Aromatase_Compare_Regressors(Step4).ipynb

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  "metadata": {
    "colab": {
      "name": "Aromatase_Compare_Regressors.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNq-XQWWYNXh"
      },
      "source": [
        "In this ipynb, I have  compared several ML algorithms for build regression models of aromatase inhibitors.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnVZXG7QY1D2"
      },
      "source": [
        "## **1. Import libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf73M_f9XwZ7"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUGwBVH7Y8Rq"
      },
      "source": [
        "## **2. Load the data set**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVojHjyWZC9D"
      },
      "source": [
        "df = pd.read_csv('Aromatase_bioactivity_data_3class_pIC50_pubchem_fp.csv')"
      ],
      "execution_count": 3,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.dropna(subset=['pIC50'], inplace=True)"
      ],
      "metadata": {
        "id": "-slfulGpUh36"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "! pip install lazypredict"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WQjlmxnaHEWD",
        "outputId": "682d5e68-bb7a-429f-d05c-abf200e1dbb8"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: lazypredict in /usr/local/lib/python3.7/dist-packages (0.2.9)\n",
            "Requirement already satisfied: click==7.1.2 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (7.1.2)\n",
            "Requirement already satisfied: scikit-learn==0.23.1 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (0.23.1)\n",
            "Requirement already satisfied: pandas==1.0.5 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.0.5)\n",
            "Requirement already satisfied: six==1.15.0 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.15.0)\n",
            "Requirement already satisfied: PyYAML==5.3.1 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (5.3.1)\n",
            "Requirement already satisfied: xgboost==1.1.1 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.1.1)\n",
            "Requirement already satisfied: joblib==1.0.0 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.0.0)\n",
            "Requirement already satisfied: scipy==1.5.4 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.5.4)\n",
            "Requirement already satisfied: numpy==1.19.1 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (1.19.1)\n",
            "Requirement already satisfied: lightgbm==2.3.1 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (2.3.1)\n",
            "Requirement already satisfied: tqdm==4.56.0 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (4.56.0)\n",
            "Requirement already satisfied: pytest==5.4.3 in /usr/local/lib/python3.7/dist-packages (from lazypredict) (5.4.3)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas==1.0.5->lazypredict) (2018.9)\n",
            "Requirement already satisfied: python-dateutil>=2.6.1 in /usr/local/lib/python3.7/dist-packages (from pandas==1.0.5->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: more-itertools>=4.0.0 in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (8.12.0)\n",
            "Requirement already satisfied: pluggy<1.0,>=0.12 in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (0.13.1)\n",
            "Requirement already satisfied: importlib-metadata>=0.12 in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (4.8.2)\n",
            "Requirement already satisfied: attrs>=17.4.0 in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (21.2.0)\n",
            "Requirement already satisfied: wcwidth in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (0.2.5)\n",
            "Requirement already satisfied: py>=1.5.0 in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (1.11.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from pytest==5.4.3->lazypredict) (21.3)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from scikit-learn==0.23.1->lazypredict) (3.0.0)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12->pytest==5.4.3->lazypredict) (3.6.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata>=0.12->pytest==5.4.3->lazypredict) (3.10.0.2)\n",
            "Requirement already satisfied: pyparsing!=3.0.5,>=2.0.2 in /usr/local/lib/python3.7/dist-packages (from packaging->pytest==5.4.3->lazypredict) (3.0.6)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyRegressor"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "BHaB7nJdHWGL",
        "outputId": "f473463f-ec8f-4d3f-a490-d5f4caa28e84"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/sklearn/utils/deprecation.py:143: FutureWarning: The sklearn.utils.testing module is  deprecated in version 0.22 and will be removed in version 0.24. The corresponding classes / functions should instead be imported from sklearn.utils. Anything that cannot be imported from sklearn.utils is now part of the private API.\n",
            "  warnings.warn(message, FutureWarning)\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbTq4zzKZpVf"
      },
      "source": [
        "\n",
        "X = df.drop('pIC50', axis=1)\n",
        "Y = df.pIC50"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Qa6WSGGZ39n"
      },
      "source": [
        "## **3. Data pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "CtC4SWpBaCVQ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "14ff350f-ece8-469b-b6c6-9fd76c4dcce4"
      },
      "source": [
        "# Examine X dimension\n",
        "X.shape"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1926, 881)"
            ]
          },
          "metadata": {},
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVHzT2f5Z8sF",
        "outputId": "e53af2fa-5d49-4e6e-9f29-24ba0ea24b15"
      },
      "source": [
        "# Remove low variance features\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))    \n",
        "X = selection.fit_transform(X)\n",
        "X.shape"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1926, 158)"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYck-_XeaHNZ"
      },
      "source": [
        "# Perform data splitting using 80/20 ratio\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ],
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkW9nKohamEj"
      },
      "source": [
        "## **4. Compare ML algorithms**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnFbUjA1aUj1",
        "outputId": "c2763861-7cb5-490c-ea95-3cdcf3006008"
      },
      "source": [
        "# Defines and builds the lazyclassifier\n",
        "clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "models_train,predictions_train = clf.fit(X_train, X_train, Y_train, Y_train)\n",
        "models_test,predictions_test = clf.fit(X_train, X_test, Y_train, Y_test)"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 42/42 [00:34<00:00,  1.20it/s]\n",
            "100%|██████████| 42/42 [00:00<00:00, 53871.79it/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cHOFkp-ka5tF",
        "outputId": "bd0f8313-5c74-4e15-de37-f28072cff55c"
      },
      "source": [
        "# Performance table of the training set (80% subset)\n",
        "predictions_train"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
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              "\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
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              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.96</td>\n",
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              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.09</td>\n",
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              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.58</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.88</td>\n",
              "      <td>2.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.57</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.89</td>\n",
              "      <td>1.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.53</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.94</td>\n",
              "      <td>2.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.50</td>\n",
              "      <td>0.56</td>\n",
              "      <td>0.96</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.05</td>\n",
              "      <td>3.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.05</td>\n",
              "      <td>2.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.24</td>\n",
              "      <td>0.32</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.18</td>\n",
              "      <td>0.26</td>\n",
              "      <td>1.23</td>\n",
              "      <td>0.81</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.16</td>\n",
              "      <td>0.24</td>\n",
              "      <td>1.25</td>\n",
              "      <td>1.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.14</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.17</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.07</td>\n",
              "      <td>0.16</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.05</td>\n",
              "      <td>0.15</td>\n",
              "      <td>1.32</td>\n",
              "      <td>0.14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.04</td>\n",
              "      <td>0.14</td>\n",
              "      <td>1.33</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.12</td>\n",
              "      <td>1.35</td>\n",
              "      <td>0.35</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.01</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.35</td>\n",
              "      <td>6.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.00</td>\n",
              "      <td>0.11</td>\n",
              "      <td>1.36</td>\n",
              "      <td>6.87</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>-0.01</td>\n",
              "      <td>0.10</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.08</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>-0.03</td>\n",
              "      <td>0.07</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>-0.04</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GeneralizedLinearRegressor</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.06</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>-0.05</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.39</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>-0.06</td>\n",
              "      <td>0.05</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>-0.07</td>\n",
              "      <td>0.04</td>\n",
              "      <td>1.40</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.11</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.55</td>\n",
              "      <td>-0.39</td>\n",
              "      <td>1.69</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-1.80</td>\n",
              "      <td>-1.51</td>\n",
              "      <td>2.27</td>\n",
              "      <td>0.51</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-19.67</td>\n",
              "      <td>-17.55</td>\n",
              "      <td>6.18</td>\n",
              "      <td>0.25</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-39.20</td>\n",
              "      <td>-35.07</td>\n",
              "      <td>8.62</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-285949936131093481652224.00</td>\n",
              "      <td>-256593152564678434488320.00</td>\n",
              "      <td>726740914913.44</td>\n",
              "      <td>1.17</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-845481dc-7e11-4eed-a972-85aab476f09d')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-845481dc-7e11-4eed-a972-85aab476f09d button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-845481dc-7e11-4eed-a972-85aab476f09d');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "                                        Adjusted R-Squared  ...  Time Taken\n",
              "Model                                                       ...            \n",
              "GaussianProcessRegressor                              0.58  ...        0.96\n",
              "DecisionTreeRegressor                                 0.58  ...        0.09\n",
              "ExtraTreeRegressor                                    0.58  ...        0.07\n",
              "ExtraTreesRegressor                                   0.58  ...        2.27\n",
              "XGBRegressor                                          0.57  ...        1.10\n",
              "RandomForestRegressor                                 0.53  ...        2.08\n",
              "BaggingRegressor                                      0.50  ...        0.29\n",
              "HistGradientBoostingRegressor                         0.40  ...        3.56\n",
              "LGBMRegressor                                         0.40  ...        0.28\n",
              "MLPRegressor                                          0.40  ...        2.67\n",
              "KNeighborsRegressor                                   0.24  ...        0.61\n",
              "GradientBoostingRegressor                             0.18  ...        0.81\n",
              "SVR                                                   0.16  ...        1.35\n",
              "NuSVR                                                 0.14  ...        0.96\n",
              "TransformedTargetRegressor                            0.07  ...        0.06\n",
              "LinearRegression                                      0.07  ...        0.06\n",
              "Ridge                                                 0.07  ...        0.07\n",
              "RidgeCV                                               0.05  ...        0.14\n",
              "HuberRegressor                                        0.04  ...        0.59\n",
              "LinearSVR                                             0.01  ...        0.35\n",
              "LassoCV                                               0.01  ...        6.15\n",
              "ElasticNetCV                                          0.00  ...        6.87\n",
              "PoissonRegressor                                     -0.01  ...        0.07\n",
              "AdaBoostRegressor                                    -0.03  ...        0.27\n",
              "SGDRegressor                                         -0.03  ...        0.10\n",
              "OrthogonalMatchingPursuit                            -0.04  ...        0.04\n",
              "BayesianRidge                                        -0.05  ...        0.16\n",
              "LassoLarsCV                                          -0.05  ...        0.21\n",
              "TweedieRegressor                                     -0.05  ...        0.06\n",
              "GeneralizedLinearRegressor                           -0.05  ...        0.06\n",
              "GammaRegressor                                       -0.05  ...        0.05\n",
              "LassoLarsIC                                          -0.06  ...        0.06\n",
              "OrthogonalMatchingPursuitCV                          -0.07  ...        0.10\n",
              "DummyRegressor                                       -0.11  ...        0.03\n",
              "ElasticNet                                           -0.11  ...        0.04\n",
              "LassoLars                                            -0.11  ...        0.08\n",
              "Lasso                                                -0.11  ...        0.04\n",
              "PassiveAggressiveRegressor                           -0.55  ...        0.05\n",
              "LarsCV                                               -1.80  ...        0.51\n",
              "KernelRidge                                         -19.67  ...        0.25\n",
              "Lars                                                -39.20  ...        0.11\n",
              "RANSACRegressor               -285949936131093481652224.00  ...        1.17\n",
              "\n",
              "[42 rows x 4 columns]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 81
        },
        "id": "KP6qT55ebcfd",
        "outputId": "dfc191f3-a4dc-4729-f6c7-a358314bb8e9"
      },
      "source": [
        "# Performance table of the test set (20% subset)\n",
        "predictions_test"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "\n",
              "  <div id=\"df-b3d5375e-f137-48a6-8ebf-58b12e4dd668\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-b3d5375e-f137-48a6-8ebf-58b12e4dd668')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-b3d5375e-f137-48a6-8ebf-58b12e4dd668 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-b3d5375e-f137-48a6-8ebf-58b12e4dd668');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ],
            "text/plain": [
              "Empty DataFrame\n",
              "Columns: [Adjusted R-Squared, R-Squared, RMSE, Time Taken]\n",
              "Index: []"
            ]
          },
          "metadata": {},
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM-lcHz6byNz"
      },
      "source": [
        "## **5. Data visualization of model performance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "MLsi5uSvbpC7",
        "outputId": "034f1553-f17f-43fd-bc5f-49ab12b0d299"
      },
      "source": [
        "# Bar plot of R-squared values\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#train[\"R-Squared\"] = [0 if i < 0 else i for i in train.iloc[:,0] ]\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"R-Squared\", data=predictions_train)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "OLkiiTTwb-Cb",
        "outputId": "fb8d5bce-6dbf-4d04-a36d-1f18d5a23090"
      },
      "source": [
        "# Bar plot of RMSE values\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"RMSE\", data=predictions_train)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 14
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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UFAMHDpTuyv7iiy9wcHAgMDCQsLAwvLy8MDY2xtXVVTrdXOa7774jJSUFU1NTzMzMHlsOMGHCBEJDQ+nTpw8Affv2lW7oelEdOnRgypQp+Pv7o9Vq0Wg0eHh44OLiQr9+/SoNWXmUCGcRBEGoXiKcRXitRDhL1ehLSpBV02N0hhyAAYa9fYa8bWDY2yfCWYQaQ4SzVE11FXNBEGoe8ddCeK1EOIsgCMKrIW6KE4Q3mAhnEQThWYkjdEF4AXc3rsZEXfzKxrfzD3plYwuCYFhEQX+NykJeHr7RYfXq1bz77rsVvj8lJQWNRoO7u3uV1yXCWQRBEP5aREF/zSoKSanM8ePHefDgQaUF/UlBIiKc5fmIcBZBEGoq8ZermmVkZDBmzBji4uJo2LAhUVFRZGRkMH78eLZs2YJOpyMpKQlPT08++eQTBgwYgI+PD8nJyfj6+tK4cWNWrFiBSqVCq9Uyfvx4PD09K12fCGcR4SyCIBgmUdBfs0mTJkmnvo2NjYmPj2fq1KlMnTqVSZMmkZiYyI4dO7C0tGTw4MFSKhvA9evXycvLo1WrVtJr9+/fJy4uDmNjY+7evYuPjw/u7u7Y2NhUOgcRziLCWQRBMDyioL9mFZ367tevH8nJyUycOJFNmzZV2jkNSgtPWQgKlLaTDQoKkrqj3b9/n8zMzCe2NRXhLCKcRRAEwyMK+htArVZz5coVrKysuHfv3hPf+9Zbb5W7rhwcHIxcLicqKgqZTEbv3r1RqVRPHEOEszxOhLMIglDTiefQ3wBLly7l/fff55tvvmH+/PlSUIqlpWWF+egPKygooGHDhshkMn777TeysrKqtG4RziIIgmAYxBH6a/bwNXQoDVo5fvw427dvx9zcnIkTJzJt2jS+/fZbevXqxc6dO/H29pZuinvU9OnTCQkJYdWqVbRq1QonJ6cqzUeEswiCIBgGEc4ivFYinKVqRDjLq2PI22fI2waGvX0inEWoMUQ4S9WIcBZBEJ6V+GshvFYinEUQBOHVEDfFCcIbTF+iqe4pCIJQQ4gj9FfAkHq2V2bWrFkkJSVRt25diouLcXd3Z/bs2RgZ/bW+I978bh4mKuUrG7/hxNWvbGxBEAyLKOiviCH1bK/MuHHjGDZsGEqlkv79+/PBBx880zPfz6K6eqprtdpX9iVIEAThVRIF/TWpqT3bK+uN/jBLS0vef/99bty4AYBSqWTx4sVcunQJlUqFm5sbgYGBGBsbk56eLvVgd3Z2Jjs7G39/f3r27Mnw4cNxdnbmzJkz2NjYsG7duirPKyoqil27dmFubo5MJuPbb7/F2tqaI0eO8PXXX6PVaqlXrx4LFizgvffeIyUlhYULF+Li4sKFCxeYMmUKPXv2fHX/EARBEF4RUdBfEUPp2V5Zb/SH3bt3j0uXLvHll18CsHjxYjp06MCiRYvQ6XTMmDGDHTt24Ovry8yZMxk5ciTe3t6cO3cOX1/fcmPl5OQQFxeHiYlJleeVl5dHbGwsv/76K7Vq1UKpVFKrVi3u3bvHzJkz2bhxI82aNWP79u3MmDGD7du3A5Ceni6NJQiCUFOJgv6KGErP9op6o5eJiYlh69atZGZmMmTIEBwcHKTxzp49yzfffANAcXExdnZ2KJVKLl++TJ8+fQAqbITTp08f6VR7Veel1Wqxt7dn5syZuLu706NHDywtLTlz5gzOzs40a9YMgAEDBhASEoJSWXrt+7333hPFXBCEGk8U9NeoJvZsr6g3etlRddk19KtXrzJkyBDc3d3p3r07er2e6OhoGjVqVG6ssgL6pIzzR+dc1Xlt27aNU6dOkZycjI+PD+vXr3/iPnp0nYIgCDXVX+uW5GpWE3u2V9Qb/VFNmzZl0qRJLF++HL1ej1wuJyYmBq1WC5SeXcjJycHS0pLmzZuza9cuAM6fP8/ly5efOOeqzEupVJKbm4urqyuTJk3C0dGRK1eu0LZtW9LS0sjIyAAgISGBli1bPvEMiSAIQk0jjtBfEUPp2V5Zb/RHffrpp3z77bccOHCAoKAgwsPD8fb2RiaTYWpqSlBQEI0aNWLJkiUEBQURExODo6Mjjo6OWFlZVThmVeelVCr58ssvKS4uRq/X07JlSz766CPMzc1ZunQpM2bMoKSkhHr16hEeHl6l/ScIgvCmE73chdeqsLCQ2rVrI5PJSE9PZ/jw4ezbt++JN/e9icr6Lb99epvBPoduyP2ywbC3z5C3DQx7+0Qvd6HGOH36NEuXLqXse2RoaGiNK+YPe2f4glcczqJBZmL6ysYXBMFwiIIuvFbu7u7P1RHvr0oUc0EQnpW4KU4QBEEQDIAo6ILwBtOVqKt7CoIg1BDilLsBy8vLo2/fvkRFRdG6dWsA1q5dy/nz51m1ahXnzp1jxYoVZGZmUqdOHfR6PV5eXvj5+QHlQ2ZUKhUffvgh8+fPx9TUlPj4eMLCwmjYsCEajYZ3332XRYsWYWtrW52b/Npd+3YsRqr7r2z85l8oXtnYgiAYFnGEbsDq1KnDvHnzCAwMRK1Wc+nSJTZt2kRwcDCXLl1i7NixjBgxgoMHDxIfH8/69evJy8srN8bKlStRKBTs3r2b9PR0fv75Z2lZ586dpWUWFhZERUW9tLnrdDqq4wGMkpKS175OQRCEl0EcoRu4Xr16sW/fPiIiIjhx4gSBgYHUr1+fxYsXM2jQILp37y69t379+kyfPr3CcVQqFSqVCmtr68eWyWQyOnTowKFDh6TXKgtVKSgoICgoiCtXrmBnZ4ednR3169cnICCAVatWceXKFZRKJTdu3GDr1q388ccfrFmzBrVajampKYGBgbRt25arV69KIS86nY7+/fvj5+dXaWhLVlYW8+bNIzc3FxMTE6ZOnUq3bt0AcHJy4osvvuDQoUN07dqVKVOmvNxfgiAIwmsgCvpfwNy5c+nZsyedO3eWmtZcuHABDw+Pp362rEFOdnZ2pXeoq9Vqjhw5Io39pFCV1atXY21tzb59+8jLy8PHx4fevXtLY509e5b4+Hjq1atHdnY20dHRbNiwAUtLS65cucLYsWM5dOgQcXFxyOVyPv/8cwCpm1xlYTIzZszA19eXQYMGkZ6eztChQ9m7dy/16tUDSnvn79ix4wX2siAIQvUSBf0v4NixY1haWnL16lXUajVmZmaPvWfhwoWcOHGCe/fusX37dv72t78B/wuZUalUfPnll8TGxjJq1CgAkpKS8Pb25vr16zg4OEhhMk8KVUlJSWHOnDlA6SWBXr16lZtHt27dpCJ79OhRsrOzGTp0qLS8pKSEu3fv0qFDB8LDwykqKsLNzY2OHTsCFYe2KJVKLl68yIABAwBo1qwZLVq04I8//kAulwNIcxUEQaipxDV0A5ebm0tYWBgxMTG4uLiwcuVKAFq0aFGuL/ucOXNQKBRoNBqpB/vDzM3N6dGjB0lJSdJrZdfQDx8+jEwmIzIyEvhfqIpCoUChULBr1y62bNnyTPO1sLAo93PXrl2lcRQKBb/++itvv/02vXv3ZtOmTdjb27Nu3Tr++c9/AqWhLaGhoZiamjJ58mS2bdv2TOsVAS2CINR0oqAbuJCQEHx9fXF2dmb27Nns2rWLc+fOMXbsWLZu3cqRI0ek96rVanQ6XYXj6HQ6Tpw4QePGjR9bZmlpSUhICJs3b+b27dtPDFVxdXVFoSi9czs/P58DBw5UOvcuXbpw9OhRrly5Ir129uxZALKysrC1tcXHx4eJEydKX04qCm2xtLSkRYsWJCQkAJCRkUFaWtoTo2cFQRBqGnHK3YDt2bOHa9euERERAYCNjQ3z5s0jKCiIHTt28K9//YvIyEiCg4OpV68epqamjB8/ngYNGkhjlF1D12g0NG/enIkTJ1a4LmdnZzw8PFi3bh2zZ8+uNFRl4sSJBAYG4uHhga2tLS4uLpWmnjVu3Jjw8HBmz55NcXExGo2G9u3b07p1a/bu3UtiYiKmpqbIZDKCgoIAKg2TiYiIYN68ecTGxmJiYsLSpUulU/uCIAiGQISzCK+VRqNBp9Nhbm6OUqlkyJAhBAYG0rlz5+qeWpWUBShYnoo02OfQDTkAAwx7+wx528Cwt0+Eswg1Rn5+PmPHjkWr1aJSqfDy8qpxxfxhjUese6XhLLoSNUYmj9/EKAiC8ChR0IXXqn79+sTHx1f3NGoMUcwFQXhW4qY4QRAEQTAAoqALwhtOBLQIgvAs/vKn3MsCSMzMzNBoNIwZM4ZBgwa91PHXrl2Lo6PjSxkvJSWFcePGlXt8LDAwUGqs8qrs37+fBg0aSCEv169f56OPPqJ58+bodDpMTU2ZM2cO7du3f6XzeNOkbvp/yNR5T3/jC2g/PvGVji8IgmH4yxd0+F83tMuXL+Pj40O3bt2ws7Or7mlVysHB4bmvQ5eUlGBiUvVf+/79+3FxcZEKOoCVlZX0THlcXByzZ89m7969zzWvimi1WoyNjV/aeM/iefePIAhCdRN/uR7i6OiItbU1t27d4vjx43z77bdoNBoAAgIC6NSpE1B61O3t7U1SUhJ37txhzJgx0jPXJ0+eJCQkBIAOHTqUSww7e/YsixYt4sGDB9SuXZvZs2fTunVrrl+/zoABA/D19eXo0aMUFxcTERHBli1bOHPmDLVq1SI6OvqJ0aRarZaIiAiOHj0KlHZYmzFjBsbGxsyaNQtjY2MyMzMpLCxEoVCQkJBAXFyc1JY1ODiYpk2bcurUKUJDQ9HpdJSUlODv74+NjQ0HDx4kKSmJ7du3M3r0aD788MNy63dzc2Pp0qXSz4cPH64wVAVg+fLl7Nmzhzp16uDq6sqxY8eIj48nJSWFhQsX4uLiwoULF5gyZQrvvfceYWFh/Pe//0Wj0TBy5EgGDBhAUVERAQEBpKenY2JiQpMmTYiMjKw0tKWwsJCFCxdKDWi8vb0ZO3YsAMOHD8fZ2ZkzZ85gY2PDunXrnv8fkSAIQjURBf0hv//+O3Xr1sXZ2ZlGjRrh5eWFTCbj6tWrjBo1qlxXteLiYrZu3cr169fp06cP/fv3x9TUlKlTpxIREYGbmxt79uxh06ZNQGm3tEmTJrF48WI6depEUlISkyZN4qeffgJKs8s/+OADpk+fzvr16xk1ahTfffcdCxcuJDg4mI0bNzJ16lSgtNOZt7c3AGZmZmzfvp2tW7dy8eJF6ci9rBPcZ599BsDFixfZuHEjtWvX5uTJk+zdu5dNmzZhZmbG4cOHCQoKYsuWLaxbtw4/Pz+8vLzQ6/UUFBRgbW2NXC7HxcVF+uJy/fr1cvvu559/lsJZnhSqcvDgQX755RcUCgW1atVi0qRJ5cZJT0+XwlVKSkrw9fUlPDwcBwcHlEolAwYMkNLWCgsL2bNnD/C/cJbKQluio6PR6XQkJiZSWFjIp59+iqOjo5Q2l5OTQ1xcnDg6FwShxhJ/vSjthqbX68nOziYyMhIzMzPS0tKYPn06t27dwsTEhLt373Lnzh3pKLmseL377rtYW1tz8+ZNNBoNb731Fm5ubtJ75s2bB0BmZiampqbSUX7nzp0xNTUlMzMTCwsLateuTY8ePQB4//33eeedd2jRooX088M91Cs65X7s2DH69+8vBa/4+Piwf/9+qaB7eHhI/coPHjxIWlqadK+AXq8nPz8fKD3SXrNmDdnZ2XTp0oU2bdpUut8KCgrw9vYmNzcXtVrN9u3bgSeHqqSkpPDxxx9Lc+nXrx/R0dHS+9577z3atWsHwLVr18jIyGDatGnSco1Gw9WrV3F2diYjI4OQkBBcXV2lfVdZaMuxY8cICgpCJpNhaWmJp6cnx44dkwp6nz59RDEXBKFGE3/B+N819L179xIYGEj79u2ZNm0as2bNolevXuh0Otq0aYNKpZI+83AzEWNj4woDTaA0K/xZPJyAZmRkVO7nJ43/rB4OH9Hr9QwYMIDJkyc/9r5Ro0Yhl8tJSkoiNDSULl26SGcGHlV2DV2r1bJ48WKmT58uhaF07dq13Cn4551n3bp1pev0j9q1axfJyckcOXKE5cuXk5iYSO/evWnbti2//fYb69atY8eOHVLr22ddryAIQk0kHlt7yMcff0yXLl3417/+RUFBAe+++y4AO3bsQK1++qNDTZs2pbi4mJMnTwKwb98+6ci3SZMmaDQakkIOeS8AACAASURBVJOTgdIjxpKSEpo0afJS5t6pUyd27tyJRqNBo9Gwc+fOSjuwyeVyFAoFN2/eBEqvv6empgKlZxLs7e0ZPHgwI0aMkK45W1paUlBQUOF4xsbG/POf/+TOnTscOHDgiaEqrq6u/Pjjj9I17h9++KHSbWrSpAm1atVi586d0msZGRkolUpu3ryJsbExvXr1IjAwkNzcXPLy8ioNbenUqRM7duxAr9ejVCrZs2dPje5QJwiC8ChxhP6I6dOn4+Pjw9y5c5kwYQI2NjZ07dqVOnXqPPWzZmZmfP311+Vuivu///s/adnKlSvL3RRXdnr/Zfj000/Jzs6Wcr3d3d3x9fWt8L0dOnRgypQp+Pv7o9Vq0Wg0eHh44OLiwnfffUdKSgqmpqaYmZlJ2eV9+/YlMDCQffv2VXhTnLm5OVOmTCEqKoqEhIRKQ1X+/ve/c/r0afr27YuNjQ1t27aVrnM/ysTEhLVr1xIWFsaGDRvQ6XTUr1+fFStWcOnSJZYtWwaUJsGNGzcOOzs71q5dW2Foy4QJEwgNDaVPnz7S9nTr1u3Fd7wgCMIbQoSzCK+dUqnE0tISnU7H7NmzadCgQaWn9d9UZQEKnFlhsM+hG3IABhj29hnytoFhb58IZxFqlICAAP7zn/9QXFzM+++/Lz0+VhO5DF3/SsNZQAS0CILwbERBF1671atXV/cUahRRzAVBeBbipjhBEARBMACioAtCDSACWgRBeBpxyv0NURYSY25ujkql4sMPP2T+/PmYmpq+9HXNnj2b/v37P3anelWsWrWKuLg4GjRogEql4v333yc0NPQv9zz3iS1+6F/xTXEAXcfueuXrEAShZhNH6G+QlStXolAo2L17N+np6fz888+vZD2LFi16oWJepl+/figUCn744Qf+/PNPNm7c+BJmV6qkpOSljVUT1isIgvCixBH6G0ilUqFSqbC2tubYsWOsWLEClUqFVqtl/PjxeHp6AqV9z8uCSJydncnOzsbf35+ePXs+cdnw4cMZM2YMPXv2ZNasWZiZmXHt2jVu3rxJ27ZtWbJkCTKZjFu3bjFz5kzu3r1Lo0aNgNLn28v6uZcxMzOjXbt2/Pnnn0Bp3/rly5dz4sQJ1Go1Tk5OBAcHY2Fh8cQxXzRExsvLi61btxIbG4uZmRk6nY4VK1bg4ODw1GAcHx8fkpOT8fX1ZciQIa/xty0IgvByiIL+Bpk0aRLm5uZkZ2fj7u6Ou7s79+/fJy4uDmNjY+7evYuPjw/u7u7Y2Ngwc+ZMRo4cibe3N+fOnSvXSOZJyx515coVYmNjkclk9O/fn6SkJLp06cLChQtxc3NjwoQJ/Oc//6FPnz64u7s/9nmlUsmJEyekZ8nXr1+PlZUV33//PQDh4eHExMQwderUp475IiEyAEuXLmXv3r00aNAAtVqNVqt9pmCcVq1aERAQ8OK/REEQhGoiCvobpKynvEql4ssvvyQ2Npbu3bsTFBREVlYWxsbG3L9/n8zMTJo1a8bly5elzmetWrXCyckJKC2wlS2rSK9evaRnqVu2bCkFs6SkpEid4ho2bCgFy5TZuXMnv/32G1lZWbi7u0tBKAcPHkSpVPLjjz8CpUfszs7OAE8d80VDZDp27MisWbPo2bMnPXr0oFGjRly6dOmJwTjm5uZ8/PHHVfhNCYIgvHlEQX8DmZub06NHDw4dOsQvv/yCXC4nKioKmUxG7969y4XEPCn85VmDYZ41aOZR/fr1IyAggDt37vDZZ58RFxfH0KFD0ev1zJ8//7Fi/SxeNEQmKiqKc+fOkZyczIgRIwgODuadd9554jrfeuutZ95XgiAIbypxU9wbSKfTceLECRo3bkxBQQENGzZEJpNJR8NQGpbSvHlzdu0qvfv5/PnzXL58+anLqsLV1ZWEhAQA/vzzTylY5lG2trbMnj2bNWvWUFxcjFwuJzY2luLiYqD0jEFGRkaVxoSqh8iUlJSQk5ND69atGTduHF26dOHixYuvPBhHEAThTSCO0N8gZdfQNRoNzZs3Z+LEiaSmphISEsKqVaseO3W+ZMkSgoKCiImJwdHREUdHR6ysrJ667FnNnj2bmTNnkpiYyLvvvkvr1q2xtLSs8L09evSgadOmbNmyhXHjxhEVFcXAgQORyWTIZDK++OILHBwcqjRmVUNkdDods2bNoqCgAJlMxt/+9jemT5/+yoNxBEEQ3gQinKUGKywspHbt2shkMtLT0xk+fDj79u3DxsbmicueVXFxMSYmJpiYmHD79m0GDhxIbGwsTZs2fe45v4oxq0NZgEJR6nKDfQ7dkAMwwLC3z5C3DQx7+0Q4y1/U6dOnWbp0KWXfyUJDQ6WC/aRlz+ratWsEBASg1+spKSnhiy++eOHC+yrGrE4dBm945eEsIAJaBEF4OlHQa7CyR9uquuxZOTs7o1AoXmiM1zHmX4Eo5oIgPI24KU4QBEEQDIAo6IJQg2hFSIsgCJV4Lafc5XI5a9euxdHRUXrNx8eHgIAAkpOTad68OZ988kmln4+Pj6ddu3blHjO6du0aX3/9NampqVhbW6PT6ejevTtTpkzB2Nj4hea7atUqHjx4QEBAAJs3b0alUjFq1KjnHi8lJQWNRlPuFLiTkxOOjo4YGRmhUqno3bu31GntZbp48SKZmZnl9q+3tzdbt26lVq1azz3u8OHDuXHjBpaWlhQXF+Pt7c2ECRNexpRrlCPbRqN7DTfFlentt+e1rUsQhJql2q+hV9Q05FEJCQnUrVtXKui3b99m6NChTJ8+nZUrVwKlzzqvW7cOtVrNW2+9Ve7zJSUlmJg836a+jL7ex48f58GDB49d096yZQsWFhYUFRXh6emJXC6XOp69LBcvXuTQoUPlCvrLuoY9Z84cevbsye3bt/nkk0/KdWx7US/yO3sRWq32hb8QCoIgVIdqL+izZs3CxcWFYcOGsX//fiIjIzEyMkKr1TJ37lyuX79OamoqCxcuZMWKFQQEBJCSkoKbmxs+Pj7SOJaWluWOcOVyOZ988gnJyck4OjoydepUpk2bRmFhISqViu7duzNz5kwACgoKmD17NpcvX8bW1pZ33nmHt99+Gyh/tA4QExPDTz/9hFarxc7OjtDQUGxtbVm1ahWZmZkUFBSQk5ODvb09kZGRZGdns2XLFnQ6HUlJSXh6ejJu3Lhy+6CoqIiSkhLpOfG7d+8yf/58srOzAfDz86Nfv34AlYaM3Lt3j+nTp3Pv3j0AOnXqhL+/PytXrkSpVOLt7U2HDh2YM2cOTk5OnDp1CgsLC+RyOd7e3iQlJXHnzh3GjBkjha+cPHmSkJAQoLTV6oEDB/jXv/5V7kwLQIMGDWjSpAk3btygTZs23L59m4ULF3Ljxg1UKhWenp6MHz/+qWM++jsLDg6uNOSlohCWJk2asGDBApKTkzEzM6N27dps2bIFKG1Tu2HDBgDs7e1ZsGAB9evXJz4+nh9++AELCwuysrIIDw+nRYsWz/mvWRAEofq8toJe1jSlzLVr1x57z8qVK1mwYAHt2rVDq9VSVFSEm5sbO3fulNLBAL755hu6dOny1HUqlUopIESlUrF27VosLCzQaDT4+flx5MgRunXrxurVq7GwsGDfvn3k5ubi4+NTYW9vhUJBTk4O27Ztw8jIiLi4OL766iuWLVsGQGpqKt9//z1WVlb4+fmRmJiIr68vgwcPLveloMzgwYMByMrKYsiQIdLjWwsXLqR58+asXr2a27dv4+PjQ8uWLWncuHGlISOJiYnY29sTGxsLwP3797GxsWHSpEkcOnRIOpNRkeLiYrZu3cr169fp06cP/fv3x9TUlGnTpvH111/z4Ycf8vPPP/Pdd99V+PnMzEzy8vJwc3MDICAggAkTJtChQwfUajWjRo2iVatWdOjQ4aljPvw7i46OrjTkpaIQlrS0NFJSUtizZw9GRkbcv38fgMuXLxMREUF8fDwNGjRgxYoVhIaGsmLFCgDOnDmDQqHA3t6+0n0kCILwpnttBb0seKTMw0fXZTp27MjixYv56KOP6Nat22NHgpWJiYlh9+7d5OXlsXz5ctq3bw8gHdVC6anUpUuXcvr0afR6PXfv3iUtLY1u3bqVCwypV68e//jHPypcz8GDB0lNTaV///7SmA93OXN3d8fa2hqA1q1bS0fYlSk75Z6fn8/IkSPZv38/vXr14tixY8yaNQsoPfrt3r07KSkp6PX6SkNG2rRpQ2xsLEuWLMHV1bVKj6yVnY5/9913sba25ubNm2g0GmrVqiXlpv/jH/+Qtq3MwoULiYiI4OrVqwQEBFCvXj0ePHjA8ePHyc3Nld5XWFhIRkYG9evXf+qYD//OnhTyUlEIS6NGjSgpKWH27Nm4ublJXwBTUlLo3r07DRo0AEq/SHl7e0vrad++vSjmgiDUeNV+yv1hQUFBXLp0ieTkZCZPnszo0aMrjP1s2bIl586dk34eN24c48aNw8fHB41GI73+cNDHN998Q35+Ptu3b8fc3Jy5c+eWCzl5Fnq9Hn9/fwYOHFjh8kdDTp51fGtrazp37sxvv/1Gr169qjSnMu3atSMhIYGkpCQUCgUxMTFs3rz5mT77vOEsZdfQf//9d8aMGUOnTp2kvvPff/89pqam5d6flpb21DEfDWepLOSlohCW7t27s3v3blJSUkhKSiIiIkLqG/8kFhYWz7C1giAIb7Y36rG1q1ev4uTkxMiRI+nbt69UtC0sLKS8a4DPPvuMY8eOsXPnTum1sl7flSkoKMDW1hZzc3Nu3brFgQMHpGUdO3YkPj4egP/+97/s37+/wjHkcjlxcXHSqVy1Wv1MRcrS0rLc/B+lVqs5ffo0jRs3Bkqvf2/btg2AO3fucPjwYTp27PjEkJGcnBwsLS3x9PQkMDCQ8+fPo9PpnrruyjRt2pSioiJ+//13APbv3y9Flz7qgw8+4LPPPiMyMhJLS0s++OADYmJipOV//vknd+7cqdKYQKUhL5WFsOTm5lJUVETXrl2ZMWMGVlZW5OTk4ObmxuHDh7lz5w4A27Zto3PnzlXeJ4IgCG+yN+oIfdmyZVLut7W1NYsWLQLg008/5auvvmLDhg0EBATQuXNnNm7cyNdff83KlSupU6cOZmZm9OrVi/fff7/CsYcPH87kyZPx8vLCzs6u3FHfhAkTCAoKwsPDA1tbW+mU8KP69etHXl6edNOYXq9nyJAh0mngyvTq1YudO3fi7e1d7qa4wYMHS4+tubq6SnfUz5kzh3nz5kl55jNmzKB58+YAlYaMHD9+nNjYWIyMjNDpdISEhGBkZESnTp3497//Td++fXF1dZUuLTyNmZkZy5YtIzg4GChNSatfv36lAS/+/v784x//4MKFC0RERLB48WJp/hYWFixatAhbW9sqjVlZyEujRo0qDGG5ceMGc+fOpaSkBK1WS7du3Wjbti1GRkbMmDGDMWPGANCoUSMWLFjwTPtBEAShphDhLEKllEqldI9AcnIygYGBHDhwACOj5z+x8yrGrA5lAQq5F5YZ7HPohhyAAYa9fYa8bWDY2yfCWYRX4qeffiI2Nha9Xo+ZmRkREREvXHhfxZjVqZvvN68lnKWMtkSNsejrLghCBURBFyrl4+NT4dMIb9qYfyWimAuCUJmae2gkCIIgCIJEFHRBqEFEOIsgCJURp9xruIqCb/R6PRs3bmTr1q2UlJRQq1Yt6tevz8SJE2nfvj0pKSmMGzdOekxOp9Ph7+8vNZgZPnw4p0+f5tdff6VOnTpAaXOWESNGMGbMGKn9btkYWq2WOnXqEBISgoODw2vfB9Xpp22j0Gpe301x/cbsfW3rEgShZhEF3QCtWLGCEydOsGHDBuzs7AA4deoUV69elbroOTg4SM/ep6enM2jQIDw8PKQb1BwdHdm9ezdDhw4FShPvHn0k8OExwsPDWbx4MevXr39p21EdQSnVFQojCILwosRfLgNTWFjIv//9bxQKhVTMobS9aVkxf1RBQQGWlpbl7jbv168fCoWCoUOHUlhYyO+//46npydqdcWnfF1dXTl06JD0c0JCAnFxcVJ73ODgYJo2bYparSY0NJTjx49Tr149WrRowd27d1m5cmWFQSlqtZqIiAgKCwuB0kyAHj16VBhGExQUxKlTpwgNDUWn01FSUoK/vz9eXl5PDLx5NBQmLCzs+X8BgiAI1UQUdAOTkZGBubm5FPTypPd5e3ujVqv5z3/+w9KlS8stb9SoEebm5mRkZPDHH3/Qq1cvTExMKizoOp2OAwcOSKfsT548yd69e9m0aRNmZmYcPnyYoKAgtmzZwtatW7lx4wa7d+9Gq9UyfPhw3nnnHWmsh4NS8vPzGTFiBDExMTRo0IDbt28zcOBAdu3aVWEYDcC6devw8/PDy8sLvV4vdcmrLPCm7FLFw6EwgiAINZEo6AYuPz+f4cOHo1arcXBwICoqCih/ujwjI4Phw4fTrl27ckf1/fr1IyEhgTNnzjBnzhx++umncmOXfSm4desWlpaWbN++HSgNVUlLS2PQoEFA6TX9shavKSkpeHt7Y2JigomJCZ6enlIrWCgflHL69GmuX7/O2LFjpeUymYysrKxKw2jc3NxYs2YN2dnZ5fLZKwu8KSvoD4fCCIIg1ETiLncD4+DggEqlkuJpra2tUSgUzJw5UzqKregzDRs25NSpU+Ve9/DwYPfu3Tx48AAnJ6cKP6dQKDhy5AjOzs5SS1e9Xs+AAQNQKBQoFAp++OGHcqfjn+ThoBS9Xo+Tk5M0jkKh4PDhw7Rq1UoKo3FxcUGhUDBixAgARo0axZo1a6hXrx6hoaEsX778mdb7cCiMIAhCTSQKuoGxsLBg9OjRzJkzh1u3bkmvFxUVVfqZW7duce3aNemu94fH+uc///lYjvujzMzMCA4O5ujRo1y4cAG5XI5CoeDmzZtA6c1tqampQOm19sTEREpKSlCpVOzdW/ld2+3atSMrK0sKowE4e/Yser2+0jCazMxM7O3tGTx4MCNGjJACfioLvBEEQTAU4pS7ARg9enS5u8ETExOpV68eY8aMQavVUrduXaytrZk4caL0nrLT5Xq9npKSEiZPnkyLFi0eG7vsuvjTvP3224wZM4aoqCiio6OZMmUK/v7+Ugqeh4cHLi4uDB48mLS0NDw9Palbt+4Tr/Xb2NgQHR1NeHg4YWFhaDQaGjVqxNq1aysNo/nuu+9ISUnB1NQUMzMzKYzmSYE3giAIhkCEswivXVlAi1qtxt/fHw8PD+l6e01RFqBw40KEwT6HbsgBGGDY22fI2waGvX0inEWoUUaPHo1arUalUtG5c2f69+9f3VN6bh/5xopwFkEQ3giioAuvXdnd8ELViWIuCEJlxE1xglCDiF7ugiBURhyhC8IL+OH7EZS8xmvoQ0b9+NrWJQhCzSKO0P8C5HI57u7uaLVa6bX4+HicnJzYuHEj8fHxTJo06bHPpaSk0KZNG7y9vfHy8mL06NFcv34dgFmzZtGtWze8vb3p3bu31G5VEARBqB6ioP9FNGjQgF9//VX6OSEh4bGwlYqUNY/ZtWsXjo6OfPXVV9KycePGoVAo2LFjB0eOHGHfvn0vbb4lJSUvbayqePhLjyAIQk0iTrn/RfTv35/4+Hi6d+9OTk4ODx48KBe5+iw6d+78WM93AEtLS95//31u3LgBlD6WtnjxYi5duoRKpcLNzY3AwECMjY1JT08nMDCQoqIinJ2dyc7Oxt/fn549ezJ8+HCcnZ05c+YMNjY2rFu3jpiYGH766Se0Wi12dnaEhoZia2vL/v37iYyMxMjICK1Wy9y5c3FzcyMqKopdu3Zhbm6OTCbj22+/xdramiNHjvD111+j1WqpV68eCxYs4L333iMlJYWFCxfi4uLChQsXmDJlCj179nwp+1wQBOF1EgX9L8LV1ZW4uDju379PQkIC/fr14/z588/8eZ1Ox48//lhh85l79+5x6dIlvvzySwAWL15Mhw4dWLRoETqdjhkzZrBjxw58fX2ZOXMmI0eOxNvbm3PnzuHr61turJycHOLi4jAxMUGhUJCTk8O2bdswMjIiLi6Or776imXLlrFy5UoWLFhAu3bt0Gq1FBUVkZeXR2xsLL/++iu1atVCqVRSq1Yt7t27x8yZM9m4cSPNmjVj+/btzJgxQ7rbPj09XRpLEAShphIF/S9CJpPx8ccfs3v3bnbv3s2WLVueqaA/3FHOycmJwMBAaVlMTAxbt24lMzOTIUOG4ODgAJSGs5w9e5ZvvvkGgOLiYuzs7FAqlVy+fFnq1taqVavHesT36dNHyiM/ePAgqamp0nPqZVGsAB07dmTx4sV89NFHdOvWDUdHR7RaLfb29sycORN3d3d69OiBpaUlZ86cwdnZmWbNmgEwYMAAQkJCUCqVALz33nuimAuCUOOJgv4X0r9/fwYNGkSHDh2oW7fuM33m4VS2R40bN45hw4Zx9epVhgwZgru7O927d0ev1xMdHU2jRo3Kvb+sgMpkskrX93BIil6vx9/fn4EDBz72vqCgIC5dukRycjKTJ09m9OjR+Pr6sm3bNk6dOkVycjI+Pj6sX7/+qdsoglkEQTAE4qa4v5BGjRoxdepUJkyY8FLHbdq0KZMmTWL58uXo9XrkcjkxMTHSDWa5ublSmErz5s3ZtWsXAOfPn+fy5cuVjiuXy6XLBABqtZq0tDQArl69ipOTEyNHjqRv376cO3cOpVJJbm4urq6uTJo0CUdHR65cuULbtm1JS0sjIyMDKL0hsGXLltLRviAIgiEQR+h/MZ9++mmFrx8+fJhu3bpJP/v4+NCpU6cqjfvtt99y4MABgoKCCA8Px9vbG5lMhqmpKUFBQTRq1IglS5YQFBRETEwMjo6OODo6YmVlVeGY/fr1Iy8vj2HDhgGlR+xDhgzB2dmZZcuWkZWVhbGxMdbW1ixatAilUsmXX35JcXExer2eli1b8tFHH2Fubs7SpUuZMWMGJSUl1KtXj/Dw8CrsNUEQhDefCGcRXqvCwkJq166NTCYjPT2d4cOHs2/fPmxsbKp7alVSFqBwNW2pwTaWMeQADDDs7TPkbQPD3j4RziLUGKdPn2bp0qWUfY8MDQ2tccX8YX0HfivCWQRBeCOIgi68Vu7u7ri7u1f3NGosUcwFQaiMuClOEGqwEq0IaxEEoZQ4QheEF7Atfjjq13gN/VF+I36qtnULgvBmEUfoL0gul0uPXhUVFeHn50dgYCD//Oc/adWqldQOFUoDTTZu3PjUMSMjI9mzZ89T37dq1SqWLFlS5WUvw6PBLcOGDZMeCxMEQRBeP1HQX5L8/HxGjx5N06ZNCQsLw9jYGFtbW1atWlXlsSZPnswnn3zyCmZZNU8LSHk4uKVNmzYsXrz4pa6/OoJSqisURhAE4UWJU+4vQVmvcLlcXi6GdPDgwWzcuJH09HSp7WgZtVrN8uXLOXHiBGq1GicnJ4KDg7GwsGDWrFm4uLgwbNgwCgoKCAoK4sqVK9jZ2WFnZ0f9+vUJCAgA4NatW4wdO5acnBzs7e2JjIzkrbfeAuDGjRuMGDGC27dv07x5c8LCwrCysqKwsJCFCxdy7tw5ALy9vRk7dizAYwEpX331FdOnT+fevXsAdOrUiaCgoMf2gaurK4cOHZJ+TkhIIC4uTmrXGhwcTNOmTVGr1YSGhnL8+HHq1atHixYtuHv3LitXriQ+Pp4ffvgBCwsLsrKyCA8PR61WExERQWFhIQCTJk2iR48e3Lt3r8J5nTp1SopyLSkpwd/fHy8vL+7evcv8+fPJzs4GwM/Pj379+gGlZ1k++eQTkpOTcXR0JCws7MX+QQiCIFQDUdBfgilTpvDZZ589lileu3ZtPv/8c5YvX87q1avLLVu/fj1WVlZ8//33AISHhxMTE8PUqVPLvW/16tVYW1uzb98+8vLy8PHxoXfv3tLy1NRUvv/+e6ysrPDz8yMxMVEKPPn999/ZuXMnb7/9NoGBgURHRxMQEEB0dDQ6nY7ExEQKCwv59NNPcXR0pHv37kD5gJTY2Fjs7e2JjY0FkLq2PUyn03HgwAHprMLJkyfZu3cvmzZtwszMjMOHDxMUFMSWLVvYunUrN27cYPfu3Wi1WoYPH84777wjjXXmzBkUCgX29vbk5+czYsQIYmJiaNCgAbdv32bgwIHs2rWLxMTECue1bt06/Pz88PLyQq/XU1BQAMDChQtp3rw5q1ev5vbt2/j4+NCyZUspcU6pVEq/C0EQhJpIFPSXoHv37uzZs4fBgwdjZ2dXbpmvry/ffPMNZ86cKff6wYMHUSqV/PhjaaMQtVqNs7PzY2OnpKQwZ84cAOrUqUOvXr3KLXd3d8fa2hqA1q1bS0egAD169ODtt98GYODAgSxcuBCAY8eOERQUhEwmw9LSEk9PT44dOyYV9IcDUtq0aUNsbCxLlizB1dW13CNnZcEtt27dwtLSUkovO3jwIGlpaQwaNAgo7fCWn58vbY+3tzcmJiaYmJjg6enJ77//Lo3Zvn177O3tgdJn1q9fvy6dPYDSPvBZWVmVzsvNzY01a9aQnZ1Nly5daNOmjbTNs2bNAkqz4bt3705KSopU0MuO1gVBEGoqUdBfgv/3//4fv/zyCyNGjODbb78tV9RNTU358ssv+frrr/nb3/4mva7X65k/f36V2qtW5OGmJsbGxqhUqhcaD8qHlbRr146EhASSkpJQKBTExMSwefNm4H/BLWq1mmnTphEcHExkZCR6vZ4BAwYwefLkKq/bwsJC+u+yhLdNmzZV+N6K5jVq1CjkcjlJSUmEhobSpUuXx856PG2bBUEQaiJxU9xL8vnnn9O/f39GjBjBrVu3yi3r06cPubm5HD9+XHpNLpcTGxtLcXExUHrKt6K7xF1dXVEoFEDpjXcHDhx45jkdOnSI3NxcAOLj4+nYsSNQer15x44d6PV6lEole/bsoXPnzhWOURaq4unpSWBgIOfPn0en05V7j5mZGcHBxpqwNAAAIABJREFUwRw9epQLFy4gl8tRKBTcvHkTKL25LTU1VdqexMRESkpKUKlU7N27t9L5t2vXjqysLJKTk6XXzp49i16vr3RemZmZ2NvbM3jwYEaMGCHdJ9CpUye2bdsGwJ07dzh8+LC0PwRBEAyBOEJ/icaPH49er2fEiBE0bNhQet3IyIhp06Yxfvx46bVx48YRFRXFwIEDkclkyGQyvvjiCylTvMzEiRMJDAzEw8MDW1tbXFxcnjkl7MMPP2Tq1KncunWLZs2aSaecJ0yYQGhoqJRL3rdv33LBLA87fvw4sbGxGBkZodPpCAkJwcjo8e+Bb7/9NmPGjCEqKoro6GimTJmCv78/Wq0WjUaDh4cHLi4uDB48mLS0NDw9Palbty5NmzatdP42NjZER0cTHh5OWFgYGo2GRo0asXbt2krn9d1335GSkoKpqSlmZmbS5Yo5c+Ywb948aZtnzJhB8+bNn2k/CoIg1AQinOUNp9Fo0Ol0mJubo1QqGTJkCIGBgZUeUdcESqUSS0tL1Go1/v7+eHh4SNfba4qyAIULl5cYbGMZQw7AAMPePkPeNjDs7RPhLAYsPz+fsWPHotVqUalU/5+9O4+Kqv7/OP4cVpVRREP7lporEqFiLrgvaIoLjAKSS6ZpUFqRlqmouSspmPuSWtHXJDdAFKVFTTEVLM2lnyuo4A5GqIOsM/P7g8P9igJisvt+nNM5zZ07934+g/rmbu8X/fv3L9fFHOCdd94hIyOD9PR0OnTowMCBA0t7SP+ap9uGEg1neVSWLgMTY+nvLoSQgl7m1axZk5CQkNIeRpHKuRtePDsp5kKIHHJTnBDlnAS0CCFAjtCFeCb/DXub9FK8hg7w4bCfSnX/QoiyoVwfoWdmZrJ8+XJ69+5Nv379cHV1xcfHh5iYmGfa7rVr13B0dASyW6sOHz78mbZ379491q1bl2vZ5MmT6dKlCxqNBhcXF0aOHMnNmzefaT/5Wb58ORkZ/zuKK2z4S0FCQkJo3bo1Go2Gvn374u3tTWJi4rMOVQghxL9Urgu6r68v58+fZ+vWrezatYuwsDDc3Ny4fPlyrvX0ej3/9mb+2rVrs2HDhmca571791i/fv1jy729vQkLC2Pnzp3Y2dmxZs2aZ9pPflasWEFmZqbyuqjCXzp06EBYWBi7du3CwsKCFStWPPM2czzLz+xZSDiLEKK8Kren3K9cucKePXs4cOCA0vpUpVLRrVs3IPuo9OLFi2i1Wm7cuMHmzZuV55czMzOxsrJi/vz5yvPiGzduJDAwELVarbRAheyjdXd3d6Kjo4HsXuN5hYXkrDd48GAOHDhAamoq8+bNo3Xr1syePZv79++j0WioXLkymzZtyjUXvV5PSkoKlpaWyrK1a9eyY8cOAJo1a8a0adOwsLAoMFhlxYoVhIeHY25ujkql4r///S+LFy8GsoNicp7Tnj9/vhL+snz5ci5fvsz9+/cfC3h5UjBMDpVKRZs2bXKFs6xdu5aff/4ZnU5H7dq1mTNnDtbW1gVuM6+f2YkTJ1i9ejUZGRmYmpri6+uLg4MDly5dwtfXl9TUVPR6PQMHDmT06NHs2bOHpUuXYmRkhE6n4/PPP8fR0ZG4uDimT59OUlISJiYmjB8/Xnn2vmnTpnz44Yfs37+fzp07M27cuH/5p1IIIUpPuS3oZ86c4ZVXXslVBB916tQpQkJCqFGjBgBeXl5KMdq6dSsBAQEsXryYc+fOsXr1aiXIZObMmXlu7969e8yYMSPPsBCA5ORkHBwcGD9+PDt27CAgIIBNmzYxffp03N3dlY5vOdauXcvWrVu5c+cOarVaaal64MABduzYwaZNm7CwsFACVT777LN8g1Vyepv/9ttvVKpUCa1WS6VKlZgxYwZBQUHKtvKSX8DLk4JhcmRkZBAZGakc9YeFhXH16lW2bNmCkZERQUFBfPHFFyxatOiJ23z4ZxYfH8+qVav4+uuvUavVXLx4ES8vL/bv309QUBBOTk689957wP/CWZYtW8bs2bNp2bIlOp2O1NRUILuRjKenJ4MGDSImJoZhw4YRERGh/NkwNzcnODg43z9LQghR1pXbgv6omJgYPv30U9LS0ujcuTOWlpZ06dJF+QcbIDIykqCgIB48eJDr1OrRo0dzBZm8+eabebYkLSgsxMrKiipVqtC9e3cAHBwcWLBgQYFj9vb25q233gKyU9WmTZvGqlWrOHLkCH379lU6wnl6eiqRnvkFq3Tq1Il69eoxceJEOnXqRLdu3QrdUS6/gJcnBcMcPnwYjUbDtWvXaNSoEX369AGyw1n++usv5fnynAjVwmzz4Z/ZwYMHiY+PZ9iwYcr7WVlZ3LlzhzZt2uDv709qaiqOjo5KG9d27drh5+dHr1696NKlCzY2Nmi1Ws6ePYu7uzsAjRs35tVXX+XEiRM4OTkBlOtn4YUQAspxQbezsyMuLo579+5RrVo1GjduTFhYGN9//z1//fUXlpaWuY5Ir1+/jp+fH9u2baNu3bocP36cCRMmPNU+CwoLuXbtGmZm/3sm2MjI6Kmuxzo7Oz9249zTMDY2ZsuWLRw/fpyoqCjc3NxYv359ngluj/q3AS8dOnRg2bJlaLVaRo8ezdKlS5k4cSIGg4ExY8bg4eHx1PN49CxC586dWbhw4WPr9e7dGwcHBw4dOsS6desIDg4mICCAKVOmcP78eaKiovj444955513CnW/gISzCCHKu3J7U1z9+vXp0aMH06ZNUzKvAR48eJDn+lqtFlNTU6ytrdHr9bmuY7dt25YDBw7w999/A+Sbi11QWEhB1Go1aWlpBRb4qKgo6tevD2QHiURERKDVajEYDGzbtk3pDpdfsIpWqyUpKYm2bdvi4+ODjY0NFy9eBLKLpFarLXCMeSlsMIxarWbWrFn88MMPJCQk4OTkRFBQkHIaPCMjg3Pnzj3VNgE6duzIwYMHlXlA9vcNEBcXh7W1NW5ubnzwwQfKPQWXLl2iadOmjBgxAldXV06fPo1arebVV18lNDQUyI59PXfuHA4ODk/9nQghRFlVbo/QAfz8/Fi1ahUeHh6YmJhQrVo1atWqhbe3N/v27cu1btOmTXF2dqZv375YWVnRtWtX/vjjDwBsbW15//33GTJkCGq1Ot+gkoLCQgpSvXp1XFxccHFxwdLSUvllIucaul6vR61W88UXXwDZ+ernz59n8ODBANjb2zNmzBgg/2CVW7du8dFHH5GWlobBYMDOzo5evXoBMGrUKN5++20qVar0VHfsP00wjK2trXKWYerUqSQnJyuXEwwGA0OGDMHW1vaptlm/fn38/f2ZOnUqaWlpZGZm8vrrr9O8eXMiIiLYuXMnpqamqFQqpkyZAsCiRYuIi4vD2NiYatWqMW/ePAACAgKYPn06gYGBmJiYsHDhwlyXY4QQoryTcBaRr+IIhqkoYTM5AQrHYxdW2MYyFTkAAyr2/Cry3KBiz0/CWUSxKI5gmIoWNvO25r+lGs4CEtAihMgmBV3kqziCYSpi2Expk2IuhIByfFOcEOJ/JKBFCCFH6EI8gzU73yYtq3SvoQNMGiwBLUI876Sgi3IvIiKCr776CoPBQHp6Oq+99hp3796lR48eDBkyRFnPYDDQs2dP/Pz8uHbtmtL6NzMzkzp16jBv3jysra1LcSZCCPHvySl3Ua4lJCQwa9YsVq9eTVhYGBEREYwePRp3d3flufMc0dHRGBkZ0aZNG6B4w2WEEKKkSUEX5dqdO3cwMTGhevXqQHYrXjs7O3r06EFcXByxsbHKuiEhIbi5uaFSqXJtIydcprjia4UQoiRIQRflmq2tLc2bN6dbt274+PgQGBjIP//8g5mZGS4uLkrgilarZc+ePXn2bH80XEYIIcojKeiiXDMyMmLVqlVs2LABR0dHDhw4gKurK8nJyXh4eLBjxw50Oh0RERG8/vrrvPjii8pnc8Jl2rdvT1JSkhIuI4QQ5ZEUdFEh2NjYMGzYML799luqVq3K0aNHsbW1pVatWkRGRhIcHKykreXIuYZ+4MABVCoVS5cuLaXRCyHEs5OCLsq127dv8+effyqvb926RVJSEnXq1AHA3d2d5cuXc+XKFXr06JHnNh4NlxFCiPJICroo17Kysli+fDm9e/dGo9Hg5eXFuHHjsLOzA6B///7ExMTQv3//XPG2j3o4XEYIIcojeQ5dlGsvv/wy33zzTb7vW1paKpGrD3Nzc8PNzS3XMj8/vyIfnxBClBQp6EI8g/ddSj+cBSSgRQghp9yFqBCkmAshpKALUQFkSjiLEM89OeUuxDNYuHsED8pAOIvfoB9LewhCiFImBV2UOicnJ6pUqcKOHTswMjJSlq1ZswYbG5t8PxcVFcWiRYvIyMggIyMDa2trAgMDmTVrFlWrVmXChAm51h8+fDhubm689NJLeHt7U79+fXQ6HdWrV2fWrFk0atSoWOcphBDFSU65izLhwYMHhIWFFXr9rKwsfHx8mDNnjhLKMnnyZFQqFe7u7oSFhaHT6ZT1r169ypkzZ3B2dgagUaNGhIWFER4eTosWLeQOdyFEuScFXZQJH374IStWrCAjI/e1YCcnJy5cuPDY65SUFB48eMALL7ygvGdnZ4dKpaJ58+ZUr16d3377TXkvJCSEPn36ULly5cf23bZtWwlmEUKUe1LQRZlgb2/Pa6+9xg8//FCo9S0tLfH09KRXr168//77rF27NldRdnd3JyQkBAC9Xs/27dsfa/2a897evXslmEUIUe5JQRdlxrhx41i3bh0pKSmFWn/69OmEhYXRo0cPTp8+Tf/+/bly5QoArq6uREZGkpycTFRUFJUrV6Zly5bKZ2NjY9FoNHTo0IHDhw8zdOjQ4piSEEKUGCnoosxo2LAhXbt25dtvv1WWGRsbo9frldfp6em5PlO3bl0GDRrE8uXLadmyJb/++isANWrUoFOnToSHhxMcHPxYV7ica+iRkZHY2toyc+bM4puYEEKUACnookz56KOPCAoKUo7S69Wrx+nTpwE4cuQId+7cASAlJYXffvsNg8EAwL1797h27ZoSygLZp91/+OEH9u/fz4ABA/Lcn5mZGTNnzuTgwYOcOXOmOKcmhBDFSgq6KFNefPFFNBoNycnZz3Z//PHHfPvtt2g0Gvbv389LL70EgMFgYOPGjTg7O+Pq6sqQIUNwcXHhjTfeULbVuXNn7t27R9u2bXPdPPeoF154gVGjRrFixYrinZwQQhQjlSHnEEcIUWjp6en89ddf7I73r7CNZY4dO0arVq2KfLtlRUWeX0WeG1Ts+eX822Jvb//UORHSWEaIZzCx73dlIpwlU5eBqfRzF+K5JqfchagApJgLIaSgC1GBSEiLEM8vOeUuxDP47KcR3NeV/jX0HN8OlJAWIZ5XZbqgDxo0iIyMDDIzM7ly5QpNmjQBslt8Flfv7StXrjBu3DgARo0ahaura7Hs52k0bdqU48ePM3LkyBL/PiD7kbDNmzfj5eWlLJs8eTKHDx/GysqKtLQ0OnXqxNSpU5VwFSGEECWrTBf0rVu3AnDt2jUlcONhWVlZmJgU7RR+/vlnWrZsyYwZM57qc8Uxlkc96ft4Ep1Oh7Gx8VPv9969e6xfvz5XQQfw9vbmrbfeQqvVMnDgQFq1alVkLVRL4vvMy7/9joQQorSV6YKeFycnJ/r27UtUVBQ2NjaMHz+eTz75hJSUFNLT0+natSsTJ04EYPny5Vy+fJn79+9z9epV6tWrx9KlS6lcuTJ79uxh6dKlGBkZodPp+Pzzz7l9+zbfffcder2e48ePs3z5cgwGA9OnTycpKQkTExPGjx9Ply5dgOwj5w8//JD9+/fTuXNnbt26hZmZGVeuXOHq1au88cYbdO/eneXLl3Pr1i1GjBjBiBEjALh06RLz58/nn3/+ITMzkxEjRii9xn/++We+/PJLzM3N6dWrV4HfR1ZWFu+99x7//PMP6enpNG/enFmzZmFmZkZISAg7duzAwsKCuLg4/P39iY+PZ/HixVSqVAlnZ2cWL17M8ePHsbCw4OTJkwQEBChNXXx8fOjWrRuzZ8/m/v37aDQaKleuzKZNm3KNQa1W89prr3Hjxg0AtFotfn5+nD9/nvT0dBwdHfH19cXY2JiYmBh8fX1JTU3F1taW+Ph4xowZQ/fu3Rk+fDi2tracPHkSS0tL1q1bx9q1a/n555/R6XTUrl2bOXPmYG1tnefPz9HRkRUrVhAeHo65uTkqlYr//ve/VKtWjcjISL788kt0Oh01atRg9uzZvPLKK0RHRzN37lzs7e05c+YM48aNo3v37kX3B1YIIUpIuSvokF0wtm3bBmQ/s7dmzRosLCzIzMxk9OjRREZGKkX3r7/+Ytu2bVStWpXRo0ezc+dOPD09WbZsGbNnz6Zly5bodDpSU1NxdHQkLi6OBw8eMGnSJCD7tL+npyeDBg0iJiaGYcOGERERQY0aNQAwNzcnODgYyD4NffHiRb777jt0Oh1OTk7cv3+f77//nsTERJydnfHw8MDc3JwJEybg7+9Po0aN0Gq1uLu74+DggKWlJZ9//jk//PADDRs2ZN26dQV+F8bGxgQEBGBlZYXBYGDSpEkEBwczZMgQAE6ePElYWBj16tXjzp07jBw5ks2bN1O/fn0CAwOV7dy7d48ZM2awdu1aatWqRUJCAh4eHoSHhzN9+vQCzwj8/fffnD9/no8++ggAPz8/2rRpw7x589Dr9UyYMIHg4GA8PT2ZOHEiI0aMQKPRcPr0aTw9PXNt6+rVqwQFBWFiYkJYWBhXr15ly5YtGBkZERQUxBdffMGiRYvy/PklJycTGBjIb7/9RqVKldBqtVSqVIm///6biRMn8v3339O4cWO2bt3KhAkTlDMeMTExyraEEKK8KpcF/eE2njqdjoULF/Lnn39iMBi4c+cO586dUwp6p06dqFatGgDNmzcnPj4egHbt2uHn50evXr3o0qULNjY2j+1Hq9Vy9uxZ5ci5cePGvPrqq5w4cQInJycABg4cmOszPXv2xMws+xGiBg0a0LVrV4yMjKhduzbVqlXj1q1bGAwGYmNj+eSTT5TPZWZmcunSJYyMjLCzs6Nhw4YAvPnmmwQEBOT7Xej1er755hsiIyPR6/XcvXuXSpUqKe+//vrr1KtXD8gu7nZ2dtSvXx/Ibo2ac+39zz//5Nq1a7lOq6tUKuLi4rCysspz32vXrmXz5s1cvnyZIUOG0KhRIwD27dvHqVOnlJ7saWlp1K5dG61Wy4ULF3BxcQGgWbNmNG3aNNc2XVxclFPt+/bt46+//lK+Y51Oh1qtBvL++el0OurVq8fEiRPp1KkT3bp1Q61Wc/LkSWxtbWncuLEy71mzZqHVagF45ZVXpJgLIcq9clnQq1Spovz/t99+y71799i6dSvm5uZ8/vnnuQI8Hm76YWxsrLw3ZcoUzp8/T1RUFB9//DHvvPPOY0eLTzuWvPb36GudTodKpcLKyirPI969e/c+1f537tzJsWPH2LhxI2q1mjVr1iiJYwAWFhaF2o7BYKBp06Zs3LjxsfeuXbuW52dyrqFfunSJIUOG0KlTJ7p27YrBYGDVqlXUrVs31/o5BVSlUuU7joe/T4PBwJgxY/Dw8Hhsvfx+flu2bOH48eNERUXh5ubG+vXrnzj3R3+GQghRHpX7W5Lv37+PtbU15ubm3L59u9AF8dKlSzRt2pQRI0bg6uqqBIA8TK1W8+qrrxIaGgpkR26eO3cOBweHZxpzgwYNqFSpEtu3b1eWxcbGotVqcXBw4MyZM0pRzjktnJ/79+9jZWWFWq3m/v37hIeH57tuixYtOHPmjHKWImdeAC1btiQuLo6oqChl2alTpzAYDKjVatLS0sjKyspzuw0bNsTHx4fFixdjMBhwcnJi7dq16HQ6AJKSkrh69SpqtZomTZooY/y///s/Lly4kO94nZycCAoK4u7duwBkZGRw7tw5IO+fn1arJSkpibZt2+Lj44ONjQ0XL17EwcGBc+fOERsbq8zbzs5OOdoXQoiKoFweoT9s+PDhfPzxx/Tv35/atWvTvn37Qn1u0aJFxMXFYWxsTLVq1Zg3b16e6wUEBDB9+nQCAwMxMTFh4cKFyvXzf8vExIQ1a9Ywf/58vv76a/R6PTVr1mTJkiXUrFmTOXPm8P7771OpUqUn3hQ3YMAA9u7di7OzMzVr1qRVq1aPRYzmeOGFF5g5cyZeXl5UrlyZbt26YWpqSuXKlTEyMmLVqlX4+/szf/58MjMzqVu3LmvWrKF69eq4uLjg4uKCpaXlYzfFQfalgf/+97/s3buXKVOm4O/vj0ajQaVSYWpqypQpU6hbty4LFixgypQprF27FhsbG2xsbKhatWq+c0tOTuatt94Cso/YhwwZgq2tbZ4/P61Wy0cffURaWhoGgwE7Ozt69eqFubk5CxcuZMKECWRlZVGjRg38/f2f8qcmhBBlm4SzPGe0Wq1yZBocHMy2bdv44YcfSmz/KSkpVKlSBZVKRUxMDMOHD+fHH3/E0tKyxMZQFHICFL677l9hG8tU5AAMqNjzq8hzg4o9PwlnEYW2YcMGfvzxR3Q6HZaWlsydO7dE9//nn3+ycOFCJcd8zpw55a6YP8y/d9kIZ8khIS1CPL+koD9nxowZw5gxY0pt/506daJTp06ltv+KToq5EM+vcn9TnBDifzIknEWI55YcoQvxDEb+8inJuvulPQxFhOa70h6CEKKUyBF6Gebk5PTYY11eXl7KY2clISQkhNatW6PRaOjbty/e3t4kJiaW2P6f1tSpU/njjz9KexhCCFHipKCXM+vWrVM6vxW3nOfOO3ToQFhYGLt27cLCwoIVK1YU2T70ej1F+aDFvHnzaN26dZFtTwghygs55V7OODk5sWbNGmxsbBg+fDj29vacOHGChIQE+vTpw4QJEwBISEhg7ty53Lhxg/T0dPr168f7778PwIIFCzh69CiZmZlYWVkxf/58Xn75ZSXFzc3NjaioKDw9PXPdwa1SqWjTpg379+9XluUXnnL//n2mTJnCxYsXqV27NrVr16ZmzZpMmjSJ5cuXc/HiRbRaLTdu3GDz5s2cOHGC1atXk5GRgampKb6+vjg4OHDp0iUlzEWv1zNw4EBGjx6dbzjL8OHDGTVqFN27d+fOnTvMmDFDOaMxevRopW2wk5MTGo2Gw4cPk5iYyKhRo5Tn3YUQojySgl7O3bx5k40bN5KSkkLPnj3x8PCgfv36TJo0ibFjx9KmTRsyMjIYOXIkzZo1o2PHjnh5eSnhM1u3biUgIIDFixcDkJycTLNmzZT3Q0JClH1lZGQQGRmpRKQWFJ6ycuVKqlWrxo8//khycjJubm707t1b2dapU6cICQmhRo0axMfHs2rVKr7++mvUajUXL17Ey8uL/fv3ExQUhJOTE++99x6A0jUur3CWR82dO5cmTZqwcuVKEhIScHNzw87OTunbn5aWxubNm7l27RouLi4MHDiw0K1yhRCirJGCXs45OztjZGRE1apVadSoEfHx8dSqVYujR4+SlJSkrJeSkkJsbCwdO3YkMjKSoKAgHjx48Fg7V3Nzc/r06ZNr2eHDh9FoNFy7do1GjRop7xcUnhIdHc20adMAqF69Oj179sy1zS5duigd9w4ePEh8fDzDhg1T3s/KyuLOnTu0adMGf39/JQ2vXbt2QOHCdY4cOcLkyZMBqFWrFl27diU6OlpZN+cXkzp16ijBOTkBM0IIUd5IQS/n8gp/0ev1qFQqtm3bhqmpaa71r1+/jp+fH9u2baNu3bocP35cOU0PULly5cfCUzp06MCyZcvQarWMHj2apUuXMnHixALDU57k0SPhzp07s3DhwsfW6927Nw4ODhw6dIh169YRHBxMQEBAkYTr5PXdCSFEeSU3xVVAarWaVq1asXbtWmXZzZs3SUxMRKvVYmpqirW1NXq9Ps++7AVtd9asWfzwww8kJCQUGJ7Stm1bJU3u3r17BYbmdOzYkYMHD3Lx4kVl2alTpwCIi4vD2toaNzc3PvjgAyVEpzDhOu3bt2fLli0AJCYmcuDAAeUIXwghKho5Qi/j3nnnHYyNjZXXKSkphfpcQEAAfn5+Sva4hYUF8+bNo2nTpjg7O9O3b1+srKzo2rXrUz3mZWtri7OzM+vWrWPq1Kn5hqd88MEH+Pr64uzsjLW1Nfb29vmmm9WvXx9/f3+mTp1KWloamZmZvP766zRv3pyIiAh27tyJqakpKpWKKVOmAIUL15k2bRrTp09XvoMJEybQpEmTQs9VCCHKEwlnEcUiMzMTvV6Pubk5Wq2WIUOG4OvrS4cOHUp7aEXiWQIUilOGLgOzImr/WpEDMKBiz68izw0q9vwknEWUOffu3cPLywudTkd6ejr9+/evMMW8LCuqYi6EKH+koItiUbNmzVyPvAkhhCheclOcEBVIhi6ztIcghCglcoQuxDN45+cFJOselPYwFLsHfFHaQxBClBI5QhdllpOTE87Ozri6utKnTx+2bt0KwN69e1mwYEGen4mOjsbNza0khymEEGWCHKGLMm3ZsmXY2Nhw4cIF3Nzc6NKlCz169KBHjx6lPTQhhChTpKCLcsHGxoZq1apx+/ZtDh06xP79+1m2bBkAixcvZvfu3VSrVo22bdvm+lzOe9WrV6dt27YcOXJEuVkvNDSUoKAgpWXtzJkzadiwYYnPTQghioKcchflwrFjx7CyssLW1jbX8n379rFv3z62b9/Oli1buHz5cq73fv31V8LCwti8eTNxcXHKe3/88QcRERFs3LiRkJAQRo8erTStEUKI8kiO0EWZ5uPjg8FgID4+nqVLl2Jmlvs56+joaPr27av0hvfw8GDVqlXKe3369KFKlSoADBgwQHlv3759nDt3jkGDBgHZXe7u3btXUtMSQogiJwVdlGk519AjIiLw9fXl9ddfL5LtGgwG3N3d+fjjj4tke0IIUdrklLsoF/o/mu3YAAAgAElEQVT06UPHjh356quvci1v164dERERPHjwAJ1OR3BwsPJe27Zt+emnn0hNTUWv17Njxw7lPScnJ8LCwrh16xaQHf36119/lcxkhBCiGMgRuig3Pv30U9zc3PD29laWde/enRMnTqDRaJSb4m7fvg1Ajx49+PPPP3F1dcXS0hIHBwclGa5NmzaMGzeOMWPGoNPpyMzMxNnZGXt7+1KZmxBCPCsJZxEVmlarRa1Wo9frmTp1KrVq1WL8+PHPvN2yG86SiZmxaZFsqyIHYEDFnl9FnhtU7PlJOIsQ+Zg0aRLXr18nLS2N1157DS8vr9IeUrEqqmIuhCh/pKCLCm3lypWlPQQhhCgRclOcEBVIhi6rtIcghCglcoQuxDN456eVJOvSSnsYit0DpTmOEM8rKeiiWDk5OWFmZoaZmRmZmZmMGjWKQYMGsXfvXv744w8mTZr02Geio6NZsGDBM+epX758mYCAAM6dO4elpSVmZma8++67XLp0ibNnz7J48eJc60+ePJk6derw4YcfPtN+hRCiNEhBF8WuNAJWEhISeOutt/jss8+U6+iJiYkcOnSIAQMGsHr1au7du0e1atUASElJ4eeff2bnzp3FNiYhhChOUtBFiSnJgJWNGzfi6OjIgAEDlO1YW1srrx0dHQkPD2fo0KEARERE0KJFC15++eWS+CqEEKLIyU1xosSUZMDKmTNnaN68eb5jcXd3z3VKPyQkBHd396KaqhBClDg5QhfFriwGrHTr1o0ZM2Zw8eJFTE1NiYmJ4Y033iiS+QohRGmQgi6KXWkErNjZ2XH69Ol8P2tqaoqrqyshISGYmZnRr1+/MtXxTQghnpacchclpiQDVoYOHcqRI0dy3eT2999/s337duW1u7s7O3bsYPv27XK6XQhR7skRuihRJRWwUrt2bTZs2EBAQABLliyhSpUqVKlSJVfr1yZNmvDSSy+Rnp4uoSxCiHJPwllEmVdcASvPouyGs2RhZlw0v6dX5AAMqNjzq8hzg4o9PwlnERXa8xaw8iyKqpgLIcof+dsvyjwJWBFCiCeTm+KEqEAknEWI55ccoQvxDEb9+A3JuvTSHoZil9u40h6CEKKUyBF6OeXk5MSFCxdyLXNzcyM6OrrAzw0fPpxff/31mfd/7do17Ozs0Gg0uLi44ObmxvHjx595u0IIIf4dKejiqWVlZZ/WrVq1KmFhYezcuRMPDw+mTp1apPvR6XRFur3CyJmbEEKUN3LKvQIaPnw4o0aNonv37nm+Pnz4MCtXruTu3bv06dOHTz75BMhOKJs7dy43btwgPT2dfv368f777wPZZwT69u1LVFQUNjY2jB07Ntc+HR0dWbhwofL6wIEDrF69moyMDExNTfH19cXBwQHIP2wlOjqauXPnYm9vz5kzZxg3bhyvvPIK8+fP559//iEzM5MRI0bg7u5OamoqkyZNIiYmBhMTExo0aMDSpUu5dOkSvr6+SiOagQMHMnr0aFJSUpg7d67SPU6j0Sh3yw8fPhxbW1tOnjyJpaUl69atK8afjhBCFA8p6OWYj49PrucUr1y5UqjPxcbGsmnTJtLT0xk8eDAtW7ake/fuTJo0ibFjx9KmTRsyMjIYOXIkzZo1o2PHjkD28+Dbtm0Dsk+5P+yXX36hb9++AMTHx7Nq1Sq+/vpr1Go1Fy9exMvLi/379+cKW6lUqRI+Pj65thMTE8Ps2bNp2bIlWVlZeHp64u/vT6NGjdBqtbi7u+Pg4MClS5dISUlh9+7dAEqzmaCgIJycnHjvvfdyLV+1ahV6vZ6dO3eSkpLCm2++iY2NDV27dgXg6tWrBAUFYWIifyWEEOWT/OtVjuX0SM/h5uZWqM8NGDAAExMTTExMlKNuR0dHjh49SlJSkrJeSkoKsbGxSkF/OIoU4P79+2g0GpKSksjIyGDr1q0AHDx4kPj4eIYNG6asm5WVxZ07dwoMWwF45ZVXaNmyJZD9C0psbKxyBgEgMzOTS5cuYWtrS2xsLLNmzaJt27Z069YNyO4e5+/vT2pqKo6OjrRr1w6AI0eOMGXKFFQqFWq1mn79+nHkyBGloLu4uEgxF0KUa/IvWAVkbGyMXq9XXqenP/kubL1ej0qlYtu2bZiamua5Tk4RzpFzDV2n0+Hn58enn37Kli1bAOjcuXOuU/CF9fA+DAYDVlZWhIWF5blueHg4UVFRREZGsnjxYnbu3Env3r1xcHDg0KFDrFu3juDgYAICAp5qv0IIUR7JTXEVUL169ZRrxTExMZw9ezbX+zt27CArK4sHDx4QERFBu3btUKvVtGrVirVr1yrr3bx5k8TExCfuz9jYmM8++4zExET27t1Lx44dOXjwIBcvXlTWOXXqFFBw2MqjGjRoQKVKlXIFqsTGxqLVarl16xbGxsb07NkTX19fkpKSSE5OJi4uDmtra9zc3Pjggw+U76F9+/YEBwdjMBjQarXs3r2bDh06FOLbFEKI8kGO0CsgLy8vPv74Y/bu3YudnR12dna53m/YsCGDBw9WborLuVkuICAAPz8/XFxcALCwsGDevHlYW1s/cZ/m5uaMGzeOFStWEBoair+/P1OnTiUtLY3MzExef/11mjdvXmDYyqNMTExYs2YN8+fP5+uvv0av11OzZk2WLFnC+fPnWbRoEZB9dsHb25vatWuzZs0adu7ciampKSqViilTpgAwduxY5syZo8zN1dWVLl26/LsvWAghyiAJZxElriyGrTwtCWcp/yry/Cry3KBiz0/CWUS5ImErxUfCWYR4fsnfflHiJGxFCCGKntwUJ0QFIwEtQjyf5AhdiGcw6seNJGdllPYwctnl/n5pD0EIUQrkCF38K3mFw3h5eREfH19iY7h79y6ffPIJ/fv3x8XFBY1Gw5EjRzh58iSdO3d+rBd8SEgIw4cPV8bv7OyMq6srffr0UZriCCFEeSVH6KLIlGQP9KysLJYsWULt2rVZtGgRKpWKf/75h9TUVF566SUsLS05dOhQrkfTQkJC8PDwUF7ndNq7cOECbm5udOnShdq1a5fYHIQQoihJQRdFxsnJiTVr1mBjY8Pw4cOxt7fnxIkTJCQk0KdPHyZMmAAUHAKzYMECjh49SmZmJlZWVsyfP5+XX36Za9eu4e7ujpubG1FRUXh6enLr1i0cHR1RqVQAWFlZYWVlBWS3wQ0JCVEKenx8POfOncPZ2fmxcdvY2FCtWjVu374tBV0IUW5JQRfF5ubNm2zcuJGUlBR69uyJh4cH9evXLzAExsvLi0mTJgGwdetWAgICWLx4MQDJyck0a9ZMeb9+/fr4+PgQHh5Oy5YtcXJyon379kB2mtry5cu5e/culpaWhISE0LdvXypVqvTYOI8dO4aVlRW2trYl9M0IIUTRk4Iuio2zszNGRkZUrVqVRo0aER8fT61atQoMgYmMjCQoKIgHDx48lk1ubm5Onz59lNft27fn119/JTo6mmPHjjFu3DhGjx6Nt7c3NWvWpEOHDoSHhzNkyBC2b9/O0qVLc23Px8cHg8FAfHw8S5cuxczMrHi/ECGEKEZS0EWxebjLkbGxMTqdrsAQmOvXr+Pn58e2bduoW7cux48fV07TA1SuXFk5vZ5DrVbTo0cPevTogb29PatXr8bb2xsAd3d3VqxYQf369VGr1bRo0SLXZ3OuoUdERODr68vrr7/OCy+8UNRfgxBClAi5y12UqIJCYLRaLaamplhbW6PX69m0aVOB2zp06BBarRbITmY7c+YMderUUd7v0qULCQkJfPnllwVGy/bp04eOHTvy1VdfPePshBCi9MgRuvjX3nnnHYyNjZXXKSkphfpcfiEwTZs2xdnZmb59+2JlZUXXrl35448/8t3O+fPn+eKLL8iJI3jllVeYPn268r6JiQkajYbvvvsu1y8Qefn0009xc3PDy8uLWrVqFWoeQghRlkg4ixD/QlkNZ4GiC2ipyAEYULHnV5HnBhV7fsUWzjJ06NDHrlnmZePGjU+1UyFE8ZGAFiGeTwX+zR80aFBJjUMIIYQQz6DAgj5w4MCSGocQoogUZSa6EKL8KPTfeoPBwNatWwkPD+eff/5h586d/P777yQmJtK3b9/iHKMQZdboH7eSnJVZ2sPIJdz9ndIeghCiFBT6sbWlS5eybds23nzzTW7evAnAiy++yPr164ttcKL8yyvEpSQYDAa+++47+vXrR79+/RgwYADTpk0jMTGRtm3bEhsbm2v9q1ev0qpVKx48eFDiYxVCiKJQ6IIeGhrKmjVr6Nevn3KjXJ06dbh69WqxDU6If2vJkiX89NNPfPfdd+zatYvQ0FC6dOlCWloa/fr1IzQ0NNf6oaGh9O7dmypVqpTSiIUQ4tkU+pS7TqfDwsICQCnoKSkp8g+geGr5BbD8/ffffPrpp/z9999AdmvXKVOmcPz4cebMmYNerycrK4sxY8bQv39/7ty5w4wZM5TI1tGjRzNgwABSUlL49ttv2b59u9L5TaVS0atXLyC7g9yYMWMYP348xsbGGAwGtm/fjr+/f+l8IUIIUQQKXdC7du2Kn58fU6ZMAbJPaS5dupTu3bsX2+BExZRfAMvOnTupV68egYGBQHbeOWTHso4ePZr+/ftjMBi4f/8+AHPnzqVJkyasXLmShIQE3NzcsLOzIy0tDTMzMxo2bJjn/u3t7bGysuK3336ja9euREVFYWZmVmGfaxVCPB8Kfcrd19eXxMREWrVqxf3792nZsiU3btzI1WtbiMKIjIzE09OT/v378/XXX3P27FkAWrRoQWRkJAsWLODXX39Vzv44OjqyevVqVq1axalTp6hWrRoAR44cYfDgwQDUqlWLrl27Eh0dXagxuLu7ExwcDEBwcHCBrWGFEKI8KHRBV6vVrFy5kl9//ZXNmzfzyy+/sHLlStRqdXGOT1QwOQEsixYtIjw8nPnz55ORkQFAy5YtCQ0Nxd7enrCwMN5++20ARo4cyerVq6lRowZz5sxR4lTz06hRI9LT07l8+XK+67i6uvLbb79x9epV9u3bx4ABA4pukkIIUQoKLOh6vf6x/2rUqIG9vT01a9ZUlglRWAUFsFy9ehW1Wk2/fv3w9fXl//7v/9Dr9Vy+fJl69eoxePBg3n77bU6fPg1kX2PfsmULAImJiRw4cIB27dphYWHByJEjmT59unI93mAwsGfPHuUmTisrKzp16sS4ceNo27at9G8XQpR7BV5Dt7OzK1Tr15xTpkLk5dEQl/wCWI4ePUpgYCBGRkbo9XpmzZqFkZERGzZsIDo6GlNTU8zMzJg2bRoA06ZNY/r06UrIy4QJE2jSpAkAn3zyCYGBgQwfPhzILuitW7embdu2yjjc3d3x9vZmxYoVJfI9CCFEcSownOX69evK/+/fv5+ffvqJ9957j5deeokbN26wbt06evXqxdChQ0tksEKUFRLOUv5V5PlV5LlBxZ5fsYWzvPzyy8r/BwYGEhwcrNyQ1KBBA+zt7XF3d5eCLkQZIm1fhXg+FfqmuPv375OampprWVpamvIIkRBCCCFKT6F/lR84cCDvvPMOI0aM4MUXX+TWrVts2LBBAlyEKGMydDrMHrpnQQjxfCh0Qf/ss8+oV68eu3fvJiEhAWtra4YNG4anp2dxjk8UMycnJ8zMzHJdq1m5ciU9evTg+PHjSnfAp3H27FkuX76cK7RHo9GwefNmKlWqlO/nJk+ezK5du/jpp5946aWXlGX29va89dZbBe5zz5491KpVi+bNmyvLEhMT8ff359ixY1SrVg1jY2M8PT1Rq9UEBgYqd8jnWL58OTdu3MDPz6/Qcx0dEVb2wlk8hpX2EIQQpaDQBd3IyIghQ4YwZMiQ4hyPKAXLli3DxsamyLZ39uxZ9u/fn6ugh4WFFeqz1tbWLF++/KmKKmQXdHt7e6Wgp6am8tZbb+Hm5sYXX3yBkZER9+/fZ9euXfTs2ZNZs2YRGxtLo0aNgOy74ENDQ1m4cOFT7VcIIcqKp7p7Jjg4mLCwMG7fvk3t2rXRaDS4u7sX19hEGfE0vdfHjBnDsmXL0Gq1aDQa2rRpw7Rp02jatKlyxB8bG8u8efNITEwEYNSoUcqlm8GDB/P9998TExND48aNc40jIyODxYsX8/vvv5ORkUHTpk2ZOXMmx48fZ9++fRw+fJitW7fyzjvvkJmZSfXq1XnvvfeUz1etWlXpLNe/f39CQkL47LPPAIiKisLU1JTWrVsX+/cphBDFodAFffXq1Wzfvp1Ro0Ypj62tX7+ehIQExowZU5xjFMXMx8dHOeVubGxMSEhIrvefpve6paUlPj4+7N+/n2XLlj22r6ysLMaOHcu4cePo06cPAP/884/yfpUqVXjvvfdYvHgxK1euzPXZ9evXU7VqVbZt2waAv78/a9euZfz48Tg5OeU6NT9z5sxcp98flfMM+ieffKLMWdq/CiHKs0IX9K1bt7Jhw4Zcj7J16tSJt956Swp6OfekU+6RkZEEBQXx4MEDsrKylOUtWrQgMDCQBQsW0LZtWzp16vTEfV2+fJmsrCylmEN217aHeXp68u2333Ly5Mlcy/ft24dWq+Wnn34Cso/YbW1tCzXHR9nZ2fHCCy9w8OBBWrduzd69eyWXQAhRrhW6oKemplKjRo1cy6pXr05aWlqRD0qUHTm917dt20bdunU5fvy4Uvhyeq8fPnyYsLAw1q5dyw8//PDM+zQ1NeWjjz7iyy+/5D//+Y+y3GAwMGPGDNq3b//Ebbz22mtK+Ep+3N3dCQkJISEhgdatW1O7du1nHrsQQpSWQj+H3rlzZyZMmMClS5dIS0sjNjaWyZMnF+qoTJRf/6b3ulqtzrc/QYMGDTAxMSEiIkJZ9vAp9xwuLi4kJSVx9OhRZZmTkxOBgYHKL5FarZbY2FiAx/bZr18/kpKSWL9+PTnNELVaLZs3b861j99++40NGzbIvSBCiHKv0AV9+vTpWFhY4OrqSsuWLRkwYACVKlXi888/L87xiRLg4+ODRqNR/ssJPwFo2rSp0nt90KBB1KlTR3nv6NGjuLm5odFoePfdd5Xe6+3btyc1NRVXV1fmzp2ba18mJiasWrWKTZs24eLigqurKwcOHHhsTEZGRnzyySe52g97e3tja2uLh4cHLi4uDB06VCnorq6uhIeHo9Fo2L59O1WqVGHDhg2cO3eOHj164OLiwvDhwzEy+t8f+erVq9OlSxcSExNxcnIqsu9TCCFKQ4G93AFu3LiR67VOp+Off/7ByspKCdzIeWZYiOdFTr/lxVdjK+xz6BW5XzZU7PlV5LlBxZ5fsfVyh+zTnPklrhkMBlQqlaStiefW1300ZTCcRTrFCfE8emJBt7W1JS0tjYEDB+Lq6iq50UKUcVLMhXg+PbGgb9++nQsXLhAaGsqQIUNo1KgRGo2GXr16FdjGUwghhBAlp1A3xdnY2DBp0iT27dvHyJEj2b9/P506deL//u//int8QoinlKHTlfYQhBCl4Klav165coXff/+dEydO8OqrryrZ6KLo5YSmmJmZodfrGTNmDP369ct3/cKEnxSl5cuXExQURK1atUhPT+e1115jzpw5VKlSpUT2X1aMjthN8kPNdsqCcI9BpT0EIUQpeGJBT05OZteuXYSGhpKSkoJGo+H777+XO9tLQE4HtzNnzjB48GDat2//WHOfHIUNPylKAwYMYNKkSWRkZDBy5Ei+//57vL29i2TbWVlZmJg81e+b5Xq/QgjxrJ74L1fnzp2pU6cOGo2GFi1aABAXF0dcXJyyTmE6d4l/z87ODgsLC65du8b48eNJSkrCxMSE8ePH06VLFwAl/KRy5crMnj2bqKgozMzMqFKlCps2bcozSGXKlCnodDoCAgI4ePAg8L8GQsbGxkyePBkzMzOuXLnCrVu3cHBwYMGCBY899WBmZkbLli25efMmkH+IioWFBbdv32bixIncuXOHunXrAv9rITx58mSMjY25fPkyKSkphIWFERoaSlBQEDqdDrVazcyZM2nYsCHHjx9nzpw56PV6srKyGDNmDP3792fz5s0EBgYqZzaWLFlCo0aNOHXqFPPmzePBgwdUqVKFqVOn0rx5c65du4a7uztubm5ERUXh6ekpiYJCiHLpiQXd2tqa9PR0tmzZ8lh+NIBKpWLv3r3FMjiRLSoqivT0dD777DPeffddBg0aRExMDMOGDSMiIiLXUfu5c+eIjo5m9+7dGBkZcffuXYA8g1QANm/ezNmzZ5VAFi8vLzZv3szQoUMBuHjxIoGBgahUKgYOHMjhw4fp2LFjrvFptVp+//13xo8fDxQcojJ37lwcHR0ZO3Ys169fx8XFJVe3wbNnz/L9999TpUoV/vjjDyIiIti4cSNmZmYcOHCAKVOmsGnTJtatW8fo0aPp378/BoNB6RK3cOFCIiIiqFWrFhkZGeh0OjIyMvDx8cHPz4/27dtz+PBhfHx8+Pnnn4Hss1DNmjVTAmiEEKI8emJB37dvX0mMQ+QhJwVNrVYTEBCAj4+P0qK0cePGvPrqq5w4cSJXl7O6deuSlZXF1KlTcXR0pHv37kD+QSpHjhxh4MCBmJmZAeDm5saePXuUgt6zZ0/lOWs7Ozvi4+OVgr59+3YOHTpEXFwcnTp1ol27dkDBISrR0dFMmzYNgJdffvmxszvOzs7Kdfh9+/Zx7tw5Bg3KviZsMBi4d+8eAI6OjqxevVoZT87Zo3bt2jF58mS6d+9Ot27dqFu3LufPn8fU1FTZV4cOHTA1NeXy5ctYWFhgbm6eKyxGCCHKI7lYWIY9nIKm1WoL9ZmqVauya9cuoqOjOXz4MAEBAYSGhv7rIJWHm6YYGxuje+gO6pxr6ImJiQwdOpSgoCCGDRv2VCEqj3r4pjqDwYC7uzsff/zxY+uNHDkSJycnDh8+zJw5c+jYsSPjx49nxYoVnD59mqioKN5++21mzpzJiy++WOA+K1eunG/zJCGEKC8K3ctdlC61Ws2rr75KaGgoALGxsZw7dw4HB4dc6yUlJZGamqpcC69atSpXr17NN0ilffv2bN++nczMTDIzM9m+fTsdOnR4qrFZW1szdepUVq9eTVpaWoEhKm3btlXmcPPmTaKiovLdrpOTE2FhYdy6dQvIbjv8119/AdkxrPXq1WPw4MG8/fbbnD59mqysLK5evUrz5s3x9vamY8eOnD17lgYNGpCZmans68iRI2RlZdGgQYOnmqcQQpRlcoRejgQEBDB9+nQCAwMxMTFh4cKFj931fvPmTT7//HOysrLQ6XR06dIFBwcHQkNDCQwMxMjICL1erwSpvPnmm8THxzNw4EAg+wY1T0/Ppx5bt27daNiwIZs2bcLb25sVK1bg4eGBSqVCpVLx4Ycf0qhRI6ZOncrEiRPZuXMnderUoXnz5qjV6jy32aZNG8aNG8eYMWPQ6XRkZmbi7OyMvb09GzZsIDo6GlNTU8zMzJg2bRp6vZ7Jkydz//59VCoV//nPf/j0008xMzNj2bJluW6KW7p0qXKZQQghKoInhrMIUZTS0tIwMTHBxMSEhIQEPDw8CAwMpGHDhqU9tKfyv3CW+Ar7HHpFDsCAij2/ijw3qNjzK9ZwFiGK0pUrV5g0aRIGg4GsrCw+/PDDclfMH/Z1n74SziKEKBOkoIsSZWtrWypNcJ4nUsyFeD7JTXFCCCFEBSAFXYgKRsJZhHg+ySn3Enb37l06d+6Mp6en0mDlUcOHD2fUqFFKU5j85AS4mJubk56eTuvWrZkxYwampqZFOuZr165x6NAh3nzzzVxjvHHjBmq1mrS0NDQaDWPHji3S/ZYH70bs4W5W2SqgOzxcS3sIQohSIEfoJSw8PJwWLVqwa9cuMjIynnl7y5YtIywsjF27dhETE8Mvv/xSBKPM7fr162zevPmx5dOmTSMsLIwNGzbwzTffcPLkySLbZ1Yp3Tmuk6NbIUQ5JUfoJSw4OJjPPvuMr776ir1799KnTx9iYmLw9fXlwYMH2NjYkJ6erqz/zTffsGvXLnQ6Hebm5sycOZNXX331se2mp6eTnp6uRNqmpKQwd+5cTp8+DWTHq3p5eQHZ4TrTp09/LOQlNTWVSZMmERMTg4mJCQ0aNGDp0qXMnj2ba9euodFoeOWVV1i2bFmufdeqVYsGDRpw48YNWrRoQUJCAnPnzuXGjRukp6fTr18/3n//fQD++OMPZs2aBWS3b927dy9fffUVNjY2ODk50bdvX6KiorCxsWHmzJn5hrzkFcLSoEGDPINpILtN7ddffw1AvXr1mD17NjVr1iQkJIQdO3ZgYWFBXFwc/v7+eX6/QghR1klBL0Hnzp0jOTmZdu3akZiYSHBwMH369GHixIkMHz6cgQMHcuLEiVxpXwMGDGDUqFEAHD58mBkzZuQKycnp9x4fH0+nTp2UHu2rVq1Cr9ezc+dOUlJSePPNN7GxsaFr165MmDABT0/Px0Jejh07RkpKCrt37wb+F+Ayffp0FixYoAS4POry5cskJyfj6OgIwKRJkxg7dixt2rRRolWbNWtGmzZt+OSTT/jyyy9p3bo1v/zyCxs2bMi1La1Wq4S6rFq1Kt+Ql7xCWPILprlw4QIBAQGEhIRQq1YtlixZwpw5c1iyZAkAJ0+eJCwsjHr16j3DT1cIIUqXFPQStG3bNjQaDSqVil69ejF37lyuX7/OhQsX0Gg0ADg4OCj92wH++usvvvrqK+7evYtKpeLKlSu5tpnT7z09PZ2PPvqIwMBARo4cyZEjR5gyZQoqlUpp+XrkyBFatWrF2bNn8wx5sbW1JTY2llmzZtG2bVu6detW4Hzmzp1LQEAAly5dYtKkSdSoUYMHDx5w9OhRkpKSlPVSUlKIjY2lZs2aVKpUidatWwPwxhtvKGcUcgwYMED5/4JCXvIKYckvmCY6OpquXbtSq1YtAAYPHqx83wCvv/66FHMhRLknBb2EZGRkEB4ejpmZmfIcdmZmptLXPL/PfPzxx3z//a8uLs8AACAASURBVPe89tpr3L59W8k/f5S5uTndunVj//79jBw58l+NsW7duoSHhxMVFUVkZCSLFy9m586d+a4/bdo0unfvzrFjxxg1ahTt27fn5ZdfRqVSsW3btsduzjt37twTx/BoOEt+IS95hbB07do1z2CaJ7GwsHjiOkIIUdbJTXElZO/evTRo0IDIyEj27dvHvn37+Oabb9ixYwc2NjZK4Tx16hQXLlwAsgt6VlYW//nPfwAICgrKd/t6vZ7ff/+d+vXrA9C+fXuCg4MxGAxotVp2795Nhw4dCgx5uXXrFsbGxvTs2RNfX1+SkpJITk5GrVYXmPbWqlUrhg4dytKlS1Gr1bRq1Yq1a9cq79+8eZPExEQaNmxIamoqx44dA2DPnj1KHGpe8gt5yS+EJb9gGkdHRw4cOEBiYiIAW7ZseeoAGiGEKOvkCL2EBAcH4+LikmtZy5YtlUARf39/1q1bh42NDc2aNQOyE9Z8fHzw8PCgevXq9O7d+7Ht5lxDz8zMpEmTJnzwwQcAjB07ljlz5ij7dHV1VY7u8wt5OXDgAIsWLQKyf0Hw9vamdu3a1KxZkwYNGtC/f38aNmz42E1xAGPGjOGNN97gzJkzBAQE4Ofnp+zbwsKCefPmYW1tzaJFi5g5cyaQnbxWs2ZNqlatmud3ll/IS926dfMMYblx40aewTRGRkZMmDBBuRehbt26zJ49+6l+fkIIUdZJOIsoUVqtVklXi4qKwtfXl71792JkVL5OFuUEKCy5eqvCPodekQMwoGLPryLPDSr2/CScRZQbP//8M4GBgRgMBszMzAgICCh3xfxh6/v0lHAWIUSZIAVdlCg3Nzfc3NxKexgVmhRzIZ5P5ffQSAghhBAKKehCVDASziLE80lOuVdwERERfPXVVxgMBtLT03nttddYtGgRmZmZrFmzhvDwcExMTDA2NqZ+/fr4+PjQuHFjQkJCmD9/PnXq1CE9PR1TU1N69erFu+++S6VKlYD/hcOYmZmRmZnJqFGjGDRoUCnPuGR5RURyN0tf2sPIJczj8achhBAVnxT0CiwhIYFZs2YRGhrKf/7zHwwGA2fPngXA19eXtLQ0tm7dSrVq1TAYDBw4cIDLly/TuHFjADp06KA8ovb3338zdepUxo0bx5o1a5R95HSqu3DhAm5ubnTp0oXatWsXyfizsrIwMSnZP6J6vV55RE4IIcoTKegV2J07dzAxMaF69eoAqFQq7OzsuHLlCnv27OHAgQNK61WVSlVgq9eaNWuyYMECunTpwsWLF2nSpEmu923+n717j+v5/v8/fnt3RNFiyb5ozFTLqfl8ihTZe0ZSqnchM4chExbGUIyM1ZoIUYStTVIootb2+RhiOpi1OcyZFDbn4xud3u9+f/Tr9Sk6OXbwvF4uu6z3+/V6v57P1yv17Pk6PO6mpjRp0oQrV65gbGzMuXPnCAgI4NatWxQUFDBy5Eip3OzPP/9MSEgIDRo0wMHBgZCQEDIzM9HT08PMzIxJkyaxZ88eevbsydixYwkMDOTkyZPk5eXRrVs3fH190dTUZMWKFSQmJqKrq4tMJuOHH35AW1u73IAZgIiICLZv3w5Ap06dmDNnDnp6eoSGhnL69GmUSiV///03sbGxGBgYPO9vhyAIwgslBvR6zNzcnM6dO9O7d2+6detG165dcXFx4dixY7z55ptPPGgZGBjw5ptvljug//777xgaGmJubk5hYSHTp09n0aJFtGvXDqVSibu7O5aWlhgYGDB37lxiY2Np06YNkZGRj7Wjq6tLXFwcALNnz8bKyoqvvvoKtVrN9OnTiYuLo2/fvkRGRvLrr7/SoEEDlEolDRo0YPfu3eUGzKSkpLB9+3ZiYmLQ09Nj5syZhIWF8fnnnwPFFfri4+Np2rTpkx5mQRCEWkEM6PWYhoYGYWFhnDp1it9++42dO3eybt06fH19y6x35swZpk2bRm5uLj179mTOnDkVbvPROkQ+Pj4UFRWRk5PDsmXL0NHR4cyZM5w9e5bPPvtMWq+goIBz586hoaGBhYWFVKLW3d2dwMDAMtt0c3OTvt61axeHDx/mu+++AyA3NxdjY2MaN26MiYkJM2bMwM7Ojt69e6Ovr19hwExaWhqOjo5SUZvBgwcTEBAgtdOrVy8xmAuCUKeJAf0VYGpqiqmpKcOGDcPR0ZGLFy+SnZ3N3bt3adKkCW+//TYJCQlERUVx9OjRCrdz584dcnJyyqTBlVxDT05OxtfXl65du1JUVIShoaEUQlPaL7/8UmV/Hw1oCQsLo3Xr1o+tt2nTJjIzM0lPT0ehULB27VrMzc2fKGCmhAhoEQShrhOPrdVjV65c4Y8//pBeX758mZs3b2JnZ8f777/PnDlzuHfvnrT8wYMHFW7r5s2b+Pn5YWNjI900V1r//v2xtbVl9erVtG3blgYNGrBt2zZp+dmzZ1EqlXTp0oVjx46Rk5MDUGUamlwuJyIiAtX/fxTr5s2bXLhwAaVSyc2bN7G2tsbHxwdTU1NOnz5dYcCMjY0NycnJKJVKioqK2LJliwhoEQShXhEz9HqssLCQ0NBQLl26RIMGDVCr1UyZMgULCwsCAwMJCwvDw8MDLS0tmjRpQvPmzRk3bpz0+dTUVFxdXcnNzUVHR4cPPvgALy+vCtubNm0aCoUCLy8vVq1aRUBAAOvWrUOtVtOsWTOWLl3K66+/jr+/P15eXjRs2JDevXujra1Nw4YNy92mn58fixYtknLktbW18fPzQ1tbm08//ZTc3FyKioqwsLCgb9++pKenlxswY2xszMmTJ/H09ASgY8eOeHt7P8ejLQiCULNEOIvw0pUOaImLi2PLli1s3Lixhnv1ZEoCFJZduFlvn0OvzwEYUL/3rz7vG9Tv/RPhLEKdsn79en766SdUKhUGBgYsXLiwprv01Nb07yXCWQRBqBXEgC68dN7e3uJ09wskBnNBeDWJm+IEQRAEoR4QA7og1EP5qtp1XV8QhBevzp9yz8/PZ8mSJezcuRMtLS0aNGjApEmT6NOnT7nrx8fH8+6779K2bVvp9Z49e6Sa5bXRxYsXcXd3JyMjAyh+lCs/P5+UlBQ0///p1fj4eHx9ffniiy/46KOPKt3eo8egIhkZGQQFBREfH//YsiNHjhAZGSndUf60zMzMMDU1RSaToaGhwcyZM7GxsXmmbVbHsmXLaN++PY6OjmRkZFBQUICdnd0Tb+eTnw5wp7D23Ve61b1XTXdBEISXrM4P6P7+/jx48ICkpCR0dXU5deoUY8eOxcDAACsrqzLrqlQqtm7diqGhYZWDWW3XvHlzfv31V+zt7YHi57k7dOhQrc8+j2PQqVOnZx7MS5SUY925cydTpkwhLS0NDY3qnTx62gCXyZMnS18fOHCABw8ePNWALgiCUFvU6VPuly5dIjk5GX9/f+lOY1NTU8aPH8+KFSuIj49n1KhRTJw4EScnJ9avX8/Ro0dZuHAhLi4upKamAsWPUU2ZMoUBAwbg6enJtWvXgOI/AIKCgnBycsLJyYmgoCCpwMmVK1cYOXIkAwYMYPz48YwfP56oqCigOBRl4sSJODs74+zsXKbAilwuZ9myZQwZMgS5XC59BiAoKAh3d3cGDhzIyJEjuXTpUoX77ubmJs2cL1y4wIMHD8pUcEtLS2PIkCG4urri7OxMUlISUPyYWHnHYPXq1Tg7OzNw4EA8PT1Rq9XSMZg7d6607OzZs0Dx7F2hUADFZxC6detGSEgIrq6u9OvXj4MHD0p9iYqKom/fvri7u7N8+XK6detW7j7Z2tpy+/Ztbt++jVwu59SpU2WOW8lruVxOcHAwHh4ezJ07l3PnzjFkyBAGDhyIk5MT69atA2DWrFlljm/p1yVfnzx5kpiYGLZt24aLiwsREREVHnNBEITarE7P0E+dOoWJiYmUJlbC0tKSZcuW4eLiwqFDh0hISMDExAQoLj06evRo3nvvPaD49PORI0fYvn07b7zxBnPmzCEqKoqpU6cSGxvL8ePHpYHTy8uL2NhYPvzwQxYuXEi3bt2YMGECly5dwtnZWZrhLVy4kPbt27Ny5UquXr2KQqHAwsJCGnBzc3OJjY3l4sWLODs74+bmhp6eHl5eXsycOROAzZs3ExwcTEhISLn7bm1tTXR0NHfu3GHr1q24urry119/ScstLCyIjo5GU1OT69evo1AosLOzw93dnW3btpU5Blu3bmXXrl1s3LgRfX19bt26Jc2Qz5w5Q2BgIF9++SXh4eGEhYWVOzO/ffs2lpaWTJ06le3btxMcHExMTAwnTpxg9erVJCQk0LRp00ofUUtOTqZFixbVqqmuVCrZsmWLdLzlcjmffPIJ8L9AluowMzPD09OTBw8eSMdeEAShLqrTM/Tq1MTp2rWrNJhXts4bb7wBQJcuXaSypGlpabi5uaGjo4OOjg4KhYK0tDSgeIZaEgfasmXLMtd909LSpIpkzZs3x97eXrr+DeDo6AhAq1ataNKkCZcvXwZg7969DB48WJpllmSXl0cmk9G/f3+SkpJISkrCycmpzPKbN2/i4+ODk5MTY8aM4c6dO2RlZZW7rd27dzN06FCp2IuhoaG0rG3btlhYWADFfyhduHCh3G00atRI+gOh9HoHDhzA3t5eGqQ9PDwe+6ynpycuLi4kJSWxcuXKCve5NFdXV+lrKysrNm/ezNKlS0lLS5MiYQVBEF4ldXqGbmpqSk5ODrdv3y4zS//zzz8xMzMDqhe6UbowiKampnRa/UUpr71Lly4RGBjIli1baN26NZmZmUyfPr3S7bi5uTFo0CCsrKzKDMJQfG+BXC5nxYoVyGQy+vXrR15e3hP3VUdHR/paQ0ODwsLCZ1qvPCXX0EvT1NSUTvsDj/W9dIBLv379sLS0ZP/+/axZs4a4uDiCg4Or3IYgCEJ9Uqdn6K1atcLBwQF/f3/pl/WpU6dYtWoVkyZNKvczenp6ZQJJKmNjY8O2bdsoKCigoKCAbdu2SYEe1tbWUrDIP//8Q3p6epnPbdq0CYBr166RkpJC9+7dK21LqVSira2NkZERarWamJiYKvvXunVrpk6dyoQJEx5bdu/ePVq2bIlMJmP//v1kZ2dLyx49Bu+99x4bN25EqVQCcOvWrSrbri5ra2v27t3LzZs3garDWEqYmJhw5MgRoPiMx/Xr1ytcNzs7GyMjIxQKBRMnTpQ+9+abb0pfX716tcxZktL09fWr/W9CEAShtqrTM3SAefPmsWTJEhwdHdHW1kZXV5fZs2djbW3NxYsXH1t/yJAhfP3116xbt67Ka6ZDhgwhJydHyue2s7Nj8ODBAMyePZsZM2awY8cOWrVqRefOnaVT1nPmzJFuJAOYPn067du3r7QtMzMzHBwccHR0xNDQEHt7+zI3llXWx/JMmzaN+fPnExoaSqdOnaQzFuUdA1dXV65cucKQIUPQ0tKiUaNGbNiwocq2q8Pc3JyxY8fi6emJvr4+3bt3p3HjxlV+bvLkydKNa927d+f//u//Klw3OTmZHTt2oK2tjUwmw8/PD4BBgwbh4+ODo6Mjbdq0oXPnzuV+vk+fPtJNcQMGDCgTUCMIglBXiHCWp5Sbm4uWlhZaWlpcvXoVDw8PIiMjeeutt2q6a7VO6TCW0NBQsrOzCQ4OruFePZuSAIXQiw/q7XPo9TkAA+r3/tXnfYP6vX8inKUGnD9/npkzZ1JUVERhYSGTJk0Sg3kFFi9eTGZmJgUFBbRu3Zovv/yyprv03Kx2sK514SxQXClOR7NOX1ETBOEJiQH9KZmbm5OQkFDT3agT5s2bV9NdeOWIwVwQXj3ip14QBEEQ6gExoAtCPSTCWQTh1SNOuddDcrkcHR0ddHV1ycvL49///jfz5s1DW1v7pbRfOvjkac2aNYvU1FQMDQ3Jzc3Fzs6O2bNno6GhwZUrV5g+fTrr168v97NmZmZkZmZWqwbBs/L+6TB3qv/I/UsT525V9UqCINQrYkCvp5YvX46pqSkqlYphw4bx3//+95kG2CdROvjkWYwbN46PPvoIpVKJm5sb//rXv3B0dMTY2LjCwVwQBOFVJU6513N5eXnk5eXRpEmTCgNbDh8+/Fjp2IEDB5KZmQkUF4MZNGgQCoWCESNGcO7cOQAyMzNxc3OTnt9OTEwEyoagVNQmwPDhwwkKCmLo0KG8//77FT7Kpq+vT4cOHfj777+B/4XBlPjPf/6Dg4MDLi4uj5WO/fnnn3FwcMDV1ZVVq1ZhZmbG/fv3ATh06BDDhw9HoVCgUCjYs2fPUx1jQRCE2kDM0OspHx8fdHV1ycnJwc7ODjs7O+7cuVNuYEvnzp1p1KgRBw4cwNramoMHD6KhoUHXrl05ePAgycnJbNiwAR0dHVJSUvDz8yMmJoY1a9YwZswYnJycKCoqKrfaWkUhMQYGBkBxlb0NGzZw//59+vTpg4eHB23atCmzjRs3bnDy5Ek+/fTTx7Z//fp1vvjiCzZu3Mhbb73FmjVryiybO3cusbGxtGnThsjISGnZ3bt3mTdvHhERETRv3lyqJZCYmChqwQuCUCeJAb2eKjnlnpeXx6effkpkZCT29vb4+fmRnZ2NpqamFNhiaWnJ8OHDiY6Oxtramg0bNjBs2DAAdu3axYkTJxg0aBBQHIhz9+5dALp160Z4eDg5OTnY2trSpUuXx/px8+bNCtsEcHBwQENDg8aNG9OuXTtycnKkAT0iIoLY2FiysrIYOnQo7dq1e2z7hw4dwsLCQqoBMGTIEGmmX7KsZHvu7u4EBgYC8Mcff3Dx4kW8vLykbclkMrKzs+nUqdOzHn5BEISXTgzo9Zyuri69e/dmz5497N69u8LAFgcHB5YsWcKxY8fIyMggICAAKB7A3d3dy70uPmrUKORyOampqSxYsABbW1umTp1aZp2qQmIqC8YpuYZ+7tw5hg4dip2dHfb29s/luBQVFWFmZvbcStwKgiDUNHENvZ5Tq9X89ttvtGnTptLAFm1tbdzd3fH29sbZ2ZmGDRsCxXfMJyQkSBGvKpWKo0ePApCVlYWJiQmenp6MGDFCCkIprbI2q+utt97Cx8eHkJCQxyJzLS0tOXbsGOfPnweKc+RLdOnShWPHjklxuKWDYd59912ys7PLhOocPny4WpG8giAItZGYoddTJdfQCwoKaN++PRMnTuTo0aMVBrZAcZjJihUrGDp0qPSelZUVU6ZMwdvbG5VKRUFBAQ4ODnTs2JH169eTkZGBtrY2Ojo6zJkz57F+VBYS8ySGDBnCDz/8wC+//IK5ubn0frNmzViwYAHjx4+nQYMG9O3bV1r2+uuv4+/vj5eXFw0bNqR3795oa2vTsGFDNDQ0CAsLY9GiRQQEBEhlaVetWoVMJnuqPgqCINQkEc4iSBISEkhKSiIiIqKmu/LclA6GiYuLY8uWLWzcuPGZt1sSoLDyorrePodenwMwoH7vX33eN6jf+yfCWYRnNmbMGHJycggPD6/prjxX69ev56effkKlUmFgYMDChQuf6/bDHTqLcBZBEGoFMaALAKxbt66mu/BCeHt74+3tXdPdeOnEYC4Irx7xUy8I9ZCo5S4Irx4xQxeEZzDxp7PcLax9fxdvcjeveiVBEOqV2vebSHjp5HI5p06deuntDh8+nN27d7+w7e/btw9PT0/69u2LQqHgk08+4eTJk4wdO/axG+OKiop4//33OXDgwAvrjyAIwoskZuhCnVVYWIiWVvn/hH/99Vdmz57NypUrpcpvx48f59q1a7i7u/Pdd9+VeTwvIyMDDQ0NrKxESpkgCHWTGNCFcgUFBXHgwAEKCgowNDQkICCAli1bcuPGDaZNm8aNGzcAsLGxwc/Pj8zMTBYsWIBaraawsBBvb2+cnJy4fv068+bNk4q7jBkzBldX10rb/vbbb0lKSkKlUqGrq4u/vz/vvPMOUByNOmnSJPbs2UPPnj3p1atXue2uXLmSCRMmlCnjWrKN/Px8/P39OXv2rFRONj4+HoVCIZ5BFwShzhIDulAuLy8vZs6cCRRXXwsODiYkJIQdO3ZgYmIiBZ3cuXMHoMKgloULF9K+fXtWrlzJ1atXUSgUWFhYYGpqWmHbrq6ujB49GoDU1FTmzZvHpk2bpOW6urrExcUBxXexl9fusWPHmDt3brnb19HRwdnZmbi4OGbMmIFSqWTnzp38+OOPz3DEBEEQapYY0IVy7d27l+joaB48eEBh4f8qp3Tp0oXIyEiCgoKwtrbGzs4OqDioJS0tjVmzZgHQvHlz7O3tycjIqHRAP3r0KKtXr+bOnTvIZDKprGsJNzc36evqBMSUx8PDg7FjxzJt2jSSk5Pp2rUrLVq0qNZnBUEQaiNxU5zwmEuXLhEYGMjixYtJTEwkICCA/Px8oLgG+tatW+nYsSMJCQmMGDECKA5qCQ8Pp2nTpixYsICQkJCnajs/P5/Jkyfj5+dHYmIia9euldou0ahRI+nritq1sLDg8OHDFbZjbm5O8+bN2bt3L3Fxcbi7uz9VfwVBEGoLMUMXHqNUKtHW1sbIyAi1Wk1MTIy07MKFC7Ro0YIBAwbw73//mw8++AC1Wk12djZt27bFxMSERo0asW3bNqD4GvumTZvw8fHh2rVrpKSkMGrUqArbzs/Pp7CwkDfeeAOA6OjoSvualZVVbrve3t588cUXdOzYkQ4dOgBw4sQJrl+/Lp1VcHd3JzQ0lL///pv333//qY+XIAhCbSAGdAGAjz/+GE1NTem1g4MDjo6OGBoaYm9vz8GDBwE4cOAAkZGRaGhooFarmT9/PhoaGhUGtcyZM4e5c+fi7OwMwPTp02nfvr3UzqxZs8qUTo2IiMDHxwcPDw9ee+01+vXrV2m/K2q3V69efPnll3z55Zfcvn0bLS0tWrVqxbRp06TPOjk5ERQUxODBg9HR0XnGIygIglCzRDiLIDyFkgCF8IsN621hmfocgAH1e//q875B/d4/Ec4iCDVkpUM7Ec4iCEKtIH7iBaEeEoO5ILx6xE+9INRDBSpxJU0QXjXilLsgPAP/n//hvkqz6hVfsuVurWu6C4IgvGRiQK8lBg0aRH5+PgUFBZw/f166E9zCwoLAwMAX2nZ8fDx79uxh+fLl/PLLLxw8eFCqEleRjIwMxo0bR5s2bVCpVLz22mvMnz9fKqUqCIIgvFxiQK8lNm/eDMDFixdxd3cnISGhRvrx/vvvV/uZ7Hbt2hEfHw/AokWLCAwMZO3atc+tLyqVqsyjdC9DZYEvgiAItZn4zVVLLV68GAMDA8aOHcuPP/7IZ599xv79+2nWrBleXl6MHDkSOzs7UlJSCA8PJz8/H21tbXx9fbG0tARg69atREdHo1Kp0NfXx9/fn7feeov8/HwWLlxIeno6hoaGUmgJlJ2tV7aNR1lbW7Nnzx7pdWVtL1iwgAMHDtC0aVPeeecdrl+/zvLly4mPj2f79u3o6emRnZ3NokWLyM/PJzg4mPv37wPg4+ND7969n2tIjFwux9HRkfT0dExNTQkICHj+31BBEIQXTAzotZSNjQ3r1q1j7NixpKenY2lpSXp6On379uXw4cP861//Iicnh7CwMNatW4e+vj6nT5/Gy8uLPXv2cPDgQZKTk9mwYQM6OjqkpKTg5+dHTEwMsbGxXLx4kaSkJAoLCxk2bBitWrV6rA+VbaM0tVrNL7/8gqOjY5Wfi42N5e+//5bS1IYPH16mhvqhQ4dISEjAxMSEu3fvMmLECCIiImjevDlXr17Fw8ODxMTE5x4So1Qq2bJly3P/PgqCILwsYkCvpbp27crUqVPJz88nMzOTGTNm8PPPP2NsbEz79u1p2LAh+/btIycnh2HDhkmfKyws5Pr16+zatYsTJ04waNAgAIqKirh79y5QfP3b1dUVbW1ttLW1GThwIJmZmY/1obJtAJw9exYXFxeuXLmCvr6+dNmgqrZdXFzQ0tJCS0uLAQMG8Pvvv5fZbxMTEwD++OMPLl68iJeXl7RcJpORnZ393ENiqop0FQRBqO3EgF5LNWjQAFNTU5KSkjAyMqJ79+4EBQXRokULunfvLq3Xs2dPvvnmm8c+X1RUhLu7O5MnT37qPlS1jZJr6Pn5+Xz22Wf4+/uzbNmyZ2pbT0+vTPtmZmZs2LCh3HW3bt1KamoqCQkJREREsHHjRkaNGoVcLic1NZUFCxZga2vL1KlTq2y3dOCLIAhCXSSeQ6/FbGxsCA0NxcbGBh0dHVq0aMHWrVuxsbEBwNbWln379nH69GnpMyUJY3K5nISEBC5fvgwU32B29OhRALp3705CQgKFhYXk5uaSmJhYbvuVbaM0HR0d/P392bdvH8eOHav0c9bW1uzYsYPCwkLy8vJITk6ucP/fffddsrOzSU9PL7N/RUVFXLhwAX19fQYMGICvry9//fUXarWarKwsTExM8PT0ZMSIERw5ckQ6liWZ6iUhMaX/MBIEQajrxAy9FrOxsWHZsmXSwNO9e3cyMzPp3LkzAG3atGHRokXMnj2b3NxcCgoK6Nq1K507d8bKyoopU6bg7e2NSqWioKAABwcHOnbsyODBgzl58qQUvtKpUyfp5rLSKtvGo15//XVGjx7NihUrCAsLq/Bznp6enDhxggEDBmBoaFjuDXYlDAwMCAsLY9GiRQQEBFBQUEDr1q1ZtWrVcw+JEQRBqOtEOIvw0imVSvT19cnPz8fb2xsHBwfpentdURKgsOVSs3pbWKY+B2BA/d6/+rxvUL/3T4SzCHXKxx9/TH5+Pnl5efTo0QM3N7ea7tJT8+/3Rq0MZylQFaGtKavpbgiC8BKJAV146UruhhdeHDGYC8KrR9wUJwj1UKEIZxGEV46YoQvCM1jz8zVyVbXvx2i6W4uqVxIEoV6pUzP0goICQkND6devHwMGDGDgwIH4+Phw5syZmu4a8fHx+Pj4AHDkyBGmTZv23LYdGhpKUFDQY+2U9ssvv0jrvCxyuRwHBwcGDhxI//79xal0QRCEdDomYgAAIABJREFUGlT7phaV8PX1JTc3l82bN9OkSROKiopISUkhKyuLt99++4W2/SShHZ06dWLx4sUvtD+PepJQlWdV+lgsX74cU1NTTp06hUKhoFevXhgbGz/3dl4WtVqNTCZDJhPXoAVBqFvqzIB+/vx5du7cSUpKCk2aNAGKy4D27t0bgPz8fEJCQvjtt9/Iz8/HzMwMf39/9PT0mDVrFjo6Opw/f57Lly9jaWlJUFAQMpkMpVJJYGAgJ0+eJC8vj27duuHr64umpibDhw/H3NycQ4cOYWBgQHh4OJ988gm3bt0iLy+Pzp07M3/+fHR0dMr0NSMjg6CgIOLj49m8eTNRUVFAceWzkydPkpCQgLm5OREREfznP/9BpVJhbGzMggULMDIy4t69e8yePZtTp05hZGREixYteP311ys9PqVDVTIyMggICKBLly788ccfyGQyQkJCpGjTioJTTp48yfz583n48CF5eXkMHjyYUaNGATBr1iw0NTXJysri/v37j6XBmZqa0qRJE65cuYKxsTHnzp0jICCAW7duUVBQwMiRI3F3dwfg559/JiQkhAYNGuDg4EBISAiZmZno6elhZmbGpEmT2LNnDz179mTs2LEVfn9WrFhBYmIiurq6yGQyfvjhB7S1tZk5cyZnzpxBS0uLtm3bsmzZMgAiIiLYvn07UPxH15w5c9DT0yM0NJTTp0+jVCr5+++/iY2NxcDA4Cn/pQqCINSMOjOgHzt2jDfffLPCX7Rr166lcePGUsDGokWLiIiIkMp+nj59msjISGQyGW5ubqSmpmJra0tgYCBWVlZ89dVXqNVqpk+fTlxcHIMHDwbgwoULREdHo6WlRVFREcHBwRgaGlJUVMTMmTOJi4tj6NChFfZ70KBB0jPWISEhvPnmm5iampKQkMCFCxfYtGkTGhoaREdH8/XXX7N48WJWrlyJnp4eP/30Ezdv3kShUNC/f/8nOl5nzpwhMDCQL7/8kvDwcMLCwli8eHGlwSktW7YkMjISHR0d7t+/z6BBg+jZs6f0h8Dx48eJiooqt0zq77//jqGhIebm5hQWFjJ9+nQWLVpEu3btUCqVuLu7Y2lpiYGBAXPnziU2NpY2bdpI4Sql6erqEhcXB8Ds2bPL/f707duXyMhIfv31Vxo0aIBSqaRBgwbs3r2b+/fv8+OPPwL/C21JSUlh+/btxMTEoKenx8yZMwkLC+Pzzz8HiivQxcfH07Rp0yc6zoIgCLVFnRnQH3XmzBmmTZtGbm4uPXv25M8//0SpVPLzzz8DxTN2c3Nzaf0+ffpIzwtbWFhI4R27du3i8OHDfPfddwDk5uaWOWXs7OwsnfZVq9V8++237N27F7VazZ07d2jQoEG1+rt582bS09P5/vvv0dDQYNeuXRw9elR6BrtktgzFM/ySCmdNmzblgw8+eOLj07ZtWywsLACwtLRk9+7dQOXBKbm5ufj7+3Py5ElkMhlXr17lxIkT0oDu4ODw2GDu4+NDUVEROTk5LFu2DB0dHc6cOcPZs2f57LPPpPUKCgo4d+4cGhoaWFhY0KZNGwDc3d0JDAwss83Sz6VX9P1p3LgxJiYmzJgxAzs7O3r37o2+vj7m5uacPXuW+fPnY21tLZ3BSUtLw9HRUTrGgwcPLhOT2qtXLzGYC4JQp9WZAd3CwoLs7Gzu3r1LkyZNePvtt0lISCAqKoqjR49SVFTEvHnzpDrnjypd/ENTUxOVSgUUD2hhYWG0bl1+Za3SA9iOHTv4/fff2bBhA/r6+qxatYrz589X2fdff/2VdevWER0dLf0BUFRUhLe3Nx4eHtU9BE+k9GUADQ0NCgsLpXYrCk5ZsmQJRkZGfP3112hpaTF69Gjy8vKk5eXNzEuuoScnJ+Pr60vXrl0pKirC0NDwsdPyUHzzXlVKt1PZ92fTpk1kZmaSnp6OQqFg7dq1mJubk5iYSHp6Onv37iUkJIQdO3ZU2WbpUBhBEIS6qM7c5d6mTRvef/995syZI2VcAzx48AAovuM6MjKS3NxcoLi86NmzZ6vcrlwuJyIiQhrgb968yYULF8pd9969exgaGqKvr8+9e/cqDDUp7eTJk8ydO5fw8PAyM0C5XE50dLR0Sjg/P58TJ04AxTXb4+PjAbh16xY7d+6ssp3qqiw45d69e7Ro0QItLS1OnTrFwYMHq73d/v37Y2try+rVq2nbti0NGjRg27Zt0vKzZ8+iVCrp0qULx44dIycnByi+nl9Vf8v7/iiVSm7evIm1tTU+Pj6Ymppy+vRpLl++jKamJn369MHX15ebN29y+/ZtbGxsSE5ORqlUUlRUxJYtW+jRo8cTHTtBEITarM7M0AECAwMJCwvDw8MDLS0tmjRpQvPmzRk3bhympqasWLECDw8P6S7lSZMmSaeLK+Ln58eiRYtwcXFBJpOhra2Nn59fuTNCV1dXfvnlFxwcHGjWrBn/+te/ysxgyxMZGcmDBw+YMmWK9F5ISAiurq7cvn2bjz76CCieiQ4dOhRzc3MmTJiAn58fDg4OGBkZ8e9//7vMNlNSUujVq5f0WqFQSBniVakscMXb25sZM2awZcsW2rZti5WVVbW2WWLatGkoFAq8vLxYtWoVAQEBrFu3DrVaTbNmzVi6dCmvv/46/v7+eHl50bBhQ3r37o22tjYNGzYsd5sVfX+0tbX59NNPyc3NpaioCAsLC/r27Ut6err0hIFarWbcuHEYGxtjbGzMyZMn8fT0BJD2VxAEob4Q4SzCS1cSzgIQFxfHli1b2LhxYw336smUBCikXTKut4Vl6nMABtTv/avP+wb1e/9EOItQp6xfv56ffvoJlUqFgYEBCxcurOkuPTWvfka1MpylUFWElqjnLgivFDGgCy+dt7e3ON39gonBXBBePXXmpjhBEKpPJcJZBOGVI2bogvAM4n66SUEtvIY+UmFU010QBOElq32/iV5xBQUFrFq1isTERLS0tNDU1KRNmzb4+Pi88Hr1TyI+Pp6AgABatmxJQUEBrVq14quvvsLISAwkgiAINUGccq9lfH19OXnyJJs3byYpKYmEhAQUCgVZWVk13bXH9OjRg4SEBJKSktDT02PFihXPbdtqtZqaeACjpACPIAhCXSNm6LVIVQE0aWlpLF26lLy8PFQqFePHj2fAgAEADB8+nA4dOnD48GEuXbrEiBEjMDY2JioqiqtXr/L5559L9eDNzMyYMmUKO3fu5Pbt2yxcuJDU1FT27dtHYWEhy5Yto127dly7do3PPvuM+/fvk5eXh729PTNmzHis3zKZDCsrK/bs2SO9V1nwjJ+fH6dPn5aeD2/WrBkzZ84sNyTlzz//JDw8nPz8fLS1tfH19cXS0pJz587h6+vLw4cPUavVuLm5MWbMGHbu3MmyZcvQ0NBApVLxxRdf0K1bN7Kzs5k7dy43b95ES0uLqVOnSs/yPxoIU7pmgCAIQl0hBvRapKoAGgsLC6Kjo9HU1OT69esoFArs7Oyk9S9fvkxUVBTXrl2jb9++jBo1ipiYGA4fPsykSZPKBLw0adKEuLg4kpOTmTBhAkuWLGHatGmsWbOG8PBwgoODadKkCatWrUJPT4+CggLGjBnD3r17yxS1geIqd3v37sXR0RGgyuCZJk2a8NNPP3H79m0UCgX9+vWTtlU6JCUnJ4ewsDDWrVuHvr4+p0+fxsvLiz179hAdHY1cLueTTz4B/hfCsnz5cr788kveffddVCoVDx8+BGD69OkMHjyYQYMGcebMGYYNG0ZycrJUva90IIwgCEJdJAb0WuzRAJphw4bh5+dHdnY2mpqa3Llzh6ysLCwtLYHi8BQNDQ2MjY157bXX6NOnDwAdOnTgypUr5OXlSc9MlwzuHTp0AOC9994Diiuo/fe//wWKy8J+8803/PHHHxQVFXH9+nVOnDghDeipqam4uLhw8eJF2rVrJ22zusEzpftYonRIyr59+8jJyWHYsGHS8sLCQq5fv46VlRWLFi3i4cOHdOvWje7duwPFZXMDAwPp27cvvXr1wtTUFKVSyfHjx6X41rfffpt33nmHP//8E7lcDpQNhBEEQaiLxIBei1QVQOPv749cLmfFihXIZDL69etXpvTsowE0Ja81NTWB4sGw5L2S/2toaFQY5PLdd99x9+5dNm/ejK6uLl988UWZ9nr06MHy5ctRKpWMGTOGZcuWMWPGjGcKnnk0JKVnz5588803j63Xr18/LC0t2b9/P2vWrCEuLo7g4GD8/Pw4efIk6enpTJ48mY8//lg6c1CZ8oJnBEEQ6hJxU1wtUlUAzb1792jZsiUymYz9+/eTnZ39Qvtz7949jIyKK6FduXKlwqQ0fX195s+fz8aNG7l69WqlwTPW1tZSCtvdu3crTV+ztbVl3759nD59Wnrv8OHDAGRnZ2NkZIRCoWDixIkcOXIEgHPnzmFmZsbIkSMZOHAgR44cQV9fn3feeUcKgjl79iwnTpyQzmwIgiDUB2KGXstUFkBz+/Zt5s+fT2hoKJ06dcLMzOyF9mX48OFMnjwZJycnjI2NK4ymBTA3N8fBwYE1a9Ywe/bsCoNnJk6ciK+vrxQ807FjR+l0/KPatGnDokWLmD17Nrm5uRQUFNC1a1c6d+5McnIyO3bsQFtbG5lMhp+fHwCLFy+WLkk0adKEr776CoDg4GDmzp1LZGQkWlpafPPNNyL/XBCEekWEswgvVUFBAWq1Gl1dXZRKJUOHDsXX17fORZmWBCicvPh/9bawTH0OwID6vX/1ed+gfu+fCGcR6oy7d+/i5eWFSqUiLy8PJyenOjeYl+bu0LRWhrOoVEVoinrugvBKEQO68FI1a9aM+Pj4mu5GvScGc0F49Yib4gShHhLhLILw6hEzdEF4Bv/98Raqwtr3Y+Qy6PWa7oIgCC+ZmKELj5HL5Zw6deqltzt8+HB27979QrYdGhpKUFCQ9DorK4uJEyfy/vvvo1Ao8PT0ZOfOnS+kbUEQhJeh9k0tBOEpFRYWoqVV9T/pq1ev8tFHH/H555+zcuVKAK5du8b+/ftfdBcFQRBeGDGgC9USFBTEgQMHKCgowNDQUIpOvXHjBtOmTePGjRsA2NjY4OfnR2ZmJgsWLECtVlNYWIi3tzdOTk5cv36defPmkZOTA8CYMWNwdXWttO1vv/2WpKQkVCoVurq6+Pv788477wCPB6v06tWr3HZL27BhA926dSvTrpGRUZX9EARBqM3EgC5Ui5eXFzNnzgRg8+bNBAcHExISwo4dOzAxMSEyMhL4X0jKmjVrGDNmDE5OThQVFUmV7xYuXEj79u1ZuXIlV69eRaFQYGFhgampaYVtu7q6Mnr0aKC4fvy8efPYtGmTtLx0sIq3t3e57ZZ27NgxbG1tn/2gCIIg1CJiQBeqZe/evURHR/PgwYMymeFdunQhMjKSoKAgrK2tsbOzA6Bbt26Eh4eTk5ODra0tXbp0AYojYGfNmgVA8+bNsbe3JyMjo9IB/ejRo6xevZo7d+4gk8k4f/58meWlg1UqalcQBKG+EzfFCVW6dOkSgYGBLF68mMTERAICAsjPzwfg3XffZevWrXTs2JGEhARGjBgBwKhRowgPD6dp06YsWLCAkJCQp2o7Pz+fyZMn4+fnR2JiImvXrpXaLlE6WKU67VpYWEi13wVBEOoLMaALVVIqlWhra2NkZIRarSYmJkZaduHCBfT19RkwYAC+vr789ddfqNVqsrKyMDExwdPTkxEjRkgDqI2NjXS6/Nq1a6SkpEjRp+XJz8+nsLCQN954A4Do6OhK+1pRu6V9+OGHpKWlsWPHDum9GzdusG3btuofFEEQhFpGnHIXyvXxxx9LsatQnLXu6OiIoaEh9vb2HDx4EIADBw4QGRmJhoYGarWa+fPno6Ghwfr168nIyEBbWxsdHR0pA33OnDnMnTsXZ2dnAKZPn0779u2ldmbNmlWmlGpERAQ+Pj54eHjw2muv0a9fv0r7XVG7pRkbG7N+/XqCg4NZunQpjRo1olGjRnh5eT39ARMEQahhIpxFEJ5CSYDCPzkt621hmfocgAH1e//q875B/d4/Ec4iCDXkA0dDEc4iCEKtIK6hC0I9JAZzQXj1iAFdEOohEc4iCK8eccpdEJ5BasJN1LXwGvr7HxrVdBcEQXjJasUMPT8/n6+//po+ffrg4OCAq6trpUEZ8fHxZGVllXnt4+PzMrr61C5evEi3bt2k13K5HDs7O1QqlfRefHw8ZmZmREVFVbm9R49BRTIyMlAoFOUuO3LkCNOmTatG7ytnZmaGs7MzAwcOxNXVlbS0tGfeZnUsW7aMH3/8ESjez19//bXMcqVSyYIFC/jggw9wcXFBoVCwatUqDh06RM+ePcsceyg+psOHD38pfRcEQXjeasXUwt/fnwcPHpCUlISuri6nTp1i7NixGBgYYGVlVWZdlUrF1q1bMTQ0pG3btjXU4+ejefPm/Prrr9jb2wOwdetWOnToUK3PPo9j0KlTJxYvXvzUny8tJiYGPT09du7cyZQpU0hLS0NDo3p/L1Y3VOVRkydPlr4+cOAADx48kCrVFRUVMW7cOMzNzUlKSkJHR4e8vDw2bdpEly5dMDAwYP/+/fTq1UvaRnx8PB4eHk/cD0EQhNqgxgf0S5cukZyczO7du6W7hU1NTRk/fjwrVqzAxcWF7du3o6enR3Z2Nh4eHhw9epSFCxeydOlSqb64UqlkypQpnD59msaNGxMaGoqRkREqlYrg4GD27dsHQM+ePZk+fTqamppcuXKFGTNmcP36dVq3bg2AnZ0dH330UaUhInK5HBcXF1JTU7l27RqjR4/mo48+AioOMSmPm5sb8fHx2Nvbc+HCBR48eFCmBGpaWhpLly4lLy8PlUrF+PHjGTBgAHFxcY8dgx49erB69WoSExORyWQ0atRIKsKiUqmYO3cuf/zxBzKZjJCQENq1a0dGRgZBQUHEx8dz8eJF3N3d8fT0JCUlhYcPH/LVV1/x73//G4CoqCh++OEHGjdujL29PRs2bCAjI+OxfbK1teX27dvcvn0bDw8PVq1aJe2TXC6XXsvlchwdHUlPT8fU1JSxY8fi6+vLw4cPUavVuLm5MWbMGGbNmkXHjh2l41v6dcnXVlZWxMTEoFarSU1NZcCAAXTs2JG///6b77//Hm1tbaC45nvJDFyhUBAfHy8N6Dk5OZw4cQIHB4cn/BcsCIJQO9T4KfdTp05hYmLCa6+9VuZ9S0tLTpw4AcChQ4eYOXMmiYmJjBo1io4dOzJnzhwSEhLo0aMHUHz6eObMmSQlJfH2229Lp61jY2M5fvw48fHxxMfHc+zYMWJjY4HioJBu3bqRlJTEF198wYEDB6T2S0JEduzYwbp16wgODi6TEZ6bm0tsbCw//PADixcv5v79+0BxiElcXBzbt2/HycmJ4ODgCvfd2tqaU6dOcefOHbZu3fpY2peFhQXR0dFs27aN7777jqCgIO7cuYO7u/tjx2Dr1q3s2rWLjRs3sn37dsLDw6UZ8pkzZ/D09GTHjh3079+fsLCwcvtz+/ZtLC0t2bZtGxMnTpT6fuLECVavXk1MTAxxcXHcvXu3wn1KTk6mRYsWNG3atMJ1SiiVSrZs2UJAQADR0dHI5XK2b99OYmLiE82UzczM8PT0xNXVlYSEBMaNG8dff/2FhYWFNJg/ysXFhZSUFClMJj4+HkdHRxo0aFDtdgVBEGqTGh/Qq1PXpmvXrpiYmFS5Tkl50C5dukgz67S0NNzc3NDR0UFHRweFQiFd483IyMDd3R2Ali1bYmNjI20vLS0NT09PoGyISAlHR0cAWrVqRZMmTbh8+TJQHGIyePBgnJycWLduHcePH6+wzzKZjP79+5OUlERSUtJjMZ83b97Ex8cHJycnxowZw507dyq8br57926GDh2Kvr4+AIaGhtKytm3bYmFhART/oXThwoVyt9GoUSPee++9x9Y7cOAA9vb20iBd3mDr6emJi4sLSUlJUsZ4VUr/AWNlZcXmzZtZunQpaWlpNGnSpFrbeFrNmjWjR48eJCYmolar2bZtm/RvQRAEoS6q8VPupqam5OTkcPv27TKz9D///BMzMzMA9PT0qtxO6eIempqaj93w9LyV115JiMmWLVto3bo1mZmZTJ8+vdLtuLm5MWjQIKysrMoMwlB8b4FcLmfFihXIZDL69etHXl7eE/dVR0dH+lpDQ6NMWtrTrFeekmvopWlqaqJWq6XXj/a9dKhKv379sLS0ZP/+/axZs4a4uDiCg4Or3EZFOnToQHR0dKXX593d3VmxYgVt2rRBX19fJLMJglCn1fgMvVWrVjg4OODv7y/9sj516hSrVq1i0qRJ5X5GT0+v3Jzr8tjY2LBt2zYKCgooKChg27Zt0ml6a2trtm7dCsA///xDenp6mc89SYgIVB5iUpHWrVszdepUJkyY8Niye/fu0bJlS2QyGfv37yc7O1ta9ugxeO+999i4cSNKpRKAW7duVdl2dVlbW7N3715u3rwJIB2zqpiYmEjhKGlpaVy/fr3CdbOzszEyMkKhUDBx4kTpc2+++ab09dWrV8u9bg+gr69f5njY2NhgbGzM119/LaWz5efnl3mCoFevXly9epUlS5ZU+CSAIAhCXVHjM3SAefPmsWTJEhwdHdHW1kZXV5fZs2djbW3NxYsXH1t/yJAhfP3116xbt066Ka4iQ4YMIScnR8rMtrOzY/DgwQDMnj2bGTNmsGPHDlq1akXnzp2lU9ZVhYiUx8zMrMIQk6r6WJ5p06Yxf/58QkND6dSpk3TGorxj4OrqypUrVxgyZAhaWlo0atSIDRs2VNl2dZibmzN27Fg8PT3R19ene/fuNG7cuMrPTZ48mVmzZhEVFUX37t35v//7vwrXTU5OZseOHWhrayOTyfDz8wNg0KBB+Pj44OjoSJs2bejcuXO5n+/Tpw/btm3DxcWFAQMGMG7cONauXcvixYtxdHSkYcOGANL3E0BLSwsXFxe+//57IiIinuSQCIIg1DqvdDhLbm4uWlpaaGlpcfXqVTw8PIiMjOStt96q6a7VOkqlUvpjJzQ0lOzs7Epv+KvvSgIU7p79v3pbWKY+B2BA/d6/+rxvUL/3T4SzPKXz588zc+ZMioqKKCwsZNKkSWIwr8DixYvJzMykoKCA1q1b8+WXX9Z0l2qFHi5NRTiLIAi1wis9oJubm5OQkFDT3agT5s2bV9NdEJ6AGMwF4dVT4zfFCYLwYqgLX9mraYLwSnqlZ+iC8Kz+2HIDCmrnj1H3Uc1ruguCILxEtfM30StMLpdLRXAePnzI22+/jZeXF127dq3prpURGhpKdHQ0zZs3Jy8vjw4dOrBgwYIyz5YLgiAIL4845V4LLV++nO3bt/Pf//4XNzc3xo0bx6FDh2q6W48pKbW6fft2/vnnn2qlxFXXkxS1eZ5qql1BEIRnJWbotVzfvn05fPgw69ato1GjRpUGlejo6HD+/HkuXLjABx98wHvvvUdoaCiXL19m5MiRjBw5Eig+C+Ds7Ex6ejpXrlxh2rRp3Lhxg8TERO7cuUNAQABWVlbMnz+fli1bMnbsWACOHTvG1KlT+emnn8r0UUdHh3fffZd//vkHKC7gEhISwm+//UZ+fj5mZmb4+/ujp6dXaSDOrFmz0NTUJCsri/v375OQkMDWrVuJjo5GpVKhr6+Pv78/b731FpmZmSxYsAC1Wk1hYSHe3t44OTkRGxtLZGQkOjo6qNVqli5dSrt27Th8+DBfffUVDx48oFGjRsyePZvOnTtLoTQKhYL09HQGDx7M0KFDX9a3VxAE4bkRA3od0KVLF3bt2lVhUZUSp0+f5vvvv0elUiGXy7l37x5RUVFcu3YNBwcHPDw8pPKs+fn5xMbGcvjwYUaMGMHnn3/Oli1b+PHHH1myZAkbN27ko48+Yvz48YwZMwaZTEZUVBQffvghMlnZO6iVSiW//fYbU6dOBWDt2rU0btyYLVu2ALBo0SIiIiKYOnWqFIgzYcIELl26hLOzsxR5CnD8+HGioqJo1KgRBw8eJDk5mQ0bNqCjo0NKSgp+fn7ExMSwZs0axowZg5OTE0VFRVKVuG+++Ybk5GSaN29Ofn4+KpWK/Px8fHx8CAwMxMbGhtTUVHx8fPjPf/4DFIfSdOrUqcoiRYIgCLWZGNDrgOrW/unTp49Uj71t27bY29ujoaGBsbGxFCDTrl074H/hMh06dODhw4f0798fgI4dO0rBNu3ataN169bs3bsXS0tLdu3aha+vr9Tetm3bpJK0dnZ2UmncXbt2oVQq+fnnn4HiPx7Mzc2B4kCcOXPmAI8H4gA4ODhI1+F37drFiRMnGDRokHQcSpLeunXrRnh4ODk5Odja2kp12Lt3786sWbN477336N27N61bt+bkyZNoa2tLbfXo0QNtbW2ysrLQ09NDV1dX2n9BEIS6SgzodcCRI0do3759lUEljwbGVBZYU7JMU1OzzOtHQ1mGDx/Oxo0bOXv2LH379i1T8tXV1ZWZM2dy7do1PvzwQ6Kjoxk2bBhFRUXMmzfvscG6OkrfVFdUVIS7uzuTJ09+bL1Ro0Yhl8tJTU1lwYIF2NraMnXqVFasWMGRI0dIT09nxIgR+Pv706JFi0rbbNiw4WNnHQRBEOoacVNcLbdz5042btzI6NGjqx1U8jzZ29uTlZXFd999x4cffljuOkZGRsyePZvw8HByc3ORy+VERkaSm5sLFJ+SP3v2LFB5IM6j5HI5CQkJUjStSqXi6NGjAGRlZWFiYoKnpycjRozgyJEjFBYWcuHCBTp37sy4ceOwtbXl+PHjtG3bloKCAqmttLQ0CgsLadu27fM5SIIgCLWAmKHXQj4+PtJja+3atSMiIoIuXbpgYmJSraCS50lDQwNXV1f27t0rnTYvT+/evXnrrbeIiYlh3LhxrFixAg8PD2QyGTKZjEmTJtGuXbtKA3EeZWVlxZQpU/D29kalUlFQUICMGkgiAAAgAElEQVSDgwMdO3Zk/fr1ZGRkoK2tjY6ODnPmzEGtVjNr1izu3buHTCbjjTfeYNq0aejo6LB8+fIyN8UtW7asTFysIAhCXfdKh7MI1fPxxx8zePDg53Kdub4E4jxLgMLLoi4sQkPr6S8l1OcADKjf+1ef9w3q9/6JcBbhhThy5AhTp07FwsKCfv36PZdtikCcl+dZBnNBEOoeMaALFerUqRM7d+58rtsUgTiCIAgvhrgpThDqKRHOIgivFjFDF4RncCr6OrL82vlj1PET45rugiAIL5GYoQtPTS6X4+DggIuLi/TfxYsXMTMz4/79+0+1zePHj/Pjjz+Wec/FxUV6BK4is2bNolOnTvz9999l3qtOffmdO3dy+PDhp+qvIAhCbVE7pxZCnbF8+XJMTU2f2/aOHz/Onj17pEp2QLWvuRsZGREaGkpgYOATtblz5046duz4Uh4DFARBeFHEgC68UEFBQRw4cICCggIMDQ0JCAigZcuW3LhxQwqFAbCxscHb25vly5ejVCpxcXHBysqKOXPmYGZmRmZmJnp6epw9e5avvvqKa9euATB69Gjc3NwA8PT0JCoqijNnzvD222+X6UdFgTGZmZns2rWL1NRUNm/ezMcff4yrq+vLPUiCIAjPgRjQhWfi4+NTpoxsfHx8meVeXl5S6MnmzZsJDg4mJCSEHTt2YGJiQmRkJAB37tzBwMAAHx8f9uzZw/Llyx9rq7CwkAkTJjBlyhTpmfhbt25Jyxs1asQnn3xCSEgIK1euLPPZygJj5HJ5mRQ7QRCEukgM6MIzqeqU+969e4mOjubBgwdlasR36dKFyMhIgoKCsLa2LpO4VpGsrCwKCwvLFLgxNDQss87gwYP57rvvHsuPrywwRhAEoT4QA7rwwly6dInAwEC2bNlC69atyczMZPr06QC8++67bN26ldTUVBISEoiIiGDjxo3P3Ka2tjaffvopS5Ys4Y033pDef5bAGEEQhLpA3OUuvDBKpRJtbW2MjIxQq9XExMRIyy5cuIC+vj4DBgzA19eXv/76C7Vajb6+vpRt/qi2bduipaVFcnKy9F7pU+4lnJ2duXnzJgcOHJDeqywwprI2BUEQ6goxoAvPxMfHp8xjayVpcABmZmY4ODjg6OjIoEGDaNWqlbTswIEDKBQKXFxcGDt2LPPnz0dDQwMbGxsePnzIwIEDWbhwYZm2tLS0CAsLIyYmBmdnZwYOHEhKSspjfdLQ0OCzzz7j0qVL0nvjxo3D3NwcDw8PnJ2d+fDDD6UBfeDAgSQmJuLi4sK2bdue9yESBEF4KUQ4iyA8BRHOUvfV5/2rz/sG9Xv/nuV3i5ihC0I9JcJZBOHVIgZ0QRAEQagHxIAuCPWUCGcRhFeLeGxNEJ7BxW+voZlXO3+M2kxpUdNdEAThJRIzdOGZyOVyTp069dLbHT58OLt3737p7QqCINRWYkAXXkmlq9YJgiDUB7XzXKFQpz1JIIufnx+ZmZksWLAAtVpNYWEh3t7eODk5cf36debNm0dOTg4AY8aMqTI45dtvvyUpKQmVSoWuri7+/v688847QPFz8ZMmTWLPnj307NmTXr16lduuIAhCXSQGdOG5e5JAFoA1a9YwZswYnJycKCoqkqq2LVy4kPbt27Ny5UquXr2KQqHAwsKi0trxrq6ujB49GoDU1FTmzZvHpk2bpOW6urrExcUB4O3tXW67giAIdZEY0IXn7kkDWbp160Z4eDg5OTnY2trSpUsXANLS0pg1axYAzZs3x97enoyMjEoH9KNHj7J69Wru3LmDTCbj/PnzZZaXRK1W1q4gCEJdJK6hC89VSSDL4sWLSUxMJCAggPz8fOB/gSwdO3YkISGBESNGADBq1CjCw/9fe3ceFlW9P3D8jSyyiYIKWmpmXUBzwxDXFEevCyogmPmkUpZYmoHbVUArrwtE6lVMkKiutmAubIMo1r3hegtcE801NNxyRUNQWYb5/eHD+UkspowOM31ez+PzOHPO+Z7Pdwbmw/ec73w/q3BwcGDBggUsW7bskc5dXFxMcHAwYWFhpKWl8dlnnynnLmdtba38X1fnFUKIukBG6EKnHlSQpVmzZgwdOhR3d3f+/ve/U1ZWRm5uLs8++yytWrXC2tpaWU+9R48ebNiwgaCgIK5evcqOHTt4/fXXqz13cXExpaWlSpW1tWvX1hjrmTNnqjyvEEIYIknootbGjx+Pqamp8ri8IIu9vT19+/Zl3759wL2CLGvWrKFevXqUlZUpBVm++uorsrKyMDc3x8LCgrlz5wIwd+5c3n//fYYPHw7AzJkz+dvf/qacJyQkpMJax3FxcQQFBTFy5EgaNWrEoEGDaoy7uvMKIYQhkuIsQjwCKc5i+Iy5f8bcNzDu/klxFiFEJVKcRYi/FknoQgghhBGQhC6EkdJKcRYh/lJkUpwQtXA17gJmd00fvKMeNPvHM/oOQQjxBMkIXdRZ+ir8IoQQhkgSuhBCCGEE5JK7MCj6LPwihBB1mSR0YVD0WfhFCCHqMknowqDos/CLEELUZXIPXRgMfRZ+EUKIuk4SujAYDyr8Ymtry9ChQwkNDeXnn3+mrKyMM2fO0KpVK0aPHk1AQACHDx8G/r/wC6AUfunevbte+iWEELogl9xFnaavwi9CCGFopDiLEI/AEIqzaEu1mEhxlmoZc/+MuW9g3P2T4ixCiEpqk8yFEIZHEroQQghhBCShC/EXpC0t03cIQggdk0lxQtTC1c9OYVZkeH8XN5veTt8hCCF0zGA+iVQqFYMHD8bb25thw4axefNmnbUdFRXFli1bat1OTk4OLi4uympldVlgYKCy7OmjyMrKolOnTvj4+DBs2DDGjh1LTk6ODiMUQgjxMAwmoQOsWLGC1NRUPvroI0JDQ8nLy9NJu8HBwXh5edW6nYSEBLp3705iYqIOoqro/lXRdOHTTz+lVatWtWrjueeeQ61Wk5aWRqdOnYiIiNBRdPdoNBqdtvdn6Pp1FkKIJ8UgL7m3a9cOGxsbpk2bRkFBQa0LdYSEhNC+fXv8/f3x9PQkPT0dBwcH4F4xEBsbG6ZMmcKhQ4dYsmQJhYWFAAQFBeHp6QncSwSbNm0iPj6ewMBAsrOz6dixIwCXL19m1qxZXLt2jZYtWwLQu3dvxo4dW+O2kJAQTE1NOXPmDIWFhajVapKTk1m7di0ajQZbW1vmzZtHmzZtqu3b+vXrWbNmDRYWFpSVlbF8+XKee+45VCoVsbGx5Ofns3DhQlJSUpTX18/Pj5CQEDw8PKo93x95eHiwfft25XF1xxUXF7NgwQL27NmDg4MDbdu25dq1a6xYsYKkpCRSU1OxsbEhNzeXxYsXU1xcXOVrrstiLCqVCi8vLzIzM3F2diY8PLz2P6RCCPGEGWRCz8zMpKioiGXLlimJtzaFOspZWVkxYMAA0tLSCAgIUJL0unXryM/P54MPPiAuLg5HR0euXLnCyJEjSUtLw87Oju3bt/PMM8/wzDPPMGLECBITE5WEvnDhQrp168bkyZO5cOECw4cPV9Yar2kbwLFjx/j666+xtrZm3759pKenEx8fj4WFBTt27CAsLIx169ZV27ePPvqI9PR0HB0dKS4urjTqdXd35/bt2xw/fhxXV1dOnDhBfn4+Xbt2rfF89ysrK+P7779XrnLUdNz69eu5ePEimzdvRqPRMG7cOJo1a6a0dejQIdRqNa1atSI/P5+AgIAqX3NdF2MpKCggISHhYX8UhRCizjCohB4UFET9+vWxtbXl448/1lmhjvuNGDGCRYsWERAQwM6dO2nTpg0tWrRgx44dnD9/nsDAQGVfExMTcnNz6dChA4mJiYwYMQIAX19ffH19CQsLo379+mRlZSkrlD399NP06NFDaaOmbXBvZTRra2sAMjIyOH78OC+//DIAWq2W/Pz8GvvWvXt3QkJC6NevH56enspVgPv5+vqSnJxMaGgoycnJ+Pr6YmJiUuP54N6cAR8fHy5fvoytrS0bN258YJxZWVn4+PhgZmaGmZkZQ4cOZf/+/UqbXbp0UW4FHDx4sNrXXNfFWKR0qhDC0BlUQl+xYoXyAXzhwgWmT59OQkICLVu25MCBA8ycORP4/0IdP/zwA2q1mri4OL755htef/11VCoVP/zwAwsWLKBXr15Mmzatwjnc3d0pLCzkxIkTJCcn4+fnB9xLSi4uLsTHx1eK69q1a+zevZtjx44RExMDwJ07d/j222/x9vauVZ/Lk3l5DP7+/gQHB1far7q+rVy5ksOHD5OZmUlAQADz5s2jb9++FY719fVl1KhRTJ8+nbS0NNavX//A88G9e+hJSUkUFxczffp05s2bR1RU1AOPq4mNjU2F/lb3mgOP/B5X5f7XWQghDJFBTYq7ny4LdfyRr68vq1evZu/evQwaNAi490dCbm4umZmZyn7Z2dlotVpSUlIYNGgQ27dvJyMjg4yMDMLDw5XJceX3ogF+++23Cm3UtO2PVCoVarWaS5cuAfcmjR05cgSgyr6VlpZy7tw5OnbsyMSJE+nVqxfHjh2r1O5TTz3F888/z8KFC3n++ed5+umnH3i++1lYWDBv3jx27drF0aNHazzOw8ODTZs2UVpaSlFREenp6dX2t6bXXIqxCCFERQY1Qr+fi4uLzgp1/JGvry/9+/fHz88PKysrABo2bEhMTAyLFy8mPDyckpISWrZsSWxsLElJScyePbtCG/379+eDDz7g/PnzzJkzh1mzZrFp0yZatGhBx44dsbW1Bahx2x917dqVqVOnMmnSJDQaDSUlJQwePJj27dtX2beysjJCQkK4desWJiYmNG/enBkzZlTZ9ogRI5g1axYfffTRnzrfHzVp0oQ33niDlStXEhMTU+1xo0eP5vjx4wwdOhR7e/sqJ9iVq+k1l2IsQghRkRRneQLu3r2r3DMun9i1Zs0a2rRpU+M2Y1VQUICtrS3FxcVMmjSJwYMHK/fbDYUhFGepiba0DBOzmi/QGXMBDDDu/hlz38C4+1ebzxaDHaEbkl9//ZXZs2ej1WopLS1lypQpSsKuaZuxGj9+PMXFxRQVFdGzZ09lMqF4ch6UzIUQhkcS+hPg6uqKWq1+6G3Gqnw2vBBCCN2RP9OFEFWq6ZKmFHcRou6REboQtXDt39mYFek7iifPaWpXfYcghPgDGaELvVKpVJw8efKxtJ2amoqvry+DBw/Gz8+PadOmcfHiRby8vNi5c2eFfQsLC+nSpQvnz59/LLEIIcTjJiN0YbBKS0sxM6v6R3jjxo2sXr2amJgYWrduDdxbpe7atWv4+fmRnJxMnz59lP23bt1Khw4daNGixZMIXQghdE4SuqhzIiMj2bNnT6WiO+fPn8ff3x8/Pz8yMzMZNWoUTZs2JSoqinr16qHRaHjvvffo1q0bK1euZOHChUoyh3vLwsK9hXSio6P5/fffadiwIQBJSUm88sor+uiuEELohCR0UecEBgYqC/XcX3QH4ObNm3To0EHZ7u3tzfz583Fzc0Oj0XDnzh2uX7/OpUuXqlyrH+4tgtO9e3fS0tIYM2YMubm5nDx5koEDBz6ZDgohxGMgCV3UOdUV3QGoX78+Q4YMUR53796diIgIBg4cSJ8+fXB2dqao6MGz1Pz9/Vm1ahVjxowhKSkJLy8vLC0tdd4XIYR4UmRSnKhTLly4QEREBEuXLiUtLY3w8HCKi4uV7VZWVpiYmCiPw8LCWLBgAebm5gQHB7NhwwYaN26Mk5MT2dnZ1Z7H09OTS5cucfz4cdRqNf7+/o+1X0II8bhJQhd1Sk1Fd6py+vRpXFxceO211/D29laKsUyePJkPP/yQs2fPKvvu3btXSfJmZmZ4e3szZ84cGjRooNSuF0IIQyWX3IXejR8/HlNTU+VxdUV3qrJ06VJyc3MxNTXFzs6ORYsWATB69GgsLS0JCgri7t271KtXD1dXV/7xj38ox44cOZJ///vfhIaGPr7OCSHEEyLFWYR4BIZenKW2/kxxl7rOmAt8GHPfwLj7V5vPFsP+jRRCPDb79++vdpuhJ3MhjJH8VgohhBBGQBK6EKJKxnpJs5wx96+qvklBHeMnk+JEBSqVitjYWJydnblz5w5TpkzB0dGRhQsXVpi4pmvlq8BlZWUpcVhYWGBhYUFJSQlvvPEGL7/8MgDff/89+/btUxaXuV9WVhaRkZEkJSU9tljvd23NHsyKZBqKqPucgvo8eCdh0CShiyrl5+czceJEOnToQFhYWIXvftdEo9HoLPGvWLECZ2dnTp48iZ+fH3369MHJyYn+/fvTv39/nZxDCCGMhSR0Ucn169eZNWsWKpWKoKAgAJKTk1m7di0ajQZbW1vmzZtHmzZtSEpKIjU1FRsbG3Jzc1m8eDG+vr5MmzaN//znP9y8eZNZs2YxaNAgAA4dOsSSJUsoLCwEICgoCE9PzxrjcXZ2xs7OjsuXL+Pk5ERSUhLbt29nxYoVACxbtowtW7ZgZ2eHh4dHhWPLtzVq1AgPDw9+/PFHZfReXZ+EEMIQSUIXlUydOpVXX31VSeb79u0jPT2d+Ph4LCws2LFjB2FhYcqiL4cOHUKtVtOqVSulDVtbWxITE9m/fz9Tp05l0KBB5Ofn88EHHxAXF4ejoyNXrlxh5MiRpKWl1RjP/v37sbe3x9XVtdK2jIwMMjIySElJwdLSknfeeafCtm3btqFWq5XvpJd7UJ+EEMLQSEIXlfTt25ctW7YwevRonJycyMjI4Pjx48o9bK1WS35+vrJ/ly5dKiRzAC8vLwA6d+7MlStXKCoq4uDBg5w/f57AwEBlPxMTE3Jzc7G3t68UR1BQEFqtlrNnzxIVFYWFhUWlfbKysvDy8sLGxga4t1hMTEyMsm3IkCFYW1sD4Ovrq2x7UJ+EEMLQSEIXlUyYMIFt27YREBDAl19+iVarxd/fn+Dg4Cr3L0+m9ytfEKH8fnppaSlarRYXFxfi4+Mr7X/+/PlKz5XfQ09PTyc0NJQuXbrQpEmT2nRN8aA+CSGEoZGvrYkqvfXWW4wYMYKAgABUKhVqtZpLly4B9ya+HTly5KHbdHNzIzc3l8zMTOW57OxsHrRY4ZAhQ+jVqxeffPJJpW3du3cnPT2d27dvo9FoSExMVLZ5eHjw7bffcufOHcrKykhNTVW26apPQghRV8gIXVTr7bffRqvVMnfuXKZOncqkSZPQaDSUlJQwePBg2rdv/1DtNWzYkJiYGBYvXkx4eDglJSW0bNmS2NjYBx47Y8YM/Pz8KlyuB+jXrx8//fQTPj4+yqS4y5cvA9C/f38OHjyIt7c3DRs2pHPnzvz+++8AdO3aVSd9EkKIukLWchdGraCgAFtbW8rKypgzZw6Ojo5Mmzat1u2Wr7fcbN9t+R66MAjG9D10Wcu9ajJCF0Zt9uzZXLhwgbt37/LCCy9UGuHXVpPXPf6SxVmE4TGGgjqiZpLQhVGLjo7WdwgGy5hHQWDc/auqb5LMjZ+8w0IIIYQRkIQuhKiSsY5eyxlz/4y5b6Cb/mlLNTqIpG6RS+7CYN1fSEZfrn+5E7MiqWIlhKFxnDJI3yHonIzQxV9SaWmpvkMQQgidkhG6MCqRkZHs2bOHkpIS7O3tCQ8P5+mnn1bKs/r5+ZGZmcmoUaNo2rQpUVFR1KtXD41Gw3vvvUe3bt303QUhhHgkktCFUQkMDFTqpG/cuJElS5awbNkyAG7evEmHDh2U7d7e3syfPx83Nzc0Gg137tzRW9xCCFFbktCFUdm5cydr167l9u3blS6r169fnyFDhiiPu3fvTkREBAMHDqRPnz56vRcvhBC1JffQhdG4cOECERERLF26lLS0NMLDwykuLla2W1lZYWJiojwOCwtjwYIFmJubExwczIYNG/QRthBC6ISM0IXRKCgowNzcnKZNm1JWVvbA2uanT5/GxcUFFxcXbt++zeHDhxk1atQTilYIIXRLErowaOPHj1dKtAIMHjwYLy8v7O3t6du3L/v27av22KVLl5Kbm4upqSl2dnYsWrToSYQshBCPhRRnEeIRlBdQaH4gT76HLoQBqqvfQ5fiLELoSeOAPlKcRQgDpC3VYGJm+uAdDYhMihNCVGn//v36DuGxMub+GXPfQDf9M7ZkDpLQhRBCCKMgCV0IUSUp8GG4jLlvYPz9e1RyD72OUKlUWFhYYGFhQUlJCW+88QYvv/wyABqNBk9PT9q3b8+qVauUY7KysggICGDGjBlMnDhReS4yMpKkpCQA4uPjWbduHSYmJhQXF9OvXz9lpbSa2gbYtWsX0dHR5OXlYWtrS9OmTZk+fTouLi41xvtXcv2r7zArMr6qTUII/SitbwpuTz3SsZLQ65AVK1bg7OzMyZMn8fPzo0+fPjg5ObFr1y4cHR05cOAA165do0mTJsoxTZs25YsvvmD06NHY2dlVaC87O5svvviChIQE7Ozs0Gg0nDp1qsI+1bW9e/du5syZQ3R0NB06dADg2LFjXL16FRcXlxrj1YXS0lLMzJ7sj2dZWRkmJiYVFp8RQghDIQm9DnJ2dsbOzo7Lly/j5OREYmIio0eP5qeffiIlJYUJEyYo+zo6OuLm5sann37KjBkzKrRz+fJlbG1tsba2BsDU1BRXV9cK+1TXdnR0NJMnT1aSOUDbtm3/VLynT58mPDycGzduUFJSwmuvvYa/vz8A3377LcuWLcPS0pLBgwezbNkyDhw4gI2NDS4uLkyZMoXt27fz0ksvMWHCBCIiIjhx4gRFRUV069aN0NBQTE1NWblyJWlpadSvXx8TExO+/PJLzM3NmT17Nr/88gtmZmY8++yzREVFARAXF0dqaioAHTp0YO7cudjY2PDxxx9z6tQpCgoKuHjxIuvXr6dhw4a1efuEEEIv5B56HbR//37s7e1xdXUlLy+PzMxMhgwZgp+fn3Ip/X6TJk0iISGBK1euVHi+V69emJmZ0a9fP2bMmMH69esrFCCpqe2jR4/SqVOnh463tLSUmTNnEhoaSmJiImvXriUuLo6cnByuXbvG+++/T2xsLCkpKVhaWlZqq379+iQmJjJ16lQiIiLo2rUrCQkJqNVq8vLySExM5ObNm6xZs4aUlBTUajVff/011tbW7N69m8LCQrZs2UJqairz588HYMeOHaSmprJu3To2bdqERqMhJiZGOWd2djZLlixh69atksyFEAZLRuh1SFBQEFqtlrNnzxIVFYWFhQWpqan069cPW1tbXnzxRTQaDQcPHsTNzU05rkmTJowaNYqYmJgKxUesra1Zv349hw8fZv/+/WzcuJH4+HgSEhL+dNsPG+8vv/xCTk4O06dPV/YrKSnh9OnT1KtXj3bt2tG6dWsA/P39iYiIqNDmiBEjlP9nZGSQnZ3N6tWrAbh79y5OTk40aNCAVq1aMWvWLHr37o2npye2tra4urqSk5PDP//5Tzw8PPD09ATgxx9/xMvLC1tbWwBGjRpFeHi4cp4+ffrg4ODwp/oshBB1lST0OqT8nnR6ejqhoaF06dKFxMRE8vLyUKlUANy6dYvExMRKSXfChAkMGTKEF154ocLzJiYmdOzYkY4dOzJmzBh69uzJqVOneOGFF2psu127dmRnZ1d7mb26eLVaLfb29qjV6kr7f//99w98DcpvDwBotVpiYmJo2bJlpf02bNjAgQMHyMzMxM/Pj88++wxXV1fS0tLIzMxk586dLFu2jE2bNj3wnDY2Ng/cRwgh6jq55F4HDRkyhF69ehEWFsatW7fYvXs3GRkZZGRkkJaWxtatWyvV7m7QoAHjx4+vMFM9JyeHkydPKo/PnDlDSUkJzZo1Izs7u8a2J02aRExMDD///LNy/PHjx9m9e3e18X7yySc8++yzWFpakpKSUiGOgoICOnXqxNGjRzl79iwAycnJNb4OKpWKuLg4NJp7s8jz8vI4d+4cBQUF5OXl4eHhQVBQEM7Ozpw6dYpLly5hamrKgAEDCA0NJS8vj5s3b9KjRw/S09MpKChAq9WSkJBAz549H+IdEUKIuk9G6HXUjBkzGD58OL6+vhVmXTs5OdGuXTu2bt3KU09V/GrD2LFj+fLLL5XHd+/eJTw8nOvXr1O/fn1MTU1ZvHgxjRs3ZsWKFQwdOrTatkeMGMH8+fOZP38+N2/exMzMjBYtWlSaeHd/vH5+fgQGBhIbG0t4eDiff/45ZWVlNG7cmOXLl9OkSRPmzZtHYGAgVlZWeHp6Ym5ujpWVVZVthoWFsXjxYnx8fDAxMcHc3JywsDDMzc159913uXv3Llqtlnbt2jFw4EAyMzNZunQpcG/G+sSJE3FycsLJyYkTJ04wevRoANq3b8+kSZMe7Y0RQog6SoqziCeqoKBAuZedmJhIQkIC33zzjZ6jenhKcZaDF+V76EIInSmtb8pvbk9JcRZR93311Vds3boVjUZDw4YNWbhwob5DeiTlfwc3eMUTCwsLPUcjhDAWxcXF/HbyJI8y1pYRuhCP4NatWxXmJwghhC45OzvToEGDhzpGEroQj6CsrIzCwkLMzc1lZTkhhM5otVpKSkqwsbGhXr2Hm7cuCV0IIYQwAvK1NSGEEMIISEIXQgghjIAkdCGEEMIISEIXQgghjIAkdCGEEMIISEIXQgghjIAkdCGEEMIISEIX4iGdOXOGV155hUGDBvHKK6/w66+/6jsknblx4waBgYEMGjSI4cOHM2XKFPLy8vQd1mOxcuVKXFxcjG7Fv6KiIj744AMGDhzI8OHDee+99/Qdks5s27YNX19ffHx88Pb25rvvvtN3SLUSGRmJSqWq9HP4yJ8xWiHEQxk3bpw2JSVFq9VqtSkpKdpx48bpOSLduXHjhjYzM1N5/OGHH2pDQ0P1GNHjceTIEe2bb76p7devn/bEiRP6DkenFixYoF20aJG2rKxMq9VqtVevXtVzRLpRVlamdXd3V96vY8eOaTt37qzVaDR6juzR7d27V3vx4sVKP4eP+hkjI3QhHsL169c5esIrGuoAAAW4SURBVPQow4YNA2DYsGEcPXrUaEaxjRo1olu3bsrjzp07c/HiRT1GpHvFxcXMnz+fefPm6TsUnSssLCQlJYXg4GBlSeImTZroOSrdqVevHrdu3QLu1VNwdHR86OVR6xJ3d3eaN29e4bnafMZItTUhHsJvv/2Gk5MTpqamAJiamuLo6Mhvv/2Gg4ODnqPTrbKyMr755htUKpW+Q9GpqKgovL29adGihb5D0blz587RqFEjVq5cSVZWFjY2NgQHB+Pu7q7v0GrNxMSE5cuXM3nyZKytrSksLCQuLk7fYelcbT5jDPdPGyHEY7VgwQKsra0ZO3asvkPRmYMHD3LkyBFeffVVfYfyWGg0Gs6dO0e7du1ISkpi5syZvPvuuxQUFOg7tForLS3lk08+ISYmhm3btrFq1SqmTp1KYWGhvkOrMyShC/EQmjdvzuXLl9FoNMC9D9ArV65Uumxm6CIjI8nNzWX58uUGfUnzj/bu3UtOTg79+/dHpVJx6dIl3nzzTXbv3q3v0HSiefPmmJmZKZdrO3XqhL29PWfOnNFzZLV37Ngxrly5wosvvgjAiy++iJWVFTk5OXqOTLdq8xljPL+pQjwBjRs3pm3btqSlpQGQlpZG27Ztjepy+7/+9S+OHDlCdHQ0FhYW+g5HpyZOnMju3bvJyMggIyODZs2a8fnnn9O7d299h6YTDg4OdOvWjf/973/AvdnS169f55lnntFzZLXXrFkzLl26xOnTpwHIycnh+vXrtGrVSs+R6VZtPmOkfKoQDyknJ4eQkBDy8/Oxs7MjMjKSNm3a6DssnTh16hTDhg2jdevWWFpaAtCiRQuio6P1HNnjoVKpiI2NxdnZWd+h6My5c+cICwvj5s2bmJmZMXXqVPr27avvsHQiNTWVTz/9VJnwFxQUxIABA/Qc1aNbuHAh3333HdeuXcPe3p5GjRqxefPmR/6MkYQuhBBCGAG55C6EEEIYAUnoQgghhBGQhC6EEEIYAUnoQgghhBGQhC6EEEIYAUnoQgghhBGQhC6EEH+CSqWiY8eOuLm50atXL0JCQpRlR0NCQnBxceG///1vhWPCw8NxcXEhKSkJuFcY5sMPP6RPnz64ubmhUqlYtGhRleco/zd//vwn10lh0KQ4ixBC/EmxsbH07NmTq1ev8uabbxIXF8e0adMAaN26NWq1WlnopLS0lPT09AormcXFxXHkyBE2btyIo6MjFy5cYN++fVWeQ4iHJQldCCEeUtOmTenduzfHjh1TnlOpVKjVan7//XcaNmzIrl27cHFxqVA85PDhwwwYMAAnJyfg3ip8xlj1TeiHXHIXQoiHdOnSJXbt2lVh9G1hYUH//v3ZvHkzACkpKfj6+lY4rlOnTqxZs4b4+HhOnDiBLNQpdEkSuhBC/EnvvPMObm5u9O3bFwcHB4KCgips9/HxQa1Wk5+fz969eyutM/7WW28RGBjIpk2b8Pf356WXXiI5ObnSOdzd3ZV/GzZseOz9EsZBLrkLIcSfFB0dTc+ePdmzZw8zZszgxo0b2NnZKdvd3d3Jy8tj1apVeHp6KgVuypmamjJmzBjGjBnD3bt3SUxMJCwsjI4dO/Lcc89VOIcQD0tG6EII8ZA8PDzw8/MjMjKy0jZvb29Wr15d6XL7H1laWjJmzBjs7Oz45ZdfHleo4i9ERuhCCPEIXnvtNVQqFcePH6/w/Lhx43B3d6dr166VjlmzZg1t27alU6dOmJmZsWnTJgoLC2nXrt2TClsYMUnoQgjxCBwcHPDx8SE6OhobGxvl+UaNGtGjR48qj7GysiIyMpLc3FxMTExo3bo1H3/8MS1btlT2efvttzE1NVUe9+zZ02jr0QvdknroQgghhBGQe+hCCCGEEZCELoQQQhgBSehCCCGEEZCELoQQQhgBSehCCCGEEZCELoQQQhgBSehCCCGEEZCELoQQQhgBSehCCCGEEfg/7S7ki1xG3ygAAAAASUVORK5CYII=\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "pCdcxpzScqXX",
        "outputId": "50f38212-e428-4a39-d78a-bb0afdcf8212"
      },
      "source": [
        "# Bar plot of calculation time\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"Time Taken\", data=predictions_train)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 15
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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++SWOjo4EBwezYMECvL29MTU1xc3NTTrdXOL7778nJSUFc3NzLCwsHlsOMG7cOMLDw+nVqxcAvXv3lm7oelnt2rVj0qRJBAQEoNPp0Gg0eHp64urqSp8+fcoNWXmUCGcRBEGoXCKcRahQxhrOYtBqja65jDEHYIBxb58xbxsY9/aJcBahyjDWcBZjK+aCIFQ94q+QUKFEOIsgCMLrIW6KE4SXJIJZBEF4E4iCLggv4c6GleJ0uyAIbwTxl6gClYS8PHyjw8qVK6lfv36Z709JSUGj0eDh4fHc6xLhLIIgCG8XUdArWFkhKeU5duwYDx48KLegPylIRISzvBgRziIIQlUl/nJVsoyMDEaNGkVcXBz16tUjOjqajIwMxo4dy+bNm9Hr9SQlJeHl5cWnn35Kv3798PX1JTk5GT8/Pxo2bMiyZctQqVTodDrGjh2Ll5dXuesT4SwinEUQBOMkCnoFmzBhgnTq29TUlPj4eCZPnszkyZOZMGECiYmJbN++HWtrawYOHCilsgFcu3aNvLw8WrRoIb1279494uLiMDU15c6dO/j6+uLh4UGNGjXKnYMIZxHhLIIgGB9R0CtYWae++/TpQ3JyMuPHj2fjxo3ldk6D4sJTEoICxe1kQ0JCpO5o9+7dIzMz84ltTUU4iwhnEQTB+IiC/gZQq9VcvnwZGxsb7t69+8T3vvPOO6WuK4eGhiKXy4mOjkYmk9GzZ09UKtUTxxDhLI8T4SyCIFR14rG1N8DixYv54IMP+Pbbb5kzZ44UlGJtbV1mPvrD8vPzqVevHjKZjN9//52srKznWrcIZxEEQTAO4gi9gj18DR2Kg1aOHTvGtm3bsLS0ZPz48UyZMoXvvvuOHj16sGPHDnx8fKSb4h41depUwsLCWLFiBS1atMDZ2fm55iPCWQRBEIyDCGcRKpQxhrNYmJoaZXMZYw7AAOPePmPeNjDu7RPhLEKVYYzhLMZYzAVBqHrEXyKhQolwFkEQhNdD3BQnCEKZnnRK06DVVOBMBEF4FuII/TUwpp7t5Zk+fTpJSUnUqlWLoqIiPDw8mDFjBiYmb9d3xBvfz8ZMpazsaVS4euNXVvYUBEF4hCjor4kx9Wwvz5gxYxgyZAhKpZK+ffvy4YcfPtMz38+isnqq63S61/YlSBAE4XUSBb2CVNWe7eX1Rn+YtbU1H3zwAdevXwdAqVSycOFCLl68iEqlwt3dneDgYExNTUlPT5d6sLu4uJCdnU1AQADdu3dn6NChuLi4cPr0aWrUqMHatWufe17R0dHs3LkTS0tLZDIZ3333Hba2thw+fJhvvvkGnU5H7dq1mTt3Lu+//z4pKSnMmzcPV1dXzp8/z6RJk+jevfvr+4cgCILwmoiC/poYS8/28nqjP+zu3btcvHiRr776CoCFCxfSrl075s+fj16vJzAwkO3bt+Pn58e0adMYPnw4Pj4+nD17Fj8/v1Jj5eTkEBcXh5mZ2XPPKy8vj9jYWH777TeqVauGUqmkWrVq3L17l2nTprFhwwaaNGnCtm3bCAwMZNu2bQCkp6dLYwmCIFRVoqC/JsbSs72s3uglYmJi2LJlC5mZmQwaNAhHR0dpvDNnzvDtt98CUFRUhL29PUqlkkuXLtGrVy+AMhvh9OrVSzrV/rzz0ul0ODg4MG3aNDw8POjWrRvW1tacPn0aFxcXmjRpAkC/fv0ICwtDqSy+9v3++++LYi4IQpUnCnoFqoo928vqjV5yVF1yDf3KlSsMGjQIDw8PunbtisFgYNWqVTRo0KDUWCUF9EkZ54/O+XnntXXrVk6ePElycjK+vr6sW7fuifvo0XUKgiBUVW/XLcmVrCr2bC+rN/qjGjduzIQJE1i6dCkGgwG5XE5MTAw6nQ4oPruQk5ODtbU1TZs2ZefOnQCcO3eOS5cuPXHOzzMvpVJJbm4ubm5uTJgwAScnJy5fvkzr1q1JS0sjIyMDgISEBJo3b/7EMySCIAhVjThCf02MpWd7eb3RH/XZZ5/x3XffsX//fkJCQoiIiMDHxweZTIa5uTkhISE0aNCARYsWERISQkxMDE5OTjg5OWFjY1PmmM87L6VSyVdffUVRUREGg4HmzZvz8ccfY2lpyeLFiwkMDESr1VK7dm0iIiKea/8JgiC86UQvd6FCFRQUUL16dWQyGenp6QwdOpS9e/c+8ea+N1FJv+V3T20Vz6FXUcbcD9yYtw2Me/tEL3ehyjh16hSLFy+m5HtkeHh4lSvmD3tv6Nzn/p/OGBi0GmRm5pU9DUEQHiIKulChPDw8XqgjnlDxnnQUJIq5ILx5xE1xgiAIgmAEREEXhCpIr1VX9hQEQXjDiFPuRiwvL4/evXsTHR1Ny5YtAVizZg3nzp1jxYoVnD17lmXLlpGZmUnNmjUxGAx4e3vj7+8PlA6ZUalUfPTRR8yZMwdzc3Pi4+NZsGAB9erVQ6PRUL9+febPn4+dnV1lbnKFu/rdaExU9yp8vU2/VFT4OgVBeLOJI3QjVrNmTWbPnk1wcDBqtZqLFy+yceNGQkNDuXjxIqNHj2bYsGEcOHCA+Ph41q1bR15eXqkxli9fjkKhYNeuXaSnp/PLL79Iyzp27Cgts7KyIjo6+pXNXa/XUxkPYGi12gpfpyAIwqsgjtCNXI8ePdi7dy+RkZEcP36c4OBg6tSpw8KFCxkwYABdu3aV3lunTh2mTp1a5jgqlQqVSoWtre1jy2QyGe3atePgwYPSa+WFquTn5xMSEsLly5ext7fH3t6eOnXqEBQUxIoVK7h8+TJKpZLr16+zZcsW/vzzT1avXo1arcbc3Jzg4GBat27NlStXpJAXvV5P37598ff3Lze0JSsri9mzZ5Obm4uZmRmTJ0+mS5cuADg7O/Pll19y8OBBOnfuzKRJk17tL0EQBKECiIL+Fpg1axbdu3enY8eOUtOa8+fP4+np+dTPljTIyc7OLvcOdbVazeHDh6WxnxSqsnLlSmxtbdm7dy95eXn4+vrSs2dPaawzZ84QHx9P7dq1yc7OZtWqVaxfvx5ra2suX77M6NGjOXjwIHFxccjlcr744gsAqZtceWEygYGB+Pn5MWDAANLT0xk8eDB79uyhdu3aQHHv/O3bt7/EXhYEQahcoqC/BY4ePYq1tTVXrlxBrVZjYWHx2HvmzZvH8ePHuXv3Ltu2beNvf/sb8L+QGZVKxVdffUVsbCwjRowAICkpCR8fH65du4ajo6MUJvOkUJWUlBRmzpwJFF8S6NGjR6l5dOnSRSqyR44cITs7m8GDB0vLtVotd+7coV27dkRERFBYWIi7uzvt27cHyg5tUSqVXLhwgX79+gHQpEkTmjVrxp9//olcLgeQ5ioIglBViWvoRi43N5cFCxYQExODq6sry5cvB6BZs2al+rLPnDkThUKBRqORerA/zNLSkm7dupGUlCS9VnIN/dChQ8hkMqKiooD/haooFAoUCgU7d+5k8+bNzzRfKyurUj937txZGkehUPDbb7/x7rvv0rNnTzZu3IiDgwNr167ln//8J1Ac2hIeHo65uTkTJ05k69atz7ReEdAiCEJVJwq6kQsLC8PPzw8XFxdmzJjBzp07OXv2LKNHj2bLli0cPnxYeq9arUav15c5jl6v5/jx4zRs2PCxZdbW1oSFhbFp0yZu3br1xFAVNzc3FIriO7Tv37/P/v37y517p06dOHLkCJcvX5ZeO3PmDABZWVnY2dnh6+vL+PHjpS8nZYW2WFtb06xZMxISEgDIyMggLS3tidGzgiAIVY045W7Edu/ezdWrV4mMjASgRo0azJ49m5CQELZv386//vUvoqKiCA0NpXbt2pibmzN27Fjq1q0rjVFyDV2j0dC0aVPGjx9f5rpcXFzw9PRk7dq1zJgxo9xQlfHjxxMcHIynpyd2dna4urqWm3rWsGFDIiIimDFjBkVFRWg0Gtq2bUvLli3Zs2cPiYmJmJubI5PJCAkJASg3TCYyMpLZs2cTGxuLmZkZixcvlk7tC4IgGAMRziJUKI1Gg16vx9LSEqVSyaBBgwgODqZjx46VPbXnUhKgYH0yymifQzfmAAww7u0z5m0D494+Ec4iVBn3799n9OjR6HQ6VCoV3t7eVa6YP6zhsLWVEs6i16oxMXv85kZBEN5eoqALFapOnTrEx8dX9jSqPFHMBUF4lLgpThAEQRCMgCjogiCUqeQapQiCEYSq4a0/5V4SQGJhYYFGo2HUqFEMGDDglY6/Zs0anJycXsl4KSkpjBkzptTjY8HBwVJjlddl37591K1bVwp5uXbtGh9//DFNmzZFr9djbm7OzJkzadu27Wudx5smdeP/Q6bOe/obq7C2YxMrewqCIDyDt76gw/+6oV26dAlfX1+6dOmCvb19ZU+rXI6Oji98HVqr1WJm9vy/9n379uHq6ioVdAAbGxvpmfK4uDhmzJjBnj17XmheZdHpdJiamr6y8Z7Fi+4fQRCEyib+cj3EyckJW1tbbt68ybFjx/juu+/QaDQABAUF0aFDB6D4qNvHx4ekpCRu377NqFGjpGeuT5w4QVhYGADt2rUrlRh25swZ5s+fz4MHD6hevTozZsygZcuWXLt2jX79+uHn58eRI0coKioiMjKSzZs3c/r0aapVq8aqVaueGE2q0+mIjIzkyJEjQHGHtcDAQExNTZk+fTqmpqZkZmZSUFCAQqEgISGBuLg4qS1raGgojRs35uTJk4SHh6PX69FqtQQEBFCjRg0OHDhAUlIS27ZtY+TIkXz00Uel1u/u7s7ixYulnw8dOlRmqArA0qVL2b17NzVr1sTNzY2jR48SHx9PSkoK8+bNw9XVlfPnzzNp0iTef/99FixYwH//+180Gg3Dhw+nX79+FBYWEhQURHp6OmZmZjRq1IioqKhyQ1sKCgqYN2+e1IDGx8eH0aNHAzB06FBcXFw4ffo0NWrUYO3atS/+j0gQBKGSiIL+kD/++INatWrh4uJCgwYN8Pb2RiaTceXKFUaMGFGqq1pRURFbtmzh2rVr9OrVi759+2Jubs7kyZOJjIzE3d2d3bt3s3HjRqC4W9qECRNYuHAhHTp0ICkpiQkTJvDzzz8DxdnlH374IVOnTmXdunWMGDGC77//nnnz5hEaGsqGDRuYPHkyUNzpzMfHBwALCwu2bdvGli1buHDhgnTkXtIJ7vPPPwfgwoULbNiwgerVq3PixAn27NnDxo0bsbCw4NChQ4SEhLB582bWrl2Lv78/3t7eGAwG8vPzsbW1RS6X4+rqKn1xuXbtWql998svv0jhLE8KVTlw4AC//vorCoWCatWqMWHChFLjpKenS+EqWq0WPz8/IiIicHR0RKlU0q9fPyltraCggN27dwP/C2cpL7Rl1apV6PV6EhMTKSgo4LPPPsPJyUlKm8vJySEuLk4cnQuCUGWJv14Ud0MzGAxkZ2cTFRWFhYUFaWlpTJ06lZs3b2JmZsadO3e4ffu2dJRcUrzq16+Pra0tN27cQKPR8M477+Du7i69Z/bs2QBkZmZibm4uHeV37NgRc3NzMjMzsbKyonr16nTr1g2ADz74gPfee49mzZpJPz/cQ72sU+5Hjx6lb9++UvCKr68v+/btkwq6p6en1K/8wIEDpKWlSfcKGAwG7t+/DxQfaa9evZrs7Gw6depEq1atyt1v+fn5+Pj4kJubi1qtZtu2bcCTQ1VSUlL45JNPpLn06dOHVatWSe97//33adOmDQBXr14lIyODKVOmSMs1Gg1XrlzBxcWFjIwMwsLCcHNzk/ZdeaEtR48eJSQkBJlMhrW1NV5eXhw9elQq6L169RLFXBCEKk38BeN/19D37NlDcHAwbdu2ZcqUKUyfPp0ePXqg1+tp1aoVKpVK+szDzURMTU3LDDSB4qzwZ/FwApqJiUmpn580/rN6OHzEYDDQr18/Jk6c+Nj7RowYgVwuJykpifDwcDp16iSdGXhUyTV0nU7HwoULmTp1qhSG0rlz51Kn4F90nrVq1ZKu0z9q586dJCcnc/jwYZYuXUpiYiI9e/akdevW/P7776xdu5bt27dLrW+fdb2CIAhVkXhs7SGffPIJnTp14l//+hf5+fnUr18fgO3bt6NWP/3RncaNG1NUVMSJEycA2Lt3r3Tk26hRIzQaDcnJyUDxEXjZcEUAACAASURBVKNWq6VRo0avZO4dOnRgx44daDQaNBoNO3bsKLcDm1wuR6FQcOPGDaD4+ntqaipQfCbBwcGBgQMHMmzYMOmas7W1Nfn5+WWOZ2pqyj//+U9u377N/v37nxiq4ubmxk8//SRd4/7xxx/L3aZGjRpRrVo1duzYIb2WkZGBUqnkxo0bmJqa0qNHD4KDg8nNzSUvL6/c0JYOHTqwfft2DAYDSqWS3bt3V+kOdYIgCI8SR+iPmDp1Kr6+vsyaNYtx48ZRo0YNOnfuTM2aNZ/6WQsLC7755ptSN8X93//9n7Rs+fLlpW6KKzm9/yp89tlnZGdnS7neHh4e+Pn5lfnedu3aMWnSJAICAtDpdGg0Gjw9PXF1deX7778nJSUFc3NzLCwspOzy3r17ExwczN69e8u8Kc7S0pJJkyYRHR1NQkJCuaEqf//73zl16hS9e/emRo0atG7dWrrO/SgzMzPWrFnDggULWL9+PXq9njp16rBs2TIuXrzIkiVLgOIkuDFjxmBvb8+aNWvKDG0ZN24c4eHh9OrVS9qeLl26vPyOFwRBeEOIcBahwimVSqytrdHr9cyYMYO6deuWe1r/TVUSoMDpZeI59CrKmAM+jHnbwLi3T4SzCFVKUFAQ//nPfygqKuKDDz6QHh+rilwHr6uUcJaKJIJgBKFqEAVdqHArV66s7CkIz6DkKEgUc0GoGsRNcYIgCIJgBERBF4TXRISaCIJQkcQp9zdESUiMpaUlKpWKjz76iDlz5mBubv7K1zVjxgz69u372J3qz2PFihXExcVRt25dVCoVH3zwAeHh4W/d89zHN/tjKOemuM6jd1bwbARBeJuJI/Q3yPLly1EoFOzatYv09HR++eWX17Ke+fPnv1QxL9GnTx8UCgU//vgjf/31Fxs2bHgFsyum1Wpf2VhVYb2CIAgvSxyhv4FUKhUqlQpbW1uOHj3KsmXLUKlU6HQ6xo4di5eXF1Dc97wkiMTFxYXs7GwCAgLo3r37E5cNHTqUUaNG0b17d6ZPn46FhQVXr17lxo0btG7dmkWLFiGTybh58ybTpk3jzp07NGjQACh+vr2kn3sJCwsL2rRpw19//QUU961funQpx48fR61W4+zsTGhoKFZWVk8c82VDZLy9vdmyZQuxsbFYWFig1+tZtmwZjo6OTw3G8fX1JTk5GT8/PwYNGlSBv21BEIRXQxT0N8iECROwtLQkOzsbDw8PPDw8uHfvHnFxcZiamnLnzh18fX3x8PCgRo0aTJs2jeHDh+Pj48PZs2dLNZJ50rJHXb58mdjYWGQyGX379iUpKYlOnToxb9483N3dGTduHP/5z3/o1asXHh4ej31eqVRy/Phx6VnydevWYWNjww8//ABAREQEMTExTJ48+aljvkyIDMDixYvZs2cPdevWRa1Wo9PpnikYp0WLFgQFBb38L1EQBKGSiIL+BinpKa9Sqfjqq6+IjY2la9euhISEkJWVhampKffu3SMzM5MmTZpw6dIlqfNZixYtcHZ2BooLbHnLytKjRw/pWermzZtLwSwpKSlSp7h69epJwTIlduzYwe+//05WVhYeHh5SEMqBAwdQKpX89NNPQPERu4uLC8BTx3zZEJn27dszffp0unfvTrdu3WjQoAEXL158YjCOpaUln3zyyXP8pgRBEN48oqC/gSwtLenWrRsHDx7k119/RS6XEx0djUwmo2fPnqVCYp4U/vKswTDPGjTzqD59+hAUFMTt27f5/PPPiYuLY/DgwRgMBubMmfNYsX4WLxsiEx0dzdmzZ0lOTmbYsGGEhoby3nvvPXGd77zzzjPvK0EQhDeVuCnuDaTX6zl+/DgNGzYkPz+fevXqIZPJpKNhKA5Ladq0KTt3Ft9Jfe7cOS5duvTUZc/Dzc2NhIQEAP766y8pWOZRdnZ2zJgxg9WrV1NUVIRcLic2NpaioiKg+IxBRkbGc40Jzx8io9VqycnJoWXLlowZM4ZOnTpx4cKF1x6MIwiC8CYQR+hvkJJr6BqNhqZNmzJ+/HhSU1MJCwtjxYoVj506X7RoESEhIcTExODk5ISTkxM2NjZPXfasZsyYwbRp00hMTKR+/fq0bNkSa2vrMt/brVs3GjduzObNmxkzZgzR0dH0798fmUyGTCbjyy+/xNHR8bnGfN4QGb1ez/Tp08nPz0cmk/G3v/2NqVOnvvZgHEEQhDeBCGepwgoKCqhevToymYz09HSGDh3K3r17qVGjxhOXPauioiLMzMwwMzPj1q1b9O/fn9jYWBo3bvzCc34dY1aGkgCFwtSlRvscujEHYIBxb58xbxsY9/aJcJa31KlTp1i8eDEl38nCw8Olgv2kZc/q6tWrBAUFYTAY0Gq1fPnlly9deF/HmJWp3cD15f5PJ0JNBEGoSKKgV2Elj7Y977Jn5eLigkKheKkxKmLMN5Uo5oIgVCRxU5wgCIIgGAFR0AVBKFNVukapE0E4glAxp9zlcjlr1qzByclJes3X15egoCCSk5Np2rQpn376abmfj4+Pp02bNqUeM7p69SrffPMNqamp2Nraotfr6dq1K5MmTcLU1PSl5rtixQoePHhAUFAQmzZtQqVSMWLEiBceLyUlBY1GU+oUuLOzM05OTpiYmKBSqejZs6fUae1VunDhApmZmaX2r4+PD1u2bKFatWovPO7QoUO5fv061tbWFBUV4ePjw7hx417FlKuUw1tHoi/npjih4vT0313ZUxCESlfp19DLahryqISEBGrVqiUV9Fu3bjF48GCmTp3K8uXLgeJnndeuXYtareadd94p9XmtVouZ2Ytt6qvo633s2DEePHjw2DXtzZs3Y2VlRWFhIV5eXsjlcqnj2aty4cIFDh48WKqgv6pr2DNnzqR79+7cunWLTz/9tFTHtpf1Mr+zl6HT6V76C6EgCEJlqPSCPn36dFxdXRkyZAj79u0jKioKExMTdDods2bN4tq1a6SmpjJv3jyWLVtGUFAQKSkpuLu74+vrK41jbW1d6ghXLpfz6aefkpycjJOTE5MnT2bKlCkUFBSgUqno2rUr06ZNAyA/P58ZM2Zw6dIl7OzseO+993j33XeB0kfrADExMfz888/odDrs7e0JDw/Hzs6OFStWkJmZSX5+Pjk5OTg4OBAVFUV2djabN29Gr9eTlJSEl5cXY8aMKbUPCgsL0Wq10nPid+7cYc6cOWRnZwPg7+9Pnz59AMoNGbl79y5Tp07l7t27AHTo0IGAgACWL1+OUqnEx8eHdu3aMXPmTJydnTl58iRWVlbI5XJ8fHxISkri9u3bjBo1SgpfOXHiBGFhYUBxq9X9+/fzr3/9q9SZFoC6devSqFEjrl+/TqtWrbh16xbz5s3j+vXrqFQqvLy8GDt27FPHfPR3FhoaWm7IS1khLI0aNWLu3LkkJydjYWFB9erV2bx5M1Dcpnb9+vUAODg4MHfuXOrUqUN8fDw//vgjVlZWZGVlERERQbNmzV7wX7MgCELlqbCCXtI0pcTVq1cfe8/y5cuZO3cubdq0QafTUVhYiLu7Ozt27JDSwQC+/fZbOnXq9NR1KpVKKSBEpVKxZs0arKys0Gg0+Pv7c/jwYbp06cLKlSuxsrJi79695Obm4uvrW2Zvb4VCQU5ODlu3bsXExIS4uDi+/vprlixZAkBqaio//PADNjY2+Pv7k5iYiJ+fHwMHDiz1paDEwIEDAcjKymLQoEHS41vz5s2jadOmrFy5klu3buHr60vz5s1p2LBhuSEjiYmJODg4EBsbC8C9e/eoUaMGEyZM4ODBg9KZjLIUFRWxZcsWrl27Rq9evejbty/m5uZMmTKFb775ho8++ohffvmF77//vszPZ2ZmkpeXh7u7OwBBQUGMGzeOdu3aoVarGTFiBC1atKBdu3ZPHfPh39mqVavKDXkpK4QlLS2NlJQUdu/ejYmJCffu3QPg0qVLREZGEh8fT926dVm2bBnh4eEsW7YMgNOnT6NQKHBwcCh3HwmCILzpKqyglwSPlHj46LpE+/btWbhwIR9//DFdunR57EiwPDExMezatYu8vDyWLl1K27ZtAaSjWig+lbp48WJOnTqFwWDgzp07pKWl0aVLl1KBIbVr1+Yf//hHmes5cOAAqamp9O3bVxrz4S5nHh4e2NraAtCyZUvpCLs8Jafc79+/z/Dhw9m3bx89evTg6NGjTJ8+HSg++u3atSspKSkYDIZyQ0ZatWpFbGwsixYtws3N7bkeWSs5HV+/fn1sbW25ceMGGo2GatWqSbnp//jHP6RtKzFv3jwiIyO5cuUKQUFB1K5dmwcPHnDs2DFyc3Ol9xUUFJCRkUGdOnWeOubDv7MnhbyUFcLSoEEDtFotM2bMwN3dXfoCmJKSQteuXalbty5Q/EXKx8dHWk/btm1FMRcEocqr9FPuDwsJCeHixYskJyczceJERo4cWWbsZ/PmzTl79qz085gxYxgzZgy+vr5oNBrp9YeDPr799lvu37/Ptm3bsLS0ZNasWaVCTp6FwWAgICCA/v37l7n80ZCTZx3f1taWjh078vvvv9OjR4/nmlOJNm3akJCQQFJSEgqFgpiYGDZt2vRMn33RcJaSa+h//PEHo0aNokOHDlLf+R9++AFzc/NS709LS3vqmI+Gs5QX8lJWCEvXrl3ZtWsXKSkpJCUlERkZKfWNfxIrK6tn2FpBEIQ32xv12NqVK1dwdnZm+PDh9O7dWyraVlZWUt41wOeff87Ro0fZsWOH9FpJr+/y5OfnY2dnh6WlJTdv3mT//v3Ssvbt2xMfHw/Af//7X/bt21fmGHK5nLi4OOlUrlqtfqYiZW1tXWr+j1Kr1Zw6dYqGDRsCxde/t27dCsDt27c5dOgQ7du3f2LISE5ODtbW1nh5eREcHMy5c+fQ6/VPXXd5GjduTGFhIX/88QcA+/btk6JLH/Xhhx/y+eefExUVhbW1NR9++CExMTHS8r/++ovbt28/15hAuSEv5YWw5ObmUlhYSOfOnQkMDMTGxoacnBzc3d05dOgQt2/fBmDr1q107NjxufeJIAjCm+yNOkJfsmSJlPtta2vL/PnzAfjss8/4+uuvWb9+PUFBQXTs2JENGzbwzTffsHz5cmrWrImFhQU9evTggw8+KHPsoUOHMnHiRLy9vbG3ty911Ddu3DhCQkLw9PTEzs5OOiX8qD59+pCXlyfdNGYwGBg0aJB0Grg8PXr0YMeOHfj4+JS6KW7gwIHSY2tubm7SHfUzZ85k9uzZUp55YGAgTZs2BSg3ZOTYsWPExsZiYmKCXq8nLCwMExMTOnTowL///W969+6Nm5ubdGnhaSwsLFiyZAmhoaFAcUpanTp1yg14CQgI4B//+Afnz58nMjKShQsXSvO3srJi/vz52NnZPdeY5YW8NGjQoMwQluvXrzNr1iy0Wi06nY4uXbrQunVrTExMCAwMZNSoUQA0aNCAuXPnPtN+EARBqCpEOItQLqVSKd0jkJycTHBwMPv378fE5MVP7LyOMStDSYBC7vkl4jn0N8CLPIduzAEfxrxtYNzbJ8JZhNfi559/JjY2FoPBgIWFBZGRkS9deF/HmJWpi9+3z/0/nfDq6bRqTEXvfOEtJwq6UC5fX98yn0Z408YUXo+qdBQkirkgvGE3xQmCIAiC8GJEQReE10QEhgiCUJHEKfcqrqzgG4PBwIYNG9iyZQtarZZq1apRp04dxo8fT9u2bUlJSWHMmDHSY3J6vZ6AgACpwczQoUM5deoUv/32GzVr1gSKm7MMGzaMUaNGSe13S8bQ6XTUrFmTsLAwHB0dK3wfVKaft45Apyn7prg+o/ZU8GwEQXibiYJuhJYtW8bx48dZv3499vb2AJw8eZIrV65IXfQcHR2lZ+/T09MZMGAAnp6e0g1qTk5O7Nq1i8GDBwPFiXePPhL48BgREREsXLiQdevWvbLtqIyglMoKhREEQXhZ4i+XkSkoKODf//43CoVCKuZQ3N60pJg/Kj8/H2tr61J3m/fp0weFQsHgwYMpKCjgjz/+wMvLC7W67NPIbm5uHDx4UPo5ISGBuLg4qT1uaGgojRs3Rq1WEx4ezrFjx6hduzbNmjXjzp07LF++vMygFLVaTWRkJAUFBUBxJkC3bt3KDKMJCQnh5MmThIeHo9fr0Wq1BAQE4O3t/cTAm0dDYRYsWPDivwBBEIRKIgq6kcnIyMDS0lIKennS+3x8fFCr1fznP/9h8eLFpZY3aNAAS0tLMjIy+PPPP+nRowdmZmZlFnS9Xs/+/fulU/YnTpxgz549bNy4EQsLCw4dOkRISAibN29my5YtXL9+nV27dqHT6Rg6dCjvvfeeNNbDQSn3799n2LBhxMTEULduXW7dukX//v3ZuXNnmWE0AGvXrsXf3x9vb28MBoPUJa+8wJuSSxUPh8IIgiBURaKgG7n79+8zdOhQ1Go1jo6OREdHA6VPl2dkZDB06FDatGlT6qi+T58+JCQkcPr0aWbOnMnPP/9cauySLwU3b97E2tqabdu2AcWhKmlpaQwYMAAovqZf0uI1JSUFHx8fzMzMMDMzw8vLS2oFC6WDUk6dOsW1a9cYPXq0tFwmk5GVlVVuGI27uzurV68mOzu7VD57eYE3JQX94VAYQRCEqkjc5W5kHB0dUalUUjytra0tCoWCadOmSUexZX2mXr16nDx5stTrnp6e7Nq1iwcPHuDs7Fzm5xQKBYcPH8bFxUVq6WowGOjXrx8KhQKFQsGPP/5Y6nT8kzwclGIwGHB2dpbGUSgUHDp0iBYtWkhhNK6urigUCoYNGwbAiBEjWL16NbVr1yY8PJylS5c+03ofDoURBEGoikRBNzJWVlaMHDmSmTNncvPmTen1wsLCcj9z8+ZNrl69Kt31/vBY//znPx/LcX+UhYUFoaGhHDlyhPPnzyOXy1EoFNy4cQMovrktNTUVKL7WnpiYiFarRaVSsWdP+XeCt2nThqysLCmMBuDMmTMYDIZyw2gyMzNxcHBg4MCBDBs2TAr4KS/wRhAEwViIU+5GYOTIkaXuBk9MTKR27dqMGjUKnU5HrVq1sLW1Zfz48dJ7Sk6XGwwGtFotEydOpFmzZo+NXXJd/GneffddRo0aRXR0NKtWrWLSpEkEBARIKXienp64uroycOBA0tLS8PLyolatWk+81l+jRg1WrVpFREQECxYsQKPR0KBBA9asWVNuGM33339PSkoK5ubmWFhYSGE0Twq8EQRBMAYinEWocCUBLWq1moCAADw9PaXr7VVFSYDC9fORRvscelVq/foijHn7jHnbwLi3T4SzCFXKyJEjUavVqFQqOnbsSN++fSt7Si/sY7/Ycv+nE4EhgiBUJFHQhQpXcje8sRPFXBCEiiRuihMEoUyv6pSm6GkvCBVDHKELwkv48YdhaMu5hi4UGzTip8qegiC8FcQR+ltALpfj4eGBTqeTXouPj8fZ2ZkNGzYQHx/PhAkTHvtcSkoKrVq1wsfHB29vb0aOHMm1a9cAmD59Ol26dMHHx4eePXtK7VYFQRCEyiEK+luibt26/Pbbb9LPCQkJj4WtlKWkeczOnTtxcnLi66+/lpaNGTMGhULB9u3bOXz4MHv37n1l89Vqta9srOfx8JceQRCEqkSccn9L9O3bl/j4eLp27UpOTg4PHjwoFbn6LDp27PhYz3cAa2trPvjgA65fvw4UP5a2cOFCLl68iEqlwt3dneDgYExNTUlPTyc4OJjCwkJcXFzIzs4mICCA7t27M3ToUFxcXDh9+jQ1atRg7dq1xMTE8PPPP6PT6bC3tyc8PBw7Ozv27dtHVFQUJiYm6HQ6Zs2ahbu7O9HR0ezcuRNLS0tkMhnfffcdtra2HD58mG+++QadTkft2rWZO3cu77//PikpKcybNw9XV1fOnz/PpEmT6N69+yvZ54IgCBVJFPS3hJubG3Fxcdy7d4+EhAT69OnDuXPnnvnzer2en376qczmM3fv3uXixYt89dVXACxcuJB27doxf/589Ho9gYGBbN++HT8/P6ZNm8bw4cPx8fHh7Nmz+Pn5lRorJyeHuLg4zMzMUCgU5OTksHXrVkxMTIiLi+Prr79myZIlLF++nLlz59KmTRt0Oh2FhYXk5eURGxvLb7/9RrVq1VAqlVSrVo27d+8ybdo0NmzYQJMmTdi2bRuBgYHS3fbp6enSWIIgCFWVKOhvCZlMxieffMKuXbvYtWsXmzdvfqaC/nBHOWdnZ4KDg6VlMTExbNmyhczMTAYNGoSjoyNQHM5y5swZvv32WwCKioqwt7dHqVRy6dIlqVtbixYtHusR36tXLymP/MCBA6SmpkrPqZdEsQK0b9+ehQsX8vHHH9OlSxecnJzQ6XQ4ODgwbdo0PDw86NatG9bW1pw+fRoXFxeaNGkCQL9+/QgLC0OpVALw/vvvi2IuCEKVJwr6W6Rv374MGDCAdu3aUatWrWf6zMOpbI8aM2YMQ4YM4cqVKwwaNAgPDw+6du2KwWBg1apVNGjQoNT7SwqoTCYrd30Ph6QYDAYCAgLo37//Y+8LCQnh4sWLJCcnM3HiREaOHImfnx9bt27l5MmTJCcn4+vry7p16566jSKYRRAEYyBuinuLNGjQgMmTJzNu3LhXOm7jxo2ZMGECS5cuxWAwIJfLiYmJkW4wy83NlcJUmjZtys6dOwE4d+4cly5dKndcuVwuXSYAUKvVpKWlAXDlyhWcnZ0ZPnw4vXv35uzZsyiVSnJzc3Fzc2PChAk4OTlx+fJlWrduTVpaGhkZGUDxDYHNmzeXjvYFQRCMgThCf8t89tlnZb5+6NAhunTpIv3s6+tLhw4dnmvc7777jv379xMSEkJERAQ+Pj7IZDLMzc0JCQmhQYMGLFq0iJCQEGJiYnBycsLJyQkbG5syx+zTpw95eXkMGTIEKD5iHzRoEC4uLixZsoSsrCxMTU2xtbVl/vz5KJVKvvrqK4qKijAYDDRv3pyPP/4YS0tLFi9eTGBgIFqtltq1axMREfEce00QBOHNJ8JZhApVUFBA9erVkclkpKenM3ToUPbu3UuNGjUqe2rPpSRA4UraYtFY5ine1MYyxhzwYczbBsa9fSKcRagyTp06xeLFiyn5HhkeHl7livnDevf/7rn/p3vbiJAaQagYoqALFcrDwwMPD4/KnobwDF7VUZAo5oJQMcRNcYJQibQ6EVwiCMKrIY7QBeElbI0fivolrqH7D/v5Fc5GEIS3mThCf0lyuVx69KqwsBB/f3+Cg4P55z//SYsWLaR2qFAcaLJhw4anjhkVFcXu3buf+r4VK1awaNGi5172Kjwa3DJkyBDpsTBBEASh4omC/orcv3+fkSNH0rhxYxYsWICpqSl2dnasWLHiuceaOHEin3766WuY5fN5WkDKw8EtrVq1YuHCha90/ZURlFJZoTCCIAgvS5xyfwVKeoXL5fJSMaQDBw5kw4YNpKenS21HS6jVapYuXcrx48dRq9U4OzsTGhqKlZUV06dPx9XVlSFDhpCfn09ISAiXL1/G3t4ee3t76tSpQ1BQEAA3b95k9OjR5OTk4ODgQFRUFO+88w4A169fZ9iwYdy6dYumTZuyYMECbGxsKCgoYN68eZw9exYAHx8fRo8eDfBYQMrXX3/N1KlTuXv3LgAdOnQgJCTksX3g5ubGwYMHpZ8TEhKIi4uT2rWGhobSuHFj1Go14eHhHDt2jNq1a9OsWTPu3LnD8uXLiY+P58cff8TKyoqsrCwiIiJQq9VERkZSUFAAwIQJE+jWrRt3794tc14nT56Uoly1Wi0BAQF4e3tz584d5syZQ3Z2NgD+/v706dMHKD7L8umnn5KcnIyTkxMLFix4uX8QgiAIlUAU9Fdg0qRJfP75549lilevXp0vvviCpUuXsnLlylLL1q1bh42NDT/88AMAERERxMTEMHny5FLvW7lyJba2tuzdu5e8vDx8fX3p2bOntDw1NZUffvgBGxsb/P39SUxMlAJP/vjjD3bs2MG7775LcHAwq1atIigoiFWrVqHX60lMTKSgoIDPPvsMJycnunbtCpQOSImNjcXBwYHY2FgAqWvbw/R6Pfv375fOKpw4cYI9e/awceNGLCwsOHToECEhIWzevJktW7Zw/fp1du3ahU6nY+jQobz33nvSWKdPn0ahUODg4MD9+/cZNmwYMTEx1K1bl1u3btG/f3927txJYmJimfNau3Yt/v7+eHt7YzAYyM/PB2DevHk0bdqUlStXcuvWLXx9fWnevLmUOKdUKqXfhSAIQlUkCvor0LVrV3bv3s3AgQOxt7cvtczPz49vv/2W06dPl3r9wIEDKJVKfvqpuOmGWq3GxcXlsbFTUlKYOXMmADVr1qRHjx6llnt4eGBrawtAy5YtpSNQgG7duvHuu+8C0L9/f+bNmwfA0aNHCQkJQSaTYW1tjZeXF0ePHpUK+sMBKa1atSI2NpZFixbh5uZW6pGzkuCWmzdvYm1tLaWXHThwgLS0NAYMGAAUd3i7f/++tD0+Pj6YmZlhZmaGl5cXf/zxhzRm27ZtcXBwAIqfWb927Zp09gCK+8BnZWWVOy93d3dWr15NdnY2nTp1olWrVtI2T58+HSjOhu/atSspKSlSQS85WhcEQaiqREF/Bf7f//t//PrrrwwbNozvvvuuVFE3Nzfnq6++4ptvvuFvf/ub9LrBYGDOnDnP1V61LA83NTE1NUWlUr3UeFA6rKRNmzYkJCSQlJSEQqEgJiaGTZs2Af8LblGr1UyZMoXQ0FCioqIwGAz069ePiRMnPve6rayspP8uSXjbuHFjme8ta14jRoxALpeTlJREeHg4nTp1euysx9O2WRAEoSoSN8W9Il988QV9+/Zl2LBh3Lx5s9SyXr16kZuby7Fjx6TX5HI5sbGxFBUVAcWnfMu6S9zNzQ2FQgEU33i3f//+Z57TwYMHyc3NBSA+Pp727dsDxdebt2/fjsFgQKlUsnv3bjp27FjmGCWhKl5ec/MQUAAAIABJREFUXgQHB3Pu3Dn0en2p91hYWBAaGsqRI0c4f/48crkchULBjRs3gOKb21JTU6XtSUxMRKvVolKp2LNnT7nzb9OmDVlZWSQnJ0uvnTlzBoPBUO68MjMzcXBwYODAgQwbNky6T6BDhw5s3boVgNu3b3Po0CFpfwiCIBgDcYT+Co0dOxaDwcCwYcOoV6+e9LqJiQlTpkxh7Nix0mtjxowhOjqa/v37I5PJkMlkfPnll1KmeInx48cTHByMp6cndnZ2uLq6PnNK2EcffcTkyZO5efMmTZo0kU45jxs3jvDwcCmXvHfv3qWCWR527NgxYmNjMTExQa/XExYWhonJ498D3333XUaNGkV0dDSrVq1i0qRJBAQEoNPp0Gg0eHp64urqysCBA0lLS8PLy4tatWrRuHHjcudfo0YNVq1aRUREBAsWLECj0dCgQQPWrFlT7ry+//57UlJSMDc3x8LCQrpcMXPmTGbPni1tc2BgIE2bNn2m/SgIglAViHCWN5xGo0Gv12NpaYlSqWTQoEEEBweXe0RdFSiVSqytrVGr1QQEBODp6Sldb68qSgIUzl9aZLSNZYw5AAOMe/uMedvAuLdPhLMYsfv37zN69Gh0Oh0qlQpvb+8qXcwBRo4ciVqt/v/s3XdYlfX/x/HnYTk4DjS0b6k5ColQMRXcA01xwBFQc0SZBqUVqblQc48UzD1SK/qa5AJEURpqjlQwNbV+agoquMVwHWSec//+4OL+igJiDDn0flxX1+W55+dzH+LNvT4v0tLSaN26NZ6ens+6Sf9YP6+1hQpnyTSkY2EuY50LIQpPCnopV716dcLCwp51M4pU9tPwAinmQogiIw/FCVEKSWiLEOJpyRm6EIXw34i3SSvEPfS8fDToxyLfphCibDPpM/SMjAyWLFlCt27d6NmzJx4eHvj7+xMbG1uo7V6+fBkXFxcga2hVHx+fQm3v3r17rF69Ose08ePH0759e3Q6He7u7gwePJhr164Vaj95WbJkCenp/zvjK2j4S37CwsJo3rw5Op2OHj164OfnR2JiYmGbKoQQ4h8y6YIeEBDAX3/9xaZNm9i+fTsRERF4eXlx4cKFHMsZjUb+6cP8NWvWZO3atYVq571791izZs1j0/38/IiIiGDbtm04ODiwcuXKQu0nL0uXLiUjI0P9XFThL61btyYiIoLt27djbW3N0qVLC73NbIX5zgpDwlmEEKbKZC+5X7x4kZ07d7J371516FONRkPHjh2BrLPSc+fOodfruXr1Khs2bFDfX87IyMDGxobZs2er74uvW7eO4OBgtFqtOgQqZJ2te3t7ExMTA2SNNZ5bWEj2cv3792fv3r2kpKQwa9YsmjdvzvTp07l//z46nY4KFSqwfv36HH0xGo0kJydTpUoVddqqVavYunUrAI0aNWLSpElYW1vnG6yydOlSIiMjKVeuHBqNhv/+978sWLAAyAqKyX5Pe/bs2Wr4y5IlS7hw4QL3799/LODlScEw2TQaDS1atMgRzrJq1Sp++uknDAYDNWvWZMaMGdja2ua7zdy+s+PHj7NixQrS09OxtLQkICAAJycnzp8/T0BAACkpKRiNRjw9PRk6dCg7d+5k0aJFmJmZYTAY+Oyzz3BxcSE+Pp7JkyeTlJSEhYUFI0eOVN+9b9iwIR999BF79uyhXbt2jBgx4h/+VAohxLNjsgX91KlTvPTSSzmK4KNOnjxJWFgY1apVA8DX11ctRps2bSIoKIgFCxZw5swZVqxYoQaZTJ06Ndft3bt3jylTpuQaFgJw584dnJycGDlyJFu3biUoKIj169czefJkvL291RHfsq1atYpNmzZx69YttFqtOqTq3r172bp1K+vXr8fa2loNVBkzZkyewSrZY5v/+uuvlC9fHr1eT/ny5ZkyZQohISHqtnKTV8DLk4JhsqWnp7Nv3z71rD8iIoJLly6xceNGzMzMCAkJ4fPPP2f+/PlP3ObD31lCQgLLly/nq6++QqvVcu7cOXx9fdmzZw8hISG4urry/vvvA/8LZ1m8eDHTp0+nadOmGAwGUlJSgKyBZPr160ffvn2JjY1l0KBBREVFqT8b5cqVIzQ0NM+fJSGEKO1MtqA/KjY2lk8//ZTU1FTatWtHlSpVaN++vfoLG2Dfvn2EhITw4MGDHJdWDx8+nCPI5M0338x1SNL8wkJsbGyoWLEinTp1AsDJyYm5c+fm22Y/Pz/eeustICtVbdKkSSxfvpxDhw7Ro0cPdUS4fv36qZGeeQWrtG3bljp16jB27Fjatm1Lx44dCzyiXF4BL08Khjl48CA6nY7Lly/ToEEDunfvDmSFs/z555/q++XZEaoF2ebD39n+/ftJSEhg0KBB6vzMzExu3bpFixYtCAwMJCUlBRcXF3UY15YtWzJnzhy6du1K+/btsbOzQ6/Xc/r0aby9vQF4+eWXefXVVzl+/Diurq4AJv0uvBBCgAkXdAcHB+Lj47l37x6VK1fm5ZdfJiIigu+++44///yTKlWq5DgjvXLlCnPmzGHz5s3Url2bY8eOMXr06KfaZ35hIZcvX8bK6n/vFJuZmT3V/Vg3N7fHHpx7Gubm5mzcuJFjx44RHR2Nl5cXa9asyTXB7VH/NOCldevWLF68GL1ez9ChQ1m0aBFjx45FURSGDRtGnz59nrofj15FaNeuHfPmzXtsuW7duuHk5MSBAwdYvXo1oaGhBAUFMWHCBP766y+io6P55JNPePfddwv0vICEswghTJ3JPhRXt25dOnfuzKRJk9TMa4AHDx7kurxer8fS0hJbW1uMRmOO+9jOzs7s3buXv//+GyDPXOz8wkLyo9VqSU1NzbfAR0dHU7duXSArSCQqKgq9Xo+iKGzevFkdHS6vYBW9Xk9SUhLOzs74+/tjZ2fHuXPngKwiqdfr821jbgoaDKPVapk2bRrff/89N2/exNXVlZCQEPUyeHp6OmfOnHmqbQK0adOG/fv3q/2ArOMNEB8fj62tLV5eXnz44YfqMwXnz5+nYcOGvPPOO3h4ePDHH3+g1Wp59dVXCQ8PB7JiX8+cOYOTk9NTHxMhhCitTPYMHWDOnDksX76cPn36YGFhQeXKlalRowZ+fn7s3r07x7INGzbEzc2NHj16YGNjQ4cOHThy5AgA9vb2fPDBBwwYMACtVptnUEl+YSH5qVq1Ku7u7ri7u1OlShX1j4nse+hGoxGtVsvnn38OZOWr//XXX/Tv3x8AR0dHhg0bBuQdrHL9+nU+/vhjUlNTURQFBwcHunbtCsCQIUN4++23KV++/FM9sf80wTD29vbqVYaJEydy584d9XaCoigMGDAAe3v7p9pm3bp1CQwMZOLEiaSmppKRkcHrr79O48aNiYqKYtu2bVhaWqLRaJgwYQIA8+fPJz4+HnNzcypXrsysWbMACAoKYvLkyQQHB2NhYcG8efNy3I4RQghTJ+EsIk/FEQxTVsJmsgMUjsXNK7MDy5TlAAwo2/0ry32Dst0/CWcRxaI4gmHKWtjM27r/FiqcJS8S2iKEeFpS0EWeiiMYpiyGzRQHKeZCiKdlsg/FCSFyJ8EuQvw7yRm6EIWwctvbpGYW/T30whjX/9nffxdClDwp6MLkRUVF8eWXX6IoCmlpabz22mvcvXuXzp07M2DAAHU5RVHo0qULc+bM4fLly+rQvxkZGdSqVYtZs2Zha2v7DHsihBD/nFxyFybt5s2bTJs2jRUrVhAREUFUVBRDhw7F29tbfe88W0xMDGZmZrRo0QIo3nAZIYQoaVLQhUm7desWFhYWVK1aFcgaitfBwYHOnTsTHx9PXFycumxYWBheXl5oNJoc28gOlymu+FohhCgJUtCFSbO3t6dx48Z07NgRf39/goODuX37NlZWVri7u6uBK3q9np07d+Y6Zvuj4TJCCGGKpKALk2ZmZsby5ctZu3YtLi4u7N27Fw8PD+7cuUOfPn3YunUrBoOBqKgoXn/9dZ5//nl13exwmVatWpGUlKSGywghhCmSgi7KBDs7OwYNGsQ333xDpUqVOHz4MPb29tSoUYN9+/YRGhqqpq1ly76HvnfvXjQaDYsWLXpGrRdCiMKTgi5M2o0bN/j999/Vz9evXycpKYlatWoB4O3tzZIlS7h48SKdO3fOdRuPhssIIYQpkoIuTFpmZiZLliyhW7du6HQ6fH19GTFiBA4ODgD06tWL2NhYevXqlSPe9lEPh8sIIYQpkvfQhUl78cUX+frrr/OcX6VKFTVy9WFeXl54eXnlmDZnzpwib58QQpQUKehCFMIH7sUTzlIYEuwixL+TXHIXooyRYi7Ev5MUdCFMQIYErgghnkAuuQtRCPN2vMODEghnmdP3h2LfhxDCtElBF8+cq6srFStWZOvWrZiZmanTVq5ciZ2dXZ7rRUdHM3/+fNLT00lPT8fW1pbg4GCmTZtGpUqVGD16dI7lfXx88PLy4oUXXsDPz4+6detiMBioWrUq06ZNo0GDBsXaTyGEKE5yyV2UCg8ePCAiIqLAy2dmZuLv78+MGTPUUJbx48ej0Wjw9vYmIiICg8GgLn/p0iVOnTqFm5sbAA0aNCAiIoLIyEiaNGkiT7gLIUyeFHRRKnz00UcsXbqU9PSc94pdXV05e/bsY5+Tk5N58OABzz33nDrPwcEBjUZD48aNqVq1Kr/++qs6LywsjO7du1OhQoXH9u3s7CzBLEIIkycFXZQKjo6OvPbaa3z//fcFWr5KlSr069ePrl278sEHH7Bq1aocRdnb25uwsDAAjEYjW7ZseWzo1+x5u3btkmAWIYTJk4IuSo0RI0awevVqkpOTC7T85MmTiYiIoHPnzvzxxx/06tWLixcvAuDh4cG+ffu4c+cO0dHRVKhQgaZNm6rrxsXFodPpaN26NQcPHmTgwIHF0SUhhCgxUtBFqVG/fn06dOjAN998o04zNzfHaDSqn9PS0nKsU7t2bfr27cuSJUto2rQpv/zyCwDVqlWjbdu2REZGEhoa+tiocNn30Pft24e9vT1Tp04tvo4JIUQJkIIuSpWPP/6YkJAQ9Sy9Tp06/PHHHwAcOnSIW7duAZCcnMyvv/6KoigA3Lt3j8uXL6uhLJB12f37779nz5499O7dO9f9WVlZMXXqVPbv38+pU6eKs2tCCFGspKCLUuX5559Hp9Nx507Wu92ffPIJ33zzDTqdjj179vDCCy8AoCgK69atw83NDQ8PDwYMGIC7uztvvPGGuq127dpx7949nJ2dczw896jnnnuOIUOGsHTp0uLtnBBCFCONkn2KI4QosLS0NP788092JASW2YFljh49SrNmzUp8vyWlLPevLPcNynb/sn+3ODo6PnVOhAwsI0QhjO3xbYmEs2QY0rGUMdqFEPmQS+5CmAAp5kKIJ5GCLkQhSXCKEKI0kIIuRCGM+fEdOXsWQpQKpfoeet++fUlPTycjI4OLFy/yyiuvAFlDfBbX2NsXL15kxIgRAAwZMgQPD49i2c/TaNiwIceOHWPw4MElfjwg65WwDRs24Ovrq04bP348Bw8exMbGhtTUVNq2bcvEiRPVcBUhhBAlq1QX9E2bNgFw+fJlNXDjYZmZmVhYFG0XfvrpJ5o2bcqUKVOear3iaMujnnQ8nsRgMGBubv7U+7137x5r1qzJUdAB/Pz8eOutt9Dr9Xh6etKsWbMiG0K1JI5nbv7pMRJCiGetVBf03Li6utKjRw+io6Oxs7Nj5MiRjBo1iuTkZNLS0ujQoQNjx44FYMmSJVy4cIH79+9z6dIl6tSpw6JFi6hQoQI7d+5k0aJFmJmZYTAY+Oyzz7hx4wbffvstRqORY8eOsWTJEhRFYfLkySQlJWFhYcHIkSNp3749kHXm/NFHH7Fnzx7atWvH9evXsbKy4uLFi1y6dIk33niDTp06sWTJEq5fv84777zDO++8A8D58+eZPXs2t2/fJiMjg3feeUcda/ynn37iiy++oFy5cnTt2jXf45GZmcn777/P7du3SUtLo3HjxkybNg0rKyvCwsLYunUr1tbWxMfHExgYSEJCAgsWLKB8+fK4ubmxYMECjh07hrW1NSdOnCAoKEgd1MXf35+OHTsyffp07t+/j06no0KFCqxfvz5HG7RaLa+99hpXr14FQK/XM2fOHP766y/S0tJwcXEhICAAc3NzYmNjCQgIICUlBXt7exISEhg2bBidOnXCx8cHe3t7Tpw4QZUqVVi9ejWrVq3ip59+wmAwULNmTWbMmIGtrW2u35+LiwtLly4lMjKScuXKodFo+O9//0vlypXZt28fX3zxBQaDgWrVqjF9+nReeuklYmJimDlzJo6Ojpw6dYoRI0bQqVOnovuBFUKIkqKYgEuXLinOzs6KoihKp06dlClTpqjzUlNTFb1eryiKoqSnpys+Pj7K3r17FUVRlMWLFytvvPGGcvfuXcVoNCrvvvuusmHDBkVRFMXd3V05duyYoiiKkpmZqdy/f19d5/PPP1e336dPH2Xjxo2KoijKuXPnFGdnZ+Xvv/9WFEVR7OzslC+//FJddty4cUr//v2VtLQ05cGDB0rLli2V8ePHKwaDQbl+/bri5OSk6PV6JSMjQ/H09FRiY2MVRVGU+/fvK127dlViY2OVxMRExdnZWYmLi1MURVFWrVql2NnZqX189HgYjUYlKSlJ/feYMWOUkJAQRVEUJTQ0VHFyclLi4+MVRVHUbV+4cEFRFEX55ptv1G3fvXtX0el0yo0bNxRFUZQbN24o7dq1U+7evZtjfw/3de3atYqiKMqtW7cUNzc3tT8TJkxQwsPDFUVRFIPBoIwcOVI97p6ensqWLVsURVGUkydPKvb29sru3bsVRVGUt956S3n//feVjIwMRVEUZcuWLcqkSZMUg8GgKIqirFu3Thk1alSe39/t27eVZs2aKSkpKepxzcjIUG7duqW4uLgo586dUxRFUTZu3Kj06dNHURRFiY6OVuzt7dVtFVRqaqpy5MgR5eOIN59qPVNy5MiRZ92EYlWW+1eW+6YoZbt/2b9bUlNTn3pdkztDB3IM42kwGJg3bx6///47iqJw69Ytzpw5o55Ft23blsqVKwPQuHFjEhISAGjZsiVz5syha9eutG/fHjs7u8f2o9frOX36tHrm/PLLL/Pqq69y/PhxXF1dAfD09MyxTpcuXbCyynpIql69enTo0AEzMzNq1qxJ5cqVuX79OoqiEBcXx6hRo9T1MjIyOH/+PGZmZjg4OFC/fn0A3nzzTYKCgvI8Fkajka+//pp9+/ZhNBq5e/cu5cuXV+e//vrr1KlTB4ATJ07g4OBA3bp1gayhUbPvvf/+++9cvnw5x2V1jUZDfHw8NjY2ue571apVbNiwgQsXLjBgwAAaNGgAwO7duzl58qQ6Jntqaio1a9ZEr9dz9uxZ3N3dAWjUqBENGzbMsU13d3f1Uvvu3bv5888/1WNsMBjQarVA7t+fwWCgTp06jB07lrZt29KxY0e0Wi0nTpzA3t6el19+We33tGnT0Ov1ALz00ks5gluEEMIUmWRBr1ixovrvb775hnv37rFp0ybKlSvHZ599liPA4+FBP8zNzdV5EyZM4K+//iI6OppPPvmEd999l379+hWqLbnt79HPBoMBjUaDjY1NrvfAd+3a9VT737ZtG0ePHmXdunVotVpWrlypJo4BWFtbF2g7iqLQsGFD1q1b99i8y5cv57pO9j308+fPM2DAANq2bUuHDh1QFIXly5dTu3btHMtnF1CNRpNnOx4+noqiMGzYMPr06fPYcnl9fxs3buTYsWNER0fj5eXFmjVrntj3R79DIYQwRSb/SPL9+/extbWlXLly3Lhxo8AF8fz58zRs2JB33nkHDw8PNQDkYVqtlldffZXw8HAgK3LzzJkzODk5FarN9erVo3z58mzZskWdFhcXh16vx8nJiVOnTqlFOftBuLzcv38fGxsbtFot9+/fJzIyMs9lmzRpwqlTp9SrFNn9AmjatCnx8fFER0er006ePImiKGi1WlJTU8nMzMx1u/Xr18ff358FCxagKAqurq6sWrUKg8EAQFJSEpcuXUKr1fLKK6+obfy///s/zp49m2d7XV1dCQkJ4e7duwCkp6dz5swZIPfvT6/Xk5SUhLOzM/7+/tjZ2XHu3DmcnJw4c+YMcXFxar8dHBzUs30hhCgLTPIM/WE+Pj588skn9OrVi5o1a9KqVasCrTd//nzi4+MxNzencuXKzJo1K9flgoKCmDx5MsHBwVhYWDBv3jyqVatWqDZbWFiwcuVKZs+ezVdffYXRaKR69eosXLiQ6tWrM2PGDD744APKly//xIfievfuza5du3Bzc6N69eo0a9bssYjRbM899xxTp07F19eXChUq0LFjRywtLalQoQJmZmYsX76cwMBAZs+eTUZGBrVr12blypVUrVoVd3d33N3dqVKlymMPxUHWrYH//ve/7Nq1iwkTJhAYGIhOp0Oj0WBpacmECROoXbs2c+fOZcKECaxatQo7Ozvs7OyoVKlSnn27c+cOb731FpB1xj5gwADs7e1z/f70ej0ff/wxqampKIqCg4MDXbt2pVy5csybN4/Ro0eTmZlJtWrVCAwMfMpvTQghSjcJZ/mX0ev16plpaGgomzdv5vvvvy+x/ScnJ1OxYkU0Gg2xsbH4+Pjwww8/UKVKlRJrQ1HIDlD49kogiz0e/wOnLCjLARhQtvtXlvsGZbt/Es4iCmzt2rX88MMPGAwGqlSpwsyZM0t0/7///jvz5s1Tc8xnzJhhcsX8YYHdvpXgFCFEqSAF/V9m2LBhDBs27Jntv23btrRt2/aZ7b84SDEXQpQGJv9QnBDPWrqEswghSgEp6EIUwuCfP8VKztCFEKWAFPRSzNXV9bHXunx9fdXXzkpCWFgYzZs3R6fT0aNHD/z8/EhMTCyx/T+tiRMncuTIkWfdDCGEKHFS0E3M6tWr1ZHfilv2e+etW7cmIiKC7du3Y21tzdKlS4tsH0ajkaJ80WLWrFk0b968yLYnhBCmQh6KMzGurq6sXLkSOzs7fHx8cHR05Pjx49y8eZPu3bszevRoAG7evMnMmTO5evUqaWlp9OzZkw8++ACAuXPncvjwYTIyMrCxsWH27Nm8+OKLaoqbl5cX0dHR9OvXL8drExqNhhYtWrBnzx51Wl7hKffv32fChAmcO3eOmjVrUrNmTapXr864ceNYsmQJ586dQ6/Xc/XqVTZs2MDx48dZsWIF6enpWFpaEhAQgJOTE+fPn1fDXIxGI56engwdOjTPcBYfHx+GDBlCp06duHXrFlOmTFGvaAwdOlQdNtjV1RWdTsfBgwdJTExkyJAh6vvuQghhiqSgm7hr166xbt06kpOT6dKlC3369KFu3bqMGzeO4cOH06JFC9LT0xk8eDCNGjWiTZs2+Pr6Mm7cOCBrJLqgoCAWLFgAwJ07d2jUqJE6PywsTN1Xeno6+/btUyNSIyIiuHTpEhs3bsTMzIyQkBA+//xz5s+fz7Jly6hcuTI//PADd+7cwcvLi27duqnbOnnyJGFhYVSrVo2EhASWL1/OV199hVar5dy5c/j6+rJnzx5CQkJwdXXl/fffB1BHjVu8eDHTp0+nadOmGAwGUlJSHjs2M2fO5JVXXmHZsmXcvHkTLy8vHBwc1HH7U1NT2bBhA5cvX8bd3R1PT88CD5UrhBCljRR0E+fm5oaZmRmVKlWiQYMGJCQkUKNGDQ4fPkxSUpK6XHJyMnFxcbRp04Z9+/YREhLCgwcPHhvOtVy5cnTv3j3HtIMHD6LT6bh8+TINGjRQ5+cXnhITE8OkSZMAqFq1Kl26dMmxzfbt26sj7u3fv5+EhAQGDRqkzs/MzOTWrVu0aNGCwMBAUlJScHFxoWXLlkDBwnUOHTrE+PHjAahRowYdOnQgJiZGXTb7D5NatWqpwTnZATNCCGFqpKCbuNzCX4xGIxqNhs2bN2NpaZlj+StXrjBnzhw2b95M7dq1OXbsmHqZHqBChQqPhae0bt2axYsXo9frGTp0KIsWLWLs2LH5hqc8yaNnwu3atWPevHmPLdetWzecnJw4cOAAq1evJjQ0lKCgoCIJ18nt2AkhhKmSh+LKIK1WS7NmzVi1apU67dq1ayQmJqLX67G0tMTW1haj0ZjruOz5bXfatGl8//333Lx5M9/wFGdnZzVN7t69e/mG5rRp04b9+/dz7tw5ddrJkycBiI+Px9bWFi8vLz788EM1RKcg4TqtWrVi48aNACQmJrJ37171DF8IIcoaOUMv5d59913Mzc3Vz8nJyQVaLygoiDlz5qjZ49bW1syaNYuGDRvi5uZGjx49sLGxoUOHDk/1mpe9vT1ubm6sXr2aiRMn5hme8uGHHxIQEICbmxu2trY4OjrmmW5Wt25dAgMDmThxIqmpqWRkZPD666/TuHFjoqKi2LZtG5aWlmg0GiZMmAAULFxn0qRJTJ48WT0Go0eP5pVXXilwX4UQwpRIOIsoFhkZGRiNRsqVK4der2fAgAEEBATQunXrZ920IvFwgILGQlMmB5cpywEYULb7V5b7BmW7fxLOIkqde/fu4evri8FgIC0tjV69epWZYv6osljMhRCmRwq6KBbVq1fP8cqbEEKI4iUPxQlRSOmGjGfdBCGEkIIuRGG8+9NcrMwtn7ygEEIUMynootRydXXFzc0NDw8PunfvzqZNmwDYtWsXc+fOzXWdmJgYvLy8SrKZQghRKsg9dFGqLV68GDs7O86ePYuXlxft27enc+fOdO7c+Vk3TQghShUp6MIk2NnZUblyZW7cuMGBAwfYs2cPixcvBmDBggXs2LGDypUr4+zsnGO97HlVq1bF2dmZQ4cOqQ/rhYeHExISog5ZO3XqVOrXr1/ifRNCiKIgl9yFSTh69Cg2NjbY29vnmL579252797Nli1b2LhxIxcuXMgx75dffiEiIoINGzYQHx+vzjty5AhRUVGsW7eOsLAwhg4q6DsPAAAgAElEQVQdqg5aI4QQpkjO0EWp5u/vj6IoJCQksGjRIqyscr7zHRMTQ48ePdSx4fv06cPy5cvVed27d6dixYoA9O7dW523e/duzpw5Q9++fYGsUe7u3btXUt0SQogiJwVdlGrZ99CjoqIICAjg9ddfL5LtKoqCt7c3n3zySZFsTwghnjW55C5MQvfu3WnTpg1ffvlljuktW7YkKiqKBw8eYDAYCA0NVec5Ozvz448/kpKSgtFoZOvWreo8V1dXIiIiuH79OpAV/frnn3+WTGeEEKIYyBm6MBmffvopXl5e+Pn5qdM6derE8ePH0el06kNxN27cAKBz5878/vvveHh4UKVKFZycnNRkuBYtWjBixAiGDRuGwWAgIyMDNzc3HB0dn0nfhBCisCScRZRper0erVaL0Whk4sSJ1KhRg5EjRxZ6uznDWczK5OAyZTkAA8p2/8py36Bs90/CWYTIw7hx47hy5Qqpqam89tpr+Pr6Fvk+ymIxF0KYHinookxbtmzZs26CEEKUCHkoTogikm7IfNZNEEL8i8kZuhCF8O6Py7hjSAVgh6cMTCOEeHakoIti5erqipWVFVZWVmRkZDBkyBD69u3Lrl27OHLkCOPGjXtsnZiYGObOnVvoPPULFy4QFBTEmTNnqFKlClZWVrz33nucP3+e06dPs2DBghzLjx8/nlq1avHRRx8Var9CCPEsSEEXxe5ZBKzcvHmTt956izFjxqj30RMTEzlw4AC9e/dmxYoV3Lt3j8qVKwOQnJzMTz/9xLZt24qtTUIIUZykoIsSU5IBK+vWrcPFxYXevXur27G1tVU/u7i4EBkZycCBAwGIioqiSZMmvPjiiyVxKIQQosjJQ3GixJRkwMqpU6do3Lhxnm3x9vbOcUk/LCwMb2/vouqqEEKUODlDF8WuNAasdOzYkSlTpnDu3DksLS2JjY3ljTfeKJL+CiHEsyAFXRS7ZxGw4uDgwB9//JHnupaWlnh4eBAWFoaVlRU9e/Z86lGZhBCiNJFL7qLElGTAysCBAzl06FCOh9z+/vtvtmzZon729vZm69atbNmyRS63CyFMnpyhixJVUgErNWvWZO3atQQFBbFw4UIqVqxIxYoVcwz9+sorr/DCCy+QlpYmoSxCCJMn4Syi1CuugJXCyC1AId2QiZV52fkbuSwHYEDZ7l9Z7huU7f5JOIso00oiYKUolKViLoQwPfIbSJR6ErAihBBPJg/FCfGMSJiLEKIoyRm6EIUw5IevuWNI+0frbvcaUcStEUL8m8kZuolydXXl7NmzOaZ5eXkRExOT73o+Pj788ssvhd7/5cuXcXBwQKfT4e7ujpeXF8eOHSv0doUQQvwzUtDFU8vMzLpUXKlSJSIiIti2bRt9+vRh4sSJRbofg8FQpNsriOy+CSGEqZFL7mWQj48PQ4YMoVOnTrl+PnjwIMuWLePu3bt0796dUaNGAVkJZTNnzuTq1aukpaXRs2dPPvjgAyDrikCPHj2Ijo7Gzs6O4cOH59ini4sL8+bNUz/v3buXFStWkJ6ejqWlJQEBATg5OQF5h63ExMQwc+ZMHB0dOXXqFCNGjOCll15i9uzZ3L59m4yMDN555x28vb1JSUlh3LhxxMbGYmFhQb169Vi0aBHnz58nICBAHYjG09OToUOHkpyczMyZM9XR43Q6nfq0vI+PD/b29pw4cYIqVaqwevXqYvx2hBCieEhBN2H+/v453lO8ePFigdaLi4tj/fr1pKWl0b9/f5o2bUqnTp0YN24cw4cPp0WLFqSnpzN48GAaNWpEmzZtgKz3wTdv3gxkXXJ/2M8//0yPHj0ASEhIYPny5Xz11VdotVrOnTuHr68ve/bsyRG2Ur58efz9/XNsJzY2lunTp9O0aVMyMzPp168fgYGBNGjQAL1ej7e3N05OTpw/f57k5GR27NgBoA42ExISgqurK++//36O6cuXL8doNLJt2zaSk5N58803sbOzo0OHDgBcunSJkJAQLCzkfwkhhGmS314mLHuM9GxeXl4FWq93795YWFhgYWGhnnW7uLhw+PBhkpKS1OWSk5OJi4tTC/rDUaQA9+/fR6fTkZSURHp6Ops2bQJg//79JCQkMGjQIHXZzMxMbt26lW/YCsBLL71E06ZNgaw/UOLi4tQrCAAZGRmcP38ee3t74uLimDZtGs7OznTs2BHIGj0uMDCQlJQUXFxcaNmyJQCHDh1iwoQJaDQatFotPXv25NChQ2pBd3d3l2IuhDBp8husDDI3N8doNKqf09Ke/BS20WhEo9GwefNmLC0tc10muwhny76HbjAYmDNnDp9++ikbN24EoF27djkuwRfUw/tQFAUbGxsiIiJyXTYyMpLo6Gj27dvHggUL2LZtG926dcPJyYkDBw6wevVqQkNDCQoKeqr9CiGEKZKH4sqgOnXqqPeKY2NjOX36dI75W7duJTMzkwcPHhAVFUXLli3RarU0a9aMVatWqctdu3aNxMTEJ+7P3NycMWPGkJiYyK5du2jTpg379+/n3Llz6jInT54E8g9beVS9evUoX758jkCVuLg49Ho9169fx9zcnC5duhAQEEBSUhJ37twhPj4eW1tbvLy8+PDDD9Xj0KpVK0JDQ1EUBb1ez44dO2jdunUBjqYQQpgGOUMvg3x9ffnkk0/YtWsXDg4OODg45Jhfv359+vfvrz4Ul/2wXFBQEHPmzMHd3R0Aa2trZs2aha2t7RP3Wa5cOUaMGMHSpUsJDw8nMDCQiRMnkpqaSkZGBq+//jqNGzfON2zlURYWFqxcuZLZs2fz1VdfYTQaqV69OgsXLuSvv/5i/vz5QNbVBT8/P2rWrMnKlSvZtm0blpaWaDQaJkyYAMDw4cOZMWOG2jcPDw/at2//zw6wEEKUQhLOIkpcaQxbeVqFCVDIVtrDXMpyAAaU7f6V5b5B2e6fhLMIk2IqYSvFrTQXcyGE6ZHfKKLESdiKEEIUPXkoTohiJiEsQoiSIGfoQhTCkB/WcSczPd9ltnt/UEKtEUL8m8kZuvhHcguH8fX1JSEhocTacPfuXUaNGkWvXr1wd3dHp9Nx6NAhTpw4Qbt27R4bCz4sLAwfHx+1/W5ubnh4eNC9e3d1UBwhhDBVcoYuikxJjoGemZnJwoULqVmzJvPnz0ej0XD79m1SUlJ44YUXqFKlCgcOHMjxalpYWBh9+vRRP2ePtHf27Fm8vLxo3749NWvWLLE+CCFEUZKCLoqMq6srK1euxM7ODh8fHxwdHTl+/Dg3b96ke/fujB49Gsg/BGbu3LkcPnyYjIwMbGxsmD17Ni+++CKXL1/G29sbLy8voqOj6devH9evX8fFxQWNRgOAjY0NNjY2QNYwuGFhYWpBT0hI4MyZM7i5uT3Wbjs7OypXrsyNGzekoAshTJYUdFFsrl27xrp160hOTqZLly706dOHunXr5hsC4+vry7hx4wDYtGkTQUFBLFiwAIA7d+7QqFEjdX7dunXx9/cnMjKSpk2b4urqSqtWrYCsNLUlS5Zw9+5dqlSpQlhYGD169KB8+fKPtfPo0aPY2Nhgb29fQkdGCCGKnhR0UWzc3NwwMzOjUqVKNGjQgISEBGrUqJFvCMy+ffsICQnhwYMHj2WTlytXju7du6ufW7VqxS+//EJMTAxHjx5lxIgRDB06FD8/P6pXr07r1q2JjIxkwIABbNmyhUWLFuXYnr+/P4qikJCQwKJFi7CysireAyKEEMVICrooNg+PcmRubo7BYMg3BObKlSvMmTOHzZs3U7t2bY4dO6ZepgeoUKGCenk9m1arpXPnznTu3BlHR0dWrFiBn58fAN7e3ixdupS6deui1Wpp0qRJjnWz76FHRUUREBDA66+/znPPPVfUh0EIIUqEPOUuSlR+ITB6vR5LS0tsbW0xGo2sX78+320dOHAAvV4PZCWznTp1ilq1aqnz27dvz82bN/niiy/yjZbt3r07bdq04csvvyxk74QQ4tmRM3Txj7377ruYm5urn5OTkwu0Xl4hMA0bNsTNzY0ePXpgY2NDhw4dOHLkSJ7b+euvv/j888/JjiN46aWXmDx5sjrfwsICnU7Ht99+m+MPiNx8+umneHl54evrS40aNQrUDyGEKE0knEWIf+BpAhRKewhLXspyAAaU7f6V5b5B2e5fsYWzDBw48LF7lrlZt27dU+1UiH8TUyzmQgjTk+9vmr59+5ZUO4QQQghRCPkWdE9Pz5JqhxCilCmrlzSzleX+Pdo3U73tI55Ogb9hRVHYtGkTkZGR3L59m23btvHbb7+RmJhIjx49irONQpRaQ3/YxJ3MjGfdDCHyFen97rNugigBBX5tbdGiRWzevJk333yTa9euAfD888+zZs2aYmucMH25hbiUBEVR+Pbbb+nZsyc9e/akd+/eTJo0icTERJydnYmLi8ux/KVLl2jWrBkPHjwo8bYKIURRKHBBDw8PZ+XKlfTs2VN9UK5WrVpcunSp2BonxD+1cOFCfvzxR7799lu2b99OeHg47du3JzU1lZ49exIeHp5j+fDwcLp160bFihWfUYuFEKJwCnzJ3WAwYG1tDaAW9OTkZPkFKJ5aXgEsf//9N59++il///03kDW064QJEzh27BgzZszAaDSSmZnJsGHD6NWrF7du3WLKlClqZOvQoUPp3bs3ycnJfPPNN2zZskUd+U2j0dC1a1cgawS5YcOGMXLkSMzNzVEUhS1bthAYGPhsDogQQhSBAhf0Dh06MGfOHCZMmABkXdJctGgRnTp1KrbGibIprwCWbdu2UadOHYKDg4GsvHPIimUdOnQovXr1QlEU7t+/D8DMmTN55ZVXWLZsGTdv3sTLywsHBwdSU1OxsrKifv36ue7f0dERGxsbfv31Vzp06EB0dDRWVlZl+iEpIUTZV+BL7gEBASQmJtKsWTPu379P06ZNuXr1ao6xtoUoiH379tGvXz969erFV199xenTpwFo0qQJ+/btY+7cufzyyy/q1R8XFxdWrFjB8uXLOXnyJJUrVwbg0KFD9O/fH4AaNWrQoUMHYmJiCtQGb29vQkNDAQgNDc13aFghhDAFBS7oWq2WZcuW8csvv7BhwwZ+/vlnli1bhlarLc72iTImO4Bl/vz5REZGMnv2bNLT0wFo2rQp4eHhODo6EhERwdtvvw3A4MGDWbFiBdWqVWPGjBlqnGpeGjRoQFpaGhcuXMhzGQ8PD3799VcuXbrE7t276d27d9F1UgghnoF8C7rRaHzsv2rVquHo6Ej16tXVaUIUVH4BLJcuXUKr1dKzZ08CAgL4v//7P4xGIxcuXKBOnTr079+ft99+mz/++APIuse+ceNGABITE9m7dy8tW7bE2tqawYMHM3nyZPV+vKIo7Ny5U32I08bGhrZt2zJixAicnZ1l/HYhhMnL9x66g4NDgYZ+zb5kKkRuHg1xySuA5fDhwwQHB2NmZobRaGTatGmYmZmxdu1aYmJisLS0xMrKikmTJgEwadIkJk+erIa8jB49mldeeQWAUaNGERwcjI+PD5BV0Js3b46zs7PaDm9vb/z8/Fi6dGmJHAchhChO+YazXLlyRf33nj17+PHHH3n//fd54YUXuHr1KqtXr6Zr164MHDiwRBorRGlRmAAFIUpaWRspTsJZcpfvN/ziiy+q/w4ODiY0NFR9IKlevXo4Ojri7e0tBV2IMqgs/9KEst2/R/tWloq5yFuBH4q7f/8+KSkpOaalpqaqrxAJIYQQ4tkp8J9tnp6evPvuu7zzzjs8//zzXL9+nbVr10qAixBlVFk9e81WlvtXEn1LNxiweujZGPHsFbigjxkzhjp16rBjxw5u3ryJra0tgwYNol+/fsXZPlHMXF1dsbKyynGvZtmyZXTu3Jljx46powM+jdOnT3PhwoUcoT06nY4NGzZQvnz5PNcbP34827dv58cff+SFF15Qpzk6OvLWW2/lu8+dO3dSo0YNGjdurE5LTEwkMDCQo0ePUrlyZczNzenXrx9arZbg4GD1CflsS5Ys4erVq8yZM6fAfR0aFSHhLOJfKbLPoGfdBPGIAhd0MzMzBgwYwIABA4qzPeIZWLx4MXZ2dkW2vdOnT7Nnz54cBT0iIqJA69ra2rJkyZKnKqqQVdAdHR3Vgp6SksJbb72Fl5cXn3/+OWZmZty/f5/t27fTpUsXpk2bRlxcHA0aNACynoIPDw9n3rx5T7VfIYQoLZ7qSYnQ0FAiIiK4ceMGNWvWRKfT4e3tXVxtE6XE04y9PmzYMBYvXoxer0en09GiRQsmTZpEw4YN1TP+uLg4Zs2aRWJiIgBDhgxRb93079+f7777jtjYWF5++eUc7UhPT2fBggX89ttvpKen07BhQ6ZOncqxY8fYvXs3Bw8eZNOmTbz77rtkZGRQtWpV3n//fXX9SpUqqSPL9erVi7CwMMaMGQNAdHQ0lpaWNG/evNiPpxBCFIcCF/QVK1awZcsWhgwZor62tmbNGm7evMmwYcOKs42imPn7+6uX3M3NzQkLC8sx/2nGXq9SpQr+/v7s2bOHxYsXP7avzMxMhg8fzogRI+jevTsAt2/fVudXrFiR999/nwULFrBs2bIc665Zs4ZKlSqxefNmAAIDA1m1ahUjR47E1dU1x6X5qVOn5rj8/qjsd9BHjRql9lmGfxVCmLICF/RNmzaxdu3aHK+ytW3blrfeeksKuol70iX3ffv2ERISwoMHD8jMzFSnN2nShODgYObOnYuzszNt27Z94r4uXLhAZmamWswha9S2h/Xr149vvvmGEydO5Ji+e/du9Ho9P/74I5B1xm5vb1+gPj7KwcGB5557jv3799O8eXN27doluQRCCJNW4IKekpJCtWrVckyrWrUqqampRd4oUXpkj72+efNmateuzbFjx9TClz32+sGDB4mIiGDVqlV8//33hd6npaUlH3/8MV988QX/+c9/1OmKojBlyhRatWr1xG289tpravhKXry9vQkLC+PmzZs0b96cmjVrFrrtQgjxrBT4PfR27doxevRozp8/T2pqKnFxcYwfP75AZ2XCdP2Tsde1Wm2e4xPUq1cPCwsLoqKi1GkPX3LP5u7uTlJSEocPH1anubq6EhwcrP4RqdfriYuLA3hsnz179iQpKYk1a9aQPRiiXq9nw4YNOfbx66+/snbtWnkWRAhh8gpc0CdPnoy1tTUeHh40bdqU3r17U758eT777LPibJ8oAf7+/uh0OvW/7PATgIYNG6pjr/ft25datWqp8w4fPoyXlxc6nY733ntPHXu9VatWpKSk4OHhwcyZM3Psy8LCguXLl7N+/Xrc3d3x8PBg7969j7XJzMyMUaNG5Rh+2M/PD3t7e/r06YO7uzsDBw5UC7qHhweRkZHodDq2bNlCxYoVWbt2LWfOnKFz5864u7vj4+ODmdn/fuSrVq1K+/btSUxMxNXVtciOpxBCPAv5juUOcPXq1RyfDQYDt2/fxsbGRg3cyH5nWIh/i+zxlhdcipP30MW/0rN8D70sD9tbbGO5Q9ZlzrwS1xRFQaPRSNqa+Nf6qrtOwlnEv5KMFFf6PLGg29vbk5qaiqenJx4eHpIbLcS/RFk+C4Ky3b+S6JsU89LniQV9y5YtnD17lvDwcAYMGECDBg3Q6XR07do132E8hRBCCFFyCvRQnJ2dHePGjWP37t0MHjyYPXv20LZtW/7v//6vuNsnRKmXbjA86yYIIUTBn3IHuHjxIr/99hvHjx/n1VdfVbPRRdFzdXXFzc0NDw8PevXqxfbt2/NdXqfTleiYAEuWLKFVq1bodDrc3Nz49NNPefDgQYntv7QYGrVDLj0KIUqFJ15yv3PnDtu3byc8PJzk5GR0Oh3fffedPNleArJHcDt16hT9+/enVatWjw3uk62g4SdFqXfv3owbN4709HQGDx7Md999h5+fX5FsOzMzEwuLp4oaMOn9CiFEYT3xN1e7du2oVasWOp2OJk2aABAfH098fLy6TEFG7hL/nIODA9bW1ly+fJmRI0eSlJSEhYUFI0eOpH379gBq+EmFChWYPn060dHRWFlZUbFiRdavX59rkMqECRMwGAwEBQWxf/9+4H8DCJmbmzN+/HisrKy4ePEi169fx8nJiblz5z721oOVlRVNmzbl2rVrQN4hKtbW1ty4cYOxY8dy69YtateuDfxvCOHx48djbm7OhQsXSE5OJiIigvDwcEJCQjAYDGi1WqZOnUr9+vU5duwYM2bMwGg0kpmZybBhw+jVqxcbNmwgODgYKysrjEYjCxcupEGDBpw8eZJZs2bx4MEDKlasyMSJE2ncuDGXL1/G29sbLy8voqOj6devnyQKCiFM0hMLuq2tLWlpaWzcuPGx/GgAjUbDrl27iqVxIkt0dDRpaWmMGTOG9957j759+xIbG8ugQYOIiorKcdZ+5swZYmJi2LFjB2ZmZty9excg1yAVgA0bNnD69Gk1kMXX15cNGzYwcOBAAM6dO0dwcDAajQZPT08OHjxImzZtcrRPr9fz22+/MXLkSCD/EJWZM2fi4uLC8OHDuXLlCu7u7jlGGzx9+jTfffcdFStW5MiRI0RFRbFu3TqsrKzYu3cvEyZMYP369axevZqhQ4fSq1cvFEVRR4mbN28eUVFR1KhRg/T0dAwGA+np6fj7+zNnzhxatWrFwYMH8ff356effgKyrkI1atRIDaARQghT9MSCvnv37pJoh8hFdgqaVqslKCgIf39/dYjSl19+mVdffZXjx4/nGOWsdu3aZGZmMnHiRFxcXOjUqROQd5DKoUOH8PT0xMrKCgAvLy927typFvQuXbqo71k7ODiQkJCgFvQtW7Zw4MAB4uPjadu2LS1btgTyD1GJiYlh0qRJALz44ouPXd1xc3OjYsWK6nbOnDlD3759gaxxD+7duweAi4sLK1asUNuTffWoZcuWjB8/nk6dOtGxY0dq167NX3/9haWlpbqv1q1bY2lpyYULF7C2tqZcuXI5wmKEEMIUyc3CUuzhFDS9Xl+gdSpVqsT27duJiYnh4MGDBAUFER4e/o+DVB4eNMXc3BzDQ090Z99DT0xMZODAgYSEhDBo0KCnClF5VHYxh6wC7u3tzSeffPLYcoMHD8bV1ZWDBw8yY8YM2rRpw8iRI1m6dCl//PEH0dHRvP3220ydOpXnn38+331WqFAhz8GThBDCVDzVU+7i2dFqtbz66quEh4cDEBcXx5kzZ3BycsqxXFJSEikpKeq98EqVKnHp0qU8g1RatWrFli1byMjIICMjgy1bttC6deunaputrS0TJ05kxYoVpKam5hui4uzsrPbh2rVrREdH57ldV1dXIiIiuH79OpA17PCff/4JZMWw1qlTh/79+/P222/zxx9/kJmZyaVLl2jcuDF+fn60adOG06dPU69ePTIyMtR9HTp0iMzMTOrVq/dU/RRCiNJMztBNSFBQEJMnTyY4OBgLCwvmzZv32FPv165d47PPPiMzMxODwUD79u1xcnIiPDyc4OBgzMzMMBqNapDKm2++SUJCAp6enkDWA2r9+vV76rZ17NiR+vXrs379evz8/Fi6dCl9+vRBo9Gg0Wj46KOPaNCgARMnTmTs2LFs27aNWrVq0bhxY7Raba7bbNGiBSNGjGDYsGEYDAYyMjJwc3PD0dGRtWvXEhMTg6WlJVZWVkyaNAmj0cj48eO5f/8+Go2G//znP3z66adYWVmxePHiHA/FLVq0SL3NIIQQZcETw1mEKEqpqalYWFhgYWHBzZs36dOnD8HBwdSvX/9ZN+2p/C+cJYHvens+6+YUi7I8NCqU7f6V5b5B2e5fsYazCFGULl68yLhx41AUhczMTD766COTK+YP+6p7DwmpEEKUClLQRYmyt7d/JoPgFCcp5kKI0kAeihNCCCHKACnoQhQTCW0RQpQkueRewu7evUu7du3o16+fOsDKo3x8fBgyZIg6KExeXF1dsbKyoly5cqSlpdG8eXOmTJmCpaVlkbb58uXLHDhwgDfffDNHG69evYpWqyU1NRWdTsfw4cOLdL+m4L2ondzNzL1wb+3jUcKtEUL8m8kZegmLjIykSZMmbN++nfT09EJvb/HixURERLB9+3ZiY2P5+eefi6CVOV25coUNGzY8Nn3SpElERESwdu1avv76a06cOFFk+8zMzCyybT0Ng5xVCyFMlJyhl7DQ0FDGjBnDl19+ya5du+jevTuxsbEEBATw4MED7OzsSEtLU5f/+uuv2b59OwaDgXLlyjF16lReffXVx7ablpZGWlqaGmmbnJzMzJkz+eOPP4CseFVfX18gK1xn8uTJj4W8pKSkMG7cOGJjY7GwsKBevXosWrSI6dOnc/nyZXQ6HS+99BKLFy/Ose8aNWpQr149rl69SpMmTbh58yYzZ87k6tWrpKWl0bNnTz744AMAjhw5wrRp04Cs4Vt37drFl19+iZ2dHa6urvTo0YPo6Gjs7OyYOnVqniEvuYWw1KtXL9dgGsgapvarr74CoE6dOkyfPp3q1asTFhbG1q1bsba2Jj4+nsDAwFyPrxBClHZS0EvQmTNnuHPnDi1btiQxMZHQ0FC6d+/O2LFj8fHxwdPTk+PHj+dI++rduzdDhgwB4ODBg0yZMiVHSE72eO8JCQm0bdtWHaN9+fLlGI1Gtm3bRnJyMm+++SZ2dnZ06NCB0aNH069fv8dCXo4ePUpycjI7duwA/hfgMnnyZObOnasGuDzqwoUL3LlzBxcXFwDGjRvH8OHDadGihRqt2qhRI1q0aMGoUaP44osvaN68OT///DNr167NsS29Xq+GuixfvjzPkJfcQljyCqY5e/YsQUFBhIWFUaNGDRYuXMiMGTNYuHAhACdOnCAiIoI6deoU4tsVQohnSwp6Cdq8eTM6nQ6NRkPXrl2ZOXMmV65c4ezZs+h0OgCcnJzU8dsB/vzzT7788kvu3r2LRqPh4sWLObaZPd57WloaH3/8McHBwQwePJhDh93MvgUAACAASURBVA4xYcIENBqNOuTroUOHaNasGadPn8415MXe3p64uDimTZuGs7MzHTt2zLc/M2fOJCgoiPPnzzNu3DiqVavGgwcPOHz4MElJSepyycnJxMXFUb16dcqXL0/z5s0BeOONN9QrCtl69+6t/ju/kJfcQljyCqaJiYmhQ4cO1KhRA4D+/furxxvg9ddfl2IuhDB5UtBLSHp6OpGRkVhZWanvYWdkZKjjmue1zieffMJ3333Ha6+9xo0bN9T880eVK1eOjh07smfPHgYPHvyP2li7dm0iIyOJjo5m3759LFiwgG3btuW5/KRJk+jUqRNHjx5lyJAhtGrVihdffBGNRsPmzZsfezjvzJkzT2zDo+EseYW85BbC0qFDh1yDaZ7E2tr6icsIIURpJw/FlZBdu3ZRr1499u3bx+7du9m9ezdff/01W7duxc7OTi2cJ0+e5OzZs0BWQc/MzOQ///kPACEhIXlu32g08ttvv1G3bl0AWrVqRWhoKIqioNfr2bFjB61bt8435OX69euYm5vTpUsXAgICSEpK4s6dO2i12nzT3po1a8bAgQNZtGgRWq2WZs2asWrVKnX+tWvXSExMpH79+qSkpHD06FEAdu7cqcah5iavkJe8QljyCqZxcXFh7969JCYmArBx48anDqARQojSTs7QS0hoaCju7u45pjVt2lQNFAkMDGT16tXY2dnRqFEjICthzd/fnz59+lC1alW6dev22Haz76FnZGTwyiuv8OGHHwIwfPhwZsyYoe7Tw8NDPbvPK+Rl7969zJ8/H8j6A8HPz4+aNWtSvXp16tWrR69evahfv/5jD8UBDBs2jDfeeINTp04RFBTEnDlz1H1bW1sza9YsbG1tmT9/PlOnTgWykteqV69OpUqVcj1meYW81K5dO9cQlqtXr+YaTGNmZsbo0aPVZxFq167N9OnTn+r7E0KI0k7CWUSJ0uv1arpadHQ0AQEB7Nq1CzMz07pYlB2gsPDS9TL7HnpZDsCAst2/stw3KNv9k3AWYTJ++ukngoODURQFKysrgoKCTK6YP2xN9y55/k8noS1CiJIkBV2UKC8vL7y8vJ51M0qEFHMhREky3VMjIYQQQqikoAtRBCSIRQjxrMkl9zIuKiqKL7/8EkVRSEtL47XXXmP+/PlkZGSwcuVKIiMjsbCwwNzcnLp16+Lv78/LL79MWFgYs2fPplatWqSlpWFpaUnXrl157733KF++PPC/cBgrKysyMjIYMmQIffv2fcY9Llm+Ufu4m2kkos/jbyAIIURJkoJeht28eZNp06YRHh7Of/7zHxRF4fTp0wAEBASQmprKpk2bqFy5MoqisHfvXi5cuMDLL78MQOvWrdVX1P7++28mTpzIiBEjWLlypbqP7JHqzp49y/+zd+9xPd///8dv744oWizZB42Zajk1nxUp4j0jKR2RmcOQaSyMoRgZK02EHMPWhhw7UWv7fAw5dDBrc5gzKWzOxzfr9H6/fn/06/UtKofQ4fO8Xi6fy6f3+/V6v57P1yvr+X6+Do+7p6cn3bp1w9TU9IX0v7CwEB2dV/tPVKPRyI/ICYIg1CRiQK/Fbty4gY6ODq+99hoACoUCKysrLly4wM6dO0lJSZFLryoUigpLvTZq1IjQ0FC6devGmTNnaN26danl5ubmNGjQgKtXr2Jqasr58+cJDg7m9u3bFBQUMGzYMLnc7M8//0x4eDh16tTBycmJ8PBwMjMzMTAwwMLCgnHjxrFnzx66du3KqFGjCAkJ4dSpU+Tl5dGpUycCAgLQ1tZm6dKlJCYmoq+vj0Kh4IcffkBXV7fMgBmAyMhItm/fDkC7du2YMWMGBgYGREREcObMGVQqFX/99RebN2/GyMjoRf86BEEQXioxoNdilpaWtG/fnu7du9OpUyc6duyIm5sbx48f580333zmQcvIyIg333yzzAH9t99+w9jYGEtLSwoLC5k8eTLz58+nVatWqFQqvLy8sLa2xsjIiJkzZ7J582ZatGhBVFTUY+3o6+sTExMDwPTp07GxseHrr79Go9EwefJkYmJi6NWrF1FRUezfv586deqgUqmoU6cOu3fvLjNgJiUlhe3bt7Np0yYMDAyYOnUqy5cv54svvgCKKvTFxsbSsGHDZz3MgiAI1YIY0GsxLS0tli9fzunTp/n111/ZuXMna9euJSAgoNR6Z8+eZdKkSeTm5tK1a1dmzJhR7jYfrUPk7++PJEnk5OSwePFi9PT0OHv2LOfOnePzzz+X1ysoKOD8+fNoaWlhZWUll6j18vIiJCSk1DY9PDzkn3ft2sWRI0f47rvvAMjNzcXU1JT69etjZmbGlClTcHBwoHv37hgaGpYbMJOWloazs7Nc1GbAgAEEBwfL7XTr1k0M5oIg1GhiQP8fYG5ujrm5OYMHD8bZ2ZlLly6RnZ3NvXv3aNCgAW+//TYJCQmsX7+eY8eOlbudu3fvkpOTUyoNrvgaenJyMgEBAXTs2BFJkjA2NpZDaEr65ZdfntjfRwNali9fTvPmzR9bb8uWLWRmZpKeno6npydr1qzB0tLymQJmiomAFkEQajrx2FotdvXqVX7//Xf59ZUrV7h16xYODg68//77zJgxg/v378vLHz58WO62bt26RWBgIHZ2dvJNcyX16dMHe3t7Vq1aRcuWLalTpw7x8fHy8nPnzqFSqejQoQPHjx8nJycH4IlpaEqlksjISNT//7GwW7ducfHiRVQqFbdu3cLW1hZ/f3/Mzc05c+ZMuQEzdnZ2JCcno1KpkCSJbdu2iYAWQRBqFTFDr8UKCwuJiIjg8uXL1KlTB41Gw4QJE7CysiIkJITly5fj7e2Njo4ODRo0oHHjxowePVr+fGpqKu7u7uTm5qKnp8cHH3yAr69vue1NmjQJT09PfH19WblyJcHBwaxduxaNRkOjRo1YtGgRr7/+OkFBQfj6+lK3bl26d++Orq4udevWLXObgYGBzJ8/X86R19XVJTAwEF1dXT777DNyc3ORJAkrKyt69epFenp6mQEzpqamnDp1Ch8fHwDatm2Ln5/fCzzagiAIVUuEswivXMmAlpiYGLZt28bGjRuruFfPpjhAYfHFW7X2OfTaHIABtXv/avO+Qe3ePxHOItQo69at46effkKtVmNkZMTcuXOrukvPbXWfbujr64sgFkEQqpwY0IVXzs/Pr9ad7haDuSAIVU3cFCcIgiAItYAY0AWhEgrUmqrugiAIAlALBvT8/HzmzZtHz549cXJywt3dnZ07d5a7fmxsLFlZWaVe+/v7v4quPrdLly7RqVMn+bVSqcTBwUF+lAuK9sPCwoL169c/cXuPHoPyZGRklJtdfvToUSZNmvQUva+YhYUFrq6u9OvXD3d3d9LS0iq9zaexePFiuZpcRkYG+/fvf67t6GrX+P+EBEGoJWr8NfSgoCAePnxIUlIS+vr6nD59mlGjRmFkZISNjU2pddVqNXFxcRgbG9OyZcsq6vGL0bhxY/bv34+joyNQ9Dx3mzZtnuqzL+IYtGvXTn48rLKKy7Hu3LmTCRMmkJaWhpbW0w2UzxvgMn78ePnngwcP8vDhQxwcHJ55O4IgCNVFjZ5eXL58meTkZIKCguTb+83NzRkzZgxLly4lNjaW4cOHM3bsWFxcXFi3bh3Hjh1j7ty5uLm5kZqaChQ9RjVhwgT69u2Lj48P169fB4q+AISGhuLi4oKLiwuhoaHyrPjq1asMGzaMvn37MmbMGMaMGSPPjm/cuMHYsWNxdXXF1dW1VIEVpVLJ4sWLGThwIEqlstSMOjQ0FC8vL/r168ewYcO4fPlyufvu4eFBbGwsABcvXuThw4elKrilpaUxcOBA3N3dcXV1JSkpCSh6TKysY7Bq1Sp5puzj44NGo5GPwcyZM+Vl586dA0rP3ovPIISHh+Pu7k7v3r05dOiQ3Jf169fTq1cvvLy8WLJkSamzDSXZ29tz584d7ty5g1Kp5PTp06WOW/FrpVJJWFgY3t7ezJw5k/PnzzNw4ED69euHi4sLa9euBWDatGmljm/J18U/nzp1ik2bNhEfH4+bmxuRkZHlHnNBEITqrEbP0E+fPo2ZmZmcJlbM2tqaxYsX4+bmxuHDh0lISMDMzAwoKj06YsQIevToARSdfj569Cjbt2/njTfeYMaMGaxfv56JEyeyefNmTpw4IQ+cvr6+bN68mQ8//JC5c+fSqVMnPv30Uy5fvoyrq6s8w5s7dy6tW7dm2bJlXLt2DU9PT6ysrOQBNzc3l82bN3Pp0iVcXV3x8PDAwMAAX19fpk6dCsDWrVsJCwsjPDy8zH23tbUlOjqau3fvEhcXh7u7O3/++ae83MrKiujoaLS1tblx4waenp44ODjg5eVFfHx8qWMQFxfHrl272LhxI4aGhty+fVueIZ89e5aQkBC++uorVqxYwfLly8ucmd+5cwdra2smTpzI9u3bCQsLY9OmTZw8eZJVq1aRkJBAw4YNK3xELTk5mSZNmjxVTXWVSsW2bdvk461UKvnkk0+A/wtkeRoWFhb4+Pjw8OFD+dgLgiDURDV6hv40NXE6duwoD+YVrfPGG28A0KFDB7ksaVpaGh4eHujp6aGnp4enp6d8jTcjI0OOA23atCl2dnby9tLS0uSKZI0bN8bR0ZGMjAx5ubOzMwDNmjWjQYMGXLlyBYC9e/cyYMAAeZZZnF1eFoVCQZ8+fUhKSiIpKQkXF5dSy2/duoW/vz8uLi6MHDmSu3fvlnvdfPfu3QwaNEgu9mJsbCwva9myJVZWVkDRF6WLFy+WuY169erJXxBKrnfw4EEcHR3lQdrb2/uxz/r4+ODm5kZSUhLLli0rd59Lcnd3l3+2sbFh69atLFq0iLS0NDkSVhAE4X9JjZ6hm5ubk5OTw507d0rN0v/44w8sLCyApwvdKFmNR1tbu9TNZi9DWe1dvnyZkJAQtm3bRvPmzcnMzGTy5MkVbsfDw4P+/ftjY2NTahCGonsLlEolS5cuRaFQ0Lt3b/Ly8p65r3p6evLPWlpaFBYWVmq9shRfQy9JW1tbPu0PPNb3kgEuvXv3xtramgMHDrB69WpiYmIICwt74jYEQRBqkxo9Q2/WrBlOTk4EBQXJf6xPnz7NypUrGTduXJmfMTAwKBVIUhE7Ozvi4+MpKCigoKCA+Ph4OdDD1tZWDhb5+++/SU9PL/W5LVu2AHD9+nVSUlLo3LlzhW2pVCp0dXUxMTFBo9GwadOmJ/avefPmTJw4kU8//fSxZffv36dp06YoFAoOHDhAdna2vOzRY9CjRw82btyISqUC4Pbt209s+2nZ2tqyd+9ebt26BTw5jKWYmZkZR48eBYrOeNy4caPcdbOzszExMcHT05OxY8fKn3vzzTfln69du1bqLElJhoaGT/1vQhAEobqq0TN0gFmzZrFw4UKcnZ3R1dVFX1+f6dOnY2try6VLlx5bf+DAgcybN4+1a9c+8ZrpwIEDycnJkfO5HRwcGDBgAADTp09nypQp7Nixg2bNmtG+fXv5lPWMGTPkG8kAJk+eTOvWrStsy8LCAicnJ5ydnTE2NsbR0bHUjWUV9bEskyZNYvbs2URERNCuXTv5jEVZx8Dd3Z2rV68ycOBAdHR0qFevHhs2bHhi20/D0tKSUaNG4ePjg6GhIZ07d6Z+/fpP/Nz48ePlG9c6d+7Mv/71r3LXTU5OZseOHejq6qJQKAgMDASgf//++Pv74+zsTIsWLWjfvn2Zn+/Zs6d8U1zfvn1LBdQIgiDUFCKc5Tnl5uaio6ODjo4O165dw9vbm6ioKN56662q7lq1UzKMJSIiguzsbMLCwqq4V5VTmQCFmqI2B2BA7d6/2rxvULv3T4SzVIELFy4wdepUJEmisLCQcePGicG8HAsWLCAzM5OCggKaN2/OV199VdVdemEK1Bpq53AuCEJNIwb052RpaUlCQkJVd6NGmDVrVlV34aURleIEQaguxF8jQRAEQagFxIAuCC9QvghrEQShiohT7rWQUqlET08PfX198vLyeO+995g1axa6urqvpP3FixfTunVruYDO85g2bRqpqakYGxuTm5uLg4MD06dPR0tLi6tXrzJ58mTWrVtX5mctLCzIzMx8qhoEleX30xHulnjkPsbLpvyVBUEQXiIxoNdSS5YswdzcHLVazeDBg/nvf/9bqQH2WZQMPqmM0aNH89FHH6FSqfDw8ODf//43zs7OmJqaljuYC4Ig/K8Sp9xruby8PPLy8mjQoEG5gS1Hjhx5rHRsv379yMzMBIqKwfTv3x9PT0+GDh3K+fPnAcjMzMTDw0N+fjsxMREoHYJSXpsAQ4YMITQ0lEGDBvH++++X+yiboaEhbdq04a+//gIej5P9z3/+g5OTE25ubo+Vjv3555/lWN2VK1diYWHBgwcPADh8+DBDhgzB09MTT09P9uzZ81zHWBAEoToQM/Rayt/fH319fXJycnBwcMDBwYG7d++WGdjSvn176tWrx8GDB7G1teXQoUNoaWnRsWNHDh06RHJyMhs2bEBPT4+UlBQCAwPZtGkTq1evZuTIkbi4uCBJUpnV1soLiTEyMgKKquxt2LCBBw8e0LNnT7y9vWnRokWpbdy8eZNTp07x2WefPbb9Gzdu8OWXX7Jx40beeustVq9eXWrZzJkz2bx5My1atCAqKkpedu/ePWbNmkVkZCSNGzeWawkkJiaKWvCCINRIYkCvpYpPuefl5fHZZ58RFRWFo6MjgYGBZGdno62tLQe2WFtbM2TIEKKjo7G1tWXDhg0MHjwYgF27dnHy5En69+8PFAXi3Lt3D4BOnTqxYsUKcnJysLe3p0OHDo/149atW+W2CeDk5ISWlhb169enVatW5OTkyAN6ZGQkmzdvJisri0GDBtGqVavHtn/48GGsrKzkGgADBw6UZ/rFy4q35+XlRUhICAC///47ly5dwtfXV96WQqEgOzubdu3aVfbwC4IgvHJiQK/l9PX16d69O3v27GH37t3lBrY4OTmxcOFCjh8/TkZGBsHBwUDRAO7l5VXmdfHhw4ejVCpJTU1lzpw52NvbM3HixFLrPCkkpqJgnOJr6OfPn2fQoEE4ODjg6Oj4Qo6LJElYWFi8sBK3giAIVU1cQ6/lNBoNv/76Ky1atKgwsEVXVxcvLy/8/PxwdXWlbt26QNEd8wkJCXLEq1qt5tixYwBkZWVhZmaGj48PQ4cOlYNQSqqozaf11ltv4e/vT3h4+GORudbW1hw/fpwLFy4ARTnyxTp06MDx48flONySwTDvvvsu2dnZpUJ1jhw58lSRvIIgCNWRmKHXUsXX0AsKCmjdujVjx47l2LFj5Qa2QFGYydKlSxk0aJD8no2NDRMmTMDPzw+1Wk1BQQFOTk60bduWdevWkZGRga6uLnp6esyYMeOxflQUEvMsBg4cyA8//MAvv/yCpaWl/H6jRo2YM2cOY8aMoU6dOvTq1Ute9vrrrxMUFISvry9169ale/fu6OrqUrduXbS0tFi+fDnz588nODhYLku7cuVKFArFc/VREAShKolwFkGWkJBAUlISkZGRVd2VF6ZkMExMTAzbtm1j48aNld5ucYDCskuaWvscem0OwIDavX+1ed+gdu+fCGcRKm3kyJHk5OSwYsWKqu7KC7Vu3Tp++ukn1Go1RkZGzJ0794Vuf4VT+1L/0eWrNeiJ+u6CIFQBMaALAKxdu7aqu/BS+Pn54efn98raE4O5IAhVRfz1EYQXTNRzFwShKogZuiBUwtifznGvsPT34i1eluWsLQiC8PKIGbqAUqnk9OnTr7zdIUOGsHv37pe2/X379uHj40OvXr3w9PTkk08+4dSpU4waNeqxG+MkSeL999/n4MGDL60/giAIL5OYoQs1VmFhITo6Zf8T3r9/P9OnT2fZsmVy5bcTJ05w/fp1vLy8+O6770o9npeRkYGWlhY2NrXnLnVBEP63iAFdKFNoaCgHDx6koKAAY2NjgoODadq0KTdv3mTSpEncvHkTADs7OwIDA8nMzGTOnDloNBoKCwvx8/PDxcWFGzduMGvWLLm4y8iRI3F3d6+w7W+//ZakpCTUajX6+voEBQXxzjvvAEXRqOPGjWPPnj107dqVbt26ldnusmXL+PTTT0uVcS3eRn5+PkFBQZw7d04uJxsbG4unp6d4Bl0QhBpLDOhCmXx9fZk6dSpQVH0tLCyM8PBwduzYgZmZmRx0cvfuXYByg1rmzp1L69atWbZsGdeuXcPT0xMrKyvMzc3Lbdvd3Z0RI0YAkJqayqxZs9iyZYu8XF9fn5iYGKDoLvay2j1+/DgzZ84sc/t6enq4uroSExPDlClTUKlU7Ny5kx9//LESR0wQBKFqiQFdKNPevXuJjo7m4cOHFBb+X+WUDh06EBUVRWhoKLa2tjg4OADlB7WkpaUxbdo0ABo3boyjoyMZGRkVDujHjh1j1apV3L17F4VCIZd1Lebh4SH//DQBMWXx9vZm1KhRTJo0ieTkZDp27EiTJk2e6rOCIAjVkbgpTnjM5cuXCQkJYcGCBSQmJhIcHEx+fj5QVAM9Li6Otm3bkpCQwNChQ4GioJYVK1bQsGFD5syZQ3h4+HO1nZ+fz/jx4wkMDCQxMZE1a9bIbRerV6+e/HN57VpZWXHkyJFy27G0tKRx48bs3buXmJgYvLy8nqu/giAI1YWYoQuPUalU6OrqYmJigkajYdOmTfKyixcv0qRJE/r27ct7773HBx98gEajITs7m5YtW2JmZka9evWIj48Hiq6xb9myBX9/f65fv05KSgrDhw8vt+38/HwKCwt54403AIiOjq6wr1lZWWW26+fnx5dffknbtm1p06YNACdPnuTGjRvyWQUvLy8iIiL466+/eP/995/7eAmCIFQHYkAXAPj444/R1taWXzs5OeHs7IyxsTGOjo4cOnQIgIMHDxIVFYWWlhYajYbZs2ejpaVVblDLjBkzmDlzJq6urgBMnjyZ1q1by+1MmzatVOnUyMhI/P398fb25rXXXqN3794V9ru8drt168ZXX33FV199xZ07d9DR0aFZs2ZMmjRJ/qyLiwuhoaEMGDAAPT29Sh5BQRCEqiXCWQThORQHKKy4VLfWFpapzQEYULv3rzbvG9Tu/RPhLIJQRZY5tXrsPzoR0CIIQlUQf3UE4QUTg7kgCFVB/OURhEoqUIurVoIgVD0xoAtCJQT9/De62qK6nCAIVU9cQ68m+vfvT35+PgUFBVy4cEG+E9zKyoqQkJCX2nZsbCx79uxhyZIl/PLLLxw6dEiuEleejIwMRo8eTYsWLVCr1bz22mvMnj1bLqUqCIIgvFpiQK8mtm7dCsClS5fw8vIiISGhSvrx/vvvP/Uz2a1atSI2NhaA+fPnExISwpo1a15YX9RqdalH6V6FigJfBEEQqjPxl6uaWrBgAUZGRowaNYoff/yRzz//nAMHDtCoUSN8fX0ZNmwYDg4OpKSksGLFCvLz89HV1SUgIABra2sA4uLiiI6ORq1WY2hoSFBQEG+99Rb5+fnMnTuX9PR0jI2N5dASKD1br2gbj7K1tWXPnj3y64ranjNnDgcPHqRhw4a888473LhxgyVLlhAbG8v27dsxMDAgOzub+fPnk5+fT1hYGA8ePADA39+f7t27v9CQGKVSibOzM+np6ZibmxMcHPzif6GCIAgvmRjQqyk7OzvWrl3LqFGjSE9Px9ramvT0dHr16sWRI0f497//TU5ODsuXL2ft2rUYGhpy5swZfH192bNnD4cOHSI5OZkNGzagp6dHSkoKgYGBbNq0ic2bN3Pp0iWSkpIoLCxk8ODBNGvW7LE+VLSNkjQaDb/88gvOzs5P/NzmzZv566+/5DS1IUOGlKqhfvjwYRISEjAzM+PevXsMHTqUyMhIGjduzLVr1/D29iYxMfGFh8SoVCq2bdv2wn+PgiAIr4oY0Kupjh07MnHiRPLz88nMzGTKlCn8/PPPmJqa0rp1a+rWrcu+ffvIyclh8ODB8ucKCwu5ceMGu3bt4uTJk/Tv3x8ASZK4d+8eUHT9293dHV1dXXR1denXrx+ZmZmP9aGibQCcO3cONzc3rl69iqGhoXzZ4Eltu7m5oaOjg46ODn379uW3334rtd9mZmYA/P7771y6dAlfX195uUKhIDs7+4WHxDwp0lUQBKG6EwN6NVWnTh3Mzc1JSkrCxMSEzp07ExoaSpMmTejcubO8XteuXfnmm28e+7wkSXh5eTF+/Pjn7sOTtlF8DT0/P5/PP/+coKAgFi9eXKm2DQwMSrVvYWHBhg0bylw3Li6O1NRUEhISiIyMZOPGjQwfPhylUklqaipz5szB3t6eiRMnPrHdkoEvgiAINZF4bK0as7OzIyIiAjs7O/T09GjSpAlxcXHY2dkBYG9vz759+zhz5oz8meKEMaVSSUJCAleuXAGKbjA7duwYAJ07dyYhIYHCwkJyc3NJTEwss/2KtlGSnp4eQUFB7Nu3j+PHj1f4OVtbW3bs2EFhYSF5eXkkJyeXu//vvvsu2dnZpKenl9o/SZK4ePEihoaG9O3bl4CAAP788080Gg1ZWVmYmZnh4+PD0KFDOXr0qHwsizPVi0NiSn4xEgRBqOnEDL0as7OzY/HixfLA07lzZzIzM2nfvj0ALVq0YP78+UyfPp3c3FwKCgro2LEj7du3x8bGhgkTJuDn54daraagoAAnJyfatm3LgAEDOHXqlBy+0q5dO/nmspIq2sajXn/9dUaMGMHSpUtZvnx5uZ/z8fHh5MmT9O3bF2Nj4zJvsCtmZGTE8uXLmT9/PsHBwRQUFNC8eXNWrlz5wkNiBEEQajoRziK8ciqVCkNDQ/Lz8/Hz88PJyUm+3l5TFAcobLvciJB+Laq6Oy9FbQ7AgNq9f7V536B2758IZxFqlI8//pj8/Hzy8vLo0qULHh4eVd2l5xbU+w0K1JKoFicIQpUTA7rwyhXfDV9biMFcEITqQNwUZjGCHAAAIABJREFUJwiVoBbBLIIgVBNiQBeEStAWs3NBEKqJGjWgFxQUEBERQe/evenbty/9+vXD39+fs2fPVnXXiI2Nxd/fH4CjR48yadKkF7btiIgIQkNDH2unpF9++UVe51VRKpU4OTnRr18/+vTpU+tOpQuCINQkNeoaekBAALm5uWzdupUGDRogSRIpKSlkZWXx9ttvv9S2nyW0o127dixYsOCl9udRzxKqUlklj8WSJUswNzfn9OnTeHp60q1bN0xNTV94O6+KRqNBoVCgUIiZtyAINUuNGdAvXLjAzp07SUlJoUGDBkBRGdDu3bsDkJ+fT3h4OL/++iv5+flYWFgQFBSEgYEB06ZNQ09PjwsXLnDlyhWsra0JDQ1FoVCgUqkICQnh1KlT5OXl0alTJwICAtDW1mbIkCFYWlpy+PBhjIyMWLFiBZ988gm3b98mLy+P9u3bM3v2bPT09Er1NSMjg9DQUGJjY9m6dSvr168HiiqfnTp1ioSEBCwtLYmMjOQ///kParUaU1NT5syZg4mJCffv32f69OmcPn0aExMTmjRpwuuvv17h8SkZqpKRkUFwcDAdOnTg999/R6FQEB4eLkeblheccurUKWbPns0///xDXl4eAwYMYPjw4QBMmzYNbW1tsrKyePDgwWNpcObm5jRo0ICrV69iamrK+fPnCQ4O5vbt2xQUFDBs2DC8vLwA+PnnnwkPD6dOnTo4OTkRHh5OZmYmBgYGWFhYMG7cOPbs2UPXrl0ZNWpUub+fpUuXkpiYiL6+PgqFgh9++AFdXV2mTp3K2bNn0dHRoWXLlixevBiAyMhItm/fDhR96ZoxYwYGBgZERERw5swZVCoVf/31F5s3b8bIyOg5/6UKgiBUjRozoB8/fpw333yz3D+0a9asoX79+nLAxvz584mMjJTLfp45c4aoqCgUCgUeHh6kpqZib29PSEgINjY2fP3112g0GiZPnkxMTAwDBgwA4OLFi0RHR6Ojo4MkSYSFhWFsbIwkSUydOpWYmBgGDRpUbr/79+8vP2MdHh7Om2++ibm5OQkJCVy8eJEtW7agpaVFdHQ08+bNY8GCBSxbtgwDAwN++uknbt26haenJ3369Hmm43X27FlCQkL46quvWLFiBcuXL2fBggUVBqc0bdqUqKgo9PT0ePDgAf3796dr167yF4ETJ06wfv36Msuk/vbbbxgbG2NpaUlhYSGTJ09m/vz5tGrVCpVKhZeXF9bW1hgZGTFz5kw2b95MixYt5HCVkvT19YmJiQFg+vTpZf5+evXqRVRUFPv376dOnTqoVCrq1KnD7t27efDgAT/++CPwf6EtKSkpbN++nU2bNmFgYMDUqVNZvnw5X3zxBVBUgS42NpaGDRs+03EWBEGoLmrMgP6os2fPMmnSJHJzc+natSt//PEHKpWKn3/+GSiasVtaWsrr9+zZU35I38rKSg7v2LVrF0eOHOG7774DIDc3t9QpY1dXV/m0r0aj4dtvv2Xv3r1oNBru3r1LnTp1nqq/W7duJT09ne+//x4tLS127drFsWPH5Gewi2fLUDTDL65w1rBhQz744INnPj4tW7bEysoKAGtra3bv3g1UHJySm5tLUFAQp06dQqFQcO3aNU6ePCkP6E5OTo8N5v7+/kiSRE5ODosXL0ZPT4+zZ89y7tw5Pv/8c3m9goICzp8/j5aWFlZWVrRo0QIALy8vQkJCSm2z5HPp5f1+6tevj5mZGVOmTMHBwYHu3btjaGiIpaUl586dY/bs2dja2spncNLS0nB2dpaP8YABA0rFpHbr1k0M5oIg1Gg1ZkC3srIiOzube/fu0aBBA95++20SEhJYv349x44dQ5IkZs2aJdc5f1TJijva2tqo1WqgaEBbvnw5zZs3L/NzJQewHTt28Ntvv7FhwwYMDQ1ZuXIlFy5ceGLf9+/fz9q1a4mOjpa/AEiShJ+fH97e3k97CJ5JycsAWlpaFBYWyu2WF5yycOFCTExMmDdvHjo6OowYMYK8vDx5eVkz8+Jr6MnJyQQEBNCxY0ckScLY2Pix0/JQdPPek5Rsp6Lfz5YtW8jMzCQ9PR1PT0/WrFmDpaUliYmJpKens3fvXsLDw9mxY8cT2ywZCiMIglAT1Zi73Fu0aMH777/PjBkz5IxrgIcPHwJFd1xHRUWRm5sLFJUXPXfu3BO3q1QqiYyMlAf4W7ducfHixTLXvX//PsbGxhgaGnL//v1yQ01KOnXqFDNnzmTFihWlZoBKpZLo6Gj5lHB+fj4nT54Eimq2x8bGAnD79m127tz5xHaeVkXBKffv36dJkybo6Ohw+vRpDh069NTb7dOnD/b29qxatYqWLVtSp04d4uPj5eXnzp1DpVLRoUMHjh8/Tk5ODlB0Pf9J/S3r96NSqbh16xa2trb4+/tjbm7OmTNnuHLlCtra2vTs2ZOAgABu3brFnTt3sLOzIzk5GZVKhSRJbNu2jS5dujzTsRMEQajOaswMHSAkJITly5fj7e2Njo4ODRo0oHHjxowePRpzc3OWLl2Kt7e3fJfyuHHj5NPF5QkMDGT+/Pm4ubmhUCjQ1dUlMDCwzBmhu7s7v/zyC05OTjRq1Ih///vfpWawZYmKiuLhw4dMmDBBfi88PBx3d3fu3LnDRx99BBTNRAcNGoSlpSWffvopgYGBODk5YWJiwnvvvVdqmykpKXTr1k1+7enpKWeIP0lFgSt+fn5MmTKFbdu20bJlS2xsbJ5qm8UmTZqEp6cnvr6+rFy5kuDgYNauXYtGo6FRo0YsWrSI119/naCgIHx9falbty7du3dHV1eXunXrlrnN8n4/urq6fPbZZ+Tm5iJJElZWVvTq1Yv09HT5CQONRsPo0aMxNTXF1NSUU6dO4ePjAyDvryAIQm0hwlmEV644nAUgJiaGbdu2sXHjxiru1bOpTIBCTVGbAzCgdu9fbd43qN37J8JZhBpl3bp1/PTTT6jVaoyMjJg7d25Vd+m5idKvgiBUF2JAF145Pz+/WnO6W5R+FQShuqgxN8UJQnUkZuiCIFQXYkAXhEoQM3RBEKoLccq9mikoKGDlypUkJiaio6ODtrY2LVq0wN/f/6XXq38WsbGxBAcH07RpUwoKCmjWrBlff/01JiYmVd01QRCE/0lihl7NBAQEcOrUKbZu3UpSUhIJCQl4enqSlZVV1V17TJcuXUhISCApKQkDAwOWLl36wrat0WioigcwigvwCIIg1DRihl6NPCmAJi0tjUWLFpGXl4darWbMmDH07dsXgCFDhtCmTRuOHDnC5cuXGTp0KKampqxfv55r167xxRdfyPXgLSwsmDBhAjt37uTOnTvMnTuX1NRU9u3bR2FhIYsXL6ZVq1Zcv36dzz//nAcPHpCXl4ejoyNTpkx5rN8KhQIbGxv27Nkjv1dR8ExgYCBnzpyRnw9v1KgRU6dOLTMk5Y8//mDFihXk5+ejq6tLQEAA1tbWnD9/noCAAP755x80Gg0eHh6MHDmSnTt3snjxYrS0tFCr1Xz55Zd06tSJ7OxsZs6cya1bt9DR0WHixInys/yPBsKUrBkgCIJQY0hCtZGUlCT169ev3OV37tyRCgsLJUmSpOvXr0tdu3aV7ty5I0mSJH300UfS+PHjJbVaLV25ckVq3769tHDhQkmSJOnw4cNS165d5e2Ym5tL69evlyRJkn788UfJ2tpa2rVrlyRJkhQZGSlNmjRJkiRJys3NlVQqlSRJkpSfny8NGTJESklJkSRJkmJiYqTPPvtMkiRJysvLkz755BMpLi5OkiRJio+Pl2bMmCGp1WpJkiRpw4YN0ueffy5JkiSFhIRIgYGBkiRJ0u3bt6UePXpI8+bNkyRJkpYsWSI5OjpKN2/elCRJkrKzs6UBAwZI9+/flyRJkk6fPi05OjpKkiRJc+bMkVauXFnq2EiSJLm6ukqZmZmSJElSYWGh/Flvb29py5YtkiRJ0pkzZyRbW1u5HXNzc2nVqlXl/2LKkJubKx06dEjKzc19ps/VJIcOHarqLrxUtXn/avO+SVLt3r/K/G0RM/Rq7NEAmsGDBxMYGEh2djba2trcvXuXrKwsrK2tgaLwFC0tLUxNTXnttdfo2bMnAG3atOHq1avk5eXJhQqKZ+tt2rQBoEePHkBRBbX//ve/QFFZ2G+++Ybff/8dSZK4ceMGJ0+elGe2qampuLm5cenSJVq1aiVv82mDZ0r2sVjJkJR9+/aRk5PD4MGD5eWFhYXcuHEDGxsb5s+fzz///EOnTp3o3LkzUFQ2NyQkhF69etGtWzfMzc1RqVScOHFCjm99++23eeedd/jjjz9QKpVA6UAYQRCEmkgM6NXIkwJogoKCUCqVLF26FIVCQe/evUuVnn00gKb4tba2NlA0GBa/V/z/Wlpa5Qa5fPfdd9y7d4+tW7eir6/Pl19+Waq9Ll26sGTJElQqFSNHjmTx4sVMmTKlUsEzj4akdO3alW+++eax9Xr37o21tTUHDhxg9erVxMTEEBYWRmBgIKdOnSI9PZ3x48fz8ccf4+zs/MR2ywqeEQRBqEnETXHVyJMCaO7fv0/Tpk1RKBQcOHCA7Ozsl9qf+/fvY2Jigr6+PlevXi03Kc3Q0JDZs2ezceNGrl27VmHwjK2trZzCdu/evQrT1+zt7dm3bx9nzpyR3zty5AgA2dnZmJiY4OnpydixYzl69CgA58+fx8LCgmHDhtGvXz+OHj2KoaEh77zzjhwEc+7cOU6ePCmf2RAEQagNxAy9mqkogObOnTvMnj2biIgI2rVrh4WFxUvty5AhQxg/fjwuLi6YmpqWG00LYGlpiZOTE6tXr2b69OnlBs+MHTuWgIAAOXimbdu28un4R7Vo0YL58+czffp0cnNzKSgooGPHjrRv357k5GR27NiBrq4uCoWCwMBAABYsWCBfkmjQoAFff/01AGFhYcycOZOoqCh0dHT45ptvRP65IAi1ighnEV6pgoICNBoN+vr6qFQqBg0aREBAQI2LMhXhLDVfbd6/2rxvULv3T4SzCDXGvXv38PX1Ra1Wk5eXh4uLS40bzEsSpV8FQaguxIAuvFKNGjUiNja2qrvxwojSr4IgVBfipjhBqCQxSxcEoToQA7ogVMJ/f7wtZumCIFQLYkAXHqNUKjl9+vQrb3fIkCHs3r37pWw7IiKC0NBQ+XVWVhZjx47l/fffx9PTEx8fH3bu3PlS2hYEQXgVxDV0odYoLCxER+fJ/6SvXbvGRx99xBdffMGyZcsAuH79OgcOHHjZXRQEQXhpxIAuPJXQ0FAOHjxIQUEBxsbGcnTqzZs3mTRpEjdv3gTAzs6OwMBAMjMzmTNnDhqNhsLCQvz8/HBxceHGjRvMmjWLnJwcAEaOHIm7u3uFbX/77bckJSWhVqvR19cnKCiId955B3g8WKVbt25ltlvShg0b6NSpU6l2TUxMntgPQRCE6kwM6MJT8fX1ZerUqQBs3bqVsLAwwsPD2bFjB2ZmZkRFRQHI1eFWr17NyJEjcXFxQZIkufLd3Llzad26NcuWLePatWt4enpiZWWFubl5uW27u7szYsQIoKh+/KxZs9iyZYu8XF9fn5iYGAD8/PzKbLek48ePY29vX/mDIgiCUI2IAV14Knv37iU6OpqHDx+Wygzv0KEDUVFRhIaGYmtri4ODAwCdOnVixYoV5OTkYG9vT4cOHYCiCNhp06YB0LhxYxwdHcnIyKhwQD927BirVq3i7t27KBQKLly4UGp5yWCV8toVBEGo7cRNccITXb58mZCQEBYsWEBiYiLBwcHk5+cD8O677xIXF0fbtm1JSEhg6NChAAwfPpwVK1bQsGFD5syZQ3h4+HO1nZ+fz/jx4wkMDCQxMZE1a9bIbRcrGazyNO1aWVnJtd8FQRBqCzGgC0+kUqnQ1dXFxMQEjUbDpk2b5GUXL17E0NCQvn37EhAQwJ9//olGoyErKwszMzN8fHwYOnSoPIDa2dnJp8uvX79OSkqKHH1alvz8fAoLC3njjTcAiI6OrrCv5bVb0ocffkhaWho7duyQ37t58ybx8fFPf1AEQRCqGXHKXSjTxx9/LMeuQlHWurOzM8bGxjg6OnLo0CEADh48SFRUFFpaWmg0GmbPno2Wlhbr1q0jIyMDXV1d9PT05Az0GTNmMHPmTFxdXQGYPHkyrVu3ltuZNm1aqfrFkZGR+Pv74+3tzWuvvUbv3r0r7Hd57ZZkamrKunXrCAsLY9GiRdSrV4969erh6+v7/AdMEAShiolwFkF4DsUBCn/nNMXFo0lVd+elqM0BGFC796827xvU7v2rTDiLOOUuCJXwgbOxKP0qCEK1IAZ0QagkUfpVEITqQAzogvACiFm6IAhVTQzoglAJqQm3+CX6upilC4JQ5arFgJ6fn8+8efPo2bMnTk5OuLu7VxiUERsbS1ZWVqnX/v7+r6Krz+3SpUt06tRJfq1UKnFwcECtVsvvxcbGYmFhwfr165+4vUePQXkyMjLw9PQsc9nRo0eZNGnSU/S+YhYWFri6utKvXz/c3d1JS0ur9DafxuLFi/nxxx+Bov3cv39/qeUqlYo5c+bwwQcf4ObmhqenJytXruTw4cN07dq11LGHomM6ZMiQV9J3QRCEF61aPLYWFBTEw4cPSUpKQl9fn9OnTzNq1CiMjIywsbEpta5arSYuLg5jY2NatmxZRT1+MRo3bsz+/ftxdHQEIC4ujjZt2jzVZ1/EMWjXrh0LFix47s+XtGnTJgwMDNi5cycTJkwgLS0NLa2n+774tKEqjxo/frz888GDB3n48KFcqU6SJEaPHo2lpSVJSUno6emRl5fHli1b6NChA0ZGRhw4cIBu3brJ24iNjcXb2/uZ+yEIglAdVPmAfvnyZZKTk9m9e7d8i765uTljxoxh6dKluLm5sX37dgwMDMjOzsbb25tjx44xd+5cFi1aJNcXV6lUTJgwgTNnzlC/fn0iIiIwMTFBrVYTFhbGvn37AOjatSuTJ09GW1ubq1evMmXKFG7cuEHz5s0BcHBw4KOPPqowRESpVOLm5kZqairXr19nxIgRfPTRR0D5ISZl8fDwIDY2FkdHRy5evMjDhw9LlUBNS0tj0aJF5OXloVarGTNmDH379iUmJuaxY9ClSxdWrVpFYmIiCoWCevXqyUVY1Go1M2fO5Pfff0ehUBAeHk6rVq3IyMggNDSU2NhYLl26hJeXFz4+PqSkpPDPP//w9ddf89577wGwfv16fvjhB+rXr4+joyMbNmwgIyPjsX2yt7fnzp073LlzB29vb1auXCnvk1KplF8rlUqcnZ1JT0/H3NycUaNGERAQwD///INGo8HDw4ORI0cybdo02rZtKx/fkq+Lf7axsWHTpk1oNBpSU1Pp27cvbdu25a+//uL7779HV1cXKKr5XjwD9/T0JDY2Vh7Qc3JyOHnyJE5OTs/4L1gQBKF6qPJT7qdPn8bMzIzXXnut1PvW1tacPHkSgMOHDzN16lQSExMZPnw4bdu2ZcaMGSQkJNClSxeg6PTx1KlTSUpK4u2335ZPW2/evJkTJ04QGxtLbGwsx48fZ/PmzUBRUEinTp1ISkriyy+/5ODBg3L7xSEiO3bsYO3atYSFhZXKCM/NzWXz5s388MMPLFiwgAcPHgBFISYxMTFs374dFxcXwsLCyt13W1tbTp8+zd27d4mLi3ss7cvKyoro6Gji4+P57rvvCA0N5e7du3h5eT12DOLi4ti1axcbN25k+/btrFixQp4hnz17Fh8fH3bs2EGfPn1Yvnx5mf25c+cO1tbWxMfHM3bsWLnvJ0+eZNWqVWzatImYmBju3btX7j4lJyfTpEkTGjZsWO46xVQqFdu2bSM4OJjo6GiUSiXbt28nMTHxmWbKFhYW+Pj44O7uTkJCAqNHj+bPP//EyspKHswf5ebmRkpKihwmExsbi7OzM3Xq1HnqdgVBEKqTKh/Qn6auTceOHTEzM3viOsXlQTt06CDPrNPS0vDw8EBPTw89PT08PT3la7wZGRl4eXkB0LRpU+zs7OTtpaWl4ePjA5QOESnm7OwMQLNmzWjQoAFXrlwBikJMBgwYgIuLC2vXruXEiRPl9lmhUNCnTx+SkpJISkp6LObz1q1b+Pv74+LiwsiRI7l792651813797NoEGDMDQ0BMDY2Fhe1rJlS6ysrICiL0oXL14scxv16tWjR48ej6138OBBHB0d5UG6rMHWx8cHNzc3kpKS5IzxJyn5BcbGxoatW7eyaNEi0tLSaNCgwVNt43k1atSILl26kJiYiEajIT4+Xv63IAiCUBNV+Sl3c3NzcnJyuHPnTqlZ+h9//IGFhQUABgYGT9xOyYo62traj93w9KKV1V5xiMm2bdto3rw5mZmZTJ48ucLteHh40L9/f2xsbEoNwlB0b4FSqWTp0qUoFAp69+5NXl7eM/dVT09P/llLS6tUWtrzrFeW4mvoJWlra6PRaOTXj/a9ZKhK7969sba25sCBA6xevZqYmBjCwsKeuI3ytGnThujo6Aqvz3t5ebF06VJatGiBoaGhSGYTBKFGq/IZerNmzXByciIoKEj+Y3369GlWrlzJuHHjyvyMgYFBmTnXZbGzsyM+Pp6CggIKCgqIj4+XT9Pb2toSFxcHwN9//016enqpzz1LiAhUHGJSnubNmzNx4kQ+/fTTx5bdv3+fpk2bolAoOHDgANnZ2fKyR49Bjx492LhxIyqVCoDbt28/se2nZWtry969e7l16xaAfMyexMzMTA5HSUtL48aNG+Wum52djYmJCZ6enowdO1b+3Jtvvin/fO3atTKv2wMYGhqWOh52dnaYmpoyb948OZ0tPz+/1BME3bp149q1ayxcuLDcJwEEQRBqiiqfoQPMmjWLhQsX4uzsjK6uLvr6+kyfPh1bW1suXbr02PoDBw5k3rx5rF27Vr4prjwDBw4kJydHzsx2cHBgwIABAEyfPp0pU6awY8cOmjVrRvv27eVT1k8KESmLhYVFuSEmT+pjWSZNmsTs2bOJiIigXbt28hmLso6Bu7s7V69eZeDAgejo6FCvXj02bNjwxLafhqWlJaNGjcLHxwdDQ0M6d+5M/fr1n/i58ePHM23aNNavX0/nzp3517/+Ve66ycnJ7NixA11dXRQKBYGBgQD0798ff39/nJ2dadGiBe3bty/z8z179iQ+Ph43Nzf69u3L6NGjWbNmDQsWLMDZ2Zm6desCyL9PAB0dHdzc3Pj++++JjIx8lkMiCIJQ7fxPh7Pk5uaio6ODjo4O165dw9vbm6ioKN56662q7lq1o1Kp5C87ERERZGdnV3jDX21XHKBw79y/0BTq8P6HJlXdpReuNgdgQO3ev9q8b1C7968y4SzVYoZeVS5cuMDUqVORJInCwkLGjRsnBvNyLFiwgMzMTAoKCmjevDlfffVVVXepWuji1hB9fX3UaklUixMEoUr9Tw/olpaWJCQkVHU3aoRZs2ZVdReqNTGYC4JQ1ar8pjhBqMk0IpRFEIRqQgzoglAJWmJmLghCNfE/fcq9OlIqlXIRnH/++Ye3334bX19fOnbsWNVdKyUiIoLo6GgaN25MXl4ebdq0Yc6cOaWeLRcEQRBeHTFDr4aWLFnC9u3b+e9//4uHhwejR4/m8OHDVd2txxSXWt2+fTt///33U6XEPa1nKWrzIlVVu4IgCJUlZujVXK9evThy5Ahr166lXr16FQaV6OnpceHCBS5evMgHH3xAjx49iIiI4MqVKwwbNoxhw4YBRWcBXF1dSU9P5+rVq0yaNImbN2+SmJjI3bt3CQ4OxsbGhtmzZ9O0aVNGjRoFwPHjx5k4cSI//fRTqT7q6enx7rvv8vfffwNFBVzCw8P59ddfyc/Px8LCgqCgIAwMDCoMxJk2bRra2tpkZWXx4MEDEhISiIuLIzo6GrVajaGhIUFBQbz11ltkZmYyZ84cNBoNhYWF+Pn54eLiwubNm4mKikJPTw+NRsOiRYto1aoVR44c4euvv+bhw4fUq1eP6dOn0759ezmUxtPTk/T0dAYMGMCgQYNe1a9XEAThhREDeg3QoUMHdu3aVW5RlWJnzpzh+++/R61Wo1QquX//PuvXr+f69es4OTnh7e0tl2fNz89n8+bNHDlyhKFDh/LFF1+wbds2fvzxRxYuXMjGjRv56KOPGDNmDCNHjkShULB+/Xo+/PBDFIrS141VKhW//vorEydOBGDNmjXUr1+fbdu2ATB//nwiIyOZOHGiHIjz6aefcvnyZVxdXeXIU4ATJ06wfv166tWrx6FDh0hOTmbDhg3o6emRkpJCYGAgmzZtYvXq1YwcORIXFxckSZKrxH3zzTckJyfTuHFj8vPzUavV5Ofn4+/vT0hICHZ2dqSmpuLv789//vMfoCiUpl27dk8sUiQIglCdiQG9Bnja2j89e/aU67G3bNkSR0dHtLS0MDU1lQNkWrVqBfxfuEybNm34559/6NOnDwBt27aVg21atWpF8+bN2bt3L9bW1uzatYuAgAC5vfj4eLkkrYODg1wad9euXahUKn7++Weg6MuDpaUlUBSIM2PGDODxQBwAJycn+Tr8rl27OHnyJP3795ePQ3HSW6dOnVixYgU5OTnY29vLddg7d+7MtGnT6NGjB927d6d58+acOnUKXV1dua0uXbqgq6tLVlYWBgYG6Ovry/svCIJQU4kBvQY4evQorVu3fmJQyaOBMRUF1hQv09bWLvX60VCWIUOGsHHjRs6dO0evXr1KlXx1d3dn6tSpXL9+nQ8//JDo6GgGDx6MJEnMmjXrscH6aZS8qU6SJLy8vBg/fvxj6w0fPhylUklqaipz5szB3t6eiRMnsnTpUo4ePUp6ejpDhw4lKCiIJk2aVNhm3bp1HzvrIAiCUNOIm+KquZ07d7Jx40ZGjBjx1EElL5KjoyNZWVl89913fPjhh2WuY2JiwvTp01mxYgW5ubkolUqioqLIzc0Fik7Jnzt3DqiQmFobAAAgAElEQVQ4EOdRSqWShIQEOZpWrVZz7NgxALKysjAzM8PHx4ehQ4dy9OhRCgsLuXjxIu3bt2f06NHY29tz4sQJWrZsSUFBgdxWWloahYWFtGzZ8sUcJEEQhGpAzNCrIX9/f/mxtVatWhEZGUmHDh0wMzN7qqCSF0lLSwt3d3f27t0rnzYvS/fu3XnrrbfYtGkTo0ePZunSpXh7e6NQKFAoFIwbN45WrVpVGIjzKBsbGyZMmICfnx9qtZqCggKcnJxo27Yt69atIyMjA11dXfT09JgxYwYajYZp06Zx//59FAoFb7zxBpMmTUJPT48lS5aUuilu8eLFpeJiBUEQarr/6XAW4el8/PHHDBgw4IVcZ64tgTjFAQpW77Shbr06Vd2dl6I2B2BA7d6/2rxvULv3T4SzCC/F0aNHmThxIlZWVvTu3fuFbLO2BeKISnGCIFQXYkAXytWuXTt27tz5QrcpAnEEQRBeDnFTnCBUgghnEQShuhADuiBUgjjlLghCdSEGdOG5KZVKnJyccHNzk/936dIlLCwsePDgwXNt88SJE/z444+l3nNzc5MfgSvPtGnTaNeuHX/99Vep956mvvzOnTs5cuTIc/VXEAShuhDX0IVKWbJkCebm5i9seydOnGDPnj1yJTvgqa+5m5iYEBERQUhIyDO1uXPnTtq2bftKHgMUBEF4WcSALrxUoaGhHDx4kIKCAoyNjQkODqZp06bcvHlTDoUBsLOzw8/PjyVLlqBSqXBzc8PGxoYZM2ZgYWFBZmYmBgYGnDt3jq+//prr168DMGLECDw8PADw8fFh/fr1nD17lrfffrtUP8oLjMnMzGTXrl2kpqaydetWPv74Y9zd3V/tQRIEQXgBxIAuVIq/v3+pMrKxsbGllvv6+sqhJ1u3biUsLIzw8HB27NiBmZkZUVFRANy9excjIyP8/f3Zs2cPS5YseaytwsJCPv30UyZMmCA/E3/79m15eb169fjkk08IDw9n2bJlpT5bUWCMUqkslWInCIJQE4kBXaiUJ51y37t3L9HR0Tx8+LBUjfgOHToQFRVFaGgotra2pRLXypOVlUVhYWGpAjfGxsal1hkwYADffffdY/nxFQXGCIIg1AZiQBdemsuXLxMSEsK2bdto3rw5mZmZTJ48GYB3332XuLg4UlNTSUhIIDIyko0bN1a6TV1dXT777DMWLlzIG2+8Ib9fmcAYQRCEmkDc5S68NCqVCl1dXUxMTNBoNGzatEledvHiRQwNDenbty8BAQH8+eefaDQaDA0N5WzzR7Vs2RIdHR2Sk5Pl90qeci/m6urKrVu3OHjwoPxeRYExFbUpCIJQU4gBXagUf3//Uo+tFafBAVhYWODk5ISzszP9+/enWbNm8rKDBw/i6emJm5sbo0aNYvbs2WhpaWFnZ8c///xDv379mDt3bqm2dHR0WL58OZs2bcLV1ZV+/fqRkpLyWJ+0tLT4/PPPuXz5svze6NGjsbS0xNvbG1dXVz788EN5QO/Xrx+JiYm4ubkRHx//og+RIAjCKyHCWQThOYhwlpqvNu9fbd43qN37V5lwFjFDF4RKEJXiBEGoLsSALgiCIAi1gBjQBaGSNIXiqpUgCFVPDOiCUAmXvr2Olo447S4IQtUTA7pQKUqlktOnT7/ydocMGcLu3btfebuCIAjVlRjQhf9JJavWCYIg1AaiUpzwwj1LIEtgYCCZmZnMmTMHjUZDYWEhfn5+uLi4cOPGDWbNmkVOTg4AI0eOfGJwyrfffktSUhJqtRp9fX2CgoJ45513gKLn4seNG8eePXvo2rUr3bp1K7NdQRCEmkgM6MIL9yyBLACrV69m5MiRuLi4IEmSXLVt7ty5tG7dmmXLlnHt2jU8PT2xsrKqsHa8u7s7I0aMACA1NZVZs2axZcsWebm+vj4xMTEA+Pn5ldmuIAhCTSQGdOGFe9ZAlk6dOrFixQpycnKwt7f/f+3deVRV5f748TcyCjjgAFrqLW8XlJxwIAsHPJYgKiI4rQavVnizDHD4KpCaU5iplzQltbqaXS0HEJAcuiscsgSHLPI6hoaiCSFOoEznPL8//LmvhGLIwcM5fV5ruRbn7L2f/Xn2gf1x7/2c50PHjh0B2LdvH5GRkQC4urrSu3dv0tPTK03oR44cYcWKFVy9ehUrKyt++eWXcstvl1qtbL9CCGGO5Bm6MKrbBVkWLVpESkoKMTExlJSUAP8ryNKuXTuSkpIYNWoUAKNHj+bDDz+kUaNGzJkzh9jY2Afad0lJCeHh4URHR5OSksLHH3+s7fs2R0dH7Wdj7VcIIWoDuUIXRnW/gizNmjVjwIABdO3aleeeew6DwUBWVhaPP/44rVq1wtHRUZtP/emnn2bDhg2EhYXx22+/sXv3bkaPHn3PfZeUlFBWVqZVWVu3bl2lsZ45c+au+xVCCHMkCV1U25gxY7C2ttZe3y7I4uLiQu/evTl48CBwqyDL6tWrqVOnDgaDQSvI8tlnn5Geno6trS12dnZMmzYNgGnTpjFjxgwGDRoEwOTJk/nb3/6m7ScyMrLcXMcrV64kLCyMoUOH0rBhQ/z8/CqN+177FUIIcyTFWYR4AHcWULC1trPIyWUsuQAGWHb/LLlvYNn9k+IsQpiQJSZzIYT5kYQuhBBCWABJ6EJUg9LLEyshRO0gCV2IarCSeuhCiFpCErqotUxV+EUIIcyRJHQhhBDCAsj30IVZMWXhFyGEqM0koQuzYsrCL0IIUZtJQhdmxZSFX4QQojaTZ+jCbJiy8IsQQtR2ktCF2bhf4RdnZ2cGDBhAVFQU//3vfzEYDJw5c4ZWrVoxcuRIRo0axU8//QT8r/ALoBV+6d69u0n6JYQQxiC33EWtZqrCL0IIYW6kOIsQD+B2AYUn2z6Jg6ODqcOpEZZcAAMsu3+W3Dew7P5JcRYhTERmihNC1BaS0IUQQggLIAldiGpQeoOpQxBCCEASuhDVYmUtf0JCiNrBbM5GOp0Of39/AgMDGThwIF9++aXR2l68eDFbt26tdjuZmZl4eHhos5XVZqGhodq0pw8iPT2djh07MnjwYAYOHMiLL75IZmamESMUQghRFWaT0AGWLFlCcnIy7733HlFRUeTn5xul3fDwcAICAqrdzqZNm+jevTvx8fFGiKq8O2dFM4aPPvqIVq1aVauNv/71ryQlJZGSkkLHjh2ZN2+ekaK7Ra/XG7W9P8LYx1kIIR4Ws/weuqenJ05OTkyYMIGCgoJqF+qIjIykXbt2hISE4Ovry7Zt22jUqBFwqxiIk5MT48eP58cff2ThwoUUFhYCEBYWhq+vL3ArEWzZsoW1a9cSGhpKRkYGHTp0ACAnJ4cpU6aQl5dHy5YtAejRowcvvvhipcsiIyOxtrbmzJkzFBYWkpSUxObNm1m3bh16vR5nZ2dmzpxJ69at79m39evXs3r1auzs7DAYDLz//vv89a9/RafTsXz5cq5du8bcuXNJTEzUjm9wcDCRkZF4e3vfc3+/5+3tza5du7TX99qupKSEOXPmsH//fho1akTbtm3Jy8tjyZIlJCQkkJycjJOTE1lZWSxYsICSkpK7HnNjFmPR6XQEBASQlpaGu7s7MTEx1f8lFUKIh02ZiT59+qgTJ04opZTat2+f8vLyUpcuXdKWb9iwQUVERCillFq1apWaPn26tuzKlStKKaVee+01tWXLFqWUUgaDQV29elUppdTUqVPVZ599ppRSKjo6Wn366adKKaVKS0uVj4+POnfunLp69aoaPHiwysnJUUoplZOTo3r27Km18Z///Ec9//zzSiml4uLi1IwZM7T9jx8/Xi1btkwppVR2drby8vLS9lfZsqlTp6ohQ4aowsJCpZRSBw4cUKGhoaq4uFgppdSuXbvUiBEjKu1b586dtZiLi4vVjRs3KhzP5557Th07dkwppdTx48dV3759lcFgqHR/aWlpasiQIUoppfR6vZo+fbpaunTpfeNcs2aNevnll1VpaakqKipSw4YNU2+++aZSSqn4+HjVqVMnlZWVpZRSlR7zqn7G4eHhKjY2VmvHx8dH63+fPn3U22+/raqiqKhIHTx4UBUVFVVpO3Ny8OBBU4dQoyy5f5bcN6Usu3/VObeY1RV6WFgY9vb2ODs788EHHxitUMedhgwZwjvvvMOoUaPYs2cPrVu3pkWLFuzevZvs7GxCQ0O1da2srMjKyqJ9+/bEx8czZMgQAIKCgggKCiI6Ohp7e3vS09O1GcoeffRRnn76aa2NypbBrZnRHB0dAUhNTeX48eMMGzYMAKUU165dq7Rv3bt3JzIykj59+uDr66vdBbhTUFAQmzdvJioqis2bNxMUFISVlVWl+4NbYwYGDx5MTk4Ozs7ObNy48b5xpqenM3jwYGxsbLCxsWHAgAEcOnRIa7Nz587ao4DDhw/f85gbuxiLlE4VQpg7s0roS5Ys0U7A58+fZ+LEiWzatImWLVvy/fffM3nyZOB/hTq+++47kpKSWLlyJZ9//jmjR49Gp9Px3XffMWfOHHx8fJgwYUK5fXTt2pXCwkJOnDjB5s2bCQ4OBm4lJQ8PD9auXVshrry8PPbu3cuxY8eIi4sD4ObNm+zYsYPAwMBq9fl2Mr8dQ0hICOHh4RXWu1ffli5dyk8//URaWhqjRo1i5syZ9O7du9y2QUFBDB8+nIkTJ5KSksL69evvuz+49Qw9ISGBkpISJk6cyMyZM1m8ePF9t6uMk5NTuf7e65gDD/wZ382dx1kIIcyRWQ2Ku5MxC3X8XlBQEKtWreLAgQP4+fkBt/6TkJWVRVpamrZeRkYGSikSExPx8/Nj165dpKamkpqaSkxMjDY47vazaIBff/21XBuVLfs9nU5HUlISFy9eBG4NGjty5AjAXftWVlbGuXPn6NChA2PHjsXHx4djx45VaPeRRx7hiSeeYO7cuTzxxBM8+uij993fnezs7Jg5cybffPMNR48erXQ7b29vtmzZQllZGcXFxWzbtu2e/a3smEsxFiGEKM+srtDv5OHhYbRCHb8XFBRE3759CQ4Opm7dugA0aNCAuLg4FixYQExMDKWlpbRs2ZLly5eTkJDA1KlTy7XRt29f3n77bbKzs3nrrbeYMmUKW7ZsoUWLFnTo0AFnZ2eASpf9Xrdu3YiIiGDcuHHo9XpKS0vx9/enXbt2d+2bwWAgMjKS69evY2VlRfPmzZk0adJd2x4yZAhTpkzhvffe+0P7+70mTZrw8ssvs3TpUuLi4u653ciRIzl+/DgDBgzAxcXlrgPsbqvsmEsxFiGEKE+KszwERUVF2jPj3Nxchg4dyurVq2ndunWlyyxVQUEBzs7OlJSUMG7cOPz9/bXn7ebif8VZPHFwrGvqcGqEJRfAAMvunyX3DSy7f9UpzmK2V+jm5JdffmHq1KkopSgrK2P8+PFawq5smaUaM2YMJSUlFBcX88wzz2iDCc2RzBQnhKgtJKE/BG3atCEpKanKyyzV7dHwQgghjEcuL4R4yFSZFHQRQhifXKELUQ15/8rAprhq27hFdKuZYIQQf2pyhS5MSqfTcfLkyRppOzk5maCgIPz9/QkODmbChAlcuHCBgIAA9uzZU27dwsJCOnfuTHZ2do3EIoQQNU2u0IXZKisrw8bm7r/CGzduZNWqVcTFxfHYY48Bt2apy8vLIzg4mM2bN9OrVy9t/e3bt9O+fXtatGjxMEIXQgijk4Quap358+ezf//+CkV3srOzCQkJITg4mLS0NIYPH07Tpk1ZvHgxderUQa/XM336dJ566imWLl3K3LlztWQOt6aFhVsT6SxbtoyrV6/SoEEDABISEhgxYoQpuiuEEEYhCV3UOqGhodpEPRs3bmThwoXExsYCcOXKFdq3b68tDwwMZPbs2Xh5eaHX67l58yaXLl3i4sWLd52rH25NgtO9e3dSUlJ44YUXyMrK4uTJk/Tr1+/hdFAIIWqAJHRR69yr6A6Avb09/fv31153796defPm0a9fP3r16oW7uzvFxfcfpRYSEsKHH37ICy+8QEJCAgEBATg4OBi9L0II8bDIoDhRq5w/f5558+axaNEiUlJSiImJoaSkRFtet25drKystNfR0dHMmTMHW1tbwsPD2bBhA40bN8bNzY2MjIx77sfX15eLFy9y/PhxkpKSCAkJqdF+CSFETZOELmqVyoru3M3p06fx8PDg73//O4GBgVoxltdff513332Xs2fPauseOHBAS/I2NjYEBgby1ltvUa9ePTp06FBznRJCiIdAbrkLkxszZgzW1tba63sV3bmbRYsWkZWVhbW1NfXr1+edd94BYOTIkTg4OBAWFkZRURF16tShTZs2/N///Z+27dChQ/nXv/5FVFRUzXVOCCEeEinOIsQDqE4BBVVmwMqm9t8cs+QCGGDZ/bPkvoFl968655baf1YRwsKYQzIXQpgfObMIIYQQFkASuhA1QAqwCCEeNknoopw751a/efMmr7zyClFRUej1+hrdb3Z2tjaT2+04/P39CQwMpH///uVKrn799dfMnz//ru2kp6cTHBxco7HeKW/1fnKW7KnwT26rCyEeNhnlLu7q2rVrjB07lvbt2xMdHV3uu9+V0ev15UasV8eSJUtwd3fn5MmTBAcH06tXL9zc3Ojbty99+/Y1yj6EEMJSSEIXFVy6dIkpU6ag0+kICwsDYPPmzaxbtw69Xo+zszMzZ86kdevWJCQkkJycjJOTE1lZWSxYsICgoCAmTJjAf/7zH65cucKUKVPw8/MD4Mcff2ThwoUUFhYCEBYWhq+vb6XxuLu7U79+fXJycnBzcyMhIYFdu3axZMkSAGJjY9m6dSv169fH29u73La3lzVs2BBvb2/27dtHQkJCpX0SQghzJAldVBAREcHzzz+vJfODBw+ybds21q5di52dHbt37yY6Olqb9OXHH38kKSmJVq1aaW04OzsTHx/PoUOHiIiIwM/Pj2vXrvH222+zcuVKXF1dyc3NZejQoaSkpFQaz6FDh3BxcaFNmzYVlqWmppKamkpiYiIODg688cYb5Zbt3LmTpKQk7Tvpt92vT0IIYW4koYsKevfuzdatWxk5ciRubm6kpqZy/Phxhg0bBoBSimvXrmnrd+7cuVwyBwgICACgU6dO5ObmUlxczOHDh8nOziY0NFRbz8rKiqysLFxcXCrEERYWhlKKs2fPsnjxYuzs7Cqsk56eTkBAAE5OTsCtyWLi4uK0Zf3798fR0RGAoKAgbdn9+iSEEOZGErqo4NVXX2Xnzp2MGjWKNWvWoJQiJCSE8PDwu65/O5ne6faECLefp5eVlaGUwsPDg7Vr11ZYPzs7u8J7t5+hb9u2jaioKDp37kyTJk2q0zXN/fokhBDmRobiirv6xz/+wZAhQxg1ahQ6nY6kpCQuXrwI3Br4duTIkSq36eXlRVZWFmlpadp7GRkZ3G+ywv79++Pj48OKFSsqLOvevTvbtm3jxo0b6PV64uPjtWXe3t7s2LGDmzdvYjAYSE5O1pYZq09CCFFbyBW6uKfXXnsNpRTTpk0jIiKCcePGodfrKS0txd/fn3bt2lWpvQYNGhAXF8eCBQuIiYmhtLSUli1bsnz58vtuO2nSJIKDg8vdrgfo06cPP/zwA4MHD9YGxeXk5ADQt29fDh8+TGBgIA0aNKBTp05cvXoVgG7duhmlT0IIUVvIXO7CohUUFODs7IzBYOCtt97C1dWVCRMmVLvd2/MtNzt4A5viin9CbmG9qr0PU7Pk+bLBsvtnyX0Dy+5fdeZylyt0YdGmTp3K+fPnKSoq4sknn6xwhV9dTUZ73/WPzlwKsAghLIckdGHRli1bZpL9SjIXQjxsctYRQgghLIAkdCGMQJXV7Fz3QghxP5LQhdm6s5CMqVxas4fcpTuwsjHO/PVCCPGgJKGLP6WysjJThyCEEEYlg+KERZk/fz779++ntLQUFxcXYmJiePTRR8nOziYkJITg4GDS0tIYPnw4TZs2ZfHixdSpUwe9Xs/06dPLlXAVQghzIgldWJTQ0FCmTp0KwMaNG1m4cCGxsbEAXLlyhfbt22vLAwMDmT17Nl5eXuj1em7evGmyuIUQorokoQuLsmfPHtatW8eNGzcq3Fa3t7enf//+2uvu3bszb948+vXrR69evXB3d3/Y4QohhNHIM3RhMc6fP8+8efNYtGgRKSkpxMTEUFJSoi2vW7cuVlZW2uvo6GjmzJmDra0t4eHhbNiwwRRhCyGEUcgVurAYBQUF2Nra0rRpUwwGw31rm58+fRoPDw88PDy4ceMGP/30E8OHD39I0QohhHFJQhdmbcyYMVqJVgB/f38CAgJwcXGhd+/eHDx48J7bLlq0iKysLKytralfvz7vvPPOwwhZCCFqhCR0YbZSU1Pv+v60adO0n8PCwgBo0aIF6enp5dYz1bSwQghREyShC1ENjUf1wt7eHlWml8llhBAmJYPihDACSeZCCFOThC6EEEJYAEnoQpgxKQojhLhNnqHXEjqdDjs7O+zs7CgtLeXll19m2LBhAOj1enx9fWnXrh0ffvihtk16ejqjRo1i0qRJjB07Vntv/vz5JCQkALB27Vq++OILrKysKCkpoU+fPtpMaZW1DfDNN9+wbNky8vPzcXZ2pmnTpkycOBEPD49K4/0zufTZV9gUmy6pur4RZLJ9CyFqF0notciSJUtwd3fn5MmTBAcH06tXL9zc3Pjmm29wdXXl+++/Jy8vjyZNmmjbNG3alE8//ZSRI0dSv379cu1lZGTw6aefsmnTJurXr49er+fUqVPl1rlX23v37uWtt95i2bJltG/fHoBjx47x22+/4eHhUWm8xlBWVoaNzcP99TQYDFhZWZWbfEYIIcyFJPRayN3dnfr165OTk4Obmxvx8fGMHDmSH374gcTERF599VVtXVdXV7y8vPjoo4+YNGlSuXZycnJwdnbG0dERAGtra9q0aVNunXu1vWzZMl5//XUtmQO0bdv2D8V7+vRpYmJiuHz5MqWlpfz9738nJCQEgB07dhAbG4uDgwP+/v7Exsby/fff4+TkhIeHB+PHj2fXrl307NmTV199lXnz5nHixAmKi4t56qmniIqKwtramqVLl5KSkoK9vT1WVlasWbMGW1tbpk6dys8//4yNjQ2PP/44ixcvBmDlypUkJycD0L59e6ZNm4aTkxMffPABp06doqCggAsXLrB+/XoaNGhQnY9PCCFMQp6h10KHDh3CxcWFNm3akJ+fT1paGv379yc4OFi7lX6ncePGsWnTJnJzc8u97+Pjg42NDX369GHSpEmsX7++XAGSyto+evQoHTt2rHK8ZWVlTJ48maioKOLj41m3bh0rV64kMzOTvLw8ZsyYwfLly0lMTMTBwaFCW/b29sTHxxMREcG8efPo1q0bmzZtIikpifz8fOLj47ly5QqrV68mMTGRpKQk/v3vf+Po6MjevXspLCxk69atJCcnM3v2bAB2795NcnIyX3zxBVu2bEGv1xMXF6ftMyMjg4ULF7J9+3ZJ5kIIsyVX6LVIWFgYSinOnj3L4sWLsbOzIzk5mT59+uDs7EyXLl3Q6/UcPnwYLy8vbbsmTZowfPhw4uLiyhUfcXR0ZP369fz0008cOnSIjRs3snbtWjZt2vSH265qvD///DOZmZlMnDhRW6+0tJTTp09Tp04dPD09eeyxxwAICQlh3rx55docMmSI9nNqaioZGRmsWrUKgKKiItzc3KhXrx6tWrViypQp9OjRA19fX5ydnWnTpg2ZmZnMmjULb29vfH19Adi3bx8BAQE4OzsDMHz4cGJiYrT99OrVi0aNGv2hPgshRG0lCb0Wuf1Metu2bURFRdG5c2fi4+PJz89Hp9MBcP36deLj4ysk3VdffZX+/fvz5JNPlnvfysqKDh060KFDB1544QWeeeYZTp06xZNPPllp256enmRkZNzzNvu94lVK4eLiQlJSUoX1v/766/seg9uPBwCUUsTFxdGyZcsK623YsIHvv/+etLQ0goOD+fjjj2nTpg0pKSmkpaWxZ88eYmNj2bJly3336eTkdN91hBCitpNb7rVQ//798fHxITo6muvXr7N3715SU1NJTU0lJSWF7du3V6jdXa9ePcaMGVNupHpmZiYnT57UXp85c4bS0lKaNWtGRkZGpW2PGzeOuLg4/vvf/2rbHz9+nL17994z3hUrVvD444/j4OBAYmJiuTgKCgro2LEjR48e5ezZswBs3ry50uOg0+lYuXIlev2tUeT5+fmcO3eOgoIC8vPz8fb2JiwsDHd3d06dOsXFixextrbm2WefJSoqivz8fK5cucLTTz/Ntm3bKCgoQCnFpk2beOaZZ6rwiQghRO0nV+i11KRJkxg0aBBBQUHlRl27ubnh6enJ9u3beeSRR8pt8+KLL7JmzRrtdVFRETExMVy6dAl7e3usra1ZsGABjRs3ZsmSJQwYMOCebQ8ZMoTZs2cze/Zsrly5go2NDS1atKgw8O7OeIODgwkNDWX58uXExMTwySefYDAYaNy4Me+//z5NmjRh5syZhIaGUrduXXx9fbG1taVu3bp3bTM6OpoFCxYwePBgrKyssLW1JTo6GltbW958802KiopQSuHp6Um/fv1IS0tj0aJFwK0R62PHjsXNzQ03NzdOnDjByJEjAWjXrh3jxo17sA9GCCFqKSullDJ1EOLPo6CgQHuWHR8fz6ZNm/j8889NHFXVFRcXc+TIEZofvmCx30M/dOgQXbp0qbH2Tc2S+2fJfQPL7t/tc0u7du2wt7ev0rZyhS4eqs8++4zt27ej1+tp0KABc+fONXVID+T2/4PrjfDFzs7OZHEU3biBlXXNzSNfXFxcY23XBpbcP0vuG1hu/0pKSoD/nWOqQq7QhXgA169fLzc+QQghjMnd3Z169epVaRtJ6EI8AIPBQGFhIba2tjKznBDCaJRSlJaW4uTkRJ06VRu3LgldCCGEsADytTUhhDJfGHkAAAg8SURBVBDCAkhCF0IIISyAJHQhhBDCAkhCF0IIISyAJHQhhBDCAkhCF0IIISyAJHQhhBDCAkhCF6KKzpw5w4gRI/Dz82PEiBH88ssvpg7JaC5fvkxoaCh+fn4MGjSI8ePHk5+fb+qwasTSpUvx8PCwuBn/iouLefvtt+nXrx+DBg1i+vTppg7JaHbu3ElQUBCDBw8mMDCQr776ytQhVcv8+fPR6XQVfg8f+ByjhBBV8tJLL6nExESllFKJiYnqpZdeMnFExnP58mWVlpamvX733XdVVFSUCSOqGUeOHFGvvPKK6tOnjzpx4oSpwzGqOXPmqHfeeUcZDAallFK//fabiSMyDoPBoLp27ap9XseOHVOdOnVSer3exJE9uAMHDqgLFy5U+D180HOMXKELUQWXLl3i6NGjDBw4EICBAwdy9OhRi7mKbdiwIU899ZT2ulOnTly4cMGEERlfSUkJs2fPZubMmaYOxegKCwtJTEwkPDxcm5K4SZMmJo7KeOrUqcP169eBW/UUXF1dqzw9am3StWtXmjdvXu696pxjpNqaEFXw66+/4ubmhvX/r3BmbW2Nq6srv/76K40aNTJxdMZlMBj4/PPP0el0pg7FqBYvXkxgYCAtWrQwdShGd+7cORo2bMjSpUtJT0/HycmJ8PBwunbtaurQqs3Kyor333+f119/HUdHRwoLC1m5cqWpwzK66pxjzPe/NkKIGjVnzhwcHR158cUXTR2K0Rw+fJgjR47w/PPPmzqUGqHX6zl37hyenp4kJCQwefJk3nzzTQoKCkwdWrWVlZWxYsUK4uLi2LlzJx9++CEREREUFhaaOrRaQxK6EFXQvHlzcnJy0Ov1wK0TaG5uboXbZuZu/vz5ZGVl8f7775v1Lc3fO3DgAJmZmfTt2xedTsfFixd55ZVX2Lt3r6lDM4rmzZtjY2Oj3a7t2LEjLi4unDlzxsSRVd+xY8fIzc2lS5cuAHTp0oW6deuSmZlp4siMqzrnGMv5SxXiIWjcuDFt27YlJSUFgJSUFNq2bWtRt9v/+c9/cuTIEZYtW4adnZ2pwzGqsWPHsnfvXlJTU0lNTaVZs2Z88skn9OjRw9ShGUWjRo146qmn+Pbbb4Fbo6UvXbrEX/7yFxNHVn3NmjXj4sWLnD59GoDMzEwuXbpEq1atTByZcVXnHCPlU4WooszMTCIjI7l27Rr169dn/vz5tG7d2tRhGcWpU6cYOHAgjz32GA4ODgC0aNGCZcuWmTiymqHT6Vi+fDnu7u6mDsVozp07R3R0NFeuXMHGxoaIiAh69+5t6rCMIjk5mY8++kgb8BcWFsazzz5r4qge3Ny5c/nqq6/Iy8vDxcWFhg0b8uWXXz7wOUYSuhBCCGEB5Ja7EEIIYQEkoQshhBAWQBK6EEIIYQEkoQshhBAWQBK6EEIIYQEkoQsh/tQGDBhAenq6qcO4q8jISGJjY00dhjATMpe7EMKieXl5aT/fvHkTOzs7bZ7sWbNm8eWXX9Z4DDNmzGDLli0AlJaWopTSJu3p0qULH3/8cY3HICyffA9dCPGnodPpmDt3Ls8884zJYvjggw/Iyspi4cKF9103MjISNzc3JkyY8BAiE+ZObrkLIf7UdDod3333HXAr2YaFhTF58mS8vLwYNGgQZ86cYcWKFTz99NP07t273Lzv169fJzo6mh49etCzZ09iY2O1Obj/qLCwMHx8fOjSpQsvvPACp06duut6BQUFvPTSS8ydOxelFJmZmYwZMwZvb2/8/PzYunWrtm5kZCSzZs1i7NixeHl5MWzYMM6ePfsAR0eYE0noQghxh507dzJ48GAOHDhA27ZteeWVVzAYDOzZs4c33niDGTNmaOtGRkZiY2PDV199RWJiIt9++y0bN26s0v569erFjh072LdvH56enkyePLnCOpcvX2b06NF07tyZadOmcfPmTV5++WUGDhzId999R2xsLLNmzeLnn3/Wttm6dSvjx4/nwIEDtGrVSp7F/wlIQhdCiDt07dqVnj17YmNjg7+/P5cvX2bs2LHY2toSEBDA+fPnuXbtGnl5eezevZvo6GgcHR1p3Lgxo0ePrvIz+aFDh+Ls7IydnR1vvvkmx48f5/r169ry3NxcXnrpJfz9/bVb77t27eLRRx8lJCQEGxsbPD098fPzY/v27dp2zz77LB06dMDGxobAwECOHTtmnAMkai0ZFCeEEHdo3Lix9rODgwMuLi7aILrbBWtu3LhBbm4uZWVl5Sq1GQyGKpXS1ev1xMbGsn37dvLz87VStZcvX6ZevXoA7N69G0dHR0aOHKltd/78eTIyMujatWu5tgIDA7XXTZo0KdePGzdu/OG4hHmShC6EEA+gWbNm2NnZkZaWho3Ng51Kt2zZwtdff82qVato0aIF169fp1u3btw5VnnYsGFcu3aNsWPH8vHHH+Po6Ejz5s3p1q0bq1atMlZ3hAWQW+5CCPEAXF1d8fHx4d1336WgoACDwcDZs2fZv3//H26jsLAQOzs7XFxcuHnzJv/85z/vut6MGTN4/PHHee211ygqKsLX15dffvmFxMRESktLKS0tJSMjg8zMTGN1T5ghSehCCPGA3nvvPUpLSwkICKBbt26EhYXx22+//eHtg4KCeOSRR+jZsycDBgygU6dOd13PysqKOXPm0KxZM15//XVsbW355JNP2Lp1Kz179qRHjx4sXLiQkpISY3VNmCH5HroQQghhAeQKXQghhLAAktCFEEIICyAJXQghhLAAktCFEEIICyAJXQghhLAAktCFEEIICyAJXQghhLAAktCFEEIICyAJXQghhLAA/w9bGhCw/IC97wAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {}
        }
      ]
    }
  ]
}