LSTM_1step_monthly.ipynb

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      "name": "LSTM_1step_monthly.ipynb",
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      "name": "python3",
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    {
      "cell_type": "markdown",
      "metadata": {
        "id": "BexZygSl10H4"
      },
      "source": [
        "references:\n",
        "\n",
        "\n",
        "*   Multivariate Time Series Forecasting with LSTMs in Keras (machinelearningmastery.com)\n",
        "*   https://www.youtube.com/watch?v=4FmVIpcwl4k&t=1117s\n",
        "\n",
        "\n",
        "*   BOOK: Hands-on time series analysis\n",
        "*   List item\n",
        "\n",
        "\n",
        "\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AF-q7YZCPKn0"
      },
      "source": [
        "from numpy.random import seed\n",
        "seed(1)\n",
        "import tensorflow as tf\n",
        "tf.compat.v1.set_random_seed(2)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_GY3HgVB7T8D"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib as mpl\n",
        "import matplotlib.pyplot as plt\n",
        "from keras.constraints import maxnorm\n",
        "from sklearn.model_selection import train_test_split\n",
        "from keras.preprocessing.sequence import TimeseriesGenerator\n",
        "from sklearn.preprocessing import MinMaxScaler, StandardScaler\n",
        "import tensorflow as tf\n",
        "from keras.models import load_model\n",
        "from keras.wrappers.scikit_learn import KerasClassifier\n",
        "from sklearn.model_selection import GridSearchCV\n",
        "from sklearn.model_selection import TimeSeriesSplit\n",
        "from sklearn.model_selection import cross_val_score\n",
        "\n",
        "mpl.rcParams['figure.figsize'] = (20, 20)\n",
        "mpl.rcParams['axes.grid'] = False"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "tj0hXjtjrJYT"
      },
      "source": [
        "from math import sqrt\n",
        "from numpy import concatenate\n",
        "from matplotlib import pyplot\n",
        "from pandas import read_csv\n",
        "from pandas import DataFrame\n",
        "from pandas import concat\n",
        "from sklearn.preprocessing import MinMaxScaler\n",
        "from sklearn.preprocessing import LabelEncoder\n",
        "from sklearn.metrics import mean_squared_error\n",
        "from keras.models import Sequential\n",
        "from keras.layers import Dense,Dropout\n",
        "from keras.layers import LSTM"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "lmNbWyCJ7n0a"
      },
      "source": [
        "df = pd.read_csv('/content/clean_monthly_df_human_supervised.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
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          "height": 225
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        "outputId": "f18dca25-f602-4d72-ee76-735767f697ac"
      },
      "source": [
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
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          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>max_clients_month_t_1</th>\n",
              "      <th>total_Holiday_Food_Related_t_1</th>\n",
              "      <th>total_consommation_t_1</th>\n",
              "      <th>max_pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>max_clients_month_t</th>\n",
              "      <th>total_Holiday_Food_Related_t</th>\n",
              "      <th>total_consommation_t</th>\n",
              "      <th>max_pdl_total_daily_load_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>28/02/2015</td>\n",
              "      <td>8</td>\n",
              "      <td>3980</td>\n",
              "      <td>2</td>\n",
              "      <td>247497.63</td>\n",
              "      <td>22257</td>\n",
              "      <td>1479</td>\n",
              "      <td>11</td>\n",
              "      <td>4023</td>\n",
              "      <td>0</td>\n",
              "      <td>263249.88</td>\n",
              "      <td>22242</td>\n",
              "      <td>1494</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>31/03/2015</td>\n",
              "      <td>11</td>\n",
              "      <td>4023</td>\n",
              "      <td>0</td>\n",
              "      <td>263249.88</td>\n",
              "      <td>22242</td>\n",
              "      <td>1494</td>\n",
              "      <td>12</td>\n",
              "      <td>4060</td>\n",
              "      <td>0</td>\n",
              "      <td>246311.89</td>\n",
              "      <td>21691</td>\n",
              "      <td>1551</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>30/04/2015</td>\n",
              "      <td>12</td>\n",
              "      <td>4060</td>\n",
              "      <td>0</td>\n",
              "      <td>246311.89</td>\n",
              "      <td>21691</td>\n",
              "      <td>1551</td>\n",
              "      <td>14</td>\n",
              "      <td>4094</td>\n",
              "      <td>0</td>\n",
              "      <td>237177.99</td>\n",
              "      <td>16752</td>\n",
              "      <td>1253</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>31/05/2015</td>\n",
              "      <td>14</td>\n",
              "      <td>4094</td>\n",
              "      <td>0</td>\n",
              "      <td>237177.99</td>\n",
              "      <td>16752</td>\n",
              "      <td>1253</td>\n",
              "      <td>19</td>\n",
              "      <td>4112</td>\n",
              "      <td>0</td>\n",
              "      <td>229106.03</td>\n",
              "      <td>12318</td>\n",
              "      <td>1123</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>30/06/2015</td>\n",
              "      <td>19</td>\n",
              "      <td>4112</td>\n",
              "      <td>0</td>\n",
              "      <td>229106.03</td>\n",
              "      <td>12318</td>\n",
              "      <td>1123</td>\n",
              "      <td>22</td>\n",
              "      <td>4137</td>\n",
              "      <td>13</td>\n",
              "      <td>223934.14</td>\n",
              "      <td>11655</td>\n",
              "      <td>1122</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         time  ...  max_pdl_daily_load_t\n",
              "0  28/02/2015  ...                  1494\n",
              "1  31/03/2015  ...                  1551\n",
              "2  30/04/2015  ...                  1253\n",
              "3  31/05/2015  ...                  1123\n",
              "4  30/06/2015  ...                  1122\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
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      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 225
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        "id": "r1Nv6sDzc6DA",
        "outputId": "3c79379e-7b5b-4d24-efcc-d71d4df16fdd"
      },
      "source": [
        "df.tail()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>time</th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>max_clients_month_t_1</th>\n",
              "      <th>total_Holiday_Food_Related_t_1</th>\n",
              "      <th>total_consommation_t_1</th>\n",
              "      <th>max_pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>max_clients_month_t</th>\n",
              "      <th>total_Holiday_Food_Related_t</th>\n",
              "      <th>total_consommation_t</th>\n",
              "      <th>max_pdl_total_daily_load_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>61</th>\n",
              "      <td>31/03/2020</td>\n",
              "      <td>11</td>\n",
              "      <td>5348</td>\n",
              "      <td>0</td>\n",
              "      <td>222261.04</td>\n",
              "      <td>23365</td>\n",
              "      <td>1451</td>\n",
              "      <td>9</td>\n",
              "      <td>5321</td>\n",
              "      <td>0</td>\n",
              "      <td>219707.61</td>\n",
              "      <td>23370</td>\n",
              "      <td>1463</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>62</th>\n",
              "      <td>30/04/2020</td>\n",
              "      <td>9</td>\n",
              "      <td>5321</td>\n",
              "      <td>0</td>\n",
              "      <td>219707.61</td>\n",
              "      <td>23370</td>\n",
              "      <td>1463</td>\n",
              "      <td>12</td>\n",
              "      <td>5324</td>\n",
              "      <td>7</td>\n",
              "      <td>207015.41</td>\n",
              "      <td>18875</td>\n",
              "      <td>1599</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>63</th>\n",
              "      <td>31/05/2020</td>\n",
              "      <td>12</td>\n",
              "      <td>5324</td>\n",
              "      <td>7</td>\n",
              "      <td>207015.41</td>\n",
              "      <td>18875</td>\n",
              "      <td>1599</td>\n",
              "      <td>18</td>\n",
              "      <td>5326</td>\n",
              "      <td>26</td>\n",
              "      <td>193552.21</td>\n",
              "      <td>15439</td>\n",
              "      <td>1400</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>64</th>\n",
              "      <td>30/06/2020</td>\n",
              "      <td>18</td>\n",
              "      <td>5326</td>\n",
              "      <td>26</td>\n",
              "      <td>193552.21</td>\n",
              "      <td>15439</td>\n",
              "      <td>1400</td>\n",
              "      <td>20</td>\n",
              "      <td>5324</td>\n",
              "      <td>0</td>\n",
              "      <td>182548.79</td>\n",
              "      <td>14697</td>\n",
              "      <td>1105</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>65</th>\n",
              "      <td>31/07/2020</td>\n",
              "      <td>20</td>\n",
              "      <td>5324</td>\n",
              "      <td>0</td>\n",
              "      <td>182548.79</td>\n",
              "      <td>14697</td>\n",
              "      <td>1105</td>\n",
              "      <td>23</td>\n",
              "      <td>5277</td>\n",
              "      <td>1</td>\n",
              "      <td>177683.15</td>\n",
              "      <td>13664</td>\n",
              "      <td>1084</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "          time  ...  max_pdl_daily_load_t\n",
              "61  31/03/2020  ...                  1463\n",
              "62  30/04/2020  ...                  1599\n",
              "63  31/05/2020  ...                  1400\n",
              "64  30/06/2020  ...                  1105\n",
              "65  31/07/2020  ...                  1084\n",
              "\n",
              "[5 rows x 13 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 29
        }
      ]
    },
    {
      "cell_type": "code",
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        },
        "outputId": "93842f81-70e3-402f-e48f-c4c49722f10d"
      },
      "source": [
        "df.info()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "<class 'pandas.core.frame.DataFrame'>\n",
            "RangeIndex: 66 entries, 0 to 65\n",
            "Data columns (total 13 columns):\n",
            " #   Column                          Non-Null Count  Dtype  \n",
            "---  ------                          --------------  -----  \n",
            " 0   time                            66 non-null     object \n",
            " 1   MIN_TEMPERATURE_C_t_1           66 non-null     int64  \n",
            " 2   max_clients_month_t_1           66 non-null     int64  \n",
            " 3   total_Holiday_Food_Related_t_1  66 non-null     int64  \n",
            " 4   total_consommation_t_1          66 non-null     float64\n",
            " 5   max_pdl_total_daily_load_t_1    66 non-null     int64  \n",
            " 6   max_pdl_daily_load_t_1          66 non-null     int64  \n",
            " 7   MIN_TEMPERATURE_C_t             66 non-null     int64  \n",
            " 8   max_clients_month_t             66 non-null     int64  \n",
            " 9   total_Holiday_Food_Related_t    66 non-null     int64  \n",
            " 10  total_consommation_t            66 non-null     float64\n",
            " 11  max_pdl_total_daily_load_t      66 non-null     int64  \n",
            " 12  max_pdl_daily_load_t            66 non-null     int64  \n",
            "dtypes: float64(2), int64(10), object(1)\n",
            "memory usage: 6.8+ KB\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DX4Wjmy08aWn"
      },
      "source": [
        "time = pd.to_datetime(df['time'], dayfirst=True).dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "s3QqtUCsyKLa",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 319
        },
        "outputId": "4baaf2ac-9f65-43c1-a09e-c8e2c043318d"
      },
      "source": [
        "df.describe()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>MIN_TEMPERATURE_C_t_1</th>\n",
              "      <th>max_clients_month_t_1</th>\n",
              "      <th>total_Holiday_Food_Related_t_1</th>\n",
              "      <th>total_consommation_t_1</th>\n",
              "      <th>max_pdl_total_daily_load_t_1</th>\n",
              "      <th>max_pdl_daily_load_t_1</th>\n",
              "      <th>MIN_TEMPERATURE_C_t</th>\n",
              "      <th>max_clients_month_t</th>\n",
              "      <th>total_Holiday_Food_Related_t</th>\n",
              "      <th>total_consommation_t</th>\n",
              "      <th>max_pdl_total_daily_load_t</th>\n",
              "      <th>max_pdl_daily_load_t</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "      <td>66.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>16.833333</td>\n",
              "      <td>4768.469697</td>\n",
              "      <td>3.500000</td>\n",
              "      <td>228764.090152</td>\n",
              "      <td>16520.727273</td>\n",
              "      <td>1264.818182</td>\n",
              "      <td>17.060606</td>\n",
              "      <td>4788.121212</td>\n",
              "      <td>3.484848</td>\n",
              "      <td>227706.295000</td>\n",
              "      <td>16390.530303</td>\n",
              "      <td>1258.833333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>5.682858</td>\n",
              "      <td>441.257442</td>\n",
              "      <td>7.190271</td>\n",
              "      <td>28276.748973</td>\n",
              "      <td>5479.954448</td>\n",
              "      <td>201.969983</td>\n",
              "      <td>5.623792</td>\n",
              "      <td>434.430822</td>\n",
              "      <td>7.194533</td>\n",
              "      <td>28864.887407</td>\n",
              "      <td>5443.529444</td>\n",
              "      <td>201.377150</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>8.000000</td>\n",
              "      <td>3980.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>141878.540000</td>\n",
              "      <td>7586.000000</td>\n",
              "      <td>892.000000</td>\n",
              "      <td>8.000000</td>\n",
              "      <td>4023.000000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>141878.540000</td>\n",
              "      <td>7586.000000</td>\n",
              "      <td>892.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>12.000000</td>\n",
              "      <td>4363.250000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>213317.625000</td>\n",
              "      <td>11801.000000</td>\n",
              "      <td>1099.000000</td>\n",
              "      <td>12.000000</td>\n",
              "      <td>4391.500000</td>\n",
              "      <td>0.000000</td>\n",
              "      <td>212747.710000</td>\n",
              "      <td>11801.000000</td>\n",
              "      <td>1096.250000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>16.000000</td>\n",
              "      <td>4788.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>228354.610000</td>\n",
              "      <td>15427.000000</td>\n",
              "      <td>1273.500000</td>\n",
              "      <td>16.000000</td>\n",
              "      <td>4819.000000</td>\n",
              "      <td>0.500000</td>\n",
              "      <td>228211.575000</td>\n",
              "      <td>15143.000000</td>\n",
              "      <td>1270.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>22.000000</td>\n",
              "      <td>5199.250000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>247201.195000</td>\n",
              "      <td>21182.250000</td>\n",
              "      <td>1440.000000</td>\n",
              "      <td>22.000000</td>\n",
              "      <td>5214.500000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>245909.882500</td>\n",
              "      <td>20605.000000</td>\n",
              "      <td>1417.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>27.000000</td>\n",
              "      <td>5354.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>313725.300000</td>\n",
              "      <td>27936.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "      <td>27.000000</td>\n",
              "      <td>5354.000000</td>\n",
              "      <td>28.000000</td>\n",
              "      <td>313725.300000</td>\n",
              "      <td>27936.000000</td>\n",
              "      <td>1628.000000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       MIN_TEMPERATURE_C_t_1  ...  max_pdl_daily_load_t\n",
              "count              66.000000  ...             66.000000\n",
              "mean               16.833333  ...           1258.833333\n",
              "std                 5.682858  ...            201.377150\n",
              "min                 8.000000  ...            892.000000\n",
              "25%                12.000000  ...           1096.250000\n",
              "50%                16.000000  ...           1270.000000\n",
              "75%                22.000000  ...           1417.000000\n",
              "max                27.000000  ...           1628.000000\n",
              "\n",
              "[8 rows x 12 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "uVuqU0u0dmbG",
        "outputId": "3299c884-4bc7-419b-cf27-babc8122fa2f"
      },
      "source": [
        "df.columns"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "Index(['time', 'MIN_TEMPERATURE_C_t_1', 'max_clients_month_t_1',\n",
              "       'total_Holiday_Food_Related_t_1', 'total_consommation_t_1',\n",
              "       'max_pdl_total_daily_load_t_1', 'max_pdl_daily_load_t_1',\n",
              "       'MIN_TEMPERATURE_C_t', 'max_clients_month_t',\n",
              "       'total_Holiday_Food_Related_t', 'total_consommation_t',\n",
              "       'max_pdl_total_daily_load_t', 'max_pdl_daily_load_t'],\n",
              "      dtype='object')"
            ]
          },
          "metadata": {},
          "execution_count": 33
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0YVVz0bEduD6"
      },
      "source": [
        "df_input=df[['MIN_TEMPERATURE_C_t_1', 'max_clients_month_t_1',\n",
        "       'total_Holiday_Food_Related_t_1', 'total_consommation_t_1',\n",
        "       'max_pdl_total_daily_load_t_1', 'max_pdl_daily_load_t_1',\n",
        "       \n",
        "       'MIN_TEMPERATURE_C_t', 'max_clients_month_t',\n",
        "       'total_Holiday_Food_Related_t','max_pdl_daily_load_t']].dropna()"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "59aKcM4mdUQU"
      },
      "source": [
        "the add of weekend feature makes the seasonal weekend decrease more visible, so increases performance"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "gdnkSpWaENYQ"
      },
      "source": [
        "scaler = MinMaxScaler(feature_range=(0, 1))\n",
        "data_scaled = scaler.fit_transform(df_input)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "rCY48qWVESHY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "d6e6f954-fad9-43f1-bf75-d03ab46c9b16"
      },
      "source": [
        "data_scaled[-1]"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.63157895, 0.97816594, 0.        , 0.23666579, 0.34943489,\n",
              "       0.28940217, 0.78947368, 0.94214876, 0.03571429, 0.26086957])"
            ]
          },
          "metadata": {},
          "execution_count": 36
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IEZuMY99zfLU"
      },
      "source": [
        "features=data_scaled[:,:-1]\n",
        "target=data_scaled[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "6x_iIIrkomf7",
        "outputId": "2c3c6ff7-bc9b-4dce-950a-037836639212"
      },
      "source": [
        "features.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66, 9)"
            ]
          },
          "metadata": {},
          "execution_count": 38
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fpuzlDtTotWA",
        "outputId": "07d755fa-72da-4af7-e676-4214c370afab"
      },
      "source": [
        "target.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(66,)"
            ]
          },
          "metadata": {},
          "execution_count": 39
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nDaUCPH6njFx",
        "outputId": "4d3ea18b-d8a9-4dbd-c67c-9cbf422b90ac"
      },
      "source": [
        "# split into train and test sets\n",
        "\n",
        "n_train_days = -7 #train months \n",
        "\n",
        "train_X,train_y = features[:n_train_days, :],target[:n_train_days]\n",
        "test_X, test_y = features[n_train_days:, :],target[n_train_days:]\n",
        "\n",
        "# reshape input to be 3D\n",
        "train_X = train_X.reshape((train_X.shape[0], train_X.shape[1],1))\n",
        "test_X = test_X.reshape((test_X.shape[0],test_X.shape[1],1))\n",
        "print(train_X.shape, train_y.shape, test_X.shape, test_y.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(59, 9, 1) (59,) (7, 9, 1) (7,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "kZthsjPVnjHh",
        "outputId": "26036970-80ef-46fb-df14-e94f26c0882d"
      },
      "source": [
        "def create_model(layers):\n",
        "    model = Sequential()\n",
        "    for i, nodes in enumerate(layers):\n",
        "        if i==0:\n",
        "            model.add(LSTM(units=nodes,input_shape= (train_X.shape[1],1), kernel_constraint=maxnorm(3.5)))\n",
        "            model.add(Dropout(0.3))\n",
        "        else:\n",
        "            model.add(LSTM(units=nodes, kernel_constraint=maxnorm(3.5)))\n",
        "            model.add(Dropout(0.3))\n",
        "    model.add(Dense(1)) \n",
        "    \n",
        "    model.compile(loss='mse', optimizer='adam')\n",
        "    return model\n",
        "\n",
        "model = KerasClassifier(build_fn=create_model, verbose=0) #build_fn is the name of the \n",
        "#function to call to get your model.   \n",
        "tscv = TimeSeriesSplit(n_splits=4)\n",
        "layers = [[100], [100, 50], [100,50, 25]]\n",
        "batches=[4,8]\n",
        "epochs=[100,200]\n",
        "param_grid = dict(layers=layers, batch_size = batches , epochs=epochs)\n",
        "gsearch = GridSearchCV(estimator=model, cv=tscv, param_grid=param_grid,scoring='r2')\n",
        "grid_result=gsearch.fit(train_X, train_y)\n",
        "print(\"Best: %f using %s\" % (grid_result.best_score_, grid_result.best_params_))\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_170 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_172 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_174 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_176 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_178 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_180 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_182 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_184 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_190 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_192 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_194 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_196 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_198 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_200 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_202 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_204 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_210 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_212 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_214 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_216 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_218 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_220 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_222 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_224 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/keras/engine/sequential.py:450: UserWarning: `model.predict_classes()` is deprecated and will be removed after 2021-01-01. Please use instead:* `np.argmax(model.predict(x), axis=-1)`,   if your model does multi-class classification   (e.g. if it uses a `softmax` last-layer activation).* `(model.predict(x) > 0.5).astype(\"int32\")`,   if your model does binary classification   (e.g. if it uses a `sigmoid` last-layer activation).\n",
            "  warnings.warn('`model.predict_classes()` is deprecated and '\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_230 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_232 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_234 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_236 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_238 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_240 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_242 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n",
            "/usr/local/lib/python3.7/dist-packages/sklearn/model_selection/_validation.py:536: FitFailedWarning: Estimator fit failed. The score on this train-test partition for these parameters will be set to nan. Details: \n",
            "ValueError: Input 0 of layer lstm_244 is incompatible with the layer: expected ndim=3, found ndim=2. Full shape received: (None, 100)\n",
            "\n",
            "  FitFailedWarning)\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "Best: -3.337210 using {'batch_size': 4, 'epochs': 100, 'layers': [100]}\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "j2wZgmBbYLOS",
        "outputId": "b2dcbf60-8df8-407d-fe5f-9f4fd608c993"
      },
      "source": [
        "model = Sequential()\n",
        "model.add(LSTM(units=100,input_shape= (train_X.shape[1],1),kernel_constraint=maxnorm(3.5)))\n",
        "model.add(Dropout(0.3))\n",
        "model.add(Dense(units=1))\n",
        "\n",
        "es = tf.keras.callbacks.EarlyStopping(monitor='val_loss',patience=20,mode='min',verbose=1)\n",
        "mc = tf.keras.callbacks.ModelCheckpoint('best_model.h5', monitor='val_loss', mode='min', verbose=1, \n",
        "                     save_best_only=True)\n",
        "model.compile(loss='mae', optimizer='adam')\n",
        "history = model.fit(train_X, train_y, epochs=100, batch_size=4,validation_data=(test_X,test_y),\n",
        "                    verbose=0,callbacks=[es,mc], shuffle=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\n",
            "Epoch 00001: val_loss improved from inf to 0.22276, saving model to best_model.h5\n",
            "\n",
            "Epoch 00002: val_loss did not improve from 0.22276\n",
            "\n",
            "Epoch 00003: val_loss did not improve from 0.22276\n",
            "\n",
            "Epoch 00004: val_loss did not improve from 0.22276\n",
            "\n",
            "Epoch 00005: val_loss did not improve from 0.22276\n",
            "\n",
            "Epoch 00006: val_loss did not improve from 0.22276\n",
            "\n",
            "Epoch 00007: val_loss improved from 0.22276 to 0.21700, saving model to best_model.h5\n",
            "\n",
            "Epoch 00008: val_loss improved from 0.21700 to 0.21467, saving model to best_model.h5\n",
            "\n",
            "Epoch 00009: val_loss improved from 0.21467 to 0.18621, saving model to best_model.h5\n",
            "\n",
            "Epoch 00010: val_loss improved from 0.18621 to 0.17193, saving model to best_model.h5\n",
            "\n",
            "Epoch 00011: val_loss improved from 0.17193 to 0.13561, saving model to best_model.h5\n",
            "\n",
            "Epoch 00012: val_loss improved from 0.13561 to 0.13061, saving model to best_model.h5\n",
            "\n",
            "Epoch 00013: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00014: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00015: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00016: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00017: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00018: val_loss did not improve from 0.13061\n",
            "\n",
            "Epoch 00019: val_loss improved from 0.13061 to 0.12479, saving model to best_model.h5\n",
            "\n",
            "Epoch 00020: val_loss did not improve from 0.12479\n",
            "\n",
            "Epoch 00021: val_loss improved from 0.12479 to 0.11920, saving model to best_model.h5\n",
            "\n",
            "Epoch 00022: val_loss did not improve from 0.11920\n",
            "\n",
            "Epoch 00023: val_loss improved from 0.11920 to 0.11492, saving model to best_model.h5\n",
            "\n",
            "Epoch 00024: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00025: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00026: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00027: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00028: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00029: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00030: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00031: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00032: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00033: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00034: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00035: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00036: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00037: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00038: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00039: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00040: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00041: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00042: val_loss did not improve from 0.11492\n",
            "\n",
            "Epoch 00043: val_loss did not improve from 0.11492\n",
            "Epoch 00043: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S2DtZsevRZXZ",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 790
        },
        "outputId": "4b50437d-a54c-44dc-c1ad-53c7d61191bd"
      },
      "source": [
        "# plot history\n",
        "pyplot.plot(history.history['loss'], label='train')\n",
        "pyplot.plot(history.history['val_loss'], label='test')\n",
        "pyplot.legend()\n",
        "pyplot.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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wPwVDL8DCfNzVSJIkSZKkCjI4SqjcYnA0mLTgqNAXXecuwplX461FkiRJkiRVlMFRQm1tb6ExFdBfSGhwBDB4KL46JEmSJElSxRkcJVRjQ4rtHa3Ja1UrHIWmNtiwDQafi7saSZIkSZJUQY1xF6Ary2fSCWxV64XsruiXwZEkSZIkSXXNHUcJls+kE7jjqA+yN0HuHpgYhImTcVckSZIkSZIqxOAowfKZNGOTc5ybnou7lMj8DIwPLAZH+6PP3HUkSZIkSVLdMjhKsPziyWoDSRmQPXYCwmIUHN14OzSlHZAtSZIkSVIdMzhKsNxicJSYOUelE9WyN0FDE2z/kDuOJEmSJEmqYwZHCZbPLu44SlxwtCu65vbD2y/DbELqkyRJkiRJZWVwlGAbW5roSDclJzga6YW2zdC6Kfpz7h4ozsOpF+OtS5IkSZIkVYTBUcIl6mS1wtGoTa2ka190HXg2nnokSZIkSVJFGRwlXC5RwVHfO21qAOkMdN7sgGxJkiRJkuqUwVHC5TNpTo5NMb9QjLeQ6Qm4ePbdO44A8gdg6BAUY65PkiRJkiSVncFRwuUzaeaLIacnpuMtpHA0umZ3v/vz3AGYGoNCb/VrkiRJkiRJFWVwlHClk9UG425Xu3Si2nt2HOXuia6Dz1W3HkmSJEmSVHEGRwmXz0TBUexzjgp9QACZHe/+PLsLWjMwYHAkSZIkSVK9MThKuK3trTSmAvqTEBxtykPjund/HgRRu5o7jiRJkiRJqjsGRwnXkAro6mhNxo6j97apleQPRDOOLhaqW5MkSZIkSaoog6MakMuk451xFIbRcOwrBUe5A9F16FD1apIkSZIkSRVncFQDurPpeHccnX8bZi9A5+6lv992F6SabFeTJEmSJKnOGBzVgHwmzfjkHBNTc/EUcOlEtV1Lf9/UClt/BAbdcSRJkiRJUj0xOKoBpZPVYmtXuxQcXaFVDaJ2tZMvwPxsdWqSJEmSJEkVZ3BUA3KLwVFs7WqFPmhYBxu7rnxP/gDMT8Pbr1SvLkmSJEmSVFEGRzUg/uDoaNSmlrrKP5fSgOzBZ6tTkyRJkiRJqjiDoxqwsaWJjnRTvDuOrtamBrDhRtjU7YBsSZIkSZLqiMFRjchn2+KZcbQwB2PHrx0cQbTraPAQhGHl65IkSZIkSRVncFQj8pl0PDuOxgegOL/M4Gg/nD8dPSNJkiRJkmqewVGNyGdaOTk2xfxCsbovXs6JaiX5e6Lr4KHK1SNJkiRJkqrG4KhG5DNp5oshpyemq/vilQRHW26F5g0OyJYkSZIkqU4YHNWI2E5WK/RBawe0Za99b6oBuvY5IFuSJEmSpDphcFQjurNtQAzB0Ujv8nYbleQOwJnXYOZ85WqSJEmSJElVYXBUI27c2EJTQxDDjqOjKwyO9kNYhKHDlatJkiRJkiRVhcFRjWhIBXR1pBkoVDE4mrkA509Bdtfyn+m6GwgckC1JkiRJUh0wOKohuUy6ujuORo9F15XsOGrZCDd8wAHZkiRJkiTVAYOjGpLPtFY3OFrJiWqXy+2PWtWKC+WvSZIkSZIkVY3BUQ3pzrQxMTXHxORcdV5YOBpdMytoVQPI3QMz52D4SPlrkiRJkiRJVWNwVENymTQAg2NV2nVU6IWNXdCcXtlzuf3RdcB2NUmSJEmSapnBUQ3JLwZH/dUakF3oW9lg7JKOHlh/gwOyJUmSJEmqcQZHNSSXaQWozpyjMFwMjlY43wggCKJdR4PPlb8uSZIkSZJUNQZHNWRDSxOZtubqBEeTBZieWF1wBJA7AGPH4cLZ8tYlSZIkSZKqxuCoxuQyaQarERyVTlTr3L2653P3RFd3HUmSJEmSVLMMjmpMdyZdnR1HI73RdTUzjgC23gEN6xyQLUmSJElSDTM4qjH5TJqT41PMLRQr+6JCH6SaoD2/uucb18H2DzogW5IkSZKkGmZwVGPymTQLxZDT49OVfVGhDzI7oKFx9Wvk9sPpl2CuwrVKkiRJkqSKMDiqMblMGqjCyWqFo6sfjF2SOwALs1F4JEmSJEmSao7BUY3JZ6sQHBUXYPTY6ucbleQORFcHZEuSJEmSVJMMjmrMjRtbaG5IVTY4mhiChRnIrvJEtZK2TsjsggGDI0mSJEmSapHBUY1pSAV0dbQyMHqxci8plE5UW2OrGkD+nmjHURiufS1JkiRJklRVBkc1KJdJV3bHUeFodC1HcJTbD5MjUeubJEmSJEmqKQZHNSifSTNQqGRw1AfNG2D9lrWv5ZwjSZIkSZJqlsFRDcpn0pybnmdicq4yLyj0RYOxg2Dta3XeDC3tBkeSJEmSJNUgg6MaVPGT1Qp90LnGwdglqRR07XdAtiRJkiRJNcjgqAblM1Fw1F+JAdlzUzA+WJ75RiX5AzD8BkyNl29NSZIkSZJUcQZHNSiXqeCOo9HjQFje4Kg052jocPnWlCRJkiRJFWdwVIPWr2sk29bMYCWCo0JfdM3uKt+a2z8EQQMMPlu+NSVJkiRJUsUZHNWoXCZdmR1HpeAoU8bgqLkNbrzdAdmSJEmSJNUYg6Mala9YcHQU1t8ALRvLu27uAAy9AAvz5V1XkiRJkiRVjMFRjerOpjk1Ps3cQrG8Cxf6IFumE9Uul9sPcxfhzKvlX1uSJEmSJFWEwVGNymXSLBRDTo1PlXfhQm955xuV5O+JroOHyr+2JEmSJEmqCIOjGpWvxMlqk6MwWSjviWol7V2wcbsDsiVJkiRJqiEGRzWqIsHR6LHoWongCKI5R+44kiRJkiSpZhgc1agbNrbQ3JAqb3BUOlGtksHRxCBMnKzM+pIkSZIkqawMjmpUQyqgK9PKYLmDo6ABOnrKt+blcvuj6+BzlVlfkiRJkiSVlcFRDctn0vQXyhgcjfRCRzc0NpdvzcvdeDs0pW1XkyRJkiSpRhgc1bB8Js1AYZIwDMuzYOFo5drUABqaYPuHHJAtSZIkSVKNMDiqYflMmvMz80xMza19sWIRRiscHEE05+j0yzB7sbLvkSRJkiRJa2ZwVMNy5TxZ7fxpmJuE7K61r3U1uQMQLsCpH1T2PZIkSZIkac0MjmpYd7aMwdGlE9V2r32tq+naF10HbFeTJEmSJCnpDI5qWK4jCo7KMiD7UnBU4Va1dAY23+KAbEmSJEmSaoDBUQ1rW9dI5/pmBsu146gpDRu2rn2ta8nth8HnorlKkiRJkiQpsQyOalwuky5fq1pmF6Sq8E8idw9Mj0Oht/LvkiRJkiRJq2ZwVOPy5QyOKj0YuyR3ILoOPled90mSJEmSpFUxOKpx3Zk0p8anmFtYQ9vX/CyM9Vd+vlFJdhekszBgcCRJkiRJUpIZHNW4XCZNMYSTY1OrX2S8H8IF6KzwiWolQRDtOnLHkSRJkiRJiWZwVOPymehktTW1q40szhqq1o4jiAZkF3rhYqF675QkSZIkSSticFTj8tkyBEeFvuia2VmGipYpd090HTpUvXdKkiRJkqQVMTiqcTdsaKG5IcXgWoOjdBbSmfIVdi3b7oRUk+1qkiRJkiQlmMFRjUulAroyrWvccXS0um1qAE2tsPVHHJAtSZIkSVKCGRzVge5Mmv7CGnccZas0GPty+Xvg1IvRqW6SJEmSJClxDI7qQD6TZnB0kjAMV/7w9Dm48DZkd5W/sGvJ7Yf5aXj7leq/W5IkSZIkXZPBUR3IZdKcn5lnfHJu5Q+PHo2u1W5VA8gdiK6Dz1b/3ZIkSZIk6ZoMjupAPrOGk9UKMQZHG26ETd0OyJYkSZIkKaEMjupAPruW4KgPCCCzo7xFLVfuQDQgezVtdpIkSZIkqaIMjurA2nYc9UF7LjrlLA75A9GMpfGBeN4vSZIkSZKuyOCoDqSbG+lcv46B1ZysVuiDzhja1EouzTk6FF8NkiRJkiRpSQZHdSKfaV35jqMwhJG+eOYblWy5FZo3OCBbkiRJkqQEMjiqE/lMeuXB0YWzMHs+3uAo1QBd+xyQLUmSJElSAhkc1Yl8Js3piSlm54vLf6jQF12zuypT1HLlDsCZ12DmfLx1SJIkSZKkdzE4qhP5bBvFEE6OTy3/oUvBUYw7jiAakB0WYehwvHVIkiRJkqR3MTiqE6s6Wa3QBw3rolPV4rR9HxA4IFuSJEmSpIQxOKoTqw6OMjujOUNxatkIN3zAAdmSJEmSJCWMwVGd2LJhHc2NKQZXGhzFPd+oJHcgalUrLsRdiSRJkiRJWmRwVCdSqYBcRysDhWUGRwvzMHo8/vlGJbkDMHMOzr4RdyWSJEmSJGmRwVEdyWfS9C93x9HEABTnkhMc5Q9E18Hn4q1DkiRJkiRdYnBUR7qzbQyOThKG4bVvLhyNrp27K1vUcm3qhvU3OCBbkiRJkqQEMTiqI7lMmgsz84xNzl375kJfdE3KjqMggNx+B2RLkiRJkpQgBkd1ZEUnq430Qks7pLMVrmoFcvfA2Ak4fybuSiRJkiRJEgZHdWVFwVGhL9ptFAQVrmoFcs45kiRJkiQpSQyO6kgu0wrA4LKCo6PJaVMr2foj0LDO4EiSJEmSpIQwOKoj6eZGNm9YR3/h4tVvnJ2Ec0PJC44am2H7Bx2QLUmSJElSQhgc1Zl8Jn3tVrXRY9E1acERRAOyT78Ec9NxVyJJkiRJ0nXP4KjO5DNpBkenrn5ToTe6JjI4ugcWZqPwSJIkSZIkxWpZwVEQBB8PguDNIAj6giD44hLf/2IQBK8HQfByEATfCYKg+7LvFoIgeGnx199f9vmOIAieW1zzvwVB0FyeH+n6lsukOTUxxex88co3Ffqia2ZndYpaidz+6OqcI0mSJEmSYnfN4CgIggbgD4FPALcCPxUEwa3vue0HwL4wDO8Avgn89mXfTYVheOfir09e9vlvAb8XhuFNwBjws2v4ObQon0kThnBy/Cq7jgpHYcM2WLe+eoUtV1snZHbBwLNxVyJJkiRJ0nVvOTuO9gN9YRgeC8NwFvivwD+//IYwDB8Pw7A0WOdZoOtqCwZBEAAPEYVMAP8Z+NRKCtfSurNpgKsPyC70QXZXlSpahZ57of97UFyIuxJJkiRJkq5rywmOtgODl/15aPGzK/lZ4FuX/bklCILDQRA8GwRBKRzKAuNhGM5fa80gCD63+Pzh4eHhZZR7fctnouDoqgOyC33QubtKFa3CzoMwPQGnnHMkSZIkSVKcyjocOwiCzwL7gN+57OPuMAz3AT8N/H4QBCva6hKG4Z+GYbgvDMN9mzdvLmO19Wnz+nW0NKUYKFwhOJochamxZA7GLtnxQHQ99ni8dUiSJEmSdJ1bTnB0Eshd9ueuxc/eJQiCjwG/DHwyDMOZ0udhGJ5cvB4DngDuAgrApiAIGq+2plYulQrIZ9KcuFJwNJLgE9VK2jrhxtvh2BNxVyJJkiRJ0nVtOcHR88DuxVPQmoF/Cfz95TcEQXAX8CdEodHZyz7vCIJg3eLvO4F7gdfDMAyBx4F/sXjr/wT83Vp/GEW6s21XnnFUOlEtycERRO1qg8/B7FVa7iRJkiRJUkVdMzhanEP088A/Am8A3wjD8LUgCH49CILSKWm/A6wH/nsQBC8FQVAKlvYCh4Mg+CFRUPSVMAxfX/zuC8AvBkHQRzTz6M/K9lNd53qyaQZGJykWw/d/WeiDVCNsyle/sJXYeRAWZmHg+3FXIkmSJEnSdavx2rdAGIaPAI+857MvXfb7j13huWeA26/w3TGiE9tUZvlsGzPzRc6cn2Zre+u7vyz0QUcPNDTFUtuy5T8CDc1Ru9pND8ddjSRJkiRJ16WyDsdWMvRko5PVTows0eZVOArZBJ+oVtKchtwB5xxJkiRJkhQjg6M61JNtA2Bg9D1zjopFGD0K2RUdbBefnQfh7Zfh4kjclUiSJEmSdF0yOKpDW9tbaEwF7z9Z7dwQzE8nfzB2yc4Ho+vxJ+OtQ5IkSZKk65TBUR1qbEiRy6Tff7JarZyoVrLtTljXbruaJEmSJEkxMTiqU93ZNP3v3XFUOBpdayU4SjXAjvvh6BMQLnFCnCRJkiRJqiiDozrVk22jvzBJeHngUuiD5vWw4cb4ClupnQdhYgDGjsddiSRJkiRJ1x2DozqVz6S5MDNP4eLsOx8W+qLB2EEQX2ErVZpzZLuaJEmSJElVZ3BUp3o60zwJqiAAACAASURBVADvblcb6a2dNrWS7C5ozxkcSZIkSZIUA4OjOtWdbQN4Z0D2/AyMD9RecBQEsPMBOPYkFBfirkaSJEmSpOuKwVGd6upoJQjgRGnH0ehxIKy94AiidrXpcTj9w7grkSRJkiTpumJwVKfWNTawrb31nR1Hhb7oWovB0Y6PRlfb1SRJkiRJqiqDozrW05l+Z8bRpeBoV3wFrdb6LXDDbQZHkiRJkiRVmcFRHctn2i7bcdQLbVugpT3eolZr50EYeBbmpuKuRJIkSZKk64bBUR3ryaYZm5xjYmoOCkdrs02tZOdBWJiJwiNJkiRJklQVBkd1rHSy2kBhMmpVq8U2tZLuj0CqyXY1SZIkSZKqyOCojnVn0wAMnT4NF4dre8dRcxvkDhgcSZIkSZJURQZHdawUHJ0/dST6oHN3jNWUwc6DcPqHMDkadyWSJEmSJF0XDI7qWLq5kS0b1jF/tjf6oJZ3HEEUHBHC8SdjLkSSJEmSpOuDwVGd68m20Tx+DIIUdPTEXc7abLsL1m20XU2SJEmSpCoxOKpz+WyajZP9sCkPjeviLmdtGhqh536DI0mSJEmSqsTgqM71ZNNsWzjJQkcNn6h2uZ0HYewEjB6PuRBJkiRJkuqfwVGd686k2RGcZqKtJ+5SymPnwejqnCNJkiRJkirO4KjO7Wq9QFsww+nG7XGXUh6du2HjdtvVJEmSJEmqAoOjOpcvngTgeHFrzJWUSRBEu46OPQnFYtzVSJIkSZJU1wyO6tz6iycAeG12c7yFlNPOgzA1Cm+/HHclkiRJkiTVNYOjelc4ygzNvHKuLe5KymfHA9HVdjVJkiRJkirK4KjeFfoorOvieGE67krKZ8MNsOVWgyNJkiRJkirM4KjeFfq4sH4HpyemmJlfiLua8tl5EAa+D3N1FIhJkiRJkpQwBkf1bGEOxk5Q7NhFMYShsam4KyqfnQdhfhoGn4u7EkmSJEmS6pbBUT0b64fiPOtu3ANAf+FizAWVUfdHINVou5okSZIkSRVkcFTPCn0AdOT3AtBfmIyzmvJatwG69sOxx+OuRJIkSZKkumVwVM8Wg6P27XtZv66xvoIjiNrVTr0Ek6NxVyJJkiRJUl0yOKpnhT5ozRC0Zcln0pyop1Y1iIIjQjjxdMyFSJIkSZJUnwyO6lmhD7I3AdDTmWag3nYcbf8gNG9wzpEkSZIkSRVicFTPLguOurNtDI5NMr9QjLmoMmpogp77DI4kSZIkSaoQg6N6NXMBzp+G7C4AujNp5hZCTk9Mx1xYme08CKPHohPkJEmSJElSWRkc1avRo9H1sh1HUGcnq8HinCPg+JNxViFJkiRJUl0yOKpXiyeq0bkbiGYcAfU3IHvzzbBhKxx9PO5K3tH3Hfj6J2CuznZ3SZIkSZKuOwZH9aqwuOMosxOAGza00NyYor/egqMgiHYdHX8SigmY37QwD9/6Agw8AyNvxl2NJEmSJElrYnBUr0Z6oT0HTa0ApFIB3Zk0J+qtVQ2i4GiyAGdejbsS+OFfQqE3+v3wW/HWIkmSJEnSGhkc1atC36XB2CXd2TYG6jE42vFAdI37dLW5aXjiK7D1TghSMGJwJEmSJEmqbQZH9SgMo1a1xcHYJd3ZNP2jFykWw5gKq5CNW2HzLfEHR4f/DM6dhB/9dejoMTiSJEmSJNU8g6N6dHEEZibeFxz1ZNNMzxU5e34mpsIqaOdB6H8G5mP62WbOw9P/Pqpj5wPQuSdqF5QkSZIkqYYZHNWj0olq2d3v+rg72wZQfwOyIQps5qdg8FA87//+H0Zzlh7+UvTnzt3R30NxIZ56JEmSJEkqA4OjenQpOHr3jKOeS8FRHc456r4XggY49nj1332xAM/8R9j7z2D7h6LPOvfAwgyM91e/HkmSJEmSysTgqB4VeiHVBJvy7/p426YWGlMBJ+pxx1HLRujaF8+co+/+LsxdhId+9Z3POm+OrrarSZIkSZJqmMFRPSochcxOSDW86+PGhhRdHa30j9bhjiOAnQ/CqR/A1Fj13jkxBIe+Bj/yU7D55nc+71xsE3RAtiRJkiSphhkc1aNC3/sGY5d0Z9vqc8YRRHOOwiKc+G713vnkb0XvfOAL7/48nYF0Jwy/Wb1aJEmSJEkqM4OjelNcgNFj0Hml4ChN/8gkYRhWubAq6NoHzeur16420gc/+AvY979AR/f7v/dkNUmSJElSjTM4qjdn34CFWdjygSW/7s62cX5mnrHJuSoXVgUNTdGQ7GoFR4//JjS2wEf/z6W/37zHVjVJkiRJUk0zOKo3Q4vH0efuXvLrnmwaoD4HZEPUrlbog/HByr7n9A/htb+Be/53WL9l6Xs698DUaHTqmiRJkiRJNcjgqN4MPh/N1unYseTX3YvBUV3POYLK7zr6zr+Flk3wkf/jyvd07omuI845kiRJkiTVJoOjejP4HOT2QxAs+XVXR5oggBMjdXqy2pa90LalssFR/zPQ909w37+B1k1Xvs+T1SRJkiRJNc7gqJ5cLMDo0Sg4uoKWpga2tbcyMFqnwVEQRLuOjj0BxWL51w9D+PaXYf2NsP9zV7+3PR/NQHJAtiRJkiSpRhkc1ZOh56Nr15WDI4B8Jl2/M44Adj0IkyNw9vXyr937KAw+Cw/8X9Ccvvq9qRRkd7vjSJIkSZJUswyO6sngc5BqhG13XfW2ns40/YU63XEEsOOB6FrudrViMZpt1LEDPvgzy3umczcMO+NIkiRJklSbDI7qydDzcMNt19wJ051tY/TiLOem56pUWJW1b48GU5c7OHrtb+DMK/DgL0ND0/Ke6dwD4wMwN1XeWiRJkiRJqgKDo3qxMA8nX4DcgWve2rN4stpAPe862nkQ+r8H87PlWW9hDh77jSiYu+3Ty39u8x4ghMLR8tQhSZIkSVIVGRzVi7OvwdzkVQdjl+QzbQD1Pedo58Hov8fQofKs94P/AmPH4aFfjWYXLVfnnujqnCNJkiRJUg0yOKoXg4sBSdfd17y1e3HHUV3POeq5D4JUedrV5qbgyd+Oho7v+bGVPZvZBQQGR5IkSZKkmmRwVC8GD8H6G2BT/pq3tq1rZPOGdfTX846jlnbY/qHyBEeHvgbnT8PHfg2CYGXPNqdhU87gSJIkSZJUkwyO6sXQoahNbZnBRncmzYl63nEEsPPBaO7T9MTq15iegO/+Lux6ONrFtBqdNxscSZIkSZJqksFRPbhwFsZORK1Uy9Sdbavv4dgQzTkKi3Diu6tf45n/CFNj8PCXVr9G5x4Y6YNicfVrSJIkSZIUA4OjelCab7SMwdglPdk0b5+bZmp2oUJFJUDX3dCUXn272oVh+P4fwq2fgm13rr6Ozt0wPwUTg6tfQ5IkSZKkGBgc1YOhQ5Bqgq3LDzfyiwOyB0breNdRYzN037v64Ojpfw/z0/DQr6ytjksnq/WubR1JkiRJkqrM4KgeDD4PW38EmlqW/UhPtg2AE/U8IBuidrWRt2Di5MqeGx+Aw38Gd/50tGNoLTbfHF2dcyRJkiRJqjEGR7VuYQ5OvbiiNjV4Jzi6LuYcwcp3HT3xW0AAB7+49hrSWWjtMDiSJEmSJNUcg6Na9/bLUTtV190reqw93UR7a1P97zjaciu0bV5ZcDT8JvzwL+Hu/xXau9ZeQxAsDsg2OJIkSZIk1RaDo1o3+Hx0XeGOI4gGZPfX+46jVAp2PBAFR2G4vGce+41oqPb9v1i+OgyOJEmSJEk1yOCo1g0dgo3bV7UzpjvbRv9one84Atj1IFw8C2ffuPa9J1+EN/4ePvzz0NZZvho698DFYZgcLd+akiRJkiRVmMFRrRt8fsVtaiU92TQnx6aYnS+WuaiE2fFAdF1Ou9pj/xZaM/Dhf13eGkonqxX6yruuJEmSJEkVZHBUy86dhomBVbWpAeSzbRRDGBqr83a1TTnI3nTt4Oj403D0sahFrWVjeWsoncw2/GZ515UkSZIkqYIMjmrZ0KHo2rW64Kgnmwagf7TOgyOITlc78d3oFLqlhCF858uwYVs0FLvcOnqgodk5R5IkSZKkmmJwVMsGD0HDOth6x6oe7862AdA/ch3MOdp5EOYuwtDzS3//5rei7w5+AZpay//+VEO062mkt/xrS5IkSZJUIQZHtWzoedh2JzSuW9XjneubSTc3cKLeT1YD6LkPgtTS7WrFhWi2UWYX3PnZytXQudsdR5IkSZKkmmJwVKvmZ+DUD1Y9GBsgCAK6s20MXA+taq0dsO2upYOjV74JZ1+Hh34ZGhorV0PnHhg7Hv3dSZIkSZJUAwyOatXpl2FhdtWDsUt6smlOFK6DVjWI2tWGDsP0uXc+m5+Fx38Tbrwdbv3Jyr6/82YIizB6rLLvkSRJkiSpTAyOatUaB2OX5LNpBkcnWSiGZSgq4XY+COEC9H/vnc9e/M8w3g8P/xqkKvy/Q+lkNdvVJEmSJEk1wuCoVg0egvY8bNy6pmV6sm3MLYScnpgqU2EJltsPja3vtKvNXoSnfgfyH4GbPlb592dviq4GR5IkSZKkGmFwVKsGD0Fu9fONSrqzaQD6EzQg+43T53j15ET5F25cB90feSc4eu5P4MIZ+NivQRCU/33vtW49bOyCYYMjSZIkSVJtMDiqRRNDcP7UmtvUALqzbQCJmnP0b/7bS/zrv3yRMKxA+9zOgzB8BM6+Ad/7fdj9Y5C/p/zvuZLNe9xxJEmSJEmqGQZHtWhwcb7RGgdjA2zd2EJzYyoxO47OnpvmyNvn6S9McnS4AmHWzoPR9Rs/A9MT8NCvlP8dV9O5B0Z6oRKhmCRJkiRJZWZwVIuGno9m9dx4+5qXSqUC8pk0/QnZcfR078il3z925Ez5X3DDbZDORrt+bvs0bL2j/O+4ms7dMHcRzp2q7nslSZIkSVoFg6NaNPgcbLsLGprKslxPNp2YHUdP9w6TbWvmlhs38NiRs+V/QSoV7ToKGuDBXy7/+tfSuSe6jrxZ/XdLkiRJkrRCBke1Zm4aTr9clsHYJflMG/2FycrMFFqBYjHk6d4R7t/dycN7t/D8iTEmpubK/6KPfRl+5v+F7K7yr30tnTdH15He6r9bkiRJkqQVMjiqNadfguIc5A6UbcmezjRTcwsMn58p25qr8frpcxQuznL/7s08dMsWFoohT/cOl/9Fm3Kw46PlX3c51m+Bde0OyJYkSZIk1QSDo1pTGoxdhhPVSt45WS3edrWnFkOi+3d3cmeug450E4+9UYF2tTgFQTTnyOBIkiRJklQDDI5qzeBz0NED6zeXbcnuTBqAEzEPyH76rRFuuXEDWza20JAKePDmLTz+5lkWinV2AlnnHhg2OJIkSZIkJZ/BUS0Jw+hEtTLuNgLY3tFKQypgIMYdR5Oz8xzuH+WBPe8EYg/esoWxyTleGhyPra6K2LwHLrwN0xNxVyJJkiRJ0lUZHNWS8QG4cAZy5Q2OmhpSdHW0xrrj6NljBeYWQu7f/U5w9NE9m2lIBTx25ExsdVXEpZPV+uKtQ5IkSZKkazA4qiVDz0fXMgdHAPlMmv4Ydxw99dYILU0p9vV0XPqsvbWJfd0dfKfe5hxdCo5sV5MkSZIkJZvBUS0ZfA6a2mDLB8q+dE+2jROFi4RhPPOEnuod5sCOLC1NDe/6/OG9Wzjy9nlOjU/FUldFdPRAqhFG3oy7EkmSJEmSrsrgqJYMHoLtH4SGxrIv3Z1Nc356nvHJubKvfS1DY5McG77I/bs73/fdQ7fcAMBjR+po11FDE2R2wUhv3JVIkiRJknRVBke1YnYSzrxakTY1gO5sGxDPyWrf7R0BeNdg7JJdm9vIZ9L1FRwBdO62VU2SJEmSlHgGR7Xi1A+gOF/2E9VKerJpgFjmHD3VO8yNG1u4acv6930XBAEP3bKF7/WNMDW7UPXaKqZzD4weg4Xq7/CSJEmSJGm5DI5qxeBz0bXr7oosn8ukCYLqB0cLxZDv9o5w/+5OgiBY8p6H925hZr7I94+NVLW2iurcEwWBo8fjrkSSJEmSpCsyOKoVQ89Hc3HashVZvqWpga0bW+ivcqvaD4fGOTc9z0eXaFMr2b8jQ7q5ob7a1TZ7spokSZIkKfkMjmpBGEaDsXMHKvqafDZd9RlHT781QhDAfTe9fzB2ybrGBu7f3cljb5yN7dS3ssvujq4GR5IkSZKkBDM4qgVjx2FyBHKVaVMr6cm2MTBa3Va1p3uHuWN7Ox1tzVe976FbtnBqYpojb5+vUmUV1rIRNmz1ZDVJkiRJUqIZHNWCwUPRtUKDsUu6s22MXJjl/HR1Bjafm57jB4Pj3L/7ym1qJQ/evAWgvtrVOnfDyJtxVyFJkiRJ0hUZHNWCwUPQvAG27K3oa7qrfLLaM30FFooh9+++cptayZaNLdzR1V5nwdHN0Y6jemm/kyRJkiTVHYOjWjB0CLo+BKmGir6mFBxVq13t6d5h2pob+GB3x7Luf/DmLbw4MMboxdkKV1YlnXtg5hxcOBN3JZIkSZIkLcngKOlmLsCZ1yrepgZRqxpQlQHZYRjyVO8wH97VSVPD8v4ZPrx3C2EIT75VJ7uOOh2QLUmSJElKNoOjpDv5AoRFyFU+OFq/rpHO9c30j1R+x1F/YZLB0Ske2HPtNrWS27a1s3nDOr7zRr0ER3ui67BzjiRJkiRJyWRwlHRDpcHY+6ryuu5sG/2jld9x9FTvMMCyBmOXpFIBD968mafeGmZuoVip0qpn4zZoXu/JapIkSZKkxDI4SrrB56Mhyq3LmwO0Vt3ZdFWGYz/11gi5TOuluUrL9dAtN3Buep4X+scqVFkVBcHiyWq2qkmSJEmSksngKMnCMNpxlLu7aq/szrRxemKa6bmFir1jbqHI94+O8NHdmwmCYEXP3re7k+aGVP2crta5xx1HkiRJkqTEMjhKskIfTI1VZTB2SU9n5U9We7F/jIuzCytqUytZv66RAzsz9RUcnRuKhqBLkiRJkpQwBkdJNrg436gKg7FLSierVbJd7eneERpSAR+5Kbuq5x+6ZQt9Zy/QX4XT3yquNCC74K4jSZIkSVLyGBwl2dAhWNcezTiqkp7FmUOVDGWe6h3mrtwmNrY0rer5h27ZApDYXUcDhUn+w7d7KRbDa99cCo5sV5MkSZIkJZDBUZINPh+dppaq3l/TpnQzG1saOVGh4Gj04iyvnJxYVZtaSXe2jV2b2xIbHP27R97g9779Fr1nl9F+ltkJQYMDsiVJkiRJiWRwlFTTE3D29aq2qZX0dLZVrFXte30jhCF8dE/nmtZ5eO8NPHdslAsz82WqrDyODl/gH19/G4CXh8av/UBjM2R2wPCbFa5MkiRJkqSVMzhKqpMvACF0Ve9EtZLubOWCo6feGqa9tYk7ujataZ0Hb97C7EKR7/aOlKmy8vjaU8dobkjR2tTAKycnlveQJ6tJkiRJkhLK4CipBp8HgqhVrcq6M2lOjk8xt1As67phGPJ07wj33dRJQypY01r7ejrY0NLI4wlqVzt7bpq/efEk/8O+Lu7oaufloeUGR7th9CgsJGv3lCRJkiRJBkdJNXQItuyFlvaqv7o7m2ahGHJybKqs6/aevcDb56a5f/fa2tQAmhpSPLBnM4+9eXZ5Q6ir4M++d5z5YpHP3b+LO7raef30ueWFb517YGEWxvsrX6QkSZIkSStgcJRExeLiYOzqt6lBNOMIKPuA7KfeGgbg/j2rH4x9uYdu2cLw+RleO3WuLOutxbnpOf7y2QF+/Pat5LNpbu/axOx8kbfOnL/2w6VT8xyQLUmSJElKGIOjJBp5C2YmYhmMDVGrGlD2OUdP946wa3Mb2ze1lmW9gzdvIQjgO0fOlGW9tfiLZwc4PzPPv3pgFwB3bI92ir2ynHa1zpuiq8GRJEmSJClhDI6SaOhQdM0diOX1mzeso7WpoazB0fTcAs8dL3D/7vLsNgLItDVzV24Tj8U852h6boGvf+849+/u5LbFwKg7m2ZDSyMvL2dAdmsHtG0xOJIkSZIkJY7BURINHorChOxNsbw+CAK6s2n6y9iqdvjEGNNzRR4oU5taycN7b+DloQnOnp8u67or8bc/OMnw+ZlLu40g+m94R1f78nYcgSerSZIkSZISyeAoiQYPRfONgrWdPLYW3dl0WWccPdU7TFNDwIGdmbKtCdGcI4AnjgyXdd3lWiiG/OlTx7h9ezsf2ZV913e3b9/EkbfPMTO/cO2FNu+B4TchTMagb0mSJEmSwOAoeabGYORN6IpnvlFJT7aNwdEpFsp0YtlTbw2zrztDurmxLOuV3HLjBra2t8Q25+gfX3ub4yMX+VcP7CJ4T9B3R1c7cwshb769nAHZe2B6HC6OVKhSSZIkSZJWzuAoaYZeiK4xDcYu6c62MbtQ5O1za28BO3tumiNvn+ejZW5Tg6gl7KFbtvDd3pHl7ewpozAM+eMnj9KTTfPx22583/e3L847enlZA7J3R1fnHEmSJEmSEsTgKGmGDkGQgu0firWMnuziyWoja29Xe7o32kVz/+7ONa+1lIf3buHi7AKHjo9WZP0r+f7RAi8PTfC/fXQnDan3txV2dbTSkW5a5slqe6KrwZEkSZIkKUEMjpJm8DnY8gFYtz7WMvKLwdGJMpys9nTvMJ3rm7l168Y1r7WUD+/sZF1jiu+8Ud3T1f7oyaN0rl/Hpz/YteT3QRBwe9em5Z2strELmtIGR5IkSZKkRDE4SpLiQtSqlrs77krY2t5Kc0OK/tG17TgqFkO+2zfCfTd1klpiV045tDY3cO9NnTx25CxhlYZLv3pygqd7R/if7+2hpanhivfdsb2d3jPnmZ67RhtdKhWdomdwJEmSJElKEIOjJBk+ArPnIXcg7kpoSAXkMq3/P3t3Hhznnd/5/fPrblyNoxtP4yIBAiBAgBRBgKKGkGY8o1tDyi5n7N11YnszO/YeM5nd2EmVqxLbcZU3ceJNxZPKprL2emxn1l7H68yOXd7NrD0mpdFIlGZGGpI6eAAUwQs3cd93H0/+6G6IEgmgATzd/TTwflWxHrLxPM/vh1JJIj/8fr8/9U3sruKo+/6cJhbW9HSL8/ONHvT8sSr1Ty3pzrhzJ8Ft5g/evKuSAp+++OmGTe9rrwsoErN14/7c1i+taCU4AgAAAAC4CsGRmwxcjF/rsl9xJMUHZPdO7i6ISfd8o6QXjlVJkl7/MP3tav2TS/qbq8P6L5+qV6Aob9N7O+riA7KvpdKuVtEqzQxIa7tvDwQAAAAAwAkER24ycFHyhySrKds7kSQ1hPzqn1raVfvXmz3jOlZTqqqyQgd39rDaYJGO1ZTqtQ9H07qOJP3RW3fl83j0jz53eMt7a8oKVVFSkNrJapWtkmxp8vbuNwkAAAAAgAMIjtxk8KJU96Rk0jMLaLsaQ8VaWotqfGF1R88vrUV0uW9Kz7amt00t6YVjVbrUO63Z5XDa1phYWNW3Lg/o75yqVXUKYZgxRh11AU5WAwAAAADkJIIjt1iaileaHHoy2ztZlzxZrW+HJ6v96O6UwlE77fONkl58rErRmK23bo2nbY1/+8NerUVj+sqzqVeFtdcGdGtsXktrkc1vtJolGWni1u42CQAAAACAQwiO3GLwUvzqouCoMVQsaefB0YWecRXmeXS6sdzJbW3o8UPlKvfn6Xs30jPnaHE1oj99u09njlerubIk5ec66gKK2VL38BYDsvMKpfIGaeLmLncKAAAAAIAzCI7cYuBHkvFKB09leyfraoNF8nqM+nY4IPutW+N66nBo0+PqneT1GD13tEqv3xxTNLbzuUwb+X8v9mt2OayvPtu8refaa+MDslOac1RxlIojAAAAAIBrEBy5xcBFqeaElF+c7Z2sy/d5dDBYqN4dVBwNzSzrzvhi2k9T+6QXjlVpeimsDwZmHH3vWiSmb3z/np46bOlU/fYqqKrKClVdVpDiyWot8ZbFWHSHOwUAAAAAwDkER24QjUhD70mHnsr2Th7SGCpW/w4qjt7qic8ZytRg7KRnWivl9Rh9z+HT1b59ZVj3Z1f01ee2V22U1F4b1NXBFMKsilYpsiLNDuxoHQAAAAAAnERw5AZj3VJ4MX6imss0hPw7qjh669aEasoKdaQq9VlATggU5el0Q7m+96FzA7JjMVtfv3BHx2pK9dwOg7COuoDuTixqfmWLE9+SJ6uNc7IaAAAAACD7CI7cYOBH8euhzuzu4xEaQ8WaXQ5rZmkt5WeiMVvfvz2hp1sqZIxJ4+4e7cXHqnTj/pyGZ5Yded9rH47p9tiCvvps846/n/a6gGxb6tpqQHbl0fh1guAIAAAAAJB9BEduMHhJKq6Sgg3Z3slD6i2/JG2r6ujq4Ixml8N6JsNtakkvHKuSJH3vQ2dOV/v6hTuqDRbpJzsO7PgdyQHZ17YakO23JH+I4AgAAAAA4AoER24wcFE69KSUheqcrTRWxId1b+dktTd7JmSM9LkjmR2MndRcWaJ6y6/XHQiOLvVO6d2+aX356cPyeXf+r0tFSYFqg0W6mtKA7FZOVgMAAAAAuALBUbYtjEvT9+LBkQslK476tlFx9NatcXXUBlRenJ+ubW3KGKMXjlXp+7cntLy2u9PJvv7GHVnF+frZzvpd76u9NqBrKQ3IbpEmbu56PQAAAAAAdovgKNsGL8avLhyMLUmFeV7VlBWqN8WKo7mVsN4fmNHTLdlpU0t64ViVViMxvX13YsfvuDkyr9c+HNMvfKZRRfneXe+pvS6g3sklzS5tNSD7qLQ0KS1O7npNAAAAAAB2I6XgyBjzsjHmpjHmtjHm1x7x9V8xxnQbY64aY14zxjQkPn/cGPO2MaYr8bWffeCZPzHG3DPGfJD48bhz31YOGbgoeXzSQfd++w0hv/pTrDj64e1JRWO2nm7JTpta0lNNlvz53l3NOfqDN++oKM+rL33GmdlTHXXxOUfXh7doV0uerDZJuxoAAAAAILu2DI6MMV5JvyfpxyUdl/Tzxpjjn7jtfUmn7YtO7wAAIABJREFUbdvukPSXkn4n8fmSpC/Ztt0m6WVJ/6cxJvjAc/+dbduPJ358sMvvJTcNXpIOnJTyirK9kw01hopTHo791q1xFed79URDeZp3tbkCn1dPt1ToezfGZNv2tp8fmlnWtz8Y1s89ecixlrvkgOyrWw3IrmiJXxmQDQAAAADIslQqjp6UdNu27bu2ba9J+qakn3rwBtu2X7dtO5ksvCOpLvF5j23btxI/H5Y0Jim7PUxuEg1LQ++5tk0tqT7k18TCqhZWI5veZ9u23rw1rs80VyhvF4OknfLCsSoNz67ow5H5bT/7jbfuSZL+ydNNju0n6M9XveXXtaEt5hwF6yVvgTTOnCMAAAAAQHal8qf7WkkDD/x6MPHZRv6xpL/95IfGmCcl5Uu688DHv51oYfuXxpiCFPayt4xckyLL0qHObO9kU42h+MlqW7Wr9U0uaWBqWc+2ZrdNLen5o1WStO12tZmlNX3zUr++cPKgaoPOVoK11wW2rjjyeBMDsmlVAwAAAABkl6NlIcaYL0o6Lelrn/j8gKT/R9I/tG07lvj41yUdk9QpyZL0qxu88yvGmMvGmMvj4+NObjf7Bi/Fry6vOGoIJU9W23xA9lu34v98sj0YO6mqrFDttYFtB0d/+nafltai+q+ebXZ8Tx21AQ1OL2tqcW3zGytaaFUDAAAAAGRdKsHRkKRDD/y6LvHZxxhjXpL0G5K+YNv26gOfl0n6G0m/Ydv2O8nPbdu+b8etSvpjxVviHmLb9h/atn3atu3TlZXuCCQcM3BRKj0oBeqyvZNNJYOjreYcXeiZ0CGraP1+N3jhWJXe75/eOqhJWF6L6k9+2KsXjlXpaE2p4/tpTwzIvjaUwoDsmT4pvOL4HgAAAAAASFUqwdElSS3GmMPGmHxJPyfp2w/eYIw5JekPFA+Nxh74PF/Sf5D0p7Zt/+UnnjmQuBpJPy3p+m6+kZw0eDHepmZMtneyqdLCPIWK89U/tXHFUTga09t3JvRMS6WMi76fFx+rUsyWLvSkVnX0F+8OaGpxTV9NQ7WRJJ1IDMi+nkpwZMekqTub3wcAAAAAQBptGRzZth2R9EuSzku6Ielbtm13GWN+yxjzhcRtX5NUIukvjDEfGGOSwdJ/IekZSb+Y+PwDY0zy3Pl/Z4y5JumapApJ/4tz31YOmB+RZvpd36aW1BDyq3di44qj9/tntLgWdU2bWtKJgwFVlBTotRtbB0eRaEx/+OZdPVEfVGdjek6FKyvMU1NFsa4ObjEgu6I1fqVdDQAAAACQRb5UbrJt+zuSvvOJz37zgZ+/tMFzfybpzzb42gupb3MPGrgYvx7KjeCoMVSsd+5Obvj1N3vG5fUY/diRUAZ3tTWPx+iFY5U6d31E4Whs09Pe/ubafQ1OL+s3f/J4Wqum2usCunRvavObQkfiVwZkAwAAAACyKPtnpu9Xgxclb7504GS2d5KS+pBf9+dWtBKOPvLrb90a16lDQZUV5mV4Z1t74Vi15lYierdvesN7bNvW1y/c1ZGqEr30WHVa99NeG9Dw7IrG51c3vinfLwXqqTgCAAAAAGQVwVG2DFySDjwu+QqyvZOUNIaKZdvS4PTD7WpTi2u6OjTruja1pM+1VCjPazY9Xe1Cz7hu3J/TV55pkseT3hlNHXVBSSnMOapslcZvpnUvAAAAAABshuAoGyJr0vD7OdOmJj1wstoj5hz94PaEbFt6prUi09tKSUmBT59uCm0aHH39wh3VlBXqpx+vTft+2g6WyRjp6mAKA7Inb0uxWNr3BAAAAADAoxAcZcPIVSm6KtV1ZnsnKWsIFUuSeicfPlntrVvjChTlrVfSuNHzR6t0e2xBfY/Y/wcDM3rn7pT+ydOHle9L/78SxQU+Haks0bWhrQZkt0jhJWluKO17+qSrgzNaixBYAQAAAMB+R3CUDeuDsZ/K7j62odyfp9JCn/qnPl5xZNu23uyZ0OeOVMib5hav3XjxsSpJemTV0dffuKOyQp9+7sn6jO2nvS6QWsWRlPE5R7dG5/WF3/2B/v2l/oyuCwAAAABwH4KjbBi8KAUOSWUHsr2TlBlj1BgqVu/kx4Oj22MLGplb0dMt7mxTS2oIFau5svih4OjO+ILOd4/oS59pVElBSocMOqK9NqCx+VWNzq1sfFPF0fg1w8HR314fkSS9vckpegAAAACA/YHgKBsGLuZUm1pSfcj/UKvXhZ5xSdLTre4cjP2gF45V6Ud3p7S4Gln/7I/evKt8r0e/+NnGjO6loy4gaYs5R8UVUmEw48HRuURwdKl3WrZtZ3RtAAAAAIC7EBxl2uxQfGZNDg3GTmoM+TU0vaxw9KPZN2/dmlBzZbFqg0VZ3FlqXjhWrbVoTN+/PSFJGptb0V+9N6T//HSdKkoye7rd8QMBeYx0bXCTOUfGxNvVJm5lbF8DU0vqvj+nI1UlGp9fVd/kw8PQAQAAAAD7B8FRpq3MSA2fleo/ne2dbFtDqFiRmK3hmWVJ0ko4qh/dm9TTLe6vNpKk043lKi306Xs34u1q3/jBPUViMX3l6eaM76Uo36vW6lJdHUphzlEGK47Od8WrjX79x49Jki71TmVsbQAAAACA+xAcZVp1m/QPvyMdPJXtnWxbg+WXpPU5R5d7p7USjunZHGhTk6Q8r0fPtFbqezfHNLsc1p+/06+faD+g+pA/K/tprw3o2uDs5u1gla3Swqi0vMUJbA45d31Exw+U6fmjVQr68wiOAAAAAGCfIzhCyhoriiVJ/Yk5R2/dGlee1+ipJiub29qWF49VaXx+Vf/DX13T/GpEX30289VGSR11AU0urml4drMB2cmT1dLfrjY2v6J3+6f18okaeTxGpxvKdbl3Ou3rAgAAAADci+AIKasqLVBhnme94uhCz7hON1jy52fuNLLdera1UsZIf3Ptvp5uqdCJ2kDW9tJeF5S0xZyj9eAo/e1qr3aPyrals201kqTORkt3JxY1Pr+a9rUBAAAAAO5EcISUGWPUGCpW3+SixuZW9OHIvJ7JkTa1pFBJgU4digc22aw2kqRjNaXyeczmJ6sFGyRvfkaCo3PXR3S4olit1SWSpM7D8Uqyy7SrAQAAAMC+RXCEbam3/OqdXFo/mezploos72j7vvJMk/7+U/X6seZQVvdRmOfV0ZpSXdtsQLbXJ1nNaQ+OZpfCevvOpM621cgYI0k6cTCgwjyPLtGuBgAAAAD7FsERtqWxolj9U0t64+a4KkrydfxAWba3tG0vnzigf/F32tcDkmzqqAvo6lYDsita0h4cfe/mqCIxW2fbqtc/y/d59PihIAOyAQAAAGAfIzjCtjSE/FqLxPRK94g+d6RCHk/2w5dc1l4b1OxyWANTyxvfVNEqTd2TImtp28e56yOqKSvUycTcpaTORktdw7NaWI2kbW0AAAAAgHsRHGFbGqz4yWor4Ziebsmt+UZu1FEXH859dWiLAdl2VJq+l5Y9LK9FdaFnXGfbqh8KAjsbLcVs6f1+2tUAAAAAYD8iOMK2NIT86z/PxflGbtNaXap8r0fXNhuQXZk4WW38Zlr2cKFnXCvh2Pppag86VR+Ux4g5RwAAAACwT+XOOepwhYPBIuV5jZorS1RVVpjt7eS8fJ9Hjx3YYkB2qCV+TdOco/NdIwr68/Rk4hS1B5UW5un4wTJdusecIwAAAADYj6g4wrZ4PUZ/91SdvvSZxmxvZc9orwvo2tCsYrENBmQXlEhltdLELcfXXovE9N0bo/r8Y9XyeR/9n4PORkvvD0xrLRJzfH0AAAAAgLsRHGHb/ref6dDff6o+29vYMzpqg5pfiahvamnjm9J0sto7dyc1vxJ5ZJtaUmejpZVwTF3Dm1RFAQAAAAD2JIIjIMvakwOyBzcbkH00XnFkb1CVtEPnukbkz/fqc5vMqzrdWC5JutRLuxoAAAAA7DcER0CWtVSVqMC3xYDsihZpbV6av+/YutGYrVe6RvX8sSoV5nk3vK+qtFCNIT8DsgEAAABgHyI4ArLM5/Wo7WCZrm42ILsicbKag+1q7/dPa2JhddM2taTORkuXe6c2nsMEAAAAANiTCI4AF+ioC6praFbRjYKZ9eDIuQHZ566PKN/r0fNHK7e8t7PR0vRSWHcnFhxbHwAAAADgfgRHgAu01wa0uBbVvY2CmdIaqaBMGr/pyHq2betc14g+11Kh0sK8Le/vPGxJki7eo10NAAAAAPYTgiPABTrWB2Rv0K5mjKMnq3Xfn9Pg9LLOtlWndH9jyK+KkgIGZAMAAADAPkNwBLhAU2WJ/PnejYMjKd6u5lCr2vnrI/IY6aXHUguOjDHqbCwnOAIAAACAfYbgCHABr8eo7WCZrm06ILtFmh+WVud3vd65rhE9edhSqKQg5Wc6Gy0NTi/r/uzyrtcHAAAAAOQGgiPAJdprg+oanlUkGnv0DRVH49ddtqvdHV9Qz+hCSqepPaizMT7n6FIvc44AAAAAYL8gOAJcoqMuoJVwTLfHNxiQ7dDJaue7RiVp28HRYwdKVZzv1aV7tKsBAAAAwH5BcAS4RPtWA7Ktw5LHt+uKo3NdIzpZF9DBYNG2nvN5PXqigTlHAAAAALCfEBwBLnE4VKySAp+ubRQcefMkq2lXwdH92WVdGZjRmW1WGyV1Nlq6OTqv2aXwjvcAAAAAAMgdBEeAS3g8Ridqy3R10wHZrdL4zoOjVxJtai+f2HlwZNvSu/1UHQEAAADAfkBwBLhIR11QN+7PaS2y0YDsFmnqrhTdWcXPuesjOlJVoubKkh09//ihoPK8hgHZAAAAALBPEBwBLtJeG9BaJKae0flH31DRKsXC0nTftt89tbimi71TenmHbWqSVJTv1YnaAAOyAQAAAGCfIDgCXKQjMSD72kbtahVH49cdzDn67o1RRWP2jtvUkjobLV0dnNVKOLqr9wAAAAAA3I/gCHCResuvskLfxierVRyJXydubvvd56+PqDZYpLaDZbvYYTw4WovGNt4jAAAAAGDPIDgCXMQYo466oK4NzTz6hsKAVFIjTdza1nsXViN66/aEzrbVyBizqz2ebiiXJF3qpV0NAAAAAPY6giPAZdrrAro5Mq/VyAatYBUt225Ve+PmmNYisV23qUlSeXG+WqpKCI4AAAAAYB8gOAJcpqM2oHDU1s2RDQZkVx6NB0e2nfI7z10fUag4X59KVAvtVudhS+/2TisaS30PAAAAAIDcQ3AEuEx7YkD2xnOOWqWVWWlhLKX3rYSjev3DMZ1pq5bXs7s2taTOxnLNr0Y2DrcAAAAAAHsCwRHgMrXBIlnF+bq2UXB08FT8+u/+ntT7gy3f98M7E1pci+ps2+7b1JI6Gy1JzDkCAAAAgL2O4AhwGWOM2msDujq0QXB06EnpZ/5YWpqW/uQnpG/9gjTTv+H7zl0fUWmBTz/WXOHYHmuDRToQKCQ4AgAAAIA9juAIcKGOuoB6Rue1Et5gQPaJvyv90iXpuV+Xes5Lv9spfe+3pbXFj90Wicb0aveoXnisSvk+5/51N8aos9HSpd4p2duYtQQAAAAAyC0ER4ALtdcGFI3Z6r4/t/FN+X7puV+TfvmydOwnpTd/R/pXp6Wr31ofnH2pd1rTS2G97GCbWlJnY7lG51Y1MLXs+LsBAAAAAO5AcAS4UEddUJI2nnP0oECd9DPfkP7ReamkSvqrL0vfOCMNvavzXSMq8Hn07NFKx/fYeZg5RwAAAACw1xEcAS5UXVagytKCjU9We5T6T0tffl36qd+TpnulP3pBnR/8hv6zJo/8+T7H99haVaqyQh/BEQAAAADsYQRHgAsZY9RRG9C1oZntPejxSKe+KP3yuxrr+Kd6KfqW/tehX5Te+j+k8Iqje/R4jE4n5hwBAAAAAPYmgiPApdrrAro9tqDF1cj2Hy4s0x/7f1E/Hv6adPhZ6bX/SfrXT0k3/tP6/CMndDZaujO+qMmFVcfeCQAAAABwD4IjwKU66gKK2dp8QPYGbNvW+esjOtjUprwvflP6B/9R8hVJ//6L0p9+QRrtcmSPnY3lkuJDuAEAAAAAew/BEeBSJ2oDkrS9OUcJt8cWdHdiUWdPJE5Ta35e+ur3pZ/436WRa9LXPyf99a9Ii5O72mN7XUD5Po8u064GAAAAAHsSwRHgUlWlhaopK9S1wW3OOZJ07vqIJOnM8eqPPvT6pCe/LP3ye1Lnl6V3/0T6V6ekd74uRcM72mOBz6vH64LMOQIAAACAPYrgCHCx9rqArg5tv+LoXNeInqgPqrqs8OEv+i3pJ35H+qc/kA4+IZ37Ven3Pyvd/u6O9th5uFzXh+e0tLaDWUwAAAAAAFcjOAJcrKM2oLvji5pfSb0iaGBqSV3Dc3o52aa2karHpH/wH6Sf/6YUC0t/9vekP/9ZaeL2tvbY2WgpGrP1fv/2K6MAAAAAAO5GcAS4WHtdfM7R9aHUB2Sf74q3qZ1t2yI4kiRjpKM/Lv2zd6TP/5bU+wPpX39aOv8bUiS1k9KeaCiXMaJdDQAAAAD2IIIjwMXaEwOyrw2lXs1zvmtEx2pK1RAqTn0hX4H02f9W+m/ekzp+Vnr7d6Ur30zp0bLCPD1WU0ZwBAAAAAB7EMER4GKhkgLVBotSPlltfH5Vl/umt25T20hJlfRTvyv5iqTxmyk/1tlYrvf6ZhSOxna2LgAAAADAlQiOAJfrqAvoWooDsl/tHpVtp9imthFjJKtJmrqb8iOdhy0th6PqHk69pQ4AAAAA4H4ER4DLtdcF1De5pNmlrQdkn+saUUPIr2M1pbtbNNQkTd1J+fbORksSc44AAAAAYK8hOAJcrqM2KElbVh3NLof19p0JvdxWI2PM7ha1mqTpXikWTen26rJC1Vt+giMAAAAA2GMIjgCXSw7IvrrFgOzXPxxTOGrrzG7a1JKsJim6Js0NpfxIZ6Oly73Tsm179+sDAAAAAFyB4AhwuYA/Tw0hv65vUXF07vqIqkoLdOpQcPeLWs3x6+R22tXKNbm4prsTi7tfHwAAAADgCgRHQA5orw1serLa8lpUF3rGdbatRh7PLtvUpHjFkbTtAdmSdOke7WoAAAAAsFcQHAE5oKMuoMHpZU0trj3y62/eGtdyOLq709QeVHpA8hVuKzhqqihWqDhfF5lzBAAAAAB7BsERkAPatxiQff76iAJFeXqqyXJmQY8nXnW0jeDIGKPTjeW63DvtzB4AAAAAAFlHcATkgBO1ZZKka4MPD8gOR2P67o1RvfRYtfK8Dv4rvc3gSIoPyO6fWtLo3Ipz+wAAAAAAZA3BEZADSgvz1FRZ/Mg5R+/cndTcSkRn26qdXdQ6LE3dk2KxlB/pbEzMOaJdDQAAAAD2BIIjIEd01AYe2ap27vqIivK8eqa10tkFrSYpuirNDaX8SNvBMvnzvQzIBgAAAIA9guAIyBHtdUHdn13R2PxHbWCxmK1Xukf1/LFKFeZ5nV3Qao5ft9Gu5vN6dKo+qIvMOQIAAACAPYHgCMgRHXUBSdL1B6qO3h+Y1vj8qnOnqT3IaopfdzDn6MOROc2thJ3fEwAAAAAgowiOgBxx/ECZPEYfm3N07vqI8rxGzx+rcn7BslrJW7Cj4Mi2pXf7qDoCAAAAgFxHcATkiOICn45UlehaIjiybVvnu0b12SMVKivMc35BjycxIHt7wdGp+qC8HqPLDMgGAAAAgJxHcATkkPbaoK4Ozcq2bd24P6/+qaX0tKklWU3bDo78+T6dOFimS/eoOAIAAACAXEdwBOSQ9toyjc+vanRuVee6RmSM9Pnj1elb0GqSpu5Jsdi2HutstPTB4IxWI9E0bQwAAAAAkAkER0AOaa8LSpKuDs7ola4RdTZaqigpSN+CVpMUWZbm72/rsc7DltYisfW2OgAAAABAbiI4AnLI8QNl8nqMvn1lWB+OzKe3TU3a8clqpxvKJUkXmXMEAAAAADmN4AjIIUX5XrVUleivr8YrgM62pbFNTdpxcBQqKVBzZbEu9zLnCAAAAAByGcERkGM66gKSpPbagOrK/eldLFAnefOlqTvbfrSz0dLl3inFYnYaNgYAAAAAyASCIyDHJOccpb3aSJI8Xqm8cdsVR1I8OJpbiahnbN75fQEAAAAAMoLgCMgxz7VW6vFDQf30qdrMLJg8WW2bOhstSdKle8w5AgAAAIBcRXAE5JhDll//8b/+bPrb1JKspnjFkb29lrNDVpGqywp0iTlHAAAAAJCzCI4AbM5qksJL0vzIth4zxqiz0dKl3inZ2wydAAAAAADuQHAEYHM7PFlNirer3Z9d0eD0ssObAgAAAABkAsERgM3tMjiSpMt9zDkCAAAAgFxEcARgc4FDkidPmrqz7UeP1pSqtMCni/eYcwQAAAAAuYjgCMDmvD6pvGFHFUdej9GnGst1uZeKIwAAAADIRQRHALaWPFltBzobLd0aW9D04prDmwIAAAAApBvBEYCtWU3S5F1pB6ejJeccXaLqCAAAAAByDsERgK1ZzVJ4UVoY2/ajHXUB5Xs9utzHnCMAAAAAyDUERwC2touT1QrzvOqoC+jiPSqOAAAAACDXEBwB2Jp1OH7d6Zyjw5auD81qeS3q4KYAAAAAAOlGcARga8EGyeOTpu7s6PHOxnJFYrbeH6BdDQAAAAByCcERgK15fVKwfscVR5+qt2SMdLmX4AgAAAAAcgnBEYDUWE07Do4C/jwdrS7lZDUAAAAAyDEERwBSYzVLk3cl297R452Nlt7rm1YkGnN4YwAAAACAdCE4ApAaq0lam5cWJ3b0eOdhS4trUd24P+/wxgAAAAAA6UJwBCA1VlP8utOT1RrLJUkXaVcDAAAAgJxBcAQgNevB0c5OVjsQKFJdeZEuExwBAAAAQM4gOAKQmmC9ZLw7rjiS4nOOLvVOyd7hnCQAAAAAQGYRHAFIjS9fCh7adXA0sbCm3sklBzcGAAAAAEgXgiMAqbOapMmdtapJH805unSPdjUAAAAAyAUERwBSZzVLU/ekHbaaHakqUbk/jwHZAAAAAJAjCI4ApM5qklZnpaWdBT/GGH2qwWJANgAAAADkCIIjAKlbP1lt53OOnjxcrt7JJY3Nrzi0KQAAAABAuhAcAUjdenC0mzlHliTpcu+0EzsCAAAAAKQRwRGA1JU3SMazq4qjtoMBFeV59cM7Ew5uDAAAAACQDgRHAFLnK5ACdbsKjvJ9Hj3TWqHvdo8pFtvZkG0AAAAAQGYQHAHYHqtJmtx5q5oknTleo5G5FV0dmnVoUwAAAACAdCA4ArA9VvOuKo4k6cXHquT1GJ3vGnFoUwAAAACAdCA4ArA9VpO0MiMtTe34FUF/vp46bOkVgiMAAAAAcDWCIwDbs36y2u6qjs621ejO+KJujy04sCkAAAAAQDoQHAHYnlBz/LrL4Ojzx6sliXY1AAAAAHAxgiMA2xNskGR2HRwdDBapoy6gV7pHndkXAAAAAMBxBEcAtievUArU7To4kuLtalcGZjQyu+LAxgAAAAAATiM4ArB91mFp8s6uX3O2Ld6u9ko37WoAAAAA4EYERwC2z2p2pOKoubJETRXFeqWLdjUAAAAAcCOCIwDbZzVJy1PS8vSuXmOM0Zm2Gr1zd1KzS2GHNgcAAAAAcArBEYDts5riV0fmHFUrErP12odUHQEAAACA2xAcAdi+UHP8OnVv1686WRdUVWkB7WoAAAAA4EIERwC2r7wxfnWg4sjjMTrTVq0LPeNaCUd3/T4AAAAAgHMIjgBsX16RVFbryMlqknS2rUbL4aje7Bl35H0AAAAAAGcQHAHYGavJkYojSXrqcEilhT690k27GgAAAAC4CcERgJ1xMDjK93n04rEqvXZjVJFozJF3AgAAAAB2j+AIwM5YTdLShLQy68jrzrbVaHoprIu9U468DwAAAACwewRHAHbGaopfHao6eqa1Uvk+D6erAQAAAICLEBwB2JlQc/zqUHBUXODTMy0VerV7VLZtO/JOAAAAAMDuEBwB2JnyxvjVoeBIks601WhoZlnXh+YceycAAAAAYOcIjgDsTH6xVHpAmnQuOHrxWJU8Rnqle8SxdwIAAAAAdo7gCMDOWc2OVhyFSgrU2WjpfBfBEQAAAAC4AcERgJ2zDjsaHEnx09V6Rhd0b2LR0fcCAAAAALaP4AjAzllN0uKYtOLcTKLPH6+WJL1C1REAAAAAZB3BEYCdS56sNn3PsVcesvxqO1hGuxoAAAAAuADBEYCds5ri1zS0q73XP6OxuRVH3wsAAAAA2B6CIwA7V344fp284+hrz7TF29VevTHq6HsBAAAAANtDcARg5wpKpJJqacq5VjVJOlpdqoaQX+e7CI4AAAAAIJsIjgDsjtXseKuaMUZn22r09p0Jza2EHX03AAAAACB1BEcAdsdqcjw4kqQzx6sVjtp6/cMxx98NAAAAAEgNwRGA3bEOSwsj0uqCo699or5cFSUFeoV2NQAAAADIGoIjALsTao5fp52dc+TxGH3+eLXeuDmmlXDU0XcDAAAAAFJDcARgd6ym+DUd7Wpt1Vpci+qHdyYcfzcAAAAAYGspBUfGmJeNMTeNMbeNMb/2iK//ijGm2xhz1RjzmjGm4YGv/YIx5lbixy888PmnjDHXEu/8v4wxxplvCUBGJYOjyTuOv/rHmkMqKfDp/HXa1QAAAAAgG7YMjowxXkm/J+nHJR2X9PPGmOOfuO19Sadt2+6Q9JeSfifxrCXpn0t6StKTkv65MaY88czvS/qypJbEj5d3/d0AyLyCUqm4Ki0VRwU+r54/VqXv3hhVNGY7/n4AAAAAwOZSqTh6UtJt27bv2ra9Jumbkn7qwRts237dtu2lxC/fkVSX+PlZSa/atj1l2/a0pFclvWyMOSCpzLbtd2zbtiX9qaSfduD7AZANVpM05eyMo6Qzx6s1ubimd/um0/J+AAAAAMDGUgmOaiUNPPDrwcRnG/nHkv52i2drEz/f8p3GmK8YYy4bYy6Pj4+nsF0BjqkVAAAgAElEQVQAGWc1SVPOt6pJ0nNHK5Xv9eh810ha3g8AAAAA2Jijw7GNMV+UdFrS15x6p23bf2jb9mnbtk9XVlY69VoATrKapPn70tqi468uLczTZ4+EdL5rRPECRQAAAABApqQSHA1JOvTAr+sSn32MMeYlSb8h6Qu2ba9u8eyQPmpn2/CdAHJEKDEge7o3La8/01ajwell3bg/n5b3AwAAAAAeLZXg6JKkFmPMYWNMvqSfk/TtB28wxpyS9AeKh0ZjD3zpvKQzxpjyxFDsM5LO27Z9X9KcMebTidPUviTp/3Pg+wGQDcmT1dIwIFuSXnqsWsaIdjUAAAAAyLAtgyPbtiOSfknxEOiGpG/Ztt1ljPktY8wXErd9TVKJpL8wxnxgjPl24tkpSf+z4uHTJUm/lfhMkv6ZpP9b0m1Jd/TRXCQAuSYZHE2mZ85RZWmBTjeUExwBAAAAQIb5UrnJtu3vSPrOJz77zQd+/tImz/4bSf/mEZ9flnQi5Z0CcK/CgOSvSFvFkSSdOV6j3/7ODfVPLqk+5E/bOgAAAACAjzg6HBvAPmY1pTU4OttWI0l6pZuqIwAAAADIFIIjAM5Ic3BUH/LrWE0p7WoAAAAAkEEERwCcEWqW5oak8HLaljjTVqPLfdOaWFjd+mYAAAAAwK4RHAFwRnJA9nRv2pY421Yt25a+2z2atjUAAAAAAB8hOALgDOtw/Jqmk9Uk6fiBMtWVF9GuBgAAAAAZQnAEwBnJiqM0zjkyxujM8Rr94PakFlYjaVsHAAAAABBHcATAGUXlUpGV1uBIirerrUVjeuPmWFrXAQAAAAAQHAFwUppPVpOk042WrOJ8ne9izhEAAAAApBvBEQDnZCA48nqMXnqsSq9/OKbVSDStawEAAADAfkdwBMA5oWZpdlAKr6R1mbNtNVpYjejtO5NpXQcAAAAA9juCIwDOsZok2dJMX1qX+eyRCvnzvbSrAQAAAECaERwBcE7yZLXJO2ldpjDPq+eOVurV7lFFY3Za1wIAAACA/YzgCIBzksFRmuccSfF2tYmFVX0wMJ32tQAAAABgvyI4AuAcvyUVBjMSHD1/rEp5XkO7GgAAAACkEcERAGdZTdJUelvVJKmsME+fbgrpfNeIbJt2NQAAAABIB4IjAM6ymjJScSTF29X6JpfUM7qQkfUAAAAAYL8hOALgrFCzNDsoRVbTvtSZ49WSpPNdI2lfCwAAAAD2I4IjAM6ymiQ7Jk33pX2pqrJCnaoPEhwBAAAAQJoQHAFwVgZPVpPi7Wpdw3ManF7KyHoAAAAAsJ8QHAFwltUcv2YwOJKkVzhdDQAAAAAcR3AEwFl+SyoIZCw4OlxRrJaqEtrVAAAAACANCI4AOMsYyTosTd3J2JJn22p0qXdKU4trGVsTAAAAAPYDgiMAzrOaMlZxJMWDo5gtffcG7WoAAAAA4CSCIwDOCzVLM/1SJDMVQCdqy3QwUKhXaFcDAAAAAEcRHAFwntUk2bF4eJQBxhidaavRm7cmtLgayciaAAAAALAfEBwBcJ7VFL9msF3tTFu11iIxvdkznrE1AQAAAGCvIzgC4DyrOX7NYHD0ZKOloD+P09UAAAAAwEEERwCcV1wh5Zdm9GQ1n9ejF49V67UPxxSOxjK2LgAAAADsZQRHAJxnjGQdzmjFkSSdbavW/EpE79ydzOi6AAAAALBXERwBSI9Qc8aDo6dbKlWY56FdDQAAAAAcQnAEID2spvipatFwxpYsyvfq2dZKvdo9qljMzti6AAAAALBXERwBSA+rSYpF4uFRBp1tq9Ho3KquDM5kdF0AAAAA2IsIjgCkh9UUv07dy+iyLxyrktdjdL5rNKPrAgAAAMBeRHAEID2s5vg1w3OOgv58nawL6L2+6YyuCwAAAAB7EcERgPQoqZLyiqWpOxlf+nBFifqmFjO+LgAAAADsNQRHANLDmHi7WoYrjiSpIeTX6NyqlteiGV8bAAAAAPYSgiMA6RPKXnAkSf1TSxlfGwAAAAD2EoIjAOljNUnTvVI0ktFlG0LFkqS+SdrVAAAAAGA3CI4ApI/VJMUi0uxARpdtsKg4AgAAAAAnEBwBSJ+snayWp9JCn3qpOAIAAACAXSE4ApA+VlP8muHgyBijxlCx+iapOAIAAACA3SA4ApA+pTWSrygrA7LrQ35a1QAAAABglwiOAKSPMfGqo2ycrGb5NTS9rHA0lvG1kV62betPfnBP9yZoRQQAAADSjeAIQHqFshQchfyKxGwNzyxnfG2k19j8qv7H/9Stf/vD3mxvBQAAANjzCI4ApJfVJE33SrFoRpdtCBVLEnOO9qDu4TlJ0pXBmSzvBAAAANj7CI4ApJfVJEXXpNnBjC7bEPJLkvqYc7TndA3PSooHSLnaivjnP+rX779xJ9vbAAAAALZEcAQgvazm+DXD7WrVpYXK93nUP8kcnL2m+3684mg1ElPP6HyWd7Mz3/j+Xf3xD+5lexsAAADAlgiOAKSX1RS/TmW2usLjMaq3/OqlVW3P6Rqe02MHyiRJVwdns7yb7ZtbCevuxKLG5lc1s7SW7e0AAAAAmyI4ApBepQckX6E0lfnqisaQX/0ER3vK3EpYfZNL+okTNSor9OVkcHR9cFa2Hf95z+hCdjcDAAAAbIHgCEB6eTzxqqMsnKxWbxWrf2pJdvJP6ch5H96Pt6adqA2ooy6oqzk4IPuDB/Z8M0db7QAAALB/EBwBSD+rSZrM/CDghpBfy+GoxudXM7420iM5GPv4wTK11wV0c2ReK+HMnti3W1cGZtQQ8qu0wKdbBEcAAABwOYIjAOlnHZam70mxzP4Bvz5xshpzjvaO7uE5VZTkq6q0QCfrAorEbN1IDMvOFVcGZvX4oaBaqkt0c4TgCAAAAO5GcAQg/awmKbomzQ1ndNnGULEkqY+T1faM5GBsY4w66oKScmtA9sjsikbmVnSyLqijNaXqGZ2nlRIAAACuRnAEIP2s5vg1w3OOaoNF8hipf4qKo71gLRLTrbF5tR0MSJIOBApVUZKfU8HRlcR8o5OHgmqpKtX0UlgTC5ysBgAAAPciOAKQflZT/DqV2TlH+T6PDgaL1Eer2p5wa2xe4ait4wfLJGm96iiXBmRfHZyRz2PUdrBMR2tKJYk5RwAAAHA1giMA6VdWK3kLsnKyWmOomFa1PaJ7OD7LqC0RHElSe21At8cXtLgayda2tuXKwKyOHShVYZ5XLdUlkjhZDQAAAO5GcAQg/Tye+IDsqXsZX7o+5FcfrWp7QtfwnPz53vXZVZJ08lBAti1dH3J/u1osZuvK4IxOJmYzVZYUqNyfpx6CIwAAALgYwRGAzLCapMnMtqpJUoPl18xSWLPL4YyvDWd135/TsZpSeT1m/bP22ngIcy0HgqN7k4uaX4msB0fGGLVWl6pndCHLOwMAAAA2RnAEIDOsJmn6nhSLZXTZhpBfktTPnKOcFovZujE8tz4YO6mytEAHA4W6kgMDsq8MfDQYO6m1ulQ9I5ysBgAAAPciOAKQGVaTFFmR5u9ndNmGRFtTL3OOctrg9LLmVyPrg7Ef1FEX1LUcGJB9ZWBG/nyvjlSVrH/WWlOq+dWIRuZWsrgzAAAAYGMERwAyI0snq9VbiYoj5hytG55Z1n//l1e0tJYbA6UlqWs4XlHU9ojgqL0uoN7JJc0uubsd8YPBWbXXBj7WateaCJFujjDnCAAAAO5EcAQgM9aDo8yerFZc4FNFSQEnqz3gW5cH9K3Lg3r7zmS2t5Ky7vtz8nriM4E+KTkz6OqQe6uO1iIx3Rie0+MPtKlJWv9+bjHnCAAAAC5FcAQgMwJ1kjc/48GRFJ9z1MeMo3Vv3ByXJL3f796g5ZO6hud0pLJEhXneh77WXhufe3TVxXOOPhyZ01o09rH5RpJUXpyvytIC3eRkNQAAALgUwRGAzPB4pfJGgqMsm1pc05XEPKD3+qezvJvUdQ3PPnK+kSQF/HlqDPl11cVzjpKDsTvqAg997Wh1qW4RHAEAAMClCI4AZI7VJE1mITiyijUyt6KVcDTja7vNW7fGZdvxAOPKwIyiMfef5jWxsKrRudVHzjdKaq8L6pqLK44+GJhVRUm+aoNFD32tpbpEPaMLiuXAPwsAAADsPwRHADLHaopXHGX46PGGUHxA9gADsnXh5rjK/Xn60mcatbgWVU8OVLp0D89Jko4f2Dg4OlkX0PDsisbnVzO1rW25Mjijk3VBGWMe+trR6lIth6MamlnOws4AAACAzREcAcgcq0mKLEvzIxldtj4RHO33drVYzNaFnnE93VKp0w3lknJjzlH3/URwtEnFUUdiQPY1Fw7InlsJ6874wkPzjZJaEgOyOVkNAAAAbkRwBCBz1k9Wu5PRZRtDxZKk3n1+slrX8JwmF9f0XGtIDWu3ZBXn58Sco67hOdUGixT05294T9vBMnmMdGXAfe1q1wdnZdvaMDhqrS6RJAZkAwAAwJUIjgBkznpwlNk5R+X+PJUW+NS/z1vV3rg5Jkl60XdF5g+f1cs1C3o/B4Kj7k0GYycVF/h0pKrElQOyryRmL518xGBsSSotzNPBQCEDsgEAAOBKBEcAMidwSPLkZTw4MsaonpPVdKFnXO21AQXW4gHSp60F3Rlf1MzSWpZ3trGltYjuTixuOt8oqb02qGtDs7IzPENrK1cGZtQY8m9aMdVaU6qbowsZ3BUAAACQGoIjAJnj9UnlDdJkZlvVpPiA7P1ccTS7FNZ7/dN67miltByvMnqsND6M+YMB91XpJH04Mi/b1qYnqiWdPBTQxMKahmdXMrCz1F0ZnFmfwbSR1upS3RlfUCQay9CuAAAAgNQQHAHILKtJmrqX8WUbQsUamFrat38wf+v2uGK29GzrR8FRfcGiPMbdA7K7EieqtdU+us3rQe2Je665qF1tdG5F92dXNpxvlNRaXaq1SEx9+zjcBAAAgDsRHAHILKsp3qqW4XaiBsuvSMzWfZdVo2TKhZvjKiv06fFDQWk5HqwUrEyotbrU1QOyu4fnFCiKzwDaymMHyuTzmPWZQm5wJVHN9fihzYOvo4mT1ZhzBAAAALchOAKQWVazFF6UFsYyumx9yC9J+3LOkW3butAzrqdbK+XzetYrjrQwqicayvXBwIxiMXfNBUrqHp5V28EyGWO2vLcwz6tjB0p1zU3B0eCMvB6jtoObB0dHqkpkjHRzhDlHAIDc8NdXh3V9yD3/zwWQPgRHADJr/WS1zM45aggVS5L6phYzuq4bdN+f09j8arxNTZKWp+LXhVGdOhTU/EpEd8bdF1hEojF9ODKf0mDspPbaoK4OzrhmQPbVwVkdqylVYZ530/uK8r2qt/zqGaPiCADgfrZt61f/8qr+5as92d4KgAwgOAKQWdbh+DXDJ6sdKCtUvs+zLyuOLvSMS5KeWw+OkhVHY3qioVySO+cc3Z1Y1Gokprba1IOjk3UBza1E1OuCf86xmK0rAzNbzjdKaqkqVc8IwREAwP3GF1a1uBbVu/3Trq1aBuAcgiMAmRVskDw+aSKzf0Pl8RgdKi9S3+T+qzh64+a4jh8oU1VZYk7QA61qh0PFChTluXLOUXdiMPbxA1sPxk5qr4vfe9UFA7J7Jxc1txLRybrU9n+0pkT3Jha1FtmfA9wBALkj+RdxM0thV1YtA3AWwRGAzPL6pNpPSffezPjSDaHifVdxNLcS1rt903r2aKLayLbjwZGJzzryxNb0+KGgKyuOuoZnle/zqLmyOOVnWqtLVeDz6KoL5hxdSYRXqVYctVaXKhKzdW9i/4WbAIDc0vvA/6su9brvL58AOIvgCEDmHfm8NPy+tDCe0WUbQn71Ty25Zv5NJvzw9oSiMfujNrXwkhRdk8oTLYOL43qivlw9Y/OaWwlnb6OP0DU8p2M1pfGB3inK83p0/GCZKwZkXxmYlT/fq5aq0pTub02crNbDyWoAAJfrm1yS12NkFefrcu9UtrcDIM0IjgBk3pEX49e7r2d02QbLr6W1qMYXVjO6bja9cXNcpQW+9VlG621qlcfi14VRnaoPyralqwPZD1uSbNtW9/05tR1Mfb5R0sm6oK4Pzyqa5ZkLHwzM6ERtQF7P1ifCSVJTZbG8HkNwBABwvb6pJdWVF+nJRkuX+giOgL2O4AhA5h14XPJXSLdezeiyyZPV+vdJu5pt27rQM67PHqlQXrJqZz04Ohq/Lozr8fqgjJGr5hwNz65oZim8rRPVkjrqAlpai2Z15sJaJKbu4Tk9nmKbmiQV+LxqDPl1kwHZAACX65tcVEOoWKcbyzUwtazRuZVsbwlAGhEcAcg8jydedXTnNSmWuUHA9SG/JO2bOUc9owu6P7ui55LzjSRpKfG3gg9UHJUV5ulIZYned1FwtD4Y+2Dqg7GTOhLDqK8MZG9u082Rea1FYzpZl3pwJMXb1W6NMWQUAOBeth2fx9cY8quz0ZIkXWbOEbCnERwByI4jL0lLk9L99zO2ZF15kTxG++ZktTdujknSR4OxpY8qjipa4teF+D1P1Jfr/YEZ18x/6hqelTHSsZrU5gM9qKmiRMX5Xl0byl7r3Qfrg7G3F3y1Vpeqd3JRK+FoOrYFAMCuzSyFNb8SUUOoWMcPlqkoz6tLzDkC9jSCIwDZ0fyCJCPdfi1jSxb4vDoQKFLf1P6oOLrQM66j1aU6ECj66MNkcFRaIxUGpYVRSdKp+qBmlsKuOdGre3hOhyuKVVzg2/azHo/RidqArmRxQPaVgRmFivNVGyza+uYHtFaXyral21QdAQBcqjfxF3CNIb/yvB49fiioy8w5AvY0giMA2VFcIR08Jd3+bkaXbQj590Wr2sJqRJd6pz7epiZ9FBwVlUsl1evBUXJ49vv92WvvelDX8NyO5hslnTwU1I3hOa1FMtcK+aArAzM6eSgoY1IbjJ10tKZEEierAQDcK/n7qOTsyM7GcnUPz2lhNZLNbQFII4IjANlz5CVp8NJHc3cyoCHkV/8+qDj64e0JhaO2nm19RHDkK5LyiqSSqvVWtSOVJSot8LliQPbsUlhDM8tq28F8o6T22oDWorGsBDDzK2HdHl/Y9nwjKf6b8HyvRz2jVBwBANypd3JRxkiHrHhV7elGSzFbrpqVCMBZBEcAsqfl85Idk+6+kbElG0LFmlpc09xKOGNrZsOFnnEV53t1OjG0ct3ydLzaSPpYxZHHY/R4fdAVFUdd9+MtZm0Hd1FxlAhtrgxm/vu5NjQr297+fCNJyvN61FRZTMURAMC1+iaXdDBQpAKfV1K83d1jpEs5NiB7fiWswem9/5eJgBMIjgBkz8En4nN2Mtiu1mDFT1br38PtarZt642b4/qxIxXK933iP/MPBUdj6186dSioD0fmtJjlUvOPTlTbeXB0yCpS0J+na1mYc3RlIL7mTiqOpPicI4IjAIBb9U4uqiFxUq0klRbm6bEDZXo3x+Yc/fbf3NBP/e4PFI2542AQwM0IjgBkj9cnNT8fD44ydJpXfeI3Ont5ztGd8UUNzSw/3KYmScszDwRHVVJ4UVqNt0WdaihXzJauZnGotBQPjqrLClRRUrDjdxhj1J6lAdlXB2fUEPKrvDh/R8+3VpdocHo56wEeAACP0je5tD7fKKmz0dL7/TMKR7MzW3C7bNvW6zfHNLm4phv357K9HcD1CI4AZNeRz8fbpUavZ2S55G90+qbccXpYOrxxM15F9NBgbElanpKKEpUwJdXx62L8/scTFTLZnnPUfX93g7GTTtYF1TM6n/Gj7a8MzKhjh9VGUrziSJJucbIaAMBl5lbCmlpcU+MDFUeS9KmGci2tRXMmhLk9tqDRuVVJ0jt3J7O8G8D9CI4AZNeRF+PXW69mZLmSAp8qSvLVN7F3K44u9IzrSFWJ6sr9D3/xY61qiWAp0a5WXpyvporirM45WglHdWtsYVeDsZPa6wKKxmx1DWfuN7Fjcysanl3Rybqd7z8ZHPWM0K4GAHCX/k+cqJZ0ujH+e4tcmXP0/dsTkqSgP0/v3M2tFjsgGwiOAGRXaY1U0y7dfi1jS9Zb/j1bcbS0FtGP7k49uk1NenjGkbQ+IFuSTtWX///svXd0I+l5p/sr5EiACAQDCIJg6umcu6e7J2l6FGxZcWzZ0l7Zclxb8pUl2Stf22f3rC3v9XrtlXxtr9e+vpJlSSNrJFnBQWFmpO7pME2yEztNgxnMAQAJgMih7h+FYrO7GRCqgAL5PufoQEOCheIMUfjq973v8+LGxBLYCrUOPsrgfATZHFuW34iHdwzdqqAgm2+NO9haesVRq0UHjVIGL3mOCIIgCIkxHuDWT27bw5tTTSYtnPVaXB2vjRDm4pAfbqsOb9/TiL6xAHmOCGILKDgiCKL6dJ4FJq8AicpUhrRZ9dtWjn1lNIBUNrd+m1o6DmQSgC4/aW01OFojyHaZEYimMBmMV+BsH4cXY5czUY3HUaeG3aiuqCB7YHIZchlTVsWUXMags8FAgmyC2MFkcyze8mfn8LX+iWqfCkE8BO+IdFker2o+5ragf7x6m0+Fks7mcGU0gNOdNpz0WBFOZHB/rjZa7AiiWlBwRBBE9ek8C+QywNj5irycy6LDbDhRcfdNJTjnXYRWKccxt+Xxb8bz5eN8xZHOCjCyhyqODru471XLc3R3JgyDWoHW9drsioRhGBxwmjBQ0YqjZfQ4jNCq5GUdhyarEcTOZjIYw6g/ijdGyL1CSItxfxSOOjV0KsVj3zvqrod/JSn5ASQ3JpYRTWXxVJcNJzzceona1Qhicyg4Igii+rSeAFRGbrpaBXDbdGBZYGpJ2gubUjg/uIgnO6zQKNcJLh4NjmRyQG9/KDjqdhigU8lxo0rBES/GlskYQY6332nGqD+KSCItyPE2g2VZDEwu40AZbWo83Q4j5sNJhGLinzdBENKDb1Ud9W/PtmqidllvohoPv2l11Sdtz9HFYT9kDPCkx4YmkxZtVh0JsgliCyg4Igii+siVgOcZYOhVoALlzS5LfrKaxHfEimXMH4UvEFu/TQ14PDgCAEPDQ61qCrkMB5xmXK+CIDubY/HmbFgQvxHPPqcJLAvcmRa/BH08EEM4kcHB1vLF3j28IHuBqo4IYifCy/FHF6OSb/shdhbjgSja1mlTA4BOuwEmrVLynqOLQ4vY5zTDpFMCAE62W9E3FkSOPEcEsSEUHBEEIQ26XgDCU8CiV/SXasuPkN1uwdF5LxcAbSjGjuUXcg8FR46HKo4AznP05mwY8VRlW/l8gShiqaygwdH+Fi7EuT0tfhA2MMm9xn5n+RVHXQ4DAFC7Wg3zO18fwHcHZqp9GkSNwlccrSQzWIwkq3w2BMERS2WwEEnCbVu/4kgmY3CkrR79Eg6Owok0BqZCeKrTtvq1kx0WhOJpvEmeI4LYEAqOCIKQBh3Pc4/Dr4j+Ula9CnqVHBPB7RUcnRtcRLtNv2EJ+foVRw5gZfGhpx121SOTY3FnpnJSaYDzGwHA7ibhgiOrQY0Ws3Z12pmY3JxchlYpR1eDoexjtZi10Kvkq1UHRG2xEEng69em8OU3fNU+FaJGGZyPwKjmHDIji9SuRkgDfsON34Bbj6PueowsRhFYkWbgeWWEm6B2putBcHSi3cp9jzxHBLEhFBwRBCENzK2AfVdFPEcMw6DNql8dKbsdSKSzeGMksHG1EbBJq9r8Qy2CB11cxcz1CjsK7s2GoZQz6M63aQnFgVZTRSarDUwtY1+LCQp5+R+tDMOgu9GIwfkVAc6MqDT9Y9x758bkEmKpTJXPhqg1UpkcRhejeP6JBgDAqJ+uA4Q08OXXTe6NNqjwwHN0TaKeo4vDfmiVchxyPagObjaT54ggtoKCI4IgpEPnWcB3GUiJH+i0WXWY2Eatar1jQSQzOTyzkd8I4IIjuQpQrtkp1DcAufSDUAmAzaBGm1WHGxX2HN2dCaOzwQiVQtiPpn0tZkwEY1iKpgQ97lpSmRzuzoRxQAC/EU93A01Wq1X6xribj3SWRf+4NG+eCOky5o8ik2PxdLcdWqUco1RxREiE8fy6ybVJxdG+FhNUcplkBdkXh/w44bFArXh4iMiJdgt5jghiEyg4IghCOnSeBbIpYOyC6C/lsuowuRRDdpssEM55F6BWyPCkx7rxk+JLgNYCMGsmlhm4He21gmwAONRqxvWJpYpKWe/NhLFHQL8RzwEnF+bcmhav6sg7F0EqkxNkohpPd6MRgWgKfomW+xMb0ze+hCNt9VDKGVwe9lf7dIgag/cb7WqsQ7tNj9FFqjgipIEvEINVr0KdRrnhczRKOfY7TZL0HE0vxzHqj+LMGr8Rz0mPFaF4GvepRZwg1oWCI4IgpEPbKa4apgLtam6rHuksi9lQXPTXqgTnvYs46bFCo5Rv/KT40sNtagDnOAIeE2QfbqvHQiSJmVBC4DNdn4VwAv6VpCjB0d58cHR7SrwKqoH8sQ8IIMbm6SZBdk3C3XiE8Uy3HYdc9bg0QsERURyDcxHIZQw8dj08dj1G/VRxREgDXyC6qd+I56jbgjvTISTSlR2ysRWXhrjr8VNdj1dnn/DwniNqVyOI9aDgiCAI6aBQA+1Pc4JskStd+FGy22Gy2kQghlF/dHO/EbBFcPRoxRH3vEp5ju7OCi/G5qnTKOGx6UUVZA9MLsOqV8FZrxXsmD1519MQeY5qimu+IFiW83yc7rDh7kwYyzHx2iSJ7Yd3PgK3VQeNUg6P3YDJYAzJjLRuwImdiS8Q29RvxHPMXY90ll2dNioVLgz7YTeqVzdm1tJi1sJlIc8RQWwEBUcEQUiLzrPA0jgQHBX1Zfj+/O0QHJ0f5EKfZzfzGwFAfHmd4IhvVXu44mhXkxEapaxinqN7+YlqT4hQcQQA+53iCrIHppax32kCs7YNsEzsRjVMWuVq2wpRG/SOBaGUMzjkMuN0pxUsSzvYRHEMzUfQ08gFxx12PXIstpWTj6hNEuksZkLxjSe3ruFIG7fWkJLnKHi5mNIAACAASURBVJdjcXnYjzOdtg0/q096LOglzxFBrAsFRwRBSIvOs9yjyO1qTSYtlHIGvmDttwCcH1xEq0WLdtsWi7l48PHgSGMC5OrHgiOlXIb9LZznqBLcmwnDZdFt6k0oh31OM+bCCSyEhW+9W0lmMLSwIqjfCOAmq/U4jBgk30JN0T8WxH6nOe/5MEOnkuPSMAVHRGHEU1n4grHV6ZIeG1cZMUKCbKLKTC3FwLKA27Z1q5pZp0JXg0FSnqM358IIRFPr+o14yHNEEBtDwRFBENLC0g5YOoChV0R9GbmMQaul9ierJTNZXB4J4Nnuhq2rXeJLgPaRcINhuHa1R1rVAOCQy4x7M+GKtEjcnQmJ4jfiWRVki1B1dHsqBJaF4MERAHQ5DBicj1RUUk6UTjyVxa2pEI63c+OoVQoZjrdbyHNEFMzwwgpY9kGrarud2xAYIUE2UWXG/fmJapatgyOA8xxd8y1JZgjJxbzf6PQmwRHvOeodo7CfIB6FgiOCIKRH1wvA+EUgLa64us2iWx0tW6v0jy0hlspu3aaWTgDp2OMVRwDXrhZdLziqRyqbw53psEBnuz6RRBrjgZgofiOe3c11kDHALREE2WKIsXl6Go0IJzKYD9NktVrgxsQSMjkWx92W1a+d7rBhdDGKuQqJ5onahm9N7c63qhnUCjjq1BiliiOiyowHuL/BQhxHAOc5iiQykhnwcHHYj64GAxpNmg2f02LWotWipfZiglgHCo4IgpAenWeBTBzwXRb1ZdqsekwEojVdzXF+cAEquQxPdlg3f2IiH5joLI9/b4OKo8MuLgi5IXK7Gl8SvqdFvOBIp1Kg22EURZA9MLkMl0UHi14l+LG7GribR6ksvInN6RsPgmGAI+4HAe2pTu69eZmqjogCGJyPQKWQrQ5wALh2tVE/VRwR1cUXiKFOo4BZV1hL+bF8gH5VAu1qiXQWfWNBnOnauNqI52S7lTxHBLEOFBwRBCE92k5z3h2RPUcuiw7RVBaBaO1OPDrnXcTxdgt0KsXmT4znw591K47sjzmOAKChToMWsxY3RJ6KcneaC3N2N5lEfZ39ThNuT4cEDwpvTYVEaVMDsDr5hYKj2qBvLIgnGusecnU90ViHep2SPEdEQXjnIui0G6CQP1iie+x6jC7W9iYHUfuMB6Jw2/QFD4Fw1mvhqFOjf7z6guxrviUkMzk8VUhw5LFiOZamwRQE8QgUHBEEIT1UOsB9RvTgiBc81upktenlOIYWVrZuUwO2CI4cQNQPZDOPfeuQy4wbIk9FuTcbhlWvgqNOLerr7HOaEYymMLUkXAvkQiSB6eX4qkNJaKwGNWwGFQVHNUAqk8P1iaVVvxGPTMbgyQ4rLo/46caf2JLBNRPVeDx2A0LxNII1vMlB1D6+QKygiWo8DMOseo6qzYUhPxQyBsfbt6jOBnDCw13DqV2NIB6GgiOCIKRJ51nAPwgs+UR7CZeFWwD5ArXpjjjvXQQAPNNdbnDUAIAFYo+30hx21WMmlBDVz3J3JozdzXWCjrJfDz7cuT0tXLvarUnuWGJVHAFAt8MI7zy1qUidOzMhJNI5nGh/vB30VIcNs6FEzTvVCHEJxdOYDSVWJ6rxdOQF2aP+2vysImqfdDaH6eU43NbCxNg8x9rqMb0cx/SyuM7Krbg07MdhVz0M6i2qswE463XkOSKIdaDgiCAIadJ5lnsUseqo1aIFw9RuxdE57wJazFp0Nhi2fnIs7xjYqOIIWLdd7ZDInqNUJoeh+RXsFnGiGk9PoxFKObMqsxaCgallyGWMqBPhuh1GDM9HyLcgcfrHuPfYUffjwRE/xefSMHmOiI0ZylcW9jQ+fE3vsHP/PEqT1YgqMb0URzbHFlVxBDy4HlbTc7QUTeHOTKggvxHPiXYr+shzRBAPQcERQRDSxNYFmF3A8GuivYRaIUdTnQYTwdoLjlKZHC6PBPB0t72wSp2tWtWAdQXZe5pNUClkonmOhhdWkMrmRJ2oxqNWyPFEUx1uCyjIvjm5jG6HcWvHVBl0O4yIprJV37ElNqdvLAiPXQ+78fGWS7dVhyaThgTZxKbwThVeis/TbNZCpZDV3GS1UCyNhQhNE9wOPJioVlzF0a5GI/QqOa5W0XN0acQPln0Q4BfCSY8VS7E0BheoTZwgeCg4IghCmjAMV3U0dh7IiOd1aLPqa7JV7ZpvCSvJTGF+I4ALjmQKQLVOdZKhgXtcp+JIpZBhb3MdrovkKLg3GwbABVSVYF+LCbenQoLsIrIsi1tTIRxsFffc+eqDIVrASpZcjkX/eBDH16k2AjjXx6kOG94YCdAONrEhg3MR6FVytJi1D31dLmPQbtVjpMaCo9/71m286y8vIZ7KVvtUiDLhK7OLrThSyGU43FaP/ipWHF0a9sOoURTlIuRbjq+MULsaQfBQcEQQhHTpfAFIrQCTV0R7iTarriZb1c4NLkAhYwrfQYsvcdVG61Un6fng6PGKI4DzHN2eDiGVyZV4thtzdyYErVKOdltxi9FSOeA0I5LMrO6eloMvEEMonsYBp3h+IwDozFcfeOeoTUWqeOcjCCcyj4mx13K6k9vBfnMuXMEzI2oJ73wEXQ4jZLLHr9Meux6j/tq6BgxMLWMunMAXLo9V+1SIMhkPRKFXyWEzqIr+2aNtFnjnIwjF0yKc2eawLIsLQ3486bE+NKlwK1otOjjrtbgyWr3AiyCkBgVHBEFIl/anAJkSGHpFtJdwWXUIRFNYST4+UUzKnPcu4qi7MNEjgHxwtMFNrUoHqOs2DI4OueqRzOTw5qzwN7z3ZsLY1WSEfJ0bJTHYn68OuiVAuxrvShJTjA0AJq0STSYNTVaTMH15v9GxDSqOAE6QDQCXh2kHm3gclmXhnYug5xExNo/HrsdEIIZ0VvgAXwxWkhlMLcWhlDP4m3MjWKKJcDWNLxCDy6ovaYjFMXc9WBa4LpIrcTN8gRimluJF+Y14Tnqs6B2jKlGC4KHgiCAI6aI2Aq6TonqO2mpwstpcKIH7cxE829NQ+A/xFUcbobev26oGAIfbxBFksyyLe7NhUcXSj9JpN0CjlAkiyL45uQyNUoauQuTkZdLlMFJwJGH6xoNoNmngrNdu+JxGkwYeux6XyHNErIN/JYWlWBrdjRsERzYDMjm2Zpx83jnuevVbZ7sRTWbwv84NV/mMiHIYD0SL9hvxHHSZIZcxuFYFz9HF/ECCM0X4jXjIc0QQD0PBEUEQ0qbrBWDhLhCeEeXwbfmF0EQNtaudH+Qqgwr2GwFbB0cGx4YVR00mLRrrNLg+Iawge2opjkgig91NlfEbAZxvYW+zSRBB9sDkMva1mIoqfy+VHocBwwsryNLOp+RgWRZ9Y0Eca7dsuRt/usOGvrGgKG2fRG2zOlFtk4ojADUjyOaDo3cdaMb7Dzvxxcs+TC3Vzucs8YBsjsVkMFa034hHp1Jgb3NdVTxHF4f8aDFrS2qH5z1HvdSuRhAAKDgiCELqdJ7lHodfFeXwfHA0XlPB0SIa6zQb3mCsS3x5i+CoYcOKIwA45DLjxqSwu4V3Z7jwppIVRwCwz2nCnZkQMmW0fKSzOdyZCYvuN+LpchiRzORqptpgJ+ELxLAYSW7qN+I53WlFLJXFLQEq3ojtBT9Rrbtx/QpGj537+uhibXiOvHPhVdH3J17oBhjgs68MVfu0iBKYWY4jnWVLrjgCgCNtFtycXK5oaJ7Nsbg84seZTltJLXatFh1azFpcGaX2YoIAKDgiCELqNOwGjM2iBUdGjRIWvQoTwdrYxc1kc7gw5Mcz3fbiFkLxYMkVRwAnyJ4MxrEYSRZxtptzbyYMuYxBzwatGWJxwGlGIp3DcBk3YN65CFKZnOh+Ix4+JOR38QnpwPuNThQQHJ30WMEwwCXyHBGPMDgfQb1OCbtBve73TVolbAZVzVQc3Z+LoLuRE303m7X4yCk3/vnGFO6THL7mKHWi2lqOuTlX4p2Z8qt9C+XW1DLCiQxOl+A34uE8R0HyHBEEKDgiCELqMAzQ+Twwcg7IiiOwrqXJajcmlxFJZIprU8ukuOl0W1UcJUNAOr7utw+5hPcc3Z0Jo8Ouh0YpF+yYhbAvP5L31mTpC9ibk1zFyMEKBUedeY/SEHmOJEffeBAWvQod9q1dV2adCnua68hzRDyGdy6Cbodx0w0Bj81QE5PVWJaFdz6CXWs2BX792Q4Y1Qr86fe9VTwzohR8+Y01t62MiiM3t/64WsF2tUt5v9HpDmvJxzjpsSAYTWFoQfrvO4IQGwqOCIKQPp1nuVBjql+Uw7dZaic4OuddgFzG4FQxosdEvi1Gu0nIYXBwjxtUHe1tMUEpZ3BjUrgWm7szYexuqmybGgC0W/UwqhW4NV3673JrahkWvWpTGbKQ6NUKtFq0q+0shHToGwvimLu+4ArA0x023JhYQixVW5McCfFgWRaD8ytbVl967PqaqDhaiCSxHEs/1E5t1qnwG8914kf3F6j1p8bwBWJQK2RwGDUlH6PBqIHbqkN/BQXZF4b82NNcB+sGVXyFcNLDhU70N0sQFBwRBFELeJ4FGLlo7Wouqx4zoTiSmawoxxeSc95FHHHVw6RVFv5D8fxCbatWNWDD4EijlGN3Ux2u+4RZ9AVWkpgLJ7CnuXJibB6ZjME+pwm3yhBkD0yGsN9pKsmbUCrdDUYMzdOup5SYCyUwEYzhmHvrNjWeU502pLMsrlZhwhAhTWZCCawkM+jewlvnsesRiKYQiqUrdGalcT/fUtvT+PDGwC+ccqOxToM/+d59sCy1/tQK4/4o2qw6yGTlfd4ddVtwzbdUkf/20WQG1yeWSpqmthbyHBHEAyg4IghC+mjNQOtxYPgVUQ7fZtGBZbkpX1JmIZLA3ZkwnimmTQ14EBzpNrm5NTRwj9GNPUeHXPW4NVWeVJrn3iznudhdYTE2zz6nCW/OhksKC1eSGQwuRComxubpbjRi1L+CtAD//glh6Bvn/UaFt0Icc9dDKWeoXY1YZXA1aNk8OOLbIUck3q7mzXuMdj3y+2iUcnzyhW7cnFzGD+7OVePUiBLwBUqfqLaWY+56BKMpjPrFr5rrGw8inWVxpgy/Ec8JjwW9Y0EKO4kdDwVHBEHUBp1ngdmBTQXOpcL37U9IvF3t9UHuRvOZ7hKDo4IqjjafrBZPZ1d3k8vh3kw+OKpCqxrACbLTWbYk2fSd6RBYtnJ+I54ehxHpLIvxCiy6icLoGwtAr5LjiabCBe86lQKHWutxmQTZRJ7ViWoNW1Uc8ZPVpH0NuD8XQYNRjXq96rHvve9wC7oaDPjT73sF2YQgxCWXY+ELRsuaqMZzNF+ZWQnP0cUhP1QKWVHVoBtx0mMlzxFBgIIjgiBqhc6z3OPwa4If2mXhdtLGA9JejJ/zLsBuVBc/vr6Q4Eif35XbYrIaAEE8R3dnwmg2ada9sagE+1q4FrmBEtrVBvK//35nZdvsuhzcTSN5jqRD/9gSjrgtUMiLW06d6rTizkwIy7GUSGdG1BKDcxE01mlg0m3egtxar4VSzmC0jImQlcA7F9mwekohl+E/vX0XRv1RvHx1qsJnRhTLQiSJRDoHlwAVRx6bHha9qiKeo4tDfhxz1wsyfONJ8hwRBAAKjgiCqBUa9wN6uyieI5tBBZ1KLmlBdjbH4sKQH0932Yv36sTyu3ubBUdyJaCzblpx5KzXwmZQ44YAnqN7s2HsroLfiMdZr4VFr8LtqeJDsIGpZbRatGUJN0uhw26AjAEGyXMkCZaiKXjnIzju3uR9tQGnO21gWeDKaOUmDBHSxTvPja7fCoVcBpdFJ+mKo0w2h6GFlcfa1NZy9okGHG2rx+deHSRJvMThN9SEqDhiGAZH2upFrzhaiCTgnY/gTGeR1dkb4KzXkueIIEDBEUEQtYJMxlUdjfwIyAkrsWYYBi6LDhNB6QZHNyeXEYqn8WyxfiOAqzhi5IB6i0olg2PTiiOGYXDYZS674iieymJ0caVqfiOA+132tZQmyB6YDFXcbwRwfhC3Vb/qQyGqy9V8gHq8CL8RzwGnGVqlHJfJc7TjyeZYDC2soLvBUNDzPXYDRiXsOBoPxJDK5B4TY6+FYRj87jt2YSGSxBcujVfu5Iii8a0GR+VXHAGc52g8EMNCJCHI8dbj0jB3XX1KAL8RwP29nvBYcGWUPEfEzoaCI4IgaofOs0A8CMzcEPzQbqt+dYEkRc57FyBjSlwIxZc4wfhWlUqGhk0rjgBOkD3mjyIYLb3F5v5cGDkWxbfcCcwBpwmD8xHEU4UHkYuRJKaX4xX3G/F0O4wYpFY1SdA3FoBKLiupZVGlkOF4u2X1BofYufgCUaQyuYIqjgBustq4P4ZsTpo3sLw3brOKI4Dz3byw24H/fW6krM8TQlzGAzEo5QyaTBpBjsd7jq6J2K52YciPep1SUIcieY4IgoIjgiBqiY63AGBEaVdrs+owGYxLdjF+fnARB1vNMOtKcALFlzZvU+MxOLYMjg67uMDk5mTpi767VRZj8+x3mpFjgbszhVcd3Zri/UbVCo4MGA9EkUgLW3VHFE/f+BIOtppLdmic7rRiZDGKuZB4O++E9OGD4B5HYcFRh82AVDaHqSVpVsh658KQMUBnARVU/+ltPYimMvjrHw9X4MyIUvAFomit1xXtcduIvc0mqBUy0TxHLMvi0rAfpzptkMmKbOvfhJPt5DkiCAqOCIKoHXQWoOUIMPSK4Id2WXVIZXOYC0vvJi6wksSt6RCe7Wko7QDxJUBbwGQRQwPXqrZJKfY+pwlyGYPrvtLb1e7NhmHSKuGs15Z8DCHgK0WKEWQPTC5DxgB7W6oTenU3GpFjgRGJy3G3O9FkBnemQzjeXvrEnlMdXPXgG6NUdbST8c5x72Vefr8VHjvXMiRVz9H9uQjcNn1BgWqXw4ifPtKKL73hw6SEW8V3Mr5ADG0C+I14VAoZDraacdUnjudoeGEF8+EknuoUpk2Np9WiRbNJg17y0hE7GAqOCIKoLTrPAtPXHgifBaItP1lNiu1qF4b8YFmU5jcCiqs4yiSA5MatUDqVArsajbhRZsXR7qa64iXfAtNQp0FjnaYoQfbNqRC6HUboVAoRz2xjuvNVCUMkyK4q1yeWkM2xOFZGcLS7qQ5mnRKXhmkHeyczuBCBy6Ir+JrisXMBk1TDY+98ZMs2tbX81gtdYBjgs68MinhWRCmwLJsPjoTxG/Ecc1twdyYsihj9whAXxJ8RyG/EwzAMTnqsuDIaIM8RsWOh4IggiNqi6wUALCfJFhB+R21CgpPVznkXYNWrsLfUKWTFBEfApoJsADjsqsfAZKiktr5MNof7s+GqirHXss9ZuCCbZVkMTC5XzW8EcC4upZyBd5t6jkLxNL5xbQof+UIfDv7hD4tqI6wk/WNByBjgSFvxE9V4ZDIGT3qsuDzspxuRHczgXGQ1EC4Ei14Fs06JUb/0NjliqQwmgjH0OAq/vjeZtPjI6XZ86+Y07uXbmAlpEIimsJLMCDJRbS1H3fXI5ljcnChv0MZ6XBz2w23VwVkv7DkDnOcoEE1hmDxHxA6FgiOCIGqL5kNcCCKw56jZrIVSzmBcYsFRLsfi9SE/nu62l96vH18uLDjS5yuathRkm7GSzGBoofjwYswfRTKTq7oYm+eA04RRfxThRHrL5/oCMYTiaRyoYnCkUsjQbtNjaBsFR5FEGt+6MYVf/mI/jn3mVfz21wcwOL+CeCqLr/ROVPv01qV3LIg9zSYY1OVVnp3qtGEmlJDcdYeoDMlMFmP+KHoaC2tT4/HY9BiVYMXR4PwKWBboKaLiCAB+/ZkO1GmU+NMf3BfpzIhS4Cuw22zCVhwdbqsHw0Bwz1E6m8OV0YDg1UY8Jz3kOSJ2NhQcEQRRW8jknCR7+DUglxPssHIZA2e9DhNBae3i3p4OIRhN4ZnuEtvUshkgGSqy4mgrQTZ3rBsl7Bbem82LsSUSHO3LS67vFFB1NJBvaTtQJTE2T7fDWPMVR9FkBt+5OY1f/cerOPKZV/GJrw3g7kwYH36yDd/6jVO4+Onn8JP7mvAvAzOSE4EnM1ncnFwuy2/Ec7qDuxG5PEKeo53ImD+KTI4tquII4NrVpOg48s5x1/diWtUAwKRT4qPPdeCcd5HeCxJi3M8F2m6BW9XqNEr0OIyCe45uTCwjlsriTGeJ66Ut4D1HV8hzROxQKDgiCKL26HwBiC4Ac7cEPazLooNPYjv/57yLYBjg6VKDo0Q+3BGwVa3NqoNFr8J1X/G7hXdnwlApZOiwF7fDLhb7W7j2v1vTBQRHkyFolDJ0FyixFYtuhxGTwbgofggxiaUy+Ldbs/j1L1/D4T96BR//p5sYmFrGh0648M1ffxKXPv0W/ME7d+OQqx4Mw+DFI05EEhm8cm/zILPS3J4KIZnJ4Zi7/OCo3aZHY50Gl8lzJDgsy0q+Mo8fXV9shU6H3YCFSBKRAiolK8n9uQi0SjlcluLbhD78pBvNJg3++/fuU+umRPAFopAxQItZ+EEWx9wWXPctIZMVbgPw4tAiZAzwZD6QFxqGYXDCY0XvGHmOiJ0JBUcEQdQenc9zjwK3q7mtOkwEYpJaEJwbXMB+pxkWvaq0A8Tz4U4hwZG2HpAptqw4YhgGh1rNuDFZfMXR3ZkQehxGKAUa7Vsu9XoVXBYdbhUgyB6YWsbeZpNgY4lLpZYE2Yl0Ft+/M4uPvnQdR/7oVXz0pevoH1/Czx5rxcu/9iTe+N3n8V9+ag+OtFkea8U86bGixazFN65NVens16dvnNttPuYu3W/EwzAMTnVacXnEj1wJzjBiY759cxovfPZ1DJRwnaoUg/MRKGQMPLYiW9Xyk9XGJOY58s5F0O0wlNRWrVHK8YkXujEwFcL37syJcHZEsYwHYmip10KlEP4z76i7HtFUFvfnhAt3Lw77sd9phkmrFOyYj3LSY4F/JSVZOT1BiIk0Vu4EQRDFYGgAmg5w7WoC4rLqEUlmEIymBD1uqSxFUxiYXC69TQ0oLjiSyQB9w5YVRwDnORpeWEEoVviON8uyuDcTlozfiKcQQXY6m8Od6VBV/UY8fHXCoESrKRLpLH54dw7/51dv4MgfvYL/+OXruDISwPuPtOCrv3ISvb/3PP7ru/fiePvjYdFaZDIG7zvcggtDi5gPJyr4G2xO31gQnQ0GWA1qQY53usOGpVgab86RGFhIvvSGDwB3MylVvHMraLfpi74x78gHR1JrV/PORYqunlrL+w470e0w4H/8wIu0gJUoRGn4AlHB29R4+IrN/nFh2r7CiTQGpkJ4SiS/EQ/vOXqD2tWIHQgFRwRB1CadZ4HJXk78LBBt+fJ6X1Aa7WoXhv3IscCzPQIER7oCqyMMDVtWHAEPPEc3ixhlPxtKYCmWlozfiOeA04SppTgCK8kNn+OdiyCZyUkiOHJZdFArZJIKjpKZLF57cx6f+NpNHP3Mq/jVL13DhaFFvOtgC77yyyfQ+3vP4zPv2YcnO6yQF1GN8P7DTuRY4Fs3pkU8+8LJ5lhcG18SxG/Ec6ozfyMyQu1qQnF/LozreQeblEW2g/MRdJcQtLgseshljKQE2YuRJALRFHoaS7++y2UMPv32XRjzR/G1/kkBz44ohfFAbHXirNA0m7VoMWtxtYSW9/V4YySAbI7FmU5xgyOXRYcmk0bS1xWCEAsKjgiCqE06zwJsFhg7L9gh+QXShEQ8R+e9izDrlOXJmIupOAI4z1EBwdH+VjNkDIryHPGjliVXcdTC/fvdzHPEi7EPVlmMDXA3V50NBnir3KqWyuTwY+8CPvXyAI5+5lX80hev4kf3F/AT+xrxj794HH2/fxb/9/v24XSnreT2PrdNj6Nt9fjGtSlJtJC+ORtGJJnBCQGDoyaTFh6bHpckXBlTa7zUOwGVQoZ3HWjG1fElSVavPBhdX3xwpFLI0FqvxYiEKo54X1OxYuxHecuuBhx3W/C5V4cQTdaWx207sRxLIRRPi1ZxBHDtalfHg4Jc2y8N+6FTyXHIVX4L8WYwDIOTHit6R8lzROw8ClrJMQzzdoZhvAzDDDMM87vrfP9phmGuMwyTYRjmxTVff45hmJtr/pdgGOY9+e/9A8MwY2u+d1C4X4sgiG2P8zigNgFDrwh2yFaLDgwDSQiyczkW5wcX8VSXvagKjccoOjhqAKKLWz9NrUC3w1iU5+juTBgMA+wqY0daDPa21IFhOOnxRgxMLqNep0SrRXhJaCl0O4xVFf/2jwdx/L+9io98oR8/vDeHt+5uxBd+4Rj6f/8s/vTFA3i62y6Yx+rFI04ML6xgoIDJd2LTN8b7jYQLjgCu6qhvLCjJgKPWiKUy+Nb1afzE3ka8Y28j4unslq2o1YB3lBU7UY3HYzdIyrNyP99qWU6rGsDdmH/6HbvgX0ni8xfHhDg1ogTG8+ugNjGDo7Z6zIeTmFqKl32si0N+nGi3iOJjehTyHBE7lS3fXQzDyAH8NYB3ANgN4OcYhtn9yNMmAPwCgJfWfpFl2R+zLHuQZdmDAN4CIAbgh2ue8jv891mWvVn6r0EQxI5DrgA8z3CeI4F2fTRKORrrNPAFqr+Le282DP9KEs+W4zcCgFgQAMOFbIVgcHCOo9zWN7CHXPW4MbFUsNT33mwI7VY99GpFYedSIYwaJTw2/aaC7FtTnN+IYcoI8QSk22HEbCiBULzyU5VYlsUf/ss96JRy/P2Hj+LqH5zFn//MATy3q0GURftP7G+CRinDNyUgye4fD8JZr0WzwFOGTnfYEE1lJS1yrhX+ZWAGkWQGHzrZttpS2DsmvbYS73xpE9V4PDY9xgNRyUjVvXMR2Awq2ARwfx1pq8fb9jjwt6+PbtpCTIgHvw5yi9SqBgBHBfIcTS/HMeqP4kxXmeulAiHPEbFTKWSFdxzAMMuyoyzLpgD8E4B3r30Cy7LjLMveArDZncaLAL7Hsmz1t/IJgtgedL0ARGaAhTcFuXj/8wAAIABJREFUO6TLopOE4+j8IFf183S5wVF8CdCaOfF1IRgauBbA+NYLosMuMyKJDEb9he263Z0J4wmJtanxHHCaN6xKiCYzGJyPYL8E2tR4uh3cFKbhhcpXHf3w3jxuT4fwiRe6cXa3A2qFXNTXq9Mo8fY9jfjuwAwS6ayor7UZLMuibyyI4wJXGwHcjQjDAJfJc1Q2L/VOoKvBgKNt9bAa1Oh2GHBFgjd4g3MRqBWykkbXA1zFUSKdw0yo/GoNIfDOlyfGfpTfedsuxFIZ/NWPhwU7JlE4vkAMDMNVYotFt8MIo0aB/vHyPEeXhrg2X7H9Rjwuiw6NdRr0kueI2GEUcifRAmCtoW4q/7Vi+VkAX33ka3/MMMwthmE+yzCMMONJCILYOXQ8zz0OC9eu5rbqJdGqds67gL0tdbAby7w0xpcKb1MDuOAIKMhzxLsErvu2rpIIxdOYWopLzm/Es99pwkIkibnQ49O77kyHkGOBg60FVm1VAL69xTtX2VL5XI7FZ18ZhMemx3sPlbIUKI33H3EiFE/jtTe3nvgnFqP+KALRlKBibJ56vQq7m+rIc1Qmd6ZDGJgK4YMnXKvVgSc9Vlwdl14boHc+gs4GQ8mtyB4JTVbL5lgMzkfQ4xDu+t7ZYMAHjrXiy1d8mJTAZs5OYzwQRVOdBhqleBsDchmDI22c56gcLgz70WBUr26oiA3nObLgyqgwfiaCqBUqIsdmGKYJwD4AP1jz5f8LwC4AxwBYAHx6g5/9VYZhrjIMc3VxcWvvBkEQOwhTC9CwGxh+VbBDuqw6+FeSVZVyhuJpXJ9YxrPdDeUfrOjgyME9FhAceWx6mLRK3JjcereQF2PvbpJmcLQvX020XrsaL8aWUsVRi1kLnUpe8clq/3Z7FvfnIvj42a6ShdelcKrDhiaTBt+8Xr12tVW/kQjBEQCc7rThxsQy4qnqVVXVOi/1TUCtkOF9h5yrXzvpsSKWyuL2JvL7asAFLaVX6DwIjqrvWZkIxpBI58oWYz/Kx5/vhlzG4M9/6BX0uMTW+AIxUf1GPMfcFgwtrGA5lirp53M5FpeG/TjTaatoK/lJjxX+laSkBPUEITaFrPqmAbSu+Wdn/mvF8DMAvsWy7KqMgWXZWZYjCeAL4FriHoNl2b9jWfYoy7JH7fbK9K4SBFFDdJ4FfG8ASWFuoPnJatWsOro07Ec2x+KZHgGuefElQFvEje5qcLR1ZYdMxuBgqxk3JrauOLo3y09Uk07Vzlr2NNdBLmPWbVcbmAzBWa8VxN0hFDIZgy6HsaLBUTbH4nOvDqLbYcA79zdX7HUBbmf6fYdbcH5wEQvhx6vCKkH/WBA2gwoemzg3U6c6rEhlc7jqk15bVS2wkszgOzem8c79zTDplKtfX/UcSahdLRRLYz6cRHcZQYvdoIZRrcCov/o3rl6BxNiP0mjS4BdPt+PbN2dwR2LB33bHF4iurofE5Ggbt7F1rYgJrWu5NxtGMJrCma7KtKnx8J6jK9SutuPpHQ3gwtDijqg+KyQ46gfQxTBMO8MwKnAtZ98t8nV+Do+0qeWrkMBw8fB7ANwp8pgEQRBccJRLA2MXBDlcm4W7KZwIVm8x/i8DM7DoVTjUKkCFi4itagBwyGWGdz6CSGJzSfPdmRAajOryW+9EQqOUo9thXK0uWsvNyWUcEOK/hcB0NxgwOF+5aoPv3JzGyGIUnzjbXd6kvxJ532EnsjkW375Z7N6VMPSOBXHMbRFtV/uY2wKFjMGlYboRKYXv3JxGNJXFB0+4Hvq6zaBGV4NBUjd4g3k3WTkVRwzDwGPXS6JV7f5cBAxT+oS4zfi1Zzpg1inxpz+gqqNKEUmk4V9JVaTi6ECrGUo5U7LniG/vPV0hvxFPm5XzHEnpukJUnlyOxX/57l38wbfvICuRQQVismVwxLJsBsDHwLWZvQngZZZl7zIM84cMw7wLABiGOcYwzBSAnwbwtwzD3OV/nmEYN7iKpfOPHPorDMPcBnAbgA3AZ8r/dQiC2HG4ngSUesHa1VxVrjgKrCTx6pvzeN+hFmFagYoNjlQGQKkrqOIIAA676sGy2HLc9b2ZMHZL1G/Ec8Bpwu3p0EO7Rv6VJKaX4zgooTY1np5GI/wrSQSjpZX4F0M6m8PnXh3C7qY6vG1Po+ivtx4ddgMOu8z4xrWpiu/sTS/HMb0cF8VvxKNXK3DIZcblEfIcFQvLsnipdwK7Go047Hr8vSo1z5F3jguOyqk4Arj3hBRa1bxzEbRZdNCqhPfhmLRKfOy5Trw+uEgOsArBr3/EnKjGo1HKsbfFVLLn6OKwH90OAxx1GoHPbHPIc0QAwL/m2/c/+UJ3Rdv3q0VBvyHLsv/Osmw3y7IdLMv+cf5r/5ll2e/m/38/y7JOlmX1LMtaWZbds+Znx1mWbWFZNvfIMd/Csuw+lmX3siz7H1iWrf4nH0EQtYdCBXie4QTZAnx4m7RK1OuUVZus9q0b00hnWXzgWOvWT96KXBZIhIoLjhiGqzoqsOKIr8S5vkmZeTKTxfDCimTF2Dz7nCYsx9KYDD6YUnRr1W8kvRa7rvzufiXa1b55bQoTwRg+9dZuyKpQbcTz4pFWDM6v4M50uKKv25/3G4kZHAGcy+n2dAih2OYVfMTD3JoK4e5MGB9aI8Vey0mPFdFUVjLtToPzERjUCjSbyrvZ9dj1mAklEEtVz8kHcMGR0G1qa/kPJ9vQYtbiT753H7kdsKtfbfjgqBIVRwBXbXlrKlT01MxEOou+sSDOdFZHZXKCPEc7mkw2h8++MogehxE/VeH2/Wqx/aMxgiC2P53PA8sTQECYsb0uqx6+QOUXAizL4p/6J3HIZV4NBcoiEQLAFhccAZznqMCKI5NWia4GA25Mbuw5GpxbQSbHYneT9MKXtRzgBdnTD36Xm5MhyBhgb4v0zr2nQsFRMpPFX/5oGAdbzXjLLgGE7WXwk/uboFLI8I1rk1s/WUB6x4IwqhXY1Shu+Hm60waWBa6MUftDMbzUOwGtUo53r530l80A974LsCxOePKeozFpeI68cxF0Owxltz167NwUqbEqeo4S6SzGA1H0iPje0Cjl+OQL3bg9HcK/35kV7XUIjvH8+qcSjiOA8xylsrmiBfbXfEtIZnI402UV6cw2h/cc9dL1ekfyzetTGPNH8ckqb6hVEgqOCIKofTrPco9DrwhyuDaLriqtatcnljG8sIIPHBWg2gjg2tSA4oMjvb3g4AjgPEc3JpY2LNe+N8stBqVecdTtMEKlkD3UdjcwuYxuhxF6taKKZ7Y+jjo16jQK0YOjr/VPYno5jk+9tbuiU2vWw6RV4m17GvGdgRkkM5WbPtY/HsRRd73obqeDrWZolXJcppacggkn0vjuwAzedaAZdZoHUmwMfg94+f8AZm5IynPEsvnR9QJU6DyYrFa94GhofgU5FoJPVHuU9xxqwa5GI/7HD7ySaTncrvgCUdiN6op97h3JC7L7i2xXuzDkh1LO4ER7dYIjt1UHR50aVyQk3icqQzKTxf/z2jAOOE14625HtU+nYlBwRBBE7VPvBqxdgnmO3FYdZpbjSGUquzh9uX8SOpUc7zwgUMlrqcGRwVFwqxrAeY6WYmmMbxC23Z0Jw6BWwGWpzO5lqagUMjzRVLfansayLAamllcrkaQGwzDodhgxOCdep3cincVf/WgYx90WnKmwfHQjXjzixHIsjR/fLzzcLIfAShLDCys4JnKbGsD9DR5rt+DSSPUDjlrhOzemEU8/LsXG0jj3GOEqVE54LOgfCyJT5dBhcSWJpVhaEJG026oHwwAjVfQc3RdpotqjyGUMPv32XfAFYvinvglRX2un4wvEKuI34rEa1Oiw63G1SEH2xeFFHHLVV21jh/McWXFlNECeox3GV3snML0cx2+/rafqG2qVhIIjgiC2B10vAL5LQDq+9XO3wGXVI8dyQtxKEU1m8K+3ZvDO/U0wCLUIKic4igeBTGHS5UMu7vgbeY7uzYTxRJOxJkp5DzhNuDMdRi7HYiIYw3IsLcmJajzdjUZ45yOiLVq/fMWHhUgSn5RAtRHPmU4bHHVqfOPaVEVej5/2c6ICwREAnO6wYnhhBfPhREVer5ZhWRZf6Z3Anua6xz1ky/l2xnz15KrnaKayfqxH4YPeciaq8WiUcrSYtVWtOPLORaBWyOCugA/n2R47TrRb8BevDSGarK7XaTvjC8Qq5jfiOea24JpvqWCHVTCawt2ZcNU3NE56rFiMJDFaxXZRorLEUhn81Y+HcaJdOhtqlYKCI4IgtgedzwOZBDB+sexD8X394xX0HP3brVlEU1lhpNg8fHCkK/KG15D32EQXC3p6V4MBRrUCNyYfD45yORZvzoaxu0nabWo8+1pMWElmMOqP4mbe23SgVXp+I57uBgNC8TQWI0nBjx1NZvA350ZwptO26nKQAnIZg/cecuLH3kVRfu9H6RsLQq2QYV9LZQJEfqz0G1R1tCXXJ5Zxfy6CD64nxQ7lg8X8dYxvZ+mtcruad16YiWo8HrsBo/7qVRx55yPochhEb+MEuAqP333HLvhXUvj7C2Oiv95OJJ7KYi6cqGjFEQAcdVsQiqcxXGD13OURP1gWONNV/eAIgCTaYInK8A+Xx+FfSeF3dli1EUDBEUEQ24W204BCI0i7Wlu+pWqigp6jr12dRIddj8OuIquDNqOciiOg4HY1mYzBgVYzrvseF2T7gjFEU1nsaZZu+LIWvrro1tQybk2FoFHKBGkpEQv+5tMrgufoi2+MIxBN4ZNv7Rb82OXy4pEWZHMsvnNzWvTX6h8P4pDLDJWiMkumJ5rqYNIqafR4AbzUOwG9So53H2x5/JuhhyuO7EY1OiXgORqci8CqV8FmUAtyPI9Nj7HFaNVaZe7PRdDjqNzGwCFXPd6xtxF/9/oI/CviB8c7jYlgZSeq8Rwt0nN0ccgPo0aB/VUeXOG26tBgJM/RTiEUT+Nvz4/iuR47jrorU4UsJSg4Ighie6DUAu6nBBFk241qaJXyigmyhxciuOZbwgeOtQq7e8EHR5oiF1arwVHhDpnDLjPuz4UfGwt9d4YTTe+WuBibp8NugE4lx62pEAYml7Gn2QSlXLofld2rk9WErTgIJ7jF0Vt2NQgbZgpEZ4MRB1rN+Ma1KVFvmCOJNO7OhHC8ggtEuYzBkx4rLo+QN2MzQrE0/vXWDN59qGX99l4+OIo+uI6daLegf3ypqp4j73xE0DC6w65HNJXFfLjyIUowmsJiJCm6GPtRfvttPUhkcvjL14Yq+ro7gUpPVONps+pgM6gL8hyxLIsLQ36c6rBCUeXPZ95z1Eueox3B318YRSiexqfe2lPtU6kK0l0NEwRBFEvnWSA4AgRHyzoMwzBos+owEaxMq9rLV6egkDF432GnsAeOBbnQSCYv7uf4VrUiBNmHXPXIsXhoIhnA+Y0UMgZdDkNx51Al5DIGe5tNuD6xhDszIcmKsXlsBjWsehUG54StOPr8xTGE4ml88gXpVRvxvHjEiftzEdwV0VlzzbeEHAscr/DUntOdVkwvx6sy3bFW+OcbU0hmcvjgcdfj30yuPAjO1wTgJz1WrCQzov7NbEYux2JoPoJuAa+HHjt3rNEqCLIrJcZ+lA67AT9ztBVf6Z2Ar4It5TsB/t9nm6WyFUcMw+CYu76giiNfIIbp5bhk/DInPVYsRJIYI8/RtiawksTnL47hJ/c1YW+VK92qBQVHBEFsHzrPco/Dr5V9KJdFt+GUMCFJZXL45+tTeP6JBsFaF1aJLxXfpgascRwVXnF0MN/idX3i4d3CuzNhdDYYoFYUGV5Vkf1OE25NhZBI5yTtN+LpchgwuCBccLQcS+H/uzCGt+1xSHpx9K79zVDJZfjmdfEk2f3jQShkDA63VTZAPJW/IbpMnqN1YVkWL/VO4IDTtP7faDjfwihTPBQcnfBwlWPValebXo4jmsoK5jcCAI+du8EfqcJNqzcfWFe64ggAfutsFxRyBn/0r28iFE9X/PW3K+OBGOp1Sph0yoq/9lG3BVNLccyGNh9MciHfxnumy16J09qSk6vXFWpX2878zbkRxNNZfELCG2piQ8ERQRDbB2sHUO8WxnNk1WEiGCt4wkep/Oj+PPwrKWGl2DylBkcKNaAxF9WqVq9XwWPT48bEw56je7PhmvEb8exbM53poIQnqvH0OIwYml8RrEz+714fxUoqI/nFkUmnxAu7HfjOzRmkMuK0HvWNBbGnxQSdqrLjnj02PRx1alwaIc/Relz1LWFoYQUfPLFOtRHwYKKaY89Dkv8GowYddj16x6pzgzeYd5EJMVGNp7FOA51KXpWKI+9cBPU6JexGgTc9CsBRp8FHn+3Eq2/O4/gfv4pPfO0mLo/4Rf/M3u74AtGK+414jrm59cpW7WqXhvxoMWsrLvDeiHabPu85oqB/uzIbiuMfr/jwvsNOdDbURgW9GFBwRBDE9oFhuKqjsdeBTHm+hzarHqlMDvMRcUdif61/Eo46NZ4WY+es1OAI4KqOimhVA4CDLjNuTCytBhgLkQQWI8ma8Rvx8O1pZp0SLos0Fqab0eUwYiWZwUyo/L9V/0oS/3B5HO/c34xdjdL/7/biESeC0RR+7C085CyURDqLgckQTrRXXoDJMAxOd9jwxkiAboTX4StXfDCqFfipA83rP4H3GzUfBpJhIP3gvXHCY0X/WLAqniPeRdYlYHDEMAzabXqMLla+4uj+XAQ9jcaqTRb62Fs68d2PncaLR5x49d48Pvj/9uLZPzuHv3xtaMuqFWJ9xv2xqgUyu5vqoFPJcc23cXCUzbG4POLHmU6bZCZa8Z6jK+Q52rb85Y+GwbIsPv58V7VPpapQcEQQxPai8wUgHQMm3ijrMLwYUkzHyFwogfODi3jxiFMcwWNZwZGjqIojADjsqod/JYWpJW7Bfi/vEdlTY8FRm1WHOo0C+51mySxMN4P3iwjhOfrf50aQSGfxW2drY3H0VJcNdqMa37gmfLvawOQyUtkcjlVpcsqpThuC0RTuC+yvqnWWoin8+505vOdQy8aVYKEpgJEDTfu5f44+7DmKJDO4N1t5z9HgfARNJg1MWmHbgDrsBoz6K1txlMuxGJyPVDVgZhgG+51m/PF796Hv98/isx84gBazFn/+yiBO/8mP8POf78O/355FMpOt2jnWEslMFjOheNUqjhRyGQ65zJt6jm5NLSOcyOBMlzT8RjwnPBbyHG1TfIEoXu6fxM8dd6G1BjYTxYSCI4IgthfuM4BcVfZ0NV4MKaZ48xvXJpFjgZ85KkKbGpAPjkq86S2h4uiQ62HPES+grbWKI4Zh8Bc/ewiffnttTM3obuAnq5UXMMyHE/jSFR/ee8iJDnttlGIr5DK891ALfnx/AQGBR3PzNy98+0SlOdXBCbkvU7vaQ3zz+hRSmdzGbWoAV3FU1wIY8xVJKw/a1U62V89z5J0TdqIaj8eux9RSHIl05QKSqaU4YqlsxcXYG6FVyfHeQ0589VdP4vXfeQ4ffa4Tg/MR/MZXruPkf3sN//Vf7q7KvIn1mVqKg2UBt616N8dH2yx4czaMSGJ9b9XFIT8YBjgtETE2z0kPd72uVhssIR6fe3UICjmDjz3XWe1TqToUHBEEsb1QG4Cmg8DsQFmHaTZroJAxolUc5XIsXr46hSc9VnF293I5ILFc0YqjHocROpV81XN0bzaMVosWdZrKSzbL5bldDTXjZjLplHDUqeEtMzj6Xz8eRjZXe6XY7z/sRCbH4js3ZwQ9bu9YED0OI8w6laDHLZRmsxbtNj0JstfAsixe6pvAYZcZTzRtEkiHpgCTEzDkW4DXVBw11GngsevRW2GRbSabw/DiiihBi8duAMuKWyH7KNWaqFYILqsOn3prDy5++i34h48cw6kOG758xYe3f+4C3vVXF/HlKz4Saq/D6kS1KlUcAcBRNzeh9VFfIs/FYT/2NNfBoq/OdXkjPDY97OQ52nYMzkfw7ZvT+Pkn3Wio01T7dKoOBUcEQWw/TM4HU3VKRCGXwVmvhS8ozkL8ylgAE8GYOFJsAEiGADZXnuMotcKNtS4QhVyG/U7TasXRvZkw9jTVRvhS63Q7jGVVHE0vx/HVvkn89NFWuCQiHC2UnkYj9jtNgrarZbI5XPct4XgV/EZrOdVhRe9oAOkq+HikyJXRIEYXo/jgibbNnxia5D4H9PkJkY+E4CfaregbCyJbQX+ULxhDKpMTp+LIlp+sVkFBNj9RTYzfRyjkMgbP9jTgrz90GL2/dxb/+Z27kcrk8AffvkNC7XUY93PrHXcVg6NDrnrIGODqOu1q0WQG1yeWJFdtBJDnaLvy5z/0Qq9S4D8+01HtU5EEFBwRBLH9MLUA4RmgzA9vl1UvWqvay/2TMGoUePveRlGOj3heLllOxRHw0E59IRx21ePeTBj+Fa7Xv9ba1GqVbocRwwsrJd8I/9WPhgAAv/mW2izFfvGIE/dmw6terXK5NxtGNJXFsSoHR6c7bYimsrg1tf7u+07jpb4J1GkUeOf+po2flMty139zK6B/vOII4MZnR5IZwf5eCoF3kAk5UY3HY+du9Cs5We3+fAStFi0M6spOHCwVi16FXzzTju99/Cl892On8dNHnXj1zYeF2jPLO1uo7QtEYdQoUK+rXpWwQa3A7uY69K8zWa1vLIh0lsVTnSIMExGAkx4L5sNJjFew8o8Qj1tTy/jB3Xn88lPtqJdYhVu1oOCIIIjtR10LkEkAsfJaEdosOvgCMcF3j0LxNL53Zw7vOdgCjVIu6LFXKTs4Wn+nfisOueqRybH4+lWu+qPWxNi1So/DiEQ6h8kSKuR8gSi+fnUKP3e8Fc1mrQhnJz4/tb8ZSjmDb14XpuqoL++pOF4lMTbPk3lvxuVhan/wryTx/TuzeN9h5+bXzcgckMtwFUdKDaA2PeQ4Ah74SCrZVuKdj3CDP0UY5axTKdBk0lR0spp3LoIeR+1d33mh9mfesw/9v38Wn/vAwQdC7f/OCbX/7dbOFGqPB2Jos+qqPhTiaJsFNyaXHqu0vDjsh1ohw9Eqeee2ohrXFUI8/uyHg6jXKfFLZ9qrfSqSgYIjgiC2H3Ut3GO4vJvINqsOkUQGyzFhXQjfvTmNZCYnXpsaIFzFUdHBESfIfqnPB6D2xNi1SpeDuxktpV3tL14bglzG4KM1LH6s16tw9gkHvn1jWpC2rr6xIFwWHRpN1XUa1OtV2N1Uh0skyMY3rk0hnWXxoc2k2ADnNwIAU/76arA/Jvp31GngsenRO1a5G7zB+QjaLDpoVeJsFnjseoxUaKJTMpPFmD+KXRL0GxWDRinHew61rAq1fzMv1P7oSw+E2t4dNNXQF4hW1W/Ec8xtQSKdWx2wwXNxyI9jbot4G25l4rHpYTOQ52g70DsawOuDi/j1ZztgrEFPp1hQcEQQxPZjNTgqT5bLL6CE9hz9U/8kdjfVYW+LiP6feL61pezgqLjJajaDGi6LDpPBOCx6FRpJJlgRuhylTVYbXljBt29M48NPttW8+PHFI04Eoimc9y5u/eRNyOVY9I8Hq+434jndacV13zLiqZ1XAcGTy7H4at8Ejrstq3/rGxKa5B754EjfAEQf/5s44bGgt4KeI7EmqvF4bAaMLq5UxK/Ct8VKUYxdKi6rDp/MC7W/+IvHHwi1/+J1fOdmec7EWiCdzWFqKQ63BBx3fEXRWs/RQjgB73wEZ7qk5zfi4TxHFvSOBslzVMOwLIs/+6EXDUY1Pvyku9qnIykoOCIIYvthygdHofIrjgAI6jm6Mx3C3ZmwuNVGwIOKI12JN786K8DIiq44AoDD+aqj3U11VS953ykY1Aq0mLUYnC/OcfK5VwehUcq3hfjx6W47bAZV2ZLskcUVLMXSkgmOTnXakMrmcNW3c8c8Xx4JwBeI4YNbVRsBa4Kj/OeAwb7udeykx4pIIoM3Z8X3HCXSWYwHYqIGLR67HpFEBv6VlGivwcNX4dR6xdF6yGUMnum2rwq1T7Rb8MmXB/D9O3PVPjVRmVmOI5NjJVFx5KjTwGXR4eoazxFfdXlGgmLstZz0WDEXTlR0wiEhLOcHF9E/voTffL5LstVt1YKCI4Igth96OyBTlF1x5LLwwZFwC4CXr05CpZDhPQdbBDvmuvDBkcZc2s/L5IDOVnTFEcB5jgDyG1WansbiJqvdnwvjX2/N4iOn3bAa1CKeWWVQyrn31Wv357EULf3muVcifiOe424LFDIGl0d2bvvDS30+1OuUhQ0TCE1x1z11PtTQN6wr+T/RXjkfyehiFNkcK27Fkd2Qfy3xBdneuQhUchnctuqHDGJi0avw9z9/DPudJvzmV6/jnLf4jZRagRc6V3Oi2lqOttXjqu9B5c6FIT8s+dZdKUOeo9qGrzZy1mvxgaMib/DWIBQcEQSx/ZDJAWNT2cGRRilHY51GsOAokc7i2zem8Y69jTCJPbUkvgSo6wB5GRNvDI6SKo74So2DrSWGVkRJdDkMGF2MFuz4+ewrgzCqFfiVpzwin1nleP8RJ9JZFt8dKP293z8eRINRvVpxWG30agUOtppxeXhneo4WIgn88O483r+VFJtneZKbqMZjcACJEJBJPvS0RpMG7TY9royKX8nFB7qiVhzlQ5zRCniO7s9F0NFggFK+/W8jDGoF/uEjx9HtMOLXvnQNb2zTAJevrJZCqxoAHHVb4F9JYTw/oOTikB+nOqyQyaRdxdxhJ89RLfP9O3O4Mx3Gb53thkqx/a9vxUL/RgiC2J7UtQDh8r0ELqsOE0FhFuI/uDuHcCJTmV2MWBDQlhncGBpKqjh6oqkO3/v4U3jbngKqAwjB6HEYkcrmCmqtvD0Vyo+Z9cCs2z5jZp9oqsOe5rqS29VYlkXfWBDH2i2SarM81WnD7ekQQnFhRf21wNevTiGTY/FzhbSpAVzFkWltcJQf3b2e56jdgr6xgOieI+99ua2xAAAgAElEQVR8BAoZI2o1R4tZC7VCVrGKo+3YprYRJq0SX/qlE3BZdPilL/bj+sTjo+JrHV8gBq1SDrtRGtWnx/Keo/7xIIYXVrAQSeIpCfuNeHjP0RXyHNUc2RyLP39lEJ0NBrz3kMhdATUKBUcEQWxP6poFCY7aLLrVEu5y+Vr/JFot2tVSZlGJL5UuxuYpseII4G7gpb4zuN3oXhVkb33j+D9f8cKsU+IXz7hFPqvK8+IRJ25Ph3B/rnh3zdRSHLOhBE5IxG/Ec7rDihy789ofeCn2SY8FHfYCx9iHpgCT88E/6xu4xw08R+EKeI4G5yLw2PWi7mDLZAzabXqMLopbcRSKpTEXTmwrMXYhWPQqfOWXT6DBqMbPf74Pd6ZD1T4lQeEmqukkE5h32A0w65S4Oh7EhaG836jLXuWzKowT5DmqSb59YxrDCyv45AvdkNP6dV0oOCIIYntiauFa1crc8Wmz6rAYSSKWypR1nIlADJdHAviZI62VCVQECY7ybhDaNasJOhsMYBhsOT76mm8JP/Yu4tee3p5jZt99sAVKOYNvllB11Jf3Gx2TiN+I56DLDI1Stm3bZDbi9aFFTC3F8cETbYX9QCIEJEOPVBzlg6MNJqsB4gdygwviTlTj6bAbRG9V4wPZnRYcAUBDnQZf+ZWTqNMo8eHP92GoyCmWUmY8EJOM3wjggtCjbfW4Or6Ei8N+tNv0aDFrq31aBfFk/rrSO7azrte1TCqTw+deG8Teljq8narlN4SCI4Igtid1LUAmwbVslQE/YWQiWN7O0devTULGAC8edW79ZCEQquIomwISy8KcEyEqGqUcbRYdhhY2v5n5n694YTOo8POnCrwZrzEsehWe62nAt27MIFOg74mnbyyIOo0CPRW4yS8GtUKOY24LLu0wz9FLvROw6lV42x5HYT/AT9J8qOIoX6WwTsVRk0kLt1W3KkQXg2gyg8lgvCJ/Ux67HhPBGFKZ4v7ui8HL+5ok9h6pFC1mLb7yyyegkDH40N/3YrwCTimxyeZYTARiaLNJw2/Ec9Rtwag/ikvDfslPU1tLh90Am0FVEX8aIQwvX53EZDCOT721h6rlN4GCI4Igtid1+f7kcHmjuXlBbjklx9nc/8/enYe3Vd/pAn+PFtuyZMmxLTmxnNhWVkKA7AkJYSstpUNZU9ayFui0pUOnnd5O57ad6TZzezu3na60QFhblkKhLIXSQllC9hAIJCF2Eu9JvMm2LEuybEnn/vGTbCfxouVI50h6P8/Dc2YSW/pR4MTn1XeR8fTudpy7wI5Ztgx9YqZUxRGQdLsaZd6CypIpK462HXFjy2E3/vG8uSguSGFwusZtXFGNnsEg3j50apXJVHY192JVbZkmf3BcP68Ch7oG0TUwpPZRMqJzYAivH+zCxpXVKDTEuRJ5NDiaqOJo4vvYmrpy7GzqRSRNc44OdYnW0QUZqNBx2c0iBFBoLt9EDnZ4UVJkwCxbUdreQ+tqK8z4/R1rMBKO4MYHdqC9L7tbkjoGhjAcjqCmTDsVR8DYnKNgKIJzsmC+UYwkSVjjKsf2RjfnHGWBoZEwfvH3Q1hZMwPnL8iOdki1MDgiotw0Ghyltlkt9oNUPAOHJ/N2Qzc6BoYyt9pTlqPBUYrtNqPBUeIDskkdC2eWoNntRzAUPuX3ZFnGT/5Wj0prIT67NjerjWIuWORAubkgoSHZXd4hNPb4RrcCas36ueLBaWuetKs9tasN4YiM61fFORQbAPpbxXX8VjWjCSgomTQAXzu3DJ7ACD5KYiZWPBo6Mleh46oQc6COpHHOUWwwtlZm4ahlfmUJHvvcGgwMjeCzD+zI6kC3pUdbG9ViljhtKDDooJOQmdmQClrrKsdxz1DK1eqUfo9ta0HnQBBfv3hh3t/XpsPgiIhyky0aHHlSqziyFRthMxlTqjh6alcbys0F+NhpcbZbpCroBeSwMq1qACuOssj8yhKEI/KEA3I3H+rBruY+3H3BvPjWmmcxo16Hy5ZW4bUDXej3D8f1PbubxaYkrQZHi6ussBYZsPVI7rerhSMyntzZinPmVaC2IoEqCE87oDOODcSOsdgnvY+tqRMPpOlqK6nv9KLIqMPssvQ/lLvs4n+rdA3IlmUZDR3evJxvNJElThsevm01urxB3PjADrgHg2ofKSmxBSA1ify3lgGFBj3W1JVhVW0ZbKbsmsd3dobmp1FqvEMj+PWbh7FhfgXWZFk4qQYGR0SUm8x2QGdIueIIEJ/CJfupUc9gEK991IkrlznTulHnBIHoA5BirWqsOMoWC0c3q53YribLYs2ss9SEa1ZlqPJNZRtXVGM4HMGLe+O7B+xs6oXJqMcSpy3NJ0uOXifh7Lnl2HI499sf3mrowjHPEG5Yk0C1ERDdqOYEdCfda82OCYdjA0BVqQk15cXYkaYHvIZOL+Y7SjKypaekyAh7SSEau6ffrJiMo/0BeIMhLJxpTcvrZ6MVNTOw6ZZVaO314+YHd8ITGFH7SAlrcftQYNBhllV77Ye/vGE57rt5pdrHSBjnHGWHB99pRp9/BF+/eKHaR8kKDI6IKDfp9EBJlSLB0Zxyc9IVR8/tOYpQRMa1mXxYD4jKiZSDo6JSQF/A4CiL1FWYYdBJpwRHr3/Uhb1t/finj82Lf15Mlju9yobTZlnjblfb2dSL5TWlMOq1+6PR+nkVONofyPn2h99vb0WFpRAfX5xglaan7cT5RjFTVBwBwJq6MuxI05yj+o7MbFSLcVWY07ZZLTY/bRErjk5w9txy/PamFWjo9OLWh3ZiMJjaFtZMa3b7MKesWJOz3WwmY9ZVGwHROUd1nHOkZX2+YTywuREXn16JM6tL1T5OVtDuT0dERKmyVgEDR1N+mZqyYhztD2AkwQ1Nsizjqd1tWD6nFPMzuYFGqeBIkkS7GlvVskaBQYe6CjPqO8YqDiIRGT/5WwNqyotx1fIMbfXTiKuXO7G33TPt2uzYjJtVtdpsU4tZF51ztOVw7rY/HOsP4I36Lly7qjrxEM/TPklwVDnpcGxAzCPxBEZwcIrB8sno8w2jyxvEwpkWRV93Ki67JW0VR7H/fTIZhGWL8xc68Ivrl+ODdg/ueGQXhkZOnTOnVS1uv+bmG+WCta4yHPcMoa03oPZRaAK/efsIBodD+NonWG0ULwZHRJS7FAqO5pQXIxyRcbQvsT/897T243DXYGarjQDlgiNAtKux4iirLKgswaGusQfgV/d34MDxAXzlovmarqZJhyuWOWHQSXhmz9RVR3ta+iDL2p1vFDPXboajpDCn5xw9uasNMoDrEhmKDQDhEcB7HLBNEI6aHeK+GJ64jSg220LpeSSxyr9MBi1z7Wb0+UfQ54tvtlci6ju8qLIVZWUFSCZ8cslM/OSas7CjqReff+zdCZcUaI0sy2hx+1FTrq35RrlgbZruK5nW7Q3mXNVU18AQHtnajCuWOhmEJyC/foIkovxic4pWtRT/wKuN/kDVkmB7yB92taG4QI9/OLMqpfdPmKLBESuOss2CyhK09voRGA4jHK02muew4LKznGofLeMqLIU4f6FDtIxOUTG4o6kXRr2EZbMV+G8mjSRJwvp5Fdh2xJ229fFqCoUjeGpXK86db098mPTAMUCOnLhRLcYSXbE8yZwjZ6kJc8qK0xYcZXKY9OiA7B7lq47qORh7WpcvdeK/rjwDbzV0454n3p/yvqMF3d4gAiNhVhylwTyHBeXmgqwNjiIRGT977RBW/fA1fOtP+3IqPPrVG4cRCsv4ykXz1T5KVmFwRES5y+oEQkOAP7XhhDXRH6ha3fHPjfAFQ3jpg2O49MxZsBQaUnr/hCleccTgKJssqLRAloHDXYN46YNjONQ1iK9cND8jw3m1aOMKJ7q8QbxzePIqnZ1NbpzhtMFUoP35T+vmlsPtG0b9NO132ejvB7vQORBMfCg2MLZBc7KKI2DaOUc7m5Wdc1Tf6UVJkQEzMzh02FUh2uKOKLxZbTgUwZHuQQ7GjsN1q+fg3z+9GH/Z34F/eXovwhoOeUc3qrHiSHGSJGGtqxzbGrMv6B8MhvCPv3sXP32tAYtmluD3O1rx0781qH0sRbT1+vH4zlZcs2o2/71PEIMjIspd1miFxUB8w3En4ygpRJFRN/oDVjz+/MFx+IbDmW9TA4BAP1BgAQwFqb+WpRLw9wAR7Zfck7AgWhFw4LgH//PaISyaWYJPLZml8qnUc+GiSswoNk46JDswHMaHRz1YpfE2tZh188Sco61HsvNT7Kk8vrMVldZCfGyRI/FvHg2OJqo4ir7eJBVHgGgr6fePKBrINXQMYmFlCSQpc6Ft9QwTjHoJjQoHR409gwhFZA7GjtNt6+vw9YsX4k/vH8O3/vShZqs1mqMfiNXyATotPnaaA8c9Q7jloZ3o8g6pfZy4NPX4cOWvtuD1g134zqWL8co9G3Dtytn4+d8P48F3mtQ+Xsp+/vohSJKEL184T+2jZB0GR0SUu0aDo9Q2q0mShDllxQltVntyVyvm2s1YPkeF1pdAnzLVRgBgtov2D1/uzlTJNTVlxSjQ6/CLvx9GU48PX/34Ak1uy8mUAoMOly914q8HOuHxnzrj5r22PoyEZazJkuDIWWpCbXkxtk5RQZWN2nr9eKuhG9eunA1DMrO4PK3iOmHFUbRVbaqKI5f4569UW4ksy6jv9I4GuZli0OtQW25WfEB2bKMaW9Xi96UL5uHuC+bhiZ1t+N5LBzQZHrW4fTDoJFSVZq4qLp9cucyJ/7rqDOxq7sWnfrYZbzVMHl5rwRsHu3DZL99Bz2AQj92+GrefUwdJkvDDK5fg4tMr8b2XDuC591L7MFZNR7oH8cc97bhpbQ1m2UxqHyfrMDgiotxliwZHntT/kKspN6O1N75PcA93ebGntR/Xrpqd0U+aR/l7AZNCq0Ut0XXYHJCdNQx6HeY6LGjvC+DMalviK81z0MYV1RgORfDiB6eGyLua+iBJwIqa7AiOAFF1tKOpV/PzUxLx5K5WSACuXZ1Emxog7vPFFYBxgoeBWMXRFPex6hnFmF1mUiw46vIG4QmMYIEjcxvVYlx2M44oHBwd7PDCoJMw1575v59s9rVPLMDt6+vw0JZm/Pdf69U+zima3X5UzzAlF9bStCRJwvWr5+DFu89BubkQtzy4E//58kcYDmnr3i3LMn71xmHc/sguVM8oxgt3nzNa3QqInyt+dt0ynO0qx9ef/gB/P5idPxP+5G8NMBn1+OL5c9U+SlbiXYKIcpfZDugMKVccAaKKo7XXH1ef+lO72mDQSeqtPley4mg0OOKco2yyoFI83H314wvUCS815vQqKxZWluCPE2xX29nsxqKZ1qzaFLV+bgUGgyHsbfeofRRFjIQj+MPudlyw0AFnaZKfAnvaJ642AoACM2A0T9mqBgBr6sqxs0mZOUejG9VUqNBx2S1o7fUrGizWd3jhsptRYOCjQyIkScK3Lz0N16+eg1+9cQS/euOw2kc6QYvbxzkvGTC/sgTP370eN66Zg/vebsRnfrMVLQnMzUwnXzCELz2+Bz9+tR6XnlmFZ7+wbsLlBEVGPe67eQVOm2XFF363B7uaU5sfmmn7j3nw5w+O4/Zz6lBuKVT7OFmJd38iyl06PVBSBQwcTfmlasqLMTQSQZc3OOXXDYcieHbPUXzsNAcq1PqDSdHgaPpP6kl7blg9B188fy7OW2BX+yiaIEkSNq6oxnut/TjcNVaJMRKOYE9Lf9a0qcWsjbZVbTuSG+1qrx3oRLc3yaHYMf1tE29Ui7HYpw3A17rK0ecfQUNX6nOORlu7VFj17KowYyQso60voNhrio1qHIydDEmS8MMrluDKZU78+NV6bNLInBhZltHS4+dGtQwpMurxwyvPwL03LkdTjw//8PN38Pz7qf98mopWtx9X37sVf9nXgX/71CL8/LqlUy6JKCky4uHbVsFZasLtD+/CR8cHMnja1Pzkrw2wmYy4Y4NL7aNkLQZHRJTbrFXKVBxFP5Gb7hOivx/shNs3jOtWpfAAlCoGR3lvjasc/+uTi1htNM7ly6qg10knVB3tO+pBYCSMVbXZFRyVWwpxepUVf9jdjn7/sNrHSdnjO1tRZSvC+QuTGIoNALIcrTiaIjgyOwDf1MFRLEDcrsDg8YZOLyosBap8su2KtpMpNedoYGgER/sDHIydAp1Owo83nolLlszE9186gCd2tqp9JPT6huENhlhxlGGXnDELL9+zAYtmluCeJ9/H15/eC18wlPFzbD7UjU//8h0c9wzh4dtW465z58b1M0O5pRCPfm41zAUG3PzgTrQmMP9TLe+29OH1g134/HmurKou1hoGR0SU22xOxSqOAKCld+o/IJ/a1YaZ1iKcq1alhywrGxwVmIGCEraqUdZzlBThvAV2PLfn6Oh67Fip/ao6FYbYp+i7l52ODs8QvvC7PZqbl5GIFrcPmw/14NpVc6BPdoh7oA8Y8U3eqgaIEHxw6la12WXFqJ5hwvbG1Fsw6jsHsUCFaiMAmGsXQYBSm9UaVKyeyiWxOTHnL7Tj3577UPUhw7FNsbUVrDjKtOoZxXjyrrX48oXz8Myednz6l+9g/7HMtB7Lsoz73j6CWx7ciZnWIrxw9/qEf2atnlGMxz63GiPhCD67aYfmN8b996v1qLAU4tZ1tWofJasxOCKi3BarOEpxm0lVqQl6nTTlJysdniG81dCNjSuqk38AStWwD4iMACYFKygs039ST5QNNq6oRsfAELZEN5LtbOpFXYUZjpLs2yi0srYMP9p4BrY1uvHtP+3T5MameDy+sxV6nYRrV01RLTQdT5u4TlVxFOd9bE1dOXY2pzbnKBKRcajTq1pwVFpcgDJzARp7lKk4OsiNaoopMOjwm8+uwNq6cvzL0x/gL/uOq3aWWAU1K47UYdDr8LVPLMTv71gDXzCEK3+1FQ9vaUrrvTwwHMZXnnof//nyQVx8+kw8+8V1Sf/zn19ZgoduXYWewSBu3rQTnsCpW0u1YMvhHmxrdOPuC+aiuMCg9nGyGoMjIsptVicQGhKbxlJg1OvgLDWheYpWtWfebUNEBq5ZmcIDUKoCfeKqVMURIAZks+KIcsDHTnPAZjLimXfbEYnI2NXch9VZ1qY23pXLqvHlC+fhqd1teGCzNuamJGI4FMEzu9tx4SIHZtpSCO9imzOnqjgyO8SfA+GpW0LWusrQ6xvGoa7kQ5ej/QH4h8OqBi2uCjOOKFRxVN/hhaXQgOoZXF+thCKjHg/cshJnVdvw5Sfew76j6gy5b3H7oZPAf64qWze3Ai//0wacM78C//HiAdz56Lvo8ynfgtzeJ+YZvbD3GL5+8UL8+sblMBemFqQsmzMDv71pBY50D+KOR3YhMBxW6LTKeLelD//67AdwlppwfSoz9AgAgyMiynVWp7gOpF4SXlMuNqtNJBKR8Yfd7TjbVY45ag6aDEQDMkWDIztnHFFOKDTocdlZVXh1fwfebe2DJzCCVVk2GPtk/3zRAnzqjJn4z1c+wt8OZNd/p6/u74DbN5zaUGxgXHA0zXBsyIB/6oHia13lAIDtjcnPOYoNxlar4ggAXHazYq1q9R1eLKi0cGaagsyFBjx062oUGfW4960jqpyhxe1DVakJhYbJhyFTZpRbCrHplpX4zqWL8XZDNy752eaU7kEn23qkB5f9cgva+vzYdMtKfOmCeYr997xhvh0/vXYpdrf04e7H92BEwW2OyRoaCeOHfz6Ajb/ZikgE+Pn1S/nvuQIYHBFRbhsNjpQYkF2Mlkla1bY3utHa60+t3UIJaas4yq4HUqLJbFxRjWAogu++uB8Asm6j2sl0Ogn/7zNLcYbThnuefC9jczKU8PiOVlTPMOHc+SnOhOtvBQxFgLli8q8xxwb9T109ObusGM5SU2rBUWcsOLIk/Rqpctkt6BkMYmAotfYRWZZxsGOAG9XSwFZsxI1ravDKh8fRNs38xHRodvtRyzY1zZAkCbefU4dnv7gOpgI9brh/O37ytwaEUghiZFnGg+804aZNO1FmLsDzX1qPCxdVKnhq4dIzq/D9y5fg9YNd+MYzH6TU6puqd1t68amfbcb9m5tww+o5ePWfz8WKmuz+c14rGBwRUW6zRYMjjwIVR2VmeAIjE24xemp3G0qKDPjkkpkpv09K0hIcOYAhDzCi7eGHRPE4s9qG+Q4L9h0dwCxbUU60aZgK9Hjg5pVi1fAju9E1oP3/Vhu7B7Gt0Y3rV6cwFDvG0y7a1Kb6BD22ITKeOUeuMuxo6k161khDpxfOUhNKitTb3uOqUGZAdsfAEAaGQtyolia3rquFTpLw4JbMt5q2uH3qVkjThJY4bXjxy+fgimVO/Pz1Q7jh/h041h9I+HWGRsL42tN78b2XDuCChQ4898V1oxsX0+Gza2vwtY8vwLPvHcUP/vxRxufuBYbD+MFLB7DxN9sQDEXw+zvW4IdXngFLiu14NIbBERHlNrMd0BkUqTiK/YB1ctWRxz+CV/Z14IqlThQZVS6FTVfFEcAB2ZQTJEnC1SvELJxVtWU5037jsBbhgVtWwhMYwZ2P7tbcrInxugaG8KXH30OBQYfPrJxiLlG8YsHRVMzRqqY45rWtdZWnNOco1tqlptgDYmN3agOyORg7vWbainDZWVV4alcbPP7MDRf2+EfQ5x9BLYMjTbIUGvCTa5bip9eehf3HPLjkZ5vx1/0dcX//sf4ArvntNjy75yi+ctF83HfTiowE2XdfOA+3ra/Fg1ua8Os3M9eCubu5F5/6+WY88E4TblwjqozWz5uiApWSwuCIiHKbTg+UVAEDR1N+qVhJd8tJJeXP7z2K4VBE/TY1YFxwVKrca8aCIw7Iphxx1TInLIUGXLjIofZRFHV6lQ3/c+1SfHDUg689/b6q7QKTaewexFX3bkWL24f7b16pzEY7T9vU842AsYqjOO5jZ6cw52gkHEFjtw8LVA5aasqLYdBJOJJicBSb18SKo/S5Y4ML/uEwHt/ZmrH3bOnlRrVscOWyarz0Txswp6wYdz32Lr7z/D4MjUz9ocDOpl5c9st3cKRrEPfdtAJfuWgBdBna9CtJEr79D4tx5TInfvxqPX6/oyWt7xcYDuP7Lx3AZ367DSPhCB6/Yw1+cAWrjNKFwRER5T5rlTIVR2Xik7nWkzarPbWrDYtnWbHEaUv5PVIW6AMMJsCoYPvN6AMX5xxRbnBYi7D7Wxfh8qVVah9FcZ84fSa+eckivPxhB376WoPaxznBe6192PibbQgMh/HEnWtx3oIUZxsBQCgo7k3TBUcFFnFv9HVP+5LVM0xJzzlqcfswHI5goYqDsQGxCXROWXHKrWr1HV5UWgtRWlyg0MnoZIurrDhnXgUe3tqE4VBmBgs3RyunOeNI++oqzPjjF9bhjnPq8Oi2Flzxqy043OU95etkWcZj21tww/3bUVJkxJ++tB6fOD3z4xN0Ogn/d+OZuHCRA9/60z68/OHxtLzPruZeXPKzt7HpnSZ8dk0NXv3KuVjHKqO0YnBERLnP5lSk4shUoIejpPCEVrV9Rz3Yf2wA163WQLURIIKjYoWHAI5WHDE4otxRZNTnTJvaye7c4MK1K2fjF38/jOfeS32+mxLeqO/CDffvgLlQj2e+sA5nzVaoKjJ2b5+uVU2Sohsip684kiQJa+rKsKMx8TlH9R2iwkfNjWoxSmxWO9jh5WDsDLhjQx06B4J4cW/qH3LFo6VH/HsR+0CMtK3AoMO3Ll2Mh25dhS5vEJ/+xRY8tat19P4UDIXxzWc/xLf/tA8b5lfgT19aj/kq3oOMeh1+dcNyrJgzA/c8+R42H5o+sI9XYDiM7714ANf8dhtCERmP37kG379iCcysMko7BkdElPtiFUcKDOo7ebPaH3a3ocCgw+VnOVN+bUUE+pWdbwSMmw2i3B/8RJQ+kiTh+1cswVpXGb7xzIfY3dyr6nmeebcddzyyGy67+OS8rkLBKof+NnEtjSO8t1TGPattrascbt8wDic456i+0wudBMxzqDvjCBBzjprcPoSTbFkMhSM40jXINrUMOG+BHQsrS3D/5saMDBVudvsx01oEUwFXlGeTCxY58Mo9G7BsTim+8ccP8eUn3sPhrkFcd992PLmrDXdfMA8P3LIKNpN6g/ljTAV6bLp1FebaLfj8Y+/i/bb+lF9zZ5OoMnpwSxNuWhutMprLKqNMYXBERLnPWg2EhgB/6g9PNeXm0dkAQyNh/Om9o7hkyUzYitX/QxqA+HtUOjjSG4HiclYcEWWRAoMOv/nsCjhnmHDXY++i1Z35dd+yLOPeN4/gX57ei7WuMjx511plZhqNF9uYOV3FEQCYHXEH4GuTnHN0qNOLmnKz+osSIDarDYciSW1kAoBmjbTd5QNJkvC5DXU42OHFO4d70v5+LW4fajgYOytVWovw2OfW4OsXL8Qr+zpw0U/eQn2HF7++cTn+5eKFqW+pVJDNZMSjt69GhaUQtz20c8IWu3j4h0P47ov7ce192xCWZTxx51p873JWGWUagyMiyn3W6ByTgdRbNmrKitE5EMTQSBiv7u/AwFAI167USJsaIFrVlByMHWN2MDgiyjKlxQXYdMtKhCMyPvfILgwMZW5rUyQi43svHcCP/nIQnz6rCg/euio9W31iwZE1jqpPiz3uiqPZZSZU2YqwvTGxDxzqO9XfqBYT26yW7IBsblTLrMuXVsFeUoj7Nzel/b1aev2cb5TF9DoJX7pgHv7w+bW49MxZePaL6/CpM2apfawJOaxFeOxzq6HX6XDTpp04mmCQvaPRjUt+thkPbWnGzWtr8Jd7zsXZc8vTdFqaCoMjIsp9sQcKJQZkRz+ha+3148mdbZhdZhr9ZFoTAn3KVxwBYkA2t6oRZR2X3YJ7b1yOph4f7n78PYTC6R++GwyF8eUn38NDW5px+/o6/OzapSg0pKkCx9MKWGYChsLpv9bsANqTia0AACAASURBVPxuIDL1ViIgOufIVY4dTe64W4eGRsJo7vFppkLHZRfBQLJzjuo7vNDrJE203eWDQoMet66rxdsN3aPb7NLBFwyh2xtETQUrjrLdipoy/PKG5Vik8TlkNeVmPHr7agwGQ7hp0w64B4PTfo9/OIT/eGE/rr1vO2QZeOLOtfguq4xUxeCIiHKfLRoceRSoOIp+Qvd2Qze2NbpxzYrZGVtzOi1ZTmNwVMmKI6IstW5eBX5wxRK83dCN7790IK3v5R0awW0P7cKfPziOb16yCN++9LT03iM97fG1qQEiAJcjIjyKw1pXGXoGh+Ou2DnSPYiIDCzQSIVOubkA1iIDGnuSrziqLS/WRNtdvrhxzRyYjHrcv7kxbe/Rwo1qpILFVVZsumUVjvYFcNvDuzAYDE36tdsb3fjk/2zGw1ubceu6WvzlKxtYZaQBDI6IKPeZ7YDOoEjFUW204ujeN49AJwEbV8b5wJIJIwEgHExvxVEGhnYSkfKuWz0Hd5xTh0e2teDRbc1peY8u7xCu/e127GzqxU+uOQufP29u+jfXJRIcjQ76j39ANgBsi7NdraEz2tqlkYojSZLgsltSqjjSeiVDriktLsA1K6vx/PtH0TUwlJb3aHFzoxqpY3VdGX5943LsPzaAux7djWDoxOpP/3AI//78Plx333YAwJN3rcV/XHY6igtYZaQFDI6IKPfp9EBJ1dja5hSUFotPcN2+YZy7wI5ZNpMCB1RIoE9c01VxFAoAwfSVzxNRen3zU6fhY4sc+O6LB/BWg7JbEhu7B3H1vVvR1OPDA7esxFXLMxCqy7IIjuLZqAaIAByIu3pyTlkxZtmK4h6QXd8xCKNeQq2SW+NS5LKbkwqOfMEQWnv9nG+kgtvPqUMoIuORbc1pef3maMURh2OTGj52WiV+vPFMbD3ixj1PvD+69XHbEVFl9Mi2ltEqI02NgiAGR0SUJ6xVilQcAWPtapoaig2MC47KlH9tS6W4cs4RUdbS6yT87PplmO+w4O7f78GhTmWC4L1t/dj4m23wBcN44q61OH+hQ5HXnZavR2zMtMV5LzZHz+WLLzSTJAlr6sqwo7E3rjlHDZ1ezLVbYNRr58fruXYLOgaG4JuiLWQio9VTDI4yrqbcjIsXz8TvtrfCP5zYP7d4tLh9qLAUpGdYPVEcrlpejW9fuhh/2d+Bf3v2Q3zn+X24/v7tkCTgKVYZaZZ2/mQjIkonm1ORGUcAML/SggpLIT52WqUir6eYQLSdIl2tagDnHBFlOUuhAZtuXYVCox63P7IrriGlU3mzvgvX3bcd5kI9/viFdVg6Ow1bHSfjaRPXuGccJdaqBoh2tZ7BII7EUbVT3+HFAo20qcW4otVPTT2JVR3FhjMvYnCkijvPdcETGMHTu5X5uWW8Zrdv9AMwIrV87pw63H3BPDy1uw2PRquMXrlnA9awykizGBwRUX6IVRwpMKPn2/+wGM99cR0KDBq7haa7VQ2Ie5U1EWmXs9SE+29ega6BID7/2LunzJmI17N72nHHI7tRV2HGH7+wDnWZbtEaDY7irDgqtAL6woTuY7FWiena1bxDIzjaH9Bchc7c6Ea0eAd8xxzs8KK4QI/ZM9jOpIYVNTOwfE4pNr3TNNrKo5QWt59taqQJX/vEAvznlWfg6X88m1VGWUBjTz1ERGlirRaDo/3xDTmdygxzAWZrcahkWoOjWMURgyOiXLBszgz8v2vOwu6WPnzzjx/GvXIeAGRZxm/eOoKv/mEvVteV4anPr4WjpCiNp51ErIo03oojSYpuiIx/vlNNeTFmWqefc3SoSwQzWqs4qikvhk5CXBVT49V3eDG/skQ7W0Pz0F3nutDa68df93co9ppDI2Ec9wxxoxppgiRJuGHNHKyqTcOIBVIcgyMiyg/WKnEdUL7sWzPSGRyZygBJz1Y1ohxy6ZlV+OrHF+DZ947i128eiet7IhEZ33/pI/yfVw7i0jNn4aHbVqk3K8XTDhjNid3zLPaEKo4kScIaVxm2TzPnqKFDWxvVYgoNelTPKEZjAhVHsiyjvtOLRRr7e8k3H188EzXlxbh/c6Nir9nay8HYRJQcBkdElB+sTnFVaEC2JgX6RBuGMQ2b3nQ6UXXE4Igop3z5wnm4fGkVfvxqPV7+8PiUXxsMhXHPU+/jwS1NuG19LX5+3TIUGvQZOukE+lvFRjUpgaoYsyOhiiNgbM5R4xRzguo7vTAZ9aieoaFNm1GJblbrHgyi1zesuba7fKPXSbh9fR32tPbj3ZbUq6UB0aYGgBVHRJQwBkdElB9s0eBIoQHZmhToE5+8J/IQlQiLg61qRDlGkiT86OozsXxOKb76h/ext61/wq/zDo3g9od34cW9x/CNTy7Cdy5drH4bk6c9/ja1mAQrjoD45hw1dHqxoNKi/v8mE3BVWNDU40Mkzlk5HIytHZ9ZWQ2byYj7325S5PVa3CJAZHBERIlicERE+cFsB3SG3K84Kk5jn7ilkhVHRDmoyKjHfTevRIWlEHc8uhvH+gMn/H6XdwjX3bcd2xt78d+fOQtfOH8upHQF1InwtCUeHJkdgK8HiETi/pba8mJUWguxvXHyqo/6jkHNzTeKcdnNCIyE0TEwFNfXx4IjVhypr7jAgM+unYNXD3SMhj6paHb7UFpshK1YpfZSIspaDI6IKD/o9EBJFTBwVO2TpI+/Lz3zjWJYcUSUsyoshdh0yyoEhsO445Hd8AVDAMQa96vv3YrGbh8euGUlNq5IMKhJl2E/4HfHv1EtxuIA5DAQiL/1R5IkrKkrx/ZG94Rzjnp9w+gZDGo2aHHZRXVJvO1qBzu8qLAUotxSmM5jUZxuObsWRp0Om95Jveqoxe1HjRaXexCR5jE4IqL8Ya3K/YqjtAZHlSI4SuCTeiLKHgtnluAXNyzDwY4B3PPk+3i/rR8b790KXzCMJ+5aiwsWOtQ+4pjYhwCJBkdmu7gmWD251lWObm8QTRPMOWroFBU68zVacTTXbgEANPbENyC7vsPLNjUNcViLcPnSKjy9ux19vuGUXqvZ7UMN29SIKAkMjogof9iceTDjqDR9r2+pjH5S35e+9yAiVV2w0IFvX7oYr33Uiat+vQWmAj2e+cezsXR2Gu8tyehvFdeEZxxFw68EqyfXukQb8ETtarHgSGsb1WIcJYUwF+jjqjgKR2Q0dHo1Wz2Vr+7Y4EJgJIzf72hJ+jWGQxEc7QuglhvViCgJDI6IKH/EKo6mWKmc1dJecRR74OKcI6Jcduu6Wnz+PBdW1pbh2S+sgytasaIpsQ8BShOtOIrex3yJbVarqzDDUVI44YDs+g4vrEUGVFq12dolSRJcdguOdE9fcdTi9iEYijA40piFM0tw3gI7HtnWgmAonNRrtPf5EZHBiiMiSgqDIyLKH9ZqIBwE/MqstdWUkQAQCqQ3ODIzOCLKB5Ik4ZuXnIY/fP5sOKxFah9nYp52QNIBJbMS+z5LrFUtsYojSZKwxjXxnKNYhY4mBoZPwmU3x1VxxI1q2nXnBhe6vUE8/35yLfctbj8AoLaCFUdElDgGR0SUP6xV4jqQg+1qgegK7XTPOAI4IJuI1OdpE6GRPsHtUEWlgL4A8CV+H1vrKkOXN4jm6AM4AMiyjPoOr2Y3qsW4Kiw45gkgMDx1tcrBDi8kCZjv0PbfTz5aP68ci2aW4IHNjRMOaZ9Oc3QrGyuOiCgZDI6IKH/YnOKaiwOyY3OH2KpGRPnA0574YGwAkCRRPTmYWKsaIAZkAzihXa1zIIiBoZDmW7tcdjNkGRMO9x6vvsOL2nIzTAX6DJ2M4iVJEu4614WGzkG81ZD4v78tbj8shQaUmwvScDoiynUMjogof1ijwVEuDsgeDY7K0vcehSWAwcTgiIjU52lLfDB2jMWeVMWRq8IM+0lzjuqjg7G1XnEU72a1+k6vZod8E3DpmVWotBbi/s2NCX9vi9uHmvJiTbdUEpF2MTgiovxhtgM6Q45WHEXnNqWz4kiSRNURW9WISE2RCOA5mnxwZE7uPiZJEtbUlZ0w56ihIzuCo7oK0Z401ZyjwHAYzW6f5qun8lmBQYdb19Vhy2E39h/zJPS9LW4/atmmRkRJYnBERPlDpwdKqoCBo2qfRHmZaFUDxJwjVhwRkZoGO4HISOIb1WIs9oS3qsWsdZWjcyA4Omi4vtMLe0khyjTe/mMq0MNZakLjFJvVDnV5IcscjK11N6yZA3OBHps2N8X9PaFwBG19ftSUczA2ESWHwRER5RdrVY5WHGUqOGLFERGpLNZunMyMI2Cs4igSSfhbT55z1JBFrV0uuxmNU8w4OhitnmLFkbbZTEZcs2o2Xth7DMc9gbi+57hnCCNhmcERESWNwRER5RebM3dnHOmMQEGay9AtlUnNBiEiUoynVVyTnnHkAOTwWOCegLl2MyosYs5RJCKjoVP7G9ViXBVmNHb7Jt3IVd/hRZFRx61bWeD29XWIyDIe3toc19dzoxoRpYrBERHll1jFURKrbDUt0CeqjdI99NJSCfjdQHgkve9DRDSZlCuO7OKaRAguSRLWuMqwvbEXrb1+DI1EsHCmJblzZJjLbsFgMIRub3DC36/v8GK+owR6HYcna93ssmJccsYsPL6jFYPB0LRf3xxtreSMIyJKFoMjIsov1mogHBThRy6JBUfpZok9cCU3H4SIKGWedqDQBhRZk/t+i0Nck2y7XesqR8fAEF77SMx7y5qKI7sIDY5MMiD7YIeXbWpZ5M4NLniHQnhqV9u0X9vS40ORUQdHSWEGTkZEuYjBERHlF2uVuObagOxAH1Bclv73sVSKKwdkE5Fa+tuSb1MDxIwjIOkA/GyXuNc+tr0FADA/a4IjURnV2HPqgGz3YBA9g0EOxs4iS2eXYnVtGR58pwmh8NTzuprdftSUmaFjNRkRJYnBERHlF5tTXHNtQLY/UxVHseCIc46ISCWe9uQ3qgEpVxzNtVtQYSlAi9uP6hkmWAoNyZ8lg2ZZi1Bk1KFxgoqjeg7Gzkp3bKjD0f4AXtnXMeXXtbh9HIxNRClhcERE+cUaDY5ybUB2xlrVYg9crDgiIpV4Uqw4Ms0QywSSHPQvSRLW1IntatnSpgYAOp2EugoLGrtPrTjiRrXsdNFplairMOOBzY2TDj2PRGS09PpRW8H5RkSUPAZHRJRfzA5AZ8i9iqNMBUdmBkdEpKKgFxjqTy04kiQxIHsw+Vlta6PtatkUHAFiztFEM47qO7woMxfAbuEMnGyi00n43Dl12Nvuwa7mibcEdgwMYTgUYcUREaWEwRER5RedDiipyq0ZR6EgMOIDTKXpfy9jEVBkY6saEakj1Y1qMRZ70hVHAHDOfDv0OglLZ2fgvquguRVmtPf5EQyFT/j1g51eLKwsgZTuzZykuKuXV2NGsRH3vd044e+3cKMaESmAwRER5R9rVW5VHAX6xTUTFUeAmHPE4IiI1KBUcGR2pHQfq6swY8s3LsTFp1emdo4Mm+uwICKPhQmAaGU61MmNatnKVKDHTWfX4vWDnRO2Iba4RYUZK46IKBUMjogo/9icuTXjKBAtT2dwRES5rr9VXFNpVQPEvLYU72MzbUVZV6HjqohuVhsXMLT1+eEfDnOjWha7+ewaGPU6bHqn6ZTfa3b7YdRLmGUzqXAyIsoVDI6IKP/EKo4mGSSZdTIdHJntnHFEROrwtIs5dSUzU3sdsx3wdefOnwNxqrOLdqXxc444GDv7VVgKcdUyJ555tx3uweAJv9fi9mF2WTH0uuwKOYlIWxgcEVH+sVYD4SDgd6t9EmWMBkdlmXk/VhwRkVo87SL81+lTex2LA4iMjN0/84Sl0IBKayEaxwVH9dHgKNsGfdOJ7thQh2Aogt9tbz3h15vdfs43IqKUMTgiovxjrRLXXBmQHegV14y1qjmAYS8wfOpmHiKitPK0pT7fCBjbEOlLfrNatnJVWNDYM9aqVt/hxZyyYpgLDSqeilI1z1GCCxc58Oi2ZgyNiOHnsiyjxe3jfCMiShmDIyLKPzanuObKgGw1ZhwBrDoioszztCsTHFns4pqH9zGX3YzGbh/kaJvewY4BtqnliDs3uOD2DeO598QHY92DQfiHw6w4IqKUMTgiovxjjQZHuTIgO9AHSHqgMEM/+DM4IiI1hEMi8E91MDYwdh/z5d99zGW3wBMYQa9vGEMjYTS7/RyMnSPWusqwxGnFA5sbEYnIo9vzWHFERKlicERE+cfsEMNVc6niyDQDyNR2H0u0xYMDsokok7zHATmsTHAUa1UbzMNWteiA7MYeHw53DSIckVlxlCMkScKdG1w40u3DG/VdaO4RLeWsOCKiVDE4IqL8o9MBJVU5NOOoL3NtasC4iiMGR0SUQbEq0VIFWtVMM0SlZh5WHM2tsAAAGrsHRwdjs+Iod3zqjFmoshXh/s2NaHH7oddJcM4wqX0sIspynIJHRPnJ5sy9iqNMMVcAki4vh8oSkYpiwZESM450OsBsz8uWW+cMEwoMOjHnCECBQceKlBxi1Otw2/o6/PDlj9AzOIzqGSYY9awVIKLU8C5CRPnJWpVbM44yGRzp9EBxBSuOiCizPNE140q0qgFiQHYeBuB6nYTa8mIc6R7EwQ4v5tktMDBYyCnXrp4NS6EBh7sGUcNQkIgUwD8liCg/WatExVF0q0xW8/cBxWWZfU+LIy8/qSciFXnaAVMZUKDQg7DZkbcBuKvCgsZuH+o7BtimloOsRUZcv1pU5tWUcTA2EaWOwRER5SdrNRAOAn632idJXaYrjoBocJSfD1xEpBJPu3LVRkD0PpZ/FUeAGJDd7PahcyDIwdg56rb1dSgy6rDEaVX7KESUAzjjiIjyk7VKXAeOipk92So8Agx7VQiOKoGeQ5l9TyLKb/1tQJlLudcz28VwbFnO3FZKjZhrtyASLbhlcJSbqkpN2P7Nj8FaZFT7KESUA1hxRET5yeYUV0+Wb1YL9IurWhVHudDqR0TaJ8uAp02ZjWoxFgcQHgaGPMq9ZpZw2cfa/RbNZEVKriotLoBOl1+hKBGlB4MjIspP1mhwNJDtwVGfuKpRcRQeBob6M/u+RJSfhjzA8KDCrWqV4pqHA7JddgsAwGYyotJaqPJpiIhI6xgcEVF+MjsAnUEMyM5mo8FRaWbfN/bAxQHZRJQJnjZxVTI4MtvFNQ/vYzaTERWWAiycWQIpz9r0iIgocZxxRET5SacDSqpYcZQsi0NcBzsB+8LMvjcR5R9Pu7ja5ij3mrH7mC//giMA+PrFC+EoKVL7GERElAUYHBFR/rI5c6jiqCyz78uKIyLKpNHgSMmKo1gAnn+tagBw7SoFQzgiIsppbFUjovxlrRp7GMlWgV5xzXTFUR63eBCRCvpbAX3B2L1HCcVlgKTL24ojIiKieDE4IqL8ZY1WHGXzZrBAn3jwKczwVhzTDEBnFK1qRETp5mkX1UY6BX901emB4goG4ERERNNgcERE+cvqBMJBwO9W+yTJC/QBRaXKPkzFQ5JEuxofuIgoE2LBkdIsDt7HiIiIpsHgiIjyl7VKXLN5QHagL/NtajEWByuOiCgzPG2Abbbyr2u2s1WNiIhoGgyOiCh/2Zzi6mFwlBRWHBFRJoSGAW9HeoIjiyNvh2MTERHFi8EREeUvazQ4YsVRclhxRESZ4D0GQE5Pq1qs4iibZ90RERGlGYMjIspfZgegM4gB2dlK7Yojfw8QCavz/kSUH/rbxDUtM44qgdAQEPQq/9pEREQ5gsEREeUvnQ4oqcruiiN/n1gprQaLA5Aj2T1cnIi0z9MurqVzlH9ti0NcfWxXIyIimgyDIyLKbzZn9lYchUNA0KNuxRHAdjUiSq9YcBRbaKAks11cOa+NiIhoUgyOiCi/WavGHkqyzZBHXNWccQQwOCKi9PK0ioDHaFL+tUcrjhgcERERTYbBERHlN2u04igbB6MG+sRV9eCID1xElEae9vRsVAPErDuA9zEiIqIpMDgiovxmdQLhYHbO6VE7ODKz4oiIMsDTnp7B2ABQXA5A4owjIiKiKTA4IqL8ZnOKazYOyFY7OCq0AAUWflJPROkjy2KrWroqjvQGER4xACciIpoUgyMiym+xYaseBkdJsTj4wEVE6ePvBUIBoDRNwREQvY+x4oiIiGgyDI6IKL9ZWXGUEkslK46IKH08beKarlY1QAze5nBsIiKiSTE4IqL8ZnYAOkOWBke9ACSgyKbeGVhxRETplIngyOJgAE5ERDQFBkdElN90OqCkSmxWyzaBPhEa6fTqnYEVR0SUTp52cbXNSd97WCo5HJuIiGgKDI6IiGzO7A2O1GxTA0TF1lA/EAqqew4iyk2edsBgAorL0vceZjsw4geCg+l7DyIioizG4IiIyFo19ql2NtFCcGRxiCurjogoHfpbRZuaJKXvPWL3Mc45IiIimhCDIyIia7TiSJbVPkliNBEcVYorgyMiSgdPe3o3qgGichLgZjUiIqJJMDgiIrI6gXAQ8LvVPkliNBEcxR64OCCbiNLA057ewdgAYLGLKyuOiIiIJsTgiIjI5hTXbNuspongKFZxxOCIiBQ2MiTCHFumKo4YHBEREU2EwRERkbVKXD1ZFBxFwkCgP70DY+Nhjn5SzwcuIlJaLMxPe3BUIa68jxEREU2IwRERkTULK46GPABk9SuODAWAqYwVR0SkPE+buKa7VU1vFPcxtqoRERFNiMEREZHZAegM2RUcBfrEVe3gCBDtanzgIiKl9WcoOALEvDZWHBEREU2IwRERkU4HlFSJzWrZItAvrpoIjvjARURp4GkHII1VhaaT2Q74uFWNiIhoIgyOiIgAMSA7q4IjLVUcOdiqRkTK87QDJTNFS2y6WSoZgBMREU2CwRERESAGZHva1T5F/DQVHEUfuGRZ7ZMQUS7xtGamTQ0QATgrjoiIiCbE4IiICBCtEAPHsif80FRw5ABG/MDwoNonIaJc4mlP/0a1GLNd3MOG/Zl5PyIioizC4IiICBDBUTgI+N1qnyQ+seCoqFTdcwCi4ghgmwcRKScSATxHM1txBHDQPxER0QQYHBERAWLGEZA9m9UCvUChDdAb1D7J2AMX5xwRkVJ83SLMz1jFUew+xnY1IiKikzE4IiICxIwjQHzCnQ0CfYBJA9VGwLiKIwZHRKSQ2My50gwFRxa7uLLiiIiI6BQMjoiIAMAabYfImoqjPm3MNwLYqkZEyvO0iWumWtXMrJwkIiKaDIMjIiJADEbVGRgcJcNUBkh6BkdEpJyMB0fRiiO2qhEREZ2CwREREQDodEBJldislg20FBzpdGLOET+pJyKleNqBgpLMLQAwFIj3YqsaERHRKRgcERHF2JxZNuNII8ERID6tZ8URESnF0y6qjSQpc+9pcfA+RkRENAEGR0REMdaq7GhVi0S0FxxZKllxRETK6W/NXJtajKVSbHMjIiKiE8QVHEmS9ElJkuolSTosSdK/TvD750qStEeSpJAkSRtP+r2wJEnvR/96Ydyv10mStCP6mk9JklSQ+t8OEVEKrE7RqibLap9kasEBQI5oMDjiJ/VEpBBPe+Y2qsWwcpKIiGhC0wZHkiTpAfwKwCUAFgO4XpKkxSd9WSuAWwE8PsFLBGRZXhr967Jxv/4jAD+VZXkegD4An0vi/EREyrE6gXAQ8LvVPsnUAn3iWlym7jnGszjEbJBIRO2TEFG2G/YBgV4VKo4crDgiIiKaQDwVR6sBHJZluVGW5WEATwK4fPwXyLLcLMvyBwDiemKQJEkCcCGAZ6K/9AiAK+I+NRFROtic4qr1drVYcKS1iqNIaOxsRETJ8rSLq02FiqPgADAylNn3JSIi0rh4giMngLZx/3979NfiVSRJ0m5JkrZLkhQLh8oB9MuyHJruNSVJuiv6/bu7u/kpEBGlkbVKXLU+IFuTwZFDXDnniIhS5Yn+2Jnp4Ch2H+NmNSIiohNkYjh2jSzLKwHcAOB/JEmam8g3y7J8nyzLK2VZXmm329NzQiIiALBG2yJYcZQ4S6W4MjgiolSNVhxluFXNHAvA+UElERHRePEER0cBjP/Ipzr6a3GRZflo9NoI4E0AywC4AZRKkmRI5jWJiNLCbAd0RgZHyYgFR5wPQkSp6m8DJD1QMiuz72uJfkDJAJyIiOgE8QRHuwDMj25BKwBwHYAXpvkeAIAkSTMkSSqM/t8VANYDOCDLsgzgDQCxDWy3AHg+0cMTESlKpwOss8RmNS0L9ItrUam65xiPD1xEpBRPu2gd1hum/1olmdmqRkRENJFpg6PoHKK7AbwK4CMAf5Bleb8kSd+TJOkyAJAkaZUkSe0APgPgt5Ik7Y9++2kAdkuStBciKPo/siwfiP7eNwB8VZKkwxAzjzYp+TdGRJQUqzMLZhz1AgUWwFCg9knGFFoBQxGDIyJKnac9821qgKg6BdiqRkREdJK4PsqRZfllAC+f9GvfGfd/74JoNzv5+7YCOGOS12yE2NhGRKQd1irg6B61TzG1QB9gKlP7FCeSJDFYdpCf1BNRijytwOw1mX9fYxFQaGPFERER0UkyMRybiCh7WJ2iVU2W1T7J5AJ9gElDbWoxlkpWHBFRaiJhcQ/O9Ea1GAbgREREp2BwREQ0ntUJhIOA3632SSYX6NPWYOwYSyUfuIgoNYOdQCSkTqsaIIIjDvknIiI6AYMjIqLxbE5x1fJmNc0GRw5WHBFRavrbxFWtiiOznQE4ERHRSRgcERGNZ60SVy0PyNZscFQpKrXCI2qfhIiylScaHJWq2KrGGUdEREQnYHBERDSeNdoeodWKI1nWcHAUW2Xdo+45iCh7edrF1epU5/3NDmDIA4SC6rw/ERGRBjE4IiIaz2wHdEbtBkfDg2L+hyaDo0pxZbsaESXL0wYU2YAiqzrvb7GLK9vViIiIRjE4IiIaT6cDrLPEVh8t8veKqxaDI3O04ogPXESULE87YJuj3vvH7mNsVyMiIhrF4IiIeuQdKgAAIABJREFU6GRWp3ZnHAX6xLW4TN1zTCTWqsaKIyJKlqddvY1qwLj7GDerERERxTA4IiI6mdWp3Va1WHCkxYojBkdElKr+NnWDI3O0VY0VR0RERKMYHBERncxaJVrVZFntk5xKy8GR0QQU2tiqRkTJGfIAQY96G9WAcQE472NEREQxDI6IiE5mdQLhoFgtrzVaDo4A8dDFiiMiSkasRVjNiiOjCSi0Aj62qhEREcUwOCIiOpktugZai+1qseCoqFTdc0zGUslP6okoOZ42cbWpWHEEiHY13seIiIhGMTgiIjqZtUpctTggO9AHGIsBY5HaJ5kYK46IKFlaCY4sDlYcERERjcPgiIjoZNZom4RWK4602qYGiIojPnARUTI87YDOKO4jamLFERER0QkYHBERncxsFw8vmg2OytQ+xeQsdiA4AAz71T4JEWWb/jZR8alT+cdTi4Nb1YiIiMZhcEREdDKdDrDOEpvVtCbQB5g0Ot8IGKsU4EMXESXK0w6UzlH7FIDZIe61oWG1T0JERKQJDI6IiCZidWp3xpHWW9UAtnkQUeI87epuVIux2MWVbbdEREQAGBwREU3M6tRwq5qWgyOHuHJANhElIjwCeI9pIzgyR+9jrJwkIiICwOCIiGhi1irRqibLap9kjCxnQXAUqzhicERECfAeB+SI+hvVgHEBOCuOiIiIAAZHREQTs1UD4SDgd6t9kjEjfiA8rO3gqLgCgMRWNSJKjKddXDVRcRRrVeN9jIiICGBwREQ0MWuVuGqpXS3QJ65aDo70BsBcwYojIkpMf5u4aqriiMERERERwOCIiGhiseBISwOy/b3iquXgCBDtamzxIKJEeGLBkQYqjgrMQIGFw7GJiIiiGBwREU3EGn140WLFUXGZuueYjsXBiiMiSoynHSguBwqK1T6JYLaz4oiIiCiKwRER0UTMdkBn1GZwpPWKI7ODD1xElBhPmzaqjWIsDs44IiIiimJwREQ0EZ0OsM4Sm9W0IluCo1jFkZY20hGRtnnatTHfKMZsZ8stERFRFIMjIqLJWJ3amnGUNcFRpdhIN+RR+yRElA1kWXvBEVtuiYiIRjE4IiKajNWpvVY1QxFgNKl9kqlZKsWV7WpEFI9AHzA8qK1WNbMDCPQC4RG1T0JERKQ6BkdERJOxVolWNa20XAX6tF9tBIytsm7bDowE1D0LEWmfp11cS7VUcWQXV1+PuucgIiLSAIPaByAi0ixbtWi58rsBc4Xap8me4KjMBUh64IUvAy9+BahYAMw6a+yvmWcARVa1T0lEWhELjrRWcQSIAdnWWeqehYiISGUMjoiIJmOtEldPO4OjRJTOBv55H3D0XeD4XuD4B0Djm8AHT459TZnrpDDpLMBcrtqRiUhFnjZx1dqMI4ADsomIiMDgiIhoclanuA4cA6qWqnsWQARHZS61TxEfa5X467RPj/2at0OESB17RaB09F1g/3Pjvqd6XJh0priWzAIkKfPnJ6LM8bQB+kKxyUwrLOMqjoiIiPIcgyMiosmMBkcaGZCdLRVHkymZKf5a8ImxX/P3Ah0fRiuTon/VvwwgOlfKbB9XlRQNk2bUZn+YtOcx0QJ5zlfUPgmR+jztok1NS/9dx1rVOOSfiIiIwRER0aTMdkBnZHCUTsVlgOs88VdMcBDo3HdimNT4JhAJid8vsokA6ZM/AioXq3LslIRDwOvfFUN3F3wScCxS+0RE6upv09Z8IwAotADGYsDHVjUiIiIGR0REk9HpxFDUgWNqn0RsJwsN5V5wNJFCCzBnrfgrZmQI6DowFiTt+yPw5n8B1z6m3jmT1bJl7GH0zf8CrnlE3fMQqc3TDsy/SO1TnMpsZ8URERERGBwREU3N6gQ8Gqg4CvSJaz4ERxMxFgHO5eIvANAXAHseAYYGsm9D2/5nAaMZWHU7sPUXIgibdZbapyJSRygIDHZoazB2jMXBGUdEREQAdGofgIhI06xObbSq5XtwdLIlV4sKrPpX1D5JYsIjwIEXgIWfBM79OlBUCrzxn2qfikg9sfur1lrVADHniBVHREREDI6IiKZkrRKtarKs7jn8veLK4EioXiUqFPb9Ue2TJKbpLSDQC5x+lZjVtP4eoOEvQNsutU9GpA5Pu7hqsuKIrWpEREQAgyMioqnZqoFwUGzAUlOs4qi4TN1zaIVOB5x+JXDk9bFQLRvsfw4otALzovNc1nxezFH5+/fVPReRWkaDI41WHPndYqA9ERFRHmNwREQ0FWuVuMYebtTCVrVTLblabFr76AW1TxKf0DDw0YvAwk+JmU0AUGAGzvmqqERqelvd8xGpob9NXK1Odc8xEYsDgKz+BwdEREQqY3BERDSV2MOM2pvVGBydatZZQNnc7GlXa3wDGPKISqnxVt4OlFQBf/+B+i2RRJnmaQMslWNhqpaY7eLKAdlERJTnGBwREU1lNDhSeUB2oE9sEjMWq3sOLZEkUXXUtBnwdqh9munte1bMNZp74Ym/biwCzvs60LYDOPyaOmcjUounXZttaoAItADOOSIiorzH4IiIaCpmO6AzaiM4Ms0QYQmNWXI1ABk48LzaJ5nayBBw8M/Aok8DhoJTf3/ZTcCMWjHriFVHlE88bRoOjhzi6utW9xxEREQqY3BERDQVnQ6wztJGqxrb1E7lWARULtF+u9qR14FhL7Dkyol/X28EzvtX4PheMQeJKB/IcrTiSIMb1YCxVjVWHBERUZ5jcERENB2rE/BopOKITrXkKtHm1d+q9kkmt+9ZwFQG1J03+deceQ1QsQB444dAJJy5sxGpxe8GQkPaDY4KSwBDEWccERFR3mNwREQ0HatTO61qdKrTrxLX/c+pe47JDPuB+leA0z4tKosmo9MDF/wb0H1Q+xVUREqIhb1abVWTJMDsAAbZqkZERPmNwRER0XSsVaJVTc3ZM4E+UbFCpyqrA5wrtBu2HPorMOITlVHTOe1yoPIM4M3/AsIj6T8bkZo87eJaqtGKIwCw2IHBTrVPQUREpCoGR0RE07FVA+GgaKtQS6APMJWq9/5at+RqMR+o57DaJznV/ufErJSac6b/Wp0OuPBbQG8j8P7j6T8bkZpiwZFWW9UAUXHE4dhERJTnGBwREU3HWiWusYecTBsZAkb8bFWbyulXApCA/c+qfZITBQeBhleBxZcDekN837PgYsC5Enjr/wKhYHrPR6QmTxtgLNb2vc1i53BsIiLKewyOiIimY3WKq1qb1Yb6xVXLD1dqs1YBNeuAD5/R1jr7hr8AoUA02IqTJImqo4F24N2H03Y0ItV52kS1kSSpfZLJmR2Av4cD64mIKK8xOCIims5ocKTSgOxAn7gyOJrakquAnnqgc7/aJxmz/znAMhOYc3Zi3+c6H6jdALz932K4NlEu8rRrdzB2jMUByBHA36v2SYiIiFTD4IiIaDpmO6AzMjjSusVXAJJeO0OyhwaAQ38DTr9CbExLRKzqyNcF7LwvPecjUlt/W3YER4D4b5GIiChPMTgiIpqOTgdYZ6nXqhb7pJvB0dTMFaJSZ98ftdGuVv+KGKp+ehzb1CYyZy0w7+PAlv8RIRRRLhkJiBYwLW9UA0SrGsA5R0RElNcYHBERxcPqBDysONK8JVcD/S3A0T1qn0QM6rY6gepVyb/Ghf9b/PPffq9y5yLSgtj9VMsb1YBxFUfcrEZERPmLwRERUTysTvVb1YrL1Hn/bLLoHwB9gfrtaoE+4PDrYii2LoU/aquWAYsuBbb9kjNWKLd4WsVV661qZru4suKIiIjyGIMjIqJ4WKtEq5oaLVCBPkBnAAosmX/vbGMqFe1d+58FIhH1znHwz0BkJPk2tfEu+N9A0Ats/Xnqr0WkFZ52cdV6xVGRTYTRnHFERER5jMEREVE8bNViXo3fnfn3DvSJNjUtr6zWkiVXAd7jQOs29c6w/zmgdA7gXJ76a1UuBs7YCOz4LeDtTP31iLSgYx9gLBahvJZJkphzxIojIiLKYwyOiIjiEXu4iX1Knkmx4Ijis/AS8UCqVruavxdofFO0qSkV9p3/TSAUBN75qTKvR6S2prfFAHi9Ue2TTM9iZ3BERER5jcEREVE8rE5xVWOzGoOjxBSYgQWfBA78CQiHMv/+H70ARELKtKnFlM8Flt4A7N6kTnhJpKTBbqD7I6B2g9oniY/ZwVY1IiLKawyOiIjiMRocqTAgO9DL4ChRS64WbYVNb2X+vfc9C5S5gFlnKfu65/0vMWPr7R8r+7pEmda8WVzrzlX3HPGy2EXYRURElKcYHBERxcNsB3RGlYKjfgZHiZp3EVBoFSFOJg12iYfi069SfiZV6Rxg5W3Ae78DehuVfW2iTGreDBSUALOWqn2S+JgdgK9b3YH7REREKmJwREQUD50OsM4CPGoER32AqSzz75vNjEVijf1HL4rZQJny0QuAHBEDutNhw9fEhr03f5Se1yfKhKbNQM3ZgN6g9kniY6kE5LC4FxMREeUhBkdERPGyVmd+xlFoGBgeZMVRMpZcDQQ9wOHXMvee+54DKhYAjsXpef2SmcDqO4EPngK6DqbnPYjSaeA44D6UPfONANGqBnDOERER5S0GR/T/2bvv+Crr8//jrzsbElYg7IQR9t4EFVSwCi5QVFxFraPW1WW1dny/Wru0/dr+Wqmj1tZRi1bBgaLWCVoIe4UNAcLem5B1//64EhkeIOO+z31Ozvv5ePA45oz7vsCc5Jz3uT7XR0Qqq37L8C9VK9xrl3Uahve8tUH7c6Fu4/DtrrZ/C6z/0p9lasc7+/s2APyzX/t3DhG/RNt8I7ClaqCd1UREJGYpOBIRqaz6La3jyHXDd86KpRHqOKq6+EToNhpWTIWiQ/6fb+lbgOvfMrUKqY0h5y4735aF/p5LxGv50yClATTvGXQllZdWHhwd0oBsERGJTQqOREQqq0FrKD1qu3WFi4KjmukxFooPw8r3/T9X3mRo2h0yOvt/riF3Q0pD+FRdRxJl1k2HNudAXHzQlVReavlSNXUciYhIjFJwJCJSWfVb2uW+jeE75+HddqngqHqyhkC9Fv7vrrZvIxTMhO5X+HueCnUawtn3WSBWMDs85xSpqb0FsGcdtIui+UZgP3/jEuHgtqArERERCYSCIxGRyqrfyi7DOSBbHUc1ExdvYc6qD6Fwn3/nyXvTLv1epna8Qd+2TohPHg3fOUVqomK+UTQNxgabWZaaoaVqIiISsxQciYhU1lfBURgHZCs4qrkeY6G0CJa/69858iZB817QONu/c5wsOQ3O+QHkf25zY0QiXf50qJPu366DfkrL0FI1ERGJWQqOREQqKzXDliuEOzhy4m2YrFRPq/7QMMu/3dX2rIdNc8PbbVRhwLegXkv45JfhHdouUlWuax1Hbc+BuCh8+ZnaFA4pOBIRkdgUhb+5RUQCEhcH9VvAvjAHR3Ua+ru9e23nONZ1tOZTOOTDYPO8yXbZbYz3xz6TxBQ490dQkAur/hP+84tU1p582FcA7YYFXUn1pDWFg1qqJiIisUnBkYhIVdRvHf4ZR1qmVnM9xoJbCkvf9P7YeZOgZT9Ib+f9sSujz43QsI3NOlLXkUSq/PL5RtEcHB3aoeeYiIjEJAVHIiJVUb9l+JeqKTiquWY9oEkn73dX27UGtiwMZplahYQkOO/HsHURLHsnuDpETmfddEhrZs/DaJTaFMqKj82dExERiSEKjkREqqJBK+s4CtenzgqOvOE40OMqWP+ltx1jeeVBVBDL1I7Xa5y9If/0V1BWGmwtIidzXes4antO9C67TWtql9pZTUREYpCCIxGRqqjfCkqPwmEfZuWEcmS3giOv9LgScCHPw+VqeW9C60HQMNO7Y1ZHXDyc9xDsWO7fEPBwOrwbJt8Jh3YGXYl4YddqOLgV2g4NupLqS82wS+2sJiIiMUjBkYhIVdRvaZf7NobnfEf2KjjySpOO0LyXd8HKjpWwbUmwy9SO120MNOsJn/4aSouDrqZmFrwCC/8Fy6cEXYl4IX+aXUbrfCM4ruNIwZGIiMQeBUciIlVRv5VdhmNAdmkxHN2v4MhLPcbCpjmwZ13Nj5U3CXCCX6ZWIS4Ohv/Udq9a8ErQ1dRMxRLADbnB1iHeWDfdfnamtw+6kupLLQ+O1HEkIiIxSMGRiEhVfBUchWFAduE+u6yT7v+5YkX3K+yypkOyXdeO0eYsqN+i5nV5pdNIaNUfPn8cSo4GXU317FkPm+aCEw8FM4OuRmrqq/lGQ6N3vhFYgO/EKzgSEZGYpOBIRKQqUjMgLjE8wVHF7j3qOPJOozY2k6imwdH2ZbBzxbEgKlI4Dgz/OezfCHP/EXQ11ZM32S4H3AK71+qNerTbvgwO74R2UTzfCKyjLzVDS9VERCQmKTgSEamKuDjrMNmn4Chq9RgL2xbDjhXVP0beJHDioNto7+rySvvzoM05MP3/onPWUd4k65rqeY19XaDlalFt3XS7jObB2BXSMuCgdlUTEZHYo+BIRKSq6rcOz4wjBUf+6D4GcKrfdVSxTK3tOccG5kYSx4Ehd8HBbbDm06CrqZpda2DLQuh+JbTsA/HJsEHL1aJa/jRomGXdftEutak6jkREosk737VdWkuKgq4k6ik4EhGpqkZt7c2tFwOWT+fwbrus09Df88Saes0t9FnyhoVAVbV1EexeY+FGpOrwDQscF00MupKqqVim1n0MJCRDy77qOIpmZWWw/ktoG8W7qR0vrZk6jkREokXJUVjwL9ul9d83KTyqIQVHIiJVNex+W7I28QYoOuTfedRx5J8eY2HXKguBqmrJJBuS2/Vy7+vySkKSBVvL34XC/UFXU3l5kyFzMDRobV9nDYbNC6D4SLB1SfVsW2I/x6J9vlGFtPIZR9UJnEVEJLy2LITSo9D5EljxnsKjGlJwJCJSVY2z4arnYftSePMu/95EHNkDOJDSwJ/jx7JuoyEuwbqOqsJ1Ldxofy6kNvanNq/0vhZKCmHZ20FXUjk7VlrQcHwnV2YOlBXD5vnB1SXVlz/NLmvDfCOwpWqlRcd2vBQRkchVsdT90j/Axb9XeFRDCo5ERKqjwwVwwcOw9E344gl/znFkjy1Ti4v35/ixrG46ZA+37qGqBH+b58He9ZG9TK1C64GQ3h4WvRp0JZWTNwlwThw4njnYLjXnKDqtmw7p2dCgVdCVeKNiptkhLVcTEYl4Bbk2XqJeMxh0u8KjGlJwJCJSXWfdBz2ugo8fhZUfen/8I3u0TM1PPcbCvgLYOLvyj1kyCeISoeul/tXlFceBXuMgf3p4dgGsqbzJ0OZs27WwQmpjaNxRc46iUWkJrP9v7VmmBpCaYZcHtwVbh4iInJ7r2muHzJxj1yk8qhEFRyIi1eU4cPmfoXkPeOM22Lna2+MrOPJX54tt167KLlcrK4O8N61TKVr+v/S6BnBh8WtBV3J625bCjuXlO96dJGuwvfgrKwt/XVJ9WxfC0f21Z5kaHOs4Oqid1UREItrutdYdmjX4xOsVHlWbgiMRkZpIqgvXvgLxCTDxOm8HESs48ldKfeh0oXW6lJWe+f6b5sD+jdD9Cv9r80p6e2g9CBa+GtkDffMmgRN34jK1Cpk59lzYtSr8dUn15U+3y9oUHKVqqZqISFSo6FQ+vuOowvHh0WvjFR5VkoIjEZGaapgFV78Au9bApDu864xQcOS/HmNt2cn6L8983yWTID4Julzsf11e6j0Odiyr3g5y4eC69m/bduixjo7jZZW/6NOco+iybjo06WyzJWqLuukWcKrjSEQkshXkQnIDyOgS+vaK8GjlVIVHlaTgSETEC+2Gwsjf2C+gz3/rzTGP7FZw5LeOF0Fi6pmXq5WV2SD0Dt+Ivl3uul9pc5kWRuiQ7K2LYfca6HGKgeONO0DdxppzFE1Ki2H9jNo13whso4K6TeCQgiMRkYi2IRcyB0LcaeIOhUdVouBIRMQrg+6APjfC54/B0hpugV5Wals+KzjyV1Jd6yBa+pa92T2VDTPgwJZThxuRrG46dLoIlrxuA4sjTd4kcOKhy2Whb3cc211NHUfRY9M8KD4E7YYFXYn30prCQS1VExGJWEf2WKd1qGVqJ1N4VGkKjkREvOI4cMn/QasBMPlOG/hbXYX77FLBkf96jLUXGWs/O/V98iZBQooFMNGo1zhbkpf/WdCVnKhimVr782wHtVPJHGxdSXrDHh3WTbPLNucEW4cf0pqq40hEJJIVlO+We/Jg7FNReFQpCo5ERLyUmALjXobkNJh4PRzeXb3jHNljl3XSvatNQssebsvPTrVcrazUOpI6XgjJ9cJbm1c6XWR/x0hbrrZ5Huxdf+ZOroo5R1quFh3yp0OzHqcPA6NVqjqOREQiWkGudTK36l/5x3wtPDrqX31RSsGRiIjX6rew8GjfRnjj1srt2HWyr4IjdRz5LiEZul4Gy6ZA8ZGv377uC9tFKRqXqVVISLbd4JZPgaMHg67mmLzJNn+pyyWnv1+LPjaYvEDL1SJeyVF70V6bdlM7XlqGdRxF8i6FIiKxrCAXmveEpNSqPe6E8OgmhUcnUXAkIuKHzEG2bG3NJ/DRw1V/vIKj8OoxFooOwKr/fP22vEk2QLtjlC5Tq9DrWig+DMveCboS47qQ96Z1fJ3p+zwxBVr2tWGXEtk2zoGSwto3GLtCalP7+x09EHQlIiJystJi+z2UVYn5RqEoPDolBUciIn7pfxMMvA3++ydY/HrVHqvgKLzaDoPUjK8vVysttkHnnUfaIO1olpUDDdvAoolBV2I2zoZ9BZXv5MocDJvnh+4Kk8ixbjrgQJuzgq7EH2lN7fKg5hyJiEScrYuh5Ii9ZqguhUchKTgSEfHTRb+BrLPgrXtgy8LKP65iNpKCo/CIT4BuY2DlByd2EuRPgyO7bZlXtHMcG5K99nPYvyXoamwodnwydL64cvfPyoGyYguPJHLlT4cWvWvvz67UDLvUgGwRkchTMQuxJsERKDwKQcGRiIifEpLgmhehbmOYeAMc2lm5x1V0HKU08K82OVGPsfYp1Yr3j12XNwmS6kGHbwRXl5d6Xwu4sPjfwdZRVgZL34QOF0BK/co9puJF4AbNOYpYxUdg46zau0wN1HEkIhLJNsyEBpnQoFXNj6Xw6AQKjkRE/JaWAde+bAOWX7vJlj+dyZE9kNzAOmEkPDIHQ/1Wx5arlRTZPKAuF9uMndqgcTa0GgCLAt5drWAmHNhStYHjqU2gcQftrBbJCnKhtMiWftZWqeXB0SHtrCYiElFc134P1bTb6HiDbreZpdptTcGRiEhYtOwLl/8Z1n8BH/z0zPc/sgfq1tKlHpEqLs6WpK3+yP79134KhfugexTvphZKr3GwbQlsXRJcDUsmQUId6DSyao/LzLEXhWVl/tQlNZM/3bZAbjMk6Er8U7cx4KjjSEQk0uwrsA+lvAyOwOaVXvJ/sPL9mA6PFByJiIRLr2tgyD0w6xmY//Lp73tkT+2dERLJeoy1OTrLpthW8ckNIPv8oKvyVo+xEJcQ3JDsslJY+hZ0uhCS06r22Kwce27sWuVPbVIz66ZbSJ5cL+hK/BOfYN1vmnEkIhJZKnZezfI4OAKFRyg4EhEJrwsegfbnw5Tv23ahp6LgKBgt+0KjdrDgFVj+LnS9FBKSg67KW6mNbWbT4tctxAm39V/am+7qDByv2F5Xc44iz9GDsGlu7Z5vVCG1KRzUUjURkYhSMBOS0qBpd3+OH+PhkYIjEZFwik+Aq56H+i3h1RvhwNbQ91NwFAzHsY6cDf+Fo/tr3zK1Cr3HWTt3/rTwn3vJJEhMhY4XVf2xjTvYUiHNOYo8BTOhrATaxkBwlJahjiMRkUizIRdaD/B3PmgMh0cKjkREwq1uOlz7ChTuh1e/GfqXjoKj4PQYa5d1GkH7c4OtxS+dRtkyvHAPyS4tgWVvQ+eRkFS36o93HJtdoI6jyJM/DeISj3WF1WapTTXjSEQkkhTuh+153s83CiVGwyMFRyIiQWjWHa54yraufu9+2wmiQlkZFO5VcBSUZt2sa6L/zRCfGHQ1/khMge6jYenbUHQofOddNw0O76pZJ1fmYNi9RkuFIk3+dPukNyk16Er8l9bUdlU7/ue2iIgEZ9MccMvCExzBieHRq9+0nXhrOQVHIiJB6TYaht4P816EOX87dv3RffbLT8FRcG6eAhc8HHQV/uo1DooP2SyncFkyCZLqQYcLqn+Mio4WLVeLHIX7YMuC2FimBpCaAcWHoehg0JWIiAjYMjUnDloPDN85B94GlzwBDVrbpiO1nIIjEZEgnf9T25J86oOw7ku77sgeu1RwJH7KOgsaZMLCMO2uVlIEy96BLhdbx1N1tegD8Uk2U0ciw/oZFnbHwmBssI4j0HI1EZFIUZBrQ7FT6of3vANvhUufgLjaH6vU/r+hiEgki4uDK5+1nbxeGw97C44LjtKDrU1qt7g46HUNrP0UDmzz/3xrP7MlmDUdOJ6YYrvfbVDHUcRYNx3ik6H1oKArCY/U8uDokJZLiogErqzUdirOjJHfQQFRcCQiErSUBnDdv6C0CF69AfZvsevVcSR+63WtdYosed3/c+VNtoHc2cNrfqzMwbY0qriw5seSmsufZi/Ya9JJFk3SMuxSHUci0e2jh2HCYAseJHpty4OiA7GxOUOAFByJiESCJh1h7HOwZRFMfcCuU3AkfsvoZN07fi9XKzlqs5S6XgoJSTU/XlaOBa2b59f8WFIzh3fD1sXQbljQlYTPVx1HCo5EotaRPZD7DOxYDis/CLoaqYmKmYfhGowdoxQciYhEik4XwfCfwv5N9rWCIwmHXtfC1kWwfZl/51j9sQ19r+kytQoVLw415yh4678E3NgZjA2Q2gRwtLOfSDSb83cbcp/SAGY/F3Q1UhMFuVCvBTTMCrqSWk3BkYhIJBl6P3QbA0lpUKdh0NVILOgxFpx4f7uO8iZZENr+XG+Ol9oEGnfQnKNIkD8dEuvPJJwcAAAgAElEQVRCq/5BVxI+8YlQN93fjqMje2yY/KFd/p1DJFaVFMGsZ6H9eTDkHljzMexaE3RVUl0bcm25tOMEXUmtpuBIRCSSOA5c9TzcM9venIj4LS0DOoyAxf+GsjLvj198BFZMha6Xefs9nZljnzK6rnfHlKpbN906wLxYghhNUpt6P+No/2aY9Vd4cTT8rgO8eiN89mtvzyEi9mHGgS0w5F7oN962Up/zfNBVSXXs3wz7NthrAvGVgiMRkUgTFw/1WwZdhcSSXuNsieT6L7w/9qr/QNFB75apVcgaDEd2w85V3h5XKu/QTti+FNrF0DK1CmkZ3uyqtnMVTH8C/jocnugK790P+zbBWfdaILf6o5qfQ0SOcV2Y8SRkdLEPTeo1tw825r8MRYeDrk6qakP5kvUszTfym4IjERGRWNflEkiqBwtf9f7YeZOgbhPvZ+BUfLqoOUfBWTfdLtvG0GDsCtXtOHJd2DQPPv4FPDkInhwAHz9iuxsO/zncPQvunQMXPAw9roI967SERsRL+dNsoH/OXceWNg28DQr32u8riS4FsyChDjTvFXQltV5C0AWIiIhIwBLrQLfRsPQtuPh3kFTXm+MWHbLdanpfB/Eev+Ro0hHqpNtsg37jvT22VE7+dJvH1rJP0JWEX1oVgqPSEhsivnyK7S64f5PNFWt7tr1h7XIxNGj99cd1GGGXaz6Bxtne1S4Sy2ZMsA8zeo07dl2bsyGjqy0V7XODZuVEk4KZNmNP4x18p+BIREREoPc4WPAyrHgPel7lzTFXfmC71nS/wpvjHc9xbCmPOo6Cs246tDkrNl+wp2ZA8SELR5NSv3578RELfJZNgZVTbdh1Qgpkj4DhP4NOI23A9umkt4eGbWxXwkG3+/P3kNpr5yrbOSw5DZr3tI6MhlmxHYrsWAGrPoDzHoLElGPXOw4MvNWWim6aB61jaNh/NCs6BFsWwTnfC7qSmKDgSERERKDNOVC/FSx61bvgKG8SpDWzcMEPWYPtTfmhneVbpEvY7N8CO1dC328GXUkw0pra5cHtkN7O/vvIHgtLl71joVHFVt+dRkKXS62DKFTIdCqOY49Z9JrtAhVrA8ilenasgGm/gyVv2NDnshJbCgmQ0vBYiNSil1026eR9R2ikmvkXiE+2Tr+T9RoHHz0Ms59TcBQtNs0Ft1SDscMkRn5KiIiIyGnFxUHPq+G/f7Y3wxVvjKvr6AEbjN3vJhv47oev5hzl2pwmCZ915YPUY3EwNtiMI4Cti2yA9fIp9m9SVgL1WkCf6+17su3QmnVkZY+w3Z4KcmP331oqZ/sy+PxxyJsMiXVtwPqQe23p8bal9r26dZF1aMz5G5QU2uPik6FZtxPDpGbdqxZyRoNDO2HhROh9begPGlLqW3g0/2W46Fdn7giU4BXk2mXrAcHWESMUHImIiIjpfS18+UdYMgly7qzZsVZMtTcmPTzeTe14LftCfJLtqqLgKLzWTbNumlgdSJqWYZevlc/XatwBhtxjuzO17GdBrBfaDbOukTUfKziS0LblweeP2Yy6pDRbtjPkXkhtfOw+mQPtT4XSEti1ykKkikBp6Vsw7wW73Ymz7+nmvaxDqUUvaN77xGNGm9nlYdmQu099n4G3Wag2/yU4+7vhq02qZ0Ou7Y6nkC8sFByJiIiIadrV3igsmljz4ChvMtRrCa0HeVNbKIkp0KLPsU8dJXzyp9tAWb+6ySJd027Q90Zo1Ba6XAYZnf2ZHZNS32Z5rf7YdloTqbBlEUx73JZGJtWDofdbKFKZN9HxCfbzvmlXm28HtuPfvgLbcawiUCrIhSWvH3tcvZbHupJalIdKDdtE/tyk4kKY/VfoeKE9V0+lWTf7uTb7bxa+eRUAi/fKymDjLOg2JuhKYoaCIxERETmm97XwwU9gx0rI6FS9YxzZa8t3Bt7u/wvvrMGQ+4y9MTh+2Kn4Z99G2JMPg+4IupLgJCTD6AnhOVf2cPjkUW+WkEr027zAlqSteBeSG8C5D0LOd6BOo5od13FseHbDrBM7OA/vLu9KOi5QWvXhsblJOXfDyF/X7Nx+W/waHNphXYFnMvBWeP1b1uXX8Rv+1ybVs3MFFO6DLM03ChfFqCIiInJMj6tsmcKiidU/xor3oLTI32VqFTJz7FxbFvh/LjH50+1SS6fCo8MIu1zzSbB1SLA2zYVXxsGz58L6L+C8n8D3FsH5P6l5aHQ6ddOh/Xk2M2nsX+HuXPjJZrjtE+h5jQ2c3jzfv/PXlOvCjAnQrKct/TyTLpfZDLPZz/lfm1TfhvIdVTMHB1tHDFFwJCIiIsfUa2YdDotes1bw6lgyCRpkQasw7ExT8aJxwwz/zyUmfxrUSYem3YOuJDY07w11m9hyNYk9G+fAy1fBX4fbm+XzfwbfWwznPQh1GgZTU2Id23nskt9Daga890D1f1/4bfXHsGM5nHVP5ZbUJSRB/5tth8Q96/yuTqqrINd+Lqa3D7qSmKHgSERERE7Ua5zNuqhOGHN4N6z9FLqPCc/ci7QMSM+2IZniP9eFddOh7Tma/xEucXGQfb51HEXqm3Px3oZceOlKeG6EdRuN+B8LjM79kQ2mjwQpDWz21sZZsOjVoKsJbcafbafD7lXogO1/s3Xezvm7b2VJDW2YacvUIn2+Vi2i3/giIiJyoi6XQGJq9ZarLZ9iW5KHY5lahawc+/TRdcN3zli1Z52FipVZ8iHeyR4Bh3fafBmp3dbPgBdHw/MX2hLcCx6xwGjoD21YeqTpfR20Hgj/+R8o3B90NSfaugTWfmbz2BKSKv+4Bq2g8yjbXa240LfypJoObrc5e1qmFlYKjkRERORESanQ7XLIe6vqL5qXTIJG7Wy3s3DJHAxHdsPOVeE7Z6xaVz7fqK3mG4VV9nC7XKPlarXWui/gH5fC30fCtjy48JcWGJ3zPUhOC7q6U4uLg1GP2fDpaY8HXc2JZkyAxLrWQVRVA2+Dw7tg6VuelyU1VLGTqoKjsFJwJCIiIl/Xaxwc3Qcrp1b+MYd22vyb7leEt328YleVgpnhO2esyp9ug2NPt6W1eK9eMxvuu1oDsmsV14W1n8PfL4Z/XAI7V8JFv4bvLrJh1EmpQVdYOa36Q98bYeZTtiNnJDiwFRb/2+qqm171x7c/Dxp3hNl/9boyqakNMyE+GVqG8QMqUXAkIiIiIbQbZnMhFlZhbsWyt8EtDe8yNbAX93Uaac6R346fb6S5EuHXYYSFo0cPBF2JeOHQLguLXrwcdq+FkY/BdxfCkLshqW7Q1VXdiP+1Jc7vPxgZy4ZnPWvLpgffWb3HOw4MvBU2zobN2rUzohTkQsu+kJAcdCUxRcGRiIiIfF1cPPS8Glb/xzqJKmPJJAtxmvXwt7aTxcVZy7o6jvy1aw0c2KL5RkHpMMLeCOdPD7oS8cLUB6BgFoz6Hdy3AHLutN3KolVaBpz/ExvivvzdYGspOgRznrd5fY2zq3+c3tdBQh2Y8zfvapOaKS60IC9zUNCVxBwFRyIiIhJar3H2RjVv8pnve2AbrP/Suo2C6EbJHAy7Vlc+5JKqy//cLhUcBSMzxzo6Vn8UdCVSUyumwpLXYdiPYPAdkJgSdEXeGHgbNO0GHzwExUeCq2PBK3BkDwy5p2bHqdMQel0Ni/4NR/Z6U5vUzOb5UFZ8bIm6hI2CIxEREQmteQ/rHlpYid3Vlr4FbpnNNwrCV3OOtFzNN+umQ72WkN4+6EpiU0IStBuqAdnR7shemPJ9aNodzvl+0NV4Kz7BBmXv3QD//XMwNZSVwcy/2NwlL8KFgbdDyRELoyR4FZ3FGowddgqORERE5NR6jYNNc2Dn6tPfL28yZHSFpl3DU9fJWvaD+CQbminec13b9andUM03ClL2CNizzpYNSnT68GdwcBuMfrJqW8RHi3bDoNsYmP6EBUjhtnKqzYwacrc3P6ta9ILWg2D2cxZKSbAKZkHjDpDaJOhKYo6CIxERETm1nleDEweLTjMke/9m2DAj/EOxj5eYAi36qOPILzuW23bbbYcGXUls6zDCLtdod7WotOZTmP+S7ZjWql/Q1fjnwl/a5Yc/C/+5Z0yABpnQdbR3xxx4G+xec2y5rgTDde13vLqNAqHgSERERE6tfgtod64FR6faKSfvTcANbplahazBNv+guDDYOmqjioHM7RQcBSq9PTRsA6u1XC3qHD0I79wH6dlw3kNBV+Ovhpkw9Ie2hHntZ+E776Z5Nmtv8J22bM4r3UZD3cbWdSTB2bUaDu9ScBQQBUciIiJyer2vhb3rT70MLG8yNOsJTTqGt66TZeZAaRFs0dbJnls3DRpkQaO2QVcS2xzHuo7WTYeSoqCrkar45FFbujX6yejePa2yzrrXQs6pD0JpcXjOOWMCJNWDfuO9PW5iih1zxXuwb6O3x5bKq3gNosHYgVBwJCIiIqfX5VJIrBt6udreAtg4C3oE3G0Exz6F1Jwjb5WVlc830m5qESF7BBQd1LLMaLJhJuQ+Y4OW25wVdDXhkZgCI39jy1zD0amzt8A+xOh/E6TU9/74/W+xrtu5//D+2FI5BblQpxE0DvhDqhil4EhEREROLznNwqO8yVBy9MTb8ibbZfcA5xtVSMuwZSB6Q+2tbUtsa2stU4sM7YZBXIJ2V4sWxYXw1j02d+eC/w26mvDqfLEFnZ/+Bg7u8Pdcs56xy8Hf9uf4jdpAp4tg7gvq9gtKQa4NKo9ThBEE/auLiIjImfUeB4V7YeUHJ16fNwla9oX0dsHUdbKsHHtxeap5TFJ168rnG2kwdmRIqW/ddas/CroSqYzPH4Ndq+CyP0JyvaCrCS/HgVGPQfFh+Phh/85TuN8CnW6joWGWf+cZeBsc2g7L3vbvHBLa4d2wc6XNMpRAKDgSERGRM2t3HqQ1O3G52u58G0Yd9FDs42UOtuGZu1YHXUntkT/dhjI3aBV0JVIhezhsXQwHtwddiZzO5gXw5f+DPjce2xEv1jTpCDnfgfkvw8a5/pxj/stwdD8Mucef41fIHmFz3mb/zd/zyNdVdBJnar5RUBQciYiIyJnFJ0DPq63j6PBuu+6rZWoRFBxVDM3UnCNvlJXC+v+q2yjSVIQQaz4Jtg45tdJiW6KW2gQu+mXQ1QTr3Afsg4f37reZaV4qLYGZT0HWEGjd39tjnywuDgbcChv+C9vy/D2XnKgg15botuoXdCUxS8GRiIiIVE6vcVBWfCwwypsErQf6uzSgqhp3tOGZBQqOPLFlIRzdp8HYkaZ5b6jbBFZrzlHE+uKPsG0xXPKE/UyKZcn14Bu/gM3zYOEr3h57+Tuwb4P/3UYV+t4I8cnqOgq3DbnQonds7EgYoSoVHDmOM9JxnBWO46x2HOfHIW4f5jjOPMdxShzHueq46/s4jjPDcZw8x3EWOY4z7rjb/uE4Tr7jOAvK//Tx5q8kIiIivmjeE5p2s+VqO1fbUplI6jYC+0Q4c7C9yAxSbZmx9NV8o3OCrUNOFBcH2edbx5HXHRxeKT4CRYeCriIY25fDtMft52PXS4OuJjL0Gmc/mz96GAr3eXNM14X/PgmN2kHnUd4c80zqpkOPsfZ7sHB/eM4Z60qKLHTUMrVAnTE4chwnHpgAjAK6Adc5jtPtpLttAG4GTo6QDwPjXdftDowE/ug4TsPjbv+R67p9yv8sqObfQURERMLBcaDXNdYy/sUTdl23McHWFErmYBtGe2hXMOc/shf+1Bc+/XUw5/dS/jRo0hnqNQ+6EjlZ9gg4vBO2Lgq6ktD+fTP8qR/s3RB0JeFVVgpv3Q1JaTDqd0FXEzkcB0Y9Dod2wme/9eaYBbNg0xwYcjfExXtzzMoYdBsUHTxx5p/4Z+siKCnUYOyAVabjaBCw2nXdta7rFgETgdHH38F13XWu6y4Cyk66fqXruqvK/3szsB3I8KRyERERCb+e1wAOLPinzZSIxIHJFXOOCgLqOvrgp7AnH2Y/ByVHg6nBC6XFsH4GtNN8o4iUPdwu10TgcrXty2Hl+3BwK/zzGu86TKJB7tMWZox6DNL0tucELftA/5sh9xnYvqzmx5vxZ0hpCH2ur/mxqqJVf9tNdPZztae7NJJVzCzMVHAUpMoER62AguO+3lh+XZU4jjMISALWHHf1r8qXsP3BcZzkUzzuDsdx5jiOM2fHjh1VPa2IiIh4qUGrY0FC9yuDreVUWvaFuMRg5hyt/BAWvAxtzrHd3Za/G/4avLJ5PhQf0mDsSFWvmS0fXR2BA7Jzn4aEFLjq79b999p4CyJru91r4eNHodNI20xAvm74z23m0dQHaha67F4Ly6bAgG9BUqp39VXWwNtgx3JY/2X4zx1rCmZCwzbqfA1YWIZjO47TAngJuMV13YqupIeALsBAIB14MNRjXdd91nXdAa7rDsjIUGovIiISuIG3Q3ID6Db6zPcNQmId+2Q73HOOjuyBd+6zOVA3vg4NMmHei+GtwUv50+xSwVHkyh5hb6qOHgi6kmMO74aFEy046XElXPYnWPsZTPle7e7OcF14+z6IT4RL/2BLs+TrUhvD8J/Zz5dlb1f/ODOftl22Bt3hXW1V0f1K63aa9ddgzh8rXNd+l2dpvlHQKhMcbQIyj/u6dfl1leI4Tn3gXeCnrut+9dGf67pbXHMU+Du2JE5EREQiXbfL4cfrreMhUmUOto6ZcC4Ve/8ncHA7jPmLhVd9b4S1n8KedeGrwUvrpkPT7vZGTyJThxFQVnIs5IsE816EkiMw+E77uu8NMOwBmP8yTP+/YGvz09x/2HPmwkehfsugq4ls/W+BZj1sWW/R4ao//sge+37qeRXUb+F9fZWRVNd+xi+fAvu3BFNDLNizDg5t1zK1CFCZ4Gg20NFxnHaO4yQB1wKViofL7z8ZeNF13ddPuq1F+aUDjAGWVKVwERERCVCkf5qelQOlR2FzmPbeWPG+bTM99Ae2VA6gzw2AA/P/GZ4avFRy1D7l1XyjyJaZA4mpsDpC5hyVllgHRtuh0LzHsevP/4nNR/vkUVj8+qkfH632bYQPfw7thkG/m4KuJvLFJ9ig7H0F8OUfq/74uf+wZbRD7va8tCoZ8C0LbqO5szTSVcwqVHAUuDMGR67rlgD3AB8Ay4DXXNfNcxznF47jXA7gOM5Ax3E2AlcDzziOk1f+8GuAYcDNjuMsKP/Tp/y2fzqOsxhYDDQBfunp30xERERiV8WLzHDMOTqyB975rn2CPuyBY9c3zIQOF9gn46Ul/tfhpU1zrWuk3bCgK5HTSUiycC9SBmQvnwL7Nx7rNqrgODD6SWhzNrz5HVj/32Dq84PrwpTvg1tqy/IiPVSPFG3Phh5XwRd/rFpXZkmRDddud67N+ApS42xbLjr377ExwysIG2ZCcn1o2jXoSmJepWYcua77nuu6nVzXzXZd91fl1/2P67pvl//3bNd1W7uum+q6bmPXdbuXX/+y67qJruv2Oe7PgvLbhruu29N13R6u697ouu5Bv/6SIiIiEmPSmkJ6+2O7sfhp6o9tW/Qxf7E38sfrNx4ObI6cN/aVlT8NcKDNWUFXImeSPcLeeO9ac8a7+i73aRti23nU129LSIZxL9vtE6+HnavDX58fFr0Gqz60oc/p7YKuJrpc+KjNKfrgp5V/TN5kOLAFhtzjX11VMfA2q2fFe0FX4o9tecF+8FGQC60HQlx8cDUIEKbh2CIiIiJhl5ljLzr9HMi7/D1YNBGG/hBa9P767Z1GQmpGdC1lKCuFBa9A1hCo0yjoauRMOoywyzUB7662eQFsmGHDik/1Jq9uOtzwGjhx8M+r4NDO8NbotYPb4f0HofUgGPztoKuJPvVbwrAfWqdaZZZbui7MeBKadLZuzkjQ6SLbCGH2c0FX4r1VH8FTZ8F79wdz/iN7YfsyDcaOEAqOREREpHbKGgyHd8EunzobDu+2naKa9YShp3hhnZAEva+DFVPhwDZ/6vDa8ndh73rI+U7QlUhlpLe3Lp6g5xzlPm3zlvreePr7pbeH6yZal8bE66G4MDz1+eG9H0HRIVuGp46I6hlyj31PvP9jW4Z2Ouumw9ZFMOQuiIuQt7Fx8TDgFuvS3LEi6Gq8U1Jk/0/iEmwp3soPwl/DxjmAC5naQysSRMgzTkRERMRjmeWfUvq1XG3qAxZMhVqidrx+N9n8kwVRMiR7xgQLIrpcEnQlUhmOY11H66af+Y23Xw5uhyVvQJ/roU7DM98/cxBc8Yx1BL55J5SV+V+j15a9A0vfhHMfhIzOQVcTvRKSYeRvYedKmPXM6e87YwLUbQK9xoWntsrqOx7iEmHO80FX4p1Zz8CuVXD1P2x+31v3hL9DsGAmOPHQakB4zyshKTgSERGR2qlJJ0hp6M+A7GVTYPG/bRh2i15nqKODDQWe96K/y+a8sHGO/Xvl3KUOimjS4QIoOnhsB6Jwm/M8lBZVbblW9zHwjV/YzJqPH/GvNj8c2QPv/tCGM5/93aCriX6dLoKOF8Fnj8GBraHvs2MlrHzfZgol1glvfWeSlmHfzwtegaO1YGzvgW32/6LjhdD1MrjyWSjca5tAhPN32IaZtjtjclr4zimnpOBIREREaqe4ONtdbYPHb6YP7bIlas17wdAfVO4x/cbDnnxY94W3tXhtxgRIbgB9bwi6EqmKtkNtScnqj8J/7pKjMPtv0OEb0KRj1R571n22pfmXf7Qt1qPFBz+17ovREyA+MehqaoeRv4HSo/DRw6Fvn/kXiE+24CgSDbwdju63DxSi3ce/gJJCuOg39nWz7jDif2wW1fyXw1NDaYnt7pmp+UaRQsGRiIiI1F5Zg63d/tAu74459Uc2tHPMU5V/09j1cgtkInlI9t4CWPoW9B8PyfWCrkaqIqW+haRB7N6XNxkObYecO6v+WMeBUb+z0GnKD4IJvqpq9Ue27PSc74UeiC/V0zgbhtwNC//19bD/0E67vvc46+6JRJmDbN7d7Ociv7P0dDbOhQUv2xypJh2OXZ9ztwXU7/8Yduf7X8e2xVB8WPONIoiCIxEREam9Kj6t9GoJz9K3bJbLuQ9aC31lJdWFXtfY4w/v9qYWr1XMFxmk3aGiUvZw2LrY5g2Fi+vCzKdsWWj2iOodIz4Brv47NO0Gr90MW5d4WqKnjh6Ad75nf99hDwRdTe0z9H6o19Lmx5WVHrt+zvPWAZNzd3C1nYnjwMBbYduS4JaM1lRZmf3bpzWDYT868ba4OPuwxImHyXee+P/HDxXhoXZUixgKjkRERKT2atXPhpZ6Mefo0E7rimjR27oNqqrfeFuKEYlLGY4egLkv2JyOhplBVyPV0aE8uFnzSfjOWZALWxbYbCPHqf5xkuvB9a/a5SvXwP4t3tXopY8ehn0bbYlaYkrQ1dQ+yWlw4aP2PTX/JbuuuBBmPWtdaU27BFvfmfS8GpLrW9dRNFo0ETbNgQseCd112jATLvm9/T798o/+1lKQC/VbQ4PW/p5HKk3BkYiIiNReiXUs6PFiztF790PhPhjzdPXmmrToBS36WEATaUsZ5r9s8zki+RN9Ob3mvW3HqdVhXK428ylIaQC9r6v5sRq0ghtes+fYK1dbmBlJ1n1pgcDgO7V8xk89xtpmAh//woaQL/43HNoBZ90TdGVnlpxmOwvmvQkHdwRdTdUU7rdgtNWA0+9a1/Nq6H4FfPpr2LLQv3oKcm2puUQMBUciIiJSu2XlwOb5NsS3uvIm25/zfgzNulX/OP3Gw/Y82Dyv+sfwWlmpDZ7NGgKt+wddjVRXXBxkn28dR+HY3n7fRtuSvt94SEr15pjNe9r239uWwuvfsgG5kaD4CLx9LzRsAyN+HnQ1tZvjwKjHLDT65Fc2sL9ZD2h3btCVVc6AW6GsGOZH8Dy7UKb9Dg5ug4sft58lp+I4cMkTkJoBb9xuzw2v7S2A/ZtsbptEDAVHIiIiUrtlDrYlYpsXVO/xB3fY1tst+8LZ1ViidryeV0NiXes6ihTLp8DeDTaYVqJb9gg4vBO2LvL/XLOfA1wYdIe3x+34DVsOs+pDeP/ByOjO+/TXsHsNXP5n70IyObXmPW23vdl/hR3LYMg9NVsKGU4ZnaDdMJjzd//nAHll52rrHuxzI7SqxIcHddNhzF9g5wr46BHv66mYEaXgKKIoOBIREZHarWK4ZnXmHLkuvPsDWzYz5ikb5FsTKfWtzX/JG3D0YM2O5ZUZE6BRW+h8cdCVSE1lD7dLv3dXKzoMc/8BXS6BhlneH3/At+Cs+yycmjHB++NXxaa5MONJ6HcTtI+Srpfa4PyfQp10SGtuy9eiycDbYF8BrPwg6Eoq54OHbFn3Bf9b+cdkD7eNFHKf8n6uWkEuJKZap5lEDAVHIiIiUrulNYVG7ao35yhvEix7G857CJp29aaefuOh6KAtfQvaxjn2In3wdyAuPuhqpKbqNbNujdU+D8he/JotJRr8Hf/OccEj0G00fPgz240wCCVF8Na9Fl5c+GgwNcSquulw4+tw3SuQkBR0NVXT+RKo1yI6hmSv/MC6+859wH5XVsU3HrEdBt+8y9vdQjfMtGXTNf2gRjyl4EhERERqv6wcC0iqsuzl4HZ4935r3T/rPu9qyRwMTTrDvAiYgTFjAiQ3gL43BF2JeCV7hHXXFe735/iuCzOfhmY9oc1Z/pwDbM7KFc9A64Ew6Q4LOcPtiydsJtmlf7Ah4BJerfpXbulUpIlPgP63WOffrjVBV3NqJUfh/YegcUfrHqqqxDpw5V9tePl793tT09EDsG0JZOZ4czzxjIIjERERqf2ycmz2S2VfxLsuTPk+FB3yZona8RzHuo42zoLty7w7blXt3WCdHP1vCr31skSnDiOgrATWTffn+Pmf29yZnDv9nzuTWAeu+xfUaw6vjIPd+f6eD6xzYuWH8PGjMO33Npes80j/zyu1S7/xEJcAc54PupJTm/mUze4a+dvqd3W17GMduUvegEX/rnlNG+eAW6Yd1SKQgiMREfUgLF8AABVoSURBVBGp/TKrOOdoyRs2NPr8n0BGZ+/r6X0txCUG23WU+4xdDq7GJ80SuTJzbD7Iap/mHM18Guo2gR5X+XP8k6U2gRtetzDslWtsiZxXysosvJ37Arx1Nzw5EB5vB69cDV/8wbqdRj7m3fkkdtRvAV0uhfkvwf7NQVfzdQe22k5qnUZBxwtqdqyzv2edtO/+0HZbrImCWYBjzz2JKAqOREREpPZr0glSGtrshDM5sM3a7lsPhLPu9aee1CY2WHjhv2y5QLgV7rfQqvsV0KB1+M8v/klIgnZD/RmQvXstrHwfBtwCiSneH/9UmnSEa1+BPevg1W9W/zlTuN8G+X72GLx0JTzeFv6SA+/cB8vfhfT2MPzncNMUeKgAvjUVUht7+TeRWHLuA7az2ktXeDsDyAsfPQylRXDRr2p+rPgEuOJpcEth8p0WyFZXwUxo2k1LQyOQJk6JiIhI7RcXZ5+IFpxhQPZXS9QOw+i/+Dswut94WPqmdTaFe9eg+S/D0f0w5O7wnlfCI3uEBTy71kDjbO+Om/usPScG3OrdMSur7dn2nJx0G7x9n71RPd1SOde1oKtglj3vC2bB9qWACzg27L77FdB6kP1saJwdPVu+S3Ro1h2umwgvj7U/N70dGcuCC2bbhxbnfN+7nw/p7WHkb+Dte22nter8bikrtdp6Xe1NTeIpBUciIiISG7IGw6oP7JPfuumh77P437DiXbjwl5DRyd962p8PDbKs8yecwVFpib2wzzoLWvUL33klfDqMsMs1n3j3xrBwvwWO3a+wZThB6HW1dR19+kto1BbOf+jYbUWHYfO88qBols0QO7zLbkuuD60HQLfLrZOw9QB1NEh4tBsK17wAE2+Af11nyy7D2a13srIymPqA7fo21KOB1hX6fhNWTIWPHrHfb826Ve3x25dB0QENxo5QCo5EREQkNnw15ygXOo/6+u0HtsJ7P7Lug5y7/K8nLg76fRM+/ZUN/U1v5/85wTqc9m6Ai34dnvNJ+KW3h4ZtbM7RoNu9OeaCV+xN3eDveHO86hp2P+zJh89/C2XFcPSgPae3LbE5SGC7RHUaCZmDrKMoo4s930SC0HmUbbIw+Q54/Ra45kWITwymloWvWMB6xbOQnObtsR0HLvsTPDXEdkK8/WNISK784ytmEGowdkTST1ARERGJDa362UDqUHOOXBfe+R6UFPq/RO14fa4HJ846OcJlxgTr1uh8cfjOKeHlONDhAsifBiVFNT9eWRnMeqa8Wyfg7dEdBy79I7Q7F6b/nw0fTq4HZ38Xrn8NHsiHe+fAmL9A/5ut60GhkQSt9zi4+Pew4j0bxF6TOUDVVbjPZhu1HgS9rvHnHGkZcPmTsG0xfFrFDyc25EJaMwu9JeKo40hERERiQ2IdaNE79JyjhRNh5VTrwmnSIXw1NWhtb/AX/NO2NI73+aVZwWxbwjPq8fCFYxKMDiNgzt/sU/x2w2p2rFUf2ryg83/qTW01lZAE35xsNTVq5//zRsQLg26HI3ttqWVKQxj1WHjnan3+OBzaCTf829/zdh5poe2X/w86XmjzySqjYKZ1/GrWWERS/C4iIiKxIysHNs07cVem/Zvh/QdtKdvgO8NfU7+b4MAWWP2R/+eaOQGSG0CfG/w/lwSr7VCIS7DlajWV+xTUawndRtf8WF6Ji7fd1hQaSTQZdj8Mucc6+D77TfjOu2Ml5D5ty6Nb9vX/fBf+yjpbJ99p89HOZP8WW0KdpflGkUrBkYiIiMSOzMFQehS2LLSvXRfe+a4t5xkTxiVqx+t0EaQ2hXkv+HuePeth6Vsw4GbvZ1tI5Empb9/va2oYHG1fBms/g4G3BjeXRaS2cBzbfKHvjfD5YzDjL/6f03Xh/R9DYioM/x//zwf2O+bKZ2H/Rpj64JnvX9EJrMHYEUvBkYiIiMSOik8zK+YcLXjFluFc8L/eblteFfGJNuto5Qf2qatfZj1r85QGfdu/c0hkyR4OWxfDwe3VP0bu05CQAv1v8a4ukVhWMUS66+XwwUP+z7hb+b4FyOf92GYQhUvmINu5beEr9qHF6RTk2s+Z5j3DU5tUmYIjERERiR1pTW0mSkEu7NsE7z9k29IHHab0Gw9uqb3A9kPhfpj7AnQbAw1a+XMOiTwdRtjlmk+q9/jDu2Hhq9Dzakht7F1dIrEuLh7GPmfh7tv3wtK3/TlPyVH7Pdeks3c7LFbFuQ/Y0rh3vmc7l57KhpnQqr/NL5OIpOBIREREYktWjr1Ifec+28579JPB77rUONtm0sx7yZ/ddua/ZFupD7nb+2NL5GreG+o2qf6co3kvQMkRyPmOt3WJiG1VP+5laDUA3rgV1nzq/TlmTIA9+TDqt8EsNY1PhCv/CsVH4K17bNncyYoOw9ZFtrRWIpaCIxEREYktmYPh8E4bRn3Bw8EtUTtZv/H2An/ddG+PW1oCM5+2zqpW/bw9tkS2uDjIPt86jqoaSJaWwKznLNBs1t2f+kRiXVIq3PAaNOkEE2+wnS+9sn8zTPs9dLnUOpuC0qQjXPgorP6P7fR4ss3zoKxEwVGEU3AkIiIisaVizlGbc2BgAK37p9L1MkhpAPNe9Pa4y9+BfRvUbRSrOlxgQenWRVV73PJ3bLCtuo1E/FWnEdw4Ceo1g3+OhW153hz3o4ctkLnwl94cryYG3gbZI+CDn8HOVSfeVjFzMHNQ+OuSSlNwJCIiIrElowtc9v/gqr8Fv0TteIl1oNc4WPa2zZbxyowJNtep8yjvjinRo6LToKq7q818Ghq2gU4jva9JRE5Urxl8801IrAsvXQG71tTseBtmwqJX4ax7Ib2dNzXWhOPA6AmQmAKT7oDS4mO3FcyyGUx104OrT84ogl4tiYiIiISB40D/m6Fe86Ar+bp+N0FpESx6zZvjFcyCjbMh5y4bxiqxJ62p7VRUlTlHm+dDwUwY/G1934iES6M2Fh6VFsNLY2ypWXWUlcLUB6BeSxj6A29rrIn6LeDSP9rStGm/s+vKymyziiwtU4t0Co5EREREIkXzHtCynw0lDjVEtKpmTLDlb32ur/mxJHplj7A3Z4X7K3f/3GcgMRX63OBvXSJyoqZd4MbXrev0pSuq1306/2XYstDmCiWlel9jTXQfA72vs9lLBbNh50oo3Kv5RlFAwZGIiIhIJOk3HrYvhU1za3acPett2Vv/myE5zZPSJEp1GGGzTiozeP3gdljyhoWNdRr6X5uInKhVf7huIuzOh5fHwtEDlX/skb3w8SOQNQR6jPWvxpoY9RjUbwWT77DB/QCZOcHWJGek4EhEREQkkvQYa3Mu5r1Qs+PkPgNOHAz6tjd1SfTKzLEOososV5vzvC2XHKzvG5HAtBsK17xgnUP/ug6KCyv3uM8fsy6lUY/ZsuxIlNIArnjKgrGPHoa6TSJnd1M5JQVHIiIiIpEkpT70uBIWv1G1T5qPV7jPdmfrfgU0aOVtfRJ9EpLsjeiZBmSXHIXZf4MO37AttEUkOJ1HwZinrFPw9VtOHCgdyvbl9oFB/5uhRe+wlFhtbc+xwd2lR22ZWqSGXPIVBUciIiIikabfTVB8CJZMqt7j570ERQdsKLYI2JyjPetOv1tT3mQ4tB1y7gxbWSJyGr3HwcW/hxXvwVt32zDpUFwX3n/QliUP/3l4a6yu4T+DbqOhr2apRQMFRyIiIiKRpvVAyOhiXUNVVVoCuU9Dm7OhVT/va5Po1GGEXVbMFDmZ68LMp6BJJwuZRCQyDLodzv8ZLHrVwqFQGycsfxfWfgbn/xRSG4e9xGpJSIZrXoQulwRdiVSCgiMRERGRSOM4NiR70xzYlle1xy57G/YVwJC7/alNolN6e2jU9tRzjgpyYcsCm22kZSMikWXY/TDkHpj1LHz66xNvKy6ED34CGV1hwK3B1Ce1noIjERERkUjU61qIT7JlZ1Ux8y/QqB10GulPXRKdHMc6ifKnQUnR12+f+ZQNre19XfhrE5HTcxy48JfQ90aY9jjMmHDsthl/hr3rYdRvIT4huBqlVlNwJCIiIhKJUhtDl0th0cTK76hTMAs2zrbZRnHx/tYn0afDCJudVTDzxOv3bYRl71iXW1JqMLWJyOk5Dlz2J+h6uXUYzXsJ9m2C6U9A18ug/XlBVyi1mIIjERERkUjVbzwc2QPLp1Tu/jOetK6RPtf7W5dEp7ZDIS7h68vVZv0VcGHQHYGUJSKVFBcPY5+D7OHwzn0w8Tpwy+DCXwVdmdRyCo5EREREIlW7c6FhG5j3wpnvu2eddY30v8V21hE5WUp92/p6zXHBUdFhmPsPG1DbMCuw0kSkkhKSYdzL0GoAbFkIZ38XGrUJuiqp5RQciYiIiESquDjo902bS7N77envm/sMOHHqGpHTyx4OWxfDwe329aJXoXAvDP5OsHWJSOUlpcINr8ElT8A53w+6GokBCo5EREREIlmfGywQOt2Q7MJ9MO9F6H4lNGgVvtok+nQYYZdrPrFtvXOfgeY9oc1ZwdYlIlVTpxEMvBUS6wRdicQABUciIiIikax+S+h4ISz4J5SWhL7PvBeh6CAMuSu8tUn0ad4b6jaxOUdrP4Mdy6zbyHGCrkxERCKUgiMRERGRSNdvPBzcBqs+/PptpSXWNdLmbGjZN/y1SXSJi7Plams+gZlPWYjUY2zQVYmISARTcCQiIiIS6TpeBGnNrbPoZMvehn0FMOTu8Ncl0anDCDi8E1Z9AAO+BYkpQVckIiIRTMGRiIiISKSLT4A+19sb/f2bj13vujDjSUhvD51GBlefRJfs4XYZl2AzUkRERE5DwZGIiIhINOh7I7hlNuuoQsEs2DQXcu6CuPjgapPoktbUwqN+46Fe86CrERGRCJcQdAEiIiIiUgmNs6HtUNtd7Zwf2qyaGU9CSkPrRhKpim9ODroCERGJEuo4EhEREYkW/W+Gveth3TTYnQ/Lp8CAWyApNejKREREpJZSx5GIiIhItOhyqXUYzX0B0pqBEweD7gi6KhEREanFFByJiIiIRIvEFOh9Lcx5HuKToPuVUL9l0FWJiIhILaalaiIiIiLRpN94KC2CooMw5K6gqxEREZFaTh1HIiIiItGkWXdocw4kJEHLvkFXIyIiIrWcgiMRERGRaPPNSYATdBUiIiISAxQciYiIiESbhOSgKxAREZEYoRlHIiIiIiIiIiISkoIjEREREREREREJScGRiIiIiIiIiIiEpOBIRERERERERERCUnAkIiIiIiIiIiIhKTgSEREREREREZGQFByJiIiIiIiIiEhICo5ERERERERERCQkBUciIiIiIiIiIhKSgiMREREREREREQlJwZGIiIiIiIiIiISk4EhEREREREREREJScCQiIiIiIiIiIiEpOBIRERERERERkZAUHImIiIiIiIiISEgKjkREREREREREJCQFRyIiIiIiIiIiEpKCIxERERERERERCUnBkYiIiIiIiIiIhKTgSEREREREREREQlJwJCIiIiIiIiIiISk4EhERERERERGRkBQciYiIiIiIiIhISAqOREREREREREQkJAVHIiIiIiIiIiISkoIjEREREREREREJScGRiIiIiIiIiIiEpOBIRERERERERERCUnAkIiIiIiIiIiIhKTgSEREREREREZGQFByJiIiIiIiIiEhICo5ERERERERERCQkBUciIiIiIiIiIhKSgiMREREREREREQlJwZGIiIiIiIiIiISk4EhEREREREREREJScCQiIiIiIiIiIiEpOBIRERERERERkZAUHImIiIiIiIiISEgKjkREREREREREJCQFRyIiIiIiIiIiEpKCIxERERERERERCUnBkYiIiIiIiIiIhKTgSEREREREREREQlJwJCIiIiIiIiIiISk4EhERERERERGRkBQciYiIiIiIiIhISI7rukHXUGmO4+wA1gddh0eaADuDLkJEqk3PYZHop+exSHTTc1gk+ul5HDnauK6bEeqGqAqOahPHcea4rjsg6DpEpHr0HBaJfnoei0Q3PYdFop+ex9FBS9VERERERERERCQkBUciIiIiIiIiIhKSgqPgPBt0ASJSI3oOi0Q/PY9FopuewyLRT8/jKKAZRyIiIiIiIiIiEpI6jkREREREREREJCQFR2HmOM5Ix3FWOI6z2nGcHwddj4icmeM4zzuOs91xnCXHXZfuOM5/HMdZVX7ZKMgaReTUHMfJdBznU8dxljqOk+c4znfLr9fzWCRKOI6T4jjOLMdxFpY/jx8pv76d4zi55a+tX3UcJynoWkXk1BzHiXccZ77jOFPKv9ZzOAooOAojx3HigQnAKKAbcJ3jON2CrUpEKuEfwMiTrvsx8LHruh2Bj8u/FpHIVAL80HXdbkAOcHf57189j0Wix1FguOu6vYE+wEjHcXKAx4A/uK7bAdgD3BpgjSJyZt8Flh33tZ7DUUDBUXgNAla7rrvWdd0iYCIwOuCaROQMXNedBuw+6erRwAvl//0CMCasRYlIpbmuu8V13Xnl/30Ae8HaCj2PRaKGaw6Wf5lY/scFhgOvl1+v57FIBHMcpzVwCfBc+dcOeg5HBQVH4dUKKDju643l14lI9Gnmuu6W8v/eCjQLshgRqRzHcdoCfYFc9DwWiSrlS1wWANuB/wBrgL2u6/7/9u5dtYowCsPwu0gUBZHgoQhsRQTBSmxFizQ2ElKJCAq5BQsRtBEEW4kXoJ0IAUVzAaawDGKRwtpCJFZiJ6ifxQy4kcF4gMwMvk81p2I1C2Z/8+/1f2kf8d1aGrYV4AbwrT0/iD08CgZHkvSP0mxP6RaV0sBV1T7gCXAtyafpe/axNHxJviY5DUxoVvKf7LkkSb+pqhaBD0le9V2L/txs3wX8Z94BR6bOJ+01SeOzVVXzSd5X1TzN109JA1VVu2hCo0dJnraX7WNphJJ8rKp14AwwV1Wz7YoF362l4ToLLFXVBWAPsB+4jz08Cq442lkbwIl2cvxu4DKw1nNNkv7OGrDcHi8Dz3usRdIvtDMUHgBvktybumUfSyNRVYeraq493gucp5lXtg5cbB+zj6WBSnIzySTJMZrfwS+SXMEeHoVqVmZrp7QJ6wowAzxMcrfnkiRto6oeAwvAIWALuA08A1aBo8Bb4FKSnwdoSxqAqjoHvAQ2+TFX4RbNnCP7WBqBqjpFMzh3hubj92qSO1V1nGbDmQPAa+Bqks/9VSppO1W1AFxPsmgPj4PBkSRJkiRJkjr5VzVJkiRJkiR1MjiSJEmSJElSJ4MjSZIkSZIkdTI4kiRJkiRJUieDI0mSJEmSJHUyOJIkSZIkSVIngyNJkiRJkiR1MjiSJEmSJElSp+/n06+boalJZgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x1440 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "S_KMCaeInjNn",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "b483a94f-a022-4b8a-ecc0-1085da2a7499"
      },
      "source": [
        "  # load the saved model\n",
        "model = load_model('best_model.h5')\n",
        "model.summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential_1\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "lstm_1 (LSTM)                (None, 100)               40800     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 100)               0         \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 1)                 101       \n",
            "=================================================================\n",
            "Total params: 40,901\n",
            "Trainable params: 40,901\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Hsk_n93VtEES"
      },
      "source": [
        "# make a prediction\n",
        "yhat = model.predict(test_X) #shape=(14,1)\n",
        "test_X = test_X.reshape((test_X.shape[0], test_X.shape[1]))\n",
        "# invert scaling for forecast\n",
        "#inv_yhat = concatenate((test_X[:, :-1],yhat), axis=1)\n",
        "inv_yhat = concatenate((test_X,yhat), axis=1)\n",
        "inv_yhat = scaler.inverse_transform(inv_yhat)\n",
        "inv_yhat = inv_yhat[:,-1]\n",
        "# invert scaling for actual\n",
        "test_y = test_y.reshape((len(test_y), 1))\n",
        "#inv_y = concatenate((test_X[:, :-1],test_y), axis=1)\n",
        "inv_y = concatenate((test_X,test_y), axis=1)\n",
        "inv_y = scaler.inverse_transform(inv_y)\n",
        "inv_y = inv_y[:,-1]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CejIp8s7rvO6"
      },
      "source": [
        "def timeseries_evaluation_metrics_func(y_true, y_pred):\n",
        "  def mean_absolute_percentage_error(y_true, y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return np.mean(np.abs((y_true - y_pred) / y_true)) * 100\n",
        "  def smape(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    return 2/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum((np.abs(y_true)+np.abs(y_pred)))*100\n",
        "  def mase(y_true,y_pred):\n",
        "    y_true, y_pred = np.array(y_true), np.array(y_pred)\n",
        "    naive_pred=y_true[1:]\n",
        "    return 1/y_true.shape[0]*np.sum(np.abs(y_true-y_pred))/np.sum(np.abs(y_true[:len(y_true)-1]-naive_pred))*(y_true.shape[0]-1)\n",
        "  #def owe(y_true,y_pred):\n",
        "   # return (mase(y_true,y_pred)+smape(y_true,y_pred))/2\n",
        "\n",
        "  print('Evaluation metric results:-')\n",
        "  print(f'MSE is : {metrics.mean_squared_error(y_true, y_pred)}')\n",
        "  print(f'MAE is : {metrics.mean_absolute_error(y_true, y_pred)}')\n",
        "  print(f'RMSE is : {np.sqrt(metrics.mean_squared_error(y_true, y_pred))}')\n",
        "  print(f'MAPE is : {mean_absolute_percentage_error(y_true, y_pred)} %')\n",
        "  #mape.append(mean_absolute_percentage_error(y_true, y_pred))\n",
        "  #rmse.append(np.sqrt(metrics.mean_squared_error(y_true, y_pred)))\n",
        "  print(f'R2 is : {metrics.r2_score(y_true, y_pred)}',end='\\n\\n')\n",
        "  print('SMAPE is : '+str(smape(y_true,y_pred))+'\\n')\n",
        "  print('MASE is : '+str(mase(y_true,y_pred)))\n",
        "  return"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U-yxUOktrvTD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "59de2c76-4aca-4730-ae3f-04603b745643"
      },
      "source": [
        "from sklearn import metrics\n",
        "import numpy as np\n",
        "\n",
        "timeseries_evaluation_metrics_func(inv_y, inv_yhat)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 10655.916648791006\n",
            "MAE is : 84.57849230085101\n",
            "RMSE is : 103.22749947950403\n",
            "MAPE is : 6.17754213551925 %\n",
            "R2 is : 0.7085492119592529\n",
            "\n",
            "SMAPE is : 0.8798685666175332\n",
            "\n",
            "MASE is : 0.6573457950843341\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "id": "TUIag9jQ7IzX",
        "outputId": "c80c9430-e26a-4b04-8397-af52a8f383fc"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[train_X.shape[0]:len(time)],inv_y,label='actual')\n",
        "plt.plot(time[train_X.shape[0]:len(time)],inv_yhat,label='Pred')\n",
        "plt.legend()\n",
        "#plt.savefig('xgboost_test')\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jrW7gDcmPPJ5"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "creouAuRJ2qH"
      },
      "source": [
        "# peak period"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Owa5qPllJ4_7",
        "outputId": "ff755fb4-5a8b-4986-aa31-1b708ddd738f"
      },
      "source": [
        "timeseries_evaluation_metrics_func(inv_y[:3], inv_yhat[:3])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Evaluation metric results:-\n",
            "MSE is : 5486.1595182088895\n",
            "MAE is : 70.78961753845215\n",
            "RMSE is : 74.06861358368259\n",
            "MAPE is : 4.787724573035385 %\n",
            "R2 is : -1.3036034181151441\n",
            "\n",
            "SMAPE is : 1.5955547026389183\n",
            "\n",
            "MASE is : 1.1700763229496223\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "smzoQvxrrvUh",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 413
        },
        "outputId": "7ce53204-d493-491b-8ef6-e8a186efc065"
      },
      "source": [
        "from matplotlib.pyplot import figure\n",
        "\n",
        "figure(figsize=(8, 6), dpi=80)\n",
        "plt.plot(time[train_X.shape[0]:train_X.shape[0]+3],inv_y[:3],label='actual')\n",
        "plt.plot(time[train_X.shape[0]:train_X.shape[0]+3],inv_yhat[:3],label='Pred')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}