Weather_Forecast_LSTM.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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    "language_info": {
      "name": "python"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zdb2j0gt6aok"
      },
      "source": [
        "# Imports "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3DTPlx7H6Chw"
      },
      "source": [
        "import pandas as pd\n",
        "import numpy as np \n",
        "import time \n",
        "import datetime \n",
        "import matplotlib.pyplot as plt \n",
        "%matplotlib inline  \n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go \n",
        "from sklearn.preprocessing import StandardScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "snxznTO76eD8"
      },
      "source": [
        "# Reading Dataset "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 254
        },
        "id": "I9IQjcbX6okY",
        "outputId": "e3a9c08f-c367-4c08-89cf-9c548dc54c1b"
      },
      "source": [
        "orig_df = pd.read_csv('/content/drive/MyDrive/delhi_weather_final.csv') \n",
        "df = orig_df.copy() \n",
        "print(f'Size of the dataset: {df.shape} \\n')  \n",
        "print() \n",
        "display(df.head(5))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Size of the dataset: (100990, 4) \n",
            "\n",
            "\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>datetime_utc</th>\n",
              "      <th>_tempm</th>\n",
              "      <th>_pressurem</th>\n",
              "      <th>_hum</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>19961101-11:00</td>\n",
              "      <td>30.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>27.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>19961101-12:00</td>\n",
              "      <td>28.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>32.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>19961101-13:00</td>\n",
              "      <td>24.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>44.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>19961101-14:00</td>\n",
              "      <td>24.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>19961101-16:00</td>\n",
              "      <td>23.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "     datetime_utc   _tempm   _pressurem   _hum\n",
              "0  19961101-11:00     30.0       1010.0   27.0\n",
              "1  19961101-12:00     28.0      -9999.0   32.0\n",
              "2  19961101-13:00     24.0      -9999.0   44.0\n",
              "3  19961101-14:00     24.0       1010.0   41.0\n",
              "4  19961101-16:00     23.0       1011.0   47.0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XvB3wqUA7foc"
      },
      "source": [
        "# Cleaning the data "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "0TpRtkRI9D5l"
      },
      "source": [
        "### Renaming he columns "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S1JDifCV9JL_"
      },
      "source": [
        "df.columns = ['Date' , 'Temperature', 'Pressure' , 'Humidity']"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "cR9hNdSz7iBB"
      },
      "source": [
        "### Removing possible outliers"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 295
        },
        "id": "AX9GYIJR7m7v",
        "outputId": "08908c8b-63d3-407c-b399-1915e57ad627"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Humidity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>100317.000000</td>\n",
              "      <td>1.007580e+05</td>\n",
              "      <td>100233.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>25.451269</td>\n",
              "      <td>1.932331e+03</td>\n",
              "      <td>57.909481</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>8.482859</td>\n",
              "      <td>3.183786e+05</td>\n",
              "      <td>23.807771</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>1.000000</td>\n",
              "      <td>-9.999000e+03</td>\n",
              "      <td>4.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>19.000000</td>\n",
              "      <td>1.002000e+03</td>\n",
              "      <td>39.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>1.008000e+03</td>\n",
              "      <td>59.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>32.000000</td>\n",
              "      <td>1.014000e+03</td>\n",
              "      <td>78.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>90.000000</td>\n",
              "      <td>1.010614e+08</td>\n",
              "      <td>243.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Temperature      Pressure       Humidity\n",
              "count  100317.000000  1.007580e+05  100233.000000\n",
              "mean       25.451269  1.932331e+03      57.909481\n",
              "std         8.482859  3.183786e+05      23.807771\n",
              "min         1.000000 -9.999000e+03       4.000000\n",
              "25%        19.000000  1.002000e+03      39.000000\n",
              "50%        27.000000  1.008000e+03      59.000000\n",
              "75%        32.000000  1.014000e+03      78.000000\n",
              "max        90.000000  1.010614e+08     243.000000"
            ]
          },
          "metadata": {},
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
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        "id": "Ttf0n5vn8vrr",
        "outputId": "4217ac0b-bb37-4cca-dd1b-46eff08b3c0c"
      },
      "source": [
        "display(df.quantile([0 , 0.25 , 0.5 , 0.75 , 1] , axis = 0 ))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Humidity</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0.00</th>\n",
              "      <td>1.0</td>\n",
              "      <td>-9999.0</td>\n",
              "      <td>4.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.25</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1002.0</td>\n",
              "      <td>39.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.50</th>\n",
              "      <td>27.0</td>\n",
              "      <td>1008.0</td>\n",
              "      <td>59.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>0.75</th>\n",
              "      <td>32.0</td>\n",
              "      <td>1014.0</td>\n",
              "      <td>78.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1.00</th>\n",
              "      <td>90.0</td>\n",
              "      <td>101061443.0</td>\n",
              "      <td>243.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "      Temperature     Pressure  Humidity\n",
              "0.00          1.0      -9999.0       4.0\n",
              "0.25         19.0       1002.0      39.0\n",
              "0.50         27.0       1008.0      59.0\n",
              "0.75         32.0       1014.0      78.0\n",
              "1.00         90.0  101061443.0     243.0"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2WhsSK-rRs9a",
        "outputId": "922c0fc8-4b42-404a-8e9e-5d6292d46a96"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(100990, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iQ3Xn4UD9zjN"
      },
      "source": [
        "df = df[(df['Humidity'] >= 39) & (df['Humidity'] <= 78)]\n",
        "df = df[(df['Pressure'] >= 1002.0) & (df['Pressure'] <= 1014.0)]\n",
        "df = df[(df['Temperature'] >= 19.0) & (df['Temperature'] <= 32.0)]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "V9KDNe9oRv4a",
        "outputId": "69cedb2c-15b1-49fd-9af5-1befd7b7e612"
      },
      "source": [
        "df.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(19874, 4)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8iugV0IR-zEz",
        "outputId": "6611ca1c-741e-46ed-9f60-090db3d5a86e"
      },
      "source": [
        "max_temp = np.max(df['Temperature'])\n",
        "max_pres = np.max(df['Pressure'])\n",
        "max_humid = np.max(df['Humidity'])\n",
        "\n",
        "min_temp = np.min(df['Temperature'])\n",
        "min_pres = np.min(df['Pressure'])\n",
        "min_humid = np.min(df['Humidity'])\n",
        "\n",
        "print(f'Max Temp : {max_temp}')\n",
        "print(f'Max Pressure : {max_pres}')\n",
        "print(f'Max Humidity : {max_humid}')\n",
        "\n",
        "print(f'Min Temp : {min_temp}')\n",
        "print(f'Min Pressure : {min_pres}')\n",
        "print(f'Min Humidity : {min_humid}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Max Temp : 32.0\n",
            "Max Pressure : 1014.0\n",
            "Max Humidity : 78.0\n",
            "Min Temp : 19.0\n",
            "Min Pressure : 1002.0\n",
            "Min Humidity : 39.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i00DKjUoBL8P"
      },
      "source": [
        "### Removing Missing values "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0P34l1guA4om",
        "outputId": "e8b8e21e-3e03-4710-aa86-0c976953e3fc"
      },
      "source": [
        "df.isnull().sum()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Date           0\n",
              "Temperature    0\n",
              "Pressure       0\n",
              "Humidity       0\n",
              "dtype: int64"
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "oI_s73h9BkZd",
        "outputId": "0f2a2407-7eb4-4475-e1f6-a103a95dfaeb"
      },
      "source": [
        "print('Dataframe shape : {}'.format(df.shape))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataframe shape : (19874, 4)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xLWLGuxsL35Z"
      },
      "source": [
        "### Resetting index "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "bknXlWNBCRuc"
      },
      "source": [
        "idx = pd.date_range('1996-11-01 14:00:00', periods=19874 , freq='1h')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "GSmduIb2Dsmw"
      },
      "source": [
        "df = df.set_index(idx)\n",
        "df.index.name = 'Timestamp' \n",
        "date_column = df['Date'] \n",
        "df = df.drop(columns = ['Date'] , axis = 1)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 234
        },
        "id": "_-eihThtLuU2",
        "outputId": "c283a05c-7752-419b-d5fa-91375c2445df"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Temperature</th>\n",
              "      <th>Pressure</th>\n",
              "      <th>Humidity</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Timestamp</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>1996-11-01 14:00:00</th>\n",
              "      <td>24.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>41.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996-11-01 15:00:00</th>\n",
              "      <td>23.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>47.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996-11-01 16:00:00</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1011.0</td>\n",
              "      <td>56.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996-11-01 17:00:00</th>\n",
              "      <td>21.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>60.0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1996-11-01 18:00:00</th>\n",
              "      <td>19.0</td>\n",
              "      <td>1010.0</td>\n",
              "      <td>68.0</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Temperature  Pressure  Humidity\n",
              "Timestamp                                           \n",
              "1996-11-01 14:00:00         24.0    1010.0      41.0\n",
              "1996-11-01 15:00:00         23.0    1011.0      47.0\n",
              "1996-11-01 16:00:00         21.0    1011.0      56.0\n",
              "1996-11-01 17:00:00         21.0    1010.0      60.0\n",
              "1996-11-01 18:00:00         19.0    1010.0      68.0"
            ]
          },
          "metadata": {},
          "execution_count": 14
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "2ygvTvCnjxF-",
        "outputId": "684da0a2-5c73-421e-a69f-a2691dcb49e7"
      },
      "source": [
        "df_array = np.array(df) \n",
        "df_array"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[  24., 1010.,   41.],\n",
              "       [  23., 1011.,   47.],\n",
              "       [  21., 1011.,   56.],\n",
              "       ...,\n",
              "       [  27., 1002.,   45.],\n",
              "       [  30., 1002.,   41.],\n",
              "       [  26., 1004.,   40.]])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "r-uFzsRpnW5x"
      },
      "source": [
        "### Reducing size for faster computation"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wG4dv42hnWc8",
        "outputId": "b87cba05-2f53-4c1d-bacf-bd56161a96fe"
      },
      "source": [
        "df_array = df_array[:5000]\n",
        "df_array.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(5000, 3)"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "NzpfxVxDkTVu"
      },
      "source": [
        "### Scaling features "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "kZ1VGjK3j_07"
      },
      "source": [
        "scaler = StandardScaler() \n",
        "scaled_df_array = scaler.fit_transform(df_array)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tga7xrH1kchd",
        "outputId": "71c85f8f-3b20-46fc-b49f-d6e1a4c1c2b6"
      },
      "source": [
        "scaled_df_array[:5]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[-0.54774188,  0.40231553, -1.48362502],\n",
              "       [-0.80245815,  0.6821288 , -0.95776549],\n",
              "       [-1.3118907 ,  0.6821288 , -0.1689762 ],\n",
              "       [-1.3118907 ,  0.40231553,  0.18159682],\n",
              "       [-1.82132325,  0.40231553,  0.88274286]])"
            ]
          },
          "metadata": {},
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YSB_PsX3khMp"
      },
      "source": [
        "### Segregating feaures "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LaZDm_8lkdtw",
        "outputId": "dd2b7c1e-7720-4aec-fe46-4976b4cd9219"
      },
      "source": [
        "  sha = scaled_df_array[:, 0].reshape((-1, 1))\n",
        "  spa = scaled_df_array[:, 1].reshape((-1, 1))\n",
        "  sta = scaled_df_array[:, 2].reshape((-1, 1))\n",
        "\n",
        "  sha.shape , spa.shape , sta.shape "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((5000, 1), (5000, 1), (5000, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rzjYn0b9n-tV"
      },
      "source": [
        "# Preparing training and test set"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vlN0368Bvoo-"
      },
      "source": [
        "### Preparing training set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "T23Tys3Ln-Tr",
        "outputId": "28728ef8-88ee-4b09-ce29-b1763272c799"
      },
      "source": [
        "train_data_len = int(np.ceil(len(df_array)* 0.95))\n",
        "train_data_len"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "4750"
            ]
          },
          "metadata": {},
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CPSbyViotTOV",
        "outputId": "2496098f-cd2a-467c-de52-183da27fe205"
      },
      "source": [
        "x_train = []\n",
        "y_train_temp = [] \n",
        "y_train_humidity = [] \n",
        "y_train_pressure = [] \n",
        "\n",
        "for i in range(48 , train_data_len) : \n",
        "  temp1 = sha[i - 48 : i, 0] \n",
        "  temp2 = spa[i - 48 : i, 0] \n",
        "  temp3 = sta[i - 48 : i, 0] \n",
        "  temp4 = np.concatenate((temp1 , temp2 , temp3))\n",
        "\n",
        "  x_train.append(temp4) \n",
        "  y_train_temp.append(sta[i, 0])\n",
        "  y_train_humidity.append(sha[i, 0])\n",
        "  y_train_pressure.append(spa[i, 0])\n",
        "\n",
        "  if i <= 49 : \n",
        "    print(x_train , np.array(x_train).shape)\n",
        "    print()\n",
        "    print(y_train_temp , y_train_humidity, y_train_pressure)\n",
        "    print()\n",
        "   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([-0.54774188, -0.80245815, -1.3118907 , -1.3118907 , -1.82132325,\n",
            "       -1.82132325, -1.82132325, -1.82132325, -1.82132325, -1.56660698,\n",
            "       -1.05717443, -0.80245815, -0.03830933, -0.2930256 , -1.05717443,\n",
            "       -1.3118907 , -1.82132325, -1.56660698, -1.82132325, -1.82132325,\n",
            "       -1.82132325, -1.05717443, -1.05717443, -0.03830933,  0.21640695,\n",
            "       -0.03830933, -1.05717443, -1.05717443, -1.3118907 , -1.3118907 ,\n",
            "       -1.56660698, -1.56660698, -1.82132325, -1.82132325, -1.82132325,\n",
            "       -1.56660698, -0.03830933, -0.2930256 , -0.80245815, -1.3118907 ,\n",
            "       -1.3118907 , -1.56660698, -1.82132325, -1.82132325, -1.82132325,\n",
            "       -1.05717443, -1.56660698, -1.82132325,  0.40231553,  0.6821288 ,\n",
            "        0.6821288 ,  0.40231553,  0.40231553,  0.12250225,  0.12250225,\n",
            "        0.40231553,  0.40231553,  0.6821288 ,  0.96194208,  0.96194208,\n",
            "        0.96194208,  0.12250225,  0.40231553,  0.40231553,  0.40231553,\n",
            "        0.40231553,  0.12250225,  0.12250225,  0.96194208,  0.96194208,\n",
            "        0.96194208,  0.6821288 ,  0.12250225,  0.40231553,  0.40231553,\n",
            "        0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,\n",
            "        0.6821288 ,  0.40231553,  0.40231553,  1.52156863,  1.24175535,\n",
            "        0.40231553,  0.6821288 ,  0.6821288 ,  0.6821288 ,  0.96194208,\n",
            "        1.24175535,  1.24175535,  1.24175535,  1.52156863,  1.52156863,\n",
            "        1.52156863, -1.48362502, -0.95776549, -0.1689762 ,  0.18159682,\n",
            "        0.88274286,  0.88274286,  0.53216984,  0.18159682,  0.18159682,\n",
            "       -0.51954922, -1.04540875, -1.22069526, -1.65891153, -1.48362502,\n",
            "       -0.43190596, -0.1689762 ,  0.53216984,  0.18159682,  0.53216984,\n",
            "        0.18159682,  0.53216984, -0.43190596,  0.18159682, -1.22069526,\n",
            "       -1.65891153, -1.39598177,  0.18159682, -0.08133294,  0.18159682,\n",
            "       -0.1689762 ,  0.53216984,  0.18159682,  0.53216984,  0.88274286,\n",
            "        0.53216984,  0.18159682, -1.39598177, -1.65891153, -1.22069526,\n",
            "       -1.04540875, -1.04540875, -0.51954922, -0.1689762 , -0.1689762 ,\n",
            "       -0.1689762 , -1.04540875, -0.78247898, -0.51954922])] (1, 144)\n",
            "\n",
            "[-0.519549215966776] [-1.8213232522961487] [1.5215686286261194]\n",
            "\n",
            "[array([-0.54774188, -0.80245815, -1.3118907 , -1.3118907 , -1.82132325,\n",
            "       -1.82132325, -1.82132325, -1.82132325, -1.82132325, -1.56660698,\n",
            "       -1.05717443, -0.80245815, -0.03830933, -0.2930256 , -1.05717443,\n",
            "       -1.3118907 , -1.82132325, -1.56660698, -1.82132325, -1.82132325,\n",
            "       -1.82132325, -1.05717443, -1.05717443, -0.03830933,  0.21640695,\n",
            "       -0.03830933, -1.05717443, -1.05717443, -1.3118907 , -1.3118907 ,\n",
            "       -1.56660698, -1.56660698, -1.82132325, -1.82132325, -1.82132325,\n",
            "       -1.56660698, -0.03830933, -0.2930256 , -0.80245815, -1.3118907 ,\n",
            "       -1.3118907 , -1.56660698, -1.82132325, -1.82132325, -1.82132325,\n",
            "       -1.05717443, -1.56660698, -1.82132325,  0.40231553,  0.6821288 ,\n",
            "        0.6821288 ,  0.40231553,  0.40231553,  0.12250225,  0.12250225,\n",
            "        0.40231553,  0.40231553,  0.6821288 ,  0.96194208,  0.96194208,\n",
            "        0.96194208,  0.12250225,  0.40231553,  0.40231553,  0.40231553,\n",
            "        0.40231553,  0.12250225,  0.12250225,  0.96194208,  0.96194208,\n",
            "        0.96194208,  0.6821288 ,  0.12250225,  0.40231553,  0.40231553,\n",
            "        0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,\n",
            "        0.6821288 ,  0.40231553,  0.40231553,  1.52156863,  1.24175535,\n",
            "        0.40231553,  0.6821288 ,  0.6821288 ,  0.6821288 ,  0.96194208,\n",
            "        1.24175535,  1.24175535,  1.24175535,  1.52156863,  1.52156863,\n",
            "        1.52156863, -1.48362502, -0.95776549, -0.1689762 ,  0.18159682,\n",
            "        0.88274286,  0.88274286,  0.53216984,  0.18159682,  0.18159682,\n",
            "       -0.51954922, -1.04540875, -1.22069526, -1.65891153, -1.48362502,\n",
            "       -0.43190596, -0.1689762 ,  0.53216984,  0.18159682,  0.53216984,\n",
            "        0.18159682,  0.53216984, -0.43190596,  0.18159682, -1.22069526,\n",
            "       -1.65891153, -1.39598177,  0.18159682, -0.08133294,  0.18159682,\n",
            "       -0.1689762 ,  0.53216984,  0.18159682,  0.53216984,  0.88274286,\n",
            "        0.53216984,  0.18159682, -1.39598177, -1.65891153, -1.22069526,\n",
            "       -1.04540875, -1.04540875, -0.51954922, -0.1689762 , -0.1689762 ,\n",
            "       -0.1689762 , -1.04540875, -0.78247898, -0.51954922]), array([-0.80245815, -1.3118907 , -1.3118907 , -1.82132325, -1.82132325,\n",
            "       -1.82132325, -1.82132325, -1.82132325, -1.56660698, -1.05717443,\n",
            "       -0.80245815, -0.03830933, -0.2930256 , -1.05717443, -1.3118907 ,\n",
            "       -1.82132325, -1.56660698, -1.82132325, -1.82132325, -1.82132325,\n",
            "       -1.05717443, -1.05717443, -0.03830933,  0.21640695, -0.03830933,\n",
            "       -1.05717443, -1.05717443, -1.3118907 , -1.3118907 , -1.56660698,\n",
            "       -1.56660698, -1.82132325, -1.82132325, -1.82132325, -1.56660698,\n",
            "       -0.03830933, -0.2930256 , -0.80245815, -1.3118907 , -1.3118907 ,\n",
            "       -1.56660698, -1.82132325, -1.82132325, -1.82132325, -1.05717443,\n",
            "       -1.56660698, -1.82132325, -1.82132325,  0.6821288 ,  0.6821288 ,\n",
            "        0.40231553,  0.40231553,  0.12250225,  0.12250225,  0.40231553,\n",
            "        0.40231553,  0.6821288 ,  0.96194208,  0.96194208,  0.96194208,\n",
            "        0.12250225,  0.40231553,  0.40231553,  0.40231553,  0.40231553,\n",
            "        0.12250225,  0.12250225,  0.96194208,  0.96194208,  0.96194208,\n",
            "        0.6821288 ,  0.12250225,  0.40231553,  0.40231553,  0.6821288 ,\n",
            "        0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,  0.6821288 ,\n",
            "        0.40231553,  0.40231553,  1.52156863,  1.24175535,  0.40231553,\n",
            "        0.6821288 ,  0.6821288 ,  0.6821288 ,  0.96194208,  1.24175535,\n",
            "        1.24175535,  1.24175535,  1.52156863,  1.52156863,  1.52156863,\n",
            "        1.52156863, -0.95776549, -0.1689762 ,  0.18159682,  0.88274286,\n",
            "        0.88274286,  0.53216984,  0.18159682,  0.18159682, -0.51954922,\n",
            "       -1.04540875, -1.22069526, -1.65891153, -1.48362502, -0.43190596,\n",
            "       -0.1689762 ,  0.53216984,  0.18159682,  0.53216984,  0.18159682,\n",
            "        0.53216984, -0.43190596,  0.18159682, -1.22069526, -1.65891153,\n",
            "       -1.39598177,  0.18159682, -0.08133294,  0.18159682, -0.1689762 ,\n",
            "        0.53216984,  0.18159682,  0.53216984,  0.88274286,  0.53216984,\n",
            "        0.18159682, -1.39598177, -1.65891153, -1.22069526, -1.04540875,\n",
            "       -1.04540875, -0.51954922, -0.1689762 , -0.1689762 , -0.1689762 ,\n",
            "       -1.04540875, -0.78247898, -0.51954922, -0.51954922])] (2, 144)\n",
            "\n",
            "[-0.519549215966776, -0.7824789811321485] [-1.8213232522961487, -1.8213232522961487] [1.5215686286261194, 1.5215686286261194]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EJX1sPdCvvlI",
        "outputId": "26bd34c0-5b4f-405d-e2ea-fb7a59525b2a"
      },
      "source": [
        "np.array(x_train).shape , np.array(y_train_humidity).shape, np.array(y_train_pressure).shape , np.array(y_train_temp).shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4702, 144), (4702,), (4702,), (4702,))"
            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ubFzKR5VwTcV"
      },
      "source": [
        "x_train = np.array(x_train).reshape((-1 , 144 , 1))\n",
        "y_train_humidity = np.array(y_train_humidity)\n",
        "y_train_pressure = np.array(y_train_pressure)\n",
        "y_train_temp = np.array(y_train_temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "i9jmG_sFvsFO"
      },
      "source": [
        "### Preparing testing set"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q7TRUtSeyw4N",
        "outputId": "1ed378e2-d6ba-4f49-fbe4-48dba750f148"
      },
      "source": [
        "test_data = sta[train_data_len - 48 : , :]\n",
        "test_data.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(298, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "QeQkDpQAvGqx",
        "outputId": "3729a379-20a6-44a2-d9ca-73db59d719c8"
      },
      "source": [
        "x_test = []\n",
        "\n",
        "y_test_temp = sta[train_data_len : , :] \n",
        "y_test_humidity = sha[train_data_len : , :]\n",
        "y_test_pressure = spa[train_data_len : , :]\n",
        "\n",
        "for i in range (48 , len(test_data)):\n",
        "  temp1 = sha[train_data_len - 48 + i - 48 : train_data_len - 48 + i ]\n",
        "  temp2 = spa[train_data_len - 48 + i - 48 : train_data_len - 48 + i ]\n",
        "  temp3 = sta[train_data_len - 48 + i - 48 : train_data_len - 48 + i ]\n",
        "\n",
        "  temp4 = np.concatenate((temp1 , temp2 , temp3))\n",
        "  x_test.append(temp4)\n",
        "  \n",
        "  if  i<=49:\n",
        "    print(x_test , np.array(x_test).shape)\n",
        "    print()\n",
        "    print(y_test_temp , y_test_humidity, y_test_pressure)\n",
        "    print()\n",
        "    "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[array([[-0.54774188],\n",
            "       [-0.80245815],\n",
            "       [-1.05717443],\n",
            "       [-1.05717443],\n",
            "       [-1.05717443],\n",
            "       [-1.3118907 ],\n",
            "       [-1.82132325],\n",
            "       [-1.3118907 ],\n",
            "       [-0.54774188],\n",
            "       [-0.2930256 ],\n",
            "       [-0.03830933],\n",
            "       [ 0.21640695],\n",
            "       [-0.03830933],\n",
            "       [-0.54774188],\n",
            "       [-1.05717443],\n",
            "       [-1.3118907 ],\n",
            "       [-0.2930256 ],\n",
            "       [ 0.21640695],\n",
            "       [ 0.47112322],\n",
            "       [ 0.7258395 ],\n",
            "       [ 0.47112322],\n",
            "       [ 0.21640695],\n",
            "       [-0.03830933],\n",
            "       [-0.2930256 ],\n",
            "       [-0.54774188],\n",
            "       [-0.80245815],\n",
            "       [-1.05717443],\n",
            "       [-1.05717443],\n",
            "       [-1.82132325],\n",
            "       [-0.80245815],\n",
            "       [-0.2930256 ],\n",
            "       [ 0.47112322],\n",
            "       [ 0.47112322],\n",
            "       [ 0.7258395 ],\n",
            "       [ 0.7258395 ],\n",
            "       [ 0.7258395 ],\n",
            "       [ 0.7258395 ],\n",
            "       [ 0.47112322],\n",
            "       [ 0.21640695],\n",
            "       [-0.03830933],\n",
            "       [-0.03830933],\n",
            "       [-0.2930256 ],\n",
            "       [-0.80245815],\n",
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            " [ 0.6821288 ]\n",
            " [ 0.96194208]\n",
            " [ 0.96194208]\n",
            " [ 1.24175535]\n",
            " [ 1.24175535]\n",
            " [ 0.96194208]\n",
            " [ 0.96194208]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.6821288 ]\n",
            " [ 0.96194208]\n",
            " [ 0.96194208]\n",
            " [ 1.24175535]\n",
            " [ 1.24175535]\n",
            " [ 0.96194208]\n",
            " [ 0.96194208]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [-0.4371243 ]\n",
            " [-0.15731102]\n",
            " [ 0.12250225]\n",
            " [ 0.40231553]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.12250225]\n",
            " [ 0.40231553]\n",
            " [ 0.12250225]\n",
            " [ 0.40231553]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [-0.15731102]\n",
            " [-0.4371243 ]\n",
            " [-0.4371243 ]\n",
            " [-0.15731102]\n",
            " [-0.4371243 ]\n",
            " [ 0.12250225]\n",
            " [-0.15731102]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [-0.15731102]\n",
            " [-0.15731102]\n",
            " [-0.15731102]\n",
            " [ 0.12250225]\n",
            " [-0.15731102]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [-0.15731102]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.6821288 ]\n",
            " [ 0.96194208]\n",
            " [ 0.40231553]\n",
            " [-0.15731102]\n",
            " [-0.15731102]\n",
            " [-0.4371243 ]\n",
            " [-0.4371243 ]\n",
            " [-0.15731102]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [-0.15731102]\n",
            " [ 0.40231553]\n",
            " [ 0.40231553]\n",
            " [ 0.6821288 ]\n",
            " [ 0.6821288 ]\n",
            " [ 0.40231553]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [-0.15731102]\n",
            " [-0.15731102]\n",
            " [-0.4371243 ]\n",
            " [-0.4371243 ]\n",
            " [-0.4371243 ]\n",
            " [-0.15731102]\n",
            " [-0.15731102]\n",
            " [ 0.12250225]\n",
            " [ 0.12250225]\n",
            " [-0.99675085]\n",
            " [-0.71693757]\n",
            " [-0.71693757]\n",
            " [-0.71693757]\n",
            " [-0.71693757]\n",
            " [-0.71693757]\n",
            " [-0.99675085]\n",
            " [-0.99675085]\n",
            " [-0.99675085]\n",
            " [-0.99675085]\n",
            " [-0.71693757]\n",
            " [-0.4371243 ]\n",
            " [-0.4371243 ]\n",
            " [-0.15731102]\n",
            " [-0.71693757]\n",
            " [-0.71693757]\n",
            " [-0.71693757]]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0mbfM4N5zlCF"
      },
      "source": [
        "x_test = np.array(x_test)\n",
        "\n",
        "y_test_humidity = np.array(y_test_humidity)\n",
        "y_test_pressure = np.array(y_test_pressure)\n",
        "y_test_temp = np.array(y_test_temp)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fh5CgdQ-z5Ot",
        "outputId": "59c71aa9-80c9-4e2a-875d-8ef770640c38"
      },
      "source": [
        "x_test.shape , y_test_humidity.shape , y_test_pressure.shape , y_test_temp.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((250, 144, 1), (250, 1), (250, 1), (250, 1))"
            ]
          },
          "metadata": {},
          "execution_count": 27
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4EtHWtTM0G7G",
        "outputId": "4c1ea862-3169-4a94-9bbd-ba4a2df9051b"
      },
      "source": [
        "print(x_train.shape )\n",
        "x_test.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "(4702, 144, 1)\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(250, 144, 1)"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Bg_mRWSb3tE8"
      },
      "source": [
        "# Creating model "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4x6RCsYK1MZG"
      },
      "source": [
        "import keras\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM  , Dense\n",
        "from keras.callbacks import EarlyStopping"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6dZO_VM74gbq"
      },
      "source": [
        "### Temperature model"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "qpi4YjHaa1h3"
      },
      "source": [
        "#### RNN's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tQry6t8ha5cq"
      },
      "source": [
        "![Unrolled RNN.PNG](data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAlUAAADRCAYAAADluEdVAAAAAXNSR0IArs4c6QAAAARnQU1BAACxjwv8YQUAAAAJcEhZcwAADsMAAA7DAcdvqGQAAHfDSURBVHhe7b0HeJXXkT6+6bubTTaJ48ROnLgXcDdu9CaKEEgC9V4oEhISEiCaEEiiShTRe++i996b6R3TDLhhGxvXxMmym9///Z937v3ElbiAygXrXs37POeRdPXdNmfOzHtm5sz3b1AoFAqFQqFQVBhKqhQKhUKhUChcACVVCoVCoVAoFC6AkiqFQqFQKBQKF0BJlUKhUCgUCoULoKRKoVAoFAqFwgVQUqVQKBQKhULhAiipUigUCoVCoXABlFQpFAqFQqFQuABKqhQKhUKhUChcACVVCoVCoVAoFC6AkiqFQqFQKBQKF0BJlUKhUCgUCoULoKRKoVAoFAqFwgVQUqVQKBQKhULhAiipUigUCoVCoXABlFQpFAqFQqFQuABKqhQKhUKhUChcACVVCoVCoVAoFC6Akqo74P/7//4//L//9/9w7do17N2zF/3794ePd0u88OxLqPbEC6j++IuoZsYzj1XDC8+9hGZNmmHIkCHYv38/vvvuO/zrX/+yv5JCcX9B3f3mm29w/PhxjBs3DmGh4Xj91TdR7akX8Oxj1fHcY8/j2cer4+XnXzU67YPBgwZjz+49+Oqrr/B///d/9lepWqDMbty4gU8++QQbN25Ez5694NXQS9Y2ZWXJrLqRYa23aqN9uw6YN28ezp8/j7///e/y/KoGfuf//d//xYcffojVq9cgPS0d9erUR/WnbbKizJ57/Hk8/8xLqFu7PhITO2Lx4sV477338I9//EPsq8K9wTmkvzt8+DCGDRsGnxYtjV15xcx/NZutMfNf7cnn8dpLNRDQOhAFBQU4dOgQvvzyS4/0kUqqbgNO9rfffoujR4+iYPhIBPmHwL9xEDoGdsGgjiMxudcCzM1ehXk5qzGl90IMTh6NTiHd0LpJMIJbhyI/Lx+nTp0Ww1EVja3ihwEN3Pfff48LFy5i2tTpiI9uCz+vALT1T0JW2yEY3XUGpvdZghlZSzG++xwM6FiA5OCuCGwWitCAcOQNyTPG8YjoflXRW35Pyu369evYsX0H+vTOQmufQAQ3j0JaWC/kdRqLST3nY2bWMkzNXISC9CnoGZOLqJbt0NLLH8mJKVi8aDE+/vhj/M///E+VkRvJ1BdffIEN6zege9fu8PcOQFiLWGRE9cXQlAnGRi7EzL5GZr0LMSx1onm8H8K8Y+DfLABdOncVEvbpJ5/K66iNdE/Qv505cwYjho9AaGA4WjcNRlJwF/RPHCH2hfM/I2sJxnabiX7t8owd6oRWjdogKjQGkyZOxtmzZ/G3v/3No8i1kion4ARz57lr5y6EhYSj4dtNkBk3EKuH78aRWVdwftEXuLD4S1w0gz853l14DYdmXMLyvG3oEz8IDd70QlRENHbs2IF//vOfajQU9xzUMRq5QwcPIaVTCrzqNEOn4G5Y0H8t3pl6FmcWfGZ093qRznKcNXp7eOZlo7fbhXQ1eMsLMZGxWLd2nawBTzJ2twMjc19//TXmzpmLQP8g+DYIQH7KeGwYuR/HZn+Ac4VfyFq/ud6v49T8q9g58SSmZS5GnG8imjX0xsABg3Dx4kWPJwn8btSLq1c/wZTJU9DcyxuBTcIxIm0yNo0+hBNzPyrSM0tm/PvkvI+xc8JJjMuYjbDmsWhUz0ucMaNcN24osXIncK64geAmJDoqBvXfbIResf2xeNAmHJxx0fjDz+xr5aYOnC383PzvEpYO3mI2K71R87U6aNe2Pd55Z79sBD3F1iipKgFGqDjBTPf5t2yNNo3CsGTIZhyf86EoxXljUC8u+eqWQaU5Z8jW6QWfGid1CUsGb0LrxsHwbtYCW7dulddUKO4VLCO3b98+tItrjyY1W2BSr3k4MP2CIVOfim6KgXOit3R4JFxHzYZhxdDtiPRpiwC/IKxevdrjI1Zc70zvT5o4Cc0btZBo84ZR+2W9c6NURA6cyI324PT8T7B70ikM6TQG9d5oiO7deuDdd9+VNKKnyo0k9PPPP8eokaPRuG4T9IzLxfYJx4RMWTIrKS9LZueMzE7Nu2quP25I/GA0qNkY/fr2wwcffCAyU7gHGCjYvn07fFv6wb9RMJblbxG/x/VwN1tDHTk6633My12DpjV9EBEaJXbLUzZxSqocQCPIUOTBAwcRHBCKkKZRWDZkq9mtvi+7+a3jjuLYnA9wYcmtCuM4aDi4K6ODivFtLzt/vqaGuRX3CjRy586eQ9e0bgjzicHMvktxaMZ7ElndNfGU6DAjrM701Rr8P40idb5DQArCgyOwbes2j60X4ndiym/hgoVo4dUS3aKysH7kOzhuyMH+aeexdwqje586dRCO42zhNbwz7ZykuPybBmJA/4F4//33Zb17GkhCSaiYWm7tHYjM+IESsTtq9Isy46DMnMnJGhYh3Wfky7IJ3yatMW7seFz77Jo4VbWRlRskvyeOnzBkKBJx/gmGUBkfafwiI1S7J50W30di5WzurUEdILkuHLAegV7h6BCfgLPGftHWuDuUVNnBhUyDwQLKlE6p8K7ji8KBG4QgcdfK+qnwZvFYNHDjbXdiJQeVa8mQTWjZoDV69ewtO+KqkE5R3F9Qd1lcnTckH4HNw6SW4ZQhRyfmfowF/dchwT8Ns/utkJTV3QgC/09itZiR1kYh6JySjlMnT3lc8Tplxu/E9Hx8dDu090/BxlEHJGLH6N6gpNHIiMwWwiDpPyeychx0Itx4kSS0bhGEuXPnSZTP08CI++ZNm9HcqwW6RPbB9vHHJPKwyzjTfu3ykd1+qHGu7zmVUclBXdsz+Qz6tsuDf4sALF+2XKKtaiMrN1g72Dk1Dc1qt8TCgeuMn7sqGZqJPeahvV8qtow5LLbG2ZwXH7aymZlZS+H1tjey+vSVVLC7k2olVXZwIdMILluyHA1qNcakXgsknM3J525/TNeZeO25tzCld2GpSZXN0F7CsM4T4d2oJZYtWyYRBYXCVaABok6xWLhls1bITRyGvVPeFR3l7pGF6W+9UFcKh7k5uBup4uBzGXkgOWvjHYKJEybhm6+/8ShnR0LFKNWA3IGI8I3D8vxtsgliBGXXpFNo55eClnUCJHLFSJQzOTkOypXrfdu4o0iL6IX2cQk4eeKkbNTc3UlY4PdgzVjP7r2khmr18F1FqeVNow4iollbRDZvh72T33UqI2fjzMLPsHH0AcS0ao/28R3kJCWJlaLygfPP6OvsWbNR6426GN1thhBobjpOGxKVFT8Eb1SrJfWZlu+82+C6oa0ZnDwGb9eohTWr1ghx57pxVyipsoPKcuXKFfTo1gvRPu3xzrTzRfVT3IGOTJ+Gl556zTiauRK2PGYcFJ0UIwJFheslFIaDDH7L2COI9U9AbHQcPrn6ie7EFC4Djc9HH32E3H658G8UhJVDd5jdn61IlEXWBWlT8eqzb2JQx9FykILEgeNOtQ8c3EHum3oWnSN6omP7ZKkT8qR0Fono3r17ERveFtkdhgoBpTxIqhidivVJRNO3WsnhFDoIm9yuimxpF24nN147q98ytG4WJKebPKW1iuVQV61chdDWERjZZapsNikHymODIZ/BXtEIMWPHhBNCtigz2sq71VlRZmMzZsG7oQ9mGYddlU6euhOox4xSxce2FRK8Z8oZsSOcQ5Kq3jED8MrTr2PJoM0STGC06uRcu60xxOt2a4brjhFLnzr+6JnRy35wwX3r65RU2fE///wf7Nq1C4GtQmR37xi+tEjVi4ZU0Umx5iQveRwGJI7EpJ4LjPM5J8TKUVGsQYWhcRmRPgl1a9aX99CdmMJVoPHZvWs3YiPikRk3CAemXywyXo6kKrfDcKwaulP0tSB9qnH8yyWiRRJRUmc5eMKNJwMn9JyLiNYxWLF8hUcdtmCPnLFjxyHavy3m918rcqDcHElVkzdbYmH/dVg4YB1GdZkup9ZWDd0l5Op2JIHrfceE42jfJgWdOqbgwoULHkFGuRFk37O8wfmIbBWPbeOPFtVOUW4WqQpqHCVEtHDABow2MmO0c92IfeJc7+RUN485hDj/REkrsWhdN56VD/RbLE73adoKk3rNtwcUbBsMi1S9/HQNs17WY+mQLTL3I9KmYH7uGqnvZCmNs/nnOD3/U+R1Godgv1Ds3LnTrW2NkioDLuC//+3vmDVzNvy9grBq2K5izoakanjnyXjqkWcR3jRe0gLNa/qh3iuNUeO5t5EU0BU7xh93ajQsQ82ida/a3nIEmbtXhaKi4G6ehZ3z5s5DuH80pmctkciApYckVdTbF558BaFNYhHiFYMmb7VEnZcbovZLDZAS1AObRh8sMowl9ZYRiDUjdqNtYEeMHDFSGoN6ArjeL1+6jDTjwDtH9MDWcUeKotIWqYrybi/p/pgWCWj2ti8a1WiOt1+oixa1/IVg0SZYzykptyOzr2Bg0igE+Ydiy5YtHpHyZ7r00qVLSOvUBd2is+Q7Wk6S35mkKrBRBOq96iV1Nc2NzBrWaCoy867pL72q5JCPU5ldF3kOSjYyax2Mo0eO6knASghGECdNmoTgFhHYNOaAWSvXZO45SKp6xfRH9cdeQnpYJrzNOvF6owVqGTtT55VG6BGVI9EoR7/qOKhLa0fsgV+TQEkvunO0UkmVAQ3GN998i5y+uQhvEVdUk2JNOBf8sNRJeOiBP+Hlp2pgQEIBluVtw6y+yxFgDAlDnqO6TLMrjHNitcsY6lDvaGT3y5GCdYWioiA5INEZPmwE2gYkYm3BXklPWXpnI1WTZDNQ/fGXkNSmqxAvOjhuDt6sXlv6MdlOuDmLvNj0tktUb/Tq3hsfffiRR6RlmMY4cvgIQoPC0L/jiGLRPYtUsT7o4d8/gmZvtTIymiBNfvu2HVJErFYO3XkbB2EczIJPMK3PYrRuFog5c+bIiWJ3B0nOwYMHER/ZHvmp423ROrvOUHasPfOvH4LHHnpSCBWJ5+zslegdO1B0L7pFB4lGMSpVUmZ8PnWQutmqqT+2bt4q/dYUlQs89ZnVJwudwrth9+TTRdmZIlIV3R9/fehxvGz8Yc/oXMzLXS2R8aZmI/ei2dgxYsWsjbXWHAf97TtTzyPCJw6DBw2RSLK7RiuVVBkwPP/Vl18hNakzOrRJFRLl6GSsSNVf/vgo0kJ6y9+sE2CNykxDrEiquob3ua3CcDBFmBzaFV3Sukp4W6GoKEgOSND7ZvVDclgXIQOOjt6KVJFURTRrJ2kYhuz5+Ky+y1D/VS+kBvfAwenvOXV2HO8Yvc1qP1jWBru0u3tahqSQmyg29vXz9kdBl8lSKGutW4tURTZvixeeeEXSF4dmXhKnv2P8CXQO6YVXnnlD7qJwuxNOtA2LBm6QHf24seOksai7k1GmftheIzooHuN7zJFaUUtm/ElS1bpBqBD1ycaRWrU0bEMT3ypZ6tPYl4gp5ZLyIhE9t+hzLBq0UVpSrFi20iOIqKeBtZtJHZOQ1WGwtM5wJNUkVSRSjz38pESqdrKuzmzwSL7HdpuFZx99HkNTJhrfecmpj6S/ZZ+8hDad5SDEF59/4ba1iEqqDLgL+9KQqoR2HZEU1KWYkeUgiWJtynOPviC1VTSa/D8LLNcM3423nq+DxDZdpHBTClqd1A/QOXWJykTnlDQpiFcoKgoanc8++wy9e2UiNaL7LeF1kieSApIDnsw5OP2i7AhJEJjipqPjKTc+j3q7f9oFaSvAyI0V8eJjOQlDkdShk/TB8gRSxU0UCYJPk1YYkzHDnpYqTqoYWan/ahMsGbxZyAEdCO1AbocREq1mJObIrPfluTztRpkxhcHXoX1gTUmYTzRGFtjSpu5OqpjC5P0QI9vEYrIhlLbo5k1SxfRfUONItKobKKf5qGeUGeXSM7o/Gr/hLSenLT3bPPqQ9E9juprXktQvzduCNs2CsXjREi2RqIRgMID3u+yfNFzmsCSpYqSq+mMvSosEIU+GLPMatiGiDcpuP0yex/VEwmXbyN3UIdqr5OBuEnj4/NrnSqrcGRKpMoYvJSn1tpEqq1Cd9wCzwp40ENyhMSXQwb+zGJA1w/fI/Y6oNJbR4eBJquTQbuia3lVONygUFYUVqWJ/l+Swrk4jVbZC9TcwOGmM/E2dJGFaV7BP6gIZReDzto49Im0XukdmS6qbDo8GkZuBvu2HIKWjLVLl7uTAilTt3LETvs39MbLrVKeRKtvpv5ZS52Ftongd+1exGJdy5Qlh9gHrFtFPiti5maLMJFI1aKNEqsaOGesxkaotW7YiKjBODi84I1Vy+q9xtOiO9Tij+ST0XoZUTeoxT2Q2oftc9IkbLHU2vK0Xo1eUO3ujMVK1fNkKJVWVEIxUdUxMQr+EIbbT8fYSGc6zrabKVqjO03+0Ndb/luVtlZPzfdvmYc+kMzLnXFf0nzdfg5Gq95EQkIYeGT01UuXusNVUfSPH0sO9Y83O80zRZHNYpIqKUZJUbRhFUlWvaMffP2EE2vl2ktMv3IWRiVOxaKhDmkchR2uqFC6C1FR9+RWG5g+VYvJ1xrHRoVt6e5NUvYkhtyFVca2SJEXD04DsCM7Gffmdxkv6iq/FmqquUZm2o84fuH9jPoLG+vChwwgJDMOApALpBE25UGaOpIr1VGtHsE6tBKl6xpCq9KliF5ganGhsAvvYUb60DVIf1GcJWjcPkqJbT6mpOnDgAOIj2mNo5wmS+rQ2npTNzZYKMbeSqrZ2UmXkdHLeRyI3ypjkfUTnyeaxq8ZWfoIZfZdKTdXmTVu0pqoSgn4rs3cmUsNtUfGSNVVFLRUGb5EIrvU/R1LFPm457YcJoV7Yf71ZKww+XBd/y5RiZMu2cg/NL69rTZVbg5PHU1SzZ82xn/4rXoRanFQtcEqq2htSxdsu0HBwZ8YaKr4GlYo/VwzdBq/azTF1ylTdhSlcAhIcnlqdM3sOwv1j5OAEa6YsvS1OqsbellRtHntY9JatRKjrTG3NzFomr7XG7CjbBSVjxLARUjzqCeB6f++9S0jt1FkadW4bd0wMO2VWFlLFvylLdqtn3QjlyxQgHx+SPAaBfiHSfdxTTv+x8Wfn5HR0j+lnvuOtp/9KQ6pIOLnZ5M2q+ycUyPF7Pka94/0TA/2D5RCBnv6rfOCJvAkTJiDUJwqbxhws8m8cpSVV28cfFyLNsoQtY45IlJLX8LVok1o3DcLMGTPx7Td6+s+twcmj4duzew+CfNmnaro9ynSTVNHR8FQDizCLk6r9qPlifUn/MV88P3et7PitQk7mjZkWGJ42UfpU7d69W/tUKVwGOh+msqLDYtGvXZ50OKbeUT9p2Oj82RpgSLIhVfbaIZKEdSP3ybFnpv/YnJaF1yONjvP5BcbgMXLFmsHJvedLn6plS5d5xH25CK537oR5Q+Bo/3ZFt6OS9Wp+klTFtewop9hsJypvkiqmUV959nWzybpJqmYZUjW660ypqeTOm89PDEhDUkIyzp075xF9qhjdY4nE4IFDEO3XTnpxkQxZjpMkKaRJjLTu4P3frMdJquhMvd5sgcm9Fkg9DUksD1DwFOoBYzPpWJl+btc6WW4R9v6V99029ePJoN/avHkzfJr4YnrWYtl0McLEeea89o4dgFefeQNLS5AqpvuYFuRtjEi4p/RaKCdDuV7oH3kNdYl2J9A3WHphubOtUVJlBw0fC8iZ5ohs0VZukGrtXmk8WQdQy5AnhvWt1CCVaovZ5Td+owVSg3uKQ6ICZcUNFiNDRaGybR5zGDG+HRAXGy/3aHP3Yl9F5QGdDwtIszL7ok3jUOkrRRJA/WTH/zHdZkqvGKZarNvU0IltGHUA/vVDkdgmHTsMCVg8aCP6dxghkVg2CmWDW0Zbu0Rmon18Ak6dOuVR0QOml9hkMCY0HgM6FgiBpIHn4HFxbpL86gbLOrZ25HQUQ1Mn4O0X62KskSs3WyQQJFp0KKwToQ3gUXLuuMeNGedxHdVJrkP8wjGu+6yijuoc7HfG3l7R3h2kJY3lUCmj3IQR8KndGtMyF0mKh6lo1qHNyFoq6Wba14k958G7YUtMnTpNSjHcNUrhyaAesx44JioGbQ0BZl0VfRznmX6unyHPrC9ekb9D1pOlA7zLAw9zDUgsEN2Ynb1CIug8FMNNCbutUy/86geiW3oGPnj/A7e2NUqq7CDR4WJevnQ5mtRvbpzRDHFCVAyLGDHEv93s0KgofJzNz1hTwbD24kGbhGSxcyxPDDEczkgW6zXyUsbCt1lruWEojbkaDIWrQL1l9+HVq1ajVXN/DEwaaQzXWdFRGjwepGABOrtc2+oXbNFTHqpgx2OmrujU+DfrAJnm5k/eaoSOLrhlOMYacuDOfWOcgemsTz/9FLnZ/RHbugOW52+X2h5GoY/OZsuJ5VJ8zpsr8zFLnquH7ZaaM8qVjoMRlnEZs2SXvWzIFvP4PmmO2S62Aw4fPiLv4ynrnfN/9uxZ9OjWE+EtYoXAUybcZNLOTe29CFMNcTpm9Ekc6pIvjU28KukfklASL54KI5liNH9Cj3nS72t9wT5x0u3i2uP06dMekS71RFCPGa2aNm263B93XPfZ4v+4PrjxYNqPa2PP5HfF1lAHWFPMspj8TuMk4MCNCYnVnOyVQrboQ0nOhxp9qPNWffG/jFK580ZESZUdVBhO5HvvvYcuaV3Qoq6fNCuz5XxthXSMAFhRKkth+D9eY6UP5G/zO5WFRnfR4A1o2aA1+vbpJ8ffNUqlcDWoU9xB9s8dIPUOTOUx/UxdJYGigZMwu/34sjg8o6fcJTp2RaZ+cyPAzcRy4wiDm0RKf6ojR454RArLEVzv/E6seYqLaIvk4AxJg3LtUm5c05SPFa22yYzpQQd5Fv1tu88d+32xiDvQJxQzps/0mBo0R7Doft3adWju1QI9Y3Pk5tPUIcs+2uylo57ZUqr8n01mtuu4USXhYlSwf+II+HsHYFHhYtkgqI2svOC6uXz5MjolpaBVgwAsGWJvOcI1Y+bZItmOOsC/Zc3YfaR1QtYac3NWw7uOn/Sn4p0O3H3+lVQ5gApDo8HbJAS1CZF0Ctk1lYbK4OiUig/Hx21GhEaWTDzcJw4xUbGya6UR53soFK4Gd/dnTp9BckIn6UpcOGA9jhtSX9LAOQ7b47caP/ZeY7+2AL9AbNywUdaEJ+otvxO7RPOAile9pugdNwDbxh8TQy/r/Y5yu/k71ztrh5hq9W0cIKlYOgdPLLbmxvPaZ9cwfvwEtPLyl5tRM01sI6C3k5l5TB63/Y/X0AEzqj8ibTJaNPTFiOEFEjmkQ1UbWbnxP/9zw2y0jqKNfyDat+mEtQV7pAbZ2mjcOv+32hr+zbZDjFQGmc1bZFg0zpx51yPqNpVUlQDD9ayD2L59B6LCYuS2NbzrPHO+NBx0PI7K4qgkVCoqF8ObPB4c1iIGYcHhshvWE3+Kewk6I+7yt2zegoS2iQj1jsLk3gvk6LMtalU8UuWot9w5kkyxHmjJ4E1ICk5HRFAUFi5YiOvXr3t05IDrnVG+USNHIbBlCLrHZEuqlKfbbFG8O6x3Qwy43nlMfFjnifDzCkRGl+44dOiwkFxPJQfcHL7//gcYmjcMrb2DkJ0wVBp+8vSjjZDe2UYen/uh3LKGtWzsS9Wnd5YU9Gvazz1AvSb5WbVqFUICwtC2dZIc9mCNFQMQdyJXVpSKafU5OSuNnYpBTESc2C1PqT9UUuUEVBruztl1OTYmDs3rtkS3yL7SJZmFl7L7L3JQtt0Zw96sF6By8dpGNZuiY0JHOcmgdVSK+wFLb3fs2IFEo3stG/kjPTxT6v1Y+8BbgVBXLd3l79RlpqkZncrpMAzedVohJjIWixcvlqaVVSEVQ2LF1Dxvdh7kH4KgppFyz0QeQiFpKu4gbOudjoENfXlKkrfWaNXEX24XdPzYcak78fT1Tr3gDZbHjBkLn6a+CPeOk5YcPDLPex/aZHZz8G8Sd6vtTLxvRzRr2AKDBg6WOi09Ee1esIjV2jVrJRPTtFYL9IkfJJEn1k0xLX5TB+yEytgaEm9mcHpE58CrZnPExcRjkz3o4Cm2RknVbUBDy74cJ0+exKTxkxAWGAHveq0Q2zJBbqzKIl8a1Dn9VmBCj7nIbp+PeL+O8Gngj/CgSIwbOx7vnnlXogdKqBT3C9zp0UBR9yZPnCKtFqiT8X5JQpp4nJldwJkenJG1RG6onBzUFa0bB0s7ER6Z3//OfqkH4mtVBd3ld7SIFRtP9uzeG62atkZgk3CkR2RKWwoW9BcOXC91llzvmXGDENGiLbzrt0Ri245YMH8hLl++UmU2UPyOJEKfXP1EHGuXzt0MsWyNkObR6B7dT9rSsBiZhx5YM8PUaI/oXIQ0i0bLxv5Sq7di+UqJEjJCVRXIu6eBOkBbc+zYMQzLHybtEPwaBUrpAFu48EAC7Qx7kfHkJ/uStfVLNn7UF6FtIjBh7EScOnlKDoh5kq1RUnUHcKEz1M1Osvv375cUQXx0WzRv7AOvOs3gVduMOs3RuE5TeHv5IDoiWq7Zv/+A9HShoVYo7jdonGikWC/0zjvvoGBEAWKj4kRHm9RtjiZ1vNGktrfocNMGzRHcJgQ52bmSpmZdS1WItDgD1zs3QSzEXbFihdwuo41vILzqNTMya46mRmb82aRuM/g080Vih46YMmUqTpw4IVG9qkJCHWHJjI1BFy5ciLTO6fBt4S8yop41rdPC2Emja+ZvPp7csRPmzpkrp/zokNVGujeo75xD2hpGyPv3H4CggGCxK7Q1nH/qAX1ls4beiAiNxFBDwPbs2SObGNYdehqhVlJVCljk6ttvv5OdFYv0eHNR1pzMmztPfj969Bg+/vhjSb9QyaqiU1JULlAHqbffGb1lL6uDBw9h7dq10gtozOgxWLpkKfbu2SsnXnm7m6pKphzB78/1zujJF198IakpOotFixbJffymT58uxfvHjh3H1atXJQVS1dc7vzsJJaN03ICeOXMG27ZtxwJjH7nJnDlzphB2Rv3pSEnCqiIB9WRwLmlrGHXipoSbuVWrVmP8uPGyblauXCmBCTZ2ZQbof2/8r8eRKQtKqkoJy9jSgNKQsonnkiVLMHbsWPmdRlgNhaKygfrIQb2lM6NTmzt3LrKzs81G4KjNwBljSN1W3b0Ja71zJ01HwWjU4MGDRXbclSsxuBWUBWVCmTEKRcealZUlESzqndpIzwfnlrbGIthDhw7FgAEDJJLJdVQVbI2SqjKCykDjwPB1hw4dULt2bTEeWmipqOygs2Nn9Pj4eLz55puYPXu2pKnVyd0elA0JwoIFC1CvXj3ExsZKJEbX++1Bp0kHyg0n9axt27YS8aNDVV2rGuBcc9NWv359WTeMkHMDVxXmX0lVGcGdFh3R6NGj8fTTT+N3v/sd0tPTJS2oBkNRWUFHxwjLyJEj8cwzz+C3v/0twsPDcejQIY0e3AaUCWXDKFVUVBQeeOABWfMjRoyQyIvKzDlIOA8ePAhfX1+xj5TZlClTxG5SDxWeD9Zm5uTk4OGHH5Z1Q2LNaBXJlqdDSVUZwdTf8ePH4eXlhV/+8pf493//d7z66qsS4mYESw2tojKC4XjezLtly5b41a9+hZ///Od46qmnMGzYMOlFRfKgKA7KxCKiTz75JH7xi1/gv/7rv9CkSRNs3bpVUoCK4iBpokMdNGgQHnvsMdGz//zP/0RISAgOHDgg0VK1kZ4Lzi2J0/r16yWLw/Vi2RpGe6tCZFxJVRlAg8H6KdajkH3/+Mc/xk9+8hPZ9cfExEiBnjonRWUDjRj1tn///njkkUfw05/+FD/60Y9kQ0CCwEJs7aVWHJQFZcI+c02bNhVCxfVO2f3hD3+QWqH3339fIy8OoMy4sdyyZYs4VG46qWccJKV5eXmSFmTNjcIzYUXEO3XqJFFKy9aQWAUGBkqTV09PAyupKiWoLDQYzA0///zz+NnPfoZ/+7d/E4Xh788++6ycDOLpP4WisoB6y+jAunXr0KBBAyFS1FnqLknCH//4R2RkZOiGoAQoC6b0e/XqhYceekhk5bje69Spg+XLl4tslVjZQJnxJGnnzp3x3//93+JQKTMORqsaNmwo9aeM9is8E9yI8AAXfaRFqKx1w81IQUGBtB/x5DSgkqpSgsaTO9OUlBRh4Ny5MkrFwd/pnNq0aYN3331Xd2KKSgPqIo/+9+zZU6IFTP1RX2nw/uM//gMPPvigFJKuXr1anZ0D6BzYKoVRKsqIsqLMuONmSoOy7Natm6S6dL3bolTUH3bif/vtt/Gb3/xGZEYCykG9Y21Vv379pPWMRkU9DyTV7LIfFhaGv/71r0WpP84/dYEZHa4n3qCd68tToaSqlGCUisWXycnJUk9Vq1YtKcJjiJsnXLy9vaXwd8OGDXoySFFpwB3h+fPn5Whz+/btERoaiueee042Bo0bN5bTbGlpaUKquINU2MATf6wLIXGKi4sTZ8CNE4v8KUOe/M3PzxfZcsNV1UGSRP0pLCyUSBXLIerWrYvf//73om+sqUpMTBSZXbhwQaJ7Sqw8C9xcsN6YG7iEhAQEBwfLmmGEirWc1IkuXbpIPaInZ3SUVJUSZOE0GgxvU3HohKgojz76qBSp86g6jQUbBmoaRVFZQOdl9ae6cuWKpF9I/qtVqyYnskgKGMnSWpfioCwoE8qGp5bYwPKNN94QQsXNFWVJmXLHrek/G6mizFiITJnRFvIQxAsvvIDIyEhp/Mh0KmVGfVRC5XngnHJuGb1ls2F2TX/ttddQo0YNSZVXFVujpKqUoMLQeFpOiiQqIiJCdq50VIxkkUypsVBUNlAnORhRIRlgxOrll1/GsmXLJCKjUQPnsOTGtNaaNWukjopHw+kwrO7zKrfioDyoT5QZa0zpUBnVo97RPqqueT44vyRNTAUyo8NDCzx5XFVsjZKqcoAGlQ0AuQNjgTp3rlWh/4bCvUEdZV0gnRxJFXePerDi7mA0igdUmM4iqfroo490vd8BdJqU2YwZM/D6669LKoh6p2Sq6oDkiYdfSKg4GLWqKjWbSqrKASVVCneEkqryQUlV2aCkSqGkSlEmKKlSuCOUVJUPSqrKBiVVCiVVijJBSZXCHaGkqnxQUlU2KKlSKKlSlAkkVbyhMgvVlVQp3AVKqsoHJVVlg5IqhZKqSgguQA5OTmUa/Ew86edIqnhPKxrZyvp5ORQKJVXlg5KqsoH2RklV1QZ9j5KqSgQuPhotGvxvv/22Ug1+Jg6LVLGlAkkVo1dstcBjo86e90MNflZ+Niq5ompDSVX5oKSqbFBSpVBSVYnAxmAkLLNnz5Y7nefk5MgNjCvLGD58OPbt2ycNQC1SxcZ2bHjGJqADBgxw+rwfYlB2/Dzjx4+Xm+ayMZ82Jq26UFJVPiipKhuUVCmUVFUisD6J99ej0X/iiScq3WBX5VmzZsn9ixybf7LTOo0Hb8ng7Hk/5KhevTp8fHywYMECfPnll2rcqiiUVJUPSqrKBiVVCiVVlQi8PxSJCW8BM2TIEIwdOxbjxo2rNIOG4tixYzhx4kSxSBVvT7Nu3TpMnDjR6fN+iEHZMbIWHx9fRKy2bNki3W7VwFU9KKkqH5RUlQ1KqhRKqioRSKjq168vt4Wg8bp27Ro+//zzSjOYQrNSlI41Vayn4uMkV86e90MMyu6TTz6Rz5eamooXX3wRI0aMEEeqBq7qQUlV+aCkqmxQUqVQUlWJ8OCDD0pkhaSAE1MZYfWpskhVZW+pQCK4ZMkSuYluRkaGpAArq2wV9w5KqsoHJVVlg5IqhZKqSoQHHngASUlJEvWprIvQ3Zp/UpYbNmyQFGBaWppE05RUVT0oqSoflFSVDUqqFEqqKhFIqpKTk5VUuRBsrbBx48YiUlWZo4CKewclVeWDkqqyQUmVQklVJYKSKtdDSZWCUFJVPiipKhuUVCmUVFUiKKlyPZRUKQglVeWDkqqyQUmVQklVJYKSKtdDSZWCUFJVPiipKhuUVCmUVFUiKKlyPZRUKQglVeWDkqqy4XakSm1O1QHv3KGkqpLAVaSKC5hNLkmAuMBdOfjZ2AA0LCwMTz/9NHbv3i33/HN2bXkHb9rMz+6K28ooqXIfUOc5Nzdu3BAdcOUggbpw4YIQA/YsW7RoEa5fv+702ooM6u39bjDruN6dfaaKjK+//loIKJ1DbGwsLl26JOvd2bUVGZxzfo/7JTe+z72QGe0X27hMnjwZr776Ktq1ayd6x15+zq6vyCC51VtvlR+WrXG1DnCuz549i7fffhs1a9bE9u3bpZWPs2srOiobPJZUUVG4O1q2fBkWLS7EkqWLXTYWFC7A6DGj0KKlN55/oTqGDR+KwkULnV5bnrF46SIsXrIYS5ctxZUrlytsNJRUuQ841yQ6W7dtxYqVy7Fq9UqXjeUrlmHGzOkIDArAazVeRU5uNhYtWeT02vKMlRyrVmDFiuU4d+6cGOr7RRC+N0b89OlT5v1tn8HZ5yvv4JocMLA/atWuiTYBrTFz1gwsM7J0dm15xsrVK+Rz8/6cdDz3gyRwXhg5OHz4sPkMq0RmnD9nn6+sg69FmXXvkYHX33xd9I165+r34M/du3eJfVOUHdQzNod29fxz0HZNnzENNWsZUmXG8BHDjE9zna3h4HssW77U/m0qDzyWVH33t++ESAQE+yOjb2f0GZRhRneXjMyBGeiRk47kbu3QvnMMemSnoY95zNm15RmZAzLQvW8aImPCsG7dWiGIFZGFkir3AXdevFl3p9SOSEpvj545XdDLRYOvxbWQkBaHuKQIpPXsaHQ33em15Rl8/a6ZKQiJCMSs2bMkWnG/SBVvaD5x0gTEtI2QtePs85V39DQySuuVhPjkSCR0jhUZunpeOnVJQHq3NJw8eVLI6L0G1//Vq1cxaPBAxCfEiMxc/Z1Suycg3ugZ9Y0yc3ZdeQc/b1JaB6R3TZPI4f3SM08C/cruPbuNj2yNLr1SjJ47l3V5B+eca4ajS+9kl9oajvReyWjWwsv+bSoPPJdUffctVhsGHhIVgPUHCrH33DrsPb/eNePceuwxr7fn7FoZLn9t85rr3ilEp64dZAdR0R2/kir3AVMnhw4dQteeqZixbCx2nl6NXWfWuHTsPLUaO06tcvlr7zy9Bmv2LkCnjPaYMnWyRNzul7O7evVjjBk32mxKumHzkWVOP19FBr8bZbbDyG6X+d3ZNeUdnA/OdffMLjh67Oh9I1Uffvgh+mZnoX9BJrYeWy6fw9nnK++gflFeHJSfs2vKO/h5Jy8sQKfOHXHh4gUlVeUASdW27VsRER+CFTvmunz+OWzz73pbw7Fy11z4Bnnbv03lgceSqm/tpCouMQLHPtqB9/9xDB/887hbjMt/P4oj729DRlaqhDmVVFUdCKk6fAiZud0NsV6IK98fFd19/x/HK/248v0xHP1wO3oPSMe0GVPuK6n6+OOPMX7iWAydkIt3v9jn9PNVznFM5njt3oXoO6AXjhlSdT/qRCxSld2/L8bOysf5rw7girE7zj9j5RsXvj6AFTvnIK1bKi4aUqX2rOygX9m+fRvaJUdj/8VNbmVrOA5f2YKIdkH2b1N5UGVIlTPyUlkHjZuSqqoJi1T1sZOq9w1RcaYjlXHQIB+rJKTK2eerjKOIVO2rBKTKfA5nn7EyDhupmov0DCVV5YWQqh12UvWenVQ5kXVlHYff36qkqjRQUqWkqirD7UnVRz8wqZqYi7NKqu6KW0iVISlKqqoWnJEqZ7KurENJVSnhDqSKhvDy34/g0neH8Z4ZwvDNY86uLc9QUlV1cS9JleXAL/3NprdMM7tSb/lankiqHNc7ZefK9W7NiaeRqmIys9tIZ9eVdyipqjjuJamypeiOyXrh/F/+2xGXrRlrKKkqJdyBVFFJ9l/ciAXrp2Dm8rFSWM7HnF1bnqGkquriXpIqOrmjH27Dih2zMX/9ZOw8vUqck7NryzNoND2RVF385qAU2i7YMMU48tliT+gknF1b1uGJpIrfiTLbcnSpsZGTsdzo24mrO13qtJVUVRz3klRJXfAHW1G4aRoWbpyC3e+uEZ1wdm15h5KqUqKykyoqHms2xs/NR2Pvenirzmvok9cVx43RcFU+WklV1cW9IlV0dGevv4Ppy8agZWAz1Gn0NnoNTDObgw0u20F6Iqli8f3By5tFVvUa14RvcHPMWjlOZOkKufE1PI1UkXDyJHOnHu1Qp+Fb8AtpgUWbpwsRcpWuKamqOO4lqaKPHDNrMBo2q4P6TWqh/yij28Y2uPI9lFSVEmUhVXf6/70iVUybcDIj2wXiDw89iN//4Xdo3KIe1u0vFAPs7DllHUqqqi7uFamiMTtwaZM4uj//9WH87ve/RSPvusbZTZMIlqsIgieRKn4fpi8Wb52BBk1r44EHf4tHjOwowwPvbXSJg/BEUsXXmrd2It40G84HHvwd/vSXh2XjedSFTlVJVcVxr0gVdZothyLbB+GhP/9BdKCZb0NsOboM7317yGXBByVVpURpSRX/x1sscDhbUPeKVJGBs+/Va2+9hDdqvSJMvNqLz2D41P64SIVxgXNSUlV1ca9IFUPvq/bMhZdPfbz8+guo51UTL9WojgGjegsJcRVBuFekivrKDtD86ew17wWpokxOfbYbA0dn4rkXnkadRm/hxVeryZpnSosOwtnzyjLuJaminCyZlQQfuxekSnTgY6MDg9Px4mvVhbg/U/0pBMf4Y+OhxUJSnT2vrENJ1d3hOP/O1sy9IlV8HZYXvF7zFdRtXFPWzTPVnpAI75nP97rER3IoqSolSkuqeM8nGoUNGzZIB2peT4Jl4V6QKioDa1IGjO5tlORJJKTHYNDYPnjWGI34ThGSQ3aFYt5rUvXFF1+4pRHiZ6aRsMj03cadDEplxb0iVSc/3YWCaQMMkXoeUR2CkT2sO2rUfFl+Z20VI7DOnleWcS9JFe9byKaoR48elU0BGxc66vC9IFWsk9xybBnC2wbiZSO33oPSEWF+f/b5pzBsci5OfrKrwg6Cz78XpIqy4T0L9+/fL13aefsbxzsz8P+uJlWMQDDqSfLENCnTPiTtbcJbolaDNzFh/jCXkTclVXcH72iwd+9eufk/12LJNXMvSBX1+dyX7yArrxueeOYx9BnSFb0HpuHxp/6KjOxOOHRli0veh0NJVSnx+9//HomJiaIQd1oodD7vvPMOoqKiEB4ejvnz58tdsUm2aDhIJFxJqizjt/nIUnj7NxYWPn7uUPmbbJw7/1W75xrnVPHdq6tJFQkn77JfrVo1dOnSxS1JFb8/nQ1vrcF55q0p7jZ478f7dS81V+FekCrqLg9TtO8cjVffeglDJ+UYXZ0H36DmUvMyfekol9QI8fmOpOqL61+4hFTxNTjv/fv3R1xcHCZNmiQ3a+W96yw9djWp4nc5a5zD5MIReLP2a/AJaIolW2Ygf2K2RK3ad47CjlMrXSIz2hVXkypuKrgGMjIy5Abas2bNkpsa87UpT8rN5aSKMjN6NGHeUDz/8nOIT44QPWOkr8bbLyO5e1ucMuTeFUX+zkiVK3TNU0BZXLx4UTbR1vxTH6z5J4RUWc0/XUSqSKpZp0lS/eKr1bF0+0wUbpqK2g3fRGPvulImw81KRdcNh5KqUuJPf/oT/Pz88O6778qkl4w+WIN3id+5cycaNGiA3/72t3LXfUa4SB5IGuhMeaNIV5EqEh02yKPBeLraEwiM8pXTLcc/3oHUnh3wwivVJGr17hcVD2/yvagw3bJS5Ca4dB7OZFCawd3JRx99hOHDhwupys7OFpLlbqSKxIg36R0wYIA41tjY2LuOjh07YvHixbJjdxeD62pSRV28ZJwYw/E8WNEqqJk4Ou4Ye+SmSCowrU+i1Fu5wqmSVPUypGrq9Mm49vk10UHqWnkH553jgw8+QKdOnfDXv/5V9Jjzu3DhQly5ckV0nATBlaSKa5AF6mm9E1D9pWfQPaeTOB528W7m20hkOWf1BElnVWS987kWqcrq3wtHjhyWm0M7k0VZBmVC4skN55///Ge88MILsh5WrFghN9Hle1CmriRVfP6+C+uR2CUOz1R/EvkTskXPlm2fJWlnDtrM81/td/r8sgwbqWJH9RRDFs+LnlmR6ao+rDXDCFVQUBAefvhh8Y9JSUlYvXq1zD/1g7bG1aSKZQYzlo+RyG6Q8ZF7z6/DvvNGJ7rG4rnnn5YNHYk3yZez55dlKKkqJby9vUUBevfuLam9Xbt2OR2bN2/G+PHjxVj8+Mc/xs9//nM8+OCDqF+/PgYOHIitW7di/oL5iOkQ5hJSxfoJGoiE9Fj85dE/I7egp4T/uZudumQU3qj1KlqH+RjlrHgBKw36octbkNI9AUOH5ct3cSaD0oxt27aJnBo3boxatWphwYIFFY58/RCwjESPHj2EdLdq1equIzQ0VHZo9/PGvhWFq0kVjdeZa3vRZ0gXqQPs2i9J9Jgh+ulLR0uE1cunnkRZWRPo7DVKO0gQeJuaHrmp6D8wxxjs7YYkHJGUXUUHbQEdxK9+9Sv87Gc/w3//93/j7bffRmZmpmyueM+8kWMKjCPPcUlHdTqH1XvnS4Etd9l0FHxdEq0uWR0lUp05OL3CDsIiVav3zEeXHikoLFwoKTtnMijLYKp0+fLlaNKkCX75y1/iJz/5iZRW1KtXTyJ+u3fvFpn16ZeJMTMrTqr4PSgzHqFn9JNjydYZIh/KrF1qFF5940UMn+KatKlFqjqldcTuPbtw7Jgh9GY4k0VVHUuXLkXDhg3xH//xH/jpT3+K3/zmN0X+kTrG6O+mzRvRNjnKJaSKc8q5jUsOl9pD+ki20uC6YTCi+kvPok1ES2lHdOHrirdXUFJVStAJkgA8+eSTePPNN1GnTh2ngwThpZdewq9//Wv827/9m4wf/ehHYnC5M2vl2wpdu3VBWGygS0jVuS/3y46rUfO6ohwrds0RosWx2ey+gqP9pNaCfVkq6pxIqqh4sR3DUOP111C7dm2nMijNoOMh8SRRZZTqvffecztCRfAzs66GKQzLgN5tsJaEuzKmhN0FriZVF785hG3Hl0tdC9NYUxePFEfHCMv2EysQnRAiNYGstzr56W6nr1HaQaNKQ5fepyNq1XkbDRs1RNOmTdGsWbMKDzqDv/zlL7K+uc456Cj++Mc/ynvQUWQZgpA3PtslpIoF6iNnDJIdN08x7TqzWurOKLtphoySaNFB7H53bYXWu0WqWPjuH+yDuvXqChFyJoOyDL4G1/9DDz0kcnK0j5RZ69atMXToUKR364zRM4ZUmFRdoUP9dBf6Ds2Q9GhcUpjcaJ6pPkb4GaFgCjAk1l9usMv3qsgpMJIqyiyufTT69s2STRQ35M5kURUH1wT9BueahNqaf+oCI1fcmHKDvWLFMsQnRbqEVNGmrN9fKAcUeFp28ebpsnmjDmw/udI8VgcvGLLFk6EsWHf2GmUZSqpKCbJnpvB69eqF6OhoRERESAi75OCutVGjRhKdclQY7mRJIJhH7tuvLyLigytMqqhsZNz9R/aS1N/bdV/HzBVjZafEhc3foxKC8cc/PYjOvTrg2Mc7KmQwSKoOXNqM9qkxaO7dTCIuzmRwp0G5cbDmjGkT1qGcP3/eJfUaPwRIqpjyZT3VqVOnSjWYLvzss8+qLKmiw6bxGjs7D6+88YIUC4+bkyenANe+s0CiCh06R+Ovj/9ZIgl0dhWJIPC5Rz7Yhq59k1G/YV14t/CGv7+/OPDyDj6fgyThiSeekIi0Rap+8YtfyAbKx8cHQ4YMQb/cvsgfX/FIFb+HVYPG9d4hLVqiLmv2zRfZTZw/TIqwSRJYcyUnmv5ZPrlZpIq2JCDMF15eXkIQnMmiLINOk6/1yCOPCJGybCRlxhKLwMBAFBQUoGtGuiFVeRUmVayTIUn3D/PBk88+Jm0nFm+ZLnpGueVP6CenpXnilFE/Rioq0oLGRqrmoG2HGOTk5og/aNOmjVNZVLVhrRn6R2sjYs0/1w/XDOW1aNEirF6zCm2TXBOp4jpg/RzbKHi39pJ5XrnbZmu4ftgf709/eUjKDRi9rOj7KakqJZjmoWOhM6RTPH36tNNx+PBhzJ07F6+++qqQKYY4aSzI0MeMGYODBw9iydIliEkIrzCp4g6VfTdYR8XeVNVeeAbe/sb4GSWhojT3a2yc1ov4ze9+jZr13sC6/QsrpDCS/ruyBZ17JmL8hHESyiVJcCaHuw3WVbCI16rTcFfws7PQcsSIEUhJSRGieLfRrVs3qSHhoYWqmP6jHu27sEFIAY3ZU889IcauTbgPAiJawj+0hdkg1DA6/XvU9aqJuWsmSiqrvMSKz+Pp2B45qeiXk4UNGzdg3759cqCkIoOvsXLlSnGa3DTRMfAnI7Cso1y/fr2kGUeNKcDQCTkVqqnid+COm93TeQDl4T//EW/Xex2tw1qIzCi7lgFNpWbokcf+hMQusRVK+VukavXu+Ujrnow5c+dI2t6ZHMoy9uzZg8LCQnGsTP/RRjL9Q3vJwyosn6BdcVX6jxG8GcvGyOlSyozH6P1CvBEQ2Urk1rRVAzz17OOiaxk5KZImrkjaVNJ/hlQld06UuiCms5zJoaoOrhlGohjhpW8ksWJWh/PfvXt37NixwyH9V/GaKurxrjNrJCL+uwd+g+ovPyv1m2JrIrluWuLVN1/CA3/4nbQkYVsi6VlVTlvDoaSqDKADdCy4czasQnXWCLBWoG7dusjJyZHFxf+xOJmF6rEuKFQ/e32f7LpYjP50tScNiWpkFKY5fIO9ZfD3pq0aSr+qx596FEPG9ZX6gvIqDJ3h4fe3SaH6ipXLhRBZhZjlGZSlO6b8HMHvQWLZtWvXUof5AwICMH36dDm0UBVJFXVw8ZYZ4uAee/KvEqlqZnS3uX9jOcHKn2xc+9Jr1SVlw2PQZ6/vrxBBOGacJQvVp0ybhM+u2aKEJfWxLMM6cMGCdBZaM5311FNPISwsDHPmzJF0NmsEWag+buKYCheq87uf/myPpLGeNiT0RUMSKCNHmXETVdvI8olnHpWftA3ldRB8Dt/TVqjeE4ePHJaDKc5kUZZBmfCwT3BwMP7whz+IzOLj4yU6wU0W9YynYxndq2ihOr/DwSub5cj8E08/KhtM2kPKiTLjYHF/7YZv4Y8PPwhfQ7YYvaDMnL1eaYZjofr5C+dFzypiIz1pWGuG9pIbEfrHZ599VuafdVY8uGQVqm/bvtUlheokyNOXjZaDL2ylUK9JLdtasc8/fzbxqS8+8rEn/4Lx84ZKZKsi76mkysVgGos7LZ5oSE1NlVMNPM1iFWG7rKXCP45LSqOfMbJ/eezPaJsSgZW75kg0imzbGjw62rN/ZyFVYfFtcPDyFqNo5VMYkipXtlTwBPD7kyizTopFtiUL8p0NEmw6Y8rPXeAqUkVHx5OpQ8Zl4clnH5edIgvTWZDOAmxrUG9zC3qZXeSLCInxt9UOldPZCam6B32q+BqffvqpFFgznT1y5EgpxObrWy1UXNVSgd+dnZ9ZI/n8K88JuVq6bWYxma3aM09kyWsefeIvcur3xCflu7edI6lyZUsFOlemytlSISYmRg6rsM6QhzYs8uGKlgr8/HSoq41MfAKaSAE/U31M+1BORTIzf7OejycAeahn1IxB4Enp8qYAbaRK+1TdDlwTVksFltGw/OPEiRNiQ601Q7voij5VtFGnr+2RtP+jhjB1SI/Bgg2Ti62Z1Xvnmfmaje45KdJhP6Vne4nwVqQZrJIqF4OGgeFLhjppHMi8HQ24K5p/WgaPhb5Mm/BoNWsonB2jZvh7zb4FqPHWy1JztXT7rHI7JyVVtwflwEEjerdhXetOcBWporNicSgLhp94+jEMHJMpJ3Ooz9Rda1z45qD0WvNp0wQ1678hu01GZnmXeWeve6fB17tXzT+5SWIjQ6b9+bokBY6v7QpSxc/P505cMByvG8ffpGUDrDEOgTJylBkH7/U5ZHxfueVPbMdQbDuxvFzpLL7WvSBV1H9GaJnmcdYcmf93FaniIR6SJJ74Co7xw47T9kJ0B3nxb96mpm9+N7xgyCobJ9MplrdnlZKqu4OthXhynBErkumSa8ZVpIp6zxuO00eyBpE3UOZBD8f552Brl7XGpjGaxQNfa42/rEhGR0mVi0HlsEKdJZWFcAWpopKxmee0JaNEEVgjsPnIEqdKQAJFhxLXKUKuzTO7NR5lL3ldaYaSqqoLV5Aq6ied1fx1k6SWhTUMPIlDXS6puzSIPFjRJStJogzdc1Oknq88ho7PuVekik7TSvE4c6CuIFVc79IyoW+S1Aax/9zBS5udkiWeqmTxLVOrlPGcNRNw8duyOwhez/d1NakiKCduNi2ZOc4F/3YFqaKtsu4pyYafcoz+k523XMfvyffgvSYbNqstrSrYeZ2EteS1pRlKqu4OysQxLVoSriJVJEY8rMWDG9ycvXNxg8xPyeuoA6y5jDGbEJYcjJwxUIIR5X1fJVX3Ga4hVbai1ULDvBm25A5Weqw4vfaoGA2eqOo1IA2zjJK9axSm5HWlGUqqqi5cRaqoQ0xT9xncRXoDHbri/LQNr6UBZO0Vb8MyavogSXfz8ZLX3m3wOfeKVN0NriFVPL24FQXTB0ivrcKNUyUK40wWlOUhQ8CGTcpGppGx1WKlrHLj9Xyte0Gq7gSXkSrKwZDwoUYOlBlrpZw5VA6SU17LrvS9B6Vh24kV5b4DhZKqisNVpIp6T7/XrV+y3IqItVK3i9qyvcZcswFhrzemgzmP5X1fJVX3Ga5M/71rlIT1KXdj1bye3YLZfsGmWOVTFiVVVReurKliGu/4x9ul3uFOaRZLb5nSYpSBp10rW/rvbnBV+o/fnTLgeiehul3Nj9znzsiUsrXZhvLdlJrvyee5K6kSmRmnSplx7q1UqfNrbcSKqSFeS50rz3tyKKmqOFxFqvg8+jvOqRSfG/91O/tB3eC881r2NaOPvJ2+3G0oqbrPcNUNlWk8aVipOJz8u/WfKnZtORwTh5KqqgtXkSoOPpe6SJ28m+Hi/23Xlt/I8XnuTKo4yiIHx2tvR77uNqzXcFdSxWH7DrbvcXeZlbSR5ZObkqqKw1WkisPS49LpQOmvvdNQUnWf4SpS9UMMJVVVF64kVfd70EC6O6m638NyMO5Mqn6IoaSq4nAlqfohhpKq+wwlVQp3hJKq8kFJVdmgpEqhpOreQElVJRxKqqoulFSVD0qqygYlVQolVfcGnkuqvv3WEJJVcu+/PWfXSZdkFpW6bPD1HIeza8ozzGvxhCFb/qf3SsbKVSuUVFUhCKk6dBA9+3bBsm0zRR9Y/OkOg/rLm+h269cJU6dNvu+kauz4MRg4qo8YW2efr1KOa0ZuZo6Xbp2JzOzuOHqfSRU7qo+YPFA64buTrpG8L1w/FaldknHBkCq1j2UH/Qo7qjPwsOPkKreafw42Kg6K8rN/m8oDjyZVjFT5Bfhg2KRcTJg7DBPmuXCY1xs3Jx/jZue79rXNa42fM9R85v6IjA/FipVKqqoSbKTqEJJSOyBnWE9MKRyJqYtcO6YUFmDywgKn/6voYEuGyLZBmDxl0n0lVWwEPGrMSCR2jpf14+yzVXRQZpSds/9VZHCOc4b3EoJwv0lVn76ZSO2eILbnXny3yQtH3BMdnmg+b9+87uiYnKCkqpxgD7Nt27bBP6iVIdYD7pFu09aMcPq/io4RU/rDy7u+/dtUHngsqaJhYlv+gYMGYFDeAOQNG+yyMTh/IHIG9EPbDnEIDQ9Crvk9b+ggp9eWa5jX4nvwsx85eqRYJ2SFZ4PN+nh7kYKRI5Cd2xcDh+RiEEdexceAwTlGb/siIakdQiOC0DOzG/oPynZ6bXkGPytfL7NPL2zeshl/+/vf7puz4+031qxZg37ZWRgwKMfp5yvv4HfqndUDEdGhIrvcgf1Els6uLeugzDhoT8aNHyv347sf653zwo7r8xfMR7+cvvJ9RNecfMayDtt3ykG3HulGzwLRsVMHZPfPctnrc/Dz5g7IxoSJ43Ht2jUlVeUA9ezMmTPok5WJ/gOzbfPjQh3gOomJi0BYZDD69OvlUlvDwdfv1r2r/dtUHngsqWIHWe76uVvmonPV+Oyzz+SGlIwm8G7vrVq1kttnMP3g7Pryjs8//1yMHsmhGoyqA0YQGJnk/DP6Qr1y1aDD5i1LsrKy5GbTy5cvB+8P5uza8g5+5k8++QR/+9vfnHZxvlegg+CN1PnerpbbpUuXhLBFRESgT58+cv9JytLZteUd/My8rQjn/n7VB1FmvH0N76vo7DOVd9A+cmMwf/58+Pn5yY3uucFlZMzZ9eUdlBntO2WmKDvoVyg7+huuG2cyLu/gXNPW8L6DoaGh2L59u9z83Nm1FR2VDR5LqggqDQeNlKsGHQVTi0uWLEGtWrXw5z//GWPGjBGD6Oz6igzr8yuqFu6F3nKQ6GzatAnNmjXDE088gUGDBslNyJ1dW9HxQ+jtvZAZ1zsdTn5+Pp577jk0adIEmzdvFgLn7PqKDGve7yes93T2eco7GG0lserZsyeefPJJ0bedO3fKJpfydPac8o4fQmaehHsx/xy0NatWrcILL7wg62bcuHFC3lw9/xyVDR5Nqu4FGDm6cuUK2rZtiwceeAC//OUvxdDSaHDnpwtcURlB3WT0oHv37rIR+K//+i94eXlh3bp19zU64k7gWuZ6J4niGv/1r3+NP/3pTxKhvnDhgshUURyUGR3q6tWrUbt2bfzqV78SmeXm5kr04n5GLxU/DKgD586dQ7t27fD73/9e1o2Pj49kd0isPd1HKqkqA6gMTMktWLAAjz76KH7605/KoNHIzMwsilYpFJUNjKww3ffmm2/i3//93/GTn/wEDz74oBAERqsYXVAUh0VEMzIyZAPFtf6LX/wCL7/8MhYuXCipM093EGUFSRNTysnJyeJQqWc/+9nP0KBBAyHwJKlqIz0XXA+c4xkzZuDZZ58tsjVcP8OHDy+KVnkylFSVElQWGlmy7ebNm4uy/OhHP5LB37kr446WCqVQVBZQb+nEzp49i6SkJPzud7/Dj3/8Y9FbOrvXXntNal9Y6K0E4SYoC6b5uYGijEioKDPKjtEX7sJZM6IE4SYoM+rR7NmzxaGSgFo28o9//CNSU1OlDoqnzhSeCfpI2hrWbP7nf/6nECrOP3+yXGbHjh0eHxlXUlVKcCfP4uHBgwfjD3/4Q5GR5eDvDz/8MFJSUqRwTg2torKAu0JGqaZNm4ZXXnlFHJ1FqqwdZFRUFI4eParOzgGUBQvS4+PjRUaWzPiTZJTRqgkTJtz3gvzKDMrs4MGDCAoKkvSyo43kxrNGjRpYvHixEngPhUWq8/LypGaT68Rx3fz2t79F586dpd7Okw8XKKkqJagEx44dQ3h4OB5//HEhUTQcP//5z/HQQw/hmWeeQcOGDaXvhzonRWUBNwM8dcN0DHXUUW9JFpjGZpSVERmmsxQ2kCwtXbpU1jRlRFmRkLKGkuudkZiOHTtKelBTpzaHSv2ZOnUq3nrrLanboxN1lFm1atUkWkV91I2n54Gbi1OnTkn9FG0No5Mk09QBpoIfeeQR1KtXT3zk3//+d/uzPA9KqkoJhjVpDBYtWoSJEydK4eXbb78tTorHrGlM5s6diyNHjqiRVVQa0NDx+H9hYaGcUqXe1qlTRwxc+/btMWLECEyZMgW7d++WiJbChu+//x7vvPOORPgKCgqQmJiIp556Sgho//79RZaUKQ+taKTKRqqoP1u3bsXkyZNFryIjI4WQUt/YVoEnwCgzFqxrpMrzwHXA1B/rqTjX2dnZcvqTIy0tTWqqaGtYQuPJZTJKqkoJ7qwYgeLpha+++krIU0hIiBhanvzjLo3/I/lSg6GoLKAukuSTJFBv2ewvNjYWzz//PObNmyc9ihiVYSRWycFNUBaUCWXD4lqmrbiJYt8dnmzigRXutilbjbrYQDnQWZJcUWbjx4+XNGlcXJzoHWvULJmpjfQ8cE7p/zjH9IdMn/NgDCOXrDe2bA11xJNtjZKqcoDGlkaCOzGmAVhHoNEpRWUHdZRRqw4dOoiz42lAGjnFncHN0tq1a1G3bl1ppcKaEF3vtwedK2XGiMXrr7+OhIQE0TslUlUHJNhMjTOyy7Fnzx6PTvk5QklVOaCkSuGOUFJVPiipKhuUVCmUVCnKBCVVCneEkqryQUlV2aCkSqGkSlEmKKlSuCOUVJUPSqrKBiVVCiVVijJBSZXCHaGkqnxQUlU2KKlSKKlSlAlKqhTuCCVV5YOSqrJBSZVCSZWiTFBSpXBHKKkqH5RUlQ1KqhRKqhRlgpIqhTtCSVX5oKSqbFBSpVBSpSgTlFQp3BFKqsoHJVVlg5IqhZIqRZmgpErhjlBSVT4oqSoblFQplFQpygQlVQp3hJKq8kFJVdmgpEqhpEpRJiipUrgjlFSVD0qqygYlVQolVYoyQUmVwh2hpKp8UFJVNiipUiipUpQJSqoU7gglVeWDkqqyQUmVQkmVokxQUqVwRyipKh+UVJUNSqoUSqoUZYKSKoU7QklV+aCkqmxQUqVQUqUoE0qSqgMHDqiRVVR6KKkqH5RUlQ0WqZo+fbqQKuob9Y6OVlE18K9//esWUlVVbI2SqjKAxoKGgYz7+PHjCAsLwzPPPCMK8/3338v/dDemqGygTnJwM/Dee++hXbt2eOmll7B48WJ8/fXXRXqrulsclAdl891332HlypXiHOLi4sRZ/POf/9T17gSUBx0qZTZlyhS89tprom8XL14UIqoy82xwbjlumLk+f/48atasiVq1amH79u345ptvqoStUVJVClABaBDogOiUdu3ahfHjJ5idaz08/PCfMHDQIGzYsAGnTp3C559/Ls7Lk5VG4R6gDv7f//2fbAIYXTl8+DDmzZuPps2a4bHHHkfv3r2x3ujt6TNncO3aNYkuqN7a5Hbjxg189dVXuHDhAnbs2IHsnBxUq14dXl5NMGv2bOzbt0/I1bfffiu2oarLjc6SMrt+/TrOnTuHzZs3Iz09HU888aTo25w5c3Hg4EGJWH2jMvNIkEwzGvXBhx/i4KFDkv597rnn8KwZ+fn5ohNnz54TH2ltSjwRSqruAMtQfPHFFzhy5ChmzJqDzl27o2VAKOo0aYka9Zrg1TpeqNvMF838ghAR2w4DBucZR7XRGI8PxEl5quIoKi9sZOpf+MpsAkiYFi9Zij79chAaFYfGPq1Ro64XXqrZALW9fNDc6G1Mu0QMzh8mBOvylStCwmggqxq4VmnsP/nkE+zesxfjJkxEx5Q0tGwTYta7D16u1dDIrgkaevujdWgk0rv1xPSZs3Do0GEhpbQVVW298/vSzn300cfYum07CkaNRtuEJPgYmdVs7I1XajfCa3Uao4G3HwLDo9GtZ2/MMcT+6LFj+PLLL0VmSq7cFzZb838ScGD2ZrYhz+kZPdAmLAqNWvjL3L9SqxFqNW4Bb/8gtEtMxsgxY7F9x07Z6DHD42m2RknVHcCIE3dWkyZNRkBwGOoY8uTfriuSB01B1vS1GFS4C0OW7EPO3K3oMnI+IroNREP/CDTx8UePnr2w7513RGnUaCjuJ2jkuBFYtHgx2icmoW7TlvAOT0Rc5nB0H7cY2XO2oP+CHeg7cwPSRsxFRNcBaBwQhaatWiO9S1ds275djGRV01uuVTqGwUOGoFWbYNT3CURgUi90GjINfaatRf/525E7byt6T1mFhP4TjC3oglpNfREcHiWR60uXLonNqCqgfpCAHzp0CDm5/dHctzUa+YcjJLUvUofOQp/p6+wy24aeE5ahQ/ZYtIxJQYMWrREWFYMZM2fiiiHx1Fe1ke4JEqKrV69i5qxZiIiOM7amFVrFpqJ99hj0nLgcucY3DphvbM2M9eg8bBZCU/uhXssQNDO2pk9WX4meM1XsSfOvpMoJOME0sKdOn0avzD7wahmA8LRsZM/ehDEbz2DKng8w/Z2rmHHgUxnT3/nEPPYhxm+7iKErDiJ9xGz4hLVFYEg4Fi9ZUpRLVijuJai3dOqsX8kfNgytgsIRnNgdvSetQMHa45i44xKm7vsY0/d/YhtGh216ewFDlx9A+nCjt+HtERoZi7nz5kuYng7P00HHQHKwZes2dOzUGd6BUUjIHo2BC3di7OazmLz7A0wzsrLkxt8n7bqC0RtOi9OI7T4YLQIj0K1HTxwyTsKqr/RkUNeY6luzdi2iYtvCOzjGbDYnmU3mXozbcl5sZDGZGb2jzEYZmfWdsQ4Raf3QxDcA/XL64/iJE/heU89uBc4VbcNp8ZGZaOzjj+iuuciZvdHM8SlM3Hm5uK0xQ2zN1gsSiOg4YAIa+4UKEWOEnLrkKbZGSVUJ0BgyJH3p8mVkZvXDWw2aIzV/BiZsv4RZhz7H7CNfmHHd6Zh1+AvMPHhNjMeABTvgF5uCZj6+UuTK2gs1Gop7BcvIceefP2w4ajVqhthe+Ri+6qgQ/1mHqbvO9ZaDuk2jl2cMXmBCd/gGhWFh4SJj7L706FQg5cY6kN179iC2XQKah8Qjc/IqTDJOgWuZa9qZvDhkvR+6hrFbziHN7MLrNvdHp87pOHLkiKQRPXW900aSONIZhkREwycy0TjTzUI+Zx78DLPvIDPaT+rjGENWOw6YiAYt/NGjVybOnTsvMlO4B2hrGJnNMBsJlsKk5E2TgAPJ011tjdGPqXs/wsAFO9GoTTQCQiOwdds2iVh5ArFSUlUCJFSffvoZBgwchHrN/dBl1AJM3vX+HY1ryUEHRcPBqFVwxx4Ssdq5a5cew1bcM5D4sLB6/ISJaBUUiQ45YzBm01khBnd2cjcHdXyGcYr5yw8gLDULrdoEYc2atR4XnncEI3uMLsW2bY9WhhwwisKoim0D5VxOJcdMcy03UkytegdFG5LQWw60eOp6Zw3V/v37Ed+hI1pFJ2Pw4j0SlbDZyNLoGsno52JXGdVv6h+KPLMR+Pjjj3Xj6QbgHLG8YNCgwWjWOgxdCuYaQv2+EOrS+kmxNcZH0tb4RHYUcn7s+HGJGLs7lFQ5gMry1VdfS8qujtnpt+87ChN3mB2r2Y1SCeigaAwcFUceN485e5w7/0GFO+EdGo/uPXsbo3HVo3f9ih8ONEbbd+xARFx7xHYfhNHcNUqE6gszbLrpqJ+Wjt583PY//k4HydSXT0QCkjun4+jRox55Wovf54MPPkDe0OFo7BeCXhOWCjkiobLJ5Va53e5x2gamPNKGzoBfWCxmzJotBwU8TWaMJFy+fAU9jD1rFd4eWdPWip2zbKQlG0suN2V2cxQ9buQ8dvM5JOaOk1T1kqXLJAKmNrJyg3O0evUaNPb2RcqgSRKp5Wbs5hzfTgeKP87fGdnqN3MD6rcMQs/emXLAi/PvzutGSZUdnEQajPPnLyA6Ng4tjENh/p+7fGvyR60/jRFrj2PK3g9FQWhI+DgdGGtWaFxKKg3TCAyNevm0lmaLnpwWUPwwYDqGp6969e4Dv+iO6D9vW5GTm3HgM0zYdhFjNr5rdPGKOH8rksoawDGb3rXtMs1jjnrLmpiMsYukVmjipEkeVxfINUiiuGHDRoREx6Nt1giRh2X4mZ6gwx+39YLUBvExyoiPU2aOjzvKrWDdCUSkZUsU5+SpU27vIEpCenatWo16jZuj0+AphkganaKuOciGcpy+3xB68xhlNoWPbz4rMptRIqVKZzxizVH4xnZCXLsO0sKiKhX7uxuoyywxCIuMkk3XiNVHxMZwTvmTc89aw8nGfliBBtoazv3oTWfsvrM44aLfTMmbjtfero3Va9a6/al5JVV2cBK5219lJrVOo+boM3W1ECZOOg3t+O3vIbb3MNT1DZeTflQOGpFhKw/DJzYVzSOSkLfsgBgNR4VhEfuINcfhF5uMdu074LPPPvMo56T4YUEjx5Q1i6xb+LWRAlAaNpvefoEJOy4hadAUNAntgOTBU6VYnTpZsPYEwrv0h09MqpwCnPbOx8X0lgZy7JbzCE/tg44pnaWRnyels0h2eMJxcF4+Wsd0RP6y/VK4T5lx5Jm/g1P6ok1iTwxatEccA4kmT/41CW2P2F7DzCbr1C0OgjYh01zjHRCOadNniE3xpPVOh9qnbzaah8Zj2IqDNpJuZED5DCzcjVZxaQhI6o2hKw+JTk3a9T4yxi2Bd3QntO07SmRYksBTZl1GzpON57x584S4KSonGHgoLFyEt+o2RHez6eJGzVozPPDCE8b1jI/sOWG5kCXaEeqCX7uu8G3bRX4nCXdcM7aI5VnU9QlCrz595TQhbZq7QkmVHTSyJDzDC0ahSWCM2XGdMYpiW/zWws+evRn1/CLwan1v9Jm+VgxvSGo/PP9WA7PTHVWUOnBUGBodYeKDJ6FeQy8cPnxEa6sULgMdNsnB2PET0KhVEHLmbC7aDDClx2jKgAU74RXcDnV8QuRo+4jVx9AhZyxeb9QKoWk5EmllxMBRb6nHfJ0uBXMQHNMeGzdtkh2kp4BGm1HpxJR0xHQfJGuXa1W+Ox3E9otIHDARNRr6IKhTH6n9IPlsFp6IGo18kDF2sUT4eK2j3CTysvooWsenoktGd3z44YceUXxLUNf27N2LyPgOSMgZYwj6e0Xfn0SJJDyqRx5eqtUYYem5yDeki0fp67YKQ4PWUdKWgjpVUmaUe77ZkPpGdUTPXr1x9ZNPdONZCcENHAlv9x490TQgCiPXnZT6Q2sep5nNWt8ZG/BK3aZoHBSPbGOLrM1b9TfrynpicKKkj2Q2iHaKbRiCI2LkXrrubGuUVNlBosPi0tQuGYgwBmGyMbKOE8+Fz9Mt7Ovz9MtvomFArDG2WXjqxdfRKr6LpP9oLKxhPY+/09Dmzt6Emg2aSr0Wc9IKhSvAzQB7qfXo3QctYzoZPTxWPBJgfmeNVLcxhXjTy88WZek9HG+Y371C2km/KivaYKW0i/TYPJd9htrEJmHK1GkeFUH4xz/+iW3btiEsLgFpw2fd4uy5wx6x5hj823czDqGeRK1aRKfg5dpeErFmHYmV3rClDG9uwCYaxxFvrgmLipUu4u6867ZgpUvZ+8w/LBY5xp4xWm/JS2Rm7Byj9Yx+Umbs20cS+txrtSRaOsHIRWRmBn86yo/R1fjeQxFhZPbuu2c9hoh6EmhruEkICYtAfM8hsqlw3IxxLpnqTR4yDc+/VR8t4zojptcwVKtRB96RSRi+6khRnWfJAw18nf7ztqO5fwiWufk9SZVU2UHDxxqIiNj2SBo4WSJTjpNORaDDoTGlUX34safwx78+idfNTjZv6TvGKNuUhcZZTg+J4pjnmp80IjwJ2LBlIMaMHSftFRQKV4DO5+TJk0jslIqILrkYt/X8Td2z6y31j4/HZY7Aky/UwJ8ef1rIQfdxS2SjwGuYvmFqcOT6k1JwbdVJ0BAGJ3TD4Lyh0kvGU8C03MLCQgTEJCBz6iox6iXlxjQpDT0jLQ8/+hT++swL8GvbVWpGuM5pD2gn2HuHMrMI6VRDNjoPnQm/oAisW7feI1oFkFSx1mnipCnwi2iH4auPSHTBkpejzHLmbMVbhrT/+Ynn8Jenn0dgcqbUp1KneEKMKWmmTmlLpS7N6Cej+WnDjMwCQwwRPeQRRNTTYNkaH7826D56gfg5xw0cB/+mHQlK7oNHnqqOh+gjG7WUBrBW6neG8ZW0N5x362Qy1w4jX80DozB5yhSp4aTOuSOUVNnBRcxbJwSExyC9YF6xsKY1aDS4o8qcuhp/ffZF/O6Pfxbl4WPc7VNReLy4x4RlRUbWet7IdSfQpE0E8vKHytF3hcIVYPSA99mKT0hCfObwYikZx8FdZa+JK1D9jXr49e8eRJOQdoboH7KTg8+FTHUZNR8JuePRdfRCSQnS6I1cewIRKZnolzsAn372mf1d3R/cCU+fMRNtYhKlqa+1Vh0HiRbLAELTsvHAQ4/gyRdrICVvhuzGud4pO5YAdDSbMHaPnrrvI5EZiULGmEK0aBOKpUuX4R8eQKqYjmNKpmDUGPhFJUhBOr9/SZnZdOmU1NA88PAjeOaVt4w9XCr2kBEpktB+szZJNIPpIDaCpN2kvWWNjrd/oNwiSIvVKx9oaw4cOIAWfgHoM3mlvQaxOKkSYm30gj6UhPpXv3lA0uc8qMB1wUjVoMLdUpIgkUt7lJx6w7qqluHtMHLUaLc+Oaukyg6LVAUKqZp7C6myTfw1qUdhMeYjT1XDY8+9hNfqe6P//B2SYqHBSMgdJ2kCGg4+Zj23iFQNHaakSuEy3CRVyXZSdUn0raTu0qhFdR9iiMHr+POTz0mtEIuuGSGgEcycukZuI8J0X7u+o4RoMLJAvbWRqv749NNP7e/q/rCRqhlCqpjKKkmqKDM6gX6zNqJWiyA8/NgzeLz6KwhM7i3Gn7JhdJrkk7ex4ZC6LPM6QqrGOpAqD6hFI6li2cKdSJUls6zp6yQ6wUjFY9Veltsg8eSXRUQps8GL96Jt1kikDp0ptpa2U0lV5catpOrWRp+zjP6zYL11h+4S2WU2h+uHhz04x9zc8cBCcEqW2BzrNSxS5RN2k1S5a12dkio7SKrYcj8qPsHsPCeKAjgqC3dT47e9J4bgr4aB+3fIkN9ZX9U0LAHDVh0W49Bn2hq07zdalIe7Vhoa/uRtQBr4tMG48RP0dIvCZbCF5E8hMTkV4U7Sf3Ty1OX0kfNRo0ELNI/oKDVV3AywzmHwkn1CrDLGLUanvOliEFn/0nuK7fQrT7cGJ3TFkPxhnpX+MwShcNEiBDL9Z8hlyfQf1/uwVUfQMrYzqr9eF2Hp/aU+6KXaXhLN4y6bz6GMGKVKGz5b6jBJHFhrRLLgFxyB9es9LP03eQr8IttJWpgEypIX0zgkmkOWvCP28IW36iO6ex4aBcSiWo3aUosq7ReMfKiP1DuSfN7mh7KewnrVYTPhGxgimwR3Tv+RDFiEgHKz/rYiLyX/dhc4pv8yRt2a/uP6oS1hBPK512qjTUJPqat7yvhIv/YZKFh3UjYeXc1zeYJ+tCHm1rqjnWL6r1lAJCZPmarpP08AWfjly5eRntEToal9pb+Ule8lKWL3X9ag1GjYErV9guW0CntW8WRDtdfrIKZnvtzzqu/MjeiQM06MBBWOLJxGI3vmBtRs0ATLV6zwqFNUih8WtkL1D9AzM0sK1VlcbUVdaKzowOi4ePqKO0amrnkiJ6bXULxavzmiMgaLMes+fomQKfZZ4s2CeTNcOs3+5rkBsUmYOn2GWxePlgSJzvbt2xFuNlFpw2cKObJIFX/yJBtPI/EkE0/48nY/lF19/0i84eWLXmanTgfBaDQ7qdsIKfszfSKEK65XPsKi49yeIFigg6ONXLxkKVqHxyF71kZxoJZDJVkavfFd+0mveojoOlCIV6Yh54xaNQqMleg9CWfe0v1Gx8aLcx2/9aKQMW4G4o3MImPi8O7Zc25ZqE4ZkXi+++67skGnvFi7x79JRmj3qQv8nYM6aJEvdwBtzUcffYTQ8EjEsVDd+ETOnaUDXAuMPlV/o66c/uOt2mhPWif0wHOGWHOjQR3pPn6pDG7gLFLFn7xZefPWIVixYqUWqnsCqDC8geyoMePQ0C8Mox160FBxeLrPOyoZdVqGiHGQOgDjdPh488iOUszazxAqnqaKNgSLhXnC5LlzJXvPGYP6jZrIzUO52BQKV4BGmS0Vxk2YiAY+AZLKomMXR2dIPdN3AR17443GvkgcOFGIANNW0l8tJlWIVtfRC+Toe3yfArTrN1oisEzPUL95k+Wg6HbYtGmzR20G6NwuXLyIxJQ0xGQMkMgyyagYeLPe6RzqGyLaIqqTyILrmNfwtlWvNWgh/asGL96HocsPScSFdSM5c7aYaz6Qlgq+sSnoktHD41oq7N27F1HxHdDBEE5JNdsdKm1cD+Mo32raWqJ7LJOgHrKWKiV/Jt5u1kYK1klO4/uMQKv4dCQPmS4y5FF81qa1ikpAj1693LalAj8zo7nDhw/HkCFDJCPB+R82bBiys7PFvzACw99zcnLkVi/u5AtIGkl22FKhSesIOWxgRSu5bmhTuF4YzSVp4v/Yt5Hrh5uR+n6R0paI7YjaZ49FjtmwyWbG2Cle2y57NIIjtaWCx4ALgjUDGzZuQt3GzYyBWFJUV8VJpzNivQl3Wdz9U4notKgMw40BYV0V61aYAmC0gDUHNM78P0/+tYzogOSUFFlI7mgwFJUTNHQkCFu3bYePfwA69h8vO0DL0NHx0dnTgDGSSuJg6S0L1bMMmRpqjCGjK9RTkiumvdjUjzUOYZ0ykWSIBztde9JmgJuob775VmocW8ckygleykTWtXEE3Cxxk0QZ3Vzv12zynLsNuWbw9Bq7rvO0GzdaBWuOy/97T1oB7zZhUghPm+Ip6526xvYdWf1y0Cwo1hBKNv+0RRpI1IcbMkl9sggV9YyyZG8ibjKph+zuz/qarOnrzWPb5VQgiWj6iDnw8vHHgoULhYzwvdwNjpGqU6dOyXrh/PPvE2YzTaLAx/g7h7tFqgiumyVLluCtOg2QMWah/QAC22J8IadgSZo417aDWrbyF/rRIWZ9sU8Zo+L8H9cbMz1ij4yO0F/WaRGAzL7Z+MSQaneO7iqpsoMLgjvKS5cuo0NikjQApZGwFEOG/Xc6LGvY/mdL8xVdZx9k6XRWSQMmomnL1nJLDC3AVLgaNMy8r2TvPlnwj+4oYXRbWN2mlzRalh4X01s6PfuQ68xPPk+iMmZzwBNsPoGRmDR5isfepoZNTUNj2qJdVoE4BStaJbIxcii55vm7JbOiYa7jBorRa26oItL6oW2HjsaxnhYn5I4E4XYg4Vm1eg3qN2mBToMmC4kUORXJxSYzS17FZGbXQ5GX0TMOEllGOHxjOhmZJeLixYtubyO5Tjg47xyOf5f8v7uBn5nEOjI6Bj6RiRhhNmBWOyGbDbnV1rAnlU0H+D+H9WW/jhHgTkOm4vWadbBm3Xohn1w37golVSXAHlLsLVOngRciuw20M/HiRqK0o6iepWUQ+mbnSGiYi0mhcDW4I961ezdCImMR2aW/RFHotJzp5Z2H7Uj0wIW70CQoDsmpabKr9pQUliPoIFgjkj9sOOo285P0FdNYxR1C6QadBCMuqXnTDBENx6zZcyQS5mmgs3v/gw/Qo2dPtAiJk5opK5LnTC53GnwOa9cScsaihX+Q3FCZhEptZOUG52jtunWo17gZkgdOKNqMOJvjOw9DtAzBZhf22s1byw2VeYNzdySbjlBSVQIMO37xxXWMnzgJ9Zu1koJKFqCW3H3daVBRaGgGL9oD/9hOiIlri8NHjohjcneFUVROULe++upr6XzeKigCbbMKMHqjreFiye7FtxvUcUZb8pbsQ2inTLQJDsOGjRuljsJT9ZYOgq1U2id0hE94e7nnp61JYemdBNc7IzYsvG4eGCn3xuOhF3dOYdwO1AOmrQ4dOoR2icnwjUnGIEPAeQPdshAryoxRfHazb+IfIrcHY8sOd43gVCVwjr788kvkDx2Gpmbuuo6cJ3cRYMTK2Vw7G9QVbt7oI1uEd0BETBxOnz4jhf3uDiVVJcAFTQf18ccfY9CQPLxRtxGSBkyQ0ynsqWGFL50pCcOZbIg2acdlDFywA82CYtDM2wdbt26VSIJCcS9hRRFYJ/RGrfpo22e4FKqzpoGpaOdOzxaaJ5liGH74qsMITOwOH/9A6Tj+tRsfbS4N+N24Nvft2ycnz5oGxqDfzA0Sobbqgm6/3m19l8Yb29B11DzUatISHTul4JghaSRrnio3OlUSKxLu1oHBaBnV0RCrnXIajI6VMispr5sys9XzsV4vNX8G6jRtia7dMvDee5fcPu1XlUAfySL89C5d8WbdxsgYNR/jtpyVE4BWnd1tdcDYIm5cuHnzCoyFX0Awtm/fIZs3T4hSKqlyAhpD7jKvvP8+Bg0egjoNveAd2VGOUdsK0K0c8s3BiACLewcW7pIoQa3GPoiIjMa2bdvFAHmyY1JUDlDHaOwYQi8YORJNvFvBJypJTuJYt1ZhNMpRb+nkGFVlXQvrGur6BCIgJEwIFVPh7lzbUFrQkHO97969G4lJyajbtBVie+abjdFOaa1CJ+AoMw6ud0awWaQekpKFBs1bIaN7D5w4eVJey9PXO78f66s2bd6M0PAI1G/RxpD4AumQXqxHXzGZsdcf70ixBgEJGajdsCn69svG+QsXPJqEeiI4V1w3vF9un6y+eLteIwR27CFzO27LOZutKTH/tDVsqcHDH2ww/GZDbwQGh2Lzlq1CqDzF1iipug2oNCRDjFgtW7ESickpaOIbhJaGXEX3GILOw2ej16QV0ruG/as6DpgoKZOmAZFoHRyOwUPyinqTqLFQ3C9Q1+jUP/vsM6xYuQqdOqejmX8wWkUnIbbXUHQpmIc+xvCxjqG32SSwdwxrsFqEtUPLwDD0zOyDnbt2SXifRrOq6C6/Jw37iRMnMXzESASERkojwpDk3kgaOAk9JyyTk23sFs6+VGwp0KZ9VzT2DUZUXDtMnTZdoi1Vab3TCVJmTAUONJtPf2P3vINjEZ7WTwg6m6LyNCl7nnUbvVAOA/jFdUZT/1C0TUjCvPkLZANAQuUJEYqqCB724Gm92XPmIKZte0kHtmnfxayP0cgYu0jWC3WAvjJ58GSzAemDJm0i4RcUJhH1I0ePyiEY6pKnrBslVaUADcf58+cxe/ZsxBvFef2tWnjmhVfwRPVX8OTzr+KJai/i+ZdfQ5Nm3hg4aJCkEqrKLl9ROUEDxbQWWyHMMQYvrm07vP52bTz9vE1neWNl6u8zz7+MWnXrSxh/zdq1UtdSldMwttq0r6Qf04CBA9GshQ+qvcg1/rKR22tGbmZUfxkvvvYG2gQGYeTIUThqdwxVlRjQzrEH044dO9AnKwsNGnuJXj1h5CQyM+Mp8/srr7+FwOAQTJo8WQ4/eFJ0oiqDtoYBiLNnz2LKlCloExCIl157HU8892KRreHP58w6aujVFJlm47Zt+3axNSRlnrYJUVKlUCgUCoVC4QIoqVIoFAqFQqFwAZRUKRQKhUKhULgASqoUCoVCoVAoXAAlVQqFQqFQKBQugJIqhUKhUCgUChdASZVCoVAoFAqFC6CkSqFQKBQKhcIFUFKlUCgUCoVC4QIoqVIoFAqFQqFwAZRUKRQKhUKhULgASqoUCoVCoVAoXAAlVQqFQqFQKBQugJIqhUKhUCgUChdASZVCoVAoFAqFC6CkSqFQKBQKhcIFUFKlUCgUCoVC4QIoqVIoFAqFQqFwAZRUKRQKhUKhULgASqoUCoVCoVAoXAAlVYry4/w0BNbNxNav7X9XQZwYVQM1athGwTH7g8cKih6rMeqE/cHy4gQKrNeqUWD+UijKAJfqomfg+srUIpmkrrxuf1ShcA2UVCnKiRs4mF9TDZPB1WWJIociUkVcXYFEVzmyfx20EyslVa7Auem+xefK0+FKXSwPzObLt7IRukM2sqmkSuFqKKlSlA9fr0eGOHozYgtx1f5wVYS18y3mqK+tQKrLHJkVrVJSVWH86zIKY0vMlafDpbpYdlxeGFf5omT2CJ6SKoWroaRKUS5cXRyHjOEFCBdn74tpZ+z/qIJQUuUm+Nd3ODE9DjVLzpWn4wckVd8dm4a4uj8cobstlFQp7hGUVCnKjn+dw7SAPBy8cVV2/TRONfMP4ob93464WXOUihXXbuDyyjzENebfNdG0bR62liLE5Vi3VGQELUchwyIb17EixeGxf13HwSmpaEqjXqMBArvNwbnv5cJitSapKy/j3KxENDC/1/TOxHrrMxknfM583sSABrZr6zZFXNYcHLxm/78dpSZVfL0NBchweL3wbtNwsJhdNzJyuKamdyqmHVpfalLlWC9ScOgqtg4IFxLRIHYaTljfnXKZlYk4b1v6tkbjQCQOXoHL1v+J706gMCsRgTJX1me1y8+p7J3P090+T6k+77WDmJMVZ59H87+AROSZObPpW2nn3LE2zWHc1dmXQaeIu82xY41Tygrz6oTje1h6VPx9D17ditwwzlcDxE05YfvuNy5j/WCHOZI1lYk5h4opVBlIlWu/q6M+3Bw2fSn+P9qGEvNj/6yO+nFz7R/EtG7hDvqQgYIN5/Ddv2z/LpVO3UKqnM2BQlF2KKlSlBk39uehgd3ofbchw26IMrD+NgXrlxeGm/8bRzg4A7kr7cbvvTm2KFfvrfjOdtkdYXsNRyNocGMP8uS9bzp2kpI9g/lYLvLyEzFu/1XcMO93Y2euPN93+jn7dQb2WpPwlFTkLjxnyEymMb4NkLuTn+g7bM0xzqJuKgrP2D+hIZF78gPlsRUOZNAy4ncmVTeMIzHPrRGHacduvt7Wwb6oETAOJ2xe0n5NIPJ22j63OK6FqeIUin/PO8CqF0kxMth5HSemNzWfORxz3jP/+9dVrEg3zrmVIY9X7DT463OYYxxKzfT1Nif/vXFwAeb9DHHec9V2zTnH1yCcyv4283Snz3O3/5s5SjXO07f3ely2f9zvzswxsq2J1LXWe5R2zm86zrI5zdK+fmnm2OBKoU33i0gVYb1H8c92cDgfS0VqjpkLQzymeZt5CpuDy/znd3uQawhVTbOG5HWs9zKyydtvvZlBqUkV4drvWkRwnLw3o938X8YG+/PNa0qdZqtpOEfdt+Oy0f9xJ+1/nDd2w+hDYP4e2FXT6EOh6EigeY+ib303nStJqgyB3JMfjrjpJ4rImUJRHiipUpQR32F9D4d0342DyLPvGOMWOw87WYbVd4oDoSlj8bX1GsWctXmms9ewdsE3jbWBVazr6MgsZ1PCiAvOTIMv/zf8oP0BOyyH6EAGS0WqPixEHP+2iIsF++sVHDK/G6IZyGuKOVuijOk/KxrSY/0thJWEmASt5oTir3Rjb555TqKQRRZy3yrrknD+mZzO0x0+j+C2/7cOQ9S86VQFdsefsKKolq90c15eUlXK1y/NHBOWbpSYZ+s9HD+b0/e9E+xkohiJKROpcu13vROpKqrLLKrJpG3hezuQQkbFUwptJLLo/zY9vQmLCBq7dN7+UCl1TvT0O0NWk+JQsLfYN1EoygUlVYqygca0RGH6uSk2J+yUnBiUhRDdDuUhVU5JjqMjsx4rSZwMrIhL4rJi1vvmcxzeszSk6rvNmXLN7Ubm5u/u4IDKR6pu+ewGFmFyPuiUSks8yk6qnH0ewW3/bxxqK8fPV2JQ3+xXlm7OK06q7vT6pZljZ8+z4Ow9bI+VJBEO+O4y9iwuQGZSuEMa0AwXkCpXfNfb6zRhkaFwFF4xfxqSlZk/B9NYUmAnQ9wEJFqbNYfo6MESdsb6zEV6V0qdCx9cgAzKLcDosWNqU6EoJ5RUKcqEIgLldJRIO9hRFkJ0O5TlNUrjFAR3cDYW+XAVqSqKINwh3Xl7B1Q+UlVcVjZY7R+KpUGL4d6RKmefR3Db/1/FigS+h0ME4jYo3ZyX/G43cPXk5dvOhyNK9fqlmGPBLZ/LBmfvYXuMNUf2Bxxw49g4SYXVDMtF4bGr+I6kwC7LYjpUUhfvAld+15I6fePqCVx2KBOwIqeMYl9enCoRcFtakOUEjFRm3iwr+G4rMvlaRudKS6rupnOBOStwYmeBRIhrpq/AVU39KSoIJVWK0oOpvsZ52HPLju5mwbqzcHtZCNHt4PQ1LENf4jVK5RSIOzgby9jfNv3n8DrWZ3P6ftZrW6mO20TzBMbQy3sO3mPcvSNcR6qK0pq3S4sYWMQ5fKEt6eIctyO0tuL3Yu9dSgfn7P/WZ7lb+qt0c34d69MdryPJKp1MS/X6pZljwpkuyme59T1s7+uMVF1GYZST/7kw/eeK73p9bXFSxbVS7HWZ3mM0sm4gAtPtEXD7a8eNKkBmsQMwlp25Xfqv5s3XLqXOWddfNZ+La69YXZbB9Q0ZaFCjATLW3ibipVCUgJIqRanBovTwec4d7Q1jpMRZ14hD4Yf2B+1wBamy0g3hs+wRFp58ygm0kRDzGo47V1eQqqKCbhaqn7c79KJC4ECMO3nT9JaKVBnYDHdNpC68eVLpxnXzPfITMU1ej06fpCQQBfut97yOc4tp2G/9nrfFHR2Kk2J4PvrhCUzrkYn1fMrXW6UAuth3l4L5DONcrNe0UnO52Gq/xHZ83kaqikX4SungnP7fSdE8/nUDV817ZfS+Wc9T2jk/McH2+XK3mQ/9/UEUxN5MId4JpX39u8+xgRVxiZpjIyTm+1xem4tAe21iUe2Vwe1JlUUwHHTl63MoHBBuq8tz3AzcSc+dwKXf9eQ42xrN2WquuYGDw+NuiTretmDdSasW2hl+PxaqX7e/p+3gAiNNDvVdpdS5m9+Rn41ru6ZZF9bnMOtRariKf2+F4k5QUqW4O+wG6OZwHhlydk3J/4mRswx00SgFsTIkZ8+ojKK6EbYamHNmj52YcdDx3NztW0OMZsnPTwN5y3dy4rhKth6QI+MFWP/eTULl9Ps5ez/79dcPzUFm26Z2MujkCHyJ9+Rx8Wn719trSTjuLCuL4N0czq4v3rbBdly++PfCd+ewwuG4fvE2BjbcOFPo8F0aIG7wVhxc5vD+xonf7fOU6vN+bxy1w9yzBUTGqPX2FhBlmHN5rXOY0y3QRlIb8+Sa4zdyhjK+vsFd59jg6s7irQhSzWZhj4Mupa68eMv73kKKrju2mrBalFzF+t729248DSfu8Dlvxb34rjdwbpY1dw7tIBzByFTJ210xohpgP+VYAt+dX48Caw7NoG5mzjpYRLLKqnM24mVt8uzD/h2vbzNkl5GqDbeXmkLhCCVVCoVCoVAoFC6AkiqFQqFQKBQKF0BJlUKhUCgUCoULoKRKoVAoFAqFwgVQUqVQKBQKhULhAiipUigUCoVCoXABlFQpFAqFQqFQuABKqhQKhUKhUChcACVVCoVCoVAoFC6AkiqFQqFQKBQKF0BJlUKhUCgUCoULoKRKoVAoFAqFwgVQUqVQKBQKhULhAiipUigUCoVCoXABlFQpFAqFQqFQuABKqhQKhUKhUChcACVVCoVCoVAoFC6AkiqFQqFQKBQKF0BJlUKhUCgUCoULoKRKoVAoFAqFosIA/n8xZyd8PE0lUwAAAABJRU5ErkJggg==)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "z6IUrfb2Wm1X"
      },
      "source": [
        "#### What are LSTM's"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "6p9iSkoaX-Pu"
      },
      "source": [
        "THE CORE IDEA "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "v_gmofpnWqT7"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CEvNFOHgYAyN"
      },
      "source": [
        "1) THE FORGET GATE "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "-lec-4N-WqG5"
      },
      "source": [
        "![Forget 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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "YbH7wT49YDdv"
      },
      "source": [
        "2) INPUT GATE "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "csok1X8kXAwM"
      },
      "source": [
        "![input gate lower 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)\n",
        "\n",
        "![input gate upper half.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "wQv3G7dEWxy7"
      },
      "source": [
        "OUTPUT GATE "
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "syhF_7Hh4l2C"
      },
      "source": [
        "![output 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)#### Defining Architecure "
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BPxGi0cr3yIv"
      },
      "source": [
        "temperature_model = Sequential()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pHnkFMVy33US",
        "outputId": "5e74442a-32c3-402e-858d-0d0280fc8df1"
      },
      "source": [
        "temperature_model.add(LSTM(128 , return_sequences = True , input_shape = (x_train.shape[1] , 1) ))\n",
        "temperature_model.add(LSTM(128 , return_sequences = False))\n",
        "temperature_model.add(Dense(64 , activation = \"relu\"))\n",
        "temperature_model.add(Dense(1))\n",
        "temperature_model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential_5\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " lstm_8 (LSTM)               (None, 144, 128)          66560     \n",
            "                                                                 \n",
            " lstm_9 (LSTM)               (None, 128)               131584    \n",
            "                                                                 \n",
            " dense_6 (Dense)             (None, 64)                8256      \n",
            "                                                                 \n",
            " dense_7 (Dense)             (None, 1)                 65        \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 206,465\n",
            "Trainable params: 206,465\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 533
        },
        "id": "EuNkKexyTWFC",
        "outputId": "9bfeb5b6-4deb-49a0-f9be-31cec1e3e9fc"
      },
      "source": [
        "from tensorflow.keras.utils import plot_model\n",
        "plot_model(temperature_model , show_shapes = True )"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {},
          "execution_count": 63
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7SZqU15v4p2l"
      },
      "source": [
        "### Compiling\n",
        "\n",
        "Compiling with Adam optimizer because:- \n",
        "\n",
        "This algorithm is used to accelerate the gradient descent algorithm by taking into consideration the ‘exponentially weighted average’ of the gradients. Using averages makes the algorithm converge towards the minima in a faster pace."
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GN6-NpRFVuz7"
      },
      "source": [
        "![adam1.PNG](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZCiAubVpV58k"
      },
      "source": [
        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)"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eNz9BzQJ4Xyp",
        "outputId": "80fdaa1d-dfd6-4879-c0ce-2ab1ab068e8a"
      },
      "source": [
        "temperature_model.compile(optimizer = \"adam\" , loss=\"mean_squared_error\")\n",
        "temperature_model"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.engine.sequential.Sequential at 0x7f788ead25d0>"
            ]
          },
          "metadata": {},
          "execution_count": 64
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VGUQ8VdS42yx"
      },
      "source": [
        "#### Training"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ABQGvdba4uxH"
      },
      "source": [
        "callbacks = EarlyStopping(\n",
        "    patience = 5 , \n",
        "    restore_best_weights = True , \n",
        "    monitor = 'val_loss'\n",
        ")"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5oKsdIBx5Oph",
        "outputId": "8c3880cf-6b3f-4ded-a19b-2ef45bbd0193"
      },
      "source": [
        "history = temperature_model.fit(\n",
        "    x_train , \n",
        "    y_train_temp , \n",
        "    batch_size = 256 , \n",
        "    epochs = 75 ,\n",
        "    verbose = 1 , \n",
        "    validation_split = 0.2 , \n",
        "    callbacks = [callbacks]\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/75\n",
            "15/15 [==============================] - 6s 165ms/step - loss: 0.7110 - val_loss: 0.8567\n",
            "Epoch 2/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.6261 - val_loss: 0.7694\n",
            "Epoch 3/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.5557 - val_loss: 0.5801\n",
            "Epoch 4/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.4448 - val_loss: 0.4306\n",
            "Epoch 5/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3962 - val_loss: 0.4464\n",
            "Epoch 6/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3909 - val_loss: 0.4361\n",
            "Epoch 7/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3907 - val_loss: 0.4292\n",
            "Epoch 8/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3876 - val_loss: 0.4267\n",
            "Epoch 9/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3866 - val_loss: 0.4237\n",
            "Epoch 10/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3895 - val_loss: 0.4208\n",
            "Epoch 11/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3841 - val_loss: 0.4295\n",
            "Epoch 12/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3833 - val_loss: 0.4247\n",
            "Epoch 13/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3825 - val_loss: 0.4247\n",
            "Epoch 14/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3824 - val_loss: 0.4175\n",
            "Epoch 15/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3813 - val_loss: 0.4262\n",
            "Epoch 16/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3861 - val_loss: 0.4225\n",
            "Epoch 17/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3810 - val_loss: 0.4230\n",
            "Epoch 18/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3802 - val_loss: 0.4171\n",
            "Epoch 19/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3846 - val_loss: 0.4219\n",
            "Epoch 20/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3823 - val_loss: 0.4249\n",
            "Epoch 21/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3787 - val_loss: 0.4265\n",
            "Epoch 22/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3808 - val_loss: 0.4207\n",
            "Epoch 23/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3794 - val_loss: 0.4151\n",
            "Epoch 24/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3774 - val_loss: 0.4244\n",
            "Epoch 25/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3809 - val_loss: 0.4184\n",
            "Epoch 26/75\n",
            "15/15 [==============================] - 1s 89ms/step - loss: 0.3772 - val_loss: 0.4160\n",
            "Epoch 27/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3763 - val_loss: 0.4204\n",
            "Epoch 28/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3759 - val_loss: 0.4146\n",
            "Epoch 29/75\n",
            "15/15 [==============================] - 1s 92ms/step - loss: 0.3738 - val_loss: 0.4135\n",
            "Epoch 30/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3733 - val_loss: 0.4207\n",
            "Epoch 31/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3726 - val_loss: 0.4145\n",
            "Epoch 32/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3737 - val_loss: 0.4173\n",
            "Epoch 33/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3707 - val_loss: 0.4111\n",
            "Epoch 34/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3687 - val_loss: 0.4149\n",
            "Epoch 35/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3766 - val_loss: 0.4227\n",
            "Epoch 36/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3774 - val_loss: 0.4156\n",
            "Epoch 37/75\n",
            "15/15 [==============================] - 1s 91ms/step - loss: 0.3724 - val_loss: 0.4221\n",
            "Epoch 38/75\n",
            "15/15 [==============================] - 1s 90ms/step - loss: 0.3710 - val_loss: 0.4135\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q5eyGWzz66Hr",
        "outputId": "4c8c89c0-5e9a-4c4a-bd19-b27ff43f8078"
      },
      "source": [
        "yp = temperature_model.predict(x_test)\n",
        "print(yp[:5])\n",
        "\n",
        "# z = np.zeros(800).reshape((400,2))\n",
        "# yp = np.concatenate((yp_copy , z) , axis = 1)\n",
        "# print()\n",
        "# print(yp[:5])\n",
        "\n",
        "# ypi = scaler.inverse_transform(yp)\n",
        "# print()\n",
        "# print(ypi[:5])\n",
        "\n",
        "\n",
        "# ypn = ypi[: , 0]\n",
        "# print()\n",
        "# print(ypn[:5])\n",
        "\n",
        "# rmse \n",
        "rmse = np.sqrt(np.mean((yp - y_test_temp)**2))\n",
        "print(f'RMSE: {rmse}')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[[-1.2728075]\n",
            " [-1.2140827]\n",
            " [-0.9442258]\n",
            " [-1.1192762]\n",
            " [-0.9304372]]\n",
            "RMSE: 0.6923593806280702\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 391
        },
        "id": "QVbG5ngd7_Qf",
        "outputId": "a82b2ee3-7086-460b-e9b5-0c5a727865da"
      },
      "source": [
        "plt.rcParams.update({'figure.figsize': (15,6)})\n",
        "plt.plot(y_test_temp[:100])\n",
        "plt.plot(yp[:100] , color = \"red\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7f789473ef90>]"
            ]
          },
          "metadata": {},
          "execution_count": 68
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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gLisMG8XnsvdEaWXuhiQDTwghZoZCjtRMUFZK7ui6HVbuXhJCms4LU/NIacDGsYG8n2/dEEAskcKzR3tznpwcT5YMOtEZqXEEwbEWzZDT8TptPRF2EslxFbk5SQgxMxRypCaSagrz0URRjxwgHDmmVhJCms2u8fygE53N64YgSb3bJxeuIuSys+SM9clNzUVbEnSi43PZeqJHTlboyBFCugMKOVITc1Gx21rYIwcAboeNBz1CSNPZPT6PZT4nVvTni5ABjwNnrOzHzh4dDC5XK61MjxGYnKvuyKkpDccX45n7tAKv09YbpZU5YpRVJoQQM0MhR2oiJIth4KV75CwsQyGENJ1dE/PYVNAfp7N1QwBPHwn15Am3HFfhcVjLXl+LI3diMQY1pWFkoIWlla7eEHJ5jpzC1EpCiHmhkCM1EYqkHblSpZUOKx05QkhTkeNJHJgOY+NYGSG3PgAlmcLvjvTePLlqYScuuxWBPochIaffppWOXL/LjsUe6JGL5owf4DGNEGJmKORITQQzjlyJsBO7jY4cIaSpPD+5AE0r7o/TuWDdECwSerK8UlYq98gBwpUzEnaiDw5vZY+c1yl65Lp9iLasqHBYxekRhRwhxMxQyJGaCEWEkCvdI0dHjhDSXHaNC6ft7DKllX63HWeN+vFYDwq5SFytKuRG/O6MSKuELvZa2iPnsiGldb/4iShJBLziGBfj5iQhxMRQyJGayDhyJVMr2SNHCGkuuyfmsbLfheW+8k7S1g0B/P7IXE/1ySnJFBQ1Ba+zfI8cAIwOuDBpwJGbnIvB47Ci311ZGDaCzyUeu9v75OS4mhFy3S5KCSG9DYUcqYmQrMDjsMJlLz658DC1khDSZHZPzJftj9PZuj4ARU3hmZdDbVpV65HTyYlGHLnFWLJq7P+xhShG/K6WDQMHkOnn63YhF1VUDPU5xb95TCOEmBgKOVITwYhS0o0DROM9HTlCSLNYjCVwcFoum1ipc/7aQVgtUk+VV4YNCrnRdArlsSqu3ORcDCMtLKsERNgJgK6fJScrSQx5xP+FxzRCiJmhkCM1EZKVkv1xgBgIrqgpJNWcuGZNAx59VFwSQkgNPDexAAA4u4oj53PZcfYqf08NBpfTyYmVUisBYGV6tt5klT65qXnhyLUSb6a0sruTKyOKCq/LBqfN0lPluoSQ3oNCjtREMJIoOUMOQGbeUSyZI+TuvRe46CLg6afbsTxCSA+xe0IEnWys4sgBorzy2fE5RJTudoN0jJZWjg4Il61ScmVCTeHEYhwjA6115HTRGe7y0ko5nkSfw8YAL0KI6WmKkJMk6ZuSJJ2QJOm5MtdLkiTdIknSfkmSdkmSdG4znpe0n5CsYNBTPHoAAFxpIZd3IrVrl7g8caLVSyOE9Bi7JxawasCNYa+z6m23bgggoWp4ukf65MJxISCqhZ2s6HdBkioPBT+xGIemAaMtduR6IexETWmIJ1PwOGxws12AEGJymuXIfRvA5RWufxOAU9Jf1wP4apOel7QZIeTKOHLpAJSYkuPI7d0rLhcXW700QkiPsXt8zpAbBwDnrxmEzSL1THmlUUfOYbNg2OusOIJgak64da2cIQcAPqfY5Fvs4h45fSPS47AKIUdHjhBiYpoi5DRNexhAsMJNrgHwXU2wE8CAJEkjzXhu0j6UZAqL8WT5Hrm0I5d34NuzR1xSyBFCamA+ksDh2UjVxEqdPqcNm8b8PTMYPBN24qg+LmDEX3kEwWTarRttcWllX9o97ObSykjagfM4RToze+QIIWamXT1yqwAczfl+PP2zIiRJul6SpKckSXpqenq6LYsjxpiLpmfIVQg7AXJKKzWNQo4QUhfPTc4DMNYfp7N1QwC7xuczblY3o/8fqoWdAELIVSqt1B25Voed2KwWeBzWrg47kXMENHvkCCFmx3RhJ5qm3a5p2vmapp2/bNmyTi+H5BCSxcF5qExpZZEjNzMDhNL9KhRyhJAa2D1Rh5BbP4xkSsOThysViHQHRksrATFL7lglITcfg89pg89Vur+5InNzwBe/CExOGrq512nr6vEDGUdOL61kjxwhxMRUP0I0hwkAq3O+H0v/jHQRQVl35EqfDOiOXObAp/fHAcDCQkvXRogZiSVU/Pn3nsbfvel0nL6yv9PLMYymaXj/t5/EwRm57G36XXZ89/0XlnXoG2X3+DxWD7lrevzz1gzCbpWw82AQl5y2vCXrahfhuAqH1QKHrfp+6+iAC+F4Eq/5woMoNe97ZjFef1nlHXcAH/848OlPA//3/wJ/8zeAz1f25l6Xrct75HQhZ4PLbs0c9wghxIy0S8htA/CXkiT9EMBmAPOapk216blJkwhFxAHNcI+cLuQsFjpyZEkyMRfFQy9O49LTlneVkFuIJfHgi9PYNObH+uG+ouvD8STu23MCjx8K4vKzV7ZkDbsmjAed6LgdVowOuCtG8XcLcjyZ6TmrxpvOHsG+42Ekcmd4FnDp6XUK2x07gJNPBs47D7jpJuA//1OIug9+ELAXb+r5XPauTq3U5/d5nFa4HeyRI4SYm6YIOUmS/gvAJQCGJUkaB/ApAHYA0DTtawDuBvBmAPsBRAD8aTOel7QXfWeybGllKUfO5QJGRijkyJJEfy/MRbqrZ2g2HAcA/OlFa/HWV44VXR9LqDj7Uzuwe2KuJUIuJCs4GoziXReuqfm+Ppetq8M2dOR4Eh4DQScAsDo4iS8++m3gC18oKa7qJhIBHnkE+N//G/jSl4C//mvhyH3oQ8BXvgL88z8D11yDXBvQ57Qh3MU9cpH02Ic+hw1uu6V8j9yTTwpxazFdhwohZAnRrNTKP9Y0bUTTNLumaWOapn1D07SvpUUc0mmVH9Y0bYOmaRs1TXuqGc9L2ksoLeQGjPbI7d0LnHYa4PdTyJElif5emI9214ltZtOmr/T8NpfdilNX+LB7ojUl03rQySaDiZW5eJ22rnaEdMLxpKGgEwDAz34mhNWjjzZ3EQ8/DCgK8Ad/IL6/8ELgoYeAbduEgHnrW4HXvAa4/34gKV5zn6u7X3/ZyPiBl14Sr8Xdd7d5dYQQkg+3kohhghEFPqetbM9GkSO3Zw9wxhlAfz+FHFmS6P023SbkZsJCyAUq9KdtXOXH7vE5aJrW9OffNS6E3NmjtQs5n8ve1T1aOrJivLQyE0Ryzz3NXcSOHaKq4tWvzv5MkoCrrgJ27xZllvv2AW94A7BsGfCud+HiJ++BJdS9YTPRnLATl6NM2Mn4uLg8fryNKyOEkGIo5IhhQrJSMXggI+QSKhCNAocPA6efLhrjKeTIEiSa3t3vNiGnO3LD3tKOHABsHPMjFElgPNT8frTd4/NYE/DA76m9TNDntHV1/L1OOK4aSqwEkBVy997b3EXcc49w3NwlglJsNuD664EDB4Cf/lS4c/ffj3ff8ve4+3PvEPf7wheAF14Qo2i6BN2R63Pa4LZbEU+mkEoVrH82PauQxzVCSIehkCOGCUYSGKxwYmWxSHDaLGIHc98+cfDWhRxTK8kSJOvIdVfynd4jVy6hFsiWPepjAprJ7on5moNOdLyu7o6/14nUUlo5lc4Oe+opINgkN+zoUSHCLrus8u36+oC3vQ345jeBqSn84Jb/xr9vfSe0cBj4278FzjoL+I//aM6a2kAkrsIiAU6bJbM5GUsWuHIUcoQQk0AhRwxTzZEDkB2gqidW0pEjS5hu7ZGblRX4XDY4beVL+05b6YPdKjVdyM2G45iYi9bVHwdkw05aUfLZTkRqZQ2O3Nq1YvPs/vubswDd3dP744xgsUA+51z8y6v/F8KPPi7E4MAA8PzzzVlTG5CVJPocNkiSlO37LiyvpJAjhJgECjlimKCslE2s1PHoA1T37hW9FKecQiFHlizRLu2Rm5WViv1xAOC0WXH6yn7sHm+ukHthSrj39fTHAYDXaUcypSGWKB/F3w0YDjvRNCHkrr5aBEs1q7xyxw5gdFQ4ajXgdYk1L8aSwNgYsHx581zCNhBV1IyAc9kLArx0KOQIISaBQo4YZi5S3ZFzOayI6I7cmjWAxyOEXCQCqJzHQ5YW3Rp2MhuOI1ChP07n7FV+7Gpy4MnRoOi5W1Nifp0RMkIi3l2veS6apkFWVGNhJ4uL4vN19Wrg0ktFX1ujvw9VBe67T7hxpSaMV8CXfv0z5a1DQ10l5MTrLv4PmdJKCjlCiEmhkCOGiCVUyIpadhi4jttuRUxRs4mVgBByABAOt3iVhJgLXcjFEqmuGiwclJWq73VA9MktxJI4Eow07bmPhiKwWSSs7HfVdf9+XUh0cQR+PJmCmtKMlVbqQSejo0J4vfwysH9/Ywt45hkhvmopq0yju4iZEQRdJuQi8SQ8aUcum8Rc4O5SyBFCTAKFHDGEPtB4sFpppcOKWDwBvPii6I8DxPgBgAc9suTQUysBYKGLXLmZsIJhb3UhpweS7GpieeV4KIrRATesltqcIJ0iIdGF6G6WodLKXCH3xjeKfzdaXrljh3Di9MerAZ9LBORkkkO7TMjpPXJAidmoOhRyhBCTQCFHDJEdEFw5Dtxlt8JzYkqMH9CFnO7IMbmSLDFyTwC7pbwyldIQihhz5E5d4YPDZsFzTQw8GQ9FMDZYIu7eILqQ6ObkSjm9dl1QVERPrBwZATZsANata3ye3D33AOeeCwwP13zXbi+tjCoqPE69R06cIlHIEULMCoUcMUQoIoRcNUfObbdi+cQh8U2hkONBjywxIkr3Cbn5aAJqSkOgr3qPnMNmwRkrfU115I4Gow0Juawj1x2vdyl0EVRzaaXuoj34IJCo8/+/sAA89lhdZZVA9vUP55ZWzs8Dye4Q1rKiZkorM2EnTK0khJgUCjliiKwjV720csXUYfENhRxZ4kQVNZMV0S1CblYWM+QCBkorATEY/LmJ+eKhyXUQS6iYCcexetBT92P4XN1fWinHhXAwXFrp9WY/Z9/4RiHGnniivid/8EEhuqrNjytD0es/NCQuQ6H61tNmRI9chbATVc3+X3hMI4R0GAo5YoiMI2dgjtzosZeBwUFg2TLxQwo5skSJKGrG2dL7TM3ObFi81404cgCwadUAFuNJHJ6VG37u8ZBIrBwbaqS0sqC0rwvJlFYaSa2cmhJunM7rXgdYLPX3yd1zjxCGW7fWdXe9HHQxXiDkuqS8UlZU9OlhJ6V65ObmRCqozcZjGiGk41DIEUPojtyAu3KPnNtuw0nHjwg3TrciKOTIEiWSUDHiF+mL3ePIpYVcDY4cgKYMBj8aEumXYw04ct6HH8S64ERXO3I1h52MjGS/HxoCzj+//j65HTvEGAOHsd9/IRaLBK/Tlh92AnSNkIsoSXgKxg/klVbqZZUnnSSSmLt88DwhpLuhkCOGCMkK+l022KyV/2TcDgvWzRyBppdVAkytJEuWmKJiRX+XCjkDYScAcMpyL5w2S1MGg+uOXN2llRMTsF19Ff7p3v/oEUfOoJDLdeQA0d/2xBOiN60WDhwQX3X2x+n4XLZsj1wgIC67QMgpyRQSqgaPvcJAcF3IrV0rRJzcuBNNCCH1QiFHDBGMJAyl2A3Ewlgmz0E9LUfI0ZEjS5RIIgmfywaf09Y9Qi4seuSqlVHr2KwWnDnaj11NcOTGQxE4rBYs9xkr6yziy18GEglsfnkXpImJhtfTKcJGUys1rbSQe+MbRS/Xgw/W9sS6i9egkPM6bfmplUBXCDndedMdOafNAkkq6JHLFXIAj2uEkI5CIUcMEZIVQyd2K44dAQDEN5yS/aHLBVitHD9AlhxRRYXbYYXfY+8aIReUFfjddtiruO+5bFrlx/NNCDwZD0axatANSz0z5GZngdtvBy6+GBZNw2kPbW9oLZ1EDzup2iO3sCBGveSWVgLAli2iz63W8sp77hEC5ZRTqt60Ej6XrTjspAuEnKzoAlq87pIkwW23li6tpJAjhJgACjliiFBEwVCV0QMAsGxcjB6IrM85EZAk4crxgEeWGBFFhcduhd/dPUJuNqwY7o/T2Tg2AFlRcXCmsTKzhmbI3XqrKHP72tfw0kln4Nzf/LKhtXQSWUnCabNULWXPGz2Qi8MBXHJJbYEniQTwwAPCjZPqG8au43XZs2Enfr94vC4QcpG0kPPklLS67VbEkvTO6o4AACAASURBVDlCbmZGXFLIEUJMAIUcMYRRRy4wfhCKxYbw6Or8KyjkyBJD0zREE2lHrouE3Ew4jmGDiZU6mzKBJ3MNPfd4qM4ZcouLwC23ANdcA5x1FnZuvQxrj7wIvPBCQ+vpFOF40njQCVAs5ABRXrl/P3DokLEnffxx4fDVOXYgF5/ThrAedmK1AgMDXSLk0qWV9qwT6rJbEVVS2RvNzorESv0153GNENJBKOSIIYIRxVCPnP/lAzg8OIpoqmBHl0KOLDHiyRQ0DV0n5IKysfd6LhuWeeG2WxsaDC7Hk5iVlfoSK2+/Xcz2+sQnAAC7Lr4cqsUKfP/7da+nk0TiSWNBJ1NT4rKwtBLI9rkZdeXuuUeIrte9ztjtK5BXWgmI8souEHJ6Sasnp6TV7bAW98gNDTHEixBiCijkSFWiiopYIoVBA6WVvkP7sT8whmiiIDGuv58HPLKkyN3dH/DYu2eOnFx7aaXVIuGs0f6Gkisn5tIz5Gp15OJxEXLyutcBmzeLn61YiSc3nCuEXCpV+f4mJBxXjSdWAqWF3GmnAWNjxvvkduwQr9/AgPGFliEv7AQQwkfvLTMxEaU4ZMZttxanVgYCDPEihJgCCjlSlWB6GPhQX+UZclAUuI8exoHA6vxSFICOHFlyZPptHDb0u+1YiCagmXzmlJrSEIoohkcP5LJxzI/nJxeg1hl4Ml7vDLnvfEc4U2k3DhBC4n/Oei3w8svAo4/WtZ5OIseT8BoZBj45KT5bdVGRiyQJV+6BB0SCZSWCQeDJJxtOq9TxumyIKCqSavo40C2OnFIcMlMy7IRCjhBiEijkSFVC6blSVR25AwcgqSoOBMbydzABcdBjaiVZQugnf3pppaKmEEuY2x0KRRRoGhDw1h7/v2nMj2hCxYHpcF3PfTSYniE3VIMjl0wCX/iCGID9+tdnfuxz2bBt/WZoHk9XllfKSg2llaXcOJ03vlGUnD79dOXHue8+McqgSULO5xKbfnqpYrcIuWh688Wd48i5HHTkCCHmhUKOVCUo645cFSG3dy8A4MDQWMaNyEBHjiwx9JM/dzq1EjD/UHDD7/USbFwlSvLq7ZMbD0XgtFmwrBYR+ZOfiAHWn/hEXtKiz2WDbHdDvfJq4Mc/BhSlrjV1irDRHrlSM+Ry0cVttfLKe+4RJZUXXGB8kRXwpde+GE//vXeJkMuMfXDkOnKW4h65QECMdwB4XCOEdBQKOVKVULq0smpqZVrIHRxalX/gAyjkyJIj0yPnsGLALd47c1FzC4qZ9DDwWnvkAGD9cB/6HFbsHq8vuXI8JGbISUaj7zUNuPlm4PTTgbe8Je8qr1MI58W3v1MIiF/9qq41dQo5noS32jBwoLqQW7YMOPfcyoEnL7wA3H23EH02A89pAJ8rLeT0wJNAAJibq17i2WFyy6F18nrkNC0r5CwWoK+PxzVCSEehkCNVyezSVyut3LsXqdFVkJ2e/J4CICvkTN4jREizKCytBIB5kwee6O/1QI3jBwDAYpFw1io/dk3U58gdDUWwupb+uLvvBnbtAv7u78RJdQ66kJi96LXA8HDXlVfKRsJONK16aSUgyisffTRfcCSTwF13CfF21llC7F53XeMLT+NNv/6ZwJOhIbHe+frDcNqBrKiwWyU4bNm/J7cjp0cuEhHhOoGA+J4blISQDkMhR6oSkhVIEtDvrhJ2smcPtNNPAwBECh25/n6xGxuLtWiVhJiLrCNn65rSytlwWsjV4cgBwKZVfrwwuZANuaiBmmbIaRrwT/8EnHQS8K53FV2tC4kFVQL+6I+Abdu6pkdX0zTIioGwk/l5IBqt7MgBou8tmQR+/WvgxAnxuq1fD7ztbWLO3M03A0ePNmV+nI4+Ay8cyxFygOnLKyPxZJ4bB6TnyOnHMz15k0KOEGISmlNHQUzPr547hqPBSF33fezgLAbcdlgtFUqeNA3YuxeW97wHkgTESjlygDjouesY+Ntj3L17ClvWB+rqRdo9Pg8NGjaNNR4TTlpHbo+cOz1g2PxCLg5JMhBsVIaNY37EkynsOxHGGSP9hu+3GEtgLpIwnlj5yCPCZbr1VsBevMHkyxUS73438O//Dvy//we8732G19QpIooKTUN1R67SMPBcLrpIfOZ+9KPA+LjoF3z968UA9SuvbFo5ZS562MlCLKdHDjC/kFNUeBz5Atptz5kjRyFHCDEZFHJLgKii4i++/3RDVY2v2hCofIOpKWBxEdIZZ8B93JpxIzLoQm5hAVi+vP6F9ADjoQg+9P1ncMpyL35w3RYs8xkvY3voxRO4/s6ncdZoP+760EUtXCVplGwCnjVTqmV6IScrGPQ4Km/aVGDjKj8AsdlQi5AbD9WYWHnzzaL/6/3vL3m1LiTC8SSwZYtwoL73va4QcnK6HLGqkNOHgVcTck4ncMUVok/wuuuAD38YOOOMJqy0PL5SpZVA1wq5hKohoaZg14Xc8LC4pJAjhHQYCrklwPRiHJoG3PSWs/HWV66q6zF0R6Es6aATnH463MFU6fEDAA96AI6kndF9J8L44zt24gfXbcZyn6vq/R7cewJ/dufTUNRUZiQEMS+5YSduuxWSVELIPfqoKHX72c9a4ozUymy4vhlyOmsDffA5bdg1MYd3XrDa8P10IWfIkfvd74Qo+dznAE/p23szYRsJkWb57ncDn/1s9XAQE6CLH69RR65ajxwA/OAHorTdVf1zphnoa1/sstLKUmMfXOljXyyhZoVcriM3Pt7OJRJCSB7skVsCTKeT6MYG3PA6bXV9Vd2hzxFyeT0FOhRyGcbT87K+/IfnYHIuimtv34njC5V7B+/fcxx/dufTOHWlF1efM4o5kzs7JCvk3HYrLBYJ/S57sZB76CFg+/asu9JhgrJSV7mvjsUi4exVfuyeqK0fTS/7NtQj961vCQH3oQ+VvUmRkHj3u0X59w9/WNO6OoEegV/oDBVRi5Cz29sm4gCxdovUjT1yxY6cK/19NKGytJIQYjoo5JYA04tCyNVSwlcze/eKuTqjo/DkpnzpUMhlGA9FYJGAq18xiu+8/0Icn4/h2tt34th8aTF37wvH8effexqnj/jw/Q9swUlDHixEE0ilmABqZqIJFS67BZb0JojfXULI6Sl+MzNtXl1pZuQ4husYBp7LpjE/9kwtQEkaDzwZD0XhtluNuYF79oikxYHyPaKZsA29tO+008TQ8O99z/CaOoVhR25qSoRI6fPMTIQkSfA6bdnXf3BQXOpCyKREEsVhJ3o1SkxJZdevC1MKOUJIh6GQWwLos6EaPUGryN69Yp6TJIm45lKplQAPegCOhqIY8btht1pwwdohfPcDF2J6MY5rb38MU/PRvNvueP4YPvT9p3HmqB93fmAz/B47/G47UhoQLhy6TkxFVFHzSpIHPHbMFY4fMJmQq8mR0zQxG2zXLuDnPxeBIh//ON5zy9/hh9/6Kyx+4HrDzzseimDM6Ay5/fuBU06peBOrRUKfw5p15ADhyv3ud0IImhjDPXKTk8bcuA7hc9mzYSc2mzgGdKEjp7+HM45cf382YIdCjhDSYSjklgC6I1dvpLgh9uzJNNC77XTkKqGftOqct0aIudmwgmtv34nJOSHmfvXcFD78/Wdw9io/7vzAhZkIe7+nO2aSLXVEcEL2ZLykIzeXHp5tAiGXUFOYiySMfU48+6wIGxkcBM45B7j6auAv/xL4t3/D8peex0B0EUN3flPE3RvgaCiK1UMG+uPiceDIEeDkk6ve1OeyZ0v7AODaa8W8OZPPlJOVGoScifv9fC5b/us/NGR6IScrSfQVOnIOcZqUEXKBnOAvn0+MgEhyU40Q0hko5JYAM+E4Bj122K0t+nUvLoqG79NPB4DSjhyFXAYxLyv/pPXckwZx5wc3IygruO5ff4VHbvgyPvz9Z7BpzI/vvv9C9LuyEevdMpNsqRNNJOHO2d3vd9uxYOLSSj1AJ2DEuf/Nb8RJ7U03AT/+MfD446LULxqF7cA+fOIPPwFJ04Bf/tLQcxdubpTl4EEglarqyAEi8GQxnvN6r1wJvOENQsg1EuHbYmoqrTSxkMsrrQS6QshF4io8BfP79LCTqFJGyAFAONyuJRJCSB5NiUmTJOlyAP8GwArg65qmfb7g+vcB+CKAifSPbtM07evNeG5SnenFeGv74156SVzqQq6UI6f3cXTJUN5WEU+qOLYQK3nS+orVA/j+Bzfjnvd/HK++9w5cf/3N+PCnP1Z0Qkch1x0URpmbvUduVhdyRkorDx0S4Rn/8A8iFTIHCYD9/PMx+6NhBH7xC+C97634UPPRBBZjSWNCbv9+cWlEyDlt+aWVgCivfO97gSeeADZvrv58HSBbWlkh7ETTuqC00oaZcE66biBgaiGnaRoiifKllbFyjhwgNigr9Gy2k6/c9xK2PTtZ9nqbRcLn3roRF6wdauOqCCGtomGLRpIkK4B/B/AmAGcC+GNJks4scdMfaZr2ivQXRVwbmQm3WMjlJFYCZRw5q1UkzS1xR25qLgZNQ9kysk1jA3j/qHALPvbb78FrL36LDngo5LqBoh45tx1z0QS0XDfITEIuXIOQO3gQWLeuSMTpnLtmEPeuOw/ajh1iAHUF9MTK1UZGD+zbJy4NCDmfq8ARAoALLhCXhw5Vf64OoadWFpb45TE3B8Ri5nbkXPaucuTiyRTUlFYcduLIEXIzM+WFnAmIKipuf/gg7BYLzhzpL/o6Y2U/XjoexhOHzPt7IITURjMcuQsB7Nc07SAASJL0QwDXAHihCY9NmsB0OI5zTxps3RPs3SuE2oYNAMo4cgAbw5E7L6u8+zA0PQnYbLA+/7woW7v22rzr6ch1B9GEmhcc4nfboaY0yIqadVl1IWeCNL9ZuYZe2kOHxJDtMmzZEMA3NlyIa3+/A3jkEeD1ry9725pmyO3bJ/ryhqq7CT6XDVOFSbD6SbgJXu9yyPGkiO+vNPLF6DDwDiIc0ZzPKJMLOX1cSF+1sBMTC7mHXjyBiKLiH686ExedPFx0vaZp+OXfTyHCoCxCeoZmNE2tAnA05/vx9M8KebskSbskSfqJJEnGJ8WShtA0DTOLCpa1OrFy/XrAKZ7DXWqOHCDSvkxywOsUR0MG5mUdOgRccQVw9tnApz5V1EivC7miBERiKkqVVgIFAtyUjlyVzwpNE45cBSF37kmDeHL9K5FwOIFf/KLiw40beU/o7NtnyI0D0j1ahaWVegy+iQVFqaHURdQyQ65D9LsKSlt1IZcyPpaineglrR5nmfED0bh4v5pYyP1i9xQCfQ5sXld6o0OSJPQ5bBnXlxDS/bQr7OTnANZqmrYJwL0AvlPuhpIkXS9J0lOSJD01PT3dpuX1LrKiIppQMdyq0spYDNi9O1NWCYhhsNGEml9CBtCRgzhptVkkrOwvM5xX04DDh4W7eeONov+wYPaV226F3SrRkTM5orQyP7USyEkbTSazIQkmEHJBWYHVImXWWf6GQdHrum5d2Zu47FacsWElfn/yK8VoggrhIuOhKLxOW6ZkuCIGRg/o+Fz2fEcIELHxPp+pHblwXK0edKILOZM7cvFkKjtPcGhIiDiTHgN0R67cQPBU4TBwwFRCLqIk8cCeE3jTxpWwlQs2U1V89JHvYfj537V3cYSQltEMITcBINdhG0M21AQAoGnarKZp8fS3XwdwXrkH0zTtdk3Tztc07fxly5Y1YXlLmxl9GHgzHblgELjzTuAd7wCGh4EXXwTOPTdztcthhaaJnoM8KOQwHopiZMBV/kB74oSIs167FnjLW8Tr+pnP5PUZSZIEv9tBIWdyIkoy35FLC5W5aPp3mRv8YwIhNyvHMehxVC7pA7L9ZRUcOQDYuiGAbWPnAgcOiM+IMhieIReLGR49AAghISsq1FSBiDR56IYcT1YOOgG6wpHzuQqGsuvlsCZ97fVyw6LxA2lHTiol5Ew0H/WBvScQTai4YmMFcX/HHfjgg9/D9Z/6IHDXXe1bHCGkZTRDyD0J4BRJktZJkuQAcC2Abbk3kCQp92hzNQBzT2TtIab1YeDVHLloFJBlMacpmSzeQT9yBLj1VtHrsnw58J73AI8+CvzJn4iI8RtuyNzUnRvXnIvPt+RTK48GI5VDHQ4fFpd6kMRnPyt+9s1v5t3M77YVR9kD4ve2YwcQCjVtzaQ+ogk1b/yA7nRlfm96WeWyZULIdTgSfyasGA86ASo6cgCwZX0A9284X3xTobxSjOMwOHpA02pw5MQJuVzYD2TyXq1wvHiWWRFTU4DfD/T1tWdRdeBNj0zJlLfqQs6kbmg5R85utcBmkSDpfzMmdeS275rCsNeJC8uUVSIUAm64AS+sOxtHTjoFePvbxTGdENLVNCzkNE1LAvhLADsgBNqPNU17XpKkGyVJujp9s49IkvS8JEnPAvgIgPc1+rzEGIYcud/+VowH8HpFpLjdLgbnWizi3243sGYN8JGPiBOIj39czI0aHwe++lXg8ssBW/bEQz8QlpwlZ4IDXiepetKqux1r14rLyy8HXvUqMa8rGs3czO+2Z52dXD7zGXGfO+5o3qJJzaRSGmKJVF5qZVGPnC7kNmwQGyiy3O5l5hGUFeNBJ0BVIffKkwYwO7QSx9eeWlbIaZqGo8GIsaCTGkYPAFkhVzSCIBAwrZgAhCNnqLTSxG4ckJ2Dl5nlZ3JHLjv2ofi1d9utsATTm2MmFHJyPIkH9p7AmzeuhLWco37jjUAohG/98cfwmY/eAlx9tTim/83fmLZvkRBSnabMkdM07W4Adxf87B9z/v0JAJ9oxnOR2sg6chVO0J58UnyQ33ijEGTJJKCq+V/LlwNXXQWcemrV59QHqEZKOXJLWMjFEipOLMYrn7Tqjpwu5HRX7nWvA/7zP4H/838ACFGg/24zfPazQsgBwLFjTV07qQ19E6Ni2EmukNu5U7hy+rzFDjAbjmPjmIFZWAcPipJq/SS2DE6bFeetGcSDp2zGtQ/8QDgCg/npuXORBGRFNR50AtRQWlngCOkMDQEvv2zoMTqBKK00IORM3B8HiLATIEdIm1zIlXPkANEuYJsp4ci5XCKxucPHtfv2HEc8mcKVm8r8TezZA9x2G3DddZg++QzMywrw058KIfelLwFHjwLf+U4msIwQ0j00RcgR8zKzGIdFqpJEd/iwOIG84Yayc6FqIW+Aai5LPLVyYk44aquHqjhyw8P5J/SXXiqE3M03Ax/8IOD1YsDjwP7pcPY2n/888MlPilLXBx80teOwFCh1Uuh12mC1SNm00VwhBwghpwv4DjArGyytrDJ6IJct6wP48cpNuFa9U5T8FozSqHn0wNCQodEDQG6PVkEJsskduXBcrS7kpqaAiy5qz4LqxKu//l0n5Eo7co75Eo6cJJlig3L7riks9zlx/poSY4Y0DfirvxJluDfdhL57jorZjVarEHdr1gB/+7di8++uu4o2Wwgh5qZdqZWkQ0yH4xjqc5QvtwDEiVmF4b61oh8ISzpy0WhRnP5SwdBJ6+HDpUvWbrpJBKHcdhsA4e5k0g+//GXgE58A3vUu4FvfEu6pCcIzljJ6f6g756RQhNTYSztyQEd/Z/GkisVY0niPnEEht3VDAL9feSqUwaGS5ZX6OI6Kmxs6NSRWAlkhsVDKkQuFTFtOJkorK4SdaBpLK1uAHnbiKfHau+1WOOZCotWg0DXvsJBbjCXw0EvTePPGkdJBRXffLTZRPv1pYNkyuB3W7LFZkkSrxPe/L3reL77Y1G41IaQYCrkeZ3pRwXC1xMrDh5vqBLgd4s+qZI8ckI1cX2IYmpd16FDp38WrXgW8+c3AF74AzM+j323HQiyJ1Fe+AnzsY8A73ylKY6xW4ehRyHUU/W8/t0cOgGmFXEgWaxqq1iOnquJEr0p/nM45YwNwOO144RUXi1Ckgk2c7HvCoCNnsKwSAHzOAkdIR4/B119/E6GmNEQTaklXKEMoJHoqTV5a6SsMO3E4hAgyqZDTZ6t57KVLK10Lc+KztXDDs8NC7v49J6AkU7jqnBLCXlGEG3f66cCHPwxADDzX+wEzvOtdQuyNjwunNxJpw8oJIc2AQq7HmQ7HsaxSYqWmZR25JuGqlFoJLNnkyqPBKOxWCSt8ZWbIpVKVT5LTzer4ylfgd9vxJ8/8Apa/+ivgbW8Ts+b0wBmTl44tBTK7+wX9Nv25Qm5uTlzqQq6Dv7OZdL9l1WHg4+NCjBl05Bw2C85fM4TtJ50rTuB37sy7/mgwin6XrfrsulhM9PHU4MjpQqJk2AlgSkGhJ2xWDDuZmhKXphdyuiNXYii4CYkoSThtlpKjYdx2C9yLc/lllTodFnK/2DWJEb8Lr1xdoiTy1lvFBsi//qtwEyEGnhdtsgKihP+WW4CJiWyvNiHE9FDI9Tgzi/HKiZXBoDgINdGR03eTo4mCEyiTJHx1ivFQBKsG3OXndE1NiR3Ucr+L884Tou3LX8aFP7oDN937NUTedCXwX/+VOUgDoCNnArKllflCbqDQkXO5RCmsxdLR31lQFgmoVVMrDY4eyGXrhgB+OHgGNJutqLxSzJAz4MbVOHoAyOnRKuyRM3EMfqXkxAxdMEMOAJw2C+xWKV9Im1rIqSWDTgDhrPeFzSfk5qMJPPzSTOmyyuPHxebfFVeIJOM0fQ4rEqqWHdSey/Ll4nKJbrYS0o1QyPUwmqZhOhyvPEMud25Zk8jOkSsxEBxYwkIuaiyxstLv4jOfAcJhbLztZty34QIcuO0bomQpl+FhIRISHBjeKcol4BWVVvr9QsQFAh0VcrOy7shVEXIGh4HnsmV9AIvOPsyeu6WEkDM4Q05PrKxByPU5rJCkEqWVZnbkMkKuQo+cLuRM7shJkgSv05b/+ptYyMlKsmxJq8tuRV943nRC7r4XjkNRU7hiUwlRf8MNokTyy1/O+3G2h71Er7rfLy4p5AjpGijkepjFeBJKMlXZkSucW9YEMkKuVGolsISFXKR6YiVQ+Xdx9tnAxz+Ombf8IT70lr9HKFnC3TPxiepSwXCPnH7i1GEXdTasO3JVSisPHhR9mKtXG37sTWN+eBxWPHn2q4Dnn8/8nWuahvFQFKuHDPbHATX1yOlComTYCdD298ehGRn/8dB+aOUGv8dikOfFLEFDpZUmd+QA4YouxnI2lEws5CJxtayAdtut8MkLphNy23dPYdWAG69cXTA25JlngG98Q4wXOO20vKv0zSW5sPUByB6jTdg/SggpDYVcDzOtDwM34sg1NexEd+RYWqkTVVTMhBVjjtyaNZUf7POfR/Br34BiyxEFuQwPi0uWV3aMcqWVfrcdC9EEUinNXEJOVmC3SpnZX2U5eBA46aRsP6YB7FYLzl87hB+t2CR+sH175jmjCYMz5PbvFyfRNUaj+5w2hAuDHfST8TaXVm77/SS+8KsXcWC6TNjTO96Bsb/4UwAGSiv9fsBjQAB3GJ/Tnv/6Dw2ZsqQVqObIWeCPmEvIzUcSeGTfNK7YNAIpN4BF04CPflR8pnzyk0X386T/toqOz0BWyNGRI6RroJDrYWbSQq5iauWhQ8DAgPhqEnarBKtFKp9auQSF3MScwcTKlSsBd/UT24HC4dK5UMh1nGzYSf6J4YDHjpSWDoAwk5BLjymRqo0gqWGGXC5b1wfwUGoAyVNOBX7+cwB1zJCroaxSx+eyF5dW6p91bXaGQhHhej52oIyQ2b0bg7++D/2xcGVHrguGgesIR65EaWU5V7KDRCv0yPmTcdjVpKmE3I4XjiGharhiY4Ez++MfA7/5DfC5z5U8rvfpjly8giNHIUdI10Ah18NMhw06ck3sjwNESZPHbi3fI7cEDxJHgw3MkCtBfyUh1yHHgWSJJEr3yOm/t4VowlRCLigrGKqWWAkIR66Oz4st60U548tbLwUeeghYXBRDiWFwhlyNowd0vC5bdo6Zjs0mXvc2vz/0QJmdB0sIyFQKmJyEJZnEaw8+XdmRm5rqGiHnc5YQcsmkKUfQyEr5sQ+DUXHM0koNo/f5RD9yPN7K5RWxfdcUVg+5sWnMn3/FzTcDGzcC739/yfvp/0e5lCO3hI/RhHQrFHI9zHTGkasQYFBublmDuBxWplbmoM/LWl3PDLkSuOxWOG0WOnImJaqokCSR3JeLP1eAz89nd8x1Idchp2ImrFT+nAAAWRZD6etw5Dau8sPrtOGhUy4Uyaz33Zdx5FYNVBFy0WjNowd0isI2dAKBjjlyOw/OFvfJTU9nZuy9cf/j1cNOukXIuWzFpZWAKfvkIkqy7Os+EBPHrORgGSEHtPW4FpIV/Hb/DK7YOJrvoh86BDz7LPDe94pe1hLom0uRUo6c1Spm/VHIEdI1UMj1MDPhOKwWCYOeMidomtYSRw4QzeFFc+ScTrEbvgSF3NFQFA6bpXyZazIpTlZr+F343XbMR+jImZGoosJttxaVKuYJubm5fEcumezYCZRw5AwmVtbxeWGzWnDB2kH8yHGS+D//4hcYD0Uw4LFn5r2VRR95UFdppa14jhzQkV6toKzAIonewJeOFzhSExMAgMjgMC458BS8UoloeEB8Zk9OdkXQCSAc0TwhZ+IgJjle3pHrD4vwj3h/iR7NDgi5Hc8fQzKl4crCtMqf/UxcvvWtZe+ri9VIqVlygCivZNgJIV0DhVwPM70YR6DPUX5u2YkTYre7BY6cx2Et7pGTJHGQWIJCTszLqjBDbmJCnMjX8LsY8JQJO3G7RRACHbmOEUmU7rcZ8KSF3HxERIPnCjmgY7+z2XC8+jDwOkYP5LJlfQAvBeOIvf6NwPbtGJ+VsdpofxxQV2mlz2XLH0it0wlHTlZw4Trh6Dx2oOD3nBZyz77pD9GvROB+7LdlHiQkHM2uceTsWIwlsg6kyR25cj1yPlkIm5i/RC95B4Tc9t1TWBPw4KzR/vwr7roL2LSp4ns0M36g1PsCEMdoOnKEdA0Ucj3MTFgxlljZAkfOZbdmzA8kHAAAIABJREFUZmnl0cGo5kJSKQ2xhFr3l5oyXgbXlBlyBeRF2RfCoeAdJaqoRYmVQNaRi8yG0j8oEHIdcFFjCRWyorZkGHguWzcIN+b5c18NHD8Oz+7ft2yGnE7Z0soOxOAHIwo2jQ1gbNBd3Cc3Pg4AePI1VyFqc0Latq30g3TJDDkdr9OGhKohrg+fNqmQS6U0RBNqJgikEG9ayEW8/uIr2yzkZsNxPHpgFldsLEirPHFChJy85S0V79+X6ZGr4MhRyJEexch5Xy3ndmbAeIY06TqmF+PVEyuBlpVWxkqVbhQIuTsePoj79hzHj/5sa9PXUI1r79iJJw7Vf0Kx3OfEwx+/FC57hX6WNEeDEWxcVeIkQKeOeX5+tx0Tc7HSVwYCLK3sIBElCY+9+ONVF3KxmfTfnS7k9JKzDojv2XQIR9Vh4AcPiv4ZXXTWyFmjfvicNtw99kqca7Hg7GcextwfvKb6HffvF89ZR7Kuz2VHNKEioaZgt+bsW7b5/RFVVMQSKQx6HNi6PoB79xxHKqVlHfqJCcBqxcTACjx58rl4zbZtwC23iCqGXHQh1yWllb70OIvFWFJ8TppUyMWSKjQtG81fSN/iHABA7usvvrLNQu7eF45DTWnFQ8B//nNReluhrBLIjkShI0eWIm/9j9/i2fHKpcPf/tMLcMlpy9u0osahkOthZsJxnLbSV/4GRueW1YHHYcWxhRJuUYGQe/xQEM+OzzX9+atxYDqMJw4FceWmEZw1WkFgleFoKIIfPH4EzxwJ4VUbKp/YhuNJhCKJ6o6cJIkZXQbpd9uxZ6rMyQMduY4STaRKOnJuuxV2q4REsIwj1wkhl063rToMXB89UG1EQRmsFgkXrhvCA9My/nbzFlzy0uN42ugw8DrKKoHsYG05nsRAbq/w0JDoUVTVsqEQzSSYDjoZ6rNjuS+A/356HHuPLeJMvTRuYgJYuRLhJPD4povxmh9/UYRWvOIV+Q+kDwPvEkdOF3LheFJUh+hzAE0m5PQo/nKOnGthDvPOPkS1En/7bRZyv9k/g+U+J84cKVFWuWYNcM45Fe/vsFlgt0rle+T8/uzfGSE9RFRR8ez4PC45bRk2rysxSiTN+mFvG1fVOBRyPYqmaZgJG3Dkli0Tu+xNxlWqRw4QB725rHAbD0UQS6SKd8xbzPZd4kB1wxVnYqXfVfP9F2IJ/PCJI9h5YLaqkJvIzMuqkli5ahXgqOKK5DDgdlQurdRL4UjbiSpJuEs4tZIkwe92IBFMnyiZQcjJusgw4MjVKah0tm4I4P69J3Bw86U467HPYS4WBLC28p327QMuvbSu58t1hIqEnKaJz6JSs8GaTCj9Gg94HBln/rGDs/lCbtUqhONJ7D7nYuC/vwRs21Ys5LrMkfM6hQOdKW91u8WXyYRcubmPOq6FOYTc/YiVaxcA2iLkNE3DzoNBXHRyIL+scnERuO8+4C/+wtBGi8dhq+zIMeyE9CAvB2UAwNvOHcPV53THZpgR2CPXo8xHE0ioWuUeuRaNHgDSpZVVeuQ0TctEkMvlDiotYvuuKVywdrAuEQcA/S47Nq7yl54JVUB2XlYF9+HQoZpLXP1uO8LxJBJqiYQ7llZ2lEil4cJuG1L6ZoZeLtjfLxJdO+LICZFRcfyAptU9DDyXLeuFaPrucuEanPz4Q5XvEI2K/rE6+uOAfCGXR5vTE4M5Ynl0wI01AQ92Hsx5f6aFnBxPIrlsObB1K/A//1P8QJOT4m/GbaC30ATojuhiLGfDqQP9idXQ+7nLvWed83OYc/vKb04CtQm5hx4CHn+8xlWKSpKZcBxb1xdsPvzqV2KOXZX+OJ0+h5U9cmTJcXhGCLl1gb4Or6S5UMj1KIZmyLVo9AAgDoglSzdyUivno4lMNHXJiPAWse/4Il48vogrNja2q71lQwC/OxoqHrNQgD5DrqIjd/hwzaLa7xYnSQvlZsnNzYlBtaTtlAs7AdJ9crqQ0x05SepYOWxQFp8VFR25EydEymaDnxdnjPSj32XDD+fdODg4iuEH76l8hwMHxGWdQi7jCBVuFOm9Wm3a7NBnyOmjYLasC+Dxg7PZpvocR87jsAFXXw0884wYSZJLF82QA3KEdOEsOZNtMmUcuTI9cva5EELNFHIf+Qjw9rfXPET8sQPiddtSKOTuukt8flx8saHH8Thtmf9zEfoxOlVmBAYhXcqhGXEutnbYQEl/F0Eh16NMp/teyjpyqRTw8sstdeRKCpwcR+5oMJr5cTuF3PbdU5Ak4M2NCrn1ASRUDU+/HKp4u/FQFG67tXyYhKII16HGk2S9VKziUHCT7XwvFSo5cgMeByR9x9uf05/ZISE3G1bgsFky7klJGhw9oGO1SNi8PoAUJDx6xlZYH3qw8glwA6MHgFxHruA90mZHLlRQvrp1QwALsST2TC0A4bAoZVu1CrKShNdpBa65Rtzx5z/Pf6Cpqa4UcnnJoSZ05Kr1yNnmggi5+0sf0+x2MSO1FiF37JgQ79/6Vk3r3HkwiBG/C2sCOSeiigJs3y7Ev8F+T4/Dmvk/F9HfLxx4Wa5pbYSYncMzMoa9juqzS7sMCrkeRXfklpXrkZuaEgeAFjlyLrsV8WQKqcIYV13IaVrGqQJK7Ji3CE3T8ItdU7hw7RCW99dXVqlzwdohWC1SfolUCY6mZ8gVDofO3uCoOHDW7MjlDJcuhEPBO0o0oZbskQPE781iIiE3E1YQ6HOU//sEGh49kIteFvbCBZeIz6B7Krhy+/eLy3oduZywjTzanJ4YjCQgSdn3rD6KYefB2cwMOYyNQY6r6HPagNNOE//nwvLKLhoGDiBzwmT+0srKPXKWkBByJZOYgdrG6qhq9n1+883iPWAA0R83i63rC/rjHnxQlEIaLKsE0nNey1WS6J9JLK8kPcahWRlre6ysEqCQ61lm0n0vZR25OuLua0F3I4pKUXw+4QZGo5n+OKDEjnmLeOl4GPtPhHFlYXRzHXidNmwa8+OxKkJOzJCrEnQC1HyS3F9JyHV4wPRSR5RWlj4p9LvtsIcXRZ+TPWdnsIOllYZnyDXh80IvC1s870KRYljoOuWyb58IZPLXniwLAD5nlR65dpVWygoG3HZY0+MGVvS7sH64T5TK6UIuXVrpddpEqe0112RP0gGx2dNljlyfUxwH8oR0B4axV6Nij5yiwLK4WL60EqhNyM3Oit/lFVcAR44Ad95p6G4vHQ9jVlawZUNBWeXPfgb09QFveIOx54eYJSdXKq0EGHhCeo7DMzLWDlPIkS5hejEOu1XK7AAX0cJh4EB2Vk1JIQcACws42gFHbvuuSVgk4PKzm7OrvWV9AM8enSsOa9GyTqThYeDNdOQo5DpGUk1BUVNlSyv73XY45UVoheKkU6WVsoJAn4HRAyMjgKfx3oLTV/qwYVkfXrFuGfDmN4uyMLXMCXIDoweAXEeo4P3p9wux1DZHTsFgQWn15vUBPHEoCDXdB5dYOQIlmRKOHCBK5RIJEWQBiLUqSlcJOafNCq/TlqkQAZB15DTzDN3Vgz88zhLv2fTfyJy7H1GlTN9YLULuxAlx+Sd/Apx/PvBP/wQkqx//9MqPvKCTVEq4tpdfXlMAjuiRq1BaCdCRIz2FHE/ixGIc6yjkSLegjx4oWy6lu0AtmCEHIDMku6h8I6cxfDwUzTiGC23okdPLKresD1RO8wREf8BnPwt86UvAL38p+glLnHhsXR9AMqXhd8+9LErEPvMZ4LLLhNNw3XVYiCUwH01g9VAVR85qBcbGavr/DHhYWmlG9JCfsj1ybjt88QhS/QVzoIaHxUljm0MGZtOllRU5eLBpmz4Wi4T7/vq1+MDF64CrrhLidefO0jfev7/uskoAcNktsFokhOMF7xGrVaQ/ttGRG/Lkv8ZbNwSwGE/ixB7hdsrDKwAgK+Re9SrxPt62TXyvjx7oIiEHABuWe7HvRDj7g6EhEfIRjZa/U5vRo/j7Srno6b+Rxb7+5jhyupBbsQL4x38U760f/KDq3R47MItVA+789OPHHxcubZUh4IV47NbySdEUcqQHOTwrej57sbSSc+R6lOnFeGWxcviw2GF3NdYnVo6KpZVAWshFcPpKH6YX4/nN8C1iz9QiDs7I+MCrq5yQJpPAH/2RcApy8XqBM88EzjpLfA0MYOtjO7Fj27049QtHhNCTJGDjRjET7sc/xvgNnweA6o7c6tUifr4GMo5cpIKQoyPXdvSxG5VSK/vjMpIBP/JuEQgIZ2p+Pjs4uQ3MGimtPHQIePWrm/acmQ2myy8Xf/fbtgEXXZR/o0ikodED+vP4XLbSny9tLPELykrR+JEt60Wf3uxLhzDi9yNsE5/XXt0VslqBK68Ujksi0XUz5HROXe7Fgy9OZ3+Q25/YBIe3GeiOXMm+1rSQi/gGKvfIGd0U0IXc8uXAa18rZgV+9rPAu99dNqwkldKw89As3nDGivwrfvYz8f654gpjz53G46zQI0chR3qQwz2aWAnQketZDA0Db1F/HJA9IBYdLNIHCW1hAUeDUZyy3AdbqR3zFrB99ySsFgmXn7Wy/I00TQxV3b4d+OpXhRD69a/Fv9/3PtGLsH078LGPAR/8IOw/+W/Iy0bwX29+P3DvvSJW/tlngRtvBBYWEH74twAMDAOvw+2wWy3wOKylHTmPR3xRyLWdSKWTQmSFnNLny7+iA+WwESWJWCKFoUqllYoiAnlaUYbt9wOXXJJ1nXLRRw80OITc67SVTsVtYwx+KFLsyC33ubBhWR9iLx8FVq3K/N305aaHXnON+Ex55BHhvABd58idusKHmXA8k9zZ7qAZI0TiSbjtVlgsJSpY0n8jUZ+/vPipx5Fbvlxs/H3yk6KE+Ec/KnuXF48vYi6SyB87oGli7MCll2bnURpE75HTSpW36iXf7JEjPUQvO3IUcj3K9GK8fGIl0NIZckCOkCvjyC1OhxBNqFg95IbXVeZEq4lomobtu6bwqg0BBCq9LjfeCHz968A//APw538udu1f8xrx71tvBR54ADh+HJieBl56CQgG8cC/fBuf3PQ2hF99SXY383WvAywW2O+/DwCwupojV6eo9rvtmCsl5AAOBe8Q1YYL+z2itDLm8eZf0QEhpw8Dr+jIHTkiyj0bHD1QlquuAvbuzY4a0NG/b8CRA9JCrlQZWZscOU3TEJITRT1ygCivdB6bRGp0NNMnnCfk/uAPRLT9tm1d68idskL8nb90PC10zCjkEmommKWI9Gdo3D/YvNJKiyX7OrzlLcDZZwtXrkxZtT4/bmtu0MmePeI9UmNZJSAcuZQGxJMlno+OHOlBDs3IWO5z5n++9ggUcj1IKqVhVlYw7CtzcpZMipOzVjpyjso9cqHj4sA0NuiB11mm9KmJPD+5gMOzkcpDwL/+deDTnwbe+17gppsqP+DwsDjBtFiwdUMAakrDk4dzTkwGB4ELL8Syx36NPoc1089WRDQqdtrrFNV+t720I6evkY5c24kmxN9yudRK0SMnI+I2gZBLuyQVe+TqTFU1zFVXicvC9Ep99ECDjly/y146FbdNMfiyokJRUxgs8Rmwdf0wli3MIDS4PNOzlDfPT08j/J//EUJucLBl5fCt4tQV4jP/Jb1PzoxCTh/EXoq0kFOqCTmjwufECZHEakmfflkswA03CGH205+WvMtjB2dx0pAHqwZyKjvuuktcXn21sefNwZPeaC3ZJ5cTSEZIr9CriZUAhVxPEoooUFNaeUduYkL04rTSkavSIzefEXJu+Fz28mEnl10G/Mu/NLyeX+yagtUi4bJyZZXbtwvX7bLLgDvuECUvBjn3pEE4rBbsPFDgfl12GUb3PYczHMnyoTNHjojLBhw5CjlzUdWRS4edhF0FB5WOOHIiTbCiS62PHmiVI7dunXAkCoVcg6MHdLwuW+lU3DY51npJYSlHbvNJ/Vgmz+GQayBzUl0UuHHNNcK1v+eeriurBIARvws+pw37Ch05E1ULyIpa9v2K2VnA5YLF21e5Ry4cNpbEeeKEKKvM5R3vELMDS7hyqZSGJw4F89MqAdEft3mz6MeuEU96s6BkcqXVKjYQKORID3F4Vsa6HiyrBCjkehJ9htxwh2bIAYDHLg4U5Rw5eWYOQFrIOW2le+RUFbj/fuCJJxpai6Zp2L57EhedPFzyZApPPAG8853AOecAP/nJ/2fvzaPcOO8r0VvYgcbWG5vd3JduUpRIcZEo0rJ2ilRi2c54ix3HS97ESexkXpzEOSfLezOZJD4Tx5nIiZ8dJbEnseJo7DgT2zqSLVHUbq2kFi5amqTYTXazV6CBxr7X++NXH1BAfVUAGju67jk8ILubQDWWqu9+9/7uLe72qgB2ixF7N3iVfXJHj8Ig5nDX3Dn1/1yj2uGxmxHSrZVthXiZGTm3CbBnkojYSuy2rbRWllPkLJbGkogPfIDmwOQqzYULNdsqAagr/n19NAdUQfR7LViSiFzpjBwADESDMIo5nBWdiCSz+eMtwr330u2FCx1J5ARBwOiQE+NzbWytTGW0iVx/P+xmjYAQl4tIXDRa/sF4RM5oJFXuzBnFvOhbsyEsx9M4tK2v8MWpKeDUqapKwOVgmwWaFQQ6kdPRJQgn0vBFUroip6NzwDp7VBW5BnfIAYDNQm+tWOkOppPsZPGlALwOM1w2M6XK8XbM5+aIzNV4wT8zvYyppTi/BPziRUr8GhoiVc7pVP5MBTi0rR/nri4jJLNwiTfeiLDVgYMXXlX/jzW+Fh67GUFeaiWgK3ItAlOh1VIrbTGymC1bSohcTw/NQ7XCWqk1I3fpEtWUqCTq1QXvfz991n/608LXaqweYHCpzeAyQhEI1PwYWgjE1BU5Vgb+SsqOoPRzilmt4WFSXtjfOxBjQ65CBYHDQRsDbUXksuqzMz5fgchpKXJAZXNyPCIHAB//OLBtG9n6ZcpeoT9uoPCzP/oR3a5gPg4o9OWploJ7PHrYiY6uwWU/JVZu6cLESkAncl0Jn2SX0lTkBIEi7xsEpkYkSnf8DAbA6UQqEMonOaqGnUhFubVe8B85OwuzUcCxXSW2yvl5slKKIpXurtVIsyyDw1v7kROBkxOFYw2lgec3XY+x0y+oW26Y2rHCBZrXUcZaGQw2XHHQUYxy1kq2QAqYS3YHBaHp5NsfScJuNqrPBwH0Hm2UrZLh4EFa3DJ7ZSxGJKceipxNI+wEaDihYESuT4PIXbH35gMtuISCzUF1oCIHAKNDLixFU3RtEoSmVj9UgliyjLWyvx82S4OJnMlEIVuvvQb85Cf5L790yY8tAz1Y65HNRv7oR8A115AdcwXIK3JJXZHT0f2Y8EmJlboip6NTkFfktIjc+vVEIBoERuS41g2XC9lQKJ/kqGp9qgORY2mV790+AE9p2MCv/AoFjTz8MDA2tuLHAIB9G72wmAz5xRgATAVieG7zPrjmZyjhkofJSVI7DCv7KHrsZsTTWaR46WNNWqjqKEaeyJlVyFGQbMV+Iye0oslEbima4hMMOepYBq4Kg4FUuZ/+lOoO6hR0AlDYSSqTQzJTci5q0qzWUpQ2WnjWSkbk5l39+NlFH0wGAVYT51zALHQbNzbqMBuKMV5yZRudl6KpDL8MHCiyVibT/FTJiolcPE4/wyNyAPDLv0wjD5Iql82JeHliKd85mD+eZ55Zsa0SKGwyxdQUOZ3I6egiTEpEblOfTuR0dAgWI0lYTAa41KwiDa4eAACT0QCL0cDdwRSlqGamyLlsZv6OOSNyNVif3pgK4mowjnv3cHayX34Z+PSngUOHVnz/DDazEfs3Fs/JTQdieGbLfvrH8eP8/1hjn1++FJynyrVg5koH8oEIatZKpsj5eESuyXONvmgKA1q2yuVlWnA3WpEDiMiFQjQrx4hcnWbkACg3i5qlyEVTMBqomFyB6WnAbMbglvVIZnLosZr4wUi7dgFPP03nqw4ES668MC9LrmwjIhdLZfN2QwXkM3K1KnKLUjG6GpEzm4E//EO6Nm3bhuzmzXj0rz+F//obR8kxMjhI1+5sdsW2SkBO5HRFTkf3Y8IfxbDHpn5N7nB0X6GCDvikDjnVpMSJCeo5azBsZgM35SvT44R9OYb1kiLnspnyO+ZWk+yDxohcMEgXrhXM6Dx8ZhYWowFHdg0VfyMWo4XEpk1V36caDm8dwNeeOI/lWBoehxlTS3FMe9ciu207jMePA//lvyj/0+RkTRdkd57IpZQK7GoicvE4LbjWr2/1kSCWysBoEGA2qnz+JCK3YFBR5E6fbuDRFWMpWqZvstHVA3IcOULR+g89VLAQ1kGRyxO5ZKY4nbNZilwshV6HmV82ffUqMDKCQ6ODeHshih6thcZttzXuIBuMNS4r3DZTsSLH3lttAAo74SyHRJGuE/39sFvKhJ0A5YmcvAxcDZ/9LHD2LLC0hElfDKdnIrh333rAbqFroNFIYxE33FD291IDs++qzsjpRE5HF2HSF+3KInCGuhA5QRDuAfA3AIwAviWK4l+UfN8K4AEABwD4AfyiKIqT9XhsHUosRpLq83GpFC0emrAwc1hMXOtGwuqAM7WEDX3SjJxsx9zq5BA5gMhcf0n8chnkciJ+cnYWt44N5JWrPKan6baOC/9DW/sgngBenvDj6LVrMR2IwWUzwXjsKPCd79BzL7ezRiK0Q1uDIueV7FpcRY49X6shufJP/xS47z6aLWnCJoUWYqksHGaj+kaKROTmBc5ntOkzcinsXOtW/4FGVw/I0dMD3HUXzcndeSctdt0ax1YhmBKmmMNtoiLXy7NVAnQuXrcOh7b245+en+zKslqAkivHhlzFityrGiFQTUQ2JyKRzvFn5JaXaROxvx82SZETRVH52a4nkbNYgK9/HQDwP/7pFVxeiuGjv3d7Zb9MhcgrcmozcnrYiY4uwqQ/pl491QWo2VopCIIRwDcA/ByAXQA+IQjCrpIf+88AAqIobgdwH4Cv1Pq4OtSxGNbYZb9yhXYZG1g9wGC3GBHnzBRErXY4U/EiRQ7gLLQY2QJWtNh6fSqA2eUE31bJ7ruOgS97N3phNRny9srpgPQ7Hj1KsdQvvFD8Hy5fptsaSLVurZRw+jSQTFIoxIsvtvRQ4qmstoVDWiBdzXEW9wMD9F7Pqiyw6ghRFOGPpCorA28GkQPo9ZuYIEJeB1slQGEnAOf84nbTbF6DidxSNMVPrAQKRG5LPwRBJeikSzA65ML5hTBEUWwrayXbbOTOyLFNMMlaCQBJ3jxyPYmchEw2h5OTAWV/XB3gqKR+IBxWdNrp0NFpWI6nsRRNdW1iJVCfGbmDAC6KonhJFMUUgO8B+GDJz3wQwHekv/87gLsE1e1qHbXCF0mqB500oXqAwabSu7NstqMnFS+kVsqsT0WYmipc8FZw0X/qnUWYDALuuoZz0WRqXx0VOavJiBs2F9LnpgIxbOi1A3fcQYlkpXNydejz0yRyTHFYDURufJye5+Fh4Od+DnjjjZYdSkyrXBgoWCsFK9LZkoXSwABttDQ4Eh+gz1sqmytfPeD10p9mgHWmzc7Wjci5rPQZUZxfDAagt7fhinUgluIHnYhinsh5HGbs3eDFkFvD5trh2DHkRDCWpjCuvj6ytycSrT6sQjgRb0auiMjRcolrr2wAkTs3E0IkmcHhbfUnckYpVEcz7KTSXjwdOtoYLOikm62V9SBy6wDIPHCYlr7G/RlRFDMAlgFwz06CIPyaIAinBEE4tcgGg3VUjGxOxFI0hUG1xVkTysAZHBYj4mnlhSJgsMKVjud3BV02WmgV7Zin07SY27NH+k/VL2xngnEMuW35+y8CU+TWlb5Va8Phrf14Zy6MpWiqoMi53cDhw0oiVwdSzYgct0vO4QDs9u63ViaT9Fzecgtw4gQ930ePAu+805LDiaezsGvF+S8vI213IGM0Kcvcm6iiFsrAy8zINUuNA2g2js3+1GE+DpAr/iqbHQ1X5NLo7eGcg0IhWihL56B//PQN+IsP7WnosbQSLPDk/HykrUrBoxLB526+yImc9H1u4AnrH62EyNntZCMuA7YheKgBihxA6q/mjBygz8np6HhM+onIbenS6gGgDVMrRVH8B1EUbxBF8YbBwcFWH07HwR9NIidqVA9MTpI6VGcCw4NdRZHzCxb0pOL5f3MXWjMztCN4/fX07xVc8OfDCfUd7qkpWjTb7VXfrxbYRffRc3OIpbJ51RFHj1I/kHxzYmKCHr+C3Vk1uKXnTrNLrtsVuYsXyQK0YweF15w4QWrLkSMtCVSIp7L53XsugkFknLSoVbxuzSRyUhl4XzlFrhlBJ3KwzrQ6WysVihxAhKKBGx2iKCIQU5mRK5nTHXBa1S2YXYDRPJELtxWRK/Q+algrBwZgM2sQOYOByFklRG7NGurSK4MXL/kxusaJAa0wohrgsBi1e+QAncjp6HhM+KJUm9ynWyu1cBWAfNBovfQ17s8IgmAC4AGFnuioM3xhWpypnvwnJmguzNT4WQwaDld67OdFC6zpFKluULFWMutjLUQulMQaFycZEKBFVAMSDves98JuNuIHr9Lx508eR48SMT1xovDDk5OkjNbgMjYZqWZiVRM51tHHugDHxoDHHyfr1l135bu6mgXVBDyG5WXk3B4AQLClihz1TQ6oKXK5XPMVOYC6tN7zHuC9763L3anO4AINV+RCiQyyOVGzDLwZm2rtgAGnBb0OMy4stCeRq3RGTjO5slIiVwbpbA6nJpcaYqtkcFiM6jNyHjo/6YEnOjodk74oRjz2/EZMN6IeRO4kgFFBELYIgmAB8HEAD5X8zEMAPiP9/SMAnhRFUazDY+sowWKkTBl4EzrkGBwWI+Il1o1cTsRsTrIZSRc9F2/HnBE5Zq1cwQV/IVRGkatj0AmDxWTADZt78foVKn3OK3IHDtDiRW6vrLFDjsFtN6sTuSb3krUE4+N0Ky91370bePRRUkCPHClWQhuMWAVhJ6InizWEAAAgAElEQVS0491KRW6pnCI3O0tJq81W5LZsAZ5/vlBBUCOsJiMsRgOfyDVYkQvG6DnmKnKrjMgJgkCBJ/ORpiWGVgJmL1SdkTMYAK83/5nmVeoAqCuROzO9jFgq25CgEwaHRbdW6uh+TPhjXW2rBOpA5KSZt98C8BiAtwH8myiKbwqC8KeCIEgeGXwbQL8gCBcB/C6AP6j1cXXw4QtLu+xailwT5uMAcAtUFyNJLJsklUy66HFT5RiR27KFLpBVXvDjqSxCiQzWuJuryAHFMw15Imc0EqE4fpyUOaBupNpjN2OZNyMHrA5FbnycQk5Ko+oPHgQefpie56NHqcKiCYiny4edCFJ4iGJGrokBNcxaqZpa2czqgQbDaTMhkmz+jFyeLGspcnUirJ2AsSEnzs+HIfb20hfagMgxe6HqjFxvL2AwFBS5JhC5l6Tk45saSOR6rBqKnE7kdHQJJn1RbK40sTKdbuqmb71Qlxk5URR/IorimCiK20RR/LL0tf8qiuJD0t8Toih+VBTF7aIoHhRF8VI9HleHEpqKXDwOzM01bYedV6A6tRRDxCp9qKSLHnfHfHqaLiZuN+2aVxl2shCmNLQhHpFjZeANUOQA5O0wXoe5OGjl6FGa/XvrLbKsBAJ1IdVeh4YitxqI3PnzxWqcHLfdBvzwh8CbbwJf+lJTDodm5LSJnKmXiJzidWtiQI0vkkSPxahuOWl29UAD4bKZ1BW5cDhv8643AkyRUyNy/f1Ugr5KMDbkQjiRwYJJ2iFvByJXrn5A2lyx1WqtFMWqiNzOtS7+BkCd4LCY8kEvCuhETkcXIBBNYTmerjyx8vRp+nw+VGoqbG90b2nNKoUvnITdbOT3EbHesmYpchalIjcdiCNqkVQq2UWPFlqyxZTc+riCzqH5EBFarrWyAWXgcuxe50GPxVhQ4xjuvptujx8vlFbXSZG7uBDhf7O/n5SoTKYpc5E1we+nwADOwjaazOArj76DKGc4/0/PvoUzB+/CuWcv4XO3ckjHPfcAH/4wqXO5HFmlGohKeuTMfUTkuGmjTSLfS9EU+rWCFC5dovnNjRsbfiyNhtNqQkSNyAF0fhkaqvvjLkXp9eXWD0jVA6sJo2so8OSdqIghk6lNiFwZRU4icpqplQAROenacmE+jH987hLk7SK2aAhfTqfx45kMnv2305rH9MrEEj5xsLGfux6tGTmdyOnoAkz4q6weOC19LneVVmG3N9p8ZaejWiy2SYccQNbKdFZEOpuD2UiL5+lADBEOkSPrU4m1khGtFRE5DUWO2TYbpMiZjQZ89ubNcJfWHmzcCOzcCTz2WOE1qAOp9mjNyLGZq6WlmtIxm4KDB4GPfAT4ylcU33rhXT8eePEyhtxWmGREzB1dRk84iBdM/fj6T97GRw6s56sfx44B3/secPZsIUCnARBFEbEKrJWG3l44LEb1IvcmELnZoMYMKUBEbv16wNr53WZOqwlhnvogn9VqAJELRJkix6kfuHq1YZtJ7YqxIYrpv7AQwW1tUgrO5sS4m59+f55sM0Wukhm5/3j9Kn7w6jRGPIXNvA2LdN15I2nOWyfVMOyx4QN7G2u5tVtM5YmcHnaio4OR75CrdEbu9GmqEukwF4pO5LoMvkgSA23QIQegaKaAEbmppTgsvVIiVokiFymdkdu3j/7e10cL8CqQJ3K81MoGK3IA8PvHdvK/cfQo8A//QJY/oG6KnCL9kEEentHORC6dJuLws59xv81OyI/+9q3FRO2FF4A/Bz7wsTvw9fPAyxN+3HPdsPIOmBr62GMNJXKpbA7ZnKieWplMUgmyxwOPQYWAN4nITfijuH1Mo+JlYqL5QScNgstmxtVgXPkNpsg1yMq6FEvBbBTyybxFmJ6mEKRVhH6nFQNOS6GCoA2IXCyZhSAAVhNHqff784Fb1aRWzocSGPHY8fwf3Fn4/s9+Bvw18N/+8x34b0fv5N9HE0GKnIq10mgkd4SuyOnoYEz6ojAIwMZKqwdOn6agtAa7duqNzjpaHWWxGNZQ5CYmAIuFgiGagHzKl+zCNx2MwT0oDbrLLhJOq2yGJZmkWQKmmPX2Vn3BXwgnYTUZ4LZzFlBMkWvFbvixY7SQ/+53aeeHLSRrgMdhRiqT4+8UM8Wh3ZMr2fGdPk32xxJM+KPw2M1KtU2qHth0eB/sZiNeuqTyPlm3DrjuOmUpe52RSNGxq87IsR1uj0ddSW0CkYskM1gMJ7V3Ki9d6ridSTW4tMJOgIYRikCUOuSE0oqRVIrOcavMWgmQvTJfCt4ORC6VRY/FpHyNgGJrZX5jUnl+AlBE5BZCSawpVbsXFui2TTbUHFZS5HI5lQBxt1sncjo6GhP+GNb12mHhbdKUQhRp/dHAjd5GQSdyXQZfJKWeWDk5SYXJTdpt4KV8TQfi6B2SFk9ya6XVXLA+McVMPiMXCBTSHivAfCiBNW4r/+I8PU2L5VaEDNx2G2A2U+DJli01dcgxeOxk21IlBUD7B56wRU40Crz7ruLblDzFIR3j44DZDMvoNtywuRcvvqtBWI8eBZ57jh6jQYil6T2sOiNXSuRaNCPHFE7VWOZwmIJ5tm1r6HE0CwrFn6HRilw0xQ+smJ2l21VI5MaGnLi4EIHYNkQuw7dCJxJ0rpCIHFPsNGfk4nEgk8F8KKF0g7QZkespN/OnEzkdHY5JX7Ty+bjLl+n6rBM5Ha1EOpvDUjSlrcg10SrFLo7Mh5/NiZgJxjE4LJELGZFzy3fMS2fY+vrIelfFAnwhlOTbKtn9N2g+rix6egpFx3WyuHYFkZNH/r7xhuLbk74otvRz7BHj40Q2TCYc2tqP8flwvuhagWPHSAl59tk6HbQSmsEJQOWKXDDYsCRFAJgsNwT+2mt0u39/w46hmWCKv6K+tNGKXCwFr0NlPg5YlURudMiFSDKDuNPTFk6BaCqrPh8H5N8jBoMAm9mgPSMHAJEIETk1RY6dk1uM0uuzAm63PiOno2MhiiKtG6qZjwN0IqejtWCdRZqKXJPm4wDAVrLjNx9KIJ0VMTzkJYtnSdhJ3lrJU+SAqhZb8+EEP+iE3X8rQwaOHqXbOpFqr512/LkJiJ1irZQTuddfL/pWIp3FzHKCr8idPw/s2AGg0N+naq+85RZSYR97rC6HzAObn6nZWgk0VK0oDIGrzA688grd3nhjw46hmXDaTMjkRCQzJbY4l4vmgRr0XKsqcquYyI0NEeHxW3vaQ5FLqihyJUQOkLpRtWbkACT8QX6H6cICjQlYGlcpUA3YHK/qnJzHoytyOjoW/mgK4WSmusRKQaAZuQ6DTuS6CIthjQ65SIRUmSYqcmwxy2bkpgMUNrChz67o3GHx4KIo8hU5oKqLPndGgaGVihxQIHJ1mj/SVORYL1m7K3Jst3pkRKHIXfbHAHBsgNkscPFivkNuz3oPHBajeiKc3Q7cemtD5+QKipxK2ImMyKn2/zVBRZ3wxTDktqof58mTtOkzqBGG0kFgfY6hRMnzLQh0fmnQRkcglkavWvUAsEqJHCVXzhkddF1KpVp6PLGUSsqsCpErp8gtzdPnVrGRWGGHXLPQY6XfmVfpAkC3VuroaJQdHyjF6dPk7nE6G3hUjYFO5LoImmXgTa4eAJTWjaklWpCv73UoiJzLZkYmJyKRzhHR6usjEgLQLiZQMZGLJDOIJDN8RS4apXm7Vipy+/YBDz4IfPrTdbk7TSIHdEYp+OIizW7eeaeCyE34VGyAly9TMI6kyJmNBty4uQ8vakV7Hz0KvP12YbOgzmDqs92icmplRM7rhcduRjydRTJTspBqApGb9JeZHTh5smvUOABwSdY57pxcf39DlKFcTkQwpqLITU+TOlyHsKNOg9dhwRqXFVcgRfMHAi09HpqRK2+tBMhlojkjByA4T+8lrrWyjYhcWUVOJ3I6OhgTK6ke6EBbJaATua5CXpHjWSubXD0AKMNOpgNxCAIw4rUpFTkbXVTCyXRxhxxQWOxUeMFfyHfIcZ4HthPeSiInCMAnPlEgqDWiLJHr729/a+XCAh3ngQMUBDE/n/9Wfp6r9IQ8Pk63EpEDyF55cSGChXCC/zjHjtFtg1S5uLQosptVlK5gkG4layXAed2aYIfVnB1YXKSNny4iciz+P8LrkmuQIhdKpJEToa7IrVtXl7CjTsTYkAuXctLz0mJ7Jc3I1U+RCy0yItfeilxFM3I6kdPRoZj0R2E0CFjfay//w+EwhazpRE5Hq+GTFDnujFwLFDmbgsjFMOSywWoy0kVPdpFw22Q75qXWxyqtlfMheh64YScNLgNvBVw2EwQBWI6pWJQ6RZFbswbYu5f+LVPlJn1R9PVY8sQnD6l6gFkrAeDwNlp0vaw2J3fttWTfbBCRqzjsxO2GW/p9QqVErsGKXCiRhj+awiY1Re7UKbo9eLAhj98KuNhGURMVOTazrDojtwptlQyjQ068k5Q+zy0mcjQjV5kiZzeXV+QiPtqs4aZWthWRq1CR49TB6NDR7pj0xbCh157vMNYE6ynWiZyOVmMxnITTauJHn09MkFWxiTMvbDHLhsOnArHC7ojbrZiRA6SFVo1EjqkximFzoCll4M2GwSDAbVOZtwI6g8gtLNB7k51IZURuwhfFZrXESq+36D193YgbTqtJ3V4pCGSvfPxxmrGrMyoick4nYDTCKyk1qopcg16zwuyARtCJIHRNYiUgU/zVKggaoMgFpI0VRfchsOqJ3NiQCwtmaSOhDRQ51Rk5h6OopsZuKR92EvcHlR2mmQzdXxsRubIzch4PVf40sK5Fh45GYUKtsogHlljJNpI7DDqR6yL4IhrVAyyxsolWHrtFaa3MEzlO2AkAxIIhurDLiZzdDlitVShyjMhxnotWloE3EKoJiEBnWCuZItfbS+9TuSLn1+iQ27Gj6D1tMhpwcEsfXirXJxcIAK++WsdfgJDIz8hpEDmPB0DBEqtIG7XZiOw1iMiVnR04eRK45ppCnHoXwC2FnXCtlQ1T5Oh17Su1VoqiTuSGnAjapfdXi4lcPJVVV+RkahxALhPNQnAAyUBQ2WHKPsttROQqUuQA3V6po+MgimL5OXA5Tp+mTeEOdWrpRK6LsBhOYMCpEm08MdHU+TgAsJkKilwmm8PscgIb+iQVgBN2AgDpy1foC/IPlCDQAr9SRS6UhN1szAccFKGVZeANhCaRGxgg4pJRuWC3AxYXC8ra3r35CoJYKoP5UBJbeCdkWfWAHIe29uGSL5on9ArcfTe9pxpQQxCrpH6ghMipJlc2TJGj0KFNfZznVBS7LugEkCv+nOe6r49Uh6RK/+AKEYgyRa7EEry0RI+1ionc9jUuLNukdLgWErlUJodUNpcvxy6C36/ofKtkRi4dDKmXgQ8N1XrIdQNT5DRn5ACdyOnoOCxGkoilstUlVl5/fcfOLOtErouwGE7yFblcrull4EChQDWezmJ2OYFsTlRV5NgMi3hFZYatr6/isJP5cBJDpTuiDK2uHmgQvA4zglpEDmj5zrcq0ml6bdlu9d69RNKi0TzpUKhH0SiRctl8HMPhrfT7qtYQDAyQbbABc3KxVBYWowEmNV9+OxA5fxTDHhtfNZyaokVntxE5m0Zq5QrqTSrBUkxlRq4dApdaDI/djJ6BPuQEQ+POS4kE8N//e9F1phTMJulQKwQvUeQ0e+TsdsBgQDYU4gedAG2lyLGN1qhO5HR0GVTXDTxkszQj16HzcYBO5LoKvkiKH3TyZ39GC8j3vKfpx8QufKxDbn1viSInigAKO+aGqyVl4Ax9fVVZK7nzcUDry8AbBHc5ayXQvvZKRljkipwoAmfP4rJfpQuGBZ1wFLldI264bCa8qGWvPHYMePHFQvhInRBPZdRtlQA9ntcLoBDw02wiRzOHGrZKoKuCTgCqprCZDQirWSuBuhOKQDQFq8mgVGfZnO4qVuQAYPuwG2GHq3HnpeeeA/7kT4Dvf1/1R6KSrZA7Izc3p1TktOoHBAFwuSCEw0pbfxsSOYNBgMNiRIz3mQAKRK7O50gdOhqN/Bx4JdbKd9+ljWGdyOloNZKZLJbjaWX1wA9+QBezz36WIu+bDLvZiFgqi6kA7ZBskBM52SA12zE3zagU5VZB5BZCCX6HHNC1ipzHblamHzI0oZesJpQucvbto9s33sBEFdUDDEaDgJu2VNAnl80CTz1Vy5EroFouzBAM5hU5k9EAl9WknJEDGkrkLqvNHAIUdGI2A3v2NOSxWwmn1awedgLUnVAsRalDTuEMWMVl4HLsGHJhydqDnL9BitzMDN2eOKH6I6rhRNPT5GIpUaZtWqmVAHJOFyyxWEcocgDNyakqctJ5SlfkdHQaJvxRmI0CVV2VAws60YmcjlbDHyEbz4DcWvnqq8BnPkNK3P33t8T/a7fQTMF0IA6DAKz1SB8sttsn2V7YjrltdoaUmdIZtgqJnCiKmA8lMcSzmLZDGXiDwGbkREnhLEK7E7nFRbplityGDTQT+frrmPRFMeC05hXbPM6fp/fz9u3cuzy0tR+X/THMBOP8xzx8mAJF6jwnF09nyytybIEEUlK5BLxBRG45lkYgllZPrDx5ki5oVpXQpA6Gy2ZSDzsB6q/IxVL5ZNIiXL1K793h4bo+XqdhbMiFoNWF5MJiYx6AEbknnlCN0GdBHz2lYSePP063d99d9GW72YhUJodsjnOeBZDp6UFPKsYvAzeZ8mp8u6DHasx3XyqgWyt1dCgmfVFs6HOojzjIcfo0YDRSNVGHQidyXQJFGfjsLPDBD9Li+Ic/bNnCjFlRpgMxrHXbYDFJbzmWiFcSeOJYmOErZhWGnYSTGcTTWb4iN61i2+wCeO1mpLMif3C93a2VbLeaETlBIHvlG29g0hfjk47xcWDjRppL4YD1yanOyVkswB131H1OLp7KqgedAAoipxpS099Pn42USjfgCpFXOHmWk1yONn+6bD6OwWUzqYedAA1S5MzKb1y9SsqMmfO9VYTRIScCdhdSCw3aYJqdpVufDzhzhvsjLHrfUVoIfvw4sHYtcN11RV+2W+j6pRZ4krL3wJmKq3fItVmYgt1s1GfkdHQdJnzRymyVABG5HTs6OgBPJ3JdgnwZuMsKxOPAL/wC2bgeeqildg6yVmYwvRTH+j7ZgpxH5KwmuBbn+ESLJcuVWdguaFUPdGGHHEPZ4Ayg/RU5+ft0717gzBlcWQjxSQerHlDBNWvd8NjN5efkLl0CLl5c4YEroWmtTCTo/Ssjcl6HCpFjr1mdyUWhQ04lBTQU6loi57Sa+GEnDVLkgrE0etUUuVVuqwSA0SEXVRAEGmitZK8tU9hKEMvPyMkUuVyO7Jgs3VYGtkmjZq+MWx3oScWVM9ptVgbO0GM1qdcPsGu0TuR0dBBEUcRlf6y6DrkOtlUCOpHrGhQUOQvwq79Ksy7f/W7L36B2iwnxdA7T8jJwgEvknDYTvP55dSIHlE2uXAjR87CmdEcUKHTIdaEip0nkHA5SrtqZyBmNpLoy7NsHJBLouTKhPCGLomr1AIOh0jk5oK6qXCydhZ3XSQUUQgNKFDlu2miDyPeELwpBQKEGRI4uDTphULVW9vSQOtaA1EpFYiVARK4LN5OqhdNqQsbjhXk52JgHmJ2lWc9rr1Wdk2MOhqL6gTfeoM9dia0SoBk5AKrJlVGLXSJyHGtlGxI5h8WoXghuMtG1Qw870dFBmA8lEU9nKyNyS0u0LtSJnI52AFPk1nzjPuDBB4Evf5lUuRbDbjYgHE9jLpQoBJ0AXCI3iCQc8Yg2kSuz2JoPkyKnmFEAujotTpPIAe1dCr6wQMTFIDsd7d0LALh24V2lejQ3R+8bTvWAHIe39WM6EMfUUoz/A9u3UyVHHefkEqksHFodckBl1soGEblJfxQjHnt+QVqEV14hUrNzZ10fs12gGnYiCHR+qePnI5PNYTmuoshNT3flOWglMA8OwBGLNKbjcmYGGBkBjhyhBMuEslcyr8jJZ3CZenfkiOLn2edGzVoZMtvhSseVHaZtSuR6LCb1OgWAzlW6IqejgzBRTWIls1zrRE5HO2AxnMQHJk/C/P/+P5RO+Yd/2OpDAkBWlCtLMeRE8BU52UViQ0RaSNVC5Jgix5uRm5riB6l0ATwOInLcBESgoSmINUNeBs6wcyeyZgt2zV9SWis1EivlKDsnJwikyj35JHXZ1QGxtEb9ACNyssADTzlrZb2JnC+qXpJ68iRw4ACpo10I1Rk5gDY66qjIUfAQp0MuHqfH0YkcAKBnLX3uM/VOrhRFUuSGh4mQxeNUN1ICpkYVKXKPPw7s3s0NoylnrQyarHCmE8qk0jYlcg6LMV/BwIXbrRM5HR2FyXzStUqglxwssVLaOO5U6ESuS2B+8xy+8sOv0HzLt7/dNkPVdosJGSnha71ckStJrQSAkZAGkWO2u7JELgGn1aRMOQS6tkMOKChymhUELSZyPzk7iz9/+C3lN3iLHLMZ/s2j2LUwoTwhV0jkxta40Oswa9srjx0DIhHuIm8liKc0UiuDkoWsRJFLZXLKXfEGEDlRFKlDjneBS6XIUtal83FAwVrJTXatsyIXkMrAe0uJHEtS1IkcAMCzbggAMDNxtb53HAgAySQpcrfdRpsTHHulYkYuFiP1jtmuS8A+22oq1pLBip5kiQMgGqU/7UjkrEZ+QBaDTuS6FuNzYXzhX19FMqPx+ncgJn1RWEwGjHj4QWhFOH2aPpdr1zb+wBoInch1ATLZHG75zteQtjmAH/9YNcmvFZAn+G3o056RWxuS0gtrnJHjBp0ApMh1OZFrZ2vlT8/N4dvPT2C5VDXkKXIAJtaPYffCJaVV8fx5eo+XeS0NBgGHtvbj5UtL/MU7ANx5Jy3y6jQnF1uBtRLgvG4spKGORC4QSyOUyPDDY86do4Vvl87HATSTlROhnuxaR0VuKUqvZ1+ptVLvkCuCc8tGAEDs1Tfqe8cssXJ4mK41hw5xiVw0lYXJIBTSlJ97jjY1OPNxgGxGTkWR88ECUzZDnyUGXphTm6DHYkJUrRAc0IlcF+O5C4v4ydm5vBWxWzAXSmCt2waDoQIxowuCTgCdyHUFXrzkx9b5SURvvrXtuolYgp/RIGCt3O7Y00OqoXxGLriAHASIvN+hYmtlQhn9zDA93ZVBJwAtUo0GQZ3ItYEix+xmL0+UEMqFBS6ROzu4Gb2x5YKKwTA+DoyOFs/UqeDwtn5cDcYxtaTSJ+fx0CKvDnNyoigintZIrayGyJnN9HN1fM0mtBIrWdBJFytyThupLtzAkzorcktRpsiVVAzoRK4IxttvxYxrAAPf/af63jE7Z4yM0O2RI8CpU4qNwHhpyuzjj1M1yS23cO/WrjEjJ4oi5iARd9l1rV3LwAFSIpMavXhwu/Wwky6FT+oenl1Wzo52MpaiKfQ7ObPJpchkgDff1ImcjvbAo69MYN3yAgYP7G71oSjArCjDHltxOaMgUCGz7ILXt7SARWcvYiLnbenx0P+pIOyEG3TSxWXgACAIgpSAqFLPMDBAv38jQgUqBCMrRVbHVIoWCpxFzosuiXS/UbJbX6Z6QI5DW/ulx9QgREePUn9ajaQpmclBFFFVaqXXThcc1Tm5OhI5Vj3ATfM6eZJUqc2b6/Z47QaXjUgVd06uzoocs1YqZuQYkevS81C18LoceHDvPRh46bmCZboekCtyABG5XA54+umiH4smM+iR2/CPHycS5+DP1+StlRwiF0lmEDRKm4gdQuR6pP481QoCPeyka7EUJdV4NthdRM4XSaG/p4Le5PFxUs51Iqej1Uhnc3jn+ddhgAjTtbtafTgKMCtKUWIlg8tVdMFz++Yw6xrgJ8sZDGVLwUVRxHwoyQ866eIycAZKQFS5IDOrXhlrak2YnARuv11V2WDzey9dkr2GjKiUKHKhRBovu6TFrpzIpVLAxETFRG50jRMDTot2n9zRoxSO8NRTFd2nGphlz25WOa0yIsdsxaig/6+eRM4fhUFQ+Sy+8gqpcW0yW9sIsCRB7vmlr48CMeIqym2VyCtypdbK6WnawGIzwqscXocZ399zDFmTGfi7v6vfHTNFjhG5m26i573EXlnU+zg7C5w9q2qrBOSKXE7xvflQEhGLND7QIUSOEVPVOTndWtm18OcVufqc89oFS9Ek+nm1L6Vg6wqdyOloNZ6/6MOamUn6R4WL22aCXSSLEisZXK6ii4TTN4cZ1wAiSRV7YBkiF4pnkMrksMa1usrAGdxqUfZAc0rBn3wSeOaZgk2vBMFYCgYBeHs2hIC00FVb5Ez6oohYHYhu3AK8/nrhG5cuAdlsxe91QRBw09Z+vKQ1J7d/P2C1Ai+/XNF9qoFbLizH8jK952WpkIzIBWMcJbXOc40TvijW9zoK80AM0ShZTLrYVglQ2AmgQuTqXAoeiKZgNxuVNQ96GXgRbGYjwr39eOc9dwP//M/0XqwHZmfps+Z00r/NZgo9KSkGj6Yyhc8rI3kqQSeALLWSQ3wWQglEtYgcxz7eavRIv7vqnBwjcmrnTh0dC590DZ7pIkVOFEX4IxVaK0+fJht1F9Tt6ESuw/HwmVlcE5JsJGV6tVoBduFbz1MB3O7CBU8UYZ+fwax7ECHeQgugXXMNRanQIbe6ysAZVDvJgMJCtZGBJ5OTdHvliuJboigilMjgpi10HPk5ORYEULLIYfNc2T17ihU5Zr+q4r1+aGs/5kIJTPpV+uQsFiJzNRI5trjTrB+QVQ8AzVfkuLbK118n21kXB50AFczIAXUjcppl4DqRK4LXbsHTRz5Kn48HH6zPnbIOOTmOHAEuXAAuX85/qUiRe/xxOg9p7NDbLLRk4lkr58MaRM7pVLVrthKOShQ5UawfwdbRNmDWyrlQ9yhyoXgGmZzIP/eW4vRpYNcu2uTpcOhEroORyuTw2JtzOJzxkdLUU0EBYpORt1b2qShy7IIXDMIUi5Eip0XkNBZa8yENItfFZQvfrMIAACAASURBVOAMXrsZyzxlB2iOIqdB5CLJDLI5EbeMDcBuNhasjqqKHJEu+40HgHffLSi3FVYPyHGYzclp2Stvuonm5Grok2OLIdWwk2CwaD4OIJVIEFRqI+pI5ERRxKQvhi39nMXkKgg6AZCvJOGeXxiRq9NGRzCWVgadADqR48DrMOONjddSd9s3vlEf9Yd1yMnBLJNPPJH/UiwlzciJIhG5u+7SDFGyGA0wCPywE01rZRvaKgHk5wM1iRygB550IfLWSjVFbmamaNOjE+CXyOmAs4IZuS5JrAR0ItfR+NnFRYQTGewIXm1LWyUADEo2x9E1LuU35UROUsxm3YP8HXOgAiJHH2Ju2EkXl4EzaCpyLSZy7LgGnFbcsLm3MCenoshN+qMY9thgPrCfvnDmDN2ePw8MDSkIkRa2DfZgwGnFyUkNteWmm2g+6ty5iu+3FGyXXlORKzlug0GA26ZRCh6N1mVuyxdJIZLMqAedbNhAz2sXg4WdhNTCToD6KXLRlHI+LpejxZFO5IrgdZgRjKeB3/xNWlzVo9ORp8jt2kV9UbI5uVhSUuTOnQPm5jRtlQBZte1mI9daOR9KQHQqa3UwP9+2RI6dq1RLwdn5Sp+T6yrEU1nEUlkYBGBmOc4fO/j1Xwc+9rHmH1wN8Et20bLWyvl5+qMTOR2txsNnZuG2GuG6fKltidz+jV4c/51bsXs9Z+HNI3KuAX6qHFCxIreGVz/QxWXgDB67GaGESuFxi62VQak7zmM349DWfozPh+GPJGm32mRSWA4nfFHqO9u3j77A5uTGx6u2EAuCgF0jbozPhdV/6Kab6LYGe2XeWqnVI8choJQ2qkLkgLq8ZpN+jcRKFnTS5cgrclrWyjp9PgI8a+XiIqXGdvl5qFr0Oix0fvjkJ0kB+sY3artDUeQrcoJA9soTJ4hUg83IGQuzcxpBJwx2i5FrrVwIJeEYkM5jnaLISTNysWQZRU4ncl0FplyNrnEhkc7lr89FGB+n2ekOmo9kKmNZa+Xp03SrEzkdrUQincXjb87jI+vNEEKhtiVygiBgbIijxgEaRE5ld7C3l2bkcsrEMICGzd02E18RmZrq6vk4gHa2szmRv1B1OEiNbJQil0oVotU5RI5ZBz12Mw5vI1L50qUlWtwODCjsTPl5ruFhUuvYnFwV1QNyjK1x4t3FiHpf0ubN9Dg1ELmCtVIj7ESFyKkqckBdXrN8h1xpGfjSEllXVwGRMxoEOCxGvrWyGYqc3iHHhddhRiCWpjmyz3wG+MEPCpbrlSAYBBIJpSIHEJFbXMwr7zQjZ6LagWuuqYhk28x8IjcfSsA1IG0IdAiRc5RT5JpJ5P7yL4Ff/uXGP46OfKrutevo9VV0yeVydB2PRkm56hBUbK3UiVwBgiD0CYLwuCAIF6TbXpWfywqC8Ib056FaHlMH4bkLPoSTGbzfJl0w2pTIaaKEyIlGIxacvdrWSlFUvajMh5L8+ThgVShy7nwCIocUCEJjS8Gnp+nkv3Yt/T1bvNBZlhG53es86LEY8dIlPy2qSmyVwVgKwVgaWwYcdNx79xKRCwTo51dC5IZcSGZyuLKkEngiCBT2URORY6mV1SlyXkfjidykLwqTQVCmx546RbddHnTC4LKZ+BtFdjsll9ZBkUtncwgnMspd4VUwp7sSeB0WLMdT5CT4/OdpTvVb31r5HZZ2yMlx1110e+IERFFELJWFW8gAzz5bkRoHkOLOnZELJzDQ20PvI3Zdy+XonNWmRI7NyPGsogCaNyMXDAJ/9mfA975XtwqQtsHzz5Oy1UZgytV1I3Q9UlQQzM9TxxpAAUEdgqWISu1LKU6fpvUg28DrcNSqyP0BgCdEURwF8IT0bx7ioijulf58oMbH1AHgkTMz8DrM2B2doy90KpFLJknNmZ6GMDICu82irsiVSZabDyewRqsMvMsVOc0ERKDucfZFYLbKW2+lhVjJLh47Jq/DDLPRgBu39FExOGe3mqlHm5l6tHcv7aCzi+EK3uujQxRDfn6+jL3ynXdWvGhJaM3IiaIqkVOtjagnkfNHsaHPAZOx5JTPgk4OHKj5MToBTquJv1EkCHUrBWdl4L1qZeA6kSuC125GOisimsqSKnbnncD99ys2gyoG65DjKXLr11Pc+IkTSGZyyOZEbD1/hshDpUTOopyRE0URC2wjUb5BGQjQ79GmRK5tFLn77wciEXqu2Dx0t+BTnwJ+//dbfRRF8EWIpLGRl5lSRU4ecnLxYrMOq2b4oym4bSZlxU4puijoBKidyH0QwHekv38HwC/UeH86KkAincXjb83jnmvXwnThAlnmNm5s9WFVD3aRCIfz1kenzaSdWgmoLrYWQkkMqc3HAV2vyDEix01ABCpT5H7wA+DrX6/+weVEDlDYK4MyRQ6gSoCLCxFk5he4QScAsIXNc+3bR2T/Rz+if6+gZmNUsveeLzcnJ4qqPXjlENOakUskiOCWzAICkrWSp6LW1VoZw2a1xModO6oKj+lkuGxmftgJUHYGt1IEonT/faW7wo8/ToEyXR4qUy3Y7nm+S/ELX6DrwcMPr+wOtRQ5gOyVzzyDWIRUiK2vP08R5LffXtHd86yVoXgGyUyOwr3kRK6Ny8ABwGqiFE7VGblmhJ0kk8Df/i1w3XX079dea9xjNRvxOF0bz59v9ZEUgVkrd6x1wWQQMBssUeTY9RzoKCLniyTRX85WmUzShq1O5PIYEkVROmtiDoDaFcomCMIpQRBeEgRBk+wJgvBr0s+eWmSJdjqK8PT4IqKpLN63Z7gQ/qARmdy2cMkSviQi57KZEVYrBNcgcqIoYiGcwJpV2iEHkNoFaChy5YhcIkHJcV/+cvUPPjlJRdc330z/LiFyy/E0zEYhT3JYJYDIVeRiEARgQ59EPPbupdt/+zcKRtm6terDc1pNWOe14/xCRP2HmL1whfZKTSIXDNKtxoycIqSmt5eUohqJnCiKuKzWIbdKgk4YXDYVRQ6g80sdFGu2SCqqH/D7iZh88pNFhfA6AI+jxBL+wQ+SmvbNb67sDpkip0XkYjFkfvY8AGD9q88Dhw8XysPLwG42Ip4untMu6jDtICInCAJ6LCZ1RY5doxtJ5B58kMj3X/0VXaNefbVxj9VsXLxIm4OTkzVV29Qb/mgKVpMBLqsJQ26bckaOKXJr13aWtTKaQn+5oJO33qLQqdVE5ARBOCEIwjnOnw/Kf06kVYhavM0mURRvAPBLAL4mCMI2tccTRfEfRFG8QRTFGwZLdup1EB45O4u+HgsthlcY/tAWkF8kpqdJkbOqzLAAtLAFuEQuEEsjnRX51QOrTJHjJiAC5a2V//qvNM8xP1+9vXBigp5fRrI4RM5jN0MQBADAtSNu9BlzMEfCSkXOF8WIx57vIMTYGM0wTU3R/a+wwHNsyIkLWtZKr5c+SyskcvF0FjazAQaDoPwmez55M3J2MzI5UdnlxNI8ayQXC+EkYqlsQeFkuHqVFlCrZD4OkKyVaueXOlsri2bkvv99Wsh9+tM133+3oaDISectk4miz48fX5mSMTtLpMylErJ1++2A0QjjU0+gL7aMvnfOla0dkMNuNiJR8lkt6jDtICIHAA4rv04BAL0WDkfjiFwuRwRuzx56Dfbv7y5Fjr1/s9lilavF8EdSGHBaIQgChj025Yzc5cu0sbV3b0cpcv5Iqnz1AAtOW01EThTFI6IoXsf582MA84IgDAOAdMuNmhJF8ap0ewnA0wD21e03WGWIp7J44u153HPdWpiyGVpAdzqRm5wkNWjDhvI75gDNHZRAswycKXJdPptSdkZuYICeuwzn+RVF4L77CiSp2l24yUlKfnS7iayoEDkGk9GAO/olwlNaBu6PFpMOo5HKgoGa3utjQy5cWowik+WnngIge+XLL68ocjmWymgnVgKqihyg8rrVIaBGMXPIsEqKwOVQDTsB6q/Iya2VDzxAi9UuWjzUC8xJwAgwAOBznyMScf/91d8hr0NODo8HOHgQtqefws2T0qKuwvk4gF8/UNRh2mFEjhQ5jXlEt7txYSePPkoKyZe+RO6D/ftpHpoFbXQ6xscLf28jZcsfTeYJz1oPR5Fj1/Pt2wuqYgfAH02hr6eCxEq7nX63LkGtfryHAHxG+vtnAPy49AcEQegVBMEq/X0AwM0A3qrxcVctnhpfQCyVxb27hyk2PJvtfCLHQizWr9deaGkocgUip6LIdXkZOEA7xWajoE3kRJFLhPH44/Q6/O7v0r+r3QlnJ36A5jXlw9IAlmPFRA4AbnbTxSHYUyA3oihSh9xAyTwX65NbwXwcw+iQC6lsDpN+leRKgIjcwoLi+CtBPJXT7pADNIkcN210YKC2KHaQwglAqcidPEmLZWZdXQVwWs3qG0VMkatx0RKQiBwjKBgfp80BXY3jgj1PRU6C4WHgQx8C/umfgJjG55UHXodcKY4cgeP0a/i58eeR8XirCvvhzcgVdZiWEjkWpNOmsFuMiKl9JgAico1S5L76VXJyfPzj9O/9+0m5brOUxxXj/HmgRzrvthGRW4oWei5HvHbMLieKrf2XLwObNgGjo/Re7oAxp1xORCCWwkA5Re70adoY7iKLe61E7i8A3C0IwgUAR6R/QxCEGwRBYPnB1wA4JQjCaQBPAfgLURR1IrdCPHJmFgNOCw5u6Svs9nQ6kXtLejtI1kpV65PVSidFDpFbkHZEuWXgU1Ndb6sEaN7BY7dop1YCfNXhvvvID/9Hf0QLj2qIHOuQkxO5MoocAOx30Ot8Nl0g30vRFMKJjFI9YmSjJkWOZmA07ZU1FIPH0xl+YiWwckVu3bpC2uEKMeGPwmI0YMRbUj3wxhvAtdd2/QaHHE5J8c/x+gT7+kgJqJY4lCAQS8NpNcFqkt4LDzxAM8y/9Es13W+3wmunhdeyXJEDaF43GAT+9/+u7g7LKXIAcOQIhFwOP3/+BUTfe1tVizqb2aCwVhZ1mJYSuf5+2jBpU2jOyAF0zmoEkTt1Cnj6aeCLXyw4QRih7pY5ufFxcjy43W1lUfRHUuiXlKthjw2pTA5+aQMqP9PHFDmgrUioGpbjaWRzYvky8DffJHdEF6EmIieKol8UxbtEURyVLJhL0tdPiaL4q9LfXxBFcbcoitdLt9+ux4GvRsRSGTzxjmSrNBooeQfoXCLHUiuLiJwZYbVUOUA1WS6/I6qmyHV50AmDx27iJyAC6imIb71FFpff/E16TTZtqu7EPTVFJ/8qidzGDAWPvBwunIYUiZUMt99OsxqHD1d+XCXYvoZVEGgEnuzZQ8RmBUSOyoVXQOS0Qmo2baLnsgaVaNIXxYY+O4yls3vvvks7rqsIbhstqCO8hWudSsEDsVQh6CSXA/7lX4Bjx8qrRKsUFpMBPRYjlYLLccsttNHw939f+Z2JYmWK3KFDyNhoYyN5x51VHa+dq8jJOkxLiVwb2yqBMjNyQOMUua9+le77c58rfG3LFjpHdsuc3PnztD7bvr1tyJAoilK6IxGeYQ99DuaYvdLvp82sTZsKRK6NSKgaWBm4ZmplJELq4jbVmI6ORAdGHa5ePPH2AhLpHO7dI+02jo+TisIIUaeBKXLvvEM7cmvWwGUjv36Wt2MOkL2SR+TCCXgd5sIuuByroAycwaPWSQaoE7mvfY3Iy2/8Bv17bKw6RY4NccuJ3NISnTQlLMfT8JbEsRv8dBxPyV7OCR+pIYqExZ07qQ+QRVSvAA6LCRv7HNpdcmYz2XtWSOTKWitV6gcAldqIjRspwrqGObnL/piSGOdy9Lpt2bLi++1EOKUCZK7qX6bepFIsRVOF6oFnnqGNDt1WqQmvw6K0FgsCJVi+9hqp/pVgeZk+L+UUOYsFC/tIfReOVB50AhCRy+REpGWztvPhRDGRi0SIVHYAkatoRq7eRG5iAvj3f6dQG/n6hc3JdQOR8/noXLJjB22YtQmRi6WySGZy+XTHYQ+9b2dYBQG7nm/aRNd0o7EjiJxPKgPXTK1kIxNsrdIl0IlcB+GRM7MYdFlx42ZpwdHJiZVAgcjFYkS0DAa4pB1zVatHXx93xku1Q26VlIEzaBI5nrXS5yPF4NOfLhC90VEicpWqQDwiB+RDZnI5EaFEGu4SRQ4LC8gZTXgzZshfRCZ9URgEYEMvp/OsDhgbcmoTOYDsla+9VnVcdCKtocgFg7RI4UScF9JGOYvVTZvodgUzewA995P+qNKqOjtLNsIVVDl0Mlw2eq65c7iMyNUYeEKKnLSYeOABWqh+8IPa/2mVw+swF3rk5LjmGpoDf/fdyu6IdciVI3IATn/4M/jn/ffCuqO63Xlmn5arcguhZMEN4nLRRkks1hFErqIZuXqHndx3HxGE3/5t5ff276c5pjaK618R5KMvo6N0nax0Q6KB8EeKU3WHvbRuygeeyMmOxVK9Q6dFYCFTmqmVpWuVLoFO5DoEkWQGT40v4OevW1uwSHU6kTObae4NyBMtRuQ0k+W4ilxS3VYJrBpFzuvQmJGTiJooH1y+/35KDP3iFwtfGxujHdhKQzZYhxx7jhmRk+yV4UQGogiFtRKLi8gODACCgJcu0eJ5wh/F+l4HLKbGnJpGh1yY8EWRypRJrkwkgDNnqrrvWCqrPSPndnP7Hp1WE4wGlZCakueyWsyHE0ikc0qFc2KCblcZkXMyayWvq7JO1sq8IheNkurw0Y9SSpoOVXgdZn5tCru+ydP/tFCuQ06G89ffjD+5+zfgUFPRVcBqUdicXL7D1CVT5ACyV3YAkeuxGJuryPn9wLe/TTOjvCTpAwdok+ntt+v3mK0Ac7WMjZFFkbkgWgxmQRyQLIgDPVaYjQJmljmKHFBIrmxz+COStVIrtVIncjpaiWfPLyKZyeHnd0sXKCbb79zZ2gOrFeyiJxE5p5UW+6qBJypEbiGU0K4eWEWKHHdnGwAcDmQsVnz3kdcoWS+ZBL7xDeCee2jnm4ElQ1a6Czc5Sc8vG+gvIR+MoCiI3MICTGuH4HWY8eK7ROQmfSrF1XXC2JATGUmlUsUKA0/iqSzsZo36Ac58HMBCalSU1BoVuQm1xMpLl+h2lVkr2UYR25UuQr0UuaikyP3wh2Sx022VZeF1WIrrBxgYkWPz4CX4xD+8hL97WqbWVaHILcfTsJgMNG9eBZh9OiGVgis6TNk1ze8nJb7NiZzDatKekWNhJzWmub52JYCDXz6B8H1fJ7Xy936P/4P790v/oTPslT98fRrv+9vnilMfAdp8MJuJNLBZ5DZQtkoVOYNBwFqPrTAjd/kykXc2BjA62hEVBP587YtGz+zkJI2RDA0156CaBJ3IdQguLdK80Z710oer0xMrGUqJnNaOOVAgcrKTSi4nYiGcXNVl4AxuuxlhlVS+b/1sAosWJ6zLAUz4o8D3vgfMzRUqBxgYkat0Tm5ioniHa2SElKcSIuflKHLC4CBu2tKHFy/5IYoiJn1RbOlvjK0SAEbX0PtN0165aRMtvqokctQjp6HIqRA5gFliOZsXvb2U1LpCRW5Sbebw0iWyejKiuEpwzVo37GYjnjnPidOuw4xcIp1FNJWlRdIDDxBRfu97V3x/qwVeu5kf0uR20/mEQ+RyORGvTC7hX16cLCyiK1TkRFHE0+cXcGBjb9XHWmqtVHSYsmsa2yxpcyLXYzEilc2puxTcbrreRjU2vyrA9165guVAGJa/kzYPWTdoKUZHyYLeIUTulYklvDkTys9o5XH+PIVqmEwFItcGyhbPgjjstmM2KCNymzbR9QEgRW55ueY+00bDH0mh12HW3piZnCz+3boEOpHrEMwsJ9DrMBesW91C5Nigs0S02I55SKtLLpmkgXYJ/mgK2Zy4qsvAGTx2M0RRaU39+2fexZ8/8jbinl70xkNYDCVoTuG664AjR4rvZONG2kmslMjJO+QAunCtW5cnH2z2y1O6UybZjg5v7cd0II43poKIprINVeS2r3HCIJRJrhSEQjF4FSibWqlB5NxqSiojWytU5Cb9UVhNBgyXfjYmJugzZykT1dxlsFuMuOuaNXj03JyyGN5upz81KHIssGMksgScOAF86lNcO62OYvQ6LAjG00pVAyDXCYfIBWJ03p9ZTuD1qSB9cXaWSAAjUyp4Zy6MS4tRvG9P9UmiTJFTErkSRY7N9bU5kbNb6Jqrqsqxa3QN9spUJofH3pzHfzr3JKxLPuD3f1/9hw0G6g3tkAqCGYkATQVKakvkoy8DA/Q8toEi54sqLYjDXluxtVJ+Pe+Q5Ep5N54qSn+3LoF+hekQzAbj+ZhYAHSSsFg6/01Zosi5tFLlgMKuuSzwpKiMtRSrpAycwcvpJPvm0xfxP376Dt63Zxgj29ejLxaC8dmnaaD8i19U7k6ZTLSTWAmRSyZpF7z0fSirIFC1Vi4uAoODOLyNZve+f5JIdyOJnM1sxKb+Hu0uOYCI3Pg4vzydg1xORDKT056RK6PIcVMrAW6dQ6WY8EWxqd8BQ2n1wKVLq24+juHePSPwR1N46RJHeWOl4CsE2+3e9eSPScX41KdWfF+rCV6HGdmciDAvdIMRuRKSJ1dAHjkjWSpnZiqaj3v4zAwMAvBz162t+ljZjBwjPooO0w4jcj3SOUs1YIwRuRoCT55/14dQLInPnfwRrmzeCdxxh/Z/2L+fei6zGpbPNsGsRICmA4XNZWSzRHwYkROEtkmu9EdScFiMRdeqYY8d86EEOXmYIsfQRmqiFqhSQWM+Digocl0Gnch1CGaXE/mYWAC0yNy+vfPb6VWslZphJ0DRYmshXLIjKsfU1KqZjwOUCYj/35MX8JePjuMD14/gb35xLyxrBuFNhLHlX/6RCO4nP8m/o7Gxyi46pR1yDOWIXCJBYQCDgxhd40RfjwUPnSZb1JbShMU6Y3RNhcmVAHDyZEX3yXbnNesHONUDDF6ttNFaFDkfJ7ESWNVE7vYdg+ixGPHI2RnlN1VmcCtFIJYCRBEbHvk/wHveU9jN1qEJVk0SjHI+Azt30udnfr7oy4thFtpgwSNnZmkROjtbdj5OFEU8cmYW79k2UH7hxwFbACdKFLk1HarIOaTN05gakWMbUDUocg+fnsXPT72ObUvT+O7NHy1vbdu/n+boqqnBaRFY2uO0XJG7fJkSKtmYAlCYNWsxlqIpRbLjiNeGdFaE/+o8fdbk1/PNm0klbQMSqgV/NKVdPRCJkD2008UPDnQi1yGYXU7kY2IBdH5iJUOpIifFg2vOyAFFi615tiPKs1auog45oLhc+m9OXMBfHT+PX9g7gr/+2PUwGQ0wDA5iw/ICtr30FPCFL6grlYzI5TTSHQH1FKiNG4nk5XJ8IseSM9esgcEg4NDWPsRSWZgMAtb3Njbhb2zIhUl/DMmMxm7vjTfSYqNCe2VM2p3XrB8oOyOnocj5fFXPqORyIi4vcTrkEglSLlZZ0AmDzWzEkV1D+Om5uaIuMAB0fqnBWrkUTeG6+XfhuDCuh5xUAa9WBYdKcqVPSqn7pYMbMRdK4LUrgYoUuTdnQpj0x1ZkqwQ41sowjT3kO0w7jMgxRS7WIGtlMpPF8bfm8FtvH0ekbxAPrLtBOzUY6JjAk0gyk990nlqSKXK80Zft29uigsAXSaKvJNmRub2W3pLImly1slrpGtQGJFQLPIJahC7tkAN0ItcRiKUyWI6nC9bKdJouEt1A5NxuIhNS9LfDbIQgVGCtlCtyEpEb5O2uTk2tLiInLYi+duIC7jtxHh/atw7/82N7CwPAAwOwZNNIm8zA5z+vfkejo2SbZDOGatAicuk0MD+P5VgaVpMhb0kCUCByg4MAgMNb6fXf0OeoOkWuWowOOZHNibi0qEGMPB5SAiokcsxmxeZNiiCKFYadpLkhNfmLarnXogQzy3GkMpzqAfaarVJFDgDet3sYwVgaL7xbQtpqtFYGYyl8+NwTEK1W4GMfq/EoVw96e+i8FeAFnrBk5pI5OabIffzgRlhMBjx8eqYiRe7hM7MwGgTcc231tkpARuRSTJFLFs9nMyI3MUHjD/LC6zYEUxijycYQuZ9d8MEzN42dp1/A9Ic/iYRgzKfpqmLnTppXbfM5udlggbwVKXLy6gGG0VHaGGXVLy3CUjSFgRLlirm9ouMSWSu1H7aJmqiGbE5EIJZSENQidGn1AKATuY4AG6YdYYrcxAQtkruByH3uc8DXvpa3WhgMApxWk3bYCVCsyIUT6O+xKLvHIhFSQlaRtZLtbL96OYCPHFiPr370+kLvIJAnzM/eeFQ7glclufLVywGqLmBgHXKlYTKyCoLleJpbPQAgv1t9eBsd1+YGJlYyjA1VkFwJFAJPKohdZrvzXEUuFqOZiTJELicCEZ69iT2XVdorWWLlptLnlC0kVqkiBwC3jg3CZTXhkTMl9kqmyK0waju4HMMH3noG4vvfXzhX6SgLj12yVvICf9avBxwOBZHzRZIU5OOx4Y4dg3jm1Lv0WdNQ5ERRxCNnZ3Dz9oFCaXuVsFnoOsM+8wvhJAZdsgWk00m36TSd39o8Ia/HUsZaWSORe+TMLP6vc8cBgwHCr30OQAXnXpMJuP76tlfkZpbZfL4VVwMlipzXm9+oBNA2s2b+iDIUhBG51KVJ+kIp2dm+nRw6bVpBEIilIIpks1aFrsjpaCVYv0dekeuWxEoAOHgQ+PVfL/qSy2pChDf0DnDDThZCCXVbJbCqFDmvw4Jehxkfv3ED/vLDe4pJHACMjSFjMuNfbvpP2nfEIXKZbA6f+MeX8O2fyXYUJyeJaJhKlCi2o6dG5EoUuW2DTmwb7MH+FcSBV4utgz0wGgRc0EquBIjI+XwV7aCyRRA37ISFBGgROWaJ5SkSsueyGrAUtU2lM3IsFn0VK3I2sxF37xrCY2/OF9u81q6lWayNG2l+9P77gTffLG8xljDw3JPoj4dg+MxnGnTk3QnW/RTkvf8NBrrWcRS5AacVgiDgfXtGYJgr3yF39uoyppbiuHf3xUctywAAIABJREFUymyVgLxHjoWdlHSYGgxUGQK0va0SAHqsLOykjCK3grCTRDqLp85M4WNnHofw/vdj0/U7YBBQPmwKIHvl669X/NlrBeakoJMbt/RhOhAvOCrOn6f3rJzEs3nZFs6aiaIoWRCLlau+HgusJgOEK5dJCR0YKP6P27fTpngNboVGgnXjlS0Dt1q7rkMOAFTaa3W0E1gsbD7spJuIHAdOm0ndWul0EmkomZHT7JBbRYqcxWTAy390RKlOMhw7hr/+7nN45c0yaYzDw7QYkV10/NEUUplcIaYYUI/zlSlyQdcGeHnVA0B+oSMIAo7/zm0o5Z2NgNVkxKZ+R+WBJy+/XJb05K2VvLCTSoicLG1U8W4dGSHVs0pFjlnPFJbjS5foYt2FF7Rq8L49w/iP16/i+Ys+3LFTWnD/7u/Se/K554AnnwQefJC+3tdHfXC33kq2r97ewh+vNz9res2JHyHQ40XvsWMt+q06E/mQJh6RA+g5f/HFoi8tRgpK2F071+D/xKVzmoYi98iZWZgMAo5eu/L3vjy1UrXD1OWimdYOIHKOfP1A/RW5Z88v4pazz8IZDgCf/3w+NViz/oVh/37gm9+kMRKmZrUZZoIJCAJww6ZePHJmFosRyWY7Pq5M5hwYoGtAC4lcOJlBKptTKFeCIGDYY4N1eoqu56UqslxNlFw97QS/VKmgWT/AEiu7sA6m+36jLgQralwrJ3KDgwV1qsvgspkRVgs7EQRFstx8KIEhXvUAmylaRYocAHUSBwCCAPfaAcRSWUTVVE/p5zA6WqTIsXS2ouJTNSLn8dACQEuRM5uL5keMBgFCk2xIO4ZcuLBQZjGxezcRngrm5DTDTqokcgqU9PJVisVwEl6HWfl+mJggW2WbW74ajfeODsBlM+FhFl0PECn7rd8Cvv99Cs64cAH4X/8L+MAHSJn70peAe+8Fbr4Z2LWLSAPrnxsZwe5TT+NnB++m97aOimEyGuCymSj1k4edO2kjQ9YfyhQ5AOixmnC7mz6D2bV8IieKIh4+M4tbRgfyKZkrgdlogNkoIJ7OqneYsjm5DiByzFqpOiNnMpG1dQVE7pGzs/js6Z9C3LYt31c6NuTE+YUKFLkDB+i2je2Vs8txDDqt+Tnk6UCMCPz0tHKjnV1TW2itZMoVj/Cs9djgnL/Kj+dvAzVRC+z30rRWdmmHHKArch2B2eU4BpyWQipWtyRWqsBpNfFnJRhkRC6TzcEXUVHkVlkZeKVgCo0vkkSPVeMUMDZWNGzO0kGZ0qPaIcewcSNw+TKWh9PYOVxS0Lu42NL5kdEhFx57cw6JdLY4hEUOk4kWE6+8Uvb+YlozcozIadUPODSIHLCiCgJfpLDQLcIqrh6Qw2oy4ti1a/HYm3NIZq4rnF8ZBIEWMNu3A7/yK/S12Vl6HYJBsnezP9K/n33jMp77+U/i/c3/dToeXodGcuuOHTSfc+ECsGcPAHp/75NZsQ/Z6Lz0atKGg5y7eGMqiKvBOH7n7jHOd6uDzWxEPJ1V7zDtICJnz6dWamzsud1VE7lEOovLT72E/VfeBL761bwSMjbkwom3F5DMZJWfOTl27aKwmNdeA37xF6t67GaB1UJt6KU55KmlOA4EpA23Mc77bPv2igO0GoElVgbOuS6MeOwY8M0Cmzkdf2zjr00DT/yRChW5ffuac0BNhq7IdQDoZFFSBt7NRM5mUu+RA8jOJBE5fzSFnKhSPXDuHJGMVVIGXikGJDtSnpCpYWyM1BspLrmgyEn/78oVfoccg9QlF1ILO5EPgjcZY0NO5ETg3cUK5uRee61sZHRCK7UyGKTblSpywIpKwRfDSaWtUhSJyK3ioBM53rdnGOFEBs+d91X2H4aHgUOHgHvuAT7xCarw+OM/poXqt76FP/7wHyC3aXNDj7lb0euwaCtyQH5OLpujWZ9B2Q789mwYUbMND03wCccjZ2ZhMRpw967aLcV2sxGJdFa9w7SDiJzFRAqj6owcQESuyhm5p8cX8aGTjyBnsRY2QkCbaGVTgwEicbt3t7kiR2szVpkzHYhpj76MjhY65loAX36WTEl4Nlqz8MZCyLGxCDlstrauIFiKpmAQoK60R6O0edylipxO5DoAs8vxgq0yGKRFcBcTObfNhLCW7a+vLx92wqoH1rg4ysPJk9QHpqMIckVOE2NjRXHJCxKR80eSyObE8nG+GzdCvHIF4WQGXnvJCXZxscVErorkymQSOHNG88fyYSc1zsipzght2kR2nazGYqsEvkgyT9rzWFqiInZdkQMA3LxtAB67GY+cnS3/wxVgKaZMhNNRGTx2s/r7f3SUFAGJyPmjSeREFKVFmufnEO0bxE/PzSNT0g+Yy4n4ydlZ3Do2oNxUWgHsFiPiqWzepdDJ1kqA5uTi5YhclYrc8Vcu4kNvPQV87KNFc1VjQ5TqWfbcC9Cc3KuvtmVaoiiKmA3GMey1wWY2YsBpxXQgXhhH4M31tbiCYElKnOb1rW2L0eZ4aI1KWBBLrmxD+KIp9DosynA3hi5OrAR0ItcRmA0mMLJKgk4Aslaqhp0ARdZKphIpLqSLi0Q0dCKnwICLTuIVKXJA/sLEFi05keJ+KyFygt8PeyoBj71EqVpYaOkiZ3N/D0wGofzQvTzwRAMVWSs1iJzdbITZKGgrcpkMWfsqBFeR0xMri2AxGXDs2iE8/tZ8PoVwpYinskikc+itYf5qNaPXYVG31DsctJkhETlfmM3EyN7fs7MwrV8HfzSFlyeK0/VenwpgZjmx4hLwUthLrJWDpRsmHUbkeixG7Zlpj6cqIhdPZeH693+DMxmD4QtfKPrelgFKDa6IyB04QJu2VdrKm4FQIoNoKosRyS21oc9OScHj43S+ttuV/6nFs2ZaFsQNIQogm+9T6Vfcvr19FblImTLwLu6QA3Qi1/YIJ9IIJzMY9nZh9YAKXDYz4uks0lmV2GE5kQurELlTp+hWJ3IK9PdYYRAqIHJsR5EROem5BqT/OzlZCOLgQbJojIQX8/H6hTtorSJnMRmwZaCnfAz2hg0USV+GyMVTWQgCYOUFzSwv03wI65fiQBAEeOwW7Rk5oOIFTSxFiwzFApMROd1amce9e0YQSWbw7PnFmu6HJaf1lr7XdVQEr8OMoNr7HyB7pUTkFqUFadH7e2YGnq0b4bAYiwNsQCXgFpMBR66pT1Kr1WxEPJ3DfCiJ/h4LzMaSz32HETm7xZgPbOKiSkXuqXfm8YuvPozozmvJiiyD1WTE5n5H5cmVQFvaK2dZmrjU77u+11FQ5NTWZy3ukvNHU3BZTdzZxKGlOQDAlEvlPTs6SuuuNqwg8EeT5asHAJ3I6WgNCh1yMkXOZOrqHXWnlaVoaXTJhUJAOo35UBKCwEkrOnWKrDjsQqAjD6NBQF+PBYuRMj79vj6yxMgUObeNXhtfJFnokDOqDKwzIhdaLLYzxeNU1t7iRc7YkKv8YkIQCsXgGoilsnCYjfzUzeVlWgiVCXbx2E0IaSlyQMVzcgXFouRzoZeBK3B4Wz96HWbF4r9avPiuHwCwc9hd5id18OB10EZGNqdio9u5k65/uRx80iZUXpETRWB2Fsb163DkmiE8em42b69ktsrbxgbhstWHZNvNBiRSWSyGVTpMGZHrkIqPHqsJ0TqGnbz1H8exa2EC9v/7t7jnvR1rXZV1ye3eTesdWehWu4ClibO12fpeO2YCMYjj4/ygE4Cup15vCxW5FPpUlKte3yySRhMmzS7u9/NqYhuqcv6o+u8FoKs75ACdyLU9Znhl4Fu3dnW8tVMiC6qBJ71SUlkwiIVQAgNOK0ylO6InT9KumFtfVPEw4LSWV+QAuiBJF52FUALXjpA9MK/Iae1wFRE52Um2pAy8VRgbcmEqENOeDQGA228nMvvCC6o/Ek9n+UEnABE5DVslg8duRjCuQq4ZkatQkeMqFgApcmvWaKqDqw1mowH3XLcWJ96uzV758JlZrO+14/r15V9rHUp47WaIIrlQuNixA4jFgKtXle/vcJgCDYaHce+eYQRiafz/7Z15eFvVtfbfrVmyZMlTPDtksANJ7AAJYaYFEiAkFCgFCm2hw+10aUtoC+W2X0fa2/b2Ai23tNDSUjpSCFAgBkKBMg9NAomdkDlxEtvybMuSrcnS/v7Y58iyrSMdWYNlef2eJ09iWbKOneNz9trvWu/7plRYbz82iO5hPzakqa0SiG6tVHBMnjdPmHXM8DVOLRY1ipxKs5MR/xgWP/4n+MwF0Hzi4zGfUz/PhqMDo4l/30wmYNmyHFXkJq7NaosscAwPgg0PKytyshPuTBVyI/6YRicAYOxsR1fhPDjdCvegXC7kPAGUztEMOYAKuZzHOTQpDHzv3nEHrzylMFEhJ+fnDQ6KDLnJN1LOyegkAWU2Y2KzE0AUcvv3IxgKo38kgGVVojCOKHLxCrmqKnCNZqoiJxdyM67IWcE5cDBRntxnPyvcCr/2NcWhe28gBLNB4XLqcsWNHpCxm+PYr1ut4rxXqcj1TlYsZOQMOWIC6xurMBoI4eV9PdN6/eBIAG8c7MP6psqsZSHmG0UF4hoxGC8UHAD27kWf2w+zXjsenyLPjlZV4byGMliNOjRLCuvmnZ0w6jS4ME1tlYBkdiLNyMXMMP3iF0WAuTFOu1cOUWDQqYsfUGE68trbe7Hu/dcwdNW1ihtGDeU2dddeIGcNT5wuLzRs3GitpsiMBYMd4pPxRl9mMEuu3xOIGT0AAKytDb2llZGW0SnIXWA5VsgFQ2G4vEEUJ2qtzNO2SoAKuZyn0+UDY1IYeCgkfonyeD4OAKxGcUP3xGutBICBAbEjOvlG2tkJdHUBq1Zl8ChnN2VqFbn6eqCjA31Osbu9sMwKk16DgQF3/Aw5ANDrMVpajurJhVyPtFie4d3qerXOlQUFwI9+BLz9NvDIIzGfMhoYg0WvoMgNDalS5OTWMkWSyJKTi/Qpbq6UIReTMxYWo6TAMO32yuff78JYmGNDo4LjG5EQ2dlW0fAkqpDr9finzMcBACorYdJrsXZpOZ6TciKf2dWF85fMi7TspwOTXpiDKGaY2myzqq3fbNBiVCkQHBDXL86F6pmAkfsegDEURNmtGxWfIztXHlATDH7qqWLzT/4/zhE6h3woLzRFuoFqisxY2N8uPqnUWgnMaARB/0hAUZHD0aNwl1ehc8gX+/Nms5gZzzHnysE4TpwRqJAjZpIulxdlVqMYpj56VFih53shF1HkFBa1UYVcT6wZha1bxd+kyClSKilyPNEup3RDcrXuAQBU2I0osxkRPqLOzne4rFJZkZvhQu6EEgsMWg32q1lM3HADsGIF8I1vAL6pN7rRQCgSrDuFJForXUpqBJBUllyvW8yOTnAnGxsT1xBS5Kagk9orX9zTE1+ZUGBzixN1xRYsr6ZW7ukiGyIpRhCUl4vfo717pbD7qHM7SpEDgA1NlXB5g/jFiwfQ6/Zjw4r0tVUCorWye9innGE6yygwqJiRAxLOyXm8Aaza8giOLj0V2qZGxeedUFoAvVaFazAwXhDn2JzchFgoANVFZiwY7MSY3jDeCh+LxYtFBIFsPJUlwmGOwREFd0efD+jqgq+6TlmRA3LSuTJeNh6AvM+QA6iQy3mcLt+ccqwEAJtUyCVS5Mb6+tA/EpiqOmzdKgakTz45k4c5qymzGuEfC8fP6wMihZz3fXHuzbOZUGo1QnNMXSE3UFKOGncvDNFujrIiN8OtlTqtBgvLCrC/S0Uhp9UCd94pCqF77pnyaV8wFDt6AFBdyBWa9Rj2jSmbPciKnIoWoz6PH8UWw8TZUTmHjhS5mKxvqoQ3GMK/9ibnXtnv8ePNQ/3YQG2VKSHHNijOiTIWca7sdSsrcgBwTn0pbCYd7n/lEEx6DS44Mb3XGrNeC/nXdIpj8izEYlQxIwckLORa/rAJ8wedGPvc5+M+T68VrsGqrr0rVojZphybk+ty+SLRA4Bw4zxpuBO9FXXxZ7FmyLly2BfEWJjHbkGUNwjr6tDj9is7hudgITeejafQWpnnGXIAFXI5T+eQF5WFcydDDgBsRnVmJyPOXnAe40a6dSuwfHnsHBcCwHiWXF+i9kp5wHm/OPfKC00osxph7pAu/Akujj2OclQM94kdSJneXmEEYFNwx8oi9WqcK2UuvBDYsEG0WfZOXOyPBkKxw8CBpBQ5II4SXVcnTB2GhhJ+rV63f+p8HGXIxeX0BSUotRrQ3JpcC9eW3d0IhXnaMsrmKg7p/B8cSRxB0OcJTCzknE6RNScVHEadFhctrUCYAxeeWA6LkhHRNIlW32O2Vs4yxIxcSLlDQy7kEhiemB/4LQYKHFjwuRsSvmd9uU1dN0RBgfh/z6FCjnOOTpd33LtAYtFAB46V1cZ/sVzIZblFsX9EwckYiNjz6xctAOdAj9K6oL4e6OtTdQ/KFnLsi2JrZZ5HDwBUyOU0nHNJkYsq5IqKgNLSmT2wDCNbRCsWcpJxhKdLLKYr7FE3Us5F9ADNx8WlzCrOqYRzcgUFQHU19IcPQqthKCkwoNRmhK2rQ6ieVfFngpz2MhhCwXEVDhgPA88B9aJhnhUdQ974YbjR/OxnolXje9+b8LBXqbWSc7GLrWZGTlrIJsySU9Fe2Td5hgigDLkEaDUMlzZW4sU9PehxK8yJxKC5tRMLSguwlGIHUqLQrAdjiJ8lt2QJ0NkJ/8DQxI2Kzk6hxkVdUy4/WVybLluR/rlFU9SmzbxYZiezDItRi1CYwz+moMSoUOTcXb1ofO9V7LnoSmjMiX8mS8ptOD7gVdfKLBuexMOvYuY7TQyNBuELhse7pQAgGERFvxP7ChUCtWWKi2ckgqBfakGMFQYuq1aWhkUAxk32ppCDzpX9iVor5UJOvn/mIVTI5TDDvjGMBkLj8v2+feJGlgML4Exi0mug1TB4/Ao3dJ0OsNvh6ugGAJwUvYA6fBgYHKT5uAREFLlEWXIA0NAA69EjmGczQqNhKLMaUdLvBI+XISdx3CbNwUUXHzMcBh5NQ4VQBQ+ocU8DxM7w5z8P3H8/sGdP5OHRgEJr5ciIaGdMQpFTLOSSiCDonTxDBAjHSp0OqKlJ+Pq5yqfOXoCxMMf9r6ibX+l1+/EWtVWmBa2GodCkVzY7ASKGJwsHOqYqcpM2lc5rKMPmL5+Di5elPztKVt9jZpjOQizS96PYXilfv+IUcu//+R/Q8TBKrr9a1XvKhieqnStlEzOZUEg4g37zm0BTk9h03LxZ1XunSqdrkps4ABw5Am1oDK0FFZEMw5gwNiPOlf2SAVbM4Oy2NkCrRUm92OSTY6+mkIuF3IgfOunaEZO2NtEBVJGgwJ7FUCGXw8hDpxMUuTxvqwQAxhisRp2yIgcAxcXwdvWi1GpERXRrJRmdqKJM2s3uVaM8NDSgpLMtMotYajOiZqgbwdrEO1xtlhLxj8mF3AzPx8k0qHWujOZ73xOLhttuizw0GhiL3b715JPibzXxA4nMHlQqcpxz9LkDsRW5ujpRzBExWVBagCtOrsaf3z6KnuHEvxvP7e5CmIPaKtNEkUWvfP4DkUJu0UB7bEVuEsur7RkpsGX1PWaG6SzEIo0zKKpjKhQ57+ZnMWK0oOGyC1W957hrsIpCbuVK8fcrrwCbNgGf/KT4/z7rLOB//keoXIsXA5/+NNDdrer9VbNnD3DJJcDu3ZGHulwTw8ABiKxRAAeLqtGV6NoxA1ly/fHcHY8eBWprUVkqiusuJcOTRUKxy6VCbmAkgKICAzQahd/zo0fzOkMOoEIup3EORV0s3JLd+xwo5ABheOJJUMiN9fWjsbpw4o162zaR3bN8eeYPchZTZDFAq2GqFTnbyDAWaMT5WGY1oma4B57KxMrOIbPkMBpdfPT05IwiV1dsgVGnwYFkCrmyMrELvHkz8OKLAABfMDyxtXJwEPjYx4CPf1wsQq68MuGXTajIlZWJczuBIjcSCMEbDMXOkKP5uIR85cLFGAtz/PqVQwmf29zSiUVlBVhSPvPznvmA3WLAYDxFbtEihLVaLOpvT6jIZRJZkcuH+ThAzMgBcRS5BIWcazSAxTvewPFTzoTGqE6hnF8sXINVXXtl47KPfhS4+mrgqaeAiy4C/vY3sTH48svAY4+JddKnP52+zDmnE1i3DtiyBfjqVyMPy4pVVXRrpeRhcLi4GscH4jg/AkKRO3Ysq+2gcguibCo0AanYKTTpYTXqlCMILBagujqnIgj6PHEiFYC8jx4AqJDLaZyRXR9zZLcn38PAZaxGXVxHxZDdAb1rEI01k5SOrVvFRV+vILMTAACNNO+mOksOwIlu0dYyTx9GuWcArvLEC6d2boTfXJCzipxWw7CozKre8ETm5pvFLt/XvoaxQBCBUHjc7OTZZ8VGwiOPAN//vmj/UVG4JpyR02hURRDI/6cxFTkq5BIyv6QAV51ajb+8cyyy8x6LnmEf3jkygPVNVdRWmSaKLPr4WYoGAzzV87Go/3ikqwBuN+DxxFTkMoU8IxczDHwWYjGK70dxVlg2plIwO3lryzuocfWg4LJ1qt8z4hqsppArLBSdELfeCrz6qtgM/POfRWEnmZ9h2TKhzj3zDPCrX6k+DkXcbmD9emHu8ZnPAM8/H9m4cw55odOwiZtl+/cjVFICl9mG9sHR+F+7vl4YgB05kvpxqmRgxA+7WT/RQVomqtipsJtmVQRBv8c/pzPkACrkchqnywsNk0J9d+4UD84hRU7RvQ+Ay2yD3TeCpuqo2aNQSAxEU1ulKkqtxkhwdDwCC0U7xaIB4eZXMSRMZnqK4y+cAmNhjAbD8JRXjatIo6NibixHFDlAzGokpcgBgMkE/OQnwM6dCP7hIQCAfcwLfPazwKWXisXFO+8A3/mO6k2FwkSFHCAKuQSKnPx/OmGR4fGIApqMTlTx5QvqEQ5z/Ppl5QXLs7u6wLnILCPSg8OcoLUSQH/NgomtlZMy5LKBrL7nQ4YcoGJGTq8XLtAKilzvY08BAGquuTyp921IxjX4u98Vhdq55yq3h3/pS6IN8utfB95/P6ljmUAwCFxzDdDSAjz6KPDLX4ow7NtvByQTuvJCE7TR7Xz79oE1LIGGAccHEyhy8qxZFpWtPqUw8EBAdHtJrfuVdlNERIjJDMz3xWNgJBB77g8Q642eHirkiDTyi18AP/+56qd3DomLhU7DgHvvFZlec0SRs5n0yjlyAHr0Fth9bjTWRBVy+/aJIoEcK1VRZjOiV0Uh111ShTGmQXVvOwCguE8UdJ1F8YeH5YLEV1k9riLlSBh4NPXlNnS6fBiOs3EQk2uvBU4/HYbvfhvnH9qKqz61Hvjd78Ts3Pbt40G2KjHptTDqNPELufnzp6fIyTu/pMiporbYgqtX1eBv/z6uuDvd3OJEQ7k1MmdJpI4jUWslgM7yOiwYdMIsdzJPypDLBnnXWmlM0FoJCMOTGIXc0GgAVe+8isHKWjC5QFFJQ7lwDY53r08KxoAHHwSsVtHaPp3WRc6BL34ReO45YWq1bp3YuPvBD8ToxqZNcMaIHsC+fdCcuAQVhSZ1ihyQ1UJOUblqbxfqoFTIVdnN8Qu5xYtFcZQgUzBb9HsCsZ04gTmRIQdQIZc9/H7g298WizwVrnOAUOQq7CbghRdEhsptt+X1wGY0VmP8GbnjMMPhc6M8erFKRidJUWo1Js6RA9DjD+O4oxylXeK8NbWLQqLNGr8YkwuSQFXNePGRI2Hg0cjzTQeSba9kDLjrLmi7uvDgpu+LXeLXXwd++lMxyzYN7GY9XPEUibo6oUDEWaDEVOQoeiBpbjp/MTg47v3X1N3nLpcPW48OYH1j9lSguYDDoofbNxbX9e9Iaa2INJFtxeVCbkZm5PJEkTPIilycgqqwMObi/YUdx3H6sVaE1qxN+n0bItfeJDsi4lFRAfz+98COHWLNlSx33CE25L79bdFSKfOJT4j2zW99Cz0DnonRA8PDwlFzyRLUFFnQnkiRKykRXRtZVLYGRhQKnknFTqXDhD6PHwGlKIoccq70j4Xg9o8pO8fOgQw5IMVCjjF2NWNsN2MszBhTlEEYY5cwxvYxxg4yxm5P5T1nLf/8p+i5DgZFoLAKulw+ET3wk5+IAdOPfzzDB5k7iNZK5ZvKoZABunBY/Exltm4VO3FzpP00VcpsRvR5AsohsBLdw360FVWh8Jik6rS1IajVoc0QPzdLLuR4ba2YMxgdzUlFTlZ13z7cn/yLzzoLvTffivtXfxhvbXpRuKilgCPRjJDsXNnerviUXrcfGjYpL4jCwJOmpsiCq1fV4u9bj6NjUq7SM61OcHKrTDuyEUO834H9RdXiH3v3ir/l1sosKnLzSy1YOb8IqxcUZ+09M4msyI344yhyCoXcvidfgDXgRcmVG5J+34bpbqIl4rLLREzM//4v8K9/qX/dH/4gWjhvvFHMN0ej1QL//d/AgQM4+9WnUBXDsRINDagpNqN9IIEiB2TdubLfE0DJZAMsYErOWqXdBM6BbiXnTVlNzIFCbiDixKmwcUqFnCp2AfgwgFeVnsAY0wK4F8A6AEsBXMcYW5ri+84+Nm0SFuSf/7yQ/hMMuXLO0enyYmX3fuCll4Rj0jR3+WcjVpOy2YnHP4bDY9Lc0cDA+Ce2bhUOgQmyzQhBqdWAQCiMYW/8tpbuYR+OFFXDeOSQaDtpa0NfcTl6RuK/blhajGnk4uP48fFCLocUufJCE1bOL8LTOzun9fpjt9yOH5//aRgcqbfY2c0JCjkVWXJ9Hj+KC4wT5zeOHBELseL8WHhmi5vOXwwGNkWVa2514sQKGxbPs87QkeUnDjmCI87vQGuBlAsnF3KdnWJ+S0VWY7ooNOnx2BfPwqKy/Pj/N6tV5CaZnQyMBFD0+ksIa7RgF6qLHYimVnINTir+RS133imKjhtuEC7CiXj+eTHjvHYt8Nvfxs7rvewyBM84E19+9S+oMUQpVnIhJylZkfMAAAAgAElEQVRyXcM+ZUVLpr4+a4VcKMwxOKowI3f0qPhea2sBSOZ6ADqVQsHlCIIccK6MG3IOzIkMOSDFQo5zvodzvi/B01YDOMg5P8w5DwB4GEByE7GznUBA5EldfrkwP9BqgR/+MO5LhkaD8AXDuODJB4UE/9nPZulgcwObUYfAWBj+sak7hLs7XBgySYtm+QIdCAhDGJqPU408Q9XriZ950z3sx9HSarDRUbFoamvD0LzqhEYpQ15xkTUuPEE8cOzYeGtlDilyALC+sRJ7u9zqwmknIc+VxAwETxK7WR93EasmS67X7Y/tWLlgQezFCaFItcOMa0+rxSNbj+O4tMveOeTF9qODZHKSARySIhcvFPwwN8NjL56oyFVW0rmdArLZSbKK3JbdXTj78Hvwrlo9HlGQBFoNw+J5VuyfxnU3IQUFwF//Kloev/AF5UgCzsUm8FVXidbJTZuUDaoYw/FvfBfzRgZx+tN/Hn983z4x9rJoEWqKzAhzxHW8BZDVCIKh0QDCHLELubY20ZZsEJ+rknKLFbPwCgrE71sOKHJyNl7c1so8z5ADsjMjVw3geNTH7dJjc4eXXgKGhoCPfET8wnzhC8BDDwGHlHOKOl1eLOo7jhNefV44Mdnm1kC9zSQupLHm5Fo7XBgySz8PWZHbtUtcEGk+TjWRQs4d31ygZ9iHoRppturAAaCtDSOVtQmNUuRZL/Ni6bXHjglFzmQSLbA5xKWNlWBMtMwli1zIReIHUqDQrI8omTGpkbL74ihyvR6FMHBqq5wW/3n+Img046qcfI6sb6L5uHQjR3AoOVf6x0JweYNw1S2M5HahszOr83H5iE6rgVGnwWgwjiIXw+zklTf2oLH7ICzrL5n2ezeU29I7IxfNypXCpOSRR4Af/1isu+64Q2yMX3SRMI8rKABWrxYb5s3NCQvSg/Ur8M/Fq7HowXuBfqkdf98+0b5nNKK2yAIAOJ7I8GTxYlFEym3vGSRuC+LRoxNaDysiilzuO1f2S2uQYiXXyjkQPQCoKOQYYy8wxnbF+JMRVY0x9jnG2DbG2LZeuQ1rtrNpk7g4rJWGgW+/Xex+3HGH4ku6XD58/t+PIWwyA1/+cpYONHewSj37sdysWjtc0JeWiA/kQo6MTpJGzmFKVJB1u33wzpeKgJ07ga4uBGrqMDQajNs+4pJaNq0L54vdclmRKyvLud3zCrsJp80vRnNL8oWcL5g+Rc5hNsRvrTQaxW5oHEWuz+2fuEPJuWitJKOTaVFpN+P61XV4dHs7jvWPYnOLE8uqCrGgtGCmDy3vkGfkBhUKObmVyrtw8VRFjkiJAqMOo0kocn0ePwwvvwgN52AXXzzt960vt8I5Hddgtdx2G3DeecC3vgV88pOiK2rzZtEm2tgoHCrvvht44w3hRZAAp8uHn513AzQejygOAdFaKc3m1xSJQiiXnCv7pN8bxdZKudMDYu1lM+kSZ8nlQGvleIEaR5GjQg7gnK/hnC+P8edJle/RAaA26uMa6TGl9/sN53wV53xVWY61X02LYBB44gngQx8an3GrqBAXjz/9aby3ehJD+w/jit0vw3fjJ3OuDS0bWE2ikItleNLa7kLVQumCKxdy27aJ+R9arKpGVm0SOVf2DPuhrasT5+8LL4gHTxAX/v4R5dcOeQOwGnXQmYxix1xW5HL0fF7fVIl93e6kd4cjilyaWis9/jEE47j2Yf58RUWOc45ez6TWyq4uwOcjRS4FvvjBRdBpGL71j1bsOD5EJicZwi7PyCm0VsrRGuGGJeJa0t9PilyasBi0GFHjWim1KD63qwvnHHkPIYcjpZGGhnkZMjyR0WqBLVuA114TKpLPJ4r/d94RGXF33gls3BiZEUtEp8uLIxULgE/cIPLljh4V67iGBgDCLESrYYmdK7NoGiLfp6cocqGQmF2PKuQAEUEQV5FbvBjo7p5oNjcD9HkC0GsZbMYYuYJzJEMOyE5r5VYA9YyxBYwxA4CPAngqC++bG7z8sig2PvKRiY/fdptoMVNQ5eoeuh8MHMZv3Jb5Y8xBbAqF3LAviMN9I1jYILWYRStyq1blnNKTy9jNeui1LLEiN+zDPIdZ3HgkBzDdQlEw98Vpy3R5g7BLrVKoqxtX5HLI6CSadcsrwBiwOUlVLjIjp1cIqU0Cu1l8jbjtlfLPMgbDvjEExsIRtRUAZcilgfJCEz52+ny8dqAPALCBYgcyQqFJB62GKbZWynO5uqUniQe2bRNh96TIpYzFoE2syIXDIqsVQPPOTpx/bAc0a9emZDC2pEIUchkxPJExmYBzzhFGHSmaxnW5fKiwm8Du+IF44HOfEz8TSZHTaTWotJsiM7WKFBeLds4sKFuycjXFFKSzExgbm1LsVDpM6BqOU4jKRWic8aBsMDDiR0mBESzWum+OZMgBqccPXMkYawdwJoBmxtgW6fEqxtgzAMA5HwPwJQBbAOwB8AjnfHdqhz2L2LRJzANddNHEx8vLgZtuEsO4couITH8/mp55BC+suADaBSdk60hzCptRmpGb1Fq5u0O0dixdVCEuzoODYudl1y5qq0wSxljCLDlvIIRh3xjmFZrEjqN0E7csEc5V8YxShmMVcjmsyM0rNOH0BcVobnUmjGSIxivtYqdFkZMUCVWh4OGpqp280J2gyFGGXFr4wgcXwqTXoLHajroSy0wfTl7CGJMMf+IrcpYVy8UDsrU8KXIpYzHoMBpMUMgBwPAwetw+DGx9D2WuPrDJa5skqXaYYdZrM1vIpRHnkE84O9bViTXc88+LT0iKHCDaKxMqckB850q/H/jVr4Drrxe5drJR2DTo8wTAGFBkmWTiMil6QKbSboYzkSIHzHh7pYhUmNsZckDqrpVPcM5rOOdGznk55/xi6fFOzvmlUc97hnPewDlfxDlXF6KWD4yNibbKDRuEPfJkbr1VPP6DH0x8/Je/hNHvxZb1N2TnOHOQcUVu4oK2tWMIANBYbRc7WgMDYm4rFCLHymlQajXGVeR63OJiXl5oGt+F0+thXygu/PmkyAHCwOJgjwf7k2jz8QZD0GkYDLrUGxwc5sQ5WqirEzf5GDPE8kI3Zhj4HLihZZJ5NhN+e8Mq/PjDjTN9KHmNw6JXnJGTz++ipfVizlwu5EiRS5kCoxajCpE/AMbjHYaHI22VAKZuUieJRsNQX27NXGtlmul0eccz5L75zfECNyq/tlZNKDgQ2zQkGAQeeEAUhjfdBDz3HPAf/yFGcs45R2TjJVlADYz44TDrodNOukcpqFZVdhP6RwKR+e8pLF4sup9aWpI6jnTTrxRyDlAhR6SJ114Ti63JbZUyZWXAV74CPPwwsFsSKUdGgHvuwetLz0Jo6bLsHWuOIc/ITVbkWtpdqHaYxS+vXMiR0cm0KbMZI4ujWHQPi8+VFxrHdxznz0eZ5GwVrwgcGp1UyAUCgNebs4ocAFyyrAIaBmxuUZ8pNxoIpUWNA4RrJRA/RyuyexpjTi6mInfkiBjiN5mmPJ9IjnPry7C8Ont5ZXMRh1kfcbydTJ/Hj0KTDkajQVyPtm8XnyBFLmUsBh1GAuoUuc0tTlzc2SJcH+VsyxSon2ebFYpcOMzRPeyLODuipAT40Y+ApqYJRik1RRZ0u30x45MmsHix2OD0+cTG/x//KH6mn/2sKNy2bBFzoDt2iKDy0VEhADQ0iKiEb30rroOxTMIw8En/hxVSoaoYoWC1CgOZRx9VjnXIAv0j/ombltHMkQw5gAq5zLJpE2CxAOvWKT/na18TvxTf/774+IEHgIEB3HPaVeO7PnMQ2bVy8oxca4cLTTXSQiq6kKusVOU4RUyk1GqImwfXPRylyMmF3AknwKTXwmbUxS0CXd5gJOB3wo0ihxW5MpsRZy4qQXOL+vZKbyCUlugBAJHCN+GMHBDzBq6oyFFbJTFLKLIYMKhkdhJt5LNkiejEAEiRSwMWgzZxIDiAwc5e7DzYhVOOtKSsxsk0lFvR4/YrFvC5Qt+IH8EQj2StARDxUDt3TpjPrykyg/MEFv6AUOQ4B+66C1i+HLjxRqF8Pv008Pbb4ufLGLBihSjk3n1XFCi/+IUYz/npT4Ub+lic/zfEUa6OHhVfZ1LHWJVDfOyMl4V3/fUiduG99+J/jxmk35NAkZsDGXIAFXKZIxQCHn8cuPRSUcwpUVIC3Hyz2NnYvh24804Ezz4H/65Ygso5XMiZ9FoYtJoJhZxrNIij/aNolAu5oqLxQo7UuGlRZjOizxNAOBy7aIkUcraoQk4qCsps8dsyp7RWRt40dxU5AFjfWIXDfSPY41S3QzwaCKUlegAYL+QSzsgBMQ1P+jx+6DQskscFgDLkiFmF3aJXNjtxB8Y3KU48UfxtMgEOR5aOLn+xGHQR46aYSIXce7uPYtXx96H3+4AUYgeiaSiXDE96cluVk+fGKu0xRmWiqC0Waz7VEQTf+pYIIX/sMbEO3LBB2bht/nzRyfXSS2LdeOAA8Le/xX2bfo8/dmi2XOxMQl57xo0guOoqccx//Wvc984U3kAIo4HQnI8eAKiQyxxvvilsv5XaKqP56lfFRXLDBuD4cXR8/mYA48GMcxWrSQePf/yGvqvTBQBoqpZu2sXFwjp33z6aj5smpVYjQmGu2MrX4/bDoNOg0KwTBdjllwPr10deq6TI+YIh+MfCkVbB2aLIAcDFy8qh1TA0t6prrxStlak7VgJRhVy8nWm7HbDZFBW5EqsBGo20CPD7gY4OUuSIWUORxaAcPxCtyMmFXFUVuRWngQJDghk5qZDbs68DV/TsEov4D3wgLe9dX24FkGHnyjQgFzaJNtnlLLnjAwnm5E4+WeQEP/ywUPU+/OHkzuUrrhBf44474qpy/SMBlMQKzZ4UBi4jF6pxFbmSEuCSS0QRGUrQQpoBIpEK8RQ5KuSIlNi0SewUXnpp4ucWFQG33CIKvxUrsP+UswFgonw/B7EadRMUuZZ2Ucgtr5Z69YuLgSFhfkKK3PSQF0VKBVn3sA/lhZK9L2PAP/4hijnIal7s18mKUkSRKyoCCqQA5RxX5EqsRpy1qASbVbZX+oLpU+QMOg0sBm38GTnGxp0rJ9HnCUycjzt6VLTukCJHzBIcZj1GAiEExmK4srr9UxU5aqtMCxajcK1U6s6QzU76OnvxwWM7hPGGfE1PkWqHGQUGbc4bnnRGFLn4a7PyQhP0WpZYkTMYgHvuAa69dnotgIyJlss4qtxYKIyh0eDUFsRwWNxDYihyZoMWDosenUMJCtHrrxcRBq+9lvyxp0gkDDxWger1CmO1GN9bPkKFXCYIh4VEfsklYudcDRs3imLkxz+GUzKYSCTf5zs2kw6eqEKutWMIdcUWOCzSBam4ePzJpMhNC3lRpFSQ9Qz7RVtlDOIZpciFXGRGjrFxVS7HCzkA2NBUiaP9o9jdOZzwuaOBsbQVcoAofuO2VgLiZ6mgyJVShhwxi3FIC87JEQS+YAhu/9jEGTmAjE7ShMWgBeeAT8mgQ1rLLBjoRNmhvWlrqwRE7ER9uQ37unJbkesa9sGo0yjPZUloNQxVDpURBKly+eVxVbkBSd2e0lrZ3S06NhSKnUq7Ob4iBwCXXSaK+Rlor+z3SIVcrNbKOZQhB1AhlxneeUe0M6lpq5RxOIB//xtYtw6dLi8MWo2yZDxHsBp1cPsnKnKR+ThgvJA74QSgtDS7B5cnJFTk3D5hdBKDUqsBbt9YTIviKYocIG4YZnPadnEzyUVLK6DTsITh4C/u6caujmFUpXHTRVUhp6DI9br9E8PAKUOOmGU4FNqL5WtU5Py22YBzzwXOPDOrx5evFEibUYpzcno9/HojNhx6S3ycJqMTmVPqHHjnSD+e3NGR1q+bTjqHvKi0m2IHUE+ipsiM44kUuXSQQJUbDwOfpFwlKHZqisw42j8S/70LCkR756ZNoijMIv3xFLk5FD0AUCGXGTZtEpL5hg3TenmXy4dyu3F8zmWOYjPpI62VgyMBtA960RRt/V1UJP6mtsppo0aRm1cY295XLgJjvVZehE0o5FavFjbNs2CepajAgLMXl6K5tVOxvfL53V34wp+346RKG765/qS0vbdqRW5gAPCMtyKFw1zYMU8OAzcaqf2MmDUUSR0Xk7PkemNFa7z6qhhLIFLGIs35jvpjF3Ltg6MYNphRPDwguipWrEjr+9968RKsXlCMW/6+A0+8157Wr50unC6f6k6pGofKLLl0EEeVU1Su5MgrBUVueZUdh/tGpkRATeH664HBQRGVkEX6petBTEWOCjkiJTgXhdxFF40HaCaJc0j9xSKfsUWZnbR2iPm4xuoYihwVctOm0KSDQaeJqch5/GPw+MfiKHJyITfVmGAoliL3ve8Bb72V+kFniQ1NlTg+4I3MZkbz3K4u/Odf3sWyKjv+9B+nT/w+U8QeJ0crQgznSpc3iGCIT1TkjhwRN7M5YMFM5AdyO/Zkw5O+WNEaRNooMApFbkQhguDZ1i64DZID99q1ab+mWAw6PPjJ1ThjYQm++shOPLY994o5p6TIqaG22Ixet185VDudxFHlxpWrqILn/feBr38dWLp0QpB5NI01heAceD/ReMHatcL4JMvtlf0jARilmfIptLUJM545soFJd/d0s22bWFwl01Y5iU6Xd05nyMlEm53Ihdyy6EJu6VKhTMTL6SPiwhhDmTV2jEBPJEMuviIXqwiMzMiZo24esmHKLOGipRXQaxmaWye2Vz7b6sSX/vouGmvs+ONnVqPQlL4iDkhCkQMmzMnJyugURY7m44hZxHghp0KRI9JGRJFTKOQ2tzoRsklGY2mcj4vGbNDidzeehrMXleLrm3bikW3HM/I+0yEU5uh2+1Gp0oSupkiOIJhZVW5cuZJ+b7q6hAmfyQQ884woeGKwXFprtbQPxX9fvR645hrgqacAd/ZmHPs9IookZpvrHMqQA6iQSz+bNgE6HfChD03r5eEwR/ewD5UOUuRksxPOOVrah7CgtGCi8lFdLRayy5fP3EHmAaUKpiXdkumOktlJvLZMlzcIxsT/4WzFbtHj3PqyCeHgm1s68aW/vYcVtQ788dPpL+IAsZBVNSMHTFDkpswQAVTIEbMO2cxqstlJnzuOuQGRMpY4M3LHB0ax8/gQzKXSOMPatRk7DrNBiwduXIVzFpfiG4+14O9bp84CzwS9bj9CYa6+tVKKIEjoXJkuFFS5fk8AGibNnno8Ij6otxfYvDmuq+M8mwmVdlNkEz0u118vnCKffDId34kq+kf88cPA50hbJUCFXHqR2yrXrBmf30qSvhE/giFOihxEjtxYmMM/FsaujuGJbZVE2iizGmK2R/a4hSI3T6G1Ul5QxSoCh71B2Iy6WT/nub6xEh1DXuw4PoSndnbi5od34NQ6Bx769GrYMlDEAUKR8wZD8Cu5xwGiZUSnm6DIjSsW0s1tcBBwucjohJhVFBi00GtZjBk5H4oseui1tGzJBLIiNxJjRk7uSnAsWyLMZTLcsmbSa/HbG1bhvPoyfOOxVvz1nZkv5jqlDDm1sVDjoeBZUuSAmKpc/0gAxQUGaMIh4KMfBXbsAB55BFi5MuGXa6y2ozXGaMEUzjpLdIlksb1yYCRAYeASdEVMJ+++K3bAU2irdEo5JXM9DBwAbEZxY2nrH0HHkBdNNVTIZQKlGIHuYbmQi93KZNRpYTfrYypyQ6MB2C2ZKXSyydpl5TBoNfjeU7ux8eH3sHJ+Ef7wqdWwGjOnNEZCweOpclotUFOjoMhJCw3ZsZIUOWIWwRiD3WyY2lrp9lNbZQaRZ+RitVY2tzixotYB6wP3Ay+8kJXjMem1uP8TK3H+kjJ884lW/OntqXEr2SSyNitUtzYrsxph0Gqy41wpE0OV6/f4UWIxiODx5mbg3nuFKqeCxmpheOL2JegQ0WiA664Dnn9eqH1ZoN8TiK3Ieb0iWoEKOSJpHn9c9B0XFEQCk6eDU9r1UTtQm8/IisebB/sBjPdsE+ml1GrEwIhoG4mme9gPs14bKahjoVQEurzBifNxs5RCkx7nNZRhZ7sLqxcU4w+fOg0FGSziAKBQKuSGk8yS6/MEYNBqUGjWie6A5mbxCVLkiFlGkUU/1exEmokhMkNEkZvUWnm0fwStHS5saKwUbtwWS9aOyaTX4r5PrMSFJ87Dt/+xK2Nulj94+n08/m78r+1MUpHTaBiqi7KUJRfNJFVuYCSAG994FLjvPuAb3wC+8AXVX0qOe9rVkThPFR/7GBAKAY8+Ot0jVw3nkkNzrOvBHMuQA6iQS53eXiFXX3WVmNl6442UMs06pV2fKpqRi6gebx7qA2PAsqrCGT6i/KTMZkSYj+fNyPS4/SgvVBgmlii1GhRn5NLp5DiTbFxTj8+cswAPfnJ1ZLGTSeQZoWSz5EQYuAGsu1vczL/7XdHmTTOkxCzDYdGTIpdl5Bk57yRFTs7SXNdYkfVjAkTnx68/vhKn1Dnwk2f3pt0F8q1D/fj9G0fw/affx3Ac5cnp8sGs1yZ1X6uZiUJukirX9MZzuP7xe8U69b//O6kvJY+ztHYkMDwBgMZGca/JQnvlaCAEXzAcO2t5jkUPAFTIpcajjwLLlgk17o47RBB4itkqXcM+GHUaFOVBW1qqyEYZ7xwewMLSgozNJM11yhRMS7qHfYrzcZHX2kyKily+FHLLq+349oalMMeyOc4AqlorAaHIdXREZiH6PH586NBb4mb6/PPA3XeLbB/d7DWcIeYmDosBg1MUOYUdeCItmPVS/MCkGbnmFidOqXNEXBhnAoNOg1svXoLuYT/+9u/0zctxznH3C/tRaNLB5Q3iD2+0KT7X6fKi0qEuDFympsiC9oEstlbKyKrc7bfj9od/gralpwIPPpi0i2OJ1YhqhzlmBE9Mrr9eiBlyMZUhxkPOqZADqJCbHj09wNVXC8vVujoxG/f//p+ijWsydEo5JclcLPIVq1TIuf1jaKpxzPDR5C+lCjECPcM+xQy5yGsVjFJc3mCkRZBIDrmQm6xITGH+fNHK0tkJDA3hxl9/G7f/7tuilfK994CNG+eM/TKRXzgmRXCM+McwGgiRIpdBNBoGi0E7YUbucK8H7zuHsaGpagaPTHDWolKcvqAYv3r5UNpUubcO9ePfRwbw1bUNWHNSOR547bDiBlrnkC/pkZfaYjP6RwKKkQ4ZQ1blOjtx3F6OLT+8T8QNTIPGajt2qXGuBITqBwAPPzyt91JLJGon1sbOnj1zKkMOoEIuOTgH/v53kV/21FPAj38MvP12WluXnC4KA5exGccLAZqPyxyxFDnOObqH/ShPsHAqsxnh8Y/BGzVXwTkXM3KkKk+LpBQ5QOy0Ll+O8959Af+85ovAm28CJ52U4aMkiMzhsOgnKHJxF25E2rAYdBNm5JqltspLZ6itcjK3rG1Ar9uPP6fB+IRzjrv+uR8VhSZ8dHUdNq6px7BvDL9//UjM5ztd3qTXZlnPkovm8svhuvc3+Ni1P0RB5bxpf5nGGjva+kfhSrSxCIhNxLPOynh7Zb9HQZH7+9+BX/5S+FXMoU3MufOdpoMdO8SOw6JFYsf79tvT3rbkHPKqDpzMd6IzyMixMnPEUuTc/jF4gyEVitzUItAbDCEY4nnTWpltCqXzXnWW3Pe+B15YiI/ccBd2fvrmtHQGEMRM4rAY4AuGI8pLxJGVFLmMYjFoMeofV4+aW51YNb8oZzaXz1hYgrMWleC+Vw5P2DycDq8f7MO2o4O46fxFMOm1WF5tx0VLy/H7149MKVqCoTB63P6kY6GyniUXDWNov+IadBWWojSF7EV57bWrM4n2ytZW8SdDyK2VE+IHHn9cGK6cfTbwl79k7L1zESrkkuGUU4BnnxU9wEuXpv3Lh8Ic3W4/qnLkojnTyO6AGgYsrSSjk0xRYNDCrNdOKMZ6EkQPyMgLq56oIlAuQKiQmx46rQY2oy5xIbdgAXDGGcAtt2DglTexo3wxLXSJvEBW8+X24nFFbvY74eYyorVSFEgHe9zY2+XGhqbcalG7ZW0D+jypqXKcc9z9z/2osptwzWm1kcc3rmmA2z+G371+eMLze9x+cA5UJmlCVzuTihyilavp3xdkwxPVc3JXXy3icaJCydNN34i4HpTI39fTTwPXXgusXi3cmgsKMvbeuQhNwSfLJZcofioc5hgcDaBkmu0fvW5hAV9B0QMAxICzUafB/BJLxi3f5zKMMZTaDBMUue5h8e9Eilystkx58UWF3PQpNOsTt7IYjcBbbwEAeruEPTS1nhH5QJHk3DrkDaDCbiJFLksUGHWRQq65pQuMAesac6uQO+2EYpxbX4r7XjmEj51RNy0n4Vf29+LdY0P40ZXLYdSNm1gtrSrEuuUV+P0bbfj0OQsiDsLOoenFQpVaDTDqNGhpd2G3WkVrEsUFhmkrojGVqyRxWAyoLTarn5ObNw9Yu1a0V37uc2IEIM1tjgOeACwGrTAge+45kd0sCy02W1rfazZAq+M08uCbbfjZlr147bYLpnXD6Uwyp2QuUF5owsr5RTN9GHlPmdU4wbQkEgauYkYOmNiWKStJDirkpo19ktlDIvrc4v+OFrpEPiBfOwZHxO9ArycAxoBiCylymcRi0MIjtVZubunEaScUJ9zMmwk2rmnAVb9+E3986yi+8IFFSb1WOFUeQLXDjKtX1k75/MY1DXhudxd++9ph3HrxiQCATpe4HyZbUDHGsKC0AJu2t2PT9ull4Bl1Grx5+wXTEggiSnYKihwANFU70KImgkDmM58RytyCBYDVKnwkli8fjyhobATKyqZ9PP0jAVGcvvACcMUVwj1+yxbAPjdHcKiQSyOPv9sOXzCM53Z34RNnzE/69V3TvFjkM3/+zOmwk2lGxim1GnG0f7yPX26VTBQ/IA8bxyrkyLVy+jgsyRVyvR5x7aDWMyIfGM9SFBsUvW4/SgoM0GlpGiSTFBh06Bn2Y3+3Gwd6PPjB5ctm+ptDLwgAABehSURBVJBisnJ+ET7QUIb7XzmEj58xP5I5q4aX9/Vi5/Eh/OTDjTDopp5PSypsuLSxEn94ow2fOWchigsM6JI22afjX3D/J1Zib5c76dcBohD71hO78NzuLnzs9OTXlAMjAeg0DIXm1Jb6jTV2NLc6MTgSQFEsy//JfOQjwLZtwPbtYlZu1y7giSeABx4Yf86ppwrVbsmSpI+nfySAczvfB753G9DQIOJ2iubuhj8VcmniSN8IdneK9qbmls5pFXKdknxPM3Lj1JXMXHbNXKLMZsS2o4ORj7uHfbAadQlvkHqtBsUFE0PBaUYudexmPQ72eFQ/n1rPiHwi1owctQ1nHotBi9HgGDa3OKFhwCXLc8OtMha3rG3AFfe+gYfebMNN5y9W9Ro5N6622IyrVtYoPm/jhfV4ptWJ37x6GLevOxGdQ+J+WDiNLNv5JQWYXzK9mS3OOX73+hE0tzinVcj1ewIoLjCkHGfVFAkGd+G8BpVK2sqV4o8M50BXlyjqduwAfvpTYNUq4Le/HY8tUMm8lm34zn23AQtPEKpcaWlSr883aHsrTTzTKmx6r11Vi3eODETMIpLB6fLBrNemvHtCEMlSajVicDSAYCgMAOgZ9ic0Ohl/7cT5Onm2i5TU6WM36zGUTGulJwCjTpPUzjRB5CryjNygdC3pdftpkyILWIxajPhDaG7pxOkLSjDPlnttlTIn1zpwwYnz8NvXDsPtU3etfHFPD1raXfjyBfXQx1F368ttuKypCn98qw39Hr8UPZD9nwVjDBsaK/H24f4pOa9q6B/xxw7NTpJlUYXctGFMZLutXQvceqtwfm9qAq67DvjiFwGfijXzyAjw85/j+/fdCndxGfDii2Imb45DhVyaeHpnJ1bNL8J/nLsAnAPP7upK+ms4XSJ6gMLAiWxTZjOC8/Hh6O5hH8pV3sTLbMYpipxWw2CjomLaJDsjJy906dpB5AMmvQYGnQZDUmtln8cfMVYiMkeBQYeBkQAO9Y5gfY65VcZi45p6DI0G8dCbbQmfK6tx80ss+PAp1Qmf/5UL6+ELhvCbVw/D6fLNmAnd+qYqhDnw3C5n0q/tHwmkRcm2m/U4ocSCVrXOlWqorQVeflkUdffdJ/LnDh6M/dzBQeCOO0Tkzi23YFf5Ijzy04fmVOh3PKiQSwMHezzY2+XG+qZK1JfbsKTcFgnSTAany0dtlcSMIF/s5V2/brcP5aoVOSN6JxVyhSYdFRUpYLfoERgbz9FKBLWeEfkEYwxFFj2GRoLgnKPX7Y/kXRKZQ3aA1DBgXQ63Vco01Tiw5qR5+M2rhzGcQJV7/v1u7O4cxpcvqFc1a7l4nhWXn1yNh95qw5G+kRlbmy2psKF+nhWbp7GmlFsr00FjjSM1RS4Wej3wP/8j4gPa2sTc3KZN4593OoHbbhPOl9/5DnDmmRh56RVce92PYZhfl95jmcVQIZcGnml1Cpve5WJ3YH1TJbYeHYiYl6jFOeSbEfmeICLukx4/OOfoHvardisrsxrR5w6Acw4AGPIGaT4uReSfn1pVjlrPiHzDYTZgyBuA2z8G/1iYFLksYDEIK/6zFpVOO0Yp22xc04Bh3xgefL1N8TnhMMfPXziABaUFuOLkKtVf+ysX1iMY4nD7xqZldJIu1jdV4t9tyY/sDMjujmmgqdqOjiEv+j3Jt3gmZMMGMTe3bJlwu7zpJtFuuWABcOedwGWXATt3Ak8/jd5GMXeXru8rH6DepzTQ3OLEafOLI9L7+qZK3PXP/Xim1YlPn7NA1dcYC4XR4/YlHThJEOkgkgfn9sPlDSIwFk7oWBl5rc0IbzCEkUAIVinImgq51JB/fkOjQVUFdZ/Hj1Pq5q5rF5F/OCx6DI4G0Sd1CZTaaOGWaSxGUcjNhrZKmeXVdly0tBwPvH54Qot/NIOjAexxDuPua1ck5XwqCr9qPPZu+4x2S21oqsTPXziAZ1qd+OTZ6taUvmAIHv9Y2jo1lkfNyX1wSQbm0urqgFdeAf7rv4C77gIMBuBTnxKtl4vGIyb65TDwWbLRkA2okEuRA91u7Ot2T7DpXVRmxUmVhWhOopDbsrsbYQ4srSzM1KEShCLyIqnX448KA1ffWgmIIjBSyFHeU0oko8iNhcLoHwmQIkfkFQ6LHm19o+OOrFbqVsk0K2ocOLnWMSvaKqP5+sVL8KkHt6K5Vbn98MyFJfjQisSzcZPZuKYee7uGceoM5tkunmfDiRU2NCdRyD3/fjcAoH6eNS3HsLxarE1b2zNUyAGieLvzTuFiWV0NVE1VT5/f3Q0NAxaVTc8JNB+hQi5FNreItsrJNr0bmirxsy370DnkRVUClS0U5vj5C/uxeJ4Va5eWZ/JwCSImFoMOBQYt+tyBqDBw9YocIIrAE0oLMOwNoq6YYiNSwWGWc7QSF3IDowFwDpRRqwmRRxRZDHhvdAh9HmF4Qopc5llebcc/bjp7pg8jaRrKbXjj9gsy8rVriy1o/sq5GfnaybC+sRJ3/nM/ulQYr4TCHL94YT8ayq1Yc1J61pQ2kx4LywrQku45uVicdlrMh3vdfvzxraO4/ORq1BTRGkOGZuRSgHOO5lYnTl9QPGXRu75RtCY8E2eHSKa51YkDPR5sXFMPrYYMIoiZocwmTEvkMPDpKHIAMDQagJ0iNFJivLUykPC5lCFH5CN2i4jg6HWLjSWakSPmMpc2qV9TPr2zE4d6R7BxTQM0aVxTNlXbsSsbhZwC979yCP6xEL58gbrcwLkCFXIpsL/bg4M9Hqxvmir/nlBagOXVhQmdhuSdkyXlNly6fPb0pRP5R6nViD63PyVFjnOOYd8YzcilSDKtlRHFgha6RB5RZDEgMBbGsQEvtBoWyZYjiLmIPLKzuaUz7vPGQmHc8+IBnFhhwyXL0tsiu7zaDqfLhx538jnJqdIz7MOf3j6KK0+pwcKy9LSL5gtUyKVAc0tnXJve9Y1V2HF8CMcHRhW/hrxzcvOa+rTunBBEskQUuWEfCk06mCUHs0QUFxigYUKR8/jHEArzSGsgMT1sJh0YA4ZVFHKkyBH5iEPazDjQ40ZJgYHuj8ScZ0NTJd49NoSOIa/ic57c0YnDfSPYmIE1ZVONAwBmRJX79SuHMBbm+MqFpMZNhgq5acI5x+YWJ85cVKK4E56ovTKTOycEkSylVhHsnUz0AABoNQzFBaIIlBUkUuRSQ6NhKDSpCwWXndpIkSPyCYekwB3s8dC5TRAYX1M+G29N+dIBLK0sxEVL07+mXFZVCMaAlnQGg6uge9iHv7xzDB8+pRrzS8jkZDIpFXKMsasZY7sZY2HG2Ko4z2tjjLUyxnYwxral8p65wh6nG4f7RrC+UTmTpK7EgqYau6KT0vjOSXr7mAliOpTZjBgaDaJ9aDSpQg4ASq0G9LoDGBoVhUchFXIpYzeLGaFE9Lr9sBi0KDDSXCKRPzgs4hridPlIbSYIjI/sPK0wsvP4ex042j+aETUOAAqMOiwus2Zdkfv1y4cQDnN8+YL6rL7vbCFVRW4XgA8DeFXFc8/nnJ/MOVcs+GYTza2d0GrYFLfKyWxoqkRLuwtH+0cmPD4WCuP/XjqAZVWFuHgZOVUSM4+8672/y4N5Ko1OZOS2zGFS5NKG3axekaOFLpFvRM/EkSJHEIINTVXYGWNkJyitKZdXF2bU/byx2p5VRc7p8uKv7xzDR1bWoK6EnCpjkVIhxznfwznfl66DmS1wztHc4sRZi0pQXBB/FuhSSQqfrMo98V4H2vpHsXFNAxgjNY6YeeRiIBAKJ63IlUlGKXLhIe+mE9NHbSHX6/bTQpfIO6KvIbRRQRACpZGdx99tx/EBL27J8JqyscaOnihTtEzzq38dQphz3HQ+zcYpka0ZOQ7gecbYdsbY5+I9kTH2OcbYNsbYtt7e3iwdXnLs7hxGW/8oNjQldpmsKbLg5FoHmqOk8KDUx9xYbceakzIUrEgQSRK9WCpPcuEkK3JDpMilDbslCUWOCjkiz4i+hpRSRiJBABC5dismjewExsK458WDWFFjxwUnZnZN2VRjB5CdObmOIS8e3noM15xWi1rKplUkYSHHGHuBMbYrxp/Lk3ifczjnpwJYB+Amxth5Sk/knP+Gc76Kc76qrKwsibfIHptbnNBpGC5WaVCyoakSuzuHcaRPtFdGdk7W1pMaR+QM0YuleUnPyBkRGAujfVC0e1Ahlzp2sx6uUZWKHIUlE3mGSa+FWS+cc0mRI4hx1k8a2dm0vR0dQ15sXJv5Dq+llXZoGNCahTm5e/91EABIjUtAwkKOc76Gc748xp8n1b4J57xD+rsHwBMAVk//kGcWEQLeiXPqSyOuWomItFe2dI7vnNQ6cP4SUuOI3CG6PU9tGLiMvNA62OOBTsNgURldQCgjt1ZyzhWfEwyFMTgaRJk1ucKbIGYDRVJ7JRVyBDFO9MiOfyyEX750ACfXOvDBhsyLH2aDFvXzbGhtH8ro+xwfGMWj247j2tNqUe0wZ/S9ZjsZb61kjBUwxmzyvwFcBGGSMitp7XDh+IA30qeshiqHGSvnF2FzizOyc3LLGlLjiNzCpNfCZhLOh2rDwGXkIvBQ7wgcFj2d22nAYdZjLMwxGggpPqdfDgMnRY7IQ+zSZim1DhPEODVFFpxSJ0Z2HtnWjk6XD1/Nghon01hjR2uHK+4mY6rc+6+DYGCkxqkg1fiBKxlj7QDOBNDMGNsiPV7FGHtGelo5gNcZYzsB/BtAM+f8uVTedybZ3OKEXsuSzuhY31iJvV1u/GzLXpxS58AHsrBzQhDJIu98T8e1EgDa+kYoeiBNyO2p8ebkImHgtNAl8hBS5AgiNusbxcjOXc/vw8r5RTi3vjRr791UY0efJwCnKzOGJ8f6R7FpezuuW12LSjupcYlIKXiIc/4ERKvk5Mc7AVwq/fswgBWpvE+uILtVnltfBnuSrnyXNlbijub3MTgaxC/IqZLIUUqtRgyOBGDUJdcaKc/XjYU5zcelCfnnODQaRJVCa0kkDJwWukQe4rDoodcyuqYQxCTWN1Xih817MDgazLhT5WQaq4Xhye9fP4KGClvM5zjMeqw5qXxaeXb/99IBaDQM/0lqnCooQTYJ9na50THkxdcuakj6tRV2E86tL8NYKJzVnROCSIaTKmzQTePCW2QxQKthCFEhlzZIkSPmOovn2XBsYJQ2PgliEpV2M86tLwXnwNmLS7L63idVFqLQpMMDrx+J+7yPnlaL/76yMali7o9vteHR7e34zDkLko5BmqtQIZcEJ1UW4rXbzk+YHafEAzeILHS6KRG5yncuWzatvneNhqGkwIAetx8OKuTSgqz6xy3kJEWOWs+IfGTjhfX48gW0K08QsfjdjacByP6a0qTX4o3bL4h7b/rbv4/hXikD7icfblJVzD34xhF8/+n3sXZpOb5xyYnpPOS8hgq5JEkly8Kgy1ZsH0FMD62GAZjeTaHMZkSP20+KXJqQf47DCRQ5m1EHk55cQon8Q6Nh0EzzekQQ+c5MriltJj1sJuV7/dcvWgKdRoNfvHgAoTDwPx9pktYXsfnd60dwx+b3cfGycvzfdafSejkJqJAjCCItyM6VVMilh8iMnDeg+Jw+j5/m4wiCIIicgjGGW9Y2QMMY7n5hPzjn+NnVK2IWc7999TB+9MwerFtegXuuOwV6LRVxyUCFHEEQaUFu7yPXyvRgNeqg1bCEM3I0H0cQBEHkIjevqYdWA/zv8/sR4hx3Xr0CuqhC7b5XDuEnz+7F+sZK/PyjJ1MRNw2okCMIIi3IipzDQplm6YAxFgkFV6LP48cSBdcwgiAIgphpvnSByE3+2ZZ9CHPg7mtEMXfvvw7iZ1v24bIVVZHHiOShQo4giLQgK3LUWpk+RCE3pvj5Xrcf5ywmF1yCIAgid7np/MXQahh+8uxehMMcDeU23P3Cflx+ctUUlY5IDirkCIJIC3KWHBVy6aPQrMcr+3pw5a/eiPn5Yd9YRAklCIIgiFzlCx9YBC1j+NEze9Dc6sSVp1TjfxXm5gj1UCFHEERaOLe+DNetro2EhRKpc/3qWmxucSp+/oIT52HN0vIsHhFBEARBTI/PnrcQhWYdjg94ccvaBiri0gCbTmZUtli1ahXftm3bTB8GQRAEQRAEQRDEjMAY2845XzX5cWpKJQiCIAiCIAiCmGVQIUcQBEEQBEEQBDHLoEKOIAiCIAiCIAhilkGFHEEQBEEQBEEQxCyDCjmCIAiCIAiCIIhZBhVyBEEQBEEQBEEQswwq5AiCIAiCIAiCIGYZVMgRBEEQBEEQBEHMMqiQIwiCIAiCIAiCmGVQIUcQBEEQBEEQBDHLoEKOIAiCIAiCIAhilkGFHEEQBEEQBEEQxCyDCjmCIAiCIAiCIIhZBuOcz/QxKMIY6wVwdKaPIwalAPpm+iCIvIbOMSIb0HlGZAM6z4hMQ+cYkQ1m8jybzzkvm/xgThdyuQpjbBvnfNVMHweRv9A5RmQDOs+IbEDnGZFp6BwjskEunmfUWkkQBEEQBEEQBDHLoEKOIAiCIAiCIAhilkGF3PT4zUwfAJH30DlGZAM6z4hsQOcZkWnoHCOyQc6dZzQjRxAEQRAEQRAEMcsgRY4gCIIgCIIgCGKWQYVcEjDGLmGM7WOMHWSM3T7Tx0PkB4yxWsbYvxhj7zPGdjPGbpYeL2aM/ZMxdkD6u2imj5WY3TDGtIyx9xhjm6WPFzDG3pGuaX9njBlm+hiJ2Q1jzMEY28QY28sY28MYO5OuZUS6YYzdIt0vdzHG/sYYM9H1jEgVxtjvGWM9jLFdUY/FvH4xwT3S+dbCGDt1Jo6ZCjmVMMa0AO4FsA7AUgDXMcaWzuxREXnCGICvcc6XAjgDwE3SuXU7gBc55/UAXpQ+JohUuBnAnqiPfwrgbs75YgCDAD4zI0dF5BO/APAc5/xEACsgzje6lhFpgzFWDeArAFZxzpcD0AL4KOh6RqTOHwBcMukxpevXOgD10p/PAfh1lo5xAlTIqWc1gIOc88Oc8wCAhwFcPsPHROQBnHMn5/xd6d9uiIVPNcT59ZD0tIcAXDEzR0jkA4yxGgDrATwgfcwAXABgk/QUOseIlGCM2QGcB+B3AMA5D3DOh0DXMiL96ACYGWM6ABYATtD1jEgRzvmrAAYmPax0/bocwB+54G0ADsZYZXaOdBwq5NRTDeB41Mft0mMEkTYYYycAOAXAOwDKOedO6VNdAMpn6LCI/ODnAG4DEJY+LgEwxDkfkz6maxqRKgsA9AJ4UGrhfYAxVgC6lhFphHPeAeB/ARyDKOBcALaDrmdEZlC6fuVEXUCFHEHkCIwxK4DHAGzknA9Hf44Le1mymCWmBWNsA4Aezvn2mT4WIq/RATgVwK8556cAGMGkNkq6lhGpIs0oXQ6xcVAFoABT2+EIIu3k4vWLCjn1dACojfq4RnqMIFKGMaaHKOL+wjl/XHq4W5bppb97Zur4iFnP2QA+xBhrg2gLvwBilskhtSYBdE0jUqcdQDvn/B3p400QhR1dy4h0sgbAEc55L+c8COBxiGscXc+ITKB0/cqJuoAKOfVsBVAvuSIZIAZrn5rhYyLyAGlW6XcA9nDO74r61FMAbpT+fSOAJ7N9bER+wDn/L855Def8BIhr10uc848B+BeAj0hPo3OMSAnOeReA44yxJdJDFwJ4H3QtI9LLMQBnMMYs0v1TPs/oekZkAqXr11MAbpDcK88A4IpqwcwaFAieBIyxSyHmTLQAfs85/9EMHxKRBzDGzgHwGoBWjM8vfRNiTu4RAHUAjgK4hnM+eQiXIJKCMfZBAF/nnG9gjC2EUOiKAbwH4OOcc/9MHh8xu2GMnQxhqGMAcBjApyA2jelaRqQNxtj3AVwL4fr8HoD/gJhPousZMW0YY38D8EEApQC6AXwXwD8Q4/olbSL8EqKtdxTApzjn27J+zFTIEQRBEARBEARBzC6otZIgCIIgCIIgCGKWQYUcQRAEQRAEQRDELIMKOYIgCIIgCIIgiFkGFXIEQRAEQRAEQRCzDCrkCIIgCIIgCIIgZhlUyBEEQRAEQRAEQcwyqJAjCIIgCIIgCIKYZVAhRxAEQRAEQRAEMcv4//PcGp9yvBRzAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x432 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eGB0QavqUHWD"
      },
      "source": [],
      "execution_count": null,
      "outputs": []
    }
  ]
}