Day_25_Normalization.ipynb

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        "<a href=\"https://colab.research.google.com/github/Manish-k723/100-days-of-ML/blob/main/Day_25_Normalization.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
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      "execution_count": 1,
      "metadata": {
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      },
      "outputs": [],
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df = pd.read_csv('https://raw.githubusercontent.com/campusx-official/100-days-of-machine-learning/main/day25-normalization/wine_data.csv', header = None, usecols = [0,1,2])\n",
        "df.columns = ['Label','Alcohol','Malic_acid']"
      ],
      "metadata": {
        "id": "EcLdGsiQmTLt"
      },
      "execution_count": 2,
      "outputs": []
    },
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        "df"
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        "outputId": "d3cf7b9a-5fb7-4327-c31c-40dc30b800e0"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "     Label  Alcohol  Malic_acid\n",
              "0        1    14.23        1.71\n",
              "1        1    13.20        1.78\n",
              "2        1    13.16        2.36\n",
              "3        1    14.37        1.95\n",
              "4        1    13.24        2.59\n",
              "..     ...      ...         ...\n",
              "173      3    13.71        5.65\n",
              "174      3    13.40        3.91\n",
              "175      3    13.27        4.28\n",
              "176      3    13.17        2.59\n",
              "177      3    14.13        4.10\n",
              "\n",
              "[178 rows x 3 columns]"
            ],
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Label</th>\n",
              "      <th>Alcohol</th>\n",
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              "      <td>14.23</td>\n",
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              "      <td>14.37</td>\n",
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              "      <th>174</th>\n",
              "      <td>3</td>\n",
              "      <td>13.40</td>\n",
              "      <td>3.91</td>\n",
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              "    <tr>\n",
              "      <th>175</th>\n",
              "      <td>3</td>\n",
              "      <td>13.27</td>\n",
              "      <td>4.28</td>\n",
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              "    <tr>\n",
              "      <th>176</th>\n",
              "      <td>3</td>\n",
              "      <td>13.17</td>\n",
              "      <td>2.59</td>\n",
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              "    <tr>\n",
              "      <th>177</th>\n",
              "      <td>3</td>\n",
              "      <td>14.13</td>\n",
              "      <td>4.10</td>\n",
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      "source": [
        "sns.kdeplot(df['Alcohol'])"
      ],
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      "execution_count": 4,
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        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Alcohol', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
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          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.kdeplot(df['Malic_acid'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 297
        },
        "id": "dULv6XOZm9Rc",
        "outputId": "96ba2afa-9298-47af-9fe1-7a159f51f0e8"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Malic_acid', ylabel='Density'>"
            ]
          },
          "metadata": {},
          "execution_count": 5
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "color_dict = {1:'red',2:'blue',3:'green'}\n",
        "sns.scatterplot(data = df, y = 'Alcohol', x = 'Malic_acid', hue = 'Label',palette = color_dict)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "sz-EUFmrm9NT",
        "outputId": "f240b8d5-d943-46b3-f1fc-85b57f6060c9"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: xlabel='Malic_acid', ylabel='Alcohol'>"
            ]
          },
          "metadata": {},
          "execution_count": 6
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# **Spliting the dataset**"
      ],
      "metadata": {
        "id": "Ukg_SFwbnyOU"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split\n",
        "x_train,x_test,y_train,y_test = train_test_split(df[['Alcohol','Malic_acid']], df['Label'],test_size = 0.25, random_state = 42)\n",
        "\n",
        "print(x_train.shape, x_test.shape)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "1s_ijJUgnyvO",
        "outputId": "2a7344a0-57ac-400b-8d49-82ae344d733d"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(133, 2) (45, 2)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Normalization"
      ],
      "metadata": {
        "id": "z7SGGaZBJXoH"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "metadata": {
        "id": "652ZNMvyoKiR"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler = MinMaxScaler()"
      ],
      "metadata": {
        "id": "IMX7dIDZJ88s"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "scaler.fit(x_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "6OfoUuORKB-t",
        "outputId": "efeaf0fb-5a75-489b-d6b4-75209ee4bea9"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "MinMaxScaler()"
            ],
            "text/html": [
              "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>MinMaxScaler()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">MinMaxScaler</label><div class=\"sk-toggleable__content\"><pre>MinMaxScaler()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled = scaler.transform(x_train)\n",
        "x_test_scaled = scaler.transform(x_test)"
      ],
      "metadata": {
        "id": "coIZb9aHKJKx"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train_scaled = pd.DataFrame(x_train_scaled, columns = x_train.columns)\n",
        "x_test_scaled = pd.DataFrame(x_test_scaled, columns = x_train.columns)"
      ],
      "metadata": {
        "id": "E56NNKG9KWv-"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "x_train.describe()"
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        "colab": {
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          "height": 300
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        "outputId": "4acad10b-e87b-4390-fd60-30b12d0b72af"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "          Alcohol  Malic_acid\n",
              "count  133.000000  133.000000\n",
              "mean    12.972857    2.386842\n",
              "std      0.829993    1.098905\n",
              "min     11.030000    0.890000\n",
              "25%     12.290000    1.640000\n",
              "50%     12.990000    1.900000\n",
              "75%     13.690000    3.170000\n",
              "max     14.830000    5.800000"
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              "      <th></th>\n",
              "      <th>Alcohol</th>\n",
              "      <th>Malic_acid</th>\n",
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              "  </thead>\n",
              "  <tbody>\n",
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              "      <th>count</th>\n",
              "      <td>133.000000</td>\n",
              "      <td>133.000000</td>\n",
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              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>12.972857</td>\n",
              "      <td>2.386842</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.829993</td>\n",
              "      <td>1.098905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.030000</td>\n",
              "      <td>0.890000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.290000</td>\n",
              "      <td>1.640000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>12.990000</td>\n",
              "      <td>1.900000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.690000</td>\n",
              "      <td>3.170000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.830000</td>\n",
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              "                                                     [key], {});\n",
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              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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        "np.round(x_train_scaled.describe(),1)"
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      "metadata": {
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      "execution_count": 25,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       Alcohol  Malic_acid\n",
              "count    133.0       133.0\n",
              "mean       0.5         0.3\n",
              "std        0.2         0.2\n",
              "min        0.0         0.0\n",
              "25%        0.3         0.2\n",
              "50%        0.5         0.2\n",
              "75%        0.7         0.5\n",
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              "      <th>Alcohol</th>\n",
              "      <th>Malic_acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>133.0</td>\n",
              "      <td>133.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.2</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>0.0</td>\n",
              "      <td>0.0</td>\n",
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              "      <th>25%</th>\n",
              "      <td>0.3</td>\n",
              "      <td>0.2</td>\n",
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              "      <th>50%</th>\n",
              "      <td>0.5</td>\n",
              "      <td>0.2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>0.7</td>\n",
              "      <td>0.5</td>\n",
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              "      <th>max</th>\n",
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              "                                                     [key], {});\n",
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              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
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    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize = (12,5))\n",
        "\n",
        "ax1.scatter(x_train['Alcohol'], x_train['Malic_acid'], c = y_train)\n",
        "ax1.set_title('Before Scaling')\n",
        "\n",
        "ax2.scatter(x_train_scaled['Alcohol'], x_train_scaled['Malic_acid'], c = y_train)\n",
        "ax2.set_title('After Scaling')"
      ],
      "metadata": {
        "colab": {
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          "height": 353
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        "id": "MGcjOFjGKuyQ",
        "outputId": "1710a1a8-46bb-494b-cb4d-845d341d643b"
      },
      "execution_count": 27,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Text(0.5, 1.0, 'After Scaling')"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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UUkpVw5gIBCdCeAW4eoDnIES0f07VjCa5qskwwVmY/EsAC0wYcGBST0SaPYyIJDs8pZRSFZhIHmbLSLA2gwmCuMG5B+SMQhyZyQ5P7Qb045BqEoyJYAquAVMCpgwIAn7wfQv+b5MdnlJKqSpM0T0QWQOmFAjZbXd4Gab4sSRHpnYXmuSqpiE0F4wvzgkfxvdxvYejlFKqesZYEBgHhKucCYL/q2SEpHZDmuSqJqJqQ1mBCdVfGEoppWrAAFY1pyL1GonafWmSq5oGd3/AGedEKpJ6en1Ho5RSajtEnOA5iNg0xQkpQ5MRktoNaZKrmgQRN5L1XyAV8EQPpoFnX0g9JZmhKaWUikOa3QeShd1uY7fZjlZI5u3JDEvtRrS6gmoyxHsotBqL8Y0BawviPRg8Q7QcjVJKNUDi6gStxoH/a0x4KeLeC1JOQCQl2aGp3YQmuapJEWcbJOOKZIeREMbKxxQ/DYHvABeknolkXIOIN9mhKaVUQogjHdLOpjEUeTQmjCl9E8pG2ZUivIcjmTchzrbJDq3R0iRXqd2QMQFM3nCIrAeiC+dKX8MEp0LOO1r3VymlGhhTeCv4fwT89gH/l5jgBGj5LeLISmZojVaNxmlFZIWIzBGRmSIyta6DUkrtgP87sHIpT3ABCEBoDoRmJSsqpZRScZjwKvD/QHmCC0AErBJM2YfJCqvRq01P7pHGmNw6i0QpVWMmOCO6qUVVFoTngWef+g5JKaVUdcIL7B3bTKDKiQCEpiUlpKZAV9wotTtydQHiLL4QFzg71Hc0SimltsfZCYhX39cFzm71HU2TUdMk1wBjRWSaiFxZlwEppXZMUk+zewUqcYAjCzyHJiEipZRS1XL1BmcPoEq7LR4k7bykhNQU1DTJPcQYsy9wAvB3ETms6gUicqWITBWRqZs3b05okEqpysSRheS8B64+2LOOXOAZjOS8bxdRV0op1WCICJLzGngPx0503eDsimS/ZpdKU3WiRnNyjTFro//dJCKjgcHAr1WueRl4GWDQoEEmwXEqpaoQ955Iy88xViHgRBwZyQ5JKaVUNcSRhWQ/j7HKgADiyE52SI3eDntyRSRdRDK3/hk4Fphb14EppWpGHM01wVVKqd2EONI0wa0nNenJbQOMjtbddAHvGWO+q9OolFJKKaWU2gU7THKNMcuAAfUQi1JKKaWUUgmhJcSUUmo3IiLHi8giEVkiIrfFOb+HiPwsIjNEZLaInJiMOJVSKtk0yVVKqd2E2KUznsOudNMHGCkifapcdgfwkTFmIDACeL5+o1RKqYZBk1yllNp9DAaWGGOWGWOCwAfAqVWuMUCz6J+bA+vqMT6llGowarOtr1JKqeTqAKyu8P0a4IAq19yDvXnPdUA6MLR+QlNKqYZFe3KVUqpxGQm8aYzpCJwIvCMiMW29buCjlGrsNMltAoz/J6y887A2n4BV9AgmkpfskJRSO2ctUHF7pI7RYxVdBnwEYIz5A0gBWla9kTHmZWPMIGPMoFatWtVRuGpnmNB8rPzrsDYfj1VwIya0ONkhKbVb0ukKjZxV8jKUPgfGZx8oW4Xxfwktv0QcOckNTilVW1OAniLSFTu5HQGcW+WaVcDRwJsi0hs7ydWu2t2ECU7GbLkcCAAGIisw/p8g523Eo9U8laoN7cltxIxVAiXPbEtwAQiBVYgpfSdpcSmldo4xJgxcC3wPLMCuojBPRO4TkVOil90MXCEis4D3gYuNMbrV+m7CFN0H+LHXDwJYgA9T/GDyglJqN6U9uY1ZeAGIG0ygyokgBCZA5vVJCUsptfOMMd8A31Q5dleFP88HhtR3XGrXGWNBuJqpCaG59RuMUo2A9uQ2Zo6WYMJxTgg429V7OEoppbZHQDKqOdUs/nGlVLU0yW3ExNUV3HsR22HvRdIvSUZItWLCq+35adaWZIeilFJ1TkQg7QLsadQVpcLu0GZb+XabHV6V7FCUAnS6QqMn2S9i8q+D0GwQFyCQeQfi2TfZoVXLWCWYgn9AcAqIB0wQkzYSybzdfhNQSqlGSjKuw1h54PsiOt0sBGlnIelXJDu0ahljMMWPQtm7IF67zfbsh2Q9iziq6ZlWqh5oktvIiSMHaTEKE9kAVj64uiPiSXZY22WK7oDgZCC4bT6x70OMszuSfk5SY1NKqbok4kKaP4DJ/CdE1oGzI+Jo2FMVjO8TKHsfu80O2geDUzGFtyPZzyQ1NtW06XSFJkKcbRF374af4Fpl4P8BCFY54YOy15MSk1JK1TdxZCHuPg0+wQWg9HXAV+VgEAI/2VV+lEoSTXJVw2LKgGqmJFiF9RqKUkqpGjDVtc0OMJrkquTRJFc1LI4W9lfsCfAeXO/hKKWU2gHPwcRNJxzNwdGm3sNRaitNclWDIiJI8/uxVxdv/evpBslAMm5MYmRKKaXikYwbQDIBd/SIA0hBmt2vi4VVUunCM9XgiPcwaPEhpvQ1CK8AzyAk/RLEqT0CSinV0IirI7T8GlP2JgQmg6szkn4Z4u6T7NBUE6dJrmqQxN0byfpPssNQSilVA+JsjWTeCpnJjkSpbXS6goqrLBRi/uZN5JaVJTsUpZRSOxCxLBbmbmZ1oS7QVWor7clVlRhjeH7qJJ6bMgmXOAhaEY7u2o3/HHMCqW73jm+glFKqXv28Yhn/HPsdgUgYyxi6ZWXz4rBT6disebJDUyqptCdXVTJm8UKenzIJfzhMSShIMBLhp+XLuOPnH5MdmlJKqSqW5W/h7998Sb7fR1kohD8cZmFeLud+9hGWMckOT6mk0iRXVfLi1Mn4wuFKxwKRCF//tYiyUChJUSmllIrn/bmzCUcilY5ZxpDv8zFl7ZokRaVUw6DTFVQl1c3BFYSigJ80nbJQp4wxEPgZ4/sMAEk9DbxHaxkepVRca4uLCFfTY7u5rLSeo2maTGgepuxdiGwG71FI2umIpCY7LIX25KoqBnfoiCNOQpXp9dA6PSMJETUtpuj/MIU3QmAsBMZiCv+JKbw12WEppRqoQzt1JtUV218VtiwGtm2fhIiaFqvsc0zeSPCNhuCvUPwoJvcMjKUfMBoCTXKTxB8OsSx/C8WBQLJDqeTmg4aQ5nbjrJDoprhc3HP4UXGTX5U4JjQPfF+DqbAHvCkD/1hMaHbyAlNKYRnDioL8Bldx5rS9+tA2IxOv01l+LNXl5qw+/ejQrFkSI2v8jPFD8T2AH7CiR30QWYMp+yB5galyOl2hnm2tXvD8lMmI2GVfzuzdl7sPPwp3hUaqIssYxi1byheLF+AWB2f17cfBHfeokyHsbtk5fDXyAp6bMolp69bSqXkWf9t/MPu375jwZ6kqAhOBePOeA/Y5d//6jkgpBfy0fBm3jfue0mCQiDHs1649Tx0/jJZpadW+Zs6mjbw3ZxZbfD6O696DYb32wlNNG78rUt1uPj/nPF6fOY1v/lpMusfDhf0HcuqeeyX8WaqK0Dzi9xUGIPAdZFxW3xGpKjTJrWefzJ/L81MmVVrc9dnC+aS63Pz7sCNirjfGcP13X/Pz8mWUhe0EaOyypZzbr3/c6xNhj+ZZPDr0uDq5d0NmInkY3/t2w+Xqg6SNQJyt6i8Ax9ZtMSNVTnhAdKqIUsmwOC+Xa7/9En+FNnvKurVc/PknfDnygridDaPmzOLBCeMJRiJYxjBx1UrenTOL9884G2+cqQW7KtPr5foDDub6Aw5O+L0bMmMC4BuDCfwMjjZI2kjE3av+ApAMYtvrree0fFtDoNMV6tnzcaoX+MNhRs2dRdiyYq6fsm4tP6/YluAC+MIh3p0zi2X5W+o83qbChJdjco+DkpcgMA5KX8LkHo8JL6m/IFKO3865E+svDqVUubdmzSBUpXpB2LJYXlDAgtzNMdcXBQI8+Ot4/OFweQkvXzjEotxcxixeWC8xNwXGKsPknYkpegACP4LvfUzeWVi+r+svCFcvcLQjJpWSVCT9gvqLQ1VLk9x6Vt18rrBlxS3R9fPyZdWU7jJMWLUiscE1YaboPjDFwNY50kEwJZjCu+stBnHkINnP270D5V/pSPYziLNlvcWhlNpmVWEhkTjVC5wOYUNJSczx6evX4XLGvrX6wiG+/mtRncTYFJmy9yG8Cti6hsEC/FB0B8YE6yUGEUGyXwZne5D0aM+uF9KvQLyH10sMavt0ukI969+mDX+sWR1zvFVaOpkeT8zxTK8Xt8NBqEovr9PhIN0de73aScFJQNU3MgOhqRhj6q2El3gPgdZ/QnCy/XzPYES89fJspVSsIZ32YOq6NQSq9OYGIxH6tW4dc326x028il4CZHlT6ijKJsj/LfaCr6rEnnLmGVgvYYhrD2g5DkIzwcoHzz6II6denq12THty69lthxxOqstFxZQpxeXirsOOjJtInbpnb5wS53+TgWO796y7QJuaahNJT73XqBXxIN5DEO+hmuAqlWTn7t2frJQU3I5t7XCqy8XIfv3jllXct2170j2x9cRTXC7O3XtAncbapDgyqzkRsXtV65GIIJ6BSMpRmuA2MJrk1rO9W7fh07PP5djuPeiQmcnBHTvxxilncFyP+Alrh2bNePyY40lxucjweMq/Xjr5VJp5NQFKmNQzgaq/Tw+knpaEYJRSDUUzbwpfjryQ8/beh47NmtGnVSvuO3Iodx12ZNzrnQ4Hb512Fq3S0kl32+21x+nkH4MPYnAHrVKTKJJ2AVB1wwUBR1twaQeQsompg72tBw0aZKZOnZrw+zZlpcEgf65ZjdPh4KCOnepkhW5TZowfk38NBKeBuMCEwT0AyX4JcVRfJkg1PiIyzRgzKNlx1CdtsxMvYllMXruGomCA/dt3ICdV25FEMsZgip+EsjdAPIABaYbkvI24Oic7PFWPttdma6a0m0j3eDi6W/dkh9FoiaQgOW9gQn9BZAk4uyHuPZMdllJqN+V0ODio0x7JDqPREhGk2c2Y9AsgNB0cOeAehMSb3qeaLE1ylapA3D3B3bCHukxwJsY3BrCQ1GGIp0l1OiqlVDlxtgbndsovNgAmvArj+xSsTYjnUEg5BpHYedsq8TTJVWo3YhU9AWVvs3VVsfGNxqSdjaPZv3fpvib0F8b3GZhSJOUY8BxS7wvulFKqsTH+nzEF1wNhIIzxfwtlr0POqF1aWGwim+3EObISce8PqSciotU7qtIkV6ndhAkvhbI32VbLF8AHZR9iUk9H3H126r5W6QdQ/BD2lsIRjH8MeIZA1jM69KeUUjvJmBCm8BYqlTozZRBajCn7AEm/aOfuG5yFyb/YXjtCwE6cS5+HFp8iDt1prSJ9B1NqdxEYj13wvKogxv/zTt3SWPlQ/CB2IxytA2rKIPhb9HlKKaV2Smg+8bf99YP/q526pTHGTpxNKeUdHqYMIusxJc/tbKSNlia5Su02vIAzznEX4tjJYargn3Y1iapMmd07oJRSaudsrfoQV9XyZzVkbYbIujgnQtENMlRFmuQqtbtIOY74DaZAygk7eVOv/foYDtD5XUoptfNce4HE2xwiFUkfuXP3FDfVJs66eVCMGie5IuIUkRkisnN97EqpXSLOVtD8McAb3dEn3f5zswcQZ/udu6l3SDUnPEjqmYA9POYr9ROJxBt2U0opFY+IINkv2YmuZICkAV57kyHvzlWEEEc2uPsTO6qXAqnnlH8X9AcJBUM7G3qjUZuFZ9cDC4BmdRSLUklRmFvEuFET2LhyM/0O6c3BpwzC6Yo3LSD5HKknYLxDIPALYMB7GOLI2un7iXgh+0VM/lXRI8ZezJBxDeLZh8nfzuCZa19l8+pcXB43w64ayuWPnI/LrWtWlVLJEfQH+fWTP/lr2lI67dWRI0cOIb1Zw9xsQ9w9ofUECEwEawt4Bu3yZhWS9V/MlnPBygdjYb8XHISkX8zqRWt54rIXWDDpL8QhDD5+IDe+cjXZrZvmgrQa7XgmIh2Bt4AHgZuMMcO2d73unqN2Fwsn/8Wtx9xHJGwR9AVJzUihfY+2/HfC/aSmN53hemOVQfAXsHzgPRhxtmX+n4u5dei9BMqC5dd50zwcde6h3PTy1UmMtm7pjmdKNVwFmwu59oDbKcotxlfiJyXdiyfFzVO/PUjHXjs5orUbMiYCwT8gsh7c/RB3b0oKSrmwx7WU5JeyNbdzupy069aa1+b/D4ejcc5Q3V6bXdOf+H/ArcRf2q3UbskYw4Mj/4ev2E/QZydyvhI/qxeu5ZMnvkxydPVLHGlIyglI2hmIsy0Aox74tFKCCxAoC/LjO79SnF+SjDCVUk3cq7eNInftFnwldlkuf2mA4i2lPHHZC0mOrH6JOBHvIUjacMTdG4Af3vmFoD9Ixc7LSDhC3vp8Zoybk6xQk2qHSa6IDAM2GWOm7eC6K0VkqohM3bx5c8ICVKqubFi+ifwNBTHHg/4Q40ZNqP+AGpg1i9bGPe7yuMhdk1fP0SilFEz8bBKRUOX1AcYYFkxaTMAXqOZVTcOqBWtiOiYArLDFuiUbkhBR8tWkJ3cIcIqIrAA+AI4SkXerXmSMedkYM8gYM6hVq1YJDlOpmjMmggmMxyp+ElM2CmMVxr3O6XZS3XQdl7thzsmtTz336444YisvWOEIbbu2TkJESqnGyoTmYxU/jVXyEia8qtrrql0vIYI00uH4mtpzUA9S0mMrLIhD6Np/1+YB7652+DfCGHO7MaajMaYLMAL4yRhzfp1HphLGWFswpW9jFf8PE5hUbWJX53EYU+fPNsaP2TICU3ADlL6IKXoUs/lITCh2qKZ1p5Z06NkuZvtab5qXE68YWqdx7g7Ov/MsvKmeSse8aV7OvHEYqRk7WeNRKbVDxgQwvi+wiv+L8X2FMbG9c/UTh8Gqh/cLq+hBTN4Ie9eukqcwuSdhlX0U99qhFxyG2+uudMzpcrDfMf3xVDne1BwxYgiZORmVPgi4vW669u9M34P3TGJkyaNLpBs5E5iEKbgqugLTjyl7E9yDIPtFJN4mAHVgU2kJd/78Iz+vWI4xhiO6dOOBI4fSJiMjoc8JhMMsWf04PbzzcTu2lk7x2wUDCm6Alj/GJLR3fXwzNx52FwFfgHAwgsPpYJ8j+3LK345LaGy7oy59O/HkL/fx0i1vs3DSErJaNePsW0/l5KuPTXZoSjVaJrIBkzccTLG9KYukQckTkPMx4mxZLzEEIxEe/30C78+djS8Uok+r1tx3xNEMbJfYhV3GGOas/Y49He/jdmxN5C0gDEX3Y1KGIo7KdWYvvu8c5v2+iJXz1xAJRXB5nDRv2YybXrkmobHtjlLSvDw3+RFevvUdfv9iCk6Xk2MvPoKL7j0n5r2vqahRdYXa0pW6DYMxYcymg8EUVDmTijS7C0k7s8r1JuH/EIKRCEe//TobSoqJbF3tKULr9Ax+uvBSvK7EJNqbSks4/cP3eOuQ1+jWLD/OFSlIy68Q1x6xMQZCTPpqGnnr8ul9YE/23L9HQmJSu6eGXl1BRI4HnsIulPmqMeaRONecDdyDXTV+ljHm3O3dU9vshsPKvypaIrDiOm8XpByPI+vJStfWRZsNcP13X/HDsqX4w+HyY6kuF1+OvIBu2fE2N6i9UCTCZWNGM7TVB4zsNhtn1XFlSUMy70LSzoh5rTGG2b/OZ/nsVbTr3oZBxw3A6dQpZk3V9tps7cltzELzgHjFoH0Y32dI2pkYE8IUPwW+98CUYlz97ATYMyAhIYxbvpQCv688wQWIGENRwM/YZUs4uddeCXnOXT+PY1NpCeFqP7MZ4u/sBR6vm0PPPDAhcShVl0TECTwHHAOsAaaIyBhjzPwK1/QEbgeGGGPyRUQnUO8mjLEg8CuxhYzC4P+h/DurbDSU/BesDRhHe8i4CUfaKQmJYWNJCd8vXUKwyuYvwUiEl6dN4ZGhiRnl+mj+XKatX8shOdU02gaQ+DMqRYQBh/dlwOF9ExKLarya9iztxm67n/Dt//Wm8HYoextMCWAgPAez5UJMeFlCQliWn48vFI45XhoKsSx/S43vY4xh3u+LeO+hz/j65R8qlbAyxvDTimVEjOHj5XviC1f97Cbg7Ii4Ou3sj6FUQzEYWGKMWWbsiZofAKdWueYK4DljTD6AMWZTPceodppQ3YfxrcetstFQdDdY0dXy1joougPLl5jNSFcWFuB1xvZ/RYxhQW7tKietW7qBj/8zho+f+JL1yzZWOvfJ/Ln4wmG+XNWToBWvvy0C3iNr9TylqtIktzFz9QWJs0BIUpG0szCRzeD/HvBXuSCIKX0lISH0zMkhNc7uWOluNz1zaja/LBKJcM8Zj3Pbcffz1t0f8uJNb3Fe52uYO3FB+TVb3xbeXdKPabltKA25CEYclITcIM2RrKcT8eMolWwdgNUVvl8TPVZRL6CXiPwmIn9Gpzeo3YCIgPcoYgdZXZBygv3Hkv8S22b7oeRJEqFrVjbBSGzHhFOEvVu3qfF9PnnyS67Y+ybeuON9Xv/3e1ze70Y+e/rr8vOOaCfMvPxWvLxwAP6wE3/EiS/swsILzR9DHE1zly6VOJrkNmIiTiTreZB0IA274UwFzxGQcjJEVoJ44rwyAqGFCYnhqK7daZWejrtCaReXw0FOahrHdOteo3v8NGoi03+cjb80gBWx8JcF8JX4ufesJ4hEIogIx3TrgcvhIGQ5ufjXk7jk15P437wDGL32HKT1r/bWiko1DS6gJ3AEMBJ4RUSyql6ktc0bJml2DzjbRdttl/1fZyek2e12dRqrmnqnkXUJeX6r9HSG9dqLlCrrJbwuF1fsu3+N7rF2yXreuON9gv4QoWCYcDBM0B/itdtGsX653aN7dt+9SY0+45n5gzhp7HD+M3swzy08HEfLn3CknpCQn0c1bU06yTXGMH39Ol6aNplPF8yjJJicMi11STz7IK0mIM3uRDJvQlq8iyP7KUQc4OwCJl7xbCe4+yTk+S6Hg0+Gj2RYr71IdblIcbk4qWcvPjv7XNw1XCjw3Rs/4S+NjTPgC7Bk+nIA7jniaNpnZpLuduMUB4uKOvH9+sMY1v+fiDSd7XlVo7cWqDjvpmP0WEVrgDHGmJAxZjmwGDvprWR3rW2+sqCA12dM461Z09lQUpzscBJOnC2Rlt8jzR9HMm5Amj+JtPwGcWTZPb2OdvFf6Kzaob/zHj76WK7cd3+yUlJwORzs374DH501gs5ZWTV6/cTPJmNFYjdINcbw2+jJAJzZuy9DOnUm1eXGKcLmQEs+WTmI4/a+G4dr9/n7qBq2JrvwLGxZXP3VF/yxZjUhK4LH6eS+X37i3TPOrtWQzO5AHBlQpZIC2I2pSTkJ/N9SafhLvEj6FQl7fk5qGk8cewJPHLtzn8y3VwFk67mWaWn8eMGl/LR8KUvzt9AjuwVHdu2Gq4kXB1eNzhSgp4h0xU5uRwBVKyd8jt2D+4aItMSevpCYSfZJ9tLUyfxv0u/ly0gfmfgr9x1xNMP77p3s0BJKxAUp1dTqzrgJiu4CfBUOptjHE8TlcHDDgQdzw4EHJ+yegL2YrMIzXhp2KjM2rGfy2jW0SEvjhB69yPDEG11Uauc02Qzg43lz+GPNKnzhEGHLoiwUojgY5G9fj0naZgnJIM0fgPRLQZpj9+AORHLeQVxdkh1aueMvOSruLi7eVA899+tW/r3L4eDY7j25ZtABHNO9R70kuMYYuxZx6WsY/7dJK9qumgZjTBi4FvgeWAB8ZIyZJyL3icjW5fXfA3kiMh/4GbjFGLPb78P8V14eT03+g0AkQjASIRD9umv8ODaXliY7vHrjSDsVmt0f7bl1gLMjNH8YR+pJyQ6t3JDTB+OIqQlm77w15PTB274XYd927bl60GCG9+lXbwmuCS/FlL6JKfsIYxXUyzNVcjTZntwPoys7q8r3+/hrSx69WtRP0e1kE3EjmTdA5g3JDqVaR59/KBNHT2LGuDkEfEE8KW7E4eCuj/+Z1NqI9u5qF0N4IZggiBckDXLej1uPV6lEMMZ8A3xT5dhdFf5sgJuiX43G138tIlSlrBXYC5jGLlvCeXsnpuzh7sCRdgokqGRYXejYsx0X3z+CN+/8ECsSAREcDuGyh8+lXdfkjZQaYzDFj0DZ+4AF4oSiByH7GcR7WNLiUnWnySa529uqsD62MVQ153Q6uXf0rcz7fRGzxs+jectmHH72QWRmJ3bHtNoyJS9FaxFH5wubMBgfpvBmpMXHSY1NqcbGGEO8ltmgbXZDNPzmUzj41P35bfRkROwe3Pbd2yY3qOAkKPuA8ul50b82puAf0PoPJF41IrVba7JJ7pm9+7J0S15Mb26mx9NkenF3JyJCvyF70W9IYjaPSAjfZ5QnuOUsCM3HWAWIIysJQSnVOB3foyevzJhaaRcusJPfoV1rVqlF1a8OPdpx9i1Vyzgnj/GPpvJc5q0cEPgdUo6u75BUHWuyc3JH9uvPPm3bkeZ2A5DicpHudvPsiSeX1+9TavtiVw/X7JxSqrZ6t2rN5QMHkeJy4RTB7XCQ4nRx+yGH0y4zM9nhqd2BiZ3uss32zqndVZPtyfU4nbx7+nB+W72KyWvX0Co9nZN77UlWig5XqBpKGQZl7wAVF5sJuLojjsTs766U2uamg4YwrNeejF26BJfDwQk9etW4rJVSknoyJvAjmLLKJ0wYPAmuJKEahCab5II9BH7IHp05ZI/OyQ5F7YYk4++Y4ASIrAVTCniAMIRXYOUOQzJuQlKOSnaY9cYYw3dv/MT7D42mYFMhvfbvzpWPXUCv/XQoWSVOrxYtdUqZ2jmew8B7HPi/w56X68QedbMwuSdi0i5C0i+x68g3EbN+mcert41ixbzVtO7UgovvG8GhZx6Y7LASpun8n1QqwcSRgbT4HGn+KHhPYGtjCX4IL8YU3IDl+3oHd2k83nvoM57/xxusX7YRX4mfWT/P4+bD72bZ7JXJDk0ppRARpPkjSM5bkHo+dgpkAUF7J7mSpzFFd+3gLo3HrF/m8e8TH2LhpL/wl/hZtWAtj170DN+9/lOyQ0sYTXJVnTNWCVbRA1gbD7S/Cu/HWI1jpyIRF5JyLESWAlVL0vmh+LFkhFXvAr4AHzwyGn9ZoMrxIG/f81GSolJK7QxjLKzSt7E2HYm1cT+s/Gsw4eXJDishRATx7GOXD4tZO+ED3+eYSNPY5vrV20YR8FWu7R4oC/Lq7aOwrMaxrkSTXFWnjIlgtpxr1yU0W+wv34eYLSOw69o3EtW9AVgbMCZUv7EkwebVefaWo1UYY/hreqPYbEupJsMUPwDFT4C1FkwxBH7C5J2FiaxPdmiJE5pBbMcEdr3z8NJ6DycZVs5bHfd4aUEpvuJ4VSh2P5rkqroVnACR1UDFRC9oz2MN/JKsqBLPWU2Bc2lOU5j6ntMum0g4/urkDj2TXBtTKVVjxtoCZR9TudSWsWuAl76WrLASz9UDe05uFSZo7yLXBLTeI/7cdk+Kh5SMlHqOpm5okqvqVmgBmDifCE0ZhBfUfzx1Jf06oGpljlTIuCZuD2djk5aZyrEXHYE3rfK2nN5UD+ffOTxJUSmlai28FCTe9rphCM6o93DqiqRfBrirHPWC5yDE1TSS3IvuPSe2zU7zcvYtpyR1N9FE0iRX1S1nJ4i3i4ykNapPy4600yHzNpBswAWSCRnXIWkXJzu0enPtM5dx8tXHkZLmxel20qpTC2579x/0P6xPskNTStWUs4PdmxnDAa5u9R5OXRFXDyTnVXB2xe7R9UDqMCT7qWSHVm8OPfNArnv2crJaN8fldpLWLJUR/zqVkf93RrJDSxgxdbAd4qBBg8zUqVMTfl+1+zEmgNl8FFh5bJvk7wBHNtLqZ0RqNyRirCJMybPg/wZwQupZSMaViHgTHfpOMcayy4lJepMqQ1NRJBzBXxYgLTN1t+zFFpFpxphByY6jPmmbrSqy8q+ydwCrtKNjCtLiY8S9Z63uZUwEU/YulL0LpgS8RyAZ1yPOhjONyVglIF5EqvbsNg3GGMqKfaSke3fLHtzttdlN811Y1RsRL5LzIbgHYc9NdYF7XyTnw9onuCaIyRsOZe+BtQms9VD6MmbL5dTFh7WdIeJAHJlNNsEFcLqcpDdL2y0TXKUUSNb/IPVE7NrfLnB2QrJfqHWCC2CK/g3FT0Jkpd3Z4fsck3c6xipIcNQ7TxwZTTbBBbviRHqztN0ywd2Rxr8iRiWduDoiLd7FWKX29470nbuRfyxENlJ5h7EAhGdDaCZ4Bu5qqEop1eSJpCLNH8U0ux+MHyRzpz60msha8H1F5TY7AlYppuxDJOOqhMWsVDxNt7tJ1TtxpO98gguY0EygLM6JCITm7PR9lVJKxRLxII5mOz8qE1pQzSI2PwQn71JsStWE9uSqBsFE1mFKnrLngTmy7ZWvKadUblydnYEU7O0YKxC3vVhCKaVUvTBWGab0FfB9ASKQejqSfnnlaWjO9kC80oIucHWpp0hVU6Y9uSrpTGQjJvdU8I0BayOEF2IK78KU/K/SdZJ6ip3QVuKwKxl4D6+3eJVSqimzN/k5H0pfBWuNXQu95CXMlosrrY8Qdx9w9iC2VJcbSbuwXmNWTZMmuSrpTOkbdt3cSp/4fVD6OsYqKj8ijuZIzihw7YndaLrBPRBp8QEiOiihlFL1IjgBwsuoXH0hAOGFEPyz0qWS8xp4D8Fusz32IracVxBX53oMWDVVmhmo5AtOovKOaFHigfBf4Nlv2yH3XkjLL+1deXAijua7/HgTXmVvTOHsCK4+WhVAKaW2wwRnEX99RABCs8F7UPkhcWQh2S/ZZbqMHxwtdrmNNVYBBKeCpINnf+3kUNXSvxkq+ZydIDwfqFIGzAShmlqK4sjZ5ccaE8YU3gr+H6LTICL20FrOa4gja5fvr5RSjZE422NIIybRlZToPNw4r3FkABm7/Gyr9C0o/k+0zTb2M7NfR9y9d/neqvHR6Qoq6STjciDeZg5OjH8cxoTr5Lmm9E3w/wgE7CLlxhedD/x/dfK87cZiQpjAH5jA+PJSa0op1SClnADxek+NDxOahbHy6+SxJjgLip9gW5tdClYeJv/SOnufqDYWYzChOfZ7VGRDvT5b1ZwmuSrpxN0fmv8HJAeoOIxVBsX/wRT8o24eXDaKmEoNhCDwi71y2EQwwSnRxLOkbmIATHAmZtMQTMHfMAU3YTYdhFX2RZ09TymldoU4MpCc98DVi8pphAVl72NyT6+TNtP4PqDyPOCtJ/wQnGL/MbQomniuT/jzyx8X2YzJOxmTdz6m8BbM5qFYhfc2mE2J1DY6XUE1CI7UY7EIQ+HtgK/CGT8EJmJC8xB338Q+1MSZU7b1VHguFNwYvUbAhDCZ/8aRPiKxIRg/Jv8yMMWVTxTdifHsjTSiveKVUo2HuHtB9quYzUOpvNlDCKwtGN8nSPrFiX2oVUjMtDY7Gkxkk70jZmiR3ctsgpjUYUizBxFJ7E5epuAGCC8FItvC8X0Gnr0h9YyEPkvtGu3JVQ1HcCqVE9ytjL2jWaJ5DwfiNH6ODlBwPVib7eEwUwIEoPghTGhuYmMI/AJYcU6EMb5PE/sspZRKpNDs6jd7CPye8MdJynFAauwJEwL/VxCabz/blABB8H2LKXs3oTGYyGYIzSK2/q8PU/p2Qp+ldp0muarhcLYn7txccYGjdcIfJ5k3gSOrwjNdIKmQfp49/BUjiCn7MLFBWCVUl+RSoXyaUko1OI42xG+/nPaC4kRLORHcvdmW6AqQAhnXQvB3Yqv0+KAswYmnKSVu5wjEjsippNPpCk2IMYYvFy/krVkzKAkGOb5HTy4bOIhm3niLvuqfpJ6OKX22ymiU2IlnHWz2IM620PJbTNlHEJoGzq5I2nkQno8hXokbC2qwoMJENtk7AQUmgrM1kn4Z4j0s/sXeA6EozpuEpCHeo2r3AymlGp1p69fywpTJrCwsYN927fnboAPonJWV7LBs7v7gaAeRFVTu2fQg6ecl/HEibsh5B/zfYvzfgqM5kjoCnJ0wJc/Ef1ENFvIaE8CUvgv+LwAXpA5H0obHL03m3AMc6WBVHXV0g/eYWv9Mqm5pktuE3P/reD6cNxtf2F6FumpaAV8uXsjXIy8k1V11R5r6J84WkP0GpuAmsPIAA64uSNYzSNwhsQQ805GFZFxZ6ZhxpNvly2KkIinHbvd+JrIZk3tK9BN9CCJL7YVlmf/EkX5B7POdHTDpl0DpW5RP1ZBUcO+ru7gp1cR9v+Qvbhz7Df5om72iIJ9vlyxm9Nnn0j2nRZKjw653m/MWpuB6CM0BnOBIR5o/XGfrCUTckHqKvQNmlDEGnG3sndcqcULK9ttRe/e2CyG0gPKFyMVLMcEJSPbzcZ7vgOYPY/Kvw+45jgApEOe9RCWfJrlNxPriYt6fO4tAZNun7UAkwsaSEj5bOJ/z9h6QxOi2Ec9AaPWT3ViJC6mm5mKdxuDIwWT8A0qew270jJ14uvaClOO3+1pT+sq2BLecD0r+g0k7C5HY+WSOzJswnoMwvo/A+JGUkyDleLsxVUo1SZYx3DV+XHmCCxAxhtJgkMd/n8iLw05NYnTbiLM10uJ9TGSTPZTv7FzvbZeI2Innliuw294w4AVJRzJu3P6LA7/ai9UqVdrxQeA3TGgO4t479nnew6HlZ5jSUfZ7lecgJO1sxJGZuB9KJYQmuU3EjA3rcTuclZJcAF84zK8rlzeYJBeiDZZrj6TG4Mi4EuMZiCn7AKwiJOUESB224x7lwG/E3b0Np70a190v7svEexBSYZcgpVTTlucroygQuzbAAFPWran/gHZAnIlfN1Gr53sGQ8svMKXvQGQ5eAYhaSMRR/Z2X2eCk4m7exsRCE6DOEkugLh6IM3v3vXAVZ3SJLeJaJWeholTesUpQvvMZkmIqOETz/6IZ//avcjZBiJ/xR43IXAkf3hRKbV7yPR44hbLAmiRmlavsewuxNUFaX5n7V7kaI29+LhK/V1xg6NlokJTSaLjoU3Efu060CItDWeVPcPdTifnN6Be3N2dpF9GbIkbN3gGIs52yQhJKbUbSnG5ObnXXnidlVfyp7pcXLlfLT98q2pJ2qkQt46uC1KG1ns8KrE0yW0iHCKMOuNs9mzZihSXi3S3m6yUFJ45fliDWMDQWIh3CGTeBpJuf+EFz/5IVjUrf5VSqhr3H3k0Q7t1x+N0kuHxkOJyceW++3Nm7wRvjNOEiSMHyX7NLocmqUCKPa84511EUpIdntpFUhfb0A0aNMhMnTo14fdVNWMiGzClr0JwOri62iWs3H3Kz68qLKA0GKRni5a4HNV/zjFWCaZsFPh/sFeOpl9YfSksVYkxAXsOriPHLlWmdhsiMs0YMyjZcdQnbbOTy1hlmLL3wf8dODKRtAuQlCPLz+eWlbGxpJguWdmke6pfF2CMBf6v7LKIRCDlNCTtDLsigdouYyy7zRanXU5S4pWRVA3R9trsHc7JFfujzK/Yk1ZcwCfGGJ1t3UCZ8CpM3ulgfEDYrvnq/wGyny1PUPdonrXj+1ilmLwzIbKOrXOVTHAKJuMqHBl/q7sfoJEQ8UKFDxZKKRWPMX7MluEQXs3WFf4mOA2TfjGOTLsyQMu0NFqm7Xgerin8JwTGRdt/IDQfE/gWsl/Xai07IOIAd89kh6ESrCZ/6wPAUcaYAcA+wPEicmCdRqV2min+T3RHlq1lZyzAjym8i9r02hvfJxBZT+XJ+D4oeQFjFSQs3mqfbwzG/yPWlsuw8kZilY6ye0eVUqox8Y2B8BpiSliVvmZvIVtDJjQf/D9uS3C33ic0M7obWN0zoXlYBTdh5Q7HKn4CE8mtl+cqVZ0d9uQaOzMqiX7rjn4lfo6DSozgn8TdZtHKBZMPklOz+wR+pnKjGyVue9/uOtyowIRXYkqeAv84yjdICM3H+EdDzvs69KaUajRM4GfK27mKxA2hGeDc/gY05YKTidv2mzJM4A/Ee8iuhLldJpKL8X0OJU9hl1C0ILzAnjbR8gudsqWSpkbjFyLiFJGZwCbgB2PMpDqNSu08R/Pqz8XZiKD6+7Qi/l8PC3ZQd3BnmcgGrNzTMbnDwP8VlRt+H4SWgH9snTxbKaWSwtGaat+Ka9PWOrIh3ja0eOusFJYxQayCGzGbD4eSx7FH/rYm2kEwRdVvt6tUPahRkmuMiRhj9gE6AoNFJKaivYhcKSJTRWTq5s01H2JRCZZ2KbElrDyQclzc3baqI+kX2K+rxGE3yK74xbF3hTEGs+UyCC8kpl5huTJM4JeEP1sppZJF0kYS29YKSHNw71fzG3mHEvctXRxI6sm7EGH1TNHD0RG3EPEHeCMQmFAnz1aqJmo1E90YUwD8DMTsbWqMedkYM8gYM6hVq1YJCk/VlqSNgLRzsLc0zLD/6zkIaXZf7e7j7g/N7gZJi94nFZzdkOzX62bVaXgBWGuw9wGvjgucWpxbKdV4iHsvaP5gtOxghj3i5uyC5LxVq8Vi4khHct60OyIkLXq/LCTrRaQO2k1jwuD7hLjT2ipyZCX82UrVVE2qK7QCQsaYArG7Ao8BHq3zyBqhpVvyeH3mdJblb2Fwh45c2H8gLWqwYrY2RARp9n+YjGvscijO9oiz/U7dy5F2Jib1JAjNA2kGrh51V1bFygPiFeSuyIWkDq+b5yulVBVbfGW8PWsmk9eupktWNpfssx89WyS+rrgj9WRMyrHRtjYDXD13qq0Vd39o9avdaWDC4O6LxJ3CkAAmyLYFztVJRdIvrZvnK1UDNfnb3w54S0Sc2D2/HxljvqrbsBqf31ev4oovRxOMRIgYw8wN63l39kzGjLyADnWwra44ssGz66U+RVLAU4shs53l7mdvfRuXx16E0ewRxNW17mNRSjV564uLOfmDdygNBglEIkxet5YvFi3gxZNO5dDOXRL+PBEvePZNwH0c4K77zSLEkYZxdoTIyjhnnfZX2vmQcmqdx6JUdXY4FmKMmW2MGWiM6W+M6WeMqd24t8IYw20/fo8vHCYSLeMViEQoDAR48o/fkhxdwyCObMi4qsriOLc99Jb1AtJ6Eo7U45IWn1INhYgcLyKLRGSJiNy2nevOFBEjIk1qY4tE+e+fv1Ho9xOI2FOoLGPwhcPcNm5srcoxNmbS7B4ghW2phNP+vtn9SOuJOJrdopsqqKSqo3EMVVGur4xNZaUxxy1j+GXl8nqNZfLaNbw/ZxaFgQAn9uzFKXv2xuPc0TSB+uHIuBbj6oMpexOsAvAeg6RfhDgS39Ot1O4oOqL2HPa0sTXAFBEZY4yZX+W6TOB6QCvh7KRfVq4o75SoaIuvjI2lJbTNyKyXOFYU5PPWzBkszd/C4A4dOG/vfchOrUWlnDok3iHQ4gNM6csQXgLuAUj6lYirc7JDUwrQJLdepLnc1VYWzvR46y2Ol6ZO5unJf+APhzHApLVr+GDubN4785wGk+hKylFIylHJDkOphmowsMQYswxARD4ATgXmV7nufuy1E7fUb3iNR6bXw+a4nROQ5q5+a91EmrRmNZeO+YxQJELYGKasW8Nbs2YwZsQFtMusnyR7R8TdB8n6X7LDUCou3eevHqR7PBzZtSseR+VEMtXl4pJ9dn0OVk1s8ZXxv0m/44smuAC+cIiFubl889fieolBKbXLOgCrK3y/JnqsnIjsC3Qyxnxdn4E1Npfssx+prsr9QG6Hg8M6d6GZt+47J4wx/Cs6zS1cYZpbgd/PE39MrPPnK9UYaJJbTx4dehz927Qh1eUi0+PB63Ry6p69Ob//PvXy/Mlr1+J2xPbWloVDfL9Uk1ylGgOxa049Cdxcg2u1tvl2jOzXnzN698XjdJLp8ZDicrF367b855iYCpp1YovPx4bSkpjjEWMYX8/T3JTaXel0hXrSzJvCR8NHsmRLHmuKiujdshVtMjLq7fmZXk/cGRMOEbJTGsb8LqXUDq0FOlX4vmP02FaZQD9gfHTBT1tgjIicYoyZWvFGxpiXgZcBBg0apCupqnCIcP+RQ7lu8IHM37yZDpnN6qR8WHVSXK5qF7hl1NN0CaV2d5rk1rMeOS3okVN/DeVWB3ToRIrLRWkoWOm4x+lk5N4D6j0epdROmQL0FJGu2MntCODcrSeNMYVAeeV/ERkP/LNqgqtqrnV6Bq3T669DYqt0j4cjunRl/IrlhCyr/Hiqy8WFAwbWezxK7Y50ukIT4XI4ePv0s2idlk66202Gx4PX6eLfhx7B3q3bJDs8pVQNGGPCwLXA98AC7Lrl80TkPhE5JbnRqUR7bOjx9G1tT3PLiE5zO7FHLy7SJFepGpG6qPc3aNAgM3Vq0+44CEYibC4tJSc1lVS3O9nhlItYFtPWr6MkGGT/9h3IrIcFFErtTkRkmjGmSdWW1TbbXui1vqSYdLeH5ikpyQ6nkgW5m1lXVMRerVrVyeZBSu3Ottdm63SFBDPG8Mr0qTwz+Q8sYzDAuf36c/shh+N0JL/j3OlwMLhDx2SHUc5YRZiyTyA8C5w9kLQRiLNVssNSSjUhv65cwW0/fk++34/BcHDHPXjyuBPIaiDrFXq3bEXvlg2jXTQmBP7vMYGfQLKRtHMQd69kh6VUXJrkJtinC+bxVLRU11bvz52N1+niliGHJjGyhsdE1mPyzgCrFPADHkzZG5AzCnH3TnZ4Sqkm4K+8PK75+otKbfZvq1dy6ZjRfHb2udt5ZdNjTBCz5UIILQTKACfG9zGm2X040k5LcnRKxUp+12Ij89yUSZUaSwBfOMxbs2cQqbB4QIEpfgysfOwEFyAIpgRT+O9khqWUakLemDmNYHTr3q1ClsWi3M0szstNUlQNlG8MhBdgJ7gAEcAPRXdjrLLtvFCp5NAkN8E2lcbukAMQCEfwV0l+m7zAeCBO4h9egDG++o5GKdUErSgsiLt9r8vhYF1xcRIiariM/2uI1zaLE0Iz6j8gpXZApyskWN9WrZm6fm3M8VbpaaQ1oAVoDYMXiPehQIC62WbYhFdifB+DlYt4jwDvUHLXFvLta+PYsHwTA4/am8PPPghPSuLrUBqrBAgjjqyE31sptXMO7NCJGevXEajSmxuIROjTqmHMg20wpLpSagakbuYvG6sAU/YphBeBux+Sega+Uifj3v2V+ZP+okufjhx/6VE0b5n4BXnGBMAqAUc29j4ranejSW6C3X7IYZw3+uNKvbYpLhd3HnoE0eLsaqu0s6H0DSBQ4aAbvEcjkvgk0/KNhcJ/Yg+xhTD+7ygt68SVgzIJlBlCgTATPv2T9x8ZzTN/PEh68/SEPNdENmIKb4XgFEAwrm5I80cRd5+E3F8ptfPO7z+Ad2bPJOL3lW+fm+pycVaffkmpj9uQSdoITOBXoEpvrqSDe5+EP8+El2HyzgYTBPwQ+J5I0XPcMmxPVi6yCJQF8KR6eP/h0fx3wv107bdHYp5rgpiiB8A3GjDgaIbJvBNH6gkJub+qP/rRJMEGtmvPh2eN4PDOXWiVls6gdh145eTTOKHnnskOrcb84RCfzJ/LbT9+z4tTJ5NbVjdzrSTjWvAcAKTYjaSkgqsn0vz+hD/LmCAU/Qt7/m8oerAMF39x+LD1hAL2hxJ/aYANyzfx0eNjEvTcCGbLuRCcDITtZ4cXYbacj7G2JOQZSqmdl5OaxlcjL+DMPv1ok55Oj5wW3HHoEdxz+FHJDq3GLGP4ZcVy/v3TDzw68Vf+ysurk+eIdwikXwZ4gPRou52DZL9aJz2dpvBOMMWUr9swPjD5jLh2AYEyu3Mk6AtSWljGE5e9kMDn3gG+z7E7YIJg5ULhvzDByQl7hqofWidXVVLg93Hah6PILSujLBTC63TicjgZdcZw+rdpWyfPNKFF9lCUszO4+9dJj7cJTsXkXwkmdi/42X+kc8uZPSoda9+jLW8tfmbXnxuYiCm4DkzVaRkpkHE9jozLdvkZKrG0Tq7anVjGcM3XX/Db6lWUhUI4RXA7ndx92FGc02/vOnmmiWy0R6YczcBzECKJn4pnTASzsS/x1m0EfMIp3ftXOuZ0Ofi84G1S0nat9ruxCjGbhgDB2JOeIThy3til+6vE216brT25qpKnJv3B+uJiykJ2b2cgEqE0FOSfY7+ts2eKe08k9RTEM6DupnSIl7iL3AB/Wew/A29qgqZLRNaAicQ54YfIisQ8I4FMZBNWyUtYhXdjfF/bPeBKqQZr3LKl5QkuQMQY/OEw9/zyE0UB/w5evXPE2QZJHYZ4D6uTBDf6FKpbmxEKxb5PiAhOVwJSGmszVPczRVbt+v0TzFhlmLKPsQrvwip9C2MVJTukBkXn5KpKvluyuNI+6VutKipkc1kprdISM0+13rn6gjQHU3nqRcDn5NtRLSsd86Z5OfnqYxPzXHdfEIGqAyaShrgb1tacdm/35WDCQBDj+wJKX4ScDxDHbvr/XalG7uu/FpUnuBW5HA5+W72KE3rsnhs1iDgwKceD/zvKp5gB4bCTnz5rUelal9vJASfth9uTgITb2ZH4HSIOcA/Y9fsnkIlswuSdBVYh9jzpFEzJs9DiQ8TVLdnhNQjak6sqcTvjf3I2xuBx7LjigbFKMabhlUoTcSDZL4Fk2yuEJRXwEnGfwZL5XUnLTCUl3Ys31cPBpwzixCuHJua57r3BPRC7ksRWLnDkQOqJCXlGIhhjMAU3RT8EbO29LYPwCkzp68kMTSm1HV6Xi3jjXwJ4nTvuxzIm0GBHbKTZ3eDqCZIGpAGpOLz9mTzhSLxpXlLSvaRmpNBxz/bc+PJViXmmpED6NVWqRQhIir2OpAGxa83nsm0hoB9Mkdaar0B7cpswyxh+W7WSX1auICslhdN792FE3/48O/lP/JFtiapThH3btd/ufu4m8Bum6B57eB4XJvVMpNn/1UmVhJ0l7r2g9QQITASrADz7k+HqxNtLI8wYN5fctVvofWBPOvdO7LbHkv0ypuQl8H0MhMB7HJL5D7sxbSgiK6K9AVUFwP81ZF5X3xEppeJYmLuZrxYvImIsTujRi+F9+vHl4oUxddhFYEin6qsNmPAqTOHtEJpuf+85CGn+EOKsm7UXO0MczaDFaDvG8HJw98Lp2puHvhYWT1vKstmraN+9DXsf2juhU90k/SpwtseUvGhPX/Dsi2T8s+H1jgbGYS9orshAaCbGBBDZtfnJjYEuPGuiwpbFFV9+zpS1aygLh3A7HDgdDp489gQ+mjeXP9euRgCHOMhJTeWDM8+hXWZm3HuZ0HxM3gi27VwGkAIpR+HI+l89/DRqV5nIWszm46lczi3KtReOlompNrE70IVnqqF6cepknp78B6FIBGMMXpeLC/oPJN3j5vkpk3CKAzvXE1475XQGd4j/gd1YpZjNR4MpYNvQvBMcbZBWP9ThPFuVSNbG/cHE65xwIW1mNZn/j9trs5t8T24oEkFEcDma1syNrxYvYvLaNfjC9lynkGURsixu+eE7pl7xN5ZsyWPOpo10yGzGwZ32wLmd348peYnYlah+8I/DRDYjTi2o3tCJswPG1RnCf1F5AnEqpI5IVlhKxbCMIRSJ4HU1rbevVYUFPDXp90qbVvjCYd6ePYNPho/krN79mLh6JeluN0d06bb9zYf834LxU3nuacROmALjIeWYuvoxVCKlng5l71H5/dcF3iObTIK7I02rlahgXXER/zfuB35bvRKAQ/bowkNHHVNtb2Vj8/nC+eUJbkUiwrT16zi40x70bd2mZjeLLCPuRH3xQGQdaJK7W5CsZzFbzrPn5W6tCOE9DEk7J7mBKYVdv/vBX3/hkwXzCFkReuW04P6jhrJfuw7JDq1e/LR8WdzjwUiEsUuXcMOBBzO8T78a3cuElwNx6p+bQIOsIKDik4wbMKGZdglOY+ztlR1t6qTW/O6qSSa5gXCYMz96j9yysvI9yyeuWsFZH7/PzxddhqeaxVeNSXULzDDUvlfb3R/CS7B3Eqt4rwC4Ou9UfHUpEo7gdDX+/8e1Ja4u0Go8BCaAtQnc+9jzmJVqAP7x3ddMWLmivCdzYV4uF47+hC9HXkC37JwkR1f3XA5H3HmnDhHctWyzxd0HI2kx1WYQD7ga3r/5SDiCwxn/52/KxJEGOR9CaEa01vwe0brFTWtkenua5G/iu6V/URIMlie4YNcWLAr4+XHZkiRGVn/O6duPNFfscIbH6WTfdu1rdS9JvwpiFlGlQtpIxJG180Em2Levj+Oc9ldwgncEZ7e7gm9fG5fskBocETeSchSSNkITXNVgrC0qqpTgbhWMRHh1etOYS3xc957EW0LjEgcn9arljpopx4CjJZX7uTzg7AKeg3YhysSa+9tCrtznZk7wjuSUZhfw4s1vEgrGjkA2ZSKCePZF0kYi3iGa4FbRJH8bKwryKY1TV9AXCrO8oKD+A0qCo7t25/TefUhxufA6naS73WR4PLx88mm17skV1x5IzofgOcQu9eJoB5k3Ipm311H0tTf27fE894/X2bKhAGMgf2MBz13/Ot+98VOyQ1NK7cDKwoK4I2wRY1iUl5uEiOpfq/R0Hhl6LF6nk1SXi9Ro2337IYfRJSu7VvcS8SAtPrbndEozkCxIPQfJebfBJEkr5q3mtuMeYPnsVRhj8JcG+OrFH3j8kueTHZrajTTJ6Qp7tmhFutsdk+imul3s1aJlNa9qXESE+48cysUDBvLb6lU0T0nhmG49tr9YYXv3c/dCcna+nqoJrwD/WMCClGMQV/edvlc8b975IYGyyovjAmVB3rr7Q46/ZPfZo16ppqh7dg7BSOzOgW6HgwFt2iUhouQ4dc/eHNKpMz8uX4plDEd16UabjIydupc4spHmD0LzB3fq9cbKB/83YG0B92DwDE7odIIPH/ucUKDye3TAF+S30ZPYsiGfnLa1S+xV09Qkk9yju3ajVXo6gaIiwtHdvdwOB20zMjmiS9ckR1e/uue0oHtOix1fWIes0jeh+AnK5/SWPI/JuAZHxjUJe0bu2ry4x/PW5mOM0bleSjVgbTIyOKnnnnyzZHF5PVgBPE4Xl+27X3KDq2ct0tI4p+/eSY1h2+6IFhCwN05wD4LsFxFJTFqxfM4qrEjsgma31836ZZs0yVU10jDGJeqZ2+nk0+HncvpefUh3e8hwezh9rz58MnzkdktlqcQz4TXRBDeAXdQ6DPih5AVMeGnCntO2S+u4x9t0aaUJrlK7gUeGHsffBg2mZVoaXqeLQ/bowmdnn0uHzGbJDq1JMSaCKbguumjNDxj7z8Ep4BudsOf03K8bDmfs+3EwEKJDz4azYYVq2JpkTy5Admoqjw49jkeHHpfsUJq2wI9Ursu6VcievpCg3tzLHzmPxy5+ttKUBW+ah8sfPi8h91dK1S2Xw8G1gw/i2sENZ2FUkxReEK2xW5UP4/sUSRuekMecc+tpjP/wd/wl257lTfNw9HmHkdWqeUKeoRo/7bZUSSbRr3jHE/fX87CzDuJfb/+Djnu2x+V20aFXO/711nUcfvbBCXuGUko1fkL8jomt5xKjY892/PeX++h/eB/cXhdZrZtx7v+dwT+evzxhz1CNX5PtyVUNRMoxUPyfOCeckJLYXvZDzziAQ884IKH3VEqpJsXVGyQ9tsYuqUhqYnpxt+oxsCtP/HxvQu+pmhbtyVVJJc72kHkb4I1+eez/Zt5ob06glFKqwRBxIFnP2Ykuadh9ZangHQKppyY5OqUq055clXSO9PMwKUdGS4gZ8A5FXJ2SHZZSSqk4xLMPtJoA/u/sEmKeweAeoIt4VYOjSe5uxB8O8cQfv/Hx/LkEwhEO2aMzdx12JJ2a7/6T8MXZHtIvTnYYTULe+nx+/3wyxfklLJy0hBk/zcHhcHDEiCFc+dj5pDdPT3aISjUKxhjenzub56ZMIreslD1btuLfhxzOAR13/w/x4siAtLOSHUaT4Cv189voyeSt38Kq+Wv548upBMoCDDi8L9f872I67dkh2SE2WGLi7RO4iwYNGmSmTm0aWy3uyPzNm/hzzWpyUtM4tvvOb7YAcMHoj5m6bm351pYOEZp7vYy78FKyUlITFbJqxL5/62eevuYVRISAr/LmGC6Piz16d+CFaY/haMKl9ERkmjFmULLjqE/aZm+zvriYH5cvxSHCMd260zp95zZbAHh+yiSem/InvmhtX4AUl4t3Tx9e6+3TVdO0ZMZy/nnUPVgRC39ZAGNty9lEhLRmqbw2/3+0aNd06wZvr83Wntw6YhnDjd9/ww/LlhCMRLCiHyb2bduex445jm7ZObW634LczUxfv67S3u2WMfjCYT6cN5er9ts/ofGrxidvfT5PX/MKQX/8vd/DwTDrl25k5s/z2Pfo5BabVyoZ3pk1g4cm/oJlDGHL4s6ff6RtegaPDj2OQzt3qdW9gpEIL0ydVCnBBfCHwzz5x2+8e0ZiF2mpxscYw91nPEZpYdVFftvOB/1Bxjz3HZc8MLKeo9s9NN3umjo2ZtECfly2FH84XJ7gAkzfsI7TPhjFhpLiWt1vcV4ujjjznfzhMLM2rt/leFXj9/vnk3c4Zy4cCrNizqp6ikiphmNVYQEPTfyFQCRCyLLKi2RtKC3hyq8+Z8KqFbW6X25ZaaW2v6LFebm7FqxqElbMXUVR7vZzhVAgzMIpS+opot2PJrl15IO5c/CF4/eY+cMh3pw5o1b365qVjRWnvXSK0CvJ2/Kq3UMkYrGj6Ukuj4uOe+owqmp6vl2yuNqkNBCJ8NhvE2p1vxapadWe69BMd2lTOxaJWDvsmHB5XHTr37meItr96HSF7cgtK+On5UsxwNFdu9MyLY3VhYU8PPEXJqxaSZrbzXl79+eaQQfgdjorvTZiYvfc3ipsTK17X/du3YYeOTnM2bSxynMMU9atrdW9GivLspj58zxWL1xL5z4dGXBEX13tW8HBpwzilVvfqfa80+Ukp00W+x3bvx6jUipx/OEQ45YtI9/v48COneiR04KyUIgn/5jI6IXzCVuG47r34F9DDqNFWuUkNGyZapNcgKX5W2oVi9fl4qIBA3l5+tSY+64sKKA0GCTd46nVPRuj5XNXMefXBWS1bsaBw/bDk6K/k6269e9MakYKvpJ4O8zZ3B4Xp193Qj1GtXvRJLcan8yfy50//4hT7M7ue8aP418HH8bTU/6gKBDAMobSUJAXp01hcV4ez554cqXXn75XH+Zu2kQgEo65t1OEPVu0rFU8IsKx3Xowb/OmmAZzxob1LMrLrfU9G5Pi/BJuPuJuNizfRCQcwely0q5bG54Yfy8ZWVotAKD1Hq245MFzeeOO94mEIpjokKwxBqfTyUEnD+IfL1yBs8oHNqV2B3M2beSC0R8TsSwilgGBU3rtxaK8XBbkbiYYXc/w+aIF/LFmNT9ecAle17a3wGO79eC5KX8SCce22QAdM2vf+3pM9568On0aVpUdwgKRCJ8vnM95/fep9T0bC8uyePSiZ5n42STA4HS5cHuc/Oene+i6t/ZMAjgcDu748Cb+78SHsCyLoC+I0+XEio7K7Tm4B9c/fwWt92iV7FAbLJ2uEMe64iLu/HkcgUiEsnCIsnCIQCTCgxPHUxYMVkoy/eEw45YvY2VBQaV7DO/Tj/3atY87j9bjdHHJPvvVOq75uZvj9jQ4RViYu7nW92tMnrv+DVYvWoevxE/QH8JX4mfVwrW8cOObyQ6tQTnrxmG8OP0xzr/zTM68aRhDTh9MZnYGGdnptO3WmpR0b7JDVKrWLGO48svPKQoEKA2F8EfC+MNhvli0oFKCCxC2LPL9Pr75a3Gle/Rs0YIr990fV5zKIikuFzcddEit41qcl4vbGXs/XzjEzI0ban2/xmTcqAn8/vlkgr4gQV8IX7GPorwS7jrtsR1Oq2pK9j60N+8se5bLHjqXs285haEXHEaL9tmkZ6XRpnMrMnN2vvpHU7DDJFdEOonIzyIyX0Tmicj19RFYMn275C9MnL25LWMIWrHTEDxOR8xCArfTydunn8WLJ53CXi1a4hRBgD6tWjPqjOF0zsqqdVy9WrTAE6eXzQBdmtf+fo2FMYZfP/6dcLByD0w4GOaXj35PUlQNV6c9O3D2rafx2+jJ/DlmKsVbSijcXMQXz37HrUPv0zcYtduZu2kjJcFAzPFAJEKoQoK7VVkoxOxNsUnmDQcezJhzzmNIpz3wOp0I0Do9nQePOobje/SsdVxdmmfF7ehIcbma/FqKr1/+AX9p7P+zgk2FrJi3OgkRNVxZrZpzxvUnkbt2C+M//I3ctVsoLShj4qd/8rf9/0X+psJkh9hg1WS6Qhi42RgzXUQygWki8oMxZn4dx5Y0YSsSt8e0urf+sGXF3ZDBIcLQbj0Y2q0HEcsibFmVhsdqa2S//rw2Y1qlXgm3w0H37Bz6t2m70/dtDKxI/DnQkWqOb09hbhGTv52B0+lg8In7NsrpDhM/m0T+xkLCoW1/l0KBECvnrWb2r/MZcHjfJEanVO2ErEi18+/jtdupLhfdqynjuFer1rxz+nCMMQQiYbxO107P7R/coSPtM5uxoiCfULSDRACP08lZffrt1D0bi1Ag/rQQcUhMh8WOBP1BJn87g5L8UgYc2Zd2XdskIsQGZcOKTUz8bFKlEpCWZQiUBvjyhe+58O6zkxhdw7XDnlxjzHpjzPTon4uBBUCj3l7j6K7d4w5ZxeMUoW+r1uzVcvtzYpwOxy4luACt0zP44Mxz2Lt1G5wiuB0Oju3eg7dPO6tJL7ASEfY7dgCOKsOCDoew//H71Ope373xE+fucTXP/P1V/nf1y4zocCUTPv0zgdE2DIunLom7mCEcCrN05oqdvu/SWSv49rVxTB83ByvOqIdSdaF/67Zxe0yr43W6OGXP3tu9RkRIcbl3qW0VEd4/82yO6toNl8OBU4QBbdvx0VkjyE5t2hv4DD3/ULxpsYvMPCkeug2o+ZzcxdOWck6HK3n84ud47vrXubzvjbx0y9uNbkRq+ZxVuL2xm0kF/SHm/744zitqZvOaPMa+NZ4Jn00i4IvtWd/d1SrrEpEuwEBgUp1E00D0yGnB5QMH8dqMqQTCYXtxTjXXel0uXjvljHqLrU+r1nwx4nz84RBOccRUdWiq/vHcFVx7wO34S/34SwOkpHtJzUjl2mcuq/E91i/fyDPXvhb9pLzt0/IjFz5Dv0N7k916998+eauOvTqQku6NGS50eVy061b7XpBQMMQ9pz/OrF/mISKICDntsnli/L1NeiceVT/cTif/O+4k/vbNGCzLELQiCPHbbQHeOu1MmnnrZ/55TmoaL5x0KqFIhIixSHHt/K6XjclJVx3L+A9/Z/mcVfhK/Li9bhxOB//33g01XvwaiUS4Y9jDlOSXVjr+1YtjGXjU3gw+YWBdhJ4U7bq1IRyK7eF2uZ3s0Xvn+h3fuf9jPnh4NA6nA4dDEIeDB7/+P/oevOeuhttg1DjJFZEM4FPgBmNMUZzzVwJXAuyxxx4JCzBZbjpoCMd278FXixdSGgrx4bw5hOP0TDXzesmsp8ayIm0oK2vTuRVvL3mGcaMmsnzuSrrt3Zmjzj2E1Iya95b88tHvWOHY+Xsi9vD+yVcfm8iQk+rIkUN4/Y73CJQFy3s8HE4HmTkZO/XG8NHjY5g5fh7BClsFb1i+kUcvfIbHfrgrYXErVZ0junTlxwsu4bMF88nzlTF6wXyK4szTdTkcdGxW/x9Y3U4nbrRTYiuP182Tv97HpK+mM33cbFq0z+aYC4+gZfua7wa64M+/CJQFY477SwN8/fIPjSrJ7dK3Ez337caiyUsIVZjO4fK4OP0fJ9b6fnMnLuDDRz+P2QHzjmEP89GGV3B7GkeOUaMkV0Tc2AnuKGPMZ/GuMca8DLwM9j7oCYswifq1bkO/1nav1h9rVrEsP7/SebfDyYk9eiUjNBVHakYqw646pvx7E8nDKnwYAuNAUiB1JJJ+MSLx/9oHfcG4c3itiCFUzVa4daloSzHv3Psxv37yJ26Pi+MvO4qzbzkVj9eNZVl8+eJYRj/1DaWFZex//D5cfP8IWneqWRm59GZpPDXxAR6/5HkWT10KwIAj+nLLG3/D6ar9G/E3r/xYKcEFiIQt5kxYQGlRGenNqi+Mr1SitM9sxrWDDwTstRLxOie6Zec0+akCDYXT6eTgU/fn4FPtbemNCWKVvAhlHwEB8B6HZF6HOOKPBgX9IbsXIo54i9rqWiQS4fOnv2HM89/jK/Fz4MmDuPi+c8hpa8c/5fuZvH33h6xfvolu/ffgkgfOpfcBNV/Q+MBXt/PU1S8z8bNJWJahY6923Pjy1Ts1+vbtaz/FtNkQrTf/01z2P75xfEDYYZIr9oSk14AFxpgn6z6khumJY0/k/M8+JmxFCEQieJxOjDF8vnABG0tLufmgIXTJ0mHZhsJYJZi808HKxV47CZQ8jQnNRrKfjvuaA08exEf/+ZJAWeXGUQQOGLZvHUdcWdAf5LoD/49Nq3LLF2F88PBo5k5YwCPf38nTf3+FH9+ZUB7ruFETmPzNdF6d91+yWtWsl6rTnh14+vcH8ZX4EIeDlLSdH5EIBqr/EFDbRSRq+0TkeOApwAm8aox5pMr5m4DLsf/ibwYuNcasrPdAk+ymA4fwy8rlbPH5KAuF8DidRCyLDSXFnP3JB9x04BAO7Ngp2WGqCkzB3yEwCYiuF/B9iAmOh5ZfIxL7waTvwb0wcUZYU9K9HH3eoXUbbByPXfQcv30+qbx3eeyb45n09XRem/dfpo6dyX8ufb783Ixxc5n/xz088v2d9BuyV43un94sjf977waCgRChQGiXOg/8ZQHiTVu2IlZM7+7urCarq4YAFwBHicjM6Fft+8Z3cwPatOXniy7j+gMOpl+r1hhjCFkWW/w+vl2ymFM/eJe1RTGzOFSSGN9nYBVSnuAC4IfAeEx4edzX9NqvO8dfeiQpaV57XqlD8KZ5GH7LKXTo0a7OY144+S9uO+5+zm53OVcOuJncNXmVEsSAL8i83xcx6dvp/PDWL5WScSti4SvxM+b572v93NSM1F1KcAEOPeMAnK7Y5iQcDPPbF5N36d5qGxFxAs8BJwB9gJEi0qfKZTOAQcaY/sAnwGP1G2XDkJ2aytjzL+aew4/iiM5d7U0ijKEwEGDqurVcOuYzfl6xLNlhqigTml85wQUgBJEt4Ps67mu8qV5ufu1veFM9uNz2CFRqRgp77t+Do86tfV3j2tq8Jo//XPY853S4kot6XssvH/9eafpEJByhtLCUb1//iZdufjtmakWgLMir/3q31s/1eN27PDp2xNkHx1345y8N8NMHvzWahcM77Mk1xkzEnqvf5LVMS+P8/vvw9OQ/ysvBgF0/1xcK8cLUSTxw1DHbuYOqN8EpgC/2uDghNBdcXeO+7O9PXcoRZx/M+A9/w+F0cvR5h7Ln/j3qNFRjDF++OJYXbnyDcNCeE5y/MX7dQ2MZJn8zA7fXHfNpO+gPMfuX5FT2u+jec/j2tZ+A2IbxqWteof9hfenYs+4/KDQBg4ElxphlACLyAXAqUP4/3hjzc4Xr/wTOr9cIG5AUl5uz+vTj9ZnTiVTptvKHwzzw63iO7NItSdGpSkLziJ9qlGFCUxHOivuyw4cfRPd9uvD9mz9TlFvEASfuxwHD9q3znRvn/bGI249/kECZHytS/QzNQFmQmePmkL+xIO75JbtQzWZXHHza/rRon8O6JbH1oid++ifj3t2XYy48PAmRJZZu61tLy/K3xC0vFjaGKevWJiGi3cu8TRtZXpBPrxYt6VWX2xC7ukLAA1Sdc2TAWf1KVBGh3yG96XfI9ssLxZO/sYDZvy4gMzudAUf0rdHc1i0b8rll6H2sXrgWY+14KrvD5aBrv05xV9k6nA469kpOIhkORaqN3wpbjHv3Fy66d0Q9R9UodQAqVspfAxywnesvA76t04h2A39V2axnq+UF+VjG1Kr8WFOztriImevX0yo9nUHtO9Td78rZHsQRpySGF5xdtvvSjj3bcdmD59b6kQFfgOk/zsGKWAw8em/SMnc8VzscCvPQuf/j9y+mEAnvuLfT5XaxR5+OzJm4AF9xbNnGlu2TM83R6XRSWlga95wVsRj97Dea5DZFbTMyKm3GsJUAnZvwrmM7UhIMcvEXn7Jg8yac4iBsLPZv34GXh522y/WD45G0kZiyt8FUTHJddkPqTvyE+lEPfsp7D36K0+UkFAxjhSO06JDDSVcOZfjNp+BJiR0WAnj4/KdZu3h9zRJcp4NmOZmccPnR/PjOryyaurTSdAa3180ZNwxL2M9UG0FfEHFU/+bnK2l89RcbOhE5HxgExH2namwVcbanRVoam0pj39CzUlI0wa2GMYZ7fhnHR/Pm4nY4MRhyUtN474yz6dCsWeIf6DkIHC0g4gcqvMeKC0mN34u7K6Z8P5P7hz8BApFQhFAgRGZOJoeeeQDn33kWLTvE35Hukye+ZPI3M2qU4AK4PE5O+dtxeNM8fPLEV5WmmXnTvJx3R+J/tpra3txbf5w66rujmu14oMq1Ts/g8M5d8FYZCvG6XFw1aP8kRdXw3Tt+HHM3bcQXDlMSCuIPh5m8dg1P/vlbQp+zYt5qfv3kD1YtCiPZr4OzM+AB3OA5EMl+BxFh5s9z+edR93Bu56u554zHWT6nZutyLMti9q/zmfDpn2zZYFfbmPnzXN5/eDRBfwhfiZ9wMIxlGTavzuO9h0Zz6zHxt8ot2lLMvIkLicQpW7aVOAS314XL46LvkL148tf7cDqd3P/lbQw+YSBujwt3ips2XVpx3+e3ssdeydmnpW3X1jRrEX8PdafLwUGnDKrniBqttUDF1VIdo8cqEZGhwL+BU4wxcT9hGGNeNsYMMsYMatVq+5vZ7O7+NugAUqt8mE51ubhqX22zq/PFooV8Mn8egUiEklCQ0lCItcVFXPX1Fwl9Tt76fCZ8+idzJy6CrHfAsz/gBjzg7I7kvI04W7Jm8ToeOvd/nNv5aq4f8m8mfTO9xs9YPncVv37yByvn24Mgxfkl3Hvmf/CV+PEV+wn6QxgDRXnFfPf6T1y9760UbI4/ZezLl8YSiFOVYCtxCC63E7fXTfvubXnom3/TrmsbLrhrOKddezzeNC/eVA/pzdO45IERSe0tPXDYfvFPCBw5ou7nNNcH7cndCf897iT+PW4s3y79C0HI9Hq47/Cj2a9do94IbqdZxvDlX4tiesADkQgfzZvD7Yfs+j9yf1mAu059hPl/LMbpchIJReh3yF7cO/pLPN4SEC/iyATg10/+4LGLny1fBJC7ZgvTxs7iyV/vo+e+1c/PW7d0A7cOvY+iLcWICKFAmLNuHsa6vzbEVGTYKugLsmzWSmb+PJeBR+0dc257vZ/eNC/Dbz6ZU/5+PC63k8zsDNb8tZ4Jn02iQ4+23Dv6VsqKffhK/OS0zUrornfGGH5+fyKf/PcrivKK2f/4fTjvjrOqrWEpIvzfqBu47bj7K20VLAIHnzaY/odVXRuldtIUoKeIdMVObkcAlcZpRWQg8BJwvDFmU/2H2PBc0H8fioMBXpw6Obplu3D5vvtx5X6a5Fbn7dkz8IUrT4uyjGHpli2sLiyMu5V9bRhjePW2dxn99Le4vS6MZWjeqhmP/fg4bTunggkhTrs3dc3idfxt/9sIlPrLOxDuP/tJrn7yIoZdWf06GF+pn7tOfZQFf257X9j7sN4cfOr+1VUeIxK2KCvy8fnT33Dx/SNjzgdKq09wnW4new3uyYNf306gLEB2myyK8or57fPJZGSnc8mDI7nw3nMoyi0iq3VzXO7EpmDzfl/EO/d9zOqFa+k2oAsX3j18u+9pVz52AVO+mxmzmUabzq0444aTEhpbsmiSuxPS3G7+e/xJPBAMUhQI0CYjo0EPeVnGMH39Ogr8PvZp256WaTu/KjNiWUxdt5aSYJBB7TvQPCWlRs8PxZniAeCLM7d0Z7z8z7eZ99uiSsMvsycs4NXb3+PvT11afswYw/M3vFFplasxBn9ZgFdvG8WjY++Me39jDP8+6WE2rcqt1Cs7+qlv6Nir/XZjC/iCLJy0JCbJbdE+h5x22WxYHpuHOJwOTvnbcZx/11k4nU57R7EzH2fKdzNxuZ1EwhY9Bnbhwa//r9KOYsYYFk5eQv6GAvYc3GOndxt74473Gf30N+W1Jr999ScmfjqJV+Y+WW2JsgFH9OXNxc/w1t0fMPe3RWS1asY5t54WfUNJ/L8PYwwLJv1FwcZC9jqgR3ktysbMGBMWkWuB77FLiL1ujJknIvcBU40xY4DHgQzg4+jvfZUx5pSkBd0AiAh/3/9Arth3f3LLSmmRmlYn06QS6a+8PJYXbKFHTgu6Zdd8g4R4FmzexOqiQnq3bF3j5LQkEP+DuwiUhqpP9Gpq4ujJjHn+e0LRclgQTUpPeZRX5lSuVvrW3R+WJ7hbBcoCvPqvdzn+kiOrTRZfir4vhCqUOJz9y3yCvhBWnJroW4UCIWb+PC/uuQNP3o8f3/k17gjc/scP5NY3/056szTSm6XxwaOjeefej3F5XBhjSGuWxqNj76Rz746VXrfmr/WsnLeaDj3b0aXvzpW1m/L9TO494/HyXubNq3OZMW42j469q9odzFp2aMG7y57j4ye+ZMInf+JwOTj+0qM5+epjqp1it6tWL1rLqgVr6bhn+5jfQ11o2P/KG7h0j4d0z47/IpSFQrw6fSpfLFqAx+lkRL/+nLf3gLgL2BJtZUEBF3z+Mfk+n937GIlw9X6Duf7Ag2t9r0V5uVw0+hNKQyFEIBSJcOuQw7hkn+3XkHU5HOSkppLni6120C4zs9ZxxPP9W+Nj5heF/CG+e/2nSkluSUEphbnFce+xaMoSADat2sy7D3zKjHFzaNk+h3P+dRpturQid21ezLQDf2mAcDAUd4vcrTwpbgo2FbJk5nJ67LOtqoOI8K+3ruX2Ex4kHIoQDobxpnlIb57OM388SOs9tg0hv//waKZ+N5OgL0gw+mtcPGUpT//tVW5/9x+AXc7mX8fcR+7aLYjD7mk+5e/HcdXjF9YqySzaUsyn//2q0u8zEo5QXFDKO/d/wnVPV79VcpvOrbj1zetq/KydtWnVZm495j62rC8o/1lPv/5ELn/4vDpJqBsSY8w3wDdVjt1V4c9D6z2o3YTH6aR95o7nkxpjGL1wPm/OnE5xMMix3Xpw9aDB9bKJRFkoxFVffc609etwORyEIxYHd9qD5048udaJeVHAz8VffMai3M04HQ5CkQjHdu/JE8eesMP3n+45OSzJ3xJzPBSJ0DMn/nzV2vj8mW9i20wD65ZuZPWitXTac9vI6NzfFlVKcLeKhCNsWpVLdtssPn3yS8aNmojL4+TEK4Zy8tXH8uPbv1RKcMGeh7p42tJqN5EAcESnic0aP49+h+5VqVLDJQ+MZMp3MyktLCNQFsDtceF0Obnvi1sZeHT/8utmjZ/Hu/d/StAfKm9L/SV+bj/+Qd5d/hwOh4NgIMQD5zzJtLGzcHlcREIR9jqwJ/ePuY3U9B13IFX03D9erzSNwhi7ssP/rnqJl2c/UW27mN48nYvvG8HF99XtwuCgP8i9Z/2HmT/Pw+V2Ei4fbb11l0tYbo/Oya1joUiE4R+/zwtTJ7G8IJ9Febk89tuv/P2bMXX+bGMMl475jHXFxZSGQpQEgwQiEV6ePoXxK+LXiq1OxLK4aPQnbCorpTQULL/X479PYMb6ddt9rWUMRdX0CmyJk/jWljEm7s4tYE9jqCg1IyVuPVeA7DZZbFqdy1UDb2Hsmz+zYfkm5v62kAdG/JfvXh+Hwxn/de5UL1333iNuzUGwE+FvXx/HjYfeyd/3/xeFudvqKfc7pDevzv0vZ900jEPPPJDLHzmfNxY+VSnBBXsv9qrzwELBML9+8kd5pYV7znictUs24CvxU1bkIxQI8fVLP/DLR7/Hjas6y2atxO2N3dIxEoow5tnvePj8p7Y7j7g+3Hnqo6xfurHSzzrmue+YOFpr8qpdd88vP3Hnzz8yd/MmVhYW8OasGZzywbuUBHe9B3NHHpowninr1uIPhykJBvFHwvy2eiVP/lH79Qu3/TiWeVvXQkTb7B+WLeHV6VN3+Nr1xSVxjztEWF8Sv6OgNjatil/xIhQMUVZU+X2hVcf4PdmRsEV68zRuPPRO3n94NGsWr2PF3NW8dvso7hv+RLWb1ISCYU6+5lhS0uMnV5Zlj4jddeqjjOhwFUtmbHu/bNEum9fn/5dLHxzJoWceyPB/nsJrC/5XKcEFGPP8dzHT2IyBkoISFk62O1Tevucjpv0wm6Df/pkDviDzf1/M89e/ETeu6oRDYdYtWR/33Ip5q7lmO3OM68vr/36fmT/NJegLUlbkI+gLMnfCAl6+5Z06fa4muTtpTVEhPy1fxtItedu97odlS1lZWECgwnC9Lxxm4qqVzN20sU5jXJSXy4aSkugctG184TBvz5pRq3tNXbeWslBsgxEIhxk1Z9Z2X2sZE7O15lbByM5PV4hYFpPXrmHCqpXVzm11VOmtcLldnHTFULyplRPSlDQv5/7fGbz/0Gf4iv2VVs4GygJ888q4uBUQPKkejjznYJ4Yfy83vXw1+w7tT0Z2Oq7op/utn559xX78pQGWzV7Joxc9W+kebTq34rKHzuOuj2/mtGtPIC0zlcLcIiZ9M51FU5fa0ymq6SW2Ihazxs/jxsPvYvG0pTFDcP7SAKOf/ibua6vTskPOdncp+/XjP/j4iS9rdc9EWrtkPWsXr4/p2fGXBvi8lj+rajoK/X7Gr1jOjPXr4i4E3Wp9cTEfzptTaT5qyIqQ5yvj4/lz6jRGYwyfLZgfd/3CB/Nq92x/OMS45Usr1XS3j4d5d87MHb6+LBw/ofc4nbuU7C/I3cxPy5cRkfj/D4xl8FTpMDj3/87EW6W3z5Pi5rCzDmTW+HmsW7Kh0shToCzI9B/n0H1A55gOWxFhnyP6cvV/LuL+MbdxxDkH06J9Nk6XE0/Ktg/3/tIAZcU+CjYV8q/j7q9UtjG9eTpnXH8Sd318M5c8MJLWnVoS9AeZ/uNsZvw0h1AwRElB/PJcIsLqhWt47OJn+ejxL2I6aEKBEONGTajVZgxOl5PU7ZQ/Wzp7BQ+O/F+N71cXvn1tXNza7mPf/Hm7/x53lU5XqKVQJMLNY7/lh2VL8DidhCyLfdu256Vhp8adujB57eq4yaFlYPr6dfRrXfs9p2uqJBjEWU3yVxioXXmQkmAwbp1uAxT4t38vl8PBgDZtmbmxctFphwgHd9q50kXzN2/iki8+pSwURjC0NqbGO5Zc8dgF9j+ut8bjdDkxwPl3nMnQCw7jvYc+i9tL6XA4GPnv03n33k/sEmERi5R0L227tGbY1cfi9rjpcFxvyrILKNzcns5pzSh88g+K/1xd6T7hUIQZ4+ZQUlBKRlZ63Pjeue9jPnhkNC6PGysSoWXHFvQ7ZC+mjZ0Vk9i16tSCu097bLurfUsKy2r4m7F17NWeHvt2Y/4fi+Im9uFQhC+e+44R/zqtVvdNlLIiH45qahCXVFP3UTVtr06fyhN/TMTtdGIZQ05qKm+ddhZd42zFPmfTBjwOZ0yi6Y92TlyyTzUr0hPAAIFqPvj7w7XbajUYicSWnI0qDe74Xsd378nLhVNjfg9up5MeOzFdId/n45Ixn/FXXi5Oh4PsYBnxxr48qR6CVXYGO3DYflz1nwt47fb3iIQjRMIWh555IDe+fBWv3jYKX5xyV8YyDD5hX9Yt3UgoECYUCOFJceNJ8fD3p+0pbB0P6AaugeSe0Jxmbg+t/sxj+bMTY7a7DQfDTP9xDoNPiF9+ctI303lw5H/LOzUcDgfHXXIk839fHDOaGA6GefYfbxD0BastHRkO2e8xVTtpqiMinHH9SXz46OeE4nVQGJg7YQGFuUU0b1kH5d9qoLr3qGAghDGmzqaZaZJbSy9MncSPy5cSiETKe2enrl/LPb/8xOPHHB9zffvMZnidzko9uQAuh9A2I37JpUTp17p1TC8uQIrLxQk9etXqXoPad4i7eCzV5eaEnju+14NHHcPZn3xIyIoQjETwOp2kuFzccdiRtYoD7Mb7gtGfkO/fNqTVrEsGKctLYhLdfofG7gnucru4/oUrueKxC8jfWECrji3KJ9l7UmOH6cEe3jruoiM54IR9+fLFseStzeeAYftx9HmH4E31MmvDes797CMCkQiWMawsKEDObEebvELS/qq83bPDIfhL/XGT3D+/msaHj31RaR7XuiUbEIG05ml270CF/6X5Gwq2W+vQ7XVz6BmV9wqIhCPM+30RVsSiz8F74okzNeG+L25lZKerCPri37usqHaJcyJ16dcJR5wPb54UN4ecfgDfv/kzY57/nqA/yJEjhnD6P04kNaPu51KqhmnSmtX898/fKrXZvlCIiz//lPEXXRbz5tomPQMrTnroFKFjs12rKLAjDhEGtm3H9A2Vh54FOLBj7RYkNfOm0Ll5FkurzKt1iHB45y47fP3l+w5izOKFbC4txRcO4xTB7XTy2NDjdmo9yY1jv2HB5k3lPcuu/tlkry3FUeVX7XA66Dagc8zrT776OI6/9Cg2rcolq1Uz0punl18vDolJGJ1uBz3368brC57iq5fGsmT6cnoO6s7JVx1Ddpss8n0+hr33Nvl+37aYukDmMe3JGVt5Cp4xpto2L299PvcPfyImifvqpbF06NGOFfNWVxphc3qc+EsC262N3mtQ90qL6YwxLJ21gvyNhey5f3ea5cSuZTn/rrP47fPJLJ+zqtr7lhX7kpbk9j+sDzN/mhPzAaLvwXsy+5f5fPDo52xauZn+h/fl3P87PWbK3s7S6Qq19O7sWfirlFUJRiJ8uXhh3CH503v3iWkQBEh1uzkiwdtJbvGVMWHVChZFd/dJcbm5/8ihpLhc5dUfUl0uWqWl0zU7O24Pc3Wap6TwryGHkepylSeSqS4Xe7Zsycm9YhPJqnq3as0PF1zMZQP348guXbl6v8H8cMGlcXtSduS31StjEu5wTvy5VW27tK72PmmZqXTo0a48wX3/4c9YvSh2frHb62K/Y/qT3SaLrnt35h/PXcG9n9/KiZcfjTfVfu79v/6MLxwu/1BhAMvtIPes2O2Ds9o0p0U1pbhGP/1NzDwuK2KxaVUuOW2zYq7fXoLrTfPQsn02Z964bYOIORMWMLzd5dx5yiPcffpjDG9zWdx6k81yMul9QPUfXgYevXe15+qa2+PmxpevxpvmKZ8n7U3z0qpjC1YtXMuz173G4qlLWTF3NaMe+JTrh9xBqAY9V6pxemf2zJhSWAbI85UxO86Usf5t2tI+MxNnleTX43RyYf99EhpbIBzmj9WrmLpuLZHo+8f9Rx1DutuDO/q+4XE6SXO7OanHnmwqjT9PtjqPDD2WVJe7/F5ep5OslBRuGXLoDl/bzJvC1yMv5NYhh3FUl26M7NefL845n6Hdar/NeaHfz5+rV1eaOhFqlUK8GQvNW2Ti9sTvbHB73HTo0a48wZ31yzy+eumHmIRRRPCmejngpH1p0S6bi+45h/vH3MaFdw0nu00WAG/NmkFhwF8pprATCo7pSCS18khROBRhwBF948b08/sT4w63iwjZbbNiPpD7ivzVJrguj5PUjBRueOHK8mO5a/O4Yu+buPHQO3lgxH8Z2fEq3rn/45jXOp1Ojjr3EKSarC41M5U2nZNXD/vaZy4jrVla+XoPt9dFWmYq+x07gDtOfphpY2exetE6vnv9J67a5xbWL0/MdE7tya2l6kqnhC2LsBWJSWhbpaXz1mln8Y/vviLf58Myhi5Z2Tx34sl4ErS3tjGGJ/74jddmTMXjdBK2LLpn5/D6qWdy+l596JXTgndmz2RlYQELcjeTW1bKjd9/Q8SyuPeIozmrT78aPefiffZlQJu2jJoziwK/nxN69uLkXnvV+Odom5HJLQdvv3EN+oN88dx3jBs1AbfHxUlXHcuxFx1eadimKBDAVOlpSZ+TH3e6wsTPJnHL63/fYWylhaW8e/+nhOIkjW06t+L2Uddv9/VzN8cvRxpqnYI73UOoNIjT5cTlcXHzq3+rdmimaEv8BR0Oh4N1SzfE2fIyvtSMFC55YATHX3pUeS9maWEp/x72UMzWkvcPf4I3/3ompgbuObeeyvw/FsesTnZ5XFzz5MU1C6SOHD78IPbYqz1fPPc9m9fkMviEgfQ9eE+uH3JHpcQ/6A+xftlGfv34T44+b8dv7Krx2eKPv7jVKUJRnGlbIsI7pw3n2m+/ZO6mjTgdDlJdbh475ji6J6CqwFY/LF3CTWO/QUQwxh5he/WU0xnQpi0/XHAx78yeyeyNG1hekE9eWRkPThzPXb+M46zefbnvyKE1Klu5X7sOfHfeRbw9ewZLt2xhv3btOXfvATWuEpHu8XDRgIFcNKD6XSKNMfz6yZ98/sw3lBaWccgZB3DmDSeVJ6IAJaGgnexV6Adq9vumuG12weZC1vy1no49d7xF+bPXvRZ30bEnxc1/f72v2mQZYMKqFTEjrABOBLpnwdw8RMCT6mXk7aeXJ8dVFeeXxl3gFg6FmTthQaW64dsjIpx67QmcecMwWnXc9vfsrlMfZfWidZV6gz967At6DuwWs5nDsRcdwbv3f1KpROZW/3r7uhpPf6gLe+zVgdfm/48vn/+OxdOW0WNgV068YijX7HtrpXgj4QhlxT7eve8Tbnljx+/dO6JJbi0d3KkzPy1fGpNr7NWiJSmu+P+g9m3XngkXX8HKwgLcTicdalDCpja+XbKYN2ZOrzQctzB3M3//ZgwfnjWCvq3b8OBRxzDkjZcpDgQqxX7X+HH0adWaPq2q7/GsaGC79gxst/26sDsrEo5w8xF3s3zOqvKhnxXzVjNt7Ez+/f6N5dcd2KFT5V5zY5BQ/En61S3YqmrprJW4vS6C/tjGIb15+g73NG/uTWFzWex80DS3hwvuOIvZ4+fRoWc7TrvuxO023oecPphV89fE9NAaY3B53ISDcRpMoVLym5Lu5YYXr+SocysndRNHT46bJFuW4ef3f2P4zSdXOr7/8QP52/8u5qV/vh3dqtii457teOibfye1R2Crrnt35oYXt/V4fPfGz3ErYPhLA8wYN1uT3CbquO49mbVhfUxvbsiyGNg2flvWJiODj4ePZGNJCaWhIF2yshNaC31NUSHXf/91pVHB0lCQC0d/zKTLry7vEPj7N2PILSsjWKFtH71wPr1atOTC7SSeFXVq3px/H3pEwmKv6pV/vcOXL4wtb2vX/LWen96byAvTHysvg9U+I5Msbwobwtt6op1l8eceO11OSvJ33GNtWRYr5q6Oey4cjuywfnn7zGbM3LA+pkl0pbq55ObTWfTJdNKbp3HSlcdsdzOb/Y8bwGf/+yrmvcbpcsZvr+PwpLg5/OyDufo/F1U6vnbJelYtWBt3QfFnT30dk+TmtM3mke/v5IERT1KwsQjLssjISuf2Udez/3H71CiWutSiXXalDTbW/LW+0oK+rayIxcyf5ybkmTpdoZb+fejhZHq95b2XboeDNLebB4+qftcVsD+ldcnKTniCC/DajGn4qixKCBvD7I0b2BAt9TJ57RrKgqGYf9ChSIT3dlAdob788eVU/r+98w6Pqkob+O9MyaRCOgmEFnrvvRcBUQQbgtjBssWy6lrWLa5ucT9d19V1FVFcRRcXBRSkiV2UYgi9Q6ihBdLL9PP9MZOYydxJJsmkTc7veebJ5M6597zn3Jlz33POW07sP+Nh22QusrB5VRoZu39Ku9sqMpJ7Bw39KU2nEFg7aPdr71FVm1IAxCZFa0YUEAIS28VXef6CgYO80oaGGgzc1m8Acx+/lr+u+y2/fHl+lasTs+6fTnxKXFn0B6ETmMJD+MUrd2k+ZIVO0L5nCkaTkbCoMEzuKBET5nqnZCzMKdJso81i87mCfPW9U1h+6W1eT3+eZecXsXj/Pys1AWlIYpOiERorFcYQAwltq76HiuBkds/etGsZXfb7FLhMrZ4cNZbIKuKct4qMJDUmNuDJfpYf2IdDY8vaKSVfZGQArni5n2cc83L8KrHbeXun/ylt65LL53L45F/rPRQ8m9nGpczLbHznm7JjQgj+NnkqYQZDmRmIuV88Uiuco4TUfh2qrFsIQXgL7cWHqBhtp97yzB8wyCvusEGno1tcPLfePpU/rX6SJ997sMpsjb1GdWfolQM9wpGFRpiYMGe0z3Pj2sQQGmEiLCoMY6iRkTOH8uBrd3uVK8wpQm/U3iktH4qyPL1HdWfpqYW8ufdFlhx7lRWX3m4UCq4WLeIiPSIZlSe2homMKqJWcqtJh+gYNt5yJ0t27yD9/Dm6xcVzR7+BtU5xWBt8RTcw6HTkWywkRUaRazFrRkdwSMnlEm2D+hO5OVwoLKRbfDzRobVz3DmWfZlLxcX0TEgkyqRtP7vzq32YtbxkkezddJDUvj85Izw0fCRD2rRh6d7dFFqtjHihP2vu+A82ix271Y4hxECIycgvX7nL63papHRtTce+7TmanuGxvRQSZuKGh6+u5EwX8wcM5mJhEe/t2YlRr8fmcDCrWw8eHjHKr/pLiWgRzuvp/8f6xV+y5dPtmMJNDLtqEONnj+KDv35McYHn1quUkrlPXMuQaQO4fDab5E5JPgNrD5jUR3OlMzTCxOAp/XzKFGIy1jgLT30y6Iq+hEeFYi40e9jI6Q16rpw/sQElUzQkYUYjK2+6meUH9rPh2BHiwsK5tW9/BtbRjpQ/5JSUYHN6r/I5pHSN1biUXF9mTQU+4o5fKi7maPZlUlq0qLWT3MWiQjJycugQHU1SpHbSngNbDmMIMXjtPFmKrWxbl841P59admxM+w58MucW/rMznZN5uQx6ZDBpB5aRez4XizvFeUiokfv/vUDTGbYiru39aax4aY3Hdrcp3OThh+CL/knJ/HXSFP7w1Rc4pBO700m/Vsm8On1GledWlOOpDx7iu+Vb+XyJK/lE37E9mbZgEkv+uIwdX3qHfuszpgePvPlzzh07T2xyjE9nsI5922vuvhndTraVyVTVSnZjoEVsFMOuGsjWNekeZnGmcBM3PTYzIHU0CiU3Mz+fzIJ8OsfGEhtW85Sz9UVCRAQPj/BeKQs0TinZe/ECTinpndjKp0frxI6pvLtrh1c8RKNOX5YOckjrFK8VAYBwg5ErKjgS5FvM3PPpJ+y+cB6jTofZbic5MorIkBAmdkzlzv6D/LbpyiouYsGqlRzNvoxBp8PmdPLg0BHcO3ioV9n4NjEYTUYvG1C9wUBMK+8Be1Tb9oxq+5Pie+2gvqx4ea3Li3ZQKtc+MJ3EaqzgPbvqcZ658UUObTuC3qhHp9Pxi5fvoucI7ZSI5dEJwVNjx/PAsBGcyc+jdVQLv1IeaxEWGcbwGYNZ/fpGsk5fYtfX+3jtobexVwhtZm9pxBYXysr3vmLSvLG0iKs8e1xq3/ZMmDuar//3fdnKS2iEiQGT+lS5WtEU0Bv0/P3rP/L0dc9z7tgFhF5HWISJJ5Y8EDBPXYWLPLOZQ5cvkRQZSbuW0Q0tTpWEGozM69OPeX18T+YCRUZOdtmE3tdK8bgOHfnowD4v518pKQurGBcWRmJ4BGcKKkRnQTCmQnQEp5T88ZsvWbZvDyF6AxaHndjQMKLDwuidkMh9g4f6nRrY5nDw+BcbWHfkMCF6A1aHncmpnfj7lOle/hexSdGaTlQ6vU5zB6xzbBx/KrfrWbxzKGsXfc7WtekkpMQx6/4r6Tqok19yAtz+9E3kXypg47vfYAgxYLfamb5gErN/7Z+CNLNbD6Z37sqxnGyiQ0N9KvNVodPpGHbVQD5f8g17vjvAwW1Hee9Py73KOU16LG3C+fr7vfwmLISOfbyjSJQnxGTk/lfn89J9b2A125BOiSkshJhW0Vz7wPQaydrYeOw/v+Cvt7xM2oZdGE0GnA4nt//xpkqV+Oog6iII7+DBg2VaWtUZVYptNu5ft5ofTp8q+2HO7d2X342dEPDtoabGrvPnuOfTTyi2WQGBUa/jlSuv9lDqSrlcXMzVS5eQay7B4nCgE4IQvZ4XrpjG9C4/KWivbNvM62nbymzTwgwGOsXG8eENczy2beavWsGmUye9lGZwefrGhIax9ubb/FJ0b1j2X3ZfOI+93PcszGDgX9NnMKFCdInL53K4o8v9HnEFhYCouCiWnl7o1+w+EGSduUz+5QLa9WhTqeNCXSGl5I5uD3Au44LmA0TqBRfmdaK4TyzC7gSjnmv79Oavk6ZUGdpHSsn3H29j/eIvcTqcTL51HONmj/BIWxkMnD12HkuJlfY9U6rtbCGE2C6lHFxHojVK/B2zpZS88MMmFu/cTohej9XhZEByMq9fNZMWPnZomgtZxUXcvfpjDl++hNE9of/VsJHcPWiIV1mnlNzx8XK2n8ssG4/DDUZu6NmLp8dPKiv3/emT3LP6Y6wOBw4pyyItrJ5zK21a/LT695+d6Tz/w3dedsfgcrAL0Rv473U30i+pameuFzdv4s0d2z3shUP1Bub17edl2yul5M7uD3Au46KH3agpLIR/bXuu3naACnIKuXAyi+SOiR4Ob/XJC/P/zVdLN/mMeJMzMZmcaSngkKAX9G3bhjdnXEtceNULe4d+PMrKl9dyKTObodMHctU9k4lo0fgXBKtD9vkcss/nktK1dbXT/FY2ZjeokvvIZ+tYe+SQh4djmMHA46PG+m1U31SwO53sy7qIUaejR3xCpYGPi6xWRi5eSEGFrDJhBgNf37GAhHDvH3Ge2cx7e3by3amTtIlqwZ39B2ommth06iTv79lJvsXC9C7duKFHLw8FN9dcwvA3F2LV2EorJUSv556Bg6tczT6Tn8cVS/6jGdx8VNt2LLn2Rq/ju77ex5/nvoS5yIzTKYlvHcPTKx9rEtvlgeLgtiM8NvkZzQDnAJdmtid/VCIy5CfFNNRg4N6BQ3hw+Mhq15ex+yTLnv+E0wcz6TWqOzc+eo2Hd29zQym5vvn44H6e+nKjhzIVotMzpn0HFs2YVYcS1j9SSo5mZ1NotdAzIdHLfrMi1y/7L3s0JvT/nn4N4zp4hxK0ORysOnyQTw4eIESvZ07vPkzq2Mnr2XA0+zKLd2wnIyebIa1TuK3/AK9nwJi3F5FZoG2jWUq/VkmsvGleVc1m4MJXy0wmyhNuNLL3Zw94Hb94Kovfz/o/zhw6i86gR2/Q8fAb9zHm+uFV1hUs2G12rmlxm9cuZClFPaO5cHsXpOmnMdug09E/KZllN8ypdn2Xz+Ww/B+fsuurvSR3TmL2o9dUawU82KhszG4wcwWL3c6aI4c0jerf2rE9qJTc706d4MH1a7A5nEgk0aGhLLp6Fj18RDT47NhRHBqTD6eUrDp0kPkDvDPutAwN5RdDhnNDj95kFuST0kLbxmd0u/aMbud7i6TA4s6SVklGQavDwVcnjlep5OaYzRj1Oiwa+vKlYm074H7je/FB5kJO7D2NIcRA226t6ywTSmMl/3KhzzTFQifIH+mp4IIrG9O7u3dUW8lN+2wXT1/3f9jMNpxOScbuU3z27tf8a+tzfoXwUTQvFqWnea0WWp0Ovjt1gjyzucYmOo2N03l5zF+9gsz8fPQ6HUhXQpsZ3bQdWU/n5bE/K8tDwYXS51mappJr1Ou5vkcvpqR25mj2ZRIjIzXHus6xcfxl0pRK5S2wVh1FZo+faeQLfYTJLLHZcErptcua2C6B19Of5+yx8xTnl9Chd1uPRAbNAavZhlPDHLCUvAmtPRRccC187blwnsyC/Go5pF88lcV9Ax+jpNCM3WrnSPpxtqxO44klDwRsiz+YaLDoCiV2m898xdVNOduYOV9YwH2ffkKu2UyRzUqxzcbZggLmrfgQi8bWErjiOmplF7M4HFz2oRya7Tbu+/QTxr/zJnd+spzhby3kT99WPyd066gowo1Vb9FrrSZXpGtsnGb9IXo9Ezv6ToSh0+lI7duedt3bNDsFFyA2ORqLRigzU3gIsx6a7qXgllLdfPJSSl66byGWYmtZumC7zU5xfglvPfl+9QVXBD05lcSc9UfRago4peSWlR+SkZNDid1OodVKoc3K419s4ICPeNg55hKMGk6dAJdKtPtMSsnLW39g6Juvc8cny5n87mJuW/mRT6eyyhjepm2VJn5VRZIoZYAPk4Zeia0qraN1pyQ6D+jY7BRccEWo0YoGIIRgwMTehLeP1jzPoNOT6+P74Yt3nl5GUV5xWaQcKSWWYiv//NkinBomhs2dBlNyW5pCaaWR1lYnBCOqmb6wMbPiwD7NVVmb08EXx49pnjMipa2mbWW40VjmlFCRp7/+km9OHsficFBgtWJ1OFi6dzdLdu/0KpuRk82eixc0FWm9TsefJlzhkSWtImEGA/MHVp2/3WQw8PuxEzyypJn0emLDwjRXo5s7eZfy+dXY3/HgyKfQVUhbExIWQmK7BO784xx6JmrvAFTXW7wgu5DLZ3O8jkunDFiMQkVwMaZdB69MYOCKB51cQ6edxkb6ubNklxR7pUS3ORy85yPcYre4eM0U6iF6PRM1VnEB1hw5xMLtaVgcdgqsViwOB9vOnuHRjeu8yl4sKmTX+XM+FeDHR4/1yJJWkVCDgVv9zNb2h3ETCTcaMbjvs14Iwo1Gni1nK6xw4XA4+Me9C7m53X0UZHvG9jUYDYS3COP+Vxdw/fCBhOi8FyeEcK3UV4f0jbu94uYClBSWkHX6cvUa0AxosCmXEII/T7yCez/9BKvDgVNKjDodoQYDj40a21BiBZwsdyDvitidTi77mMH1TEjkitTObMw4Vhb/NsxgYGBSa00l12K388mhA17ZW0rsdt4sZ/pxOi+Puz/9mFN5ueiFQC90PDd5CtM6e6Zvndq5C8tazGFRehoZOdlkFRWRZzFj1OtxOJ08NmqMpgOcFjf26kNqbCxvpW/nfGEB4zukclu//rUOSRaM/PH6Fzi49YhHCDOdXkdK12Sm3TWRq++9grCIUJ4ZP4lbV35Y5oxiEIIQg4HfjZ1QrfpM4SH4WpiJivGegCoUDw4bwcaMoxRZrdicTgSuyeyzEya5tvWDgOySYs0dJIeUXCjUTlJgMhj47ZjxZem9oTSFbhjzB2ibd7+RnuYV39zqcPDNyRPkmkuIDg2jxGbjVxvW8s3J425HPwfzBwzmkRGjPGTsGB3D+nm389aONNLOniXXXMLZwgLCDAYsDgdXdenGg8P8M2XqmZDI2ptv4830NPZcvECP+ATuHjSkRinYg52lf1nBF+9/6+FsptPriE5swbgbR3LDIzNIbBvP3W1j+PjgAXItZqwOBwLXxOP3YydUaetdkai4SC5lZnsddzqcRLQMLme0QNCg+wpj2nVg+eybWbT9RzJysxmY1IYFAwfRug4SJjQUo9q248P9e71CxQghGNYmxed5L06dzurDB/nfvj04nE6u69GL67r31FxdNdvtmqvFAHnu7UWnlMxbuYyzBQUeKw4Pf7aOTjFxdInznE32TmzFP6ddVfb/idwcsktK6B6f4Jc5Q3kGJbdh0FVtqnVOc+PCySwO/XjUKwWk0+EkISWOGx+5puzYwOTWrJ57K29s/5EDl7Lok9iKewYNqXYoJ1OYibE3jOC75Vs8BmlTuInr/YgNrGh+tI5qwYZ5d/DWju1syTxNuxYtWTBwMH1bJTW0aAFjQHJrzYWJMIOBCZWYWc3p3ZfUmFgW79zOhcJCxnfoyO39Bvic0PsyPdMLQZ7ZQnRoGL/96vOyHbrSRYy3d26nfcuW3Nirj8d5yVFR/LbcRDeruIhTebm0axntl3lZedq1jOaZCZOrdU5zZOXL67xS6DodTsxFFn7+0p1lx+LCw1k373be3pnOd6dOkBQZyfwBgxncuvrPxRsfuYaXf7bIIwqR0WRg8NT+REY3TGSJxkyDG8/0iE/gxanBEe9NiwkdUumZkMi+ixfKhe4ycmXnLnSN8x3DVScEM7v1YGa3HlXW0cJkIjkyitP5eR7HBTC0jcv0I+1sJjklJZpbcO/v2ekRtkaLDtExdPBzJp9dUsy7u3ayJfM0HVpGc+eAQXSrpK1aSFkC9uOgS0Dogz/GaW5WvmZQdYDsc7lex1JjYnlu8lSv49XlwdfvoSCnkJ1f7i2LUTx9wSSu+Vntr60IThIiInhidPDstlUkITyCuwcOZvGO9LKV1lB3OvbrulceT3pomxSGVrJ4UZ7R7dprmrOFGoyktGhBic3m0zn7jfQ0LyVXqx3+KrdWh4OVB/fz6eGDRISEMK93P69YvFUhpQPsx0CEIgzaZnXBRlGe9kSlpMCM0+n0CF8YExbGwyNGVTtBUEUm3zKWUwfOsOKlNa4x22qn18iuPP7OL2t13WClwZXcYEev07Fk1g18uH8vKw7uJ0SnZ26fvszo6l+6WS0cTifHcrIJNxpJadESIQR/mjCZe9f8ZPphEIJQo5EnRo0BXNEMhEbKM4eUnCvUTulaHQosFv63bw8bjh0pS2BhczrZfjaTVYcP8u/p1zDeh21aRZxFb0HByyD0IK1I0yhEyxcRuuCdpXbolVLm/FUeQ4iBwdP611m9YRGh/PnT33DhZBYXT12iXY82PrPvKBTNhUdGjKZ/q2Te2b2DPLOZ6Z27ckvf/oRVcxerPKfycrHYHXSKdaUJfmDYCD47dpRi20+mH6EGA8+Md5l+FPpw8gPXQkJtcTidrD16mOX797HrwjlK7PYyhfq7kye5a8BAHvEz6ZG0fI/MexRkCUgnUt8WEfMqwtCh1nI2ZroN6cT+zYe9jnfq36Ha8bn9RQjB/L/MY/avZ3Ji72niU2JJ7ugdLlThokHj5CqqzzcnjvPoxnWU2O04nJJOMTG8fvVMUlq0ZN/FCyzc/iMZOdn0T0rmvsFDy1I7ni8sYPw7b3mtCoQZDDw1Zjw31yITUL7FzIyl75FVXOQRQLw8rSIi+P6ue6v0AJbmz5B5v3YNlmWEgGkCuphXaixjU2D16xtY+OgSLO5tKGOIgciYCBbu+jsxiQ2XNrq5oeLkKgLJidwc7lvzCafy8tAJQYTRyD+mXsXItu24UFjIovQf2ZJ5hrYtWnD3wCFlDqRSSkYuXsiFoiKP6+mE4IrUTrx2Vc3Tnkop+dmaVWw6fdLLlK4Uk17Pl7fNJzmqcodC6chEZk0Hyo/ZwrULl/A1QgTvWtqhtGM8OuEP7hBiTnR6HUaTkec2/Jbeo2q+kKWoHo0yTq6i+pzIzeFna1d5KJIHL1/i5hXL+Pr2BfRKbMXLV2rbUiZFRnFrn/78d++uco4RBlpHteDaKrbgquLtnelcKCrAquHxWUqexcK5ggKPLD1ayKI3Kii4AFawfIV05iJ00bWStTEz476ppHRtzUcvrubSmWyGXNmf6381Qym4CkUTxe50Mnf5/7hYVETpclKxzcbdq1ey8dY7aR3VwsOOtjxCCJ6dMJkH1q/BYrcjoWyH7tcjx9RKrm2ZZ9h06gTFPhYlwLULuTXzDLO6V24yJ4s/AipeR4IsAuv3YBpXK1kbM90Gd+LVH//GB39bydH046T268Ccx2c1q+RFjR2l5DYhlu7Zjb1CHDynlOSUlLAt8wzDqwi99psx4xiQ3Jp3d+2gwGphepeu3N5vYK224KQzn88Of4nVUXm0BKeU/sVpdGRpHxcGcOZCECu5AAMm9mHAxMpt7RQKRdNg06mTFFptVNwvtTudLNu3l4eqSN4yObUzS6+bzevbt3E8N5fBya09duhqgpROvjnyX4rtetAwYStFJwQtQ/1Ir+o8B2itBktwXKqpmE2Gdt3b8Njbyh62saKU3CZEZmG+l5JbysUi7dA25RFCML1LV6Z36VplWX+RufcTbYwHfCu5Rp2OkSnt/MuGZBoOJZ/gnXLNCHr/HDqaGha7nTP5ecSHRwRNxiiFQuEal7Xi59qcTs5WkYa3lH5JybUyTaiILFpEtG4HIboBWJ2+VQCjTs9oP0JFipCRSPN6kBXshKUDQoInc2l5nFJyMi+XMIOBpCCJDx2sKCW3CTGmXQe+On7cK7ai3emsdiKAmiKtu5Al/wVnHoQMA+t27uiazM7LCZQ4yq8IS3RCh0mvp3t8Ai9OvdKv64uI+5HmL9wDZukWWBhE/SYobbve3pnOi5s3Aa4H37ROXXhu8hRCDTVfXVcoFI2DQcmtkV7ruBBuMDKqkvTqgUTazyCL33NFPggZBIVvMaOdjZf29tcsH240EhViYvHM6zDqtbMrehA6DYoWgf0E4A5rJcIgdBrC4DvkWlNl8+lTPPzZOvItZpxS0j0+gX9Nn1Gt1LyK+iP4tIYgZma37ixK/5HM/PyymIlhBiOzunev1faVvziL3oWCF3ANZBIs3wN2JrU+yT3dd/L6gQEY9Q6cTh2JYVYeGHkDvRJaecXgrQxhSIH4VcjChWDdCvrWiIh7Eabgy8m9/ugRXvjhuzIbaYANx46g1+n4+xT/JgUKhaLx0ik2jis7d2X90cMeSSLatmzJlZ0Dt6PmC2ndgcy5E6QVsIN1C2ChVRj8a+RGHtr8UyxcnXBy/7CpDEhuTf+k5CqdhEsRIgRi/4csfgdK1oAuFBF+M4TOqpM2NSSZ+fksWL3SY8zee/ECNy9fxle3z/e7zxT1h4qu0MQosFhYvHM7a44cIsIYwm19BzCrew/NDD35Fgs5JSW0joryb0ZeCdKZh7w4mrKZugY5FhO7sxOJD7XSs/V09C2frFWdwc41H7zH3osXvI6H6PWk3f1zv3PNKwKLiq6gCCROKflo/17e37MLs93OjK7duLP/ICI0ft8Wu51zhQUkhEdofl7turOmgSPD5+cWh470S0nohGRgUktMiatqXWcw88IP37EoPQ1bBbPBCKORN66exQiNjKSKukdFVwgiokwmHhw2stIUjWa7jSc//4x1x45gEDoMeh1PjhrHTb1r4dBk3Q7CCFJLydUBTmJMFsYlnwMRhYicX/O6mgm+7KhdGY/MSslVKIIAnRDM7tWH2ZUkb5BS8lraNv6dthUAh1NyU6/e/HbsBAw1jLcqnfngOFVJCYFJ72REq3OACRH9fI3qaU6czs/zUnBLueCHX4yi/gmOZOMKDx7/fAPrjx3F6nBQbLeRb7HwzLdf8u3JEzW/qC4SNGzLQIBxCBgHgb49hN2EiF+F0CfWvK5mwuDkNprbWya9gVaRkQ0gkUKhaAg+OrCPV3/cQrHNRrHNhsVh58P9e3nhh001v6gIwWf0BF08mCaDvh2YJiHiliJChta8rmbCyLbtCdPwl7A7nfRLSm4AiRRVoZTcICPXXMKGY0exODzjFpbY7bz645ZKz80qLuKSj3zqGAeBCNf4wISIehhd3FJ0CRvRtfwDQq+yr/jDwyNGEWYwoi+n6LqSc4yr8eqNQqFoerz64xYPO09wjdnv7dmJw8fKIbhM0s4W5KNldihEKJgmABWVslAIvx1dzKvoEj5HF/NvhLFXAFoR/Mzs1p1WkRGE6H4y/wszGJjepRsd/Ux7r6hflLlCkJFdUoJRp/PKbAb4TN97+PIlHlq/hozcHKSEbnFxvDTtKlJjYsvKCKGHmMVuJ4YSQIC0QdSvEEEaJqauSY2JZdXcW3hl62bSzmXSJqoFPx8yjDHtOjS0aAqFoh7xtbhgdTgosdu9TJfyLWYe+Ww93548jk7oiAkN5S+TpnilThct/4zMWQD2w4DeNWaHTkREKHOymhBqMPLxTfNYuP1H1h45TJjRyC19+jGnd9+GFk3hA6XkNkLyLWYsDgfxYeGaDmWV4Qpj4n2ODsHg5DZexwutVm766APyLZYyY4R9WRe56aMP+O7Ouz1CWQljN0j4DqxpIAsgZAhCp7Jx1YaO0TG8OHV6Q4uhUChqgdluI9dsJiE8An0NdmF6J7Ri29kzXscTwiOI0EjWs2D1x+w6f85tH+rkfFEhP1+7ihWzb6Z7fEJZOaFriYj7EGnbD44zYOiOMCjnqNrQwhTKr0eOqXXWOUX9oPZEGxFZRUXcsvJDhix6jTFvL2LykrfZce5sta5hMhj49cjRhBl+mr/ohSA8xMgDw0Z4lV9z5BBWh9PD2lYCZrudz44d9SovhB5hGoYInawUXIVC0ayxOhz87qvPGbDwVSa+u5ghi17jw317qn2dJ8eMI8xg8FieCDUY+N3Y8V4LHceyL7P34gUvByibw8HiHds1ry+MPRGhU5SCq2h2KCW3kSCl5OYVy9h65jQ2pxOrw8Hx3Bxu/fgjzhVomxn44rZ+A/jntKvo3yqZpMhIrurSjU/m3EIHDZuhzPw8r+QS4FJyz1azXoVCoWhO/PGbL1l+YB8WhwOz3U6uxczT33zJVyd8h+3Sol+rJD68cS6TOnYiKTKS4W3asvia65imEUv3bGEBRo3VYoc7C5dCofgJZa7QSPjxbCbnCwtwVHAgsDudLN27m4dHjKrW9SandmZyaucqy/VLSibcaKTY5qnomgwG+rZKqladCoVC0VwottlY4VZwy1Nit/PK1i1M6FC9bF89ExJ5Y8asKsv1iE/U9Lkw6fUMaxOcqc8VipqiVnIbCb7ymFsdDk7k5tRZvePbd6RjdAymcskiTHo93ePiGZHSts7qVSgUiqZMdkmxzwxXmT7G80AQHx7O3N79vEzSIkJCuK3fwDqrV6FoilS5kiuEWAxcDVyUUvaue5GaJ70TW3mt4oIrbe/QOpydWxwO3pwxiyW7d/LxoQMIBDf07MW9g4ZU2+lNoVAomgutIiIx6PSAZ+gvAfRPqrtdMKvDwf1Dh9E1Lo7FO9PJN5sZ16EjDw4bQXy4VphHhaL54o+5wn+AfwHv1q0ozZvOsXGMa9+Bb06ewOyOl2jU6YgODeXa7j0DXt+FwkIe3biOrZkuj96e8QksvuY6usbFB7wuhUKhCDaMej2/Hjmav276pizGrcAVZupXw6tnXuYPFrudZ7/9iuUH9uGUkrjwcJ4ZP8kvszSForlSpbmClPJbILseZGn2vHLlDB4aNpJ2LVqSEB7BjT37sGrOLQHJYV4eh9PJjR8tZcuZ09idTuxOJ3suXmD2Rx+QZzYHtC6FQqEIVm7p258Xp06nV0IicWFhTOiQykez53qE8QoUT3zxGSsO7sficGBzOjlfWMiD69eQXs0IPApFc0I5njUiDDod9wwawj2DhtRpPd+eOkFOSYmHeYQEiqxWfrF2FU+OHkevRJW1TKFQKKpiaqcuTO3UpU7ryCkpYf3Rw5pObg+u/5RnJkxmXPuOPm2EFYrmSsAcz4QQ9wgh0oQQaVlZWYG6bLPm4KUsnv/hO57b9A27zp8L2HVP5+Vh10gV6ZCSH86c5saPPuDlrT8ErD6FQqFoDlwsKuS1tK08882XbDx2tNKUvNXhfGEBxnLOweXJLCjg/nWfcvvHH2HTiLqgUDRnAraSK6V8A3gDYPDgwd4eVAoAnFJitttdgb8rmXW/nraNl7dtxupwIKXk3d07mde7H0+NHV9rGXolJrqy8vgYEM12O6+lbWNGtx5NJh/3ocuX+PTQQezSyfQu3eijVqIVCkUAkFJSYrcTajBUulK6+fQpFqxeiVNKLA4Hy/bvpXtcPO9fNxuToXaP2nYtozUXJkopttlIP3eWTw4d4IaeTcM/PKuoiJUH93OxqJARKe0Y36FjjbLFKRSVocwV6gmnlPxr22be3LGdEpuNhIgIfjtmPNO7dPMqeyY/j39u/cFja8pst/P+3l3M6t6j1qYEA5Na0zMhkT0Xznttf5UiJXyRcYwFAwfXqq76YGHaNv65bTO20gnBrh3c0rc/T44e19CiKRQBRwgxDfgnoAfelFI+V+FzEy5H4UHAZeAmKeWJ+pYzGFh/5DDPfvc1F4sKCTUYuGvAIB4YOsJLGXM4nTywfk2ZAxq4FM/9l7L4795d3Nl/UK3kiAgJ4e6BQ3gz/UePOspTYrc3GSV3y5nTLFi1Eod0YnE4+GDfHnrEJ/DetTfWekKgUJSnymmTEGIpsBnoJoQ4I4SYX/diBR8vbt7Ewu0/Umi14pCS84WFPLpxPd+ePOFV9ovjxzSvYbU72HDsSK1lEULw7qzruWvAIM286AA6nSDEx/ZYY+JMfh4vbf0Bs92OQ0qcuAb7Jbt3sj/rYkOLp1AEFCGEHngVuBLoCcwVQlQMvzIfyJFSdgb+AfytfqUMDjadOsnDG9dxzp2kp8hm4830NF7YvMmr7KHLlzD7yBy58uD+gMjz0LAR/GHcRJIiI32WaQpjtsPp5P51n1Jst5UtshTbbOzLusjSvbsbWDpFsOFPdIW5UspkKaVRSpkipXyrPgQLJqwOB2/v3OE1Azfb7by45Xuv8kadXtOUwaV4BmaWG2ow8uuRY1h78+2YNK4ppWRa57p1pggElU4IjtZ+QqBQNDKGAkellBlSSivwATCzQpmZwDvu9x8Bk4QKel1tXtryQ1k4x1JK7Hbe3bUDS4XjBp0OXzZ6Rl1gFE8hBLN79eG7O+7WjIcbZjAyp1ffgNRVlxy6fMlnKvlATQgUilKUAUw9kGsuQfoYAk9r5Bq/olNnNPJCoBc6rurqbd5QG9q2bMnT4yZi0usJMxgINxox6fU8N3kqiRG+VwwaCwadXtNOTqcTPh01FIomTBvgdLn/z7iPaZaRUtqBPCCu4oWUs3DlnNQYm0u5XFLs8X+X2DjiwrQVz5v79AuoXHqdjkUzriUqxESE0UiowYBJb+C6Hj2ZnNopoHXVBQadTvP5VvqZQhFIlPFLPRAbFo5Rp8eMty1VtzjveIoJ4RH83+SpPPb5evRCh0TilJLfjB5bJ45gN/Xuw8TUVL48noFOCCZ1TCVWY8BujEzp1Jk/ffu11/G6mBAoFMGEchaunO7x8Xx/+pTXcb1OR0J4hMcxIQQLr57JzSuWYXO4Yo8LAZNTO9VJMp9+rZLYuuBevsjIINdiZkRKW1JjYgNeT13QJTaOuPAwzuR7ruaGGYzM7d34V6IVTQul5NYDBp2OB4eN4O+bN3mYLIQaDDw6crTmOTO6dWdUu3Z8nnEMp5RM6JBKq0pssWpLQngEN/XqU2fXrysSwiN4bvIUnvh8g8eE4InRY5pMZAiFohpkAm3L/Z/iPqZV5owQwgC0xOWApqgGj44YzfZzyzxMFsIMBn45ZLjmLlH3+AQ233UvXxzP4FJxEUPapNCjDpJClBJqMDbJibxrQjCLecuXYXM6sTsdCCGY2DGV63r0amjxFEGGkL72DWrB4MGDZVpaWsCv29RZcWAfr2zbwoWiQrrHxfPE6HEMbZPS0GIFBZeLi/n8+DEcTieTOnaq0wmBIrgRQmyXUjbKsCJupfUwMAmXMvsjcLOUcl+5Mr8A+kgp7xNCzAGuk1LOruy6aszWJu1sJn/d9A0HLmWREB7B/UOHc32PXpWGf1T4h9lu44uMDC6VFDG0dQo9EhIbWiRFE6WyMVspuQqFQlGOxqzkAgghpgMv4QohtlhK+WchxDNAmpRylRAiFFgCDMCVkn2OlDKjsmuqMVuhUDRVKhuzlbmCQqFQNCGklGuBtRWO/b7cezNwY33LpVAoFI0N5cqoUCgUCoVCoQg6lJKrUCgUCoVCoQg6lJKrUCgUCoVCoQg6lJKrUCgUCoVCoQg6lJKrUCgUCoVCoQg6lJKrUCgUCoVCoQg6lJKrUCgUCoVCoQg66iQZhBAiCzjpZ/F44FLAhagdjU0mJU/VNDaZGps80PhkaqzytJdS1l0+1kZINcfs8jS2exhIVNuaLsHcPtU2b3yO2XWi5FYHIURaY8su1NhkUvJUTWOTqbHJA41PJiVP0yeY+0y1rekSzO1TbaseylxBoVAoFAqFQhF0KCVXoVAoFAqFQhF0NAYl942GFkCDxiaTkqdqGptMjU0eaHwyKXmaPsHcZ6ptTZdgbp9qWzVocJtchUKhUCgUCoUi0DSGlVyFQqFQKBQKhSKg1JmSK4RYLIS4KITYW+7YjUKIfUIIpxDCpwedEGKaEOKQEOKoEOKJRiLTCSHEHiHETiFEWh3K87wQ4qAQYrcQYqUQItrHuQHvo1rKE/D+qUSmZ93y7BRCfCaEaO3j3NuFEEfcr9sbgTwOd5mdQohVgZDHl0zlPntECCGFEPE+zq2XPqqGPAHvIx/37GkhRGa5uqb7OLdOxqKmRFV9IIQwCSH+5/58qxCiQwOIWWP8aN/DQoj97t/4F0KI9g0hZ03w9/srhLje/btsMl77/rRNCDHbfe/2CSH+W98y1gY/vpfthBBfCSF2uL+bmmNYY6SyZ4T7cyGEeNnd9t1CiIE1rkxKWScvYCwwENhb7lgPoBvwNTDYx3l64BiQCoQAu4CeDSmTu9wJIL4e+mgKYHC//xvwt/rqo5rKU1f9U4lMLcq9fwB4XeO8WCDD/TfG/T6moeRxf1YY6P7xJZP7eFtgA674p173pj77yB956qqPfNyzp4FHqzivzsaipvLypw+An5d+54E5wP8aWu4At28CEO5+/7Om0j5/v79AFPAtsIVKnoGN6eXnfesC7Cgd04DEhpY7wO17A/iZ+31P4ERDy12N9mk+I8p9Ph1YBwhgOLC1pnXV2UqulPJbILvCsQNSykNVnDoUOCqlzJBSWoEPgJkNLFOd4EOez6SUdve/W4AUjVPrpI9qIU+d4UOm/HL/RgBahuVTgY1SymwpZQ6wEZjWgPLUGVoyufkH8Fgl8tRbH/kpT51QiTxVUWdjURPCnz6YCbzjfv8RMEkIIepRxtpQZfuklF9JKYvd/9b7GFgL/P3+PotrAcNcn8LVEn/adjfwqntsQ0p5sZ5lrA3+tE8CLdzvWwJn61G+WuHHmDwTeFe62AJECyGSa1JXY7TJbQOcLvf/GfexhkYCnwkhtgsh7qmnOu/CNZupSEP1kS95oJ77RwjxZyHEaWAe8HuNIvXaR37IAxAqhEgTQmwRQsyqK1nc8swEMqWUuyopVm995Kc8UI99BPzSvRW2WAgRo/F5Yx2L6hN/+qCsjHtCnAfE1Yt0tae693g+vsfAxkaVbXNvA7eVUq6pT8ECgD/3rSvQVQjxvXs8qfUEvh7xp31PA7cIIc4Aa4H760e0eiFgY29jVHIbK6OllAOBK4FfCCHG1mVlQoinADvwfl3W4y9+yFOv/SOlfEpK2dYtzy/rsq4AytNeurK53Ay8JIToVBeyCCHCgd/gW9muV6opT730EfAa0AnoD5wD/l5H9SiCBCHELcBg4PmGliUQCCF0wIvAIw0tSx1hwGWyMB6YCywSPnxKmihzgf9IKVNwbe8vcd9TRTkaY4dk4rLdKyXFfaxBkVJmuv9eBFbi2k6oE4QQdwBXA/Ok20ClAvXaR37IU6/9U4H3ges1jjfU98iXPOX7KAOXDfiAOpKhE9AR2CWEOIGr7elCiKQK5eqrj/yVp976SEp5QUrpkFI6gUVof18b5VhUz/jTB2VlhBAGXFunl+tFutrj1z0WQkwGngKukVJa6km22lJV26KA3sDX7t/lcGBVE3E+8+e+nQFWSSltUsrjwGFcSm9TwJ/2zQeWAUgpNwOhgKZDbxMkYGNvY1RyfwS6CCE6CiFCcDkyBMwTvSYIISKEEFGl73E5Y2l6BQagrmm47BavKWcHVpF66yN/5KnP/nHXUX6gmgkc1Ci2AZgihIhxb0VPcR9rEHnccpjc7+OBUcD+upBHSrlHSpkopewgpeyAa7AfKKU8X6FovfSRv/LUZx9VsO+6Fu3va6MbixoAf/pgFVAameMG4Etfk+FGSJXtE0IMABbiGgObkl1npW2TUuZJKePL/S634GpjwKLj1CH+fC8/xrWKWzqedMXlXNsU8Kd9p4BJAEKIHriU3Kx6lbLuWAXc5o6yMBzIk1Keq9GVauqxVtULWIprG9CG66E2H9fD5AxgAS4AG9xlWwNry507Hdes6xjwVEPLhMvDcZf7tS9QMvmQ5yguW5Sd7tfrFeWpqz6qqTx11T+VyLQcl1KyG1gNtHGXHQy8We7cu9zyHwXubEh5gJHAHncf7QHm12UfVfj8BO5oBg3VR/7IU1d95OOeLXHXsRvXgJpcX7+zpvbS6gPgGVwKEbgerh+6v0PbgNSGljnA7fsc17Nhp/u1qqFlDlTbKpT9miYSXcHP+yZwmWPsd//W5zS0zAFuX0/ge/d4uROY0tAyV6NtWmPyfcB95e7dq+6276nN91JlPFMoFAqFQqFQBB2N0VxBoVAoFAqFQqGoFUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHUrJVSgUCoVCoVAEHf8PtBsMShcR+KoAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "Exact same distribution before and after scaling. The original shape of the data is retained."
      ],
      "metadata": {
        "id": "pdPpsoFnMWfI"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "fig, (ax1, ax2) = plt.subplots(ncols = 2, figsize = (12,5))\n",
        "\n",
        "ax1.set_title('Before Scaling')\n",
        "sns.kdeplot(x_train['Alcohol'],ax = ax1)\n",
        "sns.kdeplot(x_train['Malic_acid'],ax = ax1)\n",
        "\n",
        "ax2.set_title('After Scaling')\n",
        "sns.kdeplot(x_train_scaled['Alcohol'],ax = ax2)\n",
        "sns.kdeplot(x_train_scaled['Malic_acid'],ax = ax2)\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 350
        },
        "id": "XuqN29l4MHtV",
        "outputId": "8d25bc1f-300d-434d-8465-e6a58bd9da5c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 864x360 with 2 Axes>"
            ],
            "image/png": 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86ZBRqGUcSoWQJtDh0LjXJstxA/7cSel2qVUdgVZKKXU4e5bZ7VATaBEoPc7+vjGhj0upGKQJdDg0ln+8hV1AdrHWQCullDq8snchdzJk5B9+34FKjobmfR/OyVFKjYgm0OHQtPfDCYMDZY3TEg6llFKHZoxNoIda/xxQssRuy94NXUxKxTBNoN3W1wfNFR/WOw+UPU5LOJRSSh1a3Q5oq4XiI4f3+2OOgMR0TaCVChFNoN3W0QB9PZBRMPjzWeOgvQ662sIallJKqQhSsdpuixYM7/fjE6B4kSbQSoWIJtBua62x24EdOAK0F7RSSqnDqVgFcYlQMGv4xyg5GirXQWdLyMJSKlZpAu221mq7Tc8b/PnMQrtt2R+eeJRSSkWefatgzCxISBr+MYqPBNMLlWtCFpZSsUoTaLcdSKAPMgIdKO1orQpPPEoppSKLMbaEY+z8kR2nyPn9vStHGpFSMU8TaLcdLoFOdxLolurwxKOUUiqyNOy282kCCfBwZRTYFXH3fRCKqJSKaZpAuy1QA52aO/jzabkgcToCrZRSanD7Vtnt2HkjP1bRfE2glQoBTaDd1lptk+f4hMGfj4uHtDxo0QRaKaXUICrXgMRDweyRH6toAdRth/aGkR9LqRimCbTbWqsPXr4RkFHwYamHUkop1d/+DZA3FRJTRn6scQvttmLVyI+lVAzTBNptrTWHT6DT83UEWiml1OCq1o+sfV1/gYmIWsah1IhoAu221uqDt7ALyCjQGmillFIf19EEDXtsC7tQSMuF7BLbD1opNWyaQLutLdgR6GrbqkgppZQKqNpot2OOCN0xxxwB+9eH7nhKxSBNoN3U2w3t9cEl0D3t0NUanriUUkpFhion0Q1VCQfAmNlQswW6O0J3TKVijCbQbmqrtdtgSjhAyziUUkp91P4NkJQJOeNDd8zCI+yKhDWbQ3dMpWKMJtBuOtwiKgG6mIpSSqnBVG2AgpkgErpjBspBtA5aqWHTBNpNgUVU0kYfer8MJ8HWEWillFL9VW20CXQo5U6ChFStg1ZqBDSBdlN7vd2mHWQVwoADI9CaQCullHK01kJ7HeRPD+1x4+JtUr5/bWiPq1QM0QTaTYEEOnXUofcL1EgHRqyVUkqp2q12O3pq6I9deIQt4dDuT0oNiybQbgok0Ck5h94vPhGSs+1Ig1JKKQW2UwbYVQhDbcwR9j2nuTL0x1YqBmgC7ab2ekhMC2751bTcD7t2KKWUUjVbID45tB04AgITCbUOWqlh0QTaTe31kHqY+ueAtNGaQCullPpQzTYYPcXWLIdaYGVDrYNWalg0gXZTe/3h658DNIFWSnlIREpE5BUR2SAi60Xkq4PsIyLyexHZJiJrRGShF7HGjJotkDfFnWOnjoKsYh2BVmqYNIF2U3s9pOYEt2/aaGjTGmillGd6gG8aY2YBRwNfFJGBy9+dDUx1vm4C/hjeEGNITyfU74K8ae6do1CX9FZquDSBdtOQRqC1Blop5R1jTIUxZqXzfTOwERg3YLcLgb8ZaxmQIyJjwxxqbKjbaVcLdDOBDizp3dPp3jmUilKaQLuprW5oJRzdbdDV5m5MSil1GCJSCiwA3h3w1DigrN/P5Xw8yVahEGhh50YHjoAxR0BfD1Rvcu8cSkUpVxNoETlLRDY79XLfPcR+l4iIEZHFbsYTVsbYEejDLaISEFitUFvZKaU8JCIZwGPA14wxTcM8xk0iskJEVlRXV4c2wFgRaGE32qUaaLAj0ABVmkArNVSuJdAiEg/chq2ZmwVcNUg9HSKSCXyVj490RLauVujrHtoINGgZh1LKMyKSiE2e7zPGPD7ILnuBkn4/FzuPfYQx5k/GmMXGmMX5+fnuBBvtarZCZhEkZ7p3jtxJEJcI1RvdO4dSUcrNEeglwDZjzA5jTBfwILZ+bqCfAj8HOlyMJfyCXYUw4EACrSPQSqnwExEB7gI2GmN+c5DdlgLXO904jgYajTEVYQsyltRscbd8A+wiXqOnQPVmd8+jVBRyM4E+bK2c0wKpxBjztItxeGPICbRT6qEj0EopbxwHXAecKiKrnK9zRORmEbnZ2ecZYAewDbgT+IJHsUY3Y2wPaDcnEAbkT9caaKWGIcGrE4tIHPAb4FNB7HsTtmUS48e7sCKTGwK1zENZSAV0BFop5QljzJuAHGYfA3wxPBHFsJYq6Gx0fwQaIH8GbPgndLdDYqr751MqSrg5An24WrlM4AjgVRHZhe07unSwiYQRWU831BHolBxAdARaKaViXWACYTgS6IIZgLE110qpoLmZQC8HporIRBFJAq7E1s8BYIxpNMbkGWNKjTGlwDLgAmPMChdjCp+hJtDxCXbRFU2glVIqth1IoMNRwjHDbrUOWqkhcS2BNsb0AF8Cnsc25H/YGLNeRH4iIhe4dV7fOJBA5wT/O7qct1JKqdptkJhmu3C4LXcyxCVoJw6lhsjVGmhjzDPYSSf9H/vhQfY92c1Ywq69ARJShlZTpgm0Ukqp2m1OYhuGtc4Skuy5dARaqSHRlQjd0tHo1DUPQdponUSolFKxrn4X5JaG73zaiUOpIdME2i0djZCSNbTfScvVEWillIplfX1QvxtGTQzfOfNnQN0O6I6u5RiUcpNnbeyiXkcjpGQP7XcCJRzGgByym5RSEWlfQztPrt4HwAXzixibrW2zlPqI5gro7YRRpeE7Z/50MH22dKTwiPCdV6kIpgm0WzoaP1wcJVhpo+2Fs6sVkjPciUspjzzw3h5+/OR6Orr7ALj1X9u458YlLBwfZKcapWJB/S67DWcCXTDTbqs3aQKtVJC0hMMtwx2BBi3jUFHFGMP/vbyV7z2+liUTR/PGv53Ca98+mVHpSdx87/tUN3d6HaJS/lG/025zw1jCMXoKSJzWQSs1BJpAu6WzaegJdKou562iS1+f4adPbeTXL27hkwvGcdcNiynJTWPC6HTuuG4R9W1d/PoFnf2v1AH1u0DiIbvksLuGTEIy5E7SBFqpIdAE2g3GjHAEWjtxqMjX12f4wRNrufutnXzq2FJ+ddk8EuM/vOTMHJvFtUdP4OEVZWyravYwUqV8pG4nZBdDfGJ4z5s/Q1vZKTUEmkC7oacDeru0hEPFLGMM//7PdTzwXhlfOHky/3n+LOLiPj4x9sunTiUxPo673twV/iCV8qP6XeGtfw7InwG126FHS6qUCoYm0G7oaLTb5GG0sQNo1xFoFdl+/cIW7n93DzefNJlvnzkdOUhXmdz0JC6aP44nPthLY1t3mKNUyofqd4a3/jkgfwaYXptEK6UOSxNoNwQS6KGOQKfk2IkcOgKtIthLG/Zz6yvbuPLIEr5z1sGT54Drj51Ae3cvj7xfFqYIlfKpjiZ7/fdiBLpght1qHbRSQdEE2g0HEuicof1eXBykjtIEWkWs1s4e/uOf65hRmMmPL5x92OQZYHZRNkeWjuLeZbvp6zNhiFIpn2rYbbdeJNDaiUOpIdEE2g3DHYEGXc5bRbS739xJRWMHP7v4CJIT4oP+vWuOmsDu2jaW7dAPjyqG1Tkt7MK5CmFAYqpN3DWBVioomkC7YSQJdKou560iU0d3L/e8s4tTpuezaMLQFhE664hCslMTeWC5lnGoGObFIir95c/UThxKBUkTaDfoCLSKQU+tqaCmpYvPnTBpyL+bkhjPxQvG8fy6Supau1yITqkIUL/TlvGl5nhz/vzpdjnvHn0NKnU4mkC74UACPcQuHGA7cWgXDhWBnly9j+JRqRwzefSwfv+qJePp6u3j8ZXlIY5MqQjhVQu7gPwZ0NcDdTu8i0GpCKEJtBs6GiE+CRJShv67aU4Jh9HJVCpy1Ld28da2Gs6dOzaoiYODmV6YycLxOdz/3h6M/vtXsahupzf1zwH50+1W66CVOixNoN0QWIVwOIlE2mi7CEtXa+jjUsolL2+qoqfPcO6csSM6zpVLxrOjupXlu+pDFJlSEaK3BxrLvB2BzpsGiCbQSgVBE2g3DGcZ74BUZ/KVTiRUEeTtbTWMTk/iiKJh/rt3nDd3LJnJCdz37u4QRaZUhGgqt+UTXiyiEpCUBqMmaAKtVBA0gXbDSBJoXc5bRRhjDG9vr+XoyaMHXa57KNKSErh0cTFPramgrK4tRBEqFQG87sARkD8TqjSBVupwNIF2Q2fTyBNonUioIsTOmlYqmzo4dpiTBwf63AmTiBO48w2dyKRiiG8SaKcTR2+3t3Eo5XOaQLthRCPQgRIOTaBVZHhvp/23esyk0CTQRTmpXLxgHA8uL6OquSMkx1TK9+p2QlwiZI3zNo6CmdDXrZ04lDoMTaDd0NEIycNoYQdawqEizuryBrJTE5mYlx6yY95y8hR6+wx/fHV7yI6plK/V74Kc8RAX/AqertBOHEoFRRNoN4xkBDolGyROR6BVxFhd1sjc4uxht68bzMS8dC5ZOI77lu1hX0N7yI6rlG/V7/R2AmFA3nRAtA5aqcPQBDrUujugp2P4CXRcPKTk6Ai0iggd3b1s3t/M3OKRdd8YzJdPnYrBcOsr20J+bKV8x+tFVAKS0uxIuI5AK3VImkCHWmeT3Q43gQZbxqGTCFUEWL+vid4+w9zinJAfuyQ3jSuPHM/Dy8vYU6sdOVQUa6uzdy69XESlv4KZmkArdRiaQIfagWW8c4Z/jMBqhEr53IYK+4HxiHGhH4EG+NKpU4iPE3738lZXjq+UL/ilA0dA/gyo2WoXd1FKDUoT6FDrCNEItNZAqwiwpbKZzJQEirKHsWx9EMZkpXD9MRP4xwflOgqtolf9Trv1UwKtnTiUOiRNoEOto8FuR5RA52oCrSLC5v3NTB+TGdIJhAN9+jh7W/vhFWWunUMpT/ltBLpght1qGYdSB6UJdKgdKOEYZhs7sMt5t9WCMaGJSSkXGGPYXNnMtMJMV89TlJPKSdPyeXhFGT29fa6eSylP1O+C9HxIzvA6Eitvmt1qAq3UQWkCHWoHEugRlnD0dkJXa2hiUsoFVc2dNLZ3M32Muwk0wGWLS6hq7uS9XXpnRkWhup3+mUAIkJQOORM0gVbqEDSBDrVQJdCgnTiUr22ubAZgWhgS6JOm5ZOUEMeLG/a7fi6lwq5+t3/KNwLyZ2gvaKUOQRPoUOtohLgESEwb/jEOLOetnTiUf+2obgFgckHoViA8mPTkBI6fksdLG/djtLRJRZOeLmgq98ciKv0VzIBa7cSh1MFoAh1qnU3OaoIjmFR1YDlvHYFW/rWrto2M5ATyM5LDcr7TZhZQVtfO9motbVJRpLEMTJ8/R6B7uz7sEKKU+ghNoENtJMt4B6QGRqA1gVb+tbOmldK8NFc7cPR39CT7wXK51kGraFIXaGHnsxHofO3EodShaAIdaqFIoA+MQGsJh/KvnTWtlI52v3wjYFJeOnkZSSzfqQm0iiJ+6wEdEOjEoXXQSg1KE+hQ62iE5BG0sANIzQFEJxEq3+rq6aO8vo1JeeFLoEWEI0tzeVcTaBVN6ndBQgpkjPE6ko9KzoCc8ToCrdRBaAIdaqEYgY6Lt0m0jkArnyqrb6PPQGkYE2iAxaW57G1oZ39TR1jPq5Rr6nfZ0ec4H74d58/QBFqpg/DhKzbChSKBBmc5b02glT/tqrET+cKdQM8ZZ19b6/Y2hvW8SrkmkED7Uf4MqNFOHEoNRhPoUAtpAq23qpU/7XQS6IlhrIEGmDnW9pxev68prOdVyhXG+G8Rlf7yZ9hFvQJLjSulDtAEOpR6u6G7DVJyRn6s1FxNoJVv7axpJSctkVHpSWE9b2ZKIqWj01i/T0egVRRorYHuVv+OQBcEOnFs9DYOpXxIE+hQ6nBGxUI1Aq2TCJVP7aoNbweO/maPy9YRaBUdAh04/LaISkDedLvVOmilPkYT6FDqaLDbkCTQo2wNtK66pnyorK6d8bkjWG1zBGYXZVFe305je7cn549WInK3iFSJyLqDPH+yiDSKyCrn64fhjjHqBEoj/DoCnZwB2eOhSkeglRpIE+hQ6nBuK6eMsI0d2BHong5bEqKUj/T2GfY1tFM8KtWT808tsHXQ252lxFXI/BU46zD7vGGMme98/SQMMUW3wCIqOeO9jeNQxsyG/Ru8jkIp39EEOpQOJNAhKuEArYNWvrO/qYOePkPxKG9GoCfn29KR7VWaQIeSMeZ1QC844VS/CzKLINGbD6NBGTMbarZAT6fXkSjlK64m0CJylohsFpFtIvLdQZ6/WUTWOrcD3xSRWW7G47pQJtAHlvPWVnbKX8rr2wEoyfXmTX98bhpJ8XFs0xFoLxwjIqtF5FkRme11MBGvfqd/yzcCxswG0wvVm72ORClfcS2BFpF44DbgbGAWcNUgCfL9xpg5xpj5wC+A37gVT1i4MgKtCbTyl7I6W1bk1Qh0QnwcpXlpbK9q9eT8MWwlMMEYMw/4P+CJg+0oIjeJyAoRWVFdXR2u+CJP/S7/TiAMGHOE3e5f720cSvmMmyPQS4Btxpgdxpgu4EHgwv47GGP6T6VPByJ7xlxniLtwALTXj/xYSoVQYAS6KCfFsxgm52ewQ0egw8oY02SMaXG+fwZIFJG8g+z7J2PMYmPM4vz8/LDGGTG626G5wv8j0LmT7FLj+wedW6pUzHIzgR4HlPX7udx57CNE5Isish07Av0VF+NxX0cjSBwkZYz8WGlawqH8qby+jTFZySQnxHsWw5SCDHbXtdHV0+dZDLFGRApFRJzvl2DfP/QCNVz1u+3Wr4uoBMQn2AVVdARaqY9I8DoAY8xtwG0icjXw78ANA/cRkZuAmwDGj/fxbOWORkjOAvseMzIpOYBoAq18p7y+3bPyjYDxuWkHuoGEeznxaCUiDwAnA3kiUg78J5AIYIy5HbgUuEVEeoB24EpjtM/msPm9hV1/Y46ArS94GoIxhsqmDtaWN7KjppXKxg4qGtvp7TMkxMWRnBhHUU4qMwozWVyay7gcH0/MVFHBzQR6L1DS7+di57GDeRD442BPGGP+BPwJYPHixf69YIdqGW+wn/pTc+xKVUr5SHlDGwvHj/I0hkAP6rL6Nk2gQ8QYc9Vhnr8VuDVM4UQ/vy+i0t+Y2bDq79BSBRkFYTmlMYadNa28tb2Wt7fVsHxXPTUtH3YCyUxOoDA7haSEOLp7+2jv7uWZtRV099oUYV5JDhfOK+L8eUXkZyaHJWYVW9xMoJcDU0VkIjZxvhK4uv8OIjLVGLPV+fFcYCuRrKMxND2gA9LzoVUn4Cj/6Onto6Khg+J53o7ulDgJ9J467ZOuIlT9LlvuF5jv4mdjnIYr+9e7mkDvb+rg7e01vLm1lne217CvsQOAouwUTpyax7ySHI4Yl820MRlkpiR+7Pd7evvYvL+ZN7bWsHTVPn7y1AZ+/twmrj5qPLecNJmCLO/mbajo41oCbYzpEZEvAc8D8cDdxpj1IvITYIUxZinwJRE5HegG6hmkfCOidDQ6pRchkl6gCbTylf3NnZ72gA4Yk5VCYrxQVtfuaRxKDVud08IuFCV/buufQE8+JWSH7ejuZfmuOl7fUs1rW6rZst9ODM5JS+TYyaP5wuQ8jpuSR+noNCSIv1NCfByzi7KZXZTNzSdNZuv+Zv70+g7+9s5u7n93D587YRJfOnUKKYnezd9Q0cPVGmhnpvYzAx77Yb/vv+rm+cOuo9HOWA6VjHyoWBO64yk1QuXOiK/X9YXxcULxqLQDLfWUijj1uyBvqtdRBCc9DzIKQzKRsKq5g2fXVvLK5iqW7ailo7uPpPg4jpw4ik8uLOb4KXnMGptFXNzIP1hMHZPJLy+bxxdPmcJvX9rCra9s48k1+/jZRXM4fuqgDWSUClpQCbSIPA7cBTxrjNFp7wejI9AqylUEbqn6YIJO8ahUyuo1gR6MXrN9rq/PJtBTz/A6kuCNmT3sVnZdPX08tWYfj75fzrIdtfQZmJiXzpVHjufEaXkcPWk0aUnujeeV5qXzuysXcMXiEn7wxDquvetdLl9czH+cN2vQUhClghHsv9g/AJ8Gfi8ijwB/McboskQDhXISIdgR6M4m6O6ARK3dUt4LJNCF2d7/eyzJTWPt2gqvw/ArvWb7WUsl9HZGxgTCgDGz4d3bobfHTnIPQktnD397Zxf3vL2L/U2dTMxL50unTuX8uWOZOibT5YA/7tgpeTz71RP43ctbueO17by1rZZfXjaXYyfraLQauqBeBcaYl4CXRCQbuMr5vgy4E/i7MabbxRgjQ28PdLWENoFOdyZrtFZDTsmh91UqDCoa28lMSSAj2fMOmIzPTaOhrZvmjm4dRRpAr9k+F0kt7ALGHAG9XVC7DQpmHHJXYwxPr63gp09tYH9TJ8dPyePnl8zlpGn5QdUyuyklMZ7vnDWD02eO4VuPrObqO9/l08eV8p2zZmhttBqSoBdSEZHRwKeAzwIfAL8DFgIvuhJZpAnlKoQBgdnOrVWhO6ZSI1DR2EFRtvflGwAlzkRGnUg4OL1m+1id08LO74uo9HdgIuGhyzgqGzu4/u73+NL9H5CXkcxjtxzL3z97FCdPL/A8ee5v0YRRPP2V47nhmAn85a1dnPP7N/hgj678q4IXVAItIv8A3gDSgPONMRcYYx4yxnwZCMGye1Ggo8FuQzoC7SyB26J10MofKhrbfVG+AVCSaxN5bWX3cXrN9rn6XXbV2uwIurOYPx3ik6Fi1UF3eWd7Lef+/g3e313Pj86fxT+/eByLJnjbM/5Q0pIS+PGFR3DfZ4+io6uXS/74Nr96frOucKqCEux92DudjhoHiEiyMabTGLPYhbgiT0ej3bqRQOsItPKJysYO5owL4b/xEQgsplKuEwkHo9dsP6vfCVnFkJDkdSTBi0+0o9D7Vg369BMf7OXbj65mwuh0Hrp2IVMKwl/jPFzHTcnjua+fyE+e3MCtr2zjX5uq+M0V85hRGMJ1HVTUCbaE4/8N8tg7oQwk4rmRQAdKOFo0gVbe6+zppaali8Isf5RwZKcmkp4Uz94GLeEYhF6z/ax+F+SWeh3F0BXNh4rVtotIPw8vL+PrD69i0YRRPHbLsRGVPAdkpSTyq8vmcef1i6lq7uD8/3uTP7y6jd4+/y5+rLx1yARaRApFZBGQKiILRGSh83Uy9tagCnAjgU5MhaRMXc5b+cL+RruM7tgcf5RwiAiF2SlUOp1BlF6zI0ZgEZVIU7TAzvcJLEMO/HPVXr7z+BpOmJrPXz+9hOzUyJ7Qe8asMbzw9ZM4Y9YYfvHcZi67/W02VTZ5HZbyocOVcJyJnYRSDPym3+PNwPddiikyuZFAg21lpyUcygcqGu1I71if1EADjM1OPdBaTwF6zfa/zmZoq4msCYQBY+fb7b4PYPRk3t5Ww7ceWc2S0lz+dN2iqOlikZuexG1XL2Tp6n388J/rOft3b3DpwmK+8YlpjPXJJGrlvUMm0MaYe4B7ROQSY8xjYYopMrmVQKcXaAmH8oVAouqnN5DC7BTe3Kp3aAL0mh0BIrGFXUDBTDuRcN8HlI07h1vuW0np6HT+dP3iqEmeA0SEC+eP46Rp+dz2yjbueXs3T67Zx43HT+TzJ00mS1tnxrxDJtAicq0x5u9AqYh8Y+DzxpjfDPJrsamj0c6qTgrxBPeMfKjZGtpjKjUMHybQfhqBTqGquYOe3j4S4oPuyhm19JodAQIJdCQtohIQnwiFR9C7bxWfv/d9jDH8+YbFEV+2cSg5aUn84NxZXH9MKb96YTO3vbKdvy/bw80nTeaGYye4uoKi8rfDveOkO9sMIHOQLxXQ0QjJWRAX4jfx9HwdgVa+UNHYTlZKAuk+WEQloDA7hT4D1S2dXofiF3rN9rtIHoEGGDuf7vIP2FjRwP9eOZ8Jo9MP/ztRoCQ3jd9duYCnvnw8C8bn8PPnNnHiL17lr2/tpLOn1+vwlAcOV8Jxh7P9cXjCiWChXsY7IL0A2uugt9t++lfKIxWNHb4q34APR8P9GJsX9JodAep2QkoOpPq3P/Kh7EiayqTeVr48P55TZ4zxOpywO2JcNn/99BKW76rjV89v5kdPbuDON3by1dOncunCYuLi/LNYjHJXsAup/EJEskQkUUReFpFqEbnW7eAiilsJdIbTC7qtNvTHVmoIKhrbfdOBIyDQUk87cXyUXrN9rH5XxI4+t3T28JOVtnf1F6Y1exyNt44szeXBm47m3huXkJeRxL89uobL7niHzZWx/XeJJcHWG3zCGNMEnAfsAqYA33YrqIjk5gg0aBmH8lxlY4ev6p/hoyPQ6iP0mu1X9RHawg74r2c28lZjHn1xSaRUr/E6HM+JCCdMzeeJLx7Hry6bx47qFs79/Rv85sUt2j86BgSbQAdKPc4FHjHGNLoUT+RybQTaSaC1lZ3ykN8WUQnISUskJTGOykZdTGUAvWb7UV8vNOyJyAmEq8sauP/dPdxw3FTixs6xC6oowCbSly4q5uVvnsz584r4/ctb+dRf3qO+tcvr0JSLgk2gnxKRTcAi4GURyQd0yKe/jkZb1xZqgeW8W6pDf2ylguS3RVQCRER7QQ9Or9l+1FgOfT0RNwLd12f40ZPryctI5qunT7ULquxb9bEVCWNdbnoSv71iPv/9yTm8u6OO8299k40VughLtAoqgTbGfBc4FlhsjOkGWoEL3Qws4ugItIpilU029yrM8lcCDTYmrYH+KL1m+9SBDhyRNQL9jw/28sGeBr579gwyUxLtgipdzVC33evQfOmqJeN5+OZj6Ok1XHHHO6zcU+91SMoFQ+m5NgO4QkSuBy4FPuFOSBGotwe6WtxJoJMyIDEdmitDf2ylglTVbBPUgqxkjyP5uLHZKToCPTi9ZvtNYAnsCBqBbu7o5n+e28T8khw+uWCcfbD4SLste9e7wHxufkkOj95yDKPSk7j+rvdYW65VVNEm2C4c9wK/Ao4HjnS+FrsYV2TpdG7RuJFAi0BWETTtC/2xlQpSVZMt4SjI9N8I9JjsFPY3ddCnk3YO0Gu2T9XthLhEyC72OpKg/eHV7VQ3d/LjC2Z/2KItb5otWdyzzNPY/K54VBoP3XQMOWmJ3PCX99hR3eJ1SCqEgl0RYTEwyxij71CD6WiwWzcSaICssZpAK09Vt3SSGC+MSvNfL/KCzGR6+gz1bV2MzvDfCLlH9JrtR3U77OhzXGQse13d3Mlf39rFBfOKmFeS8+ETcXEw/mgdgQ5CYXYKf7/xKD75x7f57N9W8I8vHBfVKzfGkmBLONYBhW4GEtE6nFszqTnuHD+zCJor3Dm2UkGoauokPyMZEf8tEhAYFdfVCD9Cr9l+VLcTcid5HUXQ/vjqdrp6+/ja6VM//mTJUVCzBVp1jYLDKc1L5/ZrF1FW18bXH1qld8uiRLAJdB6wQUSeF5GlgS83A4sogQTazRHo5gqd8aw8U9XcQX6mP0d3A3XZgTITBeg123+MsSPQEdLCrqKxnb+/u5tLFo5jUn7Gx3cYf7Td6ih0UJZMzOU/zpvFvzZVcdebO70OR4VAsCUcP3IziIjndgKdWWRbH7XVfNiVQ6kwqm7upHhUmtdhDCrfKduoatYEup8feR2AGqC1GrpbI2YE+tZ/bcMYw5dPHWT0GWwru7hEKFsGM84Jb3AR6rqjJ/DWthp+/twmjpuSx6yiLK9DUiMQbBu717CrWSU63y8HVroYV2QJxwg0aB208kxVc6cvO3DAhyPQ1ZpAH6DXbB+q22G3EZBAl9W18dDyMq44soSS3IN8cE5MhaL5sEdHoIMlIvz8krnkpCXxrUdW092rd5UjWbBdOD4HPArc4Tw0DnjCpZgij+sJdJHdah208kBXTx91rV0U+LSEIy0pgYzkhAOt9pRes30pghLoO17fTpwIXzxlyqF3LDkK9q2Ebn3tBSsnLYmfXXwEGyqauPONHV6Ho0Yg2BroLwLHAU0AxpitgNYSBHQ0gsTZns1uyHQSaB2BVh6oafFvC7uA/MxkLeH4KL1m+03dDvs+kV3idSSHVNPSySMryrl4wTjGZqceeufxx0BvF1SsCkts0eLM2YWcMWsMt/5rG1VN+uEjUgWbQHcaYw4s6i4iCYBOIw0IrELoVoeCjAKQeB2BVp4IlEb4dQQabAKtJRwfoddsv6nbaZPnhCSvIzmkv761i67ePm46KYiR8pKj7Fb7QQ/ZD86ZSU+v4RfPb/Y6FDVMwSbQr4nI94FUETkDeAR40r2wIoxby3gHxMVDxhgdgVaeCIzs+rULB9jkXhPoj9Brtt/U7fB9+UZLZw9/e2cXZ84qZPJgnTcGysiH3MnaiWMYSvPS+czxE3n0/XJWlzV4HY4ahmAT6O8C1cBa4PPAM8C/uxVUxHE7gQZdTEV5xs/LeAfkZybrrdCP0mu230RAAv3Au3to6ujh5pMnB/9L44+2I9DaZnXIvnTqFPIykvnxk+vRNY8iT7BdOPqwE1C+YIy51Bhzp65w1U84EujsEmgsc/ccSg2iqqkTEcjz8Sp/BZkptHb10trZ43UovqDXbJ9pq7Mr1vo4ge7q6eOuN3dy9KRc5vdfdfBwSo+H9jqoWu9abNEqIzmBfztzOiv3NPD8+kqvw1FDdMgEWqwfiUgNsBnYLCLVIvLD8IQXIcKRQOeMh4Yy/ZSvwq6quZPctCQS44O9YRV+gfrsWC/j0Gu2T9U5C2f4eBGVZ9dVUNnUwedPHMLoM8DEk+x2x2uhDyoGXLKomIl56fz+5W06Ch1hDveO+HXsTO4jjTG5xphc4CjgOBH5uuvRRYpwJNCjJkBvJ7RWuXsepQao9vEqhAEHViOM8QQavWb7U30ggfbvCPTdb+1iUl46J03LH9ovZo+D0VNgpybQwxEfZ9sFbqho4uWN+v4eSQ6XQF8HXGWMObDupDFmB3AtcL2bgUWUjkZIyXH3HDkT7LZ+t7vnUWqA6uZOCrL828IOPpzgGOsj0Og1258CPaBHlXoaxsF8sKee1WUN3HBsKXFxw+gmNfEk2P029HaHPrgYcOH8IsbnpvH7f23VUegIcrgEOtEYUzPwQWNMNZDoTkgRprcHulrCU8IB0LDH3fMoNUBVc+eB5bL9KtCjWhdT0Wu2L9XtgKxxdvU+H/rr27vISE7gkkXFwzvApJPs++De90MbWIxIjI/jCydPZk15I69tqfY6HBWkwyXQXcN8LnZ0NtltOCYRAjToCLQKn74+44xA+zuBzklNJDFetIRDr9n+5OMOHPubOnh6TQWXLS4mIzlheAeZeKJdJGbby6ENLoZ8cmEx43JS+f3LOgodKQ6XQM8TkaZBvpqBOeEI0Pc6GuzW7QQ6KQ3SCzSBVmFV39ZFT5/x9SIqAHFxQl5GMlVNMZ9A6zXbj+p2+rZ8475lu+k1hhuOKR3+QVJH2UVVtr4QsrhiTVJCHDedOImVexr4QPtCR4RDJtDGmHhjTNYgX5nGGL0dCLb+GdxPoMHpxKElHCp8qpr9v4x3QEFmMtUtsZ1A6zXbhzqb7eRvH45Ad/f2cf97ZZwyvYDSvPSRHWzqGXZJ7+b9IYktFl26qJjM5AT+8tYur0NRQfBvX6pI0d5gt8lZ7p9LE2gVZgcSaJ+XcADkZ6boYirKfwITCH2YQL+0YT81LZ1ce/T4kR9s6ifsdttLIz9WjEpPTuDyI0t4dm0FlY16LfM7TaBHqr3ebtNy3T/XgV7Qve6fSyk4kJD6vYQDbCcO7cKhfKd2m92OnuJtHIO4/709jM1O4aRpBSM/2JgjIHMsbH1+5MeKYTccU0qvMdz3rpZr+p0m0CMVSKBTR7l/rtGToa9bVyRUYRMoifB7H2iwSX5dWxc9vbrYkPKRmkACPcQFSlxWVtfGG1truOLIEuKH07puIBGYdhZsfQm6dfR0uMaPTuO0GWO4/909dHTrYJmfaQI9UoEE2u0+0ACjp9ptzVb3z6UUdhnvjOQE0pKGOTs/jPIzkzEG6lq12YTykdpttouSz1rYPbh8D3ECVxxZErqDzjwfulthxyuhO2YM+sxxpdS2dvHk6n1eh6IOQRPokWqvh8Q0SAzDJKs8TaBVeFU3d0ZE+QZAXoauRjhSInK3iFSJyLqDPC8i8nsR2SYia0RkYbhjjDi1W31XvtHd28fDK8o5dUYBY7NDmNiXngDJ2bDxqdAdMwYdM3k008ZkcO8yLePwM02gR6q9ITzlGwBpo+25araE53wq5lVFwDLeAQdWI4zxThwj9FfgrEM8fzYw1fm6CfhjGGKKXMZA7XbfJdAvb9xPdXMnVy0JweTB/hKSYPpZsPlpu8iYGhYR4aol41lT3siGfU1eh6MOwtUEWkTOEpHNzmjFdwd5/hsissEZyXhZRCa4GY8r2uvDl0CL2DIOHYFWYVIVAct4BxToct4jZox5Hag7xC4XAn8z1jIgR0TGhie6CNRSZRfbCtw99In73ytzJg/mh/7gMy+w74s7Xw39sWPIxQvGkZQQx8MrdM6TX7mWQItIPHAbdsRiFnCViMwasNsHwGJjzFzgUeAXbsXjmnAm0AB50+wtQaVcZoyhqinySjg0gXbVOKD/O3q585gaTK3/JhDayYPVXL64hIR4F1KAKafbdRHWPBL6Y8eQnLQkzppdyOMry3UyoU+5OQK9BNhmjNlhjOkCHsSOXhxgjHnFGNPm/LgMKHYxHne014U5gZ4CLfs/XMBFKZe0dPbQ3t0bMSUcqUnxZCYnUKMlHJ4TkZtEZIWIrKiurvY6HO8EBjtG+2cE+qHlZQghnjzYX2IKzLoQNj4JXa3unCNGXHFkCU0dPTy/vtLrUNQg3EyghzpScSPw7GBP+Ppi7MUINGgZh3Jd9YFVCCMjgQbtBR0Ge4H+mVex89hHGGP+ZIxZbIxZnJ/vQplApKjdBvHJtguHD/T09vHwCrvyYFGOi11B5l5hu3FsHvQtXQXpmEmjKclN5cH3tIzDj3wxiVBErgUWA78c7HnfXoyNCX8CnT/Dbqs2hO+cKiZF0jLeAXmaQLttKXC9043jaKDRGFPhdVC+VbPNlm/E+eKtlje21lDV3Mlli11O6McfC9nj4YN73T1PlIuLE65YXMI7O2rZXauj+X7j5qs6qJEKETkd+AFwgTEmst75utugtyu8CfSoiZCUCRWrw3dOFZMiaRnvgPyMZO3CMQIi8gDwDjBdRMpF5EYRuVlEbnZ2eQbYAWwD7gS+4FGokaF2m686cDy6spxRaYmcOiMEKw8eSlwcLLwOdrz64VLmalguXVRCnMAjK8q9DkUN4GYCvRyYKiITRSQJuBI7enGAiCwA7sAmz1UuxuKOcK5CGBAXB2PnQsWa8J1TxaRIWsY7QEs4RsYYc5UxZqwxJtEYU2yMucsYc7sx5nbneWOM+aIxZrIxZo4xZoXXMftWbzfU7/RNAt3Y1s2LG/Zz4Xzb3cF1C64FiYOVf3P/XFGsMDuF46bk8cSqvfT1Ga/DUf249ioyxvQAXwKeBzYCDxtj1ovIT0TkAme3XwIZwCMiskpElh7kcP7kRQINMHYeVK6FPp2Zq9xT3dxJUnwc2amJXocStPzMZJo7enTWuvJewx7o6/FNC7un1u6jq6ePSxaGaa5+VpFd2nvlvbq09whdvGAc5fXtvL+n3utQVD+ufgw1xjxjjJnmjFb8zHnsh8aYpc73pxtjxhhj5jtfFxz6iD7jZQLd064TCZWrqpo7yc9MRkS8DiVo+drKTvlF4PrskxHox94vZ9qYDI4YlxW+kx71eWirgTUPhe+cUejM2YWkJsbzjw8+VgWrPOSPmQ2RyqsEunCu3VZqGYdyT7WTQEeSQLzayk557kAPaO8T6B3VLazc08AlC4vD+4F44klQOAfeuQ36+sJ33iiTnpzAmbPH8PSaCjp79O6aX2gCPRJeJdB50yAhFfZ9EN7zqphS1dwRUfXP0G85bx2BVl6r3QqpuZCW63UkPLaynDixpQBhJQLHfgVqNsOW58J77ihz8cJiGtu7eWWTz1r5xjBNoEeizVnxNtwJdHwCFC+G3W+F97wqpthlvCM0gdYRaOW12u2+qH/u6zP8Y+VeTpiaT0GWBy0pZ19su0e9+l86Cj0Cx00eTV5GMk9oGYdvaAI9Eu31kJACSWnhP3fpCbYTR7tOKlCh19nTS0Nbd0T1gAbITU8CdARa+UDNVl+Ub7yzo5Z9jR1cssijhX7jE+Hk79mJ7xsjq0+AnyTEx3HBvCL+tamKxrZur8NRaAI9MuFeRKW/0uMBA7vf8eb8KqpF4iqEAInxceSmJ2kCrbzV3gAtlR+uHOuhx94vJzMlgU/MGuNdEHMutYuAvfwT6NHX5nBdvGAcXb19PLde1y7yA02gR8LLBLp4sR393vWGN+dXUS2wiEqkTSIEZzEVTaCVl6o3221g5ViPtHb28Oy6Ss6bO5aUxHjvAomLhzN/BnXbYdkfvIsjwh0xLosJo9N4ao0m0H6gCfRItDd4l0AnJEPJEtjxmjfnV1GtqinylvEOyM/U1QiVx6o32W2Btwn0ixv2097dy8ULPCrf6G/K6TD9XHjtl7ZHthoyEeG8uWN5e3sttXqN85wm0CPh5Qg02Cb1VevtZBWlQiiQgEbaJELQ1QiVD1RvgsQ0yB7vaRhLV+9jbHYKiyd4+D7V39n/Y1cnfOILOqFwmM6bW0Rvn+HZdZVehxLzNIEeifZ6SM3x7vyzLrTb9f/wLgYVlaqbOhCB0c6kvEiSn5lMTUsnxuiyt8ojVRtt/XOcd2+x9a1dvL6lmvPnFREX55PFkHLG21KOXW/Astu8jiYizSjMZHJ+Ok+t2ed1KDFPE+iR8HoEOrsYipfA+ie8i0FFparmTkanJ5MQH3mXiPyMZDq6+2jp7PE6FBWrqjdDwUxPQ3h2XSU9fYYL5hV5GsfHLLweZp4PL/4n7NQ5PENlyziKeHdnHVVNukS6lyLv3dEvutvtctpeJtBge2zuXwuV67yNQ0WVqubOiOvAEZCXqa3slIfaG6B5H+RP9zSMpav3MikvndlFYVy6OxgicOEfYPRkePi6DydcqqCdN3csxsAza3UyoZc0gR6u9ga79TqBnnclJKbD2//nbRwqqlQ1d0RkBw6A/Aw78VETaOWJmi12m+/dCHRlYwfv7qzj/HlF4V26O1gpWXD1QxCfBH+7SOfxDNHUMZlMH5Op3Tg8pgn0cLV7tArhQGm5sOgGWPcoNJR5G4uKGlVNkTsCrasRKk9VbbRbD0egn1qzD2Pggvk+K9/oL3cSXPs49HbCXZ+AvSu9jiiinDd3LCt217Ovod3rUGKWJtDD1Vpjt2l53sYBcPQX7MzmF/7d60hUFOjtM9S0RN4y3gEHEmgdgVZeqN4MCamQM8GzEJ5cvY/ZRVlMzs/wLIagFB4Bn3nediy5+yxYfhfo5N+gnOfUtmsZh3c0gR6utkACPdrbOABySuCk78CGJ2Dd415HoyJcXWsXfSYye0AD5KQmkhAnmkArb1RvhHzvOnDsqmlldXmj/yYPHkzeVLjpVbu67tPfgHvO1zk9QZjo1LdrGYd3NIEerjanhCPdByPQAMd9DcYtgidugW0vex2NimBVzXZmd6SWcMTFCXkZtpWdUmFXvdnT+ucnV9v2ZudFSgINkD4arnkUzvtfqFwDtx8HD15j38t6u72OzrfOm1vEqrIGyuravA4lJmkCPVyBEo7UXG/jCIhPgKsfgdFT4L5L4dnvQs02r6NSESiwjHeklnCALqaiPNLRCE17Pat/NsawdPU+jiwdxbicVE9iGLa4OFj8afjqajjx27D7bfj7J+HnE20y/e6fYMer0Fiui7A4zps7FoCntYzDEwleBxCx2mogJccmrn6RPho+9TS89CN493Z49482xqwiSM6yM5+Ts+yo+eRT7Vd8otdRK5+pjuBlvAPyMpJ0EqEKv0BLNo96QG+qbGZrVQs/vXC2J+cPidRRcOq/wwnfgm0vwraXYOtLsOmpfjuJfT9LybH10/GJkJAM8cmQkGS3/R9LTLWLuORNtf9vRpV69B8XWiW5acwrzuaZtRXcfNJkr8OJOT7K/iJMW61/yjf6S82B8/8XTvwWbH4WqjZAS5UdGWnZD7XboHm/TbDzZ8IFv4eSJV5HrXwkUMIRqW3swMa+oaLJ6zBUrKneZLcejUAvXb2P+DjhnDljPTl/SCWm2AVXZp5vJxY2V0DNVqjdat/DOhqho8GuydDbBT2ddtvRZDt79HZ/+FhXi90/YFQpTD4Npp8Dk0+BuHiv/itH7Jw5Y/nvZzexp7aN8aPTvA4npmgCPVytNf7owHEw2cWw5HODP9fTBZufhhd+CH89Dy7/G0w/K7zxKd+qau4kMyWBlMTIfVOxy3l30ddn/LOMsYp+VZucDhylYT+1MYYnV+/juCl5jM6I3A+/gxKxd1KzimDSScM7Rlud7TddscrWVq9+EFbcBdklcMwXYeENkBR5CWgggX5mnY5Ch5vWQA9XW60/OnAMR0KSXcHw86/BmNnw0LWw932vo1I+UR3BqxAG5Gck09tnqG/r8joUFUs87MDxQVkD5fXtkdN9I9zScqHkSDuwdPWD8J2dcNk9trTjue/CrUfCusciro1eSW4a80pyeFq7cYSdJtDD1VZra44jWVouXPsYZIyBR2+0t75UzLPLeEdu/TNAvhO/1kGrsKpcB2PmeHLqpav2kZQQx5mzx3hy/oiTkAyzL4JPP2PnDqXlwqOfsQNKrbVeRzck584pZO3eRvbUajeOcNIEejiMcUagfVzCEay0XLjkz1C/C974ldfRKB+oau6I6A4c8GH9dk2zjkCrMGmuhNYqKAx/At3bZ3h6bQWnTi8gM0Unhg9Z6fG2F/UZP4WtL8AdJ0bUXdlAzbt24wgvTaCHo6MB+noit4RjoAnHwLwrYdnt0LDH62iUh4wxEb2Md8CHy3l3eByJihmVa+3WgwR62Y5aqps7/b10t9/FxcNxX4EbX7Ar+/71vIhZU6F4lC3j0FUJw0sT6OHw2yIqoXDqv9uJGq/93OtIlIeaOnro7OmL6A4cYNvYgS7nrcKoco3dFh4R9lMvXbWP9KR4Tp1REPZzR52iBfC5lyF3Mtx/BWz4p9cRBeW8OWNZu7eR3bWtXocSMzSBHo7AIirRUMIRkF0M86+BNQ9DS7XX0SiPVDst7MZkRXYNdEZyAimJcZpAq/CpXAs5EyAlO6yn7ezp5dl1FXxidmFEd87xlYwC+NSTNpl+5FP2fdHnzp5TCGgZRzhpAj0cbYEE2ierEIbK0bfYnpkr7vI6EuWR/VGwiAqAiOhqhCq8Ktd6Ur7x+pYamjp6tPtGqKWOguufgAnHwRO32FUQfax4VBrztYwjrDSBHo42Z4ZuNJVwgF2laeonYPld0NvjdTTKA/ubAiPQkV3CAbaVnXbhUGHR2WJ7DI+dF/ZTL129j1FpiRw/Ncrej/wgKR2uvA/ypsFD18H+9V5HdEjnzhnLur1NWsYRJppAD0erU+IQTSUcAQtvsDPJt//L60iUBw6MQEd4CQegI9AqfKo2ACbsI9BtXT28tGE/Z88ZS2K8vp27IiUbrnnEJtP3XQZN/h3h1TKO8NJX3HC0VEFSZkSuWnRYUz8Bqbmw+n6vI1Ee2N/UQUZyAhnJkb9IaWA1QqVcd2ACYXgT6Bc37Ke9u1fLN9yWXQxXPwztDbZXtE/v0BaPSmPBeF1UJVw0gR6Olio7ySAaJSTBnMtg0zP2YqFiSjT0gA7Iz0ihrrWL7t4+r0NR0a5yra2ZzRoX1tM+uXofhVkpLCmNsvk4fjR2Lpz3W9jzNrzyM6+jOahz54xl/b4mdtVoGYfbNIEejmhOoAHmXg69nbDlea8jUWG2v6mTMRE+gTAgL9O2sqvVUWjltsAEQpGwnbKhrYvXtlRz3tyxxMWF77wxbd4VsPB6ePM3sPVFr6MZ1Nm6qErYaAI9HK1RnkAXLYTMsbBxqdeRqDDb39QRFRMIwU4iBO0FrVzW22MnlxXODetpn1tXSXev0cVTwu3sX8CYI+Dxm3xZDz0uJ1XLOMJEE+jhaNkPGWO8jsI9cXEw41y7ClNXm9fRqDAJrEIY6T2gAwKLwVQ162qEykW126CnI+z1z0tX76N0dBpzxoW373TMS0yFy/4K3e3w5FfBGK8j+phz54xlQ0UTO7WMw1WaQA9Vdwd0NEJ6FI9AA8w4D3ratRtHDGlo66arty8qOnDAh4vBVOkItHKTB0t4VzV18M6OWi6YV4SEsWxEOfKmwun/CVufh1X+m3B/jlPGoT2h3aUJ9FAFWthFcwkHQOnxkJIDm57yOhIVJvubo6cHNNgRaBGoaNQRaOWiilUQn2x7BYfJU2sqMAYt3/DSks/bRVae+y407vU6mo8oyklloZZxuE4T6KFqqbLbaC7hAIhPhOlnw+Znobfb62hUGAR6QEdLCUdifBz5GclUNrZ7HYqKZuUroGi+vWaGydLV+5g5NospBZlhO6caIC4OLrwN+nph6Zd9V8pxjlPGsaO6xetQopYm0EPVGkig872NIxymnw0dDVC+3OtIVBgcWIUwSrpwAIzNTqGySUs4lEt6u+0I9LjFYTvlnto2VpU1aO9nP8idCKf/CLa/DOse8zqaj9AyDvdpAj1ULfvtNtpHoAEmnQwSD9te8joSFQZVTgIdLX2gwY6m6wi0cs3+9XYCYfGisJ3yn6tsucD588aG7ZzqEI680Xaueu57vlo7oSgnlUUTRvH02kqvQ4lamkAPVaCEIz0GRqBTsqFkie3GoaLe/qZOslMTSUmM9zqUkBmbnUKl1kArtwTuzhUfGZbTGWN4YtVelpTmUjwqClfCjURx8XaBlbYa+NdPvY7mI86ZM5aNWsbhGk2gh6qlyk6uS4ieUbpDmnKavUXZUu11JMpl0dQDOqAwO5Wmjh5aO/259K6KcHvftx2ZskvCcrr1+5rYXt3KhQu0fMNXiubbSYXL74Ly972O5oBz5hQC6GRCl2gCPVTR3gN6oCmn2622s4t6Vc3R0wM6oDDbfiCobNJRaOWC8uV29DlMreSe+GAvifHCuXO0fMN3Tvk+ZBbCU1+zi+v4wNjsVBZPGMXS1fswPpvkGA00gR6qlv3R38Kuv8J5kJanddAxoKqpg4IomkAIUJiVCsB+LeMIioicJSKbRWSbiHx3kOc/JSLVIrLK+fqsF3H6QludXUQlTPXPvX2Gpav3cfL0AnLSksJyTjUEKVlw1v9A5Rp4709eR3PARQvGsbWqhQ0VTV6HEnVcTaCDuBifKCIrRaRHRC51M5aQadoHWeO8jiJ84uJg8ql2lnFfn9fRKJf09RlnBDq6SjjGZtsPBNoL+vBEJB64DTgbmAVcJSKzBtn1IWPMfOfrz2EN0k/2rrTbMHXgWLajlqrmTi6aH0PvP5Fm1oUw5Qx45Wc2V/CBc+eMJTFeeOIDf/WqjgauJdBBXoz3AJ8C/LeUz2D6+qC5ArJirP5syunQVguVq72ORLmkpqWTnj5DYXaUjUA7/z1awhGUJcA2Y8wOY0wX8CBwoccx+dfeFYDAuIVhOd0TH+wlIzmB02bG0B3QSCMC5/zStjd84d+9jgaAUelJnDStgH+u2kdvn5ZxhJKbI9CHvRgbY3YZY9YAkTG02VoNfT2xl0BPPtVut2oZR7Ta54zQFmWnehxJaKUkxpOTlqidOIIzDijr93O589hAl4jIGhF5VETCM3vOj8pXQMFMSHZ/MZOO7l6eW1fJWUcURlWXnKiUOxFO+IbtC73jNa+jAeDiBeOoau5k2Y5ar0OJKm4m0MFejCNHs3NLJtYS6Ix8GDvPlnGoqLSvwfZKLsqJrgQaoDArRUs4QudJoNQYMxd4EbhnsJ1E5CYRWSEiK6qro7CDjzF2BLo4POUb/9pURXNnj5ZvRIrjvgqjSuGZb0NPl9fRcNrMAjKTE/iHlnGEVERMIvTNxThQ05QZgzOgJ58GZe9BR6PXkSgXfJhAR1cJB9g66ApdTCUYe4H+I8rFzmMHGGNqjTGBpR3/DAw6g84Y8ydjzGJjzOL8/CjsmV+3A9rrw1b//MQHeynITOaYyaPDcj41QompcPYvoGYzLPuD19GQkhjP2XMKeW5dJe1dvV6HEzXcTKAPezEOlm8uxoEEOpYmEQZMOQ1ML+x8w+tIlAv2NXSQlhRPdmqi16GE3LhRqext0AQ6CMuBqSIyUUSSgCuBpf13EJH+owcXABvDGJ9/7Flmt2FYQKWxrZtXN1dz/rwi4uPC0y5PhcC0M2H6OfDaL6Cx3OtouGj+OFo6e3hx436vQ4kabibQh70YR5ymfRCXEBurEA5UvASSMrSMI0pVNLYzNjsFCVM/23AqHpVGQ1s3zR3dXofia8aYHuBLwPPYxPhhY8x6EfmJiFzg7PYVEVkvIquBr2Angcee3W9Dai7kz3D9VE+u2UdXbx8XL4jBgZtId9Z/24Gn53/gdSQcPWk043JSeWRF2eF3VkFxLYEO5mIsIkeKSDlwGXCHiKx3K56QaK6w5RtxEVH5EloJSTDxRLustzZkjzr7Gtqjsv4ZoHiU/e/SUejDM8Y8Y4yZZoyZbIz5mfPYD40xS53vv2eMmW2MmWeMOcUYs8nbiD2y+02YcGxY3gseeb+cGYWZzC7Kcv1cKsRGlcIJ34QNT3i+GFlcnHDJomLe3Faj18IQcfXVH8TFeLkxptgYk26MGW2Mme1mPCPWtDc2658DJp8KDbtt/Z+KKvsaO6KuA0dAyag0AMrq9E1DhUDjXqjfBROOc/1UW/Y3s7qsgUsXFUfl3aGYcOxXYNREZ0Jh5+H3d9Fli4oxBh5d4X1JSTSIwaHUEWiKwR7Q/QXa2W3TMo5o0tnTS3VzZ9SPQJfXt3kciYoKe96x2wnHun6qR1aUkRAnWr4RyRJTbG/o2m3wzm2ehlKSm8ZxU0bzyPtl9GlP6BHTBDpYxjirEMZwAj16sr0lpXXQUWV/ox0VGRuFHTgActOTSE2Mp7xeR6BVCOx6E5KzoHCOq6fp7u3jHx/s5dQZBYzOiK4VQmPO1DNgxnnw+i+hwdsa5MsXl1Be38472hN6xDSBDlZ7PXS3Qnax15F4a/JpthOHD3pbqtAI1MONi9IRaBGheFSqjkCr0Nj5Oow/BuLcXdDk1c3V1LR0cdni2F2rJqqc+V92IO7573sbxuxCslISeFgnE46YJtDBathttzkTvI3Da1NOsx8kyt71OhIVIoEe0GOjbBnv/kpy07QGWo1cwx6o2w6TT3H9VI+sKCMvI4mTp8dg16doNGoCnPhN2LgUtnm3qm9KYjwXLRjHs+sqaWzTzkQjoQl0sBr22G3OeG/j8FrpCbaVn5ZxRI1oXoUwQEegVUgElmaedLKrp6lq7uBfm6q4eME4EuP1bTpqHPsVyJ0Ez/ybpxMKrzxyPF09fTzyvo5Cj4S+MoNVHxiBjvEEOiXL9oTWiYRRY09dG/mZyaQkuntL2kvFo1Jp6uihsV1HXNQI7HgVMsa43v/54eVl9PQZrlwS4+830SYhGc7+pb2L8fb/eRbGrKIsjiwdxb3LdutkwhHQBDpYDbshJRtSc7yOxHtTToXKNdBS5XUkKgR217UxITfN6zBcVey0sturEwnVcBkDO1+zo88utpTr7TM88F4Zx00ZzeT8DNfOozwy9XSYeT68/qsP72x74PpjStld28ZrW6o9iyHSaQIdrIY9Wv8cMPk0u93+irdxqJDYU9vGhNHpXofhqvHOB4Tdta0eR6IiVuUaaK12vXzjlU1V7G1o59qj9P0map353/ZD2LPf8WxhsjNnF1KQmcw97+zy5PzRQBPoYNXv1vKNgLHzIb0AtjzrdSRqhDq6e6ls6mDC6OgegZ6YZz8g7KjRBFoN05YX7HbK6a6e5u/v7qYgM5nTZ41x9TzKQzklcPJ3YfMzsO4xT0JISojj6qPG8+rmanbpdXFYNIEOhjF2BHpUqdeR+ENcHEw/C7a+5PnKSmpkyursxLpoT6DTkxMozEphe3WL16GoSLX1BShaCBkFrp2irM7eUr9yyXidPBjtjv4ijFtkVyhs8aaM4uol40mIE+5dttuT80c6fYUGo7Uaetp1BLq/GedBV7PtCa0i1u5am0CPj/IaaIDJBensqNaRFjUMrbVQvhymfsLV09z37h7iRLhqifZ+jnrxCXDhH6CrBZ75pichFGSlcM6csTy8vIymDp1gPVSaQAfjQAs7rUk7YOJJkJgOm5/2OhI1ArsPjEBHdw00wKS8DLZXt2A8qjlUEWz7y4BxNYFu7+rloeV7OH1mAWOzo7elpOqnYAac/D3Y8E9Y/w9PQrjpxEk0d/Zw3zLvJjRGKk2gg1G73W5zJ3obh58kpthFVTY/C319XkejhmlPbSuZyQmMSkv0OhTXTcpPp7mjh5oWXUVTDdGmp+28j6IFrp3ikffLqG/r5nMnTHLtHMqHjv2KLeV48mvQWB720x8xLpsTpuZx15s76ejuDfv5I5km0MGo3QoSD6M0gf6IGedCcwXs+8DrSNQw7a5rY/zoNMTFtlx+MclpCbZD66DVUHS3w9YX7fUuzp23zN4+w5/f2MmC8TksmjDKlXMon4pPgE/eCX098PhN0Bf+JPYLJ0+hpqWTR3R57yHRBDoYNVvtBMKEJK8j8ZdpZ0JcIqx/3OtI1DDtqW2jNAbKNwAmaScONRw7XoXuVtu71yXPr69kT10bnz9xUkx8mFUDjJ4M5/4adr8Fb/w67Kc/elIuC8bncMfrO+jp1TvKwdIEOhg1WyFvqtdR+E/qKJtEr33Ek0/NamS6e/soq7cj0LFgXE4qyQlxOgKthmbjk5CcDaUnuHJ4Ywx3vL6D0tFpnDGr0JVzqAgw70qYczm8+j+w++2wnlpE+MLJUyivb+fJNfvCeu5Ipgn04fT12WU3R0/xOhJ/mns5tOy3K3SpiLK7to3uXsPUgthY7SwuTpiYl862Kk2gVZB6umz98/SzXLsDuXxXPavLGrjxhEnEx+noc0w799f2bvfDN0Dj3rCe+rQZBUwfk8nvX95Gt45CB0UT6MNpLIOeDh2BPpipZ9rRmTUPex2JGqJtVc0ATC3I9DiS8JlRmMnGimavw1CRYvvL0NEAR1zq2ilufWUbuelJXLqw2LVzqAiRkgVXPWDr7h+6Bro7wnbquDjhW2dOZ2dNKw9rLXRQNIE+nNqtdjtaE+hBJabA7Avtbc4urS2NJFv325HYyQWxUQMNMLsom8qmDmpbdAEgFYS1j0BqLkw+xZXDv7ezjte3VHPzSZNITYp35RwqwuRPh0/eYSfnP/W1sC71ffrMAhZPGMXvXtpKW1dP2M4bqTSBPpyabXarI9AHN/cK2wx+wz+9jkQNwdaqFopHpZKWlOB1KGEzuygLgPX7mjyORPleZwtsegZmXwzxoW/zaIzhVy9sJj8zmeuOLg358VUEm3Gu7Q+9+gF47edhO62I8N2zZ1DV3Mlf3toVtvNGKk2gD6d6E6TkQHq+15H414TjIG8avHen15GoIdha1RIz9c8BszSBVsHa8E+7Au3cy105/FvbanlvZx1fOmWKjj6rjzvpOzD/Gnj1v8P63rq4NJczZo3h9le3U9eqPfMPRRPow6lcC4VzQFsLHZwIHPk52LcSyt/3OhoVhO7ePrZXtzB1TOzUPwPkpCUxLieV9fsavQ5F+d3Kv9nSvZKjQn7owOjzuJxUrtRlu9VgROD838P0c+CZb8PaR8N26n87czqtXT38+oXNYTtnJNIE+lD6emH/eiic63Uk/jfvSkjKhHdu9ToSFYRtVS109fQdKGmIJbOKstigI9DqUKq3QNkyWHidK4Mnz6/fz6qyBr5y2hSSE3T0WR1EfAJcejeMP8YusrLmkbCcduqYTD517ETuf28PK/fUh+WckUgT6EOp3W5v4RXO8ToS/0vJgiNvhPX/sH2zla+t22tHYI8Yl+1xJOE3uyiLnbWttHbqJBl1ECvvgbgEmHdVyA/d3tXLT5/awPQxmXxSO2+ow0lMhWsedpLoz8HyP4fltN/4xDQKs1L4/uNrta3dQWgCfSiVa+xWE+jgHPMlSEiB13/ldSTqMNbvayI9KZ6JMbIKYX9zxmVjzIcfIpT6iK5W+OBemHEeZBSE/PB/fHUbexva+cmFs0mM17dgFYTkTLj2Ubtw2dPfhNd+4Xp3jozkBP7z/NlsqmzmL2/tdPVckUpfvYdSuQbik2xbGXV4Gfmw5HOw5iHYt8rraNQhrNvbyKyiLOJicOGGRRNGAbB8V53HkShfWv0gdDTC0beE/NC7alq5/bUdXDS/iKMmjQ758VUUS0yFK/5uu1698jN4+HrodLen/Zmzx3D6zDH89sWt7K7VNrUDaQJ9KJVroWCmKy2MotaJ34K00fDcd8Pav1IFr7u3j/X7mphdFHvlG2AnEs4ozOTdnZpAqwH6+uDdO2Ds/JBPHjTG8KMn15OUEMf3z5kZ0mOrGBGfCBffAZ/4f7DpKfjz6VDt3kQ/EeEnF84mIV74yoOrtJRjAE2gD6avD/a+by+kKngp2XDaD2HPO7DiLq+jUYPYsK+J9u5ejizN9ToUzyyZmMv7u+vp0TcE1d/mZ6BmMxzzxZBPHnxs5V5e3VzN106fSkFWSkiPrWKICBz7ZbjuCWithttPgLd+Z5seuKAoJ5X/+eRcVpc18JsXt7hyjkilCfTBVG+0t/HGH+N1JJFn4fUw+VR44T90QqEPBUoXFpeO8jgS7yyZmEtbVy/rtBuHCjAGXv8ljJoIsz8Z0kPvrm3lP/+5jiUTc/n0cRNDemwVoyadBF9YBlPPgBd/CHedAXtXunKqc+eO5cojS7j9te28ta3GlXNEIk2gD2b323Y7QRPoIROBC2+zNVsPXAnt2gbHT1bsqqckN5UxMTwKtmSiHX1/b2etx5Eo39j6IlSsghO+YduHhUhPbx9ff2gVcXHCb6+YT3wMzjtQLskosHXRl94N9bvhzlPg0RuhflfIT/XD82cxKS+drz20in0N7SE/fiTSBPpg9iyDzLGQM8HrSCJTVpF9Ydfvhgeucn2ygwpOX5/hvV11HDkhdss3AAoyU5iUl85b2zSBVtjb3y//2F7v514Z0kP/37+2sXJPAz+7eA7jclJDemylEIEjLoGvfAAnfAs2PQ3/twj+cTNUrgvZadKSEvjDNYto7+rlxntWaBtQNIE+uD3LYPzRugLhSEw4Fi65E8reg3svhub9XkcU89bubaSutYsTpuV5HYrnTp1RwDvba2nRNwK15iHYvw5O/09ISArZYZ9dW8HvXt7KJxeO44J5RSE7rlIfk5IFp/0HfPl9OPKzsGEp3H4c3HOBXYCle+SjxtMLM7n16gVsrmziKw98QG9fbDcK0AR6MHU7oKlc659DYfbFcPk99pPwn06yybTyzKubqxGBE6fmex2K586YNYau3j5e31LtdSjKSx1N8PJPoGghzLo4ZIddVdbA1x5axcLxOfzXxbqWgAqT7HFw9s/h6+vg1P+Aup3w+GfhV9PgiS/ApmdGlEyfPL2AH18wm5c3VfHjJ9djYrjblibQg9n8nN1OPcPbOKLFzPPhsy9BQjLcfSY8/wO7WIEKu1c2VzG3OIfRGcleh+K5RRNGMSotkRfWV3odivLSK/8FzZVwzq8gLjRvieX1bXz2nhUUZCVz5/WLSUnU5bpVmKXl2rayX10NNzwJM86FjU/Bg1fBLybBQ9fCqgegZegDCNcdU8pNJ07ib+/s5qdPbYzZJDp0MyWiyZZnIW865E7yOpLoUXgE3PQavPQjeOdW2LgUzv0tTD3d68hiRnl9G6vKGvj2mbowEEBCfBynzRzDC+sr6e7t01XhYlH5+/DeHbD4M1C8KCSHLKtr45o/v0tnTy8P3nSUflhV3oqLg4kn2q+eLtj9pq2T3vQ0bHwSEChaAFM/Yb+KFgT1QfJ7Z8+gp9dwt7NK4X+cNxOJsZJXfccYqKPRduCYfrbXkUSf1Bw4/3/hU89AfDLcdwk88mk7+qNc9+TqCgCtxeznrNmFNHX08OpmLeOIOZ0t9tZ21jjbuz4Edta0cvkd79DQ1sW9Nx7FlILMkBxXqZBISLItZs/9NXx9gx3UOuUHdoGW138Bfz4VfjUVHr8J1j4KbQdfbEpE+I/zZvKZ4yZy91s7+d7ja2NuoRUdgR5oywvQ16MJtJtKj4Nb3oI3/xfe+DVse9lO3ln06ZDdQlUfZYzhHx+Us2B8DiW5aV6H4xsnT8+nIDOZB97bwxmzxngdjgoXY+CZb9v60E89ZT/cj9C6vY18+q/L6e0zPHDT0TG70qeKEHFxUDTffp30bZssb/8XbH0Btr1kJ9ZKHBQfCVPOsCWthXM/8h4dSKLTkuK59ZVtlNW38YerF5GdFhurN2u2MtAH90L2eChe4nUk0S0hGU7+DtzyNhTNg6e/AfdfPqx6LHV4y3bUsWV/C1ceWeJ1KL6SEB/H5YtLeHVzlfY2jSXv3Qmr74eTvgOlx4/4cA+vKOOSP75NYpzwkCbPKhKl5cKcS+GTf4JvbYXPvgwnfht6u+CV/2ebAPxmBjz9LdulrM+ONosI3zpzOr+6bB7v7azjk398i637Y6NtrSbQ/dXtgJ2vwcLrdCQ0XPKmwPVL7S2lna/btjs7XvM6qqhz91s7GZWWyIXzx3kdiu9ccWQJfQYefG+P16GocNj0NDz3XZh2tk2gR6C1s4fvPb6Gf3t0DYtLR/Hkl49n6hgt21ARLi4eihfDKd+Hm161CfVFf4SSo+wg491nwu/m2hUQK9aAMVy6qJi/33gUDW3dnH/rm9z37u6on1yoWWJ/799jb1nMv8brSGKLiO1b+bl/QUo23HuRLe3oi616KresKW/gxQ37uf6YUu0GMIiS3DROnzmGe97ZTXNHt9fhKDdte9nOuyiaD5f8eUQDJa9sruITv32dB94r45aTJ/O3z+iEQRWlMgpg/tVwxb02mb74DsifAe/cBnecALcdBa//iqNGtfDs107gyNJcfvCPdXzub+9T0Ri9d/Y0gQ5orYHlf4aZF9g+iir8Co+Az70Csy6yfVkfugbaG7yOKqIZY/ivZzaSm57EZ0+Y6HU4vvXV06bS2N7N3W/u8joU5Zb1T8ADV0LeVLjmUUjOGNZhdlS38MX7VvLpvywnNSmeR24+hu+cNUOX6FaxISUL5l0J1z4K39wC5/0W0kbDv34Kv5tLwSMXc8/cDfzkE+N4Y2s1p/36Ne54bXtUTjDUSYQBb/wautvsjFTlneQMuPRuuwrk89+3dVeX3wtj53odWUR6eEUZy3bU8f8uOoLMlNiY2DEcc4qzOWt2IX98bRuXLi7WJZejSV8vvPZz+1W8BK55GFJHDfkwu2paue2VbTy2spyUxHi+fvo0bj55EskJeldHxaj00bYF5OLPQP1uWPsIrHmIuKe/xvXxSVw6/XT+0ryEXz/bwYPLy/jKaVO4YN64qPmwKZFWo7J48WKzYsWK0B60/H24+xP2U9WFt4X22Gr49rwLj9wA7fVw7m9ggZbWDMWmyiYuvu1t5pVkc/9njyYuSi5abimvb+P037zGkomj+eunjnTl7yUi7xtjFof8wD7myjU7WDXb4J9fhLJltjTv3N9AYkrQv97bZ3htSxX3vL2b17ZUk5QQx3VHT+CWkyeTp+UaSn2cMVCxCtY8bFvhtVbRnZjFi3IMf2k+irrRC/jciVO4cP44UpMi48Pnwa7bmkC31cGdp9rWdTe/MayRCeWilmp49NOw6w1YeD2c+V+QrJN0DmdnTStX37mM3j7DU18+noKs4JOGWPb3Zbv59yfW8fXTp/HV06eG/PiaQIdJ415bn/nenyAxDc75Jcy93M63OIye3j4+KGvgqdX7eHptJTUtnRRkJnP1UeO5esl4fS0pFazeHtj5Kqx5GLPxSaS7jSrJ4/nu+SxLWEzxwjM5f9FkZhdl+XoRloNdt10t4RCRs4DfAfHAn40x/zPg+WTgb8AioBa4whizy82YPqKlGv5+MTTttZ0gNHn2n4x8uO4J20bnzd/aPt1n/MS224mLjE+v4bZ8Vx1fuG8lfX2Ge288St/wh+Cao8azcnc9v31pC+nJ8dx4/ERfX9hDzffX7EPp64Py92DF3bDuMTB9MO9qu0hK5sF7fPf09rFlfwuryhp4a1sNb2ytpqmjh+SEOE6bWcD5c4s4fdYYXalSqaGKT4App8OU05HOFtj0NPkbnuDq7a9yXc9LdLz/a95ZPos7UuaRPvVYJs87kYWTCiNmsrtrI9AiEg9sAc4AyoHlwFXGmA399vkCMNcYc7OIXAlcbIy54lDHDcloRm8PrP8HPP896GiCq+63/5OVv5WvgKe/aW8PjZoIR90Msy6ErLFeR+YLlY0d3P7adu55ZxfFo1K5+4YjtaXWMHT39vGVBz7g2XWVnH1EIf9x3iyKQlQT7ecRaF9fswdjjG09unelvUO1+VlorYKkDHu36qibYdSEfrsbqls62VHdys6aVrZXtbCmvJG1extp7+4FYExWMidOzefEafmcMqOAjGSdJqRUyPV0wq436djwLF2bXySrdRcAnSaB9UykJn0aPaOnkzZuNmOnLGDihFKSPEyqw17CISLHAD8yxpzp/Pw9AGPMf/fb53lnn3dEJAGoBPLNIYIa1sW4qQLqd9mL7Z53bCuj5n1QOAcuut12f1CRoa8XNj0Fb/0e9q4ABMbOsz0rC2ZBdontopKSDUnp9s00Promz3V099LY3k1DWzf7GtrZVNnM29treGd7LQbb1/j758zUN/8R6Osz/PG17fzfv7bS02s4eXoBJ07LY+bYLAqzUhidkURqYvyQR6d9nkD755rdWgMt+zEdjfS0NdDX3khfSy2mcQ/SWE584x7im/YQ39kIQG9iBtWFJ1BWcArbso+lrjeVxvZuqpo6qGruZH9TB5WNHbR29R44RXJCHDPHZjG/JIcF43OYV5zDhNFpMXXHQSlfaK2hfcfbVK9/DcqXk9u6nQzT8uHTJplqyaMxqYC2lEJMej6kZBOXmkNCeg5JGbkkp+eQlJJGQmIy8UkpJCWlkJCUQmJyColJKSQmJiJx8UGVcQ3kRQnHOKCs38/lwFEH28cY0yMijcBooCakkfzlLJtAg02sSk+Aeb+E6efogimRJi7ejjrPuhCqN8OGpXbxm9UPQddBVj867mtwxo/DGqZbfvb0Bu58Y+fHHp+Un85nT5jENUeN16W6QyAuTvjiKVO4cH4Rf3tnN0tX7eOljfs/ss+vL5vHJYuKPYrQFf65Zr/0n/DB3xGg/8ffdpNEuclnr8mjzBzJOjORNX2T2NJRTG9zPGwFsAviJCXEUZCZzJisFKYXZnLC1HxKR6cxKT+DiXnpjMtJ1Ym1SvlBeh6pcy5g/JwL7M/G0NtUwf7tq6jduYauml0ktFaQ1l5JUfO7ZDc2kiS9hz7mYBZ9Gs7/35CFHRFDVCJyE3CT82OLiGwe/tGagPudLwDyCPXFf3j8Egf4J5YQxfET58vrOEJi0Fh2A68A3/M4Dg94FselP//YQ8HEMuEwz0eF0F6zB/rYn/igf/etoTvpUPnl9RHgt3hAYwqW32LyWzwQdEy/c76GbNDrtpsJ9F6gpN/Pxc5jg+1T7twOzMZOTPkIY8yfgD+5EaSIrPDDLVW/xAH+iUXj+Di/xKJxfJyfYhmmiLhmD+THv7vfYvJbPKAxBctvMfktHvAuJjfrF5YDU0VkoogkAVcCSwfssxS4wfn+UuBfh6qlU0op5Rq9ZiulVJBcG4F26uO+BDyPbYl0tzFmvYj8BFhhjFkK3AXcKyLbgDrsBVsppVSY6TVbKaWC52oNtDHmGeCZAY/9sN/3HcBlbsYQhLDcZgyCX+IA/8SicXycX2LROD7OT7EMS4Rcswfy49/dbzH5LR7QmILlt5j8Fg94FFPErUSolFJKKaWUl7SHm1JKKaWUUkMQswm0iJwlIptFZJuIfNfDOEpE5BUR2SAi60Xkq17F4sQTLyIfiMhTHsaQIyKPisgmEdnoLPDgVSxfd/6/rBORB0QkLOtii8jdIlIlIuv6PZYrIi+KyFZnG5a15w8Syy+d/z9rROQfIpLjRRz9nvumiBgRyfMqDhH5svM3WS8iv3A7jlgVzOtAROaLyDvO/4s1InLI1RKHGcch30NEJFlEHnKef1dESkMdwzBi+obzXrNGRF4WEdfbKgb7XisilzivYde7KQQTk4hc3u99+f7B9glXPCIy3skTPnD+353jZjzOOQ96vXWeFxH5vRPzGhFZ6HE81zhxrBWRt0VknpvxAHZ501j7wk6Q2Q5MApKA1cAsj2IZCyx0vs/ELqXrSSxODN/ANsl+ysMY7gE+63yfBOR4FMc4YCeQ6vz8MPCpMJ37RGAhsK7fY78Avut8/13g5x7G8gkgwfn+5+GIZbA4nMdLsBPfdgN5Hv09TgFeApKdnwvC8f8mFr+CeR0A04CpzvdFQEUoryPBvIcAXwBud76/EnjI5b9LMDGdAqQ539/ih5ic/TKB14FlwGKvYwKmAh8Ao5yfXXs9BxnPn4BbnO9nAbvc/Bs55xn0etvv+XOAZwEBjgbe9TieY/v9/zrb7XiMMTE7Ar0E2GaM2WGM6QIeBC70IhBjTIUxZqXzfTOwEZu4hZ2IFAPnAn/24vxODNnYF8pdAMaYLmNMg1fxYCfaporteZsG7AvHSY0xr2O7HPR3IfbDBc72Iq9iMca8YIzpcX5chu0ZHPY4HL8F/g0Iy4SOg8RxC/A/xphOZ5+qcMQSow77OjDGbDHGbHW+3wdUAfkhjCGY95D+cT4KnCbi6jrhh43JGPOKMabN+TEcr9tg32t/iv0g3uFyPMHG9DngNmNMPbj+eg4mHgNkOd9nE4b3oUNcbwMuBP5mrGVAjoiM9SoeY8zbgf9fhOk9KVYT6MGWrPUkae3PucW3AHjXoxD+F5uI9Hl0foCJQDXwF+d21Z9FJN2LQIwxe4FfYdcGrgAajTEveBGLY4wxpsL5vhIY42Es/X0GOxIRdiJyIbDXGLPai/P3Mw04wblV/5qIHOlxPNFsSK8DEVmCHdnbHsIYgnkP+ciy50Bg2XO3DPV97Ubcf90eNibn1n+JMeZpl2MJOibs63maiLwlIstE5CyP4/kRcK2IlGO75HzZxXiC5cs8yhGOf9sxm0D7johkAI8BXzPGNHlw/vOAKmPM++E+9wAJ2Ns0fzTGLABasbdpw86prbwQm9QXAekicq0XsQxk7H0qz1voiMgPgB7gPg/OnQZ8H/jh4fYNgwQgF3sr89vAwy6PNkY1EXlJ7LyDgV8DR1QP+TpwRsTuBT5tjPFyYMBXnOvYYuCXHscRB/wG+KaXcQwiAVvGcTJwFXBnOOZ5HMJVwF+NMcXY0ol7nb+dGkBETsEm0N9x+1yu9oH2sWCWrA0bEUnEJs/3GWMe9yiM44ALnMkJKUCWiPzdGBPuhLEcKDfGBEbhH8WjBBo4HdhpjKkGEJHHsXVWf/conv0iMtYYU+EkBp6WCYjIp4DzgNOcRCbcJmM/3Kx2ctViYKWILDHGVIY5lnLgcefv8J6I9AF52LspaoiMMacf7DkRCep1ICJZwNPAD5xbzKEUsmXPwxwTInI68APgpEDJkYcxZQJHAK86r+FCYKmIXGCMWeFRTGBfz+8aY7qBnSKyBZtQL/conhuBswCMMe+Incyeh7fvAb7KowBEZC62BPVsY4ybrzUgdkegg1myNiycUaq7gI3GmN94EQOAMeZ7xphiY0wp9u/xLw+SZ5zEp0xEpjsPnQZsCHccjj3A0SKS5vx/Og1bo+6V/sso3wD806tAnFua/wZc0K+mMqyMMWuNMQXGmFLn3205dkJuuJNngCewE7QQkWnYkoEaD+KIBYd9HTjX9X9gazQfdSEGPy57ftiYRGQBcAf2dRuO5OuQMRljGo0xef1ew8uc2NxKng8bk+MJ7OgzYjv7TAN2eBjPHuz7DyIyEzvI5fWH86XA9U43jqOxJY4Vh/slt4jIeOBx4DpjzJawnNTtWYp+/cLeBtmCrYv7gYdxHI+9BbkGWOV8nePx3+ZkvO3CMR9Y4fxNnsCZWetRLD8GNgHrsLeCk8N03gewddfd2MTwRmz95MvAVmzHh1wPY9mGrX8L/Ju93Ys4Bjy/i/B04Rjs75GEvTOxDlgJnBrOf6ex9HWw1wG2JOHPzvfXOv9/VvX7mh/iOD72HgL8BJsAgk1yHnFeK+8Bk8LwtzlcTC8B+/v9TZZ6HdOAfV/F5S4cQf6dBFtasgFYC1zpcTyzgLewHTpWAZ8Iw99osOvczcDN/f5Gtzkxr3X7/1sQ8fwZqO/3b3uF238jXYlQKaWUUkqpIYjVEg6llFJKKaWGRRNopZRSSimlhkATaKWUUkoppYZAE2illFJKKaWGQBNopZRSSimlhkATaBVVROQiETEiMsP5uVRE1g3zWLucHqDB7v8pEbl1OOdSSqlYpNdsFak0gVbR5irgTWerlFLK3/SarSKSJtAqaohIBnZhmhuxqzkNfD5eRH4lIutEZI2IfNl5/DQR+UBE1orI3SKS3O/XviwiK53nAiMkuSLyhHOMZc7yoUoppYZAr9kqkmkCraLJhcBzxi7jWSsiiwY8fxNQil2RbC5wn4ikAH8FrjDGzAESgFv6/U6NMWYh8EfgW85jPwY+cI7xfeBvLv33KKVUNNNrtopYmkCraHIV8KDz/YN8/Jbg6cAdxpgeAGNMHTAd2OlcwAHuAU7s9zuPO9v3sRdysCMm9zrH+BcwWkSyQvefoZRSMUGv2SpiJXgdgFKhICK5wKnAHBExQDxggNtGeOhOZ9uLvl6UUiok9JqtIp2OQKtocSlwrzFmgjGm1BhTAuwESvrt8yLweRFJgAMX8M1AqYhMcfa5DnjtMOd6A7jGOcbJ2FuGTaH6D1FKqRig12wV0TSBVtHiKuAfAx57DPhev5//DOwB1ojIauBqY0wH8GngERFZC/QBtx/mXD8CFonIGuB/gBtGHr5SSsUUvWariCbGGK9jUEoppZRSKmLoCLRSSimllFJDoAm0UkoppZRSQ6AJtFJKKaWUUkOgCbRSSimllFJDoAm0UkoppZRSQ6AJtFJKKaWUUkOgCbRSSimllFJDoAm0UkoppZRSQ/D/Ab46g/YD/lhaAAAAAElFTkSuQmCC\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Modelling"
      ],
      "metadata": {
        "id": "6jac6VqRO-Ov"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import LogisticRegression"
      ],
      "metadata": {
        "id": "BVdmdLDoOTZl"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model = LogisticRegression()\n",
        "model1 = LogisticRegression()\n",
        "model2 = LogisticRegression()"
      ],
      "metadata": {
        "id": "gjDuAZQoT4JG"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model.fit(x_train, y_train)\n",
        "model1.fit(x_train_scaled, y_train)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 75
        },
        "id": "nfGkwMHSagdY",
        "outputId": "66d584f5-e202-443d-b63d-8a6472e15a87"
      },
      "execution_count": 43,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "LogisticRegression()"
            ],
            "text/html": [
              "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>LogisticRegression()</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">LogisticRegression</label><div class=\"sk-toggleable__content\"><pre>LogisticRegression()</pre></div></div></div></div></div>"
            ]
          },
          "metadata": {},
          "execution_count": 43
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "x_test.describe()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 300
        },
        "id": "lsdogYl1b0Zp",
        "outputId": "fc10f803-fb6c-4696-e400-d036cdab6f59"
      },
      "execution_count": 44,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Alcohol  Malic_acid\n",
              "count  45.000000   45.000000\n",
              "mean   13.082667    2.187111\n",
              "std     0.758516    1.169259\n",
              "min    11.410000    0.740000\n",
              "25%    12.370000    1.350000\n",
              "50%    13.340000    1.730000\n",
              "75%    13.630000    2.810000\n",
              "max    14.340000    5.510000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0de06871-2a18-4aa6-9b3c-54aea0134ba7\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <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>Alcohol</th>\n",
              "      <th>Malic_acid</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>45.000000</td>\n",
              "      <td>45.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>13.082667</td>\n",
              "      <td>2.187111</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.758516</td>\n",
              "      <td>1.169259</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>11.410000</td>\n",
              "      <td>0.740000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.370000</td>\n",
              "      <td>1.350000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>13.340000</td>\n",
              "      <td>1.730000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>13.630000</td>\n",
              "      <td>2.810000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>14.340000</td>\n",
              "      <td>5.510000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0de06871-2a18-4aa6-9b3c-54aea0134ba7')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0de06871-2a18-4aa6-9b3c-54aea0134ba7 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0de06871-2a18-4aa6-9b3c-54aea0134ba7');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 44
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "pred = model.predict(x_test)\n",
        "pred1 = model1.predict(x_test_scaled)"
      ],
      "metadata": {
        "id": "oBDS2aFRazfj"
      },
      "execution_count": 45,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.metrics import accuracy_score"
      ],
      "metadata": {
        "id": "GOK8ihMfbDxm"
      },
      "execution_count": 46,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "print(accuracy_score(y_test, pred))\n",
        "print(accuracy_score(y_test, pred1))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "64tv5y1CbHti",
        "outputId": "f2d71ea9-0552-4788-8a99-ef8ff3c58fbf"
      },
      "execution_count": 47,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.7555555555555555\n",
            "0.7555555555555555\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "z-eWJZ6xbSwn"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}