjtPzbvCgU4O+fNuKWdZ2u5XSjjpKz\ngkROihuDtmuHgiQrfLjDw1cH/FhMKh5caiQ5rnduA1lW+OyLJmylJgCMMW6Mse7WipJgn3lvURSF\nHV818Oq75TRa/STE6bl9aTyzZ8Ri7E516UfaOmckrxpPkwGPVQ+KCrVGZsnCBJYuTBThlRdAXYOX\nvAIreQVWCg/b8fkDv2mLWcv82bFMz7GQPT4So0HUngoEAoFAcClwTXYKGYkm/vHhQdZ9eZqiChsP\nLRlPZLh4nhIILjZClDhLx/wHfUv3C69EjLlzFkRPgyy7CqwM1smj5djhRl1QUUKjViHJnUWAlkm5\nRq3mP++dzuotxykoqifum12MPrYPZ+YIvspdCMBInZV7o45jl3Q82zARrxIYa1SYmicWRhNuUPP8\nZ00cr/Ixf3piOxHldIOOM416jNqAQ6I7QcLjVXhto5sjpyVS4gItPy2m3tXbN1p9/HXlaQoO2zGG\nqdHG2tGF+9vtEyo8NBh2p5eyGgdpCabWPzzFZ5ysfKOUoyeb0etVTJikx6Wz8tYXNWwu7P47609a\nHCKuxnB8Dh2gQqWVMUa7MVo83LRoHHHR4g9ob1AUheISF3v2N7GnwErxGVfrtmFpRqZnW5iRE8XI\n4eFi1UQgEAgEgkuUzCQz/3lvLi98dJjConp+tWoP990wjgmZMQM9NIHgskKIEmdpacu5ZFZm64RV\nr9N06W4IFmQ5eWQsV01KpqzGTnx0OO/vKOoyDDMYHp9Es8sbdJtWE1yUaDsp16jV3LNoLPfW1vD1\nMz9DFRnB1Nf+mw2bSvE7bPww9iBqFP7eOJ46KRDuY9Sq+OHCaGJNGt7Ls3OsRmb+9LR2QsyZRh2n\nzwoSOSlujN0IElaHzIvr3ZTXyowdpuGe640Y9b2b4O07YOWvL5zBZvczbbKZlJF+9p6w4zqr1xj1\nGmZNSupReKjX7+d3r+6jvNaBrIBaBYlRJlKNiXz6eT2yAjOnRRGd5uWrIxVw9hw9+c76A1lWyCuw\n8uEn1dhKAz21NQY/xmgPukgfKhXEmi/cIXK54PXJHDhytiyjwEp9YyAsVqtRkT0hktzsQNvOhDjx\neQoEAoFAcLlgCtPxg9sns2HXadZ+cZpn3spnTk4Kd1w7kjCDmCoJBBcD8T/tLOfjaGgRMm6bk0WD\nzc2WvBK+OljVmulg0Kk6daxooWMnj7ZYHR4a7cFFCa9P5qqJSRwtaeqyo4fs9XH03n9HcbkY8dxv\nMWdlMG1UM1ec+f/svXdgVOed7v+Z3mfURqOGBGp0JIoA043BgG0McTd2EseO03N3E/82v92sbzbZ\n3M3e7CZO4iS7SdzideIWOzHgJmya6QiEREcIBOrSSBqNNJo+59w/RhokVBAg+vv5R/bMmXfeU2aY\n9znP9/nuIkEV5HV3DkcCURVYpYCvL44jK1HDluNedp0O86MnZ/axr1W7NFS1adF1OyT0mqEFiYbW\nCC+u9dPukZk9Sc19i3SoLuKOcygs8ee/1rP242bUagVPPppBBy427q/vs50/GEGpUAzLwfBv/1Ma\nK2uRZfC1azl2UslRqZX0VB1fXjOK8fkmnn1h94CvH+qcjSSBoMTWnW2s29BEXWNUGUlJVeFRuVEb\nwn0uqYtxiNyKtLtD7DvoZl+Zm7IjnQSCEgBmk4pFtyUwo9DG1ElWjAZxDAWC642Kigq+8Y1v8MQT\nT/D4449TUlLCc889h1qtxmg08h//8R/YbDZefPFFPv74YxQKBd/61rdYuHDhtZ66QCC4wVAqFKyc\nO4YpOUm89MFRtpbVc/h0K0+sGM/EMcI1IRBcaYQo0c2F2nsOhU6jYvOBOrYcaOjzeCAkwyAdIc4P\nw+yNzawj3qKlbQBhIs6sZdmsTB5anIsvEB7UxVH777+h48ARkh5eSeLnlkf3w1qJRuemNJTCB55R\nsW0fn2NlcoaO8ho/f9rVweLpGX0EiZp2NafbtOhUUYeE4QKCREVNmFc/8OMPwl1ztCyerrmoz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0rcyZkt5HAOjtAAl2avA2GZAlJRpLkIwcicKJcRd1rFvagrz6dh3b97pQKGDpgkQe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\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f455fd51240>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "metadata": {
        "id": "l6D358UG55zZ",
        "colab_type": "code",
        "outputId": "24eddd9d-cec8-478b-da32-90190d49c098",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 957
        }
      },
      "cell_type": "code",
      "source": [
        "train_model(\n",
        "    learning_rate=0.00002,\n",
        "    steps=1000,\n",
        "    batch_size=5,\n",
        "    input_feature=\"population\"\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Entrenamiento del modelo...\n",
            "RMSE (en datos de entrenamiento):\n",
            "  período 00 : 225.63\n",
            "  período 01 : 214.62\n",
            "  período 02 : 204.86\n",
            "  período 03 : 196.26\n",
            "  período 04 : 189.66\n",
            "  período 05 : 184.13\n",
            "  período 06 : 181.10\n",
            "  período 07 : 178.68\n",
            "  período 08 : 177.12\n",
            "  período 09 : 176.22\n",
            "Entrenamiento del Modelo finalizado.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "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>predictions</th>\n",
              "      <th>targets</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>17000.0</td>\n",
              "      <td>17000.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>116.4</td>\n",
              "      <td>207.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>93.4</td>\n",
              "      <td>116.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.2</td>\n",
              "      <td>15.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>64.3</td>\n",
              "      <td>119.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>95.0</td>\n",
              "      <td>180.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>140.1</td>\n",
              "      <td>265.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>2904.3</td>\n",
              "      <td>500.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       predictions  targets\n",
              "count      17000.0  17000.0\n",
              "mean         116.4    207.3\n",
              "std           93.4    116.0\n",
              "min            0.2     15.0\n",
              "25%           64.3    119.4\n",
              "50%           95.0    180.4\n",
              "75%          140.1    265.0\n",
              "max         2904.3    500.0"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "RMSE final(en datos de entrenamiento): 176.22\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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jm/srs1rQbvwUzfH9OIJbYY2bCv6B7q+nBqHBRvIP7QFrBWj0zoCE1tAgdTeU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\nrkJggg==\n",
            "text/plain": [
              "<matplotlib.figure.Figure at 0x7f45623b8128>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    }
  ]
}