Compare_Regressors.ipynb

{
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4i2iuTlZYvQ",
        "outputId": "1172a063-ee94-4abe-a8a8-fa0b0ba1073e"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "Collecting lazypredict\n",
            "  Downloading https://files.pythonhosted.org/packages/5f/db/1566dca1050ea74e9474dca0f1e7bbffcb0c3e694cf92e7e6e7ef9fca3af/lazypredict-0.2.7-py2.py3-none-any.whl\n",
            "Requirement already satisfied: Click>=7.0 in /usr/local/lib/python3.6/dist-packages (from lazypredict) (7.1.2)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.7\n"
          ]
        }
      ],
      "source": [
        "! pip install lazypredict"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 2,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Vf73M_f9XwZ7",
        "outputId": "4256ee85-6e74-4322-fa05-9e79a1779dfd"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "/usr/local/lib/python3.6/dist-packages/sklearn/utils/deprecation.py:144: 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"
          ]
        }
      ],
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyRegressor"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 3,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oiess4VaZChk",
        "outputId": "b9956d3a-6631-420c-b7d5-fc69ca407cff"
      },
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "--2021-01-22 14:57:46--  https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n",
            "Resolving github.com (github.com)... 192.30.255.112\n",
            "Connecting to github.com (github.com)|192.30.255.112|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv [following]\n",
            "--2021-01-22 14:57:46--  https://raw.githubusercontent.com/dataprofessor/data/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv\n",
            "Resolving raw.githubusercontent.com (raw.githubusercontent.com)... 151.101.0.133, 151.101.64.133, 151.101.128.133, ...\n",
            "Connecting to raw.githubusercontent.com (raw.githubusercontent.com)|151.101.0.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 8363909 (8.0M) [text/plain]\n",
            "Saving to: ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’\n",
            "\n",
            "acetylcholinesteras 100%[===================>]   7.98M  46.7MB/s    in 0.2s    \n",
            "\n",
            "2021-01-22 14:57:47 (46.7 MB/s) - ‘acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv’ saved [8363909/8363909]\n",
            "\n"
          ]
        }
      ],
      "source": [
        "! wget https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 4,
      "metadata": {
        "id": "WVojHjyWZC9D"
      },
      "outputs": [],
      "source": [
        "df = pd.read_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv')"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 5,
      "metadata": {
        "id": "KbTq4zzKZpVf"
      },
      "outputs": [],
      "source": [
        "X = df.drop('pIC50', axis=1)\n",
        "Y = df.pIC50"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 6,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtC4SWpBaCVQ",
        "outputId": "1008b65f-5b93-423a-b2bf-0695dc2014ec"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4695, 881)"
            ]
          },
          "execution_count": 6,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Examine X dimension\n",
        "X.shape"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVHzT2f5Z8sF",
        "outputId": "66fc3cdb-2f97-4694-b915-58c95a49b3d2"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "(4695, 137)"
            ]
          },
          "execution_count": 7,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "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"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 8,
      "metadata": {
        "id": "MYck-_XeaHNZ"
      },
      "outputs": [],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 9,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnFbUjA1aUj1",
        "outputId": "84de30a4-1459-4506-fefd-d4495c30fbee"
      },
      "outputs": [
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            " 90%|████████▉ | 35/39 [00:57<00:11,  2.82s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[14:58:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 39/39 [00:59<00:00,  1.51s/it]\n",
            " 90%|████████▉ | 35/39 [00:48<00:08,  2.18s/it]"
          ]
        },
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "[14:59:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ]
        },
        {
          "name": "stderr",
          "output_type": "stream",
          "text": [
            "100%|██████████| 39/39 [00:50<00:00,  1.29s/it]\n"
          ]
        }
      ],
      "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)"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 10,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cHOFkp-ka5tF",
        "outputId": "7abc08e9-e5b4-44e2-955d-dec0c20b91a1"
      },
      "outputs": [
        {
          "data": {
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              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.57</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.57</td>\n",
              "      <td>5.93</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>0.86</td>\n",
              "      <td>0.57</td>\n",
              "      <td>5.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.83</td>\n",
              "      <td>0.64</td>\n",
              "      <td>4.67</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.81</td>\n",
              "      <td>0.67</td>\n",
              "      <td>0.59</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.75</td>\n",
              "      <td>6.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.87</td>\n",
              "      <td>2.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.69</td>\n",
              "      <td>0.87</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.65</td>\n",
              "      <td>0.92</td>\n",
              "      <td>2.98</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.58</td>\n",
              "      <td>1.01</td>\n",
              "      <td>6.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.57</td>\n",
              "      <td>1.02</td>\n",
              "      <td>4.82</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.46</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.72</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.46</td>\n",
              "      <td>1.14</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>3.96</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>3.83</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.33</td>\n",
              "      <td>1.27</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.30</td>\n",
              "      <td>1.30</td>\n",
              "      <td>0.74</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.36</td>\n",
              "      <td>1.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.23</td>\n",
              "      <td>1.37</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.15</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.06</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.13</td>\n",
              "      <td>1.65</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-13.70</td>\n",
              "      <td>5.95</td>\n",
              "      <td>1.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-177394519750505689776128.00</td>\n",
              "      <td>653870642365.76</td>\n",
              "      <td>1.11</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 R-Squared  ...  Time Taken\n",
              "Model                                                       ...            \n",
              "DecisionTreeRegressor                                 0.86  ...        0.16\n",
              "ExtraTreeRegressor                                    0.86  ...        0.16\n",
              "ExtraTreesRegressor                                   0.86  ...        5.93\n",
              "GaussianProcessRegressor                              0.86  ...        5.10\n",
              "RandomForestRegressor                                 0.83  ...        4.67\n",
              "BaggingRegressor                                      0.81  ...        0.59\n",
              "MLPRegressor                                          0.77  ...        6.83\n",
              "HistGradientBoostingRegressor                         0.69  ...        2.55\n",
              "LGBMRegressor                                         0.69  ...        0.50\n",
              "KNeighborsRegressor                                   0.65  ...        2.98\n",
              "SVR                                                   0.58  ...        6.45\n",
              "NuSVR                                                 0.57  ...        4.82\n",
              "GradientBoostingRegressor                             0.46  ...        1.72\n",
              "XGBRegressor                                          0.46  ...        1.28\n",
              "Ridge                                                 0.33  ...        0.05\n",
              "LassoCV                                               0.33  ...        3.96\n",
              "ElasticNetCV                                          0.33  ...        3.83\n",
              "RidgeCV                                               0.33  ...        0.15\n",
              "TransformedTargetRegressor                            0.33  ...        0.07\n",
              "LinearRegression                                      0.33  ...        0.09\n",
              "SGDRegressor                                          0.32  ...        0.16\n",
              "BayesianRidge                                         0.32  ...        0.21\n",
              "HuberRegressor                                        0.31  ...        0.61\n",
              "LassoLarsCV                                           0.31  ...        0.45\n",
              "LassoLarsIC                                           0.31  ...        0.11\n",
              "LinearSVR                                             0.30  ...        0.74\n",
              "AdaBoostRegressor                                     0.23  ...        1.15\n",
              "Lars                                                  0.23  ...        0.12\n",
              "OrthogonalMatchingPursuitCV                           0.23  ...        0.15\n",
              "OrthogonalMatchingPursuit                             0.23  ...        0.05\n",
              "LarsCV                                                0.15  ...        0.47\n",
              "Lasso                                                 0.00  ...        0.08\n",
              "ElasticNet                                            0.00  ...        0.05\n",
              "DummyRegressor                                        0.00  ...        0.03\n",
              "LassoLars                                             0.00  ...        0.06\n",
              "PassiveAggressiveRegressor                           -0.13  ...        0.10\n",
              "KernelRidge                                         -13.70  ...        1.46\n",
              "RANSACRegressor               -177394519750505689776128.00  ...        1.11\n",
              "\n",
              "[38 rows x 3 columns]"
            ]
          },
          "execution_count": 10,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Performance table of the training set (80% subset)\n",
        "predictions_train"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 11,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KP6qT55ebcfd",
        "outputId": "b25cc626-135a-4c86-8a4b-90f44a63dabe"
      },
      "outputs": [
        {
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              "    }\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>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",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.54</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.54</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.52</td>\n",
              "      <td>1.08</td>\n",
              "      <td>4.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.48</td>\n",
              "      <td>1.12</td>\n",
              "      <td>0.54</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.48</td>\n",
              "      <td>1.12</td>\n",
              "      <td>3.47</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.48</td>\n",
              "      <td>1.12</td>\n",
              "      <td>4.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.46</td>\n",
              "      <td>1.14</td>\n",
              "      <td>0.97</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.45</td>\n",
              "      <td>1.15</td>\n",
              "      <td>6.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.34</td>\n",
              "      <td>1.26</td>\n",
              "      <td>5.89</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>0.13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>3.75</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.32</td>\n",
              "      <td>1.28</td>\n",
              "      <td>3.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.18</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.08</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.45</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>1.29</td>\n",
              "      <td>0.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.29</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.73</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.28</td>\n",
              "      <td>1.31</td>\n",
              "      <td>0.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.24</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.04</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.24</td>\n",
              "      <td>1.36</td>\n",
              "      <td>0.12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>0.21</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.21</td>\n",
              "      <td>1.38</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>0.15</td>\n",
              "      <td>1.43</td>\n",
              "      <td>0.46</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.03</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.05</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.22</td>\n",
              "      <td>1.71</td>\n",
              "      <td>0.09</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-4.99</td>\n",
              "      <td>3.80</td>\n",
              "      <td>3.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-13.82</td>\n",
              "      <td>5.98</td>\n",
              "      <td>1.28</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-225543784142202044678144.00</td>\n",
              "      <td>737597884681.01</td>\n",
              "      <td>1.13</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                                                 R-Squared  ...  Time Taken\n",
              "Model                                                       ...            \n",
              "HistGradientBoostingRegressor                         0.54  ...        2.13\n",
              "LGBMRegressor                                         0.54  ...        0.46\n",
              "RandomForestRegressor                                 0.52  ...        4.56\n",
              "BaggingRegressor                                      0.48  ...        0.54\n",
              "NuSVR                                                 0.48  ...        3.47\n",
              "SVR                                                   0.48  ...        4.29\n",
              "KNeighborsRegressor                                   0.46  ...        0.97\n",
              "MLPRegressor                                          0.45  ...        6.73\n",
              "GradientBoostingRegressor                             0.42  ...        1.70\n",
              "XGBRegressor                                          0.42  ...        1.17\n",
              "ExtraTreesRegressor                                   0.34  ...        5.89\n",
              "Ridge                                                 0.32  ...        0.04\n",
              "RidgeCV                                               0.32  ...        0.13\n",
              "ElasticNetCV                                          0.32  ...        3.75\n",
              "LassoCV                                               0.32  ...        3.95\n",
              "ExtraTreeRegressor                                    0.31  ...        0.12\n",
              "SGDRegressor                                          0.31  ...        0.18\n",
              "TransformedTargetRegressor                            0.31  ...        0.07\n",
              "LinearRegression                                      0.31  ...        0.08\n",
              "BayesianRidge                                         0.31  ...        0.16\n",
              "LassoLarsCV                                           0.31  ...        0.45\n",
              "LassoLarsIC                                           0.31  ...        0.09\n",
              "HuberRegressor                                        0.31  ...        0.61\n",
              "LinearSVR                                             0.29  ...        0.73\n",
              "DecisionTreeRegressor                                 0.28  ...        0.16\n",
              "OrthogonalMatchingPursuit                             0.24  ...        0.04\n",
              "OrthogonalMatchingPursuitCV                           0.24  ...        0.12\n",
              "Lars                                                  0.21  ...        0.10\n",
              "AdaBoostRegressor                                     0.21  ...        0.77\n",
              "LarsCV                                                0.15  ...        0.46\n",
              "Lasso                                                -0.00  ...        0.05\n",
              "ElasticNet                                           -0.00  ...        0.03\n",
              "DummyRegressor                                       -0.00  ...        0.03\n",
              "LassoLars                                            -0.00  ...        0.05\n",
              "PassiveAggressiveRegressor                           -0.22  ...        0.09\n",
              "GaussianProcessRegressor                             -4.99  ...        3.88\n",
              "KernelRidge                                         -13.82  ...        1.28\n",
              "RANSACRegressor               -225543784142202044678144.00  ...        1.13\n",
              "\n",
              "[38 rows x 3 columns]"
            ]
          },
          "execution_count": 11,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        }
      ],
      "source": [
        "# Performance table of the test set (20% subset)\n",
        "predictions_test"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 12,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "MLsi5uSvbpC7",
        "outputId": "2cca0f31-9f81-4d37-8ccd-e88d35268272"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "execution_count": 12,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "needs_background": "light",
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 13,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "OLkiiTTwb-Cb",
        "outputId": "6efb0857-c5fe-4bbc-dead-1bfd98a6f5d3"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "execution_count": 13,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "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))"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": 14,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 628
        },
        "id": "pCdcxpzScqXX",
        "outputId": "62dbbf7b-d569-4280-ce60-0c191bbd8e2a"
      },
      "outputs": [
        {
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "execution_count": 14,
          "metadata": {
            "tags": []
          },
          "output_type": "execute_result"
        },
        {
          "data": {
            "image/png": 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Ora0tzZs3Z9u2bQD88ccfnD17tsQ+l6ZfGo2GjIwM3NzcCAwMxMnJiXPnztGmTRtOnz5NamoqAAkJCbRo0aLEKyRCCFHdyAz9KTGVVLXi0sse9M477/DVV1+xZ88eQkNDWbhwIX5+fqhUKiwtLQkNDaVhw4Z89tlnhIaGEhMTg5OTE05OTtjZ2RXZZmn7pdFo+PDDD8nJycFoNNKiRQveeustrK2tWbBgAdOnT0en01G7dm0WLlxYqs9PCCGqOklbExUqOzubGjVqoFKpOH/+PMOHD2fXrl0lPtxXFRUkItX7fY8sLCOqjIKFZUw5jQxMe3yStiaqjSNHjrBgwQIK/o6MiIiodsX8fg7DJpX6fzohnhZJ+3u2yU9eVChPT88nWhFPVDxTngWBaY5PivmzTR6KE0IIIUyAFHQhRJFMbfb6oMoan1GnrZTzCtMn12eEKIM//99UzHNL/sqhEPdr+OFXld0FYaJMqqDn5eWxYsUKtm3bhoWFBebm5jRu3JjAwEBllbCnac+ePfz666/Kcq1PIj4+nnnz5lG/fn3y8vJwdHQkIiKCWrVqlWNPy+7BfjZo0IC5c+fi4OBQ2V0TQohnkkldcg8JCeHMmTNs3ryZ7du3o1ar8ff3Jy0trULO37179zIV8wIeHh6o1Wq2bduGSqVi+fLlD+2j0+nKfJ6yKujn9u3bsbGxITo6utzaNhgMVMY3KqvC5yqEEE/CZGboFy9eZPfu3ezfvx97e3sgf4nRrl27AnDw4MFi08q8vLxYsWKFsk55wetmzZrxySefkJycjJWVFTVq1GDjxo3cunWLadOmKcu3dujQgdDQUOLj49m3bx9Lly7lxo0bTJ06lezsbHJzc+nSpQszZswAICoqirS0NLKyskhPT6dRo0ZERkby/PPPFxqTmZkZ7u7u7N+/nytXrjyUwObh4cHHH39MRkYGFhYWTJkyhc6dOwP/9/Ww7OxsAGV98wsXLjBv3jz+97//kZeXx3vvvUf//v25d+8eQUFBnD9/HgsLC5o0aUJkZCQXLlxQ0tEMBgP9+vVjzJgxhfqpUqlo3749+/btU94rLiUtKyuL0NBQzp07R7169ahXrx516tQhKCiIqKgozp07h0aj4erVq2zatInff/+d5cuXo9VqsbS0JCQkhDZt2hTbr+JS2C5dulTsZ+Xs7MzEiRPZt28fnTp1YvLkyeX1z1IIISqMyRT0kydP8vLLLxf7neYWLVqUOq3s9OnTpKSksGPHDszMzJQlSBMTE2nUqBGxsbEAyvv3s7e3Z8WKFdjY2JCXl8eYMWM4cOCAUkROnDjBN998g52dHWPGjCExMfGh5DGtVsvevXtxdXUFeCiBbeDAgQwaNIiBAwdy/vx5hg4dys6dOzEzM2PixIlERUXRrl079Ho9Go0GnU7H9OnTWbhwIY6Ojmg0Gvr3768UyOzsbHbs2FFoTHFxcXh5efH+++8XO1atVsuBAweUFe5KSkn78ssvsbe3Z9euXdy+fRt/f3969uyptHXs2DHi4+OpXbs2ly9fZtmyZaxevRpbW1vOnTvH2LFj2bdvX7H9Ki4dbvr06UV+VrVr1wbyw3C2bNlS7L8FIYSo6kymoD/o/PnzTJs2jZycHDp16sTQoUNLnVbWsGFDdDodM2fOxN3dXcnJbt26NbGxsXz22We4ubkV+b1qvV7PggULOHLkCEajkZs3b3L69GmloHt6eipXElq1asXly5eVY5OSkvDz8wOgXbt2vP/++2RkZBRKYNNoNJw6dYr+/fsD0KxZM1555RV+//13zMzMcHR0pF27dkB+fGvNmjU5f/48qampTJ06VTlXXl4eFy5cwMXFhdTUVMLDw3Fzc1OubLRv356FCxdy79493N3deeONNx7q55UrV3B0dFT6VlJKWkpKCrNmzQKgVq1a9OjRo9Dn1rlzZ6XI/vjjj1y+fJmhQ4cq23U6HTdv3iy2X0WlsJX0WXl5eQEofRVCiOrKZAp6ixYtuHTpEpmZmdjb29OsWTPUajXr1q3jxIkTJaaVmZubYzAYlLYK3rezs2P79u2kpKSQlJTEokWLSEhIoG3btiQkJJCUlIRarSYmJoYNGzYU6s+aNWvIzMxU1m7/6KOPCqWj3b+6mLm5eaFtHh4eLF26tFB7GRkZDyWwlZbRaOSFF15ArVYXuX3btm0kJydz4MABFi9eTGJiIj179qRNmzb8/PPPrFy5ki1btrBo0aJC/dRoNIwZM4bIyEhmzJhRYkrao9jY2BR63alTJxYsWPDQfsX1q6gUtqLWxn+QJK4JIao7k3kornHjxnTv3p1Zs2YVSi67e/cuUHJaWaNGjZQUsYMHD3Lz5k0gv4jeu3ePTp06MX36dOzs7EhPT1eSw3r37k1ISAh//PFHoT8ICs7n4OCAtbU1165dY8+ePeU6XltbW1555RUSEhIASE1N5fTp07Rp04Y2bdqQmprKkSNHgPwZ8p07d2jSpAnPPfccW7duVdpJTU1Fo9Hw119/YW5uTo8ePQgJCSEjI4Pbt29z6dIlHBwc8Pf3Z8KECUWmrdna2hIeHs6GDRu4fv16iSlpbm5uyh8UmZmZJX4uHTt25Mcff+TcuXPKe8eOHQMotl9FpbCV9FkJIYSpMJkZOsD8+fNZtmwZAwYMwMLCAnt7e+rWrUtAQAC3b98uNq1s0qRJBAcHs27dOt544w3+/ve/A/Dnn3/y0UcfodPp0Ov1dO7cmTZt2pCQkEBsbCxmZmYYDAbCw8MxMyv8t9Hw4cOZNGkSPj4+1KtXjw4dOpT7eBctWsTHH39MbGwsFhYWLFiwQLlcHRUVxaeffsrdu3cxMzMjKCgIDw8PVqxYwbx581i9ejUGg4E6deqwZMkSzpw5w+effw7kP2EeEBBAvXr1WLFiBYmJiVhaWqJSqQgNDS2yLy4uLnh7e7Ny5UpmzpxZbErahAkTCAkJwdvbGwcHB1xdXYuNMW3cuDELFy5k5syZ5OTkkJeXR7t27WjVqhU7d+4ssl/FpcOV9FkJIYQpkLQ1UaHy8vIwGAxYW1uj0WgYMmQIISEheHh4VHbXSqUgEanOb/+WhWVEqVTEwjKmuE79/Ux5fJK2JqqNzMxMxo4di16vJzc3Fx8fn2pXzO/3t/e+kLQ1USpGnRaVhVVld0OYICnookLVqVOH+Pj4yu6GeAymPAuCyhufFHPxtJjMQ3FCCCHEs0wKuhCiSKY8O4fyG59B0tNEFSGX3IUog7Nr/4GZ9uHV88Szo+X4byu7C0IAMkMXQgghTMIzP0P38vLCysoKKysr8vLyGD16NAMHDizX9u8PfimrlJQUAgICaNy4sfJeSEhIoSVZn4bdu3dTt25dWrVqBcCVK1d46623aN68OQaDAUtLS2bNmqUsNyuEEKJiPfMFHfIDPZycnDh79iz+/v507tyZevXqVXa3iuXo6PjET4rrdDosLEr/Y9+9ezeurq5KQYf8pXELVn2Li4tj5syZ7Ny584n6VRS9Xo+5uXm5tfc4nvTzEUKIyia/ue7j5OSEvb09165d49ChQ3z11Vfk5eUBEBQUpKz25uXlhZ+fH0lJSdy4cYPRo0crq6L9+uuvhIeHA/nBJvev23Ps2DHmzp3L3bt3qVGjBjNnzqRVq1ZKNOqgQYP48ccfycnJYdGiRWzcuJGjR4/y3HPPsWzZMhwcHIrtu16vZ9GiRfz4448AynK15ubmBAcHY25uTlpaGtnZ2ajVahISEoiLi1OCU8LCwmjatCmHDx8mIiICg8GATqdj3Lhx1KxZk71795KUlMTmzZsZNWoUr7/+eqHzu7u7F1pzff/+/UXGngIsXryYHTt2UKtWLdzc3Dh48CDx8fGkpKQwZ84cXF1dOXnyJJMnT+bll18ul7jX7Oxs5syZoywR6+fnx9ixY4H8Vf1cXFw4evQoNWvWZOXKlU/+j0gIISqJFPT7/Pbbb7zwwgu4uLjQsGFDfHx8UKlUXLhwgZEjR3LgwAFl35ycHDZt2sSVK1fw9fWlX79+WFpaMmXKFBYtWoS7uzs7duxg/fr1QP565oGBgcyfP58OHTqQlJREYGAg33//PZAfjfraa68xbdo0Vq1axciRI1m7di1z5swhLCyMdevWMWXKFCB/LfKCNDYrKys2b97Mpk2bOHXqlDJzHzt2LJs2beLdd98F4NSpU6xbt44aNWrw66+/snPnTtavX4+VlRX79+8nNDSUjRs3snLlSsaMGYOPjw9Go5GsrCzs7e3x8vLC1dVV+cPlypUrhT67//znP0oISkmxp3v37uWHH35ArVbz3HPPERgYWKid8+fPK/GnOp2OQYMGlUvc67JlyzAYDCQmJpKdnc0777yDk5MTXbp0ASA9PZ24uDiZnQshqi357QUEBgZiNBq5fPkykZGRWFlZcfr0aaZNm8a1a9ewsLDg5s2b3LhxQ5klFxSvBg0aYG9vz19//UVeXh7PP/887u7uyj4ff/wxAGlpaVhaWiqzfA8PDywtLUlLS8PGxoYaNWookaUtW7bkpZde4pVXXlFeJyUlKf0t6pL7wYMH6devH1ZW+YtW+Pv7s3v3bqWge3t7K4lie/fu5fTp08qzAkajkczMTCB/pr18+XIuX75Mx44dad26dbGfW1ZWFn5+fmRkZKDVatm8eTNQcuxpSkoKvXr1UvrSt29fli1bpuz38ssv07ZtWwAuXrxYbnGvBw8eJDQ0FJVKpQTrHDx4UCnovr6+UsyFENWa/Abj/+6h79y5k5CQENq1a8fUqVMJDg6mR48eGAwGWrduXWL8qV6vL7Ltx407LSjEAGZmZoVel9T+47o/HtRoNNK/f38mTZr00H4jR47Ey8uLpKQkIiIi6Nixo3Jl4EEF99D1ej3z589n2rRpfP3110Dxsael7Wd5xr0+7nmFEKI6kq+t3adXr1507NiRf/3rX2RlZdGgQQMAtmzZglb76MUjmjZtSk5ODr/++isAu3btUma+TZo0IS8vj+TkZCB/xqjT6WjSpEm59L1Dhw5s3bqVvLw88vLy2Lp1a7FrpHt5eaFWq/nrr7+A/PvvJ06cAPKvJDRq1IjBgwczYsQI5Z6zra1toVja+5mbm/PPf/6TGzdusGfPnhJjT93c3Pjuu++Ue9zfflv8d3jLM+61Q4cObNmyBaPRiEajYceOHdV6DXkhhHiQzNAfMG3aNPz9/fnoo48YP348NWvWpFOnTtSqVeuRx1pZWfHFF18UeiiuIIrVysqKpUuXFnooruDyfnl45513uHz5Mv369QPA09OTQYMGFblv+/btmTx5MuPGjUOv15OXl4e3tzeurq6sXbuWlJQULC0tsbKyYtasWQD06dOHkJAQdu3aVeRDcdbW1kyePJno6GgSEhKKjT3t3r07R44coU+fPtSsWZM2bdoo97kfZGFhUW5xr+PHjyciIgJfX19lPJ07dy77By+EEFWExKeKCqfRaLC1tcVgMDBz5kzq1q1b7GX9qqog4tDqyBJZKe4ZVxVXipNgnepL4lNFtRIUFMR///tfcnJyaNmypfL1serIafgqiU99xhl0WswkQU1UAVLQRYX78ssvK7sL4jGY8iwIym98UsxFVSEPxQkhhBAmQAq6EJVEYjeFEOVJLrkLUQZH4saA9vYTHfvG+9vKuTdCiGeZFPQqoiD1zdramtzcXF5//XVmz56NpaVluZ9r5syZ9OvX76GvnpVGVFQUcXFx1K1bl9zcXFq2bElERIQs0CKEEJVELrlXIUuXLkWtVrN9+3bOnz/Pf/7zn6dynrlz55apmBfo27cvarWab7/9lj///JN169aVQ+/y6XS6cmurOpxXCCHKSmboVVBubi65ubnY29tz8OBBlixZQm5uLnq9ng8++IDevXsD+UEmBcliLi4uXL58mXHjxtGtW7cStw0fPpzRo0fTrVs3goODsbKy4uLFi/z111+0adOGzz77DJVKxbVr15gxYwY3b96kYcOGQP6CNQUBLQWsrKxo27Ytf/75J5AfRLN48WJ++eUXtFotzs7OhIWFYWNjU2KbZU2F8/HxYdOmTcTGxmJlZYXBYGDJkiU4Ojo+MunO39+f5ORkBg0axJAhQyrwpy2EEOVDCnoVEhgYiLW1NZcvX8bT0xNPT0/u3OqRvvEAACAASURBVLlDXFwc5ubm3Lx5E39/fzw9PalZsyYzZszgvffew8/Pj+PHjxdaGa6kbQ86d+4csbGxqFQq+vXrR1JSEh07dmTOnDm4u7szfvx4/vvf/+Lr64unp+dDx2s0Gn755RdlcZhVq1ZhZ2fHN998A8DChQuJiYlhypQpj2yzLKlwAAsWLGDnzp3UrVsXrVaLXq9/rKS7V199laCgoLL/EIUQopJIQa9CCkJicnNz+fDDD4mNjaVLly6EhoZy6dIlzM3NuXPnDmlpaTRr1oyzZ88qS5m++uqrODs7A/kFtrhtRenRo4eyOEqLFi2UpLWUlBRl6df69esrSXEFtm7dys8//8ylS5fw9PRUks327t2LRqPhu+++A/Jn7C4uLgCPbLOsqXBvvPEGwcHBdOvWja5du9KwYUPOnDlTYtKdtbU1vXr1KsVPSgghqh4p6FWQtbU1Xbt2Zd++ffzwww94eXkRHR2NSqWiZ8+ehVLfSkpze9ykt8dNjntQ3759CQoK4saNG7z77rvExcUxdOhQjEYjs2fPfqhYP46ypsJFR0dz/PhxkpOTGTFiBGFhYbz00kslnvP5559/7M9KCCGqKnkorgoyGAz88ssvNG7cmKysLOrXr49KpVJmw5Cffta8eXO2bcv/6tMff/zB2bNnH7mtNNzc3EhISADgzz//VJLiHuTg4MDMmTNZvnw5OTk5eHl5ERsbS05ODpB/xSA1NbVUbULpU+F0Oh3p6em0atWKgIAAOnbsyKlTp5560p0QQlQFMkOvQgruoefl5dG8eXMmTJjAiRMnCA8PJyoq6qFL55999hmhoaHExMTg5OSEk5MTdnZ2j9z2uGbOnMmMGTNITEykQYMGtGrVCltb2yL37dq1K02bNmXjxo0EBAQQHR3NgAEDUKlUqFQqJk6ciKOjY6naLG0qnMFgIDg4mKysLFQqFX/729+YNm3aU0+6E0KIqkDS1qqx7OxsatSogUql4vz58wwfPpxdu3ZRs2bNErc9rpycHCwsLLCwsOD69esMGDCA2NhYmjZt+sR9fhptVoaCRKS8Y4tNdmEZWcu9+jLlsYFpj0/S1p5RR44cYcGCBRT8TRYREaEU7JK2Pa6LFy8SFBSE0WhEp9MxceLEMhfep9FmZWr77uonTluTlC4hRHmSgl6NFXy1rbTbHpeLiwtqtbpMbVREm9WVFHMhRHmSh+KEEEIIEyAFXQhRJFO9R1nAlMf3NMeml5TAKksuuQtRBkmbRmN4wofihKiOuv9je2V3QRRDZujPAC8vLzw9PQstGBMfH4+zszPr1q0jPj6ewMDAh45LSUmhdevW+Pn54ePjw6hRo7hy5QoAwcHBdO7cGT8/P3r27Kmsqy6EEKJySEF/RtStW5effvpJeZ2QkEDLli0feZyjoyNqtZpt27bh5OTEp59+qmwLCAhArVazZcsWDhw4wK5du8qtv5WVeva4q+QJIURVI5fcnxH9+vUjPj6eLl26kJ6ezt27d3FycipVGx4eHixYsOCh921tbWnZsiVXr14F8leGmz9/PmfOnCE3Nxd3d3dCQkIwNzd/ZAqci4sLR48epWbNmqxcuZKYmBi+//579Ho99erVIyIiAgcHB3bv3k1kZCRmZmbo9Xo++ugj3N3diY6OZtu2bVhbW6NSqfjqq6+wt7fnwIEDfPHFF+j1emrXrs0nn3zCyy+/TEpKCnPmzMHV1ZWTJ08yefJkunXrVi6fuRBCVCQp6M8INzc34uLiuHPnDgkJCfTt25c//vjjsY83GAx89913vPLKKw9tu3XrFmfOnOHDDz8EYP78+bRv3565c+diMBiYPn06W7ZsYdCgQY9MgUtPTycuLg4LCwvUajXp6el8/fXXmJmZERcXx6effsrnn3/O0qVL+eSTT2jbti16vZ579+5x+/ZtYmNj+emnn3juuefQaDQ899xz3Lp1ixkzZrBu3TqaNWvG5s2bmT59Ops3bwbyY2gL2hJCiOpKCvozQqVS0atXL7Zv38727dvZuHHjYxX01NRU/Pz8MBqNODs7ExISomyLiYlh06ZNpKWlMWTIEBwdHYH8lLRjx46xZs0aIH91uHr16j1WCpyvry8WFhZKOydOnKBfv34ASiY65KeqzZ8/n7feeovOnTvj5OSEXq+nUaNGzJgxA09PT7p27YqtrS1Hjx7FxcWFZs2aAdC/f3/Cw8PRaDQAvPzyy1LMhRDVnhT0Z0i/fv0YOHAg7du354UXXnisYxwdHYmPjy9yW0BAAMOGDePChQsMGTIET09PunTpgtFoZNmyZTRs2LDQ/gUFtKRkswfT1saNG8eAAQMe2i80NJQzZ86QnJzMpEmTGDVqFIMGDeLrr7/m8OHDJCcn4+/vz6pVqx45xvvPKYQQ1ZU8FPcMadiwIVOmTGH8+PHl2m7Tpk0JDAxk8eLFGI1GvLy8iImJUR4wy8jIID09vdQpcF5eXsptAsjPVT99+jQAFy5cwNnZmffee48+ffpw/PhxNBoNGRkZuLm5ERgYiJOTE+fOnaNNmzacPn1aSXxLSEigRYsWxYbCCCFEdSQz9GfMO++8U+T7+/fvp3Pnzsprf3//UuWZv/POO3z11Vfs2bOH0NBQFi5ciJ+fHyqVCktLS0JDQ2nYsGGpUuD69u3L7du3GTZsGJA/Yx8yZAguLi58/vnnXLp0CXNzc+zt7Zk7dy4ajYYPP/yQnJwcjEYjLVq04K233sLa2poFCxYwffp0dDodtWvXZuHChaX41IQQouqTtDVRocojBa4qKEhEyvzjC1lYRjxTqsLCMpK2VjSZoYsKVR4pcFWJxzv/fuK0NSGqI71Oi7kEC1VJUtBFhSqPFDhRMUx5FgSmPb6nOTYp5lWXPBQnhBBCmAAp6EKIIpVlhieJXEJUPLnkLkQZ7Pl6JPo8eSjuQb6jd1Z2F4R45lTIDN3Ly+uh7xv7+/uTkpJCZGQkO3bsKPH4+Ph40tLSCr138eJFAgMD8fLyom/fvvTp04fPP/+8XMI1oqKi+OyzzwDYsGEDsbGxZWovJSWlUDAKgLOzM76+vvj5+eHt7c3ixYvLdI7inDp16qHP18/Pj5ycnDK1O3z4cLp3766krS1btqxM7QkhhCibSp+hT5o06ZH7JCQk8MILL9CkSRMArl+/ztChQ5k2bRpLly4F8lchW7lyJVqtlueff77Q8TqdTllOtLSGDBnyRMfd79ChQ9y9e/ehh8E2btyIjY0N9+7do3fv3nh5edG6desyn+9+p06dYt++fbz99tvKe2q1ulzanjVrFt26deP69eu8/fbbdOzYsdz6X5afWVno9XrMzc0r/LxCCFFWlV7Qg4ODcXV1ZdiwYUUmaF25coUTJ04wZ84clixZQlBQECkpKbi7u+Pv76+0Y2try5QpU5TXXl5evP322yQnJ+Pk5MSUKVOYOnUq2dnZ5Obm0qVLF2bMmAFAVlYWM2fO5OzZszg4OPDSSy/x4osvAvmz9bt37xIUFARQbPpXVFQUaWlpZGVlkZ6eTqNGjYiMjOTy5cts3LgRg8FAUlISvXv3JiAgoNBncO/ePXQ6nbLAys2bN5k9ezaXL18GYMyYMfTt2xeAY8eOMXfuXO7evUuNGjWYOXMmrVq14tatW0ybNo1bt24B0KFDB8aNG8fSpUvRaDT4+fnRvn17Zs2ahbOzM4cPH8bGxgYvLy/8/PxISkrixo0bjB49WlnI5ddffyU8PBwAd3d39uzZw7/+9a+HUtrq1q1LkyZNuHr1Kq1bt+b69evMmTOHq1evkpubS+/evfnggw8e2eaDP7OwsDAWL17ML7/8glarxdnZmbCwMGxsbNi0aROxsbFYWVlhMBhYsmQJTZo04ZNPPiE5ORkrKytq1KjBxo0bAdi6dSurV68GoFGjRnzyySfUqVOH+Ph4vv32W2xsbLh06RILFy4sMoBGCCGqugor6IGBgYW+r3vx4sWH9ikqQcvd3Z2tW7cyevRoJdZyzZo1dOzY8ZHn1Gg0fPPNN0D+l/VXrFiBjY0NeXl5jBkzhgMHDtC5c2e+/PJLbGxs2LVrFxkZGfj7+9OrV6+H2isp/QvgxIkTfPPNN9jZ2TFmzBgSExMZNGgQgwcPLvRHQYHBgwcDcOnSJYYMGULTpk0BmDNnDs2bN+fLL7/k+vXr+Pv706JFCxo3bkxgYCDz58+nQ4cOJCUlERgYyPfff09iYiKNGjVSbg/cuXOHmjVrEhgYyL59+5QrGUXJyclh06ZNXLlyBV9fX/r164elpSVTp07liy++4PXXX+c///kPa9euLfL4tLQ0bt++jbu7OwBBQUGMHz+e9u3bo9VqGTlyJK+++irt27d/ZJv3/8yWLVuGnZ2d8nrhwoXExMQwZcoUFixYwM6dO6lbty5arRa9Xs/p06dJSUlhx44dmJmZKUvGnj17lkWLFhEfH0/dunVZsmQJERERLFmyBICjR4+iVqtp1KhRsZ+REEJUdRVW0JcuXVpoZnf/7LpAUQlajyMmJobt27dz+/ZtFi9eTLt27QCUWS3kX0pdsGABR44cwWg0cvPmTU6fPk3nzp1JSUlh1qxZANSuXZs333yzyPOUlP4F+d+xtre3B6BVq1bKDLs4BZfcMzMzee+999i9ezc9evTg4MGDBAcHA/mz3y5dupCSkoLRaMTS0lJZktXDwwNLS0vS0tJo3bo1sbGxfPbZZ7i5uZXqu94Fl+MbNGiAvb09f/31F3l5eTz33HO8/vrrALz55pvK2ArMmTOHRYsWceHCBYKCgqhduzZ3797l0KFDZGRkKPtlZ2eTmppKnTp1Htnm/T+zvXv3otFo+O6774D8tdxdXFyA/H8rwcHBdOvWja5du9KwYUMaNmyITqdj5syZuLu7K38ApqSk0KVLF+rWrQvk/yHl5+ennKddu3ZSzIUQ1V6lX3K/X3EJWg9q0aIFx48fV14HBAQQEBCAv78/eXl5yvv3p2itWbOGzMxMNm/ejLW1NR999BG5ubml6l9J6V9AoSsQ5ubmj92+vb09Hh4e/Pzzz/To0aNUfSrQtm1bEhISSEpKQq1WExMTw4YNGx7r2Af7/bgPFhbcQ//tt98YPXo0HTp0oH79+qhUKr755hssLS0L7V8QrFKSB9PWZs+eXeSa8tHR0Rw/fpzk5GRGjBhBWFgYXbp0Yfv27aSkpJCUlMSiRYtISEh45DltbGweY7RCCFG1VanvoReVoAX5v3CzsrKU/d59910OHjzI1q1blff0en2hYv6grKwsHBwcsLa25tq1a+zZs0fZ9sYbbygRof/73//YvXt3kW2UlP5VEltb20L9f5BWq+XIkSM0btwYyL///fXXXwNw48YN9u/fzxtvvEGTJk3Iy8sjOTkZgIMHD6LT6WjSpImSZta7d29CQkL4448/MBgMjzx3cZo2bcq9e/f47bffANi9ezeZmZlF7vvaa6/x7rvvEhkZia2tLa+99hoxMTHK9j///JMbN26Uqk3I/7xjY2OVJ/I1Gg2pqanodDrS09Np1aoVAQEBdOzYkVOnTpGRkcG9e/fo1KkT06dPx87OjvT0dNzd3dm/fz83btwA4Ouvv8bDw6PUn4kQQlRlVWqGXlSCFuQneX366aesXr2aoKAgPDw8WLduHV988QVLly6lVq1aWFlZ0aNHD1q2bFlk28OHD2fSpEn4+PhQr169QrO+8ePHExoaire3Nw4ODsol4QeVlP5Vkh49erB161b8/PwKPRQ3ePBgzMzMyM3Nxc3NTXmiftasWXz88cf4+voCMH36dJo3bw7k37q4/6G4yMhIrKysOHToELGxsZiZmWEwGAgPD8fMzIwOHTrw73//mz59+uDm5qbcWngUKysrPv/8c8LCwgBwc3OjTp06xSajjRs3jjfffJOTJ0+yaNEi5s+fr/TfxsaGuXPn4uDgUKo2AwICiI6OZsCAAahUKlQqFRMnTqRhw4YEBweTlZWFSqXib3/7G9OmTePq1at89NFH6HQ69Ho9nTt3pk2bNpiZmTF9+nRGjx4N5MfIfvLJJ4/1OQghRHUhaWuiWBqNRnlGIDk5mZCQEPbs2YOZ2ZNf2HkabVaGgkSkaycXycIyRagOC8vIWu7VlymPT9LWxFPx/fffExsbi9FoxMrKikWLFpW58D6NNoUQQkhBFyXw9/cv8tsIVa3NytR9UKzEpxZBIjaFqHgyNRJCFKng4cUnIcVciIonBV0IIYQwAVLQhahgEi0qhHga5B66EGWQuHkEulI+5T541HdPqTdCiGeZzNCruaKiaY1GI2vXrsXHxwdvb2/69u3LmDFjOHz4MJC/FGrr1q3x8/PDz88PX1/fQhGrw4cPx9XVldu3/69QpaSk4OzsrMTK3t+Gj48Pw4YNIzU1tQJGLIQQoigyQzdBS5Ys4ZdffmH16tXUq1cPgMOHD3PhwgVlnXtHR0dldbzz588zcOBAvL29la+QOTk5sX37doYOHQrkZ9I/uGjP/W0sXLiQ+fPns2rVqnIbR2VEmVZWbKsQQpSV/OYyMdnZ2fz73/9GrVYrxRzyA0gKivmDsrKysLW1LfR98L59+6JWqxk6dCjZ2dn89ttv9O7dG6226Pu/bm5u7Nu3T3mdkJBAXFycEmATFhZG06ZN0Wq1REREcOjQIWrXrs0rr7zCzZs3Wbp0aZFRplqtlkWLFpGdnQ3kp/Z17dq1yLjY0NBQDh8+TEREBAaDAZ1Ox7hx4/Dx8SkxkvbB2NZ58+Y9+Q9ACCEqiRR0E5Oamoq1tbUSxVrSfn5+fmi1Wv773/+yYMGCQtsbNmyItbU1qamp/P777/To0QMLC4siC7rBYGDPnj1Katuvv/7Kzp07Wb9+PVZWVuzfv5/Q0FA2btzIpk2buHr1Ktu3b0ev1zN8+HBeeuklpa37o0wzMzMZMWIEMTEx1K1bl+vXrzNgwAC2bdtWZFwswMqVKxkzZgw+Pj4YjUZlHfviImkLEv3uj20VQojqSAq6icvMzGT48OFotVocHR2Jjo4GCl8uT01NZfjw4bRt27bQrL5v374kJCRw9OhRZs2axffff1+o7YI/Cq5du4atrS2bN28G8mNPT58+zcCBA4H8e/oFISwpKSn4+flhYWGBhYUFvXv3LvR95/ujTI8cOcKVK1cYO3assl2lUnHp0qVi42Ld3d1Zvnw5ly9fpmPHjrRu3Rqg2EjagoJ+f2yrEEJUR/JQnIlxdHQkNzeXixcvAvnRrGq1mhkzZiiz2KKOqV+/vvLQXAFvb2+2b9/O3bt3cXZ2LvI4tVrNgQMHcHFxUUJXjEYj/fv3R61Wo1ar+fbbbwtdji/J/VGmRqMRZ2dnpR21Ws3+/ft59dVXlbhYV1dX1Go1I0aMAGDkyJEsX76c2rVrExERweLFix/rvPfHtgohRHUkBd3E2NjYMGrUKGbNmsW1a9eU9+/du1fsMdeuXePixYtKfOv9bf3zn/8kKCioxHNaWVkRFhbGjz/+yMmTJ/Hy8kKtVvPXX38B+Q+3nThxAsi/156YmIhOpyM3N5edO4sP8Wjbti2XLl1S4mIBjh07htFoLDYuNi0tjUaNGjF48GBGjBihRPAWF0krhBCmQi65m4BRo0YVeho8MTGR2rVrM3r0aPR6PS+88AL29vZMmDBB2afgcrnRaESn0zFp0iReeeWVh9ouuC/+KC+++CKjR48mOjqaZcuWMXnyZMaNG6fk1Ht7e+Pq6srgwYM5ffo0vXv35oUXXijxXn/NmjVZtmwZCxcuZN68eeTl5dGwYUNWrFhRbFzs2rVrSUlJwdLSEisrKyUutqRIWiGEMAUSnyoqXEGEqlarZdy4cXh7eyv326uLgojDtFMLTHZhGVOOqATTHp8pjw1Me3wSnyqqlVGjRqHVasnNzcXDw4N+/fpVdpeemO/Ar0r9P50kkQkhngYp6KLCFTwN/6ySYi6EeBrkoTghhBDCBEhBF0IUqSLvUer0kkAnRFnJJXchyuDrLcPJK+VDceJho9/7/tE7CSFKJDP0Mro/7ezevXuMGTOGkJAQ/vnPf/Lqq69y9epVZd/g4GDWrVv3yDYjIyMLpZ8VJyoqSkk/K8228iBpa0IIUbVIQS8nmZmZjBo1iqZNmzJv3jzMzc1xcHAgKiqq1G1NmjTpsb///TTpdLoStxesFLdt2zZat27N/Pnzy/X8er2+XNt7HI8asxBCVFVyyb0c3Lp1ixkzZuDl5UVgYKDy/uDBg1m3bh3nz5+nWbNmhY7RarUsXryYX375Ba1Wi7OzM2FhYdjY2BAcHIyrqyvDhg0jKyuL0NBQzp07R7169ahXrx516tRRVm+7du0aY8eOJT09nUaNGhEZGcnzzz8PwNWrVxkxYgTXr1+nefPmzJs3Dzs7O7Kzs5kzZ46yipqfn5+yXvrw4cNxcXHh6NGj1KxZk08//bTIVLMHSdqaEEJULino5WDy5Mm8++67hYo55K8P/v7777N48WK+/PLLQttWrVqFnZ2dkvC1cOFCYmJimDJlSqH9vvzyS+zt7dm1axe3b9/G39+fnj17KttPnDjBN998g52dHWPGjCExMZFBgwYB+YsvbN26lRdffJGQkBCWLVtGUFAQy5Ytw2AwkJiYSHZ2Nu+88w5OTk506dIFgPT0dOLi4rCwsCA2NrbIVLP7SdqaEEJUPino5aBLly7s2LGDwYMHF0orAxg0aBBr1qzh6NGjhd7fu3cvGo2G777LXzVMq9Xi4uLyUNspKSnK8qW1atWiR48ehbZ7enpib28PQKtWrZQZKEDXrl158cUXARgwYABz5swB8pPHQkNDUalUynroBw8eVAq6r68vFhb5/zSKSzUDSVsTQoiqRAp6OfjHP/7BDz/8wIgRI/jqq68KFXVLS0s+/PBDvvjiC/72t78p7xuNRmbPnk2HDh3KdO77VykzNzcnNze3TO1B4eSxglSzpKQk1Go1MTExbNiwAfi/CFatVsvUqVMJCwsjMjJSSVubNGlSqc9dVNra+vXri9y3qH6NHDkSLy8vkpKSiIiIoGPHjg9d9XjUmIUQojqSh+LKyfvvv0+/fv0YMWJEoZQzyJ/xZmRkcOjQIeU9Ly8vYmNjycnJAfIv+Rb1lLibmxtqtRrIf/Buz549j92nffv2kZGRAUB8fLySLtahQwe2bNmC0WhEo9GwY8cOPDw8imyjuFSz+0namhBCVD6ZoZejDz74AKPRyIgRI6hfv77yvpmZGVOnTuWDDz5Q3gsICCA6OpoBAwagUqlQqVRMnDgRR0fHQm1OmDCBkJAQvL29cXBwwNXVFVtb28fqz+uvv86UKVO4du0azZo1Uy45jx8/noiICCV5rE+fPnTu3LnINopLNXuQpK0JIUTlkrS1Ki4vLw+DwYC1tTUajYYhQ4YQEhJS7Iy6OjCltLWTZz6ThWXKQWUsLGPKiV2mPDYw7fFJ2poJy8zMZOzYsej1enJzc/Hx8anWxRxMK21tUP+1pf6fTjxMp9diYS6hNUKUhRT0Kq5OnTrEx8dXdjfK1bOetlZdVOQsSIq5EGUnD8UJIYQQJkAKuhCiSKZ6j7KAKY/vScYmiXfVn1xyF6IM/p96BLnyUJwwAR8O/a6yuyDKSAq6qPZ27tzJv/71L4xGI7m5ubRs2ZI7d+7QvXt3hgwZouxnNBrp0aMH8+fP58qVK8ybN4/69euTl5dHgwYNmDt3Lg4ODpU4EiGEeHJyyV1Ua9evXyc8PJzly5ejVqvZuXMnY8aMoX///iQkJBTaNyUlBTMzM9q3bw+Ah4cHarWa7du3Y2NjQ3R0dGUMQQghyoUUdFGt3bx5EwsLC2rVqgXkr/XeokULunfvzqVLlwqtvhcfH4+/vz8qlapQGyqVivbt2/Pnn39WaN+FEKI8SUEX1ZqLiwutWrWia9euBAYGEhsby//+9z+srKzw9fVly5YtQP5iNrt37y7yO+9arZYDBw5UiQx6IYR4UlLQRbVmZmbGsmXLWLt2Le7u7uzfv58+ffpw+/ZtBgwYwLfffoter2fnzp20a9euUFRrUlISfn5+dOjQgYyMDHr16lWJIxFCiLKRgi5MgpOTE0OHDmXNmjXY2dlx6NAhXFxcqFu3LgcOHGDLli3079+/0DEF99D379+PSqUiMjKyknovhBBlJwVdVGvXrl3jyJEjyuu//vqLjIwMGjRoAED//v2Jiori4sWLdO/evcg2bG1tCQ8PZ8OGDVy/fr1C+i2EEOVNCrqo1nQ6HVFRUfTs2RM/Pz/Gjh3L5MmTadGiBQA+Pj6cP38eHx8frKyKX17UxcUFb29vVq5cWVFdF0KIciVpa0I8gYJEpN9SF8jCMsIkVKeFZSRtrWgyQxdCCCFMgKwUJ0QZvOf3lcSnCpMgEbbVn8zQhRBF+u233yq7C0+VKY/vScYmxbz6k4IuhCiSqd6jLPA0xyfJZaIyyCV3IcpgWeIIcnTyUJwoLGRw9XnATJgOmaELIYQQJkBm6KLSeXl5UaNGDb799lvMzMyU91asWIGTk1OxxyUnJ/P555+j1WrRarU4ODgQGxtLeHg4dnZ2TJ8+vdD+w4cPx9/fn7///e8EBATQuHFj9Ho9tWrVIjw8HEdHx6c6TiGEeJpkhi6qhLt376JWqx97f51OR2BgIBEREUpsanBwMCqViv79+6NWq9Hr9cr+6enpnDx5Em9vbwAcHR1Rq9Vs27aN1q1bM3/+/HIfkxBCVCQp6KJKmDhxItHR0Wi1hR8m8vLy4uzZsw+9zs7O5u7du7z44ovKthYtWqBSqWjVqhW1atXip59+UrbFx8fTq1cvnn/++YfO7ebmJtGpQohqTwq6qBJcXV1p2bIlGzZseKz9a9asyaBBg3jrrbf44IMPiImJKVSU+/fvT3x8PAAGg4GtW7c+FM5SsG3Pnj0SnSqEqPakoIsqY/LkyaxcuZLs7OzH2v/jjz9GrVbTvXt3jh8/jo+PDxcvXgSgT58+HDhwgNu3AFfLcgAAIABJREFUb5OcnMzzzz9P27ZtlWNTU1Px8/PDw8ODpKQk3n333acxJCGEqDBS0EWV0bRpU7p06cKaNWuU98zNzTEYDMrr3NzcQsc0bNiQgQMHEhUVRdu2bfnhhx8AqF27Np6enmzbto0tW7bg7+9f6LiCe+gHDhzAxcWFsLCwpzcwIYSoAFLQRZXy4YcfEhcXp8zSGzVqxPHjxwE4ePAgN2/eBCA7O5uffvqJgmyhzMxMrly5osSmQv5l9w0bNrBv3z769u1b5PmsrKwICwvjxx9/5OTJk09zaEII8VRJQRdVyksvvYSfnx+3b+cv1jJp0iTWrFmDn58f+/bt4+9//zsARqOR9evX4+3tTZ8+fRgyZAi+vr68+eabSludOnUiMzMTNze3Qg/PPejFF19k9OjRREdHP93BCSHEUyTxqUI8gYKIwwNpC2SlOPGQyl4pzpTjRcG0x1eW+FRZWEaIMhjvK2lr4mGSXCYqg1xyF0IUyZTTyODpjk+KuagMUtCFEEIIEyAFXYhnVJ5EfAphUuQeuhBlMG/ne2RX04fiFg3YVdldEEKUo2o9Q8/LyyMqKoqePXvSu3dv+vTpQ2BgIOfPny9Tu1euXMHd3R2Aa9euMXz48DK1l5mZycqVKwu9FxwcTOfOnfHz88PX15eRI0c+tfXEo6KiCq2RHhkZyY4dO8rUZnx8PK+//jp+fn68/fbbBAQEcOPGjbJ2VQghxBOq1gU9JCSEM2fOsHnzZrZv345arcbf35+0tLRC+xkMBp7023n16tVj7dq1ZepnZmYmq1ateuj9gIAA1Go1iYmJtGjRghUrVpTpPMWJjo4mLy/v/7N352FVlevDx7/MqASiIeec0jQTiFDRFCec0BQHQAZNyykJjQbUtAg0h1TIwAgHNNSflklOgCiJDZJaKajhkMecUAE1R1LZKLDZe71/8LKOJE6JIrv7c11el6zhGdYGbtaznvXc6tdjx46tkrXLO3bsSEpKCt988w116tSp0ve4H+QzexClpaWPvE4hhKgKNXbI/dSpU/zwww9s27YNa2trAIyMjOjWrRtQdld67NgxNBoNZ8+eZfXq1SxatIhdu3ah1WqxtbUlIiKCp556CoCVK1eyfPlyrKys6Nq1q1rP6dOn8ff3JzMzE4D9+/cTHR2trmQWEhJCt27d1OMGDx7Mtm3buHHjBrNmzaJNmzZ89NFHFBQU4OPjQ61atVi1alWFvuj1egoLC7GxsVG3xcfHs2HDBgCaN2/O5MmTqVOnDoWFhcycOVNdPc3Hx4egoCCgLHCnpqZiYWGBkZERX375JTExMQAMHjwYY2NjVqxYQUREBC4uLgwdOpR58+Zx8uRJCgoKyMvLo1GjRsTGxlKrVi0KCgoIDw/n2LFj2NvbY29vT/369QkNDa3QfiMjI9q2bcvWrVsrtP+7775Dp9Nhb2/PjBkzsLOzu2OZlX1m+/btY+HChZSUlGBmZkZYWBiurq6cOHGCsLAwbty4gV6vx9fXl8DAQH744QdiY2MxNjZGp9Px4Ycf0q5dO3JycpgyZQr5+fmYmpoyfvx4unTpAoCjoyNvv/02W7dupXPnzowbN+5vflcKIUT1qbEB/dChQzzzzDMVguBfHThwgKSkJOrVqwdAUFCQGozWrl1LdHQ0MTExHD58mIULF7J+/XqefPLJ267rfe3aNaZOnUp8fDwNGjTgwoULBAQEkJqaCsCVK1dwdXVl/PjxbNiwgejoaFatWsWUKVPUHN03i4+PZ+3atVy6dAkrKys109i2bdvYsGEDq1atok6dOoSGhhIXF8d7771HXFwcer2ejRs3UlhYyMsvv4yDgwMtW7Zk+fLl/Pzzz1haWqLRaLC0tGTq1KkkJCSoZVXm4MGDrFu3jieeeILAwEA2btzIoEGDWLBgAdbW1mzevJkrV67g5+dH7969bzm/pKSE7du3q3f9KSkp5OXlsWbNGoyNjUlISODjjz9mzpw5dy3z5s8sNzeXuLg4li5dipWVFceOHSMoKIitW7eSkJCAh4cHY8aMAeDq1asAzJ07l48++ohWrVqh0+m4ceMGABMnTmTQoEEMHDiQ48eP8+qrr5KWlqZ+b1hYWJCYmHjb7yUhhHjc1diA/lfHjx9nwoQJFBUV0blzZ2xsbOjSpYv6Cxtg+/btJCQkcP369QpDq7t27aJbt27q8qAvv/wyaWlpt9Sxd+9eTp8+rd4RQ9ndaU5ODra2ttSuXZvu3bsD4OrqyuzZs+/Y5tGjRzN06FAAFixYwOTJk4mLi2Pnzp307dsXKysrAAYNGkRERARQtp55eHg4RkZGWFlZ0a9fP3bu3Im7uzuNGjXi/fffx93dnW7duqnn3427u7s6ytGiRQtyc3MByMzMZPLkyQDUrVuXnj17Vjhvx44d+Pj4cPr0aZo2bUqfPn0ASE9P5+DBg/j6+gKg0+nUttytzJs/s59++onc3FxeffVVdX9paSmXLl2ibdu2REVFcePGDdq1a0f79u0BaN++PZGRkfTq1YsuXbrg4OCARqPh999/V9OnPvfcczz//PPs27cPDw8PALWtQghRU9XYgO7s7ExOTg7Xrl3D2tqa5557jpSUFL766isOHjyIjY1NhTvSM2fOEBkZybp162jYsCFZWVlMnDjxvupUFAVHR0dWrlx5y77Tp09jbv6/xSSMjY3v63msp6fnLRPn7oeJiQlr1qwhKyuLjIwM/Pz8WLJkCU5OTnc99+aVzkxMTG7JaHY7HTt2ZO7cuWg0GgIDA4mNjeX9999HURSCg4MJCAi47378dRShc+fOfPLJJ7cc17t3b1xdXfnll19YvHgxiYmJREdHEx4ezpEjR8jIyGDs2LG89tpr9zRfoHbt2vfdViGEeJzU2ElxjRs3pkePHkyePJmCggJ1+/Xr1ys9XqPRYGZmhp2dHXq9vsJzbDc3N7Zt28bly5cBWLduXaVltGrVipycHDIyMtRtBw4cuOvkLSsrK4qKiu4Y4DMyMmjcuDEAHTp0IC0tDY1Gg6IorFu3jo4dO6r7EhMTURQFjUbDpk2b6NixIxqNhvz8fNzc3AgJCcHBwYFjx44BZUFSo9HcsY2VcXNzUx8TXLt2jS1btty2f9OnT+frr7/mwoULeHh4kJCQoA6Dl5SUcPjw4fsqE6BTp0789NNPaj+g7HoD5OTkYGdnh5+fH2+99ZY6p+DEiRM4OjoyYsQIvL29+e2337CysuL5558nOTkZKMuFfvjwYVxdXe/7mgghxOOqxt6hA0RGRhIXF0dAQACmpqZYW1vToEEDRo8eTXp6eoVjHR0d8fT0pG/fvtja2tK1a1f27NkDgJOTE2+88QZDhgzByspKnSz1VzY2NsTFxREVFUVERARarZaGDRvedXZ63bp18fLywsvLCxsbG/WPifJn6Hq9HisrKz7++GMAunbtypEjRxg8eDAALi4uBAcHA/Dmm28yY8YMvLy8APD29qZLly6cO3eOd955h6KiIhRFwdnZmV69egEwatQohg8fjqWl5X3N2H/rrbcICwvD09MTOzs7XFxcbjuM7+TkpI4yTJo0iStXrqiPExRFYciQITg5Od1XmY0bNyYqKopJkyZRVFSEVquldevWtGjRgrS0NDZu3IiZmRlGRkaEh4cDMGfOHHJycjAxMcHa2ppZs2YBEB0dzZQpU1i+fDmmpqZ88sknFR7HCCFETSfZ1sRtabVa9Ho9FhYWaDQahgwZQlhYmDpa8LiUWR3KMyJtyIsy2IVlDDmjFRh2/wy5b2DY/ZNsa+KhuHbtGkFBQeh0OoqLi+nfv/8DB96HUWZ1Cu/zRY3NtqbVlWAmSUSEMBgS0MVt1a9fn6SkpMe+TPH3SDAXwrDU2ElxQgghhPgfCehCGIgSyZ4mxD+aDLkL8QDe+H4EV3WPx6S4JB/JnibEP5kEdAN25coVvL29mT9/Pi1atABg0aJF/Pe//2XevHn89ttvfPbZZ5w8eZK6deuiKAr9+/cnMDAQAA8PD8zNzbGwsKC4uJg2bdowdepUzMzMSEpKUtfC12q1PP3008yaNQs7O7vq7LIQQvxjyZC7Aatbty5TpkwhLCyMkpISjhw5wsqVK5k2bRpHjhwhKCiI4cOHk56eTlJSEkuWLOHKlYp3m3PnzlUzqh0/fpzvv/9e3SfZ1oQQ4vEhd+gGrmfPnmzevJno6Gh2795NWFgY9evXJzIykoEDB1bILFe/fn0mTJhQaTnFxcUUFxera77fTLKtCSFE9ZOA/g/w4Ycf0r17dzp27Kiua37o0CE8PT3vem5ISAgWFhbk5ubi7u6Ou7v7LcdItjUhhKh+EtD/AXbu3ImVlRUnTpygpKSkQhKZcjNnzmT37t1cvnyZtWvX8u9//xsoC5AODg4UFxfzzjvvsHz5ckaOHAlItjUhhHicyDN0A5efn09ERATx8fG4uLgwd+5cAJ5//nk1oQnA5MmTSUlJQavVotPpbinHwsKCbt26sWPHDnVb+TP0bdu2YWRkRGxsLICabS0lJYWUlBRSU1MrJMO5k8qyrZWXk5KSws8//8yTTz5J7969WblyJY0aNWLx4sW89957AISHhzNjxgzMzMwYO3Ysa9asuad6JduaEKKmk4Bu4KZPn86gQYNwcnJi0qRJpKam8ttvvxEUFMTq1avZvn27emxJSQl6vb7ScvR6Pbt371Yzwt1Msq0JIUT1kyF3A7Zp0yZOnTpFdHQ0UJYtbsqUKYSHh5OYmMjnn39ObGws06ZNo169epiZmfHGG2/QoEEDtYzyZ+harZZmzZrx1ltvVVqXZFsTQojqJdnWxCNlaNnW5v8RZbALyxhyRisw7P4Zct/AsPsn2dZEjWFo2daEEOJxIQFdPFKGlm1t0UuPT/rUEl0J5pJBTYh/LJkUJ4SBkGAuxD+bBHQhHoBWJ0vFCiEeDxLQhXgAZiby1EoI8XiQgC6EEEIYAAno4rHl4eGBp6cn3t7e9OnTh7Vr1wKwZcsWZs+eXek5mZmZ+Pn5PcpmCiHEY0HGC8VjrXwt+aNHj+Ln50eXLl3o0aMHPXr0qO6mCSHEY0UCuqgRHBwcsLa25vz58/zyyy9s3bpVXZc+JiaGTZs2YW1tjZubW4XzyvfVrVsXNzc3du7cqb42l5ycTEJCgpo8Ztq0aTz77LOPvG9CCFEVZMhd1Ai//vortra2ODk5Vdienp5Oeno669evZ82aNZw8ebLCvh9//JGUlBRWr15NTk6Oum/Pnj2kpaWxcuVKkpKSCAwMVJePFUKImkju0MVjLSQkBEVRyM3NJTY29pbUr5mZmfTt21fN0hYQEEBcXJy6r0+fPmomtQEDBqj70tPTOXz4MAMHDgTK1pu/du3ao+qWEEJUOQno4rFW/gw9LS2NsLAwWrduXSXlKoqCv78/Y8eOrZLyhBCiusmQu6gR+vTpQ6dOnfj8888rbG/fvj1paWlcv34dnU5HYmKius/NzY1vv/2WGzduoNfr2bBhg7rPw8ODlJQUzp07B4BOp+PgwYOPpjNCCPEQyB26qDEmTJiAn58fo0ePVrd1796dffv24ePjo06KO3/+PAA9evRg7969eHt7Y2Njg6urq5qjvW3btowbN47g4GB0Oh1arRZPT09cXFyqpW9CCPGgJH2qMGgajQYrKyv0ej2TJk2iQYMGjB8//oHLfZAUhzWFIaeoBMPunyH3DQy7f5I+VYjbCA0N5cyZMxQVFfHCCy8QFBRUpeVrdaVYYJgBXQhRs0hAFwZtwYIFD7V8WctdCPG4kElxQgghhAGQgC6EqJShPqMEKJG0t8IAyXihEA/gtW9juaK7Ud3NEPdpk+/U6m6CEFVO7tDFQ+Xh4cHRo0cfeb2KovDFF1/Qr18/+vXrx4ABA5g8eTIXL17Ezc2N7OzsCsfn5eXx4osvcv369UfeViGEqApyhy4M0meffcbu3bv54osvePLJJ1EUhe+//56ioiL69etHcnIyEydOVI9PTk6md+/e6jKxQghR00hAF4/c7Nmz2bVrF1qtFltbWyIiInjqqae4fPkyEyZM4PLlywB06NCB8PBwsrKymDFjBnq9ntLSUoKDg+nfvz+XLl1i6tSp5ObmAhAYGMiAAQMoLCxk2bJlrF+/nieffBIAIyMjevXqBYC/vz/BwcGMHz8eExMTFEVh/fr1REVFVc8FEUKIKiABXTxyQUFBhIaGArB27Vqio6OJiYlh48aNNGrUiOXLlwOoq7otXryYwMBA+vfvj6IoFBQUADBz5kyaNWvGggULuHDhAn5+fjg7O1NUVIS5ufltU6G6uLhga2vLzz//TNeuXcnIyMDc3NygJ4EJIQyfBHTxyG3fvp2EhASuX79Oaen/Zhu3bNmS5cuXM3v2bNzc3HB3dwegXbt2LFy4kNzcXDp16kTLli0B2LlzJx988AEADRo0oGvXrmRmZqr778Tf35/ExES6du1KYmIifn5+D6GnQgjx6MikOPFInTlzhsjISObMmUNqaioRERGUlJQA0KpVK5KTk3FxcSElJYXhw4cDMHLkSBYuXEi9evWYMWMGMTExd6yjadOmFBcXV8iN/lfe3t78/PPP5OXlkZ6ezoABA6quk0IIUQ0koItHSqPRYGZmhp2dHXq9nlWrVqn78vLysLKyol+/foSFhfHf//4XvV7PyZMnadSoEYMHD2b48OH89ttvQNkz9jVr1gBw8eJFtm3bRvv27alTpw4jR45kypQp6vN4RVH44YcfyMvLA8DW1hZ3d3fGjRuHm5sbDRo0eMRXQgghqpYMuYuH7rXXXsPExET92tPTk759+2Jra0vXrl3Zs2cPALt27WL58uUYGxuj1+uZPn06xsbGrFixgszMTMzMzDA3N2fy5MkATJ48mSlTpuDl5QXAxIkTadasGQDvvvsuy5cvZ9iwYUBZQG/Tpg1ubm5qO/z9/Rk9ejTz589/JNdBCCEeJsm2JsTfUJ4Rac6ZLbKwTA20yXeqQWfsMuS+gWH3T7KtCVFNlvUea7DpUw2ZLP0qDJE8QxdCVOrXX3+t7iY8NOaSJU8YIAnoQgghhAGQgC6EqJQhPaOUIXbxTyDjTkI8gFGbl3BFV1TdzRB38Y3fhOpughAPndyh/8N5eHjg6emJj4+P+u/06dM4OjpSWFj4t8r8/fff2bRpU4VtPj4+FBXdOfB98MEHNG/enLNnz1bY9tVXX921zh9++IEDBw5U2Hbx4kXef/99evToga+vLwEBAaxZs4ZNmzYxaNCgW8qYN28eYWFhd61LCCEeRxLQBXPnziUlJUX99/TTTz9Qeb///jubN2+usC0lJQVLS8u7nmtnZ8e8efPuu86/BvQbN24wdOhQmjZtyvfff09ycjLLli1Dr9fTs2dPcnJyKqRQVRSF5ORk/P3977tuIYR4HMiQu7ir+8mOFhwczNy5c9FoNPj4+NC2bVsmT56Mo6MjWVlZ1KlTh+zsbGbNmsXFixcBGDVqFL6+vgAMHjyYr776iuPHj/Pcc89VaEdJSQkxMTHs3r2bkpISHB0dmTZtGllZWaSnp7Njxw7Wrl3La6+9hlarpW7duowZM0Y9/4knnmDw4MEA9O/fn6SkJN577z0AMjIyMDMzo02bNg/9egohxMMgAV0QEhKivkttYmJCUlJShf33kx3NxsaGkJAQtm7dyty5c2+pq7S0lDfffJNx48bRp08fAP788091f+3atRkzZgwxMTEsWLCgwrlLlizhiSeeYN26dQBERUURHx/P+PHj8fDwwMXFhaFDhwIwbdo0WrRocds+l68S9+6776p9lgQtQoiaTAK6YO7cuTg4ONx2//1mR7uTkydPUlpaqgZzKFtX/WaDBg1i2bJl7N+/v8L29PR0NBoN3377LVB2x+7k5HRPffwrZ2dnnnzySX766SfatGnDli1bmDhx4t8qSwghHgcS0MUdlWdHW7duHQ0bNiQrK0sNfOXZ0Xbs2EFKSgrx8fF8/fXXD1ynmZkZ77zzDp9++in//ve/1e2KojB16lQ6dOhw1zJeeOEFEhMT73iMv78/SUlJXLhwgTZt2mBvb//AbRdCiOoik+LEHf2d7GhWVlYUFBRUWl6TJk0wNTUlLS1N3XbzkHs5Ly8v8vPz2bVrl7rNw8OD5cuXq7PlNRqNOrHtr3X269eP/Px8lixZQnm6Ao1Gw+rVqyvU8fPPP7NixQqZDCeEqPEkoAtCQkIqvLZWnp4UwNHRUc2ONnDgwAoz4Hft2oWfnx8+Pj68/vrrana0Dh06cOPGDby9vZk5c2aFukxNTYmLi2PVqlV4eXnh7e3Ntm3bbmmTsbEx7777LmfOnFG3jR49GicnJwICAvDy8uKVV15RA7q3tzepqan4+Piwfv16ateuzYoVKzh8+DA9evTAy8uLYcOGYWz8v2/5unXr0qVLFy5evIiHh0eVXU8hhKgOkm1NiL+hPCPSp6czZGGZGqCyhWUMOWOXIfcNDLt/km1NiGryf56vS7a1GqBEVyoJWYTBkyF3IUSlDCnbmgRz8U8gAV0IIYQwABLQhagiktFLCFGdZBxKiAcwavNXXNGVAPCNX3A1t0YI8U8md+hCCCGEAZA7dPFQeXh4YG5ujrm5OVqtllGjRjFw4EC2bNnCnj171DXib5aZmcns2bNvWVP+fp08eZLo6GgOHz6MjY0N5ubmvP7665w4cYLff/+dmJiYCsd/8MEHPP3007z99tsPVK8QQlQHCejioStfK/7o0aP4+fnRpUsXevToQY8ePR5anRcuXGDo0KG89957apKXixcv8ssvvzBgwAAWLlzItWvXsLa2BqCwsJDvvvuOjRs3PrQ2CSHEwyQBXTwyDg4OWFtbc/78eX755ZcKGdliYmLYtGkT1tbWuLm5VTivfF/dunVxc3Nj586d6t17cnIyCQkJ6HQ6rKysmDZtGs8++ywrV66kXbt2DBgwQC3Hzs5O/bpdu3akpqbyyiuvAJCWlkbLli156qmnHsWlEEKIKifP0MUj8+uvv2Jra3tLhrT09HTS09NZv349a9as4eTJkxX2/fjjj6SkpLB69WpycnLUfXv27CEtLY2VK1eSlJREYGAg4eHhABw6dOiu6VNvHtJPSkqS9dyFEDWa3KGLhy4kJARFUcjNzSU2NhZzc/MK+zMzM+nbty916tQBICAggLi4OHVfnz59qF27NgADBgxQ96Wnp3P48GEGDhwIlGVju3bt2j21qVu3bkydOpVjx45hZmbG8ePHeemll6qkv0IIUR0koIuHrvwZelpaGmFhYbRu3bpKylUUBX9/f8aOHXvLPmdn5wpJZv7KzMwMb29vkpKSMDc3p1+/frKEqxCiRpMhd/HI9OnTh06dOvH5559X2N6+fXvS0tK4fv06Op2uQh5zNzc3vv32W27cuIFer2fDhg3qPg8PD1JSUjh37hwAOp2OgwcPAvDKK6+wc+fOCpPcLl++zPr169Wv/f392bBhA+vXr5fhdiFEjSd36OKRmjBhAn5+fowePVrd1r17d/bt24ePj486Ke78+fMA9OjRg7179+Lt7Y2NjQ2urq5cvXoVgLZt2zJu3DiCg4PR6XRotVo8PT1xcXHB3t6eFStWEB0dzWeffUbt2rWpXbs2QUFBar3NmjXjP//5D8XFxbi4uDzaCyGEEFVM0qeKx55Go8HKygq9Xs+kSZNo0KAB48ePr9Y2/S996j6DXSnOkFNUgmH3z5D7BobdP0mfKgxaaGgoZ86coaioiBdeeKHCXXZ1+z/PoeoPnaToFEJUJ/ntIx575QvDPO4kmAshqtMdfwO98sorGBkZ3bWQlStXVlmDhBBCCHH/7hjQy9/vFULcngy1CyEeB3f8LeTr6/uo2iFEjRS4eQ1f+Qyr7mYIIcS9P0NXFIW1a9eSmprKn3/+ycaNG9m9ezcXL16kb9++D6VxAwcOpKSkBK1Wy6lTp2jWrBlQtmhIZGTkQ6nz1KlTjBs3DoBRo0bh7e39UOq5H46OjmRlZTFy5MhHfj0Arl27xurVqytMRvvggw/YsWMHtra2FBUV4e7uzqRJkzA2lqUNhBCiOtxzQI+NjWXHjh2MGDGCqVOnAvCvf/2LyMjIhxbQ165dC8Dp06fx9/cnJSWlwv7S0lJMTat2qPO7776jVatWah/v1cNoy1/d7XrcjU6nw8TE5L7rvXbtGkuWLLlldvno0aMZOnQoGo0GX19fXnzxxSr7XngU17Myf/caCSFEdbvn35jJyckkJydTr149pk2bBsDTTz9NXl7ew2pbpTw8POjbty8ZGRk4ODgwfvx43n33XQoLCykuLqZr1668//77AMybN4+TJ09SUFBAXl4ejRo1IjY2llq1avHDDz8QGxuLsbExOp2ODz/8kPPnz/PFF1+g1+vJyspi3rx5KIrClClTyM/Px9TUlPHjx9OlSxeg7M757bffZuvWrXTu3Jlz585hbm7OqVOnyMvL46WXXqJ79+7MmzePc+fOMWLECEaMGAHAiRMniIiI4M8//0Sr1TJixAh1tbLvvvuOTz/9FAsLC3r16nXH61FaWsqYMWP4888/KS4upkWLFkyfPh1zc3OSkpLYsGEDderUIScnh6ioKHJzc4mJicHS0hJPT09iYmLIysqiTp067N+/n+joaAoLC4GyNdi7devGRx99REFBAT4+PtSqVYtVq1ZVaIOVlRUvvPACZ8+eBcreG4+MjOTIkSMUFxfTrl07wsLCMDEx4fjx44SFhXHjxg2cnJzIzc0lODiY7t27M2zYMJycnNi/fz82NjYsXryY+Ph4vvvuO3Q6Hfb29syYMQM7O7tKP7927doxf/58UlNTsbCwwMjIiC+//BJra2u2b9/Op59+ik6no169enz00Uc888wzZGZmMnPmTFxcXDh06BDjxo2je/fuVfcNK4QQj4pyjzp16qQUFRUpiqIobdu2VRRFUQoKCpQuXbrcaxF/W15enuLm5qYoiqJ0795dmTowWgXlAAAgAElEQVR1qrqvqKhI0Wg0iqIoSklJiTJs2DBl27ZtiqIoyty5c5WXXnpJuXr1qqLX65XXXntNWb16taIoiuLl5aVkZWUpiqIopaWlSkFBgXrOxx9/rJYfEBCgrFmzRlEURTl27Jji5uamXL58WVEURXFwcFA+//xz9djQ0FBl8ODBSnFxsXL9+nWlffv2ygcffKDodDrl3Llziqurq6LRaBStVqv4+voqx48fVxSl7Dr26tVLOX78uHLx4kXFzc1Nyc7OVhRFUeLj4xUHBwe1j3+9Hnq9XsnPz1f//9577ykJCQmKoihKYmKi4urqquTk5CiKoqhlnzx5UlEURVm2bJla9tWrVxUfHx/l/PnziqIoyvnz55XOnTsrV69erVDfzX1dsWKFoiiKcunSJcXT01PtT3h4uJKcnKwoiqLodDpl/Pjx6nX39fVV1q9fryiKohw4cEBxcnJS0tPTFUVRlKFDhypjxoxRtFqtoiiKsn79emXy5MmKTqdTFEVRVq5cqbz77ru3/fz+/PNP5cUXX1Ru3LihXletVqtcunRJadeunXLs2DFFURRlzZo1SkBAgKIoipKRkaE4OTmpZd2roqIiZc+ePcqr67+8r/Nqkj179lR3Ex4qQ+6fIfdNUQy7f+W/W8rj7f245zv0rl27EhkZqaanVBSF2NjYarmbuTnHtU6n45NPPmHv3r0oisKlS5c4fPiwehft7u6OtbU1AC1atCA3NxcoWz88MjKSXr160aVLFxwcHG6pR6PR8Pvvv6t3zs899xzPP/88+/btw8PDA7h14mDPnj3VbGJNmjSha9euGBsbY29vj7W1NefOnUNRFLKzs3n33XfV87RaLSdOnMDY2BhnZ2eeffZZAF5++WWio6Nvey30ej3/93//x/bt29Hr9Vy9ehVLS0t1f+vWrWnUqBEA+/fvx9nZmcaNGwNla5mXP3vfu3cvp0+frjCsbmRkRE5ODra2tpXWHR8fz+rVqzl58iRDhgyhadOmQFkWtAMHDrBs2TIAioqKsLe3R6PRcPToUby8vABo3rw5jo6OFcr08vJSh9rT09M5ePCgeo3Lc55D5Z+fTqejUaNGvP/++7i7u9OtWzesrKzYv38/Tk5OPPfcc2q/p0+fjkajAeCZZ56hVatWt73GQghRE9xzQA8LCyM0NJQXX3yR0tJSWrVqRadOnZg9e/bDbF+lylNpAixbtoxr166xdu1aLCws+PDDDykuLlb337x0nomJibovPDycI0eOkJGRwdixY3nttdcYNGjQA7Wlsvr++rVOp8PIyAhbW9tKn4Fv2bLlvurfuHEjv/76KytXrsTKyopFixZx6tQpdX95StK7URQFR0fHStcUOH36dKXnlD9DP3HiBEOGDMHd3Z2uXbuiKApxcXE0bNiwwvHlAfROaxvcfD0VRSE4OJiAgIBbjrvd57dmzRqysrLIyMjAz8+PJUuW3LXvf/0MhRCiJrrnKclWVlYsWLCAH3/8kdWrV/P999+zYMEC9Y6puhQUFGBnZ4eFhQXnz5+/54B44sQJHB0dGTFiBN7e3pWm2rSysuL5558nOTkZgOzsbA4fPoyrq+sDtblJkyZYWlpWyPyVnZ2NRqPB1dWVQ4cOqUG5fCLc7RQUFGBra4uVlRUFBQWkpqbe9tiWLVty6NAhdZSivF8ArVq1Iicnh4yMDHXbgQMHUBQFKysrioqKKC0trbTcZ599lpCQEGJiYlAUBQ8PD+Lj49HpdADk5+eTl5eHlZUVzZo1U9v43//+l6NHj962vR4eHiQkJKjJWEpKSjh8+DBQ+een0WjIz8/Hzc2NkJAQHBwcOHbsGK6urhw+fJjs7Gy1387OztX+vSuEEFXpjnfoer3+lm316tWjXr16FfZX56tKw4YNY+zYsfTv3x97e3s6dOhwT+fNmTOHnJwcTExMsLa2ZtasWZUeFx0dzZQpU1i+fDmmpqZ88sknav//LlNTUxYtWkRERARLly5Fr9dTv359PvvsM+rXr8+MGTN44403sLS0vOukuAEDBrBlyxY8PT2pX78+L774YoURips9+eSTTJs2jaCgIGrVqkW3bt0wMzOjVq1aGBsbExcXR1RUFBEREWi1Who2bMiiRYuoW7cuXl5eeHl5YWNjc8ukOCh7NPDll1+yZcsWwsPDiYqKwsfHByMjI8zMzAgPD6dhw4bMnj2b8PBw4uPjcXBwwMHBgSeeeOK2fbty5QpDhw4Fyu7YhwwZgpOTU6Wfn0aj4Z133qGoqAhFUXB2dqZXr15YWFjwySefMHHiREpLS6lXrx5RUVH3+akJIcTj7Y7Z1pycnO5p6dfff/+9ShslHp7yzGUAiYmJrFu3jq+//vqR1V9YWEjt2rUxMjLi+PHjDBs2jM2bN2NjY/PI2lAVyjMixZw+ZLALyxhyRisw7P4Zct/AsPv30LKt3Tx8vXXrVr799lvGjBnDf/7zH86ePcvixYvvegcpHi8rVqxg8+bN6HQ6bGxsmDlz5iOtf+/evXzyySeU/x05Y8aMGhfMb7bUc5As/SqEeCzc8bfQU089pf5/+fLlJCYmqjPGmzRpgouLC/7+/rzyyisPt5WiygQHBxMcXH15u93d3XF3d6+2+h8GCeZCiMfBPT/8Ligo4MaNGxW2FRUVUVBQUOWNEkIIIcT9uedbC19fX1577TVGjBjBv/71L86dO8eKFSskgYv4xyvR6TCX5WKFENXsngP6e++9R6NGjdi0aRMXLlzAzs6OV1999W+9uy2EoQhMS+GrAfIzIISofvc85G5sbMyQIUP44osvSEtL48svv2TIkCGSyOIh8vDwuOU97aCgIPU98kchKSmJNm3a4OPjQ9++fRk9ejQXL158ZPXfr0mTJrFnz57qboYQQjxy9/UCeWJiIsOHD6d3794MHz6cxMTEh9UucRuLFy9Wl3J92MoXkunYsSMpKSl888031KlTh/nz51dZHXq9nju8OXnfZs2aRZs2baqsPCGEqCnuech94cKFrF+/nlGjRqmvrS1ZsoQLFy5U66zpfxoPDw8WLVqEg4MDw4YNw8XFhX379nHhwgX69OnDxIkTAbhw4QIzZ87k7NmzFBcX069fP9544w0AZs+eza5du9Bqtdja2hIREcFTTz2lpmX18/MjIyODQYMGVXgP0sjIiLZt27J161Z12+2yoRUUFBAeHs6xY8ewt7fH3t6e+vXrExoayrx58zh27BgajYazZ8+yevVq9u3bx8KFCykpKcHMzIywsDBcXV05ceKEmp1Nr9fj6+tLYGDgbbOtDRs2jFGjRtG9e3cuXbrE1KlT1RGNwMBANQ+Ah4cHPj4+7Nixg4sXLzJq1Ch1ARshhKiJ7jmgr127lhUrVlR4lc3d3Z2hQ4dKQK9Gf/zxBytXrqSwsJCePXsSEBBA48aNCQ0N5c0336Rt27aUlJQwcuRImjdvTqdOnQgKCiI0NBQo+1yjo6OJiYkB4MqVKzRv3lzdn5SUpNZVUlLC9u3b1ZznKSkp5OXlsWbNGoyNjUlISODjjz9mzpw5LFiwAGtrazZv3syVK1fw8/Ojd+/ealkHDhwgKSmJevXqkZubS1xcHEuXLsXKyopjx44RFBTE1q1bSUhIwMPDgzFjxgCoy8DOnTuXjz76iFatWqHT6W55AwNg5syZNGvWjAULFnDhwgX8/PxwdnZWE/EUFRWxevVqTp8+jZeXF76+vve89r0QQjxu7jmg37hx45YlT+vWrUtRUVGVN0rcO09PT4yNjXniiSdo2rQpubm5NGjQgF27dpGfn68eV1hYSHZ2Np06dWL79u0kJCRw/fr1W9Znt7CwoE+fPhW27dixAx8fH06fPk3Tpk3V/XfKhpaZmcnkyZOBsu+Tnj17ViizS5cu6vfTTz/9RG5uLq+++qq6v7S0lEuXLtG2bVuioqK4ceMG7dq1o3379sC9ZcvbuXMnH3zwAQANGjSga9euZGZmqseW/2Hy9NNPq5nwyjPGCSFETXPPAb1z585MnDiRCRMm8J///IczZ87w2WefGdwiITVNZdnc9Ho9RkZGrFu3DjMzswrHnzlzhsjISNatW0fDhg3JyspSh+kBatWqdctyvx07dmTu3LloNBoCAwOJjY3l/fffv2M2tLv5651w586d+eSTT245rnfv3ri6uvLLL7+wePFiEhMTiY6OrpJseZVdOyGEqKnueVLclClTqFOnDt7e3rRq1YoBAwZgaWnJhx9++DDbJ/4GKysrXnzxReLj49Vtf/zxBxcvXkSj0WBmZoadnR16vb7SRCt3Knf69Ol8/fXXXLhw4Y7Z0Nzc3NT0sNeuXbtjFrxOnTrx008/cezYMXXbgQMHAMjJycHOzg4/Pz/eeustNSvevWTL69ChA2vWrAHg4sWLbNu2Tb3DF0IIQ3PXO/SzZ8+q/x83bhzvvPMOf/75J7a2tpiYmKDRaNTlYEXVe+211yq8GlhYWHhP50VHRxMZGYmXlxdQdkc8a9YsHB0d8fT0pG/fvtja2tK1a9f7es3LyckJT09PFi9ezKRJk26bDe2tt94iLCwMT09P7OzscHFxuW260saNGxMVFcWkSZMoKipCq9XSunVrWrRoQVpaGhs3bsTMzAwjIyPCw8OBe8uWN3nyZKZMmaJeg4kTJ9KsWbN77qsQQtQkd8y2BnfOuKYoCkZGRpJtTdxCq9Wi1+uxsLBAo9EwZMgQwsLC6NixY3U3rUqo2dbysg12YRlDzmgFht0/Q+4bGHb/Hlq2NSgL6EVFRfj6+uLt7U2DBg3+dkPFP8e1a9cICgpCp9NRXFxM//79DSaY32xpHx9Z+lUI8Vi4a0Bfv349R48eJTk5mSFDhtC0aVN8fHzo1asXlpaWj6KNogaqX79+hVfeDJkEcyHE4+CeJsU5ODgQGhpKeno6I0eOZOvWrbi7u/Pf//73YbdPCCGEEPfgvpZ+PXXqFLt372bfvn08//zzMhlOiJuUyGtvQohqdNch9ytXrvDNN9+QnJxMYWEhPj4+fPXVV/znP/95FO0T4rEWuCmNK/9/cZ7Ugff/Pr4QQlSVuwb0zp078/TTT+Pj40PLli2BsneDc3Jy1GM6dOjw8FooHkhaWhqff/45iqJQXFzMCy+8wJw5c9BqtSxatIjU1FRMTU0xMTGhcePGhISE8Nxzz5GUlERERARPP/00xcXFmJmZ0atXL15//XV17oSHhwfm5uaYm5uj1WoZNWoUAwcOrOYeCyHEP9NdA7qdnR3FxcWsWbNGXaTjZkZGRndcNERUnwsXLjB9+nSSk5P597//jaIo6iuGYWFhFBUVsXbtWqytrVEUhW3btnHy5Emee+454H8rxAFcvnyZSZMmMW7cOBYtWqTWMXfuXBwcHDh69Ch+fn506dIFe3v7Kml/aWkppqb3vJhhlShfZe92r2oKIcTj6q6/LdPT0x9FO8RDcOnSJUxNTalbty5Q9seXs7Mzp06d4ocffmDbtm3qPAgjIyO6det227Lq16/P7Nmz6dKlC8eOHbtlgRYHBwesra05f/489vb2nDhxgoiICP7880+0Wi0jRozA398fgG+//ZaYmBgsLS3x9PQkJiaGrKws6tSpg6OjI2+//TZbt26lc+fOvP7660RGRnLkyBGKi4tp164dYWFhmJiYMH/+fFJTU7GwsMDIyIgvv/wSMzMzQkNDOX78OKampjRp0oTY2FigLDPchg0bAGjevDmTJ0+mTp06lWZ/s7GxqeqPQwghHqpHe/sjHiknJydatGhBt27daNeuHa1bt8bHx4dDhw7xzDPP3HfQsrGx4Zlnnqk0oP/666/Y2tri5OREaWkpEydOJCoqiqZNm6LRaPD398fV1RUbGxumTJnC6tWrady4McuXL7+lHgsLCxITEwGYNGkSbdu2ZdasWej1eiZOnEhiYiK9evVi+fLl/Pzzz1haWqLRaLC0tOTHH3+ksLCQTZs2Af/LzrZt2zY2bNjAqlWrqFOnDqGhocTFxfHee+8BFbO/CSFETSQB3YAZGxsTFxfH0aNH2b17Nz/88ANLly4lLCyswnHHjx9nwoQJFBUV0blzZzVLWmX+urBgSEgIiqKQm5tLbGws5ubmHD9+nOzsbN599131OK1Wy4kTJzA2NsbZ2ZnGjRsD4O/vT2RkZIUyy7O3QdkI0YEDB1i2bBlQlvLU3t6eJ554gkaNGvH+++/j7u5Ot27dsLKywsnJiezsbKZPn46bm5s66rBz50769u2rLj87aNAgIiIi1Hpuzv4mhBA1kQT0fwAHBwccHBx49dVX6du3L6dPnyYnJ4dr165hbW3Nc889R0pKCl999RUHDx68bTlXr14lNze3QqrS8mfoaWlphIWF0bp1axRFwdbWVk3OcrN7mW9Ru3Zt9f+KohAXF0fDhg1vOW7NmjVkZWWRkZGBn58fS5YswcnJidTUVDIyMti+fTsxMTFs3LjxrnVKHnQhRE13X++hi5rl/Pnz7N27V/363Llz5Ofn4+7uTo8ePZg8eTIFBQXq/uvXr9+2rPz8fMLDw+nQoYM6ae5mffr0oVOnTnz++ec0adIES0tL1q9fr+7Pzs5Go9HQsmVLDh06RG5uLgDJycl37IOHhwfx8fFqatP8/Hzy8vLQaDTk5+fj5uZGSEgIDg4OHDt2jHPnzmFiYkLPnj0JCwsjPz+fK1eu0KFDB9LS0tBoNCiKwrp16wxyKVohxD+X3KEbsNLSUubNm8eZM2ewtLREr9czbtw4nJ2diYyMJC4ujoCAAExNTbG2tqZBgwaMHj1aPX/Hjh0MGDCAoqIizM3NeemllwgKCrptfRMmTMDPz4+goCAWLVpEREQES5cuRa/XU79+fT777DOefPJJpk2bRlBQELVq1aJbt26YmZlRq1atSssMDw8nKioKHx8fjIyMMDMzIzw8HDMzM9555x2KiopQFAVnZ2d69epFRkYGc+bMAcpmrI8ePRp7e3vs7e05cuQIgwcPBsDFxYXg4OAqvNpCCFG97pptTYiqptFo1GfZiYmJrFu3jq+//rqaW3V/1GxruXkGu7CMIWe0AsPunyH3DQy7fw8125oQVW3FihVs3rwZnU6HjY0NM2fOrO4mCSFEjScBXTxywcHBBjPcvbRvH/WvaEmjKoSoTjIpTogqIsFcCFGdJKALIYQQBkACuhBVTNKoCiGqgzxDF+IBvJ6WztXSigF8Q0D/amqNEOKfTAK6ASpPa2phYUFxcTFt2rRh6tSpmJmZPZL6Y2NjadasGX379v3bZXzwwQfs2LEDW1tbioqKcHd3Z9KkSRgbG3P+/HkmTpzIihUrKj3X0dFRTfYihBD/FBLQDVT5kqw6nY5XX32V77///oEC7P0YO3ZslZQzevRohg4dikajwdfXlxdffJG+fftib29/22AuhBD/VPIM3cAVFxdTXFyMtbU1O3fu5OWXX2bAgAF4eXnxzTffAGWZxvr3rzhM7O3tTVZWFlC2POvAgQPx8/Nj+PDhnDhxAoCsrCx8fX3x8fGhX79+pKamAmV311999RXAbesEGDZsGLNnz2bIkCH06NGD6OjoSvtgZWXFCy+8wNmzZwE4ffo07dq1U/d/9913eHp64uPjw4IFCyqc++233+Lp6cmAAQNYtGgRjo6OFBYWArB//36GDRuGn58ffn5+bN269W9dYyGEeBzIHbqBCgkJwcLCgtzcXNzd3XF3d+fq1askJCRgYmLCpUuX8PPzw93dnRYtWlC7dm127dqFm5sbe/bswdjYmNatW7Nnzx7S0tJYuXIl5ubmbNu2jfDwcFatWsXixYsJDAykf//+KIpSYV34cs7OzpXWWZ669Y8//mDlypUUFhbSs2dPAgIC1Exs5S5fvsyRI0d45513bin/0qVLfPjhh3z99dc8++yzLF68uMK+26VqvXbtGlOnTiU+Pp4GDRpw4cIFAgICSE1NVXPECyFETSIB3UCVD7kXFxfzzjvvsHz5crp27Up4eDg5OTmYmJhw9epVTp48iaurK8OGDSMhIQE3NzdWrlzJq6++CpSlLz18+DADBw4EyrKfXbt2DYB27dqxcOFCcnNz6dSpEy1btrylHeVJXSqrE8DT0xNjY2OeeOIJmjZtSm5urhrQ4+PjWb16NSdPnmTIkCE0bdr0lvL379+Ps7Mzzz77LAAvv/yyeqdfvq+yVK179+7l9OnTFdamNzIyIicnh+bNmz/o5RdCiEdOArqBs7CwoFu3bmzdupUff/wRDw8P5s+fj5GREb1796a4uBgoC6yffvophw4dIjMzU80VrigK/v7+lT4XHzlyJB4eHuzYsYMZM2bQqVMnxo8fX+GYadOm3bbO8vaVMzExUbOqwf+eoZ84cYIhQ4bg7u5O165dq+S6KIqCo6MjK1eurJLyhBCiuskzdAOn1+vZvXs3jRs3pqCggKeeegojIyN++eUXcnJy1OPMzMzw9/cnODgYLy8vNfuZh4cHKSkpnDt3DgCdTqfmTD958iSNGjVi8ODBDB8+nN9+++2W+u9U57169tlnCQkJISYmhr/mEnJ1deXQoUOcOnUKgLVr16r77pSqtVWrVuTk5JCRkaFuO3DgwC3lCyFETSF36Aaq/Bm6VqulWbNmvPXWWxw8eJDp06czb948mjdvjqOjY4VzBg4cyPz58xkyZIi6rW3btowbN47g4GB0Oh1arRZPT09cXFxYsWIFmZmZmJmZYW5uzuTJk29px4QJE+5Y5716+eWX+fLLL9myZQtOTk7q9vr16zNjxgzeeOMNLC0t6dWrl7rvTqlajY2NiYuLIyoqioiICLRaLQ0bNmTRokUYGRn9rTYKIUR1kvSpQpWSksI333xDfHx8dTelyjysVK3lKQ4/y7tgsAvLGHKKSjDs/hly38Cw+yfpU8UDCwwMJDc3l4ULF1Z3U6rUw07VuqSPxy0/dJJ1TQhRHSSgCwCWLl1a3U14KKojVasEcyFEdZBJcUIIIYQBkIAuRDWSzGxCiKoiQ+5CPIDRab9wtVT/t89fH9CzClsjhPgnkzv0GsrDw4OjR49W2Obn50dmZuYdzxs2bBg//vjjA9d/+vRpnJ2d8fHxwcvLCz8/P3XtdyGEEI+eBHRx30pLSwF44oknSElJYePGjQQEBDBp0qQqrUdXDcPR5X0TQoiaRobcDdCwYcMYNWoU3bt3r/TrHTt2sGDBAq5evUqfPn149913Abhw4QIzZ87k7NmzFBcX069fP9544w2gbESgb9++ZGRk4ODgwJtvvlmhznbt2vHJJ5+oX2/bto2FCxdSUlKCmZkZYWFh6vrtMTExbNq0ibp16+Lm5sbOnTtJSkoiMzOTmTNn4uLiwqFDhxg3bhzPPPMMERER/Pnnn2i1WkaMGIG/vz83btwgNDSU48ePY2pqSpMmTYiNjeXEiROEhYVx48YN9Ho9vr6+BAYGUlhYyMyZM9XV7Hx8fNR13IcNG4aTkxP79+/HxsamQoIXIYSoKSSg12Dlq8GVK1/+9G6ys7NZtWoVxcXFDB48mFatWtG9e3dCQ0N58803adu2LSUlJYwcOZLmzZvTqVMnoGyRlnXr1gFlQ+43uznfem5uLnFxcSxduhQrKyuOHTtGUFAQW7duJT09nR9//JGUlBQsLS0JCQmpUM7x48f56KOPaNWqFaWlpQwaNIioqCiaNm2KRqPB398fV1dXTpw4QWFhIZs2bQLg6tWrACQkJODh4cGYMWMqbI+Li0Ov17Nx40YKCwt5+eWXcXBwUNeGz8vLIyEhAVNT+ZEQQtRM8turBivPqFbOz8/vns4bMGAApqammJqaqnfd7dq1Y9euXeTn56vHFRYWkp2drQb0AQMGVCinoKAAHx8f8vPzKSkpUddR/+mnn8jNzVUztkHZUPalS5fIzMykT58+1K5dWy0zLi5OPe6ZZ56hVatWQNkfKNnZ2eoIAoBWq+XEiRM4OTmRnZ3N9OnTcXNzo1u3bkDZUrVRUVHcuHGDdu3a0b59e6AsL3t4eDhGRkZYWVnRr18/du7cqQZ0Ly8vCeZCiBpNfoMZIBMTE/T6/828vjm72e3o9XqMjIxYt24dZmZmlR5THoTLlT9D1+l0REZGMmHCBNasWQNA586dKwzB36ub61AUBVtbW1JSUio9NjU1lYyMDLZv305MTAwbN26kd+/euLq68ssvv7B48WISExPVdKr3Wq8QQtREMinOADVq1Eh9Vnz8+HF+//33Cvs3bNhAaWkp169fJy0tjfbt22NlZcWLL75YYR33P/74g4sXL961PhMTE9577z0uXrzIli1b6NSpEz/99BPHjh1Tjzlw4AAAbm5ufPvtt+oz7g0bNty23CZNmmBpacn69evVbdnZ2Wg0Gs6dO4eJiQk9e/YkLCyM/Px8rly5Qk5ODnZ2dvj5+fHWW2+p16FDhw4kJiaiKAoajYZNmzbRsWPHe7iaQghRM8gdugEKCgpi7NixbNmyBWdnZ5ydnSvsf/bZZxk8eLA6Ka58slx0dDSRkZF4eXkBUKdOHWbNmoWdnd1d67SwsGDcuHHMnz+f5ORkoqKimDRpEkVFRWi1Wlq3bk2LFi3o0aMHe/fuxdvbGxsbG1xdXdXn3H9lamrKokWLiIiIYOnSpej1eurXr89nn33GkSNHmDNnDlA2ujB69Gjs7e1ZtGgRGzduxMzMDCMjI8LDwwF48803mTFjhto3b29vunTp8vcusBBCPIYk25p45MozoOn1eiZNmkSDBg0YP358dTfrvpRnRJqbd9VgF5Yx5IxWYNj9M+S+gWH3T7KtiRolNDSUM2fOUFRUxAsvvKC+PlYTxffpdN8/dDeTzGxCiKoiAV08cgsWLKjuJjw2JJgLIaqKTIoTQgghDIAEdCEekRLd33/WLoQQdyND7kI8gDc2/8rVe1z+PclfXpMTQjw8cocuKs3c9ihUVea32/npp58YPHgwvXr1ws/PjzFjxnDkyBFef/11vv766wrHKvm3M+QAACAASURBVIpCjx492LVr10NrjxBCPExyhy5qrNLS0tsu1/rzzz8zadIkFixYQPPmzQH4/fffuXjxIv7+/ixbtowhQ4aox2dmZmJsbEzbtm0fSduFEKKqSUAXlZo9eza7du1Cq9Via2tLREQETz31FJcvX2bChAlcvnwZKFuBLTw8nKysLGbMmIFer6e0tJTg4GD69+/PpUuXmDp1Krm5uQAEBgbesib8X/3f//0f33zzDTqdDgsLC6ZNm8bzzz8PgKOjI2+//TZbt26lc+fOdOnSpdJ6FyxYwJtvvqkGc0Ato6SkhGnTppGdnU3Tpk0BSEpKws/PDyMjoyq/lkII8ShIQBeVCgoKIjQ0FIC1a9cSHR2trpfeqFEjli9fDvwvm9nixYsJDAykf//+KIpCQUEBADNnzqRZs2YsWLCACxcu4Ofnh7Ozc4WkMn81YMAARo0aBZSlep06daq6RjyUrUqXmJgIQHBwcKX1Hjp0iClTplRavrm5OV5eXiQmJvL++++j0Wj44Ycf1MxtQghRE0lAF5Xavn07CQkJXL9+ndLS/836atmyJcuXL2f27Nm4ubnh7u4OlOVDX7hwIbm5uXTq1ImWLVsCZVnOPvjgAwAaNGhA165dyczMvGNAP3jwIJ9//jlXr17FyMjolrSwvr6+6v9vV+/dBAQE8PrrrzNhwgTS0tJo3bo1//rXv+7pXCGEeBzJpDhxizNnzhAZGcmcOXNITU0lIiKCkpISAFq1akVycjIuLi6kpKQwfPhwAEaOHMnChQupV68eM2bMICYm5m/VXVJSwtixYwkPDyc1NZUlS5aodZe7OTPa7ep1dnZWE8JUxsnJiQYNGrB9+3YSExPx9/f/W+0VQojHhdyhi1toNBrMzMyws7NDr9ezatUqdV9eXh7/+te/6NevH23atOGll15Cr9eTk5NDkyZNaNSoEbVr11YzpHXo0IE1a9YQEhLCxYsX2bZtGyNHjrxt3SUlJZSWlvLvf/8bgISEhDu29eTJ/8fefcdVXf7/H38cpgJqODBTyJHgRksRR6LHUkA2pGRqTkoz1PSjoKSoqJmYe7YotwICotYnxZEpaFqOXEnmVhyhgso45/z+4Mf7KzEcION8XvfbzduNc97juq43cl68B9fzfIHtDh8+nE8//ZQWLVrQvHlzAE6fPs2tW7eUqwq+vr4sWrSIq1ev0r179+c+XkIIUR5IQRcADBo0CMPHpiF1dnbG1dUVS0tLnJyc+PXXXwE4ePAgERERGBgYoNVqmTp1KgYGBqxatYqkpCSMjY0xMTEhJCQEgJCQECZPnqyknI0bN47GjRsr7QQFBeWZC33lypUEBgbi5+fHSy+9RM+ePYvsd2HtdunShWnTpjFt2jRSU1MxMjKiXr16jB07VtnWzc2N2bNn07t3b0xMTIp5BIUQomxJ2poQzyE3EWnx5Qy9nVhGnxOtQL/Hp89jA/0eX3HS1uQeuhBCCKEH5JK7EMWw3PmNp/4tOlOjxcRQfocWQrwY8ukiRCmRYi6EeJHkE0aIEiBJakKIsiYFXYhi+OiH07wTdUzOvoUQZU4+hYQQQgg9IAVd5KOPcaqLFi1i9uzZyuvz58/z0Ucf0b17d3x8fPD392fHjh0vpG0hhCgN8pS70BtFxak+LiUlhX79+vGf//yHJUuWAHDz5k1++eWXF91FIYR4YaSgi6dS0eNUH7dmzRrat2+fp91atWo9sR9CCFGeSUEXT6Wix6k+7uTJk3Tq1Kn4B0UIIcoRKejiqeh7nKoQQlR08lCceCJ9iFN9XLNmzTh+/Phz9UcIIcorKejiiZ4Up2phYUGvXr0IDg7mjz/+QKvVcv78eWxsbPD392fAgAFKAc2NUwWUOFVHR8dC236eONWC2n1c3759OXDgAFu2bFHeu337thK9KoQQFZFcchcF0rc41cfVrl2bVatWER4ezvz58zEzM8PMzIxhw4Y9/wETQogyJvGpQjyH3IjDZZeNuJutYpNvq7LuUonT54hK0O/x6fPYQL/HV5z4VDlDF6IYljg3wdTUVJLUhBBlTj6BhCgBUsyFEGVNPoWEEEIIPSAFXYhyJFMjj7QIIZ6P3EMXohg++eEK9zSGT17xKa3yebXE9iWE+N8iZ+jiuRSUyDZs2DBljvbScPfuXT755BPc3Nxwd3fH09OTAwcOcPToUd588000Gk2e9aOjo+nfv7/Sf2dnZzw8PHBxcWHTpk2l1m8hhHgR5AxdlJgvv/yy1NrKzs5m/vz51K5dm7lz56JSqfjnn394+PAhr7zyCtWqVeOXX36hS5cuyjbR0dH4+fkprxcuXIitrS1nz57Fx8eHLl26ULt27VIbgxBClCQp6KLEqNVqli9fjq2tLf3796dFixb8/vvvpKSk4OLiwrhx44Cc+NKwsDCuXr1KRkYGvXr14sMPPwQKT3W7fPkyvr6++Pj4kJiYSO/evbl+/Trt27dHpVIBYGlpiaWlJQA+Pj5ER0crBf3ixYucPn0aZ2fnfP22tbWlatWq3LhxQwq6EKLCkoIuXphr166xZs0a0tPTeeutt/Dz86N+/fpMmDCBESNG0K5dOzIzMxk4cCAtW7akU6dOhaa6AaSmptKyZUtlef369QkMDCQ+Pp42bdqgVqvp0KEDAJ6enixatIi7d+9SrVo1oqOjcXV1pVKlSvn6efjwYSwtLWnSpEkpHRkhhCh5UtDFC+Ps7IyBgQFVqlShUaNGXLx4ESsrKw4ePMidO3eU9dLT00lOTqZTp06FprpBTkyqi4uL8rpDhw7s2rWLpKQkDh8+zOjRoxkyZAgBAQHUqFGDjh07Eh8fz7vvvktMTAwLFizIs7/AwEB0Oh0XL15kwYIFmJiYvNgDIoQQL5AUdPHCPD5toaGhIRqNBq1Wi0qlIjIyEmNj4zzr56a6RUZGYm1tzZEjR5TL9ACVK1dWLq/nsrCwoHv37nTv3p0WLVqwbNkyAgICAPD19WXx4sXUr18fCwuLfFGquffQt2/fTnBwMK+//jo1a9Ys6cMghBClQp5yF6XKwsKCN954g5UrVyrvXbt2jZs3bxaZ6laQX375hbS0NAB0Oh0nT56kXr16yvIuXbqQkpLCF198gY+PT6H7cXFxoVOnTqxYsaKYoxNCiLIjZ+jiuf07kS09Pf2ptgsPD2fWrFlK4pq5uTkzZszAzs6u0FS3gpw5c4bPPvuM3HyhV199lcmTJyvLjYyM8PT05LvvvsvzC0RBxo4di4+PD8OGDcPKyuqpxiGEEOWJpK0J8RxyE5G+uWyptxPL6HOiFej3+PR5bKDf45O0NSHKyBfOdZ/5h64omRodJoaqJ68ohBD/IvfQhShHpJgLIZ6XFHQhhBBCD0hBF6KCypZkNiHEY+QeuhDFsOTHFB5qyubHaJJ3nTJpVwhRPskZeim7e/curVq1IiwsrNB1+vfvz65du564r9zEME9PT5ydnQkJCSErK6skuwvA5cuX2bBhQ74+du/eHU9PT3r27MnSpUtLvF0hhBBPTwp6KYuPj8fe3p6tW7eSmZlZ7P0tXLiQ2NhYtm7dyrlz5/jpp59KoJd5XblyJV9BBwgJCSE2NpZVq1bxzTffcPTo0RJr89/TvpaWf0euCiFERSGX3EtZVFQU//nPf1ixYgU7d+7ExcWFc+fOERwczIMHD7C1tSUjI0NZ/5tvvmHr1q1oNBpMTU0JDQ2ladOm+fabkZFBRkYGVatWBXImeQkLC+P48eNATljJsGHDALhw4QKTJ0/mzp07GBkZMWbMGLp06cLDhw+ZMGEC586dw8jIiAYNGrBgwQKmTZvG5cuX8fT05NVXX2XhwoV52raysqJBgwZcvXoVe3v7ItPUfv31V6ZOnQpA+/bt2blzJytWrMDW1ha1Wo2rqyuJiYnY2toSGhrKvHnzOHToEJmZmdjZ2REaGoq5uTkbNmwgIiICExMTtFot8+fPp0GDBkybNo3ExERMTEwwMzNTZpuLiYnh66+/BsDGxoZp06ZRo0YNoqOjiYuLw9zcnAsXLjBnzpwCj68QQpR3UtBL0enTp0lNTcXR0ZGbN28SFRWFi4sL48ePp3///nh7e/P777/z7rvvKtt4eXkxePBgAPbv38+UKVPYuHGjsjwwMBBTU1MuXrxI586d6dy5MwBLly5Fq9WyZcsW0tPT6dOnD7a2tjg5OTFu3Dh69+7NO++8w7lz53jvvffYvn07hw8fJj09nW3btgE5twcAJk+ezOzZs4mOji5wXOfPnyc1NZX27dsDFJqm1q5dOz755BO++OIL2rZty08//cSqVavy7CstLY3IyEhlDFWqVFFez5kzh5UrVzJmzBg+//xztm/fjpWVFZmZmWg0Gk6fPk1SUhLbtm3DwMBA6f/Zs2cJDw8nOjoaKysr5s+fz/Tp05k/fz4AR48eJTY2Fhsbm2J8d4UQomxJQS9FkZGReHp6olKp6NGjB2FhYVy5coWzZ8/i6ekJQOvWrbG1tVW2OXHiBCtWrODu3buoVCr+/vvvPPvMDRjJyMjg448/JiIigoEDB3LgwAEmTpyISqXCwsKCXr16ceDAAd544w1OnTqFr68vAK+99hpNmzbl999/p0mTJiQnJzN16lQcHBzo2rVrkeMJCwsjPDycv/76iwkTJlC9enUePHhQaJpajRo1qFSpEm3btgXg7bffVq4o5PLy8lK+TkhIIC0tjR9//BGAzMxMJeLU0dGRoKAgunXrRteuXbG2tsba2prs7GwmTZpE+/bt6datGwBJSUk4OTkpU7r6+/srxxvg9ddfl2IuhKjwpKCXkszMTOLj4zExMSE2NhaArKwsNm/eXOQ2o0aNYvXq1TRv3pwbN27QpUuXAtc1NTWla9eu7N69m4EDBz5XH62trYmPjycxMZG9e/cyb948tmzZUuj6ISEhdOvWjcOHDzN48GA6dOhA3bp1C01TO3369BP7YGZmpnyt0+mYMmWKknH+uMWLF3P8+HESExMZMGAAoaGhODk5sXXrVpKSkti/fz/h4eFFHt9c5ubmT1xHCCHKO3korpTs3LmTBg0asHfvXhISEkhISOCbb74hLi4OW1tbpXAeO3aMs2fPAjkFPTs7mzp1cv48ae3atYXuX6vVcujQIerXrw/kZIVHRUWh0+lIS0tj27ZtdOzYEQsLC5o2baoUuuTkZE6fPk3r1q25fv06hoaGvPXWWwQHB3Pnzh1SU1OxsLBQUs0K8sYbb9C3b18WLFhQZJpaw4YNefjwIYcPHwZgx44d3Lt3r9D9qtVqIiIiePToEZBzOT45OZns7GwuXbpEq1atCAgIoFOnTpw6dYo7d+7w8OFD3nzzTcaNG0eVKlW4dOkS7du3Z8+ePdy8eROAjRs30rFjxyK/X0IIUdHIGXopiYqKUtLFcrVp0watVktQUBBz5szhyy+/xNbWlpYtWwI5UaOBgYH4+fnx0ksv0bNnz3z7zb2HnpWVRePGjfnoo48AGDFiBNOnT1fa9PDwUM7uw8PDmTx5MhERERgZGfH5559TvXp19uzZw9y5c4GcXxACAgKoXbs2NWrUoEGDBri5udGwYcN8D8UBDB8+nLfffpuTJ08WmqZWq1Yt5s6dS2hoKAAODg7UqFGDKlWqFHjMAgICWLx4MX5+fqhUKlQqFSNHjsTa2pqgoCDu37+PSqWiTp06jB07lqtXr/Lpp5+SnZ2NRqOhS5cutG7dGgMDA8aNG6c8i2Btbc20adOe6fsnhBDlnaStiVKVlpaGhYUFAImJiQQHB7Nz504MDCrWxaLcRKQ9V6z0dmIZfU60Av0enz6PDfR7fJK2JiqM//73v0RERKDT6TAxMSE8PLzCFXMhhCiPpKCLUuXj44OPj09Zd6PEfNTTqkTjU59FtkaHkaSzCSH+Pzk1EqKCkmIuhHicFHQhSoAknwkhypoUdCGKYeMPt/kmOkXOloUQZU4KuhBCCKEHpKCLCkutViuT8AghxP86Kejif1JZxbMKIcSLIn+2JvTK7NmzOXjwIFlZWVhaWjJz5kzq1q3L5cuX8fX1xcfHh8TERHr37k2tWrVYsGABBgYGaDQaPv30UyUxTgghKhop6EKvDBs2jAkTJgCwadMmwsPDmTdvHgCpqam0bNlSWe7h4cG0adNo06YNGo2Ghw8fllm/hRCiuKSgC72yd+9e1q5dy4MHD/JdVjc1NcXFxUV57ejoyKxZs+jRowddunTJE1srhBAVjdxDF3rjypUrzJo1i7lz5xIfH8/MmTPJzMxUlleuXBmV6v/+vGzixIlMnz4dY2NjRo0axcaNG8ui20IIUSLkDF3ojbS0NIyNjalVqxZarZb169cXuf5ff/2FnZ0ddnZ2PHjwgOPHj9O7d+9S6q0QQpQsKeiiQhs0aBCGhobKa2dnZ1xdXbG0tMTJyYlff/210G3nzp3LhQsXMDQ0pGrVqsyYMaM0uiyEEC+EFHRRYSUkJBT4fkhIiPJ1YGAgAPXq1SMpKSnPekuWLHlxnRNCiFImBV2IYujtXANTU1NJPhNClDl5KE6IEiDFXAhR1qSgCyGEEHpACroQJUQjEapCiDIk99CFKIb/bvsHTXbOj5HXOzXLuDdCiP9l5eIMPTMzk88++4y33noLZ2dnvLy82LFjR6HrR0dHc/78+Tyvc59mLq8uX76cZ55wtVpN586d0Wg0ynvR0dHY2dmxevXqJ+7v38egMElJSfj4+BS47Pjx44wdO/Ypel80Ozs73N3d8fDwwMvLiwMHDhR7n09jwYIFbNu2DcgZ5759+/IsT0tLY/r06bz99tt4enri4+PD8uXLOXr0KG+++WaeYw85x7R///6l0nchhChp5eIMPTQ0lAcPHrB161ZMTU05e/YsQ4cOpVq1arRr1y7PuhqNhs2bN2NpaUmDBg3KqMclw8rKin379uHk5ATA5s2bad68+VNtWxLHoGXLlsydO/e5t3/c+vXrMTc3Z8eOHYwePZoDBw5gYPB0vy9mZ2djZPTs/xVHjRqlfH3w4EEePHhA586dAdDpdAQEBNCkSRO2bt2KiYkJGRkZbNy4EXt7e6pVq8Yvv/xCly5dlH1ER0fj5+f3zP0QQojyoMwL+pUrV9i+fTu7du3C1NQUAFtbWz788EMWL16Mp6cncXFxmJubc+HCBfz8/Dhx4gRhYWHMnz9fCdpIS0tj9OjR/Pnnn1SpUoVFixZRq1YtNBoN4eHh/PzzzwC8+eabjBs3DkNDQ27cuMH48eO5desW1tbWAHTu3Jl+/fpx69YtpkyZwsWLFwEYMmQIXl5eQM7ZtaenJ/v37+fmzZsMHjyYfv36AYWnfRXE29ub6OhonJycuHTpEg8ePMgzn/iBAweYP38+GRkZaDQaPvzwQ3r16kVUVFS+Y9CxY0dWrFhBfHw8KpUKMzMz1q5dC+T8EjR58mR+++03VCoV8+bNo1GjRiQlJTF79myio6OVNDJ/f3/27NnDw4cPmTFjBm3btgVg9erVfP/991SpUgUnJyfWrFmT7++6ATp16kRqaiqpqan4+fmxfPlyZUxqtVp5rVarcXV1JTExEVtbW4YOHUpwcDAPHz5Eq9Xi7e3NkCFDCAoKokWLFsrxffx17tft2rVj/fr1aLVa9u/fT69evWjRogVXr17lu+++w9jYGMiZyz33DNzHx4fo6GiloF+8eJHTp0/j7Oz8jP+DhRCifCjzS+5nz57FxsaGl156Kc/7rVu35vTp0wAcPXqUCRMmEB8fz8CBA2nRogUhISHExsbSsWNHIOfy8YQJE9i6dSuvvfaactl6w4YNnDp1iujoaKKjozl58iQbNmwAICwsjPbt27N161Y+/fRTDh48qLQfFhZG48aN2bJlC19//TXh4eGcPXtWWf7o0SM2bNjA999/z9y5c0lPTwdy0r6ioqKIi4vDzc2N8PDwQsfu4ODA2bNnuXv3Lps3b1Z+YcjVrFkz1q5dS0xMDN9++y2zZ8/m7t27+Pr65jsGmzdvJiEhgXXr1hEXF8eyZcuUM+Rz587h7+/Pli1bcHFxYenSpQX2JzU1ldatWxMTE8NHH32k9P306dOsWLGC9evXExUVxb179wod0/bt23n55ZepXr16oevkSktLIzIykpkzZ7J27VrUajVxcXHEx8c/05mynZ0d/v7+eHl5ERsbS0BAAH/88QfNmjVTivm/eXp6smfPHu7evQvknJ27urpSqVKlp25XCCHKkzIv6Drdk58Mfv3117GxsXniOnXq1AHA3t5eObM+cOAA3t7emJiYYGJigo+Pj3KPNykpCV9fXwDq1q1Lhw4dlP0dOHAAf39/IOfSuJOTU54zUldXVyBnBrKqVaty/fp1ICftq3fv3ri5ufH1119z6tSpQvusUqlwcXFh69atbN26FTc3tzzL79y5Q2BgIG5ubgwZMoS7d+8Wet98165dvPvuu1hYWABgaWmpLGvQoAHNmjUDcn5RunTpUoH7MDMzo1u3bvnWO3jwIE5OTkqRLqjY+vv74+npydatW596BrbHf4Fp164dmzZtYv78+Rw4cICqVas+1T6eV40aNejYsSPx8fFotVpiYmKU/wtCCFERlfkld1tbWy5evEhqamqes/Tff/8dOzs7AMzNzZ+4n9zL9QCGhob5HngqaQW1l5v2FRkZibW1NUeOHGHcuHFF7sfb25t33nmHdu3a5SnCkPNsgVqtZvHixahUKnr27ElGRsYz99XExET52sDAIF+s6LOuV5Dce+iPMzQ0RKvVKq//3XczMzPl6549e9K6dWt++eUXvvzyS6KioggPD3/iPgrTvHlz1q5dW+T9eV9fXxYvXkz9+vWxsLDA3t7+qfYthBDlUZmfoderVw9nZ2dCQ0OVD+uzZ8+yfPlyRo4cWeA25ubm3L9//6n236FDB2JiYsjKyiIrK4uYmBjlMr2DgwObN28G4Nq1ayQmJubZLjdO8+bNm+zZswdHR8ci23rWtC8Aa2trxowZw4gRI/Itu3//PnXr1kWlUvHLL79w4cIFZdm/j0G3bt1Yt24daWlpAPzzzz9PbPtpOTg4sHfvXu7cuQOgHLMnsbGx4fjx40DOFY9bt24Vuu6FCxeoVasWPj4+fPTRR8p2r776qvJ1SkpKgfftASwsLPIcjw4dOlC7dm0+++wzJUI1MzMzz18QdOnShZSUFL744otC/xJACCEqijI/QweYMmUKX3zxBa6urhgbG2NqasqkSZNwcHDg8uXL+dbv06cPn332GV9//bXyUFxh+vTpw8WLF/H29gZyHnrLjcicNGkS48ePZ8uWLdSrV49WrVopl6xDQkKYPHky7u7uAIwbN47GjRsX2Zadnd0zpX093seCjB07lqlTp7Jo0SJatmypXLEo6Bh4eXlx48YN+vTpg5GREWZmZqxZs+aJbT+NJk2aMHToUPz9/bGwsMDR0ZEqVao8cbtRo0YRFBTE6tWrcXR05JVXXil03e3bt7NlyxaMjY1RqVRMnDgRgHfeeYfAwEBcXV2pX78+rVq1KnD7t956i5iYGDw9PenVqxcBAQF89dVXzJ07F1dXVypXrgygfD8BjIyM8PT05LvvvmPlypXPckiEEKLcUeme5ia2nnr06BFGRkYYGRmRkpKCn58fERERNGzYsKy7Vu6kpaUpv+wsWrSICxcuFPnAn77LyMjgxIkTXL1YV28nljl8+DBvvPFGWXfjhdHn8enz2EC/x5f72dKiRYs8t3afRrk4Qy8rf//9NxMmTECn05Gdnc3IkSOlmBdi7ty5HDlyhKysLKytrZk2bVpZd6lc6OFqqfzQaTQ6DCWkRQhRRv6nC3qTJk2IjY0t625UCFOmTCnrLpR7UsyFEGWpzB+KE0IIIUTxSUEXohgkYU0IUV5IQReiGOQyuxCivKjwBV2S2nJIUtuzeVJSmxBCVDQV/qE4SWqTpLaSTmoTQoiKqEKfoecmtYWGhhaY1BYdHc3AgQP56KOPcHNzY9WqVUpKWW5aGvxfUluvXr3w9/fn5s2bQM4vALNnz8bNzQ03Nzdmz56tnBXfuHGD999/n169evHhhx/y4YcfKmfHt27d4qOPPsLd3R13d3diYmKUPqvVahYsWECfPn1Qq9V5zqhnz56Nr68vHh4evP/++1y5cqXQsecmtQGFJrX16dMHLy8v3N3d2bp1K0CepLbHj8GKFSuUM2V/f39lutXcpLbcZcnJyUDes/fcKwjz5s3Dy8uLnj175plQZ/Xq1fTo0QNfX18WLlyY52rD4x5PalOr1XnCcB5/rVarCQ8Px8/Pj8mTJ/PXX3/Rp08fPDw8lDn0AWVSm1yPv879+syZM6xfv16ZlEYmmBFCVFQV+gy9qKS2BQsW4OnpydGjR4mNjVXCXXbu3MngwYOVEJLo6GiOHz9OXFwcderUISQkhNWrVzNmzJg8SW2Qk6S2YcMG+vbtqyS1jRgxgitXruDu7q6c4eUmtS1ZsoSUlBR8fHxo1qyZUnBzk9ouX76Mu7s73t7emJubM2zYMGXmu02bNhEeHs68efMKHLuDgwNr167Nk9T2xx9/KMtzk9oMDQ25desWPj4+dO7cGV9fX2JiYvIcg8eT2iwsLPjnn3/yJLXNmjWLadOmsWzZMpYuXVrgmXluUtuYMWOIi4sjPDyc9evXK0ltsbGxVK9enbCwsEK/n8+T1JZ7vNVqNR988AGAkqD2NHKT2h48ePDEWQeFEKI8q9Bn6JLUJkltUPpJbUIIUR5V6DN0SWqTpDYo+aQ2IYSoiCr0GboktUlSG5R8UpsQQlREFfoMHSSpTZLaXkxSmxBCVDT/02lrxSFJbU9PH5PaipOIVFHoc6IV6Pf49HlsoN/jk7S1MiBJbU9Pn5PaZOpXIUR5IQX9OUlS29PT56Q2mfpVCFFeVOiH4oQQQgiRQwq6EKVImy2X6IUQL4ZccheiGI5uug1ZT/9j5DDI6gX2Rgjxv0zO0IUQQgg9IAVdlKl/h7CUpLi4nnJ9YAAAIABJREFUOLy8vHB2dsbHx4cxY8Zw9epVXF1d2bt3b55109PTef311wucu0AIISoCueQuKqyiolM3bdrEt99+y9KlS6lfvz6QM/9+blDN5s2b6dKli7L+Dz/8QMuWLalXr15pdF0IIUqcFHRR7syePZuDBw+SlZWFpaUlM2fOpG7duly+fBlfX198fHxITEykd+/e1KpViwULFmBgYIBGo+HTTz+lffv2LF68mLCwMKWYA0ps6yuvvMKSJUu4e/cu1apVA3JS9wqbdU8IISoCKeii3CkqRjY1NZWWLVsqyz08PJg2bRpt2rRBo9Hw8OFDbt++zfXr17G3ty9w/zVr1sTR0ZH4+Hjee+89Lly4wNmzZ+nRo0fpDFAIIV4AKeii3Nm7dy9r167lwYMH+VLbTE1NcXFxUV47Ojoya9YsevToQZcuXbC1tX2qVDVfX1+WLVvGe++9R3R0NK6urlSqVKnExyKEEKVFHooT5UpujOzcuXOJj49n5syZZGZmKssrV66MSvV/s7NNnDiR6dOnY2xszKhRo9i4cSM1atSgdu3aHDt2rNB2unbtyvXr1zl9+jSxsbFKtr0QQlRUUtBFufKsMbJ//fUXdnZ2vP/++3h4eChxqSNGjOCzzz7j4sWLyrqHDh1SiryRkREeHh5MmjSJKlWqFJrEJoQQFYVcchdlbtCgQRgaGiqvnyVGdu7cuVy4cAFDQ0OqVq3KjBkzAPD396dSpUoEBgby6NEjDAwMaNKkCf/5z3+Ubf38/Pjmm28IDg5+cYMTQohSIvGpQjyH3IhDzak6ejtTnD5HVIJ+j0+fxwb6PT6JTxWijNi/U+OZfui02ToMjCShTQhR8uQeuhClSIq5EOJFkYIuhBBC6AEp6EIUk0SiCiHKAynoQhTDudW35DK6EKJckIIuyq0XmcQmhBD6Rgq6EEIIoQfkz9ZEhVJYEtvt27cZO3Yst2/fBqBDhw5MnDiRI0eOMH36dLRaLdnZ2QwfPhw3Nzdu3brFlClTlJnkhgwZgpeXV1kOTQghikUKuqhQCkti27JlCzY2NkRERABw9+5dAL788kuGDBmCm5sbOp2O+/fvAxAWFkbjxo1ZsmQJKSkp+Pj40KxZM2xtbctkXEIIUVxS0EWFUlgSm729PREREcyePRsHBwc6d+4M5GSgL1u2jIsXL9KpUyclUvXAgQMEBQUBYGVlhZOTE0lJSVLQhRAVltxDFxVGUUlsbdq0YfPmzbRo0YLY2FgGDBgAwMCBA1m2bBnVq1dn+vTpSq66EELoGynoosIoKont0qVLWFhY0KtXL4KDg/njjz/QarWcP38eGxsb/P39GTBggJLG1qFDBzZu3AjAzZs32bNnD46OjmUyLiGEKAlyyV2Ua0+bxHbw4EEiIiIwMDBAq9UydepUDAwMWLVqFUlJSRgbG2NiYkJISAgAISEhTJ48GXd3dwDGjRtH48aNS3+AQghRQiRtTYjnkJuIVOn3l2k+pG5Zd+eF0OdEK9Dv8enz2EC/x1ectDW55C5EMbzWr6ZM/SqEKBekoAtRTDL1qxCiPJCCLoQQQugBKehCFINOI5fbhRDlgxR0IYpBZSiX24UQ5YMUdPHc1Go1zs7OeHp6Kv8uX76MnZ0d6enpz7XPU6dOsW3btjzveXp68ujRoyK3CwoKomXLlly9ejXPe6tXr35imzt27ODYsWPP1V8hhCgv5O/QRbEsXLiwRKdLPXXqFLt378bV1VV5LzY29qm2rVWrFosWLWLWrFnP1OaOHTto0aIFrVq1eqbthBCiPJGCLl6oZ0lHGz58OAsXLiQtLQ1PT0/atWtHSEgIdnZ2HDlyBHNzc5KTk5kxYwY3b94EYPDgwXh7ewPg7+/P6tWrOXfuHK+99lqefmRmZjJv3jwOHTpEZmYmdnZ2hIaGcuTIERISEti/fz+bNm1i0KBBkromhKiQpKCLYgkMDFQmPzA0NCQ6OjrP8mdJR6tWrRqBgYHs3r2bhQsX5msrOzubESNGMHr0aFxcXAD4559/lOVmZmZ88MEHzJs3jyVLluTZ9quvvqJKlSpERkYCMGfOHFauXMmYMWNQq9W0aNGCfv36lcxBEUKIMiAFXRTLky65P2s6WlHOnz9Pdna2UswBLC0t86zTu3dvvv32W44ePZrn/YSEBNLS0vjxxx+BnDP2Jk2aPNUYhRCiIpCCLl6Y3HS0yMhIrK2tOXLkCOPGjQP+Lx1t//79xMbGsnLlStatW1fsNo2Njfn444/54osvqFOnjvK+TqdjypQpdOjQodhtCCFEeSRPuYsX5nnS0SwsLLh//36B+2vQoAFGRkZs375dee/xS+653N3duXPnDgcPHlTeU6vVREREKE/Lp6WlkZycDFBkm0IIUVFIQRfFEhgYmOfP1nLjSQHs7OyUdLR33nmHevXqKcsOHjyIj48Pnp6eDB06VElH69ChAw8fPsTDw4OwsLA8bRkZGbF06VLWr1+Pu7s7Hh4e7NmzJ1+fDAwM+OSTT7hy5YryXkBAAE2aNMHPzw93d3f69u2rFHQPDw/i4+Px9PQkJiampA+REEKUCklbE+I5FCcRqaLQ50Qr0O/x6fPYQL/HJ2lrQpQRmfpVCFFeSEEXohhk6lchRHkhBV0IIYTQA1LQhSgGnUZb1l0QQghACroQxaIylB8hIUT5IBPLlDNqtRoTExNMTEx4+PAhr732GsOGDeP1118v667lsWjRItauXYuVlRUZGRk0b96c6dOnY2ZmVtZdE0KI/0lyelEOLVy4kLi4OH766Se8vb0JCAjIN5VpeeDl5UVsbCxxcXFcu3btqaJKn9bj08SWprJqVwghikvO0Mu5Hj16cOzYMb7++mvMzMzyhIgEBQUpr4OCgjAxMeHvv//m0qVLvP3223Tr1o1FixZx/fp13n//fd5//30g5yqAu7s7iYmJ3LhxQ0k9i4+P5+7du8ycOZN27doxdepU6taty9ChQwE4efIkY8aM4YcffsjTRxMTE9q0acO1a9eAwpPNzM3NuXHjBuPHj+fWrVtYW1sD0LlzZ2UMhoaGnD9/nvT0dGJjY9m8eTNr165Fo9FgYWFBaGgoDRs25MiRI0yfPh2tVkt2djbDhw/Hzc2NDRs2EBERgYmJCVqtlvnz59OoUSOOHTvGjBkzePDgAWZmZkyaNIlWrVpx+fJlfH198fHxITExkd69e/Puu++W1rdXCCFKjBT0CsDe3p6EhIQn5nX/+eeffPfdd2g0GtRqNffv32f16tXcvHkTZ2dn/Pz8MDc3B3KK7oYNGzh27BgDBgzgP//5D5GRkWzbto0vvviCdevW0a9fPz788EOGDBmCSqVi9erV9O3bF5Uq759qpaWlcejQIcaMGQMUnWwWFhZG+/btGTFiBFeuXMHd3T1PMMupU6dYvXo1ZmZm/Prrr2zfvp01a9ZgYmLCnj17mDhxIuvXr+fLL79kyJAhuLm5odPplKlbP//8c7Zv346VlRWZmZloNBoyMzMJDAxk1qxZdOjQgf379xMYGMh///tfAFJTU2nZsqWSCieEEBWRFPQK4Gkn83vrrbcwMTEBcuY9d3JywsDAgNq1a1O1alWuX79Oo0aNAHB1dQWgefPmPHz4UEkwa9GiBRcvXgSgUaNGWFtbs3fvXlq3bk1CQgLBwcFKezExMfzyyy9cuHCBzp074+joCBSdbJaUlERISAgAdevWzReW4uzsrNyHT0hI4PTp07zzzjvKcbh37x4A7du3Z9myZVy8eJFOnTphb28PgKOjI0FBQXTr1o2uXbtibW3NmTNnMDY2Vtrq2LEjxsbGnD9/HnNzc0xNTfMkuAkhREUkBb0COH78OI0bN8bQ0BCt9v/+TCojIyPPeo9PE2hoaJjvtUajybeuoaFhntcGBgZ57iP379+fdevWkZycTI8ePahSpYqyzMvLiwkTJnDz5k369u3L2rVree+994qVbPb4Q3U6nQ5fX19GjRqVb72BAweiVqvZv38/06dPp1OnTowZM4bFixdz/PhxEhMTGTBgAKGhobz88stFtlm5cuV8Vx2EEKKikYfiyrkdO3awbt06Bg8ezKuvvqqEn6SkpJCUlPTC23dycuL8+fN8++239O3bt8B1atWqxaRJk1i2bBmPHj0qMtnMwcGBzZs3A3Dt2jUSExMLbVutVhMbG8v169cB0Gg0nDhxAsjJRrexscHf358BAwZw/PhxsrOzuXTpEq1atSIgIIBOnTpx6tQpGjRoQFZWltLWgQMHyM7OpkGDBiVzkIQQohyQM/RyKDAwUPmztUaNGrFy5Urs7e2xsbEhMDAQV1dX6tev/8R76iXBwMAALy8v9u7dq1w2L0jXrl1p2LAh69evJyAggMWLF+Pn54dKpUKlUjFy5EgaNWrEpEmTGD9+PFu2bKFevXq0atUKCwuLAvfZrl07Ro8ezfDhw9FoNGRlZeHs7EyLFi1YtWoVSUlJGBsbY2JiQkhICFqtlqCgIO7fv49KpaJOnTqMHTsWExMTFi5cmOehuAULFii3J4QQQh9I2pp4okGDBtG7d+8Suc/86NEjjIyMMDIyIiUlBT8/PyIiImjYsGEJ9LT0SNpaxafP49PnsYF+j684ny1yhi4Kdfz4ccaMGUOzZs3o2bNniezz77//ZsKECeh0OrKzsxk5cmSFK+ZCCFEeSUEXhWrZsiU7duwo0X02adKE2NjYEt1nWZK53IUQ5YU8FCdEMchc7kKI8kI+jYQQQgg9IAVdiGLSZctldyFE2ZOCLkQx3Pr6JCoj+TESQpQ9+SQSxaJWqzl79mypt9u/f3927dpV6u0KIUR5JQVd/E+SmFQhhL6RP1sTJW727NkcPHiQrKwsLC0tmTlzJnXr1uX27dtKVCtAhw4dmDhxYqFRqLdu3WLKlClKWMyQIUPw8vIqsu1vvvmGrVu3otFoMDU1JTQ0lKZNmwJgZ2fHyJEj2b17N2+++SZdunQpsF0hhKiIpKCLEjds2DAlinTTpk2Eh4czb948tmzZgo2NDREREQDcvXsXoNAo1LCwMBo3bsySJUtISUnBx8eHZs2aYWtrW2jbXl5eDB48GID9+/czZcoUNm7cqCw3NTUlKioKgOHDhxfYrhBCVERS0EWJ27t3L2vXruXBgwd5Lm3b29sTERHB7NmzcXBwUHLQC4tCPXDgAEFBQQBYWVnh5OREUlJSkQX9xIkTrFixgrt376JSqfj777/zLPf29la+LqxdIYSoiOQeuihRV65cYdasWcydO5f4+HhmzpxJZmYmAG3atGHz5s20aNGC2NhYBgwYAOREoS5btozq1aszffp05s2b91xtZ2ZmMmrUKCZOnEh8fDxfffWV0naux+NZS6pdIYQoD+QMXZSotLQ0jI2NqVWrFlqtlvXr1yvLLl26xMsvv0yvXr1o27Ytb7/9NlqtlgsXLtCgQQNsbGwwMzMjJiYGyLnHvnHjRgIDA7l58yZ79uxh4MCBhbadmZlJdnY2derUAWDt2rVF9vX8+fMFtiuEEBWRFHRRbIMGDcLQ0FB57ezsjKurK5aWljg5OfHrr78CcPDgQSIiIjAwMECr1TJ16lQMDAwKjEIFCAkJYfLkybi7uwMwbtw4GjdurLQTFBSUJ41o5cqVBAYG4ufnx0svvfTEQJnC2hVCiIpI4lOFeA65EYcvJxlQd0Sbsu7OC6HPEZWg3+PT57GBfo+vOPGpcg9diGKoOaSZTP0qhCgXpKALUUwy9asQojyQTyIhhBBCD0hBF6IEyGV3IURZk4IuRDHc+vYwNxb8IpfdhRBlrsJ8CqnVapydnfHw8MDNzY2tW7eW2L4XLFjAtm3bir2f5ORk7OzslKlNy7Nhw4Ypc6Q/j6SkJOzt7fH09MTNzY1+/fqRnJxcgj0UQgjxLCpMQQdYuHAhcXFxfP755wQHB3Pnzp0S2e+oUaNwdXUt9n4iIyNxdHRU5govSSWdDvbll19iY2NTrH00atSI2NhY4uPjsbe3Z9asWSXUuxwajaZE9/c0JIVNCFFRVciJZZo1a4a5uTljxowhLS2t2KleQUFBtGjRAl9fX7p27cr27dupXr06kJMcZm5uzsiRIzl69Cjh4eGkp6cDEBgYSNeuXYGcQrBlyxbWrFnDsGHDOHbsGK1atQLgxo0bjB8/nlu3bmFtbQ1A586d6devX5HLgoKCMDQ05Pz586SnpxMbG8vmzZtZu3YtGo0GCwsLQkNDadiwYaFj27BhAxEREZiYmKDVapk/fz6NGjVCrVazfPly7t27R1hYWJ5Z0nx8fAgKCsLBwaHQ9v7NwcGB3bt3K68L2y4zM5Pp06dz8OBBqlevTtOmTbl16xYLFy4kOjqauLg4zM3NuXDhAnPmzCEzM7PAY16SyW1qtRpXV1cSExOxtbVl5syZxf9PKoQQpaxCFvTExEQyMjKYN2+eUniLk+qVq3Llyrz11lvEx8czYMAApUivX7+ee/fuMWXKFFauXImVlRUpKSn4+fkRHx9P1apV2b17N6+++iqvvvoq3t7eREVFKQU9LCyM9u3bM2LECK5cuYK7u7sSTFLUMoBTp06xevVqzMzM+PXXX9m+fTtr1qzBxMSEPXv2MHHiRNavX1/o2D7//HO2b9+OlZUVmZmZ+c5627Zty4MHDzh9+jRNmjThzJkz3Lt3j3bt2hXZ3uO0Wi07d+5UrnIUtd2GDRu4evWqEnHav39/Xn75ZWVfR48eJTY2FhsbG+7du8eAAQMKPOYlndyWlpZGZGTks/5XFEKIcqNCFfTAwEBMTU2xsLBg0aJFJZbq9Thvb29mzJjBgAED2Lt3Lw0bNqRevXrs2bOHy5cvM2zYMGVdlUrFhQsXaNmyJVFRUUqSl5eXF15eXkycOBFTU1OSkpKUaUXr1q1Lhw4dlH0UtQxyplHNDRRJSEjg9OnTvPPOOwDodDru3btX5NgcHR0JCgqiW7dudO3aVbkK8DgvLy82b95McHAwmzdvxsvLC5VKVWR7kPPMgKenJzdu3MDCwoJNmzY9sZ9JSUl4enpiZGSEkZERvXr14vDhw8o+X3/9deVWwG+//VboMS/p5LYn5awLIUR5V6EK+sKFC5UP4CtXrvDJJ58QGRmJtbU1R44cYdy4ccD/pXrt37+f2NhYVq5cybp16xg4cCBqtZr9+/czffp0OnXqxJgxY/K00bZtW9LT0zlz5gybN2/Gx8cHyClKdnZ2rFmzJl+/bt26xb59+zh16hRLly4F4OHDh/z44494eHgUa8yPp4PpdDp8fX0ZNWpUvvUKG9vixYs5fvw4iYmJDBgwgNDQUJycnPJs6+XlRe/evfnkk0+Ij49nw4YNT2wPcu6hR0dHk5mZySeffEJoaCgLFix44nZFMTc3zzPewo458Nzf44I8fpyFEKIiqlAPxT3uSaleFhYW9OrVi+DgYP744w+0Wi3nz5/HxsYGf39/BgwYwPHjxwvct5eXF99++y2HDh1SAj7atGnDhQsXSExMVNY7duwYOp2OmJgYevbsye7du0lISCAhIYGZM2cqD8fl3osGuHbtWp59FLXs39RqNbGxsVy/fh3IeWjsxIkTAAWOLTs7m0uXLtGqVSsCAgLo1KkTp06dyrffV155hddee42wsDBee+016tat+8T2HmdiYkJoaCg///wzJ0+eLHI7BwcHtmzZQnZ2NhkZGWzfvr3Q8RZ1zJ/1e5yb3AYoyW2Ojo6Fti2EEBVNhTpDf5ydnV2JpXr9m5eXF927d8fHx4fKlSsDUK1aNZYuXcqcOXOYOXMmWVlZWFtbs3z5cqKjo5kwYUKefXTv3p0pU6Zw+fJlJk2axPjx49myZQv16tWjVatWWFhYABS57N/atWvH6NGjGT58OBqNhqysLJydnWnRokWBY9NqtQQFBXH//n1UKhV16tRh7NixBe7b29ub8ePH8/nnnz9Ve/9Ws2ZNBg8ezOLFi1m6dGmh2/n7+3P69Gl69eqFpaVlgQ/Y5SrqmJd0cpsQQlR0krZWCh49eqTcM859sCsiIoKGDRsWuUxfpaWlYWFhQWZmJsOHD8fZ2Vm5315RKGlrhzIwytBRe1Snsu5SidPnRCvQ7/Hp89hAv8dXnLS1CnuGXpH8/fffTJgwAZ1OR3Z2NiNHjlQKdlHL9NWgQYPIzMwkIyODjh07Kg8TVkQ1B72BqakpumytzBYnhChTUtBLQZMmTYiNjX3mZfoq92l4fSLFXAhR1uRTSAghhNADUtCF0GO67NKfPlcIUTbkkrsQxXD7u30YZZTf6FSrj98u6y4IIUqJnKGLPNRqNWfPngVyJscZMmQIwcHBLzwo5fLly7Rv3z5PP3LT9VxcXPLcd9+5cyezZ88ucD9JSUnKZEBCCPG/RM7QRYHu3btHQEAALVu2ZOLEiahUqqfaTqPRYGhoWCJ9yJ0Z8OzZs/j4+NClSxdq165N9+7d6d69e4m0IYQQ+kIKusjn9u3bjB8/HrVaTWBgIFB4elpBCWleXl6MGTOGn376idTUVMaPH6/MuFdUYl1hbG1tqVq1Kjdu3KB27dpER0eze/duFi5cCMC8efPYtm0bVatWxcHBIc+2ucteeuklHBwcOHDgANHR0UWOSQghKiIp6CKf0aNH07dvX6WYPyl17fGEtFwWFhZERUVx+PBhRo8eTc+ePYtMrCvK4cOHsbS0pEmTJvmW5U61GxMTQ6VKlfjoo4/yLNu1axexsbFUqlRJGc/TjEkIISoaKegiHycnJ7Zt24a/vz+1a9d+Yura4wlpuXKjVFu3bk1KSgoZGRlFpqdZWlrm60dgYCA6nY6LFy+yYMECTExM8q2TlJSEq6urEuri5+enBOQkJSXh4uKiBK94eXkpy540JiGEqGikoIt8hg4dyq5duxgwYADff//9E9PTHk9Iy5U7ZWHu/fTs7Owi09MuX76c773ce+jbt28nODiY119/nZo1axZnaIriJMIJIUR5JE+5iwJ98MEHeHt7M2DAgKdOXXuSotLTiuLi4kKnTp1YsWJFvmWOjo5s376dBw8eoNFolIQ7yEl2+/HHH3n48CFarZa4uDhlWUmNSQghygs5QxeF+vDDD9HpdISEhDx16lpRikpPe5KxY8fi4+OT53I9QLdu3fj999/x9PRUHoq7ceMGkJN499tvv+Hh4UG1atVo3bo1d+/eBZ4tSU4IISoCSVsTei032U2r1TJp0iSsrKwYM2ZMsfebm4hU53Cq3k4so8+JVqDf49PnsYF+j0/S1oQoxIQJE7hy5QqPHj2iefPm+c7whRBCX0hBF3ptyZIlL3T/Nd7v/My/RZcmXbYGlVHJTPQjhCjf5KE4IfSYFHMh/ndIQReiApIUNSHEv8kldyGK4faqnzDKKP3iavWRZ6m3KYQo3+QMXQghhNADUtDLiaLiQjUaDW+++SbDhw/Ps01SUhJ2dnasXLkyz3uPx4euWbMGd3d3PDw8cHZ2zhc7Wti+AX7++Wf8/f3p0aMHPj4+fPDBB5w5c+aJ/RVCCFH65JJ7OVJYXOjPP/+MlZUVR44c4datW3mmP61Vqxbfffcd/v7+VK1aNc/+jh07xnfffUdkZCRVq1ZFo9Hw559/5lmnsH3v27ePSZMmsWTJElq2bAnAqVOnuHnzJnZ2dkX2tyRkZ2djZFS6/z21Wi0qleqpo2KFEKI8kYJeDv07LjQqKgp/f39+//13YmJiGDp0qLKulZUVbdq04csvv2Ts2LF59nPjxg0sLCyUcBJDQ8N8iWWF7XvJkiWMGDFCKeYATZs2far+/vXXX8ycOZN//vmHrKws3n//fXx9fQH48ccfmTdvHpUqVcLZ2Zl58+Zx5MgRzM3NsbOzY+TIkezevZs333yToUOHMmvWLM6cOUNGRgbt27cnODgYQ0NDFi9eTHx8PKampqhUKr7//nuMjY2ZMGEC586dw8jIiAYNGrBgwQIAVq5cqUz92rJlS0JCQjA3N2fRokX8+eefpKWlcfXqVTZs2EC1atWK8+0TQogyIZfcy6HH40Lv3LlDYmIiLi4u+Pj4KFnejxs+fDiRkZGkpKTkeb9Tp04YGRnRrVs3xo4dy4YNG3j48KGyvKh9nzx5Ent7+2fub3Z2NuPGjSM4OJioqCjWrl3LypUrSU5O5tatW0yePJnly5crcaf/ZmpqSlRUFKNHj2bWrFm0a9eOyMhIYmNjuXPnDlFRUaSmphIREUFMTAyxsbGsXr0aMzMz9u3bR3p6Otu2bSMuLo5p06YBsGfPHuLi4li/fj1btmxBo9EoqWuQcyUjPDycH374QYq5EKLCkjP0cqSguNC4uDi6deuGhYUFb7zxBhqNht9++402bdoo29WsWZPevXuzdOlSXFxclPfNzMzYsGEDx48f5/Dhw2zatIk1a9YQGRn51Pt+1v6eO3eO5ORkPvnkE2W9rKws/vrrLwwMDGjWrBn169cHwNfXl1mzZuXZp7e3t/J1QkICx44d49tvvwXg0aNH1K5dmypVqmBjY8P48ePp3LkzXbt2xcLCgiZNmpCcnMzUqVNxcHCga9euABw4cABXV1csLCwA6N27NzNnzlTa6dKlC9WrV3+qMQshRHklBb0cKSguNCoqijt37qBWqwG4f/8+UVFR+Yru0KFDcXFxoXnz5nneV6lUtGrVilatWvHee+/RsWNH/vzzT5o3b17kvps1a8axY8cKvcxeWH91Oh2WlpbExsbmW3/nzp1PPAa5twcgJ+J06dKlWFtb51tv48aNHDlyhMTERHx8fPjqq69o0qQJ8fHxJCYmsnfvXubNm8eWLVue2GZB8a9CCFHRyCX3cig3LnTixIncv3+fffv2kZCQQEJCAvHx8fzwww95Lp0DVKlShUGDBrFs2TLlveTkZM6ePau8Pn/+PFlZWbz88sscO3asyH0PHz6cpUuX8scffyjbnz59mn379hXa3xUrVtCgQQMqVapETExMnn6kpaVhb2/PyZMnuXjxIgCbN2/16s93AAAKWklEQVQu8jio1WpWrlyJRpPzd9537tzh0qVLpKWlcefOHRwcHAgMDMTW1pY///yT69evY2hoyFtvvUVwcDB37twhNTWVDh06sH37dtLS0tDpdERGRtKxY8dn+I4IIUT5J2fo5dTYsWNxd3fHy8srz1PXtWvXplmzZvzwww+88sorebbp168f33//vfL60aNHzJw5k9u3b2NqaoqhoSFz5syhRo0aLFy4kF69ehW6b29vb6ZNm8a0adNITU3FyMiIevXq5Xvw7vH+5sabLl++nJkzZ/L111+j1WqpUaMG8+fPp2bNmoSGhjJs2DAqV65M165dMTY2pnLlygXuc+LEicyZMwdPT09UKhXGxsZMnDgRY2NjPv74Yx49eoROp6NZs2b06NGDxMRE5s6dC+Q8sR4QEEDt2rWpXbs2Z86cwd/fH4AWLVoU+Gd6QghRkUl8qihVuXGmkPOEfWRkJOvWrSvjXj07JT71t2t6O1OcPkdUgn6PT5/HBvo9PolPFRXGqlWr+OGHH9BoNFSrVo2wsLCy7tJzyf09uEofJ0xMTEq9/UcPHqAyfPHBKxkZGS+8jbKkz+PT57GB/o4vMzMT+L/PmGchZ+hCPIf79+/neT5BCCFKkq2tLVWqVHmmbaSgC/EctFot6enpGBsby8xyQogSo9PpyMrKwtzcHAODZ3tuXQq6EEIIoQfkz9aEEEIIPSAFXQghhNADUtCFEEIIPSAFXQghhNADUtCFEEIIPSAFXQghhNADUtCFEEIIPSAFXYhndP78efr06UPPnj3p06cPf//9d1l3qcT8888/DBs2jJ49e+Lu7s7IkSO5c+dOWXfrhVi8eDF2dnZ6N+NfRkYGU6ZMoUePHri7u/Ppp5+WdZdKzK5du/Dy8sLT0xMPDw/++9//lnWXimX27Nmo1ep8/w+f+zNGJ4R4Jv3799fFxMTodDqdLiYmRte/f/8y7lHJ+eeff3SJiYnK688++0wXHBxchj16MU6cOKEbMmSIrlu3brozZ86UdXdK1PTp03UzZszQabVanU6n0928ebOMe1QytFqtrm3btsr369SpU7rWrVvrNBpNGffs+R06dEh39erVfP8Pn/czRs7QhXgGt2/f5uTJk7i5uQHg5ubGyZMn9eYs9qWXXqJ9+/bK69atW3P16tUy7FHJy8zMZNq0aYSGhpZ1V0pceno6MTExjBo1SpmSuGbNmmXcq5JjYGDA/fv3gZw8BSsrq2eeHrU8adu2LXXq1MnzXnE+YyRtTYhncO3aNWrXro3h/086MzQ0xMrKimvXrlG9evUy7l3J0mq1rFu3DrVaXdZdKVELFizAw8ODevXqlXVXStylS5d46aWXWLx4MUlJSZibmzNq1Cjatm1b1l0rNpVKxfz58xkxYgRmZmakp6ezcuXKsu5WiSvOZ0zF/dVGCPFCTZ8+HTMzM/r161fWXSkxv/32GydOnKBv375l3ZUXQqPRcOnSJZo1a0Z0dDTjxo3j448/Ji0tray7VmzZ2dmsWLGCpUuXsmvXLpYtW8bo0aNJT08v666VG1LQhXgGderU4caNG2g0GiDnAzQlJSXfZbOKbvbs2Vy4cIH58+dX6Eua/3bo0CGSk5Pp3r07arWa69evM2TIEPbt21fWXSsRderUwcjISLlca29vj6WlJefPny/jnhXfqVOnSElJ4Y033gDgjTfeoHLlyiQnJ5dxz0pWcT5j9OcnVYhSUKNGDZo2bUp8fDwA8fHxNG3aVK8ut3/xxRecOHGCJUuWYGJiUtbdKVEBAQHs27ePhIQEEhISePnll/n666/5f+3d30tTfxzH8WdwHDIqWkatEvtxEeRFtHRFuWUMQVGzoIQgBCsY0Y9R4MUYMRjtokLaRQRFhn9AN5I4TIgy0H54502BrR+GEKYtNnPRan4v4juUr/BVW999Pb4eV2fsc87en3NxXuycz/l8XC5XvkvLidWrV7Nnzx76+vqAX6Olx8fH2bRpU54r+312u52PHz/y5s0bAGKxGOPj45SUlOS5stz6nWuMlk8VmadYLIbf7yeRSLBy5UquXr3K1q1b811WTgwNDVFfX8/mzZspLCwEoLi4mJs3b+a5sj/D4/Fw69Yttm3blu9ScubDhw8EAgG+fPmCYRhcuHCBysrKfJeVE/fv3+fOnTvZAX8+n4+qqqo8V7Vw4XCYnp4exsbGsNlsrFq1iq6urgVfYxToIiIiJqBb7iIiIiagQBcRETEBBbqIiIgJKNBFRERMQIEuIiJiAgp0EVnS6urqeP78eb7LmJXf7ycSieS7DFkkNJe7iJiaw+HIbqdSKSwWS3ae7FAoRFdX1x+vIRgM0tnZCUA6nWZqaio7aU9ZWRltbW1/vAYxP72HLiJLhsfjIRwOs2/fvrzVcOPGDd6/f09ra+u/tvX7/axbt46LFy/+B5XJYqdb7iKypHk8Hvr7+4FfYevz+WhpacHhcHDw4EHevn3L7du32bt3L5WVlTPmfU8mkwQCAVwuF263m0gkkp2De658Ph8VFRWUlZVx/PhxhoaGZm03MTFBU1MT4XCYqakpYrEYJ06cYPfu3VRXVxONRrNt/X4/oVAIr9eLw+GgsbGR4eHhBZwdWUwU6CIi0zx69IhDhw4xMDDA9u3bOXXqFJlMhidPnnD27FmCwWC2rd/vxzAMenp66OjooK+vj3v37s3r9/bv38+DBw94+vQppaWltLS0/KNNPB6nubmZXbt2cenSJVKpFCdPnqS+vp7+/n4ikQihUIjXr19n94lGo5w7d46BgQFKSkr0LH4JUKCLiExTXl6O2+3GMAxqamqIx+N4vV4KCgqora1lZGSERCLB2NgYvb29BAIBrFYrRUVFNDc3z/uZ/NGjR1m+fDkWi4Xz58/z6tUrkslk9vvR0VGampqoqanJ3np//PgxGzdu5MiRIxiGQWlpKdXV1XR3d2f3q6qqYseOHRiGQUNDAy9fvszNCZL/LQ2KExGZpqioKLtdWFiIzWbLDqL7e8GayclJRkdH+fHjx4yV2jKZzLyW0v358yeRSITu7m4+f/6cXao2Ho+zYsUKAHp7e7FarRw7diy738jICIODg5SXl884VkNDQ/bzmjVrZvRjcnJyznXJ4qRAFxFZALvdjsVi4dmzZxjGwi6lnZ2dPHz4kPb2doqLi0kmkzidTqaPVW5sbCSRSOD1emlra8NqtbJ+/XqcTift7e256o6YgG65i4gswNq1a6moqODKlStMTEyQyWQYHh7mxYsXcz7G169fsVgs2Gw2UqkU169fn7VdMBhky5YtnD59mm/fvnHgwAHevXtHR0cH6XSadDrN4OAgsVgsV92TRUiBLiKyQNeuXSOdTlNbW4vT6cTn8/Hp06c573/48GE2bNiA2+2mrq6OnTt3ztpu2bJlXL58GbvdzpkzZygoKODu3btEo1Hcbjcul4vW1la+f/+eq67JIqT30EVERExA/9BFRERMQIEuIiJiAgp0ERERE1Cgi4iImIACXURExAQU6CIiIiagQBcRETEBBbqIiIgJKNBFRERM4C/ZAXaBm1PvOQAAAABJRU5ErkJggg==",
            "text/plain": [
              "<Figure size 360x720 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "output_type": "display_data"
        }
      ],
      "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))"
      ]
    }
  ],
  "metadata": {
    "colab": {
      "collapsed_sections": [],
      "name": "CDD_ML_Part_5_Acetylcholinesterase_Compare_Regressors.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "display_name": "Python 3",
      "name": "python3"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}