stock_forcasting_by_implementing_LSTM_and_RNN.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "stock forcasting by implementing LSTM and RNN.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "metadata": {
        "id": "m9Q4XvnvOWHM"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "%matplotlib inline\n",
        "\n",
        "from matplotlib.pylab import rcParams\n",
        "rcParams['figure.figsize']=20,10\n",
        "from keras.models import Sequential\n",
        "from keras.layers import LSTM,Dropout,Dense\n",
        "\n",
        "from sklearn.preprocessing import MinMaxScaler"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": 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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": ""
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 73
        },
        "id": "p9I0rcY2Ui9z",
        "outputId": "20b62063-b7c3-49e8-caae-b962565eebab"
      },
      "source": [
        "from google.colab import files\n",
        "uploaded=files.upload()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-7d59211e-0a22-4b9b-8f5f-c8d8aed241dd\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-7d59211e-0a22-4b9b-8f5f-c8d8aed241dd\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ],
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Saving NSE-TATAGLOBAL.csv to NSE-TATAGLOBAL (1).csv\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 195
        },
        "id": "aXcq4iZYadZf",
        "outputId": "cde4c99c-7c52-470c-cab6-30c3cb5bccf5"
      },
      "source": [
        "import io\n",
        "df=pd.read_csv(io.BytesIO(uploaded['NSE-TATAGLOBAL.csv']))\n",
        "df.head()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Last</th>\n",
              "      <th>Close</th>\n",
              "      <th>Total Trade Quantity</th>\n",
              "      <th>Turnover (Lacs)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>04-01-2019</td>\n",
              "      <td>210.60</td>\n",
              "      <td>214.90</td>\n",
              "      <td>210.00</td>\n",
              "      <td>213.50</td>\n",
              "      <td>213.80</td>\n",
              "      <td>1213181</td>\n",
              "      <td>2583.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>03-01-2019</td>\n",
              "      <td>214.95</td>\n",
              "      <td>215.35</td>\n",
              "      <td>209.90</td>\n",
              "      <td>210.55</td>\n",
              "      <td>210.05</td>\n",
              "      <td>2096553</td>\n",
              "      <td>4433.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>02-01-2019</td>\n",
              "      <td>219.00</td>\n",
              "      <td>220.35</td>\n",
              "      <td>213.60</td>\n",
              "      <td>214.20</td>\n",
              "      <td>214.60</td>\n",
              "      <td>1081778</td>\n",
              "      <td>2340.31</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>01-01-2019</td>\n",
              "      <td>219.95</td>\n",
              "      <td>221.05</td>\n",
              "      <td>218.05</td>\n",
              "      <td>218.95</td>\n",
              "      <td>219.10</td>\n",
              "      <td>716275</td>\n",
              "      <td>1571.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>31-12-2018</td>\n",
              "      <td>222.00</td>\n",
              "      <td>223.65</td>\n",
              "      <td>218.85</td>\n",
              "      <td>219.55</td>\n",
              "      <td>219.40</td>\n",
              "      <td>965858</td>\n",
              "      <td>2131.09</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "         Date    Open    High  ...   Close  Total Trade Quantity  Turnover (Lacs)\n",
              "0  04-01-2019  210.60  214.90  ...  213.80               1213181          2583.05\n",
              "1  03-01-2019  214.95  215.35  ...  210.05               2096553          4433.50\n",
              "2  02-01-2019  219.00  220.35  ...  214.60               1081778          2340.31\n",
              "3  01-01-2019  219.95  221.05  ...  219.10                716275          1571.63\n",
              "4  31-12-2018  222.00  223.65  ...  219.40                965858          2131.09\n",
              "\n",
              "[5 rows x 8 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "TczD7vVAchK1"
      },
      "source": [
        "# **Analyze the closing prices from dataframe**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 500
        },
        "id": "7cbDOdfVctDX",
        "outputId": "ac0d0fb2-fa7a-42ae-fcfd-4b7de27a4e0d"
      },
      "source": [
        "df[\"Date\"]=pd.to_datetime(df.Date,format=\"%d-%m-%Y\")\n",
        "df.index=df['Date']\n",
        "plt.figure(figsize=(16,8))\n",
        "plt.plot(df[\"Close\"],label='close price history')"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff09a6d9a20>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1152x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CYnXmtxoktj8"
      },
      "source": [
        "# **Sort the dataset on date time and filter “Date” and “Close” columns**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 402
        },
        "id": "W06jL-EskyFa",
        "outputId": "ad761127-ec98-4a32-8e8c-38566bfef8b1"
      },
      "source": [
        "data=df.sort_index(ascending=True)\n",
        "\n",
        "new_Dataset=pd.DataFrame(index=range(0,len(df)),columns=['Date','Close'])\n",
        "for i in range(0,len(data)):\n",
        "  new_Dataset[\"Date\"][i]=data['Date'][i]\n",
        "  new_Dataset[\"Close\"][i]=data['Close'][i]\n",
        "new_Dataset"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Close</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2010-07-21 00:00:00</td>\n",
              "      <td>121.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2010-07-22 00:00:00</td>\n",
              "      <td>120.9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2010-07-23 00:00:00</td>\n",
              "      <td>120.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2010-07-26 00:00:00</td>\n",
              "      <td>117.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2010-07-27 00:00:00</td>\n",
              "      <td>118.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>...</th>\n",
              "      <td>...</td>\n",
              "      <td>...</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2095</th>\n",
              "      <td>2018-12-31 00:00:00</td>\n",
              "      <td>219.4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2096</th>\n",
              "      <td>2019-01-01 00:00:00</td>\n",
              "      <td>219.1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2097</th>\n",
              "      <td>2019-01-02 00:00:00</td>\n",
              "      <td>214.6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2098</th>\n",
              "      <td>2019-01-03 00:00:00</td>\n",
              "      <td>210.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2099</th>\n",
              "      <td>2019-01-04 00:00:00</td>\n",
              "      <td>213.8</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "<p>2100 rows × 2 columns</p>\n",
              "</div>"
            ],
            "text/plain": [
              "                     Date   Close\n",
              "0     2010-07-21 00:00:00  121.55\n",
              "1     2010-07-22 00:00:00   120.9\n",
              "2     2010-07-23 00:00:00  120.65\n",
              "3     2010-07-26 00:00:00   117.6\n",
              "4     2010-07-27 00:00:00  118.65\n",
              "...                   ...     ...\n",
              "2095  2018-12-31 00:00:00   219.4\n",
              "2096  2019-01-01 00:00:00   219.1\n",
              "2097  2019-01-02 00:00:00   214.6\n",
              "2098  2019-01-03 00:00:00  210.05\n",
              "2099  2019-01-04 00:00:00   213.8\n",
              "\n",
              "[2100 rows x 2 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "JrkaR6nZoIqU"
      },
      "source": [
        "# **Normalize the new filtered dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 284
        },
        "id": "OVl3Cq3woMp5",
        "outputId": "de5ef01f-a6e3-433d-9e8f-57bb846b29eb"
      },
      "source": [
        "\n",
        "df.describe()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Open</th>\n",
              "      <th>High</th>\n",
              "      <th>Low</th>\n",
              "      <th>Last</th>\n",
              "      <th>Close</th>\n",
              "      <th>Total Trade Quantity</th>\n",
              "      <th>Turnover (Lacs)</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2100.000000</td>\n",
              "      <td>2.100000e+03</td>\n",
              "      <td>2100.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>151.818810</td>\n",
              "      <td>154.145190</td>\n",
              "      <td>149.361619</td>\n",
              "      <td>151.584762</td>\n",
              "      <td>151.561595</td>\n",
              "      <td>2.327641e+06</td>\n",
              "      <td>3919.237300</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>49.350352</td>\n",
              "      <td>50.127368</td>\n",
              "      <td>48.597083</td>\n",
              "      <td>49.420366</td>\n",
              "      <td>49.401844</td>\n",
              "      <td>2.081348e+06</td>\n",
              "      <td>4547.900775</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>81.100000</td>\n",
              "      <td>82.800000</td>\n",
              "      <td>80.000000</td>\n",
              "      <td>81.000000</td>\n",
              "      <td>80.950000</td>\n",
              "      <td>3.961000e+04</td>\n",
              "      <td>37.040000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>120.750000</td>\n",
              "      <td>122.675000</td>\n",
              "      <td>119.000000</td>\n",
              "      <td>120.600000</td>\n",
              "      <td>120.550000</td>\n",
              "      <td>1.147086e+06</td>\n",
              "      <td>1462.815000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>142.250000</td>\n",
              "      <td>144.300000</td>\n",
              "      <td>140.425000</td>\n",
              "      <td>142.000000</td>\n",
              "      <td>141.950000</td>\n",
              "      <td>1.768579e+06</td>\n",
              "      <td>2552.165000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>159.000000</td>\n",
              "      <td>161.500000</td>\n",
              "      <td>156.825000</td>\n",
              "      <td>158.712500</td>\n",
              "      <td>158.762500</td>\n",
              "      <td>2.795726e+06</td>\n",
              "      <td>4548.082500</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>327.700000</td>\n",
              "      <td>328.750000</td>\n",
              "      <td>321.650000</td>\n",
              "      <td>325.950000</td>\n",
              "      <td>325.750000</td>\n",
              "      <td>2.919102e+07</td>\n",
              "      <td>55755.080000</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "              Open         High  ...  Total Trade Quantity  Turnover (Lacs)\n",
              "count  2100.000000  2100.000000  ...          2.100000e+03      2100.000000\n",
              "mean    151.818810   154.145190  ...          2.327641e+06      3919.237300\n",
              "std      49.350352    50.127368  ...          2.081348e+06      4547.900775\n",
              "min      81.100000    82.800000  ...          3.961000e+04        37.040000\n",
              "25%     120.750000   122.675000  ...          1.147086e+06      1462.815000\n",
              "50%     142.250000   144.300000  ...          1.768579e+06      2552.165000\n",
              "75%     159.000000   161.500000  ...          2.795726e+06      4548.082500\n",
              "max     327.700000   328.750000  ...          2.919102e+07     55755.080000\n",
              "\n",
              "[8 rows x 7 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qRjQrahDse4R"
      },
      "source": [
        "\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "\n",
        "final_Dataset=new_Dataset.values\n",
        "train_Data=final_Dataset[0:980,:]\n",
        "valid_Data=final_Dataset[980:,:]\n",
        "\n",
        "new_Dataset.index=new_Dataset.Date\n",
        "new_Dataset.drop(\"Date\",axis=1,inplace=True)\n",
        "final_Dataset=new_Dataset.values\n",
        "scaler=MinMaxScaler(feature_range=(0,1))\n",
        "scaled_Data=scaler.fit_transform(final_Dataset)\n",
        "\n",
        "x_Train_Data,y_Train_Data=[],[]\n",
        "\n",
        "for i in range(60,len(train_Data)):\n",
        "  x_Train_Data.append(scaled_Data[i-60:i,0])\n",
        "  y_Train_Data.append(scaled_Data[i,0])\n",
        "\n",
        "x_Train_Data,y_Train_Data=np.array(x_Train_Data),np.array(y_Train_Data)\n",
        "x_Train_Data=np.reshape(x_Train_Data,(x_Train_Data.shape[0],x_Train_Data.shape[1],1))"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "F49wSViiieXK"
      },
      "source": [
        "# **Build and train the LSTM model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "galJE89wLdNS",
        "outputId": "ff1c57b9-9950-4ae7-9999-bdabb9c31d8c"
      },
      "source": [
        "lstm_model=Sequential()\r\n",
        "lstm_model.add(LSTM(units=50,return_sequences=True,input_shape=(x_Train_Data.shape[1],1)))\r\n",
        "lstm_model.add(LSTM(units=50))\r\n",
        "lstm_model.add(Dense(1))\r\n",
        "\r\n",
        "inputs_data=new_Dataset[len(new_Dataset)-len(valid_Data)-60:].values\r\n",
        "inputs_data=inputs_data.reshape(-1,1)\r\n",
        "inputs_data=scaler.transform(inputs_data)\r\n",
        "\r\n",
        "lstm_model.compile(loss='mean_squared_error',optimizer='adam')\r\n",
        "lstm_model.fit(x_Train_Data,y_Train_Data,epochs=1,batch_size=1,verbose=2)\r\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "920/920 - 17s - loss: 9.9906e-04\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<tensorflow.python.keras.callbacks.History at 0x7ff0a1e19390>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 25
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "gVhyFWeGyNtD"
      },
      "source": [
        "# Take a sample of a dataset to make stock price predictions using the LSTM model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qC5A3VUtLjCE"
      },
      "source": [
        "x_test=[]\r\n",
        "for i in range(60,inputs_data.shape[0]):\r\n",
        "  x_test.append(inputs_data[i-60:i,0])\r\n",
        "\r\n",
        "x_test=np.array(x_test)\r\n",
        "\r\n",
        "x_test=np.reshape(x_test,(x_test.shape[0],x_test.shape[1],1))\r\n",
        "predicted_Closing_price=lstm_model.predict(x_test)\r\n",
        "predicted_Closing_price=scaler.inverse_transform(predicted_Closing_price)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ahJrIrP8yVdK"
      },
      "source": [
        "## **Save the LSTM model**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BX-oC1jTLwdA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "290b98ff-e067-4f3b-d4c2-cf7fe26d2927"
      },
      "source": [
        "lstm_model.save(\"saved_model.RS\")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: saved_model.RS/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "ZFx8VorUybV7"
      },
      "source": [
        "## **Visualize the predicted stock costs with actual stock costs**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 726
        },
        "id": "dWpiXUGWvvCW",
        "outputId": "e44b9dfe-8f76-4132-825b-5e8fceada338"
      },
      "source": [
        "train_Data=new_Dataset[:980]\r\n",
        "valid_Data=new_Dataset[980:]\r\n",
        "valid_Data['predictions']=predicted_Closing_price\r\n",
        "plt.plot(train_Data[\"Close\"])\r\n",
        "plt.plot(valid_Data[['Close',\"predictions\"]])"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/ipykernel_launcher.py:3: SettingWithCopyWarning: \n",
            "A value is trying to be set on a copy of a slice from a DataFrame.\n",
            "Try using .loc[row_indexer,col_indexer] = value instead\n",
            "\n",
            "See the caveats in the documentation: https://pandas.pydata.org/pandas-docs/stable/user_guide/indexing.html#returning-a-view-versus-a-copy\n",
            "  This is separate from the ipykernel package so we can avoid doing imports until\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<matplotlib.lines.Line2D at 0x7ff0a247b278>,\n",
              " <matplotlib.lines.Line2D at 0x7ff0a2a30be0>]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 28
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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KoUYVcgUOzZ2kr9nkM4eeAeDzUw9v/rO1YxkISZIkSZLa18N/mx4/9ANQL5/20XTffmAN284DhDkGgojJ6uR6V7i8ZhWiIofKJ7i83mBvs8k/m5zmUJSnMXty85+vHclASJIkSZLUvoIgPZbHoZ5VCOWKAMz07gPWGAgFIUUCanFtI1a5tDiG2ZPQqKUVQqWjXNZowL7ncGulQi0M+MRTf7t5z9eOZiAkSZIkSWpfrUAIoDGXHruHAZjpSLdtX2vLWJ6AWnMTA6FP/zy872oYe4RyLuJY+SSXH3w5/OjHeOlchZsrVX77/vfTjJubtwbtWAZCkiRJkqT2FSz6s7h+eiA0ne8A1hoI5dIKoc0MhB78VHpMmnwxmSUh4XnP+0no3U8AvKpU5mRlnLlW0CUtYiAkSZIkSWpjiyqEWlvOd49QB6bD9E/mtbWM5ShCGghNPA0f+/GFwGmjZC1tAH9XH2eoY4hb9906fy6fpDumbWrbmnYsAyFJkiRJUvtaXCFUSrdtH+vo5bsuu4RfKD8EwGDH4OrvG+bIE9BIGjT/7t/Ct/4cHr19I1a8IJt5VAO+UB/nVZe/ilyYSz97yb+mQBYIbWaVknYsAyFJkiRJUvtaPEOonG4R//muTmZy6Z/L1wxeQ3+xfw33DSmmeQy1Vkhzxrb261aZAuBYFFEl5saRGxc+23s9+ez59WZ9Y5+rXSHa7gVIkiRJkrRtFlcIlU8BcHLwEhiF97/if7Cv/+Da7hvmKGQva1GRTpgPcDZMdr+jURo4XdR90enPz1rG6rGBkM5mhZAkSZIkqY0tqhCaPQHAaG2KweIg33bpS7ms77I13jZHoVUhFGQvZo6tY51nqFegWYWrX82RfFrrcXHPxWc83xlCOjcrhCRJkiRJ7WtRhVB94inywFh1kqHOofXdd1GFTq0ynZ6bOb6+ey7Wqja6+tU81gmd5afOqBCKFoZKO0NIS7BCSJIkSZLUvrIZQh/u7eHW5EmORDmm66W1zQ067b7ptvMA1WoW3mxkhVA1DZkaHf3c3hjnxpGbCBe3v4XRQiBlIKQlGAhJkiRJktpYwJEox38e3kMcwJc6Oyk35ujOd6/vtmFIIYkBqFUm03OzJ9e51kWyCqEn4wqjc6O84ao3nPH8aKFlzZYxLcFASJIkSZLUvoKAr3V0zL99sFigtBGB0OIZPlk1D63WsY2QhUzfqo4CcMPQDad/HobzLWONuLH25zxwG5TG1v59XbAMhCRJkiRJ7SsIOB5FBEnC8yoVHs3nKTXKG1AhlKOYBTLVufH0XHUDdxnLdkT70uRD7O3ayxX9V5zx/IgC62wZq0zDn70N/vgH17NSXaAMhCRJkiRJ7StJOBblGGk2ubTe4ESUo1Qv0x2tNxBamOFTDwIIclCdgezcuk0dpg7cMXYv33ngOwmC4PTPN3Ko9OjD6/u+LkgGQpIkSZKk9hU3OR7l2B91M9RsMpbLUWlWNqZlLM4qhIIABg9CEkNt9tzfadTg7/7dylq0Jg9zT/8IpUaZl17y0iWf3wqE6nF9DT+AdL0Ajcravq8LmoGQJEmSJKl9xQ2O54vsz3Ux1IyJs0qbg/0H13ffMEchm91TAxi4ND1/vjlC934EvvJb8IX3LX//qSM82TsEwPVD1y/x/Ih8Voy05hlCrUBIu5KBkCRJkiSpPd3zp/CPv8+JXMD+A9/O/sZCcPLKy1+5vnsHIcVmFgiFAXQMpOer5wmExh9Pj52Dy99/6jBjHT0EBAx1Dp39eZgjYp1DpQ2EdjUDIUmSJElSe/qrn6IawFwQMDh8LS+dq3Bzpcr7vut95MP8+u4d5sjPVwgF0Hdxev58FUJPfC499uw9/72TBKaOMJbvYLBjcOm1hhFRq0IoWWMgFDfX9j3tCAZCkiRJkqT2dMkLmAnSP4t78j10JAl/fOwErzn4mvXfO8hRbKaze6rdQ3DDG/lMVyefP3bnuSt2xh5Nj8l5gpj39sOnfhZqs4zmwqWrgyCtEFrptvOH7kzve+L+08+fbx3a8QyEJEmSJEntKd9JKUz/LF73EOkzhTmKjXR3r9plL2QsSPjZfSO846E/4M8f+fOlv1Mvp8fmGQFOksAzd0Et+/yuDwAwToPhjuFzPD8iat12uaHSD34yPT7+2TOea8vYbmYgJEmSJElqT/Uys5c8D4DeQu/G3jvIkW+kQUyNhIfnTsx/dHT26NnXx4vClzO3ib//L+H3XwFf/Z3TTo815xjpGln6+aupEGptWX9mi5gtY7uagZAkSZIkqT3VyswUOoG0ZWxDhSFRs0YuSagkTQ5VTwGQJ2R8bvzs66tTC6/PDITGn0iPj31m/lQMjNWmGeo4R8tYkCPXut1yrV9Z29xZFUFWCO1q0fKXSJIkSZK0C9XLzOQiqG9OhVDQrNMbF5mOayTVSaIk4epCP+OVLBB6/HNpGPPI38ILfmLhu80zWryiYnqcPDR/6mSxi3pc50DPgaWfH0YEQES4ggqhLDo6syLIQGhXs0JIkiRJktR+KlNQmWIiTNul9nTs2dj7hzloVumLY6bjGifnRhmOE4aCiInKBBy7F/7oDfChH4A7fxtGHwHgnXuH+a4jH+OTj39y4V6tQGjq8PypJ/v2AXBF/xXneH5a/xEFKwmEsmjgc78Kf/fvFs4vDoT+8HXw6O3L/27tGAZCkiRJkqT2818ug7lTjGfjcwY6BqB7me3eVyNXgEYlC4SqHJo+xMVxSE+cMFufhae+dPr1J+8nAb7Q1cmpuMpnDi20h3FGoHMoivhsZxoSXT149dLPD9Oqn1UFQgD/+AeLnruoYujpL8Gf/ND576MdxUBIkiRJktS2Jgod9BX6yId5eOc34T2Hl//SSlx0CwB9zZiJxhwPTzzM9RTojWNma7Mw/Ux63Rv+Z3o88QDTYUgjG/A8W5tduFdrdzFgKgz4oQP7+UiuzDWD1zDYMbj081uBEOHyu4wtDoQGLl94bcvYruYMIUmSJElSe1m0o9fJKGK4M9u6vdC1cc+46rsBGG42uWPuOADX5/bweLPBDGWSqSMEQ1fBFd/Jn/d20zP+TS6NFv5En6nPLNxrUTh0Z0cHc2HIu4oHecNr3n/u58+3jAWrqxAaXBwIucvYbmaFkCRJkiSpvTSr8y+fqJ7iYN/BjX/G0FUA3FpZeNaNhT30NGo04gbVqUPQd4A4V+BXhod4d0eVJwp5AK4Nu0+vEKovVAgd2ncNAG96zW/SX+w/9/PXMkMIoGt44bUVQruagZAkSZIkqb3U59IDcHjmCFcOXLnxzwgC2Hs93zdb4jev+hE+8rqPcLBjmN56GhDNjj0Ce6/jSGVhC/o/7O8ll8D1ue50zlDLopaxw+UTDHcO0zV4jmHS889vtYwFK9h2ftHrxe1li2YINYFjuRzaPQyEJEmSJEntpZGGModf9Ys0kgZX9m9CIASQy5MHXj54AzcM3wAdffTU0jBqOq7A3us5PHdy/vLHCgUuSQIGkoCZ2tItY6MjV7Gva9/yz25VCLGClrHFw6MXb3mfJPMv/7C/j1dfdoDHJx9f/tnaEQyEJEmSJEntpVEB4MlGGrRsWiCUhTKtAc8U++itlQCYDUPYez2HZo8C8FMTUwDsS3L0xjH1uE611dq2qGVsLMovzDw677NDICBiBS1jWQh0R2cHH6wdXTifNGkAP7d3mN/YMwDAF498cflna0cwEJIkSZIktZcsEHqiNgnAwf6Dm/OcrG1r/tjRR28jDV9mw5Bk5Fpue/w2Lq/X+YnJKX5uao5fYYierGJnvkooC5EAxqqTDHUOrez5YW5lQ6XjOkQdvGvfPt7XPM7T00+n55OYY1HE7d0Lw7ZPlE+s7Nm64BkISZIkSZLaSxYIHapPMdI5Qne+e3Oes0SFUE+ctmHN9OzlrslHuG/8Pv7p1AwF4J/XQg7k++htpAHO/GDpLBA6HEWMVca5om+Z+UGLnh8B9WSZbeebDQjzzATp2o5mVUvETZ6J0rW/Y2KSS+p1jpeOrezZuuAZCEmSJEmS2ks2Q6gU1+kr9G3ec1pBUGsXr45+erMt72e7BvnwQx9msDjI989mFUBRB+Q7F6qI6qcHQp/qSSt1vueK71nh86MVzhCqk+QWtrw/VTmVvkhijkfp+dfOlrmy3lioHtKOZyAkSZIkSWov2S5j5aRBZ9S5ec9pBUFBto1XsY+eViAUBjw68Si37r+Vztbw5qgDCt301NMKpvmWsXoZBq/gb/Zdwbft/zb2d+9f2fPD3MoCoWadewuF+bcTlYn0RdJkOkx/Q1/c5KpajSenn6IeL1NxpB3BQEiSJEmS1F6yCqG5uE5XvmuZi9ehVSGUhUB09NGVJARJwkwAJ8snT98xLCpCvpOe2hmBUG2W5KpXcag+zS0jt6z8+UGOCM4OhO76ADz1pYX39Tn+uqtIPtt//lTlFDz5BfjQ69Ph10BPnHBNrU4jbvDU1FMrX4MuWAZCkiRJkqT2ks0Qmotrm1sh1JohlGTbuhd7CUnDleM0KTfKjHSNLFyf74R8N71ZhdBCy1iZcr5AM2mursUtaxlrLt5WHuCT74QPvG7hfXmM24sBLwv76Euy5379/QDMhAFdcUwOuLqWVgY9OvHoytegC5aBkCRJkiSpvbQCoWaNrmgTK4Rau4u1KnSKaZjTG8c8SRqu7O3aCz/w/6WfDx7MKoTSIGimNgONGsR1prNZPn3FVQZCCTSS87eM1ctjjAUJ1+S66UmgVC9B5yCQ7obWanO7ol4nCnI8MvHIytegC1a0/CWSJEmSJO0iWSBUblbpzG9mhVArEMoqdDrSMKcnjnk8Sdewr2sfXPE6SBJ4zg/C136P7mZ6/Wx9FurpQOnp7F6rqxA6R8vYGSbLp6AIg2GRnjgLojrSre1LYZgOws53k6+XONh9MY9PPr7yNeiCZSAkSZIkSWovrRlCzcomt4ydUSGUzSvqjWNKpFU3e7v2pkOnn//27JpuckB31JVuO18rAzCdDabe0EBo8hCMPsJkbQroZyDXQU+SMDtzDO7+IJC2jPXECXQPw2SJwaibmfrMytegC5YtY5IkSZKk9pLtMlZp1ujIdWzec+ZnCMWnvX91qTx/yVk7hmUVSz35rrRSp9aqEEo/XkvL2Gm7grUGXAP8/S/Cn/wTJuK0Wmkg6qQnjpmdOjR/yXzLWPcwAIUgpNasrXwNumAZCEmSJEmS2kujSkIalBRyhWUvX7OXvhtGroNnvSJ9n1X5/PDMLFclES+75GUUc8XTv1PoBqA313l6y1gWKq1+qHRyeoXQ4jDn5IMAPJJtOX95vp/BZpMT1EmyS2aDLBDqMhDabQyEJEmSJEntpVGhnlXrbGogtPfZ8DN3Qtee007ngD+vD/DrL//1s7/TqhDKdWQtY1kgRDpXaFWBUJBLh0qfFghVF15HRR7J5/mvQ4Nclu9nf76XG6s1TiV1nonSdreZMKRn4CDc/MMAFAmpLr6HdiwDIUmSJElSe2lUqWbBSz7Mb8sSolyRXGvG0GLZnKGeXDGd1ZPNEJpK6oRBSFd+FbuiZTOEmsmibecbC9U9ldIo796bDo/+Nxd/N+QK3DKXPu/uYlq5VAoDei/7Dth7PQAFAiuEdgkDIUmSJElSe2nMUYvSwOOslq2tksRLn28Nng7zaYVQq2UsrtFb6CUMVvFnfBgRJWe2jC1U93w5nubxQoHfPDHKS4duhFyBq6oVusnxzY4i5SCgEob05Dog+/cqBCG12EBoNzAQkiRJkiS1l0aVej4dJr2pLWPnc66dvwpZhVAQpTOEWi1jzerq2sUAwhz5MwOhbIe1agCf7SySTxJeXJ6DXAFyeXLAjRS5t1jko309ALxg6DkQpf9eRbBlbJcwEJIkSZIktZdGhWo+rXjZrpYxmvWlz2cVQkNhkcnqJJXb3gHAdGNuDYFQRC6JlwyE/uueQW7r7eG5tSYFgPIY5NJ/i6tKkzxYLPAH/X28eODZPPfaN8wHQoXElrHdwkBIkiRJktRe6hVqWWXQBVchlAVCz37yTuIk5pFCGtLMNMprCoQioJE0SJJs37CsuudTPeluZu+89kegZz9c/Zq0Sgi4fG4WgOlcjv/zxe9Nv9dqGUsSas3awv20YxkISZIkSZLaS0pmVv4AACAASURBVKNCLUqDlm2bIbRMy9j1Y08DcH+2Jfx0fYa+4upbxqI4nVXUSLLnNWpMhwGVIOCnJya56eB3w7sehv4D8xVC3zk3xyuGbuZ3Xvk73DB8Q/q97N+pmCQkZ25lrx3JQEiSJEmS1F4aVepZwFEIt7hC6LXvS4/LtIztbzYZbDZ5oJgFQrWZ1VcIBTmirJJnPsBpVrmnWCQJAp5fqcLilrmsQuhAo8lvvPK3ecmBlyz6LIIgRyG7n3OEdj4DIUmSJElSe2nMUc1FAORzWzxD6Oa3pscX/czSn2frCYDrqzUeKBRIgOnq9NpaxrLdzOYDoUaNbxaL5JKEG6u1NOiZv37Rv0XHEs+KOihkFUfuNLbzRctfIkmSJEnSLtKoUuvqhsY2zBAq9sB7p1Z06bW1Gl/t72M2CGgkjTW0jEXzFULNuJmea1SYyIX0xzGdSXJGhVD2Oupc+n5RkWKS3sfB0jufFUKSJEmSpPbSqFDLKmO2bYbQClxeb9AIAh7O2sZWXyEUkj9zhtCRr1EOQ7qy84SL6kRa4Vh4jtqRRRVCtoztfAZCkiRJkqT2Uq9QC3PANswQWoUr6+mcof+yZxBYSyC0RMvYl3+DUhDQHWe7hC1umcsGWsM5dhCLihRiK4R2CwMhSZIkSVJ7aSwEQls+Q2gl/u1RAG6u1vi/Tk1yKJ9W7Ax2DK7uPosCoXpch9IYAHNhSFeyRIVQx0B6PNfA66iDYjMNlgyEdj5nCEmSJEmS2kuzTi1M6yMuyJaxQjeQDpb+ialp3jA7y5fe8n6et/d5q7tPGJGLF1UIjT0AQCkM6G9mgdDiQKyjPz2ea0v5qEihmVYI2TK281khJEmSJElqL80atSB9eSG3jLWMNGPeePUbyWVVTSsW5IiyIdCNuAEnskAoCOmenyG0RCCUfecsUZFCtruYgdDOZ4WQJEmSJKm9xHVqpInQlu8ytlpDV8Hz/unavhvmiOIYyKWB0Mn7ASiHAV1Ja4bQolhguV3MoiLFxixEWQuadjQDIUmSJElS+0gSiBvzFUIX5AyhxX76ztPbulYjjMjHTSBPM2nCyQcBKAfhwlDpxRVC+Y70+PJ/v/T9og4K1VMQWSG0GxgISZIkSZLaRzYfpwaEQUgUXKB/Fv/Ul+HwOsIgyCqEFrWMzZwgoVUhtMQMIYD3Tp37flGRQqMORQOh3eAC/V++JEmSJEmbINsdq0Y6PygIgu1dz7nsf07633qEUdYylrV41WapXvf9NCvfpGupCqHlRB0UG2kQVD/XTmTaMRwqLUmSJElqH1mQUQuSC79dbL3CM4ZK18uU+g8ALBoqvYpYICpSaDhUercwEJIkSZIktY/5lrFkR+wwti5htNAy1qhCo0I5C8Hmh0qvRtRBsVEBDIR2AwMhSZIkSVL7aFUIJTHFXHGbF7PJghy5LABr1EsAlKM0EJqvEFqNqINCPWsZc5exHc9ASJIkSZLUPuZnCCUX/pbz67VohlCjlgZCpTAHQNdz3gy9F6/uflGRvBVCu4aBkCRJkiSpfSxqGdv9M4Qi8qStYa0KoVI2M6jn234cfv7B1d0v6iBImhRzRQOhXcBASJIkSZLUPrKWsWrSbIMZQiHd2W5is5UJAEpZCtCd7179/aK0xa4Q5t1lbBcwEJIkSZIktY9s9k29HWYIhRF7mulQ6bG5MQBKWcXQ2gKhDiANhKwQ2vkMhCRJkiRJ7aM1QyhptknLGAwWBxifGwdgdl2BUBqgFQ2EdgUDIUmSJElS+2hmM4TaoWUsSAdIDxUHGa1mLWNJWjHUFXWt/n7zFUKRLWO7gIGQJEmSJKl9xK1t55ttsMtYGggNdwwyVpsCoJQ06Iw6yWWfrcr8DKHICqFdYNlAKAiCS4Mg+FwQBA8EQXB/EATvzM6/NwiCZ4IguCf777WLvvMLQRA8FgTBw0EQvGYzf4AkSZIkSSs2fRRon23nAUaKexivzwJQSur05HvWdr+sQqgYRFRjA6GdLlrBNQ3g55Mk+UYQBL3AXUEQ3J599t+TJHnf4ouDILgeeAtwA3Ax8L+DILgmSbK6NEmSJEmStsvYIxDk0kBot7eMZYHQcLGfsUY6TrrUrK1tfhDMVwjlgxy1bBaTdq5lK4SSJDmWJMk3stczwIPAgfN85fXAR5IkqSZJ8iTwGPCCjVisJEmSJEnrMnsSuoepxfU2qBDKZggV+qkmTWbCkNnm3DoCoVaFkIHQbrCqGUJBEBwEngt8NTv1jiAI7g2C4P1BEAxm5w4Ahxd97QjnD5AkSZIkSdoa9TnIdzLXmGuLbecBhgv9AIx19FKul9ddIVQMQgOhXWDFgVAQBD3Ax4CfTZJkGvgd4FnALcAx4L+t5sFBEPxkEARfD4Lg66Ojo6v5qiRJkiRJa9OYYzYLhEa6RrZ7NZurNVS60AfAeLGT2frsuiuE8gQOld4FVhQIBUGQJw2D/iRJkr8ESJLkRJIkzSRJYuD3WWgLewa4dNHXL8nOnSZJkt9LkuTWJEluHRnZ5f9PKEmSJEm6MNTnOF5IW8X2d+3f5sVssta289kQ6fFCB5PVSXoLvWu7X6tlDCuEdoOV7DIWAP8LeDBJkv930fmLFl32RuC+7PVtwFuCICgGQXAFcDXwtY1bsiRJkiRJa1Sf43iUB2B/9y4PhLKWse4w/b2jUYGT5ZMc7Du4tvu1WsYIDIR2gZXsMvZi4G3At4IguCc792+BtwZBcAuQAE8B/xIgSZL7gyD4M+AB0h3KfsYdxiRJkiRJF4R6mROdeYjbJxDqygKh+6IAgGcNPGtt98sqhArP3EV1YHj969O2WjYQSpLkS0CwxEefPs93fg34tXWsS5IkSZKkjVef43h3niAO2mCGUBYIfeAH4IpL+VbYAOCqgavWdr+sQqhQn6PmDKEdb1W7jEmSJEmStKPVyxwnZqRzhHxWObNrhemf/HkSoiThMHWKuSIHeta4EXirQihJqMWLWsa++VF48gvrXa22mIGQJEmSJKl91Cscp77728VgvkIIoDNOALii/wpy2e5ja71fMUloJjGNBz4B9Tn4+E/CB79/3cvV1jIQkiRJkiS1j7jB8aTGvu59272SzbcoEOpKYoC1D5QGCNJpMh1ZuFT+i7fD7b+09vtpWxkISZIkSZLaR9xkPK4y3NkGQ5GDhUqgrizE2Yjfva+Z7ht1IhfBzLHzX1yfW/fztDkMhCRJkiRJbSNJmswlTbrz3du9lM23qEKoJ04rhIY6h9Z92/2NdDj1sSgH+c5zX/jQp+HX9sPxb637mdp4BkKSJEmSpLZRj5s0SOiKurZ7KZtv0ayg1h//Qx3rD4QubqQVQsejaH7Q9JK++jvpceLpdT9TG89ASJIkSZLUNuZIw4zO6DyVLbvFogqhK+p1AJ6959nrvu1ws0lEwNEoB8F5YoVj96bH3C7fzW2HMhCSJEmSJLWNMuksna58e1UI/etTk/z+gddx3dB1678tsI+IY1EEQUAdGAvPiBfiGCqT6WvnCF2QDIQkSZIkSe0hSZgL0kCo3SqE9rz83/PC73j3ht36oiTkeJQGTh/s7+Pll1/CE5NPLFww+hAACfB06eiGPVcbx0BIkiRJktQe4iblrMWpPWYILQRCfOfPQ9ee9d/zLX8KwEX1WlohdPhrfKUznSN0x9E7Fq6bfgaAv+3u4vse/B0+e+iz63+2NpSBkCRJkiSpPSRNymEAtEmFUNf6B0ifpaMfgP1zs5zM5WicuI98klZdfWts0W5itVkgDYQA/vqJv974tWhdDIQkSZIkSe0hblDKZt20xbbzHX0bf8+s6uiieoVmEDCay3Eiax377KHPMl2bTq+rlQH4x460eugrx75CI25s/Hq0ZgZCkiRJkqT2EDcpBWmFUFsMld4MuTQQam09/+meLp7K53lxI0elWeGTj38yva5WohwEzORCnlPYw0xthntO3rNdq9YSDIQkSZIkSbvfsXvhH/+AclYh1JPv2eYFbZG3fxJ+4nMbd7+sQujSelrt8+t7BmkEAW+r5bhh6Ab+4pG/IEkSqJcYz6X/1t/XcYAoiE6fMaRtZyAkSZIkSdr9fvc74TO/TCmbIdQWLWMAV7wUDjxv4+4X5gG4rNHgh6dnAPiRZiff0Uh487Vv5rHJx7j75N1QKzGeVRNdFodc2ncpj08+vnHr0LoZCEmSJEmS2sZsEBLQJkOlN8Oincv+/fgEtx96hvdEBwjiJt9z8hA9uQ5+797fI6mWuLsrrcI62Ii5sv9Knph64lx31TYwEJIkSZIktY1yGNAVFgiyWUJapVx02tv9zSZB5wDETbpu/yXecfIoXz76ZT4+9QC39XRxUxxxaW2OK/uv5MjMEepxfZsWrjMZCEmSJEmS2kYpDOkOC9u9jJ0rjM4+V+iGWgmAt07P8vx9z+eXqk/wWBTw+twgVKa4ov8KGkmDw9OHt3jBOhcDIUmSJEnS7hekW6OXgoDuXHGbF7ODLfVvl8vD3CkgDRl+6Ko3zn/0yo6LoTLFlf1XAizbNnZ4+jDHZo9t2HJ1bgZCkiRJkqTdL0qDjFIYGgitR7TEv10WtrW8IuznR6emedeBV7GncxjmJjnYfxCAh049dM5b15o13njbG3n1x17NXz76lxu5ai3BQEiSJEmStPvl0t2xDITWaalA6Iw2sq4PvZ5/c2qSt1/zZujoh8oU3Z/5Nb594Nn87r2/y9s+/TbmGnNn3ebhUw9TbVYB+KU7foknp57clJ+glIGQJEmSJGn3izoAKIUBXQZCa7f4327fc+Da10KYW/raYh90DkBchzv/B//p3s8AcM/oPTwxmbWO1Svwwe+Hw//Io5OPAvCh7/0QURDx8cc+vpm/pO0ZCEmSJEmSdr8syCgHIT05t5xfs3BRjPCvvgxv/dPzBEK9aYVQZm+zyR8fPQ7AeGU8PXn0bnjyC/DX/5pHn/4HOpKAm/fcwA3DN/DA2AOb9SuEgZAkSZIkqR0MX00CTOZCevNd272a3SWX7dqW74aLblk4f0YgBDDUbAIwPpcFQmMPp8euIR598jM8q1YhnDnG/u79nCifWPji2KNwn3OFNpKBkCRJkiRp9+vo41QYUgpDLu0Y3u7V7C5XvyY91kvQNbRwvtgLxTMDoRhYVCF0/D4AavU57s2H3FSpwdwE+7r2cbx0nCRJ0ut+61b4i3++qT+j3RgISZIkSZJ2t9t/Ce7/OE8U0sHSl4VWCG2oS25deF3oTo/57rSVLN9x2qWdSUIhSZiuTacnTqSB0DdH72EugBdVKlAaZbBjkEqzMj9kel4rINK6GQhJkiRJkna3L/86AF/p7CCXJNxy8BXbvKBdJgjgrR+Bn/yHhfaxi25Kj9HZ85p6E5ipzaThzon74dYf4yuXP49cknDrXAUmn6Y7nwZLpXoJ4njhy43qWffT2hgISZIkSZLawpf6Brl53/Ppu/SF272U3efa74WLn5sGPAAv+bn0mD8jELr6NfTGWSA0NwHVaRh6Fnd1dnFDtUZvksBf/zw9YRosleolqEzOf/3wxGPceezOrfhFu1603QuQJEmSJGmzjeVCHgxj3nnJd273Una31/wqPPw3cPWr0vdntIzRdzF9c/engVCr2ifq4HBtkpfU6/OXdc2lIVCpXoJqBYDJMOS1f/NWAO552z3kzrW7mVbECiFJkiRJ0q73aD6dH3TzyM3bvJJd4Af/AN7y4aU/u+qV8Lr/lraRwektY79wBMIcvXGcBkLZfKBKGDI6N8qBb38HfP9vANAzMwrAbH0WymMAvL+/b/5WT049ucE/qv0YCEmSJEmSdr1TubSaZKhzaJkrtayb3gTPft3Krl3cMlbshWBRINSoAXC0OQfAgaFr4ZYfhVyB7qlnACjXy1BKw6GHs6HgAEdLRzfgh7Q3AyFJkiRJ0q73x/29AAx1GAhtqTNnCIURvXG2y1hWIXSkMQvAJT2XQC6C4WvonzgEwER1AkpphdCThTw39z0LgFOVU1v0A3YvAyFJkiRJ0q5WCQLuKxYB6Cv0LXO1NlRr17GWMKS32WSmNkNSTwOho/U0ELq45+L0mr3XMTz6OABjc2NQGqMcBByLIp7XdwUAE5WJrVn/LmYgJEmSJEna1Vrzg15RDwlas220NYIA9t0Ir31f9j5Hb9ykHtepZkHQTJK2jg0UB9JrRq6lc+owvfleTpZPQnmMw/l0T6zri8Pkw3xaOaR1cZcxSZIkSdLulSRM5NJaiB+rGgZti3/1pYXXYY6+ZgOAmeokHUApbpAP8xRa1US9aaXQ3uJAGgiVJjlV7AZguNlksGPQCqENYIWQJEmSJGn3qpWYCtM/ffvjZJsXI4Ic+7Lt5Z/OBkPPxnW6890L1/TuA2BvvofR8mjaMtZ/CQDd1TJ7OvYYCG0AAyFJkiRJ0u5VnWE6qxDqSwyEtl0YcUs1bRH7xuSjAJTi6umBUM9+AEbCIifnTkJpjFJnPwDd1RkGigO2jG0AAyFJkiRJ0u5Vm2UqTLec77VCaPuFIf1xzFX9z+IbM08CMNus0pPvWbim9yIA9iYhY+VR4tEHKUXpUPCuuSlbxjaIgZAkSZIkafeam+D+YoED9QaRFULbL0jDuefvfS53zx6mAZSaZ1QIde2BMM/eZkwjaXIqDCmF6fyn7rkJW8Y2iIGQJEmSJGl3Ovkg5fe/mjs7Onh5uQxJvN0rUlatdeOeZ1OOaxyJImabc/QUFlUIBQH07GNvrQLAaJSjTEwO6CidYrA4yGx9lnqzvg0/YPcwEJIkSZIk7U6Pf5Y7OjuohQEvL8+BFULbL6sQGukYAmA8l6PUmDu9Qgigc4CRRrob2clcjlLSoCuICEqjDHYMAjDxn/bBJ96xdWvfZQyEJEmSJEm704Of4lvFAlGS8NxK1QqhC0EYATBUSIdEj+dCSo2502cIZdf1xen/vabDkFJcpzssQGmUwezaiVwId//R1q19lzEQkiRJkiTtTqcepxyG9MQxeTAQuhBkLWNDhT4gqxCql5YMhHqyIeDlMKScNOiOugC4NP2/Jo/k81u06N3JQEiSJEmStPskCZTHKQcBXfO7i9kytu2CNIYYLPQQEnAyylE5c6g0QC5PV9IEoBQGlJIG3dmcoWvikMHiAHd2dmzp0ncbAyFJkiRJ0u5TnYa4wVwY0tWqDLJCaPtlFUK5BAbCAo8V0u3kTxsqDRBGdMYxYZJQCsJ0hlBxIP1o6gjfvu9WvtLZYcS3DgZCkiRJkqTdpzyeHkauobP/stPOaRtlQ6VJmgwFBf6hK63yuXH4xtOvCyOCZoOuJKEUhpTiJt2dg2mF0dRhXrT3+YxGEU/koy3+AbuHgZAkSZIkafcpnwJgLpenq/8yCPPwInek2nbZUGniBnvDAgADxQFuGLrh7OviBl1hgVIYMF3ooLvQC30HYPIQ1/VfCcDvDfSf/Yzxx+GT74RmYzN/yY5nICRJkiRJ2n1aFUJJk858F/ziKLzm17Z5UWq1jBHHvDLaA8BQxxC51vmWXB7iOt1BxFOdvZysjHPt4LUwcBlMHuLSjmEAPt3TzURl4vTv/tEb4K4PwMRTm/tbdjgDIUmSJEnS7jMfCNXpjDohCLZ5QQKgMw2BePyzvDbs59YGvOfb33P2dWEO4iY9hNwTpZOCnr//+dB/KUwepifMs7+RVgDdcfSO076aTB7i3SNDfO/nfooHxx9c1fIenXiUqerU6n/XDmQgJEmSJEnafbKWsXKzRle2XbkuAAOXpse/+b/puv+v+MNKFy+86IVnXxfmoVmnK4stuvPdaYVQ58D8wPC/PXyUoUaTD9z/AWrNWvq9JGEsF/I3Pd0cKZ/gx//+x3li6okVLa1UL/GDt/0gP/2Zn96IX3rBMxCSJEmSJO0+5XEII+aaFbryBkIXjP5LFl4nMeQKS1+XzRDqJq3sumXkFqIwgqgjDYS++afkgPeOneKhUw/xW3f/Vvq9yac5mUvbz9597duoNqt89KGPrmhpXz/+dQDuHb13TT9tpzEQkiRJkiTtPuVxkq4hyvWyFUIXkkL36e9Lo0tfl8tD3KCYvb1u6Lr0RZTuSsYX/xsAL5ub49WXv5pPPP6J9PzTX2EsC4Ru7ruCF170Qj5/5PMkyfIb1D8y8cj86ziJV/RzdjIDIUmSJEnS7lMep9I5SEKSzhDShSluLn0+zEGzzrGkDsCV2a5i5DvOuvSmkZs4VTnF+Nw4PPQpnsrnAdhX6ONll76MZ2af4a8e+6uzh0+f4a6Td82/boc5QgZCkiRJkqTdp3yKctcggC1jF5rrX7/8NWFaIXQ9aUvZd1z8Hen56OxA6OaRmwH48tEvQ2WKv+/u4tpqjX1RDy+79GUUc0X+wx3/gZ/7h5875+Om7vhNvnrkyxzovhiAsbmxVf6oncdASJIkSZK0+1SmmOvoBbBl7ELz5g/Bj/zZ+a8JI4jr/FwywO3N/Qx1DqXnlwqEhm/iQM8BbnvsNo6MPcC9HUW+t1SCuM5w5zB/9n1/xuuufB1fP/F1SvXSko/7/B3/hUYAb7/oJdw0fJMtY5IkSZIk7UhxnXKY/slry9gF6FzDpOc/z0PcpBg32R8WF84vEQgFvzLI90R7+Prxr3F7Lt1t7JWlOWim29JfOXAlLz3wUgBOlE4s+bhvdHYy0GzyluOH+JPX/QnX7rl2DT9qZzEQkiRJkiTtPnGDcrpBlS1jF6LlAqEwgmY9nTEU5k4/v4SXPXU3TRI+2ttLlCRc0mjAxJPzn+/r3gfA8fLxs7/8yN9zMgzY32gSPHgbVHb//CAwEJIkSZIk7UZxg7nspS1jF6CoeP7Ps23niRunh0CVySUvv3H4JgaIeCYfcXGjQQ7g0++CRg0+8n+w75l0K/klK4Q+/CbGcjmGu0agUYHj31rbb9phDIQkSZIkSbtP3LRC6EK2kpaxpJkGQkHu/NcCub6L+LZoAICbK7WFD6YOw0OfYu9tPwssUSE0lwZMo7kce/suS8/V52gHBkKSJEmSpN0nblAmAZwhdEFatkIoC4Ea1dMrhJ73dui75Ozrm3X6spDpRXOLAp2//AkACsBQx9DZFUKTT9METkURw4PPSs8ZCEmSJEmStEPFTeaCNBCyZewCtOwMoXx6bFQgXBRdRAV428fT12/6AFyRDoumWeVf9V7H26ameU3ngYXrn7lr/uW+rhFOlM8IhCaeYiIXEpMw0rl34ZltwEBIkiRJknTBu/vk3bzlU29hfG58ZV9YPEPIlrELz0pmCMHZFUIAI9fAe6fghjfC2z8JI8+GRpV9+R7efWqSwg9/eMlb7sv3crx0RsvYxFOM5tJqpJHuLBCql1f7a3YkAyFJkiRJ0oWtfIpf/vuf5v7x+/nCkS+s7Dtxk/L/z959x8d1lnn//5zpGvVq2XJ3bMeJnd4dh0ASQjqEtpulL7CwQNhd2AfYXQJPQtiwy0MJ7A8WQgmEklBCeoB0x7ETxy2JW9xtybYkq46ml/P7454maWTJjjwj2d/368VrRmfOzNxSZKHz1XVfFykAfM7ho8qlxKxR4ghnukIoHspVC414rgeSMRMeldVCWU3B06ZY3uEVQt272FVRC8D0qtnp91SFkIiIiIiIiEjpPXMHyXAPAC8dfGlsz0klCJHC5/ThdIzelFiKzFNhbk99e+HHM//Nov1Q3nD413J5TRiUiIDLN3g72qyl2bvTbQeBWIBD4UO5x3t2s6WiFrfDzdz6ReZY4sToIeQa/RQRERERERGR0oknwuxzm8vXFw+8iG3bWJZ1+CelAyFtF5ugvBXw+e2moqeQ/Kqg8sbDv1Z0AFpXw6k3mnCorAY+9hQMdML8KyDYCd9ezHkp8z20cv9Krpt3nXlu71421/o4qWY27kxIpQohERERERERkdLbm4qStCzOKptGZ7iT1kDr6E9KJQjbKU0Ym8gqGsE5Qp1Kft+giqbDv07nZnO75RFTIQTQcjYsfJupNKpshpoZLBzopc5Xxwv7X8g+tSc+wIZUkNMaTzPNq53eE6ZCSIGQiIiIiIiITGi7UuYC/UK/mR7VE+0peF57sN1sB0qlAJuQnVQgNFk58yuERgmEMpJR8FYWfszfgCPSx4XTLuSF/S9g22YC3c98ECHFTSffZM5z+zR2XkRERERERGQi2JkMAnCq02zpCSWGT4Ha07+Hy39/OR96/EOQSpjz7IS2jE1W+X2fRushlK9iSuHj6T5DC2sX0h3pJpQIcTB4kF/7PVxdNoO5NXPNeZE+eOlHsH/90a99klAgJCIiIiIiIhOWbdusinXQEk/QEDO9XYLx4KBz+mP9fPrJTwMmGFrfsQ6AkJ3E71IgNCnl9xAabcvY9d8f/VyXDxIRqjxVAATC3Tyy8xGiDotP1Z7xBhc7OSkQEhERERERkQlrxf4VrE708b7+AOWhbgBC8cEVQn/a9id29+/mfy77H5r8Tdy++hskMBVC2jI2SeX3EBqtqfTCq3P3R6kQqvSYLWV9v3on6zvWMicWZ4avfvj5nvIjXPDko0BIREREREREJqZwLw9vuIv6ZJL39gfwBzqB4RVCWw+9RqOvgUumX8L/Off/sKV3G0+U+2mN9zOtYlopVi5vVKaHkK/ahDmHk9836HAVQskoVd50hVDfbjZ3vsqiWCzXiDrfCbDVUGPnRUREREREZGL64cW0+WPMs23cQHm4B7zDA6GNrz/EwkQcgEumXwLAE/4ywnaCJQ1Lir1qGQ+ZHkJjaSjt8mS3hI1YIeT0QNd2KlOmmfQel4v2aA+nRGOFAyfP8R8IqUJIREREREREJqa+fex3OZmaSEDNTHwpG5fDRX+sP3dKtI8dHhdnRaIAlLnKqPZU8tdyc0GvQGiSyvQQGm27WEa68mfEQKhtDQBVK74HwCMVZkvYmdHoCBVC2jImIiIiIiIiUhJxoNPppCWRgObTsFIJ6nx1dEe6s+e83vM6AKfEYtljM/xTSVkW1U4fMypnFHvZMh4yPYQquAfrnAAAIABJREFUxhgI+TKB0AgVRUkTGFbFzffJ6jIfM+JxlkRH2DLm8hzJaiclBUIiIiIiIiIyIR10ubAti6mJpBk9biep99UXDITmx+Lw27+Dzq28Y+blANQ4y7AsqyRrlzco00NoLFvGINdHaMTzzfdBhTMX/lw7EDJHR+tRdJxSICQiIiIiIiIT0n6X6SMz7eQbTMVIKkFdWR3d4XQg1LObbQfXUJ1M0phMwpaH4Zn/5EoqOCcc4fZZN5Rw9fKGZCuExhoIVZkG1O4C1T4AqQQAzrzKnwvCEcCChgW586aefhSLnZzUVFpEREREREQmpB6nCYTq/E0QCUMqQb2vnl29uyAZx/7u6axpmcopyQTZOqDa2VQ/8Gl+BrBsbolWLm9YJhAaaw+hiilQM3Pkx2ecB337wJmrBpr74b9Aw6LBFUIf+TPEw0ex4MlHFUIiIiIiIiIy8cSC9DvMJWu105euEEpS56ujK9KFHe5js8fNbo+bK4IheO895nmOvLoHV1kJFi7jomYGNC+BGeeP7fwrb899DxRy/ffNrcPBR/tDvK98HjXNZwzfLuYuA3/d0a15klGFkIiIiIiIiEw8BzbQmw2EvOAIZQOhaDJKKNLNoxXluGybK4JhmHKq2TYUyxtJP9L2IZn4fNXwiefHfv5oW8s8fqifD7Egn+3pg0VnvrH1HQdUISQiIiIiIiITT9ta+pwOylIpPDiyPYTqy+oB6A62s97r5Qx81Lzp36BuLnjKITaQew1VCEk+XxVE+kw/IYfqYxQIiYiIiIiIyMTTtoZeh4PqVMp8nGkq7TPbebrCHbS6XMxsWgJv+ldzjqd8SIWQAiHJU9EM/QcAW4EQCoRERERERERkItq/jv6KRmo8VXDOh8HhBGzqPLUA7O7fQ5fLyfSyvK1CnnKI5lUIKRCSfJXNprE0pL+fTmwKhERERERERGRiSUShZzd9vgqqm5aYfjLpC/g6bxUAD6z7XwDOrTs19zxvFUT7cx+71ENI8lQ257YUqkJIgZCIiIiIiIhMMH/5MmDTS4qqdACUuYCvd1fhweJln4e6ZJIl9YtyzyurhVB37mNVCEk+X3XuvtNdunVMEIrEREREREREZGLp2g5Ajx2nxltjjqUDIXcizG9a97OyzMfceBxnfuNofx0EO3MfDx0pLie2TLgIqhBCgZCIiIiIiIhMNNUtHKhqpjfWz7yaeeaYle75sv1JFsTjLIjH0yfbueeV1UE4r0LIU1GU5cok4csPhNRDSIGQiIiIiIiITCypJGu8ZkvP2VPONscyFR0P/KO5vf57JiSasjj3vMrm3P0rvw6WVYTFyqShCqFB1ENIREREREREJpZknHVui3J3OfNr5ptjQys6qlrgzL8bHPqcckPuvtNz7Ncpk4tPgVA+BUIiIiIiIiIysaQSrHVZnNF4Bs5MEDT0At5XM/x5lc0w62JzPxE9tmuUyUcVQoMoEBIREREREZEJxU7G2OuwmV87P3dwaIWQt7Lwk9/0r+a2rPbYLE4mr/wpYwqE1ENIREREREREJpZAKk7MgoayhtzBoRfwI42Un3sp3LweamYeq+XJZJUfImrsvAIhERERERERmVgOJcPAUQZCAHVzjsGqZNLLD4Fch/n+OUGMumXMsqwZlmU9bVnWJsuyNlqW9dn08TrLsv5qWda29G1t+rhlWdadlmVttyzrFcuyzjrWn4SIiIiIiIgcP7pSMWBoIDRky5jLV8QVyXHHre+fsfQQSgCfs237FOAC4FOWZZ0CfBF40rbt+cCT6Y8BrgLmp//3ceAH475qEREREREROW512QUCIad38EmHqxASGY0qhEYPhGzbPmDb9tr0/QCwGWgBbgDuTp92N/D29P0bgF/YxiqgxrKsqeO+chERERERETnu2LbNy3aBLWOVUwafOLRiSORIqELoyKaMWZY1GzgTeBGYYtv2gfRDB4HMv84WYF/e01rTx0RERERERERGZNs2317zbe51BAGo8uSNCa/SZaWMI1UIjb2ptGVZFcAfgH+ybbvfsqzsY7Zt25Zl2UfyxpZlfRyzpYyZM9X9XURERERE5EQWT8b53LOf4+l9TwPwYauO/OtOyhtLtDI5LqlCaGwVQpZluTFh0K9s2/5j+nB7ZitY+rYjfbwNmJH39OnpY4PYtv0j27bPsW37nMZG/cMWERERERE5kT3T+kw2DFoVquRf3EMqgrRFTMaTKoTGNGXMAn4CbLZt+1t5Dz0IfDB9/4PAA3nHP5CeNnYB0Je3tUxERERERERkmFX7VwHwn8v+k/JUUgGQHFuqEBpThdBS4P3AWyzLWp/+39XAHcAVlmVtAy5PfwzwKLAT2A78GPjH8V+2iIiIiIiIHC9C8RCP7HqE62oXc+1T34FEBJzu4Sf6qs3tO35U3AXK8eO8j5tblwKhUXsI2bb9PGCN8PBlBc63gU+9wXWJiIiIiIjICeLhnQ8TjAd5z+bnIDxgDs44f/iJ5U0Q6YP6ecVdoBw/3vYNuOJWVaBxhFPGRERERERERMbbAzseYEHtAk5P5M0qchSoEMo0lo6HirMwOf44HOBW/yBQICQiIiIiIiKl1L6JgwfWcoq7BstTnjvuLLChZeHbzK2vpjhrEzmOjXnsvIiIiIiIiMi4+8GFDMyaTsWh7WCncsc9FcPPvehmWHg1NMwv3vpEjlOqEBIREREREZHS6NlDEgg5HFQmk5CM5x7LrxbKsCyFQSLjRIGQiIiIiIiIlMaOJwk6zAyj8mQckrHcY4UqhERk3CgQEhERERERkdLY/iQDDnNZWpkYGgj5S7QokRODAiEREREREREpvr5W2PIwActcllb07Qc7mXs8ERvhiSIyHhQIiYiIiIiISPH96t0ABNMVQhV2euS8Iz37KNJXilWJnDAUCImIiIiIiEjxhboAGEj3EKqommGOL3mPuV3w1lKsSuSEobHzIiIiIiIiUnxOLwCBTIVQ9Sxo3wZNi+Crqg4SOdZUISQiIiIiIiLF5zT1CZktY5XeanM8ES3VikROKAqEREREREREpPjsFACBzNh5b5U5noiUakUiJxQFQiIiIiIiIlJ8KRMIhSwHlm1T5q0xxxUIiRSFAiEREREREREpPo8fgJhl4bVtLF+mQkhbxkSKQYGQiIiIiIiIFN+0MwGIpgMhFl1njp/23hIuSuTEoSljIiIiIiIiUnzJGNTNIzpjLt7ujVB/kqaLiRSRAiEREREREREpvmQcnB6iDSfhSQXBskq9IpETiraMiYiIiIiISPEl4+B0EUvG8Dq9pV6NyAlHgZCIiIiIiIgUXypdIZSM4nUpEBIpNgVCIiIiIiIiUnzJGDjcRBNRVQiJlIACIRERERERESm+ZAKcbqLJKB6np9SrETnhKBASERERERGR4kvGsoGQKoREik+BkIiIiIiIiBRfuodQOBGmzFVW6tWInHAUCImIiIiIiEjxJePgcNEV6aLOV1fq1YiccBQIiYiIiIiISPEl48QdLgKxgAIhkRJQICQiIiIiIiLFl4zRnb4iVSAkUnwKhERERERERKT4Ugm6LBuA+rL6Ei9G5MSjQEhERERERESKLxmjm3Qg5FMgJFJsCoRERERERESk+JJxuq0UoC1jIqWgQEhERERERESKLxmniwSgLWMipaBASERERERERIovFafbjuN1evG7/KVejcgJR4GQiIiIiIiIFJdtmx5CqTj1vnosyyr1ikROOAqEREREREREpLhSSQC67Jj6B4mUiAIhERERERERKa5kDIDuZJRaX22JFyNyYlIgJCIiIiIiIuPKtm3ag+0jn5CKAxAhid+t/kEipaBASERERERERMbV8rblXP77y/nqC18lZaeGn5A0gVDMTuF1eou8OhEBBUIiIhOabdu83h4o9TJEREREjsiGzg0A/GHbH/jDtj8MPyEbCCVxO9zFXJqIpCkQEhGZwO5ZtYe3fvs5Vu/uLvVSRERERMZsV98uZlfNpsnfxNr2tcNPSPcQitpJVQiJlIgCIRGRCWzd3l4A9naFSrwSERERkbFrD7bTXN7MgtoFbOvZNvyERBSAWCqhQEikRBQIiYhMYImUDYDLaZV4JSIiIiJj1x5qp8nfxPza+ezs20k83UQ6KxHGxlQIeZyekqxR5ESnQEhEZAJLpgMhh6VASERERCaHVKSfQ6EOmsoaObn2ZOKpOK93vz74pESUBGBjKxASKREFQiIiE1gmEHI6FAiJPLnnSb64/Iv0RftKvRQRETmM0GP/ShKb6oFDnD/1fABWHVg1+KR4mFj6D17aMiZSGq5SL0BEREaW2TKWCYZETmS3rrqV7kg3bYE2fnn1L0u9HBERGcHAwQ3gh0p3OfVl9TSXN/N6z/AKoWg6EFKFkEhpqEJIRGQCS6ZSAEQTqRKvRKS04qk4vdFeLCzWd65XlZCIyEQV7KK/ezsAlTgBWFy/mJX7Vw7+2Z1QhZBIqSkQEhGZwJLpwqBoIlnahYgUQTJl0xmIFnysM9hByk5x+azLAdjeu72YSxMRkbH6wYUEHOYys3L9bwH4+Gkfpy/Wxw83/DB3XjySDYTcDnfRlykiCoRERCa0TIVQTBVCcgK47eFNnHv7E/RH4sMeO/DcfwJwWt0pAHRHuou6NhERGaOB9lwg1LsPgEX1i3jrrLfy6K5Hse30X7sSkeyWMVUIiZSGAiERkQkskS4R0pYxORE8/MoBAELR4RVxB3b8BYCFcfNYb7S3eAsTEZEj0uY2W8WmJBPZYxdNu4juSDc7eneYA4mItoyJlJgCIRGRCSyV/itaNK5ASI5/mYq4pD28ifpBl9lOsMhdA6AeQiIiE5bFLrebymSKhmTu95dzm88F4OX2l82BvAohNZUWKQ0FQiIiE1gkHQRFDtNDqC8UJ6UpZHIcyEzTK7RF8gAJqpNJaiwnPqdPgZCIyESUDvR3Ns5jDi6svGMtFS3UemvZ3L3ZnBuPEDN5kAIhkRJRICQiMoGFYqbUOhRNFHx8f2+Y02/9Cz9dsauYyxI5JjKBUKEm6gesJFMTSUhEqPJU0R/rL/byRERkNIkoYLMzEWBu9dy8Y2BZFgvrFrKle0v6eJiY01R/asuYSGkoEBIRmcBCMXNhHBghEHqtzVRJfO2RzUVbk8ixkkiNsEWyZw9tLhfNiQQkIvjdfoLxYAlWKCIihxUP0eew6EqGmetrMMeeug3uugJiIRbVLWJbzzYGYgOQiBJNVwapQkikNBQIiYhMYJlAKDhCINTWG87eH2lct8hkkemZFUsODoQ2vHI3Oz1uzo5EIRGlwl3BQHygFEsUEZHDiYfZ5TZVP3NS6UvNld+H1pegYxPLpi8jnopz4W8u5P/1vULUZYIgVQiJlIYCIRGRCcq27eyWsYERAqEdnbmL4nhSjadl8npww37iycIVQj/a/Si1KZv3BAYgEaHcU04oHhr3NYQTYX644YeE4iH6on3s7ts97u8hInJcS0TodJoJY1N99YMfsyzObT6XWy68BYD7Im3E01vGPA5VCImUgqvUCxARkcIi8VT2AnmgwBhugO0duUAoHB+58bTIRHfPyj3Z+/k9hMKJMC/YA7zPOw2/vQ8SUcpd5XRHusd9DY/vepz/Wf8/BEKHWL3rz2yO9/LoOx5lRtWMcX8vEZHjUjxE2GFqDvzn/D3sXQP7VpnHYibIf/fc6wl1beebr/+adpcLSGrLmEiJqEJIRGSCCkTi2fsDeffzDQqEYgqEZPJK2jYNFeaCIH/K2KM7HyVhWZxbNg2cXkhEqPBUEIyNfw+h3mgvAKu3P8zmuLm/vnP9uL+PiMhxKx4mnB4lX+arhituzXssXdn5wKdpev5OAA66zOWotoyJlIYCIRGRCao/HQKVuZ0Ft4z1heMcGohlP1aF0MQXiSd5emtHqZcxISVTNmUes80gmg6ENnRu4KsrvwrAmeXTweUzFULucoKJcQ6Eunawdc/TAGxO5V57Q+eG8X0fEZHjWTyUC4RcZTD19NxjWx6BXc/BlkeoS5rfWXY6wGk58boUCImUggIhEZEJyLZtXk1PEJta4yNYYMtYpoLo1GlVgCqEJoM7HtvCh3+2mldb+0q9lAnHtm38brOTPROAnlJ/Cn8bd/OlQ91UeirB5YV42ARCsSB2ugn1eEj+/BpWtL+MyzKh1JJIlLfULeHx3Y8TS8ZGebaIiAAQCxF2mEDI5/SB2wefTQfra++G+z4I8SB16b6H6xxxZlfNxu1wl2rFIic0BUIiIhPQmj09/PO95heo6bV+BqIJUqncxW9/JM69q/cBcMmCRgD++89bi79QOSIbWs02pF1dGpk+VMqGaTU+qsvc2cDM7XDzb607uCkwAIkw+OsheIhydzkJO0E0OX6T9V6NddPrdPL1WW/nl/sP8ssD7by36Tz6on08te+pcXsfEZHjWjRA2HLgcbhxOkzAjrs897jDBP8tiQRlKRMKza+dX+xVikiaAiERkQkoMz3sHy6Zy0XzzJSOUN6WsM/+Zh3fe2o7ANVl5q9qmYoimbgyvXG2tQdKvJKJJ5mycToczGssp603bA7m9wmKhaBmBvTtpcJdATCuo+efr2vGadtctO9VzojGcAIXlM+g3F3O2va14/Y+IiLHtWg/YYdltotleCtz9+Pm57vftvmbfvMzvKGsoZgrFJE8CoRERCagg32m8uFf3rqAKp8JfAYiuT5Cq3bmJizNrvdn7yc0en7Csm2b3YdMwLH1oAKhoVK2jcMCn9uZmzLWtSN3gq8KamZB107KMdsRxnP0/G6Xk+mJBNW7V2SPOZIJpldMp22gbdzeR0TkuBYNELGGBEJuH3zqJVj2eYjl/v/vH3r7uMZVz7sXvrsECxURUCAkIjIhdQQi1PrdeF1Oyr2m5Dq/sXR+A+nGSi//9a7TANjXEy7uQmXMgrEkwXSfp2wFjGSlbBunw8LrchCJp3jklQOE6xblTlj2eTjr/ZAIU77pEWB8K4QO2HGaE0mzNS0jGaWlooW2wLEJhKLJ6Lj2QRIRKblogLDDiS8/EAJoXAiVzYMOlds2d1QsZm713CIuUETyKRASEZmAIvEUfo/ZZ1/pG9xoNzJkmliZ28VJTWYLzY6OwRfIr7X1cdfyncd6uTIGoVheoKcG4MOkbHBYFj63k1fb+vjUr9fy7Se3w/v/BJ9YYf7CPPV0WPzObBVPT6Rn3N7/gMNmWmLINL9ElJbKFvYH9497cLO8dTmX/PYSfrDhB+P6uiIiJRXtJ+x0D64QyvDXDT/mLnCeiBSNAiERkQkonkzhdpptMeXpYCiYDoSGbjcq8ziZ15AOhDoHB0If/OlLfO2RzfSF48d6yTKKUHpSnMflIBhLjHL2iSeVsnGkK4QyBqIJmPdmaF6cO7G8iRkxs6Vyb2DvuLx3LBmj0+lg6tBAKBmnpaKFcCLMofChcXkvgEQqweef/TyhRIj7tt43bq8rIlJy0QBhp6twIFRWO/yYxs2LlJQCIRGRCSiRSuFymh/RFekKoUC6h9C2IVVAZW4n1X43DRVetg95LPPcTfv7j/WSZQTBaILuYIxQuiqoodyTvS85+T2EMpoqC1woOF00xmOUucrY2z8+gVB7sB3AbBnLl4xyRuMZOCwHX1j+hSPqWdQaaCWRKhz8tQ20EUqEaC5vpifaQzypwFZEjhPRABGHk7JClT9lBSqECgVHIlI0CoRERCageNLG5TAVQhXewRVC/elqn/eeM4OrFjczpcpcNJ8xo5qHXtnPgb5cD5QpVT6AQcekuK65czln3fZX9nabMKGh0ksollTvmCGSto3DGlwhlEoV+Bo53FipOLOqZrGnf8+4vHf7tkcBmJLpye5JT8RJxDi14VS+fvHXWdO+hn988h9HDHnyPbHnCa7641XcsuKWgo/v7tsNwCUtl5CyU+PStLo70s1AbPx6KomIHJVowEwZcxYIenxV5jY9eh5QhZBIiSkQEhGZgOLJFJ70hXEmEMr0EMr0ornt7Yv5wfvOxrJMcPR3588iEk/xzNbO7OtkRtJ3BKJFW7sMtrvLBEGfuGcNAA0VXpIpm5gmwg2SSpkeQt68CqFgoUoqpxvsFDMrZ45bINT95FcAqHekL0w86cl9z3wdgGvmXsO/nfdvrGlfw7qOdYfdPtYX7eP/rvy/ADy086GCVUW7+3cDsGz6MuCNb32LJCJc88dreO/D731DryMi8oZF+wkPnTKWUTkNvFVw7bdzx9RDSKSkFAiJiExAibwKofIhgVAwlsTjdGQDo4wL5tYD0BOKZY9l+hB19CsQKrZnX+/kJ8/vGnY8E9JlegqJYae3jB3oi2SPhQr1Wkr/ZXlWRQttA23EU29wu5Vt0+M0IVSdlQ6jnJ5hp10x+woAPvLnj3D57y5nfcf6gi/3fNvz9EZ7+cjijwCwtWfrsHN29e2i1lvL4gbTG2lfYN8b+hRe2P8CA/EB9gb20h3pfkOvJSLyhkT6CVt24UDI7YMv7YPT/zZ3TBVCIiWlQEhEZAKKJ3M9hLwuB06HxUA0QXt/hN5QDL/XOew5ZR4nXpeD3lDuAjmWMFUoP12xi1sf2lScxQsA963ex51Pbht2fHqt+SU5FFcglC+ZHju/tyuYPRYsFJo5TaA2q6KFpJ1k/8D+N/bGffvoTv9bq0mmt6g5PTD/SnD5sqfV+ep436L3cX7z+TT6G/nKC18Zvn1s9wpWrPoWNd4a3jX/XQDs7B0+5W93/25mV82k3ldPmauM1kDrUS//W2u+xb8v/1L245X7V4588u7nIRbkBxt+wCW/vYS7Xr1rzO9j2zYHgwePep0icoKIBgjbNr68n5/DpH+Om/sKhERKSYGQiMgEFE+m8KQvUq10X5UfPLOD87/+JL95aV928thQdeUeeoK5CqFoIrct6acrdpHQNqWi6QpG6QvHmdtYnj123uw6Fkwx/WlCUU0ay5eyzff6bW9fzLvPns7pM2rY31ug95XDXEhMLWsAoCPUAZgmzr2R3iN/49bV9DicVCZTuO30vw+XF5pOhiF9nr5w3he468q7+OTpn2Rn385hW9ZS97yTF8IHuLDhdKZVTMPlcLEnMHxb2+4DLzN75wqs3r3MqJxx1FvGdvft5mev/YwFA93c2tnFFP8UHtv1WOGT+/fDz69h1/1/z49e+RE90R6+u/a7Y55ydvuLt3PF769gQ+eGo1qriJwYUtEAYZKUu8tHPxkGh0MiUnQKhERESmQgmiAyQpVIImXjSm/3AgY12gVoK3ShDNT4PYO2jMUSKWbW+bMft6uXUNH0BE2lVn7F1qFgFL/HVHdp0thgqZSN0wGnTa/hv999OhfNq2f9vt7sVsms9MVDhcP89TkQC3AofIgbH7yRa+6/hgMDB47sjfetpsfpoC6VhEwg5HSbyTfJqGluNMS8mnnmqUO2em11O+lyOVlaPgOnw8miukU8s+8ZIolItpfQwcB+uhwWC2Mx6N3DnOo5bO7aTDJ15N8PL+x/AYDbO7t4x0CQq+ZcxYr9K+iL9g0/ORak3enkY6GNVHmqeOQdj7C4fjG/f/33o77Pq52vcu/WewF4dt+zR7xOETlBJKKEUuZ3kDEHQo7Cf+ASkeJQICQiUiJL73iKt377uYKPmSljuR/RXpcJES6aV895c+q48cyWgs+r9bvpyQsgookUs+r9XDDXjHotWHEhx0R3OpjrDsY4d3YtADPr/PjT1V3BQv1xTmCp9JSxjGUnNZBI2aza0TX4xPTFQ2V6O8JAfIAVbSsIJ8L0x/r5wvIvjH2CW3QA1t1Dj8tNbTIF899qjju9uUanieH/ZmZVzgJgY9fGQccfrDDh60UpswXiA6d+gF19uzj3V+dywa8v4KN//ij3bfk1AOeEoxDu4bKZl9EZ7mRN+5qxrTltIDbALzb9gtlVs2lJmDDpwinnkUgl2NYzfKsits0DFeW0O+B/r/hfZlbN5Pyp57OtZ1vBxtf5Xu95PXtf28ZEJJKI8M3V3+RXm381+IHoAAPp310q3BVje7ECPdtEpHgUCImIlIBt2/SF4+ztDhUcrW2mjOUujn1u8+O63Ovi3o9fwLfee0bB160tH14h5HU5+NrblwAKhIrFtu1BW/diiRT3fvwCvvPeM7IVQmFVCA2STA0OhM6eXcuHl86mpXZIY9J0hVClw1xEBGIBdvTtwO1wc8uFt7CuYx1P7X1qbG96aCvEAnRXNlF70pUw7y3muCsvEIpHhj2txlfDhVMv5P5t92f7CK1qW8k9FT7e3R+gsc+Mkb9i5hUsrF2I1+nl2rnX8uLBF/nxpru5NBhiYTwO4R4unXEpfpefh3c+PNYvFQDffPmbHAge4Nalt5L5qjWkm7h2RXIhWttAG//89D+zoXsTW7weZqTg5LqTAbhw2oUk7AQrDxym71C4l/aeHVhYLK5fTHuo/YjWKSLHn++u/S53b7qbO166g+0923MPRPsIZgZiaMuYyKSgQEhEpMjW7e3hY794Oftxfp+fjEQyVbBCqNzjzI6ZL6TW7x4cRKTH10+rMdUUrT0KhIqhP5IgkRf0bWjt4/y59dT4PdlAqOBI9THafKCfvtAbnK41wdg2gwIhr8vJV647lUVTqwafmO4hVJ4XCO3s3cmsqlnceNKNVHurWd62/LDvlR0bn0wQB1qTIZorpuYuTJyeXEPp/AqhDffCqh8C8J6F76E91M6KthWk7BTffOkOpsfj/J/uXnj9z5BK4nQ4+cVVv+DhdzzM7Rffnn2Zm3vSW7rCPZS5yrhw2oWs61g35q9Vf6yfB7bfz3umX8aZtSdnjzc4vIM/P+DOtXfyxN4n+OmO+9nrcjEnmfu5ctaUs6j0VPL03qcLv9GeF+Abs2jf8Evqy+qZVjEt27NJRE5Ma9vXcs/me7hy9pVUuCv4r9X/lavKjAayFUJj3zKmQEiklBQIiYgU2W0Pb+KJzbmLqnCBPkLx5OAeQlvbAwAMjDKqvM7voTcc54fP7qAgfwTdAAAgAElEQVQ/EieWMM2p/R4XNX43B/oUCBVDfigHZBuEA/i9ZstT+Ci3jNm2zVXfXc77f/oiuw4FR+xDNdkk02PnR5UObdy2GWscaFvNzu6tzK2ei9Ph5JS6U9jUNfJEvd+//nvefN+bTe+cZIyXfV7CqRgXTbsod5K3Etzp3lvxvH8z938cHv8CAG+a/ibcDjdr2tfw2K7H2Nq/k0/19OE78/3Quwd2mV47fref5vJm0zDb2cI7+weYH0+Hef2m39G0imkcDB4c81a35a3LSdgprtn1MgRyW7hqbAun5aQrbCqE9gX28fjuxwFY07OV/S4XU/Pewu1ws6xlGc+1Pkc8VSBg3PBbANqTYaaUNdLkb6I91D72LXkictx5aOdDVLorufWiW/nMmZ9h5YGV/GXPX8yD0QBBK71lzDPWLWPqISRSSgqERESKrNI3+K9hoQLBQP6UsXyrdnYNO5bvnWdP5+KTGrjjsS2c9tW/sLc7xNQas42kzu8Z1OBYxl8imeLuF3az5WBg0PH8puDl6Qqh/b3DtyKNRSDdZPmV1j7e/M1n+NmK3Ue32AkmlR47P6pMFU8qTp27glf3Pce+0EEW1S8C4NSGU0fsi7P64GpuW3UbAL/Z8htIxXnOX4bHcnFe83kQTf9381aCO10hVKi/TrgXt9PNgtoF/Gzjz7h15a2c7Gvi6mAIzv6QOefgq8Oe9nb/TL7a1Z070GGCq2Z/M5FkpHAz6CE2dm3krld/zLREgiX7NphR8mmOWJA6X112y9g9m+7BaTn5zJmfoS8RJOB0MC2V/hr3tUIqxVVzrqIn2lO4WXR6O1y7y0mzu4op/imEE2EG4gOjrlNEjk+vHXqNJY1L8Lv9vHfhe2nyN/Hk3ifNg9FAdsuY3+U/zKvkUYWQSEkpEBIRKbJDA4MnfT2wfj/x9Dj4Wx54jT+ubR02ZSzjvDl1h33tWfXl/PzD5w06tqSlGoCqMjc9odhxU1FytF5t7eO3Lx3dmO/RvLS7m688uJFP3DO4QXAsmdsWWF3m5rTp1Ty55eh6sXQNDK4+Gvr9NFllxs6PKnPxkIzT4PCxzufDYdtc23IpAEunLSVpJ7n5qZtpDw7+Gj+19ym8Ti83n3kzr/e8zlOd61lZ5uPc2pPxu/15gVAV1JlJYtlgJxbMvVDnFgCumnMVYHryfK/hYvNL1ZQlUN4Eh9KNmP/nAnjg0+Z+Ii8ErJ5hAiHbpsnfZF423HnYT31T1yZueuQmdvfv4T8OdeMEeP7buRNCXTSUNXAofAjbtnlm1+MsqzqJD5zyAeaVTQFgWcKCrh3w7VNhxbdZ1rKM5vLmwuPnk3FsoN3posnhzq5T28ZEJr9ALMB31nyHX23+VeEKwQKC8SCv97zO4obFADgdTqZXTKczlP7ZlbdlbPQKofTPe/UQEikpBUIiIkUWiiW5anEzt1x7CgD//eet3LvajK/+xco9/Mt9G4jEk9m+Qfm++zeFm0nnczosvv6OJSxpqeaXf38ebz3FXAjW+N2s2N7FW775zPh9MpPMjs4Brvv+83zxj8OrN8ZDaMiWvhl1pjorPxCyLIszZ9Sw51DoqLbedA0JgJIFmpJPRpmx86PKbC9IJah3miqei8MRmtvWA3BO8znctvQ2Vrev5ro/XTeoP86O3h3MrZ7Lh079ECfXncxXdtzLDo+HBVVmahiVzeZ2yqnQeDLUzoYHPgW/fi/07MmtoWMzAO9b9D7uv/5+fnrlT2lOJMBdDi4PNCyAQ9vNyPrOzbDul+Z5ibwwb95bINQFwU5qfDUA9EZ7D/up/3XPX7GweOzyn7AsHDET17p35E4IdlJfVk9XuIvWQCv7o91csH0FPpeP3y36Bx7f18b8zp3w6OfN+btX4HQ4uWbONaw6sIp4cshFYSpBr6+KgNPBjM1/zgZCaiwtMvk9tusxfvLaT7jjpTv42qqv0R3pPuz5vZFerv7j1aTsFOf6Z2SPN/mbcmF2pI9QukJo1CljjvTvOBo7L1JSCoRERIosHEtS5XOzsLkyeyw1JBgIxZI0VHizH9eVmwa6Q7ebjeSm82fy0GcuZtn8Rlzpq+xdh0yFw/6+CL2h2OGeftx6duvhKzDeqEhicCD08GeWAaZhcr6Z9eUEogm6g0f+3+HQkAqh/LBpMhs6dn5EeRVCTtt87hdFYrDx/uwpN5x0A3+47g/Mq57HF5Z/ITuGfWfvduZWz8HtdPO1pV+jN2G2g033TzVPXPJu+ODDcMZN4HDAjXeZ468/Dr+4IbeGdIWQ0+HkpNqTcDqcEOkFn6nGo+EkUyEUytvimYhCMgot58AXdsPid5rj7Rup9dYCjLpl7Pm25zlrylk02+mv00lXDD4h2MnU8qnsDezlh6+Y5tdLwxFIJnCnktnx9OxIT2HzmKav0yunY2MPmk4GQCrOnmoTKM+KBJniN/ez1QAiMmmt7VhLY1kj1829jj9u+yOX/+5yPvjYB9nTv6fg+be8cAvdkW7+vreP83/zwezxJn8THaEO8weOvAohv3uULWOZIEgVQiIlpUBIRKTIQrEEZR5ndpQ8mLHkQ9WV535Jevpzl/LSv132ht73I0vnZO9v6zgxe4Ds6ynQD2YcReKD/ztW+Vx86KLZ3PcPFw46Prve/KK8p/vI19MVHFwhFI1P/kDItu2xbxlzpYPSZJTPVp/GWZEIV7Ysg7a1g047qfYkvnfZ97Btmx+/8mMGOrfQHu5kbq9pwrywbiHnVszCl0qxJD2GHcuCOcvMLcCMc+FjT8M5fw9zL4WzPgjNS7IVQsTD8OPLYOczEOnLC4QWQLgbVnwnt6DAARMKuXxQVmuqkAA6NlHtNc/rifaM+GknU0l29u5kcf1i6ExvRzvvo7kTPBUQ6mJR/SICsQAP7niQT/b0MTORgP62wc2xs88xgdCIW8GSCXal+1/Njido9JiJbweCB0Zcp4hMDtt7trOwbiG3X3w7v7/u99w4/0bWdqzlD9v+UPD8tR1reeesK/mnnj7yf1I3+ZtyvcWiAYIOF16nF/dovYEygZB6CImUlAIhEZEiC8eTlHmcg7aE9YeH79+38n7lqva7aaryvaH3/eBFs3n0ZlOxcihwfPSdOVJbDpgeMYUado+HTH+m6bVmq5hlWXz1+lOH9X6alQ6E9nYdRSCUrhCaVe9nTkP5hKkQiidT3L+ulb/90apRz00kU2zNa7ydqaByjiUQyvzVORZilu3g7vZuGmrmQujQsFKshrIGrppzFY/tfox/XPElABa1b8s+/pO5f8sLe1pZVLtg5PdrOQuu/Ra888dw/Z3QfFq2QojuXdD2MvzyRoj0g88EJlRPN7crv597nf5MIGSq/ShvAKcXBtqp8aa3jEVG3jK2P7ifWCrG7OrZ0LERnB6Ycymc/0l4zy9MuBMLctnMy5hfO5+PLP4In+xNVxz17DYBFcDf/GbY17KhrAEo0MMolWCPA1w4mJZI4Iv0M696HmvaB/fIEpmsArEAf9r+JxKpo5v6OFklIn3s7NnG/Kq5WJbFwrqF/McF/8FJNSexs3fnsPN7Ij30RfuY66rKHew1vfgayxqBdOVgNEDQ7RnbyHkr/TuQKoRESkqBkIhIEcWTKeJJG7/byeKWan7+4XMp9zjpj5hfRp0Oi7+/eA6fu2IBN5w5bdzfv6HCXIweL42Ix2J7RwDbtrFtmy0H+wEz4vxYyARC9//jUl756ltHPG96rR/Lgj1HFQhF8bkdPPjpi/G6HETjSTbu7+OWB14rWGlWDO39Eeb/+2P8870bWLmza9TG5X9Y28qV33mOp9KNtTP/PcY0dj5d1UI8ZBo9e8qhogmSMYj2Dzv9i+d9kbOazmJd33aqk0nOzKuoslIJ3HBkFySNJ8NAO4S6TQgFYCehdTX4683Hcy8d/rzOLSYQcua2gmZCHJ/LR5mr7LAVQrtfNUHO7MpZ0L8fKqeafkpX3QGn3GDCnXiIOm8tf3x9I/8cdeUi5d49MNBhmmWffPXg9ydXITRsK1gqzmuOJHP9U3ABDLSztGUpa9rXFJziJjKZ7O7bzfsffT9fXvFlHtrxUKmXU1Q7n7mVOClOGhi8TfT0xtN58cCLHAofGnR8d/9uAGY787aB7TD92Rr9JhDqCHdgRwdY53Ezo3IGo1IPIZEJQYGQiEgRhdMXymXp0eOXLmyittxDbyiGbdskUzYVXhefuWx+wabSb1SmF9HQPjTHq1dae7n8W8/xk+d30d4fpScUp9bvJpmyj6qh82ii6UCmqsxF1WH6PfncTpqrfOzpDo54zki6Q3Gaq3xUl7nxuhw8t62Ta+58nl+s3MPr7YHRX+AYONAXGfRxR//hA8fM998PnzV/ic700HKMJRHKVggNQHQAPJVQbi5I6Gsddrrf7efuq+7mdws+ym/3H8Sf3zg5mf534PSM/r4ZTWa8PZ1bTMiSEQ/BqTea+75qWHR9+gELXGXw8D9B+6u5CiEw27zS08tqvbWH7SG0Y9WdAMwuazQXYhVTBp/gKYdYOiQbOAh/+Y/cYz17TIiVec4Vt5nbru2w5ufUemtxWI5hFUKRVJx1VozzG9PN7PtaubjlYuKpOKsPrj7sl0lkovvi8i9mG6Rv6tpU4tWMr8y/0S8t/xKff/bz3L/tflLpnmvPtT7Hh9qfwmXbnOFtHPS8Dy/+MPFUnJ++9tNBx3f37QZgjp0X3jx0M2y4d1CgvDXWxTaXxbVzrx19kQ5VCIlMBAqERESKKBwbHAgBzKj1s6c7RCI9LcpdYNz8eHE5HdT63SdMhdD+XhNUrNrZTVuvqWiY02CqIsZ7OFc0keS7T5rtSGPZkjajzk9rd4G+LqPoD8epLjO/QO86FBzUt6j1GPdIGsnQSWcdgcgIZxrRdDD60q5u7lq+k1T6UxhTU2lPbssYsQHwVsDMC8yxLY+M+LSTcTE9kTRVOhmZbSJH8hfqxnS/oc4tEDg4+LHMOsBsCQMTwrztP3PHXXlbPz3+bIhV7a2mJ1K4Qigc7uVX1ZUsisao3feyqUxKb9fIcvshHsxtDcs/3r0D9r6YC4SW3mzub30UHvoszoEO6n31wyqE1qVCxCy4YMal5kB/G2dPORsLi9e6Xiu4VpHJ4GDwIBu7NvIPp/0Di+oWsW9gX6mXNK5+9MqP+MifP8LDOx/mmX3PcMsLt/DgjgexbZs7XrqDOsvJvW0HmZkc/LN7VtUsLm65mOWtywcdbxtow2E5mJpIB+p1c83tyz/NbhlrDx7k0faXcNnwttlvG32RmS1jx6hiV0TGRoGQiEgRtfaYAKDck7sAndtYzs7OIIn0L2ZOx7H90dxQ4c32oTneeVwmYHhic3u2+XK513ztx3tc+183tWe3bI2lOXJjhZdDwSMP5vrCcarSgVBmq+F/v+s0APYeRZPq8RAf0seofZQKoZ5QHJ/bwZyGcv60vi1bITSm1k7u/C1jA6YypnY2eKvNNq6M8JBwJTPxK3gIMlVCmdsj+Qt11TTAMmFQ52bwN8BV/w0X3ZzrHQTmOJjtbOd8GN787+ZjK6/yr3ML7F4Oe16g1ldbcOz8uo51nHffMg66XHyhqwfrkc+ZB/JDJjDhUiw0+GvgcMP0c80EtsB+iOVVkLnytq4dfJVpFdNoHRhcYfUiYVzAOTPeZKqc+trwOD2HDa9EJoMXD7wIwLnN5zK1fCrtwfYSr2j82LbNIzsf4ayms/jZlT9j1U2raKlo4ck9T7KpexP7Avv4UMLHgngcuof3C5pTPYf9A/uzFUUAncGD1HtqcMXDJkB/3x/NA5aF3+2n0l3Jzp1/Yb3Pw5JohBpfzegLnX2xuXWXjcenLSJHSYGQiEgRPPbqAU655XHe+YMXAKj05QKheY0V9IXj2aoK15gaqRy9+grPCVMhlD+B6+U95gI2E8aNZyC0+1CQn63YfUTPqSv3HNXY+f5ILhDKeOdZ06kv97Cj48i3oI2HTCD0w/edDZieQofTG44ztbqM+U0VJJJ2bsvYWCqEnOl/O0/fnt4yVmE+9laYaV/bn4BVP4BvzB5cRZMJhOJB+PZiSCXhr182x45kyo3DaaaEhbqgfaOZFnb+x+Gtt+Wmk0Gun1DmYsebbsaaKPC12fooNd6agoHQIztN1dO1A0HOjkZNBVB5Eyy+cfCJ7nITkuVXCKXicNLluY+Def1C8iuV+luZXTWbPX2Dx02/ZEVZYpXh95RDdQv0m8BopLWKTBYP7niQ6RXTOaX+FKq8VWZC1nHAtm1+u/W37Avs44aTbuCc5nNwOVyc13wez7Q+wy0rbqHSU8kV3ekA7MArw16jpaKFWCo2qI9Qx74VNPYfMM3xXWVQNwdOebsJ2IGr517NQ31bWOfzcUp0+JCMgm74H/jE8+CvG/1cETlmFAiJiBTBHY9vIRTLNdqtzOsvM7fRVDxkpi65juGWMUhXCB1FEDEZBfO+5t/6qxnV7feaCo3xbCx99Z3LWbOnh3KPk1VfumxMz6kr99AbipM4wilh/eHEsP5EDofF/CkVvN5Rmh5CmUBoSpUXt9OiY5Qpdr2hGNVlbtwuB/FkKrtlbExj5/PFgnmBUKWp2LnnnfDXr5hj6aangLlwKW80I+EHDpox8RlH2sPCX2968nRshimLRz4HcoFQpndQofdKxk2F0JApY7Zt89qh1zjNVc3XO/PCnKoCDec9fvP16M3b+nLFbdAwP/dx7ezc/fwKoWAXs6tn0xHuyDaLHogNsMlKcq6jMv2eLdDXBigQksltY9dGXjr4EjdOOR/Lsqj0VBKIleZn53i746U7+PqLX2dpy1Kun3d99vhVc64CYF9gH/9+1ueozvQ/G2iHNXfDX28xP88i/UyvNJWOrYFWs8X2f99ER2A/TYkkHNiQ+5nmr88G7f901j9l3+uU2Bh/v3D7oHnJG/yMReSNUiAkIlIEQ8dp51cITUmPk89cRB/rCqGGCi+HAtFRJ0EdDzI9dTJj3uHYVAhlwr6Z9eU0V/tGOduo9ZtgoC88xr+mYqbDdQWjTEu/x9Ofv5QHPrUUgAVTKtnePnBMmmXna+0J8YNndmDbNp/97Tpuf2QTsYR5T4/LQVOlj45RKoR6QjFq/W48TgfxvAqhMWehLeeY21jAVAYBxPP6MSXTgdTz34a7roCfvBWCnab/z8X/Yh7rb8ud7zjCBu7lDbDlUVPtM/3swuek0v9dM8FQZitXeV4T1w88YG4j/dR4awjEA8RTue+HF/a/wMaujVxtVWA1LMxV+9QUmODjqYBoAA6+ahpt39JjegVltq4BvCuvUez5nzTbyQBCh5hVNQuAPf2mSmhdxzqSFpznrDbnVM/Ifs1qfDXDwiuRyeJ7675HjcPL3z75Hdj2BJWWm2A8OOlHz/dF+7hv631cP+96vv+W7+PK64124bQLeeCGB1jxNyu4piodEtfMMj8zHroZVnwX/r8L4MdvpqWiBTB9g+hrhQPr6XQ5aUwmoWOjCXIAymog0gu2TYWngl/OvJE3hcIsDR15bzwRKR0FQiIiReB0jBwIZQKKTDBw7HsIeQhEE5z85cfZexRjzyeT7zxhmjxnQhPIVQilxrurNFBXPvZKE096ilziCNbx1JYObBvefLKZ6jKnoZzTZ5heDfObKghEE8Mmfo23rzywkW88voUNrX08sH4/P16+K1sh5HE6aKz0jloh1BOMU+v34HJYxJOp7HQ215iaCAHz3mJu87eM9e4Zfl7PLmh9Cfa9CPvXm79Gl6V7W7S/galC/nozat5dDgtGaJ465xKzFe3CT5uPF10PWHDWB3LnzL0Umk6FSB+13loADoVy2zTuevUuWipaeHfUMkHSu++Gxe+Ct90x/P3Kas3FWeCACYwyP0fyt2NU5k0mO/Pv4KNPmKqh4CEW1i4E4NnWZwFYfXA1bhtOc6W3ulW3mL5JyTi13lp6ouohJJNPR6iDFW0ruMnZQIVtw6/eSdVz3wJMVdxktqFzAwk7wdtPevugMChjbs1c3KkE/PHj5sDU0yAxJLzp2s60ClOBuH9gP4R7iQE9znQgBLlJj55ysFPZRv1nVM7i++2d1KeOrOpVREpLgZCISBEMDYQqvLlf1jIBRX/EBELHestYXXluq0ipmhAXW3Vez51MAHckQcxY1frHPr48Uwl2JOt4cnM7U6t9nDqtathj86eYrT3bOo7tRU2mKffmA/3ZY5kAyO10MKXKe9geQuv39dLWG6YjEE1vGbPZ3WV6H82s84/4vEEy26/C3eBLfy3KB49PZs6bBn+cjJoKm0yz080PmtuPPc0Ry/QDWnSduSgqpHo63HIIWs4yHzcugK/2QuPCwef5qiDazznNpurpgR2maiiWjLGhcwNXzLwcT7jXBFneCnjXTwY3r84oqzVT0/r359YHuWlnI/FVQzTAzKqZXDr9Un656Zfs7d/Ls63PsiQBZZleQ1UtgA2BA9R4a+iL9o1ajXasq9VExmzFnbDnBVa0rQDgLdFcNVBV0tyf7CHnq4dexcLi1PpTIdxreqj9v0Vmm1fGSz82W2sBmk8v+DpenFR6Kk0PoUgPbW7zM785kQ6EMtWYmQb/sXTvOv17F5mUFAiJiBRBfiB07WlTqckLDjIBRX+6QuhYbxnLD0cGxtr8cRKybRuXw+KTl84b1JvG70lXCB2DX17PnFk75nMz3xPJ5NjXsXp3D8vmNxTstbMgEwi1H9teGHXl5ns3PxC67WFTbeN2OWiu8rGjc4B//d2Ggs9fsd1UwEyvLUtvGUux65C5oJjTMEK4MpQzr/9NJgj6+DPwoUfN/arpcOGnBj/HVQazluYqZrY8bCZ+NZ0ytvfMl2ncnN+w+Wj5aiDYybyaeSxrWcavN/+aaDLKpq5NxFNxTl/za+jYNLgJdCGZyqfePbmQDEzgczhuv2lGDXzijE/QH+vnmvuvYW//Xt4TSZqJQpAbWT/QSY2vhmgySnhodUGeZCrJR//yUW5deauCISm9v34ZfnYVz7c9T2MiwYI9q7MPTU1XvhwIHijV6sbF9p7tzKyaid/pg2/Mgu8sMdMFn/2v3En5Te0rmwu/ULCDxrJGEwiFe1ntMz97zoqkKz8z1ZiZMDyeDoQyUxtv+t04fUYiUgwKhEREiiA/ELr5svmDHvO5HVhW/paxYxsIXXnqFL7+DtPI8Uj610w2gWiCRMqmbkjVTlk6EDqaHkIvbD9ENDG891JLTRkNFV4+fNHsMb9WphIscQTl9cFoYsQqpLpyDw0VHlbu6Cr4+Hj7xcrhW7TcTosPL53DObPr+OO6toJfY6/L/OrxxatOzm4Z6wmZJqQNFd5h5xeUH45keuRUT4fZS+Hqb8KHHhpeuTNnmel90bAgd6zx5Fw/jCOR6TVS3XLkzx1q1oVm/HzXDt6z8D30RHt48cCLrD5oLlhP79xlzhs1EEqHkaGuwRVCo3H7s3/hP7X+VG4+82aumXsNf7jhD1wTjuUCIW+6uXQskN3eNlJjadu2+fnGn/PSwZf43eu/45YXbhn7ekSOkQSw8sBKloYj5P+/7FSHaZJ8MHiwJOsaL9t7tzO/Zj7senbwA/GQqd7Z/uTgSYiZ/msZVenKw2iAxrJGOsOdEOljVZmPqd46Zn50yOt6hlQIZXq3zbpofD4hESkKBUIiIkWQH/I0DrnotSyLco+L/rC5yHQd4x5CLqeD606fChzfgVBv0HxuNf7BfX086T41RxoI7ewc4Ka7XuTLf3pt2GOBSJxrT5uK4wjCvGyF0BjXYds2sWQqG6gUMqXKx5NbOugNHbspckObkT9687LsfY/TweyGcq5ZMpVkys4GPfn6IwksC6p8ZspYImkTiCRwOSx87jF+77vyQrGhW6LO+xjUzR0eCM19s7m1LFhgJu4w9bSxvd9QV38TLvgUTD/v6J6fb/G7AAteuZfzms+jxlvDzU/dzJ3r7uScBDRmptC5RgnLambm7vuGBEKf3w6f21r4eR7/oIbcHzvtY9yx7A7mlk83F3qZiUKZQCgaoMZrqpEKbbGxbZsvLv8i31n7HS6Zfgk3nXwTf9r+J3b17Tr8+kWOsVe9HgKxABfnNz2espimWASH5TA9cyajRIxI+0b2BvZyUu1JsPYXgx+PBeH1P8M9N8JTXzPH5lwCMy+EhVfDTffBLd1wVbo3WSJCg7+BQ+FDJCN9vOjzckHLUqzm9ETFTFVlNhBKbz3v2m5uR/tZJSITyqi/eVmW9VPLsjosy3ot79hXLctqsyxrffp/V+c99iXLsrZblrXVsqwrj9XCRUQmkzJ3bopR/patjHKvMxvOHOseQmB6GDkssiHU8SgTRmQqam5/x2LOnFlzxEFMxsF0s+YnNncMOp5K2QxEE4MahY/FkfYQiidtbNtM8hrJ288wFSuHBg7f1PmNCA8JhKbmTVVzp8O2pkpzQdDRP3wd/eE4FR4XDoeF2+kglkwRiMSp9LnGPnY+f8tY3ZzC5/z/7J1neBzl2bbP2b6rVS+2bLn3bgwG22DABdPbS+ihmBJKEkj5QmgJJbQkkPImpBHeAAm9hW6KsQF3jHuvQrKt3rW9zPfjmdkircrKK1myn/M4fOzu1GfXq9mZa677ui0t7n7HZvfoZRP9uygI5QyDsx4DY3L/5wnJHCguzPZ8hsPs4KVzXuKmSTdxwYgLuL86RnDpyCGUNxp030NLh5Azv+3yEHNatOQDRNehr56CR/KFs0DPYooIQs1kaTlMDd6GVpvbWrOVD/d/yMKJC/nfOf/LFWOvAETgrURyJFllt6GoKjP00qesITD2PMxBLwX2gr5bMrbyT6x5bi5hNczk9GGiHDYWvwt80RJfDGa4+g1xTLjyZRh9pui0aNLE36CPPJsQhA64K2k0Gjmuv9aR8O4SuGmxeB4RhJrBVR0VohIEWkskkt5LZ27FPQckaqHxe1VVp2r/PgRQFGU8cAUwQVvnL4qiJNnLVSKRSI4+Yh1CiVwkBek2Nh8UF1fdndFXZxIAACAASURBVCEEwpWUYTf3CodQaa2boXd/wKfbKlK63VpdENIyb64+aQhv335yVBBKMtfky90i+6bW5afJG/3cXP4gYVU4XpJB7ybXWWHKrzlFrKa2f1ZHFggRpMnbfUKfxx8i027mne+fzJOXTol8vhAVhPI1QagqgTDV5I2KZ2bt/6LOHSA9mc8v9g50dhuCkLlFQHWs+KNnYHTVIZRq0nIjd9kHZQziB8f9gEezT2CEJyYPqqO77mY7OEX3uUir+85gtsc5hPjySVj8cPS13tGtkw6hrw5+hYLCTZNuwmgwUpRehFExUtJY0vkxHUMEQgG+KP2CsCo7M3U3q+w2xvv9ZOplut76iAOu0NGHBSF3LR+nOUhXzMzY/hmE/DBoRnS+v1n8Azj1LvhFVeLjiT4t6CXfkY8v5GOj+yAARelaOZktU7gK9ecgMtWW/yG6nc4K+xKJpFfQoSCkquqXQG0nt3ch8Iqqqj5VVfcDe4AU+KklEomkb+MLtH+yPzQmTLelA6O7yOwlgtDab8VPzHsb27frL9lZSVlD2yG2LamPOITihYauOoRW749m80x68BNeXiMucHXxpbsdQj7te9GeQ0gfQ7OvGwWhQCjS7v47x4uLhFGaEGXW3G0F6cLNUpmg21iDJ0CG5pIza++lttlPhj2Jz0+/cMkd2fbFhx4ePeuHcH+lcMno6OVV/SZ2fp/didEC4RZ/i7s/FY96V7SOHEIAaZog1FFnsVgsadGSD4DmCsgcDPeVw093RcvzdEFo/QtkWzRByNtaENrfsJ8BzgFkWIRLyWww0z+tPweaD3R+TMcQj695nB98/gNWHFpxpIdy9BIO41IUNlmtzPDEHJO8DVFByF7QZ0vGAo5cljgczGmsw7z2WRG0P+Hi6AKNZVC9Wzw/9f+1fczUjzFBPzMHzMRkMHFf4waASCv6OHJHAgpU7YSNr6buDUkkkh7lcIIqfqAoyiatpExvqzIQKI1Z5oA2TSKRSI5pvFoQ8awRie/cD8uNuhkGZNl7ZEyZdnOk1f2RxOMXYllsWV1L9lQ2s/BfX3PHy+s7vd06LUOoZQizUemaIOTyBRka8//09npx51T/DJNyuBArTHXOGRB1CLX90+3UBKF6d4B7397M3qrUt6Bv9AZJs8b/X716y0xevnlGpOSrPYdQrcsX6VSmO4pqXX7SrUl8fs1a2d7AE9pexmyHew/BGb9qfTf8kmfh2nejnbmONEaLuKvfcpojF0adIV53JpdDF71alsu1h95lLByCUBA2viyCts12SO8XXU7ff/lmMmr2UOAoYGnp0labK24oZmjm0Lhp+fZ8ajw9E3bel2jyN/Fx8ccALNq/SHZj6y7UEFusFoKKwonemGPSRX+LCkK2bCpcFYTCPXNDJpWU+mppMhqY4dXErtPvhuk3ityw21aIEPxVfxGlpO0dR3TxN+hldPZofjztx5FZhWmFrZc320VeW8UWcFen8B1JJJKepKuC0F+BEcBUoAx4KtkNKIryPUVR1iqKsraqqqqLw5BIJJK+gS8Q5swJ/Xjp5hkJ5w/JjTqEpiXRuvxwyLAJh5Cqqjz+4Xa2HGydB9ITuP3CzaJ3/0rELq2V+tfFdZ0ScgKhMDvLmzAoRNwoOoYuOoQ8gRAj8qMX2vq4XT5xAeGwJlchHXEItWg7HwqrPPPlPpbtjj/B1l1m7TmEnFYhCK0rqeOl1SXc+UrnBbTO4A2E2H6okQkD4luZ56RZmBkjdtotRtKtpkjuUiy1Ln+MICQ+gxqXLzmH1ZhzYMgpMPf+9pezpCW+G56WB8NP6/z+uhuDKdqyWcddrTl+tPF3xiGk5/3YkziGZA0GVKgrhm3/FdOqd7U/3FCAa8Zdw+ry1Wyt2RqZHlbDFDcWMywjvowvz54nBaEWBMNBfrL0J7gDbsbljOOdve/w0o6XjvSwjk7CQSq1UtuBaL8HV7wEU6+M5OYMsOYQVIOis1Yf45Bf/HYXBYIw4X9g+k1gNIuMoH4TYOb3xYKxOUKJ0I8xr14NwJVjr+QcJYMfBmwYlDZ+dwrGicBqNSzK0b7/dSrekkQi6UG6JAipqlqhqmpIVdUw8AzRsrCDwKCYRYu0aYm28Q9VVU9QVfWE/Pz8RItIJBLJUYM3GGo3+yW2ZKyn0EvGvIEwf/9yH9/5W8+WLFQ0ehl69we88Y0oJTG0kzsQm9mzvayDk1rgofe28uraUsxGQ1x+E0QdQuEk78Z7/GEKMqIX5boQ5A927NxJhLGNkrH7/7uZRz/czlOfxneF8gU7zhDSXTZVTT5tzKm9273pQAP+UJgThnQsOEwqymTVvtYiQI3LT64mCOmiXHWzPzmHVXo/WPgBZA3qeNm+gNHSWhBqOBBf5tYZh9CsO2DhIhg2u+NldfppHYP+NA3evLH9ZW8QbhZ8TXxn9Hdwmp08v/X5yOx99fvwBD0MzxoO1Xtg6RMQ9JFrz6XaKx0Esaw4tIJVZau456R7ePW8VxmbM5ZPv/30SA/r6CQcosYgjpu5dq2cUg+m1x1CZlHi2Bdbz+/3i9LNAcFQNDA/lhNu6NyGYo8x7lrMRjO/DmfzPWM712kF46LuxqxBkD+6k6OWSCS9hS4JQoqixPoGLwb0DmTvAlcoimJVFGUYMApYc3hDlEgkkr6PLxBut6X2sCMgCKVZjbh9oUgpkreDnKNUo4sFO8qF+0d33CQiNiR5T2XHZVBLd4q7vPp7i8VoTC67R8cbCOGIcTHpOT2dKeVKhKmNcXyyVYRrW4zx2yupFTkv7TqENJfN+5tEOGqyLqiO+LpY5D1NH5rT4bJzxxawq6KZ0tpoPo0/GKbJGyQnTVx4nDE+WpKUbAbTUYUuCKkqLPsDHNoAZRtF97GA9vl1RhAyGGDIzOT2nT+u9bTz/tB6GkTDqn1NOC1O5g2ex6pDqyKlTq/teg2zwczcQXNh2e9h6eOw8yNy7bk0+BoItMxJOoZZW7EWk8HE+ZWlKN8u5/h+x7OtZpsMl+4OwkFqjEasipE0p3bM0W9ARAQh8RvcG3KE6r31SXXl+8JzkJF+PwWhEAw4rvUCmUWd21CsC3Hv5+LR1xTtJpaIgpjjh+wjJJH0STrTdv5lYCUwRlGUA4qi3Aj8RlGUzYqibALmAD8GUFV1K/AasA1YBHxfVdW+V4wrkUgkKcYbDGFrJyNHDz5u72I/1TgsJsobvSz4/Rc9ts9YYoUCgHc3HiKQQMCZ9MDHPPLBdkCcw3dGEMrSPs9EJqCIQyhGLNlf7cLVThCzqqq4/cF4QUgTqXSHkMWY3Mmw3mWs0RNgxV7hnnD5gtS4xN3Wxhadwm5+YS3Qfhc6o0GJG2OK9SDWl9QxssAZ11msLeaNExdeS3ZWRqbVaUHfOU6xflG2gx/PF3eUdQfUMYnRJO6yV2yFzx6Af5wmSjBGnylEIYg+phqLg0hZGkB6IZywMPGyerC01m5+SsEU6nx1rClfgyfo4d09/+WsoJHc5y+EDf+JLJtrE0JSraezPUqOfr4p/4aJuROxL34YnjuXEVkj8AQ9fbfTVW8mHKLaZCTX6EBJ1+5pe7RAdE0QGmDUBCHXkReEHlz5IN/98Lt8U/FNp5bfHWxkciCM8sN1cMpPWi9g1NyXQ05pf0OxorMeau93dSAIjY8+n3x5p8YrkUh6F53pMnalqqqFqqqaVVUtUlX1WVVVr1FVdZKqqpNVVb1AVdWymOUfVVV1hKqqY1RV/ah7hy+RSCR9A18g3K6DRFEUXr55Bp/86NQeG5N+g7SisXXwb0+wvawp7rXbH2LOk0vZUFofN71JE2qcVhPDctM6DEpucAfYclCUlc0dW9BqfssuY8FQmDlPLuUHL61LuL09lU1sPdRIWAWb2ci5k8UFhScQIhRWo4JQsg4hbRw/fHk9Vz2zmgZPgEP1ooua2ajQ6AlQ0ejl/D8tY09l9LPyBdu/zxLbpS7TnlzQdUfUuQP0y+iEUwXhehuWl8bi7UIQavIGePj9bQCRkjGA08aIcoRwqtWrvoTRAkEP/C3mgi2tAAqPgxm3wV37IWdY2+sfNjGffWY7ZXgxrecBzht+HumWdN7f9z67q7fhCnqYV1UK5Zuj6wTc5GllOrJsTLC/YT+bqjdx2qBojtXgdNH5rjc4VI461BA1RgO5Jgec+TiMv1CIrRARhBzhMJnWTMqaj6wg1+RvYnHJYgBWHlrZ4fINvgZqw36GhYDcEUJcTsQ9B+Cat9vfWKxDqFLLButIEMoZEX3e1r4lEkmvpuduRUskEskxiqqqHTqEAGaOyO3RLKGWYcY9zbe1LoBI63KAA3UeLnp6eeR1bNedDJuJ4fnODh1Cn22viDx/8tIprebrna104aS0Togwa/YL98KiLWVx7dKvfXYN5/1pGSA6of3piuP4yRnC1VLV5MMf6rgdfCL0kjGdJm+ABo8oqRmc4+BgvYfXvi5l88EGfv7mZk4bLYSTM8b3b3e7+kfWL8MaEatShT8Yjnx+nWHu2AJW7qvB7Q/y0Hvb+EArZcuJEYSmFGXy56uO4+dnj03pWPsUBl24i/mbHL1AlIAZjODouETvsDj1Z9HnztYiagSzQ4xVc1fYTXam95vOhsoN7Dm4CoBRfq0sLENrMut3kW8X392DTQljJbuMN+iltLG04wV7GRsqRSvv+YPmRqbpn1GVu++FGvdawmHe+/xent/xEodMJnLNTsgohMteiIqbZq1zZNBDYVrhEXdofVL8SeS5/j2JpcZTw6XvXcqjqx4lrIbZ37AfgGHhtp2jgHi/pg6cnWa7CNsePkeIum/cKMLt2+taaLIIV9Jl/25/2xKJpNciBSGJRCLpZgIhFVVNPmOmuwkmaHcebFGy9e7GQyza0j0hm83eIBdOHZBQtHlrnQiajs0AmjuugJEFToprXK3GGUtxjSvyPCdBadOYfukoCmwsbeCyv63k9heFM6hfpo3iahe3/mcdP3ktmt8QO4bCTBsGg8KEASKAdHtZI6v2CiGpqw4hHZcvFMlKGpYnTsCf+lR0e9p8sAFfMMT0odmtQrLb4oShObgDbZfBdQV/MNwq26g9Zo/Kwx8Ms7G0gW2HomHgsQ4hRVE4b/KAhP9Xxwx6SceoM+HCp2HIyXB8G2Vb3cGc++C838ePJRGKAs5+0BwtAxydM5qSphI+P/glWaEQRUHtOzfvl+Jx8xuMzR1LpjUz4nxIBe6Amxs/uZHz/nseW6q3dLxCL0FVVT799lPSFTOD/nZ6ZHquXZTVVXukiypZNlRu4I7P7+DlHS/Hz1j/Ar/69h2e3Pos35rN5JkTCBu6KybgocBRcGS7jPldvLfrDYZlDmPhxIV8XfE1X5fHd+16b+977KjdwSs7X+EP6/7Axl3vAjDM70/NGMaeG3UjbnlDlLK25xACmP8AjL8gNfuXSCQ9Tu+6OpFIJJKjEK9W4tORQ6inSeQQWlcSX651x8vrufU/ncsxSJZmXzDSJv1/jhsYN+/1tUIQis2VuW7mUEbkpxEIqRFXTyK2HGxod7+ZDjNDchzsqmhiTXFtpGtZ/wwbGw+I9x/rQnJaTVwwZQBr7p3HmROEO6cwU5QZLHzua15dKxwKyQglEM0Q0mn2BWnUuqmNLIheuFw0dQD+YJiyBi8OS8eW/EkDM8lzWsiwmfH4u+4QKq114w3El6cFQuGkhK+ibHH3vbLJG3E/QWKh7pjGqH0eFgcc911Y+CEUndBz+1cUMJjix9IWzgJojrrw5g6ai0kx8UXdNk7xeDHozqAB08Rj1XbMVbuZP3g+S0uX4k3UBakLPL/1eTZVbSKshlMqNHU3n5d+zlcHv+J71ZUYvdFjVYYlA4vBIgWhLvC7b37HktIlPLb6MXbWRrszNtXswWMwcLophxvrG7g6/8TWK+sOoYCHLGsW9b761sv0FC9dzq7KTcwonMEtk29hcPpg7v7qbuq90TGtLFvJ8MzhXDr6Uv615V88ufd1TvB4GeSqS904dPeUTnsOIYlE0ueRgpBEIpF0M7rrw27pXYJQoovyxTsqEizZPTR5o4LQE5dM5oaToxkpustHFyT6ZVgZ1S+dPKfIr6l3J74bqqoqGw+0LwgBDM5NY1+1K26axWTArbVpL48pGXP5Q6RZjRRkCHcQCKdQSw7XIfTexkPc+YooEdAFoR/MGcl8rRPXtzVu0qwdf4fe+f7JrLxnHg6LEU87ndvao97tZ/ZvlvArLfNHxxdMThDS84YqGr1x4lKWQwpCceiuHHPPdxuMMFTLL5p2XfvLpfePE4TG5Y7jT/P+xHzHYO6orRc5JQsegbxR0XVWPc2CIQtwB92sq0ic1ZUs6yrXMS5nHCOzRrKnfk9KttkT/Hn9nxmZNZKrPfFiraIo5NnzpCDUggpXRZvd6VRV5aP9H7G+cj23TL4Fk2Lig/0fROaXeYST7Vz7YH5U18BIR4JyW7PuEHKTZc2iwdfx70d34fv2K5qMBvIsmaSZ03j45Iep9dRy/aLrWVW2iuKGYtaWr2XWgFncOOnGyHq/qKmlc77RTmJpIQi1FIgkEslRhRSEJBKJpJvZXSECWEfk9667bD8+YzSPXjyRU0bmRab9/Yt9fLS5dYZCS6fI4eIPhvEFwxFByGIyMCArKrKUNQhBplx7/OmCMQCRDlq6cNOS8kYvta6OrfNDchzsLBfOoPOnDBDb9IXi3mdNswjbdvmCrZw5ehezWJItCWxZ+vXciuLI8/OnFPKPa47nzvmjIm4koFMOIYNBwWw0YDcbcQdCcTlMAFsPNfDMl/va3caKvTUAEfeUjj+UXMmY02rCaTXx2Ic7It3ToPV7P+bRW41bHEduDDnD4cEGGHpy+8u1cAgBzBowi9/bRlJozYb8MTDrh9HUeoDNbzI0YwgA5e6ul6DWeGoIq2HCapit1VuZmDeR/mn9qXD1nJB9OHiCHvbW72VB1njModZh/nmOvCNbstTL2FC5gflvzOcP3/yh1bx6bz13fH4Hd315F0MzhnLDxBuYWjCVb8qjjtYSrziOFdmyxQRDAkFdLxkL+sm2ZeMJelLmYkuWGq1TZa5RjOm4guP449w/4gq6uPmTmzn/v+fjMDu4dvy1DHQO5Nz84zmr2cXwFJcGxwlA066DiZekdvsSiaRXIQUhiUQi6WZ2aYLQmH696y6bzWzk6pOGRC7OdXHmthdb38H3BVIbTqy3eHfaogJHS+fJW+sOcMGfRcC0LrakaWNsq0W8LiD96sIJrLl3Xpv7H5LriLRkP3FoNnPHFuAOBOM6dO0obyIcVnH7Q5H96ihKa0Ej2ZKxRO3jTQaFomw7VpORBRP6YzYamDgwgzRNCMuwdb5rmN1iRFVbt3O/5tk1PPrhduraEc5Kat0AFGbZ46YnWzKmKAozhndzIPLRQLXIiiK7OzuJpQhnf3BVQygIG1+BBzNFyHRjmQjsjeXs30LhFAh6yLdkAlDh7pp48+L2F5nz2hweWfUIJY0lNAWaIoJQuat7cs5STXFDMSoqI75+HsItjmGbXifPJh1Csehdtl7Y9gLv7X0PgLAa5qm1T3HGG2ew9MBSvj/1+7x1wVs4zA7G5oxlV90uQmFxHN/jF2VUw03iu4eSQBDSSyXDATKtYrkjVTZWq4U+5xL9vTm16FT+e+F/I69/MeMXFDrF39kTDT5+W53CUjEda8zNqwv+t/tD7SUSyRFFCkISiUTSzewob6Ig3Up2L81N0YWJ9hwu/nZCnLtCrVbyFdsWvaXIEhvsrItWujDTlkOoskncdT9ucDYFGa3LunQG50SdGFazEYfFKBxC/nhBSHfKpHWi3M+QpOslI+a9D8yyYzYqmIwK50yKv6i2moyM10KsY11UHaF/ti0dU01aTtHWQ42t1tE5UOeOe93gCVDr8icdKg1EyvxmDhehuRdojixJDKPPFo9jzz2y4+gMzgJABVcVLH5YTGuugsZD0c5iOid9DyZfAYA55CfHltMlN8/Wmq08seYJVFTe2PUGi4oXATDBnEW+PZ86Xx3BlgJLL+SQS7SUH+hP4EB56yYGZwympLGkzRKpYwlVVdlUvYn+af05sf+J3LfsPvbV7+OTbz/hua3PMWfwHN6+4G1unXIrZq3kclzuOLwhL8WNxaiqyvJwEyP9fhyaQBQRf2JRFNE5L+Qn2yqcREdKEKrXytey9n0ZNz3NnMY1477L5LyJzB8yPzrDXQNDZ6d+ILJETCI5ppCCkEQikXQzO8ubGNO/955g/eqiiVwyrYgFE9puZx5IsSB0QAuF1kOHAVrqKeMLMyLP9RwmXZhp9gVZsbeauU8tjXML6e3iC9Kt7e5/SG40q8VuNpJmMeH2h/AGw1hNBowGhVqXj+8+uxqIdzK15LGLJ3HR1ORFDpvZyB+vmMpfrp7GVScNJhBS8QaiZXSxTC7KEmNNIodKL1HcrQVkN7gD/Gv5fgJamHhVc+KyiJV7a3hNC/V2+YJUNno55defc84fvxKCUJKlcbpQds6k/mx96Ex+f/nUpNY/JhhzlijXyh5ypEfSMenacaK5HBq1NvJ1+6Fis+hA1hJLNLR3aMZQ9tbvTXqX/9n2H9LMabxz4TsYDUae3vA0WaEQI567mCyr+Ns4ktkvnaW8SQhChVqjAUbEuxjH5YzDH/azr779ks6jnaWlS5n96myWHVzGadnj+fWpv0ZRFB5a+RBPrH6CkVkjefyUxxmZPTJuvSn5UzAoBhYuWsgVH1zBBrx8p6kZ9PK8RIIQiAyv0JF3CDVmid+RjOrWfyN3bVnKi19/iOGL34IeRu53d9wBrCtIQUgiOaaQgpBEIpF0I6Gwyu7KZsb2YkFoQJadpy6bEpeLEwiF41q7p14QEg6UouxoSZISE4t57qRCtpW1drA4Ig6hIHe8vIF9VS72VUXDoauafBgUyHW2LwgNyonu1242YrcYcfmDePwhHBYjTqspIkIBZCcIQZ47tgCAK6YP4g9XHNfu/triwqkDOWdSIXnO6PZblqcB3Dl/FDecPCwpd83ofpogpJUsLnxuDQ+9t43+mnOq3i1cCKqqsqG0PpI19Ncv9pLvtDKywMnSnVWc+NhimrxByhu9BMMq5iQdQrrQFwqrpFlNMj+or6OLPt+uiE5762bxqAtEsUS6OLkZnT2a3fW7CaudP540+htZVLyIi0ZexPCs4dx70r2cEzTz28pqTEC27ci6OpKhbOc7WMNhsvVYr6GnwMwfROaPtQuxbXvt9iMwut7DaztfIxwOc3dNLbcvf4E8ex6zBsxiXeU60sxpPHnakxgT5AENyRjCX+f/lTE5Y9hTt4fvhOxc1dgMeiZQogwhEIJQOHhkHULhMA313wKQUb4NAi06aZZof29LH4PPHhTPA67o31cqaRkqLZFIjmqkICSRSCTdSIMngD8YZmCLLJbeiM0UPVmuc/nxBrtTEPJgNir0iynr0ivGLj9hUJxActVJg7lkWhEADrMRRYFGT5BqLfQ5NvensslHrtPaoegQG85stxhJsxrx+EN4AiFsZiPptnhBKMveOrvnL1dP46u75iRdKpaI/BhHkzNBJ7EMm5lfnj+e9CQyhHKdVnLTLKwvqcftD7KuRFzknKRl+uiC0EPvbeOip5ezZn+t6NJWWs+po/NIb8MVVdmUXOBqmvZZSyHoKEEXhD6+NzpNdyyc9vPWy+sXrH4XY3LG4Aq4ONicQDhqg521OwmGg8weKEpjLh19Kb8+WMIMr/j71x1Ctd7a5N5HT7PxVcrK1tE/FELRP8OAJ86NMcRVi91kZ0ftjiM0yF5AUzl76/dySuEMrm5sJicsfnsenvUwP5/+c147/zVGZI2IX6dkNTSJHKlZA2bxzIJnWDvqZh4o2SluM5QIp2ebgpBWMpZlE9+lGk9NN7yxDlj2Oxo1sT0z6IMDa9tedr9WUuZ3g9kOd24U/1KFdAhJJMcUUhCSSCSSbiSoncyaknRVHAmafdHcihMfW8yLq76NvPYH1USrdJnSWjcDsuxxIoH+TEWNuGTSrSYeuXBipEzJYFAYlO2ItKWHaCYOCIdQfgfuoJbYzEYcFhPBsMreqmbsZiPpNjOltdEcnURCjM1sZFBOau7O5sWMeeqg7JRsE2B0v3Q+2FzGrCc+j5ueYTPR4AlQUuOOdDcrrfPQ7AvS4AkwJDeNRy+axHMLp0fWue10cREW68jqDLfPGcn1s4byneMHHd6bkfQOnAXR57EhvcdfD4NObL18TMnYmGzRLbCzgoeqqry983UAxuSMic6ICa8ucgqxuDtbz/95/Z+57qPrul6WVlcMb3+PcpOJ/sFQVFQLuKMd5gBj+RbGZI9hW822wx90X6RkFaGnxlDuKmOQOV6UyHfk893x3yXNHFMiFfTDtnfh/xbA27fGLa8sihEna3aLR19T4v0aLRAKkGvLJd2czv6G/al4N50n4IHlf6TBYMAeDmMGKFkFqgreFk5ZWxbUl0I4LL4/ljTIHir+pQo9VNrcDeVoEomk19H7r1AkEomkD6PpQQk7SvU2mn3xQc2PfxS9aOsOh9Cg7HgxRXcIqWq0vfyofs5WDpyRBU72xogSjZogtHJvDYt3VMYFVXcGm9kQ2d/6knrq3H7SbSb2VDVHlnEkcO2kkiG5afTLsPLwhRNSmjell43pbiAQpVu5TitVTT5W7ot2NCpv8ERCuftlWBk/IIPTx0Qv/udpJXLXzRqa1Bgy7WYevGBCUvlHkl6MyQoX/Q3GXwTXvhOdPvD4xMtHSsZcjM4ZjUkxsbV6a6d29f6+93mv+CNurWsgj5jvT+6oyNMig42BzoGRjlSpxh1w8/dNf2dd5ToeWPFApLQyKbwNuBWFnRYzI/0BOONhGHsenHynOODp1JcwOGNwn+malnLqS6k1GgijUhBq8TkH/VFnjM4bC+G1a8TzhtKOt99/cuLpRhOEAiiKwqjsUeys3Zn82A+Hqh3ga6TRYCATAxSMFyVi7/8YnhgM1bujsjLsAQAAIABJREFUy2YPEZlIrkrwd1PJmOaU4vjrUr9tiUTS65CCkEQikXQjukOoL5TL/HTBaH5+1tg48UoPce6ODKHY/CAQjhsQGTr686Ls1ie7eU4LtS5f5LVe2vXAu1vEa19yHXr0UGkdXzBMutUUEVGumTEkEtDcXWTazay+dz7Xzhya0u3GhmfrTC7KpCjbTmmdmy0HG3FaTWQ5zBxq8FLZKD7XgvRoKd+tp41gbP90Jg7MpPiJc1t1QZMcg0y9Ei57Pj5EuiNByO/GarQyMW8iKw6tSLysxrqKdTT4Gnhr91sMVWzcXt8An9wPbq0szBNtta3U7GHmgJl8Xf51t3Qae2evEL1OHngyi0sW8/qu1ztc59drfs2pr5zK27vfFhOCfpbbbfgMBua63TB4JlzxYrRjm463njRzGq5Aci68owZ/M5VGcezPq2sh8Hz5W3j+/Gj5l7sWdnwAxy+E4aeDEnNJk0i0O/VnkDMs8X4NZtA6u43JGcOuul1J5VwdNlpeUMPAqWRkj4Ci6XDgG/jmX4AKVTEC1Yi54rF0NaihqAMvlVid8LO9sOCR1G9bIpH0OqQgJJFIJN1IKCxOTPuCIJTntHLb6SN48tIpkWkXaN2zUtV2fsmOSmb/5nOqm/2tBKFzJxXy0zNG8//OHBNxkyTqaJVpN1PR2FoQsutZNUpyn7UeKK3zz2tPwGqO7vfm2cOT2l5vIjYoHOCus8Zw0ynDGZLrYH+1i6+La5kwIIMBmXbK6j1886244B5ZEBXA7j57LIt+dGpEpJNIIpijwiEZbQSe612QtIve0wedzvba7W26YL4u/5rrFl3HKa+cwtqKtZznV0U56fr/wIYXYc9iOLQexl8oVqjZzczCmTQHmlNealXSWMKTXz/JKQNP4S/z/sLQjKF8dfCrdtdxBVy8svMV6nx1/HLFL1lauhSCXj5Lc5BlSmPa9zeDKSakPlZ48NThNDtxBVxdcyL1dTx1lJjFMauwPMZFFg5FA8u/+Re8vhB2fgioMOEiUS7liQmCDkZ/HyL0m9D2fo0WCPkBGJ09GnfQzcGmzudcHTYBUZ7cqChk2nOE0BpbnthUBnljwGiF0+8R0/YtFY/21JUYx5GW13bmkkQiOaqQgpBEIpF0I8E+JAjpXHTcwEjLdz30OdDSvt9F/m/5fkprxYVhfovW8CajgR/OG4XTaorc4E3U0SqjRZ6PVwuVtmrLjihIzs1jNxs5d1IhV0wfxKYHFzBrZB7WmIDtgozkMol6E7Gf1fK753LbaSMwGBSOH5JNkzfIjvImTh6Zx4AsG6v317KhtIHBOY64sG+JpE1MMaKupY2/O7O2jOZ6mTN4DiBaiyfi8xKRdzWjcAanDpzNd8tjnCJ1xbDkUcgdARc+LS6Qq3dH2o+XNnWibCgJ/rP9P6ioPDTrIQyKgXG54zosJ9JDsP9w+h8Ynjmcf2z6B4R8rLdZmZkzAVN6v/gVYi/oPfU4zA5CaghfKIGocbTjqWO1zYYzHGZ0ecznHPSBI1c83/gybH0L3vm+eF04BWyZItg8FITPH4U3bhDzTorJFSpoRxCq/xa2vwfehkjO1c66Hiwb08TSxpCHDEsG2LPi5zcegnAQxp0nSjZNtqhrKFNms0kkksNDCkISiUTSjYQ1Qchk6FuHW73UTS8dCqbAIVTR6GX5nmhmTXuOE71EzWJsLaQ1a26eG04eht1sjAhC2WlC/PjVhROTGpfNbKQgw8YTl0yOCChWzZmUaTf3aWeMWXsfs0flMTDLjqK5p04bHc0GOmdSIZMGZuH2h/hse0UkT0ki6ZDYchVjG9ldMSVjAMMzh5Nvz2dz9eZWizb4GlhUvIjZA2fzzIJneHrKj0gLxnS1q94NrmoYeILohJQzHKp3U2AX3+cqd1VK3haAGvCyaNebzC86nQKH2P7YnLGUucraDZfeVbcLgAl5Ezh72Nlsqd7CV9WbqDAaGZSWoNzypFvh3KdEJpPmEAJoDjS3XvZox1vParuVEzxe4nochnyt27ADTLhYCGr2bLHMwW/gy9/Azg/E/P6TosvmtOP01Bw6fHgXQzKHACTVCe+w0QWhgJtMa2ZURB15BuSPg/JNwsFk1G5OmB0idwggY2DPjVMikRyV9K0rFIlEIulj9EWHEESdQdlayVEqMoTe3XCIcIzRyN6O0BLUHEmJHEJ63s/MEbnYzAa8ATG2Bk+AE4fmRDqUdRZrgrI0fVq/PuwOAjBr37uW1Sc5adGSlaJsO3fMGxkp4evLApikh+lMoG2LkjGAwrRCKt2Vkdefl3zOj5b8iNs+u416Xz23TblNzKhsET5dvRtcVdEuSHmjoGY3aeY07CY7Fe6Kw3k3EVRVZeXq31EX9nNC5V749AHwNUfcI7rok4hddbvIsGTQz9GPK8deybDMYdy+81+EFYUiZ4KyOqMZpt8kXCH+5kgXLXfA3XrZo5yDrgoOmM3M8GoioEE7lgd9cblRFGnd7PTw43Ttc63aHr9BS0yGmrETvwtb3iQ9FMaoGLveUa4raP/XDYFmMi2ZIiwaIHckjJgDez6LD822pIG7Rjj08kYl2KBEIpF0nuTOmiUSiUSSFH0pQyiW318+lc+2VTCqn+h45faHOlijY5buEh3AGjwivLO9rlPHDRYn+qeMyms175bThjO6n5P54wqwm414NIdQvTvQpTbwSoLMIasmirTshNbXmD4sh8tPGMT354xsNe/pq6bx+Y7KiAA0rjCDA3UebGZ5r0jSSTqT16U7h5Y8AlMuh6zBFDgK2NewDxCZQXcuuTOy+J2Tb2WS1kqeA2vjt9V0SNumJmjmjYKdH6KEgwxOH5yyduHPbnmWP+59mcxQiNm7voLQUhgwlTHDZwOwo3YH0/tPb7VeKBxi5aGVTMidgKIoZFozeencl/jFB9ezrmYrpxbOaHun5jQIeCKC0NHuEKpwVVDlqWJiXtTRudNXDQpM9oo8HwaeAKWrhCDUHCP2zbkHipfDcVeL13p+VWW0MyYAaQUkRTiAsuN9Mq2Z1PnqOl4+VQQ8eBUFX9hPhjUD8seL6aMXwJCThQD05W/hwBoxXRdih54sSsgkEonkMJBnfRKJRNKNBCMlY31LEMpzWrnixMFYNIfOA+92rk10e5Q3eJkyKJqN0J4T5YShOWx+cEFc23Mdq8nIWRMLURQFW0zJWKMnkHTL+bbQ/7+G57fu0tWXMBsN/Po7kxmc21rYOndyIU9dFg0Q18Uv6RCSdBur/w5AgaMg4hBaXLIYq9HKyCwhWp7z2W/g10PB1wQr/5x4O35NLMkdJbJV6oqZkDeBTdWbDjt7p9HfyLObn+WUzDG8f6CMwpAmhqsqefY8sq3Z7K3fm3DdxSWLOdB8gEvHXBqZlmZO43eDzuOz0oPkOvolXA8QZUJ+F05NEDqaO41VuCq47P3LuOqDq/jn5n9G3mt5oAmAwqAW8j9wmnjc8xkUfwX2HNFRbNAMmPeLaBlYplY21dIh1D+58mEAmsrJsmb1uEOoUSsrz7BkwJBZcHep6ChmssKsH2oLaucRuvNp+JyeG6NEIjlqkYKQRCKRdCN91SGkM7BFJ7DDobLRx/C8qMDSXskYQLqtY3EnVhCq9wTISkIQ+tf10/nx/NEJ5+mOqP6ZqXv/vZ1BOeK9un2H7waTHEPYsqKOnY5Y+Wf46ncUOApoDjTT7G9m0f5FnDzgZJ5Z8AzvXfQeA1xat6hqrSxrgCYKKDHHC58QDsgdIR5r93Pe8PNo8jfxi2W/IBAKdPntvL37bZoDzdw5cB5Z4ZhSWU2EKkovajNf5q3dbzJQsTJ3xxfxM4I+Yclvz81hcQAqaYo4hh2tgpA36OXmT2+m0ddI/7T+/HHdH3ls9WMAlIc8mFHI0T93XfD54CficfRZcO07rVut6yVjeuetuffDmY+JsOnOkDlYPCoGCHjIsmZR76tvf51UEvDQoAtCVtHQAVtGdL4tEy7+O1z1qnitC0J6C3qJRCI5DKQgJJFIJCkgGAonzNnp64KQ0aBwxfRBcV23uoLHH6LJF4zrLJaK0iQ9Q8gfDOP2h1q1WW+POWMLuHN+4vwFvZV9uu3YqawerJXbVTUfg92NJF3npzvh7pL2lxlzbvT54ociIc3LDi6jxlvDvCHzyKvYwdDfRx1rHNogHi/6C5zyE7hjvXCGAGQPE49WUdKKv5np/afzo2k/4qPij3h2y7NdfjubqjZR5CxirEXr/jX/QfGoiVBFziL2N+xP2BZ+e8U6pjfWYFz5p/gZumupPUFIcwalacLX0SoIbanewv6G/Tw2+zE++c4nXDDiApaWLiUYDrLFEGKQ0RG9OBl7XrS72Gl3w1mPJ96o2QaOmPLimT+Emd/v/KB+8DXcW6aV7bnJsmZR5+3BkrGQn31WIcgPcrbRNWzKFVEB1JoBzv5QMK6HBiiRSI5mpCAkkUgkh4mqqsx96gsu/svyVvP0bl19VRACKMiwUePyHVawdJNP3LGPLelKRWmS3SIcQnouUapKxpq8YnvOJAOq+zJ6/lJVkxSEJElgtkW7IrXFlS/BsFMjL4vSRUbQqzuF42F6v+mw8ZX4dTa+Anmjxb/5D0D2ELjxY7juPTj9HrGMSYTfo3Uiu3HSjRSmFXa5/XwgHGBD5QbG5Y6LhmBP/I549AmH0PTC6VS4K1oFS5c2lVIb8jLWr+XfhGOcdkHtb8rYniAkPsM0rSzoaBWE9OyoKflC/JtdNJtGfyNnvnEmX1tNTLLkRBdOyxeC3Kw7RG5Qy3bssejioMkmvpM65zwJ13/Y/qDMNuE6sjgg4Cbblt2zJWOhIJtsVqxGK6OzE7tW45h7H1z2QucyvCQSiaQDpCAkkUgkh0lFo4+SWjdbDjZS1hDfGld3vve1DKFY+mfYUNXDEwr0MqQ0q5FRBaJDkCVBB7FkybJbqGr20eARF2GZjk6WrnSAResylozjqK+jdxlr9gWP8EgkRyVp+ZGn47PHoqCwtmItY7JHU2h2Qnr/+OUPrYPRZ4KhhXA87FQwaX/nuhAV08HMaXHS7O9aIPOi/Yuo9FRy0ciLIiJTpDxn6WMQ8DBn0BwUFD4v+Txu3Y+LPwbgdLc2lnUvRGf6m0VZnbGd44m2nzTtN+NoDZXeWLWRLGsW/dPE//ecQXO4YeINjM4ZzZkuD3dkxbjEjCaYdi0s+FXHG9a/A5e0cIedeLMIX+4MZjv4Rev3el99QhdYtxDys8lqYVzOOMztfUd0+k2AwSd1/7gkEskxgRSEJBKJpBO0d2K4typ64r58T03cvKPBIaS3Xq9o9HZ5G7rI4LCYeOHGE7n/3HFx5WNdZUR+GqW1biobhViVKofQQxdM4OdnjWXm8NyUbK8v4LCYuPqkwbxww4lHeiiSoxF71PlhDXh4bPZjXDX2Kp4pr4bHi6DlMTYcFF2m2qOFQwgg3ZzeZTHllZ2vMDJrJLMHzo4KDKYYt8m+L8iz5zG1YCpLSpfErbv84HLG+0MMnHq9mHDga1j3b9Epzdsgynzac3RonaPsoQAGxdBlUas3o6oqKw+t5KTCkzAo4hLEarTy4+N/zF/nPs2TlVUUWLKgcEoHW0qA/v91OG3YY0rGAuEA7qC769tKgkDIx3azgcn5k3tkfxKJRBKLFIQkEomkA/ZUNjHsng/5eGs5vmDrwN1YoWT5nuq4eX09QwigX4a4IDocQUgPaU6zmCjMtHPT7OEJ270ny4gCJ2EVNhwQAaDJhEq3R67Tym2nj0jJGPsSj148iVNH53e8oESSLI6YUiBPHecNP497nGPJ1rOCvAlCfItat3aPI4FDKN2STpO/KenhhdUwu2p3MrPf8SgBN/gaxQyTDS59XtuPKOOamj+VvfV7CWllYaqqsqtuFxO8bnD2g9yRsOFFePcH8M95UF8SHxKciDSRgaO4a8iwZNDob0z6PfRq6ks5sOxJqjxVnNg/geis5ywZzXDDx/Dz4uS2f9It4jF7aNfHqJWMZVlFaVpPBUvvCzTgUxQm5U3qkf1JJBJJLFIQ6qNUNnpxSVu/RNIjLN4u2iPf8u9v+O4/V7eaX+sS5UpzxxawZGdlpOsVRNvOHx2CUHzJWDis8u7GQwQTZAt5/KGIGAbg8msOIWtqW5qP1MrPvikWAaDHUomXRNKnGH9R9Lm7VtTTvn59dFpTOWQNgcyYUF29nXhbGC2AIhxCqgqqitPi7JIgVO4qxxvyMWz5X+CxAbDs92BJB4MBBmnlOR4hEAzJGII/7KfcXQ7ANxXf0OhvZIrPL9wtWUPiN77nMzB0cGxKLxSPTYeOTkFo5dMcXP5bAIbuX9V6fkjLXjJZhdBnz05u+3Puhfsr2w/u7gitZKynBaEDQSE0Ds4Y3CP7k0gkklikINQHCYVVTnxsMWf98csjPRSJ5JigpDZqG/+6uC4SOKxT4/JjNircNHsY9e4A7244FJkX1kQRk6HvHm5z0yyYjQrlmkOoutnHrMcXc99/t3DHy+t5YeW3ccurqsq4Xy7i3rc2R6ZFMoQsqQ1pHpEvBKHFO4RodyyFQEskfYp+4+GGT8RzXyPsXxo/v3q3cBF9963Ob1NRxEV8wAN/mQlvLMRpdnapZGx/w34AhgVibrbN+qF41MOMvVFBCODbBnHse2XDX8lQFRY4BifOPQKo3tn+ANL7Awo0aoKQ7ygThCwOyk3i+Nx/7fPg1d6fqsLW/0ZdXsYu5sApyuGJQaCVjLnIsmmCUCLXWjegC0ID0gb0yP4kEokklr57hXKMEgqr/PQ1Ya8urfXw1roDR3hEEsnRz6YD8d1GPt5aEfe6zuUn22Fh5vBcxvRL59W10Q43R4NDyGBQKEi3RUrGth5q5FCDl5fXiFbTB+vjg7S9AeEYiv0cXJEModQ6hFp2KktF5zKJRNJN6GHQIb8QAWKp3Qe2TNFae+x5optYp7ZpEw6hqu2w9W0yzKJkLNlA4P3V2wAY5tcE/+xhMPun4rnZLjqEeYQTcWjmULFO434qG0pZXL6aixsbsBeMF8JEWoKyy8mXtz8Ao1kES/uayLRmHn0OoYCHPRYzJlWlXygIrioxfds78Pp18MrV4nVXBaFUYHFAwNOjDiFXwMWLwUoGhw1kWjO7fX8SiUTSEikI9TG+2FXJf2PcB2+vP3gERyORHP14AyG2lzVy2+kj2P/4OQzJdfD2+nghtsblJyfNgqIoTBuSxbc10XbBR0OGEEBBhjUiCMWWxAEcqIsP3myMcVDtqmjC7Q9G3EWpCJJuyUnDYsJqTfJnTSLptegX+0Ef1OyJDw8OB8CWJdw1V7wY16a+XXSHkIYz6CekhvAEPe2s1Jr9B1aQEQqRo7eGvPw/osuVTuEU2PwGeBvJteXSP60/nxR/whs7XyEEXN7YDKq27pmPwZWvRNcdfxH8zz86HoTJCkHfUVkypvqa+MyZzknZ47CqwJ+mwe7PROA2wMG14vFwXT6Hg1YylmMTvyk1npoOVugaDb4G3t37Lg+vfJjz3z6fMgL8KpB2zGXWSSSS3oE8c+5jmFu0aZY5QhJJ97K3qplgWGXigEwUReGsif1Zta82Lly6VhOEAPKdVmpc/kiuTjBSMta3T/T6Z9gipXMNnviSuS0H4y9cYkvqFvz+S+58ZQMH6zzkp1u7xcHz4k3R9rumFLSyl0gk3YRRu9j3NUHlNug3Ea7/IDrf1gWHhCUt6jYBnJo4lGzZ2L7GbxkWCBI5Ujv7xS9w1hMi52j131AUhYUTFrKuch3Pbv83J3h9DAoG4fiFYll7Fow5O7ruJf/s3CCMmiBkzaDB19Dx8n2Ibd4qDhoVzhw4OzrxxUvg6xafTU+1ek+E1mUsw5KB1Wil0l3ZLbu5/bPbuW/ZfSzav4jROaN5kHymGdK6ZV8SiUTSEfLMuY/hD8aHt3bkOvhwcxnVzb52l5FIJG2jtzMvzBLByuMLMwiFVfZXR11AcYJQhg1VjQZNh48Sh9ChBi+ltR4+2lzGXW9sAuCNW2dyz9ljOVjvoSbmONPojReqP91WweIdFYzI754TXikCSSR9BL1kbP+Xovxq1Bkw5GSwiCywSFZPMgQ8sPuTyMsMTYxPKlhaVdnvrWJYSIVp14ppsV3RAIqOh/4TYdu7AFw+5nJ+MPkWwmqYOW6PcAQNPy3x9o2dDLs3WSEkHEJdKXvrzXwVqEZRYe6E78L8B6Ph4eWb4hcMuFqu2nNoXcYURaHAUUCFu6LjdZKktKmUTdWbuH3q7Sy7chl/m/83LgnbO/8dkUgkkhQjz6L7GJ4WpRrt2UsbPAFuf3EdNzz3dXcPSyI5atFLnfROW2P6pwOwszx6sVHT7ItzCAFUNgmBxK9dnPR1h9DM4bkA/P3LfZFp0wZnM6lI3NHffDB6N7vJ29q5GAipPHLRxG4epUQi6dXoJWPuavGYM0Jk7mRo3cS64hBqKI176dRawScjCFVvfZMaAwz3eeDc38PdJYmDoTMHQ8VmqNyO0WDkljFXsezbA1zd2ATW9NbL37wEblvR+fdiskLQS6Y1k5AawnUkxZEUszPUzCCMZNpz4JQfw42ftO7GBuB3t57WU5gdIt8qFGRoxlC2VG9JuSj3ecnnAJw//HwMinYZFg5IQUgikRwxpCDUx/D44wWhQIJ2zzq6m6i4+ug5oZBIeooPN5cx7VefsreyGUWJCj3D85yYDEpEEGryBmj0BinMtAPRjJzV+2v5wUvr2FvVjMmgRASjvsp1s8SJe7+MaL6DwaAwcaC4gNsSIwiV1bfO7vj+nBGMLEhwwSSRSI4ddEFIC2fGorkG9fbyti44hFrgDAp3ZmdLxkLhEL/Y+L+YVJVZbq/IDWpLmJp6lXhs1pwjAQ9pqipOpq0ZrZcfOA36Tej84E1WCPrJsIhtNfj7btlYnbeOh1Y+xKyXZjH/9fl8hpsJij26QMYAOO2u1itajmDplNkhHgNu5g+Zz4HmA2yr3ZbSXSwuWcyY7DEUpRdFJ4b8YJCCkEQiOTJIQaiPoTuEnrxUBDG6fEGKq12s2Fvdalk9+PUochxLJD3G00v2UOvy889l+xmQaceihRVbTAYKs2wcqvfQ5A2wel8tAENyxYlkgSYI/er9bby/qYx/LS9mcI6jz5c1tcwv08mwmRma64hzCO0ob31n/n+mFbWaJpFIjjH0wOCWgtDhOIR0ik4Um/ILd2ZnHUJv7niVZcE67qmpY0wg0P7CunClu1iC3ug8WwJBKFmMwiGkdzFbU7bm8Ld5BPAEPVz63qW8uetNhmUNw6gIt9WZthZt1fvFuEbvq4ALn4bjr++5gbbErAlWATfzBs/DpJj4uPjjlG1+feV6NlRuYF5QgQatOUXNXji0/sh2V5NIJMc0ffsK5RhEdwidM6k/l0wroskb5PQnl3LVM6tbLauH3oakIiSRJE2WI3q3bnCOI25etsNCnTvArf/5hpteEJ1RJgwQFwOJumgNzLa3mtbX0AUhly/Uat7EgZlxwdLbylp3x8lzHsHOMRKJpHfQ0iGkOzIyNcG4KxlCOgs/AkcezmDnBSFVVXlr6wuM9Pu5bPAC0VmsPcyagBVwxz8C2HNaL58sJiuE/EzNn8rIrJG8vOPlPpkjtL5iPRXuCn53+u948ZwXefuCt/h3RR3zssbGL5gf89psg+O+m7hUr6ewRP9/M62ZzBgwg0+KP0nJ/0Gtt5Y7Pr+DwfZ8rtryGbx3p5jx6S/FY8h/2PuQSCSSriAFoT6G7hCymYwMz0+jrCF6dyrYonzMGxCv9bbXEomk81Q3iZMzgwIXHRd/VzPbYaHe7WdXhShJuPvssQzJFSeSibpoFaTbunm03Y9FE4SatM6GPztzTGTekFwH5Y1ewmEVVVXZ0UIQevnmGT03UIlE0nsxmAAl2mq8lUOoC4LQVa/DcdeIUi+zHWdInCe1VzJW7irnlk9v4aw3z2Kr+yBXNTbBxX+Hcee3vy+LJmD5tVL8QIxDKFGGULJoGUKKonDl2CvZXrudjVUbWXloJdd+dC3lrvLD30cPsKVmCwAzB8wEwBEOMdXd1Lpzm9kGU66Ck27t6SEmRhcoNQfYjMIZHGw+mJKOb2/tfot6Xz1/GHUNmeEwoEBTBexaJBZQW99skUgkkp5ACkJ9DI8/hNVkwGBQuOXU4fwqJqS1rMHLv5bvj3Q10kvGwn3w7pJEcqRp9gW5ZFoRWx46k8unD46bl+0wU+v2c+LQHLIcZm49bUS720rkGuprmI0iFNulCUJnTewfmZfntBIKq9R7ApQ1eGn0BiMOq5nDc5k5IrfnByyRSHofihIfnqu7QUYtEN29+nUheH70Arjwz+K5yYo96MeoGNt0CHmCHh5c+SCry1YzMW8il9gGcZHfGO2A1h66YNBcAc2V8Q6hdpp8dBqTDbQMpPOGn4fT7OTF7S/y27W/ZX3lep7f+vzh76MH2FO3hwFpA0jTHVWuKvHYUhACuPivcPave25w7RGTIQSQaxe/XXW+usPabCgc4r297zE+dzwjTVpHPUsabHwZwloTBmPfP0+QSCR9E9ORHoAkORo8gciFlslo4JoZQ0izGPnJaxuZ/ZslAAzIsnPmhP4Rh5A0CEkkydPkDZBuM+GwtD5MDs1L478bDlFa64mUirVHy5KzvojRoKAoUUHIEpMppJeDVTf7OFAnTqRPGJLNZ9sr40rvupNNDy7okf1IJJLDJFFpTHo/uOBPh79tkw0l5Cfdkp5QEKr11nLLp7ews3Yn95x0D1eOvRLe+h7YSxNsLAEW7WJ+yaPi35WvHv6YYzFaIrlEDrODy8Zcxv9t+b/I7Be3v0hRehFXj7s6tftNMSVNJZEcJCAawu0sOCLj6TSWeEEoxyrKAOt99W2uoqoqW2u2sr12O0tLl3LBiAs4c+iZccusKlvFvoZ9PDH+e9B4SEz01sNnD0D6AGg6BJO+k/r3I5FIJJ1ACkK9iHBY5WC9h0HtXDzWuwNk2ePvYp08Mo+pg7KnWk5UAAAgAElEQVTYUCp+sPR7VB7pEJJIuoSqqjT7gjitiQ+Rt542gq2HGvl0W0Wkm18sD184gV++sxWAT398KiMLnN063p5AURRUlUiZqtUcFYRyneKYVNPsZ3uZuAibUpTFZ9sr2/wMU02GTXZokUj6FNe9n/ptaiVXToczYcnYW7vfYkftDp6e9zSnFp0qJnrqwJ7dye1bRNmb7upY/gfxmCphyJIWLUcDbp50M+sr1zMpbxLfm/w9frH8Fzyx5glmFs5keNbw1Owz1RQv42DVFsaPvDA6LSIIJXAI9SZ0h9An98Pl/yFLK2Gs8yZ2CO2r38fDqx7mm4pvItPWV6wXgdQG8dvX4Gvg4ZUPM8DRn3kf/iLa6WXfUvG44Fcw+iyw9v3zBIlE0jeRJWO9BF8wxPB7P2T2b5awr6rtuvd6j5/MFnfc+2XY+MvV0yKv9W5IssuYRNI1PIEQYRWctsRihs1s5NqZog377srWf6/XzhwaeT6qXzpKKkoJehlWUzQrKU1zUbn9QfZWNVOYaYtkDbUncEskkmOQS5+HW5fBsNmp37bJBkFfmw6h1WWrGZU9KioGAbiqwZFEWas5pi16XTHMugNGzu/6mGNx5ICnNnLi5rQ4eeHsF/jZ9J+Rac3klzNFAPHSA0tTs79uwLXkEeoNCgPDiPcR8IjyOuj9DiFdECrfDB/9nFyb+F5UuataLRoMB/npFz9lT/0ebptyG/8++9/89rTf0hRoYmPVxshyH+z7gEOuQ/w6fTK2lifkI+cLZ5AUgyQSyRFECkK9hEZPMPL8vY1lbS5X7w6QaW99J7x/RjS0Vi8V0zuSSSSS5Nh6SIQit+duyUlrP2/i+CHZDM09esUQqyn68+GwCHHI7Q9R6/KT57Ry6qh8QHRElEgkkggTLoL+k7pn25pDKN2STrM/XqyvclexoXIDJ/Y/MTqx9Gs4tA6cSRynYgOGZ9wuHB7GFDkh7dmipC7GJRRLnj2PwrRCdtTsSM3+uoEDYVESOLBiF6z/DzzaHw6uA8WYmk5s3Ykl5jfb20C+Ix+r0UppU+uSwhWHVrCnfg/3z7if26feztSCqZHv1pryNZHlttVsI8eWw5RV/xQT+sV896de1S1vQyKRSJJBCkK9BN3NA/DOxoNttrhs8ATISiAIGQwKz99wYty26j3iR9loOPrcCRJJd7K2WNjDpw5qu+NNRyVKb942i6U/m5PScfUmYjOE7Jog5PGHqHOLnLNTRuWx//FzGFmQgs47EolE0hlMdgj6cJqdNAWiDiFVVbl/+f0AXDbmsujy79wuHrva8tuU4iBgXTDZ/Hp02pLH4LnzoFSIDIPSB1Hu7r3dxg4QAKBoz1LY86mYuOkV4Q4y9PLLjtjSwVAAg2JgUPogSppKWi264tAKbEYbcwZFf+dzbDnMLJzJi9tfjHQm21G7g7E5YyNxDpz5SHQjI8/ohjchkUgkydHLj8zHDrqIM3N4LvuqXBGHQkvq3YE2Q1pH9xOWUz07qM4diMxrS2CSSCSt0f+G2guMPpYza35yxmgMMUKzHry9priWerc/4p46GkvlJBJJL0ZzCGVaM6nx1ETOfSrdlaw4tIKbJ9/M8MyY7B1FK32de3/X9mdIcUaaQxOE3v8R7P0cvI3wxa+h+KtI5ozT7Gyzg1qq0IOSy13lSZ8/vmVwkxkKMTwQgG3vRGf09nIxEBlOP9sHo8+GkA8QAlxLh5A74GZJyRKO73c81hbdwX56wk9p8DXw+q7X8Yf87G3Yy9icsTH7SIcBx0H+OLB13JRCIpFIuhspCPUS9DKv86YUArBmf22CZUJ4AiGyHIlLVexmY2S5V78u4a9L9wIQCqv4Q62DbyWSVPPZtgreWnfgSA/jsPEGQtjMhnYFjbbyhY4F7pg3Ku61XjL2xjcHqGn2k93GMUoikUi6FS1D6LiC46j11rKrbhcA22u3A3CCowg2vAwf3wdb3oTqnXDqXZAzrPP7GDwr+jwtP5Wjjy+peu36aPYOiPBrEOVwCQKzkyEUDvHU2qdYX7k+4fyXdrzEFe9fwRlvnMEDKx5oczstxaKtNVv50uDn+oAFR0shKWvwYY25x0jLFcJi+WYIeBiSMYTSptK4EsRHVj1Cubuc6yZc12r1MTljGOgcyK66Xeys3UkwHGRC7oToAhYH3LwEblveE+9GIpFIOkQKQr0Eb1A4EoqyHRgUqHML+/I339Zx1xsbCYVVGj3C8ZMoQwhE0C3A00v28PM3N8fNq2ryddfQJZIIN72wlp+8trHjBXs5QhAytruMXoppMcnDaGyeULMv2GOt5iUSiSQOzSF08sCTAfjq4FcArC1fi8VgYfyLV8F/b4WVf4Y3bgA1DINOSm4f17wN/28PXPgXGHd+asfviBGEfA2w+MHoa4/oJJsoHylZXtj2As9tfY5rP7qWsBp/w9AX8vH81ucZljmMeYPn8faet6n3tm67/qf1f+K0V0+LC1D+ZrfoHHfx0LPhO/8SLhidEXMPa8w9SoPmCProLuYNnkcwHOS+ZfcBEFbDfPrtp/zPqP9h5oCZCVcfmjGUffX72FwtzsUnr3wmOlMxgqKAof1zDIlEIukp5JVML0EvGXNYjGQ7LNS4/Ly17gCX/HUFr609wNriWqqahajTliBkNRlQFKhujtbCnz1RBCUu+P2XNHoDCdeTSCTxePyhiOOuPZ5bOJ1Pf3xqh8sdLbx2y0wW/ah1Z6CWTirpEJJIJEcEzSFU4ChgbM5Ylh1cBoiQ36kFU1t3eQIoOj65fZht4MyH464WF/appGXo8vb3os81UcZpceIKuFoJOZ1lddlqfvfN7yKvl5QsiTwvbihm4aKFlLnKuO+k+7h63NUAEWEDoNpTzVUfXMU/Nv2DOl8dd37+/9u77/jI6nr/468zLZPek81ms73vsgWWZelLERYEqQLqFSmKXP3ZBbGi3uvV61UuWK6KiCIoKKAivffqwrKwvfeSsumTzGRmzu+Pc6Ylk92USSaZvJ+Pxz5mctp8z55MZuYzn8/n+wX2tFqZwbv+9SvyQ2FKiqfC/Ivhs2/EHng09cvptNs21G1kUcUirltwHc/tfo5aXy0H2g/QGepkbunc2Pb1W+C9+6HJCiQtrljMxsaN3LXm95QHg1RufCq2bVlihq2ISLopIDRCRErGvC4nxbkeGtr8CZkWT6w9wKPv7cdhwOKJyRvdGobBV8+aFZ0Z6ZMnTeGXHz2aL505E18gREPbAJsmigyTzq4Qd7y8jWCaSxw7g+EjZggBLJ9VwaTS3CNulymWTilh9rjkPQ9uOHtW9L4yhEQkLewMIYAllUtYW7uaQ3/7JBsObWBp5bHJ94lvJJxuhxtLRxM07qAgGMTEpMWfvNfkkdyz7h5KvaW88dE3mFo4lW+/+m22NlktBm575za2N2/n5uNv5riq45hXOg+H4UjIArp/0/2sqV/DTUtv4oHzH6Aj2MGvVv8KQkG2ud1M6erCKBjf84GLagY03rSIZO84rNeyE8ZbZYJr6tewzZ7hbWrhVCsQtOdteOpb8LdPwq3zoX4L5045F4fhYJ/vAP+vsTnWUPqKe1MfRBQRGaSx2wRjhIlkCHndDioLsnh2fW3C+n++u4+waXLmnEomFPc+lfVnT5vOZ0+bzrp9LUwszcHhMJhdZc3y4wsEe91PJJUCwfCASql+98p2/ufJjWR7nHzsuElDMLLD29vUAVgZQn0JCEnMZ0+bzk+f2kjYJNpUWkRkWLm8VjNg02Rm8Uw6zSD/t/cZzIJ8jnv827Htqo+BvW/DZX9M31iTcfXyt7Og2soQum0h1TnZUFnOnrY9FHl7nwkzmQ2HNvDinhf55FGfJNedy6/P/DVXPHoFX3rhS0wqmMQLu1/g6vlXc+nMSwHIceewqHwRf97wZ86fdj41+TU8tOUhjqs6Lpo9dOakM3l+9/M0L/gMGzwezmlvB3fc+9TPvDmg/4q0Muz3L04rIDS7ZDYuw8Wa+jUUr/k7AFNe+w2s/kvPfVv2UjP1VP507p8ofPk2avY/CTfbJXcKBonICKQMoRHggbf3sKXWqgf3up0cM7GYYDgxrbmhPUCjr4sPLUryrUsSc8cXRDOFIg1ffYHQ4XYR6RNfIMiPn9gQDWIm09wxsPLEDvt3NF3ZbCf+6DlO/NFz+IMhst3689hfP71sIVPKcplVqanmRSQNItPAB/3MKLZKc/5SkM8pQQcL/XGvK6d+Db6xH+ZekIZB9sP0M61bTx7UrgNgYpf15d7Olp39OpSvy8ctK2/B6/Jy1fyrAKjKq+LrS79Ona+OnS07uXzW5Xx6wacT9vvBST/AYTj42ktf4/V9r7O3bS8XTr8wun7F5BW0Blo57aHzaXcYnNPlgClxpdQVs61/o4oduHFaATqvy8uc0jm8uu9VNrXsoCQUojhZMAjA7u80v3QeNbv+BROWWoEgBYNEZIRShlCatfuDfPV+KxV3fKGXcYVe5lQllmTMqsxn40FritGB9OaITAnd7leGkAzer1/cxv+9sJWyvCyuOSn5zCwtnV2U52clXXc4XjsI03GYYNNw8ClDaEAuWjyBixZPSPcwRGSscnmt22An04qmRRffbJZisAOufQbeuQumntZ7Ns5IcuGv4K3bIeCzZkQDaowsDAx2te7q82Ga/c1c++S1bG7azDcWfo6Cl2+DU28EVxYrpqxgxZQViTs07rD+L/PHMSF/At9a9i1uePEGrn/mekq8JZwx8YzopidWn8g5k89hX8sOrln/EkvO+O/R3zDZtN+DGLEvhs6adBY/ffunrMvP40RfR6wMbOl11jVyZlnZaX7r/Tq166BxO5z4hWEduohIf+kr8DSLb/R8/fJpuJ2OHj1Jqouzo/dzs/ofw8vNsl6YO5QhJCngt4M1kZnxkhlohlAkCJPu39VNB1rHVG8gEZGMEJchlO3KZkYgQF44TEVnq5UNVHMsXPCL0REMAsirgNO/BTmx3kJZDieVuZXsaul7QOj53c+zsXEjt5x6C5fX74OXfwJv/6Hnhi37IByG2xbCL5dGF5896WxuWHIDH5/7ce459x68kcAb4DAc/PjUH3PPCT/kdF8HuLN7Hne0CUXew8Sy9ZfXLMdhB4hO7uiIbTv+aPjEw3Dtk9bPkYDQ+kcAA2adO/TjFREZBAWEUuiWpzbyx9d39Guflg4ra2dqeS6XLbEa7s0al8/1p07jpnOsFFu3M5ZmmpfV/29dctx2hpACQpICkRmlVu5opK7Vn3SbgQaEXPZU7ocrRxsK4bBJbWtn9OdWf5Djp5UO6xhERGSQ4jKEAO7dd4Cndu2F1oOQlbwh/oiTk+S1J77ZtL+VSfkT+5UhtKVuDR6Hm+U1y2H/e9bCTU8mbtS4A26ZA49+2fq5szm6yjAMrpx3JTceeyM1+XZz6P8cB89+P7a//X8eDcqNZvMvsW69hRCy3qdPLpzMIxc+zA9r67msxSoL4xOPwMIrrBK58jnWshd+ZN3ueBmqFkJ+5TAPXkSkfxQQSqE3th/igbf39GufVjtD6Lvnz4tmRzgdBjedM5vp5XmA1aA3IlL+1R85dhCpTdPOSwrYMRue21DLsT94hpbOLrq6zQrW0s+A0J2vbOfy37xOp/27PtCA0kD98vktLP3BswnLlk0p6WVrEREZkeIyhACyTMg3TfA3Wx/uR4PPr7ICDdfETVU+72JYcDksuALMMDW5Vf3KEHp/zb3M7GjDaZqw5Wlr4bYXwHcottHBtdbt27+PLXvkS9C8N/Fge9+Ghq0Q7ICXfxpbHg0IeRn1ln/dOo+OJviPUnjnbgBqDA/ntftwA5z3vzDl5FhvoMjvnq8eTNP6/6xamJbhi4j0hwJCKXTS9DLe39vMxgOtfd4nUjJWkN1zmuZxhdaL6jQ7MAQDKxkryfGQl+Via117v/cV6a57X8Rzbn2ZGd98nN2HfNFl+5tj2TZbaluPOI389x9Zx5vbD+HvsrZraB/eptJPrTuY8HOB10VFQQa8qRURGUu6ZQglyBslmRreQivQMPG42LKcErj4dpi4DIAJWaU0+Zto7zr8+7pgOMgd79/BKrfBso5O6IgLAJkhaN1vZQz9+QrY9ETPA6y8E967L/bz+w/Ab0+Hnx/dc9uuDAoIORyQUxbt28Tqe63bA+9bt1c/DkuuSdzHMGJZQpuftv6vy2YMz3hFRAZBAaEUumxJDaYJr22t7/M+rZ1WKmq+t2egZ351IX/+1HHcsGJWjxnD+sPhMJhTlc/db+zk7td3JGQcifSXQWJEKDJV+8+f2xxd9ux6K8Cyq8HHmbe8xP88ubFPx470JTrYkuTN/DDK9/YM0IqIyAgXDQglKWee/cHhHctQ8Fplb4UO6z1ha+DwX0De9s5t3PbObZzm6+C6phZo2m2tWPxx67azxSpt2vQ4vPPH2I41y2L3w6HY7YPXJj6A2+619+Zv4P5PWPczISAE1pTzpt1DaOer1v391iQwVM5Lvs+ij1i3f/6wdVtQPbRjFBFJAQWEUqiyIIvCbHd0Cvm+iJTWFPTyAfSEaWVkuZw8/LmT+K+LjsLtHNglc9p1Pt9+aC3/WLX3CFuL9LStro3JNz3Kuv0t0WULa4qi9/+6MlYuebDFejO+v9kKFr29s7FPjxGZCW9/cyem/UaspbOLUNg83G798u7uJn78xAaafbGyNJPE4//v5YtS9ngiIjJMoiVjnbAlrgz4M29C6bTk+4wmWVbZW579vV5boPf3m2EzzN+3/J0zx5/ErbX1ZJsmvPUba2XxZOvW3wpv32Xdn3iCdXvSl+CS38Z+bj1g3W54JHbwSPldoT2r5OM3QnuddT8TegiBNeV8V1zzaF+DlSFUPLn38sPsbqXm7pwhG56ISKpo2vkUMgyD6RV5/QsIHSZDKN6UslymlA181qNvnjuXnzy1kRc31Q17OY5khle3NgBW76CIiiRTy7scBo0+63fMb2ejeVw9A5lNvgB7mzqYNz72xmpPo/XmKxAM09AeoMDrZsF3n+KqEybz3Q/18o1cP134y1cBWLe/hT9cbc2iYsbFg648fhJL1T9IRGT0ic8Q+pPdGHjZZ6BidvrGlEp2hlCe/aLV1tX7+83tzdtp9jdzSltbLK/3vb9Yt5FSpl2vx8qirnk88QDXPA6/PA7a7df8NQ9CfhV88X0wnPDETfDun60XUG8RdDYljHHUc3rAH/sCjJZ9VkBo3ILe98mrSPx58klDMzYRkRRShlCKTS/P483th3jo3b5l4bR2BvE4HdGG0kPlqAmF/OHqY3E7jYSp7kX6aly3njofXFDFp06eyr8vT/zWtSI/i9bOIMFQOJrxkywgdPUf/sUHf/ZKQvbPcxtq8dhZcI++t599djnag/1s1t4X7+5uSrrc5dCfRRGRUcltv04deC+2zDlKppjvC3umtLyQVcZ1uJKxf279Jw7DwbItr8K8i+ArG+G6F+Cj98OEY62NXrnl8I/nLbJmaPvDebDuIWs2Lafb6rFTMQcCrdb/dWcTHH2ldeziKSk40RHA6U7sRVW/CQ5tO3xAaOIysMv5mH0eZOX1vq2IyAihTz4pNq3CyuL5wn3v8r2H1zL5pkeTbmeaJjc/tIZn1x+kIHt4ErUMwyDf647ObCbSH5Ep4cHKaPvZFYtZOqWEjy+blLBduZ019MPHN/C9h9cBJC11fG+PNaXtgW79gr5y1kwAbv7nWnbZjaojM+WlQlme9eHgqOpYZlL8ubmdRo99RERkFIhkCEUyYQA8A8+uHnHs7Jt8OyDUW4ZQR7CDBzY9wBk1p1PV3gClMyB/HIxfDDPPigaWjii7CPa8ZfUZAiisia0rt7OuXvyxdTvzHOvY3WeeGK0q5iT+vPNVwITymb3v4y2E6iXW/Uz6vRORjKaAUIoVxs0W9vtXdwDgtxvlxjvUHuCu13eyubZtWDMSCrwuWjqCw/Z4kjmCcZk8BV53tC9VricxoBkJCP3ule3RYE92kgy40lwrMPPc+sQZvj60aHz0/k47INT9MQbKFwjSaPcOim9cHSndhOTBKxERGQUi/WvqNsSWZVIfF4f1HjPPsF5TewsIra5bTUughYunnGstyMpP3CArD077pnXfcMAX1yR/vO69chxxr+VFE63bSG+hTJtiPdJ4O2LlndbtkYJpU5dbtyF9+Soio4M++aTYinlVPZY1tPXs2bOjITZF96XHTBjSMcXL97pp6tCLlPRf/NTxWXElYNndZr4rz+85w0jI7NkUenaV9abq2w+tjS6bVJpDZdz+6/ZZ9fuuFGTtvLCxlk/f/TahsMm08lz2NHbEGlfHPSdS8VgiIpIGyWa4cmRQu0w7IBMNCPXSVLq2xWpbMMlTbC1IVrp0yg1WUOizb0FRTc/1EAsIFdjvU72xiSTIHxe7f9HtUJhhM2pNXJZ8uecIZWBTT7Vu961K7XhERIaIAkIpVpjj5k+fPC5hWX1bz+lPL/nVawC8dMNpfPXsWcMyNoAFEwp5c1tD0qwlkcOJzxCKzwj3uBx889xYanV5kkbTHYGev2+maTK1PJc5VbFv2y5YOB6Hw+DGFdZzItKLq90/+N/Xq37/L17eXI/TYXDR4mp8gRCvb23AFwjSHBcQCoZSN6OZiIgMo2QzXBkZ9FbXPpccHDgMR689hGqfvBGAMof9/5Esq8Uw4NQbYw2mk7Ezkjj6Srj0Tlj6qbh1TvjYg1aT6YWX9/tURjzDgFNv6rn8SKVgkZKxJVenfkwiIkMgg14lR47us4F1zxCKNNoFmFg6vKnMMyry8AfD+FLwAVvGlvjmz/Xdfqc/dcrU6P34mcd+cNF88rNc+AI9yxR9gRBVhV4e/dxJvPfds7jtikV8/gzrjekiezp7XyBESa6HvU0dfOqPK+nsGvzv7aSSnGgQ6qN3vMnn711FMGwFpwD2NPoOt7uIiIxUyTKEMqWnDUQzhAwzTK47N3nJWFcHtYTID4XJ9tvrj5TV0pvJJ1q32cUw/xKr0XK8GWfGSscy0Wlfh+80Ji47UqNolwduboITvzB04xIRSSEFhIbA+KJsTp9dwfkLrV4okSm4IyIZQ99L0TTa/eG2S30CceU/3X3j7+9zwS9eGa4hySgRnyHUfJiyw/gMoXPmV3HM5GJ8STKEfIEQOR4XDodBgdfNBYuqcdn9eyaVxoKqH15ipao/ve4gW+t6n2K3r46aUMikuEDsM+utKXUX11ip9fuaO5PuJyIiI1z8jGJzzrduMyogZJe/hYPku/OTl4w17qTO5aQiFITfr7CWde8h1FezPwifeBiWXDOw/TOBw5FYdtiX4Fom/c6JSMbLoMLqkeXOq46lyRfg4dX7ok1sI+parYBQ90yi4RCZ0jsQDPPNv7+P2+lg5c5D/PCiBUwsyeHT96zkjW2HAKukx9CLmtiChwkigtU4uqMrRFFcY/XCbDc5Hid7GnsGhFo7u8jt5Y1VZIr7ZVNLKMqOvcHv7Dr8GA7H63bQ2RXmPy+cn7Rx9FnzKjEx+dTJU5PsLSIiI178e5Y8u8eNw51829HI7h2EGSLPk5c8Q2jvSmqdTspzKoED1rLIjGADMeWUge+bKa56FO4827qv2cNEJMMoIDSECrxuHAY0dcsQigSEkvVaGWoeO0Oors3Pn97cFV3+m5e2MqeqgDe2HaI8P4u6Vj8tncGEWdNkbIvPEEomN8sKCLnjGk47HQbZbhcdgRC/fWkbJbkeLjlmAv5giH1NHUxcnLwJpdNhsPJbZ1Kc4+HWZzZFl7d2Dq4h+nWnTCXfm/x3elyBl1suWzSo44uIyAiQWwGnfcPqubMgg/rbRGb5CofJc/cSENr6PHVuD8dOOhmm/xvsfgNyS4d3nJlm4jK45Hfw+i8Ss9BERDKASsaGkMNhUJjtptEXYH9zB9/+xxra/UHq2tIXEIpkRmytTXwT0REI8eqWeuZWFfC1FdY3Sd0DWTK2hY4YELLiy25H4p+VHI8TXyDIDx5bz1fuXw3A9vp2wiZMr+w9jb0sLwunw4hOTw/Q2tmzF1FfmKaJPxiOZsglU12cPaBji4jICHLdC/Dvr0JOCZz7Y3An6Ss0WkUaZEcyhLqXjIXDhLc9T53TQUVOBRx3ndUMWgbvqEut3y1lzotIhlGG0BArzvHQ6Ovi+B8+B0BVkZeOQAinw6A4Z/i/ZYgEhPbbfVLcToNZ4/JpDwTZdLCNs+eNozjHyqBo9HUxSV8qie1IGUI5nuR/TnI8Ttq79RDafNB6Ezuj4si1+P+2bBI1JTlce9dK2vwDCwgFwyamCVlx2Us/vmQB7+xqJBQ2uezYGsryhj9AKyIiKTZ+cbpHMHQMwwoKhUPkufPYFtiWuP7g+zR2NhIkl/Ls8vSMUURERhUFhIZYUY6bg3FNag82d/K3d/ZSkuvB6Rj+bxncTusx39vTBMDzX13Ot/+xhl2HfBxqD1BTkh39YFzbEhv3P1btZVFNEZPT0PdIRob4HkLTkwRy8rKsVHZfIMg/PnsiobC1fY7HRSCY2Ptnc20bDqNvfbRcTgdLp5QAAy8Zizy+Jy4gdNmxNVx2bM2AjiciIpIWhtNqKu3Jp72rPXHdthfY77Le2lfkVKRhcCIiMtooIDTEinM8PLuhNvrzXa/vBKB1gJkOgxUpmXlmfS1ZLgdleVnkZLnYWme9qaguymZiiTUD0/Mbazlr3ji6QmG++Jd3yfE4Wff9FWkZt6RfJEPoiS+eTFVhz/Kqa0+awr92NDKlPJeK/FiKfp438c/M7kM+tta2MbEkB6/b2afHzrWzjzbsbx3Q2P1JAkIiIiKjjsNllYy5i2ntak2cAKR5L38sLsHr9LKgfEF6xykiIqOCPh0NsQMtI2sK6/iGv/ddtwyv20leXKnPwglFFNklY/e+tRvTNKNNsJNNHS5jR6SH0IyK/KTNxlfMr2LHjz6YEAwCogHGiG/9Yw1b69qYVt6HqVttDjub7m+r9rL7kO+w2zGHuVsAACAASURBVK7a1cjKHYd4c1tDdFkkQyjL1bcAlIiIyIjkcEI4TL4nn2A4SEewI7rqvY79PJ7j4cp5VypDSERE+kQBoSH28WWTmFaeyw1nz+Jjx01M93CiPYRyPU4WTywGIMcu9akuymZSaQ6GYXDVCZMB2NvUwcG4oJZpHr6PjGSuSIZQfysdu5eFNXd00dLRRUnuwHpoNXccvmzsov97jUt//TqX3/5GdFmykjEREZFRx3CCGaIsuwyAho7Ylx8PdOwh34Rr5l+TrtGJiMgoo09HQ+yKpRN59ivL+exp05lTVRBdfvrs9HxzEykZy47LCopkeyyYUBhNOz5/YRUAGw+0RhtQw5E/jI8lobDJmr3N6R7GsAmFw7idRiw1vY+6Zwj5AkE6ukJkewaWrdM9Jlnb0slD7+5Num0kEBQIWdltCgiJiMio5rCaSkeaRtd21NIV6qIr1MU7oRaOCbvIdavfo4iI9I0+HQ2j+Ea8t12xKC1jiDSVzon7MH7cFGsqsZNmlEWXzbSnA99woJUdDbGmhfuaRlYJXDr94rktnPfzV3h/z9gICgVD5oAaoXcPwrT7Q/gCIbL72D+ou46uxNLFT/5xJV+4710a2vw9Mtja7V5dnV2RkjH9yRMRkVHMzhAqz7ECQu/VvcfF/7yYo+85mp0EONrIOcIBREREYtRUehjFB4Rye5mie6h1hawPzPEBoeOnlfLkF09hZmVsfPleN9VF2Ww40Eq2O/YhumWAszxlolW7GwGobe0ECtM7mGEQDJu4HIMLqCyqKeLd3dYMd31tKN2dL5DYkH3DAavR9I6GdnK6Pa/a/EGKcz0EQioZExGRDOBwQTjI1MKpzC2dyy1v3wJARXYFtR21LHcWp3mAIiIymujT0TAqjeuZ4kjDlPMA+faMTyfHZQMBzBqX36MUaE5VPqt3N7HpYFs0gNSikrGoSJNll3NsPI3a/UG87oGda+RX6wNzK6PL+lsy9vfPnABAZ1yG0KpdjdGysF2HfNFp6U+cbmW9tdkZQtGm0mPkWomISIaym0o7HU5+ePIPmVwwme8c/x2eOO2XPLZ7L1O2vZzuEYqIyCiiT0fDqL+9V4ZCTUkOT3/pFL62YvYRt73k6AnsOuTj3d1NHFVtZcC0dgaPsNfYEQwNrMnyaLV+fwszKvIHtO+D/34Clx4zIaHBdGdX/2atK86xAqrxs91d9H+vRe83+bpotQNAlQXWTGeRkrHITHk5WUqKFBGRUcwuGQOYWjCFh6d9nA9PPhf33lXUBENQPDm94xMRkVFFAaExaEZlfp+yWs45qiqh4TQMf8nYHS9vY/JNj0YzPUaC1bubaO7oimYI+e3+NJnMNE221rUza9zAAkJHTyzmJx9eyAnTSqPL+tugPJJR1L2HUPzxGtoCANQUWz0UIr83/1i1l4r8LOaPL0i6r4iIyKhgN5UGYN1D8LdPwWu/gMYd1rJPv5S2oYmIyOijr8uH2dNfOoWDLf50D6PPIh/a59sZQi0dwxuY+e3L26LjyBsB2R2maXLBL1+luiibyoIsADqD/ct0GY1aOoO0+YNUF2UP6jhFOR5uvXwRX/zLu/3eN1K26PNb/9/BUGIgrrmji12HfADRGf3a/EEONHfy/MZarj912pgp7xMRkQwVlyHEdjv4YxjgbwNPHngzv6ehiIikTvo/YY8xMyrzmVE5sCyLdJpZmU9xjttuoDx8IrNDdQVHRhZOpDnx3qYOyvKsEqbOMZAhtK+pA4CqIu+gj3Xegiq217dzzUlT+rVfXpYLj9PBI+/v5zcvbeWeTx4XXecw4OHV+6MZbZFMpr/8azcHW/yETbj0mAmDHruIiEhaOZwQtr+c2/u2dRsOQbATXFnpG5eIiIxK+rpc+mR6RR7VxdnstQMDw8VvZ9/0ViY03CLZKQCba9uA2BgzWaR3VFG25whbHpnL6eBLH5gZDd70lWEYlOZ5WL27ifq2AD9/dgsA3zlvLtluJ/Vtfg40d5LvdVFi9xt6eXM9r29toLoom6nleYc7vIiIyMjncFkBoI4m2L/aWtbZZAeEBv+ljYiIjC0KCMlh3XnVEm44exZup4Pqomz2NsYCQi2dXTT5AkP6+JHsm/42IB4qvrhxRJob17X6ufetXZimma5hDblg2LoOLmd6O2iX5cW+/Xz0/f04HQYXH13NTefOAWB7fTuF2W5ys2IzmL20qY4JxYMrdRMRERkRDCeYYWjeA9jvO978NQT94Bz8lzYiIjK2qGRMDuv02ZWcPtuaKry6KIeXN9djmiaGYbD8f17gUHuAoycWAXDfdcfjcQ1NjHHkZAj17KF06zObAZg9Lp/FE4uHe0jDItJA25XmKdUiZXoR/3bcRIpyPIyzZxXbXt9OWV5WQq+gQCjM+EH2PhIRERkRHA4IdUH9xsTl+95VhpCIiPSbMoSkz8YXefEFQtFG04fareygd3Y18c6upiHNFhoxGUJ2VtDVJ07usW6kBK2GQtAOCDnTHBAqz0/sj/CxZZMAKMm1AkW1rf6kpWjjU9D7SEREJO0MJ2x5Gh64xvr5kt9Ztw2b1UNIRET6TQEh6bNI2c2exsQ+Qp862WoOHAiltrly/BT3HYHhbdwcCIZpbO8Z4PqPR9YBMGecNYtV1hBlRI00wVAkQyi95xtfMuZxOZhpN2ifWJJDJFZVkG0lPv76345h2dQSAGUIiYhIZnA4E3+umBu7rwwhERHpp7HxaVZSorooB4CtdW189LdvAHDTObOZN96a4jSQ4pnAWjpiAaFkGULdpx1PpU/fvZLF//F0j+UrdzYCUFlovemKlFJ1v59pQnYPoXRnCEUCQuMKvLz7nQ9El5fnZ3HGHKu0sSLfujYr5o/jqhMmA1CtgJCIiGQCo1tAKLcMcius+y71EBIRkf5RQEj6rNrOEPrCfe/y2tYGppblcukxE6J9g7pCqQ2IRMqzAA60JE53v6vBx/RvPs4j7+1L6WMC/HP1Pp7fWAeQ0Cg6GArjdhrMrMzj5OllAHzy5KlJx5tpIiVj7nQ3lbZLxtwugxxPYgu0a060MtWOn1YaXXbmnEr+59IFnGRfLxERkVHN6PbW3VsI4xdZ9x1qDSoiIv2jgJD0WXFOrDfLcVNKeO6ryynLy8LtjASEUpux0x7XwPkdOzMnYntDOwA/fWpTSh8T4PP3roreD8Zl/Wyvb6crZHL9qdNwOAy2//BcvrZiVnS9L9Cz4XSmCI2QHkLdm0rHO35aKW994wzOmT8uuszldPDhJTUJTaZFRERGrfP+F8YdFfvZlQXHXGXd3/ZiWoYkIiKjlz4lSZ8ZRiwYsG5/S/R+JGvEn+KSsUjGzZSyXJ7dUMvH7niDR9/bz4HmTvY3WX2MGoegkXV80CNyTi9uquOJNQcAmDve6h9kGEbC/0kmZwh1jcAeQslUFHgTromIiEhGqZgN591m3S+otm5nnG3dTl2ejhGJiMgoptxSGZCff2Rx9H6sZGxoAkInzyhje307r25poN0f4pfPh9lwwApItSeZBn6wirLdNNgNpTu7Quxt7OATd74FgMfpYFp5XtL9WjoyOUPI7iGU5pKxcjsgdM78qrSOQ0REJG0iX3xkWxMn4HTBjdvVVFpERPpNGUIyIMtnVUTvewZRMhYMhXljW0NCr56ISAnWKTPKo8s2HWxlS20bkUqurpCZdN/BqIqbovzh1fs4+9aXoj+PK/RGS+Qivn2eNcPHql2JZW2ZJNpDKM0lY8W5Ht78xhl8bcXstI5DREQkbYonW7cnfzm2LKcEPDlpGY6IiIxeCgjJoEUCJMlmGatv8/PGtoZe9/38fau44vY32HSwrce6lk4rIDS/upC/feYEvn/BPHyBEIFQmGnludHtmuNmI0uFxvYuXHbg46VNdQnrklUjXXvSFC5YNJ61+1p6rswQI6WHEEBlgXdEjENERCQtckrgu80w/+J0j0REREa5IwaEDMO40zCMWsMw1sQtKzEM42nDMDbbt8X2csMwjJ8ZhrHFMIz3DMM4eigHL8Pv4f93Evdce1zCssOVjF3yq9e44vY3kh5r9yEfj71v9eVJFtR5et1BxhV4Kc/P4uiJxcyze/cA/OKjR3Pr5dasGvVtqe0j1OQLUFlgZQlFZhuL6K1P0MzKfPY2ddA2BCVsI0FwhPQQEhERERERkdToy6e7PwArui27CXjWNM0ZwLP2zwDnADPsf9cBv0rNMGWkOGpCISfNSJzCO5ohlGTa+Z0NPoCkZV3xGTXdZ+ja1eDjpU11fGTpxGg2yIzK/Oj6OVUFlNtTkNe1+gGrn9BPn9pIZ9fAmzsHgmHaAyGqCpPX4ft6CfjMqLD6Cm0+2Drgxx7JgiOkh5CIiIiIiIikxhEDQqZpvgQc6rb4AuAu+/5dwIVxy/9oWt4AigzDUPfXDBftIXSYWca6kgSL/MFY4KZ75s1Lm63MnAsXj48uK/C6E7aZUZlHlsvB717Zjmma/OzZzfz8uS089O7ePo17X1NHtBQqosmetSySIRTx3fOtPkG+XoJNs8ZZwapNGRsQimQIKSAkIiIiIiKSCQZa/1FpmuZ++/4BoNK+Xw3sjttuj71MMlikZGx/c0ev2yQrJ/N3xZZ1ny2spdMqIesemLnzqiU8+O/HA1CR7+UrZ83kmfUHeX1bA1vrrD5EXrfziGN+bsNBTvjRc/xjVWLwqNFnPW4k+2hiSQ6Pff5kzjnKimv21r+6pjgHr9uRtBdSJgiFFBASERERERHJJINuCGJatUD9nubJMIzrDMNYaRjGyrq6uiPvICNWWZ6HhRMK+clTm3h5c/JrmSwg1BmXIbS1rh2AP76+g92HfLR1BnE5DLJcib+ip8+u5JhJJdGfrzx+MlkuB8+ur432IVq1q+mIY165w5oRbGO3jJ5GO0PoA3Mrufvapbzw1eXMHV9ASa7nsMdzOAymV+RlfIaQmjmLiIiIiIhkhoEGhA5GSsHs21p7+V6gJm67CfayHkzTvN00zSWmaS4pLy9PtomMEi6ng/uuOx6nw+Ct7d2rCy2BI2QI/frFrTT5AnznobV87I43ae0Mku91YSSb1iuO1+1kSlkuOxt80bK0P7y2g2CSx4t3oLkTgJU7DiVsGykZK8pxc/KMchx2AMTtdHD81FJ+fOmCXo85szI/YwJC3Xs+BcNhnA7jiNdDRERERERERoeBBoT+CXzCvv8J4KG45Vfas40tA5rjSsskg2V7nFQXZbPDbiLdXbIeQt2bP//4yY0A7Drk4+43dpLndfXpsSsLvBxs6UzIQlpzmCngn1hzgHX7rfXv7Grilqc3RddFSsaKc3pmBN173TIuW1LTY3nEzMp8Drb4o+Vuo9XuQz6mfP0xHns/9tQNhk2Vi4mIiIiIiGSQvkw7fy/wOjDLMIw9hmFcC/wI+IBhGJuBM+2fAR4DtgFbgN8CnxmSUcuINLkslx317UnXJWs47Q+GcRiwfJaVIfbnN3clrM/PcvfYJ5lxBV7W7mumvs0fXfba1vqk29a3+bn+nrfZcKA1btuG6P1IyViygNCRlOdZfYea2kd3QCgSLPvLv2LtwNr9QQWEREREREREMsgRUzBM0/xIL6vOSLKtCXx2sIOS0WlyaQ6rdjZimmaP0qLItOXxOrtCeN1OTppexgsbY72HzpxTwTPraymzGzsfSWWhl7AJB1v8LJ1cwvt7m3ns/f0cagvwlbNmke2JNZlONiV9fJyjtsVPrseZsE9f5WZZT6dW/+gOCLV1Wg2+w3bZ2L6mDh58ey8nzyhL57BEREREREQkhQbdVFokYnJpLq3+IIfaAz3WBYLJpp0Pk+VycMacSk6ZWc5b3zyD9d9fweTSXADm2FO5H0mBXVp2ydET+Munl3HpMRNYs7eFO17Zzn3/Ssw6ShYQip95fvchHzUlOX163O7y7XG0+5NPTT9aRGaLiwT17nptB6GwybfPm5vOYYmIiIiIiEgKKSAkKTOlzArk7GjoWTb2hftWcf/K3QnLnttQG20K/cdrllKR7yXb44yWfk0tz+3X45fleTAMgzlVBdFlGw8kNnnuCMQylW65bCGQOHPWnsaOAQeEIhlCbaM8Q2i/3XA70lh67b4WZlflD/j/RUREREREREYeBYQkZSbbAaFfvbCVt3c2Eo5Lvdlc28Z3/7k2+rNpmhxo6YxmA8Ursvv3TK/oW4bQZcfWcPHiaj596jTAKjnLdlslX29sa0jY1hcIRu/PG1/IhYvGU9ca6z3U2tlFUXbfehd1l5dlPWbbqM8QsgJCkWyqDQdamVXZt2shIiIiIiIio4MCQpIyk+wMkmfW1/LNv7/Pk2sPJKzPyYq1rPIFQoTCJqfaDaXj3XD2LH7z8WM4ZlJxnx63wOvmlssXUZJrBZIqCrys+/7ZfOuDc9jR4GNfU0d02w47yDG9Io9p5bmU52dR1+qPZsO0B0LRTJ/+yrObYLeOslnGOrtCfPrulazd1wwQ/f/q6ArR0Oanvs3PrD6W74mIiIiIiMjooICQpIzDYfD506cDkONxcqClM2F9pFkxQKt9Pz/J1PK5WS7OnjduUGMxDCNacnYwbhyRrJeff2QxLqeD8vwsOrpCtPmt8fgCQXIG0FAaiGYldXb1bKAN4A+GErKmRoo3tx/iybUH+c9H1gOxDKGOQIgdDT4AppXnpW18IiIiIiIiknoKCElKffmsWZw5p4LOrjC7D3UkrIufaSySRZPvHVh5Vl/keno2efYFrPuR4E25PZNZXaufQDBMV8gccIaQ22X1IuoK9QwI7W/uYN53nuR7D6/tsS7d9jRaQZ+Kgix8gSDNHda16ewK02D3cyrL69uMbyIiIiIiIjI6KCAkKZeb5cIXCLKlrg2ATxw/iU+fOpWukBktzWqxA0IFSTKEUjkOgDZ/kGAozHH/9Qy3PrMZiGUmled5ASsgFOkvFAkW9ZfHaT2duoKxgFAobBIOm9z71m6CYZNH398/sJMZQi0d1nl3BEIc+5/PAGAYsLepg9V7mgAoyfOkbXwiIiIiIiKSekP3aVzGrNwsF23+EFsOtnLhovF874L5/PxZKxATDJu4ncZhS8ZSJS8rkiEU5JAvwMGWWPPoSL+hSIZQbaufCXYPpNysgQWEnA4Dw4hlCPmDIS74xatMLMmhosB6HIdhHO4QaREJhD217mB0mR2345fPbwWgNFcBIRERERERkUyigJCkXG7c1PHTK6zeMy47eyYYMnE7Y312vAPMxunTOCIBoUCQlo5Yo+cTp5di2IGZ8UVWhtDuRh/VxdlArDl0fxmGgdvpIBCyoimbD7ax4UArGw60km+PpbbVT12rnyy3g72NHWyubeNDC8f3+THCYZPWziCFOakrtYuU0cUbX+hln91LKN/rGtLrJCIiIiIiIsNPASFJufgePJGAkNtp99cJh8nGiT9oBSGGMtCQF1cyFumLc/vHj+H4aaXRbfK9birys9ha286htgAel4MTp5cmPV5feJwOAkGr985rW+ujy1v9sYbaJ/7oOQJxfYb6ExC69ZlN/Oy5Lay++SwKs1MTFIpkCL14w3J+/+oOguEwnz1tOsf/8DkAilIYfBIREREREZGRQQEhSbn4HjyxgFBifx2/nSGU5Rq6NlZetwOHAT5/iCafFRCqLPD2aGRdU5LDg+/soTDbzZJJxRTlDLw8yu002NfUwTF2Lx6Ar62YzX8/sQEAh0FCMKi//vzWLgC21LZyzKSSw2776pZ6JpflUl2UfdjtfIEQk0pzmFSay3c/NC+6/C/XLePy29+gNFcNpUVERERERDKNmkpLysVn/UwqtaZ+d9kZQkF72vXOYcgQMgyDXI+LNn8w1sQ6SVZNsR0AKsvz8P0L5vVY3x9up4Pt9e0Jy64+cTIAMyvzmGz/fwxUpNRt1a6mI277sTve5MQfPXfE7dr9IXI8PWPDx00t5f7rj+fWyxf1f6AiIiIiIiIyoikgJCnndcd+rSKZQW6HdXv36ztZ+oNneH5DLTC0GUJgla+1+4PRPjm5np4BqMhMZ+fMr2J6Rf6gHs/jckSncY/wup288NXl3PPJ41hUU9Rjn7AdJDuScNik3S49+89H13PjA6tZv78l6baR2dwArr/7bTqS9AmKaO4IkJPk/wXg2MklTC4bXBBLRERERERERh4FhCTlkmX9RDKEfvH8Fmpb/Ty/sa7XbVMpN8tJeyAYDYh4kwQ+alutBtgTS3MG/Xgep4P2uOBLJNg0uSyXinwvN50zm0+fOjVhn65w7yVkf1+1h9oWq7nz3qYOfIEQU8utAM1fV+7hJ09uTLpffFnaE2sP8NaOQ0m3e2VzPf/a0cgJ0wbeN0lERERERERGHwWEJOWyXD2DLpFMobI8T9LlQyUvy0WbP0RnlxWkyU4SgLpwcTUAZ86pHPTjdT+f7rOBVRR4ufHs2fz40gWcNqscgK5Q8gyh3Yd8fOkvq/nqA+8BsPFAKwA/vmQBq79zFmfMrmC/PRNYd5EA2EI7I2nljkOs39/Ca1vraWjz8/NnN9PuD/KJ379FttvJ9adOG+AZi4iIiIiIyGikptKScvElYxGRWcbq2wIsnVzSa8ZKqkVKxjq6QrgcRtIA1KXHTODixdU4HMagH8/tso7hchgEwyYF3p49i5wOg8uW1ODzB3l+Yx2BYBiS9G1eu88qBwvY/ZY2HrQCQjPH5VPgdVNR4GX1nuS9hDrsANgVx9awra6Nv67czc+f2wLAh4+ZwP1v76Eox00obPKp06YmzAwnIiIiIiIimU8ZQpJySUvGHLFftWOnFA/bWCIBoc6ucNLsoIhUBIMgliH0wQVVHFVdyH9cOL/3be3+SYFgOGkfoffsYM+EYquUbfPBVqoKvdEg0/hCL/VtAQ61B3rsG+mZlONx8qUzZ3KwxR9d57AbU//6xW0AzK0aXN8kERERERERGX0UEJKUO1wPIYDrT53GGbMrDhugSZW8LBcbDrTS5OtK2j8o1SJNn4+qLuThz53E0RN7D3557ODRtXf9i6nfeCxhnWmavLenGQB/0OoHtOlgGzMqY8Gbs+aNA+Dvq/b2OHakZCzb7WTZ1MT+QOX5VjrS3qYOgKRZTCIiIiIiIpLZVCciKZesZCyS/3LCtFLyvW5+e+USjNQk5fRpLA++s4eJJYNvGn0knV1W8CY+cNMbj50hFCkNi3fF7W/w5narrK61s4tQ2GRrXVtC8+dZ4/JZOrmEW5/ZxPJZ5Uwrz4uui2UIuZg1LnEsITMxG6kgWwEhERERERGRsUYZQpJy1UXZHFVdyO+vPja6LDJT1viibMAq0TKGISIUjGvY7HYO/eNFppWf2oep2rv3M4o0vgaiwSCAFzbWsX5/C/5gmJndAk23XL4QgF/Y/YEiIj2Esj0OnN3K4Q61BSjwuphQbF2L3qacFxERERERkcylgJCkXL7XzcOfO4nTZlVEl62YX8XyWeV8+QMzh3UsobjePPuaks/IlUo/vPgo7r52KTV9yEbydAsINfp69gKaXGod54WNtYDVUDrehOIc5lQVRMu/IjoCVulatttKAnz8CydH19W2dpKb5eKea4/jyuMnMan0yMErERERERERySwKCMmwKMx284erl0YzhIZLMC4g9L+XLxryx8vNcnHyjPI+bdtm9xuKiDSHDtg9gwCuO8WaDn613U9oSpLgTXl+FvWt/oRl8U2lAeZUFXDLZVY2UW2rnxyPk8lluXz/gvk9MohEREREREQk8ykgJBktkiH0s48sZsX8cWkeTaJIc+ePLK0B4P6VewDY0dAOwI8uPooFEwoBa4axLJeDguyebb/K87Ko6yUglB1XDlZVaAXj1u5roVB9g0RERERERMY0BYQko5051ypbG4lTq584vYyXbzyN/7jAmpr+D6/toCsUZkttGwDzqwujgZsdDT4qCrKS9l0qz8+i1R+MziwGsX5E8QGhxROLoo2sI1PZi4iIiIiIyNikgJBktIsWT2Dt985mesXICwgB1JTk4HI6uPn8uQCs3t0UDQhNLc+lNM8T3XZ8YfJyu0imUX1bLEvIFzftfITX7WShnXEUaSgtIiIiIiIiY5MCQpLxcrN6llmNNBctrsYw4OXN9Wyta6O6KJscj4scj4tjJxcDcNM5s5PuGwkI1bYmBoTcTqPHTGaRzKDZVQVDcRoiIiIiIiIySoz8T8oiY0BRjocFE4p4ZUs9nV0hplXkRdf99solmCYU53qS7lthB4QifYS6QmF+/eLWpNvedM5sSnI9nDW3MsVnICIiIiIiIqOJMoRERojFNUWs29fC1ro2ppXHZhMryvH0GgyCWIZQnV0y9vrWBgBmj+tZJldZ4OXb583FG1dKJiIiIiIiImOPMoRERoip5bl02M2gp8dlCB1JaW4WDsPKEKpv87NyZyMOAx789xOGaqgiIiIiIiIyyikgJDJC1JTEZv6aXt73gJDTYRA24WfPbuZnz24GYE5VwajonSQiIiIiIiLpoZIxkRGiPC8rer+/TZ+/8oGZCT/PVdNoEREREREROQwFhERGiEgvIIDCbHe/9v3cGTP45UePjv5crWnlRURERERE5DAUEBIZIUrsxtH53oGVen1wQVX0/nWnTE3JmERERERERCQzqcmIyAjhdjr4+UcWs6imaMDHuPrEySycUESe+geJiIiIiIjIYehTo8gIcv7C8YPa/+bz56VoJCIiIiIiIpLJVDImIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIinXS5SAAABv9JREFUIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGKCAkIiIiIiIiIjLGGKZppnsMGIZRB+xM9zjksMqA+nQPQoaMrm9m0/XNbLq+mU3XN7Pp+mY2Xd/MpuubuTLt2k4yTbM82YoRERCSkc8wjJWmaS5J9zhkaOj6ZjZd38ym65vZdH0zm65vZtP1zWy6vplrLF1blYyJiIiIiIiIiIwxCgiJiIiIiIiIiIwxCghJX92e7gHIkNL1zWy6vplN1zez6fpmNl3fzKbrm9l0fTPXmLm26iEkIiIiIiIiIjLGKENIRERERERERGSMUUBojDIMo8YwjOcNw1hnGMZawzC+YC8vMQzjacMwNtu3xfby2YZhvG4Yht8wjK92O9adhmHUGoaxJh3nIj2l6vr2dhxJrxReX69hGG8ZhrHaPs730nVOEpPKv8/2eqdhGKsMw3hkuM9Fekrx6+8OwzDeNwzjXcMwVqbjfCRRiq9vkWEYDxiGscEwjPWGYRyfjnOSmBS+/s6yn7eRfy2GYXwxXecllhQ/f79kH2ONYRj3GobhTcc5SUyKr+8X7Gu7drQ/d1UyNkYZhlEFVJmm+Y5hGPnA28CFwFXAIdM0f2QYxk1AsWmaXzMMowKYZG/TaJrmT+KOdQrQBvzRNM35w30u0lOqrm9vxzFNc10aTktsKby+BpBrmmabYRhu4BXgC6ZpvpGG0xJbKv8+28f7MrAEKDBN87zhPBfpKcWvvzuAJaZp1g/3eUhyKb6+dwEvm6Z5h2EYHiDHNM2m4T4niUn132f7mE5gL3CcaZo7h+tcpKcUvr+qxnpPNdc0zQ7DMP4KPGaa5h+G/6wkIoXXdz5wH7AUCABPANebprll2E8qBZQhNEaZprnfNM137PutwHqgGrgAuMve7C6sJwCmadaapvkvoCvJsV4CDg3HuKVvUnV9D3McSaMUXl/TNM02+0e3/U/fEqRZKv8+G4YxAfggcMcwDF36IJXXV0aeVF1fwzAKgVOA39nbBRQMSr8hev6eAWxVMCj9Unx9XUC2YRguIAfYN8TDlyNI4fWdA7xpmqbPNM0g8CJw8TCcwpBQQEgwDGMysBh4E6g0TXO/veoAUJmmYUmKpOr6djuOjBCDvb6GVU70LlALPG2apq7vCJKC5++twI1AeCjGJ4OTgutrAk8ZhvG2YRjXDckgZcAGeX2nAHXA7w2r5PMOwzByh2qs0n8pfP98BXBvSgcngzaY62ua5l7gJ8AuYD/QbJrmU0M2WOm3QT5/1wAnG4ZRahhGDnAuUDNEQx1yCgiNcYZh5AEPAl80TbMlfp1p1RMqW2AUS9X1PdxxJH1ScX1N0wyZprkImAAstdNgZQQY7PU1DOM8oNY0zbeHbpQyUCn6+3ySaZpHA+cAn7VLuGUESMH1dQFHA78yTXMx0A7cNBRjlf5L4fsrD/Ah4P6UD1IGLAWvv8VYWSdTgPFArmEY/zZEw5V+Guz1NU1zPfDfwFNY5WLvAqGhGe3QU0BoDLN7hjwI/Mk0zb/Ziw/a9ZWROsvadI1PBidV17eX40iapfr5a5ciPA+sSPVYpf9SdH1PBD5k95m5DzjdMIx7hmjI0g+pev7a30JjmmYt8HesfgaSZim6vnuAPXFZmw9gBYgkzVL8+nsO8I5pmgdTP1IZiBRd3zOB7aZp1pmm2QX8DThhqMYsfZfC19/fmaZ5jGmapwCNwKahGvNQU0BojLKbyf4OWG+a5i1xq/4JfMK+/wngoeEemwxeqq7vYY4jaZTC61tuGEaRfT8b+ACwIfUjlv5I1fU1TfPrpmlOME1zMlZJwnOmaeobyjRL4fM3126KiV1KdBZWGrukUQqfvweA3YZhzLIXnQFoQoc0G4L3zx9B5WIjRgqv7y5gmWEYOfYxz8DqVyNplMrnr91wGsMwJmL1D/pzakc7fDTL2BhlGMZJwMvA+8R6S3wDq47yr8BEYCdwmWmahwzDGAesBArs7duwOue3GIZxL7AcKAMOAjebpvm7YTwd6SZV1xdYkOw4pmk+NkynIkmk8PpOxmqe58T6guCvpml+f/jORJJJ5d/nuGMuB75qapaxtEvh87cMKysIrPKiP5um+YPhOg9JLsXvrxZhNYT3ANuAq03TbBzO85FEKb6+uViBg6mmaTYP75lIMim+vt8DLgeCwCrgk6Zp+ofzfCRRiq/vy0ApVsPpL5um+eywnkwKKSAkIiIiIiIiIjLGqGRMRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSMUUBIRERERERERGSM+f8o1xR53MFQJgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1440x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0UK6BTs5y_j3",
        "outputId": "1d23b7ab-2c78-485e-ef8d-6b4c3019c7a5"
      },
      "source": [
        "lstm_model.predict(x_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([[0.36300233],\n",
              "       [0.36230323],\n",
              "       [0.36314383],\n",
              "       ...,\n",
              "       [0.58011895],\n",
              "       [0.5777515 ],\n",
              "       [0.5707615 ]], dtype=float32)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 32
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "N33GeMUI59Bv"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0RtpGJ_g8G2T"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}