5_comparing_regressors.ipynb

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      "name": "python3",
      "display_name": "Python 3"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ChidhvilasTanay/drug-discovery-CHEMBL/blob/main/comparing_regressors.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xNq-XQWWYNXh"
      },
      "source": [
        "# **Bioinformatics Project - Computational Drug Discovery [Part 5] Comparing Regressors**\n",
        "\n",
        "Chanin Nantasenamat\n",
        "\n",
        "['Data Professor' YouTube channel](http://youtube.com/dataprofessor)\n",
        "\n",
        "In this Jupyter notebook, we will be building a real-life data science project that you can include in your data science portfolio. Particularly, we will be building a machine learning model using the ChEMBL bioactivity data.\n",
        "\n",
        "In Part 5, we will be comparing several ML algorithms for build regression models of acetylcholinesterase inhibitors.\n",
        "\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "OnVZXG7QY1D2"
      },
      "source": [
        "## **1. Import libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "B4i2iuTlZYvQ",
        "outputId": "70a7318e-23a5-4212-b9fd-0eb235c11c6f"
      },
      "source": [
        "! pip install lazypredict"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting lazypredict\n",
            "  Downloading lazypredict-0.2.12-py2.py3-none-any.whl (12 kB)\n",
            "Requirement already satisfied: click in /usr/local/lib/python3.10/dist-packages (from lazypredict) (8.1.7)\n",
            "Requirement already satisfied: scikit-learn in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.2.2)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.5.3)\n",
            "Requirement already satisfied: tqdm in /usr/local/lib/python3.10/dist-packages (from lazypredict) (4.66.2)\n",
            "Requirement already satisfied: joblib in /usr/local/lib/python3.10/dist-packages (from lazypredict) (1.3.2)\n",
            "Requirement already satisfied: lightgbm in /usr/local/lib/python3.10/dist-packages (from lazypredict) (4.1.0)\n",
            "Requirement already satisfied: xgboost in /usr/local/lib/python3.10/dist-packages (from lazypredict) (2.0.3)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from lightgbm->lazypredict) (1.25.2)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.10/dist-packages (from lightgbm->lazypredict) (1.11.4)\n",
            "Requirement already satisfied: python-dateutil>=2.8.1 in /usr/local/lib/python3.10/dist-packages (from pandas->lazypredict) (2.8.2)\n",
            "Requirement already satisfied: pytz>=2020.1 in /usr/local/lib/python3.10/dist-packages (from pandas->lazypredict) (2023.4)\n",
            "Requirement already satisfied: threadpoolctl>=2.0.0 in /usr/local/lib/python3.10/dist-packages (from scikit-learn->lazypredict) (3.3.0)\n",
            "Requirement already satisfied: six>=1.5 in /usr/local/lib/python3.10/dist-packages (from python-dateutil>=2.8.1->pandas->lazypredict) (1.16.0)\n",
            "Installing collected packages: lazypredict\n",
            "Successfully installed lazypredict-0.2.12\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Vf73M_f9XwZ7"
      },
      "source": [
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "from sklearn.model_selection import train_test_split\n",
        "import lazypredict\n",
        "from lazypredict.Supervised import LazyRegressor"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lUGwBVH7Y8Rq"
      },
      "source": [
        "## **2. Load the data set**\n"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Oiess4VaZChk",
        "outputId": "b9956d3a-6631-420c-b7d5-fc69ca407cff"
      },
      "source": [
        "! wget https://github.com/dataprofessor/data/raw/master/acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv"
      ],
      "execution_count": null,
      "outputs": [
        {
          "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"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "WVojHjyWZC9D"
      },
      "source": [
        "df = pd.read_csv('acetylcholinesterase_06_bioactivity_data_3class_pIC50_pubchem_fp.csv')"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "KbTq4zzKZpVf"
      },
      "source": [
        "X = df.drop('pIC50', axis=1)\n",
        "Y = df.pIC50"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "7Qa6WSGGZ39n"
      },
      "source": [
        "## **3. Data pre-processing**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CtC4SWpBaCVQ",
        "outputId": "a79ec4b2-b50c-4000-b130-3a931846ef15"
      },
      "source": [
        "# Examine X dimension\n",
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 881)"
            ]
          },
          "metadata": {},
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mVHzT2f5Z8sF",
        "outputId": "66fc3cdb-2f97-4694-b915-58c95a49b3d2"
      },
      "source": [
        "# Remove low variance features\n",
        "from sklearn.feature_selection import VarianceThreshold\n",
        "selection = VarianceThreshold(threshold=(.8 * (1 - .8)))\n",
        "X = selection.fit_transform(X)\n",
        "X.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(4695, 137)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MYck-_XeaHNZ"
      },
      "source": [
        "# Perform data splitting using 80/20 ratio\n",
        "X_train, X_test, Y_train, Y_test = train_test_split(X, Y, test_size=0.2, random_state=42)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zkW9nKohamEj"
      },
      "source": [
        "## **4. Compare ML algorithms**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nnFbUjA1aUj1",
        "outputId": "777dd47b-bfa6-4823-e2d0-0c22d7efb42e"
      },
      "source": [
        "# Defines and builds the lazyclassifier\n",
        "clf = LazyRegressor(verbose=0,ignore_warnings=True, custom_metric=None)\n",
        "# predictions on both train and test datasets to find out the best model.\n",
        "models_train,predictions_train = clf.fit(X_train, X_train, Y_train, Y_train)\n",
        "models_test,predictions_test = clf.fit(X_train, X_test, Y_train, Y_test)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 98%|█████████▊| 41/42 [08:19<00:04,  4.86s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.015861 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 1488\n",
            "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 496\n",
            "[LightGBM] [Info] Start training from score 5.814494\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 42/42 [08:20<00:00, 11.92s/it]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "'tuple' object has no attribute '__name__'\n",
            "Invalid Regressor(s)\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            " 98%|█████████▊| 41/42 [06:49<00:03,  3.68s/it]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[LightGBM] [Info] Auto-choosing row-wise multi-threading, the overhead of testing was 0.016039 seconds.\n",
            "You can set `force_row_wise=true` to remove the overhead.\n",
            "And if memory is not enough, you can set `force_col_wise=true`.\n",
            "[LightGBM] [Info] Total Bins 1488\n",
            "[LightGBM] [Info] Number of data points in the train set: 3756, number of used features: 496\n",
            "[LightGBM] [Info] Start training from score 5.814494\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "100%|██████████| 42/42 [06:50<00:00,  9.78s/it]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "cHOFkp-ka5tF",
        "outputId": "46693c6b-3c94-4741-a356-5b5d0e054cba"
      },
      "source": [
        "# Performance table of the training set (80% subset)\n",
        "predictions_train"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                         Adjusted R-Squared  \\\n",
              "Model                                                         \n",
              "ExtraTreesRegressor                                    0.91   \n",
              "DecisionTreeRegressor                                  0.91   \n",
              "ExtraTreeRegressor                                     0.91   \n",
              "GaussianProcessRegressor                               0.91   \n",
              "RandomForestRegressor                                  0.85   \n",
              "BaggingRegressor                                       0.82   \n",
              "MLPRegressor                                           0.81   \n",
              "XGBRegressor                                           0.77   \n",
              "LGBMRegressor                                          0.63   \n",
              "HistGradientBoostingRegressor                          0.63   \n",
              "KNeighborsRegressor                                    0.55   \n",
              "SVR                                                    0.51   \n",
              "NuSVR                                                  0.49   \n",
              "Ridge                                                  0.40   \n",
              "TransformedTargetRegressor                             0.40   \n",
              "LinearRegression                                       0.40   \n",
              "RidgeCV                                                0.39   \n",
              "HuberRegressor                                         0.36   \n",
              "GradientBoostingRegressor                              0.35   \n",
              "ElasticNetCV                                           0.34   \n",
              "LassoCV                                                0.34   \n",
              "BayesianRidge                                          0.33   \n",
              "PoissonRegressor                                       0.31   \n",
              "LinearSVR                                              0.30   \n",
              "OrthogonalMatchingPursuit                              0.25   \n",
              "OrthogonalMatchingPursuitCV                            0.25   \n",
              "TweedieRegressor                                       0.19   \n",
              "GammaRegressor                                         0.19   \n",
              "LassoLarsCV                                            0.10   \n",
              "LassoLarsIC                                            0.10   \n",
              "AdaBoostRegressor                                      0.02   \n",
              "LarsCV                                                -0.30   \n",
              "Lasso                                                 -0.31   \n",
              "ElasticNet                                            -0.31   \n",
              "DummyRegressor                                        -0.31   \n",
              "LassoLars                                             -0.31   \n",
              "PassiveAggressiveRegressor                            -0.66   \n",
              "KernelRidge                                          -17.93   \n",
              "Lars                                                -156.56   \n",
              "SGDRegressor                    -51773293695446967910400.00   \n",
              "RANSACRegressor               -6134536495799827550961664.00   \n",
              "\n",
              "                                                  R-Squared             RMSE  \\\n",
              "Model                                                                          \n",
              "ExtraTreesRegressor                                    0.93             0.41   \n",
              "DecisionTreeRegressor                                  0.93             0.41   \n",
              "ExtraTreeRegressor                                     0.93             0.41   \n",
              "GaussianProcessRegressor                               0.93             0.41   \n",
              "RandomForestRegressor                                  0.88             0.53   \n",
              "BaggingRegressor                                       0.86             0.58   \n",
              "MLPRegressor                                           0.85             0.60   \n",
              "XGBRegressor                                           0.83             0.65   \n",
              "LGBMRegressor                                          0.72             0.82   \n",
              "HistGradientBoostingRegressor                          0.72             0.82   \n",
              "KNeighborsRegressor                                    0.66             0.91   \n",
              "SVR                                                    0.62             0.95   \n",
              "NuSVR                                                  0.61             0.97   \n",
              "Ridge                                                  0.54             1.05   \n",
              "TransformedTargetRegressor                             0.54             1.06   \n",
              "LinearRegression                                       0.54             1.06   \n",
              "RidgeCV                                                0.54             1.06   \n",
              "HuberRegressor                                         0.51             1.09   \n",
              "GradientBoostingRegressor                              0.50             1.10   \n",
              "ElasticNetCV                                           0.49             1.10   \n",
              "LassoCV                                                0.49             1.10   \n",
              "BayesianRidge                                          0.49             1.11   \n",
              "PoissonRegressor                                       0.47             1.13   \n",
              "LinearSVR                                              0.46             1.14   \n",
              "OrthogonalMatchingPursuit                              0.42             1.18   \n",
              "OrthogonalMatchingPursuitCV                            0.42             1.18   \n",
              "TweedieRegressor                                       0.38             1.22   \n",
              "GammaRegressor                                         0.38             1.22   \n",
              "LassoLarsCV                                            0.31             1.28   \n",
              "LassoLarsIC                                            0.31             1.28   \n",
              "AdaBoostRegressor                                      0.25             1.34   \n",
              "LarsCV                                                 0.00             1.55   \n",
              "Lasso                                                  0.00             1.55   \n",
              "ElasticNet                                             0.00             1.55   \n",
              "DummyRegressor                                         0.00             1.55   \n",
              "LassoLars                                              0.00             1.55   \n",
              "PassiveAggressiveRegressor                            -0.27             1.75   \n",
              "KernelRidge                                          -13.49             5.91   \n",
              "Lars                                                -119.59            17.05   \n",
              "SGDRegressor                    -39626217331748220436480.00  309038827760.53   \n",
              "RANSACRegressor               -4695248439128815705260032.00 3363961585157.28   \n",
              "\n",
              "                               Time Taken  \n",
              "Model                                      \n",
              "ExtraTreesRegressor                 26.24  \n",
              "DecisionTreeRegressor                0.70  \n",
              "ExtraTreeRegressor                   0.49  \n",
              "GaussianProcessRegressor           173.52  \n",
              "RandomForestRegressor               17.95  \n",
              "BaggingRegressor                     2.44  \n",
              "MLPRegressor                        19.36  \n",
              "XGBRegressor                         1.32  \n",
              "LGBMRegressor                        0.91  \n",
              "HistGradientBoostingRegressor       10.16  \n",
              "KNeighborsRegressor                  1.79  \n",
              "SVR                                 26.24  \n",
              "NuSVR                               17.38  \n",
              "Ridge                                0.39  \n",
              "TransformedTargetRegressor           0.91  \n",
              "LinearRegression                     0.91  \n",
              "RidgeCV                              2.84  \n",
              "HuberRegressor                       3.92  \n",
              "GradientBoostingRegressor            5.07  \n",
              "ElasticNetCV                        54.26  \n",
              "LassoCV                             56.49  \n",
              "BayesianRidge                        2.29  \n",
              "PoissonRegressor                     0.77  \n",
              "LinearSVR                            8.33  \n",
              "OrthogonalMatchingPursuit            0.52  \n",
              "OrthogonalMatchingPursuitCV          2.56  \n",
              "TweedieRegressor                     0.79  \n",
              "GammaRegressor                       0.80  \n",
              "LassoLarsCV                          1.27  \n",
              "LassoLarsIC                          1.15  \n",
              "AdaBoostRegressor                    6.88  \n",
              "LarsCV                               3.63  \n",
              "Lasso                                0.29  \n",
              "ElasticNet                           0.38  \n",
              "DummyRegressor                       0.27  \n",
              "LassoLars                            0.62  \n",
              "PassiveAggressiveRegressor           0.60  \n",
              "KernelRidge                          2.61  \n",
              "Lars                                 0.86  \n",
              "SGDRegressor                         0.43  \n",
              "RANSACRegressor                     41.83  "
            ],
            "text/html": [
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.41</td>\n",
              "      <td>26.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.70</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.41</td>\n",
              "      <td>0.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>0.91</td>\n",
              "      <td>0.93</td>\n",
              "      <td>0.41</td>\n",
              "      <td>173.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RandomForestRegressor</th>\n",
              "      <td>0.85</td>\n",
              "      <td>0.88</td>\n",
              "      <td>0.53</td>\n",
              "      <td>17.95</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>0.82</td>\n",
              "      <td>0.86</td>\n",
              "      <td>0.58</td>\n",
              "      <td>2.44</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>0.81</td>\n",
              "      <td>0.85</td>\n",
              "      <td>0.60</td>\n",
              "      <td>19.36</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>XGBRegressor</th>\n",
              "      <td>0.77</td>\n",
              "      <td>0.83</td>\n",
              "      <td>0.65</td>\n",
              "      <td>1.32</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.82</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>0.63</td>\n",
              "      <td>0.72</td>\n",
              "      <td>0.82</td>\n",
              "      <td>10.16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>0.55</td>\n",
              "      <td>0.66</td>\n",
              "      <td>0.91</td>\n",
              "      <td>1.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>0.51</td>\n",
              "      <td>0.62</td>\n",
              "      <td>0.95</td>\n",
              "      <td>26.24</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>0.49</td>\n",
              "      <td>0.61</td>\n",
              "      <td>0.97</td>\n",
              "      <td>17.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.05</td>\n",
              "      <td>0.39</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TransformedTargetRegressor</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearRegression</th>\n",
              "      <td>0.40</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.06</td>\n",
              "      <td>0.91</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>0.39</td>\n",
              "      <td>0.54</td>\n",
              "      <td>1.06</td>\n",
              "      <td>2.84</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>0.36</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.09</td>\n",
              "      <td>3.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>0.35</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.10</td>\n",
              "      <td>5.07</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.10</td>\n",
              "      <td>54.26</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>0.34</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.10</td>\n",
              "      <td>56.49</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>0.33</td>\n",
              "      <td>0.49</td>\n",
              "      <td>1.11</td>\n",
              "      <td>2.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>0.31</td>\n",
              "      <td>0.47</td>\n",
              "      <td>1.13</td>\n",
              "      <td>0.77</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>0.30</td>\n",
              "      <td>0.46</td>\n",
              "      <td>1.14</td>\n",
              "      <td>8.33</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>0.25</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>0.52</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>0.25</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>2.56</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.79</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>0.19</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsCV</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.28</td>\n",
              "      <td>1.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLarsIC</th>\n",
              "      <td>0.10</td>\n",
              "      <td>0.31</td>\n",
              "      <td>1.28</td>\n",
              "      <td>1.15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>AdaBoostRegressor</th>\n",
              "      <td>0.02</td>\n",
              "      <td>0.25</td>\n",
              "      <td>1.34</td>\n",
              "      <td>6.88</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-0.30</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>3.63</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.29</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.38</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.27</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-0.31</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.62</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-0.66</td>\n",
              "      <td>-0.27</td>\n",
              "      <td>1.75</td>\n",
              "      <td>0.60</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-17.93</td>\n",
              "      <td>-13.49</td>\n",
              "      <td>5.91</td>\n",
              "      <td>2.61</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-156.56</td>\n",
              "      <td>-119.59</td>\n",
              "      <td>17.05</td>\n",
              "      <td>0.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>SGDRegressor</th>\n",
              "      <td>-51773293695446967910400.00</td>\n",
              "      <td>-39626217331748220436480.00</td>\n",
              "      <td>309038827760.53</td>\n",
              "      <td>0.43</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-6134536495799827550961664.00</td>\n",
              "      <td>-4695248439128815705260032.00</td>\n",
              "      <td>3363961585157.28</td>\n",
              "      <td>41.83</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
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              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
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              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
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              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
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              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
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              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
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              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "models_train",
              "summary": "{\n  \"name\": \"models_train\",\n  \"rows\": 41,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"OrthogonalMatchingPursuit\",\n          \"Ridge\",\n          \"LGBMRegressor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R-Squared\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 9.578856064175163e+23,\n        \"min\": -6.134536495799828e+24,\n        \"max\": 0.9099364678421538,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          0.34613876483657946,\n          0.30960411707938507,\n          -0.3001274829650462\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R-Squared\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 7.331460007573747e+23,\n        \"min\": -4.6952484391288157e+24,\n        \"max\": 0.9310672193284554,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          0.4995480186791823,\n          0.47158514846502075,\n          0.00490908494233222\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 526371907986.7184,\n        \"min\": 0.40760043434719895,\n        \"max\": 3363961585157.2754,\n        \"num_unique_values\": 34,\n        \"samples\": [\n          1.0982543844235153,\n          1.1285199654259532,\n          1.5486494401060327\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Taken\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 29.406803622186207,\n        \"min\": 0.2669227123260498,\n        \"max\": 173.51955199241638,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          0.516085147857666,\n          0.3860352039337158,\n          0.9075422286987305\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "KP6qT55ebcfd",
        "outputId": "84b5378a-4ca4-4b6a-f5a6-3af50fa32208"
      },
      "source": [
        "# Performance table of the test set (20% subset)\n",
        "predictions_test"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "                                                Adjusted R-Squared  \\\n",
              "Model                                                                \n",
              "LGBMRegressor                                                -6.14   \n",
              "HistGradientBoostingRegressor                                -6.14   \n",
              "XGBRegressor                                                 -6.36   \n",
              "RandomForestRegressor                                        -6.64   \n",
              "SVR                                                          -6.91   \n",
              "KNeighborsRegressor                                          -7.10   \n",
              "NuSVR                                                        -7.15   \n",
              "BaggingRegressor                                             -7.19   \n",
              "GradientBoostingRegressor                                    -8.08   \n",
              "LassoCV                                                      -8.36   \n",
              "ElasticNetCV                                                 -8.38   \n",
              "RidgeCV                                                      -8.50   \n",
              "HuberRegressor                                               -8.52   \n",
              "BayesianRidge                                                -8.53   \n",
              "PoissonRegressor                                             -8.61   \n",
              "MLPRegressor                                                 -8.83   \n",
              "Ridge                                                        -9.19   \n",
              "OrthogonalMatchingPursuit                                    -9.47   \n",
              "OrthogonalMatchingPursuitCV                                  -9.47   \n",
              "LinearSVR                                                    -9.53   \n",
              "GammaRegressor                                               -9.82   \n",
              "TweedieRegressor                                             -9.88   \n",
              "LassoLarsCV                                                 -10.44   \n",
              "LassoLarsIC                                                 -10.44   \n",
              "ExtraTreesRegressor                                         -10.52   \n",
              "ExtraTreeRegressor                                          -11.00   \n",
              "DecisionTreeRegressor                                       -11.14   \n",
              "AdaBoostRegressor                                           -11.59   \n",
              "LarsCV                                                      -15.43   \n",
              "Lasso                                                       -15.46   \n",
              "ElasticNet                                                  -15.46   \n",
              "DummyRegressor                                              -15.46   \n",
              "LassoLars                                                   -15.46   \n",
              "PassiveAggressiveRegressor                                  -20.22   \n",
              "GaussianProcessRegressor                                   -160.06   \n",
              "KernelRidge                                                -239.68   \n",
              "LinearRegression                      -222596304322469512085504.00   \n",
              "TransformedTargetRegressor            -222596304322469512085504.00   \n",
              "SGDRegressor                          -226788356998671414329344.00   \n",
              "RANSACRegressor                    -273091981535757934474035200.00   \n",
              "Lars                          -16905073721568946760211945750528.00   \n",
              "\n",
              "                                                        R-Squared  \\\n",
              "Model                                                               \n",
              "LGBMRegressor                                                0.57   \n",
              "HistGradientBoostingRegressor                                0.57   \n",
              "XGBRegressor                                                 0.55   \n",
              "RandomForestRegressor                                        0.54   \n",
              "SVR                                                          0.52   \n",
              "KNeighborsRegressor                                          0.51   \n",
              "NuSVR                                                        0.50   \n",
              "BaggingRegressor                                             0.50   \n",
              "GradientBoostingRegressor                                    0.45   \n",
              "LassoCV                                                      0.43   \n",
              "ElasticNetCV                                                 0.43   \n",
              "RidgeCV                                                      0.42   \n",
              "HuberRegressor                                               0.42   \n",
              "BayesianRidge                                                0.42   \n",
              "PoissonRegressor                                             0.42   \n",
              "MLPRegressor                                                 0.40   \n",
              "Ridge                                                        0.38   \n",
              "OrthogonalMatchingPursuit                                    0.36   \n",
              "OrthogonalMatchingPursuitCV                                  0.36   \n",
              "LinearSVR                                                    0.36   \n",
              "GammaRegressor                                               0.34   \n",
              "TweedieRegressor                                             0.34   \n",
              "LassoLarsCV                                                  0.30   \n",
              "LassoLarsIC                                                  0.30   \n",
              "ExtraTreesRegressor                                          0.30   \n",
              "ExtraTreeRegressor                                           0.27   \n",
              "DecisionTreeRegressor                                        0.26   \n",
              "AdaBoostRegressor                                            0.23   \n",
              "LarsCV                                                       0.00   \n",
              "Lasso                                                       -0.00   \n",
              "ElasticNet                                                  -0.00   \n",
              "DummyRegressor                                              -0.00   \n",
              "LassoLars                                                   -0.00   \n",
              "PassiveAggressiveRegressor                                  -0.29   \n",
              "GaussianProcessRegressor                                    -8.79   \n",
              "KernelRidge                                                -13.63   \n",
              "LinearRegression                      -13526641094222562721792.00   \n",
              "TransformedTargetRegressor            -13526641094222562721792.00   \n",
              "SGDRegressor                          -13781382035100502327296.00   \n",
              "RANSACRegressor                    -16595141735115355206975488.00   \n",
              "Lars                          -1027280599285106652369882447872.00   \n",
              "\n",
              "                                             RMSE  Time Taken  \n",
              "Model                                                          \n",
              "LGBMRegressor                                1.02        0.78  \n",
              "HistGradientBoostingRegressor                1.02        9.29  \n",
              "XGBRegressor                                 1.04        1.22  \n",
              "RandomForestRegressor                        1.06       20.70  \n",
              "SVR                                          1.08       15.92  \n",
              "KNeighborsRegressor                          1.09        0.42  \n",
              "NuSVR                                        1.09       11.17  \n",
              "BaggingRegressor                             1.10        1.86  \n",
              "GradientBoostingRegressor                    1.15        5.11  \n",
              "LassoCV                                      1.17       55.55  \n",
              "ElasticNetCV                                 1.17       57.55  \n",
              "RidgeCV                                      1.18        1.58  \n",
              "HuberRegressor                               1.18        3.17  \n",
              "BayesianRidge                                1.18        1.65  \n",
              "PoissonRegressor                             1.19        0.80  \n",
              "MLPRegressor                                 1.20       21.21  \n",
              "Ridge                                        1.22        0.41  \n",
              "OrthogonalMatchingPursuit                    1.24        0.50  \n",
              "OrthogonalMatchingPursuitCV                  1.24        2.22  \n",
              "LinearSVR                                    1.24        9.34  \n",
              "GammaRegressor                               1.26        0.56  \n",
              "TweedieRegressor                             1.26        0.47  \n",
              "LassoLarsCV                                  1.30        1.12  \n",
              "LassoLarsIC                                  1.30        1.09  \n",
              "ExtraTreesRegressor                          1.30       25.77  \n",
              "ExtraTreeRegressor                           1.33        0.61  \n",
              "DecisionTreeRegressor                        1.33        0.45  \n",
              "AdaBoostRegressor                            1.36        7.01  \n",
              "LarsCV                                       1.55        5.66  \n",
              "Lasso                                        1.55        0.24  \n",
              "ElasticNet                                   1.55        0.21  \n",
              "DummyRegressor                               1.55        0.15  \n",
              "LassoLars                                    1.55        0.36  \n",
              "PassiveAggressiveRegressor                   1.76        0.55  \n",
              "GaussianProcessRegressor                     4.86       99.01  \n",
              "KernelRidge                                  5.94        1.96  \n",
              "LinearRegression                  180633881681.82        0.87  \n",
              "TransformedTargetRegressor        180633881681.82        0.88  \n",
              "SGDRegressor                      182326845104.54        0.28  \n",
              "RANSACRegressor                  6326954542714.10       41.72  \n",
              "Lars                          1574159643906769.50        0.84  "
            ],
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              "\n",
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              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Adjusted R-Squared</th>\n",
              "      <th>R-Squared</th>\n",
              "      <th>RMSE</th>\n",
              "      <th>Time Taken</th>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Model</th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "      <th></th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>LGBMRegressor</th>\n",
              "      <td>-6.14</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.02</td>\n",
              "      <td>0.78</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HistGradientBoostingRegressor</th>\n",
              "      <td>-6.14</td>\n",
              "      <td>0.57</td>\n",
              "      <td>1.02</td>\n",
              "      <td>9.29</td>\n",
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              "      <th>XGBRegressor</th>\n",
              "      <td>-6.36</td>\n",
              "      <td>0.55</td>\n",
              "      <td>1.04</td>\n",
              "      <td>1.22</td>\n",
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              "      <th>RandomForestRegressor</th>\n",
              "      <td>-6.64</td>\n",
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              "      <td>20.70</td>\n",
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              "    <tr>\n",
              "      <th>SVR</th>\n",
              "      <td>-6.91</td>\n",
              "      <td>0.52</td>\n",
              "      <td>1.08</td>\n",
              "      <td>15.92</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>KNeighborsRegressor</th>\n",
              "      <td>-7.10</td>\n",
              "      <td>0.51</td>\n",
              "      <td>1.09</td>\n",
              "      <td>0.42</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>NuSVR</th>\n",
              "      <td>-7.15</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.09</td>\n",
              "      <td>11.17</td>\n",
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              "    <tr>\n",
              "      <th>BaggingRegressor</th>\n",
              "      <td>-7.19</td>\n",
              "      <td>0.50</td>\n",
              "      <td>1.10</td>\n",
              "      <td>1.86</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GradientBoostingRegressor</th>\n",
              "      <td>-8.08</td>\n",
              "      <td>0.45</td>\n",
              "      <td>1.15</td>\n",
              "      <td>5.11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LassoCV</th>\n",
              "      <td>-8.36</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.17</td>\n",
              "      <td>55.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>ElasticNetCV</th>\n",
              "      <td>-8.38</td>\n",
              "      <td>0.43</td>\n",
              "      <td>1.17</td>\n",
              "      <td>57.55</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>RidgeCV</th>\n",
              "      <td>-8.50</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.58</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>HuberRegressor</th>\n",
              "      <td>-8.52</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>3.17</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>BayesianRidge</th>\n",
              "      <td>-8.53</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.18</td>\n",
              "      <td>1.65</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>PoissonRegressor</th>\n",
              "      <td>-8.61</td>\n",
              "      <td>0.42</td>\n",
              "      <td>1.19</td>\n",
              "      <td>0.80</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>MLPRegressor</th>\n",
              "      <td>-8.83</td>\n",
              "      <td>0.40</td>\n",
              "      <td>1.20</td>\n",
              "      <td>21.21</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>Ridge</th>\n",
              "      <td>-9.19</td>\n",
              "      <td>0.38</td>\n",
              "      <td>1.22</td>\n",
              "      <td>0.41</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuit</th>\n",
              "      <td>-9.47</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.24</td>\n",
              "      <td>0.50</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>OrthogonalMatchingPursuitCV</th>\n",
              "      <td>-9.47</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.24</td>\n",
              "      <td>2.22</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>LinearSVR</th>\n",
              "      <td>-9.53</td>\n",
              "      <td>0.36</td>\n",
              "      <td>1.24</td>\n",
              "      <td>9.34</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>GammaRegressor</th>\n",
              "      <td>-9.82</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.56</td>\n",
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              "    <tr>\n",
              "      <th>TweedieRegressor</th>\n",
              "      <td>-9.88</td>\n",
              "      <td>0.34</td>\n",
              "      <td>1.26</td>\n",
              "      <td>0.47</td>\n",
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              "      <th>LassoLarsCV</th>\n",
              "      <td>-10.44</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.12</td>\n",
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              "      <th>LassoLarsIC</th>\n",
              "      <td>-10.44</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.30</td>\n",
              "      <td>1.09</td>\n",
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              "    <tr>\n",
              "      <th>ExtraTreesRegressor</th>\n",
              "      <td>-10.52</td>\n",
              "      <td>0.30</td>\n",
              "      <td>1.30</td>\n",
              "      <td>25.77</td>\n",
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              "      <th>ExtraTreeRegressor</th>\n",
              "      <td>-11.00</td>\n",
              "      <td>0.27</td>\n",
              "      <td>1.33</td>\n",
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              "      <th>DecisionTreeRegressor</th>\n",
              "      <td>-11.14</td>\n",
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              "      <td>1.33</td>\n",
              "      <td>0.45</td>\n",
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              "      <th>AdaBoostRegressor</th>\n",
              "      <td>-11.59</td>\n",
              "      <td>0.23</td>\n",
              "      <td>1.36</td>\n",
              "      <td>7.01</td>\n",
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              "    <tr>\n",
              "      <th>LarsCV</th>\n",
              "      <td>-15.43</td>\n",
              "      <td>0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>5.66</td>\n",
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              "    <tr>\n",
              "      <th>Lasso</th>\n",
              "      <td>-15.46</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
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              "    <tr>\n",
              "      <th>ElasticNet</th>\n",
              "      <td>-15.46</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.21</td>\n",
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              "    <tr>\n",
              "      <th>DummyRegressor</th>\n",
              "      <td>-15.46</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.15</td>\n",
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              "    <tr>\n",
              "      <th>LassoLars</th>\n",
              "      <td>-15.46</td>\n",
              "      <td>-0.00</td>\n",
              "      <td>1.55</td>\n",
              "      <td>0.36</td>\n",
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              "      <th>PassiveAggressiveRegressor</th>\n",
              "      <td>-20.22</td>\n",
              "      <td>-0.29</td>\n",
              "      <td>1.76</td>\n",
              "      <td>0.55</td>\n",
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              "      <th>GaussianProcessRegressor</th>\n",
              "      <td>-160.06</td>\n",
              "      <td>-8.79</td>\n",
              "      <td>4.86</td>\n",
              "      <td>99.01</td>\n",
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              "    <tr>\n",
              "      <th>KernelRidge</th>\n",
              "      <td>-239.68</td>\n",
              "      <td>-13.63</td>\n",
              "      <td>5.94</td>\n",
              "      <td>1.96</td>\n",
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              "      <th>LinearRegression</th>\n",
              "      <td>-222596304322469512085504.00</td>\n",
              "      <td>-13526641094222562721792.00</td>\n",
              "      <td>180633881681.82</td>\n",
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              "      <td>180633881681.82</td>\n",
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              "      <th>SGDRegressor</th>\n",
              "      <td>-226788356998671414329344.00</td>\n",
              "      <td>-13781382035100502327296.00</td>\n",
              "      <td>182326845104.54</td>\n",
              "      <td>0.28</td>\n",
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              "    <tr>\n",
              "      <th>RANSACRegressor</th>\n",
              "      <td>-273091981535757934474035200.00</td>\n",
              "      <td>-16595141735115355206975488.00</td>\n",
              "      <td>6326954542714.10</td>\n",
              "      <td>41.72</td>\n",
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              "    <tr>\n",
              "      <th>Lars</th>\n",
              "      <td>-16905073721568946760211945750528.00</td>\n",
              "      <td>-1027280599285106652369882447872.00</td>\n",
              "      <td>1574159643906769.50</td>\n",
              "      <td>0.84</td>\n",
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              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-95453fb7-e2d8-4c81-aadb-e12786603ddb button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-95453fb7-e2d8-4c81-aadb-e12786603ddb');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-79f206e1-3d4c-4e84-a0dc-eaa3de84b0cb\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-79f206e1-3d4c-4e84-a0dc-eaa3de84b0cb')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-79f206e1-3d4c-4e84-a0dc-eaa3de84b0cb button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "variable_name": "models_test",
              "summary": "{\n  \"name\": \"models_test\",\n  \"rows\": 41,\n  \"fields\": [\n    {\n      \"column\": \"Model\",\n      \"properties\": {\n        \"dtype\": \"string\",\n        \"num_unique_values\": 41,\n        \"samples\": [\n          \"ExtraTreesRegressor\",\n          \"BayesianRidge\",\n          \"GradientBoostingRegressor\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Adjusted R-Squared\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 2.6401278895771767e+30,\n        \"min\": -1.6905073721568947e+31,\n        \"max\": -6.137139007660367,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          -1.6905073721568947e+31,\n          -8.527989140718226,\n          -11.590438881368163\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"R-Squared\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 1.604342107738796e+29,\n        \"min\": -1.0272805992851067e+30,\n        \"max\": 0.5662932585963316,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          -1.0272805992851067e+30,\n          0.42100705648087533,\n          0.23490936435182797\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"RMSE\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 245817588993848.44,\n        \"min\": 1.022827315164185,\n        \"max\": 1574159643906769.5,\n        \"num_unique_values\": 36,\n        \"samples\": [\n          1574159643906769.5,\n          1.1817915125242313,\n          1.3585032271461175\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"Time Taken\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 20.047845165848614,\n        \"min\": 0.15308356285095215,\n        \"max\": 99.01277351379395,\n        \"num_unique_values\": 41,\n        \"samples\": [\n          25.774198055267334,\n          1.6481876373291016,\n          5.108869791030884\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CM-lcHz6byNz"
      },
      "source": [
        "## **5. Data visualization of model performance**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 794
        },
        "id": "MLsi5uSvbpC7",
        "outputId": "fc16bb4f-dd26-44dd-dcec-21795f03839b"
      },
      "source": [
        "# Bar plot of R-squared values\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "#train[\"R-Squared\"] = [0 if i < 0 else i for i in train.iloc[:,0] ]\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"R-Squared\", data=predictions_train)\n",
        "ax.set(xlim=(0, 1))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 1.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 11
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ],
            "image/png": 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CyM/Pf6ZtAFi4cCEPHjyQjhcvXoyGhgZz586VzhkaGj7zeqsza9YsnJycKCgoICUlhfj4eO7evcuHH374QuoXBEEQBOHpiAC3gZLJZNja2krHbm5uDB8+nIkTJzJ//nzs7e1p1arVM6uva9eudcrfunXrZ1b3ozp06KB0rK2tjZaWllJf/F1hYeFzGVVt06aNVK+LiwuXL1/m888/Z9GiRTVusfssPK/P9Sy9DG0UBEEQXj9iFYWXSOPGjfnggw8oKSlh9+7d0vmkpCTefvttrKys8PDwIDo6mrKyMqVr79y5w5w5c3B1dcXa2pq+fftKI8RQMUXh0ZHJCxcuMGHCBJycnLCxsaFPnz6sXbtWSg8JCWHAgAFKdZw/fx4/Pz9sbW1xcHBg+vTp3Lp1SymPXC5n7dq1xMbG4urqipOTE/Pmzav1aPCxY8eQy+UcOnSI6dOnY29vz4wZMwDIzc1l0aJFuLu7Y2lpiY+PD//973+rlHHo0CEGDRqEtbU1zs7OLFy4sFb1d+nShcLCQu7fvy+dq02dCoWCuLg43NzcsLOzY/r06fz888/I5XKOHTum1Ddr1qwhIiICNzc3XFxcpOsTExPp06cPlpaW9OrViw0bNijVkZ6ezowZM3B1dcXKygovLy8+/vjjWqcD/PbbbwwdOhRra2vpvmRnZ0vpN2/eRC6Xk5SURGhoKE5OTrXa3lgQBEEQXjQxgvuS6dChA82aNSM1NRWA9evXExERwejRowkJCeHSpUtSgFu5kUBWVhZDhgwBIDAwkJYtW3Lt2jWuX7/+2HomTZpE06ZNWbJkCdra2ly/fp309PTH5r99+zYjR46kVatWREREUFRURHR0NCNHjiQ5ORltbW0p79atW3FwcGDp0qVcvXqV8PBwjIyM6rTxwQcffMA777xDfHw8MpmM4uJixo4dy71795g5cybNmjUjOTkZf39/kpKSkMvlABw4cIDAwEB8fHyYNm0amZmZREZGkpubS3R09BPrvHXrFk2aNMHAwACg1nVu3ryZuLg4xo8fj7OzM0ePHiU0NLTaOjZt2oSNjQ1LliyhtLQUgCVLlrB7924mTZqEjY0NJ06cYNmyZTRu3Jhhw4YBMGfOHDIyMggNDcXIyIjbt29z+vRpqdya0k+fPs3YsWNxcnJi+fLl3L17l8jISC5evMiOHTtQVVWV8kZFReHp6UlkZCTl5eW1vmeCIAiC8KKIAPclZGpqyt27d3nw4AErVqxg/PjxzJo1C6iYyqCmpsbSpUvx8/PDwMCADRs2cO/ePb7++mtpa9jK0cHq3L9/n5s3bzJ//ny8vLwAcHZ2fmKbNmzYQGlpKevWrUNfXx+oGPH09vZm7969+Pr6SnmNjY2JjIwEoHv37pw5c4ZvvvmmTgGul5cXs2fPlo4/++wzzp07x+effy5Nc/Dw8ODatWusXLmS5cuXo1AoCA8Pp3///ixZskSpPRMnTiQgIICOHTtK58vLyyktLZXm4B48eJCZM2dKwd7+/ftrrLOsrIw1a9bg4+MjfT53d3eysrLYs2dPlc+lp6dHXFyctI7i9evX2bJlC4sXL5b+SHF1daWwsJD4+HiGDBmCTCbj1KlTzJo1i/79+0tlvfvuu9L3NaUnJCRgbGxMQkICampqQMXPmZ+fH4cPH5Z+DgA6d+6s1H+CIAiC0NCIKQovIYVCgYqKCqmpqeTn59O3b19KS0ulr8oA6MKFCwD88ssvODs7S8FtTQwMDDAzMyMqKoq9e/c+ceS20vHjx3FycpKCW4D27dvTuXNnfv/9d6W8rq6uSsft27evVR2P6tGjh9LxkSNH6NSpE23btq3SF5Vrrl65coW0tDT69eunlKdbt27IZDKlEU2oGO22sLDA0dGRuXPn0qdPHyZMmFCnOtPT08nMzFQKEAGlVRoe1b17d6VFwn/++WcAevfuXaWOzMxMbt++DVTMoV63bh3btm3j2rVrVcqtKf348eP06tVLCm6hIhDX1dWtcv/+3veCIAiC0NCIEdyXUHp6Om3btiUrq2J5pffee6/afJXBT3Z2ttLIZE1UVFRITEwkOjqaDz/8kPz8fCwsLJg3bx5vvPFGtdfk5ubSpUuXKueNjIzIyVFe5/TvqzWoqalRXFxc6/ZVlvuorKwszpw5g4WFRZW8lSOulf01ZcqUasus7K9KwcHBODs7k5eXx5YtW/jyyy/p1q0bQ4cOrXWdmZmZQNWVH/7e/id9LoVC8dgR9Nu3b2NmZkZ0dDTR0dHExMSwePFizM3NmTVrFr179waoMT03N7faNlV3/x7XdkEQBEFoKESA+5K5cOECd+7c4b333kNPTw+oWOKrefPmVfJWjtjq6+uTkZFRp3rMzc1ZsWIFJSUlpKamEhUVxaRJk/jxxx9p0qRJlfx6enrcu3evyvl79+7Rtm3bOtVdG3/fClFPTw+5XP7ER+eVo8sLFizA2tq6SrqJiYnScatWraTdVZycnHj//feJiYnhnXfeQUtLq1Z1GhsbAyi9mAZU21eP+1wqKips27ZNaXS1krm5udT2sLAwysvLOX36NKtWrSIwMJADBw7QqlWrGtOfdP8qf84e10ZBEARBaGhEgPsSKSoq4j//+Q/q6uoMGjQIXV1dNDU1SU9P56233nrsdS4uLqxbt45bt27RokWLOtWppqZGt27dmDhxIpMnTyYjI0MKqh7l4ODArl27yMnJkQKiy5cvc/78ef71r3/V7YM+BVdXVw4fPoyJiQnNmjWrNk+7du1o3rw5N27cYMSIEXUqX1VVldmzZzN27Fh27drFmDFjalVn8+bNMTY2JiUlhTfffFM6/91339Wq3sq50tnZ2VWmOVRHJpNhbW3NzJkz+f7777l27ZrScnKPS3dwcCAlJYWQkBAaNar4b+HIkSPk5ubi4OBQq7YKgiAIQkMhAtwGqry8nD/++AOA/Px8aaOHGzdusHTpUml0dvr06URERJCenk63bt1QVVXlxo0bpKSkEBsbi6amJmPGjOHzzz9n5MiRTJ48mVatWnHjxg2uXr2q9KJWpXPnzvHJJ5/Qv39/WrVqxYMHD1i9ejVmZmaPXf92zJgxJCUlMW7cOCZPnkxRURExMTGYmpo+dgrFs/Tuu++yY8cORo0axbhx42jbti15eXmcOXOGkpISgoKCUFFRISQkhODgYPLz8+nRoweamprcunWLw4cPExgYWG3wXsnV1RUHBwc2bNjAiBEjalWnqqoqEydO5OOPP6Zp06Y4OTlx7NgxfvnlF4Aa19M1NzdnxIgRzJkzBz8/P2xsbCgpKeHq1ascO3aMlStXkpeXh5+fHwMHDsTc3JySkhI2b96Mrq4uXbt2rTEdKlbNGDp0KP7+/vj6+kqrKFhbW+Pp6fnsbpQgCIIgvAAiwG2gCgsLpbfmtbS0aNmyJS4uLsTFxSlt1Ttu3DiaNWvG+vXr2bJlC40aNaJ169b06NFDeqRtYGDA9u3biYyMZNmyZRQUFGBmZsbw4cOrrdvY2JimTZuyevVq7ty5g46ODo6OjkRERCgtF/UoU1NTNm/eTHh4OMHBwchkMtzc3AgJCVFaIux5UVdXZ9OmTcTGxpKQkEBmZib6+vp07dpV6XP269cPXV1dEhIS2L9/PwBmZmZ4eHjQtGnTGuuZOnUqY8eOZf/+/fj4+NSqTl9fX3Jzc9m2bRubN2/GxcWF2bNnExgYiI6OTo11hoaGYm5uzs6dO4mPj6dJkyaYm5vTt29foGJ95E6dOrF582Zu376NhoYGlpaWJCYmYmhoSHFx8RPTASwtLVm3bh1RUVFMmzYNLS0tvLy8mDt37mPvuSAIgiA0VCoKhUJR340QhNdNTEwM69ev59ixY6/lTmCVq0z8eDqXtIycGnILgiAIL4qZiR5Thrm9kLpOnjyJioqK9L7LsyRGcAXhObt06RLJycnY2dmhpqbGr7/+SmJiIsOGDXstg9tHvaj/RAVBEITaKy9XIJO93C8UiwBXEJ4zDQ0NUlNT2b59Ow8fPqRZs2b4+fkxbdq0+m5avSouLqagoABNTc36bspLoaCggCtXrmBubi76rA5Ev9Wd6LOn8yr128se3IIIcAXhuTMzM2PTpk313YwGScyQqj2FQkFBQYHoszoS/VZ3os+ejui3hkXsZCYIQr0Ra+rWnoqKCpqamqLP6kj0W92JPns6ot8aFvGSmSAIL1zlS2bP48UCQRAE4Z95UXNwxUtmgiC8kuK3HxGrKAiCIDQgL3IVhedJBLivqdjYWOLi4qpNCwoKYuLEibUux83NDXt7+2fSrpCQEPbu3fvEPN26dWPz5s3PpL7Hkcvl0vdqamqYmpri6enJ1KlTpS1/hX8uLSOHq2lZ9d0MQRAE4RUjAtzXmIaGBhs3bqxy3tTUtNZlxMXFoaWl9cwC3ICAAIYOHSodr1y5ksuXL7Ns2TLp3IvYOAIqNmgYMGAARUVF/PrrryQkJHD16lU+/fTTF1K/IAiCIAhPRwS4rzGZTIatre0LqauwsLBWa762bt1aaTtgQ0NDbt269cR21rbsujI1NZXqdXJyIiMjg127dpGRkYGJickzr+/viouLadSoUY3b+dan59X3giAIgvBPNNzfnEK9+u6775DL5fzwww/SuezsbDw8PJg1axbwf4/xw8PDkcvlyOVyjh07JqWtWbOGiIgI3NzccHFxASA1NZVJkybh7u6Ora0tAwcOZN++fbVu182bN5HL5SQlJREaGoqTkxODBg0CKgLCqKgoevbsiaWlJf369ZO2431Uamoqo0aNwtbWFgcHB4KCgrh3716NdXfp0gWA27dvS+dqW+eOHTvo2bMnNjY2jB07ljNnzkifo5KXlxcffvgha9eupWfPnlhbW5OdnQ1AUlISb7/9NlZWVnh4eBAdHU1ZWZl0bW5uLqGhoXh4eGBlZYWnpyeBgYG1Tgc4f/48fn5+Ur9Mnz6dW7duKeV53H0VBEEQhIZEjOC+5kpLS6uca9SoEW+++SbvvvsuoaGh7N+/H0NDQxYvXgzAwoULAdi5cydDhgyRHuUDdOjQQSpn06ZN2NjYsGTJEqmeW7duYW9vz7Bhw1BXV+fEiROEhoaiUCh47733at3uqKgoPD09iYyMpLy8HIAZM2Zw4sQJpkyZQvv27Tl8+DCzZ89GV1cXT09PoCK49fX1xdPTk+joaAoKCoiJiSEgIICdO3c+sc5bt24hk8lo0aKFdK42daakpLBw4UIGDRpEnz59OHv2LDNnzqy2joMHD9KmTRvmz5+PTCZDS0uL9evXExERwejRowkJCeHSpUtSgBscHAxAWFgYP/30E0FBQZiZmZGZmcmPP/4olVtT+u3btxk5ciStWrUiIiKCoqIioqOjGTlyJMnJyUrTQqq7r4IgCILQkIgA9zWWn5+PhYVFlfNbt27F0dGR0NBQ3n77bRYsWEDfvn356quv+PTTT9HT0wOQHt8/+ij/UXp6esTFxSmtCejt7S19r1AoeOONN7hz5w47d+6sU4DbuXNnlixZIh0fPXqU77//nsTERNzd3QFwc3MjMzOT2NhYKdiMjIzE0tJSqV2dOnViwIABHD58WMoHUF5eTmlpKcXFxRw7dozt27czZMgQjI2N61TnqlWrcHZ25qOPPgLAw8OD0tJSli9fXuVzlZSUsHbtWrS0tAB48OABK1asYPz48dLIuZubG2pqaixduhQ/Pz8MDAw4deoUAwYMUOrDR/u6pvQNGzZQWlrKunXrpJfounTpgre3N3v37sXX11fKW919FQRBEISGRAS4rzENDQ22bNlS5Xy7du0A0NHRISwsjLFjx3L48GGGDRuGh4dHrcvv3r17lSAoJyeH2NhYUlJSuHPnjvSYva4rE/To0UPp+MiRI+jr6+Ps7Kw0qujq6sqiRYsoKyujuLiYEydOMGfOHKXH+23btsXU1JRTp04pBbjLli1TernNwcGB0NDQOtUJcPbsWebMmaPU3l69elUb4Do5OUnBLVSMOOfn59O3b98qdRQWFnLhwgW6detG165d2bt3L8bGxnh4eNCpUyelcmtKP378OE5OTkr3oX379nTu3Jnff/9dKcCt7r4KgiAIQkMiAtzXmEwmq3FxZQcHB1q0aEFaWhojR46sU/lGRkZVzoWEhJCamsqUKVPo0KED2trabN++na+//voflZ2VlUV2dna1I9IAmZmZqKioUFZWRlhYGGFhYVXyPDq3FmDUqFG88847FBQUkJyczO7du1m+fDlBQUG1rlNVVZXS0lIMDQ2f2P4nfS7gsaPblW3+4IMP0NPTY/369YSHh2NqasrEiRMZPnx4rdJzc3OlOcZ/b09OTk6Vc4IgCILQkIkAV3iiFStWkJWVRdu2bVm8eDGbNm2q9ejd3/MVFRVx6NAhQkJClEYEt23bVud2/b1sPT09DA0NWbNmTbX5DQ0NKS0tRUVFBX9/f958880qeQwMDJSOmzdvLv0B0K1bN+7evcv69esZPnw4pqamtapTVVWVRo0acf/+faW0x73UVt3ngorl2Jo3b14lf8uWLYGK0fb58+czf/58zp8/z6ZNm1i8eDGdOnXC0dGxxnQ9Pb1q23Tv3j3atm37xDYKgiAIQkMjVlEQHuvEiRMkJiYSEhLCsmXLOHHiRJV1c9XU1CgqKqpVecXFxZSXl6Ompiade/DgAd9///0/bqurqyv3799HTU0NKyurKl/q6upoaWlha2vL5cuXq81TGSw+zpw5cygvLycxMbHWdaqqqtKlSxdSUlKUyvruu+9q9bns7OzQ1NQkPT292jr+HpRDxUoH8+bNA+DSpUu1SndwcODo0aNKo7WXL1/m/PnzODg41KqtgiAIgtBQiBHc11h5eTl//PFHlfNGRkYYGRkxd+5c3N3dGTJkCACTJk0iKioKDw8P2rdvD1TM101JScHR0RFNTU3Mzc0fuxGDjo4OVlZWrF27FkNDQxo1asSaNWvQ1tauMsJZV25ubvTs2ZPx48czfvx45HI5BQUFXLx4kWvXrkkvpM2ZM4fRo0czc+ZMvL290dXVJT09nZ9//hkfHx+cnJweW0e7du3o378/e/bsYcqUKbWuc/LkyQQEBBAaGkrfvn05c+aMtDRaTWvc6urqMn36dCIiIkhPT6dbt26oqqpy48YNUlJSiI2NRVNTk6FDh/LWW2/RsWNHVFVV2bdvH2pqajg6OgLUmD5mzBiSkpIYN24ckydPpqioiJiYGExNTev08p8gCIIgNAQiwH2NFRYWSsHro95//31UVVXJzc1VWqlg8uTJHDp0iDlz5rBz504aNWrEggUL+Pjjj5kwYQKFhYVs2rTpiUFiZGQkCxYsICQkBH19fXx9fcnPz2fdunX/+POsWLGCNWvWsH37dtLS0tDR0aFjx474+PhIeezt7dm2bRuxsbHMmzePkpISmjdvjrOzM23atKmxjoCAAL766iu2bNnCtGnTalVnr169WLRoEatXryY5ORkbGxsWLVrEuHHjarUr27hx42jWrBnr169ny5YtNGrUiNatW9OjRw9pNNze3p59+/Zx8+ZNZDIZnTp1IiEhQfpDpKZ0U1NTNm/eTHh4OMHBwchkMtzc3AgJCXlhO8cJgiAIwrOiolAoFPXdCEF43ezevZvQ0FBSUlJqnBrxKjp16hQAP57OJS0jp4bcgiAIwotiZqLHlGFuL6SukydPoqKiUuML709DjOAKwnOWnZ1NXFwczs7ONGnShFOnTpGQkECvXr1ey+D2US/qP1FBEASh9srLFchkL/cLxSLAFYTnrFGjRty4cYMvvviCvLw8DAwMGDhwoLQL2euquLiYgoICNDU167spL4WCggKuXLmCubm56LM6EP1Wd6LPns6r1G8ve3ALIsAVhOdOW1ub1atX13czBEEQBOG1IebgCoLwwlXOwX0e864EQRCEf+ZFTVEQc3AFQXglxW8/Il4yEwRBaEBe5Etmz5MIcAVBqDdpGTlcTcuq72YIgiAIrxgR4Nbg8OHDbNmyhVOnTpGXl4eenh6Wlpa888479O/fv8aF+l80X19ftLS0nsucz6SkJGkHLKjYuKF9+/ZMmDCh2q1vX2ZyuVz6Xk1NDVNTUzw9PZk6dSr6+vr11zBBEARBEGokAtwniIqKYvXq1bz11lssWLAAY2Nj7t69y3fffcfs2bPR09PDw8OjvpupZOHChc896P7000/R0dHh/v37rF+/nilTpvDpp582uL74p3x9fRkwYABFRUX8+uuvJCQkcPXqVT799NP6bpogCIIgCE8gAtzHOHToEKtXr2bq1KlMmzZNKa1fv36MHj2aRo0aXvd16NDhuddhYWGBoaEhAN26daNHjx5s2bLlsQFuYWEhGhoaz71dz5qpqSm2trYAODk5kZGRwa5du8jIyMDExOS5119cXEyjRo0a3FOCR72s91YQBEF4tTXc35z1bP369RgbGzN58uRq062trenatStQEQyPHTsWFxcX7O3tGTRoED/++KNS/tjYWOzs7KqU4+joSGxsrHT8+++/M2LECBwcHLCzs+Ptt99m7969tU739fXF399fOr506RKBgYF4enpiY2ND//79WbduHeXl5VKemzdvIpfL+fzzz/nwww954403cHd355NPPqG0tPSJ/aStrY25uTk3b95U+pwnT55kyJAhWFlZsXXrVgB+++03hg4dirW1NU5OTsybN4/s7Gyl8oqLi4mOjqZXr15YWlrSvXt3QkJClPKkpqYyatQobG1tcXBwICgoiHv37inlWbNmDW+99RZWVlY4OzszZswYbty4Uev06nTp0gWA27dvK7U3KiqKnj17YmlpSb9+/di/f3+Va3fs2EHPnj2xsbFh7NixnDlzBrlcTlJSkpTHy8uLDz/8kLVr19KzZ0+sra2l/klKSuLtt9/GysoKDw8PoqOjKSsrk67Nzc0lNDQUDw8PrKys8PT0JDAwsNbpAOfPn8fPz0/q1+nTp3Pr1i2lPHK5nDVr1hAREYGbmxsuLi5P7DNBEARBqA8NbwiyASgtLeXEiRP06dOnVqO0N2/epGfPnowbNw6ZTMaPP/7IxIkT2bhxI05OTrWu98GDB/j7++Pg4EBUVBTq6upcvHiR3NzcWqVXJyMjA3Nzc95++22aNGnC2bNniY2NJT8/n6lTpyrljYmJoVevXsTExJCamkpsbCytW7dm2LBhjy2/rKyM27dv07FjR+lcSUkJQUFBjBkzhsDAQPT19Tl9+jRjx47FycmJ5cuXc/fuXSIjI7l48SI7duxAVVUVgGnTpnH06FH8/f2xtbXl/v37HDx4UCo7NTUVX19fPD09iY6OpqCggJiYGAICAti5cycA+/btY/ny5UyfPh1bW1vy8vL4/fffefjwYa3SH+fWrVvIZDJatGghnZsxYwYnTpxgypQptG/fnsOHDzN79mx0dXXx9PQEICUlhYULFzJo0CD69OnD2bNnmTlzZrV1HDx4kDZt2jB//nxkMhlaWlqsX7+eiIgIRo8eTUhICJcuXZIC3MrNIsLCwvjpp58ICgrCzMyMzMxMpT+yakq/ffs2I0eOpFWrVkRERFBUVER0dDQjR44kOTkZbW1tKe+mTZuwsbFhyZIlNf4BJAiCIAj1QQS41cjOzqa4uBhTU1Ol8wqFQmnUTCaTIZPJGDlypHSuvLwcJycnLl68yK5du+oU4F65coW8vDxmzZolveT06AhZTenVcXFxkfIoFAocHBwoLCxky5YtVQJca2trQkNDAXBzc+PYsWN88803VQLc8vJySktLuX//PqtWrSIzM1NpGkdJSQmBgYH0799fOjd16lSMjY1JSEhATU0NqJgC4Ofnx+HDh/Hy8uLIkSMcOnSIyMhIBgwYIF376PeRkZFYWloSFxeHikrFGn2dOnViwIABHD58GE9PT06ePIlcLlcayX70Jbia0v/+OYuLizl27Bjbt29nyJAhGBsbA3D06FG+//57EhMTcXd3l/otMzOT2NhYKcBdtWoVzs7OfPTRRwB4eHhQWlrK8uXLq9RZUlLC2rVr0dLSAir+qFmxYgXjx49n1qxZUh1qamosXboUPz8/DAwMOHXqFAMGDOC9996TyvL29pa+ryl9w4YNlJaWsm7dOuklui5duuDt7c3evXvx9fWV8urp6Sn1vyAIgiA0NGKKwhP8/Rf4N998g4WFhfRVGbCkp6czd+5cPDw86Nq1KxYWFvz3v//lypUrdaqvdevWaGtrs2jRIr766ivu379fp/TqFBUVsWLFCulxvIWFBdHR0WRmZlYZsawM0iq1b9+e9PT0KmW6ublhYWGBh4cHSUlJTJ48mcGDByvlqQzuKh0/fpxevXpJwW1lfbq6uvz+++8A/PLLL2hqaioFXo8qKCjgxIkT9O3bl7KyMkpLSyktLaVt27aYmppKmwd07dqVM2fOEBYWxvHjxykpKVEqp6b0SsuWLcPCwgI7OzsmTZqEXC6X/gAAOHLkCPr6+jg7O0ttKS0txdXVlbNnz1JWVkZZWRlnz57Fy8tLqexevXpVW6eTk5MU3ELFiHV+fj59+/atUkdhYSEXLlyQPtPevXtJTEzkr7/+qlJuTenHjx/HyclJaYWI9u3b07lzZ+n+VOrevbsIbgVBEIQGTYzgVkNfXx91dfUqwZ2Liwt79uwBkObmlpeXM3nyZPLy8pg+fTpt2rRBU1OTFStWKM3VrA09PT3Wr1/PihUrmDNnDmVlZTg6OhIaGopcLq8xvToRERHs3r2bKVOmYGlpiY6ODikpKaxatYqioiKaNGki5dXR0VG6Vk1NjeLi4iplbtiwAW1tbfT09GjRokWVaRyamppK5ULFHFAjI6MqZRkZGZGTU7HQf3Z2NsbGxo8NnnJzcykrKyMsLIywsLAq6ZX97ePjw8OHD9m1axcbNmxAR0eHd999l+DgYDQ0NGpMrzRq1CjeeecdCgoKSE5OZvfu3SxfvpygoCAAsrKyyM7OxsLCotr2ZmZmoqqqSmlpqfRS3qOfuzp/P5+VVbFG7KMjr9V95g8++ED6+QgPD8fU1JSJEycyfPjwWqXn5uZKc4z/3p7K+1NT2wVBEAShoRABbjUaNWqEvb09v/zyC2VlZdL8UD09PWk7OXV1dQCuXbvGmTNniI+PV3rMXVhYqFRm48aNq4wUlpSUkJ+fr3TO2tqaTz/9lMLCQo4dO8Ynn3zClClT+O6772qV/ncHDhxgyJAhTJw4UTp3+PDhp+kWiVwurxKwPaq6AFVPT6/Ki2AA9+7dQ09PD6j4wyIzMxOFQlFtGTo6OqioqODv71/tlAIDAwOgYurI6NGjGT16NHfu3OHLL78kMjISAwMDpkyZUmN6pebNm0v3u1u3bty9e5f169czfPhwTE1N0dPTw9DQkDVr1lTbD4aGhqiqqtKoUaMqo+3V9UV1fVfZN3FxcTRv3rxK/pYtW0p9M3/+fObPn8/58+fZtGkTixcvplOnTjg6OtaY/qT707Zt2ye2URAEQRAaGjFF4THGjh1LRkYGCQkJT8xXVFQEoPToPS0tjdTUVKV8zZo1o6SkhOvXr0vnjh49qjSn91EaGhp4enoybNgwbt68KdVT2/RH2/do28rKyvjyyy+f+JmeBwcHB1JSUpReSjpy5Ai5ubk4ODgA4OrqSkFBAV9//XW1ZWhpaWFra8vly5exsrKq8lUZ7D2qWbNmjBs3DrlczuXLl+uc/qg5c+ZQXl5OYmKi1N779++jpqZWbXvU1dVRVVWlS5cupKSkKJX1uD9I/s7Ozg5NTU3S09OrraMyqH+UXC6XNuS4dOlSrdIdHBw4evSo0mjt5cuXOX/+vHR/BEEQBOFlIUZwH6NHjx5MnDiRFStWcO7cOfr164eJiQl5eXkcP36czMxMmjRpQrt27WjevDmRkZGUl5eTn5/PihUrqqyT2r17d7S0tAgNDWXChAmkp6ezadMmGjduLOU5dOgQe/bs4c0336RFixbcvXuXLVu2YG9vT+PGjWtMr46rqyu7d++mQ4cOGBgYsG3btmqnHTxvkyZNYujQofj7++Pr6yutomBtbS3N13V1dcXT05N///vfXL9+HRsbG7Kzs/nmm2+IiYkBKoLM0aNHM3PmTLy9vdHV1SU9PZ2ff/4ZHx8fnJycWLBgAbq6utja2qKrq8uJEyc4d+6c9LJcTemP065dO/r378+ePXuYMmUKbm5u9OzZk/HjxzN+/HjkcjkFBQVcvHiRa9eusWTJEqBiOktAQAChoaH07duXM2fOsG/fPoAa17jV1dVl+vTpREREkJ6eTrdu3VBVVeXGjRukpKQQGxuLpqYmQ4cO5a233qJjx46oqqqyb98+1NTUcHR0BKgxfcyYMSQlJTFu3DgmT55MUVERMTExmJqaPnZ6hCAIgiA0VCLAfYKgoCAcHBzYunUrixcv5sGDB+jp6WFhYcHHH3+Mt7c3MpmM2NhYPvzwQ2bMmIGpqSmTJ0/m6NGjnD59WirLwMCAFStWSFMKunTpQnh4uNLb6a1bt0YmkxETE8O9e/fQ19fH3d1denu+pvTqfPDBByxcuJD//Oc/aGpq8t577/HWW28pvSz1IlhaWrJu3TqioqKYNm0aWlpaeHl5MXfuXGkKCFSsoxsXF8fOnTuJi4vDyMgINzc3Kd3e3p5t27YRGxvLvHnzKCkpoXnz5jg7O9OmTRugYtRz165d7N69m4KCAlq1asW8efMYNGhQrdKfJCAggK+++ootW7Ywbdo0VqxYwZo1a9i+fTtpaWno6OjQsWNHfHx8pGt69erFokWLWL16NcnJydjY2LBo0SLGjRuntPzW44wbN45mzZqxfv16tmzZQqNGjWjdujU9evSQRuft7e3Zt28fN2/eRCaT0alTJxISEmjfvn2t0k1NTdm8eTPh4eEEBwcjk8lwc3MjJCSkVm18WmYmes+tbEEQBKHuXpX/l1UUCoWivhshCK+b3bt3ExoaSkpKSrVTK151lSteVM5xFgRBEBqO8nIFMtnzf9/i5MmTqKioPJffBWIEVxCes+zsbOLi4nB2dqZJkyacOnWKhIQEevXq9VoGt5WKi4spKChAU1OzvpvyUigoKODKlSuYm5uLPqsD0W91J/rs6bxK/fYigtvnTQS4gvCcNWrUiBs3bvDFF1+Ql5eHgYEBAwcOlHYhe52JB0i1p1AoKCgoEH1WR6Lf6k702dMR/dawiABXEJ4zbW1tVq9eXd/NaJDEkmO1p6KigqampuizOhL9Vneiz56O6LeGRczBFQThhRNzcAVBEJ6PFzV/9lkQc3AFQXglxW8/QlpGTs0ZBUEQhBqZmegxZZhbzRlfAyLAFQSh3qRl5HA1Lau+myEIgiC8YkSA+5xVrutaSV9fn3bt2jFp0iRpg4MXYeDAgXTp0oWlS5e+kPqOHTvGqFGjqk375ZdfnrjV74ty8+ZN9u7dy+DBg2nWrJnS+V69eknH6urqmJmZ0b9/fyZOnIiGhkZ9NFcQBEEQhFoSAe4LoKGhwcaNGwGk7X8nTZrE1q1bsbe3r+fWPV9hYWG0a9dO6Zyurm49tUZZWloacXFx9OjRQynArTRr1iycnJwoKCggJSWF+Ph47t69y4cfflgPrRUEQRAEobZEgPsCyGQybG1tpWMbGxs8PT3Zt2/fKx/gduzY8ZlOHi8rK6O8vFzawet5atOmjXTfXFxcuHz5Mp9//jmLFi2qcYvdZ6GwsLDBjxa/DG0UBEEQXj/P/7e0UEWzZs0wNDTk1q1bQMWo7rx58+jVqxfW1tb07t2bqKgoiouLla6Ty+WsXbuW2NhYXF1dcXJyYt68eeTn5yvlO3HiBD4+PlhZWTFgwAAOHz5cbTsOHjzIwIEDsbKywt3dnbCwMIqKiqT0Y8eOIZfL+emnn5gxYwZ2dnb06NGD/fv3A7Bp0yZ69OhBt27dmD9/fpX21iQ7O5t58+bh5OSEtbU1Q4cO5bffflPK4+vri7+/P3v37qVPnz5YWVlx7tw5AA4dOsSgQYOwtrbG2dmZhQsXKvVFSUkJn3zyCT169MDS0hJ3d3cmTZpEXl6e0hSK999/H7lcjlwuf2J7u3TpQmFhIffv35fO5ebmsmjRItzd3bG0tMTHx4f//ve/StcpFAri4uJwc3PDzs6O6dOn8/PPPyOXyzl27JiUTy6Xs2bNGiIiInBzc8PFxUW6PjExkT59+mBpaUmvXr3YsGGDUh3p6enMmDEDV1dXrKys8PLy4uOPP651OsBvv/3G0KFDsba2ln62srOzpfSbN28il8tJSkoiNDQUJyenWm1vLAiCIAgvmhjBrQcPHz4kJydH2sUqKysLfX195s2bh66uLlevXiU2NpbMzEzCwsKUrt26dSsODg4sXbqUq1evEh4ejpGRkbRpQGZmJn5+fsjlcmJiYsjNzWXx4sXk5+fTpUsXqZyUlBSmT5+Ot7c3QUFBXL58mejoaG7fvs2KFSuU6ly0aBHvvfcegwcPZteuXcyZM4dz585x4cIFFi9ezI0bN1i6dCmtWrVi0qRJSteWl5dTWloqHctkMmQyGWVlZUyYMIEbN24QHBxM06ZN2bx5M2PHjmXHjh1YWlpK15w+fZq0tDRmzJiBrq4upqamHDhwgMDAQHx8fJg2bRqZmZlERkaSm5tLdHQ0AKtXr2bHjh0EBwfTsWNHsrKyOHLkCMXFxVhYWLBgwQI+/PDDaqdRVOfWrVs0adIEAwMDoGInrrFjx3Lv3j1mzpxJs2bNSE5Oxt/fn6SkJClg3rx5M3FxcYwfPx5nZ2eOHj1KaGhotXVs2rQJGxsblixZIvXbkiVL2L17N5MmTcLGxoYTJ06wbNkyGjduzLBhwwCYM2cOGRkZhIaGYmRkxO3btzl9+rRUbk3pp0+fZuzYsTg5ObF8+XLu3r1LZGQkFy9eZMeOHaiqqkp5o6Ki8PT0JDIykvLy8hr7TRAEQRBeNBHgviCVwUpGRgYRERE0adJEGkGUy+XMnTtXymtvb4+mpiYhISEsWLBAacs/Y2NjIiMjAejevTtnzpzhm2++kQLcjRs3oqKiwtq1a9HR0QGgefPmjBkzRqk9cXFx2NraKpWlqanJggULOH/+vNJoZt++fZk6dSoA1tbWfPvtt3z55Zd8++230lSBX3/9lQMHDlQJcAcPHqx0/P7777NkyRIOHTrEyZMn+fTTT/Hw8ADA3d2d3r17s3r1amJjY6VrcnJy2LNnD6ampkDFiGZ4eDj9+/dnyZIlSn0zceJEAgIC6NixI6dOncLd3Z0RI0ZIefr06SN936FDB+Dx0ygqg/PKObgHDx5k5syZUrC3f/9+zp07x+effy6V5eHhwbVr11i5ciXLly+nrKyMNWvW4OPjI90jd3d3srKy2LNnT5U69fT0iIuLkxYKv379Olu2bGHx4sUMGTIEAFdXVwoLC4mPj2fIkCHIZDJOnTrFrFmz6N+/v1TWu+++K31fU3pCQgLGxsYkJCRI99TU1BQ/Pz8OHz6Ml5eXlLdz585K/S4IgiAIDY0IcF+A/Px8LCwspGNVVVVWrlwpjRoqFAo2btzIrl27uHnzptI0gRs3btCpUyfp2NXVVans9u3b8+WXX0rH//vf/3BycpKCW6iYP6qvry8dP3z4kLNnzyoF1QD9+/dnwYIF/P7770oBrpvb/62pp6Ojg6GhIY6OjkrzYNu2bav0uL3SJ598Qvv27aXjytUTjh8/jra2thTcAqipqfHWW2/xxRdfKJXRqVMnKbgFuHLlCmlpafz73/9WGh3u1q0bMpmM06dP07FjR7p27UpiYiKxsbF4enpiaWlZp7mzgYGBSsfe3t5MmDBBOj5y5AidOnWibdu2Su1wdXUlOTkZqJgakJmZqRQgAvTq1avaALd79+5Ku+D8/PPPAPTu3btKHWvXruX27duYmZnRtWtX1q1bh6qqKm5ubrRp00ap3JrSjx8/zoABA5Tuqbu7O7q6uvz+++9K7e/Ro0f1HSYIgiAIDYQIcF8ADQ0NtmzZgkKh4OrVq0RGRjJ37lz279+PiYkJGzdu5JNPPmH8+PE4OTmhq6vLqVOn+PDDD5WCXai6AoGamprS3NfMzMwqwQugtCxXXl4eCoUCIyMjpTw6Ojqoq6uTk5NT5fyj1NXVa2xHpfbt21c7Opqbm1ulfoCmTZtWqb9p06ZKx1lZFeumTpkypcr1ALdv3wZg8uTJyGQy9u7dS1xcHIaGhowYMYIpU6bUaivF4OBgnJ2dycvLY8uWLXz55Zd069aNoUOHSu04c+aM0h8vlSpHeTMzMwGqLItW3Wev7nxWVhYKhQJnZ+fHflYzMzOio6OJjo4mJiaGxYsXY25uzqxZs+jduzdAjemPux9GRkZV7sfj2i4IgiAIDYUIcF8AmUwmBXnW1taYm5szePBg4uPjWbx4MQcOHMDLy4ugoCDpmkuXLj1VXcbGxty7d6/K+UdfjNLR0UFFRUXpHFQEvsXFxejp6T1V3XWhp6dXbTvv3r1bpf6/B6OVo9ELFizA2tq6ShkmJiZARSA+bdo0pk2bxrVr1/jss8+IjY2lZcuWSo/nH6dVq1bSfXNycuL9998nJiaGd955By0tLfT09JDL5U98XG9sbAxQpa+r++zVfVY9PT1UVFTYtm1btStHmJubS585LCyM8vJyTp8+zapVqwgMDOTAgQO0atWqxvTH3Y979+7VeD8EQRAEoaERqyjUAysrK7y9vUlKSiIzM5PCwsIqwUvlSgV1ZW1tzbFjx8jLy5PO/fLLL0pvwzdp0oQuXbpw4MABpWu//vprABwcHJ6q7rpwcHDgwYMHSisOlJaW8t1339VYf7t27WjevDk3btzAysqqyld1a9q2adOGWbNmoa+vz+XLlwGkPv/7KHl1VFVVmT17NllZWezatQuomCZw48YNTExMqm0HVMx/NjY2JiUlRam87777rsY6AWklhezs7Grr0NbWVsovk8mwtrZm5syZlJaWcu3atVqlOzg4kJKSojQN4siRI+Tm5r6QnwdBEARBeJbECG49CQgI4KuvvmLjxo24urqyadMmtmzZQtu2bUlOTq4SmNTW6NGj2bZtGxMmTGDChAnk5uYSGxurNAcXYOrUqUyZMoXg4GDeeecdrly5QnR0NH369KlxuaxnoUePHlhbWzN79myCgoKkVRQyMjKqrOLwdyoqKoSEhBAcHEx+fj49evRAU1OTW7ducfjwYQIDAzE3NycgIAALCwu6du2KpqYmP/zwAzk5OdLj/rZt26Kqqspnn31Go0aNUFVVfeKava6urjg4OLBhwwZGjBjBu+++y44dOxg1ahTjxo2jbdu25OXlcebMGUpKSggKCkJVVZWJEyfy8ccf07RpU5ycnDh27Bi//PILQI1zgs3NzRkxYgRz5szBz88PGxsbSkpKuHr1KseOHWPlypXk5eXh5+fHwIEDMTc3p6SkhM2bN6Orq0vXrl1rTAeYNGkSQ4cOxd/fH19fX2kVBWtr6xe6454gCIIgPAsiwK0n7dq1o3///mzfvp1Dhw6RlZUlBXZ9+vQhNDS0yooEtWFiYsLatWv56KOPmDFjBq1bt2bBggXS0lmVevXqxfLly4mPjycgIAB9fX0GDx6sNE3ieVJVVWXNmjWEh4cTEREhvYi3bt06pSXCHqdfv37o6uqSkJAgjXabmZnh4eEhzdm1t7fn66+/Zv369ZSVlWFubs6yZcukF/UMDQ1ZsGABn376KcnJyZSWlnL+/Pkn1jt16lTGjh3L/v378fHxYdOmTcTGxpKQkEBmZib6+vp07dqV4cOHS9f4+vqSm5vLtm3b2Lx5My4uLsyePZvAwMAq85urExoairm5OTt37iQ+Pp4mTZpgbm5O3759AWjcuDGdOnVi8+bN3L59Gw0NDSwtLUlMTMTQ0JDi4uInpgNYWlqybt06oqKimDZtGlpaWnh5eTF37lylJcKeNTOT5z8dRhAE4XUh/k/9PyoKhUJR340QhNdNTEwM69ev59ixY6/lTmCnTp0CeKa73AmCIAhQXq5AJns53pU4efIkKioqz+V3gRjBFYTn7NKlSyQnJ2NnZ4eamhq//voriYmJDBs27LUMbisVFxdTUFCgtM6z8HgFBQVcuXIFc3Nz0Wd1IPqt7kSfPZ2G0m8vS3D7vIkAVxCeMw0NDVJTU9m+fTsPHz6kWbNm+Pn5MW3atPpuWr0TD5BqT6FQUFBQIPqsjkS/1Z3os6cj+q1hEQGuIDxnZmZmbNq0qb6b0SCJJcdqT0VFBU1NTdFndST6re5Enz0d0W8Ni5iDKwjCCyfm4AqCINTdyzS/tjbEHFxBEF5J8duPkJaRU3NGQRCE15yZiR5ThrnVdzNeGiLAFR4rNjaWuLg46VhdXZ2WLVvi4+ODn59fjWu4Pi/Hjh1j1KhR7Nmz57n81ff3z62vr0+7du2YNGmSWBP2GUvLyOFqWlZ9N0MQBEF4xYgAV3giDQ0NNm7cCEBhYSHHjh0jMjIShULBxIkT66VNFhYW7Ny5k/bt2z+3Oh793BkZGSQkJDBp0iS2bt2Kvb39c6tXEARBEIR/TgS4whPJZDJsbW2lY2dnZ/766y8OHjxYbwGutra2Upueh79/bhsbGzw9Pdm3b98LC3ALCwsb/DJiL0MbBUEQhNdP/TxjFl5qTZo0obS0VDpetmwZb7/9NnZ2dnh4eDBr1iwyMjKUrlEoFMTFxeHm5oadnR3Tp0/n559/Ri6Xc+zYMSlfXl4ewcHB2NnZ4eLiQlRUFOvWrVPaPvjYsWPI5XLpRSUAuVzO2rVriY2NxdXVFScnJ+bNm0d+fr5SO44fP867776LlZUVb7/9NkeOHGHgwIGEhIQ88TM3a9YMQ0NDbt26pXQ+NTWVUaNGYWtri4ODA0FBQdy7d08pT3p6Ov7+/lKQvGHDBpYsWYKXl5eUJykpCblcTmpqKmPHjsXW1pbw8HDp+uDgYJycnLC2tmbEiBGcPn1aqY6UlBR8fHyws7PD0dERHx8fDh8+XOv08vJyVq5ciZeXF5aWlvTt25cdO3Yo1REbG4udnR0nT55kyJAhWFlZsXXr1if2myAIgiDUBzGCK9SoMpitnKJw8OBB/P39pfR79+7h7++PiYkJ9+/fZ/369fj6+vLll1/SqFHFj9jmzZuJi4tj/PjxODs7c/ToUUJDQ6vUNW/ePI4ePcrs2bMxMzNj165d/Pnnn7Vq59atW3FwcGDp0qVcvXqV8PBwjIyMCA4OBiqmGkyYMIGuXbsSExNDXl4eixYtIi8vjy5dujyx7IcPH5KTk0PLli2lc6mpqfj6+uLp6Ul0dDQFBQXExMQQEBDAzp07gYrAPiAggLt377J48WJ0dHRITEzk1q1b1c5hDgoKYsiQIfj7+6OpqUlOTg7Dhw9HS0uLDz74AB0dHTZv3szo0aM5ePAgRkZGXL9+nRkzZuDt7U1QUBDl5eWcO3eOnJyKl7dqSgcIDw9n06ZNTJ48GTs7Ow4dOsTChQspLS1l5MiRUr6SkhKCgoIYM2YMgYGB6Ovr1+reCIIgCMKLJAJc4Yny8/OxsLBQOte/f3+l6QlhYWHS92VlZdjZ2dG9e3eOHj2Ku7s7ZWVlrFmzBh8fHynYdHd3Jysriz179kjXXrx4kW+//ZZPPvmEd999FwAPDw/69etXq7YaGxsTGRkJQPfu3Tlz5gzffPONVOeGDRtQVVVl9erVaGtrA9CyZUtGjBhRbXmVgX1GRgYRERE0adKEUaNGSemRkZFYWloSFxcnrXvYqVMnBgwYwOHDh/H09OTHH3/kzz//ZOvWrTg6OgIV0zw8PT3R1dWtUufQoUOV+nbFihXk5uaye/dujIyMAHBxcaFPnz4kJiYyZ84czpw5Q0lJCR988IH0uTw8PKQyakq/f/8+W7ZsUdp8ovL+xMfHM2zYMFRVVYGKADcwMJD+/fvX4o4IgiAIQv0QUxSEJ9LQ0GDPnj3s2bOHbdu2MX/+fH766Sel0dfDhw8zdOhQHBwc6Nq1K927dwfg6tWrQMUj9szMTKVH8gC9evVSOq6ccvDoeZlMRs+ePWvVVldXV6Xj9u3bk56erlS+k5OTFOQBODo6VjsKWRnYW1hY0LNnT7755hvCw8Np164dULEl44kTJ+jbty9lZWWUlpZSWlpK27ZtMTU1lT7LqVOn0NXVlYJbqJji4eLiUu1n6NGjh9LxkSNHcHJyQk9PT6pDJpPxxhtvSHXI5XJUVVUJDg7m+++/Jy8vT6mMmtJPnjxJSUkJffv2VTrfr18/7t+/L93HSmIlCUEQBKGhEyO4whPJZDKlpbgcHBwoKytj6dKljB07lsLCQgICAujVqxcTJkzAyMgIFRUVBg8eTFFREQCZmZkAGBoaKpVdOSJZKTMzEzU1NXR0dJTO//26x/n7iKiamhrFxcVK5bdt27bKddWVr6GhwZYtW1AoFFy9epXIyEjmzp3L/v37MTExITc3l7KyMsLCwpRGsCvdvn0bqBj9ra78x32mpk2bKh1nZWXxxx9/VBlFB2jdujUA5ubmJCQksHr1aqZOnYpMJsPd3Z0FCxbQokWLGtMrpyr8ve7K4+zsbOmcpqYmTZo0qbbtgiAIgtBQiABXqLPKUcyLFy9y7tw5tLW1iYmJkeaUpqWlKeU3NjYGKh6FP+rvL2MZGxtTUlJCXl6eUpD79+uelrGxcbVlVXfu0cDe2toac3NzBg8eTHx8vDSXVkVFBX9/f958880q1xsYGABI85JrU2d19PT08PDwYMaMGVXS1NXVpe+7d+9O9+7defDgAT/++CNhYWHMmzdPWursSemVI9j37t2jWbNmUpl3794FUBrhFltQCoIgCC8DMUVBqLMLFy4AFUFcYWEhampqSoHP/v37lfI3b94cY2NjUlJSlM5/9913SseWlpYASvnKy8v54Ycfnkm7raysOHr0KA8ePJDOHT9+XGmE8knXent7k5SURGZmJlpaWtja2nL58mWsrKyqfFW+jGZlZUVubi6//fabVNbDhw/55ZdfatVmV1dXLl26RPv27avU8ejKEpW0tbXp378/3t7eXLp0qVbpVlZWqKmpceDAAaW8X3/9NUZGRtWOeguCIAhCQyZGcIUnKi8v548//gAqXjD6888/WbVqFR06dMDR0ZHi4mI2btzIf/7zH9566y1SU1P5/PPPlcpQVVVl4sSJfPzxxzRt2hQnJyeOHTsmBXmVI78dO3bkrbfe4qOPPqKgoIAWLVqwa9cuCgsLn8nI4ZgxY9i+fTv+/v74+fmRm5tLfHw8BgYGtSo/ICCAr776io0bNxIcHMycOXMYPXo0M2fOxNvbG11dXdLT0/n555/x8fHBycmJ7t27Y2FhQVBQELNmzUJXV5dPP/2UJk2a1KrOMWPGsH//fkaOHMmoUaNo0aIF9+/f53//+x/NmjVjzJgx7Nixgz/++AMPDw+MjY25efMmycnJuLlVbOlYU7qhoSEjR44kMTERdXV1bG1tOXz4MF988QUffPCB9IKZIAiCILwsRIArPFFhYSFDhgwBoFGjRjRv3px33nmHqVOnoqamhqenJ8HBwWzZsoWkpCTs7e1ZvXo1ffr0USrH19eX3Nxctm3bxubNm3FxcWH27NkEBgYqTUf4+OOP+fDDDwkPD0ddXZ333nuPjh07PpP1Vk1MTFi7di0fffQR06dPp3Xr1syfP58PP/ywyrzf6rRr147+/ftLQbK9vT3btm0jNjaWefPmUVJSQvPmzXF2dqZNmzZAxSP9lStXsmDBAhYsWICuri6jRo3iypUrnD17tsY6DQwM2LlzJzExMSxbtozs7GyMjIywsbHhrbfeAipeIvvhhx8ICwsjOzsbY2NjvL29pWkNNaUDzJkzBx0dHfbs2UNCQgJmZmYsXryYoUOHPk1XC4IgCEK9UlEoFIr6boTweoqJiWH9+vUcO3bsibthjRgxAplMxubNm595G65evUq/fv34+OOPee+99555+dUpLi7G29sbR0fHal9Qex1UrgDx4+lc0jJyasgtCIIgmJnoMWWYW30345k6efIkKioqSi+zPytiBFd4IS5dukRycjJ2dnaoqanx66+/kpiYyLBhw5SC22+++Ybbt2/TqVMnCgoK+OKLLzh+/Djx8fHPpB2RkZHI5XJMTEy4ceMGq1evxtjYmN69ez+T8quzc+dOysvLMTc3Jzc3l+3bt5OWlkZUVNRzq/Nl8ar9Zy0IgvA8lZcrkMnEy761IQJc4YXQ0NAgNTWV7du38/DhQ5o1a6a0sUAlLS0tPv/8c65evUpJSQnt2rUjIiKi2pUKnkZJSQnLli3j7t27aGho0K1bN+bMmfNcl75q3Lgxa9askVaX6Ny5M6tXr34uf7G+TIqLiykoKEBTU7O+m/JSKCgo4MqVK5ibm4s+qwPRb3Un+uzpvIh+E8Ft7YkpCoIgvHCnTp2iuLiYLl26oKWlVd/NeSnk5+dz9uxZ0Wd1JPqt7kSfPR3Rb3X3PKcoiGXCBEGoN2Jd3dpTUVFBU1NT9FkdiX6rO9FnT0f0W8MiRnAFQXjhKl8ye92naQiCIDzJqz7nVrxkJgjCKyl++xGxioIgCEI1XsVVE14kEeAKDVpsbCxxcXGYmJhw+PBhaVOISkOHDiU1NZX33nuPpUuXkpSUxLx58/jll18wNDSstkwvLy/phS9VVVVMTU1xd3dnxowZ0jUhISHs3btXusbIyIjOnTszbdo07OzsntOnff2kZeRwNS2rvpshCIIgvGJEgCs0eGpqamRlZfHbb7/h5OQknU9LS+OPP/54qsn8ffr0Ydy4cZSWlvLHH38QFxfHX3/9xdatW6UgulWrVixbtgyFQsGNGzeIjY1l7Nix7N+/n1atWj2zzycIgiAIwrMlAlyhwVNTU8PFxYUvv/xSKcD98ssv6dixY5VR3dpo2rQptra2ADg6OlJUVMSKFSv4888/pblAGhoaUh47OztatmzJsGHD+Oqrr/D39//Hn6s2CgsLn7gJRn1TKBSUlJSgrq5e300RBEEQBIlYRUF4KQwYMIBvvvmGkpIS6dwXX3zBgAEDnkn5lpaWANy8efOxebp27QrArVu3lM4fOnSIQYMGYW1tjbOzMwsXLiQ/P18pz4ULFxgxYgRWVlb07t2b5ORkAgIC8PX1lfLExsZiZ2fHyZMnGTJkCFZWVtIWxZcuXWLy5Mk4ODhga2vLxIkTuX79ulIde/bswdvbG2tra5ycnBg2bBgnT56sdXpRURFhYWG4u7tjZWXFwIED+fbbb5XqCAkJYcCAARw+fJh33nkHKysrvv/++xr7VxAEQRBeJDGCK7wUevbsyfz58zly5Ag9evTg4sWLnD9/nvj4eL766qt/XH5lYGtiYvLYPJXzdlu2bCmdO3DgAIGBgfj4+DBt2jQyMzOJjIwkNzeX6OhooGIUdty4cejq6hIREQFAfHw8ubm5tG7dWqmOkpISgoKCGDNmDIGBgejr63Pjxg2GDh1Kx44dWbp0KSoqKiQkJDBmzBgOHDiAuro6v/32G/Pnz2fcuHF4enpSWFjIyZMnycvLA6gxHSA4OJiffvqJmTNn0q5dOz7//HOmTZtGfHw8vXr1kvJlZGTw0UcfMXnyZExNTWnRosU/6XpBEARBeOZEgCu8FDQ1NfHy8uLLL7+kR48efPHFF9jZ2T31XFiFQkFpaSmlpaX873//IyEhgVatWmFhYaGUr7S0FIVCwc2bN1m8eDFmZmb861//ksoIDw+nf//+LFmyRLrG2NiYiRMnEhAQQMeOHfnss8+4d+8e27dvl4JjS0tLevfuXW2AGxgYSP/+/aVzc+fORU9Pj/Xr19O4cWMA7O3t6dWrF7t372bEiBGcPHkSfX195s6dK13Xo0cP6fua0s+dO8fBgwdZvHgxQ4cOBaB79+6kpaVVCXBzcnJYu3YtNjY2depzQRAEQXhRxBQF4aUxYMAAUlJSKCws5KuvvsLb2/upy9q2bRsWFhbY2NgwatQomjVrRmxsrNJ81wsXLmBhYYGlpSV9+/bl5MmTxMbGSistXLlyhbS0NPr16ycFy6WlpXTr1g2ZTMbp06cBOH36NJ06dVIa+W3ZsiWdO3eutm2enp5Kx0eOHMHLywtVVVWpDl1dXbp27SrV0bVrV7KzswkJCeHIkSMUFBQolVFT+u+//w5A3759lc7369ePM2fOKE250NfXF8GtIAiC0KCJEVzhpeHu7o6amhrLly/n5s2b9OvX76nL6tevH35+fqipqdG8eXP09fWr5GndujVRUVGUl5dz7tw5IiIimDlzJsnJyWhqapKVVbG81ZQpU6qt4/bt20DFI/3qliwzNDSkqKhI6ZympiZNmjRROpeVlcXGjRvZuHFjlTLU1NQAcHFxITw8nE2bNuHn50fjxo3p06cP//73v9HX168xPScnBzU1tSr90LRpUxQKBXl5edJqFU2bNq328wqCIAhCQyECXOGloaamRu/evdmwYQMuLi7/KNAyNDSsceeUxo0bS3lsbGwwMDBg2rRpbN68mYkTJ0rB4IIFC7C2tq5yfeV8XhMTE86ePVsl/f79+1WC2eq2eNTT08PT05Phw4dXSXv0+oEDBzJw4EDu379PSkoKYWFhNGrUiI8//rjGdD09PUpKSsjJyUFPT08q8+7du6ioqKCjo/PENgqCIAhCQyICXOGlMmjQIO7du8fgwYNfeN29e/fG3t6ejRs3Mnr0aNq1a0fz5s25ceMGI0aMeOx1lpaW7Nu3jxs3bkhzhm/evMm5c+dwcHCosV4XFxcuXLhA165dUVVVrTG/oaEhgwYN4scff+Ty5cu1Sq9sx4EDBxgyZIiU98CBA3Tt2vWp1hoWBEEQhPoiAlzhpWJtbc3KlStrzPfDDz9UGR3t2LEj7du3/0f1T5s2jbFjx5KUlMSwYcMICQkhODiY/Px8evTogaamJrdu3eLw4cMEBgZibm7Ov/71LxISEpg0aRLTpk0DIC4ujqZNm9ZqNHT69Om8//77+Pn5MXjwYJo2bcrdu3f59ddfcXR0ZMCAAaxYsYLs7Gy6deuGkZERf/31Fz/99BNjxowBqDG9c+fO9O7dm6VLl1JYWIi5uTnJycmkpqbWqr8FQRAEoSERAa7wSvr3v/9d5dyMGTMICAj4R+W6urri4ODAunXrGDx4MP369UNXV5eEhAT2798PgJmZGR4eHtIUCg0NDdatW8fChQsJDg6mWbNmBAQEsG/fPqVH/4/Tpk0bdu/eTUxMDIsXLyY/Px9jY2PeeOMN5HI5AFZWVmzcuJGvv/6aBw8e0Lx5c/z8/Jg8eXKt0gEiIiKIiopi7dq1ZGdn065dO1asWIGXl9c/6jNBEARBeNFUFAqFor4bIQivm+zsbN58803GjBnD1KlT67s5L9ypU6cA+PF0LmkZOfXcGkEQhIbHzESPKcPc6rsZz9XJkydRUVGp8Z2YpyFGcAXhBVizZg1NmzbFzMyMzMxM1q1bR1lZmbSm7uvqVf/PWxAE4Z8oL1cgk4kXe5+GCHAF4QWQyWSsWrWKO3fuoKqqio2NDRs3bsTU1LS+m1ZviouLKSgoQFNTs76b8lIoKCjgypUrmJubiz6rA9FvdSf67Ok8j34Twe3TEwGuILwA48ePZ/z48fXdjAZHzJCqPYVCQUFBgeizOhL9Vneiz56O6LeGRexkJghCvRFr6taeiooKmpqaos/qSPRb3Yk+ezqi3xoW8ZKZIAgvXOVLZs/jxQJBEISGTsytrSBeMhME4ZUUv/2IWEVBEITXyuuwOkJDIAJcoUHy8/Pjxo0bfPHFF6irq0vnT58+zeDBg/n3v//NyJEjAcjKyiIxMZHvv/+etLQ0AFq1aoW7uzsjR46kZcuWQMXuYb169ZLKUlFRwdjYmG7dujFr1izMzMyktJCQEPbu3SsdGxkZ0blzZ6ZNm4adnd1z/eyvk7SMHK6mZdV3MwRBEIRXjAhwhQZp4cKFDBgwgISEBKZPnw5AWVkZCxYsoGvXrgwfPhyAa9euMXr0aEpLS/H19cXKygoVFRX+/PNPduzYQWpqKjt37lQqe9asWTg5OVFeXs7169dZsWIFEydOJDk5WWkr3FatWrFs2TIUCgU3btwgNjaWsWPHsn//fmnLXUEQBEEQGh4R4AoNUuvWrfH392fVqlUMGDCAdu3asXnzZs6dO8eePXuQySrejwwKCqK0tJTPPvuMZs2aSde7uLgwatQokpOTq5Tdpk0bbG1tAbC3t0dbW5spU6Zw5coVOnToIOXT0NCQ8tnZ2dGyZUuGDRvGV199hb+///P78I8oLCxEQ0PjhdT1NBQKBSUlJUqj7IIgCIJQ38QqCkKDNWHCBFq2bMmiRYu4ffs2y5cvZ+TIkXTt2hWA48ePc+rUKSZPnqwU3FZSV1fn/fffr7GeJk2aAFBaWvrEfJX13rp1S+n8oUOHGDRoENbW1jg7O7Nw4ULy8/OV8ly4cIERI0ZgZWVF7969SU5OJiAgAF9fXylPbGwsdnZ2nDx5kiFDhmBlZcXWrVsBuHTpEpMnT8bBwQFbW1smTpzI9evXlerYs2cP3t7eWFtb4+TkxLBhwzh58mSt04uKiggLC8Pd3R0rKysGDhzIt99+q1RHSEgIAwYM4PDhw7zzzjtYWVnx/fffP7HfBEEQBOFFEyO4QoOlrq7OokWLGD16NCNGjEBXV1eargBw7NgxANzd3etUbnl5OaWlpZSXl3Pjxg3i4uJo164dHTt2fOJ1lfN7K+f0Ahw4cIDAwEB8fHyYNm0amZmZREZGkpubS3R0NFAxCjtu3Dh0dXWJiIgAID4+ntzcXFq3bq1UR0lJCUFBQYwZM4bAwED09fW5ceMGQ4cOpWPHjixduhQVFRUSEhIYM2YMBw4cQF1dnd9++4358+czbtw4PD09KSws5OTJk+Tl5QHUmA4QHBzMTz/9xMyZM2nXrh2ff/4506ZNIz4+XmnuckZGBh999BGTJ0/G1NSUFi1a1Kn/BUEQBOF5EwGu0KA5Ozvj7OzM0aNHWbZsGdra2lJaRkYGQJXdwMrKypQW2m7USPnHPDAwUOm4RYsWrF27Vmn+baXS0lIUCgU3b95k8eLFmJmZSdvrKhQKwsPD6d+/P0uWLJGuMTY2ZuLEiQQEBNCxY0c+++wz7t27x/bt26Xg2NLSkt69e1cb4AYGBtK/f3/p3Ny5c9HT02P9+vU0btwYqJha0atXL3bv3s2IESM4efIk+vr6zJ07V7quR48e0vc1pZ87d46DBw+yePFihg4dCkD37t1JS0urEuDm5OSwdu1abGxsqvSXIAiCIDQEYoqC0KBdvHiR33//HRUVFX799ddaXTNw4EAsLCykr/v37yulBwcHs2fPHnbv3k18fDwmJiaMHz+eO3fuKOW7cOECFhYWWFpa0rdvX06ePElsbCyGhoYAXLlyhbS0NPr160dpaan01a1bN2QyGadPnwYqVn7o1KmT0shvy5Yt6dy5c7Xt9/T0VDo+cuQIXl5eqKqqSnXo6urStWtXqY6uXbuSnZ1NSEgIR44coaCgQKmMmtJ///13APr27at0vl+/fpw5c0ZpyoW+vr4IbgVBEIQGTYzgCg2WQqFg0aJFtGnThuHDh/Of//yHf/3rX9KLXyYmJgDcuXNHaVWD6OhoCgsLOXToEHFxcVXKbdWqldKi0vb29ri5ubFhwwalEc7WrVsTFRVFeXk5586dIyIigpkzZ5KcnIympiZZWRXLW02ZMqXa9t++fRuoGGmuDIofZWhoSFFRkdI5TU1NaU5wpaysLDZu3MjGjRurlKGmpgZUvFQXHh7Opk2b8PPzo3HjxvTp04d///vf6Ovr15iek5ODmpoa+vr6SuU3bdoUhUJBXl4eWlpa0jlBEARBaMhEgCs0WElJSRw/fpzNmzfj6OjI/v37WbRoEZ999hmqqqo4OTkB8N///pdhw4ZJ11XOpb1w4UKt6jE0NMTAwKBK/saNG0uBsI2NDQYGBkybNo3NmzczceJEKRhcsGAB1tbWVcqtDMBNTEw4e/ZslfT79+9XCWar2+JRT08PT09PaWm0Rz16/cCBAxk4cCD3798nJSWFsLAwGjVqxMcff1xjup6eHiUlJeTk5KCnpyeVeffuXVRUVNDR0XliGwVBEAShIRFTFIQGKSsri/DwcN577z3eeOMNVFRUWLRoEX/99RebN28GwNHRESsrK1atWiXNx30ad+/eJSsrCwMDgyfm6927N/b29mzcuJGioiLatWtH8+bNuXHjBlZWVlW+Kld2sLS05Pz589y4cUMq6+bNm5w7d65W7XNxceHChQt07dq1Sh3t2rWrkt/Q0JBBgwbh5ubG5cuXa5Xu4OAAVLw096gDBw7QtWtXafRWEARBEF4GYgRXaJDCw8MBmD17tnSuc+fOjBw5khUrVtCvXz+aNWtGZGQko0ePxsfHh1GjRkkbPaSlpbFjxw7U1dWlx/iVrl27xh9//IFCoeDOnTskJiaioqLC4MGDa2zXtGnTGDt2LElJSQwbNoyQkBCCg4PJz8+nR48eaGpqcuvWLQ4fPkxgYCDm5ub861//IiEhgUmTJjFt2jQA4uLiaNq0aa1GQ6dPn87777+Pn58fgwcPpmnTpty9e5dff/0VR0dHBgwYwIoVK8jOzqZbt24YGRnx119/8dNPPzFmzBiAGtM7d+5M7969Wbp0KYWFhZibm5OcnExqaiorV66szS0TBEEQhAZDBLhCg3P8+HH27t3Lf/7znypzV6dPn87XX39NWFgYMTExtGnThqSkJBITE9m7dy9xcXGoqKhIW/VGRUUpPV4HiIqKkr43MDCgc+fObNy4kTfeeKPGtrm6uuLg4MC6desYPHgw/fr1Q1dXl4SEBPbv3w+AmZkZHh4e0lxVDQ0N1q1bx8KFCwkODqZZs2YEBASwb9++Km2rTps2bdi9ezcxMTEsXryY/Px8jI2NeeONN5DL5QBYWVmxceNGvv76ax48eEDz5s3x8/Nj8uTJtUoHiIiIICoqirVr15KdnU27du1YsWIFXl5eNbZREARBEBoSFcWj6ykJgvBCZGdn8+abbzJmzBimTp1a38154U6dOgXAj6dzScvIqefWCIIgvDhmJnpMGeZW381oEE6ePImKiorSi9/PihjBFYQXYM2aNTRt2hQzMzMyMzNZt24dZWVl0pq6ryvxn7wgCK+j8nIFMpl4Yfd5EgGuILwAMpmMVatWcefOHVRVVbGxsWHjxo1VNql4nRQXF1NQUICmpmZ9N+WlUFBQwJUrVzA3Nxd9Vgei3+pO9NnTqUu/ieD2+RMBriC8AOPHj2f8+PH13QxBEAThOVEoFBQUFCBmfjYMIsAVBKFeqKuri9GhOtDU1KRr16713YyXjui3unvZ+0w8/hdABLiCINSj+O1HxEtmgiA8M+IFLqGSCHAFQag3aRk5XE3Lqu9mCIIgCK8YsZOZ8ELExsZiZ2f3xDxZWVksW7aM/v37Y2Njg42NDQMGDGDp0qXcvHlTynfz5k3kcrn01blzZzw8PAgKCiItLU2pzJCQEORyebWbOCgUCjw9PZHL5cTGxiq19dHynZycGDZsGIcPH/6HvSAIgiAIwosgRnCFBuHatWuMHj2a0tJSfH19pR3J/vzzT3bs2EFqaio7d+5UumbWrFk4OTlRXl7O9evXWbFiBRMnTiQ5ORlVVVUpn5aWFv/73/+4ceMGrVq1ks4fP36ce/fuoa6uXqU9GhoabNy4EYCMjAxpJ7KtW7dib2//nHpBEARBEIRnQQS4QoMQFBREaWkpn332Gc2aNZPOu7i4MGrUKJKTk6tc06ZNG2xtbQGwt7dHW1ubKVOmcOXKFTp06CDlMzMzQ1VVla+++gp/f3/p/BdffIG7uzvHjx+vUrZMJpPKBrCxscHT05N9+/a9sAC3sLAQDQ2NF1LX03oZ2igIgiC8fsQUBaHeHT9+nFOnTjF58mSl4LaSuro677//fo3lNGnSBIDS0tIqad7e3nzxxRfScWlpKd988w0DBgyoVRubNWuGoaEht27dUjqfmprKqFGjsLW1xcHBgaCgIO7du6eUJz09HX9/fylI3rBhA0uWLFHaAjcpKQm5XE5qaipjx47F1taW8PBw6frg4GCcnJywtrZmxIgRnD59WqmOlJQUfHx8sLOzw9HRER8fH6UpFTWll5eXs3LlSry8vLC0tKRv377s2LFDqY7KaSYnT55kyJAhWFlZsXXr1lr1nyAIgiC8SGIEV6h3x44dA8Dd3b1O15WXl1NaWkp5eTk3btwgLi6Odu3a0bFjxyp5vb29iYqK4uLFi3To0IEjR45QVFSEl5cXixYtqrGuhw8fkpOTQ8uWLaVzqamp+Pr64unpSXR0NAUFBcTExBAQECBNp1AoFAQEBHD37l0WL16Mjo4OiYmJ3Lp1C5ms6t+XQUFBDBkyBH9/fzQ1NcnJyWH48OFoaWnxwQcfoKOjw+bNmxk9ejQHDx7EyMiI69evM2PGDLy9vQkKCqK8vJxz586Rk1OxOkFN6QDh4eFs2rSJyZMnY2dnx6FDh1i4cCGlpaWMHDlSyldSUkJQUBBjxowhMDAQfX392t4uQRAEQXhhRIAr1LuMjAyAKrt6lZWVKS2Y3aiR8o9rYGCg0nGLFi1Yu3at0vzbSmZmZtja2vLFF18wc+ZMvvjiC7y8vNDS0npsuypHgjMyMoiIiKBJkyaMGjVKSo+MjMTS0pK4uDhUVCrWXOzUqRMDBgzg8OHDeHp68uOPP/Lnn3+ydetWHB0dAXB2dsbT0xNdXd0qdQ4dOpSJEydKxytWrCA3N5fdu3djZGQEVEzb6NOnD4mJicyZM4czZ85QUlLCBx98gLa2NgAeHh5SGTWl379/ny1btuDn58e0adOAij82srKyiI+PZ9iwYVKflpSUEBgYSP/+/R/bb4IgCIJQ38QUBaHBGjhwIBYWFtLX/fv3ldKDg4PZs2cPu3fvJj4+HhMTE8aPH8+dO3eqLW/AgAF89dVXFBYWkpKSgre392Przs/Pl+rt2bMn33zzDeHh4bRr1w6o2JLxxIkT9O3bl7KyMkpLSyktLaVt27aYmppy6tQpAE6dOoWurq4U3ELFVAoXF5dq6+3Ro4fS8ZEjR3ByckJPT0+qQyaT8cYbb0h1yOVyVFVVCQ4O5vvvvycvL0+pjJrST548SUlJCX379lU6369fP+7fv8/Vq1eVznt6ej623wRBEAShIRABrlDvTExMAKoEptHR0ezZs4epU6dWe12rVq2wsrLC2tqaN998k1WrVnHnzh02bNhQbf6+ffty8+ZNli9fjpqamtIo5t9paGhIwXNERATGxsbMnTtXGm3Ozc2lrKyMsLAwpSDcwsKCW7ducfv2baBi9NfQ0LBK+dWdA2jatKnScVZWFt99912VOj7//HPS09MBMDc3JyEhgby8PKZOnYqLiwuTJk2S5gvXlF45VeHvdVceZ2dnS+c0NTWluc6CIAiC0FCJKQpCvXNycgLgv//9L8OGDZPOV86lvXDhQq3KMTQ0xMDA4LH5mzZtirOzMxs2bOD9999HTU3tsWXJZDKsrKwAsLa2xtzcnMGDBxMfHy/NpVVRUcHf358333yzyvUGBgZARfD+95FnoNpz1dHT08PDw4MZM2ZUSXt0ebPu3bvTvXt3Hjx4wI8//khYWBjz5s2Tljp7UnrlPNp79+4pveR39+5dAKV5tpVTMQRBEAShIRMBrlDvHB0dsbKyYtWqVfTq1Usa0a2ru3fvkpWVJQWX1fH19UVDQ4NBgwbVqWwrKyu8vb1JSkpi6tSpGBsbY2try+XLl6VA+HHX5ebm8ttvv/HGG28AFS+s/fLLL9XOwf07V1dXkpOTad++/RPnC1fS1tamf//+nDx5UmnViCelW1lZoaamxoEDB5T2n//6668xMjKibdu2NdYrCIIgCA2JCHCFF6asrIwDBw5UOW9tbU1kZCSjR4/Gx8eHUaNGSRs9pKWlsWPHDtTV1auMuF67do0//vgDhULBnTt3SExMREVFpdpdyyr17NmTnj17PlX7AwIC+Oqrr9i4cSPBwcHMmTOH0aNHM3PmTLy9vdHV1SU9PZ2ff/4ZHx8fnJyc6N69OxYWFgQFBTFr1ix0dXX59NNPadKkSa1GQ8eMGcP+/fsZOXIko0aNokWLFty/f5///e9/NGvWjDFjxrBjxw7++OMPPDw8MDY25ubNmyQnJ+PmVrEfe03phoaGjBw5ksTERNTV1bG1teXw4cN88cUXfPDBB9W+tCcIgiAIDZkIcIUXpqioqNpH7eHh4QwcOJCkpCQSExPZu3evtDJBq1atcHd3JyoqCh0dHaXroqKipO8NDAzo3LkzGzdulEZKn7V27drRv39/tm/fjr+/P/b29mzbto3Y2FjmzZtHSUkJzZs3x9nZmTZt2gAVj/RXrlzJggULWLBgAbq6uowaNYorV65w9uzZGus0MDBg586dxMTEsGzZMrKzszEyMsLGxoa33noLqHiJ7IcffiAsLIzs7GyMjY3x9vaW+rqmdIA5c+ago6PDnj17SEhIwMzMjMWLFzN06NDn0JP/x8xE77mWLwjC60X8nyJUUlE8ug6TIAjPXXFxMd7e3jg6OhIWFlbfzakXlStAPGl6hyAIwtMoL1cgk7349wXy8/M5e/YsXbp0qdWUMqFiFR8VFZXn8rtAjOAKwnO2c+dOysvLMTc3Jzc3l+3bt5OWlqY0Av06Ki4upqCgAE1NzfpuykuhoKCAK1euYG5uLvqsDkS/1d3L3mf1EdwKDY8IcAXhOWvcuDFr1qwhLS0NgM6dO7N69WoxegmIB0i1p1AoKCgoEH1WR6Lf6k70mfAqEAGuIDxn7777Lu+++259N6NBEsuO1Z6Kigqampqiz+pI9FvdiT4TXgUiwBUEoV6oq6u/lI8/64umpqbSMm5C7Yh+q7uXvc/qaw6u0LCIAFcQhHoTv/0IaRk59d0MQRBeEWYmekwZ5lbfzRAagAYV4MbGxrJu3TpSU1OfmHbz5k169erF8uXL6du3b63K3rBhA+bm5nh6elZJKykpYdeuXSQnJ3Px4kWKioowMTHB0dGRIUOG4ODg8I8/25MEBASQl5fH5s2bgSf3wz+Vm5vLxo0b6devHx06dFBKk8vlSsdGRkZYW1sTGBhYJe1Fedx98/LyokePHixYsOCZ11n581VJXV0dMzMz+vfvz8SJE9HQ0Hjmdb6u0jJyuJqWVd/NEARBEF4xDSrArS0TExN27txZpx2WNm3aRI8ePaoESkVFRUyYMIHU1FSGDBnCpEmTaNKkCdeuXWPfvn0MHz6cU6dOKW2L+rwNGjSo2kD8WcjNzSUuLo6OHTtWCXChYqevAQMGoFAoSE9PZ/Xq1fj5+fHVV1/VauetZ+1x9y0uLu65t2fWrFk4OTlRUFBASkoK8fHx3L17lw8//PC51isIgiAIwj/zUga4lbstPQsxMTH89ttvrFu3DhcXF+l8t27dGDRoEJ999tkTry8sLHzmI3rNmzenefPmz7TM2jI1NVXqW3NzcwYOHEhqaupzC7qfxouYH9amTRupL1xcXLh8+TKff/45ixYtQiaTPff6n8fP1rP2MrRREARBeP08/9/Sz8HNmzeRy+VK276mpKTg4+ODnZ0djo6O+Pj4cPjwYaDicXZaWhpbt25FLpcjl8tJSkqisLCQHTt20Lt3b6Xg9lH/+te/lEZv5XI5a9asISIiAjc3N+m61NRUJk2ahLu7O7a2tgwcOJB9+/ZVKe/SpUuMHDkSKysr3nzzTfbu3VslT2xsLHZ2dkrncnNzWbRoEe7u7lhaWuLj48N///tfpTy+vr74+/tz4MAB+vTpg52dHaNGjeL69etSv1U+ep8xY4bUFzdv3nxsXzdp0gSomMbxqIMHDzJw4ECsrKxwd3cnLCyMoqIipTxpaWlMnz4dBwcHbG1t8fPz4/z580p5nua+VaY9OpIaEhLCgAEDOHbsGO+++y62tra8//77nD59Wqm+vLw8goODsbOzw8XFhaioKNatW1erKRhdunShsLCQ+/fvS+dqc18UCgVxcXG4ublhZ2fH9OnT+fnnn5HL5Rw7dkzK97ifLYVCQWJiIn369MHS0pJevXqxYcMGpTrS09OZMWMGrq6uWFlZ4eXlxccff1zrdIDffvuNoUOHYm1tjZOTE/PmzSM7O1tKr/x3l5SURGhoKE5OTgwaNKjGfhMEQRCEF61BjuCWlpZWOVdeXv7Y/NevX2fGjBl4e3sTFBREeXk5586dIyen4uWVuLg4Jk6ciL29PePGjQOgdevWnD59mvz8fNzd3evUvk2bNmFjY8OSJUuktt66dQt7e3uGDRuGuro6J06cIDQ0FIVCwXvvvQdUTIcYN24cmpqahIeHA7BixQoePHjwxOkWxcXFjB07lnv37jFz5kyaNWtGcnIy/v7+JCUlKQVnZ8+e5f79+wQHB1NWVsbSpUuZPXs2O3fuxMTEhLi4OKZOnSo9foeKKR+P9nNpaSkKhYI7d+4QERGBgYGBlBcqgtLp06dL/X358mWio6O5ffs2K1asAODBgwf4+voik8lYvHgxjRs3ZtWqVYwcOZLk5GRMTU2f+r49TmZmJh999BETJ05ER0eHyMhIpk6dyrfffouamhoA8+bN4+jRo8yePRszMzN27drFn3/+Wav7fuvWLZo0aYKBgUGd7svmzZuJi4tj/PjxODs7c/ToUUJDQ6uto7qfrSVLlrB7924mTZqEjY0NJ06cYNmyZTRu3Jhhw4YBFVvtZmRkEBoaipGREbdv31YK7mtKP336NGPHjsXJyYnly5dz9+5dIiMjuXjxIjt27EBVVVXKGxUVhaenJ5GRkU/8dykIgiAI9aXBBbj5+flYWFhUm/a4re/OnDlDSUkJH3zwAdra2gB4eHhI6V27dkVdXZ2mTZsqPX4/evQoQJXpAOXl5Uq/uFVVVZXWA9TT0yMuLk7pnLe3t/S9QqHgjTfe4M6dO+zcuVMKcJOSksjIyODrr7+WAtquXbvSt2/fJwa4+/fv59y5c3z++efSvFkPDw+uXbvGypUrWb58uZQ3Ly+Pffv2YWhoCFT057x580hPT6d58+Z06dIFUH78/qhly5axbNky6VhfX5+4uDh0dHSkc3Fxcdja2hIZGQlA9+7d0dTUZMGCBZw/f14a5bt16xZffvkl7du3B+CNN96gZ8+ebNy4kZCQkKe+b4+Tk5PDli1b6NixI1Cx1M2oUaP43//+h6OjIxcvXuTbb7/lk08+kdal9fDwoF+/ftWWVxnsV87BPXjwIDNnzpSCvdrcl7KyMtasWYOPjw/BwcEAuLu7k5WVxZ49e6rU+fefrevXr7NlyxYWL17MkCFDAHB1daWwsJD4+HiGDBmCTCbj1KlTzJo1i/79+0tlPbr2bk3pCQkJGBsbk5CQIP0xYGpqip+fH4cPH8bLy0vK27lzZ5YsWVLj/RAEQRCE+tLgpihoaGiwZ8+eKl+DBw9+7DVyuRxVVVWCg4P5/vvvycvLq1Odf1/M+qOPPsLCwkL6+uabb5TSu3fvXuWanJwcPvroI3r27Cldt3PnTq5cuSLlOXnyJB07dlQKZtu0aUPnzp2f2L4jR47QqVMn2rZtS2lpqfTl6urKqVOnlPJ27txZCm4BKfBKT0+vuSOAUaNGSX2+Zs0abG1tCQgI4Ny5cwA8fPiQs2fP0qdPH6XrKgOn33//HYDjx4/TsWNHKbiFimDZ1dVVyvNP79vfmZiYSMEt/N9nv3PnDoDUV4+ukCCTyejZs2e15QUGBmJhYYGjoyNz586lT58+TJgwQUqvzX1JT08nMzNTKUD8exse9fefrZ9//hmA3r17V6kjMzOT27dvAxV/DKxbt45t27Zx7dq1KuXWlH78+HF69eolBbdQEYjr6upK96tSjx49qm27IAiCIDQUDW4EVyaTVbuF6aFDhx57jbm5OQkJCaxevZqpU6cik8lwd3dnwYIFtGjR4rHXVT6a/3vwN378eN577z0yMzOZPHlyleuMjIyqnAsJCSE1NZUpU6bQoUMHtLW12b59O19//bWUJyMjo9prjYyMqsxffVRWVhZnzpypdmT70UfHQJWVBSoDlieV/6jmzZsr9b+Liwvdu3dn5cqVrFixgry8PBQKRZXPoaOjg7q6ujS9IDc3l6ZNm1Yp38jIiAsXLgBPf98ep6bPnpmZiZqamtJoNKD0B8GjgoODcXZ2Ji8vjy1btvDll1/SrVs3hg4dCtTuvmRmZlZbR3U/B9Wdz8rKQqFQ4OzsXG3+27dvY2ZmRnR0NNHR0cTExLB48WLMzc2ZNWsWvXv3BqgxPTc397E/m5X3tKa2C4IgCEJD0eAC3KfVvXt3unfvzoMHD/jxxx8JCwtj3rx5bNy48bHXWFpaoqWlxZEjR3j//fel8y1atKBFixaPffnq76O3RUVFHDp0iJCQEHx9faXz27ZtU8pnYmJS7XzPe/fuSY/oq6Onp4dcLq+Xx8Lq6uq0atVKCkp1dHRQUVFRetEKKqZGFBcXo6enJ7X50dHrSvfu3ZPywNPdt6dlbGxMSUkJeXl5SkHu3z9LpVatWknBvpOTE++//z4xMTG88847aGlp1eq+GBsbV1vHvXv3qs3/958tPT09VFRU2LZtm9LoaiVzc3Og4mcrLCyM8vJyTp8+zapVqwgMDOTAgQO0atWqxnQ9Pb1q2/T3+1VdGwVBEAShoWlwUxT+KW1tbfr374+3tzeXLl2SzqupqVUZxdTQ0GDo0KF88803Sm+z11VxcTHl5eVKAciDBw/4/vvvlfJZWVlx4cIFpUfE165dkx7/P46rqys3btzAxMQEKyurKl91UdcR3aKiIq5fvy69WNWkSRO6dOmitIIFII1UV26K4eDgwF9//cXly5elPDk5Ofz888/VbpxRl/v2tCwtLYGKl+QqlZeX88MPP9R4raqqKrNnzyYrK4tdu3YBtbsvzZs3x9jYWKlOgO+++65Wba5cSSE7O7vaOv7+h5FMJsPa2pqZM2dSWlpaZTrC49IdHBxISUlResHzyJEj5ObmPveNTgRBEAThWXslRnB37NjBH3/8gYeHB8bGxty8eZPk5GTc3P5vu7527dpx9OhRjhw5gq6uLi1btsTAwICZM2fy559/MmHCBIYMGYKbmxtNmjTh3r170tzbx73cVklHRwcrKyvWrl2LoaEhjRo1Ys2aNWhrayuN3Pn4+LBq1Sr8/f2ZMWMGULGKQnWP8h/17rvvsmPHDkaNGsW4ceNo27YteXl50ktaQUFBte4rY2NjdHV1+fLLL2nZsiXq6urI5XJpKbTbt2/zxx9/ABWjjlu3biU7O1t6LA8wdepUpkyZQnBwMO+88w5XrlwhOjqaPn36SCsH+Pj4sGHDBvz9/Zk5c6a0ikKjRo0YPXo08M/u29Po2LEjb731Fh999BEFBQW0aNGCXbt2UVhYWKtRSVdXVxwcHNiwYQMjRoyo1X1RVVVl4sSJfPzxxzRt2hQnJyeOHTvGL7/8AlDjerrm5uaMGDGCOXPm4Ofnh42NDSUlJVy9epVjx46xcuVK8vLy8PPzY+DAgZibm1NSUsLmzZvR1dWla9euNaYDTJo0iaFDh+Lv74+vr6+0ioK1tXWDWv9YEARBEGrjlQhw5XI5P/zwA2FhYWRnZ2NsbIy3t7cURELFrlSLFi1i2rRpPHz4kLCwMHx8fGjcuDGJiYns3LmT5ORk9uzZQ0lJCSYmJjg4OLBt27ZajWBFRkayYMECQkJC0NfXx9fXl/z8fNatWyfl0dDQYN26dSxatIjZs2fTrFkzAgICSElJeeILVurq6mzatInY2FgSEhLIzMxEX1+frl27Mnz48Dr1lUwmIywsjKioKMaMGUNxcTEpKSm0bNkSqFjSqnLLYF1dXdq3b098fDxvvvmmVEblNsnx8fEEBASgr6/P4MGDlQJtbW1tNm/ezNKlS/nggw8oLy/H3t6eLVu2YGpqCvyz+/a0Pv74Yz788EPCw8NRV1fnvffeo2PHjmzdurVW10+dOpWxY8eyf/9+fHx8anVffH19yc3NZdu2bWzevBkXFxdmz55NYGBglfnA1QkNDcXc3JydO3cSHx9PkyZNMDc3l7apbty4MZ06dWLz5s3cvn0bDQ0NLC0tSUxMxNDQkOLi4iemQ8Xo9rp164iKimLatGloaWnh5eXF3Llzq8zzFgRBEISGTkWhUCjquxGCUJ9GjBiBTCaTAvsXISYmhvXr13Ps2LHXciewylUmfjydS1pGTg25BUEQasfMRI8pw9xqzvgc5Ofnc/bsWbp06VLjk1+hwsmTJ1FRUanzdMvaeCVGcAWhtr755htu375Np06dKCgo4IsvvuD48ePEx8c/tzovXbpEcnIydnZ2qKmp8euvv5KYmMiwYcNey+D2UfX1i0gQhFdXebkCmUy8DPu6EwGu8FrR0tLi888/5+rVq5SUlNCuXTsiIiKUpmA8axoaGqSmprJ9+3YePnxIs2bN8PPzY9q0ac+tzpdBcXExBQUFaGpq1ndTXgoFBQVcuXIFc3Nz0Wd1IPqt7l72PhPBrQAiwBVeMx4eHkq7pb0IZmZmbNq06YXW+bIQM6RqT6FQUFBQIPqsjkS/1Z3oM+FV8MotEyYIwstDrKlbeyoqKmhqaoo+qyPRb3Un+kx4FYgRXEEQ6oW6uvpL+fizvmhqakrLugm1J/qt7p53n4k5ssKLIAJcQRDqTfz2I2IVBUF4jdTnKgfC60UEuC+p2NhY1q1bR2pqqtL5pUuXsmHDBv7zn/+Qnp5OXFwcjo6OVdZ5XbJkCSkpKVV2W6uJr68vWlparF69+plfd/PmTWmN3co1XutDbGwscXFx0rG+vj7t2rVj0qRJYtODZywtI4eraVn13QxBEAThFSPm4L5CIiIi2LBhA4sXL2bQoEHS+ePHj/+jrYgftXDhQubOnftMymrINDQ02LlzJzt37uQ///kPRUVFTJo0iRMnTtR30wRBEARBqIEYwX1FREdH8+mnn7Jw4UKGDBkindfS0qJDhw6sXLkSJyenf1xPhw4d/nEZ9aWwsLDW687KZDJsbW2lYxsbGzw9Pdm3bx/29vbPqYXK6tLe+vIytFEQBEF4/YgR3FdA5VaxH3zwQbVb9wYEBHD06NEaRx9zc3NZtGgR7u7uWFpa4uPjw3//+1+lPL6+vvj7+yud+/bbb+nTpw9WVlYMHjyYP//8E0dHR2JjY6vUceDAAfr06YOdnR2jRo3i+vXrVfIUFBTw73//GwcHB7p160ZYWBilpaVKec6fP4+fnx+2trY4ODgwffp0bt26pZRHLpezZs0aIiIicHNzw8XFBYALFy4wYcIEnJycsLGxoU+fPqxdu/aJfdOsWTMMDQ2r1JGamsqoUaOkdgQFBXHv3j2lPOnp6fj7+0tB8oYNG1iyZAleXl5SnqSkJORyOampqYwdOxZbW1vCw8Ol64ODg3FycsLa2poRI0Zw+vRppTpSUlLw8fHBzs4OR0dHfHx8OHz4cK3Ty8vLWblyJV5eXlhaWtK3b1927NihVEdsbCx2dnacPHmSIUOGYGVlVestjgVBEAThRRIjuC+5VatWERcXx7x58xg5cmS1eXr27EnXrl2Jj48nMTGx2jzFxcWMHTuWe/fuMXPmTJo1a0ZycjL+/v5S8FWdM2fOMGPGDHr27Mm///1v0tLSCAwMpLi4uEres2fPcv/+fYKDgykrK2Pp0qXMnj2bnTt3KuWLiorC3d2dmJgYzpw5w4oVK1BTUyM4OBiA27dvM3LkSFq1akVERARFRUVER0czcuRIkpOT0dbWlsratGkTNjY2LFmyRAqSJ02aRNOmTVmyZAna2tpcv36d9PT0J/bzw4cPycnJoWXLltK51NRUfH198fT0JDo6moKCAmJiYggICJA+k0KhICAggLt377J48WJ0dHRITEzk1q1byGRV/74MCgpiyJAh+Pv7o6mpSU5ODsOHD0dLS4sPPvgAHR0dNm/ezOjRozl48CBGRkZcv36dGTNm4O3tTVBQEOXl5Zw7d46cnIqXt2pKBwgPD2fTpk1MnjwZOzs7Dh06xMKFCyktLVX6uSopKSEoKIgxY8YQGBiIvr7+E/tNEARBEOqDCHBfYvn5+cTExDBo0CDGjBnzxLyTJ09m2rRpnDx5Emtr6yrp+/8fe/cel/P9P378cV0plU6iHHKK6ZpSJKQUOUay4WMOc4xNzsfmMGYYY84kE3PO2XLKxjCyGe1j7IOFzXGESVJZ57p+f/j1/rpcUSGF5/126/ZxvV6v9/v1fL+ua5+eva7X+/Xes4cLFy6wa9cuZRmCt7c3169fZ+nSpSxatCjX84aGhlKpUiWCg4OVhK1UqVKMHTtWr21SUhI7d+7E2tpaiX/ChAncuXOH8uXLK+2qVKnCzJkzlRhSU1NZvXo1H3/8MZaWlqxZs4bMzExWrVqlJFi1atWiXbt27Nixg169einnsrS0ZMmSJcp+jvfv3+fmzZtMnDhRmUFt1KhRrteWkxDfvXuXOXPmUKpUKXr37q3Uz5s3j9q1a+uc38HBAX9/fyIjI2natClHjx7ljz/+YMOGDdSvX1/pr2nTplhYWOj12a1bNwYMGKC8Xrx4MYmJiWzbto0yZcoA4OHhga+vLytXrmTs2LFER0eTkZHBZ599piT3jz/MIq/6+/fvExYWpvN0NS8vL+Lj4wkJCaF79+4YGBgAjxLcUaNG4efnl+uYCSGEEMWBLFF4jRkbG9OgQQMiIiL47bffntm2VatWODg4EBISkmv9sWPHcHBwoFq1amRmZio/np6enD179qnnPXv2LD4+PjqzkS1atMi17bvvvqskt/B/63mfnD1t1aqVzmtfX19SUlL4888/gUc3zbm7u+vMHtaoUYN3331XbxyaNGmis1l56dKlsbOzY/78+ezYseOpM7fJyck4OTnh5OREs2bN2L9/P7Nnz6Z69erAo2UUp06dok2bNmRlZSnjVa1aNSpUqKCM2dmzZ7GwsFCSW3j0B0DOcokn+fj46Lw+duwY7u7uWFpaKn2o1WoaNGig9KHRaDAwMCAoKIgff/yRpKQknXPkVX/mzBkyMjL0dq5o27Yt9+/f59q1azrlspOEEEKI4k4S3NeYWq3m66+/plq1agwcOJCLFy8+ta1KpWLgwIEcOXKEP/74Q68+Pj6e6OhoJanL+fn666+f+fV9bGysTtIKYGZmRsmSJfXaPjljaWhoCEBaWppO+ZPnK1u2rNIXPFornFP2uDJlyuh87Z5T9jiVSsXKlSupXr0606ZNo2nTpnTq1In//ve/Ou2MjY3Zvn0727ZtY86cOdjY2DBu3Dju3r2rxJCVlcXMmTP1xuzWrVvcvn0beDT7++T15HaNT15rjvj4eA4ePKjXx65du5T3xd7enmXLlpGUlMTQoUPx8PBg4MCBynrhvOpzxuzJvnNeP3jwQCkzMTGhVKlSucYuhBBCFBeyROE1l7Oms3v37vTv359NmzZRuXLlXNu2bduW4OBgli5dSsWKFXXqLC0t0Wg0zJgxo0D929jYcP/+fZ2yhw8f6iWtBfHk+e7du6f0lRPrkzdyAcTFxVGtWjWdstweNWlvb8/ixYvJyMjg9OnTzJ8/n4EDB3L06FEleVOr1Tg7OwPg4uKCvb09Xbp0ISQkRFlLq1KpCAwMpGXLlnp9lC5dGgBbW1u968ntGp/G0tISb29vRowYoVdnZGSk/LtJkyY0adKEhw8fcvToUWbOnMmECRNYu3ZtnvU5M+FxcXGUK1dOOWfOuD8+Uy6P7hRCCPE6kBncN0CZMmVYtWoVKpWKfv36KTOdT1Kr1QwcOJBDhw7pzfZ6enpy48YNbG1tcXZ21vt5GmdnZ44cOUJ2drZSdvDgwRe6ngMHDui83r9/PyYmJjg4OADg5ubGiRMndGZrr1y5wsWLF3Fzc8t3P4aGhjRs2JABAwbw8OFDZXY2N87OzrRr147w8HBiY2MxNTWlbt26XLlyJdfxyrkZzdnZmcTERJ0Z4n///Zfjx4/nK0ZPT08uX75MjRo19PrI7cY/MzMz/Pz8aNeuHZcvX85XvbOzM4aGhuzbt0+n7ffff0+ZMmX0/mgQQgghijuZwX1DVKpUiZUrV9KzZ08++ugjwsLCcm3Xvn17QkJCiIqKws7OTinv0KEDmzdvpnfv3vTr149q1aqRlJSk3KA0ZsyYXM8XGBhI586dGTZsGF26dOHWrVusWrWKkiVLPvds399//82ECRPw8/MjOjqa5cuX06dPHywtLQHo27cv4eHh9OvXj0GDBpGWlsbChQupUKECHTt2fOa5L1y4wFdffYWfnx+VK1fm4cOHhIaGYmdnR5UqVZ557ODBg/nuu+9Yu3YtQUFBjB07lj59+jBy5EjatWuHhYUFd+7c4ZdffqFTp064u7vTpEkTnJycGDNmDKNHj8bCwoJvvvmGUqVK5Wt8+vbty549e+jZsye9e/emYsWK3L9/n//973+UK1eOvn37snnzZn7//Xe8vb2xsbHh5s2b7N69m8aNHz0OM696a2trevbsycqVKzEyMqJu3bpERkYSERHBZ599ptxgJoQQQrwuJMF9gzg4OBAaGkpAQACBgYG4urrqtTEwMGDAgAFMmjRJp9zIyIh169Ype+rGxsZiZWWFo6Njrnvr5nB0dGThwoXMmzePoUOHUrNmTWbNmkXv3r0xNzd/rusYNWoUv/76KyNGjMDAwIAPP/yQUaNGKfUVKlRg/fr1zJ49m6CgINRqNY0bN2b8+PE6W4TlxsbGhrJlyxIaGso///yDubk59evXZ86cOXkmctWrV8fPz49NmzYRGBhIvXr12LhxI8HBwUyYMIGMjAzKly9Po0aNqFq1KvDoK/2lS5cyefJkJk+ejIWFBb179+bq1aucP38+z7EoXbo0W7ZsYeHChcydO5cHDx5QpkwZ6tSpo9yMp9FoOHz4MDNnzuTBgwfY2NjQrl07ZVlDXvUAY8eOxdzcnO3bt7Ns2TLs7OyYOnUq3bp1yzNGIYQQorhRabVabVEHId4sx48fp2/fvqxfv56GDRsWdTjFTnp6Ou3ataN+/frKdmhvm5wdII6eSyTmbkIerYUQbwo7W0uGdG9c1GEUiuTkZM6fP0+tWrUwNTUt6nBeC2fOnEGlUj1zKeTzkhlc8cKmTJmCh4cHVlZWXLp0iaVLl+Lo6KizNdbbbMuWLWRnZ2Nvb09iYiKbNm0iJiaG+fPnF3VoRe5N/UUnhHi67GwtarXcsCoKlyS44oUlJibyxRdf8ODBA8zMzPD29mbcuHG5PqnrbVSyZEmWL19OTEwM8Gg/4NDQ0EL5i/V1kp6eTkpKCiYmJkUdymshJSWFq1evYm9vL2NWADJuBVfYYybJrXgVJMEVL0xmIp+tQ4cOdOjQoajDKJZkhVT+abVaUlJSZMwKSMat4GTMxJtAptiEEEVG9tXNP5VKhYmJiYxZAcm4FZyMmXgTyAyuEKJIGBkZyVfGBWBiYoKjo2NRh/HakXEruPyOmaylFcWZJLhCiCITsumY7KIgxGvoTd4NQbwZJMEV4g2ze/du1q1bx9WrV9FqtZQrV4569eopD5rw8vKiZcuWT30s87Bhwzh37hw//vgjO3bsYMKECUqdmZkZVatWpXfv3i9lXXHM3QSuxcS/8HmEEEKIx0mCK8QbZMWKFcybN4++ffsyfPhwtFotf/31F3v27OHu3buUKVMGX19f9u3bx+eff46RkZHO8Q8fPiQyMpI+ffrorL/75ptvMDc3Jz4+nvXr1zNu3DgMDQ1p167dq75EIYQQIk+S4ArxBlm/fj0dO3Zk/PjxSlnTpk356KOPyM7OBh49rnnLli0cPXqUli1b6hz/ww8/kJaWhr+/v065k5MT1tbWALi7u+Pj40N4eLgkuEIIIYol2UVBiDdIYmIitra2udbl7Etcv359KlSowN69e/Xa7N27FwcHBzQazVP7MDU1pWrVqty6devlBC2EEEK8ZJLgCvEGcXJyYvPmzWzbto3Y2Nhc26hUKvz8/Dh8+DD//vuvUh4XF8fx48dp3779M/vIzs7mzp07VKpU6aXGLoQQQrwskuAK8Qb5/PPPsbS0ZNKkSXh5edGiRQumT5/OzZs3ddq99957pKSkcOjQIaXs+++/Jzs7W295AjxKajMzM4mNjWXWrFk8ePCAwMDAQr8eIYQQ4nnIGlwh3iAODg5ERERw/Phxfv75Z/773/+yfv16wsPD2bBhA7Vq1QIePS74nXfeYe/evbz33nsARERE4ObmRsWKFfXO27ix7nZAU6ZMoX79+oV/QUIIIcRzkBlcId4wRkZGNG3alIkTJ7Jz506++eYbUlNTCQkJ0Wnn7+/PsWPHiI+P5+bNm/z+++9PXZ6wZs0atm3bRnBwMDVr1mTGjBlcuHDhVVyOEEIIUWCS4ArxhvP29ubdd9/l8uXLOuX+/v5kZGSwf/9+vvvuO0qUKEGbNm1yPYdGo8HFxYXWrVuzYsUKSpQowdy5c19F+EIIIUSBSYIrxBvk3r17emWpqancvn2bsmXL6pRXrlwZV1dXIiIi2LNnD15eXlhZWeXZR4UKFejTpw8//fQT0dHRLyt0IYQQ4qWRBFeIN0j79u359NNP+e677zh58iR79+4lICCA+Ph4+vTpo9fe39+fkydP8ueffyprcfMjICAAMzMzVqxY8TLDF0IIIV4KSXCFeIMMHTqUu3fvMmvWLPr27cusWbMoVaoUa9as0XuoA4Cfnx8GBgaYmprSvHnzfPdjZWVFz5492b9/P3///ffLvAQhhBDiham0Wq22qIMQQrxdzp49C8DRc4nE3E0o4miEEAVlZ2vJkO6N8274FklOTub8+fPUqlULU1PTog7ntXDmzBlUKhXOzs4v/dyyTZgQosjIL0ghXl/Z2VrUalVRhyFErmSJghCiSKSnp5OSklLUYbw2UlJSiI6OljErIBm3gsvvmElyK4ozSXCFEEIIIcQbRZYoCCGKhJGRESYmJkUdxmvDxMQER0fHog7jtSPjVnC5jZksRxCvG0lwhRBFJmTTMbnJTIhiTm4oE68jSXCFEEUm5m4C12LiizoMIYQQbxhZgytEEQkODkaj0dCjRw+9uhkzZhRoX9ocu3fvpnPnzri5uVGvXj3atm3LxIkTiYuLIyMjA3d3dyZOnPjU44cNG0azZs3QarWEh4ej0WiUHzc3Nzp16sTOnTsLHJcQQgjxKkmCK0QRO3nyJFFRUS98nhUrVjB27Fjq16/PggULWLBgAf/5z384d+4cd+/exdDQEF9fXw4cOEB6erre8Q8fPiQyMhJ/f39Uqv9ba/fNN9+wZcsW5s6di5WVFePGjWPv3r0vHK8QQghRWGSJghBFyNTUlHfeeYelS5fi7u7+Qudav349HTt2ZPz48UpZ06ZN+eijj8jOzgYePcp3y5YtHD16VO/JZj/88ANpaWn4+/vrlDs5OWFtbQ2Au7s7Pj4+hIeH065duxeKVwghhCgsMoMrRBEbPHgwJ06c4NSpU7nW5ywVuH//vk75+++/r5PMJiYmYmtrm+s51OpH/6nXr1+fChUq5DoDu3fvXhwcHNBoNE+N1dTUlKpVq3Lr1q08r0sIIYQoKpLgClHEmjVrhqOjIyEhIS90HicnJzZv3sy2bduIjY3NtY1KpcLPz4/Dhw/z77//KuVxcXEcP36c9u3bP7OP7Oxs7ty5Q6VKlV4oViGEEKIwSYIrRDEwaNAgfv75Z86cOfPc5/j888+xtLRk0qRJeHl50aJFC6ZPn87Nmzd12r333nukpKRw6NAhpez7778nOztbb3kCPEpqMzMziY2NZdasWTx48IDAwMDnjlMIIYQobJLgClEMtGrVCgcHhxeaxXVwcCAiIoLly5fTu3dvzM3NWb9+Pe+99x7nz59X2r377ru88847OssUIiIicHNzo2LFinrnbdy4MU5OTnh5ebF27Vo+/fRT6tev/9xxCiGEEIVNElwhigGVSsXAgQM5cuQIf/zxx3Ofx8jIiKZNmzJx4kR27tzJN998Q2pqql7i7O/vz7Fjx4iPj+fmzZv8/vvvT12esGbNGrZt20ZwcDA1a9ZkxowZXLhw4bljFEIIIQqbJLhCFBNt27bF3t6epUuX6pSXLFkSgIyMDJ3yxMTEPM/p7e3Nu+++y+XLl3XK/f39ycjIYP/+/Xz33XeUKFGCNm3a5HoOjUaDi4sLrVu3ZsWKFZQoUYK5c+cW5NKEEEKIV0oSXCGKCbVazcCBAzl06BAXL15UysuVKwfAlStXlLLLly9z+/ZtnePv3bund87U1FRu375N2bJldcorV66Mq6srERER7NmzBy8vL6ysrPKMsUKFCvTp04effvqJ6OjoglyeEEII8cpIgitEMdK+fXsqV66s8+CHOnXqUKFCBb788kuOHDlCREQEo0aN0ktI27dvz6effsp3333HyZMn2bt3LwEBAcTHx9OnTx+9vvz9/Tl58iR//vkn7733Xr5jDAgIwMzMjBUrVjz3dQohhBCFSRJcIYoRAwMDBgwYoFNmaGjIkiVLKFmyJCNGjCA0NJQJEyYoM7s5hg4dyt27d5k1axZ9+/Zl1qxZlCpVijVr1ug91AHAz88PAwMDTE1NC/RYYCsrK3r27Mn+/fv5+++/n+9ChRBCiEKk0mq12qIOQgjxdjl79iwAR88lEnM3oYijEUI8i52tJUO6Ny7qMIq95ORkzp8/T61atTA1NS3qcF4LZ86cQaVS4ezs/NLPLY/qFUIUGfmlKcTrITtbi1qtKuowhMg3WaIghCgS6enppKSkFHUYr42UlBSio6NlzApIxq3gchszSW7F60YSXCGEEEIotFotKSkpyApG8TqTJQpCiCJhZGSEiYlJUYfx2jAxMcHR0bGow3jtvMnjJssGhHg6SXCFEEUmZNMxuclMiOcgN34J8WyS4AohikzM3QSuxcQXdRhCCCHeMLIGV4hiKjg4GI1Go/y4u7vTvXt3IiMjddppNBpWrlz5zHOdP38ejUaj8wAJIYQQ4k0lM7hCFGPGxsasXbsWgLt377Js2TIGDhzIhg0bqFevHgBbtmyhYsWKRRmmEEIIUaxIgitEMaZWq6lbt67yuk6dOjRt2pSdO3cqCe7j9UIIIYSQJQpCvFbKlSuHtbU1t27dUspyW6KwdOlSGjdujKurK0OHDiUuLk7vXElJSQQFBeHq6oqHhwfz589n1apVaDQanXaJiYlMmTIFLy8vateuTadOnfj5558L5wKFEEKIl0BmcIV4jfz7778kJCRQqVKlp7YJCwtj0aJF9OvXD09PT3755RcmTpyo127ChAmcOHGCTz75BDs7O7Zu3coff/yh0yY9PZ2AgADi4uIYOXIk5cqVY/fu3QQGBhIeHq6XDAshhBDFgSS4QhRzmZmZwKM1uHPmzKFUqVL07t0717ZZWVmEhoby/vvvM27cOAC8vb2Ji4tj165dSrtLly5x4MABvvrqKzp06KC0a9u2rc759uzZw4ULF9i1axfvvPOO0u769essXbqURYsWvezLFUIIIV6YLFEQohhLTk7GyckJJycnmjVrxv79+5k9ezbVq1fPtf2dO3e4e/curVq10in39fXVeX327FkAWrRooZSp1WqaNWum0+7YsWM4ODhQrVo1MjMzlR9PT0/lHEIIIURxIzO4QhRjxsbGhIWFodVquXbtGvPmzWPcuHHs2bMHW1tbvfaxsbEAWFtb65SXLVtWr52hoSHm5uY65U8eFx8fT3R0NE5OTnp9GRgYPNc1CSGEEIVNElwhijG1Wo2zszMALi4u2Nvb06VLF0JCQpg6dapeexsbGwDu37+vU37v3j29dhkZGSQlJekkuU8eZ2lpiUajYcaMGS/leoQQQohXQZYoCPEacXZ2pl27doSHhyuztY8rX748NjY2HDhwQKd8//79Oq9r164NwKFDh5Sy7OxsDh8+rNPO09OTGzduYGtri7Ozs96PEEIIURzJDK4Qr5nBgwfz3XffsXbtWoKCgnTqDAwMGDBgADNmzKBMmTI0btyYY8eO6T3BrGbNmrRq1Yrp06eTkpJCxYoV2bp1K6mpqahUKqVdhw4d2Lx5M71796Zfv35Uq1aNpKQkoqOjycjIYMyYMa/kmoUQQoiCkARXiNdM9erV8fPzY9OmTQQGBurV9+rVi8TERDZu3MimTZvw8PBg+vTpfPTRRzrtvvzyS6ZNm8bs2bMxMjKiY8eO1KxZkw0bNihtjIyMWLduHcHBwSxbtozY2FisrKxwdHTkww8/fOFrsbO1fOFzCPE2kv92hHg2lVar1RZ1EEKI4qFHjx6o1WrWr19fqP3k7MAgyxyEeH7Z2VrUalXeDQsoOTmZ8+fPU6tWLUxNTV/6+d9UMm4Fd+bMGVQqVaH8LpAZXCHeUvv37+f27ds4ODiQkpJCREQEJ0+eJCQk5JX0n56eTkpKCiYmJq+kv9ddSkoKV69exd7eXsasAN7kcSuM5FaIN4UkuEK8pUxNTdm1axfXrl0jIyOD6tWrM2fOHFq2bPnKYpAvkPJPq9WSkpIiY1ZAMm5CvJ0kwRXiLeXt7Y23t3eRxvD4DW3i2VQqFSYmJjJmBSTjJsTbSRJcIUSRMDIyeuO+Mi5MJiYmODo6FnUYr503ddwKa/2tEG8KSXCFEEUmZNMxYu4mFHUYQrxW7GwtGdK9cVGHIUSx9sYmuBqNJs82M2fOpFOnTq8gmqe7ceMGkyZN4syZMyQnJ7Nz505q1apVpDE9j4MHDzJkyBAOHTrEjh07WLJkyTPb29nZ8eOPP76i6J4uKiqK06dPM3DgQJ3y8PBwJkyYoLw2MzOjatWq9O7dmw4dOrziKN9cMXcTuBYTX9RhCCGEeMO8sQnuli1bdF537dqVXr164e/vr5RVqVLlVYelZ9GiRdy4cYPFixdjbm5OtWrVijqkF/bBBx/orO3ctm0bERERrF27VikzMjIqitD0/Prrr6xatUovwc3xzTffYG5uTnx8POvXr2fcuHEYGhrSrl27VxypEEIIIfLrjU1w69atq1dWoUKFXMtzpKamYmxsXHhB5eLKlSvUr1//pdzsUxTx56Z8+fKUL19eef3TTz+hVqufOfb5kZ6eTokSJVCrX90Tpp2cnLC2tgbA3d0dHx8fwsPDX1mCW1ze02d5HWIUQgjxdnl1mUIxExwcjKurK2fOnKFr1644OzsrT3CaO3cu7du3x9XVFW9vb0aPHs3du3d1ju/VqxeBgYHs27cPX19fXF1d6d27N3///bdOu+XLl9OqVSucnZ1p1KgRffv25caNG9y8eRONRsMff/zBrl270Gg0NG/eXDlu8+bN+Pr6Urt2bZo3b87SpUvJzs5W6sPDw9FoNJw+fZqAgADq1q3L7NmziYqKQqPR8NNPPzFixAhcXV3x8fFhz549AKxbtw4fHx8aNmzIxIkTSU9P14n3zp07BAUF4e7ujouLCz169ODcuXM6bTIyMpgxYwYNGzbEzc2NTz/9lH///TffY5+cnMy0adPw9fWlTp06NG/enMmTJ5OUlKTTrnnz5kybNo0VK1bQrFkzXFxcePDgAVqtliVLltC4cWNcXV0ZPnw4v/zyCxqNRueRtFqtlpUrVyrj2KJFC9asWaPUBwcHs2TJEpKTk9FoNGg0Gnr16vXUuE1NTalatSq3bt3SKb98+TKDBg3Czc2NunXrMmDAAL3PQVJSEkFBQbi6uuLh4cH8+fNZtWqVzlKanPfuyJEjDB8+nHr16jFixAgAEhMTmTJlCl5eXtSuXZtOnTrx888/6/Tx22+/0aNHD9zc3HB1daV9+/bs2LEj3/Xw/J87IYQQojh5Y2dw8yMjI4MxY8bQt29fRo0ahZWVFQBxcXEEBgZia2vL/fv3Wb16Nb169WLv3r2UKPF/Q3b+/Hnu379PUFAQWVlZzJo1i08++URZHrFz504WLVrE8OHDqVu3LklJSfz222/8+++/VK9enS1btjBu3DiqVq3K4MGDla/t169fz/Tp0+nVqxc+Pj6cPn2aJUuWkJSUxLhx43SuYcyYMXTt2pXAwEBMTExITU0FYMqUKXTs2JEuXbqwdetWxo4dy4ULF/jrr7+YOnUqN27cYNasWVSuXFn5ej4hIYEPP/wQU1NTPvvsM8zNzVm/fj19+vThhx9+oEyZMgDMnz+fTZs2MWzYMBwdHdm7dy/z5s3L97inpqaSlZXFqFGjsLa25vbt2yxbtozBgwfrPUHrhx9+oGrVqkycOBG1Wo2pqSnr169nyZIlfPTRRzRq1IgTJ04wadIkvX5mzJjBtm3bGDhwIHXq1OHUqVPMnTuXkiVL0r17dz744APu3Lmjs3zCzMzsqXFnZ2dz584d3n33XaXsxo0bdOvWjZo1azJr1ixUKhXLli2jb9++7Nu3T3lPJ0yYwIkTJ/jkk0+ws7Nj69at/PHHH7n289lnn/Hee+8REhKCWq0mPT2dgIAA4uLiGDlyJOXKlWP37t0EBgYqCefDhw8JDAzEzc2N+fPnY2RkxKVLl0hMTATIsx5e7HMnhBBCFCdvfYI7atQo/Pz8dMpnzpyp/DsrKwtXV1eaNGnCiRMn8PLyUuqSkpLYuXOn8hV2cnIyEyZM4M6dO5QvX54zZ86g0WgIDAxUjnl8E/26detibGyMtbW18vV9VlYWISEhtGvXTknavLy8yMjIYNWqVQwYMIDSpUsr5+jWrRsDBgxQXufMYLZp04ahQ4cC4OLiwoEDB9i7dy8HDhzA0NAQeLT+dN++fUqCu3btWhITE9m2bZuSzHp4eODr68vKlSsZO3YsDx48YOPGjXz88cfKdXl7e9OzZ0/++eeffI27tbU1U6dOVV5nZmZSqVIlPvzwQ+WJQzkyMjJYsWKF8tjDrKwsli9fTqdOnQgKClLGJz4+nu3btyvH/f3334SFhTF16lS6du0KgKenJ6mpqYSEhNC1a1dlKcWzlk9kZ2eTmZlJfHw8K1as4MGDBzrv55IlS7C0tGT16tWULFkSgHr16tGiRQu2bdtGjx49uHTpEgcOHOCrr75SblDz9vambdu2ufbZvHlzPvnkE+X1t99+y4ULF9i1axfvvPOOcvz169dZunQpixYt4urVqyQlJTF69GhlVtjDw0M5R171L/q5E0IIIYqTt3aJQo6mTZvqlUVGRtKtWzfc3NxwdHSkSZMmAFy7dk2n3bvvvqskt4CSfNy5cwcAR0dHoqOjmTlzJidPniQjIyPPeK5cuUJ8fDxt2rTRKffz8yMjI4MzZ87olPv4+OR6nsaN/28LGXNzc6ytralfv76S3AJUq1aN27dvK6+PHTuGu7s7lpaWZGZmkpmZiVqtpkGDBpw9exaAP//8k9TUVFq1aqXTX+vWrfO8tsft3LmTDh064OrqipOTEx9++CGgP8bu7u46z/S+c+cOsbGxOss5AFq0aKHz+pdfflHiyrmWzMxMPD09iY2N1bnuZ2ncuDFOTk54eXmxdu1aPv30U+rXr6/UHzt2jObNm2NgYKD0YWFhgaOjo7K0I2fsHo9RrVbTrFmzXPt88j09duwYDg4OVKtWTe9acs5dpUoVzMzMmDJlCt999x3379/XOUde9S/rcyeEEEIUB2/1DK6JiQmlSpXSKTtz5gyDBw+mRYsWfPzxx5QpUwaVSkWXLl1IS0vTaWthYaHzOid5zGnXqVMn/v33X7Zu3cqaNWswNzenQ4cOBAUFPfWmnISER3uC5syg5sh5nVOfo2zZsrmex9zcXOe1kZFRrvE+vgY3Pj6e33//HScnJ73z5ew4ERsbm2t8T4sjNwcOHGDcuHF07dpVWRoSGxvLkCFD9Mb4yX5y+n/8D4vc2sXHx6PVamnUqFGuMdy+fRs7O7s8Y12zZg2lSpXizp07LF68mBkzZuDq6qosU4iPj2ft2rU6O0TkyPk8xMbGYmhoqPeePHkNz7qW6OjoXN8XAwMDAGUWefHixYwdO5asrCzq16/PpEmT0Gg0eda/rM+dEEIIURy81Qlubo9uPHjwIGZmZixcuFC5Wz8mJua5zq9Wq+nTpw99+vThn3/+Udaqli5dmiFDhuR6TM464Cdn2OLi4oBHiUxhsbS0xNvbW7mx6XE5a0ltbGyUeMqVK6fU37t3L9/97Nu3j1q1ajFt2jSl7Ndff8217ZPvUU7/TxufHJaWlqhUKjZu3Kgza53j8WUQz6LRaLC2tsbFxQVnZ2fatm3L3Llz+eabb5R+mjZtqsxAPy7njycbGxsyMjJISkrSSXKfvIYcT16zpaUlGo2GGTNmPDNWFxcXvvnmG1JTU4mKiuKrr75iyJAhHDx4MM/6ovzcCSGEEC/bW53g5iY1NRVDQ0OdJCNnB4IXUa5cOfr160dERARXrlx5ajt7e3usra3Zt2+fzjKA77//HkNDQ1xcXF44lqfx9PRk9+7d1KhRQ2dZwOMcHBwwNjbmwIEDOo+//OGHH/LdT84YPy6/Y1y+fHlsbGw4dOiQznrmnCQuR8760gcPHugtZ3jck7PYz1KhQgX69OnDsmXLiI6OxtHREQ8PD/766y8cHR2V2dQn1a5dG4BDhw4pa3Czs7M5fPhwvvr19PQkMjISW1tbnT8qnsbY2JimTZvy999/M2PGDNLS0pT1wU+rL8rPnRBCCPGySYL7hMaNG7N27Vq++OILWrVqxenTp9m1a9dznWvy5MlYWFhQt25dLCwsOHXqFBcuXKB79+5PPcbAwIDBgwczffp0rK2tadq0Kb///jsrVqygT58+Ojf6vGx9+/Zlz5499OzZk969e1OxYkXu37/P//73P8qVK0ffvn2xsrKiW7durFixAmNjY2UXhSe3xXoWT09Ppk2bRkhICK6urkRGRnL8+PF8HWtgYMCAAQP48ssvKVu2LO7u7kRFRSnH58y629vb06NHD8aOHUv//v2pU6cOGRkZXLt2jaioKJYuXQpAjRo1yMzMZO3atbi6umJmZkb16tWf2n9AQABhYWGsWLGCBQsWMHz4cDp37kz//v3p0qULZcuW5d69e/z666/Ur18ff39/atasSatWrZg+fTopKSlUrFiRrVu3kpqamuu3CE/q0KEDmzdvpnfv3vTr149q1aqRlJREdHS0shPIkSNH2L59Oy1btqRixYrcu3ePsLAw6tWrR8mSJfOsB4rscyeEEEK8bJLgPqFp06YEBQURFhZGeHg49erVIzQ0FF9f3wKfy9XVla1bt7Jt2zZSUlKoXLkyEyZM4IMPPnjmcb169aJEiRKsWbOGTZs2YWNjw9ChQ5/6tK2XpXTp0mzZsoWFCxcyd+5cHjx4QJkyZahTp47OrN6YMWPIysrim2++ITs7m1atWjFmzBjGjh2br366devGzZs3CQsLY+XKlXh5eTFv3jy6dOmSr+N79epFYmIiGzduZP369Xh4ePDJJ58watQonSUAkyZNwt7eni1bthASEkKpUqWwt7fXuZGqWbNmfPjhhyxfvpy4uDgaNGigt1XZ46ysrOjZsycrVqzg77//pmrVqmzbto2FCxcydepUkpOTsbGxoUGDBjp73H755ZdMmzaN2bNnY2RkRMeOHalZs6ay9/KzGBkZsW7dOoKDg1m2bBmxsbFYWVnh6OioLI2oUqUKarWahQsXEhcXh5WVFV5eXowePTpf9TnjWhSfOyGEEOJlU2m1Wm1RByHEi1q4cCGrV68mKirqtXmqVo8ePVCr1c9MqN9UObs/HD2XSMzdhDxaCyEeZ2dryZDujfNu+JySk5M5f/48tWrVeupyNaFPxq3gzpw5g0qlwtnZ+aWfW2ZwxWvn8uXL7N69G1dXVwwNDfn1119ZuXIl3bt3L7bJ7f79+7l9+zYODg6kpKQQERHByZMnCQkJKerQilRh/pIW4k2Wna1Frc57iZMQbytJcMVrx9jYmNOnT7Np0yb+/fdfypUrR//+/Rk2bFhRh/ZUpqam7Nq1i2vXrpGRkUH16tWZM2eOzo1yb5v09HRSUlLkSWj5lJKSojwIRcYs/97UcZPkVohnkwRXvHbs7OxYt25dUYdRIN7e3nh7exd1GMWOrJDKP61WS0pKioxZAcm4CfF2euufZCaEKDr52UVCPKJSqTAxMZExKyAZNyHeTjKDK4QoEkZGRm/UV8aFzcTERGfvaZE/b+q4yRpcIZ5NElwhRJEJ2XRMdlEQooAKexcFId4EkuCK5xYcHMyqVas4ffq0Xt3Nmzdp0aIFixYt0tl3trho3ry58ghmAwMDypcvT4MGDRg5ciQVKlQo4uhevqioKHr37s327dsLZTuW5xVzN4FrMfFFHYYQQog3jCS4olDY2tqyZcsWqlWrVtShPJWvry/9+vUjMzOTs2fPsnjxYqKjowkPD9d7lPDrzsnJiS1btlCjRo2iDkUIIYQodJLgikJhZGRE3bp1izoMPampqcpeuWXLllVirF+/PmlpaSxYsIBz587h6upa6LFkZWWRnZ39SpJpMzOzYvl+CCGEEIVBdlEQheLmzZtoNBr27dunlDVv3pxp06axYcMGmjVrhpubG4MHD+b+/fs6xyYmJjJlyhS8vLyoXbs2nTp14ueff9Zpc+TIEQICAvDw8KBevXp88MEHHD16VKdNeHg4Go2G06dPExAQQN26dZk9e/ZTY65VqxYAt2/fVsq0Wi0rV67E19eX2rVr06JFC9asWaN37IEDB/D19cXZ2ZkuXbrwxx9/UL9+fYKDg5U2vXr1IjAwkB07dihtL1y4oFzPBx98gIuLC40aNeLzzz8nOTlZOTYjI4OvvvoKHx8fateujZeXFwMHDiQpKSlf9VFRUWg0GuUJYgBpaWnMnDkTLy8vnJ2def/99zlw4IDOdY0fPx5/f3+ioqLo0KEDdevWpXPnzpw7d+6p4yiEEEIUNZnBFa/Ujz/+yPXr15k8eTLx8fHMnDmTL774ggULFgCPNv8PCAggLi6OkSNHUq5cOXbv3k1gYKCSsMKjBLpZs2b069cPtVrN0aNHGTBgAGvXrsXd3V2nzzFjxtC1a1cCAwOfedf+rVu3AKhUqZJSNmPGDLZt28bAgQOpU6cOp06dYu7cuZQsWZLu3bsDEB0dzYgRI2jWrBmffvopMTExjBo1ivT0dL0+zp07R0xMDCNGjMDCwoIKFSqwb98+Ro0aRadOnRg2bBixsbHMmzePxMREZVxCQ0PZvHkzQUFB1KxZk/j4eI4dO6b0kVd9boKCgvjpp58YOXIk1atXZ9euXQwbNoyQkBBatGihtIuNjWX69OkMGDAAc3Nz5s2bx9ChQzlw4MAbt5RDCCHEm0ESXPFKabVavv76a4yMjACIiYkhNDSU7Oxs1Go1e/bs4cKFC+zatYt33nkHePSQhOvXr7N06VIWLVoEQM+ePZVzZmdn4+7uzqVLl9i6dategtutWzcGDBiQayyZmZlkZmZy7tw5QkNDadq0KS4uLgD8/fffhIWFMXXqVLp27QqAp6cnqamphISE0LVrV9RqNaGhoVSqVIng4GDU6kdfipQqVYqxY8fq9ZmQkMD27duVG9m0Wi2zZ8/Gz8+PGTNmKO1sbGwYMGAAgwcPpmbNmpw9exYvLy969OihtPH19VX+nVf9ky5cuMAPP/zA1KlT6datGwBNmjQhJiZGL8FNSEggLCyMmjVrAo+2Xerduzf/+9//qF+//lP7EEIIIYqKLFEQr1SDBg2U5BagRo0aZGRkEBcXB8CxY8dwcHCgWrVqSvKZmZmJp6enztfrd+7cYdy4cXh7e+Po6IiTkxM///wzV69e1evTx8cn11g2btyIk5MTderUoUePHpQsWZL58+cr9b/88gsArVu31oslNjZWWcpw9uxZfHx8lOQW0EkQH+fg4KCzS8PVq1eJiYmhbdu2On00bNgQtVqtLAVwdHQkMjKS4OBgzpw5Q3Z2ts5586p/0m+//Qagt8NF27ZtiY6O1lkeYWtrqyS3gPKHxz///PPMPoQQQoiiIjO44pWysLDQeZ2T7KalpQEQHx9PdHQ0Tk5OescaGBgAj2ZsBw0aRFJSEsOHD6dq1aqYmJiwePFinfWzOcqWLZtrLG3btqV///6kpaVx9OhRQkNDmTx5spLkxsfHo9VqadSoUa7H3759Gzs7O2JjY7G2ttapMzMzo2TJknnGEh//aIusIUOGPLUPgEGDBqFWq9mxYwdLlizB2tqaHj16MGTIEFQqVZ71T0pISMDQ0BArKyu9+LRaLUlJSZiamgL671nOsoSc90wIIYQobiTBFcWKpaUlGo1G5+v6J12/fp3o6GhCQkJo2bKlUp6amlqgvqytrZU9YevXr09ycjLr16+nT58+1KlTB0tLS1QqFRs3bsx1ram9vT3waDnBkzfKPXz4MNcE8MlkMyfBnDx5srI04nG2trbAoz8Ehg0bxrBhw7h+/TrffvstwcHBVKpUiQ4dOuRZ/yRLS0syMjJISEjA0tJSKb937x4qlQpzc/NnjJwQQghRvMkSBVGseHp6cuPGDWxtbXF2dtb7gf+bOXw86YyJicn1gRMFMXToUMzMzFi2bBkAHh4eADx48CDXWMzMzABwdnbmyJEjOssCDh48mK8+q1evTvny5blx40aufZQrV07vmKpVqzJ69GisrKy4cuVKgesB3NzcAHR2uch57ejoqMzeCiGEEK8jmcEVLyQrK0svSQIoU6bMc52vQ4cObN68md69e9OvXz+qVatGUlIS0dHRZGRkMGbMGCUpnDdvHtnZ2SQnJ7N48WJltvN5WVlZ0bNnT0JDQ7l8+TI1atSgR48ejB07lv79+1OnTh0yMjK4du0aUVFRLF26FIDAwEA6d+7MsGHD6NKlC7du3WLVqlWULFky1+UBj1OpVIwfP56goCCSk5Px8fHBxMSEW7duERkZyahRo7C3t2fw4ME4OTnh6OiIiYkJhw8fJiEhQVk+kVf9k959911at27NrFmzSE1Nxd7ent27d3P69GnluoQQQojXlSS44oWkpaUxYsQIvfLcyvLDyMiIdevWERwczLJly4iNjcXKygpHR0c+/PBDpU1wcDDTpk1jxIgRVKhQgUGDBnHixIkX3p81ICCAsLAwVqxYwaxZs5g0aRL29vZs2bKFkJAQSpUqhb29vc7NWY6OjixcuFDZPqtmzZrMmjWL3r175+ur/rZt22JhYcGyZcvYs2cPAHZ2dnh7eytrduvVq8f333/P6tWrycrKwt7enrlz5+Lp6Zmv+tzMmTOH+fPns2LFCh48eED16tVZvHgxzZs3f5EhFEIIIYqcSqvVaos6CCHeNMePH6dv376sX7+ehg0bFnU4xU7OjhhHzyUSczehiKMR4vViZ2vJkO6NC+38ycnJnD9/nlq1aslypQKQcSu4M2fOoFKplCWIL5PM4ArxEkyZMgUPDw+srKy4dOkSS5cuxdHRUfaJzUNh/pIW4k2Wna1FrX72Eigh3maS4ArxEiQmJvLFF1/w4MEDzMzM8Pb2Zty4cTp74wpd6enppKSkPPPpcuL/pKSkcPXqVezt7WXMCuBNHTdJboV4NklwhXgJHn9AhMg/WSGVf1qtlpSUFBmzApJxE+LtJNNLQogik9cuE+L/qFQqTExMZMwKSMZNiLeTzOAKIYqEkZHRG/WVcWEzMTHB0dGxqMN47byJ4ybrb4XImyS4QogiE7LpmOyiIEQBFPYOCkK8KSTBFeIlCA4OZsmSJcprKysrqlevzsCBA2natKlSrtFolAdHPM358+fp0KED69atw93dvVDi1Wq17Ny5k23btnHx4kXS0tKws7OjWbNmBAQEEBsby3/+8x9mz57N+++/r3d8VlYW3t7eeHl5MXv27OeOI+ZuAtdi4l/kUoQQQgg9kuAK8ZIYGxuzdu1aAO7evcuyZcsYOHAgGzZsoF69egBs2bKFihUrFmWYaLVaxowZw/fff0+nTp346KOPMDMz49KlS2zevJkbN24QEhKCvb09e/fuzTXBPX78OHFxcbRv374IrkAIIYR4NklwhXhJ1Go1devWVV7XqVOHpk2bsnPnTiXBfby+qGzcuJG9e/cyY8YMOnfurJQ3bNiQrl278vPPPwPg7+/PsmXLiI+Pp3Tp0jrniIiIoEyZMs98UpoQQghRVGQXBSEKSbly5bC2tubWrVtKmUajYeXKlTrtli5dSuPGjXF1dWXo0KHExcXpnSspKYmgoCBcXV3x8PBg/vz5rFq1Co1Go9MuMTGRKVOm4OXlRe3atenUqZOSsOZYvXo1Tk5OOsltDgMDA2VJRfv27cnIyGDfvn06bdLS0jhw4AB+fn4YGBgUbFCEEEKIV0ASXCEKyb///ktCQgKVKlV6apuwsDAWLVrEe++9x+LFi6lcuTITJ07UazdhwgSOHDnCJ598wqxZs7h8+TLr1q3TaZOenk5AQABHjhxh5MiRfP3119SoUYPAwEAuXrwIwJ07d7hx4wbe3t55xl+1alWcnZ2JiIjQKT9y5AgPHz6U5QlCCCGKLVmiIMRLlJmZCTxagztnzhxKlSpF7969c22blZVFaGgo77//PuPGjQPA29ubuLg4du3apbS7dOkSBw4c4KuvvqJDhw5Ku7Zt2+qcb8+ePVy4cIFdu3bxzjvvKO2uX7/O0qVLWbRoEf/88w8AFSpUyNf1tG/fnpkzZ3L79m3lmL1791KlShXq1KmTz1ERQgghXi2ZwRXiJUlOTsbJyQknJyeaNWvG/v37mT17NtWrV8+1/Z07d7h79y6tWrXSKff19dV5ffbsWQBatGihlKnVapo1a6bT7tixYzg4OFCtWjUyMzOVH09PT+UcOfK76b2fnx9qtZrvvvsOgIcPH3LkyBH8/f3zdbwQQghRFGQGV4iXxNjYmLCwMLRaLdeuXWPevHmMGzeOPXv2YGtrq9c+NjYWAGtra53ysmXL6rUzNDTE3Nxcp/zJ4+Lj44mOjsbJyUmvr5y1suXKlQPQWRf8LDY2Nri7uxMREUH//v05cOAAaWlpsjxBCCFEsSYJrhAviVqtxtnZGQAXFxfs7e3p0qULISEhTJ06Va+9jY0NAPfv39cpv3fvnl67jIwMkpKSdJLcJ4+ztLREo9EwY8aMp8ZYvnx5qlSpws8//8yoUaPydV3t27dnwoQJXLlyhYiICJycnJ46Ky2EEEIUB7JEQYhC4uzsTLt27QgPD1dmax9Xvnx5bGxsOHDggE75/v37dV7Xrl0bgEOHDill2dnZHD58WKedp6cnN27cwNbWFmdnZ72fHH379uXcuXPs2LFDL6bs7GyOHj2qU9a6dWtKlizJ2rVrOXHihMzeCiGEKPZkBleIQjR48GC+++471q5dS1BQkE6dgYEBAwYMYMaMGZQpU4bGjRtz7NgxoqKidNrVrFmTVq1aMX36dFJSUqhYsSJbt24lNTVVZy1thw4d2Lx5M71796Zfv35Uq1aNpKQkoqOjycjIYMyYMQB8+OGH/Pbbb0ycOJFTp07RokULTE1NuXLlCps3b8bOzo4mTZoo5zUzM8PHx4ctW7agUqnw8/MrxBETQgghXpwkuEIUourVq+Pn58emTZsIDAzUq+/VqxeJiYls3LiRTZs24eHhwfTp0/noo4902n355ZdMmzaN2bNnY2RkRMeOHalZsyYbNmxQ2hgZGbFu3TqCg4NZtmwZsbGxWFlZ4ejoyIcffqi0U6lUzJs3Dy8vL7Zt28bevXtJT0/Hzs6O5s2b069fP70427dvz/79+3F3d1fW8QohhBDFlUqr1WqLOgghRMH16NEDtVrN+vXrizqUAsvZ1eHouURi7iYUcTRCvD7sbC0Z0r1xofaRnJzM+fPnqVWrFqampoXa15tExq3gzpw5g0ql0llG97LIDK4Qr4H9+/dz+/ZtHBwcSElJISIigpMnTxISElLUob2Qwv5FLcSbKDtbi1qdv63+hHhbSYIrxGvA1NSUXbt2ce3aNTIyMqhevTpz5syhZcuWRR3ac0tPTyclJQUTE5OiDuW1kJKSwtWrV7G3t5cxK4A3cdwkuRUib5LgCvEa8Pb2ztfjdYUQQgghCa4QoogYGRm9MTNqr4KJiQmOjo5FHcZr53UZN1l2IMTLJQmuEKLIhGw6JjeZibfeq7hxTIi3jSS4QogiE3M3gWsx8UUdhhBCiDeMPMlMFKrg4GBcXV0LXPc853uZevXqhUajQaPR8O6779KkSROGDh3KpUuXCr1vIYQQQrwYSXCFeIp69eqxZcsWNmzYwNChQzl16hR9+/YlIUG+UhdCCCGKM1miIMRjUlNTMTY2BsDCwoK6desC4ObmhomJCUFBQfz000/4+/sXeixarZaMjAyMjIwKva/nlZWVRXZ2NoaGhkUdihBCCKGQGVxRLERFRaHRaJQnXOUYPHgwvXr10mt/5swZOnfujLOzM23btuXw4cN6bY4cOcIHH3yAi4sLjRo14vPPPyc5OVmvzyNHjjB8+HDq1avHiBEjnhpjzp3Yt27d0ikPDw+nffv2ODs74+3tzYIFC8jKytJpc/LkSTp06ICzszPt27fn2LFjvP/++4wfP15pM378ePz9/YmMjOS9997D2dmZH3/8EYDTp0/Tu3dv6tati5ubG2PGjCEuLk6nj+XLl9OqVSucnZ1p1KgRffv25caNG/muf/DgARMmTMDd3R0XFxe6devGf//7X50+evXqRWBgIDt27MDX1xdnZ2cuXLjw1DETQgghioLM4IpXIjMzU68sOzv7uc6VkZHBqFGj6NevH5UqVWLTpk0MHTqU8PBwNBoNAPv27WPUqFF06tSJYcOGERsby7x580hMTGTBggU65/vss8947733CAkJQa1++t98MTExAFSqVEkpW716NXPmzKFPnz6MHz+ey5cvKwluUFAQAHfv3uXjjz/G0dGRhQsXkpSUxJQpU0hKSqJWrVo6fdy9e5fp06czaNAgKlSoQMWKFTl9+jS9evWiadOmLFiwgJSUFBYuXMjgwYPZsmULADt37mTRokUMHz6cunXrkpSUxG+//ca///6br/qsrCw+/vhjbty4QVBQEGXLlmX9+vUEBASwefNmateurcR47tw5YmJiGDFiBBYWFlSoUOG53kchhBCisEiCKwpdcnIyTk5OudY9z/O6MzIyGDRoEJ07dwbAy8uL1q1bExoayvz589FqtcyePRs/Pz9mzJihHGdjY8OAAQMYPHgwNWvWVMqbN2/OJ598otePVqslMzOT7OxsLl26xNy5c3F0dFSeHvbw4UMWL17MRx99xOjRowFo3LgxhoaGzJo1i/79+1O6dGnWrFmDgYEBoaGhmJmZAY+S5B49euj1mZCQwIoVK6hTp45SNnHiRGrXrs2SJUtQqR7tk+ng4KDM9jZt2pQzZ86g0WgIDAxUjnv8KWd51R85coQzZ87wzTffKA+UeHxcg4ODdWLcvn27JLZCCCGKLVmiIAqdsbEx27dv1/vp0qXLc5+zVatWyr8NDAxo2bIl//vf/wC4evUqMTExtG3blszMTOWnYcOGqNVqzp07p3MuHx+fXPuIjIzEyckJZ2dnOnbsyL179wgJCVHWxJ4+fZrk5GTatGmj04+npyepqan89ddfAJw9exZ3d3cluQWoX78+VlZWen1aWVnpJLcpKSmcOnWKNm3akJWVpfRRrVo1KlSooCzpcHR0JDo6mpkzZ3Ly5EkyMjJ0zptX/cmTJzEzM9N5WpqhoSGtWrXit99+02nr4OAgya0QQohiTWZwRaFTq9U4OzvrlR85cuS5zmdoaIilpaVOWZkyZYiNjQUgPv7RvqpDhgzJ9fjbt2/rHZsbNzc3JkyYQGZmJqdOnWLBggWMHj2ajRs3olarlX46duz4zH5iY2OpVq2aXr21tbVeWdmyZXVeJyYmkpWVxcyZM5k5c+ZT++jUqRP//vsvW7duZc2aNZibm9OhQweCgoIwNjbOsz4xMTHXcShbtqzerhFPxiiEEEIUN5LgimKhZMmSAHozi4mJicrX8jkyMjJISEjQSXLj4uKwsbEBUGZGJ0+ejIuLi15ftra2Oq+fPH8Oc3NzJTF3dXVFrVYza9Ys9u3bh5+fn9L/kiVLKF++vN7xOWt1bWxsuH//vl59bmVPxmJubo5KpSIwMFBnSUGO0qVLA4/+iOjTpw99+vThn3/+Ye/evcybN4/SpUszZMiQPOstLS31bloDuHfvnt4fE08bLyGEEKK4kCUKoljISRAvX76slN2/f58//vgj1/YHDhxQ/p2VlcXBgweVr/arV69O+fLluXHjBs7Ozno/5cqVe64Ye/bsSaVKlQgNDQUeJb0mJibcuXMn135ykk9nZ2dOnDjBw4cPlXOdPHmSBw8e5NmnqakpdevW5cqVK7n28fgNbznKlStHv3790Gg0XLlyJV/1bm5uPHz4kJ9//llpl5mZycGDB3FzcyvQOAkhhBBFTWZwRbFQvnx56tSpQ0hICObm5pQoUYIVK1Zgbm6u19bQ0JCvv/6atLQ0ZReFO3fuEBISAjyaYRw/fjxBQUEkJyfj4+ODiYkJt27dIjIyklGjRmFvb1/gGA0NDRk4cCCTJk3i6NGjNGnShOHDhzNnzhzu3LlDw4YNMTAw4MaNGxw6dIjg4GBMTEzo27cvmzZtIjAwkP79+5OYmEhISAilS5fO12zo2LFj6dOnDyNHjqRdu3ZYWFhw584dfvnlFzp16oS7uzuTJ09W9u21sLDg1KlTXLhwge7duwPkWe/j44OLiwuffPIJY8aMUXZRuHv3LosXLy7wWAkhhBBFSRJcUWzMnTuXSZMmMWHCBMqWLcvIkSPZu3cvSUlJOu0MDQ2ZP38+U6dO5c8//6RSpUosXryYd999V2nTtm1bLCwsWLZsGXv27AHAzs4Ob2/vF1pD2qFDB5YtW8aKFSto0qQJ/fr1o1y5cqxevZqwsDBKlChBlSpV8PHxUR5+YGtry4oVK5g+fTrDhw+nSpUqTJw4kWnTpuWawD+pXr16bNy4keDgYCZMmEBGRgbly5enUaNGVK1aFXg0m7x161a2bdtGSkoKlStXZsKECXzwwQf5qjcwMGD58uXMnj2bOXPmKDtfrFq1SmeLsJfNztYy70ZCvOHkvwMhXj6VVqvVFnUQQrxtrl27Rtu2bfnyyy+fepPamyxn94fcbj4U4m2Una1FrS4e69uTk5M5f/48tWrVeq6tHN9WMm4Fd+bMGVQqVaH8LpAZXCFegXnz5qHRaLC1teXGjRuEhoZiY2ND69atizq0IpOenk5KSgomJiZFHcprISUlhatXr2Jvby9jVgCvy7gVl+RWiDeFJLhCvAIZGRnMnTuXe/fuYWxsTMOGDRk7diylSpUq6tCKlHyBlH9arZaUlBQZswKScRPi7SQJrhCvwPjx4xk/fnxRh1HsyJZj+adSqTAxMZExKyAZNyHeTpLgCiGKhJGRUbH+yri4MTExwdHRsajDeO0U53ErTutuhXjTSIIrhCgyIZuOEXM3Ie+GQrxh7GwtGdK9cVGHIcQbSxLcx0RGRhIWFsbZs2dJSkrC0tKS2rVr89577+Hn54daXTjPxVizZg0zZ87k4sWLAERFRdG7d2+2b99eKHcWrlmzBnt7e5o2bapT3qtXL3799VfltZmZGTVq1GDAgAG5PkXrVTh48CD//PMPPXr00CkfP348586dIyIiolD6bd68OTExMcCjLbTKly9PgwYNGDlyJBUqVCiUPt9GMXcTuBYTX9RhCCGEeMNIgvv/zZ8/n9DQUFq1asXkyZOxsbHh3r17HDx4kE8++QRLS0u8vb1fSSxOTk5s2bKFGjVqFMr5161bh4+Pj16CC4/2XB03bhzw6DG527dvZ9iwYYSFhRXJE60OHjzIuXPn9BLcwYMHk5ycXKh9+/r60q9fPzIzMzl79iyLFy8mOjqa8PBwZY9bIYQQQhQ/kuACR44cITQ0lKFDhzJs2DCdurZt29KnTx9KlMh9qLKyssjOzn6pCY+ZmRl169Z9aecriJynXeXw9PTEy8uLQ4cOFatHtlapUqXQ+yhbtqwyFvXr1yctLY0FCxZw7tw5XF1dC73/wvhsvWzp6emUKFGi0L7dEEIIIZ6H/FYCVq9ejY2NDYMGDcq13sXFRblJoVevXgQGBrJjxw58fX1xdnbmwoUL3L17lwkTJtCiRQtcXFxo3bo18+fPJz09XedcDx8+ZOzYsbi6utKoUSNmz55NVlaWTpuoqCg0Go2yGT482upm5cqV+Pr6Urt2bVq0aMGaNWt0jgsODsbV1ZWLFy/SvXt36tSpg7+/Pz/99JPSJuer9w0bNqDRaNBoNISHhz91bEqUKIGxsTGZmZk65RcvXqR///7UrVsXNzc3hg8fzq1bt3TapKWlMXPmTLy8vHB2dub999/nwIEDOm3++usvPv74Y9zd3alTpw6+vr6sWLECeLQMYceOHfz1119KrDk7EYwfPx5/f3/lPOHh4Wg0GqKjo/noo4+oW7curVu3ZufOnTr9abValixZQuPGjXF1dWX48OH88ssvaDQaoqKinjoOALVq1QLg9u3bOufL630BOHDggPJ56dKlC3/88Qf169cnODhYafO0zxY8+iPsgw8+wMXFhUaNGvH555/rzGBnZGTw1Vdf4ePjQ+3atfHy8mLgwIHKU+DyqgeIiYlh+PDhuLm5UbduXfr3768sm8nRvHlzpk2bxooVK2jWrBkuLi48ePDgmeMmhBBCvGpv/QxuZmYmp06dwtfX96mztE86d+4cMTExjBgxAgsLCypUqEBcXBxWVlZMmDABCwsLrl27RnBwMLGxscycOVM59tNPP+Wnn34iKCiISpUqsXHjxnytI50xYwbbtm1j4MCB1KlTh1OnTjF37lxKlixJ9+7dlXYZGRkEBQXRu3dvBg8ezIoVKxg+fDg//vgjpUuXZsmSJQwYMIB69erRr18/QHc2VKvVKslsYmIiW7Zs4Z9//tF5IMHt27fp2bMnlStXZs6cOcrMZs+ePdm9ezdmZmYABAUF8dNPPzFy5EiqV6/Orl27GDZsGCEhIbRo0QKAgQMHUrZsWWbMmIGZmRl///03d+7cAR4tQ7h//z5Xrlxh7ty5AFhbWz9znIKCgujSpQsBAQFs3bqV8ePH4+zsrCz3WL9+PUuWLOGjjz6iUaNGnDhxgkmTJuU5/oCSwFeqVKlA70t0dDQjRoygWbNmfPrpp8TExDBq1Ci9P34g98/Wvn37GDVqFJ06dWLYsGHExsYyb948EhMTWbBgAQChoaFs3ryZoKAgatasSXx8PMeOHVP6yKv+4cOH9OrVC7VazdSpUylZsiRff/218p4+vu74hx9+oGrVqkycOBG1Wi1P7BFCCFHsvPUJ7oMHD0hPT9e7cUir1erMrKrVauVr2ISEBLZv365zTNmyZZW1q/BoLauJiQnjx49n8uTJmJiYcOnSJX744QemT59O586dAfDy8srzaVZ///03YWFhTJ06la5duwKPlg6kpqYSEhJC165dldhyEtyc9bX29va0aNGCo0eP8v777+Po6IiRkZHO1++Pi4yMxMnJSXltYGDA+PHjqV+/vlK2Zs0aMjMzWbVqFVZWVsCj2c127dqxY8cOevXqxYULF/jhhx+YOnUq3bp1A6BJkybExMQoCe79+/e5efMmEydOpHnz5gA0atRI6adKlSpYW1tz69atfC/Z6NGjh7Je19XVlcjISPbv38/gwYPJyspi+fLldOrUiaCgIGX84+Pj2b59u965cpL9zMxMzp07R2hoKE2bNsXFxaVA70toaCiVKlUiODhYeZ9KlSrF2LFj9fp88rOl1WqZPXs2fn5+zJgxQ2lnY2PDgAEDGDx4MDVr1uTs2bN4eXnprFX29fVV/p1XfXh4OLdu3WLv3r3KHwMNGjSgWbNmrF27VmcP34yMDFasWCGJrRBCiGJLlij8f09uAr5//36cnJyUn+nTpyt1Dg4OuSbEa9aswc/PDxcXF5ycnAgKCiIzM5MbN24Aj5IMrVZLq1atlOMMDAzy3KHgl19+AaB169ZKwpWZmYmnpyexsbE6X5mr1Wo8PDyU15UqVcLY2Jh//vknX+Pg5ubG9u3b2b59O2vXrqVPnz7MmjWLHTt2KG1OnjyJu7u7ktwC1KhRg3fffZfffvsNQPnfNm3a6Jy/bdu2REdHk5ycTOnSpbGzs2P+/Pns2LFDmbl9EV5eXsq/TU1NqVixonLeO3fuEBsbqyTTOXJmk5+0ceNGnJycqFOnDj169KBkyZLMnz9fqc/v+3L27Fl8fHx01qk+rc8nP1tXr14lJiaGtm3b6vTRsGFD1Go1586dA8DR0ZHIyEiCg4M5c+YM2dnZOufNq/7kyZPUrFlT58ZGKysrPD09lfcyh7u7uyS3QgghirW3fgbXysoKIyMjveTKw8NDmdV7cm1u2bJl9c6zdu1avvrqKz766CPc3d2xsLDg7NmzTJs2jbS0NABiY2MxNDTE0tJS59gyZco8M8b4+Hi0Wq3O7Objbt++jZ2dHQDGxsYYGRnp1BsaGiox5MXc3Fxna7JGjRpx9epVvvrqKzp06IBKpSIxMVFZj/rkdSQkPNrTNCEhAUNDQ50kGB6NnVarJSkpCVNTU1auXMmCBQuYNm0aycnJODk5MWHCBBo0aJCveHOL/3GGhobK1/CxsbGA/jKHp41/27Zt6d+/P2lpaRw9epTQ0FAmT56sJLn5fV9iY2P1+jQzM6NkyZJ6xzz52YqPf7SF1pAhQ57aBzz6jKrVanbs2MGSJUuwtramR48eDBkyBJVKlWd9YmJirp/rMmXK8Ndff+mVCSGEEMXZW5/glihRgnr16nH8+HGysrIwMDAAwNLSUkn0nkwYc3vk4759+2jevDljxoxRyi5fvqzTxsbGhoyMDBISEnSS3Li4uGfGaGlpiUqlYuPGjbneUW9vb5/HVb6YGjVqcPjwYeLi4ihbtiyWlpa5xhwXF0e1atWUmHO71nv37qFSqZRE1N7ensWLF5ORkcHp06eZP38+AwcO5OjRo5QqVeqlXoeNjQ0A9+/f14s7N9bW1spnoH79+iQnJ7N+/Xr69OlDnTp18v2+2NjY6PX58OHDXP/oePKzlfMHwuTJk5WlEY+ztbUFHn1Ghw0bxrBhw7h+/TrffvstwcHBVKpUiQ4dOuRZb2lpydWrV/XOHxcXp/cHmTzyVAghRHEnSxSAgIAA7t69y7Jly577HKmpqXpJzp49e3Re5yRLj+8kkJWVxcGDB5957pwlBw8ePMDZ2VnvJ+emrvwqyIwuPNrpwNDQUOnHzc2NEydOKLO1AFeuXOHixYvKVmI5/7tv3z6dc+3btw9HR0e9r7gNDQ1p2LAhAwYM4OHDh9y9e/e5Yn2W8uXLY2Njw6FDh3TK8xr/HEOHDsXMzEz5nOT3fXF2dubIkSM6ywLy22f16tUpX748N27cyLWPcuXK6R1TtWpVRo8ejZWVFVeuXMlXvZubG3/++adO+4SEBH755ZditT2cEEIIkR9v/QwugI+PDwMGDGDx4sVcuHCBtm3bYmtrS1JSEidPniQ2NjbP2URPT0/WrVtHWFgY1apVY/fu3Vy/fl2nzTvvvEOrVq348ssvSUtLU3ZRyMjIeOa57e3t6dGjB2PHjqV///7UqVOHjIwMrl27RlRUFEuXLi3Q9VavXp0TJ05w7NgxLCwsqFSpEqVLlwYe7Zzw+++/A/Dvv/8SGRlJZGQkXbp0wdjYGIC+ffsSHh5Ov379GDRoEGlpaSxcuJAKFSrQsWNHAN59911at27NrFmzSE1Nxd7ent27d3P69Gkl3gsXLvDVV1/h5+dH5cqVefjwIaGhodjZ2Sk7O9SoUYNvv/2WiIgIqlatSunSpXV2MSgIAwMDBgwYwJdffknZsmVxd3cnKiqK48ePA+S5l6uVlRU9e/YkNDSUy5cvU6NGjXy9L4GBgXTu3Jlhw4bRpUsXbt26xapVqyhZsmSes6EqlYrx48cTFBREcnIyPj4+mJiYcOvWLSIjIxk1ahT29vYMHjwYJycnHB0dMTEx4fDhwyQkJCjLJ/Kq79SpE2vWrCEwMJCRI0cquyiUKFGCPn36PNd4CyGEEEVFEtz/b8yYMbi5ubFhwwamTp3Kw4cPsbS0xMnJiS+//JJ27do98/ghQ4YQHx/P4sWLgUd3qE+aNImBAwfqtPvyyy+ZNm0ac+fOxcjIiI4dO9KwYUNmz579zPNPmjQJe3t7tmzZQkhICKVKlcLe3l7vJq78GD16NFOmTGHYsGH8+++/zJw5k06dOgFw6tQpZUcAY2NjKleuzNixY+nVq5dyfIUKFVi/fj2zZ88mKCgItVpN48aNGT9+vM5s8pw5c5g/fz4rVqzgwYMHVK9encWLFys3ednY2FC2bFlCQ0P5559/MDc3p379+syZM0dZKtK5c2fOnDnDF198wYMHD+jYsSOzZs0q8DXn6NWrF4mJiWzcuJH169fj4eHBJ598wqhRo/TW7+YmICCAsLAwVqxYwaxZs/L1vjg6OrJw4ULmzZvH0KFDqVmzJrNmzaJ379756rNt27ZYWFiwbNky5VsBOzs7vL29lXWz9erV4/vvv2f16tVkZWVhb2/P3Llz8fT0zFe9mZkZ69evZ9asWXz22WdkZ2dTr149wsLC5NHEQgghXjsqrVarLeoghChKCxcuZPXq1URFRSmz1IXt+PHj9O3bl/Xr19OwYcNX0mdxkvMQk6PnEom5m5BHayHePHa2lgzp3riow8hVcnIy58+fp1atWrJjSgHIuBXcmTNnUKlUOje3vywygyveKpcvX2b37t24urpiaGjIr7/+ysqVK+nevXuhJrdTpkzBw8MDKysrLl26xNKlS3F0dNTZX/htVFx/wQvxKmRna1Gr5aZNIQqDJLjirWJsbMzp06fZtGkT//77L+XKlaN///4MGzasUPtNTExUllmYmZnh7e3NuHHj8lz3+yZLT08nJSUFExOTog7ltZCSksLVq1ext7eXMSuA4jxuktwKUXgkwRVvFTs7O9atW/fK+338ARHi/8gKqfzTarWkpKTImBWQjJsQb6e3d/pICFHkZE/d/FOpVJiYmMiYFZCMmxBvJ5nBFUIUCSMjo2L3lXFxZmJigqOjY1GH8dopjuMma2+FKHyS4AohikzIpmOyi4J4qxTn3ROEeJNIgitee8HBwSxZsiTXujFjxjBgwAA0Go3yQIaX5fz58xw8eJCPPvpIZyYyPDycCRMmcPz4caytrfN1rpxrqF+/Phs2bNCpmzFjBocOHeLHH38sUHzh4eEYGhrSvn17vTqtVsvOnTvZtm0bFy9eJC0tDTs7O5o1a0ZAQACxsbH85z//Yfbs2bz//vt6x2dlZeHt7Y2Xl1eeezg/S8zdBK7FxD/38UIIIURuJMEVbwRjY2PWrl2rV16YDyk4f/48S5YsoUePHjoJro+PD1u2bMHCwqLA5zx58iRRUVG4u7u/cHw7duzA1NRUL8HVarWMGTOG77//nk6dOvHRRx9hZmbGpUuX2Lx5Mzdu3CAkJAR7e3v27t2ba4J7/Phx4uLick2ehRBCiKImCa54I6jVaurWrVvUYQBgbW2d75nbx5mamvLOO++wdOnSl5LgPs3GjRvZu3cvM2bMoHPnzkp5w4YN6dq1Kz///DMA/v7+LFu2jPj4eOVRzjkiIiIoU6aM8iQ0IYQQojiRXRTEW+nIkSMEBATg4eFBvXr1+OCDDzh69KhOm8TERCZNmoS3tzfOzs40bdqUUaNGAf+3DAHAw8MDjUajPII4PDwcjUbD/fv3lXOlp6ezYMECWrRoQe3atWnSpAnjx4/Xi2vw4MGcOHGCU6dOPTP+xMREpkyZgpeXF7Vr16ZTp05KYgqPHkn866+/cuTIETQaDRqNhuDgYABWr16Nk5OTTnKbw8DAgKZNmwLQvn17MjIy2Ldvn06btLQ0Dhw4gJ+fn/JIZSGEEKI4KdAM7s6dO5+rkw4dOjzXcUIURGZmpl5ZiRK5f8Rv3rxJs2bN6NevH2q1mqNHjzJgwADWrl2rzJ7OnDmTn376iTFjxmBnZ0dsbKySBPv4+DBo0CC+/vprvvnmG8zNzTEyMnpqbMOGDePEiRMEBgZSt25d7t+/zw8//KDXrlmzZjg6OhISEsLKlStzPVd6ejoBAQHExcUxcuRIypUrx+7duwkMDFSS688//5xPPvkEY2Njxo0bB0D58uW5c+cON27cYODAgc8eTKBq1ao4OzsTERFB9+7dlfIjR47w8OFDWZ4ghBCi2CpQgpvbjFNeVCqVJLii0CUnJ+Pk5KRXvmHDhlwfh9uzZ0/l39nZ2bi7u3Pp0iW2bt2qJLhnz57F39+fjh07Km3btWsHPFqGUKVKFQCcnJyeuSTh2LFjHDlyhHnz5uHv76+UP/7vxw0aNIhhw4Zx5swZXFxc9Or37NnDhQsX2LVrF++88w4A3t7eXL9+naVLl7Jo0SLeeecdzMzMMDU11Vm68b///Q/I/9rk9u3bM3PmTG7fvq0cs3fvXqpUqUKdOnXydQ4hhBDiVStQgnvo0KHCikOIF2JsbExYWJheefXq1XNtf+fOHRYsWMAvv/xCbGys8pSjx5NkR0dHduzYgY2NDd7e3jg4ODxXbMePH8fExERJjvPSqlUrHBwcCAkJITQ0VK/+2LFjODg4UK1aNZ1Za09PT3bv3p2vPvK76b2fnx9fffUV3333Hf379+fhw4ccOXLkpe5GIYQQQrxsBUpw7ezsCisOIV6IWq3G2dk5X22zs7MZNGgQSUlJDB8+nKpVq2JiYsLixYu5ffu20u6zzz7D0tKS1atXM3v2bCpUqMCAAQP48MMPCxTbgwcPsLGxyXdSqVKpGDhwIKNHj+aPP/7Qq4+Pjyc6OjrXGeu81sSWK1cOgFu3buUrFhsbG9zd3YmIiKB///4cOHCAtLQ0WZ4ghBCiWHspuyikp6fzxx9/EBcXR7169Z7rDnIhXpXr168THR1NSEgILVu2VMpTU1N12pmbmzNx4kQmTpzIxYsXWbduHVOnTsXBwSHXZQ9PY2VlpcwS5zfJbdu2LcHBwSxdupSKFSvq1FlaWqLRaJgxY0a+Y8hRvnx5qlSpws8//6zcMJeX9u3bM2HCBK5cuUJERAROTk5PnRkXQgghioMX3kVh3bp1eHl58eGHHzJs2DAuXrwIwP3793F3d2f79u0vHKQQL1NaWhoAhoaGSllMTAynT59+6jEajUbZNeHy5cs6x6enpz+zP09PT1JSUvj+++/zHaNarWbgwIEcOnRI+W/q8fPduHEDW1tbnJ2d9X5yGBoaKtf6uL59+3Lu3Dl27NihV5edna23m0Tr1q0pWbIka9eu5cSJEzJ7K4QQoth7oRncb7/9li+//JJ27drRuHFjPv30U6XO2tqaRo0a8d133+W6HZEQL1N2dja///67XnmZMmWoXLmyTln16tUpX7488+bNIzs7m+TkZBYvXoytra1Ou27dutGqVStq1qyJgYEBO3fuxNDQUJm9rVGjBvDoRraWLVtibGyMRqPRi8HT05OmTZvy6aef8vfff1OnTh0ePHjA/v37Wbhw4VOvqX379oSEhBAVFaWzPKhDhw5s3ryZ3r17069fP6pVq0ZSUhLR0dFkZGQwZswY5Tp37tzJjz/+iI2NDba2tpQrV44PP/yQ3377jYkTJ3Lq1ClatGiBqakpV65cYfPmzdjZ2dGkSROlPzMzM+XhFSqVCj8/v2e/GUIIIUQRe6EEd/Xq1bRo0YJ58+YRH6//uE0nJyfWr1//Il0IkS+pqal07dpVr7xz5856X+UbGRkRHBzMtGnTGDFiBBUqVGDQoEGcOHGCc+fOKe3q1avHzp07uXnzJmq1GgcHB5YtW6Ykto6OjgwbNoxt27bxzTffUKFChac+TjfnUbxbtmxhyZIllClThsaNn/08egMDAwYMGMCkSZP04l+3bh3BwcEsW7aM2NhYrKyscHR01Fkf/PHHH/P3338zbtw4EhMTGTp0KMOGDUOlUjFv3jy8vLzYtm0be/fuJT09HTs7O5o3b06/fv30Ymnfvj379+/H3d1dWccrhBBCFFcqbc7t48/B2dmZSZMm0bVrV+Lj4/Hw8GD16tV4eHgAsHXrVr744gvOnj370gIWQrz+cv4/4ei5RGLuJhRxNEK8Ona2lgzp/uw/botacnIy58+fp1atWpiamhZ1OK8NGbeCO3PmDCqVKt83iRfEC83gWlhY5Dpzm+PSpUvY2Ni8SBdCiDdYcf9FL0RhyM7Wolbn74ZTIcTzeaGbzJo0acLWrVtJTEzUq/vrr7/Ytm2b8vhSIYR4XHp6OikpKUUdxmsjJSWF6OhoGbMCKo7jJsmtEIXvhWZwR44cSZcuXfD396dZs2aoVCp27tzJt99+yw8//ICNjQ2DBw9+WbEKId4wL7BC6q2j1WpJSUmRMSsgGTch3k4vNINbrlw5wsPD8fb25vvvv0er1bJr1y4OHz5Mu3bt2Lp1q+yJK4R4qvzuCywejZWJiYmMWQHJuAnxdnrhBz2UKVOGGTNmMGPGDO7fv092djbW1tao1S+8xa4Q4g1mZGSEiYlJUYfx2jAxMcHR0bGow3jtFMdxkzW4QhS+l/IksxwyWyuEKIiQTcdkFwXxVnkddlEQ4k1QoAR3yZIlBe5ApVIxZMiQAh8nxOsmODiYVatWPfOJaMXJxYsXWb58OVFRUTx48AALCwvq1atHjx498PDwoH379pQpU4Y1a9bkevysWbPYvHkzx44do1SpUs8VQ8zdBK7FPH0nFiGEEOJ5vHCCm7Ou6ckF/CqVCq1WKwmuEMXQwYMHGTVqFDVr1mTUqFFUqVKF+/fv88MPP9CvXz9+/fVX/P39WbRoEbGxsXrb/WVnZ/Pdd9/RvHnz505uhRBCiMJSoAT3woULOq//+ecfBgwYQM2aNenTpw/29vYAXLlyhbVr13L58mVCQ0NfXrRCiBcWGxvLuHHjcHNzY/ny5RgZGSl1vr6+fPDBB5QoUQJ/f38WLFjAd999R58+fXTO8d///pd//vkHf3//Vx2+EEIIkacXuhNs6tSpVK1alblz5+Ls7IyZmRlmZma4uLgwb948qlSpwrRp015WrEK81ubOnUv79u1xdXXF29ub0aNHc/fuXZ02v/32Gz169MDNzQ1XV1fat2/Pjh078l0PsHnzZnx9falduzbNmzdn6dKlZGdnK/Vbt27l4cOHTJgwQSe5zdGoUSNMTEyws7PD1dWVvXv36rXZu3cvVlZWeHt7v+iwCCGEEC/dC91kduLECYKCgp5a36hRI+bOnfsiXQjxxoiLiyMwMBBbW1vu37/P6tWr6dWrF3v37qVEiRI8fPiQwMBA3NzcmD9/PkZGRly6dEl5kEpe9QDr169n+vTp9OrVCx8fH06fPs2SJUtISkpi3LhxwKPZV1tbWzQaTZ4x+/v7M23aNG7cuEHlypUByMjIYP/+/bRp0wZDQ8NCGCkhhBDixbxQgluyZEl+//13Pvzww1zrT58+TcmSJV+kCyHeGDNnzlT+nZWVhaurK02aNOHEiRN4eXlx9epVkpKSGD16tJJ8enh4KMfkVZ+VlUVISAjt2rVj0qRJAHh5eZGRkcGqVasYMGAApUuX5p9//qFixYr5irlt27Z8+eWXREREMGjQIAB+/vlnHjx4QPv27V9sQIQQQohC8kJLFNq3b8+ePXuYPn06165dIzs7m+zsbK5du8YXX3xBRESE/BIU4v+LjIykW7duuLm54ejoSJMmTQC4du0aAFWqVMHMzIwpU6bw3Xffcf/+fZ3j86q/cuUK8fHxtGnTRqfcz8+PjIwMzpw5o5Tld9N7a2trGjdurLNMISIigooVK+Lm5pbvaxdCCCFepRdKcIOCgvD39ycsLIy2bdvi7OyMs7Mzbdu2ZcOGDfj5+T1zCYMQb4szZ84wePBgbG1tmT17Nlu2bGHr1q0ApKWlAWBpacnq1aspVaoUY8eOpXHjxvTq1YuLFy/mqz4h4dF+smXKlNHpO+d1Tn25cuW4detWvmP39/fnr7/+4sKFC6SkpPDjjz/i7+8vT4YSQghRbL3QEgUjIyPmzJlD//79iYyMVH5p2tnZ0aRJE959992XEqQQr7uDBw9iZmbGwoULlaf8xcTE6LVzcXHhm2++ITU1laioKL766iuGDBnCwYMH86y3srIC0JvZjYuLAx4lyAANGzbk+PHj/PXXX9SsWTPP2Fu2bImpqSl79+7l3XffJTk5WXZPEEIIUay9lCeZvfvuu5LMCvEMqampGBoa6sx67tmz56ntjY2Nadq0KX///TczZswgLS1NZz17bvX29vZYW1uzb98+WrVqpbT9/vvvMTQ0xMXFBYAPPviAlStXMnPmTEJDQ/VuFIuKisLFxUV5jK6pqSnNmzdn7969/PXXXzg4OOTrBjUhhBCiqLyUBPfGjRscPXpUZwbX29tbuetaiLdFVlYW+/bt0yt3dHRk7dq1fPHFF7Rq1YrTp0+za9cunTZHjhxh+/bttGzZkooVK3Lv3j3CwsKoV68eJUuWzLMeYPDgwUyfPh1ra2uaNm3K77//zooVK+jTpw+lS5cGwMbGhq+++oqRI0fSvXt3evToQeXKlYmPj+fgwYPs2bOHqKgondj8/f2JiIjg1q1bjB49upBGTwghhHg5XjjBnTVrFuvWrdPZZxNArVbTp08fZWsiId4GaWlpjBgxQq989uzZBAUFERYWRnh4OPXq1SM0NBRfX1+lTZUqVVCr1SxcuJC4uDisrKzw8vJSEsq86gF69epFiRIlWLNmDZs2bcLGxoahQ4cycOBAnXhatmzJ9u3bWbFiBfPmzSM+Ph4LCwvc3NxYtWoV5ubmOu29vLywsrIiISFBlicIIYQo9lTaJ5+xWwCrVq1i9uzZ+Pr60q9fP2rUqAHA5cuXWbNmDfv27WPcuHH07dv3ZcUrhHgDnD17FoCj5xKJuZtQxNEI8erY2VoypHvjog7jmZKTkzl//jy1atXC1NS0qMN5bci4FdyZM2dQqVQ4Ozu/9HO/0Azu1q1bad68OYsWLdIpr1OnDgsWLCAtLY3NmzdLgiuEyFVx/0UvRGHIztaiVssuJEIUphfaJiwmJgYvL6+n1nt5eeV6p7gQQqSnp5OSklLUYbw2UlJSiI6OljEroOI4bpLcClH4XijBLVOmDBcuXHhq/YULF7C2tn6RLoQQb7AXWCH11tFqtaSkpMiYFZCMmxBvpxdKcNu0acP27dtZvnw5ycnJSnlycjLLly9n+/bt+Pn5vXCQQog3kzwsIv9UKhUmJiYyZgUk4ybE2+mF1uCOGDGC8+fPM3/+fBYvXoytrS0Ad+/eJTMzE3d3d4YPH/5SAhVCvFmMjIyUvXZF3kxMTHB0dCzqMF47xWXcZN2tEK/WCyW4JiYmrF27loMHD+rsg+vl5UXTpk1p3ry5/NUshHiqkE3HZBcF8cZ7HXZOEOJN81Ie9NCyZUtatmz5Mk4l3kLBwcEsWbJEeW1kZESlSpXo1KkT/fv3Vx5tW5xERUXRu3dvtm/fXijbmzRv3ly5QdPAwIDy5cvToEEDRo4cSYUKFZR24eHhTJgwgePHjz9zvfuMGTM4dOgQP/7440uP9UXE3E3gWkx8UYchhBDiDVPgBPfJDePzolKp+PrrrwvajXjLGBsbs3btWuDRY22joqKYN28eWq2WAQMGFHF0+pycnNiyZYuy93NhyNlfOjMzk7Nnz7J48WKio6MJDw9XHq/r4+PDli1bsLCwKLQ4hBBCiNdNgRPcI0eOULJkScqWLZuvu1JliYLID7VaTd26dZXXjRo14s8//+SHH34olgmumZmZTryFoWzZskof9evXJy0tjQULFnDu3DlcXV0BsLa2lp1KhBBCiCcU+LvfcuXKkZaWRunSpenduzdbtmzhxx9/fOrPoUOHCiNu8RYoVaoUmZmZyuu5c+fSvn17XF1d8fb2ZvTo0dy9e1epX79+PXXq1OHhw4c657l8+TIajYbIyEil7MiRI3zwwQe4uLjQqFEjPv/8c52dQDIyMvjqq6/w8fGhdu3aeHl5MXDgQJKSkoBHSxQ0Go3yRC549GS///znP7i5ueHh4UFgYCBXr17ViWX8+PH4+/sTFRVFhw4dqFu3Lp07d+bcuXN5jketWrUAuH37tlIWHh6ORqPh/v37Stk///zDwIEDqVOnDt7e3qxYsSLX8508eZIOHTrg7OxM+/btOXbsGO+//z7jx4/XaXf69Gl69+5N3bp1cXNzY8yYMcTFxeUZrxBCCFFUCpzgRkZGsm7dOhwdHfn666/x8fGhb9++fPvtt3qJhRAFkZmZSWZmJg8fPuTQoUP88MMP+Pr6KvVxcXEEBgYSGhrKxIkTiYmJoVevXkoS/N5776HVaomIiNA57/bt2ylXrpzyUJJ9+/YxaNAgHBwcWLJkCZ988gkHDhxg4sSJyjGhoaFs3ryZjz/+mFWrVvHZZ59ha2tLenr6U+O/c+cOPXv2ZOnSpUyfPp3s7Gy6devGgwcPdNrFxsYyffp0+vfvz8KFC0lLS2Po0KFkZGQ8c3xybuKsVKnSM9sNHjyYc+fOMWXKFD7//HMOHjzI/v37ddrcvXuXjz/+mFKlSrFw4UL69+/PlClT+Oeff3TanT59ml69emFubs6CBQv44osvOHv2LIMHD35mDEIIIURReq6bzBo2bEjDhg357LPPiIyMJCIigi+++IKpU6fSpEkT/P39ad68OUZGRi87XvGGSk5OxsnJSafMz89PZ3nCzJkzlX9nZWXh6upKkyZNOHHiBF5eXlhaWuLr68u3335Lt27dgEdJ8+7du+ncuTMGBgZotVpmz56Nn58fM2bMUM5nY2PDgAEDGDx4MDVr1uTs2bN4eXnRo0cPpc3jyXZuPv30U534GjdujIeHB/v376dr165KXUJCAmFhYdSsWRN4tBtJ7969+d///kf9+vWVdlqtVkn6z507R2hoKE2bNsXFxeWpMRw9epRz586xZs0aPDw8AHB3d6dp06ZYWVkp7dasWYOBgQGhoaGYmZkBjxLnx68XYN68edSuXZslS5Yoy40cHBzw9/cnMjKSpk2bPnNMhBBCiKLwQrenGxoa0rJlSxYuXMixY8eYNm0a9+7dY9SoUU/9WlSI3BgbG7N9+3a2b9/Oxo0bmThxIj/99BOTJk1S2kRGRtKtWzfc3NxwdHSkSZMmAFy7dk1p06VLF86cOcNff/2lHBMXF8d//vMfAK5evUpMTAxt27ZVksfMzEwaNmyIWq1Wlgo4OjoSGRlJcHAwZ86cITs7O89r+P333wkICMDd3R1HR0fq1KlDcnKyTnwAtra2SnIL8M477wDozZ5u3LgRJycn6tSpQ48ePShZsiTz589/ZgxnzpzB3NxcSW4BzM3N8fT01Gl39uxZ3N3dleQWHq3zfTwJTklJ4dSpU7Rp04asrCxlrKpVq0aFChV0lmcIIYQQxclL2SYsPT2dn3/+mUOHDhEdHU3JkiWxs7N7GacWbwm1Wq2z3ZabmxtZWVnMmjWLgIAAUlNTGTx4MC1atODjjz+mTJkyqFQqunTpQlpamnJcgwYNsLe3Z/v27UyYMIFvv/2WBg0aUKVKFQDi4x9tSTVkyJBc48hZ3zpo0CDUajU7duxgyZIlWFtb06NHD4YMGZLrjZO3bt2iX79+1K5dm6lTp2Jra4uhoSGBgYE68QF6Ox7k7IjwZLu2bdvSv39/0tLSOHr0KKGhoUyePPmZSe7du3dzvemsTJkyOq9jY2OpVq2aXrvHj01MTCQrK4uZM2fqzJ7neHwtsBBCCFGcPHeCm52dzbFjx9i7dy8HDx4kNTUVDw8PvvjiC1q1aoWpqenLjFO8hapXrw7ApUuXuHDhAmZmZixcuFDZFzdnn9gnffDBB3zzzTcEBAQQGRmpsxQhZ4Zy8uTJuX7Vn/M0PiMjI4YNG8awYcO4fv063377LcHBwVSqVIkOHTroHffTTz+RnJzMkiVLlAQ2MzOThITnf4iBtbW1kvTXr1+f5ORk1q9fT58+fahTp06ux9ja2urccJbjyZvCbGxscm33eJm5uTkqlYrAwMBc97kuXbp0ga5HCCGEeFUKnOCeOnWKiIgI9u3bx4MHD6hTpw6jRo2ibdu2sl2ReKlylhmULl2a1NRUDA0NdWZP9+zZk+txHTt2ZMGCBQQFBWFsbEybNm2UuurVq1O+fHlu3Liht970aapWrcro0aPZsmULV65cybVNamoqKpWKEiX+7z+p77//XmcXiBc1dOhQduzYwbJly566t7SzszNJSUkcP35cWaaQlJTEL7/8orP8wNnZmS1btvDw4UNlmcLJkyd1bogzNTWlbt26XLlypVAeZiGEEEIUlgInuB9++CHGxsbKzWQ5SxFu37791K8sn7x5SIgnZWdn8/vvvwOPtuj6448/+Prrr3nnnXeoX78+6enprF27VvmG4PTp0+zatSvXc1lbW9OiRQv27dtH165dMTY2VupUKhXjx48nKCiI5ORkfHx8MDEx4datW0RGRjJq1Cjs7e0ZPHgwTk5OODo6YmJiwuHDh0lISKBRo0a59plTPmHCBLp168Zff/3F6tWrX+oDGKysrOjZsyehoaFcvnw514dMNGnSBCcnJz755BOCgoIwNzdn+fLlOmttAfr27cumTZsIDAykf//+JCYmEhISQunSpXX+iBg7dix9+vRh5MiRtGvXDgsLC+7cucMvv/xCp06dcHd3f2nXJ4QQQrwsz7VEITU1lR9++IEDBw48s51Wq0WlUnH+/PnnCk68PVJTU5WdBkqUKEH58uV57733GDp0KIaGhjRt2pSgoCDCwsIIDw+nXr16hIaGPnVng1atWrFv3z46d+6sV9e2bVssLCxYtmyZMgtsZ2eHt7c3ZcuWBaBevXp8//33rF69mqysLOzt7Zk7d67ezVo5NBoNM2fOZMmSJQQGBlKrVi0WLVrEyJEjX8Lo/J+AgADCwsJYsWIFs2bN0qtXqVQsXbqUzz//nMmTJ2NhYUGvXr24d++ezp7Utra2rFixgunTpzN8+HCqVKnCxIkTmTZtGubm5kq7evXqsXHjRoKDg5kwYQIZGRmUL1+eRo0aUbVq1Zd6bUIIIcTLotLm53Fkj9mxY0eBO+nYsWOBjxHiRYwdO5bz588/dRmD0Hft2jXatm3Ll19+Wej/zebswHD0XCIxd59/nbIQrwM7W0uGdG9c1GHkW3JyMufPn6dWrVpyP00ByLgV3JkzZ1CpVIWyDK7AM7iSrIri7OLFi5w/f57vvvuOzz//vKjDKdbmzZuHRqPB1taWGzduEBoaio2NDa1bt35lMbxOv/SFeBHZ2VrUanl0vRCvykvZJkyI4mLQoEHcv3+fDh06KHvfitxlZGQwd+5c7t27h7GxMQ0bNmTs2LGUKlXqlfSfnp5OSkoKJiYmr6S/111KSgpXr17F3t5exqwAisu4SXIrxKslCa54o/z4449FHcJrY/z48YwfP76owxBCCCFeOklwhRBFwsjISGYiC8DExARHR8eiDuO186rHTZYiCFE8SIIrhCgyIZuOyU1m4o3xut1MJsSbTBJcIUSRibmbwLWY+KIOQwghxBtGXdQBiNdPcHAwGo1G+WnUqBG9e/fm5MmTBTqHq6trIUb5/G7evKlzfc7OzrRp04bFixeTmppa1OEJIYQQIg8ygyuei7GxMWvXrgXgzp07LF26lL59+xIeHo6Dg0Oex3/wwQc0bdq0sMN8IaNHj8bd3Z2UlBQOHTpESEgI9+7dY9q0aUUdmhBCCCGeQRJc8VzUajV169ZVXru4uNC8eXM2b97M5MmT8zy+fPnylC9fvhAjfHFVq1ZVrtHDw4MrV66wa9cupkyZglpd+F9+pKam6jxmuDh6HWIUQgjx9pElCuKlqFixItbW1ty8eZPs7GyWLl1K8+bNqV27Nm3atGHz5s067Z9copCRkcFXX32Fj48PtWvXxsvLi4EDB5KUlJSveoCYmBiGDx+Om5sbdevWpX///ly8eFGn3+bNmzNt2jQ2bNhAs2bNcHNzY/Dgwdy/fz/Pa6xVqxapqak6bRMTE5kyZQpeXl7Url2bTp068fPPP+scp9VqWbJkCY0bN8bV1ZXhw4fzyy+/oNFoiIqKUtppNBqWL1/OnDlzaNy4MR4eHsrxK1euxNfXl9q1a9OiRQvWrFmj08edO3cYMWIEnp6eODs707x5c7788st81wP897//pVu3bri4uODu7s6ECRN48OCBUp+zdCM8PJxJkybh7u7OBx98kOe4CSGEEK+azOCKl+Lhw4c8ePAAW1tbZs+ezbp16xg0aBCurq4cOXKEzz//nMzMTHr27Jnr8aGhoWzevJmgoCBq1qxJfHw8x44dIz09PV/1Dx8+pFevXqjVaqZOnUrJkiX5+uuv6dmzJ7t376ZChQpKXz/++CPXr19n8uTJxMfHM3PmTL744gsWLFjwzGu8desWpUqVonTp0sCjBxUEBAQQFxfHyJEjKVeuHLt37yYwMJDw8HA0Gg0A69evZ8mSJXz00Uc0atSIEydOMGnSpFz7WLduHXXq1GHGjBlkZmYCMGPGDLZt28bAgQOpU6cOp06dYu7cuZQsWZLu3bsDjx5NfPfuXSZNmkSZMmW4ffs2586dU86bV/25c+cICAjA3d2dRYsWce/ePebNm8elS5fYvHkzBgYGStv58+fTtGlT5s2bR3Z29jPHTAghhCgKkuCK55aTgN25c4evvvqKrKwsPD09GTt2LP3792fYsGEAeHl5ER8fT0hICN27d9dJlnKcPXsWLy8vevTooZT5+vrmuz48PJxbt26xd+9eatSoAUCDBg1o1qwZa9eu1XmggVar5euvv8bIyAh4NPMbGhpKdna2ztKD7OxsMjMzlTW4P/zwAyNHjlTi37NnDxcuXGDXrl288847AHh7e3P9+nWWLl3KokWLyMrKYvny5XTq1ImgoCCd8di+fbveOFhaWrJkyRJUqkf7aP7999+EhYUxdepUunbtCoCnpyepqamEhITQtWtX1Go1Z8+eZfTo0fj5+Snn6tChg874Pat+2bJl2NjYsGzZMgwNDQGoUKEC/fv3JzIykubNmytt3333XWbMmKEXuxBCCFFcyBIF8VySk5NxcnLCycmJFi1aEBUVxeTJkzE1NSUjI4M2bdrotG/bti3379/n2rVruZ7P0dGRyMhIgoODOXPmjN7MYF71J0+epGbNmkpyC2BlZYWnpye//fabTtsGDRooyS1AjRo1yMjIIC4uTqfdqFGjcHJyon79+owbNw5fX18+/vhjpf7YsWM4ODhQrVo1MjMzlR9PT0/Onj0LPEr+Y2NjdRJEgBYtWuQ6Dk2aNFGSW4BffvkFgNatW+v1ERsby+3bt5XxWbVqFRs3buT69eu5ju+z6k+ePEmLFi2U5BYeJeIWFhZ64+fj45Nr7EIIIURxITO44rkYGxsTFhaGSqWidOnSVKhQAbVaza5duwAoW7asTvuc14+v6XzcoEGDUKvV7NixgyVLlmBtbU2PHj0YMmQIKpUqz/rExES9PgHKlCnDX3/9pVNmYWGh8zon2U1LS9MpDwoKolGjRiQlJREWFsbevXtp2LAh3bp1AyA+Pp7o6GicnJz0+s2Z5Y2NjQXA2tpaL67cPFkeHx+PVqulUaNGuba/ffs2dnZ2LFiwgAULFrBw4UKmTp2Kvb09o0ePpnXr1gB51icmJuYaU5kyZUhISNArE0IIIYozSXDFc1Gr1Tg7O+uVW1lZARAXF0e5cuWU8nv37unUP8nIyIhhw4YxbNgwrl+/zrfffktwcDCVKlWiQ4cOedZbWlpy9epVvfPGxcVhaWn5XNdYuXJl5Rrd3d3p3LkzCxcu5L333sPU1BRLS0s0Gs0zv663sbEB0LuJ7cnZ4hyPz97CoyULKpWKjRs36syu5rC3twfA1taWmTNnkp2dzblz5/j6668ZNWoU+/bto3LlynnWW1pa5hpTbuP3ZIxCCCFEcSNLFMRL5ezsjKGhIfv27dMp//777ylTpgzVqlXL8xxVq1Zl9OjRWFlZceXKlXzVu7m58eeff+q0T0hI4JdffsHNze3FLopHM7KffPIJ8fHxbN26FXi0FvbGjRvY2tri7Oys9wOPtkOzsbHh0KFDOuc7ePBgvvrN2UnhwYMHufZhZmam016tVuPi4sLIkSPJzMzUW47wtHo3NzcOHTqkrKuGR0swEhMTX8r4CSGEEK+SzOCKl8ra2pqePXuycuVKjIyMqFu3LpGRkURERPDZZ5/leoMZwODBg3FycsLR0RETExMOHz5MQkKC8tV8XvWdOnVizZo1BAYGMnLkSGUXhRIlStCnT5+Xcm2enp64ubmxZs0aevToQYcOHdi8eTO9e/emX79+VKtWjaSkJKKjo8nIyGDMmDEYGBgwYMAAvvzyS8qWLYu7uztRUVEcP34cIM/9dO3t7enRo4dy416dOnXIyMjg2rVrREVFsXTpUpKSkujfvz/vv/8+9vb2ZGRksH79eiwsLHB0dMyzHmDgwIF069aNwMBAevXqpeyi4OLiUuwfyCGEEEI8SRJc8dKNHTsWc3Nztm/fzrJly7Czs2Pq1KnK2tXc1KtXj++//57Vq1eTlZWFvb09c+fOxdPTM1/1ZmZmrF+/nlmzZvHZZ5+RnZ1NvXr1CAsL09ki7EUNHTqUgIAA9uzZQ6dOnVi3bh3BwcEsW7aM2NhYrKyscHR05MMPP1SO6dWrF4mJiWzcuJH169fj4eHBJ598wqhRozA3N8+zz0mTJmFvb8+WLVsICQmhVKlS2NvbKzfylSxZEgcHB9avX8/t27cxNjamdu3arFy5Emtra9LT059Zhf9bUAAAv5dJREFUD1C7dm1WrVrF/PnzGTZsGKampjRv3pxx48Y99Y+Sl8HO9vmWjwhRHMnnWYjiQ6XVarVFHYQQb5uFCxeyevVqoqKi3songeXsMpHbOm4hXmfZ2VrU6td7nXpycjLnz5+nVq1amJqaFnU4rw0Zt4I7c+YMKpWqUH4XyAyuEIXs8uXL7N69G1dXVwwNDfn1119ZuXIl3bt3fyuT2xzp6emkpKRgYmJS1KG8FlJSUrh69Sr29vYyZgXwqsftdU9uhXhTSIIrRCEzNjbm9OnTbNq0iX///Zdy5crpPAjjbSZfIOWfVqslJSVFxqyAZNyEeDtJgitEIbOzs2PdunVFHUaxJFuO5Z9KpcLExETGrIBk3IR4O0mCK4QoEkZGRvJVewGYmJgou16I/HsV4/YmrLsV4k0jCa4QosiEbDpGzN2EvBsKUUzZ2VoypHvjog5DCPEESXCFeEJwcDCrVq3i9OnTenU3b96kRYsWLFq0SNmmqziJj4/n66+/5siRI9y+fRszMzOqVauGr68vffv2Zf/+/QwfPpwNGzZQv359veMfPHiAl5cXH3/8MSNGjKBXr178+uuvwKOvem1tbZUHRbzzzjsvHG/M3QSuxcS/8HmEEEKIx0mCK0QB2NrasmXLlnw9ke1Vy8zMpE+fPiQlJTFgwACqV6/OvXv3OHXqFIcPH6Zv3774+PhgZmbG3r17c01w9+/fT0ZGBu3bt1fK6tWrx7hx48jKyuLy5cssXLiQvn37snfv3ud+DLIQQghRmCTBFaIAcp7OVtykpqZy6tQpLl68SFhYGA0aNFDq2rVrR3Z2NvDooRCtW7dm3759TJw4kRIldP8vICIiAicnJ6pXr66UWVhYKNfs5uaGiYkJQUFB/PTTT/j7+xf+xQkhhBAF9OznhAohdNy8eRONRsO+ffuUsubNmzNt2jQ2bNhAs2bNcHNzY/Dgwdy/f1/n2MTERKZMmYKXlxe1a9emU6dO/Pzzzzptjhw5QkBAAB4eHtSrV48PPviAo0eP6rQJDw9Ho9Fw+vRpAgICqFu3LrNnzyYh4dFaVhsbG724H38kcPv27bl//z6//PKLTpt//vmHkydP6sze5ibnhp1bt249s50QQghRVCTBFeIl+PHHH/nxxx+ZPHkyEydO5L///S9ffPGFUp+enk5AQABHjhxh5MiRfP3119SoUYPAwEAuXryotLt58ybNmjVj9uzZBAcHU69ePQYMGEBUVJRen2PGjKFRo0YsW7aM999/n1q1aqFWq5k0aRLHjx8nPT0911gbNWqEjY0NEREROuV79+4FwM/P75nXGhMTA0ClSpXyNzhCCCHEKyZLFIR4CbRaLV9//TVGRkbAoyQwNDSU7Oxs1Go1e/bs4cKFC+zatUu5Ocvb25vr16+zdOlSFi1aBEDPnj2Vc2ZnZ+Pu7s6lS5fYunUr7u7uOn1269aNAQMG6JSNHz+eOXPm0LdvXwwNDXFxcaFt27Z0795dWY6gVqvx8/Nj+/btpKWlUbJkSeDR8oSGDRtSrlw5vWvLzMwkOzubS5cuMXfuXBwdHWnZsuVLHEEhhBDi5ZEZXCFeggYNGijJLUCNGjXIyMggLi4OgGPHjuHg4EC1atXIzMxUfjw9PTl79qxy3J07dxg3bhze3t44Ojri5OTEzz//zNWrV/X69PHx0Svr06cPhw8fZtq0afj6+nLt2jWmT59OQECAsg4XwN/fn3///ZfDhw8DcO3aNf74449clydERkbi5OSEs7MzHTt25N69e4SEhOhcrxBCCFGcyAyuEC+BhYWFzuuc5C8tLQ14tH1XdHQ0Tk5OescaGBgAj2ZsBw0aRFJSEsOHD6dq1aqYmJiwePFibt++rXdc2bJlc43FxsaGrl270rVrVzIyMpg8eTLh4eEcPnyYFi1aAODi4kK1atWIiIigTZs2REREYGRkhK+vr9753NzcmDBhApmZmZw6dYoFCxYwevRoNm7cqLO2VwghhCguJMEV4hWwtLREo9EwY8aMp7a5fv060dHRhISE6Hz9n5qa+tz9Ghoa0rdvX8LDw7l8+bKS4MKjWdzly5eTlJREREQEPj4+mJub653D3NwcZ2dnAFxdXVGr1cyaNYt9+/bluV5XCCGEKAoy/SLEK+Dp6cmNGzewtbXF2dlZ7wf+b7bX0NBQOS4mJibXB07k5sGDB2RmZuqVX7t2DdDfXcHf35/09HQWLlzI1atX89w9IUfPnj2pVKkSoaGh+WovhBBCvGoygytELrKysnS2AstRpkyZ5zpfhw4d2Lx5M71796Zfv35Uq1aNpKQkoqOjycjIYMyYMVSvXp3y5cszb948srOzSU5OZvHixdja2uarjxMnTjB37lw6duyIi4sLJUqU4Pz584SGhlKxYkVatWql097e3h4nJyc2bNiAubl5rmt6c2NoaMjAgQOZNGkSR48epUmTJgUdDiGEEKJQSYIrRC7S0tIYMWKEXnluZflhZGTEunXrCA4OZtmyZcT+P/buOyzKY238+BcQBCwgCoodo2ykSVERBLFjgWiMRg2iggkItlhiORpjDbEXwKBGFEussaMmUY8aTcRYctBYorFErIggKB3294c/ntd1UdCoWO7PdXm97DPzzNw7y3lzMzvPTGIipqam2NjY8Mknnyh1wsPDmTRpEkOGDMHS0pKQkBAOHz7MqVOniuyjQYMGeHt7s2fPHmJiYsjKyqJKlSr4+voSFBRE2bJlte754IMP+PPPP2nbtu0zPTTWuXNnoqKiWLx4sSS4QgghXjs6arVaXdJBCCHeLQU7Rxw4lcq12/dKOBohnl81CxMG9Gxa0mG8UOnp6Zw5c4b69etjbGxc0uG8MWTcnl18fDw6OjrKUr0XSWZwhRAl5m1LDMS7KT9fja6uTkmHIYR4hDxkJoQoEdnZ2WRkZJR0GG+MjIwMTp8+LWP2jF7FuElyK8TrRxJcIUSJkRVSxadWq8nIyJAxe0YybkK8myTBFUKUGB0dmfkqLh0dHYyMjGTMnpGMmxDvJlmDK4QoEQYGBhgZGZV0GG8MIyMjbGxsSjqMN05xx03W0QrxdpEEVwhRYiJXH5JdFESJext3QhDiXScJ7mO2bt3K999/z7lz5wCwtramZ8+edO7cuch7ExIS2LRpEx9//DGVK1dWrsfFxdG7d282bNjwUrbCeB0kJCTQqlUr5s2bR7t27QDw9/fnyJEjdOjQgTlz5mjUv3//Pu7u7mRlZREWFkaXLl2K3deZM2fYvXs3n3766TPPALZs2ZLmzZszfvz4J9Z52Z9XeHg4ERERyusKFSpgbW3N4MGDadiw4Qvv799QqVSMHDmSfv36AbBx40b09fWLfepZUa7dvsfla8kvpC0hhBCigKzBfcTkyZMZOXIk7733HvPmzWP+/PnUq1eP0aNHM3ny5CLvv3btGhEREdy+ffsVRPtmMDY25r///S/p6eka13fv3o2ent5ztXnmzBkiIiJe2lPRtra2rF27lvfee++ltA9gaGjI2rVrWbt2LRMmTCAlJYW+ffvy119/vbQ+n8fatWs1ktlNmzaxffv2EoxICCGEKJrM4P5/e/bsYeXKlQwcOJBBgwYp1z09PbGwsCAyMpKmTZvSsmVLrXvVajU5OTmvMtw3hrOzM3/++Sd79+7Fx8dHub59+3Zat27N1q1bSzC6wpUtWxZHR8eX2oeurq5GHw4ODrRs2ZI1a9Y8dXa5KJmZmRgaGr6ACB962eMghBBCvAwyg/v/xcTEYGJiQmBgoFZZv379MDExISYmBoDRo0fj4+PD/v37+eCDD7C3t2fv3r307t0bgK5du6JSqVCpVBrtpKamMnz4cJycnGjRogWLFy/W6uunn36iU6dO2Nvb4+HhQVhYGFlZWRp1zp8/j5+fH/b29rRt25atW7cSGhqKv7+/Rr3ff/+dHj164ODggKurK2PGjCElJUUpT0hIQKVSsWXLFiZNmkSjRo3w8PBg2rRp5ObmKvX+/vtvhg4dipeXFw0aNKBDhw5ER0eTn59f5LiWKlUKb29vjVm/u3fv8ttvv2kkvAX27dtHQEAAbm5uODs7061bNw4cOKCUb9y4kTFjxgDg5uaGSqXS+KPj1q1bjBw5End3dxwcHGjXrp3yuT1q1apVtGjRAhcXF0JDQ7l7965SFhcXh0qlUk7bgodf1S9evJjw8HDc3d2V8Xx8Zvro0aN07twZe3t7fH19OXToEJ06dWL06NFPHaeqVatiZmZGQkIC8HB5R3BwsEadM2fOoFKpiIuL04hr0aJFzJgxg6ZNm+Lm5gY8/B357LPPcHV1VY7wffT37VnaX7JkiXLPkSNH2Ldvn/L7HR4e/tT3JYQQQpQEmcEFcnNzOXHiBM2bN6dMmTJa5WXKlMHV1ZX9+/crid/t27eZMmUKISEhWFpaUqFCBcaPH8+kSZMICwujTp06Wu189dVXdOrUicjISHbv3s3MmTNRqVQ0a9YMeDiLPHjwYDp27Mjw4cO5ePEic+bM4caNG8yfPx94OEMXGBhI+fLlmTFjBgCRkZGkpqZSs2ZNpa9Tp04REBCAq6sr8+bN486dO8yaNYsLFy6wZs0ajeUBc+fOpVWrVsydO5cTJ04QHh5OzZo16dmzp/Jerays8PX1pUyZMpw5c4bw8HDS09MZOHBgkePr4+NDQEAA9+7dw8TEhJ07d1K5cmWcnJy06iYkJNCiRQsCAwPR1dXlwIEDBAUFERMTg6urK82bNyckJIRvv/2W7777jnLlymFgYABAcnIy3bt3B2Do0KFUr16dK1eu8M8//2j0sXfvXq5cucL48eNJTk4mLCyMyZMna60TftyqVatwcXHhm2++4fLly0yfPp2KFSsyYsQIZZw+++wzbGxsmDt3LmlpaUyYMIG0tDTq16//1Lbv379PSkoKFhYWRY7n45YvX06DBg2YOnWq8vvZv39/KlWqxNSpUylbtiz//PMPN2/efOa2H/XVV1/xxRdfYGhoyKhRowCoUqXKv2pTCCGEeBkkweVhYpSdnY2lpeUT61haWpKVlaXMgN67d4/FixfToEEDpc69ew+fBq9Xr16hDye1bdtWWf7g5ubGvn37+PHHH5UENyIiAkdHR2bNmgVAs2bNMDIyYvz48Zw7dw6VSsUPP/xAUlISq1evpnr16gDY2dnRtm1bjQQ3KioKc3NzoqKi0NfXV95Dv3792L9/v8asp4ODA+PGjQOgadOmxMXF8eOPPyoJrpubmzIzqFarcXFxITMzU1nSUZSGDRtSsWJFfvrpJ7p160ZsbGyhs7cAvXr1Un7Oz8/H1dWVCxcusG7dOlxdXTEzM1Pep62tLWZmZkr9ZcuWkZSUxM6dO5WxKYj7UWq1mm+//VZJjK9du8bChQvJz89HV/fJX2qYm5trfDanT5/mxx9/VBLcZcuWoaenx8KFCylbtiwA1atXx8/Pr9D2CpLRmzdvMm3aNPLy8vD29n5i/09iYmJCRESEss/n3bt3SUhIYOzYscrn3KRJk2du93F169albNmyGBsby9IFIYQQrzVZovCcTE1NNZLb4vDw8FB+1tHR4b333lNm1R48eMCZM2e0EpwOHToAcOzYMeDhzKy1tbWSwMHDJOr999/XuO/o0aO0atVKSW4L+i9fvrzSVmFxARpxAWRlZTF//nzatGmDvb09tra2zJkzh8TERB48eFDk+9bR0aFDhw7ExsZy48YNjh8/TseOHQute/PmTUaNGoWnpyc2NjbY2tpy8OBBLl26VGQ/v/32G02aNNEYm8I0atRISW4L3m9OTg5JSUlPvc/d3V3j9ePjdPLkSVxdXZXkFh4m96amplptpaenY2tri62tLa1atSIuLo7x48fj6en51BgK06xZM41N7CtUqEC1atWYPXs2mzZt+tczt0IIIcSbRmZweZgQGBgYcOPGjSfWuXHjBqVLl1aSlUqVKj1zP+XKldN4ra+vT1paGgBpaWmo1WoqVqyodY+BgYEyO3z79m2NWcsCZmZmGmt1U1NTtdoCqFixotLW0+LKzs5WXs+YMYP169czYMAA7OzsKFeuHHv27OHbb78lKyur0GUdj+vYsSMxMTEsW7aMevXqoVKpSE1N1aiTn59PSEgIaWlpDB48mFq1amFkZMT8+fOf+tkUSElJoV69ekXWK1++vMbrgmT38bXORd33+DglJiZSu3ZtrfsK+7wMDQ1ZuXIlOjo6VKhQAUtLy6fOHj/N45+zjo4OS5YsYc6cOUyaNElJpseMGUOjRo2eqw8hhBDiTSIJLg8fhHJycuLIkSOkp6djbGysUZ6ens6RI0dwcnKiVKmHQ/aij30sV64cOjo6Gg87wcPENzs7GxMTEwAsLCw4c+aM1v13797VSDRNTEwKnZFMSkpS2iquXbt20b17d4KCgpRr+/fvf6Y27OzsqFGjBsuXL2fIkCGF1rly5QqnT58mMjKS1q1bK9czMzOL1YepqWmJbtFmbm6u9fkBhV7T1dV96h67BgYGWjtzPP6HSYHCfhetrKyYP38+OTk5nDhxgtmzZ9O/f38OHDhAmTJlnql9IYQQ4k0jSxT+vz59+pCSkkJ0dLRWWXR0NCkpKfTp0+epbRQsByhqJrAwZcqUoX79+uzatUvj+s6dOwFwcXEBHiaK586d4+rVq0qdhIQEzp49q3Gfi4sLe/bs0dgN4dChQ6SmpiptFVdWVpbGUoe8vDxiY2OfqQ2Azz77jBYtWjzxkICCcXu0r2vXrnHixAmNegXlj86ewsP1tocPH+b69evPHNuLYG9vz+HDh7l//75y7ejRoxo7VxRXlSpVuHTpEmq1Wrl26NChZ25HX1+fxo0bExQUxP3795U/AJ63fX19/ef6/RZCCCFeJZnB/f9atWpFr169iIiI4ObNm8ppXD/99BPr1q2jV69ehe6B+6jatWujp6fHDz/8QKlSpdDT03umk7AGDhzIgAEDGDFiBB988AGXLl1izpw5eHt7K1uOffTRR0RFRdG/f3/lgbWIiAgqVaqkMZPXv39/evToQXBwMP7+/souCg4ODnh5eT3T2Li7u7N+/Xrq1q1LhQoV+P7777WSy+Lo2rUrXbt2fWJ5nTp1qFKlCrNmzSI/P5/09HTmz5+vtbNAwQEMq1atonXr1hgaGqJSqejbty9btmyhV69ehISEUKNGDa5evcrly5f54osvnjneZ9W3b19Wr15NcHAw/fr1IzU1lcjISCpUqPDMM/7e3t5s2LCByZMn07p1a44fP86PP/5YrHvPnj3LtGnT6NChAzVq1OD+/fssXLiQatWqKQ/oPW/7derUYfPmzezduxdzc3MsLCw0Tu0TQgghXgcyg/uIL7/8kunTp3PhwgUGDRrEoEGDOHfuHN988w1ffvllkfebmZkxfvx4fv/9d/z8/J6azBWm4Kjbv/76i9DQUBYvXszHH3+sbAcGD9duRkdHY2JiwogRI5gxYwYBAQHUqlVLYy2tnZ0d0dHRPHjwgEGDBjFjxgyaN2/O4sWLn/kEsS+//JJGjRoxefJkxo4di7W1Nf3793+mNorDwMCA8PBwDAwMGDJkCPPnzyckJITGjRtr1LOxsWHQoEFs3bqVHj16EBISAjxcS7169WqcnZ2ZOXMmQUFBREdHv7KtrCwsLFi8eDEPHjxg8ODBLFq0iLFjx2JsbKy1zrkozZo144svvmDv3r0MGDCA8+fPM3HixGLda25uTqVKlVi4cCGfffYZ48ePx9LSkujoaOWzf972P/vsM5ydnRk1ahRdu3Zl3bp1z/S+hBBCiFdBR/3od5TijZSSkkLr1q3p27dvsbbtEq/O5cuXad++PV9//TUffvhhSYfz2ig4ROPAqVSu3Za1v6JkVbMwYUDPpiUdxmsjPT2dM2fOUL9+fa1nUsSTybg9u/j4eHR0dJ7p2+7ikiUKb6BFixZRqVIlqlWrRmJiItHR0eTl5fHRRx+VdGjvvFmzZqFSqbCwsODq1assXLgQc3Nz2rZtW9KhvZYkqRCvi/x8Nbq6L/bhYSFEyZEE9w2kq6vLt99+y61bt9DT06NBgwbExMQ89aAK8Wrk5OQwc+ZM7ty5g6GhIY0bN2bkyJHF2krtXZOdnU1GRgZGRkYlHcobISMjg0uXLmFlZSVj9gyKO26S3ArxdpEE9w306aef8umnn5Z0GKIQo0ePZvTo0SUdxhtDVkgVn1qtJiMjQ8bsGcm4CfFukofMhBAl5kXvJ/0209HRwcjISMbsGcm4CfFukhlcIUSJMDAwkK/an4GRkRE2NjYlHcYbp7jjJmtwhXi7SIIrhCgxkasPyS4KosTJLgpCvH3e6AR369atfP/995w7dw4Aa2trevbsSefOnYu8NyEhgU2bNvHxxx9rbFQfFxdH79692bBhw0vZtuJ1kJCQoOy5W3Cghb+/P0eOHKFDhw7MmTNHo/79+/dxd3cnKyuLsLAwunTpUuy+zpw5w+7du/n000+febauZcuWNG/enPHjxz+xzsv+vMLDw4mIiFBeV6hQAWtrawYPHkzDhg1feH//hkqlYuTIkfTr1w+AjRs3oq+vX+jJcWq1ms2bN7N+/XrOnTtHVlYW1apVo0WLFgQEBJCYmMhHH33E9OnT6dSpk9b9eXl5eHp64uHhwfTp05875mu373H5WvJz3y+EEEIU5o1dgzt58mRGjhzJe++9x7x585g/fz716tVj9OjRTJ48ucj7r127RkREhHJ0qQBjY2P++9//kp6ernF99+7dz3w4RIEzZ84QERFBRkbGiwhRi62tLWvXrlVON3sZDA0NWbt2LWvXrmXChAmkpKTQt29f/vrrr5fW5/NYu3atRjK7adMmtm/frlVPrVYzfPhw/vOf/2BlZcWMGTOIjo6mT58+/Prrr0yaNAk7OzusrKyeeCTzb7/9RlJS0hOPXRZCCCFK0hs5g7tnzx5WrlzJwIEDleNqATw9PbGwsCAyMpKmTZsWerSuWq0mJyfnVYb7xnB2dubPP/9k7969+Pj4KNe3b99O69at2bp1awlGV7iyZcvi6Oj4UvvQ1dXV6MPBwYGWLVuyZs2ap84uFyUzMxNDQ8MXEOFDxR2H77//ntjYWKZOnapx2l7jxo3p3r07Bw8eBMDHx4eoqCiSk5OpUKGCRhvbt2+nYsWKuLu7v7D4hRBCiBfljZzBjYmJwcTEhMDAQK2yfv36YWJiQkxMDPBw2yYfHx/279/PBx98gL29PXv37qV3794AdO3aFZVKhUql0mgnNTWV4cOH4+TkRIsWLVi8eLFWXz/99BOdOnXC3t4eDw8PwsLCyMrK0qhz/vx5/Pz8sLe3p23btmzdupXQ0FD8/f016v3+++/06NEDBwcHXF1dGTNmDCkpKUp5QkICKpWKLVu2MGnSJBo1aoSHhwfTpk0jNzdXqff3338zdOhQvLy8aNCgAR06dCA6Opr8/Pwix7VUqVJ4e3trzPrdvXuX3377TSPhLbBv3z4CAgJwc3PD2dmZbt26ceDAAaV848aNjBkzBgA3NzdUKpXGHx23bt1i5MiRuLu74+DgQLt27ZTP7VGrVq2iRYsWuLi4EBoayt27d5WyuLg4VCqVcjIWPPyqfvHixYSHh+Pu7q6M5+Mz00ePHqVz587Y29vj6+vLoUOH6NSpU5HbfFWtWhUzMzMSEhKAh8s7goODNeqcOXMGlUpFXFycRlyLFi1ixowZNG3aFDc3N+Dh78hnn32Gq6srDRo0wNvbW+P37VnaX7JkiXLPkSNH2Ldvn/L7HR4eDsDSpUuxtbUt9ChpPT09vLy8APD19SUnJ4ddu3Zp1MnKyuLnn3+mQ4cOzz2zL4QQQrxMb9wMbm5uLidOnKB58+aFbp5fpkwZXF1d2b9/v5L43b59mylTphASEoKlpSUVKlRg/PjxTJo0ibCwMOrUqaPVzldffUWnTp2IjIxk9+7dzJw5E5VKRbNmzYCHs8iDBw+mY8eODB8+nIsXLzJnzhxu3LjB/PnzgYczdIGBgZQvX54ZM2YAEBkZSWpqKjVr1lT6OnXqFAEBAbi6ujJv3jzu3LnDrFmzuHDhAmvWrNFIIubOnUurVq2YO3cuJ06cIDw8nJo1a9KzZ0/lvVpZWeHr60uZMmU4c+YM4eHhpKenF+sYXx8fHwICArh37x4mJibs3LmTypUr4+TkpFU3ISGBFi1aEBgYiK6uLgcOHCAoKIiYmBhcXV1p3rw5ISEhfPvtt3z33XeUK1cOAwMDAJKTk+nevTsAQ4cOpXr16ly5coV//vlHo4+9e/dy5coVxo8fT3JyMmFhYUyePFlrnfDjVq1ahYuLC9988w2XL19m+vTpVKxYkREjRijj9Nlnn2FjY8PcuXNJS0tjwoQJpKWlUb9+/ae2ff/+fVJSUrCwsChyPB+3fPlyGjRowNSpU5Xfz/79+1OpUiWmTp1K2bJl+eeff7h58+Yzt/2or776ii+++AJDQ0NGjRoFQJUqVbh58yZXr16lf//+RbZRq1Yt7O3t2b59u/L7BQ//sLl//74sTxBCCPHaeuMS3OTkZLKzs596apelpSVZWVnKDOi9e/dYvHgxDRo0UOrcu/fwye169eoV+nBS27ZtleUPbm5u7Nu3jx9//FFJcCMiInB0dGTWrFkANGvWDCMjI8aPH8+5c+dQqVT88MMPJCUlsXr1aqpXrw6AnZ0dbdu21Uhwo6KiMDc3JyoqCn19feU99OvXj/3792vMejo4ODBu3DgAmjZtSlxcHD/++KOSgLi5uSkzg2q1GhcXFzIzM5UlHUVp2LAhFStW5KeffqJbt27ExsYWOnsL0KtXL+Xn/Px8XF1duXDhAuvWrcPV1RUzMzPlfdra2mJmZqbUX7ZsGUlJSezcuVMZm4K4H6VWq/n222+VxPjatWssXLiQ/Px8dHWf/AWEubm5xmdz+vRpfvzxRyXBXbZsGXp6eixcuJCyZcsCUL16dfz8/AptryAZvXnzJtOmTSMvLw9vb+8n9v8kJiYmREREKHty3r17l4SEBMaOHat8zk2aNHnmdh9Xt25dypYti7GxscbShf/9738AxT71ztfXl7CwMG7cuKHcExsbS82aNTX+9ySEEEK8Tt7IJQrPytTU9Jn/Y+zh4aH8rKOjw3vvvafMqj148IAzZ85oJTgdOnQA4NixY8DDmVlra2slgYOHSdT777+vcd/Ro0dp1aqVktwW9F++fHmlrcLiAjTigodfH8+fP582bdpgb2+Pra0tc+bMITExkQcPHhT5vnV0dOjQoQOxsbHcuHGD48eP07Fjx0Lr3rx5k1GjRuHp6YmNjQ22trYcPHiQS5cuFdnPb7/9RpMmTTTGpjCNGjVSktuC95uTk0NSUtJT73t8bejj43Ty5ElcXV2V5BYeJvempqZabaWnp2Nra4utrS2tWrUiLi6O8ePH4+np+dQYCtOsWTONDecrVKhAtWrVmD17Nps2bfrXM7fFVdxN7zt06ICuri47duwAHs5e79u374l/9AghhBCvgzduBrdChQoYGBhw48aNJ9a5ceMGpUuXVpKVSpUqPXM/5cqV03itr69PWloaAGlpaajVaipWrKh1j4GBgTI7fPv2bY1ZywJmZmYaa3VTU1O12gKoWLGi0tbT4srOzlZez5gxg/Xr1zNgwADs7OwoV64ce/bs4dtvvyUrK6vQZR2P69ixIzExMSxbtox69eqhUqlITU3VqJOfn09ISAhpaWkMHjyYWrVqYWRkxPz585/62RRISUmhXr16RdYrX768xuuCZPfxtc5F3ff4OCUmJlK7dm2t+wr7vAwNDVm5ciU6OjpUqFABS0vLp84eP83jn7OOjg5Llixhzpw5TJo0SUmmx4wZQ6NGjZ6rj6cp2BLv+vXrxapvbm6Oq6sr27dvp1+/fvz8889kZWXJ8gQhhBCvtTduBrdUqVI4OTlx5MgRrYeG4OFs25EjR3BycqJUqYf5+4s+orFcuXLo6OhoPOwEDxPf7OxsTExMALCwsNCqA2hdMzExKXRGMikpSWmruHbt2kX37t0JCgrC3d0de3t7ZRyKy87Ojho1arB8+fInzt5euXKF06dPM3r0aLp160bjxo2xt7cnMzOzWH2YmpqW6BZt5ubmxfps4OEuCvb29tjZ2VGtWjWt5NbAwEBrZ47H/zApUNjvopWVFfPnz+fIkSOsWLECAwMD+vfvr8y4P0v7RalSpQo1a9ZUdkooDl9fX06fPs3FixfZvn07tra2ha5bF0IIIV4Xb1yCC9CnTx9SUlKIjo7WKouOjiYlJYU+ffo8tY2C5QBFzQQWpkyZMtSvX1/r6fKdO3cC4OLiAjxMFM+dO8fVq1eVOgkJCZw9e1bjPhcXF/bs2aOxG8KhQ4dITU1V2iqurKwsjaUOeXl5T9zL9Gk+++wzWrRo8cSZuoJxe7Sva9euceLECY16BeWPzp7Cw/W2hw8fLvZM4otmb2/P4cOHuX//vnLt6NGjGjtXFFeVKlW4dOkSarVauXbo0KFnbkdfX5/GjRsTFBTE/fv3lT8Anrd9fX39Qn+/+/bty6lTp9i0aZNWWX5+vsZOGPBwPXrp0qWJiYnh8OHDMnsrhBDitffGLVEAaNWqFb169SIiIoKbN28qp3H99NNPrFu3jl69ehW6B+6jateujZ6eHj/88AOlSpVCT0/vmU7CGjhwIAMGDGDEiBF88MEHXLp0iTlz5uDt7a1sOfbRRx8RFRVF//79lQfWIiIiqFSpksZMXv/+/enRowfBwcH4+/sruyg4ODgoWzYVl7u7O+vXr6du3bpUqFCB77//Xiu5LI6uXbsWuo1UgTp16lClShVmzZpFfn4+6enpzJ8/X2tngYIDGFatWkXr1q0xNDREpVLRt29ftmzZQq9evQgJCaFGjRpcvXqVy5cv88UXXzxzvM+qb9++rF69muDgYPr160dqaiqRkZFUqFDhmWf8vb292bBhA5MnT6Z169YcP36cH3/8sVj3nj17lmnTptGhQwdq1KjB/fv3WbhwIdWqVVMe0Hve9uvUqcPmzZvZu3cv5ubmWFhYULlyZT755BOOHTvG2LFjOX78OK1atcLY2JiLFy+yZs0aqlWrpjxMCQ/3Gm7evDlr165V1mgLIYQQr7M3cgYX4Msvv2T69OlcuHCBQYMGMWjQIM6dO8c333zDl19+WeT9ZmZmjB8/nt9//x0/P7+nJnOFKTjq9q+//iI0NJTFixfz8ccfK9uBwcO1m9HR0ZiYmDBixAhmzJhBQEAAtWrV0lhLa2dnR3R0NA8ePGDQoEHMmDGD5s2bs3jx4mfeZ/TLL7+kUaNGTJ48mbFjx2JtbV2sLaGelYGBAeHh4RgYGDBkyBDmz59PSEgIjRs31qhnY2PDoEGD2Lp1Kz169CAkJAR4uJZ69erVODs7M3PmTIKCgoiOjqZKlSovPNbCWFhYsHjxYh48eMDgwYNZtGgRY8eOxdjYWGudc1GaNWvGF198wd69exkwYADnz59n4sSJxbrX3NycSpUqsXDhQj777DPGjx+PpaUl0dHRymf/vO1/9tlnODs7M2rUKLp27cq6deuAh8skZs2axZQpU7hw4QLDhg0jMDCQpUuX4ubmxoQJE7Ta8vX1Ra1W07hxY42jrYUQQojXkY760e89xUuXkpJC69at6du3b7G27RKvzuXLl2nfvj1ff/01H374YUmH81YrOJjjwKlUrt1+vvXEQrwo1SxMGNCzaUmH8dpIT0/nzJkz1K9fH2Nj45IO540h4/bs4uPj0dHReaZv0IvrjVyi8CZZtGgRlSpVolq1aiQmJhIdHU1eXh4fffRRSYf2zps1axYqlQoLCwuuXr3KwoULMTc3p23btiUd2jtDkgrxusjPV6Or+2IfSBZClBxJcF8yXV1dvv32W27duoWenh4NGjQgJiam2Bvti5cnJyeHmTNncufOHQwNDWncuDEjR44s1lZq4t/Lzs4mIyMDIyOjkg7ljZCRkcGlS5ewsrKSMXsGxR03SW6FeLtIgvuSffrpp3z66aclHYYoxOjRoxk9enRJhyGEeInUajUZGRnIajwh3i2S4AohSoSBgYHMRD4DIyMjbGxsSjqM144sLRBCFEYSXCFEiYlcfUgeMhPPTR4OE0I8iSS4QogSc+32PS5fSy7pMIQQQrxlJMEVT1RwYMXThIWF0aVLl1cQTfGEhoaSlpbGihUrAAgPDyc6OlrrhLWijB49WuOkr4oVK/L+++8zaNAgnJycXmjMQgghhHixJMEVT7R27VqN1927d8ff3x8fHx/lWsFpW6+rbt26PfNpcAVq1KjBzJkzUavVXL16lfDwcAICAti2bRs1atR4wZEKIYQQ4kWRBFc8kaOjo9Y1S0vLQq+/rqpUqfLcp6MZGhoq79XJyYnq1avTs2dPduzYQXBw8AuM8skyMzMxNDR8JX09D7VaTU5ODgYGBiUdihBCCKF4Y4/qFSVr8+bN2NnZkZmZqVzz9fXFxsaG+/fvK9e6d++ucaxsamoqEyZMwMPDAzs7O7p06cLBgwe12t+3bx/dunXDwcGBJk2a8NVXX5Genq5R5++//6ZXr17Y29vTunVrjSUFBcLDw7WWFBQ3hscVPMF+/fr1Z471/Pnz+Pn5YW9vT9u2bdm6dSuhoaH4+/trxRofH0/37t2xt7dn1apVynsNCQnBxcUFR0dHgoKC+OeffzT62LBhAx07dsTBwQFXV1d69uxJfHx8scuzsrIICwvDw8MDe3t7OnXqxM8//6zRx+jRo/Hx8WH//v188MEH2Nvbs3fv3iLHTgghhHiVZAZXPJdGjRqRk5PDH3/8QZMmTUhOTub8+fMYGBhw7NgxvLy8yMjI4M8//6RPnz7Aw439AwICSEpK4vPPP6dy5cps3bqV4OBgNm7cqKz53bVrF0OHDqVLly4MGjSIxMREZs2aRWpqKnPmzAEeJmOBgYEYGRkxffp0AObPn8/9+/epXbv2E+MubgyFuXbtGgDVq1dXrhUn1szMTAIDAylfvjwzZswAIDIyktTUVK0lHjk5OQwfPpy+ffsydOhQTE1NuXr1Kj169KBevXp888036OjoEBUVRd++fdm1axcGBgb8/vvvjB07lsDAQLy8vMjMzCQ+Pp60tDSAIssBRowYwS+//MLnn39OnTp12LJlC4MGDSIyMpJWrVop9W7fvs2UKVMICQnB0tKSqlWrPuU3RQghhHj1JMEVz6VatWpUrVqVo0eP0qRJE44dO4aFhQUODg78/vvveHl5ceLECXJycmjYsCEA27Zt4+zZs2zZsoW6desC4OnpyZUrV1iwYAHz5s1DrVYzffp0OnTowNSpU5X+zM3NCQoKIjQ0lHr16rFx40Zu377Nzp07lYTWxsaGdu3aPTXBLU4Mj8rNzUWtVpOQkMDEiROpVq2acsxycWP94YcfSEpKYvXq1UpybGdnR9u2bQtNcIcOHUqHDh2Ua6NGjcLExISlS5dSunRpAJydnWnVqhXr16/Hz8+P+Ph4TE1NGTVqlHJf8+bNlZ+LKj979iw//fQTEydOpEePHgA0a9aMa9euaSW49+7dY/HixTRo0OCJ4yyEEEKUJFmiIJ5bw4YN+f3334GHM4SNGjWiUaNGGtdq1aqFhYUFAIcOHcLa2pratWuTm5ur/HN3d+fkyZMAXLp0iWvXrtG+fXuNOo0bN0ZXV5dTp04BDxO2evXqaSSztWrV4v33339qzMWJocD58+extbXFzs6Odu3aER8fT3h4OGZmZs8U66lTp7C2ttaY+a1evfoTY338obhDhw7RsmVL9PT0lD7Kly+PjY2N0oeNjQ0pKSmMHj2aQ4cOkZGRodFGUeXHjh0DoF27dhrX27dvz+nTpzWWXJiamkpyK4QQ4rUmM7jiuTVu3JipU6eSk5PD77//Trdu3WjQoAEzZswgIyODo0ePKrO3AMnJyZw+fRpbW1uttvT09JQ6AAMGDCi0zxs3bgAPvyavWLGiVnnFihXJysp6YszFiaFAzZo1mT17Nvn5+Zw9e5YZM2bw+eefs3XrVoyMjJ4p1oKk+FFmZmZasRoZGVGmTBmtmGNiYoiJidFqQ19fHwA3NzemT5/O8uXL6devH6VLl8bb25v//Oc/mJqaFll+79499PX1MTU11Wi/UqVKqNVq0tLSMDY2Vq4JIYQQrzNJcMVza9iwIRkZGcTFxXH27FkaNWpEnTp1MDQ0JC4ujv/97398+OGHSn0TExNUKpXG1/mPK0iwxo8fj4ODg1Z5wWywhYUFf/75p1Z5UlISZcuWfWL7xYmhQOnSpbG3twegQYMGVKhQgUGDBrFixQqCgoKeKdYzZ85old+9e1crmdXR0T5y1MTEBC8vLz755BOtskfv79SpE506deLu3bvs2bOHsLAwSpUqxddff11kuYmJCTk5Ody7dw8TExOlzTt37qCjo0O5cuWeGqMQQgjxOpEEVzw3KysrzM3NWbhwISYmJsqaVhcXF5YsWUJWVhaNGjVS6ru7u7N//34sLCyoXLlyoW3WqVOHKlWqcPXqVfz8/J7Yt729PZs3b+bKlSvUqlULgCtXrnD27FmNWePHFSeGJ2nbti3Ozs7ExMTQp0+fYsdqZ2fH5s2buXr1qrJ/bkJCAmfPnsXFxaXIft3c3Dh//jw2NjZas8yFMTMzo1u3bhw4cICLFy8Wq7wgjl27dtG9e3el7q5du7CxsVFmb4UQQog3gSS44l9xcXFh165dtG3bVrnWsGFDZs6cSZUqVTQOROjcuTNr1qyhd+/eBAYGUrt2bdLS0jh9+rSye4COjg6jR49mxIgRpKen07x5c4yMjLh+/Tr79+9n6NChWFlZ0aVLF7799luCg4MZMmQI8HAXhaK+Pi9ODE8zaNAgAgIC2LhxIz179ixWrB999BFRUVH079+fQYMGARAREUGlSpWKNRs6ePBgunbtSr9+/fj444+pVKkSd+7c4ciRIzRs2BAfHx/mz59PSkoKjRs3pmLFivz111/88ssv9O3bVxmbp5W///77tG3blm+++YbMzEysrKzYunUrJ06cYMGCBUXGKIQQQrxOJMEV/0rjxo3ZtWuXxqxp48aNAbRmUg0MDFi+fDnh4eFERUWRmJiIqakpNjY2Gl+/t2/fnvLlyxMVFcW2bduAh7s2eHp6KgmsoaEh0dHRTJgwgS+++ILKlSsTGhrKnj17NLa+elxxY3gSd3d3XFxciI6O5uOPP36mWL/66itGjBihxLp582aNr/6fpFatWqxfv565c+cyceJE0tPTMTc3p1GjRsq2Zvb29sTExLBz507u379PlSpV6NevHyEhIcUqB5gxYwazZ89m8eLFpKSkUKdOHebPn0/Lli2LjPF5VbMwKbqSEE8gvz9CiCfRUavV6pIOQoh3TUpKCq1bt6Zv374MHDiwpMN55Qp2rChY4yzE88rPV6Or++RvQtLT0zlz5gz169eXpTbFJGP2fGTcnl18fDw6Ojov5b8FMoMrxCuwaNEiKlWqRLVq1UhMTCQ6Opq8vDxlT913UXZ2NhkZGRgZGZV0KG+EjIwMLl26hJWVlYzZI56W3Aoh3l2S4ArxCujq6vLtt99y69Yt9PT0aNCgATExMVhaWpZ0aCVKvkAqPrVaTUZGhoyZEEIUgyS4QrwCn376KZ9++mlJh/HakS3Hik9HRwcjIyMZMyGEKAZJcIUQJcLAwEC+an8GRkZG2NjYlHQYr52i1uAKId5NkuAKIUpM5OpDXLt9r6TDEG+oahYmDOjZtKTDEEK8hiTBFc9k//79rFy5kpMnT5KWloaJiQl2dnZ88MEHdOjQAV1d3ZIO8V9LSEigVatWymsDAwOqVatGhw4dCAoKwtDQsASje7tcu32Py9eSSzoMIYQQbxlJcEWxzZ49m4ULF9KmTRvGjx+Pubk5d+7cYffu3XzxxReYmJjg6elZ0mG+MMOGDcPV1ZWMjAz27NlDZGQkd+7cYdKkSSUdmhBCCCGeQhJcUSz79u1j4cKFDBw4UDmNq0D79u3p06cPpUq9Xb9OtWrVwtHREXh4XO7FixfZsmULEyZMeCUz1ZmZma/9bPGbEKMQQoh3z5v/fbJ4JZYuXYq5ubnGyVePcnBwUB6A2bdvHwEBAbi5ueHs7Ey3bt04cOCARv2NGzeiUqk4efIkgYGBNGjQAG9vb3799Vfy8/OZM2cO7u7uuLu7M2vWLPLz85V7w8PDcXJy4vTp03Tv3h0HBwc+/PBDTp8+TVZWFl999RWNGjWiWbNmLFu2TKPfEydO0L9/fzw8PHB0dKRTp05s3ry5WGNQv359MjMzuXv3rnItNTWVCRMm4OHhgZ2dHV26dOHgwYMa96nVaiIiImjatClOTk4MHjyYX3/9FZVKRVxcnFJPpVKxaNEiZsyYQdOmTXFzc1PuX7JkCd7e3tjZ2dGqVSut93Xz5k2GDBmCu7s79vb2tGzZkq+//rrY5QC///47PXr0wMHBAVdXV8aMGUNKSopSnpCQgEqlYuPGjYwbNw5XV1e6detWrLETQgghXqW3a8pNvBS5ubkcP34cb2/vYs3SJiQk0KJFCwIDA9HV1eXAgQMEBQURExODq6urRt1Ro0bRo0cPAgICWLRoEQMHDuTDDz/k/v37TJs2jf/973+Eh4djbW2Nr6+vcl9OTg6jRo2ib9++VKpUiZkzZzJw4ECcnZ2pWLEic+fOZc+ePYSFheHg4ICzszMA169fx9nZmZ49e2JgYMDx48cZN24carWaDz/88Knv6/r165QpU4YKFSoADw8qCAgIICkpic8//5zKlSuzdetWgoODlQQeYMWKFURERPDpp5/SpEkTDh8+zLhx4wrtY/ny5TRo0ICpU6eSm5sLwNSpU1m/fj39+/enQYMGHD9+nJkzZ1K6dGl69uwJwMiRI7l9+zbjxo2jYsWK3Lhxg1OnTintFlV+6tQpAgICcHV1Zd68edy5c4dZs2Zx4cIF1qxZg56enlJ39uzZeHl5af3hIYQQQrwuJMEVRUpJSSE7O1vrUAK1Wk1eXp7yWldXF11dXXr16qVcy8/Px9XVlQsXLrBu3TqtBLdXr1588sknAFSuXBlfX19OnTrF2rVrAfD09GTv3r3s2rVLK8EdMWIEXl5eSj8FCeCYMWMAaNKkCbt27WLXrl1KgtuxY0eN+Bs1asStW7dYu3atVoKbn59Pbm6usgb3p59+4vPPP1eSvW3btnH27Fm2bNlC3bp1lXivXLnCggULmDdvHnl5eSxatIguXbowYsQIADw8PEhOTmbDhg1aY21iYkJERISy1+k///zDypUrmThxIt27dwfA3d2dzMxMIiMj6d69O7q6upw8eZJhw4bRoUMHpa3OnTsrPxdVHhUVhbm5OVFRUejr6wNgaWlJv3792L9/Py1btlTqvv/++0ydOlUrdiGEEOJ1IQmuKLbHN5j/8ccfGTJkiPLaz8+P8ePHc/PmTebMmcOvv/5KYmKicvKSra2tVptNm/7fFj+1a9cGHiamj7KysuLSpUsa13R1dZWv8B+9193dXbmmp6dHzZo1uXnzpnLt3r17hIeHs2fPHm7duqUk6KamplqxDR06VON1x44d+eyzz5TXhw4dwtramtq1ayuzrQUxbN26FXi4NCAxMVEjQQRo1apVoQlus2bNNMb5119/BaBt27ZafSxevJgbN25QrVo1bGxsiI6ORk9Pj6ZNm1KrVi2NdosqP3r0KD4+PkpyCw8T8fLly3Ps2DGN+Js3b64VtxBCCPE6kQRXFMnU1BQDAwONRBEePnhVkKQVrM3Nz88nJCSEtLQ0Bg8eTK1atTAyMmL+/PncuHFDq+1y5copPxsYGABQvnx5jTr6+vpkZ2drXDM0NFTqF9R5vL2C61lZWcrr0aNHc+LECQYMGEDdunUpW7Ysq1evZufOnVqxjRgxgiZNmpCWlsbKlSuJjY2lcePG9OjRA4Dk5GROnz5daOJeMMubmJgIgJmZmUZ5xYoVte4p7HpycjJqtVor6S9QkODOmTOHOXPmMHfuXCZOnIiVlRXDhg2jbdu2AEWWp6amFhpTxYoVuXfvntY1IYQQ4nUmCa4oUqlSpXB2dua3334jLy9PSd5MTEywt7cH/i85vXLlCqdPnyYyMpLWrVsrbWRmZr76wB+TlZXFvn37GD16NP7+/sr177//vtD6NWrUUN6fq6srXbt2Ze7cuXzwwQcYGxtjYmKCSqV66tf15ubmABoPpgEkJSUVWv/xWXITExN0dHT4/vvvNWZXC1hZWQFgYWFBWFgY+fn5nDp1im+//ZahQ4eya9cuatSoUWS5iYlJoTElJSVhYmLy1BiFEEKI143soiCKJSAggNu3bxMVFfXUegWzpY8mY9euXePEiRMvNb7iyM7OJj8/XyO2+/fvs3fv3iLv1dPT44svviA5OZl169YBD5cJXL16FQsLC+zt7bX+AVSpUgVzc3P27Nmj0d7u3buLFXPBMoyUlJRC+yhbtqxGfV1dXRwcHPj888/Jzc3lypUrxSp3cXFhz549GssgDh06RGpqKi4uLsWKVQghhHhdyAyuKJbmzZsTFBTE/PnzOXv2LO3bt8fCwoK0tDSOHj1KYmIiZcqUoU6dOlSpUkV5wj49PZ358+djYWFR0m+BcuXKYW9vz+LFizEzM6NUqVIsWrSIsmXLas2wFsbd3R0XFxeWLVuGn58fnTt3Zs2aNfTu3ZvAwEBq165NWloap0+fJicnh+HDh6Onp0dQUBBff/01lSpVwtXVlbi4OH777TeAIvfTtbKyws/Pj5EjR9KvXz8aNGhATk4Oly9fJi4ujgULFpCWlka/fv3o1KkTVlZW5OTksGLFCsqXL4+NjU2R5QD9+/enR48eBAcH4+/vr+yi4ODgoDzIJ4QQQrwpJMEVxTZ8+HBcXFxYtWoVEydO5P79+5iYmGBra8vXX39Nx44d0dXVJTw8nEmTJjFkyBAsLS0JCQnh8OHDGttSlZRZs2Yxfvx4Ro8ejampKf7+/qSnpxMdHV2s+wcOHEhAQADbtm2jS5cuLF++nPDwcKKiokhMTMTU1BQbGxtlZwgAf39/UlNT+f7771mxYgVubm588cUXDB06VGvNcGHGjRuHlZUVa9euJTIykjJlymBlZUW7du0AKF26NNbW1qxYsYIbN25gaGiInZ0dS5YswczMjOzs7KeWA9jZ2REdHc3s2bMZNGgQxsbGtGzZklGjRmlsESaEEEK8CXTUBY+4CyFemblz57J06VLi4uLeyZPATp48CcCBU6lcu32viNpCFK6ahQkDejZ9ap309HTOnDlD/fr1MTY2fkWRvdlkzJ6PjNuzi4+PR0dHR1nW9yLJDK4QL9nff//N1q1bcXJyQl9fnyNHjrBkyRJ69uz5Tia3jyoqORGiKPn5anR15cFHIYQmSXCFeMkMDQ05ceIEq1ev5sGDB1SuXJl+/foxaNCgkg6tRGVnZ5ORkYGRkVFJh/JGyMjI4NKlS1hZWcmYPUKSWyFEYSTBFeIlq1atGsuXLy/pMF5LskKq+NRqNRkZGTJmQghRDLJNmBCixMieusWno6ODkZGRjJkQQhSDzOAKIUqEgYGBfNX+DIyMjJRt3d4kskZWCFESJMEVQpSYyNWHZBeFt1hxdjkQQoiXQRJc8VYIDw8nOjr6tTgx7Wni4uLo3bs3GzZseCnbohTX3bt3WbRoEf/973+5fv06+vr62NjY0KlTJ7p06cLXX3/Npk2b+PXXXwvd6WHv3r2EhISwbNky5bS153Ht9j0uX0v+N29FCCGE0CIJrhDvmCtXrtC7d2/y8vIICAjA1taW7OxsDh8+TFhYGBUqVMDHx4eVK1fy3//+l/bt22u1ERsbi4WFBa6uriXwDoQQQoinkwRXiLdEZmZmsfbVHTFiBHl5efzwww9UrlxZud6sWTN69epFWloaKpWKGjVqsH37dq0ENz09nb1799K9e/cijxoWQgghSoL810m8E2bOnImvry9OTk54enoybNgwbt++rVHn2LFj+Pn54eLigpOTE76+vmzatKnY5QBr1qzB29sbOzs7WrZsyYIFC8jPz3+mWDdv3kzPnj1p3LgxjRo1wt/fn/j4eI064eHhODk5ER8fT/fu3bG3t2fVqlUALFq0iDZt2mBvb0+TJk3o27cvV69eBeDo0aPEx8cTHByskdwWqFq1KiqVCoCOHTty4MAB0tLSNOrs3buX9PR0fH19n+l9CSGEEK+KzOCKd0JSUhLBwcFYWFhw9+5dli5dir+/P7GxsZQqVYr79+8THByMi4sLs2fPxsDAgAsXLpCamgpQZDnAihUrmDJlCv7+/jRv3pwTJ04QERFBWloao0aNKnasCQkJdO7cmZo1a5KdnU1sbCx+fn5s3boVKysrpV5OTg7Dhw+nb9++DB06FFNTUzZv3sy8efMYPHgwjo6OpKWlcezYMR48eADAkSNHAPD09CwyDl9fX6Kiovjxxx/p2rWrcn379u3UqVMHW1vbYr8nIYQQ4lWSBFe8E8LCwpSf8/LycHJyolmzZhw+fBgPDw8uXbpEWloaw4YNU2YwH314qqjyvLw8IiMj6dixI+PGjQPAw8ODnJwcoqOjCQoKokKFCsWKdeDAgcrP+fn5NG3alPj4eDZt2sSwYcOUspycHIYOHUqHDh2Ua+vWrUOlUhEcHKxca926tfLzrVu3gIcztUWpW7cu9evXJzY2VklwU1JSOHjwICEhIcV6L0IIIURJkCUK4p2wf/9+evTogYuLCzY2NjRr1gyAy5cvA1CzZk3Kli3LhAkT2LFjB3fv3tW4v6jyixcvkpycTLt27TSud+jQgZycHK0lBk/z999/M2DAANzd3alfvz62trZcunRJifVRXl5eGq9tbGw4ffo0YWFhHD16lJycnGL3WxgfHx/i4uK4c+cOAD/99BM5OTmyPEEIIcRrTRJc8daLj48nNDQUCwsLpk+fztq1a1m3bh0AWVlZAJiYmLB06VLKlCnDyJEjadq0Kf7+/pw7d65Y5ffuPdzLtWLFihp9F7wuKC/K/fv3CQwM5Pr164wePZpVq1axYcMG3n//fSXWAkZGRpQpU0bjWpcuXRgzZgwHDx7Ez88PNzc3pkyZQmZmJoCy7vbGjRvFisfHx4f8/Hx27NgBwLZt23B0dKRmzZrFul8IIYQoCZLgirfe7t27KVu2LHPnzqVVq1Y4OjpSqVIlrXoODg589913HD16lKioKJKSkhgwYECxyk1NTQG0ZnaTkpKAhwlycfzxxx/cvHmTsLAwPvjgAxo2bIi9vb3Wg15Q+DG3urq69OnTh9jYWA4cOEBoaCirV69myZIlADRu3BiAgwcPFiueKlWq0KhRI2JjY7l16xZHjx7Fx8enWPcKIYQQJUUSXPHWy8zMRF9fXyMh3LZt2xPrGxoa4uXlRc+ePUlISNCaOS2s3MrKCjMzM3bt2qVRd+fOnejr6+Pg4FDsWAH09fWVa8ePH+fatWvFuv9RlStXJjAwEJVKxcWLFwFo2LAhDg4OREVFae0iAQ9ndgtmpQv4+Pjwxx9/sGjRInR0dDTW/AohhBCvI3nITLw18vLytBJMeLguNSYmhsmTJ9OmTRtOnDjBli1bNOrs27ePDRs20Lp1a6pWrcqdO3dYuXIlzs7OlC5dushygNDQUKZMmYKZmRleXl788ccfLF68mD59+mg9YHb48GGtpLV69eo4OjpibGzMxIkTCQoK4tatW4SHhxe6pVdhxo8fT/ny5XF0dKR8+fIcP36cs2fP0rNnT6XOzJkz8ff356OPPtI46OH3339n1apVTJs2TXmQDsDb25vJkyezatUqPDw8tJZhCCGEEK8bSXDFWyMrK4shQ4ZoXZ8+fTojRoxg5cqVbNy4EWdnZxYuXIi3t7dSp2bNmujq6jJ37lySkpIwNTXFw8ND2bWgqHIAf39/SpUqxbJly1i9ejXm5uYMHDiQ/v37a8U0c+ZMrWtdu3Zl6tSpzJs3j+nTpxMaGkrt2rWZOHEi3333XbHGwMnJiXXr1rF+/XoyMjKoUaMGY8aMoVu3bkqdWrVqsWnTJhYvXszq1au5ceMGBgYG2NjY8J///IcWLVpotGlqaoqnpyd79+6V5QlCCCHeCDpqtVpd0kEIId4tJ0+eBODAqVSu3S7eA3jizVPNwoQBPZuWaAzp6emcOXOG+vXrY2xsXKKxvClkzJ6PjNuzi4+PR0dHB3t7+xfetszgCiFKTEknP+Lly89Xo6ur/UCkEEK8TPKQmRCiRGRnZ5ORkVHSYbwxMjIyOH369Bs3ZpLcCiFKgiS4QogSIyukik+tVpORkSFjJoQQxSAJrhCixBS2l68onI6ODkZGRjJmQghRDLIGVwhRIgwMDDAyMirpMN4YRkZG2NjYlHQYz0zW4AohSoIkuEKIEhO5+pDsovAWex12URBCvJskwRVvtfDwcKKjozlx4kRJh/JUcXFx9O7dmw0bNryU7VKKQ6VSMXLkSPr166dxfc+ePaxatYpTp06Rnp6OhYUFHh4eBAQEYGVl9a/6vHb7HpevJf+rNoQQQojHyRpcIcQTzZw5k9DQUMqWLcvkyZNZunQpAwYM4MKFCwwdOrSkwxNCCCEKJTO4QrzlMjMzMTQ0fOb79u/fz+LFiwkNDdU4Ia5Ro0Z89NFH/Pe//32RYQohhBAvjMzginfazJkz8fX1xcnJCU9PT4YNG8bt27c16hw7dgw/Pz9cXFxwcnLC19eXTZs2FbscYM2aNXh7e2NnZ0fLli1ZsGAB+fn5zxTr5s2b6dmzJ40bN6ZRo0b4+/sTHx+vUSc8PBwnJyfi4+Pp3r079vb2rFq1CoBFixbRpk0b7O3tadKkCX379uXq1atP7C86OppKlSoRGhpaaPnjR/oKIYQQrwuZwRXvtKSkJIKDg7GwsODu3bssXboUf39/YmNjKVWqFPfv3yc4OBgXFxdmz56NgYEBFy5cIDU1FaDIcoAVK1YwZcoU/P39ad68OSdOnCAiIoK0tDRGjRpV7FgTEhLo3LkzNWvWJDs7m9jYWPz8/Ni6davGWticnByGDx9O3759GTp0KKampmzevJl58+YxePBgHB0dSUtL49ixYzx48KDQvnJzczl+/Dht27ZFX1//OUdXCCGEKBmS4Ip3WlhYmPJzXl4eTk5ONGvWjMOHD+Ph4cGlS5dIS0tj2LBhqFQqANzc3JR7iirPy8sjMjKSjh07Mm7cOAA8PDzIyckhOjqaoKAgKlSoUKxYBw4cqPycn59P06ZNiY+PZ9OmTQwbNkwpy8nJYejQoXTo0EG5tm7dOlQqFcHBwcq11q1bP7GvlJQUsrOzqVq1arFiE0IIIV4nskRBvNP2799Pjx49cHFxwcbGhmbNmgFw+fJlAGrWrEnZsmWZMGECO3bs4O7duxr3F1V+8eJFkpOTadeuncb1Dh06kJOTo7XE4Gn+/vtvBgwYgLu7O/Xr18fW1pZLly4psT7Ky8tL47WNjQ2nT58mLCyMo0ePkpOTU6w+5VABIYQQbyJJcMU7Kz4+ntDQUCwsLJg+fTpr165l3bp1AGRlZQFgYmLC0qVLKVOmDCNHjqRp06b4+/tz7ty5YpXfu/dwj9eKFStq9F3wuqC8KPfv3ycwMJDr168zevRoVq1axYYNG3j//feVWAsYGRlRpkwZjWtdunRhzJgxHDx4ED8/P9zc3JgyZQqZmZmF9mdqakrp0qW5fv16seITQgghXieyREG8s3bv3k3ZsmWZO3cuuroP/9a7du2aVj0HBwe+++47MjMziYuLY9q0aQwYMIDdu3cXWW5qagqgNbOblJQEPEyQi+OPP/7g5s2bLFy4kPfff1+5npaWRpUqVTTqFjbrqqurS58+fejTpw+3bt0iNjaWWbNmUaFCBQYMGKBVv1SpUjg7O3P48GFyc3MpVUr+X4UQQog3h8zgindWZmYm+vr6Ggnhtm3bnljf0NAQLy8vevbsSUJCgtbMaWHlVlZWmJmZsWvXLo26O3fuRF9fHwcHh2LHCmg88HX8+PFCE/KiVK5cmcDAQFQqFRcvXnxivYCAABITE4mKiiq0fP/+/c/ctxBCCPEqyLSMeOvl5eVpJZjwcF1qTEwMkydPpk2bNpw4cYItW7Zo1Nm3bx8bNmygdevWVK1alTt37rBy5UqcnZ0pXbp0keUAoaGhTJkyBTMzM7y8vPjjjz9YvHgxffr00XrA7PDhw1pJa/Xq1XF0dMTY2JiJEycSFBTErVu3CA8Pp3LlysUag/Hjx1O+fHkcHR0pX748x48f5+zZs/Ts2fOJ93h5efHpp58SHh7OhQsX6NixIxUqVCAhIYEffviBtLQ0rbW+QgghxOtAElzx1svKytI4qKDA9OnTGTFiBCtXrmTjxo04OzuzcOFCvL29lTo1a9ZEV1eXuXPnkpSUhKmpKR4eHsquBUWVA/j7+1OqVCmWLVvG6tWrMTc3Z+DAgfTv318rppkzZ2pd69q1K1OnTmXevHlMnz6d0NBQateuzcSJE/nuu++KNQZOTk6sW7eO9evXk5GRQY0aNRgzZgzdunV76n1ffPEFTk5OrFq1iv/85z9kZGQoR/U+fqSvEEII8brQUavV6pIOQgjxbjl58iQAB06lcu128R60E2+eahYmDOjZtERjSE9P58yZM9SvXx9jY+MSjeVNIWP2fGTcnl18fDw6OjrY29u/8LZlBlcIUWJKOvkRL19+vhpdXdluTgjxaslDZkKIEpGdnU1GRkZJh/HGyMjI4PTp02/cmElyK4QoCZLgCiFKjKyQKj61Wk1GRoaMmRBCFIMkuEKIEiMnpRWfjo4ORkZGMmZCCFEMsgZXCFEiDAwMMDIyKukw3hhGRkbY2Ni80DZlfawQ4m0lCa4QosRErj4kuyiUkNdhhwMhhHhZJMEVfPDBB5w7d45Vq1bRsGHDIus3bNiQPn36MGjQoGL3ERcXR+/evZXXenp6VK5cmWbNmvH5559rHXjwKqSmphITE0P79u2pW7euRplKpVJ+1tfXx9LSEi8vLwYOHKgcvyv+vWu373H5WnJJhyGEEOItIwnuO+78+fOcO3cOeHhMbXES3H8jLCyMOnXqkJuby4ULF5gzZw4JCQksWbLkpfZbmNTUVCIiIqhXr55WggsPD2jw8fEhKyuLI0eOEBUVxeXLl4t9uIIQQgghSoYkuO+4bdu2oaurS6NGjdi1axfjxo1DX1//pfVXr149ZUPnhg0bkpWVRVhYGA8ePKBMmTIvrd/nYWlpiaOjIwCurq7cvn2bdevWcfv2bSwsLF56/9nZ2ZQqVQpd3df3WdDMzEwMDQ1LOgwhhBBCw+v7X07x0qnVarZv306TJk0ICAggJSWFX375RaPO7t27adeuHfb29nTt2pX4+Hitdvbt20dAQABubm44OzvTrVs3Dhw4UKwYypQpg1qtJj8/X7mWn5/PggULaNmyJXZ2drRr1441a9Zo3fv777/To0cPHBwccHV1ZcyYMaSkpGjUWbRoEW3atMHe3p4mTZrQt29frl69SkJCAq1atQJgyJAhqFQqVCoVCQkJT4y1fv36ANy4cUO5lp2dzezZs2nRogV2dna0b9+ebdu2ad27Zs0aWrRoQYMGDQgICOD06dOoVCo2btyo1GnZsiWTJk1i8eLFtGjRAgcHB+X9bNy4EV9fX+zt7fH09GTOnDnk5eUp96ampjJu3Dg8PT2xt7fHy8uLoUOHFrsc4Ny5c/Tr1w9HR0dcXFwYPHgw169f16ijUqlYtGgRM2bMoGnTpri5uT1xvIQQQoiSIjO477Djx49z7do1BgwYgIeHB6ampmzfvp2WLVsCcObMGQYPHkyzZs0YM2YMCQkJfP7552RnZ2u0k5CQQIsWLQgMDERXV5cDBw4QFBRETEwMrq6uGnXz8/PJzc0lLy+P8+fPEx0djbu7O+XKlVPqTJ8+neXLlxMSEoKTkxP79u3jq6++Ijc3l169egFw6tQpAgICcHV1Zd68edy5c4dZs2Zx4cIF1qxZg56eHps3b2bevHkMHjwYR0dH0tLSOHbsGA8ePKBOnTpEREQwcOBAhg0bpsT5tJnZ69evo6urS9WqVZVrQ4YM4fjx4wwYMID33nuP/fv388UXX1C+fHm8vLwA2LNnD1999RXdunXD29ubM2fO8Pnnnxfax08//UStWrUYO3Ysurq6GBsbs3TpUmbMmEGfPn0YPXo0f//9t5LgjhgxAni49OOXX35h+PDhVKtWjcTERI0/Mooqv3HjBr169aJGjRrMmDGDrKws5syZQ69evdi6dStly5ZV6i5fvpwGDRowdepUcnNznzheQgghREmRBPcdtn37dkqXLk3btm3R19fH29ubrVu3KssFFi1ahKWlJZGRkejp6QFQunRpxo4dq9FOQdIJDxNYV1dXLly4wLp167QS3I8//ljjtUqlYtq0acrru3fvsnLlSvr166c8xObh4UFycjKRkZH07NkTPT09oqKiMDc3JyoqSllSYWlpSb9+/di/fz8tW7YkPj4elUpFcHCw0n7r1q2VnwtmZGvVqqUsRXhUQTKenZ1NXFwcq1evpnv37pibmwNw+PBh9u7dy5IlS/Dw8ACgadOmJCYmEh4eriS43377LU2aNGHKlCkAeHp6kpuby7x587T6zMnJYfHixco55vfv32f+/Pl8+umnDBs2TOlDX1+fb775hn79+lGhQgVOnjyJj48PH374odJWx44dlZ+LKl+2bBm5ublER0crD9HVr1+fjh07smnTJvz9/ZW6JiYmREREyH6sQgghXluyROEdlZuby65du/Dy8lJmT319fcnIyODnn38G4H//+x8tWrRQkluAdu3aabV18+ZNRo0ahaenJzY2Ntja2nLw4EEuXbqkVXfatGls2LCB9evXM3v2bHJycvj000958OABAPHx8eTk5Gj10759e+7evcvly5cBOHr0KK1atdJYL+zh4UH58uU5duwYADY2Npw+fZqwsDCOHj1KTk7OM43RzJkzsbW1xcnJif79+6NSqRg3bpxSfujQIUxNTWnSpAm5ubnKP3d3d86cOUNeXh55eXmcOXNGmRUvULA84nGurq5Kcgtw4sQJ0tPTadeunVYfmZmZnD9/XnmvmzZtYsmSJfz1119a7RZVfvToUVxdXTV2iHjvvfd4//33lfEs0KxZM0luhRBCvNZkBvcddejQIe7evUuLFi1ITU0FwNraGnNzc7Zv307nzp1JTEykYsWKGveVLVuW0qVLK6/z8/MJCQkhLS2NwYMHU6tWLYyMjJg/f77GWtUC7733nvKQmYODA7Vr16ZLly5s2rSJXr16ce/ewz1RK1WqpHFfweuCNampqalasQFUrFhRaaNLly48ePCAdevWsWzZMsqVK0fnzp0ZMWJEsR6M6t27Nx988AEZGRls3bqV9evXM2/ePIYPHw5AcnIyKSkp2NraFnp/YmIienp65ObmYmZmphVnYR6/npz8cAutR2deH1Uwxl9++SUmJiYsXbqU6dOnY2lpSVBQEJ988kmxylNTU5UZ7cfjKRjPomIXQgghXheS4L6jCh6EGjNmDGPGjNEoS05OJikpCXNzc5KSkjTK7t+/T1ZWlvL6ypUrnD59msjISI2v/zMzM4sVx3vvvQegzEQWzCAmJSVRuXJlpd6dO3c0yk1MTLRiK7jPxMQEAF1dXfr06UOfPn24desWsbGxzJo1iwoVKjBgwIAiY6tSpYqSjDdu3Jg7d+6wdOlSPvnkEywtLTExMcHMzIxFixYVer+ZmRl6enqUKlWKu3fvasVZmMdnRgveS0REBFWqVNGqX716dQDKlSvH2LFjGTt2LOfOnWP58uVMnDgRa2trGjZsWGT508azdu3aT41RCCGEeN3IEoV3UEZGBnv27KF169YsX75c49/s2bPJzc1lx44dODg48N///lfjaf1du3ZptFWQ7D66VODatWucOHGiWLEUfF1ecNCDvb09+vr6Wv3s3LmTihUrKsmWi4sLe/bs0XjI6dChQ6SmpuLi4qLVT+XKlQkMDESlUnHx4kWNmB9N2J9m5MiR5OfnK3v2uru7c/fuXfT19bG3t9f6Z2BggJ6eHvXr12fPnj0abe3evbtYfTo5OWFkZMTNmzcL7aOwAzJUKpXyR8vff/9drHIXFxcOHz6sMVt78eJFzp07V+h4CiGEEK8zmcF9B+3Zs4f09HT8/f21HgID+O6779i+fTtfffUVXbt2ZcCAAfTs2VM5kOHRJQp16tShSpUqzJo1i/z8fNLT05k/f/4TdyM4f/48eXl55Ofnc/XqVRYsWICRkRGdO3cGHs569urViyVLlmBgYICjoyP79+9n+/btfPnll8p64P79+9OjRw+Cg4Px9/dXdlFwcHBQHu4aP3485cuXx9HRkfLly3P8+HHOnj1Lz549ATA3N6d8+fLExsZSvXp1DAwMUKlUGBgYFBp7nTp16NChAxs2bGDAgAE0bdqUFi1a8Omnn/Lpp5+iUqnIyMjgwoULXLlyhalTpwIQEhJCaGgo48aNo127dpw+fZrNmzcDFLnHbfny5Rk8eDAzZszg5s2bNG7cGD09Pa5evcqePXsIDw/HyMiIHj160KZNG+rVq6fsIKGvr68c3FFUed++fdm4cSOBgYGEhISQlZXF3LlzsbS0fOLyCCGEEOJ1JQnuO2j79u1UrVq10OQWoHPnznz99deULVuWefPmMXPmTAYOHEi9evWYM2cO/fr1U+oaGBgQHh7OpEmTGDJkCJaWloSEhHD48GFOnTql1XbBzKGOjg6VKlXC3t6eefPmaXwNPnLkSMqVK8eGDRuIioqiWrVqTJw4kR49eih17OzsiI6OZvbs2QwaNAhjY2NatmzJqFGjlCTYycmJdevWsX79ejIyMqhRowZjxoyhW7duwMPkMiwsjNmzZ9O3b1+ys7PZs2eP8rV/YUJDQ9mxYwcrV65k0KBBzJ8/n0WLFrF69WquXbtGuXLlqFevHl26dFHuadWqFRMmTGDhwoVs3bqVBg0aMGHCBAIDAzW233qSwMBAKleuzNKlS1m5ciWlSpWiZs2aNG/eXJmFdnZ2ZvPmzSQkJKCrq4u1tTVRUVHKEpCiyi0tLVmxYgXTp09nxIgR6Orq0rRpU0aPHl2sGIUQQojXiY5arVaXdBBCvGvWr1/PuHHjikyo31YnT54E4MCpVK7dvldEbfEyVLMwYUDPpiUdxkuXnp7OmTNnqF+/vsYOJeLJZMyej4zbs4uPj0dHR0d53uVFkhlcIV6ylJQUIiIiaNKkCWXKlOHkyZNERUXRqlWrdzK5fdS7kGC9zvLz1ejqykODQoi3jyS4QrxkpUqV4urVq2zfvp20tDQqVKhAp06dlFPI3lXZ2dlkZGRgZGRU0qG8ETIyMrh06RJWVlYvbMwkuRVCvK0kwRXiJStbtiwLFy4s6TDEG06tVpORkYGsKhNCiKLJNmFCiBJhYGCAkZER+fmSsAkhhHixJMEVQpSYTbtPytfkQgghXjhJcIUQJSYx5UFJhyCEEOItJAmuECUgPDwcJyenEo3h3LlzDB8+HA8PD+zs7HB3d2fgwIH89ttvAPj6+tK3b98n3v/NN9/g6OjIgweSpAohhHi9SIIrxDto9+7ddO3alUuXLjF06FCWLl3KV199RenSpQkMDCQtLQ0fHx+OHDlCYmKi1v35+fns2LGDli1bUqZMmRJ4B0IIIcSTyS4KQrwlCo5ALjjd7EkSExMZNWoULi4uLFq0SONoYm9vb7p160apUqXw8fFhzpw57Nixgz59+mi08fvvv3Pr1i18fHxeynsRQggh/g2ZwRXiNTRz5kx8fX1xcnLC09OTYcOGcfv2bY06/v7+BAcHs2nTJry9vbG3t+fs2bOkpqYybtw4PD09sbe3x8vLi6FDhyr3rVu3jvv37zNmzBiN5LZAkyZNMDIyolq1ajg5OREbG6tVJzY2FlNTUzw9PV/8mxdCCCH+JZnBFeI1lJSURHBwMBYWFty9e5elS5fi7+9PbGwspUr93/9sT506xbVr1xgyZAjly5fH0tKSsLAwfvnlF4YPH061atVITEzkwIEDyj2///47FhYWqFSqIuPw8fFh0qRJXL16lRo1agCQk5PDjz/+SLt27YqcLRZCCCFKgiS4QryGwsLClJ/z8vJwcnKiWbNmHD58GA8PD6Xs3r17bNiwAUtLS+XayZMn8fHx4cMPP1SudezYUfn51q1bVK1atVhxtG/fnq+//prt27cTEhICwMGDB0lJScHX1/e5358QQgjxMskSBSFeQ/v376dHjx64uLhgY2NDs2bNALh8+bJGPWtra43kFsDGxoZNmzaxZMkS/vrrr0Lb19Ep3t6zZmZmNG3aVGOZwvbt26latSouLi7P8I6EEEKIV0cSXCFeM/Hx8YSGhmJhYcH06dNZu3Yt69atAyArK0ujbqVKlbTu//LLL/nggw9YunQpvr6+NG/enO+//14pr1y5MtevXy92PD4+Ppw/f56zZ8+SkZHB3r178fHxKXaSLIQQQrxqkuAK8ZrZvXs3ZcuWZe7cubRq1QpHR8dCE1kofCa2XLlyjB07loMHD7J161aaNm3KxIkTOXr0KACNGzfm1q1bnD9/vljxtG7dGmNjY2JjY9m7dy/p6emye4IQQojXmiS4QrxmMjMz0dfX10het23b9lxtqVQqxowZA8Dff/8NQLdu3ShbtixhYWHk5ORo3RMXF0dGRoby2tjYmJYtWxIbG8u2bduwtrYu1gNqQgghREmRh8yEKCF5eXns2rVL67qNjQ0xMTFMnjyZNm3acOLECbZs2VLsdnv06EGbNm2oV68eenp6bN68GX19fRo2bAiAubk506ZN4/PPP6dnz574+flRo0YNkpOT2b17N9u2bSMuLk6jTR8fH7Zv387169cZNmzYv3vjQgghxEsmCa4QJSQrK4shQ4ZoXZ8+fTojRoxg5cqVbNy4EWdnZxYuXIi3t3ex2nV2dmbz5s0kJCSgq6uLtbU1UVFRvPfee0qd1q1bs2HDBhYvXsysWbNITk6mfPnyuLi4EB0dTbly5TTa9PDwwNTUlHv37r3Q5QnmpnIKmhBCiBdPR61Wq0s6CCHEu+XkyZMA2Nvbk5+vRldXHlgrSnp6OmfOnKF+/foYGxuXdDhvDBm3Zydj9nxk3J5dfHw8Ojo62Nvbv/C2ZQ2uEKJEZGdnk5GRIcmtEEKIF04SXCFEiZEvkIQQQrwMkuAKIYQQQoi3iiS4QogSoaenV9IhCCGEeEtJgiuEKBF6enpyGpoQQoiXQhJcIV5j4eHhODk5lXQYQgghxBtFElwhhBBCCPFWkQRXCCGEEEK8VSTBFeINNnPmTHx9fXFycsLT05Nhw4Zx+/ZtjTrHjh3Dz88PFxcXnJyc8PX1ZdOmTcUuB1izZg3e3t7Y2dnRsmVLFixYQH5+/it5j0IIIcSzkqN6hXiDJSUlERwcjIWFBXfv3mXp0qX4+/sTGxtLqVKluH//PsHBwbi4uDB79mwMDAy4cOECqampAEWWA6xYsYIpU6bg7+9P8+bNOXHiBBEREaSlpTFq1KiSeutCCCHEE0mCK8QbLCwsTPk5Ly8PJycnmjVrxuHDh/Hw8ODSpUukpaUxbNgwVCoVAG5ubso9RZXn5eURGRlJx44dGTduHAAeHh7k5OQQHR1NUFAQFSpUeBVvVQghhCg2WaIgxBts//799OjRAxcXF2xsbGjWrBkAly9fBqBmzZqULVuWCRMmsGPHDu7evatxf1HlFy9eJDk5mXbt2mlc79ChAzk5OcTHx7+8NyeEEEI8J0lwhXhDxcfHExoaioWFBdOnT2ft2rWsW7cOgKysLABMTExYunQpZcqUYeTIkTRt2hR/f3/OnTtXrPJ79+4BULFiRY2+C14XlAshhBCvE0lwhXhD7d69m7JlyzJ37lxatWqFo6MjlSpV0qrn4ODAd999x9GjR4mKiiIpKYkBAwYUq9zU1BRAa2Y3KSkJeJggCyGEEK8bSXCFeENlZmair6+vcRrYtm3bnljf0NAQLy8vevbsSUJCgjLL+7RyKysrzMzM2LVrl0bdnTt3oq+vj4ODw4t9U0IIIcQLIA+ZCfGay8vL00owAWxsbIiJiWHy5Mm0adOGEydOsGXLFo06+/btY8OGDbRu3ZqqVaty584dVq5cibOzM6VLly6yHCA0NJQpU6ZgZmaGl5cXf/zxB4sXL6ZPnz7ygJkQQojXkiS4QrzmsrKyGDJkiNb16dOnM2LECFauXMnGjRtxdnZm4cKFeHt7K3Vq1qyJrq4uc+fOJSkpCVNTUzw8PBg2bFixygH8/f0pVaoUy5YtY/Xq1ZibmzNw4ED69+//8t+8EEII8Rx01Gq1uqSDEEK8W06ePAlA3bp1MTIyKuFo3gzp6emcOXOG+vXrY2xsXNLhvDFk3J6djNnzkXF7dvHx8ejo6GBvb//C25Y1uEKIEpGXl4f8fS2EEOJlkARXCFEi8vLySjoEIYQQbylJcIUQQgghxFtFElwhhBBCCPFWkQRXCFEi9PT0SjoEIYQQbylJcIUQJUJPT0/jkAohhBDiRZEEV4hChIeHo1KpCv23aNEiAFQqFUuWLHmh/Z45c4bw8HAyMjI0rm/cuBGVSqV1ZG5x3oOfn59W2dSpU2nZsuUzx7dx48annpYmhBBCvA7koAchnsDQ0JCYmBit65aWli+tzzNnzhAREYGfn5/G/rDNmzdn7dq1lC9f/pnbPHr0KHFxcbi6uv7r+DZt2oSxsTG+vr7/ui0hhBDiZZEEV4gn0NXVxdHRsaTDAMDMzAwzM7Nnvs/Y2Ji6deuyYMGCF5LgCiGEEG8CWaIgxAuyb98+AgICcHNzw9nZmW7dunHgwAGNOqmpqYwbNw5PT0/s7e3x8vJi6NChwMOv/8eMGQOAm5sbKpVKWUZQ2BKF7Oxs5syZQ6tWrbCzs6NZs2aMHj1aK67Q0FAOHz7M8ePHnxp/amoqEyZMwMPDAzs7O7p06cLBgweVcn9/f44cOcK+ffuU5Rrh4eHPN1hCCCHESyQzuEI8RW5urta1UqUK/59NQkICLVq0IDAwEF1dXQ4cOEBQUBAxMTHK7GlYWBi//PILw4cPp1q1aiQmJipJcPPmzQkJCeHbb7/lu+++o1y5chgYGDwxtkGDBnH48GGCg4NxdHTk7t27/PTTT1r1WrRogY2NDZGRkU9cM5ydnU1AQABJSUl8/vnnVK5cma1btxIcHKwk11999RVffPEFhoaGjBo1CoAqVao8fQCFEEKIEiAJrhBPkJ6ejq2trdb1VatW0bBhQ63rvXr1Un7Oz8/H1dWVCxcusG7dOiXBPXnyJD4+Pnz44YdK3Y4dOwIPlyHUrFkTAFtb26cuSTh06BD79u1j1qxZ+Pj4KNcf/flRISEhDBo0iPj4eBwcHLTKt23bxtmzZ9myZQt169YFwNPTkytXrrBgwQLmzZtH3bp1KVu2LMbGxq/N0g0hhBCiMJLgCvEEhoaGrFy5Uut6nTp1Cq1/8+ZN5syZw6+//kpiYiJqtRpAI0m2sbFh06ZNmJub4+npibW19XPF9ttvv2FkZKQkx0Vp06YN1tbWREZGsnDhQq3yQ4cOYW1tTe3atTVmrd3d3dm6detzxSiEEEKUFElwhXgCXV1d7O3ti1U3Pz+fkJAQ0tLSGDx4MLVq1cLIyIj58+dz48YNpd6XX36JiYkJS5cuZfr06VhaWhIUFMQnn3zyTLGlpKRgbm5e7H1kdXR06N+/P8OGDePPP//UKk9OTub06dOFzljLgQxCCCHeNJLgCvECXLlyhdOnTxMZGUnr1q2V65mZmRr1ypUrx9ixYxk7diznzp1j+fLlTJw4EWtr60KXPTyJqampMktc3CS3ffv2hIeHs2DBAqpWrapRZmJigkqlYurUqcWOQQghhHhdyS4KQrwAWVlZAOjr6yvXrl27xokTJ554j0qlUnZN+PvvvzXuz87Ofmp/7u7uZGRksHPnzmLHqKurS//+/dmzZw/nzp3Tau/q1atYWFhgb2+v9a+Avr6+8l6FEEKI15XM4ArxBPn5+fzxxx9a1ytWrEiNGjU0rtWpU4cqVaowa9Ys8vPzSU9PZ/78+VhYWGjU69GjB23atKFevXro6emxefNm9PX1ldnb9957D3j4IFvr1q0xNDREpVJpxeDu7o6Xlxf/+c9/+Oeff2jQoAEpKSn8+OOPzJ0794nvydfXl8jISOLi4qhWrZpyvXPnzqxZs4bevXsTGBhI7dq1SUtL4/Tp0+Tk5DB8+HDlfW7evJm9e/dibm6OhYUFlStXLtZ4CiGEEK+KJLhCPEFmZibdu3fXut61a1etr/INDAwIDw9n0qRJDBkyBEtLS0JCQjh8+DCnTp1S6jk7O7N582YSEhLQ1dXF2tqaqKgoJbG1sbFh0KBBrF+/nu+++w5LS0v27t1baHzh4eFERESwdu1aIiIiqFixIk2bNn3qe9LT0yMoKIhx48Zpxb98+XLCw8OJiooiMTERU1NTbGxsNNYHf/bZZ/zzzz+MGjWK1NRUBg4cyKBBg54+kEIIIcQrpqMueNRbCCFekZMnTwJQt25djSOJxZOlp6dz5swZ6tevj7GxcUmH88aQcXt2MmbPR8bt2cXHx6Ojo1PsB7qfhazBFUKUiLy8POTvayGEEC+DJLhCiBKRl5dX0iEIIYR4S0mCK4QQQggh3iqS4AohhBBCiLeKJLhCiBIhJ6QJIYR4WSTBFUKUCD09vWKfwiaEEEI8C9kHVxRLwZ6rADo6OpQpU4aqVavSqFEj/Pz8lH1c3wYbN25UThgDKFu2LLVq1aJ379507ty55AITQgghRLFIgiuKzdDQkJiYGAAePHjAX3/9xdq1a1m3bh1Tp06lU6dOJRzhi/Xdd99Rrlw5kpOTWbFiBaNGjUJfX5+OHTuWdGhCCCGEeApJcEWx6erq4ujoqLxu2rQpn3zyCUFBQYwdOxZnZ2etI2zfZLa2tpiZmQHg6upK8+bN2bhx4ytLcDMzMzE0NHwlfT2vNyFGIYQQ7x5Zgyv+ldKlS/Pll1+Sk5PD+vXrAVCpVCxZskSj3rJly1CpVMrruLg4VCoVv/zyC0OGDMHJyYnmzZuzbds2AJYvX07z5s1p3LgxY8eOJTs7W7l348aNqFQqTp48SWBgIA0aNMDb25tff/2V/Px85syZg7u7O+7u7syaNYv8/HwAzp07h0ql4tChQxqx5eXl4enpyfTp05/4Po2NjalVqxbXr1/XuP73338TEhKCi4sLjo6OBAUF8c8//2jUSUtLY8SIETg5OeHm5sbs2bOJjo4udDz27dvH4MGDcXZ2ZsiQIQCkpqYyYcIEPDw8sLOzo0uXLhw8eFCjj2PHjuHn54eLiwtOTk74+vqyadOmYpcDrFmzBm9vb+zs7GjZsiULFixQxu7RcT9x4gQBAQE4Ojo+dcyEEEKIkiIzuOJfq1u3LpUrV+bEiRPPfO+ECRP48MMP+fjjj1m3bh0jR47k7NmznD9/nokTJ3L16lW++eYbatSoQf/+/TXuHTVqFD169CAgIIBFixYxcOBAPvzwQ+7fv8+0adP43//+R3h4ONbW1vj6+qJSqWjQoAE//PADTZs2Vdr55ZdfuH37Nh999NET48zPz+fmzZu8//77yrWrV6/So0cP6tWrxzfffIOOjg5RUVH07duXXbt2YWBgAMCYMWM4fPgwX3zxBdWqVWPdunX8+eefhfbz5Zdf8sEHHxAZGYmuri7Z2dkEBASQlJTE559/TuXKldm6dSvBwcFKwnn//n2Cg4NxcXFh9uzZGBgYcOHCBVJTUwGKLAdYsWIFU6ZMwd/fn+bNm3PixAkiIiJIS0tj1KhRGjEOHz6c7t27ExwcLMfsCiGEeC1JgiteCEtLS+7cufPM97Vr146BAwcC4ODgwM8//0xsbCw///wz+vr6ABw5coRdu3ZpJbi9evXik08+AaBy5cr4+vpy6tQp1q5dC4Cnpyd79+5l165d+Pr6AtCtWzcmT57MvXv3MDExAeCHH37AyclJ60G5/Px8cnNzSU5OZvHixaSkpBAcHKyUR0REYGJiwtKlSyldujQAzs7OtGrVivXr1+Pn58eFCxf4+eefmTZtmvKAmqenJ+3bty90PFq2bMkXX3yhvP7hhx84e/YsW7ZsoW7dusr9V65cYcGCBcybN49Lly6RlpbGsGHDlFlhNzc3pY2iyvPy8oiMjKRjx46MGzcOAA8PD3JycoiOjiYoKIgKFSoo9Xv06EFQUFCh8QshhBCvA1miIF4ItVr9XFs+PTqTWq5cOczMzGjYsKGS3ALUrl2bGzduPPXe2rVrA9CkSRONOlZWVhr3duzYkVKlSrF9+3YA7t69y3//+1+6du1aaPu2trZ4eHgQExPDf/7zHxo2bKiUHzp0iJYtW6Knp0dubi65ubmUL18eGxsbTp06BcDJkycBaNWqlXKfrq4uLVq0KHQ8mjdvrvH60KFDWFtbU7t2baWP3Nxc3N3dlbZr1qxJ2bJlmTBhAjt27ODu3bsabRRVfvHiRZKTk2nXrp3G9Q4dOpCTk0N8fPxTYxRCCCFeNzKDK16ImzdvKknmsyhXrpzGawMDA8qXL69xTV9fX2MNbmH3FiwHKOpeY2NjfHx82LBhA35+fmzduhV9ff1CZ1SXLVtGmTJluHnzJvPnz2fq1Kk4OTkpyxSSk5OJiYlRdpZ4vF+AxMRE9PX1td5nwcNrj6tYsaLG6+TkZE6fPo2tra1W3YKDEgpmkefPn8/IkSPJy8ujYcOGjBs3DpVKVWT5vXv3Cu274HVBeYFKlSoVGrsQQgjxupAEV/xr58+f59atW3z44YfAw2QzJydHo86j6z1LWrdu3Vi7di1nz55l48aNtG/fnjJlymjVU6lUmJmZ4eDggL29Pe3bt2fmzJl89913wMPE0svLS1km8aiC9szNzcnJySEtLU0jyX18FrXA47PgJiYmqFQqpk6d+tT35ODgwHfffUdmZiZxcXFMmzaNAQMGsHv37iLLTU1NC40pKSlJiUEIIYR4k0iCK/6VrKwsJk+ejIGBAd26dQOgSpUq/P333xr1fv3115IIr1D29vbUr1+fKVOmcO7cOb766qsi77G0tKRPnz5ERUVx+vRpbGxscHNz4/z589jY2Dzx2Fk7OzsA9uzZo6zBzc/P57///W+xYnV3d2f//v1YWFhQuXLlIusbGhri5eXFP//8w9SpU8nKylLWBz+p3MrKCjMzM3bt2kWbNm2Uujt37kRfXx8HB4dixSqEEEK8LiTBFcWWn5/PH3/8AUB6erpy0EPBTgfVq1cHwNvbm5iYGOzt7bGysmLr1q3cunWrBCPX1q1bNyZNmoSVlRUuLi7FuicgIICVK1eyePFi5syZw+DBg+natSv9+vXj448/plKlSty5c4cjR47QsGFDfHx8qFevHm3atGHKlClkZGRQtWpV1q1bR2ZmZrHWLHfu3Jk1a9bQu3dvAgMDqV27NmlpaZw+fZqcnByGDx/Ovn372LBhA61bt6Zq1arcuXOHlStX4uzsTOnSpYssBwgNDWXKlCmYmZnh5eXFH3/8weLFi+nTp4/GA2ZCCCHEm0ASXFFsmZmZdO/eHXi4lrV69eq4ubkRERGhsQNBaGgoSUlJREZGoqOjQ/fu3enduzfffPNNSYWupU2bNkyaNOmpW4M9ztTUlF69erF48WL++ecfatWqxfr165k7dy4TJ04kPT0dc3NzGjVqpLHH7ddff82kSZOYPn06BgYGfPjhh9SrV49Vq1YV2aeBgQHLly8nPDycqKgoEhMTMTU1xcbGRlkaUbNmTXR1dZk7dy5JSUmYmpri4eHBsGHDilUO4O/vT6lSpVi2bBmrV6/G3NycgQMHau1cIYQQQrwJdNRqtbqkgxDiVduwYQNfffUV+/btw9zc/JX37+fnh66uLitWrHjlfb8OCnaAqFu3ruylW0zp6emcOXOG+vXrY2xsXNLhvDFk3J6djNnzkXF7dvHx8ejo6GBvb//C25YZXPFOSUhIUPaQbd++/StJbn/88Udu3LiBtbU1GRkZbN++naNHjxIZGfnS+36d5eXlIX9fCyGEeBkkwRXvlIiICLZv346TkxOjR49+JX0aGxuzZcsWLl++TE5ODnXq1GHGjBm0bt36lfT/usrLyyvpEIQQQrylJMEV75Rvvvnmla8F9vT0xNPT85X2KYQQQrzL5CQzIUSJeNLWakIIIcS/JQmuEKJE6OnpPdfxzkIIIURRJMEVQgghhBBvFUlwhXiC8PBwnJycSjqMIsXFxaFSqZStt4QQQoh3nSS4QgghhBDirSIJrhDiiTIzM0s6BCGEEOKZSYIrxHOaOXMmvr6+ODk54enpybBhw7h9+7ZGnWPHjuHn54eLiwtOTk74+vqyadOmYpcDrFmzBm9vb+zs7GjZsiULFiwgPz//mWLdvHkzPXv2pHHjxjRq1Ah/f3/i4+M16hQsyYiPj6d79+7Y29srxwkvWrSINm3aYG9vT5MmTejbty9Xr159phiEEEKIV0X2wRXiOSUlJREcHIyFhQV3795l6dKl+Pv7ExsbS6lSpbh//z7BwcG4uLgwe/ZsDAwMuHDhAqmpqQBFlgOsWLGCKVOm4O/vT/PmzTlx4gQRERGkpaUxatSoYseakJBA586dqVmzJtnZ2cTGxuLn58fWrVuxsrJS6uXk5DB8+HD69u3L0KFDMTU1ZfPmzcybN4/Bgwfj6OhIWloax44d48GDBy9uMIUQQogXSBJcIZ5TWFiY8nNeXh5OTk40a9aMw4cP4+HhwaVLl0hLS2PYsGGoVCoA3NzclHuKKs/LyyMyMpKOHTsybtw4ADw8PMjJySE6OpqgoCAqVKhQrFgHDhyo/Jyfn0/Tpk2Jj49n06ZNDBs2TCnLyclh6NChdOjQQbm2bt06VCoVwcHByrV3/RQ2IYQQrzdZoiDEc9q/fz89evTAxcUFGxsbmjVrBsDly5cBqFmzJmXLlmXChAns2LGDu3fvatxfVPnFixdJTk6mXbt2Gtc7dOhATk6O1hKDp/n7778ZMGAA7u7u1K9fH1tbWy5duqTE+igvLy+N1zY2Npw+fZqwsDCOHj1KTk5OsfsVQgghSoIkuEI8h/j4eEJDQ7GwsGD69OmsXbuWdevWAZCVlQWAiYkJS5cupUyZMowcOZKmTZvi7+/PuXPnilV+7949ACpWrKjRd8HrgvKi3L9/n8DAQK5fv87o0aNZtWoVGzZs4P3331diLWBkZESZMmU0rnXp0oUxY8Zw8OBB/Pz8cHNzY8qUKfIAmhBCiNeWLFEQ4jns3r2bsmXLMnfuXHR1H/6deO3aNa16Dg4OfPfdd2RmZhIXF8e0adMYMGAAu3fvLrLc1NQUQGtmNykpCXiYIBfHH3/8wc2bN1m4cCHvv/++cj0tLY0qVapo1C3sZDFdXV369OlDnz59uHXrFrGxscyaNYsKFSowYMCAYsUghBBCvEoygyvEc8jMzERfX18jIdy2bdsT6xsaGuLl5UXPnj1JSEjQmjktrNzKygozMzN27dqlUXfnzp3o6+vj4OBQ7FgB9PX1lWvHjx8vNCEvSuXKlQkMDESlUnHx4sVnvl8IIYR4FWQGV4inyMvL00ow4eG61JiYGCZPnkybNm04ceIEW7Zs0aizb98+NmzYQOvWralatSp37txh5cqVODs7U7p06SLLAUJDQ5kyZQpmZmZ4eXnxxx9/sHjxYvr06aP1gNnhw4e1ktbq1avj6OiIsbExEydOJCgoiFu3bhEeHk7lypWLNQbjx4+nfPnyODo6Ur58eY4fP87Zs2fp2bPnswylEEII8cpIgivEU2RlZTFkyBCt69OnT2fEiBGsXLmSjRs34uzszMKFC/H29lbq1KxZE11dXebOnUtSUhKmpqZ4eHgouxYUVQ7g7+9PqVKlWLZsGatXr8bc3JyBAwfSv39/rZhmzpypda1r165MnTqVefPmMX36dEJDQ6lduzYTJ07ku+++K9YYODk5sW7dOtavX09GRgY1atRgzJgxdOvWrVj3CyGEEK+ajlqtVpd0EEKId8vJkyfJy8vD2toaY2Pjkg7njZCens6ZM2eoX7++jNkzkHF7djJmz0fG7dnFx8ejo6ODvb39C29b1uAKIUpEXl5eSYcghBDiLSUJrhBCCCGEeKtIgiuEEEIIId4qkuAKIUqEgYFBofvuCiGEEP+WJLhCCCGEEOKt8s4kuOHh4ahUKuVfkyZN6N27N0ePHn1lMYwePRofH5+X2sfdu3extbXFycnprT9KNTw8HCcnp5fS9ujRozV+X9zd3QkMDOTEiRMvpT8hhBBCvDjv1D64hoaGxMTEAHDz5k0WLFhA37592bhxI9bW1i+9/9DQUNLT019qHzt27CA3N5fc3Fz27t1Lhw4dXmp/Jalbt254eXm9tPZr1KjBzJkzUavVXL16lfDwcAICAti2bRs1atR4af0KIYQQ4t95Z2ZwAXR1dXF0dMTR0ZF27doRFRVFbm4ua9aseSX916xZk/fff/+l9rF9+3bee+89KleuzNatW19qX4V5lbPGVapUKfZxtc/D0NAQR0dHnJyc+OCDD5g2bRoZGRns2LHjpfX5uNd9Fl6tVpOdnV3SYQghhBAa3qkE93FVq1bFzMyMhIQEoqOj+eijj3BxccHNzY3g4GAuXbqkUf/8+fN89tlnuLq60qBBA7y9vVm8eHGxyx9dopCQkIBKpSr0GNguXbponGZ18+ZNRowYgaurKw4ODvj5+XHq1Cmt+65evcqJEyfw9fWlY8eOHDx4kJSUFK16P//8M97e3tjb2/Pxxx/z559/0rBhQ8LDw5U6arWaiIgImjZtipOTE4MHD+bXX39FpVIRFxen1FOpVCxatIgZM2bQtGlT3NzclPuXLFmCt7c3dnZ2tGrVimXLlmnEcfPmTYYMGYK7uzv29va0bNmSr7/+utjljy5RSE9Px9HRkSVLlmi938GDB9O9e3fldWpqKhMmTMDDwwM7Ozu6dOnCwYMHte57nI2NDQDXr1/XuL5v3z66deuGg4MDTZo04auvvtKaqT9//jx+fn7Y29vTtm1btm7dSmhoKP7+/lrvJz4+nu7du2Nvb8+qVasA+PvvvwkJCcHFxQVHR0eCgoL4559/NPrYsGEDHTt2xMHBAVdXV3r27El8fHyxy7OysggLC8PDwwN7e3s6derEzz//rNFHwe/w/v37+eCDD7C3t2fv3r1Fjp0QQgjxKr1TSxQed//+fVJSUrCwsODmzZv06tWLqlWrcv/+fdasWUOPHj348ccfMTU1BaB///5UqlSJqVOnUrZsWf755x9u3ryptFdU+aOqV6+Oo6MjO3bsoF27dsr1y5cv8+effzJw4EAA7t27xyeffIKxsTFffvkl5cqVY8WKFfTp04effvqJihUrKvdu374dAB8fH1JTU4mOjmbXrl306NFDqXP69GmGDBlCixYt+M9//sO1a9cYOnSo1izcihUriIiI4NNPP6VJkyYcPnyYcePGFfpeli9fToMGDZg6dSq5ubkATJ06lfXr19O/f38aNGjA8ePHmTlzJqVLl6Znz54AjBw5ktu3bzNu3DgqVqzIjRs3NBL3osofZWxsTMuWLYmNjaVfv34an/G+ffv44osvAMjOziYgIICkpCQ+//xzZaY7ODiYjRs3olKpCm0f4Nq1a8pnV2DXrl0MHTqULl26MGjQIBITE5k1axapqanMmTMHeDgLGxgYSPny5ZkxYwYAkZGRpKamUrNmTY0+cnJyGD58OH379mXo0KGYmppy9epVevToQb169fjmm2/Q0dEhKiqKvn37smvXLgwMDPj9998ZO3YsgYGBeHl5kZmZSXx8PGlpaQBFlgOMGDGCX375hc8//5w6deqwZcsWBg0aRGRkJK1atVLq3b59mylTphASEoKlpSVVq1Z94pgJIYQQJUL9jpg/f77a0dFRnZOTo87JyVFfvXpVPXDgQLW1tbX6wIEDGnVzc3PVGRkZakdHR/WaNWvUarVanZSUpLa2tlbv2bOn0PaLKler1epRo0apO3bsqLyOiYlR29vbq9PS0pRr4eHh6kaNGqmzsrLUarVaPW/ePLWLi4v6zp07Sp2srCx18+bN1dOmTdNov2PHjuqPP/5Yed2uXTv1J598olFn8ODB6jZt2qjz8vKUa5s3b1ZbW1ur58+fr7z/pk2bqseMGaNx73/+8x+1tbW1+vDhw8o1a2trdYcOHdT5+fnKtStXrqhVKpUydgVmzJihbtq0qdK3o6Ojevny5U8cr6LKCz7TArt371ZbW1urL126pFzbtGmTun79+urExES1Wq1Wb9iwQW1jY6M+f/68RlvdunVTDx48WHld8Fnl5OSos7Oz1RcvXlT7+/urW7RooU5KSlKr1Wp1fn6+ukWLFuphw4ZptLV//361SqVS//XXX2q1Wq1euXKlun79+uqrV68qda5evaquX7++ulevXhrvx9raWh0bG6vR3siRI9WtWrVSZ2ZmKteSkpLUjo6O6pUrV6rVarX6u+++Uzdu3PiJY1VU+ZkzZ9TW1tbq1atXa1zv3r27+sMPP9QYF2tra/Uff/zxxLaKIz4+Xh0fH69OT0//V+28Sx48eKA+evSo+sGDByUdyhtFxu3ZyZg9Hxm3Z/e///1PHR8f/1LafqeWKKSnp2Nra4utrS2tWrUiLi6O8ePH4+npyR9//EFAQACurq7Y2NjQoEED0tPTuXz5MgAVKlSgWrVqzJ49m02bNmnNzBZVXpj27duTk5PD7t27lWs7duygbdu2GBgYAHDo0CFcXV0xMTFRHh7T1dWlUaNGnDx5Urnv7NmznD9/XmOXho4dO3Ls2DGNr9RPnjxJ8+bN0dX9v4/+0dk5eLg0IDExkZYtW2pcf7xegWbNmmnsZ/rrr78C0LZtWyXm3Nxc3N3dSUxM5MaNG8DDr/yjo6P5/vvvuXLlila7RZU/ztPTk/LlyxMbG6tci42NxdXVlUqVKgEPx9Pa2pratWtrxfboeMLDZQW2trbY2dnRrl074uPjCQ8Px8zMDIBLly5x7do12rdvr9FW48aN0dXVVWabT506hbW1tcbMb/Xq1Z+4HvvxB+cOHTpEy5Yt0dPTU/ooX748NjY2Sh82NjakpKQwevRoDh06REZGhtZYPq382LFjABrfJsDD39HTp09rLLkwNTWlQYMGhcYuhBBCvA7eqQTX0NCQDRs28MMPP7B3714OHz6Mn58f169fJzAwkLy8PCZOnMjq1avZsGEDFStWJCsrCwAdHR2WLFlCnTp1mDRpEl5eXnTp0oXff/+9WOWFMTc3x9XVVUnIzp49y99//62RpCYnJ7N7924lMS/4t2XLFo0keuvWrejq6uLh4UFqaiqpqal4eXmhVquVpQsAiYmJSoJWoGzZspQuXVqjDqBV79HlEE+7npycjFqtpkmTJhoxBwQEACgJ7pw5c2jSpAlz586lbdu2tGvXjp9++klpp6jyxxkYGNC2bVvlIbDk5GR+/fVXrfE8ffq01nh+++23Wn+U1KxZkw0bNrBu3TomTZpEqVKl+Pzzz5XkMDk5GYABAwZotNWgQQPy8vKU93n79m2tsSxsfAGMjIwoU6aM1njGxMRoxXz06FGlDzc3N6ZPn8758+fp168fTZo0YeTIkcoa7KLK7927h76+vrIcp0ClSpVQq9UaSxkK/lgQQgghXlfv1BpcXV1d7O3tta7/8ssvpKenExERQfny5QHIzc3l3r17GvWsrKyYP38+OTk5nDhxgtmzZ9O/f38OHDhAmTJliiwvTMeOHZk4cSLJycnExsZibm5O48aNlXITExM8PT0ZMmSI1r0Fs7xqtZodO3aQn5+vNQMHsG3bNoKCgoCHSfXdu3c1yu/fv68k8gV1AK16SUlJhb6Hx0+jMjExQUdHh++//x59fX2t+lZWVgBYWFgQFhZGfn4+p06d4ttvv2Xo0KHs2rWLGjVqFFleGB8fHzZs2MDZs2f5448/0NXVpW3bthqxqVQqpk6dWuj9jypdurTy+9KgQQMqVKjAoEGDWLFiBUFBQUoyOH78+EJ3c7CwsFD+75kzZ7TK7969q/V7UdjJXiYmJnh5efHJJ59olT16f6dOnejUqRN3795lz549hIWFUapUKeXBvKeVm5iYkJOTw7179zAxMVHavHPnDjo6OpQrV+6pMQohhBCvk3cqwX2SzMxMdHR0KFXq/4Zj586dygNTj9PX16dx48YEBQUREhLC7du3laStOOWPatu2LRMnTuTHH38kNjaWDh06aCwfcHd3Z+vWrbz33nsYGxsX2kbBTN6gQYNo1KiRRtkvv/zC4sWLOXfuHCqVCnt7e/bt28fo0aOVfh5dIgEPt98yNzdnz549tG7dWrn+eL0nKdhJISUlRWuZQ2F0dXVxcHDg888/Z+/evVy5ckUjgS2q/FGNGzfG3Nyc2NhY/vjjD5o1a6aRnLm7u7N//34sLCyoXLlysd5PgbZt2+Ls7ExMTAx9+vShTp06VKlShatXr+Ln5/fE++zs7Ni8eTNXr15V4k5ISODs2bO4uLgU2a+bmxvnz5/HxsYGPT29IuubmZnRrVs3Dhw4wMWLF4tVXhDHrl27NHac2LVrFzY2Nk/83RNCCCFeR5LgAk2aNAFgzJgx9OjRg/Pnz7N06VJlNhceLh+YNm0aHTp0oEaNGty/f5+FCxdSrVo1atasWWT5kxTM0EZGRnL79m2tk8769u3Ltm3b6NWrF71796Zq1arcvXuX//3vf1SuXFkpNzY2JiAgQGtGsF69eixbtozt27ejUqkIDg6ma9euDBo0iI8//pjr168THR1N6dKllZk5PT09goKC+Prrr6lUqRKurq7ExcXx22+/AWgk4IWxsrLCz8+PkSNH0q9fPxo0aEBOTg6XL18mLi6OBQsWkJaWRr9+/ejUqRNWVlbk5OSwYsUKZW1pUeVPoqf3/9q787ga0//x469KSVooZYkh0hESirSQMnbGMshWlkLD2DP4aQYzY98aQjKJMvZtKmMZzXwxjGYsM7bGWIaxS7slbef3h4fzceZEREq9n4/HeTw6131d1/2+r8mcd9e57uvWoUOHDuzcuZPExEQWL16sdrx79+5s2rQJHx8fhg4dSq1atUhPT+f8+fOqHQxeZvTo0QwZMoQdO3bQr18/pkyZQkBAAI8ePaJ169aUK1eOW7ducfDgQcaPH4+VlRUff/wxISEh+Pv7M3r0aACCg4OpVKnSK82Gjhkzhl69euHr60ufPn2oVKkS9+/f57fffsPR0ZEuXbqwdOlSUlJSaN68OWZmZvz9998cPnyYwYMHA+R7vF69erRr1465c+eSkZGBlZUVUVFRnDp1ihUrVuQboxBCCFGcSILL071c58yZQ3BwMCNGjMDW1pZvvvmGcePGqeqYm5tTqVIlVq1axd27dzEyMsLR0ZEFCxago6OT7/GX6dKlCz/99BMffPCBxlfdFStWZPPmzQQFBbFw4UJSUlIwMzPD3t6etm3bkpWVxb59+/jwww/zXAZhamqKu7s7MTExTJgwgfr16xMUFMSiRYv49NNPVVtP+fj4qM10ent7k5aWxoYNG4iMjMTZ2ZlJkyYxfvx4tXovEhgYiJWVFZs3b2b58uWqJRzPllCULVsWGxsbIiMjuX37Nvr6+jRs2JCwsDBMTU3JzMx86fH8xjMyMhIDAwM8PDzUjunp6REREcGyZcsICQkhISGBChUqUL9+/TyXAPyXi4sLDg4OrFmzhj59+tCxY0eMjY0JCQkhOjoaAEtLS1q2bKlaq6qvr8+aNWuYPn06AQEBVK5cmZEjR7Jr165XGsuaNWuydetWgoKCmDlzJo8ePcLc3JxmzZqptjWzs7Nj3bp17NmzhwcPHlClShV8fX355JNPXuk4wIIFC1i8eDGrV68mJSWF2rVrs3Tp0leahRdCCCGKEy2lUqks6iBE0fr1118ZPHgwkZGRaut//ysoKIjw8HDi4uLQ19d/hxGWPCkpKXz44YcMHjxYtedxafJsxwpra2vKlStXxNG8Hx49ekR8fDy2trayZOQ1yLi9PhmzgpFxe32nT59GS0srz/uj3pTM4JZCM2bMwNnZmQoVKnDp0iVWrFhB/fr1cXR0VNW5fPkyUVFRNGnSBF1dXX777TfCwsLo16+fJLcFEBoaSqVKlbC0tCQhIYE1a9aQk5PDxx9/XNShCSGEECWOJLilUFpaGl999RUpKSkYGhrSsmVLJk+erLa2Vl9fn1OnTrFx40YePnxI5cqV8fX1Va0hFa9HW1ublStXcvfuXXR0dLC3t2fdunVUrVq1qEMrMpmZmcgXSEIIIQqDJLil0H9vvMqLpaUlERER7yCa0sHPzw8/P7+iDkMIIYQoFUrVgx6EEEIIIUTJJwmuEKJI6OnpyUMjhBBCFApJcIUQQgghRIkiCa4osZYtW0aTJk00yufOnUu9evXYunVrEUSVtylTpqg95CMuLg6FQqF6NW7cmK5du7Ju3TpycnLU2np7ezNixIh8z+Ho6MiyZcveeuxCCCFEcSM3mYlSZcGCBaxdu5aZM2fSu3fvog4nX3PmzKF27dqkp6eza9cuZs+ezZMnTxg+fLiqzvTp0/N9upwQQghRmkiCK0qNJUuW8O233zJ9+nS8vLwK3E9mZiZlypR5J0ll3bp1VRtgu7q6cv78ebZv366W4FpbWxd6HEIIIcT7RKZ9RKnw7NG8n3/+udojee/cuUNAQABOTk40atSIAQMGcPbsWbW2np6efPnll6xevRoPDw8aNWpESkqKamnA3r17ad++PU2aNMHHx4d///1XrX1mZiaLFy/Gw8ODhg0b0rFjR9VjfV+HtrY2CoWC27dvq5XntUThwIEDdOjQATs7O3r16sXp06c1+lMqlQQHB+Pq6kqTJk0YM2YMR48eRaFQEBcXp1YvLCyM9u3b07BhQ9q0acPatWtfO34hhBDiXZEZXFHirVy5kuDgYKZOncrAgQNV5ampqfTv3x8DAwM+//xzjIyMiIyMZNCgQezfvx8zMzNV3f3791OzZk2mTZuGtra26jGM8fHxJCUlERAQQE5ODnPnzmXSpEls3rxZ1Xbs2LGcPHmSUaNGUadOHQ4ePMikSZMwNjbG3d39ta7l1q1bVK9e/aV14uPjGTNmDK1atWLq1KncuHGDcePGkZmZqVYvMjKS4OBg/Pz8aNGiBceOHSMwMFCjv1mzZrF161b8/f2xt7fn5MmTLFy4kLJly9KvX7/Xil8IIYR4FyTBFSXao0ePCAoKonfv3gwePFjt2Lp160hLS2Pr1q2qZNbZ2Zn27dsTFhbGZ599pqqblZXF6tWrNZ4v/mxtrKmpqep8U6dO5c6dO1SpUoVjx47x008/ERYWhpubG/B0qUFCQgLLli3LN8HNzc0lOzub9PR0duzYwenTp/N9UEdoaChVq1Zl+fLl6OjoAFC2bFmmTZumqpOTk0NoaCg9e/YkICAAADc3N5KTk9m2bZuq3r///sv69euZOXOmalmHi4sLGRkZLF++HC8vL1n/K4QQotiRTyZRounr69OsWTNiYmI4ceKE2rEjR47g5OSEiYkJ2dnZZGdno62tTbNmzThz5oxaXScnJ43kFqBevXqq5Bb+tx72zp07qnNUqFCBFi1aqM6RnZ2Ni4sL8fHxGjsi/FefPn1o0KABLVq0YP78+QwbNoxOnTq9tM2ff/6Jh4eHKrkF6NChg1qdO3fukJCQgKenp1p5mzZt1N4fPXoUgHbt2mnEn5CQoLFcQgghhCgOZAZXlGja2tqsXLkSb29v/P39Wb9+PQqFAoDk5GT++OMPGjRooNHugw8+UHv//HKF5xkbG6u919XVBeDJkyeqc6SkpOR5DoCEhASqVKnywvjnzZtHnTp1SEpKYtWqVaxevZpmzZrRqlWrF7ZJSEjQiNfQ0JCyZcuq1QHUknPQvM7k5GSUSiUtWrTI81y3b9/G0tLyhbEIIYQQRUESXFHiGRkZERYWRr9+/fD19WXjxo3UqFEDExMTWrZsydixYzXa6Onpqb0v6BO3TExMMDU1JTQ0NM/j/00w/6tOnTqqXRQcHR3p0KED8+bNo2XLli+MydzcnMTERLWyBw8eqJLuZ3UAkpKS1Or9t52JiQlaWlps2LBBlbw/z8rK6qXxCyGEEEVBliiIUsHMzIw1a9agpaXF0KFDSUhIwMXFhcuXL6uSyOdfz2Z535SLiwtJSUno6upqnMPOzk4jkX6Z8uXLM2bMGC5dusSBAwdeWK9Ro0b8/PPPassf9u7dq1anSpUqmJubExsbq1b+336dnZ0BSElJyTN+Q0PDV45fCCGEeFdkBleUGtWrVycsLIyBAwfi5+fHihUriI6OZuDAgfj4+FCtWjWSkpL4888/qVy5ssZNaQXh6uqKh4cHfn5++Pn5oVAoePz4MZcuXeLatWvMmjXrtfrr3r07ISEhrF69mrZt2+ZZZ/jw4fTq1YtRo0bRr18/bty4QVhYmNoSBR0dHYYPH87s2bOpVKkSTk5OxMXF8euvvwKobhyzsrJiwIABfPbZZ/j6+mJvb09WVhZXr14lLi6OFStWFHBkhBBCiMIjCa4oVWxsbFi1ahVDhgxh0qRJbNq0iW+++YaFCxeSkpKCmZkZ9vb2L0weC2Lp0qWEhoayceNGbt68iZGREXXr1qVnz56v3Zeuri7+/v4EBgYSFxeHk5OTRp369eurrunTTz+lbt26LFmyBF9fX7V63t7epKWlsWHDBiIjI3F2dmbSpEmMHz8eIyMjVb3AwECsrKzYvHkzy5cvp3z58lhZWWncuCaEEEIUF1pKpVJZ1EEIIYqHoKAgwsPDiYuLQ19fv9DO82yXCmtra8qVK1do5ylJHj16RHx8PLa2tnnu6CHyJuP2+mTMCkbG7fWdPn0aLS0t1b0mb5PM4ApRSl2+fJmoqCiaNGmCrq4uv/32m+pmvMJMboUQQojCJgmuEKWUvr4+p06dYuPGjTx8+JDKlSvj6+vL6NGj38n5MzMzkS+QhBBCFAZJcIUopSwtLYmIiCjqMIQQQoi3TrYJE0IIIYQQJYokuEKIIqGnp1fgB2gIIYQQLyMJrhBCCCGEKFEkwRWihFi2bBlNmjQp6jCEEEKIIicJrhBCCCGEKFEkwRVCvFBOTg5ZWVlFHYYQQgjxWmSbMCFKiYULF3Lw4EFu3LiBoaEhzZo1Y8qUKVhYWKjqeHt7Y2BgQIcOHQgJCeH69ets3ryZmjVrMn/+fA4ePEhKSgqmpqY0bdqUJUuWFOEVCSGEEHmTBFeIUiIxMZERI0ZgYWFBUlIS4eHheHt7s3v3bsqU+d//Cs6ePcvNmzcZO3YsxsbGVK1alTlz5nD48GEmTpyIpaUlCQkJHDp0qAivRgghhHgxSXCFKCXmzJmj+jknJ4cmTZrQqlUrjh07hpubm+pYamoq27Zto2rVqqqyM2fO0KVLF3r06KEq69y587sJXAghhHhNkuAKUUocPHiQlStXcvHiRR48eKAqv3r1qlqCa2Njo5bcAtSvX5+dO3dibm5Oy5YtsbGxeWdxCyGEEK9LbjITohQ4ffo0I0eOxMLCgvnz57N582a2bNkCwJMnT9TqVqpUSaP9559/zkcffUR4eDhdu3aldevWbNiw4Z3ELoQQQrwumcEVohQ4cOAAhoaGBAUFoa399O/amzdv5lk3r6eLGRkZMW3aNKZNm8aFCxeIiIhg5syZ2NjY4OjoWKixCyGEEK9LZnCFKAUyMjLQ1dVVS16jo6ML1JdCoWDq1KkAXL58+a3EJ4QQQrxNMoMrRAmSk5PD3r17Ncrr16/PunXr+Oqrr2jbti2nTp3i+++/f+V++/btS9u2balbty46Ojrs2rULXV1dmb0VQghRLEmCK0QJ8uTJE8aOHatRPn/+fAICAli/fj07duygadOmrFq1ivbt279Sv02bNmXXrl3cuHEDbW1tbGxsCAkJoU6dOm/7EoQQQog3pqVUKpVFHYQQonQ5c+YMANbW1pQrV66Io3k/PHr0iPj4eGxtbTEwMCjqcN4bMm6vT8asYGTcXt/p06fR0tLCzs7urfcta3CFEEIIIUSJIgmuEKJIZGZmIl8gCSGEKAyS4AohhBBCiBJFElwhhBBCCFGiSIIrhCgSenp6eT5UQgghhHhTkuAKIYQQQogSRfbBFcVSVFQUERER/PPPPyiVSipXrkzTpk2ZMGECZmZmqnpZWVls2bKFqKgoLl26xJMnT7CwsMDR0REvLy8cHBxUdT09PVWPpy1TpgzGxsZYW1vTpk0b+vTpo7aty40bN2jTpo3qvZ6eHpaWlnTq1Inhw4ejr6//DkZBCCGEEAUhCa4odlavXs2iRYsYPHgwY8aMQalUcvHiRaKjo7l3754qwX3y5AnDhg3j1KlTeHl54e/vT/ny5bl27Rq7du2if//+nDlzBj09PVXf7du3Z+jQoeTk5JCUlERcXBxBQUFs3LiRdevWUaVKFbVYJkyYgJOTE48fPyY2Npbly5dz//59vvzyy3c6JkIIIYR4dZLgimInMjKSHj16MGXKFFWZu7s7fn5+5ObmqsqCgoL4/fffWbNmDc7Ozqry5s2b07t3b7Zv367Rd6VKlWjcuLHqfdu2benZsyf9+/dn6tSphIeHq9WvWbOmqr6zszNXrlzh+++/Z8aMGWhrF/4Kn4yMjGI/W/w+xCiEEKJ0kTW4othJS0vDwsIiz2PPksqMjAw2bdpEu3bt1JLb53388cdqs7cvUr9+ffr378/Ro0e5cuXKS+va2tqSkZFBUlKSWrwzZszAzc2Nhg0b0rNnT3755Re1dkqlkuDgYFxdXWnSpAljxozh6NGjKBQK4uLiVPUUCgWhoaEsWLAAV1dX1bUplUrCwsJo3749DRs2pE2bNqxdu1btHHfu3GHs2LG4uLhgZ2eHp6cns2fPfuXjAL///jt9+/alUaNGODk5MXXqVFJSUlTHb9y4gUKhYMeOHQQGBuLk5ETv3r3zHWMhhBDiXZIZXFHsNGjQgE2bNlG9enVat26Nubm5Rp2zZ8/y6NEj3Nzc3so53dzcCAsL488//6R27dovrHfr1i3Kly9PxYoVgacPKxgyZAiJiYmMGzeOypUrExUVxYgRI9ixYwcKhQJ4OisdHByMn58fLVq04NixYwQGBuZ5joiICOzt7Zk1axbZ2dkAzJo1i61bt+Lv74+9vT0nT55k4cKFlC1bln79+gHw2Wefce/ePQIDAzEzM+P27ducPXtW1W9+x8+ePcuQIUNwcnLim2++4f79+yxatIhLly6xadMmdHR0VHUXL16Mu7s7ixYtUptVF0IIIYoDSXBFsTN9+nQ+/fRTVQJYvXp1PDw8GDx4MNWrVwfg3r17ABprZnNzc9USLh0dnVfaiupZPwkJCRr9ZWdnq9bg7t+/n3HjxqmSvejoaP766y++//57rK2tAWjZsiXXrl1jxYoVfPPNN+Tk5BAaGkrPnj0JCAgAnibUycnJbNu2TSMWExMTgoODVXH/+++/rF+/npkzZ+Ll5QWAi4sLGRkZLF++HC8vL7S1tTlz5gwTJkygU6dOqr66d++u+jm/4yEhIZibmxMSEoKuri4AVatWxdfXl4MHD+Lp6amqW69ePWbNmpXvuAohhBBFQZYoiGLHxsaGmJgYQkND8fHxwcjIiMjISD766CPi4+PV6v43ef36669p0KCB6rVv375XOuezR8b+t7/x48fToEEDHB0dmTx5Mu3bt2fYsGGq40eOHMHGxoZatWqRnZ2term4uHDmzBng6dKAhIQEtQQRUNul4XmtWrVSi+Po0aMAtGvXTuMcCQkJ3L59G3i61GLNmjVs2LCBa9euafSb3/Hjx4/Tpk0bVXILTxNxY2NjTpw4oVa3devWecYuhBBCFAcygyuKJT09Pdzd3XF3dwfg8OHDjBgxguXLlxMcHKxao3vnzh21dn5+fvTo0YOEhAQ++eSTVz7f3bt3ATSWQwQEBNCiRQvS09NZv349u3fvpnnz5vTt2xeA5ORkzp8/T4MGDTT6fDbL+2xW2NTUVO3489udvaw8OTkZpVJJixYt8qx/+/ZtLC0tWbJkCUuWLCEoKIiZM2diZWXFhAkTaNeuHUC+x9PS0vKMyczMjNTU1FeKXQghhCgOJMEV74WWLVtSr149Ll++DEDDhg0xMDDgyJEj9OrVS1WvWrVqVKtWjRs3brxW/4cPHwZQ22EBoEaNGtjZ2QHg5OREr169CAoK4qOPPsLAwAATExMUCsVLv65/ljQ/f2MaQGJiYp71/zuLbGJigpaWFhs2bFCbXX3GysoKAAsLC+bMmUNubi5nz55l5cqVjB8/nr1791KjRo18j5uYmOQZU2JiIiYmJi+NUQghhChOZImCKHbu37+vUZaRkcHt27epVKkSAPr6+vTt25d9+/ap7UJQEPHx8WzcuBE3Nzdq1ar1wno6OjpMmjSJ5ORktmzZAjxdC3v9+nUsLCyws7PTeMHT9b3m5ubExsaq9XfgwIFXiu/ZTgopKSl5nsPQ0FCtvra2No0aNWLcuHFkZ2drLEd40XEHBwdiY2NVN7bB0yUYaWlpag/MEEIIIYo7mcEVxU7Xrl3x8PDAzc0NCwsL7t69y/r160lOTmbQoEGqeuPGjePcuXMMGzYMLy8vXF1dKV++PImJiaq1t88/nQyeJs9//PEHubm5JCUlcezYMbZt20aVKlU0tszKi4uLCw4ODqxdu5YBAwbQvXt3Nm3ahI+PD0OHDqVWrVqkp6dz/vx5srKymDhxIjo6OgwfPpzZs2dTqVIlnJyciIuL49dffwXIdz9dKysrBgwYwGeffYavry/29vZkZWVx9epV4uLiWLFiBenp6fj6+tKtWzesrKzIysoiMjISY2Nj6tevn+9xAH9/f/r27cuIESPw9vZW7aLQqFEj1VIRIYQQ4n0gCa4odj799FN+/vln5s6dS1JSEhUrVkShULB27Vq1dahly5YlLCyMzZs3ExUVxbZt28jKysLCwgIHBwc2bNigMfO4b98+9u3bR5kyZTAyMqJu3bqMHz+e3r17ayTDL4tvyJAhREdH07NnTyIiIli2bBkhISEkJCRQoUIF1d66z3h7e5OWlsaGDRuIjIzE2dmZSZMmMX78eIyMjPI9Z2BgIFZWVmzevJnly5dTvnx5rKys6NChg2osbGxsiIyM5Pbt2+jr69OwYUPCwsIwNTUlMzPzpcfh6bKPNWvWsHjxYkaPHo2BgQGenp5MnjxZbYswIYQQorjTUj67fVwI8U4FBQURHh5OXFxcqXsS2LMdJqytrSlXrlwRR/N+ePToEfHx8dja2r7yH2NCxq0gZMwKRsbt9Z0+fRotLS3Vkr63SWZwhXgHLl++TFRUFE2aNEFXV5fffvuNsLAw+vXrV+qSWyGEEKKwSYIrxDugr6/PqVOn2LhxIw8fPqRy5cr4+voyevToog6tyGRmZiJfIAkhhCgMkuAK8Q5YWloSERFR1GEIIYQQpYJsEyaEKBJ6enqyn64QQohCIQmuEEIIIYQoUSTBFUIIIYQQJYokuOKlli1bhkKhUL2cnJzo168fBw8ezLP+Rx99hEKh4Pjx4xrHbty4gUKhoH79+ly9elXtWHx8PAqFQu2pZI8fPyY4OJhOnTphb2+Pk5MTH3/8MUuWLMnz3ElJSTRo0IAmTZqQkZGRZx2lUsnOnTvp378/Dg4ONGzYkPbt2zN37lzu3r2rqvf8NTds2JC2bdvy9ddfk5KSks+ICSGEEKKoyU1mIl/6+vqsW7cOgHv37hESEoK/vz/fffcdTZs2VdW7ePEiFy5cACA6OhpHR8c8+8vJySEkJIS5c+e+9Lxjxozh9OnTjBgxAltbW9LS0jhz5gwHDhxg/PjxGvV/+OEHsrOzyc7O5qeffqJTp05qx5VKJRMnTmTPnj307NkTPz8/DA0NuXTpEps2beL69essX75cVd/b25suXbrw5MkTfvvtN0JCQrh69Srffvvtqw2cEEIIIYqEJLgiX9ra2jRu3Fj13t7eHnd3d3bt2qWW4EZHR6OtrU2zZs3Yu3cvgYGB6OrqavTn5OREdHQ0o0aNokaNGnme89q1axw6dIh58+bRvXt3VXn79u2ZMGFCnm1iYmKoU6cODx48ICoqSiPB3bBhA7t372bWrFn06tVLVd68eXO8vLz45Zdf1OpXrVpVdd1OTk7cu3ePLVu2cO/ePSwsLPKM4W3KzMykTJky+T7KtyhlZGTIPr5CCCGKneL7ySmKrcqVK2NqasqtW7dUZUqlkpiYGFq0aMGQIUNISUnh8OHDebbv1asXpqamrFq16oXnSE1NBcDc3FzjWF4J3/Xr1zl16hRdu3alc+fO/PLLLxrLCcLDw2nQoIFacvuMjo4O7u7uL4wHwNbWFoDbt2+ryjIzM1m8eDEeHh40bNiQjh07Eh0drdF206ZNeHh4YG9vz5AhQzh//jwKhYIdO3ao6nh6evLll1+yevVqPDw8aNSokeoaduzYQdeuXbGzs6Nly5YsWbKEnJwcVdu0tDQCAwNp2bIldnZ2uLu7q81y53cc4MKFC/j6+tK4cWMcHBwYM2aM2n9jeLp0IzQ0lAULFuDq6oqzs/NLx0wIIYQoCjKDK17bw4cPSU1NpXr16qqykydPcvPmTUaNGoWbmxsVKlQgJiYGT09PjfZ6enr4+fmxYMECRo4cSbVq1TTq1K5dGwMDA+bOncuECRNo3rw55cuXf2FMMTExAHTp0oW0tDTWrFnD3r176du3LwB37tzh+vXr+Pv7F/i6b926hba2tlq8Y8eO5eTJk4waNYo6depw8OBBJk2ahLGxsSphjo2NZfr06fTu3Zv27dsTHx/PuHHj8jzH/v37qVmzJtOmTUNbWxsDAwPCw8NZsGABgwYNYsqUKVy+fFmV4AYEBAAwZ84cDh8+zMSJE7G0tCQhIYFDhw6p+s3v+O3btxk4cCA1atRgwYIFPHnyhCVLljBw4ECioqIwNDRU1Y2IiMDe3p5Zs2aRnZ1d4PEUQgghCoskuOKVPEtk7t27x4IFCyhfvjw+Pj6q4zExMZQtW5Z27dqhq6tL+/btiYqK4uHDh3kmpl5eXoSGhhIaGsqMGTM0jhsaGjJr1iwCAwPx9/dHR0eHevXq0bZtWwYNGqTxnO/du3fTuHFj1ZKH2rVrEx0drUpwn91AVrVq1Ve+5tzcXLKzs8nMzCQuLo6NGzfi5eWlmlU+duwYP/30E2FhYbi5uQHg6upKQkICy5YtUyW4K1eupEWLFnz99dcAtGzZkuzsbL755huNc2ZlZbF69WrV9T148IClS5fi5+enWprh6uqKrq4uc+fOxdfXl4oVK3LmzBm6dOlCjx49VH117txZ9XN+x9euXUt2djZr1qyhQoUKwNMZ686dO7Nz5068vb1VdU1MTAgODpY9bIUQQhRbskRB5OvRo0c0aNCABg0a4OHhwb59+5g/fz61a9cGnia/e/fuxd3dHSMjIwC6du3K48eP+fHHH/PsU19fnyFDhrB9+3a13Que16lTJ37++WfmzZtHt27dSE5OJigoiI8//phHjx6p6v31119cvHiRLl26qMo6d+7MiRMnNL5if52kbOHChapdGfz9/VEoFAQGBqqOHzlyhAoVKtCiRQvVzW3Z2dm4uLgQHx9PTk4OOTk5xMfHa8xkt2nTJs9zOjk5qSXvp06d4tGjR3To0EHjHBkZGVy8eBGA+vXrs3PnTsLCwvj77781+s3v+PHjx3FyclIltwB16tShXr16nDhxQq1uq1atJLkVQghRrEmCK/Klr6/Ptm3b2Lp1KwsWLMDc3JzJkydz79494Gmil5SUhIeHB2lpaaSlpWFjY4O5ublq6UBe+vXrh4GBAatXr35hHRMTE7p3786cOXP46aefGDlyJFeuXGHbtm2qOlFRUWhra+Pm5qY6v7u7u2pdMDxdNwxoJLwv4+Pjw7Zt24iMjKR3796cOHFCbdY1OTmZlJQUVfL/7BUYGEh2djYJCQkkJSWRnZ2NqampWt9mZmZ5nvO/5cnJyQD06NFD7Rzt2rUD/rce+PPPP+ejjz4iPDycrl270rp1azZs2KDqJ7/jaWlpVKpUKc94nq2Hzi92IYQQoriQJQoiX9ra2tjZ2QHQqFEjrKys6NOnD8uXL2fmzJmqm6qmTp3K1KlT1domJyeTmJiYZ1JUvnx5hgwZwsqVK2nVqlW+cWhpaeHr68uKFSu4fPky8PTmth9++IHc3Fw6dOig0SY6Oprhw4dTpUoVPvjgA3755Zc8txjLS5UqVVTX3bx5c+7fv094eDj9+/enatWqmJiYYGpqSmhoaJ7tTU1N0dHRoUyZMiQlJakdS0xMfOE1Ps/ExASA4OBgqlSpolH/2TpoIyMjpk2bxrRp07hw4QIRERHMnDkTGxsbHB0d8z1uYmKSZ0yJiYnUqlXrpTEKIYQQxY3M4IrXZmdnR+fOndmxYwd3794lNjaWDz/8kIiICLXX4sWLyc7O5ocffnhhXwMHDkRPT4+wsDC18gcPHuT5sIZnD4h4tg72+PHj3L59m9GjR2ucf9iwYfz999+qvXkHDx7M2bNn2blzp0a/ubm5ajdd5eWzzz4jNzdXFauLiwtJSUno6upiZ2en8dLT00NHRwdbW1tiY2PV+jpw4MBLz/VMkyZNKFeuHHfu3MnzHBUrVtRoo1AoVH9oPPtDIL/jDg4OHDt2TG229sqVK1y4cAEHB4dXilUIIYQoLmQGVxTIyJEj+eGHH/Dx8eHRo0d4e3vj5OSkUe/bb78lJiZG7Sal5xkaGuLj40NwcLBa+T///MMnn3xCjx49cHBwwMDAgEuXLrF69WqMjIxUN0tFR0djYGDAkCFDNG5mq1u3LmvXriUmJgaFQkH//v05ceIE06ZN4+TJk7Rp0wYDAwOuXLnCpk2bsLS0fOlMcu3atenUqRPbtm1j1KhRuLq64uHhgZ+fH35+figUCh4/fsylS5e4du0as2bNAuCTTz5h5MiRBAYG0qFDB86fP8+uXbuAvLc8e56xsTFjxoxhwYIF3Llzh+bNm6Ojo8P169eJjY1l2bJllCtXjr59+9K2bVvq1q2Ljo4Ou3btQldXV/WwjfyODx48mB07djB06FA++eQTnjx5QlBQEFWrVlW7MU0IIYR4H0iCKwrkWbIXGxuLsbFxnsktQPfu3Zk9ezb//vvvC5M5Hx8f1q5dy4MHD1RlNWvWxMvLiyNHjrB161YePnxI5cqVadGiBf7+/lhaWpKVlcW+ffv48MMP89ypwdTUFHd3d2JiYpgwYQJaWlosWrQINzc3tm7dyu7du8nMzMTS0hJPT0+GDh2a73U/S+zXr1/P6NGjWbp0KaGhoWzcuJGbN29iZGRE3bp16dmzp6pNmzZtmDFjBqtWrSIqKgp7e3tmzJjB0KFD1bbfepGhQ4dSuXJlwsPDWb9+PWXKlOGDDz6gdevWqgdpNG3alF27dnHjxg20tbWxsbEhJCSEOnXqvNLxqlWrEhkZyfz58wkICEBbWxtXV1emTJnySjEKIYQQxYmWUqlUFnUQQpQ2W7duJTAwkNjYWLX9hEuLM2fOAGBtbU25cuWKOJr3w6NHj4iPj8fW1lZjmzzxYjJur0/GrGBk3F7f6dOn0dLSUt3v8jbJDK4QhSwlJYXg4GBatGhB+fLlOXPmDCEhIbRp06ZUJrfwdL9fpVLJxYsX5aa1V/RsLuLSpUsyZq9Bxu31yZgVjIzb68vKyiq0sZIEV4hCVqZMGa5fv05MTAzp6elUrFiRbt26qZ5CVho9+x+afAi8Oi0tLfT09Io6jPeOjNvrkzErGBm316elpVVonwOyREEIIYQQQpQosk2YEEIIIYQoUSTBFUIIIYQQJYokuEIIIYQQokSRBFcIIYQQQpQokuAKIYQQQogSRRJcIYQQQghRokiCK4QQQgghShRJcIUQQgghRIkiCa4QQgghhChRJMEVQgghhBAliiS4QgghhBCiRJEEVwghhBBClCiS4Aoh3qrLly8zZMgQGjdujKurK/PnzyczMzPfdkqlktDQUFq3bk2jRo3w8vLijz/+KPyAi4GCjNm9e/eYP38+3bp1o0mTJrRq1YqJEydy8+bNdxR10Svo79rz1q5di0KhYMSIEYUUZfHyJmN29+5dJk+eTIsWLWjUqBEdO3YkKiqqkCMuHgo6bsnJyXzxxRe0bt2axo0b06VLFzZu3PgOIi56165d44svvqBbt27Ur1+fLl26vFK7t/VZUOa1WwghxAukpqYyaNAgatWqxbJly7h79y5z584lIyODL7744qVtV69ezdKlSwkICEChUPDdd98xdOhQvv/+e2rUqPGOruDdK+iYnTt3jh9//JGPP/4Ye3t7kpOTWblyJb179yYmJgZTU9N3eBXv3pv8rj2TkJDA8uXLMTMzK+Roi4c3GbN79+7h5eWFlZUVX331FYaGhly8ePG1/6B4H73JuI0dO5YrV64wYcIEqlatyqFDh5gxYwY6Ojr06dPnHV1B0bh48SIHDx7E3t6e3NxclErlK7V7a58FSiGEeEtCQkKUjRs3ViYnJ6vKNm3apLS1tVXeuXPnhe0yMjKUTZs2VS5atEhV9uTJE6WHh4dy+vTphRhx0SvomKWmpiqzsrLUym7fvq1UKBTKsLCwwgq32CjouD1v0qRJys8++0w5cOBA5fDhwwsp0uLjTcYsICBA6eXlpczOzi7kKIufgo7bvXv3lDY2Nsrt27erlQ8YMEDp4+NTWOEWGzk5OaqfJ0+erOzcuXO+bd7mZ4EsURBCvDWHDh3C2dmZChUqqMo6duxIbm4uR44ceWG7kydP8uDBAzp27Kgq09PTo23bthw6dKgwQy5yBR0zY2NjypRR/xKuSpUqmJqacu/evcIKt9go6Lg9c/z4cQ4cOMDEiRMLMcripaBj9uDBA/bs2UP//v3R0dF5B5EWLwUdt+zsbACMjIzUyg0NDV95NvN9pq39+inm2/wskARXCPHWXLlyhdq1a6uVGRsbY25uzpUrV17aDtBoW6dOHW7dukVGRsbbD7aYKOiY5eWff/4hMTGROnXqvM0Qi6U3GbecnBy++uor/P39sbCwKMwwi5WCjtm5c+fIysqiTJkyDBw4kAYNGuDq6sqCBQvIysoq7LCLXEHHrWrVqri5uRESEsKlS5d48OABP/zwA0eOHGHAgAGFHfZ76W1+FsgaXCHEW5OWloaxsbFGuYmJCampqS9tp6enR9myZdXKjY2NUSqVpKamoq+v/9bjLQ4KOmb/pVQq+frrr7GwsKBz585vM8Ri6U3GbcOGDTx+/JjBgwcXUnTFU0HH7P79+wAEBgbSp08fPv30U06fPs3SpUvR1tYu8bPgb/K7tmzZMsaPH6/6N6mjo0NgYCDt27cvlFjfd2/zs0ASXCGEKAGWLVvGsWPH+PbbbzEwMCjqcIqtxMREli5dyrx589DT0yvqcN4Lubm5ALi4uDBlyhQAWrRowcOHD1mzZg2jRo0qsX+AvgmlUsnUqVO5evUqixYtwtzcnKNHjzJ79mxMTExKxR+iRUkSXCHEW2NsbEx6erpGeWpqKiYmJi9tl5mZyZMnT9T+ck9LS0NLS+ulbd93BR2z523ZsoXly5cza9YsnJ2d33aIxVJBx+2bb75BoVDg6OhIWloa8HStZHZ2NmlpaRgYGGisbS4p3uTfJzxNap/n7OxMSEgI165dQ6FQvN1gi5GCjtv//d//sXfvXqKiolTj4+TkRGJiInPnzpUENw9v87NA1uAKId6a2rVra6xJS09PJyEhQWNN1X/bwdM1pM+7cuUK1apVK9GzQwUds2d+/PFHZsyYwZgxY+jVq1dhhVnsFHTc/vnnH37//XeaNWumep08eZJffvmFZs2acfTo0cIOvcgUdMysra1f2u+TJ0/eSnzFVUHH7dKlS+jo6GBjY6NWbmtry71793j8+HGhxPs+e5ufBZLgCiHemlatWnH06FHVzBjA3r170dbWxtXV9YXtmjZtiqGhIXv27FGVZWVlsX//flq1alWoMRe1go4ZQFxcHBMmTKB3796MGjWqsEMtVgo6bv/v//0/IiIi1F716tWjcePGRERE0KhRo3cRfpEo6JhZWlpiY2OjkfwfPXoUfX39fBPg992bjFtOTg4XLlxQKz937hxmZmaUK1eu0GJ+X73Nz4KS+T2MEKJI9O3bl8jISEaNGsWIESO4e/cu8+fPp2/fvlSuXFlVb9CgQdy6dYsff/wRgLJlyzJixAiWLVuGqakpNjY2bNy4kZSUFHx9fYvqct6Jgo7Z5cuXGTVqFLVq1aJbt25qT/oxNTXlgw8+eNeX8k4VdNxsbW01+jI2NsbAwAAnJ6d3Fn9RKOiYAYwfP56RI0cya9YsWrduzZkzZ1izZg2+vr4lfs13QcetVatWVKtWjTFjxjBq1CgsLCz45Zdf2LlzJ6NHjy6qy3lnHj9+zMGDBwG4efMmDx48YO/evQA0b94cU1PTQv0skARXCPHWmJiYsG7dOr766itGjRpF+fLl6dWrF+PHj1erl5ubS05OjlrZsGHDUCqVrFmzhqSkJGxtbQkLCyvRTzGDgo/Zn3/+SXp6Ounp6fTr10+tbo8ePZg7d+47ib+ovMnvWmn1JmPm6enJ4sWLWbFiBRs3bsTCwoLRo0czfPjwd3kJRaKg42ZoaMjatWtZsmQJCxcuJD09nerVqzNlyhQGDhz4ri/jnUtMTGTs2LFqZc/eR0RE4OTkVKifBVrK0rDbsBBCCCGEKDVkDa4QQgghhChRJMEVQgghhBAliiS4QgghhBCiRJEEVwghhBBClCiS4AohhBBCiBJFElwhhBBCCFGiSIIrhBBCCCFKFElwhRBCCCFEiSIJrhBCCCHUxMXFoVAoiIuLK+pQhCgQeVSvEEKIEmfHjh1MnTpV9V5HRwczMzNcXV0ZP348lStXzreP48ePExISwoULF0hJScHMzIx69erRuXNnunbtWpjhCyHekCS4QgghSqwxY8ZQvXp1MjMz+eOPP9i5cycnTpwgJiaGsmXLvrDdnj17GD9+PLa2tvj4+GBiYsKNGzf4/fff2bJliyS4QhRzkuAKIYQosVq1aoWdnR0AvXv3pmLFiqxevZrY2Fg6der0wnbBwcFYW1uzefNm9PT01I4lJiYWasxvIjc3l6ysrJcm70KUBrIGVwghRKnh6OgIwPXr119a799//8XOzk4juQUwMzNTe5+WlsaUKVNwcHDA0dGRyZMnEx8fj0KhYMeOHap63t7eeHt7a/Q3ZcoUPD091crCwsLo27cvTk5ONGrUiJ49e7J3716NtgqFgi+//JKoqCg6d+6MnZ0dhw8fBuDu3btMnToVFxcXGjZsSOfOndm2bZtGH3fu3GHkyJE0btwYZ2dnZs+eTWZm5kvHR4jiTmZwhRBClBo3b94EwNjY+KX1qlWrxq+//sqdO3eoUqXKC+splUpGjhzJiRMn6Nu3L3Xq1OHHH39k8uTJbxRnREQEnp6edO3alaysLHbv3s3YsWNZtWoVrVu3Vqt77Ngx9uzZw4ABA6hYsSKWlpbcv3+fPn36oKWlxYABAzA1NeXQoUNMmzaNBw8eMHjwYAAyMjIYNGgQt2/fxtvbGwsLC77//nuOHTv2RvELUdQkwRVCCFFiPXjwgKSkJDIzM/nzzz8JDg5GT08PDw+Pl7YbNmwY06ZN48MPP6Rp06Y4ODjg6upK06ZN0db+35efsbGx/P7770yaNAk/Pz8A+vXrh4+PzxvFvW/fPvT19VXvBwwYQM+ePQkPD9dIcP/55x+io6OxtrZWlU2bNo2cnByio6OpWLGiKq4JEyYQHBxM37590dfXZ/PmzVy9epWgoCA6duwIQJ8+fejWrdsbxS9EUZMlCkIIIUqswYMH4+zsjLu7O2PGjKFcuXKsXLnypbOyAL169eLbb7/FycmJkydPsmLFCgYMGEC7du04efKkqt6hQ4coU6YM/fr1U5Xp6OgwcODAN4r7+eQ2NTWV9PR0HBwcOH/+vEbdZs2aqSW3SqWS/fv34+npiVKpJCkpSfVyc3MjPT2dc+fOqeI3NzenQ4cOqvblypWjT58+bxS/EEVNZnCFEEKUWF988QVWVlakp6ezfft2fv/9d9W62szMTFJTU9Xqm5qaoqOjA0DLli1p2bIljx8/5ty5c/zwww9s2rQJf39/9uzZg5mZGTdv3sTc3Jzy5cur9WNlZfVGcf/888+sXLmS+Ph4tfWwWlpaGnWrV6+u9j4pKYm0tDQ2b97M5s2b8+w/KSkJeLpko2bNmhr9vmn8QhQ1SXCFEEKUWI0aNVLtovDhhx/Sv39/Jk6cyN69ezl79qzGUoLY2FiNhLFcuXI4Ojri6OhIxYoVCQ4O5tChQ/To0eOtxJiTk6P2/vjx43zyySc0a9aM6dOnY25ujq6uLtu3bycmJkaj/fOzvfB0JwWAjz766IUxKhSKtxK7EMWVJLhCCCFKBR0dHSZMmICPjw/fffcdXl5ehIeHq9UxNzd/aR8NGzYEICEhAQBLS0uOHTvGw4cP1WZx//nnH422JiYmee7ecOvWLbX3+/bto2zZsoSFhant4rB9+/Z8rvApU1NTypcvT25uLi4uLi+ta2lpyd9//41SqVSbxc0rfiHeJ7IGVwghRKnxbNutdevWoa+vj4uLi9rr2f6xv/76a57tDx48CPzvK/xWrVqRnZ3Nxo0bVXVycnJYv369RtsaNWpw5coV1fIAgL/++kttTS88TcS1tLTUZnZv3LhBbGzsK12jjo4O7du3Z9++ffz9998ax58/f6tWrbh3757aFmSPHz9my5Ytr3QuIYormcEVQghRqvj6+jJ27Fh27NihdnPY80aOHEn16tXx8PCgRo0aPH78mKNHj/Lzzz9jZ2en2oXB09OTpk2bsmjRIm7evIm1tTX79+8nPT1do89evXqxdu1afH196dWrF4mJiWzatAlra2sePnyoqufu7k54eDh+fn506dKFxMRENmzYwAcffMCFCxde6RonTpxIXFwcffr0oXfv3lhbW5Oamsq5c+f49ddf+e2334CnOyZ89913TJ48mXPnzmFubs7333+vsexBiPeNzOAKIYQoVdq1a8cHH3zAmjVrNNa/PvP1119Tt25d9uzZw9dff83ChQu5fv06/v7+rF27ljJlns4PaWtrs3LlSrp27UpUVBRLliyhcuXKzJs3T6PPOnXqMG/ePNLT05kzZw4//fQT8+fPp0GDBmr1nJ2dmTVrFvfv32f27Nns3r2bgIAA2rZt+8rXWKlSJbZu3UrPnj358ccf+eqrr4iIiCA1NZWAgABVvXLlyrF27VpcXV1Zv349K1euxMHBgUmTJr3yuYQojrSUSqWyqIMQQgghSpIbN27Qpk0b5syZQ8+ePYs6HCFKHZnBFUIIIYQQJYokuEIIIYQQokSRBFcIIYQQQpQosgZXCCGEEEKUKDKDK4QQQgghShRJcIUQQgghRIkiCa4QQgghhChRJMEVQgghhBAliiS4QgghhBCiRJEEVwghhBBClCiS4AohhBBCiBJFElwhhBBCCFGi/H8yKziWQ9OM2AAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "OLkiiTTwb-Cb",
        "outputId": "2097a67b-d2ea-4c13-b4c2-d06583d902da"
      },
      "source": [
        "# Bar plot of RMSE values\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"RMSE\", data=predictions_train)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 12
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 798
        },
        "id": "pCdcxpzScqXX",
        "outputId": "30557edb-2720-418b-f47b-b3fb0f6a5fa2"
      },
      "source": [
        "# Bar plot of calculation time\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "plt.figure(figsize=(5, 10))\n",
        "sns.set_theme(style=\"whitegrid\")\n",
        "ax = sns.barplot(y=predictions_train.index, x=\"Time Taken\", data=predictions_train)\n",
        "ax.set(xlim=(0, 10))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[(0.0, 10.0)]"
            ]
          },
          "metadata": {},
          "execution_count": 13
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 500x1000 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}