TheMathCompany_Hackathon.ipynb

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  "metadata": {
    "colab": {
      "name": "TheMathCompany_Hackathon.ipynb",
      "provenance": [],
      "collapsed_sections": [
        "KhzPwHig6EkR",
        "tc-H5BuDh3Fn",
        "mZ7R_8ozzRq3",
        "dM_5HKsCSOpg",
        "jcPxQPYg5Ft1"
      ],
      "mount_file_id": "1vaayduVomS5aGeHgfshNlzpPRFFnn2Y5",
      "authorship_tag": "ABX9TyNSKYSfkFBb1ZRwf8YPyZT9",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/ArpanSM/Machine_Learning_Hackathons/blob/master/TheMathCompany_Hackathon.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vE-499WrBJ6H",
        "outputId": "f493b480-870f-45df-c4e9-aad6551d3845"
      },
      "source": [
        "import warnings                                 \n",
        "warnings.filterwarnings('ignore')\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "trainx = pd.read_csv(\"/content/drive/MyDrive/Projects/TheMathCompany_.DSHH/fe_train.csv\")\n",
        "testx = pd.read_csv(\"/content/drive/MyDrive/Projects/TheMathCompany_.DSHH/fe_test.csv\")\n",
        "subx = pd.read_csv(\"/content/drive/MyDrive/Projects/TheMathCompany_.DSHH/submission.csv\")\n",
        "pd.set_option('display.max_columns', 500)\n",
        "trainx.shape, testx.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((19229, 189), (8245, 188))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 1
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "3bGsX8Z_5nAR"
      },
      "source": [
        "#Scikit-Optimize"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zgVq77xk5mN5",
        "outputId": "eb7b151a-df75-4d72-e8d8-a81065e1b82a"
      },
      "source": [
        "!pip install scikit-optimize\n",
        "\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "from sklearn.model_selection import cross_val_score, train_test_split, KFold, cross_validate\n",
        "\n",
        "from skopt import gp_minimize, forest_minimize, gbrt_minimize\n",
        "from skopt.plots import plot_convergence,plot_evaluations,plot_objective,plot_gaussian_process\n",
        "from skopt.space import Real, Integer, Categorical\n",
        "from skopt.utils import use_named_args"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-optimize\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/8b/03/be33e89f55866065a02e515c5b319304a801a9f1027a9b311a9b1d1f8dc7/scikit_optimize-0.8.1-py2.py3-none-any.whl (101kB)\n",
            "\r\u001b[K     |███▎                            | 10kB 17.2MB/s eta 0:00:01\r\u001b[K     |██████▌                         | 20kB 22.0MB/s eta 0:00:01\r\u001b[K     |█████████▊                      | 30kB 24.1MB/s eta 0:00:01\r\u001b[K     |█████████████                   | 40kB 25.3MB/s eta 0:00:01\r\u001b[K     |████████████████▏               | 51kB 26.6MB/s eta 0:00:01\r\u001b[K     |███████████████████▍            | 61kB 28.1MB/s eta 0:00:01\r\u001b[K     |██████████████████████▊         | 71kB 28.5MB/s eta 0:00:01\r\u001b[K     |██████████████████████████      | 81kB 27.7MB/s eta 0:00:01\r\u001b[K     |█████████████████████████████▏  | 92kB 28.8MB/s eta 0:00:01\r\u001b[K     |████████████████████████████████| 102kB 11.1MB/s \n",
            "\u001b[?25hRequirement already satisfied: numpy>=1.13.3 in /usr/local/lib/python3.7/dist-packages (from scikit-optimize) (1.19.5)\n",
            "Collecting pyaml>=16.9\n",
            "  Downloading https://files.pythonhosted.org/packages/15/c4/1310a054d33abc318426a956e7d6df0df76a6ddfa9c66f6310274fb75d42/pyaml-20.4.0-py2.py3-none-any.whl\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-optimize) (1.0.1)\n",
            "Requirement already satisfied: scipy>=0.19.1 in /usr/local/lib/python3.7/dist-packages (from scikit-optimize) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.20.0 in /usr/local/lib/python3.7/dist-packages (from scikit-optimize) (0.22.2.post1)\n",
            "Requirement already satisfied: PyYAML in /usr/local/lib/python3.7/dist-packages (from pyaml>=16.9->scikit-optimize) (3.13)\n",
            "Installing collected packages: pyaml, scikit-optimize\n",
            "Successfully installed pyaml-20.4.0 scikit-optimize-0.8.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "NpX1D7Gw5mYM",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "18d20fd7-33b5-4d2d-d1ea-d5a374c5b06e"
      },
      "source": [
        "X = trainx.drop(labels= ['ID', 'Price'], axis = 1)\n",
        "y = trainx['Price']\n",
        "\n",
        "# split dataset into a train and test set\n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.3, random_state=0)\n",
        "X_train.shape, X_test.shape\n",
        "\n",
        "X_train.reset_index(drop=True, inplace=True)\n",
        "y_train.reset_index(drop=True, inplace=True)\n",
        "\n",
        "dft = testx.drop(labels= ['ID'], axis = 1)\n",
        "dft.reset_index(drop=True, inplace=True)\n",
        "\n",
        "X_train.shape, y_train.shape, dft.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((13460, 187), (13460,), (8245, 187))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "NrTCv34TGeuD",
        "outputId": "5e5551d2-4026-4546-a4f9-2cd2b20f014c"
      },
      "source": [
        "trainx.columns[trainx.isna().any()].tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 4
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "P_hDzU5lMCpp",
        "outputId": "0aa4d9e5-57ae-4da8-e83d-ff0a5aa11e7e"
      },
      "source": [
        "testx.columns[testx.isna().any()].tolist()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "tc-H5BuDh3Fn"
      },
      "source": [
        "####XGBoost GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "y24sDIpf5mbi",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "35e451f5-3d17-4f44-c822-8010e3549ae3"
      },
      "source": [
        "# defining the space\n",
        "param_grid_reg = [\n",
        "    Integer(100, 1000, name=\"n_estimators\"),\n",
        "    Real(0.05, 0.3, name=\"learning_rate\", prior=\"log-uniform\"),\n",
        "    Integer(3, 10, name=\"max_depth\"),\n",
        "    Real(1, 12, name=\"min_child_weight\"),\n",
        "    Real(0.5, 0.9, name=\"colsample_bytree\"),\n",
        "    Real(0.5, 0.9, name=\"subsample\"),\n",
        "    Real(0.001, 100, name=\"reg_alpha\", prior=\"log-uniform\"),\n",
        "]\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "xgb = XGBRegressor(random_state=0, xgb_gpu_hist = 1)\n",
        "\n",
        "@use_named_args(param_grid_reg)\n",
        "def objective(**params):\n",
        "    \n",
        "    # model with new parameters\n",
        "    xgb.set_params(**params)\n",
        "\n",
        "    # optimization function (hyperparam response function)\n",
        "    value = np.mean(\n",
        "        cross_val_score(\n",
        "            xgb, \n",
        "            X_train,\n",
        "            y_train,\n",
        "            cv=3,\n",
        "            n_jobs=-1,\n",
        "            scoring='neg_root_mean_squared_error'\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # negate because if you need to minimize\n",
        "    return -value\n",
        "\n",
        "# gbrt_minimize performs by Bayesian Optimization using GBMs as surrogate for f(x)\n",
        "\n",
        "gbm_ = gbrt_minimize(\n",
        "    objective, # the objective function to minimize\n",
        "    param_grid_reg, # the hyperparameter space\n",
        "    n_initial_points=60, # the number of points to evaluate f(x) to start of\n",
        "    acq_func='EI', # the acquisition function\n",
        "    n_calls=100, # the number of subsequent evaluations of f(x)\n",
        "    random_state=0, \n",
        "    verbose=True,\n",
        "    n_jobs=-1,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration No: 1 started. Evaluating function at random point.\n",
            "Iteration No: 1 ended. Evaluation done at random point.\n",
            "Time taken: 67.8772\n",
            "Function value obtained: 10676.8751\n",
            "Current minimum: 10676.8751\n",
            "Iteration No: 2 started. Evaluating function at random point.\n",
            "Iteration No: 2 ended. Evaluation done at random point.\n",
            "Time taken: 60.6581\n",
            "Function value obtained: 10622.3888\n",
            "Current minimum: 10622.3888\n",
            "Iteration No: 3 started. Evaluating function at random point.\n",
            "Iteration No: 3 ended. Evaluation done at random point.\n",
            "Time taken: 58.7005\n",
            "Function value obtained: 10555.7793\n",
            "Current minimum: 10555.7793\n",
            "Iteration No: 4 started. Evaluating function at random point.\n",
            "Iteration No: 4 ended. Evaluation done at random point.\n",
            "Time taken: 126.5677\n",
            "Function value obtained: 10744.8969\n",
            "Current minimum: 10555.7793\n",
            "Iteration No: 5 started. Evaluating function at random point.\n",
            "Iteration No: 5 ended. Evaluation done at random point.\n",
            "Time taken: 61.2306\n",
            "Function value obtained: 10721.9188\n",
            "Current minimum: 10555.7793\n",
            "Iteration No: 6 started. Evaluating function at random point.\n",
            "Iteration No: 6 ended. Evaluation done at random point.\n",
            "Time taken: 131.8255\n",
            "Function value obtained: 10570.4727\n",
            "Current minimum: 10555.7793\n",
            "Iteration No: 7 started. Evaluating function at random point.\n",
            "Iteration No: 7 ended. Evaluation done at random point.\n",
            "Time taken: 133.2604\n",
            "Function value obtained: 10378.5412\n",
            "Current minimum: 10378.5412\n",
            "Iteration No: 8 started. Evaluating function at random point.\n",
            "Iteration No: 8 ended. Evaluation done at random point.\n",
            "Time taken: 34.7160\n",
            "Function value obtained: 10353.1421\n",
            "Current minimum: 10353.1421\n",
            "Iteration No: 9 started. Evaluating function at random point.\n",
            "Iteration No: 9 ended. Evaluation done at random point.\n",
            "Time taken: 89.5550\n",
            "Function value obtained: 10211.4662\n",
            "Current minimum: 10211.4662\n",
            "Iteration No: 10 started. Evaluating function at random point.\n",
            "Iteration No: 10 ended. Evaluation done at random point.\n",
            "Time taken: 195.2731\n",
            "Function value obtained: 10630.4521\n",
            "Current minimum: 10211.4662\n",
            "Iteration No: 11 started. Evaluating function at random point.\n",
            "Iteration No: 11 ended. Evaluation done at random point.\n",
            "Time taken: 157.5855\n",
            "Function value obtained: 10206.4884\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 12 started. Evaluating function at random point.\n",
            "Iteration No: 12 ended. Evaluation done at random point.\n",
            "Time taken: 50.6907\n",
            "Function value obtained: 10679.8636\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 13 started. Evaluating function at random point.\n",
            "Iteration No: 13 ended. Evaluation done at random point.\n",
            "Time taken: 30.2125\n",
            "Function value obtained: 10362.8146\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 14 started. Evaluating function at random point.\n",
            "Iteration No: 14 ended. Evaluation done at random point.\n",
            "Time taken: 48.1507\n",
            "Function value obtained: 10534.3689\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 15 started. Evaluating function at random point.\n",
            "Iteration No: 15 ended. Evaluation done at random point.\n",
            "Time taken: 75.8426\n",
            "Function value obtained: 10904.2423\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 16 started. Evaluating function at random point.\n",
            "Iteration No: 16 ended. Evaluation done at random point.\n",
            "Time taken: 76.3802\n",
            "Function value obtained: 10363.5351\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 17 started. Evaluating function at random point.\n",
            "Iteration No: 17 ended. Evaluation done at random point.\n",
            "Time taken: 36.4066\n",
            "Function value obtained: 10716.2558\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 18 started. Evaluating function at random point.\n",
            "Iteration No: 18 ended. Evaluation done at random point.\n",
            "Time taken: 104.8768\n",
            "Function value obtained: 10773.6888\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 19 started. Evaluating function at random point.\n",
            "Iteration No: 19 ended. Evaluation done at random point.\n",
            "Time taken: 102.1800\n",
            "Function value obtained: 10223.8526\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 20 started. Evaluating function at random point.\n",
            "Iteration No: 20 ended. Evaluation done at random point.\n",
            "Time taken: 152.0228\n",
            "Function value obtained: 10218.0643\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 21 started. Evaluating function at random point.\n",
            "Iteration No: 21 ended. Evaluation done at random point.\n",
            "Time taken: 46.4325\n",
            "Function value obtained: 10777.6148\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 22 started. Evaluating function at random point.\n",
            "Iteration No: 22 ended. Evaluation done at random point.\n",
            "Time taken: 49.1150\n",
            "Function value obtained: 10734.3731\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 23 started. Evaluating function at random point.\n",
            "Iteration No: 23 ended. Evaluation done at random point.\n",
            "Time taken: 20.3387\n",
            "Function value obtained: 10590.2781\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 24 started. Evaluating function at random point.\n",
            "Iteration No: 24 ended. Evaluation done at random point.\n",
            "Time taken: 163.5364\n",
            "Function value obtained: 10401.5797\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 25 started. Evaluating function at random point.\n",
            "Iteration No: 25 ended. Evaluation done at random point.\n",
            "Time taken: 13.3620\n",
            "Function value obtained: 10593.2096\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 26 started. Evaluating function at random point.\n",
            "Iteration No: 26 ended. Evaluation done at random point.\n",
            "Time taken: 183.6802\n",
            "Function value obtained: 10556.4266\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 27 started. Evaluating function at random point.\n",
            "Iteration No: 27 ended. Evaluation done at random point.\n",
            "Time taken: 89.4701\n",
            "Function value obtained: 10325.5176\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 28 started. Evaluating function at random point.\n",
            "Iteration No: 28 ended. Evaluation done at random point.\n",
            "Time taken: 184.6139\n",
            "Function value obtained: 10764.3868\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 29 started. Evaluating function at random point.\n",
            "Iteration No: 29 ended. Evaluation done at random point.\n",
            "Time taken: 12.3472\n",
            "Function value obtained: 11553.3959\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 30 started. Evaluating function at random point.\n",
            "Iteration No: 30 ended. Evaluation done at random point.\n",
            "Time taken: 29.1412\n",
            "Function value obtained: 10921.1117\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 31 started. Evaluating function at random point.\n",
            "Iteration No: 31 ended. Evaluation done at random point.\n",
            "Time taken: 91.8314\n",
            "Function value obtained: 10333.5415\n",
            "Current minimum: 10206.4884\n",
            "Iteration No: 32 started. Evaluating function at random point.\n",
            "Iteration No: 32 ended. Evaluation done at random point.\n",
            "Time taken: 69.7716\n",
            "Function value obtained: 10185.3865\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 33 started. Evaluating function at random point.\n",
            "Iteration No: 33 ended. Evaluation done at random point.\n",
            "Time taken: 105.7698\n",
            "Function value obtained: 10678.2022\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 34 started. Evaluating function at random point.\n",
            "Iteration No: 34 ended. Evaluation done at random point.\n",
            "Time taken: 45.0313\n",
            "Function value obtained: 11195.5668\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 35 started. Evaluating function at random point.\n",
            "Iteration No: 35 ended. Evaluation done at random point.\n",
            "Time taken: 125.2300\n",
            "Function value obtained: 10728.5241\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 36 started. Evaluating function at random point.\n",
            "Iteration No: 36 ended. Evaluation done at random point.\n",
            "Time taken: 14.6010\n",
            "Function value obtained: 11304.0762\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 37 started. Evaluating function at random point.\n",
            "Iteration No: 37 ended. Evaluation done at random point.\n",
            "Time taken: 61.1862\n",
            "Function value obtained: 10638.0590\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 38 started. Evaluating function at random point.\n",
            "Iteration No: 38 ended. Evaluation done at random point.\n",
            "Time taken: 155.7466\n",
            "Function value obtained: 10325.0892\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 39 started. Evaluating function at random point.\n",
            "Iteration No: 39 ended. Evaluation done at random point.\n",
            "Time taken: 74.5193\n",
            "Function value obtained: 10701.4945\n",
            "Current minimum: 10185.3865\n",
            "Iteration No: 40 started. Evaluating function at random point.\n",
            "Iteration No: 40 ended. Evaluation done at random point.\n",
            "Time taken: 106.4387\n",
            "Function value obtained: 10158.1313\n",
            "Current minimum: 10158.1313\n",
            "Iteration No: 41 started. Evaluating function at random point.\n",
            "Iteration No: 41 ended. Evaluation done at random point.\n",
            "Time taken: 143.0775\n",
            "Function value obtained: 10274.5850\n",
            "Current minimum: 10158.1313\n",
            "Iteration No: 42 started. Evaluating function at random point.\n",
            "Iteration No: 42 ended. Evaluation done at random point.\n",
            "Time taken: 130.5696\n",
            "Function value obtained: 10151.2064\n",
            "Current minimum: 10151.2064\n",
            "Iteration No: 43 started. Evaluating function at random point.\n",
            "Iteration No: 43 ended. Evaluation done at random point.\n",
            "Time taken: 28.7716\n",
            "Function value obtained: 10571.5091\n",
            "Current minimum: 10151.2064\n",
            "Iteration No: 44 started. Evaluating function at random point.\n",
            "Iteration No: 44 ended. Evaluation done at random point.\n",
            "Time taken: 81.7851\n",
            "Function value obtained: 10100.4915\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 45 started. Evaluating function at random point.\n",
            "Iteration No: 45 ended. Evaluation done at random point.\n",
            "Time taken: 64.9147\n",
            "Function value obtained: 10349.6061\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 46 started. Evaluating function at random point.\n",
            "Iteration No: 46 ended. Evaluation done at random point.\n",
            "Time taken: 85.8679\n",
            "Function value obtained: 10367.6666\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 47 started. Evaluating function at random point.\n",
            "Iteration No: 47 ended. Evaluation done at random point.\n",
            "Time taken: 120.3194\n",
            "Function value obtained: 10617.3011\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 48 started. Evaluating function at random point.\n",
            "Iteration No: 48 ended. Evaluation done at random point.\n",
            "Time taken: 187.1470\n",
            "Function value obtained: 10625.4040\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 49 started. Evaluating function at random point.\n",
            "Iteration No: 49 ended. Evaluation done at random point.\n",
            "Time taken: 85.5680\n",
            "Function value obtained: 10270.1950\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 50 started. Evaluating function at random point.\n",
            "Iteration No: 50 ended. Evaluation done at random point.\n",
            "Time taken: 108.7097\n",
            "Function value obtained: 10389.0172\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 51 started. Evaluating function at random point.\n",
            "Iteration No: 51 ended. Evaluation done at random point.\n",
            "Time taken: 61.3567\n",
            "Function value obtained: 10225.8786\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 52 started. Evaluating function at random point.\n",
            "Iteration No: 52 ended. Evaluation done at random point.\n",
            "Time taken: 44.4305\n",
            "Function value obtained: 10410.9779\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 53 started. Evaluating function at random point.\n",
            "Iteration No: 53 ended. Evaluation done at random point.\n",
            "Time taken: 82.9880\n",
            "Function value obtained: 10374.8973\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 54 started. Evaluating function at random point.\n",
            "Iteration No: 54 ended. Evaluation done at random point.\n",
            "Time taken: 24.3706\n",
            "Function value obtained: 10140.8720\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 55 started. Evaluating function at random point.\n",
            "Iteration No: 55 ended. Evaluation done at random point.\n",
            "Time taken: 41.9684\n",
            "Function value obtained: 10140.0631\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 56 started. Evaluating function at random point.\n",
            "Iteration No: 56 ended. Evaluation done at random point.\n",
            "Time taken: 118.7383\n",
            "Function value obtained: 10856.3050\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 57 started. Evaluating function at random point.\n",
            "Iteration No: 57 ended. Evaluation done at random point.\n",
            "Time taken: 137.3773\n",
            "Function value obtained: 10282.1892\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 58 started. Evaluating function at random point.\n",
            "Iteration No: 58 ended. Evaluation done at random point.\n",
            "Time taken: 105.9943\n",
            "Function value obtained: 10517.1169\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 59 started. Evaluating function at random point.\n",
            "Iteration No: 59 ended. Evaluation done at random point.\n",
            "Time taken: 71.1544\n",
            "Function value obtained: 10321.6480\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 60 started. Evaluating function at random point.\n",
            "Iteration No: 60 ended. Evaluation done at random point.\n",
            "Time taken: 56.2471\n",
            "Function value obtained: 10179.2018\n",
            "Current minimum: 10100.4915\n",
            "Iteration No: 61 started. Searching for the next optimal point.\n",
            "Iteration No: 61 ended. Search finished for the next optimal point.\n",
            "Time taken: 26.9602\n",
            "Function value obtained: 10075.4140\n",
            "Current minimum: 10075.4140\n",
            "Iteration No: 62 started. Searching for the next optimal point.\n",
            "Iteration No: 62 ended. Search finished for the next optimal point.\n",
            "Time taken: 19.0443\n",
            "Function value obtained: 10274.8188\n",
            "Current minimum: 10075.4140\n",
            "Iteration No: 63 started. Searching for the next optimal point.\n",
            "Iteration No: 63 ended. Search finished for the next optimal point.\n",
            "Time taken: 51.0478\n",
            "Function value obtained: 10062.5301\n",
            "Current minimum: 10062.5301\n",
            "Iteration No: 64 started. Searching for the next optimal point.\n",
            "Iteration No: 64 ended. Search finished for the next optimal point.\n",
            "Time taken: 67.7106\n",
            "Function value obtained: 10143.7058\n",
            "Current minimum: 10062.5301\n",
            "Iteration No: 65 started. Searching for the next optimal point.\n",
            "Iteration No: 65 ended. Search finished for the next optimal point.\n",
            "Time taken: 79.7420\n",
            "Function value obtained: 10093.3275\n",
            "Current minimum: 10062.5301\n",
            "Iteration No: 66 started. Searching for the next optimal point.\n",
            "Iteration No: 66 ended. Search finished for the next optimal point.\n",
            "Time taken: 83.9962\n",
            "Function value obtained: 10088.0018\n",
            "Current minimum: 10062.5301\n",
            "Iteration No: 67 started. Searching for the next optimal point.\n",
            "Iteration No: 67 ended. Search finished for the next optimal point.\n",
            "Time taken: 47.6888\n",
            "Function value obtained: 10024.6934\n",
            "Current minimum: 10024.6934\n",
            "Iteration No: 68 started. Searching for the next optimal point.\n",
            "Iteration No: 68 ended. Search finished for the next optimal point.\n",
            "Time taken: 61.3469\n",
            "Function value obtained: 10092.7961\n",
            "Current minimum: 10024.6934\n",
            "Iteration No: 69 started. Searching for the next optimal point.\n",
            "Iteration No: 69 ended. Search finished for the next optimal point.\n",
            "Time taken: 51.6844\n",
            "Function value obtained: 10095.4210\n",
            "Current minimum: 10024.6934\n",
            "Iteration No: 70 started. Searching for the next optimal point.\n",
            "Iteration No: 70 ended. Search finished for the next optimal point.\n",
            "Time taken: 65.1534\n",
            "Function value obtained: 10069.0822\n",
            "Current minimum: 10024.6934\n",
            "Iteration No: 71 started. Searching for the next optimal point.\n",
            "Iteration No: 71 ended. Search finished for the next optimal point.\n",
            "Time taken: 69.2836\n",
            "Function value obtained: 10010.8244\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 72 started. Searching for the next optimal point.\n",
            "Iteration No: 72 ended. Search finished for the next optimal point.\n",
            "Time taken: 99.3273\n",
            "Function value obtained: 10062.6681\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 73 started. Searching for the next optimal point.\n",
            "Iteration No: 73 ended. Search finished for the next optimal point.\n",
            "Time taken: 123.4358\n",
            "Function value obtained: 10119.6694\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 74 started. Searching for the next optimal point.\n",
            "Iteration No: 74 ended. Search finished for the next optimal point.\n",
            "Time taken: 98.5836\n",
            "Function value obtained: 10075.9703\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 75 started. Searching for the next optimal point.\n",
            "Iteration No: 75 ended. Search finished for the next optimal point.\n",
            "Time taken: 106.1032\n",
            "Function value obtained: 10116.5853\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 76 started. Searching for the next optimal point.\n",
            "Iteration No: 76 ended. Search finished for the next optimal point.\n",
            "Time taken: 116.7845\n",
            "Function value obtained: 10117.0107\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 77 started. Searching for the next optimal point.\n",
            "Iteration No: 77 ended. Search finished for the next optimal point.\n",
            "Time taken: 61.4060\n",
            "Function value obtained: 10083.9438\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 78 started. Searching for the next optimal point.\n",
            "Iteration No: 78 ended. Search finished for the next optimal point.\n",
            "Time taken: 55.2550\n",
            "Function value obtained: 10072.5126\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 79 started. Searching for the next optimal point.\n",
            "Iteration No: 79 ended. Search finished for the next optimal point.\n",
            "Time taken: 56.8294\n",
            "Function value obtained: 10038.7887\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 80 started. Searching for the next optimal point.\n",
            "Iteration No: 80 ended. Search finished for the next optimal point.\n",
            "Time taken: 76.7714\n",
            "Function value obtained: 10075.2744\n",
            "Current minimum: 10010.8244\n",
            "Iteration No: 81 started. Searching for the next optimal point.\n",
            "Iteration No: 81 ended. Search finished for the next optimal point.\n",
            "Time taken: 33.0242\n",
            "Function value obtained: 9989.2983\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 82 started. Searching for the next optimal point.\n",
            "Iteration No: 82 ended. Search finished for the next optimal point.\n",
            "Time taken: 66.6204\n",
            "Function value obtained: 10071.0890\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 83 started. Searching for the next optimal point.\n",
            "Iteration No: 83 ended. Search finished for the next optimal point.\n",
            "Time taken: 81.9330\n",
            "Function value obtained: 10091.6048\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 84 started. Searching for the next optimal point.\n",
            "Iteration No: 84 ended. Search finished for the next optimal point.\n",
            "Time taken: 34.6002\n",
            "Function value obtained: 10067.1427\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 85 started. Searching for the next optimal point.\n",
            "Iteration No: 85 ended. Search finished for the next optimal point.\n",
            "Time taken: 8.1937\n",
            "Function value obtained: 11810.5437\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 86 started. Searching for the next optimal point.\n",
            "Iteration No: 86 ended. Search finished for the next optimal point.\n",
            "Time taken: 207.5221\n",
            "Function value obtained: 10184.0451\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 87 started. Searching for the next optimal point.\n",
            "Iteration No: 87 ended. Search finished for the next optimal point.\n",
            "Time taken: 29.0114\n",
            "Function value obtained: 10161.4698\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 88 started. Searching for the next optimal point.\n",
            "Iteration No: 88 ended. Search finished for the next optimal point.\n",
            "Time taken: 8.0125\n",
            "Function value obtained: 11651.5883\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 89 started. Searching for the next optimal point.\n",
            "Iteration No: 89 ended. Search finished for the next optimal point.\n",
            "Time taken: 51.3806\n",
            "Function value obtained: 10211.4021\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 90 started. Searching for the next optimal point.\n",
            "Iteration No: 90 ended. Search finished for the next optimal point.\n",
            "Time taken: 7.3479\n",
            "Function value obtained: 11739.7473\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 91 started. Searching for the next optimal point.\n",
            "Iteration No: 91 ended. Search finished for the next optimal point.\n",
            "Time taken: 24.8294\n",
            "Function value obtained: 10077.5599\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 92 started. Searching for the next optimal point.\n",
            "Iteration No: 92 ended. Search finished for the next optimal point.\n",
            "Time taken: 8.5453\n",
            "Function value obtained: 11111.1690\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 93 started. Searching for the next optimal point.\n",
            "Iteration No: 93 ended. Search finished for the next optimal point.\n",
            "Time taken: 23.0233\n",
            "Function value obtained: 10118.7427\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 94 started. Searching for the next optimal point.\n",
            "Iteration No: 94 ended. Search finished for the next optimal point.\n",
            "Time taken: 49.0691\n",
            "Function value obtained: 10028.6392\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 95 started. Searching for the next optimal point.\n",
            "Iteration No: 95 ended. Search finished for the next optimal point.\n",
            "Time taken: 20.9839\n",
            "Function value obtained: 10094.6776\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 96 started. Searching for the next optimal point.\n",
            "Iteration No: 96 ended. Search finished for the next optimal point.\n",
            "Time taken: 13.1206\n",
            "Function value obtained: 11368.8129\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 97 started. Searching for the next optimal point.\n",
            "Iteration No: 97 ended. Search finished for the next optimal point.\n",
            "Time taken: 19.9973\n",
            "Function value obtained: 10029.0185\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 98 started. Searching for the next optimal point.\n",
            "Iteration No: 98 ended. Search finished for the next optimal point.\n",
            "Time taken: 66.3566\n",
            "Function value obtained: 10021.0391\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 99 started. Searching for the next optimal point.\n",
            "Iteration No: 99 ended. Search finished for the next optimal point.\n",
            "Time taken: 8.7407\n",
            "Function value obtained: 11239.7621\n",
            "Current minimum: 9989.2983\n",
            "Iteration No: 100 started. Searching for the next optimal point.\n",
            "Iteration No: 100 ended. Search finished for the next optimal point.\n",
            "Time taken: 6.8945\n",
            "Function value obtained: 11350.7461\n",
            "Current minimum: 9989.2983\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "UkBCoefY7lZb",
        "outputId": "9a034191-d327-4618-d182-9210871a4038"
      },
      "source": [
        "# function value at the minimum.\n",
        "\"Best score=%.4f\" % gbm_.fun"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Best score=9989.2983'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 912
        },
        "id": "R1KLdsZQ7oT5",
        "outputId": "bc823322-f811-440c-b4c6-2afd7f64a9f1"
      },
      "source": [
        "plot_convergence(gbm_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fe9265d90>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x1080 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Y0MhzaBFmVbe",
        "outputId": "81d89c4b-4d34-4c1b-aa82-edc53d674ce5"
      },
      "source": [
        "from xgboost import XGBRegressor\n",
        "xgb = XGBRegressor(random_state=0, \n",
        "                  xgb_gpu_hist = 1,\n",
        "                  n_estimators= gbm_.x[0],\n",
        "                  learning_rate= gbm_.x[1],\n",
        "                  max_depth = gbm_.x[2],\n",
        "                  min_child_weight = gbm_.x[3],\n",
        "                  colsample_bytree = gbm_.x[4],\n",
        "                  subsample = gbm_.x[5],\n",
        "                  reg_alpha =  gbm_.x[6],\n",
        "                  )\n",
        "\n",
        "xgb"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
              "             colsample_bynode=1, colsample_bytree=0.6285516313782635, gamma=0,\n",
              "             importance_type='gain', learning_rate=0.05878316665242285,\n",
              "             max_delta_step=0, max_depth=10, min_child_weight=6.540977065739142,\n",
              "             missing=None, n_estimators=147, n_jobs=1, nthread=None,\n",
              "             objective='reg:linear', random_state=0,\n",
              "             reg_alpha=2.0042764073285015, reg_lambda=1, scale_pos_weight=1,\n",
              "             seed=None, silent=None, subsample=0.7126789003396041, verbosity=1,\n",
              "             xgb_gpu_hist=1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 21
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "o4TaEm6B7P-8",
        "outputId": "80fb23d1-bc1f-4701-b914-7234e2097793"
      },
      "source": [
        "dim_names = ['n_estimators','learning_rate','max_depth','min_child_weight','colsample_bytree', 'subsample' ,'reg_alpha']\n",
        "plt.rcParams[\"figure.figsize\"] = (15,15)\n",
        "plot_objective(result=gbm_, plot_dims=dim_names,size=3)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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ZVe0OXNZcU1V/X1X7VtW+wEeAH1fV4iTjgc8BrwT2Ao5Nslcz1yeBT1XVbsCDwKpqoP85cCGwa5KfAl8G3jMEzyhJkiRJkiStNZPpvZo1q+0IpJ5V1RXA4gHNRwFnN+dnA69ewdBjgXOa8/2B26vqjqp6GjgXOCpJgEOA8weZqz+Wa4GXAC8A3g48p6puXOOHkiRJkiRJkoaQyfQBksxMMjvJ7EWLFg0+4O1vH/6gpN5t0/973RwzV2PM1Kpa0JzfC0ztvplkYzq72b/RNE0H7unqMrdp2xp4qKl93t2+QkneBWxaVbdU1c3ApkneuRrxSpIkSZIkScPGZPoAVTWrqvqqqm/KlClthyMNlfv7f6+bY43+SUVVFU0N8y5HAD+tqoE72nt1QlU91LX2g8AJQ7yGJEmSJEmStEZMpktamfuSTANoPhcOuH8Mvy3xAjAP2KHrevum7QFgiyQTBrSvzPimNAzN2uOBVb2wVJIkSZIkSRp2JtN7deGFbUcgDZcLgeOa8+OAC/pvJJlMp675BV39rwF2T7Jzkol0ku0XNrvaLweOXtFcK/B94GtJXprkpXQS9t8fgueRJEmSJEmS1prJ9F7NmNF2BFLPkpwD/AzYM8ncJMcDpwIvS3Ib8MfNdb/XAD+oqsf7G5qa6O8GLgFuBc6rqlua2x8G/jzJ7XRqqJ+xinA+TCf5/r+b4zLgQ70/pSRJkiRJkrT20tk0qhXp6+ur2bNnr7pTAv4MNcIlmVNVfW3HIUmSJEmSJK2vJgzeRZLWnSQvBE4GnkXn/1Gh8w7UXdqMS5IkSZIkSWObyXRJI80ZwAeAOcCylmORJEmSJEmSAJPpvTvhhLYjkEabh6vqe20HIUmSJEmSJHUzmd6rWbPajkAabS5P8vfAN4Gn+hur6tr2QpIkSZIkSdJYZzK9VzNmwJw5bUchjSYHNJ/dL0wt4JAWYpEkSZIkSZIAk+m9u9bNstJQqqo/ajsGSZIkSZIkaaBxbQcgSd2STE1yRpLvNdd7JTm+7bgkSZIkSZI0tplM79W0aW1HII02ZwGXANs1178C3t9aNJIkSZIkSRIm03s3f37bEUijzTZVdR6wHKCqlgLL2g1JkiRJkiRJY53J9F6dfHLbEUijzeNJtqbz0lGSHAg83G5IkiRJkiRJGutMpvfqlFPajkAabf4cuBDYNclPgS8D72k3JEmSJEmSJI11E9oOQJK6VdW1SV4C7AkE+GVVLWk5LEmSJEmSJI1xJtMljQhJXruSW3skoaq+uU4DkiRJkiRJkrqYTO/V7NltRyCNFkc0n9sCLwB+2Fz/EfBfgMl0SZIkSZIktcZkuqQRoareCpDkB8BeVbWguZ4GnNViaJIkSZIkSZIvIO1ZX1/bEUijzQ79ifTGfcCObQUjSZIkSZIkgTvTJY08lyW5BDinuX498B8txiNJkiRJkiSZTJc0slTVu5uXkb6oaZpVVd9qMyZJkiRJkiTJZHqvTjqp7QikUaeqvokvHJUkSZIkSdIIYs30Xp18ctsRSKNKktcmuS3Jw0keSfJokkfajkuSJEmSJEljm8n0Xm23XdsRSKPN3wFHVtXkqtq8qjarqs3bDkqSJEmSJEljm8n0Xi1Y0HYE0mhzX1Xd2nYQkiRJkiRJUjeT6ZJIcmaShUlu7mrbKsmlTcmVS5Ns2XXv4CTXJ7klyY+72g9N8ssktyc5sat95yRXNe1fSzJxFeHMbvoc25R8eW3zQlJJkiRJkiSpNSbTe7Xffm1HIA2Fs4BDB7SdCFxWVbsDlzXXJNkC+DydUizPAf60aR8PfA54JbAXcGySvZq5Pgl8qqp2Ax4Ejl9FLJsDTwAvB45ojsN7fD5JkiRJkiSpJxPaDmC9N2dO2xFIPauqK5LsNKD5KODg5vxs4EfAh4E3AN+sql83Yxc2ffYHbq+qOwCSnAscleRW4JBmXP9cJwOnrSSWt/b6PJIkSZIkSdJQc2f6AElmJpmdZPaiRYsGHzBz5vAHJfVum/7f6+ZYnV/cqVXV/1KAe4GpzfkewJZJfpRkTpI3N+3TgXu6xs9t2rYGHqqqpQPaVyjJHkku6y85k+QPkvzlaj2lJEmSJEmSNExMpg9QVbOqqq+q+qZMmTL4gNNPH/6gpN7d3/973Ryz1mRwVRVQzeUEYAbwKuAVwF8l2WMIYz0d+AiwpFn7RuCYIZxfkiRJkiRJWmOWeZG0MvclmVZVC5JMA/rLucwFHqiqx4HHk1wB7NO079A1fntgHvAAsEWSCc3u9P72ldm4qq5O0t22dGWdJUmSJEmSpHXBnemSVuZC4Ljm/Djggub8AuAPk0xIsjFwAHArcA2we5Kdk0yks5v8wmZX++XA0SuYa0XuT7IrzU74JEcDC1bRX5IkSZIkSRp27kzv1bxVbbCV1g9JzqHzstFtkswFTgJOBc5LcjxwN/A6gKq6Ncn3gRuB5cAXq6q/vvm7gUuA8cCZVXVLs8SHgXOTfAy4DjhjFeG8C5gFPDvJPOBO4I1D+LiSJEmSJEnSGktn06hWpK+vr2bPnr3qThddBEccsW4CktZSkjlV1dd2HGsiySbAuKp6tO1YJEmSJEmSJMu89OrII9uOQBpVkmyd5DPAT4AfJfl0kq3bjkuSJEmSJEljm8l0SSPNucAi4E/o1FlfBHyt1YgkSZIkSZI05lkzXdJIM62q/qbr+mNJXt9aNJIkSZIkSRLuTO/dF77QdgTSaPODJMckGdccr6PzUlNJkiRJkiSpNb6AdBVW6wWk0npgfXoBaZJHgU2A5UAB44HHm9tVVZu3FZskSZIkSZLGLsu89CoB/yAhDZmq2qztGCRJkiRJkqSBLPOyCkuXmySX1rV0/K8kf9Vc75Bk/7bjkiRJkiRJ0thmMn0VbrvvUa65a3HbYUhjzeeBg4A3NNePAZ9rLxxJkiRJkiTJZPoqjUs4ZtaVfPEnd7DS2vKHH75ug5JGvwOq6l3AkwBV9SAwsd2QJEmSJEmSNNaZTF+F3aZuykufvS0f+86tvOvfr+XRJ5f8bqeLLlr3gUmj25Ik4+m8fJQkU+i8jFSSJEmSJElqjcn0VRif8IU3zeD/HvZsLrnlPo4+7Wc8uWTZ/+x0xBHtBCeNXp8BvgVsm+TjwH8Cf9tuSJIkSZIkSRrrTKYPIgkzX7wrp71xP35536N8+Wd3/c8OF1/cRljSqFVVXwU+BHwCWAC8uqq+3m5UkiRJkiRJGutMpq+mlz/n9zh4zyl89oe38+DjT7cdjjTqJNmq/wAWAucA/w7c17RJkiRJkiRJrTGZvgb+72G/z+NPLeUzP7yt7VCk0WgOMLv5XAT8CritOZ/TYlySJEmSJEmSyfQ1scfUzXj983fk3352N3fe/3insardoNZQVXHmf97Jd25cwJJlvtNRI0dV7VxVuwD/ARxRVdtU1dbA4cAP2o1OkiRJkiRJY53J9DX0gZftzoYTxvHJ7/2i0zBrVrsBraHr73mIj178c97179fyh5/8IZ/+j9tY+MiTbYcldTuwqr7bf1FV3wNe0GI8kiRJkiRJEhPaDmB9s+1mG/GOl+zKP176K665azHPf/vbYebMtsNabedefQ8bTxzPP/zpPnztmnv41H/8is/+8DZeuNs2bLrhqn8dNtlwPO//4z3YbotJ6yhajVHzk/wl8JXm+o3A/BbjkSRJkiRJkkymr40/e9EufOWqu/nYd27lgraDWQOPPrmEC2+Yz5H7bMdhz53GYc+dxp33P85Xrrybn9y2iOWDVKyZ9+BvuOrOxZw780CmTTahrmFzLHAS8C2ggCuaNkmSJEmSJKk1JtPXwqSJ4/ngy/fkL86/EYD3nHNdz3PuOXVT3nnwbowbl57nWpkLb5jPb5Ys49gDdnymbedtNuGvDt9rtcZf9+sHedMZV/OG06/i3JkHMnXzjYYrVI1hVbUYeF/bcUiSJEmSJEndTKavpT/Zb3v+678f4P+99ePcMu/hnuZasnw5F90wnyXLig+8bI8hivB3nXP1r3n2723GPttPXqvxz9txS85+2/N58xlXc+zpV3LuCQeyrQl1SZIkSZIkSWOAyfS1NG5c+NTr94UXbQvbbdfTXFXFB79+I5++7DZ23XZTjtynt/lW5OZ5D3PzvEc45cjnkKz97vcZz9qKs962P8ed2Umof/XPDmTypA1Ysnw5S5cVS5cv7xTm6LLJhhPYZJB67JIkSZIkSZI0kpnh7NX06VCDFBsfRBL+9rV7c8/iJ/jg129ghy0n8bwdtxyiADvOufrXbDhhHK9+3vSe53r+Tlvxpbc8n7d86RoO/MRlg/bffKMJXP7Bg9l60w17XluSJEmSJEmS2mAyfYTYcMJ4/vVNMzjqc//JCV+ewwXvfiHTtxial3w+/tRSLrh+Pq/6g2lMnrTBkMx5wC5b8/V3HMSPfrmQCePHMWFcmDAujB8/ju6y708uWc7Hv/NzTv/JnZz4ymcPydoaeknOBA4HFlbV3k3bVsDXgJ2Au4DXVdWDSQ4GLgDubIZ/s6o+2ow5FPg0MB74YlWd2rTvDJwLbA3MAd5UVU8PiOGz/M6/a/itqnrvUDyrJEmSJEmStDZMpo8gW20ykTOPez6v/fx/8Wdnz+b8dxw0JOVRvnPjAh57ainH7r/j4J3XwN7TJ7P39MHrr9849yG+/LO7mPniXdhqk4lDGoOGzFnAvwBf7mo7Ebisqk5NcmJz/eHm3k+q6vDuCZKMBz4HvAyYC1yT5MKq+jnwSeBTVXVukn8FjgdOGxDD7CF+JkmSJEmSJGnIjGs7gPXeCScM6XS7T92Mz77hefzy3kc47DM/4ZJb7qV6LCPz71f/mt223ZS+Zw1t6ZjV9Z5Dduc3S5Zx+k/uaGV9Da6qrgAWD2g+Cji7OT8bePUg0+wP3F5VdzS7zs8FjkqnSP8hwPmrmquqzl7VsZaPJkmSJEmSJA0Jk+kDJJmZZHaS2YsWLRp8wKxZQx7DwXtuy1eOP4ANJ4zj7f82hzecfhU/n//IWs31i3sf4fp7HuKY5+/Q04tHe7HbtptyxB9sx9n/dReLH3968AEaDtv0/143x8zVGDO1qhY05/cCU7vuHZTkhiTfS/Kcpm06cE9Xn7lN29bAQ1W1dED7CiWZkuQfknw3yQ/7j9V5SEmSJEmSJGm4WOZlgKqaBcwC6OvrG3xL+IwZMGfOkMfxgt224bvvfRHnXP1r/unSX/Gqz/6Ew547jSmDvMRzeRUPPP40ix59ivsffYp7H3mSiePH8dr9th/yGNfEe1+6GxfdOJ8v/uQOPnSotdNbcH9V9a3t4KqqJP3/PVwLPKuqHktyGPBtYPehCLLxVTq12l8FvAM4DliNv2xJkiRJkiRJw8dkeq+uvXbYpp4wfhxvOmgnjtxnOp++7Da+ff08li5bvsox48aFrTaZyJRNN2Sv7TbnJXtO4YCdt269Vvlu227G4c3u9BNetAtbWjt9fXBfkmlVtSDJNGAhQFU9888kquq7ST6fZBtgHrBD1/jtm7YHgC2STGh2p/e3r8zWVXVGkvdV1Y+BHye5ZoifTZIkSZIkSVojJtPXA5M33oC/PmIv/vqIvdoOpSfvPWQ3Lr5xPl/8zzv4i1cM7+70J5cs45HfLOHh3yzhsaeW0v1PDKrg4d88za8feIJfL/4Nv178OAsffYq+Z23FUftuxx9sP7m1kjgjzIV0doWf2nxeAJDk94D7mt3q+9MpF/UA8BCwe5Kd6STLjwHe0PS7HDiaTh31Z+ZaiSXN54IkrwLmA1sN9cNJkiRJkiRJa8Jkeq+mTWs7gvXG7lM341XPncZZP72L416wEw8+voRf3vcov7r3Ue564HGWD3jR6tNLl/Pok0t57KnO8fhTS1k+SOGdquLxp5fx9NJV7+DvN2mD8Txr643ZYuMN+MqVd3PmT+9kl2024ch9t2O/Hbfk3oef5J4Hn+CexU8w98Hf8NRqzptAmpM016vsDzxvxy159b7T2Xv65us8mZ/kHOBgOrXV5wIn0Umin5fkeOBu4HVN96OB/51kKfAb4JjqvCV3aZJ3A5cA44Ezq+qWZsyHgXOTfAy4DjhjFeF8LMlk4P8AnwU2Bz4wZA8rSZIkSZIkrYVUDV4WfKzq6+ur2bNntx3GqPKr+x7lFUc/jMcAACAASURBVP98Bd2/duPHhe23nMTE8f/zfbgbjB/HphtNYLMNJ7DpRhPYZMMJjF+NJPPGE8ez+aQNmDxpAzaftAGbbTjhd5LZm200gR232oRtNp34TOL64SeW8P1bFvDt6+Zz5Z0PPBPjuMC0yZPYfstJbLLh4H9/qiqKzg74aq4H89TS5Vz36wdZsqzYZcomvHrf6fzx709l0sTxg45dHbtM2XROLzXTJUmSJEmSpLHOZPoqrFYy/eSTO4dW279deTcLHvoNe/7eZuwxdTN2mbIJG04YmqTxULn34Se54/7H2H6LjZm2xUZsMCDRPxweeuJpvnvTvXz7+nlcfefiIZ377k8ePuKT6Uk+VFV/l+SzwO/8j6mq3ttCWJIkSZIkSRJgMn2VViuZnoA/Qw2xeQ/9htl3LR6yX63X7Lf9+pBMP6KqLkpy3IruV9XZ6zomSZIkSZIkqZ8106URaPoWk5i+7/S2w1inquqi5vSJqvp6970kf9pCSJIkSZIkSdIzhr92hSStmY+sZpskSZIkSZK0zrgzvVe+oFQaEkleCRwGTE/yma5bmwNL24lKkiRJkiRJ6jCZLmmkmA/MBo4E5nS1Pwp8oJWIJEmSJEmSpIbJ9F719fkCUmkIVNUNSW4GXuHLRiVJkiRJkjTSWDNd0ohRVcuAHZJMbDsWSZIkSZIkqZs70yWNNHcCP01yIfB4f2NV/VN7IUmSJEmSJGmsM5neq5NOajsCabT57+YYB2zWciySJEmSJEkSYDK9dyef3HYE0qhSVae0HYMkSZIkSZI0kMn0Xm23Hcyf33YU0qiRZArwIeA5wEb97VV1SGtBSZIkSZIkaczzBaS9WrCg7Qik0earwC+AnYFTgLuAa9oMSJIkSZIkSTKZLmmk2bqqzgCWVNWPq+ptgLvSJUmSJEmS1CrLvPRqv/3ajkAabZY0nwuSvAqYD2zVYjySJEmSJEmSyfSezZnTdgTSaPOxJJOB/wN8Ftgc+EC7IUmSJEmSJGmss8xLr2bObDsCaVRIslGS9wOHAscAv6iqP6qqGVV1YcvhSZIkSZIkaYwzmd6r009vOwJptDgb6ANuAl4J/GO74UiSJEmSJEm/ZZkXSSPFXlX1XIAkZwBXtxyPJEmSJEmS9Ax3pksaKfpfPEpVLW0zEEmSJEmSJGkgd6b3at68tiOQRot9kjzSnAeY1FwHqKravL3QJEmSJEmSNNaZTO/VnDmw3XZtRyGt96pqfNsxSJIkSZIkSStjmZdeHXlk2xFIPUtyZpKFSW7uatsqyaVJbms+txww5vlJliY5uqvtuKb/bUmO62qfkeSmJLcn+UySrJsnkyRJkiRJkoaGyXRJAGcBhw5oOxG4rKp2By5rrgFIMh74JPCDrratgJOAA4D9gZO6EvCnAScAuzfHwLUkSZIkSZKkEc1kuiSq6gpg8YDmo4Czm/OzgVd33XsP8A1gYVfbK4BLq2pxVT0IXAocmmQasHlVXVlVBXx5wFySJEmSJEnSiGcyfYAkM5PMTjJ70aJFgw/4wheGPyipd9v0/143x8zVGDO1qhY05/cCUwGSTAdeQ2e3ebfpwD1d13ObtunN+cB2SZIkSZIkab3hC0gHqKpZwCyAvr6+GnTAzNXJSUqtu7+q+tZ2cFVVkv7/Hv4Z+HBVLbf0uSRJkiRJksYKk+m9SqAGz7lL66H7kkyrqgVNqZb+ki59wLlNIn0b4LAkS4F5wMFd47cHftS0bz+gfd7whi5JkiRJkiQNLcu8SFqZC4HjmvPjgAsAqmrnqtqpqnYCzgfeWVXfBi4BXp5ky+bFoy8HLmlKxTyS5MB0MvBv7p9LkiRJkiRJWl+4M10SSc6hs6t8myRzgZOAU4HzkhwP3A28blVzVNXiJH8DXNM0fbSq+l9q+k7gLGAS8L3mkCRJkiRJktYbJtN7dfjhbUcg9ayqjl3JrZcOMu4tA67PBM5cQb/ZwN5rG58kSZIkSZLUNsu89Oqii9qOQJIkSZIkSZI0zEym9+qII9qOQJIkSZIkSZI0zEym9+rii9uOQJIkSZIkSZI0zEymS5IkSZIkSZI0CJPpkiRJkiRJkiQNwmR6r6rajkCSJEmSJEmSNMxMpvdq1qy2I5AkSZIkSZIkDTOT6b16+9vbjkCSJEmSJEmSNMxMpkuSJEmSJEmSNAiT6ZIkSZIkSZIkDcJkeq8uvLDtCCRJkiRJkiRJw8xkeq9mzGg7AkmSJEmSJEnSMDOZ3qvp09uOQJIkSZIkSZI0zEymS5IkSZIkSZI0CJPpkiRJkiRJkiQNwmR6r044oe0IJEmSJEmSJEnDzGR6r2bNajsCSZIkSZIkSdIwM5neqxkz2o5A0hBK8tg6WOMdSd483OusZO23JNmujbUlSZIkSZLWZxPaDmC9d+21bUcgaQRKMr6qlq3oXlX9a1trA28BbgbmD2cMkiRJkiRJo4070yVpJZL8RZJrktyY5JSu9m8nmZPkliQzu9ofS/KPSW4ADmquP57khiRXJpna9Ds5yQeb8x8l+WSSq5P8KsmLmvaNk5yX5OdJvpXkqiR9q4h14Np/3cR+c5JZ6Tga6AO+muT6JJOSzEjy4+Z5LkkybXh+mpIkSZIkSes3k+m9mmbeSRqNkrwc2B3YH9gXmJHkxc3tt1XVDDqJ6fcm2bpp3wS4qqr2qar/bK6vrKp9gCuAlb2xeEJV7Q+8HzipaXsn8GBV7QX8FTBYTamBa/9LVT2/qvYGJgGHV9X5wGzgjVW1L7AU+CxwdPM8ZwIfX72fkCRJkiRJ0thimZdezbdSgjRKvbw5rmuuN6WTXL+CTgL9NU37Dk37A8Ay4BtdczwNXNyczwFetpK1vtnVZ6fm/A+BTwNU1c1Jbhwk3oFr/1GSDwEbA1sBtwAXDRizJ7A3cGkSgPHAgkHWkSRJkiRJGpPcmd6rk09uOwKpZ0nOTLIwyc1dbVsluTTJbc3nlk37UU3Zk+uTzE7yh11jjmv635bkuK72GUluSnJ7ks+kydyOcAE+UVX7NsduVXVGkoOBPwYOanacXwds1Ix5ckCt8iVVVc35Mlb+B8ynVqPPYJ5ZO8lGwOfp7Dh/LnB6V4zdAtzS9YzPraqXr+X6kiRJkiRJo5rJ9F6dcsrgfaSR7yzg0AFtJwKXVdXuwGXNNc35Pk2ZkLcBX4RO8p1OiZID6JRGOak/AQ+cRqfEye7NMXCtkegS4G1JNgVIMj3JtsBkOuVXnkjybODAYVr/p8DrmrX3Ap67BmP7E+f3N/Ef3XXvUWCz5vyXwJQkBzXrbJDkOT1FLUmSJEmSNEqZTJdEVV0BLB7QfBRwdnN+NvDqpu9jXbutNwH6z18BXFpVi6vqQeBS4NDmhZabV9WVzbgv9881klXVD4B/B36W5CbgfDpJ6O8DE5LcCpwKXDlMIXyeTqL758DH6JRpeXh1BlbVQ3R2o99M548C13TdPgv41yTX0ynrcjTwyebFpdcDLxiqB5AkSZIkSRpN8tucmACSzARmAuy4444z7r777sEGgD9DjXBJ7gbu72qaVVWzBvTZCbi4eWElSR6qqi2a89DZjd1//RrgE8C2wKuq6mdJPghsVFUfa/r8FfAb4EfAqVX1x037i4APV9Xhw/S4o0KS8cAGVfVkkl2B/wD2rKqnWw5NkiRJkiRpTPIFpAM0CcZZAH19fYNnyWfPHu6QpKFwf1X1re3gqqok1XX9LeBbSV4M/A2dGuIaWhsDlyfZgE5t83eaSJckSZIkSWqPyXRJK3NfkmlVtaAp1bJwYIequiLJLkm2AeYBB3fd3p7OrvR5zXl3+7xhi3qUqKpHgd/5A0iSq4ANBzS/qapuWieBSZIkSZIkjVHWTO9V31pv9pVGuguB45rz44ALAJLs1pR9Icl+dBK7D9Cpzf3yJFs2Lx59OXBJVS0AHklyYDPuzf1zac1V1QFVte+Aw0S6JEmSJEnSMHNnuiSSnENnV/k2SeYCJ9F5ueZ5SY4H7gZe13T/E+DNSZbQqYn++ubFoouT/A2/fdnlR6uq/6Wm76Tz4stJwPeaQ5IkSZIkSVpv+ALSVejr66vZg9VE9wWkWg8kmdNLzXStnSS7AP8PmFxVR7cdjyRJkiRJktaeyfRVSLKIzo5caX33rKqa0nYQ65MkZwKHAwurau+u9kOBTwPjgS9W1amrMdf5JtMlSZIkSZLWb5Z5WQWTj9LIlOTVwKuAzYEzquoHw7DMWcC/AF/uWnc88DngZcBc4JokF9JJrH9iwPi3VdXvvLRVq2ek7+of6fFJkiRJkqSh5wtIJQ0qyRZJzk/yiyS3JjloLec5M8nCJDev4N6hSX6Z5PYkJ65qnqr6dlWdALwDeP3axDKYqroCWDygeX/g9qq6o6qeBs4Fjqqqm6rq8AHHepNIT7JDksuT/DzJLUne18NcQ/Ud31FVxzfjNkpydZIbmvhOGUnxSZIkSZKkscFkuqTV8Wng+1X1bGAf4Nbum0m2TbLZgLbdVjDPWcChAxu7dny/EtgLODbJXkmem+TiAce2XUP/shm3rkwH7um6ntu0rVCSrZP8K/C8JB8Z7uB6sBT4P1W1F3Ag8K4ke3V3aPE7BngKOKSq9gH2BQ5NcuAIik+SJEmSJI0BlnmRtEpJJgMvBt4C0OzIfnpAt5cA70hyWFU9leQE4LV0EpPPqKorkuy0gmWe2fHdrNm/4/sTdOqWD4wpwKnA96rq2rV/uuFVVQ/Q2T0/olXVAmBBc/5oklvp/JHg513d1ul3PGDOAh5rLjdojoEv/GgtPkmSJEmSNDa4M13SYHYGFgFfSnJdki8m2aS7Q1V9HbgE+FqSNwJvA/50DdZYox3fwHuAPwaOTrIuk9XzgB26rrdv2kaNJtH8POCq7vZ1/R0P3NWfZHyS64GFwKVVNaLiW4N1JEmSJEnSesqd6ZIGMwHYD3hPVV2V5NPAicBfdXeqqr9rdvOeBuxaVY/97lRDo6o+A3xmuOZfhWuA3ZPsTCeJfgzwhhbiGBZJNgW+Aby/qh4ZeH8df8cr2tW/b5ItgG8l2buqbh4wpu34JEmSJEnSKObOdEmDmQvM7doJfD6d5Pr/kORFwN7At4CT1nCNEbfjO8k5wM+APZPMTXJ8VS0F3k1nB/StwHlVdUubcQ6VJBvQSaR/taq+uZI+rX/HVfUQcDkrrnveenySJEmSJGn0MpkuaZWq6l7gniR7Nk0v5X/W0ibJ84BZwFHAW4Gtk3xsDZZ5Zsd3kol0dnxf2HPwPaiqY6tqWlVtUFXbV9UZTft3q2qPqtq1qj7eZoxDpalBfwZwa1X900r6tPYdJ5nS7EgnySTgZcAvRkp8kiRJkiRpbDCZLml1vAf4apIbgX2Bvx1wf2PgdVX131W1HHgzcPfASVa02xtgNO/4Xk+8EHgTcEiS65vjsAF92vyOpwGXN79/19CpmX7xCIpPkiRJkiSNAamqtmOQJEmSJEmSJGlEc2e6JEmSJEmSJEmDMJkuSZIkSZIkSdIgTKZLkiRJkiRJkjSIMZdMT7JLkjOSnN92LJIkSZIkSZKk9cOwJtOTbJTk6iQ3JLklySk9zHVmkoVJbl7BvUOT/DLJ7UlOXNU8VXVHVR2/tnFIUrckM9uOQZIkSZIkScNvuHemPwUcUlX7APsChyY5sLtDkm2TbDagbbcVzHUWcOjAxiTjgc8BrwT2Ao5NsleS5ya5eMCx7dA8ljQ2JTmi7XnWduyajFvDNUymDzBUvyfDaX2IUZIkSZIkjSzDmkyvjseayw2aowZ0ewnw7SQbAiQ5AfjsCua6Ali8gmX2B25vdpw/DZwLHFVVN1XV4QOOhUP0aNJYNVQJyF7mWduxazLORGtv1oef3/oQoyRJkiRJGkFSNTC3PcQLdHaOzwF2Az5XVR9eQZ8PAS8Avg68G3hZVxK+u99OwMVVtXdX29HAoVX1Z831m4ADqurdK4lna+DjwMuAL1bVJ1bQ5wjgiPHjJp6wycStVvhcyyeOX8VTj0zjnl7WOVmytPfJNpjQ0/DlE8f/Nh4Nu0eevPf+qprS6zybb7557bHHHms/wZIlsMEGPPzww0yePHmtpljbsWsybk36Llq0iClTev7Rjiq9fL/rypw5cx6pqpEdpCRJkiRJGlF6y4iuhqpaBuybZAvgW0n2rqqbB/T5uyTnAqcBu64okT6E8TwAvGOQPhcBF02eNO2Eg3Z72wr7PLXd5sMQ3fDacP4jnZO59/U+2fZTexr+1Hab/zYeDbtLbv7bu4dinj322IPZs2ev/QQJDPMf8KTVkeS2tmOQJEmSJEnrl+Gumf6MqnoIuJwV1z1/EbA38C3gpDWceh6wQ9f19k2bVmJpLWk7BEmSJEmSJElarwxrMj3JlGZHOkkm0Smt8osBfZ4HzAKOgv/P3p3HyVVX+f9/naSzQsKShIQkLBFClCBhCYiMW0QRlEVHhwFUUPkSl68zbvNDna8OcXRcGB3H7asGZYBxQVyQwBcUjCgqoiQMicQIiawhJAQCJNBJdyp9fn90deh0utNbVd+q7tfz8ahH3/rUvbfelYU8OPfUubwdmBARn+rF29wBzIyIGRExEjgbWFSJ/ACl7c2VOlVNWPbQNSx++kqWPfvLoqNIkiRJkiRJUt2odmf6/sAtEbGc1qL3zZl5fYd9xgJnZeZfM7MFOA/YZSRFRHwf+D0wKyLWRMQFAJlZonXO+s+BlcDVmbmiEuEbm59i8crPs+yhaypxusKVtjezbtNKANZtu98OdQ28Cy8sOoEkSZIkSZLUJ1WdmZ6Zy4Gju9nndx2ebwMu7WS/c3ZzjhuAG/oYs0ullq0ArNu0ktnbX0fD8JGVfosB1TB8JFPGv4B1m1YyZcQMGmJE0ZE01CxcWHQCSZIkSZIkqU+qfgPSetYwbDSllq1MGf+Cui+kt5lz4BuY/fBxRcfQUHXssbB0adEpJEmSJEmSpF6zmL4bY0fuzXEz3jJoCultVjT+hnXb7mfKiBnM2eOVRcfRUHLnnUUnkCRJkiRJkvqk2jPT695gK6SXtjezbtv9gHPTJUmSJEmSJKmnLKYPMQ3DRzJlxAwA56Zr4O2/f9EJJEmSJEmSpD5xzMsQNGePVzI7t1lI18Bbu7boBJIkSZIkSVKf2Jk+BJRKTbusWUhXIRYsKDqBJEmSJEmS1CcW03ugtL256Ah9tmLZ9/jtLy9mxbLvFR1Fgk98ougEkiRJkiRJUp9YTO/GsoeuYfHKz7PsoWuKjtJrpVITG9YvB2DD+uWFXBSo5wsRkiRJkiRJktTGYvpuJMm6TSsBWLdpZd0VhhsaRjFp8pEATJp8JA3DRw7o+9fzhQhJkiRJkiRJas9i+m4EwZTxLwBgyvgXDHgxuhJmzzmXl7zyE8yec+6Avm9pe3NdX4hQlSxZUnQCqUci4rKIeCwi7m63tm9E3BwRq8o/9ymv7xUR10XEsohYERFvb3fM+eX9V0XE+e3Wj42IP0XE6oj4ckTEwH5CSZIkSZLUWxbTuzHnwDdw0gv+iTkHvqHoKH3W0DBq4N9z+Mi6vxAhaUi7HDilw9pHgMWZORNYXH4O8L+BP2fmHOAVwBciYmRE7AtcDLwIOB64uK0AD3wduBCYWX50fC9JkiRJklRjGooOUA8sBPfNnAPfwOztr/PXT8+ZOxcyi04hdSszb42Igzssn0lrsRzgCuBXwIeBBMaVu8v3BDYCJeA1wM2ZuREgIm4GTomIXwHjM/P28vqVwOuBG6v2gSRJkiRJUr9ZTFdVWUivfxExH5gPcOCBBxacRqqYiRHRfu7Qwsxc2M0xkzPz0fL2OmByefurwCJgLTAO+PvMbImIacDD7Y5fA0wrP9Z0si5JkiRJkmqYxfQBUio1FTJuReqvcoFxIcDcuXNtK9dg8Xhmzu3rwZmZEdH29+E1wF3AK4FDgJsj4jcVyChJkiRJkmqIM9MHwIpl3+O3v7yYFcu+V3QUqVgXX1x0Aqk/1kfE/gDln4+V198O/CRbrQbuB54PPAIc0O746eW1R8rbHdclSZIkSVINs5heZaVSExvWLwdgw/rllEpNBSfSYFHa3lx0hN5bsKDoBFJ/LALOL2+fD1xb3n4IOAkgIiYDs4D7gJ8DJ0fEPuUbj54M/Lw8KmZTRJxQnrN+XrtzSZIkSZKkGmUxvcoaGkYxafKRAEyafKSjXlQRyx66hsUrP8+yh64pOkrvTJ1adAKpRyLi+8DvgVkRsSYiLgA+C7w6IlYBryo/B/gkcGJE/AlYDHw4Mx8v33j0k8Ad5ce/tt2MFHgP8C1gNfBXvPmoJEmSJEk1z5npA2D2nHMpld5oIV0VUdrezLpNKwFYt2kls7e/rn5u9Proo93vI9WAzDyni5dO6mTftbR2nXd2nsuAyzpZXwIc0Z+MkiRJkiRpYNmZPkAspKsrvR3X0jB8JFPGvwCAKeNfUD+FdEmSJEmSJKmO2ZkuFWjZQ9ewbtNKpox/AXMOfEOPj5tz4BvqqyO9zTHHFJ1AkiRJkiRJ6hM706WCdBzX0pcO9bqzdGnRCSRJkiRJkqQ+sZguFWRIjmuZP7/oBJIkSZIkSVKfOOZFKlDdjmvpq0svhYULi04hSZIkSZIk9Zqd6VLBhkwhXZIkSZIkSapjdqb3QdPU8YW8b+PkEX06buz6bRVOIkmSJEmSJElDi8X0XhroQnpfC+hVM31y0QlUzx55pOgEkiRJkiRJUp9YTO+hQVVEtyCuoixdClOnFp1CkiRJkiRJ6jVnpvfAYCmkFzWeRtrhjDOKTiBJkiRJkiT1icV0SZIkSZIkSZK6YTFdkiRJkiRJkqRuWEyXNHC++c2iE0iSJEmSJEl9MuSK6RHxvIj4dkT8qOgsUk+VtjcXHaEy5s8vOoEkSZIkSZLUJ1UtpkfEARFxS0T8OSJWRMT7+nGuyyLisYi4u5PXTomIeyJidUR8ZHfnycz7MvOCvuZQsSpRVK63wvSyh65h8crPs+yha4qO0n8RRSeQJEmSJEmS+qShyucvAR/KzDsjYhywNCJuzsw/t+0QEfsBWzJzc7u1QzNzdYdzXQ58Fbiy/WJEDAe+BrwaWAPcERGLgOHAZzqc4x2Z+VhlPlrt2b6tieEjRu2y3jh5BGPXbysgUeeapo7f6WdPrVj2PTasX86kyUcye865fXrvSpxjIJVKTay7eyUA6zat5ND9zqahYdff427tcglKkiRJkiRJUm9UtZiemY8Cj5a3N0fESmAa8Od2u70ceFdEvDYzmyLiQuBvgVM7nOvWiDi4k7c5HlidmfcBRMRVwJmZ+RngtL7kjojTgdNHj5nQ64JvfzVOHtGn41bfeiUbH1zGvgfN4dCXndfpeceu30bT1PGMWrupvzF3GKhfn1KpiQ3rlwOwYf1ySqU39rqoXIlzDLSGhlFMmnzkjgsAtZ5XkiRJkiRJGqyq3Zm+Q7kQfjTwh/brmfnDiJgB/CAifgi8g9Yu856aBjzc7vka4EW7yTEB+Dfg6Ij4aLnovpPMvA64btxe0y/sRY5+62shffu2JjY+uAyAjQ8u67ZDfaAvEHSWo/dGsO9Bc3ZcMGietie9H9ZSiXMMvBknn8+B5d/TxqLD9Ndpfbq+JUmSJEmSJBVuQIrpEbEn8GPg/Zm5S1t0Zl5S7ij/OnBIZj5TrSyZ+QTwrkqdr68F8EoaPmLUTkXizgrp3SmVmqra9VyJX6dDX3ZelxcKBvIcRai3vF267rqiE0iSJEmSJEl9UvViekSMoLWQ/t3M/EkX+7wUOAK4BrgYeG8v3uIR4IB2z6eX1wZMLRRn+1Mkrqc54pX4dS7692pIO/10C+pSL0XEZODTwNTMPDUiDgdenJnfLjiaJEmSJElDyrBqnjwiAvg2sDIz/6OLfY4GFgJnAm8HJkTEp3rxNncAMyNiRkSMBM4GFvUvec+tvvVKll71z6y+9crud66yvnak7zxHvKnSsaTnXH990QmkenQ58HNgavn5vcD7C0sjSZIkSdIQVdViOvA3wFuBV0bEXeXHazvsMxY4KzP/mpktwHnAgx1PFBHfB34PzIqINRFxAUBmlmjtZP85sBK4OjNXVO8jPadU2nVWeb1pu8El4A0uJak2TczMq4EW2PHv3vZiI0mSJEmSNPRUdcxLZv4WiG72+V2H59uASzvZ75zdnOMG4IY+xuyzhob+zyqvBbPnnEup9EYL6ZJUFhGXAacBj2XmEeW1fYEfAAcDD9B6IfjJiPj/gDeXD20AXgBMysyNEXEK8CVgOPCtzPxs+VwzgKuACcBS4K2Z2dU9kZ8t3zw7y8eeADxd2U8sSZIkSZK6U+3O9EHv0Jedx7Fnf5pDX3Ze0VH6xUK6BkRm0QmknrocOKXD2keAxZk5E1hcfk5m/ntmHpWZRwEfBX5dLqQPB74GnAocDpxTnncO8Dngi5l5KPAkcMFusnyQ1vFlh0TE74ArgX+owGeUJEmSJEm9YDG9Auq1I10acAsXFp1A6pHMvBXY2GH5TOCK8vYVwOs7OfQc4Pvl7eOB1Zl5X7nr/CrgzPL9RF4J/Kibc7VluRN4OXAi8E5gdmYu7/WHkiRJkiRJ/WIxXdJuRcT8iFgSEUs2bNjQv5O9852VCSX138S2P9flx/weHDM5Mx8tb68DJrd/MSLG0trN/uPy0jTg4Xa7rCmvTQCeKs8+b7/eqYj438CembkiM+8G9oyI9/QgryRJkiRJqiCL6ZJ2KzMXZubczJw7adKkouNIlfJ425/r8qNXX5vIzKQ8w7yd04HfZWbHjvb+ujAzn2r33k8CF1b4PSRJkiRJUjcspkuS1DPrI2J/gPLPxzq8fjbPjXgBeAQ4oN3z6eW1J4C9I6Khw3pXhpdHw1B+7+HAyD59AkmSJEmS1GcW0yUNnEWLik4g9cci4Pzy9vnAtW0vRMRetM41v7bd/ncAMyNiRkSMpLXYvqjc1X4L8KbOztWJnwE/iIiTIuIkWgv2P6vA55EkSZIkSb1gMV3SwDn2jxMZIAAAIABJREFU2KITSD0SEd8Hfg/Miog1EXEB8Fng1RGxCnhV+XmbNwA3ZeazbQvlmejvBX4OrASuzswV5Zc/DHwwIlbTOkP927uJ82Fai+/vLj8WAxf1/1NKkiRJkqTeaOh+F0mqkGnTIDuOmZZqT2ae08VLJ3Wx/+XA5Z2s3wDc0Mn6fcDxPczSAny9/JAkSZIkSQWxmC5JUg2LiL8BFgAH0frvdtB6D9TnFZlLkiRJkqShxmK6atL2bU0MHzGq6Bjdqpeckurat4EPAEuB7QVnkSRJkiRpyLKYrpqz+tYr2fjgMvY9aA6Hvuy8ouN0qV5y1pQLLyw6gVSPns7MG4sOIUmSJEnSUGcxvULsUK6M7dua2PjgMgA2PrisZn9d6yVnzVm4sOgEUj26JSL+HfgJ0NS2mJl3FhdJkiRJkqShp8fF9IiYDHwamJqZp0bE4cCLM/PbVUtXJ+xQrpzhI0ax70Fzdvx61mqBul5y1pxjj4WlS4tOIdWbF5V/zm23lsArC8giSZIkSdKQ1ZvO9MuB/wL+T/n5vcAPaJ3lOmTVW4dy4+QRjF2/regYu3Xoy86r+V9HqJ+cNeVOG2ml3srMeUVnkCRJkiRJvSumT8zMqyPiowCZWYqIIX8jtMHWoVwqNdHQsPvP0Dh5RNVz1MuvY73klFS//GaYJEmSJEm1oTfF9GcjYgKtXy0nIk4Anq5KqjpTbx3KXRXDqz2upp5+jVQl++9fdAKpHl2O3wyTJEmSJKlwvSmmfxBYBBwSEb8DJgF/V5VUdajei8TVHlfjXHkBsHZt0QmkeuQ3wyRJkiRJqgHDerHvCuDlwInAO4HZwF+qEUoDr21cDVDxcTWdFeobJ0bFzq86smBB0QmkeuQ3wyRJkiRJqgG96Uz/fWYeQ2tRHYCIuBM4puKpVIhqjavpaq5848Rg7ONZ0fdSjfvEJyyoS73X2TfD3lRsJEmSJEmShp5ui+kRMQWYBoyJiKOBtpbi8cDYKmZTAao1rqbe5spLqq6WluS/b3+QPz6wsegoNS8z74yIlwOzaP03+J7M3FZwLEmSJEmShpyedKa/BngbMB34j3brm4F/rkImDVJ9KaT3tABvoV6qH488tYUPXX0Xt9+3kQP2HcPI4b2ZODZ0RMTfdvHSYRFBZv5kQANJkiRJkjTEdVtMz8wrgCsi4o2Z+eMByCQBPb9pqTc3rSNLlhSdQAPkF39ez413r9tlPTO5eeV6WlqSS950JH937HQiBv4eCvFPA/6WfXF6+ed+tN6v5Jfl5/OA2wCL6ZIkSZIkDaAez0zPzB9HxOtovfHo6Hbr/1qNYBraOrtpaWed5z3dT9LA+vxN9/DgE43su8fIXV47+sB9+NSZR3DgBCeF7U5mvh0gIm4CDs/MR8vP9wcuLzCaJEmSJElDUo+L6RHxDVpnpM8DvkXrzc/+WKVcGuK6umlpX/dTjZg7F9Kbzg52W5q3s+qxZ3jPKw7hQyfPKjrOYHBAWyG9bD1wYFFhJEmSJEkaqnpcTAdOzMwjI2J5Zn4iIr4A3FitYFJPb1pa1M1N7YKXOrdi7dNsb0mOnL530VEGi8UR8XPg++Xnfw/8osA8kiRJkiQNSb0ppm8t/2yMiKnAE8D+lY8kPaenxeqBLmo7p13q2rI1TwMwZ/peBScZHDLzveWbkb60vLQwM68pMpMkSZIkSUNRb4rp10XE3sC/A3cCCVxalVRSDavVOe21kmO3Lr646AQaAMvXPMWU8aPZb/zo7ndWj2TmT/CGo5IkSZIkFapHxfSIGAYszsyngB9HxPXA6Mx8uqrppBpUi3Pa66ZTfsGCohNoACxf8zRH2pVeMeWu9M8B+wFRfmRmji80mCRJkiRJQ0yPiumZ2RIRXwOOLj9vApqqGUzFq4tO54IUNae9M7XaKd+pqVNh7dqiU6iKnt6yjfsff5Y3HjOt6CiDySXA6Zm5suggkiRJkiQNZcN6se/iiHhjRETV0qhmrL71SpZe9c+svvXKoqPUrFopWLd1ygM10ynfpUcfLTqBquxP5Xnp3ny0otZbSJckSZIkqXi9mZn+TuCDQCkituLXzAetuup0FlBbnfIa2pateQqg7se8RMRlwGnAY5l5RHltX+AHwMHAA8BZmflk+bVXAP8JjAAez8yXl9dPAb4EDAe+lZmfLa/PAK4CJgBLgbdmZnMXcZZExA+An9LuW2HlOeqSJEmSJGmA9LgzPTPHZeawzByZmePLz3cU0iNidnUiaqDVVaezdqiL36djjik6garsT2ue5qAJY9l77Miio/TX5cApHdY+Quv9Q2YCi8vPKd+c+/8CZ2TmbODvyuvDga8BpwKHA+dExOHlc30O+GJmHgo8CVywmyzjgUbgZOD08uO0fn4+SZIkSZLUS73pTO/OfwNWygYJO51VFUuXFp1AVbZ8zVMce/C+Rcfot8y8NSIO7rB8JvCK8vYVwK+ADwPnAj/JzIfKxz5W3ud4YHVm3gcQEVcBZ0bESuCV5ePazrUA+HoXWd7e388jSZIkSZL6rzcz07vjLPVBxkK6ACJifkQsiYglGzZs6N/J5s+vTCjVpA2bm1j79Fbm1MeIl4ltf67Lj5784ZycmW2D/9cBk8vbhwH7RMSvImJpRJxXXp8GPNzu+DXltQnAU5lZ6rDeqYg4LCIWR8Td5edHRsTHevQpJUmSJElSxVSymJ4VPJekGpGZCzNzbmbOnTRpUv9OdumllQmlmrR8x7z0urj56ONtf67Lj4W9OTgzk+f+3WsAjgVeB7wG+HhEHFbBrJcCHwW2ld97OXB2Bc8vSZIkSZJ6oJJjXoacUqmJ1nvNSZKWrXmaYQGzpw7a+1Kvj4j9M/PRiNgfaBvnsgZ4IjOfBZ6NiFuBOeX1A9odPx14BHgC2DsiGsrd6W3rXRmbmX+M2OkLYKWudpYkSZIkSdVRyWJ6cwXPVfNWLPseG9YvZ9+D5nDoy87r/gBJqnONzSVWrX+GxzY37bQ+esQwRo8Yzu33PcGh++3JHqMG7XXaRcD5wGfLP68tr18LfDUiGoCRwIuALwJ/AWZGxAxai+VnA+dmZkbELcCbgKs6nKszj0fEIZQ74SPiTcCju9lfkiRJkiRVQY8rHhHR2c1FnwYezMxSZp5QuVi1o1RqoqFh1C5rG9YvB2Djg8u8UecgM5h+P2vuszyyu+ZbFeXaux7h/y3vuja7bXsL9z3+LA9tbCS7Geh19nEH7H6HOhER36f1ZqMTI2INcDGtRfSrI+IC4EHgLIDMXBkRPwOWAy3AtzKzbb75e4GfA8OByzJzRfktPgxcFRGfAv4H+PZu4vxvYCHw/Ih4BLgfeHMFP64kSZIkSeqByO4qI207RtwOHENrsSCAI4AVwF7AuzPzpmqFLMqIEWOzVNrCpMlHMnvOuTu9Zmd6/zRObB1XMPbx2hq1v/rWK9n44LJB8fva/rNsfHDZ0syc299zzp07N5csWdL3E1x3HZx+en9jqEK2tySf+9lfWHjrfUzbewzjRnd+fXVYBAdPHMusyeOZNWUc0/YeQ9vEkUxoKm1ny7btbN3WwtyD9mGfPUYO4Kfom4ioyN+JgRQRewDDMnNz0VkkSZIkSRqKevNd/LXABW1ddRFxOPCvwEXAT4BBV0wvlbYAsGH9ckqlN+7UoT57zrmUSm+kedqeRcVThW3f1sTGB5cB9f+Ng46fhcrebLjvzjiDblubNSA2b93G+6+6i8V/eYzzX3wQHz/tcBqG18YfE+0sIibQ2hn/EiAj4rfAv2bmE8UmkyRJkiRpaOlNMf2wdl9PJzP/HBHPz8z7OtwUbdBoaBhDW2d6x1EvgIX0PmrrSu+4XbzRjD/sKDbdexfjDzuKpv1HFx2oH3b+LJvuvaul6ETqvcc2bd1lPnlfbdveQmPzdhqbt/NsU4mv/+qvrN7wDJ88czZvffHBFXkPVc1VwK3AG8vP3wz8AHhVYYkkSZIkSRqCelNMXxERX6f1f+oB/h74c0SMArZVPFkNGD12X46aO7/TQrr6plrF8+3NTQwf2f/fpwNfdx7bX/33FTlX0dp/lrvvvavoOOqlOx7YyLmX3s627dXp5B8/uoEr3n48L5k5sSrnV0Xtn5mfbPf8UxHx94WlkSRJkiRpiOpNMf1twHuA95ef/w74J1oL6fMqG6t2dFVIb5w8YoCT1L9qFdIf+n9X7ujAPvB1/Z9zPlCF9EpdANidmrso8M1vFp2gLjz+TBPv/d6dTNt7DB997QuoxN+cEcOHMXbkcPYY1cDYkcOZPH40e4zqzT8BKtBNEXE2cHX5+ZtovampJEmSJEkaQD2upGTmFuAL5UdHz1QsUR0oqpDe22J0rd3csxq2Nzexqdx1veneu+qmq7zSFwDqxvz5RSeoedtbkvdfdRdPNW7jv95zPIdPHV90JBXvQlovZH8HSGA48GxEvBPIzPQPiSRJkiRJA6DHd5uLiL+JiJsj4t6IuK/tUc1wek5furobJ0bNzCSvVo7hI0cx/rCjABh/2FF1UUjf5QJAc2VmYteFQXp/hUr60uJV/Hb143zyzCMspAuAzByXmcMysyEzR5S3x5Uf/iGRJEmSJGmA9OY7/t8GPgAsBbZXJ07tGuhu9EoWn9ufqxLd6pXItnUSjN7Q79MA9TfnvO0CQFtner3kVvXdeu8GvvLLVbzp2OmcddwBRcdRjYjWu3y/GZiRmZ+MiANonaP+x4KjSZIkSZI0pPSmmP50Zt5YtSQ1qKUhBrSIPhBd5EV3qm+d1Pl2/42qq7vg7ve285jY9PcMGzWKrUWHUU149OktvP8HdzFr8jg+eeYRRcdRbfm/QAvwSuCTtI5W+xpwXJGhJEmSJEkaanpTTL8lIv4d+AmwYy5FZt5Z8VR1oujCdD2pbOF8cBg2agh2pJ92WtEJatK27S2893v/Q9O27XztzccwZuTwoiOptrwoM4+JiP8ByMwnI2Jk0aEkSZIkSRpqelNMf1H559x2a0lrp9yQMpBF9EoVoSs1UgUqk2nbxFL/T9KFEY/35o9119oyVup8PX2/SmnZ2sSw0TVWsL/uuqIT1KRb793A0gef5D/OmsMhk/YsOo5qz7aIGE7rv7lExCRaO9UlSZIkSdIA6nGVMDPnVTNILRvoDvRqdHG3P2dbYb2IbvFqFtE7e49KFJQHInOlbVj4HRqXLGfs3COZNP8tRcd5zumnW1DvxB8f2MjI4cN47Qv3LzqKatOXgWuA/SLi34A3AR8rNpIkSZIkSUNPt8X0iHhLZn4nIj7Y2euZ+R+Vj1UbWhq6L6TX4/iSwVpE76hmC8qdqGQXecvWJhqXLAegcclyWs5r6uaIAXT99UUnqElLHniSF07fi9EjHO+iXWXmdyNiKXASEMDrM3NlwbEkSZIkSRpyetKZvkf557hqBqk39VCQruZ4kv4Wx0dP2NLjfbc+MabX5++soFxzI0/K2or+406czdQPnVWRc447cTabb1vBuBNnM3aa0yBq2dZt21m+5ine8ZIZRUdRjYmIfds9fQz4fvvXMnPjwKeSJEmSJGno6rbampnfLP/8RPXj1L6BKqJXopO74zm6Kq4PZNd4b4ro/TkGOiso9+081dSy5bmi/+bbVtDyniaGjel/0X/qh86q2LlUXcsefopt25PjDtq3+5011CyldU56AAcCT5a39wYeArwCI0mSJEnSAOpx63L5hmcXAge3Py4z31H5WLUhRwxsB3q1i9rVOn9PR5T0tSjeV/VQUB42ZtRORf/dZW3Z0rvPUpOfO7PoBDVnyYNPAjD34H0KTqJak5kzACLiUuCazLyh/PxU4PVFZpMkSZIkaSjqzRyQa4HfAL8AtlcnTn2pxxtTVtLoCVtY+4WrdxSCKzWipJJqsqDcQWdF/xkTn9hpn7sW3Mi6W1YxZd5Mjlpwaq/f455+p6yQhQth/vyiU9SUOx7YyGGT92TvsSOLjqLadUJmXtj2JDNvjIhLigwkSZIkSdJQ1Jti+tjM/HDVktSRWh+L0pndzR3vzc0v2+dp2dLE5ttWAL0bUdKxUKw2z3S6WmpsZt0tqwBYd8sqShedREO9Fl7f+U6L6e1sb0mWPvgkp8+ZWnQU1ba1EfEx4Dvl528G1haYR5IkSZKkIWlYL/a9PiJeW7Uk2kUlx6J0da4NC7/Dw//4cTZefgWjJ2zp9tFe24gSoNsRJeq7hrEjmTJvJgBT5s2s30K6dnHPus1s3lriOEe8aPfOASYB1wA/KW+fU2giSZIkSZKGoN50pr8P+OeIaAK20XoTtMzM8VVJpqpr2dr/m19WYi55qbHZAnE3jlpwan13pKtTSx7cCMBcbz6q3cjMjbT+GyxJkiRJkgrUo870iBgGnJKZwzJzTGaOz8xxFtLr27DRoxg790igf53l/Smk37XgRn5x6je4a8GNfT7HUDEoCumLFhWdoKbc8cCT7L/XaKbv0/UYJkmSJEmSJNWGHnWmZ2ZLRHwVOLrKeaomIp4H/B9gr8x8U9F5asWk+W+h5bwmxk5rGfD3HlSzwNUzxx5bdIKakZnccf9GjpuxLxFRdBxJkiRJkiR1ozcz0xdHxBujgKpPRFwWEY9FxN0d1k+JiHsiYnVEfGR358jM+zLzguomrU89vflopRU5C7zU2Dxg76V2pk0rOkHNeOSpLazbtNV56ZIkSZIkSXWiNzPT3wl8EChFxFYGdmb65cBXgSvbFiJiOPA14NXAGuCOiFgEDAc+0+H4d2TmYwOQU71UxCzwuxbcyLpbVjFl3kyOWnDqgL2v1N4dDzgvvZZFxGXAacBjmXlEeW1f4AfAwcADwFmZ+WREvAK4Fri/fPhPMvNfy8ecAnyJ1n+bvpWZny2vzwCuAiYAS4G3ZuZOV/ki4itAdpUxM/+xEp9VkiRJkiT1TI8708sz0odl5siBnpmembcCGzssHw+sLnecN9NalDgzM/+Umad1eFhIr2ED3ZG+02gZO9RVkDseeJJxoxqYNWVc0VHUucuBUzqsfQRYnJkzgcXl521+k5lHlR9thfS2i76nAocD50TE4eX9Pwd8MTMPBZ4EOvvm1BJaC+1dPSRJkiRJ0gDqTWc6EbEPMBMY3bZWLnQXYRrwcLvna4AXdbVzREwA/g04OiI+mpkdu9fb9psPzAdo2NvxC4NN22iZts50Z7QPsAsvLDpBzVjywEaOPXgfhg9zXnotysxbI+LgDstnAq8ob18B/Ar48G5Os+OiL0BEXAWcGRErgVcC57Y71wLg6x0yXNHX/JIkSZIkqfJ6XEyPiP8FvA+YDtwFnAD8ntaCQM3LzCeAd/Vgv4XAQoDR0w/o8uv1qp5SY/OOInf77UopYrRMV6rx+Sqt/QWmAw88sH8nW7iwAonq31ONzdy7/hnOPMoZ8gWaGBFL2j1fWP7v/+5MzsxHy9vrgMntXntxRCwD1gL/lJkr6Pqi7wTgqcwstVvv8g9DREyitWh/ODtfzK6Lf38lSZIkSRosetOZ/j7gOOD2zJwXEc8HPl2dWD3yCHBAu+fTy2uqY+3nmQNVm21eCwXsepnd3v4C09y5c/t3genYY2Gp0ymWPPAkAHMP8tsvBXo8M+f29eDMzIho+/twJ3BQZj4TEa8Ffkrrt7gq5bu0zmp/Ha0Xhc8HNlTw/JIkSZIkqQd6PDMd2JqZWwEiYlRm/gWYVZ1YPXIHMDMiZkTESOBsYFGBedRPHeeZD+bZ5kN2dvuddxadoCbc8eBGRgwP5hywd9FR1DvrI2J/gPLPxwAyc1NmPlPevgEYERET6fqi7xPA3hHR0GG9KxMy89vAtsz8dWa+gzr5VpgkSZIkSYNJb4rpayJib1o77m6OiGuBB6sTa2cR8X1aR8rMiog1EXFB+evx7wV+DqwEri5/rb7mtWxtKjpCTWqbZw4wZd7MnbZroZO8kjp+1sH2+bR7Sx54khdO24vRI4YXHUW9s4jWrnDKP68FiIgpERHl7eNp/bf1Cbq46JuZCdwCvKnjubqwrfzz0Yh4XUQcDexbuY8lSZIkSZJ6osdjXjLzDeXNBRFxC7AX8LOqpNr1vc/pYv0G4IaByFApGxZ+h8Ylyxk790gmzX9L0XFqTsd55rUy27waaml2+4DZf/+iExRu67btLF/zFO94yYyio2g3yhdxX0HrbPU1wMXAZ4GrI+ICWi8mn1Xe/U3AuyOiBGwBzi4XzEsR0XbRdzhwWbuLvh8GroqITwH/A3x7N3E+FRF7AR8CvgKMBz5QsQ8rSZIkSZJ6pDcz04mIlwAzM/O/yjdEmwbcX5VkNWrbxFL3O3WhZWsTjUuWA9C4ZDkt5zUxbPSoXfYbPWFLn99jd0ZP2MLWJ8ZU/X12Z8bEJ7rdp31xebAXmgf759vF2rVFJyjcsoefYtv25LiDbCyuZV1dxAVO6mTfrwJf7eI8nV70zcz7gON7mOX68ubTwLyeHCNJkiRJkiqvx8X0iLgYmEvrnPT/AkYA3wH+pjrRipcN2a/ieUfDRo9i7Nwjd3SmdyykD0Rxu4gCepueFNI1yC1Y0PoYwpY82Hrz0WO9+ai6EREXZeYlEfEVYJeb/2bmPxYQS5IkSZKkIas3nelvAI4G7gTIzLURMa4qqepIb4vTB3z0jbRsOY1hY0bROg2gvlkgV6984hNDvpj+h/s3MnO/PdlnjyH2rQT1xcryzyWFppAkSZIkSUDviunNmZkRkQARsUeVMtWNvnZ5txbS65+F9Op44V6VH4UyIDc3ULe2btvOH+57gnOOP7DoKKoDmXldebMxM3/Y/rWI+LsCIkmSJEmSNKT1pph+dUR8E9g7Ii4E3gFcWp1YxYqI04HTGyZN6HKfWp033pn7H+/6cwxkjvaqUTDuiz89PXWXtVrJ1hPNjSVGju3VrQ9UoD/ev5GmUgsvnzWp6CiqLx8FftiDNUmSJEmSVEU9rsJl5ucj4tXAJlrnpv9LZt5ctWQFKncDXjfq4OkXdvb6QBXSK9X5PWPiE10W1Fu2NDFszKgB7TKvpWJ1LWXprUUX3c49N61h1snTOeOSE4qO0zNLhva0ilvv3cDIhmGcMKOyF7g0OEXEqcBrgWkR8eV2L40HKndDD0mSJEmS1CO9amktF88HZQG9p+qtkN7xfO2L6mu/cDWbb1vBlHkzYcGpFX2/rtRz8bqWNDeWuOemNQDcc9MamhfYoV4Pfn3vBo4/eF/GjBxedBTVh7W0zks/A1jabn0z8IFCEkmSJEmSNIR1W32LiM1AdvYSkJk5vuKphriB6BJv2dLE5ttWALDullWULjqJhrHeELFejBzbwKyTp+/oTK+bQvrcuZCd/edk8Fv71BZWPfYMZ809oOgoqhOZuSwi7gZek5lXFJ1HkiRJkqShrtsKXGaOG4ggta6IGenV0DbyZdiYUUyZN5N1t6xiyryZA1JItyu9d7qbh37GJSfYkV5Hbr13AwAvO8x56eq5zNweEQdExMjMbC46jyRJkiRJQ5lVuN2IhhzwIvpAzS6fMfEJWHBqXXekD+abb/Z0Hvpg/fyD0a2rNjBl/GgOm7xn0VFUf+4HfhcRi4Bn2xYz8z+KiyRJkiRJ0tAzrOgAg1HLlqaiI+xW+4J9vRbSF110O1868acsuuj2oqNU3C7z0BsH0X0GL7646ASFKG1v4berHudlh00kIoqOo/rzV+B6Wv/NHtfuIUmSJEmSBpBtrRXWdlPPcSfOZuqHzio6zqA02G++Wbfz0HtiwYKiExRi2Zqn2LS15IgX9UlmfqLoDJIkSZIkyWJ6RbW/qefm21bQ8p4mho0Z1ePjB2rESxEqOS99UBebywbtPPSpU2Ht0Jud/+t7H2dYwEsOnVh0FNWhiJgEXATMBka3rWfmKwsLJUmSJEnSEDTIKnXFGjZmFONOnL2jM703hXT1zqAtNrczKD/bo48WnaAQv753A3MO2Ju963Sskgr3XeAHwGnAu4DzgQ2FJpIkSZIkaQgahNW6ymrZsvvu8o7d5DM+cxKlxpeWZ5EP3k7zWjAoi80adJ58tpnla57iH185s+goql8TMvPbEfG+zPw18OuIuKPoUJIkSZIkDTXegHQ3tq3byKq3fJq1X7i6V8fV6009pao75piiEwy4365+nEx4+SznpavPtpV/PhoRr4uIo4F9iwwkSZIkSdJQZGtvJyLidOD0tud9mX+uoaO5cXCPm6mopUuLTjDgfn3vBvYaM4I50/cuOorq16ciYi/gQ8BXgPHAB4qNJEmSJEnS0GNneicy87rMnD9sj9b7vDn/XF1ZdNHtfOnEn7LootuLjlIf5s8vOsGAykx+s2oDL5k5keHDoug4qjMRMToi3g+cApwN/CUz52XmsZm5qOB4kiRJkiQNORbTd2PElH2Z+Z1/ZuqHzio6impQc2OJe25aA8A9N62hubFUcKKd1VoeAC69tOgEA+qe9ZtZv6mJl890xIv65ApgLvAn4FTgC8XGkSRJkiRpaLOY3g070tWVkWMbmHXydABmnTy9pka92DFfG359zwYAXnrYxIKTqE4dnplvycxvAm8CXlp0IEmSJEmShrLaqf5pyBkMs8bPuOQEmhfU1ufYpWN+QQ12qA8Rt67awKzJ49h/rzFFR1F9arvxKJlZinBUkCRJkiRJRbIzXYUYTJ3TtVRIh9rumOeRR4pOMGAam0vccf+TvMyudPXdnIjYVH5sBo5s246ITUWHkyRJkiRpqKmhKpuGis46p2uq4DsI1GLHPABLl8LUqUWn6LWWluRHd65h05Zt3e9c9vDGRpq3t/Dyw/arYjJVS0RcBpwGPJaZR5TX9gV+ABwMPACclZlPtjvmOOD3wNmZ+aPy2vnAx8q7fCozryivHwtcDowBbgDel5nZPkNmDq/Sx5MkSZIkSX1QY5U2VUupsZmGsSOLjgE81zl9z01raq9zehCpyV/XM86AneuFdeH6Pz3KRT9a3uvj9hs3irkH71OFRBoAlwNfBa5st/YRYHFmfjYiPlJ+/mGAiBjpGehTAAAgAElEQVQOfA64qW3ncvH9YlpvIprA0ohYVC7Afx24EPgDrcX0U4Abq/yZJEmSJElSP9RgtU2VdteCG1l3yyqmzJvJUQtOLToOUMOd01IH21uSL/3iXg6bvCc/eveJ9GZq9egRwxkx3Gla9Sgzb42Igzssnwm8orx9BfArysV04B+AHwPHtdv/NcDNmbkRICJuBk6JiF8B4zPz9vL6lcDrsZguSZIkSVJNs5I5yJUam1l3yyoA1t2yitJFJ9VUh7pU665fvpa/bniWr517DONHjyg6joo1OTMfLW+vAyYDRMQ04A3APHYupk8DHm73fE15bVp5u+O6JEmSJEmqYbZMDnINY0cyZd5MAKbMm1kzhXTVj4iYHxFLImLJhg0b+neyb36zMqEGSGl7C1/6xSqeP2Ucpx4xpeg4qqyJbX+uy4/5vTm4PN+8bWbRfwIfzsyWiqeUJEmSJEk1w9bg3cjScwMdWrY0MWzMqALT9N1RC06tqY501ZfMXAgsBJg7d27/Bp7P71W9snCLlq3lvsef5etvPoZhw3oz4EV14PHMnNvLY9ZHxP6Z+WhE7A88Vl6fC1wVEQATgddGRAl4hOfGwgBMp3U0zCPl7fbrj/T6E0iSJEmSpAFlZ3oPrP3C1ax6y6dZ+4Wri47SZxbSVROifgrSpe0tfHnxKl6w/3heM9uudAGwCDi/vH0+cC1AZs7IzIMz82DgR8B7MvOnwM+BkyNin4jYBzgZ+Hl5VMymiDghWivw57WdS5IkSZIk1S6L6Z2IiNMjYmFL41YaHxnG5ttWALD5thW0bGkqOJ06am4sFR1Bg9BP71rLA0808v5XzbQrfQiKiO8DvwdmRcSaiLgA+Czw6ohYBbyq/LxL5RuPfhK4o/z417abkQLvAb4FrAb+ijcflSRJkiSp5jnmpROZeR1w3aiDp184bPQoxp04m823rWDcibPrdtTLYLXootu556Y1zDp5OmdcckLRcaqmubHkDVsH0LZyV/rsqeM5+fDJRcdRATLznC5eOqmb497W4fllwGWd7LcEOKKv+SRJkiRJ0sCzOtcDUz90Fi3vqd+Z6YNVc2OJe25aA8A9N62hecHgLDgPqgsGp51WdIIeuebOR3hoYyOXnjeXqKPRNJIkSZIkSaqewVd5rLCWra1jXboqpN//+ISKvdeMiU9U7FxDwcixDcw6efqOQvNgLKQPugsG111XdIJubdvewlduWcULp+3Fq16wX9FxJEmSJEmSVCOcmb4bpQ0befgfP84jl1xVdBR14YxLTuB9t72+/ju2u9B2wQAYHBcMTj+96ATd+vHSNTy8cQsfePVMu9IlSZIkSZK0Q51X5qqrpXELAM/8YSWPXPJ9pl3U1QhdFanuC8zdOOOSE+q/I73N9dcXnWC3mkstfOWXq5lzwN7Mm2VXuiRJkiRJkp5jZ/puxJjRO7af+cNfaNnSVGAaKDU2F/r+Ks6gKKTXgR8ufZhHntrC+19lV7okSZIkSZJ2ZjF9N0bsN4ExR88GYNyJs3eZm96X4npfC/J3LbiRX5z6De5acGOfjpe0e/dteIav/XI1Rx2wN684bFLRcSRJkiRJklRjbHftxn7vPp+RY5/apZC+9gtXs/m2FYw7cTZTP3RWj87Vl2OgtSN93S2rAFh3yypKF51Ew9iRPf8QUq3ILDrBLlY/tpmv/HI11y1by8iGYfzn2UfblS5JkiRJkqRd2JneA511pG++bQUAm29bsVO3eVed57s7pjsNY0cyZd5MAKbMm2khXfVr4cKiE+ywav1m/uH7/8Orv3grN61Yz4UvfR6/ueiVHD9j36KjSZIkSZIkqQbZmd4Hw8aMYtyJs3d0mbcV23fXed7VMT111IJTB1VHenPjILmhpnrnne+E+fN3Wtq8dRs/WroGgPGjRzBudAPjx5R/jh7B+NEj2HN0A8OHVaZb/C/rNvGVxau54e5HGTNiOO982SFc+NIZTNizd38nJUmSJEmSNLRYzeyjqR86i5b3NO0oiu/Sed7uta6O6a3BUkhfdNHt3HPTGmadPJ0zLjmh6Dgq2IJFf+bHd67pdr89Rg5nzMjhQBABAeWf7Z93X3B/5Kkt7Dmqgfe84hAueMnz2HePwfH3SpIkSZIkSdU15IrpEfF64HXAeODbmXnT7vYfPWFLl68NGzOKGROf2PF887yZrLtlFVPmzeSQA54BnuniyK7W688L91oLwJ+entqj/ZsbS9xzU2vh9J6b1tC8wA713Tluz/uKjlBVv//rE/z4zjW86+WHMP9lz2PTlm1s3lpi89ZtbNq6jU1bS2zeWtqxvrW0vTx2PclsHcGebdv0bCT7QRPGct6LD2LvQXJxSpIkSZIkSQOj6lXMiNgb+BZwBK31rndk5u/7cJ7LgNOAxzLziA6vnQJ8CRgOfCszP9vVeTLzp8BPI2If4PNAl8X0aNh9ZW7GxCcY/9QWNu09Bqi/USylxuZ+ZW0rpHfc3p2RYxuYdfL0HZ3pFtJ3NagL6IsW7dhsLrXwsZ/+iQP2HcP7TprJmJHD7RKXJEmSJElSzRqISuaXgJ9l5psiYiQwtv2LEbEfsCUzN7dbOzQzV3c4z+XAV4ErOxw/HPga8GpgDXBHRCyitbD+mQ7neEdmPlbe/lj5uD5p60h/15d/zSX/csqO9XoppN+14MYdXfRHLTi118f3tHjemTMuOaEuOtIHdVG7KMceu2Pz0t/cx183PMt/ve248vgWSZIkSZIkqXZVtZoZEXsBLwPeBpCZzUBzh91eDrwrIl6bmU0RcSHwt8BOFd7MvDUiDu7kbY4HVmfmfeX3vAo4MzM/Q2sne8dMAXwWuDEz7+z7p4NpDz/JvMX3csU5x7B+5n79OVW3+lO87jiCpdTYzLpbVgGw7pZVve6m70+WNn+z30P9Pkel3PHM83ZsW0CvsmnTIJOHnmjky4tX8doXTmHe86v7d0eSJEmSJEmqhGq3Bs8ANgD/FRFzgKXA+zLz2bYdMvOHETED+EFE/BB4B61d5j01DXi43fM1wIt2s/8/AK8C9ip3wH+j4w4RcTpw+ogp++683tLCmKZtHDRhIzTCy37ZWpA+4H9dxW9eeghH/nNr7KbRI8hh3d8IsTuVKFq3P09bUb1h7EimtJvv3jB2ZMXeqydqrWBda3kGu8zk49feTcOw4F9Om110HEmSJEmSJKlHql1MbwCOAf4hM/8QEV8CPgJ8vP1OmXlJuaP868AhmVm1O3Rm5peBL3ezz3XAdaMPnXZhy5Ymho0ZBUAkfPAXv+Cc/76D4dufm6f+SeCTv/kr2193H987/3iueutxJP0rplejuP3CvdbuKKi3zXc/ev/HgaFbSNfAu/Hudfz63g38y2mHM2Wv0UXHkSRJkiRJknqk2sX0NcCazPxD+fmPaC2m7yQiXkrrDUqvAS4G3tuL93gEOKDd8+nltX7btm4jq97yacadOJupHzqLgyY/yXfffgLLjjmA/++TP2e/Dc/V/NeMauA///1MVsyZ1q8be1a7Q3yn8+/Vu2ObG2t/zrlqW/M7LuAT161g9tTxnPfig4qOI0mSJEmSJPVYVSujmbkuIh6OiFmZeQ9wEvDn9vtExNHAQlrnm98PfDciPpWZH+vh29wBzCyPinkEOBs4txL5W57dCsDm21ZwwMcfBVoL5HfPmcaiN87hf33jdzv2/dk7TmDFnGn9urHnQI5a6Y3mxhI/W7CEe25aw6yTp3PGJSf06Tx2pffO34x+tOgIFfeZ13+Ax257gG++dS4Nw4cVHUeSJEmSJEnqsYFoM/4HWgvkI4H7gLd3eH0scFZm/hUgIs6jfMPS9iLi+8ArgIkRsQa4ODO/nZmliHgv8HNgOHBZZq6o1odpc+Jv/srWUQ0sfe10jr/hIV5120rufutEftbuxp6z/m32Lp3c9dbdveii27nnpjU7nt9z0xqaF/T+M1hI77m2Ivqzz7awxx6Dp+B89yNP87cXnknpGz/lqAP2LjqOJEmSJEmS1CtVr+pm5l3A3N28/rsOz7cBl3ay3zm7OccNwA39iNmpYXuMpuXZrTtu0tlmwoZnGLNlG1/77jzWH7oXt795Jud+9I9MfGYbs06evqODu2PBua0w3Z/u7mpobiwBdFr4b19IB3b6XIO9QF5kZ/i73v0k1123ldNPH803vr5PYTkqZXtL8n+u+RPXrv8rB75mVtFxJEmSJEmSpF6LzOx+ryFqr+dPzuP/8293KqSXGpuZumkrT+0zlln7PbZjvWHrdvZ8somn9h+7U/d523ZzY4kvnfjTHfu/77bX10SHevvO886K/O0vAJyyYK6F9AHw7LMtHDZr/Y7n994zud8d6tOmP7o0M7u8qNVTc+fOzSVLlvT6uP++/UE+/tO7eeBzp4H/zVENiIiK/J2QJEmSJElDx+CZIVEl7Qvpdy24kV+c+g1u+sbvaB61cyG8NHo4T+0/Fniuw3vRRbfzpRN/yqKLbmfk2AZmnTwdoNOu9d5o6yTvr46d5/fctGaXc59xyQm877bXc8YlJ9RE8X8o2GOPYZx++mgATj99dN2Penls81Yu+dlfeMmhE8n99y86jiRJkiRJktQn9V2lG0ClxmbWtZuHXmps3u3+7QvVbUXq9oXpvmhuLO1UoO+v9gV+6LrIbxF94H3j6/tw7z2TB8WIl3/7fytpKrXwydcfQaytzZvsSh1FxGUR8VhE3N1ubd+IuDkiVpV/7lNePzMilkfEXRGxJCJe0u6Y88v7r4qI89utHxsRf4qI1RHx5YiIgf2E9SkiHoiIiX089m0RMbUS55IkSZIkDU0W03uoYexIpsybCbDLDPXOdNWJ3tfCdFsRvWOBvk1fu9XPuOQE3v2L0/pV5B9stj67vegIAHXfkQ7w21WPc+1da3n3yw9hxsQ9YMGCoiNJPXU5cEqHtY8AizNzJrC4/Jzy9pzMPAp4B/AtaC2+AxcDLwKOBy5uK8ADXwcuBGaWHx3fS5X3NmBqdztJkiRJktSV+q/WDaCjFpzKq258F0ctOLVH+/elE72zonh3NwLtT7f6ootu5+uvup6fLej9HOzeqJUCdXe++I/3cd6cZbzr3U8WHaXubd22nY9fezcHTxjLu19xSOviJz5RbCiphzLzVmBjh+UzgSvK21cAry/v+0w+dwOSPYC27dcAN2fmxsx8ErgZOCUi9gfGZ+bt5eOubDtXvYiIgyPiLxFxeUTcGxHfjYhXRcTvyl34x5cfv4+I/4mI2yJiVvnYD0TEZeXtF0bE3RExtov3mRARN0XEioj4FhDtXntLRPyx/I2Ab0bE8PL6MxHxxfIxiyNiUkS8idaboX/3/2fvzuPkKqv8j39Or5V0moR0FrIQshBCwpIILQEdBEEUMCwqM8LoAA4DyiIobvj7qTCKDurMKBGFH4IDzDgIg0QSJggZRAgiaIJJAGNIAkPIAh3ShCSV9H5+f9StTqVS3V3dtdxb1d/361Wvrvvc53nuubeqSHP6qXOD/kOCaT5rZs8H3xI4vJDXTEREREREREqfkum98K79b5TY14r0dP1ZiZ6aFE9Nqqevck9N0GcqJ5OtXMb2RzJB/YOro33T0pZ4J79fvB2ARYtaiMe7Qo4oerrc2dXakdXjJ0+s49W34nzr3COJVVeGHbpIPox19+Tdid8AxiZ3mNlHzOwvwH+TWJ0OMAF4PWX8xqBtQvA8vb3UHAr8C3B48Phb4K+ALwL/B/gLcKK7vwv4BvCdYNzNwKFm9hHg34BPu/vuHo5xPfC0ux8BLAAmAZjZTODjwHuDbwR0Ap8IxtQBy4IxTwLXu/sDwDLgE+4+x933BH3fcvdjSHxT4Iu5XhAREREREREpbyqG3Yuda7ey4oZHsl6J3l9tuzu6k+3pie01j21kxgcndifNz/7e8bTd0LFPcj45fsYHJ3b370/yPpex2UpNUP9+8XYu/6dOYnXRTKzG6io54cwR/H7x9rK48We+mNllwGUANQcdypHXP5r12LNnj+fE6aMLFZpILkaZWepXcm5399uzHezubmaesr0AWGBm7wO+BXwgf6FG1qvu/gKAmb1EogSOm9kLwGRgOHC3mU0nsVq/GsDdu8zsYmAV8P/c/Xe9HON9wEeDcf9tZsmvDZ0KHAv8MSg3PwRoCvZ1AfcFz/8DeLCX+ZP7liePIyIiIiIiItITJdP78MYTa+n48qn9XpHel1994fesfXxTd8I8U5mVNY9t3CeBnprsXvjlZ/dJuKcn2vuSTMQPZGx/pCaoTzhzRGQT6Umfnz+Vy/+pk1MbmvruPEgECcbbASYffpT/3zNnZjWutrqCj7wrbbHtssKWExLph7fcvbGfY940s3HuviUo1bLffyjc/Skzmxrc2HITcHLK7onAb4P2iWntm/oZSxS0pjzvStnuIvH7xbeAJ9z9I2Y2mcS5J00HdjHwGuYG3O3uX82i7/5fM9srGXMn+p1IRERERERE+qD/cexD8majHbvb8pZQ/9W1z7D2N5uBRMJ815db9qmJPv3UCd2J9kxJ7v3Ks9yw7wr3vhLj6Yn4QiXSk5IJ6kIl0lvi+Z076gn/MI0aVsul75sadhgiYVkIXATcFPx8CMDMDgXWB6uyjwFqgW3Ao8B3Um46+kHgq+7ebGY7zOx44DngQuBHxT2VohjO3j8SXJxsNLPhwHwSq85vMbPzgjIsmTxFonzMjWZ2BpC8lo8DD5nZD9y9KbjZa727v0aihN15wC+CsU8HY3YC9fk6ORERERERERl8lEzvRf300cy54QxW3PAIbzyxloPePz2nki/JmuTJRDokEue/+d6K7u2q2krO/ZcTek2K91SeJT1J3lMMPSXiC6lQCeofXP1K96r3z88vTJI3Hu9SyZd8aWwE722RqEg0mNm9JFaVjzKzjSRqd98E3G9mlwCvAX8TdP8YcKGZtQN7gI8HNxZtNrNvAX8M+n3T3ZM3Nb0CuItEeZJHgke5+R6JMi9fI1FLPukHwI/d/eXgWj5hZk+5e6avBP0jcG9QRuYZYAOAu/85mPcxM6sA2oErSbwuceC4YH8TidrqkLjet5nZHuCEPJ+riIiIiIiIDALmSmz1aPjhY/24H36U/znjtu62Dzzyme4V6kcN39zT0P2kJrohkciefsp4zrzxOG5+z6/26Xv5/8xj2KhYn3Om11xPnae3ObJJuvfl3cPCv5loS7yTC2ev7N6+Z+XsvCXt3xtL3GPwM5e/zaJFLZx1Vozbbj2wj1HRNWHiluUDKGmxn8bGRl+WS6kWMyXTJRLMLC+fCYkeM9vl7sPCjkNERERERETKj5bb9qFqaA0HvX86sLfkS3+lrwY//YZGrnnmXM791/d0rzJPqh87JKtEOuxbQz11nvqxQ7j1Aw+z8MvPZhx39veO55pnzh1wIj0qkvXYgYLUY4/Hu1i0qAWARYtaiMe78jq/iIiIiIiIiIiIlA6tTM/AzM4CziLx1fC1QXMFiZuqZTIKeKuPaaeSqPX6NpBpWXdF8OjIMsyejlkFzE7Z/hM9xz0Q2ZxrIfR23N5em1zNBIbS8+tWKPm+zoe4++hcJzGzrSTKKIiUurx8JiQ/zOxTwDVpzb9z9yvDiEdEREREREQkEyXT88DMlhW7XEAYx9Rxy/+4IlJazKwSWAZscvd5afsuBr7P3puA3uLudxQprhHAHcCRgAN/7+6/T9lvwM3AmcBu4GJ3f77AMc0A7ktpmgp8w91/mNLnZBI3ln01aHrQ3b9ZyLhSjn0NcClgwE9T4wr2F/2aiYiIiIiIyL50A1IREZHSdQ2wGjigh/33uftVRYwn6Wbg1+5+npnVkPiGT6ozgOnBYy5wa/CzYNx9DTAHuv8IsQlYkKHr0vQ/TBSamR1JIpF+HNAG/NrMHnb3dSndin7NREREREREZF+qmS4iIlKCzGwi8GESK8Ajw8yGA+8D7gRw9zZ3357W7RzgHk94FhhhZuOKGOapwHp3j0rZqpnAc+6+2907gCeBj6b1CfuaiYiIiIiIDHpKpufH7YPkmDpu+R9XRErHD4Ev0/s9Iz5mZqvM7AEzO7hIcU0BtgL/ZmZ/MrM7zKwurc8E4PWU7Y1BW7GcD9zbw74TzGylmT1iZkcUKZ4XgRPNrMHMhpIo5ZL+eoV9zURERERERAY9JdPzwN2LnvgM45g6bvkfV0RKg5nNA5rcfXkv3RYBk939aGAJcHdRgkuUkDsGuNXd3wXEgeuKdOw+BWVnzgb+K8Pu50ncnHY28CPgV8WIyd1XA98FHgN+DawAOotxbBEREREREcmekukiIiKl573A2Wb2v8AvgFPM7D9SO7j7NndvDTbvAI4tUmwbgY3u/lyw/QCJ5HqqTey78noie2+UWmhnAM+7+5vpO9x9h7vvCp4vBqrNbFQxgnL3O939WHd/H/A28HJalzCvmYiIiIiIiKBkuoiISMlx96+6+0R3n0yiZMlv3P2TqX3S6mmfTeJGpcWI7Q3gdTObETSdCvw5rdtC4EJLOB54x923FCM+4AJ6KPFiZgeZmQXPjyPxe9K2YgRlZmOCn5NI1Ev/z7QuYV4zERERERERIfFVbBERESkDZvZNYJm7LwSuNrOzgQ6gGbi4iKF8Fvh5UFLlFeBTZvYZAHe/DVhMoi74OmA38KliBBXUbj8N+HRKW2pc5wGXm1kHsAc43929GLEBvzSzBqAduNLdt0fhmomIiIiIiMheVrz/RxQRERERERERERERKU0q8yIiIiIiIiIiIiIi0gcl00VERERERERERERE+qBkuoiIiIiIiIiIiIhIH5RMFxERERERERERERHpg5LpIiIiIiIiIiIiIiJ9UDJdRESkzJjZZWHHkElU44LoxhbVuERERERERAYjJdNFRETKT1QTsFGNC6IbW1TjEhERERERGXSUTBcRERERERERERER6YO5e9gxRJaZ7Xdxjjiiiqo9TuXrnVj73vY24FVgV0rfmTOrqKqGrk546aWOrI87Y0YlNbXWvd3T+COOqAJ6n3vmzCpWr+7ocbs/cx5xRBUVlX3Hnyo99vTj9zRnNuMyxTmQGAeDF1Z1vOXuo3OdJ9Nnoi/DgClATUpbG7DnkFra6yp57c+7u9sPmTWUin78ia+riz7Hp/dJ1dPxdnXGsg8iC7s7qjO2t3fk+Gbtctpe29K9WXPIOKjY+98O7+yifcMb3dvVkw7CKoMT7qjYO8emTXvnmDBhnzmyiiFlfKp+z1VEba9vzMtnYtSoUT558uQ8RJRfW7duZfTonE8v76IaF0Q3tmLFtXz58rx8JkRERERERMpZVdgBlIJYDFpaYN5ZMX5y6wgA6m+NM+LbO7v7fBFYc1aM5cvb2LK5i3HjK3h0yaju/Vdcvp2HF7UAiXkAHl7Uss/z5LGWPL7//8smx48bX8GWzV37xJK+L71Pcn+m7WS8ybh6mjN1X3/1dvze5sxmXPp1HWiM5W7SxDdeK+T8FRWJpHWq5OdmF/Ah4Psp++YfPoRRi45KPL9mHc8tbmbumSO5+uZD+33sbMan9gH67P/0jun9jqM3q5rHZ2zfvDX392vT/F8Qf/YF6o4/ijFXn7/f/g1XfY/O5neoHDmciTd9NmXg3j8YNN11D/EVK6mbM5sxF1/Y/xhSxgM5zVUsr37uC3n5TEyePJlly5blYyqRUJlZQf+dEBERERERKQdamd6LI4+s9rv/fQRjxlQRj3dRV7d3CeuYc7ZR/VI775w7hOEL9tAyq5ptixoAaGrqYMyY/f9OEY8nso3JeVLnjMe72Lq1k8mTM69gTe2fHkumfel9ettuauqgrq6izzlz0Vc8uYxLv66yv0kT31ju7o25zjNrVrX/+89H8PrrnRx7bC1NTYlvBowZU9X9Pkqqq6vobpt0fjOx1R28c+4Q6he08PYRdSy5b1Z335Z4J7G6ga/SzmZ8ap+++pdSMh2gq6WVilhtj/s7tu+kakT9vo1N+66+72ptpaK25zn6jCFlfK5zFcOrn/tCXj4TjY2NrmS6lAMzy8tnQkREREREpJxpZXovKirpToqnJgkrt3Ricafp4QbaD6+m9R+GMvKqd6h4o5OugyozJtLT50jfzpTM7ml8pn7p+3o7Vvp2X/HmI0ndVzy5jFMSvXiqqhPvl+R7JvW9k+l9NGZMFZVbOqnaQ/fn5Zm/m8Z7rl3PkDfb2DM2Ufwll0R6tuNT++R6vKjpLZEO7J9IzzRHjsnv1PFRT6SLiIiIiIiIiAyEkukD0QlNixrwIYlawO2HV9O0qIGK5q4+BooMQmmfl3cOG8pjDxxBbXN7HwNFRERERERERESiQ8n0AeicuP+qVh9idE4or9WuIvmQ6fPSGatg93itXhYRERERERERkdKhZLqIiMgAvd68m6vv/VOP+ysrjM+cNI0ZB/VdakdEREREREREok3JdBERkQHa3d7JC5ve6XH/q2/FOXjkUCXTRURERERERMqAkukiIiIDNGNsPU988eQe9x91w6PsbNH9AURERERERETKgZLpIllYumdS2CHk6I2wA5CBaIqFHUFBxZos7BAKrr62ip0tHWGHISIiIiIiIiJ5oGS6hKb0E9TSX6Xwmj+9Y3pRjrN564ied5ZBEn0wJMqzUR+r1sp0ERERERERkTKhZPogVQpJTSkvpfCeK0QifVXz+Ow7l0ESXfZVH6tiV6tWpouIiIiIiIiUg4qwA4iyXV01JZEA7I+leyaV3TlJ9JXCe66YifSMq9LLKJGuVel71cdU5kVERERERESkXGhlehZSE4EnDtkQYiS5KYWEpkixFausS6/KKJEu+xoWq+Z/t+0OOwwRERERERERyQOtTBeRgtIfcWQw08p0ERERERERkfKhZLqIiEiBJJLpugGplD4z+5mZNZnZiyltI81siZmtDX4eGLQPN7NFZrbSzF4ys0+ljLko6L/WzC5KaT/WzF4ws3VmNt/MVC9KREREREQiR8l0ERGRAqmvraK1o9HNf5IAACAASURBVIu2jq6wQxHJ1V3A6Wlt1wGPu/t04PFgG+BK4M/uPhs4GfgXM6sxs5HA9cBc4Djg+mQCHrgVuBSYHjzSjyUiIiIiIhI6JdNFREQKpD5WDaDV6VLy3P0poDmt+Rzg7uD53cC5ye5AfbC6fFgwrgP4ELDE3Zvd/W1gCXC6mY0DDnD3Z93dgXtS5hIREREREYkMJdNFRET6wcwuM7NlZrZs69atvfatjyXu872rVXXTJfJGJd/XweOyLMaMdfctwfM3gLHB81uAmcBm4AXgGnfvAiYAr6eM3xi0TQiep7eLiIiIiIhESlXYAYiIiJQSd78duB2gsbHRe+u7d2W6kukSeW+5e+NAB7u7m1ny8/AhYAVwCjANWGJmS/MQo4iIiIiISKi0Ml1ERKRAhtUm/ma9Q2VepDy9GZRoIfjZFLR/CnjQE9YBrwKHA5uAg1PGTwzaNgXP09tFREREREQiRcl0ERGRAkmWedHKdClTC4GLgucXAQ8FzzcApwKY2VhgBvAK8CjwQTM7MLjx6AeBR4NSMTvM7PigzvqFKXOJiIiIiIhERlkk083sGjN70cxeMrPPZdhvZjbfzNaZ2SozOyaMOEVEZHA5ICjzskvJdClxZnYv8HtghpltNLNLgJuA08xsLfCBYBvgW8B7zOwF4HHgK+7+lrs3B/v+GDy+GbQBXAHcAawD1gOPFOnUREREREREslbyNdPN7EjgUuA4oA34tZk9HHytOOkMYHrwmAvcGvwUEREpmGHdK9NV5kVKm7tf0MOuUzP03Uxi1XmmeX4G/CxD+zLgyFxiFBERERERKbRyWJk+E3jO3Xe7ewfwJPDRtD7nAPcEtTufBUYka3z2pSXemZcg4/GuXrfT23vany8t8c7uc0t/HrYoxJCqkPFkO3c2/Ypx3bpSDhGPd3U/MkltzxRb1F7nbLXvbqd9d9+J0Wz65FtXa2tB+g5EV1t+5h/oPPk6fq5U5kVERERERESkfJT8ynTgReDbZtYA7AHOBJal9ZkAvJ6yvTFo29LbxE0bWrlkznLmnjmSq28+dMABXnH5dh5e1MK8s2L85NYR+22n9xs3voItm7v2258v869Zx3OLE9+qHjmuhuYtbfs8z/V88xFbmDEUK55s586mX7Gu20svdXDF5dsBeHhRS3d7T+/leWfF2NrZtl9sUXuds/XYdUtZv2QDANNOm8QHbzqx134NJ83g8K+dXZTYmu66h/iKldTNmc2Yiy/MW9+B2LjgHnauXkH9zDlM/MjA5x/oPPk6fj5UV1YQq65gZ6uS6SIiIiIiIiKlruRXprv7auC7wGPAr4EVwICXvJrZZWa2zMyWxXckpnlucfOAV9HG413dSceHF7XQ1NSxz3bqSvRk+5bNXfvtz5eWeGd3Ih3oTqSnPs/lfPMVW1gxFCuebOfOpl+hr1vqZwIS78vURHqyLdN7+eFFLfvFFrXXOVvtu9u7E+kA65dsyLj6PLXftifX0Lmnbb8++dbV2kp8xUoA4itW9rrqvD99BxrLztUrANi5ekVOK8sHMs9AxxVSfaxaK9NFREREREREykDJJ9MB3P1Odz/W3d8HvA28nNZlE3BwyvbEoC3TXLe7e6O7N9YdUAnA3DNHEqurHFBsdXUVzDsrBiRW744ZU7XPdl1dxX79xo2v2G9/vsTqKpl75sju7ZHjavZ7nsv55iu2sGIoVjzZzp1Nv0Jft9TPBCTel8n3alJP7+V5Z8X2iy1qr3O2qodWM+20Sd3b006bRPXQ6l77NZw0g8ohNfv1ybeK2lrq5swGoG7ObCpqa/PSd6Cx1M+cA0D9zDlU1Axs/oqagc0z0HGFVF9bpZrpIiIiIiIiImXA3D3sGHJmZmPcvcnMJpFYoX68u29P2f9h4CoSJWDmAvPd/bi+5p16VJ1/7T9m7pPsO3HIhl5G9Cwe79onMZ6+nd7e0/5cLN2zNxGYXA0cq6vc73nYyc0oxJCqkPFkO3c2/Xrr84npf1ieTIbn4sgjq/2RR0cB+9ZE7+29vHTPpIyxReV1fnrH9H71T65Gz5RIT++3uuWQjPs2b00r39QUy9ivv7paW7NOjvenb3/Fmoyutta8JLIHOk9f41Z/59q8fCYaGxt92bL0ymL7OueWpxkxtIa7/77Pf3ZEQmNmeflMiIiIiIiIlLNyqJkO8MugZno7cKW7bzezzwC4+23AYhKJ9HXAbuBT2U6cr2RferKxp0R56ureQko9r56ehyUKMaQqZDzZzp1Nv2Jct4qUQ/T1Hk3d39OK+lLUVxJ9n34tfffLp/4kxwuVSO+eP08rwnNZ2R4V9bFqdqlmuoiIiIiIiEjJK4tkurvvdxfAIImefO7AlUUNSkREBBhWW0XTziL/ZUVERERERERE8q4saqaLiIhEVX2sSjcgFRERERERESkDSqaLiIgUUH2sWsl0ERERERERkTKgZLqIiEgB1ceq2NXaQVdX6d/wW0RERERERGQwUzJdRESkgOpjiduT7GrT6nQRERERERGRUqZkuoiISAElk+kq9SIiIiIiIiJS2pRMFxERKaD6WDUAO1vaQ45ERERERERERHKhZHo/Ld0zKewQREQiK9ZkYYcQOd1lXrQyXURERERERKSkKZkuIiJSQMNqVeZFREREREREpBwomT4Apbg6vRRjFhEpB8kyLztU5kVERERERESkpCmZLiISpqZY2BFIgR2QLPPSqpXpIiIiIiIiIqVMyXQREckL1UvPbFhMZV5EREREREREyoGS6SIiIgU0pLqSygpjp8q8iIiIiIiIiJQ0JdNFREQKyMyoj1VpZbpInpjZWDO708weCbZnmdklYcclIiIiIiLlT8l0ERGRAquPVbFLyXSRfLkLeBQYH2y/DHwutGhERERERGTQUDJdRESkwIbVVrNDyXQpYWb2MzNrMrMXU9pGmtkSM1sb/DwwaP+Sma0IHi+aWaeZjQz2nW5ma8xsnZldlzLXFDN7Lmi/z8xqeglnlLvfD3QBuHsH0FmQExcREREREUmhZLqIiEiBJcq8qGa6lLS7gNPT2q4DHnf36cDjwTbu/n13n+Puc4CvAk+6e7OZVQI/Bs4AZgEXmNmsYK7vAj9w90OBt4HeyrbEzawBcAAzOx54Jw/nKCIiIiIi0isl00VERArsANVMlxLn7k8BzWnN5wB3B8/vBs7NMPQC4N7g+XHAOnd/xd3bgF8A55iZAacAD/QxV9K1wEJgmpn9DrgH+Gz/zkhERERERKT/qsIOQEREpNwNq61iV6uS6VJ2xrr7luD5G8DY1J1mNpTEavargqYJwOspXTYCc4EGYHtQriXZPqGng7r782Z2EjADMGCNu+urHyIiIiIiUnBamd5PLfHyKslZbueTjXyfc67z5TI+qq9fPN4VdghA/65P++522ncrF5MvXW2tRR1XTGZ2mZktM7NlW7duzWpMfaxaZV4k6kYl39fB47L+DHZ3Jyi7kuIs4Hfunr6iPSdmdiUwzN1fcvcXgWFmdkU+jyEiIiIiIpKJkun9MP+adVwyZznzr1kXdih5UW7nk418n3Ou8+UyPqqv3xWXb2fmjCauuHx7qHH05/o8dt1S7jjxfu448X4eu25pEaIrbxsX3MOaf/4qGxfcU5Rxxebut7t7o7s3jh49Oqsx9UGZl0S+USSS3kq+r4PH7VmMedPMxgEEP5vS9p/P3hIvAJuAg1O2JwZt24ARZlaV1t6TS929+x8Zd38buDSLeEVERERERHKiZHqWWuKdPLc4sbDqucXNkVl5O1Dp5xPVFc75lO9zznW+XMZH9fWLx7t4eFELAA8vagntc9Kf69O+u531SzZ0b69fskEr1HPQ1dbKztUrANi5ekXWK80HOq5U1Meq6ehyWtpL+98OkTQLgYuC5xcBDyV3mNlw4KTUNuCPwHQzm2JmNSSS7QuDVe1PAOdlmiuDyqDOevJYlUBNjuciIiIiIiLSJyXTsxSrq2TumSMBmHvmSOrqSvvSpZ9PrK4y5IgKL9/nnOt8uYyP6utXV1fBvLNiAMw7Kxba56Q/16d6aDXTTpvUvT3ttElUD60ueIzlqqKmlvqZcwConzmHiprago4rFcNiiQW3O1v1hxopTWZ2L/B7YIaZbTSzS4CbgNPMbC3wgWA76SPAY+4eTzYENdGvAh4FVgP3u/tLwe6vANea2ToSNdTv7CWcXwP3mdmpZnYqidXvv87HeYqIiIiIiPTG9JXznk09qs5vXHDkPm0t8U5idZWcOGRDD6OiaemeSRnbk+czmOT7nHOdL5fx2Y79xPQ/LHf3xgEdJMXRs6v9vxeP6rNfPN7VnUjv6b1XDNlcn6d3TAfoXo2ez0T6qubxGds3bx2xd6MplrfjhS3W1L1QlK621gElxAc6rr9Wf+favHwmGhsbfdmyZX32e2jFJq75xQoe/8JJTBs9LNfDiuSdmeXlM1EMZlYBfBo4NWhaAtzh7tH4mpaIiIiIiJStqr67SKpySzyX2/lkI9/nnI8V7mEdu1Ci8s2N/lwfrUbPr4EmxMttRXpSfXJlektHyJGIlD537wJuDR4iIiIiIiJFo2S6iIhIgQ2rTfyxZpeS6SI5M7P3AjcAh5D4XdYAd/epYcYlIiIiIiLlT8l0ERGRAtu7Ml0100Xy4E7g88ByQKVdRERERESkaJRMFxERKTCVeRHJq3fc/ZGwgxARERERkcFHyXQREZECq48lyrzs0Mp0kXx4wsy+DzwItCYb3f358EISEREREZHBQMl0ERGRAhtWm/jndlerVqaL5MHc4GdjSpsDp4QQi4iIiIiIDCIln0w3sxnAfSlNU4FvuPsPU/qcDDwEvBo0Peju38zluEv3TALgxCEbcplGRELy9I7pYYcgg0hlhVFXU6kyLyJ54O7vDzsGEREREREZnEo+me7ua4A5AGZWCWwCFmToutTd5+X7+Mmkej4oMS/lJp+fj3waSCJ9VfP4vBx789YReZknn2JNFnYIg0J9rFo3IBXJAzMbC3wHGO/uZ5jZLOAEd78z5NBERERERKTMlXwyPc2pwHp3fy3sQAaiUKvdo5rQlPLWn/edVonLYDAsVqWV6SL5cRfwb8D/DbZfJvEtRSXTRURERESkoMotmX4+cG8P+04ws5XAZuCL7v5S8cLqn6V7JmmVukTSrq6avP9xRon0cGlVevHUx6pUM10kP0a5+/1m9lUAd+8ws86wgxIRERERkfIXuWS6mR0GfAk4hJT43L3Xm0qZWQ1wNvDVDLufBw5x911mdibwKyBjBs/MLgMuAxg1vmYgp5AXSqhLVETlMyFS6upj1byzR2VeRPIgbmYNJG46ipkdD7wTbkgiIiIiIjIYRC6ZDvwXcBvwU6A/q4zOAJ539zfTd7j7jpTni83sJ2Y2yt3fytD3duB2gKlH1Xl/gxcpN/pMiORHfayKjW/vDjsMkXJwLbAQmGZmvwNGA+eFG5KIiIiIiAwGUUymd7j7rQMYdwE9lHgxs4OAN93dzew4oALYlkOMIiIi/VJfW8Uu1UwXyZm7P29mJwEzAAPWuLu+9iEiIiIiIgUXmWS6mY0Mni4ysyuABUBrcr+7N/cytg44Dfh0SttngnG3kVitdLmZdQB7gPPdXStsRUSkaOp1A1KRnJjZR3vYdZiZ4e4PFjUgEREREREZdCKTTAeWk6h9mbwb3pdS9jkwtaeB7h4HGtLabkt5fgtwS94iFRER6af6WDV72jtp7+yiurIi7HBEStFZwc8xwHuA3wTb7weeAZRMFxERERGRgopMMt3dpwCYWczdW1L3mVksnKhERETyY1ht4p/cXS0dHFinm/mK9Je7fwrAzB4DZrn7lmB7HHBXiKGJiIiIiMggEcWlcc9k2SYiIlIy6mNBMr1VpV5EcnRwMpEeeBOYFFYwIiIiIiIyeERmZXpwk9AJwBAzexd7y70cAAwNLTAREZE8qI9VA7CjRfdJFMnR42b2KHtvPP9x4H9CjEdERERERAaJyCTTgQ8BFwMTgX9Nad8J/J8wAhIREcmXA4KV6boJqUhu3P2q4GakJwZNt7v7gjBjEhERERGRwSEyyXR3vxu428w+5u6/DDseERGRfBqmZLpI3rj7g+iGoyIiIiIiUmRRrJn+WzObb2bPm9lyM7vZzBrCDkpERCQXyTIvu1pV5kUkF2b2UTNba2bvmNkOM9tpZjvCjktERERERMpfFJPpvwC2Ah8Dzgue3xdqRD1oiXcW9XjxeFdO+wulENehv3MW+7WQwmnf3U777vJINnbuaev3mK7W1gJE0o/jt+V+/HzMUY7qtTJdJF++B5zt7sPd/QB3r3f3A8IOSkREREREyl8Uk+nj3P1b7v5q8LgRGBt2UOnmX7OOS+YsZ/4164pyvCsu387MGU1ccfn2Ae0vlEJch/7OWezXQgrnseuWcseJ93PHiffz2HVLww4nJ3+5cSHPnn0zf7lxYdZjmu66h9e+8n9ouuueAkbWs40L7mHNP3+VjQsGfvx8zFGulEyXUmZmPzOzJjN7MaVtpJktCVaJLzGzA1P2nWxmK8zsJTN7MqX9dDNbY2brzOy6lPYpZvZc0H6fmdX0Es6b7r467ycpIiIiIiLShygm0x8zs/PNrCJ4/A3waNhBpWqJd/Lc4mYAnlvcXPBV0fF4Fw8vagHg4UUt+61A72t/oRTiOvR3zmK/FlI47bvbWb9kQ/f2+iUbSnaFeueeNrY9uQaAbU+uyWqFeldrK/EVKwGIr1hZ9BXqXW2t7Fy9AoCdq1cMaHV5PuYoZ7VVldRUVrCjpTTf1zLo3QWcntZ2HfC4u08HHg+2MbMRwE9IrB4/AvjroL0S+DFwBjALuMDMZgVzfRf4gbsfCrwNXNJLLMuChPsFQcmXjwY3JBURERERESmoKCbTLwX+E2gDWkmUffl0lOphxuoqmXvmSADmnjmSWF1lQY9XV1fBvLNiAMw7K0ZdXUW/9hdKIa5Df+cs9mshhVM9tJppp03q3p522iSqh1aHGNHAVQ6poeGkGQA0nDSDyiG9LbBMqKitpW7ObADq5symora2oDHud/yaWupnzgGgfuYcKmr6f/x8zFHu6mNV7NLKdClB7v4U0JzWfA5wd/D8buDc4PnfAg+6+4ZgbFPQfhywzt1fcfc2Er/jnWNmBpwCPJBhrkwOAHYDHwTOCh7zBnhqIiIiIiIiWTN3DzuGyJp6VJ3fuODIHve3xDsLmrw9cciGfbbj8a5eE+U97V+6Z1KG3vlTiOvQ3zkL/VqUuk9M/8Nyd2/MdZ6+PhMD8fSO6ftsJ1ejRy2Rvqp5fL/HdO5p2y+RvnnriH07NcX22exqbS1qIj3WZPsev6015yR4PuYotNXfuTYvn4nGxkZftmxZ1v1P/v4THD1xBPMveFeuhxbJKzPr8zNhZpOBh939yGB7u7uPCJ4b8La7jzCzHwLVwBFAPXCzu99jZucBp7v7PwRj/g6YC9wAPBusSsfMDgYeSR5HREREREQkKiK3Mt0SPmlmXw+2Dzaz48KOK5NiJ2/7WnFerBXp6QpxHfo7pxLp5aN6aHXkEukDlc2K9HTFXpG+3/HzkASPeiI9V2Z2mZktM7NlW7du7dfYYbEqdqrMi0TTqOT7Onhc1p/BnlidkVyhUQUcC3wY+BDwdTM7LF+BmtlhZvZ4sn67mR1tZl/L1/wiIiIiIiI9iVwynUSNzRNIfEUYYBeJ+poiIiKhc/fb3b3R3RtHjx7dr7H1tdXsalWZF4mkt5Lv6+BxexZj3jSzcQDBz2Q5l43Ao+4ed/e3gKeA2cAm4OCU8RODtm3ACDOrSmvvyU+BrwLtAO6+Cjg/m5MUERERERHJRRST6XPd/UqgBcDd3wb6v7xTREQkYupjVexUzXQpHwuBi4LnFwEPBc8fAv7KzKrMbCiJUi6rgT8C081sipnVkEiALwxWtT8BnJdhrkyGuvsf0tr0wRIRERERkYKLYjK93cwqCb4qbGajga5wQxIREcldfaxayXQpSWZ2L/B7YIaZbTSzS4CbgNPMbC3wgWAbd18N/BpYBfwBuMPdX3T3DuAq4FESyfX73f2l4BBfAa41s3VAA3BnL+G8ZWbT2Pu74nnAlryesIiIiIiISAZVfXcpuvnAAmCMmX2bxCol1cEUEZGSVx+rYodqpksJcvcLeth1ag/9vw98P0P7YmBxhvZXgGzvkXMlcDtwuJltAl4FPpHlWBERERERkQGLXDLd3X9uZstJ/M+ZAecGK5xERERKWn2sil2tHbg7ZhZ2OCIlKUi8f8DM6oAKd98ZdkwiIiIiIjI4RCaZbmYjUzabgHtT97l7c/GjEhERyZ/6WBXucPI//5beUum1VZV89JgJfPL4Q6irjcw/1SKRYGYNwPXAXwFuZk8D33T3beFGJiIiIiIi5S5K/4e+nETtSwMmAW8Hz0cAG4Ap4YVWupbumRR2CCI9enrH9LBDyMqq5vFhhyBl4oOzDmLNG7vo6Or9ViBb3mnhnx75C7c9uZ5L/moKF75nMgfEqosUpUjk/QJ4CvhYsP0J4D4SddtFREREREQKJjLJdHefAmBmPwUWBDU1MbMzgHPDjK0UKYkuIhI9k0fV8S9/Mzurvs9veJtbfrOOf37sZW5/6hU+MHMsVZW9l4aZMGIoV596qErISLkb5+7fStm+0cw+Hlo0IiIiIiIyaEQmmZ7ieHe/NLnh7o+Y2ffCDKjUKJEuIlL6jpl0ID+7+N28uOkdfvzEOn7/Su8VLNo6utgWb+PEw0ZxzKQDixSlSCgeM7PzgfuD7fOAR0OMR0REREREBokoJtM3m9nXgP8Itj8BbA4xntAoKS4iIkdOGM6tnzy2z347W9p597f/hweWb1QyXcrdpcDnSPyu6EAlEDezTwPu7geEGZyIiIiIiJSvirADyOACYDSwAHgweH5BqBGJSN6pXrpIftXHqjnjyHEsWrmZlvbOsMMRKRh3r3f3Cnevcvfq4Hl98FAiXURERERECiZyyXR3b3b3a9z9Xe5+jLt/zt2bk/vN7EdhxiciIhJV5x07kZ0tHTz25zfDDkWkYCzhk2b29WD7YDM7Luy4RERERESk/EUumZ6F94YdgIiISBSdMLWB8cNj/HL5xrBDESmknwAnAH8bbO8CfhxeOCIiIiIiMliUYjJdREREMqioMD527ESWrt3KG++0hB2OSKHMdfcrgRYAd38bqAk3JBERERERGQyUTBcRESkjHztmIl0OC/60KexQRAql3cwqSdx8FDMbDXSFG5KIiIiIiAwGpZhMt7ADEBERiarJo+p49+QDeWD567h72OGIFMJ8EjeqH2Nm3waeBr4TbkgiIiIiIjIYRC6ZbmaxDG2jUjZvLmI4IiIiJee8YyeyfmucFa9vDzsUkbxz958DXwb+CdgCnOvu/xVuVCIiIiIiMhhELpkO/NHMjk9umNnHgGeS2+5+V6ZBZjbCzB4ws7+Y2WozOyFtv5nZfDNbZ2arzOyYQp2AiIhImM48ahyx6goe0I1IpYyY2cjkA2gC7gX+E3gzaBMRERERESmoqrADyOBvgZ+Z2W+B8UADcEoW424Gfu3u55lZDTA0bf8ZwPTgMRe4NfgpIiJSVupj1Zx+xEEsXLmZr8+bRay6MuyQRPJhOYk66QZMAt4Ono8ANgBTwgtNREREREQGg8gl0939haD+5b8DO4H3uXuvS+vMbDjwPuDiYI42oC2t2znAPZ4oIPtssJJ9nLtvyTXmlngnsbrSSVQMNN5CnWfUrt/2rW2MGF1T8OOknndf12Ag16iY17UQ8adr391O9dDqnOYohs49bVQOyfz+6dzTRldLKxWx2v32dbW2UlFb22dbFHW1tVJRE904ox5foZx37MH8asVmfrDkZQ4bW5/zfIc0DKVxshb/SnjcfQqAmf0UWODui4PtM4Bzw4xNREREREQGh8gl083sTmAacDRwGPCwmf3I3X/cy7ApwFbg38xsNomVS9e4ezylzwTg9ZTtjUFbTsn0+des47nFzcw9cyRX33xoLlMVxUDjLdR5Ru36ffZ9K2je0sbIcTX86Kk5BTtO6nkDvV6DgVyjYl7Xvo6Vj1geu24p65dsYNppk/jgTSfmGnLB/OXGhWx7cg0NJ83g8K+dnXEfQN3xRzHm6vO79zXddQ/xFSupmzObMRdf2GNbFG1ccA87V6+gfuYcJn4kenFGPb5COmFaA5MbhvL/nnolL/NVVxov3PAhrXKXKDje3S9Nbrj7I2b2vTADEhERERGRwSFyyXTgBeAfghXkr5rZXOBf+xhTBRwDfNbdnzOzm4HrgK/39+BmdhlwGcCo8b2vTm6Jd/Lc4mYgkQxt+U60VlinG2i8hTrPqF2/7VvbaN6S+EJD85a2gq1QTz/vpEzXYCDXKNOYXPT2megrvny8xu2721m/ZAMA65dsoP0b0Vyh3rmnrTtZvu3JNXR+Ye8K9dR9APFnX6Drso9QEaulq7WV+IqVifYVK+lqbe1+ntoWxRXqXW2t7Fy9AoCdq1fQ9eGPR2oFeNTjK7TKCuO/rz6RbbvSv6jVf799uYlvPPQSr2yNM2v8AXmITiQnm83sa8B/BNufADaHGI+IiIiIiAwSkbsBqbv/MEikJ7ffcfdL+hi2Edjo7s8F2w+QSK6n2gQcnLI9MWhLP/7t7t7o7o31I3v/W0OsrrJ7ZfHcM0dGOpEOA4+3UOcZtes3YnQNI8clkp8jx9UUrNRL+nn3dg0Gco3yfV17+0z0dax8xFI9tJppp00CYNppkyKZSAeoHFJDw0kzAGg4acY+pV5S90FiZXqy1EtFbS11c2Yn2ufMpqK2NmNbFFXU1FI/M/ENjvqZcyKXqI56fMVQV1vFpIahOT/mTmkAYG3TzpDPSASAC4DRwALgweD5BaFGJCIiIiIig4Kl5K0jwcymA/8EzAJiyXZ3n9rHuKUkVrSvMbMbgDp3/1LK/g8DVwFnkrjx6Hx3P663OaceVec3XR++JQAAIABJREFULjiyz5ijVvO7L6qZ3rtyrJn+iel/WO7ujQOPNqGnz8RA4n96x/R+HTusmumrmsf3q39fNdO3vDV835rpTYn/zIVVMz3WZDnPEfWa5JniW/2da/PymWhsbPRly5blOk3ktXV0MfMbv+YzJ03lSx86POxwpADMLC+fCRERERERkXIWxTIv/wZcD/wAeD/wKbJbQf9Z4OdmVgO8AnzKzD4D4O63AYtJJNLXAbuDefMiSongbAw03kKdZ9SuXzES6bDvefd1DQZyjYp5XQsRf7qorkhP11MiPbkv081HgYxJ86iuSE8X5UQ6RD++UlBTVcHkhqG8/OausEMRERERERERCU0Uk+lD3P1xMzN3fw24wcyWA9/obZC7rwDSV1TdlrLfgSvzHq2IiMggcNjYelZv2RF2GCIiIiIiIiKhiVzNdKDVzCqAtWZ2lZl9BBgWdlAiIiKD2fSx9Wxo3k1Le243NZbSZGY/M7MmM3sxpW2kmS0xs7XBzwOD9pPN7B0zWxE8vpEy5nQzW2Nm68zsupT2KWb2XNB+X/BNQxERERERkUiJ4sr0a4ChwNXAt0iUerkw1IhEREQGucPGDqPLYf3WXRwxfnjY4Ujx3QXcAtyT0nYd8Li73xQkxq8DvhLsW+ru81InMLNK4MfAaSRuHv9HM1vo7n8Gvgv8wN1/YWa3AZcAt6aN/xHQ481+3P3qHM5PRERERESkT1FMpjvw78AhQLJI8k+Bo0OLSEREZJA7bGw9AGvfVDJ9MHL3p8xsclrzOcDJwfO7gd+yN5meyXHAOnd/BcDMfgGcY2argVOAv02Z6wbSkulA+d/tV0REREREIi2KyfSfA18CXgC6Qo5FREREgMkNdVRVGC+/uTPsUCQ6xrr7luD5G8DYlH0nmNlKYDPwRXd/CZgAvJ7SZyMwF2gAtrt7R0r7hPSDufvdeY5fRERERESkX6KYTN/q7gvDDkJERCQTM7sMuAxg0qRJIUdTPDVVFUwZVcfLb+4KOxQpjFFmlrry+3Z3vz3bwe7uZpYswfI8cIi77zKzM4FfAdPzFaiZjSaxAn4WEEuJ4ZR8HUNERERERCSTKCbTrzezO4DHgdZko7s/GF5IIiIiCUGC8XaAxsbGHus3l6PpY4fx0uYdYYchhfGWuzf2c8ybZjbO3beY2TigCcDdu98k7r7YzH5iZqOATcDBKeMnBm3bgBFmVhWsTk+29+TnwH3Ah4HPABcBW/sZu4iIiIiISL9VhB1ABp8C5gCnA2cFj3m9jhAREZGCmz6mng3Nu9nT1hl2KBINC0kksgl+PgRgZgeZmQXPjyPx++Y24I/AdDObYmY1wPnAQnd34AngvPS5etDg7ncC7e7+pLv/PYma6yIiIiIiIgUVxZXp73b3GWEHISKF8/SOvH3bv6BWNY8PO4SCijVZ2CFIiTlsbD3usH7rLo6coJuQDiZmdi+Jm42OMrONwPXATcD9ZnYJ8BrwN0H384DLzawD2AOcHyTMO8zsKuBRoBL4WVBLHRJlW35hZjcCfwLu7CWc9uDnFjP7MIm67CPzc6YiIiIiIiI9i2Iy/Rkzm+Xufw47kF2dsYxJv786YG0I0URLf5OhumaDU6kkzZMKmTzfvHXE/o1Nsf3bAuWe6B7SFHYEMhCHjR0GwNqmnUqmDzLufkEPu07N0PcW4JYe5lkMLM7Q/gpwXJbh3Ghmw4EvAD8CDgA+n+VYERERERGRAYtiMv14YIWZvUqiZrqRuK/V0eGGtVchE4SFTDqHmdgstaRqmKL8h4ee/sA0EOW+6jtpsCbRlSwvT5NH1VFdaboJqYTK3R8Onr4DvD/MWEREREREZHCJYjL99LADCJOSzlKY98AfCjBndko5aZ4xEZ6rHhLp5ZJEl/JWXVnBlFF1rH1zZ9ihyCBkZl929++Z2Y+A/W7+6+5XhxCWiIiIiIgMIpFLprv7a2HHkLS7ozpjIvDokZtDiEYkfD19JnJRkIR1FA2C1egyOEwfW88LG98JOwwZnFYHP5eFGoWIiIiIiAxakUuml4JVzeNLOqEexkrhUr5eUhiDJokOvSbSy5FKvJS36WOGsfiFLexp62RITWXY4cgg4u6Lgqe73f2/UveZ2V+HEJKIiIiIiAwyFWEHICIiIqXjsLH1uMO6JtVNl9B8Ncs2ERERERGRvNLKdBEREcnaYWOHAfDymzs5auLwkKORwcTMzgDOBCaY2fyUXQcAHeFEJSIiIiIig4mS6SIiIpK1QxrqqK401mpluhTfZhL10s8Glqe07wQ+H0pEIiIiIiIyqCiZLiIiIlmrrqxg6qhhrH1zZ9ihyCDj7ivN7EXgQ+5+d9jxiIiIiIjI4KOa6SIiItIv08cO4+UmJdOl+Ny9EzjYzGrCjkVERERERAYfrUwXERGRfjlsbD0Pr9rC7rYOhtboVwkpuleB35nZQiCebHT3fw0vJBERERERGQy0Mj0LnXvawg4ho/bd7WGH0Kewr10pXKOBCvvcwn5tS1VXa2vYIZSEzva+r1Pb7h1FiEQymT4mcRPSdaqbLuFYDzxM4vfY+pSHiIiIiIhIQWk5WR/+cuNCtj25hoaTZnD4184OO5xuj123lPVLNjDttEl88KYTww4no9Rrd/S/Nhb9+KVwjQYq7HOL6uci6pruuof4ipXUzZnNmIsvDDucyHrl8XvY/uoKRkyZw9RTM1+nF+79Ju3x7VTXjeCoC75R5Ahl+thE3vLlN3dx9MQRIUcjg427/2PYMYiIiIiIyOCkZHpv3Nn25BoAtj25hs4vtFE5JPwSne2721m/ZAMA65dsoP0b7VQPrQ45qn117mnb59q1755d1BhL4RoNVNjnlv7aRuVzEXVdra3EV6wEIL5iJV2trVTU1oYcVfR0trey/dUVAGx/dQWd7R+nsnrf69S2ewft8e0AtMe307Z7BzVDDyh6rIPZ5Iah1FRWcN0vV/H1X71YkGNMG1PH3CkNHD+1geMmj2R4mfw3XHJnZqOBLwNHALFku7ufElpQIiIiIiIyKCiZ3hszGk6a0b0CNyoJw+qh1Uw7bVL3yuQoJokrh9Tsc+2KHWMpXKOBCvvc0l/bqHwuoq6itpa6ObO7V6YrkZ5ZZXUtI6bM6V6Znp5IB6gZegDVdSO6V6YrkV58VZUVfP+vj+alzYUptdPZ5fx58w7+/dnXuPPpVzGDqaPqqKmq7HVchcG44UOY3DCUQ0bVccjIoYw5oBbDeh134NBqxhwQ67WPRMrPgfuAecBngIuAraFGJCIiIiIig4K5e9gxRNawww7yOT+5kM49+6+8PXrk5pCi2qt998BWJK9qHl+AaDJLXruwrtdAr1Ep6M+53Xrsz5e7e861dpKfCSDj56I/Nm8dRKUhmvYm6TKtSI819Z7oKzVDmnKfo7O9NWMiPVUuK9Kfv+PavHwmGhsbfdmyZblOIz1oae9k5evbefaVZv685R26+viVpaOzi83bW3itOU5Le1fWx6kw+Mezj+DvTpicW8AlzMzy8pkohiDWY81slbsfHbT90d3fHXZsIiIiIiJS3rQyPQtRXXlbCknisK9dKVyjgQr73MJ+bUuVVqRnp69EOqAV6YNArLqSuVMbmDu1oV/jurqcpp2tvLYtzrZ43zdL/uXyjXz9oZdYvzXO1+fNorKivP7AVYaSd+DeYmYfBjYDI0OMR0REREREBgkl00VERKSsVFQYBw2PcdDw7Eq3fOiIg/jO4tXc+fSrbGjezfwL3sWwWv2KFGE3mtlw4AvAj4ADgM+HG5KIiIiIiAwG+j9FERERGdQqK4yvz5vFlFF1XL/wJc679Rm+9KEZVFVW5Dz3mPpaZo7TtyjywcxiJGqkHwpMAO509/eHG5WIiIiIiAwmSqaLiIiIAJ88/hAOaRjKFT9/nkvuzl8t/KtPOZTPfeAwKlQ+Jld3kyjxshQ4A5gFXBNqRCIiIiIiMqgomS4iIiISOHH6aJ744sm8tm13Xua7748bmP+bdfx5yw5+8PE51MfK914eRTDL3Y8CMLM7gT+EHI+IiIiIiAwySqaLiIiIpBg1rJZRw/Jzs+BjJo3giPHD+ebDf+YjP3mG2//uWKaOHpaXuQeh5I1HcfcOM630FxERERGR4iqbZLqZVQLLgE3uPi9t38XA94FNQdMt7n5HLsdb1Tyeo0duzmWKUKxqHh92CCJs3joi7BBERIrCzLjoPZM5bGw9V/7n85zz49/x0XdNKEjJF8P46DETOHLC8LzPHRGzzWxH8NyAIcG2Ae7uKk4vIiIiIiIFVTbJdBI1M1cDPf2P1H3uflU+D1iIxHShEvRhJ9FL9Y8Pkl+DMoneFOtxV6xJqypFBosTpjXw0JXv5fP3reDBP23qe8AA7Gnr5KXN73Dfp0/I+9xm9jNgHtDk7kcGbSOB+4DJwP8Cf+Pub6eMeTfwe+B8d38gaLsI+FrQ5UZ3vztoPxa4CxgCLAaucXdPjcHdK/N+YiIiIiIiIv1QFsl0M5sIfBj4NnBtyOHkJOykt0ihKJG+r3JNpA9pCjsCkeg6eORQHrj8PQWb/0ePr+VflrzM6827OXjk0HxPfxdwC3BPStt1wOPufpOZXRdsfwW6vzH4XeCxZOcg+X490Ag4sNzMFgYJ+FuBS4HnSCTTTwceyfdJyP9n797j5CqrfP9/Vlenu5MmF3IhCQkYLoEQAmmhRcSDigElJgIiiIwcovIziAZkRo/IGR2ijo44OodxcAIRGcjAIIo4QmJUzERBAbGDHRKSSDBiyKXJjVzoTl+qev3+qF2dSqUvVd1VtXdVf9+vV726atfez7N2V++CrFq1HhERERERGYiKsAPIkzuAzwOdvezzQTN7wcweMbPjshm0Ix5j285Rh92k//RBweBUFtfOjpr+3XpQrol0EQnX5WdPxgx+/PyWvI/t7k8CezI2XwrcH9y/H7gs7bkbgR8D6R+xvRd4wt33BAn0J4CLzWwiMMLdnw2q0ZdkjCUiIiIiIhIJJV+ZbmaprxyvMrN39bDb48BD7t5mZteT/Affu3sYbz4wHyA29sieo6mk4LHj9g449rBs2zmqpOOX4sq8Jko+MZ6NXhLhAxXVRLoqykVK36RRQznvpDH8+Pkt3PTuqQXpy55hvLtvD+43AeMBzGwS8AHgAuAt6SECr6Y93hJsmxTcz9wuIiIiIiISKeVQmf524BIzewX4AfBuM3sgfQd33+3ubcHDe4CzexrM3Re7e72718eG1/Y4aaknFMOKX9XppSfba6JsFDCRHlVKpOfGzOabWYOZNezcuTPscEQO88GzJvPqnoP84ZXMIvI+jU39XQe3+bkcHFSUp3qc3wHc4u69fWNQRERERESk5JR8Mt3db3X3ye4+Bfgw8D/ufk36PsHXh1MuIblQqYiISM7SP2AaN25c2OGIHObiGROorYrxyKqcW73sSv1dB7fFWRzzWur/sYKfqY/m6oEfBIUOVwD/bmaXAVuB9FZ7k4NtW4P7mdtFREREREQipeST6T0xs6+Y2SXBw5vM7EUzWw3cBHw0vMhERERECmNYVSVzzpzIz9Zsp6U9XujpHgPmBffnAT8FcPcT3H1KUOjwCPApd/9v4BfAe8zsaDM7GngP8IugVcx+MzvXzAy4NjWWiIiIiIhIlJRVMt3df+3uc4P7/+DujwX3b3X30919prtf4O4bwo1UREREpDA+eNZkmtsT/HxtU97GNLOHgGeAU81si5ldB3wDuMjMNgIXBo975O57gK8CfwhuXwm2AXyKZCu+l4E/A8vzFryIiIiIiEielPwCpCIiIiJyyFumjOb40cN4ZNUWLj9rct8HZMHdr+7hqVl9HPfRjMf3Avd2s18DMKO/8YmIiIiIiBRDWVWmi4iIiAx2FRXGB8+azDObdrPl9ZawwxERERERESkbSqaLiIiIlJnLz5qEO/zkea3jKSIiIiIiki9q8yIiIiJSZo4bPYxzTxzNI89v4czjRoUdjoiIiIiISFlQMl1ERESkDF31luP424dXM+/e58IORUREREREpCwomR6CxMF2YkOrQpl7287wqtMKdd4dLR0MGTak4McUW2aMucYchXPsbG2joqa66OPle16AzrY2Kqq7H7O/z4Wls72NiqpoxSQi+XdZ3SSmHjOctnhnn/vW316EgEREREREREqckukDsG3nKI4dtzenYzb842Ps/s2fGPPOU5n2xUsKFFl2+hN/f6Wf95n/Up+3cX/5haf48xObOemi43nPN84v2DHFlhljrjFH4Rx3fOcHND+7htpzz+CYmz5ctPHyPS/AjvuW0Ny4mtq6mRzz0Wvz8lxYtvxkCQfWNzL8tDomfyAaMYlIYZgZMyaNDDsMERERERGRsqEFSIsocbCd3b/5EwC7f/MnEgfbQ46oODLPu6OlIy/jdrR08OcnNgPw5yc2ZzVuf44ptswYW3a15BRzFM6xs7WN5mfXAND87Bo6W9uKMl6+54VkZXlz4+rkmI2r6WxrG/BzYelsb+PA+kYADqxvpLM9/JhEREREREREREqFkulFFBtaxZh3ngrAmHeeGlqrl2LLPO98tR4ZMmwIJ110PAAnXXR8VuP255hiy4xx2NhhOcUchXOsqKmm9twzAKg994wBt1zJdrx8zwtQUV1Nbd3M5Jh1Mw9r2dLf58JSUVXN8NPqABh+Wp1avYiIiIiIiIiI5MDcPewYIqv6xEk+6Wufzvu4PfVzLnTLlZ76pRer1UuqZ/qZo7fldVz1TO/7+EVnP7jK3QfcXyfXa6Ike6bvqOl+zDz1TK/ZYf2LK48ye6YP3RFiMCF5/p6/y8s1UV9f7w0NDfkISSRUZpaXa0JERERERKScqTI9BPleGLFUFKoSvz9J8agn0uHIGHONOQrnmO+/9WzHK8Q11ltleX+fC4sq0kVEREREREREcqdkuoiIiIiIiIiIiIhIH5RMFxERERERERERERHpg5LpIiIiIiIiIiIiIiJ9UDJdRERERERERERERKQPSqaLiIiIiIiIiIiIiPRByXQRERERERERERERkT4omS4iIiIiIiIiIiIi0gcl00VERERERERERERE+lAZdgCRFq+AHTVFm24bo4o212Hz7iz8vMeO29t1/4U9xxZ8vv46c/S2sEOItiJfE6WuZofltP/QHQUKREREREREREREBkzJ9F5UdOSeDBuQHUO77rYe48Wbtz+Oac1p91TCPj2pHkVRTvRHQeVBOHptEa+JAjl4THHmUXK8d0c1xcMOQUREREREREQka0qm96IiHl4ybGiQxC9W0i8Xrcd47tXJQfJ9oEn1QlbRRz3RHwUVHV4WCdCjmg5//MaEwr0V5vr7KmQsxVAOfx8iIiIiIiIiIt0p7axNgVU2Jxj3h9e7fe7gccOLEsNRTVFMriUT/TlVz6eS7xlJ9SiJYkxRU9HeydBXD4QdRt4NfbV413RflIwWEREREREREYmmqGVpo6W9AzZv7/apoZu3w/ETixJGlBJ9b0yoZOiOZMV8zQ7LvR3NjpqcW8RIhPRyTZS6oX3vUhRRudZFRNKZ2b3AXGCHu88Ito0GHgamAK8AH3L3183sUuCrQCcQB252998Gx8wDvhgM+4/ufn+w/WzgPpJvxz8DPuPuket5Z2aXANPd/Rt5Gu/XwOfcvSFjez1wrbvfZGYfBerdfUE3x7/h7kflI5aMcX8G/I279/i1vV5irwOOdfef5TsuEREREZGwVYQdgIiIiIhE3n3AxRnbvgCscPepwIrgMcH9me5eB3wcuAe6ku+3AW8FzgFuM7Ojg2MWAZ8Apga3zLkiwd0fy1civY95Gtz9pkLP08v87+stkd6HOuB9+YxHRERERCQqlEwXERERkV65+5PAnozNlwL3B/fvBy4L9n0jraq8Fkjdfy/whLvvcffXgSeAi81sIjDC3Z8NjluSGquYzGyKmW0ws/vM7CUze9DMLjSz35nZRjM7x8w+amZ3BvvfZ2bfMbOnzWyTmV3Rx/i3mNkaM1ttZukJ+SvN7LlgzvODfd9lZku7GeMEM3smGOcf+5jvu0ElPWb2k+DbBZjZx83sa8H9a4K5G83sbjOLBdtfMbOxwf0vmdmfzOy3ZvaQmX2up9jNrAr4CnBVMOZVvf/WRURERERKi5LpIiIiItIf49091furCRifesLMPmBmG4BlJKvTASYBr6YdvyXYNim4n7k9DCcD3wamBbe/Af4X8Dng/3az/8Tg+blAjxXrZjab5IcPb3X3mcA3056udPdzgJtJVu735l+BRe5+BtBX37WngPOD+5OA6cH984Enzew04Crg7cG3CBLARzLifgvwQWAmMBuoz5jjsNjdvR34B+Bhd69z94f7iFFEREREpKQomS4iIpIDM5tvZg1m1rBz586wwxHJl7Gpv+vgNj+Xg4OKck97/BN3n0aywvyreY61kP7i7mvcvRN4kWQbGwfWkOwNn+m/3b3T3deR9mFCNy4E/sPdWwDcPb3K/9Hg56oe5kj3duCh4P5/9rHvU8D5ZjYdWAe8FnwL4G3A08As4GzgD2bWGDw+sZv5furure5+AHg84/lcYhcRERERKXlagFRERCQH7r4YWAxQX18fuQUSRfppl7tnVh335TUzm+ju24Mk7Y7MHdz9STM7MWgZshV4V9rTk4FfB9snZ2zfmmMs+dKWdr8z7XEn3f9/c/r+NsA5Ez3MkSmr9x1332pmo0j2n38SGA18CHjD3Q+YmQH3u/ut/Yg5JdfYRURERERKmirTRURERKQ/HgPmBffnAT8FMLOTg0QtZnYWUA3sBn4BvMfMjg4WHn0P8IugVcx+Mzs3OO7a1Fhl5AngY2Y2DLoWY+2P3wEfDu5/pLcdA8+SbMHyJMlK9c8FPyG5UOwVZnZMKiYze1M3873fzGrM7CiS7Wz6cgAYnsV+IiIiIiIlR8l0EREREemVmT0EPAOcamZbzOw6kj3CLzKzjSTbmKR6hn8QWBu0DvkucJUn7SHZ8uUPwe0rae1OPgXcA7wM/BlYXqRTKwp3/znJDx8agt/L5/o4pCefAT5tZmvIrq/8UyT7mr8MPE+yOv2pIKZ1wBeBX5rZCyQT/hMz4v5DEPcLJF+TNcC+PuZcCUzXAqQiIiIiUo4s2QZSujOycpyfWXsB1RXDAKi0IYc9H580hspY1eHbEu2HbevrcbYOHtf/Ap9EvI1YZXW/j0/3xoTkN3gPHpN83HpMP/5+jmkFoLO1jYqa3uPqbZ9sji+kztbkN5sLFUM+zi81xl/+5u9X9ePr+0cYWTnO3zbisl73iXvHEddKNuLeARx+nbV2tlATXH/Zjp/t/Kn9uvY/fmKfx3Qd28/rOBuZ13p7635ildU5XcP9vebz+V5RTH3F3d3zTy79fF6uifr6em9oaBjoMCKhM7O8XBNSfszsKHd/I6iqfxKY7+7Phx2XiIiIiEgY1NuwF/sTu/jt/h91PZ4w5ARmHjULgNVvrKBp7V+YMHI6M6d8MLntlR/TtG9d17a+HhfD+lUPsHP7C4ybeCannX1NUebMxo7v/IDmZ9dQe+4ZHHPTh3PeJ5vjCyk1P1CQGPJxfuljFMvqN1bQ1PGXw66VXI6DQ9fZb/Y+RKs3U2O1vHPU1VmNn+38qf1qrJZWb07uT3bXRzGv49//6mu0tSYLALO9hvt7zUf1vaIvfcVdquclIhIhi4NFTGtI9lhXIl1EREREBi1VpvfCzLr75fwx+PnmtG2vArsytq0GZvby+I8kF7Pqr7HBnL2pyIhpoHNmO29fsokrc59XObSwWSHOqyfdnW/m/PmOIR/nlznGbncfO9DAzGwfsDHLOXuLeySHvibe3e9zDZD+KcDqYKzexu9r/tSc3c2XuX96fOm6m2M0A78mulPJ4e8Zqfl6+1vo799Ovq+pnn5/+dZX3L09P9XdRw40ADPbCfx1oOOIRMCb3H1c2EGUOjM7A/jPjM1t7v7WcppTRERERGSwUjI9D8ysodhfjQ5jTs1b/vP2xcwWu/v8MMfp77G5HJfjvpF8rcKUr7+TQiqFGEUGCzM7DlgCjAccWOzu/xpSLDGgAdjq7tkstpnPuUeR7Bs/g+Tv4ePu/kyRY/hb4P8L5l8DfMzdWws8570kFzbd4e4zgm2jgYeBKcArwIfc/fVCxiEiIiIikg0tQCoiuXg8AuP099hcjsvXeQ5WpfD7K4UYRQaLOPBZd58OnEtygc3pIcXyGWB9SHP/K/Bzd59G8ptJRY3DzCYBNwH1QVI7BhSjl959wMUZ274ArHD3qcCK4LGIiIiISOiUTBeRrLl7XhKQAxmnv8fmcly+znOwKoXfXynEKDJYuPv2VB9udz9AMok8qdhxmNlkYA7J6vBizz0SeAfwfQB3b3f3vcWOg2SLsaFmVgkMA7YVekJ3fxLYk7H5UuD+4P79QO+rn4uIiIiIFImS6fmxeJDMqXnLf17JnV4rEZE8MbMpJNc6+H0I098BfJ7CrcPSmxOAncB/mNkfzeweM6stZgDuvhX4FrAZ2A7sc/dfFjOGNOPdfXtwv4lkCyARERERkdApmZ4H7l70ZFoYc2re8p9XcqfXSkQkP8zsKODHwM3uvr/Ic6d6dq8q5rxpKoGzgEXu/magmSK3NjGzo0lWhJ8AHAvUmtk1xYyhO55c4EmLPImIiIhIJCiZLiIiIiKhMrMhJBPpD7r7oyGE8HbgEjN7BfgB8G4ze6CI828Btrh7qiL/EZLJ9WK6EPiLu+909w7gUeC8IseQ8pqZTQQIfu4IKQ4RERERkcMomS4iJcfMLjOz75nZw2b2nrDjkfwzsxPN7Ptm9kjYsXQn6vGJlBIzM5K9wte7+7+EEYO73+ruk919CslFN//H3YtWle3uTcCrZnZqsGkWsK5Y8wc2A+ea2bDgNZlFeIuxPgbMC+7PA34aUhwiIiIiIodRMl1E+mRmo8zsETPbYGbrzext/RznXjPbYWZru3nuYjP7k5m9bGa9frUl+jhuAAAgAElEQVTd3f/b3T8BfBK4qj+xyCFmdpyZrTSzdWb2opl9ZgBj5es13uTu1wXH1ZjZc2a2Oojvy1GKT0QG7O3A/yZZDd4Y3N4XdlAhuBF40MxeAOqArxdz8qAq/hHgeWANyX8nFLyVmZk9BDwDnGpmW8zsOuAbwEVmtpFkxfw3Ch2HiIiIiEg2LNmGUESkZ2Z2P/CUu99jZlXAMHffm/b8McBBdz+Qtu1kd385Y5x3AG8AS9x9Rtr2GPAScBHJr7r/AbgaiAH/lBHOx919R3Dct0m2BHg+f2ebP2Z2IvD3wEh3vyLseHoSfIV+ors/b2bDgVXAZe6+Lm2fsF7jR4ArgVp3fyNoBfFb4DPu/mwU4ovyaysiIiIiIiIi+aPKdBHplZmNBN5B8iv4uHt7eiI98E7gv82sOjjmE8C/ZY7l7k8Ce7qZ5hzg5aDat51kv9pL3X2Nu8/NuO2wpNuB5YVKpPdUwVyO1cvuvj31ewyS0euBSRm7FfU1zhjT3f2N4OGQ4Jb5SXBo8YmIiIiIiIjI4KBkuoj05QRgJ/AfZvZHM7vHzGrTd3D3HwG/AB42s48AHydZTZytScCraY+3cGQyN92NJL/2fYWZfTKHeXJxH3Bx+oagevm7wGxgOnC1mU03szPMbGnG7ZgCxVVQZjYFeDPw+/TtxX6NzWyMmd0FvNnMbjWzmJk1klyE7om0RfoiEV8O84iIiIiIiIhIiaoMOwARibxK4CzgRnf/vZn9K/AF4EvpO7n7N83sB8Ai4KS0SuK8c/fvAN8p1PjBHE8GieV0XdXLAMH5Xuru/wTMLWQ8xWBmRwE/Bm529/2Zzxf5Nd5Nsid+ujozGwX8xMxmuPvajGPCjk9EREREREREypgq00WkL1uALWmVwI+QTK4fxszOB2YAPwFuy3GOrcBxaY8nB9uipmyrl4Ne5D8m2YP+0R72Cf01DloMrSTjWwNRiU9EREREREREypeS6SLSK3dvAl41s1ODTbOAden7mNmbgcXApcDHgDFm9o85TPMHYKqZnRAscPph4LEBBx8yd9/t7p9095OC6vVIMjMj2RN/vbv/Sw/7hPYam9m4oCIdMxtKcpHQDVGJT0REREREREQGByXTRSQbNwIPmtkLQB3w9YznhwEfcvc/u3sncC3w18xBzOwh4BngVDPbYmbXAbh7HFhAsuf1euCH7v5iwc6m/8q1evntwP8G3m1mjcHtfRn7hPkaTwRWBn9/fyDZM31phOITkTwxs/mDef4oxBD2/FGJQURERESkO+buYccgIhJJQc/0pe4+I3hcCbxEsjp/K8nE7t8o6Soikh9m1uDu9YN1/ijEEPb8UYlBRERERKQ7qkwXEelGdxXMql4WERERERERERm8VJnei6rKYT50yEgAOqtiXds7K43OyuR9H5L/eb3y0GtilQN/fTxuybHihnVARTy5PXUO/Rozx/P2Su86l+rKeP8nLgLvdKzCwg6DobGOvI312rrXd7n7uIGOM3p0hU+eHDtie6ITYll8NLe/s2agIRTMgUTPsR2MD/xCT3Qc+gVZx6G/r4q0l7kify95aGLtib53agv/RPcnduXlmqiqqPGhFcN736l6CImqGJ1DoHMI+JDi/Xc3NqSzIOMOrUy+hsNjrTkd19npVETg/XUwGFGR22uzZk08L9fE2LFjfcqUKf0+fufOnYwbN+AwSnb+rGPo6IAhBfif0GznL7AoxLBq1aq8XBMiIiIiUl4GkE4tf0OHjORtJ38cgLZjR3Rtbxk/hJaxRqK9jY5J1Xmft2PsoWRzzZiDAx6vdfdQAIbsqqRmJwzblUzktIztf0KjNcd/WnSMjXedywljd/d73kJrXLicppUbmXDBVOoWzi74fPGWdiqHVXX73Bkjt+Vtnn+ue+SI3tH9MXlyjMd+NvawbQtu2Muypa3MmVvDnYtG9Xr8r1pOzkcYBfGb10/t8bk1uyZ23U8cbCc2tPvXrDevNx1KuFY3HUqADGs6tM9RW7tPRCfibcQq8/9eUwi1rzb3uY/9Jfw287/Y/b28XBM1FUfxtlEf6HUfP2ESzcfV8sakGC0ToG1C8T5MOHrCgYKMe8bY7QC88+g/ZX3M3TdvoGH5Lupnj+X6O6YVJK5y1NqcoKb2yA8x+3LhsJdz2v+E45ryck1MmTKFhoaGfAwlvTEDFcQUlJnl5ZoQERERkfKiNi/9tHnZEtZ/91aaHloSdiiSJ/GWdppWbgSgaeVG4i3tWR3TX40Ll/Or2XfRuHB5v8cIW3NzJ8uWJqsfly1tpbm5MFWwUfHK7Y+y9spv8srtjxZtzg3PPcCzj3+RDc89UPC5EvG2gs9Rbg4kdrP6wIqww4i81uYEDct3AdCwfBetzVl8g0G4++YN3HjWM9x984awQxERERERERGUTO+XREcb+19qBOCNNY10tikBVQ4qh1Ux4YKpAEy4YGqPFeMpA0mG9ydxH0W1tRXMmZtsjzJnbg21teX7lpI42M6+p9YBsO+pdSQOFv41S8Tb2L11NQC7t64uaLK7mEn7ctPUvom4h9+6JspqamPUz05+q6V+9th+VVoPNvoAQkREREREJHrKN/NVQLEh1Yw4pQ6Ao86oo6K6NNovSN/qFs7mwuWf7LPFy0CT4dkk7ttbot1bPuXORaNYu+GYPlu8RFW2CarY0CpGnj8dgJHnT+9Xq5dcxSqrGTNpJgBjJs0sWKuXYibtZfC6/o5p/Nvzb1OLlyzpAwjp1Sc+EXYEIiIiIiKDknqm99Pxc64lcdFVBemZHhWJ9jZiVeV7fj3pqyI9tc+EC6Z29VfP5phMdQtnE//8rG6PbVy4nJ+v3Mip75nMJd88N+exi61UK9LTezhPu63nnukpU265nMRNc4uSSE+Zds41JOJX9ppIH2hP9VTSfvfW1QVN2permopaKq0wCwGWGyWEc3P9HdOY97X+9Uzvh9J8Ix+sFi8OOwIRERERkUFJyfQsxeNtVGYkmGJV1ZTrF/s3L1vC/pcaGXFKHcfPuTbscCKpt2R4tro7Nr3q/U+/3EL7wjhVw6J1qTY3d5ZsAj0ls4XCSf+ngyHD+k6IFjOR3jVnL8ntDc890JUEn3bONf2eI5ukvXSvtbOZuHcooS4FUYxE+oIb9gK8ueATSf6cfTasWhV2FCIiIiIig05pZ8OKZPXmn/Db/7mNF1f/V9ihFEWi/VBP+P0vNZJoL92WD4XuRT6QRHpvY6ZawJz6nsmRS6QvuGEvM6btSCVfSlZmC4VsEun50Nmav+sp3+1ZlEjvnwlVJ5ZFIr0Y6wBI9KQvJC0l5Pnnw45ARERERGRQUjK9D/FEO0371wOw87UXiA+CXsKxqkM94UecUleyrV4GskBo2OoWzuYzT18WuRYviU66ki7LlrbS3NwZckQDU+wezrvuepAtC/6B7T9YkpfxitVTXXo2PDaGmcNnhR3GgL1y+6OsvfKbvHL7o2GHIiIiIiIiIhJZ0Sp5jaDKWBUTRpxG0/71jBt/JpWV1bQDw3Y5LWONmp35m6t1XDfbdg+lZszBAY89ZNeRL3XqHLqT6glfqER6vKW9IFXd6eMftkDoANuxhCFqFekAsQqYM7eGZUtbmTO3Jm+tXlqbi9YT+AjFmreztY2WhhcAeGNtI51tV+Vl8WK1ZwmX0f176GH7/GUrtUwCammZEL2e4YmD7ex7ah0A+55aV/R1ASRctbUVXe/rUkImTgw7AhERERGRQSl62boImnn8Bzg9MYfEcWPDDiXvekuo5zuRnvpgoHHh8q6FO+sWzs7rHCn5WCA0bGv2HRt2CN26c9Eobv9W/nqmpy8CWqwK8Uy/eb3vxUcH4vWm4VTUwLD6M2lpeIGjZtTlJZGeokR66RjWBG0Two7icLGhVYw8fzr7nlrHyPOnK5EuUgq2bQs7AhERERGRQUnJ9CxVxqpIFGmuIbsq6Rgb73qcr+r03qroE+1tRWnn0nmw7YiKcShM7/F8LBAq3csmkZ65SOmvWk4+Yp/MRUDnfa34Fer5TqQnDrb3mIwc+8mP0Nl6BUP3HpXXOUUGasotlxelIj3Mb6GUqkL/ztQzvUQtXJi8iYiIiIhIUalnurDt0ftZ/91b2bws+z7O/W1vUzG0umtxzQkXTGXtN1cUtK95Non0v+wa03WT/Mh2kdLMRUCjmEhfsyv7r9Jn03c6PZE+rCnroaWMVDdFc7HSQifS7755Azee9Qx337yhoPOUk2L8zlJtXqTEfPnLYUcgIiIiIjIoKZnem474YQ+rt+0v6HT57L+erURHG3v+uhqA/S81kmgv/AKrIxdcw4XLP8mMz886vEq9pb3gc6dTAr0w0qscs1mktNiLgKYUoiL9sL7TB4v79ywSZZnfQmltLtZ3vUpXMX9ndy4aBfDHgk0gIiIiIiJSJpRMH+RiQ6oZ/aaZAIw4pa4orV4gWTGe6msOlGxfczlSepVjtouUlkPbh1TfaUB9p0UyhP0tlFIUwu+s908+ReQwZnavme0ws7Vp20ab2RNmtjH4eXSwfaSZPW5mq83sRTP7WNox84L9N5rZvLTtZ5vZGjN72cy+Y2Z9r3gtIiIiIgWnnul5Vqze4/l08juu5cCI9lDi7quvebylvWBJ9hPG7lZleoH0Z5HScuilXKy+07lKxNu0SGmBOB52CCXj+jumhbIuQikr8u9MBRalpKEh7AgE7gPuBNL7JH4BWOHu3zCzLwSPbwE+Daxz9/eb2TjgT2b2IHAUcBtQDziwyswec/fXgUXAJ4DfAz8DLgYK0xdRRERERLKmfzjl0eZlS3LuPR4VYX4A0F2yPN7STuPC5QXtpy6FlUsivZx6KUctkb7huQd49vEvsuG5B7LaPxEvfKuncnIgsZvVB1aEHUbJKFYivZzayBTjdxasb/Hmgk8kUkbc/UlgT8bmS4H7g/v3A5eldgeGB9XlRwXHxYH3Ak+4+54ggf4EcLGZTQRGuPuz7u4kE/aXISIiIiKhUzI9TxLtbex/qRHof+/x1nH5jqo0pZLoYfZTl+JRL+XCScTb2L01uSbC7q2r+0yU55p4l6Sm9k3EvSPsMCRQTh/OFUP6OhdSQurrw45Aujfe3bcH95uA8cH9O4HTgG3AGuAz7t4JTAJeTTt+S7BtUnA/c7uIiIiIhEzJ9DyJVVUz4pQ6oLi9x8tNvKW9K4meon7q5S2sXsrvPPpPRZknTLHKasZMSq6JMGbSzF5bveSaeJdDJlSdSKUNCTsMQR/O9Uf6Ohf9ZWbzzazBzBp27gxhNXWRwhib+rsObvNzOTioKE/1Ansv0AgcC9QBd5rZiPyGKyIiIiLFoJ7peXT8nGtJXHRVaIn0zoNtVAwt7SR+alHSppUbmXDBVGb00k89KgrZ132wKFRf4HLowz5Q0865hkT8yj57pqcS77u3ru4z8d6XeKKNylhpvxdla3hsDDOHzwo7DAmkPpxrWL6r4B/OldP7y52LRrFsadMf+3u8uy8GFgPU19drIQEpF7vcPdevALxmZhPdfXvQqmVHsP1jwDeCBPvLZvYXYBqwFXhX2vGTgV8H2ydnbN+a+ymIiIiISL4pmZ5nYSXSt337hxx4+kWGn3c6x372Q6HEkC99LUoaJY0Ll3cl/usWzg47nJKW76TU3Tdv6EqoXX/HtLyOXWqyTYxnm3jvzeo//4jXXn+R8UefzsyTruz3OKXCsLBDkBCU6ftLZ9gBSA5uuy3sCKR7jwHzgG8EP38abN8MzAKeMrPxwKnAJuBl4OtmdnSw33uAW919j5ntN7NzSS5Aei3wb8U7DRERERHpidq8lIHOg20cePpFAA48/SKdB0u/PUMpJNLTW9JEta97FGMqhnJq9VDsdisDSaQn4m289nryvei1118knij996K+OCrCjZJiXPvl9P4iJWzhwrAjGPTM7CHgGeBUM9tiZteRTKJfZGYbgQuDxwBfBc4zszXACuAWd9/l7nuC5/4Q3L4SbAP4FHAPyYT7n4HlRTo1EREREemFKtPLQMXQaoafd3pXZXqpt3opFZktaaL2AUB61fxgU8xWD4W04bkHutquTDvnmrDD6VOssprxR5/eVZk+GFq9HEjsZvWBFWr1EhHFuPbL5f1FStyxx8K2bWFHMai5+9U9PHXEfxDcfRvJqvPuxrkXuLeb7Q3AjIHEKCIiIiL5p2R6mTj2sx+i81Ol3zO91ES1JU1m1TyD8FsoherDXixHLgg6sPYrxTLzpCuJJy4ZFIn0lKb2TZzu79AipBFRjGu/1N9fpAxs3x52BCIiIiIig9KgS7CVMyXSwxG1RDocqpoHUj8HZS/cUk50pRYEBQa8IGixDaZEOsCEqhOVSI+YYlz7pfz+IiIiIiIiIv2jynQpC/GW9kgmtcOUXjX/86BKXaIvEW/rSpznY0FQKazhsTFq8RJBrc2qGpcyd9ZZYUcgIiIiIjIoqTJdSl7jwuX8avZdNC7UukyZ9AFDadnw3AM8+/gX2fDcA13blEiPNsPCDkEy3H3zBm486xnuvnlD2KGIFM6qVWFHICIiIiIyKCmZLiUtszd4vKU95IhE+ifRntkjvS3kiERKT2tzgobluwBoWL6L1uZEyBGJFMj8+WFHICIiIiIyKCmZLiQ6Sjdpl9kbXJXYEhWJg7l9sBOrKt0e6SJRUVMbo372WADqZ48ti1Yv+kBAuvW974UdgYiIiIjIoFR2PdPN7F5gLrDD3WcE20YDDwNTgFeAD7n764WKIdHeRqyqNBJhLz+5hD1/Xc2IU+o4fs61YYfTL+m9wUWyUeh+yq/c/ij7nlrHyPOnM+WWy7M+Tj3SRQbu+jumMe9r5dEz/e6bN9CwfBf1s8dy/R3TCj2dCixERERERET6UI7/cLoPuDhj2xeAFe4+FVgRPC6IzcuWsP67t7J52ZJCTZE3iY429vw12VZi/0uNJNpLu0JdJBuF7qecONjOvqfWAbDvqXW5V6grkS4yYOWQSC9my5oFN+wFeHPBJhARERERESkTZZdMd/cngT0Zmy8F7g/u3w9cVoi5E+1t7H+pESiN5HRsSDWj35RsKzHilLqSqaYX6a/M5FRHS0fe54gNrWLk+dMBGHn+dGJD9UGPiOSuWC1rmps7Wba0tSBjSwFt3Rp2BCIiIiIig1LZtXnpwXh33x7cbwLG97Sjmc0H5gPUWG1Ok8SqqhlxSh37X2osmeT0ye+4lkRHG20Ta8IORSIq/Zo4dlJpf/6WSk6l2iYMGTakIPNMueVyEjfNVSK9TB3234mKo0KORspZMVrW1NZWMGdujRLqpWbVKjj22LCjEBEREREZdAZLMr2Lu7uZeS/PLwYWA4ysHNfjfj05fs61JC66qiQS6SmxIaUTqxRf+jVx5plDcr4moiY9OfWbgq2cgBLpZWyg/50QyUUxWtbcuWgUy5Y2/bHgE0n+XHIJuN5+RERERESKrbTLTLP3mplNBAh+7ijkZKWUSBcZjMqhn7KISJ51hh2AiIiIiIhI1A2WZPpjwLzg/jzgpyHGIiIiIiIiIiIiIiIlpuyS6Wb2EPAMcKqZbTGz64BvABeZ2UbgwuCxiIiIiEjpufvusCMQERERERmUyq5nurtf3cNTs4oaiPRbvKWdymG59ZvuzzEi/ZU42F5SPdET8TZilWo/JVKKWpsLuwBpmrIrsChr8+eHHYGIiIiIyKCkfzhJpDQuXM6vZt9F48LlBT1GpL9euf1R1l75TV65/dGwQ8nKhuce4NnHv8iG5x4IOxQRydHdN2/gxrOe4e6bNxR0ngU37AV4c0EnkfwyCzsCEREREZFBScl0iYx4SztNKzcC0LRyI/GW9oIcI9JfiYPt7HtqHQD7nlpH4mC0/94S8TZ2b10NwO6tq0nE20KOSESy1dqcoGH5LgAalu+itTlRkHmamztZtrS1IGOLiIiIiIiUGyXTJTIqh1Ux4YKpAEy4YGpWbVv6c0zU6AOA0hEbWsXI86cDMPL86ZFv9RKrrGbMpJkAjJk0syRavcS9I+wQRLJSqOR2Sk1tjPrZYwGonz22YK1eamsrmDO3piBji4iIiIiIlJuy65kedYn2NmJV0U9ohaVu4Wzin5+VU1K8P8dERePC5TSt3MiEC6ZSt3B22OGEroi9gfttyi2Xk7hpbuQT6SnTzrmGRPzKkkikrz6wgqb2TUyoOpGZw0tjmQvHww5BQnD3zRtoWL6L+tljuf6OaQWb5/o7pjHva4V/X7xz0SiWLW36Y0EnkfyaOzfsCEREREREBiVVphfR5mVLWP/dW9m8bEnYoURaf5LipZhIV4uawxWrN3A+lEoiPaUUEulx76CpfRMATe2bSqZC/UBiN6sPrAg7DCmiYrVfSSniB4ydxZpI8uDxx8OOQERERERkUFIyvUgS7W3sf6kRgP0vNZJoV+/iwa4cWtTkS7GTUxI9lTaECVUnAjCh6kQqbUjIEWWvlJL/MnDFar8i0qv3vz/sCCRCzGy8mX3fzJYHj6eb2XVhxyUiIiJSjtTmpUhiVdWMOKWO/S81MuKUupJs9aIWNflXyi1qctHc3AnW8/Op5FSqbYKSU4PTzOGzON3fUVKJdCi95H+YSqGVUzaK1X5FpEdLl4YdgUTLfcB/AH8fPH4JeBj4flgBiYiIiJQrVaYX0fFzruW0T/8Tx8+5NuxQcqYWNYVT7on0BTfsZca0HX22b7n+jmn82/NvK2j/YYm+UktKD4+NKZn+7mErpVZO2VAiXWRwM7N7zWyHma1N2zbazJ4ws43Bz6OD7f/HzBqD21ozS5jZ6OC5i83sT2b2spl9IW2sE8zs98H2h82st/9hHOvuPyRo1+TucUBf8xMREREpACXTi6zQld2dB/PfPkYtaiRdc3P2bXWbmztZtrQVyK59i5JTUmqst69cSBe1cioJ+n9CkdzcB1ycse0LwAp3nwqsCB7j7v/s7nXuXgfcCvzG3feYWQz4LjAbmA5cbWbTg7FuB/6fu58MvA701ral2czGQHJVbDM7F9iXh3MUERERkQz6h1Mf4onSWRRy27d/yMZrvs62b/8wr+OmWtQAJduiRvJj8+YEM6btYMENe7Pav7a2gjlza4D+9RZWwk2izpN5C+mD+oxHW/Ce/uaw45AcuN57wubuTwJ7MjZfCtwf3L8fuKybQ68GHgrunwO87O6b3L0d+AFwqZkZ8G7gkT7GSvk74DHgJDP7HbAEuDG3MxIRERGRbEQ6mW5mV2azrVBaOvezYv23WL35J8Wast86D7Zx4OkXATjw9It5r1Av5RY1kj/79iX/8b5saWvWFep3LhrF2g3H5Ny+pdxaQkh5OpDYzeoDK8IOoySolVM0pX+DSErI4sVhRyDdG+/u24P7TcD49CfNbBjJavYfB5smAa+m7bIl2DYG2Bu0a0nf3i13fx54J3AecD1wuru/MLBTEREREZHuRDqZTvJrkNlsK4i4J6vSm/avj3yFesXQaoafdzoAw887nYqh+a8eV0W6jByZbGkxZ24NtbXJt49skuqpfbOllhBSSpraNxH3jrDDKAmqSI+e9G8Q9ZeZzTezBjNr2LlzZ54ik15df33YEQwGY1N/18Ftfi4Hu7vDEV9fej/wO3fPrGgfEDP7NHCUu7/o7muBo8zsU/mcQ0RERESSKsMOoDtmNht4HzDJzL6T9tQIIN79UYUzYcRpVMaiv0jksZ/9EJ2faitIIl0E4PjjYzz0o9FdyfEFN+xl2dJW5syt4c5Fo/I2T6olRMPyXWoJIZE3oerEkls4VSSf3H0xsBigvr5e/UekXOxy9/ocj3nNzCa6+3YzmwjsyHj+wxxq8QKwFTgu7fHkYNtuYJSZVQbV6antPfmEu3839cDdXzezTwD/nmP8IiIiItKHqFambwMagFZgVdrtMeC9xQ7m9Elzij1lvymRLoWWXpGeag2QS9uXbKklhJSC4bExzBw+K+wwRPpNbV5E8uoxYF5wfx7w09QTZjaSZCuWn6bt/wdgqpmdYGZVJJPtjwVV7SuBK7obqxuxoM96aq4YEP1KIBEREZESFMlkuruvdvf7gZPd/f6026Pu/noxYymVqnQpbfGWaLcR6k56a4D0ti/5VG4V6Z2t+V3LQMJnWN87SRe1bIqefLR5kRA89ljYEQx6ZvYQ8AxwqpltMbPrgG8AF5nZRuDC4HHKB4BfuntzakNQdb4A+AWwHvihu78YPH0L8Hdm9jLJHurf7yWcnwMPm9ksM5tFsvr95/k4TxERERE5XCTbvKQ5x8wWAm8iGauRbEF4YjEmHx4bw8zjP1CMqWQQa1y4nKaVG5lwwVTqFs4OO5yc3LloFLd/q7MgifRys+uuB2lpeIGjZtQx8cNayFcGn7tv3tDVuknfOImWOxeNYtnSpj+GHYfk4Oyzw45g0HP3q3t4qtuvK7n7fcB93Wz/GfCzbrZvAs7JMpxbSC48ekPw+AngniyPFREREZEcRD0D9n3gX4D/BbwFqA9+FoUqDqXQ4i3tNK3cCEDTyo0lW6EuvetsbaOl4QUA3ljbSGebKtRlcNGiwiUhv726pLAmTQo7AokQd+9090XufkVwu9vd9UYrIiIiUgBRz4Ltc/fl7r7D3XenbmEHJaUtKgnreEs7lcOqmHDBVAAmXDCVymFqKVSOKmqqGVZ/JgBHzaijovrItQ0S7eWVYI8nyut8ZGBSiwoDZbOosD4QEJGoMLO3m9kTZvaSmW0ys7+Y2aaw4xIREREpR5Fs82JmZwV3V5rZPwOPAl2ZGXd/PpTApOhSCed8iUpLlcw44p+fpUR6CUkcbCc2NLfXa+wnP0Jn6xUM3XvUEc+98vMl7Hu5kTGTZjLtnGvyFWZoVv/5R7z2+ouMP/p0Zp50ZdjhFJTjYYcgIVDLGhGJmO8DfwusAvRJn4iIiEgBRbUy/dvB7a0kW7t8PW3bt0KMS4qoceFyfjX7LhoXLs/LeFFpqdJdHEqkl45Xbn+UtVd+k1dufzTnYytquq9I31kgoJIAACAASURBVPdyIwC7t64mES/tiu54oo3XXk+unfba6y+WfYX6gcRuVh9YEXYYkVdObV7K6VykhH3iE2FHINGib/OKiIiIFEkkk+nufkEvt3eHHZ8UXiES31FpqRKVOCR3iYPt7HtqHQD7nlpH4uDA/y5jVdWMPLkOgDGTZhKrPDLhXkoqY9WMP/p0AMYffTqVsdI+n2w0tW8i7h1hhxFp5dTmpZzORUrY4sVhRyDRstLM/tnM3mZmZ6VuYQclIiIiUo4i2eYlxcz+rpvN+4BV7t5Y7HikeFIJ51QrlHwlnKPSUiUqcUhuYkOrGHn+dPY9tY6R50/PudVLT6ZcfC2J9qsYuTPSb8lZm3nSlcQTlwyKRDrAhKoTqbQhYYcRedffMY15X0uURfK5nM5FStTZZ8OqVWFHIdHx1uBnfdo2B1SEJCIiIpJnUc/c1Ae3x4PHc4EXgE+a2Y/c/ZuhRSYFV6iEc1QS2FGJQ3Iz5ZbLSdw0N2+J9JRYVTXl1OZ0sCTSh8fGMHP4rLDDKBnllHwup3OREvS8lg+SQ9z9grBjEBERERksItnmJc1k4Cx3/6y7fxY4GzgGeAfw0VwHM7O/NbMXzWytmT1kZjX5DVfyTQlniaJ8J9KldBkWdggiIjLImdl4M/u+mS0PHk83s+vCjktERESkHEU9mX4MkL56XQcw3t0PZmzvk5lNAm4C6t19BhADPtzXcfFEOItUiogAJb8gablzPOwQRGQwmjgx7AgkWu4DfgEcGzx+Cbg5tGhEREREyljUk+kPAr83s9vM7Dbgd8B/mVktsK4f41UCQ82sEhgGbOtt55bO/axY/y1Wb/5JP6YSERmYDc89wLOPf5ENzz0QdijSgwOJ3aw+sCLsMERksNnW6//CyuAz1t1/CHQCuHuccuodJyIiIhIhkU6mu/tXgfnA3uD2SXf/irs3u/tHchxrK/AtYDOwHdjn7r/s7Zi4J6vSm/avV4W6iBRVIt7G7q2rAdi9dbUq1COsqX0Tce8IOwwRGUwWLgw7AomWZjMbQ3LRUczsXGBfuCGJiIiIlKdIJtPNbETwczSwCfjP4LYp2NafMY8GLgVOIPkVyFozu6ab/eabWYOZNVRYcn3WCSNOozIWfo/kzoNth/0UKYb0a2L3ns5e921u7qS5ufd9JDuxymrGTJoJwJhJM4lVDo4FPUtB+jUBMKHqRCptSNhhiQxU1Bell3Rf/nLYEUi0/B3wGHCSmf0OWALcGG5IIiIiIuUpqv9w+i9gLrCKZIWFZfw8sR9jXgj8xd13ApjZo8B5wGH9E9x9MbAYYOTQif6WE67pSqS3HTuiP+fSo0R7G7Gq7BJk2779Qw48/SKVY0YQ372f4eedzrGf/VBe4yk18ZZ2LVBaBOnXxJlnDjmsQXRzcye1tcnP5BbcsJdlS1sBmDO3hjsXjSpypLlpbY7+t5+nnXMNifiVSqRHTPo1MbxyjM8cPivkiEQG5rxzdgDMDDsOEekfd3/ezN4JnEry30t/ctdXpkREREQKIZKV6e4+N/h5grufmPmzn8NuBs41s2FmZsAsYH1fBxWqIn3zsiWs/+6tbF62pM99O1vbOPD0iwDEd+8H4MDTLw7qCvXGhcv51ey7aFy4POxQBq0FN+xlxrQdLLhhL83NnV2JdIBlS1sjXaF+980buPGsZ3jq7/8n7FD6pER6tL2R2KOe6VLSdu6Is317dN+vRaRnZnZ56gZcQjKZfgrw/mCbiIiIiORZJJPpKZZ0jZl9KXh8vJmd05+x3P33wCPA88Aakue+uK/jCtErPdHexv6XGgHY/1Ijifbek+IVNdUMP+90ACrHJKvjh593OhVDB2eSLd7STtPKjQA0rdxIvEX97IstPXmeXo2eMmduTVfFemr/qGhtTtCwfBcAf13xFzpaVLglA6Oe6VLKhtVG+n8FpScNDWFHINHw/uB2HfB94CPB7R7g4yHGJSIiIlK2otrmJeXfSa5K/27gq8AB4MfAW/ozmLvfBtyW7f4t7XtZsf5bTBhxGjOP/0B/puxWrKqaEafUsf+lRkacUpdVq5djP/shOj/VRsXQajoPtg3aRDpA5bAqJlwwlaaVG5lwwdTQWr0M5jYztbUVzJlbw7KlrV2J8zsXjeL2b3V2PZ+Sav9SP7uT6++YFlbIXWpqY9TPHkvD8l28adYJDBlWfr2uE/E2VbQXkXqmSymrra1g4sQKVaeLlCB3/xiAmf0SmO7u24PHE4H7QgxNREREpGxFPZn+Vnc/y8z+CODur5tZ0bKX8c5kxW3T/vWcnpiT17GPn3MtiYuuyrpnOtCVQM81kd7Z2kb0X+rc1C2cTfzzs0JLZjcuXN6VzK9bODuUGMKWSp6nJ85rMyoc0yvYG5bvYt7XEtTUxooaZ3euv2Ma876W4Pft08MOJe82PPcAu7euZsykmUw754g1lstS3DuUzBbpp+bmTiXSS1F9Pbj3vZ8MFselEumB14DjwwpGREREpJxF/bu9HWYWI7noKGY2jmSlelFUViTbVkwYcVpBeqfnkkjvr23f/iGv3vQlmh7qvTd7X61moijMinS1mUnKTJ5393yq/Uv97LGRSKSnRCmWfEnE29i9dTUAu7euJhEv3nVdzLnSrT6wghV77gu1b7navEgpS3+fFpGStcLMfmFmHzWzjwLLgF+FHJOIiIhIWYp6Mv07wE+AY8zsa8Bvga8Xa/JhVaOYddrn+mzxEtVEdOfBQwuXvrGm597sLz+Z/WKocqjNDBBqm5lSceeiUazdcExBW7y0NicKNnYpiVVWM2bSTADGTJpZtFYv65+9n2cf/yIbnnugKPOlxL2DpvZNgBLapULXqohI/rn7AuBuYGZwW+zuN4YblYiIiEh5inTvD3d/0MxWAbMAAy5z9/Uhh3WYzcuWdPU+P37OtWGHc5iKocmFS1MJ9a1PPHxEjImONvb8NVnJuv+lxpxbzwxWYbeZKTW1tRXQUpix7755Aw3Ld1E/e2yvCfvW5mi0mCm0aedcQyJ+ZfES6b+/nz3b1wLJavj4Me+jMlacuSttCBOqTqSpfVOofcvVMz072V6rUlzp7bikhNyW9RJAMki4+6PAo2HHISIiIlLuIl2ZbmZfBY4D7nP3O4udSE8tQLp680+6fT7R3sb+lxqBIBGdZYV6MSvZJ3zq0q773cUYG1LN0cfNAMh6MdRcdLZFs2o/H5RIP1xzc/F77rY2J2hYvgtI9mTvqer17ps3cONZz3D3zRv6PVfiYOm08ylWIj0Rb2PPtrVdj0dPnFG0RDokK9NnDp/FrNEfZebwWUWbN93w2JiCz51c96LwCvk3nu21KiJZWrgw7AgkQszscjPbaGb7zGy/mR0ws/1hxyUiIiJSjiKdTAc2AVcDDWb2nJl928wu7eugfElfgDSeODLJEKuqZsQpdUD2iejNy4rbUqViaDXD6s8Euo/x5SeX8Pqraxl+8hl5r6xvemgJm758a5/92gdqMPcsD0tm4nzBDXuZMW0HC27YW9Q4ampj1M8eC/Tckz0fSbxXbn+UtVd+k1duV8FXuvS2MqOPncFp584r2tzpvdLDrAo3rKDj77rrQbYs+Ad23fVgQecp9N94NteqiOTg2GPDjkCi5ZvAJe4+0t1HuPtwdx8RdlAiIiIi5SjSyXR3/w93/zhwAfAAcGXwsyiyWYD0+DnXctqn/ymrRHR/K9kHatz8azjxtiNjTG/xcuDlNXmNp7OtjTfWJM/1jTWNBausbFy4nF/NvovGhcsLMr4cKTNxnt4iYNnS1qJXqF9/xzT+7fm39dg2YqBJvMTBdvY9tQ6AfU+tK6kK9WKYds41nPv+f+S0txYvkT5YeqV3trbR0vACAC0NLxTsfbRYf+N9XasikoPt28OOYNAzs3vNbIeZrU3bNtrMngiqxJ8ws6PTnnuXmTWa2Ytm9pu07Reb2Z/M7GUz+0La9hPM7PfB9ofNrLevRL4WtVaYIiIiIuUq0sl0M7vHzJ4GFpHs734FcHTvR+VPtguQZtsapT+V7PlSUX3kXLEh1Yx+08yCxFNRXc1RZyTP9agz6qioyf+5xlvaaVq5EYCmlRtVoV4EiU6OSJzX1lYwZ27yg6c5c2uS/dGLrK8E+UCSeLGhVYw8fzoAI8+fTmyo2vtkKlZbmZRUr3Qo737lFTWHvlk0rP7MgryPQnH/xlWRLiJl5D7g4oxtXwBWuPtUYEXwGDMbBfw7yerx00kWCGFmMeC7wGxgOnC1mU0Pxrod+H/ufjLwOnBdL7E0BAn3q4OWL5eb2eX5OEkREREROVykFyAFxgAxYC+wB9jl7vFiBtBTRXp/HT/n2kgt8nnyO64l0dFG28SavI894epr6bz8Kiqqq+kg/y9b5bAqJlwwlaaVG5lwwVT1MC+CWEUyYb5saethifM7F43i9m91hpJIz9ZAknhTbrmcxE1zlUiPkJnDZ3G6v6NsE+kpYz/5ETpbryhYIj1Ff+ODV+oDUS1CWmLOOivsCAY9d3/SzKZkbL4UeFdw/37g18AtwN8Aj7r75uDYHcE+5wAvu/smADP7AXCpma0H3h0clxprIckCo+6MILnU+3vSQ0QLkoqIiIjkXaST6e7+AQAzOw14L7DSzGLuPjncyAYmCon0REcbsSHJOFI/C6G7ivh8qls4m/jnZymRXkR3LhrFbV+OM+6Yw98+opxIzwclGaOn3BPpKYVOpKfob3zwunPRKJYtbVoddhySg1Wrwo5Aujfe3VM9eJqA8cH9U4AhZvZrYDjwr+6+BJgEvJp2/BbgrSQLivamFRFtCfbtlrt/LG9nICIiIiK9inT2y8zmmtntwL3A9cD/AP8QblSl7+Unl7DqB/+Xl58sziKohaZEenEtuGEv55y9q+iLjYqISGEE7+cz+3u8mc03swYza9i5c2f+ApOezZ8fdgSDwdjU33Vwy+mX7u5OsjockgVMZwNzSBYIfcnMTslXoGZ2ipmtSPVvN7MzzeyL+RpfRERERA6JdDKdZB/C54EPuvtp7v4xd7837KAKpbOt8AuSJtoPLTq656+rSXQUZxFUoGCL50nxZPZM37mjqF2XJE8ScV2LpUTvnVJI6YtI95e7L3b3enevHzduXJ4ik15973thRzAY7Er9XQe3xVkc85qZTQQIfqbauWwBfuHuze6+C3iS5AdYW4Hj0o6fHGzbDYwys8qM7T35HnAr0AHg7i8AH87mJEVEREQkN5FOprv7And/2N23dfe8mT1T7JgKZfOyJWz68q00PVTYavFY1aFFR0e/aWZBW7yk27n4AV696Uts+/YPizKfFEaqZzrAxIkVqlAvQRuee4BnH/8iG557IOxQJAu77nqQLQv+gV13PRh2KFKmamsrmDgx0v87KFJKHgPmBffnAT8N7v8U+F9mVmlmw0i2clkP/AGYamYnmFkVyQT4Y0FV+0rgim7G6s4wd38uY5sqHkREREQKoNT/9ZT/VTNDkGhvY/9LjQC8saax4BXqJ7/jWs7+8Nc5+R3XFnSelM62NloaXgDgwNMv0nlQVZal7M5Fo3hu1Vi2b+8EkhXqzc2dIUcl2UjE29i9NfnNlN1bV6tCPeI6Ww+9d7Y0vKAKdSmI5ubOrvdzEcmemT0EPAOcamZbzOw64BvARWa2EbgweIy7rwd+DrwAPAfc4+5rg57oC/j/2bv3+CjrM///rysTkpBwlCAgimJFURRQU6u2tlWslYLabT3U6mK3/RW1RWvXfrVuuytda11d3VpLF2XbbmV1PbW1VZBai7S6VatRAQVREI9IkPMhIQkzc/3+mDthCEnIYWbue2bez8djHknuuee+r0kyA3nPNdcHHicVrj/o7suCU1wL/KOZrSI1Q/0XnZSzwcw+QjBWxszOBdZ2sr+IiIiI9FC+h+m+712ya9DOHb0+RqysnAGHTwSg3zETs75oJ2R30dG2SsrLqawZD0D/k8dR0jf8BVild4buX9raoT5lakWXFx8t37Qrm2XJPsRKyxkyMvXOlCEjJxAr1WMxykoqdj93VtaMz9lCpFJcqqpKmDK1gljYhUj3rOls4ofkgrtf6O4j3L2Pux/o7r9w943uPsndx7j76e6+KW3/f3f3o9z9aHe/PW37Y+5+uLt/xN1vTNu+2t1PcPfD3P08d+/sFdVvAncBY81sDXAVcFkW7raIiIhI0Svd9y7SmesWPcy1n/v7Xh9n1JRpNFx4QU6C9DAMnX4xyWlNVI5U91uhmDV7EDffmuxykA5w0o2r+fNtR2SxKtmXsSdcTCJ+noL0PFF92UUkG89VkC5ZNWv2IE6fV7c57DqkG158EQ44IOwqJCLcfTVwuplVASXuvj3smkREREQKVb53pluYJz9484d87vWXGbV5fUaOV6hBeguFQYWnO0H6gLd2cti89Qx4e2cWK5KuUJCeX/TcKdlWujrOIBgcdh3SDWefHXYFEiFmNsTM7gCeBv5sZj8xsyFh1yUiIiJSiPI9TO99S3g3mCepiDfRd1fqcubrqTnnk19/mb7NTa0Xc3Vfi5B0rD7Zejn0sdSLTofOX09pfaL1QjL0aU0iIsWlzfNz1SONYVckIr1zP7Ae+CKpRUvXAw+EWpGIiIhIgYrkmBcz204n89DdfUDw8dWcFUWqDf6Ct5/i4kWLKE0LzGc8+wdmPPsH4lbCXR/7DHM+9hk81J55kQhwGHhXPQPvqMcScHCwueaOd6m5412SMXj5G6NYfPlB4S9+ICJSTNo8P4tI3hvh7jekff1DM7sgtGpEREREClgkO9PdvX8QmP8E+C4wEjiQ1Kr2t3d222xKWgl3H/YZvvmZy6irHLjHdWv7DeRr536DO0/6LMmSSH5bpYviDc1hl1AYYsaWf+xP3f370Txiz8fE9uFlzJ97DC/PGIXH9MpTRxLxztYaE8k/jfVKbiOhk+dnyRN33RV2BRItfzSzL5lZSXA5H3g87KJEREREClHU/4I6293/0923u/s2d58NnBNmQfF4E4uHfYSHxp6yx/Z7j/0kLx14aEhVFbdMht+LZy7gT5PvZPHMBRk7ZjGqr9/9zo2mE8vY8dWqPa5fPu0A1tUMbHuzfSqmIG7F8/fw3KPfZ8Xz94RdioQs2VgYL6rcddUKrjjuWe66akXYpWREITwftff8LHli+vSwK5Bo+Trwv0Az0ERq7MulZrbdzLaFWpmIiIhIgYl6mF5vZheZWSzosrgIqA+rmGVL/pf/e/J6Vj01l0+99wo7S/tw/7iP0RgrZdKqV8Iqq6hlMvxO7myibtFKAOoWrYxUh3qUatmXGZdv4eixHzLj8i2t2yr/0Ei8ooRXzh1GvLyEQ57Y2O3jFloQ15lEvImNa5YAsHHNkrzsUI8n8q/mKNpw5728P+Nf2HDnvWGX0iuN9QlqF2wAoHbBhrwPonP5fJTt71XlHxpJVsBG2JDVE0lmmd7VJbsF7+otcfdSd+8TfN4/7d2+IiIiIpIhUQ/TvwycD6wLLucF23Iunmhm/bqlAFS8s4S+zU18+cKruPGMC7jwy9+malcTQ3dsDaO0ohVvaM5o+F3St5zhp44BYPipYyitLOt1jZnQ0QsGUQzY6+uTzJ+XWshu/rxG6uuTxNYmKKl3fv/QBP524xh+/+sJ9KlPULmu62FroQVx+xIrLWfIyAkADBk5gVhpecgVdc+SNx/iyZdvYsmbD/X4GHHflcGK8lOysYmG2tS/Ow21S7PeoZ7Ymb3nlIqqGDWTqwGomVxNRVUsa+fKtlw+H2U7tG95fl776BDehneychIRyTpLudjM/jn4+iAzOyHsukREREQKUaTDdHd/293Pcfdqdx/q7p9397dzXUc80UxprIyhw8YDUD3ySL72uatYVT0CgFXVI/jyhd8ilkx2dhjJsNLKsoyH3xNnTub0BZcxcebkXh8rEzp6waC9gD0K4XpVVQlTplYAMGVqBVVVJZCAtY8MYfPhqVECmw+v4vcPTcASXV92tJCCuK4ae8LFnHjWDxl7wsVdvk0UOtjjiSbWbV4GwLrNy3rUob5k+0IWbvoVS7Yv7PxcBR64l1SUU1mT+nensmY8JRXZe1Hl7Zt/y6vn3cLbN/82a+e49Pax/PSlk7j09rFZO0cu5Or5KCehffD8vGtsn8wfW0Ry6T+Bk9jddLQD+Fl45YiIiIgUrtKwC2iPmf0U6DBpc/cre3jcQcDPgaOD43/V3Z/t7DZL3n2Yum2vMXzAkYw78RLi8S/SPLIfbeOhptIy6gZEo5O5mEycOZn4NZMy2kUelY502P2CQd2ila0vGOwVsF8ziVdvWdi6T9gvBMyaPYibb02mgnQgcWAQNDXs3idREaP+gO4FUJfePpZLbkwURZDeojsd6Suev4eNa5YwZOSEbgXwmVYaK2fY4HGs27yMYYPHURrrXgAc913UNa8GoK55NeP8k5Ta3kHfku0LqWtezfCyQ5nQf1JGao+i6ssuItl4blaD9MTOZrY+vRyArU8vJ3HlVGJ9s/M8WCiP31w8H1VUxRg8vIzNdc0MHl6WlXO1Pj9L/pk6NewKJFo+5u7HmdnLAO6+2cyi8x9aERERkQISyTAdqM3ScX8C/MHdzw3+g1nZ2c6OU7ftNQDqtr3GYfEmSkvLCb//V9JFKfzOhrYvGLQN2IG9wvWwvyctQXqmFUoQl2l7z1g/L9TRMBM+ch7xxNndDtIBSq0Pw8sObQ3K2wvSuxq4F4psBunSc9l+PmqsT7C5LvU/js11zTTWF9eLibIPjz4adgUSLbvMLEbQjGRmQwG9ZVZEREQkCyIZprv73Zk+ppkNBD4JfCU4RzN0nosbxvABR7Z2ppfm2dxiKRxtw/G2AXvb7nUpLi0z1ls606MwY70nQXqLCf0ndRqQdyVwl66L9S1j4ClHsfXp5Qw85aisdaVL97SMk6ldsKFoxltJN5x1lgJ1SXcH8DCwv5ndCJwLfD/ckkREREQKUyTDdDO73d2vMrNHaWfci7uf3YPDjgbWA/9tZhOAF4FvuXt9ZzeaMOrvGJeYQmmsbK/RLiJhSg/NszHuRvLL2BMuDr0jPZP2FZDvK3CX7jnk2i9kdbyL9EwxjreSLpo3L+wKJELc/V4zexGYBBjweXd/LeSyRERERApSJMN04H+Cj7dm8JilwHHAFe7+NzP7CfBd4J/TdzKz6cB0gIo+A1I3jGU/XEg0NwGFEYJJOLIVpKc/Jg4YmRrfUl+fzNooF+m5QgnSuyqsIH2PfydK+oVSQzYoSI+mHAbpelIXyTNmtl/alx8C96Vf5+6bcl+ViIiISGGLZJju7i8GH/+SwcO+D7zv7n8Lvv41qTC97bnnAHMABvYd0eEiqJn07vy5bHtjMf2OmcjwC6fl4pQiXZb+mBg/vo/PuHwL8+c1MmVqBbNmDwq5OpHc2+PfidKhOfl3QiSbZly+BeDYsOsQkW57kdS7eA0YBWwOPh8EvEvqnbkiIiIikkGR7kIys4+b2RNm9oaZrTazt8xsdU+O5e51wHtmdkSwaRKwPGPF9lCiuYltbywGYMcri0k2aZiMRFciCfPnNQKpj/X1WttKRCSf1dcnW5/XJY+4XscTcPfR7n4o8CfgLHevdvchwFTgj+FWJyIiIlKYIh2mA78A/gP4BPBRoCb42FNXAPea2VJgIvCjXlfYS7GycgYcPhGAfsdMpKS8eMY0xBs6Xf9VIihWAlOmVgCpj1Ec9dJYnwi7BCkivveyHiJ5paqqpPV5XfLInDlhVyDRcqK7P9byhbsvAE4OsR4RERGRghW9JGxPW919gbt/6O4bWy49PZi7L3b3Gncf7+6fd/fNvSkuNee890ZNmcaR37ypqEa8LJ65gD9NvpPFMxeEXYp006zZg3h1xf5ZG/HSmzD8rqtWcMVxz3LXVSsyWJFIx7YnNrJk+8Kwy5A29KJa9wTP5y+HXYd0w6WXhl2BRMsHZvZ9MzskuHwP+CDsokREREQKUSTDdDM7zsyOAxaZ2b+b2Ukt24LtoVv11Fxe+9l1vDt/bkaOFysrno705M4m6hatBKBu0Up1qOehbHWk9yYMb6xPULtgAwC1CzYoTJOcqWteTdx3hV2GBPSiWo9pbpdI/roQGAo8DPw2+PzCUCsSERERKVCRDNOB24LLx0iNdvlR2rZbQ6wLgMSuJja9swSAbW8szliHerEo6VvO8FPHADD81DGUVpaFXJFEQW/D8IqqGDWTqwGomVxNRVUs4zWKtGd42aGUWp+wyxD0opqIFCd33+Tu33L3Y939OHe/yt03hV2XiIiISCEqDbuA9rj7qWHX0JlYn3L2O3gCm95ZwoDDJxZVV3mmTJw5mfg1kxSkS6uWMLx2wYYeh+GX3j6WS25MKEiXnOkfG8KE/pPCLkMCmXgeEckLjzwSdgUiIiIiIkUpkmF6CzP7EXCLu28Jvh4MXO3u3w+3Mjjsk9PYPqBZQXovKEjvunhDc1F8vzoLwxvruxaSKzyTXDIs7BLySlcfx72hF9WkKBx/fNgViIiIiIgUpaiOeWkxuSVIBwgWDP1ciPXsIcpBenKnRs8Uiigt1prIwUTd9gIwzUAWyX+5fBwrSO+RqP+fUNKNHBl2BUXPzH5pZh+a2atp2/YzsyfMbGXwcXCw/dNmttXMFgeXf0m7zZlm9rqZrTKz76ZtH21mfwu2P2Bmhd9VISIiIpIHov6HU8zMWhNrM+sLRDfBjogPbnuQlRf/iA9uezDsUqSX4g3NkVqsdfmyODMu37LvHTNIM5BF8p8ex9EWPK8fG3YdInnmV8CZbbZ9F1jo7mOAhcHXLZ5294nB5V8BzCwG/AyYDBwFXGhmRwX73wz82N0PAzYDX2tbgJn91Mzu6OiSwfsqIiIiIoGoh+n3AgvN7Gtm9jXgCeDukGuKtOTOJrY/swyA7c8sU4d6niutLIvcYq3z5zVSX5+DFvWAFhYVyX96HEdXfX2S+fMawy5DJO+4+1NA20U+z2H33yp3A5/fOOoh1wAAIABJREFUx2FOAFa5+2p3bwbuB84xMwNOA369j2PVAi92chERERGRDIv0zHR3v9nMlgCnB5tucPfHw6wp6kr6ltP/5HFsf2YZ/U8eR0nfcmgIuyrpiZY56VFbrHXK1Aqqqrr2Olx9fbLL+3ZGM5ClmCUbmyipyP83ZelxHE1VVSVMmVpR8IF6czzJS+9u5pk3N7K1l+/ySuWcLZ/3trKemXrGuTz6yDLcHQeS7rj37FgOJJNOouXiTrKHxwpbMuk0xZM0xRM0x5PsSiQZs39/Pn3EUD4+ppoBFX2yXcIwd18bfF4HDEu77qTg75oPgO+4+zJgJPBe2j7vAx8DhgBb3D2etn2v2T7uriYjERERkRyLdJgO4O5/AP7Q3nVm9qy7n5TjkiLvgKvPJ/mNplSQLnlp8cwF1C1ayfBTxzBx5uTIBOlHjStl1uxBXdp3xuVbmD+vkSlTK7p8m84ogJNitOHOe2moXUplzXiqL7so7HJ6TY/jaJo1exDz59W93NPbm9l0YDrAqFGjMlZXdzTFE/x04So2NzQzsG8fBvbtw6DKPuxoSvDXVRt4bvVGGpoTlBgM6NvzQNUdPEitw8ybf33S/wcvvU+JGSWWCviNnof7JWaUlhglJUasxCix/Fxe2QzKSmOUlZZQXlpCeWmMx15dywO17xErMY4fNZiPHbofFX269FxUbWa1aV/Pcfc5Xa3F3d3MWn5NXgIOdvcdZvY54HfAmC7fsX0ws6HAtaRGxVSk1XBaps4hIiIiIimRD9P3oWLfu0RLorkpJwuXKkjPX3vNSY9QV3qsi03m6WMD5s9r5OZbk+TlX+UiIUo2NtFQuxSAhtqlJBvPLYgOdSk8QcA4B6CmpiaUjPk/F73JrEWr2K+qjK07d5FIa60+ZEglXzzuQD4xppqTPjIkF93J2Xf88fCipnh0RTyR5OX3tvDn1z/kz6+v56dPrurqTTe4e003T7fOzEa4+1ozGwF8CODu21p2cPfHzOw/zawaWAMclHb7A4NtG4FBZlYadKe3bO/IvcADwBTgMuASYH03axcRERGRLsj3MD2v3oT67vy5bHtjMQMOn8ioKdPCLkciqmVOektnelSC9O6oqiphxIgS1q5NMmJESWrUi8YNiXRLSUU5lTXjWzvTFaRLtuT7AqRvrNvOf/55FedMPICffOlY3J0dTXG27txFrMQYMbBv2CVm3ksvhV1B3iiNlfDRQ/bjo4fsx//77Fh2JZJdGolTfnOPTvcIqSD734KPvwcws+HAuqBb/QRS61ZtBLYAY8xsNKmw/EvAl4P9FgHnkpqj3nqsDgxx91+Y2bfc/S/AX8zshR7dAxERERHpVL6H6Xkj0dzEtjcWA7DtjcUkPnNBTjrUo6JQZv7mStTmpHemvbno9fVJ1q5NLVK6dm0ytWCpOtNFuq36sovUkS5Zle8LkCaSzrW/WUq/8lL+ZepRQGrkSf+KPvQvhA50ybg+XX2b3T6Y2X3Ap0mNg3kfuJ5UiP6gmX0NeAc4P9j9XOByM4sDO4EveWpeUNzMZgCPAzHgl8EsdUiNbbnfzH4IvAz8opNydgUf15rZFFJz2ffLyB0VERERkT3ke5ieN/FcrKycAYdPbO1ML6Ygve6+uex4ZTGVNeM56Lovhl1O3siHIL2juejpC9q1LliqznSRHlGQLtmU/k6iTFizeSff/c3SXh/nU4cPZfIxI/a53z3PvcPL727hP86fwJB+RfRYGbHv741kl7tf2MFVk9rZdxYwq4PjPAY81s721cAJXSznh2Y2ELga+CkwAPh2F28rIiIiIt2Q72H634ddQHeMmjKt+DrSm5rY8UqqI7+hdinJnVM1z71AtDcXPb1DfdbsQXttExGRaEl/J1EmbGvcxaLXP+zVMZriSe5/4T2mjB/BDecczX5V7b+4vGbLTm75wwo+efhQ/u7Ykb06Z9754IOwK5AIcfd5wadbgVPDrEVERESk0EU6TDezLwA3A/uT6kI3wN19AKlPXg2xvG7L1eKjUVJSXk6/Yya2dqYrSM9vibS8pd3u8zYUpIuIRFv6c3kmHDliAH/7p9N7dYx4IsldT63m9j+9wd9Wb+LfvnAMpx81bI993J3vP/wKSYcbP380ZnnzZsXMmDkzdZGiZmbXuPstZvZT2llLyt2vDKEsERERkYIW6TAduAU4y91fC7uQ7mgvNC/mxUeHXziNpq9/MRhVsDPscqQXli+LM+PyLa0jXbrSfd7eTHUREZGOlMZK+Oaph3HqEfvzjw8u5v+bW8tZEw7g8P37te6zbnsji15fzz9PPYqD9qsMsdqQ/OAHCtMFoOVvpNpQqxAREREpIlEP09dFJUiPJ5q7tF97oXmxLz4KmvlbSNqOdOksKE+fqf752zJz/sb6BBVVscwcTKSXfO9GQJG8EuUFSI86YACPzPgEdyxcyV1PvcmjiT0fb6eMqeYrJx8STnEiEeDujwafNrj7Q+nXmdl5IZQkIiIiUvCiHqbXmtkDwO+AppaN7v7bXBax5N2Hqdv2GkM3jWfchC93uF9HoXlUFx9N7Goi1icatUj+6GikS1ttZ6qf+a97huA9CcXvumoFtQs2UDO5mktvH9u9wkWyYHtiI0u2L2RC/73WmxPJiGJ/AbGstITvfPYIvv2Zw3HfM0yPlVjxjXcRad91wENd2CYiIiIivRT12QsDgAbgDOCs4DI1lwXEE83UbUs1x69ft5R4vKnDfVtCc2Cv0HzUlGkc+c2bIjPiZdVTc3nx/n9i1VNzwy5F8shR40pbR7zsS8scXkgF8Olh0F1XreCK457lrqtWdPncjfUJahdsAKB2wQYa6xPdqFwke+qaVxP3XWGXIQWoJ8+V3ZX+XB1lsRKjNFayx6Wog/RaTfUQMLPJwbz0kWZ2R9rlV0A85PJEREREClKkw3R3/4d2Ll/NZQ2lsTKGDzgSgKHDxlNa2nknd2eheZQ60je9swSATe8sIbGr4xcIRNLFuvmMMWv2IF5dsf8eAXxPQ/GKqhg1k6sBqJlcXdSdmhItw8sOpdT6hF2GFJhcvoAYPEe/nLUTiEi2fEBqXnoj8GLa5RHgsyHWJSIiIlKwIjnmJWor008Y9XeMS0whcVB1u9e3XXA0KqF5R2J9ytnv4AlsemcJ+x08QaNeJKvaGwlTM7m6dVxLd0LxS28fyyU3FvfIA4mW/rEhGvEiWdHyAmJPniulCNTUgGvNhmLn7kvM7FXgs+5+d9j1iIiIiBSDSIbp7LkyfST+UiiNldFeT1h7C47mg8M+OU0z0yXn0mee//Slk3oUDilQkigxinjMhGRdrl5AnHH5FoBjs3oSEckKd0+Y2UFmVubuzWHXIyIiIlLoIhmmp61Mvxz4J+AQdtfqQCQGfXe04GhvJJuaKCnPTcDdXpDetsteZF/q65NdWpC07ciCS24ck+3SpBOJeBOxfYytEpHwNTVkN0xPXyxaRPLWW8BfzewRoL5lo7v/R3gliYiIiBSmSM9MB+4B/hv4AqmFR6eSWoS0x8wsZmYvm9m83hbX2YKjPVF331xW/+A66u7r2msFyZ2ZnXX+7vy5vPaz63h3fiReq5CIq69PMuPyLRw99sOWrsZOaeZ5dKx4/h6ee/T7rHj+nrBLkQhJ7FRDY9Rc86nn+c4nnueaTz2ftXPkywKk0sb114ddgUTLm8A8Un/b9U+7iIiIiEiGRbIzPc16d38kw8f8FqkxMgMycbBRU6ZlpCM90dzEjldSXe47XlnM4MYvUlLR8TE/uO1Btj+zjP4nj+OAq8/v1blbzp/pLnspXDMu37JHJ+P8eY3cfOu+O9Q18zx8iXgTG9ekFiDeuGYJifh56lAX3r75t2x9ejkDTzmKQ679QtjlCLB1fTOb61IvcGyua2br+mYGDi3LyrlmzR7E/Hl1WoA0n8ycGXYFEiHu/oOwaxAREREpFlHvTL/ezH5uZhea2RdaLj09mJkdCEwBfp65EjOz4GisrJx+x6S63PsdM7HTID25s4ntzywDYPszyzLSod5Zl32iObMd8JLf2hsJMGVqRbtBen19cq9tCtLDFSstZ8jICQAMGTlBQbqQ2NnM1qeXA7D16eXqUI+IgUPLGDw8FZ4PHl6WtSBd8tQBB4RdgUSImQ01s383s8fM7MmWS9h1iYiIiBSiqHem/wMwFugDtKRyDvy2h8e7HbiGiL7tcfiF00h+4QJKysvZRbzD/Ur6ltP/5HGtneklfTMThrXXZZ+vC6wKxBuaKa3MfPjSMhJg/rxGpkyt4OZbB7QbpLd0r0+ZWsGs2YMyXkdnGuvV/d6ZsSdcrI70PJJsbOr0BdbeivUtY+ApR7V2psf6KrSNilv+ckJWO9JbaAHSPLR2bdgVSLTcCzxAaiTmZcAlwPpQKxIREREpUFEP0z/q7kdk4kBmNhX40N1fNLNPd7LfdGA6QEWfjEyC6ZauLj56wNXnk/xGU8aC9BZtO9I1+iU/LZ65gLpFKxl+6hgmzpzcq2OlPyYOGJkKzWfNHtTpWJf07vWWETBYr8rosruuWkHtgg3UTK7m0tvH5uakeUhBes/t8e9ESb+snmvDnffSULuUyprxVF92UdbOc8i1XyBx5VQF6RGU7SBdC5CKFIQh7v4LM/uWu/8F+IuZvRB2USIiIiKFKOpjXp4xs6MydKyPA2eb2dvA/cBpZrbX6nvuPsfda9y9pixWmaFTZ0emg/S2Mr3AquRGvKGZukUrAahbtJJ4Q+9GNqQ/Jobst/spo7P56OkL2nU0AiYbGusT1C7YAEDtgg001idycl4pLnv8O2HZW7gx2dhEQ+1SABpql5JszO7ILQXpxUkLkOap444LuwKJll3Bx7VmNsXMjgX2C7MgERERkUIV9TD9RGCxmb1uZkvN7BUzW9qTA7n7de5+oLsfAnwJeNLdL85ksYVo1JRpHPnNmzTiJY+UVpYx/NQxAAw/dUxWRr10xazZg3h1xf45HfFSURWjZnI1ADWTqzXqRfJaSUU5lTXjAaisGZ/VUS9S3ILnaS1Amk9efDHsCiRafmhmA4Grge+QWh/q2+GWJCIiIlKYoj7m5cywC5DMLLAquTVx5mTi10wKLUhvkauO9HSX3j6WS27UzHQpDNWXXUSy8VwF6ZILe68YLdE1fTrMmRN2FRIyM6sgNSP9MGAk8At3PzXcqkREREQKW6Q70939nfYuGTjun919aiZqFImqsIP0MClIl0KiIF1E9vJf/xV2BRINdwM1wCvAZOC2cMsRERERKXxR70wXERERERGRvR3l7scAmNkvgOdDrkdERESk4EW6Mz0q4oneLeAoIj2jBUQl6hwPuwQRESleLQuP4u7xMAsRERERKRYK0/dhybsPs/C1W1ny7sNhlyJSVO66agVXHPcsd121IuxSRDq0PbGRJdsXhl2GiBSbNWvCrkCiYYKZbQsu24HxLZ+b2bawixMREREpRArTO+E4ddteA6Bu22vE400hV1QYRldv7PFt4w2ZfZdAei29qSuKMv29yqXG+gS1CzYAULtgQ0F1qCebdj+PNAzf87odIwtj1ns8UVzPlXXNq4n7rn3vKAX1WC5A+j9hPnnxxbArKHpm9ksz+9DMXk3btp+ZPWFmK4OPg9vc5qNmFjezc9O2XRLsv9LMLknbfryZvWJmq8zsDjOztjW4e8zdBwSX/u5emvb5gGzddxEREZFipj+cOmEYwwccCcDwAUdSWhrOInAVQ3Zm5DiNQ/feltjVREP1Xv8379Gxsm3xzAX8afKdLJ65IKPHHV29seCC9Gx9r3KloipGzeRqAGomV+dkQdFdDe2HocdUr83YOTbceS9v3nAda++fm7FjRs2SNx/iyZdvYsmbD3W4j48emcOKsm942aGUWp8u7980PL+D954+Jgrt3SaF9MLAjMu3ABwbdh3SDWefHXYFAr8Czmyz7bvAQncfAywMvgbAzGLAzcAf07btB1wPfAw4Abg+LYCfDXwdGBNc2p5LREREREKgMH0fJoz6OyYd+R3GnnjJvnfupZaO1V3VuRl5uOqpubx4/z/x7vzoB3vxhmbqFq0EoG7Ryrzuus62XHyv6uuTGT9mW5fePpafvnQSl94+Nmvn+NTg1wF4+ntP8sBpc3n6e09m7VzJxiYaapcCsOPVxXt0qBeKRLyJdZuXAbBu87Ki6FDvHxvChP6Twi6jXYOHbw+7hFaF9m6TQnphoL4+yfx5jWGXIZJ33P0pYFObzecAdwef3w18Pu26K4DfAB+mbfss8IS7b3L3zcATwJlmNgIY4O7PubsDc9scS0RERERCUhp2AfmgNFZG2z/7W7q5M9WhXXffXHa8sph+x0xkyBVfbt2eqa70XdVx+mzY/eNO7Gpi0ztLANj2xmISn7mAWFl2O+97c19KK8sYfuoY6hatZPipYyitLMtgZdF0zMAPenbDgfDeGQfy+h/f54gzDuTYERv4UwbrmnH5FubPa2TK1ApmzR6UwSPvLVcd6e8sfAuAdxa+xYnf20Wfyq53GXdVSUU5lTXjaahdSr+jJ1JSHs47XbIpVlrOsMHjWLd5GcMGj6M01vF99NEjsbfyf+av0f139hSjlneb1C7YkLN3m2RL2xcGLrkxkdf3p6qqhBEjSli7NvsvkooUgWHu3vL2nTpgGICZjQT+DjgV+Gja/iOB99K+fj/YNjL4vO12EREREQmZwvQISDY1seOVxQDseGUxgxu/SElFdoO2WJ9yBhw+kW1vLGbA4ROzHqRnwsSZk4lfM0lBeprmhjhllXs/jM++5USaZ7Z/XW8kkrR2MM6f18jNtyapqsrvN7icPnI1z00azTsL3+KgTx+clSB98PDtbK7rT/VlF5FsPJe+W/p1uO+OkTH6rcnfrt0JHzmPeOLsToN0KU6X3j4274NnKKwXBiDVmd7bIN3MpgPTAUaNGpWJsmRf7ror7AqKQbWZ1aZ9Pcfd53T1xu7uZubBl7cD17p7sp3R5yIiIiKSR/I7BcuBeCL740RKysvpd8xEAPodM7HDID25M7MjE0ZNmcaR37yJUVOm9eo4uRxXUQxBelc9cs1z/OTk3/HINc+1e32mg3SAWAlMmVoBpD5mK0jP9QiIU248jYM+dTDv/fmdjI56Sezc+/kj2y+URUHUgvRsLg7q+L53kla5Cp63rtcosK6qqippfV7vKXef4+417l4zdGgIi6oUo+nTw66gGGxo+b0OLl0J0tcFI1oIPraMdKkB7jezt4Fzgf80s88Da4CD0m5/YLBtTfB52+0iIiIiEjKF6Z1oaN7CwtduZcm7D2f9XMMvnMah19/E8AvbD7Y/uO1BVl78Iz647cGMnbNyg/e6I73uvrms/sF11N0X/bnrhaS5Ic7rf0y9+/f1P75Pc0Nu5uwDzJo9iFdX7J+1ES9hzCLe1bCL9/7yDpAa9dLRYqTd8fbNv+XV827h7Zt/263b5XNXehQt2b6QhZt+xZLtC7Ny/O2JjVk7tvTMNZ96nu984nmu+dTzWTl+oc1/B1qez18Ouw7pBnU3R9UjQMtCS5cAvwdw99Hufoi7HwL8GviGu/8OeBw4w8wGBwuPngE8HoyK2WZmJ1qqlX1ay7FEREREJFwK0zsRT6bGWdRte414vGfd193p2u5ohnJyZxPbn0kt6rf9mWW97lBP7MpMJ3nb8TT7uq+NG/tm5LyS6jo/4oxUw9IRZxyYlS70znTWkd6bxUnDCqn6VPbh4EmjATh40uhej3pJ7Gxm69PLAdj69PJ2O9Ql++K+i7rm1QDUNa/OWod6No9daLL9mN66vpnNdanH2+a65qx0qLeMeQEKYsxLGg1NF+kGM7sPeBY4wszeN7OvAf8GfMbMVgKnB193yN03ATcALwSXfw22AXwD+DmwCngTWJCVOyIiIiIi3aKZ6Z0oLakgnmxk+IAjKS3dM+iu3OCti5BCKlhuG4anLyraUcd5V5T0Laf/yePY/swy+p88jpK+3e8mb1l89N35c9n2xmL2O3gCh32yd+NdWsbTtNzHQlxQMcqyNRe9N3q7OGmYs4hPufG0jC0+GutbxsBTjmLr08sZeMpRxPqWwdYMFJlFiXgTsdLCegyXWh8qSqpoTNZTUVJFqWV+Hj6Q1WMXkruuWtH62L709rFZOcfAoWUMHl7G5rpmBg8vY+DQ7IwGK5T5722owUKkG9z9wg6umrSP232lzde/BH7Zzn61wNE9rU9EREREsiM6KVwEVZYN4qOjL6Y0Vkbbnuv0IL290Hyvru0vXNCrsPmAq88n+Y2mHgXpALuq48TWJNj2RqqmTe8sYfuAZmKUk2hu6vG4l+EXTuvyfasYsrNH5yg2XV18FLIzF72n6uuTGVmcNIyQ6lODX+cvm4/I6OKjh1z7BRJXTk0F6d0QxgKkK56/h41rljBk5ATGnnBxTs+dTXHfRWOyHoDGZD1x35WV0Dubxy4Ubd91ks3H+C1/OYGt65uzFqS3KKQgfcblWwCODbsO6YapU8OuQERERESkKEUniYuoxEHVpMdaDcP6tAbpjUM7Ds172rW9qzo1+7pt8Dy6emPw2Y5u34e3GELjxr6UlJcz4PCJbHtjMQMOn0isrLy1U33A4RO7tBBpY9q6Yi21QowEXZvZ3XI/uhMY98YrWw/o9m1yVVt3NTdEqwu9rZZF7FoC9Wu/s22P7vTTK1fxp4bDunSsMEKqlkC9PcdUr+WVDSO6fcxY3zISO5uJ9S1j8PDtbK7rD0DT8F2U13UcvOYyUE/Em9i4ZgkAG9csIRE/r1cd6vUHVVH1Xn2X9vXRI3t8nq6IAcN8HOs2L2PY4HHEDj1k93KhGzu5YTcNLzu0y0F6w/DuHz/Z2NTthWsHD9/e/RNlUa7fdZLtID3qTq9c1eV9018IlTzy6KNhVyAiIiIiUpSim8xFQLJs9x/7DcP2DEpaQuXEyBiVNeNpqF1KZc14EiN3B8tDrvgygxu/SElFObvahM2dhSPpQfruEL13KobspJG+7P+VaYxccwGxsnIaBjS1dqpve2MxDRd2vXt+d5De9fPnOkjP9bky5aP9Vu+17cdXrubZx7Zw0ucG8e07Dg2hqr3V1+/deX7zrQMy0p0els4C9Z54++bfto56OeTaL7S7T8NwqKzbe/uOkdkNG1vC+lhpOUNGTmjtTC+0US8TPnIe8cTZlMayc7/6x4YwoX+n7+hv1ZOf6YY7723996X6sou6dJtcBemfGvx6t/Yv0NEoWddYr++ZtOOssxSoi4iIiIiEQGH6PrQN0RuqrTVIbwmUh06/mOS09sPx9ratn3NPazgydPqeIxVagvS2IXpvQ+G3Ngxp/XzXyHJ2ASV0v3s+PUTvydiWlvvRXmCcDS/s6H7wnKvauqqxPsGzj20B4NnHtnD5TeEHKx3NRk/vTp8ytWKvIL073elRc0z12m7tv6thF0vSFyG9cmq3utOzrSXY7bcmwdgTLu51R3q67nSn50K2gnQAw/a5j48eSf1BVd0+drKxiYbapQA01C5lwMAzuz02KNO6+zhoK+znrnzTkznz3elKh73fVSR5Yt68sCsQERERESlKCtM7kSzdHZKkz0iHjsax7DtcTu7cMxwpu2rLXnPQ04P0THVWj67emBr3Qt/WxUh3Vcc77Z7vTHeD9NHVG3MepOf6XJny8Yo2YVUFnHVWBY8+2shZZ1UwaciH4RQW2Nds9FmzB3XakR71QD1T3el9Kvtw8KTRvLPwrd2LkMIegXqLjrrTc2F3t3Rlt27X0tme3m2d61nv+ayr3ePb2y5kK3mvq2F3fX2Sr6fNmf/Vj9/I2jt9Zs0exPx5dS9n5eAiIiIiIiIFRGF6F6QH6ekzw3uipG85/U8ex/ZnltH/5HE5CdKPGfjBHrPD245o6e4s3kyMofl4xVr+2tj9GdSFbq8gPXDn7MHcFpGRKfvqPm/ZR+CUG0+j/2VDCjIEbW9kSRiLpxa6ni5km6633eQSjq4812ZYMtsnEBERERERyXcK00NwwNXnk/xG015BukhnohRQ76v7XHYrxCBdcku/Q8VLz7XSIfd97yMiIiIiIhmnv85CoiBd8p3CHRGR7NNzrbRrzpywKxARERERKUr6C01EREREJJ9cemnYFYiIiIiIFCWNeemCyg3eOje9Yn3v56Z35q0NQ1rnkL+y9YCMzE1Pn5cO7LEAaU80buzbOjf9rQ1DunSbtrPVNS+9fX9tHNHh3HSRMGkWev56ZUPmnm81f11ERERERESKmcL0LkoP1CEVSO+qjtO4sW/GzpG+sGeLTAXq7YXevQ3VuytT96XQtbzQoFC9MG2u67/Xtsq6EAoJKCQXya0/NRyW1eOfXrkqq8cXEREREREpZgrTO1ES33Nxp7aBerakd6dD5kLojoL/noTq6d3pXZF+n17YcSgf7be6y7fNhhd2HNrhdWHX1iKz3fsK5mVv2QrSq96rz8pxC0l7L6q0Z/Dw7VmuRArNnxoOU6BeDB55JOwKRERERESKUtGE6WZ2EDAXGAY4MMfdf9KTY2Vr1Evb8Sm9DdTbjneB3cF5ezq7Ll1L6N7dQL0znYXbudY27G+sT1BRFQuxouhIJMOuoPioc7y4tYTuCtWLl/4NknYdf3zYFYiIiIiIFKWiCdOBOHC1u79kZv2BF83sCXdf3tmNKtftomFYn3avaxn1kivtheP5Jv1FgSgF6G211PbINc/x+h/f54gzDuTsW07M+nmj0hXfkeXL4sy4fAuzZg8Ku5S8lN6NXF7X/vNKOgXphcHeWgMHHR52GZKH7rpqBbULNlAzuZpLbx8bdjkSJSNHgvu+9xMRERERkYwqCbuAXHH3te7+UvD5duA1YGS4VUmUNTfEef2P7wPw+h/fp7khdy+cRNn8eY3U16tFXUQkmxrrE9Qu2ABA7YINNNbrxTUREREREZGwFU2Yns7MDgGOBf4WbiUdS+5sCruEoldWWcoRZxwIwBFnHEhZZTG9kaNjU6YHPN78AAAgAElEQVRWUFWVeupQqC4ikh0VVTFqJlcDUDO5WqNeREREREREIqDo0kEz6wf8BrjK3be1c/10YDpAeUU4oyw+uO1Btj+zjO2njmHizMmh1CApZ99yIs0z40UdpKc/JkYcUNI64mXG5VuYP6+RKVMrNPZFikr6Y6KipF/I1YhIUfr618OuQERERESkKBVVZ7qZ9SEVpN/r7r9tbx93n+PuNe5e06esKrcFkupI3/7MMgDqFq0k3tCc8xqKQXdGthRzkA57Piaqh+zuSJ8/rxHQ2BeAXQ27wi5Bcij9MVFmFVk/X7JR71QqRhrzIp2aMyfsCkREREREilLRhOlmZsAvgNfc/T+6ert4PDMhRlfDkJK+5fQ/eRwAw08dQ2llWUbOL7s9cs1z/OTk3/HINc+FXUreqqoqYcrUVIiYPvalI4Uctj/9vSd54LS5PP29J8MupSDFE8UdJG+4817en/EvvH1zu6//SgHTmBfp1PHHh12BiIiIiEhRKpowHfg48PfAaWa2OLh8rrMbNDZs4v+evJ5VT83t1YnXz7mH9678Z9bPuadL+x9w9fmMueefNOIlC7SoaObMmj2IV1fsv88RLzMu38LRYz9kxuVbclRZ7uxq2MU7C98C4J2Fb6lDPcOWvPkQT758E0vefCjsUkKRbGyioXYpAFufXk5ip96pVGzefHnbHh9FWr30UtgViIiIiIgUpaIJ0939/9zd3H28u08MLo91dpt4fCcAm95ZQmJXz7oj08OQhtql3epQl8zToqKZ1ZWO9EIeB9Onsg8HTxoNwMGTRtOnsk/IFRWOeKKJdZtTI6/WbV5WlB3qJRXlVNaMB2DgKUcR66t3KhWTreub2VyXegFlc10zW9frxRQREREREZGwKUnsRGlpX+Lxnex38ARifXoWbreEIQ21S6msGU9JhULysGlR0dxpGQfTslDpvsL3fHTKjadx4vd2KUjPsNJYOcMGj2Pd5mUMGzyO0lhxPndWX3YRycZzGXKIgtRiM3BoGYOHl7G5rpnBw8sYOFQvpkiaESPCrkBEREREpCgpTexEReV+TKyZTvPIfr06ztDpF5Oc1qQgPUIUpOfOrNmDuPnWZEEG6S0UpGfHhI+cRzxxdtEG6S1S/3YoTC9Gt/zlBLaub1aQLnv74IOwKyh6ZvZLYCrwobsfHWzbD3gAOAR4Gzjf3Teb2TnADUASiANXufv/Bbe5BPh+cNgfuvvdwfbjgV8BfYHHgG+5u+fkznWTmc0Edrj7rTk+71eAGnef0c3bzaQb9ZrZROCAfb2rWURERIpD4aZbGVJampkQR0G6FLNCDtIlu4o9SBdRkC7tmjkz7AokFXSf2Wbbd4GF7j4GWBh8TfD5BHefCHwV+Dm0hu/XAx8DTgCuN7PBwW1mA18HxgSXtueS3JkItLvWlpmpQ0dERKTIKOGKkK7OUxcJU6HNPRcREUD/J8wvP/hB2BUUPXd/CtjUZvM5wN3B53cDnw/23ZHWVV4FtHz+WeAJd9/k7puBJ4AzzWwEMMDdnwtuN7flWLlkZtPMbKmZLTGz/zGzQ8zsyWDbQjMb1c5trjSz5cE+9wfbTjCzZ83sZTN7xsyOCLZ/xcx+Z2ZPmNnbZjbDzP4x2O+54MUGzOzPZvYTM1tsZq+a2QntnHeomf3GzF4ILh/fx92bENS00sy+Hhxjrpm1fp/N7N7gXQX/ClwQnP8CM5sZfD/+CvxPR+c2syoz+6WZPR/cp3N69pMQERGRKNEr6RGxfs49NNQupf/J4zjg6vOzfr5kUxMl5er4lO55990ER4/9kClTK5g1e1DY5Yh0KJ5oUle7FITG+gQVVbGsnmPG5VsAjs3qSUSKwzB3Xxt8XgcMa7nCzP4OuAnYH5gSbB4JvJd2+/eDbSODz9tuzxkzG0dq/MzJ7r4hCLbvBu5297vN7KvAHewd8n8XGO3uTWbW8p/FFcAp7h43s9OBHwFfDK47mtTzTwWwCrjW3Y81sx8D04Dbg/0q3X2imX0S+GVwu3Q/AX7s7v8XhPyPA0d2chfHAyeSenHjZTObD/wC+DbwOzMbCJwMXAIMJm2cTDAm5ijgE+6+08z+t4Nzfw940t2/GnwvnjezP7l7fSd1iYiISMSpCykCko1NNNQuBWD7M8tI7sxuh3rdfXNZ/YPrqLtvblbPI4Vn69ZUI9X8eY3qUJfIWvLmQzz58k0sefOhsEsR6ZW7rlrBFcc9y11XrcjaOerrk8yf19irY5jZdDOrNbPa9evXZ6gykdBVt/xeB5fp3blx0FHuaV8/7O5jSYXPN2S41mw4DXjI3TcAuPsm4CTgf4Pr/wf4RDu3Wwrca2YXk5oPDzAQeMjMXgV+DIxL23+Ru2939/XAVuDRYPsrpGbPt7gvqOMpYEBaUN/idGCWmS0GHgn26Wzhq9+7+87g/i0CTnD3vwBjzGwocCHwG3ePd3D7R9x95z7OfQbw3WD7n0m9YLBXN7+IiIjkF4XpEVBSUU5lzXgA+p88jpK+2eumTDY1seOVxQDseGUxySaNlpGuGzjQAJgytUJz0CWS4okm1m1eBsC6zcuIJ/QcJ/mpsT5B7YINANQu2EBjfSIr56mqKmHK1IpeHcPd57h7jbvXDB06NEOVSadqa8OuoBhsaPm9Di5zunCbdcGIFoKPH7bdIQiDDzWzamANcFDa1QcG29YEn7fdng+mAD8DjgNeCGaK30AqND8aOItUqNwi/R/qZNrXSfZ8F3XbxVfbfl0CnOjuE4PLSHff0UmdHR1vLnAx8A+kOuA7kt5d3tG5Dfhi2vZR7v5aJ8cUERGRPKA0LCKGTr+Yg+64IesjXkrKy+l3zEQA+h0zUaNepFtGjYrx6or9NeJFIqs0Vs6wwamGt2GDx2nUi+StiqoYNZOrAaiZXJ3VUS/Bc/rLWTuBSPF4hNRYEIKPvwcws8PMzILPjwPKgY2kxoGcYWaDg4VHzwAeD0bFbDOzE4PbTWs5Vg49CZxnZkOCuvcDngG+FFx/EfB0+g3MrAQ4yN0XAdeS6kjvF3xseTHgKz2s54LgHJ8Atrr71jbX/xG4Iq2Wifs43jlmVhHcv08DLwTbfwVcBeDuy4Nt24H+nRyro3M/DlyR9rPXOC0REZECoJnpEVJSUQ7s3Od+7Yk3NFNaWdalfYdfOI3kFy5QkC490lFHen19Ut3qEgkTPnIe8cTZCtIl7116+1guuTH7M9MlD9XUgLdtrJVcMrP7SIWw1Wb2PnA98G/Ag2b2NeAdoKVL5ovANDPbReo/+xcEY2A2mdkN7A5y/zUYpwLwDVLBbl9gQXDJGXdfZmY3An8xswSpF9yuAP7bzP4fsJ5U93a6GHBPMG/cgDvcfYuZ3QLcbWbfB+b3sKRGM3sZ6AN8tZ3rrwR+ZmZLSf2N+xRwWSfHW0pqvEs1cIO7fwDg7uvM7DXgd2n7LmL3uJabunHuG0jNfF8avNDwFjC1i/dXREREIkphegFYPHMBdYtWMvzUMUycOblLt1GQLpk04/ItzJ/XqIVJJTIUpEuhyEWQrgVIRbrP3S/s4KpJ7ex7M3BzB8f5Je2ME3H3WvZeZDOn3P1uUouOpjutnf1mpn251xx1d38WODxt0/eD7b8i9YJBy36HpH2+x3XAPe5+VZvjtu4TzD6/oP170mm9ezCzSmAMwYz2YP9NwEc7OV675w5mql/alZpEREQkf6iNNM/FG5qpW7QSgLpFK4k3NIdckRSb9MXrtDCpiEh+ycQCpCIihcDMTgdeA37azhgZEREREQDM9RbRDpnZelJv0dyXamBDlsvp7JyHAoOBzcDqHJ43l3Te3jnY3Xu9KpyZbQVWtnNVd38HBwI9/SOlp7ftzu26s29YvyNR1pufb66McfeBvT1IN/6dyLWo/l5GtS6Ibm25qOtQYLC7W28PFOHHhEh3ZeT/ThIeM/sH4FttNv/V3b8ZRj0iIiJSGBSmZ4CZ1bp7TaGfU+ct/PPui5nNcffpYR6np7ftzu26uW8kf1ZhytTvSTblQ429EdXfy6jWBdGtLap1iUjumNmZwE9IzST/ubv/W5vrvwL8O7sX+Zzl7j8PrhsF/Bw4CHDgc+7+djbrCfY5H5gZnHOJu3857boBwHLgd+4+I5u1mNmPgVODLyuB/d19ULBI6GxgAJAAbnT3B3pTSxfrGUVqdM6gYJ/vuvtjwXXXAV8L6rnS3R8Pqx4z60Pq9+Y4UqNh57p7e/PiRUREio5mpotIdzwageP09LbduV2m7mexyofvXz7UKCIiRc7MYsDPgM8A7wMvmNkj7r68za4PdBBMzyUVFD9hZv2AXs3j60o9ZjYGuA74uLtvNrP92xzmBlKLdPZKV2px92+n7X8Fu9eHaACmuftKMzsAeNHMHnf3Ldmsh9S8+AfdfbaZHQU8BhwSfP4lYBxwAPAnMzvc3RNh1AOcB5S7+zHBHPnlZnZfb1+IERERKQSamS4iXebuGQkge3Ocnt62O7fL1P0sVvnw/cuHGkVERIATgFXuvtrdm4H7gXO6csMgHC119ycA3H2HuzfkoJ6vAz9z983BeT9Mq+l4YBjwx17W0dVa0l1IsLCou7/h7iuDzz8APgR6O9anK/U4qW54SI3F+yD4/Bzgfndvcve3gFXB8cKqx4EqMysF+gLNwLZe1iMiIlIQFKZnxpwiOafOW/jnle7Tz0qiKKq/l1GtC6JbW1TrEpHcGAm8l/b1+8G2tr5oZkvN7NdmdlCw7XBgi5n91sxeNrN/D7qVs13P4cDhZvZXM3suGDWCmZUAtwHf6WUN3amF4NwHA6OBJ9u57gSgDHgzB/XMBC42s/dJdYFf0Y3b5rKeXwP1wFrgXeBWd9/Uy3pEREQKgsL0DHD3nP+hG8Y5dd7CP690n35WEkVR/b2Mal0Q3dqiWpeIRMqjwCHuPh54gtQMbEiN9DyFVHj9UVKLDX8lB/WUAmOAT5PqBv8vMxsEfAN4zN3fz0ENbX0J+HXbsSlmNgL4H+Af3L1XI3C66ELgV+5+IPA54H+CFxnC0lE9J5Ca3X4AqRchrjazQ8MrU0REJDoUpouIiIiIiETTGlKLh7Y4kN0LjQLg7hvdvSn48ufA8cHn7wOLgzEfceB3pBaUzGo9wXkfcfddwciSN0iF6ycBM8zsbeBWYJqZ7bV4aYZrafElghEvLYKFUOcD33P353pRR3fq+RrwIIC7PwtUANVdvG0u6/ky8IfgZ/gh8FdAi2GLiIigMF1E8pCZfd7M/svMHjCzM8KuRzLPzA41s1+Y2a/DrqU9Ua9PREQKxgvAGDMbbWZlpELhR9J3CLqrW5wNvJZ220Fm1jIL/DSg7cKlGa+HVGj/6aC2alJjX1a7+0XuPsrdDyHVLT/X3b+b5Vows7HAYODZtG1lwMNBDZn6t7wr9bwLTApqOJJUeL0+2O9LZlZuZqNJvfjwfIj1vEvq9wUzqwJOBFb0sh4REZGCoDBdRPbJzAYFMzhXmNlrZnZSD4/zSzP70Mxebee6M83sdTNbZWad/mHl7r9z968DlwEX9KQW2c3MDjKzRWa23MyWmdm3enGsTP2MV7v714LbVZjZ82a2JKjvB1GqT0REJFuCjvIZwOOkQvIH3X2Zmf2rmZ0d7HZl8O/jEuBKglEuwUiT7wALzewVwID/ykE9jwMbzWw5sAj4f+6+sTfn7UUtkAqR73d3T9t2PvBJ4Ctmtji4TMxBPVcDXw9+VvcBX/GUZaQ6xJcDfwC+2XYkTS7rAX4G9DOzZaRC+f9296W9qUdERKRQ2J7/pxAR2ZuZ3Q087e4/DzpbKt19S9r1+wM73X172rbD3H1Vm+N8EthBqgvo6LTtMVJvAf4MqbcGv0BqhmMMuKlNOV8N3m6Kmd0G3OvuL2Xu3mZOMFvye8BAdz837Ho6EnS0jXD3l8ysP/Ai8Hl3X562T1g/418D5wFV7r7DzPoA/wd8K/0t2WHWF+WfrYiIiIiIiIhkjjrTRaRTZjaQVOfOLwDcvTk9SA98CvidmZUHt/k68NO2x3L3p4BN7ZzmBGBV0O3bDNwPnOPur7j71DaXDy3lZmBBtoL0jjqYC7F72d3XtnwfgzD6NWBkm91y+jNuc0x39x3Bl32CS9tXgkOrT0RERERERESKg8J0EdmX0aRmJ/63mb1sZj8PZie2cveHSL2F9AEzuwj4Kqlu4q4aCbyX9vX77B3mprsCOB0418wu68Z5uuNXwJnpG4Lu5Z8Bk4GjgAvN7CgzO8bM5rW57J+lurLKzA4BjgX+lr491z9jMxtiZncCx5rZdWYWM7PFwIfAE+4eqfq6cR4RERERERERyVOlYRcgIpFXChwHXOHufzOznwDfBf45fSd3v8XM7gdmAx9J6yTOOHe/A7gjW8cPzvFUECyna+1eBgju7znufhMwNZv15IKZ9QN+A1zl7tvaXp/jn/FGUjPx0000s0HAw2Z2tLu/2uY2YdcnIiIiIiIiIgVMnekisi/vA++ndQL/mlS4vgczOwU4GngYuL6b51gDHJT29YHBtqgp2O7lYBb5b0jNoP9tB/uE/jMORgwtos27BqJSn4iIiIiIiIgULoXpItIpd68D3jOzI4JNk4Dl6fuY2bHAHOAc4B+AIWb2w26c5gVgjJmNDhY4/RLwSK+LD5m7b3T3y9z9I0H3eiSZmZGaif+au/9HB/uE9jM2s6FBRzpm1pfUIqErolKfiIiIiIiIiBQHheki0hVXAPea2VJgIvCjNtdXAue7+5vungSmAe+0PYiZ3Qc8CxxhZu+b2dcA3D0OzCA18/o14EF3X5a1e9Nzhdq9/HHg74HTzGxxcPlcm33C/BmPABYFv38vkJqZPi9C9YmIiITKzKaHXUM61dM51SMiIpK/zN3DrkFEJJKCmenz3P3o4OtS4A1S3flrSAW7X1bo+v+zd/fxUdV33v9fn9yHIIRbDXgDFsuNiLHmKrJd111pt8IVsLWW6toL7foTxVrS2m2pP3up7aqt1dUrdAvW1lZZuWppS7eQBW0Xt5TtIm2o4UaUbSpYlSC3QZlkcjPzvf6YCQ4h95k558zM+/l4zIOZM2fO+UzInMl8zmc+HxEREfGTmdU65yr8jqOD4umZ4hEREUlfqkwXEelCVxXMql4WEREREREREcleqkzvQV5xicsfPvLk7WjBe/dZfjTp+yvKbzvl9rC8cNK2/U57EQDhtvxTlre9fZRoKExOSRH5Z47s6qFd6hxrX3Q8n+E5vT+vSNSRm2P93od0bdfOtsPOuTGD3c7IkTnu7HNyT1v+59cjHD/uGD7cOPe80+/vcCLxRRRAJyJFXS5vas/vcnl/tLUn/Fza3zuPmdPFSymnfdC7GzDnHLEW6gOT05Ye7yknjr+VlNdEQd4QV1xQ2ut60YIcovlGezGQl/z3j67k50VSst0hee/90g7NTd77VDJFo5CTxeUCQ3Na+7V+xzHcOTfoN96i0iJXUnbGYDfDGZ1+t4Z18bfDO9HYMfvdSBHNfTxOR9qy7xej/dBRok3N5AwpJm9M3//WS0cu6rAu/n7MHcDf7c31DYN+nxg9erSbMGHCYDbRZ4cOHWLMmEG/rQ1MWxvkn/oa9DWeLiienvUlnm3btiXlbycREZF0l+d3AEGWP3wkE677LLkFhQA0jX8vSZQ3PjTo7UebW8gpLjx5e+pZb59y/5WjX+38kAF74fAUAF45cOYp+//Twgdi10Nhzr7vM6fE05POsfZFx/OZO3R3j+tVLT7GhpowcyqLqF4xot/7kdO9/5yG03pHD8TZ5+Tyb+tHn7wdCsU+nE6dfBCA48cdq38ykpKSrpMVm5vPTUYYKfOf71zQ5fIdR8cNetv7DyUkXA++l7QvOnj6h/7ig4Pe3YC8tnEljXvrKJ1YzvmzFw5oG0MP+HgmoBuR9hZy8049tv2m5stJeU0U5Q9n1vv/v17Xaz7nDE6clcex6Q7GepOAHjemMSXbnTFy/8nrfznsjynZx2Asq6pn6/qjzJw7kiXVk/wOxxeXF/+5z+uGQtGTx/BkKCk7g7lPXT3o7VwxYs8ptz88pP60df69Kfb/u+nYZHYeLju5PNLcSm5x1ydvjx0YfKI/nUTDLbx5xz2x603NjP3SreQUdf23XjTc0u196eDw46toqt3BkIoZjL7thlPuG3HWu/3e3vbK+wf9PjFhwgRqa2sHu5ngMwMVaGU8M0vK304iIiLpLvvKc/qhtfEIr1bfxRtrVyZ92w2PruZPCx+g4dHVSd92X+UUFzJ01nQAhs6a3udEeiqFQlE21MQSTRtqwieTtb09Rrx3++JGpk4+yJf+4R0q58WSw5XzirpNpCdDOJSaSluBSFsLjXvrAGjcW0ekrcXniJLjlW3P8Nvn/jevbHsmJdt/N3yA7ft+lpJtd4iGM+P/wgvhUISt648CsHX9UR0z+qCkJIeycZnz5+C+h9aw65PfYt9Da/wOJakGehzIKSpkSMUMAIZUzOg2WX748VW8ecc9HH581YBj9FM03EJT7Q4Ammp36LgpIiIiIimTOZ+eUiDa0gzAO3vqiLQm74/yaHMLJ7bsAuDEll1Em/37g7/szgW8b+XdlN25wLcYEpWU5DCnMpaYnVPZe2K2avExLpnyNlWLj3kRnsSFQlFq1sVOetSsC/PwI8N4Zc9Ylq/ovd3FQC2rqufm8m0sqzq9OlEGLze/kNKJ5QCUTiwnN9//k2uDFWlv4VBDLLlyqGEHkfbUHGsPHN9Ne6R/bTX66uCyZ3n977/OwWXPpmT7maaoJJeZc2NtLGbOHUlRSfdtpyQmFIrSsD8zTkpHmls5vjn27bfjm3cTaU7N69Jrg010j77tBs7+56+fVq3dIRMS0X09aSAiIiIiMlhKpvcgp7AYgGGTy0+2eknKdgNWEe73/jurXjGCl149s9cWLwOpYpfkKCnJOa0aPdUV6ao2Tb3zZy/k4hu/MeAWL0GTm1fImLJYcmVM2YzTWr0ky1nDp5GXm/x5ANFwC6EXdwIQenFnWia4/LCkehJP1l2atS1e+ivxeJ7ucosLGH75NACGXz6t21Yv6SRZie6eksuZkoju7aSBpNAtt/gdgYiIiIhn0qpnupldBVQDucD3nXPf7HT/ucDTQGl8na8459abWT7wfeADxJ7zSufcN3rbX0HpqFN6pnenc+/zvii7cwHR5quTnshuCbVTWJJW/61d6ktitqOKvaO/eiqTufKeUChKSUkOy1eU8vAjUU9+7h3Vph19kFVtmjoDqUiPtLUEtpJ96qWf5v1d9ExPljOKzuLiCZ9IybZzigopuewiQi/upOSyi9I2wZUoHIp48vrVMaJ/lq8opWbdge1+x5EME5ZeQ2RJZUYk0uG9RHdHL/DBHge664s++rYbiIavTdpxxq/+65lwnExLTzzhdwQiIiIinkmbrKuZ5QLfAT4CvAn83szWOucSp1l+FVjtnFthZtOA9cAE4JNAoXPuIjMbAuw2sx855/b1tt/eEukNj67mxJZdDJ01vd+tUpKdSH/qCzupe+4g5VeN5abHLkrqtoOqesUIHvQooSvw59cjTJ18kMp5RSxfUerpz31J9STCD3qTiJO+S8bQ0lRLVSIdwOz0AbLJNHbJdUQXfTwjEkQaDBpcty9uBLjY7ziSJVMS6R2SlejuaUAnJC8R3dt+JANdeils2+Z3FCIiIiKeSKcM5AeBeufca865VuBZ4OpO6zhgWPz6cGB/wvISM8sDioFW4J3BBhSk3uctoXbqnjsIQN1zB2kJtfsWi9eUSO9aKtreHD/ugFifdD/a6mRCIj2TWnVk6tDSoMmERLpaNQVX4gyMTJbu/dOTUZHuRV/0TOi/LgPwhz/4HYGIiIiIZ9IpCzkeeCPh9pvxZYnuAz5tZm8Sq0r/XHz5T4EQ0AD8GXjEOXd0sAEFqfd5YUke5VeNBaD8qrEZ0eolGdKhj3oqYkzVYNbhw2NVuB190qV/Tg6TfGql36EkRSqGlqZqSKj0LpXJxqKSXEaWxaqFR5YVZMSJsUw5IZBJPdO7s++hNez65LfY99Aav0PxjVd90XvbTxCS6+l+YkVERERE/JVpGdfrgaecc/9kZrOAfzGz6cSq2iPAOGAEsNnM/t0591rnDZjZImARQP6wngdgQup6nw/ETY9dRMv9mdEzPRmqFh872U+9t2GmfklFjJ0Hsz74yOCS9YmvifHjc3jxd2OUSB+AU4ZJ1m0n2tJCTqH/x43BOn/2QiJtn0pKIv2Vbc9wqGEHY8pmMPXSTychutRIfE0U5Q/3OZrkePX+tRzZtIdRV0xmylfnJ3374VCEow2xBNbRhlbPeqenSqa1rIn3TH9poI9PfE2UnFWStLiSIdLcyvHNsY6Axzfv5oxP+dPLOwiS3Re9v/sJQvuXfQ+t4fjm3Qy/fBoTll7jSwwZqazM7whEREREPJNOGbG3gHMSbp8dX5boZmA1gHNuC1AEjAb+DnjOOdfmnDsI/Bao6GonzrknnHMVzrmK3OK+fSAMQiK9gxLpMZ0TykGsUE9VjB2DWYGkDGZNfE2MHJWjRLqcJlkV6YcaYq0BDjXsCHSFeuJroiBviN/hDFqkuZUjm/YAcGTTnpRUbXYMEQbSfohwBresGfCbUOJrorC0OJkxDVpucQHDL58GwPDLp2VtIr2DV8+/q4p0v9u/dD6xogr1JNq/v/d1RERERDJEOmXFfg9cYGYTzawAuA5Y22mdPwOzAcxsKrFk+qH48ivjy0uAy4BXPYo7q7U39f+DSjKSyslOKKdCKmOsXjGCl149M7AV+eluIB/Ac4oKKbksNhi4pPzijKhKT6bcvELGlMVaA4wpm9GnoaFBTrink9ziAkZdMRmAUVdMTtnwxiXVk3iy7tK0r+T2+sRABiXrfTNh6TVM/8mXs74S2c8WK121f/E6ns4nVjJtUK2v7rvP7whEREREPJM2ZczOuXYzuwN4HsgFfuCce9nMvg7UOufWAl8Evv4bmkQAACAASURBVGdmXyA2dPQm55wzs+8APzSzlwEDfuic2+HTU8katfc+z/4X6hl35SQqvvbRPj0mmW1PqleM4MFHooFMpHdIZYxBft7pbDDtMMYuuY7ooo+T805mtAZJtqmXfpr3t7f0KZGeLi1h5FTpXJGeaEn1JMIPpr5VTaa1k/FTtidOg9BiJbH9i1/xTFh6DZEllVn/+5B0X/uaEuoiIiKSNdIq2+acW++ce79z7n3OuQfiy+6JJ9Jxzu12zn3IOXexc67cOffL+PITzrlPOucudM5Nc8497OfzyAbtTa3sf6EegP0v1PepQj0VbU/SIaGcDjFKTDLaYWR7i4He9LUiPV1awqQDL9q8ZCIvKtIztJ2MeCwILVY6dFSk+xmPEukiIiIiMhjK4klK5A0pYNyVsSq6cVdOIm9I7x9c0qE1i2Q3r9phSM8G0hJGuqff62DKpD7z4q+uWqwoHhFJNjP7gZkdNLNdCctGmtmvzOyP8X9HxJcPN7N1ZrbdzF42s88kPObG+Pp/NLMbE5ZfamY7zazezJaZmXn7DEVERGLSps2LQEuo3dMBo9HmlkENV6342kdpX/o3fUqkd0iH1iySeuFQ6tsnDNSUr84n8sXWQCUcI20tSRkCmk760xJGehfE3+vBCPIxpD+8aicTlzFvvJHmzPldTpbEFitBEJR49LuSJLW1fkcgwfAU8M/AyoRlXwE2Oue+aWZfid9eCnwW2O2cm2dmY4A9ZrYKGArcC1QQa9u6zczWOueOASuAW4CtwHrgKmCDJ89MREQkQcZ8cMp0T31hJ0srNvHUF3Z6sr+GR1fzp4UP0PDo6kFtpz+J9A5KpGe3ZVX13Fy+jWVV9X6H0q0gffB+beNKtj99F69tXNn7yhlGifTkCtLv9WCkwzGkP7xIpN++uBHgkpTvyAP7HlrDrk9+i30PrfE7lMDxO3Hdmd/x6HdFJLmcc78BjnZafDXwdPz608DHOlYHzohXlw+NP64d+CjwK+fc0XgC/VfAVWZWBgxzzr3onHPEEvYfQ0RExAfKWqaBllA7dc8dBKDuuYO0hNpTur9ocwsntsS+nXdiyy6izepHLN5Qj+D+ibS10Li3DoDGvXVE2vRaleymY0j/hUJRataF/Q4jKSLNrRzfvBuA45t3q/+/dEu/K0lWUeF3BBJcZzrnGuLXDwBnxq//MzAV2A/sBKqcc1FgPPBGwuPfjC8bH7/eebmIiIjnlExPA4UleZRfNRaA8qvGprzVS05xIUNnTQdg6Kzpg2r1ItIf6hHcP7n5hZROLAegdGJ51rV6EelMx5D+KynJoXJekd9hJEVucQHDL58GwPDLp2XMty0k+VL5u2Jmi8ys1sxqDx06lLTtigTA6I7f7fhlUX8eHK8od/GbHwXqgHFAOfDPZjYsueGKiIikhnqmB0h7U2u3bVFueuwiWu73rmd62Z0LiDZfrUS69CgUSn5/e497BKe982cvJNL2KSXSReJ0DOm/5StKqVl3YLvfcSTDhKXXEFlSmZWJ9Gi4xffWKekkVb8rzrkngCcAKioqXC+ri6STw865/n4N4W0zK3PONcRbtRyML/8M8M14gr3ezPYCU4C3gL9OePzZwK/jy8/utPyt/j8FERGRwVNlekA0PLqa9R/9HrX3Pt/tOl4OHwWUSJce3b64kamTD3b02k0qJcH6R4l0kVPpGNI/8eP4xX7HkSzZmEg//Pgq3rzjHg4/vsrvUNJKNv6upMS99/odgQTXWuDG+PUbgV/Er/8ZmA1gZmcCk4HXgOeBvzWzEWY2Avhb4Pl4q5h3zOyyeJ/1hQnbEhER8ZSS6QGQ2KN8/wv1tDepb6MEW2KP3Zp1YUKhqM8RiYjIQGRSz/RsFQ230FS7A4Cm2h1Ew5qfIR677z6/I5AAMLMfAVuAyWb2ppndDHwT+IiZ/RH4cPw2wD8Cf2FmO4GNwFLn3GHn3NH4fb+PX74eXwZwO/B9oB74E7DBo6cmIiJyCiXTAyCxR/m4Kyd12+plMINHMz1Br2SutxJ77FbOKzrZ6sWv/wcNGeyZBpOKSHcyqWd6tsopKmRIxQwAhlTM6LXVS0/Jdi8T8Ur6Z5Bx4/yOQALAOXe9c67MOZfvnDvbOfekc+6Ic262c+4C59yHOxLjzrn9zrm/dc5d5Jyb7px7JmE7P3DOTYpffpiwvDa+7vucc3fEW8SIiIh4Tj3TA6LszgVcMGxWt4n0p76wk7rnDlJ+1Vhueuyifm079tiNjLtyEiWLFyYj3ECpWnyMDTVh5lQWUb1iRNK3n4q+4OkqmpCzXr6ilIcfee9nc/viRmrWhamcV8TyFaWexbSsqp6t648yc+5IllRP8my/6eK1jStp3FtH6cRyzp+dea9/kUwXDqW+//u//yp4lelePO9MMvq2G4iGr+01kX748VU01e5gSMUMRt92Q5/vSzYv9yUeaGjwOwIRERERzyhDGCA9VaTXPReb1VL33MF+VagnPnb/C/VEmzOrCigUirKhJpYE2FCT/HYjVYuPccmUt6lafCyp201XL7/cfkqP9MSKdD/avoRDEbauj33zc+v6o6pQ7yTS1kLj3joAGvfWqUJdJM0sq6rn5vJtLKuqT9k+9u1rIxywXPrmu1/gcx/Ywnc//6rfoaSVvlSkd9cOxstWMWpLIyIiIiLpTMl0n/QnqV1Ykkf5VWMBKL9qbJ8GkXYk3BMfO+7KSRk3VLSkJIc5lbGvp8+pLEpqBXmqE/XpqiNZnvjz6K7tS6oVleQyc+5IAGbOHakqxk5y8wspnVgOQOnEcg0qFUkjXp0snDAhn6IAdXlpa2rj9Y17AajdcLjX550OJ1GDkizuqR1Mf1vFpCoOSVMf+IDfEYiIiIh4Rm1e+iDS2kJuQfL+0G94dDUntuxi6KzplN25oE+Puemxi2i5v71PifTOLWFueuwifvmF88kbUsArBwYbffBUrxjBg48kvxVLR6K+o4WMWr3ElI3L4Uv/8M5pLV06t33xypLqSYQfVDuA7pw/eyGRtk9lZCI90t5Cbl7mPS+/RJpbyS3u+htS4r2iklxGlhVwtKGVkWUFKT3GffgjRYEZQpo/JJ/zZk/k9Y17qZgzusfn/d3Pv0rthsOcN/sApV8IZquQwbYziYZbkpps7qkdTF9bxaQ6DklD27b5HYGIiIiIZ5Qd7MUba1fyavVdvLF2ZVK2F21u4cSWXQCc2LKr3xXqvemuJUx3LWQyRaoSuNUrRvDSq2empBd7umrY331LF79OOCiR3rNMTKS/su0Zfvvc/+aVbc/0vrL06tX71/Li/GpevX+t36FIXDgU4WhDbHj40YbWlFVgJ7bpCorLH7iSb/9hFrf+nyndrhMKRandcBiA1zfuJdIcvEHrg21ncvjxVbx5xz0cfnxVUuPqKYHtZXJbifQMsmiR3xGIiIiIeEbJ9J44xzt7Yv2G39lTR7Rl8F/TzSkuZOis6QAMnTU96W1XBtISRnqmivRTVc4r8qWli0iHSHsLhxpiCapDDTuItAejhUK6ijS3cmTTHgCObNoTyKRkNvKqjVVim64g6e35lpTkUDFnNADnzZ4YyG9VDKadifqKS1r53vf8jkBERETEM8q09sSMYZPLeWdPHcMml5NTmJzEd9mdC4g2X52y/uX9aQkj0h8XXph3sq2LHy1dRABy8woZUzaDQw07GFM2Q61eBim3uIBRV0zmyKY9jLpiciCTktnKqzZWy1eUUrPuwEsp3UkK3Pp/pnDjAxG2tk5j52G/o+naQNuZdCTiO1rEqIpbRERERCQYlG3txTnzFxJp/RS5BYU04ZK23VQPAlUiXVIhJyGfo0S6+GnqpZ/m/eqZnjRTvjqfyBfVMz2IPGxjlZZTtotKciHgX6YYaCJcfcVFRERERIJH2bA+SNbw0f70RxcJomhqWvaKDIgS6cmlRHrW09+E/eRF6xUl0iUtvPWW3xGIiIiIeEYfnDzS8Ohq/rTwARoeXe13KCID9vLL7dy+uNHvMEREJInix/VL/I4jnaRqOKhIWtq2ze8IRERERDyjZLoHos0tnNiyC4ATW3apQl2SIhTy5xv5NevCvu1bsoeGinpPg0ezUygUpWZd2O8w0oqXw0E1eFTSwvz5fkcgIiIi4hkl0z2QU1zI0FnTARg6a3rK+6VL5qtafIxLprxN1eJjnu+7cl6R+qVLSr2y7Rl++9z/5pVtz/gdStZ49f61vDi/mlfvX+t3KOKxkpIcKucV+R1GWukYDgqkdDioqt9FRERERIJHUyo9UnbnAqLNVyuRLoMWCkXZUBOrItxQE+bBR6KeJbcvvDCP5StKPdmXZKdIewuHGmIVn4cadmjIqAciza0c2bQHgCOb9mgQaRZavqKUmnUHXvI7jnSS6uGgp1e/axCpiIiIiEgQqLzUQ0qkSzKUlOQwpzJWRTin0tsq8Zxcz3YlWSo3r5AxZbGKzzFlM5RI90BucQGjrpgMwKgrJiuRnr3Uv6ufUpnc9qr6XSQpvvtdvyMQERER8Ywq07NAS6jd7xAkyapXjPC0Il3ES1Mv/XS/KtIjPlavO+d82W+yTfnqfFWkiwRMKqvfo+EWJegleRYt8jsCEREREc8oE5fhnvrCTpZWbKL23uf9DkWSTIl0yWR9TY773V/93fABtu/7mS/7TjYl0kWCJxUJb/Vil6Qz8zsCEREREc8oG5fBWkLt1D13EID9L9TT3tTqc0QiIsnTub96pL3FlzgOHN9Ne0THVxEJvtN7sftz3BQRERERSVdKpmewwpI8yq8aC8C4KyeRNyQ4VYehkFqzynvCoYjfIUgaCkp/9bOGTyMvNzjHV5EB0t+EWSDbe7FHmnXiU0REREQGJ616ppvZVUA1kAt83zn3zU73nws8DZTG1/mKc259/L67gJuBCLDEOZcVfU9ueuwiWu5v57fN0/0OhZZQO4UleVQtPsaGmjBzKouoXjHC77DEZ8uq6tm6/igz545kSfUkv8PJCpG2FnLzMyOB0t/+6sl2RtFZXDzhEyndh3obS6rdvrgR4BK/45DB68vxIpW92INs30NrOL55N8Mvn8aEpdf4HU5mqaz0OwIRERERz6RNFZKZ5QLfAeYA04DrzWxap9W+Cqx2zl0CXAcsjz92Wvz2hcBVwPL49rJCYYn/50xq732epRWbePJz29lQEwZgQ01YFepZLhyKsHX9UQC2rj+qCnUPvLZxJdufvovXNq70O5Sk8SuRDmAp7hN7cNmzvP73X+fgsmdTuh/JXqFQlJp1Yb/DkCToTy/0bEukR5pbOb55NwDHN+9WhXqyrVvndwQiIiIinkmbZDrwQaDeOfeac64VeBa4utM6DhgWvz4c2B+/fjXwrHOuxTm3F6iPb0880N7Uyv4X6gHY+e+H+ds5sQ9wcyqLNEQzyxWV5DJz7kgAZs4dSVFJ1pzj8kWkrYXGvXUANO6tI9KmXrlBFg23EHpxJwChF3eqt7GkRElJDpXzivwOQwZJvdB7lltcwPDLYzU4wy+fpoHLyTZvnt8RiJzCzM40syfNbEP89jQzu9nvuEREJDP4X7Lcd+OBNxJuvwnM7LTOfcAvzexzQAnw4YTHvtjpseNTE6Z0ljekgHFXTmL/C/WUXzWWf34il1AoqkS6ALCkehLhByNKpHsgN7+Q0onlNO6to3Rieca0eslUOUWFlFx2EaEXd1Jy2UVZV0kq3lm+opSadQde8jsOGbiOXuhNtTuyshd6X0xYeg2RJZVKpKdCTY3fEYh09hTwQ+Du+O3/Bn4MPOlXQCIikjnSKZneF9cDTznn/snMZgH/Ymb9ahZuZouARQD5w9TPO1kqvvZRPvSt8fGWM7uVSE8jia+J8eNT8/+mRLp3zp+9kEjbp5RIH4TE10RR/vCU7mvskuuILvq4EmPihQH3XUt8TZScVZK0gKR/srUXen8okS6SOmb2A6ASOOicmx5fNpJYEnsCsA9Y4Jw7ZmZfAm6IPzQPmAqMcc4d7W5OmplNJPbt9FHANuB/xb+x3pXRzrnV8blpOOfazUz9JEVEJCnSKaP5FnBOwu2z48sS3QysBnDObQGKgNF9fCzxxz3hnKtwzlXkFusDYTIFoXe79F/ia2LkqHQ6ZEh3lEgfnMTXREHekJTvT4kxCbrE10RhabHf4WQ1HS9ExEdPEZtPlugrwEbn3AXAxvhtnHMPO+fKnXPlwF3Apngivac5aQ8BjznnJgHHiH32707IzEYRawOLmV0GHE/CcxQREfEvmW5mxWY2uR8P+T1wgZlNNLMCYgNF13Za58/A7Pj2pxJLph+Kr3edmRXGz2hfAPxusM9BREREREQkqznndwQSAM653wBHOy2+Gng6fv1p4GNdPPR64Efx613OSbPYxPcrgZ/2sq0OdxLLAbzPzH4LrAQ+179nJCIi0jVfkulmNg+oA56L3y43s86J8VM459qBO4DngVeA1c65l83s62Y2P77aF4FbzGw7sTfkm1zMy8Qq1nfH9/lZ55y+5iUiIiIiIjIYTzzhdwQSXGc65xri1w8AZybeaWZDiFWz/yy+qKs5aeOJtXZpjOcEEpd3yTn3B+AK4C+AW4ELnXM7BvdUREREYvzqu3EfsbPOvwZwztXFK8Z75JxbD6zvtOyehOu7gQ9189gHgAcGHLGIiIiIiEgPEmcInHvuuT5H45Fbb4VFi/yOQlJvtJnVJtx+wjnX5zMpzjlnZp2/xjAP+K1zrnNF+6CY2WeBVfGiOsxshJld75xbnsz9iIhIdvKrzUubc65zzzJ9P1BERERERNJW4gyBMWPG+B2OSDId7vjdjl/6kkh/28zKAOL/Hux0/3W81+IFup91dgQoNbO8Tsu7c4tzrrHjhnPuGHBLH+IVERHplV/J9JfN7O+AXDO7wMy+DfyXT7H0KNLa4ncIImkvFIr6HYKISFoKhzK7K11bU5vfIYiISOqsBW6MX78R+EXHHWY2nFgrll8krN/lnDTnnAP+A7i2q211ITfeZ71jX7lAwSCfi4iICOBfMv1zwIVAC/B/iU3W/rxPsXSrtfEIr1bfxRtrV/odikjaun1xI1MnH+T2xY29rywiIictq6rn5vJtLKuq9zuUlNh89wv8+MqVbL77Bb9DEZHBWNvj6CvJEmb2I2ALMNnM3jSzm4FvAh8xsz8CH47f7vBx4JfOuVDHgu7mpMXvXgrcaWb1xHqoP9lDOM8BPzaz2WY2m1j1+3PJeJ4iIiK+9Ex3zjUBd5vZA/HrgRRtaQbgnT11RFo/xWBOZkebW8gpLjz5r2SOUChKSYlf56WC7eDBdmrWhQGoWRfm4/8Yoagk17P9h0Pe7k+SL9LeQm6ejpkSPKk+voRDEbauj7WQ3br+KOEHU3488/SNrK2pjdc37gXg9Y17uezuNvKH5HsZQkpEwy3kFOmY5SX9zAPg0kv9jkACwDl3fTd3ze5m/aeAp7pYftqctPjy14jNXeuLpcQGjy6O3/4V8P0+PlZERKRHviTTzewviL2ZDQXONbOLgVudc7f7EU93cgqLibY0M2xyObkFhQy0rXvDo6s5sWUXeaOG0X7kHYbOmk7ZnQuSG6z4omrxMTbUhJlTWUT1ihF+hxMoty9upGZdmLJxOTTsj1I5r8jTxPayqnq2rj/KzLkjWVI9ybP9SvK8su0ZDjXsYEzZDKZe+mm/wxE5yYvjS1FJLiPLCjja0MrIsoKUHj/j3xy6JGU76MKLD2w+ef282RMzIpF++PFVNNXuYEjFDEbfdoPf4WQF/cwDYvx4cBp/JcHhnIsCK+IXERGRpPKrnPYx4KPEBongnNsO/JVPsXSroHQUU6q+wTnzFw54G9HmFk5s2QVA+5F3ADixZRfRZvViT3ehUJQNNbGq6w014cD1BfcznlAoerIivWF/lNo/jGb5ilLP9t94qPXUis4M7zmciSLtLRxq2AHAoYYdRNqz95gZDWfvcw+i0yrGU3R8CYciHG1oBeBoQ2vK9pN4vPZKYlU6wGV3X+7p/rszmNdaNNxCU23smNVUu0OvWw/oZy4i3TGzD5nZr8zsv83sNTPba2av+R2XiIhkBt96Uzjn3ui0KJDZrlhF+sDlFBcydNZ0APJGDQNg6KzpavWSAUpKcphTWQTAnMqiQLV6qVp8jEumvE3V4mO+7L+kJIfKebGfTeW8IsaO9e5LMMuq6vnsX9QxsizWlmnm3JFq9ZKGcvMKGVM2A4AxZTOyttXLwWXP8vrff52Dy571OxSJKyrJZebckUBqjy9e7SfxeO2V/CH5nDd7IhCcqvTDj6/izTvu4fDjqwb0+JyiQoZUxI5ZQypmqO2IB/QzF5EePAk8Cvwl8D+Aivi/IiIig+ZLmxfgjXirF2dm+UAVsQEjGanszgVEm68OdM/0oMYVdNUrRvDgI8Hqmd65Yv7BR/ypUH/4kWE8/MgwT382iRWjRxta+c5/lVM6ZuCzDsRfUy/9NO/P4p7p0XALoRd3AhB6cSfRRR9XsiggllRP8qKHOUuqJ9H41daUH8eWryilZt2Bl1K6k04uf+DKwPRJP73C+doBvdZG33bDgB8rA9PXn3mkuZXcYv09kDK33OJ3BCKdHXfObfA7CBERyUx+ZQBvAz4LjAfeAsrjtzNWR6I6iAnrhkdX86eFD9Dw6Gq/Q0lLQUqkQzAq5m9f3MjUyQf50j+84+l+O1dyKpGe/rI1kQ6xqsuSyy4CoOSyi5SgCxgvvvHS8U2bZVX1Kd8X4PmZ1yAk0iG5Fc56nXqvt5/54cdXseuT32LfQ2s8iigLPfGE3xGIdPYfZvawmc0ysw90XPwOSkREMoPnlelmlgtUO+c0JSgAEnu6x3q5Xx3IhL/0j58V84n9d2vWhXnY4zi8qhgV8cLYJdepIj1LndabXce1lFJVeWZK/NbB8c27iSypVIV6Klx6KWzb5ncUIolmxv+tSFjmgCt9iEVERDKM55k251wEOM/MsvYv2SANH03s6a5e7pnFr4r5zv3S/YgjFQmnSHNr0rcpItIdr3qmy3syOZGercM5E791MPzyaUqkp8of/uB3BCKncM79TRcXJdJFRCQp/OqZ/hrwWzNbC4Q6FjrnHvUpHs80PLqaE1t2MXTWdMruXOB3OMCpPd1FkmH5ilLPK9JT6dX713Jk0x5GXTGZKV+d73c4kkUOLnuW0Is7KbnsIsYuuc7vcMRj+qaNJMPhx1fRVLuDIRUzGH1b9n0xdPRtNzBs+FVKpItkETM7E3gQGOecm2Nm04BZzrknfQ5NREQygF+Zrj8BNfH9nxG/DPUpFs+c3lIlOFVCSqRLsmVKIr2tqY0jm/YAcGTTHlWoi2dOG0CapZWl2U6JdBmM04erZudxRIn0FCsr8zsCkc6eAp4HxsVv/zfwed+iERGRjOJXtmu3c+5riRfgFZ9i8Yxaqoikn/wh+Yy6YjIAo66YrA/k4hkNIBWRwUrmcFWRbu3f73cEIp2Nds6tJj5c2znXDkT8DUlERDKFX21e7gJ+0odlGUctVUTSz5SvzifyxVYl0sVzGkAqIoOl4aqScvfdF7uIBEfIzEYRGzqKmV0GHPc3JBERyRSeJtPNbA4wFxhvZssS7hoGtHsZi5+USBdJP0qki1+UABORwdJxRFLqa19TMl2C5k5gLfA+M/stMAa41t+QREQkU3hdmb4fqAXmA9sSlr8LfMHjWCSDhEKZM+xSslu0tQVQ0kNkMMIhDe0UyUTRcItODIhIr5xzfzCzK4DJgAF7nHNtPoclIiIZwtNkunNuO7DdzI4BNc65qJf7l8xUtfgYG2rCzKksonrFCL/DERmwN3++kndfqaN0Yjnnz17odzgiaWlZVT1b1x9l5tyRLKme5Hc4IpIkhx9fRVPtDoZUzGD0bTf4HY6IBJCZXdPNXe83M5xzazwNSEREMpJfpbwLgD+a2bfMbIpPMYjPQqHBn0sJhaJsqAkDsKEmnJRtSnoLh9JztlC0tYV3X6kDoHFvHZG2Fp8jkr5wzvkdgiQIhyJsXX8UgK3rj6bt8UCyQzSs43xfRcMtNNXuAKCpdod+dkFTW+t3BCId5sUvNwNPAjfEL98H/t7HuEREJIP4kkx3zn0auAT4E/CUmW0xs0VmdoYf8Yj3qhYf45Ipb1O1+NigtlNSksOcyiIA5lQWqdVLlltWVc/N5dtYVlXvdyj9llNQyBlTywEonVhObr6+xp4O3g0fYPu+n/kdhsQVleQyc+5IAGbOHalWLxJYhx9fxZt33MPhx1f5HUpayCkqZEjFDACGVMxQqxcR6ZJz7jPOuc8A+cA059wnnHOfAC6MLxMRERk0r3umn+Sce8fMfgoUA58HPg58ycyWOee+7Vdcknqdq8kffGRw/c6rV4wY9DYk/Z1Wkfpg+vVMPvvjC4n+z09R0qgkQTo5cHw3F0bmkZerIbVBsKR6Ulq+/iV7nF5lfa2Sw30w+rYb9LMKqooK0De1JFjOcc41JNx+GzjXr2BERCSz+JJ9NLP5ZvZz4NfEzhB/0Dk3B7gY+KIfMYl3UlFNrkS6ZEpFak6BkgTp5qzh05RID5h0ff1LdlCV9cDpZyUifbTRzJ43s5vM7Cbg34B/9zkmERHJEH5Vpn8CeMw595vEhc65JjO72aeYxEOqJpdUUEWqeO2MorO4eMIn/A5DRNKMqqxFRFLHOXdHfBjp5fFFTzjnfu5nTCIikjl8SaY75240s7PMbD7ggN875w7E79voR0ziPSXSJRWUSBcvmZnfIYhImlIiXTLGvff6HYHIaZxza4A1fschIiKZx682LzcDvwOuAa4FXjQzTdf2UbS5xe8QRCSNRdp0DBFJczrDnUWiYR2zJYnuu8/vCEROYWbXmNkfzey4mb1jZu+a2Tt+xyUiIpnBrzYvXwYucc4dATCzUcB/AT/wKZ6s1vDoak5s2cXQWdMpu3OB3+GISJp5beNKGvfWUTqxnBkX/p3f4YhIP92+uBHgEr/jEG8cfnwVTbU7gF1A5wAAIABJREFUGFIxg9G33eB3OJIJxo2D/fv9jkIk0beAec65V/wOREREMo9fVUhHgHcTbr8bXyYeiza3cGLLLgBObNmlCvWACoWifofgu3Ao4ncI0oVIWwuNe+sAaNxbR6RdxxDpnl7HwRMKRalZF/Y7jIwXlErwaLiFptodADTV7ghMXJLmGhr8jkACwMx+YGYHzWxXwrKRZvareJX4r8xsRMJ9f21mdWb2spltSlh+lZntMbN6M/tKwvKJZrY1vvzHZtbT9Pe3lUgXEZFU8TSZbmZ3mtmdQD2w1czuM7N7gReB/+7D47t8Y024/7H4G3Kdmf23mTXGl5eb2Zb4G/UOM/tUsp9busopLmTorOkADJ01nZxi9e8MmqrFx7hkyttULT7mdyi+WVZVz83l21hWVe93KNJJbn4hpRPLASidWE5uno4h0jW9joOppCSHynlFfoeR0Q4/voo377iHw4+v8jsUcooKGVIxA4AhFTPUt11Ekukp4KpOy74CbHTOXQBsjN/GzEqB5cB859yFwCfjy3OB7wBzgGnA9WY2Lb6th4DHnHOTgGPAzT3EUhtPuF8fb/lyTXwgqYiIyKB53ebljPi/f4pfOvyitwcmvLF+BHgT+L2ZrXXO7e5Yxzn3hYT1P8d7X1luAhY65/5oZuOAbWb2vHOucVDPJkOU3bmAaPPVSqQHUCgUZUNNrGJwQ02YBx+JZt3g1nAowtb1RwHYuv4o4QcjGjIaMOfPXkik7VPk5hfCgXa/w5EA0us42JavKKVm3YGX/I4jE51eCX6t7wns0bfdEIg4JIN84AN+RyAB4Jz7jZlN6LT4auCv49efBn4NLAX+DljjnPtz/LEH4+t8EKh3zr0GYGbPAleb2SvAlfHHdWzrPmBFN+EMI5YD+NvEENFAUhERSQJPk+nOua/1ZT0z+7Zz7nOdFnf5xgrs7vz4uOuBe+P7PVn17pzbb2YHgTGAkulxSqT3XyiU+sR2SUkOcyqL2FATZk5lUdYl0gGKSnKZOXckW9cfZebckUrA9UGkrSWW2PaQ1/sLCuec3yGkBb2O04L6iaVARyV4R4/yoCSwc4oKiYZbAhOPpLlt2/yOQILrTOdcRx+gA8CZ8evvB/LN7NfECu6qnXMrgfHAGwmPfxOYCYwCGp1z7QnLx3e3U+fcZ5L2DERERDrxawBpbz7UxbLu3lhPY2bnAROBF7q474NAAadWxifevwhYBJA/bERXq4hQtfjYyQR39YrU/p5Urxjha0V64mti/Hh/YlhSPUmVrH2UOAz0/NkL/Q4nIyW+JgC27/sZF0/4hI8RpQe9jjNX4mui5KwSn6MJniBWgmsIaeokvh7OPfdcn6PxyKJF8MQTfkchqTfazGoTbj/hnOvzf7xzzplZRxVCHnApMBsoBraY2YvJCtTM3k+sav1M59x0M5tBrKXM/cnah4iIZK9MLXO9Dvipc+6USWdmVgb8C/AZ51yXFVjOuSeccxXOuYrcYm8/ELY3tXq6PxmYzq1XvBgO6mdFeuJrYuSoHN+GoSoB17vThoG2abBcKiS+JgAOHN9Ne0TH777Q6zjQBvxGk/iaKCwtPu3+tqa2QQWWCYKUSNcQ0tRKfD2MGTPG73C88b3v+R2BeONwx+92/NKXRPrb8c/gHZ/FO9q5vAk875wLOecOA78BLgbeAs5JePzZ8WVHgFIzy+u0vDvfA+4C2gCcczuI5QhEREQGLZ2S6d29sXblOuBHiQvMbBjwb8DdzrmknfVOloZHV7P+o9+j9t7n/Q5FetHRegXIutYrf349wtTJB7l9sTokBdFpw0CztPWK184aPo283AK/wxAZsPgx/ZLe1huIzXe/wI+vXMnmu0/7sqD4RENIRcRDa4Eb49dv5L1Zab8A/tLM8sxsCLFvnL8C/B64wMwmmlkBsc/1a12sr95/ANd2sa2uDHHO/a7TMg32ERGRpAhqmxfrYtnJN1ZiSfTreG8AyXsPNJsCjAC2JCwrAH4OrHTO/TQlEQ9CtLmFE1t2AbD/hXral/4NeUOUmAkyv1uv+OX48dg3M2vWhXk4C59/OjhlGKik3BlFZ6nFi6S1UChKzbpwSrbd1tTG6xv3AvD6xr1cdncb+UPyU7Iv6Z8gtp4RkfRmZj8iNmx0tJm9SWx+2TeB1WZ2M/A6sADAOfeKmT0H7CA2s+P7zrld8e3cATwP5AI/cM69HN/FUuBZM7sfeAl4sodwDpvZ+4gNHcXMrgUaelhfRESkz3xNppvZEOdcUxd3VXde4Jxr7+qN1cy+DtQ659bGV70OeNadOhVuAfBXwCgzuym+7CbnXF2ynstg5BQXMnTWdE5s2cW4KycpkZ4msjGRPHy4cfy4o3JedlXkpxsl0r1j1tW5X5H0UVKSQ+W8opQk1POH5HPe7Im8vnEv582eqER6wCiRLknzVk/dNiRbOOeu7+au2d2s/zDwcBfL1wPru1j+GvDBPobzWeAJYIqZvQXsBTQgQkREksKXZLqZ/QXwfWAocK6ZXQzc6py7HcA591RXj+vqjdU5d0+n2/d18bhngGeSEXuqlN25gAuGzUrLRHp7U2taxi39d+55uaz+yUgl0vso0txKbrFeGyJeC4c06LQ/lq8opWbdgZdSse3LH7jSl4p0HX9FPLRtG4wb53cUIifFE+8fNrMSIMc5967fMYmISObwKyP2GPBRYoNEcM5tJ1Y5nhGizQMb5JSOCenae59Xr/cskwmJ9HAo0vtKg/Tq/Wt5cX41r96/tveVRSRpllXVc3P5NpZV1fsdisR5nUjf99Aadn3yW+x7aI2n+5Xk03DUNDF/vt8RiJzCzEaZ2TJgM/BrM6s2s1F+xyUiIpnBt6yYc+6NTotSn93yQMOjq/nTwgdoeHS136GkXHtTK/tfiCUr9r9QT3tTq88RifTOi0RbpLmVI5v2AHBk0x4izXptiHghHIqwdf1RALauP+rJibNMkMoBpF6LNLdyfPNuAI5v3q3jbxo7/Pgq3rzjHg4/vsrvUEQk/TwLHAI+QWxo6SHgx75GJCIiGcOvZPob8VYvzszyzewfiE3vTmuJg0RPbNk14Ar1dJE3pIBxV04CUK/3FAuFon6HkBG8SrTlFhcw6orJAIy6YnJWthqItGf28U+Cqagkl5lzRwIwc+7IjGj1kuoTAqkcQOqH3OIChl8+DYDhl0/LiONvNlZnR8MtNNXuAKCpdkdW/gxEZFDKnHP/6JzbG7/cD5zpd1AiIpIZ/BpAehuxIaPjgbeAXxIbEpLWEgeJDp01nZzizB/sVPG1j9K+9G+USE+hqsXH2FATZk5lEdUrRvgdTlrrSLRtXX805Ym2KV+dT+SL2dmz95Vtz3CoYQdjymYw9dJP+x2OZJkl1ZMIP5gZPdOXVdWfPF4tqZ6Ukn0kewDpGblhrhixJynbGqgJS68hsqSyy+PviLPSq23uvofWcHzzboZfPo0JS6/pdr1jB87wMKrUyykqZEjFDJpqdzCkYoaGpQbdd7/rdwQinf3SzK4DOr4ufi2gvqQiIpIUviTTnXOHydBp2mV3LiDafHVWJNI7KJGeOqFQlA01sQTHhpowDz4SzYie5X7yMtGWjYn0SHsLhxpi1YSHGnbw/vYWcvOy53gowZAJifTTvkmTwuNWKgeQ+iUTjr+ntazp5gQBJOckQdAS8qNvu4Fo+Fol0tPBokV+RyDS2S3A54FnAAfkAiEzuxVwzrlhfgYnIiLpzdNkupl9m9ibWZecc0s8DCdlsimRLqlVUpLDnMqik5XpSqQnRyYk2oKq+ewSxpTNOFmZrkS6yMB4+U2aOPUTC5iOljUdlempPkEw4qx3A5dQVyI9TZiB6/YjnojnnHPBOpiJiEhG8boyvdbj/YmkveoVI1SRLmll6qWfVkW6SBJkUssaGZieWtakQhAT6iIi/WVmRuyb8BOdc/9oZucQ66P+O59DExGRDOBpMt0593TibTMbFlvs0quBpUhcKORNkluJdPFCpK2F3PzkJMCzJZHuVIknKaZEumRCy5qgizRn54wTkQy2nNg3rq4E/hE4AXwH+B9+BiUiIpnBlwydmVWY2U5gB7DLzLab2aV+xCIyUFWLj3HJlLepWnzM71BEBu21jSvZ/vRdvLZxpd+hpJV3wwfYvu9nfochIiIDtO+hNez65LfY99Aav0NJX5WVfkcg0tlM59xngTCAc+4YoDNmIiKSFH6Vu/4AuN05N8E5dx7wWeCHPsXSL9GWFr9DkADoPBg0FFKr2UT6eaSXSFsLjXvrAGjcW0ekTce5/jhwfDftkVa/wxCPhUMRv0OQLBNp1nEm2U4b8qqf8cCsW+d3BCKdtZlZLvF5bWY2Bs0GERGRJPErmR5xzm3uuOGc+0+g3adY+uzgUyt5fen/z8GnUlO52d6kP+A7C2pStmMwKJCVg0F7+n+5fXEjUycf5PbFjR5GJD3pLTmem19I6cRyAEonliet1Uu2OGv4NPJyVeyUTZZV1XNz+TaWVdX7HUoyeT1HxzOZkCBV9XTfRcN9PyHcMeQV8GTIa8aaN8/vCEQ6Wwb8HBhrZg8A/wk86G9IIiKSKfzKAG4ys++a2V+b2RVmthz4tZl9wMw+4FNMPYq0thCq2w5AqG470ebkVm42PLqa9R/9HrX3Pp/U7aazoLdRqV4xgpdePZPqFSP8DsVTPSXLQ6EoNetiFfs161SxHwR9bd9y/uyFXHzjNzh/9kKPIssMZxSdxcUTPuF3GOKhcCjC1vVHAdi6/mhGVKjP/OBBgIv9jiMVMiEJrerpvjv8+CrevOMeDj++qs+PmbD0Gqb/5MtMWHpNCiPLcDU1fkcgcgrn3Crgy8A3gAbgY865n/gblYiIZAq/kukXA+8H7gXuA6YClwD/BDziU0w9yi0opKQ89jmzpPxicoqTV7kZbW7hxJZdAOx/oV4V6qRPG5Vsq0iPRugxWV5SkkPlvFjFfuW87KvYD5r+tm9RRXr/mZnfIYjHikpymTl3JAAz545M+wGhBw+207A/mO+xg5UpSWhVT/dNNNxCU+0OAJpqd/S7Ql1E0p+Zjey4AAeBHwH/F3g7vkxERGTQfPlKr3Pub/zY72CNvWkh0ZYWcgoLgVDStptTXMjQWdM5sWUX466cRN4Q/UHf0UZlQ004K9uoBFVObixJXrMu3G2yfPmKUh5+JKr/swDoaN/SuLdO7VtEkmhJ9STCD0bSPpEOMHZsHmXjcjIyod6RhD6+eXfaJ6EnLL2GyJLKtH4OqZZTVMiQihk01e5gSMUMcor8f8/b3fAO5V//Zb8fd96oEn5y6ywK8vS3lEg/bSPWJ92Ac4Fj8eulwJ+Bif6FJiIimcKXZLqZlQILgQmJMTjnlvgRT3/EEunJV3bnAi4YNkuJ9ATVK0bwYA9J2VBICVs/9CVZng7/L+FQZiTCenP+7IVE2j6lRHqKOOf8DkF8kknHj62/G8u5Zx/Y7nccqZBJSehUPodIc2tG/IxG33YD0fC1gUikA5QW53P1xeP69ZjG5jZ+Ubeff9u5n49fcnaKIksyvRdKQDjnJgKY2feAnzvn1sdvzwE+5mdsIiKSOfwaNrUeeBHYiaZqn6RE+um6S8pWLT52smo923qWB0E6JMt7sqyqnq3rjzJz7kiWVE/yO5yUUyI9dd4NH2D7vp+pb7pkgsAPgh+oTEgSp9K+h9acrN6fsPQaRpz1LscOnOF3WAMWlEQ6wLjSYr529fR+PSYadex66zhP/udePlY+Pj3aiT3xBCxa5HcUIokuc87d0nHDObfBzL7lZ0AiIpI5/EqmFznn7vRp35LmOvdT76l6PZ1lQuV9EJ/DacMDM6RVg/jnwPHdXBiZR15uahJ20XBLoJJDItK7dKn0Pq2vfLyKf8RZ7/oc2cCk80mADjk5xt//5UTu/vkufrf3KDPPH+V3SL279VYl0yVo9pvZV4Fn4rdvAPb7GI+IiGQQv7Jc/2Jmt5hZWachISK96uinDqSkn3oQhp1WLT7GJVPepmrxMb9DGbDbFzcydfJBbl/c6Hcop0in4YG9DQyVYDhr+LSUJdIPLnuW1//+6xxc9mxKtu+1cCjidwgiKbfvoTXs+uS32PfQmqRuNxUDVDNtuGm6ngTo7JpLzqZ0SD5P/udev0MRSVfXA2OAnwNr4tev9zUiERHJGH4l01uBh4EtxIaEbANqfYpF0lD1ihG89OqZSW/xEoQkdufK+yAk9/srFIpSsy72HGrWBe85LKmexJN1lwa6xctrG1ey/em7eG3jSr9DkR6cUXRWylq8RMMthF7cCUDoxZ1Ew+l9cmVZVT03l29jWVW936GIpMxpld5JSoCnKkEPsb7y03/yZSYsvSbp2/ZD54R6OibYiwtyuWHmufzqlbd5/UjI73BE0o5z7qhzrso5d4lz7gPOuc875476HZeIiGQGv5LpXwQmOecmOOcmxi/n+xSLpKlUVKQHIYmd6sp7L5SU5FA5L/YcKucF8zkEvSK9cW8dAI1761ShHmCp7GWbU1RIyWUXAVBy2UVp3erltPZKqlCXDJWKSu9UJegTpXtFemfpmEDvbOGsCeTlGD/87T6/Q+nd2rV+RyAiIiLiGb8yXPVAk0/7FulSkJLYqaq899LyFaW8smcsy1eU+h1K2snNL6R0YjkApRPLNUA0i41dch3n/eAexi65zu9QBiWd2iuJDFayK70zrRWL9M2Zw4qonDGOn9S+wTvhNr/D6dmll/odgYiIiIhn/BpAGgLqzOw/gJMll865JT7F061Iawu5BUpkZYvqFSMCM9A0CDEMVsdzCOIg0qA7f/ZCIm2fUiJd0roiPdGS6kka+BtsOkgnUbIT3hOWXnNyOGiypcuw1Gx0819O5OcvvcWPf/cGt/xVgL/EO348OOd3FOIzM/sBUAkcdM5Njy8bCfwYmADsAxY4546Z2V8DvwA6BgOscc59Pf6Yq4BqIBf4vnPum/HlE4FngVHE2sT+L+dc8r+qIyIi0gu/Pjj9K/AA8F+81zN9m0+xdKu18QivVt/FG2v73rM42qx2DOkuKEnfoPUZH6i+DiL1q+1DkNtNKJEefFEX3N+fIFIiPZjix+dL/I5DepaKhHcqe7H7pT9tcFLRMieZpo8fzsyJI3nqv/bRHsmMvwsloz0FXNVp2VeAjc65C4CN8dsdNjvnyuOXjkR6LvAdYA4wDbjezKbF138IeMw5Nwk4BtzcOQAz+7aZLevuksTnKiIiWcyXrKFz7umuLn7E0pNoSzMA7+ypI9Lae5K84dHV/GnhAzQ8ujrVoSWVTgAETxAGoSZD50Gk3SWu/RpMmMr9Bv0DuiTHifBBNu2u9jsMyWCpPuGXeJyW7OJFL/bB6m9M/Tk5kC4nEm7+y4m81djMmpfe8jsUkR45534DdB7yeTXQ8Tn/aeBjvWzmg0C9c+61eNX5s8DVFhtScyXw0162VcupxXqdLyIiIoPmSzLdzC4ws5+a2W4ze63j4kcsPckpLAZg2OTyXlu9RJtbOLFlFwAntuxKmwR1up4AyGRBGYSaDJ0HkXZVlerXYMJU7vfV+9fy4vxqXr1fA7myQbjtHcKtJ/wOQzKQFycaE4/Tkl2C3ou9v8nuzicHouHu/xZPhxMJHWZPPZMLxw3jyz/dwX1rX6aptd3vkE53yy1+RyDBdaZzriF+/QBwZsJ9s8xsu5ltMLML48vGA28krPNmfNkooNE5195p+Sm6K9oLavGeiIikJ7/6WfwQWAG0A38DrASe8SmWbhWUjmJK1Tc4Z/7CXtfNKS5k6KzpAAydNZ2c4uC3Z0jXEwCZrj+DUNMh0d7bIFK/BhN2td+2psEP+Io0t3Jk0x4AjmzaE+gP6JIcRfnDKCoY6ncY4rFUn/jz8kRj/Pj8Usp24BMdf3uX7GGpyTKQZHfnkwM9zZtI5YkEM1tkZrVmVnvo0KFBby83x/jJbbO46S8m8NR/7WNu9WZ+v69z8a/PnnjC7wjEG6M7frfjl0X9ebBzzgEdzfX/AJznnLsY+DaxNrBJY2ZjzOwRM1tvZi90XJK5DxERyV5+DSAtds5tNDNzzr0O3Gdm24B7enpQd8NIOq2zALiP2Bv1dufc3yXcNwzYDfyrc+6OvgTan+GjZXcuINp8dVok0uG9EwAntuxKmxMA2aIvg1CrFh9jQ02YOZVFVK8Y4WF0/ddbH3q/BhMm7ndZVT1b1/+O933kXP72m5cPeJu5xQWMumIyRzbtYdQVkwNX6SfJNbRoLB+afKvfYYjHYseLo8ycO5Il1ZNSso+OE34d+/Hg+Bj8s7P9sO+hNRzfvJvhl08LXKI4aIL4PtWR7O74P+xrjImDWo8d6Pu6yeScewJ4AqCioiIpUzmHFORx3/wL+eiFZ/Gln25nwXe3cP0Hz2XiqJJkbH7QPnbLfP71ewP7Np7DEYlCWyRKeyRKa8T1qT+8AyJRR0t7lLZI7NIe1RDUFDvsnKvo52PeNrMy51yDmZUBBwGcc+90rOCcW29my81sNPAWcE7C48+OLzsClJpZXrw6vWN5d1YRG3z6P4HbgBuBwZ/dEhERwb9keouZ5QB/NLM7iL0R9ljWlzCM5CPEvtb1ezNb65zbnbDOBcBdwIfiU8LHdtrMPwK/SeLzOCna3EJOcWHaJaTT7QRANumtIj2xFUxvifd04NdgwqKS3FMqQP/0qz/Tdk8b+UPyB7zNKV+dT+SLrYFMUHQn0taigacDkGMaqJltTqsYT+GJQI9PNKb1m0ik+b1j7mlVzSlImErqDTTZ3Z/10+33Ytb7RvH85/+Kb2x4hVVb/4wLSO74lv9+mQfWv5KUbRXk5ZCXY1gf1s3LzSE/N4eCXKMgL4ecPj5OPLWWWCL7m/F/fwFgZmcBbzvnnJl9kNh70BGgEbjAzCYSyxFcB/xdfL3/AK4l1kf95La6Mco596SZVTnnNgGbzOz3qXmKIiKSbfxKplcBQ4AlxBLcVxJ7Q+zJyWEkAGb2LLGBJrsT1rkF+I5z7hiAc+5gxx1mdimxHm3PAf09o96jhkdXn6zuLrtzQTI37Qkl0tNPRyuYjsr0dE+k+y2xAvR9Hzl3UIn0Dun0Af21jStp3FtH6cRyzp/de1srkWzmdcW4F4n02xc3AlyS8h2lyOa7X+D1jXtPVqEPtKpZgkf/d6crKcz7f+zdeXxU9b34/9c7C0kYlrBFFkVQkEWEKFTx23JtoS5QxNbWpdUit15t0RZau6i/eqvWpbXaBW4rrVqL1F6X9np7geJ2sbXU6wYaFlkqAiKbYYcMySSZef/+OGfiZMgyM5mZc2byfj4e88jMmbO8J8k5M/M+7/P+cPdnz+C2z4z2TyX2fbDuzgtTXryoQCgqEAoLBGecSeNH8p12nhd5AvgkTjuYHcDtOEn0p0XkWuB9IPpl+QvAbBFpBGqBK902MI1usd3zOFejP6qq77jL3Aw8KSJ347Qm+20b4UR7N+4Wkc8Au4Deib9aY4wxpnWeJNNVNXpWuMZ9Y+0We6lXK1oajOScuHlOAxCRV3DefO9Q1efcKvifAlcDn+5o/LGO7ztuVd4mOxJpBWMSN2feMP5665C0JNJzSbghxKGtVQAc2lpFuOEKq1A3ph1etabKhGAwwtIldV6HkbK6YJj3l28FmlehZ6qFhzF+UVrso+PPgAF0K/GqRsv4hap+sZWnprQw7y+BX7aynmXAshamb8EpsEvE3SLSE/g2Tk/2HsC3ElzWGGOMaZMnWTgR+U8R6SEiAWAdsF5EvpuGVRcBw3HOiH8ReFhEyoEbgGWquiOB2JoGDQrXBtvdYJ+GxpwbeNTkj2wk0mP3iQP7O9ZSt+RAxwf4zKTOlkgHKCwuoXxoJQDlQystkZ6A2H2ivvGY1+EYj+RDIh2c95HpF5fSkVcTu08cPZjd43xpoJCTpwwFjh9IMp8T6eWH7dhjfGTXLq8jMKYZVV2qqodVdZ2qfkpVx6tqao39jTHGmDhelRCMVtUjInIV8CxwC7AKuL+NZVobjCTWDuB1VW0AtorIP3GS6+cCk0TkBpze7F1EpEZVb4nfSOygQWX9T2r32snbHl3Gt63vuMljsfvE2HHFHbqeePxd7/N/P8/MYH0mdadMmWkV6UmI3Sd6dh3ok2vsjUndgwvK+dSSPQdTXT52nxgypnvW94lJ90ym+9f65HXyPN7NDz3Prd/9nNdhGOO44w7nZozHROR7qvoTEfkPnHFqm1HVOR6EZYwxJs941R+iWESKgc8Ci93kd3tfvt7EHYxERLrgDEYSf3b5zzhV6bijgZ8GbFHVq1R1sKoOAb4DLGopkZ6sIbv2cfErazl5935LpBvTjqItjQxZeoDu23K3nUA+s0S6MZ1X0ZZGyqGX13F0RGdKpA/euZ+pL7/D4J0HvA7FGMedd3odgTFR0ZFwV+IU68XfjDHGmA7zKpn+G2AbEAD+LiInA232TFfVRiA6GMkG4GlVfUdEfigiM9zZngf2i8h64K/Ad1V1f7qClkiErqGQc6sNMe3/nF7pn3llLV1rQ003iXSsFUZnEwza7ysvRRQJRppuXRc7SfSTl+6nKBhuuuGXwbPSJFxb73UIxvhWXTDsdQgGWj0+G3+SiFJWW990u/Dv6wG48O/vNJsuefZ+mi72vmxM56GqS9y7x1T1sdgbYP2xjDHGpIVXA5DOB+bHTHpfRD6VwHLHDUaiqj+Iua/ATe6ttXUsBBYmF7FDgOte+hs3vvi/FMUkzL/51Et886mXaCwo4MEvnMcvp02EQFmL64jUhjpcxR4KNlISyI9BfubOPsizS+uYOr2UeQtyuijOuIJBd1BUhe6/DtJjfhCJyZ+NnbeTsfN2EimEdTcO4p0bBjZdllIXzO0B/TbevZj9L2+iz3kjGHnbjPYXMDnPedsxiZg/dzOvLzvAOdN6M2eetXvyVCvHZ5O4cG191qrhRZVrnnmVf3vqHxTFJMxv/MPL3PiHl2ksEB6+chIPX/EJFOmqh1YnAAAgAElEQVTQtjL5urL5O4va9+s/sH3lGnpOGs2Qmy/N6raNMZ66FfhjAtOMMcaYpGW1Ml1EbmrtBvi+f1mkoIB5Uy/kqhu/xq7ePZo9t6t3D2beMYvvf7CXd2f9iN0/e/q45Xf/7Gnem3lPi88lauG31nLzhJdZ+K21Ka8jKlIb6vA6OiIYjPDsUqca7tmldVahngdumH2IUSOquWH2ISgUrt4c5rwwVJc2n6+mfzHPPnwa674xCC10vvjPn7uZaytXMX/uZg8i77hwbT37X94EwP6XN1klXCdxtG4Pq7f9l9dh+F5dMMzry5yWFK8vO2AV6knIyO+qUDjy7e7sfao3DQO8ukgxN4Vr69l23zOsu+wnbLvvmaxsM1JYwK+vOo/r772a3X26N3tud5/uXHfvl/nNl/6FSGHH/paZfF3Z/p0BROpCHFu5BoDDK9bb+3ImrVzpdQTGACAiU91+6YNEZH7MbSHQ6HF4xhhj8kS2v0F1b+eWE9489VR+P21is2mLPjORN4YMoOZVp/VLzavrmiWrI7WhVp9LVCjYSNVz1QBUPVdNKJj654Fd9z/R4cR+RwUCBUyd7mRZp04vdaqZTU5o6cRHMBhh6RLn5MjSJXVUVzeydEkdK4D74zoIPFpexPSv/LMpcZ4PibbCsi70OW8EAH3OG9Gp+vd2dnsOr6cxbEmatpQGCjlnWm8AzpnWO6evQMmmTJ9kDE3swoeXl7Y/owE+SggfXuG0Wcl2gvatMSfz5Iyzm0174pKzeXvM4GbTUokpXFufsdeVyXW3paC0hK4TxgLQc9Joe182pnPYhdMvvY7mvdIXAxd6GJcxxpg8ktXspare2dYtm7F01PlvbKC2SzF/mHwWdcVFnP/GBgrKSuh27hgAup07plk7l7aeS1RJoIjKiyoAqLyoIuVWL7vuf4LgG87YLKkm9tNl3oJevL3xBGvxkkOaVZ/HCAQKmH6xk5SZfnEpFRVFTY+/0kuIlMKGy/rSWCKM31gLfJQ4z5dE28jbZjBx8Vxr8dLJ9O85mqJCS9K0Z868Yfy2ary1eElQtk4y9vtHA8eAvRlZe/6ITQhHeZGgnfzqJmpLinjq/ErquhQx+dVNzZ5PtQK8sKwLPSeNBtL/ujK57vb0/dpVjPnj96zFS6ZNmOB1BMYAoKqrgceBV+J6pj+jqge9js8YY0x+8KTxtoicCPwH8HF30gpgrqru8CKeZJ1w6DBd6+r5wo+u593BJ/CfnzmXn877ExUHjsBNlxOpvaTFZPmANp5L1Kyfn0Ho7tR7pkdqQ02JdIDA2aM63MO9o6wiPabPuM9FwjSrPr//geZxP7igvNm0BxeU8/PvNjD4+sNU/7Inb598Cltm9WfElRsYcDTM4JjE+Zx5w6i7N7d7pgNtJgkidSEKSr3d30x6dS/tz7ghn/c6DJOHoicZo33mM3FsLNwdRo9GOBvYnva155doQvjwivX0nDSak+ZMz3oivWLfEcrq6rn6p1/hvSEVPH3J2fzogT/Tb/9R9vbpfnwFeJIxDrn50qSXgcR6oae67nSwinRjOhdVDYvISSLSRVXt0kFjjDFp59Uolr8D/hO4zH18tTvtfI/iaVW4PkRhl+bJr8JImMvuvZ5QSTEA7w4+gcvuvZ7eR4IAbSanE01cNx5r/X2/I4OPRivka15dR+DsUQz87hdTXpdJj1wahLWg0Kk6X7qkrqnqPF78SYGuXQqoXtIHLROohcOndWXV/53Jz3eECA9rPlBvfLIo1wckjVU9/0mCr60lMPEMKq6c5XU4xmSdDUCavDnzhnHotnrK+2UoGRiGfX/py9CbDvPOkrr2588wvx/zvUwIAxRGInz5p//a9PnzvSEVfPmn/0qvw8ec5+MS/qnEmewy2+57pml7sdXfLSXYLaltjMmircArIrIYCEYnqurPvAvJGGNMvvCqFLafqv5OVRvd20Kgn0extKr+0H42zruVDxYvajZ9V+/eTV9kokIlxezuV56W7a68/XmWXfhwWgYZbcmAmy7n1EXft0S6D+TiIKwPLihnwyan3VBL7V7ihU8sdBLpsdNKC45LpMfL9QFJY4Vr6wm+5uzPwdfWEgl5O/ivSR8bgDQx+TAughfmz93Mjf+vKmPHwejx+cEF5QBvZ2QjCfrNNzfyjbNe5Tff3OhlGO3yMiG8u6K8xc+feyp6Nj0ecvOlWWtr0lovdC8GGzUeu/12ryMwJt57wFKcfEfOjdFmjDHG37xKpu8XkatFpNC9XQ3s9yiWVkVCTl/nI5uqCNdnJ/nVeKyeXS85X5o7OshoW7xu7WIcuTwIa2y7l3SfBMi3xFthWRcCE88AIDDxDApKbP/LJzYAafvyZVyEbPLgOOjZ2dy6YJiVz+4DYOWz+3L+mO+1bCX8W+qF7tVgo8Zjd9zhdQTGNJMPY7QZY4zxL6/avHwFp2f6zwEF/g+Y5VEsrSooKSMSqqXHiEq31YtmfJtFXbswcPIwdr20uUODjJrcMW9BL+59IDM90zPViz062Gi03Uu6t5GNXsHZVjHnSiLXf87pmV6d+HLhhhCFxZZ89zMbgDQx+TIuQrbk43GwNaWBQiZM7cvKZ/cxYWrfvH6t+Sa+9U06Ws0kI5F+7SYLBg6EXbu8jsKYJiLSD/gecDrQ1JtSVSd7FpQxxpi84VWm9ofANdERtUWkN/AATpLdN7qU92HIlTce1zM90ybceSGNN3+KCwZvyep2/ShXBubsqEy8xthe7JkQP9houvk58Zbql/dkBx/dsnwRh7ZWUT60klOmzEx6e8b4jR/3Zz/z83Ew3b76i5Fcc0/neK25Lv49MP79MFu95Vvr1248sHu31xEYE+8PwFPAdOBrwDXAXk8jMsYYkze8ylKOjSbSAVT1AHCmR7G0KduJ9KiirlZlM3f2Qc4c+SFzZx9sf+Yclake6fG92MnQvp7pEx1+TKpsuPPPvDZjHhvvXpzR7YQbQhzaWgXAoa1VhBusz7pfWZsXY9LDj8d801yi/dCzUZGeiXYy1pbGmLzRR1V/CzSo6suq+hXAqtKNMcakhVfJ9AIR6RV94FamWz8T0yQXB+ZMViZPFsT3YsfDXrj5ZMOdf+bAP94FYP/LmzL6pbuwuITyoZUAlA+ttFYvxnRC+TQQs8l9fuqH3lK/9o6ygVM74KyzvI7AmHgN7s/dIvIZETkT6O1lQMYYY/KHV8n0nwKvishdInIXTs/0n3gUS+s08z3S0ylTg5V6IZcH5kxENk4WzFvQi7c3nsC8Bb3an7kD8vFER0vCtfVNiXSA3p8YnvHKu1OmzGTcNT/KuRYv4cbOVUVvPdP9Jx8GsPRgANL8eqM1addeAjvbyfUhN1/KmD9+z2kr08Ft++lEQU5atcrrCIyJd7eI9AS+DXwHeAT4lrchGWOMyReefHFS1UXApcCH7u1SVf29F7G0pa56Jx8sXuR1GAlZ+K213DzhZRZ+a63XoaRNtpLBXsjEyYKWktqZPglxw+xDjBpRzQ2zD2V0O35QWNaFPueNAJxE+qjbP5ud7SZRke6HVjBbli/ilef+nQ2rHvc6lKzoXtqfcUM+73UYJka+VHNHByAFMj4AqXsM92W7vc7Mjwnd2AR2LK+qugvLuqRl25modO9Urr/e6wiMAUBESkXkm8BFwJXARlX9lKqOV9XM9mg0xhjTaXhWhaSq61X1l+5tvVdxtOfIpirC9d4nqNoSCjZS9Vw1AFXPVeddhXq+SufJAi/6yweDEZYucarrly7Jz1Y88UbeNoOJi+dmLZGejC3LF7H6sVvZsty7E4Cxfd737l7TKSrURcTrEEwMD6q5c17ssdz4h59bjrRUke5VVXc6t93aiQKTgIcf9joCY6IeAyYAa4GpOFfEG2OMMWmVv5nKNOkxojIrg5BGalNPOpUEiqi8qAKAyosqKAlY+/lcka6K9Gz3lw8GIwQCBUy/2Kmun35x/rXiaY0fq9X8MlhpbJ/3fgPGUlhkfd5NdmWzmjvTsnViIPZYbrwTmwTOtZYjXlZ1p3vbfnyPN8YkZbSqXq2qvwG+AEzyOiBjjDH5x7KubSitGMRJMzLfq3j3z56m5tV11E4exoQ7L0xpHbN+fgahuxstkd4JRVvGPLu0Liv95W+YfYilS+qYfnEpDy4o5/4HIp0mke5X0ST2oa1Vng9WesqUmZTt+Lwl0o1n5swbRt294ZxOpMNHJwZeX3Yg508MmLZtu+8ZDq9YT89Joxly86VNCeLotFxI8A65+VLCc6Z7EquX2zbG+E504FFUtdGuIDTGGJMJlnltSxbefCO1IWpeXQfArpc203jzpyjqmtqXgVQS6ZHaEAVllvTKdfMW9OLeLCS1I2GatXbJp0R6rreDOGXKTMINV3iaSI+yRLrxWjYSz3XBzCfss3FioLO0eQnX1vsy2XpcFbqbFM7FBLGXsebS7ykv7dzpdQTGRI0TkSPufQHK3McCqKr28C40Y4wx+SI/smA5rKCshG7njgFg4ORhKSfSU7H7Z0/z3sx72P2zp7O2TZM52UhqFxSSl61dogMWvnDLCq9D6RA/JNKN6QyyOchpphP2naHNi9/7j7fWpsQSxCZnrFrldQTGB0TkURGpFpF1MdN6i8iLIvKu+7NX3DIfE5FGEflCzLRr3PnfFZFrYqaPF5G1IrJZROZLC2Xnqlqoqj3cW3dVLYq5b4l0Y4wxaZEfmbAcN+Cmyzl10fdTbvGSitiK+JpX13WoZ7vpXB5cUM6GTRU8uKDc61DSIrYv8XsvbqfhWEM7SxhjOrN8HOTUPZ6/7XUcmZAL/cdt4EuT82bM8DoC4w8LgYvipt0CLFfV4cBy9zEAIlII3Ae8EDOtN3A7cA5wNnB7TAJ+AXAdMNy9xW/LGGOMyQpLpvuEF61WohXx3c4dY61eTFLypSIdmg9YeOr5gynuWuxxRCYdwo12gtBkRrYHOc1isj4jo1d7fbLBy8Exk+HXuIwxJlGq+nfgQNzkS4DH3PuPAZ+Nee4bwH8B1THTLgReVNUDqnoQeBG4SEQGAD1U9TVVVWBR3LqMMcaYrLGe6Z1QdMDTbueO4dRF37dEuun0on2JV4ZHeh2KSYMNqx5n7+419BswllHjr/Y6HJOHsjXI6fy5m5sGIJ0zb1hGt5UJv/nmRlY+u48JU/vy1V94d3zNxf7jxhiTJ05Q1d3u/T3ACQAiMgj4HPAp4GMx8w8CPoh5vMOdNsi9Hz/dGGOMybr8KS81CYlv72KMcWRjwEKTeeHGEHt3rwFg7+41VqFuMiYbFem53E6mLhhm5bP7AFj57D7P47dEuskWEbleRFaKyMq9e/d6HU52/OY3XkdgsqNv9H/bvV2fzMJuRbm6D38B3KyqGbkqyhhjjMkkS6Z3MrEDnlp7F2NMviksKqHfgLEA9BswlsIiO8aZ3JTtdjLpVhooZMLUvgBMmNo35+I3JlWq+pCqTlDVCf369fM6nOy4Pqmcqsld+6L/2+7toQSW+dBt0YL7M9rSZQLwpIhsA74APCginwV2AifFLH+iO22nez9+ujHGGJN11ualExpw0+VEai9pMZEeqQ1Zgt2YTiDcEKKwOD/39VHjr+a0xpAl0k3OmzNvGIduq6e8X25WVX/1FyO55p7Mt8PJlHBtvVW0G5MIEVBtfz7TGS0GrgF+7P78HwBVHRqdQUQWAktV9c/uAKT3xgw6egFwq6oeEJEjIjIReB2YCfxH9l6GMcYY8xGrTO+kWkqY7/7Z07w38x52/+xpDyLKnmDQriY0nduW5YtY/ditbFm+KGPb8Lq9iiXSTaZlo23J/LmbufH/VTF/7uaMbytTcjWRvu2+Z1h32U/Ydt8zXofiS+Haeq9DMMb4jIg8AbwKjBCRHSJyLU4S/XwReRf4tPu4Vap6ALgLeNO9/dCdBnAD8AiwGXgPeDYjL8QYY4xphyXTMyxSmxv9euN7qedK3MmaO/sgZ478kLmzD3odSt6ykxX+Fm4IcWhrFQCHtlYRbkj/vr5h1eO88ty/s2HV42lft9+oVeJ1SvPnbubaylUZTXLnes90r6QjyRuurefwivUAHF6x3hLHcexEgzGmJar6RVUdoKrFqnqiqv5WVfer6hRVHa6qn45JjMcuN0tV/xTz+FFVHebefhczfaWqjlHVU1X162ofwowxxnjEkukZlEuV3pnupd54zPsvosFghGeX1gHw7NI6S/qmSezv8YbZhxg1opobZh/yMCLTlsLiEsqHVgJQPrQy7a1ewg2dawDQo3V7WL3tv7wOw2RRtpLcHvRMz/nPhOlK8haWdaHnpNEA9Jw0OuFWL50h6W4nGkyLpk/3OgJjjDHGmKzJqS9OInKRiGwSkc0icksLz88Skb0iUuXe/i3mucEi8oKIbBCR9SIyJJOxplrp7WXSecBNl3Pqou8z4KbL07relbc/z7ILH2bht9amdb3JCgQKmDq9FICp00sJBHLq39+XYpPnwWCEpUuckxVLl9jJCj87ZcpMxl3zI06ZMjPt6y4s7nwDgO45vJ7GsCWUOotsJrnnzBvGr/6vkjnzhmVsG0D0BOiZGd1IhqU7yTvk5ksZteibDLn50oTm7yzV2qmeaDB5bskSryMwxhhjjMmanMkmikgh8CtgKjAa+KKIjG5h1qdUtdK9PRIzfRFwv6qOAs7mo5HEMyKVSu9o0nnl7c9nMrQ2ZaIifddLzmXwVc9VEwo2pnX9yZq3oBdvbzyBeQt6tT+zaVN88hxg+sWlTT/tZIW/ZXLw0VHjr+bjF93FqPFXZ2wbftK/52iKCi2h1JnMmTeM31aNz3iSOxs902OP5bks3Unebfc9w4aZv0goOd7ZqrWH3HwpY/74vYRPNJhO4OKLvY7AGGOMMSZrcinbdTawWVW3qGo98CRwSSILukn3IlV9EUBVa1T1WOZCdSRT6R2pDTUlnXe9tNkXbVHSoahrFwZOdpINlRdVUBIo8jgiLMmbQQ8uKGfDpgoeXFDudSjGY52hIh2ge2l/xg35vNdhGA9kuu1KttrJBAIFTSdCc126krzJJsc7Y7V2Z3iNJglLl3odgTHGGGNM1uRSVnEQ8EHM4x3utHifF5E1IvInETnJnXYacEhEnhGRt0XkfrfSPeMSrfQuKCtpSjoPnDyMoq758yVlwp0XMu3565j18zO8DiWn+a1tSmwCJrYS3U5WNJfvFYqdnYhkfBuRuvzuO29als12Mu4J0LcztoEMij/JkI4kbyrJcavWNsYYY4wxpnPwvkw4vZYAT6hqSES+CjwGTMZ5nZNw+oFuB54CZgG/jV+BiFwPXA9Q3CO7rUAm3HkhjTd/Kq8S6VH5+Jqyae7sgzy7tI6p00uz3qImdp8YNKh5ovzBBeXc/0DEEuit2Hj3Yva/vIk+542gx1fT35/ceCN2nygt7pnRbVXPf5Lga2sJTDyDijlXZnRbxn/mzBtG3b3hbAw+CpDyGdvYfaL3wOxdlfKbb25k5bP7OHnKHsq/dVVa1z3k5ksJz5meVHLeqrWNMcYYY4zJf7mUAdsJnBTz+ER3WhNV3a+q0RK+R4Dx7v0dQJXbIqYR+DNwVksbUdWHVHWCqk4oLAuk9QUkwpLObfNbdXY2BIMRnl3q9LN9dmn2B/aM3Sd69zn+kGGJ9JaFa+vZ//ImAPa/vKlTVReHG/P7tcbuE12KumZsO5G6EMHXnIGbg6+t7VT/Q+YjWUqkQwc+E8buE917FaczplYFgxFWPrsPgPeXb233KqBUrhKy5LgxCVL1OgJjjDHGmKzJpSzYm8BwERkqIl2AK4HFsTOIyICYhzOADTHLlotIP/fxZGB9huM1aTZ39kHOHPkhc2cf9DqUrAoECpg63WmnMnW6DeyZKwrLutDnvBEA9DlvBAWlnaOH+Jbli3jluX9nw6rHvQ4l5xWUlhCY6LTHCkw8o9P8D5nsu2H2IXCu3ssZgUABE6b2BeDkKUPbTHxvu+8Z1l32k4QGEzXGpOChh7yOwBhjjDEma3KmzYuqNorI14HngULgUVV9R0R+CKxU1cXAHBGZATQCB3BauaCqYRH5DrBcnAa3q4CHk9l+uD4E+KtCKRRs9MWAntkQX519bydrLTJvQS9fveZodbxf4vGrkbfNIPztegrLurBrr9fRZF64IcShrVUA7N29htMaQ51mINJMqZhzJZHrP5eVRHq4tt4qcTuhYDDC0iV1XoeRkq/+YiTX3BPm9frRrN3X8jzHDSaaYOsW2x9y28E93dudx/7GafTVr8L113sdhTHGGGNMVuRUJlZVlwHL4qb9IOb+rcCtrSz7IjA2le1+sHgRRzZVEagcR8Usf/Q9XvittVQ9V03lRRUJD+y5Yc8JADTudNrXFA0Kprz9DXtOYFT/D1NePlnR6uxo3/B0JXGX1Yxu9nhaN/9esOCXxPUNsw81JV6mX1waHbgu5/3jyPCMrLczfVEvLC6h34Cx7N29hn4DxiaUSC/74GgWIstt2Uikx/b3H3nbjIxvz/hHIFDAgIEF7N6VnhZiR8OlvHxwRFrWdV6vTU33//fYMD7ddfNx87xeP/q4abGig4keXrGerhPGcuRwHzjc9nb3/foPHFu5hq4TxtL3a+ntxW6yLzax3qu/856z7b5nOLxiPT0njbZBY40xxhhjTFJyKpnuhXB9iCObnErLYNVqIqEQBSUlNO4MdCgZ3ZL4BPVL+0YCMLnvxmbzhYKNVD1XDUDVc9WE7m67Qj26nniZeA1teWnfSCb33ciymtFJJ62X1Yzmwvvhk3c4r3VZTWZijCbX/ZxU91IkTLMKxqVL6hIegHRF7eBMhpayTCXRY+3am/gJh7LqDAaSBaPGX51wRXpnTKSXVgt1FV5H0Vx8f//o1RQmd62oHcyksu0JzRsMRtKWSE+3lw+OOC6h3pK1+5wuf61VI/e85hq6XxFK6MRUpC7EsZVrADi2cg2Rui9Yi6U8cnBPdyJ1oWZXK+y/4kr7GxtjjDHGmIRZMr0dhV1K6DGikiObqugxopKCksx+2G6p4juahI4qCRRReVFFU2V6R1q9dKRKvaPV6fFV4YnIVlubVGJLFz8n8gsKnWr02Mp0v1TMpyKRRPqaAwOzEEl+sUR6bon2949WplsivXMJBAqaHdf9Jj6hHv9cohJNlhaUltB1wtimynRLsuYf+xtnwOLF7c9jjDHGGJMnLJnehsIG5+dJM2YSrr+Cwi4lsBOODcr+iPXxCfVZPz+j3Yr0lkST54lOjxefdI+2jmlPfNLdy2R1e7zuRe/36vgHF5Rz/wPNe6YHg/7p556IbFSjJ6K0Wpo9zvWq9HiWMG9FdSlUZD5xuWtvOQP7HUpo3tj+/u1Zc2AgY3vvSimmumCY0kBhSsuazHlwQTlLl+x5Ox3rqm0sbqoU74gz+u5uut9WQj0qkR7ZACV7itudZ9BnZxGZ6lyJyJ6EVmt8LtS/odnjvl+7qsWrDiJ1iV3BYOKMH+91BMYYY4wxWWPJ9HZ02+kkDWsGHf/BOlNtUlqr+G6xXUtt4utMh1Rfc/Q1xZ8U8JtUetFnSnpPOOxuf5YkxCbOoz3U2+qf3laLF78ktluSkar06tIWJ1si3XgtlYr0ZPbfF25ZwXsvbufU8wdzwY8nJb2tRHyix7sZWW8n4ateL9GEfDSp3loV+tp9A5oS6YkkyhOV6SsRTXa1/L/RfNruJxdRs66KbmMqGXClP8ZIyhmDBoFmv9jIGGOMMcYLlkxP0EdJ9QK67nSqSWv61iVc0Z2oaKI6Nvnd0YE+4wcejcYflWylfexrTiax3lpCvbWe7pnUeKyeoq5djptW9dxy4Phe9NmK0c8nGloSDEaaWgO01D+9vT7p+ZJID9e2XtGbTL/0VtffEKKwOLOJnW57GjO6/nyl9fWwvZ2TVYM7VqXbkUrJdPz/xUq00j1ew7EG3nvR6eH93ovbafhBA8Vd05f4jErHMSVfEvLJ9E1Pp3BDQcJV4tD+//fadpY/uKd7U6K0q1WRmxSF60PUrHPGSKpZV0XJ9is8jsgYY4wxxviVJdPbUBCK0H2rU/p9dGgZ4CTVawYV8MHiRRzZVEWgchwVs9JXvdJSD/N0VJW3lkhvbVpbYpPvyZxMKBoUbJZQ98rK259n10ubGTh5GBPuvPCj+Lp2YeDkYU3PvVI7JuHK/1S0lNDP3O9leVrWUhPp0jxBXgDnTGvk9WUHOGdab94qGJLw7yxfEukb717c1Gt65G0zmj13XCIzpio9tsVLWXXrCfMtyxdxaGsV5UMrOWVKeivlMpVA70xV6UfD+1lds5xx3aYkt2ArVygcN9vCRQSrVjvvNd+7PIUIMyPZVi/FXYs59fzBTZXpmUikp0umjk35kqRvjzRIwhXiiVQCH6TtxHxsIr3bzjAANYOatxKKTjfpEU5wkGm/iv//AGeMpOJu5TTUHKK4W7nT2tEYY4wxxpgWWDI9QbFJ9bJttRzZ5FSvBKtWU7K1Pm0fuqOJ6lSrv1sSn0iPVtlH1QxKrtd1bPI9mar2aIuY+DY26WpBk4hIbYhdL20GYNdLm3ln1hUUlH30twvMnsmps0KcPjS16stEtZbQz0Vz5g2j7t7E+yD7OYkOrSfSW6rwjdSF2P+y08d3/8ub2HFNWesVlm20d2kpYd5tTyPhxhCHtjrHmkNbqyjb8fmcTmDkqz0NWzldGyiSVhKI23fDSYlX6kZFQiGCVasB570m8sEVibeeyFBP9mT6sJvmYo99+ZxYL2hIrEK8pUrg+M9Sx/q337oluq3YhHl88jzwQfpb8nVWq9/7Ix8efIcTep3OuFMv8zqcFB1fCBJuDNFQ4xzbGmoOUfb+sWwHlduuu87rCIwxxhhjssaS6W2QUANF7+0EoPHUQYCTVD86tIzeg8dxYPtqeoyoTGv1SjRRnWr1d3vrjU+ktzatLbHJ90Sr2mNPEkQT6l4oKCuh27ljqHl1Hd3OHdMskR47Tybjay+hn4tyPfrKyrIAACAASURBVJHeXiV6a60yCkpLCEw8g+BrawlMPCPhVhyxVenhhuYJ83DDFfTc7/w+C4tK6DdgLHt3r6HfgLE5kUhPuiq9vRYpOaB/8dDWE+ku5/fSndqKxN92C0pKCFSOa6pMT6qHcxYGOU2mOj1bbV5yRaaPhV4m6wsaEq0EL6LPoHHs37maPoPG0XNvERC/XPP3lmP9mz8bW43eXsJctu5MICbTlkZt4MOD7wDw4cF3CG/5WLvHPr/RoYNa/V85odfpTScKeuy21mdJeeghryMwxhhjjMkaS6anoPvWWoZ/4suEGy6ndkiZ1+FkXbTVTTK67pSke7NnyoCbLidSe4lnCexEEvomd1TMuZLI9Z9Luad1YXEJ5UMrmyrTo4n0qFHjr+a0HL+kvlV5kEjvXtgn+RYvSaiYNZNIKJTzgyHmUpsXkz0jz76acONlrR7fuu0MN2vJkWpPdEukp0eRFNO/yynsqd9C/y6n5FwiHZz/BR06qMXnxp16GY3hGRQV5vbx1hPjx8OqVV5HYYwxxhiTFZZM74BMDwiYLdkY3NBvvE5ge53Qz7S6YOJtX/JBqon0qFOmzCTccIWzH7bQxzwvE+l5QkhuzIlU5HoiPeqCH0/q9BXpPpfcWfI0ScfxLdwYSkMkJhHjuk/hdP2XnEykJ8IS6Sl66y2vIzDGGGOMyRpLpndy7/7j9xzYvpreg8cx/BNf9jqcTiVfE+nz525uGpB0zrxhXoeTMzrbCS3TOVki3Z9umH0I4Eyv40jFxjceZ//O1Tnewzu35Gsi3RhjjDHGmER4UoWUixrD+Vf1FG4IcWC7M7jdge2rCTfk32s02VUXDPP6sgMAvL7sAHXBRPrmJqfhWEPa12lMqhR/tK8yJlXBYISlSzLbXz9TwvUh9u90Psd8ePCdrH5Wa1R7LzKmyYABXkdgTF4RkTtE5Ds+iGObiPT1Og5jjPEbS6Yn4O1Dz/O3N+5m7T+f8jqUtCosLqH34HEA9B48rt3KWEu2m/aUBgo5Z1pvAM6Z1rup1UuqSfX4xPkLt6zgkUlP88ItKzoWqDFpcjS8n9U1y70OI2fYyTD/CQQKmH5xqddhpKSwSwl9BjmfY07odXrCLTo6mghffXQ5yw8sZPVR2/cTYSceOoFdiQ1GbfKbiDwqItUisi5mWm8ReVFE3nV/9nKnXyIia0SkSkRWisgnYpa5xp3/XRG5Jmb6eBFZKyKbRWS+iGS+154xxhjTAkumt+OtQ8/zYf1WAD7cv67Fqqdwfe4mmYd/4stMuOyedlu8vPuP37Pyj9/n3X/8PkuRmVw1Z94wfls1vqnFy/y5m7m2chXz524GEk+mxSfOG4418N6L2wF478XtlpQzvrGnYaslixJgJ8P868EF5QCrvY4jFSPPvpqJF9+dcIuXVBLhsft3ozawp34LAHvqt9i+3w478dBJ3HGH1xEYf1gIXBQ37RZguaoOB5a7j3Hvj1PVSuArwCPgJN+B24FzgLOB26MJeGABcB0w3L3Fb8vXRCQgIn8RkdUisk5Eroit/BaRCSLyt5hFxonIq+5JhevceQaIyN/dkxDrRGSSO32Be1LiHRG5M2ab20TkRzEnLc4SkedF5D0R+Zo7zyfddf5FRDaJyK9F5Lg8kYhcLSJvuOv6jYh0ngGyjDEmjiXT26Ao1W4iHaBf79HHVT19sHgRG+fdygeLF2U7vLRJpCLd2sGYZMRWpMe2fXnuOy8nlExrKXFe3LWYU88fDMCp5w+23svG5BA7GeZvbs/0cV7HkapEBzFNJREenwwukmL6dzkFgP5dTrH+4W2wEw+dyJ13tj+PyXuq+nfgQNzkS4DH3PuPAZ91561R1WivvAA09c27EHhRVQ+o6kHgReAiERkA9FDV19zlFkXXlUMuAnap6jhVHQM81878Y4HJwLnAD0RkIPAl4Hn3JMQ4oMqd9/uqOsFd5jwRGRuznu3u/CtwTnh8AZgIxO64ZwPfAEYDpwKXxgYiIqOAK4CPu+sKA1cl8dqNMSavWDK9DYLQv+RUAPr1HsW4EV9s9ny4IcSRTc7715FNVZ5WqEdCiW872WR4su1gsi1Sa8n9bGupbUtL02Lbvnzsgl5s/esOoP1kWmuJ8wt+PIl/W3E5F/x4Uodfg+k8Mp3A6V881BJq7bCTYf6V9p7pGknfulLQVt/0ZBPhrSWDx3WfwpTesxjXfUqaos4sr5LYduLBGAOcoKq73ft7gBOiT4jI50RkI/AXnOp0gEHABzHL73CnDXLvx0/PJWuB80XkPhGZpKqH25n/f1S1VlX3AX/FSXi/CfyriNwBnKGqR915LxeRt4C3gdNxkuJRi2O2/7qqHlXVvUBIRMrd595Q1S2qGgaeAD5Bc1OA8cCbIlLlPj4luZdvjDH5o8jrAPyusuf5NEY+CcOHHPdcYXEJPUZUcmRTFT1GVFLYxZskc/XCRQSrVhOoHEfFrJltzvvuP37Pge2r6T14XLutXaLCDSGGf+LLhBsu910ifffPnqbm1XV0O3cMA2663OtwOoX5czfz+rIDnDOtd7NWLvHTwEmwz5k3jLp7w5QGCvnBDXt478XtCSXTLvjxJBp+0HDcfJaEM8lYXbOcPQ1b6V88lHHd0p/46l7YJyPrNSZbAoECSkuhLk359ODhXWx843FGnn11elaYhNXv/ZEPD77DCb1Ob7Xty7juUzhd/6XVxG6jNjQ9F00G76nfclwyOFcSw6uPLm+K34vkf3u/73wkItcD1wMMHjzY42iMSau+IrIy5vFDqvpQogurqoqIxjz+b+C/ReRfgLuAT6cvVP9R1X+KyFnANOBuEVkONPJRgWP8ACbxo9yrqv7d/X19BlgoIj/DqTj/DvAxVT0oIgvj1hU9yxyJuR99HM0HHbetuMcCPKaqt7bzMo0xplOwyvR21IWPUVTQ+heAk2bMZOTcH3HSjLaT2JkSCYUIVjktWIJVq9usUA/XJ9+uJbZXut8S6ZHaEDWvOuPb1Ly6zirUsyASoVnblrpg+LhWLtEK9dhe6dG2L8lWlqczcZ6rbSXCjfZ/napGbWBPg9Oqy/qae8vavPhXdXVj2hLpUft3rs76sasxHOLDg+8A8OHBd9qtUG9JS/29c60KPVZLlfWJHgfTebzsTIl0AFV9SFUnqOqEfv36eR1Odqxc2f48Jh/si/5vu7dEEukfui1acH9Wx8/gtoc5xe0dvhM4KebpE91pO9378dNzhtum5ZiqPg7cD5wFbMOp+Ab4fNwil4hIqYj0AT6JUxV+MvChqj6M02f+LKAHEAQOi8gJwNQUwjtbRIa6vdKvAP4R9/xy4AsiUuG+lt5uLMYY0ylZMr0NRxv387f9i/jbvrYH3fSqIh2goKSEQKXTgiVQOY6CktZjKeySXLsWv/dKLygrodu5YwDodu4YCsr8lezPRwUFNLVtOWdab0oDhc1auUSntZZgB28qy3N14MMNqx7nlef+nQ2rHvc6lJxUJMX0Lx4KZK4Vy9HwflbX2MB67bE2L/4VCKT/o2CfQeMS7mOeLkWFJZzQ63QATuh1+nFj3LSnrf7euZoMjm+z8k7N3xMaDNQGDTXGpNFi4Br3/jXA/wCIyDAREff+WUAJsB94HrhARHq5A49egNMjfDdwREQmusvNjK4rh5wBvOG2SbkduBunb/k8t+I/vmfmGpz2Lq8Bd6nqLpyk+moReRsn6T1PVVfjtHfZCPwn8EoKsb0J/BLYAGwF/jv2SVVdD9wGvCAia3B62Q9IYTvGGJMXrM1LGxSn72ddJEhd/VFKu3T3OKKWVcyaSSQUajORHpVMu5Zor/RoWxi/VaYDDLjpciK1l1giPYti27a0Ni2aYI+2fomdN9uOq4htoXWMH4UbQ+zdvQaAvbvXcFpjKOvJqXwwrtsUTo9p25AJexq2Znwb+aC11k0mvwR6DvSkxQvAuFMvozE8I+lEOrTd0iWXRdusACw/sBBwTha01nol/qRCZ2vRYlI0YQJofFcI09mIyBM4yd6+IrIDJ2H8Y+BpEbkWeB+I9uX8PDBTRBqAWuAKd2DRAyJyF05yF+CHqhod1PQGnAE0y4Bn3VvOUNXncU4WxDuthXnvaGUdj/HRgK6x02e1Mv+QmPsLcX5/zZ5zz2kcUdXp7Sz/FPBUS9sxxpjOxpLpbRAKUCKUFgRaTKQ7ldpl2Q+sBYkk0qOSSYr7tVd6LEukZ19LyfH4aS0l3RPRcCy9ybZoRWyivdr9orCohH4DxrJ39xr6DRhrifQOyHQiyAYgTVy29r90H0fyWSBQwPSLS9M3CKl4e9FjKon0qGz2927M4gm46HYSOVmQrycVjDGZp6pfbOWp4/pkqep9wH2trOdR4NEWpq8ExnQkRmOMMSYdLJnehu5FfTir51RKC7vSGPdcdCDPHiMqPeuXni1+TqQbf0s2kf7CLSuakt6J9lVPRK5WxI4af7VVpPucDUDqP5k6juSzBxeUs3TJnre9jsMPspE89mpQ0ERPFnTGQUONMaazUtW/AX/zOAxjjMkponZJXqtEZC/O5WjxCoAzYx5/QAuDqWRYX2Bflrdp283d7Z6sqh0eBUtEDgPvpiGensDhuGnx+9Xb4PZaan/ZVLeZjnm9+h/xs1T/Rtk0XFV7dnQlbbxPeM2v/5eZjivR40hLOuvvLCpd7xMd3Se8/jv44X80338HuRJDh/cJH79HGJOKtLxPGGOMMbnOKtPbkOiHBRFZqaoTMh2P19u07eb/dhPwlKpe39GViMhDqa4n1WWTWS7Jef36t/JMR/6+2SIiD6VjPX79UunX/0u/xgX+jc2vcbUmDclHT1+v19v3Qwxeb98vMaSDX98jjMl1InIRMA8oBB5R1R/HPT8LuB/Y6U76pao+4j43GHgEOAlQYJqqbstkPO48lwN3uNtcrapfinmuB7Ae+LOqfj2TsYjIz4FPuQ+7AhWqWi4ilcACoAfOYKj3uH3SOySBeAbj9IEvd+e5RVWXuc/dClzrxjPH7TvvSTwiUozzf3MWTh5tkar+qKPxGGNykyXTjTHJWOKD9aS6bDLLpet1dla58PvLhRiNMcYYY0wMESkEfgWcD+wA3hSRxaq6Pm7Wp1pJTC/CSRS/KCLdSPwKtpTjEZHhwK3Ax1X1oIhUxK3mLuDvHYkj0VhU9Vsx83+Dj66WOgbMVNV3RWQgsEpEnlfVQ5mMB7gNeFpVF4jIaGAZMMS9fyVwOjAQ+F8ROU1Vw17EA1wGlKjqGSLSFVgvIk909ESMMSY3eTtKlDEmp6hqWhKQHVlPqssms1y6XmdnlQu/v1yI0RhjjDHGHOdsYLOqblHVeuBJ4JJEFnSTo0Wq+iKAqtao6rEsxHMd8CtVPehut6lFrIiMB04AXuhgHInGEuuLwBNuTP9U1Xfd+7tw2th29OqaROJRnGp4cFpF7nLvXwI8qaohVd0KbHbX51U8CgREpAgoA+qBIx2MxxiToyyZnh5paReQA9u07eb/dk3y7G9l/Miv/5d+jQv8G5tf48oUr1+v19sH72PwevvgjxiMMf40CGfMsqgd7rR4nxeRNSLyJxE5yZ12GnBIRJ4RkbdF5H63WjnT8ZwGnCYir4jIa26rEUSkAPgp8J0OxpBMLLjbPhkYCrzUwnNnA12A97IQzx3A1SKyA6cK/BtJLJvNeP4EBIHdwHbgAVU90MF4jDE5ypLpaaCqWf/A78U2bbv5v12TPPtbGT/y6/+lX+MC/8bm17gyxevX6/X2/RCD19v3SwzGmJy2BBiiqmOBF3F6YIPT5nYSTvL6Y8ApwKwsxFMEDAc+iVMN/rCIlAM3AMtUdUcWYoh3JfCn+LYpIjIA+D3wr6raoRY4CfoisFBVTwSmAb93TzJ4pbV4zsbp3T4Q5yTEt0XkFO/CNMZ4yZLpxhhjjDHGGGOMyQU7cQYPjTqRjwYaBUBV96tqyH34CDDevb8DqHLbfDQCf8YZUDKj8bjbXayqDW7Lkn/iJNfPBb4uItuAB4CZInLc4KVpjiXqStwWL1HuQKh/Ab6vqq91II5k4rkWeBpAVV8FSoG+CS6bzXi+BDzn/g2rgVeAnB8o2xiTGkumG2Nyjoh8VkQeFpGnROQCr+Mx6Scip4jIb0XkT17H0hK/x2eMMcYYk6feBIaLyFAR6YKTFF4cO4NbXR01A9gQs2y5iER7gU8G4gcuTXs8OEn7T7qx9cVp+7JFVa9S1cGqOgSnWn6Rqt6S4VgQkZFAL+DVmGldgP92Y0jX59tE4tkOTHFjGIWTvN7rzneliJSIyFCckw9veBjPdpz/F0QkAEwENnYwHmNMjrJkujGmXSJS7vYb3CgiG0Tk3BTX86iIVIvIuhaeu0hENonIZhFp80Okqv5ZVa8DvgZckUos5iMicpKI/FVE1ovIOyIytwPrStffeIuqXusuVyoib4jIaje+O/0UnzHGGGOMyQ63ovzrwPM4SfKnVfUdEfmhiMxwZ5vjfmZcDczBbeXitjT5DrBcRNYCAjychXieB/aLyHrgr8B3VXV/R7bbgVjASSI/qaoaM+1y4F+AWSJS5d4qsxDPt4Hr3L/VE8AsdbyDUyG+HngOuDG+JU024wF+BXQTkXdwkvK/U9U1HYnHGJO7pPnx0xhjjicijwErVPUR9yx+V1U9FPN8BVCrqkdjpg1T1c1x6/kXoAan4mFMzPRCnMsdz8e5DPJNnH51hcCP4sL5intpHSLyU+APqvpW+l5t+rh99L4P9FTVL3gdT2vc6p0BqvqWiHQHVgGfVdX1MfN49Tf+E3AZEFDVGhEpBv4BzI29/NTL+Pz8tzXGGGOMMcYYY0z6WGW6MaZNItITp0rhtwCqWh+bSHedB/xZRErcZa4D/iN+Xar6d6ClUc/PBja71b71wJPAJaq6VlWnx92qxXEf8GymEumtVTDnY/Wyqu6O/h7dZPQGjh/ZPqt/47h1qqrWuA+L3Vv8mWDP4jPGGGOMMcYYY0znYMl0Y0x7huL0ifudiLwtIo+4feKaqOofcS6Xe0pErgK+glNNnKhBwAcxj3dwfDI31jeATwNfEJGvJbGdZCwELoqd4FYv/wqYCowGvigio0XkDBFZGneryFBcGSUiQ4Azgddjp2f7bywifUTk18CZInKriBSKSBVQDbyoqr6KL4ntGGOMMcYYY4wxJkcVeR2AMcb3inBGuf+Gqr4uIvOAW4B/j51JVX8iIk8CC4BTYyqJ005V5wPzM7V+dxt/dxPLsZqqlwHc13uJqv4ImJ7JeLJBRLoB/wV8U1WPxD+f5b/xfpye+LEqRaQc+G8RGaOq6+KW8To+Y4wxxhhjjDHG5DGrTDfGtGcHsCOmEvhPOMn1ZkRkEjAGZxT425Pcxk7gpJjHJ7rT/CZvq5fdXuT/hdOD/plW5vH8b+y2GPorcVcN+CU+Y4wxxhhjjDHG5C9Lphtj2qSqe4APRGSEO2kKzqjqTUTkTOAh4BLgX4E+InJ3Ept5ExguIkPdAU6vBBZ3OHiPqep+Vf2aqp7qVq/7kogITk/8Dar6s1bm8exvLCL93Ip0RKQMZ5DQjX6JzxhjjDHGGGOMMZ2DJdONMYn4BvAHEVkDVAL3xj3fFbhcVd9T1QgwE3g/fiUi8gTwKjBCRHaIyLUAqtoIfB2n5/UG4GlVfSdjryZ1+Vq9/HHgy8BkEalyb9Pi5vHybzwA+Kv7//cmTs/0pT6KzxhjjDHG+IiIXO91DLEsnrZZPMaYXCKq6nUMxhjjS27P9KWqOsZ9XAT8E6c6fydOYvdLlnQ1xhhjjDHGP0RkpapO8DqOKIunbRaPMSaXWGW6Mca0oKUKZqteNsYYY4wxxhhjjOm8rDK9DVJUpITDFHQto6hf72bPFRZHkl5f/Z6DhGvqKOxWSpf+vVqcp6yoAYDuhXUJr3fvB3UcO9JI1x5F9DuptNX5joZbfy5VycQZ1aMg+WVMx6xd27hPVft1dD29exfoiScVHjd9+/thDh9WevYUBp9c2Oq0WDWRLh0NJytq0rjfHGssbva4oTHm99KYH+c2Cxrcn43exhEVOrKfcH0thV3KKOnRp2n6sX070rJPiIgWSRe6FvRoc75ItxIixUKkCCJlyb9/JKu4KJyxdXd136cAuqXwHmAyr1tBfVLzb9zQSEMDqKp0dNsiomU9iuh7UllHV5WwI40fHaeP7TpCw9EQxd1L6Dqw7f0y3bzctkm/w5v2dvh9orB7QIv6lTeb1lh9kEiwloJAGUUVLX8fgI+O49Fjrh1v/a96e4jgkTCBHoVUDC7xOpwOaem1bF13rMP7RN++fXXIkCHpCLFde/fupV+/Dn/USxtfxNPQAMXF/oknRq7Fs2rVqrR8lzDG5CZLprdBRJp+OSf+8ocUlH70oahX/6NJrStcW8+6y37S9HjMH79HYdnxycQz+u4G4LxemxJab10wzDfOerXp8X+8dS6lgeMTlwAvHxzR4vSOSDTOWJ/uujntcZi2DT1pz6p0XKY2dlyx/mVZ32bTgsEIo0ZUNz3esKkC4LhpgUDzZPGK2sEdDScr/nFkeNrWtebAwGaPd+2N+YJdnf6TXV4orXZycWXV7cyYBeGGEKsfu7Xp8bhrfkRhsXMcf+uRm9KyT0TfJ6aUX0ORFLc6X+3HT6OmfxG1FVA3prajm23XwH6HMrbusb13Nd3/RI93M7Ydk7pJZdsTnre6upEJZ+0D0pdMB7hv5XmUBIo6urqEvLRvJACNx+pZduHDTdOnPX8dRV2zc+LWy22bzFg86Vcdfp8oOWWQDrrnxqbHkboQ73/lh02PT370B82+X8SKHsejx1w73vpbXTDMtZWrmh7/tmp8q9/J/K6113LV8Dc6vE9MmDBBV65c2eEYTYpEwPI/aSEiafkuYYzJTflRCpkhBaVOVVXXCWNb/aCbqMKyLvScNBqAnpNGt5hIT0VpoJAJU53k5oSpfXP2Q1uuCgYzX2Hqd4FAAdMvdhLB0y8uJRAoaHGa8b9IKOR1CGlVWFxC+dBKAMqHVjYl0tOtf/HQNhPpxvhZRUURAwam9xhdeVFF1hLpsYq6dmHg5GEADJw8LKvJbC+3bTqu8VhyV3OkqqC0hMDEMwAITDyjw98vOpu6YOauuuqo0kAh50xzrmQ+Z1rvnP5Olk+vxRhjjMmE7H/TySHFvftw4rU30nByer5kDrn5UsJzpqctkR711V+M5Jp7wvZBJ8u+PvsQf1lax2eml/LLBeXtL5DHHlxQzv0PRJolzVuaZvyreuEiglWrCVSOo2LWTK/DSZtTpswk3HBFxhLpxuSD19+oYPCJe1anY12DRnVj1s/PSMeqUjLhzgtpvPlTniSzvdy2Sd3K259n10ubGTh5GBPuvDDj26uYcyWR6z9nifQkzZ+7mdeXHeCcab2ZM2+Y1+G0aM68YdTdmx/fyfLptRhjjDHpZlmudhSUpPeDbroT6VH2QSe7gsEIf1nq9K78y9I6q1CHFpPmlkjPDZFQiGCVk0cLVq3Oywr1TNrTsJVGbWh/RmP8LS0jHRQUdLhTTId5mcy2RHpuaTxWz66XnPaDu17anNUKdZO4umCY15cdAOD1ZQd8X6GeL/LptRjXddd5HYExxuSFnMp0ichFIrJJRDaLyC0tPD9YRP4qIm+LyBoRmeZOLxaRx0RkrYhsEJFbj1+7MYkLBAr4zHSnjclnplsbE5PbCkpKCFSOAyBQOS7tJxHznbV5McaY3GTteXKDtR0xJk0eesjrCIwxJi/kTJsXESkEfgWcD+wA3hSRxaq6Pma224CnVXWBiIwGlgFDgMuAElU9Q0S6AutF5AlV3dbedp0KTUuUZlIwmJutQH65oJz7rI1JRtUFc+/y0oZjDRR3zb3EasWsmURCIUukJ6lbYW/GdZvidRg5Ixf3aWNM8hqP1fsmMd1eLNaeJzmtHcczfXy3tiPGpMH48bBqVfvzGWOMaVMuZQHPBjar6hZVrQeeBC6Jm0eBHu79nsCumOkBESkCyoB64Eh7G2w4sJ/37rqVfb/+QzriNy34+uxDjBlZzddnH/I6lJRYIj1z5s/dzLWVq5g/d7PXoSTshVtW8Mikp3nhlhVeh5ISS6QnryZ8gNU1y70OIyfk4j5tjEneytufZ9mFD7Py9ue9DiXhWCyRnpjWjuPZOr5bIt2YDnrrLa8jMMaYvJBLmcBBwAcxj3e402LdAVwtIjtwqtK/4U7/ExAEdgPbgQdU9UB7G4zU1QJwbOUaInWt9xAO12anv2K+sb7jnVMif+dc6o0Z1XCsgfde3A7Aey9up+GY9dDuLKxnevtycZ/uhHLpM2FWZauPdj7wqge532NpSVvfLfyoteO4Hd+NMcYY09nk2xenLwILVfVEYBrwexEpwKlqDwMDgaHAt0XklJZWICLXi8hKEVkpxU6rhq4TxrY6UNC2+55h3WU/Ydt9z6T/1eQ56zueG2L3iQP7O3bC44bZhxg1opob2rkSIRd7YxZ3LebU8wcDcOr5g3Oy1YtJTOw+AdYzPRG5uE93Ju4x+cxUl4/dJ2oO5teJJT9VWecCP/Ug9yqW2P0hfDTY4jzV85/k/a/8kOr5T2YlpnRo7Thux3djcsiAAV5HYIwxeSFneqYDO4GTYh6f6E6LdS1wEYCqvioipUBf4EvAc6raAFSLyCvABGBL/EZU9SHgIYDSQSfpidfeSMPJLSd56w/UcHiF07L98Ir1hOdMp7DMLhNNhvUd97/YfWLsuGJNdT3BYISlS5wrEZYuqeNzd7Xd9zIXe2Ne8ONJNPwg/T3TrZe5v8TuE92L+qj1TE9MLu7TnUHssTlVsfvE4DE9Un6f8JvjKputr3ZC/NSDPNlY0tHrPXZ/KDll0HH7Q6QuRPC1tQAEX1tL5PrPtVq0A/4ai6W147gd343JEbt2tT+PMcaYduVSBvNNYLiIDBWRLsCVwOK4ebYDUwBEZBRQCux1p092pweAicDGRDbaWgJr233PsGHmLyju67Ro7zlpRgDQcAAAIABJREFUtCXSU2SJ9M4hEChg+sXOlQjTLy5N6AtXLn4pS/cX3uqFi3j/5v+P6oWL0rpekx7WMz05ubhP57vYY7Npzk9V1rnGT7+rRGPJ1lUIBaUlBCaeAUBg4hltJtI33r3Yd2OxtHYct+O7MTngjju8jsAYY/JCzmQxVbUR+DrwPLABeFpV3xGRH4rIDHe2bwPXichq4Alglqoq8Cugm4i8g5OU/52qrkk1lkhdqKkivWHfEUYt+iZDbr405dfmFevpbLLtwQXlbNhUwYMLyq2nZgIioRDBqtUABKtWEwnlVn/VzsJ6pptMysax8sEF5QBvZ3xDOWjCnRcy7fnrmHDnhYD1T89X2e6vXjHnSk5+9AdUzLmy1XnCtfXsf3kT4IzF4sfPTX6MyRjThjvv9DoCY4zJCzmTTAdQ1WWqepqqnqqq97jTfqCqi93761X146o6TlUrVfUFd3qNql6mqqer6mhVvb8jcRSUltBz0mjAqUjv0rtbB19Z9q34/ks8NXkRK77/ktehmE4mECjghtmHuLZyFfPnbvY6HF8rKCkhUDkOgEDlOGv14lPWM91kyvy5m7N5rLRRwFsRrWy2/un5y4urENqqSAcoLOtCn/NGAM5YLH6r/M7y8ckYY4wxxjdyqWe6rwy5+dKc7ZHecKyB95dvBeD95VuZ+H3/9GI0+S+2P+/ryw5Yj812VMyaaT3Tfax7YR+sZ7rJhLpgmNeXHQDsWOkH1j89//mp13vUyNtmMOru993P6e96HU4TOz4ZY0z7RORRYDpQrapj3Gm9gaeAIcA24HJVPSgiPYHHgcE4eboHVPV37jLXALe5q71bVR9zp48HFgJlwDJgrtuZwRiTYTlVme6VSF3LrRVyMZEOTk/nk6cMBeDkKUMtkW6yKrY/7znTetuXrwRYIr1l4Qbv294I4nUIaROutfYVflIaKOScab0BO1b6gVf9062tTHb5KZEe5cfP6XZ88p612DEpWbnS6wg6m4XARXHTbgGWq+pwYLn7GOBGYL2qjgM+CfxURLq4yffbgXOAs4HbRaSXu8wC4DpguHuL35YxJkOsMr0du59cRM26KrpOGEvfr13ldThpM+meyVaRbjzz4IJyPnfXIPvyZVK2ZfkiDm2tonxoJadMmel1ODlv492L2f/yJvqcN4KRt81of4H/n717j4+qvhb+//lmEiYkXAJB5KIIggKKEitVz2mRFkStovYq2lq09Wir5Wgvp0d99Hirtlh9fn3kWGi19lGOtujT1hYQBQoW6cUL1KAoUrmXSwwEiDAhk8nM+v0xM2EymSQzk32bPev9euVFZs/MnjVDZs+etddeSznilkfHaMWnhzhdubz2nmXsWbWZYVPHtPVsV8ordPvknrm3bub1pQc495KB3PLoGLfDUUp1QkReNcaMTFt8BfFkOcDTwJ+A2wAB+hpjDNAHOAC0AhcBK0TkAIAxZgVwsTHmT0A/EXktsXwB8FngJduekFKqjVamd0WEIxtqAWha+3anFeqFShPpyk365UvlKxoJc2hbfNt8aFutJyrUC1nqkLuG1Zu0Qt1jdFvpLU5WpDs5EFOpfOj2yXkdWuxohbrKxaRJbkeg4HgR2Zv4vQ44PvH7Y8B4YA/wDvGWLTFgOPDPlPvvSiwbnvg9fblSygGaTO+KMfSZUANAxaQzux0UpJRSyn6BsiBVo+Lb5qpRNQTKdNvcE6lD7qqnjC3YFmZK+YlbbWWUUt5WiC12jDE3GmPWGmPW7tu3z+1wlLLKoOTfdeLnxlxXkOhvnuxxfhFQCwwDaoDHjDH9rAtXKWUlbfPSjaFXzeJo1ec1ka6UUh5y8rRZRCMzNZFukXF3XU70ey2aSFfKQ7w4EFMp5b5Ca7EjIo8DjwNMmjRJhyMqv9gvIvmU+n9ojBkqInuNMUOB+sTyrwFzEgn2zcaYbcA4YDfH2sIAnEC8NczuxO+py3fnEY9SKg9amZ6FkvKg71q8+EkoFHM7hKLj1muup7KqVMWSSHfq80cT6UVP9wk9KJlI1zYv1ivk11T3h5TdiXT9G/Ope+5xOwIFi4BrE79fC/wh8ftOYBqAMeZ4YCywFVgGXGiMGZAYPHohsCzRKuYjY8x5iT7rs1LWpZSymX5xysL+nz3Lrtl3s/9nz7odikoz+6ZDTBhXz+ybDrkdStG4+aZDjB9bz80Ov+Zzb93M9TXrmHvrZkcfVyk31c9dyI6v30/93IVuh6J8LLE9P8vtOFRma+9ZxtKLnmDtPcvcDsU3Cvk11f0hZTf9G/Oxe+91O4KiYoz5NfA3YKwxZpcx5npgDjDdGPMBcEHiMsAPgH81xrwDrARuE5H9icGjPwDeTPzcnxxGCtwM/ALYDGxBh48q5RhNpncjFg7TtPZtwJ9DSAtZKBTjxSXNALy4pFkr1NPY8XrEorBkcfw1X7LYudc812FLWk2j/CDWHCb02jsAhF57Rz9/lC1CoVjbdr3YebFSWQeRWq+QX1MdPulNfvp/0L8xnxs2zO0IioqIXC0iQ0WkTEROEJEnRaRBRKaJyCkickEyMS4ie0TkQhE5Q0QmiMgzKev5pYiMSfz835TlaxO3HS0isxMtYpRSDtBkejdKgkEqJp0J6BBSr6msLOHSGeUAXDqjnMpK/XNOsqtivyQAMy6Lv+YzLnPuNc9l2JJW0yi/KCkPEhjYH4DAwP76+aNsUVlZ0rZdL2ZerVTWQaTWK+TXtBCHT/qd3/Y79W/M5/budTsCpZTyBR1AmoVB3/wKseYvZp3IiB7VIW5OeWx+FQ89EtNEeor0iv2HHrG2enze/CoezuE1D4Ws+f/JZthSh2qaAhrOpFS6WHOY6IFGAKIHGok1hzWhrmwxb34VSxbXrXc7Drd0qFTOYuhna1OLY0lYHURqvUJ+Tb04fLI55K14nOLX/U4v/o0ppZRSXqIZyCxlm8DY/tDv2PClH7P9od/ZHJFK0kR6e05U7Ge7Tqv7q3e3U6/VNMpPSsqDVJ53BgCV552hiXRlm8Q2eqLbcbgl10plN6rYCzHp63WF/Jp6af/Gb5XZufDzfqefnotK8bGPuR2BUkr5glamWyh6tIXGNe8B0LjmPaK3zNAKdeUKL1Tsp/bhXbK4Oadq9p7QahrlJ4NvuYrYjZ/TRLqyjfZMj8u2UjmfKnal/Mqvldm50P1OVVDWrXM7AqWU8gUt6bVQoHcv+k8+DYD+k0/rkEiPHi2cAUeq8NmRuI7lMIMotQ+vk/3VQatplL9oIl3ZSXumH5NNUjy1in3o+SdrIl0VNT9XZufC7uetQ0CVZW680e0IlFLKFzSZbrGRt32eCf/vPxl52+fbLdf2L8oP3n23NaeWLfPmV7Fx02Dmza+yMSqllFLKOZPuu4gh55/M3le3em5gqVJOu+XRMTxZeza3PDrG7VB8qZjb6CgbPPGE2xEopZQvaDLdBpkq0tu1f9EKdVXAlixuJhTKfqip9rRXSinv0jYvuWttaqHu1a1AotVLk+7XqeJWrBXpduvQRkcr1JVSSilP0CyXA7pr/6JUvnJJalvF6ZYtTtIvKUqpbPlle+HnNi92JblzHViqVLHzy/bSadpGRymllPImHUDqkJG3fV4HkqpOhUK5D+ecfdMhXlzSzKUzynnMoTYqp59e6tuWLXNv3czrSw9w7iUDbTtVOXq0RbcBqqg1h/wxpM2J7YWT5s2vYsniurfcjsNKa+9Zxp5Vmxk2dQyT7rvI8vVnO7C0GLQ2tejrUGRy2Zb7bXvpNB1wqiy1e7fbESillC/4s7zUozSJpjKZfdMhJoyrZ3YOvchDoRgvLomflv/iktzarvREiU/34504jXb57Wt47fJHef+BRZavW6lC4Je+rz4+7d75U51s0trUwp5V8b8zO9uwaAI5ftBi6UVPaO/4IpLLttzH20tHaSJdWWbdOrcjUEopX9BkulIuyjcpXllZwqUz4qflXzrDv21XnGL3abSRpghbVuwEoGH1Jp2boIqOnxIqetq992kbFmc4ddBCeUeu23LdXirlMZdf7nYESinlC9rmRflaPu1TnJRMiifbteQS62Pzq3joka6fn9efv5fYeRptWUUZo6ePYMuKnQz85Ck5naUSC4cpCQYtj0lZQxC3Q7CMnW2IkgmV5Kn+hZ5Q0dPuvU/bsNgvedAi2U7Hjdc6nxYz2pYmN6ktXfLZluv20tv80n5NKaWUcpIm05VvudFTPB+dJcWzSYR3dX3q81edS/+SaJcL50zmuaNvcODPH/D+A4sYd1f3lSH1Ty0gVLueypqJDL5ulm2xqfwdjjaw/shKJvaZ5nYoPfL+A4toWL2J6iljs/rbzIffEip+eR5+pglT+7l50CKfvvjp99HEetcy9TvPZ1ueeltN3nqH9rNXSiml8qMlqwqIt6HwE7d6iucrPSmeTx/1VOnPHxfe615/zcHZHs6RpggH/vwBkF2rl1g4TKh2PQCh2vXEwmHbY1T5qYtso1UKdxsaPdpCw+pNgP1tiDSBkrtCbomjioNbFem5tJhpbWrpcJ837nxJ+713oauWLvluy/0yOyOpkLfPfmq/pnLw85+7HYFSSvmCJtM95IxBe1153DV3ruK5qQtYc+cq2x/rggpndp4Luae4FQcC0p8/Dg+Wu/mmQ4wfW8/NeR4McILTXyLKKsqonjIWgOopY7ttp1ESDFJZMxGAypqJ2upF2SbQu1dOf5vKOX5LPClllVz64ieHpNY+9ErbfYaefzJ1r24FtN97Z6zud+635G2hb5+1n32RuvFGtyNQSilf0DYv3QgP8U61Yb6J6D82dX7aXqQpwo6V2wDYsXIb590ZoayirO265O+ZTBmwKa94nJJNT3Ev6kkf9VSpz//FJXUWR9m5UCjGksXxgwFLFjfzsEf/D9zo4TzursuJfi/7vtSDr5uVVc907avuriFloyg1nW8rC0Guf5vKfh0ST/a3yPHehlqpLmTTYia9Gv2SZTdQk7hPassXu6rry0qjDDvOu4UF3bGyPZefZme4sH3usUztdfzWfk1lwRgQ/8z7UUopt2gyvQtSlvmDZsCQw7Y+bqYktdUV3clEeVlFGSdNG8WOlds4adqotuT5mjtXtS2b/OBUSx/bSV5M4mbDqgMBdj3/rvq5V1aWMOOycpYsbmbGZd4+K8CNLxGdJisHN0N9x/723SXJvdpX/ejgrq/vXe9MHHbrG6gu+H7pSZpI9xYnE0+Js4jOsu0BLHBJn/c6LFt65DQApg56n1X7xzkdkmcVUx/w7p6nF4ak9sSZA/d0ef3k3jvtD6J35sX5DLmf/HgvQqHBiftZE/uaoyMsWU8uCu3AQFe90b0eu1JKKeVFmkwvAFYl0qcM2MTqg2M7JMonPzi1Q0V6Z9XqqetS9vJqEvrmmw61JcrndTLYdd78Ks9WpKfz1JeIThLqncnUV71QKtSPDvZHQt1g3A7BUamJnU/2+8DFSIpDPgf8ck2upZ5NpApfPkM5/S5TBXuHnusuDVHNVqbtrSOJ9E5ksy/YGav3Dd16Hew4MGCHUCjWror+Yz/ZXhD750oppZSXFVQy3RhzMfAoEAB+ISJz0q4fATwNVCVuc7uILE1cdwdwPRAFbhERS6YN2d3n3I6K9EyJ8tRkeWfV6j3lVL90ZZ9YlKxbuOiOep5ySKgn+6onK9O9kEhvHiyU12eXYO6uel0pv8op+dNJVWgxyVSVnlyu1enHFFqC2EnJ12HqoPfblu25eDC1L9dTc/FgLhyxtcN9FjkWXe7cTKQXSjs/JxTC8y6kM0aVA2bMcDsCpZTyhYJJphtjAsBPgenALuBNY8wiEUn9hnUX8LyIzDfGnAYsBUYmfr8KOB0YBvzRGHOqiFg2+aa7/uJekW2iPL1aXSmAkgC6Q+4x2fZV704hVbUrpayTmmgpZOFQq9shuK7QW5o47bqfnEH4gVaClQXzdcgTck3O5tMORlmrkM4YVTZbvNjtCJRSyhcK6RP1HGCziGwVkRZgIXBF2m0E6Jf4vT+QPB/9CmChiIRFZBuwObE+S6y5cxXPTV3AmjtXWbVKW01+cCozV83qthe6nYn0UChm27oLTaG9FvPmV7Fx0+CcT+vtSnPIsuNaRamnSfD6pxaw47b/Rf1TCyyKKH+xlrDbISiVFd1uecdT33mH2yatZu09lpx0WNAm3XcRlyy7wbMtXlqbWlx9/PSDLppIz0+2+4I333SI8WPrk3MZVCec+C7QVSK90L6LqB647DK3I1AuMcYcb4x50hjzUuLyacaY692OS6lCVUjJ9OHAP1Mu70osS3UvcI0xZhfxqvR/z+G+eUlvmxJpilixWtu5WXE++6ZDTBhXz2zdsS7Y18LKypa5t27m+pp1zL1V2wC5IVPfdbfsemEBmx65g10vuJ/UV6orftpuFXrP9HColdqX4wMY9qza7Hqy1gu8WpG+9p5lLL3oCdcOeqy9Zxm3TVrNU995x5XH95tsKtJT28FowjYztw84uP34ymFLlrgdgXLPU8Ay4p0aAP4BfNu1aJQqcIWUTM/G1cBTInICcAnwP8aYnJ6jMeZGY8xaY8za6OFQt7dPtk0BLO0v7lehUIwXl8R3rF9c0v2OtZ93vHN9LdyS+p440GBtjM2haLuhSIVS6VkoB82ykey7Drjadz3WEubwxloADm+s9XSFeup7oiV21O1wlMMKdbtlp9T3xJGDzm4fg5Wl1FwcH8CgrU2ckc8Biw793B0+6JH6+LUv19vaFij1/dDaWPifEfnunybbwQDaGrATbh9wcPvxlfI7Y8wvjTH1xpgNKcsGGmNWGGM+SPw7ILH8+8aY2sTPBmNM1BgzMHHdxcaYTcaYzcaY21PWNcoY83pi+XPGmK52ggaJyPNADEBEWonPE1RK5aGQ9mp2AyemXD4hsSzV9cDzACLyN6AcGJTlfUnc73ERmSQikwJ9K7MKLNu2KSq+Y33pjPiO9aUzut6xLtSq7Wzl8lrkw6od4tT3xMBqa2Msrwxw7iUDATj3koGUVwYsXb8dlt++hl9Mfp7lt6/J6/7Ro96rmhx83SxOeuiHDL5ulmsxlPQK0nd8DQB9x9dQ0su7/dtT3xPNEmL9kZVuh1Qw/JB4LsTtVldSE175Sn1P9BngfFHBdT85g4fWTumytYlWrFsj3+ryZD93cOegR+rj11w82Nb2Lqnvh9L+hT1BuKdVy3a0BvQTtw84uP34ShWBp4CL05bdDqwUkVOAlYnLiMjDIlIjIjXAHcBqETmQMjvwM8BpwNWJmYAADwE/EZExwEHi+bDOhIwx1cRbI2OMOQ9otOA5KlWUCukT803glMTRt17EB4ouSrvNTmAagDFmPPFk+r7E7a4yxgSNMaOAU4A3rAxOK9Kz99j8Kja8P5jHutixLpSqbehZ0jqb1yIfhXQg4pZHx/Bk7dnc8ugYy9dtdeIu0hRhy4qdAGxZsTPnCvX3H1jEa5c/yvsPpG+63OeF4aMnfG4WY//jR5zwOfeS+vmoi2yjVfxztoJd/NQa5ZZHx/DTv9bYst1yQyLR9ZbbcfREV8lRt9uL+EVPq8vt6ueebRyT7ruIh9ZO4bqfnGHp4/uVVVXLxZSgzec10gMOylEibkdQVETkVeBA2uIrgKcTvz8NfDbDXa8Gfp34PePsQGOMAaYCv+lmXUnfJZ4XG22M+QuwgGNtkZVSOSqYvZvEaSizifd52gg8LyLvGmPuN8ZcnrjZ94AbjDHriW98rpO4d4lXrL8HvAx8S0QKvzyugHW3Y2131bZVrEha21GRXigHIpLsqOy0I3FXVlHG6OkjABg9fUROB9EiTREaVm8CoGH1Jk9WqHuBlyvSOzOkbBSlRg+odsVvrVHm3rqZb/1rrS8ODKTw/odFHtxuL+InVlSXW12RnuuBEh04mj2tWs5NT6r43Xpttc1LEXr8cbcjUHC8iOxN/F4HHJ96pTGmgng1+28Tizqb/1cNHErkyVKXZyQifwemAP8KfAM4XUTe7tlTUap4FdRekYgsFZFTRWS0iDyYWHa3iCxK/P6eiHxCRCYmTpFZnnLfBxP3GysiL7n1HFT3kjtydlVtW8WrSetCORBhJzsTdxfOmcy1yz7HhXMm53S/sooyqqeMBaB6ylgCvbWvrx/0DVQzsc80t8PwPD+1RnH6wIBXPlsKldvtRfzGyurynh7Y0AMl9tOq6ewUWlI6GZ9TB0xS5wjs27fPlsdQWfrGN9yOwE8GJf+uEz835roCERESbVdSXAb8RUTSK9p7xBjzLaCPiLwrIhuAPsaYm618DKWKSfFl2ZSnpVd6ezkR7OWktdcPRNjNzsTd8tvX8PRFL+TVM33cXZdz3qJbGXfX5d3fWBUEg3E7hIJhZ0snJzl5YKCn/YpVnF3tRYqVFQckrGi9owdKnOGl/VuvKqQq/vTPFScOmKTOETjuuONsexylHLY/+Xed+Mm27P9DY8xQgMS/9WnXX8WxFi/Q+fy/BqDKGFOatrwzN4hI2w6liBwEbsgyZqVUGu9+0qui49VK7654OWnt5R15J9iRuOtpz3RAK9JVUSvkivRUThwYKLRKR6/TRKt3WFlRrgdKlFcUQhV/Z58rxf6dQSmHLQKuTfx+LfCH5BXGmP7EW7H8IeX2GWcHJqraXwG+mGldGQQSfdaTjxUAdOdIqTzpJ6fyDC9XenelUOIsRlYn7nrSM10p5S92HxgopEpHpXJhdUW5HihRXuH17bR+rigWLXI7gqJijPk18DdgrDFmlzHmemAOMN0Y8wFwQeJy0ueA5SISSi7obHZg4urbgO8aYzYT76H+ZBfhvAw8Z4yZZoyZRrz6/WUrnqdSxUin8ChPeWx+FQ89EtOdO+VZF86ZTOTuiCbSlVK2mze/iof1M1H50KT7LqL1tk9rIlwph+nnSpE7+2y3IygqInJ1J1dlHLgkIk8BT2VYvhRYmmH5VuCcLMO5jfjg0ZsSl1cAv8jyvkqpNJpMV56jO3fK6zSRrpRyin4mKr/SRLpS7tDPlSI2fDhI+rxLVQxEJAbMT/wopXpIk+lKKaWUUkoppZRSSvmQMeYTwL3AScTzgAYQETnZzbiUKlSaTPeRUEhP2VNKuS/WEqakV9DtMFQRaw5FfTPsVCnVvdamFq10t4h+n1BKKV96EvgOsA6IuhyLUgVP95R8YvZNh5gwrp7ZNx1yOxSlVBGrf2oBmx65g10vLHA7FFWk5t66metr1jH31s1uh6KUcsDae5ax9KInWHvPMrdDKXg333SI8WPruVm/TyjlTzfc4HYEyj2NIvKSiNSLSEPyx+2glCpUmkz3gVAoxotLmgF4cUkzoVDM5YiUUsUoFg4Tql0PwOGNtcRawi5HpIpNcyjK60sPAPD60gM0h7TwRik/a21qYc+q+IGzPas209rU4nJEhSsUirFkcfz7xJLF+n1CKV96/HG3I1DuecUY87Ax5l+MMR9L/rgdlFKFSpPpPlBZWcKlM8oBuHRGuZ6aqZRyRUkwSGXNRAD6jq/RVi/KceWVAc69ZCAA514yUFu9KOVzpRW9GDZ1DADDpo7RVi89UFlZwozL4t8nZlym3yeU8qWzz3Y7AuWec4FJwA+B/534ecTViJQqYNoz3QWRpghlFWWWrvOx+VU89Ij2OFTKLna8b/1o8HWz6LWrRRPpFoo1hykp19czW7c8OobmH2rPdKVS+bmn+KT7LqL1tk/79vk5ad78Kh7W7xNd0p7yqqD9/e9uR6BcIiKfdjsGpfxE9wQctubOVTw3dQFr7lxl+bp1x87f9HRb9yy/fQ2/mPw8y29fk/N9I00RGyLyNk2kW6d+7kJ2fP1+6ucudDuUgqKJ9OIRDrW6HYLnFUNP8WJPpFu5j6jfJzqnPeWVUoXKGHO8MeZJY8xLicunGWOudzsupQqV7i05KNIUYcfKbQDsWLmtKJNsKj/fvPGgDph1SaQpwpYVOwHYsmJnTu/bniThC532S++5WHOY0GvvABB67R1izfqaFiM9kNq5W286yG2TVvPUd95xOxRb9aQPuPYU9z9N8DqzndSe8soXhg51OwLlnqeAZcCwxOV/AN92LRqlCpwm0x1UVlHGSdNGAXDStFHaMkJl5Zs3HGTZS/Ekmg6YdV5ZRRmjp48AYPT0EVm/b3uShC90u15YwKZH7mDXCwvcDkWpgqZJss6FQjFeSgxfr3253rcV6j2tKtee4v6mCV7ntpPaU175wp49bkeg3DNIRJ4HYgAi0gpE3Q1JqcKlewEOm/zgVGaumsXkB6e6HYoqAKFQjGUvH6tGvegzQd15d8GFcybzb2uu5MI5k7O+T75J+EIXC4c5vLEWgMMba7VCvQdKyoNUnncGAJXnnaF904uMJsm6VllZwmcSw9drLh5MsNJ/Y4CsqiqfdN9FXLLsBibdd5GV4SkPKPYEr9PbyXnzq9i4aTDz5lfZ+jhK2ebee92OQLknZIypBgTAGHMe0OhuSEoVLlf2uEzcNcaYuxOXRxhjznEjFjcUS1JN9VxlZQmXJpIFF10c5GePD3A5ouKVz/s2nyR8oSsJBuk7vgaAvuNrtH96Dw2+5SpO+uXdDL7lKrdDUQ4r9iRZNh6dP4CH1k7hup+c4XYotrCyqlwr0v2rmBO8bmwndVusCtp997kdgXLPd4FFwGhjzF+ABcC/uxuSUoXLrTKeecRPL5kK3A8cBn4LfNyleJTyrMfmV/HQIzHdeS9QxXjw7ITPzSJ26UxNpFtEK9KL17z5VTys2/8u+bEiPdWk+y6i9bZPazJcdamYtxG6nVRKqe6JyN+NMVOAsYABNolI8fQhVcpibn0DOVdEPmaMeQtARA4aY/RbguogFNKdYyjuL0mq52LhMCVBZxOymkhXyhoObv/1g8ajNJGem9amFn3NfCSb7wK6n6yUUpkZYz7fyVWnGmMQkd85GpBSPuHWnkfEGBPgWL+m40gMQlAqafZNh5gwrp7ZOnhNqbzVP7WAHbepcii5AAAgAElEQVT9L+qf0mGgSqnMEoP7znI7DqV6qqcDW5W36BBmpSy2dq3bESjnXZb4uR54EvhK4ucXwNddjEupguZWMn0u8AIw2BjzIPBn4IcuxdKlWLMOz3NDKBTjxSXxgUIvLtHBa6rnmkPFN6w8Fg4Tql0PQKh2PbGwbs+UUu2lDvBT/pfvENNCYNXAVjtFj3ovJq/SIcxKKdVzIvI1EfkaUAacJiJfEJEvAKcnliml8uBKMl1EngX+E/gRsBf4rIj8Pzdi6UrrvgPsmn03+3/2rNuhFJ3UwZuXztDBa6pn5t66metr1jH31s1uh+KokmCQypqJAFTWTHS81YtSyvsqK0sYOkw/Y4uB36u2rRzYaof3H1jEa5c/yvsPLHI7lIKgQ5iVssGkSW5HoNxzoojsTbn8ITDCrWCUKnSO9kw3xgxMuVgP/Dr1OhE54GQ83Yk1HQWgae3bxJq/qEPgHKaDN5UVmkNRXl8a37S8vvQAzT+MUl4ZcDkq5wy+bpYrPdOVUoUhFIqxd49WfPpdh6ptnw419erA1ujRFhpWbwKgYfUmot9rIdDbWzF6kQ4XVUopy6w0xizjWA5uJvBHF+NRqqA5vWeyDlib+Df9x3MNvEoqegNQMenMokmkR5q8NdBZd55VT5VXBjj3kvhxvHMvGVhwiXQrWrNkSqRryxdrSHz0h1K2sbu1QWr1p2rPi21C8pVr1XYhP3evJdIBAr17MfCTpwBQPWWso4n0Qm+Pot8FlFKq50RkNvBzYGLi53ER+Xd3o1KqcDlamS4io5x8vB5L7HuWNLffiTtY15cBQw4D8M7+oZY+5BmD9rL64FimDIhXr/yxaYxl6159cCxwLOYzBu1td/2aO1exY+U2Tpo2iskPTs1qXdmaMmBT23O5oKK4Wm2ojtYcdfaMslseHZNXRfqfPzrFshjePjCs3eU9+6ra36C+YzKr/qkFhGrXU1kzkcHXzbIsFrvWC1BebwDoXW/paj3rcLSB9UdWMrHPNLdDaWfPviqGHWf9wLa3DwzjzIF7gPj745P9PrD8MYpBttvAubdu5vWlBzj3koHc8mhu+wOTe+/M+rbz5lexZHHdWzk9gIOWHjmty+tX7R9n+WOuvWcZe1ZtZtjUMUy67yLL1++GbKu2/fjck3L7W1nZ48eLtAbYs6+K+rkLCb32ARUfP41+3/gKe/Z1f99hxx1q2+Ym90eS21wntiFKdVTndgCqp+65x+0IlItE5HfA79yOQyk/cDSZnsoY83ngk4AAa0Tk927F0plYc7zNy5ENtRy/Y2ZbdWd4SISDdX0tf7wBQw7zzv6hbQn1VMnkej7Sk+hJqZejR1vYsXIbADtWbqPvN6u7rZpJT8ZnE0NqUl05SXd+PZtIz5BEB+i1q6Xd8NBeu1oo6dXzM2RiLWFb1puqWBLpSXWRbZwuEUpNljN86sthsP0DH51KqPuF1w4M9LRFVTLZlkNS3ZLy1Y9ay21JbndlY93xlq4vdjTcriXKu9fNpKS3j85Q/Kjzq3z/3J3WWkLsn4bQa+8A0PTme8T+abJqvbaH9gfdU5Pq2Yg0RXh96RtAfBvyyh0jKavQWXOqJ95wOwDVU/fe63YEyiWJ/NtDwGDAJH5ERPq5GphSBcqVZLoxZh4whmP9mr5pjJkuIt9yI57OBIK9iYaP0n9MDX0Oxnd6m4ZAsO7Yjmh4SHZtUWLN4W5bxSQT9O8kLqcmq3OtBE+XTJx3dRCgYtKZNK19m4pJZ/JRYzU0Zr5dvlX5qQcJkgcHevq8VLb+7HYAbZyuSs+HVQnCXKrRkxXdbXoF6Tu+hsMba+k7vsayhHdJJ+u1OgHep67VkvVEW8MESr2dyBlSNqrbRHrvfx7myJABxxZ0chClU3km3zv8zfVQMjmfmlD3CycODOSSsE+2qEpWlebboir7ba41B12bI2VZJ7djR8M5JWrHD/mw3eXUx2ndXZn1erpXSWXNxLYzeGIHBlpzpKEgFPNzt15JBCoay9t97lY0Zrf9byZ+u/Skei6qp4ylYfUmqqeMZWPzSWD/cdyiFj2qvfCVxw0bBnv8tf+msvZj4DIR2eh2IEr5gVuV6VOB8SIiAMaYp4F3XYqlU+W9qznjwm/GEzm7oxwZHqAi5btmemK9M3sXLuDIhlr6TKhh6FVdt1RIVr0nq9StkppET485eUBg0De/ktWg1Xyq8lOfT6bKey+zupVPV3Kp9lfW6yqZlp4Yz1Vn1egdkugcS2yf8olZRM+ZSaAsGB/ZbBG71gu5JdF7//Nwl9ev3/5b6hrfY0j/05g48gs9Dc0WfQPVWbd46VPXytHBeX7sOpR8745d1e7FIteE/f5IHXCA/ZE+Od/XrUp7iZRkldjOtd1U6fBQhyR96uNU7O64Le2JkdOvJTolTKBXEHZbumrPK+bnbrWS1vhnen6fu/G/6Z4k1ft9YxZ9ro0X9GTTWkblL97K5x0qzzuDwbdc5XY4SmW2V79rOskY80tgBlAvIhMSywYCzwEjge3AlSJyMHHdp4D/A5QB+0VkSmL5xcCjQAD4hYjMSSwfBSwEqonPIfyqiHQ29ORDTaQrZR23kumbgRHAjsTlExPLutTZRiTl+p8An05crAAGi0iVMaYGmA/0A6LAgyLyXHePF2iJtquI7LM7CsCR4fHqsIosiriiLWGObKiN329DLcGdM+NfTjJITc4fpGPCOlkRnqtk8ruzxH/75dmf/pltVX5qDFYfJPAbv782Xq1KtzOJDtkl0jurDA+U2VOVnbpeq6rIU3WXKO9Oa7SFusb3AKhrfI/To5dRGvBetZchtwRe73poHmxTMKmSf2c2JNWTCXU/Vqd7SaQpwpYV8RYtW1bsJHJ3JKcWDW71tC9p6T6xHU1rNxXc1tL5vtHw+JDfzhL0qY/VZ7fVNdRlWNT9pgAV83O3R66f58f2C9on1XNVQnmXrX1Uz8XC4bZWPqHX3iH2uauzauWjlPK9p4DHgAUpy24HVorIHGPM7YnLtxljqoB5wMUistMYMxjAGBMAfgpMB3YBbxpjFonIe8TbtvxERBYaY34GXE8875XJWmPMc8DvgXByYaKPulIqR24l0/sCG40xycZrHyf+5l4EICKXp9+hm40Iift9J+X2/w6clbjYBMwSkQ+MMcOAdcaYZSLSbXldcPsBSgNBQice+xKXnlTvSqBXkP5jamjcXEv/MTWdflmEeHK+aUjn67KqT3t6dX2+sqnKT0om3lOHtxYaO/rkpyrU1yUboVCMysqS7m/oIFd6PeeYSLcjye2EnibSAUoDvRjS/7S2ynQvJtLzVV5vaB4szjyYzf3ZrTjYlK1iS9yXVZQxevoItqzYyejpI/LqdZxjQt2xjXSgV5B+Y2v4aFMt/cZ2s2+URcW59Un04hKNhG07cFvsSiKS92f5kSHtv6Y5+tnhgFg47JuEc0kw2K49kl+el/Khj33M7QiKioi8aowZmbb4CuBTid+fBv4E3AZ8GfidiOxM3Df5DfEcYLOIbAUwxiwErjDGbCTe8eHLKeu6l86T6f2I58UuTA0RHUiqVF7cSqbfncd9Mm5EgPc6uf3VwD0AIvKP5EIR2WOMqQeOA7pMpjeFD7LqrR9x/IDTmciXADok1UsHHuVQ7z5dBj7y4llEWzqvSE+VTKgH68o6VH5n03e9M8nEd3o1fTbV9enyScCnPp/0hLrdSepC4dfX4eabDrFkcTMzLivnuruGEh7YMSGUTGwnkz7NodwG7eUilyS65VXpGfgtkW6liSO/4NmK9IKSa4uYrjgwOLUzubwfe9q31iuJ+wvnTM65Ij1dNtu85bevIXCsAKFHApHsktvjz76G6JlfiidxO7n9keHd5/czPVZ6crjvtqPdrqfQ9IuE+Kis533i3/nHc3zYsIHjqydwxqkzLYhMWaVPXStHhpTSux6OJs5oSj0Q76XE+oAjRzjYp+vvQ6lybfNUCAZfN8uzBwi8Gpdywbp1bkeg4HgRSfbbqQOSPexOBcqMMX8iXnz6qIgsAIYD/0y5/y7gXOKtXQ6JSGvK8uGdPaiIfM2yZ6CUcieZLiKrAYwx/VJjEJEDXdyts41IB8aYk4BRwKoM150D9AK2dHLfG4EbU5d9ePBdWqOXUxoIUvnPULuE+p3Lfsd9H/9KF2EnlRLvMNO59Er31Mrv3b9/qm046KBvZvN4HdeTT+I8k+R6ckmqx8JhgnXBdgl1VRhS3xPDMyQ2uqo6D4ViLFkcT74tWdzM/4ls5/X/bp/YSU30/PmjU1h++5q2SswL50y26mnkzPKK2276pCcVehI9q6r0ndn3a3TriG9XUt8T5SXZJw9SdfU3kImXkibg7d7p7z+wqG3g3ri7OpzolhU7K+5zTdT3JJGejWQ7mVE9WEfqe6JXxYBubn1Md9XQqYnyTIn11OuTCXM3k8Ot0TClAWcSVt/a9iI/OvXKHq2jNRrmw4YNAHzYsIHx0c86Fr+ftfuMKOuf99laR0/smzGhntTV50isJWzZ4PJs3Pf73/Ofn/1qVreNpbV56rWrxdFY7WXhwWuL7HphQdvw2xM+586Bi9T3xIgR3mz5WDRuvBEef9ztKPxikDFmbcrlx0UkpxdXRMQYk9zJLwXOBqYBvYG/GWNesyZUMMacSrxq/XgRmWCMORO4XEQesOoxlComruQpEh+o9xOfKR8j3gxQgJMteoirgN+ISLvstTFmKPA/wLUikrEMKrEBfBygrCQordLCkF4nt/tykUyon3h4H9N31fLk+AvZ1ee4HgfdJ2XIaWqiOhYO07T2bQCa1r5N2Y6e9eHLpU1NV7JNzm9/eQGNm48NYM2l17rf9eRsA6ekvifOnFjWLqOXWnU+b37HCuzKyhJmXFbOksXNfPH8/ox++SDvbW/m8MjMXzZ62iPYKl0l0nKpeM1Uld5Ze5dCT6J3kEPCvNCkvif6lx7nSJY7l+S7rYl3m9vG9FT0aAsNqzcB0LB6E9Hv9axC3Q65JurzqbLPJWFfVlHGxOpyShvy/39NfU/0qT7Rlj/Arqrdk4n0rpLDpVvsnaRZ27iCuvAWhgRHU9N/uq2PNbz1EFMPvMOzm05nT2nuQymTSoEhwdFtcZdv329dkB7WOrrTwj1LDoi0+4yoGJb3+6H3Pw93SKgnpSfWU1mVPM02IX9SQz0z3nuLn55/MTsGdv99qKRXkL7ja9pi9E8i3XtiLWEOb4zP7jq8sZbYpTNdeb1T3xOTJk3yVnVAsXniCU2mW2e/iEzK434fGmOGisjeRH4quXXfBTSISAgIGWNeBSYmlp+Ycv8TiI8HbwCqjDGlier05PLOPAF8H/g5gIi8bYz5FaDJdKXy4FbR3/eBCSKSy177bjJvRDK5CvhW6oJEFfyLwJ0iktURvoqSfny8/wxKTRls2w0jhxKMxRPB5dvDTG2O75xM21XL82POb7tfc6AMMT1rPZqaUC8JBukzoYYjG+IJ6XwS6ZkS38mkOvQ8sd6ZaEuYxs3HBrDGwjPbVai7LZe+71bbu3BB2//p0KuOfdnxymvTnfSq84cfSatQjwnmqDD/kX48cl8fwk8H4dVGTlrSwPtfi/9x/+3waEy5ICXxRKEVPYLtZEXFqxdZ0eNceYff+urmItC7F9VTxra9T72WSM+VXdscExN6NccP4EWaIlzW0MxLFq27JByztK3K4VG9u7w+9bHKt+93JTncGotQF46f8FgX3kJr7FOUllj3+WVECMqxA67nJx5rSngLL5Sc2bY8bEoRk9tZLzX9p1ser9d1dmDFyQMiPZWpUh2sS552lZA3EqN35Ni+6mcSj/eZ995iwTlT2pYfLev8+9AJn5vlWmK3mOiBC6U8aRFwLTAn8e8fEsv/ADxmjCkl3knhXOAnwPvAKcaYUcTzX1cBX05Utb8CfBFYmLauTCpE5A3Tfj/BZ9VcSjnHrWT6FuLDD3LxJhk2Iuk3MsaMAwYAf0tZ1gt4AVggIr/J5UFLTfzLhYwaTonE+PzeNVxT9xcCHEtU3LBxOTdsXE6rKeHpsdN4etwFWJ3GGHrVLGLhmT3ud5eaQM9meSa5JN5TB7B2dSDAzaS2G2LhMEc2tD/IUGi9DFOrzmdcVt4ukR4KxagsN/T9WYh+c0OYlD+vMx/dzZmP7iYWgIp/a+Gl6ycQPzklzooewXYohIpXpRSMu+tyR96fPe3Lns36bdvmiHDB/2zk4ic3EIjG91isSqa7zY3kcGlJWbskvtWPbRC+0FTL1U1/b7f/OSv0JrNCbxLF8KvKs1lY8TGE3JLpQFEl0juT6YCIF6RXp7e7LkNC3YrkaXcJeSPwtdde4Rt/+SOlKSf63vLqy9zy6su0mhJ+9snp/OwT05Eu/hw7i62zWTI6KDc/p3xiFtFzZsZfu05eW6WUPYwxvyY+bHSQMWYX8Zl+c4DnjTHXAzuAKwFEZKMx5mXgbeLdG34hIhsS65kNLAMCwC9F5N3EQ9wGLDTGPAC8BTzZRTj7jTGjiXeEwBjzRcC/pxIrZTO3kul3AH81xrwOhJMLReSWzu4gIq2ZNiLGmPuBtSKyKHHTq4CFIpKaz74SOB+oNsZcl1h2nYjUdhllsAwZdexU0Le2/paXD77LbwaN5okj+xnS3Nh2XV15f+7/+Jd5e1D+nWrSk9Tp7V7yTbZa1Ss9X8kBrOERx+IvtuR5uq7ONsg0fDYTL7SImTe/qkNFenrrl/AnglTMPkyfumPP6ciQMv72/41h6dgzM63Wc4l08F/Fq9e1SqTtYKZyhp8GlNn9/nTiLBU7tzkSKOHFb5zJpo8fz7V3/pXq+lzrG7zNjeSwXUn81liE0pIyvh89xOMIvyLACSkzeOpLKvlxvwt4t9dQSx+32Nh9QMQumRLPPU+eBqkaVcOhbbVUjaqh8lD650IJvxx3MW/1P4Uf/OkZhoaOfR/aW9mfuz79VWqHnEzQwhNDtq5c0BbPydP8MbA0G1YdQNCDEKrNbntbnqn2ROTqTq6a1sntHwYezrB8KbA0w/KtwDlZhvMt4u2WxhljdgPbgNyG8Sml2riVTP858eGg7xA/6paVTBsREbk77fK9Ge73DPBMPoEmtUbDfHgwfgBwyf4t/Gb8RczeuKzt+l997Hz+OvGUzu6et/SEuhcke7vnItAr6Mnn4qaenG2w/2fP5j2Q1mrpFenprV84rxe7Zw3hrB8fmx/8j2uHsO/jfeEjx8PtEacqXp3kxRYv64+spC6yjSFlo5jYJ+O+prJY/VMLCNWup7JmIoOvK55ERT6cPEvF7m3O5o8dz+qrx/L5R9+yZf1W6LvtaLetXrzC6gRssu3I4F6jqG/ZRh3wKNF237IXVZyhiXSL+KnlTU+TpydPm0U0MrPL9bw1dDTPnT6Zb7+xpG3Zc6efT+0Qq0ZgxUUjYQ5ti9c/HdpW221cflGsBxCUzdatg2H2DVlX3pVIvF9gjKkESkTEe18ClSogPWvsnb8yEfmuiPxfEXk6+eNSLFkpDQSpHj4RgOrhE5ny4fscLS3jufHn0hwoZerWd2x7bKsqy3Np5eLkuopZPon0WHP7gbSx5nA393BOsvUL0K71ywkrDtJaXsLGLw2iNWg4ccVBN8PsUnfDAfNKatVnHriqOmqVCHWRbQDURbbRKoUxQ8BrchlYGguHCdWuByBUu55YOLttSqYBu8UgWTEOOHKWit3rr3nln7QEAzRAwU2fTO2XbveAUaelth2pb9nG8b1GAXClCdJMKYvLxxEmwL+Et7kZpu9YnkhvicQHcufz47JsEtaf3r6B5kAZz58a/z70qe3Wfx8KlMUr5QGqRtUURSK94wEE7+zrqwJ3uX9mPqncGGOqjTFzgTXAn4wxjxpjqt2OS6lC5VZl+kvGmBuBxbRv83LApXi6FDqxEoBxJ15DtPVLHB9pJvjmk1zzpW+zpXooz9ecz4+WP8NxRxrZ16e/LTH0pKo72hImoANnfKOkPEjFpDPbKtPdbvWSLr31S2BvlNKmKMt+exqNp1aw9boh/Ot3t9D7wxYojGJD5aBSU8aQslFtlenZtHpxsyWMWD4hw3klwSCVNRPbKtP90urFTn45S6V/fRPBplYeWnAx22e+uMPteNQx6W1HavpPp3/kEIM+Ws63+1/AjtKBvFhxJrc1/pGB0RAHApU5rT/ZPkapfB0XOkRFJMysK77NloFD+c2E83nwlWcYFGpkf6W134eyqZT3i2Rrl9RWO8XwvJVStlsIvAp8IXH5K8BzwAWuRaRUAXMrmZ7sHXVHyjIBrD0v0AaB0iCBlia++qVbCZfGv0RvqR7KV790KwOOhlyOrqO9CxdwZEMt/cfUMOEMbYnlF4O++RVizV/0XCI9KbX1C1FY/pvTiZbHlzWeWsHy35xO8IBWHKvMJvaZxulZJsjdbglzONrA+iMrC74dzeDrZvmqZ7oTCj2RDhCIxvjx0xcRKXdrd1B1Jb3tSLAkwLcHfp4WE///2lE6kG8P/Dz9Y0e7Wk0HyfYxySS9UvkIxGJce0XK96GBQ7n2ilsZ0GzP96FiSCint3YplgMISilHDBWRH6RcfsAYM9O1aJQqcK60eRGRURl+PJ9IT/qwYkDbjmNSuLQXdX0HuBRRZrFwmCMb4qcINm6uJdqa3ymC+d5P2curifRUoVCM6AmBtkR6UrS8hKZh3o9fuSfbinQvtITxSzsaTaQXnwND+6Qm0t1q/ecprTFvvZdTq8frA33bEulJLaaUfYG+Wa8vtX1MXXiL555vMfDD5wVAXd+Bmb8P9fHW96FCkam1iybSleV+/nO3I1DuWW6MucoYU5L4uRJY1u29lFIZufbFyRgzwRhzpTFmVvLHrVj8qiQYpM+EeI/B/mNqCJTmvkP2/hvP8Nriu3j/jR7Nb1VF6OabDjF+bD0333TI7VCUTyVbwgBZt4Sxg5uPrZQVlt++BuAst+NwW23jCv64/0lqG1e4HYptku1jAIYER2urF4etP7KSlYeeZv2RlW6HojymGHvDKxfceKPbESj33AD8Cmgh3mp5IfANY8xhY8xHrkamVAFy5bxeY8w9wKeA04ClwGeAPwML3IjHz4ZeNYvgzpnxnuk5Dg2NtoZp2B0fStewez3R1i/llZBXxScUirFkcTMASxY387kfRCmvDLgclfKjXFrC2KFvoLrgW7yo4hZpirBlxU63w3Bdx4rtT/k20ZzePkY5I/1sKjc/u5Q3aWsXZTtjQAp/3o/KnYhkfxqbUqpbblWmfxGYBtSJyNeAiYA9kztV3sNHA6VBqodPBKB6+ERNpKusVVaWMOOycgBmXFauiXRlKzeTEQbj2mMrZYWyijJGTx/hdhiuK7aKbb8/Py/yytlUyts0ka6UsoOJu8YY81+JyycaY85xOy6lCpVbE6eOikjMGNNqjOkH1AMnuhSL6sK4c67RinSVl3nzq3j4kRiVlSWsyW02mlJKKQddOGcy81c8+5bbcbhNK7aV3dw+m0oppVTRmgfEgKnAD4AjwE+Bj7sZlFKFyq3K9LXGmCrgCWAd8Hfgby7FYqtoS+EP79REuspFKBRr+72yUufZKX8T9FRZ5Rux7m/ifT0dqKmJdGU3pxLp0UjhfwdRSllsxgy3I1DuOVdEvgU0A4jIQaBX13dRSnXGlUyXiNwsIodE5GfAdODaRLsXz4q25r5Duv3lBWx4/A62v5xdK3g/JN5VcUlNnIMzQ0cjTT1LlChlpcPRBh0kp/yi4I9+OjlAtKdJezt4MSbVvVax/v9t68oFrH/6Drau1HFUxUoPpqiMFi92OwLlnogxJgDxSiBjzHH4pJBCKTe48sXJGPMJY0xl4uIngeuMMSe5EUs23n/jGV5bfBfvv/FM1veJtoRp3FwLQOPm2oyJ8tRluSbelXJbeuI8fehoeqLdCstvX8MvJj/P8tvXWL5upfJVF9lmSzJEKacktqlnuR1HT3QcIGrfe9LJpH22rIhJk/HOW39kJSsPPW3pQdloJMyhbfHvIIe21WpStQhZeTBF/3585rLL3I5AuWcu8AIw2BjzIPBn4IfuhqRU4XKrCmk+0GSMmQh8D9gCeDKL3BoN07B7PQANu9dnXaEe6BWk/5gaAPqPqekwBDQ1eZ5N4t1O+VTdq+KWKXGeOnT04s8ELW/xEmmKsGXFTgC2rNipFepKKWWB1G1roWqNhh0bIOpk0j5bVsTkxQMEftcqEeoi24D4QdnmWJMl6w2UBakaFf8OUjWqRgdaFhkrD6boGQ4+tGSJ2xEol4jIs8B/Aj8C9gKfFZH/525UShUutwaQtoqIGGOuAB4TkSeNMde7FEuXSgNBqodPpGH3eqqHT8ypf/jIi2cRbZnZIZGenjw/cepM+o+poXFzbcbEu53ef+OZtuc27pxrHHtc5U/z5lfR2nqQl18Kc/NNh5g3v8qydZdVlDF6+gi2rNjJ6OkjKKvQvrbKG4aUjdJhckq55J1/PMeHDRsYEhztyADRZNK+LrzF1qS9kzF1TMbrEFYnlJoyhpSNoi6yjXJTyerGXzGkbBQT+0zr8bpPnjaLaGSmJtKLUPJgyqFttT06mNIxKT/TkvjqPmrmuTd3clJ1JSOrKxncN0hJibFk3W5rjkSJRL3VNSNYGqBXacF3cVM9YIwZmHKxHvh16nUicsD5qJQqfG4l0w8bY+4AvgpMNsaUAJ7dax93zjVEW7+U1yDOTInxZNV6avK8s8S7naKt6VX3+T1HVXySVehLFjcz47Lytir0UCjGyy/FK2CWLG7m4UdillaoXzhnMpG7I5pIV57RN1BtSeJDKZW71miYDxs2AM4mgZ1I2ueqJzF58QBBsZjYZxpjY02sbvwVEK9QP10ilhyg1UR68bLiYIpVSfl0+w+Hue2377RdLi8rYfzQfkw+5TjOP2UQNSdWURoorOTv5vojPLbqAxat30PMY3PpywKGiSdUcd7J1Zx3cjWfdDsg5YZ1xPukG2AEcDDxexWwExjlXinbgQIAACAASURBVGhKFS63kukzgS8DXxeROmPMCOBhl2LJitVJ5kzJcycT6RB/TvlW3Ss1b35Vh2R5Z0l2K2kiXXmJwR/VVKp4pZ71U2hKA0GOr57QVpnuZBLYiwnnnsTkxQMExaK8pKKtQl3PdFJWsSL5bccZDqcP788L3/80Ow6E2N7QxPb9If6+8yCPrfqAuSs/oG+wlHNPrqYqbX+/sleAsUP6MW5oX8YN6UtFL7fSGMd88OFh5q7azJK391BeGmDWv4zkhAG93Q6rnX1Hwryx7QDzV2/hsVc2U3rHi/zXX7dz7b+OdDs05RARGQVgjHkCeEFEliYufwb4rJuxKVXIXPkUSiTQfwWcY4y5DHhTRIquGZvTyfNU0dYwgdJgj6rui10sHKYkWNyvW6ZkeaYku3JW8v1dzFotquzrjuCxEiTlO9GjLQR697L1MS6cM5n5K559y451t0bDlAbs2x6dcepMxkc/S/n2/bY9hl+1xiLtkueaSHfPxD7TLKtIz0Y0Ei6qyvVie75Wsvp1M8CI6gpGVFcw+ZRjyw81tfDXLQ2s+WAfr287QDjSvl1K49EIR8I74uswcNLACvpX2PvZ2JVoLMa7ez6ioizAN6eM5t8+OYrqPt79GzsSbmXdjoNsuv8RHl4W4LKJwxhY6d7rp1xxnojckLwgIi8ZY37sZkBKFTJXkunGmH8D7gZWEf9M/W9jzP0i8ks34ik22ie95/YuXMCRDbX0mVDD0KtmuR2OJySHkELmJLtyxsZ1z7Bv79scN/RMxp9dnO/v9UdWtlX42d2C5XC0gfVHVmqrF2WL9x9YRMPqTVRPGcu4uy53O5ycJfuZH189gTNOtabfbiZ2Juv9Ij1xXtu4oq2tS03/6S5GppKcSqRvXbmgrXXHydP8vw9bbM/XLj0ZZJqNqopeXHLGUC45Y2jG60WEXQePsnHvR2zce5hNH35EKBy1NabuzP70GL7+iVEMKICkdJ9gKVNOPY4pz/6YObefz89f3cIdnxnvdljKWXuMMXcBzyQufwXY42I8ShU0t86P+j5wlog0ABhjqoG/AppMt1l6n/SNr0c5sGdDwSfWnawSj4XDHNkQH8hzZEMtsfDMoq9Qv/mmQ22tXawcOqpyE20Ns2/v2wDs2/s2pyYq1IupUr1VItRFtgHW9p7tilOPo4pL9GgLDas3AdCwehPR79lXob789jUAZ1m5ztR+5h82bGB89LOa9HZJeuLcKwNH0xP8yn6Zhkr6uWK7mJ6vndX3yQMSwMm2PEAWjDGcOLCCEwdWcOHpQ9wKwxc+WzOcp/+6nes/OYrBfcvdDkc552rgHuAF4j3UX00sU0rlwa3y0QbgcMrlw4llymbJPukAA4dO4MCe+Bfd+ABSeysO7LJ34QK2/OAO9i50plNQSTBInwk1APSZUFP0ifRQKMaSxc1AfOhoKOStKfbFJFAa5LihZwJw3NAzCZQG2bjuGf7y8n+xcd0z3dzbH0pNGUPK4nN0nOo9qz1ulR0CvXtRPWUsANVTxtqWSI80RWzpl57sZw5wfPUETaS7pGPiPNI2cBRwbeBobeMK/rj/SWobVzj+2MUsOVQSsHSopFcVy/PdunIB65++g60rrf8ulHpAAhhg+QMox916wSlEosK8V7a4HYpykIgcEJFbReQsEfmYiHxbRA64HZdShcrRynRjzHcTv24GXjfG/IH4UbErgLedjKWYpfZJT235UoiVq25ViQ+9apZWpCc4MXRUZW/82de0q0jPVKnud072nu0bqNYWL8o24+663NaKdLsl+5lrIt09ycR5sjI9mTh3c+CoVyrji5UdQyW9zO/P1+7q++QBicRjHLRsxcodixZxUnUlV046gV+9vpMbzj+Z4VXeGpqqlFKFwOmsV9/Ezxbg99A2ue0PwDaHYylqyYTauHOu4bzLHijYFi9uVolrIv2YefOr2LhpsLZ48Yjk+ztTpXqxcKpS3GAceRxVvOxOpJdVlDF6+gjb1q+JdPfV9J/OBYOu79Ab3a0Ethcq44udXxPLnfHz83Wi+v7kabOYeO2PALZavnLlrLPPBmD21Pj018dWfeBmNEopVbAcrUwXkfucfDyVnUJPsGmVuDdoRbo3pVaqK6VUJhfOmcz8Fc++5XYcyj5eS1i7WRmvlN84UX3v5wMSRWX4cBBheFVvvnzuCP7ntR184/zRjBxU6XZkvmSM+SUwA6gXkQmJZQOB54CRwHbgShE5aIz5FO2LTH8nIvcn7nMx8CgQAH4hInMSy0cBC4FqYB3wVRFpceTJKVXkXMl+GWNeMcasSv9xIxanRVsKsy+512ki3R8iTRG3Q/AlTaTbR9pOsFKq4OnAC2Wr1lj7z3hNpPtHNKLfb9ymyW6Vq5s/NZqygGHuSq1Ot9FTwMVpy24HVorIKcDKxOWkNSJSk/hJJtIDwE+BzwCnAVcbY05L3P4h4CciMoZ4G6br0wMwxvy3MWZuZz8WPlelioqjlekp/iPl93LgC0CrS7E4ZvvLC2jcXEv/MTWMvHiW2+Eo5SnLb1/DlhU7GT19BBfOmex2OEpl5XC0gfVHVmrfdKWU6kJt44q2vu3p7WZUYdu6cgGHttVSNaqGk6fp9xulCsXgfuVc+y8jeXzNVr4z/VROHFjhdki+IyKvGmNGpi2+AvhU4vengT8Bt3WxmnOAzSKyFcAYsxC4whizEZgKfDllXfcC89Puvzav4JVSXXIlmS4i69IW/cUY84YbsTgl2hKmcXN8OEzj5lqiLTMJ9NIKAqUgXpG+ZcVOALas2Enk7ghlFVqxpgpDXWSbYwNPlVKq0OjAUf+ye/ilUspiN9zQ7uIXzj6Bn7+6lde2Nmgy3TnHi8jexO91wPEp1/2LMWY9sAf4DxF5FxgO/DPlNruAc4m3djkkIq0py4enP5iIPG1x/Eop3GvzMjDlZ1CiB1R/N2JxSqBXkP5j4sNh+o+p0US6UilSB+CNnj5CE+mqoAwpG6WJdKWU6oQOHPUvJ4ZfqsJnjLnRGLPWGLN23759bodT3B5/vN3FMcf1oV95Ket2HHQpoII2KPl3nfi5MdcViIhAW8/IvwMnichE4L+B31sYK8aY44wxjxhjlhZbq2Wl7OBWm5d1xDcaBogQH7zQob9Tus4GL6Td5krip7cIsF5EvpxyXT/gPeD3IjK7x88iRyMvnqUV6Up14sI5k7UiXRWcvoFqbfGilFLd0IGj/uXE8EtV2ETkceBxgEmTJumwGTedfTasO9YkoKTEcPZJA3hz+wEXgypY+0VkUh73+9AYM1RE9hpjhgL1ACLyUfIGIrLUGDPPGDMI2A2cmHL/ExLLGoAqY0xpojo9ubwzzxIffHop8E3gWkCPbimVJ1cq04n3hKoRkVHA/wAhoKmrO3QzeCF5m1OAO4BPiMjpwLfTVvMD4FVLnkGeNJGuVOc0ka4KjcG4HYJSShUETaT7lybSlSoQf/97h0WTRg5ky74QB0MtLgRUlBYRT2ST+PcPAMaYIcYYk/j9HOK5ugbgTeAUY8woY0wv4CpgUaKq/RXgi+nr6kS1iDwJRERktYh8nXjPdaVUHtxKpt8lIh8ZYz5J/A38CzoOSkjXNnhBRFqAhcSHN6S6AfipiBwEEJH65BXGmLOJ96NabtFzUEoppZRSSimllCpIk04aAKCtXmxgjPk18DdgrDFmlzHmemAOMN0Y8wFwQeIyxJPiGxI90+cCV0lcKzAbWAZsBJ5P9FKHeJHqd40xm4n3UH+yi3AiiX/3GmMuNcacBQy07MkqVWTcavMSTfx7KfCEiLxojHmgm/t0Nngh1akAxpi/EG8Fc6+IvGyMKQH+N3AN8Q2WUkoppZRSSimlVHEYOrTDooknVlEWMKzdcZALTjs+w51UvkTk6k6u6tAjUkQeAx7rZD1LgaUZlm8lXnSajQeMMf2B7xHvyd4P+E6W91VKpXErmb7bGPNzYDrwkDEmiDVV8qXAKcCniPeMetUYcwbxJPpSEdmVOHOmU4nBETcClPfy9UxUpbKS+p4YPtytk1mU8o52nxMlfVyORin36b6TUsfoZ4RSyrP27OmwqLwswOnD+rNuh/ZN9zMRWZL4tRH4tJuxKOUHbmXGriR+mspFInKI+Okl3+/mPp0NXki1i3j/qIiIbAP+QTy5/i/AbGPMduARYJYxpsPwUogPSBGRSSIyqVdpRY5PS7khFg67HYKvpb4nBlbHNxmhUMzlqJRyT7vPCVPudjhKWSXvfcLU90RZWaWVMSkLtMYi3d9IWaa7z4hW0f8PpZRL7r034+JJJw1g/a5Gwq3RjNerwmWM+c/Ev/9tjJmb/uN2fEoVKleS6SLSJCK/E5EPEpf3ikh3vcwzDl5Iu83viVelk5h8fCqwVUS+IiIjRGQk8B/AAhG5PZtYW6POJGqjLZoQzsfehQvY8oM72LtwgduhFI2bbzrE+LH13HzToazv0xzSHTOllMpH9Kj9A8GW374G4CzbHygDp/azvM6uhHdt4wr+uP9JahtX2LJ+lZv1R1ay8tDTrD+y0u1Q2olG9H1oNX1NlSfdd1/GxZNGDqSlNcaG3Y0OB6QcsDHx71pgXYYfpVQeCqZnQ2eDF4wx9xtjLk/cbBnQYIx5j/hk4++LSEO+j9kUPsiqt37E+28809Pwu7T95QVsePwOtr+sCeHupFahx8JhjmyoBeDIhlqtUHdALApLFjcD8X+zqVC/+aZDXF+zjrm3brY7POURrVH7k39KFYP3H1jEa5c/yvsPpNcOWCfSFGHLip22rb8r7/zjOf70xgO884/nbH8sL1dn25Xwbo1FqAtvAaAuvMXTr0ExaJUIdZFtANRFtllWod7TpO3WlQtY//QdbF2p30Osoq+pKjRnJ4aQrt2uQ0j9RkQWJ35tEpGnU3+AJjdjU6qQFUwyHeKDF0TkVBEZLSIPJpbdLSKLEr+LiHxXRE4TkTNEZGGGdTwlIrOzebzWaDxp2LB7PdFWexK10ZYwjZvjCeHGzbW+q1C38vmkV6GXBIP0mVADQJ8JNZQEg5Y9lrJGKBRrS76/vvSAVqgXgfXbf8vKDQ85UnWnp8pbSw9Iekv0aAsNqzcB0LB6kyMV6k5qjYb5sGEDAB82bLC1Qt3L1dl2JrxLS8oYEhwN0Pavck+pKWNI2SgAhpSNotSU9XidPU3aRiNhDm2Lfw85tK1Wq6ktUCivqVfjUu44rm+QkdUVrN2hyXQfuyPLZUqpLBRUMt1ppYF4n8Pq4RMJlHZM1FqRKA70CtJ/TDwh3H9MDYFe/kkIW1lx31kV+tCrZjH6v37E0Ktm9fgx/n/27j0+qvpO/P/rncmNBAgQLgEUQVDukAq1drfU7qoUW7W1tdWuLvZbt3hZq34f9vut7tpqW7fFrd1ftVYqVb/KaottrV2kaqVsvbRbq6AJIBcFQQUJhACRJGSSmfn8/piZOJlc5nbu834+Hnkkc+bMOe85k3PmzPu85/1RmZWE4Lzz4/vFeedXUl09+CGkurqkZ/6PfGoUldWhjOvo7tAEqV9Fol00tW4FrK26649XvyrvVwcfWsXb3/gXDj4UjCq6ICSeQ0PKqT1zOgC1Z04nNKTc5YisVRqqYFztHADG1c6hNGTP+Y9Xq7OTcaQnvEtLCk+wpqqvOYezR18B4NkLCkGX+l44f+hZnDXicuYPPavg5VqRtA2VVTBiSvxzyIgp9YTKgvM5xC1+2KZaOV/ENmwY8K4FJ41i49tHMMY4GJCym4icKyI/Biam9Ut/CIi4HJ5SvlXqdgBeVlUxkoXTv0x48qg+9+15ZhWtOxuomVbP5CWFJXInL1lKtOviQCXS+1bcF/b8klXobVsa+lSha0W6s+5dMYIf3BnLmEhPnf/C707MKpH+7E0vsmvdO0w9ZxKLly8qNFTlsNJQOXU1s2hq3WpZ1V1/0r8qP9t027auYhALh2lvaASgvaGRWDjs6+Pq9tvX0PL8DmrPnM6MWy7I/AAPm3HLBURv7LI1kV5WVcbUcya50upl7qkXMzP6WdsS6fBBsropvMuWZHU+GlrX9cRTX3MO9TXnEIl9wtbYel9QsHdd6gMdsfdZf/Rh6sqm9CTQrXq/SiZtj+5uKChpe/JZS4l2X+zJpK9feXmb9r0I4804lfMWTh7J46/u5a1D7UwdM9TtcJR13iPeL/0CevdIPwb8b1ciUioANJmeQWmogvQ6j2jE2kQxEKhEOnxQcZ+84GDF8xt/yVJi4Yt9neQJimwT6UnZVqQnkzm71r1D97e6KavSD/t+M3/y55kdPZ/SfXkPV5FR8qvyTd27bU3aF4uSigqq6+fT3tBIdf18Xx9j+7RGsTkR7QQn4l+8fBEr1j36mu0r6oedifQkJ5LV2epbKR+Py87YvHhBoVhETPxbMnZd+LUqaavJVOt5dZtadRFG+dTChTBA5fmHJ8f7pm/cc0ST6QFijGkUkS3AJxN90pVSFtBkeh5CpdYnijPpqOs7zevVg3ZU3Hv5+Rab9vbsq9OzkVodOfWcSa4l0ueNeo9NhydYu9CxnXCw0tplelhpyP7k3/yhZ2lFuoXGfnkpkfffp3T4cLdDKUiyNUqyMt3viXSHZR5R2sdyTSBHYt22JJ3dSmx76YJCMSmVciKmy/ILv9HucE8SVJOhKlderpxX7jl59FBGVJWx4e3DfPHDJ7odjrKQMSYqIieKSLkxxv+9EJXyAE2mZ9B+YnW/01MTxdGusK0J9f4S6ftXr+ppeZJtv/C2iSGG7rNmAMi2iZkrjcHfFffhum4qmpz90Bmu80Yv10yuufooa5/s5LzzK7l3xQjLlrt4+SKtSHfY8ROHMeTdYwUtIxLtciSBnk4T6QPrHJtbv8uDD63qqUwf++Xs3lMmjDmac1zR4/ZXijvRGkU559iUIYPeN2z3cQAiUydSumufJetMb8NiNbcS26UlZbZdJFD9q6qs5cNTl1r6HvnW+lU9VcUnn6VjBqn8DJRIT71Qo4pLSYmwYNJIHYQ0uHYDfxaRNUB7cqIx5j/cC0kp/9Jk+iCi5f0njJOJ5FB5haW907PVdzBObX1iF78kt53U3h5j7ZOdAKx9sjOn/unZ0ES68wpJqDfueTzeI71mFvMnf97iyJQTnOqZ7mQvc02kB8NgifRMIlMnAuScYB+oDYvV3Eho232RQPWvkET68ROHAdBWF//Ilt7vun3ExZT4uHBFecveJ1ZxbFsDw2bWc8KFeqEmkG69ddC7F0weyfrtB2lpC1M71BvHlp0H27j3uZ00H8t9kGXVy67ETwkwzOVYlPI9TaYXwOpBNvvTX1X6YINxOiHbqvR0/T0Xr9Ik+sCqq0s47/zKnsp0KxPpyj3JD+yZpCbdI9Eumlq3AtDUujXeK92FCnVVGCd6pgexl7lXzRv1ntshuCaZQE+flktCPaj9xZ26SKDsldrvetjMek2kK8vEusIc2xb/XHtsWwOxT+uFmkC67bZB71540igANr59hMWz3f3w3tIW5kd/eJOfv/wOQ8pCnDJO+7gXwhjzbbdjUCpINJlegFB576R2eJJzJxy5DMbZUQdVTQ4ElYNkstrpNirZ0ER6ZveuGGFJRfrHhr/Jn94/xaKorGNL3/SASE+6j2mZR/P+TYwZP08T6T429stLbR2HQ3uZF49YRUlBFeW5Sm31MpBcE+pB7C8e1IsE+ejvoku/DtobR75OPmupVqQry5WUVzBsZn1PZbr+fwXUhAnw3sAX3eedUEN5qIQ/7jjIlNH9t7u1mwHWbzvIvX/cSUd3lH84fRI3nH2KZyrlk+Sf3Y4gNyIyBvi/wGygZzAvY8zfuxaUUj6myfTBmMxjceWS1LZaEFq7uNGXXFkjmUi3eiBS5S8zF1zGqZEwodIKjkPB/deVe+x+T9Fe5h/waPV4oA/k+VSoB43dFwn80I8960S6x9mZ6Ix1hTWRWqROuHCpVqQH3f79g95dWRZi/ok1/OLld/nFy+86FFT/zp45lpvOncG0sdqRxCKPAo8B5wFXAZcDza5GpJSPaTJ9EO2t77H95UeYcfplg84XhKS2UvmwayBS5S+hUj0GquxoIt2bnr3pRYAPuR2Hsp9dyW7txx4M2jNbaSJd/ccX62ncm/sg81aaOGIIH5o00tUYAqjWGPOAiFxvjHkeeF5EXnE7KKX8SpPpGbTsayQa+YImi5RKY/dApEoppezX3dHNrnXvuB2G8jHtxx4M2jNbqSJw2mkZZzlxVBUnjqpyIBjlsGQv2/0i8mngPWCUi/Eo5Wua+cqgduJ8TaQr1Y/kQKSADkSqlFI+VVZVxtRzJrkdhvKxZD92oOj7sftZsmc2oD2zlQqqjRvdjkC553YRqQFuBL4O3A/8b3dDUsq/tDJ9ENU1EzK2eHGDnYPEKZWL/gYi1R7qSinlL4uXL2LFukcb3Y5DuS/fvudBHLS1GOXbM1v7rCvlE8uWwcqVbkehHCQilcR7pE8DJgIPGGP+zt2olPI/zXgNRry3efavXsWu797M/tWr3A5FKYBeifNrrj7KzOkHueZqd/vsKaWUyl6iZ/p8t+NQ7mpoXccfDj1AQ+u6vB6vifRgyDUpvveJVey482b2PqGfTZTyvJ/9zO0IlPMeBhYCm4FzgR+6G45SweC9bLEaUCwcpm1LvJdh25YGYuGwo+uPRpxdn/KX9B7q7e2xnvs626NuhaWU8pDo8S63Q1BptGe6gv76nndneERuy1YDi0T9e1zs02e9Sz8rKKWUx8wyxlxmjLkPuAhY5HZASgWBJtMtZmeCu6SigqFz4r0Mh86pd7TVy/aXH+GlJ29h+8uPOLZO5S8D9VC/5uqjXFG/kbuv3zno47s79MO28h+DcTsE39h++xpeuuAutt++xu1QVArtma7Avr7nhVa758NPyfvGPY+zfssdNO553O1Q8pJPn3VNuCullKN63hSNMRE3A1EqSLRnuoX2r15F25YGhs6pZ/wlS21Zx/hLlhILX2x5Ij0aCQ840Go0EqZlX7yVasu+RsZ3hQmlnCxH026r4pXeQz21Wv2vTx2m83tRKqtDfR737E0vsmvdO0w9ZxKLlwfnYnmsM0xJZXHsG4MdQ4LsWLSFxrb1zB96ltuheFr0eBctz+8AoOX5HURv7CI0pNzW9dm5/KAJYs/0SDRMaaj4jknZ6q83utV9z/tWu9vfU72hdR1N4V3UVUylvuYcW9dVqEi0i6bWrQA0tW5ldvR8SkP+O27l0md97xOrOLatgWEz6znhQus/K+XSu137vKuitG+f2xEo580XkfcTfwswJHFbAGOMGe5eaEr5l1amW8TJFixWJtKjkXDGqvNQaQW1E+OtVGsnzu+VON/zzCq2rLyZPc9Y2yfR6RY2yjqpPdRTq9U/8qlR/SbSU1sM7Fr3jmsV6lavd/vta3j7K9/h4N2rLV2u3fJp57Rt4yP8+Zlvsm1jcX5zpal7NxHjn0pIN4SGlFN75nQAas+cbmui26kK+CC1rAlaz/TNbzzGcy/fzuY3HuuZFonqeUXSYNXiVia77ap2H4idrWrsUBoqp65mFgB1NbNcSaRbVSWebUW6nS1hcundrn3ec6PfJgiQjRvdjqCoiMiDInJQRLakTBslIutE5M3E75Fpj/mwiERE5KKUaZcn5n9TRC5Pmb5ARDaLyE4RuVtEJD0GY0zIGDM88TPMGFOa8rcm0pXKk1amWyTZgiVZme5kC5Z8bX/5kZ6Kc4hXnUcjX+i3unTG6Zf13NeWmBbtCtO6M35S3LqzgWjXxZZUqDtR4T+YYqomtkp7e6xXEj3VvStGcOF3J/abSIcPWgwkK9PLqpwfwMzqyvjUKtz2lzYTW3YhJVQWvFy7bdv4CM37NzFm/DxmLrgsq8dEI2Ga928CoHn/JiK15/qysq4QdWVTKBUdeC+TGbdcQNc1bZSPGmrbOpyqgN9++xpant9B7ZnTmXHLBZYv30lB65keiYY50BL/zHqgZQszo59l267fcqBliy+qle3mdLW41dXug9ly7Lmev/NN3jv9jYb5kz+fdUV6tDtMqMy62OyuEk+XbAmTXKeVVeF9EvWDVMrnMq/q/X+iAuCCC8Boi0IHPQTcA6ReubsJWG+MWS4iNyVufwNARELAHcCzyZlFZBRwK/FBRA2wUUTWGGOOACuArwJ/BZ4ClgBP2/yclFJoZbqlxl+ylKnf/H5WCeCBKq8LrcjO9vGprVuSaifOH7RNQ/p9ofIKaqbFT6xqptVbkkh3e5DV/atXsffab3Hop486ul4/SQ4smvy97KtHmDn9INdcfXTAxwyUSE9avHwR//TiF11p8WJHZXxqFW71GXN9cXEmPSmebYV6qLSCMePnATBm/LyiS6QPC9Vqi5csbb99Da9cvML3PdP7JOxtrlDvONRh6/L93jM92t37WFUaqmBc7RyAnt/J5Hqu1cper2zOh1PV4qnbzolEeupFAoA5wz6R8zL6+0aDE7J533xr/SoaH76Zt9ZnrqTOporYrYFDT7hwKdO//n3Lk/e59G7Pp8+7l1n52qUvK/3/BM0dKJUTY8wLwOG0yZ8BHk78/TDw2ZT7vgY8DhxMmfZJYJ0x5nAigb4OWCIi44HhxpiXjDGGeMI+dVlKKRtpZbrFsqlIH6jyutCK7Fwen2zd0rKvkdqJ8znltP4r0jOZvGSpZRXp4G6Ff2oiv2PDJmKdF/kiCeqkd96OMnP6QcZPKGH/ezHq6kpoaoon1dc+2dmrX3qu3KhIT67Xjsr4GbdcwN7Lh/jmfyiZFE9WpudyPJi54DJOTfZMf/eYjVF6j9Dn25SqH05VjCcvZCWrxv26jqRVn3qC9gMdVI+rYulTF9q2Hr9680//yeF3GhlXO4e5p17cM33uqRczM/rZngrjcbVzeirTs03s+qn3dq7srhZ3Y9slLxIk15vrc+vvGw1eEY2EObo7fn56dHcD0e6Lgf7fo7OtNrezSjwTu9aVS+/2XOb1Miu/XdDf6dgPZAAAIABJREFUstL/T45ta4hZEbdSRW6cMWZ/4u8mYByAiEwELgT+DvhwyvwTgXdTbu9NTJuY+Dt9ulLKAZpMd1jfyuv4YKIDTS90uYNJbd1SCKsHH7VrkNVMUhP5VQvn+SYJ6qTW1vjXAve/Fz+XTibSAZacW5F3It1ti5cvovtb3ZYn9P32P9QrKZ6jYhx8VGXPyQT0jFsusH2AUyfW0XGog/YD8ar09gMddBzqoGp0leXr8Wubl2h3mMPvxL9hl0x+prbnSP07mVyv3HMoq2W7MXCm0+ysSHdr2xVykSD5jYYDLVsYVzvHU4PXhkorGDGlnqO7GxgxpX7AVi+5ti8JSkI5VS7Pxe/P28p2NYMtK/X/ZFtinlyJyDJgGcCkSf79JlQg3Hef2xEEyWgR2ZBye6UxZmUuCzDGGBFJ9t35EfANY0ysn9bnSimP0WS6wwaqvC60Ijvfx3s1AeZWz/nxlyzl+IjP+S4J6pSaGqG11fRUpid/Lzm3gpU/G5l5AR7mVmW813j1mKD8z4kEdFIQ1lE1uorqcVU9lel2JNKh97dz/CRUVsGoSfN7KtMzJT9zSY4WWuVczNzedoWsL/0bDV5y8llLiXZfPGjP9Hyqzf2eUC5mVn67INOyCv0/SSQYVwIsXLhQG3a7adkytyMIkkPGmIV5PO6AiIw3xuxPtGpJtnRZCKxOJNJHA58SkQiwD/hEyuNPAJ5LTD8hbfq+POJRSuVBk+kuGKjyutCKbLcquoPGi4l0rwyKOumkEL/81Siqq0t6Bh0dbPBRK3V3WF85rpRSXrb0qQttq0hPtXj5Ilase/Q1W1dig1M+9o9Eu7/IiL3Wdx6wuhVKJNZdNEl5JwcdtZoXE+lJ2Qw+GsRqczvEusKB2EZWvt76v1MkRHQAUvetAS4Hlid+/xeAMWZKcgYReQhYa4z5bWIA0u+JSLJybTFwszHmsIi8LyJnEB+AdCnwY+eehlLFTZPpLhko4V1oIlwT6cFz6KeP0rFhE1UL5zH6qkvdDqcncZ7+207P3vRiT09zNwYpVUoVbvvta3ravMy45QK3w/ENuxPpKXzZCzeeYDxuy7KtSgYHuf/6QPyYSA8KTYYOzso+415g5eut/ztKWUtEfkG8qny0iOwFbiWeRP+liFwBvA18cbBlJJLm3wVeSUz6jjEmOajpNcBDwBDg6cSPUsoBmkxXKo1XqsAhHkvHhk1A8Q6KmtrPd9e6d2zpbe5V0Tz7lyvlNU4NQKqU11jZQ7yYqttV8XGiWtzKPuNKKZWJMeZLA9x1VobHfTnt9oPAg/3MtwGYk298Sqn8+XO0QKVssn/1KvZe+y0O/fRRt0MB4i1nqhbOAyjaQVGT/XwBpp4zqWgS6ds2PsKfn/km2zY+4nYoShUsNKSc8jHDACgfM0wT6apoJHuIAwX1EG9oXccfDj1AQ+s6K8NTyhP2PrGKHXfezN4nVtm6nmRvcKDgPuNK+dJ557kdgVJKBYKvKtNFZAlwFxAC7jfGLE+7/8vAD/hg4IV7jDH3J+6bBNwPnAgY4FPGmD3ORK78IBYO07YlXq3ipSrw0VddmlUsXqqot9ri5YuKriK9eX/8GwnN+zdxapYV6lrJrrwqeryLruZjAHQ1HyN6XCvTs6XjRfhfvj3Ek5XoVla3KzUYN3qJO10trr3BVVF78km3I1BKqUDwTWW6iISAnwDnArOAL4nIrH5mfcwYU5/4uT9l+irgB8aYmcDpfDBqslJAvN/80DnxahWvVYFniuXQTx/1VEW9HYopmRQqrWDM+Pg3EsaMn5dVglwr2ZWXhYaUU3vmdABqz5yuifQsPXvTi9y/6Jc8e9OLboeiCpRr8ju1Et2q6nalBuNUdXg6N6rFNZGuitb557sdgVJKBYKfKtNPB3YaY94CEJHVwGeArZkemEi6lxpj1gEYY9rsDFT51/hLlnJ8xOc8lUjPRPuqB9PMBZflVJGeTyW7Uk6accsF2is9B8U8XkSx668SPd/qdqWy4XYvca0WV8oha9e6HYFSSgWCbyrTgYnAuym39yampfu8iGwSkV+LyImJaacCR0XkNyLymoj8IFHprlQffktEa1/14Mo2IZ5PJbtSbtBEevaKdbwINXCfdU2kK7t4oZe4JtKVUkop5Rd+qkzPxpPAL4wxYRG5EngY+Hviz3MR8CHgHeAx4MvAA+kLEJFlwDKAiiEjnIlaqQJl21c9H6n7xMSJfrr+VlxyqWRXhUndJypLhrocjQoyv4wXkbpPlFeNdDmaYNBKdP/q9R5RVuNyNNnT6nCllFJKqez4KTO2j/jgoUkn8MFAowAYY1qMMeHEzfuBBYm/9wINxpi3jDER4LfAaf2txBiz0hiz0BizsLRCkyReFguHM89UROyqSE/dJ0bV+umQMbjujm63Q7CcJtKdkbpPlIluc2UvryfSIW2fqKx2O5zAcDKRHokF7z3RLan7Q3lpldvh5MQLifRYl57fK2UbY9yOQCmlAsFPmbFXgFNEZIqIlAOXAGtSZxCR8Sk3LwC2pTx2hIiMSdz+e7Lota68a//qVez67s3sX+3sIEkqGHRQP2WVY9EWGtvWux2GUkrlLXWwU6Xc5NYgqEoVjZUr3Y5AKaUCwTfJ9ERF+bXA74knyX9pjHldRL4jIhckZrtORF4XkUbgOuKtXDDGRIGvA+tFZDMgwM+cfg7KGrFwmLYt8UGS2rY0aIW6ykmfQf0CWKGunNXUvZuI0f8jpZT/9B3sVI9lyh19BkHVCnWlrHfllW5HoJRSgeCbZDqAMeYpY8ypxpipxph/S0z7ljFmTeLvm40xs40x840xf2eM2Z7y2HXGmHnGmLnGmC8bY7rceh5uiAbohLSkooKhc+KDJA2dU09JhftfSVXuyiUhroP6KavVlU2hVPT/SPmer84JVf5SE+YDDXaqlNO8MAiqn+nFB6WUUso5QRuA1Ndi4bAtieH9q1fRtqWBmmn1zJl7qeXLd8P4S5YSC1+siXSXtLfHqK72Rt7l2ZteZNe6d5h6ziQWL1+U1WP8Mqif8r5hoVrmDz3L7TCUKkii5dWH3I7DLpFomNKQni9AvKVLU3gXdRVTqa85B9DBTpV39DcIaqwrrIn1DPY+sYpj2xoYNrOeEy5c6nY4SimlVOB5IxumbOsBntoSpXVnA9GIe1ULVrdj0US6O665+igzpx/kmquPuh1KQS1bijmR7uZxIGgEsX0dWm2m7JR6HA2izW88xnMv387mNx5zOxRHDNamZbCWLppIV16RmjgPeg91K97ftT2OysmaNZnnUUoplZEm0z3Azh7gqS1RaqbVEyp1JwG95xkdMDQIYlFY+2QnEP/d3h5zNR5t2ZK7bRsf4c/PfJNtGx9xOxSVhaAnEpSyUyQa5kDLFgAOtGwJfD/wTAOJaksX5SdBTxJb9f6u7XFUThYscDsCpZQKBE2me4DdPcDHX7KUOcu+z+Ql/X/tz+4q1WhXmNad/V8sqGjSD3J+UhKC886vBOK/vdDqZfHyRfzTi1/MusWLU6LHvTcsQzQSpnn/JgCa92+yfN8f8u4xS5dX7IKeSFDZsXuQ5NSLkkFTGqpgXO0cAMbVzgl08jjbgUTra87h7NFX9LR48TqrL4CU7tpn6fJUbnJ5Hwtyktjq9/cTLlzK9K9/X1u8qMwmTnQ7AqWUCgTtmW6DfHqf290DPDTACej2lx+hZV8jtRPnM+P0y2xbd820elp3NqRcLOj2bCK9oqmMcF2wq9cKce+KEfzgTu/0TIfcW7Z0d9jbL3377WtoeX4H1WfMZex1l9i2nlyFSisYM34ezfs3MWb8PNe+qaKyk0wkJPugBimRUOw2HZ6Q1XzJY0ntmdOZccsFWT1m3qj3co5n8fJFrFj3aGPOD/SBuadezMzoZ6ncc8jtUGyVrDpP9kMf7MKBXy4q9NffXflXPn29++uhPhi/9Fe34/3dD89bKaWUCgpNplssOdjn0Dn1jL8kt+oAp3uARyNhWvbFPzu37GskGvmCbcm1yUuW0jbyg4sFXk2kq+x4KZGeq2wGLM020dWf6PEuWp7fAUD7S5uJLbuQkkrvfMCZueAyTo2ENZHuE7kmElRwpB5LWp7fQfTGLkJDym1ZV2IA0vlWLKskHGPY7uMFL+fYlCEWRBNXLIOPBmkg0b6V9tY9L61Od16fSuwc3teync9vg3Dq+7tSSinlX/7NiHmQnb3P7RAqraB2Yvyzc+3E+Vkn14bui+a1Ph0wVKX70/unOLq+QgYszVZoSDm1Z04HoPqMuZ5KpCdpIt1f9IN2cUo9ltSeOT3rRHquFwO9OgBptgn5bOYrpuRpEBLpKnjsbtni17Zo+v6uHPfVr7odgVJKBYJWplso2fs8WZnuh+TxjNMvs7UiPVVVE3TU2b6ajLJpw6OtXoIp2Rs4WZluV6uXGbdcQPTGLg60jbVl+W7Kti96xHRTKprUUcEVPW5fpXhS8lhi53pSj4tKZSsS67Y1cZ9L25piNOQgHM/iFMNLbU/srMTWtmhKZWnlSrcjUEqpQNBkusXs7n1uhWhai4diqlItpA2PCobFyxfR/S17e6ZDvKqUNltX4VmNex6nqXUrdWVTmD/0LLfD0cS+hziRgHZCPr3M8+XE9kr0TH/N9hU5IBINF01bF7c41cs8SG1rUkWiXZSG7N+vvdj2xM4kt7ZNUSoLCxbAxo1uR6GUq0TkNqDNGHNnIfOo4qZtXmzg5UT69pcf4aUnb2H7y4+4HYrj/NaGR9nH7kR6MYtEu2hq3QpAU/duIsbdb3g0tq1n/dGHaWxb72ocKp6AfumCu9h++xq3QylIn17mx7tcjsgyMbcDKNTmNx7juZdvZ/Mbj7kdSmD17WVu7zE+aIn0xj2Ps37LHTTuedzW9fi17UmhNJGuVAavvup2BEplJHGaq1Sepv+gHmJ3cjfalT7gaHGcWCcl2/AAnmjDE+v05/Zvb/d9vsWTgrI/lobKqauZBUBd2RRXK8Ijppum7t2ANxL7xSxICeh8e5n7gG3nhJGo/ce3SDTMgZYtABxo2eLIOotRaUkZ48qnAGj7lRz1utjcupVINPNxMN9zA7t7lPtFsVxEUEopvxORySKyQ0RWAVuAb4rIKyKySUS+nTLfNxPz/UlEfiEiXx9kmV9NLKNRRB4Xkap+5nlORO4SkQYR2SIip6fcPStx/1sicl3KY34rIhtF5HURWWbRJlA+o21ePMKJ9iOh8viAoy37GnMacDRIvNKG59BPH6VjwyaqFs5j9FWX9jtPrDPsucEr33k7yszpBznv/EruXTEi4/zt7TGqq/WaXSZvrV/F0d0NjBk/j5kLLnM7nILNn/x5Zr/9Yddbq5RKGXVlU2jq3u16Yr8QXup5m6/QkHLKxwyjq/kY5WOG+T4B7UQvcyc9e9OLAB+yY9mb33iMAy1bGFc7h7mnXmzHKgAoDVUwrnZOz7rcbvVid09xtzS0ruNA127Glk+xtcVLECUvNje1bqWuZlbGVi+Nex6nqXErY8bP48RPfznn9fmx7YmV73debHOjlOvGj3c7AqUGcwpwOTAcuAg4HRBgjYh8HDgOfB6YD5QBrwKD9S36jTHmZwAicjtwBfDjfuarMsbUJ9bxIDAnMX0G8HfAMGCHiKwwxnQDXzHGHBaRIcArIvK4MaalkCeu/EeT6R7Qt/1I/MOmHQlfJwcc9ap8t2tFkzUfimPhMB0bNgHQsWETZW9/iZKKil4DnmaTbHdDa6sBYO2TnfzgzniF+kDJ8muuPsraJzs57/xKvvQfkxyL0W+i3WGO7o7v/837N3Fq2pgGfuWVxPX8oWcx28c904OSDIge76KrOT54bVfzsUD0Tncqfru3VXdHt22Dj6ZXi8+MfnbQJHeh/c7nnnpxxnU4wame4k5LbfFysGt3YC8Y2Gn+5M8zO3p+xkR6ahV78/5NTOgOEyrL/f/a6kS6nRd3rXy/69PmxmcXFZSyzXvvuR1BURGRB4HzgIPGmDmJaaOAx4DJwB7gi8aYIyLyGeC7xNvuRYAbjDF/SjzmcuCWxGJvN8Y8nJi+AHgIGAI8BVxvjDGOPDl7vG2MeUlE7gQWA8nxfIYST7QPA/7LGNMJdIrIkxmWNyeRRB+RWMbvB5jvFwDGmBdEZLiIJKsGf2eMCQNhETkIjAP2AteJyIWJeU5MxKbJ9CKjJaMFqmoq/GfokQpqpsW/ilkzrZ5Dv3iMXd+9mf2rV+UdV2rLmKqm3vcFIVHnpIqmMssS6TBwu5nkOmKdvZPtXmoHM3y4AHDe+ZX8n6+/z8zpB7nm6qN95mtvj7H2yU4gnnjvbI86GqefhMoqGDEl/v8wZvw8T++fQ9495nYIefFrIj1IPW8D3BrFVk70mS+rKmPqOfZc8ExWiwMZq8Wt6HdeumsflXsOUbprH6W79vVMc1J/PcXt7ivulNKSMuoqpgLa4qUQ2Qw+mtoybcz4eXkl0q2294lV7LjzZvY+kf/nk4FY/X5XLG1u/HxeoFxy221uR1BsHgKWpE27CVhvjDkFWJ+4TeLv+caYeuArwP3Qk3y/FfgI8UrtW0VkZOIxK4CvEk/mntLPuvymPfFbgO8bY+oTP9OMMQ/ksbyHgGuNMXOBbwOVA8yXfgEieTv1IBsFSkXkE8DZwEeNMfOJJ/wHWq4KMK1M94jJS5YS7YpXpG9ZeTPwQZV6MtkaC4ezqqp2omVMMbAygZ5uoHYzFU1lhOugauG8nsp0L7V6ef99w5JzK/jBncOZOf0g8EGVenV1SU9bl+rqEs47v5K1T3bykU+NorI65HLk3nbyWUsZsvfznk6kK+clkwHJSj2/JwOC1hrFbn36zPt022VTLT5QBfuw3cc5NmVI3ut2OpEOHySck5XpW44956sq9UzV5vU15xCJfUIT6Q5IVrF3T66Fpghtdb0/tg05CMfHOhOL3ZXe+b7fDTk48H2n/O1SoqdfHL8QMch8fpVsEThiSj0nn7WUaJ7fXlBF5tvf1oS6gxKVzpPTJn8G+ETi74eB54BvGGPaUuap5oOE7ieBdcaYwwAisg5YIiLPAcONMS8lpq8CPgs8bfXzcMHvge+KyKPGmDYRmQh0A38G7hOR7xPPZZ4HrBxkOcOA/SJSBlwKDHRieDHwRxH5GNBqjGkVkYGWWQMcMcZ0iMgM4Ixcn5wKBk2me0goceJYM62e1p0N1EyrZ+iRCjrqsk+Q998yJr7cofusqw4eui9K20RNkBZisAsjo6+6lFjnRZ5KpCc983SY/+9H9CTLzzu/kurqkl5tXe5dMYJ7V4zgB3fGeLVkcr/L+dP7pzgbeJY2HZ5g/UIPZr5YrYl01R8/9rwdjB+TwW5JVvO3PL/D1mp+O9u8JGVqu+K1fueFSiacAf5wKF5IFa9S93YSOtv2NF5+DkFTGipnsO81DJZMtlb8W3TJ5G31Uev3UTuS30FNLqe2CDy6u4Fdf4jSumdzT2JdKeVp44wx+xN/NxFvHQJAonXI94GxwKcTkycC76Y8fm9i2sTE3+nTfc8Y86yIzAT+kkhqtwGXGWNeEZE1wCbgALAZaB1kUd8E/go0J34PG2C+ThF5jXgf9q9kCO8Z4CoR2QbsAF7K7lmpoNFk+iBCXf0nn+1OJCer1JPJ9f4S5AMlYpMtRJKJd7cH2hxMtpX2QVt3JvHq9G5PJtKBnuR5MlmerEhPbeuSnF5dXRIfJsRB3R3dlFU5+0E/CP2fVX5Mn28FekPlQaFzrL2xvdc8gglj+rZ5UtYphmr+ZJ/0gSrYC61Od0sy4ZxapZ6ehPZSz/G+7Wm8nfgvFpFol9sh9IhXP19sa4LaC8lvP1R4J1sEHt3dQM1Jc2ndsxmIJ9btfo3yISLLgGUAkybpOEoqMEaLyIaU2yuNMYNVSfdhjDEiYlJuPwE8kRgI87vE24kUBWPMHj4Y+BNjzF3AXf3Meqcx5jYRqQJeYJABSI0xK4i3wkmfflvapEeMMTcMNk+y533CuQOtUxUPTaZ7VCilCnHokdwS5AO1EPESq1vR5JIc1zY4+Zs9u5R7V4zouZ0cfDS1rUsy2Q7w4nFnT5ifvelFdq17h6nnTGLx8kWWLvu95hH9Tj9492raX9pM9RlzGXvdJQWvJxqQAUiLxbFoC41t65k/9Cxblh+UAUidFqQLXHY/j2TPdLur0/uz+Y3HeqrR5556cc4V6cN2f3C11o2WLtkYqC2K1wYpTW9P49VEupcuQGSlqxve2Z95vn40xv6HptatjGmZx8wFl1kcWH68lqS1WmrrlJM+7r2kdKrUixupcXsx5kSCcSXAwoULvVmFUCw2bMg8j8rWIWPMwjwed0BExhtj9ovIePr5Lk6iPczJIjKaeGuST6TcfQLx1jD7En+nTvfmyZC1VorILOJ9yh82xrzqdkCqOGky3SdyTZB7OZGeS6V9NnJJjlu9brskq9O9pmSQL2SkVqq7IbVVwa5179D9Lfsr1GOdYdpfilcDtb+0mdiyCwv6RsG2jY/QvH8TY8Z754OzyqypezezTbflA53a3aM2qLbfvqanNcqMWy5wOxxfWLx8ESvWPfqak+scqE96EPVXke7FKnCv90P32gUIO0VMN02tWwFo3r+JUyNh9GObvdJbp6T2I/eqZOLciW8NKKUsswa4HFie+P1fACIyDdiVqFY/jXiv3hbi/cO/lzLo6GLgZmPMYRF5X0TOIN7CZCnwY2efivOMMf+QPk1EfgL8bdrku4wx/y+L5X3CotBUkXEn6+Uj1e+2E414Y6R0LyZ985FsRQMU3Iqmb3J88Ncqn3VnWmYxiWVou291Ir27I/sLCsnqSoCp50xypNVLSWUF1WfMBaD6jLkFJdKjkTDN+zcB8Q/OXjnuqMzqyqZYnkiHDwZkAwIxAKkT+gzaedw7LRLy5eBziDm1IvigTzrQb5/0SNS+Y2Ak5u7F6mQVOOC5KnAvxZKq7wUI7xUc5CNi+n8epVJGXc0sAMaMn6ffWHNAsnVKqnjbFD0fUwGyMJ9CapUvEfkF8BdguojsFZEriCfRzxGRN4m3cVmemP3zwBYRaQB+Alxs4g4Tb/nySuLnO8nBSIFrgPuBncAugjH4aM6MMf9sjKlP+8mYSFeqEFrikEHjrl9xYMPr1E6cz4zT3asUrWqCjjrXVm85q1rR5NMjPpd1u9kSxovV6a+/HuGaq4/2avWSj2wGH82nZcvi5YscqUhPNfa6SwquSIf4AKRjxs/rqUzXD84KgjcAqd2cGrTTKflW2W86PIF5o96zMTJrDNQnPb39i5V9071S3ez1KnCv8Usbmlw0tq2nqXs3dWVT+m0VNn/y55kdPZ/uybUuRFeckhXeb7/wmKfbpqRLbfPi5Up6pYqNMeZLA9zV56BvjLkDuGOA5TwIPNjP9A2k9BlXSjlHk+mDMMZw4MjrALTsayQa+YImuFIUOhCrVZX2+STms61Id7slTEWT9z4spg4wOphC+qUX0rKlkET6psMT8nqcVYPFzlxwGadqz3TfsavNS5Im0nMTlEE7+1TZB+A59ae/inS72r94rb1KEBLCTgrSBYiI6aapezcw+HtIaagcb5VVBF+orMJXbVPS29P4JW6lVGYiMhO4HhgNrE8MqKkSRORk4F+BGmPMRW7H4zbdHs4SY3QMkIGISDMQAkYCR4C3Bph1NHDIqbhcXGcxrncmUMXgr78drH6+JxljxhS6kMRo41ZsixqgNcM8J9P/vpfNY/NdZz7zuvW/6WX5vkZOOsUYU1PoQizcJ6zm1f9Lr8YF3oxtoOOgHax6n2gG3i5gEaOB4dj3vDNtUy/8H7gdg9vr90oMBe8TGfYHq/dvL2yzVBrP4KyMx4r/pWzisXufUMpPBt0fRORB4DzgoDFmTsr0JcBdxPNM9xtjlg+wiNRllQCrjDGBGVTL4u3z66Amj/PZTkHeHl6ilemDyPZkQUQ25DmSc97cWKeuN/jrzcLPjDHLCl2IiKzMdzn5PjaXx+U4r1dfK9cU8vo6RURWWrEcY4xYsRyrefX/0qtxgXdj82pcA7Eg0bLBGDPZonDyXb+r29vtGNxev1disIIVF6iy5bVtpvEMrljjcXKfUMplDwH3AKuSE0QkRLwf+jnAXuAVEVlDPCH6/bTHf8UYc1BELgCuBv7TiaAd9BAWbB9nQnXVQ2S5nYwxW12JsEhpMl0plYsnPbCcfB+by+Osep7Fyg/bzw8xKqWUUkoppXzGGPOCiExOm3w6sNMY8xaAiKwGPmOM+T7x6uP+lrMGWCMivwN+bl/EzrJq+wRdLtsJ0GS6gwZveqyUUimMMZYkIAtZTr6PzeVxVj3PYuWH7eeHGJVSSimllFKBMRF4N+X23sS0fonIJ0TkbhG5D3jK7uA8INftUysiPwU+JCI32x2ch/S7nYp4e7hCK9OtYUm7AB+sU9cb/PWq3OlrpbzIq/+XXo0LvBubV+Oyi9vP1+31g/sxuL1+8EYMfuO1babxDE7jUUoNyhjzHPCcy2F4ljGmBbjK7Ti8QreHs3QAUqWUUkoppZRSSimlLJJoz7E2OXCkiHwUuM0Y88nE7ZsBEm1Mio5un+zodvImbfOilFJKKaWUUkoppZR9XgFOEZEpIlIOXAKscTkmL9Htkx3dTh6gyXSllO+IyGdF5Gci8piILHY7HmU9ETlZRB4QkV+7HUt/vB6fUkoppZRSyh0i8gvgL8B0EdkrIlcYYyLAtcDvgW3AL40xr7sZp1t0+2RHt5N3aZsXpVRGIjICuB+YAxjgK8aYv+SxnAeJj8R9MPk1pZT7lgB3ASHgfmPM8iyWNxK40xhzRa6xqA+IyInAKmAc8dd3pTHmrjyXZfVr/GvgMuAFoIL4WB+/Nsbc6pX4jDEX5ROLUkoppZRSSiml/EWT6UqpjETkYeBFY8z9ia8SVRljjqbcPxY4bow5ljJtmjFmZ9pyPg60AatSE5kiEgLeAM4hPhr1K8CXiCc103t/fcU69pIHAAAgAElEQVQYczDxuB8CjxpjXrXu2VpHRE4G/hWo8XLCVUTGA+ONMa+KyDBgI/BZY8zWlHnceo1/DXwBqDbGtIlIGfAn4HpjzEteiM/Lr61SSimllFJKKaWso21elFKDEpEa4OPAAwDGmK7URHrCmcBvRaQi8ZivAj9OX5Yx5gXgcD+rOR3YaYx5yxjTBawGPmOM2WyMOS/t56DE3QE8bVciXUQeFJGDIrIlbfoSEdkhIjtF5KbBlpF4Pp6vmjfG7E9ux0QyehswMW02R1/jtGUaY0xb4mZZ4if9SrBr8SmllFJKKaWUUqo4aDJdKZXJFKAZ+H8i8pqI3C8i1akzGGN+Rbxn12MicinwFeLVxNmaCLybcnsvfZO5qb4GnA1cJCJX5bCeXDwELEmdkKhe/glwLjAL+JKIzBKRuSKyNu1nrE1x2SoxWviHgL+mTnf6NRaRWhH5KfAhEblZREIi0gAcBNYZYzwVXw7rUUoppZRSSimllE+Vuh2AUsrzSoHTgK8ZY/4qIncBNwHfTJ3JGPPvIrIaWAFMTakktpwx5m7gbruWn1jHC4nEcqqe6mWAxPP9jDHm+8T7cPuaiAwFHgduMMa8n36/w69xC5B+oaQ+0b//CRGZY4zZkvYYt+NTSimllFJKKaVUgGllulIqk73A3pRK4F8TT673IiKLiA9Q+gSQ6+CQ+4ATU26fkJjmNYGtXk70In+ceA/63wwwj+uvcaLF0B9J+9aAV+JTSimllFJKKaVUcBVNMl1EPisiPxORx0RksdvxKOW2bPcJY0wT8K6ITE9MOgvYmjqPiHwIWAl8BvhfQK2I3J5DOK8Ap4jIlMQAp5cAa3J4vCcZY1qMMVcZY6Ymqtc9SUSEeE/8bcaY/xhgHtdeYxEZk6hIR0SGEB8kdLsN8c0XkdUi8ivgn7KNT6mg0nMnpXrTfUKp3nSfUEopVYx8nUzPZYBAY8xvjTFfJf61/IvdiFcpu9m4T3wNeFRENgH1wPfS7q8CvmiM2WWMiQFLgbf7ie8XwF+A6SKyV0SuSMQSAa4l3vN6G/BLY8zruT17RwS1evlvgX8E/l5EGhI/n0qbx83XeDzwx8T/3yvEe6avzSa+9H0iJb4ZItItIgdE5KZEfF8h/q2LhcARj/4PKlUQPXdSqjfdJ5TqTfcJpZwjIsvcjsHLdPsMTrePe8QY43YMeRORjwNtwCpjzJzEtBDwBvHKxb3EEy9fMsZsTdz/Q+JtDF51J2ql7KP7hLUSPdPXpmzLUuLb8iziSfRXgH/QpKt36T6hVG+6TyjVm+4TSvWm+4RSzhGRDcaYhW7H4VW6fQan28c9vq5MN8a8ABxOm9wzQKAxpgtYDXxG4u4AntY3eRVUuk9Yp78Kax9V0KsE3SeU6k33CaV6031Cqd50n1BKKaUG5+vKdOi3cvQiYIkx5p8St/8R+AjxK+mXE7+K3mCM+ekAy1sGLAMIlZQtqK4cTbQ81HN/rKz3b1Nm7fYLlcV6/h5S2m3Zco9Hyoh2lyDdUvCyUp9zaryZJJ/PsFAnw0s6AXg/VllwPHZxI8bkOgvR1WXYsyfKqaeWAtDaGuOdd2KdxpghUNg+UVXFgqlTS/OKy4uv9bFo5piOR8oKXk+0u/d1y+R+WDLALj7QdL8IdUXdDqGXWCxCR/gIQ4eMAaA7cpzjXUct2SdClC6oDo3IHERFGdHyEJEq6983BpLL8TkXqe9Nw0KFH7OU9TK9l9j5PiEVZQvKTxhj1VOhojQCwJBQ3wPj8WgZ4UgpdJYg3VASsWy1vlES8fd5vJNipQOfA8eiEcJthxhSUwdApKuDrrbDee0TqftDSah8QdXQ/PaHWJkQK0185iiNUVPZe78eqsdfZaPurhgH3g5zwilDAGhvjXDw3a6C94kQpQuqS0cOuF5TWfh5t9Ok0z8n7unbN1pRkvgNptxQXh5heGknI0MdboTXx5FoldshDOi9ra2HjDEFn/AMHTrUzJgxw4qQAqm5uZkxY6w7r3RUdzeU2XtM89L22bhxoyX7hF/klxXzIWPM3cDdWcy3UkT2A+eXlw1bcMasK2k/sbrn/raJ8cR6R/xcm3CdtW+eI+uO9fw9d/R+y5a7+dB4jjQNo6Kp8J059TmnxptJ8vmcOXIHZ1ftBOAPHdMKjscubsSYXGch9r4b4YovH2XNU6MBeOp3nfzzVUfb0ufLZ58YMya0ILncXHnxtX7+yPSM82w+NL7g9RxpGtbrdnI/rGrqf/6h+7yVjM5V9bvtbofQy/HwEV598+ecMetKAJoOv86mt35lyT5RXlK14KMjLswYg5kykfYTqzm4IGT5+8ZAcjk+5yL1venMkTtsWYcqTKb3EjvfJ0pHDlsw+d+vzC/wfkwZ3QLA3Jr3+ty3uXUCuw/VEt0xjMpmqDpUfInlqgP+SeK4rWPcwOfA4bbDvPHfDzDn0/8bgMNvN7LzhVV57RO93iMqhi04bdH1ecXbVlfK8bHQOdbA2E6WTO819jsfG/5mXstVKhvNe8PcuewNbn9iDgB/ffowd1+3s+B9oiJUteBvai8acN6uGScUFrgLyrfvdTuErKVv39ap8cKiYycJ4UldTDrxEGeP28FFNRvdCK+PX7cucDuEAd0698k+Y0flY8aMGWzYsMGKRSmvEQGfFy/nQkQs2Sf8wtdtXgZQ8ACBxpgnjTHLykIVlgamlBvq6koAylMm5b1PDB9e+DcblHJbZflwsGifKJPyzDMr5XFWvk+UVHvvW0hK5aqsqgYK2CeS+0Npme4PKhhG1ZWBFfuE6OdrpZRS/hfEZPorwCkiMkVEyoFLgDW5LEBEzheRld3RsC0BDiTW6ez6VHGYN78MoNKKfeL99+NXVtvb7WkfoZQThldPAIv2iW7TZUeISjnKyveJWLv3Wk9Eu/X8SuVmaO2JUMA+kdwfIt3e2x/8prPd39/WC4qT5w4FK/YJY/3xOOLwZ3allFLK18l0uwYIdKMy/dBPH2Xvtd9izx2/cWydKniu++ejfO6zh3nrrQgf/fBBHlvdQWm8R+g7WLBPDB8uXHv1UebMOMi1Vx+14RkoZa1Nb/2av25/gI5wC883/pC9za9SIiGwaJ/QynTlN/29T9zwtVaIVxwWvE94rTJ95wur2Lj6X9j5wiq3Q1EetfPF/2TrM3fT+f5BXnv8OzS/+VekpLD3Ca1Mt8bd1+/kivqN3H194W0QVfbuuWEnt31xK/t3d3Ltx17juV81Eyrw84Rdlelbtq7mhT99hy1bV1u6XKWUKthXv+p2BMpGvu6Zboz50gDTnwKeyne5InI+cP6QilH5LiInsc4wHRs2AdD64lai151HaIgmaCBejVJZHco8owLg7p8MOBBiqzFmYb7LTe4TJ04KsXltvMrqd2s7uePOGNXVvr4ml5fo8S7dR31i3skD9uW0ZJ8YUjIs47xKeUn6+0R7e4yb/s/7ABhjTs13ucl9oqzOmXOnbES7wxx+uxGI98COdocJlWmLgUJFImFKS4OzHact+seB7sr7fSK5P1RW1eYdl1e4dS7e2R7lr08dBuCvTx2m83v6mcAp1/5owHGOCt4nqkLD844rXSQa5mDzZgAONm8mEr2QUm3T6ivt7cX5WVIViZUr3Y5A2UiPXP1wujK9pLKCqoXzAKhZNMuVJF0s7L2vx913w3a+dtpfuO+G7W6HUvSS+8SIGuHT58WrrD59XmVRnvzsueM3bPnCv+u3SNJEI947htgpuU8cjx2j8dh6t8NRKm/V1SU9x/VCeLEyPVRWwaiT5gMw6qT5mkhPEcnzmP1648/503/fyuuNP7c4omAJSmW6m5XhldUhPvKp+MW5j3xqlOWJdG0f4yw7KtNLQxWMHTMXgLFj5lqSSNeWMc658ZrDnD6ziRuvOex2KErZY4F3B9BVhfN1ZXqQjL7qUmKdF1E72fn+u/tXr6JtSwND59Qz/pKljq+/P53tUTY8fQiADU8f4vJ/02oUr7hnxYiirkhvfXEroN8iSbX95Udo2ddI7cT5zDj9MrfDcVxT11vMNh+nVMrcDkUplWbax5dqRXqa1xt/TvOBTYwZN4/Z8/8h68dFImGaD8S/Sdl8YBORyOcDVaGuevNCZfh1d02zZb13X7+Tvz51mI98ahTX3TVgFbbygTmzLrGsIn3L1tUcbN7M2DFzmTPrEguiUwNpb4/xTOLbzs+s7eQ7PyjOz5Yq4F591e0IlI30iNUPtwYgLal0/gNJLBymbUsDAG1bGjxToV5ZHWLhuaMBWHjuaE2kuyx9ANJiPdkJDSmnZtEswL1vkXhNNBKmZV+8jULLvsaiqVBP7hMAdeUnayJd+VZ7e4zfrS18kEQvD0CqifQP9E2IZ3/MLi2tYMy4+Dcpx4ybp4n0QQRhAFK7K8NzicNKfS4SaIW6I+wcgNSqivTeLWOK43zWLdXVJSxJfCtuSZF+21kp5W961OqHGwOQuqWkooKhc+oBGDqnnpIK7zznK380gx+/+lGu/NEMt0MpeqkDkBa7yd/4HHN+9X+Z/I3PuR2KJ4RKK6idGG+jUDtxPqEiSa4k94lhoVrmDzvL7XCUyluQ27yovgpNiM+e/w987O+/nVNFezEKSpuX6+6axgMNCwJVve2ViwTFxq4BSK1iR8sYNbgf3juKl7fV8cN7vTPWilKWGj/e7QiUjbTNi2L8JUuJhS/2VCI9SU9wlRdpRXpvM06/jGjkC0WTSE8l6AUm5X/3rBjB79Y2veZ2HMoZs+f/Q0EtWrQivbgE8VzcrvYxyt+sbBmjsqMV6SrQ3nvP7QiUjfTo1Q832rzEOt39KpkXE+nKO9LbvLS3x1yOSHlNsSXSe94njPPjXCjlRV5u8+IH0W5nzwM1IW4vv7V5KcZWJ5pId5adbV6sZGUiPRLrtmxZSikfuu02tyNQNtJkej+cbvNy6KePsvfab3Hop486sj6lcpXa5uXaq48yZ8ZBrr36qNthKeWanvcJ0W8pKP9LHM8/VMgytM1L/na+sIqNq/+FnS+scjsUZRE/tXm5+/qdXFG/kbuv3+l2KCrAvN7mxWoNR5/lD83303D0WbdDUUq55dvfdjsCZSNNphfAisE6Y51hOjbEB4Lq2LCJ6HGtclTeFY3RM1Dd79Z22lKhXozVUUopa+lxJHtWDUCqcpOsRI92hzn8dnwQ58NvNzpeoa7s5+WBuXUwTqWsFYl1E4l10xTeBUBTeJdWqCulVABpMr0f2bR52b96Fbu+e3PB1eQllRVULYwPBFW1cJ72YlaelNwn2ttMz0B1n7Zh5PX7btjO1077C/fdsN3S5SplNW3z4l16HMmNVQOQapuX7KVWoofKKhh1UnwQ51EnzSdUVhxVm0GX3B862pr58zPfZNvGR9wOqV86GKdyil/avBQiWY2+5f0/UlcxFYC6iqmUlpS5HJlSSnmDiDwoIgdFZEvKtFEisk5E3kz8HpmYXiMiT4pIo4i8LiL/K+Uxlyfmf1NELk+ZvkBENovIThG5W0RsG+BMk+n9yNTmJdoVpm1LAxCvJs+l33l/846+6lJOuOc7jPryRfkF7EPZVjRrhYw3pLZ5uWfFCLZsH8s9K0ZkfB1zeZ0726NsePoQABuePqSvvYW8XBXnV9rmxZv0OJKfe1aMANhWyDK0zUtcpsry/irRp318KQsu+R7TPr60oHVH9FgPDL4dnKr8T+4PsWj8gmvz/k2efS++7q5pPNCwgOvumuZ2KP3y23E8n3jteI5e225Bb/OSXo0+Z/jfcfaYf6J+xGLb1ucku8fL8tt4XOGOiNshKD/YsMHtCLzoIWBJ2rSbgPXGmFOA9YnbAP8MbDXGzAc+AfxQRMpFZBRwK/AR4HTg1mQCHlgBfBU4JfGTvi7LaDI9D6HyCobOqQfi1eQlldmdFAzWG/3wQ79m77XfYs8dv7E0Vi+674btWfXc1upC76quLsnYOz3b3urJ1/nhf32TheeOBmDhuaO1Osoi219+hJeevIXtL3uzKk4pK1VWh/Q4koepJzUBzHQ7Dr/Lpvf5QJXohVakv974c/7037fyeuPPC1qO3w22HdzoTV9aNgSAMePneXqgbq8eK/3Wzz2feO14jn7bbkFQWlLWpxrdrop0p/ux33jNYU6f2cSN1xz25fKt9suvb+R7H3ka4GS3Y1HKb4wxLwDpO/tngIcTfz8MfDY5OzAsUV0+NPG4CPBJYJ0x5rAx5giwDlgiIuOB4caYl4wxBliVsizLaTI9T+MvWcrUb36f0VddmtX86b3RUyvUU+9rfXFroPumd3d091QN/m5t54BVE16qLjy7Sk9E06X22O2vd3qm+5PSX+fL/+0UfvzqR7nyRzNsjN47OursXX40EqZlX7wCsmVfo2er4pSy0pU/mlFUx5FCbdvaRcxfBWGelEvvc6sq0ZMikTDNB+Lnkc0HNhVthfpg2yEScac3fWXVKP52yXeZueAyS5f7seFvWro8L/JbP/d84rXjOfptuwVJ/YjFtlajQ98KeLsr1NvbYzyT+Ez3jA3jZdm9fKuFOyK8/vv3kjdHDjavUixc6HYEfjHOGLM/8XcTMC7x9z3Ei33eAzYD1xtjYsBE4N2Ux+9NTJuY+Dt9ui00md6PbHqmA5RUZF9hkt4bPbWaPfW+mkWzBu2bfubIHYP+eF1ZVVlP1eCnz6scsBLG7erC9AS6Ewl1Lyftk/vE++8boHeP3f56p2e6P6m/19mr1VF+FCqtoHZivAKyduJ8T1fF+Y32TPc2PY5kb+asckosOBss9p7pufY+t6o3ese4MromDu217q6JQ+kYV1bQjx+VllYwZlz8fHrMuHmUprznpW8ju3vT9/SH7u605L13wpjBv+UXRH7r555PvHY8R69ut2LomQ7Y3h+9vwp4O1VXl7Ak8ZluiQ3jZdm9fKtVVJUy+5MTkjeP5LscEVkmIhtEZENzc7M1wSnlvtHJ/+vEz7JcF5CoKDeJm58EGoAJQD1wj4gMty7cwkg8VtWfmuoJ5oxZV9J+YnXPtLaJ8ROSZEVpuC77q8HJavSB2sLEOsPUTv4gMTN39P4+82RKmD9/ZHq/0zcfGs+RpmFUNBX+hpv6nEfWHcv6ccnnc+bIHXzUvEF1dQl/6Bi8P2Nne9SVk8D+EtuZYrVjnVaZcmLTRmNMwZdGZ88pM797ZnTP7fb22KAnPan3D7b93HqdB9pfUm0+NL7g9RxpGtbrdup+WNXUd/6h+6ytIopGwo4m0qvfbXdsXfl6dsNtluwTNaVjzEdHXJhxPjNlIu0nVnNwQSin941C5HJ8zkXqe5MfLuIWo3zeT6ac2LTNGDOr0HVXTptoJv/7lYUupseU0S0AzK15r899m1snsPtQLdEdw6hshqpDzp7TRrvDfZKy/U1zM55iU3Wgm0gk3CuRDvRcIMhlG738nzcW/D4xbMQJ5rRF1+f12La6Uo6Phc6xhgmzm5g36oN9oBgq05PcOkfMVz7xZnqMHcvMx6WnvFzwPlFTNtb8Te3A44R1zTihkMW7onz73swzWSwS684rkZ6+fVunxhPZx04SwpO6mHTiIc4et4OLajb2mi/TZ75CDbT8X7cuyOrx4Y4IFVWlVoc16Pq+95GnLfkssXDhQrNBe2sHkwgUUb5VRLLaJ0RkMrDWGDMncXsH8AljzP5Eq5bnjDHTReR3wHJjzIuJ+f6beD/1qYn5r0xMvw94LvHzR2PMjMT0L6XOZzVvX/oLgGQCPdkv/fBDvx5w3mx7rwdBtm/Gfjp5LgZbX4/06oHe3+uY+vU8fZ29QSvS7WMonhMkFVyJ43ogeqY71b5joP7bbiaziz2RnpSeSE9VTNsoKO09/HaOmE+8gz0m3/7nfttuKjelJWWODkJqd8V4IctP9jD/5dc3Zp7ZIk4m7pWP3Xqr2xH4xRrg8sTflwP/lfj7HeAsABEZB0wH3gJ+DywWkZGJgUcXA79PtIp5X0TOSPRZX5qyLMtpMt1GyQR6809WDdgvXSm/GawHeraDjioVFMeiLTQeW+92GErlLXWMC79zaoDJXPqjK+UGHYAyGLT/uRqI04OQelVqD/PXf/8e4Y6IyxEpleK229yOwHNE5BfAX4DpIrJXRK4AlgPniMibwNmJ2wDfBf5GRDYD64FvGGMOGWMOJ+57JfHzncQ0gGuA+4GdwC7gabueiybTbZI6qOjx115nyGlzgL790pXym4F6oGc76KhSQdPU9RYR41x1kFJWSh3jws+iXc4luHPtj66UkzQBGxxe7X+u3OX0IKReltrDfPYnJ2jFuPKWCRMyz1NkjDFfMsaMN8aUGWNOMMY8YIxpMcacZYw5xRhzdjIxbox5zxiz2Bgz1xgzxxjzSMpyHjTGTEv8/L+U6RsS8041xlxrbOxrrkcbmyQHFe3YsImqhfMYfdWlxDrDmkhXvjZrdin3rBjR733JhMzv1nYOOuioUkFTV34ypeLPAfuUArhnxQh+t7bpNbfjKESoPJ7gPvx2oyMJ7mkfX6o9ypUnJROwf33qsCZgA+C6u6bR+T1/9Y1X9koOQtoU3uXIIKRe98U7FxD+znxNpCvv2d93DEQVHHrEsVE8gX5RTwJdE+nK70IZ8uP3rBjBHXfaO0CNUl4yLFTL/GFnuR2GUlbw/deJnE5wayJdeZUmYINFX0eVrn7E4rwHIQ0iTaQrpZymR51+iMj5wPlDKkYVvCxNoKsgSO4TJ52U+WReE+mqGPS8T5QMdzsUpTwhuU+U1RV+7lQITXArL0juD5VVta7FoAlY5SXJfaIqpOdNVtFEulIed9ppbkegbKRZr34YY540xiwrC+kHMqXgg31i+HBxOxSlPKHnfULK3Q5FKU9I7hMl1f7vv65UoZL7Q2mZ7g9KQco+Ifr5WilVJDZudDsCZSNNpiullFJKKaWUUkoppZQVli1zOwJlI02mq6LU2R51OwQVINHjXW6HoAIs1hl2OwSlbBM7rv/fSg0k1qX7hwq2SKzb7RCUUsoeP/uZ2xEoG2nPdFV07rthOxuePsTCc0dz5Y9muB2O8rk9d/yG1he3UrNoFpO/8Tm3w1EBc+inj9KxYRNVC+cx+qpL3Q5HKUu998Nfcux/XmfY38xmwo1fdDscpTzlrfWrOLq7ger6+Uz4wWK3w1HKcg1Hn6UpvIu6iqnUj9D/caWUUv6hlemqqHS2R9nw9CEANjx9SCvUVUGix7tofXErAK0vbtUKdWWpWGeYjg2bAOjYsEkr1FWgxI6HOfY/rwNw7H9e1wp1pVJEI2GO7m4AoL2hUc8vVOBEYt00hXcB0BTepRXqSimlfEWT6aqoVFaHWHjuaAAWnjuayuqQyxEpPwsNKadm0SwAahbNIjREB6NU1imprKBq4TwAqhbOo6RSB+1SwVEypIJhfzMbgGF/M5uSIfr/rVRSqLSCEVPqAaiun6/nFypwSkvKqKuYCkBdxVRKS8pcjkgppSy2b5/bESgbFU2bFxE5GfhXoMYYc5Hb8dhBq6yzc+WPZnD5v0WLPpFeDPuEEyZ/43NErztPP+imiETDlIb8lxjz4j4x+qpLiXVepIn0HHW26zHeCnbvExNu/CKxa8KaSFcFi3aHCZXZ/3/k5PvEyWctpX3ExXSdUA402bmqXvT4qXJRyD5RP2IxkVi3JtIDqL09RnW11m2qIrdxI0yY4HYUyia+PsKJyIMiclBEtqRNXyIiO0Rkp4jcBGCMecsYc4U7kdrvvhu287XT/sKL//rfbofiC0H9kPB/b2xlYf1BPnnWofS7hhfbPuEkTaR/YPvLj/Dfr32fxl2/cjsUALbs/i1/bPh3/rzlJ+l3+Waf0ER6bpLvh/fdsN3tUDzJa+8TmkhXhdr5wio2rv4Xdr6wKq/Hv/U/q3n1l7eyec0P0u9y/X2ipNzZ/ePu63dyRf1G7r5+p6PrVd6y8qa3uPojr/KNT21Ov8vyfUIT6cFz4zWHOX1mEzdec9jtUJRy1wUXuB2BspGvk+nAQ8CS1AkiEgJ+ApwLzAK+JCKznA/NOal9wN9ev5vuDu05V6w+/4UhPPSfI3tNi0YNwCSKaJ9Q7ohGwrTsawTgwJHXiUTd74E8YXQ9C075/9k78/C2qjP/f44kW95iO14Sx9lJgKx2IGEp00AhLKEkZaCshQmdYQhLU+BXOqVMmULptFMKzLQMZUmXgRRKSjswJQkEaNpC6EKbgLMnELKRxbGdxHYiR7Ilnd8fWiLLWq6ku0k+n+fxY+vq3nNfXevcc+97v+f73thvmZRBUH2iIFF1MdKjxglFIRHo83F4d2jcObx7HX5/5uNO3YQzOHXOLf2WyeDgGye8ngDvvRZKfr332mF1/hzEzL6yjq/97NR+y4JqnFBowOMJsnK5F4CVy714PEGLI1IoFApjyOtkupTyHSD+keeZwPbwU/JeYClwuenBmUisD/jYOeMpKlNP+AcrZ51dTHW16LdsXUsfgG8w9QmFNThdbmpHNgMwfOhUW1i91AwZR5GrtN+yLs8+UH2iIFF1MdKjxglFIeEsclMzNjTu1IxtxuXKfNypHD4Bl7us37Jjh/bAIOsTJeVOzvpsDQBnfbZGnT8HMZPPrKSiqr8b7Mfrj8Eg6xOKzCkvdzB3XgkAc+eVKKsXhUJRsBSiZ/pI4JOY13uBs4QQtcB3gNOEEPdJKf8j0cZCiIXAQoCS4iqjY9WNiA/4e71KIKDoT2trEKA3ZlHWfaJxpLogUqRm0pk34t71WVsk0pPh7e0GnfpEiaPC4GgVmaLqYmSOnuOEqy5/rp0UhcHEcxec8Ew/qM/szL6eLsiyT8T2B3dptS7xmMWdP5yI97vq/KkYyOHWPtChT6jrpsLnsSdreOgR5ZmuUPDMM1ZHoDCQQkymJ0RKeQi4TcN6i4UQB4D5QjhnGh+ZfpSUO/tf4igUKcimTxS5RF71CYU12DmRnoqsxgkcqk/YEJUI0oes+oQrv66dFIWBGcVHQVufyOd7CVDnT5yLB5IAACAASURBVEVmZNonHEJdNw0GVCJdoQAWLrQ6AoWBFOJZbh8wOub1qPAyzUgpl0kpFxblaUJIoYilocEBEFshM+s+UVkp0q+sUNickuJK0KlPFAlVfFaR/+g5TjjKS/QMTaGwhKKyKsihT0T6g6tI9QdFYVDTUAR69Amh7q8VCsUgQajcSSFTiMr0vwEnCyHGExrgrwO+kEkDQoj5wPxSd40B4eXG20dOTb9SnvDbnolWh5CU3/ZM5MKy7dG/zd6nnjQ1FwGU6NEnxo5VaiVF/lNZ3gg69YlSR6X+ASoUJqPnOOGqr8V7qDTt+ukoqT2ecxuJKNPJBkRR2FTUjoYc+kSkP5SU1eoSz/72appq9kdfv9t9Mp+u/EiXthUKLZw0vQJ06BNlTnXdZCXFW/fSO2mU1WEoFApF3pPXyXQhxIvAZ4A6IcRe4AEp5U+FEIuANwAn8DMp5aZM2pVSLgOWVZU33gJQ/okHz+hyACr2BTg20riE4pHWIQxtOArAho4RTK87kHObGzpG9Htd1hr63dOQc9McaR2iab3IZ4pg50R6BCtizDWhfueXOvnLX3o5cjjIp85o4+57Krj2ujKAPejQJ5qaim7JOjiFwgLW7/g1h4/uos/fw9vrHmNC4/mMqj8ddOoTVa561ScUtiPVWGL0OOEeN0qXPuE9VEpJ7XF2dtQyvu4QG7oaB6yzsyOUuNdSdl0l0Qc3kf9/z/CB35btq3/O0YMf4/d6+OB/H2JU0yXUn3wW5NAnIv1hSLU+/QFg/eHGfgl1xUDe7T7Z6hAKgrf+9V32rzmIt9PHwr/bwBm3NjH5708G/pZzn6gqGpayTxRv3Zv0PTsmgVPFq8iNX3cpRyCFQmFf8jqZLqW8Psny14DXsm03E2W6u1XLLZw2fA3m3uiVtWaXUHe3FmUVq14PB+LxegqrUFIkiZ9NUv3xHyUtdNUlpZyVbUzxynSPRxWVMYqKfQGrQ8iJ8k88Ga0vdmbkJJExzeJTUPmpEwuOAcf2gU59IlNlelkr+HR4kKlQZEuKceKolPL0bNuNVabrRXxCPRVlHVIlzJPg9/twubRZK7j3dxscjTX4GkPn6kTfkaZTroNT4haG1st6nIiOEcVDKf2kv6DEH+jF5dRiETaE48P636rFJtRV4lhhFBd999PJ3sq5T+SiTI9NXNshsa4lke4P9uFy6Jcv0AMt6nSVyFYodGDePKsjUBhIXifTjSJemQ791emQfSJaC0ao05MpyDNRqQd9Phxue/ncPXP3Vta83sGsS+u49QeTrA5HV3JVxvd/yNCaU1uxyvRFt3eyYrmXy+aV8MRTSZMy/fB4gmCiZVihPWApRIxOohuNmcr0oNeHo8Re595sUX3Tniy6vRPgtFzaiPSJklGjbynq0Ofysq/OnzahXtThoqRdW3vpEsXak5z5w7o9r9DavYWGysk0j7nCkhjscFzd+7ujCXUziI4RZY39xoh1u/6X1q7NNFRNoXnc54HUx6e0DUDgHXZimVKo24++nj6KyuyVMLUbWpXpWjFaEa5HEryl801afR/T4J7AjOqLdYrMeH578FQuHL7N6jByxtfjx12m0l0KC1m2zOoIFAaipKWp8PVXr2SquNSLeJuWbEmloi9rTf3T9uwSPv72fRxYuiRtW+nQy/fd6wmw5vUOANa83oHXk7mi9+0jp/b7KRSeuXsrXz79zzxz91Zd2w0EYcVyLxD67fEE026z6PZOpk1q0z2WZBj12RWZI3buS/qj0EbH0y+wd9E36Xj6hYy31WrDlSnZjkmqb9oTjycYPa/bjfik/M6O2n4/sd7sqVTp7v3daRPp6/a8wqotj7Juzyu5BW0j/IFeWru3ANDavQV/oNf0GOx0XLV8D4zEH+iltWszAK1dm/EHelm3639ZtfFh1u3639Qbt5Wwv/2EgGH94YG2RwprePPrq/nJ7Jd48+urrQ5FoRMtnW/y2/af0NL5ZtZt+IN9tPo+BqDV9zH+oD1nTlV93H/83/NJnUWR6MtLX13Ld896nZe+utbqUBSDmfnzrY5AkQIhxHAhxE+FEK+HX08RQtysdXv1qC4B6abvR3zT9VZqp7JP0UuhXpaFQDnQ66NrewsAxza2EPRdm/HnjlXbZ0OyRPfYOa3sXrWTsXPG817vFMjxPvHtI6dy3tD8fhIf/5Dhpu/kbhsSa/Ny2bySqDI9ndVLbJImEouRqtREnz2fVLDJLF4Cfh9OjVP07UKhJ8xPjBOZJ6y1WmUFvT561qwHoGfNeoLeq/JWoZ7vfbOQKS93RM/ruRDpE8VVdZqV4qnw1sf8HVanJ6OsQwIaLUr2HhywyC/7+iWdp35yBi6R/ypTF9BQNJ7Wvp00FI3HdeCIqfu31XEdNTz6Z6rviV4q+liblwguZzENVVOiynSgX3J9amB+wn0He31AYc2Y0BMrHywEjvfy8Vt7APj4rT188OU6nKXqf5WIfClAmigJno1C3eUoosE9IapMt5vVSzxDdkugGN8Y8x+66o2vx8+mN0Kzdza9sR/fQ82aFepKza7QleXLrY5AkZpngf8BvhF+/SHwS+CnWjZWZ4oExE7fFzv3IcePHLDOrpVL6NreQsW0GYy4boHuMSRKPmtNqOulZI/gLHZTNXEGXdtbqJo4I5pIz8Y7PdFnyEURPvs7F3D2N/SdWpksoW60cl2vJH5JuZNZl9ZF7W/0SFjF2rw88VQ1Dz+qzTM9NkmjVyypMOKzW83Wvz7PoX3rqB3ZzKQzb7Q6HEWYSJ8QQtyy7ugqmofM0X0fjhI3ZbOa6FmznrJZTXmbSIfC7JuFxBNPVbNieesHubQR6ROlw0frMoW/pD2UUC/qcPWze4klE4uXREn0CC5R1D/pXACJ9AjN5RcwVfZZ8plsdVwj//+YpHo8elri9LMC23PiurfZcQ5Tq8MPFfYd6n989vW3MSoFtqz9De0H1lM+o5lhX7uG/e3VNNZ3AkqdbgecpcXUnncqh97eRu15p6pEegr0tnkxCj2T4DOqL7alZzpo803PV9xlLqZe0simN/Yz9ZJGzcnxl766NrrNNY8qz3iFwgiEED8D5gFtUspp4WU1hJLY44BdwDVSyiNCiH8Bbghv6gImA/VSysNCiLnADwkVxP6JlPJ74bbGA0uBWmAt8A9SymRPCeuklC8JIe4DkFL6hRCalagqmZ6AdMr0gD93pXa+MW7uAgK91+IsdtNjdTBxKI/Cgdz6g0m6Kj/jC5BmUnw0knz/s4iv7mUMen92Kwn4fRzatw6AQ/vWEfBfnXcK9UIl0icAWnt3MFWea0iiqO62G/JakR5LIfXNAiW9b1cKYpXp+YiVSWejsfIz5ctxjbfEmRq4LKf2Us1eij0WzRVzkh4ff6CX9gOh2UmelnUEvZcXxFhQaEy6/3ME7ulVifQ05IsyHfRNgtsxkT4YuObRmRkr0rNVsysUiox4FngCWBKz7OvAKinl94QQXw+/vldK+QjwCETHkP8XTqQ7gR8BFwF7gb8JIV6VUm4GHgb+S0q5VAjxNHAz8FSSWDxCiFpAhvdxNtCl9YMoz/QESCmXSSkXFonEF0VOV0ipDVAxbUbBJ9IjOIsHx+csFPRMWEX6RGVldlVEM0m+60GhJOucLje1I5sBqB3ZrBLpNiLSJwAaik8yNFFUSMmTQumbioFE+oTDXWJ1KFlj94RvvpIPx9XlLKahcjIADZWTc7Z6OXEvkf78nez4uJzF1I9oAqB8RnNBjQWFhkqkpyfSJ1wa+oQdUEnw/CeTZHhEzQ5kpGZXKFIipdUR2A4p5TvA4bjFlwPPhf9+Dvj7BJteD7wY/vtMYLuUckdYdb4UuFwIIYALgF+naSvCV4BXgQlCiD8SSvB/WetnUWeJLIkotX1j8uOCQKFQ5CeTzrxRKdJtzBBnrSEWLwqFQqEwl+YxVzA1cJkunul6MXnmjdRMCtA7qhiwZ6FghUKhKAQyVbMrFGlZvBgWLrQ6inxguJQy4onXCvTz5BNClAFzgUXhRSOBT2JW2QucRcjapVNK6Y9ZPtCzO4yU8n0hxHnAqYAAtkkpNftYK2V6DiiltkKhMAOVSLcvguxmaygUCoXCftgpkR7Boe43FAqFwhTMSqQLIRYKIdYIIda0t+tQtV1hT2691eoIzKYu8r0O/2T8JEFKKQnbrsQwH/ijlDJe0Z4TQogvARVSyk1Syo1AhRDiDq3bq2R6AoQQ84UQi/uS+tQrFIOLSJ/o7lZTlRQKUOOEQhFPpE8EfUo9q1CcGCN8VoeiUNiCSJ/wqz6hUCClXCylnCWlnFVfX291OAqFXnREvtfhn8UatzsohBgBEP7dFvf+dZyweAHYB4yOeT0qvOwQUC2EcMUtT8YtUsrOyAsp5RFAc5FslUxPQDrPdIVisJGrZ7pCUWiocUKh6E8heKYrFHqRiWe6QjEYyDfPdIVCoVCYxqvATeG/bwJ+E3lDCFEFnBe7DPgbcLIQYrwQophQsv3VsKr998BVidpKgDPssx7ZlxPQfHOvkukKhUKhUCgUCoVCoVAoFAqFQqEHr75qdQS2QwjxIvBn4FQhxF4hxM3A94CLhBAfAReGX0e4AnhTSumJLAh7oi8C3gC2AC9JKTeF374X+IoQYjshD/WfpghnJfBLIcQcIcQcQur3lVo/i6quoFAoFAqFQqFQKBQKhUKhUCgUejBzptUR2A4p5fVJ3pqTZP1ngWcTLH8NeC3B8h3AmRrDuRe4Fbg9/Pot4Ccat1XJ9EQIIeYD80sdlVaHolDYgkifGDvWaXUoCoUtUOOEQtGfSJ8orqqzOhSFwnJOjBFDrA5FobAFkT5R5lTXTQqFYpAwciRIVXPOrkgpg8BT4Z+MUTYvCSh0L9xAryr8Ugh4PQHT9qU80xVm45d9tmwrQiGOE4HjxhdTNfO8pTAXu3im+wOFVRTYiPOXwnhSeaar/6lCK2aMy2aRrWe6P6j6ix6o46hQKBT9EUL8nRDiLSHEh0KIHUKInUKIHVq3V8r0HBnqOcaR8gqrw9DMrpVL6NreQtXEGYybu8DqcBRZ8szdW1nzegezLq3j1h9MsjoczbgP9+GrKbI6DIXNWXd0Fa29O2goPonmIQlnfFnSViGz6+GX6Vq9marZUxh375WG7CNfz1uDBVcBXBOu2/MKrd1baKicTPOYK6wOJ2fWeX5Ha99OGorG01x+Qb/3KoNeuh2q2Gu+se7YqhP/04rCGZOqunroqiqzOoyCYuu/v8qht7dRe96pTLr/c1aHYwktnW/S6vuYBvcEZlRfbHU4eUvscTy3cjbdjlKrQ1IoFAo78FPg/wFrgYwVX3l/42Q19694hXuu+Qerw+jH9LoDbOgYMWB50Oeja3sLAF3bWwj0Xouz2NiK6kGvD0dJYVRtP2/oNqtDAELKzjWvdwCw5vUObvpOgJLy/LBf+dR3dvCHx061OgyFjfHLPlp7Qw+EW3t3MFWei0skfgDjl31J38u0rcFM4HgvXas3A9C1ejOBO+fhLNVXcZ/P563BwjgYbXUMueD3+2jt3gJAa/cWpgYuw+WM+R6PGg57D1oUXeb4ZR+tfTsBaO3bydS4893tx//Mw+XnZ9zmoD4Hjhpu6e7T/U/zmbt/9Du+9a/zrA6jYAgc7+XQ26H7jkNvbyNwT6/u43LguP5t6ok/2Eer72MAWn0f4w/24XLkR3+xU6zxx/HWbnhEPZhQKMzhllusjkCRmi4p5evZbqyS6WmQ40cmfW/MkTbmr/+Axy+Yy+66esNjmV53IKt1I4l1h9tN1cQZUWV6Non0ngbt63Y8/QI9a9ZTNquJuttuGBBXhERJaq+nf6Ll7SPmJWDtkjRPRkm5k1mX1kUVnvmSkKrceZyJy9t5/8tj6B6nFBF2JeD34XRZ9wDMJYpoKD4pqiZPlmjQojjX2tZgx1laTFFdJX0d3RTVVRpyc232eSt+DFGkxrXDTxXUWB1HIvrq/ACU1B5PuZ7L5aahcnJUmd4vkR4hjxLqLlFEQ9H4qIo59vw1MtDF+X07+HngdPY7qzS1l0rlXkgkfWBgcSIdUv9Ps6WpZr8OkeXGsN3dXPT7Lbx750Tax5jvh73+cKPp+zQaZ2kxteedGlWm6z0uZ6p6tyLx7nIU0eCeEFVUm5Gc1iMJbjc1fexxPKdoNBf4PuYFfyf7XdVWh6ZQFD6LF1sdgSI1vxdCPAK8DES9sKWU72vZWCXTM0DIIKX+0DEO9Dq5JKzyvmzDBzx7znnR9TwyiCixZ7Jw3NwFuinSfQ3JvdeCXh89a9YD0LNmPUHvVZrbTWQFoHeC28zkvBHc+oNJ9ld2BiXi+ImCGye91h76vaKdjV888ZDKX+oAh7le7CrRlpitf32eQ/vWUTuymUln3mhZHM1D5qRVpGtVnKdrSxG6Se7r6Aagr6PbsJtms85byk5GA3Hn5/JXvbo1LYvAa7y+AAgl2kvaT1zKNo+5op8i3ddYiXt/d/+NbJBU1Uoz1zM10EuRowh3jN/teYc+gqPwmdI2Xq4dF13ucxQhxcDx1B/opXVLjCJ6xNDEDxtsiD/QqznWATY/kQcnCf7nvkYNSd+NmUSahOIiGHNitmgzNzI1g88UT2N9pw5B5YYISoq9/ujrmW/uBmDWG7v53Q0nzrm9JS6kCdd3sQ8U+nr6KCorjPG+6T9n0dfTHP48+j006evp448xqvfJ/7475TF78+ur+fitPUy4aIxuMWhlRvXFAxLcRqm+9UiC201NL6TELf2cXXk+fvlprj6+Gfo+4Tzvdn552qWUBEJ5jW5ZjBSqlJ5CoTszZ8LatVZHoUjOWeHfs2KWSUCT6kQl01Ph7j/4CeD6j95mwbZVuGQwuvzuVSu5e9VK/A4HD9cN45ttrZRNm8GI6zLzJE+VnM5ElZ6ORIn0TBTnWnCUuCmb1RRVpoesXtIX0THLCiCSnI9PqttdlR6L7ZPBEqqe8VD1uAcRgLHhxbMe38Osx/cQdMIHd4yh5fbRmFnjOjbRNumB/H6ooicBv49D+9YBcGjfOgL+q3NSqMvxIxE792W9farkd6aKc5VIT42ztJiq2VOinulGqs/MUKQrOxkNxJ2fC4VIgjQQt2xAQj2PcDmLETLIVR1/4fr2P+KMGTEXtL3DgrZ3CCD4xbBP82L93yEZmLx0OYvTq/ZtSCYe+P5A70CbnyQPTjQl0g1E6/E/PnoIxxqS36pZpkqXkgt/voW5P92IM3Di+zj/6fXMf3o9Aafg9X+exus3T4ME30ejiE36Xvy92abt10iMeDBQVFbEhIvGRI9Vqn309fTx8Vt7ACK/Tc+4xiajjVJ965UEt0JNnwqB5PM9LVzvWdt/7PD8jQXv/o0ADn4+9jM8OfFSuseZK2xSKAYF72sSOCssQkqZmV9iHCqZngAhxHxgfqm7/4zno2OG8DMu5t3Jp/CdN59nhKcr+t7+yiq+csW1/PK50FSOYxtbCPquxeHOLhk1tOFo1vGnoqcByloNaXoAdbfdQNB7FY4St+bPY7YVQGxSPdNE+mBSN0f6xNixGXxep6DzK0M4fo6b2js7KT5w4gHU0YZi/vDYqRycpW16ul7EJ9om/EvhKJhiycaqxelyUzuyOapM18PqJZlNVi5J9ghWK86j44RjiCX715tx915piFe62eSrDZbpxJyfaxZ14j4YTL9NGiJ9oqimLvf4wsRbvIyvO9Tv/S2HRoVV8AIoouxgYlFCvifUg8LB88PPpaViHF/75DcM85+4pmpzDeHh0ZezqTy1ajRetQ+Zqb7NJmFyPEWsWh8YmJVIj44RxUNN2Z+ZSKeDFbc2se2M4dz0jT9R29YTfe/QsDKe/e7f8fFpw0yNKT7p2/fN/td3haRY14OLvzd7wDFKRHzi/eO39mQ9WET6RJkzuz5opOpbzyR4IjV9LuTSVlA4eKHiDNYVj+Rfun7LsKAn+t5BdyX/MelaNlSPI+hQqnSFQjH4EEIMB74LNEopLxVCTAE+JaX8qZbt1ZkzAVLKZVLKhUXOxMmk90dOYGlzf8XDknPO5f2TJ1ExbQYAFdNmZJ1Ij0dPVXoi9Falx5JN8dFbfzCJ/37/U6ZOz880kf7M3Vv58ul/5pm7txoUkb2I9InKysxVC76zizn2T+X9lm1e0Gh6Ih1OJNoAZl1aZ7sbq2Mjc0/8bf3r8/xl2f1s/evzGW876cwbOXv+v1tq8ZIJVirOI33iePAo646usiwOPcn3RHoEK8aQfMV3djGefy5Pv6IGIn3CUVqiS3t6Y7UaWQ82lo/h1doz+i37Td0ZaRPpEWITzOv2vMKqLY+ybs8rusaoF5HkOKBZTd885grmTP5qUhW7md+BE/cS+vUHO1i8xLL99OG8fX3/GX5vX3+q6Yl0OJH0BQaord/8+mp+Mvsl3vz6atPjsjNar4Ev/t5s/nn1NTmr/SN9wiWyuz+OJLwBQ1TfM6ov5sL6f9ZF8a5XbC2db/Lb9p/Q0vlmTu1sLG7k1bLp/Za9MvIcNlSPy6ldhUKRhhEj0q+jsJJngTeASPGVD4G7tW6slOlZcsGOjRwvKuLlptP5/Lq1XLR5Az/79PmMuG5BTop0RQg7qwmVjUDmlK304i9xsGVePZOXtTPurUNsuHmUJbH0923elvf++bHoYdViRvHRXC1g7EY633aF+ahzsnbKVnoJlsAhL4fSr53f5LtCHeCc7m14hYu3qqZycdcmzun+kP+tOzujNjJVfVtFIjV9Ouz4OQqZGb//hF63kz/NHcc5K3fR/IdP+O2CKZbEkkhtnU6xrtCGXY5ZvOq7d5L+9xLpTUlPULx1r+77j6C3Ev8c3068uHirZCIXez/i0x2b+dXowrBDUihsy37ri4QrUlInpXxJCHEfgJTSL4R2A0yVTM+CYcc6Ke3zcdVtd7OxuY5n953D44tfpKrkEG3VlYQE/8n9z+1Etqr0VP7udsIIKxYzbQQuLNtuWNtm4TwQwOGR/OZXzRw5pZwPb2rk/Hu2UXbQR89wax46WZloC3p9gDE3JUZYtSjSo8W3PR8wqvCowr5Ezs8HltWy66JDu/RoU7pk1J5FT+ItXrIlnxXqdd4u3LsC3H72InYNaeDlo63cv2EpFTVwqCSzz1V/uIn2g+upH95EYHQd8XcOdnnooFdyPPb/7vf7cOX5+GiZX3oMVW09uHv8PLxkLgcmVrP6C5P4p2/8kar2HrrqyyyJKT7pm4k/+GAl3yxwjEyk2wk9rWdqA8colX3cXXslu121rChv4mvet6n1dXPInV9joq/Hj7tMpbAUecKDD4Z+FHbFI4SoJVR0FCHE2UBX6k1OoM5EWeAMBvmHq+/iyPBioI8PRzZw+TcWUXv0mO77MtripZCJLTSp93T//upmYyiERDoAATjwai1HZMhK4Mgp5fzmV82UHM6PBzJ60vH0C/SsWU9FFgWKtTLpzBtzLh5qBoWiTh/irKV5yByrw8iZXQ+/HC1AOu7eK60OR2EW4fOzLLVv4bGIX3om9Azvn3RI5qWebzhlkDvO+hK9ztDn2zWkgTvO+hLVvZ40Ww5kavMX8Ps/nzSpnM8PHVKxad0vaD+4npqxzUw815hxeLDgDAT5/nOX0FcSup08MLGa7z93CRVHfBZH1h+t/uCDkXwt2lroifQIevmvO5HcXfN5ekWor+521fKl026nui/12HFV1dqc9psLv+6aOWDZS19dy6Y39jP1kkaueXTg+wqF7fjWt1Qy3d58BXgVmCCE+CNQD1yldWOVTM+CA5U1A5b5iovYX5tfRYaODfXhIPeEW6S46JFW+xTiM8OKRdkIaCMwKnycemKWlTg5VAX2dNbtz/S6A2zoyN3vLOj10bNmPZB7geJ02D2RXkgI7JuE1ErgeC9dqzcD0LV6c0EUIi0kjCx2HT0/FwjxSXQjsFLVfLB04HVmr7OIttLqrNrLd3V2pvj9PtoPhsbhw7vXEejz4SzK72Pw6cqPrNt5ZYLzUyVgsmX6u90np11HJdIHkq8WOIMlkR5BD//1NufAe/ReZxFtzuzGDivw9fjZ9EZoRs6mN/bje6hZN4W6UrsrFIMTKeX7QojzgFMBAWyTUmpW4Jh+1hBCDAVOJiaPJqV8x4T9lgNPErJC+4OU8gUt2/kDPlxON57R+hToygQjVem7Vi6ha3uLrgrZSFLdDphpxWIEZqjSs+0TemDkrAG74ihxUzarKapMV3UVQup0M4icx/uRwDEimz4hQ7PC8hpnaTFVs6dElekqkW4frD5XWjlOJGJ6VX9riy0kTqrEJ0l7hhfpok6PqJrrhzcxtfkLObenMBeXy03N2GYO715HzdjmrBLpdusTVvL4Xdt577XDnPXZGu784UTL4rD0gUI+UwkbPlsT/R+e37BrwCrxD0ueStCMmX3Cbon03kmjDPVNtxIrVemJcJe5mHpJY1SZrlfyO1u1u69Hfzs7hUJhDkKIZNOwTxFCIKV8WUs7pibThRD/DNwFjAJagLOBPwMXZNnez4B5QJuUclrM8rnADwEn8BMp5feAK4FfSymXCSF+CaQd6Ht8R/jdB//B8KFTmTj6iynXDXp9OEryIzl2bKiPru0tob8NVsgm48Ky7fy2x9gLbzOsWOzG1+7p4nerfNTWOnhjVV3sW5VCiG3k2Cf0YDAXcK277QaC3qso7aywOpQBBPy+glS0v7P+B3h7O3E63Mw5/b7Yt3TpE0cDh1h3dFXeW72Mu/dKpUi3GUadK1OME9V2GSciZGLxEkmgb39nSTRZqqeNR6yquf3g+pQWKQr7MvHcBQMetuz401I6926hqKSC6Z/7l9jVbXPtFIsd/NK9ngDvvXYYgPdeO4z3u8nPT0bOrlHkxp0/nJjwf7f46zv447JD+Htl/MMS0/qE3RLnhYRdj63HE6S83JH0/Wsenam7Ij0btXskAQ+cpEsgisJlzRqrI1AkZn749zDgHOB34dfnA38CNCXTk5+tjOEua5bElQAAIABJREFU4Axgt5TyfOA0oDOH9p4F5sYuEEI4gR8BlwJTgOuFEFMIJfA/Ca+mqUKrP+AF4OCRTQT8yf3/Op5+gb2LvknH0/khTnG43VRMmwGQlUI2tvhoLmp0M9TXRl+8ez2ai/2awuevLuXZn/efBh4ISIAx6NAn9CAyawDIy1kDuWLHh25b//o8f1l2P1v+8pzVoeiKP+DD2xsaYgJBH/5A6DwuZRB07BOtvTvwa58RZltUIt1eGHWuTDROfOm2IwATsMk4kSl7Vixh7dJ/5aM//A+Hd68DTth4RMjVAsblclM/vAmA+uFNKpGeh0S+A/GK9LoJZ3DyeTf1WyaD+o4ThUZJuZOzPhuyvTzrszVJz0+P37Wdm2es5fG79Lnmt9t1dyGQ6H931mdr8PeGZt6999phvJ4AQRPvJ+ya7FUYxz13HObMya3cc8fhlOvpaccSUbsDmtTuvh5/vwQ8kF8evwqFAgAp5T9KKf8RKAKmSCk/L6X8PDA1vEwTZtu8eKWUXiEEQgi3lHKrEOLUbBuTUr4jhBgXt/hMYLuUcgeAEGIpcDmwlxOKeE0PEVzOEvwBL8OHTk2q2Iz1Qe5Zs56g9ypbJsviGXHdAksU6fGYoVA3CqOm3+fykOGss4vZ+0n/aWfrWvoAfHr0Cb0YjLMGtHJspJOKfebdLAb8Pg7tCyefDmxky3vPMfmsm9JslR+4nG6GD53KwSObcDrcUauXLs8+0LFPlDjKcQn7+4wq8g8jzpXx44THE+S1FZEHTfYZJ2IZX5fAlylM0Oej+8PQbLsjn2xk6OjpHPlkQ9Y2HqlIV7RTkZ+0bfsjh3evw1l0opLLsUN7QIdxIljs4Pho+9QU0pNkquYImajXtWAXW5nBQPO51cz4TBUtf+iKPiz56IOjYLP7CUVh4PEEWbk8JGJcudzLQ48EuapqbcIipHqjVe0eawcTsZsBjhgeoCK/mTULZP5bghYwo6WUsd7aBwk9NNaE2QPeXiFENfB/wFtCiN8Au3Xex0hOPB2H0AA/kpBU//NCiKeAZck2FkIsFEKsEUKscTndXHDafTRPuDr6/rGR/S8CIz7IAGWzmtIm0oPe3Crcnzd0G2cVb+a8oduS/mjF6kR6BDMU6noTP/3ezkqZ1tYghHwMI2TdJw4dDqbdn8eTfh1IrISx83EsVJwuNzUjoi5ZHN6/MeVMnHyjecLVfGrK7ZQUV0aXeXu7Qac+AeANegpCma6wJ0Y/dCwvd3D66QMeBmXdJwJHPbrEpdXixeF2U3lKaLZdzdhmTv7MF5l53XcTWrzoUaC00BPpfgPP/0a2nY5k//tAny86myHQ543OZujr6YIsx4nY/tDXq09/sCuR81Nne2/C97So17UwIDGvrhcN54sPjKNxQkn0wcXh1j7QoU/0BrXbd9mByKzGZOihovcHB/c1ZHm5g7nzQg8z584rSWn1YgRaFOmxdjCXP9TMv753KcAO46NTKBQGskoI8YYQ4otCiC8CK4Dfat3YVGW6lPKK8J8PCiF+D1QBK03atwf4Rw3rLQYWA1SVN0otxUcjPsjpEukdT79Az5r1lM1qou62G5Kul8o6RYsa+ryh23j7SNaCf0tIllC3UrWeSjWf7wVOIbs+0dRUlPLR6qLbO1mx3Mtl80p44qnMKsRbXWhvMDP57JvY8t5zHN6/kdqRzQXnne5yarMvyaZPCCGkUqYrCpVs+kTJxJEyE69zPRhz2QICF13LkO5QX0+lSNcjoV6oRPzmjSiwatfirc6iE0VJnUUlaWczaOkTsf1hSPWogpekffncFg4f6KVmRDH//c6Mfu+lU69rJZKYjyjT8/G6Ox9xOETadTLtE1VFw1L2CTtZvGzcvJS29g0Mq5/OtCnXGbKPls43afV9TIN7AjOqLzZkH7HY6fjG8tiTNTz0SGrPdKswqvipQqGwFinlonAx0tnhRYullK9o3d70M0HY03w4sDO8qAHYo+Mu9gGjY16PCi/TjBBiPjC/1F2jeRstivRc7WD6evo0FyPLx4R6IiJJdquS6qkS6kZMvzdCpd/Q4ACIzSZm3SfGjk3+WT2eICvCU/RWLPfy8KPaL4gGc1FSuzD5rJsKtghpPGGVui59Ak4o07Um1GPrTigUVuPxBHn//QHfyaz7RFGD9msnPXEWu4ETOZqeulASqKyj4HOZuhCr0G4/uJ6jNdfpZpMT6OtfvFXPtlNRdjD0vU70ACXy/QBovPImhna0sve1JdHlwd5qyGGciPSHkrLaLCJPjh2Kj8bS2d7L4QMhsfLhA710tvdSXd//AbZe13N6JeYV2VHTUAQ69IkyZ2Xade2AP+CjrX0DAG3tG/AHrojaBeq2j2Afrb6PAWj1fYw/2IfLMXgf+NoxkR5B7+KnikHCAw9YHYEiDVLKl9FYcDQeU89YQogvE/KheYuQhH4FsFzn3fwNOFkIMV4IUQxcB7yaSQNSymVSyoVFOg6YmdrBJKKorCijYmSZ2r7YmQvLtmv+MZN8uKBvai4CKNGjT1RWJleolJc7uCw8Re+yDKfoDfaipHZhMCTSASrLG0GnPgHQUHySUqYr8pbycgeXfjbU9/XoE47ykvQrG0xsolShjYhCG9Ddb97ItlPRM7xI80wEZ1H/BHBpw2jIYZyI9AdXkfX9wUiq64upGRE6djUjigck0vVGXR9ax0nTK0CPPiGS9387qaZdTjfD6qcDMKx+uu6JdACXo4gG9wQAGtwTDE+k2+n45iMqka7ImAcftDoCRQqEEFcKIT4SQnQJIbqFEEeFEN1atzf7jHAXcKqUMnklqQwQQrwIfAaoE0LsBR6QUv5UCLEIeANwAj+TUm7KsN2Mlela0GoHkwq91dBBny8j7/TBqKjMpyKpd36pk7/8pZcjh4N86ow27r6ngmuvK4PQ7I+c+0QqZTrAE09VZ6RIj8XqoqR9PX0UlamEaKGxfsevOXx0F33+Ht5e9xgTGs9nVP3poFOfKHUMoXnIHP0DV9geryc/FZKJxoknnxnK+NGtH6NDn7BCme6th5L2xEn0njpBWYck0OczLYmbr0w8d4Fhx8nItjMl/nvyyWs/x/PJdvxeD1t//C2GfeoSaqadDTmME0Yp0/VGj/PYf78zI6EiXZG/PHH3drb89ShHj/hZ9OkPuOquUXzm6nrQoU/YXZnuD/iiifNpU67TpEjvnTSK4q17s9rfjOqLB70iXaEoWBobYb+9ZpQp+vF9YL6Ucks2G5udTP8E6NKrMSnl9UmWvwa8lkO7y4BlVeWNt2TbRjJySaRH0Ovm/cDSJRzb2EL55Ok03pDWEjVvMCL5bUZCXQ9V/eM/SupT3iWlnJVtu5E+0dRUlLZPlJc78HiyS6hblZha/Y3fsXvVTsbOGc/s71xgSQwKY2g66apkb+nSJ6pc9bqPEwr7k881HlKME91SytOzbTfSJ0omjtS1T4yvS6+/CPp8QPLrq4gXeM3Y5oSFSRUnMDLZbYdEeiJGf/Yfkr2V9TgR6Q9Dqkdp7g9mW609ftf2qAd5pMhktqhEemGx6AdJvw8594mqomG2vW5K5JFuhCI9nkwS6YMp8X5V1Vp+3TXT6jAUiuw5cMDqCGyHEOJnwDygTUo5LbysBvglMA7YBVwjpTwSfu8zwA+AIqBDSnleePlc4IeEHu7+REr5vfDy8cBSoBZYC/yDlHJglfQQB7NNpINJyXQhxFfCf+4A/iCEWAFES2NLKf/TjDi0YpQy3U4EfT6ObWwBwLNlA/t/8T/UfuVGi6OyN2ZbyNgJrcp0yK0IqRX09fSxe1WohMPuVTs5+xtKoa5Izwllur0VVgr9KcQaD4tu7wQ4LZc2rFKmt764hGMbWqg8ZQZjLhuYKA/0nvACP7x7nW3U0YrCJlNl+pa1z9N+YD31I5qYPDPx9biefuleT4D3XjsMwHuvHVZe5ArDSadMt9qCxAyP9FzJpVip1cdXoVAowjwLPAEsiVn2dWCVlPJ7Qoivh1/fK4SoBp4E5kop9wghhkG0DuePgIuAvcDfhBCvSik3Aw8D/yWlXCqEeBq4GXgqSSxrhBC/BP6P/vlpTR7qZnmmDwn/7CHkl14cs2yISTFoJtYz3TO63OpwDMHhdlM+eXr0tWfzBoJeX4otFIMZLZ7pMLAIqccTNCO8nCgqK2LsnPEAjJ0z3haJ9GMj1Q2t3YmOE0Kp8QYbhVbjIfa8nQtmeKafUbGj30/Q6+PYhpAwoPvDFgK9A69jnMXW+HUrBjeZeKYH/D7aD4QLtB5YT8Bv/PV4SbmTsz4bevB11mdr8v48prA/WjzTrcQMj/RcSFSsVKFQ2JzTs57wWbBIKd8BDsctvhx4Lvz3c8Dfh//+AvCylHJPeNu28PIzge1Syh1h1flS4HIhhAAuAH6doK1EVAI9wMXA/PDPPK2fxRRlupTyW7GvhRCVocXyqBn7z5R8VqZn4n3YeMM/sv8X/4Nn8wYqps3QxYLGLuSTz3k+EKtMT2XhEilCGlGm27kqeyyzv3OBUqQrMkIp0wc3Vtd40JPY83YuWKFMd5S4qZg+I6pMdxYnvo5pvPImxh/wqkS6idh1BoBZxWkzUaY7XW7qRzRFlelarV4+XflRTjHe+cOJSpGu6IeRtUBSKdOtVk1HfNK1eqQnIhffdC1EipVGlOmDxepFochr1q61OoJ8YbiUMuKJ0woMD/99ClAkhPgDIRH2D6WUS4CRhCzEI+wFziJk7dIppfTHLB+ZbKdSypy8rk31TBdCzAL+h7AaXQjRBfyTlNJW3zIjPdONJBsP18Yv/GO0CKkP9YQ7gh0tXbL1IdeDSJ+ornbcMm1SW0oLl1yKkFqJUYn0wPFkFl2KfMZMz/Sg11dQDzsLBZWA6o9RnunpGHbltQy78lrKulP3ETsmdgsV5VGfuWf65Jk3ckoOnunZJkHVeUwRQU8P/UTY1TM93ifdbor0WLItVqr1YYVvTHb3LFbeo+YrQoiFwEKAMWPGWByNwjAWLoTFi62OwkzqhBBrYl4vllJmdACklFIIIcMvXcBMYA5QCvxZCPEXfUIFIcQphCxghksppwkhmoDPSSn/Xcv2Zp/1fgbcIaUcJ6UcB3yJUHJdoYHzhm5L+l68h6vXE9DcrsNt34uGXLBjQjxbFt3eybRJbRFfW8vo6gqd19JZuKgLqhC7Hn6ZjVd/n67nnku/skKRgI6nX2Dvom/S8fQLVoeiKFD0snkxmulVA/2i2xc/z45v3Ufby7+0ICJFIgJ9Az3qFdrINpH++F3buXnGWh6/q3CuexXmMsBDP4P7yHxmoE+6/c9XdlOk33PHYc6c3Mo9d8S7NihSIaVcLKWcJaWcVV9fb3U4CqP48Y+tjsBsOiLf6/CP1kT6QSHECIDw74idy17gDSmlR0rZAbwDNAP7gNEx248KLzsEVAshXHHLk/Fj4D4IqYqllOuB6zTGbK4yHQhIKVdHXkgp3xVC+FNtYAWxNi+93m6KS+w/jT/i4RpRpiulSf4Tecof70Nuheo7OjWzTNDTIzO2cGlv80OFcfHlQl+PMfYugeO9dK3eDEDX6s0MuVapiwuJEzYvxpX9CHp99KwJ+ej2rFlP0HuVLb5D0+tUZXozMXLaPVhv8xI87sNRmvh7Pb7uUNLtvJ5AtH8c29BC4Nxrk9q8QMjeo6xDJn1/MKOnJYuzKORRH1Gm231GQKDXl/J7ky2ZFiDNlnwsJBo5pxl9brOafPp8EQ/9iDLdiLiT2bxYafES8UmPKNNzVaXHf5aIfYydCT1AyK7WiccTZGX42mHlci8PPWLcPaqvx4+7zOzUlUKhMIlXgZuA74V//ya8/DfAE+HkeDEhK5f/ArYCJwshxhNKll8HfCGsav89cBUhH/XYthJRJqX8a8hqPYrm/LTZZ6S3hRDPAC8CErgW+IMQ4nQAKeX7JseTkMg0NIfDdcvfXv82xaVVnDH3fqvDSku+eLimUtjrTb56py+6vTPqO/7EU9WW+5BH+kRTU9EtL/6qJqMYzjmzjQMHggxt+Cvff/tM44LMgpA10ruMnTOe2d+5QNe2naXFVM2eQtfqzVTNnmKLJKhCPyJ9Qghxy7qjq2geMkf3fThK3JTNaqJnzXrKZjVl/B0a2mDLsiSKDMjGvs0qsrF52f/YSxz90yaGnDOVxnuuyWh/JeXOaP+omJ7cL12RGiMsWSaeu8C2numx7FmxhO4PQ377Yy7T144mU5uXbLmobh/z5pewfJmXefNLuKgulQDLeu64vZPly7yMaHRwYH+QefNLeDKJbaAWVh+3pz2C0ZYpRmC0h75dbV5y8UlPRbx9jB2JxDh0XzP1X7wp4+3Lyx3MnVfCyuVe5hp0j3pV1VquuQU2vbGfqZc0cs2jM3Xfh0KhMA8hxIvAZwhZwuwFHiCURH9JCHEzsBu4BkBKuUUIsRJYDwSBn0gpN4bbWQS8ATiBn0kpN4V3cS+wVAjx78AHwE9ThNMhhJhAKDeNEOIqQLNqzOxkenP49wNxy08j9AH0zWbliJShKW69x7tMU6jnmvxIdgEU9PoAe00LM4tEdi92TrAnUqLbyYc8U0X6gQMhO5gjrb10tfdSVV+se0zZqH9irZF2r9ppSAHScfdeSeDOeThLiznSqmvTChvR2ruDqfJcXEL/c2zdbTfYRpGuMJd4+zajHpZbZfMSPO7j6J9C171H/7SJ4B3JFerJqF94I87LAiG7unYjoixMIonuRJYseirU7UZs8dFAr4/uD1sA6P6whcBFqWc22Jknn6rmEZtcI6bC4wmyfFnoXHNgf+jacPkyb06xzy7dE/3bLol1q2YL6KGEN1uQpZcqPVcFuN6J9IH2Mfon63MlNsYjO9dR6/MBmRdofuzJGkMV6R5PkE1vhG6iNr2xH99DzUqhrsgf9tn7AbcVSCmvT/JWQmWalPIR4JEEy18DXkuwfAegVcH5JWAxMEkIsQ/YCdygcVtzk+lSyvPN3F+uCOFEygDFpVV5YfWSjF0Pv0zX6s1UTJvBiOsGZyGofCJ2yn2sEt3uN0mJqB/mYsQIR1iZXmxIIj1b5WasNdLYOeMNK0DqLM3uMx8b6aRi3+DwrMx3GopPMiSRHkEl0gcnZtm3lZc7oudpM3GUuhlyztSoMj3TRHq0nQKt+2IU8Ur0fLJk0RNnsZvKU2ZElen5mkiPkA/XiOXljqiKPlaZrlfssYl1SynF9NkCEcV/rkr/fMSOCnC97WOMIDbGoeObw2NpdgVIjTz/lJc7mHpJY1SZrhLpirxi7VpobLQ6CkUSwon3C4UQ5YBDSpmRstmUs5EQ4iup3pdS/qcZcWgl6nNYXsv0c+/I60S61xOI+jYf29hC0HdtwhtPX0Of2aEpUmAnJTqc6BNjx2aezPnTX4fR3uZnXYX+FgW5Kjdv/cEkJvzLSYYl0hWFS6xnuhEWLwoFmGPf5vEEdUmkZ+OZ3njPNVkp0rNlsPumJ1Ki54slixGMuWyBYYp0szzT841YFX2kNlAhYuZsgVjFf65KfyNJ5pmeC3ZWgBtlH6MnkRg9p1RhZ2PAax6dqRTpivzkc58DOXivO+2OEKKWkGvKpwEphHgXeEhKmbxoUwxmjbRD0vzYCinlMinlQmdRaV4n0iGkbKuaPQWAimkzlILLADweY9R8droQjvSJysrMp/9BSKFuBBHlJpBQuen1pFd2q0S6IhsifaJIqHPqYEXL+UUPjJ52H5kNlSuRPuEoz6wtsxLpihPFQYF+SvR8T6QH+nxZb2uUIj3SH1xFufetQiOfZ1ymI/aewKzPF1H8A7oq/fUm2ifC1016WLxE1NWALRXgdosnEZ5TqqzZb4b3zyqRrlAoDGApIaPIzxMqWtoO/FLrxqaclaSU3zJjP4rEjLv3Sg5dex2lnRVWh5IXJPJYT0Z8oVCF+SRTbuZT4T6FQpFfFNr55YmnqlmxvPUDq+OIZXydJlGIIkMKTYluRAFVhSIbrLRayRfffCPIBwV4PjFmdIfh+7jnjsPRoqWPPal9NptCoVDozAgp5bdjXv+7EOJarRub+ohPCFEC3AxMBaJSDSnlP5kZx2Aknzx3zZj2eWHZ9pyLkCYqFJpJ3IU8vdVsEinSjSzcFzjem7UXetI2e31579mqUKRDjwJpVmNWYdDY/Zl0zHSZZuV2+W2dCI8tQjl4SayW1mqBo1cyPtd2Mimgmq//95DqXl0b2B07WK0U4j2I1sKiKpGeP7S3+VkZvn9eudyrqXjpVVVr+XXXTDPCUyj05ZlnrI5AkZo3hRDXAS+FX18FvKF1Y7NH0Z8DDcAlwNvAKLCfRZcQYr4QYnGg77im9YPe7KeXmkk++KIvur2TaZPaWHR7p9WhpCV2anxsoVAt5NPnhBN9ors7d88vM6wR0tm/5MKuh19m49XfZ9fDL+vX5solbFx8H7tWLtGtTYWxRPpEn8yuWNNg5Jm7t/Ll0//MM3dvtTqUnDDy/BKPyccsp2vCSJ/wH7Pmmshbb1zbgd78uM7LlZ46Ef1JxvZ3lrB26b+y/Z3cxis92klmW2MHov2hz5t0nYA/9fdqy9rnWffcfex9RV0b2J18sVqBkIJ+8qlt3GHyPUi0T0ifJouXjZuX8s67D7Fx81ITolOYwT13HOYzs9poGBHqH3MzvH82C1+P3+oQFIXCwoVWR6BIzS3ALwhVX/YRsn25VQhxVAjRnW5js82nJkoprxZCXC6lfE4I8QtgtckxpEVKuQxYVjF09C3p1u14+gV61qynbFYTdbfdYEJ0hUuuSm8ryKZQaD5+zkifaGoqStsnUmGmNYIRhfsCx3ujBX27Vm8mcOe8nBXqgV4fXdtbQm1ubyHQe6IY2rGR+ibpKvaZ4/E8GIj0iSpXfU59YrBgtprbaMwoDGrmMQs/2D0tlzaifWLScNP6REntcQIdxpbe2bNiCd0ftlB5ygzGXDZ4bEQSJdQDvf2V4Ecre7OaUaVXOwCNV97E8PDMrp7wMjsUmY30hyHVoxL2hy1rn6f9wHrqRzQxeeaNA94P+H20H1gPwNEtLdRUfs7QeBW5kw9WK1Yq6CN9onLIyLRjhJ0Liyqyw+MJRhXprQeC/GHNMMNqauXCS19dy6Y39jP1kkaueVSp4RU5IoQqQGpjpJQ53USYPdpHpNGdQohpQBUwzOQYsiY+sRX0+ehZE7rQ7VmzPm8U6lYROJ5awZmL0ltP4guipCuQkmmcdvmc2RDIwQQgPjFklkJdT5ylxdGCvlWzp+hi9eIsdlM1cUaozYkz+iUU9FZDRs5h6dRwCntRCGOLmWpuszD6M5h1zGIf8NoJf0/usz5yVasHen10fxh62Nn9YYvlCnUt+zcyRmexm8pTQuNV5Skzsk6A69VObHuxaLF08dan/zGK2ER5+4H1Ccdkp8tN/YgmAIZMnpFXdo3ZkmlBQjuS6TW9np9ZS1t2UdD7A6nPU1oLi6ZrR69tFLlTXu5gbvj+d+68Elsm0n09fja9sR+ATW/sVwp1haLAESFuFEL8W/j1aCHEmVq3N/sstlgIMRS4H3gVqAD+zeQYdMPhdlM2qymqTB8MF7rZsuvhl+lavZmjc8Zz3pMjk66XjdJbT565eyu3vN4RLShqVIFRqz9ntmze5GfR7Z1ZHYtIYiiiTM/XZNq4e6/URZHer825C/op0iFk/dK1vYWqiTMYN1c/NeTGDS/Qtb2F2pHNTDpzoBpOYS/U7CeF0UQe8Nopod7y4Ou0/v4jGs4/mRkPXmpZHJGkb0SZbmVdCy0KeTNU9GMuW0DgomtzPhZ6tZOMnjoRVajbzS89kiiPKNOdrsTHYPLMG2msDdA70o0XbdaT+YqVxTutQs/PnElbVivoj3uP8M67DzGsfjrTplyXdL10hUU3bl5KW/uGtO3kuo1CPx57skaTR7pVuMtcTL2kMapMd5fZL+GvUCh05UlCNaMuAL4NHAN+BJyhZWNTzxBSyp+E/3wHOCn+fSHETVLK58yMKVfqbruBoPcqlUhPQaw1xu5VO/F6GqAs+fpmDbDxRUhjldMrlnt54Ft+Q+1Y7HohkY5cjoUZ1ghmoHfxUWCAIj2Z9UsuxLZ7aN86Av6rk97EK6wn6I2f/ZS/Y02h2byYgZnH7ImnqlmxvPUDQxrPEH9PL62//wiA1t9/hP9rc3CVZX7O1UtZbHTSVwsDFPIJ4tGyjl7o1a7Rx9RuSfRYJs+8kVP8vrRjsJ184I3CDsU7zUbPz5xNW1YeX78/9GBIi31LKkV6pjYwyjrGHmT73buqaq3OkSTZz4/B42mgvBwg9T4fMCUiRV4zb57VEShSc5aU8nQhxAcAUsojQgjNNx12u1K5y+oAssGs5IYZthhGEGuNMXbOeNsmUGKn1F8Wnn6Wr3YsRpLrsdDy/8/X77pepLJ+0avd2pHNKpFucxwlodlPQN7PfjLb5qUQziEWWOPYwmPBVVZMw/knA9Bw/slZJdL1tjqxMpEe2X86WxS9rVMUxqPG4BB2sR4xEz0/s5a27GSh43KVAqntW9K2odEGJtdtChlld5OcwXAOUpjEsmVWR6BITZ8QwglIACFEPRncD9lt7ootJCRCiPnA/JLyWlP3O7ThaNL3Vn/jdzy/aqcphRuNIGKNMWP0IWCb1eEk5VeLXXg8w6KDaL7asehNpE+MHuPU1e4mEWYWKbUziaxf9Gy3qt1up//8ItInSh2Vhu6nkGY/mTUzpZDOIfk0myfSJ8pGVuXc1owHL81akR6xOqmYPoOG6wunYKgWhbwdVPSKENF7iTJz7yXyFautR6xAz8+cqi27WOhEr5tKajjn7H/JOZmdzgZGr20KkVi7m9ETvmh1OApF4TJ/vkqo25vHgVeAYUKI7wBXEbIk14TdrlhsUepWSrlMSrnQWVRqdSgA9PX0sXvVTsC8wo1GYIQ1hhHEX4gaeWFvJ6VIKiJ9orrK2OddVhQpzZR0hXT1xO5T9AczkT5RpH0mWNYUQiI9ghmw1nVWAAAgAElEQVSKdLufQwqVSJ9wVejzfc1WkR6xOjm2oYWgr7CUd1rO3er8nht6fWei/aGoRJf2rMDsa9TBlEiPYLR9ZLwFjJX3HdE+4SrRLZmdTTuDPZEeb3cT6Bt4zrtwuH2FbwpFXrF8udURKFIgpXwB+BrwH8AB4O+llL/Sur3drlpsoUyPJeDvP8DoOXU46NXWVlFZEWPnjAdMm+qtMIFFt3cybVIbi27vtDqUrEl2UZ7txboFtgYZsevhl9l49ffZ9fDLpu1Tb7sChb5IezwDVoQx8xxiRqL+mbu38uXT/8wzd281fF/Y75pQE311/ujfsVYnFdNn4HAXVtLE6PEgtv3BOPa0vriEHd+6j9YXlxjSfvw9hZ254/ZOJp/axh02ukbNFwGKndDDTkYdd3uSqU1LZP2t216JLhtWPz1hXQZfj3/AMoVCoSgUhBA1kR+gDXgR+AVwMLxME3ab5/9HqwOIxes5xF+W3U/tyGYmnXkju1YuoWt7C1UTZzBubm5ThzuefoGeNespm9VE3W03pF1/9ncu4KuPbLZdcjHfiS9CahYeT9DQ4qZmsOj2TlYs93LZvJJ+1i/JlmvFrrYGsYV0u1ZvJnDnPMNnW+h5zlEYw9HAIdYdXUXzkDlWh6IIY8Y5xAwrGTMLkIYf6p5mSOMmM+yLC6jzXVtwifSIfU3lKTMYc5n+40Fs+4Ch+7IjQZ+PYxtiZjVcea2u7W9Z+zztB9ZTP6KJyTNv1LVtvbFjQVC7WJXkI7nYyajjbk9ibVqmTblO8/r1dVNo79gcXT7p1CvwxK275dvLWP32Nt6fV8JjT2rOKSkUCkU+sZaQK4oAxgBHwn9XA3uA8VoaMfXKSAjxlQQ/NwshZgBIKReZGU86An2hauOH9q2j19tN1/bQRXbX9pacFDv+rm561qwHoGfNes0KdaNuogttGnQ+UF7uyOvipvEPAzyeYPQnfnk22C2RHiFSSLdq9hTDE+mBXp9u5xyFsbT27sAv+wxrX+sYoTiB0Yp0M6xkzFLZx5638x1vfeh3oSXSY+1ruj/MbjzoO9atuf1c95WPONxuKqYbM6sh4PfRfiB03d9+YL3tFep2KwhqJ6uSSDz5RraK9Njj3tam1Mpmk0h9Hm/Tkk6hHrt+e8dm6upC9zKJirAGvT463g5ZvKzM4D4uH/uEQmEKUs1gtiNSyvFSypOA3wLzpZR1UspaYB7wptZ2zL46mgXcBowM/9wKzAV+LIT4msmxpCXimV47spnikkqqJoYusqsmzsjak7Lj6RfYf893cA4NFegqm9VkqR/ugaVL+Pjb99Hx9AuWxTBYeeKpajZuHWZ4QU8jiH8YcO9Xu5k2qY17v9qd1w8JkhGxdwGY9quvMe7eKw3fp7PYrcs5R2E8DcUn4RJFhrTd8fQL7F30TXWOthFmWsnc+oNJ/Pf7nzK0kGp5uYMRIwrjXF2oxNrXVJ6S+Xiw9ccPse3HD7L1xw9paj+XfeUzDdcv4KQH/kP3wrVOl5v6EU0A1I9owumy/zF98qlqtmwbZgs1sp2S+3a0vzGK2OM+otHBrNM7AE6yNKhBxMbNS3nn3YfYuHlpv+Uup5th9dOBxAnxeOLXb5p6A+d++psJFe2OEjd1550KwFyN93H33HGYMye3cs8dhzV9LoViULF4sdURKFJztpTytcgLKeXrwDlaNzbb5mUUcLqU8hiAEOIBYAVwLiGp/fdNjiclJeW1TJ99W/Sid9zcBQR6r015YxH0+pImx4NeX1SRHjjSReNj38BVVal/4BoJ+nwc2xhSH4UU8lcVVKG7fCCfk81PPFXNw4+GlAjTJrUBITX6xq3DePjRyow/m9djP2sXCBUAjrV3GX3nPNP2reWco7CWCmeNYRYvsWOGOkfbCzPtqIzeh8cT5MAB+6vKzqjYYXUIljLmsgUELsp8POg71o3/WCjx5z/WSd+xbooqBl57xrefzb7sQqDXl3XsRs1qmDzzRk7x+/IikR7BqGtUjydzy5FcrEr0wo72N0bz5FPVPPgtfySRDjDUynjMxB/wWVasdKD6/Ip+sUybct2AZamIXz/VdpP/bT6zv38SN4xYl7ZdjyfIyvDMtpXLvTz0SOH3CYUiI269FRYutDoKRXL2CyHuB54Pv74B2K91Y7PPdsOA2LlIfcBwKeXxuOW6I4Q4SQjxUyHErzPZLv6iN9XFeToFoaPETdmskDKlbFaTqYn0oQ1HB8bjdlMxbUY0HpWkGVxk2ydiKS93JLSsyfRCyuQiexlRVFZkqr1LPPmazMhHsukTxwKHWXd0lSHxxI8Z6hxtL+z48C8bYs/h8egxTij0I5vxoKiiEldFSF3sqqhOmEhP1H6+jj17Vixhy4/uY88KY4qI5tIn8imRbhS5KLutTtLZSSFvJsOGuaKfm5CvbD8KcZxIpgo3Cy3q80wT/Zms7y7TprcsL3cwN3z9oFXJrlAoFDbieqAeeAV4Ofz39Vo3NluZ/gLwnhDiN+HX84FfCCHKgc3JNhJC/IyQf02blHJazPK5wA8BJ/ATKeX3krUhpdwB3KzXQN/T0P+1VgVh3W032EpdOOK6BQR919I31vjBb3rdAcP3kQ2RIqQXlm23OhTNfO2eLn63ykdtrYM3VtVFl7/9ex/ferCbYACuvb6U279UkbQNPftERKWezUWUmUX2smXcvVeaUnBUkT0bd/4f7V0fUuwq5++mfSm6vKPrI7buWYkkyKi60xk/YnbSNrLtE629O5gqzzXE6sVuY4Yif8hknHjiqWpWLG/9IL4Nva+dFNYw6ZZvJlWkFxIDvOXj1PV731zK0R2bcZVVMPqeE+6Sng+30LH8/yAYpPKMsxl6XvLZRlb0Ce+wwvBcLQRltx0U8nry1Xu6WPVbH7V1Dn4bM0784fc+Hnygm0AArru+NPq5J5/aNmCKUKGNE+lU4WaRqfrcKh57skYp0hUKRV4ipTwM3JXt9qae9aSU3wYWAp3hn9uklA9JKT1SyhtSbPosIW/1KEIIJ/Aj4FJgCnC9EGKKEGK6EGJ53M8wQz5QDJkoCO2WFCm0Ql2Dgc9fXcqzP+8/0zIQkHzz/m6eXTKUN39Xx6u/8fLRh362bukDmGh0n8j2IspM/+FcUIl0e9NYN4OZJ9/Yb5mUQbbseY3TT7mBv5v6JQ4c3six420c7TkIOvYJIz3TwX5jhiI/yGKcOMnsayeFeRR6Ih3Se8sPnXIG467oP91aBoO0v/oyjV9cyJi77+XouvfpPdiKr3U/mHDtNJgoFGV3vsadiKuvLmXJ8wPHifvv7+a5nw9l1e9D48SHH/r5ZE8ABkGfyNST3OhY8oFC6hMKha68+qrVESgMxGxlOkAJ0C2l/B8hRL0QYryUcmeqDaSU7wghxsUtPhPYHn4ajhBiKXC5lPI/CKnYTSHWPmXog58jcHxuOOk20FZFoUhGpv6RZ51dzN5P/P2WrWvpY+w4J2PGhrr1/M+V8NabXu5YVAGhvmKe2XeGmOk/bCWpaioocqNmyDiO+/rPPu7y7KPMXUOZuwaAhppptHVu46SQOl2XPjHEWWuYZ3ohYtfaCIVIoY0TCvuSi0e53qTyli8fNYHerv5F8rx791BUW0dRTS0AFU2ncWzLRmo+cyGoPpGUbHzPofCU3fnOWWcX80ncONHS0se4cU7GRsaJy0t4800viwbROJEvqvDBTLbnIIXCVGbOtDoChYGYegYKFxy9F7gvvKiIE2bvmTIS+CTm9d7wsmT7rhVCPA2cJoS4L8V6C4UQa4QQa/y+YxkHpZd61a6WKLEk8mHPV6y0eFl0eyfTJrWxKAv/yFhaW4OMaDyRpGoY4aS1NXlRuWz6xKHDxhWpK/QEW7qaCgr98fZ2U1J8Qo1ZUlyJr7c76frZ9Ik+aWi5j4LCzrURBgtmjBO9ncd1jTkV4+sOmbYvRWKM9ijPhkwS+4GuLoqqqqOvXVXVBLq7kq6vpU/0GyN6PdoDzxNy8T0HpWK1O60HgjSOODFOjGhwcjBFkepM+0RvX370CZVIty/33HGYMye3cs8dh9OvrFBYycik6clBixDiZ0KINiHExphlNUKIt4QQH4V/Dw0v/4wQoksI0RL++WbMNnOFENuEENuFEF+PWT5eCPFeePkvhRCG2QuYfTVzBfA5wAMgpdwPDDFjx1LKQ1LK26SUE8Lq9WTrLZZSzpJSznK5k/tNKxR64PEEWRGugr5iuRePx7hkdTzZ9InaGnUDlA2B4739ayr4VALWjmTTJ4pF4sKNiv7E10bwegIWR6TQQlZ9orrUzBAVFjLAo7y38Mc2LX0itj8UFZebHaKhxPuem3ndqrAnmfaJ4qLC6hMKc/F4gqwM3zuvNPneWaFQ6MKzxFl4A18HVkkpTwZWhV9HWC2lnBH+eQiSW36H138Y+C8p5URCRbNvjg9ACPHfQojHk/1o/SBm27z0SimlEEIChAuPZss+YHTM61HhZTkjhJgPzC8pr9WjOYUiKeXlDi6bV8KK5V4uy7EKekODgwP7TySoWg8EaGjQJ/kd6RNjxxa2etwonKXFlM1qomfN+lBNBVWnwBRKiivxxijRvb3duIv18Q2O9IlSR+H7EOtBpDbCmtc7bF0bodAxY5woG1mlS3sK+xPxKO/+sCWhR3k+4Kyqoq/rhMLa39WJszL373D0XqKssO4l/j97Zx4mVXnl/8/pnW7oZqcRRVyQRTYDQRMlKm4QUBMTI0aDmTGiIEmckIkxY4xbhpDojIkISqKJREcSM/6MgKJGicGMxoCyCSioiAttszbSTW/V7++Pe6upLqqra7lr9fk8Tz9ddeve9z21nPu+97znfk9U93zZ0vpQ654r7VPZP4+Pdx4eJ3ZWRejXP/vvuXXeVNIz67aUzktZWR6TppawYlk9k7K8dlaUVDDG0Bhpob6xhUNNEQ41RahrbKa+KcKhmG2HGps51BjhUJO1rb4pwo+AOX9cR739PFV6dy3mX84YxNDK3LvObEfC+2LgLPvxw8BfsRRN2iOh5LeIbAYmAl+PaetWYGHc8aszMj4Oz4LpIiLAMhF5AOguItcA/wr8OsMm/wkMFpHjsILo0zj8oWWFMWYpsLRrj2OucaI9Jzizx1uOttfS0KABvYAwf2F35jmgHzlqdCHbt0f4YEcz/SrzWfpUPb+815mgRtQnRo0qzNgnzi3dxl/qTnTEnjDS+7oraKn/qqWZXuW3NblPc6SB8rKjqKvfQ13DPkoKu1G1dyOjjv+KI+1HfaKioE9gxomg01lqIziNkzrzXowTFUP7tfGJ5rpGCkq1gHOukkyjPKiYGHcqGXAMTbt30bR3DwXlFRxc/wb9LvtG9n3Y/tCt+9GBHCOy0RtW3fPcZvToQt57L8KOHc1UVuaz9M/1/Gp+9uNE1CfKuw0IpE8EleZIg0rOxHH3gp7c/gs9BylWoLuhucUKbDdFONQYoa4x0uZ5m/9NEeobowHxSGvQO9F+hxoPtxtpMWnb1qUwn6FjJ/Pqu3soKcyjpDAfkdSOffXdPfxh9QecP7wf3544mJFH53yiSj9jTFTnugroF/Pa50RkHfAx8H1jzJsklvw+FegF7DfGNMdsP0JrxxjzsBNGexZMtzPSLwW+BxwAhgC3GGOe7+hYEXkMa6Wit4h8CPzEGPOgiMwGngXygYfsDzZrss1MjxxqdEw73Q12LlnMwY1r6TpiDP2nTffbHIXE+pGxFzrxFz3fuX4/r77ayL69LXzus9XcMKcrl00r5bY7ypl+5T5aInDpZV04aUihI/ZlmpkevfVOJzsWnan4qNeT//Xv/om9n26nqbmOv6y5kxbTTL8eJzN04Bd5/e3fYzAM6HUKXbv0daQ/LzPTc6lwrQbS0+OBG7a0ZvNfe8/QtI7NYJzI6kSdKDN97a3PULVyK5VnD2bMrZMzbntkxcfZmOYKHRXddLIoZ5AKfCYiyLbF8sHTv6f2g20019fy3s9uo9e5F1A+7jT6XHQJH/92Eca0UD52PMX9KrPuy+3M9DPKt2Z87KyZ+1szyxcs7N7xAQmIn9c5VQww03a0GGFmzL5+P6+8Yo0T48dV8705XZl2eSl33FHON67YR6QFLrusC0McuJ5IJzNdA8gWGzctoXrXBvr2GcmI4dP8NucI/PS7ZP2G4XwgIjOAGQADBw702Rp3iAa62wao2/6vjwa14wLbdTFB7/aOiz5ON84tYgW6uxTmU1KYT5ci63GXony6lxbR335cUphPacxrsc9jjystimunMJ+SwjxEBO6YxCUZfHb76xr53f9t56GX3+O5TZ9w1pA+fHviYMYe2yOD1jylt4jEZn0vMsYsSqeBWCUT4HXgWGPMQRH5IvAkMNghWxGRPlgZ8MOBVu1WY8zEVI73WubldayVgn9P5yBjzOXtbH8aeNoJw+LazTgzffu8J6hZtYmKCcMZdGMmruM8I3vvZMPu/oCVkX5wo6VveXDjWloaLtMM9QAye+b+VukXoPXxfPui51f3Jb74OXtiMWdP7OO4PZlkpkffA9DGdiX3WffO43yy70369TiZ0Sdc6kmfo47/KmBdgL34hiXZ+cm+Nzl50EWcMfI7jvfnVWb67vsfbZUH6n3dFW52pQSMeJ35dLP60xkn7ALYp2RqKxyZmd5c10jVSivgV7VyK80/OCdnMtR3LF/cKm0ycMqRSQkdve5kX0rqHPNFK+O8Pm6aVDZkOGVDhic4InOCmpker3nuRIa5E8H5bNpxqv/OyPx2xomJ5xQz8RxnrydSzUwPegDZK5ojDVTv2gBA9a4NNEe+HKgFhjmz9rZKrdy9IDjSPUG1Kx47wLgIYNy4cemnPWffP/VNLW2D1XHB6/p2srdbM7/bCXa3ZoU3RTAZBLpLY4LXXWKC1D3LiujS3Q5WF+Un3K80wXGxQe4uRfkUF9iBbi8YOxbWrEn7sO6lRdxw7klcfcZxLH7lfR58+T2+svD/GD+oJ0f3dL420cH6ZnYfbMCBH+JuY8y4DI77RET6G2N2ikh/oBrAGNOq12qMeVpEFohIb9qX/N6DpYJSYGendyQF/ijwB2AKcB1wFbArVaO9DqafClwhIu9jFyEFMMaM8tiOpGSamR451EjNqk0A1KzaROQ7UwOXoZ5XXEzXEWNaM9M1kB484ouSRlm+rN4ROZhMiM9M72jFP/Y9wGHb8WjcUvyjOdLAJ/usm4Q+2fcmzZGLPJ38F+QX06/Hya3BfLf69iIzvaW+oW3h2qhMkKI4SPz5OlPiM9MLSouoPHtwa2Z6rgTSjyi6GSdx0tHrTvaV7LiwZIvnKkHVTHdS8zx696ETwflMg/xuLA4o7pBKZnrQA8heUpBfTN8+I1sXFoL0OcQXAXVLciXdDHOv7HKblhZDfXMH2dsx2w5LmrRwqKk5JsjdQn1jhDp7W2vw3H49XfKiGd1FBXQpymuT3d27a1HbwHY0YzsukB37vyQmAB597mmg2wtefz2rw7uVFHL92SfyL6cP4n/+sYM//PMDPq455JBxh+lSmE/f8mLy/Pvsn8IKZP/M/v9nABGpBD6xs9XHY91FuwfYTwLJb3u/lcBXgSWxbbVDL1vx5LvGmJeAl0Tkn6ka7XUw/QKP+8uIVDPTGyqbKI15nt+liIoJw1sz04MWSFfCQXxRUsCRAqXZEJuZHps13162eex7AA7bXueh0YoveBXMTsboEy51PYjvRWZ6Xklx28K1GkjvVHhVtLWsLI/+/fPYubMlq3YSaaaPuXVyTmWkQ8dFN50syplJW5rJHgyCmpkOzmiex2aDOxGczzTIrwVRw0MqmelBDiD7wYjh0wK5oOBFEdBMMszDWpz07U8+5fNzXzis6d2U/nwsT6C0qKCtFElRPl0K8+jTtbj1tdggeJeiAroU5sVImBTY2/Nag+LRbSVFeRTl51igO0SUFhXwrQnH860Jx/ttSlIe+VbH+ySS8MYKov9RRK4G3ge+Zu/+VWCmiDQDh4BpxhgDNCeR/L4RWCIidwJvAA8mMafJ/r9TRKZg6bKnfEuLp8F0Y8z7XvaXKdlopg+68ZJAZqRHSSTz0nRsOAaaZIRBGy0d4ouS+pWRHiXqE8cMzGdDTNZ8Mrui7wFUMz0dciGj0Itgdke43bdXmultCtcqKeNk0U4/8aJoa21tS9aBdEismQ7kVCA9SkdFN50syplOW05mxQeVsIyRQc1Mj5JtRnpsNvjmt/ryi7vKs57rZRrkd6sgaq5dW/hNosz0RNroQQ0g+0VQPwc3i4Bmk2EexuKkJYX5fP7E3gm1uQ8HxZNLmBTmiwa6g0j//n5bEDjak/AGzkmw73xgfjvtJJT8Nsa8C4xP0Zw7RaQCmAPcC5QD/5bisZ5npoeCbDTTgcAG0qE9mZemDo8LMtfN2MezzzSEVpe7vcl67Da/JwSxmemjYrLmO7LLabtzJUDWHttXLKZm21oqThzDoEnhzijMdPIflqJTXmmmK+mTTdHOdPDqfOR2H/F3EmVKosz0XKajgK6TAd/8ouKUgshOZsW7RTbB8DBl3TuVmX4oSc3sCV12ZNN0xriZDZ5NZruTdDYd9kTXIk4vJsRnpifTRg/DPFBx3u+iv7lsM8zd/i07zcCepdx16Wi/zVDc4OOP/bZASYIxZpn9sAY4O93jg3tWUVyj/7TpnPDjufSflvxCpKW+wSOLMue6a6xAOliZ0lH9xrAwe+Z+RgytjhZ/CwXzF3Zn45a+ni9cPHDDFr79mVd44IYtnvbrFZHGBmq2WRmFNdvWEmlM3f/S2TfIrHvncV58Yy7r3nncb1Nco64yvf133/8oH86+hd33P+qOQTlGfNHO+tr0NSFTIdfOR/b5fJ3fdiiJ2bF8MZvvu4kdyxd3uO/AKdMZdv3cQAab03kf8RyRde/QuNfSkBvjp9csWNidzW/1zclAc3zmfdiuLdqjvfcxa+Z+hg2pZlbMtUiibU5ypDa6+mFnZ86svYwfVsWcWXsBK8P8r6v7Zl1ENL5dRfGUW2/12wIlASLyA/v/vSLyq/i/VNvRYHoCRORCEVkUaXJe3D8odFR4NAwBnNraFp5dcXjydcHk4kCvOscTX2g0yJP1qE8cOGDVePb6c/YqQOYn+UXFVJw4BoCKE1PPKNy+YjEbF93E9hXpByeCxJGFS4N9YRX1iSbT6FofRxYgDfZnEgSiOuOAazrjuXg+shd0s0qLivpE88Fg/U7r+/htQXZkEkQOakZ6NsHwaNY94FjWfdVji3n3tpuoesz58bPVH5qyL+4bVMI0506HaOY9kDM67O0FxxMtHLi1mNDqE831rdrogGqjK0fIutTWtjBn1l7OGledVRA8UbuK4im33ea3BUpiNtv/VwNrEvylRPhnBy5gjFlqjJmRX9jFb1N8ISwBnOit6QAXTCrm/kU9fLYoPWLt97O4aCpEfaK83B8tNi8CZEFg0KTpjJgxN2WJl2yy2YNGtHAp4Fvh0nSI+kShuCfrFS1ACmgB0jS49p6h3Pv651yTeAnD+ejc0m0p7xu7sJsNUZ8o6Kq/UydxI4jsB068Dyez7lsaGji4wa4htGGt4xnqrf5QWOJou4o35FLmfbLgeKKFA7cWE1p9osBqe8TwaXzhjFuOkHhROh9RWReg9b8TQfD4doN8ra0oinfYsmMAdcaYh2P/gLpU21HN9BDhlT5rNIBTt3p94AM48YU6w0bY7Y/HTU06LwrxBYF0AgzRbPaoznpYgyxRglC4NGhoAdLMcPs8kUvnI6c00xX3cLKgaUe4WeDTiffhlG15xcV0HTmGgxvW0nXkmA7v2FQ6H2GbmyerwZRM4z5RAVe3irrGEz/fC0vdHMV54guHZqOZnqxdRVGUGG4C4vVlE21LiAbTExCtNl5S1stvU1rxqqBalDAFcMI+OIbB/qhPHHts+4Gj2TP3txYmdUtPPRcCV04zaNJ0Io3eBFlSofaYMso+qM34+LBcREV9okteuet9heE83BnR81Fboj5ROqDCb1NyEi/O8V4U+AzKWAVQefl0Wi65zJVAeuu1RGlwriWU3KWjgqkdBcfbC8I7Seu8qSSxBnaygqRK5yD2N+dkEDwM19pKjrJ6td8WKAkQkcnAF4EBcRrp5UBzqu3omSUBQZN5aapr8kWfVQM4SpSOZF7CpP+eiwQpONFZ8ELmRVG8QGVeFHCvwGfQcSsjXWVeFK9IVePc74BivMxLLFqQ1F8+PdYfGc+O8Ps3qyhKzvIxll56PW210p8CLki1ET1DhYDC0sLA67NmQrYBVw3YBocg6b+nutiUC0UDFf9pQc9DSriJPX8r3hK0gHUuaLMr4cfv+b3f/adL2AumRqVdnCxIqsF4RVECwbhxflugJMAYsw54BPh7nGb6E8aYfam2E67R1gcizcEYjN0uqOY1s2fuZ8TQambHVZX36njFeeYv7M7GLX0dlXhJN+D9wA1b+PZnXuGBG7Y4sp+idMTByF5e2vc/fpuhKFlhn7ff8NuOzsSO5YvZfN9N7Fi+2G9TWm0BHCvwGbSFAsU53Aw2z5q5n2FDqpnl0/ze7/4zJawFUzduWsLfXr6djZuWOFaQNLZNRcmGsC2sKYqSOsaYCHCMSOa3mWswPQn1tXt4denNbHntEb9NAXJHn7W+NpKVJIhKigQXJ7Nh0g1419dGUpJDit+vqa7JGYOVTkt9Sy31LSkX/laUoKKDqUcESVIl3hYnCNJCgeIsbgabU5UrcQu/+8+WMGakx0u7OJGRrnIxihPMmbWX8cOqmDNrr9+mKIriHu8BfxeRH4vI96J/qR4crlHXI0TkQhFZFGk6BMCej9YFJkPdLVrqvXt/JWX5WUmCBElSpLMQ9YkDB0zKx2RzEZJqYDyWkrL8lOSQ4vcrLC3M2E6l8xL1iejzAtHfkeIOYZGkivpE88Hcni85QX5RcWAkVZy2JUgLBbF4bUerPzRlX48gKLgdbPZbriST/sMWcPeTVp9otn5DTv1OOR4AACAASURBVEu7uNWm4j9+LKytsBP3VmjinpINP/mJ3xYoyXkHWIYVF+8W85cSBS4ZFWqMMUuBpQVFpddEmg7Ra8Bo8gtydzDeff+j1K1eT+m4UfS+7gpP+py/sDvzklSVd/t4JT2iPjFqVOE1qew/e+Z+li+rZ8rUkoxkX6IB79XP7E6rTsC19wzlqp9GOtw/dr+XUlbFUpTDRH1CRK6pLDpeg+mKKzxww5bW82DQZd6iPlExtF9K40RnZ+CU6UTOuywQ2uRO2hINzh94e63vCwVRdixf3GqPExI2qRD1h27dj84Zf4gGm5ctrXct2L1gYXd+4eP8Pp3+Z83c3/pZhE1exQ+iPlHebUCrT4wYPo3myJcdDXq70abiH3Nm7WXFsnomTS3h7gU9PemzrCyPSVNLWvvVeIOSMbfe6rcFShKMMbdlc7yeGZJQUtaL0y68k6Hjr/TbFNdoaWigbvV6AOpWr3c9Q/3MHm+1Ps52YNKBLZg4JcOTaZ2AVAPv0f1if5N+UFfpa/dKlnTL78Xobuf4bYaSg2Ryh44SLoIQaI7ipC0Dp0x3THs9W4KaKZ8KByuDl/PkhTa33/P7VDPSwywJEyTcCHprID038DND/O4FPXltc6VnAXwlRznqKL8tUJIgIn1E5Bci8rSIvBj9S/V4jUZ2gFMZ6ZFDjY604zR5xcWUjhsFQOm4UeSV6ORDyQ4nZXhypU6AkjlB17sUxG8TlBwlVemqMNNcF8y5kZI9QVkoCJKkTq7gd7A7CLQnCaNBdSWstDQEb74dzRAHfMkQ13OdkjU7d/ptgZKcR4EtwHHAbcB24J+pHhy8lIccZPu8J6hZtYmKCcMZdOMlfptzBL2vu4KW+q9qIF1xDJXhUZxg3TuP88m+N+nX42RGn3Cp3+YoiuIga299hqqVW6k8ezBjbp2ccTslvQ4R2Z2yvKHSCQmSpI6SO8RLwqjsixJWPv7jYg6+uZbSz45i4Nzz/TanDXcv6Mntv9BrSkVRXKGXMeZBEfmuMeYl4CURSTmYrmcll4kcaqRm1SYAalZtCmSGekt9gwbScxC/s2M6+6SnPV/3sthvmGmONPDJvjcB+GTfm4HPUFdSQ6VKUieXZV6a6xqpWrkVgKqVWz3JUA+TvIdyGKe+Nw2kBwO/56ZOE5uRHmbZl7DZC8G/czEsRJoaOPimJYVV98/1nsYqUv3ddfZrSiXEfOYzflugJKfJ/r9TRKaIyClAytpOemZKQLTaeKTpUNZt5XcpomLCcAAqJgwnv0tR1m06yc4li/lw9i3svv9Rv01RHGT2zP2MGFrN7Jn7HWkv6hMHDhhH2st1ts97go2X/pzt855os333/Y/y4exb+OjJ3/ljWIgoyC+mX4+TAejX4+TA6V9GfaLJBG+BNKg8cMMWvv2ZV3jghi1+mxIKSsry6VFpzRl6VBYFXuYl6hPNBzsOcBSUFlF59mAAKs8eTEFpanOj00t2tvkbWfFxSsftWL6YzffdxI7li1PaXwkGYf7eWv2hqd5vUwLDrJn7GTakmlkOzU2DRHuyL2HAq++l1Seas/eJjZuW8LeXb2fjpiUOWNa5yS8spuvJlhRW6WdHeRarmDNrL+OHVTFn1l5P+lMUX1izxm8LlOTcKSIVwBzg+8BvgH9L9eDwjPQeYoxZaoyZkV/YpXVbpDnz1e9BN17CiMd/EDiJl5aGBg5utFeiPSg+qniDUwVAY4n6RHm5//rQQc/ObO9ulJb6uGK/AdQmDBqjT7iUiafcFEiJl6hPFEih36aEglzOsnaL+toI+6qs88e+qsbAf2atPtE1tYWvMbdO5txnrstK4iUVWhrcKUCpme7uEubCoRDjD4UlHe6bzTVGWAh75nYqeFGc1Wm8/F5afaKgY59IRnOkgepdGwCo3rWh02aoO/m+j/radE780Vz6zPq6Y20mw8/CooriKTNm+G2BkgARKRGRG4BJwDRgizHmbGPMWGPMU6m2o8H0Dog0N7DltUd4denNbF+RODMmlduhgpaRDlbx0a4j7JVoD4uPnlu6zZN+OitOFgANGtlktnoViGrvbpS8krhiv8XByrTOBjcDAUHLSI/n08ge1n36gt9mBJ7OUEzTaXz4zDwfLFLNSM+GvGLnC1CGOWO66eABv01IiVwtHBo/Xm5e8wh/X/FjNq95xCeLvCHMmdvpELb3leh7CXpgsyC/mL59RgLQt8/IQMwTvQ7ou5GZn+y6xOnfhN+FRRXFM379a78tUBLzMDAO2ABMBu7OpBEtQJqE+to9vLr05tbnNdvWEmlsW8DIreKiTXVNFJa6n/HYf9p0DnW/RDXTcwyvCoDW1npXECY+s/Wqn0ZSDi49cMMWVj+zm3GTe3PtPUPdNBOw7kaJfGfqEYtobYr9Vrluhidsee0R9ny0jl4DRjN0/JV+m+MLVY3vcrL5ApqlrjjNtfcMZdp/NFLRx92gsy0JdoqrnaRAc12jKwF2JwtQHpExHaLCllt+fTvNB/dT0LU7Q6+5xW9zOiTXCoduXvMIu3aup0//UQwbeyWR5gZ27bTuWNu1cz0nNTeQXxDs95rNvC++YKcSDGK/lyAUUW2ONHQYIB8xfBrNkS8HIpC+cdMSqndtoG+fkYwYPs31/o7MzHf3c5gzay8rltUzaWoJdy9IWUq4Q7SwqKIoPjLcGDMSQEQeBF7LpBE9eyUhXjO94sS2mTEt9Q2uFBfdPu8J/jBxMav+40VH2usIDaTnJm5PTpzWZe+ITLM0/ZKXaO9ulFzyt0hzA3s+WgfAno/WdYpb1RNRWXS8BtI7QGVeMuOBG7bw/TNec1VnPlYazE/W3voMf5l8P2tvfcaV9p0KyIY1Y7rp4AGaD1rjdfPB/aHKUM8F4gPnETtw3qe/dcdan/6jAh9Id0JbWwNnwSSake63FE86GddBCKT7ITnjZGZ+zQnJJXfclmPR84GiKD4RLTyKMaY500b0DJaEqGZ6rwGjOe3COxk0aXqb1/NKih0vLhqrt/z+C+/RVNfUwRGK4j1u6LKnwrX3DOXe1z+XVna5yku4R35BMb0GjAas82TQAwFu0C2/F6O7neO3GYFH/TB9vFqAiJUG84vmukaqVm4FoGrlVprrgl3Yd+CU6Qy7fi4Dp0zveOeAUNi1nIKuVqZpQdfuFHYt99mizkV7gfNhY6/k9El3MGxs8ju7jurjb9HOIARaFXfxW4onjFrofknOjBg+jS+ccYvrmfAqx6IoWfLRR35boCRmtIgcsP8+BUZFH4tIytkmKvOShJKyXoyccB1Au0Gi9uQcMiWqt1yzahPHnnNcxlIv9bWpS2AoSqpE7GunaPBl+bJ6V3XZE/2OM/ldX3vP0LRkYZTUGTr+SiLNl3bKQLqSHuqH6RFdgIhKVLn5uc1f2J3ly6rWudZBBxSUFlF59mCqVm6l8uzBaUu9NPVupnB36lPaSGND1hnP6RzvRH9OMPSaW2g6eEAD6T4xbOyVR0i5REIg7QKHA61RCRANqgUDp+UWvZTiiZdzKcgvpk/v4ezavSkwWuip4JfkjFf9qRyLomTBmjVw1FF+WxEoROQhYCpQbYwZYW/rCfwBGARsB75mjNkXc8xngVeAacaYP9nbrgKimtx3GmMetrePBX4HdAGeBr5rjDGxNhhjHLmo6jTBdBH5EjAFKAceNMY8l8pxW19/nD0fraNn/xEc85V/TbiP08VFB914CUNvG9caSD+zx1tpHe+1PrQSTjLxiU1vNjN75n7m3VXuui67079jDeC5RxgCAamQiU9EC5BqdnpqqB+mh1cLELZc1+j47ZnOnTJhzK2Taf7BOa4XJd2xfDEH3l5L+UljPMks97q/jtBA+mFaGhrSLgaerU/EjpfxGurpUF8bsS4TPUQ1z4OFW/rm6X6/mfhEIp3xjZuWsGv3Jnr3Hu6J9riThCXwnylh93kva3wpShsuugjaxnEVK9A9H1gcs+2HwAvGmJ+JyA/t5zcCiEg+MA9oHVvs4PtPsIqIGmCNiDxlB+AXAtcA/8AKpk8CXNGQDMVZRUQeEpFqEdkYt32SiLwlItvsD71djDFPGmOuAa4DLkupY9PSqge8d+dG3nv6t5m9gQzIJiNddWlznx/MqWHcmGouOGd3m+0vrWxg4pm7OOuMXSy872DSNjLyCSxZl6hWupsZ6fo7VtJh43tPsnLtz/n7xvvabN9ds5WXN9zLqg2/5L2dq5K2kalPVDW+S7NRSS7FHTINpKc6TiTTTM/UJzLF7UD6EcVDG92VEfC6PyU5Hz63hM3338KOe35O1WOLefe2m6h6bDG1b2/m/f+ay/t3/ZR9L72QtA2nfCKRhnqqPPfDVVw9Zk1W2uWZogGpYOCU7M7359Rwyuhqzo0bJ/66soGzvrCLCafv4r75zl5PGMwRci6xEi+7d28KhcSLEg7mzNrL+GFVzJm1129TFEUBjDF/A+Id8mLgYfvxw8CXYl77NvC/QHXMtguA540xe+0A+vPAJBHpD5QbY161s9EXx7XlKGGZEf0Oa0WhFXuF4j5gMjAcuFxEhovISBFZFvfXN+bQm+3jOkby6Nl/ROvTA+9uCPyFkOrSdg6+cmkXfvf7Hm22RSKGW24+wO8W9+C5F3vz1J/r2fp2M1s2NwGc6IhPxOCmVrr+jpV0Oar3GMYObpvZZ0wLm3c8zWdOuoLTT76enXs3cvBQNZ/WfQIO+oQWIFWCSKrjxDvbDtfdcXqccIKRFR871pbXxUPDWqw0V+kx/LMM+vIMjDEc3GAtchzcsJZdf/5fjvrmDAbecCOfrnudxk+qaKj6GFyYO0XJtPhoU10T7zy/A1Dt8s6MU/rml17ahcWPHDlO3HzzAR7+fQ9eWGmNE287eD0hyBE6435pjyu5jdsFVBVFcYx+xpid9uMqoB+AiAwAvoyVbR7LAOCDmOcf2tsG2I/jt7tCKGRejDF/E5FBcZvHA9uMMe8CiMgS4GJjzFwsDZ42iIgAPwOeMca8nmrfw067is3/eJi9H2+k4kTnLoQihxodl4eJorq0uc+ppxXx4QdtCw+vW9vEsYPyGXis5dYXXlTC88/VM2t2V7B8pY1fZOITw08u4LjjCli+rJ4LJhe7mqGkv2MlHXp2G8Shhn1tttXUfkRpcU9Ki3sCUNlzBNX73+L4/hPAIZ/QAqSK22RaAyXVceLlVY2tNTCc8AmvaTnUQDp6FwOnTCdy3mWeBba97k9pn7KjT6CxZi8iQteRYzi4YS1djh8M+XkU9uwFQNdRp3Bw80Z6nnUuODROtEciDfWOKCwt5ITzBvLO8ztUu7yTE5XdyYZTTyvig7hxYu3aJgYNyufY6DhxcQnPPVfPbAevJxLpjPulPR4lXsNdCT/RAqorltUHroCqiMwAZgAMHDjQZ2sU13jgAb8t8JreIrI65vkiY8yidBowxhgRiWrj3APcaIxpsYaaYBGKYHo7JFqNODXJ/t8GzgUqROREY8z9iXaKPbEVd7H054adehWR5gYOHVvqhN1sn/cENas2UTFhOINuvMSRNuPRAGTno6qqhf5HHf7eK/vns/aNpNITafvEUQPymL+wO83N+3j2mQZmz9zPfAd1GuPR37GSDfWNBygpOqwPXFJUTs3BD5Mckb5PlOR1dc5gB+lR+anfJigO4HTtiPbGCbsA6RsJDknfJ/p1y9rOVHnqB6+y9bk/UTpuFEdd8s2Uj/M6sK2B9OBRefl0Wi65jLq3t1C3dUvr9oKK7jR88H6yQzv0iUTXEu2RSc2R2xdUUl/bh/N6f5T2sUpu8e/fP+C4bnrVzhaO6n94nOhfmf31RBufKK4AEuuM+xXMTqThruQGQS2gagcYFwGMGzdORbVzlRkz/LbAa3YbY8ZlcNwnItLfGLPTlmqJSrqMA5bYgfTewBdFpBn4CDgr5vijgb/a24+O2+7aZClYZxUXMcb8yhgz1hhzXXsXg/Z+i4wx44wx4wqKDwdJnCqwFznUSM2qTQDUrNpE5FCjI+1mSkOl6vzmMs1N7Y/NmfhEr5551Na28OwKS+7ITakXxSLo0lK5RCY+USgaJFPcwYfaEUeczDPxiaLu3lRFrK1t4a3nrMWxutXraWnQc2VnJJsxMt3io5CaT7QZI4rKMrYvGV4mG+g8L5g4pZueLen6RFGhOz6RKbF67VENdyWYZPobD1ogXelEBDCbOqA8BVxlP74K+DOAMeY4Y8wgY8wg4E/ALGPMk8CzwPki0kNEegDnA8/aUjEHROQ0+66p6dG23CDMZ5aPgGNinju26iAiF4rIokjTISeaa0N+lyIqJgwHoGLCcNekXtxGsx6DR2VlHjs/PhxsWXR/LY/9zyFmO1CgKuoTBw4YysrymDLV0mmcErBb5nKN7SsWs3HRTWxfsbjjnZUjKCkqp77xQOvz+sYDFMdkqmdD1Cc+jexh3afJC9YpSia4UTsifpyo2hmhstKZc3jUJ5oPehOIKCvLY8j5VvJJ6bhRGQVGlXCzY/liNt93EzuWZz5G5ldU0FRzeJ7UXLOf/PKKrG1r9YemxMV9w8KsmfsZNqTal2KnSnKc0k2Pp7J/Hh/vPDxO7KyK0K9/9m23+kRzsHxC9drDgRYSVZTcQEQeA14BhojIhyJyNZZU2HkishXrTqefJWvDGLMXuAP4p/13u70NYBbwG2Ab8A7wjCtvhHDLvPwTGCwix2EF0acBX/fXpNQYdOMlRL4z1dFAeqaaqkruMGp0Idu3R/hgRzPduglvbbE0EJdbhVccjXjPX9ideXe1vWWutjZ4t9CFmUhjAzXbrAJpNdvWEmlUzd10KS87irr6PdQ17KOksBtVezcy6vivON5PVeO7nGy+oEVIFcdxunZE7DjRrzKfpU/V88t7WwOHjpzATYt3dytf9PPTeHL6STTWdYfdnnWrBIBIYwMH3rbGyANvr81Yl75kwDE07d5F0949FJRXcHD9G/S77BtOm3sEkTS10v0gPvP5F3fpPC9oRHXTnfxeRo8u5L33IuzY0UxlZT5L/1zPr+Znv8AUZPzWa1eSE19INAiyLXrHjqJkhjHm8nZeSlqEzBjzzbjnDwEPJdhvNTAiU/vSIRQzokSrF8aYZmA2Vor/ZuCPxpg3nejPGLPUGDMjvzDxrcp1ldn34WQg/YEbtvDtz7zCAzds6XBflXXJDb5z/X4u+dJe3n23mc99tpo/LKmjoEC47Y5ypl+5j4un7uWkIdZamZ1FntWIH/WJ8vLDtyrFTmJmz9zPiKHVjmTBKxb5RcVUnDgGwNHix7nK+nf/xD+2PEhdwx5eWnc3H+56nTzJZ+jAL/L627/n72/eR2WPk+napa8j/UV9AqCy6HgNpCuukWkgvaNx4ryzdzNlagknDSmMnrtPycbOqE8c2nmAtbe6lgRyBHld9NzYGckvKqb8JGuMLD8ptTHyg6d/z7tLfknj7mre+9ltHFj9KpKfT5+LLuHj3y7i/Xvm0XXkGIr7ZT/Rj/pDQWHJEa9tXvMIf1/xYzaveSTrftzErcxnxVmy+V5mX7+fL128l3ffaWb8uGqWPGaNE3fcUc43rtjHxLN3M/XCEoYMyX6O0+oTBUf6RBDQQHpwiRYSBQJRSDSaJQ8c76shSvCZOrXjfZTQEorM9PZWL4wxTwNPe2xOoIjXVHUyg62zEoYM61/dl7jI0NkTizl7Yp/W59H3snxZlWu21Na2RLPfWb6s/oiMdSVzBk2arhnpKTLq+K8m3N6n+0n06X6Sa/12y+/F6G5JF9KVGPQuKu9IZ5yInsOdomrlVpp/cA4FpeGUslPCwcAp09PKSD/mi1bGeX2fttvLhgynbIglwei29n6kuYFdO9cDsGvnek5KI0O9qa6JwlJvF27dyHxWgsP8dsaJiecUM/GcPglfy1WaIw0aUA8wQSkkGpslD/Tw0xYlBCxd6rcFiovozCgBbmqmO40bmqpuMLL3Tr9NSIlcy7B2asIRq5kez43fP6xJrRrqzqOB9GDSqv1p9G6fVEnnLirFO2LrYGRD1CcAKs8erIF0xROcHCOrHlvMu7fdRNVj2dcpaU8zPb+gmD79RwHQp/+olAPpW+58it9M+CPP/XBV1rali87rFCcIqmY6wMZNS/jby7ezcdMSv01RkhCEc1Fsljywz09blBBw4YV+W6C4iP9npADSkcxL0Lj2nqHc+/rnuPaeoX6b4jpu6pPFZ1irFtphEsm8wJEZjfPucqa4oxJumiPeFCD0k6hPFIoGDFMh/i6q+tpIB0coXjJ/YXeAddm0EfWJboP7MObWyc4Y5iCRxtw/LymZ09LQwMENlgb7wQ1rs85QTybzMmzslZw+6Q6Gjb0yZdv2vPQWAO88v+OI86fOV5UwEFSZl+ZIA9W7NgBQvWuDY3PYzjAX7qzcvaAnr22uBHjXb1uUgLNsmd8WKC6iwfQEhCkzPUpQM9KdxO2s8djsvKBlWPt9odReZrqXn5kG38LBltce4cU35rLuncf9NsVVoj7RZBr9NiUUhOUuqs6KPa6OzqaN1rlTXfB8YsfyxWy+7yZ2LM8+4zhTNJgfbPKKi+k60tJg7zpyDHnF2WW8t5eZHiWd4qN5xcX0OnMIACecN7DN+XPWzP0MG1LNrBy5o9Ir/J5Xd0aClJkeG+guyC+mb5+RAPTtM9IRqRfNdM99ghQnUBTFH/QskICwZaZ3BjrKGndqUjx/YXc2bukbzdILBEGQnmkvMx28+cxUHiI7vAriRJob2PORldz6yb43czorRzPTlVzBKc301qzDrsGSpmppaODA21bG8YG31/oS1A5CMD+IBG2BofLy6Rz/k7lUXj4967aSZaZnwtCbL+Jbq77G+T+b0LqttraFZUst3122VO+oTJVcXYAI+vcflMz0RIHuEcOn8YUzbmHE8GlZt+9Upnsuz6EVRVFyAQ2mp8jBAZpF5yfJMqCdDjYHaaU5LNIzbmekqzxE5mxfsZiNi25i+wr3gzj5BcX0GmAlt/brcbIWclJaUT8OLmVlefTvH5xxz2nyiospP8nKOC4/aYzndSgijf4H84NIUBcYss1Id5P44qNlZXlMvdCaG0+9MFh3VAaVXF2AyNUFAqdJFuh2as7qRKa7ZrYrSo5gjqw3p+QOOutKQBhlXoJOU132RfoSZUCHJdicKUGRnklWgNTtz1zlITIn0thAzTYriFOzzZsgztDxVzLxlJsYfcKlrvflJyrzkh7qx8GltraFnTuzP4+33sJ/MHjB4oFTpjPs+rkMnJJ9xnEyEp1j84v8DeYHkc6wwNCRzItTLFjYnc1v9WVB3N2BuTYfdopcXIAIywJBEGRe3JB0SUQ2me5uabjH96EoigcsWuS3BYqLhH8G4QIq8+Is2+c9wR8mLmbVf7yYdVvxk96gBJvdJAjSM+3JvHglQdOZiuw6SX5RMRUnWkGcihO9C+J0hox0lXlJH/XjYOJUZnpQZV6iuH3+S5Zp7VUwPyx0hgUGp2VekhE/99Us5eS0twARVsKyQBAUmRcnJV2Skelc2O2Av2a9K4qHXHut3xYoLlLgtwFK5tTXRgKf3Rc51EjNqk0AvP/Ce9TXVkKps33MX9ideXe1BHby6ARBfG+7qpvb3BXg9ncQ9N96UBk0aTqRxstyMlihOEvkUCP5XdxdHFA/Dh5OZaa7yWe7vtvua8f13sPmPUc73meksSHl8+YRmdbnHXnO1XNwWwZOmZ7wc1JSo742AglyfuKzlH+R4/PjTMm1z2TBwu76XaeB0wHq5kiDo22OGD6N5siXXbEzNuv9qKYG8gv1HKwoipIJOuImIAwyL2EpyJjfpYiKCcMBOPac41wLpOjk0d1beuNlXmbP3M/4sbtbsxlz9a6AsNHerfIarHCeXJN52T7vCTZe+nO2z3vCb1OUONzWl4+9wysbgizzUt8nvf3T1fN2K9M6F+VPYsnlsclNmZdffXcbV49ZkzDzPNss5aBKhCgdE/R5uJ8yL27KmriV6e3GXZ7xWe8aSFcURcmcYI+6PuG3zMvI3juTvh62Qm6DbryEy16czoSfTvTblJzFbbmVWJmXWJ36nTtbeG1Nb18laBQLLwuNKrkl8xJ7B1HNqk1EDuXGAkEu4NXCuX0OfyObNoIu85Iqmep5Oy3lEtQCnUpquCXz0lTXxD+e3gu0r4+dqYyJysMobuKXzIubsiZe6Js7jVcyN4qiAE895bcFiotoMD2EBK2QWyqBl8LSQg8s6XzU1rZ4XoQ1Xqe+T9/cUYtyolBuLF4FJf0oNKrkDrF3EFVMGO661IsXeLHI7HYfPiyc51RKaktDAy0N6Z8Ls8kydzIjPdcLdPpFuncpBI3C0kJO/WJPIHnmeSYZ6W4WsfQq410z65VYMg12p7qfVwVNnSYsdnaE+rsSeMaO9dsCxUVyJwrWybj2nqFc9VP/NdO3z3uCmlWbqJgwnEE3XuKrLZ2N2TP3s3xZPVOmljBlaknrYy9u88xFnfpV//Ei77/wHseec5wjd1F46RvRQqM129Z6WmhUSY+DA4KrFz7oxkuIfGdqTgTSH7hhC6uf2c24yb1dK3bqRR/RhfNoP36P92Gi6rHFHNxgBaO7jhxD5eXpZYv7recdDegfeHttzhboVNKgb1tZjO/88kTq/zPCeb0/cqyLqDzMsqX1jhexnDVzf2u7bhb99KofJTxEg93VuzakHOzeuGlJ6/6pZG+7pW+uJGfOrL2sWFbPpKkl3L2gp9/mKEpiBgwAY/y2QnGJ3ImEdULSvbDuUfmpo/2rNIB/xGejz7urnI1b+noqt5JLgfSmuibef+E9wCqUm22Guh++MWjSdEbMmMugSc5IDCidj1wIpHuRze1lxvi19wzl3tc/51rAPhdpaWhoDaQDHNywNuMMdT9xWjZGyS3cWFzLVB4mGW5nvHvdjxI+0pE1yTSTXQPp3lJb28IK+zp4hQd3ZSuKoiQid6JhDtJaIKWxzm9TAk0uSgOEhXiplbKyQhxh4AAAIABJREFUPFeD2/EFSGPJhQlMYWkhx55zHGAVys1Wlsgv3/A7+NOZyLUCpJlwZo+3/DbhCLyQQfNaas3DjPSsBpGgFCDNKy6m68gxrc+7jhxDXnE4z41hO6erHM1h3CxA6iZlZXmOzuuyLYgatH6UzPGzAGmqwe5UZFvCoIue65SV5THJvg6e5NFd2YqiKPGozEsCjDFLgaUics2W1x5h6Pgr/TbJV1rqG8grSTwJySVpgLDhpdRK1CdGjSq8JnZ7rNRMoqz42trwSMFM+OlETvuPJsf0/dU3cpuoT1QU9Lmmw50VT/FCBi0oUmtOYRevPiWbNlp9Ymg/332i8vLptFxyGUDagfRIY0PogthBYMfyxa2yNJpNf9gfunU/+gh/iDQ3kF8QzN+YG1IpCxZ25xcezFe96kfJjKhPlHcb4PsYkYxksi3pSsD4RXOkIeez5e9e0JPbf6H+rgScawJ9ulOyRM8+HbDno3VEmr1ZgQ6iTMru+x/lw9m3sPv+R9vdR4OF/uHHBCKasdRR4dPZM/czYmh1NEgTCpwulKu+oSj+4EWQO1cC6bHncrdorvN+fpNXXJx2IH3H8sVsvu8mdixf7JJVuYkWTE2dzWse4e8rfszmNY/4bcoRuCmV4tV8Nb6fXLh7srPiZwZ4exnpmUjAeM3GTUv428u3s3HTEr9NcR0NpCuBZ9Eivy1QXETPQB3Qa8BoT7JHts97go2X/pzt855wva9UaalvoG71egDqVq+npT6YkwbFO3bsiLQGyBNJzUTpKNCuKIqidA7W3voMf5l8P2tvfcZvU5KiAeHMiRZMBbRgahIizQ3s2mnNq3ftXO9Zsk6q5JpUyqyZ+xk2pJpZIUrqUCyCGBBORQLGb8IS8FeUTsPYsX5boLhIuGdJLlNWcZQnEi/xxQqzLX7oFHklxZSOGwVA6bhR7Uq9KJ2HmhpLMz0aIJ+/sHvCwqfJAu2KoihK56C5rpGqlVsBqFq51ZcM9VTRgHB2aMHUjskvKKZPf2te3af/qEBKvbhRhNQPtCBpeAlyQDidYqZ+EIaAv6J0Kl5/3W8LFBdRzfRkiDcBwGixwppVm6iYMNxxqYls6H3dFbTUf1UD6QoAFRVCTY1pEyBvL1Dupaa7oiiKkhnRxU83pF4KSouoPHswVSu3Unn2YApKgy19NXDKdCLnXaaB9AzRz61jho29kpMCrJkOuSGdEM2yj+q/58J76ixEA8JRbfKgBYSDZk88yTTfFUVRFOfQYHoCRORC4MKSsl6e9dm2WOFOR9vONtNdA+lK1CeOPTafv/+jV8oXJXrxoniBH4WWoj7RJa+bp/0qihvMX9id5cuq3simjahPlA6oaLN9zK2Taf7BOYEPpEfpLAFhLbTqLq3XEqVHXksEOZCeS4S1IGltbfhsToXWeVNJzw731YBwdhTkF3eKIqSKEnj69/fbAsVFcm+kdgBjzFJjzIz8wi6e9puoWOGZPd7Kqs0HbtjCHyYuDpQWuxI+oj5RXi45OcFXwsu6dx7nxTfmsu6dxz3tN+oTh1o+Zd2nL3jat6K4RFY6CFGfKOh65MW724H0kl6HXG0/19BCq+7T6g+FJX6b0qkJ25w1l3XeW32iIDWf0EBw5gRRc15ROiUff+y3BYFDRB4SkWoR2RizraeIPC8iW+3/PeztF4vIehFZKyKrReSMmGOusvffKiJXxWwfKyIbRGSbiPxKRMSt9xKuGYZPBK1AUKrU10ZY/cxuwNJijxwKrlapoihKutQ3fson+94E4JN9b/qmq1nV+C7NJhi1LrJBxwhFCR5OF0LVQquKEkzS1XlXHXglEUHWnA8S6j+KJ9x6q98WBJHfAZPitv0QeMEYMxh4wX6O/Xi0MWYM8K/Ab8AKvgM/AU4FxgM/iQbggYXANcBg+y++L8fQYHoHbHntEV5dejPbV4Qve6ekLJ9xk3sDUDFheMLMd0VROicHB+T7bUJWrHvncf62/m6KC8sB6NfjZN+ymCqLjqdAglPrIhO2z3uCjZf+XO9iUpQA4UYGuRZaVZRgEtV5BzrUec/lDHYlO7QIacfMmbWX8cOqmDNrr9+mKLnObbf5bUHgMMb8DYh3vouBh+3HDwNfsvc9aIwx9vYyIPr4AuB5Y8xeY8w+4Hlgkoj0B8qNMa/axy2OtuUGqpmeDNPCno/WAVCzbS2RxvAVpbr2nqGc8O/Hs6VuoN+mKIqiOEJzpKE1I72h6QBfGDWHkiJ/tMu75fdidLdzfOnbKSKHGqlZtQmw72Jqrd+hKP4ysqLz3h57RAa5g4VRtdBqeKjvazreSckZUtF5j89gD6MuvOIuqjnfPrW1LaywC66vWFbP7b9Q/1GUANDPGBMtHFkF9Iu+ICJfBuYCfYEp9uYBwAcxx39obxtgP47f7gp65kiG5NFrwGgAKk4Mb/ZOYWnijMmGyiNlCVrq9VYwRekMhPnW/oL8Yvr1OBmwMtL9CqQDCK7JsHlGfpciKiYMB/QupiBSXxvx24ROi5/nSbczyMM6p1XSR+UMwkVHgb10MtiVzks2gfSGumYHLQkWZWV5TJpq+c+kqf75j4jMsDWgV+/atcsXGxTFBXpHf9f234x0G7Azyk3M8/9njBmKlWF+h4O2Zo1mpnfA0PFXEmm+lEPHlvptiuvsvv9R6lavp3TcKHpfd4Xj7bdXTDVXq9YrnZPIocbAByO3r1hMzba19BowmqHjr/TbnIwYfcKlNEcu0qwbhxh04yWeZKTX10YoKQu3xJCXPHDDFlY/s5txk3tz7T1D/TanU7Fj+WIOvL2W8pPGMHDKdF9s0AxyZ4g0NnTaz3DWzP0sW1rP1AtLWLCwu6t96XzeO1LJYFeUTNh8x1JWvfQWr08t4e4FPf02xxXuXtAzpYx0N89pxphFwCKAcePG6S1Iucrq1X5b4DW7jTHjMjjuExHpb4zZaUu1VMfvYIz5m4gcLyK9gY+As2JePhr4q7396LjtH2VgT0p0mhFYRIaJyP0i8icRmZnOsfkF4Z+Aj+y9M+nrLfUN1K1eD0Dd6vWeZajPnrmfEUOrme2h5p9m6Fhk4xNKYsKgOx1pbKBmmyUdsOejda4VWPai4JHTgfTO7hNuB9IfuGEL3/7MKzxwwxZX+/EKtzPGY4uIr35mty8Z6p3VJ4JUpDOoQeCw3N3ktO683z5xRvnWlPdNt6BlNqiGt/cEJZDuhU9oEU1vaKlvYPdLVvLbimXunjP8piP/UV11RfGUp4Cr7MdXAX8GEJETRUTsx58BioE9wLPA+SLSwy48ej7wrC0Vc0BETrOPmx5tyw2CMQp3gIg8JCLVIrIxbvskEXlLRLaJyA/bOx7AGLPZGHMd8DXgdDftDSN5JcWUjhsFQOm4UeSVuH/xVlvbwnJbs2y5RwO2H8F7N/jBnBrGjanmgnN2t9n+0soGJp65i7PO2MXC+w4mbUN9wlmO0J0+1OizRYnJLyqm4kRLOqDXgNGuLBaue+dxXnxjLuveedzxtttj43tPsnLtz/n7xvvabN9ds5WXN9zLqg2/5L2dq5K2oT7hHkEIDDuJFwsDsUXEx03unXZGv44TmaNFOpPjRmFUN4hfFPngmUd576e3sOOen7fZr/btzbz/X3N5/66fsu+lF5K2GSaf+PfvH2h97KYciJdBe8VZvj+nhlNGV3Nu3Djx15UNnPWFXUw4fRf3zfd3nNi4aQl/e/l2Nm5a4nTTShx5JcX0PnMI4K8Eit/E66rrOU3JmHGZJGnnNiLyGPAKMEREPhSRq4GfAeeJyFbgXPs5wFeAjSKyFrgPuMxY7MWSfPmn/Xe7vQ1gFvAbYBvwDvCMW+8lLGfI3wGTYjeISD7WBzoZGA5cLiLDRWSkiCyL++trH3MRsBx4OluDEumNO4GfAbje113B0fNvd0XiJRFlZXlMsTXLpngwYPsRvHeLr1zahd/9vkebbZGI4ZabD/C7xT147sXePPXnera+3cyWzU0AJ7rlE2H+HJ0kTLrTgyZNZ8SMua5IvESaDxcH/WTfm55lEx3VewxjB7d9P8a0sHnH03zmpCs4/eTr2bl3IwcPVfNp3Sfgok8oR5JtYDhIeLkwcO09Q7nr5fEZSbwEaZzwgqbezmq8DpwynSHX3OqbxEtQCVLWfkfEL4r0HHka/b/ZVr7TtLSw66knOOqbMxh4w418uu51Gj+poqHqYwipT9TWtrQJcAP84q5y1/pTDe/wcumlXVj8yJHjxM03H+Dh3/fghZXWOPG2T+NEc6SB6l0bAKjetcG1OaVmvh9m2I8v5Ef/mJyzEi+pEBRddUXJRYwxlxtj+htjCo0xRxtjHjTG7DHGnGOMGWyMOTcaGDfGzDPGnGyMGWOM+Zwx5uWYdh4yxpxo//02ZvtqY8wIY8wJxpjZtga7K4RCM93WxxkUt3k8sM0Y8y6AiCwBLjbGzAWmttPOU8BTIrIc+B/3LM6M7fOeoGbVJj495zgm/HSiLzZ4kZEey/yF3ZnnkeZfNHi/fFm9J8F7Nzn1tCI+/KBt4GDd2iaOHZTPwGMtt77wohKef66eWbO7guUrR/hFtj4xe+b+1s9zvstanGHAK91pJ7AyLZ0PAuYXWMVBP9n3Jv16nOyZpnnPboM41LCvzbaa2o8oLe5JabF1QVDZcwTV+9/i+P4TwCWfUNrn2nuGctVPw6+ZHl0YiGqZu/l+stFMD8o4EVaCoJkeRKIB6uhnE/Ss/Xjd+U8L2t6yX//hDgp79aawZy8Auo46hYObN9LzrHMhhD4Rq5E+9cKS1sduz3lVwzucnHpaER/EjRNr1zYxaFA+x0bHiYtLeO65emb7ME4U5BfTt89IqndtoG+fka7MKTduWtLa/ojh0xxvP4wUl4YiROQqqeqqK4rSeREXA/WOYgfTlxljRtjPvwpMMsZ8y37+DeBUY8zsdo4/C7gES2dnvTHmvnb2mwFE01aGAImrZralN7C7w72SkwecEvP8DSBZyq8TfWZCLvSbR/LP1q1+0yGVfouAwcCb9vMeQDnwvv28J9AV2AEca4zpE3twhj5xckx/6f5mY6kAalLc16lj0zkunX39+o0EmQrgU1L/PThFOj4x2BhTEXuwy+OE1wT1dxlUuyB929IZSzIhm3NsFD/GiWx9wu/fSG9gL9l/9tna4Pdn0FH/bv/+3foMXPcJH8eIvsAxMc/fsP/7dQuh37/jeNSexER94hMse4LoE5mebzr6jJ0YZ520x2vUnuR0ZM8R/pAJIrKLw/6mKGHGEZ8IC50mmO6ybaszrFobqj6132D164dPiMgiY8yMjvd0r51Mj03nuDT39eU3EmSc+p1k0O8gUvQJv2z0iqD+LoNqFwTXtmzsCvLcqT38/h787j8INvjdv5s2hNEnUiUI31ssak9ygmJP1CeAemPMOPUJ91B7kqP2KIoSJsJ838pHtM2+ONrepiidFS98YmkA2sn02HSOc+p9dlaC8vkl84mg2KgoXqJzJ0Vpi/qEorRFfUJRFEVROiDMwfR/AoNF5DgRKQKmAU/5bJOi+InrPmGMcSQAmU07mR6bznFOvc/OSoA+v3Z9IkA2KoqX6NxJUdqiPqEobVGfUBRFUZQOCEUwXUQeA14BhojIhyJytTGmGZgNPAtsBv5ojHkzWTsusqiT9Kn9BqTfEPhEZ8Kv34gSg/rEEQT1dxlUuyC4tmVkV4h9wu/vwe/+wX8b/O4fXLAhxD6RKkH43mJRe5Ljuz2xPgGcoD7hOmpPctQeRVFCQ2g00xVFURRFURRFURRFURRFURTFL0KRma4oiqIoiqIoiqIoiqIoiqIofqLBdEVRQoeIfElEfi0ifxCR8/22R3EeETleRB4UkT/5bUsigm6foiiZISLiY99lfvWtKG7jp28p6aPfl/voZxwe9LtSFCUeDabnEHqSV9xCRLqLyJ9EZIuIbBaRz2XYzkMiUi0iGxO8NklE3hKRbSLyw2TtGGOeNMZcA1wHXJaJLcphROQYEVkpIptE5E0R+W4WbTn1Hb9rjLnaPq5ERF4TkXW2fbcFyb4gIiLFftsQNjSQ6Q8icqKIjPPzNysiZ4jINwCMMcaP+ZSIXAzME5G+Xvdt93+aiHzD/l/khw2KM4jIqSJyjN92RBGR/uCfbyWwJ99vG4KMiHQD6/vy2xanUJ/o0B71iXbIRX9QFMUZNJjuACJS5MeFh4icLiLnisg54M1JXkTOEpGvicjX3e4rIP2eJyLXi8i37eeeTHj8er9J+CWwwhgzFBiNVZCoFRHpG51sxGw7MUE7vwMmxW+0J3H3AZOB4cDlIjJcREaKyLK4v9hAw832cYFBRMJ4Xm0G5hhjhgOnAdeLyPDYHXz8jgEagInGmNHAGGCSiJwWIPsChYhMBL4VxICYiIy3x65T/bYlFhE5F7hJRLr4bUssQf28nEJEpgJPAL8AficiJ3ncf56IdAUewPr+r4PWAIdn53IROROYB/zZGFPtVb8x/V+EVWjtXOD7wLFe29AeQQg0hQkRmQw8CRwXDZD5+RmKyATgLyIyC/wPHtqfz90i8rCIHGVv89OeqSLynyJyr4j0FpFCv2yJ2gM8KNadn0fb20Ltg+oTHdqjPpHEFnLMHxRFcY4wBn0ChYh8BfgfYJmITBGRHh71ez7wGHA+8FMRWehBn2fbfQ4EviciC6KDbo72ewbWd1sPXCYi9wKni0iBy/368n6T2FMBfAF4EMAY02iM2R+325nAk2JnForINcC98W0ZY/4G7E3QzXhgm53t2wgsAS42xmwwxkyN+6sWi3nAM8aY1x17sxkiIl3sYATGmJawBdSNMTujn6Mx5lOsxZIBcbt5+h3HtWmMMQftp4X2X/zioW/2BQkRmQTcA6y330dgEJELgKeAKcBjIjLbDmT6in0hOQ943hhzyG97ogT183IKEfk8VhD9KmPM2cA+IOkdIU5jjGmxzy0PY41xnxeRf4u+5qEpY4HfGGOeF5GjxFrIP9Uef11FRHoB1wNfN8ZcBRwAxoi1QFnidv8J7DlVRM4Ukc+C/4GmsGDPi/oBPwG+YY910fmqn3OST7HG3MEi8j3wL8PTPtffBTwLRIDfikiBj/aMBe4HXgXKsOYsU7zw+3bsmQD83LajFvgZhDcjV32iY9QnktqSU/6gKIrzhCrgEzTEyqC6E7gb+C1wLTBdRE5wud984ArgNmPMD4CJwCgRmR+zj2MXHvZkRLAyNn9ujLkLOAOoAH5oT1RcWan1q1+b8cB8Y8yDWNlaNcBX7e2u4PP7bY/jgF1YE6w3ROQ3EieHYIx5HGsi9gcRuQL4V+DSNPoYAHwQ8/xDjgzmxvJtrO/kq2JnEvqF7Y93At8RkR/A4SBMGAMAIjIIOAX4R+x2r79jEeklIvcDp4jITSKSLyJrgWqsoGeg7EujH9cQkVFYC3G3G2NW2Tb2FpHjfLZL7EWOy4HvGGN+BFwCXAxcJyKlPto2BPh/wH8ZY16yA4iDRGSEjzYF9vNygXnGmDfsxz8Beoo/ci/NwDFYQfXxIvJfIjLX/i68mCs3xzz+E9b5azZwn7ifpNEMdAGGikg5cBYwHWtR7ub48d5N7MDOI1hz3B+JSHQRXwPqHWAHeGqBN40xfxFLRuIhEfk1MEc8vOsj+l3Z86NDwEHgdeAEEfmmWNJOniWJ2H5cgeVX/2GMecYY869Yn9dXvLIjAUOA54wxT9n2/BVr8fRMESnw4Tc/EXjSGLMK+E8gT0RuFpEx9rkhVKhPJLdHfaJDcsofFEVxHg2mZ0dP4BNjzCvGmMeAucAIrBXUbskPTZ/oAGKMiQCvYV10lhhj6oALgLEicp+9j2OrpnZWqAHWACeJSD9jTD1wDdAX6wLYlZVau83XgSFe9RszUK8DThORk+x+7wDqANekV/x4vylQAHwGWGiMOQVronVE9qAx5udYWfwLgYtiMokdxxjzK2PMWGPMdcaY+93qJ0XygfuMMecCR4nITBGpFJHSsGUviJX1+r/ADcaYA/Gve/wd77G/3xOMMXONMRFjzBjgaKxg1xHBTj/tc6ufNCkB/ghUisg44FGsxd5nReSbfhllDyENWHc8jBKRrsaYtcANwBeBf/HLNqwMsfnAqWJlSv8PlnzUCyIy0w+DAv55Ock/sCReogGGYix5kXJ7Wy8PbfkzUGWMeQFYjVWPo9z+LrzIUF8JXCMiS4BfG2MuxxrzD+LiAj6AMaYG+BVwE/Ac8FtjzIXAb7DOt4nkshzH/g1chbUYOMN+PFTsIs8aUG+fmAWoOiwpi5uw7vp4CXgea2y4UkSKPfoMK8C6XjHGvAX8HcvfH8MK1D0L9LFt98KePvbvfC6wQg7fYfoxUOlB/+3xKta88fMAxpgHsK4BrgTKfJhDrsG6lrwJWAW8j/U9fRcrySI0SSLqEx2iPtExOeMPiqK4gwbTMyAmK+w14D2xtK0LjDGvYGWoTwYyKtDYAbE6vRuwMnOPBbCDRhdgXXg7pq0qImNF5Hj76Tag1O6jix3E/xesIMRFTvVp93uMPcHpArwCdPOo31OxbvMWYJP9d4aI9LeDG7djBfK+6XC/vrzfFPkQ+DAmE/hPWMH1Noh1O9wIrCzPn6TZx0dYWYFRjra3BRoREWNJabwHYIyJBruWAL39tC1dxNIk/F/gUWPME+3s4/t3bCyJoZUk1j333T4/Mca8BizGCn79BUsi5GqszKM7ReRkP+yKudhYD/TCysQqMMa8Cfw7lpzVaD9sM8Z8jFUT4iBWBtSfjTHfwsqEulPitPk9JnCfl5PYQYXoop0A+4G9xphdYt1dcqd4p2F/CGsRO1rY+mfAQBG51ovOjTEbsLTKT8W6GwxjzLtYi7V9POj/T1hzylXAG/a2F7HmIp7op9uJIm/EPD9gjDkd6CciD9jbQrVA7QVyuEZGqb3w8yOs7M6exphFxpg/Av8HDDLGNLj9GYolT/Ubse7yiV5nHo8VoCsAxgFvAdFgmdv2TAJ2iMhZxpjXjTH1xpjonSCbsO8KEUuneaSbttj9fF5Evioi59s+vgbrOmMogDFmIdCIda53nRh7zjLGLMNaRCsDXjTG3GSM+S7WXXtX2fYF3gfVJzq0R32iY1tyxh8URXEPDaaniT0gXh9zgbcGK2voDBEpNMb8H1Yg7VvioLa2WAUwnhSRX4vI7cBarIDRYhEZagddDwBbOKwHl22fFwB/wLqYwhizBmsVfTYwwQ4wHwJewNJZcwQRmQI8g6VR9hDWAPoY1kqwm/1egHWLd72djbYTeBk4HfiiiAw1Vqb4UsCxTDW/3m+qGGOqgA/EkkMAOAdrstWKiJyCVbzsYqzAfy8RuTONbv6JpR14nFhFE6dhBQIDi4jk2Zlyx2AHP+xMmA+BG40xO3w1MA3sYOeDwGZjzH+1s49v37GI9BGR7vbjLsB5WOe6QNgXBKLjjTHm71jn7auNMQuAiDHmZWAFViFXL20qs20y9v9nsILW3wFGiJVxvca2zbPsHhE5WmKKsxpjPgAWYN3NcK+9SLYa6zzs+Tk3uvgQlM/LC4wxzXZSwAciMhf4HrDAeKRhby+qfAD8GPieMeZ24L+Ap73o3+YZrEXAK0XkahG5Giv77RUvOjfG7ANeBL4iIufbi/fHYS3quIa0lVr4CLhRRAbGbPsy1vncl8XAICNta2TU2ZvfwlpwHiUi37e39QD6iMu6w2IV0n0A6+6K6pi7Oh7CGpd/D/wb8N/AiSLS02V7JmHJ8P0/rESYfGkr21QAFIvI17A+x1qX7Tkfqwj6ycDjIjIG626yE4Av2Z8fWMladQkbcceeEcBTInKasSTz7gNqY3zubaBZAljUPB71iQ7tUZ/o2Jac8QdFUVzGGKN/Kf5hZZyvA86K2dYV6+Lnv7GKm4ClNbkYyHeo3xOAd7F0LCcAt2AFeXtg6bQ/bdvwY3u/Exzo82xgK3C2/bws5rXL7Pe3GOv2sA+Bkxx6rwOAjfZ77Qf8AOsCdwBWpqBb/Z4G7Ix5v+Uxr52OJZXwElYG4yfAMAf6FKxM2A1ev98MbB2Ddev7euBJoEfc66cDI2OeFwLXJGjnMftzbrLfz9Uxr30Ra4LyDpZ+n+fvM43PI8/+fxzWIscFMa9VRL9fv+1M4/2cgVXQcz3WQt1a4ItB+Y6BUVgZi+vt88MtCfbpVL/BmO/tGzHPC2IeF8U8vgxr4XeAh7ZdjCWf0td+nhfz2jys4PV/YwVNP8LKEPPCrguwgpPH28/zY16L/fy+jjXeD/TIrsFY2Wl5xM0d/Py8PPy9CFBk+94OYLAPNhwDjI15nue1DXa/n8HSZ7079pzmUd/dsRZvXsKSHRjtcn9TsQIkS2K23YE1DxoYs20JMN6P7yOof/a4uA/4qv28F9ZdDAPs5+dgXSv8FmvxeYQHNs0AZtqPK7EybT+PdbfU/wKT7NfKiJlnu2TLmVhzmdOwCvz+A+htv5Zv/59uzwNeAoa7bE+f/9/enYfJVVdpHP++EDQkURFBBxw1DgIKAhFCNCAYFXFjkV0FNUQUXFDEiCguIDAgA2JAVhWCbIOCbEYJgoSEIEtYQiAILkQYQQVlCxDWd/44v6JvKlW9JFXV3enzeZ48VFfdqnuq6aq6de75nUO0sPhQ+flrxHet1YA1ie9xV5W/9Xvb/dpvEM9k4rvIuuXnE4kChZOJ466Ovhct5XPK10T3seRrovexDPrXQ/7Lf/mv/f9k5+qU3pC0HjANOML2qYpenq8BnrS9QNIeRNuB/yQ+vD/prqFay7rvVYAjbe9Tq1gjelt+gPgisj7wZuKL+I9s39mCfR5BfLn/BPEB822iYvphYur3yLK/jYDzHP3flnWfKxMV3z8kvkw9YNuKSedfJj78BWza4v2OJgaLbkJ8gX2MSF4/QVTlf9X23xQtJNYFZtj+07Lut+x7RaK/8yHA/Z14vqk1JL2JSHL9zPbF/R1PGjpKFdEI4ouQgONcZgeUFVLPlsvDiKTwZOAeMZ9eAAAczklEQVRjjjYhnYivVom1r+3fNtnm3cQX33WImQPzG23X4ri2Bo4iEoYXO5br1m/zEuJEwHeB3TrxO5P0EeIz4E9EEvFu4AzbT1S26fjvqz8oWqjd2Km/1SYxyEP84Fgx90duMDujhfsYSSSTfkkkl17q6BWPpEOB7YgExmrEQNIP276nXfEMNpLGEW285hFJoMOIAd3vAA61fWb5DHgt8JTtf3YgponESd7Dif+31wAfIVoknmv7CUkrOlr6tDuWTxGr7W4oP59OtE2a5NLSQtHa8TRgl3a/pypa6f2AMp8BmEkUQ70XOKD8//pPYjXKXLd5dWOTeKYR7Z72J1rFvYv4fnme7T+2M55WyNdEj7Hka6JvsQzq10NKqf0ymd5LkjYhPqBvJs7YTgb+RSSab7T99bLdBsRQ0mX+gC7LiVYj3tTPBH5u++hy2wrEl/1FLsPvWvEFsCSMVyWWkR9KJB22JBIj9wMbEBXx+zt6iLeEpO2BrYlE/ZHE8rzDK7d/g0juf67F+30/UQ3+NaJdz7gSx1HEgdjWxGqAXVr5pVLS2sTv9g/EQcs1tqdUbm/L801Lp/61VQ66DgHmOYYPZwImdZykA4g2JBsBt9g+tsE2OxB/py05AdjLuPYnqnqPlrQmccL3MeAuR8/76rbD3NWrs50xbUVUFG1PJKunAYfZntlg2/HEydwFHYjrVcBZxEnb+ZImAZ8jWgxNqf/c6dTvq7/k++jQUt4fHiOGAZ4MPFtJqO9AVHJuAvzQ9u39FugAJWlzYBdgItEX+lRiZtO5xMmHuR2OZ31i5sA/gCdsH1u+F/2SGGw+rZPxlJiG2X5O0sbAF4BDbN9bipNWIT6r/tWhWI4g5l+NJxJyh0jalFgFsr3tWZ2IoxfxXE6suLyhk/G0Qr4mehVTvib6FsugfT2klNqrZT29l1eS1rF9t+2bJA0nKphrE8FPISrRp0ra0vZMxxCpVuz3g0TV6wLgQeBA4HhJi2z/yPYLkq4lzm4DyzYEo1LpeDLRHmFl4FtEYvukWqJX0gNE1X0rE9rvIp7rl23fI+lrwCxJT7mrf/O5xEHRMy3c79Zlv6sCO9k+qFQl/t72j8s29xNDYlr5fGtViH8hWlfcC3xP0rOOHsfQhuebll7ttVVONq1I9O47xva/ymvHmQBK/eA54PXErIe9JP0AeNr2NyRtBjxs+8J+iqvWS/J84K/lOknak0iuv7Z8iexUT/IVic+uO8pqr7tKHDNrCdxSkfUy21d0KCaI38soImk43/Zp5fN/NWLl2Tklub9qh39f/SLfR4cWR696gIWKYa+nSjq3JNTvBm52DJ9LFYqZLS/Yni3pBWCW7QvKe9ksSZfR5l7HjZT310eIVQUzFHMe5kn6BfDSTsZSe1+vnHy8k6hI/jxwYHmvebhDsdT+f32jfM/YG7gDwPaNks4iPqM6opfxjOxUPK2Qr4me5WtimWIZVK+HlFJnZDK9G4qhnz+XdIntj5YP6GeBmZUExX2S7qO1Sd4JRG/uPWzfIOlS4HGi5covSvLueGANYB1JL7P9+LLs0zEQZaGkM4gv69sCI2zvrxiqWPMGYM1yMLBwWfZZsQnwE9vTFUOnRhGJ/BMlLSJ6Uo8n+oiuQgs+6EuV4olEleIfgV8rVh+cxOIf3u8ikukr04KEeqlC3JtouTBf0meJ538JcISi1c0lRP/nlj3ftOwkHUWs0niWWEI6X9Iptp8t1RwpddrFxKqZKxVDmw4jhidBtIc6r5/iugo4X9JYYujW6ZL+C/g60bN8BDALOpc8tT0dXvzS9IikacSJ8GvKF9uViPf6GZ2IpxLXo5LOBiZJei2xhPhpYsjz+4BziM/dq8v2mWxOy6Vycnpv4H8k3UUci03o36gGDsUQ+FWJ+TW1IYbY/n1JAFFOCu5GtEVo6/De+nhc2lTY/rakZ4gTvV+StBD4GDHkvKPxVE6UrmD7KUlfIk5Qjmt3lWmj/18lnmfKMeOnJf0fsfp1K2ImxkCJ571Ey4sBLV8TfY8nXxNLFcugeD2klDov27w0oSV7Ob7E9sfLbSvbfqpc3omoGt/Z9l9btO+3AP9h+ypJ/0G0lplDDLwQ8eViHvEGv2urquHLvvcnPuwvBT5DDDp7ppyt3Y8YTPIJt7Cfafkgf4mjJcC1RDuZPxPP759EVeNmRE+3VlX+vx943Pa1iirFQ4k+cidWDjQ+Qyx/271Vz1cxNf5XwHdt/65cdxExEG8Ykcx5gRj42bLnm5aNpFcSSa0PO1aF7ET8fT4IHOXSozqlTlK0SDgcuJZoV3Um0arqHKIXZ799wEvaFjgOONP2d8p1PwF+Y/uC/oqrStL3iC/X3y+v635pMVI+F7YhvrA9Ynv/cv004oTv85lET0OFpK8QJ97el8dAQdKOxEyfv5V/c4Cpth+rHLN2bEZGd/FUtnkPsBawHnCqWzDPaWniqVWdlkKkVwL7EXNGHuyPeCrbnEOcOF0b+Kzb2J96oMXTCvmaWPp4hvprYiDFklIa3DKZ3g0t2cvxadu7V27/FPBFYE+3qZejpIOI/0+HSdqLGEB2PDGkbJTth1q8v7WISscjJX2VSDKfbvsLikElx7T6uSp6x51PtDyZXqoY1wH2BK6zfbGkV9pueYV25YDiA0RF5/tKleJIoif96a0+2JG0DzGMZjpRhTiaSIa90fbkss0qrustnPpHqVBYhRhGs7/t35aKlwnEEOBrbZ/fjyGmIawkhCcCX7B9qWJQ5Z9s39fPcdW+xB5MJPwheoF/1B3s396dclLsK8AED4A+5LXPo3L5k0Sf1a1buAospQGtnLj+OTFD4Lb+jmcgUKyaOYtIds0u71vvIFbEHmX70cq2bZ+R0Zd4yvZtnfOwFPG8WBDV3/GUZOZIL+Pq4sEUTyvka6Ll8QyZ18RAiiWlNPit0N8BDGS277e9sCSs9waGK/pm1arHRwK7tSuRXmI43PZh5fJPgHWAl9te1OpEevEUsG6pyt6H6Jk+WtKuwKfb8VxL5dFk4O3AG8t1dxNDQF5RNmtLYrmWuLB9GTGkZhvFVPUniP5x7agaOBf4DfBuYGXbu9s+AVivVMmTifT+U5LnL3J4mBhKu5ekjWw/A1xDnNR6Tz+EmVLNj4EdbF9afr66vxPpALafs/0zYs5IrRJrz4GSSAcoFfL3E7NP+l0lkT6JWPG2dybS01BSPmu3zUT6El5OVEgCXEiscFyJaBWBpHGS3mL7wg69x/YUz6aK4YbQmTkPfYln0QCI5+2Sxjj6NHciUTfQ4mmFfE20Lp6h9poYSLGklAaxTKb3kmOq9d7As4pejhcDF9r+S7v2WZ/UK2dPVyeWJLWFYxjUfcC3iSrc7wHHEEM5X+j2zsvmN0Ql+B6SPi3p08BGwOwSVyeWUMwFPky00qFdz9f2o7bPBvZy13L+TxLVz9kupJ/V/tYkHSDp25WbZgA3AZ+TtLHtJ4m+++soeh2n1HG273MMyG7r+9bSsn2z7W/a/qoHUMuGyu9rV9sL+jmcelcC2w2k31dKnWK7E4mdQcPRRu4HwI6Stijv8dcAtwJbKmbtbE6bik6WMp53Eicq2378Pkjj2Qz4ezvjGKjxtEK+JpbLeDryNziQYkkpDX7Z5qWP1A+9HBUDQPcA9qfNlfBlf68DXm37pvLzi0vP262cJd+ZmHA+tdPJBEk/Bw7oZHKlVCFOJv7fZvJkAJD0eaIy9O/AFba/Wa5fnxig+BHgFOJgfTTRSz3fTFNKKaXUUpKGA7VWj2fZnlmun0GsGv1zxpPxDJZ4WmGgPaeMZ/DEM5BiSSkNbsP6O4DBpPRy/BDRw7STSc8XgAeAHW3f1e6dlRYB90kxwKWTlY62byYGrnZU5bnu2ul9E1WIMwdS+4PEQqJn/3zgPElH2j7Q9h2SFgB/BN4HPAdsb9udPOmUUkoppaHB9iJJZwMGviHpzcRwvNWJ45WMJ+MZNPG0wkB7ThnP4IlnIMWSUhrcsjK9jyQNzyWoKS3/JI2w/aSktYGfANfbPqDctqLt5yvbtnWYUEoppZSGNsXw882JtpOLgCm2b8l4Mp7BGE8rDLTnlPEMnngGUiwppcEpk+kppdQDxcDhE4ArgFHAAtun9m9UKaWUUhpqJK1ItDoeEKvhMp7uZTztN9CeU8bTvYEUz0CKJaU0uOQA0pRS6kZpAXQnsCsxJHdr4LT+jSqllFJ/kLSdpANb+HgzJI1tcP1YSceVyxMl/ajJ/duyLF3SryWt0sM2zWIfI+lD7Ygrge3nB1LiJ+PpXsbTfgPtOWU83RtI8QykWFJKg0v2TE8ppW5UBot+GbgRmGD7ufpWLymllJZ/ti8BLunAfuYAc9q9n272vyzJ8DHAWODXLQonpZRSSimlASMr01NKqXdupyuRPiwT6Sl1nqQFklZbyvtOlLRmKx4rLZ8kjZb0B0lTJd0t6WxJW0maLemPksZVq8TLdsdJulbSXyTt3MPjf13SPElzJR1ZuWkXSTeUfW5Rtp0g6VcNHuONkn5fHuewHvZ3gqTtyuULJZ1WLk+SdHi5vEfZ962STilL3hd7fUj6tqS7JF0j6VxJk5vFXvrQfg/YrTzmbt3/1lNKKaWUUhpcMpmeUkrdkCQA2+dVKtJz2GhKg89EYM2eNkpD3puAY4A3l38fB94JTAa+2WD7Ncrt2wBHNrgdAEkfBLYH3m57I+Coys3DbI8D9iPaiXVnCnCS7Q2AB3rYdhawRbn8WmC9cnkLYGaZB7IbsLntMcDzwO51cW8K7ARsBHyQqDivWix2288A3wHOsz3G9nk9xJhSSimllNKgksn01Db1PTNb2WdU0n6SRrTisVLqTqXNS+3nrEhPQ14vK3jHlQraW0rl7rrlvl+pVMhuIOn2Zu/nkl4l6XJJd0j6CaDKbc0qahdKOrbc50pJq5eK4bHA2WX7lcvD7Cvp5lLl++Z2/s7SoHGP7Xmlh+odwJXlc2AeMLrB9hfZfsH2fOA13TzuVsDptp8EsP3vym2/LP+9qck+qjYHzi2Xz+xh21nAFpLWA+YD/5C0BjAeuBZ4L7AJcKOkW8vP/9VgfxfbXmT7ceDSutv7EntKKaWUUkqDXibTUzuNAV5Mptu+xHbTqq0+2g/oUzK9lmhJKaXUEj1V8P4B2ML224hK1f8u95sCvEnSDsDpwN61BGMD3wWusb0+cCHweoAeKmpHAnPKfa4mqmXPJ/pP716qZZ8q2z5ke2PgpBJ3Sk9XLr9Q+fkFGs8aqm6vBrf3ZZ/PN9lHPfe8Cdj+G7AK8AFgJpFc3xVYWBLjAs4or4kxtte1fXCbY08ppZRSSmlQy2R6qlUY3inpx6WS7/JK1V79tmtJukzSTZJm1Sr5JO1SqgvnSprZqGdmgz6jJ0m6rvQZnSDptBLH1Mr+TpI0p8R1SLnuS8RS/askXVWu+1ipLLxd0vcr918o6RhJc4Hxko6UNF/SbZKObs9vNKWUhoSeKnhfAfxC0u3AscD6AGX7iURV7dW2Z3ezjy2Bs8r9pgEPl+u7q6h9Aai1ljiLSPA3k1W1qVN+C+xZW4UhadWlfJzZwEfL5d2727C4jihAqCXTJ5f/AlwJ7Czp1bWYJL2hwf62lTRc0iiinU1PHgde1ovtUkopNSDp4Lr5FP0VR86XSSmlBjKZnmrWBk4olXyPEP0xGzkV2Nf2JsQXshPL9d8B3l/6gG7Xy56ZrySWGn8FuISuZMsGksaUbQ6yPRbYEHiXpA1tHwfcD7zb9rsVA+W+D7yHqIbfVNJHyv1HAteXuO4EdgDWt70h0O3grpRSSt3qqYL3UOAq228FtgWGV7ZfG1jI0vcw70tFbXdVvFlVmzrC9mXEsc6ccgJoaZMkXwa+IGke0Qe9J7OIvuZ/Am4GVi3XUVrTfAu4XNJtRMJ/jbq4byxx3wb8hjhZ9mgP+7wKWC8HkA4d/ZX4qxbq9PF+fYq3vnVlSimllIa2TKanmnts31ouN6zQKxVJmxGVhrcCp9D1pWs2MFXSZ4DetlO5tFLF+I+6Csfa/neVdDNwC5FoX6/B42wKzLD9YBkMeTZRzQiRILmgXH4UWAT8VNKOQLO2AimllJbdK4C/lcsTa1dKegVwHPE+/arSz7yZmUT7mNoAx1eW67urqF0BqD3mx4FryuWslk3dsr2gnPyp/TyxtAh68TbbU21/sf728vOoHh7/SNvrlRNA3yzXTbA9p1x+yPbocnmG7W3K5eo+77E93vYGtr/Vi33+1Paa5fKztkfa/mXl9lrRw4a2N7F9Xbl+tO2HymZH214HeD/wBuI4sbvY/2170xxAmpYji7WurJKUJ2JTr0gaKWlaWcl9e1m5/WLlt6SxkmZU7rKRYvbMH8t3bCStUVaB31oeY4ty/RKrucv1CyQdUbafI2ljSdMl/VnSPmWbCeUxp0m6S9LJkpbIE6nJrJqUUhqKMpmeaqoVhs0q9FYAHqlUAo6x/RYA2/sQ1U2vA26S9Ko+7LNa0Vj7eZikNxKVW+8tleTTWLyysTcW1QZGlkT7OOB8YpnyZX18rJRSSr13FHCEpFtY/DPlWGIl1N3Ap4Eja0nxBg4BtpR0B7AjcC/0WFH7BDCutJd5D9FyDGAqcLIWH0CaUurZqaWI4mbgAts393dAqTMkfbK0Rpwr6UxFa8jfleuulPT6Bvf5UqWl4v+W65oNpJ4o6SJJvy1Jvy9K2r9sd12tHZKkGZKmVBKI4xrsd3VJF0i6sfzbvIen1yhR+bPK6lYUw7W3Z8nWlQeX38ds4Mxm+y7J09NKAvKW8lhp6PoAcL/tjcqJ256+i25IHMeMB76jWI39cWB6mRezEVArhltiNXflce4t288ijoV2Bt5BHGPVjAP2JQrX1iKOuV6k7mfVpJTSkJNn0lOv2X5M0j2SdrH9C0kCNrQ9V9Jatq8Hri/Vg69j2asAX04kRR6V9Brgg8CMclvtsR8CbgCOK2f1HwY+Bhxf/2CKyvoRtn9dDn7/sgyxpZTSkGV7AbBYBW+T29ap3O1b5fZJlW3vIwaZNtvPv4Ctm9x2Hl290etv27/BdRfQtVIJKiuwSnXthGZxpNRbkjYg5gFUPW377YN1n7Y/3orHSYOLpPWJ9+3NbD9UEttnEC22zpA0iVhl9JG6ux4IvNH205JWKdfVBlI/J2krYiB1raXkW4G3EQUzfwK+bvttko4FPgn8sGw3wvYYSVsCp1H5DCqmAMfavqYk+acDb+nmKW5IJBRHArdImgb8lGg/eZFiFdVmwKeIVVFjaytEJB1MJB3fafspSec02fdBwO9sTyq/ixskXWH7iW7iSsuvecAxivlev7I9K75ON3WxY2D6U4o5YeOAG4HTJK0EXFRZWb6rpM8S+Z01iL/P28ptl1T2P8oxgPpxSdXX6A22/wIg6Vxi3syLK69YfFYNwMrAP5fqt5BSSsuBTKanvtodOEnSt4CVgP8F5gL/I2ltoo/tleW6e4EDSzXTEX3dUUnS30IcgN9HtJKpORW4TNL9pW/6gUSPTgHTbF/c4CFfBlwsaXjZbolkS0qSFva0bL4F+9gHeNL2z9q5nyb7nghcbvv+Tu87pZSWd7bnES0hlut9piHhPcAvau1+bP9b0ni6KlbPJFYg1bsNOFvSRcBF5bpXAGeU7womvkPUXFVJ7j0KXFqun0ckvGvOLXHMlPTyShKwZiuiV3/t55dLGmV7YZPnt0Si0vZFkk6UtDqR7L+gnABodP9Lyv2b7ps4GbyduvqzDwdeT8xxSkOM7bslbUy0DDpM0pXAc3R1C6hfgV0/88Xl739L4MNEi9Uf0DVcelPbD0uaWvdY3a4Gb7avup9rs2q+0cPTTCmlISGT6alRheHR3Wx7D7FErf76HRts/m+in3nV1LL9xG72P7HR5br9HU+l+tz2uZSD7LrtRlUuP0Cc0U+p7SStWGsxVM/2yf21b6J39e3EEN+UBgRJexKDFatm2/5CXx+r3SfDUkopdevDxEyMbYGDysqJ2kDqHSSNpmulKfQ8zLqmp2TfCsA7bC/qZZzNHu9nwB7AR4E9u7l/tbq84b7LKt6dbN/Vy5jScqy0afm37bMkPQLsBSwgKr5/Q9dqjZrtJR1BrJ6YQBSpvQH4P9s/lvRSYGOiiK3Zau7eGqdosfpXop3LqXW3X0kUpR1r+59lpcrLbP+1j/tJKaXlQvZMTymlJiR9rfS+vE2LD/O5SNJNiiE/n61cv1DSMZLmAuPLz4creo1eVw5wKb02J5fLMyR9v/TTvFtdg4RGSPq5ou/ohZKulzS2m1jr9/2dEvvtkk5V2BkYS1SM3SppZUmbSLq6PJ/pktZoto+U2sX26XXzOMYsTSI9pZRSy/wO2EVlDlJJnl1LJJkhVqvOqt5BMbTwdbavAr5OVKSPoslA6j7arezjncCjth+tu/1youdzLZaeVmtsL2l4eX4TiPYZEIU/+8GL8zmg59aVzfY9Hdi3JNWR9LYeYkrLtw2IVj+3At8FDiP6lk+RNIfoQ151G7Hy+jrg0LKqdAJQW729GzDF9lygtpr7HBZfzd1bNwI/IlZN3ANcWL2xh1k1KaU05GRlempI0glA/eCeKbZP7494Uuo0SVsDaxOrGQRcImlL2zOBSWW588pE78ALSm/nkcD1tr9aHmMkcJ3tgyQdBXyGOHCuN8z2OEkfIg6utwI+Dzxsez1Jb6VrwFAz9fueb/t75fKZwDa2z5f0RWCy7TmKfovHA9vbflDSbsDhwKQm+0gppZTSEGD7DkmHA1dLep5I1u0LnC7pa8CDLFm5vSJwlqLfuIDjbD9SjoHOULSJnLaUIS0qCcSVaHyc8iXghJLoGwbMBPbp5vFqicrV6EpUYvsfku6kq0UNZbvuWlc22/ehRM/328qJhnuAbXr5fNNyxvZ04gRLvXUabHtwk8c4g5hdUH/9xCbbj65cnkpZJV69rZzrecz2En+bdfdvOqsmpZSGGtn1K9xSSmnoUumZLuloYtr9I+WmUcARtn+qGDy1Q7l+NPB+29dJeg54aa3FiqSngeG2XRLV77O9V7n/QttHS5oBHGR7dqlcn237TYpeo1NKdReSbgY+WwYlNoq7ft87AQcAI4BVgeNtH1n2V0umv5WoMqsN410ReMB2w4GPKaWUUkqdVj126cC+RhD92jduUP2e0nJH0gTi9ZUnelJKqZeyMj2llBoTkTw/ZbEr44BzK2C87SfLF7zakJ9Fdb3Kn3XXGcvnaf6e+3QvtunJokoifThwIjDW9n0leV8/1AjiOd5he/xS7jOllFJKabkgaSvgp8CxmUhPQ4XtGfS9x3pKKQ1p2TM9pZQamw5MkjQKQNJrJb2a6Pv5cEmkvxl4R5v2PxvYtex7PaLPYm/VEucPlfh3rtxW7ft5F7C6pPFlPytJWn+Zok4ppZRSaiHbE5amKl3SnmVGTPXfCd3s5wrbb7D9w2WLOKWUUkrLs6xMTymlBmxfLuktwO9LL8GFwB7AZcA+pZ/mXcRQoHY4kegvOp8YKHQH0KsqqdKf9MfA7cDf6RqqBdEr8WRJTwHjiUT7caW/6TCit+cdrXoSKaWUUkr9ocx6ynlPKaWUUmqp7JmeUkoDkKQVgZVsL5K0FnAFsK7tZ/o5tJRSSimllFJKKaUhKSvTU0ppYBoBXCVpJaK3+eczkZ5SSimllFJKKaXUf7IyPaWUBhFJ1wMvrbv6E7bn9Uc8KaWUUkoppZRSSkNFJtNTSimllFJKKaWUUkoppR6s0N8BpJRSSimllFJKKaWUUkoDXSbTU0oppZRSSimllFJKKaUeZDI9pZRSSimllFJKKaWUUupBJtNTSimllFJKKaWUUkoppR5kMj2llFJKKaWUUkoppZRS6sH/AwMkySdi4lC/AAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1512x1512 with 49 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "k22Qa5bua74F",
        "outputId": "ca57ddf4-be4d-401b-f67b-4dbc97d57e2b"
      },
      "source": [
        "plot_evaluations(result=gbm_, plot_dims=dim_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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zRCxMLi0Cdq6kj9WSrgS+DhwqqRFQvL2Bm5mZmZmZVcaJfhlJsjcGYPDgwZHncCx3lkbE4KoqSWoFjAO+FxGrJJVei4iQVNnfyOnA14DREbFIUnfgV9sZt5mZmZmZWaWc6JtVQFIxqST/gYj4W1L8qaQuEbFQUhdgcUX3R8Qi4Oa084/wGn0zMzMzM8sxr9E3y0Cpofs7gVkRcXPapceBc5Ljc4DH6jo2MzMzMzOzynhE3yyzg4CzgGmSpiRlPwZuAMZKGg18CJyWp/jMzMzMzMwycqJvlkFE/AtQBZdHVHavpPERMULSjRFxefajMzMzMzMzq5gTfbPs6yLpQOBESQ9S5guDrY/tMzMzMzMzywUn+mbZ9zPgp0BX0jbjSwQwvM4jMjMzMzOzBsOJvlmWRcQjwCOSfhoRv8h3PGZmZmZm1rA40TfLkYj4haQTgUOTohcj4sl8xmRmZmZmZoXPj9czyxFJ1wPfBWYmr+9K+mV+ozIzMzMzs0LnEX2z3DkO2DciSgAk3Qu8ReoxfWZmZmZmZjnhEX2z3GqXdtw2b1GYmZmZmVmD4RF9s9y5HnhL0gRSj9g7FLgivyGZmZmZmVmhc6JvliMR8VdJLwL7J0WXR8SiPIZkZmZmZmYNgBN9sxyKiIXA4/mOw8zMzMzMGg6v0TczMzMzMzMrIE70zczMzMzMzAqIE32zHJBUJOmdfMdhZmZmZmYNjxN9sxyIiC3AbEnd8x2LmZmZmZk1LN6MrxLTPl5JjyueKlf+wQ3H5SEa2wHtBMyQ9DqwdmthRJyYv5DMzMzMzKzQOdE3y52f5jsAMzMzMzNreJzom+VIRLwkaXegd0T8U1ILoCjfcZmZmZmZWWHzGn2zHJF0AfAI8MekaDfg0fxFZGZmZmZmDYETfbPcuQQ4CFgFEBHvAZ3zGpGZmZmZmRU8J/pmubMhIjZuPZHUGIg8xmNmZmZmZg2AE32z3HlJ0o+B5pKOAh4GnshzTGZmZmZmVuCc6JvlzhXAEmAacBHwNPCTvEZkZmZmZmYFz7vum+VIRJRIuheYSGrK/uyI8NR9MzMzMzPLKSf6Zjki6TjgD8BcQEBPSRdFxD/yG5mZmZmZmRUyJ/o7iB5XPJWx/IMbjqvjSBoGSXcBxwOLI2JAUtYeeAjoAXwAnBYRyytp5jfAERExJ7l/D+ApwIm+mZmZmZnljNfom2V2D3BMmbIrgPER0RsYn5xXZvXWJD8xD1idtQjNzMzMzMwy8Ii+WQYR8bKkHmWKTwIOT47vBV4ELi97r6RTksNJkp4GxpJao38q8Eb2ozUzMzMzM/uCE32z6ts5IhYmx4uAnSuod0La8afAYcnxEqB5jmIzMzMzMzMDnOiXI+lC4EKAojad8hyN5VBHSZPSzsdExJjq3hwRISnjDvoRcd52R2dmZmZmZlZLTvTLSJK9MQBNu/T2o9AK19KIGFzDez6V1CUiFkrqAiyurLKknsC3SW3eV/pvLSJOrGmwZmZmZmZm1eVE36z6HgfOAW5Ifj5WRf1HgTuBJ4CS3IZmZmZmZmaW4kTfLANJfyW18V5HSQuAq0gl+GMljQY+BE6ropn1EfHbnAZqZmZmZmZWhhN9swwi4owKLo2oQTO3SroKeA7YkNb2m9sTm5mZmZmZWWWc6JvlzpeAs4DhfDF1P5JzMzMzMzOznHCiXws9rngqY/kHNxxXx5FYPXcq0CsiNuY7EDMzMzMzazga5TsAswI2HWiX7yDMzMzMzKxh8Yi+We60A96R9AbbrtH34/XMzMzMzCxnnOib5c5V+Q7AzMzMzMwaHif6WeS1+5YuIl7KdwxmZmZmZtbwONE3yxFJq0ntsg/QBCgG1kZEm/xFZWZmZmZmhc6JvlmORETrrceSBJwEDM1fRGZmZmZm1hA40a8DntKfPTvqZxkRATwq6SrginzHY2ZmZmZmhcuJvlmOSDol7bQRMBhYX4371kREq5wFlurjYmBdRNyXy34q6Ptc4LmI+KSu+zYzMzMzawic6Jvlzglpx5uBD0hN368TkooiYkumaxHxh3z1DZwLTAec6JuZmZmZ5YAT/TyqaBr6jtBnfZsqXx+n9EfEedvbhqQfAqcBTYG/R8RVSfmjQDegGXBrRIxJytcAfwSOBC6R9AxwK3A88DlwUkR8KunnwJqI+LWkF4GJwBFAO2B0RLwiqQVwDzAAmA3sClwSEZMqiLVs38NJfdnRHPgPcBHwFVIzGx6Q9DkwDOgP3Ay0ApYC50bEwu397MzMzMzMGion+mZZJulnlVyOiPhFNds5GugNHAAIeFzSoRHxMnB+RCyT1Bx4Q9K4iPgMaAlMjIjLkjZaAq9FxP+TdBNwAXBthu4aR8QBkkYCV5FK1r8FLI+I/pIGAFOqCLls3zMj4prk+H7g+Ih4RNKlwA8iYpKkYuD/SH0BsUTS6cB1wPnV+YzMzMzMzKw8J/pm2bc2Q1lLYDTQAahWog8cnbzeSs5bkUr8Xwa+I+m/k/JuSflnwBZgXFobG4Enk+PJwFEV9PW3tDo9kuODSc0GICKmS5paRbxl+z5C0o+AFkB7YAbwRJl7+pKaMfB86sEEFAEezTczMzMz2w5O9M2yLCJ+s/VYUmvgu8B5wIPAbyq6LwMB10fEH7cplA4nNeI+LCLWJVPvmyWX15dZG78p2fEfUol4Rf/mN1SjTlVK+5bUDPg9MDgi5idLBZpluEfAjIgYVss+zczMzMysDCf6VtDysQ8CgKT2wPeBM4F7gUERsbyGzTwL/ELSAxGxRtJuwCagLakp9esk9QOGZjP2NP8mtT/ABEn9gS/V4N6tSf1SSa2AUcAjSdlqoHVyPBvoJGlYRLyaTOXvExEztj98MzMzM7OGyYm+WZZJ+hVwCjAG+FJErKlNOxHxnKS9gFeTae1rgK8DzwAXS5pFKlF+LSuBl/d74F5JM4F3SE29X1mdGyNihaQ7SO2uvwh4I+3yPcAf0jbjGwX8VlJbUv9N+t+kLzMzMzMzqwV9MavXymrapXd0Oed/8x1GpWq6q/yOvut+tuL/8MbjJ0fE4Kw0VoakElJT4TcD6f/ARGozvja56DfbJBUBxRGxXtIewD+BvhGxMc+hmZmZmZlZJTyiv4Orj4+Vy6SmCXp9i78mIqJRvmPIkhakpu0Xk/qS4ltO8s3MzMzM6j8n+maWUUSsJvXM+21Imgg0LVN8VkRMq5PAzMzMzMysUk70zaxGImJIvmMwMzMzM7OKOdG3WsnXbvZmZmZmZmZWuUJZS2xmNSSpl6Q7JT1SdW0zMzMzM9tRONE3q6cknSzpDkkPSTq6zLW7JC2WNL1M+TGSZkuaI+mKytqPiHkRMToXsdcHdflFhr80MTMzM7P6xIm+WUJSO0mPSHpH0ixJw2rZTsYkPLlWk0T80Yi4ALgYOL3M5XuAY8q0XQTcBhwL9AfOkNRf0pckPVnm1bk2760ikrpJmiBppqQZkr67HW1V9fm9K2m9pIVJX1dnaqc6X2Rk8XdV0F+amJmZmdmOxWv0C5TX0NfKrcAzETFKUhPgBEmzgSLgT8BdwOfJbvQASNozIuaUaece4HfAfemFaYn4UcAC4A1JjyftX1+mjfMjYnFy/JPkvlIR8bKkHmXuOQCYExHzkv4eBE6KiOuB46v1CdTeZuCyiHhTUmtgsqTnI2Lm1grJlwvZ+vyWAy8CZwF/ljQCWJFW/UfA/Dz8rszMzMzM8s6JvhkgqS1wKHBuUrQFuIm0RA/YABwvaWREbJB0AXAKqRH0UhUk4VDDRFySgBuAf0TEm9V4G7sB89POFwAV7pAvqQNwHTBQ0pVJHLUSEQuBhcnxakmzknhmplU7DLg4y5/fyXzxJcPEtPd2KvB/kkYm5zn9XZmZmZmZ1SdO9M1SegJLgLsl7UMqYX6/TKLXDHgWeEjSw8D5pL4IqK4aJeLAt4EjgbbJaPQfatBXlSLiM1LLArIqSZwHAhPTyyPiYUk9ycLnl4y4Xwx0AW5JT/LT+tobeAdoAewP7F2bvhJ19qWJmZmZmdn2cqJfhqQLgQuT0zUf3nj87HzG01Dpxpx30VfSpLTzZ4FBwLcjYqKkp4AOadcXAEMi4tIk6b8d2CMi1uQqwIj4LfDbGtzyMdAt7bxrUlZnJLUCxgHfi4hVZa9HxE3Z+PwiYouknwCHAAdIGhAR08vU+bmkfsBIYECOf1c5+dLEzMzMzKw2nOiXERFjgDH5jsPqlqRdgDPTRoZf5YsvfNLrHQIMAP4OXAVcWoNucp2IvwH0TkbNPwa+Cnwti+1XSlIxqST/gYj4WwV1sv35vU/qS5hjgLJPIKjPvyszMzMzs5zxrvtmQEQsAuZL6psU9SC1Tn+rrkAJqS+BTgLOAzpIurYG3ZQm4slmf18FHq9NvJL+SurLiL6SFkgaHRGbSSWzzwKzgLERMaM27dciHgF3ArMi4uYK6gwkO5/ffpI6kfr8niU1/f+dHPW13b8rMzMzM7O65kTf7AvfBh6QNBXoRCp/TU/0XgdOi4i5EVECnA18WLaRTEk4QDYT8Yg4IyK6RERxRHSNiDuT8qcjok9E7BER19Wm7Vo6iNQO+MMlTUleI8vUaUF2Pr9xpEbxdya1W/7zEfFkjvqq8y9NzMzMzMy2lyIi3zGY1UtJovq/pB6pdlcdJ85mZmZmZma14kTfzMzMzMzMrIB46r6ZmZmZmZlZAXGib2ZmZmZmZlZAGlyiL6mXpDslPZLvWMzMzMzMzMyyLaeJvqRmkl6X9LakGZKu3o627pK0WNL0DNeOkTRb0hxJV1TWTkTMi4jRtY3DbEci6cJ8x2BmZmZmZnUr1yP6G4DhEbEPsC9wjKSh6RUkdZbUukzZnhnaugc4pmyhpCLgNuBYoD9whqT+kr4k6ckyr87ZeVvW0Eg6IRf1q1OvqjpVXN8hE/2aft47Sl9mZmZmZnUhp4l+pKxJTouTV9lt/g8DHpXUFEDSBcD/ZWjrZWBZhm4OAOYkI/UbgQeBkyJiWkQcX+a1OEtvzRqemiaD1a1fnXpV1SnERLUu31Mhfn5mZmZm1oDl/PF6yYj7ZGBP4LaIuDxDnR8BBwIPA5cCR6V9QZBerwfwZEQMSCsbBRwTEd9Izs8ChkTEpRXE0wG4DjgK+FNEXJ+hzgnACS1bFV3Qa89dKC7qhCiq2RvPkrXrNzJ/yQpKkt9TkRrRrXM7WjQtzks8hWLy5MlLI6JTdeu3adMm+vTpU+32V65cSdu2bbNSr6o6lV1fsmQJnTpV+23WG9X9/LJh8uTJqyKibjozMzMzM6sDjXPdQURsAfaV1A74u6QBETG9TJ2bJD0I3A7skSnJz2I8nwEXV1HnCeCJvb7c9II7H2tM8+Kd2XuXJ5Fy/nFto6QkOP6nd9J82erUNAgBAb12ac+4q86p01gKjaQPa1K/T58+TJo0KVfhWB5Jei/fMZiZmZmZZVOd7bofESuACWReZ38IMAD4O3BVDZv+GOiWdt41Kcuazze9w4rPX8hmk9XywafLWLhsNZubwqaWyasFzF28jEXLVtd5PGZmZmZmZlb/5XrX/U7JSD6SmpOaLv9OmToDgTHAScB5QAdJ19agmzeA3pJ6SmoCfBV4PBvxp9uweX62m6zSTq2aE02hpJjUaD5AI9jcHIqKVNmtZmZmZmZm1kDlekS/CzBB0lRSCfnzEfFkmTotgNMiYm5ElABnA+WmVUv6K/Aq0FfSAkmjASJiM6l1/c8Cs4CxETEj22+kdbMh2W6ySju1bkHTlk3KXxC88X5WJy2YmZmZmZlZgcjpovOImAoMrKLOv8ucbwLuyFDvjEraeBp4upZhVqlzq7Np2WRA1RWz6NPPVvP3CVOhJLebJZqZmZmZmVlhqdvd5XYwmza0461Xr2C/vUaye/u66/eDT5ZxwS/+yqq1G9jYEmi17TT9Ns2bcli/nrVqe/aMj1m7Zj0D9u1OE+/cb2ZmZmZmVnCc6Ffi08VNuP/BNdzPWC742sGcferQOun37scnsmrtBgCK10I0CjY3ByR279iOX5x2NC2bZZjSX4nPlqzmqv/5C+/N+gSANm1bcOX1oxg0ZI9sh29mZmZmZmZ5VGe77u/o7n3kNVau+rxO+nrvo8WlxwKaroYWS+Ab+w/iyR+dy6CeuyYAht8AACAASURBVNW4zdt//Y/SJB9g1cp1/PLKh9m4YVM2QjYzMzMzM7N6wol+NW3cuJkPFnxWJ3317t6pXJkC9u/bDal2u+2/9tI75cpWr/ycaW/W6HHyZmZmZmZmVs850a+mxo0b0W3Xneqkr3NPGELrFk23Kdu/f3eGfal26/IBmrXIPNW/RaumGcvNzMzMzMxsx+Q1+tV02gn70b5dy4zX5s1eyJ9ueY6ZUz6iS7edOOOCwzn06Nrv0t9ztw7cf+1Z/O2Ft1m0dBWD9urGcQf3p1Gj2o3mAxz3lcE8eNcr25T16rMLe32pW63bNDMzMzMzs/rHiX4l2rZuzoiD+3H4gX04fFifjHVWrVjH5RfczeqVqfX777/7Kdf/aCwtWzVjvwP3rHXfXTq24ZLTDqn1/WWdfdERRMBT4ybx+doNDD20L9/60cistV+f/OuxSbz48Gs0KmrEiDMOZMgx++Y7JDMzMzMzszrjRL8Su3Ruw88vO77SOhOenlqa5G8VETzx0MTtSvSzrahxEedfeiTnX3okJSUlNGq0467aKCmJCmc3PHDDY9x37d9Kz18aN5GLbvwap1zyX3UVnlk5kr4L3A2sBv4EDASuiIjn8hqYmZmZmRWkHTfbqydWrVyXuXxF5vL6YEdN8t/9YDHfuuYhDjrzZk685I88/Oxb21xft/pzxt7yVLn7/nLjY366gOXb+RGxCjga2Ak4C7ghvyGZmZmZWaHaMTO+emTIoX0zlg89LHO51c7adRv4znUP89asBQAsWbaGm+95gWdemVlaZ/H8z1i/dkO5e1cvW8uKxavqLFazDLZOQRkJ3B8RM9LKzMzMzMyyyon+duqz926c9a3hNCr64qPc/+DenHzmsDxGVXhemPguK9esL1f+6Pippce79upM6/blN0zsuOtOdOjSLqfxWeGS1EzS65LeljRD0tVJeU9JEyXNkfSQpMyPtkiZLOk5Uon+s5JaAyV1Eb+ZmZmZNTxeo58FZ150BEefNIhZU+fTpetO9O6/W75DKjjr1meeer/2842lx02aNeH8q0/jt9+5h4gAoFFRI75x7ekUNS6qkzitIG0AhkfEGknFwL8k/QP4PnBLRDwo6Q/AaOD2CtoYDewLzIuIdZI6AOfVRfBmZmZm1vA40c+STru0pdMubfMdRsE6ZL89+O39L1KSJPBbHbb/thsejjzvcHrvuzsTkl33h58+jF4DutdlqFZgIvWt0ZrktDh5BTAc+FpSfi/wcypO9APoDxwPXAO0BJrlJmIzMzMza+ic6NsOYdfObbn8G0dyy30TWL9hMwAHD+rF10/cv1zd3gN70ntgz+3uU9KFwIUA3bv7y4IC1lHSpLTzMRExJr2CpCJgMrAncBswF1gREZuTKguAyqby/J7UVP3hpBL91cA4oPwfsJmZmZnZdnKibzuME4d/mSOG9GHm3EXs3KE1PXbrkNP+kmRvDMDgwYOjiuq241oaEYMrqxARW4B9JbUD/g70q2EfQyJikKS3kvaWV7Gm38zMzMys1pzo2w6ldctmDPlyj3yHYQ1URKyQNAEYBrST1DgZ1e8KfFzJrZuSWQEBIKkT3ozPzMzMzHLEu+5bwdhS4rzJsk9Sp2QkH0nNgaOAWcAEYFRS7RzgsUqa+S2pmQCdJV0H/Av4Zc6CNjMzM7MGrcGN6EvqBfw/oG1EjKqqvsGHsz/hs0Ur6DeoFy1aV7x/2PzVK/j91NeYunQRvdt15JtfHkLfnTrlPL6X5r3PTS+9wuylS9mjfXsuO+Qgju7TO+f9WoPRBbg3GZFvBIyNiCclzQQelHQt8BZwZ0UNRMQDkiYDIwABJ0fErDqI3czMzMwaIEXkbumxpG7AfcDOpKasjomIW2vZ1l2kdqxeHBEDylw7BrgVKAL+FBE3VKO9R6pK9AcPHhyTJk2qrErWzFq6hNsmvcbsz5ayd6fOXDp4KHu2z+0a9KqsX7uB60b/kTf+OR2A5i2bcumvzmTEaUPL1f1s/TqOefRulny+trSsVXETnjjhbHq2bZ+zGN9dupST7v0zm9JG84skxp75Vfbp0qXC+yRNrmpddrq6/FuwulXTv4Uatl3pH39ELMtFv2ZmZmbWsOV66v5m4LKI6A8MBS6R1D+9gqTOklqXKdv2mWkp9wDHlC1MRtluA44l9fiqMyT1l/QlSU+WeXXOztvKrg9XruC0cQ/y1Jx3mbN8GY+9+w6jxj3IojWrM9Zfvnwta1avz3lcf/nNU6VJPsDnazdw83fu4bOFK8rVHfvutG2SfIA1mzZy76y3chrjuGkztknyAbZEMHbq9AruMKtTk4FJyc+yL39zZGZmZmY5kdOp+xGxEFiYHK+WNIvUI6hmplU7DLhY0siI2CDpAuAUUol7elsvS+qRoZsDgDkRMQ9A0oPASRFxPakZAFm1fN3nTF2wiN3atWHPztkZcb9/2hTWbNq4TdnKDet5cMY0vjfkwNKyhR8v56ZrH2f61Pk0KhKHHrEX37/8eJq3yM3m3f9+qnySvmVzCa89+zbHnXvYNuUL1qzM2MaCNeW/FMimtRs31qjcrC5FxPY/59HMzMzMrIbqbI1+kqQPBCaml0fEw5J6Ag9Jehg4n9RmV9W1GzA/7XwBMKSSODoA1wEDJV2ZfCFQts4JwAl77rntxII/T5zCr557mQ2btwAwot8e3DxqJE2Lt+9jXFjByP0na1aVHkcEP7t8LB+8vwSAki3Bi/+cSdOmxfzgxydsV/8VadEq83r8TOUH7NyVB2ZPKVc+ZJduWY8r3VG99+TBqdMylpvVJ5JOAQ4mtYzplYh4NM8hmZmZmVmBqpNd9yW1AsYB34uIVWWvR8RNwHrgduDEiFiTq1gi4rOIuDgi9siU5Cd1noiIC9u2bVtaNm/JMq77x4TSJB9g/DtzufvVN7c7pmG7ZU6Gh3XtXnr87jsLS5P8dC88P53NaTFl08hzDi1XtlPnNgwbuW+58uN69mN4117blA3stCtn9i1fN5sO69WTi4bsT+NGqT/lIolzBg1kZN8+Oe3XrCYk/R64GJgGTCc1i+m2/EZlZmZmZoWq2kPRknYm9TioXSPi2GSt/bCIqHCn6eS+YlJJ/gMR8bcK6hwCDCD1+KmrgEurGxepZ1enZ8pVPc+6Vl6YPZdM+xaOf2cOFx96wHa1fWr/ATwz9z3+veCj0rLhPXpxQu9+peclJZk3TYySyBhXNhx79iGsXfU5437/HMsXr2Kfg/ty8S9Pp1mLpuXqNm7UiDuP/Aovffw+0z/7lD3bdeDIbnuWJuC59MNDD+HsQQOZvWQpe3bowK5tWld9k1ndGg7sFcnup5LuBWbkNyQzMzMzK1Q1mXN+D3A3qUfTAbwLPEQlj5SSpOT6rIi4uYI6A4ExpNbTvw88IOnaiPhJNeN6A+idTP//GPgq8LVq3lttrZpmXgdfUXlNNC1qzP0njeLljz7k3WWpXfcPTBvNB+i7167suttOfPLx8m3KDz6sH8XFRdsdQ0VGXXo0oy49mi2bt1DUuPJ+JHF4114cXmZkvy7s3KoVO7dqVef9mlXTHKA78GFy3i0pMzMzMzPLupoMt3aMiLFACUBEbAaqmjN+EHAWMFzSlOQ1skydFsBpETE3IkqAs/nif4ZLSfor8CrQV9ICSaPT4rgUeBaYReoZ11kfKTt2QF/aNi8/kv3V/ffJSvuSOGz3HlwwcHC5JB+gUSPx8+tPpUfPL55LP3hIL759WbkHEeREVUm+mVWqNTBL0ouSXiS1IWkbSY9Lejy/oZmZmZlZoanJiP7aZCO7rVNPhwKZt1pPRMS/AFVR599lzjcBd2Sod0YlbTwNPF1ZP9urbfNm3HX2V7jx2Zd548MF7Nq2DRcdcgD/1b93LrvdRs89OnPHny/iow+X0qxpMZ13aVv1TWZWH/ws3wGYmZmZWcNRk0T/+8DjwB6S/g10Ak7NSVT11N677sx95+X/LXffvWO+QzCzGoiIlwAktSHtv7sRsSxvQZmZmZlZwapJoj+D1DPv+5IapZ9NHe3ab2a2I5N0IXANqaeLlJD6b2gAdb+hhZmZmZkVvJok+q9GxCDSdoqW9CYwKOtRWd58uHg5D7/8NouWr+aAvt05edjeNCmuyZ9Jzc2Z+Qnr1qyn377dadIkt32Z5ckPgQERsTTfgZiZmZlZ4asyq5K0C7Ab0DzZIX/rmvs2pDbSa1DWf76R/zz9NquWr2X/4f3ZrVfnfIeUNbPnL+b8W8aybsMmAMZPmcMLb8/h9ktPIfUAhexavnQ1V19yP7OnzgegXYdWXP7rr7Lv0D2y3pdZns0F1uU7CDMzMzNrGKozfPpfwLmknk+f/oi81cCPcxBTvfXxvMVcftr/8dmi1B6EY67+OxdcdTL//Y0j8hxZdtzxzMTSJH+rie98xMR3PmLoXrtnvb8/Xv9kaZIPsOKzNVz/P3/h/hevoEnT4qz3Z5ZHVwL/kTQR2LC1MCK+k7+QzMzMzKxQVbnGPiLujYgjgHMj4oi014kR8bc6iLHe+NO1j5Ym+QARwV3XPc6yTyt9+EBObNywmaWLVxERWWtz7sLPMpbPqaB8e/3nnzPLla1asY5pk97PSX9mefRH4AXgNWBy2svMzMzMLOuqvSA6IsZJOg7YG2iWVn5NLgKrj97+z3vlyjZv2sL01+dy6Al1t1XBA396iUf+/Crr1m5g127tueSHxzJ42J7Vvn/j+k28+9YHtN+lLbv2/GLpQb9unfng0+Xl6vfrlpvlCc2aF7Np4+Zy5c1bNM1Jf2Z5VBwR3893EGZmZmbWMFR713xJfwBOB75Nap3+qUD253PXY5123alG5bnw/FNvc98fX2Td2tTs30/mL+PqHz7E0sWrqnX/v56YzJn9f8APRt7I+YN+zNVn/o7161JtXXDsENq2bLZN/SP22YPBvbtm900kRp4+pFxZjz67sNe+3XPSn1ke/UPShZK6SGq/9ZXvoMzMzMysMNXk8XgHRsTZwPKIuBoYBvTJTVj106hvjihXNmDIHuy1X886i+H5J6eUK9u4YTMvPT8jQ+1trViyihsvuIPVy9eWlr369BT+8qsnAei1SwfG/vgsLh45lJMPHMAvzzuWm0Yfn73gyzjr20cyavShtGzdjEaNxJAj9uKaP5yTk43/zPLsDJJ1+nwxbX9SXiMyMzMzs4JVk2eZrU9+rpO0K/AZ0CX7IdVfR506hGbNm/D43S+zavlaDhixN1/9ztF1GkPJlsxr8rdsKany3tefm8qmDeWnyr/y2CTOv+orAHRu14rzRgzmlSfeZNFL85j2OQw8pN82/bw5fT6fr9/I4C/vTovmTWr5TqCocRGjf3As5192DCVbSihqXFTrtnJt2scr6XHFU+XKP7jhuDxEYzuaiKi7bwPNzMzMrMGrSaL/hKR2wK+AN4EA7shJVPXYIccP5JDjB+at/8OO3ptpb324TVlRUSMOGd6/ynubNMuclDdNS9ZXLF3Nj/77Zua/92lp2RFf2Z8f3XYen3y6gu//YhwLFqbW8bds0YSf/8/xDBvUqzZvpZSkep3km2WDpAFAf7bd4+S+/EVkZmZmZoWqWlP3JTUCxkfEiogYR2ptfr+I+FlOo7NyjjtlMCeffgDFxanEuO1OLfjRNf9Nl65V7xMw9Jh9aNepdbnyY84+pPT44d89t02SDzBh3Bu89co73HzH+NIkH2Dtuo384tan2ZBhQz0z+4Kkq4D/S15HADcBJ+Y1KDMzMzMrWNUa0Y+IEkm3AQOT8w2kPQva6k6jRuKbPziWM79xGEsWr6J7z06lSX9VmrVsyi/HfZ/f/eDPzHx9Lq13aslJF43gpAu/2HtgxutzM9479T/vMXFK+cferVqznhmzP2HQl2q+gd6yRct5+o7xLHp/MQMO7seIrx9CcZPiGrdjtgMYBewDvBUR50naGfhznmMyMzMzswJVk6n74yV9BfhbZPPh7VYrbdq1oE27FjW+r9eXunHzs1eyfu0GipsVU1S07aSOXbp3ZPabH5S7r8vuHWk180NWr1lf7lrr1s3KlVVl4fuf8t0D/x/LP10JwLP3TGD8X17hhmd/QlFR9qbxr1n1OdPfmMdOHVvTdx/v5m9583nyhelmSW2AxUC3fAdlZmZmZoWpJon+RcD3gc2S1pN6xF5ERJucRGY51axl5mfVf+WbR/Kff0zZZtO+3Xp15rCT9uODki3cN+61bep/ea/d6N2jc437f+iGR0uT/K2mvDCdiU+9yYEn7l/j9jJ54dHJ/Pb/PcKGzzcB0H+/Hvz8T+fTum3NvyAx206Tkj1O7iC14/4a4NX8hmRmZmZmharaiX5ElF/cnUbS3hFR9TPeLGsWrVnNH954gzcXLqRHu3ZcOHgwAzrvvF1t9t6nO79+7DLG3f5PFn6whAFD9+TUS4+mafMmjD79QJo0KeLx56ay7vONHDq0N5ecfVit+pmTYRkAwJw3389Kor98yWpuuWIsmzduKS2bOfkD7vvNM1xyzSnb3b5ZTUTEt5LDP0h6BmgTEVPzGZOZmZmZFa6ajOhX5X5gUBbbs0qs2biR08Y+xIJVqwCYvvhT/jlvLuNOP4O9OnXarrb77Ls7V/5xdLnyoqJGnDtqGOeOGrZd7QP0HNCd2W+U3w+gZy3W+mcy+ZXZ2yT5W702foYTfasRSd2A+4CdST1tZExE3CqpPfAQ0AP4ADgtIpZX0MZBwJSIWAscDAySdGtEfJipvpmZmZnZ9qjWrvvVpCy2ZVV47J1ZpUn+Vus3b+aut97MU0Q1c/rlJ9N6p5bblPUf1ocDT8rOtP1WbZpnLm+dudysEpuByyKiPzAUuERSf+AKUk8j6Q2MT84rcjuwTtI+wGXAXFJfHpiZmZmZZV02R/S9QV8dmr9yZcbyBRWU1zdd++zK7W/+isd//ywL3/+ULx/Sn2NGD6eocXY24ht8WD926daeRfOXbVN+3JnbPxvBGpaIWAgsTI5XS5oF7AacBByeVLsXeBG4vIJmNkdESDoJ+F1E3Cmp/LQZMzMzM7MsyGaiX/CiZB2wBTWqdLuCOnFA166MmTwpQ/lueYimdnbevRMX3Pj1nLTduLiIX95/EWOufZxJL79D+46tOfm8Qzn+rIOq3YakC4ELAYrabN9yCKvXOkpK/8c0JiLGZKooqQepx4xOBHZOvgQAWERqan9FVku6Evg6cKikRoCfJWlmZmZmOZHNRH9jFtuqV6JkDZtX/pSS9U8Dm1HTQyluez0q2iVvMR3Roycje/fh6ffeLS3r27Ej5w/cL28x1TddunfgqjHn1fr+JNkbA9C0S2/PWClcSyNicFWVJLUCxgHfi4hV0herlZLR+sr+Rk4HvgaMjohFkroDv9rOuM3MzMzMMqp2oi8p00Z7K4EPI2JzRAzNXlj1xOb3KFn+LbbEeko2vFhaHBteYtPyb9Gk49/yFpokfnfc8fz7o494c+En9GjXjv/aszdNsvgM+mzYuGULd78ziefmv0vr4qac2WcgR3XrXedxrFy7nt/94z+8PHMebVt4nb7VjKRiUkn+AxGx9R/+p5K6RMRCSV2AxRXdHxGLgJvTzj/Ca/TNzMzMLEdqMqL/e1K76k8ltfHeAGAG0FbSNyPiuRzEl1+xATb8k0YBJWUvbZpCyaY5NCreMy+hbXVQ9+4c1D07O9Xnwvf+9ThPfzS79PzFT+Zx07BjOW3Pfeo0jm+O+TvTP1oEwMLlq+u0b9uxKTV0fycwKyJuTrv0OHAOcEPy87E8hGdmZmZmVk5Ndt3/BBgYEYMjYj9S61TnAUcBN+UiuPpCgkYZP6qCXa1QqY9WrOC52XP4YFnGJ4mVmrPys22S/K1+N+3VXIWW0VvzPi5N8s1q4SDgLGC4pCnJaySpBP8oSe8BRybnZmZmZmZ5V5MR/T4RMWPrSUTMlNQvIualr1UtVGXfoYp6osZ75SWWfLrm+Qn8efIUgtRncuo+A7j2mCPJ9Dcwf82KjG3MX7OCiMh4T0XWb9zMy1Pnsn7jZg75Ui92qsFj8pauXlftumZlRcS/qPjxoSMqu1fS+IgYIenGiKhoR34zMzMzs6yqSaI/Q9LtwIPJ+enATElNgU1Zj6yeiaKusOUjAFTUi8Y73VajRLUQjH9vLvdPnlJ6HsDYt6czdPdunNC/X7n6X+7QhSaNithYsmWb8sGdu9bos3tvwRIu+d+/8dmqVMLetLiIa78xkuEDq7dsYr89dqO4qIhNW7ZUXdksu7pIOhA4UdKDlPnCICLezE9YZmZmZlbIajJ1/1xgDvC95DUvKdsEHJHtwOqV4sE07vQPijv9k+KOT1Hc6XkaFffNd1R17oU58zKWj38vc3mHZi24fNDh25S1Lm7KT/ardBC0nOsfGF+a5ANs2LSFa+97ng2bNlfr/vatWvDjUUfQuFFN/tzNsuJnwE+BrqQ24/tN2uvXeYzr/7N33/Ftldfjxz9Hsry3neEMxxnOJItMshgJkATC3ntDoTSl0JZO2jJK15fSX2kpo+y9CZBAIWEkZO+997Rjx3tJOr8/JA9FsmPHdpw45/165YV17nOfe66tuj66zzDGGGOMMa1YvZ/oq2oJ1X+gHq6wyTI6njjbI0nPQ/gYRAQJ69bSGbWo+MiIkPGEWuIAt/YZxuj2Xfjfzo3EuiK4oGtfUiKj633NkrIKlm/eGxTPKyplzbb9DM7sWK9+Lh3ZnzG9uzJn3TYSYyIZ//ef1DsHY46Wqr4LvCsiv1HVh1s6H2OMMcYYc3JoyPZ6o4HfAV1qnqeqrbf6daQiEWNbOovjxuUDTuHlRcsorzEE3uVwcEHf3qzZf4D0xERiI8KDzuud1JbeSW2P6prhLifx0RHkF5cFHUtNiKl3P6XlbjbuzSajbRKDu3Y4qlyMOVqq+rCIXACM84e+VtVPWjInY4wxxhjTejVkjv7zwH3AYsAmO5+EuqUk8/wVF/O3b+awev8BerVJoU+bNtz65gcUV1QQE+7injEjuW3k0Ca7ptPh4KqzBvPMJ/MCc0lO5NC+Ajq3TTxiH/M37uCBlz/lUFEpAD3apzRZfsbUh4j8ERgOvOYPTRWRUar6yxZMyxhjjDHGtFINKfTzVHV6s2ViTggju3TmnRuuAmDO1u3c/Mb7VceKyiv488zv6NOuDaO7dmmya94xZSRxMRG89/UK9uw7hOSWk7NhL/fNfYWJkwfywM/Pq/XcCreHn70yvarIB9i072CT5WZMPZ0HDFJVL4CIvAQsBazQN8YYY4wxTa4hq5PNEpG/iMhpInJq5b9my8wc9z5ds6FB8aMlIlwz/lTO7ZBOzIZiorPciPqOzfhsOSuW+3ZDWJt7gJUH96KqVecu27aXnELbXs8cF2oOP0losSyMMcYYY0yr15An+iP8/605LluBs5ouHXMkXlWKKyqIDQ+eC1/TnB07+Nvs2azNyqJ3air3jxnDmC5N95QdIMwReou82uKNtXzZ9pDxb5du4Nd7vmJlzj4AMuKS+PfYS+id1Ja4qLq/T8YcI38ElorILHxb7I0DHmzZlIwxxhhjTGtV7yf6qnpmiH9W5B9DL6xYwvCX/s0pz/+DSW+/xLw9O0O225CdzW0ffMDyffso93hYsX8/t3/4Ieuzs5s0n4v69+Xwkl788ebQrn3oh6CfRW+tKvIBthXkcu/sDwHo3bEtAzPSmiUfY+pLVd8ARgLvA+8Bp6nqWy2blTHGGGOMaa2OWOiLyHX+//4k1L/mT9EAfLp5Pb+fM5PsEt8w9LUHs7j50/c4UBS8s+Hbq1YFrIwPUO7x8NbKlSH73nool2eXLOLN1SvILwte3b42p3bqwGPnnUObGN92eW1jY3jsvHM4tVPzrGp/+ZUjcLmcAbG2XRJYWxH8Acam/IOsP5QFwD9uuYDJp/YmIsxJUkwUt40f1iz5GVMXVd2rqh/7/+078hnGGGOMMcYcnfoM3a/cwyyuORMxdXtrbXCRXuJ28/Gmddw2MHCV+7zS0qC2tcVfWbGMh775ispZ7X/+/jtevehy+rap33Z4lw7sx4X9+3CwqJiUmGjCHA1Z9qFh+vTtyN/+fi1vvTGPffsOcUr/zlx09TDO+upZ3L41zgJEOH0fCiTHRvOn6yYBk6qO/bjZsjTGGGOMMcaYlnXEQl9V/+P/7++bPx1Tm1K3u97x8d268f6aNcHx7t0DXueWlPDo7G/QmrHSUh6Z/TWvX3xFvXMLczhoFxcLwOq5G/j46S/Jy85n+LkDOf/OCYRHuOrd15H0PaUTv3/0soDYeV368NG21QGxEW3TyYhLbrLrGmOMMcYYY8yJot6L8YlIG+B2IKPmeap6S9On1fREpBvwKyBBVS87UvvjzeTuPVmwd1dATICJ3TKD2p6bmcm1Awfy+vLlqL/dVQMGMCkzsO3SfXsp8wR/UDB/966gWH0smLGMhy57Aq/H93R96czVLP16DQ+/f/9R9Vdfjww/lzCHg2nb1uBRL+M7ZvLI8InNek1j6ktEnMBqVe3d0rkYY4wxxpiTQ0NW3f8I+A74EvAcoW2TEpH/AucDB1T1lBrxicCTgBN4TlUfr60PVd0C3Coi7zZ3vs3h+n6DWJN9gHfXr8arSqwrnF+NOoMeSSlBbUWEP4wfzy2nnsr67Gx6pqbSNSkpqF3H+PiQ12obF01BeRlx4RENyvG1xz6sKvIrLZi+jPWLttBraLcG9dUQsa4I/nra+Tw6fCJeVaLCmm4EgTGNpaoeEVkvIumquqOl8zHGGGOMMa1fQwr9aFX9ebNlUrcXgX8CL1cG/E/JngLOBnYBC0XkY3xF/x8PO/8WVT1wbFJtHk6Hgz+fOZH7ho1mV0EefVLaHnGLvYykJDJCFPiVeqWkclZGN2Zu2wKAOhUiPewljwGv/p2EdR7OKEjjB/eeS6++R15gb9em0OuL7d64t9GFvterTP9qFd/O3UBkhIvzzunP8MFdA9pEOBvydjbmmEoCVovIklIOewAAIABJREFUAqCoMqiqF7RcSsYYY4wxprVqSGX0iYhMVtXPmi2bWqjqtyKScVh4OLDJ/6QeEXkTuFBV/4jv6f9REZE7gDsA0tPTG3x+WVkFefklpKbE4WiG/eTTYuNIi226dRGfmnQ+/1m8kM+3bGRDxQHc/gn76oRD/Zx8u2gv26e+ygtv30NiUkydffUZ3p2Fn68IiIkIvYZ1r+WM+vv7f77kw+nLql7PmrOeB380kckT+je6b2OOgd+0dALGGGOMMebk0ZAl0qfiK/ZLRCRfRApEJL+5EquHjkDNjeR3+WMhiUiKiDwNDBaRX9TWTlWfUdWhqjq0TZs2DUroxdfmcNE1T3H5jU9zzW3PMG/h5gad3xIiw1xMHTGKH542IuTK9QU9HRQVljHrf6tDnB3o5j9cQexhHwZc8qOJdOzRvlE5HsguYNrny4PiL7zxPaoa4gxjji+q+g2wDXD5v14ILGnRpIwxxhhjTKtVryf6IuIAJqrqnGbOp9mo6kHgrubqf8ZXq3jhtepvz959efzmkQ957bnbadsm9Fz444mnloJZ/YMSigpDb9lXU/cBXXh2yeN89foc8rLzGTZxEAPH9Wl0brv35uLxBue3PyufsnI3kU24qr8xzUFEbsc3UigZ6I7vQ8mngfEtmZcxxhhjjGmd6lXoq6pXRP4JDG7mfBpiN9C5xutO/liL+PzLVUGx8goPM79dx1WXDm+BjBrmzE7diA+PIL+8LCAev9H3lH/0uF716ie5fSKX/+S8Js2te0YbwsPDKC8P3CGgW5fUFi/yMx78tNZj2x5v2u+DOaHdg2+60XwAVd0oIm1bNiVjjDHGGNNaNWTo/lcicqmINP3E86OzEMgUka4iEg5cBXzcUsl4Qzxxrit+vIl1RfDMmZfQKSbBF/Aqces9tFsBd/7obLr2aNdiucXHRXHbtWMCYuHhYdxzy5ktlJExDVamquWVL0QkDDgxfjkYY4wxxpgTTkMW47sT+AngFpFSfNuzq6o2+7h0EXkDOANIFZFdwEOq+ryI/BD4HN9K+/9V1SNPJG8m40/vw7KVOwNiTqeDM8bU70l4fRUUlyIixEY1bOu7+hjZPp1vL72TLfk5FO0voqxXGT1/2uGIi/AdC1ddPIwB/TrxzfcbiIwI49wz+9GhfWLItuVlbqa/NZ9F364jMTmWKdePomf/ziHbGnOMfCMivwSiRORs4G5gWgvnZIwxxhhjWql6F/qq2nRLvTeQql5dS/wz4JjvAhDKlEkD2bErh48+XUp5hYfkpBjuvXM8HdJCF6MNlZ1XxMMvfsH3q7biEOH0wT349Q1nEx8T2ST9V3KI0CMhBRJSoGeTdt1ofXum0bdn2hHbPfrDl1nw9bqq17OmLeXh529l8KjM5kzPmLo8CNwKrMT3oelnwHMtmpExxhhjjGm1GrTxuIgkAZlAVXWpqt82dVItTUSmAFN69OjRkHP44R1ncePVo8g6WEDnjsm4XM4my+mX//mUJRt2Ab6F82Yu3oh6lb/cc/Juw+31elk2azU5e3MZeEY/2nRKYe3S7QFFPoDH7eX1f35phb5pMf51Tl7CN0dfgfVqW0YYY4wxxphmUu9CX0Ruw7fFXidgGTASmAuc1TyptRxVnQZMGzp06O0NPTcuLpK4uKZ9yr47K6+qyK/pm2WbyS8qbfKn+rXZu+8QB3MKyezRnojwBn1G1OTyDxbw4KTH2LR0KwAOp4M7/nQdcZ1Cr2+2c/OBY5meMQFE5Dx8q+xvxjftqauI3Kmq01s2M2OMMcYY0xo1pFqbCgwD5qnqmSLSG3isedI6/i3P2sura5eTV1bC+PQeXJbZD6ejIWsb1p/bE7y/PYBXFY839LGmVFZWwaN/+YTv5mxAFeLjo/jp1ImMHd1yY/tf/v07VUU+gNfj5ZmfvcIjn/8mZPteA2yOvjk6IvJf4HzggKqe4o8lA28BGcA24ApVza2jm78BZ6rqJv/53YFPASv0jTHGGGNMk2tIZVqqqqUAIhKhquuApl1p7gQxc+cWLvn4dd7esJLPt2/iZ9/N4CffNN9SAV3aJ9Gzc5ug+LDenUmKi26261Z65Y25fDvbV+QD5OeX8PCfpnHoUHGzX7s2Cz9fFhTzepV963dz/rWnBcRj46O44b6Jxyo10/q8CBz+BnoQ+EpVM4Gv/K/rUlBZ5PttAQqaLENjjDHGGGNqaEihv0tEEoEPgf+JyEfA9uZJ6/jg9eylrOQTVAP3b39i8RzcGvgk/cPNa9mQm91suTx+1/kBxX7/bmn87pZjU7x+/d26oFh5uZvv528K0frYSGyTEDreNoF7fncxj/z3Vi68cQw33T+R/0y/n+59OxzjDE1r4V+HJOew8IXAS/6vXwIuCnWuiFwiIpcAi0TkMxG5SURuxLfi/sLmytkYY4wxxpzcGrLq/sX+L38nIrOABGBGs2R1nPB6syjMvQtX+BjiUl5BxAXAxkMHQ7bfeOggPZNSmyWX9HZJvP7Q9WzZcxCnw0GX9knNcp1QapuPH96C8/QvuncSa+dvDIh16N6OEeedCsCQsb0YMvakHHBijo12qrrX//U+oF0t7abU+Ho/cLr/6ywgqplyM8YYY4wxJ7mGrro/BshU1RdEpA3QEdh6hNNOeBXlsykv+ZSIaN9Du/6p7ViwL3BxPIcI/VNq+1u/6XTrkNLs1zjc5HMH8P+e/ioglpAQxejT6r8rQV3mzt3IrFlrCXM6OHfiAAYOTD/iOWdeOQqP28N7T3xK9p4chkwYwC2PXIWrCT98EJE7gDsAnPHBUydMq5EqIotqvH5GVZ+p78mqqiIScgV9Vb250dkZY4wxxhjTQA1Zdf8hYCi+efkvAC7gVWB086R2fHFXLCLCPzr3Z0PHcv2MdylxV1Qdv7nfqaTHJ7ZUes3qkguHkF9QynsfLaKwsIw+vdO4755ziIoMb3Tfr7wymxdf+K7q9eefr+T+ByYzefLAI5474dqxTLh2bL2u4/Z4Wb1lL1ER4fRMr1/R7i/2ngGISMu0rdBar2xVHdrAc/aLSJqq7hWRNKDObR1EpCtwL77F+6p+76rqybs/pjHGGGOMaTYNefx5MTAYWAKgqntEJK5ZsmphIjIFmNI1w1kVczi7VX09rH0nvrj0Jt7ZsIpDZaWM79ydMzp3bYFMG2/P3kOsXLmT9u0TGDgg9JN0EeHm68dw/TWjKCutICYmokmuXVhYyhuvzw2Kv/jCt5x7bn+czqbZxWD5xj388t+fcCC3EIB+3do3Sb/mpPYxcCPwuP+/Hx2h/YfA8/jm5jf/VhnGGGOMMeak1pBCv7zmEFURiWmmnFqcqk4Dpg0aGH47gMOZTkT0ZQFt0uMSuX/ImJZIr8m8+NJ3vPLanKrV9Puf0ok/Pno50dGhC/kwp4OwJiryAfbsOURZmTsofvBgIXl5JSQnN/4t5nZ7+MW/ppF1qKgqtnrLvkb3a04eIvIGcAa+If67gIfwFfhvi8it+BYlveII3ZSq6j+aNVFjjDHGGGP8GlLovy0i/wESReR24Bbg2eZJ6/ggEkNkzF1Exd6BwxF/TK/t9SpzNmxj+Y69ZLRJ5pz+PQgPa7r55xs27uPlV+cExFau2sVbb8/n5pvGNdl16tKpUxJRUeGUlJQHxNu2jSchoWnWKVu5ZW9AkW9MQ6nq1bUcGt+Abp70T3/6Aiir0feSxuRmjDHGGGNMKA1Zdf+vInI2kI9vnv5vVfV/zZbZccAZ1p2YhF8f8+t6vF6mvjKNWWu2VMWe/yaVl+68nPioyCa5xsKFW0LGFyzccswK/ejoCG68aSxP/7t6oT+HQ7j99jOabNh+ZLirSfoxppH6A9cDZ1E9dF/9r40xxhhjjGlSDXpE7C/sW3VxfzyYuWZzQJEPsGFvNq/OXsrdZ5/WJNdISIwOGU9MPLYzMi6/fDi9erZn1qw1hIU5Ofuc/vTs2XRz6PtktKN3l7as217nWmnGNLfLgW6qWn7ElsYYY4wxxjTSER+bikiBiOSH+FcgIvnHIsmW4lU3uwu/Iq9swzG97pKte0LHt4WOH40zT+8TNAdeBC65uKGLjzfegIHpTP3xRO754dlNWuRX+tvUCxk7qBsOEaIiXFwxflCTX8OYI1gFtM5tOYwxxhhjzHHniE/0VbVVrqxfH/nlG5m//wEA0qLPYHj7P+GUxm8pdySdUhJCx5NDx49GTEwET/ztWp7/7zes8K+6f81VpzFsaO27B5S53UxfvYGNWQfpl9aWs3v3wOV01tr+eNE2KY7/m3oR5RVuHA4HYU4HP7u+pbMyJ5lEYJ2ILCRwjr5tr2eMMcYYY5pc063u1ipVb52+t/hrNue9Sc/EG5r9qhcM7sOL3y5mT271gInocBfXjxncpNfp3CmZ3/324nq1LSwr5/qX32HNvuoh8MO7dOL5ay5u0kUCj8a27FxenrOE7QcPMahzGtePPpXE6OC1DMJd9nY3Leahlk7AGGOMMcacPKzyaYB9Rd8ck0I/LiqCV39wJf/9ZiHLd+yja5skbh43lO7tUpr92rV5c/GKgCIfYMH2XUxbtZ5LB/Vroaxga1YOV/37TQpKfQ9J527aweerNvLOPdcQ1cIL8WU8+GnI+LbHzzvGmZiWpqrftHQOxhhjjDHm5GGFfggiMgWY0qGLi1hHKSVeFx6cuI7hFnvtEmL5xQVnHrPrHcnSXaHXB1i6a0+LFvovzl5SVeRX2pKVw2cr1nPp0FNaKCtjAolIAdVDhMIBF1Ckqsd2305jjDHGGHNSaJo9zFoZVZ2mqnfExjsIEy8xDl8h2S3hiibpv8zjZsbOdby9eRn7iguapM/m1iU59DpitcWPlW3ZuQ2KG9MSVDVOVeP9hX0UcCnwrxZOyxhjjDHGtFL2RL8eHAKqYfxj7V6c8imXdBnIoJROQe1KyitYt+sA7RLj6JAc+kHd9oJcrpv1GnuKffPvw8TBo8MmcVm3gc16D4113bBBvLdsNYdKSqtiafFxXDaoZZ+aD+jcnoVbdwXHOzX96v2m+ZxM0xxUVYEPReQh4MGWzscYY4wxxrQ+VujXkwcv3x+Yze7iJN7auoQ/DZ3CeR3CcTiScDrTmL54HY++PZOCkjJE4NzBvXjkunNxhQWuSv/o0i+rinwAt3p5aPHnTOjYk8SIqGN9W/XWISGed2+9mv/OW8LGA9n0S2vHLSOHkBTdsjnfNGYIX6zayM6cvKrYmMwunNW3ewtmZUwgEbmkxksHMBQoraW5McYYY4wxjWKFfj2oQqEngoIK30ruA+L2kOG9iqwD+YCA8xwefqMfReXOqvYzlqyne1oKd5w7IqCv2fu3BvVf6nGzMGsnZ3fq2ez3UulgYTEvfr+YZTv3kpGSxM2jh9CtTXKd53ROSuShSWcdowzrJyU2mvfvvY5pS9ey/eAhBqanMaFvD5wOm5VijitTanztBrYBF7ZMKsYYY4wxprWzQv8IPCpke+JYndeR/IooIh0VPNJrOnFh5f4WCp7PuWZMNs/ODCyCv1y2MajQbxsZy86iQ0HXaRsV21y3EKSorJxrnn2LHTm+PBZt282MVRt4+86r6XqEYv94FBMRzlUjj++pD+bkpqo3t3QOxhhjjDHm5GGFfh28xJAnI/hyn4s5BzoDMDxxR40iv9rpvdcFFfoRIfZtv6XXcH6/5IuA2JDUTgxM6VBnLqWeCqbvWsXmwiz6J3ZkfFpvwhzOOs+pzScr1lUV+ZUKy8p5ae4SfnfBhKPq0zSf2uavQ+1z2Os6p6EaOk/+aPJtrUTkt3UcVlV9+JglY4wxxhhjThonXaEvIhcB5wHxwPOq+kVtbRPCu3JZxktklc7mu/2zAPCoA0eOF29y4NBwrwYX3RePDN527oaeQ4l0hvHqpsXklZcyvmMmPz5lXJ0551eUcMN3L7CxoHof+xGpXfnPadfhOopif/vB4BEFdcWNMUetKEQsBrgVSAFOqEJfRLYBQ1U1+yjOvQn4QlX3NLYvY4wxxhhTt2Yv9EUkEXgOOAXfPtK3qOrco+jnv8D5wAFVPeWwYxOBJwEn8JyqPl5bP6r6Ib4Vr5OAvwK1FvqVftB7DP2T0vjXutdQp5eoXxdS9K/AVfVjYq6kX3o7Vu/YT2JMJDecOYRLRvUP2d8V3QdxRfdBR7pslde3LAgo8gHmZ2/l892rOb/zgHr3U2lQ57Ra4nWPKjDGNIyq/q3yaxGJA6YCNwNvAn+r7bxW6iZgFbCnhfMwxhhjjGn1jsUT/SeBGap6mYiEA9E1D4pIW6BEVQtqxHqo6qbD+nkR+Cfw8mHnO4GngLOBXcBCEfkYX9H/x8P6uEVVKyvmX/vPq1VR+UZWHfgxmcm/ZF3Bx/RMWUzKtkISPi5iz33JJGS68Wo4sbHXkJbwa15/wEVRaTmR4WFNuhjc8tzg7eMAluXurCr0Vb1kl8ylzJNFStQIosJCF/MA4/t0Z2xmBt9t3FYV65aazA2jBjdZzqZ2TTmsviW1lvtobiKSDPwEuBZ4CThVVXMb2WcGMAOYB4wCFgIvAL8H2vqvBb7fv5FACXCzqq4XkfuA/qp6i4j0B94AhqtqcYjrpPiPdwTmAlLj2HXAj4BwYD5wt6p6RKQQeBY4B9gHXAWcjm+ngddEpAQ4zd/NvSIyBXABl6vqusZ8X4wxxhhjjE+zFvoikgCMw/ckB1UtBw6f4H46cJeITFbVMhG5HbgEmFSzkap+6//j9nDDgU2qusV/zTeBC1X1j/hGAByekwCPA9NVdUkteU8BpnTsEkZu4TQWZH3OwcJ0wiWMfjN2A5D9XgSzb+yCV8Ppm5zIiJgywl0uIlx5+B7cRdbjO1Q/6TGhF8jrEpMCQFHFAb7feysVHt+K/oKT3skP0DXh+pDnOR0O/n3dhcxat4Xlu3yr7k/u34uocFeT5XysqCpZOYUkxEUScQLmb1o3EfkLvt9nz+ArrgubsPsewOXALfgK/WuAMcAFwC+BG4CxquoWkQnAY8Cl+Ir/r0XkYuBXwJ2hiny/h4DZqvoHETkP35QDRKQPcCUwWlUrRORf+D5ceBnf1IRFqnqff42Ch1T1hyLyQ+ABVV3k7wMgW1VPFZG7gQeA25rw+2OMMcYYc9Jq7if6XYEs4AURGQgsBqaqatW8VVV9R0S6Am+JyDv4/mg9uwHX6AjsrPF6FzCilrYA9wITgAT/yIGnD2+gqtOAab0HRN6OQvqz+xn9z3U4PNVtxv5zE2P/uQmvU1j4gy18d/86ukVsody9AYfEkhR3GynxP638Y7ZRru82ko93Lie/onrb7URnNKdEdWZB9mZm7r6fAfHV2/YpHtbm/IV20WcR7eoYsk+nw8GEvj2Y0LdHo/NrKQtWbONvz89k595coqPCuWLSqdx+5agm+Z4b00TuB8rwjSD6VY33puBbjC++thPrYauqrgQQkdXAV6qqIrISyAASgJdEJBPftCkXvot6/fPlVwD/UdU5dVxjHL4PKlDVT0WkciTCeGAIvhFUAFFA5WgpL/CW/+tXgffr6L/y2OLK6xhjjDHGmMZr7kI/DDgVuFdV54vIk8CDwG9qNlLVP/ufxP8b6N7ET70CqOo/gH/U+wSnsOPHyewekUC/n+wnYV9J1aGCtAi++HNfsofGMcbxBeVuLwBeLeRg/t9xOtuRFHtjrV2XeYop8xYR72pTZwqdYpJ4c9zt/GnldOZmbcIjHvKLirnmrVeJ71XMj3rsDnGWbyh/uuuyet/qiSQ7t5Cf//kjysrdABSXlPPi+/NolxrHhRMavm7BiepkHT5/oty3qjbdHJ5gZTW+9tZ47cX3u/dhYJaqXuwfDfV1jfaZQCFwtAtzCPCSqv6iHm21jmOVOXs4CReHNcYYY4xpLs35Ryj4nq7vUtX5/tfv4iv8A4jIWHyL9X2Ab6hoQ+wGOtd43ckfa1KbB7Vl/jVdA2IrruvE3qGJtA0rIEy8QefkF70dsq9dxat4beuP+ceGK3hq4w08u/kH7C6ue2pqudfNvIMb8To8iIAjxkN4jwLc4qbQHRHynAhn6CH/rcHMuRuqivyaZny7pgWyMea4lED178KbKoP+KVX/wPe0PkVE6vo08Ft8UwIQkUlAkj/+FXCZf40VRCRZRLr4jzmAyj6vAWb7vy4A4hpxP8YYY4wxpp6atdBX1X3AThHp5Q+NBwIqMREZjG/+6oX4VqNOEZFHGnCZhUCmiHT1L/Z3FfBxo5OvocwbxptrRtLhk0OURThZc1VX3BEOun2Z5buHWh5YqVYExf639wne2f4TDpSuIYxywvGQXbaN17f/kg92PcfS3Dl4NLiAfX/HIiJdJXRLyKZP8n46x+US7vTNJZib0z2ofYwrgzbRYxtz201m7eKtfPDc1yyYuQavN/gDkaPh9Yb+nrs9TdO/Ma3An4E/ishSAp+WPwE8paob8M25f7yyYA/h98A4/9SAS4AdAKq6Bt90hC9EZAXwP6ByBdAiYLiIrALOAv7gj78IPC0iy0Qkqonu0RhjjDHGhHAshkrei2+l5XBgC75ivqZo4ApV3QwgIjdQ4+lTJRF5AzgDSBWRXfgWeHrev9DUD4HP8a20/19VXd0UiXtU2FDQjg82nUr2pjicRV6m/Hwqv7nzcj65/neMuW82MQfK2BmfRGZYFmHOwCIzLvoCcssLeXv7XNbl7yEjuhgv0wPaOATC1ItbS1mY8xlzDv6PeTn9uKPbL3FK9Y8nu3wPmUlZOPxTfKNcFcRHlLIhuw0rCzohe5TRyZuJCyvF4RjAWe0fxyHBi9PNPbiA+QcX4hQn49qMZmBi6C0Am4Kq8rf7XuOr9xdVxfoMyeDRV39AVEzoUQj1dcbITP712rdBhf3Zo3s3ql9jTgSqug3fKKjK1zfVcqxnjdN+7T9+S422O/Et6lfbdQ7iWz0/1LG3qJ6Lf/ixn4SIvQe8VyOUUePYIny/340xxhhjTBNo9kJfVZfh21aptuNzDntdgW9rpsPbXV1HH58BnzUizZBc4qGgMJo12Z1I8+Zx0YP3UhERTvf4voya9Am/3voXcj7Ywarcbqzr2YsbL/ofCXHFeNXBqpI+fLffzabCx3E6ckmLyifHnU9iiO+447ARAZsLV7Pi0HwGJ42uikWFZwcsBggQ4fSQWC50bNeZFfmwqag7l6QP597u5xDmcAZd560d7/LJ3hlVrxfkLOLGjGuZ0O7Mxn2jarFw1tqAIh9g7eJtTHvxO664Z0Kj+m6fGs8ffnw+f33uS3LyiglzOrhwwgAum2hbBDa1E2U+vDHGGGOMMcbHFj+qgwDjO64lpzSGl9VXdF/cvQ8dYn0LZZ897FJ+OfcTABavyWTZ+m70uGYD+VFh5HuicMpW4l2ldIjKRwTctazLpQhehXKt/nHsKN5UVeirKk4OkhBWgleFYk84Hv+siyv79uX23tdR5C4j3OHE5Qj9Iy10F/L5vi+D4h/s+pgz247DKcEfDATlqcqKvPXsLN5D99h0+sTXvWL/8u83howvm7Oh0YU+wBkjMhkzpBvb9+SQmhRLQpyNBjbmaIjIzcDUw8JzVPWehvalqrFNk5UxxhhjjDlaVujXwYES5/BwS+Z8xrY7wI6Kh7ihf3WBetbwnjx010Re+WQhe3PySTgTdoXHgQcELwPj93JF4np6RByiwOvi+6IO7PBE43JUDzdXhXJ1UOCJwvfRgs/qvN2Mb1dMbFg0H+z6F+XeLcT4a/EYZxkHK2Ko0DDGdBjii4XVPRQ+qyybihBz//PdBRS6i0hw1b3LV4W3gsfW/psVedWLBo5MHsxPet2KU0J/gJHaPiEoFplSTvvBJVR4i3A5Yuq8Zn2EhTnpnl73rgXGHM9ExAksAnar6vmHHbsJ+AvVi+r9U1WfO4prJALP4RvSr8Atqjq3RpMXgcHAZKAYuElVlzTwGr0IHMrfDfitqv69RpszgI+Ayv1A31fVP9AAIjIVuB3fL8xna/bvPy7Ak425F2OMMcaYE11zr7p/Qqu5G3tm/FamdHoX12FD4ieN6cuvHzwH90Xl7EvaXxVPiSji3jZL6BWZi1OURGc5k+O30cFZSr47gvJSF1lL41n9YjoHchNw13iaX+F1sPzQdqYu/T3bizawOPergGs6BOLCyhiTOoY+8X3qdS9pke2JdEQGxdtEpBIXduQHcF/u/z6gyAeYl7OUeQeX1nrO+EuHkdTG9wGCOJSh923lgjeWkXDRe7yz9ULW531Yr9yNaWkiMlFE1ovIJhF5sIm7nwqsreP4W6o6yP+vwUW+35PADFXtDQwMcb1J+LbcywTuwLfVaYOo6vrKPIEh+IrsD0I0/a7G/TS0yD8FX5E/HN99nC8ihw8tavS9GGOMMcac6KzQb4DikhkhV9L/1ZKPyC4rxKuC+qfbj4jdQ6KzPKjtkKj95Lhj2T19CEv+2Ivd09ux+bfp5BREUeIOI68ikgOlcSBCoTuPl7e9HDKXxLAIbu16a71zj3RGcmX6pQExpzi5Jv1KHLU8ka9pZV7o7f8OL/5rik+K4a/v/Yjxlw5jyI0l9Dg/i8rPSSq8xcw78Bdyy7bU+x6MaQn+J+5P4Ssg+wJXi0jfJuq7E3AevqftzcK/nd444HkAVS1X1UOHNbsQeFl95gGJIpLG0RsPbFbV7Y3oI5Q+wHxVLVZVN/ANvt0AamrqezHGGGOMOeHY0P0GEAnj8M9GDpQUsC7P9yS/zO0i0uUmzOHFReht3qLEw+jUCcRknsIG/y6AFVlhZHliiQ6xL/ymwv10CjH1PC2qa4Pzn9DuTLrFdGV+zkLCxMmo1JF0jOpQr3NTwpMaFK/UISOVB564ls93zWVfyeFHlR2FX5MU0a1eORjTQoYDm1R1C4CIvImvmFxT51n183fgZ9S9v/ylIjIO2ADc518pvyG6AlnACyIyEFgMTFXVohptOgI1+93lj+1t4LUqXQW8Ucux00RkObAHeKCBu6SsAh4VkRSgBN/w/EWpAggZAAAgAElEQVSHtWnqezHGGGOMOeGIauj9yE9mIjIFmAJcCYRaUS4VyG7CS1p/x76/Lqpa5+R+EbkD39BfgF7A+qPIzRz/Dv/ZPqOqz1S+EJHLgImqepv/9fXACFX9YWMuKiLnA5NV9W7/3PUHQszRTwEKVbVMRO4ErlTVsxp4naHAPGC0qs4XkSeBfFX9TY02nwCPq+ps/+uvgJ/7t71r6H2F4yvi+6nq/sOOxQNeVS0UkcnAk6qa2cD+bwXuBoqA1UCZqv64Oe7FGGOMMeZEZU/0Q1DVacA0qou8ACKySFVr3TKwoay/46u/Sv5i75kjNjTm6IwGLvAXvJFAvIi8qqrXVTbw72Nf6Tngz0dxnV3ALlWd73/9LnD4OgO7gc41XneiegHAhpoELDm8yAdQ1fwaX38mIv8SkVRVrfcHdar6PP5pCCLyGL77q6kp78UYY4wx5oRkc/SNMaZuzVI4quovVLWTqmbgG+o+s2aRD3DY3PILqHvRvtqusw/Y6V8VH3zz5w+fdvAxcIP4jATyVPVoh7pfTS3D9kWkvX9VfERkOL7/DzoYqm1tRKSt/7/p+Obnv35Yk6a8F2OMMcaYE5I90TfGmLotBDJFpCu+Av8q4JrmupiI/AFYpKofAz8SkQsAN5AD3HSU3d4LvOYfVr8FuFlE7gJQ1aeBz/DNd9+Eb7X8m48y9xjgbODOGrGa17kM+IGIuPHNsb9KGz5/7D3/lIYK4B5VPdQc92KMMcYYcyKzOfpHQUTuqDmH1/prXf0Zczj/8Pq/A07gv6r6aAunZIwxxhhjTK2s0DfGGGOMMcYYY1oRm6NvjDHGGGOMMca0IlboG2OMMcYYY4wxrYgV+sYYY4wxxhhjTCtihb4xxhyHROSO1nCN1ngdY4wxxpjjnRX6xhhzfDoWReuxKoxb23WMMcYYY45rVugbY4wxxhhjjDGtiG2vV4eUZId27hzme1HoxbnTg1RUH/e6hLxOkUgMRIo74FwH4JTgPr0q5HgjguKK4FEHiu8kj/peO1HCHJ6Q+VV4nVR4Hbi9zsBri4Ne8WkBsXJvObtL9obspzg/AlSIiQjHmVtGaVF5UBtpDxIT4n5KHHSMbUt0VHjIvo9XixcvzlbVNvVtn5Ls1PTO1d9nt0LN/+U4iry4Dnt/eMKEHckpFDsjARCEvh3aBvS7P6+Q7IKioOt1SCghLjI/KF7sdrG9KAmnw0Hf5LZBx5tLUXk5W3JzQx5zFQbHnB4vlLqD4rEJUaR1SQ2Ibdq4D68n+PeQVLjB4yWlfSLJ7ROOLvF6aOh7ITU1VTMyMpotn0pZWVm0aVPvtI7ba5xo12no+8EYY4wx5ngU1tIJHM86dw7jq+m+YqrY68H7lJsuj2dXHZ/z424svSWdGCljVPTWgHOdQBtHGHJYsb+2LJHvS9sHxCrUgVsd5LpjUP8giwOlsZR5XZTnhdG73V4czsBCqNjj4pA7mo25qZR4Aovsc9MG8NigKwNiHvVw39IHya0ILNaKDkWydnZ3AH4+cRyueft59S+fBH0vnGM8RNzrDIqXzujEe7/6KZERrqBjxzMR2d6Q9umdnXwz3fdz86qy06M48QZ8mJP0dCFtHyuoev3q7cN4OP1yHEW+79tZvbvx1LUXBvQ7b9MObn32vaDr/fHStozu9POg+D/Xn8Z/Nw/n4h59+fsZ5zXkFhql1F3Bac8+Q15ZWUC8E9F4vwuu9Ee3TWLFtOVB8R//9WrOveq0gNijv32fr79aE9jQqzg270a8Xp78+Bf0OrVr42+iFg19L2RkZLBo0aLmSse0sIa+H4wxxhhjjkc2dL8eytVLhaTimu6mItLJoivScUc46PZlFgA5FTF8dmAgHq2u+jaVJvPhoR7UfFC5uzye0oibgOp2XgUPDoq8EVVFfkFFBGVeF+5yBzsWd2TFou6ot/pHVeF1kFcRyYH9iRzclYR6q6+RHp3C1F4Tg+7BKU7u6H4zUc6o6n5Kw9i2ogMAYzMzuHr4QC69azz9T8sMOFe9XtxfKe4FgX2WrY7np5MvO+GK/MZQVXK9XjwqbClPpchb/SFL7IxSPJHCzmsSqYhw0O+LfVVFfuekBB6cdHpQfyN7pHPd6EEBsTE9M5h46pVExv6Amu+VBdmdeH3rYHokpvCLYcF9NafIMBd/PudcosKqPxtMi4vjmWsuoUNMZEDbTk4nf3jies6+ckRAfMx5g5hw2fCgvm+/Zzyd01OqA15F9ufgUOWq+yY1a5FvjDHGGGNMa2RD9+swcFCKzvx6KlERI4g62BOdNIU5T9zJ984wui/1MuHpPzHzufsZNeRaEiMTmLN3NuG6nPT43mwoTiEqLJK9RYuhYjYdYgZxWvu7cTgcHCrfw+7ilRR7ioh0JtA1ZjAHyw+yr3QXB0sr2FOWS4/YLqR5u7J2dzZdU5Pp2iGCzYUrWJ29k9W7DtHT0ZPrRk1gZ2E+3+3aTKGjgMFtOzIitQdOqf3zmxJ3CcvzVhImYcRXdGTNnmwyUpMZ1DlwqP+q+ZvYuy2bfsO7sX/nQQ7uPcTAsb3JiT7E1+tXklCezORBQ4iPi6rlSsc3EVmsqkPr2777KQn6/97vx86KjoSHxVMhcTgdLsKkDRHeUtplb2D4LTNY9OQVbOt2JmdltyfttruZ+cQ/CU/vzJgeGYQ5a/+5bDmQw4qde+mamszALtU/C497CxXli8l3t2P2/hRSoqIZ06ELTkfLfEaXV1rKN9u2Ee1yMS4jg3Cn74OMmV+uZPHiLQwZnMFZ5wysar917R42rdpJRq80Mgek19qvx+Nl8YIt5OeXEOOAwoMF9BvRgw5dm396QkPfC0OHDlV7ot96NfT9YIwxxhhzPLJCvw4Bf9Bv3w5t20JUjcK2pASysiC99gLGHJ+avLiz98cJywp9U5MV+sYYY4xpDWyOfn116RIci4qyIs742PvDGGOMMcYYc5ywQt8YY8xJJ+PBT1s6BWOMMcaYZmOL8RljjDHGGGOMMa2IFfrGGGOMMcYYY0wrYoW+McYYY4wxxhjTilihb4wxxhhjjDHGtCJW6BtjjDHGGGOMMa2IFfrGGGOMMcYYY0wrYoW+McYYc4yIyFQRiRef50VkiYic09J5GWOMMaZ1CWvpBJqCiEwFbgcEeFZV/37YcQGeBCYDxcBNqrrkSP3mlW9m1u676JV4PR1iRtfabt+uXF7591esWbaD6IGRVJztJddVRL+EdCa0G8THu78jp2I5MWGldI5O55JOV9Ettle97+9/Kzby7/lz2BKzF3dEBZ5yJ54sF4k7XJwT25F77zyHdmmJ9e6vNrmlJTy5fA5f795CSmQ0t/YdyuSM3o3ut9K+4iWszH2Z/IpdtI0cwKCUW4lzdazXuWVlFbz8zjy++X4DkREuppwzgAsnDQJgfVYW//h+Hmv2H6Bnm1TuPW0kp7RvR15OEa89+TmLv91AYmosF98yjjGTBhxV7gfLNvGnVZewszQaN07QtpwaPZGlKwvJLVvHuH4rSUvKIy6iB3OyejE3u4zOMUnc3nMsw1O7HtU1G6qwqIwH35rGN8Xb8YQrp6Wm83/nXUBCRCRvrV3Ja6uXUVhezjndMvnRkJFEu8KP6jorF2zmrX/NZPe2bPoM7sJ1U8+hQ0Zq1fGZ01fw8TsLyc8rZsSYnlx72+nExkXWu//Xli7n7ZUrKa1wM7FXJj8YMZxIlytkW6/Xy4evzeN/Hy/B4/ZyxqQBXHHzWMJczoB2ny/fwCvfLSGnqIQxvTK4++yRJMZEHdX9G9NIt6jqkyJyLpAEXA+8AnzRsmkZY4wxpjURVW3pHBpFRE4B3gSGA+XADOAuVd1Uo81k4F58hf4I4ElVHXGkvrv3j9HHPugLCOPS/k5azKigNkWFpdx58f8j+0A+ng5eiu91Q42aJEw89E3cS7jTWxVzipP7ej5M5+gjF4DTl63ngQ+moYMKkRrjL9QDxfuiidzjYOAqJy++fg9R0UdXuAF4VZnyyUusztkfEH9y7BQu7Nb3qPutdKBkJTN23Y3iqYpFOVO5qMvrhDtjj3j+rx77gO/mbQqI3XH9WM46tx/nvfgyBWVlVfFol4sPrruG/7vpJbas2RNwzoP/uI7Tzx+EiCxW1aH1zb9r/1j93fsDKPOGsaa4A4qQWxbFtrVp3DZyJhEud1XbUk8YT28eR35FFGHi4KUxNzMouXN9L3XULvzjsyzvkBMQy3AlcF2/oTzy/dcB8XGdM3j5/MsafI31y3fwwBVP4a6o/jkmtYnjP1/8lLiEaGZ8tIQnHpkWcE7fAZ154vlb6tX/v+bO5/9mzwmInZvZg6cuuiBk++ee+Jx3X5wdEDv7gsHc//AlVa8/W7qOn70+PTCnjm15a+o1iEiD3wtDhw7VRYsW1be5OU5lPPhpyPj2P53foPdDQ4nIClUdICJPAl+r6gcislRVBzfXNY0xxhhz8mkNQ/f7APNVtVhV3cA3wCWHtbkQeFl95gGJIpJW/0so6w+9FvLI19NXkn0gH4DyMd6AIh8gJaIooMgH8KiHb7Nm1OvKL36zGNLKA4p8AHGCK86NO17YHlnKN/9bVb9bqcX3e7cHFfkAz65e0Kh+K6099HZAkQ9Q4slmS8GRH2Lt3JMbVOQDvPXRIt5aviKgyAcorqjgyenfBhX5AO89+3XDEj9MhMNNYlgxAEkRJYzuuyagyAeIdLo5NWkHAG718srmeY26Zn2sXreHNTE5QfFtFXk8tSz4+t/u3Mb6g1kNvs5HL84OKPIBcrMKmPnBYgDefXVu0DlrVuxk9fKdR+zb4/XywuLFQfHPN25ie+6hoHhZaQWfvBX8/vzq0+XkHiysev3CN8F9rtl9gHkbdxwxJ2NqEpFIEVkgIstFZLWI/N4f7yoi80Vkk4i8JSJ1feq6WES+wPfB8+ciEgd462hvjDHGGNNgraHQXwWMFZEUEYnG98fT4Y9POwI1K41d/lgQEblDRBaJyKL8nOoCrsQduijKycqv+lrjg0dHuByeoBjAoYrgoiyUA/mFEBH6b0BxKOoAd6SQfaCgXv3VZl9x6PP3FReGjDdUsTu7lviRi82DOaFzyMsvYW9+LXnXEj+4Pz9kPJSa74WCnIqquEuq3xdxEaUhz40Lq47vL63/NY9WVk4BnlpGoueWFYeM7ytq+M8250BeyHj2Pl/8YFboe60tXlO5x0NuSejv54EQuRYWlFJaUh4U93q8AYX+/rzQ93kgv2ne2+akUgacpaoDgUHARBEZCfwJeEJVewC5wK119HEr8CAwTFWLgXDg5uZN2xhjjDEnmxO+0FfVtfj+yPoC37D9ZUDo6rp+/T2jqkNVdWh8cvUSBu2ih4dsP2hE96qvwzYFfzvzK0LPTe4d179e+YzskQ6HQi+l4Cl34igXorI8nDqiW736q81p7bvgFAmKj+nQpVH9VkqLHhIy3qGW72tNvXq0IzYmIijev09HxnbLCHnOhH6ZOBzB9zN4dOYRr1ep5nshLrl6qEaBv6L2eIWNWe1Dnru1qHrO+qg23UO2aUoD+3YiKiv4fh0Kw9p2CopHh7kY3K5Dw68zKvT379QxPQEYNCz4fehyORlwasYR+45yuRiUFjzQJiEygv7t2gXFU9rE0aV729Dxbm2qXp+WmR7UxukQhvcIjhtTF/+osMpPiFz+fwqcBbzrj78EXFRXN0Bf4Ef+1zFA/RexMMYYY4yphxO+0AdQ1edVdYiqjsP3NGXDYU12E/iUv5M/Vi8J4d3pmxz6AU3/IRlMudI33d81x4Fzc2CxFelI40BJ4Bz0rjE9GZNav0WWp04aTafyVDgUuLiYuzgMzQ0jbmsFV58xmL4DGjcHvGNsPD8/9XRqZp8em8hPB5/eqH4r9U28mtTIfgGxXgkX0z76yNNSoyLDuf/ucwgLq367JsRH8eM7xzO5V0/O7RlYfI7rmsFNY4dz008nIzU+vEjrksKN909q1H3sK4+nxBuOV2F7XjsWLOnNhj2Bxena/PaszvPF+id25IbuIxt1zfpISozh/gFjcBbVCCrc23sUj447hzbRMVXhMIeDP4wdT3xE8IcnR3LRTWPpOyQjIDbxyhEM9hf6t089m7btE6qOORzCnfedS2JyDPXxu7PPIimqemiCy+nkD2dPqHUxvh/95gJiaiz0FxHp4ke/vRBnWPX/Xn48aTSdkqtzEoH7Jo8lLTGuXjn5zqke3ZGV1fApD+aEklr5s/b/u6PmQRFxisgy4ADwP2AzcMg/dQzqGDHm9y/gNOBq/+sC4KkmvQNjjDHGnPRO+MX4AESkraoeEJF0fE/2R6rqoRrHzwN+SPVifP9Q1SM+Su4/uLvOmP1f0qJH45C6NyjYtHYPa5btoF3HJLSPsqskh74Jnemb0JnludtZlLOUCGcRQ5P70yuuf0ABeiQVbg+zVm9mQc42cl1FlJUorv1OunlimXxaP7r3DP1U+Whsy8/luz1bSYmMZkLnTMKdziOfVE+qXnYXz6egYjdtI/uTEln/nQcAsg8WMmfhJqIiXIwZmUl0VPU02MW7drP6wAF6pqYyMr36Q48927JZ/O16ktrEMWJ8X1zhvp9jQxdg6zeomz427W9sLsrCq256xQ/i3LThLNm+l01ZB8lot592CXkkRvQiu6wtS3J2kB6TzOi23XEcvsBCM9q+N4cX5s2nwgU3jxpGj2TfyIKSigq+2LqJgooyxnfpTlps/Yvcw3m9XpbO3sjubVn0GZxBZv/AEQPlZW7mfrue/Lxiho3KpH2Hhu0IUVRezhcbN1HmdnNW9260ja17scbC/BK+n7UWj8fLaWf0CfmhQoXbw6w1m8kpLGFUzy6kp1bnZIvxnZwauxifiCQCHwC/AV70D9tHRDoD01X1lFrOW6Kqp9ZcgE9ElvunAxhjjDHGNInWUuh/B6QAFcBPVPUrEbkLQFWf9m+v909gIr7t9W5W1SP+pW5/0LdeVtyZSvZeODk1xar7IvJboAT4OdBeVd0ichrwO1U9t5Zz5gOjgIX+gr8N8IWtum+MMcaYplT3Y+oThKqODRF7usbXCtxzTJMyxhjTqviL8gpVPSQiUcDZ+NaImQVchm+r1xuBj+ro5h/4RgK0FZFH/ef9ulkTN8YYY8xJp1UU+sYYY8wxkAa8JCJOfGvcvK2qn4jIGuBNEXkEWAo8X1sHqvqaiCwGxgMCXORfVNYYY4wxpslYoW+MMcbUg6quAIKG2KvqFqDOdV9EJLnGywPAGzWPqWr99lw1xhhjjKkHK/SNMcaY5rcY39Z6oVZiVaBxe6QaY4wxxtRghb4xxhjTzFS1a0vnYIwxxpiThxX6xhhjzDEkIpcAY/A9yf9OVT9s4ZSMMcYY08ocuw2+jTHGmJOciPwLuAtYCawC7hKRp1o2K2OMMca0NvZE3xhjjDl2zgL6+Ld9RUReAla3bErGGGOMaW3sib4xxhhz7GwC0mu87uyPGWOMMcY0GXuifwTvbVjFW+tXUub2cF63ntzSfyhhjmP/+YjXq7w9cymfL1iPwyGcP6ovF48bcMzzqMuOgkM8tXwuKw7uJzMxhbv7j6R3cps6z/F4vHz4wSJmzVqLM8zBxIkDmDRpYMi2ufnFvPzRAhav3kG7lDiuOX8Yg/t0CmpXVFDKO89+zaJv1pGYEsuFN45h2Om9m+QeK+3IP8RTS+exInsfmYmp3D14xBHvtb4KC0t58415LFy4haTkGC69dBjDhlUvyP3FvHX8+81vyMktor0jnHuuOp1x5/Zvkmu3tHKPh+dXL2LG9vVEhbm4uudALuzeN6jdjO/W8PGslZSWuzlreCZXTR5CWJizBTI2psHigLUissD/ehiwSEQ+BlDVC1osM2OMMca0Glbo1yGrpIj7v55e9Xp51l7W5WTzf2dOPua5/OWNmbwza3l1Lpv2sD+ngLsuGn3Mcwklu6SISz59jeySIgDW5hxg5s7NTJvy/9m77/ioyqyB47+THggkhFBCDb1LC0VRimBnrdhx7W6z67uLu2tZt+i+vuuurq4u9rUAiisiiKCCgiIl9F6lBJIQkpBK+nn/mEmYyUz6JBPC+X4+82Huufd57pk7NyHnluf+lB6R0ZW2e/HFJSz4bEP59NYtiRw/ns0tt5zrtlxxcQn3/vFD9iemAbDnYCo/bDrAi7+dxoiBXcuXU1WeuOsNtq8/WB5b/90efvfSLYy7cLDvPuun73P8ZJ7js6alsvTQPj67+qf0iGxTr75VlRkz5rBj+9HyWMLa/fzh6WsYN64vC1Zu56k3v3DMCIZ9FPKbfy/gmcJiJv7E4/Hep51Hv/uc+ft3lE+vSj5MWn4edwyKL4/NWpjAi+99Wz69Y18yew8d56l7G//n0pg6eMLfCRhjjDGm+bNL96uQmpfrEZu3dztJOdmNmseJ7JN8snyLR3zWVxvILyxq1Fwq8+GeLeVFfpmcokLe2bGhkhaQkZHLos83ecTnfrSWwsJit9jydfvKi/wyJSWlvP/ZWrfYljX73Yp8cBTPH/17WY0+R018uGtreZFfJqeokHe2ra933xs2HHQr8gFUYfasVQDM/OR7jzZF0aG8+YbvPp+/HM4+wWcuRX6ZV7asxnk7M6Wlyrvz13oss2TlDpJSsxo8R2PqS1W/VdVvgQ04BuTbAmxxiRtjjDHG1JsV+lUoKS31iJWqcjS3cQuK45k5FJd45pKbX0hWbn6j5lKZIznet0liTmalbVJTsynx8rlycvLJzS1wiyWleu+nYnGXciTD63KVxesiMdt7LonZ9d8vUlK8910WP5bpefAJ4NiJnHqv298Sc7JQL/HUk7kUlDgO/OQXFpGRleexjCqkpFmhb5o+EblHRJKBzUACsM75rzHGGGOMzzS5Ql9E+orIayKyRESWlr38kUtYULBHrHVIKAPbtm/UPLp3jKZt6xYe8W4domgXFdGouVRmdEfPe+UBxnbs6jUOEBcXQ2RkuEe8W/e2tGnT0i02fID3fireoz84vgci4rHckNE9PWJ1NSbWey5jY71vg9oYMqQrXtLnrLMc6xzaK9ZzZqkyrE/neq/b34bEdKCll5+5oTGx5T+LLcJC6N+jg8cyLcND6BvXuD+XxtTR/wCDVTVOVXuqag9V9d0vKGOMMcYYmmChD3wErAd+j+MPorJXo+sU0YqI4JDy6UARnjznfMK9FCMNKTgokF/ffD5Bgae+rtCQIH5902SvRa0/XBbXn8lde7nFRrTrxE39vA+sBxASEsR9919IoMvnCgsL5v77LvRYdmCvjlxz4TC3WJeOUdx+9Vi3WGy3ttx83xS3WNsOrbntkYtr/Fmqc1nPfkzuVuGzdujETQMq/6w11aVLNDdPdx93ISamFXfcOQGA/5k+mRZB7kNrtDtWyC9/M7Xe6/a3iOBQnhwzmQCXfbpViCPm6qHbJtEy/NTPZYAID906iRZhIRhzGtgHeF6WYowxxhjjQ1J272tTISLrVHWkv/MAiI+P18UrlrNw/04KSkq4OK4PXVtH+S2f5PRsvk7YTWBgAFPi+xIT2bL6Ro1IVVlx9ABbnCPRT+7ai8AaPKEgJTmT5St2ERwUwPgJA4iOrvxzbdubRMLWQ3SMac3E0X0IDfE+nuT+HUdJWL6LqLYRnHfJWYS3DHWb79zP4r029iI+Pl4TEk5dXauqrDhygC2pKfRp05bJ3Wr2WWtq794U1q5xjLo/YUJ/wl0K25y8AmYvXMuObYkM7daBa649m5atwny2bn87mJXB4oN7CA8OZmpcf9qEeV71cSIrj69X7Sa/sIiJo/rQuUPdfy7ruy+Y01PcjIVe4wf/OrVW+0Ntichw4C1gNVB+j5Kq3t9Q6zTGGGPMmafJFPoiUjY0+/3AMeAT3P8ISm/snOwP+ubLijtTxvaFM5MfC/01wHc4BuErH6REVd9pqHUaY4wx5szTlB6vtw5QoOy6XdfL9RXweg+jiPQD5riEegJPqOo/XJaZCHwK/OgM/VdVn/ZN2sYYY0yNBavqw/5OwhhjjDHNW5Mp9FW1B4CIhKmq21DyIlLpdcmqugsY5lwuEDiC42qAilao6ul/I7MxxpjT2SIRuQf4DD9ftWaMMcaY5qvJFPouVgIjahDzZjKwT1UPVrukMcYY0/hudP77mEus0qvWjDHGGGPqoskU+iLSEegMhDsHKyq7hL814PlsOe9uAGZVMu9sEdkEHAUeVdVt9cnXGGOMqa2yq9eMMcYYYxpSkyn0gYuA24AuwPMu8Wzgt9U1FpEQ4HLcz5KUWQ90V9UcEbkUmAf0qaSfe4B7ALp161aL9E1zY/uCMaYhiMhgYCBQfluaqv7HfxkZY4wxprlpMoW+c8Thd0TkGlX9uA5dXAKsV9UUL31nubz/XET+JSIxqnrcy7IzgZngGF27DnmYZsL2BWOMr4nIk8BEHIX+5zj+7/oOsELfGGOMMT7juwd/+843IvKiiKwXkXUi8oKItK1Buxup5LJ9EekoIuJ8PxrH507zXcrGGGNMjUzDMZ5MsqreDgwFIv2bkjHGGGOam6ZY6M8GUoFrcPxBlIr74/M8iEhL4ALgvy6xn4vIz52T04Ctznv0XwRuUFU7Q2uMMaaxnVTVUqBYRFoDx4Cufs7JGGOMMc1Mk7l030Wsqv7RZfpPInJ9VQ1UNRdoWyH2qsv7l4CXfJqlMcYYU3sJIhIFvAasA3KAH/ybkjHGGGOam6ZY6C8RkRuAD53T04DFfswHVWXugY18nridoIAAruk+lIu7DKxzf98n/8j7e9eTWZjP5M59mN57JCm5OczcsJZdaccZFNOeu4fH06lV6zqv40hWFq8lJLAjNZUBMTHcFR9Pl8jqrw7ddjCZD5ZtIDkjm9F9u3Lz+SOICA8tn5+Ulc3rqxPYnpJKv/Yx3Dl6JF2j7KrTyuQWF/Litm9ZeHgbRaWljG7bnceGTaFTy8bfZot272butm0Ul5ZyWd++XDt4MM47WgDYezyNN2+tw70AACAASURBVNes58f0DIZ3juXO0SNp27KmD7zw7mRhEbO+2cDK7Qdp26oFN0wYxvDenb0uW6rKnO1bWLh3F6GBQVw3YDAX9fI6ZqYxpy1V/aXz7asi8gXQWlU3+zMnY4wxxjQ/0tSuYBeRbKAlUIrj2cKBQK5ztqpq3avfWoqPj9eEhAT+tHEx/9m3xm3ejCEXcEffsbXuc8Gh7Ty4ch6uW31Cx15s2ptGev7J8liHlhEsuP4WYlrUvtBKzc1l6rvvcjwvrzzWtkULFkyfTvuIiErbbdx3hHte/Jii4pLy2KDuHXjn0RsIDAggLTePK956j5Sc3PL5bcLD+fT2m4lt3arWefqTiKxT1fiaLl+2L9SGqjJt2ZtsyTjqEoPWgS1YfMnPiAmr/LvwtdcTEnhm+XK32G3Dh/P4pEkA7EtL55p3ZpFbWFg+v1tUJPPvmE7LkJA6r/eeF+aydvfh8unAAOGFn1/JuEFxHss+/u1XvLdlk1vsifMmcfvQEXVef000xr5gmp64GQu9xg/+dWql+4OIdMUxaF4HHP8/zVTVF0QkGsctZnHAAeA6Vc2opI9xwEZVzRWR6cAI4AVVPVi/T2SMMcYYc0qTu0dfVVupaoCqBqlqsPN9K+er0Yr8MukFucza7/lH/au7vqOotMRLi6q9tPU7Kh5a+TZ5H+lFuW6xlNwc5mzfUuv+AT7YtMmtyAdIy8vjg81VnzR6c8latyIfYNvBFL7fdgCAOZu2uBX5ABknT/LBBvfizDh8f2y/W5EPIAJZxXnM2rux0fIoKinh1TVrPOLvb9pEmnM/eWvNOrciH+DQiUzmb9tZ5/Wu25PoVuQDlJQqbyxe7bFsam4us7d57u8vJ6ympLS0zjkY42PFwCOqOhAYC/xKRAYCM4CvVbUP8LVzujKvAHkiMhR4BNiHjbhvjDHGGB9rcoW+OEwXkced012dI+X7xZG8TIrUs9A4UXiS7KL8Wvd3MMfrSR4I9Lyy4sCJSpatbh0nTniNH8iour/Dqd7bHXLGD2Z4n19Z/ExX6XctcCAnvdHyyCwoICPfc18tKi3laHY2AAczMr22rc93W+n+dMwznpidSbGXgj7tZB45FQ5AGOMvqpqkquud77OBHUBn4ArgHedi7wBXVtFNsXMw2CuAl1T1ZeD0uiTKGGOMMU1ekyv0gX8BZwM3OadzgJf9lUzvVu1oHRzmEY+LiKZNSO0vqx8e43l/cgACxZ5fxYjYTrXuH2BEJ+/tKouXGdrD+/yhPWId7Tt7nz+8mn7PVMPbdvEaVxVGxnif1xDahofTPSrKI946NJTe0dEADO8S67Xt8M7e4zVxVk/vbYf29Nxf+kbHEBHseYtA7zbRRIZ5/vwZ428iEgcMB1YDHVQ1yTkrGcel/ZXJFpHHgOnAQhEJAIIbMFVjjDHGnIGaYqE/RlV/BeQDOO9zrPtNwvUUHhTMb4ZMQVxiIQGB/G7oRW4DmdXUjGGTaRUc6hb7xYBzOCumo1ssPrYzV/er24B/0wYNYnise5E1rGNHrh08uMp291w6hvZR7veNX3H2IIY4C/0rBw8gvot7kXZWbAeuG1Z1v2eqgVEduTZuuFtMFQa2juXquCGNloeI8PjEiYQEBpbHAkT47YQJhAc76ovbR42gV9tot3YTevVgSp9edV5vz45tufl89/vr20SE86vLz/FYtmVICI+Nm+D2cxYaGMTj502q8/p9SUTuEZEEEUlITU31dzqmYcWUfdfO1z0VFxCRCOBj4EFVzXKd5zxbX9XgN9cDBcCdqpoMdAGe8136xhhjjDFNczC+1cA5wFpVHSEi7YAlqjq8mqY+5zro1p6sYyxK3EFwQCBTuw6ia8s2de73eH4O8w5sI7PwJJM792FY284UlBSzaO8edqcdZ2C79lzUszfBLoVZbRWVlPDFnj3sTE2lX7t2XNynj1uhV5nskwUsWrvTMep+v66M7d/do98vd+9le0oqfdq15eJ+fQgNaooPb6haYw7A9l3yPt7bl0B+cTGXdh3EVXFDCA6o+3dbV4czM/ls506KSkq4pG9f+sbEuM0/WVTEgu27OJhxgmGdYpnUuweBAfU/FrhuTyIrtx+gbeuWXDqqP1ER4ZUuu/N4Kl/s20NIYCCX9x1Al9YNPyyHDcZ3ZqrLYHwAIhIMLAAWq+rzztguYKKqJolILPCNqvZrgLSNMcYYY2qkKRb6N+M44zECx72O04Dfq+pHjZ2L/UHffFlxZ8rYvnBmquOo+4Lj/6V0VX3QJf4ckKaqz4rIDCBaVX/dAGkbY4wxxtRIkzsVq6rvi8g6YDIgwJWqusPPaRljjDHjgFuALSJS9uiM3wLPAh+KyJ3AQeA6P+VnjDHGGAM0oULf+RziMseAWa7zVLXxhik3xhhjKlDV74DKBmeZXFVbEflaVSeLyF9V9Te+z84YY4wx5pQmU+gD63AMYCRANyDD+T4KOAT08F9qxhhjTL3Eisg5wOUiMpsKBwzKHttnjDHGGOMLTabQV9UeACLyGvCJqn7unL6Eqp9JbIwxxjR1TwCP4xhl//kK8xQ4v9EzMsYYY0yz1WQKfRdjVfXusglVXSQi/+vPhIwxxpj6UNW5wFwReVxV/+jvfIwxxhjTvDXFQv+oiPweeM85fTNw1I/5GGOMMT6hqn8UkcuB8c7QN6q6wJ85GWOMMab5qf9Dsn3vRqAd8AnwX+f7G/2akTHGGOMDIvIM8ACw3fl6QET+4t+sjDHGGNPcNLkz+s7R9R+obL6I/FNV72vElIwxptyWI5len8F+4NnL/JCNOQ1dBgxT1VIAEXkH2IDjMX3GGGOMMT7RFM/oV2ecvxMwxhhj6iHK5X2k37IwxhhjTLPV5M7o15WIRAGvA4NxjGB8h6r+4DJfgBeAS4E84LbaPs4ot6CQWWs3sfbgETpHtmb62GH0jIn23YeoxNcb9rA4YRciwtQxAzhvSE+PZbIKdnIwezYFxanEhJ9N19bXESgh1fZdqsrHO7bx5f69RISEcuPgIYzq1MVnuWdm5PL07E9ZczKJyLBw7jtvAhcNGVTrftbsPMSn320jIzOXkJxSWuQpQ87qyhVXjSQt/yTvrdzIwbQTDOsay01nD6V1eFh5W1VlwbodfLV5L+EhwVwzdkidP8+J49l8+tZy9m07Qve+Hbnyjgm0jMnnQNb7ZBfuITJ0IN1b30xooPf9oqS0lP+u28Y3O/cTGR7G9aPPYmi32DrnU530k3m8s2kDW46l0Du6LbcPG0GLkkDenv09S/f8SElYAJPj+3Ll2MEsWLmdPYnH6dslhlZ9w/gu9SARwSHc0GcoYzp0a7AcD6Wf4N1VGziUnsnwbp24efRQWoWFNsi6UjNzmL1sI3uOHKdP5xhuOn94g6zHmCo8A2wQkWU4HrE3Hpjh35SMMcYY09yIqvo7h1oRkfWqOsJL/B1ghaq+LiIhQAtVPeEy/1LgPhyF/hjgBVUdU9W64uPjNSEhAYDiklJufnMOmxKTy+e3CAlm1p3X069jO198NK9e+3wVr3z2g1vs4WkTmD751CZIz1/PmuS7KNXC8lhM+DmM7jiz2v5/8/ViPty+tXw6QISXLp7KJb371jv3/LwCJj/zd464dlWq/GXwFG4aOarG/Xy2chtPvb3ELRaaXkyL1CK6DGjPzs6FZJ0sKJ/Xt2MMs39xI2HBjuNYf/p4KXO+31Q+XwS2PP/wOlWNr2kO8fHxuuyrb7n/sudJPpxWHu/SP4TrZq6hSI+Xx1oEdWFcpzkEB3qeqPufOYtYuGln+XRggPDP6Zczsb/nwZv6yi4s4PLZ73Egs/zHgLbhLei6qpR9LQrQoFOP8Q5CkBxFgJzeRRTElpbPE+CFcy/n8h4DfZ7j/uPp3PDabLLyT31//Tu248O7byAkyLfHIdOz8rj52fdJycgpj3WMbsWiv9xdq30hNLaPxt76D4+4Xbp/evF2+wXAwb9OrdX+UBciEguU/RJco6rJVS1vjDHGGFNbp+Ol++IREInEcVbkDQBVLXQt8p2uAP6jDquAKOcfWzWydNc+tyIfIK+wiNe+S6ht/jWWm1/I20s8+3/981UUFhWXT+898apbkQ9w/ORK0k9WndvBzBN85FLkg+MM/99Xr6xH1qd8NO97jvSucCApQPjbhhU17kNVeXX+Dx7xgjaBlAbC1oIMtyIfYHfycRZt3gVAckY2H63cXKHPGq/ezeLZq92KfIAu5253K/IB8ooTOZwzz6P93pQ0tyIfoKRUeekrz8/nC3N3bHMr8gHSTuaxq91JtyIfoBilNAhKQpWCjqVu8xR4flPNv7PaeOv7dW5FPsDO5FS+2LbH5+uau2KzW5EPkJye7fP1GFMdVU1S1fnOlxX5xhhjjPG5Jlfoi0iYl1iMy+QLXpr1AFKBt0Rkg4i8LiItKyzTGTjsMp3ojFVc1z0ikiAiCampqeXx/cczvOa7LzXNa9wXktOzOFlQ5BHPyivgeFZe+XRO4X6v7XOKvMfL7EtPx1vNuzfdN59p69GjEOBxXIa00EJqeiVJfmGx92JMhJKQAIpbePYPsD81HYADqRmU1rGyr7gvHN6X4rFMm265XtvmFO6rNKeaxutrX7r3fosjvC+vAVASrl4OpcGB7AyKS0s9Z9TTvuPec9zXANvkQHLDbGdjjDHGGGOamiZX6ANrRWRs2YSIXAOUn2JW1be9tAkCRgCvqOpwIJc63vOoqjNVNV5V49u1O3VJ/uBOHbwuP7iz97gvdGkXRWRLj+MetItsSfuoU9VaZOhgr+0jQ6u+F35Qu/YEBXjuAkPad6xlpt6N7dUDSjyL7E6FYTiGTKheeGgwPWK93O9eqgQWlBKc4734HOT8Xvp2iiE4MLDmSbuouC/0OaurxzLHdrX22jbKy3cyqHN7Arx87kENtA+d1cF7vyGZ3g98BJRAUK6Al006sI33faW+hjTiz9XA7g33s2r8L27GQq8vY4wxxpgzUVMs9G8C/ikiz4nI+8DdwPnVtEkEElV1tXN6Lo7C39URwLVS6+KM1ci4Xt2Y1M/9Pup2rVrys/NG17SLWgsNDuL+K8/FtTYMEOHBq8cTFHjqq+vb5lcEB7gXnJ0jrqi20O8QEcHPR7rnHxYUxGPjxtc/eeAnU8cycJ/7gIABxcofzr2oVv08OG08wUHuxXp4WjEBpXBexy50jnL/7GN6dmXKwN4AREe04OcXug/FEBJUt8J/yrTRHsX+ia0jaRHYwy0WGTKYzhGXe7Tv3CaSW8913y3DQ4J5+KJz65RPda7oO4DhHd3vTukR1YbRedEEFLhX8y0Cg5ASCCgSwg+5b5+QgEB+O7K6H8G6uWNcPJ0iW7nFzunVjfP7+X7MgqvOHUKfzjFusX5dGm58DWMqEpFAEdlZ/ZLGGGOMMfXTJAfjE5ErgXeBbGC8qu6tQZsVwF2quktEngJaqur/uMy/DLiXU4PxvaiqVVbproPxgWPE9C937GXNj4l0btOaq4cNok3L8Dp8wtrZfjCFxQm7CAgQLhnVn75eipP84lQScz6hoOQ4MeHn0D58Qo3Pmv+QeIgl+/fSKiSUaQMG0S0yqvpGNVRUWMzMeUtZmrSPmMgIHrroAvrH1v6KgQPJ6Sz8YQdZOScJy1MC80oYMrQb48f3J7eokE/Xb+fA8RMM6x7LxUP6epzFX7c/ka8276VFaAiXxw8grn10rQfjS0hIoCC/kG/mrWfv1kS69+vI5KtHERxewpGc+WQX7iUydCCdWl5GYEDlo8av3HuQZTsco+5fNXIgnds03NO1CoqLmb97J5uPJdM3Ooar+g8khAA+/2ITCzfuQsMDuHTcIC4Y1ocla3ez58hx+nVtR0yPCJYm7SMiOIRreg4hrnWbBssx82Q+n2zYzqH0Ewzv1omLB/Wp81UY1TlZUMTCNTvYk+j4nJeM7k+L0BAbjK8ZqOzsfWXfi78G4xORT4H7VPVQQ63DGGOMMabJFfoi8gbQC7gd6Ivjnvx/qurL1bQbhuPxeiHAfmf76wFU9VXn4/VeAi7G8Xi921W1ytHqKhb6pvkQkToV+qb5qe2+YIV+03QaFfrLgeHAGhy3mQGgqp6XARljjDHG1JFvn1/lG1twnJlX4EcRGQM8X10jVd0IVPzj7FWX+Qr8ypeJGmOMMbX0uL8TMMYYY0zz1+QKfVX9R4XpTOBOP6VjjDHG+Iyqfisi3YE+qvqViLQAGuZeFWOMMcacsZrcYHwi0kdE5orIdhHZX/byd17GGGNMfYnI3TgGjP23M9QZmOe/jIwxxhjTHDW5Qh94C3gFKAYmAf8B3vNrRsYYY4xv/AoYB2QBqOoeoL1fMzLGGGNMs9MUC/1wVf0ax0CBB1X1KcBGuTLGGNMcFKhqYdmEiAQBTWtUXGOMMcac9prcPfpAgYgEAHtE5F4cz7qP8HNOxhhjjC98KyK/BcJF5ALgl8Bnfs7JGGOMMc1MUzyj/wDQArgfGAlMB37q14yMMcYY35gBpOJ4wszPgM+B3/s1I2OMMcY0O03xjL4C7wLdgWBn7DXgLL9lZIwxxviAqpaKyDvAahz/3+1yPv7VGGOMMcZnmmKh/z7wPzjOdpT6ORdjjDHGZ0TkMuBVYB8gQA8R+ZmqLvJvZsYYY4xpTppioZ+qqvP9nQRASmoWf37hcyae049xo3r5vP+kw+ks/GgNx5MzGTqmJ1MuH05wcMN9Jft2JbFo3nrycvI5e+IAzps8sN59njiZz5wNm9mRksqADu24fvhZRIWH+SDbulu/bBvLPvyBgMAAptxwDkPO7V/vPouKSnjzH4tJOXqCs+J7cMGVIwgJaYo/PjVzMP0EszZs5lh2Duf06MYVgwcQHHh6Pcq7tLSUb+auZu3iTbRu24pLb59I9wGdSTqawQsvLWH/kTS6dIrm/l9MIa5bjL/TNabM34BJqroXQER6AQsBK/SNMcYY4zPS1K4YFJHJwI3A10BBWVxV/9vYubRq01WHTXwAgJ9OG8vdN5/rs77370ri0VtfIy+3/CMSP64Pf3r1Np+tw9XqFbv5wyOzKCk5dZHEFdeP4Ze/vrTOfWaezGfa27M4mHGiPNa9TRRzb7uRSD8V+3NfXMRrv53tFnvgxdu49I5JbjERWaeq8TXtt01EZx3T47by6WFjevKXmbcTENAUh7mo2rbkFKa/9xG5hUXlsfP79OTVa6/wY1a199w9M/nqg+/Lp4NDg3notXt45q1vKQiS8ngg8O/nptOvd0ev/dR2XwiN7aOxt/7DI37gWXs4iD/FzVjoNV7Z91LZ8gf/OrXK/UFE3gSmAsdUdbAzFg3MAeKAA8B1qppRSfu1qjrKZVqANa4xY4wxxpj6aopVyu3AMOBi4CfO11S/ZgTMmreWjMw8n/X3wb+/cSvyARK+38PG1ft8tg5Xb738lVuRD/DZR2tISTpRSYvqzdm4xa3IBziYcYI5G7fUuc/6yM8t4P1n5nnE3376Y4oKi+vVd2mFbbdx9X7Wfre7Xn36y0srVrkV+QBL9+xn7aFEP2VUewe2JboV+QBFBUX888XFbkU+QAnwj1e/asTsTDP3No7/n1zNAL5W1T44DlLPqNhIRK4WkauBBBH5XERuE5FbcYy4v7aBczbGGGPMGaYpFvqjVDVeVW9V1dudrzv8nVRRcQmHjqT7rL8fdydVEk/22TrKqCo/7knxiJeWKgf3Hatzv7uOHa9VvKElH0glLzvfI555PJuMlEyfr+/Abs9tejpoat9bXezfeshrPLvE+xVKh5O9nlw1ptZUdTlQ8T+DK4B3nO/fAa700rTswHUYkAJMACbiGIE/vCFyNcYYY8yZqyneZLxSRAaq6nZ/J+IqJDiQ7l2ifdZfz36xHDmY5hnvH+uzdZQREXr27cj+CgcRAgKEuF7t69xv//YxfLbNMz6gQ7s691kfHXu0o0XrcPKyTrrF27SPJLpjpM/X17Of90vBm7p+HdqRmJnlEffX91YXPYd08xpvHRiAt8MV3TrV72dXRO4B7gEIbH36bCdvanuJ+xkoRkQSXKZnqurMatp0UNWyo7fJQIeKC6jq7b5K0BhjjDGmOk3xjP5YYKOI7BKRzSKyRUQ2+zupm64aTVTrFr7r72eTaNnK/T72MRP6MXRUT5+tw9Ud900hKMh9sLUrbxxL+9ioOvd5/fAh9Ihu4xbrEd2G64YNrnOf9RHWIpSf/u5qt5iIcNuT1xBUz0EOAwPdf1RGnN2bkeP61KtPf7nvvLG0DAlxi13Qtzcju3b2U0a1FzewCxf9dLxbLDQ8hAceuoTQYvez+oEiPPyLKfVan6rOdF5pFB/YwvcHjUyTcrzsu3a+qivy3TgflVfp4Dci0kNEnheR/4rI/LJXvbM2xhhjjHHRFM/oV7z30W+iIltw2ZQhTDq7L2NG9PBp3z36duRfc+9l0dy1HE/JYujonky6dKhP1+Fq1Dl9eOm9n/HFp+vJyy3g7An9OWdi/Uajbx0Wxke33cBHG7ey41gqA9q349phg2kd5r9R96/61YX0HNKVbz5ahQQIk28cx6Cx9S/Iu/Vqz413TeRY0gnOGtWD86cOOy0H4gMY2KE98++azpwNWziWk8M5cd2YOqj+TyZobA+9fAfxU4awZvEmWreN4JLbJtK1bywDRvfipVe+ZO/hNLp2iea+n0+ma+e2/k7XNG8pIhKrqkkiEgtUdU/UPOANHPfm2yNkjTHGGNMgmlyhr6oH/Z1DmQ4xrZjxq4sarv9Obbjt/gsbrP+KevTpwC8evcSnfbYOC+POsTUesLxRDB0/gKHjB/i0z6DgQG697wKf9ulPXaMieXSS754i4Q8iwvirRzP+6tFu8Q6xUfzx6Wv9lJU5Q80HbgWedf77aRXL5qvqi42SlTHGGGPOWE2u0DfGGGOaKhGZhWMQvRgRSQSexFHgfygidwIHgeuq6OIFEXkSWIL7I2TXN1jSxhhjjDnjNJtCX0QCgQTgiKpOrTDvNuA54Igz9JKqvt64GRpjjDndqeqNlcyaXMMuhgC3AOdz6tJ9dU4bY4wxxvhEsyn0gQeAHUDrSubPUdV7GzEfY4wxpqJrgZ6qWujvRIwxxhjTfJ2eo4lVICJdgMsAO0tvjDGmKdsK1P1xJ8YYY4wxNdBczuj/A/g10KqKZa4RkfHAbuAhVT3sbSHX52V36+b9Wd3mzGD7gjGmAUQBO0VkLe736F/uv5SMMcYY09yc9oW+iEwFjqnqOhGZWMlinwGzVLVARH4GvEMl90M6n5k8EyA+Pr7SZyGb5s/2BWNMA3jS3wkYY4wxpvk77Qt9YBxwuYhcCoQBrUXkPVWdXraAqqa5LP868L+NnKMx5gwVN2Oh1/iBZy9r5ExMU6Cq3/o7B2OMMcY0f6f9Pfqq+piqdlHVOOAGYKlrkQ8gIrEuk5fjGLTPGGOMaVQiki0iWc5XvoiUiEiWv/MyxhhjTPPSHM7oeyUiTwMJqjofuF9ELgeKgXTgNn/mZowx5sykquVjyYiIAFcAY/2XkTHGGGOao2ZV6KvqN8A3zvdPuMQfAx5r6PWfyMxj4eLNJKVkMmRgFyZP6E9QUGBDr7ZWSktLWblgA+uXbSe6YyQXTT+Xdp2ja9Q2p6CAjzduY29qGgNj23PlWQMJDw52Wyb5YCqL3/6WzLRsRl04lLGXDcfxt6zvFZeUsvSHXWzYngjRQnanYoJCApjabSDx7bu4Lbt2+yGWrdtDeGgwU88d5LMcjqScYMGyrWRl53POyJ6MG9Gz2jaFJSUs3LqT9YeP0i06imuGDSa6RTgA6Vl5fLp8K0nHMxnerwsXjO5HUGD1F94kJZ/g8yVbOJF1krNH9eScMb1Zu3gTqz7fQERUSy786XhCOrVkzr5NHM3NYmyHblzabQBBAb69qCfjWCaL3/2OlMNpnDWuH+Oviiewif0MGNNUqKoC80TkSWCGv/MxxhhjTPPRrAp9f0pOyeSXj7xHWnouAJ8t2sSSpdv436enEViDQq2xPPfzN1j20ery6U/+9SXPLfw1PQd3rbLdiZP5XP/mbA6kZzgCG+CjDVt5/9bryov9nWv3MePSZzmZkw/AwteWculd5/PAP2/3+edQVWY89ykr1+/nZKcSTowsgR8d897ZtY7HR07mjv6jAXht3g/M/PSH8razvtzgkxy27D7KA3/8iPyCYgA++XIT0y4exsN3TK60TXFpKXd98AmrD5x66MN7azcy5/YbKM4v4c4/zyY9K8/R37dbWLxqJ39/8MoqD5Zs23mUR347h5P5RQDM/3wjfaJbcvjjVeXLzJ79NSlP9SGz1PHo7ll7N/LZwR28NmFa3TdABUd/PMbDFz3DidRsABa9s5xvPl7NU7Pua7CDPcZ/fDX2wJk2hoGIXO0yGQDEA/l+SscYY4wxzVTTqUBPcx/MXV1e5JdJ2HCAVQn7/ZSRp13rf3Qr8gFys07y3rPzq237QcKmU0W+07akY8zbvL18+u2nPiov8st8/vpSDu08Uo+svVu96SAr1+9HUbIHlXjsyX/ftIKcogLSs/J4a+Eat3lFxSU+yeHfs74rL/LLfLx4I4eTMippAV/v2udW5AMkZ+Xw+g8JvL1wTXmRX+b7zT/yw9YDVebx2tvLy4v8MnvScigNDzm1jimR5UV+ma8S9/B9ctV918bsvy0sL/LLrF68mfXLtlfSwpgz0k9cXhcB2Tgu3zfGGGOM8Rkr9H1k956USuLJjZxJ5fZuPOg1vqeSuKttSd4/37akY6f62XDA+3oridfHrv2OfEpDoKSF5/yc4kJ+zEpnb+JxnxX2Fe3c77lNVGHXj963FVS9HXceOOZ1XmXxMrv3etnHRNBW4eWT+d3DvLbdkua7/bOy/Wjvpur3L2POFKp6u8vrblX9s6pW/UNujDHGGFNLdum+j8R1j2HH7iSv8aaie/9OtYq76t2uLV/t2uc1Xt7PgM5sW7nbY5luAzrXIsua6dHVsd6AIgjIgy/u7AAAIABJREFUh9IKdWxYYBDdIqLIl2ICA4SSUvV9Dl3asm2P53feo0vl37nr9nLVp11bKCxi1yHPv/d7dvbepkxc9xi27TjqEZfcgvL3IUcLKOzsWez3jfLd/tm9fyd+3JboEe/Wr/r9y1TvTLvEvbkRkSeqmK2q+sdGS8YYY4wxzZ6d0feRG6eNJqJlqFtsQN9Yzju7j58y8jT4nL6MmjLYLRYSFsxNv/5JtW2njxpGh1YRbrG46CiuGXpqYLufPn41QcHuA6+dd/Voeg+Lq3vSlRg3sidD+nVCVGi103Owt7sHjCEyNJwO0a2Ydv5Qt3kBPrpf/K7rzvEYKO+Ccf3p1a3y4vniAX0Y1LG9WywyLJQ7z47n1stG0dLlcnuAIb1iOW9Y1QP83X7zuQQFuefRKTyYgNxTt1HEfJFOGO7baVS7rkzs1KvKvmvj+ocupUUr94MJA8f0ZvRFZ/lsHcacxnK9vADuBH5TXWMRuVxEfDZgn4h8IyLxXuLxIvKi8/1tIvJSJe1zfJVLhX4/F5GoapapLPdhInJpQ+RljDHGnG7sjL6PdO/altdevJVPFqznaNIJzhrclZ9cMrTJjbr/xPv3suS971i3bBvRHaKYeudE4mpwxr1dREvm3nkj7ydsYs+xNAbFtuem+KG0Cjt1cGPYxEG8uOIPLHh9KZnHshh9yVAumH5eg3yOwIAAXvj9ND79egsbth2muFjJ6VJCcGggU7sP4KKu/cqXfeSmSZzVuxPL1u0lPDSYK8YPZthbD9c7hzFD43jtzzcx76vNZGad5JyRPblkwsAq24QEBfGfn17L7HWbWX/4KN2jo7h51FC6REUC8N5T0/nw640cTc1kRP+uXD1hCIHVjIw/akQcr/z9FuZ/vpHMzJOMHd2TCycN5JsPV7FqwXoi2rTksjsnEdwniv/sXlc+6v6NvYf57KAHQI9BXXjpm8eZ/9oyjh1O46xz+3HxT89rUoNRGuMvqvq3svci0gp4ALgdmA38rbJ2Lu3nA9UPqFJPqpoAJDT0eqpYf30K9WE4Bjf83EfpGGOMMactK/R9qFNsFL+6+3x/p1Gl4JAgLrtjIpfdMbHWbdu3iuChSeOqXKbX0O4NMsq+N2GhwVx/6Qiuv3RElcuJCBeO6c+FY/r7PId+PTvwm3suqFWbiNAQ7jrH42QUAF3aR/HwjRNrnUffXh149L6L3GIX3HwuF9x8rlvsyfja5VpbnXp24OfP3NCg6zDmdCUi0cDDwM3AO8AIVc0QkTgR2QmsAs4B1gJvAX8A2juXHwjEq+q9IvI2kIWjqO0I/FpV51ax3t8A04FSYJGqll0ZcK2I/AuIAu5U1RUiMhF4VFWnVuijB/ABEAF8Ws3nfBlYrKrzReQTIENV7xCRO4Beqvo7EZkO3A+EAKuBX6pqiYgccH7O4yLyuDPvVOAwsE5V/89b7s4+ngbCReRc4BlVnVNVnsYYY0xzZqfajDHGmAYmIs/hKOCzgSGq+pSquj6iozeOM/v9na+bgHOBR4Hfeuky1jl/KvBsFeu9BMeo/mNUdSjwvy6zg1R1NPAg8GQ1H+EF4BVVHQJ4Dk7ibgVQdjlXZxwHKXDGlovIAOB6YJyqDgNKcBzMcM17FHANMBS4BMdBDVduuatqIfAEMEdVh1mRb4wx5kxnhb4xxhjT8B4BOgG/B46KSJbzlQ1sAX5U1S2qWgpsA75WVXXOi/PS3zxVLVXV7UCHKtY7BXhLVfMAVDXdZd5/nf+uq2QdrsYBs5zv361m2RXAeSIyENgOpIhILHA2sBKYDIwE1orIRud0xcFIxgGfqmq+qmYDn1WYX5vcjTHGmDOOXbpvjDHGNDBVrfTAuojEAQtcQqVAgct7b/9XF7i8r+tgG2V9lFSyjopq9PgSVT3iHFDvYmA5EA1cB+SoaraICPCOqj5Wh5zL1DZ3Y4wx5oxiZ/SNMcaY5utL4HYRaQHl4wTUxfdA2QAcN1e1oNMqHJfVL8dxhv9R578AXwPTRKR9WU4i0t3L+n4iImEiEoHjFoXqZAOtarCcMcYY0+xZoW+MMcY0U6r6BY7R+hOcl8k/WseuHgB+JSJbcNx3X50VOO6j3wusx3FWf4Uzp+04bmFYIiKbcRyMiK2Q91pn3puBRThuYcisZp3LgIEislFErq/pBzPGGGOaI7vczRhjjPEjVT0ADHaZvq2SeW9XnO+cjqim/2epMGCfqk50eX8c533uqvoN8I3z/dsu6/wRxz32ZX5fzTrfAN5wvi8CWlaYPwfwGDBPVeNcJv9PVZ9yXo2wHMf9+FXlng6MqiovY4wx5kxhhb4xxhhjmqKZzgH9wnDc07/e3wkZY4wxpwsr9I0xxpjTnIgMwXM0/AJVHXO6rlNVb/JFP8YYY8yZyAp9Y4wxpoGISFfgPzgegafATFV9wUd9BwIJwBFVnQoM80GfUcDrOG4XUOAOVf3B27KquqWm6xSRh4C7nH1uAW5X1fw65PcmjoH5jqnqYGcsGsdtAHHAAeA6Vc2obd/GGGNMc2KFfhUKi7axI3E0hcHXMaDNVAIKloAWEhB+CUWlWeTnf0VRcSTLNwwiMSWQsNahZJcU0bFNK34yagCtW4TVed3Zufks+n4HSWlZjOzflXHDeuB4IpHvlJYqP6zay+Yth4ntGMmUKYOIaBlGbm4BX3+xheQjGQwa2pWzz+tHdk4+S77ayvG0HOJHxDFqZI96rz83K49PX/2SdSv3EdWxDVffM4lBI+Jq3D4rLZsv311OWlIGI6YMIf6CoVUuv25fIsu3/0hkPb6XMvkFRSxeuZODSekM7NmRiaP6EBToPrZlaUk66Zmz2Xt0O/uO9SEqNpjWrdcQFhBOXOubyCk5Rk7hQaLDhhDbchJFhcrSZTs4cPA4fft0ZML4fgQFBQKQnZPPoi82se67XYTlFzBhwgDGTxtLULD7j3DSkQy+XryFwoJixp8/gN793Ma3qrFDSStJS32PNmFHad26N1HRNxIQMrJ8vqqy9ouNbFy6hZgubZlyy3haR3sOdp2bk8+y+RtISsxg8Mg4xkzqT0BA44wBumrzAdZsO0i7Nq24dNwAIluFk1OwiYyTXxAoLWjb8ipCg7o0Si6m+RCRi4EXgEDgdef971UpBh5R1fUi0gpYJyJfOgekq68HgB1Aax/0VeYF4AtVnSYiIUCL+nYoIp2B+4GBqnpSRD7EMYL/23Xo7m3gJRwHT8rMAL5W1WdFZIZz+jf1StoYY4w5zVmhX4UQlH5BJ8gv+Tclx/+FOuvs4tx/kVtaRAElAAzuHsr7y69n74kO5W3f+motbz94PV3aRtZ6vcnHs7j7T7M5lp4DwAeL1nHB2H786ZeX1f9DOakqT/9pHstX7CqPzf5wNU8/fhV//O1cUpJOADB31iqGju7B3pQMMk7kATDn4zX85JKhPPLAxXVe//Gj6dx73pOcCGqBBATArlRWfrubW+6fwk2/mFxt+8TdR3l40h84ccwxCPPc5xdwyZ3n89Cr93hd/oWF3/HGV2vrnK+rzJyT/OzpORw4ml4eix/YlX/8+urywry4eD/HUq4A0ujWBrq1gYySYFKKwigCtqQu5WRpMAUaDJnQNmQM7/15JPv2pZX3Of+zLjz31xtIS8/l3offJS09t3zeilWfMWzmVzz7xe8ICQ0GIGHVPp78zRyKCh375Zx3v+eXD13MFdfWbmyqjdufZ3CbV+nS3jGtuo3itE8IiLiPoFYPAvDcHS/z5TvflreZ89d5/H3FH+nUq2N5LO1YFg/f+ArHjjr2pf++tYJzpgzi9y/e7PODVhX95Y0lfPrt1vLp/yxcw/MzCskpeb48lpT1Mn3avdmgeZjmxXkG/WXgAiARWCsi86sq2lU1CUhyvs8WkR04Rq2vV6EvIl2Ay4A/Aw/Xpy+XPiOB8cBtAKpaCBT6om8cf2+Ei0gRjoMHR+vSiaouF5G4CuErgInO9+/gGEzQCn1jjDFnNHu8Xg2EBQhBLoWJAC3k1DGSiBYF3DLlO7c2qZm5zPxidZ3W99Znq8uL/DJfrtrF+p2H69SfN+s3HHQr8gGOHcviuf9bWF7kl1m35VB5kV/ms0Wb2LMvpc7rn/PXeZwoCXQU+S4+eHkp6anZ1bZ/949zy4v8MoveWMreDT96LHs0PYu3vk6oc64VfbRko1uRD5Cw/TBL1+wpn87O+huQ5rZMm8AiQp0HhwDCpAhBAUgrXA2RG92W37I1ka+Xbue92SvdinyA0o7RbN56mGWzvi+PvfrCkvIiH0AV3nx1Kbm5BTX+bIVFmfRs+SYBLnW4iBBIACU5/0JLktmxeo9bkQ+QnnyC9/441y320evflhf5ZVZ+tY313++hIe34McWtyAfIL0wns/CfbrFSzedwxp8aNBfT7IwG9qrqfmcRPBtHkVkjzgJ1OFC3/xzc/QP4NVDqg77K9ABSgbdEZIOIvC4iLatrVB1VPQL8H3AIx0GPTFVdUt9+XXRwHlABSMZxm4QxxhhzRrNCv4YE9zOQASIEucQGdPI8ObHlQJJHrCa27vXebsueuvXnzfbtR7zGj1QozABKg7zvJtt31OmEDAA7Vu2GMM9L6EtKStm7zXtubute5b1Y9BbfeiiZUtXaJ1mJSr+fvae2R2HhOq/LhAecKsRFINDlb/SOPY57LL99xxG27/S+nbV1S7av2g04LpE/dMCz/cm8Qg7sO+a1vTcpaZuICPU8gec4A19MadEWx3fnxfYf3OM7Nh7yutzOTd7jvrJ1n+f307ljKoGBRR7xvCJfXD1tziCdAdcjronU7JnyiEgE8DHwoKpm1ScJESm7R937L5q6CwJGAK+o6nAgF8dl8PUiIm1wHBDpAXQCWorI9Pr2642qKuC7X/jGGGPMaUrUhwVQcyMiqcBBL7NiAM+qqu6sv8bvr7uqtqvpwiKSCdTmVHQkkFntUjVbrrplqprv623dWGq6/Xyhj6pWeY+NiNwDlN0X0g/YVcXi5vRW8fudqaozAURkGnCxqt7lnL4FGKOq91bVoYgEAwuAxar6fFXL1oSIPAPcguP+/zAc9+j/V1XrVTyLSEdgVdmz7EXkPGCGqtbrvjERuRbHdrvTOf1TYKyq/rKO/cUBC1wG49sFTFTVJBGJBb5R1X71ydkYY4w53dk9+lWorBAUkQRVjffVeqy/ptVfJeaoqvcBALwQkZk1Wb4my1W3TFXzG2nb+FxNt5+v1lXdMs5Cr9rlTLN3BOjqMt3FGauUOC6HeQPY4YsiH0BVHwMec/Y/EXi0vkW+s99kETksIv1UdRcwmXqOJeB0CBgrIi2Ak85+fXc/FcwHbgWedf77qQ/7NsYYY05Ldum+MTXzWQMtX5PlqlumtrmdDhrzMzXH7Wcaxlqgj4j0cI5IfwOOIrMq43CcfT9fRDY6X5c2dKL1cB/wvohsxvHovL/Ut0NVXQ3MBdbjeLReAHU8cCYis4AfgH4ikigid+Io8C8QkT3AFOe0McYYc0azS/froKmfkbb+TBnbNsb4lrNI/weOx+u9qap/9nNKxhhjjDEe7NL9uvH1JbzWX9PqrzmxbWOMD6nq58Dn/s7DGGOMMaYqdkbfGGOMMcYYY4xpRuwefWOMMcYYY4wxphmxQt+YJkpErhSR10Rkjohc6O98Tjci0lNE3hCRuc1pXcYYY4wxxlTHCn1jnEQkSkTmishOEdkhImfXsZ83ReSYiGz1Mu9iEdklIntFZEZV/ajqPFW9G/g5cH1dcmksItJVRJaJyHYR2SYiD9Sjr+q2324RyReRJOe6/uCtH1XdX/bc7nqsq6bfVbXrMsYYY4wxprFYoW/MKS8AX6hqf2AosMN1poi0F5FWFWK9vfTzNnBxxaCIBAIvA5cAA4EbRWSgiAwRkQUVXu1dmv7e2c6nfHwWuhh4RFUHAmOBX4nIwArr89X2uxiIBY4BNwJXich3FbbfQD99V8Z4JSL3WL8N029D5WqMMcaczqzQNwYQkUhgPPAGgKoWquqJCotNAOaJSKizzd3APyv2parLgXQvqxkN7HWe/S0EZgNXqOoWVZ1a4XVMHP4KLFLV9RXy9Xom2l9noVU1qSxHVc3GcZCkc4XFfLn9MnBsvys5dZChfPsBg3y4rmq/q+q2jzFAQxWj1m/D5WqMMcactuzxesY49ABSgbdEZCiwDnhAVXPLFlDVj0SkBzBHRD4C7gAuqMU6OgOHXaYTgTFVLH8fMAWIFJHeqvqqy7y3gZeA/5QFXM5CX+Dse62IzMfxvO9nKvR9R0MVqCISBwwHVrvGfbn9nJ/15zjO7P9dVb2taxCwE2gBjMJR/Nd6XU5Vflci0hb4MzBcRB5T1Yrb2xhjjDHGmEZjj9erQkxMjMbFxaFFO4GiCnMFCe6PHSupXFpyJunHsjziLVuH0SmuHVkZuaQcdT9pHtEqjNhubd1ixcWl7DuQCrjvq8HBgXRuH0lJUQlhEaEEBNT8ApV169YdV9V2ZdMiEg+sAsap6moReQHIUtXHwbEvtAiOJDMtx6OvVm1a0LFrW494QyguLKYwv4iQ8BCCggPL4wWFxRw4dNxj+bDQYLrXITct3gOa7xGXoL7gOElerfyThST+mIrrr5iQkCC69W6PiNQ6p4aybt26DOCoqg4ui4nINOBiVb3LOX0LMEZV742JDtTuXR0/9xLUC6SFP9Kus6MHUsnN8vxuo9u3pm3HSD9kVHcZmXkcS/X8HdMmqiXtY1p5aVG9ir8bqlP2/0R1UlNTadeuxt3WmPXbcLnWdl8wxhhjmhKrUqsQF9edFcv/QEDmfV7nB7d9m4CQYY2c1Sk7UlP53+9XsP5oEnFtorhv9Fim9Orlt3wqev/vX/De37/wiI+75Cx+/+87uP3C50gO8bxq+tVZD9K9T4fy6Q1bDvHA7+Z4XUfo4vVIqdKiVTj3vXQnk286t0a5icjBCqFEINHlzPBcoPzS9+7dO/LbX17PB88nkZcd4tbwwuvH8NBzN9ZovXWlqrz8wFss+PeXlJYqQcGBXP/rK7j1qesA+Ob7XTzx1/ke7cJCg1ny0YO1Xl9BUj88D27B7EO38tLGloQHB3P94CH8YtRoAiop2p956AOWf7HFI/7I49cy5coRtc6poYhIIp63MR0BurpMd3HG6N41iJVfOO5KSEr7BXGDvf9+aKqevvsNfljs+b3c8sgl3PTARX7IqO6ef/VL5n2+0SN+dnxP/vrENTXuZ0dqKi+vXkhe/kbWXbuu4u+GKsXFxZGQkFCbJuY04eX/CWOMMea0YffoV6GwaDsp6XdRrKWeM6UFEtSz8ZNyOp6Xx01zP+TbAwfILixgS0oKP18wnzWJiY2aR35RMR9v2safv/yGjzdto6C4uHzepKviCQoJ9Ghz4fVjATh2JMNrnykV4j27tyMkxPOYlGSfREodp4vzsk/yt7teIS3Je5/VUdVk4LCI9HOGJgPby+YXFe1i5EX/4plP5zPu8n2nchDhgmuruvreN5bN/p75ryyh1Pl5i4tKeP/P/2XDUkfB1rdXBwICPAvufr07eMQA0nLzeH1VAn/56luW7d1PxSt7JHiIR5tSFWZuyiXt5EkSs7L428rv+ccPKyvNObnS79fbLfFNzlqgj4j0EJEQ4AbA40jK3p3RjZ5YVn4+/1m7gb98+Q2Ld+6hpNTL76cqXHid5/4aHBrEpCtH+irFRjOgT6zXeL/eHWvcR1peHnM3Psj9Q37PE2O9H1A0xhhjjDndWKFfFXUUrVmlRR6FUGDEg0hAa39kBcC8nTvILChwi5Wq8p9Nnme3vFFVvttzgJeW/sCCTTspdCnQayq3sJCb3p3DYwuX8M7aDfw/e+cdHmWVPuz7TMmk954QCL33LoJ0KRZEEbChrtjX76eua13L6qpb3NVV1t4bgoiVJr33DiFAgCSQSnqZZMr5/pi0YSbJpE0CnPu6ciXvM6c8M2/JPOc85alfVzL784WUlNt2gqM7hPKXD+4mtpMtKXlQmD8PvnIjQ8fZkrH3GtTBYUy9h45u/drZyQL8vZg3e+SFbwBdgv2ihtlkYfuve2gCDwNfCSEOAP2Bv13YwMPTwtwndhMSVURYdCCP/msOvYc6X/A5lp7F/9Zt58tte8ktLm2KXmz+cadT+aYfbPLoyEBuutbeUPPy1HPvHaMd+iSdz2HaB5/z9zUb+XTHHu797kee/nWVXRud3xOAp53sk4T+pJXYu0N/vn8f1lrCf3oPjm+QvBXpDnQTQqQKIe4GkFKagYeAFdgSC34npTxcs9PvyzpwPiXIrYqmFxRyzYdf8vKqdXy6cy8PL/mFh5b84vB8qovhE3vz4Ms3EhRme3616xzOXz64m6j2oS2ldosxfnR3enWPtpO1iwli5nTXPUZWn/iJ23qswUNraW71FAqFQqFQKFoN5brvAmVYybaW4SV0+PvehdZrOhqPwa2mT16pkcRMx3hsgNzS+g1Kq1Xyx29/ZvXR6p3pDzaG8sXdN+Hv5VlHT3u+33+YQ+n2+dwOp2eyeP8hbh8yAIAhY3syZGxPivJL8PL1RKutXlua/+dpPHX3RxTlV+t812NXExDk4zDXrTcOo2/PGNZvScTToGfbguWkZDvG5nr5ebms/4VIKfcB9Z5YjUby718GEhJyH4Vl5Rw6l0GHkCB8DdUu/Z9s3s3fl2+oOv7v6q18PG8mvWKc77DXh7ev8/fl7Vd9vh68ayxDB8SzZedJ/P08mTK+N5HhjjHXb23YSk6J/XXy/YHDzB3Ulz5Rtp1QjWEoHmHLsZQuRloLSCzsyz8PnHYYq7CsDLPViofW0XPjpj+MZuf6BFKSsqpk468bQL9hbSe8pIIDUkqH8y6l/A347UJ5fp6BV54dxfZfvHnuXfcax+9t3UlaQaGdbHXiSTYmnWF0pw4ujzP99lFMuWUkpUVGfAMurhwDNfHQ63jzlZv5be0hth9LpldcBDdMHIC3t0f9nSvw1WxqQQ0VCoVCoVAoWgdl6LuIGUkRBoL9n8OW8Lt1+OfvG/ls+16MmMHRHmZsfP27pWuPJVUZ+RKJxRsOlmdy7Tdf8uToMVzduYtLuuw/l+6y3Jkx0blXDB8vf5z1yw5QUmRk2NgetK/F1Rygb89Y+vaMBSDaWMabu07avR4cGcjIa92zAOPlHcqCjdv5YPMujGYz3h56HrlqJPOGD+R8UQn/XrXZrn2BsYx/rNjIp3fd2Kj5rr5rLKu+WF/lug8274eJt4+xazdkQAeGDOhQdZySn8+C3ds5mJlB5+AQ7h80lAO1nbez6VWGPoDQxaHzexSAzj5mgjzfJ9don8Ttirj2To18gMBgX95e8jCbVh4iIzWXXoM60LcW74eLiaxzBrYthNAwDUOnDnDr3LXfc2kNMvQPZ2Xw7u6dnMrLZUBkFA8MHkaUb+OS17U23+09xBt7NlNcXs6vhafJ8jPzf+OucLm/jovzfSsUCoVCoVDUhTL0G4Cfzy2tauT/ciiBD7bYkj5pEMgyidUDqAjNvqpDPLf1qz854O4zZ6v+NgVIrBVJ1M8U53P/sp94ddxEZvfqW+848cHO3ZY7hrjuzuwX6M30OcNdbl/JtHsmUFJYyuI3fiE3I5/+Y3vxwL/nYfByfSevsWg0IWxP7cF/1/9eJSspN/HqyvX0jgqnsLQMk8XRDXjXmcbnT+g1shtPf/0In/5lIamJaXTsE8cfXr+FuO4Xlqqv5nxpCTMXf0NWia1C4OGsTFYnnaR3QDip+Y7eEB1Dao83N+h0/GPy1Tzy268Um2yhGbH+/rw4dlydensY9Iy7xr3GcItjsTBgVDce/M88PAx6t07dMTiIQ2kZjvI6zt2FJGRncdPibymtCNc5nJXJmtNJLJtzB/4G1yoqtBX2p6bx1+Vrq47LzBbe3bSDbhGhTO3VrY6e1ZzIHEGM99f4eJS3lJoKhUKhUCgUbkcZ+nWg0QQBBjTCC1+fWwgK+HOr6vPb4US7Y22ZQFMuGdM9noeuHEG/SNcSUMUG2dy5rdpqI78mC3btcMnQnzOwLwv3HSSjsLrkXISfL7MH1N+3Objp0Wu46dFrsFisdiEBLYLwADR4Gq4gOPAl3tyW5LTZb0cSmTPQ+fuv/Nwby+iZwxk9c7jL73fRkUNVRn4lRaZygoK80Z/V2i1GDI2LZUSHdhcOYce4+I5s+cN8NiafwVunZ1T79ugaUNLwUqHLwI68vvyZVpn7nhFDWH38JMXl1RURekSEMbFbZ5fH+GT/niojv5JzhYUsPXaE2/teXIsyy44kOpcfTnTZ0A/168Czv87iDyPW0iPibP0dFAqFQqFQKC4ClKFfBzpdHB1iHcsmWSyZFOS/QlnZajSaYHx8/4CPz+0tr48To0pIQc+QcJeNfIBr+3Xn4027SC7Jd/p6akE+sya/TGFCJn0GxnPPCzPp0i/OoV2IjzeL5s3hsx17SMjMont4GHcMHUiIj3tjflvcyAc89D3srgWd5rTTdnqNli4RoYzv0QmN5Udm9dtJiHcxe87GERT4bLPo4ur7TS1w3LUHKJdmFt0xmy927SOjsIgr4uOYO6ifS7Xt/QwGpnbpaic7vOMkH//1BxL2nCa2UwS3/mkaV15TnQytxFjOgVNpBPt70zXm4i9JbTIdJjfnAfwDnkWrja6/QzPSLTyURfPm8PnOvaTk5TOkXSy3De5fa/iEM2q7LlIKnD8P6uLc+QJOZ+TQJSaUsADfBvdvKrUtNOkb8HlM69udBau78PiPlefy0WbQTKFQKBQKhaJ1UYZ+A5HSyvns2ZjNCQBYrTnk59nKrbe0sT+zfy9WHD1uJ9NpNFzbt0eDxvH1NPD1PTfz9rqtfHnmABfm69aUwPHOPogO7SnZl8GpWW/y4ZYXCQhx/CIf6efLn8c7Zna/1JnRrxdLDxy1k2mE4LqKc/HqtWYK85dVvTa28zG02meQchRCuMc9emhMDF8d2u8gHxbdjp6R4bw6fVKT58jorOt/AAAgAElEQVRMzeHZ2W9jLLFVgEhOTOPV+R8RuMSPPiO6sGpPIi99tYoio80temjXdvxz/jX4eV1cLuL2WCgtXUq56QDh4esQwr2P0c6hIbw0ZUKj+w+NjmFLarKDfFhM3R4dNZFS8tp3a1m88QBWKdFpNMybNIQHrxlZf+dm5Lq+Pfhk2x7MF5QYnNGvp8tjlOWV4v9lIqWdvTFGND6Zp0KhUCgUCkVb4vLzu20i5WWbqoz8mhQXfdSkcQvKjLy/ZyePrVrGx/t2U1TuGC86pks8z109lmBv25fR2EB//nPjNDqFNryWd7i/Ly9dO5HnRo+l5j6usIBXhk0idRoKBoaTbzKx7gfn5d2ag3Nnc/n2220sXrSD7OzC+ju0AYbHt+Pl6RMI87V5L0T5+/HPGVPoGWUrJWgs+cShj8WSjNG4ykHujISj5/jyi838+ss+iovL6u/ghKmduzGug33yu/4RUdzSp1+jxnPG6kXbq4z8SqSU/Pb5RnIKS3j2s+VVRj7AjsQUFvy8pdbxNqWc4ak1K3lhwxoOZTrGorclLOYkyoxr62/Yxriz/0B6htp7Vkzp3NXhWqmLZTsT+HbTPkqCrBRHWSkOsPDBim1sSzjTIF0sFiubNyfy+Wcb2bQpEYvFWn+nGnQJD+WNmVOJCbSVCgzx8eb5qeO4snMHl8dY8/0OTNklBG/LJvrHlAbNr1AoFAqFQtFWUTv6DcRiPe9UbrU6L3fnCnnGUmYs+obT+bkALDl2hO+OHGLxjXPw9bBPLnfr0P7cPKgP+aVGgn280bjgbl0Xd/YbyJXt2rMq6QTfrtlHXkoxGmuNMTUCY5QPxQVNqwNfG6tXH+a1V3+uyib/8cfr+evLNzJoUPPXWk/PLWRTwmkCvF0vIVgXNw3sw4z+vcgrKXU4F9barhOLc3lNPvxwHd98vbXq+LPPNvLv/9xCTEzDFnR0Gg0fTr+eDcmnOZCZQdfgEMbHd3Iprj6/2Mi6wyfRCMFVvTvVugNflF9Si7yULUdOU252TEq4dv8J/jxrrIP8nV3b+ee26lJnXxzcx5uTpjG9i2ux1q1BU+771sLf4MnSWbewMukESXm5DIyM4op27Rs0xvL9xyiKr0gGCpiQlAXCyn2JDO/u2ljl5WaefHIh+/dVexf06dOOv/9jNh4erv9rmtyjCxO7dyanuIRAb68G541oqWebQqFQKBQKRWuidvQbiMFwJeCY2d1gGN/oMb88tL/KyK/kWE42i44ectper9US6uvTZCO/ks7BIdw/eBgdNAH2Rn4FGpOV4ZObP8FeebmZt/+7yq5kXFmZmf++tbLZ51q05QBTXv6Il777ncc+/aXZxtVpNE7Phaens2z0GgyeV9U5Xkryeb79Zqud7Pz5Ij7+aEOj9BNCMKZ9PA8PGc7kTl1cMoK2HTvD5L9+yHPfrOSZr1dw9UsfsSfJeZKy2q6LEVf3xcfTeQUEbyfyfKORt3dts5NZpeT1LRuQ8sLgkraBVWrrPZ9tFb1Wy7Qu3Xh4yPAGG/kAyRRWGfmVWD3htNn1OP9VKw/ZGfkABw+msHzZgQbroxGCUF+fRiWHHD7JPclDFQpnCCEeEUL4CxsfCSH2CCGaHlelUCgUisseZeg3EK02lMDA16hp7Ot03fAPaHwW7iNZWbXIMxs9ZmOYOaqPg0xTZuGe28bSsVdss893+nQ2BU5201JScsjJKXbSo3GcLyzhtSXrsFjdZzD6+T+GXl/TRV6Hf8AL6HR1G1WHDqfizK49cMAxprolsFitPL9wFSVl1VndC41lvLjwd6ft+4zowtxHp6LVVT9Kxs4cwuRbruCKnh2IDHKsUX7jKEfDKikvB+MFmeABUgsLKChrXOhCS2KyaFjw+zgy8n1aW5VWwRDovKygT7DruRcOHnTuJu+ua72SbgM7cMdT16LTt17pVMVlzV1SygJgEhAE3Aa81roqKRQKheJSQLnuNwJvn9kYPCdQVrYRrSYED8MohGj8mkn3kFCWnXQsE9U91L0Zyq8d3oui0jI+XbWLrPxiuoYG8qcbxzK4T4eqNkaTmZ+PJHAkI5OuYaFc16sH3h6NqyUeHu6PTqfBbLaPy/Xz88TPr3nc6wF2n0x1Wte+JdFogggN+43y8q1YLOkYDCPRauuvjBAZGehUHhXlXN6cFBrL+GDrTs6YC9F4gtZIVf6GU5k5pOUWEBXk79DvtiemM+W2UZw4mExsxwhiO0cAtuoA7z48k9cXrWVbwhmCfL2ZO3YAc67q7zBGfGAQBq2OMou9sR/t54dfG6vtnp4XwO3v3UtuiS+Duydzw/Dera2S2xnarh07Mhy9PAbHur4gGBnlvNxkVHRQo/VqLLMfuZpJs0eQuP8Myyf/z+3zKy5rKh+zU4EvpJSHhSslUBQKhUKhqAdl6DcSrTYUb+8ZzTLWrX36sejoIVILq8tedQwM4qYe7jcg5o4dyNyxA7FYrWgvcIMtNZmY+9V3HEqv9jT4cs8+vr3lZvw8G26MBQZ6M3Vaf376cY+d/KZZw9A34+5aeCuU/QKb27zB0LAs5P37x9G7dyyHDqVWyTQawZy5I5pbPTuyi4u56fNvSc0vAG+wABYv8Mi1fQs16LV15jYIjQok1MliRPuIIBY8dIPT66kmgZ5e3DtwMG/trHbfF8Djw0c1W4hKc1Fo9CS3xHZNhQVcnjv6M7r24N1dOzBTvUjnqdExvZPr+RSmTx/ATz/uIT+/2qvHz9+Ta64Z0Ky6ukpwRIBy41c0CCGEJ7ABMGD7PrVYSvm8ECIe+BYIAXYDt0kpHTPs2tgthFgJxANPCSH8gIZlpVQoFAqFwgnK0G8DBHt58+OsW/jq0AGOnc+id1gEc3r1xd/JTuaZ/DySC/LoExZBoGfLlYJyZpQtOXjEzsgHSMw6z7f7D3LPsMGNmufhhyfRvn0oa9ceQa/TMnlyHyZOcgwhaAr946Pp2z6KA2fSmnXclkAIwauvzeK777azc0cSwcG+zLhhMAMHdmjy2Aez0iksL2dQZDQGrf2t//GOPTYjvwZWD7AYQFcGM4f3wdvgPObeFeoy8iv5v2FX0Dc8kl9PJGLQarmpR28GRrm3Tn1D6BIVyshuDY9vvxRYnnAcWSTR6AVSIxFWgclkYfWJk8zq59r9Gxrqx9tv38E332zl5MkM4juGM2fOCMLDHb1G6iO7pJij57PoGBhMjF/D+ysUjaQMGCelLBJC6IFNQohlwKPAv6WU3woh3gXuBmpzFbkb6A8kSSlLhBAhwJ3uUF6hUCgUlzbK0G8kHx7cxWdH9pBfXsb4dh15euhVhHk3fncv2Mubh4cMr/V1k8XCY2uW8fOJBCTgqdPx5PDRzOszsNFzNpQDaelO5QdrkbuCRiO4/vpBXH/9oEaP4QoL5l/PguVbWX84iUAfLw626GxNw9vbwLx5o5k3bzQA6cWFPLz2F9alJBHs6cWdvQYxr5fr5z2zpJh7lv3A/kzbeQr18uatidMZGRNX1eZgmvNSdv6BnswfMpg7xrbs+alkfHwnxsd3cstcjUWv03LTiD48MGWkSwsYlyIH0zIQCIQJqFGg80BahsuGPkB0TBCPPT61Sbq8tWsrb+/eRrnVgkYI5vToy8ujJ9AQ7+cj5zN5bed6dmeca5IuissLacsUWlRxqK/4kcA4YG6F/DPgBWo39CXQE5gOvAT4AM0Xu6ZQKBSKy5Y2/S1VCHGTK7ILXv9YCJEphDhUQxYshFglhDhe8btJQaAfHNzJX7evJbkwn/wyI0tOHOH2FYtbNDv4Z4f28lOFkQ9gNJt5cdMajp13nsivJegU4ry8W23ytoS/tydP3jCWZc/dzTePzq2/QxvBYrVyy7JF/HTyKAXlZZwuyOP5rav5/Mhel8d4cdPqKiMfILu0hIdW/mwXD98p1Pk5fGzKldw1fshla9A6o0tUKM/NmkCIn3drq9JqtJVnwbZzKbyxczPlVlsODquUfHVkP0sSj7g8RnZpMbN/W8j61NMUmWrzrlYonCOE0Aoh9gGZwCrgJJAnpax8wKYCMXUMsQAYAcypOC4E3mkhdRUKhUJxGdHWv70/5aKsJp8CV18gexJYLaXsAqyuOG40nx52NLKOnM9kp5PkVM3FiqTjDjIJrDh1osXmvJBZ/foQG2DvFhvh68PcAf1q6aFoKpvPneFE3nkH+aeH9zhp7YiUkpVOrpEcYym70qqv1zuHDCTQ034TqUtoCNN7tt0a9orW49ZB/QnzsV/oiAsMYGafnm7Vw9lzEWDFKedyZ/xw4gj5ZcYGzSuEmC+E2CWE2JVVS9UUxSVBaOV5rviZf2EDKaVFStkfiAWGAt0bOMcwKeWDgLFivFyc1fBVKBQKhaKBtEnXfSHEFGwZaGOEEG/VeMkfcKzBVQMp5QYhRIcLxNcBV1X8/RmwDvhzY/XLK3MsCQeQYyxp7JD14uPh/P++j9593wcCvTxZdPtsPt+9jyMZWXQLC+H2QQMI8708E5K5g9xaDJDcWq7BCxFC4KXTYyp3LFHnXePaaR8UyJJ5c/l8115O5eYxIDqKWwf1w0vfuIoKikubSD9fvr9jLp/v3svx7Bx6R4Rz2+D++Hu61+PYtxmei7nGhhn5AFLK94H3AQYPHuy+up0Kd5MtpXQpAY2UMk8IsRbb7nygEEJXsasfC9S1C2ASQmixrd0jhAhDJeNTKBQKRTPQJg194BywC7gWW8baSgqB/2vEeBFSyspMbOlARG0NK1bs5wPExcU5bTO2XUd+Tkqwk3np9IyIct6+OZjbsx/rkk/ZyXz1HlzbpaGbB00j1MeHR0df4dY5WwtXroWWZlR0ewxaLWUXlAcc166jy2PM6dmX9/bttJP1DA1nQESUnaxdYADPTLiq0bpeyrSFa6GtEeXvx5/Hjm5VHWZ268V7+3ZSXuP+EMDsHq7nCRgX15F39m+rv6FCcQEVRrmpwsj3AiYCrwNrgRuxZd6/A/ixjmHeAn4AwoUQr1T0e7ZFFVcoFArFZUGbdN2XUu6XUn4GdJZSflbjZ0mFW1tTxpZArTswUsr3pZSDpZSDw8Kc17F/dthVdA0KrTr21Or4x5VXE2Boud2sSfGdeXXMJGJ8/QAYEBHFZ9NvbFICQGcYzSa2pSdzIt/RXbySpOQsDiWew2K5tDcdXLkWWpoQL29eHTUJg7a63GDP4DCeHDKm1j7H87LZnp6CsSIG//Gho7i3/xD8PAxohWBih858PKV5SkNWkp1TxP4jqRQWN3x39GKgLVwLCkc6BATx0ZQZdA0JBY0k1t+PNydMZ1h0O5fHGBwRw0P9h7e5Mo6Ki4IoYK0Q4gCwE1glpfwFm8fgo0KIE9hK7H1U2wBSyq+AJ4BXgTTgeinlohbXXKFQKBSXPG11R7+SoUKIF4D22HQV2Gx117czbWQIIaKklGlCiChsSXMaTaSPHytumMfWtGTyjEZGxbRvUSO/kjk9+zKnZ1/MViu6FkiOtjL5OE9s+Y28Cnfx0dHxLLjqOnz1tjJ/ufnFPP36jxw8ZstMHR7qx0uPXUPvrm23BNqlwMwuvRnXrhObz50hxNOb4VHtnGYULygv4/51P7A57QwAQQYv3hg1jbGxnXhqxBieHD4aq5TNmlhPSsmbH6/hh+X7sFglngYd98wZxc3XNK7cokLRUA7kpXG6LBs8raRZ8tmdk8I1sluDsu53yval/RYdBV4mkltQV8WlhZTyADDAiTwJW7x+rQghamauzAS+qfmalDKnufRUKBQKxeVJWzf0P8Lmqr8bsNTTti5+wuY+9xr1u9G5hEYIrohunRraLWHk55WV8sjGnyk1m6pkG86d4p97N/LC0AkA/OuD1VVGPkBmdiHP/uMnFv/vHnQ6rcOYzY2Uki17T7HnaAoRIX5MHtWDAF+vFp+3LRDk6cX0jnWHafx9z/oqIx9scfwPrf+JbTc9gJ+HASEE2mbetVy29jCLf6tOTmksM/PfT9fRp3sMPbtE1dGzaZzLzGf5piOUlZsZO7Qr3TvWGo2jaMOUm8ys3pbI8dOZdG4fxvjh3TB4uP5vaUdGCv/Ys6Hq2CIlnyXsoX9oNDM69XJpjOOnM/n3h6uRErzz1K6+wm3sxuZd6Oyik0BDNzQUCoVCobCjrRv6+VLKZQ3pIIT4BlvivVAhRCrwPDYD/zshxN3AGWBWcytaUGLk87V72Hk8hchAP24dO5A+7SObe5oWY/3ZU3ZGfiXLzxzjhaETsFisbNzhmL09O6eIQ8fO0b+X666yjeXFd5axYvPRquMvftrJey/MJjo8oMXnbiyHk9P5Yu0e0nIKGNylHXeMG4S/d8t4fyw/c8xBVmwuZ+O5U0zt0DK5HNZtS3Qu35rYYob+jgOn+dM/f6TcZAtN+PzHHfy/28dy85SBLTLfhZzOyOW95du4bewgvA0qWWFjMZaZePCv33HkZHXpx0XL97LgLzfj5ena57r8jPPrb9mZYy4b+hu2HacFK6MqFE6RUsa3tg4KhUKhuLRpk4a+EKLyG/taIcQ/gCVAVdpwKWWttcWklHNqeWl882loj9liZf7b33M0tToiYPX+E3zw8EwGdKyrfG7zIqUFqyxHq2n4Lrenzvml4K2zZa8WQuCh11LqJC7fYGj5y2h/QqqdkQ+QnVvEJz9s45l7J7f4/PUhpcQiS9FpqkuOHTiVxl1vLcJUkShs36k0Nh4+xVePz0GvbX4PCE+dc+PIqxZ5c1Db7mtLXhNvfrm+ysiv5N2FG5k6uid+Pi0fQlNSXs6C37ay/VgyHz/S7GuGbsPZNetOfl1/2M7IB0g4lcHP6w4y62rXFm1qu7a9G1AtwqMBHgQKRUsghLgBGIVtJ3+jlHJpK6ukUCgUikuANpmMD/hXxc8wYDDwtxqyf7aiXk7ZcDjJzsgHMFksfLxqZy09mhcpJcdzF/B78pWsODOEredupaAsof6ONbgqpiNR3n4O8tld+gKg0Qimjevt8HrnDmH06NxyLtqVHDqR5lR++LhzuTs5W/Qr61KvZuWZoaxPnU5G8RoAPl69s8rIr+TY2SzWH0xqET3mdO3nIIv1DeDK6JbbOJo+3jG7uYdey9VjXNtNbSglxnKSUrId5MYyMyedyFuS3SfPsvN4ilvnbC6SCxazNmUCK88MZUPqDLJLt7pdh9ru6UOJ55zKnTGzU288NPaLZgK4uYvjvVAbE6/s0aBwAYWiORFCLADuAw4Ch4D7hBDvtK5WCoVCobgUaJOGvpRybB0/49ylh1UascryetulZOc7lafWIq+NErOR3zO28fPZ9WQYa896fyFnCr7ieN4CTNYCAHLL9rEjfT4Wq2u11gEMWh1fTJzFiEhb6bAAD08e6XcF9/Sqzif0wO1jmHF1fzwNOoSA4QPief2p5s3eXhtxkUFO5e2inMubm3xTEcvSNpFXXmgnzzHuYX/Wk5SabWWSi02n2ZP5KIXlx52e/yC/QtLyDyJl81csuL/3cB7qMwJ/DwMCGBXVns8nzGqRnA6VDBsQz5P3TyY8xLZI1CE2hL/9+XpiIgNbZD4vg56wYF8HuVYj3BbCodeZ0Qjb+UvJynPLnM1JZskGDp1/AaMlA4Ai03F2ZTxEiamuUt/NT233dFx0sFO5MzoGBPP+uBvoGmirghLrG8Abo6ZXPcdcITLMn78/fQPt+3liGFDgcj+FopkYB0yWUn4ipfwEmFohUygUCoWiSbTpbQwhxKNOxPnAbinlvpaev9h0gi0pY+kW+iJh3hNqbTcg3nnW+f4dXc9Gf7r4HM8e/C/5piIAPkxawoNdZjMpckS9fVMKv3eQlVtzyChZS7TvVJd16BwYyjeT52C0mPHQaB3KTXnodTx2zwQeuXMsZosVTzfGJ48c2JGenSLtXH099Dpuv67OxMbNRlZZDgtOLOTDpCU80/MeBgb1ACC1cAkXVmuUmEktXEq/+AEcP2fbZfY0lHHb1N/pGW/bAd529n/0CvsX/oa+zaajRggeHziaRwdciclqwaB1z+09fUIfpo3vTanRhLeXR4vOJYTg7htG8NqHq+zk143rS3iwo0dKSxAZkstz93zJtyvGNugebys4e15YZRnnin6hc9C9btPjuvF9WfL7frJzi6pkIYE+zJjg+m482LyRrorpiNFsqjV8pT42GTZSMPYYrRPEoLjMOQHEYcsfBNCuQqZQKBQKRZNo04Y+Nrf9wcDPFcfTgQPYXNsWSSn/3pKTCyTl1iwOZ/4fI2LXYNA5r5/dNz6K64f3Yum2w1WyqCA/5k8e5vJcH5xcXGXkA1ix8v7JxYwM7Yevru6vnxbpvHa5RZY5ldeHZz0Gok6ndUuW/ZpoNRreeuYmFq3Yy+7DyUSE+HHzlIF0aR/uNh00WOlkOMPms8/Szfsv+Bj61fHZG5k/aRhbjp7hXE4B14/ZUmXkA5SakzmQ+QAjY9eiEc27YKIRwm1GfiVCiBY38iu5bnxfwkP8+GntQYxlJsYO68r0MY5hJS1JoG8J91z/O+3CXnDrvM2BtZbngkW67gHUHAQHePPRy3P59tfdJJ7JonNcKHOmDSIk0KdR4zXWyN+bm8DvGduI9cghzpDDL40aRaFoNH7AUSHEjorjIcAuIcRPAFLKa1tNM4VCoVBc1LR1Qz8WGCilLAIQQjwP/AqMxlaapkUNfS2SAGGkQEJWySpi/efW2vbFuZOYOrg7OxNTiAzyY8qg7vh4umb4SCk5mO+4gF9mLedYwWkGBfess3+kzySS8j+yk2mEB+HeY1ya/2LBx8uDedcPY971ri+gNBcaJPdHrSPKw+baeyJjOiG+dxDpM4m04uUO7SN9JhHq5cfiJ29j2e4EAqM+dmhTbskiz7iLYK/6vTYU9ozoH8+I/q2btFqrLeN86Voifa9rVT0aSqTPBLJKNzmRT3K7LuHBfvzxtqvcPm9NDuQlMiNkD4N8kwF4ulW1UVyG/KW1FVAoFArFpUlbN/TDqZFtHzABEVLKUiFE47arG4hOSDwxubTrOqxrHMO6uh4bWokQghCPALLLHeN9Qw31x6B3CbyPYtMpMkpsSeD0mgD6hL6AQet6rOvFwulTWXy/aCfpabn07tuOmTcOxdev5TOt+2qNVUZ+JeeLPqOzzww6BtzJqfwvkJjRCA86B9xLqJdtMcLH04Mbr+jL+jMeWJzkexDC/bdgbk4RixftJDHhHHHtQ5k5ayjR0e7JdXCpIZrZG8MdxPrewNnzezhv+QWhsWK1eNDJ9yECDHUvKF6qxOhTCNMmt7YaissUKeV6ACGEPzW+k0kpc1pNKYVCoVBcErR1Q/8rYLsQ4seK42uAr4UQPsARdynhAYR5t+xu1w2xE3g/abGdbHBQT9r71J/RXqvxYlDEWxSbkimzZBPg0QutxtBSqrYaSScz+eODn2EsNQGwd88ZNm9M5J337kSvb9lQAoPG7FReZNxE9+DHiA+4g+Ly0/h6dMZD65iILsp3BqmFX9jJvHTtCTQMahF9a6Oo0MjDD3xOepptUWnvnjOsWX2E/71/J5FRLZNA71JFrwki1Gtsa6vRYM6m5vLqgyEI3bUEhBeRnRpIWEg5//ugHC83hV+0Jbp455BXVH87haIlEELMB14CjIAVW+EICXRsTb0UCoVCcfHTpg19KeVfhRDLgCsqRPdJKXdV/H2Lu/Tw9xyKXtuyGb2viRmDj86L5embKDEbGR7SlxvbNWxxwUcfh4++4R4FziguLiMnu5DomGC0OteytpdZzPyclMDB8+l0Dgzhhk698NE3j+GQW1jK395fTp6nRG8WaE22BHhJJzPZtCGBseNbppxbJbKWW0WvtSVjM2hDMXiF1tq/U9CfsMhS0ot+RGIiwDCQ7qGvIETzZ8Q/n11IebmZKCe79CtXHKgy8ispLCjlh+930mt0J06kZtO1XRgje3VAoxEO/d3B7oyzLDtzDC+dnpmde9PBv+15G/h69KR7yEtoNV6trUqDWfzdDoqKyjD5+pKV7Y9OWilLyWHN74eZds0At+piNJtYmnSEI+cz6REczvWdeuLVyFj7xuKjj+Piq52guIT4E9BbSune+qAKhUKhuORpk4a+EMJfSlkghAgGkip+Kl8LdqdLm0BPdOATbplrXMRQxkW4J4t8bUgp+fh/a1j63Q7KysyEhPrxwKOTuXJsjzr7Gc1m5i7/lj1Z1TWwPz2ymyXTbiXA0DTX+v0nz/Hwmz9QbCyHcD2lYTq8000YCmw16lNSWv5y8PeIxbbhUr2z76GNI9B7ukv9tRoDPUJfoWvwM1ikEY8WCKsoyC/l7y8tZefWE0gJXXtE8eQLM4iNC6lqk+rks5ICvj+SyIcHDlXJhvWI482Hr0fv5qSL/zuwndd3r686fu/QDj4YfwNjYlo3Hr8mGWeCWPDH8Tz6jD993WsXNwtnUrIpbG/A4lm9yKQvMJOS7HpJz+ag2FTOzcu+4dD5jCrZ5wl7WTR1Dr5693kkBfnMILPgbUyW9PobKxTNz0mgpLWVUCgUCsWlR8sV2G4aX1f83g3scvLbLWg0/nQM/xofN7tXtybLf97Hwi+2UFZmM2jPZxfy6l+WkHYut85+PyUdsTPyAU7m5/D50T1N1unv36y1GfmVCEFJuB5ZseHco2dMk+eoD43woVP4V/h5XoVB15lg31voFLEYTQN3dLUa7xYx8gHe+vuv7NhiM/IBEo+m8eJTi+zaOPusygO05Jrt8wdsP5rMsu0JLaJnbeQaS/nPvs12snKLhb/tWOtWPerDatFw7mwuzz+xkOJit6QKaVZKA3V2Rj6AyV+HDHTvTvqi4wftjHyAozmZfJt4wK16aDX+dI5YQpDPLAy6Tm6dW6EAngK2CCHeE0K8VfnT2kopFAqF4uKnTRr6UsrpFb/jpZQdL/ztLj0Mug74eg5313RtgjUrDjrIzGYrG1bXnRJhX3aaU/n+WuSuUlpmIiE50/EFrcDsqWHUlV0ZPMQ9u72+niPpGOhnVy8AACAASURBVP4F3aPX0i74NTx09edPcBdlRhOb1x9zkJ9JyuJkYvVO5VXjetKvv314h0eo88WK3YmpzatkPSTkZlFmccyFcCwvG6PZ5FZdXKG4qIztmxJbW40GY/Z2/tg3ujmv4IFs5zvo+7Oa9sxoDB66dsSF/Ivu0evcPrfisuc9YA2wDdtmRuWPQqFQKBRNok267lcihBDYYvHjK+L144BIKeWOeroqGotwHpctapFX0ikgpEFyVzHodQT7eZNT6OjZ+Pj/u5ppk/rVq9tlT42PR6/X8vq/5rBxfQKJx9KJax9CQnE+X/7u6HkRHeLvRiUh3j8IrRBYKl0SKoj1DWh0jfSW5mK89uIigth74pyDPCasZfOQXEinAOeeLZ0Dm/bMUCguMvRSykdbWwmFQqFQXHq0yR39GiwARgCVBewLgXfcNrv5JNa8J5Bmxxr3lyrjJ/dxkOn1WsaMr7v01k1d+tDBzz5re6inN/N6Ni3sQaMR3HH1YAf5lGHduebq/q2WMK6tYfDUM2psdwd5fKdwOnWJtJPpdFrGju/FvQ+MZ8q0/swePwA/7+qYaI2wcvuVCcwb9m+sOXcgS39qcf0BIn38uKVbfzuZAB4dMMot87tKu5h8bpuzj9AwDcOu6NLa6jSYOeMHYtDbr/FGBPkxZVjdeTiaXY9u/YjxsV9MivT2dbgGFIpLnGVCiPlCiCghRHDlT2srpVAoFIqLnza9ow8Mk1IOFELsBZBS5gohGp3GXQjxf8AfsJWuOQjcKaU01tpBloJxKbJsFYT8gNB1aOzUdlilhfzyNLy1Qei1nmiEexOe1cXV1/Qn7VwuPyzcjrHURFiEPw8+ejUR9ZRe8/cw8P30W/n0yG4OZmfQJTCEO3sOIsrHr8k63TpxEMH+3izdeAhjuZkJg7owZ3zrZkEzW8spMGXgowvGoPVpVV0q+eOfplJebmbbxkSkhB69Y/jz89fX2y8qxJ9P/jybT5bv4ERqNv83eTWD4jbb7pJykOVbwZKB8L2nxd/Di8Mn0C8simWnE/HW65ndpS8jo9u3+LwNwcvTzC2zDjLjeite3hdfObousaF89MQsPl+xizMZufTpGMWdU4baLfa4g2BPb36YfiufHNnN0ZwsugWFclevQYR6tc79ZJFmcsvdG66iUABzKn4/VUOmyuspFAqFoskIeYGbbFtCCLEdGAnsrDD4w4CVUsoGW3lCiBhgE9BTSlkqhPgO+E1K+WltfQb385Q7VlTEM3vfisb/Ly7Ndbo4ne3nj+Cr8+aq8P746Kqzzp8o2MTajAUUmW2VdMxSQ4z3ACZHPUiIIbahb6vFKC0pJy+3mPDIALTaascPi8Vqd3yxIoTYLaV0dBWohcGDB8ufN65gU9ZBjOaD5JavxCLLkBL8PdpxQ9xfCfaIbkmVAdc+/7zcYkwmC2HhDXe9l5YsZNZowGL/gghEhG9GiLbpQt8UGnwt1HguiKDPEIYRLaabwj1syfyS7ee/Aiw82nNVg58Nu3a5LUeswo009NmgUCgUCkVboq3v6L8F/ACECyFeAW4Enm3CeDrASwhhArwBx0DV2rCkuNTsu+R1vHey2tX546Tf+NeAB+jgE0l+eRq/nv0b1hol2nTCSmrJXr498wz3dfkQbRsxpLy8Pex2K79dtJ2FS3aSm1vMgH5x/PH+CcR3CGtFDd1LgamE27e/ip+2iFEhSVWpDISAQlMKX596jPu6fIxWeCBawENjyU+7+XrhNrLPF9GnVywP3zeerhe45FcSGNSEHVHLORyMfACZB7IIRNurad+qWFKwRRcpLlZSig6wLftzhAAN1tZWR3EZIoToDfQEqnYFpJSft55GCoVCobgUaNNbs1LKr4AngFeBNOB6KeWiunvVOtZZ4J9AcsVY+VLKlRe2q4iV2yWE2JV1vtrgEfoh9c5xvqyAj5J+tZPlmYp4/8TPACQWbrQz8ivRICkwZ3GyqG3uCv34617e/WgdubnFAOzdn8zjT39HWVnTM6HnZeVzYu8pypthrOam5rWQmnEWq7QS45XvkK/QQ5horzvE1uS+bE0eSFLOa0jpeJ4by6o1h3lrwe9kny8C4ODhVB5/eiGFRbVHnTQafTcQTjwBdJ0RGnsj32wyc2LfKXLS6y69eCng7LlgtYJJDGxlzRRNZWX6h/hrS+lpSGWg1+nWVkdxmSGEeB74b8XPWODvwLWtqpRCoVAoLgnatKEvhPgr0A74VEr5tpTyaBPGCgKuA+KBaMBHCHHrhe2klO9LKQdLKQeHhdh2ZousYPSoPyHY4fxTmKXjbuj+vJMu6VhurTbcyk1mVm5L4PNfdnDopPvLTdXkl9/2O8jO5xSxeVvjkxRarVb++9CHzIm9l/sHPcGc2HtZ8/XGpqjZ7NS8FnQBnrW1oqtHOoHaUkBikcWcLfiQM3nNVwb5l2WOn39BoZH1Gx3L6TUVITwR/s9g92gQXgi/5+zabflpJ3Pj7uP+gU8wp919/OOudzCbmm9xozk4djqTL37dyfItRzGWN20hydlzYeGyPmzb7cT7QXFRYTRn09kjHW9NeWurorg8uREYD6RLKe8E+gHuLYGhUCgUikuStu66n4QtUc1bQohCYCOwQUr5YyPGmgCcklJmAQghlmCL//+ytg7lEhLKreRYIbZ4BR08bJnnLVYrWo3jGkmEp/NEueGetp3Qrn6j2Zr1GRZpb3RYEeiEgY4+tgz1OQUl3P/KdyRl5GDVg+Z7mDW2Hw/OHMUnH6xj07oEDJ56pl4zgBvnDG/xzPMlpc6/AJeUNP6L8S/vruKnBSuqjgvOF/L6HW/TfVgXojs5d0lvXWyf8dnSQOK9z1ft6vtpjHhqHI3I9MKFdAhyvWJSdmExR89m0j4siLgQ+8SHtX3OpbWcl6YivGaAfgAYV4DwAM+pCG1E1es56bm8fPO/MVV4YVgtVlZ+uo6YzlHMffqGRs35y9LdLF28k8ICI8NGduaue8c2KQThnYUb+fzXnVXH0WEBvPvMLCKCm54cMqvAh7v/O4PEhFCeCC1s8nitSW3PssuJOE9vtLTdXDWKS55SKaVVCGEWQvgDmdg2OBQKhUKhaBJt2tCXUn4CfCKEiARmAY8D84HGfFtPBoYLIbyBUmwr6HX6ykusSMwItFhlKQfPpvPq8vXsST5HVIAf80cNYc7QflXtu/m3Y2BQV/bkJtqNM7f9eAACPCKZFvMMq9P/S7H5PFKCBYFO48e06P+Ht87mMv3Jj9tILMyhPBxbELiUfLlzP6d2pXB8d3VW6A8WrKa0tJw7/jCmER+H64wa0YWF3++wk+l0GkYM69ToMdct3Owgs1qsbFi0ldlPzmj0uC2Fv94bgHyzF4cKoujpn45WSEQtySytdRRzuJD31mxnwe/bMFusCAHXDOjByzdNQkMZxpKvefgPy9m208TytX3IyfUFbGUHrxjRcqXdhK4D+N7r9LXNS3dWGfk1Wbdwc6MM/ec+/IUlicewdNLgkW8hbcMBTiSm885HdzeqTn1SaradkQ9wLiufD5Zs4dk/TG7weBeSXeDNkZQI8JbkJGc2ebzW4Kf9R/nv2q2k5ObTJyaCJyaNZkiHtpMM1J3Ee3Qir3wtQRoLXkLF6Cvczi4hRCDwAbAbKAK2tq5KCoVCobgUaNOGvhDiQ2wJajKw7ebfCOxpzFhSyu1CiMUV/c3AXuD9uvpoBUTqzARaLaAZyezPl1BgLAMgLb+QF39dQ6C3J1N6dwPg6LE0DMvDCfLPxxpfSlxoGDd2GM2osOra9J38RhLvO4wCUwYl5gLKpZEYr+7oNdWu4RuPnqLcr4aBIwRmb9iXe47ISTnoQs2UJXlSstOPHxfv5La7Rte7q2+xWPnyvbX8+v0uSorLGD6mO/c/PoWQsPrXTO649QpOnclix65TAHh763n04asJCfatt29taPXOE9Zp9W3zkozwDGZ+x+msz9qPEKGU0pMoD+ju1wVhfBmLtcCufYi3awbl3tPneGvFlqpjKeGnPUcZ2D6cSZ1ewGzaQ0wEzJwOE8Yc4qmXZ5FfGMpD944nukbJw207T7Lk570UFJQyfEhHbp45BC/Plin9pqvl3NUmr4t1iUksSjkOXrZd5fIALVkDPEnYksHB/cn07d/w0npfLdrmVL4noZlLpwlBVsXzwFX2H0ph0dJdZGYVMrBfHHNvGoa/n1eDxljx016++WgDGedy6dU/jnsfvZouPVyv+LD55BmeWLK86vjg2Qzmf/kDvz08j6iApns8XGxs3xzG5BHleGnUrr7C/UgpH6j4810hxHLAX0p5oDV1UigUCsWlQdu0qqoJAbRAHpADZMsmZDmTUj4PPN/Qfp4ayeGz2ykwOhpOC3cdZErvbiQkpvHHJ76m3GTB5ubtjVe0B4Pf7u7QRyO0BHpEE1hLOTazQUCJvczDu5yOU0+h97a9fZ+hhfgMLiTrnWjMJgsehrpP5ZfvreXrjzZUHW/8/TBpKTm88/V9db95wNvLg7+/PIufjm7gl7xVnJfnWe6zlLA8Qb/AHvX2d8akO65i35pDdjIPTz1jZ49s1HgtzdmSNL47u5gwgydl1lySiiGpGLbmJDK//cNojO9hstpKJgYYhtIx+Kl6RrSx5ojz/A2ZOUswx9mvaQX4GXnt+XzCop7H16e65vmaDQm8+Fp1pYejiWkcOJzKG3+7uaFv0yVG3TCMdx/7jJKCUjv5pHljGzzWwl0HHWRSJyiO1JKbU9zg8bZvTGTt0r3Q3tHtPyKk+Y3YEWP71N+ogr0Hknnsme+wWGy7xseOp7Nzz2nee/N2dC6WrNyyLoE3XlxadXxwzxmevP8zPln6CP6B3i6N8Z2Tz7zUZObH/Ue5b/RQl8a4lCi2HlFGvqLVEEJcAeyTUhYDo4CBQog3pZRnWlk1hUKhUFzktOngTCnlDCnlMGxZaAOBtUKIZt6Wcw1PjXODrLjcFie9cMnOCiO/mtRzuazb1PCEaYO6ObrQRnfLrDLyKzF0NtL9Wp96jXyAXxY7RimcOJZGwkHXPs7EwlN8kbuQ8/I8AKeKU3n16P84W5rhUv8LmXjbGO58eQ6+gTaDLLZrFC8u/TOhMSGNGq+lKbOaEEiMFvsM81asLElPYEjsevpGfsvA6N/oG/U1eq1rZeh8DM533dsFOv9cgwPO2hn5AF9957iDvXvfGY4kuF49siH4Bfnyyi9P0aG3LYzU28+LOU/N4LoHr27wWMVlzvMMCIOWAYM6NHi8X7/fia7IjKbU/l4UwO3T6q+c0RA6RgQyapDr4StfL9peZeRXciIpk207XEvWCfDL4p0OsqJCI2tXOBrvtVH5zHKQ13IuLnU6dnGtdKpCcSFCiHZCiLVCiCNCiMNCiEcq5MFCiFVCiOMVv+v6h/A/oEQI0Q94DDgJqNJ6CoVCoWgybXpHXwgxHbgSGI3N0F+DzYXf7UQFD0OIAi4MyZ7QvTMAaen5TvulZziX18W9k4fz88EEyszVxopvUAkGjQmrFJhk9WkbOC3C2RAOlBQ7dzEudrFE26qMTVgvSFhlkmbWZG7ltvbXuzTGhcx9+gZuevwaCnOKCIoIbFQ8trsQSPx0peg0VszS3kU9syyTcmklwHNwg8e9dmAPPly7g9IaGeuFgI5RVwDLHNpr9b0cZGnpeU7HTsvIp2d31126G0LvUT344MAb5Gbk4RPgjUcjwwQmdO/EjtOOi033TB2Bf4BrO9Q1KS4qQwC+Z4opCzVg8tWhMVmZNrw7I/rGN0rHCzHoddw6dTDzrhnaoGu2tmdBWgOeESW13K+u3sdg+8w3nXDcLJzYo7PLY1xK5PtcfuEKimbDDDwmpdwjhPADdgshVgHzgNVSyteEEE8CTwJ/rm0MKaUUQlwHvC2l/EgIcbdbtFcoFArFJU2b3tEHrsYWUz9TStlDSnmnlPJjdyuh1fclNnwuf5k6Ds8aMeQTe3TmzpG2Otr9+jhPZNW3dyyFpmJMVtcjDqKD/PngDzfQp50t+3y/3sV0jswmRF9CmEcxofoiRIXR3Tuqq0tjDh/TzUHm5aWnR1/XkvuWWpwbEqXmUqdyV9F76AmODGrTRj6AQWsm2rOAcI8igvXFVZ8/QKhHKJ6a2srv1U10kD/v3T2j6ly3Dw3ktZun0KvDreg8htm1FZowvHzvdxijb2/Hc6jVCPr0jGmUTg0hKCLQzsiXUrJ3/VGWf7GRc0n1e3vMHdqf6X26U3n2dULDH68cxt2z6i9n6YwRFde5xiLxyjDif7II3+QSbr6++VzSO8aG8PDs0fj5NOyc9+3t/BnRr4/rCbZHjHEMBQIYWYvcGTcN6sP1/XtWVY7w0Gl5bMIo+sa2xWoXLU8m/Uku929tNRQXIVLKNCnlnoq/C4GjQAy2Ur6fVTT7DKhrNbxQCPEUcCvwqxBCA+hbTmuFQqFQXC606R19KeVDdb0uhNgqpRzRUvNrNEH4BLyMwWsWQngxZ2g/pvbpxoGz6cQE+tMxtLqc3pwbh7Flx0mSU3KqZCOuieUTy3ec2JqKt9aTa6Kv5I4O01wyagfFx/LtQ3MoMuXzj4T5mGX17r6HxoK/zkiooTcDgwa69F4e+NMU0lJzOHks3SawWClNSuPRiX/j1aWPEhRed9neYcH92Xp+r4N8aEg/J60vbTw1Zny1ZRRaPBEIZsbObNJCReW5llLajeMf8hVlJd9jLt+JRtcBT+9b0GjDHPrfd9cYEhLTyM2rTuxw562jCA9zr/FSnF/C0zP/zbHdtqSNQgjm/mk6tz11Xa19dFoN/7xxCg+PHUFybh69oyII8mlYcrqaXDd7GAkHU9m4+ggAer2WOx4YT6duUY0es7m4cWp/Vvy2D1ONcna9YoLo2sk1rxyAGbeM4Njhs2xeexQAvYeOux6eQIfOro+h1Wh4bcZkHhwzjNM5efSKCifYp+HeE5cKQ0P68sO5rvQ0ZBOtLwTO1ttHCDEfWwUY4uLiWlhDRSsSKoSoGff2vpTSaRJfIUQHYACwHYiQUqZVvJQO1HWD3gzMBe6WUqYLIeKAfzRVcYVCoVAohKylPNjFgBBir5RyQEuNP3jwYLlrV50V+OwoKzezYXMi59Ly6N47nDeKPqbAbJ9QbH7HGcyIvcrlMfflrmdRypsOcg+NN0/2+AiD1uCkl3PSz2Rx54jnsSLAWIaoOPVT543hj/++rc6+Uko+SPq2yoVfKzRcFz2RW9rXbsS1ZYQQu6WULvvaR/UKknd9U51srsyqxU8/kFva30Rn39Z3eS4qLmPNhqPkF5QyYkgnOncMd7sOH7+wmO/eXO4gX7DheTo2YNe6OUg+lUVaai7dekUTWE91iIZeCw19LlTy3tPfsuS91VhC/JAeOjT5JWiLjHyw/a+069qwhYgzSZmkn82jW+8YAoMcEw8qXGdp6ofszPmt6vhv/X5wy/WgaPu4+mwQQvgC64FXpJRLhBB5UsrAGq/nSildS9yiUCgUCkUz0aZ39F2gdVYpsrMhNNRBbPDQMXFsTwDWZOyiIM8xa/jKjG0NMvQ9tc6/xAfogxpk5APsWXsEWVrOhXvPu1Yfctq+JkII5neaw/Uxk0gtTaeDTwzBHoH19rtUMVm1nC/TOzfya7k+WhJfHwPXTunv1jkvZPeaw07lu1YfcruhHxcfRly8o/dDa7J7zWGEVaLLKnCQN9TQb98xnPatsJhzKZJSkmN3rLv4/y8q3IgQQg98D3wlpVxSIc4QQkRJKdOEEFFAZutpqFAoFIrLlbYeo9+mKDEXciBvPQX334Klnip/5lpeb0isPkAXv/4Eezh6/Q0NaXiG86Bw567cwRF1u+3XJNwzhIFBvS5rIx8gq8zX4Vzml2exL3cNRQ/cxsXsKdNYAmu5voIiVPwzQGAtoRS13ZcK9+Cr64RFCkAisNIB3LsqpbhoEbZYq4+Ao1LKN2q89BNwR8XfdwA/uls3hUKhUCgudkPfbRncjuRv5Y1jd7F+00v4L17J5yvncr6s9vJlw0J6Y9A45tMZHdawSAOt0HFn/PN09xuMBi1+umCujrydkaHTGvwehkzsQ2wXx4RbM+6f0OCxLjesUoOUUGbRklwSRK7Jx+5cbsn+kf8kzmfj5lfwXbScb1ffRYm5oI4RLz1m3DfRIVdBaHQQV17b8GoElyIz7p/oIItsH8qIaS0WfaRwgbERIyg0G9AJKxHJhQRAcP29FAoArgBuA8YJIfZV/EwFXgMmCiGOAxMqju0QQqyu+P26OxVWKBQKxeXDxe6iWHdgeTOQVVTIb0d2cFa+g0aU0Wu5LVFT+58PsDL2bea0f9rW0NsbaiTZCtD78mSPebyZ+A15piI0CK4MG8DsuMkN1iHYEMlt8U83+b3o9Dpe//FxPn15CXvXHSUoIoAbHpjImBtcz0ieV2okOSePjqFB+BpcCx3Ym3WOlSmJ+HkYmBHfiyifi28HM8gjkqSSHuRXnMux4YMIoyNv7dpEB72RQ2UfoEdWXR8RS7eysf2nTI66yzbABddHSbmJE1nniQ7wI9S37cVY789OY0VKIj46PTM69ibahXM2eEJv/vLlA3z3n+Vkpp6nz8iuzHtuBp4+DQsxuVQZMbU/z352P1++t4L0/EKG9+7EXc/MwMPg/gTbJaZyfjx9hFMFOQwMi2FibBe0Gvev+1qsVtYkJ3E0J5PuwWGMj+vkXj2sVjRFZ4mx5EMpVfevQuEKUspN1L7hML6e7lFCiJHAtUKIby8cpzKbv0KhUCgUjaVNJuMTQhRSR/y9lNItlmKPfn2l7o/zeGTZD9y1dDMai6NKUqtFPPccPPssaLUOr5usZpKKzxKs9yfM8+LOxfP/2Tvv8KiqtIH/zsyk914gkBBIQg9VuoCoIMWG2EDsuu6q69pdV91P3XXXsu66qy4uKjasqChNpCm99xIgEAgJ6X0ykynn+2OSkGEmYSZMGpzf8+TJ3HdOeWfuvTPznvOWf6xax/sbtlFjseDv5cWDlw7nzuGDmuzznz0beHXnmvpjf50XH112I4OjnZcaay6ZxcXsz8snJTKSlKhzx8c3JwHb+s0bOVZ1ilBdEE+tXsWqk5lorFb+vOY7bv1qk9PrA60Wzro+vtqxl78t/4UKoxGdRsONA/vy7MRxaNpJicH/7tvEX7evqj/203rxwWU3MCymdbKLm61W1mVlYTCZGZnYlUBv73N3Og9aKxlfjcXCsz8s54e9B7FKSZi/H3++6jKu7NnD7bHOh2KDnunLPiGz/Exs+qXx3Xh/3PRWNbKNFjN3LPmG9Tkn6mVD4zrz0aQb8NW5twZ9vKSEvafzSI6IoGe0G7kZLBYyn5xO4pvf19+/g4GtUrp8M6pkfBcu7n42uDn2dOAuYBRw9gUkpZTjW2JehUKhUFw8tMsdfSllEIAQ4kUgF/gY22r3rUCr1ck6VVFOuMXCf665DO0UyXVPbyfk9Jm68eWx/gR/uRRGj250DC+NjtSgrq2hbouy/OAR3l27uf5YbzLxt59/oV+nGAZ3cW60F1RX8ebuX+1kerOJl7et5NtJt3lELyklzy1fwfxdu+tlQ3zCSViWh0YjmHDLSCbcPNIjc3nXnsvvj+xn1clMAKwaDf+bPgbzlVqH66MyLojALxbZXR9HCor404/L61exzFYrn27dRWpMJDcO7OcRPc+HYoOe13f+Yiertpj4y7aVLLzq9hafP7O4mDu/XkB2mS3sIdDbm39Nm8KYpESHtsbqGhb8exlbftpFcEQQ0+6bwMBxvQHQV9dw8MhpoiKCSIhvHwts72/Yxvd7DtQfl+irefTbJaR3jiMmqOnKAA05vPM4H/9jMadySxg0qBu3PjqFkMggl/vPPbDFzsgHWJOTyU8nM5jUNc3lcc6Xbw/vtzPyATbnZvN1xl5m9nI9seSLK1bx0fYd9ffUpNQU/jHlKnSuLFpoteQ9dQdr+hQ43L8KRUsipfwa+FoI8Scp5YttrY9CoVAoLjzae4z+NCnl21LKCilluZTyHaDV6rkZLbZkayX6ADb26MamW5Lsni+877omjfwLicX7DjUiz2i0z97i05isVgf5zsIcjyWr+/nIUTsjH2CLsZi1JbnsWHOA1/4wn+v6PMNvp7zBzws8s+u2KTfb7vhkcTh7+nRyuD4qHpjlcH0sPXDYqatKU+9ja7KvOI8aq8VBvrvoNObac7nt10M8NuM/3DL0z/zf/R+Sdfi0x+Z/bvmKeiMfoLKmhscXL6XG4qjTCze+yYd//pp9Gw6z4cftPDPtVdYt3MrS1fu49q53efi5L7nlt3P54yvfY6xxLwlmS+DsHjJZLPx86KjLYxzYepT7fjuXVWVVZAT4MH/vCe66/nVqDDUuj7Gj0Hluke2NyFuKzbknG5FnO5U745djx5nXwMgHWHIog692n7uSSB39QsdScEmSw/2rULQGUsoXhRDThBCv1f5NaWudFAqFQnFh0N4N/SohxK1CCK0QQiOEuBVwrFnnIkKIUCHE10KIg0KIA0KI4U12aPDrceXBnnRdWkSNj5ZdN/XE6utN0vLM5qrS4fDWOYYlNCUHSAxyvpOaGBTmkLStuazKdH4OqnoEI0JDEH6+VOtryNyfw+uPfe4RYz8hyL5KgVVqWLa3Dz1/LsXkq2XfzX2x+noTt2SnQ19vrfNbrqn3sTVJDApzGnCaEBiCTqNh/7bjPHfnXPZtPUZJYQUbftrLEze/Q1lxs2/LegwmExtPOBp/RXo9e3LtFxMObjnK9rPK+Ukp+fC1H3jl38vQV58xfH/ZdJjPvt1MW9PoPeQk5Kcx3njtR0zRIVB3/+i05IUFsOCjX5ru2IDG7sukoNbNQdc5yHm1j7Pvr6ZYsuegU/mPu/a7PEaALpjbk15mwOpKTL5aiqHA5c4KxXkihPgr8DCwv/bvYSHEX9pWK4VCoVBcCLR3Q/8WYAaQV/t3Q62sufwTWCqlTAP6Aweaaqw1nnkcXliFtUzwr3fePJ7dgQAAIABJREFUov/8/Wi2bENUVEBO6+6CtRXX9e/tINMKwTV9ezXaJyk4nGuSHPs93G+Ux/QK9fVzKtcarQgncb7fffCrk9buMSO1L1F+9gn0YoutxJhD8dqyk96f7UazZRs4uT4m907Dx4nBd72T97ctSAgK5frkvg7yunO2cN5arBZ7L43y4ipWfrftvOf20mobjccP87c/zznHnJelzq40YLE4epGs3tD2HhPO7qEgHx+u7Nnd5TFOVBochUKwZfcJR3kj3NVzCEFe9gkSuwWHc3VS4/dyS3BLz/6E+dif11AfX27t1d/lMQqPFjmVl58sc0uX6CItUaYQvLbs5Bi4/mYqFOfPZOByKeX7Usr3gYmA2tVXKBQKxXnTLmP065BSHsdDrvpCiBBgDHB77dg1QJP+rtoaQfhOqEoAvzIri9//kN9PqC2R1acPbNoEBc43f6SUfHXyVxblbMFkNTMupj+3JV6Gj7b1M2x7gksSE3h56uX8c/V68iuq6BQazBOXjSYttunEV6+NmMygqE78dDKDQC8fbumRzuh4z7nIzujXh4+278BgbuCabbYSvKsMcMz2XlpY0ax5TuoLuGHdy/QO7spdyVey4JpbeXvHRvYU5pESFsHvwuPx/s0T4FdruDRyfXQKDebtGVfz0rJVHCsqIczfj/tHDmVir5Rm6dUS/G3YJNIj41l2IoMALy9u7pHOpfHdACgtcv7+lRQ0731tiFaj4Zb0/szZvMVOPrJrV7qF2+829xraHY1GYLXaB0J0SozmkJOwEL82yGx/NjcP6kdRlZ55m3ZQYTSSFhPFC1eNJ9jX1+UxgoP97LwV6khIjnF5jOSQCL6bdBtzD2zhWHkxA6I6cVfPIQR4tWzSw7OJDQji22tu5e2dm9hfZMu6/0D6JcQHup5rNbncm9U6K9K7wZq1RdLVzWiSnIo8Pnj/EXaWqXLnijYhFKhLnOG6S4tCoVAoFE3QLg19IcRbNJ11/6FmDJuEzSXzAyFEf2Ab8LCUskmf44BcQUAu9Eztw6NXnFUaz88PujjPRP6/zKV8fHxl/fHHx1eQrS/kz31nNkP19sH09D5c268X5QYjIX6+LmWJ12k0zEodyKzUgS2iU2JYGPNuuJ7X165jf14+McIH46e78TlViYzwdggRGDSmecnG9BYjeYZS8gyl7Cg5ykfDH+OvY85RKrGR62NUcleWPnA7Jfpqgnx9XEsa1opoNRpmpgxgZopjffeBo1PZtcExpnzwpakemfvR0SPx9/Lii917MJjNTEzpwROXOubBiE2M4qbHp/HZ384YZiERQfzhj9fy+JuLKT9r53vqFW2f6FAIwYOXDue+UUOpMtY4eCm4wm0zR/P391bYyXTA7DvGujVOckgEfxk20e35PU1iSBh/v7T5eowb1Ztlf9xO4SXBGKO88C41E76lgivvGubyGOUmPQ8U/EBxTWWz9VAozoO/AjuEEKuwJR0eAzzVtiopFAqF4kKgXRr6OJaa8QQ6YCDwoJRykxDin9i+TP/UsJEQ4l7gXgBf/zC0GsGood35/T2Xsb80Fy+Nlh7B0U1OZLKa+ebkOgf56vzd5BlKiGknZfYKCiv45JN17N5zktjYUG6acQn9+zddQk2r0TTLQGlJBnXuxGc3zag/zpmSx5pvNnNoXw5b1h6p3/XtmhLD7MdcNyoaXgs+0WeympeYKlmWu50ZXVxLxFhjMXOgLI9InwA6BYTWy89+H48cyeOTz9aTlVVIjx6xzLx1BF0SIlzWtzW4+vbR7Fh7mJ3rD9vJ+g1z3f28KbQaDb8bMYzfjTi3oTb7T9dxyaT+bFm2m+CIQMbNGE5weCBvvDCdN/+3kr0HcwgP9eemq4cw+TLHcAR3aHgtBASG8/a7K5h160iCglzfja/DW6vFu5n30NSrBqCvMTPvi/VUGEx0iQ7hyd9PIiw04NydW4BiYxVZlSUkB0US7O3+e3G+DB3fk2vGD2Tp55vOyMb1ZOJNl7g8xvLT25WRr2gzpJTzhRCrgSG1oiellJ7LcKpQKBSKixbhqezn7R0hRCywUUqZWHs8GnhKSjm5sT6DBw+WGzduIrOqiAc3fsWxSptnXd+weP4z7AZi/Z27mFaY9Ez+5Xmnz/13yEP0DE44vxfjAQwGE3fdO5fc3NJ6mVar4Y3XbqZvn7bXz1MU5Jayc91hwqKCGDAqBW1tMjx36yMHpcbKAW/Pqj++uetYftO90UunnuWnDvLs9kWU1OgRwMROPfn7kGvw0dqvsZ3MLua+33yAwWCqlwUH+zF3zl1ERLheeq212LvlGKeOFdBzYFe6dHfdbbw1qTGZ8dJpz5n40d1rITi4sxx8ye9I6RHL2/+ejUbjmcSS7iClpMZkwce77dZqX9u7gg8Ob8JkteCn9eLBnmO4O3VEm+hy7GAuGbtP0qVHDD0HuFfOdM6RJXySdcb76tcJr7l1PQwePFhu3doSa9OKtsbdzwaFQqFQKNoT7ctnuBYhxJu1/38QQiw8+685Y9aukJ8UQtT5GF+GLcNtk2i1Gh7e9E29kQ+wpySHZ7b90GifIC9/0oIca8uHewfSPTDOXdVbhDW/HLQz8gEsFitffb2lkR4dk6i4UC6fPoTBl6bVG/meYGj4uWPqiwxV/GHzt5TU6AFbLMqSUwd456BjQsDvv99mZ+QDlJdXs3jpLo/o62n6DEniyhlD262RD+DtpfNYdQdnZBw+zfYdx1ts/KYQQrSpkb/s1AHmHFqPqbYUY7XFxN/3rmBLYdvksUtKi+PKGUPdNvIBBof3aAGNFAqFQqFQKNqW9uq6/3Ht/9c8PO6DwKdCCG8gE7jjXB0yyvM5WlHoIF+Xn0l5jaFRd9VH067nsZ3vUWayGXk+Gi8eT7sBL037eMvzC8qdy/Ody9szBpOZJbsPkVVUSnqXOMakJLXoLuvVnYYxyAXjYGVuBkarY/32747vQZvjQ4i/L5P7pxER6E9+I8nsCjyQ5E7RcnTE+wVs98zSPYc4Xti8e2ZxtvM10iXZ+xkS2XT4T3tjYHh3ru08gm+z17e1KgqFQqFQKBQeo31YnWchpdxW+3+Nh8fdCbjlhufTiGGuExq0TSRRSw3uzBcjnmF94X5qrGZGRPYi1LvpOFopJSvz17KhaBtewoux0cO5JKJlktgN6N+VD3DcWR7QjB2xtqRUX83M/35JZsEZj4txPbvx1q3TPGrsB+i0TInvydWdriA12NFbwxlnu+fXkVNcwTsHbDHFb6/YyPt3Tyc9vQtr1zmWgBuQ3rHOx8WEEJDeAc9Pqd7ArDlfcDT/zD0zNi2Jt2ZOa/IzrSGNfS42Jm/v3NVtAt6aCg5UHHHyqahQtAxCCC2wr7bkr0KhUCgUHqVduu7XIYQYKYRYLoTIEEJkCiGOCSEyW1OHxKAIBkU4xqxP6tyLAF3T5aj8dT5MiB3AVfFDzmnkA3x4/EvmZH7KnrKDbC/dwxsZc1iU83OzdW+KPn06c/VU+0WEbklR3HSj69mq2wMfrt1uZ+QDrDqQyepDnr1Maqw1bCvdzLK8ZS73uSw+lVBvx6RrmsIzxlC5wcjrS39lylXppJ+VCHHkyB6MGe2ZbPYKz3P7baOJjws9d8N2xsfrttsZ+QCrDx5j1UHX75nrEx1r3euEhmu7tn11A3cxWIw8t/c1filcT4Exv63VUVxESCktwCEhRMdyg1EoFApFh6C9b7/MBR7BVgrP0lZK/GvYdP60/UdW5x5BKwRXJfTm+fRJHp2jzFTO8rxfHOTfnlrKlbHj0Gm0Hp0P4OGHrmDilX1rs+6HMGJ4D4/Gsbc0JeV6ftjk3IV4R1YO43sme3zOdYVbuKHzFOL8bLHp67Zn8s3SHZRVGBg5qBu3TB2Mb23N9gCdN++PupXndyxmT0kOfhovDNkgCuxruu/IysHbW8frr97M1q3HOHa8gJSUWNL7d7zd4ouBqKgg3v/f3SR2jXS7784D2Xz+4zbyiyoY1CeBWdcMJTiwdatYbM/KcS4/nsOEXq5VT7gkKpG/DprKP/ev4XR1OV0Cwnii7wRSQpquSNIeWVe4hVxDXlurobh4CQP2CSE2A/XlfqWU09pOJYVCoVBcCLR3Q79MSrmkrZWI8g3k3RE3UWWuQSsEvlovp+0slhKqa7ah08bj693LrTkKjMVYpONaRoW5Er2lmmBNy2ReT02NIzW1fSQIdIcak5kHnv+CIlMFBDu66CeEh7TY3KcNBcT5xfDz+oM89+YiunXJxd/PwEffnWTXgWz++acb6tv2CYvjm/F3UV5j4ERBKTO2zG9UVyEEQ4Z0Y8iQbi2mu+L8CQsLaJaRv33fSR5+8SssteUeD2bmsWlXFu+/MhNdKy6wdYkIYVPmSadyd7g+MZ1ru/anwmQg2Mu3RRMftiR5hgIAAjQGOnmXnqO1QuFx/nTuJgqFQqFQuE+7NPSFEHU+5auEEK8CCwBj3fNSyu1toVdTrvqllR+TX/o8UhpsbX3HER/xHhqNv0tjJ/jF46/1Q2+ptpPH+cYQpPNcjezThjw2F21Dq9EyPHwI4T7hHhu7NVm96TBZp4rx8YKaQKBBPH6XiFAm92+ZkEcvoeFQxUEKjfl8vWIPzz38CUldbLuB+mof5nw2if1HRtOre6xdv2BvX/p0imVcz26sOnDGRVoIuH+c6zW/FR2XT77fXG/k13Ekq4D12zMZM8S1nXRPcNvIgSzenUGVsaZelhAewtT0nm6PpRGCECfhKR2J1KBkRgdlMC74EDoheamtFVJcVEgp1wghugI9pJQ/CyH8Ac+78CkUCoXioqNdGvrA62cdN0ygJ4HxrajLOakxZ5FX8jRgrZdVGVZRXPFvIkOecGkMH603s7pOZ07mJ0hsxoCX0HF74gyP7ZStLdjAe5kfYK3V85uT3/FI6u/oG9LbI+O3JqdrqwZoTRCULTGGgsULonz8+OTeGQT4NJ0/oTlosCKEmUW5SwGYdv0ukoLOuPz6+xm579bFnC64x8HQr+MfN0/mkw07WX0gk2A/X24Z1p8RPZSL/sVATn6ZU3luI/KWIjk6gvn338QHa7eRVVhCv4Q47hw9qEXumY5Aqr+JwJCDba2G4iJFCHEPcC8QDiQDnYB3sZUAVigUCoWi2bRLQ19KOa6tdXCHqurlNDTy66ioXuayoQ8wPmYkKUFJbCjajk6jY1TkEKJ8IjyiY421hk+y5tcb+QAmaeLj4/P5e/+Ot4fVv2en+sfaGvDPty2OTJ2USmSQ5zwg6gjRBeGtkXZnOTXAMa7Xz7eG1OTjwACn43jrdNw5ejB3jnar+IPiAiC9Z2dO5JQ4ynu5VsXBk3SPieDl669o9XnbI8dLP8N5MJZC0Sr8FhgKbAKQUh4WQnS8ZBcKhUKhaHe068xrQoi/CCFCGxyHCSHahVVqsdaQWbGOI+VrMEvnO2FaEeT2uJ3947khYQrXdproMSMf4FR1DlUWvYM813CaMlPHqwXeP60zU8f3tZMlxIUx+9qWcYMvrjFgxd7t2iidr5OFBce0iA6Kjs1dNwwnPto+Dn76xHRSk9T10pbk1zh+LioUrYhRSlkfRyOE0MFZXzYKhUKhUDSDdrmj34BJUspn6g6klCVCiKuAZ1tj8ipTNouOTyDAqzNpoXcTGzAKgALDEX7Ifhq9uQgAjdQx1NeHIJ3Rrn9o4G0e1cdoMVForCTGN9jtLPzh3uFohdYh4V+gLoAArWt5BNobT99/BZMu7cWO/dnERQUzblgKPt4tc0mbrY6/u7ZVdWFccIadrMrqS6HZB62+hHDvAPwayeuQr9/IgZI5VJpOEO7Tl17hvyXEp/XitBXNp6wmg635z9E7/EH8dFEu94sKD+KT12ezckMGeUUVDO7bhb4p8S2o6cWD2Wohz1BOpE8gPo0kS22MGu1l1Jh/wFvj6JWlULQCa4QQzwB+QojLgQeAH9pYJ4VCoVBcALR3Q18rhPCRUhoBhBB+gE9rTW6ylmO0FGG0FLH+9MOMiZ9DpN8gVp5+rd7IB7AKM5v0XYmkir5BORTWBPL96XSeib7SY7p8eGQ9/834hXKTgSifQB7rcwVTOrteszrEK5ixUWNYkb/KTj45biI6TXu/DBonvWdn0nu2vOuzVQrKDL6E+BrqZesru9PN30Ss5gRewkKBJZBDNTGUHH+OeUcHE6Dz4fbkETyQNtZurGLDbtblPojEDECufjVFhh1MSFiAr65jJke8mJDSzImKHygx7GVCwlcI4fqim6+PF1eN7Xg5MdozP57czWv7fqLAWEmwly/3pYzh9u4jXO6frTdSIBPo4Z1PqKb63B0UCs/yFHAXsAe4D1gM/K9NNVIoFArFBUF7t/A+BVYIIT6oPb4DmNc2qlg5WvY5AV7JFBgyHJ6VWg3/zhxLofFMGbz7jVVE+Jx/vPjq04d4dd9P9ccFxkqe3vYtPYKiSQ1xnvTNGbcl3kxn/3g2FW1BJ3SMjhrJiEiV8d1VjhRHEBNYSfcQLT1DEhgTOYiNBc+SQQ+7dv66asK99RTXCP5zaDUJAWFMTehf//zRsi/rjfw6aqxlnKz8kR6hnvUCUbQcFaZj5OnXExswuq1VuWg5WHaap7d/Wx9WU24y8Oq+n0gMjGBsbKpLY1SZDmDQ+LHVUJcUM7eFtFUoHJFSWoUQ87DF6EvgkJRSue4rFAqF4rxp14a+lPJvQohdwIRa0YtSymVtpU+xMZfHNi+hR6gWncbeBV5KqLaccRmN9w8hOch1t96mWHhyl4PMimRR9h63DH2N0DAhZhwTYjpUrsN2g0TD6cpgnutzM+PiUjBZq9lS6I3lTHglAFYJBsuZW2vhyd12hr7RUoQzDGbnckX7xdDIuVS0Dj9m73bInQHww8ndLhv6SYFdOKFf72nVFAqXEEJMxpZl/ygggCQhxH1SyiVtq5lCoVAoOjrt2tAHkFIuBZY6e04IsUFKOby1dFmcreWnnKMYrXGkR2bbPXeiKoYqsy2qIEDnzUsDpqERgozyHOYcWcGh8hySA2O4u/tl9AlNsOsrpWyyhJ61kcX95Yt3EfCTlRtnjyI03POZ5hVnI+kZFsqbh77jw2MB3Nh1BL1CJrGn9Hu7VpkVkegtPg162Z+/GP/h5FdvdBg9xt91d+P2zJb1R1j41WYqyqoZOqoH198yHB/fCy+vuUBLtP+wNpv/XJ8bFwO298CKt9aCRiOxSoHJrHVq/DfGJVHTyMj8Bl+tqQU1VSga5XVgnJTyCIAQIhlYBChDX6FQKBTnRbs39M+Bb2tNZLB0Z8XpJAB+ye2OwaKjV9hpAr10DAifyKxuMxkbk4VFSsbFpRDk5cvp6lLu2/weVWZbkr48Qxnbio/x0YjfkhQYzfefbuCrD36lKL+c/kOTuP/JKST2cMzAPaVzP5bnHrAXSjD8UsmC0xvZvO4w73x6P94+Hf10tl9CpR8RVRay/fIByDWU8sKer3i29zUMiQhhQ8G3GCxGjpRHs6Woq13fyZ3sqwN0C76RPP1G8qs32Mmi/Tt+GMXqn/by1z9+U398YE82e3ec4C9vzWxDrTyPQEu/yEfx17nuUeMpli7YyudzVpOXU0qvAV2474mrSOnd+iX62gPp/p340stM3XqHRki0Xma6acJcHmNZ9km+PjqAEbFHifMvayFNFRciQoj3gSlAvpSyT60sHPgCSASOAzOklI51Nc9QUWfk15IJVLSIwgqFQqG4qOjolqHbcWzCljlrK3BKSjmlqbbB3skMjHqBQK/O/GNfLibrztpJNWzK78am/G5c2SmNh3pOB2Bygr1B9332lnojvw6j1cQ3JzbR/3An3nnlx3r5zk2ZPHX3XD5Y8ih+/vb5BifE9+TBtHHMPbwOvaUGjR5CVoL3aYHVX3KwXx4TV/yN2OAQbuw6jKsTVI12Z+QUluHr7UV4sPtVBqqK9NREWhzk7x1dRYRPCAfK0tHXSExmLRqNRAhbOEefkM5c23WAXR+txodR8W9TVL2TClMW4b59CPZOrn++stpIcZmeTtEhaDXtugKmA59/uNZBtm3jUTL255DS68LIMO+vi+fKrovw1zWvLF6l3khJhZ5OUaFoNO7tyK9fsZ83n/+2/njf9iyeuecD5i76AyFhF59Xz9Id2xBnvWwhYOOh/Tw46HKXxigwVFBS48/PeWloNVZguecVVVyofAj8G/iogewpYIWU8hUhxFO1x0+e3VEIcV3tw61CiMXAl9h+09wAbGlJpRUKhUJxcdDRDf3m8DBwAAg+V0ON8CEx+GoARsTs48tjOx3ajIrt1mj/AqPzRflCYwWLvtzsIC8trmLdiv1MmDrA4bn7Uy9lVrdhPPjIXLK35iMsAomkfJYZcyeJAT1lFXr+b++3WKSV67oMPdfLu2jIPFXE8+8t4WBWPhohGDMg+dydzkI2klj9dHUZOdXlAHh7gU5rpVLvjS3UEh7s2Xg+hAi/dCL80s/MISVvffUrX67YibHGTEx4EE/MGs+YdPf1bStOn3K+cZV7quSCMfS9taHNMvItViv/mL+Gb1fvpsZsIT4ymKdnT2BYn0SXx1j0lePnRmWFgTVLdjPtllaLYmo35FeXgZP1jVKz3uUx+kXE4e9npIOtqSnaAVLKX4QQiWeJrwbG1j6eB6zGiaEPTG3wOA+4tPZxAeDnKR0VCoVCcfHS0Q19t7bDhBCdgcnAy8Af3Ok7sXNPJiccYtHJ/fWyS2O7c11i/0b7XBLRnYXZWx3kwyJ7sLjKefInfaXRqRwgwMuHS5KTObWpAABzV4m5k6NTwyfH1ylDvxarVfLYv77nZH6p7VhKVm8/co5ejggjYMbhjjn73ddoJDqdFbNZx4ykdEbHum6kL1i9m4+XnLle8oorePo/P7Lgb3cSEx7kts5tQZ/0LmxZb//+arUaevdPaKTHxcPny3fwxc876o9zCst5/K2FLHz1bsJc9DKpqjA4leurGv/cuJAZFpXCIXnaQd7H1/XrrbCmxG0jXwhxL3AvQJcuXdzr7ITEpxY5lR9/ZfJ5j604LyKFEA2/xOdIKeeco0+MlLKudMNpwOmqoJTyDk8oqFAoFApFY3T0PYxZbrZ/E3gCsDbWQAhxrxBiqxBia0FBQb1cKzS8Oew6vhx/Oy8MnMSnY2fxv9E34a1pvIb2ZbF9uCLOvtb9qKg0pnQayIjxPR3aa7QaLrm06UzRN84eRecuEQBYA51HLhQZK5sc42Jib2ZuvZHvLg2vBVONHv+FQAPvfS0ap7EjEzun8d2Eu3h5cJORIQ4s2XDAQVZjtrBi62H3FG9D7npwAiGh9kbrzHsvJTL6nA407ZrGPhfcYcl6x/NrqDGzyo2FpxHjezmVDxvn+HlyMXDf+MuJzw61kwXn+vL0pGtdHqM5n5dSyjlSysFSysFRUZ6prqJolxTWnefav3MZ+XbUlslrMsRQCJEkhHhDCLFACLGw7u+8tFYoFAqFgna+o18bw/Y3IBrb7r3A9t0ZjO3BXjfGqkuYs00IMbaxdrVf5HMABg8e7PAFPSCiMwMiXEt8pREaXup/E7ckjuJg2SmSg2LpH2ZL1HbT3WM5eiCXLWszAPDx8+KBp6cQE990EqnQ8ADe/vQ+flmxn8zcPN7nV8xnrVsMj+zRSG+FOzS8FuKjk6TvBoHXfgl9tEy8fBCyp475WRsc+v02bRzdg5oXv93RSeoew9xvfsfqZXspL9NzyegUuqfGtbVa5825Phdai2tvG0HGvmzWLt8HgJe3jrv+cCWJ3S/O603npWXBnY+z4NeNbDl1hLTIzsyaNQadrvEF2LMZFtmdDzLXtKCWiouMPCFEnJQyVwgRB+Sfo/13wFzgB5rYhFAoFAqFwl3ataEP/B2YKqV03Apzn5HANCHEVdiy9QcLIT6RUjaaDrzcdIrF2b8nKXAsaSHTEKJ5DhC9QjrTK8R+ccDX35sX35nNsYzTFOWXk9YvgcBg18LyfHy9uHyyLWQg6mQYr+xbiEXafh8k+EfwcNqVzdLzQqRPtzi6xIRyIq95u/p1xHeJ4L9fPEx+Tglp/RIICvGn3FTNzpIsDpTnACAQ3NvduZFfZSpgT8l8Co0ZhHon0i/sZoK9O9m1mTS8J7sO59jJvHVaJgzpWAs3QcF+TL1hSFur0WKU1ZxgZ9FH9AmbgU7jeuGPSSN6cuiE/W9+X28d4wZ2d3kMLy8dz75xCycy88k7VUJKn84XZRK+hmg0gumXDmc6zctRMDiiGzd3HcH8LOfhVAqFmywEZgOv1P7/vunmGKSU/2pxrRQKhUJx0dHeDf08Dxn5SCmfBp4GqN3Rf6wpIx+gxlJJjn4bOfptFBkPMyrmcU+oYkdSSixJKc0v0XVdwhBGR6WyofAwoV7+jIhKQddEOEFzqDGY0FdUExrV8dyvNRrBaw9dzXNz7JPxbf3Q/bG6JkfTNTm6/jjYy4+PRvyGzUWZnK4uZVB4EgkBEQ79DJYyFp68nyqzzcg7Xb2LYxWruKbr/wjyOrPbfd3YfpwqKLNLxvfkrMuIDusY8fmewlRjprJUT2hUULusE2+yVrO16D1O6bcyOcH13+c3Xz6Q3MLyM8n4okJ4+rbLXI7Pb0iXbtF06RZ97oYKl3is12SuTRjMxpMZzOQvba1Oh+NizTEghJiPLfFepBAiG3gem4H/pRDiLiALmHGOYf4phHge+AmoT7YhpdzeIkorFAqF4qKhvRv6W4UQX2BzbWv4BbigtRU5VLaIAeG3E+DlXjym2Wqm3FxGiFcoWnF+BvjezFyWbT6EViOYNKwnqV1sP/SjfIOZ1nnQeY3tDKvVyocvfccPc1dRXWUksVcnHnp9Jr2GdJws8ABhIf50SY0iw1CKr7cXiT0imzVOcU0RwboQdJozt41GaBgW2fSObEbZonojvw6jtZz9pQu4JOq39TIhBA/NGMNdU4dRXF5FfJTz8nrZBaX8sG4f5VUGxvRPZrgbWdvrWLYzg3d/2sjXVp2MAAAgAElEQVSJwlLSE+P5w9RR9E5o/ZrwZ/P5m0v4+t/LqCzV0yk5hgdeuYlB43q3tVpOya3eQa5+B3H+jlUynKHRCB67dRz3Xzui2eX1PMXyXYd556eNZBWU0L9rHI9MGU3frq1//quNJhat38+hk/mkJEQxeXgv/H29W12PTT/t5t1nPye/4lxe1grFGaSUNzfy1GVuDNMXW76h8Zxx3Ze1xwqFQqFQNJv2bugHA3rgigYyCZyXoS+lXI2t5I3rfbBQYcpxy9BfU/AzP+QsoNJcQYhXKNd2upFhEaPcU7aWr1ft4pVPV9Qff7Z8O/9390QmXtJySbi+n7OSL/+1tP74+P5TPHfTW8zb+VcCgjpO9Z+H31/I9mM2l3h9jYl3f9rk9hjZ1Sd4Zs/vCdIFMTX+esZEuf47rtx0yrm8JtupPMDPmwA/58bOjoxsfvfmAow1ZgC+WrWLWy8fxCM3Xuq0vTM2ZGTx+MeLkLWR5puPnOSedxaw8OnZRAa1nRv4yq828eFLZ2rEnzqax59nvc3czS8RdY7cFW1FmSmbOFwz9OsI9Pch0N+nhTQ6N1uOnOTRj36sP/9bjmZzz7vfsPCp2USHBLaaHlWGGu752xdknDyT3PDr1buY+9RNBPq13vtzOquQ1955i5DnjSTEShjcalMrFAA3AN2klDVtrYhCoVAoLizaddZ9KeUdTv7ubAtddMKPcB/XY2kPlu9j/ol5VJorACgzlTLv+ByyqjLdnttgNPHvBWvtZFYp+dfXv2Kxtlzunp/mO8asVpbpWb9oh5PW7QdTjZkfP13P8/d+wJ+e/LTeyD8fLNKWbr/CXMFnJz7kUIXrESXRvn2cy/2cy5vi3wvW1hv5dXz28zZyCstcHuPLdbvrjbw6KgxGFm076LY+nuSn+escZDUGE2sWbGkDbVwjppFz2575Yr3j+a8y1vDDVo9ESbnMwrV77Yx8gKOnivjulz2tqsfiRT8T/qQBXWyb5VhUXNzsBULP2UqhUCgUCjdplzv6QognpJR/F0K8hZPSNFLKh1pDDx+NiVhdKQXmEIZG/QZvreu7nRuL1jrIJJKNxevQynC+ObabcpOR8fHdGRmb1ORYJ/NLqax2rJOdX1JJYWmV0xrrO3NzeXP9eg4UFNAzKoqHhw9nQHy8y/oDWEwWp3JzI/L2wssPfsymlTajxRDhDSMc4+bPl41Fv5Ia5Jo3RZz/GDR8iZUzZdQifHrQM+Qat+c9mJXnIJMSflyyg3tnjW20n8VqZc7WrXy9dy+nyyowh0i8ymwJBOso0zuv0d5aNHq9mT1zvVmlifyKeZTol6DR+BMVeDPh/le5PY6PxkSMrozIgNsI82n63m2PlDdynsur3Tv/v2Qd4/82/kyuoZyuAeG8NOIKBsZ3OnfHWnZnOPd02ZVxiplXtt62+unI4wivVptOoTibUOCgEGIL9iGK09pOJYVCoVBcCLRLQx+o21rayjlq0LYkGiQxugoSfKPpGTLVaZvsvFK++XkX+UUVDOyVwNRLe+PtpcPaSJWcPH05Eze+R7XFBMCHGVu4N20YT6Y3Ho53LK8Y4aXBYrUirCBq35HQQD/CnSTyOlZSwsyvvqLabNv5/TUri62nTvHDrFkkhbnuAn3ptUP4+G/25Xy9fb0YPind5TFam4M7T9Qb+QA+JTVojBasPp5NUFhtrOG/X60jK7eYXt1iuXZ8PwKcuGNXmIzctGI+h0qTSQkNJNa/HF9tJ/429Bm3Fo7qiA8P5lheiYN868LdTRr6f/v1V+Zu23ZGEARSCz7FZ0Rje3dzWx9Pcum1Q9iz4bCdTKPVMGaaZ/JPHCt6nGL9d/XH5YZfKff7k9vjaJDE6soJkBuQUrbLhIFNcWnvbmzIOOEgd+f8b83J5o5fPwedBB84ZM7lxhWf8NOUe0gKC3dpDGOe8/r1jclbii5pcZww7WvVORWKBjzf1gooFAqF4sKkXRr6Usofah/uB54BEjmjqwQ+ak19asxHKa1eSZj/FWRl5LL0sw1UlFSRMLgr/12zG73BFlq3YnMGq7Yc5q2nrmdI+HA2Fzu6vm/MsdQb+XX879AmZvYYRKeAEIf2HyzbzFvf1bo0awRSA1gkwgr3TBuGl5N60Z/v3l1v5NdRbTYzf/dunrnU9VjuGQ9PJOdYPqu+3oTVKgmLDub3/7iN0Mj2mwX+ZKZ9Mi1hhfAdpRQPDseqsxlkidFh7D3PeZYvqOTUPlus/8rNh1my7gBzX7gZXx/7rcEvju7kYGk+oOFQaRyHSm1Z9i+JOsDsFPdL0I3vlsDc08XQwLj0OV1NYXZ1o32Mtef+bCx+YNVKvKSG+y6/hH5dPVvvvqq8mqXzN3Bkz0kS0+KYdOtIgpsoBXfV7WM4kZHL4nm/YDZZCA4P4L6XbiTeA9nljeaTFOsdq1wdLXiz2WNW1WyjwriBYN8R56NaqzM6NZF/oMFYtxgpIcrfn16dHctCNsbLW1fajPwGWL0s/GXLSt67YrpLY0RUSjTVZqx+DZJbGixEVLRuKfHLekxg7b4VZ1ZQFR7Lot9RsvE3pmdrIKVc02aTKxQKheKCpl0a+g34BHgc2AONbJG3EkbzCXavP8yzs97BZLQZ0ZUHjmOKtjdctu47waY9WQzrl8418Tew5PQPGK0G/LT+TIufzgOrHSvmWKVkb8lpB0Nfb6hh7pLN9ccy3IQ1tRqCLSQHRNO9v/Pd+UK93rm8qsqt1+zlrePxt+/kzueuoyS/nMSe8ei82uclY7ZaWZ93nKIEiVUHmgbrHL4FNUzWh3DlI+Pw9/FiULfOaJ++w63xNbXpLHw0voTk9WXHfhPmvtWYk4yggQNZehau28OM8QPt+u0pznU63u5G5Ofi6gnpLPjPKqrj/LDqNPgUGfHJqyZlWOOVEKpMJvQm2+KS1FlBK8GkQVgFj149mqv6pBEb6tnFG32lgUevfZOsQ2de59LPNvDmj48SEu484ZtGo+GBV27mlsemUHCqmK6p8Xj7esan2mg+iTPnoODAivMaNzNnD+nd3DP0dxXlkFtVweDozkT6tn7yw89X7cRabUUnQAqbfVtq0LNs6yGmDXetwkGu0XlOiCy9o7dJY/Tp14VfluyiOj4Ac6AOXZUZv1N6+j52ictjeIJIbQyW5Z2wjs3By7tNv2YUFyFCiArOfDh5A15AlZSy49WzVSgUCkW7on1abWcokFIuPHezlifIdxivvrqo3sgHsAQ4MULijLyW+xWGEgO9ghO4t9szhHrriPKJxlvjQ3JwFgdKHUs4dQ92jCPPLS5Hb6w10AItWIdWQO0G/lFjHr/ZNI8vxjxAUqB9JYAxiYl8d8AxsdaYxEQ3XvEZImJDiYhtv7mCDpcVcseqLzlVZTM+/J6KJOqjEvxP2GK7A4J8ufORiXTv7Xr88Nn4a8MoN6ZgMMOJIjOmQXos3c8kSbakGvm4dC0zsDf0k52cV4Aewc0r8RffNZIbpg/j2w9+rZf5BXhz+6OTGu0T7udHr5go9hpPgU/t70kJQTX+3DZiEDonJfzOl+VfbLIz8gFOnyjix3m/cusjjesKEBoZ5HGvEX/vPpgt3ui09omtj+XEYCt17T5WK2TndCXdRY/3SpORe9Z8zcY8m9u8t0bLMwPHMzu1ddO8H80tAmwGfsNN7KM5RS6PkRYaTUGR4yLJgCjX77Erpg9h+YKtHN1/JllmUlocE2cMdXkMgIzyU8w5+hMHy7NJDIjmjm6XMSjc9cSpSxdvZks3DRQl4KsznbuDQuFBpJT1H3bCFgd0NTCs7TRSKBQKxYVCu866DzwvhPifEOJmIcR1dX+trURU4EwCvPtwbL998ihd5VnVcCJNaCaUkqsppKSmknWFB3h0+zx8NaF4a2zx27/vOwbtWTG913TtQ7ITw69TZChBtWWmZIKh3sivw2g1882JrQ79pqSmMjU11U42OTWVqWlpLr3ejsZTGxfXG/kA1ToL1ffEMnXWCG575EreXfzoeRn5AAXGcrKqKskzVpIfnYdXqqN3xEnvAvKq7Xc6b+k+kFg/e6M13j+YG5Obn+fg3mem8tL7d3P1bSOZ+dDlvLv4UdLSuzTZp1dixBkjH0BAhY+erErXd2DdIXO/80Rrx/affwWE5qDTBFNQMBtrg7eg2ujNlz+PafaYK34dRPdOrpfW+8/e9fVGPkCN1cL/bfuZrIqWOQeNkZbgPBSiZxfXQyReGn4l3metEwdofHhm8DiXx/AL8OG1zx/g4ZevZ+rMETz44nW88eVv8Q/0dXmMAkMZD26bw4bCg5TUVLKjJJM/bH+fQ+XOrz9nLMvfiwzQIBFUm52XtVQoWgNp4zvgyrbWRaFQKBQdn/a+o38HkIbNla3Op1ICC1pjcm9tNGnRXxHka4ulTurVif1bzpTH880qwxTuh/SyWeAiVY84a+mkwlzNkpxtzEwaC8CETil8ftks5h/ZQbnJwGXxPbg+qZ/T+X29dfzu6pH89fOV4O08frS0xtFNX6vR8Obkydw1eDAHCwpIi4qib4zr8bcdiRKjnu2Fjj/qCyx6xv5uOL3CW+Z163QSYZVIeWbRRgKlJj0xfmdCMCJ9A/j2ijuYl7GFg6X59AqLYXbKYMJ8HJMousOg0SkMGp3icvs9ZY6hAhJYeepIo14H50O3Xs4XVpJ6uVf5wZOM6/sYj83xITJsG0aTF1v2pxAT2vQCiTPKygN47e0ZpHUZS1qS69fXilNHHGRWKVl56gh3pLmfr6G5zJwwkOXbMzhdfGZHPj05nssG9HB5jM6Boaycdj+vbl/NwdJ8BkR24g/pYwjx8XNLF18/bybOaL6r/qKcrVSa7asFmKWFBSc38HRv13IF+Ma6p3N7oKPEvrc32jIWvzHO2rzQAIOBti2BolAoFIoLgvZu6A+RUqaeu1nL4KWNqTfyAWY/PtkuRl+rNzElLpbo0cnkFVVwKPkgmVbHHcviGnsX14GRnRkY2dklHW64tD+pCdG8u30N63DMDD06unFjr29MzAVr4Nfhq/XCR6vDaLFPPiiAEB/Xdwabg0AiG5Sni/MLoUeQ4/sd7RfI4/1d3+lsCUK8nb8XYd4tY+RcfuMlLP50HScyTtfLYrtEMGX26BaZzxWC/Hx4476H+GHDPg6fKuSBqVFMHdaLr56b7dY4gkjuueYuxg91faEFmjgHbhrH50tUSCDzn5nJ9+v3kpVXQt9ucVw1JM1pYs+miPMP5o1RbVsBrLjGeYb+kkbkzrhp2CiWb87wlEoKhbs0LOljBo5jc99XKBQKheK8aO+G/nohRC8p5f62VgSg34gevLXk8fqs+0Mn9Gb0lAH15bW+PBHAPw85GvrDIs9vraJftzj+nTSD53d9x8LsHfXyaxIGckVcn/Mau6Pjp/Pihm79+OSwfZLD8Z26O61i4CkCdb744k+h0WZQBHv58XL6dDRnu3S0E25LGWTnNg42b4NJXVomnMM/0Jc3vn+EpZ+5nnW/NQjw9eamca672zsjLjKYCcPcv6dnpwxia0G2nSzGL5ArE1p/LTMkwJfbLm/d3AAtwbCIVL456VjdxJ3P3MHRSdyeOJJ5x9YhO1alRMUFgJTSvcywCoVCoVC4SHs39IcBO4UQxwAjto1aKaV07uveCnRNieO+F5ynCbim8zA2FWawsehQvey6hOEMjXBv588ZGqHhxfTrmJ08ksPleaQEx5IcdP5lxy4E/jRoAoFe3nyduQeT1cKUrj15aoBnd9B1mjO7nb4aL57tfSPDIlPYUHAUs7QwPKo7ftr2G987qUsarw6bzLv7N5JTVc7wmC48NXA8AV4tp3NAkB/X3ze+xcbvaExJ7IXeYuK/+zeSW1XBiNiuPD1wPH46z1QWuBgZHpnK1Z0u4ftTm+plIyN7MrWTe6EQj/SZyA3dLmFPyUmu4iVPq9lsPOVqrlz92x9CiOeaeFpKKV9sNWUUCoVCcUHS3g39iW2tgDt4a3S8PvBO9pZmkaUvoFdwAkmBnnWd7x4UQ3cn7uEdAas0c7LyV8pN2UT79SPGr79HxvXWanlywDie9LBx35B4X29e6D0aKToxLDKVYC9bjP2YmDaLLHGb6cn9mJ7cZmtkCmBGcn9mJHvmuleAEIInel3HhJggjlZsJd4/jeFR09AI98IQADr7h9HZ33nJUoWiBXBW7zYAuAuIAJo09IUQLwCVUsrXPK9ak/PeDgyWUv7OzX4v4Ia+Qoh0IF5KudhtJRUKhUIBtHNDX0rZvLpXbUyf0K70Ce3a1mq0K4yWcpZlP0hJzeF6WXLQJEbF/qkNtXKdaksBOVV/IS1kOsFe5+f6rVAoPINVWlid+0dOVv0CwJGyHygzLuHyTv/ES9PxkuwpLh6klK/XPRZCBAEPY0tA/DnwemP9LiLSsSUmdDD0hRA6KaXZsYtCoVAoGtI+A4pbACFEghBilRBivxBinxDi4dacP1t/nLWFyzlcsQ8pnWfQv5DZVzLfzsgHOFqxhBz9ljbSqHkcLPualXkfUW4qbWtVFIqLnhOVa+qN/DoKDHs5VPZtG2mkULiOECJcCPESsBvbxstAYB/wsxBilxDiYyFEohBipRBitxBihRDCoVSIEOKh2t82u4UQn9fKhgohNgghdggh1gshUmvltwshvhNCLBdCHBdC/E4I8YfadhuFEOG17VYLIf4phNgphNgrhBjqZN4oIcQ3QogttX8jz/GS+9fqdFgIcU/tGB8JIa5pMOanQoirgf8Dbqyd/0YhxAu178c64OPG5hZCBAgh3hdCbK59TSqxoUKhuGhp1zv6HsYMPCql3F67er5NCLG8NRL9fX7iPTYUraw/7h7Yi/uSn8Bb49PSU7cb8qp3OJWf1m8n3r/1Sot5go0Fn/Nj7k/c0uU3DA4/1+8ahULRUpyu3u5UnqvfSp+wW1pZG3ucxcW3dEx8S5ePa+mcAe7S2PvZHsvonY0Q4lXgOmAO0FdKWSmE6A08C4yQUhbWGt3zgHlSynlCiDuBfwHXnDXcU0CSlNIohAitlR0ERkspzUKICcBfgOtrn+sDDAB8gSPAk1LKAUKIfwC3AW/WtvOXUqYLIcYA79f2a8g/gX9IKdfWLkAsA3o28bL7Ycu9FADsEEIsAuYCjwDfCSFCgBHAbCCMBiECta7/vYBRUspqIcRnjcz9R2CllPLO2vdisxDiZymls1AJhUKhuKC5aAx9KWUukFv7uEIIcQDoBDRq6NdYyyms3kXkecSSHyjfZWfkAxyp3M/aguWMj5nS7HHbKwc3H2Hhf3+mvKiCIVf056q7x+PlrSPAKxYMux3aB3p1vHwDNVKHRVr48uRc+oYMwkfrS5nxKGU1Rwn1SSHYO7GtVcRitrDkg9VsWryDwNAAJt89nj4jO04+gQuVzD0n+P6d5RRkF5F+aS+m3T8B34CWLQN5IVNhtnnvCmR9uUuJqJcrFO2YR7ElGX4W+GNt9R4vbJ6WmUCwlLJYCDEc24IAwMfA352MtRv4VAjxHfBdrSwEmCeE6AHI2rHrWCWlrAAqhBBlwA+18j3YjPE65gNIKX8RQgQ3WESoYwLQq67yEBAshAiUUjZW3/J7KWU1UC2EWAUMlVJ+J4R4WwgRhW0h4pvaxQln/RfW9m90buAKYJoQ4rFauS/QBTjQiE4KhUJxwXLRGPoNEUIkYlvN3uTkuXuBewEi471ZcepuIn3TGRP/D7w0gU7Hyz5eyIf//pn9O08Q1zmcm+4ew5BRtkz7hyr2OO1zsGL3BWfob12+m+euewOL2QLAlmW72b5qH3/+6hF6hd5IVuVqrLKmvn2ALpakoMvbSt1z0vBaiIi3Zac3WHWUmm2J+IzWajIrD1Jm+JasijNhhN2Cr2Vw1NM08kOlVfj7ne+y+quN9cerv9zAn+Y/xIhpHb+kWkMsFitfffAry7/fgcViZeykvtxy3zi8vT370dbwWujSxcFz1iUObjnK41e8TI3BBMC25XvYuGgHr/38RzSaiyaKyqOUmoPRSCtewkLd7WaWGopMwW2rmEJxDqSUDje9EOJBIFZK+Uc3h5sMjAGmYls06Istmd8qKeW1tb95Vjdob2zw2Nrg2Ir978Kz4wzPPtYAw6SUBhf1bGy8j4CZwE3Y8hQ0RsNdeadzC9sX7/VSykMoFArFRc5FZ+jXrvh+A/xeSll+9vNSyjnYXOlI7hsgAQoNO9lf/AH9Ix90GK+yvJrH75xLSZFtAbu4oILnH/yEV967g36Dkwjxcp7FOdjr7IXxjs8nL39bb+TXsfHH7WRsyyRlUE8mdvo3e0o+odJ0iii/fvQLm42Xpm3rqjdFw2uha59gmV8TxGlTCJIzBrzRvNfOyAfILP+WOP/hdA5suSoATZF14JSdkQ9gtUo+emnBBWfo/++NpXz7yYb648//9wt5OaU8+dcbPDpPw2th8ODBzUqy8fnfF9Yb+XXs25DBtuV7GHKlysTfHLQ15Xhr7D9zdMKKV01Jq+mw51RZh3AVd0ZH1buOjq6/E1YC3woh3pBSFtW67q/HZgB/DNwK/NqwgxBCAyRIKVcJIdbWtg3EtqN/qrbZ7c3U50ZglRBiFFAmpSw7awH7J+BB4NVaXdKllDubGO9qIcRfsbnuj8UWcgDwIbAZON0gnLICCGpirMbmXgY8KIR4UEophRADpJTOYwcVCoXiAuei2kYSQnhhM/I/lVIucKdvjn6tU/mqJbvrjfw6rFbJ95/ZDK0hYaMJ1NnvLmmFljFRV7ozfYfgZEauU3l2rTzKrw/j419hWtePGR79OAFe0a2p3nnhq40huyYcszxTtistqB/VJufegDn6da2lmgPZjZ6H062sSctiqK5h8ddbHeRrlu6hqKCiDTRqmsbuj5OHclpZkwuH6lMnncplcX4ra6JQnD9Syn3Ay8AaIcQu4A1sxuwdQojdwCxs2fkbogU+EULsAXYA/5JSlmJz8f+rEGIHzd/UMdT2fxdb2b+zeQgYXJsEcD9w/znG2w2sAjYCL0opcwCklHnYXOs/aNB2FTbX/J1CiBvdmPtFbGEKu4UQ+zhHmUKFQqG4kLlodvRr3bnmAgeklG+4299H43wHvrTIeShanfEfoAvi4W53sTx/FYerson2jeOKmGvo4p/srgrtnrQhyWz9yT4OXwhByuBubaSR5wjyCuCWztewpmgHRouBvqGDmRQ7nb3F/3La3kcT0soanqHHgEQ0Wg1Wi9VOnnoBnIeG6KuMGM/aIQfbQltZSRURUU1tBrU+aUOSnS7CpA658D4LWouaUj90nRzlxiJ/t8eS0oy0ZHtAK4Wi+Ugp52FLwNeQ8U7avdDgcJST5zcAKQ1Ez9bKP8S2g17XLrHBY7vngE+klL8/a9z6NlLKQmy7/ufkLH3tEEL4Az2ozQlQ274YaDRTb2Nz18bw3+eKTgqFQnGhczHt6I/Etho+vnaFeKcQ4ipXO/cIdf5dNmhEj0bk3TEbN1KZPwbfsulM9XmPJzuH8JvkJ+ge1Mtt5fecPM3q/ZlUVBvP3biNuP2FGwgIsf+BffUDV9C5R1wbaeQ5LKYD9DQ9zm8jDvDH1N9zTaeZ+Gh9SQ6+Do1djiPQCh+6BbddRZ/oLpHc8Af7bNR+gb7c9ZJLv8c6DOGRQSSlOCZzjIoNoWuyo7fInhNtew/d8vQ1hMXYLwCNu3E4vYenNNJDcS5SIq7FVK21k1nNgljhXnZ7U/UiKvOGU5k/xpPqKRSKc1BbEeAA8JaUsqyt9VEoFIoLiYtmR19KuRZwKzuaQEOIdzJpobeREOiwoA5Ar/QuXD97JN/MO+Oq3XtAV669tQ/64kuhPvlsDSb9R2i0sfgE/c5lHcr0Bh788Hu2H7O59/p56Xh++gSmDGyqgk3b0GNAIu9tf4Xln/5KaX45QyelM3D82dV4Oiq2sGyLaQf64vsIjF4GQKhPd8bE/5M9xe9SZjxKmE8qfSN+Q5B38xK2eYo7/28Ggy/vy4YfdxAU5s+EW0fz/+ydd3iUVfb4P3daJr2QRhICoYbeOyhNQRQEFRVs2FjX3nbXvup3/e3ay64NUbEjVhBBbPROqCbUhAAJgfReZ+b+/phJmcwkmSSThvfzPPMk75l7z73z9nPvueeEdunUpn1qCe7/5xyevOsT8nOLAfD0MvDAM3PRaqvHMPOKS7nrg+/Zd9I6m+5p0PPMVdOYOTS2Vfsa2SOMd3f/m18+3URGSjZDJ/dj1CVDWrUP5xvjxo/j81cvp8uwLUSEZpGR48+hncP43521s4/Vjdl0gpKce7BmYFUoFABSyklNqSeEuBnH5QVbpJR31dHOr0DXprSlUCgUivr50xj6TSHAow8zopc1WO72B2cwY+5w4veeonOXIAaPjKG8+GtMTjLMVJR82yhD/78/ba0y8gFKKkw8ufwXxvSKJti3/QWy6xQRyLV/m93W3WhRLKZDmCsOodVbB1vCvEYS5lWnh2GbMWhiXwZNbH8DQu6kz4AoPl7zEDs3HcFksjD6gj54+9qnq3ttzeYqIx+gpLyCJ5b/zJhe0QT5NN7Fuzn4B/ty1f0uOxIpGmDH8dNsSDPCj1Pt5Kv3H+WKUa4NMppKVqKMfEVrIISYgTX3vBZYIqX8T63vF2INLlcZRO9/Usoltu+igSVAF6wjzzOllMmN0W8rczXwtE3Hfinlghrf+WFNOfx9Zf76Rvb/VaAyCq0XECqlDBBCDBFCbAP8ADPwnJTyyyboj8a6rCHAVuYRKeVq23ePYo0jYAbulVKudZd+W3ynJcAwrO/NH0sp/11bv0KhULQ3lKHvJrrEhNAlJqRJdY8XxvPruW/JKDtDtFdPpodfQ7gxCoB1CYkO5SvMZrYcSebyEf2b1WdF01mc+BwWXQ+mhM6hr9/Qtu7Onxqjl4ELpg+s8/v1CUkOsnKTmS1HTjJr+Pk9EHK+4+z+CLAuPtFlQ1+haA6uGNe2clrgTeAiIAXYJYRYWSPKfCVfOjOysaagez31gc8AACAASURBVE5K+Yste5BdEBZX9AshegGPAuOllDlCiNprnP4P2NjU/kspH6hR/h6saYwBioEbpZTHhBARQJwQYq0taGBj9s8TwHIp5dtCiH7AaqCb7f9rgf5ABPCrEKK3lNLsDv3APMBDSjnQFk8gQQjxRe2BFoVCoWhvKEO/hdAbL6JU+NRw3bfJPefabZ8uTuS9pOcw255HB/N2klh4iL/1eQlffQC+Rg/O5Tl6BvgajQ4yRetwzuRFYlkFlB3mgxPPs6j7E/TyVUZFe8XH6EFGfpGD3NfTow16o3Anvkbnx7Axx9akn4pZvoJWNClrYqM4D9PB/alphPEOMAo4LqVMstVdBlyOdQa9oXb6ATop5S8AUjpxF3RN/+3Am1LKHJueqvQUQojhQBjwE+AsD2tj+z8f+KetnaOVQinlGSFEOhAC5NYo74p+idUrAKzpAyvdHS8Hlkkpy4ATQojjNn3batRtjn4JeAshdIAnUA44pGdWKBSK9safKRhfqyI0/ngFLUFoK9dqG9B7XY/B56925bZkrq0y8ispNhewO8c6qH7tOMf82pGBfkyM7dYS3VbUiTW8Q2qFD8vzqoOnSSSbM9e0VacULjDfyTUUFeTP+D5qWWhHZ87I/hj19uPVGiGYN2aQyzr2FJzmu4KeFJj1DRdWKOypMh6llOVApfHojEigZj7IFJusNlfaUsZ9LYToYpP1BnKFEN8KIfYKIV60DTI0Vn9voLcQYosQYrvNGwEhhAZ4GXi4nt/qav8RQnQFYoDfnXw3CjAAtd1xXNH/NHC9ECIF62z7PY2o2xz9XwNFQBpwCnjJlhVAoVAo2jVqRr8F0XmMwyd0ExZzMhpNIMJJir4CU66TmlBQYZVfO24w5SYzH2/aQ1ZBMeP7dOXvsy5Er6v9jFe0JFp9X/Zp7mJl7q8O3+VX5LRBjxSusmD8EMpNZj7ZtIfswhImxnbj77MvRK9V11BHJyrIn3dum8urqzcTf/ocMaFB3D19LEO7Rbiso8CUS0JZMIfLggjQlmFvCygU9eLMeBzdDH0/AF9IKcuEEH/Bul58CtZ3tYlYXeFPAV8CC7GmDG4MOqxp7CYBUcBGIcRA4HpgtZQyxZqJuNlcC3xd03UeQAjRGfgEuElKaXFas37mA0ullC8LIcYCnwgh3OlOV5f+UVjX/kcAgcAmIcSvld4BCoVC0V5Rhn49xMXFZQohTjr5KhjIdGNTDvpeZjlwk0PBfVj9BBurr5mcj/oaNZ0bF7c//8LOi47V9f39OMTl8QdcSRXkSrmGytT3vbv3dWvh6v5rNPuA/9qLnOfIrIN67gvupjWOXWudH63Szn4I/v7hZrfT4L1BCLEIWGTbLDz5/GVHmtmmon3SRwixu8b2Yinl4ibqSsUaSK+SKKqD7gEgpcyqsbkEeMH2fwqwr4bb+ffAGOwN/Qb12/TskFJWYHVxP4r1/jcWmCiEuBPwAQxCiEIp5SON1F/JtYBdlH1boL8fgcellNud1HFF/63ADAAp5TYhhBHrvcWVus3RvwD4ybbf0oUQW7Aub1CGvkKhaN9IKdWnkR9gt9J3/uqro43FLVHelXINlanv+9bYN+1hf3eUthrZr9Y4r1vl/Djf2lEf9ZFSgtVAXltj+1Hg0TrK6rAahjFYXdf3A/1rlelc4/+5wHbb/1pb+RDb9ofAXU3QPwP4yPZ/MFZvhE61yizEGu2/0f23lYsFkgFRQ2YAfgPur2dfutL/NcBC2/99sa6hF1iD8O0HPGz1kwCtG/X/A/jQJvfGuq5/UFuff+qjPuqjPg191Bp9hcI1fmih8q6Ua6hMY/vWEWjN33Q+7j+FQtHy7AJ6CSFihBAGrDPZK50VlFKagLuBtcAhrNHd44UQzwohKnPS3iuEiBdC7AfuxWp0I60u8A8DvwkhDmI1Pt9rgv61QJYQIgFYB/xN2nsR1ImL+rHtg2VSyprRLa8GLgAWCiH22T5DmqD/IeB22/75AqtRLqWU8cByrAb4T1gHQczu0o/VkdJHCBGP9Zh/KKU84Mp+UygUirZE2N+LFa4ghNgtpXQWlVbpOw/0nU+ofdNxaY1j11rnx/nWjkJRiRBiJvAa1ln3D6SUz7VxlxQKhUKhANQa/abS1DV6Sl/H0Hc+ofZNx6U1jl1rnR/nWzsKBQBSytVYI7QrFAqFQtGuUDP6CoVCoVAoFAqFQqFQnEeoNfoKhUKhUCgUCoVCoVCcRyhDX6FQKBQKhUKhUCgUivMIZegrFO0UIcQcIcR7QogvhRAXt3V/OhpCiO5CiPeFEF+fT20pFAqFQqFQKBQNoQx9hcKGECJACPG1EOKwEOKQEGJsE/V8IIRIF0L84eS7GUKII0KI40KIR+rTI6X8Xkp5O3AHcE1T+tJAP91mnAohuggh1gkhEmzpoe5rhq6G9t9RIUSpECLN1tYzzvRIKZOklLc2sy1Xj1WDbSkUCoUrCCEWKf2tr7s19CsUCkVrogx9haKa14GfpJSxwGCsuXarEEKECiF8a8l6OtGzFJhRWyiE0GLNx3sJ0A+YL4ToJ4QYKIRYVesTWqPqE7Z6NXU5NVDb0Dg1AQ9JKfsBY4C7hBD9avXNXftvBtAZSAfmA3OFEJtr7b9+bXSsFAqForm0tLHZkfV35L4rFApFq6LS6ykUgBDCH7gAWAggpSwHymsVuxC4QwgxU0pZJoS4HbgCqzFYhZRyoxCim5NmRgHHpZRJtjaXAZdLKf8NXOakTwL4D7BGSrmn1tdLgf8BH9coX2mcXgSkALuEECux5nf+d636t0gp0530sUlIKdOANNv/BUKIQ0AkkFCjWEvsvzlUDzLsqCwohJgH/NeW45qWPlYKhUKhUCgUCkV7QqXXq4eAIK3sHKVDCB1e+j405ABhkmZOFKYhsd+n3jpPIj2Dm9WXtNI0ysxljn00BBCgD2iW7kqSzmZTWl7hIO8c5IeflweJmdmUm81VcqNOT/fgQDRCuKX91iQuLi5TShlSuS2EGII1B3cC1tn8OOA+KWURgEeAlzSG+aLXmJ3qi/aORuOCg4wEkrNyKCqvHkPQaTT0CA5Cr9U24xe1P/KLS0nJzHOQG3RaekY073poCqUVSZith7MKvTaEg/tSc4AzUsoBlXIhxFXADCnlbbbtG4DRUsq7K+8LGmHAU98bcO38LzOZSMrMwSwtVTI/owfRge65fluT1Mw88opLHeRBvl6EB/o6qdF+SS/LoNhUfV5kHc6yuzc0RHBwsOzWrZtb+lJuTqPCnGkn0wofjPoYt+hvDBYpScrMptRkqpJ5aHV0Dw5Cq6k+58ssZaSVpDnVUWHRUvsNw0fnRYRnp5bosgPpuYVk5hc5yH09PegS0vB1V/s50RDuOhcyMjIICXG52T+V/rbqe2PPBYVCoWgPqBn9eugcpePjVZ0B6N7pKXSaGXy4NY5dySlEBvixcNxw+nWu9trdmL6fZ+I/ctBj1Bj48cL/NNielJJD+ScoNpcwwL8XRq2h6rsvT3/JT2d/cqjzcO+H6e/fvyk/z4EF//mMQ6cdJ3mfmD+VNEsx/92wzbH9S6awYMRgt7TfmgghTtYS6YBhwD1Syh1CiNeBR4AnATzCfBn97rV09spHU8uui/aK5pn+TpeJO7Am4Sj3f/Mjtc3ceSOH8MSMyU34Je2XtXFHeOSD1Q7yqGB/fnjmlqrto+cy+XBrHKeycxkWHcHN44YT5O3l1r4Ulx8m/uwMwM9OrhW+DI9OTaERy5hq3hd6Bv+HQK+LXKr34Ler+TH+iIP8rRvnMaprlKvNu4XEjCw+2BJHclYOQ7p05uZxwwn28Xa5/mMfrmHN7sMO8uumDOPhKy90Z1dbnBcOvcehwq1V20tHLa19b6iXbt26sXv37mb3Q0oTe1KGYJF6h+8GdF6Gp97ZypOW46Mde/h/P29wkP91yngWjR9VtV1uKefBfQ9SZLY3qLVmb06UGGpXx0/vzXcT/q/etotMJSTkJxKo96Onb3QTfwG8+cMWlvy000E+eXAPXlk0u8H6Tp4T9eKuc0HR/mjsuaBQKBTtgQ61Rr+h9cdCiGhbQLC9QogDNdx29UKIj4QQB21B1h5tbNulFVnc8OFXvLNxJ3GnzrDywGHmL1nGwdSzVWXCjEFO64YaAxvUn1mWw717/8M/DrzKM/HvcPPOJ9mTU71EfGb4TCKMEXZ1xgSNoZ9fv9qqmszMUX0dZEaDjilDenHorHMv74Q65O0Rk9mCxVKnB0sKkFLD/ftrrIY/ABoh8dBaKKzwoKYTjIfGg+uir3O5D3Xtx/g22o8VJuceCu5gYv8Y/Lw8HOSX1jjPjpzL5Noly/huXwJxp87w3ubdXPfBckqceJY0B7PF0bMAwCwL66qSCnSpsR1lk9lhsuS43Ie6rpWEtNY99kkZ2Vz93jK+2RtP3KkzvL8ljgXvf0lhWe2VKnUzc1Ssg0wIuGREH3d2tVWI9e1Hiantx7ylNGGp43wsrchq5d7AobMZTuXxtc5Xg8bAgugFiBqvE2aT4FC88+demEf9z8N16bu4accTPBv/Lg/se5FH9r9Ooam4kb23MmN4Hzvvg0pmjnR81ikUCoVCcb7R9m83LlLX+mMpZc01wE8Ay6WUb9sCga0GugHzAA8p5UAhhBeQIIT4QkqZ7FrrGg6m9uDoub120jKTmfe37Oa1q61Ldvv4dWFYYC/25ByzKze/65QGW1ic+DXJRWeqtgtNxbx0eClLR/8Lg0aPr96Xp/s/ze6c3aSXptPbtzd9/dz7srJg0lDOZufz9eYDlFWYiQr25/H5Uwn08aR3aDC/Hkl0qNM7NJhTObkczciid0inFnVDNpnM/LT5EHEJpwnr5MucqYMID/ZrsF56XiH/79t1rI9PxEOv44rRAxzKSCnPCiFOCyH6SCmPAFOxX18OQLlFT3aZFo2QjOnUn7/2vB5/vb/Lv6F3qHOX9brkLcV3O/7g9Z+3kl5URN+QYB6dM5kRPdw7q+xlNPD6X+fwzKc/k3wuB71Oy+zR/bh1RvVs4Idb4yiuZdSfyMxhdfxRrhxat6fKkRPnWLX+D0rLTUwZ3ZuxQ+p3bfb2GIJO0wmTxd5g8jdeACQ7q7IL6CWEiMFq4F8LLKhZQKDH33NSve3WpHdoMCeyHAcGeoc23o35SHomKbl5DI4Ib9RMPMDSbXsoqmXUn8rOY9WBw1w7cpBLOib0j+GhKy9k8ertFJSUEeTrxX2XT6B/1/BG9aWwuIwVvx/g+MkMekSHcPmUgfh6Gxulo7l08wmlyGSk3GJCJywNV2ghNBojfh7jyS/bYifPL/Zm8lObGNLtJI9eMZk+Ea3jPdyYe9W44HF8ui6ZwyUHsVgEWWcCqCjTE9ypAg9/+3OtvudhTnk+bxz9HJOsXi4Qn3+cF3Z+juFAFN6eBmZNGkjPrq7tgx4RwfzfTTN45duNZOYV4WM0sPDikUwb2guAkooKdpxMwdugZ0SXSEQHXIamUCgUCkVddBhDnzqCY2FvjEmqfXP9gTM15N5CCB3giTXIWr4rjQoMdAl8nLgTnk6/P5mVa7f97MBbWHbyd7ZnJeCn9+LyyPFMCKn/5VlKyc5sh+xeFJiKic9LZGigdfZMr9EztlOTMr65hEYjePiqSdxx6Vhyi0qI7ORf9eJz3YjBfLc/gbT8gqryMUGBHM3M5Llf1yOxrlSeP2ww/5w+2e0vTFJK/vbS92zfn1wl+/aXfbz79Hxiouo3lO55fwUJKdZZqOKyCj7duLfOosBnQggDkATc7LQvaFgQPYero6c2+ndc3LcXA3fs4eCZc1WyAE8jt40d3mhdTWXt/iM8suYXpAHwg4Olmdy69Bt+fuhWwgJ83NrWkO4RfPfUQlIz8/Dz8sDXy96IO5Wd67TeqSzncoB1O47y5BurMNu8M1at/4NbrhjD7fPG11lHIzzo3ulVEjPvxiytl75R153ooP8DPo7FGvswBfinlPJ9KaVJCHE3sBZrMMMPpJTxlfqE8KBb4L8waF0PuH/nhNFsSky2G9gY3z2asTGuuyaXmUzc992P/H4sCQC9RsODkydw62jXz58DKWedyhNqnJOucP2UYVw1YRCZ+YWEBfii1zUuxkRhcRm3Pfk5J89k2ySH+GHdQd57dgF+Pq1n7O/PtS6nqLDocK8fSePpGvQcRzMWUmZKBqCw1IN311yM2aIlLimVRe98y5rHb8HLw9G9391cNbQ/X+45QHKNazTS34/5dSzVOp1u4ky6/fWQdSCEaRf6U+aVS4Deh7lRExkbXPcAXlxOgp2RX8nu3D/I2WD1yvn2l/08/9DljBva3aXfccmIWKYN7cW5nAI6+XnjabDuu81JJ3ng+x/JK7XGvukV3Ikl186ls1/HijGhUCgUCkVddCRDPxI4XWM7BRhdq8zTwM9CiHsAb2CaTf411kGBNMALeEBKmU0DeOi6MjhyG3ptJ4ZGn3ZaZmi0vTu9p9aDm7tfws3dL3Fa3hlCCLy0RgqcuCd665wPMDSFn76P48dvdlNcVMbYC2O57vYL8azlWp1eXMjmtGQ6Gb2IwL8qzFiwjzff3LaAL3bv52hGFgM6hxLi680/Vv1cVVcCn+/Zz9huXZge28tt/QbYdfCknZEPUFBUxoffbefZey6ts15CyrkqI78hpJT7gBENldMgmB4+qqFiTjFotXx8wzyW7z3IntNn6BoYwIIRg+ns756XS7PFwqa0ZHJKSxgf0ZVQT0fD/eV1W6xGfiUCSowWvti2j/svmeCWftQmMti518OwLhHsOXXGQT40urPT8lJK3vxiU5WRX8knK3dx1fShBPp5sW/XCZZ9uJGzKTn0G9yFG++YQnhkIP6eFzA4cjsFpVvRaLzw9RiDEBqAA1JKh+MupVyN1SvIDqOuK0MidqDTNs57JTY8hO9vv54v4vaTllfA2Jhorhjcr1GDYh/u3FNl5ANUWCw8/9tGJsR0pY+LXiGmMkdDCqC0xLm8PowGHVHBTfPiWfn7wRpGvpVTaTms+P0AN8xu3PV1LDeT/Zlp9PTvxJCQiIYr1MBb17oeBPVh1HdjYOffKCjbzr+/W8PPe/0oN1Ub9dmFxaz7I5FLhzsunXA3fkYjX94yn2VxB0g4m07v0GAWDB9EkJfzZ1KU3ptj2HvMSLOGhd0uYXjXSJfa9NY61y3LrcsCKrwlxf4mXvj+d7530dAH0Gu1dudpmcnEgytWVxn5AMcys3h27e+8Pe9yl/UqFAqFQtGe6UiGvivMB5ZKKV8WQowFPhFCDMDqDWAGIoBAYJMQ4tdK74CaCCEWYcujGh0djV5rnS0eHdOFSwf24ceD1cG0IgP8+MvEphl8tbmk80SWn15rJ+vpE01v365u0f/d59t45+XqYH5ffbyFxCNn+fdbN1bJPj2yl6d3/IrJFhW8T0Awn1x8TZWx2Mnbi7svrPYo+PsPjsEBAX47luh2Q//oSefrRY/XIa+kuKzpc3Q1zwXPsGqDeVzwEPwNTTfMvQx6Fo4exsLRwxou3AjOFhVw3S/LSMyzGk96jYbnxkzn6l72HiVnSx2jUCPgUGb9+7IluGX8cH45fNzOM2ZabA8u6OncFb+4tILUc46z/RUmMyfPZHMmKZPH7voYs9l6Dqel5rB/dzKLv7oLbx8jWo0XAV7THOo3RO37QmON/Eq6BgXwyEVND1a3aq/DahIA1iYccdnQDzN6c9ycY/VTsCFMEO7tXm+Ohjh20vkA3LFk12MWSCl5fPvPfH50X5VsWpeevHXhHAwuZrGYFDqSZafWUmJ2zCJQF7XPB3cihBY/43j+SD5GuclxqUdxueuxFJpLgKeROyY0/IzbteUYxz7+A+1YH8xe1Wv1Zw3s47KRDzAiqD8hHoFklNn/7tI/fMnrZ6Y42jrAt48sbvvtG96a5PpxrsmelDPkljge73XHT2C2WNBqOlT4IoVCoVAonNKRnmauBMe6FVgOIKXcBhiBYKxra3+SUlbYcodvoY6ZWynlYinlCCnliNopVl668hLeu2EuiyaO5JlZU1lx5w2E+bnn5XhB15lc02UG/nof9ELHxOBhPNX/Lw3WqzCbScrOprCBl7+vP93qINuzI5HEo1Y33nPFBXZGPsCR3ExeiHOMulyJn4djoLX65M2hVx1rMntE12/cDO7WmRC/xq1hrqTmuWAM8MZTa+SS8Anc19v14HutyX/2rK8y8sE62/vE9p/JKrX3FKlrRm54N9deyE/n5ZFR5GSwoAkEeXvx3R3X86/ZF7Fo4kjeXnA5b1wzC42TAFoAXkY9kaGO3gF6nZbozkF89/m2KiO/ksz0fNavPdisftZ3X2hNCjKc7/ezSQ06KFUxfXAfdMWgLQFNGWht/1882L2Dcw1R1zrrHtGu79/1qUl2Rj7Ar6eP8+Wx/S7rCDL4838D7qKfX3eXUmRC65wPUwc6RtnXaTVc2M/1mezW4utPt6ItsRCxMZ+gA8X4HSsldHshl/p2abhyDfQaHf8aeA8jgvqjFRoCdf4UbQki95xPlZFfya8pjTvONfEz2j+jJBIpJN4eemXkKxQKheK8oSPN6DcYHAs4hTWI2lIhRF+shn6GTT4F6wy/NzAGeK2xHRBCMLFnNyb27NbkHwGw8mQ8nx2LI6+ilKkRvbiz33i89Qau73Yp13er2w29Nj8cOcz/bVhPZnExnjodtw0fzgNjHdcpSynJzihwogGyMwro0TuczWkn7Yz8SjacOVFn+/OGDOSLPQeosFTX02s0zBsy0OXf4CqjBnZl1MCu7DxYneHGx8uDhXPH1FtPr9Xy0o2X8tBHq8gssBq8/aJCcYyIUD/dfaJYPu7FxnYbgMO56bwZv5nDuen0DQzj7v4T6O3vfuNgfaqDgwrlFjNb0pKZHVOdneGuC0bz+Opf7cr5GgzcNLZ+D4Pj2Vncv2Y1CRkZCGBa9x68OH1Gswd2vAx6rhruGCDRGUII7rh2Iv/8749YaqQ/WHDZCIL8vcjKdH6eZ6Y7l3c0wtMFabViJmrKLMSUu77E58oxA4g/fY7vd8ZjMUk8dFrunjWOAdGNC6T3R/ZZ3krYwrG8DAYEdebu/uPp4ed6UMnZkwey8veDnEqrnr2NDAtgzjTXAgKC83MeYEPqCW6Idd1j5lhaGet3eJFR7B4PqqZSZjbxTsJW1qYcwUOvo/ugAJIOWD1YfIwGnrhyKqH+ret54QqVzxeNCXxPVQ86Z9dxPdZHhGcI/+x/R9X2q0d/571y5ynr1qcmNeo4V9I/PIzBEeHsO5OGxQBSCwiwGGBnSgqjolo33aVCoVAoFC1BhzH06wqOJYR4FtgtpVwJPAS8J4R4AOuS8YVSSimEeBP4UAgRjzVm3IdSygNt8TuWJe7l8V1rqraP5WWyP+sMn05p3CxxYnY2D/60BrPN2Ckxmfjvjh3EBAYxJ9Y+Gr8QgiEju7Nnh33UfA+jnn6DrDMunYzO85bXJQfoExrMu1fP4ZX1mzmcnklsaDAPTprgsgtxYxBC8NLf57JmYzy7408THuzL3GmD6RzScMT7Yd0j+fmp29ifnIaXh55+UWEsf+h6t/fRGacKc7jmt08orLCuBU0qyGZjWhKrZ9xGhLfr0fpdoZPRi9wyR3fUYKO9R8O8IQMpM5lZvG0XGYVFjOnahSenT8ZDV/ftwCIlt69cwclcq9EhgV+SEnlm/e+8PN31eBTuYNrYPkSE+luj7pdVMHl0byYO7wHA0FHdSdjvGE9j2JgerdrHlmJ6v96c/m07ebFGKrw1GDNNBB0oZfxrrqe102o0PHPNRSy6aBSnM/OIjQwhwLtxsUCS8rOY//snFJusS2OSCrLZlJbE6ktudxoXwhm+3kbee3YBK34/wLHkdHpEhzBn2iD8fVzvS533Lc+671u1ScrO5oGfVlfdS9uSB7atYG1K9fIwjLBo/hgmBfRkYHQ4Xh6OeenbA0NGxXDqhOPSn2Gjm3/dPXDTFE79WMh3mYccvgv2bJq3FsA78y7nhq++5nBuZpUsr7yM21euYPOtt+HbAp5pCoVCoVC0Jh3G0AfnwbGklE/V+D8BcJjSllIWYk2x1+pkleWxP/cYnTz8GeTfk8WHtjuU2ZZ+koPZaQwMch6AzBmrjh5x+mK64tAhB0Mf4I6HZ/DIXz+ummHRajXc/Y9L8fa1BqKa2Lkbvfw7cSzPPpjSLX3rj003oXtXJnRvnVkwvU7L7CmDmD3F9Rm/qrpabbPTx2WU5XIw9zjBHgEM9O/hUhC1z4/vrTLyKymoKOOLxL08NGhSs/pTm1v6juTx7fZxHvoGhjI23HEN8fUjhnD9iCEu695/Nq3KyK/Jj0eP8vxF09G1srtrvx7h9OvhOAN95fXj2L31OEfiq1f1zLp6FAOHtu1Mrbu4+qbx7NmeyLGfqwMYzpk/pmrArjFEBvkTGdS0waZPjsVVGfmV5JSXsDxpH3f3dz2go5+PsdGB92oyr+dA3ovfSUFF9SyyXqPhhj5DXdZReS/VGyrw9ClruEILkVyQbW/k2/gm5QB/H+H+TCbN5VB+MmklGcT6xXDd7ZM4sDuZ5MTq+Arzb72Abj3D3NLW3ydN4tcVSRTUuJc29jjXppO3FyXSMYZLQXkZv59I4nInz1GFQqFQKDoSHcrQ72isSN3Ie0nfYba5xPfy6cK5EufB4dKK8xtl6Nf1ylfXy2DX7qF8+P29bNtwhKLCUkZP7E1IWPVLvlaj4bOLr+WFPRtYn3qCYE8vbuk7wiGQ25+VnPICFu54Bgu2QIW+XfnXwDvw0dU/c5hW7DyLY1qx+13Jr+szBI2ApYfiyC4rYXJkd/4+7EK3GAiizjOufeHtY+S1pbcRty2RMynZDBgSTY8+rl9X7R0fX0/e+Ph2dm89TlpqDgOHdaV7r8a53LuDus9rl7KWuo3O3n58MX0+L+/dxIGss/Tw78T9g8czoJPr+0QIQXBEDkFh+QgBTVv13XzO1nFPyCorf2Ch0wAAIABJREFUptxixkPbPh7X5ZYKno1fQlzOYcB6b7iqyxTe+vwOdm45Rsa5PIaM7E50jPuWJ1Uf541NPs5OqePeqGlngyoKhUKhUDSF9vHmcB5yrjSLxYnfYqF61v1Y4WliQ8M4eNZMJ2MRBRVGSkwGDBotw4OdzzYXlpbhZTA4BCe7rE8s/9u5A5PFfl29s9n8SoyeBibPqHv9fKiXDy9NaDhGwNmMfEpKy4np4n4X/fZKZnlulZEPcLwwiU9PfMLC7vMxav3qrDcmtCurTjlGSh8b5jjDXNexbgzzew9hfm/XZ+pdZXB4ODGBgZzIsY+GPat3H3QaDQVFpZzNyCe6cyAerZDjuz40Gg0jx7duYLnWRKPRMGpC7zbtw5iwrvySetRBPjasW6v3ZUCncD6c1nSHrdhII53K8tFiwVvXdjP6A4M646MzUGiyD6w6LDiq3Rj5YB3ArjTywRrI7qvTvzEmaCBjL7Sm/SssLcNikVX3slJzPrnlaQQaovDQNs3dfkCnMN6acDkpZ3PpHOKPj3fzXevnxPbl9e3b7GR+Hh5Mjml/AQ8VCoVCoWgs7eft4TwjLueInZFfSaRPCiP7n8RDW4FZCg5ld2ZS6F10qrWOevORZJ5ftYGk9GxC/bz567QxXD26ena9e2Agr82Yyb82rudsYSE+BgO3DRvO7NiWy6+cX1jKM6+tYsfeZAC6Rgbx9AOX0Ssm1KX6uWUnSMhdRkFFKiHGgfQLvBaj1r3r1FsKX20p/X1SOVoURmdjHj29MygxHWXxsZUMD7qK8aE3O613Zcwg1qYcYdPZ6qBhkyN6Mrtr/6rtho51e0AIwXuzLueBtWs4eO4cGiG4uEdPnpo0mcWfb2bZD7spLzfh62PkzhsuYFYjAqopOh7zewzll5SjbE+vDo45PaoPl0S1fH732pSYsojPXUZW6SH8DV3pFzAfP4Pry3RSK1KINObS2/sceo3FmrbFzZgtZRzK+4bUom0YtYH0DbiKUE/7a8Rbb+CZETP4x45VVYFRAwye/HPYxS3Qo6azK9t5ised2fHknNPw/A/rSay8l00dQ1T3PezO/gqzrEAvjIwNuZHhna5qdLvfrNnL4s83UVRcjtFDx/VzR7Nw3tiGK9bDnSNHkV5UxDcJ8ZSbzXQPDOQ/F12Mj6F9xkJQnN8IIe4DPgQKgCXAUOARKeXPbdoxhULRYVGGfgsQn3eKxIKzDnIvbRndPJOqvAW1QjKg0xkGBadVlbFIC9vOxPPQd2sozLHOjKbnF/HMt7/R2d+XibHV+cVn9u7NxT17kpqfT4i3N176lp1Jfe3936qMfICTqdk8+vz3fPnmbWi19a/RzilLYvXpRZikNfL92ZI9nCrawGVdPkSnaf9Bj/QaMwN9zxBtzMYsbCGaAbOsYGfWF4Qae9HLz3FtskGr5cMLr2HruWSO5GXQNyDUbtbzdFYu93y8knKTGaj7WLcHugcFsWL+dZwpKMBDq6WTlxe/bDrEx99Ux50oKCzlhXd+JrZHuMsDQIqOh4dWx6eTF7Dp7AmO52fSPzCM0aGtHweh3FzA6tOLKDRZ76FnS/aQXPAbl0Z/gK8+wiUdOTln6eeTVpcXt1v4Pe0fnCneWbV9snAdUyNeJNJ7DBZpYVf2cXLLC5nYuRfrLruTX1OP4qXTM71LLL56998fk4tSMEkT3b2j0YjGxdfw19cRbLFCy90frbC7l30U9xnT/PdWF5GlbExfTLhnLJFermXaAPjjyBleXfJb1XZpmYkly7bQo2sIE0c5piF0Fb1Wy3NTp/GP8RPIKysjys+v3cVCUPypuEVK+boQYjoQCNwAfAIoQ1+hUDQJZejXQ1FxGRu3H2PE4K54eTY8wl9uMfH4/k/YmnkYkPgYBBpRPasfZihw+jJ5pGADg4Nmk1yUwitH3uVcWQbdLoSSXA9O7YikosRqwH+z6w8H40+n0dA1IKBZv9MVzGYL67Y6uuqezcjnj6NnGNy3/hm0+JzPq4z8SvLKk0ku/I2efjPd2teWxF9fSoHZSIW0v3SO5m9wauiDdTZ8fHgM48MdDfcf9hyqejGuibNj3V6I8PWt+v+3LYcdvpfSKm9LQz8zu5D9CSmEBvsyMDayzfrRUiQcTSMtPY8BsRGEBde9dKQlEUJwQefuXNC57dycj+evrjLyKymz5JOQ8yWjQx9wSUf62SOEtuClll5y0M7IB5CY2Z/9IR66ftwft4QTRecA0AktD8XO4cbeTQ9QWB+ZZdm8dOQdThSdAiDMI5gH+9xBN2/H+3dKWg6Hj5+jW1QQPWtcy7MiJrI1c7+dx5qfzpucU14O97KYGMcBb7DeLxtj6Du7zwD8uvlQswz9SvyMRvyMxmbrUfx5EUIYgY2AB9Z366+llP+0pYReBnQC4oAbpJTldamx/Z0JfGLLLKVGnhQKRZNRhn49pKTl8vjzK/D2MvDs32Yzaki3esuvSNlhM/IBBEXlBoy6CvwNBkI9ApkY3JWzJT851NMKHVJKXjv6HufKqlMUeQaUETn0LMlbrRG1y5wYhK2FEKLOteMNzeYDFFSkOJXnl59qVr/aAi0WaodU1IimXUp1HdO2PNaNQVNHtH1XzomW4suVu3n7442YzVb350F9I3nhiSvw9mr/niMNUVpWwWP//p5d+60u81qN4OZrx3FTM12YOypHC5xnST1e8AejXRxnMgtLw4WaQX6FY7rHSvk7x36qMvIBTNLMy4e/Z0JIX4I8fJ3Waw7vJn5aZeQDnCvL5LWji3l1yDN2M9lvfLCOr1fFUZnYZfK43jz14GXotBoGBfTkyf63sezUz5wpyaCvXwwLYy5jxaak2s1hMdfxzBCN8z5rzrNHoWglyoApUspCIYQe2CyEWAM8CLwqpVwmhHgHuBV4uw4dcUKIn4EY4FEhhC/QsjcohUJxXqOeki5QVFzOv15bTUVF/cbX9kz7WQeJoMRk4MLgCbw94hFmRd6ITjgaG/39Z3CyOJW00nMO33mHFKPRWdu9eEDbBRjTaATTL+znIO8aGUT/Xg1HNQ829ncqDzHWHRywvVIutQ6y/gFNW0d70UDns1Fteawbw8zJjsdVq9Vw8cS2SU11+kwOby5dX2XkAxw4lMpHXzmmteyILFuxu8rIBzBbJEs+38LRJMd7x5+BlBLngd1S65A7IzS0BxbZcpNmIXXe+wawLdNxptokzezKPu72fhSbSjiY55iLPq00nZPF1QOxO/cl89UP1UY+wLqtR1n1S/WgyphOA3ht6IMsH/dvnhmwiBjvCC5ycs86etzRU0Cgoa//tEb1fcaF/Z0a+5dMcr5vFYrWRloptG3qbR8JTAG+tsk/AubUo+ZW4BFgpJSyGDAAzgMAKRQKhQu0maEvhPAUQvRpq/YbS05eMYcTnbshVqKvY5Yi0GBd0+ijD2Z2l2cIMlhn6I1aXyaE3kas/2QMGud1pQSt0LJg3BDmjHA0tFuTexZOYvoF/apmUQbGRvL8o3NdWtM4IPA6fPX2LtRdvCcQ5d2xZiJTSwPYkR1Dkcm6lMNbF8S08AeI9m5aPucBUeE8OnsSngbr8ddpNO3iWLvKhJE9uffmyfj5WN1ew4J9eeaBy+ga1alN+rM9LsnOQKlkW5zjbGNHpK7fcb78vsbirRtGaol9QM/sci8sYqTLOnqGd+dIURgl5paJcWINEHiNncxD48ewTn+pejbUJtDQtMj0NUkvKeBofjoW2wWhFVq0wnGQEsCgqV6atm13086x/lFhPFbjXqbVCCbFTGFa+P1464IA8NOHMTPyMUKMjVvu0SsmlCfvnUlIkHV/Bfh58uDtUxk5uFuj9CgULYkQQiuE2AekA78AiUCulNJkK5IC1LeWTAL9gHtt296AWlOiUCiaTJu47gshZgEvYR2tjBFCDAGelVLObov+uIq/r6dT+bmSfB7c+TX7c5Px87RPzeur8+SSiOFV2129h3FTj/cpMeVh0Hqjtbl8R3iGEevbk8MF9jM5A70G8dbfbyTEr44ASK2Ip9HAk/fN5MHbp1JeYSLQ3/WXUU9dELOiPyKp4GcKKlIJNQ6ki/cERCMDQbUVZRYdu/K6cabUn3MlvoR7juGhvvPw0PqgqePl2VWuHz+Uy4f14+jZTKI7+beLY90Yrr5sOHOnDyE3v5hOgT7NSg/YXPzquEbrunY7Gn6+zt/5zpff11gujRzDDds2E2o4Q4hHITkVnpwsDuetkZNd1jEheBTLT69if4EnuhZy4x8Zch9dvCeSWrwdozaQ7r7T8dQFcXX0eJ4/9K1d2e4+4YwIavq682JTOY/FreDnMwlIINIrgOeHz2V4cDTjg0ewIcPeuyXWtycRnmFV23WeY34Nn2PXjR/KbCf3sv4B0ykzF2LU+jb5nn/RxL5MGdeH7NwiAv290Omad991F0KIRcAigOjo6DbujaIFCRZC7K6xvVhKubhmASmlGRgihAgAvgMam4bkLayu+lOAZ7FG3/8GcH3kUqFQKGrQVmv0nwZGAesBpJT7bAFL2i0jh3QjOjLI6XePxH3PnuzTgJaCUg88DRV4aGF0cG8W9ZxOsIdjsCxPnWNauQd6L2LJic+Jyz6AVmgYHzyKm2OuwahtX2uLvb088KbxfdJrvOjjX5/Xmns4eSiVDd/sQKPVMHneGCJ7hjdbZ6lZz6mSILJLPJkUNoTH+l+Jp859x8XX04PhMR03aJxeryWkk/vXFDfEwS1H2PXzfvw6+TL1mnFcOLYX737qQ0ZWoV25Ky9tmsdFe+PKS4ayY88JO6+FAD9Ppk5s/bR27YFQYwBvDP8ri4//xKH8FLp5h/HCkGnE+rmeXi+3zMzxLH/CffPw1JsartBEwr2GEe41zE42O2o0IFh+ajM55YWMDY7lr70uaXQk/Jq8lvA7a89Up8BLLc7lru3LWDfjAW6JmY8GDZszd2KWFoYHDuS27tfZ1Z85ZQBffL+LktLqSCRajWDO9CEute/sXqYRWqfPvMai1Wra5D5THzZjbzHAiBEjnPgTKc4TMqWUI1wpKKXMFUKsA8YCAUIInW1WPwpIrafqaCnlMCHEXpueHCGEyvWoUCiaTFsZ+hVSyrxaLt/t7gGp02nw9/Vk0rje3HHDBU7LZJcVsT3jRNW2yayloERLAXDP2Mvp6uN8cMAZAQY/Hu5zB+XmcjRCg06jYiU2ll+/2MLLf3kPi8V6On3xwkoeXXonEy536flcJyUmPccyQwDw1wTj7UYjX9E03n9qOctf+bFq+4sXf+CF1Y/w+rPX8PbHG9hz8BShnXxZMHcUk8d1mFVC9dIrIpCQ1BwyfI1ITwOanCL6GfX4nAeBBptKH79IXh52a5Prr02Np6DcQEFWCJoWDsznjNlRo5gd5b4o+2tS/nCQ5VWUsCU9kWkRsdzR80Zu674Ai7Rg0DraEGEhftwxaxj/Xfw7Jl8jmqIydEnpJK5PoF/vhuOxKBR/RoQQIVjfbXOFEJ7ARcDzwDrgKqyR928CVtSjpkIIocX2PmzTqYLxKRSKJtNWlmS8EGIBoBVC9MK6HmlrG/WlTnp0DWHVx3fVW0YjNAicj1LomjgrY9AaKCs38evuQ5zNzGdobBSDe3fc2d7WorysgsWPflFl5AOYKsy8+8jnjL1smNsiNGs7yHKD85m0E+l89epqO1lhThEf/vMr/vXtQ/y/R+YQn5jGroTTmHXWaPVGD/eswc7KK+Jw8jliu4U1XNjNfPnyjxTGp1DTiXrfvpPsWRfP8CmupytTVFNz9twiO/61XZc3QM37VkODyGteWY3h8BlqDgO8/9Ryplw7Dm8XXPiPnkxn24FkAv08mTqqD94upKdVKDo4nYGPbIa6BlgupVwlhEgAlgkh/gXsBd6vR8cbWF3+Q4UQz2EdIHiihfutUCjOY9rK0L8HeBxrOpLPgbXAv9qoL80iwODJ5PA+/H72iJ18RKeuRHo3Lb99Vm4Rf3nuS06fy62SzZ08iEdublyk4j8bZxLPkZdZ4CBPP51F1pkcQrs0P0CcVghmRQ9qth5F8zi0KxHpJOpewg5rjIs3vtjAZ2viquTvfbeNdx67mnA35JxPzy7kpqc+4+bZo7njqvHN1tcYDu10Ho390PZjytBvIpdGDeDtIxupsHSMlJYNMSd6MIuPbraThRh9GB/qWgC8orxiTh0+4yAvKSwlOSGF/mPqzwiy5LttvPfdtqrtd7/ZytuPXU10eKBL7SsUHREp5QHAYY2YlDIJ61JVV3R8JoSIA6YCApgjpXRMlaFQKBQu0ibTF1LKYinl48CFUsqRUsonpJSlbdEXd/CvYbOZ2rkPGgQCmBDag5dHXdlkfR+u3GFn5AN8t+4AB487vnw1h4yCItYfSSI5K8etetuKkMggPJzMHPkEeBEQ0vx1nZ09/XhxxJX09nMtQXdphYnNx5LZdzqt2W0r7ImqI+5CZM8wElMy7Yx8gLTMfDvjwx0s/WEHp8627rVTV7wJd8Sh+LMS7RPE66Pm0dmz+WvI2wN39Z3ENTHDMWiswer6BXTm3bHXYdC6Nq7v6WskKNxxkFqr0xLeLaTeuqnpubz/vX2wv8zcIt7+anMdNdqO4vIKNh1L5mBK/dl0FIqWRggRVPnBGrH/C6yTYOdsMoVCoWgSbRV1fxywBPABooUQg4G/SCnvbIv+NJdADy/+N+Za8spLsEhJoIdXs/TtPZLiVL7nUAoDe0Y0SzfA4d1JvPbTJjaX5WC2LTqYNSiW/3fFdHRucm9vC7z9vZh713SWvfSDnfzqBy7FYGye62hvvzB+mX6fy277G4+e4O9fryGvpAyAfhGhvH395YT6dqyI+o2lrKScTSt2c+5UFoPG92Hg+N4t0k7vYTGMmjGYnT/tr5JpNILrHpnDnsN1XD91yJuKlLD3cEqjZipLi8rYuGI3GanZDJkY2+DsaG3mPTCTraviKCsur5LF9I9iwhwVlLk5HD+WT8Z2AZqOf30aNFqeHnIZfxtwMcWmckKMjftNGo2GBf+Yzf8e+NhOPvPmSXSqMQDwx7Zj7N98mJDIIC6YMwKjlwf7jqRWpfOryV43X3vN5bdDiTz67VoKSq3350FR4bx9/eUEeTfv2a1QNJE4rCtAnaWskUDj8lEqFAqFjbZy3X8VmA6sBJBS7hdCOI92VwMhxAzgdUALLJFS/qfW99HAR0CArcwjUsrVtu8eBW4FzMC9Usq17vs5VvwN7klx1TnYj+OnMx3kESHNn3F678mv+PyrjZydZ//c+OHAYYZ2jWD+qMHNbqMtufnpq+jaN4Lfv9yGRqth2vzxXHBF8wNd6TVal438kvIK/vbVGvJtL5EACWfS+ffqDbx6zaXN7kt7JetsLn+79AXOJKVXyabfMIEH3ljYIu09+dk9rHrvN3b8tB//Tr7MWjSVgeP7YNrnPN93REjz3fYddbp+TWakZvPwzBc4d8p6bX/CCmbdNpm7XryugZrV9BgYzRvr/sm3b64l7UQ6A8f3Ye5d09EbVODOpvJH6lle+cU242zuuAOdtfHWGfDWNW2Ac9btUwmJDOKnjzdSXlrBBXNHcvENE6u+/99Dn7Lqg/VV25+/8AMv/vh3OtdxjdUlbwsKS8v4+9drKC6vzipwIOUsL67dxL+vmN6GPVP8WZFStuusUwqFouPSZm+HUsrTtaLu17tA0hbg5E2skUxTgF1CiJVSyoQaxZ7AGgDlbSFEP2A10M32/7VAfyAC+FUI0duW87Tdcf3MkWw9kIzZXB1stVtEEJNGND23MkDyoVS++d9aisc6dz3/7VBihzf0AaZcM44p14xrs/Z3JafYGfmV/H44sQ1603ose/lHOyMfYO0nm7l4wfhGz1y7gsFDzxV3z+CKu2fYyccO6kafrqEcOVndF40Q3HCpe2e9+/cIZ0S/Li6X/+yFH6qM/Ep+WLKOi68bT68h3VzW061/FA++1fQo8wp7fjvkfGDoz86YmUMZM9MxLeWRPSfsjHyAsycz+fzFVdz32o0M6hXBgWPVy8yEgBsvdV9WgeayLem0nZFfyW+Hzu/7s6JjIIS4ApiAdSZ/k5Ty+zbukkKh6MC0laF/2ua+L4UQeuA+oKGAI6OA47bAJgghlgGXAzUNfQlUTh34A5VvG5cDy6SUZcAJIcRxmz73Ltp1E0P6RPL2o/P4bM1uzmYWMCw2iptmjUKv0zZLb/x2ayAvTbnzbC2+HioysjvwNTpPdebrcX6nQPtj27E65S1h6NeFVqPhf49cxcerdrLzj1OEBPowf8YwRvSLdot+o4eem2aN4sZLR1JrsLJe6to/B7cebZShr3AvPs1c1vNno+7r/CgArz18BZ/8uIutB04Q5OfFNRcPZeyg9jNh6VPHc64uuULRWggh3gJ6Yl2jD3CHEOIiKWX96Z8UCoWiDtrK0L8Dqwt+JJAK/Aw0dCOLBE7X2E4BRtcq8zTwsxDiHsAbqAxTHwnUjBCUYpO1Wwb3jnR7Sr2waGvUee/DueQND0YaqgcOhIBrRqlo8u5gSJfOxIaHcPhshp38mpED26hHrUN412BOxDuuxQ3vGtzqffHzNnL3NRfANe7XHRMRxJ3zJjS6Xnh0MCnHHAN/dW4gwJmiZZk1KJa31++gqKy84cKKOq/n8K7W89jb08AdV41v9YwUrjI6pgvdg4NIysy2k89Xzz9F2zMF6CttKWWEEB8B8W3bJYVC0ZFp9QWJNhf816WU10kpw6SUoVLK66WUWW5QPx9YKqWMAmYCnwjRuKTnQohFQojdQojdGRkZDVfoQAyb3I8+w2PQFZkIXXkSj7RikJLOnl68eNUljOnunhnP84WmngtCCN69YQ4X9+uJXqsh0MuTOyeN5s7JY1qwt23PvHtnoNPbe51Ex0Yw7rJhbdQj9+GO+8K8+2egreWV031AF0ZdrAyMtiTUz4clN13BsOgIXHXQOJ+fEw0xZsZgYvpH2cm0Oi3z7ptRR432hUYjeO+muUyN7YFOo6GTtxf3Th3HrRNUQEtFm3McqPki1sUmUygUiiYhnOWibvFGhdgMTJFSujyFIoQYCzwtpZxu234UQEr57xpl4oEZUsrTtu0kYAzWIHxVZYUQa2266nXdHz5imNy1aycacf4EuirKK+ar/65lz7p4gsICmPvXaQyeGNvW3Wp1hBBxUsoRrpYfMWKE3L17d53fS2mhwlKIXuPbKHfu841DuxL55n8/c+60Ner+1ffPwL9T81MbtiTuPhfq449tx/j2rZ/JSM1h6AWxzLtvBr6BHT/Se1sjpaTCUoBe40Mjx3YdcOf5UG7OR6fxOq+eIQAFOYUsf+0n9m06TGhUEFfceVGrLs9pLVrz3qBo3zT2XGhiGxuAkcBOm2gksBvIA5BSzm7J9hUKxflHW719JAFbhBArgaJKoZTylXrq7AJ6CSFisLr7XwssqFXmFDAVWCqE6AsYgQys0f0/F0K8gjUYXy+qb6R1klt2hBUnptM7YAH9g86P4Ffe/l4sfGIuC5+Y29ZdOW9Izl/Nwey3KDadw1sXweDge+jiM63hiuchfUf24ImP/trW3Wi3DBjbiwFjzz+DqC1JLdrAvszXKaw4jac2hP5Bi+jhP6dN+5ReHMeezBfJK0/EQxNAbOCNxAbe0KZ9cie+gT7c+sxVbd0NheJ846m27oBCoTi/aCtDP9H20QCV0331uhZIKU1CiLuBtVhT530gpYwXQjwL7JZSrgQeAt4TQjxg07fQttYpXgixHGvgPhNwl6sR98st+fyR/Q5GbRA9/J0bx6YKMxt/XY9Fv5jQqOP4+nUjNPBuvD0nu9LEn56DB0+zft0htDoNF188kJ49w9q6Sy6TmvMa2fmvES0s5GuNnDVZ2Hb2CXy6dCHQo0+TdEop2bzpKLt2JxEY6M3MSwYTFt781IrnK4mH0/j9x/2YzRYumD6AfoPVEpSOQklJOWt/OkBiYjrdu4cyfcZAvLxcD1qZX36CLWn/QNqStpSYM9id8Rze+s6Ee9UO4VI35aaTZOW9Qkl582djS0yZbDhzPxZKASiz5LI/6w08dSF09W199/ZyU7Ltt8Vh0MUQ5HcPXh6u75uOzLZtx9i+PRE/P08uuWQQERGBbd0lhaJOpJQbAIQQftR4P5dSZtdZSaFQKOqhrQz9BCnlVzUFQoh5DVWSUq7GmjKvpuypGv8nAE4jAEkpnwOea1JvgaT8lU4NfbPZwj/v+5AZN75EaGQ+AOXmNFIytxMV/BnenpOa2uSfgm++2cVbb/5atf3dt7t57PHZTJ7crw175Rq5hZ9RWPgC3jZPYaOmEC9NOccrQjiRv4rAkKYZ+i++8CM/rT1I5QKA777dzUsvL6B373D3dPw8Yv2aA7zw2NdYLNZxwu8/28adj1zK7PntOx6ClPJPvcQDoLi4jHvv/YQTSdVr3H/4YS9v/PcGfHyMLulIzFlVZeTX5Gjmd4RHu2bMmi15nE6fi8nsGCixKew++hUWQ6mDPCl/hdsN/YbOI7Mll1Pn5mC2WFNNVphOUFy6meiwFRgNHT+Van288fpaVqzYU7X97Te7eOHFa+lfK76AQtFeEEIsAp4FSgELILBOWnVvy34pFIqOS6sH47PxqIuydoNFOuZFB9ix4TBmsa7KyK9Gkl3wTst3rANTXFzG0g832sksFsnid9dVGW7uICuviMfe/pFxt7/OlLve5L/LNzZcyQWyC952kPloyvES5VhcDz9hx7tfbeHrP45QEG2gKEyP2SAoKipj6VL39Pl8wmy2sOTVtQ7nytL//UppcfuMoL78171c9tB7jL71Vf76wlccP/3nCuRWkzWr99sZ+QAnT2ay+sf9Lus4eSLVqTz1tOtGe37Rt24z8gF2bU1wKi8pKXIqbwq/7z7G1Y8vZdQtr3LD05+yM+GU03L5Rd9UGfmVSMrJKVjitr60R06fzmLlyj12stLSCt5/f0Mb9UihcIm/AQOklN2klN2llDFSSmXkKxTgAAmpAAAgAElEQVSKJtOqhr4Q4hIhxH+BSCHEGzU+S7G61Ldbuvhc5FR+LOEMAcHOX+Dc+fJYybYTp7jho6+Y8Mq73LFsBYfPdVxDISUlh2InBll6ej65ucVua+fhN1bwy84jVJjMFBSX8fEa9wRLKq9wbmTohZkuPlMbre/33UdZsnoHUidACMxGDUWheqQGjh11/7nU0cnNKiTzXO0BNiguLOPMaXck8XAvq7cm8OJn6ziXXYCUsPvQaf4/e+cdFtWVNvDfYRh6L9IVFSxgQ7HFHjXRRE1MMTHRmL5pm00vmy89m152N8mmJ6Yak1ijMSb23huiIoggCIjSO1PO98cAgjMDzDDMgLm/5+HR+869574z99xz73vOW+5/exGV1R1zUqK9SU09Y1J+/Hhuq9vIO2g6TOP03qhWt6HVtf58reHQKi/0JgLDZL5tVtAPpeXw9P9WcDLH4M17LDOfh99bwqm8IqN9zX239ng2dSTS0s5gKs9w6vE8Dmbncsf3ixn1zifc8d1iDmTb9vorKLSBE4DtXn4UFBT+8th7RT8HQwbRamBvo7/lwOV21qVVCJzo5jWF3v5zTH7etWcXjh8MN/mZh5vltbab43DOGe76fgm7MrM5W17J+uPp3PL1z+SXldv0PPYiPNwPV1fj6JGAAE98fd1tco6UU/kcTrf9S+3x/HPszTZ2AdVLQVffewjxGGZxm4vWHzIWqgQaDyeiuyu11i/E198TX39PI7mrm5qQDhiLa+r6FpZWsn7vX7N6Urdo0/XYu1vQ1z09+vH7rgRqNIZxRKtzYktyH2pqh7S6DVuP0wFePdj6v/7UVBh00usEx9dE0ifwBpu0v3RjEvoLrNharY5ftxiX2zb33Wz9nTsa0dGm+1CXngHc+u0itpzI5FxFJVvSM5n3zS+cLDCeJFFQcABPA9uEEJ80XgxztFIKCgqdF7vG6EspDwIHhRBFwAoppd6e57cUb5dopnVbhofafGz06ElxLP6mP2t+TmfS9edf5IsKQ3DnVrChvfH97gNo9E1/spLqGpYePMLdoy03LB2Nl5cbN9wwgm++2dJEfuutY1GpbDMHVV5pOuSirSzYc4gNx8cTHXCGIE/DRIteCo6du4WZXR+wqs3yKtO6OqmduOWW5l/Mf/39IIuX76OktJIRQ3ty961j8fP1sEqPzoKzWsWceyfw4asrmsivv200nt6ti/G2J+aub4UZ+cXOlVcOYuWKA+TmFjfIQkJ8mTY9odVtFIar2La1L3szexLsU0pRuRcVNW5M76NudRuebmPx9byJkoofLNLfHHPuvZTn7j/NyW2hBHQro/ycG0OHJtLzHtvEhleY8QAx1Y88XMfh63kjJRU/NsjcXIbg73WnTXSpR0rJT0t2s+L3Q1RVaxg3qhd3zB1tUWJFW9K9ezATJ8axdu35MApnZyd8hwZRmV7cZN9qrZaFew/x1GXj7K2mgsKFfAKsA5IwxOgrKCgotAlHJeObBbwnhFiEIXv+MQfp0SzOwr1ZIx9ArXbmjc9v55MPuvLCixvp0zuLwkJvDiX1wM93Nd9+GoWXp21eds5VmPboOmtG3hmYd+sYYmJDWL/uCM5qFZdfPoCEhG42a39ATDgBPh4Ultr2NyqoqCCnNIB7Ft3JiG6peLlUsze7B+N6jWbmYOvaHD84hiMnjd2Z//XUTPr3N++KvGzlft798M+G7ZWrD3E8LY/P/jvvok/4Nv2G4UR0DeTP5fvR6/SMm9KfSy7tmIkcewQHkH7aOKRgeLzt+ntnwsfHnQ//N49lS/eSlpZPj57BXHXVEHwtmKAqrTUYvTUaF7ILznsIlOs0FukSGvA2vp5zqK7dA9xl0bEXMnhET/793d2s/Hk3JUUVDLupN5NnDGpTm40Zl9CTdXtSjeWDY4xkQghCA97F13Mu1bV7UTv3wNNtPELY1plv/vdbmf/DtobtX5bt5VR2IW+93GKO3XbjyaemM3hId3bsSMPX14Np0wbxefIBk/ueLe+8z1CFiwq1lPIRRyuhoKBw8eAQQ19KOaeufMhsDDXvJfAVsEBKWeYIndqCh6cr52pqyMkJIifn/MtmYVEF6zYeZcYVtnnJG92zG5vSMozkY3pG26R9RzFqVC9GjerVLm2rnVX8654rePp/KykurwIgJjKItkbpj+4ZzeqjadTq1GxKP29YtuVa3Hz5EI6czGPDvhMAuKpVPDhrLKOHGr/AN+aXZXuNZKkn8jmQlEXCgIu/1NzgkTEMHtn8b9QRKMspw6lGj961zsiSEpcyPWnHz9AtLMCxyjkIX18Pbpk3xurjR/fsxne7jY23MTHRFrfl7joId9dBtNXQB4jpG84/nruqze2YYurIvhw4fpqlm5KQElQqJ+ZOSWx2wsjdNQF319Z7SliCXi9ZdEHiO4Bde09yKruArpGB7XLellCpnJgyZQBTpgxokI2ujmbpoaNG+46J+WtOtil0OFbVZd7/FWhw0VHK6ykoKFiLo1b0kVKWCiF+AdyBh4CZwONCiP9KKd93lF7WUmgmeVxhse0yLc8eMoDNaZlsPpHRILtxyADGWvFS+1cisW9XVr57F/uPn8bDTU2/HmH8+Mq8NrU5c2AcG1NPsiblRBPZ5D7WG5wuamfe+vtVpJ8uIPdcCf16huHr1XKuAnN9rMiGCQ0V2k5pcRVuxXr0Kj1SJXDSSISEoiLbjRF/NcbHdmfW4H78tO9wE9n1g/s7UKv2RQjBP2+dzLwrh5GRU0Bs12C6+Hs7TB+NVkdZuXE5QYCiokqHGfqmuCK+FxtS01lxOKVBNjWuF9P79XGgVgoKDcyu+7dxFSqlvJ6CgoLVOMTQF0LMAG4DYoBvgGFSynwhhAdwBOh0hv7wId05cizHhNx247OLszOf3zyTfVk5pJ8rZEBEKL26mE5o1V4UVlbx3d4DHDmTT68uQcwdPIhgL+OEaB0NF7WzTV2k1SoVH94wg0On8zief474sC70De1ik7Z7RATSI6L1L8fDhnRn3cam0S9qZxWDB1q2mp9TUso3ew+QUVhEQkQYNyUMxNvNMTG2FyPDhnQnM6sAJx2gO59MbVhid8cp1QbOlJXz3b4DpJ4tID40hDlDBuLvbpskmq1FCMHL0yYzZ2gCSTl5xAQHMigyzK46OIqIYF8ign0drQauLs4MGhDFgUNZTeQ+3m706W2/ayGlZMXRFP5IScNd7cz1A/sxNKppXgSVkxPvXHMFt48cwtG8s/QOCaJ/ePPheQoK9kJK2TkfBgoKCh0WR63oXwu8J6VsUhxcSlkphLjDQTq1iVnXDGXvgUwOJWcDIATcdN1w+rbDi87gqHAGR5nO9G8pBQXlJB85TViYL7Exzb/wlFRXc903C8gqLgFgbVo6S5KOsOTWmwjy7PjGfnswICKUARG2e1E8daqAjMxzxMaEEBbm16pj7r1jAqlpZ8g6bcgc7ezsxMP3T7YoGV9WcQnXfr2AoipDeMPatHR+PZLCL7fMxk3tMMefDkl5eTWlpVX4+Fhm1M6bfQmHkrNJSTVUgXByEtwxd0yHWvFsLWfKyrnm6x/ILzd4I6xNS2d58lEWz7vJIZNDvUOC6B1i30nPiwW9XpKUlEV5RQ2DE7rh7u5icRsP3zeZx575ibMFhsSkbq5qnnxoKq4u9hs7XlmzgW/2ng/jWHr4KO/MmMr0OOPV+viwEOLDQuymm4JCaxFC9APigIaMslLKbxynkYKCQmfGUTH684QQoXUr+xLYLaXMq/tsrSN0aise7i68/9ZNHEzK4nRuMQPiI4iM6Nhxtz/+tJMvv9qIVmtI7jpsaA9efH4mrq6mM1b/dPBwg5FfT15ZOT/sO8SDY0a2u74XM3q95K23V7L6T4MLspOTYObVQ7j/3kktHtslyJv5H9/Bnn0nKS6tYmhCNIEBXhad/8tdexuM/HpSzp5jxdEUrhsQb1FbFzs5OcXMmv0hD//jci6/rPUu4t7ebnzy77nsO3iK/LOlJAzoSmiI41dkreG7fQcajPx6MoqKWZSUzK1DrcxGqWB38vNLefKfP5GZeQ4ATw9XnvnndEYMtywEKbprED98eTc7d5+kuqaW4Yk98PG2n3dHXlk53+872EQmgfc2bTNp6CsodESEEM8D4zEY+r8BU4EtGDxfFRQUFCzGtql3W0ndqv0u4BrgOmCHEOJ2R+hiawb2j+KKy/p3eCM//WQ+n362vsHIB9i1O52fftll9pi0c8YZwwHSCkzLFVrPmrWHG4x8qEtwtXgPO3aeaOao8zirnBgxtCdTJvaz2MgHSDtnOtePuWv+V6e2Vss7762isLDcouOEEAwZ1I2pk/t3WiMfmukvBUrOqM7E+//7s8HIB6iorOHV13+lutqyqgVgCI8ac0kskyfE29XIB8goLEInpZE8q7iEGq3WrrooKLSB64CJQJ6U8jZgINB5HxQKCgoOxyGGPvAEkCClvFVKOQ8YAjzpIF3+kuzYYdqA3L4jzewx/UJNuzrGm5ErtJ5t203/7jt2mr8etiTeTH4Bc9dcAbRaPbt2pztaDYdgvr/YJk+Fgn0w9RwoL68h6XC2A7SxntjgQNQqlbE8KBBXZyX0SKHTUCWl1APauspU+YD52roKCgoKLeAoQ78AaFxGr6xOptACOp2eI4ezOZFqXG/dEry83EzKvc3IAa7tH09cSNMX+ZigAGYPungzXNsLb2/Lr4ctuX3YYCJ8fJrIhkZFcHlv+5Wty8w4x+GDp9BodHY7Z1sxd90uduYMHkT3AP8msv6hIVwV39dBGrWd8rJqDu7PdLQadsXsuNPGfp2VWUDSgVPU1tpnNT3Qw4P7LhnWRKZWqXhygvWlGxUUHMAeIYQf8BmwF9gHbHesSgoKCp0Zu051CyEeqftvGrBTCLEMQyjdVcAhe+rSGTmafJqXn13E2TOlAPSJC+eF164nMMjy0koTxvfly/mbKC1tGpd91Qzz8bUeLmp+nDOLpYePGrLuBwcxs18cXq6WJ2+yJXq9ZEtqBpkFxSR0DaNfZMfOony6qISNx07i6+HGxLgY3NTOTL8ygd9XJ6HTnQ+lcHV1blIDuj3p4uXFsttv5peDyWQUFTEoPIxpcb1NrpLZmvKyal76v1/YvycDAD9/T554dgZDh/ds93O3hbBQX4YP69g6mkOvl2xNyyTjXBGDuobR38J7xs/djUXzZrM46Qhp5wqID+3C1fFxnTZx49JfdvPFR+usclnvjJwuKmFTSgYx47qye/kxRCOv9z59wqzOll9RUcMrzy5iz06Dp4uvnweP/XM6I0bF2kLtZnlg1AiGRkawOiUNdxdnrukfT8/Ajh1Cp6DQGCnlfXX//VgI8TvgI6VU3o0VFBSsxt5vZfUW6Ym6v3qW2VmPTodOp+fl//uFs/nnHSGOHcnhv2+v4sXXZ1ncnre3G++8NZtPP1vPoaQsQkN8uWn2SC4Z2fwLmbtazewE+xifraGippY7v1zMwazcBtm1if14+ZrJDtTKPD/uOMgrv65HXxdPGuLjxVd3XkevXqG8/OK1zP96MxmZ5+gVG8odt48lIty/hRZth6+bG3cMH2K389Xz+UdrG4x8gOKiCl55djE/Lv0H7h6OnUQyhXASjB7Vi3v/dinOzu0/EWJrKmpqufurxew/df6emTkknleumYwQotXteLu6Mi8xoT1UtCsnUs/w4XurHa2G3Vi48xAvL1/XMAZ5jfAiLAN0JbVcMjKWe/92qdVtz/90Q4ORD1BSXMm/nl/MgiX/wMsO3i/Du0UxvJvi6axgf4QQURiS5oVgWMD6VEr5HyFEALAQiAYygFlSyiIzbYwCDkgpK4DRwGAhxH+klH8tVyMFBQWbYVdDX0r5Ymv2E0K8L6X8uwn5FOA/gAr4XEr5+gWfvwdMqNv0ALpIKf2EEIOAjwAfQAf8S0q50PpvYn+OHTndxMivZ8e2VGprtbhYUcaoZ48uvPHaDbZQz2F8u3V/EyMfYNGew0zt34tLYrs5SCvTFJZX8trKjQ0v2ABnSst567dNfHjLVYwY3pMRHXwVuz3YtP6Ykayyooa9u9MZPa7jZcyOjQnhpReucbQaVvP99gNNjHyAJXuTmdq/F6N7RTtGKQeyecNRR6tgN4oqqnht5YYmY1C5VkP4xO58NO/qNre/ab3xb1ldpWH3zhNMmKRU71C4qNECj0op9wkhvIG9Qog/gVuBtVLK14UQTwFPYT4n1UfAQCHEQOBR4HMMkwfj2l17BQWFixJHxei3xKgLBUIIFfAhhnIjccBsIURc432klA9LKQdJKQcB7wOL6z6qBG6RUsYDU4B/18VBdRrMlbxzUTujcuqol7F5/vx2I7f1eZCprjfy+MQXSNt/0uI2dpw4ZZHckezNOI1GZxx/3hF1bYnMo9k8PfUVprreyNye9/Prx39Y3Zabm+lJKlc3033e0aTuS+fZGa+TcyLP0apYhbn+tr0T9kNb4NZB+1l7sDfzNLVa4zHIVtfe3G+ZdbRzJfdTULAUKWWulHJf3f/LgKNABIbQ1K/rdvsaaG5GTSulrA9n/UBK+SHnPWEVFBQULKYzWYjDgDQpZbqUshb4EcNgaI7ZwAIAKeVxKWVq3f9zMGQyDW5nfdHrCqgq/j/Kz4yn4twsNNXWG0MxvULp1cc4bnLyFQNQOXemy2hgx4q9vDnvA7KP56LV6DiwPpknJr1IaYGx10JzhPiaLiXXxcfyEnPNodemUp4/iZqy95Cyxqo2QnxNP69trWt7U1VRzRMTX2TP6oNoNTryTubz3/s+Y813m6xqb+p0Y/fv0HA/Bg/p3lZV2wUpDf33yckvoantfDHdIWb6W2gn64e24tLL+hEZWckD96/ng//+aFUbutqDVBbeQfmZsVQWPYBOY59qGZZi7hqb6xOWMnWGiVCOmlp+eHw+J5MuTu9jIcTdQog9Qog9Z8+edbQ6Cu1HUP11rvu729yOQohoIAHYCYRIKetdqPIwuPabo0wI8TQwB1gphHAC/jozkQoKCjanM1mIEUBWo+3sOpkRQohuQHdgnYnPhgEuNM0RYHOk1FFZcCOaym/Q69LR1e6gqvAuNFXWG/svvj6LEaNicXISuLo6M33mEO55oGPGorfE8o+MY2LLiipYt2CLRe3MvSTBKGFcsLcn0xNsm/1byir02uPUlL1HVfFjVrUxICqUxGjjLnv72MS2qmdXti7ZRWFesZH8VxPXtDXcNG80N8y5BA9PVwASEqN57d2bOvwEVl7GWXb9tt/RaliMqXsmyNuD6QlxZo64uAkOhnfe/p0J41MJCyu1vAFZTUXB9Wir/0Svy0BbtZzKgmvR69pWGaU96BcZyrDukUby28fYZgyaddNIeod5Q33t+pJyOJqOrlbLb5+vtck5OhpSyk+llIlSysTg4HZfP1BwHOfqr3Pd36emdhJCeAGLgIeklE0GlLrVemnquDpuAGqAO6SUeUAk8JZt1FdQUPgr0lFTJLc+I5RpbgR+kVI28VEUQoQB3wLz6mqVGp/YMEt7N0DXrl2tVkBbsx69NuUCqaS24mPU7pdZ1WZQsDcvv3kDtbVaVE5OHd4Qao6K4goz8kqL2ukXGcoXt1/DZxt3k1lQzKCuYdx36Qh83due+KlxX4iKOG8YaauWo/d+Cidnk/NMzfK/W67io/U72XA0HR93N24aOZDpgzpXSbJyM9fOnLwlVCon7rz3Um67ezw6rR4X1443LDXuC254NMit/c6OJC4ihK/uvJZPN+wm41wRA6NCuW/iSPw8/pqlAjVVS3FWnbPomCZjQ6QPSN8mn0t9EZrKn3D1Nko143A+vOUqPlq3g/V1Y9DsEQOZYaOJUScnQVdvFSm7k8FJgP68TVNRYtnYrqDQ2RBCqDEY+d9LKetDR88IIcKklLl176D55o6vM+7fbbR9CkOMvoKCgoJVOPSNWgjhIaU09fT/jwnZaaBxOt3IOpkpbgTuv+BcPsBK4Bkp5Q5zOtXN0n4KkJiYaHbmdWduFt8ePUBJTTWXdu3JnL4DUTudNwalLtfkcXpd2+N6W5N4b2tmJm9v2cLRs2fpExzMo6NGMSY6us3nthUjpydyZPtxY/kMy1eWErtHkmhilaqtNO4LCQNdGvUFiV6fj5Nph5Jm8XJz5fGpY3l86thWH1Oj0/LtkQNsyE7H382DeXEJJIZYdu4arZZ3t25lUXIyGr2eK3v35umxY/F2dbX0KzBi2hA+eugr9Pqmt8clM4Za3FZjVConVKqOOXnVuC/4iAAJ4KxWMWxq58w6P7hbBB/Ps7z/NmZdejr/3raN1IIC4rt04YkxYxgWafv7sL3Rmxmrm6Nxfxg8KMjkc8Lasf6zPXv4ev9+CisrmdCjB8+MG0e4j49VbZnC09WFx6aO5TELxiBLGDl9KH9+vbGJkW+Qt4/n0oH8XOYf2cfZygrGRHTj1vjBuDkr3s4K9kUYSpZ8ARyVUr7b6KPlwDzg9bp/lSpTCgoKdsMhb9VCiEuEEEeAY3XbA4UQ/6v/XEo538Rhu4FYIUR3IYQLBmN+uYm2+wD+wPZGMhdgCfCNlPKXtuq/OiOVG39byK/px9h0OoMXtq/l7+tWNNlH5ToKU44Jzq6j23r6Fjl+7hx3Ll3KoTNn0Oj1JJ05w11Ll5JyzrJVq/bkmoenNTHqndUq7n5zLj0GWJcpP7ukhD9T08goMlm1xqYIJ39Uavu5Od+zZhkv71zP5tOZLD9xlFkrFrA+K73lAxvxyoYNfL53L0XV1ZTX1rIwKYmHf/vNKn1Co7vw9w/vwqVR4q2Eif256RnbZKLXS8nOrCw2pp+kWtMxY+DdPFx55PN78Q/pVDk9bcahvDzuWbaM5Px8anU69ufmctvixXa5/0yRU1rGn6lppBcWWnysznlEm84tnEzHtzu7GuWUbZGv9u3j9U2byC0ro0an4/fUVG5ZtAid3qQDWrtzrqKCP1PTOJbf+tjz0TOHMeO+yxtKNQohmH7v5Yy+ZrjN9duWc4rrVvzAkrQjbMnJ5LXdm7jtj8UtH6igYHtGAXOBS4UQB+r+rsBg4E8WQqQCk+q2FRQUFOyCo1b03wMup85Ql1IeFEI0u7wgpdQKIR4AVmMor/ellDJZCPESsEdKWW/03wj8WBcLVc8sYCwQKIS4tU52q5TygDXK/3v/tibliQBWZRznWOFZ+gQYYvRUzj1w9X6YmrLzE7tOqu64ej9izSkt4qfDh6m9ILu7Rq9nYVISz02YYOYo++LiqualpU9yMimT3PR8+o6ItdpoemXdBr7Ztx+9lAhg1oD+vHLZJItqgrceF9x8X0UIy1fCreFAfi7rLjDqdVLyn/3bmBDVo1VtVGk0LEpONpKvP3mS7JISIn19TRzVPNP+Npmx143g8NZjBEcGEju4dbq0xKniYu5ctIT0QoPB6O/uxn9nTGNkG8JobE1ETCgLdn+Cl5+no1VxGAsOHUJ3wRhYrdXyS3Iyj41u/8nMxry5cTNf7N7ToM/M+Dhen3JZq6uRrMgKpKIojuu6HrHq/MIpEJVLLLrabQ0yZ7dpOLtNsbit7w8eNJKdLCpi26lTdvfI+nz3Ht7ZvLWhUsiEnj34YMY0XJ2bf20QQvD3D+7kukemczLpFN37dyWsR3P5x6znv/u3o7lgEmRbzil25GYxIizKzFEKCrZHSrkF82GnE5s7VgixVko5UQjxhpTSXOk9BQUFBYtxmOu+lDLrAkPMuOaP8TG/Ab9dIHvugu0XTBz3HfCdVYqa4GSJ6VWrEyWFDYY+gKv3Qzi7XYm2ZjNOqlCc3SZhcC5oX0qqqy2SO5Lu/bvRvb/19e7XnUhn/t59DdsSWHgoiRFdo5je13Y12J1UUbj5voCz22U4qdrnpdUU6SWmVyjN9UFTVGu11Jgo6wdQWmNdBQEAn0DvNrvrX8izf6xtMPIBiqqqeXTFKjb+7U6jBHKOwtPX4y9t5IP5ftOW/mQN2zJP8emu3U1kS5KPMCwqkuv792tVGydLCvno8ER+yerHIP9c4AcLtRB4BC5AV7sFneY4KvUAnF2tuy/Mjt12/l1Tzp7l9Q1Nq2isP5HOl3v2ce+IYa1qI6xHSLsZ+PWcNDs+FiqGvkJnIkwIcQkwQwjxIxdMGNSX7VNQUFCwFEcFxGbVDWpSCKEWQjyGoeZop2BQsHGZOychTMpV6lhcvW5H7X6FXYx8gIk9TK+uTurZ0y7ntydr00wXT1hjRm4twskfF8+5djXyARK6hJtcIjDV18zh7+7O4PBwI3mEjw99OlCW6GqNhm2ZxiW48isqSMrreBnM/8pcamaMMTf2tBe2uP8TuhjujeSSEL7PGGSVHkIInF3H4Op1h9VGPpj+/VxVKkbb2aNl7QnToUFr0jpW2cD6a3chloyPCgodgOeAZzHknnoXeKfR39sO1EtBQaGT4yhD/x4MyfIiMCTUG8QFyfM6Mk8PG4u3uqnRfu+A4UR42S5hUlu4PDaWOQMHNhiIArhpwACmxMY6Ui2TrPppFw9c8z63XfYWn76+gopyy7wOfMwkk/O1IslcR6S7rz+39xvSRObj4soTQ8dY1M7rl11GVCMX/QB3d9674gqc2iW84Tx7tx7nyXmfMe/SN3jziYXkZpmPoXZWqfBQm06iZU3SwPYi43ge/31+CcUF5Y5WxWHMjIvj2vj4hm0nIbhjyBDGde9uVz3M9QtL7v9JXXsyPtK+epvjibFjGRByfjLRQ63mzSlT8HN3t6se5sZVc3JH8eiQ0QS4Nf1t5vQZSN/ALha39ev327nv6v9wx+Vv8+U7v1NdVWsrNRUUmkVK+YuUcirwppRywgV/lzpaPwUFhc6LkLK5kp5/bRITE+XHK5byc8phanRarujRm8uiDcbymYpyFqclU1RdxaRuMQwL7XjZpjOLi0k5d45egYFE+/s7Wh0jlny9hU9fX9lENnB4D16ff1er20gvLGTa/G+b5CRwdnLilzmz6WziyfQAACAASURBVBdifvVdCLFXStnqNNCJiYlyz549DdslNdX8cPQgSWfziPEPZG5cAsEe7efOvT3nFOuy0gl09+CamDi6eJhOANYcOr2eHdnZaHQ6RkZFtRhr21YO7Urn6ds+b5KdPzDEh09XPoKHp2mD4dX1G/lyz94msuFRkXx/46x209PSvuDrHiYvib6V6NgQPljyYIetFGAP0gsLSSssJC442KpcD0XVVXx35ABHCvLpGxjMnLhBBLh5tHxgHVnFJVwx/2uqNNoGmUoIFsy+gcERpld7TaHT6/kjM409Z07z3MhL2zQ22IJ9OTkUVlUxPDLSIZNcxVVVTPr8K4qqq5BqiXQGJDw/5lJuHTTY7vo0R0FVJYtSkzlbVcHYiGjGREZb3MaCj9fzzX/+aCIbNr4PL318q8P7gkLHwNLnRBvOMwNDTimADVLKFc3tr6CgoNAcdo3RF0K8jyGM2iRSygftqE6LFNdUc/3yBQ0KL007yv2DRvD4sDGEeHpx78Dmswinlh1jRe5icqqyifKIZkb4dUR72s+1tZufH938LE9wdzaniN++2UzeqQLih/Zg8o0jcXW3fdjB4vlbjGQHd6aTdiSHmLjWvaT3CAjgi2tn8vbmLRw5k09sUBCPjLmkWSPfGvJr8nj84H2Eu0cxMXg6j67ZRWpRgeHDk/BzymGWzZxjlQHeGkaGd2VkeNvcd1VOToxqBxfgqopqVv+wjZR9GUT2DOGKeWPwD/Zh6TdbjUrwFZwpZePKg0ydZTrO9/Gxo3F1VrHwUBLVGi1TesXy9IRxNtfZFmSknmHf1lSGju3taFUcRo+AAHoEBFh1bElNNVcv/Y7M0mIAVp08zs8ph1k+cy7+bq1bwY7y8+XL667hrY1bSMrLIyYwkAdHjbTIyAfIKjnFjpNfc9bF8lJ77cHg8HCOlh7mk4y3OFOdQ7RnT64Kv45ID+vzmViCn7s7395wPfNW/ky+pqJB/uqeDfQKDuSSCPvo0RoC3T24e0Dz4RJ5medY+c1mzuUWM2h0LyZePwJntSHnh16vZ+nXxs+iXRuOtYu+CgrmEEK8BgwDvq8T/UMIcYmU8p8OVEtBQaETY+9kfJ1qqjuvooygC2SfHdrNHQOGtLjqdLoqi/+kvolWGsqDHSlN4kR5Ks/G/YsgV8vdCu1FTsZZHr7iLUoLDW7JGxbvZsPSPbyx6CFUzrZNhlZ4tsyMvBRo/Yv6yG5dWdTtJhtpZZoqXRVl2jJSyo5wvCyFnKqegFvD57kVZXx9eD+PD7PMpb6zU1NVyxMz3yPtUFaDbNV3W/j3qifrrqMx5q47gFql4tExo3l0jH0zt1tLYb7p76jQMguOHmww8uvJKivhh6MHuT+h9SXvhkZG8tPNN1qtR1FlEa8nvQC+LeaDtRsZFem8n/o2+roctUklBzhRfpzn49/AV22fko7CmSZGPkCtXse7e7Z2KEO/JU4eyeaxq96lsswQFrZh8W62rjzAS98bogW1Wj2lxZWOVFFBoZ4rgUFSSj2AEOJrYD+gGPoKCgpWYVefUynl143/MNS2X9xou0OhMZGpvFav42RxyxnPN51d22Dk11Ojr2bruY020689+Pn9PxqM/HqSd55gx+pDVFbVkm/GeLOGhJHGyQFd3dXED4622TnaA4mOrsEFRvKUwtbXmraGqmrD79+Rwm02LNnTxMgHKMgrYemn60gYaTonxOBLYuyhWrvj5CQYNPLi+C6O4HjROZPyY+18H13Iwq0/g2fHMfIBNuT/2WDk11Opq2R7wWa76ZBSaPr6pJi5buaortZwJt9x49b376xqMPLr2b02mYNbUgBwcXEmfki00XFePm5GMgUFO9B4Js/yeCgFBQWFRjgkuFQIkSiESAIOAYeFEAeFEENaOs7euJgo5+WqcibGP7DFY8s0pg3iUm3HXgE8eeS0kUwCPyzdw8wb32fW3I+Yd/fnHDqc3eZz3fPP6QSGnE9gqHJ24oHnrsbT27IXrOLSSjRa+76ouzhrjWTxQe2TkV9KyadfbmTmjR8wa+5HzLnjM/bsy2iXc1nKyaPG/QUg/Ug21905lt4DmuaumDlvFH0TOs9qoCn0Liqqw7254tbRhERYnvtCo9VRXKqsIMYFmr5f+rXTfWSOM0X5dj1faygz85wo1ZS0qd2qag1lFa1LeBofZNrzLL6Vie6klHz21UauvvF9brilftw62WpdbUWG2THqvPz+567CP+h82JWzWsWDL17T7ropKFzAa8B+IcT8utX8vcC/HKyTgoJCJ8bervv1fAncJ6XcDCCEGA18BQxwkD4mCfX0Ri8EukYrEQ8OHomva8uGaLzvQPYV7zaS9/PpUF/RiO7xEaTsz2gi03bxIblRtvTMUwU8/fwv/PTNvXiaSarWmNyKUtaePoG32pXLomJxdzZkVo/sHsyXqx9j5/pjVJRVM3RcbwK7tL5ywf4jWbz9+VpOZhfg7enK7OmJ3HpN611+24KrvqkBq6qEg8vTSQ/pTY+oCwM+2sayFfv54acdDdunc4r4vxcXs+Drv+HfTvXc8yrLWJudhqezC5dFxeKhNp2joUec6SSUPeMj8fRy490F97J/Wxp52UX0GxJNt1j7GnHthVSrWL0rlduqavFoZf4KKSVf/LyNhSv3UVFVS0y3YB6/cyL9e0e0s7amddmcm8HJ0kIGB0fQPzDU7jrc0Kc/P6UkNVnZ7+kXwI197DtG9vXqRy4pdj1nS8T7DiC59JCRvJ/vQKvaq67R8PYXa/lzyzE0Wh2J/bvy9N8uI6yL+QXDGL9AZvcZwIKUg+BkeAa6CRceG9q6sJrlvx3g+4VNx61nXlzMgvn3EODffolLL6R7XASn040nc3rGnx+7uvcK5cs/HmfHuqPUVNUybHwf/IO87aajggKAlHKBEGIDUJ904kkpZZ4DVVJQUOjkOMrQ19Ub+QBSyi1CCOMlUgfj6+rG1zPnsuh4ckPW/VGtjE0cETiawyUH2d/I2B8ZOIZBfu2etLVNXP/AZWxfdZCSRqXD1BH+aC/wuqyoqGHr9lQum9Sv2fZ+OZHEU9tXoTWEnBHs7skPk2YT62cwhl1c1YyZ0t9iPQtLKnnstSVU1RjCI8oqavj0x60E+XsxbULzOrWVwf7DmN3/Th77cSWb0k+iLhd45MBxbT6PvrqYn9+/A2cb5jP4Y22ykay6RsPGzSlcPd32GbCXpB/mie2/odEbrlmQmyffTbqBPv7GK3njZyay4quNpB461SALDPPj6rsNFYGcnJwYMrqXzXXsCJSWVbN9ZxoTx8e1av8lfx7ky1/OGz5pmWd59LUl/PLBnfh42c9NuFJTy7x1P7E7/7xXzg0xA3h9xFREO5dbbIy3iyuLr7qZn48nceRcPn0Cg5nVuz/eLvbNMj9z0jQ2fbwRzchziA5SQGFM0KUcKUnicOnBBtm44EnE+Vg+VgL8e/56fttwfhzZk3SKJ99axjdv3dLscYPDw1h46gD6usluT1cVvm6t66t/rDlsJKup0bJh8zGumWE/B76bHr2CfRuPNnHfHzapHwNGNR2X3NxdGH+ldRMpCgq2QkqZCyx3tB4KCgoXB44y9DcKIT4BFmDwDL8B2CCEGAwgpdznIL2MiA8KscolWyVU/K3ng2RWpJNTfZoo925Eetg+47mtCY8O5sO1/+S3b7aQm3mWfsNjWLDpGFnZxvXPdfrmYy5La6t5ducfDUY+wNmqCl7as4ZvJ1mfPAtg3faUBiO/MSs3HG4XQz/MLYJ50XcT7hZJN09Dze28bQX4Fza1DM4UlLE76RQjE2xXl/vCzPUtydtCuaaGZ3f90WDkA5yrruDF3WtYcJlxwkMXNzVvLnmY1Qu2kbI/g6iYUKbOGY1f8F9jNcySa7ByvfGETXllDRt3pTL9UusMOGuYn7K3iZEPsDDtEFd268vYcPvWk/dyceG2fo6N2lK7OPPOXW+wZMnvJJ8xNk4dgdpJzQOxj5FensaZmly6efQg3N06zw+tTs/vm44aydMyz3Is/Qx9eph+vpXUVvPcrj8bjHyAgppKXtqzlm8n3dDiec3dG7Idxq3m6N43gv+tfYbfvq3Put+bCdearvqhoKCgoKBwMeEoQ79+2vz5C+QJGAz/S+2rjmmqtdnklH5DiNd1qJxaX9u5Md08e9DNjiX1bEFgqB9zn5jWsH1W5cRX3zYtP+Tmpma0mWRr9ew7m0OVztgY35qXiZSyTauH5mLytVq9SXlbcXFyYWRg04z6jXWIj0+nb1wmVVWuaOQgwHYG08TxfTl2vGnZL7Vaxdh2WCk/cC6Xck2tkXz7mVNo9XqcnYyXPN08Xbnqzgk216Wj4+nhwiXDW5+MT2umz9o7v8TW3AyT8i25J+1u6HcUnF31jJtWTkJNIa884mhtztPDK4YeXm1M+CglOr3pcbG5vrfv7Gkz43dGq047eXIoYbEr8A8qJSMtjAM7YnESLowdbf9ylCFdA7ntmavtfl4FBQUFBQVH4hBDX0rZKawCrb6YE4UvcaZ8EQNCF6JyMrgsSqmnqmoZNdXrcHIKwMNzDmp180avrSiqriKvopwefv64quxz+W6+YQRn8ktZveYwOp2ekC4+PP7QFLxbSJoXYqamfBd3rza7CI8fFsv/vttk5FUwcaT9XiInjuzNotUHuOqqzVwy6vxKoOABiqq+wFsdTkXFt+h1Z3B1HY27x7UIobb4PNdcNYTTucWsWHUArVaPCzCmeyDOFq6M5ZSXUlFbS4x/oNnfP9TMNQt29zRp5P8V6RJUwuRxx5k86clW5aio59KRvUnNbJpRXu2sYuxQ+2buD/Ew7W1hTt4ZqNJoyCgtJsLLBx9Xy1z/9VLD4TO3UFrTYRzJbIpzXR9bv+N4E3lYsA/xMWFmjwtxNz0WmJM3pkqTQdiAZwjuZ6hQM2hEGkNGZhDl8RHB7RD7Xq3VkF5cRIS3j8kcOlrNCSoqv0OvO4ur23jc3WcihCG8qrSwnOX/W82xXWlE9gpj5t+nEtIt2OY6Kig0hzB0yGQpZR9H66KgoHDx4BBDXwjhB9wCRDfWQUr5oCP0aYny2mTOVvxKqPf1ABQXPUxV1c8Nn1dUfEtg0AJcXYe3mw5SSl7etoHvDh+gVq8jwM2d50dfylWxfdvtnPU4O6t44uGp3H37OIqLK+kaFYiTU8uGel//LowJ687m3KaZlu+Oa7vbZGFaHm57T1IRFwGuatBLQrU6rpua0Oa2W8s9N42hrPokI0Y2dfeVaDhd+BKB4gRSGupQV1Utprr6dwIC51t8HpXKiTlXD2HbW0soKq5EVlazeS2c+OMAH+x4Fa8WEvKV1FTz0JqVrD9luA49fP357+Rp9As2dtmN8Q1iQkRP1p8+0UR+V1/F1bUeL68abrtlFWl7U4FNrT7u5hmJnMopZPXmo+ilxM/HnSfumkyQf8uGky25rU8iKzKOUqs/v5ob5ObJNT3aN7dFe/Fd8gHe2LGZstoa3JyduXvgUB4ZNqrVx5+rWH3RGvn1PHbnRErKqtiXbCiFGRnqx8sPTWt2HI8LCGFMWDSbczOayO+Oa/k5l1XyCVp90zK0UT2ziOtyGrCtHfPjkUO8un0jpbU1uKqcuWtgIo8NP58wsLZ2LwXnrkdKQ4x+VdViaqrX4h/wEVUV1Twy/nlO1WXm37VqP2u+28wHO/5FaHTrqgsoKNgCKaVOCJEihOgqpTzV8hEKCgoKLeMo1/3fgB1AEtA+vtY2prz2CAAazdEmRr6BaspK38Q1eFG7nf+HI4f48tDehu3C6ioeWfsbA7uEEu1reYkva/Dz9cDP17IQho/HzeT9pG38kXUcb7UrN/caxKyYtic8+uG1JYiMs3ieOofexx1RVUt5jZbkLUcJ7xXB56+vYPfGFPwCvbj61tHMmNv6F//W4unuwgO3duWoibLfrvojSKem+SWrq/+gpmanVRNCy/63muLThQgAlRPC3588jZrbJ77B7AcmMfO2sWaPfWnr+gYjHyC9pIi7Vi1l85y7TK7S/2/s1byftI3fT6XgpXbhptgEboxVklRdSMyQNI7tWUafxKtatb+zs4pnH5jKPTeN4VxROTHdglHbMGlja+kfGMr3k2/kw6TtpNdl3f/HgFH4u7rbXZdDO0/w9bu/k34sl+69w7jl4csZNLL1Hg4H83N5dtMa6n1bqrVa/rt3O30Dg5nas3WhLRV1Y3tHobZGwzfvrWbt0n1IKRk/PYFbH52CWyurO5jC38eDD56fRVZuEdU1GmK6BbfKq+rjcdfw36St/JmVWjd+JzArpuWqCBW1xjkB6uWBHhMt1t8ch8+e4emNfzRc/xqdlg/27aB3YBDTYwwTCmWlbzUY+fVUVS3Ds/Ze1n6f32Dk11NaUMaS/67i3nfn2UxPBYVW4g8kCyF2ARX1QinlDMeppKCg0JlxlKHvJqXsQJGQLePpYnAJzzqxCTcTnocajekXG1vxa9oxI5lOSlaeOM79g9vPk6CteKpdeGrweJ4aPN6m7Z46Zng5E3qJqvh8TfKTh7N4/18rOX3SULKrqqKGj15ahrOziitm2770nqdLL0AATd3o3ZxUgHEhCa3miFWGfv33BXAK6YJwMbz0l5VW8+mrKwDBzNvGGB0npWRFmnHpsNyKMnbnZjMywjhBpLuzmicSxvFEwjiL9WxMSWE5K+Zv5kRyNl17hTHj1jEEhJgv59UZKSnYA7TO0K8nOMCL4AD7ruJfyNAuUcyfGOVQHbJO5PN/t3+BptZwnxzdn8mzd3zBB8v+QbfY1pX7W5GWgqkAluVpR1tt6Hu6dAxP2a2rDrLp1/2kHskh93Rxg3zZ11soKSjnyfeMk2FaSlSYZZPCnmoXnh48gacHWxZt5+nSh/Ja48SGHmrbhlatPGHm+qceazD0NRrTEzlazRGyUmpMfpZ5NNukXEGhnXnW0QooKChcXDgq6PZbIcRdQogwIURA/V9LBwkhptS5NqUJIZ4ys88sIcQRIUSyEOKHCz7zEUJkCyE+sERZT3Vfunhexd6NR3n93h0m91GrW1diy1pUZlZfnO1YDqsj0Tuxp0m5VLs2GPmNWf7t1nbRw10dTahX0wzUAjVqtelVL2cr+0nD93VzbTDyG2Pu+wkhzPeddoy5Ly+p5JEZ7/Hdu6vYvjqJhe//wYNXvk3R2dJ2O6cjCI60vafIX4Xff9rZYOTXo9XoWPXjzla34WSmb6ss6Ns+qokUnHRsTPZ37/zGK3d9wabl+8jNLjL6fNOqQxQ3Knna0Yn0/RvOTn5NZD6uQwn0sG2eXZWZeojOjUIS1Op40/uo4+g1xPRzxNzzRUGhPZFSbgQyAHXd/3cDF3dckYKCQrviKEO/FngL2A7srfvb09wBdYlKPgSmAnHAbCFE3AX7xAJPA6OklPHAQxc08zIWBNU6O/nS3f9pBoQtQOXkzrdv/0ZWqjdbV3Rrsp+Urnj7PN7aZq3iuj7G8bMuKhXTYjrGapS9ueW56/G+YFV00s1j8OniY3L/0kar/rYmJvBl+gT/h2DPGYR5zyUhfAmBfv9CiKb6ubldbnUeh+n3TKZr3wgwY8CUNfP9rultPLnQ3defIaHWletqDX8s3EFORtOYhoK8En6dv7ndzmlvTh2Op0f8FEer0WkpK64yKbfkXp3ZK87kRNa1vUwbd6bYujKZL+4czB/vxZG8xnxyuvaivKSSXz5aa9gQgInvo9fpKS81/Xt1RDzU3UkIX06U7z0Ee06jZ8AL9Av5qiEBnq24uldfkxOW1/Y+/7z09nkcIZqGpbi7X42LywDGXjeC/mOa5rkJ7xnCzAevsKmeCgqtQQhxF/AL8EmdKAJY6jiNFBQUOjuOct1/FIiRUhovvZpnGJAmpUwHEEL8iMFntrFf3l3Ah1LKIgApZX79B0KIIUAI8DuQ2JoTujlHEel7R8P2qeN5ACx6MZ6k7aH0vySP8mIXeg98lIjL29d9fmavOE6XlfLJgd2U1dbQzcePF0ZfSoS3acP2Yqdr3wg+O/g2q7/eQGFOEYMn9WfEtCEUF5TjrFah1TQtGzV8QvslLRRCEOx5JcGeVxoE585BUBDBXdZQWfEtOl0erm5jcXefafU5vP29eH/bv1jx2Vrmf7zJqEb1sGa+3zMjx1Ol1bI89SgavZ7BIeG8NWGK2dVQW5CZkmdSXn8PdWZqq9w5l/kowyb/w9GqdGqGju/Dn4uN53eb68sX0icwmH9PupLXtm8kp7yMADd3HkwcyYRurS9pmnk8D22Nij2LotmzKBpnttj1uZiXVUBNdV0ZOwno9UYTepE9gomIDrKnWuapG99aws05nGj/x9pVlRj/QN6fNI1Xtq3ndHkZ/m7u3D94OJOiz6/Iu7gMJrjLn1RUfN8o674h3Ebt4szrvz/DhoXbSNmdRmSvcCbfMhZPH+vK6SootJH7Mbzr7gSQUqYKIZSskAoKClbjKEM/DbB0iTUCyGq0nQ1caF33AhBCbAVUwAtSyt+FEE7AO8AcYFJzJxFC3A3cDdC1a9P45ZiEcE5VHeGBXRv415qp7F4ThRCCzzYaYr/1eklOYQl+nu54ubdc4qlKV0aVrgx/dVirEiM9MGQEdw5MpLi6ihDPtpeoA9DodHxz6AB/pKfh7erCzf0GMiG69S/JjiQg1I/ZTzatjewf5M2DL1/LB88vprbG4BYc2y+CeY9YvvLaXF8AqNSWUqOvwN/FsApYpSsnq/IYUfe/hPvC5Tg7d8XH9xkqqmspKKskwk1larGu1bh7uXH9w1cSHBvBv5/+ucE46N4njDufvNLkMbnlZXy6bzeZhcXM6N6Xm/oPYEhY+63k1xM7IIo/FhqHucQOdGxcuLU07gsRkVH0H/mITe4/R3G84Byf7d/DyeIiEkLDuDthKMGezVdvsDWjp/Rn6g3DWbXwvKv+ZdcNZdyVliV+nB7Thyt69OJsZQUB7h64qCxbNY4dYOiTLkFa/IZVEv0ZLXbSlsYGS4jo3gV3L1eqyuvixWs14OrSsLLvF+jFo2/c4PD+VqEtJbsyha73v4L7wmUWHVutq6BSW4yfSyhOVqzqF5RVotXqCPE3TpAztWcvLuse0+z1d3buga+vIfz5j/Q0fjy8jEqthik9Y5nbfxCT545l8lzzCU0VFOxEjZSytv5eF0I4c2ECIAUFBQULEFLafwwRQiwB4oH1QEM2nObK6wkhrgOmSCnvrNueCwyXUj7QaJ8VgAaYBURicNPvj8HA95BSvimEuBVIbHycORITE+WePYYVpxPlB1h48g28M/P5+/R1fLxwDFUxLqhdnOgTOBGX4hm8/tM2TheU4qpWcf2ogTx69ViT5Yt0UsPvuR9yuHgtenT4u4QzLfxhIj2adzfV6nWUaarxc/Gw2UvfA6t+ZWVa0/rKb0+awrV9W+/62hEpLarg4I4T+Ad5E58YbfR7CSH2Silb5dkBTfuCVl/Lqtz3OVKyAT1afFVuICvRSQ0eJ2u448pt/Lzmb1w++i0+W7WfnzYfpFqjJTzAh2dmXcrouO5t/n5lJZUc3H4CH38P+g/rYbI/FFdXccWCb8gtPx/b6+Pqyoob5hLl275J8aora3j82v+QlnQ+qVVUTAjvLH0Yb7+OtVpmaV/wCImSk/7+Mi/Mnszgnu0/aWJr0goLuPqn76nQaBpkXX18WTn7FrxM5H9ob7LT8xuy7kf1tG7xSi/1lNRW4a12w9nJMkNSp9Xx6m/z8I7NJTCjgv9csZM9UrZ6gG08NljLsi838vFz56u2CJUT1903ifihPUgYFYuLq7rVbeVVprAp/0Pyq5PxVocwJPAm+vlNa5N+uwpWsjrvK/xOFvH36etYvu5Bpo57E7VT8xPaUkrWnfmCfUUr0MpafJyDuSzsPmK9W+cBV1pZzf99t5pNyelICQOiw3h17hSigv1aPtgEPxw+yDPr1zSRXdsnnrcnm54IbstzQuHiwtK+YOU53gSKMZSf/jtwH3BESvlMe55XQUHh4sVRK/pLsTzu6DQ0WWmJrJM1JhvYKaXUACeFEMeBWGAkMEYIcR/gBbgIIcqllCYT+jVBr0dbXsLy46+Dvoz43w2nHLQlm4M9I6EGTuSsJPPsfk4XGF5eajQ6vtuwj/AAb24eP9ioyS1nF3Co+I+G7aLaHH4+9SL39/oaFyfTJa5+TN/BNydXkV+tIdClC4/HX86k8La5o58oLDAy8gE+2L2j0xv6Pv6ejJnachmo1iKRZFem4q0OYG/hUg6XGGJq3aQGVbmhCo4KiP89F4DAJRt4RPcQh5K74lThilCrySks5ZEvfuW35+8gyKdtq6fevh6MntK/2X1+OnK4iZEPUFpTw9eH9vN/Y8a36fwt4ebhyluLH2Ldot2kJWXRrXcYk2cNx8PLrV3Pay8y84t48NNlrH7xTjzd7G8ct4UvDuxtYuQDnCotYVnKUW7ub/8yipE9uhDZw3rv1DU5R3kreTXZlcUEunryQJ8JzIpu5fu4Xk9yzjKCw08hKiF2VX7Lx7QDV90+jl4Du7Jx2T5UahUTrx1KjzjLJ5EWpG8kp+pVXFWG61umyWND3ru4OXkT42NFFQ29nnNFaaw58REqZMPzz2vRarZ3G8SIoOnkV2fj59cVLxfjfLr7ilawq3Bxw3ap9ixLsl/l3pgv8VYHtnj6l39cw8bD6Q3bhzJyeeSLX/n5qbkWfxUpJR/sNvYyWnwsmYeGjyTS5+KqCKLQKXkKuAND6em/YShF/blDNVJQUOjUOMTQl1J+bcVhu4FYIUR3DAb+jcCF9YaWArOBr4QQQRhc+dOllDfX79BoRb9lI9+gLGWv/5N/vPkTTrrz3g8j3j/JiPdPolcJdt/bDe09Kry8KikvP79auXLPMZOGfnLJeiNZtb6cE2W76etr7D64NOs3dhXPJyFYi5SQW5nD43uL+dnrPmJ8rH9BziwpMSMvNinvCOi0enbsSCM3p5gBA6Po1ds+hJzYbwAAIABJREFUybPyqjL5KO1JBE74qOvj/yVqqSPhqywSP85s0j8u/TCFSz9MQesk+GTcRD6Pm4IUhkmgPw+kMnvsoHbX+ZSZ65tRUkTSoSxSjuXStVsgiUN7mPQ8aStu7i5cMefizUpfVlXDpuR0pg7pXAkxm+sXnY200nwe3fMzWqkHoKCmghcPriDCw49RXWJabkBKNG+9yN0fJTW5fx1B3yHd6TvEem+fLflpLD71PeNCNUafHS5ebp2hLyVVbzzHU+/+ZjS+8eHt6FWClLt7s+nufgwNnsKV4bc38S5KLtlg1KROajhWupmhgefDrkpLq9iyKQWtVsfoMb0JCPSiqlbDukMnjI4/nnOO1JxzxIZblrOgVqczmvgEg190VmmJYugrOBwppV4I8TWGGH0JpEhHuN0qKChcNDjE0K/Ljv8ahuz5DUt8UkqzweFSSq0Q4gFgNYbF0y+llMlCiJeAPVLK5XWfXSaEOALogMellAXW6llRm0pK8cu4//N2lvZK4pqn9+Gbdz7zcVmYK3+8GUduosGN0MVVC43eIxonTCvXlJJdlUGwayiC1htV1boKdhXOx93ZEG8uBIR7llKpzWFF9iEeims25UCzDAgJxcVJRa2+aeI6e8RwW0NZWRWPPfQDJ9LONMimXzWYf1gRf28pegy/kUSPRl+DkzAkyMZZsOuB7mQP92fyk0fwzj1fl7kwxIN/PDiL/SE9UR10IrHvcS4ZmExA0ApOFF1JtO89qJzaz419SFg43x8+aCQvOVzMw29927A9YGAUr755I25urXcP/isTGljIzPFbWL0jkc74CjYgKIRt2aeM5Ikd9L5vjl+zD6GVelyctHi51FCpcaFap2bJqQOtM/RVKvb8PZ7MYSqj+9dWVOsqyahIw08dQKh7pM3br2fpqf24OOlMflajr7CuUZWK/KduZ01cttHzrzjUnZ9eSyRzcBCgY3vBSkLdu5EY0Jpn0vlnYNLBUzzz1E9UVtYC8PPSn7jjibO4eJ9mzhVOrN4xhNxzTVf/9VbceK7OzvTvEkJS/pkmcndnZ+KDlXxnCo5HCHEl8DFwAsNN0l0I8Tcp5SrHaqagoNBZcZTr/lfA88B7wATgNlpR6k9K+RsGV6bGsuca/V8Cj9T9mWtjPjC/NUrqqeF02QI8qneiHTOMnTcVcdm755P8H5oT2WDkl5Z4UljQNFHQlUMNrvV/5i1jVd4v6KQWgaCXVxegafZxNycvenoPM9LheNl+hNAaycM9S9DoTb/UtZYgDw8eGTmK17eerzjo7eLCM6OtWPmxAwsX7Ghi5AP8umwfEyfH06+//ZK8aaUKF6FDItBJgUpIcob6cejmSEa9fX4FasnVg9gX3w1RKBk18DDXTdzS8FlmySeU1SQzKPSLdtNzWmxvFh1LZmvWeaMu2t2HgqW5TW62QwezWPnrfq693rj/KRijdtYxdvBhYqLOMCbuwgqeHZ/AdIm6RI/G93wv8MjR4XtWQCcrH67R6+juc47uvgU4CZASTpf7otG33svC36U3OUPzjO5fW7C7cDM/Z31Jjb4agDifQdzW/SFcWohttwaNXkdGeSAjgk8aJf3s4WW9Z0287yjWDu9u9PzbflOPOiP/PEnF25oY+vG+EzhddbTJPiqhpo/P6Ibtf7/7e4OR7xtUzuzHV6L30FCtg4G9oFe3bN7+9joKSw0VZmLCAukdEWzVd/m/0eO57dfFVNaFrgjgyUvG4uNqm5CipNMlRD+10kie8brpZKkKFw9CiC+BaUC+lLJfnSwAWAhEAxnArPqqUGZ4B5ggpUyrO74nsBJQDH0FBQWraNG4bifcpZRrMSQDzJRSvgD/z955h0dVrH/8M1vTe4eEEAi9Su+CIIgFBWwXLNi7Xu+9lmvvXuvP3gs2rIiooCJdeglFOoZACqT3ZPv8/thN2eyG7CabAp7P8+TJnjkzc2Z3z55z3pn3/b502DthlTmdqdFjGbK6ErOfmh2XdsOiV5Hyuz1PeKAmkqHB/yQ+3P4gotOouXz8IOZMGExG5SF+Ov4lVmk31iWSAxUniPPrj8oxzxKh68zFSY+iU7k+bKiF+7kYmxRMTWh5HP2NZwzjp8vmcsewkTwwdgLLr7iGQXHxVJQbyEjPw2Jp2WSCL9mx/ajb8rRtGR61/2NfBk9/t4I3lq5v0ThMNg0mmxopoVpqAftKeMrv+Zj9VGyanYxRp+aM1fYkEdoCPZOG7XDpp8jwBxWm/S0aizvyckvJzixCq1Yz/4JZvD39Am4ZOoL/O3s6F1R0RuU6b+TyGf557ATPL1rNi4vXsD+7feKWOzoJ0fmYxLb2HobXHNiZTeflZmI2mwnbZyFunZm49RZ2prn/fXVkBkb40S3MbuSD3eOpc3ApAyJsHvcRE9CXCquu9vdbBPm+GFuJqZAvjr5Ta+QD7C3bwW8nWict9tSEvpSYA1ib1x2Lre7WHqkfzOCIS5vdr586kCu7PsGgVWWY/dTsuqw3Fr2GPiuOu9TVNLhfnRF+LiMiZ6ER9omNUG0MMzs/UBufn5NVzNGMuiy7gycewC/QOfTAX29i9ED7BEPfpFhevu6CJsdss0l+33WIJ79dzrvLNpFfane1G96pMyvmXsP9Y8Zz1/BR/Hz5lVw1cLAXn4aCQqN8DDR0L7wPWC6lTAWWO7ZPRnmNke8gHSj32QgVFBT+drTXir7RkfLukMMdPxu7SF6HRZN9lEhTCGzZwaB+/ZC7dxF1+Wxmau4ltttZqIWGsx45k8yCUiKC/AkJsBvtu0rcKfAKjLZw7uj5OQZrBWHauEZV9HsEn0GQJowKi3PcfNfAEQyI8I0baN/oWPpGxwJ2waJ3X13GD99twWyyEh4RyK13T2P8WX18cqyWEBUdjHQsDtX/tKJjQpps+9z3q/hsTZqPRiIos/hjlYKrkm9lUNhYTvz1O5HmW1m96B0esmYQM+4Yz7+0kOsDU5h36yVsy/vIbU8GywmCdL6J8S4tqeLZR79n66Z0BNAtNZb7H5/J1G6pTO2WCsCCre4XE+p/ht+u38UT3y6vdUv/dNV2npozlXOHtEz88XTEaHE1djo60dHBqGwQfMzZGI6Jbvp31NGotGW4LZcqz7+XgWHj2bprPqJS8v7nYzgya61rXEMz+LNse23IT312lWzmvITmG96NMb1zf/aXnuDT9I0cLoumS1A1FyWeyeVdmzaMmyKuUAvmcNiylAH9+lGatgr9pRcSnFdNeUydgOwZEZOc2gkhmBR7LWOiLqfKWkqYNhb7rR++/GQdX3y81u6G4bj/BYe7z7o7c0wct0y6hk6RnsXR3/fZUn5JO1C7/dnq7Xx8+yWkxEYSGxTEDWcMa7RtTUh0e6czVDi1kFKuEUIkNyieAZzpeD0fWAXc27CtEGKm4+VWIcQS4GvsMfoXY9enUlBQUGgW7WXo3wkEAHcATwCTgKvaaSweEaoZCJs2gb/9oUb0H4B2y04S8vPBsYqhVqlIjgl3auendu8SqFf74a8Oxl/tmhe4PlqVjqu7PsQP2W+TWXUIjdAzJHwK53VqnY9ryQ9pfLugTpm4uKiSZx75ntRe8cR3Cj9Jy9alqLiSYqsZc4j9s1aZbKgNNuLiQpkw8eQGaGZBCZ+vbamRb3/okxJMUo1F2lN4Reg6IYSKeG1P2LKLyf7+jLGYODwqn8grH+OusmoI8CPcbyTFBmdPApXwI1Tvu9WkZ59cxIZ9mdii9CBhf3Yhj933Ne8vuLn2oXXaOQP47uvNlJTUPVDr9RpmXDQEAIPJwv/9/IdT7LlNSl5avJapg3qiUbeXE1DHw2YT5OQmk3Dyn3CH46JZw1i5Yi9mU50RGhkZxOSp/dpxVM1D30iWEr0b76jGCNfFMCv+Jr7+pjO5IrfpBh7i19jY1O7LfcHdfadwTeoYMiuLSQmKIlDroxABi8Xp/hc6+Exy1y4hau+7lFNGoDqEM2Nn0zd0pNvmenUAenWdHsnGPw7y4Vsr7BtqYf8Dju6Lo/+YdJf28cHjSAj2zMjfmZHjZOQDlFQaeOuXjTx/VeOOg1XVJl7/YCXLVu1FSslZ45WJTYUWEyulrJl1PAHENlLv/Hqvc4Ga+Ml8oPUuGAoKCqc97aW6XzNDWSGEuBYIklKWtcdYToYKiQYrCSE3EdhgpQKwP/QkJZ20j6ERY/ntxCLM0uRUPjryLKftPfm5vLZlIwcKC+gTHcMdw0bRM9Ie/xjv35Wbuv+PaksFWpUejar1RNNWLfvTpcxqtbF6+V4uu7L9FNQfePp79hw4XrvyY9OrSe4azXOPX4y//8nTm236M6PFommBmjD8hZF+/tkEqw2cMIdSIEfTJdAR1NylS21df42O/uEOYTNHuufUiPtJO3E1ZluNNqSK1IgH0Kp9o/RcXWVi/b5MpN6RQ1yANVDDkfwS/jp4gu6O7AThEUH83+tX8On8Pzh44DhJSVH844oxJHe1x7xmFZZQVuUqSJZfVkleaQUJEfZVX5PRTGlhJZFxIahUvjH+jVYL72zbws+HD6BXa7i4Tz/m9hvYYVfWlm4cSkRVIUM90HzrSKT2iOPFl+ew4PMNZGUV0rtPJ+ZeNZbgYO+eJ3flnuD1rRs5VFRIv5hY7hw2iu4RTadM8yVJloHYLItQaep+4FJCTPnJU082pHPPIcws20iFMZ2nfTS2/qFDCdKEUGFxvrWNjjqrkRa+IUwXQJiu5SKfVRUGqiqMRMWFOl3faugRO5rUmFFUWyvQqwNQC7XHfa9ctqf2tbBKkDZ0gWa6dSkl2CpAU41BajGjJtJ/PHFBMzzue88x+2SNRGIKB0ug/dz4I+8oUspGryfPvf4rK9bWhVItXe56L3SHEOIG4AYAdUjztAMUTgmihBD1XTTflVK+62ljKaUUQrh9EpFSzmvx6BQUFBTc0F6q+18AN2FXxt8ChAghXpFSPt8e42kMFZIwlY2EwCnN7iNCF81N3e5lUfZnZFYfIVIXwznxs+kZUvcgeqSkmEsXflWb2zqjtIQ/Mo+y9LIrSQiuc6f117R+dENjD0GtkX7NU/46kmc38htwvKiciMjGP5P9O47yyn3fcOh4AUxqbCLdM0I0es4L3YNwuOJGaSrRqLdgkwZUounVwyBdD0Z1XkZ+1TIstnKi/Cfir/WdAvfOvVlIN6vtFj8NqgblnRMjuf9B9w/O8eEh+Ou0VJuc42RDA/yICrEbD1+/tYKv315BZbmBmE7h3PDgBYyZ6p1h5Y57fv+VxYfqHrR3r86lqLqKO4ePbnHfvqS4PIinP7mUvOJw7hrVoSOOGqVPv8488czFzW7/V3Ehl33/FdUWu+BDRmkJfxw7yi+XX0VsUNt9JpvW5JO+tRsJZ2cTEF+NoUDP8RXxbIkux9NT0mqr4HDuTEzWTJ+OTa/249buD7Awaz6HKvYSogljUux5jIqc6NPj+BqzycLbjy1i2XdbMZsspPRO4K5nLyG1v+v1SghBgMZ7l5b69xMBYLNx470/0SW1Tg9EJ0xEhdxLp7Bbveo7Jc4+2WSIkVjqDe0E1by8eT13j3CdsC4tq2b1ugMu5Z7gMPbeBdDHp56CeTgUPKRASjnUyza5Qoh4KeVxIUQ8cFLBG0f66Nuxi/fVPp9LKVsef6OgoPC3pL1c9/tIKcuEEHOwq4neB2wDOpShDyAxk1/+Hl30r3vVzmKz8tGRZSw9vgWzzcLEmKE82e9BgrWuKy2f795Za+TXUGY08uXe3W4fSlqTyecMYEcDYTatVs2Zk1su/NdcqqpNbsuNJgs2mw3cGLiGKiOPXPsBZcVVaAH/rCqqOzd/lavUmI3AefXdYj1KadVSwgMv8qgPjSqQ+KALm67YDMyNiCaq1IKU7p5PcgT66bh28jBeX+IcZnDj2SPQaTSsXbKTj56vS3yRl13Ms3d8xju//oeEZO/yWtcnp7yMHw+5ChN+uHM7twwZgVbt+Wpha1NR5U9ecTgB5ZLLz/t7Zir4ZNeOWiO/hhKjga/27eaOYaPabBwncoopTw/hwNshICRIuwGZ083zrKollYt8buTXkOCfxG2pD2GTNlSiY4e9fHVsNQsz11NYWYY1wIzOH1QmSN+XwyPXfsDHa/+LTu8bb7KzpvVn+S+7a7e798txMvLtSKqNywHvDP0RqYkM6B7POlu2y76Pd27ntqEj0TW4nhhNFqw2xUZX8DmLsYelPuv4/0MT9RcBHwA/Ap4riiooKCg0Qns9eWiFEFrgQmCxlNKMXXikQ2KyeP8Q+Obhn/g0YzkFxjJKzVUsyt7Ak3u+dFv3eKV7UdUTFc7l2fmlfPrrVj7/bRt5xa0jxHr2uQOZe+14AgLs7vCx8WE89PRsYuJ842LeHHr3TCAqwnWVcOSQFHRa93NVm1fuo6y4Lg49fFsxYWnFhJVauWiE95MWKtxI1QMmi090u7yirNLAt6t28uHPmzicZRcIHzKwCwFuQhjGjUr1uv8bpozglWsu4OyBqUwb3JM3briQuRPOAOD371zFJS1mKysXb/f6OPXJrax0ewEoMxqptpjd7Gk/hFWSXKbhvdtn4x9w8rCR05UTlRVuy3Mr3Jc3htVmY/WOv3jvxw2sSjuM1ebds62o9xuvMfIBVKUGN7XdU2ZyjQn3Nc018o+eKObjpZv58vftFJW5F6rzBV8fW8Mbh37iuKEIk9qCdaTAeGfdTbm4oJxta5q34u2OoSO6cevd0wgJtYeLJHZ1P5nbnHuvEIK7LhrnrNjqoNxkotLkeqyYqGB6dY/z+lgKCjUIIRYAG4CeQogsR1jqs8AUIcQhYLJj+2QYpJSvSilXSilX1/y18tAVFBROY9prRf8d7DlFdwJrhBBdgA4Xo19DoH6EV/VNNgs/ZW9yKd9QuI+c6iIS/COcysd0TmLJ4YMu9cd0rouL/H3rQR54bwlWq/1B+M3v/+CFW2cwql+yV2PzhCuvm8Alc0dTWlxJdGxou7rtA2jUKh6953we/t9iioorAejeNZq7b248pMJmdTYbBRB4rIo46cdjl53N45d7NwaDdP9T8fbcaCmHMvO56cVvKK2wGzJvfr+OO2aP48ppw3j0nvN58qWfKSu37+vXK4F/3Xp2s44zsX83JvZ3Tapua8QQa/h5e0uf6GjC/fwoNjgbaP2jY32W49pXpHaKZvEHt7f3MNqV0Z2T+C39sGt54sk1S+pjMlu445Xv2bq/zpg7o0cnXvvnLPSNTOA1JCY4AG1uBeboQFAJkBJNcTWxXuRZ31MZSqLHtduORWt38/Qnv2NzCIy8+f06Xr1rJoNSO/n8WN9lrnMpsyUKbD0kasetyWrx7QLjjIuHcc6MwRQXVhAUnsfhvGUudZp7fR0QG0ekvz+F1dVO5X2iogn3d69F8d9/Tuf+JxeSfdye4SY+tv0mtxVOPaSUjT1VeCPK8YoQ4hHgN6BWLEdK2bKZdAUFhb8t7SXG9yrwar2io0KIDhm4qNf2ICbkJq/aGK1mDDb3q5Bl5koXQ3927378mn6YNccyasvOTunOuak9AXtO8/8u+AWDnw2VBdRGMJqtPPfFChY+Na9VxMr8/LT4xYf5vN/mMqBPZ7794EZ278vGz09L79T4k9YfNrE3AUF+VFU4G44Tzm+eyn2ZxY9cUzCxujpPivDASwjyc68y3Vq88u2aWiO/hje/X8f0UX0YOSSF7z66mT/3ZRMS7EdqSst0Cdwx4fzBbF3tvLKnUgnGnzewRf3q1RqeOnMKd/22BJPNHoYQqvfjsQm+ES7bl53HgvU7KaqoYmzPZGYN74dW07xwAI2244QRtBeX9enPsvTDrMuq82g5p1sPpqV47kGyZMM+JyMfYPvBbH5ct4fZZ3p2Pk04bxDffrqW6mg1xnAd2jIz+uIqJs0Y4vE4jptTyTMkMSS4ed45Npvk3eWb2XE0h6TIMOaMHURiZMuunVUGEy9/tbrWyAeoMpp5YcFKPnt4bov6dkeZ2b23gAy0/w8K9WfohJ4+P65OpyE2PgwIIybkVvLK3qjdp1XHERf2n+b1q1bz1JlTuOO3nzFZ7deTYJ2eJyZMbrRNcmIkn791HXsO5CClpG/PBL5+/8ZmHV9BoZn0B67AnomqZmZNOrYVFBQUvKZNDX0hxN1NVHmpTQbiITpNEj3ilqAS3qUoCtb60z80md2lGU7l0fpQUoNdV2N0ajXzL5jF+qxjHCgsoG90DMMT7MJHW/7K4vr3vsOisoFOYNOBTSPRVUJmXgn5JRXEhJ9i+b2aiUajZnB/z1YMA4P9ePidq3npnq/Iyy5GpVYx8YLB/OP25gkr2lDx7LFpDAzKJFJTyflJN5AU2Trx9icj7WCWS5nFamP3XzlMPCMVvU7DkIGuCtm+YvLMoWQezmPRR2swGS2ERARy/X/Pp0tqy91ez+negyHxCSw78hd6tZqp3VIJ1rU8Pdjmw5nc8P5CLA5vmFV701l3IIPX5nmu5K3gjF6j4dMZs1mXdcyuuh8dw7AE78Qltx9yPZfBfo57auh36hGLcVZXyoz2yS9jjB5bj1BSRiZ7PI5RUX24Zeto1pV2I8WvAHA/rsY4kl/Mq7/UrYgv3raXBbdfTpfo5qcjPXAsj0qDq4v5/mN5VBvN+PsoVr6GUVG9+T23QQpSI6j3Q1xiBP96/jL8AnyUqq8R4sPuIzRgOuXVq9GoowkLOB+1KrDZ/U3tlsofV17PsiOH0ars15MQ/cnfg0ol6N/b9x4TrU3yfT+7Lc94tvF0ggodkouBFCml+1gWBQUFBS9p6xX9U8oiVavCvDbya/hP79n8Z8f75BrsboDBGn8e7Hs56pPEao7unMTozs6G7FvLNtYaKDXYdAKbQRKs0xEapKRYbYyBo7rz0er7yTycR0h4IOHRLTv9LFJNWnlX5iRPZGDUOT4apXckRIVy5HiR2/K2Yt4907n4pokUHC8hITkand53l5GYwCDm9GuZd0BD3ly2weU3tHJvOruPnaB/khKX21yEEIxN7MLYxOZNLHVq5Jz15lz+YdteCozOHi5VNiuf/5HGv88b71Ef/UKTuSp5Mp8dXcGh6ljs+rCeY2gopFpt5OM123hkVuOrx00RHxmCSginFX2AyJAAj8MavOGW1HNJrzxOesUJAPxUWm5LPZ+Bi7qS2C3GZyk0myJAN4AA3QCf9RcdGMg/fHw9UVBoRf7EnpT3pOr8CgoKCp7Spoa+lPKxtjxee5IcGMuCUfexvfgwRpuZYRE98FN7L9yVnudePdqmhrByyYevLOOyeeMIP0maubak1GjgUEkBySHhRPk3fzXGV6hUKrr0aLkxlxwYy6P95tI7JJH4BqEXbcm86cN5+INfnMpG90umZ1JMm44jKMSfoBD3k0wb1x5k8debKS+tZsS4VGZfMQY/P89XIG1SsrvgBAFaLalhzVfyryE913ViBOCv3ELF0G9HLhrfn29W7qTIUIXVH9TVEK73Z5aHq/nQ+Hebnue+vDGu7TaVcxOGs7fsGGfxgldtvRmXp8RFhnDOyN78vGGvU/lV5wx30UxZ8ctufvlhO0aDmbGT+nDR5SPQeBmWEqUP5aPhd7OjJJ1ScyVDwlMJ1rb+JLLVZuPPwlyf/dYVFE5xwoD9QogtOMfoK+n1FBQUmkW7xOgLIToDrwE1uePWAndKKb3zmewg7DyYTV5hOQN7diImom7VWKNSMzyyZXGN/RLjWLXXVRU6MMuA4Xg5i/ZsZsuGw7z1+U3ovTCmWoN3dm/i5bR1GKwWtCoV8/oM4b/DOqT0gtfoVVomxTobIAcy8jiaU0TvlFgS45rvpusN00f1ITjAj29W7qCsysC4Ad2YO9XzeOTWZuWvu3n2wYW12wf35fDnjkyefeMKj9rvzD/OrasWk1VRCsCQmE68PelColswadQ3MY61+4+4lPdLbJ6GQVmlgaLSKiJCm5+uUQFiwoMZP7sH8w9tx4pEjeC8bqnERXjueZMS6f53lxjkvfdOnH84cf6++R0399wymMxs2nUUlUpw75xJ9EiMZvm2g/jptFw4rj9nD3e+nyz8YgPvvPxb7fb+P7M5cjiXex7zLOVnfYQQDA53FeBsLXbkH+fWVT+QXWHX4R3q+K13hAliBYV24pH2HoCCgsLpRXup7n8EfIE9HglgrqOseQHU7URllZG7X1jEzoP2fL1qtYpbLh3LnOlDfXaM26aOZlt6NuWG2sldAnJMBB2vcxfNPlbEmuV7mXJu0ythW4q2sbFwC2qhZnz0GPqF9vHJODefyOSZrXVZYMw2G+/+uYUBUfGc17WXT47R3lillbX560kr3sn+g0XsX6nGmOePEHDp1DO4a+6ZbTKOcQNTGDcwpU2O5S1ffvSHS1na5nQO7MmmZ9+Tx75abTZuWfkD2ZV1CTi25WXz0IZlvD2p+XoId0wbTVpGNhX1Yp4vHtmf7nHNW0HMyStlxp3v8a+rJnLhRN+5Gf/dWJ19hA8PbavdtiL55K80zuyawqREzwxOkVGBrtSCKbTuVqapsqJN9y4VXbWlmt/zVnKo/C+v2gEE6Z09teLDgrl6gveTb7sO5vCflxbVim1GhAbw0r8vYs7Z7vuy2SRfzXdVy1+xdBdX3TSR2PgwbNJmv2aV7CRQE8hZMRNICerq9dh8jcVm4+YVizheVSduujUvm4c3/s6bExXtDF+hxO43TmOfTXuipNJTUFDwNe1l6EdLKT+qt/2xEOKudhpLs/l48eZaIx/AarXx+oI1jB2UQpcEu3v35sLDvHnwNw6U5ZASFMtNqVMYF+O54dsrIZpF/76SRVv2UFBeyZ9LDlB40DUTYW5OcZN9fZP5PYtz6m5uGwo3MS/5CibFTvB4PI2xJMM1PaC9/MBpY+i/fOAtdpbusG/EQOLFkP1jItXHgvnyl+2MHtSV4f1aTwjvVCDXkZqqISdySpo09HfkH3cy8mtYduwQJqsVnbp5ave9O8Ww6F/231BhRRVjeyUzvldVXvu1AAAgAElEQVTLjB2zxcrzH69gzKAUosM7RtjMqcaSI/vdlv+cccBjQ7/wRBlhJRVUjrYiY2xQrCJgmYrS3PKmGzsw2cw8ue85jlV5n7MdICk6nMcun0ZaRg5dosKZMbQPoQHepYSUUvL420udMmoUlVbx5Hu/8tnTV7ptYzSYKS6qwDDeinGEFRkA2gMqApZoyDtRSmx8GG//9QEbCutSva4r2MA/e9zGwLD+zXqvviItP8fJyK/ht6OHMNusaFVKZguFvx9CiHLsKvsAOkALVEopQ9pvVAoKCqcybaOw40qhEGKuEELt+JsLuA9Gr4cQYpoQ4oAQ4rAQ4j43+68WQuQLIXY4/q6rty9JCPGbEGKfEGKvECK5pW9i/Q5Xl3opYf1Ou5vw0Yp87t72CXtLs7BKG4fKj3NP2mfsK812aXcyYkODuHHyCB64aBIT+3bDXTK9/mckn7SPCksFS4//5lK+MHsxVmn1ajzu0DdihDVW3hqUmQwUGbxbyfMUk81UZ+Q7ECqInFCnmbN+h6t7eEfDYDGTW1WOlC3Le98Y/Qe7TnSo1Sr6Dmw6U7le437eUatSo2phCsm4sGBumjKSBy6axITeKT5JSWm12ti0K8PrdjXfwd8dP437UCNvrhnxgyKpmGtGxtnsd7NIG1WXWIgY5rnr/ubCLc028gEEcN4ZvXlo5llcOf4Mr418gIycIo6WliBVzr/Lw8cKyCt0f674B+gImhmIYYoVGQJowNzXRuU8Cyk9YsmqynYy8sHulbQw6wevx+drGvuOtWr1SQVrFRROZ6SUwVLKEIdh7w/MAt5s52EpKCicwrTXiv412GP0X8Y+e7keuPpkDYQQauAN7O79WcAWIcRiKeXeBlW/klLe5qaLT4CnpJTLhBBB1OUobTYhjSjehwXby3/I3orJZnHaZ5U2FmVupneo9zGUAJdePZYt6w9x7EhBbdmUcwcycEjySdvlGwswS7NLeam5lApLJaHaxieMTUYzhkojIRGNr1zO6t6PD/ZsxSKdP9ZLUlvftbnUWM19m5byW9ZBbFIyJi6Z50edS3yA7ybByxvJM60LqXMHDw3uuBkQpJS8uHMNHx/YSqXFREpIBE8Mm8rouGSfHufa2ydzcF8OJUWVtWVX3TSRqJimv4t+kbH0i4zlz8Jcp/LZqf3QtJHqt7d4851LKXl+x2rmH9xKlcVM95BInhg+jZGxnqWMPN2Y3b0fn+5Pc1KWVwnBxamerzbnJZdBw/k1FRT18nzCL7s6x+O6rcHK7MM8tPlXCiaZEGYIOKIm6C/7rVmrUeOnU1NaWE5IRJDLBFX1UAs0mKe1RNnYa8hG4t7LK6ud3y/AgKh4ekfEsK/IWVz84u79Wjypp9A0HcmlvyONpSMh7bPxi4QQjwAuC1sKCgoKntBehv7jwFVSymIAIUQE8AL2CYDGGA4cllKmO9p8CcwAGhr6Lggh+gAaKeUyACllRcuGb+eSqYNJ2++sHxgdHsTEYakAVJoN7ppRbnFf7gmhYQG88dmN/LFiHydyiuk/qAv9z2jaXTzeLw4/lR6DzehUHqWPIljj3oCXUjL/mR9Y/N4KqsoNdOufyO0vzKHXENf48B7hUbw1aQZPb1nFkbJiEgKD+efgsYyKb30j5v7Nv/BL5oHa7XUnMrh17fcsnHqVz46hEhqkhIbPoEaz/ScU6K/j3LG+0TtoDb44lMYbe9bXbqeXFXH96m9ZM+MWIv18JyqX3C2GD7+7jdXL9lBeWs3wsal07e65MNmMYxFkHj9KaYoelQ3OCu/Cgx1U0LFzbBijBnoeAjD/4Dbe2ruhdvtwWSHXr/qGtRfeQpi+404StRb9o+J4bcL5PLt1NZkVpXQOCuWeIeMZHJ3gcR9VDa5nNVTjeRrqroHJHtf1NdmVpdy0ZiEmm91al1qo7GFFXS3wz1HTJyqU64c9RGlBOZ1SYrjxqUsZPqVuIsQkXCdvASrM1fQN64JAIHH2EmjP91uf98+ayX/X/8qa7CPo1Rpmdu972oi3Kig0ByHEzHqbKmAo0PwHRgUFhb897WXoD6gx8gGklEVCiMFNtOkE1PevzAJGuKk3SwgxHjgI/FNKmQn0AEqEEAuBrsDvwH1SuvqsCyFuAG4ASErqRGn5O6iEPwEBF6BWhTnVnTgslUdumsZnP28lt7CcIX0SufWycfjp7S6p42P78H3WFpcBjo/pXfs641AuBbml9BqQ2Gi6sobodBomTfMuxtJP7cfsxIv47OiXtWUqVFyeOBtVI66SP7y7gi9fWlK7/dfuTB689FXmpz1DoJuVzClJqUxO7E652USQVtcmKzMVZiO/1TPya0gryCG9rJCUkMhm913/XOicGMUw/0xyrMHkmO3ngU0KijMiGdYviVsuGUtMpPdK323Ft+m7XcqqLGaWHtvP3B5n+PRYgUF+TL/IezGyld9u4rtHfyQeiNUKsEnydMeoGDMVv/i2yWrQGPXPBb+gcCaP7Mltl41Do/bc02Chm++gwmLil8wDXNZ9kFfjyUzP50R2ET37JxISduqq/5/btRfTk3tSbjYRrNV5HVIxPqYPXx3d4Ka8t5va7ulUkYh5l6D70EKSdMV85kEb5/tE8yczf8zYV2vk18faBc4K68LWl39FOOz07PQ8nrjqLd5d/xjxydGA/f3/nLPdqa2/WsewyO4Eaf04O+4sfj3xe+0+nUrHJYkz6Qh0Cgph/tkXU2U2oVGpm63B0VIMxg0YjdvQaFMI8DsbIdrrsUhBgfPrvbYAGdgXtBQUFBSaRXvd0VRCiPAGK/q+GMuPwAIppVEIcSMwH5jk6HscMBg4BnyFPVTgg4YdSCnfBd4FGDBAJ4tLHwWguPRpYqMXoNc5z0ecM7YP5zSykjsmuidzkseyIGMdNiQCwYWdhzI1fiCGahNP3b2ALX/YRez0/lpuuf88pl7kO8X+hkyNm0xKYFc2FdlV98dGjSIxoHOj9X9b4KroXFFSxYYlO5h86Si3bYQQhOj0PhtzU9ikdHL9rY+1hXHozueCVs6I3AXAipLufFMwmDh9Cgsvu4EwXcc3tGzSfaRKw1CL9uS3BXUeByqz/bszVptZvXALs249u72GBTifC0OHDpVP3ua9W2lj56PV5vl3YDZb+N+9X/PHsj0A6PQarvnnVC6cM9rr8XQUWnLNGBbZjatTJvDpkbVYpQ2BYHrCYM7r5Pnk1dqFW7kwMI2R0Z5rpzQ8H7weuIPGfpcDenZCN/9ErZFfg9lkYcW3m5jz7/MwGsxUf1yGuhdYHXMNeqnh0QEXE6S16wTM7XIZA0L7kVaykyBNIOOixhDjF93c4bYKAVpd05VaASklBUW3Uln9fW2ZTjuAuOhvUKnaRvvMW+X3v7s7+8noiCr63iKlnNfeY1BQUDi9aC9D/0VggxDiG8f2xcBTTbTJBuorenV2lNUipawv6Pc+8JzjdRawo57b/yJgJG4MfWfqnrJsspTCkgdJiPHuZnJnr+lc2mU0h8qP0zUohs4B9hXmL99bVWvkg92geeWxHxg0ohuxCa23epka3I3UYM8UrS0m9yJ9ZqPFbXljWK02Ck+UEhYZhM7PvQBXcwnR+XFmQjdW5jinxeoVFkNqaPPSpzXFpLDDrH29D4/867JTwsgHOC+5D7uKTjiV6VRqpiX2bKRF22MxuT+vzI2Un2qc36U3e4ud9Qd0KjVne/EdLPp0fa2RD2AyWnjnf0sYNKIbyV6ESACYDGZKCiuIjAtF7YVnQkfjlh5TmZU0koNlOXQJjCYp0LvffVDkTvpN8k4g1Veck9SLl3atcZkEOj+5D3tM7jNt1fwevv5gNTuW/UXoMoElUWILBH2GlV4L453qDwjrx4Cwfq0y/lOZasNyJyMfwGTeRUnpu1iqriU8uuN6aCmcXgghHj7JbimlfKINxvAoUCGlfKG1j9XEODKAoVLKgqbqKigoNE27PN1JKT8BZgK5jr+ZUspPm2i2BUgVQnQVQuiAy4DF9SsIIeo/4VwA7KvXNkwIUbOUMQkPYvsbYjJtxya9D5eK8w9jXEzvWiMfYP2KfS71bFYbm1a7uqG3F+NmuLpf6/y0jDpnoMd9rP15B1ePfpyrRj3GnKEP881by305RAD+N3I6I2Lq3Gf7hMfyxrjm5133hD7RJ7hr1mv89Ilr3viOyDU9h/GP7oPROkTtov0CeXXsDOICOs7D7LgLXb1ZVCrB2PO9DwPoiFzXewSXdhuIxhEqE+MfxBvjLiLaP9DjPtYtd71sSSnZuNL1enIyvn7zd+YMfZirRj3GvDGP88eSnV6172jE+oUyLqa310Y+QL8x7ZcBoWtIBC+OOp8Ih0aDTqXmml7DuKzbQMZfOMylvhCC8RfYfw/17yGaTIFuv0AaYNNq92kLFZwxGNe7LU/b8AVXjXqMucMfaeMRKfyNqXTzB3AtcG97DUpBQeHUp92C0Rxq+R4b21JKixDiNuBXQA18KKXcI4R4HNgqpVwM3CGEuAB7bFMRDiV/KaVVCPFvYLmwB4FuA97zZrz+Qk2A8MNS+A9UftNRB17doli+gED3rqoBQW3n9t4Ul951Djnpeaz+fgs2myQsOpg7X7yCsOjG3RqllOz+Yz+l+eVEJUXxv9s/wWqxu6dWlFXz4TM/kpAcxRgvJgtqKK00YLZYiQp1Noyi/YP4csocMitKMNmsdGtBXL6nFOUHYag08saD39K5WwyDxvRo9WN6S0VJJTtW7SUoLIAB43vz1Ihp3D1wPHnVFXQPjXTJVW22WCkorSQqNBCtRo3V8CvWys9AlqHST0YddD1CeJ86zFPOmzeBo/ty+OWztVgtNoJCA7jxqUvo7OVKdWsjLYexlD2HOugmhBcuvhqVimdHTucCSyLpx/M4e+RgYhIivDp2Y9eHxq4n7lj78w4+evYnpApsfmryckp49rb5vP37fXROifFqPKcDEbG9KCrxfb9mk4W0FX9is9oYPKkfen/3LuozuvZlWlJP0suKiA8IrhVmnHLZKI78mcmPH67CYrYSEOzHtY/MIqWf3bHNv7F7iBfnQkcgt7icQD8dQf5tO26NxjVsLUCo6ZVUyTPvrmX1L535+tSe/1I4RZBSvljzWggRDNwJzAO+xO4B2yyEEIHA19g9YNXAE8D/cKyYCyGGAi9IKc90NBkohNgARAHPSSnfcyygfQU4knhys5RyrRDiLWAY9jSA30opH3EcMwNYAJyD/Vn8BuAZoDvwvJTybSHEmdhFucsd5SuBW6R0jmVypN6+A9ABmxx1Wp4PWkHhb8QppTojpVwCLGlQ9nC91/cD9zfSdhnQrFxvgUJDiEoL2JDmHVjNO5CWQ2jD/tec7gA495Lh7N/lnLs5LCKQMWd1HOV2nV7Lve9cxzWPzKI4t5SufTuj1TV+yhTnlvLAjOf5a+dRANQhQUi968Pb8u+2eGXoV1QbeXz+MlbtOIzVJhnYLZ7H5k0jMcZZHDExKKyRHnxLztEwdq5PBEdWheXfbelwhv7ahZt5/vp3MFbZ1ceTenfimR/vIapThFuV/W9X7+StHzZQUlFNRHAAz1xpoH/MK7X7rebdSHMa2ogPW23MKpWK21+Yw5z/nEd+djHJvRMaNY7aFVmNtfJtbKaNaCO/81hArrKsmodnvsif6+xeOwu0n3Ld05dz0W1TPT70uZcMZ/v6w05lgcF+TDjH80vbioVbqewaQlVKKFKrQlVtIWh/MasWbWPu3ed43M/pQlDATErLX8Nq9Z37/l87j/LQRS9SeNyuORsaFcyj3/yTPiNT3dbXqzX0DneeZBFCcONTl3LpP6eTe6yQpB5x+AfVTbSde/Fw9qYddWoTGh7AmCl9ffY+WpO9GSd4bP4yDmcXoNWoOX9UH+65fCJaTduI8gUGzKK0/A2sVnu6wWChIUilhUgzUZHF9B5YzF2vNNGJh/gqfry149B91b+iJeA9Dq2qu4E52PWlzqgvWt1MpgE5UspzHccIxW7oN8YA7GGtgUCaEOJn4HLgVynlU4401zUPEA84hLTV2BfRBkgpdzn2HZNSDhJCvAx8DIwB/IA/gbcddYYDfYCjwC/YvXy/rRmIEKI3cCkwRkppFkK8if2z+aT5H4eCwt+PUzcwsw1Qq+PRafoRpHJdxbRVL0Rac9208owpM87g5vvOIyo2FCEEg0ak8Mx71+Af0PFWY6ITwukxOPmkRj7ABw99VWvkA9gs7iderVbvtKueW7CS5dsPYbXZ2+386zj/enNxE618i9UaSE5GBMu/78NL/5qKrcxEjXln8/L9tDaVZdW8eON7tUY+wLF92bx9z+du6287kMUzn6+gpKIagKLyKkLV813q2YyrsJm9cxFvDhGxofQ8I7ljGvn1kOYdSJN79193LPjfD7VGPoDFbOWdez4n+y/PryNjzurLXY9dRGwnu45H3zO68PS78wgN99z9P8tmorJnOFJrv/zb/DWUDYyi0OA+Vd3pjkoVTHz0DwQFXoFW67la/8l46ab3ao18gNKCcp679m1kMwRCw6KC6XlGspORD3DW+YO45b/nEx1nv4cMHG6/hwQGtZ7Xja8wmi3c+doPHM62h+GaLVYWrt3Nez9tbLMxqFWhju99DjpNTwLo2NcbhdMXIcTz2ENMy4H+UspHfWDkA+wGpggh/ieEGCelLG2i/g9SympHfPxK7Mb4FmCeI4a/v5SyJtbpEiHEdiAN6IvdaK+h5gFtN7BJSlkupcwHjEKImhWZzVLKdMcK/QJgbIOxnAUMAbYIIXY4tl1zOysoKJyUU2pFv61Rq2OIj/kGU647BWcr0nocoW6+W/GMOaOYMWcUUkqv00p1RDYv3eG0LY0mCPB3eW8TvUi9ZrZY+W3rQZfyv3IK2Xc0l95d2sat29+/O6H6r/nm+VcRgKDOw8yb99MW7Fqzj+oKVy2Jzb/scFMbft7karzHh5a5rSutWeAjY+h0QFqzPK67aanr5y+lZMsvO+h0q+er+tNmDmXazKHYbDZUKu/nag2dAiGvgVGvEhjiTw1hydZAo+lEVHiNdmvLrsWFOcUc3nHUpfx4eh6ZB3JI6tWpRf3X54LLR3LB5SNPuXvIhj1HKSqvcin/acM+brlwTJuNQ6PpTFT4C0jLUUz5E9vsuAoKDfgXYAQeBB6o91sW2MX4mpUGQkp5UAhxBjAdeFIIsRy7O33NjaPhrGDDmUgppVzjSFl9LvCxEOIlYC3wb2CYlLJYCPFxg75qbjC2eq9rtmvsDpdjNdgWwHyHp66CgkIzUQz9JhCqMISmJ9LSQCRPhCK0vXxzDA8f0HYXZ/HWwRUcLDtBz5A4bu45iX5hjafHa2uCI4IoLagnbGW1IssrCUyIpKrCiN5Py8wbJnLmBZ6nvpJSYrO5XwVrLK1ea9F3WAq3PD6LT174mYpyA8Zz/RFTgnnEtpRp+7O4IfVM9GrfZhVoDiERQY2Uuxfes7lJ75Z2tBPDu2U2KNWh0nr+3Z3+CIRuuMe1G/9e3Jc3RXOMfLB7TJBX5FIe3MxxnA5kVOTz+oHl7Cg61uK+/IL80Oo0LtkiVCpBYKh7z4ul2bv4NH09BcZyRkV35/aek4ny81wo81Qy8sH9NQcaTzfY6qgTQZ0ADjd+hY7L6ZBGryFSylbxrhVCJABFUsrPhBAlwHVABvaV8qXArAZNZgghnsHuun8mcJ8QoguQ5YjX1wNnADuxCwaWCiFiscfjr/JyeMOFEF2xu+5fiiNdaT2WAz8IIV6WUuY5QhuCpZSus6gKCgqNorjun4RqSwHrjt9Njmo4iPqrXWo0oY+0qjBZQ45WFHLdxg9Zl3+IfGM5f+Qf4roNH3Gssi6jYJFhO8crfsFoLTxJT63HjJunuJQldY3k862P8/aye/l86+Nc+e/pXvWp02qYdEZ3l/IuseH0aaPV/PpccPU4PtvyOCM+H0vRdA2F2mqyqor5MfMnXt/7JBXmjEbbGqwlHK9Ko8pSgMlaxqGST9ie/yQZZT9glaZG23lLn1GpdB+c7Dr2mya7rT91mOuE1Wu/jsViC3UqU4fcg1C3vtDhqYIq8GZUmq4e17/gJtffR1RCOGPcqKu3JlOH29P5xccW0K93OmGh5aiEYOqwjqUz0VaUmKqYt/4Dlh3fQ76x5Qr8gSH+TJ7b0AsVxlw4jMh4Vx2Rpdm7uC/tG/aUZpNrKGNR5nau3/gR1gZGb4HhIHnVe5BNGMOV5hz2Fr1JWv5THK9c3axwgdZmVN9kQgNd75/nDHe+FpUYd5NTsZRqy/FWHY8QKjQhj0M9931J+0/aKii0kP7AZofr+yPAk8BjwCtCiK1Aw/jKXdhd9jcCT0gpc7Ab/DuFEGnYDfJXpJQ7sbvs7we+ANY1Y2xbgNexZ8c6AjjlunQIdj8I/CaE2AUsA+IbdqKgoHBylBX9k2C05pFbtZLcKghURTE+6mLKTBZ2V/Yl0TSAnv5tN5Zvj23BYDU7lVVbTXx3dCu39hzJltybKDXuBkCFlj6R95MUcknbDRC7ISNtkkVv/kZJfhnDpw3i+qcvwy9AT5eezb8+3z/nLCqrTazfkwFAauconr5uevutYukEv5TbE0YIJBd32sbgMPvq95qshdjEFPpHPUiXoDpF9bTCT9hRNB+rNKFCEKmRWGUFABl8x9HyHxiX8A4q0fKHSyEET37/b95/4Es2/rydoLBAzr9xMrPudC+0NqpvF+6aPZ73ftpIpcFEkL+ei86cTUD8vdgMPyNtZaj0Z6HSuk64/B0xSBVrq4PQ2A5xphcOlRNmj8BkMPH1Sz9TkFXIoDP7ct3Tl7W5FsG04d2p0m8lINQuKW6zCUTVDLoleJ+a7nTg5+ydFJkqm67oBbe8dCXB4UH8/vlarFYbEy8ZzbzHL3Zb99N0V52H9Ip81uUdYnxsTyrMuSzLuZ9C4yEAgjTxTE54kii/uokZm5RsyDtCsWEPVuuzWKXdLf5I2bd0DZnF4OgHffr+Woq/XsuLt1zAox//SlZ+KWqVYMrQntx4wWgArLZqtuXdSUG1/bMRqEkNu4Xu4Te22pjUfpNQxazCalgKSNR+01HsCoVTGSnlr9gzVTXEZVZXSvloI33Mxy4O2LD86kbqJ9d7/TF2MT6nfY5ntzIp5XlNtP8Ku+K/goJCM1EMfQ+ptJUyP3MPL+5JwD7ZuZHzk/ry/PAZqEXLHCOklByq2E2eMYekgO4kBbgaVAWNrDQVGMs5VPJGrZEPYMPMnsKniA4Yj78mrkVja4jFZqXMUk6oNsTt+55xy9nMuOVsnx4zNNCP1+68iNzicowmC0mx4T7t31OKTCWEaoM5UnGYaP88qi1akvSFtUa+HYlK/sZd66sZETuL+wZOJqcqjW2Fddkc9cKAVTpP2hQa0siu+J3EYN+onofHhvKf9z1/KL7i7CHMnjCAnMJSEiJD8dfbJxzUAZf7ZDynE0YpKJNqMGwmt2orsQFDPW47Ze44pswd14qja5qjZV/WGvkAKpWEoEUUGS4iwq9j6U20BYVG+4RbsLaacH01u5qo35AKSxlFpjwidHWq+Tq9lmufvJRrn7y0yfaNX9vt41qb+79aI99+vOOsOP4IFyd/gRCCE9VlzFvzBYfLCriu+1pSQ5xj34+UfUe30H8QomtdHSuj1USVtZpwXWjTlYHBqZ1Y9OQ8Mk4UExKoJzKkLqzhSNkntUY+gMTKwZLXiA4YT6i+9TRChDoOTeC8VutfQUFBQUGhLVEM/ZNgXy+Wta8s1r1AQu3+H4/tYWxsCjOTvc8JX4PJZuT99GdIr6wTRBsaPoFLE29GCIHZYuXDXzazJf2EW73R4ZEppJe+hrrB4rbESkH1HyQGz2722Bry64k1fHnsJ8osFUTowriyy0WMi247t+PYcM9jVn3N0apsrt/6X+L9zPipy+jlmGvoqXefkqtPWA4fHNzIxIRUrHKV0z6NcO96W2zc62LoS2nDZjuBShXR6qEi/nrtab+quyfjBO/8vJFD2fn06BzNjeeOalF/GeVLvTL0OwL5VWvcludVrf5bGvpR2hD6R+YQF1BOsMrET162LzEX8My+O5jR6WrGRk3z+vijoruzKHO7c6GEY3+WUxVXRnbVVpc2ZeYs1uat4qecE2wtyCC32gBo6BTgXqi7xLiv1Qx9KSULMn/k55yVGGxGUgNCuCJ5Fn3Dmr43CCHoGh/hUp7XyDmaX7WmVQ19Bd9wOsbRK/gOKeUqvI/pV1BQaAaKoX8SQlRWpviXsd+sI9PiT77RVaxq1fHDLTL01xX86mTkA2wtXs3AsFH0DhnMU18sZ/GGPUgk+Gshvm4l+Oz4vizP203/AAud3IQRaFW+yyu/q2Q/76Z/WbtdZCrh1UMf08k/jpSgRJ8dp6NisVnRq+xGvhONhA9UWuxpElcdP8zEeGcD3SpVaIVr6sEQXTenbZNhGZWlD2OzZiJEMH5BNxMQfEcL3oVnSCmh+nukYQkILcJ/FsLPfXz/qcSxvGJu+L9vqTbaf0O5xRVsO+i5an4NgSob3bUm0s1aArUdRwzTU6oM7ieMLJa/pxjf/rIdjA07ypSgI4SpjTzajD4kkh9zPmFg6EiCtd5dd2/vOZldxZmkV+TXdIbYF8AnR9KoLjMQO0SDrYEHEMDju3+k2Gy/8PvpQa22kWsIoWuQq0ZLw2uLL/k1dy3fZf1CqNrATbHb6eNfgK3yM4osEwmLeBWVyj4rKo2rkFXfgDQg/KaC/2xEI95wWpV7rwCt2nf3NAUFBQUFhdMdRYzvJAgEgSoNg3VWwoWFX3P60jADSLi+ZSmpDpXvbrS8uLyKJY7UZwKBenswqj9C0O0J4v1h1zKv+1jW5R9kY7HrSo2/pjMxARNaNLb6rMx3zW9sQ7I6f5PPjtHR8Ve7Pmxnm8KxSWdj32hVs7HALtIWrvMnNWQaot6cmkFqXdqE6FJJDKpbDbRajlFedCM2qz0sQMpyqsufw1jlpFfTKsjyZwbBVywAACAASURBVJBl94FpDRiXI0tuQVZ+0urHbW2+W7u71sivocro+p02hRbopzMz0s9Mr7B/+Gh0bcemtF7YbM7nX7VBx8693mswWKWNYmNVhxR88xQpNjMz5ABhamPTlU+CVVr5q9I1VWVTRPkF88HQ69BsC0HsCkS1MhTVEftkzA/rDpLkP8mlTYkputbIr0GrsbH8RC8sDb7bToGTCdM3niHGJiXFxioX8T9PWZVnvzdcH72VPv4FAKgESNNKKorvBkBWfYUsvgGMy8C0Fln2ILLssUb7TA75Bw3THOpU4SQEeu8xoaDQURFCTBNCHBBCHBZC3Odm/9VCiHwhxA7H33X19iUJIX4TQuwTQuwVQiR727+jziWO9nuEEF802BcihMgSQrzejLG/XG/cBx2q/wghBgkhNjiOt0sI4Ta+yYP+k4QQK4UQaY5+ptfbd7+j3QEhhNvctc3tXwihFULMF0Lsdnz2Svo/hQ6NsqLvASoh6KwKpMgc5Hj2kCBBq9JwWUrL0o2Fal3dFgFCtOEUV1RjaZCGSJRqsJZCojaSnZVHANhRloRaSMZEHCZIYyCjKo6be3/gE2G3Ghp7CGy3dEjtgMVNBpwKmz9rSnqQos8jRl9OjiGMJTn9KDQGE6zV0ysshmv++J1qa38mxR8iQl9BoKYTfcJnY7XlUGE6SoRfP1JCLkGtqltpNVUvBlyV+I3V36IPuKjV3qO0FkLV567llW9BwD8Q4tS9ZBSU+lZwLUZtRlh2ga5tVfNbypFjsexIn8zE0WlERZSSmRPDb2uGMmWAd9/tN0fSeGXvavIM5XQOCOPeAZOZ2unUc6se6H8ctfDNREWopnn6IeWVRuQJDaoGt2STxUpPv+vRa3UcLvsVm7TSJWgsz+8D17TTcKQqijcOTGJap+OcGRdLXOBYugRf0Ohxl2Tt4YXdK8iqKiHWP5h/9pnotYeaVVqJ1FTSw881ZaPZuBybtQgqXOwEqP4aGXQzQu2qIxMdMJbBMS9wuORdqs1ZhPsNoVfE3WjVnsX/K5z6nIru/0KIacArgBp4X0r57EnqqoE3gClAFrBFCLHYoTZfn6+klLe56eIT4Ckp5TIhRBD2HPVe9S+ESAXuB8ZIKYuFEDE48wTgEkfjSd9Syn/Wq387MNixWQVcKaU85Ej/t00I8auUssTLz+ZB4Gsp5VtCiD7AEiDZ8foyoC/2WNvfhRA9pJRWX/QPXAzopZT9hRABwF4hxAIpZUbDz0lBoSNw6j61tzFqaQDqPLXDdQG8PGImvcNaluJtbNQ0tpf8gVXW5VwO1IQwNGICAapg4iNCOF7k7C7eNS6CuIhgbFWJqBDYkGwr7cK20i4AjIpKJUDbqUXjasi4qKGsK3CNFR0T9feJ6a206AnXVqNR1d1PbRLSiruw3FS3YiZNQYyN7cKtvcdwy8ZvKDFVA9Gkl0ejEVb+028afcJHnvRY0iXrTc2ORsp9hTULcLPKbSsEWQ6ifYQQfcGI3kks3bLft51ajpxyhv7I3l14dVEmB/5Kciof1buLx31szMvgge110exZVSXctek7vj/renqFtn3ay5bQJSAFvJbgc9dPKl2DGl85PxmJ0WF0jgolq6DUuc+YcDpHxpAo7mFMzN1IJGqhZXHOfAryD7r0E6gKYETMYK7vfRZRficPxdhbcoJ/bf4eq8MbI7e6nPu3LSYxMIxh0Z6fC2OjhrI0uzFPBhvIKrDlutlnBWsmuDH0AeIDpxIf6HYxTkGhw+GF4V7DcOCwlDLd0f5LYAbQWP36x+oDaKSUywCkdKTw8b7/64E3pJTFjn7y6h1jCBAL/AI0FKLxduyXY0/vh5Sy9sIlpcwRQuQB0UBJvfqe9C+Bmrw3oUCO4/UM4EsppRE4IoQ47Ohvg4/6l0CgsK96+GNfkWkQ06mg0HFQXPc95I8SZ8O53GJgYGTLjelOAV25qdtD9AoeRKQulkFho7mt+2MEaUJQqQQPz51MgL5uZT7YX89Dc+3x0gkB4czr5uyeH6L15/aevlW9BxgWMYDLEs9Dr7KnAgtQ+3Ft14vpFdJ6sZ8dCZUQSARFpiii9d2J1MWSFNCLzPJUSkx1LrTnJgxi+4z7+Wj8P8iuLnUY+XVYpJqFR3c27N4Fvf/52BcFnNEFXNji93JSNN1BuDEQ1MkgTu342OnDezNxkLN7+lmDW5gyUDuoZe3bgUsnDmJoD2dtgVnj+jPCC0P/u6M7XMqsUrLoaMsN5rZmUOztWGXzU3VqhJZxUedwXUrzPThVKsFDc6cQ6FeXajHIX8+DcyfXphFVCQ1qh5fWbT3OJkTr7Lo/L2UCW2fcw3PDZzRp5AMsOrqr1sivQQLfeXB9qs95CZMYFDmNDKPr9UGrH49K0xk0Ltm8QPiDpqdXx1JQ6MDUGo9SShNQYzw2RiegfsqeLEdZQ2Y5XMe/FULUCCL1AEqEEAsdruXPOyYavO2/B9BDCLFOCLHR4ZGAsItnvAj8u4VjRwjRBegKrHCzbzigA/5qRv+PAnOFEFnYV9tv96JtS/r/FqgEjgPHgBeklK7uTAoKHQRxKsdWtjZCiHzgqJtdUUCBDw+l9Nf2/XWRUkZ7WlkIUQocarJiHaFAaZO1PKvXVJ2T7ff1Z91WePr5+YJUKaXHPsEnuS74mrb47trq/DiVjuPttcHT86G1PgOl39Yba5PnghDiBuAGx2ZP4EArjEOh/Wn43b4rpXy3ZkMIMRuYJqW8zrF9BTCiEbd7j+oLISKBCimlUQhxI3CplHKSo+0H2N3hj/H/7J13fFRV9sC/d2omvfdKCb13EOnSRFBExe6qrK51V/en66699+6uqKhYUayIUqT3jkBCSQg1vZKeTLm/PyZ9JslMMkkIvu/nkw+8M/ede1+77517zz3Hmmv+VynlR07q/wWrC99VQCRWN/1+wPWAu5TyJSHEzcDQBvs5fKxCiIeASCnlPQ3kYVgj798kpdze4DdH2v4PrDbMq0KIUVXnoy/wFrBdSvl5VbmPgN+klEtdpH8U8DfgZsAP2ARMr/YOUFA431Bc95ugsRe8EGK3lNJlObUUfeeXvkZYIqVc0HwxK0KIhY6Ud6Rcc2Wa+r2dzo3LcfT8uaouZ8o7YwS2hva4du11f1xo9dTF0fuhrdqm6O3Yfq7K2HOqD1FQAFKBuimLIqtkNUgp66bQ+BB4qer/Z4H9dVzPfwRGYjVGHdZfpWeHlNKI1c39GNAdqzE7VgjxN8AT0AkhiqWU1UHrHNFdzTXAXXUFQghvYDnw74ZGvhP6bwWmAUgptwlr/uFAB/dtjf5rgRVV5yxLCLEF69IGxdBXOC9RXPcVFBxjWRuVd6Rcc2WcbVtnoD2P6UI8fwoKCgoK7Yczxi/ALqC7ECJOCKHDahD/XLdA1ax3NZcBh+vs6yuEqB5knIjt+vhm9QM/AuOr6grE6sqfIqW8TkoZLaWMxeq+v7iOke+oboQQPbHOem+rI9MBP1TpXNpwHyf0nwYmVensBbgB2VXlrhFC6IUQcVgHLna6UP9prOcbIYQH1gEWFwf/UVBwHYqhr6DgAFJKp4xBR8s7Uq65Ms62rTPQnsd0IZ4/BQUFBYV2xSHjtxoppQm4G1iJ1YD/RkqZIIR4SghRnSrjXmFNQ/cHcC9Wd3GqIsg/CKwRQhzEmg/qgxboXwnkCiESgXXAPxt4EbSm7VSdg69l/TXCVwEXAzeL2vR79YLdOKj/AeD2qnPzFXCztJIAfIN14GMFcFfdiPut1Y814KKnECIB6zX/WErZ+YLTKPxpUNbotwAhxIK6a7MUfReWvgsJ5dx0Xtrj2rXX/XGh1dMS2qptit7z+7or/HkQ1lzrb2CNpLtISvlsBzdJQUHhT45i6CsoKCgoKCgoKCgoKCgoXEAorvsKCgoKCgoKCgoKCgoKChcQiqGvoKCgoKCgoKCgoKCgoHABoRj6CgrnKUKIOUKID4QQS4QQl3R0ezobQoguQoiPhBCNRfbtlHUpKCgoKCgoKCgoNIdi6CsoVCGE8BVCLBVCHBFCHBZCjGqhnkVCiCwhxCE7v00TQhwVQiQLIR62t381UsofpZS3A3cAV7ekLe2FECJKCLFOCJFYFSX4vlboau78HRNClAsh0qvqetKeHillipTy1lbW5ei1arYuBQUFBQUFZxBCLFD0X7j6FRTaGsXQV1Co5U1ghZSyJzCA2py1AAghgoUQXg1k3ezo+QSY1lAohFBjTc0yHegNzBdC9BZC9BNC/NLgL7jOrv+p2s+luHgW2gQ8IKXsjTWv7F1CiN4N6nPV+ZsGhAFZwHzgciHE5gbnr3cHXSsFBQUFBQVX0daGpqK/Y/UrKLQpiqGvoAAIIXyw5nb9CEBKWSmlLGhQbBzwoxBCX7XP7cDbDXVJKTcCeXaqGQ4kV83+VgJfA7OllAellJc2+MsSVl4EfpNS7m3QXrsz0R01Cy2lTK9uo5SyCOsgSUSDYq48f/lYz98cagcZas4f0MeFdTV7rZo7PwoKCgoKCgoKCgrtiZJerwl8/dUyLFIDgF4TgUblb7ec2ZKH0XTWRq4SBnTa7k3WkVuZSZm5xEburfHDW+vXglZ3HsxmCxln8igtqQBAo1ETGuWPwV3X5nXv2bMnR0oZVL0thBgILAQSsc7m7wHuk1KWAAQGBsrASD8KjLk1OvzVJaiwfX502jhU9SeT/7RkpuZTWFAKgEolCArzxdvXvd3bYZGllBmP28gFgsMHK/KBNCll3xq5EFcC06SUt1Vt3wCMkFLeXb9fiEKj8m2XY1BoG0qMp1DLczXbCQeN9fqG5jD46aVPuAcAgbpQ3NQtvb8lpZUJSDt9ikHbDZUwOK3RZM7EZM60katU3ug0sS1p5AWJ2WQh/UwuZaWVAGi11ndRQuJBp+4FT38P6Rmmt5H7agPx1Hi7rsHtSGlxBZmpeZhMFgA8PPWERgWgUgmX1mOxnKPSdMpGLoQOvbanS+tqsh2NvCuOHKx06l4IDAyUsbGxrW5PdnY2QUEOV6vobyf9Db8hFRTOVzQd3YDzmbBIDYt/CQMgxu9pgr2us1suv+hjsgr+bSPXa/sQG7q6yTo+SnmRw0V7beTjg2Zxafj1LWh15+GVh75hzc/7oE5X6evvyeK1D6HVte2tKYRo+EWhAQYD90gpdwgh3gQeBh4FiI2N5aXl/+HXjK9qdrjRdxt6lclGd0Tgu3gaJrdZ2zsLP322hf899wvUsYNVKsH73/6dyLj2fT8WV+zjcOblNnKBjmExSWdxwrupbr8QZniOyKCrXNZOhfZne+ot+FlW1mz3jE63tTaawCfcg+u/nALATTH/oJ/viBa1wyIr2HumLxKjzW+9Q77CQ9/faZ05514it/ANG7m723iigr5sUTsvRJ6593O2rE6oJwsO8SUh8WGn7gX/CG+u+vxiG/nMsGuZEDy7dY3sAMpKKrhhwgvEiPJ68kvnjOSux1x7PIWlP5Oee4eNXKMOo2v4bpfW1RTFFXs4nDnXRj485pRT90JsbCy7d7dfuxXaFzvfkAoK5yWK674DqIQBP3ebZbw1eLlPR2A7C+3lflmzuvv5Drcv92nZx2JLyCsr5Ycjifx+4jhGs7nd6t208qCNrCC3mIO7TrRbG+pwFjgrpdxRtb0Uq+FfQ1+f4QhqZzFSKgNtlKhV/ri7XWS3AousJKtkFelF31NhynZVu5vlaG4OSw8fYn9GervVCbBppU18OywWafNB3R546Aag18TZyP3dL21sl1Qgqs52ZJWshrJiLUe3N1yd0HnYk57K0sOHOJ6X23zhC5hw7xuwuMCxzU1loLuX88Z4NSqhx899uq1eTTfcdf1apNPLMAuwnXn1NjT/bupICisOkFr0Decq/nC57gqziVXHk/jx6GEKysswVprYtvawTbms9IYrt5rHoPawkQkEfX3sv+fbgnJTGmlF35JTug6LNHHm3Dm+O5zA1jOncdaDc+/WZEqKym3k9t7drcXDbYJdTzhHvqNc2g7dIPSKt4uCgsIFQqea0RdCTMMaME0NfCilfKHB79HAp1jnENXAw1LKX4UQWuBDrIabBlgspXzekTotUuDtfiladQAAp4rzSCzIpLt3EN28rYaeRh1KWMB7ZOY/jNmSA6jx9rgSf687m9U/zG88Z0qPsyN3DRYsaIWOqaFXEePRtMu/q/jl2BEe+H0FlVUGfqS3N5/PmUeMT9u7I+v0GiorbGfEdW7aNq+7IVLKDCHEGSFEDynlUWASVjf+GoLdwpkbeTvL0hZTYSlnZ2kXwvRe+IokADTqSIy6p1iZeoL+fmFEeNSew1LjSfZl3EyF2WpsC7T0CnyOUM+2+4iRUvLIutV8nVD7UTYlrivvTp+FVq1us3qr0TXilaHTt3+3I4SK7kEfkJJzH6XGBEDga5hCtP8TwOv2dtkFdBdCxGE18K8Brq3+0SwFe37vytD4zueOW2EysWD5T2w8fbJGdsuAwTx28YSOa1QHEu01kV3Ft6Cv/Myuh44jeGi8uTbqLtzUzrvX1yXG/2ksspSCsjWAxF3Xjy4BbyBE027SUkp25pzmXGUZo4Jj8dK6AaDX9SLE7yWyC57BIs8h0OHreQveHudnEg8pzSRk/4Os0hU1siD3qfQJeg2VaH2/kZyXy40/LSW9uBgAg0bDa5Ono9WqqTBbWq3fS+PDYN+L2FewBYlEjY6podcRpA9rtW5HOFP4Gcl5zyOxvs8rTGH8e90k8suty0kGhoTx6ewr8Na7OaSvsb66LfpwtcqL8MAPyci7H5M5HRB4GmYS6P1Pl9fVFLXvivur3hUqfA2TgQ/atR0KCgoKrqDTGPp1omBPwTr7uksI8bOUsq4x9h/gGynlf6sifv8KxALzAL2Usp8Qwh1IFEJ8JaU82VSdFgQlUktJ8c9Eet/BmwkJLEnZQ6nFaoheHtOfF4bNQiUEXu4z8DRMpsJ4BI06FI3asUDcQgjmRt7GpODLya3MJMwtGneNp1PnpqUUV1by8NpVNUY+wNnCQp7auI6PZtm6ObuaS+YO4/uPN9WTRcYF0XtQdJvX3Qj3AF8IIXRACnBLwwIjAyYx0HcUqWUn8dMF4q8Lxmg6S4Upj4f3HmFl6m5gNyoEf+05mn/0sxpPSXnP1hj5ABIjR3MfJ9B9IhpV21zvtSdT6hn5AKtPHOfbw4e4tu8Ah3RIWQGyEqFyPubAJXOHsm9bcj2Zm7uOcdNbPuvZGgzabvQJW06F6Qwq4YZWXbN8oCcghBBngcellB9JKU1CiLuBlVgHDRdJKRMAJIJSqaXfzJMMCPXpkGNpDYsP7K9n5AN8/MdepnTpyqjIDnv2OpRteZfwSZKKeK8MrGPCjhOkD+fRXu+hUbV+gFKj8qF70IcYzVlYZAV6TVSz+2SVFXHrpq84cs4aE9JdreWFYbOYHmVNeuHreR3e7pdTaUpGo45AUzVofT6SWfJLPSMfILt0JZklvxDmOafV+h/fsLbGyAcoM5l4eN1qbpw9kNVLdgGgd7MO9kTGRcMRZ2sQXBp2O7+cUJNSfJpz5QZ+Sd7Ho4OCuaZLrYOYtBSCMGCdg3AN5aaMekY+gF6TzuU9d7Jo/3gA9mem8+7uHfxrzDiHdA4a1Y3gMF8b74apc4e6rN118XAbS5ewnVQYE1GrAtBqwtuknuYwaLvbeVcohr6CgkLnozO57tuNgt2gjASqp9h8gLQ6cg8hhAYwAJVAoWPVClRIcrPv5t7gf7B91Ge813sVQbpSfjh1gJ9P1xpSQuhw0/V32Mivi68ugK6evdvNyAfYnZZKidF2PeiGU+3jOn/z/Zcw69qR6Ktm8AeO7MpT79+MStUxt6WUcr+UcqiUsr+Uck5VZHcb3NTudPXsjb/Oep21mki+P1PBytRjNWUsSP57ZAu7sk8DkFe2xUaPWZZSUL6nLQ4FgPWNXMf1J5u/vlJWYjz3OJUZg6jMHEBl7nwsJufui/EzB3D7QzPw8be6s8bGh/Lkf2/CP7hjZ8H1mqi6Rj7AASmlVkoZKaX8qFoopfxVShkvpewqpXy2Wu4hJP21Zrw0FootO9uz6S5hw+lG7ot2eu7PN/blnuXtxE0UGfXsyYtxen+9ys0lRn5dtOpgh4x8gOf/+L3GyAcoNRt5eNcyioy1LtcqlXvVu+n8NfLBfj9plW9ute4Ks4mtZ0/byM9VlDPspkHMvr4PDz25jS+Xfc9Xy77nhXcPtKieNxM2sCs7j9wyT0xSTaXFzBN7fyO9tBBL5QEqc2ZTmTmQyswhmIpec9qdvjHyy7fVM/Kr6RN0pt72hlMnHdap0ap5+oNb6DskFgCDu465fxnLNX9tO+8fIdS46fp1mJFfFzvvCgUFBYVORaeZ0ceaqqvuG+ss0HAh+xPAKiHEPYAHUB0RbSnWQYF0wB34u5TSXkothBALqMqbGRxuPT1dNWaCVUdrylzsf5bXe67h+gOzWJuWxJyY9pmhTCnKJqUoh96+YYS7t961PsDdfnToxuSuRqvT8LdHZ3P7QzMxGc0YPGyjFXckde+F6OimZzrXpSfV2ZIIIQHB2vQkhgVFo1MHUmEn+rVebbvO31UEGuxfx0B323WkDTEXvYal9LOabVm5A2PeX9AF/Y7VucYxrrh5LLNvGEN5aSUeXo65i56P1L0XoiLUeKkkvbUmKlSdz3W/0fvC0Px9cb6SVlpAYkE6XbwC6eLl3Id5/WfXMZzpG9oa2/ZLyiwV/Homgau7DOmQNrUUXSP9oc4FxpZWpcbXzY2Ccts152E+3oy8cz2WitqBABVrHNLb8F6wdz+ZpWRLxkFmud8L1ePHshhz8Tug8kPjYeM85jSNnbvCSgPWuQ7r8o/Gnv/GiO4azMuf/5XSkgp0Og0abdsv+1JQUFBQcA2daUbfEeYDn0gpI4EZwGdCCBVWbwAzEA7EAQ8IIbrYUyClXFg1qzvUN0CNCkmgynbEfaB3Nl0MBfjqWrcm0xHM0sJDe75j1tp3uW/XEqaufpNXE5qO5u8I/YJDGBZuG0zslgGD7ZRuO7Q6zXln5EP9e6G59C0+OqsRq1JZ0GrNaLXWfzdnH6HSbCLK+2abfXz1w/HS92mLpgMwr3dfvHT1g0TqVGqu79e827659Fs7wlPIyl1Ot0OtVnVqIx8a3AsB1g9dnQA/VWUHt8x5buw/CHWDNd++bm5c3rN3B7WodbySsIqpq9/kvl1LmLX2XR7e8z1m6fh6ax+t8324M31DW1Pd94BErTaj01n7n6cP/uKS90R7Eu51NWpR3xBVCQMRXq2PKaASgpv6D7KRj42OoauPxFKxrkV6G94LjX0TdNXvrzXy62Cx19e2AH+3MXho423k6wvicQsvQWits/03D2zZ+93dQ68Y+QoKCgqdjM5k6DcbBRu4FfgGQEq5DXADArEG0VohpTRKKbOALUCzi8ykRU1e6pB6kdbrYlBLruna9kbxD6f38cvZ2iUCFiSLkrewPTul1bo/mDmH6/sNINjDg65+/jx28QTuGNJ+EYIvFK7tOgSVAI3GQl0bKqU4my9P7CTa5y/E+z+OhzYevTqcSK8b6B/yXpu2KdzLmy8vv4rxMXEEGAyMjIji09lz6R3kyNKSRgxYWeHSNrYFJrOFlesTeeGdFSz6egtZOUVtV5m0XfrS1uTml/Dpt9t4/u0VLF9zEKPRuUwZg8PCWXTZFQwNiyDAYGByXFe+vuJqAtvJk8eVbMs6zsfJW7HUyT2/7OwBfjy932Eds2P64q3tvANR13cbBoBKJVGra8+DbMV7orzCyA8r9vP82yv48oedFBaVuay9TeGujWFQ6KcEGMahUwcTYLiYwaGLcdfGukT/fcNH8ciYccT5+hHq4cnNAwbx3vTLACPgGhf667vZflpEuPvQ28ff/g7SNYOFQqgYFPoxYZ7zKDJ5crrUn09PjWRXfhwqjcQnxMS702cxOa6rS+o7n8nIOseHX27mhXdWsGbzEcwuCLSooKCg0BnpTK77TUbBruI01mjpnwghemE19LOr5BOxzvB7ACMB2+TCDUhP9ef1V4YQ+Ld0RvStv7YvrcKfBwbeSW/f0FYeVvOszzhmV74u4ygjg+w6JjiMj5sbT4+fzNPjlbzvrWF4UAwzo3uyMt02pdy6jKPc3G00kd7XEund8JZtW/oGh/DxZVc4vZ/KbQaWsgYzTSp/hH6ki1rWdjz64k9s3nW8Znvp8n2899x8YqNcvD5ZGFC5tW+k+rSMAu54+Evyz5UC8OvaQ6zeeJjXHp+HStV0ZPa6XBwdy8XRsW3UyvZjfWbjfePcGMcGYQPdPFk87jpeOrCWfblnXdm8duG2+JEI4N2ja6m048ng7HuiosLIPf/5miPJtUuNflyxn/dfvA4/37Zf3uGt78+AkIVtolsIwe2Dh3L74IbGeBhCOwhp3NfqOq6IHUCF2cSiYzvIKitiTGgXHuo/CZ3BQmXRU0D9wVKVYUar66xGpw7Ey/AAj2yx/bSrVFXSP+zCX2+edCKLe/7zNSWl1gGU5WsOMfGiHjz5wKwObpmCgoJC+9NpZvSllCagOgr2YazR9ROEEE8JIapzlD0A3C6E+AP4CrhZWiPdvAt4CiESsA4YfCyldDjSzmtfjOfwiVqDXqjjiIn4jLFh3VxybM3R2GxTS1xOFdqOQQGRduWdcbZQ4/0IQje2VqAKQ+v3HkKcf0ss6rL34Ol6Rj5AUXE5n367zbUVqQLR+L6NULV9Gsq6fP79zhojv5o9B06zbU/rvXs6I409W84+c338wvh03HUcuOIhVzSrXRFCcFuPUUyLtL/0wtn3xOpNR+oZ+QDpWYUsXd56I/h8Ruv7GkLTq2ZbaHq2WNf8rkNYPf1v/HHFQ7w3eh4xnv4IdSAavzdBVT3gKFC5zUTteVcrW14fD43OZmkOgFoIPDQ6O3tcWCz6akuNkV/N2s1HSTyW3sgeCgoKChcunWlGHynlr1hT5tWVPVbn/4nA0WIbrgAAIABJREFUGDv7FWNNsec0fj5FVBg13PPyFSx8fiQ9uvohNL2bzWvsSq6KHcovZw9grhOd16DWMjvasRRpriSzsJiU3DzigwMJ8Oh8rr4tx4LRdAaNOrLRaz8zsh/vHV1PobF+sKdr4obZlD1XVk5iRhZRfj5E+p5/KdqEygddwKdI00mkpRCh7eNUEL6O4lhKll15UiPyliA03dAFb8aahdF5Tubmk1FUTL/wEDx0zulIOmEb0BHgWEomY4Zd+C65DZkTPZCPk7dSZq5dQqEWgqtjnU//JaUZk7nharDOw1WxQ/jlzIF6yxgMai1zogc6pScppf49ZtGAyQ0OpKQ1skfnRkoTJnMaanUwuqDlWIxHAIlK2wsaWbbXUtRul6DSj0caExHqIIQ6AoulFJP5NBp1hEv6WD+9B1PD+/Bran3vsmkRffHTd96Am45y7IT9vv5YSia948Mc1pNbUsqxrBy6BPoT4tV+2ZAUFBQUXEmnMvTbm/CQXN56ZiEWi2DPgXhCQq5HpW17V/2GDPSP4o1hV/Pu0fWkFGXT1zeCv/eeTIS7X7u249mV6/li137MUqJVq7ljzDDuHjeqXdvQUVQaE0jNGI5GHUuA34sY3C62KeOv9+Cj0TfySsJq9uWdJsrDnzviL2ZMcH3Pj8U79/Hqms2Um0wIYE7/3jwzawqaDkor2BRCE+viT922pUuM/cjTcdEuzG4gDC0y8itMJh74/jdWH00GwEOn47HpE5jT3/EgeHHRgTazrQBdXHl8nYgIdz8WjrqB1xJXk1CQRhevIO7qMZ4B/o6lpqumpGw5eQWPYTZ3XmN2kH80bw6/mneOrONEcU7Ne8LZDC11n5XiMEFpiACV4HdSeXH1Rh6aYtv3dVZKSn8h79xjmM3pCOGFj9ff8PW+v03rFEKH0FkHXwoKX+Nc0X+Rshi1Ohx/36fwMMxsdR1PDbwMP507y85aHRdnRfbn773/HMvzukQH2o3L0iXG8WULb2/YxvtbdmE0m1ELwXXDnBssU1BQUDhfUAz9JtDrrLNEKpVk2MCjmOXzwJsd0paJYT2ZGNZyV8LW8mvCURbvrHXdNJrNvL1xO0OiIxgV17HppZrDaDLz9eq9rNuTjJtey+Xj+jmvpGrtq8l8kqzcm4kM3YnaTjqj3r7hLBpzU6NqjmRk8+zK9bVqgR8OJNI3PITrhw3k4PE0Pl+xh/ScQgbFR3DTzOH4e/+ZPCdax7ABMQwdEMPuP07VyNwNOm6a1/GxBRZu2VVj5AOUVFbyyLLVjIiJIszHyyEd118xgs07j1NUXOs10rdnOGOGt88yovORwQHRfD721hbvbzSdIDv3DsDkuka1McdTc/h0+S5OpOXSMzaEm2cOJyLIxyXvianje/PDb/s5XJBDaVjt4KMFWLR9D4Miw7ikV/dWHkHHYzQmk513J9XXXcoiCgpfRKvpgof7ZU3v7AKKS7+noPDlmm2zOY3s3DvRhW5Eq4ltlW6DRscj/WfwSH/Xrf9vS0xmC9/8vo/fdx1Dr9Vw2cV9mT6qV/M72uGWq0ez79AZKiprn+cxw7rSv5dthiF7bDtxmnc2bq/ZNktZ79tHQaE9EELcB3wMFAEfAoOAh6WUqzq0YQqdDsXQd4KS0p8I9HsVITSYpYmzJXuxSBNRHkPQqM7vtcutZeVh+7mmVx1JPu8N/Sc+XMGqHUdrtnclnm6idPNIWUZp2W94ed4AUHUv7MEizUR5DEWjany2t66hV5dVR5IZ4B/MX1/4FqPJGkX98MlMth48yRdPXo9OqzyqjiCE4MV/X86vaw6x9+BpggO9mDNtIJFh7ev9Yo+f9ifayMwWC6uPJHPjCNu0X/aIjvDno1dv4McV+0nLOMeAPpFcOrkfGvX55w3SWSgp/YHOZOSfyczn1me/pqTMug75yKksNu1P4aunbsCvalCw2JhDRtkhvLShhBicM/wNbjrefW4+t3/8HXvzbb1HVh1JviAM/eLSH7F33YtLl7aLoV9SstSO1EhJ6Y8u8yowWso4U7IXjUpHpPtgVOfp8qvnPlnNss0JNdu7j5whp6CEG6Y7vwSnd3wYH716Az+t/IPc/BKGDohh+gTH09g29q2joNDO/EVK+aYQYirgB9wAfAYohr6CUyjWg1NYAEluxQmWnfkXxSbrWjA3tTczIp4m3L0FM8WdBHed1q7ccJ4boGezCli982jzBZ1EYp3hzylP4Zez/6LYlA2Am9qHmRFPE+be1+5+Bq398+iu1fDZb7trjPxqTqbnsW5PMlNHdpw3R2dDp9UwZ9pA5kw7v9wtC4vL7cpTMwqc0hMW7MOdN45zRZMUOiHfrNlfY+RXk3uuhJ83HeKmmcPZm/s127M/woK1L4lyH8KMyKfQqhwPyufpoadffDh7d9ga+o29CzofjaXTa59UbLKN6z9bspffUp+kwmJ1Y/fWhjEr8nn89OfXwHxmXhHLt9gOgi7+dRfXTh2MugVL2mIiA7j31oktao/hgrm/FTo51asmZwCfVQUf70wrKRXOE5RpICfwcJ+FEFrWpr9SY+QDlJsL+S31Gd5IXMNrCb9zuCCjA1vZNswb1A9Vgz5Gq1JxxQDHR8o7grScQqRr0iPXIHDDoyol0tqMl2uMfIBy8zlWpz+PtJPmCmBW35642zH2rxrcj7TsQrv7pOWcc0GrFToazzLb2TRhloRqL/wAWeczJareWFzcR7Ql6TmN9ROF5FacYGv2whojH+BM6R725i5xup4rBvSxiRuiEoIrB9ofxOxsWGftbZ9JD3fn05G2BE/3y+1I1XgYZrdat1maWJX2XI2RD1BoTGd9ZrNZhdudzLwiLHZe0gXFZZSVG+3sYZ/TxXm8c3g9Lx9azR95LU+TOXdAH7R27nsFBUcRQrgJIXYKIf4QQiQIIZ6skscJIXYIIZKFEEtE08F+9gghVmE19FcKIbxor1FIhQsKxdBvAiGqZ0AEBrcp+Ps+R5npHJnlh23KlpmzWXpyBR8c28Lcde/z3ckLa03X4KhwXr18OtF+1gjx3YMCePeqy+gW5OLc5C6md2wIbrrWex3IqsHVApMPfv4LUauDKDXlkVVu6y1QaEwnr/KUjRwgxNuTD66dQ5+wYADCvL14euZkJsZ3ZVAP+2sIB8XbT9un0LmY1K0r+lyJMFk/atVlEvd0GN0ntmMb9ifnVOlZ9leEU245P92aGzIwvrF+IoJTxTvs/rYjZ6XT9fQMCeLteZfSNdAfgFh/P16/Ygb9I9o/IG1boNP2IND/TVQqa6wVIQz4eP0dz/Yy9D2uwsfrXoSwpoJUqYII9H8Hrbb12TOyy5MoNefZyFNL91NpKWu1flfSPSoIT4Pt0sfuUUF4uju2JHJL5nFmrXmPd49sYFHSVq7Z8BEfJ21tUXu6VX3bdK/6tonx9+XVy6e3SJfCn5YKYKKUcgAwEJgmhBgJvAi8LqXsBuQDTQWXuRV4GBgmpSwFdMAtbdtshQuR89vvuoPRauOJCF2KEAY06hAANJSjFjrMsr7rpJRQYbaeTgm8fGg1l0b1Q6++cE7xjD49mNGnB5UmEzrN+XtcFouFTb8dYPeGo/gGeHLLJUP53/LtNTP7HgbnI6anlAZw+8GrSSv34dkhwcyJBo3KgFpoMcuGsw4CvarxdDxDoyP5/rbrbM7jzTOHs/XgSU5n5NfILhvbx+bDXkrJlpWH2LnuMF5+7kybN5yorsFOH5NC+3LbZSPZmXia1NPnqB46mjdxAD1i/rzXrqSojBXf7CTlcDpxPcOYdtVwPL2dy/veWs6UlJNt8iHd5I27qAQ63iMrce9J1vy4F6Rk/KxB9Bvepea3uRMGsGbXMQ6l1LZzVL9YJg/vwdGiFLv6UksqOV2cR7Snv1PtmBjflYnxXc/7Pr+leLrPxcMwC6PpFBp1KCqVY0ExXYWfz7/w8bobkzkDrSamxSk7G6JX23//aKveWTvWJrJ1dQIGDz1TrxxGXE/H0865GoNeywPXjufpRatqZvYNei0PXjfBYR0vHFxJpaX+sre3Dq/jiphB+Oic70/GdY9jXPe4C/a+V2hbpJQSKK7a1Fb9SWAicG2V/FPgCeC/jakBegOXAk8BHoBb27RY4UJG6cGaoWH0W63KjV4+0zhU8HM9+YniAIpNtc/gOWMZp4pzifcJaY9mtisd+eJLOpbB0m92kJ5WQL/+UVx1zUh8fOtHpX/1/75h7U97a7b1Bi0vvDGfM2WlGNx0XDK8B37/vcepei0IzpZbA7odyDvLnOgB6FQGevpMI6FgWb2yXTzH4KltPpVPw/MY4OPBl0/dwJpdx0jLKWRIz0i7s/lvPfodK5bsrNn+5fOtPLPotnrGQFNkZxXyzdfbOXokneiYQK6eP5Ko6PPbM+NCIMTfiyVP38jvu46RmVfEsN7R9O8W3iFtOXkim2++3s7ZM3n07hPBVdeMxD+gfXNFlxSV8fd573LmeNUyqJ9gxTc7eP3bu/Hyab9ME2dLIpFCg5vaRIns+KCqvy3ZwduPfo+sMnp+/XoHdz42m8tuGANYjaAPHrmGjfuOk5KWS6/YEEb1jUWlEnTzGsevZ9/GoKk/EH0gP5yDBWlOG/rVtHWfv/b3BFauOIDZZGH8xN7MnDWQ9lqOKoQOnbbjgguqVF7oXDjAkJyUydIl+xDDwvGIqZ8uso/PpXzy8iqWfrC+RvbLF1t57L2bGD6hZVHuXcGlF/VhQPdw1u1JRqdVM2V4DwJ8HFvSVGqqJLko20ZebjZxtDCT4YGxLW6Xs/e9EGIBsAAgOvr8ioWg4HIChRC762wvlFIurN4QQqiBPUA34F3gOFAgpayOAHoWaCoVxHtYXfUnYjX0i4DvgGEuOwKFPwWKod8CxobchV7tyeFzKzFbjBzI82FDRky9Mm5qDWHuPh3Uwvan3GjCrY0D8yUdy+C+uxZTWZU2JzEhlW1bk/jfB7ei01vrPp6YVs/IB6goM7Lms+08+/FtLmlHF6/atHoXh9yNXuXBkXOrsGCmu9cERgXf3mLdeq2GGaMbz6t+5nhWPSMfoLLCxOLXV/LyV3c2q7+wsIx771pMdlYhEkhITGXTxiP8d+FfCI/o+Mj0Fzpuei2XXtT6uBZSSirNZvQtMMBOnczmnjs/pawqoFtiQiqbNx1j4aJbcXfQVdYVrPx2V62RX0XqiRxWLNnJvAXj260dcV4xvJ7Qn1HBJwhz79h4GMZKE5++tqLGyK/mszdWMXXecPRu1vgeGrWKiUO7M5H6Bqpe7UlC/mQC3bYT7n6OIqOevbnRnCwOJM7z/BzM++qLrXy0cH3N9v59pzh9Koe/3TOlXeqvrMqV3pKgb+cbSccyuP/uxVRUmNBsiqfPXEHU0Gy8vbzo4zuDLubZvPDxS/X2MZssfPzKbx1q6ANEhfhx4wznbRiDWkuYwZv0svqxK9RCEOPh+MCWRUpMZnOrBrWqDL2FAEOHDu1EkT8UWkCOlLLRtBBSSjMwUAjhC/wAOBtReYSUcrAQYl+Vvvxm1vQrKNhFMfRbgFpoGRV0G6OCrIbjx0lb+T19db0yN3cbhZf2wveyWXU0mVfXbeZEXj5x/n48MOEiLunRNjm9v/lqe42RX83pU7n8vHQHc68djRCClMNpdvc9diyVZ5auYVPiCXzcW35doj38mBNdG81dLbSMDl7A6OAFLdbpDMcOnLErP3441aH9V/12gOysQs7FaSiK1WDRCfR5ZhZ+s4kn/m5NKWU0mVm4ege/7T2KEIJZw3rxl4nDlBRuVRxLy+axr1Zx78wxBHq3fyC9z3fv539bd5JVXELfsBD+M2U8gyMd9wz47ttdNUZ+NRnpBaxZncCs2YNd3dxGaexZPZ7o2L3sKmZE9uXjpC4sO1M9q7qmXeuvS15WIefySmzkxYVlZJ7NI7pb8x5iN3e/jLu3l2KuM1gwKawnvX07zj27MSorTSz5cruNfNlPe7nuhjE23lqOklFQxDu/bmVH0mmCvD25ecIQLhkYX79MYRFPrFzL+uQTuGk0zIjtwr+mjcfLq/28SVzFodMZvLdiG3uOnMEcqcIjXUCphj++6MUfX/TioUdmMWJqP/ZsOobZZBvP6+SxDMxmC+pO2McLIbir53j+s6++l+VVsUMIMXg3u79FSt7ZtJ3Pdu/jXHkFI2OieHzqxJrYFAoKrUFKWSCEWAeMAnyFEJqqWf1IoKmXnbHKK0ACCCGCUILxKbSAztern4fc0n0074y4mslhPZkY2oNXh83lvt4tS+3iCIfSMvn54GFO5OY3X7gNScjI5L7vf+FEnrUdJ/Lyue/7X0jIsE3J5ArS0uwf7/v/WcItve7njw0JxPW0Hygqc7Av32w5QHp+EUdSbd38msNDo+ev8Rfx1bhb8dTWznoezsji54OHOZ6d67ROZ1n6+nLe+uv/7P4W18Oxj/i0tHyKotSci9di0VldYyv81XxXcoLSSmusgae+/Z33V+3gdE4Bp7LzeefXrbz84wbXHMQFgMls4cedCdz46peYLe373v3t8DGeWrWOrGKrMXgoPZNbv/6B3JJSh3WkN/IcpaW2b38SG2//WW3JeuGUnDyWHTxCQrrzfY+bWsvnF9/C3T3HMTrYseUvbYV/sDfefraGpoeXG8EOetyMD43ns7G3MCuqP2NDuvGf/tN5ffiVrm6qSyguKqfYTtpJo9FMVpb97ALNYTSZue3dpfy8K5HMgmIOnc7gwU+X8/uB2vzox7JyuGbxEtYmpWCRklKjkaVJR5ly0zN89cIPLT6ejuBMTgG3vruUzYdPUibNVPqoye+ux6SvXfpQ/WxHdwtGZc+YN5n4/Oml7dVkG/JLy/g14Sibj59qUZ86N3YQH46+nmkRvRkX0p3nBs/m0QEzHNp30Y49vLN5O+fKKwDYfuoMf/n6eyrN5mb2VFCwjxAiqGomH2GN6j0FOAysA6o745uAn5pQ8xZWT4BgIcSzwGbguTZrtMIFizKj7yImhfdkUnjb5jqvNJu5f+ly1hw9DliTbN4wYhAPTrqoRS68rWXpHwn1Zo0AzFKy9I8E+oS6PjZBn36RHD2SbvtDUSlpZ4t5/PJX+DzlHS6eOYCNy/+o+VmGGCjQtM4gi/UM4P4+k2q2zRYLD/7wG78mHKuRXT24H0/OnNQma0t3rtjHwv/7DACh0qLyql1PrdVpuPHvUx3S06dvFMWZCTbyCpWFNUnHGRMTzS+7bbNKfL/jIPfOHIOHmw6j2YzqAnF1bQ1nC4tZt+8Yk4e07XNflyX7DtrISiorWZ54lBuHDXJIR5++kezba5sVom+/9s3uMO3qEaz4diepJ3JqZGHRAcy4ZqRTep7+bR2f79pfs31Jz268NncGWrXjUfR9dAbu6jWeCpOJRdzoVP2uRKvTcMN9U3n3ifrG5nX3TMbNiSCigwKiGBQQ5ermuRw/fw/CI/xsBpm8fQzExAQ2slfTrE9I4XROgY38s/V7mdivG/9etprv9ifU+/qpzmmf39efRY8uIapHBBddPrxF9bc3S7cdpKyyQUBYtaAsUI1XqtUDrm8/670QFObL7BvH8MPHm2qKSimx5ObzxbPfE941lCk3XNxubQdYfugo//p5JRUmq2EdF+DHx9fPJczHubgFY0K6MibE8YwFZosFCXyz37ZPTS8sYtPxk0yKb30GBIU/JWHAp1Uz8irgGynlL0KIROBrIcQzwD7go8YUSCm/EELsASZh/dyfI6W0/ThTUGgGxdDvBFSYy9iU/RPbMjZT7ldOSFA4mdl+mNWSjw7t5aPDewn38uKeESO5um+/dmtXmdF+jtvG5K1l/rWj2LE1mdS6H4XZBYhz1uCmpUVlbP15N//36nyGj+/J7o3WqPs+Q8N5bsWmRrQ6RqExl/eS/omPNpAxQZexJ8lSz8gHWLL3IBd3j2NyD9d/HKz5YnPN/2VuHpbycjAY6Noviv975+ZGZ0cbMm5CT/Tb12DEdraitNJIQUkZZjtJxSuMZlJy83h37w7WpKTgptEwr09fHrpobIcMMp0v7NpypF0N/caerVInnrkr5g1n86ajnKxjYI8a052Ro9s3IJmnt4HXv72b377eQcrhNGJ7hDFz/ki8nHDX3ph8sp6RD7DqSDJL9x1i/tABDuvZcDqBL49/gtC2jTeSM1x63SiiuwWz9qe9WCySCZcNYlA7XxtHKDTmsTHre06VHiVQF8bY4DmEG5zziBBCcPe9l/D4o0sxVlr7JJVacOddk2virjhLbpHt0gervJQVicf4fn8CVdlSkUikBixaQIBFJ6gMceP3LzZ1GkM/p9D2eMNicoidkIZemvAriaf/4NqYXwsemUX+mSzW/rgXLBZkcTFUDRT8/sWmdjX0C8vL+feyVTVGPsCJ3HyeX7WBt+Zd2iZ1lpuMPLNhA98fTsRksaCxqJBIBPUH6NvqO0bhwkdKeQCwGXmXUqYATXYsQoi6a0aygK/q/ialtM2bqaDQBH/eL/ROxOKTz3KyJBFUEBYKoSF5bN3Zi9PFATUfLGlFRfzr99UEe3gwIa593E8v6dGN7w8k2pW3BX7+nry/6DbWrUnko6eWcu54Opwrrl9IStRqFZPmDGHSnCEA5BeX8crvW6k0tdwVr8hUQGrZcVLLjnOkcDfFZy+xW25j0ok2MfSrlmnVbpWUQkkp/foOd9jIB9Bo1MwfM4gPtu+uJ9eq1UzoHkeguwfhft6k5dd3m+0a4s9/Nq7hYKbVECoxGvlk/z4AHhvveBqkCwqLJN67fdNxXdKzO/tS63u1CGBKvOPPnLe3gffe/wsb1h/m7Nk8evWOYPiIrqhU7RPlvC5ePu5c9deW3z8bkk7YlyefdNjQP3Uuix/SnyUwwNaFvKPoP6Ir/Uecv7OJFeYyFiY/Qr7RGkwxrew4hwt3cke3Fwg1xDqla/jIrnzy+R2sW5OI2Wzh4nE9W5UFZGR8DEJAA2czRveMqb1fpPVPasFSx1FCagRpt/fEdLDzxFEb3TOGZXW8sGJ6pNN/dHLNtol9LD37FvNjHqyRRcf4I3PafrlZc+w8eZYyo8lGvjHZ/nPtCp5Yt45vEg7VbJuwgB40FbVlDFoNY7vEtlkbFBSaYA/WHsreC1kCHbu+TKHT8ef2vW0hZ49nkrg7BWOl7QvK1ZwoTrAa+XUQAnr2OGO3G1hyyNYNrSkaRnd2hondu7Jg1DC0VS7cWpWKBaOGMbF7232ganVqeg+KYuZlAxHniuudAoOnG6Nn20bt9fM08MjciWhc5GpuwYxHkO0AB4Cfe9vkAJ907VgbmRCCifMvclrX3WNHMrF77bvCS6/nxUunEuzpiUoleHL+FDz0tV+/3u56bpg6tMbIr8u3CYeaXFNZmF/CoR3Hyc9u2XrbpsjOKyY109ZFt12wSAL2pDN97uh2rfbGoQO5rE/PmvveXavlyWmTnA4cpdNrmDK1H7fcOo6Ro7p1iJFfTXmlkZOpuZRXOD+D1tjz5u/Ec/ht8g94GDreyLdYLBzdf4rjCWc7uinNsr9gY42RX41RVrIlZ1kjezRNSIgP11w7iutuGNOokX8mI5/cAvuz9XWJDfbjnhljUNVZQhUfHsgdU0fi7271FhEAZrDYmeow++jwn92xEeidYdqgHkytE2iwe3/bgK2Hzm0lp8Ia/LKksIyw+AjUOq1NuUnzx7RdQ+3Q+PPbvFdPS75dyk1Gfjxix/tZDVJY9fm46Xlt9gx8DBd+MGWF8w8pZZyUskvVvw3/FCNfwWk61Yy+EGIa8CagBj6UUr7Q4Pdo4FPAt6rMw1LKX6t++xdwK2AG7pVSrnS2/pLCMp796yL2bToKgG+gFw++eT1DxrXdR0F+ZZZduYehwq68xEF3sz2nUnl55SYOnE0n2t+XuyeM4tIBzrsgPzjhIm4aNojknFy6BQYQ5Nl2Uci37E3hpY9Wk5VXjFajJva6MeR8uwNTpYmwLsHc/78FePrar/+KkX25qFcsW46cxNfdwMQ3/tGqtnh7VqJVqzHWCdjjrtVy5SBr6rTMwmKeXb6OdUdS8HTTcc2wAdw9cWSL17UPnz6I21+8ji+f+4GSc6X4BHrxl2fn02OY84MqBq2W/82bzYncfLKKi+kXFop7nY++Ed2jWfX4bWxISEGlEozv04VDOfbvw3KTCbOU2FsN/dWbK/nqrZUYK0xotGrm3DqeW/8z2+n2NuRccRlPvfsb2/44gZQQHxvMk3fPIDaifdKHqYoq6LMxlX+9dRvuXm0zsNMYWrWaV2ZP575xo0ktKKRPaDBebh2f+72lfLtiLx8s3UpRSQWe7npunTuKa2YMcXj/uQP7sGjbHkoqa7MIaFQq5g/t77COctmxQU0BThxO45nbPyLtpDVQaJfeETz20W2ERJ2fKfHyK+0vcWjsfdUakk5l8cS7v5FyJgch4OKh3Xj0jml4NJEK8rbJw5k2MJ4dyWcI9vFkTI9YVCrBvMF9+WL3fipMZrvTZdXEDolz+XG0FWqVipdvmslNE4ZwODWT/R72l6nlV2ax9ctkPn7+Z8pLKxEB/hikibKMXPTueq64dzqX3DS+Xds+JDqCPmHBJKTXv29uGD7QbnkpJQs37uKzbfsoKCtjbPdY/j1zApF+jqUyNpotjQbZC87SUFFkxNNi5EyXHFDW5yt0MEKIK4CLsM7kb5JS/tjBTVLohHQaQ78qqMW7WKNXngV2CSF+llLWnVr9D9agF/8VQvQGfgViq/5/DdAHCAd+F0LEV+W5dJiPnv2pxsgHKMgp4vk7P+GzXU9h8Gibj+1Yj14IRE2woGry833tlp/atfm1nBnniliw+IeaKOuncgv4v+9+I8DTnVFdo5FSsn/PSTLSCujTP4ro2KaDIgV5erSpgQ+QW1DCI28so7LKzc9oMpNkMvPXT//GlAFxhHcLbTYIXrCPJ5eP6OuS9sT79OODa2fw9vptJGXn0icsmPsnjCHKzxcpJX9d/ANHM61OqRZnAAAgAElEQVRroAtKy/nfhh2oBNwzqeUzwPP+MYtZd1xCztlcgmOC0OltZ2Qqyo3s2JJERYWREWO64+3T+MxIXIAfcQH2I3l7GfRcOrR2AGtQaBghHh5kltSfUZsY1wWdnaBn+zYfZfHLy2u2TUYzS/+3hp6DYxkzw/G10/Z4ddEatu6vde08djKLh177ia9fuaVNAiE2JDjYjzd+fZSQMPvPYHsQ5etDlK9jH7fnK3sTz/Dap+tqtotLK3jzs/V0iw5iaN9oh3SE+XjxyQ1zeWPdFg6mZdI10J+7x42iX7jjy1lCK+I4odvpdPtdyfN/+7jGyAdISUzl9Qe+5IVv7unAVjVOrEdvNmXbfnPGevR2Sk/iwbOcOpFN9x6hdLOTOcRssfB/r/xERo7VI0hK2LArGW/P9TyyoOkApJGBvkQG1n9GuwT689G1V3DbFz9QbjKhqgRLg4lbvVrN+LjOY+iDtd/PS8rDtwIi/LuTWpFU73et0GE85c5/H11UI5MWSSVqbn7xRubcPqHdBy2r+fDay3l5zWbWHUvBx03PtcMGcNMI+2k+P96yhzd+31Kzvf7oCVKy81h+780OpX/10usZEx3NltOn68nV5RJzhhEtUI6J95dspmtkAGOHts0yRAWF5hBCvAd0o3aN/h1CiClSyrs6sFkKnZBOY+hjDWCRXBXMAiHE18BsoK6hL4HqxKk+QHWi5tnA11LKCuCEECK5St82Zxqwefl+G1lJYRl7Nx5hzPTWGS+N4a8PZXzwlazL+rZG5qnx5aVxj/NTYDqvbt1i/WARgit69WZ+v+aD8f28/3CNkV+NlPDNrgMMCA3h3//4ioQ6+dqvvHYUC+6Z7LqDssOJE9lo1KpG3TbX70yqMfLrsmFvCjdf2bTxXFph5P3V21lzMBl3nY65I1tn7PvpQpgQMg8fbQCj4mwNkv1n0muM/Los2XXQYUPfZDKTkpKNn587QUG1uYDd3PVExtvPmX48KYNH7v+S/Ko83Ho3LY88dQWjxsbblN2VfIYPft/J6ZwC+seEcfe00UQHNW64atVq3pk5i7uXL6sx9vsGh/DUxEl2y9t7VgA2Ld/XKkO/0mhi7c4kG/nptHyOpGTSq6vjBl5Lyckp5qZ57/DXe6Zw+dUj2ry+tmD38bN88PtOTmXn0z8mjL9NHUVssGPp21zF6q1H7MpXbjnssKEP0D8ilEXXz21xO/auq6BoSCDREbbPbHtw6mg6Z5JsZ8j/2JrEsm2H+HZ3AlnnihkZH83d00YT6N22g6qO0MNrCH19RnPo3NYaWYhbDGMCZzm0v8lk5ulHlrJtU21A08nT+vHgo7PrLSP540hqjZFfl9VbjzRr6AOYzRZSUrLw9jIQEmodGDOZLVSWm1ABwgRGFciqlUreej0vTLkEf0PHGL0toWG/79tdQ68H3DAJ63IUgWBa2E3s/ti23wQ4tPsE1/7DsVR0bYG/hzvPX1Yb86akpIKjR9MJD/fFq8Hgw5JdB2z2P513jm0ppxnbPdah+l6YfAm3/fwjR3Osz7uXSosmuZJKT4HJAEKCpkyyYnOiYugrdCQTgV6yao2KEOJTwDZlkoJCM3QmQz8CqLv47CzQ8Cv7CWCVEOIewAOotk4jgO0N9o3ADkKIBcACgOjo+h+beoOOogLbfNXOpD1qCZND59PLezjHivbhqfGhn+8Y3NTu3Do4nLm9e5OYnU2Mjy8R3t7NKwOKKuy7/RdXVLL0y20cOnCmnlvj0i+3MXZCT3r1dX36rVOncnjqyR84edL60u3dO4LHHr+coKD6Qc4am6gVDqwt/udny9mYWDsD/Mx3ax1qW917ISIqnMkh8/HRBtLXdzQ6VeMeHEXljZ1f+/KG7NqVwssvLSc3t9jqqnpxTx56+FL0dmbw6/LWS7/WfOxJrLM8rz2/jC9H3o9WWzvrfuBUOgv+9z2mqrX1qXmF7Eo+w48P3YSPe+PrEoeEh7Pp1tvZl56Ou1ZLn+DgRss29kzo3Vr/rDR2xdtynXnde8FN54PFLHn/7dWMGdeT4NDONbOecCaD2//3HSZz7fXfkXSGnx66EV+P9jNwGvO+ULXQK0NK2SKPDrNGxe6tvTgZkk9AoGOxJJp6TziLzs3+c10R5cEj366u2f5u+yH2pqTx3T+vdyp1YFsghOCa6AdIKb6EU6VHCNSH09t7BBpV031UNSuW7a9n5AP8vuIgo8f14KLxtZ5E9q6nvQjp9vjjj9O88PwysrKs13T06O488u/LKK6oXeYhEOiKwKKSuLtp2XrXAgxax46hThtddi+0hLr9PkBBkhtHnu7PPe/2xSwq6eU9nGC3SE4ZVtndX9/I/dcRLPl6O4sXb6a83Iher+Ga+aO48cbaODR1r11diht559ojwtub366/kQOZGVSazWxec4xPtHsxG6z3lAQqtYIj55Tg5godSjIQDVTnwo2qkikoOMWFFoxvPvCJlDISmAF8JoRw6hillAullEOllEODgoLq/TZt/iib8qHRAQy8yHbG1NVEuHdlQsiVDAuYgpu61h3b183A6Khoh418gEm97I9SD+gayjvF+0haoOX4jVryBqpqFgzs3nG8Nc1vlKef+rHGyAdITEzl1VeW25QbPzweNzvplqZf1HR8hJTMvHpGvjPUvRdCg8OYEDKPwf4TmjTyAYbFRuJjsC0zsWfza/6Ki8t58okfyM0trmoDbNhwhMWfbm5yv9KSChIPpZI7WMXxG7UkLdCSOl1DjqmU5KP1o7R/vnFfjZFfTU5RKcv32J9hrYtGpWJYRESTRj7A5HkjUGvqP3pCCKbOdy5HekN0Wg2TRvawkXeJDKBHXEirdDdF3XtBq7XOqFrMkr272i46dFvxxab9NUZ+NXnFpfUid7cHsY14kMQEOjdwsjX9FLN++ZS4xS8x6ccPWH6y+fu4LtdPs44X52T6cTQhxqF9mnpPOEtYTCADxti+Q8QIW8+dE1kt789cjRCCrl79mRhyFf19L3LYyAfYuzPFrnz39vry/j3CiQi23g8V/hZyRpnImGYmb7ylyetcXm7k8ce+qzHyAbZuTeLDD9YzqktUvZgkACqLYHqPeKeNfHDtveAspSUVHD6UaiPPTzXinzWQccFXEOxmHaCfcPlQu0b9VDvfNR3B/v2nWLhwHeXlVm/DigoTn36yiW3baj0R7L1DDVoNY7o59tzWpX9IKAfPpbNYfZCCESZKuxgxu9X2i6dLi6g0tX3AZQWFRvACDgsh1gsh1mP1XvYWQvwshPi5Y5um0JnoTIZ+KtYRrWoiq2R1uRX4BkBKuQ1wAwId3LdZrrlvKlfeOQl3L+us54Ax8Tz9+Z2oNR07u+IsA6PCuG/SaLRVa9qEgJn9e/B1xgGy/CtBJTC7C3JGaijoZy3j5+/p8nacPJnDiRPZNvLdu09QVFRWT+bv486L/5hNRIjVONDrNFx76VCunGqTqrQe2YXFTf7eFhh0Wl6eN6Oesd8nPJh/zRjf7L7btydTVmY7a7F+fdNGmE6voWSYjtzhGszuAlSCkhgVZy/V4uVXf5Y2207eZYCshqkKW0Fcr3D+/f5fiOhiHRAIjQ7gn2/dQO+hrQ8a++BfJjF+ePeamd++3cN44YHWB/lrCf4BHe9G7SyNXWdXXn9HOJuUja7QBJaq4USLRFto4mySbZ/QGKcK87llzVIO5mYAcPxcHvds/JldmY5Hrh87sDvz43siLB2XUu3h925izIwBqNQqtHoNU64agTbAfnyNxp7fzoSvv/3npuHzpFapeOnBOcTEB5A3xIKxagyoQFQ0eZ337DlBUZFtJoX16w/j6abn5Sun1wtiOSAqjAen2mY2Od/R6TW4NxIfyK/BOQ6O8OfxTxYQ29M6gBQQ6sM9L1zNyCnNL/drD9avs/+Oqyt/4JKxDI6uHQDz1Ot4Ye40vFsQIX9p8kGe3LmGPGMZCDB7Scq6mJAqaz9QbjRRXG7fg0BBoR14jP9n77zDmrzaP/45WYSNTFEQHIgD99571tYOrVU7bO16O+z8db0db/devm21tna8HWqtbbW12uWoW1EUB4KioDIF2YSR5Pz+CCIxCRAMBGw+15VLc/KMm+R5znPuc+77e8MU4Lmq19SqtrerXi5c1IuWFLq/B4gSQrTH5KTfAMy5aJtTwDjgCyFEV0yO/llgDfCtEOIdTGJ8UYDd6ktKpYL5/57OvMenUVlpaPSQ/cbk7tGDmNE/hsPp2UQGtOJYaQ7fb7AszVfQTUnHdA/GTOjucBvUauvzTAqFAqUVYZ2BPSNZ+e5tZOYU4uvtjkc9wsB7tAvFS6tp8gf2iKhINv3fnexNTcNb60bPsPrljqttTBqp6phMUqmU6Pq4AeYhjJW+gmQKCOOC9sGQzu2ITbYcIA+Ntn9VpDaGTOrJkEk90ZWUo/XQOEwoz8vDjVcfuor8wlIq9AaC/Zu2lv15ItoH0q8Z1zq3xZDOEew6ZlmCa4iDf/+6UKkUqHRGlDojUgHCaErLUKnrP3G6KvkQ5QbzVTejlCxL2s+AkPqnGj170xTuLR7FvhNnmPjSpVXkaAh+Ad48vWQ+FWWVCIVArVFRvPJPVu4w75OFgMFRTR8e7miuvLY/v/9ygIoaJWo9PN2YfKXlxG2H8EAGXt2RnfHmOga1/c62+svz7WO7dmTT/93BvtQ0/Dy0xLRtfG2PxkClUjJ95gCWfWEe8TVkZGdC21pqbvQZHs2iP59AV1KOm7sahYNKzjoCW/d9zXY/Dy3f3DGLw+lZ5JXo6BvR1iI6o758k2SpIyPVoPc1os5T0rVtMP5edZf5c+GiMZBSbgYQQvhQw1eTUrpySlzYRfPp5etASqkH7gN+AxIwqesfFkK8IIS4qmqzR4A7hBAHMClVzpMmDmNa6T8CrAfutVdxvyZKlbJFO/nnCfTyZFTn9kQE+FFUYT3HTeWt4u1FN+Pp5fiasm3b+hPTw3KQNmJkNB42SicJIQgN8q2Xkw/g4abm2ZnjzXJaA7yb5uGtrQoprK+TDzBocCf8/Cztmzy57nJhwt367VxcaT7JcePIPvRpbx4WfP3QngyMCqcxcPd0axQ1fD8fD6c4+WqVgknTevP6f2+yOiHV3Jk9vDf9OphLlFw3OMbhEz11MXFiDxQKU7a1osrJFwImTar/CmNhpfV+q6jS/om9AC8PJvRs/DSs2tBo1ag1pjHdPZOH0CHEv/ozIeDeyUObXDSxMWjfMZjXF95I34EdCAj0ZtCwKN768Cabehf2/s79+kUSHGyZzjZ58oVry0OjZnhUZIt18s9zyx2jufP+8bSLDCQk1I+Zc4fw5PPX1rqPu6dbs3LyASZOjLHQWTH1B5bPvu5tQhgeFdlgJx+wOeaRCvBxd+PpGWMbfGwXLi4VIcSdQohMIB6IBfZW/evChV2IKkHHpj2padQ/F+ggpXxBCNEOaC2ldG6No4vo37+/jI1tmvtKbzTy18lkEnNz6BYUzNjIDg0WpWoI58pKGfL9IovVsZui+/Di4Ik29rp0cnOLeevNtezZcwKFQsHIkdE8+NBkvBw8sZBTWMLmIyfwcNMwunsHPNw0e6WU/eu7f1NeC0lJmbz7zjqSkjJxd9dw5ZV9uP2O0XU6lU9sX8fyY+aqxO4qNTtn3oOvxvz7lFKyI+kUp3Ly6R0ZSpe2tefcNxUn8/P4LfkYbioV0zpFE+TZ+KHxQohmey00FlJKdh07TcrZPHpFhNI1zP7fX2808seJ4xw7l0tMcAijI9rb3Wf9+echln66mezsQgIDvZl/+ygmTqy/o789I5U5vy+3aH9z2FRmdmpYSHJzuh70BiNbEk5Wq+5HBLV8J78h7Mg8xezfllm01/Y7nzx5lhcW/kx86VnUCgVXd+vGo/+aXGd0VE2a07VgjYPZWWxKPUmAuwfToqLxcWucMr9NxYa/DvPJJ5vIzi4kIMCL224bxeQpdU9yN4Q39m3mo4M7zdoUCJ6LGseMvj3wvGgxoblfCy6aFnuvhwYc/xgwRErpnHIwLi4bnBW6/xFgxFQ+4gWgCFgFDHCSPU5FV1nJzWu+JzYjvbptWFg7PrvyWqs1yhsDf60H7wy/gse3r6teAR4UEs6jfUc26nkDArx49bVZFBeXoVQqcG+kSIlAH0+uG9w8chHronPn1ixafCsFBaW4u2vQaOp3mz7RbzTHC3KJzTbJT3ir3Xhr+FQLJx9MkRFDoyOafBW3NlYlHOaxDb9hrJp8fGvnVpZecQ2Dwxon0uCfjBCCwZ3bMbhzw8LASysrmfvTSvZnXRB6HNkukk+vuNouRfjx42MYO7Y7hYU6vL21dkdIDA2NYEGvoSw6uJNKoxEBzIrqyXUdL62EZnNBpVQwJqblpYc4miGt29n9O++vOMuWTvkYpKn//J/qOCMz0xga1vJTHwDe3rmND2IvOKrv7d7O8muup0Mr/1r2at6MHded0WO6Nbg/sIf7egzhyLlsNqWZBCDdVWqeHzie66MaZ2LBhQs7SQYsy3y5cGEnznL0B0kp+woh4gCklHlCiJYfC99Alh85aObkA2w7c4rViQnM7NZ0A9YrIrswum0HYrPPEOjuSXf/xlMxvxhHr+BfDvj62pdi4OfmzvdTbuRQbhbnykrpH9wWD3XLuK10lZU8v2VjtZMPJmfyP1s2sH72LU60zIU1vjl0wMzJB/j7VAq/HE/kmuhudh1LoRBW01Xqy8O9R3Bj5z4cPpdFJ98Awr2tq/m7aNnY8zuX6St5/u+NGGr0Jzq9nuc2/8Ufc29tCnMbldSCfD6MNV+NPltawps7trJo6lU29moZXGp/UF881Bq+GD+TxLyzZJQW0TeoLT6alh0R4eKy4klguxBiFzXEl6SUC5xnkouWiLMc/UohhBJTyVKEEEGYVvhbNJWVBrZsTyI9s4CeMWH07F4/MajYDOsFAHann2lSRx/AU61hVNtLV0Z3BpmlReiNBsK8/nkD/dLScjZuSaSgUMfgAR2IiWxvdbvCijLOlpUQ6dUKZTPK0Tyae9ZqzmRibg4FZWX4ai+PiaDy8krWbT1ESk4e43tFE9Olbd07NUP2pFtXO9+Tnma3o+8Igj28CPZwfGWQpqLSaOBUUT7BHl54q1ums1FQqGPjlqOUl1cyfEgUbUMdn2ZQ3985MTeXgnJL1f3jeec4pyvF3715iqyllxRilJIwr9pLTMamp2Et6XJPRv2rTTQHUk7lsmP3cby9tIwe0QUvGxUEGpPoVkFEt2rasoguXNSDj4ENwEEuA//IhfNwlqO/EPgRCBZCvAzMAJ52ki0OoaBQxwOPL+Nk6oV0mikTevDEQ1Pq3Dfcx/pDPdzXvnrS/1Ryykp4aNsatmamANArIJT3h00nwvufkc966kwuDzy+nHN5prJbH3++mbtuHcWcmYOqtzFKyYt7/+TbY3FUGA2Eenjz4oBJjAuLcpbZZrTx8kEphNkKHIC/1h1PTcuISqiLrJwCpi9dSlZoOSjhg793MX1bZ96ef52zTbObdj7WJ9PCfSwF0FzUzq+njvJ87B9k64rRKlXMi+7PY71HN4qAZWNx5Gg6//fMSopLTJN1iz/bzKMLJnHFROeEQbfx9kalUKA3mo+P/dy0eDfDVdtsXTEPblvDjqxUAPoFtuW9YdNtOvy2xgZhNsYSzZGVP8XywZIN1e8/+XIL7746iw6RLqfbhQtALaVs+hIwLi47nLKkJ6X8BngMeBXIAK6WUq50hi2OYtn3u8ycfIB1fxwkLv5UnfveGNMbXzfzFcsAdw9md3PlitWHuzf+UO3kAxzIzeDeLT86z6AmZvFnm6ud/PN8+uXfZOcUVb//MjGWLxJjqTCaik1klBZxz5YfydY1be10W4R4eTGjq2X0SrinL8Xl1tWRWxqP/LiarDCTkw9g8IQfVUnsPpriVLsaws09e1s4TEEenlzfrel1MPalpTN72Qq6v/s+V335NX8dT25yGxpKalEeD2xdXX0flhn0LD6yk5Un4uvYs/E5mn2WeStX0f3d95n02ResPmK9zjnAwsV/VTv5AEaj5IOPN1Ba6px7N8jDk+ut9Cd39R1gl4ZEU/Hojl+qnXyAvTlpPLBttc3tB7YJY2Ab84hBAdzTb5D1HRzIF3v3MXrJp/R4byH3rv6ZtIJCu49xLq+Ejz/bbNaWX1DKoqWbHGSlCxctnnVVyvuhQgj/8y9nG+Wi5dGkjv5FF2s2phJ43wJZLf0C3h9vWZca4MBB6+01CfPxYdWM2czs2p2ewSHc0K0HP8yY3SSK4y2dg1mZ7M21DFc8nJfF8YJ/hljpfiuTSQaj5ODhC9/LTymHLbapMBpYd+poo9pmDy+PHs+Q1uFgAAwgKuDQmSzuW/OLs01zCPsNWRZtUgUrDlrWc27utPP144cZs7mui6nPmtO9Jz/MmE1AE4dEnyko4JaV37PnTBrlegNHsrO556c1xKWn171zM+DXU0fRS8uozNUnLe/XpqSgrIybvlvJ1pRUyvUGknPP8cjadWxIPmGxbXl5JQlJGRbtpboKkpItr/mm4oVR43huxBj6h7ZlaFg73pkwhbv7DXSaPbbILStlS8ZJi/Z9OWmcLs63ud9n067hgQFD6BXSmrGRHfjyquuY2KFTY5rK13H7eWnDJs4UFKKr1PNb0jFu/u57i8iJujiUkEal3rLCsbVnmQsX/1BmU5Wnj6m0nqu8nosG0dSh+3sx5eVbi0mUQMtMDgdah/hYHeyEWKnla42Orfx5Y9xkR5t12bP6yBGbn7Wk0NdLoXWIL8knz1q017z2bH0TTVnCsS4kcDgtG2WFuU07T53meG4unQICnGOYg1CrlYDeot3X273pjXEAnfwDeGu8c/usVYcOo6s0/04NUrJsfzx92rRxklX1x9bd5+y+65eERPJ0ljnuX8ftZ2xH88e0RqPCv5WnRVSREBAS5LxUDqVCwbxefZnXq6/TbKgPouplLee+tqvAU6PhwUFDeXDQ0MYxzApfxx2waEvNz2fLyRTGdKz/8K11iPUUg/qOl1y4uNyRUloXWnLhwk6a1NG/nC/cWdcOZOuO42az1G1D/RgzoosTrWoYCXtP8v2Hv5OZmkv3gR254cHJ+Nt4MDub0go9lCnA/aJcTIU7HX1atmNYX+ZeP5gXXv/ZrK13z3Biul4QerumfQwHcs0norRKFZPDL/36zDqdy4qFv5G4L4WwjsHMvH8SnXrYXxJPbzBQWlFh9bMCK05HS+OayO58nrnPrE1hEPxr5HAnWdTyKSizfl0U2JnucTY9jxXvrych1nJltTG5IqIr78ZvqU6pOc817Z1bHtCakB1Y/76FEMyZOcgs3xpgzMguhLZuHsKoB7Ym8sPHf5GTkU/vYdHMemASPv7NQ7zRX+vBqDYd2JRuHi0xICjcqcKymak5rFj4G0n7UwnrFMKsBZNs32822m3RuWMIA/u1Z/de8/tt7vWDG2yvCxeXG0KIGKAbUJ3bK6X8n/MsctEScZYYH0KIa4HhmCayt0gpf3KWLbYw6E9QUvgy7p53olDWLhDTNTqU916/geWrdpORWUCP7mHcNGswWq26iax1DIn7UnjsmnfRV5hWyU4cPsOeDYdZtPFptB7NTxRtbMeOrDh4EKPQg5vRtARSLljQd4SzTbtkjMY8yoo/obJiD0pVBFrPO1GpO1tsN25UV7RaNT+s2UdBYSlDBnRk9kzzXM2bO/cjraSQr5L2UmbQ087LjxcGTCLI/dLSQ4rySnjkqrfJzTCFmJ44fIZdfxzi/XWPEdHFvhVVrVrN0Ih2bEs1D98M9PCgZ2jrS7KzOfD02Ink/F7Kr9mJGBQSP6Mbrw2bSpCHK0WnoYzt2JH/7bNMfbh41bk2Sop0PDv3ZQaM3csNC87x0Ya693EU4V5+/Hf41Twf+wfppYV4qjTM7zqA6zo0vdZBTcZ26MA7W7ZZtI/r2NHq9jOv7o+frzupKZ/TrdM+WrXyJiKyeUxyx205ytM3fIDRYJoMPnHoDPv+TuCDP55EqXJsvr40FlCYOw8w4OY+HTePGfXa780h03h0+y/8nXECCQwJieCdoVc61DZ7KMgt5pGr3uZcVgFg6td3/3GIga+NZm2JuQaGWqlkRGSk3ed48d9Xs3zVbrbtMqnuXz2tDyOHWj7fXLj4JyKEeA4YjcnR/xWYAmwFXI6+C7twiqMvhPgI6IQpRx/gbiHEBCnlvc6wxxZSFlNWvIgK3Vp8g9ajUNQeVhbTtS0vPX1NE1nXOPyw+M9qJ/88mak5bFmzlwk3DHGSVbYZ36kjN/fuw9dx+zEqTMGPM7vHcEszD9esCyl1FOZci0F/DAB9xQ7KdT/jG7gGlTraYvthgzoxbJDt/EwhBE/1HcsDPYaTV15KW09fh4QH/7lyV7WTf55yXQWrl25iwZtz7D7e8xPGMf/7H0nNNx3TV+vGO9OmNksBLXtRCMHCSdfwSmU5+eU6h/0G/2SGR0Zw+4B+fBa7D2NVxYarunZhRkz3eh/j75+28q+XfqFte+cIU04M78y4tp1ILy0kUOuJu8r5k8NdgoN4bOQI3tm6rTr/elT7SG7rb7tfHdpvDX2jl1e/1xXuRcjTuHs797G+8oPfq53886QkpLP7z0MMmdzLoecyGFKpLNcBUFm+Eb3+GJ4+T9a5X6DWky/GziKnrAQpJUHuzo02+GPFjmon/zxlpeWExxXTs1sI8Zkm7QWtSsVLE8cT4Gm/NodWq2be3GHMmzvMITa7cHGZMQPoBcRJKW8VQoQAXzvZJhctEGet6I8FukppGpkJIb4EnKs+ZAUFAi0qyg2nKC/9Hnev25xtEgA52YWsXRVLZno+3Xu3Y+K03mjcHPNTZp05Z739dK5Djt8YPDd+LPP69eXo2bNEBQbQwb9F6zoCUK77pdrJr0aWUFb8MV6t3mnwcT3VGjzVdUdmyPJtyLJfQWgQ7tcg1NYrQGRbuV6kSsmeuFO88dyPDFXYWoEAACAASURBVBremZHju9XboY1s1Yrf589j1+kzlOn1DGkXjrva+Y6PGYYzSN3PoJ2KEPZPQHip3fBqobXSmyMLBgwm8JQk9uRpuoQGM2/YcJSK+uvMqt3+dJqTfx6lQkF4A8O0k46k8/vPcZSV6RkxriuDhjtmVfTOQQO4untX4tIzCPP1pXtIsM1tjcYCyoo/tWjXFX+E1ms+QmiRlUeQulUgdQjtZITbSIfYWRfW+ihonGeaAoGH0GBEUi71lBV/irvXPSgU9Ut9C9ReiO4x6I38tS6e/XtO4h/kxRXX9Cc0rGlKxmaftv6dFZ7K54dX72VfWjq5paUMDA/DV6u1uq0LFy4uCZ2U0iiE0AshfDAJmNufE+niH4+zHP3jQDvgfD2Z8Kq2ZoUCgbtCg0aqqNDbFn1rStJOn+Oh25ZSkF8KwIb1B9n02yFeX3QzSuWlF1HoMbgTiftSLNuHNI9667aIaOVHRKvmkQ/qCIx6S3VrMKWTNDayeBGy+N0L70uXge9bCPdpFtvGDO7ETzVyc6WbGhnUiuyCcv76NZ6/fo1n364+PPT0VfU+v1KhYGhEu0v7IxoTYz6y4BEo34Dwe7fu7V00GmVllTxyx+ecOGZaYTzJCWLXHGHhF3fgH1i/VdH2XSob08RGZctfR3jlqe8xGk3RDH/8sp85t43gln+Ndcjxg728mNS57r7faEgHLHURpCzAaDyHovIgMv9BTCU1QOq+R3rei8L7AYfYWRs9BkeRlpxt0d6zEcLEFQjchGlY5YaKIlmG0ZBeb0e/Ji88/h07/06sfr92VSxvfjyPTtGhDrPXFj2GRPHz55uttgP0bdv8hS5duGjhxAoh/IBPMAmZFwM7nGuSi5ZIk5bXq4E3kCCE2CSE2AQcAXyEEGuEEGucZJNNlEKBxlj7ik9RoY59O5PJsLF64Ci++3JrtZN/noNxqezakuSQ48+8byIRFw0kxl8/mF7DLcPFXTQeKk0/u9odhTQWIosXXdRqRBa9hbRSBmzI5J4Mu6LPhf19vUxy2zVYvzqOUymNV+pQSklC/GkO7UvFYLCvzNMlUbYWWXmw6c7nwoINv8ZXO/nnOZtVyE8rdtX7GGFREx1tFgAnj2VyYPcJyssaZyJBSsnSD/6sdvLPs/Kr7eRfpIDf2ChVHRAKy9VmhTIMhaI1sugtzjv51ZR8gjQ27vMSYO6jV9A6ItCs7eo7x9Khe5iNPRyDQgi0Ci+UKvuLCR2MSzVz8gFKSyr45tO/HWVerQy9ojdDppinNUT3jWTarY0fhZF7toh9O5PJzS5s9HO5cNFckVLeI6XMl1IuBiYAt0gpb3W2XS5aHs5a0X/WSedtMEorZbHOs/rbnXz2/u+Ul1UihGD0lB48+uK1Dhf6ATiRZL0u8YljmQwdfeniRz7+Xvz3jyfZ/ut+Mk/l0H1QJ2Jqyf120Tio3cai1k6ksuz36jaFMgJ3r7sb98T6k4AVBWVjOsgCEOaDeYVCwdNL7yBuy1GS4lL4ankslZWW9ZFPHsuiXWSgRfulkn4ql/888A2nTpjKCwa38eO5d+fQsUvjr3oBUJkAaucKp/2TSU7KtNp+4pj1dmtotBNRu42nsvxPh9hUVKjjxYeWEV+l4O/t684jL1zLYAf0zzXRlVaQcSbPor2y0sDpkzn4tWo6kUch3PD0eY7i/IeB85Ntajx9/gNSB4ZUK3tVgD4ZNI2bahUY6sfiTU+z9Zc4cjPz6TU8mug+kY16zvNoFCEIYX+azsWTV9XtNq53R6NUKnjmszvZvyWRpP0phHVqzeCJPRplTFOTzxf+wfdfbsWgN6JUKZg+ezB3PjqlUc/pwoWjEUKEYxLNC8EkOL5ESvm+EMIfWAFEAinA9VJKy07cdIxhwH4pZQkm4fK+Qoj3pZTWOlMXLmziFEdfSrkZoCrvRFWjvdbpfSHEZOB9QAl8KqV87aLP3wXGVL31AIKllH5CiN7AIsAH07LCy1LKFXYZrbY+SDt5LJNFr6+t+bex8dd4OndvyzU3Or6+bYfOISQlpFu2RzlOlVytUTHq6v4OO97ljtFYTlrhZ+TqNqBS+NLGey7+HmPq3rEWhFDg3eoTKsv+rFLdj8TN/WqEopFFmlSRgBsWYbiK1iBsh5/2GdGFPiO6sCs+ncMHTlt83j4qxKFmnuftZ3+sdvIBstPzefXx7/jkpwVNI3SnckW6OBNbVU0UtVYgN0cIBW4+r5Kfp8SgPwCkXZJNn733e7WTD1BUoOP1J1fy9e//h6e34/KZ3T00hIa1snD21Wol4e0dP6lWF24eM1Cqe1KhWwNCiV7ZheSiXynL+5huCm/UFF20hxqU9q92N8g2dw3jLqpE0hQITcPO2cFGf9mhs2Orj5Tp0zlTsJii8kN4qDsR7nsnHhrTxL4Qgj4ju9BnZNNUT9i9JYkVSy9ELBj0Rn74ajvderdj+Pj6i2u6cNEM0AOPSCn3CSG8gb1CiD+AecBfUsrXhBBPAE8Aj9s4xiKglxCiF/AI8CmmyYNRjW69i8sKp4TuCyHuFEJkAvFALKb8k9g69lECH2IqMdENmC2E6FZzGynlQ1LK3lLK3sB/gR+qPioFbpZSdgcmA+9V5b7UiwqpwOBWQ00/50IY8vYNCVb32faX7Zz+LF0RHyfs4I0DG4k9a+kU1cb1twzHx9fdrK1HnwgGjXCVpXEWCWfvIyX/bYrK48jTbSLx6G1kF1+oFllu0PPDyYO8EvcXP6UcotxgOzqkJkIo0bhPwtP3abSeNza+kw8IhS/C666LWxHeDyNE3d3FzXePQa0xX/WZPL1Po6zm558r4XCc5eT2mZQcM+e/0XCbjNA4VrW7JVGmr+T7Ewd4Je4v1qQeptJoGcnR2JSXlsNFoetIibGifvcYgMFYQnzWHFJLN3OmIh/VJU6AW3sm6Eor2LujfjI08ecyeOvAJj48vI20kgKb2wkhmH/feBQK80mNmTcNbdzV/BzbaTgqdWc8fB7FoBlHfPYj5JT+SnHFQZIrCpAX/Ux43oZQBjSenc5GWOtL60ePPhEMHmk+iejhqWHu7Q0Mnbfym1UaznEgYyYZRd9SXBFPdskP7M+Yga7SOQuG2zdYHzPVNpZq8LmyUnht/wY+PbqL3LKmTXNxcfkjpcyQUu6r+n8RkAC0BaYDX1Zt9iVwdS2H0VcJlk8HPpBSfogp7dmFC7twVuj+/wExUkp7EncHAsellCcAhBDLMd0Atp4Cs4HnAKSU1QnsUsp0IUQ2EATk29gXgErgVKWRTIORcN1m2mo6UqIvQHv/PSiXfQeA1t26grm7p/VwvQO56dy88VuK9RUAfJywg/u7D+fBHvV7gLcN92fRt3ezdlUsGWn59OjbjglX9K6XEF+ZoYwVp1exK3cPSqFkZNAwrml7FSqFsy6D+mM0Sn798yCbdyShdVNz5cSeDOzbvkltyK/IZ9mplezPj8dL5cWEkLEMDwjjnG6j2XYdn8/h1EeLCPa6mlJ9BXM2fMPBcxnVn391LJZvxsxF6+AyWnq9gdXrD7B9TzI+XlquntqHXg3IQxVe94E6Bqlbd0F1X1O/coW9+7fnw6/uYt1Peyks0FWr7jcGao0SpUqBQW+Zl2/rvnQICl+E72ugrb/A4OVGUWU5N/z1FUfzL4icfXt8H1+Ono2bsun6Ey8fd6TRiN5bg1GjRFQaURWW4+NX/3Jf2SU/U6a/MOEaeYnKxu4eGgqs5Mh72Hgm1OSzxN28HHchheCjI9v4dOT1DAmJtLr9iHHdeP/z222q7udXFLD81Eri8uPxUnkyPmQMU0IbpkkgpaRYn4fn/fehWLa81m3PFHyCpKL6fa4R4iuMdPYcgrsqFKGdhNCOa5AdjcmmbYn8tvEIEsmEkV3tP4CyDWivAGVbhMcchNJ+0brUklOsOL2K9BuOE32NN94H2tHeGGVVdV9KyfoNh9m4LRG1SsnU8TEMG2iealdhLEPcfzfqZd+btWcVr6LCYJ4iYJDFpBd+RceAp+22+1Kx1WfX576xhxf2/c6XSRfWlRYf2c63426ks2+QQ8/jwgWAECIS6APsAkKklOcHg5mYQvttUSSEeBK4ERgpTCstzawEkYuWgLM8vGRMq+z20Baoufx9BrAaFyeEiADaAxusfDYQ0FTZUCvlEk5XLVIlF+xjfU4ipQmx3L98A7/f829GD3uWMVN78vWiDehKK8z2nXqd9dD31/dvqHbyz/PRkW3M6tibUA+fukwCIDDYp0GqyouOf8q+/P3V79ek/4rOUMbNkfbXO29q3ln8B2vWH6h+v3l7Eo/fP5krJjRdfvQbR9/ltM4U1ltq0PHNqRXkFofRrkbX636iguA1xaQ8mISxjZEVyfvNnHyA/bnprDoZz9woxwrrPf/WL2zefkGUceO2RF58YjojBttfMUG4jUa4jW6QHREdgrj74ckN2tcePL20jJrUgw1rD5i19xvaiZA2jViBQRmOcL+28Y7fAvj2+D4zJx9gz9nT/Jx6mBkd7ItyyNGVkJSXS5RfAEEe9q1EDxvfnc9/3YtUVU10asHgoWLA2PqHGxeUX3gUuJ+owBcuKWl8ynX9+XzhH2Ztrdu2os+g2sPU88t1vB2/yaytzKDnlbi/+HnyfJv7de7Whs7dLB1KKSVvJr7HqVLTY7PUUMq3p0wT1PY4++UGPRvS/iahdAXq48e5f/kGdiwYwODBD9tMj6k5cXKeYgm5qkG087uv3uduSr5euZMlX22pfr99d51DBEsU/iguoRJHQWUhrya8RYnBNDwqU5eT3z+XuV3GEuprKXb44Wcb+W713ur3W3Ye44E7x3HdtL4YpIH1GZ+SGvcj9yz/jWXz5zF62LOEupuuQ52V3wigTH/K7H1+mY6Ec2eJ9GlFqFfjLShOvLovP6/YZTZxq1AqmHSN456TxwtyzJx8gLwKHe/Eb2bxiBmXfHwhxJ3AnQDt2jXjyjEuHEGgEKLmxbRESrmk5gZCCC9gFfCglLKwZn8ppZRCiIvjnGoyC5gDzJdSZgoh2gFvOs58F/8UnKW6/ySwXQjxsRBi4fmXA49/A/C9lNIsllQIEQp8BdwqrUmIU51WECuEiM3PNYBRoigxEpd2hKzcQ3Rfb3L0VCtW8VfyIvy1glfenkF0N5P4V1BrXxY8cxVDxlhfDYjLtcz/NEhJ/EXOoKPJKc8lLv+ARfvms1upMFZY2aP5kJ1TxC+/x1u0f75sG9IiHhR+/3oLT01/k39f8xYbv2t4NZKa10J6dka1k292roxUZBEoSowoSowE/mKqzuC9Wsee1O84fPo4HmXlCKP55bbPynVwKSSnnDVz8sEUBfHFsu0OPU9zoqKsgrZ+Gvw9VCiESUBqzNSePPHaTIefq+a1cPZsE6QF1ELywVO8eecSHpn4Mp/9ZyWF55q+Bvy+HOvXr7X+rTbe2rOFId8sZs4vKxj67WJe22VZ0qs2DhxLv+Dkn0ep4HCqZTk1qxiNnDyrs7h/66K262HmrcOZe9dovH3dEULQb2gnXll8S51CZgn5WZRZSes5kp9V73SfmiQWHat28mvye5bF/LdVtq2JZf5dbzLyv8+zO+1dygrPVD//dN98Qtzp1VBcbHpd1L/5aK1PdPu4NU/tl/LySr5ZVf9KDTVxZN+wPWdntZN/Honkz6yNFtvmF5Tyw9o4i/avlm9DX1DItpP/48CZNXRZlwJAyE87WXHkaSoLz0FxMb5q61FavtqB1f//JH4Pg75ZzOxfVjBs2cc8teV3jFaeuY6gQ+fWPPvOHNp1MK2sh0UG8tQb19O5e9ta90tLzuL9BV/w8ISXWfR/X3M2zbbU034b/ZO9/ZYtpJRLpJT9pZT9g4JcEQKXOTnnf+uq18VOvhqTk/+NlPJ8GnFWlR9y3h+x+aCSUmZKKd+RUm6pen9KSvm/xvlTXFzOOGtF/2NMq+0HuSDRWxdpmIdUhmFbNekG4N6aDVXCf2uBf0spd9o6SdXNugSgS0+tRILbQgP3L92EwnDhATf2w0T48CGk8lG6PvMM73/9NHojqNS1D+Yivf1JKrAcDLT3blzl4RJ9CRLLB3SFsYJyQwUaRSOGOl8iaRl5FiWkwDQBUF6hR+t2YUl96bPf8d07F8QRY/84SEbKWeY8Zn+Ydc1roWvvblZHNxUoMbyrZMT/khE1ppW6vp8F789mgFJB2NWj+fDaMWbfvqN/79M2Bjen060Kul4WPD97IbF/XChvJxSCES9Mx9u3/mHb9aXmtdC/f//GGenWg+MHUnl4/EuU60yTc4e2J7FzbRwfbH0ejVvTRfXZun4j7biuN546wQdxF7riSqORxQd20y+kLRMi61fp45St6z6tnte9lAR+tIV+i06isENioLbrQaFQcNM947jxX2MxGoz1ViqP8GqFQggLR6qNh0+D0iGK9dYnLYor657MWL34D/77729JeSaK53b9ydxHd1l5/l0DSiU88ww8bR7qHe57N3mlm9DpU6rbgj2vxs99sN1/R1OQm1dCSWnDJrwd2TfY/M2stKdnFaC3kraUX1CK/rXXGf7mq4y86Dcb+2EiUvkdPPMMgf9+nGzdGvJ0FwTwvDQ9CfWeDcD+7Axe3rmp+jOjlHybcIBeQa2Z1aVnQ//EWhk0KppBo6LRVxrqHEsBZJ3K4YExz1N0zpQqc3hHElvX7OWjbS/gG2gZfWCrf2rs8ZeLfxbCtHS/FEiQUr5T46M1wC3Aa1X/rnaCeS7+YThrRV8tpXxYSvm5lPLL86869tkDRAkh2gshNJic+TUXbySE6AK0AnbUaNMAPwL/k1J+f/E+tsgr8+CtA1O5b8yNfPnpUApam4vgFbR2R274C557DpTKej2YFsQMt9CDnhrexWp+WMLJLDbFHiO34NLFYsI9wgiwUsaok1cHvNWNL/J2KXRqH4ybxnKg2zEyyMzJL84v4aePfrfY7vv311U7Rg3FXam1OhlyrtSbV66axorP+lMcav55UagXBet/ZfmNV2FUXLjVQty9uKFjn4sPdUl0iWptIcoF0LVzE5WZa2KOxiabOfkA0ihZ9saFLiG/SMfmvcc5nOzYaJmi0nIKinUOPWZ9WfnuWotrOTUhja2ra9UydTg3R/XD3828P2zj4cPM9vUP219/Mslq+zob7dboHm09B7pbfa97pZJTj83iy0+HUtTasbnAQgi7ypG18fRlhpXvb0HMiAadv6tPF6t9Vp9WtTtpBoORZW/8TGm0F3oPNd/OHmz1+Vcc6g0bN1Y//2pSVuZFSdb7uOufop3fAnqEfEV00FsN+juaguAgHwL9nf8c7O1n/f7pbeU3iwwPwMNKXnubtgG4vfIS330+2eqYJeXnhfDccyhUWroHf0q34CW081tAl6CF9ApdgVJhSp9xxP3ZUOozlgL44r/ryNWqMLpfGAfkpJ3jt6/+trp9/6BwhoeYa/uohIJ7uw1ruLEuXFgyDLgJGCuE2F/1morJwZ8ghDgGjK9678JFo+IsR39dVbhbqBDC//yrth2klHrgPuA3TAqW30kpDwshXhBC1FyuvQFYLs1juq8HRgLzatx0vesysqDCkwM57cgq9OVwjzbsmmP+gDg1fzyKkfZVupgS3pWvx8xlanhXhoe057m+E3lnyHSzbcrKK1nwxirmPfcNjy/8mase/IRl6/fZPObpklyWp+xgXfoBygyVVrdRCAV3dZyPl+pCDqy/phW3tb/FLvudgbeXljtuMh/sajQq7ptvXsIuJz2PijLLv7+koJSCnItLO9mHQii4vf0tZgPnEp2GE+lBZOq8OdQ7jO2zzVchs++cTqvxk/hhwjzmdR7A4OAI5kcPZNWEeQRqHauK3TrYl7nXmUtWeHpouHteA1WamznpJ6xHvKUlm8SlftoYz5UPLuGx99dw2/PLuPvlFRTryq3uYy9nsvK58oElrN1y2CHHswdbf3f68aapr32eNp6+/DDhVm6K6sfg4Ahu7zKIVRPm4XeR818b7jbEKD3U9Y9MGDu8C71jzLXz2rcL5Npp9ROPBBgRNI3U/kHsmNOx3vs0Fi8PmMLLA6YwonUHJoVF89moWcy0oXlQZqhkffoBlqfs4HRJrsXnnioP7ugwz6zPCnNvy+x219dqQ1lxGXnZBYgK02px6rkATvQNtHj+5dx1HYywnIRYvz2BaQ8s4bH3f+PeF/J5/r1AhL7Ox61TUSkV3Hf7GJQ1JkutTZzWh6TCDL49uY0NmYfR21mJIsq7I9PbTEPUWA7o7deTCSGWmjwe7hruvmUkNWUS1Col988fgxCCVuNnWvxme2+Mps3EW6vfC6EgwGMsEX4LCPKcikJcuPds3p8OFpFtCHq9gSf++zM/ZJ2ldHAkhZO6out6QdcsPTnL5r5LRs7kid5jGRoSyfSI7iwfdxMjQpumzKOLfwZSyq1SSiGl7Hm+EpiU8lcpZa6UcpyUMkpKOd5aSXEhxF9V/77e9Ja7uBwR1nKcG/2kQpy00iyllM2qtw3t0lm6PXwHAP6exax6/UPCjuWz/6ow+qxOQ/Tth3K7zSyABrPkh+0s/cn8uELA8lfnEdnGfD7k25TtvJPwa3VYfojWl48Hzifc03rZonJDOYcKjqBSqOju07VFKO6fJ+FYBn9vP4bWTcWE0d1o09pccK2yQs/czg9aOPUh7QL54tCbKGqsqgsh9kop650w2r9/fxkbG0uJvoS/Mnbzwtat5Jao8QkuQaU2EqwtYvGD39AmqYADV4XTZ3Uain79EduaNkf+wOEzbN99HG9vdyaP6U5ggPNXqRqDzJSz3Nrz/yxSOgZO6sW9H9/OtY8uxXDRZ3Mm9+OBOZYTc/ZeC54B4TLmiodQKRWsefcOAvwasZTZRSx67BurUSuv/vwYfce0rFrTh3KymP7jVxhqPIOUQvDj1TfSM6j+9cL1egObtyeRcCyTyPAAxo/qahbpUx+eP/gG8256nTaJBUSXGc6ekDK4vvue7xuaktMludy1eylZZabyewLBw12mMKe95cpkib6EwwUJeKm86OLTGUU9ymTeOeApUhLTOPlkZyoDNLTzz+GrFz+lTVIB+68Mp+8a6/1bXmEpVz30CRWV5g7u9NE9eOq2CZfwFzcNKadz2bAlAaMRxo6IpmNksF19Q3hMlAx+88JESkevED4eNJ9WGvv6iJzyXI4XJ9NaG0KkZ0St2yYlZ7FpWyJqtZIJo7oR1sYk2ldhLKdwQGd8j6RVjVnOoO/dA+1Oy7x+a5wuzGf8ys8t9CH+N3UGI8OatuLNxaz4fR/vfL3Jot1r0zFU50p5ZPHtTLyxYZEwtmjomMHF5Ym914Mdxz0C3I4p9H8OmAcBny/b58JFfXHKir6Usr2VV7Ny8gHaeHpzY7feaJUqtGlG1MVenP7rR6K+2oMqNg5lcSmkpzv8vFv2War9Sglb958waztbVsh7R9eZ5d5nlRXwfuJ6m8d2U7rRz78Pvfx6tCgnH6BrVCh33TKSW24YauHkA6g1Ku5+Yy6KGqUGVWqlqU3hmEvdU+XJVeFjeHPYXEKDFKjUplUveQYqCpXcuXAOqkXrUMbuQxQVN8r1URu9uofxr1tHc+OMQZetkw/QOjKImQ9fYdbm7e/Jrc/PYHv8SQsnH2BLXANUtGtBbzCy42CKQ49ZF9c/fAWh7c3TfEZeO5A+oxunhGFjEhMYwkcTptOhSk28vW8rPhh3pV1OPoBKpWTcyK7cN38M0yb2tNvJ35lznB37z1BRYLp/T8KpuvdyLgsTf6t28sEk2PZe4nrOlhVabOup8mRgQH+6+Xapl5MPcPcbc3HTqAlbkoJHYjHlx1WU5Wr43+dv0uWbOJv9286DKRZOPsDWuBMWbc2RyPAAbpsznNtvHE6HCPvF1M6Vm+fSJxdn8XmyfQKTAIFuAQwOGFinkw/QuWMId948kltnD6t28gE0GTkEVvpSvn0T4V9uQrFnH9pSQ72fSeE+fiyddA1d/E3fQ1svH94YNdnpTj7Yvp4qQ33oOaILY64f0sQWuXDhMJ4FnsGkQ/YO8HaNV/PNf3LRbHGapyeEiAG6Adrzbc1NUVIIwUvDJ/CfoeMwpqSguf8pcK8KTY0Jgl27oBEUuH28tNbbL6onG5eXgsFK8YDduY51aFoSY68fQlTvSDav2oVCqWDMzMGEtq/34ly9GdOuA2GpbiRXjeuUBiO3vDuPcjc18fln6R0zotGuDxcmbvvPTAZN7s2e3w7gE+jN2OuH4Bfkw4ld1vPnfTyt31eXgreHY/O66yKgtR+LdrzExpU7yUzJpsewaPpP6GmzzFlzZ1JkFJMioyjTV6J1Ukjwntxks/u3JbDHSh9vkEb2nUthUptLF0rrO6Y7S+NeZ+N3O6goq2Ro7yDaJ/2baI8qocsYP6v9m61nl7eDa6G3JJz2PNbrYdcuvNzd8QLogd3PpOFhkayfMY8yfSVuSlWz6We8bfTl468dxL8fmW6XNoYLF82JKh2x74UQz0gpX3S2PS5aPk5x9IUQzwGjMTn6vwJTgK1As3L0z6NSKKCDlYADd3fWphTx1QdfkH2uiL5dw7l/9kgi2lyaguuMcb2JPWJeFqmVtztjB3Y2awvW+lrdP8RG+z+F8M6h3Pjk1Y16jq1xJ8g6XWKSfQQyQy5859Xfv7s7uGrpNirdB0fRfXCUWduIPh0J9vci+6KyczPGOzZPOMTfm2G9mn51y91Ly9RbRzf5eRsTZzn5YLpfa96/zqKsvJKPVmzl9x1HUSgEU4Z3464ZQ9GoLR/TwVpfiorLrLT7OMye4PAAZj0yzfYGVvq3QTGRhIX4cSYr36zd0fdeS6Ihv8lPG+P5du1ecvKLGRATwYI5I2kbbBnFVisRVqIBGvhMcub9aY1rx/Zkw54kamaeerpruOeuiS4n38VlgZTyxSr9sfNCS5uklL840yYXLRNnrejPAHoBcVLKW4UQIcDXTrLFJiWVZ1ibMgEvdRjRrW6n9abe1gAAIABJREFUtYd5/uOG3Um8uOS36vdb406QkJLJTQ9Hs7cgEbVCQbG+iOTiM/hrfLg2bAzXhI2u87xjBkTxzB2T+N8vu8nMKaRv13AW3DASL3fzVZHerSLo1SqCA3mpZu03t3dsbpoLqDBWcueeVzmty0SFipIEd3THvGAMZgkw7TwCGBNSdxh1dukujuYtoajyFP7aHnT3vxcfjfPFwC4H3DQqPnxiJguXbWb3oVSCWnkxd2p/pg53THh7cEgeU6/aTsJe1312OTClTS8+S/4NgyIPpXBa5UReXPIbf+26oGj+zdpYCot1/PuOSRbb3tJhBM/GmxeQ6enXjj7+kaQUria54FvKDXm09hhON//70KocWz5sQ1Ys355aT7ouh24+7ZnfYTpdfSJRKRV88PgM3vt2EzviU2jlp6XbJA92B+4g8fBBrm47nBg/54d+NxbqGulwoW75jAo8RpTXdram76Sr/90EaOuuSrH278O8tvTP6vebY4+TmJLNyrduRe1yYgHo360dr9w3jaU/7eRUZh4xnUK57/oRBPrZTldbnbaZVWc2kFteSO9Wnbmr4zW087CdJpRaksWq05tJ0+XQ3TeyEf4KFy5sI4R4FRgIfFPV9IAQYqiU8iknmuWiBeIsMb7dUsqBQoi9mFylIkz1Jrs0uTG10KmHh3zjp2gABEpGtFlCoPsFNed/vfQdcUfPmO3jNjYXVXsdIFErjFwc6bYgahZTQoc2yJ7c8mKWndxJYmEm0b6tmR05GI1CxafJG9manYifxoNZEUOYGNqjQcf/J2GvkIpfl9ZyyJJZZm2lqR4UVbhBKz1CQne3drw9/gaC6ljBOVd2kM1ptyG5IHKkUfgxPnyVwwfkLurG3mvhfL+Qk+1LtGYJo/p1rnsnF82WdF0O8/e8BFVaJ+tHLWxy0a3daSf518pvMGqNKDNVKE+4IYwClVLB+kX/wstKisgfGQdZnrqD/IpShgdHc3vHMeTq1rHv7Atm2/lqOjM2bLnDwq73nkvg6UOLzdrclW58OuBp/DXmfd8TB5aw59zR6vcKoeClHvMZFNDVIbY0Nvb2Db379ZEzv3yOxIJ4poX8gkpcqAJjlCo6tFpI34Da88dvfeYbEk5aqsa/smCaRVSfi/rxW+ZO3ktaZtbmr/Fh6YBn0CotSxSmlmRx3973KDVcqNSyYey7LjE+F9U0lhhfjePHA72lNOXnCiGUmBZHLz03y8U/Cmet6McKIfyAT4C9QDE16t43RyQGkguWmTn6hRfV0hatKqqcfFAIaeHkA6zN2NYgR/9ceQlzty4mQ2cSYPo7O5G1Zw7w7Yi7eajLFB7qMsXuY7qoPzUFD8+jDS8lv1jJeVFUUaaq08kHSC5YYebkA1QY8zld/AtRfjc7xF4XjU9gcAEFObsB1+C7JfNp8hqwcn83FfvOpXLP/i/RdzKJ2BnDKjG0qcTtb2/0BiOlugqrjv6E0B5MuGhSd2eGZWBcQUUSZ3V7CPYY6BB712Zss2jTGcrZkBXLjPALZeASClLNnHwAozTyVcrvLcbRtxeVUPLvmKs5lHuKpHzzUq8KoWfFibcoNbzK8GDbfUZhiWVKBkChlVQNF/VjbfpWi7ZzFYXszD3I6OB+Fp+tPL3JzMl34cJJ+AHnS/A5P7/MRYvEWar790gp86WUi4EJwC1Sylvr2s/ZlBnMaxUP62Oet6/wNXferFGity4UVhf/PbCh2sk/T7ounx9OtewZ47ysfBJjkyl3UI3zpkQI87onR9xPUVhR9+9bbrCseQ1Qprfe3lyprKgkaW8yOekWpWCbJftOpvHnweMUlDpuwNwhonnlrv5TOXOugD/ij5GcZf89lFycccnnz8gv4o/4YxxNt1988+OkjVRKc6V6Yxs9hgA9nSOCCA7wrvexyg3W70VbfU5DKNVbv39KDeZ9X3LuGavbpelyHGZLc8XW7+ChLOOjxA217ju0t2Vqg1Ih6NQ+uMHXWFMgpeREfCppxy/9fnI0tpx2W+OxM6XN8zt28Y/iVSBOCPGFEOJLTIuiLzvZJhctEGeJ8Q0D9kspS4DhQF8hxPtSytQ6dnUqIR7mK/G3XDWIg8cyqsP3xTktQgqkkBilQErLVf2hAfaF1ReUlnH3pz9yyPM4ihCJt6YcN6We4go3ygxqjhdlX9Lf5CyMRiMf3LeUXz/9C4PegLe/F/e8dyvjbxxZ985ORmBEq9SjNwoEElnl7ldIA2m6PHw07rXuH+wxhGzdTov2i6+v5sy2n3bz3l0fk3+2EIVCMO7GkTz8yd2orAiHOZu8Eh3/Wvojh06bwmHdVEqemzGeq/pdWs6+NCroEDjKESa6uATe/PlvvtqyD2NVGtrU3tG8OnsyynqW1PRVBZBdkY1KGFArLEvD1UV2YQmTX11aXdJxbPeOvH3jFfXOp0620Yd7hip5erZlfn5thHgM4XTxOrM2gYogd8es5gMMDozhQMExi/Zefp3YmrMVpVByfOkZln/yCyz2B4X5Q7CrT90l41o6IR5DSC1abdGeXBJEcnHtz+w7rhvK0ZNZHDxmcpjVKiV9hkQwd/Hy6mtsdLcOvHPTFWhUzaO/TT6Qwkuz3uFMksnmXqO78/SKh/ALah6LkIMDYlh1xnyCRSkUDAyIsbp9N98IDhaYSviphAGtsu5FHBcuHImUcpkQYhMwoKrpcSllphNNctFCcdZTYhHQSwjRC3gE+BST4n6zHTUHavvRyXeuWZunu4ZFT1/PoeMZZOUW0Tu6LWvO/c3XqX8AAoMUqGqIO/Xyi2JuhH0h9u+v28bB05ko2wk6tzqLl6ai+rOsEi+6+7a9pL/LXnbvT2HNHwcoLatk1KAopo3rgVJpf2DIz4t+5+fFv1e/LzpXzJu3fkjXwVG07RTqSJMdgrfKAylBq6zET6OrnsDx0ZRzPC8QnV6Dl8qNCM+AOo/V0WcW2aU7zJz9Dj6zCPYY1FjmO5TcjDxenv0eleWm0FS9u4a1B1LZe8dixk7oxfXT+tHK18PJVl7g/XVbq518gHK9gedW/smw6EgCvBpmp1EPu98OZuLrXtWVF1w0PduTUvny771mbb/uT2Rgp3BmDKrfpOoVoUNZkrIfH03DoorOFhbjY7zQz284nMyy7Qe4eWTfWva6QHe/tmzKOmrR/t87Z9HZ377SoN0DHiCvPIHiyhTA5OT3Cnwcrarufqm+TAsdQUJhCn+fjQNM4eqTWvfko+R3qDCank+GPhKpLoNVxTDzQkSCt8qD+R2mNui8uXklrPg5lsQTWbQPD2DWlf0JDW4ejuTFtPWcgL/2D86V/VXddqSwNfEFbenrX/sz28dTyyfPzeZAUhq5+SW4eWu467MfzbbZdOQE3247wLxRlmHnTY3RaOQ/175J5skLExgHNh1m4T2f8OzKR51omYkd+05wfIMezyg/SlqZqkG4KTTc0+k6gtysVzKYGT6arWcPUqQ/ja+mzGoapgsXjY2UMgNY42w7XLRsnOXo66WUUggxHfhASrlUCDHfSbbYxEfTkb5Bz+KpDifI3bbmRkynUGKqnNNb/aYwwL8LO3IP46P2YGhADBnlZwnQ+NLRK8xuGzYnmGaVQ3yKzJx8gBDPYnr521ly5xJYt/EQL3+wvvr97v0pHDmWwZP3Trb7WJu/227RZjQY2fL9Tm544ppLsrMxaK0N4JWYe/jwxFsYa7SrFUbCvfNJygvm3ujxeKjqrhetVLgxvM0icnRxFFem4K/t2aIU97ev3lPt5Bs93SgfHQMaFWlFZXz1wy42bk/ks7duxsPdUuTIGWw6csKirdJgYFtiSoNW9QvT1Hw9JoKSTDW7Ru5rEVEolyvWfluAzUdO1NvRD9LQYCffFhuPJNfb0b8neix7c1MoqhESf1VYH3r42/+88FCFMD58JVmlO6gw5BHsMRh3lX2TBXWhUih5sus8boyYQoYuh/aeobyU8Hy1kw+g9BO0esKN7Hn5yO065CAtXaLa89KCBbTS1D8V4TyFxWXc9eQ3ZJ4tBGDvwVP8ufUon75xE62DHFdS0FEIoWB027d4/8gnxOVuJavch/QyP7RKNQu6TKjXMXp1Nk0IfPCb5bMSYNOR5Gbh6CfuSTZz8s+z7ac9VJRXonFzXnrTz3/G8/qiqgWFHd4oAt0YMqIdz9xwLV4q25O8rTTePB8zlxcS/tM0hrpw4cJFI+EsR79ICPEkcBMwQgihAJpdsqtCuBHpY7/TGePX3qyEULhnwwdaPu5asgqK8Q0utvp5ckkSPfwcUzasLj6z4pz/uvEQ82YOsXtlRam2Htaq0jSPUERreGsUGLEM4fPSVLB0yC30D+hk1/EC3fsQ6N7HUeY1Gaoav52+Y2u46Dc7k5nPb5uPcM3k5lE729dDS05RqUW7n0ftKRa2KMtTUiJM3ZW6GV+v/wR8PbR2tVvj98RtUPf8nF342XH+aJ9QVo26jx9P7yO3vJihQZ0YHdLwAjQKoSLUs/FLP4Z7hBDuEcLJkpMU6gstPtd0VSJ8gPhyRHw5ne8KapCTD7B2w8FqJ/88+YU6flgXxz03N9tAQB7odgfbskezOesovhoPpof3JczDvhAgH3db13jD+i9Ho7LxLFeqlCgUzlsKl1Ly+XfmGs/GHA07V2dROsWAVx1BLskllukpLly4cNHScIoYHzALKAduq8o5CQPedJItDiMlO4+V2+LZcvgkBqOx7h3qwZxhprq7FWXWHQpfddOELur1BjKyLQdzUsKZjHy7jzdp3hiLNjd3DaNnNd88dVvftYfSgz7+1mtDG42SrUdOsnJbPCezWoZoXV0Mv3YQnlWh+dLL+mDzVDMS6Js91HLCISLQj6GdG5YrbNSqMHi74Rfkw6Bpzl9R+ydzzYDuuGvM54iVCsH1Q+pfgUiXf2nOyMVaAELADUPrrpdekxB3X+7uPIZ/97iSMa27OqwUXlPgq/ZFYGmvsUQiq7TOhBBMaKBDXlpewZaEFCo9BMaLRiyn0/MadMymZFhwFE/1uJJ7o8fZ7eQDXNG3Cz7u5jNRQsAN9bjGS8oqWLsngdW7DpNf0jAh4LqI6tuBDr0s+9Jxc0c4VbOlrLyS7Nwii3aDUZKWWfeYxU/ddNGSLlzURAihFEJY5nO5cNEAnNILSykzhRDfAgOFEFcCe6SU/3OGLY7io1+3s+S3XVTpQRHdNogl912Hn+elzbrPHNwTvcHIsoMbkW3iETUGOn5qPwYHDL6k49cXlUpJdMcQEpPN6/tq1Eqi2gfZfbzxN44k50wu3725mqK8EsK7tOW+hbcR2NZxuaSOJlgbTF+/vuzL32fWPiFkAkphuapRUFLGXR+uIuHMhbDG2ycO5P5pwxrd1sbEu5UXL699ioX3fELiuSKMIZYTIDHRbZxgmXVuGNoLg9HIV1viyC0uZUSXSB6dNhJVA7QlAIw+buRe35crBsSgtVL2zEXT0aaVD0vuuLZah6FDiD/3TBhCz3b11/kYETyML4v3ovJqmOBWZKAfg6PasT8lnXaBftw9fhCDOrVr0LFaIv4af/q36s+evD1m7cb1KqiEwLb+3PbKHLoNibb72Amns/jXRz+SV6KDVgqQEvdzRlRVWQ7dOjc/PRdHE+DlwdK7ZvD22i3s/3/2zjs+yiJ94N/Zlt4raQRCgNB774INsIAKoqiI5ezld55659mueaeeZ++iqFhAAZEO0nsILbTQ0knv2STb5vfHpi27m2xCKu738+FD9tmZ2dl933dmnpmnJGcSGWC+x0Y3slF5LCWLRz5cXptlxFWj4o0FMxjf1/am9OXw6opnefO+Dzm0+RgqtZLJt4/j4bfbN5GSm6uGmKhAzqVaZnlwdVHRI7rxNcsg30GEuISQXZXdaFknTloSKaVRCHFaCBElpUxt7/446dwIKds+f7AQ4j7gReA3zBnKJgKvSim/aPPONMCwYcNkfHzj6evOZOZxy2tfW8nvnDSYZ2ZNarH+JBYnsvrianKqcujp2ZObw28m2LVl/S8b4tDxNJ75x09UVtUtiB+eP4F5NzU/orNep6e8WNvm0XmFEAellPYDL1xCzb2gM+lYmbGS+MJ4NAoN4wPHMy1kms0TuDdXbGfxbwet5D8+eye9wpu+OdIRSUvO4dk3V5Fa72Rt2ICuvPHC7GYr0m1NU+8F95BIGTvnaRRCsPqlewnz73g+wk4cx2g08cw7X5Pqn4BreAXfTvu0WWPD7xm9Sc+qzFXsL9iPSqFifOB4pgROobxQi5e/J0qlYxkILmXeG0s4nmqpaAmjxCPLRI+uQbz/t7l4erTeZltz54mOwJz/fMupdEvf+SBvD9a9cl+rjc1lReUo1UrcPBx3XWlNDhxJ4dnXlqPT1a1ZHl8wmdsctMQq1BWyPGM5J0pO8OagNzvtveCk5Wnq2NCM9rcDg4H9QHmNXEp5Q2t9ppMrk/ayq3oGGCylzAcQQgQAu4EOpeg7yp5TtrMC7rYjby79fPrRz8d2Opi2YHDfSL55+142bD+BtkLHxFGxxF1mhHy1Rt1hUvA4gkah4dbIW7k18tZGy+61c/33nEq5YhT9yOhgPn99Ppt2niIlo4A+sV2YMDK20yj5l4NJSvadTuXm0e33TDq5fJRKBa8/Pp+d8WM4diqDb/m0vbvU6VAr1MyKmMWsiFkW8ssZ20u0lVZKPoBUCubfPooFM0fi0o6B3joyBaVaKyUfILeknDOZecRFts4BgaevR6u021yGD+zKt28vYP32E1RVGZg4KpbeMaEO1/fT+HFvt3sBeJM3W6ubTpzY4q/t3QEnVwbtpejnA/Wdp0qrZQ0ihLgWeBtQAp9JKV+zUeY24GVAAkeklPPqvecNnABWSCkfvZwvUJ8gH0/bcu+ONem1BKFB3tw1u23cBTo7gd4eJGXm2ZRfSbi5apg51XGf6CuJIJ8r61r+XlEqFUwcGcvEkbE8dk9798YJgJuLGk9XDWWVltlmhIAbpgxwKvkN4OGqwd1FjbY6O0oNCiEI8O44qU/bgi7BPtxzy+j27oYTJ01CSrlNCNEViJVSbhJCuGPWfZw4aRJteuwmhHhaCPE0cBbYJ4R4WQjxErAXSGqkrhJ4H7gO6APcLoToc0mZWOB5YKyUsi/w5CXN/A3Y3iJfph5TBsQQHmBpvisEzJ/sDNT1e+bOyUOs8u+G+XszdWBs+3TISYvSo0sAo3s3L6CfEydOGkatVHL7BOtAmlMHxjrdZRrBRa3itnHWASGvHdKLYDsHE06cOOk4CCHuB5YBH1eLwoEV7dcjJ52Vtj7Rr8mtc676Xw0rHag7AjgrpTwPIIT4HrgR8wl9DfcD70spCwGklLW2a0KIoUAIsA5oUb8aF7WKLx6/jffX7OZAUhqhfl7cc9WwVgl6096cLUshTXuRGI8oojw6TsC11kZn0rElZy9d3cPo7ulYoK2xcdG8ff+NLNp0gKzCUobFRvDI9WNwdaZk69SolUpmj+nPI9ePtoq47qTzklmRzenSC+3djU6JlJLEkiTyqwrp4x1LsGvLBFV9ZPoYfD3cWL43EZ3ByNWDe/LANSNbpO3WosqoI6HoOFJKBvv1wU3ZPv7qT8wch7+XOyv3HUdvMHLNkF48cHXz4+k4ceKkTXkEs96zD0BKeUYI0XZBuZxcMbSpxiGlfOUyqocDafVepwOXzvg9AYQQuzCbuLwspVwnhFAAbwJ3AlMd/UCtIYekou+J9roejbLhE4RQPy/+dsc1FrJNWYdYc/EAepORScEDuCliNErR+RSDUwfPk3OxgJ1R+zlYllgrvyp4DA/F3NGpUkE1lzRtFu+dNSeGGOY3gD/1fqD2WhpNVaSWbaRYdxZfl15Eek5FWZ1nfWK/7kzs173d+t0ZOXs0layUXOKGxRDQpeOlOIoNC+TFuQ4PI05aGZM0kF72GwVVJ/HWRBPleQ0qRdOUq4/Pfc+G7JYz9pLSRGb5DnIrD+Oh6kJXr+vRKJt3knrhRDoZ57LpNaQbQeH+LdbHS9mXf4rl6bsp0WsZHRjHbZETcFE2bh5fbqjg7yfeI6nMvEmiQHB71ExmRVx72X0SQnDn5CHcOXnIZbfVFlSZdPzh4AuUGMoA8FC68VzcQ/Tx7tFgPb3OwOHtJxFCMHB8b9QNbAYbTEaWpe1kR24i7ioXbggfxfgg6zghCoXg7ilDuXtK57MsLNadJ7V0AwJBlNc1eGui27tLTpy0NVVSSl3N+loIocLskuzESZNol6NFIcQWbNywUsopl9m0CogFJgERwHYhRH/MCv4aKWV6Y0qpEOIB4AGAwDANh/Le5FThYqZEfIqnOtzhjnyXspUPz66ufX2k6DwXyrP4Y+/ZTfxKLceZk5msW5GAtryK0RN7M35qnwaV9PISLS/Ne5/EvWdQTRO4PGbpHrQ5ZzfD/Pszwr9pOaM7C/XvBbeQukV6fOFRNmfv4urQ8ehNWrZkPEhhVV3K07PFS5kU9kGTlY3fO7pKPX9f8BH7Nx4DQKlSMP+5G5n75HVNaic3u5hVSw+QlVFInwGRXHvTEFzdNJfVt/r3QlRU+6ZOy7lYxK/LDpCVWUTfQVFce+MQXFx/n/7KRqlne+Zj5FTUZbdIKvqOKeGfNLo5W8OhwhNNVvIbuh+klOzOeo708i21slNF33BVxGe4qxw/kDHoDfzr/k/Z9eshABRKBXOevI67n7+xSX11hN+yj/By4je1rxOLUzhalMzrgxY2Wven9LW1Sj6ACcm3qb8w2Lsv3bwjW7yvHY3694JXFx96Viv5AOXGCt47s5j3hryMws4m/5nDKbx0x3sUZBcD4B/iwytLHiXWRm56gH+c+J7N2YdrX+/LP80zvW9hZnjHtnRwlOTSNezPfgWJCYAThYsYHfp3Ij1bfnPVaDSxZd0xDuw6g4+vO9fPHkZ0jPPQ1EmHYJsQ4s+AmxBiGvAwsKqd++SkE9Jex8t/xBx5/xnMkSUPA43lIckA6q8aIqpl9UkHfpFS6qWUFzD7/ccCo4FHhRDJwBvAXUIIq0B+AFLKT6SUw6SUw7z9zfsgFcZcNme8xTMHVvDng6s4kNdwWkuDyciSlC1W8tWZ+8mvKmnka7YOe7ef5om7P+XXZQf4be1R/vHcj3zw+poG63zzn19J3HsGAOVQ2xsCCYXHW7yvHYX694KLr5vFe+uzdgNwvmSFhZIPkF95jJRS82+rNxn58cIhntj3E/88soELpY3GnPzd8vNHm2qVfACjwcSXf1/O2aOOp5HNSMvnkXkf8cOiHWzbkMiHb6zluYcXY9AbL6tv9e+FoKD2y5iQnpLHw3d8xA9f7mTbhkQ++M8ann9kMUbD5X2/5pBfWc67J7bxxN5lfHJ6FyW6yjbvQ1rZRgslH6BYd44zxUsdbuPbk2ub/LkN3Q/ZFfstlHwAreEiJwu/atJn/LpoW62SD2AymvjuzdUc33e2yf21R7Gugo9P7+K14z9Zvbcv/xSnS9IbbeNQ0Qmb8n9/tYT2SN/b1tS/F5Te1huK2VV5ZFZYR8Cv4fVHvqhV8gEKsot589FFNsumaXMtlPwaFidvakbPOx5Gqedw3tu1Sj6AxMih3LcwSUMDNZvHGy8t5/UXf2br+mOs/GEfj975MccSklv8c5w4aQbPAbnAMeBBYA3wQrv2yEmnpF0UfSnlwXr/dkkpn8Z8Ct8QB4BYIUQ3IYQGmAv8ckmZFTXtCCECMZvyn5dS3iGljJJSRmPeZFgspXyuKX1OKzvIytRjLEs+zJ3bvuKnZOvJtgatsYpivdZKbpQmsiuLmvKxLcaX72/GaDRZyH5deoCsevnPL2X/hqO1f8ti22V81F6237jCMVavX/Mrj9l8P69a/uiepbyQ8Ctr00/w5dl93LT5U44WZLZVNzsV9e+3+uxbb1tui2WLd1FcZPnsnTyaxu6tp+zU6Fz88OVOSosrLGTHD6eyd3uDsUxbnLzKMmb99hnvntzO2oyTvJH4G3O2LqJMX9Wm/bD3/OVXJtqU2yyb17Kbr/mVtu9Xe321h737vinPQ0OU6iu5bcsi3kz8Da2pwmaZzIrGNybtzQFpiQUc2n/+svp4JaBAgZfKdnaOi8m5pJ6+aCVPPplJVop1xpaLFQU228muLMIoTTbf60yU6zOoMlp/xwpjDlqDdarFy+FcUha/rbV8lvQ6A4s/sj6kceKkrZFSmoCvMAcRfwX4Sv4edk6dtDjtougLIfzr/QusTpvXYMJdKaUBeBRYD5wEfpRSHhdCvCqEuKG62HogXwhxAtgCPCOlbJEj1LyqOtNtCfzr8EbKqmwvar3V7nR1tzb/8lS50d2zLofr6h/3cc+1bzBj8Iv85Q9fknre/q7/5ZJ8zrptk0mSej7Xbh1P37o0PPq1JqTecoxxVbhwVfCYlutkJ0FKGIDZJ9JLbduM20sdxYG8VLZknbGQVxj1vH+yxRM/dGqq9AZWHjlBcncXKiI9rHx6vPwcTwd14aztZ+jCmazL6GHHIfms7cXuhbNN+346g4FVR0/yzm+72XEmucknr9+cO8DFCksF+VxpHj+nHGlSO5eL/efPcZPx2KJumFpw+eSltm1yba+vdtuxk5Pcq964vGbpfhZc9yYzBr/Inx9cZPf+sMVPyUe4UGaeHvUG66xNSqGgn090o+1M7zLZSmYoVVKW7sGFMy2rnHV0DCbrJdW4oGH4aKw3Q6SUbFx1GIIDIDQQfL2hOrinQqnA3cva9au3dyQahbXHZV/vqE4Z/+dS3FRBKIX191YrPHBVtkyAxxrsPSvHcnJ4d8sefkpIRKvT2yzjxElrI4SYjjlo+TvAe8BZIUTT/BidOKH9TPcPYjbVPwjsBp4GGnUGlFKukVL2lFLGSCn/US17UUr5S/XfUkr5tJSyj5Syv5TyexttfCmlfLQpndWbFGzPsQymU2Ks5MZPFpNTWmazzpO9bsJFUec3q0DwWOxMXJVm077Nqw7z7t9+ISujEIPByMFdZ3h24ec6i6QvAAAgAElEQVRUanU227tcuvcMsZIplAqie1jLa5i5sG4BJ5Oh8kUjitNKvFQeDPLtwyt9n2ix6ModHQmYJOiMCkpPuLHuiUOUl1bSw+cWXJSWAbLclMF097mJpGLbSmdSSett6HQ2CssrmPXxtzz783pOBEiyb4gi95rwWmXfy8+DSbMcjxQd0zPUtrx3lxbobfvjaWPxDxAW4XiQtuKKSm755Due+WkdH2zbx/3fLOex71dhaoK2e9rOvX2mxP7GYWsQ7TUDD5Vl9g+NwoeevnMdbuPBa26m/JALVUZliyj84Z6T8dFYptBUCld6+93VpHamL5hoFUPF3cuVKbeNAmDr2qO88+pKLqYXYDAYSdh9lucWfo623DGrivrjUGmFKyaT5Wct6HY1Qa4N7r8DMNx/AN3i+2JIU2GoVFB80ZPzhyLJHubFxkrr0+orGb1RSXapJzqDEl+1DzeHX81DMXfYLPvz4l0s+WQrKBXmfLxuLhBg/r0nzRqOt7918EZvtTt/6DHdQuahdOXRnjdYle2MqBUe9PK1/r16+97d4jFvbM0VeQPcSB6o4f2te/nLyo3c8MFisktsr/GcOGll3gQmSyknSSknApOBt9q5T046Ie2V5+tZYJ2UskQI8VdgCGBt697OqBQehHtcxytHdKRrLzld0QnS80r5ZMcBXrje+kRjqH8s3415ls3Zh9GZDEwKHkCke50v5+of91nVKcwrY9fmE1w10zp38OVy76PTeOmpJRjq+fLeNHckwaH2F3JT54xGV6ln2XsbyM8uYnBkHPdPuIXwGPubA1cqpjwlxSu8UCYrUJwSlFDB9g3HuG72cKZFfMnpom8p1p3Dz6UXPX3n4ar0I87X9u8U52tbGf098sXueM7lWppqant4YzhfxsjwMO57+RabC1573Hr3WHZtOUlhft3irP+Qroye0KvF+tye6LU6s0lJfQXQZKKk0PHh86s9CSRlW5oFbzp1ji1J57mqd4xDbcT5hrL5orW7QJxP244NGqUXUyO+4HTREgqqTuCt6UYv33l4qB1P/RkQ4sN7457gv4u+47zp8k+glULNlPCPSSr6jtzKQ3iow+npMxdfl4Yjr1/KwHG9+MuiB/n29V/JPJ9N76HduffF2QSEmjNR/Pq99RxSVFDOzo3HufqmxqPU9653rQwmJbmlnrip9YwJ6coTva+ll3eEw319dPqt3P7aVxSGWS4ptqalkpCayZCo308q1pwyL3S6YH4a+yQapf0llq3rh0rFmJtH8MR/59utd0vkOIb69WB7biLuShemhg7GT9O8jA4dkf4Bf8Bb042U0nUIBNHe17dKIL7oHiFcfcNgNvxijoNR6aekPMrFokx6YQkfbd/HSzOuavHPv1yin1ttU5782nSbcidtixDiC2AGkCOl7Fct8wd+AKKBZOC2mnTgNiiVUtYPyHIeKG21Dju5YmkvRf8FKeWPQohxwBTMAfI+xDpdXrviqQplXJdXmVOym/8kbq57Q4LI0iAQHEy5NB5gHYEuPsyJmmjzvbJS24Gryktt+0peLsPG9OD9JX9g/coEyssqGTMpjlEOKD/X3z2B6++e0Cp96kyIctCsU+EXUopf/3IyzvpTVmK+hh7qLgwJ+qNVnSEBkVwbHse6jJO1Mk+VC4/FOX/PGg6m2o5XMONvN/HU1LFNbi80zI8PlvyBtcsPkplWQN9BUUydPhClyto0uTNi0BtBbwSFMCv7UoJJUlHueCC8BDu/eXxKhsOK/p0xw1mZeoy08ro1SpxPCDd1HeBwP1oKV1UAAwMfu6w2eg6K5v23HqZCdwKv996+7D5plF70C3jgstsZN2MI42bYVtrL7MwVjs4hs6MHsfTCIU5Xn+xLKQjWhPKPgfPwc3HcXQYgOiYY335BFBZYr1kTUjMcVvRNpgq0uiOolEG4qh27FzsaAni675QGlXywf/0m3TISl0ayhHTzDKWbZ2j173WYKn0oLupuze1yh6Or1zV09bqm8YKXydMv3siIsbHs25lEklsF2Vrrjb74BtZ4Tpw0wJeYTe4X15M9B2yWUr4mhHiu+vWz9SsJIWZV/xkvhFgD/IjZqPRWzLHKnDhpEu2l6NccK08HPpVSrhZC/L2d+mKXSv0pUvOfYmHP1ykrMvLBkZ0gBaJQhagwKw6R/o2bNtpi1KTepF7iN69QCEZM7H3Z/bZHdEwwDz59+bmNf5cImPvHHQyceAGFAqq0KvxcewDjG6z21shZXJ1+gl3Z5wl28+K26MGEe3S83PDtRaSfj03FM9Kvec8VgH+gF3fcP+kyetVxGTU5jlNH08x+JPWiGYyaFOdwG5F+Puy9kGYlj2rCb+7v4s7PUxay9MIhzpbm0cc3lNnRg3BXXV4aw/Yit/QLsopewyRbZ6O1NRg1KY7kS3zgFQrByEmOzSEeKg3fT17AT8mHOVGURax3ELdGD8Zb0zwT6e7B/lywoehHOHhfFWnXkl7wDEaTOfKrl+sUugZ+gFJhO1ZBR8TfxZ3vJy1gcEDj1hCjJsWxcWWChczFTc2Q0Y5ZfhSWryS94M+YpDlWhrfbNLoGvI9C4dZITSc1CCEYP7Uv46f2Zd3xJLb/aH1KHuXvnK+dNB0p5XYhRPQl4hupCzz+FbCVSxR9YGa9v7OBmtPCXMD5cDtpMu2l6GcIIT4GpgH/FkK40H7xAhqksHwZbup+PDz0bjbHX+BMTl1sP7VSycKxw5rV7u0PTOLM8QwO7T0HgMZFxYN/mk5ouF+L9NtJy+IfbGDw5Lpc0S7uBrT8kyr9NFzU3e3WUwoFMyL7MSOyX1t0s9OxYMxQ1p84Q6W+LnVSV39fru9/ZZjatzSz7hrLqSOp7K3OIqBWK7nniavpZic2gS3uHj2E1YmnLQJNRfh5M3NA0zYZfTRu3Ner8wfjrNAdJ7PwpfbuRpOZe99Eko6nk7DbbN2p1qh44JnrCIt0PG6Kh0rDXT0cj4HREAvHDmP7mQvo62V36RkS6JCViMFYQGr+Y0hZF1+gtPI3sov/S5jfX1ukf21BmLuPQ0o+wMKnryXlXDZJieYTYzd3DU//bTae3o2v5fXGbNLyn0JS9wyXVGwku+Qduvheqjc4cYSresfQMyTQwq1JrVRw3zjH13hCiAeABwCiopoWfNNJpyNQCFE/LfgnUspPGqkTIqWsCVySBVj5ukkpF7RUB504gfZT9G8DrgXekFIWCSG6AM+0U18apbhiDUHeC1l8z618sTue/cnphPt6s2DMUPqHN8/fukxxkUmvVtKnUIFHSQxX9Z6Nj+/vM1VdZ8DTRw9cetJlYlfWswR6PUA/nykoxJVhHt6W9A4N4ruFc/hi90FSC4oYHBnGfeOG4a5RN175d4hGo+Lld+eTfCabi+kFxA2IxDegaf65PYID+O6+uXyxK57k/EIGRXRh4bhheLq6NF75CqRYu7a9u9AsXN01/PPjBZw7lUluVjFxA6Pw8Wu/0+8hUWF8c+9tfLk7gcziUkZER7Bw7DDKjDkk5P5KsT6bKI8BDPS9BrXC8l4rqfjNQsmvoVi7tlMp+k3B19+Dd757mFNH0yguKqf/0G64ezj2DJZUbLJQ8mso1q61UPRTy49xtGgjRqknzmcCPb1Gt1j/rzTUSmXtGm/fhXTCfLy4Z8wQBkY4Hsi1WtH7BGDYsGHOVGhXNnlSyuad9GEOHi6EsHuPCCG6AY9h9udX1at3ZUTedNJmtIuiL6XUAj/Xe30R6LDheRXC7K/o5+HG/02rM9U2GnMxmbQoFE3zZ0wrT+S71L9glNUTtfdBKE1nlu8Lze6jwaTlYvl6qoy5BLqNwdfFeYLcstg2OMmqTGFf6X9JLllBH+8BeGv6EOQ2FlGd6kjKCkymUpRK63SLTszEdQnm9dmdKWuMofEirUx0bAjRsc0PfBcb7MOT1wjK9SX4unQjwLXzmEe3NJLOvcER0zuMmN4dI9jdwIguvHVbXTCw3Mpkvj7/R6pM5mCRp0t3c6pkB/O6vmaxMWrP3NyIkbNFH+PvMhR/t2avqQEwmbRIWY5SGdR44RbA0Tm59wDHU0HWoBC2f6+atQrA0aINrM78X+3rEyXbGBt4OxOC6wL9SWnCZMpFofBHCOfmqq+7K09PHdfe3XBy5ZIthOgipbxYfcDZUAqmFcDnwCrA1EA5J04apL1O9DsV/p7zLF7rdYkUFf0fev0xwBV3j7n4+Lzi8ES5I/fbOiW/mtOlu8msSCLMrWeDdQ0GIycTM3BxVdOzOmWYVp/B3ot3U2k059JOKnyH7j73Ul45l1O5ufQKDGRYeLiD39aJLVRKf8DyxEkvFWTpvemhyUFlTCOpcBsAgW5jGRr8DmWl/0Fb/hVSalGpeuPr+280LsPbofdmynQ6Np09i8FkYkpMDP5uHcvd68K5HEqKtcT1jUDj0nGHJr3+BDnZU/H1ewONpuUzZLQ2elMp+y4uoER3qlYW6n41g4PfqN2gcpT96emcyc+nX3AwA7t0zhSGeaZQDFKgsn+40qbojEa2nD9PQUUFE6KjCff2bu8uNZnk87kUF5VzPvD7WiW/hjTtcc6VxRPrVRd719t1CiplMAaj5bo3X19AeeG7AIR73sjAoH80uS9SGigpfhWt9hukrESl7oOv75toNAOb8c0cw96c3Nv/6RZp39vtGlSKAAymfAu5v+ftAJikkW05X1vV25f/E8MDbsJN6UVFxa+UFL+M0ZiJQuGPp9dTeHo2muW4lrzycn47fx43tZqpMTG4qS3XP+fOZFNWVklc33A0mo47njtx0ob8AtwNvFb9/8oGylZKKd9pk145uaJxjr4NIISacL9/4Oted9ooZRX5+XdiMtUsSCrRln+JQuGHt7dj3ge5VSk25XlVKQ0q+p8u3cXXv+xDrzOgKTbSNyyIV/99G+ny3doFRQ3ni7/g75uKyC41B5KZFhPDezNnolI0vJAvq6hibfwpsgvLGN4zkpG9nX5mAEqFL2F+j5BT8h4GYw5FRjdOVHXBS1mJh9JyAyCvYhdZhX9CVC6tlRkMp8jPv4uQ0H0oFJe3cI8/lcZPW49QXF7JhEEx3DJxAKpGosofuXiRe5cvp6jSHJ3dVaXivRkzmNzdfnyBluLQ2Qx2n0zG38ud64fH4eNh6QJRWlLBy39eytFDqQB4+7jx7F9vZISDQanaA4PhBAX5dxIcsr/JFj3tTXLx1xZKPkCWdgM5FdsJcZ/kUBs6o5EHlq9gR2rdWDajVy/euv56FJfkfm+I7IJSvtlwkKS0HHpGBnHHtKGEBrStYpunLyOpIpo4l4v4KB3PXtAaZJaUcMfSpaQWmwPSCSAOP3q7BHDj+H6MG9D6z+vlUF5WySt/Wcah+GQARr1yFHcbxga5VckWir5C4UZM0BLSC1+gvGov4EqJSVIu64I7ZpStJMLzRgLcmhZToKTkPcrLP6t9bdDXPLv7Wu3ZTSq0PSdHeN6Ip6b52QRSsgpZsvEgKdmFjOr7R0YNXEaV4SBKhT9BXvcT6GU+ra80llFmyLeqb5A6CnWZqFRQWPAQNXGRTaYCSor/ikrVDVfXKY32Y01SEv+3di06o7l+oLs7X82eTe+gIIqLtLz0/I8cP5oOgK+vO8+/fBNDhnfse9cWFwtKWBt/Cr3BeaDqpGkIIb7DHHgvUAiRDryEWcH/UQixEEjB7MZsj7eFEC8BG6h3yiSlTLBfxYkTa5yKfgO4quMI9LrLQlZVubWekl9HhfZHvL2fwWAysivvJKnlufTxiWKov/WkHuoaw/nyg1byENcYLpRnUGGooqdXV1SKOuXtnaXbWbw+HlwAFxV6LxXHsvN4+/W1TH403qotgB4BWbWK/sZz5/jl1Clm9elj9/tm5pdw739/ILvInIP88/X7uXlMP168Y5rdOo1h0BvZueYwpxJSiIgJZsqsYbh7Ni+qc3uiNVSyPDsYN/k6BUVL0HqYI8V31eTaLG+s2mz1cElZTGXFOtw9GhrbG2bzwSSe++hXZPXh4/6TqRxKSuffD81ssN4LmzbVKvkAlQYDz2/cyI777kOtbL3YAv/9eTtfb6671z9bt5/PnryVbqH+tbJPP9hcq+QDlBRX8I8Xf+a7FU847LPaHphMBVRVbsTN/cb27kqTKKi0HnsACioOOKzof3PosIWSD/Dr6dNcHdOD6b0dC6SYX1zOPf9cQm5ROQAHT6ez4UAS3/71DgJ9HY87IKXkYME5TpakEe0RzJigOJRNsUwoDKDA5MmuilgUmIB0x+u2AFpDFVtyjlGkK2PbiZxaJR/MeRVOyEIyjhWy5dBZ/jx/KrMmtn0KQ0f54uOttUo+QPEFN9zDyqzKhbpab+K5anrRI2QpJlnJ7oy7KDWesCpTUBnfJEXfYDSRlrUI/0tuJ5Mpn6qqzbi5WY6buioD235J4FxiOl17Nd9CpaDS9pxcUHkQT00MKUlZbFlxEJPRxISZg+nRr/EAfqnZhdz9jyWUVZjX/PGn4JedN7D4ha/xcPWwsMZxU3rhow6mWG+5VlELV/w1EVSUvU1d8qM6tNofGlX0K/R6/rJxY62SD5Cn1fLKb7/x3Zw5fPjOhlolH6CoSMvfX1zOdysex8Wl7dwDqox6tuYcI7eyhGEBPejt7ViQxDRtFiX6MgoyBM98spoqvfXv5MRJY0gpb7fz1lUONtEfmI85BXnNTpOsfu3EicM4Ff0mIu3450ppoNxQxRMHP+FkSd0kN8i3O1q9kaTSi8R4hfCH2GlMCJ5PesoJdKa6VE69vCby5ukVnCwxR3b313jzp94L6OvTg5LySr7bZL2JVxmoZt/es1z3RJjV6QFAvtZydbMjOblBRf+TtXtrlfwalu9OZNbY/vSLbnrQQYPeyAvzP+LI7jN17X22lTeXP9nkAGLtTWZlDqsursNoEihwYaSbQKWQ6EwqsKEnK+z49Nu7fxzls1V7a5X8GjYfPMPZjDx6hAfarJOv1XIi13pDIre8nNN5efQLab6vd0NcyCqwUPIBCkq1vLdqF2/eX7fA3rnttFVdrVZHQvwFxrViusmWQNpYLHd0lEbbPsquCsf9vJcdTrQp/+7gEYcV/eU7jtUq+TXkF5fz07ajPHijY9H8jdLEY/GfcKSoLiNGpHsQi0Y9jpvSsVR/yTs9KOrugW9oOaY2Tv6Srs3jkfiPyasyp0hDDR5BHpTn1hsfBRjcQVMCn/26l5sn9EdUW01IKfn6wg5+TN1DsU7LuOBePNl7OiGuzU9PeTns3GZpKZK8OoKA/oVovOvGve6ew+jmMcRuG/vyT3O0pJBoG55FbqqmuaBtOXKWQBfrIH9gnrPrU1Wp49k573P6kG2Lu6bgpgq3OSe7qcLZsfow/35sMcbqU+JlH/3G46/dxrW3j6ZYp+Xt02v4LSsRF6WaGyOG8UCPqagUSpZsTKhV8mtIzS5i3b7z3DLJ0g1BCAWTQxayMv3fyHruveOD7sBV6UGVjUB+AMjGx7OjWVmUVFn/pvszMqgyGNhlYzwvLangSEJKm1lp5VQW8Uj8x2RWFJgFZ+H2ruN5tOcMu3W0hgpeO/kFh4qq72GdChEUAJm/3/glTtqVW4HuUkpde3fESeemQ6a068i4uExGCOu8qm5uN7IsbZeFkg9wuOg8J0rS0Esjp0oy+b+ErynWebCw+weMCZzDIN9ruTniz5wpDapV8gEKdCW8dvIL9CYD2QWlFimLapBKgcpNRXfvhVx6KVMKAjmdY7koCvZoeMI6ct46nznAYTvyxti19oiFkg+QmZzHis+3Nau9joBSITGiZFt2LKeLg0lMDMNksFQoNAp/PDzmWNUVwhVX12su6/NTs63zVAOkZtmWA3hqNHiorU9SFEIQ6N56Zuf27qcj5yzlrm62T3nc3Dp2TnYhPHB1ndre3WgyJ1fFoK+wHC9KslxJ29PN4TaqtLY3rCrK7SgQNrB3L6fYkdtiRdo+CyUfIE2bywdJjkfSr/Cq5KQ6hLNlQWRXtm3mkw/PrK1T8qvxDClHobZUuET1z51TWEZFVd1v/G3yTt5LWkdOZTFVJj2bsxJ5In4RJtk+psaXPssVuW7sf3UQ3SquZ6Dv1cwM+yO3RL5Yu1FxKcX6Mt44/RUHS8IwSssy7qooung0bfw8cj6TbSfirORVelerZ3fzT/EtouQDxPjex6Vzso+mH36akXz295W1Sj6YN2s+/+cqqip1PHd4Cb9mJKA16ijUlfPl+W28l7QesP+8pGUX2ZTHeY9nQfe3Ge5/E0P8pnNH138zMnA2AG5uN2F2DLHEEeukYE/bm/R+rq6olUpcXdt/PP/83KY6Jb+a71J2cK7Ufsznxcmr6pR8AI0B/9E5CHXznqVjGcVEP7fa6p8TJw6SCFgrG06cNBGnot9EFAp3/AM+R6msMQNT4Oo2Ey/vP3G48LzNOvUzaBilieXpB/DVhDAx+G6uC3ucXl5j2Vtw1Kpekb6UE8XniAr1s/JrBlDoTFx39QBCvSYyIvQTgt0m4q3pQ6DrfBbtn4GsN5G7q9XcPqBhk8/IINtjSmRQ806Hko6k2pS31GKqhiq9gV8OneDdjbvZfvoCJlPrBtVSKSRVUs3J4i7sLI3l1KJ5hHvOxFsTR6TnbEaHfYOv9zO4ud9GzXG/QhGKn/+nKJUBHM/I5v3Ne/hm9yEKyysa/rBL6Nfd2pxUqRD07Wbf4sJFpeLOQdZB46b37EmoV+spNvbvJ0v59BsGW5UJC/dj0NDo1uhWi6BQdsHP/7PLjrfQHhzfpmPZ48M4sTeci2m+JGzoyrJHh3E6wbYbii1GBUdYxwE2wsSIrg630d/GvQwwwI7cFqszbZtIb8857nAbqv5VSAQ5Vd6cK2/b7BiHbMwZQoDGo+4QR1kBqup4dj3CA3F3rVOYlqXutap/viyHhIILVvL6nMjI5oPNe/l69yEKyrQNlm0K02+wPqn3dQ3kloEPcX3Yk/TznYJS2DckPFhwHJ1JT47emxW5gzmnDSJX58kZbQ9Gd1mMUtE0t6/IIF+W7BjD1uNxGE3m+TCn2IstJ59EobAc+04fbrl5Kdh9vMWcHONzHyNCP6Eor4ycDGuFvaxYy/6kJA4WWN8PK9L2YzAZ6R9j+7mwJwezO+DU0Ae4pssjRHn0r5VrNAPx8f0XQpjHLyFc8fR60sqVwRbd/PyYYiO2y4KhQ1EIwbSZAykPUVESrabST4EEoqID6Tew6dkFmou9tViCHTnA7rwjVjKFWuIa2nLPhxMnTcAXOCWEWC+E+KXmX3t3yknnw2m63wxcXEYTHLIXg+E0CoUfSqVZyQp19XOofqneUrkTQuCi0GAwWit9LkoNLmoVT902kVe/3ICpxm5bSib16MqDj5n95wPdRhHoNqq23hezsnl3715O5OQQFxTEY6NHE+3XcP8WTBvOnpMpGOpZD/SJCmFcX8dP+uoT3t28aNZ7Kyjr5YKyUuJ5spLIHi23mC7SVjL/kx84l1O3e39VnxjenjcThcLxoGBNQUowmQQSwF0Q5BfHwKDpVuX8/P6Ht/efMZlyUal6IYSKj7bs452Nu2vLvLd5D4sW3kJcmGO/yeO3TODh/y6jvKJOEbh3+khC/BtW2P84bhwB7u4sS0xEbzIxvWdPHh450m75HG0ZmzPO4q7ScHVkLG6qpvtWDo2NYFhsBPFn6qxcFEKw8FpLH9u588ei0xn45eeDlJdVMnREdx57+lqUyo65D6lS9SQkZD9CtF5sg9YksHsga/x6sv9cX0zpEqVW4NGnkvDujqcde2TaKDa/eYZCNx1GDSirIMzgzl0ThjrcxswxfVm95wSJ5+tMnPtEh3Dj+P4N1LJErbA9hSmbcG38vDzBOm5ZmxDq6kex3lqR6OYVRJbWgDarEtdc89mri1rJU3MmWpS7dC5pTA7wydb9/G/DrtrX72/awxf33UIfO2NQlraU39LP4aVxYVpED1wbGAtuuX0UVVV6li89QFlpBYOHduPRp69tNFhoDS6Kuk2MHJ0PGwrMm8yR7qG4qGy7JjXE9BFxfL35IK+vnMFnmybh464lvyyMxc/cYVU2MqZlXZgunZMBfPwNePm6U1pkec1dXNVo/DVgwwiqwqjHII3cPnUIm+KTSKlnvTWqb1cmDW6eObyHx124ud2C0XAepSoShDdbMs6RXlbMiOBIevnZHw/emT6dd/fuZd2ZM7ir1czp35/5gwaRV1rOz2UpFPWui60SaXLln0/OtWvF0RqEuvmRXmH9UDe0RnNRarDl0SCNbddvJ07q8VJ7d8DJlYFT0W8mQihQqy1NAm+NGsuGi4eoNNXNFlKC6RITxAnB1qaE00JHsyLjNwtZtHsYvbyiAZgxti9x0SFsPHAaIQTXjOhNdBd/q3Zq6BcSwsc3Ni1I2OAe4Sx6eg7fbkkgu7CUEb2iuGPKEJSNROq3x+SbhvLB1q2cHCtAaf4N1GUmBk1subRGX+08aKHkA2w+cY5tp88zOa750Y0bQm9SYpIKQOB5WjDjPvt5d5XKYJRK8wI6u7iMDzZbnsCVVFTx5rodfHbvbIc+u2+3UH762z2s3nOSUm0l4wfGMLBH477VCiFYOHQoC4c2roitvHCcP+5ejd5k3vAJcvPgm6vmNrjws8c7D9/Ekt8OsetEMoHe7sydNIghPSyDIimVChY8MJl77p+EwWBCre7YCrQQrp1WyQcoH+xLWb7A5GEep/SA3k+NIs7+eHIpYQE+/PDkHXy96SDnLuYT1z2Yu6cOw9fT8ZSNri5qPv3THDbFJ5GUlkOPiCCmDeuJRu34tDQnajzHjiZTX4eQEmaEO57GclLwcH5IXU+Vqe1dIed3m8QLR7+1kA3wjebDaQ8AcCo1h83xSbhq1Fw/Oo4ul2QkGBccx9rMQxYyN6WG4QG2lb+ckjLe27THQlZSWcUba7fzxcJbrMovO3eM5/asxVDtChDi5sm30+bSw8e20q1QCObfO4E7F4xv1o5PMIEAACAASURBVLM8PKAf/hpvCnSW7gxXh45uUjs1eLq58OX/zWXx5oMcu3CRriF+3DV1KNEh1vf6NXNHsmrxDnLSHXcdaSpqjYrbH7+aT15dYSG/+f5JDA2LIfCMF3lVpRbvDQ+IwVWpwdUTvvnrnazfd4qU7AL6de/CpME9mj0/g9lCUaHpR7Gukjs3fsWxgrpNt/v7jOAvQ23H/XJTq/nT+PH8afx4C/ln2w+QWlBsIUtTVHKhvISQNrRCnhc9kYSCc5ios+7r4dmFMUH2Y75cHTqaxcmrLGSewpOu/n3Qe5o43EJ9s2e+n/ya9WFBZ6K9vteV+ntKKTuvj6uTDoVT0W9BunuG8uHwh/g6eWt11P1IdEbJ6oxDmJAIBDMjhnJ1F2sT+ruiZ2KSJjZm76HKqGOIXxwP9ZhjsQseEx5IjJ2Aay1Fv+hQ/rXg+hZpS6+WnJvsAsa6jQ+9p4IPMxMY0ye2RT4jIcW2H3hCSiYTe3Vj9/pEDu44jV+gF9fMGUFIuOPKjCXm61BlUFKqc8EoBUFlHrx91z0EhDrm2nAk7SIGk7W/n73vYI9AX0/uvs5xRaYplOt1/GXf+lolHyC3opxX4jexZJo5iGxxQTnrf9xHZnIecYO7MvmmoXbz3rtp1Cy8doTVKb4thBAdXsm/EthWlorJw9K9xehnYnVKEtcOdjz4YddgP16Yd3kxCtQqJdeNiuO6Udabn44wJbQf12UPZ01WPEJIpBQM94nl7u6THG4jyMWPl/r+gc/O/8z58raNuD85ZACvD1KzNG0XhbpyRgf24s7oybXv944KpneUfWufx3tdy4WybE6VmMcQd6WGF/vfgqfaton70fQsm2PQwWTLMehiaj4rl+7hjcDjGBR190p2RRmvxm9m8VXWMUjq09xnWaNQ82q/R/jw7FKOl5zFS+XOzLBJ3Bg2ufHKdgj08eDpWRMaLefl68H/Vj7Nis+3cjYxneheXVj34tvN/lx73HzfJLp0DWTTsgOYjEYm3jCEidUuD68OuI3nDi+hpNoiI9I9gOf61m3Yu7mouWmC2eLlzLF0Pv37KkwmE5NmDqbPZbg6fZS410LJB/j0xH6md+3NoEDHg3QmJNuZj5MzGRXTdql6Rwb05H9D7+P7lB3kVBYzPKAH86MnN5iNY3bEVKqMOn69uJ1yQwUDfGL5Q4/biBxnttj85Mm26r0TJyCEKIXanSoNoAbKpZSdz1/QSbvSqRR9IcS1wNuYHZ8/k1K+dsn79wCvAxnVoveklJ9VvxcFfAZEYn54rpdSJrd0H3t6h/O3AZZmgffGTOFMaRbdPIPp6mFbUVcrVNwfM5t7u9+MSRpRK9ouDU1rkZCbSYXR2hZuV1YyUsoWMeWL9PfhwAXrxXmEnw9vPfsjm36q8+Fd8eUO/v3tQ8T2dyzNTn1iPCP5eexbVBkNJBRewFftwQC/pi1cIv1tbwhE+LVPhGxbHMrLpExvfbK5OysFg8lEUU4pT89+l9yL5gBQ63/cz6af4/nXN39A5VTSOwUVLjpbmbUoVjYtXkRHoKpST95LGXilGzBGCpRZklKXHEp/0jYps0d/31jeHvIslUYdbrzXij22ZkxQHGOCmrfREeDixeIxj3K0MIVivZbB/t3wVNn3Y4+0M9bUH5tOHU7h+Ts/Ji9MYphvXX7nxeRm9dVRunqE8drAJ6gy6lArVCiakirxMvEL8mLBc3V+6g+82DqfM2paP0ZN62clHxYQw6+TnuNgwXlcFCqG+Hez+f1/W3GQN//4fW08mlWLd/HQyzdxw132rcsawt413XkxuUmKfqS/D4kZ2VbyCDtzX2sy1L8HQ/0dd2tQCAV3Rs/g9q7XY5RGNFfAGsxJ50VKWeuPKcyL5RuBUfZrOHFim06j6Auzrez7wDTMiY4PCCF+kVJemmz3BynlozaaWAz8Q0q5UQjhiXUoqVYj3N2fcHfHTpKVQtG0HNAdmGA321H+g908W8xf7+5xQ1h7LIkKXd2GQqS/D31cvfnyJ8tAXRVlVXz91jpe/eK+Jn+OwLwZo1aobLpeOEJcWDCTendj6ynLQFl/mGzfV76tCXGzrRwFunqgUij4+bNttUp+DYkHLrBr/TEmzrAO+Oek49E3OISLF0ut5KOjHQ+k11HYujKBpKNpKAFlgVnpySKfXxbv5K6nrm1ye64OpuTraAzwc+za9eoSxOS47mw5aRmUrP4Y9NUb66jU6lCV2d64szdGtDQunfRaXC6uSjVjg+ynqDQajHz+2mqroLOL31zH1beOwLUZ0e1D3D05VmAtD27itb53wjB+O3mOKkPdTmL3IH+u6dcyFnxtwZW0BqtPZzdxb6+MBR0hU4KUUgIrhBAvAc+1d3+cdC46jaIPjADOSinPAwghvse8w3Wpom+FEKIPoJJSbgSQUpY1UsXJJSQcTmHp8gPkF5QzdHBX5t02Ci/PhiMg9/EPYVxoNDuzki3k98W1nNl5bEgg3/1hLot2xJOcV8Sgrl24d/wwEtbajrp99niGTXlb8b95M/h69yG2nDyPj5srt48ayLie0Y3WO3Y8nR9+2k9OTgmDBkQxb84ofH1aPjVerG8gk8K6szXTUhG4v4/Z9P7scdumzWcT0383in5Kaj6fLtrGvNtG4eHh0niFDsbjg8eyNet8rd81gL+LG3N7tlzsjLbC3vN8LrF9n/OOzFu3T+fr3YfZcvIcPm6uzB01kPH1xqCaZ9w1y4j7OR3aGEvF8b4+jbvh1HDseDo//nSA7JxiBvaP5I45o/H1bb2Unr8HCnJLKcgpsZKXl1ZyMSWfbr0dz1pRw8K44fyWca4u2C/gplNiTKzE1F06HNi2b3gI3z44h0U7E0gvKGZYt3DuHT8MlybE3XDixAkIIWbVe6kAhgGV7dSdZiOEeBkok1K+cTllnDSfzjT6hgNp9V6nA7aOQmcLISYAScBTUso0oCdQJIT4GegGbAKek1LaMGB1cin74s/z/IvLak8Qks5kkXAohQ/fvqvRBcDHk2bx9tGdbEw7g6fahTt6DmZubMsqFD1DA/nXrZand9G9bKebi+5pPw1dW6BRqVg4YTgLJzi+2XE0MY2nnv0eY3U2hKSz2eyPP8+n7y9oFZ/2DybcxNvHdrEhNQkPtYbbYwdxR09zCrzonqEc22edoqhrO/+ubUlllZ5vf9jLoSOpvP/WnW0aTbolGBDQhW+nzeX9xD2cLy5gSFA4Tw4ch5+L44H0Ogr27ruo2JaNoH4lYR6DhrFwwjCb70f3DCXxgNnqKOL7UvImuVHa24VukUHcO2Ckw+P30cR0nnr2O8tx6+AFPmulcev3gm+AJz4BHhTnl1vIXd01hEQ4lvnnUkaHdmXRlFt5afN60kuLcckBv2NGPi7fQk5mMU88PM3htvqEh/D6nOua1Q8nTpzUUj/XpQFIxny42aJUuwUIKWWbWTk7aVs6k6LvCKuA76SUVUKIB4GvgCmYv+d4YDCQCvwA3AN8fmkDQogHgAcAoqIuP3hMlbGSQn0BAZpA1ArHTerKq3Qs25/I0dSLRAf5MXfUAIK828Zk8lKW/LDXykzw9JksDiRcYOQw63y69fFQa/jz0Cn82U703tai18AoRl/djz0bEmtlao2KeY9f7XAbl94LWZWZ+GsC0Cha9hS3sWv93dJ9tYvlGpJT89m15wyTJjgePM1R3NUanh8ymeeHWAfAmnXfRLatPkJJQd0is3ufMCZM73ynwU2h/r3g4mqOHn3iVCYJh1MYOji6HXvWPEaGRDEypO2CY7UWV908lF8W7yTtbE6tzD/YmxvvGd9ALWu0Oj1L9x/m8MWzDpVv6XnCHpXGSor0BQRogtosbsudT17DCws+xaAzotRJQjZouVUdy/NPzm+0bv2x7PzxLHTCRH2VPiU1nx27k5gysXnuTx2RtroXalBrVNz5xDW8/+LPFvLbHpqCeyNWdg0xKjAK9+WVRGgtNy7X7d3H7Dn9iQjonJu5u5JSWH34FEohmDkkjhExke3dJSdOGkVKuaC12hZCRAPrgX3AUOBHIcQMwAVYLqV8qbrcX4E7gVzMB60H7Z26CyHuxzwOaoCzwHwppfaSMluBI8BEzHrZvVLK/dVv96l+Pwr4n5Tyneo6KzDHVnMF3pZSfnL5v8Dvi86k6Gdgvtg1RFAXdA8AKWX9xKmfAf+p/jsdOFzP7H8F5qAWVop+9U30CcCwYcPkpe83hQ1Zq1lzcSWVpgo8lJ7cGH4LE4KuarReld7A3R8t5WRm3eJ12f5jfP/oPLr4Npwv3RG0pZWcij+Hf6gv0XHhjZbPyi62Lc+yLe8o/Pnd+Wz6OZ74bafwD/Ji+h1jmnTyXP9eCOkTJF8+/iyuCjdmhN3M1JCWObFw5Frb+/0v2pG3JqGRAbz3y1Os+noXGRdyiRsazfW3j0LjUqeEpJ3JIictn15Du+HZCu4F7UH9e8HLJ6J2XLjYjGcg9XQmeZmF9BraHQ/vzneK3pFw83Dhv8seY/U3u0k6mkZUbAgz54/FP9jxwMQ6g5EHlr2DJioRTXeDQ3Vacp6wx+qLK9iQ9StVpiq8VF7Mirid0QFN28BoDgNH9+Cd5U+weskeivLLGDE5jqtmWZ/+lxdrOZ1wgaBwfyJ7dqFKb+Cej5dyIqNuLBM91fid0aOsF5PV3njWWWmLe+FSZtw5hvBuQWxeHo/RYGLyDYMZMaXPZbVZUlKBVlsXiNUlrIKwW9JxCaniH8l/YnDxcO7uej8uyuZvJrQEWSm5ZJzLIWZAFL6BDa+HvtgWz5trdtS+/jn+OC/ePIU5o67sjemWoCP4pv8eEUI0FAJUSin/1kIfFQvcDXgDt2B2jxbAL9VW0RXAbGAg5oj/CcDBBtr7WUr5KYAQ4u/AQuBdG+XcpZSDqj/jC6AmKmlvYDLgBZwWQnwopdRj3gwoEEK4YY7N9tMlup6TRuhMiv4BIFYI0Q2zgj8XmFe/gBCii5TyYvXLG4CT9er6CiGCpJS5mE/5LSO1tTDHi4/yc8b3ta/LjWUsSf2SCLcouns2HJhmzeHTFoofQF6plsU7Enjo6lHkaMvo6u2LWtl088cty/bx7tNfoy01u/oMmdKXv371EG4NnAQM7B/Jhs3WPu8D+3fsnXGVWsm1c0Zy7ZzLD3Znqo7dWGmqYFn6EsLcIujj3f+y223oWj87cyIAA/tFciE5z6puU3//HG05Wr2OaJ/mmXfWEBTmy73PWgfw0VXp+ff9n7JrVQIALu4aHvzHHK6/Z+JlfV5HRYimXYOqCh3/vPdj9q07AoCrhwsP/2ceV88b21pd/F3g6e3GTQ9OJK2kmC6eXniomxaM7Oeje/CIPUxHir91sHA/qzJ/qn1daihlcfKnRLp1JcK99U+Nu8WF8ejfZtt9f8O3O/ng2e+oLK8CYNR1g+j76HgLJR9AqgUVQUo8M+u85Ab2a/l5o8poaPb176wMHhvL4LGXF+Qup7yMSqOBKG9fAgM8CQ/zIyOzEBSSiPkpqL3NG18SSULhfrxUXtwedU8L9L7pGI0m3n7iKzYu2Y2U0mzZ8NwNzHnKdjrg8iodH23eZyV/b8Mebh7WD43K6T7ipENSbkPmgVlxDgBaStFPkVLuFUK8AVwNHKqWe2LeBPACVkopK4FKIcSqRtrrV63g+1a3sd5Oue8ApJTbhRDeQgjfavlqKWUVUCWEyAFCMB/SPi6EuLm6TGR135yKfhPoQEubhpFSGoBHMd88J4EfpZTHhRCvCiFuqC72uBDiuBDiCPA4ZvN8qn3x/whsFkIcw7xr9Wlr9nd/wW478j2N1k3KslbqJJI1GUmMWPwhU79fxNhvPmHNudNN6lNBdjH/fWRRrZIPkPDbcb75d8PP74L54wkOstw5nzN7BNFdbacK/D2wP9/29W0qtq61WZ5b+/f828cQHmapnM+8fhB9ejuW9qhUV8WD61Yy8qsPmbTkc6754UtO5uc2XrGJ/PTu+lolH6BKq+O9//uGzPPW6ZauBO6YM5rICMeyaQD88NaaWiUfoLK8iv89/hU5ac4563L48eQxRi3+mKnfL2LEVx/y/sG9Tap/rDS+Qyn5YHv+kEgOFDY+f7Q2Wal5/O+JxbVKPsDetYdZvc72YY/Btc4UfOZ1A+nbp3Ersqaw7FQiY6qv//AvP+Tt+JYZm69kiqsqWbjmZ0Yu/ogJ337G9KWLOVdUwJOPTMPFRYV7t/JaJb8+jqxfWov1X+9gw7e7kNUBA/U6A4te/ZlT8dYxYwAyC0sor7JOFVtQXkFeqS1dyomT9kdK+WbNP8yWQm7AAuB7oGFf2aZR8xAI4F9SykHV/3pIKa2snR3gS+BRKWV/4BXMpva2uNTqqeZ1VT2ZEVAJISYBU4HRUsqBmDcj2tekqBPSmU70kVKuAdZcInux3t/PA8/bqbsRGNBSffk8MZ6vThyiqKqSqVExPD98IkHuttPJ1ceRwF29w4KsZAZPSYosNofkwHw6+/im1fQLCiHK29eqvC32bziKXmc9ee/+NYH7/3ar3XpdQn346tP72Lr9NHn5pQwf0o3evZoe2fdKoqUCsNm61mBOxVeD3lXiNy+UhNRy3KSK26L78n9XOR7z4O+7trL+wpna16cL8rhv7XK2z7sPpaLlNJzdqw9ZyUwmyZ41h5n96DUt9jntTVCAF5+8ezc9Y5vms7r7Vxu/j9HE3rVHuOGBto1hcaWQmJvNs1vX164UyvV6Xt+/kx7+AVzTzbHTzkAvT/LbxODacQS2xxfRAfbm9607gsloHbep+Ggm9LKeA8f268ao8aEMHhhFvz4RLdqXk/m5/HHbOqRCggrKpY7/HthFrF/A/7N33tFRHEkD//VG5SwkgZBACRA552CCCcYYjM85Z5/TfQ7nHO58PvsczvEczvmcjQ3YmGgbk3POiKiAJCShHDb398ciwWp30a60Snh+7/EeW+rpqZnp6enqrq5iWrL7FHV/dJ5a9Su/ZZ4xkPcWFXD7kh/57cqb+OrjO/h+0zK2c9zpuNp2mVFSxIubVrEpP5vOwaHc3W8Y05N8Hy/mbFz1nwBrF2yju4tYQfERoQTpdVTWM/YjgwKIDml4rNbStPf0d+7wdgtAc19ve9iSIISIAB4ArsEea2yAlLKkmU63FHhOCPGllLJSCNEJMANrgfeFEC9gtxWnc3qLkhuCgTwhhPa03u5S31wB/C6EGAWUSSnLzjGeDgVKpJTVQoju2LdcK3hJ648a2iEf7N7M3zf8TmZ5KWVGAz8c2sv1S7+vm2kGGBrp7I4rEAyJGNFg/VP7ptGns6MRoQp1flQWm42fD3u+qu8uUM+53PbryvjpmHphb667asQf3sgHGBrhG3drV886NjSI60cPAMAmJdcunsOC4weptlk4JQ28e2wrn+9zPeipj5SSHw/vd5KfqChnc75vU5C5a0d+gefXBGxERKDXRj64vz/+we0vRV9b4adD+52WBwB+zHBu8+64sMsF2NqYoe+qf1GhYnBE649z3LXjRIOavgmO34aYkCCevuZCrrtqhM+NfICXNq1CqqV9TUpgH9FoYG5Gg1l3/7CYrFYWH81wkh8tLWZXYT6RkUHcOuUSwrWRTmWGRoyg3GTkqoXf8lv2ESrMJvYVF3L38gUsz3a9su4r/IPd9Z9u5Dot9052HG8JAf83ZVSjtj0qKLQEQoiXsW83rgB6SymfbUYjHynlMuArYP1pj+fvgWAp5WbgJ2AXsBjYDZwrwMpT2IP7rQUOnKOcQQixHXgP+3aEc7EE+8r+fuBFwDt3PQWgna3otxU+3etsZO07VcDmkycYEmsfzKSH9Obyztfyc+48qq1VBGtCmNnpcroGJjdYv06j4ePbL+OnbfvYmZWPWgqWlhzG4BjAEgCVFyvLQ6f0JSI2lOJ6AcSm3TjG4zrOxab8bP61dRW7i/JJCYvkgf6jmJiQ4pO6Wxu1sA8MAtVBTO84i+4hPX1Sb/1n3TU6nNmDexERZA9it+bEcQ6VOrt2f7J3G9el92+wfiHcrQ2Cyu1fGse0G8eya43jxFNQWABjZrlO4+WKEkMN/9j8O0uOH8Rfq+OqtD7c328kGh95HpRW1vD96l0cOlFEWqdoLhvjMyefBpl20xj2bz7iIAuNDGLk9IEtpkMthaWVzFm1i8yCEvp0jWPWyF4E+LX83ua1uZm8tHUl+4sL6R4RzUMDRjOmU1ePj3e3EuCNx836EyXsOJZIj/hc/HXmhg9oRjbuz2TR5gMIBIP6T2avdTU11mpCtWHMjr+KTv6tHxdl5PQBfPjUHMqLKx3k028cy6jZQ/hx616XfVl9Sgw1PLdpOUszM/DX6rg6rQ/3efmu7z6V7ywUcMrg/K1sKRZvOsDq3UcJDfJn9qjepHTyfoubTUre2rmOLw/soNJsYlJCCk8OGU+0v29Wot19FWrHE2qh5u6U/+PT4/8lpyYLFWoGRwxjVvwVzDm4nyIX9/ezvdsY39mXnsWOTL1hDKvnO4ZW0vvrmHD5cLfHXDuyP93jolm08yAqIbi4fw/6JioLFQptmgexu7E/CTxx1rdMYA/G53mkWTdIKY9zJggeUso3gDdcFH1FSvmsECIAWMU5gvFJKd8F3nUhf7ae6Asp5V/OVUZK2eusn0quziaiGPqNoNRY41JeXO/jN77DZEZHXUCpuZRwbQQalee321+n5YphfTFVWnjth1UYw22OOQcAnUrNRV64J+r9dbww70HeffRrdq46QFh0MJf+eRLTb3FOo+YtWRWlXL9sDgarfWvAvuIC7lg+jx8uupZ+0U3/sJrMFtbvzcRgsjCiVyLBAS27StzJvzPP9XqVMG24z9Nc1T5rV5GAS4wGF0dAiZs26IqZael8vW+XgywhJJRBcY57ZQ9mF3Aop4hunaNJjXe9peBcjJs9hPLiSr57fTFFuSX0HJbKnS9cQXCY54PTO3+fz8b8bACqLGbe2rkek9XKY4PHea1PfUora7j+pa/JKbRPdP2yNYMFG5yDTDYXk64aSWVpNXPeXEJxfhm9R6Zx1wtXEeBmRaq5yCsu5/p/fc2pcnt/9cvWDBZt2s/HD12BXttyn4RDpUXc9Mv3mGz2QG27ivK55dcfWHDxDXSP8Kz9XZLagw92bsEmHZfkL03zPAJ5saGGk2WhFJSF4Kdz3tPbUnz52zZe/X7lGcF6ePCKW7lweCIRusi6ycbWJjDEnxfmP8B7j37D7nUZRMaF8af7pjDxSvvqqbu+rD63L5/H5pM5gP1df3Pnesw2G48M8jx4p7sJnV4dOriUNze5p8p54pPFdb/nrtnN2/fMYnA3+8fbYLKwbs8xrDbJiF5dCHQzufbmjnW8vmNt3e8fj+7nWHkJP118fZN11KnVXJTSjXn1vB5SwyPpFRVT9zs+IJEn05+n2FSEXuVHoMae8tUX36TGMGBcOg+/dwtfvPgTeccLSe2XyG3/uJyYBGfPg7MZlBTPoCTfe5O0FN66mrcH1/SWoL3eByllW/K0/q8QIh37vvjPpJTbGjpAoe2hGPqN4ILOSSw46uiZ4q/RMjzOMRqyzZqPpfwl/IxrMKg7oA+6Ha3/DDylxmjmvwvtnir6EhVWnQ1DlAQ1xAUG87fRE+gcEuqV7ondO/Li/AexWm2o1b7rT+Yc2l1n5NdilZKvDu5osqF/LO8Ud78+l5Ml9hUkf72WF26bxpDunVmwfA/b9+UQGxXM7Cn96djBu/vhKdJyCL/SWVj9pqEJfgChsqdFO3S8gPm/7qK8ooYRA5OZPKoHKpXvVspHdUxEr1ZjtFod5N6snDw5YhyVJhOLjhzEKiW9omJ4bcLUutUbq83GUx8vYemmM6vx04en88wNF3p9LTNuG8+M28Y3qn1llBTVGfln89XBHTwyaKxX3iuu+GH17jojv5asgtIm1ektg2YOIitAy8micgb1TiC+e8uvLn22bEudkV/L/qwCftmawfRhTUvR5Q3fZOyqM/JrMdtsfJOxk2eHTfSojvSoDrw6fir/XL+SwuoqQnR67hk4jAldGvacqmV8QhKLDi/inrQN9AvPpzFxzC3GjRgrXsdmOYRa2wd9yEOotZ7fyxqTmfcXOnslfvjzFv40sr9PjPw9GbksWL4Hg9HMuKGpXDAsrdF1JfdO4OWFf230d2R/cUGdkX82nx/YzsMDx3j8rl/YJZUv9u9wkAng7n6ts8WhtKqGszf1mC1W3l2wjsHdrmB/5knue3M+xRX2dy/IX8+rf76YQd2cvTQ+P+DsNbirKJ8dhXk+mTj/26gJ1JjNLDt+GJuU9OsQx2sTprFpVybLVtu3vVw4ugdD+3YhQufokTC+cxIvbzmTsi5CV829aeuZ0imfysKv0QXejC7gsibr6IoJVwxnwhXDfT5+UVBQcEZKeXV9mRDiP0D9vWVvSCk/8aC+cT5STcELFEO/ETw5dBwHigvrXKr91BpeGj2ZUP2ZlTkpzVQXXYnNat+3Jm351JTcw/KTi+ge/TCJblz4zTYze8sy0Kg06CvDqTKcWWEKOKnCv0Bi08B3T11B52jPgvC5wtcfyTI3s/zu5N7wwpfL64x8sE+APPvpMlKCQtl9ILdOvmD5Ht7/x1V0jT/3DH9jkNKAtGZjqnqfA6ULydc9SHhhXx5+YQGW04GpflufweZdmTx9j+88jSL9A/jnyAt5fO2yOmO/R0Q0jw72fNUrUKvjrUnTeW70BKotZjoGOXp+Ldl4wMHIB/h5/T5G9e7KpEGNMwYa077KTK7bSqXZhNlmRa9uWnd1ONd1hoOWIuN4AX9++huqDXb38BUbD7Fy4yFef/IynwV39IT1+zJdylfvPtqihn65yehS7q4duGN0YiT36STHq/KI8+vElDjvXKUHRAfw7agF+KvLvTquDmmg+tS11AYNthh/xVK0iaAOv1AtA1mWP5+Mij2EaSMY12Ea3UOct4ucLK6gssb5fpRVGSgorSS+CX09wG/rDvLMcwUcFQAAIABJREFUGwvtng9CsipzG6tzUnjs0sua5KHU2O9ITonrrZ5VJhNWaUPl4cTGQwNHsaswn11Fdhd+rUrN34aPJzbw3PnVW5JDJ+z9znP/+6XOyAeorDHy7KfL+On5m50mVN1+Txt4N3aWbmJ14VKqLJX0DB3ApJhLXOa9D9HreW/KJRTXVGO0WokLCubbhVt547MVdWUWr9rH/TeM44qLHLcWpUd24KGBo3ht21ps0sZ/h8ynW4h9LGQzF2MofQCQ6ALcB/htKp60OyklByuOUG2tIT0kDT+1EgtFQaGpSCnvbm0dFLxDMfQbQWxgMMtm38S63CzKjAZGdUp0MPIBLIbldUb+2cSzis+P5RGjDyZS35nBkVfQObAfAAfKD/Pqwfcpt1QAEK2LIjQ6grLCM4MAIQXRfgHERrSdgQzAhIQU/udiFaKpe/RrjGa2Zjiv/JRW1rAzr8ohmmRltZHP5m7k2ftc59X1FV01OawseR8DOoZcHsr+lcmU5No9CZas2se1lwwmqbPvUg9eltaL8QlJrMvNItIvgGFxnRtlGIb5+ROGv5N87d7jLsuv3XOs0YZ+Y+gbFUeUfyBFNY6pj8Z06tpkIx+gW3w0S7d4l5LSl3z6w4Y6I7+Wzbuz2Lw7kyF9urSYHhaL1aXcYGzZ/ekTOicz59BuJ/nEzp73GZXmcl7LeJpKi91ILzLlcrByFw92e55YP8/SuJmr5zbeyAdstlNAPWNZlmOs+oa3T+SRZ7B7qeQZsjlQsYs7kh+hR4ija3tcZAghAXrKqx2N/bAgf2LCm97Xv/f1GmxSogkzETUrF22Ymb2c4M4tW3i4+110D2m+WCr5NRlsKvqSU8ZMOvinMizqWgyFJoQZZL3b5lehRqvy3Hsh3M+fny65lk35ORTWVDEsrjNRPtrH7iu6dY6msLSSg9nOKU3zTpVzNO+U0z7+CZ2TWZp1yEEWotMzJMa9C/rm4tV8kflO3e9cQxbHqjK4N/Upt8dE+NvjJxhNZj6a45w67+Pv1zNzUh/0OscHdW//4cxO7cnRwp/opneOIWOq/LBZDf2GKDaW8MKBt8mqPkG0rpxYfSXRulB6hY1nSNRVaFXnV4BYBQUFBXcovk+NRCUEozolclFSNycjH0DaXOcpD1UbCVTXUGkpILNqK3OzHuNE9V5s0sbbhz+uM/IBCk1F9JjsOAAVAu6ZObLNRY0d26krt/ca7OByeWlyTy5NblrQOp1WTZC/m5l4F5Gyj2U3/6qtEBCpqUGnNdGpeyHjbtpMcNQZj4OjzaBDhF8A05O6M7xjgldGvrHGhNWNYVdLeJCz8Q8QEew6iFZzoVOreX3MRYTqzrxPSaERPDd8kk/qnz26N11iwh1kXWMjfFK3J7hrF0eznAfKzUm3+GiXmWz7pfg2v3lDTE5I5fru/evCggng6m59mdbF87gjG4tX1Bn5tZhsRlYVLvG4jqKacwUI9gDpnLIUoNiwr87IryuK5LeTC5zK6rUa/jzDOdL+3TNGoNU0ra83msycOGnfohI+qQBt2JkJnUprFW8d+hibdE6X5wuKjVnMyXyQI5XrKTXnklG+ku8yHyAsxELQITXirFunroaU8nD3lblBCMHQuM5MT+re6kZ+/b5Ur1Xz54tHEOinQ691fo4qIQh1kZXkmaET6BZ2xvgP1up4fcx0/DXuvS9+Pfmjk+xw5T6OVx1uUO+CU5VUVjt7lFRUGSk4VeniCOgYFMKQGNffDnfjn5bi0+NzyKo+Qay+lOSAIgLVBqqtJ9l06msWnni+VXVTODddHl3o8t/5ihCihxDiPSHE90KIu1pbH28RQiQJIT4SQnzf2rp4SnvUuSkoK/rNhEY/mtNBMh3kJyyOAxEbVrYV/0Cv8BspNBY71XNKlc9bf7mGFVszUQkVFw3tQZ8k3+zrzco+xYcfrWTX7mziYkO56srhjBnd8CC7wmDkaHEJieFhhPmfGaQ8OnAsfotzWbBiCxwpR93dwok3+5PQrWOjdVSrVFw+ri8fL97kIE9PiOHIrjyn8mldmz8IU41NzSnrmevW6KykDMli+6J0hIC0Lo3TYcfOTD79bA3HM4tIS43l5pvGNDqVYe7Rk7x57yfs+H0ffoF6pt58Abc+fwVqF0bD7DF9mLd6N0azFZvKvsrmj5pZo3s36txNYVTHLmy44i7W52Xhr9EyNLZzk/fm1xIc4Mdnj1zF/LV7OJRTSFrnDswc0ZO5z97oVT1HjhbwzxcXcNut44iO8ny1Na1LB7JynbPkdEtq2cBhN0wezOpdRzk7FXqov55LRvRyf5AbSmpqyCopIykynGC9d66xQgj+PnwSt/QcxP6SQrqHR9MlxDtDr8TkepLEndwVWyurGNcEr16hCgKcJ9N+XWHC1Yb/ErNr3S4f25fUTlEs3nQAIQTThnSnb3Lj+85a9DotiZ0iyCoqxC/eOWBakamY6x9+k5osNaNGpHLLzWMJCXFtwHnLzpIFWKSjAWmwVqCJ2k16UAcObS3EHCIRNtCUC2661vMMHa6QUrK/oJAArZYuEd5PGjSVuIgQ/nX7dFbvPkZYkB+zRvYm8fTk4oyRvZizYqdD+eEpnYgOC3Kqp2NQCEtm3sSmkzlUmIyMiEsgQHvurBglJufxg11eRJfAc3tsxEQFExrsR1mF49aA0GA/Ys7Rx2n0I7EPIy315I6ZfBYv2cn3P2zhVHklXft35M5rxtKtc/P1e1tL7MFnO+qdt4gcq9xIsTGLCH2C098UFLxBCPEx9vzyBWdHihdCTMEexV4NfCilfNFdHVLK/cCdQggV8D9cRK5vLnyk/1HgltY2mr25lraic0uhGPrnwGAtY1/pPBKDRhOo8c4VW6VJRB/yOIbyFxDYR9SFFj+2GZ3rqTQXEaB2PbDSCi2DUxIZ2a0xIaLcU1Vl5IGHvqK42O4mXV5ew9+em8eLz1/O4MHuA729v2Ez/1m7gRqzBb1Gze1DB3PfaHt6m29f+ZmlLyyhdjiyq2A/T1z8Ep/sfQXNWdG8bdLq8R5MgLsuGUGgv455q3djMFmYMDCVu2aM4OnXfmbjzuN15cKC/bnh0qGe34RGYJYqVtfEYauXnsg/xD6YnTmxLwkdXa8S59fsoshwkHBdVzoGDHRYmT9y5CSPPPYdZrPdYNiy9Rh7953go//eTGysd/tzbTYbT17yCicO2/et1lQamPvmYvT+Wm581tmdMqljJG/ffykPzV9MtrUSBEititUnMrm6Q9P2BjcGf42W8Z09D6bmDcH+eq6b2LR0dhr/Gjbu3sjBv+bx8Qe3erxPOUWl5leTBXRn3gW/U5Wkxftum4cn9E3uyBv3zOLDhRs4nl9Cn+Q47p45kogQ7zw4/r1yLR9t2orJaiVAq+X+0cO5eYj39zYxJJxELw38WlKC0lldtOz0Lwmn38vUIM9jDZySEewxhtNL37hUxUIVgUbfG4txeZ1s3+befP83C0lf1C8tz6lb/5RO9HfhWWG0VpBZuQqrNJMYNJoAjXdxSO6+ZgyPvT4fm1mg0jq7QuVnVWAr0bJg4Q6OZxbxxmvXelW/OyrMrld2q6xFvPN/t/HW3DWs3X2c8GB/rpzRj0ubkO5yT/5J7p+/kKxSu3E3pHM8b866iMiAlvVMmtA/lQn9U7HYDByvXMW+0ko6Bw4n9ZQJ/515GJMjQAj0x0vI//Uohrtn4BfgPNMkhGBorOfpFFODerCn3DEotlqoSQpqePJep9Vwx1WjefmDX6hNYCEE3HHVaHTnyMShUsfiF/oMhrJnqZ3sUmmS0Yc8WldmydJdvPzqYio7qijtrWEvOSz84kuuGdiXpydd0CzxSQI1/pSZy9GpaifgzvQNABWWIsXQV/AFnwJvYzfQARBCqIH/AJOAHGCzEOIn7IbmC/WOv1lKWSCEmAHcBXzeEkqfxaf4QP+WUbVBPsXDa5FS7nNZw3mKYuifg0pzPusK/s2GgjcZG/ckycETvDpeH3QHav1U1uQ+x+7KbHLMgcTqK5zKJQT2J8Yvmn5hPdlR6pjua1yH4ejUvs9v/fvK/XVGfi1Swtz5W90a+uuPZ/HKijV1v40WK2+t3UDP2A5MSE1myScrnI4pyD7Fll92M2xafw6V/cyu4k+otOQR5deTwVH30cG/4VVjlUpw45TB3DhlsIP85Udm8su6g2zfm01cdAgXT+hNVLjz6ogvqLJpWFPdgV010eg1zqt3HTS9ePHhSxg9yNlAtUkrv+c9y7HKFXWyjv4DuLDTS2hU9gHeTz/vqDPya6mpMbF4yS5uutFxdaQhdq0+UGfkn83iT1a4NPQBjhnKyLZV1o2Fqs1mnl26nP4dO9IjxvtUe+czgeEGLn5mJTt+7MamzeMZPsyz/c1bv99I4O4szAmRSH8t6qIKNLmlrJm/mcnXe/eMm8qIXl0Y0atLo49fevAQ764/42VTbTbzwvJV9ImLZVDnltsC0CdsMH1DEjCaNxKgNmGwabCIHoyM8ixqP8DQiMkszdvEQVMYHdQ1gHPmh3MjCIj8FItpKzZzBgZDKn+75i1sVhunvpBEXivRCzPx+mJC1AakeS67irX0Dr/BIyOnoGYvS048hMlmd6HeUPgW42Kfpmuw5wE5Rw1K5tN/Xs+bez8jP8zRldt0yB9byRmX8N17csg4lE9aamz9arwmIbA/RyrXuZAPIDIkkGdvnNzkc4A9c8jdcxeQW37m+7opO4e/LVvOmzOn++Qc3lBqymRR9v1UW+3eG4I3OJbdlcBdVgLP8kQrAzYs3M64PzU9S8CMTleTVX2UcsuZTCLT464kVOvZJNrMiX1ISYhi2ZrTUfdH9aBXWsMeJbrAG9D4XYjFuAqhikKjH4sQZ4aWc+dtwRQoKOmusc8eAFLAF9t20iMmmsv7+t5zbFLMWL7P+RmDVUO0rhytsGGVAoNNh02EEOfX3efnbK+cz27xzY2UcpUQoks98RDgMJAGLAaigH9LKadgX3F2Vc9PwE9CiIXAV82msPN53ep/etUbIcQ3wCVSyhdwo39bwJtrARRDX8ERGxbWnXyVxMBRdYaZp2i0CYxL/ID4yv1kVh2i2LCFnOozaZQ6+KUwMNJufN2feiufZ37P+lNb0Qg146JHcGXCJT69llrKSqtdyktLq1zKARYdyHApX3wggwmpyRhrXOegNtWYyK5cy7qCf9bJigx7+TX3/5iZ+A0BXnpL1KLRqJk6Jp2pY5o/UrgZFYVSR0c/E1aRQqn5WN3fOgf0Z+b0B9CoXE/IHK9c5WDkA+TWbONg2c/0DJ8NuL/vpW6e07kwVrt/Du5Y7OLZytNyxdB3TZ+LD1KUkwl4Zugba0yoDGb0GY6TMEYXe2PbOov2u+4LFh3IaFFDv9Kci1ZuRK22t20/lQXYQ6npADH+/TyqIz1sNPvLR5FVtYZSW+N9+DW6gaAbSFVlGbbT+yIK31FTscLG1PcL8A+0uzebbGVsP/Vf1MKPnuFXNljvmoJX6ox8AKs0sbbgFRKCRqAWnkfMT06I5tX4v/BN9o+sKFiP2WamdIuWyiXOhmBj+h1X9AqbyuGKdWRXnwnUmho8mpRg53gETWFnbr6DkV/LLxlHMFutLR7TZkPB23VGPoDERufrjrLzf/GYyhx1MRnc98veEOPXiSfTX2NH6UaqLBX0DO1PjIcBKWvpldbRI+O+Pip1HLqAK1z+raS0mpoOqjoj/2wW7c9oFkN/dvw0BAbKat5AI+zvolpIAtVGkoPHoVO3rJeHwh+KTthXj2tXkscArwgh0l2tJAshxgGXAnpgUQvq6Y5OOM525wBuXWWFEJHA80B/IcRjpycE2gour6WN6+xzFEPfQ4y2CoqMB4n1b5xrYUpQD1KCegAzOFlziBM1ewjTxtElaHCdG3uAxp87kq/jjuTrfKi5a4YMSeKjT1Y5yYcOce8yrXMzWNKdjog+auZgFrz/q8Pf/IP8GDipDxvKn3E6zmyr5ljFMnqGO6XqbLNIahgZPY1AbQonDRlE6hNJCOh/zpW53OqtbuW1hv7QIcmsXuNsPA05x/NwR79x6QRHBFFR7BhEaeTMwW6OwO1AuK0FfWxLqFQQ18Nzd+9RswZzbI/jarFao2b49KZtJWgN3PcFLdtejlQswibrG0qSjLIfPTb0AWYnPE1+zUFya/YBvzRJp/CYUHqOSGPvOvv7HBldg3+oc8C+jLL5DRr6BmsZxUbnYGoGaynFxsNE+/XwSjeNSsO1ibO5NtHe7zy+YA4bDEccygQG6Ondy310d+/Op2N2wotkVW/nlDGTGL9UOgV4HwuiIXRuAhZqVCqfxfjwhtwa5z5frZdE9jOSt/KMkanz0zJkiufttCH0aj+GRnru6dESDB2SzLF9e1z+rbn6C5VQ0TtYzQaDs/ddlcU5G5KCgo+J5vRKshBiJHAINyvJUsoVwIoW1c6HSClPAXe2th7e0B51bgpK1H2PEV7v03dHjH8qAyJmkRQ8zKu96r4kNSWW668b6ZC/t0/vzlxxufs97pf2TncaNInTcoCb/n45AyedmZ0PjQ7miS/uITDEH4vN9QqR2Y28LWO2VdMpoCcDImaRGDigQffbQI3rFfGz5ZMv7O0UCPGiaX0ZMdz7tFd6fx1PfnkPEbGhdbK+Y3tw+4vuJ1Qu6+OcHUGrVjOjp+LieC5iwzzfQ3v5g9MZfemQuvYSFBbAQx/cTnR8y0X+9xWzXbQXtRDM7OWd4dlU3PUfjelXYv27MSBiVlNVAuChD26nay9729AEuo5o74mOWlUAOpXzdiSBigB1079H99w9ibi4M3E49HoNDz04FX9/320XE0KQGDiAARGzmsXIB+gVG0P3aOf7MbNXD9Sqlh/muOvzU7qdeW+CI4J45JO7CIsOaSm1WoVbbhpDd30YWJ1jQ1zWp3naA4BZnj9jDoV2xQkggTMryfGn/9+yaW0azwng7IFN/GlZe+R8upZGI6R0kaNMAQAhRCGQ6eJPUYAvc6gp9bV8fYlSSo990oUQZdhnZT0lFPsWTF+Ua6jMuf7u63vdUnh6/3xBqpQytOFids7RL/ialnh2LdU+2tN5vO0bPG0PzXUPlHqbT9fmagsN0dzvS3uuv7V0b7AtCCFuB24//bMbcNDHuim0HWqfrw57fpX7pZT/FfYAFSeA34Hrgc3A10C8lPKe1lLWHaf3tf9cG6n+tP4ZwATs17EZuFpKudddHW2F8+lafIniun8O3HXqQogtUsqm5QFS6muz9bnhWynl7Q0XsyOE+K8n5T0p11CZc/29he6Nz/H0/vnqXN6U92bg3xRa4tm1VPs4385zNp62h+bSTam37fRzvuobmvt62nP9bVl3KeV/Aa++JwrtFyHE18A47LbU00IIq5TyIyHEv4DngP3Ax9idX9vcSvJZ+kcJIXKAZ07rfw+wFHuk/Y/bg2F8Pl2Lr1EMfQUFz1jQTOU9KddQGW91aw+05DWdj/dPQUFBQUFBoZmQUl7l5k9vAvcAEzlrJbml9PIUd/pLKRfRNgIDesz5dC2+RjH0FRQ8QErplTHoaXlPyjVUxlvd2gMteU3n4/1TUFBQUFBQaHmklBZlJVmhraAY+o3D165ZSn1tq77zCeXetF9a4tm1VPs4387TGJpLN6Xetv3cG0NzX097rr89667wB0FZSVZoKyjB+BQUFBQUFBQUFBQUFBQUziOU9HoKCgoKCgoKCgoKCgoKCucRiqGvoNBGEULMFEJ8IIT4VghxYWvr094QQiQJIT4SQnx/Pp1LQUFBQUFBQUFBoSEUQ19B4TRCiDAhxPdCiANCiP1CiOGNrOdjIUSBEGKPi79NEUIcFEIcFkI8eq56pJTzpZS3AXcCVzRGl5ZCCNFZCPG7EGKfEGKvEOL+JtTV0P3LEEIYhBB5p8/1N1f1SCmPSilvaeK5PH1WDZ5LQUFBQUFB4Y+FEKJFUgU3F4r+7RvF0FdQOMMbwBIpZXegL/YcqHUIIToIIYLryVJc1PMpMKW+UAihBv4DTAXSgauEEOlCiN5CiJ/r/etw1qFPnj7Op/h4FdoCPCilTAeGAXcLIdLrnc9X928KEAcUAFcBs4QQa+rdv/RWelYKCgoKCgoKCrW0d0NT0b8doxj6CgqAECIUGAN8BCClNEkpS+sVGwvMF0LoTx9zG/BW/bqklKuAYhenGQIcPr36awK+AS6RUu6WUk6v969A2PkXsFhKua2evi5XoltrFVpKmVero5SyAvskSad6xXx5/0qw37+ZnJlkqLt/QE8fnqvBZ9XQ/VFQUFBQUFBQUFBoSZSo++cgKipKdunSpUl11FirMdvM6FQ6/NT+vlFMocls3bq1SEoZXftbCNEPe1qdfdhX87cC90spqwDCI8NldHwUWpUWf3VAq+is0Dxs3bq1BMiVUvaqlQkhLgOmSClvPf37OmColPIeX/QLCm0Po82A0Wrk0K7DDn1DQzRne6i2VmOxmdGp9fip/JrlHAruqf+daIjzrW8wSzM1lmpUQkWAJhDVH3htqLnbgtFmxGg1oBYaAjQBCERj1FRoIbxpD0FBQbJ79+7NrVKzUVhYSHS0x02/zdHc+nvbN7Q0mtZWoC3TpUsXtmzZ0qhjTTYTbx16iUOVB+tkvUL6clfKX1CLtn/by8tqKCosJz4hEp2u7evrLUKIzHoiDTAAuFdKuVEI8QbwKPAUgCZGxfiPhwGQGtSd+1IfRqvStaTKHnMyrxSj0UJCl6jWVqVdIITIwQvvpri4eDZu3IRa/ccd9J5PSCn59Pj7bCxeC8ChQYfr9w3npCnfCXcYrDW8nvEix6uP1sn6hw3mtqR7UInzp91VVRk5mVdKXKdw/P3bXn/q4jtxTpqjLXiKr/v95QVLmZP9JRL7YlCwJpi/pD1GJ//OPqm/vdGcbeGbrP+xovCXut8x+lge6PYEodow75T8g5CdWYRWpyE2rvXujzftoXv37q3WLyg0P972DS3N+WfBtRHWFq1wMPIB9pTvZEvxRoZGjmxS3QaDGZVKNIsBLqXkvdeX8fO8rZjNVkLDA/jzXyZzwYW9Gj64fZMD5EgpN57+/T12Q9+JQ5UHWFu0inEdJraYcp5QWlLFP5+ay46txwHomtyBx5+7lMSubXai0QGLxYrZZMU/oE0M+E8AZ49o40/LyDxWyPWz3+LBxy9mwJCkVlFOwXfsK99dZ+S3FVYU/OJg5ANsL93MrrJt9Asb1EpaeY4n7/KXn6zmm/+txWgwExCo56Y7LuCSPw1uQS3PD0pLqnjh6Xls33IMgC5J0Tzxj9lN6vcrzOXMzfmmzsgHqLBU8EPO19yX+leP6jCbrVgs1jY5gdOWOFZ1xMHIBzhpzGdx3k9cmXB9g8fXVJvQ6tRoNOrmUrHNkHW8kOefnMuxI/bdcn0HduHxv19KeERgK2umoNB2OX+WBtoYGRUH3Mj3u5R7QmFBOU889A0zJv6LWZNf4fWXFmI0mhtdnysWztvGvO82YTZbASgrqealv/9Ibo6rbcznD1LKfCBbCNHttGgCdjd+l2RUNv45Nhdv/GthnZEPcOxIAX97dA5tfXuO1WLjv2//yqVTXmXGpJd46J7Pyc481dpqbQZShRBdhRA64Ergp9o/Fp4s5++PzaGq0tBqCir4hrb4LmdUuvt+uJa3FWw2yUfvLmf2tH8zY9JLPPDnzzh+tNCp3PrVB/nsvyswGuzfr+oqI//59xL27spuaZXbPW/8a2GdkQ9w/Ghhk/v9I1WHsEiLk9yT8YvFYuWd15dx6dRXmDHxJf56/xfn/fihKRxq5Fgx83gRD9z9P2ZMeonZU1/lg3d+w2q1NYeKbQIpJX9/7Ps6Ix9g59bjvPGvha2olYJC20cx9JuJSJ1r97kIfePd6p559Ds2rT+MlGAyWVj443bee/OXhg/0ghW/7nWSWa02Vi1ve4PhZuBe4EshxC6gH/BPdwXdPd/WwmAws35NhpM8J+sURzLyW0Ejz/nys9XM+XoDNTUmAHZuz+TxB7/CammxQUt3oJsQIkcIcQuAlNIC3AMsxR5Y8DsppcPLUV1tYsPaQy2lo0IzoTO0vdUgt98PXWQLa+Id336xjm++WEd1lRGA3TuzeeyBr+omjmtZ8YvzdwZcf38U3GM8R79/uAn9fpTb9tfwd+/TD1Yyb84mDDX2SZztW47zxEPfYLO17Qnn1sLdPY08x1jRbLby2ANfsXtHFmD/Fn335Xq++bxteSb5kqOHTpJ1vMhJvmFNRt3YQUFBwRnF0G8mxnaYgJ/KMfhekCaYUVHjGlXf4Yx8Dh10/nD/sniXTw0ilcp1AJg/wn5kKeUOKeUgKWUfKeXM05HdnfBT+TM2ekJLq3dOVEKgEu3z2S1esMNJlp9Xxratx1yUbhZ2SSm1Usp4KeVHtUIp5SIpZZqUMllK+byrA9v6vVVomOL1gZiL29Yutgs6XIiuXgyQUG0YwyNHt5JGnrF4wXYnWVFhBZs3HHGQqdy8N8r75B2imfr9+IBEeob0cZJPjp3e4LGu2kBOdjG7d2Y1Wp/zmX5hA4n16+ggU6FiUsw0t8ds23yUwpPlTvIlPzt/S88X3I1NVSr374CCgoKyR7/ZiNbHMCnmGpbmL8AiK+nkn8CtXW9qdHAVdy76ZosVq82G2kdzNhde1NfB/RtAq1MzdmK66wP+IKiFDkEA4doO3ND1eqL0bSt1uk6vYcyEdJYvdci2R0paLF1TYlpJK88wmZxdRAFMRtfytkJIqD9DR6a2thoKTcRmUJHzXjzhVxXhF9M2tmJ09I/noW5PsSR/AScNeXQJTGZq7AwCNUHNds5dpceYn7OOcnM1w6N6cEmn4WhU3u37Nbp5Z031vl+TpvbhtyW7HWQqlWDClN7eKf0HxCptLMzdxOrCPQSo9fSc3Ymd3zpueUhOjSGpif3+Hcn3sSx/ITtLtxGgCWBs9CQGhDccQ6G99uethUal4YG0x1mSv4CDFfuI0EW5Py+HAAAgAElEQVQyKWYaacE93B7j7j1zJ6/lSEUuc7LXUGAspV9YMpd1HkmApn1k8+iaEkNKt1gO11vwGj0+Hb2ftpW0UlBo+yiGvgeYbVY+PrSBRdl70ajUzE7sy1VJAxHnmEWcm72W1zN+BgQQzJ7SEkI1m7knbUajdOie3okOMSEU1JvFHT4yzadB+SZO7cPJ/DK+/2o9VZVGOnYK588PTqFDTKjPztEeqbJI9paGAAayqubzwZD78Vc3HGSo2FjNO/tXs7bgKB38grk5bRhjY1OaRcd7H56G1Wpj9e/7sVklfQck8uCTjWtvLcnocT1Y+OM2B1lgkJ4Bg7u2kkYNk5TSgb88Ov0PHWhqX2ke7x5Yw6HyQnqFxfHnHqNJCm5bW1o8YciYZD4OXERueBCYms+Q9paEgC7cnnRvs57jSHkR7xxYzc7Sw5jUZ/a+birOYEfJUZ7r03AwsLMZM74H8+dsdpD5++sYPCzZQTZgSBL3PjyV/324krKSaqKig7nl7gmkdotr/MW0AY5UFPHpoY1cnzKk2VYZX97/PYvyzrrHvaG3Jpkj3xT5tN/XqfRM73gp0zte6tVxo8f14JfFuxxkoWEB9O2f2GSdzldCtKFc3vlaj8sPGpJEQICO6mpHl/XR41xPDizJ2ceHh1dSYDkGwr6FYkvxIdYW7eWdQfegbieZPJ554U+88vwCdm49jkotGD2uB/c9PLW11VJQaNMohr4HPLVtIXMzd9b93l2SS6GxkvvTxzmUk9KG2ZoFhPG/47+e/RcA5uas45ou4wnXeT+YVKtVPP38ZTz35A+czC8DIL1XPPc91PhOrtpk5mjhKTqFhRIeeGabwTU3jeayq4ZRdLKcjgkR55zQ+OMhyao+yW/525neaSgAVmsxVlmJTpPgUNJstXLl8k84Xm0PRHSkoogNhcd4b8QVXBCX5nPNAgP1PPHcbKoqDZjNVsLCW3bvsaHahN5f63V7ue3P48nPK2XrJnuU8fCIQB59+pI2a0QnpcTw3ud3tLYarcrRiiKuXvEZ1Vbz6d+nWH3yKAsm3k4H/+BW1s47jgXlITvYUGEjUlvZ2uo0C1JaMFuy0ahjUKkCAMivKeeqlZ9SaqohMqiS+mtiKwt3c6Qil+Tgjs4VYl9ZrrEaCNIE1Mluvv0C8k6UsHHdYQDCwgN5+ImLCQxyXjW8+NJBTJ3Rn9LiKsIjg5rdbd9ms2E2WtCf7lesNhsH84sIDfCjU1iIT85RYzHzz13LyK0u4/G+F/qkzrPJrSlmcZ5zmq5TA0r44caHmqXf97Zfv/O+SRQWlNd5BkZGBfPYszPR6T0fbposmahVoahVrj0gc0vLKa020C02CrXK83ZjsVkx2swEtpNVbHcEBOp58h+zeem5nygtqQJg8LBkbr7zAqey8zJ38siWnwgLqMZP5xgnYX95NuuL9jMquqdH55VSUmWtIUDt1+Q0n1JKjDVm/LzIshMTF8bLb19HaUkVRpuVQkM1lhZMNnCmH21bHp0KCudCMfQboKCmgh+zdjnJ/3d4E3d1G4VObb+FVTUrOFnyCGZrNqBnYmhXfijsh1ZtQ62SSGn3DMitPtUoQx+gW4+OfPbd3Rw6mIefn44uSY1Pn/Pd5l28smwNFQYjWrWaa4b25a9TxiCEYMHHK/nmjSUUnywjpU9n7njuT/Qa2jyr0O0JP7W57ln+enIj0+L6kF/yMBXVPwJWdNruxEW8jp+uDzuy87h7wVwKYxyjDUvgw4z1zWLo1+JqUN2cbFu5nw+e+YHjB3Lp0CmCax66iAuvGu7x8YFBfrz42tXkZJ2ivLyGtO5xbTpVkEbTPlY/mpMvj2ypM/JrKTFV80PmDu7q3rb3kdcnq7qQQcHHuSJ6G2GaGua2tkI+prz6RwpKnsVqO4kQgUQE30lU6IPMObadUlMNAGq16zgvWdWFLg39n06s4pusZZSYK0gIiOXO5EvpH94N/wAd/3j5Sk7kFFNWWk1qtzi0WvfvskajJqqDb4zsc/HdW8v44d1fKS+upPvArox9YDxvbt9OXlkFABd0S+LlP00lUO+bycWvj27lvvSxBGn1PqmvlpzqQoeUd7Xk1hTjF6gj0Icrs1uW7+WDZ+eSlZFHh/gIrvvrdCZePqzB40JC/Hn5zWvJyiyissJAt+4dUXvYZ9YYt5Bf/AAmy2FAS2jgbGLCX8Se+MS+OPHI90v47YA9KHFsSBAvzp7C0KTO56xXSslXWUuYf2IllZYaUoM6c0/q5aQFJ5zzuLbM4KHJfDXvPg4dzCMkNID4zhEuy31wcB3g/h3PrnbOiuGK1YU7+PjYT+QbTtFBH84NXS5ifEzj0mH++OHvfPvmUkoKykntm8Bdz19Oj0Gep6qdt+8Aby9fT7XJjF6j5rbRg7l7vOdjjsZQUf0zBaVPY7HmI0RAwwcoKLQRlBFrAxQYKrG6SFNTYTZSabFHFrZYizhx6ubTRj6AkUnhB7gwYj9qlf1YIUCntrG3/HCT9FGrVXRP79QkI/9AXiHPLviNCoNdf7PVyqfrtvHjjv2sXrCNdx7/luKTdq+Bw7uyefrq/1BcUNYkvds7KqTDszxYeYTNufdSUT0XsEeUNpkPkFN4A0ZzDfd+9RNFpiqXdeVVOwfRaa/kHi/k2evf5fiBXAAKThTz2v99zraV3mdpiE+IJL1XfJs28hXs5NW4bsO57bBta8tOcGvsOsI0Na2tis8xmQ+Td+oerLaTAEhZxanyVymvmufwDM0ulsVUCHqEOBtC64p28e6RHygx243krOp8nt37AQWGM5OaneIjSO8Vf04jv6VY/MUaPnl+PuXFdm+NfTuO8+TSFXVGPsDvB4/y6rI1Pjun0Wah2Oi6/28KacGd0Arne9otON6n7tc5R07ytxvfJysjD4CCnGL+ff/n7Fhz0OM6EhKjSO8V77GRb7NVc6LohtNGPoCZsqpvKCr7d12Z139dy6/77UY+QH55Jfd+vYAq47mjri/IXc0XmUuotNjf8UOV2Ty5+12qLW0jJkdj0WrVpPeKd2vkw5m+2tU7DpDu4h2vz+HKHF7c/xn5Bnva2wJjCa8c/JJ9Zd4HzF0xfwvvPTmHkgK7Xod2ZvHElW9TWlTRwJF2Nh3L4aUlq6g22SeajRYrb/++gd8PHGngyMZjMh8l99SfsVjt8QGkrG62cyko+BrF0G+AtJBoIvTOs3fdQjoQobe7yFXWLEJK5w/GoGDnKLO/F252krU0S/Zm4CrF7uI9GSz50jk9S02VkZXznN0F/1C48FrUW5c7yay2k+zM+pGiympEpesP67AOXXysXOuxfM5GzC4CAC354vxN86MAw6K7eCVvy5gqF6MW51fqr4zyAv6xcxHfH3qB2onIsymvnuvwrCoMftTPfnZV4jhi/cOdjl2av8FJZrKZWVGwtalqNwv1+yJDpwCseuehz+I9nhuxDREfEEZ8oPO9ayphuiBuSnLcEqBXabk7teFo+N7w25yNWOoF1ZNSstTF+MBXVBl+x2pzTnRTXn3Gx2bxbudnVGEwsvZwJjuLc3hm+wL+umUev+U65qZ31WYrLNWsP7XbSX6+UfueVxn1WG2OA5kJMf3oG97wSvqvJzdhw9EjQCL55eRGr/VxNTaoqTSw6kfP+g937+ni3c5pJgF2FOfw9Ol2sTyvce94RfVPgBJMUqF9orjuN4BOreFv/afxwKZ5mG32AVOQRs8z/e174w9nF7IwYwNTPPTENttav7PQutnTplWrMLrJR2pQ8pQ6YbNJl1NlmtN7TYVRhcjXImPPuDgnBIZzX/rYllKx2XHXXtzJzwey80v5evFWLp3QF70PA2G2Jy7v2p9fcg+wsTCzTjapYzemxLuPFN1WkW3IyP91w0GWrT+ASiWYNiqdMQO93zK1sfAYt637ErPNypUdCxjoJmTCtPieLM7Zx295GVisaorKg+gWHsTETqmMju5FPzcGgNnmOgOMqQ1821zh1Be5yeeuVfvG+8BPreHvA6Y1KRjfweMFzFm2naKSSgb3TmT2xL746exRFK7tMp4B4SmsLNxNgFrP5NiBLidkmkLr9OsNv4funtGuqmw+WbkG2+k6FmTv4tbUkTzYayLgftxlctOWzyf+2nsiu0vyKDBUUFQRhL/WxPCYBK5LGsXwSM/6a3fvfLnB+5Vtt2PMas/altvxqwtPwIXZu/nrlnkO7eKOtFH8pWfbSo+soNCc/DFHqV4yuVMPlk+J55fcA2hVaiZ36kGYzp99R/K58/lvME8L5QKrBr3a8WOyo6qLU12jovq1kNbumd63B++v2ozZ6rjSc2n/npRVBrF3o6MLlBCCkdNaX+9WxcUYZMOxFCak7nWQqVUx9Ok4g4SIr8gqLkOVr0eWapDBVsZ2TeLtSbPq4jqcD4yc3p/v3/nVWX5R/1bQpmWoqjHyxpcr2bDrOG88Mru11WkV/NRaPht9HWtOHrFH3Q+PY2g7XM0HMKgmYpWbWn1V/4Mf1vHRvDMrjys2H+aeK0dz7XTv9sG+ue/3uknp1cXJ/Clum9O1hQRcikal4t0RV7Ch4Dh7S/NIC+nAqJikBoOujYruz/ZSx9UzgWBUdF+v9GwpRl7Un8yDeXW//U9Uo6k0YwlyDD94aX/PApKdi44Bofw+5T4i/RofEG/HgRzuffEHzBb7M9ywO5N1O47xn8f/VFcmPTSB9NDm218+8qL+zHvf2WOtOfv1QL/xqFTh2Oqt6ocEnIn6P6t/Ou+scFxFjg4OYGnJ7jpjrpbPDq/nhpRhRPkFMSq6H19nLXX4u1ZoGBbZy8dX0fboGhzJssl/ZtmJA5SaahgXm0KX4Eiv6hgV1Y9Feeuc5JsWlVDatYawYH8XR7lm5EX9OLDV0eVfpRKMmOZZ/3FJ/3S+3LgTWz231Jn9HVNASyl5bd9yp3bx8eH1XJ8yrM4j1xOCA2ZQVP5vlFV9hdZACHE/8AlQAXwI9AcelVIu8+R4xXXfQ2L8g7k2eTBXdB1AmM7eqX22YBMGtYVStR8vHplEgdEeZM9kU7PgZC+6hj9MiNbemQgEo6L6cXXi5Fa7hloSI8N446rpJETYI9pGBPrz2LSxjO+RzIybxzLhT0PrBnt+AXrufvEKEtLad9qjpmKxqc64vUkwHQ7gmy8nsG5HD6xWu9xgTCA++jM0aj/eu24WAxPtQaz0Fh1XJg7k7annl5EP0H1AV2595lL0/vZBs0qt4qIbRjPpyoaDNrV3Nu7OZMfBE62tRquhEoIxsSnckja83Rr5ADKsG+9kj6HM0nqRuKuqjXy5yHl71Gc/bcLoJi+5OzLKz6TJO2EI57Wj4yk22befCRFIZMgDhATOqiszrEMXbkkbzujYZI8iq0+JHcZFcSNRnR4++Kv13JPyJ7oEuo7O39pccd9kRl50ZqI6KNifRwcNoW9n+zfNT6vh6qF9uXt80/usCH1Ak4x8gE9+3Fhn5NeydV82W/c5bwVsLnoOSeamJ2ai8zvTr8+4ZRzjLxvSbOdUqQKIj/oUrabWk0RLSOAVRIb+X12Zu8YN47rh/fHX2r+jfeJjeeuai8mpLnWqzyxtHK0sAuDKhEmMie6POL0HL0QTyF+7X0e4rvkDQbYFAjQ6Zib24cbUoV4b+QD9w7uRXtUbabHfP2mBmm0hFO4R/Pi7d9sfZt42ngtmD67ra/wD9dz78tXEJ8d4dHzPjjG8dNlU4kLtrkrRwYH8Y+YkBneJdyhXZTFxwlW7sFk5VnHKK5112iQ6Rv4HtcquoxKMT6GFuVlKWQ5cCIQD1wEvenrw+WV1tDCZucUIg0AYBNtJ4I7dVxOjL6fc4odKhLCy3whmxI/iSGU2EboQYvy872Cbiwu6JTEurSvFVTWE+OvrXOLUGjUPvXUD1/11OiezTpHcpzOBXszWnq/YpCCnJAydxkLSyThKVtgDGb7/7UV8sWA8AXoj4waPo29KHwC6RoXzxa1XUFptQK9R46+rn7zq/GH2XRO58MrhHNt3griu0UR39P3e1LZKZm4x/bp1am01FJpA97BYPjzUnRUFqcQHlABvt7gOJ4srMLiIdVFRbaS4vJq4KM8Nku6hsWw5dWZLxZqSFNaXdmX+2BkkhfZApWqaIaoSKu5JvZyrEyaTbyimS2AcAW04XZnOT8uTH91O7vFCinJLSOubiF+gnmuAkuoa/LVa/LRtZyiUmee8Tx3geG4JA9NbLkr85fdeyJRrRnB8fy4dkzoQFec61Z0v8dcPpmvsaszWTNQiFLXa8VuiUat4fNo4Hpg0ihqzmfAA+9gkMTCCzCrHDDdalZqUYHvQYp1Ky2M9buTmrsUUm8pIDopHpzp/v8nNgd/hWMp2lKAKM2Mr1SKN9jFjZl5xA0c6otGq+et/buKGR2ZwMucUKX0SCPAyU9BFfboxtVcaJdU1hAX4uUyxGKjRkRAYTlaV4/ukU6lJCo7y6nwAwQEXE+Q/FbMlC406Bmhc9iwFhUZQOwM/DfhcSrlXeJHHWlnRbwI9k+MQUuC3Rw8SJIJ8YyjVVj339hhHgEaHTqWhR0jXNmXk1yKEIDIowOW+t5jOkfQZmaYY+WchESQHJTAxvI+DvKran8KSMHomO3s9hAX4nddGfi3B4YH0GZn2hzLyAXqmxLa2CgpN5MKOPRgQ0RkrajKrvR8A+oJOHcIIdTHYjQ4PIjrcuwHlX9LH41fPc2hWwiBSwgc12cg/mwh9KOmhXdu0kX82HbtE02dEGn6BZ1LehQf4tykjH6Bnkus+pWdyy/c1IRFB9BmZ1iJGfi1CCHSaLk5G/tn4aTV1Rj7Ag70moq437r0tbaSTe3aMXwQ9QroqRn4j6JkchzSqsZ70qzPyAdLdtNeGiEmIpM+INK+N/FpUKvv41ZWRD/Z29GBP53Zxe7fRhLsIsO0JQmjQaZN82o8qnP8IIfyEEJuEEDuFEHuFEH87Le8qhNgohDgshPhW1OYRdWarEGIZdkN/qRAiGHCdL9MFbesL1864aeZQNuw6zqnMKlTlKsyJZuJjw3hmyjSGRHVpbfVahQObj/Dzf3+lrKiCwZP7Mu2WC9C0sYFUYwjTBfBkn6nMSuyHucbKyg2HHVZe+nePZ8JQDyMytgBWi5Wln61kw6LtBIUFctGt4+k5vO3odz4wa3wfUjp7l+by+N5sfnz3FwpzTtF3bDoX3zERvwDf5tpW8A6tSs3Ho67nx6ydbC/O9twfrgE2LNrOr1+sxmaVjLt8GGNmD3VbVq/TcO9VY3j+w2V1GVHUKsF914ytC+7pKQOjEph7wZ18n7mNUmM1Y2NTmdSx/QVJPB85vOM4P733C8X5pQyY0Jvpt41H5+c4trt19nC27s+mtOJMuseLx/ake1fPXJv/iEzq2IM5425nXtYODFYzF3bswagY7wNZ+hKrxcb/nvuBg1uO0jktjpn3TCY2sfFpkVubSy7ozeI1+ziUVVgnS0+KYfqYpse2aC4u7JTOd4G3MT9rBwarhckd0xkZk9zaain88TAC46WUlUIILbBGCLEYeAB4TUr5jRDiPeAW4F0Xx98C9AOOSimrhRCRwE2enlxIV3nWFAAYNGiQ3LLl3GnlyipqWLh6HydPlTMwPYFR/ZNQqRofbbc9s/XX3Tw161WsZ+0vHD59AM/O+b9zHNU6CCG2SikHeVq+fluoNphYsnY/mbnF9EiKZcLQNJdRX1uLF298h9+/XV/3W6USPPXN/Yy4eGAratU28bYtdE7qJr+bt4Thfbt6dZ6DW47y0KR/YDKciWDcc0Qar/zyBCo3qxIKLU9T+waAuW8u5v1HvnKQXfP4TK5/6tzBGw9lFvLLhgMIIZg8ojtJ8a3jYaBgxxdtoZbdaw7w2EX/wnxWzIUBE3rxws+POJUtLqtm0eq9FJVWMaRXAsP7dvUofoJC8+FtW4gKiJUD5aS63yGRQby19u/t2tg3mMwsW3eAI9lFpCZ2YNKwbn/YzDPetAdPbAmF9os3bUHYAzysAe4CFgKxUkqLEGI48KyU0imQ22k3/WuAJCnl34UQCaeP2+TJOf+Yb6gPCQ325+ppvjGebFJSYTQSote3y4/65/+Y62DkA6z/eRuHth0jdYB3RlFbJ8BPx6UT2maU6awDJxyMfLCnAvz8H3PbhaFfZTKhVgn8NG3TvTImIthrIx/g63/96GDkA+xdl8HWX/cw+MI+bo5q27T3Pqs5MBnNfPWvn5zk37++iNn3TSUw1L3baGpiNKnt2BBoi1SbzagErd6ffPnCfAcjH2Dbb3vYveYAvUd1d5BHhAZ4nG2hwmREr9ag81F6QAXfYDaY4SxnrfJTlcx/eyl3vnxt6ynVRPx0WmaM690i56oxm5FAgLZtjgMUFLxBCKEGtgIpwH+AI0CplLL2o5ADuAv49A52V/3xwN+xR9//AfDoI6EY+m2EuQf28eqGNeRWVtA5JJT/Z++sw6M61j/+md1NNu5ClBBCkODuWqBQCtSwGoW6X6jcut7b/npr1KHe0pZSWgqlSHF3CAQJhAhxd9usnN8fG2HZ3WSTbDYB8nmePO0OZ+bM7pkzM+/MO9/32eGjuCGia2tXq1GknM8wnX4u/aoz9NsyKefMPQfT6W2FrNJSnt++me0XE7CTybkxshuvjR6Ps725Y0tXFmbfj9i0K9LQX3X2FO8d2EtmWSkd3T14bvhoJnfu0trVanUKs4spyS81SleVV5GTmlevod+O9cgpK+Pf2/9hx8VE5EIwrUs33hgzodX6E/P9crqRoW8Jsbk5vLBjM8cyM3C2s+OOXn15euhIs2eW22l9ks+lt3YV2jzFqkpe3LGFDfFxSJLEdZ06899xE/FyvLL6TSHE/cD9AKGhthPRbKdV8BFCXOqysUySpGWXXiBJkhboK4TwAFYDjen0h0iS1F8Icby6rIJ6zvMbcc2MCEKIcCHE10KIVa1dl8s5kpHGU1s2kF5aAkBKcRFPbPqb0zlZrVyzxtF1YLhRmhCCLgOM01uThIQEgI5tsS1Ygy79wkweH4kc0LYXWx7asJZtFxOQgCqdlt9jT/PKLuNYzi2AvS36BlPvhz79yjszeCA1hae3biKzTG/QXiwq5NFN6ziXl9vKNWse1ugbvAM98QnyMkp39XIhILz9nLWteHjjWrYlJaCTJNQ6HavPneHFnVsszm/tccLc+x/ZhPFRpdVw99rfOZapXzwoU6tZeuwwS48dblYd26mXZo8TV2Jfb2ue2bqJv+LOodHp0EoSmxIu8OQ/61u7Wo1GkqRlkiQNlCRpoK9vu5fWVU5uzbOu/ltm7kJJkgqB7cAwwEMIUbPhHgyYi9esrvYIkACEEL40Qozvijb0hRDfCCGyhRCnLku/XghxrlrJ8N8AkiQlSJK0sHVqWj+/nT3F5UoJWkni99gzrVKfpnLPa7NwcjNU6Z/+4HWERBqr0VubBQsW4OfnR8+ePQ3SN27cSNeuXYmIiODtt/UyW+Hh4QAXjUu5OvAL9eG2RTcYpDk4K1n45uxWqlHDXMjP43iW8Y7XX+djqdSoTeSon8a0B6DKFn3D7c/fhIefYZi0sbcNJWr4lSeS+NvZU0ZpGp2O32NPt0Jt6sfWfYNcLuO+t+Yiu0RETwjBwjdno3S8OrxT2joJBfkcyTDePf3m5dfwbaVx4q6Xb8HVyzCCwuS7RxPRN6zRZe28mEh2eZlR+koT72U75mnJccLxMjX5oIgOzHx4UnOrfFVTUFHB5sR4o/TdKRdJLyluhRq1007zEUL4Vu/kI4RwBCYCZ9Eb/LdWX3Y3sMZMER+h9wLwE0L8B/0Z//9aev8r3XX/O/RBj3+oSahe9fgU/Q+ZChwWQqyVJKnNWs2qy86116Ubx1Vuy3TpF8aXx97mn+W7KcopYfD1fRhwnW3Oc82fP59HH32Uu+66qzZNq9XyyCOPsHnzZoKDgxk0aBDTp0+nR48eNqlTa7Lgjdn0v64XB9Ydw8XTmYm3j8K/Y9sV9lJpTbd1jaRDq2u8YGhj2oOtCOrsz9Ijb7H5x93kpObRd1wUQ2/oZ7P7WxNzz6st9lmt0TeMvW0oYVHBbFuxD51Gy5jbhtGlX5hVym6nYVRa02Oq05AB/PrBEh6+997aNFuNE2E9gll2VP/+52cW0v+6Xgy+vmk6L1fLnKG1aclxIqhLB55e/ADnjyQQHBnAdbePxKk9XHG9aHQ6dGYEwqvMvNPttHMFEAB8X22fyoCVkiStE0KcAVYIId4EjgNfm8osSdJPQoijwARAADMlSTpr6c2vaENfkqRdQoiwy5IHAxckSUoAEEKsAGYAFhn6rXGuZmpEJGvOGz+zG7pcWWf0AXyCvJj37Ayb33f06NEkJSUZpB06dIiIiIianRnmzJnDmjVrLJ7AXelnrPqO6UHfMVfGokYPHz/C3D1JKiowSB8TGtakM7WNaQ+WYK224OHrZuRtcSUyNaIrf184b5TeFvus1uobwnoEs+D1WU2veDtNppu3D509vYgvyDdInzR+AqEdDON+23Kc8Orgweynb7T0a5hlTMdOONvZUaY29Ha60nR9WpuWHieumzeS6+aNtGqdr2Z8nZ0ZGBDEkQxDD+YoXz/CPDxbqVbttNM8JEk6CRjt6lTbqYPN5RNCXHoGMBv45dJ/kyQp3ziXMVe0674ZgoCUSz6nAkFCCO/qOIX9hBDPmcvcGudqJoVH8OjAobWquQ4KBU8PHcnw4LZnXFZoK0kuT0elrWrtqjRIWloaISEhtZ+Dg4NJS0sjLy8PIJRGtAUfXx+Sy9Op1KpavuLXIEIIPptyI2HudYN5P/8A3hpnPVdHc+0BkDfUN7SftzNkakQkD/UfjL1M32c5KhT8e/hoBgcGt3LNLMNUW0hJTeFEyiloZN/Q3h70lKjLSCnPQCu1/s6bEIJPJk+j0yXGQW+/DvzfeOP+xJrjRE1byK7MI6uy5fQq3JRKPr3+Rnyc6gTKruvUmUVDR7TYPa8VroVxIr0im6571DMAACAASURBVDxVQcMXtgIfTJxCD5+6366LlzdLJl35i+PttNMEjgJHqv97+Z/F8Rqv6B39xiBJUh7wYGvXwxyLh47gnj79SCospLOnF+4ODg1nsjF/pG7i99SNVOpUOMsdub3jDCZ3GN3a1Wo03t7eAMmNiYmbVJbGv6LfxFHuwK3BU5gZNLHhTO00iu4+vmy74x5O52SjVCjo4uVtq1trJUlqs31DW+WZ4aNY2G8AFwsLifDywk3Z9vosSzlXnMD27ANk6ZeIG9U3XOtoJR3fJK5kS9ZeNJIWL3t3Hgifx0Av2xzbMkc3H1+23n4PZ3KzsZPJifTWH11Kys2zKH9TxgmNpOXFU+9ztvgCAF1cwlgUuRA/B+v3ZWM6dmLf3fdzKicbHycnQtzcrX6Pdgy44seJlPIMPjj/DRfL9Tvm/TyieKLLfFztnFu5ZnUEu7nz95y7iM3NQSdJ9PD1a+0qtdNOqyBJklUUtK9GQz8NCLnkc31Kho1C0hWCpEbIW2al1svRqckhRNJK/yax6HtU2hx8HIfT1fMJHBTW6yCP5sfwU3Kd+1qZtoJlCSvo5BxCpGvbVHMPCgoiJSWFotIKPl65m2+X/YZCIeertQcaXZZO0gtcVmgr+fHiasKcg+jroXftzK88RlzBZ5SqL+Bm351Iz0dxV0Y1qc4b95/l278PkZFbTP+uwTw2axSdg1rvbL2kKwapAiG3jVq4EIKefi1zr5r2UENqaipBQebCltZH48UBa/hrz2l+3HCYrPwSBnYL5fHZo+nYwfYuibkV+7lQuJQydRIeyj5Eej6Gq31Eo8vxdnTC+woLewSGbSG1PJM1MRuRe185Qnn7YhJZunofien5dAvz45FbRtGnS2Cr1GV9xnY2Zu6q/ZxfVcS7577k8wFv4GnfdOMzq3wHCYVfUa5JxdNhAF09H8fZrmOjyhBCEOVbf39yeb+QknKewMCmvZPZlXm1Rn64QzbdlcfYm/Yb4W6TDMZkSZsBwg0ha56BZSeX069Dy4vdtjSl5So+/m03Ww+fx95OzvTRPbl3+jAUcts7nVpvnGh5dJKa+MIvSSv9C5AIdJlGhMcDyIRx7HlJkngndinpldm1accLT/Nl4q8silxgw1pbRjcf68+zN+w/y3fV86sB3YJ57LbRhAcZLsJJkhZ0mSDzQQil1evQTjtNQQhxMzASvfL+bkmS/rQ079Xoun8Y6CKE6FQdZ3AOsLY5BUq6YtQFD1GVNZCq7CFU5c1B0lhl7cAqpJeu50TOsxRXnUGlzSGtdA0HMxegk6wnzLMr13TYnt05bTecz6BBg4iLi+OB179i9Y5oMs4fwc6nC0tX72t22TXfu6QqjkOZ95JXeQCVNpecit0czFhAhbrx8XJ3Rcfz0rINJKTlUaFSs/dkIg/932+Ultv+uIAkVaIufJqqrEFUZQ+jKnc6OvU5m9fDmtS0h8TERKqqqlixYkWTRJYkdSxVOdejqzreqHybD53j9a83kZieT3mlml3R8Tz8zm9Uqpq+cNAUCipPcDjzIfIrj6DS5pJVvpUDGfNRaS067nVVcGlb2JVxkJStcQSMDGvtalnE2aQsFi1Zw5nELCpUao6fS+PRd1eRllPUKvUxNQaoJQ0H8qKbXGZuxUGOZj1OgSoalTaXzLJNHMi4G7WutDlVNUlNW0i4cITSzLtY8dM7TBn+HVX58xtdVoW2EtAb+eM8Y/G1L0Upq6gdkzWqA1RlT6QqewRVWYPQFL+hNyyucZ77fB1/7DhJUVklOYVlfL32IB+v3NVwxhbAWuOELTid91/iCj+jXJNCuSaVC4VfcCr3NZPXxpdeNDDyaziYdxy1zrZjUGuw89gFXr5kfrXnRCIPvfMbpRV18yttxUaqskdTlT2KqqwhaEq/aMUat9OOHiHEZ+g90mOAU8CDQohPLc1/RRv6QohfgP1AVyFEqhBioSRJGuBRYBP68AUrJUlqVswnTdGL6Co3URO2UKo6hLrgoeZV3ookFv9olFamTiKnfLfV7iETppuKuXRbM3fuXIYNG8a5c+cIDg7m66+/RqFQ8Pwr/+HPpa9z+s938Azri6Nnh4YLs4Ca732x+Fd0kqFegUYqI6X0j0aX+dtW44lxQUkFmw/b3sDWFr+DruJ3anavJfUp1AULkay4eNSSmGsPn3zyCZMnT6Z79+7MmjWLqKimeV5ImvOo8xci6YxDXJnD1PPNLihl+7ELTapDU7lY/DMShs9RrSus3hW6+mioLSwa/yBB4zvjFu7VcGFtgN+3n0CrNQyhW1mlYe3u1gmtZm4MEEI0ucyk4uVcHiZYpc0lo3RDk8uEhtrCeHoP/YVbbnSmR1c7JFXTDc0oZ+ONgEp1Ipr8hUjamvBhlWjLvkVbZlJo+ZohJauAA6eMoxj+uSsGtZnoAtaipceJlkStKyGtxHhTL610HVXaQqN0YXauJqr/rm5Wmhh/84vL2XpYLyqr0ySgKXwcdNVhfqVitCXvoK1Yb8tqttOOKcYDkyVJ+laSpG+BqdVpFnFFu+5LkjTXTPp6wCpvp6QrR1e50Thdcwqd+hwyu9ZXua3Smj5zqNJaTwxojO9gduUcMkiTIRjta1Yw0qb88ssvJtP7DRlJr5vN6ig1mTG+QwDr/vZFpZWNSm9JtBWrTSSmI1UdRCjbvuCTufYwdepUpk6dap2bSIXoVNuQO1qmol1o9vlWWKc+FqIy02arrNhftCUaagsZFdk8Gf0GmitkZ7Ut9ROg7wvjSpMM0pQye4Z5Nz10pPl+1bLz9eYw1xamTB7EhD3NPyLlpNDrVDjKjMVqfWVaBMbpuoo/wOX+Zt/7SsVcuy2vVFOl1mKnkLfYvW0yTrQQam0xOhPHyCQ0VOkKsZd7GKSHO4cQ4hhASkWGQfpInwHYya5oU8Aiisvq7zd1FX8BxhsZuorVyB3bdlto56rnAnph2JoV0ZDqNItoG9uxbRqJy3cW6jCXblt8HE0ZXjJ8HIdb7R59PXqwsNNtuCr0Zwo97Nx4JOJOOru0vcgAl9KzcwDuLs0XCZML/WTDTeHCfeFz6OkeCZj77cHXTHp9jOoTbjJ9pJn0lsWc0dM22nzbwXLjcFQfYy0LIWz/fH3N9As+jtdmGKgARz8WRS7Ex/7K2NE310+Yal+24PoOo5kZOBGlTK9x0MHBl2e7PYi7nWuTyzQ3dvk6tdQio3X6NT+lD/09okhTGbcl8x4O13af2q2jHz4exloF/boG4ex45ehm2BonuyCc7YzfeSdFCM4KYy0LIQTPdnuArq76/kOGjGHe/VnY6doIATrCTP9YN/6aew+vjAXgdq5qXIGzQogdQogd6MPFuwkh1gohGjyafvUv4zUTIXNGppyATrXZMF0RicyueyvVypBIz0cprIymRB0HgEBON6/FONlZV0BmasA4JvqPpKCqGC97DxSylltptxZKOwWv3TeFF774m7IK/W5KiJ+H5XEpqunoHMTn/fXiUpeufge7ziCnYhdZ5dtq04JcpuPvNKHRdb1zykCi49I4dCYZALlcxkM3DSci2PZifDLHG9GVX7bbIfND2A+xeV3aLMIFmdJi7ynuuXEIMQkZHD+nd+tVyGU8MXs0wX4eDeS0Lh3d5pFTsY+8ygMGaT6O1+6zHeLdl4FevclTFfAHn7d2derlhpE9OHQ2mU0HYgH9YtGsCf0Y0SoLgnoD4s6wm7gtZColmjJ87D2b5bYPEO5+D3kVBylQ1ehgCMLdF+ChbBklfyH3R9gPRqo61PDF9SAXMl7o8Qg5ldM4nfM45Rq9m75Ajqfbo1D5CUjlBnlkDpZ5BF2tKBRyXr9vCs9++hcl1Xo0gT5uPH93e2Sbhujt8zpHsh5BrSsGQCFzpbfPG2bfvwBHP/7b6ykKqopQCEWbUttvae6eOpiTFzI4fMn86pFbRtSK8ckcbkBb+imXG/wyx7apz9DONcXLzcksJEmyVkWuOgYOHCgdOXIESZuLuvBfSFV7ARCKHig8P0KmaJ2JlSkkSUtuxX4qtTn4OA7FUXHlK/Fak7KKKg6fTcZRacfA7iEo5PKjjQmbVNMWzFFYeZKSatV9d2XzFoBOJ2SSkVdMn4hAfD1dmlVWU5F0pWiKnkZX+Q8gIeSdUHi8j8y+T6vUpyURQjSqLQzoo5T2bR6Mnfv/IVM23msmJj6drPxS+kUG4e3eehOt/Mpj1ar7vZukuH+10tj20FDf0FJcSM0lKSOfrqG+hPjbPnJDSyNJEvmVh6tV9/vjYhfWsvfTpqMueAJJfRQAYdcfpe/vTW4LpsZknWoX6sLnqs8BK5A53oTC/U2ECZX0a40KlZpDpy+itFcwsHtoqyju10db7Rc0unKyy3cCEn5OY1A0M5LD1c7phAwy8kpMzq+05X+gKfkv6PIBB+TOC1C4PWWynMa0h9YaI9qxDY3tG5pxHzcu2aCXJMkiBeX2HX0LEHIf7L1/RNKmIUlVyBRtL5ycEHJ8na5N11tLcHa0Z2z/ljNmPBx64+HQ2yplRYV3ICrcOqKBTUXIXLDz/BxJm4UklSLk4c3epbtaEIpI7H131iNuVD+9OgfSq7OVK9UEvBz64+XQv7Wr0U4TiQj2aRVvH1shhMDbcTDe2EYHRsgDsff5DUmTAkgIRSjNESkzNSbLlKOx99uFpIlHyLwRcm8zua89HJV2jGnBMfpqRSFzItBlSmtX44ohKjyAqHDTG2Fyp5uROd6ApElCyAMQMjcb166ddowRQtwPvA5Uonc5EejPlVu029xu6DcCIQ9qFW3SgtIKdp6IRwjBuD6dcXNu/pnzq5nKMhWH/zmJTC4YOLE3yvZzfk1GyP0RtExce2tQmF3MsR2ncfd2od+4KGSyho3vo+dTiUvLoUuwLwO6BDf+pkLZZCPfWpzef56MpBx6DoukQ1jj4w3rdBIHYi+SnFVA7/AAenRs3YWldvQUllaw40Q8CBjXJwL3Vuzrq1Rqjm6JQVWhZtDEXji7O7VaXWyJUIQ0K39abhF7Tyfh4eLAmN6dUdoZTrOEkCPsIpt1j3YMiYtO4uLZNCL6dCSsRxP6dBuiUmvYcSKe4rJKRvQMI9DbvbWrZBUkSeLk7lhy0/PpPao7vkFXhubJ5QihRLQBke122rmEp4GekiQ1STG53dBvCrm54GObnZR9Z5J4aulfVFbp1UDf/c2eDx+e0TQD5RogZu85Xp2zhNICfdgzdx9X3vxjMZH9beiFYcP2cS2z6cddfPzE96ir343QboG8/dczeAeYdmNWa7U8tfQvdsck1qaN7dMGttYbQWWZipdufZ+Tu/Xns2UywdxnpnPXizdbXEaFSs0jH/9BdHx6bdqM4VG8fMfEdq8NQNFK4+KBsxdZvPQvKlR6Je13V+7gg4dmMKhr8wzPppB0JpUXZr5LbnoBAE6uDjz/wyMMmti7vX+rh/yScma8/C266iORHbxcWfbkrQT72laHw4ir9JlpNVremv85u/88XJs2Zf4Ynvj4njbZlyVnF/Lgh6vILCgBQCYEz8wex6wxV/aRuJKCMl646V3OHUkAQCaX8dCzk5n+/JxWrlk77VwVxAPlDV5lhrZ1COoKIF+VgfrRBwEoqVKRVVbaYvfS6nS8uXxLrZEPUFZZxX9+2kJjtRW0Wh3ffbaV28b/HzcMeZ03n/mVvJwSo+sqqzT8fegsX286RExihomS2i46nY73Hvyy1sgHKMot4f2HbROnWCfpyFWlonnsoRYpP7OshNIq4/BMbZ1zKdl8888h1uw/TXmldepfmFNsYOQDJMem89WLv7L5r2jmz1jC9QNfZdGCr4k9lQrA3wfPGhj5gH739Api1ZL1nDyRCp2CILIjuiB/fvpgA3HHExvOXM0v248bGPkAa/ad5sDZ5EbVRavTsf1EPF9vPMS+M0mN7pPaGmqdmjP5sYTpQ9fYFJ1O4s2fttQa+QDlKjVvNqGvtwYfPfF9rZEPUF5SybsPLiOjNAnNYw+bzLN25UHuvOF9pgx6lWcf/I7EuCxbVZcydRWZZcbjWQ0HY5P5euMhthyPQ6NtOZX7rILSWiMfIDO/hCWrd7fY/SyhTFNM5SP3Wr0dSZJEemkxlRrjEG+2YvNPewyMfIAN3+1k/9/HzeQwJDOtgJef/Ikpg19jzqT/seKbXQa/U35JOSt2RPPDlqNWqe+SP3bVGvkAWnS8s2YH2YWNn0fmFZfxy47j/Lj1KOl5xfVeq9Pp+OnLHcye+A5TB7/Gq4t+ITujsNH3NMdPb/9Za+QLuYR9sBq3158jLd52fUA77VzFPAfsE0IsFUJ8VPNnaWab71wIITyBLkCtT6IkSbtsXY/GklWZxO8p76M9d5rHft3GB3Pu5suiKMrUgp7e/rw7+nq6e/tZ9Z4XswoMBoUakqrTA7wsPz+0fOl2fvm67mfevfUMGakFfPrzg7Vp2YWlLPxgJam5RbVpc8f25ZnbxjXxG9iW1POZZCTmGKUnnkohJy2/RV3J4kujWZv2KYq4BB5bsY1tD7/KmJEv1Yblaw4xuZk8s2sjZ/JzUMrl3NalJ68On4DdFRD14PN1+1i24WDt58/W7eOrJ28jpJk7XNE7zhgY+TXs2xHLtui6BarTJ5J5/pEf+Wb1YxyOTWnWPdsCW/8+AQGX7Mw5OUCIP7vWHqNLP8u8Vg7FmjboD5y9yLAexmGZTFFRpeahj3/nRELdbz0yqhMfPDC9zYloWcLPF1YSU/IbgWn5uIPNfU5TcgpNTtZr0oN8bOfeW15Swen95w3SPEaoCX8mlVU7FvLYim3sfPRNRg1/Hln1EZat60/w6f+tr70++nAi/374e75b8wSOTsoWq6tWp+ONg9v5OfYkKq2GSE8f3h45iQH++ogzOp3EM1+vY2t0Xcjh7qF+LHv8VlwcrV8vCWNj+mAjF9CshVpXxV/pn5IWvZ7HVm7h+4XzGDfiZUKdmx8taEdKIi/t20xySREudvYs7DmARQNsrxF0aNNJk+mH/znJ8Gn1649oNVr+/fAPZKTq9awK8kr59tOt2NkruOWO4RyNS+Xxz/+kXGW9hYyDl/S9lT46ygN1SHZarvvzG14ZMZ5buvS0qJxD55J54os1tZtAH/25hzfnX8/kAaZdzld+t4cfvthe+3n/zljSkvNYuvJhi467NcThf2IA8BpfRadFFQSUljD2xiR+3v4f5oYvaZPeFe20cwWxFNgGxNCEeKw2nZEJIe4FdgGbgNeq//uqLevQaHQ6dCVFrDrzGoUF8URt1IfG6rPlIEN8zuBUqSIhLZkFG1eh1lk33qanqxMKE52wg50Cd6fGnd1ct8pY8fPCuQzOxtQZP8vWHzAw8gF+2RFNbEp2o+5lKVqtloN/H+XPjzcQfyKp2eW5ejojM2Fk2CntcHZzbHb5JtHpKC9M5/ezr1NRlF7bPvh1BQeSfobSUv2frmm7SJUaDfM3/s6ZfP0ChkqrZXnsCZYc22+tb9BkLp5J4c9PNrD3z0NoNcZtPzm7kC83HjRIyy4s5ZO/9jb73u4+ZmJ0uxtHKSgrrWTbxhh8rKRwX5hdTGpc63i7lJlaOJLLySqw3KvLz8N0JAdz6ab4fU+MgZEPsOd0IpuPnTeTo42i03Hq4mES8r7HVV1e9/7aGE8XR+wUxs9WaSe3+Tl9ewc7nFz19xSShKuXil4vFuHqqq79fbS/LOdI8qra/u3v3w4alVOYX8aSf6/gxM7TLVbXL04e4tvTx1Bp9QbP+YJc7vnnD8rUes+hnTHxBkY+wNnkbFbsjG6xOl2ObyPeK6ug00FpKbsTvyU2fQtRG/VjfMja46w6+ypVxXnNGpMyy0q4f8ufJJfo5wql6iqWHN/P1wcPsG7pZjb/uJPykgqrfZ36MDcOeJgbHy7h2MGEWiP/Utat0nsIvL1ym1WNfKjrY9WuOso66pCqgy0UaVQs3rmBEzmWjStv/7rdwNNTo9Pxfyu3U6U2XvwG+Pt34/lfcmIOJ48k1XuflHNp/PnJBnb/cRCNmbIBPLyd8QisJOrfxbg41fUTHaL/4WTqumbPg9pp5xrHTpKkRZIkfStJ0vc1f5ZmtvXWyxPAIOCiJEnjgH6A9fyHWgJJovit53lo8Pc8P2Q94z89B8D4T8/x7bTviXniFe77ZxeZZSXsTbPuyr2niyM3Du9hlH7L6N44OTROYK68TGU6vbQu/eiFVJPXHI0znd4cSgvLeGzo87x449t8+sQ3PNjvaT59/Jtmlenp786YW4xjgU+6YyROri1k6EsSRW/9m6eG/WnUPkaE3wUeHvDee9BEt8mdqYnkVhobcX9caLnJsyV899IK7u25iE8f/4ZXb/4fD/R9ioIsw1f5eHyaya99zArtqc+Y7nTqaexh7RVo+nx+eWklt47ujZPSMIyVSyPfI4DslFwWdHuCFf/3Z6PzNhc3X9NePEFdLA+nOWdcPyOj0tvNiRuGWL7TZ65PaIm+okWRJArfep7nR2wweH9tjZuzAzOGRRml3zSiV4vsPNeHwk7BDfeOB0AgcbvXKZ4ft8GofxscNru2f7t0HLmUrb/u46lxr/LSjLfrNRSayu9xxv1gkaqSLcn6Izm2bqdO9sZh8u6a2OJRlwyRJHj3XcZ2fcjomS0atBI7L/9mjUnrEs7VLqxcyru/r2fJQ8t45+5PuKvzI1xoxHGipjLtvvHY2Rs6pjq6ODD57tEN5i0zMycqK1VRVFbJhfQ8q9TxUu68bgAAKi/j314C/og702AZecVlJGYZL1AUlFaQkGk62laZmffTXDrA8jdWsaD7k3z6+De8fuu73NdrEbnppsuf+eBE5rmf4vnRxv1En9DpzZ4HtdPONc4GIcT9QogAIYRXzZ+lmW1t6FdKklQJIIRQSpIUC7RteUu5nOIXHuX7r4ZT1MHQWCzs4MicxfezZPpEdDIZWsn6q5XPzZnAozNG0DnAmy5BPvzrltH86+aGB7HLGTbG+Gd2dXekZ786V91AM0cBAr3NHxGoUKtZcfwkr27cyorjJ6lQW7YCvvJ/a4g7mmCQ9ucnGzi9r3kT7UWfLeC2f03FJ8gLvxBv5j0znYffvaNZZdaLXE7mv+8y2T5KAlxg+3Z45RWQN83N3lyb0jZzZTyntIzP9x7itU3b2HzugsG50oZIPJXMT//53SDt4plUlr+xyiAtwMv0rkqAFVSGZTIZb615mol3jMTdx5XQroE89uHd3DB7qMnrh43tToivB18tnsW4Pp0J9nFnfN8Ivlo8q0n3lySJb1/4mcyklvF2McfEGf3R2guKuijJ7etIabAdkoAx11se2rFHR3++eOIWRkSFEezjzpTB3fh68Sw8XCxfDDPXJwTU01e0SeRy9jw4yeT7a2uemT2Ox2eOJCJQ39c/cdMoFt82plXqcs+rtzH/lVvxD/fnn9GDTP4+pQGutf3b0HHGC9KSJEGR/ujZgb+OsvUn659Vb6h/NNdOa9IPXUzlP5t38OHOfVzMb/6eQ6ifJ7dP6E+onwe9wwP4zz1TmDHceAGnRZHL4dVX+e27KUbPrKiDIxfXfdwiY5J0iXd2UW4JHz/W8to4nXuF8ubqp+g1oituXi70Hx/F2+ueoUPHhiOR9B8SjrJ64VfjKCjopiSvjyNBY0NwcrBrEU+am0b24s17rsfDzMaDJXNIV0elycU/hUxm1ivL1PzP0cmefkNMR+dKjcvgh1dXGqadz+CHV1aavH7UrUMpeHuhyX6iLMCt2fMgS6nSaPjj5Gle3biNH49EU6oyv5DRTjtXEHOpPqcPHK3+M3bTMYOtz+inCiE8gD+BzUKIAuCijevQaIKdulIyvCcH5xUw6f26Fdd103tzOFLfUfo4ODEyyLLzrY1BIZex4PrBLLh+MCcLUkkoySWhNIcIt8bpATz09BQyUgu4cE7vGubq7shz/7kVpUPdDsTdEwdy8FwyWl2d0dcl0IdRPU0PBqWqKub9uJLY7Lpz8T8fPcnPd87CRVn/TunxbadMph/bfJKo4U1f+7F3sOfeN2Zz7xuzm1xGY+nmNpQNg4ON2kf8PVPoO2pUs8oeG9wJd6UDRapKg/TpnZt+zvJCTh7zlq+ksEJf5k9HTzC9ZzfenW5ZLN5oc89ui+F5yUGRIfTqFGAg6igE3DNxUBNrboinvztPfXGfQZparSH2VBq7t+h3+uzs5Nz98AQ6R+rDx3UL8eO9B6db5f46ncTxrTFMWTjBKuVZwtBpPflv6jEqZPoJYUlnJV6OHoSGNy7EXr+IID5+9KYm12P26L6s2X+askvEFb1dnZhpYlfaFlwozuZUYTrhrj709mxcRJKJQTewRraXg/M6Gby/tkYhlzF/8iDmT27e+3EyP5WE0lx6egQ2epyoQS6XMffpG5n79I2UqAtYcv5+o98n/4HbcKnu3+bcM4q4s+kc2ad3k5d0OqTUTKiqW/g9vjWGyfOtq/cyo3N3lhw3PMbkYmfPhFB9NI1pQ3rww5ajZF0iduZgr2De2H58vHs/H+8+UJv+1YEjLJ01gxGdmj6Oy2SCxbeOYfGtrbNAcynuE27j4Lwkg2d27I7ujJy0oFnlTu3Ulf8d2Y36ssVm10OGO+Bn9p2jslyFQwtqNAD0HdOdvmMaPx66uTvx9Bs3858P1pA6SIlkp1+p+JtM3P7ZwZ0TBvDJ2uYfM7ucqYO74xigZP6m343+bYYF47q9nYI7xvfni78N2/2M4VF4uZoOgXnfvyaTejGvVpjW2cWBp1+/CSdn08/m+NYYk+KNx7ea1kQAmDHqdj6OM+5Hix6ci3Mz50GWUKXRcOdPqzieVjffWH4kmhV3zcbTqeFF3AJVGXuz43GxUzLCL+KK0EBq59pAkqRmhQ2zqaEvSVLNzPJVIcR2wB3YaMs6NIZKdTyphe/Rwe1e5oa+gHb7ENQOck5MD6HPmhS6b8uEQRDm5sEHY29AKW+Zn7NKp+Ffh1eyI7Pu/OvssIG83GeaxWV4+7rx6c8PQi91vQAAIABJREFUcjYmhbISFb36dzQw8gEGdw3li8dv5cetR8kqKGFw11AWTBpkVlxr1YlTBkY+QGx2DqtOnGL+4PqFcLzNuFh7X4GxVx3lLswNfQGH7RNRO8g5fmMIfdekIP2xn3umfsdnQ+bhqGi8iziAk509X028iUU715NSUoRMCKaFd2PxgBFNru/Hu/fXGvk1rD0Vy50D+9EnsOF46uaenc9lz04IwWeP3MS3/xxm39mLeLs6MW9cP4b3CGty3RvCzk7Bi/83i+TEHDJSC+jaMwgPT/3ZfEnSkFXyA4UVG5EJJ3xd5uDpdH2j7+EbXsH4G9PY81UHo+/c0nx7pM7Ir+FcRSG74pMYG2G7EJKhfh58t3g232w6TEJmHt1C/Lj3+sF4u1lHB8FSJEni9ZPrWJlUp4o9tkMkHwyahb3Msv54gG8PTubNpeuWR1A7yMmv1BoretqQ/PIN5JauQCdV4uk0BT+XOxEWiHpW6TQ8eWglO7OaPk6YwtXOkzmhz+G0bQpqBzknp3ek79oUQv6JhVf01zg42vOfj+8kMS6L5W//ya4ftsFl6vbegdZ/Vx7tO4y00mJWXziDVpIIcnHjnVHX467U78a6OTnw7eLZfL3pEKeSMuno58k9kwbh7ubIF3sPGZRVpdXyzrbdrFlo/QV7W3M8P5k3jmfw3T85VDnIOTE9mL5rUhm+R8JO1jzDO8TVnSXjpvHKvi3kVJRjJ2Q4b0nHfZehd5Obtyv2DsZHGdoSoyb0IDDrLAlJhscuf42OZtV8e7qGbyUzX8Op+IFEf2a9+44LCefZQaP5JHo/ZWo1bvZKFg8YyaAOli1SPjB1KP4eLqw9cAaNTsfk/pHMGdvX7PUens4s+f4+zp1Oo7ionF79OuLgaH5OYm5cq29+5mnvz20hT+O+bQZqBzkxM8LosyaZwE0x8LJFX6tZrDtzzsDIB0jML+DHI9E8PnpYvXk3pJ3i+WOrqarW2Qp28uCr4XcR4lz3fTXaAjJKllFSeRCloj28dTu2RQjRE+iBoZD9D5bkbQ3VfTngD9Qc4OoAtI4sbQNopXIyij+mqGI7PbSfITQ+7HzrVf7Yk8vqrqk8kbmOv3uPJWrQwBZVFf394jEDIx/g16QjjOvQlVH+XRpVVvde9UeOGtglmIFdLOvETmWYDp0SYyb9Um55chr71x5Bh0C4uSJkMtxdFIybM9yie7c1OhV5UaHy5M53p3E2JICwkXm88/7vJJ6P4UffA9wf2fjjFjUM7hDMrln3caEwD0+lI75OzTOmYjLNPLf0TIsM/WHTBxIcGUDq+Ut36gW3LjbcKU84k8b376zjTHQKLp7OjL9zBMO622YSHdrJl9BOhrvcifnPkFf2R+3nosoddPR8s9Fl2zvruG5ROlGTqxgwybbxj0+afecybWroA0QE+vDfeyzzAmkpdmfHGRj5ADsyz/P7xWPM7TTY4nKuz++GLM2B58beQ+LGr2w+HlWp1Kz46B/yKpYzcn7dTl2J6iDlVWfo5P1Og2WsSjpmYORD08eJy4ko9kfS+lO871d69h6C/Gw8zJsH6ekQGFh7Xacu/tz74kyOrNprIMjm5OrItAcmNqsOprCXy3lvzFSeGzyG/MoKIjy8kV02Fgd4ufHi3OsM0vYkXDTakQY4m5XD9x/9w6hJvQjvZrnuRVtCkiRePLYG+8wcdCWCuf9bQHInHwZdJ/Hhe2v4+O4POHy2kMBOvsx5fBJ9R0Q2WObZ4xc5tPMcbp5OjLuxLzd06sqkjhEkFhXg5+jMh+s/Ya9k+Nrc8q9pVlF0b2nOZBuv690/bDMV6liUSugYAB0DYnnNyvd9uM8Q7u7Rj9SSIkLdPHBUNG5RZObwnswcbplKfw1do4Isum7wlH6ERYWQdNowUs1ti+v3huteElLXT/QZivzMBZP9RHMpzCvhx3fXc2T7WTy8XZh571hOOpoOMXjKzHynhlJ1JS8fX1tr5AOklhfyVsxGPhs6D4CTh89T5DAfB3d9WNqyqmNW+ibttNMwQohXgLHoDf31wBRgD2CRoW9r1f3HgCxgM/B39d86W9ahKZSrT1FcuptN/1rC2++e4vyBTI7lKbhPMY1Vz69p8dAhu7MumEmPa9H7NkS4t+nV3Qifhndueo3qzsNfPIh9l07I/XyQ+XhR4uDGX78cajBvm0Sj4dvl7xMTHIxGknOhox9z37kXhVZn9vk1BpkQRHr6NNvIh/qem7dF+e3s7Xh3+2vccN91BEZ0oPeYHryx9lmGTK3z4shOy+epW5ZwaH8CpSotmZnFfPm/Dbx037etEhdcpUkhr2y1UXp68SdNLjMgKpfSKmO18ZakczOf3dWGuXdrT7bl71xJQRlv3fcl9yunczqmdc7pf/jUL6z4aCO9bzCeQOaWrUKlSW+wDHPf2SrjhEaDOHgQ935jUcodoWdPOHgQNMaibAGd/Hl/1+uMvHkIgZ39GXHTYN7b+RoB4f7Nr4cZfBydifT0MTLyzdHJ29PktXbFGlZ8vp1HbvqINcv3WbuaNiG1vICksjzkWh1z37mX2JBAyjX27PRTcpffNA5vOklOegEn9p7nxds/4+zR+kXzvv/wHxbN+4IVS7ez7O2/uX/q+yTEZmAnkxPp6YOHgyPPLX+c21+4hdDuQXQZEM7jn93HvOdvttE3bh6Xj4e+zkWM6BRrk3s729nT1cu30UZ+SyNXyHln6yvc+OAkAiM60Gt0d15b/QwjZjaweHpJP2Evc6i3n2gqOp2OF+Z+xvof95Kdms/5E8m889gPaFONQ1GD+TGzhmP5yZRrq4zS91b3p0vfWsePX79aa+S3004rcCswAciUJOkeoA96j3iLsPWO/hNAV0mSrC9n2sJUBFaw6jvD81pVQsHB2GJOHYonalA4u04ncjguhQ4ertw4uEe9Yi5ZlfEcy19PmbaAcOcB9PGcjFyYfhzeStPGnbfSxmF7LmNOv178Gh1DRnFdBxvg5srsvr0syr97+3l0l9l8P368mYk39cfTgvA4rUlGRRzHC9ZToS0m3GUgfUIn4a7NNLhGpbQjw9cdt2I1767eycCIYEZHhSOTtW5M2UdGDOHgxRRUl4TEGxYWwtCw+r09LsU7wJMnlz5g9t83/ryfiiodXKZCfXRvHMf3XaD/iObtMDYWlSYFTMS4Vl/2zBpLenoMbuH1uwVak/uHDWTL+XjKquomJj0D/LkusrPN6tCW8DLXN9pbviC2bfUREtQSgXNKcOtRwcZ7rVU7y8jPKmLnmqMolFrcfE2JR+mo0qSgVNS/I+alNH0+1yrjREcTnjiOjhAaavLyzn3CeGXVUwDkqVI5WrCO48lZhDr3op/nVL0RYCEpOYX8feQsKrWWiX270CO0+QsGQe5uzOrbkxXHY+oSJQmv6LLaj9++32ZPFdaLq50DCiEjw8+DDo5FdHbLRSZ0JJd4U5AhkCnqxlatRsfab3bRfYBpb6CstAJWfrnDIK2kqIJv39/IG8vuqU1TOiqZ/8Yc5r8xp0W+U0vy2Kih3Ltida2Hh59rEa08RDcao/mIxyRkFhz3uZSzKdlsjj6PnVzO1IHd6OjnyeOf3ddwxktpZD/RFKL3nCfhjHEY1Ow/Yuk03Z/E/ILaNG8nJ+4a1K/e8rzMjBVeSmeS47P584e9jJpdavKadtqxERWSJOmEEBohhBuQDVg8Ybe1oZ8CFDV4VRvERTmI/OyfTP5bQXYxz/+wkfVH61aBv9t6hO+enEWwj4fR9RfLTvJr8otoJf0qZ1zJARJKj3JrqOmDTHM7DWJtygk0lyiyuiqUzAw1fybLFng5O7Fq/lx+OHKc2Kxcuvn7cNfAfng5m55wXs6ZY8Y6jBq1lrhTaQwe283a1bUaiaXHWJn8Cjr0hvL5kv0klR1nWvC/+OLcTvKrLgmHJ8GFg8XEFx7jx+3HmNw/knfm39BKNdfTLziQVfPnsfxINNmlpQwPC2VOf8tV2y2hILsYzGg7nI2+aHND38k+CplwRCdVXJbei+bogSaf9qObaa3KFqGLrw+rF8zjh8PRpBYVMTAkiHn9+2DXwmrGbZWbQvvy3YV9lGrqDGSFkDGnk+WCdvm5+fR5Lw2Xzsa7OragILcEnU5CV6kg47wbAZGGLqgy4YyTvbGq/eXM7TSYv1JOtqlxIrsykR+TnqJKp3/vLpQe5FzxXu4Ie8ciQ2TPmUSe/PIv1Fp9X/vNlsM8d+s45oxu/nd69foJ9AsOZNOZOA5tPoNbXAWO2XXigaoK68ZQtxUe9k5MD+nDycJNDPVPqk3v6FpA/AP+pH1ouIien21+SnY2Ohnd5avxwJnjbV5D2WKGhYXy691z+PnYSfLLyxnTeTBCbKA6QFSbx9x85Kbg5y0uY+XuE/x31bba6Hdfbz7MewunMcaMEHNrkp9t2kW/JKOYFXc9zo9HojmVmUVnby/uGtSPALf6N416egbRzyuE4/mGxxTuDB/K2ep2nnKmcWK37bRjZY5UC9l/iV5xvxTYX3+WOmziui+EWCSEWAQkADuEEM/VpFWnt2l8XebiouxDv1HGavAKezkEuRgY+QA5xWUs3WTarXdPzs+1Rn4NcaUHSCs37S7WwyOQpcPuYIBXKK4yB3o7BfPlsLvwd2z9UFa+Ls4sHjuSL2fPZPHYkfi6WL6TFhhq2qUqwEx6W2F3zk+1g2oNscV7qNSl893Ie7guoBte9k54a12RnXBCFNatp206dp4jzYzhLEkS0XFp7IqON1A9bwxd/Xx4Y+p1LJ01k7sH90epsO6aX7/RXTFy16gmINT2buYKmTvBHs8BdVs1MuFMqEfTVYI2He6PWhnW/Mo1kjAvT16ePI5ls2Zy/7BBDUa4aOukZhey/VgcKVkFDV98GR0c3fluxHxG+3fBy96JQd4dWTrsDnp4WH4e1G9cWasZ+QBhXQPw9td74W34sCeq8ksNYIGq7D6Ony8waXBdSlT1ODHQuyNe9k6M8e/CtyPmt+o4sS93Za2RX0NaxVkulFp2ROt/f+ysNfJrWPLXHkormh82SyYEN/XqweezphMVLwyMfKDVPa/MEZ+Wy/ZjcWTmm3ZVBnih1/X09TB2NQ6blIPMw1CboP8Y84vqgR1N99VBHX0srO2VQc8Af/57w0S+uG0Gc/uPIMTEWNEWOZWQwbqkr0zOR7Iq4y0qo1xVxYdr9xiEuFdrtby7emerHLNriD7DuyAzsYnQf3Q3PJ0ceXz0MJbNmsmzE0Y3aOTX8OmQecwOG4iv0oVwFx+e63k9C7qMqJ2rJMX4cXxzmDW/RjvtWIwkSQ9LklQoSdIXwETg7moXfouw1Y5+zduWXP1nX/3XprGX+9HVbwVuDvrY3Pe9NJP4U6lkJutPHsjkMh56/VYSCsyIgCSZdgvOVZnWesqrSiHIyfSg61LiSMaaSlQeErHuGTx26lc+uWUuPUOvTMEggHkPT+B/zxrGZR19fS9CwpsWEspWmHt+uapkotzHsWSw3n3xlrd+oCjP+JTKyaQMiwUPje5RWMrjS1ZzPkUvIOTsYM9rC69nbL+IJpXXUoy8oS8DfjvM0UOJ+ph61YR09mPkpMYJCFkLf9e7cFUOobBiEzLhhJfzdOzljW9rRaXOvPXzbcSnB9JjzrW5k24NJEninZ+3sWrHCSRJ30xuGt2b5+6Y0Cjdk+4eAXw+9PYm18MpVA2teJhMrpBz9zszePP3VUQ7BZP2ujsjOmcwYFpXvt7oTFyKDlhFqL8HHz1xM8F+xl5iNQz1DWeob9vZhcurp6+MdK3/yEtxeSVJ2caLP+UqNQmZ+fTuZJ2xTwjB3IfG8/ErhhoeE2b2Z70Voi3uzY5jV/Y53O0cmR7Sn2An01FLGkKj1fHSl+vZfEQvuCiXCe6ZOpgHZxpHYMkrLcBBabx4JVfosO8sUVmtX9lzSGdmLDAfCjCyZzCDRnfl8K5ztWlCCOY8aN0wiW0Nf9e7cVUOrR0rvJ2no9eQbhuUVqhY9PGfHDufxo0Pp2Jv4iRMrioFf4eGj3QlZOZTpjJuK8k5hRSWVeLp4khmRRF/phwlX1XOSL8ujPKLbHFtKnP4Bnpy30sz+fL11bWLn0GdfLn72aZHF3G3d+TlPtOMIpT0HhxOr0GdiDmcyOp3h3FiSyc69clh7OSJwJPN+RrttGMxQogRQLQkSWXASKC/EGKJJEkWuVbZxNCXJMlAsLT6jIEkSZL5Jek2gJ3cv9bIB/AP8WbZjhc4tOU0xQVlDBzXHd9AT3aeSjCZv6Of6QE9wLEL8aWHjdI7OJg21iRJ4oVv/ya7Xza461duC6hi4cGv2Rb4NM6Klo1T21KMn94PN08n1v96iLKSSoZN6MG0uUMbztjKBDh2Iaks2iBNkuBobjlRl8hjhPl5ciHD2III82/aRA/gg5U7a418gLLKKl7+agMb3nsAZ4e2s3Ymk8l484cH2PbnUdb+vJ8qlYbB47pz890jsVe2nvCQk31XnOyNPXMaQ2GZM/Hp+h3j5jzLa50dxy/w2/YTtZ8lCf7YeZLB3UO5bmDDSuDWwk/ZuvoG54ozeE2ziZKp+vfiIkp6+I/jfysKyCuqOzOenFXIW8u38umiW1qrqo2mg2MXslXGYm8BDg0f3XF2sMfHzYnc4nKDdIVcRpC3db0Ups4ajJevKxt/O0xlRRUjJ0YxZfYQFv+3eeX+7/QGlifWifr9kLCPL4bcTV+vxp9Z/mPnyVojH0Crk/hq3UGGRoXRt4uhmrqviw/lJU44uRr+duoqOS+88jBZpwsI7ORL3xENG2wvfnQ7a5fv59DOWNw8nblx3lD6DLn6NUGsMVa0FMvW7ufYef059cIsN/w65l92hTA7n7ycQC83FHIZmsvCYXq5OuHqqORMYRr3HviWsurjUb9ePMitoYN4qXf9Cvwtycx7xzJsci+O7ozF09eVQeOjUNi1zKL761/MZ83yfRzZfR4vn15MmTScqH5htBv67ViKECIEvUK+P3qxqGWSJC0RQngBvwJhQBIwS5IkU66NnwN9hBB9gMXAV9XlmV+lvQSbntEXQgwEvqV6h18IUQQskCTpaL0Z2xB29gpGTDUMqTWyRxi9OnYg5mLdDr7STs6CiabPiY72vZPU8tOodHWDcB+PyfgqwzhTlECFtpIo9wgc5HrDLTEjn2SnOiO/hkpHFcvP7+eBHmOt9O1sz8BRXRlo4khEW6NCW8mJwnP0dI9glO+dXCiJQSGrex5xxb78nXKcWzteh1P1wsuCiYPYcyaJSnXdMY2oUH9GRzV9x233CeNFpXKVmiOxKYzp2/YmX+NnDmD8zAGtXY0WYWjXUPp3bo+n21R2mWjLADuj421q6KeVuZNV4Yq/Y+usO392bhslasPzwBuyYlDgirhsiD509iKVVWoc7NuWSrc5hvvMJr7kMGXaurlLhMsQwpzrF8gq01RwpjieWydH8MVvJw3+bfbIPni7Wd+Veui47gwd191q5aWU5fNTouExygptFR/Fbuab4QsbXd7uE6ZdsXdGxxsZ+vYKBaG6mWRrf0Emr3O/digcy8CxUdD/8lLMY6+049aFo7l1YdNDxF5JnC+5SLG6lB5u4TgpWicKR0PsOh2HMqgcXaWcU3s7MyqgCDv7uvlIf88b8FZaNjZ5uToxZ1Rflu8wjPhx/+QhKOQyPjm3tdbIr2FV8mHmdhpChGvreTn4h3gz9Q5jbxZr4+Bkz+z7xzL7/rEtfq92rlo0wGJJko4JIVyBo0KIzcB8YKskSW8LIf4N/Bt41lR+SZIkIcQM4BNJkr4WQlg8iNhajO8b4GFJknYDCCFGojf8rasEZmPkMhnLHr2FX3ZGcyguhQ6erswb04+uQaYFPDo4RnBv5y84WfgPZZoCwl0G4mkXyePH3yapTH+uzkXhxNPd5tPfszsujvbgaTo8yYXyhmPWXwvszUzit/iTaHQ6pnXszvWh1l08SKvI5sWYT/BRevJYxB2sT+1OuGse9jINGeXuZFS4AyouluXR3V2/2xsV2oHli+eyfMcxMgtKGBgRzLwx/VCYEamzBFcnJeUqY5EoN6eGvToSivP44fxR0suKGeofytyIfm0urM+VgLPSnkUzRzNnVJ+GL76M+CL9M8goL2aYf0fmdemHUm7rbrht4GqmzVrSlq3JmeIsjuWH0NktFzc72wtwnSo0VpAGkDy0iCLDtuGktEdxBQkvetoHcG/nzzhR+A9F6mw6OvWiq9uIeneRd2Qf5pO4Fah0enfi4XODsI+NRKuSMalfF24c3LAwYVvgbFE6kolIH6eLTD/vnekJrE6MQStJTA+LYmKwodeDq5koPm5m0u8bdgfrY8LYl7YeCQ29Pccye+zURn6La4cSdRmvn15KbIneA8VRruSxLvMY5duIVREbsDvnGNpxp/BS6Hfgq7KVbPllMB0jMxkzMIhx4RPp7Gq5GCnAUzeNpluwL5uO61X3Zw6NqhXiO2OmvZ4tSreaoV+pUfPzheMcyEomwMmNu7oOoLPbtRkytp2rD0mSMoCM6v8vEUKcBYKAGcDY6su+B3Zg2tAvEUI8B9wBjBZCyACLJ++2nmFqa4x8AEmS9gghrBdg08qUVcVxIutxIr1ewNGu/g7NSWnPwkmDWTipgTij1bjZ+TDSd17t5/+e+bLWyAco1ZTzbux3fDfkTfw8Xenk4ks8xuI6Xd07WPhtrj5UFVXsWX2I9UXx/OFW99v8nXyWx3qOYFEf6+9A5KoK+CZxFQ4yL04V6A0SGVpmdIimu0smZWUXKVb+CzcH/Y5Vl0AfXps3ySr31ul0DAn0ZW2BYaiXLsE+Rjs6l3MmP4tZm5dTptFPnjenxrEh+Ry/TrzD4tjT7ehxk2SM9vVFade47jMmL4PZm3+iQqtfqNmcGsc/qef5ecK8Vjvv2JrMHNWL37afQH1JmEeFXMZNoy0Lz1mDJElkVRwkv/IMbvZhBDqPRmYmVKkpXO3B17GUzspsgpSNFwQ0R3bFMXIronG2CyTYZTxyYXy0pkqrpVRlegjs5tmB8xcN63PT6F7NWii0JRXqJNKLf6BSk0KwwyAGe81HLqt/J76gqpgl539Gc4lYbaomjWmjI3igc8vFZT+56wyxhy4QHBnIkBv6I7fCbxzuanqhv7OLsS7It7GHef3oltrP6y6e5ak+Y3ik5/DatFvG9GbL4fPoLhFIc3aw54Zh5r0QpvYaydReI83+e8z+w6QUfIdrUDaBQQMI9piPUtH6c4pydSYpZdsACHG5DidFy+v2fJ+0ttbIB6jQqvjw/HL6enTF1a5tiPEVq8v48PxyUNS52dv7qVB3qSA/tg933rWg3nGpUpNPculmdFIVwS7jcLHT7/oLIbhxcA+Ti2jhLn4czU8ymV4f5eoE0ot/QKVJw91hCAGutyOXGXtI6CSJu7b9yuGcOtX7VQkn+W3infTwatxCwtmDccTsicU/1IfhMwZhZ996i+hCiPuB+wFCrRhesJ02iY8Q4sgln5dJkrTM1IVCiDCgH3AQ8K9eBADIxLwQyGxgHrBQkqRMIUQo8D9LK2frGcNOIcRSIcRYIcQYIcRn6FX4+wsh2tayKSChorhiPQfTxlNa1XLhZCRJ4lD+KaP0Ek05+zNPo9Zq+Xj6HJx0hjtdHRzcuaXjwBarV1smJzWP+/o8xdv3fMIaE3oUy84epEhVYSJn87lYnsEd4foVc4XQ8lLkeib6nCXYoYCKqn1EZ84ip+yfRpUpSRJ5peVGCtM1qCqqePq6N9j9zK84Hk1BlFUhlySuGxjJR0/e3KCh+OnpfbVGfg1HclLZkW6ZMm87deSk5vFA/2f48Y1Vjcr36el9tUZ+DQeyktmdaXyG+VogPNCbJU/cRFSnDtgp5PQI8+fDx2cSEWx5KCOdpGVPxmJ2pj9GTP7n7M18lq2p96LWlTecuRqFrJi5fgcZ43GeCMechjNYwKGs19me9gAx+Z9zIOslNiXfTqXWeBFhU8o58kp1XC5u7WPnwefz53DjiCiclHZ4uTqx4IYhPHrLKKvUryWRJIm0wpMcT59BeskP5FdsJ6ngHU5m3olOqj9k3dGCMwZGfg0H806auLr56HQ6/jPvQ56a8DpfPfczr97yLv8a/TLlJc0fOyJc/ZkSaLhopRAyHoocb5Cm0mr4KGaPUf7PT++nVF3nMj2gawhvPziNiCAf7BRy+kcG8emiW/DztExZ/HI+eOhD4ivn49pjE7gfJ730K46lzUSlMS0ibCtSS3fw/+ydd3hUVfrHP3dmMiW99waEEgi99y4ICthQbKtY1lUs67qW3VXXuupa1t5ZOyCgIFVq6D2hBAghlfReJ5lMO78/hpTJzIRJFffH53l85J6599w7uWfOOe857/t9N2Rdz/GSdzhe8g4bsq4jT7uvQ3VW19VTo2s9U8Oh0lM2ZXqzgYTysx26d1vQGYxUaB23vYTys+jNdjz6etbz8RM3tWrkF9UlsCFrAYklb3Ki9D02Zt1IZvXGSz7TH/tMRdEiFebUoFgGeDveXKipP0Ni3gLyq7+jrG4nGeWvkVR4F0LYzm/i89KsjHyAWqOBD0/vtzm3Nd5/eCmPTniWL57+nldufZc/jXiKimL7QtndgRDiMyHECCHEiICAK+n5/scpaXjXF/9zZOS7A6uBx4QQVo1TWFJc2E1zIYQoEEK83bBRLoS4IIT4xtmH6+7lrgZf1+dblA/F8gWncRkip56k4mcZE+b037VNSJKEq1xNtdF2cvrnrzbhZjzIA9NHs37WYyzLOMi5qgL6egWzKHoMPsrLY6W5u/nmnyspyCjC5KHA5GXrwVJvMnKhpoKBqs6PsZMhcWPkKGK9okgoepsApfUOu4QgrewVAtyc28nfeSaN19ftIrusEl83DX+cPprbx1vHsG78fBun9pxFAjRJ+WiSLIuAs64aS4C3+yXvkVpZYrc8paKEaWGXl2L/74XvXlrN9FsnENrLuR2w8w7ewfmFK5/VAAAgAElEQVSKEiaFXD5K6d3JqNhIRv391kuf6ICcmh3k1e6xKiurP01a5Wr6+dzhVB3+8mQ8pc6Lzy+qO0ZG9TqrsmpDJsnlXzPE31rA6XxlCSaTHG2NGqXSiCQTGI0yPBV+eLlreP7uWTx/96xOe7auJv5sOq+ti2f64NWM6au1+qxGf5LS2m0EuF3t8Ho3uf3+2lF5Rzm4PoFdKw9alSUfTuWXj7dwy5PzO1z/K0NvZJR/L3YXJuOldOWmqJHEeVvHTpfotFTobUNGtEY9edoq+ng3GQnThvdm2vBLCxleisQdpyjRr2VED2vj1yhKyKv6hh6+T3b4Hu3BLIwkFL+BGUOzMj0JxW8Q4vozFo9V5ymp1vLc6q3sTs5AJklM69+LF26YiZerbbiDq0JNhcG2H3Drhjh9g8nE6+t28dORJOqNJgZHhvDiDTOICbZOY+imsB+mEezpTeQlhGETiv+NUTQtIghMJBa/RbjbNBQy+/UCjPbvyXcT7ufHzMOU6bVMCOzDgojW9+WyKz/BLKzns1X1xyiri8fPdbpVucNx0UG5Pc4cSGHdJ9abKxfO5rL89TU88OadTtdzhSt0FZIkuWAx8r8XQvx0sbhQkqQQIUS+JEkhQFFX3LtbDX0hxO82J4vWkHLpkzrA1SET+TH7V6syXYWK+ko19dTx6i/xhPp4sqT/jC59jt+KvNxy5AoZQUFelz4ZOLHbkvdIXmNEUaLH6G/tFuuuUNKzi2K8xvkPwUvpwbgAD4S+BjsL7BhM+baFdsgsLuex79Y3Kt6Waev41y/xhHp70svdCy8vV7y9XTmx2/6uwon400y9eZzdz5oz0C+EFDsD5yC/395N8/eKEIITu844begP9A0hvaqlOrLl3fx/RqczkJ9fQXCwFxpN2zJHFOsS7JYX1SU4begHyurRmS99ntPPVOf4mVoS52tpO2azDJ2u6bsP8gvtvAfqJrJKynn023UYTWZCfGzbOUCdoXUPohG+AwhQ+VBcb+39cHWIY/fz1hBCkJ1dhru7Gl9f20XxE7tO273uRPzpTjH05ZKM6yOHc32kY0HSII0HgRp3iuqsF4y9lWoi3R2nUuwIJ3adwb+XfT2KWkNql9zTGWoMOdSZbL1qtMY8tMZ83F1aD1FryV+XbeJwmmW32CQEW5NSMQvBe3faKsbPCZnIF+k/WZWFqP0Z6mM/5XFn8uHWAyw70JSB5MSFfP64dA2bn7obl2aaHMN8YglR+5Ovsx7L54S07umjN1VRqbd9r3pzFRX68/irWw+XivUK5fnBC5z5KoDjNlRrSMMPa0N/kK/98W+gr+24ajKZyc4uw8fHFS8v18byhvlgS07Ed0J+zCtcoYNIFpfbL4GzQoi3m330C/AH4LWL/1/bFffvFkNfkqTHW/u8xRe/LHGRt22AaSu3Rs1BQmJzwV6q9bVU5WkoPWNtqK45eoap/S8/ZfWOkJtbxisvr+XcOYu74NChUfzt7/PtTsqaExjhR0FGEZIA/xV5FDwQBfIm9/XHB0/CzaXz0s3JkKGRq5kSMIK7ezYNeK4ufajW76el57xc7pzxtj7xrE1aG4B/fLIezelaZDKJ6TMG4Bfma/f6wEh/u+UtWRI3jvjcNErrm1bZZ4b3ZlxwtFPXX8E+zv79AR4eOJ7d+emUNwspuTqyH6MCI7ri0X4XrF59hK+/2oNWW4+bm4rb7xjPwoWjnb7eTWHfIHZzcX7x5HxuABGdKB7t6uiZ7JRPD4thQnA0ewsyG8uCNR78sf/ln2a0JesTkxv7sqziQKICbQ02d2Vcq3W4yBS8PHAJn6f/RGL5WXxcPJkfPpU5oW0PWTh9OofXX1tPbm45MpnEpMn9+Otf56JWN3mABUbY//06Ku8KFDIZzwydyl8OrG+Mv5eAvw6ZgrqLxFIDI/w5s93V7mfuqrZpZHQm2zZewNRXhtzFekxUSBrUcvtjoCPyyqsajfzm7DyTTkWtDu8Wu/rzQqdgNJv4JS+eKkMNw336c2+v65FLXS9++fNR2wWngspqDqZmM7FvdGOZXJLz4sCH+CLtJ46Vn8HTxZ15oVO4NrT1LFsKmStKmRd6c6VVuYQcty7QZPBQDaTWzuaYvd//2OAorgrvw5acpvP9VK4sibNW1D96NIO33txIUVEVcrmMmVfF8ec/z0ahkDv+HUdeEfS7wmXBeOAO4JQkSQ25uf+GxcD/8aKCfhawsPlFkiRtF0JMlyTpdSGEPZE+p+iuHf32BZFdJuiFggG+f208Ngkz8ja6kF0KuSTj9ui53B49l/9s3svniUdsznEUv91ZFNXW8EbCLuJzM/DXuLI4dgQLe3dtQoQXX1hDampT5oDExCzeenMjr7x6k9V5OcUVbDx4Fp3eyLRhvVn4xDxO7UlGCIHHkUqU+SnI5kcx5bbxXBvdn2EBnZv2rKd7OD+Os9W+6OOzmL3a1bjS5PInhESM7z+cqtdotr+VqNMb0ABms2DrliTmzRmExl1NXU3TLoxXgCez77Z2klmWcpyvzh6jrL6OqWE9eXLYZPw1bkR7+LJp7r2sTDtBXm0VY4KiuDqib7P7Gdly5Bznc4rpExHArJF9UbZRbO7/G32G92TYdOcnxb08/fh17r38mHaS/NoqxgVHMyu8+9LINUer07Px4FkuFJYzsFcI04b27naRt+PHs/jowyYBMq22nk8/2UFMTBDDhkU7VUcPz2s5XfYdRtG0gyyhprfXwlausqa8ahhaQxj9wu2rS7eVCPfpnClfSo3hQmOZDBf6+dxuc65cJmPp1IVsyDrLkaIcIj28WdhrED4q+0bYpeiKsclZmi9Ybk4YxqDoDLzdmhYWfTRT8NFYC6QeTr7A3lMZ+LhruGZsfwK83QnVBPL8gAds6k8qLeStxN2cLC2gl5cfjw0ez7iQKLvPUl9v4Nl/rKKy0rKoZjYL4neexc/PnQcfbPKKm3nHJFa+vZ6y/CYPApVGyYKHHYcXtIWW7yMjv4xNh5Mxm83MHNGHvhEWQbMFPeLo4xXAz5lJmIVgXnR/BnehV8eUm8fx49t9yDleQviQphALuTGcUA/nPGE6m1OnsvnwvX2Muakng2Za7wj39r4ZhR0ht9Yw2FlAB4v4m/HiXOqXjDN8lnSYgtpqxgZH8eTwydwQ0XVek0kZBexIOI9aqWDOmFjCAyweG/YW+8H+nC9Y7c8/BtzfpvvKJAX9fO7gZOkHVuXRHnPRKDo/fjzC60HKauMxmEsby/xcZ+Kjse99+NHE69iUfY6DhVmEunpyU6/BBGiaNnv2Z2Vy/7YfqZ0iUFZI+J4ws3nTSYKDvbjjjglMvH4U370cTO75Jn0JuULOjY9f0+7vYBbiilDxFToFIcReLOu39pjuoBwgRJKkccA8SZKWt6xDCGHffbAF3TKTF0K80B336WwEClAMZYDvEwS7jmZ3QRr/Prmd5Moienr48XjcVGaFd75b16yBffgi/oiNQNPswV1nFJjMZm7bspzzlZaOuUSn5cn9mwA6xdjXGvQoZDKrVGJZWSVWRn4Dhw+nUVOjw93dsuJ+4HQWj3+4Fv1Fde6vfz3Kn2+axCsbnuan/2ykJLeM4TMHsejpBXj6de+akptLKGPDfiKp5F9U6U5QpFOxLGswmrQLPDU4i9EB9ieiDTh61+oy6wH+SOIF3o5/geWv/0zmmRx6D+3JbX+7Dt/gJtfO788d5+8Hm8I/fkw9xanSQjZeexeSJBGgcePBONuBVqvTc9+/V3Iuuyk8aGX8Cd5+ZD4eGtX/2/RvjlBplNz48DUsenpBm9XyAzTuVkravwXl1bXc/foKLhSVI+Qg2y4xOjaS9x65rluN/R077LtVbt922mlDv0wn453ECYwKPkW4ezlFdR7szYulv4+aOCc3c2YN7Md1X8xl6LBkevkXAh0TqFTI1EwL+5Sz5V9RrDuOuyKUvj534Ke2v5vtIpOzoEccC3q0vtvtCLMQvHd6F9+nHaNKr2NySAzPDZ1FuFvnuX3b679bctWg3nwefxghoKzGk5d+vIVx/c4yd6g3g8OvIsDtaqsY67dWxPPD9sTG4682H+XTv9xIv0hbNe98bRWLfv2BaoNFULRUV8sftv3Iz3PuIM5O+NGxo5mNRn5ztm8/bWXoe/p58M6uF/jh1Z9IPpJKRN9QbnlyAT3iOuZl88W5AyxNOUSJroYxgdE8O3QWF85X8MznGzFdXNz9avMR/nHHTBZMsLz3/r5BbVYZby+uHhr+veVllr2+kgt7txExVE/fuMkMGHAfdSYlLr9BBscd2y272gdXDaSqyI2Y0dkIIeFSNYaBCx9sc31R/t70DwvkTK512OvInuH4e7ixOescj+xu0tJYl3mWxJI8ti24F3UXjHnfbjnGf1btbjz+76YjvLNkPmP6RzF7cF+WN3PdB/ByVTOud9P8oc5oQIJ2e3nE+vwBtdyfjKpfMAsDEe7T6e19c7vquhQalyiGhW2ksGZVo+q+v+tsh+fLZTKuiYrlmijbDBLZ1RXcvWsV9SEAEjoN5AdC2HrB9m2nueOOCSjVSt7c/jzL/vUzJ3efJSg6gJv+ci0DJzjOSOGIgroqXkz8lR15KbgqlNzScxiPx01FIft9ZDu5wv8UzwHPAuFAS893p3XtunUGL0mSGrgHGAA0+k0JIRZ353M4i4eyHxPDVwJwvqqYB/auwCAsg3R6dSmPHFjNiml3McSvc936Y8MCeW7BdN7etJdqXT0ucjm3jx/CvGEdzx9cUVzFd6+vI2HnafyCvbn+oZmMnTOUPfmZjUZ+c5aePdohQz+7uoJnDm5mb0EmPkoTt/fTMybIlWDXQShaSbTQ3IB6+8f4RiO/gY/W7GfzG/fx6sy25zJvD1pjARnVW4lyn4ashSufp7IHZvnTPHzsh2al+dy3ZzmbZz9AqKtj3YGW71ohSSjzDGhKrVf4ZTKJXoOj+PsPjzmoCZaetfUCOVtexIGCCw53vgDW7EmyMvJNKsEB92xG/vw+SrmCG3sN5NkR052a/BzbcZqV722m8EIJgyb05fan5hHgIOzg90pU/3Duf912d9YZDm85yeoPtlCcW8bgif24/el5+AV3TRyuI77flsB5eSm1o40IJchrJfakZRJ/PJUZw7vPw0Aus79IInNQbo+VqacoqJWTWe2PXigoqXOnuE7Dt+cSeX2cc7uy7l5KzP1MHND25IC2J3BpJepLoVH4MyzgicZjIQTZNXvJrT2IWu5NjOc1uLt0jsvsR2f38uHZJtX2nfnnyaopY9OsBzq8I3WhuoJnDm5iX0EWarmCm3oN4h8jplFTVMN3r//CiT3JBIT5cuPDsxg5cyDPXTedtzda+jK9wZ0wj/uY0XeizYLYhcJylu1ItCqrqavnozX7eO+R62yeY2XqqUYjvwGD2ezwPcvk9r+33M5kPaRHIH/53NaDoL38kHaM109ubzw+UJTJH3Z9j9d+t0YjH0AIeHfVbmaP6of6N0gDFhDuxyPvPwBYvvtnpw9x30//pay+jn4+Abwwciajg7ovLZis4d0IiTO7enFmlyVMcdasgY3t58yhVJa9tYGc1AL6jejJ7U/NI6yX48WRNxbN4q2d76FQp1NV7YahehSvLrSIWy49c9Tm/JyaSrZeOM+1PdpuILZEayzgbMVKennMQaeT8/FaaxV5vdHEOyt38+VjN+BzpBCf4nrKAyyZlYK9PHj9ltmoXRQU12l55uAmduSmIZckro2O5cVRV+HuorJ321bp4TmXHp5zO/zdnEEp9yPC648drmf5+ZPUt1DrF3KJqr4gK2j6PfuF+LDkvY6bEvfvXcHZCssGVLWhns/PHUAmSTwx8LLUCr/C/zBCiFXAKkmSnhVCvNTeerp7dPkWSAZmAS8CtwHdl7ukA6zOONFo5DdgRrAyI7HTDX2AhWMGcc2wWNKLSgnz8cLHrePKryaTmafnv0XmWYuLal56Eaf2p/Dstw9SGms/nr1U53yaqpYIIbh750pSK0vxVNZx94A9eKl0pFZDavVaQjQjiO0/lLNnrNP5jB/fBzc3yyBWq9OTnm8r7FRvMJKaW8KwPp3rou8InamC3QXPE+G2jWmhr9t8/mN6ok1ZncnAmqxTPBjbupBU83ftp9bwp3uXUo11iqmZV116t8/RuyrRae2WN5CU0SQcKBBUDzBgcrO4GNSbjHyfkoiLJOOfo2a2Ws/x3Wd59qb/YDZbrs3PKObE7mQ+PfAiqjaKrP0vcnRbEs/f8j7iovtGXnoRJ/ed45N9L3Rrvt8duWloY5ral8nV8s73pmZ2q6E/Y0Yc69Yl2nizzLzK+XCIMl0ld8XtI8KjyfV6VHAGKaWte9I055esJPR20j51JgeL3iClqkln50zFcmaFfYCfuuMeYcvTbb330qtLOVScydjAHu2u1ywEd+9YSVqVZQFYZzLybUoCCiQuPBRPbpplMpyXXsTJved4aeWjLJw+iGuGXnrcSsossHnvAKfS7YuYOuzb6uz3bcOGRePn505pqbXA3cyZ7fOaaAsr7IwFxboaahQGXFpMuapq68kuKqd3G1JKdgVr0k/zasLOxuPk8mIW71hF/II/WrlQdyUzZg5g7dpjDvuD1JMXeGremxj0lr4rP6OYxPgzfHbwJTx9bTPPCCFIN7zOyKFNqfnU8lw83K8GPClx0KY6Mudpjs5UweHid0iuWE1Y/T+pN9imjUzNLeHvN7/LuUNpuANqdxfMKjl//efNjOhpmds8tHsNh4ssWgNmIfgp/TQms+DdibaCgv+LlDqYv5jUzs2L2sLx0txGI785y9MSrhj6V/jNEEK8JEnSPKAh9i1eCLHe2eu72xclRgjxLKAVQnwNzAWcV176DWmZg/xS5a2xOuMk12/5iukbPuHVxG1UG+znd3VVuhAXHtwpRj5YDI0GI9/qed7/lQkhUSjsxHZODm3/RPFYcS6pF70Exoek4qWyVvnNrzvK/c8EM3iIZddAJpMYN643j/+laYdGo3IhyMd2EJfLJCICu3cnFCBbu4f8WtudAIftw6AnqayAP+5ZxZR1H/Gnvas4W247kDS86xB/L159dSFRURZxGRcXOfPnD+PWWy/t7j0lzDZFm1ImZ3xIdKvXRQU3peUxuotGI785K9NONRqojvjpw62NRn4DBVkl7P3lWKvX/X9h9Ye/2vwNc1MLObjpuIMruoYyLzt5mmVQ4FpjW94KJ0rzuG/3j0xZ9xFL9v1ESmXbctAPiAvn8b/MwdvHEo/u7e3KY3+ezaBBzrtNDw8qtDLyAfw0WsaGZjpdR3v68LZQXp9mZeQDGMy1JJbaptqtMxp480Q8Mzd8wrxfv+SH1EuH4GkNjvuejnCsOKfRyG/OipQTjUZ+A0IIfvrQkt7KmXEr2kEqsKhg+94/9vq21sqVSgWvvHoTvXpZwgBcXOTMmTOYP9zVdlG/tqI12h/P1a62i3kqFznBvr+9hNGK1BM2ZVqjng1Z3bcPExsbxhNPzMXHx7Kw4OWl4dFHZzF0qGXRbu2n2xqN/AYqiqvZttx+vvW82kPkaPdZlelMZRwt/pJ/HvuVMlMVMhcTyJr6ZAmY1IE5jz2qDBcwuu+268Hk56Yh+VBTqJCixoCyVMdPH1h+S5lV5Y1GfnM2ZCVT42De+FsjhOCrlCNcs/kLrtr4Kf85tZt6k+0ih7M4+o1PDIpqk3CrMzj67dYa9Zec/1zhCl2FJEn/Ah4Fzlz871FJkl519vru3tFvSERWIUlSHFAA2AbkXS4IPULo0BpkjAvsYXfnZGZYXzsXOub788d47lhTHPWX5w5zqiyfZdO7XgCnJK/cYXmQqwf/HD2Dfx7ahvGi50Ifb3+eHN66mmtrNO/cQ9wq7Z5jVGXx9tsPUFamRS6XrFKmgMWF//5rx/LSN1utykfFRDiVP74rKNUlE+I6ArMQlNbV4qPWMCO0D7sLbGN743xCuHXHd43GRLa2gv0FmWyYfS/hDlIn9e8fxtL/3kdRURXu7ipcXZ1z0Xs8bjx7MzIouZgrV47Ey2Ouwk/durDXDZMG8fOeJEoqtVZLf0EqLUazjFKDBr3ZiMCxmgg4bl+l+RVOPf/vBtE+A6q13193EuTvQWaJ7TuJCmk9D3NzzlcWc+uO79Bd/I1nayvYX5jJxtn3EezqvOEyZ85gps/oT1p+CT2D/VGr2hZ/GuBaSpmdriXU3fk2NzOsLx81c33vbErrz9ktL6lPtil77MAatuWebzx+9uhmKvU6/tTf8ULfzLA+/Jx1yqrMXaFibFDHDJZ6B+KvjrwfnGnHwlwGQk//6GAmDerJ7pPpjZ/JZRL3zbU/cZ8a3ovb+gzh+5SmRbEZ4THc0sdx6Fbv3sF89vk9FBdX4+qqbPQSa431W06yJf4MQghmTG6f+/YNEX58cq6IWnNTW3aRZNw9bBRfZBy2Ove2GcPxsJPT3VlMJjM/b0xk595zlsWM6XFcNXVAm+upN9t/pzqTsXGc81arcZF1bfD+7KsHMWPmAEpLa/D1dcelmVhAW/tPR7+7E2WH+Pa8pV5JBnKZGbNBQhJynhw2mZ5enR9qppPSuW7iTFbtOmlVPrNnONux3TgoybP0X44MZJMwYzJfnobnn7euYV1Z0wLR+6f3kl5dynvjrkOYK0DUIsmdF5qcFdmHm2IGsjK1qY+7KjyGD6ZeZzcUpyOM8I/ER+lKud7aq2N6WN826/Fc4QqdyFxgiBAW40ySpK+BRCzK/Zekuw39zyRJ8gH+gSV/oDsWoYHLE2MKtXmjeP/cYD5NHURUgC+FpgpMwowMidtihjMn3Dpuvqy+ik/SfuFg6Rk8FK7MD5vATRGTGzuJL5IP2dzmcHE2J0vzujx38pBJ/ZAkyWZlcsgUy3e4ve9QZkbEsC8/Cz+1KxNDe3QoxnNkYAT+ajdKdFqK6zyI8rR1wfdWWlZrW0unt2BCHNt3nOFQqmVlW14vOLE7ndX9ErjhGsdx/l3FJ2kH8MyEXck15NZU469x5dFh47g+ehA/Z55EYBHYenTAJBJL8mx2DGuMepanHeeJwVNavU9goGebnuv9d3egSdARECRhVoG6EHyjXaB369cFeLvzzd8W8f22BFJyislQJ/FS3DZG+BRhFrCjJIKtlfdcsi0MmdyP9CTb3YehUzoe73hZYUzBXDwLyetVJKXj/NgtGTIpluyUApvyoZM7rr3RFm7oHcehEuv3JAHX9nT+PX2fmtBo5DdQqdexMv04D8c5v2v68/nTvH54NwXaGoJc3Xli5ERu6uu8O6bKbD9sSqF3Pr1enE8I98cOZ21ePC4KA52dRdxbGe2g3NoQz6gqtTLyG1h67jAPxI51ONF8evBMsmrKSSjNuVivhn+Pmo+bomPhMqMCI/BRaazSQQJM9o0iF9vnHGLHMC7WVfBJ2i+cLj/F4yGJDHfNRpIEwmUwb9z3Gqv2RrIvyaK6v3DqEAb2dPzeXhk7iz/0G8bJ0gJivPwYEuDceBkQ4NzC0+ff7eHbHw82Hp84nePUdVYYz/FH/+e4y9eFr3L78nbmUNwUKl4cPod5kXHEBgWz/sAZTCYzs0b1ZfaojoVuvP3JVtb92mQ8Jpy8QHFZDbfd0LadzjmRfUkotvb2k0kSHjI1U5Z/zoXqSnzVGh4eNpa745zv89qDQiEnKMhW12bIpFgS4209DBz1n45+d9k1tp4mEV6e/DDtdsLdm+67p/gk32ZuIbeuhAGe9utyFm9lD55aNI0B0cFsv6i6v2DCQPr6e7PrpXUYDdYLLUMmW9pFX58AYrz8Gj0jGxgfHI2Xqv0LRO1hQ95BfrywkzJ9FUN9evNAzDxCNU1p7c6UFrFk01oy5cU26YZ35JxCW7ITjXErYEIo+iF5vYHkcun2L0kS/x4/h8WxIzhdVkgfb38G+Tvfv7cFlVzBu2Ov47GDP1N2MRVxnE8Izw65qkvud4UrtAFvoMGIciz8ZYduNfSFEF9c/OduwMYfR5KkP1x06b9s0Mh1PNX/EOervdhRGEU/v2D+PmEyvTz9bUTWTp3O4W/pn1Lrbokp0hp1fJr2CwALI6cAUKiz7x5bUFfNpSTv6kx1HCw9SHF9Mb3dezPYezCyNqRSCusVxB1/m883r6yxKrvzmfmNx0GuHlzfq3PinpRyOZ9Ovo6H9/zC/vxe9PfLw1VhaPzcV9WHKPcpl6wnv7CSk8eyULdYwP525UGunzu0W1dai+o9SK2V4arYh2eAklJ9ICV18Oy+bXw35yYeip1IVk0Z/X2C8VO58dj+NXbrKairtlveXlb8dIRDCRlISGiaedV+u/KgUztTQT4ePH6TxXujpuC/uGIR55NJMCMgm0mhe4FbW63jlsfnkhh/loxmE+QbllxF7yHRbf4+lz2mDET5HyEgHknmnGfJbU9ey/6DqeQZTQilC7KqWhZeO4zo/p2v8dEaN/UaxOmyQr5POY5RmHFTKHlq2GTifJ0Xhyuotd9+C+ucd/9PLMrj8Z0bafhZF9bW8OSuTfT08mF4sHN/k7OHwqjwd8c7sOm+tVUqTp7qwVQnN7TL9dXsLd+Dt1qLSt5+F1NH+Kv7E+E2iWxtk+K2THJhsK+1cJSjv11ZfS16s8mh4r2vypUV0+7idHkBlfo6hvmHo5Z3Uv71ehmYafL0MUEP/yAm/HkOK95pEi2M6heK2yQ/7v/qRUwGwWjfMdx57TSePPEJF2qLeCo0kRFuzQxJwwnk1Q+zaPpGFk0f6vTj9PEJoI9P58ez1+n0rOqMECNhGd9UMgN/jEhiRsQ0QvzuaxROmzKkF1OG9Or4fYDS8ho2bj1lU77sp8PcPH8ECoXzu+939RvBuYpiVqdb0vt5uKh4cMBY/rlvO4aLAoJlujpe2L+DcHcvZkbHdPj5dToD3688yP5DaXi4q1lwzVCmTHDsITn//ukc3nKK0webFplmLBrHiBn25yvhbuMJUA+gWNc8R72afQW2f3+92YMJt+0AACAASURBVGhl5CeWn+eFpK8RCNRyA2na9odXaeT+9PO+AZlMYt74Acwbb+1xcf/LN/PJM8saw94Cwny598Wm9MIfTlzAn3b/THqVZY4/0DeYN8bNaffzOEt2bTZHy48il+SYzG58cH5D42f7SpI4X53L12OeQSlToDMa+cPGVRTX1SC3sz/xRM+jaIzNPJiMyRfHz+1IkgKzWbBmQyJbd5xGCJg5rT/XXTPMSpg11jeQWN+udwAeG9iD3XMfIaE0G3eFioG+XbsBd4UrOMG/gERJknZyMcIIeNrZiy+3vFmPApeVod/AgvBUdhRGkVxaSqDSk1BXL4QwAAokSeJ0ch6PffA1LLQVDlmTu6fR0B8XFM3OPOs9I5VcwciA1uNSK/QVvJr8KsX1ljjYTWximPcwlsQsaZOhe+sT1zBx/ggSd57BN9iL0bMHd6kQ2PDAcPZc9wBJZYUoZIuoNW2jypBLkHoQfb2vQy679K5TQVGlXeGmsnIt9Xpjm91924PeLOdIRRR59V6EXIxl9lLqCPCs5lR6BOXV7qxKSeI/064h0r3JBXpcUBQbs09hEtaTrgnBnRcHuHXnGT78cifYUZrOL7QfMuEIYTiPq53dOqVhJ8KsRZI59rzw9HXng/hnObzlFEU5pQwc14eeHUxTdVkjqqA+HjTO5epNzSkjz8u1cUJnclNxsLiae42mNk3KO4okSbww6ioeGjiO7OoK+ngH4KFsm4Lz+OBotuam2C13lp9STtPyZy2AVSlJThv6Bfn1bPxhMv3Hp+MfWU5FoQdn9vZi8CW8WJqzNf8oGkUJmmaLkJ3NlJCXOV+1nlztAdRyH/p5X4+vylr4cJBfCB4uKhvNlpEBEU6luBzg0zkq/g3sycmkvE4HyJpidoTEhrRzHHjuAabeNJoTe5IJDPdlr+w4O32WI12ch+8zZHF8ZRIXQotQYGayZ57tDYypCMNpJJe2u5q3B4snmxFJsh0vyitqqdN1/vvv5bIfmcuSTq8XoLC42q77dlW1Dm2tHi9P57R9hNCjkCn597i5/GXIJHK1VfT3CeTj44cajfzmrEpJ6hRD/x8v/8yRhMzG48STF3jqsauZ40CMU+2m4s2NT3Jsx2ly0wrpN7wHfYfbj98GS+74mWHvkVK5hsK6RNxdQvBRzuLlhLXQoucZ32I8Xpu7D4HAV6XF3aV9oVpKmTsDfe6kn/cNuCr8HZ437/5pjJo1kCNbk/D0dWPsnKEo1U1ttK9PANvn3cfpskJcZHL62lnoEkKPJHWe4G18UTzfZH2DaPw7SajlbuhMTc9VVF/OgZIkJgcOYVdOBsV1WkBCmCQkufXf97rgdGww54P+MKjG8fGXO/nx56YQhrMp+eQXVLLk/t9GAE8lV3RIyPQKV+hMhBDLJEmKB0ZeLHpKCGHrHuqAy83Qv2yDYGRSU8dlMmZQWvIw9fV7kCRv3Nzv4sefYjApTdibrlcZmuJ9/j5kOmfLCxt3dOWSxHPDZuKjaj2OelPBpkYjv4GEigSSqpIY6OW8SjVARO9gInp37qSwNeQyGYP9Q4AQLJkV20afXkFo1C42E7F+McHdYuQDVBvVJNcEE+ZmHfsrlwliwgo5kuxuMymqrv6Aia6fET+hhOOVofwndRJptf7MDOvDNZGd5679w8pDlnmLELT0mRvU5t1iR3F/opXPmpAr5IydM6SN9/w943yc5PLVh23ECjOySth/KI1J47tP7b6BQI07gZr26Vws7DmE7bnn2VOQ0Vg2NzKWWeHOuyIb7RgRgF3jwhGD+4ezZuNxjm+zvu+gAc5n48isS+1SIx8sRkdfrwX09Vrg8BxXhZKXRszmr4fWNf4NfFWuPD/st3EbNTTGbUtWzbyhPDo2jOjYMHRGHZ8e/JDmzmUyF0FVcDLgjiSJVgZ25991exFCUFP9H7TaLzCby1GqJuDl9QouLk2rQcGBXgQHelJQVNXJd++679cryh93NxU1WuuFoegIP6eMfJ1uF1WVL2E0nkEuj8LD868Eu17fqLHh+PfZ8QwVKakFVkZ+A9//eNChoQ+WRcoR0+MYMd05r0MXmYYBPosY4LOosezJwVW8fnxHY5OOdPfhLwOnWF1XZdCilBnbbeQDeLiEM8zfubSNwVEBXHvvVIefS5JEnJ/tnK2+/hBVlf/EYDiBXB6Ku8djuLm1L/VrY52men7M+bGZkQ8g8FHVkl/rSfNpesPctnlbMdfJkLmamvoDE6gcajsItLX1rNlg6zGxduNx7rptPO5OaGtc4Qr/6wgh8rGEvLeZy83QvzzVRYA1OZYV7N7enviZH6beYIlxFaKcmup36BV9NfFHeiP0EpLS+muM8Wsy6np4+rF97gNsyztPlV7HlJBehLpdOtwitcZ+5GhqTWqbDf3fG26uKpbcM5W3Pm5SdXfVKHn4vu5b7VVIcpQyE/ZSfLtr6pHLTFzTs8ntUKv9luqqJlHMIV55fDZsE6XKtQwP7FyjrrCoEgkQZmi+0uSqUfKnu6e0qS7JpQ9C0Q+MLYTCVNOddlH//4JZgEw51unVyUIHRkRhcWcbF12PSq7gqymLOFCYSWpVCQN8ghnm37ZUl3N79mVZ8kmb8mt7Ob9YMHl8X8bGn+HA0aYdo34xwVx3tfPu4B6Ky2d9+dqoAYwOjGJ77nlcFS7MDO+Dawdj7dvLpHBLHHBlvXW2lGt6Wr+frOpsJJWt8efiaURZI0MvJPZUhTDVq8WuvrwnKLo+3Z1W+znV1f9uPNbX76W09BaCgvYjSRYjQiaTeOyPM3jutbXoL8ZLKxQdF/qSNPMvfVI7UalcePT+6bz27qbGnX31xbJLYTRmUFZ6F2BZJDCZsqgofxi5PBiVyiL8OLdnXz4+fshmUtby/bcHRwsqRd3QF97XbwxXhfVlb0E6/mo3pob2Rim3NkTH+A0gTXvaQQ2XByZTIWWltyOE9uJxHpUVTyKT+aPRzG53vbl1udSZbDOzuMjMyBCYL454MiRG+VnawuTwHngoVVTr68Esw1wjIbkI1MVwt/8QlG56qPvBukJZEChHUZ5fg15vGzKl1xupqNBeMfSvcIUOcrkZ+l0245IkqSfwd8BLCHGjs9fVmlR8lDKQbQXRxHj78fFUb0x6W8GxsSMT+PK7vpg3eyKbXdVo7AfL/XggxjrfqVrh0uYd3WB1MOlaW/enIFVQm+r5vXLtrMEMHhDBpm2nOH+uAI1cRnpyPn16BqJq465+eno6QJQkSaucbQvBGj+UMleEqLYRmqk3KHhw8Hiubmbo12q/s6lDKVUS654EdK6hP2RQJPsOpiIJEEYBkmXi+vEbt9EjytZlUKutZ+P645xLziMyyp9r5w9rTGkEIHm/j6h8HAwX4z+VE5G8XurUZ77MUEqS9CVt6BuMwkylWYe7/jAajXPxkkMGRnAhx1aQcuigyDY9bGdQWlrDujXHyMkpo/+AMK6eMwSNa9sNyrFB0YwNim7XM0wIj+bpUZN4L+EAtUYDGoULS4aOYXKE8y6TCrmM1569nkMJGSSfLyA6wo8Jo2PaFAoxwKsPh8stabiqc6uhjX1DZxOocWdRjPMLFV2Fq4uST2cu4PGdG8jTViMBV0X35slR1mKLIe5BYJKghbuuSavgTz1v4OPMNbxXEIdGZmSUe5FlsVTRH8n7rcaws1Mns9my+RQmk4kp0/ozanTnxLGD/b7YbMpHp9uBRtOUynXcyF788Ol97NybjFkIeoapGPNzG9tCY1iAClxvBc0tnfEVHDJr6gDi+oUSvz8FF4WcaRP7IYxmln4eT15eOXEDI7h67mCbMbK2diUNRn4TglrtD42G/gD/IF6ZeBX/OrSLan09SrmcuwcM4/o+HQ+1iIsNw0Uhx2BsIULXTX1hlIcPUR6ORQUXhE/gcNlRSg1Wxn6bx4mupK7250Yjvzm12h86ZOj7q/wtcfktsmtIKBqNfBdJzoO9FxCktmQocFcq+XTmfP4Sv4l8bTUSErHCn5evmcGw/pEI8wSEKAfdr4AZFDFIXm8iSS6EBnvb9aYJCvAkJLh9KZTz8spZvzaR4qIqBg+NYtbVg6wyOFzhCv+fuNwM/X32CiVJWgpcAxQJIeKalc8G3sWyj/mFEOI1RxULIdKBeyRJWuX00yh64xZykIXueuYPNtLXN4C62l8ot+PN5e5momd0AOnnijFlqpCi9IwZ0It/3Xtrp4jFzQ6ezbHyY9SbmwbnME0YI31HtnLV/xYy4NXnniS/4DRKFzfGjHiUndvP8NZ/bmfrti08+uijmEwm7r33Xp5+2rFORc+ePQGy2nJvV7mKZeNe4P3zH3O22jrn8O3RNzEn1Hrya7YzAAMIUWu3vCP8afEUklPyKS3TWoZhAX/8wyS7Rn5dnZ7HlnxDRnpTGMim9cf54NO78fOz7NhLiigkv9UIYw5ILkjyy3cxafHixaxfv57AwECSkpIayzdv3ux0ewD0Qgin+wYDZkrMlvdob6LliLtuG8+xE1nk5jWFfyy8bgQxPbs3w2hhYSVLHviK8jLLs8fvOMu2LUn85/07Uaq6d0h4YMhobus/hMzKcqK8fPBso1YAWNxaxwzvyWcfvsyT7WgLo31Hs7lgM/m6fDzCPKCNfcP/MmNCI9iz6H6Sy4rxUWsIdbdV2vJ08WS4ZizH9Nb5zIdLE5gXNY4poYNJLE/FXemOzN0dCT2Sokm7Y9OGE7z97w2NOixbNp9i8X1TuPV2xykFL0XzfmHbtqY2tXOnjueer8JsgrsXf89zz11tdV2gvwc3L7AaU9vWFhR9kPxXgiwASeZ8msmOEBbi06iyn5tTxsMPfk1VpWVHNn7HWXZuP8Pb796OvJmHgjA7Gp+sy2+NHcyCmFjSKsoI9/DCR+1c3P+l8PVx44F7pvDBZ9sb37uPtysPtuK+3hHaOk4oZQreGPw4z53+J7l1jRs7bRonuhpHc4m2jEn28HTxZFrgNLYWWqc0XhS5kFB1DCX1lQzy7oWP0rp9jwuLYu+i+zlbVoxvi75Ckrkheb+LMJWCqEFSRDV+JpNJ/Pmhq3j2lTWNO/tKpYLHl8xELm+7V016WhGPPfwttRdDWnbuOMOe3cm89u9brqTIu8LvDkmS5MBpIUS7Xam6dVYnSdLjdoorgWNCiONCCEeqNV8BHwDfNKtLDnwIzARygCOSJP2Cxej/V4vrFwshitr+wCokmRvRXk27nSr1FCTJ1aaT1bjO4fP37mTvwVTy8isYFBdOXGznqWlHuEbwbP9n+bXgV4rri+nj0YeZQTNxkXVPjPrlwIofDuDvM4gg/xGcOWcZa5NO5bB3TzIPPfQQW7duJTw8nJEjRzJv3jxMJhPPPPOMVR1Lly4lMLB9hpVGruKJvo+wp2QPR8uOopQpmRgwkSHetjHpGvVcamreb1Hqglo9s133bo2IcF+++/w+du09R2VVHWNG9iI60s/uudu3JlkZ+QDFxdWsWX2Ue+6fYlUuKdrmiv1bcNddd7FkyRLuvPPOxjKTydSm9tB+1KhUzoeP+Pm689+PFrNnfwpFxdUMHxJF327Uymhg9Y+HG438BlLOFRC/8yxXze7+MCAPpYqBAR3/O3SkLRiFkYWvLKRYXWyv6v/XyGUyBvi3vtj30KB72Z4Tw7YLuxAmiasipjG9h2Xx09PFjcmB9vPdm4xm/vtFvI3Y6g/f7WP+dcNxa6fbbvO2oFHPRav9ApNJ8Pd/VLHsB19CQ92YN/84N954ppPHCQlJ4VggrqtZsexgo5HfwOmkHPbtS2HS5KZ5olozB632c5vr1Xa8k1xdlJ3y+2zJjfOHM2p4NAcOp+HurmbqhL64unaNm3Z7+wazMFNcX8zsF9q/Q95VqDVXU139Fi0jXtWaq+1f0AYWRSwi0jWSw2WHkUtyJvpPZJjPpVMZy2Uy4lrpKyS5H2A7NxkzsifLl97Prn0WgdfJ4/vg59u+UMHvv93XaOQ3cOxIBglHMxk+8orA3hV+XwghTJIknZMkKVIIcaE9dXT3jv6Ii/+tu3h8DXASeECSpJVCiDfsXSSE2C1JUnSL4lFA6sWdeiRJWg7MF0L862K97UKSpPuB+wEiI23dyGQyT3x8PqK8/M8WVyRAqZqAp+czyGTyVtPDtJevdh3jm93HKKupY2yfKJ6Zfz2R/u1zafo9k55WhI93D+p05Vbl27fvIiwqmhSDEWpquOWWW1i7di3PPPMM69evb/f97LUFmSRjcsBkJgdMtjrXJAwIIVBczCLg7vEoBuNZ6nXbLtblgZf3a8i7aHfcVaPk6pmXNtLS0+yvd6Wnt30drKswmc0cys6hzmBgXFQkGhfHi1mTJk0iMzPTquzw4cPExMQ0eG5wyy238Ow7H1IWOZjyATMZ3zeKp+ZNadNvqHlbCAuTI0leePu8iVxuf0HFESqlghlTOk+IsT1kOHjXjsovd46k5fCv9TtJLi7B36SnRtfkcmWvLbTWN+jNdTztRNaa5u0hJCyMOz5cwYmsfCL8vPjTzDFcM+zSKS27Gr25DhdJhdSGFKwtKaypITE3jwhvbwYEOTZ8JUliRsRUZkS0bUe2orKWsjLbHUhdnYG83HJ692mfgdm8X/Dw/CtG43n27d9KdLSc6Gg/vH3e4pZbTnTZONHZnLpQwL/X7+J4pqWNPTBjDNcOt21j9vpxmcJEenqelaGvUo3Gw/NpqqveweLCL8PV7Q40Guc80o/l5FJaW8voyAi81O3P5x4Z7kdkeNv60Pbg7DjRWnt4mZcveZ/uaAsNuLjE4un1ItVVryCEDpDQaG7Aze2uDtctSRJ9lUP4+VAFO06ns0p9mIVjdDx41Rjkso5rV7Qko6yc5OJihk3oQbSPz6UvaK0uB3OcjPQiBg8PRZLkyKXLzZn5CldoFR/gtCRJh4HGAVMIMc/xJU10d2sPB4YJIWoAJEl6HtiAJSfgMcCuoe+AMKB5sHwOMNrRyZIk+QGvAEMlSXrm4oKADUKIz4DPAEaMGGFXHFCmmsgZbqW0biM6s4S/iGWqWaDp/P6PZfuO8+b6pvzLe5IzSCssZcOTd+HSjSm5Lgd6xQRxLjm/8dgsl9B7yPn61EHKa2pY8otlcO5XWkZUnWMX+dLSUoBIwKejbeGHDXs5rV9KYO8LyGQQoRnDrIi/olF44ef3DUZDKiZzAS4uw5DJWs+s0B30irG/0NArpnvdxx2RWV7OPat+JqvC4t7upVbx/rxrGRfl/KQpNzeXiIgm1+AsrZH4oycI8bYIau46a/kNrXvyLlwsIkxySZI+oZW+oXlbGD68nwgKPtau97l+ZxLf/3KEopJqhsdFsOSOyUSG+ra5no7QKyaIxARbj+SY3pdviIYjsksruOuHVdSrzOAFWWXVFFRXczQ9mxE9I2zaQnh4OIcOHbKpJ0ebwJ6ij7hQcA7a2De4hUSKxEyL0FxWSQVPL9uMj5uG8X2jO/vrOkV+bRK7C9+nuP48GrkPw3xvZpDPjXy1+iC/bD+Frt7AlNG9eej2yXi6OzbSPjt0hLf37mtU1J7cI5oP51+LupWFt7bi7e2Kn787pSU1VuUajZKw8I5N+BuQyTzw819Gbe179OixlaDgFchkroSHl9ptCw105jjREUqqtNz3+erGBayskgqeWW5pYxP6RVudGxMTRPIZS1tUedYz5LazBA8qQSvtZUd+AhODluAis7xzD49HcHW9DaPhDApFT4zmQN77Op5f954FYNaEWB64dQIqZdP7Lqut497VP3GyoBAAtULBS1fN4LoBv+3iZXtwtm9oRpvGiZZtYfn6Y6zanEhFdR3jhvbg4TsnE+DbsfAOd/d7cHW9AYP+FHJFFApF5y0uPLR0LWdyLUZzubaOT7cfQiB4ZPb4TruHEIJnt2xjxclTCCwiXYsGD+KFmdPb7WbfKyaICxdKrco0vnXo4r7nk5S/oZBUxHrNZkLQn5DbSbV5hStchjzbkYu7wDRtlUCsFWAMQJAQog5bZZhORQhRKoR4QAjRy9GA7SzxBe9wtmo7RUYVVWYl6TV72Jz3Ymc9qhUrDpwESRDeo5DhE5KJG5FGlamAPcmZXXK/y5lbbhuLt3eTcaXzcaEuSE59i7DRIzm5ZJZX4Ag/Pz+AC21pC1pjFSuy3mZz/jeU6S2TnBUbjpGg/YSQ2EzkCjOSzExO/X5+zWsSrlO4xKBSTbgsjHyA6TPjbAy6oGAvrrt+xG/0RNY8v3V7o5EPUKmr5/H1GzGY2p/S6VCqrXhmTlkV+85lNhya2tI3SJJ7u97n9v3nePXjX8nKLaOu3sDeY+k8/OJKdPVdm9qtJTfcNAr/AOsJZr/+oUya8tvvQreV93bup15ttpJxFRK8uWOv03VUGwpZl/N3SuvTcfNxgTb2DeaWfudc7Ld/A+qMlazLeYbi+vOWY1M5+4o/4ZMtH/HlygMUl9VQra1n3Y4k/vaW40w9yUXFvLF7j1XarF0ZmSw9mtB4rDfr2F+ygeVZb7G14AeqDLZCk5dCLpdx7/1TbQRO77hrQqe7cSsUocjlQU7/dtszTnQF6xOTrbxUGlhx4IRN2c23jsXbxxXXCC2T/nGEkCElSDIQkoEzlRvZU/iB1flyuR8q9UTkijBe+3QrKzYmUFFVR0VVHSs2JvDap9Zx2m/t2dto5APojEb+9utWSrWdrz1zGdKmcaI5KzYc471v4skrqqS2Ts+2/ed47JXVNulW24NM5o1KPbFTjfyTF/IbjfzmdHa/tvFcCssvGvlgCUL44cRJfk0573Qd5foifs3/luVZb3Go9FcW3TkKd6sFTMG0J89SIU8CBEah41TFGvYX2YauXOEKlyNCiF1AJuBy8d9HgIRWL2pGd+/ofw8ckiRp7cXja4EfJElyA860sa5cIKLZcfjFsi5Fb6oltWqn7cPUHqdSn4eXMrRT71etq2f4+HOE92iKHY3uXUBe/TCg85SJnSWnopIvDh/jTGERffz9uG/MSKJ8uieMIDTUh0+X3st/l27gbNoykEvUe4NC6YWxssk4NFZWUh5gK0TXESoMxZystBgPh0u3cH+vV/hpxyHGL863OTenNoEqfQGeyu6Pv74UarUL77x/B5s3niDlXAGRUX7MuWYIXl6upBSX8MWhY2SWlzMkNIR7Rw8n0L37UurpDAb2Z9mGIJXU1nIyv4Dh4c5pXoSFhZGd3WTcV5YWo/CwTWFZXdf+HMntYdXmRJuy4rIadh1OZdbE7jOyAwI9+fjzxWxcf5yc7DJi+4e2S5U4o6yczw8eJbW0lAFBgdw3egShXrZibV3JufJSu+UXtBYF55ZtIScnh7Aw63aUXLkVk+jcdWZ7hll3kFodj96O0FqxtJuWDm8Jp7NJzy6hZ4RtX7kj3TbDC8D2tDQeHDsao9nAF2nPkVvXlPb1SNlW/hTzOj7KtnkHzZw1kPAIX7b+egqTycyUaf0ZOiy6TXWAJZf3d8eOsyUlFVcXFyb5WXsEONMWLkdqdPbbZrWdNhYS4s1fPxjKxrJ3cFPqbD4/V7WVyUGPIJdZZ9iorK5j275km/O37Uvmsbum4uVhEeHbkWbbLgwmE3syM1lwiV39Eq2Wzw8eJTEvnyhvb+4ZPZx+gQGtXtOVdHV7KKrRcvO3ywlwdyNtb57N5xnZpSScyWZEXPdnXbkUjvovbb0eIUSnidrZa08N5bP7Xjo7UZEuh0/TnkFnsvR5pyr30dMtjk+XPs7G9ScoKqyi31gz2T7bba49W7mJiUEPduwLXOEK3YAkSfdhCQnyxWL4hQGfAJfOpUo3G/pCiJckSdoENPj+PCCEOHrx37e1sbojQG9JknpgMfBvAW7tnCd1jBkjZuzvLho7ebIIMGWoB7oIa4EoucJMjXofcDVCCEpqa/FSq21ywXY2yYVF3Pzdj9QaLDuQibn5bElJZe3dtxPi2fUKw6UVWpauPcjulAuYEAhAZgRVeASGkhIMZaUoPL2oOZHIoH92jYcFQL25lviildTWScjk9lfkW2sLNfX1LDt2koTsPKJ8vbltxBAifGwN0a5Co1Fy3Q3W2RrOl5Sy8NvlaPVN73ZrSirrFt+Bu8p6UlheV4eLXI67snPzeyvkclyVSrR620mGl8baxfiXnadYt/MUunojA3u4W4l5jRw5kvPnz5ORkUFYWBi150/iMdU69lSpkNu4vXY1VTU6TCrQ+UqYXMBFK1CXWSbZ3Y2Pjxu33dF+F8zsikpu/HoZVfWWdt7QXn5ZfAe+rs4rc6dnl/Dfnw+SklVE78gA7rpuDDGRzk/++wX7c6bCVkCv4ffUsi0sX76cH36wzuecX5fm9P2cZVpc9y/CAhjN9vsdSW7fa6Sy2tYYBPBS2Xfp1ygsrq5JlfutjHwArbGSvcVruTbsPmcft5HY/mHE9rdvZOnqDXy99jC7j6biplFy/YzBzJ5oa1T+beMWfk4623i87VgCJl3T92utLeiMBmr0BvxdLw/Pq+ZMHdCLT7bZupRPd9DGDmp/QpLMdj8zCxNmTLScKWjr9Jjs7C6bzIKa2vpGQ99LraZYa7uQdKk4fa1ez83fruBCRSXQNHdYeect9OnkRXln6TdoEOdSUsjIyMAk0/Duh58zdPpiqt75heuvHsKhwlxOFxTRO8CP20faCu5eiqKaGhJyL24E+INXBSirrc+p+g36fmcY1iMML1c1lbXW/cPU/j07Vbne20G78bxEeyqt0LJ09QGyfdfi0cu6PaZrkygPSOXueyw6SjnaBLJtnfqoN9bx7PotLBo+hP4hl0fo4hWu4ICHsOjSHQIQQpyXJMnpRvtbKFKogSohxH8lSQqQJKmHECKjtQskSVoGTAH8JUnKAZ4XQnwpSdIS4FcsSvtLhRCnW6mmcx5e7km461Byaq1353yUUfipOl/Rc9oIXzYW2paXGXLYnp7OS7t2cqGyEm+1mgdHjuLe4V3jgl1UXcOib3+k1mg9YSyv0/F9wgmemDKhS+7bgMFoaFVjbQAAIABJREFU4sEXV7B99ftUFaVhrNeSsP4lQgZfhWLBaPznX0/+F58hzALvUaN5ZL5TGhXtpkB3gRGxsynMOEBQD2uXVS9FJL6qKLvX6Y1G7vhmFWcKmtzifjp+mh8XLyLar3NiUtvDV0cSGo38BnIqq/jlzFluHWpRzL5QWcFTW7ZwKDcHF5mMa/r25aVpM3DtpJhdhUzGosED+eLIMavysZERxPg1CTZ9+8thPlq2B4Dz+79jRVEaJn0t4eHhvPDCC9xzzz188MEHzJo1C5PJxH133UlWYD8SMiy7Kq4qF164cSY+bp2TKspZYgYEcTSnDEsicTB4SRg8BWOH/v6UgL87drzRyG+gsEbLqpNJ3D/GuZSfBSVVPPDCcqovKiRfyCvn4IlMvnntTkIDnVv4emTSONalnMNgNlP4/bfUpadh0mqJf+rPfGnQ2rSFxYsXM2BAizzgkjdCYOM+7ixBXu4o5DKMJotxNXtwH24eO6h9lXWQnh7j2V/8GQJrQ89cZmsYe3tqGOAg68M1sX15e+8+KnXWE/2U/BJKtbUU6OxnnSvUtUsUuFX+/u569ic27fydSsmjuraem2YNbSzLqahkTTMjv2DZt9RlpGLWah32C4sXL6ZfbCyv7N71f+ydd3hUZfbHP3d6Mum9EdKB0Am9VxHEgr0LKvau6+66Rberu3Z/9r4qKiBKVzpI7ySUQEhCGuk9M5l27++PSSYZZiaZSQPcfJ7HR+47t03m3vd9z3vO+R6+PnoEvdnMwNAw/jFzJkPCL55orIEx4Tw5dyJv/7TLlsI0e0gyN493rGQgShZKDfmAHLMkQ3GewR+rHY1S5tjvRYX5Ex8TTE6BfYRMfEww0eEtEXu3Dx/KCxs22e3TNyCASfFxbX6HVcczbUZ+MzqTiU/2HuTFKy5r89iu4JZbbmHLli2Ul5cTHRND/+uuIy8hjsZZMxkyfjwWnZmQviOpNnqxaf9pfqw8g6VpHXvTqWyWH/U04PQ8BNBFCKjqWhZT1CoFIwdffN58AI1Swcu3zuU3X62mVm/tn/tFhvK7q7u2/OFNQwbz9ZGjdql5Krmcm4a4FhY2msw89NdvyTtXxai7q5zuU9J4lv5+1rlwpPdgvOVB6Cz287TTBRH8dOgYy4+c4INbrmF8gvM5Wy+9XAQYJEkyNi+yCYKg4PxyG23Q0+X1nsequt8P+BRQAl/S4uF3iiRJt7hoXwOs6eLbbJfpkb9hTcGfKTdYPRr+ymhmR3mmlSBKFmRC+x74BL9kcGLoByr78PDqlRibOsjqxkb+uX0b0X5+zEluP+TJU77ad4R6o8mpqkNetet8+K5i674s8s5VkTz+drt2ATCeAmVMf3x/8xwDwkN5evJEUttQiO4KIr3iue32iTz3Zg4qzXoCI62hwkpLGHPjn3d53Nrjp+2MfLDmoX+y+wB/vWJmt95zW+SfNwlrJq/K2i5JEvet+JFTVoEqTKLI8hMnUMsV/HNm15UMfGbyJDQKBd8ezaDRbGZ2ShK/n9pS4cAiiny9ar9tu/l5EARY+vq9NgNx7ty5zJ1rXyrqWEEJFXU60uKj0Wq6NhrBHUq0RpuR34zJR+CsrpY+XLhFno5w/qS9vXZn/LjxqM3Ib6ZBb+SHjUd46JbJbp0jxt+fr26+kX9s2sKJOxbQNzCA30yZxPTElhJnzp6F1vjKBlBr+Rlfuf78n8ctQny1rPvDvWTklxAbEkBCWM+KK7bGXxXN9MjfsL3kbYxiAwIyUvymc9Wk+zi+/UcKS6x9tY+3mj8/MheV0vkUwF+j4br+qXxy4KC1z5dAMENNYyOL9x9h0hDni1ORXnEu783dMa812fnldkZ+M1+t3Gdv6NfU2s16Im65w/bvjGceRa2wfs/zn4V39u7l44MtC4vHykq5+4flbLv73i5bwOwK7pk2iqtHprb7jMkEOeGaWEoa86gyexOo0NmM/WB1ItMinnR5jT8+fDnPvvQDFdVWD2lwgJY/PmxfVu724cOwiCKfHThEhU7HpPg4nps6BUU7Suyu5gg9MXcA+Prrr5EQkQlyHl+7mpWZmSBJeKcOwDt1AIp6ieAmN5ExAJuR30xpXedq0wPItALNc3O1SsFzD87Gz6dnF5s9YUK/vmz6030cyC5Aq1YxLK5r01IBUkJD+Ojaa3j1lx1klpXTPzSUpydNIDnEdSWGbfutc0GAugotvuGO+hDltd5WRTBALiiZE/0CPxX9jXqzNfqrqCyIrYcGAda5zFvbdvca+r1czGwVBOE5wEsQhFnAQ7RUr2uXnvbozweG0yQiIElSkSAI3R/z3cX4KSO4Of4DyhvPYJFMhGn6uR3OdE5/gq0l73FOfwJfRSijQ25lSOAVLveP9IpnWMAUDldvtbUpBTX66uEYLbkO+y87frxbDP2s8goEESQn4/mI6K4fAM6ntKLOaXtMZCAP3DiRyFA/BiT2jBdGK1egM6XzZfH1jL8tmijjXYh1/vRPCiEuYHCbz8KZcuc5xafLnLf3FMOjo9h11jG+LS3G+tseLSmxGfmtWX7iOH+dPqPdiZ67KGQynpg4gScmOl/7MxotVDsJd5QkKK2sa9MTPDDmwqrK51Y69z5klVUwMfHSmmSMiIliw2nHkPc0D/qCEhfvdHG583aX9xIdxbI7Op61dazAh1KzH0p/MyqZ85Dn9gjx1TI19cLVT2/NAP/ZJPlOpqwxCx9lKH5Ka7/4zRsLOZiRj95gYtTgvnhp2jZka3UG5CbHvux0WQUP+M8mtqIfebpMW7uvIpAJIVc77H+qdhs7yz6jylhAiDqBSWH3EufjXuSZq2ektLLOLlc4NTwUL6UCvclst9/giHCbke+MZSccgwAr9Xo252RzRUrXl8rtDO4+Y5dF3M7XZ1/GLEGZyRelYGF44Ayujnm8zeMGJEaw7P/uZX+6NSpj5OBYpwtBd6WN4K609muqt8bVHGGkm7orHcUimdlR+gnp1WswiY3EaceyNU8D2FvyZh8Bs5eEQg8WddeFpgP0jy1gZEoWflo9vtfEE6G7hikDpuGr7XhZwp5Co1R0e/WQCXF9mRDn/vjXuk84froPwUlVqFUt7/25igC2Fli4LqnlmEjvQdyZ+DXLjv3EKxv2U1Vnb3ZkXeD5Vy+9tMPvgHuAdOB+rA7uj9w9uKcNfaMkSZIgCBJAkwjfJUuIxrM8TJ25iu/zfo9RtK5A1pnL2Fj8Bt6KAJJ8XQc1XNfnUfr5pXG67hBahT+jgmbxxcEcrCKM9phFz9TJKyrqOXa8gIiIAFJchHECDIwMZ33mGRCx8+r38ffjxqHt12/vLGkD+zhtHzO4L9PHdv3CxvkEKsMYHjgVjUzJmdqVVButnohqUyE1wvvcOPAVorwHtnMW69/RGYOa2vPyKsg9W0ZSUgRRkT0jcgiwcNQI1p/K4nSrhYgZSQlMT7JOLFurb7dGlCQkJ6rj3YWXRsmAxAhOnCm2a/fxVtM/vmcM+fr6Rmpq9fj7eeaNGRgZTk6Fo7E/KOrSyw+8dfgQ1p44RXorBe7xcbFckeq+cZQ2sA9rtzuGxI508a53FyaxlmjfapQyCYmuneRfKJQyL6K87ftluUzGqCGOE2pRlDiankdDg4Hhw/ra1O4HRoY5DVkeFBWOQqbk7oQXOFi1hXxdJsGqSEYFzcJHad9nFemOsabwn7ZUgnJDNisKnuf2+HcJUrcftjwoORKNWkGjwd6AH5Eaa7eg6qfR8OzUSfx1/WabZ1+rUvLcjCm0RXO6xfmYXPR3lwL9/UbyUNLLHKjaiMHSSKr/GFsYc3uolArGj+j6BatpSQmMiYhmT3GLXnJicBALR3u2YOApO0o/4UDlUtt2TsNO5gz149vd4x32lZoeJ3ljc6G3zhMbVsr04em27Tohh0afd5mqGo01i7UXTxk5sKXfaKj3Yu2+4SRFFaPVGCip9udscSiXJTrOSWSCnBERY6mqy3T4bJCLeVkvvVwMSJIkCoLwOdYcfQnIlDyYePeYoS9YR+VVgiC8DwQ0qQjeDVy0NS4skg6LaEAu65oyPydrt9iM/NakV61u09CXCTKGBExkSEBLHvwVKUr+b+8eh9JOnnghvvl2Nx9/ug1L02Rn9KgE/vL8fNRqR09PWJ0SL5MMPSKI1kExNsCf5QtvxVvV/SGO/eLDueWKNBavbgmz7BsVxMJrx3X7tQG8Fb5c3+cxfin9GBH7SaeESHr1WrcM/XEJMq4YJLDmmAWpKTwiws+HhWNG8OLLq/h5fQZgDUWff81IHnnIGs6/cctxvv5uD6WltQwbEsv990whJrrrQoQDvDQsX3Ara0+eIqeymuHRkUxOiEPWNJkeFhFBjJ8fBbW1dsfNTkpurkXfYzyzcAZP/GsZdQ3W/GG5XMYzd89A4+S57Q7KKsq57tbXuW/BdG68fkz7BzTx8KQx/HIml2p9S97z7P5JjIyN6Y7b7Fa0KhXf3HETP2daF4cGRYQxPSkBuQeRHbMnDGDT7lPsPNwi0TJmSF/mTG7/PepK+ked42RDx8s3XsqUlNbwu+eWcPZsOQBabzXP/f5Kxo1N4tphA1l2+BgnSlrEDpNCgrhphHUBQSlTMyZ4NmOCZ7s8/88nv0HysjeaLZKJYzXrmRR2j8vjTJZqdKZsvDXxPHnnNF76aINtrAv08+KJO6Y6HHN72jDGxMbwU5Pq/rzUfu1WDZnXrx/v7ttr16ZVKpked+npZjQjSka8ZDVMD70ML6Vni2Zms4XPv9rBuvUZiKLE9Cn9ueeuyWjaif5oi5paPc/9YQmFJ4sICZZhCZIzb9IgfnvDjDajLTqLJEmkVztmdob51xLqV0NZbUv0l5+kRKmzisCqq0EeoaROaNGsiQ8O5JSH1/dHxdBQR80Kk9TIydqNjAy+0elxW7ad5Mtvd1FSUsuQwX24/+4pxPZxHcr+v0a/+HBumZvG4jUH0FRAZaSa9Bz7Bcx5/ZzPgxNDgrglbQiLD7SUCfRWKXlymuP8W5Ik6o3HkQlqtKokh8976aWnEAThCqwq+2ewrkLGC4JwvyRJa905vscM/SZP/g3AU0At1jz9P0uStL7tIy8cOlM2Owom0y/4ecK1rnM83cXkxMgHMIqeK6+mBIfwr5mz+Oe2rdQYDKjkcu4YOozrU92bJJ/JLuWDj7bYte3dl813S/dyx232nd7PG4/x9pvr8ZeDKkyG2VsgRO7FsidvbVcdtSt57PapXD4xlQPH8ggP8WNSWiJKRc8ama5+K1M7v6HJUkVG2RNUNe5m3nCYOzSEw7l3EqBJ47phA9n7S5bNyAdrKPr3y/czMi0esyjytxdb0nG27zzFicwi/vvxIry6MNdcrVBwzSDnJZLkMhnvzbuKR9euJqfK6pWeEBvLX6ZN77Lru0tqYgTL3riHzXtPYzCamTwykfDgnivrFhJZzWP//IaVXxQydfIAwsLcu3ZCSBCrHriTpYcyOFdbx7j4WC7rf+lOIFRyOfM88OCfj0Ih5z/Pzmf/sTyyzpaRGBvKqEGxXarq7A7+GgE6n4J7SfL2OxtsRj5Ag87Av15ayXeLH0GrUfH1gpv4Mf04J4rLSAkL4ZohqQ5VOFyxc3cWGTk5RDsRLHc1FgLkVL/N2ZoPECUDMkHN4BH3seT1e/jl4Bm0Xiqmjk5B6+X8HpJDQ0j2QMX9sTFjya+pYc3pU4iSRISPDy/NuqxHx7WupEK3lePlv8ckWiOzQrxmMDD0P8hl7lUTeOu9jfy4qkVoeMny/ZRX1PP8c44pGe7yyafbOHGyCAHwqhChQuSXnCM8MnsC6qDuK98qIWISnVeVGBIRyMZa6wJUSnAwb82dR91sHZk5JcRFBzNkQDSrjp3k2Dmr6v78oan4PrzQo+vraw1oLGann7maR+zck8UL//zRtr1j12lOnCziy48X2SJteoHH7pjK7IkDOHg8n2PmSlYUnqLeaEQtl7MobSRz20hffWHuDKYkxbM1K4cgby+uGzaI6AD7MbzOeJKM0sfQm62io36qoQwOewu1otfz38sF4RVgmiRJWQCCICQCq4GLy9Bv4iBQLUnSb3r4uh3GLFZzvOxZAtRpnX7JE33Gs7PsC84XS2zLm38+mzLP8Pa23WSXVzI4KoL3516F1ktNlK8vgV7uhxLv2eO8pNTOXVkOhv7yFVYvuswC2nPWwdFEPUcO5TFpQveHzbcmJS6MlLgLF+qc5DuBI1UrHNrXHZQTYSlgZF/n3tnTlf+iqnG3bVsmK2ds0heM77MQmaBi1+4sp8ft2p1F/jnHcO/yinq2/XKK2TMHdfCbeE5qWBgb7lzAqYoKvJVK+vj3XEnA8/HVarhqWvenjLjCx6+R6xZtZumPm3lokfuT4FAfLQ9Ocj8K4NeOIAiMGtSXUYM6rlFwKL+IVzftIKOohPiQQB6bMo6pKe6HHyf6TmBPxdft7/grZPdux3Ggvt5Aeno+o0Yl4K1Sckuao7q7Oyz7cT/FhBI9rNThsyRf51VaKnTbyKl+y7YtSgZyqt9iaNhgbry87TB8d9mTm89rm3ZwsqSMfmGhPDFtPM9NnkylTk9KSEiX6Y30NCZLLRllT2CRWhZRyvUbya5+i+Sg37Z7vE5vZM1PRx3at/6SSXlFHSHBHZNTcja2mc0i+/bnMPuywZTVN/DS+m1sPpWNv0bDraOGcs+4tE4v+MkEOYm+48mq+8Wu3VseyHtzHqZgfANGi4Xk5oouwcGMSG2JgLhpROcrZxQfDSW0n+P4nejjmDoAsPzHAw5tlVUNbNmeydzZF6aSx8VKv/hw+jWl6z1UO46//bSJvTkF/LDnOCqTnAcnjXYZYTYtJYFpLsYISZLIKH3cZuQD1BqPcLLizwwNf7/rv0gvv3oEQfgEmAeUSpI0qKktCPgWiMOah32jJEnOhZygrtnIbyIbcFvMqKdHtDHALkEQzgiCcLT5vx6+B4+RMFGm29DufmI7eX0hmnimhT+EQmhZme3nN5VhQde4dR8H84t45LuVHDtXit5kZu/ZAu5f/CNBai+PjHwAHx/nHgtnecfVNc69L67af83EaoczJuR2hKZKxKIEGVmxrD+s4Z6vlnO20rmKcKlunUObUSynunEfAL6+LurJ+mqouYj+/oIg0C8k5IIa+a7oSa0AAJXajE9oRvs79tJtFFbXcveX37P3bAE6k4lj50p56NsVHCk45/Y5wr2SmRx2H3Kh6ysx9PQz6Sku+x0P9SecUV2jp/BQONnbY5CahkbRLOBTOpVY7XCnxzjrJwFKXLR7ypmyCu79ajmHCs6hN5k5XHiORYt/oFZnIDUs7JI18gEq9FvtjPxmShvccvqg1xkxmRxTWERRorbOuWfcHfxcPGO+vhokSWLR18tZmX6SeoORwppa/r1hOx/u2Nfh67VmWvjDhGlaoqa85QFcEf0H5IKSvgEBLUZ+N3F2RxT5eyNszz+igslh9xHulex0/5pa557+S32u1d394LPfr2XTiWzqG42U1NXz1tZdvLllV4fOVW88gd6c69Beod+GWfwfDf3qpbN8Blx+XtvvgI2SJCUDG5u27RAE4VpBEK4F9guCsEYQhAWCINyFVXHf7U6ypz36rpP5LnJkguuwqZNH83n/P2s5cSSfiOhAblk0hdnz05zuOyzoavr7T6NEfxp/VQQBKvdVZxfvP4LlvA5TZzSx/MhxHprsmZdw+rQBfPr5Nmpq7AeWq69yFMcZMyqRH1YetGuTyQRGpV26eYydYXzonfx0wI9fcvdRXaelXm+dFDeazSw7lMFTMxy9VTJBjSgZnLYDXHnFcNb9lG7TSwBQqxVcfvkQLMCZnDKHY8eOujhUvi8kjXojH736ExtWHkYURSZfNogHnp2LTxcYKu4wdlT/HrlOL875/vAxdCaTXZtFkvjmwFGGxkS6fZ604OtJ9Z9FaWMWT9G5bDJJkvj6gy2sWLyHuhodaROSeeh3VxAZc+HK7rni6qtG8PkX9h7PAf2j6NfP/b+dK8aNTuRMdinp3/cja1NffMIaqD3nw3v/XuTyGFfjbFctwnx3MMNWkrYZk8XCdwfT+ePlXVsjvKdx9bdra+7SmuBgH5ITwzl9xr6eb0S4P3Gx7qdDnM81V6fx6uv2CzVRUQGMHpXAgbxCThQ7jm2vr9rGgVf3sOCxmYybOqDD1/ZRBnNb/DsU609hEvVEeaciF3qobKIAkijj8JepnFobT0CUkb89/RDxwa51E8aMSuBUlmM95bGjPBN+vlgoLqjinRdXsX9nFr5+Gq66eSy33j+1S9OzMkvK2ZdX6ND+9f4jPDFtvMfXcvW+CMhtDp5eevEESZK2CYIQd17z1cDUpn9/DmwBzg+9urLVv0uA5rC2MsDtSW6PGvqSJJ1tf6+LD4XMjzCt8zWKqop6nnvwc3T1ViOuuLCK1174Af9ALWOnOjcCNHI/+vo4Xwhoi6zaSowBFiQZyMwgb5AhswjU6D1fbffx0fDKv2/h/Q82czQ9n4iIAG67ZRzjxjrmDC+8YyInThaRedqqdK5QyHjovulEhF98Xl1XbDl2ho3pZ/BSKZk/uvNiX9X1CgpKHSc/1S5+i0if68iv/dSuzVsRj7/aurCSkhLB3/96HZ99vp3cs+UkJ0Vw7z1TiI4K5LabxpJ+rID0YwWAdZHl3gWT6evB5Ouz9IN8dewIDSYjl8UnsaD/cNYeyORsWTXD4qO4ZtRANKqeXvfrPG//cxUbVrTklG5YeZiaah1/e/uONo7qGixGPxJjXZfGvJgpr2tgyc50zpZVMTQukmtGD8SrB0Q1u5rCsmpEuYRFKyIqQLCAQicjv9hVBJxrvBT+HeqXz2fp5zv47zubbNv7tp/iD2fL+XD5Y8g91BQpqKhhya6jlNU2MCa5D1eMGIBC3nVe5ztum4BcJmPFqkPodAYmjE/mwftndMm5b71xDEcz8jmaUYC+WoOh1ot77ppMfFyoy2MifeZTWPcN0Do6Tkakz3UujzlZUcYre3dwuPQciQFBPJY2jvExzhX9axqd98+u+u1LiWCvKajkoRgt9oZzlK/j306SJD4+eoDFx4/SaDYxJzGFp0ZN4Nkn5/DbPy2hssrqufT10fD7Z65AJuu4YTbvimGYTBaWfr+PqqoGRo2M58H7p6NQyKludFz8BhBVMs6eKeVvT33DG1/dT/KAzpXwjfBqP8VwZ+ZZfjp8CoVcxlUjUxka17nFruioQFKSIzibV05iZDL33j2F+Gh7Iz+/toZ/79nO7qJ8In18uWdMGkMz+nAk3VrqViYTWHjHRBLiXb8zFysWi8hzD35GUV4lADVVOv777iZUagU3LJzUoXM6G7dczX/rGg2YRdFjsWCtKhE/9TBqDYft2sN9rkQuc1+7QxCE+4D7AGJj268w0sslTYggCPtbbX8gSdIH7RwTLklSc+hhMeCQGy5JkmfCIC649Gb2PY43KuUI6k1FBKitwlNVOj0+ahVKuZzNa47ajPzWrFm6z6Wh3xF2FeZxWHcOqWkuLspBVIqoKmVMTemYZz0hPoyX/nVTu/v5+3nx3pt3cvhoPhWV9QwfEktwcPeJ6HQ1r63aziebWt7B73Ye6fQ5pybHs+74aYd2V3lfiYFPIUoGztV/jyg14qsaiiREsaVwAUGawaQE3MWY0YmMGe24cq/Vqnnrlds4mlFAaVktQwbFEBbqvvjcu4f28NLu7bbtz9IP8c3+oyibBIFXHzzJyv0n+OzhG3pc3LAZs0WkzmAg0Nt9T7yuwcCWNY6ZP/u2n6L0XDVh3VieUC6EMyL2DY8G/ouF4qo6bntjMSW11sn8qubf/5EbUHWjCnZ3YC6oxxQg2pLQJDmYlCLG4voLdk9rlux1aCvKq+TQnmxGTnAesuuMk4WlLHh7CQ0Gqxr4yv0n2Jh+hjfvvqrN40p1uzlT8w0GSxUR2okk+d+OQub8vZLJBG6/bTy33+Y8Z7gjmCwW6g1GAr29ePM/t5F+rICSUvf6LT/1EAaG/Icz1a/QaC5EI48mIfAp/NTO85NLdQ3c9OO31Bisk/0yXQMHigtZNv9WhoQ5loudmhzP8iOOJQOne6Dp4C4NpkJOVX9KteEU/qokUgIX4KPsvgm/XKZmWPhHnKx4nlrDYeSClmjfW4n1u9dh39f37+SN/S2hzR8dOUB2dRWfzL2WxZ8/wP4DOVgsIqNGxneJ4Ov8a9KYf43jItqYvjF4K5UOUTne56zPvGgR+en7AyT/oXOGfnt8tHEvb6zeYdv+btdR/nbTZVzTCaeAVqvmvXcWuPy80Wzi5h+/obDemmpbqmvg8dLVfPzIfBY1TKa4pJbBA2MId1Ps1RU6owlRktwW0XRFrfEMp6o/p86YS5BmIMkBC/BuQ7fq0O4zNiO/NauX7OuQod88bpU2jVvN85YPHriWQG8vqnT20akTE+M6XBFocOibnKz4MxX6bQjICfe5kn5Bf/LoHE2G3gcAI0eOvLhzuHrpLOWSJLlXw9QJrcvOO0MQhHjgUaz5/IpWx7U9GWji0prV9TBqRCJlteTqd7K18CjBwsu8tCaH06UV+HtpuHt8Gj4Nzlekdee1VxsryddnE6aOJFzjfrh+M5+mH8ThKZDB2AGxjIvv/tVCQRAYPvTSW5Usq63niy32aQcWsWN9riRJZDdkYrA0MmdQf3bnDOCHoycAa72L20YNc2noywQV/YKfJzno99QYMtle9ABmybpQUGVI51zDFmb0+RalrGUBxWI+i67uVczG/cgVfRmQ/AhDBnk+If/4iKPAT6PagkwlQ260emqOnj3HhvQs5gzvuIp6R/ls50He376XKp2ehJAgnpszlYlJ7YuzGQ1mzGbnZdHOf/+6Eosko8Jcx5GKt5gc9XGPq8R3li+2HeScvgFRi9VAFuFIYTE/HT7NlSM7HiZ7ISgRdCADjcpIgFZHnV5DQ6OGKpVnv7/FnNv0rjm+K56iazDabcu0FrySGyk05DIXTNxcAAAgAElEQVQS9w39d3/abTPym9mccYaD2YWMSHA+hhQ1bGZ38dM0C75WGo5Spt/PpKjuF5GSJBNb05/DV74ekyiwv2gU4/r/ibSBfRjsgb0U7nMFYdq5mMU6FDIfBMF1BMOyzAybkd+MSRT5IuMQ/5k+x2H/2QOSuSltMN8dSLeNpzcMH8Sc1K4RlRXFMqpLZyFKRs4aasg1gYRAlSGdoobNTO+zGG+F4wJEV+Gj6k+w399Qm0pJ9BmCt8LRSLSIIp8ePejQvulsNtnVlSQEBDFhnPvPaWfw1ah58ZrZ/O6Hn2zGvrLGTNDRlpz0hvrujbao0xv4YP0ewOpAsagBOfx+9c8U6ep4aOrYLrlOkT6fCmMJfb2T8FMGsDb7tM3Ib0YCPjl6kC+vvAEXBXDcplbfyAurNvLz8SxESWJqSjx/u2oWwT7uVWBoTb3xLFsKF2AWrQuo1ud5CzNivkUld74Qodc574OT+p+kpvw6REsxSvUkvH2fRiZvP2Lh860HbEZ+M+l5xWzOyObf11zOk8vWUGewXjMuKIDn53Y8FUetCGdo+PuYxQYEFF1WYruXXlpRIghCpCRJ5wRBiAQcVWtb+AH4GGtuftticE7oNfTbQCZAmMJAgNzI/kYZG7Lf5HSp1dCq0Tfy2sYdPD3O+SAwfnpLL7323FJ+Lv4BEatRMiJgHLfHPYxccH+1sVznXIxleN/O51L+mskqrsDcjkiiO1gkMy+efJbiRmv4vJdcy8Lpj7FowihOl1WQGhFG36D2PcgyQUVO7Q+YzxNN0pmLyKtbQ6K/tbauKNZSU34tkmjN1xMtZzEZduIXsgylyv3wYkmSqGx0LvAjKYBWdsTJwtIeN/TXHTvFiz9ttW1nl1fy8OIfWfvoAqIC2vZkBARpSR0Wy/HD9rWKo/sG0zex+yszVDQeorxxP6Feo7r9Wl3JzjN5iK0DEWRg8YJ9OQWXnKHv18ePAerDJEUV0xxdnFcWhKXQ/cV1Uaympvw627vWWcZPH8DaZdYIIv8pNYTdVo5MJbGJT8g/vYd7E55GI28/ciWzyDF3ubndlaF/suojzq/qUqbfS0XjEYI1HVPPd5eMM/czOORn23Zy8BqWZJSTEPIVgVrPNDMEQUDpwoBojatxsUznXDRLEAT+esVMFo5Na1LdDyEhpOu0E0TLOSxNi4+xClCi4ZTJqlZvFKvJrlnCoOBHu+x6rakz1fB+9svk67IBUMu8uK3vAwwNGG23n9Fiodbo3Agr1+lICOhZLYnZA5IZHx/LF6t38907W9CUm2m9dDphRict3nbIK69CbzQjCdZ+sPniEvDm5l2E+/lw3YiOV7cxiyY+y32T9BprnyAX5MyLvJlynXNvuKtn11P+uGI9Px9vEerelJmNbukaPltwvcfnyqpZbDPym9Gbi8mrW0lSwG1OjxkxNgmNl4pGfcskY8SYPB58ZiPmpiaD7ixm4x78Q9cjCG2bI6eKyp22ZxaV8czIyWx98l525eSjVSoZHRfjUnHfExQybafP0UsvLlgB3AW82PT/H9vYt1GSpDc7eqFLV2K2B1EJEhGKRvy1NQ6fbTtXwH3PzEHZKr95yuxBXHWzVRwvuz6TdcXLbEY+wMHqXewq3+RwLoAifQnLC9aysuhnKgwteaaT+8Q53X9Kn/9NQTx3SYoI7hIl5dLGYpuRD6C3NPBF7lv0DfZlTmqKW0Z+Mw3mAuftpnzbv436H5wYHmYa6z/y6L4FQWBijBPvuAXk59n//aJ6Pg/w+0PHHNoMZgur0zPdOv6pv8wnum+LcnJIuB+/e+nGHvOy17f6zS4VdJgcGwWoNl16ecqThmhJiW4x8gFiQyuZMtz9d96gX95lRj7A3Y/PYuDwviiCTYTfWYZM1WJ4n64/xs/Fy906T4qL9zElyrU2h87kKEoF0GBq6XMqDVWsLPqZ7wvWUKQvdute2kO0VBCucaxMM6/fQdaf6L7COpNcjYuxbY+L8cGBzElN6VIj3xnh8kaUrRwwrX+HrubHoq9sRj6AQdTz5dl30FvsF0O8lEpGRzqWgQ3UeDlNd+gJfDVqHr5uCnfMG4u8SYNCJpdx9a1jmTizY+HztaY61pzbyNL81eQ2uP679wkJwEulQFQAToaNZU7GKE/YUrbWZuQDWCQLPxZ9RWq4c02UKS6eaU+o0Tey4YRj6czdOfkUVDnOY9vD1ZylrfFP66vht/+6Hp9WVRduuMsx3dFiPo2psf2qVimRzvu9fk39oValYma/RMYlxHaJkd+Lc+J+t9rpf724RhCExcAuoJ8gCAWCINyD1cCfJQjCaWBm07Yr3hAE4XlBEMYJgjCi+T93r9/r0XcTlWAht9TRS2g0m7l24XhmzBvK6eNFREQHEhPX0iFl1DgPBU2v2c/E0Fl2bVtLd/HumS+Qmjwy3+at5Nn+DzIkIJVFw0ayszCPveesHa4A3DdsFCMiujd37VIn1M+HO6YM59PNLb+DvAPCQgJ6klWlFJr80UnWMK4GSz3ZDafo59v+an+FIYsD5R9TbsjER+Y8dSDEq+W9FS3ODQ9R9Hxi/sLE6dyxcoktTFAlk+NfraRRajH4hsRGMGtIz4RrtsboIvT+fGVsV8TEhfDhD49x4kg+FovIwGGxHguedYYQjdt97UVDZKAveTWOk73YkO7TNOgulJoicJKO7+PbVhScPaK5a4zdZnz9vXnls3tZnr6ULWZH/dn0mgNcFX1ru+d58LKx7Dmdh85gZMTQ06T2O4uPlxzRzxuzuACFk3DSIM1QinXbzmuVEawZBsDR6hP8O/MdjKL13f8ufyUPJN7B1LDO5eiLYhlymWPklJfSjCTWdurcbTE1Np7bUofy1fEW3ZXJfeK4baD70Qsmcz5G4yEUigTUqo57bp0hE0ApiJgkq+ER4uVYVtBgTMdszkGtGoFC4WiAu0tGjWM4vlE0cKouw8Gr//fJM7l95RJKm7zHGoWCl6bORnOBNTrufXI219w6jtysEvomhhEa0THB3zP1Z/n78dfRWayr2UsKVnJ732u5Muoyh339vDQsmjmG19bvcPgMwGA2d+geqgzZLMm5jbN6M9YZm/28o17I5slR43l9305bDM7QsAgeTut8qoBFFBFdlLRzd2xtTbBmOCU6x79P6zmLM8ZNG8CX63/D8cN5+AV4Exy0CdHJ5d2Z19w1NY11hzMpr2tZuBrYJ5zZwzxPuzFbzmEw7EOh6INa5bzUZy+9dBWSJN3i4iN3lW8HA3cA02kJ3Zeattul19B3E1+hkex8x0F4zkBrqLN/oNap0JKX3Hk+lJfcPiTIYDHyee4Sm5EPYJJMfJr7Ha8NewGtUsV319zMrsI88mprGBkRTWLgxVeu6WLkqSsnMzw+mg3pWXirlMwfM5CBrzzp0TmC5Drm+R/HIglsb0jkkN76LHi7+H1b02AqZXX+oxibQt/0SPjJ5MiFlhEv0nsqkd6TbdtK9QT09W84nEup9lzEJiEgiM233svmvGzqjUamxsYjmkS+23nUqrofF8X8MQMviBDf5QNT2J1j7xWQCQKzU91fdJDJZAwc3n5Of1cjCSFola5LJV2sXD0slT259h4aAbhicM/rM3QWL4Xz0EofhftiobmmaLrDjxkbHQlO6sy4GhPOZ0BMGEufuZ0Vp/6NOjjD1n6k8ktqjYXMiPqrwzGDgh+jqjEdg9gSDdY/8B60Smuo/2e539qMfAAJiS9ylzIueCRqecfFuuSKZPTmYLwUFXbtuVXBTErxrPSrp/xjyizuGDSMI6XFJAYEMTLSfQ2cqpp/UVP3Ns1zJy/NbMKCP0DoonJ+jaIMnWTtVwXBl76+V9s+kyQDpRWL0Dc2l3OU4e/7OIH+z3boWl5yb/QWx7BvZ89bSlAI225bxOaz2ejNJqb3TSBA0zMlSdsjJNyPkPDOCdD9N3epzchv5pu8FUwOHYu/0vHci2aOJjzQh9+u+MlBC2nuwI71ixbJRI0pjwAFmCUfzhntF1K95FoeHzmea5JT2VOUT5SvHxOiY7skGi1I683ouBj2ntfP948I7VAUS6L/TRTWb6DGeNLWFu49kSht+3aGxkvFiHHWak711RMx6M7vFAWU6smOB55HRKAvS56+ne92HiW3SXV//uhBHgvIVte+RXXty4B1AUejnkxY8KfIZJ5rF/TSSw9xA5AgSZKx3T2d0Gvou4FJEpEJEv+44iCLvr2GGr0BuUxg/rCB3D5mmMP+p2oLydOV0d+vD6OCJrG+5EcMYktYrIDAxJCZdsfk64tosDjmGxbpi6k11eOntE5cx0XHMs5zLb//eaYNSmTaoM7XopULElO0WeQYg/BXD6GPd/tqzZm1q21GPljFmWpEDXHew4jyHkiQejDh3uPtRKeU6vGove/CoPvc1qZQDkejdV2Dui1Ucjmz41sZz17w8OVdp7TdUW5MG8ypknK+O5COWRTx1aj57WWTSQoLbv/gC4SIQI3FCwuNFDbspY/PuAt9Sx4xf1gqmcVlfL3vCCaLiI9axTOzJjIgsvt1DbqaMUFT2FSyClOr8U9AYHyI+yXizjSUEaQQUbUh+tYRBgeMxK8wgFpztV37xBD7SC5JkjhUlU2VsZ60oCQCVC2LF1GB3viE7MN0nvWRU7+FelMJPkr7PF8/VSKzYn+goH4dBksV4d4TCdJYQ5/rTPUUOgnVb7DoyNcVkuTb8TQwQZATHPIqNRWLUMqtv0WDUY1O/sd2tTa6gv7BofQP9iz1qNGwh5o6+7RHfeNP1NZ/hr/vfR28k5bFUrMkcMQQhF5UYZbkmBCpMuYSqrHqYNTWf9LKyAcQqal7DS/NDDRqz8s8TgyZyYqixXZtEZpoknyc57hrFArmJHaNCOHFxsm6LIc2s2Qmqz6XtEDnFRyuSktFoZbzl1WbqNE3IhMErh46gLvGdT5qK0jRQLHRD6kpW1aUBNICreNvX/8A+vp7Fk1lsJjYW2ENgx8dnIxa7pgG8OL82Tz67UqOFVmjm5LDgnn1hrkdun+lzIdpMV9QWL+ROlMOgepBRHhPaFMo0xnevk9jNu7BYm7+fQS8fJ9CrnCv6kWIn5aHLu/4eGs0Hqe69p92bY2GbdTWv0uA39MdPu/FgqsQ+twXL80ywL3YyAACaFuwzyW9hn4bSBI0iBZbnISfMoMtTy3ndGkd4X4+hPvZe43MooU/pX/FtlJrTpeAwM19J/Jg0u9ZUfg1uQ1ZhGkiuTziOpJ87QffEFUQckGGRbIPf/RVaPF2Q7ipl/Y5VVTOtuPZBHh3riSaIMAIrZrpsc+4tb/OXOGkVUAS/EgNetDlcT4B/0CjvQWTcR9yeRxK9RSPB9aOcq6qlp8Pn0YmE7hsaArhAd1TTlEmE/jzvOk8NHUMRdV1JIUF432R13MXJRmWpgm9zuJYPuhiRxAEfj9nKvdNGk1hdS2JoUFoO1l66UIRrA4jRDmJ0w2/4K0w0GhREqwaQh8v9xf1+ijPYBAlTJIFubMk3Q6ikql5JPmP/FD4JZl16QQog5keNo/RwS3eq0pDHU8d+oTTdUVNxyj4zYBrmRtlNfRMUiMmyZmYpoTeUuVg6AOo5H4kNIl6tsZb4YWvwoc6s32ug1yQEaLu/MKal/cM1Or9lFevoFZvIibyOmLVFy7q7EjuOfacziPc34dZQ1PwVtv3KzoXecH6xg0dNvQVyv5oA37PjpKXKDSpMWIfJdV6LNC7vP56jw19UZSQlaYSVj+eWp90zEIjA/2HMz/6TmQ9NGZcTISpQygxOIpZhqtd61sAzB3Ujxn9EsksKXc6x+soMkFCkqyxtkZRjloejVbh26FzHa/J59nDn1FltL7HgSofXh62gFR/++iyqAA/lt1/G1mlFVhEkX4RndPgkQlK+vhe3rlzyMPwD12PqXEjomhV3ZcrOu+AacZksbA5/Qy5pVUM7hvB2BT7CAlX77xOv+FXYej38qslADgpCMI+wKak2lterwuQkOzqGAiCDxqlF0NinIeL/lCwx2bkNx+/+Ox2xoX05/GUF9q8VoDKj+lhE1lfYp9feVXUbBSyC1Pb/NfExxvs6+R2lkNVKubEu6fIGu09ipM1joKa0dr21doVykEolF2bN9oeG46e5tkv1mC2WJ/+N1f/wqsLr2TSgO4Tfgzx0RLic2kp3EqSQLR3h0unXnCCfbw7VGrpYmJPeSYri04DrQ3eMlYV7eOaGPdyXWO1qdTVLUcERMcipp0iXBPN/Ym/dfn5e1nrbEY+gFE08/LxZYwL6UegygeN3I8QdT/KDfbilF7yIILVSR7di1yQc3X0ZXx59nu79ulhEwlQdY3XXSYPIix4ARc6NuTl5Vv4ctsh2/a763bz6aM3EBnY8j3lskCnx8pctLuHHI33jZhVBzCadtp/IqiJ8GrRDnB1HU+vbzSbeeiDH9h7ujkFagAjE2O47f75qJX/m1M8P0WUg6GvNysRaL9MmlqpYEhM1ybz1JnVFBtafteH+87v8Ln+cew7m5EPUGWs5x/HvuOr8c4N1YstOk4QlKi8Ordg4Iz6RgP3/t9Sjhe0OD2nDUrk1YXzbOJ8rt55V+299HKR8HxnDv7fHAU6iJ/PgjY9qrvKTzht31l2krQg15OyjJp9/Fy8lDJDEUP8QzCIAahlAUwNG8+EkEurdNfFSEl1PW+v3dn+jm5Sa1HToDDw0sknuSLyJkYETmxz/zifSST5ziar7idbWx/tOPr5z+uye+oqTBYL/1y6yWbkAxhMFv61bDMTnotD1gEhw18jogg7dg9iVqAKnwtt1fwPs7P8pPP2spNuG/oBPjdTW/cGgjNVPzco1Ofy5dnXuTLqTvyVnnmwnd2/SbKwr+I0l0VaRaImhv+GdYVP02ixCigqBA2Twn+LrJ1yVM64MuoyglSBbCndhVkyMTY4jVnh7efHXkpkFpbZGfkARVW1vLtuN3+9pUWMTet9PdW1byBJrcUCZfj5LOjU9Q9WbSejrh5vQY6ySYdFhoIJYU+jlrd4cX21C9Dp19C6HKJMCMDH+zqPrrdi34lWRr6V/WcK+HHvMW6c0L0lFS9WsurqKTd4o1UakQkSerOSepOK3RWZXOfdsylrKpkvsT7XUyVWoJV7MytiMiMCB3foXIW6CnIbHKN3cxtKKdRVEO19cRn1Pcni7UfsjHyAzRln2JxxhplNQsNa76upqn0JUbSPsvT1ubvd859tOMXqc4vJ12URpvl1iGD3RKh/Vyry/6+mIEiStLX9vVzTa+i3gST4IqFFLvPDz+d2/H0fb3N/f5Vzj6SrdoCc+pN8nvuKTYSvylSEWlbFw0mvEKC69Dttk9HMtpWHOHnoLDEJocy8fjRav55NRTiSW4RF7JynzijJ0VsUFJr92a2LRxJk1JhK+TrvLTRyb1L9XOfxCYKMqZF/ZFDgDZQ3ZhKgjiPCy3me4IUmr6zaTtW2mYKKGkpq6uw8Yv+r1NZ58/lXl1NX783B/oXEhV163oD6Gh0blu6jMKeMASPimDRvmF2J0EsFf6XziISANvrc89GJCpbXDmGk5gQRSs8V4iVEDlfv4lxjPk+n/NujUGl/pbedd87W3ur+QzT9uCl+CXn1O7BIJmJ9JqBxo8a8KyaEjPpVLyAfOOO8FNiBbPt2hTyMiNAlVNX8A4PxAEpFAlrNo2xdLpCVsZS+/Tz36uotOr7Oe7tpKwwfWSPecjX3Jr5CkDrWtl/OySI2fV9JSMx9DJ68BbmyALVqFIH+f0Aubzu8/HwOZjsvqXggu/B/1tAPUGrJ1ylptNina/grey5qzFcZybSI54n1GY9S5o1nyzfO8VFonKZ4ygUZPorOpSRe6pz/ftvazxTaDH2ZzI+I0GVU1fydRsMuFIpY/H0fxdtrptNjm6kylvN+9t8xitao6UJ9bpfeey+9tIUgCHW0rAirACXQIEmSWxOBS29m14OolQnEx+xvf8cmro0Zx4biI3adsFahZk6k63y7nRU/2yntg7X+7YGqbcwI73h4V1dSWVJDVWktfftHovAgFNBssvCH298lfXdLPdcfPtnGaz88QUBIx/LTOkJ0UOeN0wJDAH/In0eCr2NO9vbitW0a+s2EaPoRorm4lc1D/bSoFHKHsnfeaiUB2p5ZoKmpqKOssIrYlEhUmosvX7+xUUVdvdXAjA6+9BY+qsrqeOqa1ynOt3o1Vn3xCz99u5t/fPkgCuWllSY0N2okX5/dht7SIsYnF2TMd9ObD3CgajtFJgUrTM1etu86dC8ljQWcqT9OshvlNpu5vs8E/nNyuV1bnDaMka0iwOqqGijJr6BP8mTUXpemlkJPEh3svCRbdJBju1o1hIjQbwFo1Bt59oa3OH10Y4evXW9uXbZSoF70ol6EU/WnGNtk6G9dcZCXH/8SsSlqShCG8/Df/8GQsYmU1QjEeJaR4XJ8c/Z9/1e4PnY86en26u7hmgAmhToXJuwO1HI/Ev3aNiA9xV+lZbgUx36y7dpTasLbdCg1I0kSuScK8fb1IrzPpe9Iao2r5/3890Ol7Ed4yH89Ove+yi02I7+XXnoaSZJsBpNgFZ24GnB7ktNr6HchgwL68uLQO/kkewN5ujIG+PXhgaQ5hGpcGwM6J+VwrO0dCyPtSkxGM68/8QWbl+1FtIgEhvnxxGt3MnLmIERRQtmOUfDLmsN2Rj5AcV4Fyz/awsLfXdll91lZr2Pp/gzOVlQzLDaSq4YNsMtNHBgbwbiUWHadyuvwNULUfmhx/n2LSovBA/HiTSfOsOVENv7eGq4bOYi4kIvHI+znreHG8UMcQl9vnzwCrzZE8iRJwmg0o1Z33DAXRZH3nvuWNZ9vw2yy4Bek5YF/3Mz0G7q3PFdHGRoXyeikS6+83vcfbqawtIr6gX6YfJWoyw0c2ZPFjrVHmHJV5xWme5IIr0DeTLuP97LWcrK2gL7aMO5JmMUAf/d/F2clyTqKp/32/D5jkZD4Lu8Xqo0NjA3px8PJc5ELMiRJ4qMXlrHiw02YjGZ8/L259y/Xc/ntbacKucPmE2fYcjIbPy8N16YNJD700inVajCY2uxnJg6Io390GCcLW8J45TKBu6e3raexcek+Th/Nb3Of9pDO87Q202CuA8BiEfnoHytsRr71GIl3/rQUS731OUwe1pc/fHI/EbHuefavHzeEb3ccobqhpbKPf1M/fiEoq2tg2b4M8qtqSIuLZt7Q/qh6uHTrzIhhGEQzX+dupdxQy4jARO5PuNypOv2lQnpBMSsOnSD9xxpkESANtTqIlLstlK0tpG6GDt8A15ormQdzefH+DzmXY9UuGDljIL/7YBE+/s6PEUUJk9mC+hKJ9Lpt8jBW7T+B3thSPjTM34erRnV+cUd/EczHe+kFQJIkCfhBEITngd+5c8yl8QZfQowPHcD40AFu75/qN4LMusMO7QPc8BB3N9+9sY6N3+22bVeW1fGHvy6BzzdhNFkYNyKBp++bSViwc++8q0nTqSOdm0y1primjlve/YaSWmtHvPzAMX48eJxP770epbxlcvH6PVfx+eYDbDuejb+3F+keXidE7cfgrYMonbfZ4TPLcX9wc+799xWb+Xp3y+/95c5DfLDgWkYlxHh4R93HM1dPITY0kLUHTyKTCcwbOYDrxrrOKVyzKYOPv91BSXkdibEhPLJgKqOGxnl83TWfbWPFRy1/39rKBv7zyKf0T4snKuHiSYTXqJQsmjWahdNHdknN454m/Vge5+ZGYtFau/+GJB8aErScOJJ3yRn6AKn+fXgzraPl0CCoOrb9ndxAKahI9vFcOPPaPuO4to9jyaiN3+1m2f/9bNuur9HxxpP/pd+IOOJTO95f/GvVFv67s2Uh78udh3jvrvmMSby4F612HjjDO19sJbegkqhwfxbdMpFZkxzHWrlMxkcPXccnG/ex+1QeYQE+3DFlBKPaWZQ7nd75cUnjpGY9QKqfNaqvqqyW8nPVDp+LomQt5yJJnD58lpcf/JhXV7sWcWxNeIAP/338Zj7esI/MojJSokK4e8YoIgJ7LmqumfzKam5991sqGqzpX8sPHGP1kZN8uODaHtd3uSJqJDNDhvF/n29hzeYMdpr+y/iRiTx930xCArunikx3seLQCX6/dB2SBIQpQYwg8INK/DKtC0hGLJzNPMegMc4V7M0mM3+98x0qiluevf0bj/H+H7/j6bcWOOz/xbLdfLvyADV1eoYMiOape2eSFNc51f7uJiE8mP8+fhOfbNxPTmklg/tGcM+MUfh1ssoSWN/f7eVrO3x8emGNx/nwXZk/35V58hfyGj1BW9/jQmkECIJwbatNGTASaHSxuwO9hv4FZmzwDM7UH+dojdWgFhCYFnYVST4Du+V6erOeozUZyAUFQwIGoZK5XuHeunyf3bYxIQRThD8YzADs2H+Gsoo6PvnPnU6P75PkWPoJIDbZeXtH+OyXAzYjv5mDZ4tYn5HF3KEtYfJeKiUPzB7LA7Ot0S7vPeD5tRKCU0j/Zh9hN9QjNK0h1B1QM6hhtFvH51VUs3iP/aKOwWzh9Z9/4asHbvb8hroJmUzg5olDuXli+/md+47k8s//W2fbPpNXzm//tZz/vr6Q6AjPagOf/7wBiBaRbSsOcPMTczw6V3eSEB7Eo3MnXOjb6DClcRosksmuzRCmoTL00grb7yoOLjlHgV8k0VPO0dFKZEqZmptiHsBb0XUGhLP3QZIktv14wCNDv9ZUR3rNMXwUWgIs0Xy5yz5ax9oH7WDxgxdPH3Q+uQUVPPfyj5jNVk94UUkNf31jNWHBvgx18rfw89bwxJWTPLpGTGLnFxN9lQH08x1KZt0RAGTImRN5E5Fe1sUk/yAf/IK01FbaR5FIkmSt59tEZn4Wa06vJykinhTf9mP5+4YG2gkNXig+2rrPZuQ3sysrj62Z2Uwb0HVl1Nzl7c+3sHxdy5i7fW8W5VX1fPji7T1+Lx3FbBF5Zd321o8HANVDA/DJqhyA9L0AACAASURBVEdmkZArZETFuzbEM3Zl2Rn5zWxdvs/B0P9+3SE++PoX2/bRE4U8+dclLHl3ERo3I/ZyG/Io0BXQVxtLH++ec2KkRIXy4h1dP1dI9h3M1NAr2Vq2yiHVtpdeeoDWIdBmIBdr+L5b9Br6FxC9zsDWdenISoZw9eix+CfKiPFOIEjVPSunGTXHefP0u+gt1rrM/kp/ftPvcfpqnXu05K1C8yWwGvnncSqnlJNZxfRPchQumnr1CJZ/tJW808W2Nr9ALfPvndq5L9KKE0WOtXIBTpwrtTP0O4skShRFFyANEig3aVFaLBhL5IiLo7jqq6lunePkuTKHwdp6r86/w6XAyg2OsRFGk4Wfth3n7hs9UzeWu0gFUfRw2OfFjqHRxPafMygurGTg8DiGj/VsAi1EaqGozrE96tLycnUV2dX1NASoKDNqUcks7R9wHsGqcP6c+i5e8q4V+pK7eO5bvw+NOiNbfzpKWXENQ0clMHikfQnMX8p28UnO55gk6+KsnxCEQh2CqdF+wn68yFHJ+2JizeYMm5HfjCTBqo3pTg39jjD7prGs/u8OivMq2t/ZBQICixKeo0CXTaWxjDhtCn7KltQspUrBrY/P5r3nrSUOJQCVwlrGw6QAsxntQhGva2Fx5bdQCYP8Unmy3yOoZBe/PoPL8biorMcNfbPZwppNGY73crqYrNyyi95D3UxFvY6yOsf0Ikklw+yrQFVt4sq7JhEU5pgiarGI7Nlykl0bMkCjgkaj3efOxlZnY3pVjY7te7OcRtC0RpRE3sn6kD2VLYuUk0LGsyhh4SUZ/daaeVG3My74Mgr1OYRponilg1ouvfTiKZIkLezM8b2G/gWi9Fw1zyz4kNKiplXWd2DeTWN45I/dk49skSx8cOZTm5EPUGOq4ZOcL/jLoD86PWbWzeP44E9LWhpchN7pDSan7RpvNf9Z9hgrP99O5qGzRCeEcvXCyV0qApMcHsy+HEe11ebasScOneXgtkwCQnyYeuXwDiv+n80pYW/gNgQliMgwiDIIhRGvRjgdYJ3hqp7txVbntj2MBhO/rD1KYU45xUVVTvdpbHT+TLTFrJvHc3ibfbkxlUbJlGt/vQrhnlJdUc/TCz6kMLfc1jZ93jCe/dcNbp9jYN8IDhUVO7T3j7540iM8QRRF9m0+yen0fGISw5gwe7BHFQT8hysJSKlEan6vPcRLru1yIx9g1i3j2b3uiF2bQiln2nXWCKKy4hqeWfAhJYXWd/BLNjHn+lE8/vw1ANSb6/kk5wubkQ9QK1USM8hMzn77MPbkcPs+qK5Gx9YVh6ipamDklP70G9o16Q0dxWAwO213NfZ0BN8Ab1774QlWfLadMxkFxKZEsO4Pb3ToXDHeCcR4Jzj97OqFk4mKC+HHz3/hyJECTM3Cp1ovFFG1eF+vt9s/o/Y4PxdvZF6UvaeyrKiarasOIVokJs4dQlRfz9T6u4Ok8GAyCkuctvc0oiS1/G3Po7ELn5vupLq8jl9WHEQrU9Ag2r8DCgkmjOvPtDnDmHq1Y8qV0Wjmjw98xtF9OdaGQH8wmqCixbM/8xbHxXi9i7Hbnb/ZjvJddkY+wPbynQwPHMqoINei1JcKweowgtVdO056GvL+awmR76V9BEH4cxsfS5Ik/c2d8/Qa+heIr97b1GLkN7Hq2z3MvjaN5NToLruOKEnIBIF8XSFVJkejLLshlzpTHb5Kx3y+a+6fQXVZHSs/3oy+wYCfyUKt2v6RCQ7UMrif65qivgHe3Pr47M5/ERcsmJjGuvRTVDa0TI4GRodx+eAUPnlpNUveb8n5/urN9by8+CFiEjxfyTfLzQhOotYyDZlunyMhLIj5aQNZfuCYrU0hl/HYrJ6t6ytJUodX1+uqdTx78zvknrIaiqYgL+jrGOkxZawH6oRNzLhxLGVFVSx96yfqa3REJ4bz4L9uJjTq4hErvNB8+/E2OyMfYNOqw8yen8bQ0c4Ni/O5a2Iaa4+esguxHRAVxtwhHYuAae5jLgQWs4UXFn3K/q0tC0TJg2J48esH8PZxLzczdJhIpmNFyQvOuLnDuO9vN/DNa2uorWwgMi6E+/9+k02v4uv3N9uM/GbWLt3H5dem0W9wH07UZmKSHCfnwZE6clptK2T2fVBeVgnP3vIONRVWL+KXr//MrY/O4o4nu68fb48pY5NZtvaQQ/vUDvQzbREQ4sudz8y1bd/7hy49vY1R01JZuXS/gyGqGu38PTpSnW5n6O/bcoK/PfA5JqPV+PvitXX85tVbmTJvWPfcsAvOH0sWTRnFphNn+H/2zjPMiiJrwO+ZnIecc46ShxxEwBwBUVFEwZwVA7uuu+awn+u6xjViRsUFMYEJJYnknHPODHHyPd+PqoE7M/fO3DQJ+n2efu7t6u5T1dXV1XWqTp06knbKQ3mHejXpXwpm+1GREXTv2IhZ8/M6BK5WJZGWHiwQyxprlmzlL9e9xYlj6UQ3juN4l7zfwTvO7ckt/VK8fs9/mbzolJKfS1QkxMUQmZ1N/6HdGPXYFQWu69etKR9PnJsnLDIinJ6di36GS1ILWlAALE5ddloo+g4OJYwnT8HxwCigMuAo+mWZ5Qu3eA5fsDkkiv60jRt5YcYM1h44QNPKlbm9+1kIUmB+UVRYFNHh0R5lhIWFceNjV3DtQxdz/EgarshwHnl+IqusKX6VSgk88cDFpWpaXadSMhPuHM5nc5ac9Lo/oEEDnr/nE/74cbmxQlAFNcuKffivH/jLq559ChSKF4vexAj/HB49eflAujWuy2+rjMfrK1Pa0rKWfz3EqQeOMe7FH5g7bRWJFeK55LoeXHhNQWde7qRnZfHc9Ol8tXIl2S4XFzRrxt/69aNCrH8WDhPfm35SyQeIOJhGZFwkOdXicakSFRXB6GE9aN2spl9yc7nq3vMZfPtAjh46TsVqSeXe3C/ULF+42WP4sgWbfFb0a1dMMu/Mn0vYvP8Q7erW5MqUtnlWqvCFKWvX8q/Zs9lw8CCtqlbl4T596FW/vl8yVi3awof/nsrGVTtp2KIW190ziNadGvh8/YwfluZR8gHWLd/Otx/P5spb+/sko36V6swMfEGOkKKqvPLHHN6eM5cTmk3FPS6uGtWH667pQ9XaFQkLO2VxsGKR52/IsgWbad62Lkle6qYqsRV54crzmbZqA0mxMQxNaUsrtzro/Re+P6nk5zL+tZ8ZNLQL1euUjnf+jm3qMfrqnnw4YQ6ZWTmEh4cx+PwODOjVolTSEwo8PT895tmiJCnylNWYy+Xi9X9MOqnkA+Rku3jz8Un0GNSmSGuW40fTGPfiFGb/tJzY+GguuKobl9/Q26+6dv/x4zw+bRo/rV9PdEQEw9q2ZUyvXjSsWomv7Pd4+6EjdGpQmyGd2xARHqDziyAZc/MA9h86xpoNxsqgWuVEnnjgYsJLKT3+8NZTkzlxzPjbStxwgsij2ZxonEDP89tyaZc2tKtXg/u//54f1q0jMiyMK1q3ZmyfPkRHmOfv7VvR/bIUHnpmCLFeOkJHDu3Opm0HTnaQJMRH8+AtA6lUoWirpSQPA0aA17qoOPn6w1l898kfHDuaRrf+rbhhzPmFrkrg4FDWUNUXc/+LSCJwD3ADMB540dt1+XEU/VKiZu2KBUbmAGqEoCG1at8+bp08mWyXmdO47sABHvz+d647pxVrTqzIc27/an2LnPsXFRN5cj3zt5+7lnWb95KWnkWrpjVL7QPuTo3kRO4717i9z8lxcftFL7F1/R7jxRjMr8sFCsvz93D7iKYLWVsiiKyfjZlZaWSfW8O/dXLDwoSL27fk4va+r8yQJx2qPHrjO2xYuROAQ/uP8erfJ+JyKRdf690y4PFp0/hi+ane9kmrVrH/xAk+GDzYr/hXzM+bfwLEbD/C+QPa0uuKTjSpX5WkxMCmR+QSGRVBpepn7hrQhVGzTiXWrdhRINzfeqN6cgL3DgrcoeDCnTu567vvcFmnEyv37eOmSZP47rrraFTJt7Ts2LyPsde/RUaaGXVe8sd6Vi3czKtf30tdHx2jrfDyPq+Ytwl8dLjZs0o3vt75Hceyj0EpO1p6Z8ECXp7zh9kR4VCNcN45vA7XKxk89EJeZ3k1aldk64aCc+tzy0KzxKY0jK/PpuN5FcrzagxgUI0WXNTes5K8fN7GAmEul7Ji/uYSUfS9jVCOHNKdSwe2Y9O2/dSrVYkqlcq3T4katSuyYfWuPGHpc2KpelUOaZyyUBOEQdVPdVod2HMknx8B8z1KPXCM7Rv30bBF4Z2sT97+IUtOLnt7lLef/ZaMtCyuvuMcn9N+6+TJLNpl0p6Vmcm7CxbgUuXRfv2oXTGZMef38VlWcVK1ciLvvnAd6zbtJT0ji5ZlpM1SFC6Xi5ULNucJi9mbSczeg1xzc1M6N2/IiAkTmLXV9FBm5uTw0eLFZGRn8+ygQaiq129CvcbVvCr5ADHRkTw/9nK27jzIgUPHadmkhs9O+PpX68tve6fnmS4UHRZNv2r+OcYMlq/e/Z13njtl4v7D53+yee1u/vXFHSWajvJKeZseUBY95YcKEakE3A8MBz4AOqqq5zmzXij7NV4pkp61mgPHxheL7KGj+hAekTf7G7esSUof/81nT2RnMHvfWlYeNnPVv1i27KSSn0u2y0XmvtacX2MgFSMrUjW6CoPrXMpV9Yb4HV/TBtU4q0XtMvnBXDRrnVHy82MbjjXrBTZXUERotnQ/o5OmM6bmj1yVNI8b6nRjYI3+rD+6h5l713A4s/jtf5fN3XhSyXdn0rgZXq85kZXFpFWrCoTP3LKFrakFPfEWhreGfpMmNejYpl7QSr5D4Qy5oVeBEbs6DarQZ5D/y7oFw+fLlp1U8nPJzMnhqxUrvFxRkCmfzz2p5J+UkZHN9+PneLmiIDXqeS6PNfx4zxMjE3moyXmMrraUMTV/LPqCYuSTxQWXWs1KjuC7RStJPZB3dZErb+yDq34Ymc1AbV9tw2Y16N7PKPAiwpjm99K3ai+SI5OoFVOT6xsMZ1CNwhW6ml58qNT0ktehIiNrIxv2Dmfptgas2N6JvUfeLHBOxeQ4OrapV6aV/NTME8zcu4YNRz18h9y46qZ+BTo02rdpymNtH6FjhfYkRiTQJKER9ze7i+ZJp6YoJFeKJy4hhmoNDjPqxd945tcvGPvlZPpctY4qHhzmurNp9S43Jf8Ukz6Y6eFsz6zat++kku/OF8uWkZOv3VFWaNqwGm3LaJvFE2FhYVSr7XnKWo26ldmSmnpSyXfnj61/sH7PdSzd1oD2Fz3A+SNX4955mVQhjouG+bZKUL1alejQum6hSn6WK4e5Bzaw4MAmctRFnbjaPNziflomNicxIoE2Sa14pOUDVI8pWf8vk8YVLM+rFm1hzZIyYrrl4OADIvJPYB5wFGirqv/wV8kHZ0S/UFQz2X7wQSLCKpEc5//SNa6cXWQceY7sjBlIWHWiEm4iKs7MiWrXpREvvDeaiR/NYt/uw7Tv2pghI3v7bVI2bc9K/r50AsezzZy4thXqUiHTsxfiE5kurqk/jGvqD/P7XsoLhw8e83osLEwYdptv5rz5adAklXtv+5PjCodcUCf+IGGul3hwQRa/7jUdLNFhETzQ8kIG1/PtQxoIhw96mrIDqQc8hwNkZmeTmeN57sHxLP+cEl0xqg/Tv1ucR0GrUbcS/S7p4Jcch8Bo1roOL354E1+Nm8mu7Qdp26khQ2/sQ5SPIy6h4nhmpsfwY17CPZFfcS0q3BODhqQw8b0ZHNh9+GRYXEIMl4zw3VohK3s3x1LvoFlkJjEBzhTJzphNxtF/48peR3jkWUQnjiE8qq3fco5leM6/nHA4dvgEFSobBTc18wQvZk7lwB1GsQrPFAbta8nDQy/P460/KTKR0Y1G+pWGYXecw9O3f2iWfLO0696Elh0b+HczfuDSDDbsvYqsHKNAZrv2siv1acIkliqJ1xdbvKHmUOZxLpj2PBnWcVrvqs15vsPVRIcXfD97D2rDk29czzef/cHhQydI6dOcK0b0JCY2ivua3+k1jqjoSAbf0pW6Pe4nqbIx7U6umsYFty4kM2Iy4H2ZRG/v1tHUE+TkuHxqf+R/9yvHHOeus2bRs+ZmTuybTHTCKKLiy+5SjaVFdsZMW0dsIDyyHdFJDxIe6X0Z5avvHMDLY7/ME9b7grOo06gqK/cWtOQJlxzG9viC4xlHAHBxgH5DD1Cnfm1+/6oFDZvV4Mob+1DVz2VvvbHq8A7uX/Axe218tWMr8XLn62ie1Iy/tHowJHEESii+LQ4OZYAHgAzgUeCvbh3DgnHG55MncEfR94EDxz7xW9FXzeLE/mG4cjabfdd+0lPvRSSCyNhLAGjdoT6tO/g3p9Wdo1np/G3Jl6TlnPrwLkvdRqckz2ZZg5oUvR5vead99yaER4SRk28ppqq1KvDA88No1z2wPBDJJkKEZIEIlL0ucOkRNOtnwIygZbiyeW7FZFIqN6ZufPF4GT6ra2MioyLyzM8E6NzXuyVIhdhYUmrXZu6OvCbf9ZKTaVHFP0/NDZrX5KUJdzHh7d/YsWkfLTs2YOgtZ/vs+MwheJq1rsPYf5ZuQ3pgkyb8sG5dgXB/6phOfZrz88QFBcK79PV93nVihThemnAXX/73V9Yu3Ua9JtUZfHM/aheypnR+1h54gzoRmYQH6g9C0zhxYARg6uHsjGlkZ84nodpPhIV7d1TqiXObNWX8srzLW0mWi0Yan+ee/rNmCktTT41O5UQp0+qt4+G44H1a9Dy3LU+Nu4lvPppF6oFjpJzdkstHFa8p9pG0X08q+e4cOPZxuVL0d6elUsHNO/qMfWv4YOMMbm7quYO5c8+mdO7Z1O94zh+ZzdYD6QXCDxz7iMoJ3uuGlh3qE58Yw/Gjea/t0LOpz4MM7WrUoFp8PHuPH0dQ3uz3P5pUMFMJNGcN6YcfAhGi4k7fAQV/yclcYusIUzayM34le/98Eqr9Qlh4dY/XnDesK8mV4vn+0zmcOJZOj3PbcMkIM0WxRdWq1EtOZuvhUx2c7atvoUbCkQJyWvVYzOAh/wnp/agqf13yxUklH2BH2kH+vvQrPuxxW0jjCoQufVvwx895rcti4qJo06WhlyscHMoeqhoSEyRH0fcBlxZcc7oostN/Oanku5N57N2Tin5RLFu/k937j9KuWS2qVSrozGTugfV5lPxc1qVtZ1Sn3oxbuJAcVcJFuL5DhzNC0a9cPZnbH7uM15+YdFLZb9CsBk+Pu4lKVUPjECY+TIhwKdlAbHjeEXEXyvS9qxneMPD5z4WRXCmeu568gv88+hXZWWaUvk6jqox+uPB5SM8OGsRNkyax8ZCx+qmRkMDLF14YkLO7hi1r8eC/rvE/8SXMgcPHWbR6O1UrJtCuWehWsjh6PJ1DR05QMenMdexzSYsWzN+xg/HWhD8iLIybu3Shpx/O+PpccBbzf1/NL5MWngzrd1F7vz2HV61VgdsfL+g92lcy0/8kPDJwBdnlOgDk83OiR8k68TnRiff5JevBXr1YtnUXKw4b/y1hGS4a/JnGQ09fm+dd/W1Pwak46TlZzNm/noE1/bckyE/H3s3o2Dtwj/apR9NYsGobFZNi6dC8TpH1jMvl+Rub4yr/I3C/7V3pVdEPFG/54ioiv2LiorjvuSv55wOfkWGXUatWuyK3//1Sn+OODA/n5Qsv5K5vv6Vp0sqTSr47mcfeLRFFf83mvWzdfYjWjWtQq2rZ9euSefxDcpX8k+gRsk58SXSid+uN7gPb0H1gwWlZYSL8+4ILuG3yZPYcN9Z89St49rVUVJkIhM3H97HleEEfUysOb2d/xlGqRJe88z13bv7LxWxeu5td1pdFZFQE9zw9hHhnaqFDEJQ33wW5OIq+DyTF+r+skLoKfvwAXK6ClWN+TqRnMualr1mwahsA4eFh3DakJ9ddmHc98fgIz6OoCREx/KVvX27o2JG1+/fTtEoVaiWWbsVbklxwdTe6ndOKRbPXUaFyIh16NsnjrToUhGGc+S86UrfAsYQIz6sYhIqBV3SmS98WLJy5lsQKcXTs1azI0ZgGFSvy48iRLNy1i6ycHDrXrk1EiPOkLDHh58W89MlvZOeYzp62TWry0gOXkxgfvOXB9r2Hufjet3no+v5c0jd4pao8IiI8OWAAt6aksP7AAVpWrUq1BP/mTYeFhTHmn1dxxY192LBqJw2b16RJ69B1yPiMBLn+uHqeFuPK8fwNKIwKsbFMHjWCRdt28Oef62gSm0yve1oTE5e3EZ8QEcPhrII+QRK8fBNKku9mrOC5cT+TaTsim9evxssPXlFox1hi7NkIUSh5O66T40pvSb9QURzPJCl2ADsOPUZ+5dGXtkrPQW04a8ZfmT99DbHx0XTu05yISP9WzkmpU4cZN93Eup3vAZMKHFcf2jnBkJWdw9hXvmXGIuNvIEyE6y7qwu1DexVrvIHirT0YTD61q1mT30ePZsHOnUSGh3NWtVhW7foG1bzWGsXxDsVHRHtcxQkXHElNp0r10m1v1qhbibenjmHBjLUcO5JGp97NSa5U9KoBDg6nI46iXwTr1rVlxZG2XHlxjl/LyEVE98Gog6584f2KvPbDb+edVPLBeJJ/9fMZ9GzfiEa1T5mEd6nciPrxVQr0rA6xc8RrJiZS8wxS8N2pVC2Jcy4rnnVbs1XJkiiOhN3Arsy0PMcqRMZxTo3id4xWoXIC/S/t6Nc1IkKnWv6ZEpdH1mzew/99+GueJsiy9bt4Z9Ic7hveLyRxZGXn8Ny4X+h+VkOqVixZx2Ar1uzks0nz2LnnMGe1qs11g7tRuWLpNGJqJyVRO8mnaWJeadSyFo1all65rFtxFBydHvD1EpZIgdE6ICKmb8AyO9StTYe63js9htRL4eU1U/KE1YurTEqV0KxXvmvvYT6a8Cer1++mfp1KDL+iK00aFD0d4sDh4zz7/s951odfs2Uvr385k7+O8j79LTK8KnUrv8j2g2NxqRmBTIjuSY3kB4K/mRIkXAp2ng6p1zXk8URF1KRupRfYcehRXGo6fBJi+lA9+V6frk+sEMfZQfpViQoPp2WtSzi25zkgr2WbL+2cYJjw85KTSj6AS5UPvplLj7Ma0r55beYv2cIX3yxg/6FjpLRrwPDBKSHp5A2UiOi+ZGf86iH87KDkRoaH063uqcGGepX/w7YDD5y0Qo2PTqFGhYeDisMT1WKS6VG5KbMOrM0TLluieXP9bF64x3cLkeIiPCKclLOLXt1o2uw1fD11CcdPZNA7pSnDLu1MdBFLVDo4lCec0lwIO3dV5uVX+wOz2bAllb/de4HP14ZF1CU66W9kHHmK3EXYwyJaE51YdMNl1uKCyxvlhrsr+uESxmtdRvLPld8yc99akiNjuap+d0Y0LNmlTM4oJJaIhNtpFT+CiPCKPJLzJx9sms6etMN0qtyQB1pcSEJk6Y+qnalkZ+fw4HMTPS6SNnPxxpAp+mA64OYs28zFfUrO4/2Ktbu469HPTypS6zbt5c+Fmxj30vVEl7BDvtOFKolnsy3tahKzPiMsAAt+CatIRExHstOnngyLjLuGiGj/lt70h+sa9iJbcxi/+Q9Ss07Qq2ozxrS8yKOi6S+Hj6Rx2yOfcuCQMQlet2kvs+Zt4O1/Xkv9OoX7Hpm7fEseJT+XWYuLXta0YvxlJMUO4HjGfCLDqxEb1SqwGyhF6sVXoXOlRiw4uImasRUY2agPg0IwlcITlRKGkhx3ns2vGsRGBbZkazCEhVcnJvkJ0g8/Rq6yHxbRjOik0CuX7hTWRko7lsEjz5glZwHWbdzLgmVb+O/z1xIWyAseAiLjh5OdMZ3sjF9OhcWNCKoz0BMV4s4nMaYvxzPmEhFehbio4vs2XRbVjdkbtqD1M0AF2RRF2OJ4ZhHYEsalweQfl/LPN06ttLJ6/R5Wrd/Ns2MvK8VUOYSS8mpuH0ocRb8QsrNPZc/PM1Zx8/BeVK/q++hVdMIoImMvIDtjFmHhNQiP6unTnOjkBM/ziHLDt27ax8Txf7J7xyHatK/Hk1cNJb6To1wWJ2HhDYit+H9ERPdDwk6VgaH1uzK0fuhHbMozK5Zs49sJ8zhyOI2uvZpywRWd/LKGCYZZ8zZw4MAxiC2o8CQXg8NAb+9qcTF+0rwCitT2XalMm72W88727sH5dCb14HEmjp/D2pU7qdegCpdf050atfzzLF232vPkZN9CTuYC4Eo/UyDEVXqbnMwl5GSvJTzyLMIj/V8m1a8YRbixcT9ubNyPBXM28MOEhbyR/gO9+7di4EXtAvK9kcv3vy4/qeTnkpaexVffLeL+WwrvvPD+7fLt3QsPSyAptp9P55ZFYsIj+W/XUSGRlZ6exTdfzmXR3E1UrprIpcNSaNK8Zp5zwsMSSYoNblQ4WKLihxMRM4DsjJmEhVUlPLoXEoIOp8LwVp6SEmL45H9zTyr5uaxev4d5SzbTtUPpOGMTiSKu8vtkZy7Clb2e8Mj2hEf674TRF8LD4krkHaqRlEz43ASYm9eiLSnZ87NZtmgL3321gGNH0+jauxkXXNapwBLTJc3HXxVcynXm3PVs2rqfhvWCnNLl4FBGcBR9H3G5lD37jvil6AOEhdckKs6/teqHDmzPvJV51/usnBzPOSnN2LR+D/eNeo+0E2Yu48I/NzJr2mpeHjeaSD/n2QHsPX6Mx6dP4+eNG0iIimJ423bck9Kd8ADmb6sqv+/YxLL9u2laoQoD6jU5beaBS1iSz04UyxJztm/j2VnTWbZ3N00rVeb+bj05t3HxNDAA5kxfw+MPfn6yoTX/j/UsWbCZvz3vu/K06fBBpm5dR1xEJBc1bEGlGN+d3u3aewTJBlxK/uHZKweGdgnAOtUr0OOsBiGVWRS79x32GL5rr+dwb3yzdjX//nM2m1IP0bFmLcb27EOnmiU/P96lyq/bNrDq4F5aVKpK/zqN7KsZxQAAIABJREFU/ap7jh9L595R77Jru3EyufDPjUybupxXPryJ6jX9U/bDIxoRHtHIr2vyXB/VjvCodgFfHwhTvl7IS099c3J/7sx1rF21kzsf8t36LD+7vZSlXV7Knjtd29anfs2KbNmVd6nfoQM9O1jcePggPwb4rgdCVk4O/5ozi/Erl3EiK4vzGjflsT5nUzm2bDnWVFUeuv8j5pzYiStKSJiZw7Qpy3j+jRG0blfPZzmr9+/jyRm/MWfHNmomJHJbpxSGtw19GQ0Lr05U3OCQy/XGkAHt+XXeOlxuS0AmxkVzQc9WfP31Qo/X7N5b0CN9SRMR1YGciHZM276RVQdn07JSNfrXbUxYEB1zpUWrRjVo3bgGKzbszhPu6Ts7a9oqnnrky5Ptgnmz17N80VbGPl1yZcYTu/d5LhNrd+7j5dVzmbJ+HTEREQxr3YYx3XoRGV4yAxYODqHk9NDCfEBEWorImyIyQUT8Xv8jPi6Kpo2q+R1vtiuHX3Yv4/W1U5myczFZroJzOfPTt1MTHr/1fBrXqUxcTBS9OzTi9bFDiY+N4ssPZ59U8nPZsHY3s6YV9MLsCzd+M5Hv168l05XDwfQ0Xpk3h5fn/uG3nGyXi1E/f8XInybw4qKZ3DptEkO+/4QTWb6vq11SrFq1CqBeIGVh6aGtvLH2R8ZvnkVqpve168sCm1MPMXLy/1i6dzcKrD14gNt/+IaFu3YWW5wfv/N7gdGUmb+uYuO6PT5dP37tEs6Z+C7Pzf+dx+b8TN8Jb7Fg746iL7R0aFMXASJPKJKlxmNijtK5UW3O6+HVtDXGn7ohPEw4J6UZrz48pMQsFXJp37qg88fCwj0xa9sW7pn6HRtTD6HAgl07uf7rr9h1zP/VRYIhIyeb66Z+wehf/seLi2Zy0y8TuWbq56RnF11H5vLTt0vYueMgmS1zSBuQTUa7HFKPHWfS+D8DSlMwdUNJo6p89NbvuOKU9O7ZpJ2TTXYdF9/9bz4H9gX+LIMpY+FhYbzy8BAGdG1OfGwUdapXYMyI/lzRv6By+emaxQzI964v2ld8dRPAC3/M4M2F80hNTyczJ4fJa1dzy3dfezy3tMrC+qO7ue/nj/il13b2nx3B/u6RbL4qmr0t4LP3Zvgs52hmBtdO+pLZ27fiUmXH0SM8+tvPTF4bWFuhLNGhRR2eu/timjeoRlxMJClt6vPqI0OoXCGedq28ld86wUbr13fCExk52VzrVueN/uV/XDNlPBk5vtd52a4cftq1lNfWTOXHXUvIdnl2BloS/Ov+y7mwd2sS46KpWSWJO67sxciLUwqc99FbvxVoF/z243K2bNxXUkn1SLtWBctEVFQEb29dzNdrVpGRk83hjHTeWjifZ2cF7sfF4cxGRM4TkTUisl5EHinp+MuFoi8i74nIXhFZni/c58xT1VWqeivGLtOvtc/Cw4S7R/UnNsbz8iXeyHRlc8e8dxm7+DPGbfydx5Z+weg5/+VEdkaR157XoyWfPnM90966k/+77zIa1KoEwLYtnr20bvcSXhiLdu9kxb69BcI/WbbEb1nfbVrNr9vzzptbvG8Xn6xZ7LeswrjxxhupVq0abdrknXs2ZcoUmjdvTpMmTXjuuecKldGyZUuArfhZFl5fO5XRf77J+xt/41+rv2PojJdYf3R30ReWEhNWrSjQgHCp8tmKpcUW5/Ytnr0Lb9tcdPk8mpnBk3/+mmeU5mhWJk/8+YvXa/KXh+aNqzP4wg6k7l7N0m+fZ+nEZ0nfMJvH7y50+cF0f+qGZvWr8cydF1GzSnBO6AJh+BUpNKibd570Bf3b0KGN74r+p8uXFvBhcDwri4mrV4Yghb4zccMKZu3akifsz93bmLB+mZcrCrJ1y16Ojcji+DXZrFowmVkTXmTmttfYtPNUvVYSdUNpkJ6Wxe6wVI7ck0naBTmk98vh6C1ZHO+bxc7tBwOW26dbU3ql5F2KtVWzmlx+nm/LHlavlMjTd1zIr/+9k6/+eSNDBxS87khmBk/OnVbgXX+8kHfdHzx9J3JcLt7+/HO2P/U82594ltSfjHO0Bbt2smp/QYWjNMrChK1zGD7rFWbnrCa+UjqVqx0hMioLRNjXPZKNO3xXjH5Yv5YDaWkFwj8O4PteFunbqQkfPnEt0966i1ceGkyLBmY9+tHX9KR6vqV0cw78SpeOLYNqN+Dnd8ITE9Yt54/dea015+zexlfrl3u5Il8CcrK4Ze7b/HXJeD7Y9DuPLvmcW+a+TbqHZZZLggqJsTx207n8/OYdTPrXaEZclOJx2pC3dkEg7dZQcteNZ5PkNg1EBK64uhML9hTscPx85TKyXa4C4Q4OhSEi4cBrwPlAK+BqESlRBzTlxXR/HPAq8GFugFvmDQS2A/NEZDIQDjyb7/obVXWviFwC3AZ85Euk1askcvv1fenbrSm1avhnBgowZediFh3anCds1ZEdTNw2j+ENA1sGpkWb2qxdWbASah5Ab/WRDM8fh6OZRXdE5Gfunu0ew//cvY2b2hTs4Q2UkSNHcueddzJixIiTYTk5Odxxxx389NNP1KlThy5dunDJJZeQk5PD2LFj81z/3nvvUa1aNYBk4Dt8LAtZrmw+2Ji3R/dw1gleWzuVlzpdH+RdFQ/enuPRDP+fr680b1WbxfPzOuMJCxOatyraq/ryA3s4np1VIHzJ/t2kZ2cRE1HQ2Zyn8nDXDf148YlRPPn8mzRv2oi/PDCCvbu3smuH5/IA4G/dUFpUSIrjvRdH8Pucdezck0r7VnU4y8OoRGGURrnwxNzd3uuMa1v4Ns0is62SHWaUxSoD21Ltkk5s+r9vSOtkOrhKom4oLWLjouDSMDSf1Xl63xxi6wS+xGd4eBjPPHIpC5ZuZeW6XTSoU5keXRoTUcQSnv6wfP9u0jy864v37SIjJ5vo8OCaJp7qhczsbHaM/5Lqt99MRIVkdv7fy8S1aQWq3HTV1VSIOdXgL42ycCw7nVfWTMmzZJkIJCSlcWh/JBohJHeo5Lu8TC/fdy/f/dOFmtWS+fiVG5k2aw0HDh2nS/sG7NmRQkJCQlDtBgj+OzGvkHbSNc2L7kj7dscClqXm7ShYlrqVb7YvYGj97oEkqURo3qY2yxflTXdYuNC0FFdbAWjWqDrj3xjNr7PWcOxEBr1TmrA/LB22zS1wblpWFtmunNNmOqpDiZECrFfVjQAiMh64FCixkZVyoeir6nQRaZAv2GPmqeqzwEVe5EwGJovId8CnRcVbITmOqy/rUtRpXllyaIvH8GWpW4DAFP0rR/RizvS17N19ar5ktz7N6dzd/2WVutauTXJ0DIcz8q67OqBREy9XeKdOQrKXcP87SAqjT58+bN68OU/Y3LlzadKkCY0amfm1V111FV9//TVjx47l22+/9SbqsKqe72tZSMvJJNqDL/elXp5xWWBgwyZ8uLSgRcXAAJ6vr9x45zk8csdHnDh+SmkcfG13atSuWOS1dRKSECiQy1Vj4702/L2Vh1Ytm/PA7VcAsOzqq4ssD/7WDaVJZGQ4A3q3CPj6gQ2bMGNrwXI7qHHxlQtP1EnwbBFRN9H3OiO7rgvszI7EtvXI2JMKgKuBCSuJuqE0yazjwWw3DDZl76UJNQKWKyJ0blefzu3qB5E679T28q5Xj0sgKiz46TCe6oXFCxZQrU4dIqsYi5j4ju05sWwFTS67mF+fep4oz/NvS6wsrDuyizQPI7ORkS5EFFXh6st8V+b6N2jE0zN/z2M1ATCoUWiWYCzLxERHcn7/U6P3zRtXD0m7IdjvhLd2Ul0f20lLD231HJ66tUwr+qPuHMBf7vo4z7TTYdf3oloNz/lRkiQmxHDpuaemFtVyuagen8Ce48fynNerXn2Pgw0ODkVQG9jmtr8dKFEP3qLqaSGqsodV9L9V1TZ2fwhwnqqOtvvXAV1V9U4v1/cDrgCigaWq+pqX824Gbra7zYE1Hk6rAoTS5siR5x9RQFNghd2vBwiQq71UAhIwZpeeSATqYnrlfS0Lrd3i84VkwBcPab6cV9Q5hR0P9bMrKXzNPyhYHioCSfheHloA71FI3eBjvRBqSuLZlVT5KKl4agCVCbwsBFI3+FoeiisPHLmeZQZbLxRnWSiK4n5fyrP8QGX7Wh5OeJHfBGNVWtLfifL8rE5n+fVVtaq3i0qpzeBQOuR/vm+p6lvgv65aLKhqudiABsByt/0hwDtu+9cBr5ZQWuY78kpPnoeysKG4ywLmxQ35+b6cV9Q5hR0P9bMrqc2f/A62bvD32ZZgHhT7syup8lGC8SwtK9+JksoDR65nmWWpzVAW8uh0kR+obF/LQ1n7ZpbnZ+XId7YzfQO6A1Pd9scCY0syDeV5sskOTG97LnU4acTpcIaRSfGXhW+KPiWg8305r6hz/E1beSCYe/K3bjgd88/B4HwnHHJxyoKDO055cHBwKG7mAU1FpKGIRAFXAZNLMgHlWdEv9cxzKDMcp5jLgqr6pQz6er4v5xV1jr9pKw8EeU9+1Q2nY/45nMT5Tjjk4pQFB3ec8uDg4FCsqGo2cCcwFVgFfKGq/kwDDppyoeiLyGfAH0BzEdkuIqNKOfPecuSVjjxPZcHKK9UXqQwT6mdXpiiDdUMoKYlnV1Llo9jjsWWhHmW3LBRXHjhy88k8DeqF4n5fyrN8v2X7WR7K2jezPD8rR77DGY+qfq+qzVS1sao+XdLxlxtnfA4ODg4ODg4ODg4ODg4ODkVTLkb0HRwcHBwcHBwcHBwcHBwcfMNR9B0cHBwcHMohIiKlnQYHBwcHBweHsomj6JdBnMabA4CIXCYib4vI5yIyqLTTU94QkUYi8q6ITCiPcYlIdKhklTYiEl/aaThdEJFedi1eVFVD+b0ojuckIt1E5Dr7GxVq+Q7B47Q5vHOm5c2Zdr/+4uSPQ3nDUfQDRESiQtVoEZGeIjJARM4B03gLUl4/EblSRK4JUfpCLW+giNwhInfZ/aAqzlClT0QqiMgEEVktIqtEpHuAct4Tkb0istzDsfNEZI2IrBeRRwqTo6qTVPUm4FZgWCBp8RB/sbzzIlJXRKaJyEoRWSEi9wQhq6j8Wysi6SKyy8b1uCc5qrpRVUcFGZevz6rIuPxBRPoDo4tbMRKRFFv/dC3GOAYAY0UktrjisPEU+734i4g0EZHOoei0EZEwEUkA/ovJz1vhpLIf9HstIpcCz4tItWBlucm8BOPMagAwBqgfKtle4jutGuEi0lVE6hZ9ZsDya0LoO4ys7PBQyitpRCQRgm+PhYryXBasfKc8ODiUAo6iHwAiMhj4FPhWRC4UkYpByBoEfAYMAp4WkTeCTNvZVl494H4ReV1EapUheb0weZcODBORV4CeIhJRBtL3MjBFVVsA7TCeeN3jqpZb2buFNfEgZxxwnoe0hgOvAecDrYCrRaSViLQVkW/zbe6N7UftdQEhIrG2wY2quopJ2c8GHlDVVkA34A4RaZUvHaHKv/OAmsBe4GrgchGZmS//WpXSswoaETkP+DewVFUzQyk7XzznYpaTuhD4TETutIpkKOM4H3ge+ElV00IpO188xX4vAaTpIuB/wD+BcSLSLBh5qupS1WPAB8C7QA8RuS/3WJBp7Yt5Tl+r6t5gZLnJrAzcAVyjqtcDR4D2th6ICVEcXUWkr4h0geJTUkoD++5MAhrmKkmhvDcR6Q38LCK3Q2jzzqb9RRH5IPd7HOK0XyQiz4jIKyJSRUQiQyU7Vz7wrhhrujo2rNTKVXkuC1a+Ux4cHEoLVXU2PzagGUYB7I5RMiYD9wCNA5AVjmm0jbL7ccAs4FW3c8RHWWK3F4D7bFgM8AnwH6C6P/LcZIZMnj3/fuDvbvKewig1PQLIv5ClD0gGNhV2PjAU+AWItvs3AT94ObcBsDxfWHdgqtv+WGBsEff3PDAgiPIaDrwI/Aw8lF9+oHJ9iPdrYGAJ5N9jwEKgq7e4gAnF/azczpsQZL6dBRwChtj9ykAVoGEIn43YfBkHXGnD2gM/YUZd40IUT3NMh95wu1/N5nWb8nYvAaSrB+Y70cHuvw68FyLZ92PqzHMwnZz/Ap61eREWhMwx9n8tYCDQFUgOIp3JwHRgCJAEbAS+wXT0PgXEB5kP5wPrMBYDE4F33ctFaTz3ED1fAaoDc7B1v1udGR7CeNoDM4CXgPtDKPd8YIX9fQ+zfF1ECOV3ArYDl1j5nwGXBVNW88nvDay0v+8BHztlwSkPZaE8OJuzBbI5I/r+UwnYo6p/qOpnmAZWG+DC/COI3sjtCVTVHGAuUElEYlT1BHAu0ElEXrPn+GQmpBZgAdBMRKqrajpGwakG/N0feW7nLsSsPRuUPLfezyVANxFpZuU9CZwA/Da7D2X6gIbAPuB9EVkkIu9Ivvmqqvol5iP1uYgMB27EKJS+UhvY5ra/3YZ54y6MyesQsWa6ARAOvKaqA4BaInKbiNQQkTh/yoI/iEgDoAPwp3t4KPPPjmrcirF2+ElVPcU1A1iN+UA/GmhclkKflYhUFpE3gQ4iMtaPePITA3wB1BCRzpiOqxeBqSIyMgi5J7FVRQZGET1LRBJUdTFwL3ABcEMo4gGOAq8CXUWkB0bBexT4RURuC0UEJXgvgfC8qi6y//+OqedD4Xfha2C3qv4CzMe8B0k2LwId2c92+z8B827eCbwmAVqsqephTKfrWOBH4H1VvRh4B6gDeLKw8Qn7/l8PPKGqN9v/LcT6yFAtvyP7tl4+DqxQ1Z/FmFS/JyJvAw8EYxmSmyc2/9KAY5hvaGMRGSlmqklAFnFiSMaUnb+q6g+qeqO9l8GBptkDzYEfVXWylf8bxpKnr4hEhOC59wcmqeoM4BkgTEQeFZH2IpIUpGy/KK9lIVe+Ux4cHEofR9H3ERGJs3/nApvEzAmPUNU/gPcxvZW+zul2N/VdhlHm6gOoMc08F9No9WmuqYh0EpFGdnc9xjLgLBGJtZ0HN2Aa25f4KK+uiESLmVP7B5AYpLyuGDNTwfSMrgR6iUhN20h/AkjxVZEJdfosEUBH4A1V7YD5GBWYl62qL2BGKd8ALrHPq1hQ1f+oaidVvVVV3/T3ehERNabfm6y8XOVnPGaUOOSIMZf+CrhXVY/kPx6q/LOdZI9iRnJTRKSNh3P+gelsiAU6F/OzOmCfU2NVfTYIOXOBDzFK0M8Yi6FRmMbSUyLSOti0ujV8lmIsBhrbumwF8CBmCky7YONR1Z2Y6TDHMI2vr1V1NKYR9pSIdAs2DjeK9V4C4E+M2X5uQzoaU8cn2bDKQchOw3Ru5vrveA6oJyK3BCFzGnCTiIwH3lbVqzGdE8eAlECFquoEzPdtBrDIhv2KqbMDnq9v3/9FbvtHVLUnUF1E/mvDyt1cWreOoBMYM+2xmKkfv2OsVGKAa+33LxAFJhlM/qnqGowF4Z+YUdDBmI7YqjYt/sqvajt3ngWmyKnpeDuBGgGk1RtzMJ3WPQBU9b8YBfVajJVIsM99AWawZSym3G7B5Mk9mA7sEjHbLudlAZzy4OBQJnAUfR8QM//zDjnlTGoBpvHTS0QiVXU2RnkaLUXMNRcz12eSGG/qTwCLMWaHH4pIC6usHsGMRBY5b92m7XNMwwlVXYCprO8EeltlOg1jxpzjg7wLgR+AVzBmSpmYiv+eAOWdi5mekG5HnHYBM4GewAUi0sKOxH8DFDkaFer0ubEd2O42MjwBo/jnj783xoJjItZqwA92AO7OdOrYsJAjImF2VKsuxloht+GwHXhYVbcWQ5yRGCX/E1X9n5dzQp1/mzBKiqd59uXiWeWSW3eo6izMOz1KVV8HclR1JjAFyAhCfryVr/b3B4widzfQRsxo+AIbT0ANFxGpI24OBFV1G8Zs/RJVfcV2Ps3HvLP+vJ/e4su1jgr5vQSDbTzndnQJkAocVNV9YqxZnpIAnRPaDpRtwN8wZrZPYMz3vw8ivcswUx26YusLVd2IsQiqGqhcK+cQ8CswWEQG2Q7YhpjOGb/IN4K5A3hYROq5hV0OVA5Fh1hJI6cccMZZy4y/YEYrK6nqW6r6BTAbaKCqGf4qMPZb/I4YHwm5bb9GGKUrAugMrMFMO/Gro0SMX5GtItJPVReqarqq5lqJrMRajIiZS93Wn3Tb63qIyBARGWTL5QJM+6uFTesbmLbAg/7Kzie/n6p+i7E6iQd+VdWxqnoP5p273sZXrJ1I5bksWPlOeXBwKCtoGZg/UJY3zEj9EqCfW1gCRnF4CbjOho3AjMR5nTcFNMbMU+yHMSd+DKP0VgRuwTTU/o5pwG2kiHn/wNmYOYpn2/14t2PDbHo+xPSobgeaFSGvNrDcpq868BCmMquNGYXzV143YJdb+pLcjvXEmCT/jhn12wO0LESWYBSvZaFKn4c4ZgDN7f9/AP/Md7wDxkS4MaaT7DPgKS+yGlBw3neEfa4NgShbrloXQ5kNs78NMR0e57odS87NzxDHKTbv/13IOaHKv04Y5WMJpjNmBnBROX1WvbB1SG68bv+j3P4PwzRmagcYz6UYM/pq7mXE/n8eo4y/hJmrvQPTgPQ3jnMxFjaN7H642zH3+7rG5me9AO+lKaYhGka++jZU91IcG8b65Fn7HNsGKasu0MltP6C5+flkRmC+YxsxViSjMFMD/PY/40F2BUwnzO+YkcJ2Aci4CDO6Od4t7EnMN6CeW9h4IKW0n7ef93YepuOjt1tYRYwisZ1T/hOGYjq6/Zp/DPQFNuP2LbDhvTAWiduAqzC+GV7EKJT+pD238+4hTOeQe/1yt30Xr8RYHDbyM+2DgLWYttFhzHzydpjVJx4B+rrF85cA8j5X/j8wDiO72fCaGP8Pre3+tXY/yt84zpSy4JQHZ3O2sreVegLK8obxtr0JuNnuV7ZhDez+tcDHGLPUZVjHS4XIqwC8af/nOs/7C8ZhURLG9P8GjLdvr0qvm7xnMSPPsRiv82/ba5+ycdXGNPAfwyqwhciKxZiXvoFxxiQ2/H6MqVJNG+6rvAaYEaLPgLYYM81PbRrHYxUWTIfHaKCJD/cbbivW2sGmz4v89vYDtRTj4bZivuM9cWugA5HATR7kfIbp4MjCfJhHuR27wH5ENmDmrRVX2W2CGV2/tITelV6A2rxbbLcLiin/NmNGtndjOqYe8yCjTD8rjJKagHFStBK41T2tbv9zla+lBNjRgGnYrSafc8R855yNsYp5DWgVQByD7DPfDLzs5ZwoTON0eRD3chmmk+ArjEO628nn1C3Yewn1hqnno2w52go0DaXsYkhvR8xc1BcJskPCg+xE3Dp8/bguHmOdcTOmw+Qzt2NP2jJxC/BX+z6FzHllCZQPTw44q3LqG3kOZkDgffse++3M0ubbbfZ/DcxIbQ9OfSfOc8tnn5+PrVsWYzr1O2GsCavYY+H2d4StW3/39320+TAH+y3BjNCejZl6Vguj7E3DtCm2+ltePcgfgxlIyO3wfx3T5njT1lshfR9Op7LglAdnc7ayuZV6AsryZiuq1zGK6HmYObOfY8wQn3c7ry12pMyLnNa2AmyOmeM/xu1YGPA4bh69KaLxhlGOL8Uo5i/Yimc1cB9m5O8pTCM32sf7vNSe39De31/zHR+LMZP3Vd65mJHkjpg5pO/Ziv1uzJSHRzGjOj59RDAjeF0wjcQvgXuCSd/ptuUvLxil9hngal/LlLOV2rN7CHgAYw1xn5dzLseHjrBC4vDmUb2Ch3P99oaMmYO93tZzkRjHa328nNudAEfYMY3eH7CNQ4zfgnkYC6gCdUkg91LMz3okxWAVcqZstuwmYBr1E8ir7F8O3IYxsQ3Zqg4ldF8pmNHIOzGWKlNsfbCWUxaDEZjOcq/tjCLiGGnzpiFm/vJ/MIrQaGxHGQF4cceMMqe47b9v0+5uwdMV06EZSAdiJGaa3gDMNKyDmMGVXW55Uwe4mAAshLzI/8jKvxqj+A3BtFlC1kF3OpYFpzw4m7OVza3UE1AWN9xMvjEjgy9hRmNu5ZQJ+S94aczmk3U+ZjRuMmbt4/62krvT7ZxzMU7gipLlPgq4FmNeFYWZn3mP23l9gHd8vNfc0b5z7X49zAj5/W7nNMB0JhSpLHJqZG8r8LQNuxu30VRbEfukmHNqBG8iZqT+RYy51u2BpO903jAdQP0wzhgru5WZMzpfyvKGl6XS7LEe+GDZ40Mcd3NK0Z9t4/nINpAiMZYsF9rjfpcVW3/1sP8r2IbSbe7yMI27gJeJtDKSMdM0+ruFfYmZ+nON3e8ezL0U87MuU+kpzxum0+crrLKP6WSqX9rpCuJ+etp6IBVjpRJh6/PtBDDNwYP81va9fJRTy9G2xUz9uzAE8iPsb0dMO6ee3ReM2XnlIGQ/a2Wu5NTSvF0wSljvQOX6IP8QpTAFpLyXBac8OJuzla2tSGdvZxrWWd4XIjJZVa9S1VkikgVMV9WJ9rRtIrIN4+yjMFn9MI3Qa1V1roh8g1ly6jrgS+sE5RWM2XkzEUlU1aPe5KlxynJMRD7AOLK6GLNO9P2Sd9mm+hgvpAlatKfxTphOganWqVEC5gPwuoikYzo0umMq7AqYys7b/Q7AWEBcivlofC8inTDTAcLdTu2LcfwSSyHOxaxn6lswI9MrReRmm97JwLPWmdVkzIexyPSdzojIC5gOnizMNJKVIvJfVc1yPMKWab4GhqrqLyLSHmONM84e64KxsAmWacAEMcv1va2q74tZpeNhjJIeh1GgUVX1V7iqToWTDiBTReQ7YJyIzFTVZdZJYyPMFKeAUdXDIvIJcKOI1AZaYOqPlRgrhU8xdd/vgd5LcVLW0lOeUdUDYlYZ+KeIrMF8X/qVbqr8x74zLtvOcAEzVPUr67RyhohMwawAExSqukJEUjFrjf9m2wbLRORLjGVgoOkXNeQ6WluFmVp3O/CILfMBfZPd8masdfB5C2aQA1WdJyIfk7ddURzy4wsVEkLKe1mw9+CUBweHMoaj6LthvVLfiVmDuYfJrQa1AAAXJklEQVSIfKqq11glPdbtvMGYXtFdRYjcA9xir6+BUVL/hpnb8wXGFKgNprf2ysKU/HxkY0be38MsidQa0+kwVkTuxcyBus4HJT9XVq6X7PGYpU82YJTFQZjpBj2AG9R4Ty6McGCE/ZBUwFTyXVV1gYjkelm9CbgDGK6qqT6kLQEzj2ylqr4lIhdg8m81xl9CC8yIpC/pOy0Rs851W8yoqsuWz97AQyLygqpmlW4KHQoh/1Jpz2KWC7wG+E8olEPbiBuDMdFca8M2il32TY0H56CxHZGo6hQReQu4SERW2M6m8SFSdD/DdJaeA6Sq6rUAInKJmFULPncU6jMDVd0vIksxVnMDVXV7aafJF0SkOVAJ4w/m5EozqvqHVTBQVRWRYRinomnByFezFCGq+jcRycS0He4WkWOYNsi7wcjOVe6sopQmIncDn4pIiprlQgNOuw0TVc20HdajRGQ75vs2AGNtWVzyz8FYWBUb5bkseJPvlAcHh7KFOG2ivIhILYynzRiMw40MVR3udvx6TGfADaq63A+5f8Xk91MiMhrjdOUVjIfTBFXd74esxphRwOdE5AGMM6L3VfUOEXkfeNHXtIlZ2mQCZk3iqXa0rxnGKeAcVf1aRCr6o0Tn9oyKWWJlHKYRtsx2pPzdpnWVj7JuxTh6m4pR6htgzI8bquoYe04FHzoNTkvsx6gCxqHj/ar6k20g9MP4lZitZi1rhzKKmGU2RwJ3qOo3InI2sF7N0nShiiMC4+3+H8DTNvg24CpVXR+qeNziG4zxGdLPbXQnlPLDcjsWRGQEppNkkI+dmw6nAbaD8wvgAVX1e6m+0kBErsD4T9lht/nAOFU94qYg5b6rYzDWbCtCId/tnP6Y1UhaAW/58S0uLO253/wwjGn2vZiOyn0hTvunGCuephgnySvLinx/Kc9lwYf0O+XBwaGM4Cj6hSDGdPwtIE1VrxWRlhgPn1PUrN0ZjOwpGKd3CwK4thamsT4b48zrI8wc2A+ACbkNYD/kXYwZ7ftIVR+zYe8Cv6vqh7kfHX/TaeU8gemFfsH29ob5kz4RScYsq5Q7gne/Df8eMy/3jFLwvT0LEbkOk0/PqOoSEYnDmJ41VdXbSzqdDr4jInUxjpUW2H2/3hE/4+qIcSYUjWnULCuOeGxcXwAPqermYozjRkwjeFhx3otD2UREYlQ1vbTT4QtiprB8jFF4ZtnOsG4Ya7wXVPWw27mXA8v86YTzR749P8LXTrgAZMeqqs+jz37mTRjGaZyvFpDFLt9fynNZCFC+Ux4cHEqJsNJOQFlGVQ9glKUsMfMAvwYm+qvk21FX9/3BGO+dOwJM106MJcDfMKO4T2Cc1P0RoILwA2ak/VoRGSUiozDrks6y8QXTG7QEs8a9WFl+pU9VD6vqJ8BoNyV/BGYU+4wzSc99FiLykIj8ze3Qb5j1uW8TkY6qegLjL6GZmPnMDmUUVd2mZnpLQO+In3EtVNW/qOoDxaUYu93HlcWp5Ft+AS5xlPwzk/Ki5LuRhBkdBONg9luMQ8yrAUQkRURaqurEAC1tipLfxXb2gfHzU1yyA3kuRcnvKiLt1cyjDkTpKm75oU5PWS4L/sp3yoODQynhKPpFoMakfinG4/NgVS1qXr4nGbnKWbRVop8ArlfV3UEk7W3gclX9xu7/pgGa+qpqtqp+iBnpyzXjukFVNwSRvlzZX2Hm/dcJUk6ume6NwCMY3wdBO6Ypj4jI7ZjpIxeLyDNglEXgO8wc7H+LyHDMfLVMTP47lHGC7FArM5TkfajqlgAbwQ4OJYoaXyn/Aq4Qkd72mzYTs0pNHzF+gHpivK0Xl/xe2O+BP+9pccr2Q34PIKA2U3HLL6b0lMmyUIbknzblwcGhOHFM94tAQjgP0JoLDQQ2qOqaEKUvYLP64qY40iYi9YHIM7lxby0admC8jX+OmYf/iD0Wj1nCcaA9/QE1ztCKzRzcwcHBwaFoRCQGs175WcDHqjrdhv8GjAq2c7045ZfntJeE/LKWHkd+6cp3cCgrOF73i0BVD4nIxaEwEbS9iN+HIFnuMsukkg/FkzZV3RJqmeUNNX4T4lT1hLUQeUeMd/2HVPW4iHzvZunh9/w7BwcHB4fQo6rpYpaIVMwqOblLRFYFgnYkWZzyy3PaS0J+WUuPI7905Ts4lBWcEX0Hh3KOGCeRrwE/Y5Yj3Kyqb5VuqhwcHBwcPCFmZZSeGB9A6cDLqrqoPMgvz2kvCfllLT2O/NKV7+BQ2jiKvoNDOSZ3eoSIVMGY8y8Dujkj+A4OpxcicgnQSlWfC5G834Axqjo/X3hnYISq3i0iI4HOqnqnh+uPqWpCKNKST26RK6oUkvb2QC1VDanlXHEhIuEY47fiWmWj2OSX57SXhHx/Ke/368h3cCibOKb7Dg7lGLfpEfcA87DrlotIuKoG4knXwcGhDKKqk4HJJRDPfMya0qWCql4QxOXtgc6EeIpccVHcdXRxyi/PaS8J+f5S3u/Xke/gUDZxvO47OJweLOeUkh/hfLQcHMoPItJARFaLyDgRWSsin4jIABGZJSLrxCy1NVJEXrXnjxOR/4jIbBHZKCJDipD/sIgsE5ElIuJuETBURObaOHvbc/uJyLceZDQUkT+snKeKiO81a4GAiEwUkffs/xtF5Gn7/1ob92IR+a8dUUNENlsLJUTkbyKyRkRmishnIjLGW9qtCe4TwDArc1jhue7g4ODg4HB64yj6Dg7lGJGTa5Z/7jaS75jtOxQL7kpYANeOFJFaoZB1mtIEeBFoYbdrMEtUjQH+4uH8mvb4RYBXc34ROR+4FOiqqu2AF9wOR6hqCnAv8Pci0vcy8IaqtgWKWmZ2BtDb/q+NWbIVGzbd+hUZBvRU1faYdbyH50t3F2Aw0A44HzNS706etKtqJvAY8LmqtlfVz4tIo4ODg4ODw2mNo+g7OJRj8q9s4IzkO5RhRgK1ijrpDGaTqi6zc0RXAL/Y93sZ0MDD+ZNU1aWqK4HqhcgdALyvqicAVPWg27H/2d8FXuJwpyfwmf3/URHnzgB6i0grzDKge0SkJtAdmA2cA3QC5onIYrvfyEN8X6tquqoeBb7Jd9yftDs4ODg4OJxxOIr+GYCItBeRC9z2LxGRR0Ik+14RiQuFLAcHB9/w0dQ7xZpaL7Im3s3ttfe5mVK3FZHl3t5hEaksIj+KyAoReQcQt2PeTK+PichL9ppfRKSqNS3vDHxiz4+1Yu4SkYXWHLxFceZZOSDD7b/Lbd+FZ3867ueLh+P+xJnjJY78+OS9V1V3ABWA84DpGMX/SuCYVdoF+MCOvLdX1eaq+o9iTruDg4ODg8MZhaPonxm0B04q+qo6OVSemzFmk34p+rkKgYODQ1AUZeq9Guitqh0wJs3P2OteBpqIyOXA+8AtuaO9Hvg7MFNVWwMTgXpwcklHb6bX8cB8e83vGLPqCRgHb8OtYpdmz92vqh2BN2y6HULPT8ANuZ05IlIpQDmzgKvs/+GFnWiZg/k+5Cr6Y+wvwC/AEBGplpsmEanvIb6LRSRGRBIwUxSK4iiQ6MN5Dg4ObojIP/L5wCitdDhTuhwcQoij6Jch7CjdKhF5246G/eg28pX/3MYiMkVEFojIjNzRMBEZakfolojIdE8OiqSgU6c3RGSOGKdO/UTkPZuOcW7xvSEi8226Hrdhd2NMcaeJyDQbdrUdnVsuIs+7XX9MRF4UkSVAdxF5TkRWishSEfm/4slRB4fTmqJMvZOBL0VkOfAS0BrAnj8SY379u6rOKiSOPsDH9rrvgEM2vDDTaxeQOz/6Y0zngzcc8+tiRlWnYLz1z7fPKtDG/D3AHSKyDDPvvihmYObRrwcWApVsGHa6waPAjyKyFNMZUTNfuufZdC8FfsCU68NFxDkNaHUmOOMrLcXMvf3g53V+pTe/JaKDg4ODQwCoqrOVkQ3T0M0G2tv9L4BrvZz7C9DU/u8K/Gr/LwNq2/8V7O9I4FW3a0/uA+OA8RhTykuBI0BbTCfQAre0VLK/4cBvwFl2fzNQxf6vBWwFqmJMKX8FLrPHFLjS/q8MrAHEPZ3O5mzO5ttm64rlbvvjgCHux2zY3W5hm93OPxfYDXxWRDyLgUZu+weBKsBdwLNersnBKHhglP9F9v9vmDXZc89zrzs6A7+Vdr46W9nagAT7G4exCOlY2mkqKxvwD2BMKcSbpz1RXOktLJ7c+sXZiszDeOA7YIn9JgzzVu/a5/MR8AewDrjJhtfEWOUstjJ62/A37Du5AnjcLc7NwLP2/PlAR2AqsAG41Z7Tz8r8zrYF3wTC3K7PTd+1wFwr679AeGnnqbM5W3nbnBH9sscmVV1s/3sc5bJmjD0wo3W5FWDuaMgsYJyI3IRRyn3hG1XNHQnco3lHCXPjv1JEFgKLMCODrTzI6YL5aOxT4/n9E8yIIJjG/1f2/2EgHXhXRK4AvJkNOzg4BE4ysMP+H5kbKCLJwH8w72ZlKXxptumYKQG53tsr2vDCTK/DgFyZ1wAz7X/HrNrBX96y37iFwFequrC0E1TciMgIa+m2REQ+spZ+v9qwX0Sknodr7nazkBtvw7z56BgpIpNE5CdrJn2niNxvz5uTO7VDRH4TkZetdcRyEUnxEG9VEflKRObZrWcRt9fOpmmdbaMgIh+KyGVuMj8RkUspaIn4D5sfs4CPvMUtIvHWKnGuvadLA3sSpwXnATtVtZ2qtgGmFHH+WUB/jNPMx8SsknINMFXNFK12GKUb4K+q2tle01dEznKTs9WePwPbCQ10Ax53OycF02HcCmgMXOGeEPFhZQ4HB4eicRzYlD3cHSzlAJ5M98OAVFv55UFVbxWRrsCFwAIR6eRHnO4OoHL3I0SkIcbcs4uqHrIm/TE+yHUnXa1HeDXLwKVgzH2HAHdiPi4ODg6h4wXgAxF5FDNykstLwGuqulZERmGm3kxX1b0eZDwOfCYiKzDe0reCMb22cn8UkTAgC7gD2AIcB1Ls8b2YxhqYBt+bIpKGaUg6hBARaUtBb/gZqtq1vMapqteEQk55QURaY6Y09FDV/Vbp/gDjuPADEbkR00l3Wb5LHwEaqmrG/7d3/7FW13Ucx5/v5A6EG7bKtXKWzbRk5MQ1ls0xcmR/ZLGiYq2lgGWk0lbNfow2XehsYQgSrDQExcmaMpFZA0ZBCsUPywGhuTVxYxOdmGI3wNRe/fH+HM+Xc8859wJduJz7emxsl+/5nu/ne7mXc87n8/7xiYh3lGO1Hh1vRMQkskfHlPLYWGAc+T7+D+AHksZFxO3AlcD8ct5ISRdFxATg7vK8qgXA7ZI2lQWItcAFbb7FC8kJ3yjgiYj4LbAE+A6wqixCfgK4ilxU/Jik68u/zU3kpPBSSYci4v4WY88mMxxnlH+LbRGxXtK/29xXp9oF/DyyjPIRSY9FtO3b+bCyf8qhyHLM8cB24O6I6CJ3+qhN9L8cEdeQ84j3kj+bneWx1ZXxu5UNOP8VEdXfz22SngGIiBVkideDlXuplodBfhZu9h5lZm14on8KkvRqROyJiC9JeiDyVfBCSTsi4lxJW4GtJQJ3NscfSRtNfng/EBHvIfc03lgeq117P5lidUdkI5WXga8ACxsvVjISRkr6XVmdf+Y47q3jRESPpO4BHmMmcFDSvQM5TouxpwHrJD13osfuFJKepfKhW9K0Fo+dX3naj8vjMyrn7iWb+rUa5yXg8haP/YZ6LX7jY99tcmwl9aweqGQrSXqcTOe0YyRpF9l4taPH7HCXAQ9I2g+5FWJEXEI92rmcXMBrtJPc0WIVsKocO4Nc6DuPLJ3rqpy/oTL5OkB968Jd5GS8ZkW5j0cjYnRlklYzieyJUPv76IjoltTT4vvrNZGUtCoiFkfEmeRCxMqyONHs+atVb+TZdGzy9epzUe8HMIJsIvpUi3vqWGUx92KyGfPNEfF7sjy0ls3bGLBp3FVD5Wc/gQweLYuIedSba7YK/rQNHrUaq+HvtZ05ftTHt2lmbTh1/9T1VeDqyOZ2u8n6eoC5UZrhkRG4HRxngyJJO8iU/b8D95PlATV3AmsiYoOkfWRkYUMZ9y+SHm5yybcDj0Q2YdoE9JoU2PGLNrsbSPrlQE7y242N91M3M/t/+gywiKyH3h4Rw4A55IR+LPBZmk/EoP1Wjn1Nxt4GfFz1bRLPajPJb3e9e8l67Olk5kAr1ah8q7EDmFI5/n5JQ26SD1BS7w9Kug+YS/5+PEtGyqGe4VEzOXKXi3eRC6/bI0uyXpB0F/Drco1mwZ+jNT4iPlgywqZSL/Gq6c/OHGbWB0f0B5EmUbqW3egl7SHrrxqPf6HJ6f8k6+erlpXzp7UZf1qzrxvGW0glai9pBSUK0HBed+XrfWRKmPUhIm4g958eDjwk6cZyfBWZrTECWCDpznK8h+zZMInskL2GTK+8AjgETJb0QkmD7JF0W0RsBLYCnyT3vr66pPiNJH9PxpINc94HXFeir83utXHsy8gPmKeTi07fJD9Y1PZTr6VwjwHmAd1kZsi08jtiJ0hETCe7qldtlnTd0V5roLNRzDrYH4CHImKepJdK6v6fyG0Nl5ML/I9Vn1AmSmdL2hARm8q53bTo0XGUppKlPZcCByQdaIi0ryPrrOeWe7moktrdzOSIuJVM3Z9IBgYg32e2Ac8rd2SAvjMRW429FpgVEbMkKSLGSXqiv99wh/koGfz5L1le9S3y/XhJRMyhnplZs5MM1LwbmCPpuYi4CrghIl4HeoArJe2JiFrwZy9HBn/6azvwCzKjbAO5fetb+igPM7N+8kTfbJCKiMuB88hFkQBWR8QESY8CM0pa5+nkqvvKkmY9Ctgq6XvlGqOALZJmR8TPgG8ANzcZbpik8ZHbGd1ITtavBV6WNCYixlJvwtNK49hPSvpJ+Xo5cIWkByPierL78uOl7m8huQDxYsk4uQWY0WIMGwCSlgJLT/Z9mA1lknZHxC3AHyPiTTKTbhawtCz6vkhGvatOA+4r9e0B3CHplfJ6f0/07tFxNA6XCV0XzV+Tvw0sKtl5w8jmnTPbXK/XRBKgLD4/Rb3sgHLeDyObMd56FGPPIXsM7CwTxD3kQveQI2ktufDR6Pwm597U4hr3kH0iGo9Pa3H+OZWvl1GCStXHymLRq5J6/Vwant+yPMzM+qe2vZkNUhGxCGjsZLugfDC3DlSr0Y+I28hmha+Uh7rJLc2WlIj858vxc4BPS9oSEW8Aw1UaH0bEa8CIEtmYCnxK0tebRPRnS9pc0vA2S/pQyRpYIGlDudZfgWvaRPQbx54CfJ/cGuudwEJJPy3j1Sb6Y8mIVa1Pw2nAPklN68LNzGzgVV+nT8BYI8n+ABdLOjDQ49nJFRETyd+tIbkAY3YiOaI/yB1L6qx1jCAn9r864mC+SU4CLpF0sHwgq9VfvrW7QfG66qt5b9L6//xr/TinL4crk/wRwGKya/LesrDQbKeGAHZLchd2M7MhJnJHgCVkB31P8ocASRvpXTZgZgPAzfjMBq+1wIzSSZiIOKs0pjmDTKk/GBEfIbcrGgibyf4ARMQYst6vv2qT+v3l/qt7tVdrL58GzozsLE1EdEVuMWVmZieJpInHEs2PiOml8W/1z6I246yX9AFJ81udY2Zmx8YRfbNBStK6iLgA+HOpaeshOxOvAWaWmsangS0DdAuLyRrPJ8mmO7uBfkVcSo3oXcDfgOfJxjs1yzhyP/UvktsynkG+Js0vY5mZ2SnE/T7MzAYP1+ibWVNli7wuSYcj4lxgPfBhSf85ybdmZmZmZmZtOKJvZq2MJLdW6iJr6a/1JN/MzMzMbPBzRN/MjkpEbAWGNxz+mqRdJ+N+zMzMzMzsSJ7om5mZmZmZmXUQd903MzMzMzMz6yCe6JuZmZmZmZl1EE/0zczMzMzMzDqIJ/pmZmZmZmZmHcQTfTMzMzMzM7MO8j/dXMlnLPidGgAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x1008 with 49 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PXBfNVeIeCcX",
        "outputId": "cd3973f2-9b27-44f5-a22a-e5f90a7279e8"
      },
      "source": [
        "# K-Fold Cross-Validation\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=4)\n",
        "\n",
        "# estimate generalization error\n",
        "clf =  cross_validate(\n",
        "    xgb,\n",
        "    X, \n",
        "    y,\n",
        "    scoring='r2',\n",
        "    return_train_score=True,\n",
        "    cv=kf, # k-fold\n",
        ")\n",
        "\n",
        "clf['test_score'], clf['test_score'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "[16:36:10] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:36:32] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:36:54] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:37:16] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "[16:37:39] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.7690048 , 0.73091247, 0.70238767, 0.69878413, 0.66369031]),\n",
              " 0.7129558748385806)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "q18Pn0JPdT54",
        "outputId": "2a63bf04-aad5-44b3-b8b7-7e849fa5c671"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.7024950192396608"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 42
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DHosvCgUdxDa"
      },
      "source": [
        "'''\n",
        "# forest_minimize performs Bayesian Optimization \n",
        "# using Random Forests as surrogate\n",
        "\n",
        "fm_ = forest_minimize(\n",
        "    objective, # the objective function to minimize\n",
        "    param_grid_reg, # the hyperparameter space\n",
        "    base_estimator = 'RF', # the surrogate\n",
        "    n_initial_points=15, # the number of points to evaluate f(x) to start of\n",
        "    acq_func='EI', # the acquisition function\n",
        "    n_calls=20, # the number of subsequent evaluations of f(x)\n",
        "    random_state=0, \n",
        "    verbose=True,\n",
        "    n_jobs=-1,\n",
        ")\n",
        "\n",
        "# gp_minimize performs by default GP Optimization using a Marten Kernel\n",
        "gp_ = gp_minimize(\n",
        "    objective, # the objective function to minimize\n",
        "    param_grid_reg, # the hyperparameter space\n",
        "    n_initial_points=15, # the number of points to evaluate f(x) to start of\n",
        "    acq_func='EI', # the acquisition function\n",
        "    n_calls=20, # the number of subsequent evaluations of f(x)\n",
        "    random_state=0,\n",
        "    verbose=True,\n",
        "    n_jobs=-1,\n",
        ")\n",
        "\n",
        "from xgboost import XGBRegressor\n",
        "xgb = XGBRegressor(random_state=0, \n",
        "                  xgb_gpu_hist = 1,\n",
        "                  n_estimators= gp_.x[0],\n",
        "                  learning_rate= gp_.x[1],\n",
        "                  max_depth = gp_.x[2],\n",
        "                  min_child_weight = gp_.x[3],\n",
        "                  colsample_bytree = gp_.x[4],\n",
        "                  subsample = gp_.x[5],\n",
        "                   reg_alpha =  gp_.x[6],\n",
        "                  )\n",
        "\n",
        "xgb\n",
        "'''"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KhzPwHig6EkR"
      },
      "source": [
        "####CatBoost GPU"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "OILosInE7DEK",
        "outputId": "8933d730-1669-4568-99f1-ea33cc8b51dc"
      },
      "source": [
        "!pip install catboost"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting catboost\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/5a/41/24e14322b9986cf72a8763e0a0a69cc256cf963cf9502c8f0044a62c1ae8/catboost-0.26-cp37-none-manylinux1_x86_64.whl (69.2MB)\n",
            "\u001b[K     |████████████████████████████████| 69.2MB 39kB/s \n",
            "\u001b[?25hRequirement already satisfied: plotly in /usr/local/lib/python3.7/dist-packages (from catboost) (4.4.1)\n",
            "Requirement already satisfied: pandas>=0.24.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.1.5)\n",
            "Requirement already satisfied: graphviz in /usr/local/lib/python3.7/dist-packages (from catboost) (0.10.1)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from catboost) (1.4.1)\n",
            "Requirement already satisfied: numpy>=1.16.0 in /usr/local/lib/python3.7/dist-packages (from catboost) (1.19.5)\n",
            "Requirement already satisfied: matplotlib in /usr/local/lib/python3.7/dist-packages (from catboost) (3.2.2)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from catboost) (1.15.0)\n",
            "Requirement already satisfied: retrying>=1.3.3 in /usr/local/lib/python3.7/dist-packages (from plotly->catboost) (1.3.3)\n",
            "Requirement already satisfied: python-dateutil>=2.7.3 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2.8.1)\n",
            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas>=0.24.0->catboost) (2018.9)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (1.3.1)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.7/dist-packages (from matplotlib->catboost) (0.10.0)\n",
            "Installing collected packages: catboost\n",
            "Successfully installed catboost-0.26\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "B2n4qXB86JDb",
        "outputId": "9580cdb0-9a72-470b-ba4f-031b2f12dc0f"
      },
      "source": [
        "# defining the space\n",
        "param_grid_reg = [\n",
        "    Integer(100, 600, name=\"n_estimators\"),\n",
        "    Real(0.05, 0.3, name=\"learning_rate\", prior=\"log-uniform\"),\n",
        "    Integer(3, 15, name=\"depth\"),\n",
        "    Integer(3, 30, name=\"l2_leaf_reg\"),\n",
        "]\n",
        "\n",
        "from catboost import CatBoostClassifier, CatBoostRegressor\n",
        "cb = CatBoostRegressor(task_type='GPU', random_state=0, verbose=False, max_bin=254)\n",
        "\n",
        "@use_named_args(param_grid_reg)\n",
        "def objective(**params):\n",
        "    \n",
        "    # model with new parameters\n",
        "    cb.set_params(**params)\n",
        "\n",
        "    # optimization function (hyperparam response function)\n",
        "    value = np.mean(\n",
        "        cross_val_score(\n",
        "            cb, \n",
        "            X_train,\n",
        "            y_train,\n",
        "            cv=3,\n",
        "            #n_jobs=-1,\n",
        "            scoring='neg_root_mean_squared_error'\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # negate because if you need to minimize\n",
        "    return -value\n",
        "\n",
        "# gbrt_minimize performs by Bayesian Optimization \n",
        "# using GBMs as surrogate for f(x)\n",
        "\n",
        "gbm_ = gbrt_minimize(\n",
        "    objective, # the objective function to minimize\n",
        "    param_grid_reg, # the hyperparameter space\n",
        "    n_initial_points=60, # the number of points to evaluate f(x) to start of\n",
        "    acq_func='EI', # the acquisition function\n",
        "    n_calls=100, # the number of subsequent evaluations of f(x)\n",
        "    random_state=0, \n",
        "    verbose=True,\n",
        "    n_jobs=-1,\n",
        ")\n",
        "\n",
        "# function value at the minimum.\n",
        "\"Best score=%.4f\" % gbm_.fun"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration No: 1 started. Evaluating function at random point.\n",
            "Iteration No: 1 ended. Evaluation done at random point.\n",
            "Time taken: 4.0820\n",
            "Function value obtained: 11464.8788\n",
            "Current minimum: 11464.8788\n",
            "Iteration No: 2 started. Evaluating function at random point.\n",
            "Iteration No: 2 ended. Evaluation done at random point.\n",
            "Time taken: 26.3920\n",
            "Function value obtained: 10222.1213\n",
            "Current minimum: 10222.1213\n",
            "Iteration No: 3 started. Evaluating function at random point.\n",
            "Iteration No: 3 ended. Evaluation done at random point.\n",
            "Time taken: 12.5146\n",
            "Function value obtained: 10211.4969\n",
            "Current minimum: 10211.4969\n",
            "Iteration No: 4 started. Evaluating function at random point.\n",
            "Iteration No: 4 ended. Evaluation done at random point.\n",
            "Time taken: 84.5550\n",
            "Function value obtained: 13224.5300\n",
            "Current minimum: 10211.4969\n",
            "Iteration No: 5 started. Evaluating function at random point.\n",
            "Iteration No: 5 ended. Evaluation done at random point.\n",
            "Time taken: 43.4458\n",
            "Function value obtained: 13885.9222\n",
            "Current minimum: 10211.4969\n",
            "Iteration No: 6 started. Evaluating function at random point.\n",
            "Iteration No: 6 ended. Evaluation done at random point.\n",
            "Time taken: 12.7067\n",
            "Function value obtained: 10200.8128\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 7 started. Evaluating function at random point.\n",
            "Iteration No: 7 ended. Evaluation done at random point.\n",
            "Time taken: 15.6717\n",
            "Function value obtained: 16203.6720\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 8 started. Evaluating function at random point.\n",
            "Iteration No: 8 ended. Evaluation done at random point.\n",
            "Time taken: 6.3595\n",
            "Function value obtained: 10516.0934\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 9 started. Evaluating function at random point.\n",
            "Iteration No: 9 ended. Evaluation done at random point.\n",
            "Time taken: 16.9834\n",
            "Function value obtained: 13950.6117\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 10 started. Evaluating function at random point.\n",
            "Iteration No: 10 ended. Evaluation done at random point.\n",
            "Time taken: 6.1946\n",
            "Function value obtained: 10912.6520\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 11 started. Evaluating function at random point.\n",
            "Iteration No: 11 ended. Evaluation done at random point.\n",
            "Time taken: 36.1632\n",
            "Function value obtained: 10309.4899\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 12 started. Evaluating function at random point.\n",
            "Iteration No: 12 ended. Evaluation done at random point.\n",
            "Time taken: 58.6568\n",
            "Function value obtained: 10693.6269\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 13 started. Evaluating function at random point.\n",
            "Iteration No: 13 ended. Evaluation done at random point.\n",
            "Time taken: 3.5992\n",
            "Function value obtained: 11603.5575\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 14 started. Evaluating function at random point.\n",
            "Iteration No: 14 ended. Evaluation done at random point.\n",
            "Time taken: 24.4588\n",
            "Function value obtained: 14488.3762\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 15 started. Evaluating function at random point.\n",
            "Iteration No: 15 ended. Evaluation done at random point.\n",
            "Time taken: 6.4967\n",
            "Function value obtained: 10495.5029\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 16 started. Evaluating function at random point.\n",
            "Iteration No: 16 ended. Evaluation done at random point.\n",
            "Time taken: 7.4221\n",
            "Function value obtained: 10600.8091\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 17 started. Evaluating function at random point.\n",
            "Iteration No: 17 ended. Evaluation done at random point.\n",
            "Time taken: 31.6315\n",
            "Function value obtained: 10369.9836\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 18 started. Evaluating function at random point.\n",
            "Iteration No: 18 ended. Evaluation done at random point.\n",
            "Time taken: 6.4997\n",
            "Function value obtained: 10638.4508\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 19 started. Evaluating function at random point.\n",
            "Iteration No: 19 ended. Evaluation done at random point.\n",
            "Time taken: 10.0340\n",
            "Function value obtained: 10372.2268\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 20 started. Evaluating function at random point.\n",
            "Iteration No: 20 ended. Evaluation done at random point.\n",
            "Time taken: 52.5885\n",
            "Function value obtained: 10930.3632\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 21 started. Evaluating function at random point.\n",
            "Iteration No: 21 ended. Evaluation done at random point.\n",
            "Time taken: 6.4941\n",
            "Function value obtained: 10534.5765\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 22 started. Evaluating function at random point.\n",
            "Iteration No: 22 ended. Evaluation done at random point.\n",
            "Time taken: 45.4205\n",
            "Function value obtained: 14143.4256\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 23 started. Evaluating function at random point.\n",
            "Iteration No: 23 ended. Evaluation done at random point.\n",
            "Time taken: 30.0072\n",
            "Function value obtained: 10783.6988\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 24 started. Evaluating function at random point.\n",
            "Iteration No: 24 ended. Evaluation done at random point.\n",
            "Time taken: 6.6665\n",
            "Function value obtained: 10846.1679\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 25 started. Evaluating function at random point.\n",
            "Iteration No: 25 ended. Evaluation done at random point.\n",
            "Time taken: 6.6331\n",
            "Function value obtained: 10225.8166\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 26 started. Evaluating function at random point.\n",
            "Iteration No: 26 ended. Evaluation done at random point.\n",
            "Time taken: 2.9983\n",
            "Function value obtained: 11585.4042\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 27 started. Evaluating function at random point.\n",
            "Iteration No: 27 ended. Evaluation done at random point.\n",
            "Time taken: 3.6898\n",
            "Function value obtained: 11399.6674\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 28 started. Evaluating function at random point.\n",
            "Iteration No: 28 ended. Evaluation done at random point.\n",
            "Time taken: 3.0461\n",
            "Function value obtained: 11758.9947\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 29 started. Evaluating function at random point.\n",
            "Iteration No: 29 ended. Evaluation done at random point.\n",
            "Time taken: 12.1410\n",
            "Function value obtained: 14960.7013\n",
            "Current minimum: 10200.8128\n",
            "Iteration No: 30 started. Evaluating function at random point.\n",
            "Iteration No: 30 ended. Evaluation done at random point.\n",
            "Time taken: 15.3063\n",
            "Function value obtained: 10052.6381\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 31 started. Evaluating function at random point.\n",
            "Iteration No: 31 ended. Evaluation done at random point.\n",
            "Time taken: 4.2587\n",
            "Function value obtained: 10866.1516\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 32 started. Evaluating function at random point.\n",
            "Iteration No: 32 ended. Evaluation done at random point.\n",
            "Time taken: 16.5709\n",
            "Function value obtained: 14370.7933\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 33 started. Evaluating function at random point.\n",
            "Iteration No: 33 ended. Evaluation done at random point.\n",
            "Time taken: 23.8167\n",
            "Function value obtained: 10311.3308\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 34 started. Evaluating function at random point.\n",
            "Iteration No: 34 ended. Evaluation done at random point.\n",
            "Time taken: 7.6608\n",
            "Function value obtained: 10728.6845\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 35 started. Evaluating function at random point.\n",
            "Iteration No: 35 ended. Evaluation done at random point.\n",
            "Time taken: 7.1118\n",
            "Function value obtained: 10362.7088\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 36 started. Evaluating function at random point.\n",
            "Iteration No: 36 ended. Evaluation done at random point.\n",
            "Time taken: 4.2530\n",
            "Function value obtained: 10571.7186\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 37 started. Evaluating function at random point.\n",
            "Iteration No: 37 ended. Evaluation done at random point.\n",
            "Time taken: 13.0193\n",
            "Function value obtained: 10303.7701\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 38 started. Evaluating function at random point.\n",
            "Iteration No: 38 ended. Evaluation done at random point.\n",
            "Time taken: 3.3846\n",
            "Function value obtained: 11439.9948\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 39 started. Evaluating function at random point.\n",
            "Iteration No: 39 ended. Evaluation done at random point.\n",
            "Time taken: 2.9630\n",
            "Function value obtained: 11598.8765\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 40 started. Evaluating function at random point.\n",
            "Iteration No: 40 ended. Evaluation done at random point.\n",
            "Time taken: 19.5779\n",
            "Function value obtained: 10409.4808\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 41 started. Evaluating function at random point.\n",
            "Iteration No: 41 ended. Evaluation done at random point.\n",
            "Time taken: 16.6050\n",
            "Function value obtained: 14500.7677\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 42 started. Evaluating function at random point.\n",
            "Iteration No: 42 ended. Evaluation done at random point.\n",
            "Time taken: 7.6780\n",
            "Function value obtained: 10162.8720\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 43 started. Evaluating function at random point.\n",
            "Iteration No: 43 ended. Evaluation done at random point.\n",
            "Time taken: 8.9614\n",
            "Function value obtained: 10594.5315\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 44 started. Evaluating function at random point.\n",
            "Iteration No: 44 ended. Evaluation done at random point.\n",
            "Time taken: 21.2556\n",
            "Function value obtained: 10366.6227\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 45 started. Evaluating function at random point.\n",
            "Iteration No: 45 ended. Evaluation done at random point.\n",
            "Time taken: 19.3826\n",
            "Function value obtained: 10532.7800\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 46 started. Evaluating function at random point.\n",
            "Iteration No: 46 ended. Evaluation done at random point.\n",
            "Time taken: 21.3474\n",
            "Function value obtained: 14489.5635\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 47 started. Evaluating function at random point.\n",
            "Iteration No: 47 ended. Evaluation done at random point.\n",
            "Time taken: 5.3648\n",
            "Function value obtained: 10788.6630\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 48 started. Evaluating function at random point.\n",
            "Iteration No: 48 ended. Evaluation done at random point.\n",
            "Time taken: 7.4254\n",
            "Function value obtained: 10820.1404\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 49 started. Evaluating function at random point.\n",
            "Iteration No: 49 ended. Evaluation done at random point.\n",
            "Time taken: 31.9784\n",
            "Function value obtained: 10266.6219\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 50 started. Evaluating function at random point.\n",
            "Iteration No: 50 ended. Evaluation done at random point.\n",
            "Time taken: 33.7063\n",
            "Function value obtained: 11066.2684\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 51 started. Evaluating function at random point.\n",
            "Iteration No: 51 ended. Evaluation done at random point.\n",
            "Time taken: 3.2103\n",
            "Function value obtained: 11551.4852\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 52 started. Evaluating function at random point.\n",
            "Iteration No: 52 ended. Evaluation done at random point.\n",
            "Time taken: 19.1014\n",
            "Function value obtained: 10354.7285\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 53 started. Evaluating function at random point.\n",
            "Iteration No: 53 ended. Evaluation done at random point.\n",
            "Time taken: 89.0357\n",
            "Function value obtained: 13461.4969\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 54 started. Evaluating function at random point.\n",
            "Iteration No: 54 ended. Evaluation done at random point.\n",
            "Time taken: 21.5777\n",
            "Function value obtained: 13479.0901\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 55 started. Evaluating function at random point.\n",
            "Iteration No: 55 ended. Evaluation done at random point.\n",
            "Time taken: 7.4897\n",
            "Function value obtained: 11306.1599\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 56 started. Evaluating function at random point.\n",
            "Iteration No: 56 ended. Evaluation done at random point.\n",
            "Time taken: 5.5411\n",
            "Function value obtained: 10615.0386\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 57 started. Evaluating function at random point.\n",
            "Iteration No: 57 ended. Evaluation done at random point.\n",
            "Time taken: 17.5730\n",
            "Function value obtained: 15652.4313\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 58 started. Evaluating function at random point.\n",
            "Iteration No: 58 ended. Evaluation done at random point.\n",
            "Time taken: 5.0232\n",
            "Function value obtained: 10464.9603\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 59 started. Evaluating function at random point.\n",
            "Iteration No: 59 ended. Evaluation done at random point.\n",
            "Time taken: 5.2914\n",
            "Function value obtained: 10975.2752\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 60 started. Evaluating function at random point.\n",
            "Iteration No: 60 ended. Evaluation done at random point.\n",
            "Time taken: 4.0198\n",
            "Function value obtained: 10833.8028\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 61 started. Searching for the next optimal point.\n",
            "Iteration No: 61 ended. Search finished for the next optimal point.\n",
            "Time taken: 28.4591\n",
            "Function value obtained: 10088.3123\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 62 started. Searching for the next optimal point.\n",
            "Iteration No: 62 ended. Search finished for the next optimal point.\n",
            "Time taken: 47.0881\n",
            "Function value obtained: 10311.6511\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 63 started. Searching for the next optimal point.\n",
            "Iteration No: 63 ended. Search finished for the next optimal point.\n",
            "Time taken: 163.0735\n",
            "Function value obtained: 10442.1067\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 64 started. Searching for the next optimal point.\n",
            "Iteration No: 64 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.0882\n",
            "Function value obtained: 10125.2878\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 65 started. Searching for the next optimal point.\n",
            "Iteration No: 65 ended. Search finished for the next optimal point.\n",
            "Time taken: 13.1236\n",
            "Function value obtained: 10099.1952\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 66 started. Searching for the next optimal point.\n",
            "Iteration No: 66 ended. Search finished for the next optimal point.\n",
            "Time taken: 12.7844\n",
            "Function value obtained: 10088.2919\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 67 started. Searching for the next optimal point.\n",
            "Iteration No: 67 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.8151\n",
            "Function value obtained: 10088.8574\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 68 started. Searching for the next optimal point.\n",
            "Iteration No: 68 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.3049\n",
            "Function value obtained: 10056.1312\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 69 started. Searching for the next optimal point.\n",
            "Iteration No: 69 ended. Search finished for the next optimal point.\n",
            "Time taken: 11.2002\n",
            "Function value obtained: 10069.9765\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 70 started. Searching for the next optimal point.\n",
            "Iteration No: 70 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.0971\n",
            "Function value obtained: 10272.7436\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 71 started. Searching for the next optimal point.\n",
            "Iteration No: 71 ended. Search finished for the next optimal point.\n",
            "Time taken: 12.5180\n",
            "Function value obtained: 10056.8901\n",
            "Current minimum: 10052.6381\n",
            "Iteration No: 72 started. Searching for the next optimal point.\n",
            "Iteration No: 72 ended. Search finished for the next optimal point.\n",
            "Time taken: 14.7543\n",
            "Function value obtained: 10002.0853\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 73 started. Searching for the next optimal point.\n",
            "Iteration No: 73 ended. Search finished for the next optimal point.\n",
            "Time taken: 11.2244\n",
            "Function value obtained: 10104.0194\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 74 started. Searching for the next optimal point.\n",
            "Iteration No: 74 ended. Search finished for the next optimal point.\n",
            "Time taken: 12.6497\n",
            "Function value obtained: 10110.3177\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 75 started. Searching for the next optimal point.\n",
            "Iteration No: 75 ended. Search finished for the next optimal point.\n",
            "Time taken: 12.9745\n",
            "Function value obtained: 10091.5206\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 76 started. Searching for the next optimal point.\n",
            "Iteration No: 76 ended. Search finished for the next optimal point.\n",
            "Time taken: 12.0829\n",
            "Function value obtained: 10067.3507\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 77 started. Searching for the next optimal point.\n",
            "Iteration No: 77 ended. Search finished for the next optimal point.\n",
            "Time taken: 21.8232\n",
            "Function value obtained: 10174.1049\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 78 started. Searching for the next optimal point.\n",
            "Iteration No: 78 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.6467\n",
            "Function value obtained: 10061.8245\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 79 started. Searching for the next optimal point.\n",
            "Iteration No: 79 ended. Search finished for the next optimal point.\n",
            "Time taken: 5.2783\n",
            "Function value obtained: 11316.2570\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 80 started. Searching for the next optimal point.\n",
            "Iteration No: 80 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.1383\n",
            "Function value obtained: 10017.0626\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 81 started. Searching for the next optimal point.\n",
            "Iteration No: 81 ended. Search finished for the next optimal point.\n",
            "Time taken: 14.3753\n",
            "Function value obtained: 10105.2684\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 82 started. Searching for the next optimal point.\n",
            "Iteration No: 82 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.8385\n",
            "Function value obtained: 10036.4731\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 83 started. Searching for the next optimal point.\n",
            "Iteration No: 83 ended. Search finished for the next optimal point.\n",
            "Time taken: 20.1910\n",
            "Function value obtained: 11028.4698\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 84 started. Searching for the next optimal point.\n",
            "Iteration No: 84 ended. Search finished for the next optimal point.\n",
            "Time taken: 13.4492\n",
            "Function value obtained: 10106.1730\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 85 started. Searching for the next optimal point.\n",
            "Iteration No: 85 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.3997\n",
            "Function value obtained: 10053.1037\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 86 started. Searching for the next optimal point.\n",
            "Iteration No: 86 ended. Search finished for the next optimal point.\n",
            "Time taken: 14.0258\n",
            "Function value obtained: 13888.3431\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 87 started. Searching for the next optimal point.\n",
            "Iteration No: 87 ended. Search finished for the next optimal point.\n",
            "Time taken: 14.0095\n",
            "Function value obtained: 10089.0234\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 88 started. Searching for the next optimal point.\n",
            "Iteration No: 88 ended. Search finished for the next optimal point.\n",
            "Time taken: 13.3640\n",
            "Function value obtained: 10095.5202\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 89 started. Searching for the next optimal point.\n",
            "Iteration No: 89 ended. Search finished for the next optimal point.\n",
            "Time taken: 16.2668\n",
            "Function value obtained: 10093.4825\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 90 started. Searching for the next optimal point.\n",
            "Iteration No: 90 ended. Search finished for the next optimal point.\n",
            "Time taken: 11.9088\n",
            "Function value obtained: 10053.0061\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 91 started. Searching for the next optimal point.\n",
            "Iteration No: 91 ended. Search finished for the next optimal point.\n",
            "Time taken: 22.3117\n",
            "Function value obtained: 10197.9571\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 92 started. Searching for the next optimal point.\n",
            "Iteration No: 92 ended. Search finished for the next optimal point.\n",
            "Time taken: 15.8506\n",
            "Function value obtained: 10007.3428\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 93 started. Searching for the next optimal point.\n",
            "Iteration No: 93 ended. Search finished for the next optimal point.\n",
            "Time taken: 6.3048\n",
            "Function value obtained: 10886.7880\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 94 started. Searching for the next optimal point.\n",
            "Iteration No: 94 ended. Search finished for the next optimal point.\n",
            "Time taken: 6.0115\n",
            "Function value obtained: 11250.4559\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 95 started. Searching for the next optimal point.\n",
            "Iteration No: 95 ended. Search finished for the next optimal point.\n",
            "Time taken: 97.0334\n",
            "Function value obtained: 10291.4047\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 96 started. Searching for the next optimal point.\n",
            "Iteration No: 96 ended. Search finished for the next optimal point.\n",
            "Time taken: 6.5957\n",
            "Function value obtained: 10402.5063\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 97 started. Searching for the next optimal point.\n",
            "Iteration No: 97 ended. Search finished for the next optimal point.\n",
            "Time taken: 6.3740\n",
            "Function value obtained: 10996.4212\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 98 started. Searching for the next optimal point.\n",
            "Iteration No: 98 ended. Search finished for the next optimal point.\n",
            "Time taken: 9.6983\n",
            "Function value obtained: 10095.2390\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 99 started. Searching for the next optimal point.\n",
            "Iteration No: 99 ended. Search finished for the next optimal point.\n",
            "Time taken: 5.7266\n",
            "Function value obtained: 10328.3237\n",
            "Current minimum: 10002.0853\n",
            "Iteration No: 100 started. Searching for the next optimal point.\n",
            "Iteration No: 100 ended. Search finished for the next optimal point.\n",
            "Time taken: 75.1700\n",
            "Function value obtained: 15779.3850\n",
            "Current minimum: 10002.0853\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'Best score=10002.0853'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_reWQRQjFrkA",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2e3ee873-8848-4aa5-8a7b-106a983befa0"
      },
      "source": [
        "gbm_.x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[447, 0.21817467080001723, 10, 30]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "A1VBlZ_T6JBL",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 314
        },
        "outputId": "1e8f468a-5763-4958-c5f4-8e7dcfaff11b"
      },
      "source": [
        "plot_convergence(gbm_)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.axes._subplots.AxesSubplot at 0x7f8fe9177210>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aEf2PBh56I_B",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "ec559fbc-096f-4c0e-d7b5-8b347a3a7b12"
      },
      "source": [
        "dim_names = ['n_estimators', 'learning_rate','depth' , 'l2_leaf_reg']\n",
        "plt.rcParams[\"figure.figsize\"] = (15,15)\n",
        "plot_objective(result=gbm_, plot_dims=dim_names,size=2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SdEMmVLJ6I8J",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 627
        },
        "outputId": "f30da0c2-4db8-492e-d3fd-e8697e66b1ea"
      },
      "source": [
        "plot_evaluations(result=gbm_, plot_dims=dim_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
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\n",
            "text/plain": [
              "<Figure size 576x576 with 16 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "qkpmGon06I5q",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4ad7e0ae-d915-4a51-a98c-740a729248f3"
      },
      "source": [
        "cb = CatBoostRegressor(task_type='GPU', \n",
        "                       random_state=0, \n",
        "                       verbose=False,\n",
        "                       max_bin = 254,\n",
        "                       n_estimators = gbm_.x[0],\n",
        "                       learning_rate = gbm_.x[1],\n",
        "                       depth = gbm_.x[2],\n",
        "                       l2_leaf_reg = gbm_.x[3])\n",
        "\n",
        "# K-Fold Cross-Validation\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=4)\n",
        "\n",
        "# estimate generalization error\n",
        "clf =  cross_validate(\n",
        "    cb,\n",
        "    X, \n",
        "    y,\n",
        "    scoring='r2',\n",
        "    return_train_score=True,\n",
        "    cv=kf,\n",
        ")\n",
        "\n",
        "clf['test_score'], clf['test_score'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.73456194, 0.70231735, 0.69004596, 0.66902429, 0.65566848]),\n",
              " 0.6903236040818476)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 12
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "R8hbsvzT-dWU",
        "outputId": "23350a31-9057-480b-8ee1-a022a47c9aee"
      },
      "source": [
        "x = cb.get_params()\n",
        "x"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{'depth': 10,\n",
              " 'l2_leaf_reg': 30,\n",
              " 'learning_rate': 0.21817467080001723,\n",
              " 'loss_function': 'RMSE',\n",
              " 'max_bin': 254,\n",
              " 'n_estimators': 447,\n",
              " 'random_state': 0,\n",
              " 'task_type': 'GPU',\n",
              " 'verbose': False}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "M24M6odAh8DN"
      },
      "source": [
        "####Random Forest Regressor"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "_gwhgERFh73m",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "ee453f05-26fb-4c19-99e0-a91fedc7e020"
      },
      "source": [
        "# defining the space\n",
        "param_grid_reg = [\n",
        "    Integer(100, 1000, name=\"n_estimators\"),\n",
        "    Integer(3, 15, name=\"max_depth\")\n",
        "]\n",
        "\n",
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0, n_jobs=-1,)\n",
        "\n",
        "@use_named_args(param_grid_reg)\n",
        "def objective(**params):\n",
        "    \n",
        "    # model with new parameters\n",
        "    rf.set_params(**params)\n",
        "\n",
        "    # optimization function (hyperparam response function)\n",
        "    value = np.mean(\n",
        "        cross_val_score(\n",
        "            rf, \n",
        "            X_train,\n",
        "            y_train,\n",
        "            cv=3,\n",
        "            n_jobs=-1,\n",
        "            scoring='neg_root_mean_squared_error'\n",
        "        )\n",
        "    )\n",
        "\n",
        "    # negate because if you need to minimize\n",
        "    return -value\n",
        "\n",
        "# gp_minimize performs by default GP Optimization using a Marten Kernel\n",
        "gp_ = gp_minimize(\n",
        "    objective, # the objective function to minimize\n",
        "    param_grid_reg, # the hyperparameter space\n",
        "    n_initial_points=30, # the number of points to evaluate f(x) to start of\n",
        "    acq_func='EI', # the acquisition function\n",
        "    n_calls=50, # the number of subsequent evaluations of f(x)\n",
        "    random_state=0,\n",
        "    verbose=True,\n",
        "    n_jobs=-1,\n",
        ")"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Iteration No: 1 started. Evaluating function at random point.\n",
            "Iteration No: 1 ended. Evaluation done at random point.\n",
            "Time taken: 275.7069\n",
            "Function value obtained: 10544.1930\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 2 started. Evaluating function at random point.\n",
            "Iteration No: 2 ended. Evaluation done at random point.\n",
            "Time taken: 377.5950\n",
            "Function value obtained: 10553.1409\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 3 started. Evaluating function at random point.\n",
            "Iteration No: 3 ended. Evaluation done at random point.\n",
            "Time taken: 178.2411\n",
            "Function value obtained: 11238.7479\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 4 started. Evaluating function at random point.\n",
            "Iteration No: 4 ended. Evaluation done at random point.\n",
            "Time taken: 51.4425\n",
            "Function value obtained: 13408.4658\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 5 started. Evaluating function at random point.\n",
            "Iteration No: 5 ended. Evaluation done at random point.\n",
            "Time taken: 105.1041\n",
            "Function value obtained: 11028.2778\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 6 started. Evaluating function at random point.\n",
            "Iteration No: 6 ended. Evaluation done at random point.\n",
            "Time taken: 251.7660\n",
            "Function value obtained: 11003.4931\n",
            "Current minimum: 10544.1930\n",
            "Iteration No: 7 started. Evaluating function at random point.\n",
            "Iteration No: 7 ended. Evaluation done at random point.\n",
            "Time taken: 196.2672\n",
            "Function value obtained: 10536.8505\n",
            "Current minimum: 10536.8505\n",
            "Iteration No: 8 started. Evaluating function at random point.\n",
            "Iteration No: 8 ended. Evaluation done at random point.\n",
            "Time taken: 149.0794\n",
            "Function value obtained: 10715.7889\n",
            "Current minimum: 10536.8505\n",
            "Iteration No: 9 started. Evaluating function at random point.\n",
            "Iteration No: 9 ended. Evaluation done at random point.\n",
            "Time taken: 199.4786\n",
            "Function value obtained: 10473.6822\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 10 started. Evaluating function at random point.\n",
            "Iteration No: 10 ended. Evaluation done at random point.\n",
            "Time taken: 98.5800\n",
            "Function value obtained: 10572.8267\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 11 started. Evaluating function at random point.\n",
            "Iteration No: 11 ended. Evaluation done at random point.\n",
            "Time taken: 226.9032\n",
            "Function value obtained: 10554.5397\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 12 started. Evaluating function at random point.\n",
            "Iteration No: 12 ended. Evaluation done at random point.\n",
            "Time taken: 209.1708\n",
            "Function value obtained: 10713.1753\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 13 started. Evaluating function at random point.\n",
            "Iteration No: 13 ended. Evaluation done at random point.\n",
            "Time taken: 249.6564\n",
            "Function value obtained: 10831.8104\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 14 started. Evaluating function at random point.\n",
            "Iteration No: 14 ended. Evaluation done at random point.\n",
            "Time taken: 233.1924\n",
            "Function value obtained: 10611.3319\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 15 started. Evaluating function at random point.\n",
            "Iteration No: 15 ended. Evaluation done at random point.\n",
            "Time taken: 59.2670\n",
            "Function value obtained: 11023.6908\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 16 started. Evaluating function at random point.\n",
            "Iteration No: 16 ended. Evaluation done at random point.\n",
            "Time taken: 107.6223\n",
            "Function value obtained: 10630.7143\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 17 started. Evaluating function at random point.\n",
            "Iteration No: 17 ended. Evaluation done at random point.\n",
            "Time taken: 51.8620\n",
            "Function value obtained: 12540.7001\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 18 started. Evaluating function at random point.\n",
            "Iteration No: 18 ended. Evaluation done at random point.\n",
            "Time taken: 67.2073\n",
            "Function value obtained: 12534.9979\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 19 started. Evaluating function at random point.\n",
            "Iteration No: 19 ended. Evaluation done at random point.\n",
            "Time taken: 80.3126\n",
            "Function value obtained: 11271.6801\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 20 started. Evaluating function at random point.\n",
            "Iteration No: 20 ended. Evaluation done at random point.\n",
            "Time taken: 244.4094\n",
            "Function value obtained: 11247.8053\n",
            "Current minimum: 10473.6822\n",
            "Iteration No: 21 started. Evaluating function at random point.\n",
            "Iteration No: 21 ended. Evaluation done at random point.\n",
            "Time taken: 297.5211\n",
            "Function value obtained: 10466.8284\n",
            "Current minimum: 10466.8284\n",
            "Iteration No: 22 started. Evaluating function at random point.\n",
            "Iteration No: 22 ended. Evaluation done at random point.\n",
            "Time taken: 92.4101\n",
            "Function value obtained: 10461.6190\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 23 started. Evaluating function at random point.\n",
            "Iteration No: 23 ended. Evaluation done at random point.\n",
            "Time taken: 117.4728\n",
            "Function value obtained: 12521.0887\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 24 started. Evaluating function at random point.\n",
            "Iteration No: 24 ended. Evaluation done at random point.\n",
            "Time taken: 169.3014\n",
            "Function value obtained: 10609.3159\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 25 started. Evaluating function at random point.\n",
            "Iteration No: 25 ended. Evaluation done at random point.\n",
            "Time taken: 151.9755\n",
            "Function value obtained: 11555.6222\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 26 started. Evaluating function at random point.\n",
            "Iteration No: 26 ended. Evaluation done at random point.\n",
            "Time taken: 49.6032\n",
            "Function value obtained: 10755.8230\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 27 started. Evaluating function at random point.\n",
            "Iteration No: 27 ended. Evaluation done at random point.\n",
            "Time taken: 353.0641\n",
            "Function value obtained: 10713.9568\n",
            "Current minimum: 10461.6190\n",
            "Iteration No: 28 started. Evaluating function at random point.\n",
            "Iteration No: 28 ended. Evaluation done at random point.\n",
            "Time taken: 325.4510\n",
            "Function value obtained: 10447.8822\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 29 started. Evaluating function at random point.\n",
            "Iteration No: 29 ended. Evaluation done at random point.\n",
            "Time taken: 167.5099\n",
            "Function value obtained: 11247.0595\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 30 started. Evaluating function at random point.\n",
            "Iteration No: 30 ended. Evaluation done at random point.\n",
            "Time taken: 176.7870\n",
            "Function value obtained: 10835.2727\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 31 started. Searching for the next optimal point.\n",
            "Iteration No: 31 ended. Search finished for the next optimal point.\n",
            "Time taken: 262.4940\n",
            "Function value obtained: 10451.2297\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 32 started. Searching for the next optimal point.\n",
            "Iteration No: 32 ended. Search finished for the next optimal point.\n",
            "Time taken: 261.0477\n",
            "Function value obtained: 10451.5341\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 33 started. Searching for the next optimal point.\n",
            "Iteration No: 33 ended. Search finished for the next optimal point.\n",
            "Time taken: 267.0367\n",
            "Function value obtained: 10451.3156\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 34 started. Searching for the next optimal point.\n",
            "Iteration No: 34 ended. Search finished for the next optimal point.\n",
            "Time taken: 270.2689\n",
            "Function value obtained: 10451.0798\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 35 started. Searching for the next optimal point.\n",
            "Iteration No: 35 ended. Search finished for the next optimal point.\n",
            "Time taken: 278.7710\n",
            "Function value obtained: 10449.8803\n",
            "Current minimum: 10447.8822\n",
            "Iteration No: 36 started. Searching for the next optimal point.\n",
            "Iteration No: 36 ended. Search finished for the next optimal point.\n",
            "Time taken: 290.9883\n",
            "Function value obtained: 10445.5554\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 37 started. Searching for the next optimal point.\n",
            "Iteration No: 37 ended. Search finished for the next optimal point.\n",
            "Time taken: 441.9335\n",
            "Function value obtained: 10451.7431\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 38 started. Searching for the next optimal point.\n",
            "Iteration No: 38 ended. Search finished for the next optimal point.\n",
            "Time taken: 484.0976\n",
            "Function value obtained: 10454.8716\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 39 started. Searching for the next optimal point.\n",
            "Iteration No: 39 ended. Search finished for the next optimal point.\n",
            "Time taken: 481.4408\n",
            "Function value obtained: 10454.4599\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 40 started. Searching for the next optimal point.\n",
            "Iteration No: 40 ended. Search finished for the next optimal point.\n",
            "Time taken: 51.6141\n",
            "Function value obtained: 10481.6353\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 41 started. Searching for the next optimal point.\n",
            "Iteration No: 41 ended. Search finished for the next optimal point.\n",
            "Time taken: 483.7755\n",
            "Function value obtained: 10454.3506\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 42 started. Searching for the next optimal point.\n",
            "Iteration No: 42 ended. Search finished for the next optimal point.\n",
            "Time taken: 478.7915\n",
            "Function value obtained: 10454.4599\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 43 started. Searching for the next optimal point.\n",
            "Iteration No: 43 ended. Search finished for the next optimal point.\n",
            "Time taken: 483.8114\n",
            "Function value obtained: 10455.5937\n",
            "Current minimum: 10445.5554\n",
            "Iteration No: 44 started. Searching for the next optimal point.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "weDebBZniC-J"
      },
      "source": [
        "# function value at the minimum.\n",
        "\"Best score=%.4f\" % gp_.fun"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4lqOQ1Ge3JEA"
      },
      "source": [
        "plot_convergence(gp_)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "V5fVFx0X3SMY",
        "outputId": "2e113a72-0822-41e9-db48-ae923e634ba4"
      },
      "source": [
        "dim_names = ['n_estimators', 'max_depth']\n",
        "plt.rcParams[\"figure.figsize\"] = (15,15)\n",
        "plot_objective(result=gp_, plot_dims=dim_names,size=2)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 362
        },
        "id": "hg1MZGzy3iQI",
        "outputId": "71dcafe7-ad14-4f3d-a7ba-133ea0396c34"
      },
      "source": [
        "plot_evaluations(result=gp_, plot_dims=dim_names)\n",
        "plt.show()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 288x288 with 4 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ep7xFdMeiC76",
        "outputId": "a2b0dd39-a516-4f95-d233-bdff14a43a26"
      },
      "source": [
        "from sklearn.ensemble import RandomForestRegressor\n",
        "rf = RandomForestRegressor(random_state=0, \n",
        "                           n_jobs=-1,\n",
        "                           n_estimators = gp_.x[0],\n",
        "                           max_depth = gp_.x[1]\n",
        "                           )\n",
        "rf"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
              "                      max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
              "                      max_samples=None, min_impurity_decrease=0.0,\n",
              "                      min_impurity_split=None, min_samples_leaf=1,\n",
              "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
              "                      n_estimators=720, n_jobs=-1, oob_score=False,\n",
              "                      random_state=0, verbose=0, warm_start=False)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 67
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "lqifKaWZ30UN",
        "outputId": "1bce9c47-93cb-4079-c453-a883414004fb"
      },
      "source": [
        "# K-Fold Cross-Validation\n",
        "kf = KFold(n_splits=5, shuffle=True, random_state=4)\n",
        "\n",
        "# estimate generalization error\n",
        "clf =  cross_validate(\n",
        "    rf,\n",
        "    X, \n",
        "    y,\n",
        "    scoring='r2',\n",
        "    return_train_score=True,\n",
        "    \n",
        "    cv=kf, # k-fold\n",
        ")\n",
        "\n",
        "clf['test_score'], clf['test_score'].mean()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(array([0.76123013, 0.6914934 , 0.67926326, 0.6756818 , 0.66067344]),\n",
              " 0.6936684045990527)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 73
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "mZ7R_8ozzRq3"
      },
      "source": [
        "#Stacking"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "l5x4WXCDzgLO",
        "outputId": "df7c5749-ce6b-4b73-ec1e-3d22f8dc673c"
      },
      "source": [
        "!pip install vecstack \n",
        "from vecstack import stacking"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting vecstack\n",
            "  Downloading https://files.pythonhosted.org/packages/d0/a1/b9a1e9e9e5a12078da1ab9788c7885e4c745358f7e57d5f94d9db6a4e898/vecstack-0.4.0.tar.gz\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.7/dist-packages (from vecstack) (1.19.5)\n",
            "Requirement already satisfied: scipy in /usr/local/lib/python3.7/dist-packages (from vecstack) (1.4.1)\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.7/dist-packages (from vecstack) (0.22.2.post1)\n",
            "Requirement already satisfied: joblib>=0.11 in /usr/local/lib/python3.7/dist-packages (from scikit-learn>=0.18->vecstack) (1.0.1)\n",
            "Building wheels for collected packages: vecstack\n",
            "  Building wheel for vecstack (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for vecstack: filename=vecstack-0.4.0-cp37-none-any.whl size=19877 sha256=0d6d4556de9f609fb7c68c61864bfa64ebb9468b0d9c06f45a457635ea3b32a8\n",
            "  Stored in directory: /root/.cache/pip/wheels/5f/bb/4e/f6488433d53bc0684673d6845e5bf11a25240577c8151c140e\n",
            "Successfully built vecstack\n",
            "Installing collected packages: vecstack\n",
            "Successfully installed vecstack-0.4.0\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9T2Q6CskzgI0"
      },
      "source": [
        "models = [\n",
        "    CatBoostRegressor(task_type='GPU', \n",
        "                       random_state=0, \n",
        "                       verbose=False,\n",
        "                       max_bin = 254,\n",
        "                       n_estimators = gbm_.x[0],\n",
        "                       learning_rate = gbm_.x[1],\n",
        "                       depth = gbm_.x[2],\n",
        "                       l2_leaf_reg = gbm_.x[3]),\n",
        "        \n",
        "    RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
        "                      max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
        "                      max_samples=None, min_impurity_decrease=0.0,\n",
        "                      min_impurity_split=None, min_samples_leaf=1,\n",
        "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "                      n_estimators=720, n_jobs=-1, oob_score=False,\n",
        "                      random_state=0, verbose=0, warm_start=False),\n",
        "        \n",
        "    XGBRegressor(base_score=0.5, booster='gbtree', colsample_bylevel=1,\n",
        "             colsample_bynode=1, colsample_bytree=0.6045963719621126, gamma=0,\n",
        "             importance_type='gain', learning_rate=0.05058083584390974,\n",
        "             max_delta_step=0, max_depth=10, min_child_weight=7.917729929664523,\n",
        "             missing=None, n_estimators=303, n_jobs=1, nthread=None,\n",
        "             objective='reg:linear', random_state=0,\n",
        "             reg_alpha=18.858834096247904, reg_lambda=1, scale_pos_weight=1,\n",
        "             seed=None, silent=None, subsample=0.6791237313942322, verbosity=1,\n",
        "             xgb_gpu_hist=1)\n",
        "]"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pwILDJs60oTE"
      },
      "source": [
        "from sklearn.metrics import mean_squared_error, neg_root_mean_squared_error, r2"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "pqXJwmTizgGT",
        "outputId": "6dd8faef-fda0-4ceb-cd31-868325c180c0"
      },
      "source": [
        "S_train, S_test = stacking(models,                     # list of models\n",
        "                           X_train, y_train, X_test,   # data\n",
        "                           regression=True,            # regression task (if you need classification - set to False)\n",
        "                           mode='oof_pred_bag',        # mode: oof for train set, predict test set in each fold and find mean\n",
        "                           save_dir=None,              # do not save result and log (to save in current dir - set to '.')\n",
        "                           metric= neg_root_mean_squared_error, # metric: callable\n",
        "                           n_folds=4,                  # number of folds\n",
        "                           shuffle=True,               # shuffle the data\n",
        "                           random_state=0,             # ensure reproducibility\n",
        "                           verbose=2,)     "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "task:         [regression]\n",
            "metric:       [mean_squared_error]\n",
            "mode:         [oof_pred_bag]\n",
            "n_models:     [3]\n",
            "\n",
            "model  0:     [CatBoostRegressor]\n",
            "    fold  0:  [94879894.02469654]\n",
            "    fold  1:  [114259421.13803849]\n",
            "    fold  2:  [88429419.62781414]\n",
            "    fold  3:  [110883354.74354795]\n",
            "    ----\n",
            "    MEAN:     [102113022.38352427] + [10770478.89650570]\n",
            "    FULL:     [102113022.38352428]\n",
            "\n",
            "model  1:     [RandomForestRegressor]\n",
            "    fold  0:  [102762837.82078305]\n",
            "    fold  1:  [124529209.65040855]\n",
            "    fold  2:  [89288620.82068892]\n",
            "    fold  3:  [114720269.89285755]\n",
            "    ----\n",
            "    MEAN:     [107825234.54618451] + [13188993.57161109]\n",
            "    FULL:     [107825234.54618451]\n",
            "\n",
            "model  2:     [XGBRegressor]\n",
            "[05:41:47] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  0:  [94726948.07347646]\n",
            "[05:42:03] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  1:  [102446076.29177609]\n",
            "[05:42:20] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  2:  [81085637.38254151]\n",
            "[05:42:36] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  3:  [105307384.81240478]\n",
            "    ----\n",
            "    MEAN:     [95891511.64004970] + [9383377.67279553]\n",
            "    FULL:     [95891511.64004971]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "uTgWraV31H2K",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4e750029-f7a7-44a6-bc57-2f3d4e496f56"
      },
      "source": [
        "# Initialize 2nd level model\n",
        "model = RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
        "                      max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
        "                      max_samples=None, min_impurity_decrease=0.0,\n",
        "                      min_impurity_split=None, min_samples_leaf=1,\n",
        "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "                      n_estimators=720, n_jobs=-1, oob_score=False,\n",
        "                      random_state=0, verbose=0, warm_start=False)\n",
        "    \n",
        "# Fit 2nd level model\n",
        "model = model.fit(S_train, y_train)\n",
        "\n",
        "# Predict\n",
        "y_pred = model.predict(S_test)\n",
        "\n",
        "# Final prediction score\n",
        "print('Final prediction score: [%.8f]' % np.sqrt(mean_squared_error(y_test, y_pred)))"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Final prediction score: [10453.86100004]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HDSuO0fMRoEM",
        "outputId": "1876a737-b91e-4199-ec84-f272982eead2"
      },
      "source": [
        "X.shape, y.shape, dft.shape"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((19229, 95), (19229,), (8245, 95))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 56
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "AUSrGPNWPnPz",
        "outputId": "250cdb41-971d-4f5e-fbee-665812b388ed"
      },
      "source": [
        "S_train, S_test = stacking(models,                     # list of models\n",
        "                           X, y, dft,                  # data\n",
        "                           regression=True,            # regression task (if you need classification - set to False)\n",
        "                           mode='oof_pred_bag',        # mode: oof for train set, predict test set in each fold and find mean\n",
        "                           save_dir=None,              # do not save result and log (to save in current dir - set to '.')\n",
        "                           metric=mean_squared_error, # metric: callable\n",
        "                           n_folds=4,                  # number of folds\n",
        "                           shuffle=True,               # shuffle the data\n",
        "                           random_state=0,             # ensure reproducibility\n",
        "                           verbose=2,)   "
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "task:         [regression]\n",
            "metric:       [mean_squared_error]\n",
            "mode:         [oof_pred_bag]\n",
            "n_models:     [3]\n",
            "\n",
            "model  0:     [CatBoostRegressor]\n",
            "    fold  0:  [113904305.42026265]\n",
            "    fold  1:  [78273607.01914431]\n",
            "    fold  2:  [95765860.78531845]\n",
            "    fold  3:  [122840053.20083949]\n",
            "    ----\n",
            "    MEAN:     [102695956.60639122] + [17145619.68981308]\n",
            "    FULL:     [102696539.49415523]\n",
            "\n",
            "model  1:     [RandomForestRegressor]\n",
            "    fold  0:  [113604420.63007101]\n",
            "    fold  1:  [79715265.34809770]\n",
            "    fold  2:  [97591282.89357610]\n",
            "    fold  3:  [123132850.75054786]\n",
            "    ----\n",
            "    MEAN:     [103510954.90557316] + [16493742.79179847]\n",
            "    FULL:     [103511479.81408243]\n",
            "\n",
            "model  2:     [XGBRegressor]\n",
            "[06:19:22] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  0:  [107420522.60664625]\n",
            "[06:19:45] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  1:  [74987116.56600708]\n",
            "[06:20:07] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  2:  [85790099.47465546]\n",
            "[06:20:31] WARNING: /workspace/src/objective/regression_obj.cu:152: reg:linear is now deprecated in favor of reg:squarederror.\n",
            "    fold  3:  [114936463.37584908]\n",
            "    ----\n",
            "    MEAN:     [95783550.50578946] + [16082707.47734675]\n",
            "    FULL:     [95784155.68401511]\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jnxI5U_zP0W0",
        "outputId": "6030a996-c6ce-4bd5-dbd5-42a18489259f"
      },
      "source": [
        "# Initialize 2nd level model\n",
        "model = RandomForestRegressor(bootstrap=True, ccp_alpha=0.0, criterion='mse',\n",
        "                      max_depth=15, max_features='auto', max_leaf_nodes=None,\n",
        "                      max_samples=None, min_impurity_decrease=0.0,\n",
        "                      min_impurity_split=None, min_samples_leaf=1,\n",
        "                      min_samples_split=2, min_weight_fraction_leaf=0.0,\n",
        "                      n_estimators=720, n_jobs=-1, oob_score=False,\n",
        "                      random_state=0, verbose=0, warm_start=False)\n",
        "    \n",
        "# Fit 2nd level model\n",
        "model = model.fit(S_train, y)\n",
        "\n",
        "# Predict\n",
        "y_pred = model.predict(S_test)\n",
        "print(y_pred.shape)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "(8245,)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "61UTkpo8Pxo4"
      },
      "source": [
        "result = pd.DataFrame({'Price':y_pred})\n",
        "result.head()\n",
        "result.to_csv('stacked_ensemble_regr_1.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dM_5HKsCSOpg"
      },
      "source": [
        "#AutoGluon"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IeSq37AB5IAV"
      },
      "source": [
        "!pip uninstall -y mkl\n",
        "!pip install -U pip\n",
        "!pip install -U setuptools wheel\n",
        "!pip install -U \"mxnet<2.0.0\"\n",
        "!pip install autogluon\n",
        "!pip install -U ipykernel"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "VkS6J9Vj8c7g"
      },
      "source": [
        "import pandas as pd\n",
        "from autogluon.tabular import TabularDataset, TabularPredictor\n",
        "train_data = TabularDataset('/content/drive/MyDrive/Projects/TheMathCompany_.DSHH/fe_train.csv').drop(labels=['ID'],axis=1)\n",
        "test_data = TabularDataset('/content/drive/MyDrive/Projects/TheMathCompany_.DSHH/fe_test.csv')\n",
        "\n",
        "#create temp dataset to hold ID\n",
        "test_data_tmp = pd.DataFrame()\n",
        "test_data_tmp['ID'] = test_data['ID'].copy()\n",
        "test_data = test_data.drop(labels=['ID'],axis=1)\n",
        "\n",
        "label = 'Price'\n",
        "print(\"Summary of Price variable: \\n\", train_data[label].describe())"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "K6vgtKpH9a_y",
        "outputId": "6f819da0-ab29-4f6c-c2f1-d9f48877d91a"
      },
      "source": [
        "save_path = 'agModels-predict'  # specifies folder to store trained models\n",
        "predictor = TabularPredictor(label=label, \n",
        "                             path=save_path,\n",
        "                             eval_metric= 'r2').fit(train_data, \n",
        "                                                    presets='best_quality',\n",
        "                                                    excluded_model_types = ['NN', 'FASTAI', 'KNN'],\n",
        "                                                    auto_stack=True)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Warning: path already exists! This predictor may overwrite an existing predictor! path=\"agModels-predict\"\n",
            "Presets specified: ['best_quality']\n",
            "Beginning AutoGluon training ...\n",
            "AutoGluon will save models to \"agModels-predict/\"\n",
            "AutoGluon Version:  0.2.0\n",
            "Train Data Rows:    19229\n",
            "Train Data Columns: 95\n",
            "Preprocessing data ...\n",
            "AutoGluon infers your prediction problem is: 'regression' (because dtype of label-column == float and many unique label-values observed).\n",
            "\tLabel info (max, min, mean, stddev): (308906.0, 1.0, 17114.46565, 18280.79671)\n",
            "\tIf 'regression' is not the correct problem_type, please manually specify the problem_type argument in fit() (You may specify problem_type as one of: ['binary', 'multiclass', 'regression'])\n",
            "Using Feature Generators to preprocess the data ...\n",
            "Fitting AutoMLPipelineFeatureGenerator...\n",
            "\tAvailable Memory:                    12446.8 MB\n",
            "\tTrain Data (Original)  Memory Usage: 14.61 MB (0.1% of available memory)\n",
            "\tInferring data type of each feature based on column values. Set feature_metadata_in to manually specify special dtypes of the features.\n",
            "\tStage 1 Generators:\n",
            "\t\tFitting AsTypeFeatureGenerator...\n",
            "\tStage 2 Generators:\n",
            "\t\tFitting FillNaFeatureGenerator...\n",
            "\tStage 3 Generators:\n",
            "\t\tFitting IdentityFeatureGenerator...\n",
            "\tStage 4 Generators:\n",
            "\t\tFitting DropUniqueFeatureGenerator...\n",
            "\tTypes of features in original data (raw dtype, special dtypes):\n",
            "\t\t('float', []) : 86 | ['Levy', 'Category', 'Engine volume', 'Cylinders', 'Cylinders_cb', ...]\n",
            "\t\t('int', [])   :  9 | ['Prod. year', 'Leather interior', 'Fuel type', 'Mileage', 'Gear box type', ...]\n",
            "\tTypes of features in processed data (raw dtype, special dtypes):\n",
            "\t\t('float', []) : 86 | ['Levy', 'Category', 'Engine volume', 'Cylinders', 'Cylinders_cb', ...]\n",
            "\t\t('int', [])   :  9 | ['Prod. year', 'Leather interior', 'Fuel type', 'Mileage', 'Gear box type', ...]\n",
            "\t0.3s = Fit runtime\n",
            "\t95 features in original data used to generate 95 features in processed data.\n",
            "\tTrain Data (Processed) Memory Usage: 14.61 MB (0.1% of available memory)\n",
            "Data preprocessing and feature engineering runtime = 0.36s ...\n",
            "AutoGluon will gauge predictive performance using evaluation metric: 'r2'\n",
            "\tTo change this, specify the eval_metric argument of fit()\n",
            "Excluded Model Types: ['NN', 'FASTAI', 'KNN']\n",
            "\tFound 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'FASTAI' model in hyperparameters, but 'FASTAI' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "Fitting model: LightGBMXT_BAG_L1 ...\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's l2: 5.08862e+07\ttrain_set's r2: 0.84819\tvalid_set's l2: 1.04435e+08\tvalid_set's r2: 0.678587\n",
            "[1000]\ttrain_set's l2: 5.16822e+07\ttrain_set's r2: 0.846238\tvalid_set's l2: 9.13965e+07\tvalid_set's r2: 0.711053\n",
            "[1000]\ttrain_set's l2: 5.18448e+07\ttrain_set's r2: 0.844957\tvalid_set's l2: 8.45575e+07\tvalid_set's r2: 0.745332\n",
            "[1000]\ttrain_set's l2: 5.12029e+07\ttrain_set's r2: 0.847805\tvalid_set's l2: 8.82051e+07\tvalid_set's r2: 0.718853\n",
            "[1000]\ttrain_set's l2: 5.12155e+07\ttrain_set's r2: 0.847628\tvalid_set's l2: 9.2602e+07\tvalid_set's r2: 0.707509\n",
            "[2000]\ttrain_set's l2: 3.74272e+07\ttrain_set's r2: 0.88865\tvalid_set's l2: 9.11647e+07\tvalid_set's r2: 0.71205\n",
            "[1000]\ttrain_set's l2: 5.05175e+07\ttrain_set's r2: 0.84737\tvalid_set's l2: 1.21611e+08\tvalid_set's r2: 0.664806\n",
            "[2000]\ttrain_set's l2: 3.74133e+07\ttrain_set's r2: 0.886962\tvalid_set's l2: 1.18789e+08\tvalid_set's r2: 0.672583\n",
            "[3000]\ttrain_set's l2: 3.07815e+07\ttrain_set's r2: 0.906999\tvalid_set's l2: 1.18158e+08\tvalid_set's r2: 0.674322\n",
            "[4000]\ttrain_set's l2: 2.63605e+07\ttrain_set's r2: 0.920356\tvalid_set's l2: 1.17344e+08\tvalid_set's r2: 0.676566\n",
            "[5000]\ttrain_set's l2: 2.32783e+07\ttrain_set's r2: 0.929669\tvalid_set's l2: 1.17346e+08\tvalid_set's r2: 0.676562\n",
            "[1000]\ttrain_set's l2: 4.99707e+07\ttrain_set's r2: 0.848829\tvalid_set's l2: 1.24794e+08\tvalid_set's r2: 0.659439\n",
            "[1000]\ttrain_set's l2: 5.08053e+07\ttrain_set's r2: 0.849191\tvalid_set's l2: 8.78342e+07\tvalid_set's r2: 0.716418\n",
            "[2000]\ttrain_set's l2: 3.75774e+07\ttrain_set's r2: 0.888456\tvalid_set's l2: 8.44444e+07\tvalid_set's r2: 0.727363\n",
            "[3000]\ttrain_set's l2: 3.0578e+07\ttrain_set's r2: 0.909233\tvalid_set's l2: 8.26408e+07\tvalid_set's r2: 0.733186\n",
            "[4000]\ttrain_set's l2: 2.61347e+07\ttrain_set's r2: 0.922422\tvalid_set's l2: 8.23601e+07\tvalid_set's r2: 0.734092\n",
            "[5000]\ttrain_set's l2: 2.29911e+07\ttrain_set's r2: 0.931754\tvalid_set's l2: 8.26383e+07\tvalid_set's r2: 0.733194\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\t0.6912\t = Validation r2 score\n",
            "\t159.43s\t = Training runtime\n",
            "\t2.23s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L1 ...\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's l2: 3.15194e+07\ttrain_set's r2: 0.904094\tvalid_set's l2: 1.32833e+08\tvalid_set's r2: 0.653955\n",
            "[1000]\ttrain_set's l2: 3.2325e+07\ttrain_set's r2: 0.903332\tvalid_set's l2: 7.42375e+07\tvalid_set's r2: 0.776413\n",
            "[1000]\ttrain_set's l2: 3.22522e+07\ttrain_set's r2: 0.904134\tvalid_set's l2: 7.73692e+07\tvalid_set's r2: 0.753393\n",
            "[1000]\ttrain_set's l2: 3.22485e+07\ttrain_set's r2: 0.904057\tvalid_set's l2: 8.51876e+07\tvalid_set's r2: 0.730928\n",
            "[2000]\ttrain_set's l2: 2.05593e+07\ttrain_set's r2: 0.938834\tvalid_set's l2: 8.44631e+07\tvalid_set's r2: 0.733217\n",
            "[1000]\ttrain_set's l2: 3.1378e+07\ttrain_set's r2: 0.905197\tvalid_set's l2: 1.08584e+08\tvalid_set's r2: 0.700713\n",
            "[2000]\ttrain_set's l2: 2.07587e+07\ttrain_set's r2: 0.937281\tvalid_set's l2: 1.06993e+08\tvalid_set's r2: 0.705098\n",
            "[3000]\ttrain_set's l2: 1.5632e+07\ttrain_set's r2: 0.952771\tvalid_set's l2: 1.05441e+08\tvalid_set's r2: 0.709373\n",
            "[4000]\ttrain_set's l2: 1.25926e+07\ttrain_set's r2: 0.961954\tvalid_set's l2: 1.05523e+08\tvalid_set's r2: 0.709147\n",
            "[1000]\ttrain_set's l2: 3.22129e+07\ttrain_set's r2: 0.90438\tvalid_set's l2: 7.07426e+07\tvalid_set's r2: 0.7716\n",
            "[2000]\ttrain_set's l2: 2.10017e+07\ttrain_set's r2: 0.937659\tvalid_set's l2: 6.88267e+07\tvalid_set's r2: 0.777787\n",
            "[3000]\ttrain_set's l2: 1.59592e+07\ttrain_set's r2: 0.952627\tvalid_set's l2: 6.8223e+07\tvalid_set's r2: 0.779735\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\t0.7243\t = Validation r2 score\n",
            "\t137.26s\t = Training runtime\n",
            "\t1.79s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L1 ...\n",
            "\t0.7027\t = Validation r2 score\n",
            "\t57.98s\t = Training runtime\n",
            "\t1.81s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L1 ...\n",
            "\t0.7168\t = Validation r2 score\n",
            "\t214.53s\t = Training runtime\n",
            "\t0.08s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L1 ...\n",
            "\t0.7108\t = Validation r2 score\n",
            "\t34.55s\t = Training runtime\n",
            "\t1.82s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L1 ...\n",
            "\t0.7214\t = Validation r2 score\n",
            "\t355.99s\t = Training runtime\n",
            "\t0.95s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L1 ...\n"
          ],
          "name": "stderr"
        },
        {
          "output_type": "stream",
          "text": [
            "[1000]\ttrain_set's l2: 1.12294e+07\ttrain_set's r2: 0.966499\tvalid_set's l2: 8.6596e+07\tvalid_set's r2: 0.733489\n",
            "[1000]\ttrain_set's l2: 1.09977e+07\ttrain_set's r2: 0.967281\tvalid_set's l2: 8.26551e+07\tvalid_set's r2: 0.738928\n",
            "[1000]\ttrain_set's l2: 1.11274e+07\ttrain_set's r2: 0.966381\tvalid_set's l2: 1.08102e+08\tvalid_set's r2: 0.702038\n",
            "[1000]\ttrain_set's l2: 1.13117e+07\ttrain_set's r2: 0.966423\tvalid_set's l2: 7.00345e+07\tvalid_set's r2: 0.773886\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "stream",
          "text": [
            "\t0.7178\t = Validation r2 score\n",
            "\t99.64s\t = Training runtime\n",
            "\t1.31s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L2 ...\n",
            "\t0.7392\t = Validation r2 score\n",
            "\t1.37s\t = Training runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "Excluded Model Types: ['NN', 'FASTAI', 'KNN']\n",
            "\tFound 'NN' model in hyperparameters, but 'NN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'FASTAI' model in hyperparameters, but 'FASTAI' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "\tFound 'KNN' model in hyperparameters, but 'KNN' is present in `excluded_model_types` and will be removed.\n",
            "Fitting model: LightGBMXT_BAG_L2 ...\n",
            "\t0.7249\t = Validation r2 score\n",
            "\t29.7s\t = Training runtime\n",
            "\t0.3s\t = Validation runtime\n",
            "Fitting model: LightGBM_BAG_L2 ...\n",
            "\t0.7256\t = Validation r2 score\n",
            "\t23.47s\t = Training runtime\n",
            "\t0.16s\t = Validation runtime\n",
            "Fitting model: RandomForestMSE_BAG_L2 ...\n",
            "\t0.723\t = Validation r2 score\n",
            "\t130.46s\t = Training runtime\n",
            "\t1.88s\t = Validation runtime\n",
            "Fitting model: CatBoost_BAG_L2 ...\n",
            "\t0.735\t = Validation r2 score\n",
            "\t21.3s\t = Training runtime\n",
            "\t0.04s\t = Validation runtime\n",
            "Fitting model: ExtraTreesMSE_BAG_L2 ...\n",
            "\t0.734\t = Validation r2 score\n",
            "\t54.47s\t = Training runtime\n",
            "\t1.91s\t = Validation runtime\n",
            "Fitting model: XGBoost_BAG_L2 ...\n",
            "\t0.7159\t = Validation r2 score\n",
            "\t124.51s\t = Training runtime\n",
            "\t0.77s\t = Validation runtime\n",
            "Fitting model: LightGBMLarge_BAG_L2 ...\n",
            "\t0.7208\t = Validation r2 score\n",
            "\t36.38s\t = Training runtime\n",
            "\t0.2s\t = Validation runtime\n",
            "Fitting model: WeightedEnsemble_L3 ...\n",
            "\t0.7412\t = Validation r2 score\n",
            "\t1.36s\t = Training runtime\n",
            "\t0.0s\t = Validation runtime\n",
            "AutoGluon training complete, total runtime = 1517.43s ...\n",
            "TabularPredictor saved. To load, use: predictor = TabularPredictor.load(\"agModels-predict/\")\n"
          ],
          "name": "stderr"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gFyR9zeR_svZ",
        "outputId": "03825ce0-a7ee-44c7-84ac-1eca506759c8"
      },
      "source": [
        "print(\"AutoGluon infers problem type is: \", predictor.problem_type)\n",
        "print(\"AutoGluon identified the following types of features:\")\n",
        "print(predictor.feature_metadata)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "AutoGluon infers problem type is:  regression\n",
            "AutoGluon identified the following types of features:\n",
            "('float', []) : 86 | ['Levy', 'Category', 'Engine volume', 'Cylinders', 'Cylinders_cb', ...]\n",
            "('int', [])   :  9 | ['Prod. year', 'Leather interior', 'Fuel type', 'Mileage', 'Gear box type', ...]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Pn4SKsN-_hkW",
        "outputId": "05ac36e0-f9fe-4ee8-ddfe-804184fc81cd"
      },
      "source": [
        "results = predictor.fit_summary()"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "*** Summary of fit() ***\n",
            "Estimated performance of each model:\n",
            "                     model  score_val  pred_time_val     fit_time  pred_time_val_marginal  fit_time_marginal  stack_level  can_infer  fit_order\n",
            "0      WeightedEnsemble_L3   0.741196      12.240751  1166.213134                0.002116           1.358785            3       True         16\n",
            "1      WeightedEnsemble_L2   0.739180       7.761212   901.318407                0.002100           1.365046            2       True          8\n",
            "2          CatBoost_BAG_L2   0.735000      10.026905  1080.683011                0.035208          21.300593            2       True         12\n",
            "3     ExtraTreesMSE_BAG_L2   0.734025      11.904963  1113.851896                1.913266          54.469478            2       True         13\n",
            "4          LightGBM_BAG_L2   0.725621      10.151747  1082.854987                0.160049          23.472568            2       True         10\n",
            "5        LightGBMXT_BAG_L2   0.724928      10.290162  1089.084278                0.298464          29.701860            2       True          9\n",
            "6          LightGBM_BAG_L1   0.724305       1.788998   137.261818                1.788998         137.261818            1       True          2\n",
            "7   RandomForestMSE_BAG_L2   0.723045      11.868083  1189.845041                1.876385         130.462623            2       True         11\n",
            "8           XGBoost_BAG_L1   0.721431       0.953700   355.987681                0.953700         355.987681            1       True          6\n",
            "9     LightGBMLarge_BAG_L2   0.720753      10.191156  1095.758404                0.199459          36.375986            2       True         15\n",
            "10    LightGBMLarge_BAG_L1   0.717796       1.308111    99.644713                1.308111          99.644713            1       True          7\n",
            "11         CatBoost_BAG_L1   0.716791       0.080181   214.527202                0.080181         214.527202            1       True          4\n",
            "12          XGBoost_BAG_L2   0.715913      10.766404  1183.894189                0.774707         124.511771            2       True         14\n",
            "13    ExtraTreesMSE_BAG_L1   0.710813       1.821428    34.554889                1.821428          34.554889            1       True          5\n",
            "14  RandomForestMSE_BAG_L1   0.702715       1.806694    57.977058                1.806694          57.977058            1       True          3\n",
            "15       LightGBMXT_BAG_L1   0.691207       2.232585   159.429058                2.232585         159.429058            1       True          1\n",
            "Number of models trained: 16\n",
            "Types of models trained:\n",
            "{'StackerEnsembleModel_RF', 'WeightedEnsembleModel', 'StackerEnsembleModel_XGBoost', 'StackerEnsembleModel_CatBoost', 'StackerEnsembleModel_XT', 'StackerEnsembleModel_LGB'}\n",
            "Bagging used: True  (with 10 folds)\n",
            "Multi-layer stack-ensembling used: True  (with 3 levels)\n",
            "Feature Metadata (Processed):\n",
            "(raw dtype, special dtypes):\n",
            "('float', []) : 86 | ['Levy', 'Category', 'Engine volume', 'Cylinders', 'Cylinders_cb', ...]\n",
            "('int', [])   :  9 | ['Prod. year', 'Leather interior', 'Fuel type', 'Mileage', 'Gear box type', ...]\n",
            "Plot summary of models saved to file: agModels-predict/SummaryOfModels.html\n",
            "*** End of fit() summary ***\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 562
        },
        "id": "fsU78DGi9bEl",
        "outputId": "c22e03e6-718f-4d41-bf68-8040b3bf2e36"
      },
      "source": [
        "predictor.leaderboard(train_data, silent=True,)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
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              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>model</th>\n",
              "      <th>score_test</th>\n",
              "      <th>score_val</th>\n",
              "      <th>pred_time_test</th>\n",
              "      <th>pred_time_val</th>\n",
              "      <th>fit_time</th>\n",
              "      <th>pred_time_test_marginal</th>\n",
              "      <th>pred_time_val_marginal</th>\n",
              "      <th>fit_time_marginal</th>\n",
              "      <th>stack_level</th>\n",
              "      <th>can_infer</th>\n",
              "      <th>fit_order</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>ExtraTreesMSE_BAG_L1</td>\n",
              "      <td>0.951816</td>\n",
              "      <td>0.710813</td>\n",
              "      <td>11.773576</td>\n",
              "      <td>1.821428</td>\n",
              "      <td>34.554889</td>\n",
              "      <td>11.773576</td>\n",
              "      <td>1.821428</td>\n",
              "      <td>34.554889</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>RandomForestMSE_BAG_L1</td>\n",
              "      <td>0.950733</td>\n",
              "      <td>0.702715</td>\n",
              "      <td>11.379278</td>\n",
              "      <td>1.806694</td>\n",
              "      <td>57.977058</td>\n",
              "      <td>11.379278</td>\n",
              "      <td>1.806694</td>\n",
              "      <td>57.977058</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>3</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>LightGBMLarge_BAG_L1</td>\n",
              "      <td>0.941654</td>\n",
              "      <td>0.717796</td>\n",
              "      <td>12.672306</td>\n",
              "      <td>1.308111</td>\n",
              "      <td>99.644713</td>\n",
              "      <td>12.672306</td>\n",
              "      <td>1.308111</td>\n",
              "      <td>99.644713</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>7</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>WeightedEnsemble_L2</td>\n",
              "      <td>0.933433</td>\n",
              "      <td>0.739180</td>\n",
              "      <td>67.636794</td>\n",
              "      <td>7.761212</td>\n",
              "      <td>901.318407</td>\n",
              "      <td>0.006021</td>\n",
              "      <td>0.002100</td>\n",
              "      <td>1.365046</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>8</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>RandomForestMSE_BAG_L2</td>\n",
              "      <td>0.928190</td>\n",
              "      <td>0.723045</td>\n",
              "      <td>93.683156</td>\n",
              "      <td>11.868083</td>\n",
              "      <td>1189.845041</td>\n",
              "      <td>2.024987</td>\n",
              "      <td>1.876385</td>\n",
              "      <td>130.462623</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>11</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>XGBoost_BAG_L2</td>\n",
              "      <td>0.924183</td>\n",
              "      <td>0.715913</td>\n",
              "      <td>99.865195</td>\n",
              "      <td>10.766404</td>\n",
              "      <td>1183.894189</td>\n",
              "      <td>8.207026</td>\n",
              "      <td>0.774707</td>\n",
              "      <td>124.511771</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>14</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>ExtraTreesMSE_BAG_L2</td>\n",
              "      <td>0.922771</td>\n",
              "      <td>0.734025</td>\n",
              "      <td>93.704950</td>\n",
              "      <td>11.904963</td>\n",
              "      <td>1113.851896</td>\n",
              "      <td>2.046781</td>\n",
              "      <td>1.913266</td>\n",
              "      <td>54.469478</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>13</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>XGBoost_BAG_L1</td>\n",
              "      <td>0.918588</td>\n",
              "      <td>0.721431</td>\n",
              "      <td>10.644222</td>\n",
              "      <td>0.953700</td>\n",
              "      <td>355.987681</td>\n",
              "      <td>10.644222</td>\n",
              "      <td>0.953700</td>\n",
              "      <td>355.987681</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>6</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>WeightedEnsemble_L3</td>\n",
              "      <td>0.917171</td>\n",
              "      <td>0.741196</td>\n",
              "      <td>95.861156</td>\n",
              "      <td>12.240751</td>\n",
              "      <td>1166.213134</td>\n",
              "      <td>0.011286</td>\n",
              "      <td>0.002116</td>\n",
              "      <td>1.358785</td>\n",
              "      <td>3</td>\n",
              "      <td>True</td>\n",
              "      <td>16</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>LightGBMLarge_BAG_L2</td>\n",
              "      <td>0.910860</td>\n",
              "      <td>0.720753</td>\n",
              "      <td>93.641155</td>\n",
              "      <td>10.191156</td>\n",
              "      <td>1095.758404</td>\n",
              "      <td>1.982985</td>\n",
              "      <td>0.199459</td>\n",
              "      <td>36.375986</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>15</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>10</th>\n",
              "      <td>LightGBM_BAG_L2</td>\n",
              "      <td>0.910333</td>\n",
              "      <td>0.725621</td>\n",
              "      <td>93.149377</td>\n",
              "      <td>10.151747</td>\n",
              "      <td>1082.854987</td>\n",
              "      <td>1.491207</td>\n",
              "      <td>0.160049</td>\n",
              "      <td>23.472568</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>10</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>11</th>\n",
              "      <td>LightGBM_BAG_L1</td>\n",
              "      <td>0.909696</td>\n",
              "      <td>0.724305</td>\n",
              "      <td>20.160880</td>\n",
              "      <td>1.788998</td>\n",
              "      <td>137.261818</td>\n",
              "      <td>20.160880</td>\n",
              "      <td>1.788998</td>\n",
              "      <td>137.261818</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>CatBoost_BAG_L2</td>\n",
              "      <td>0.904196</td>\n",
              "      <td>0.735000</td>\n",
              "      <td>91.796186</td>\n",
              "      <td>10.026905</td>\n",
              "      <td>1080.683011</td>\n",
              "      <td>0.138017</td>\n",
              "      <td>0.035208</td>\n",
              "      <td>21.300593</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>12</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>13</th>\n",
              "      <td>LightGBMXT_BAG_L2</td>\n",
              "      <td>0.890435</td>\n",
              "      <td>0.724928</td>\n",
              "      <td>93.665072</td>\n",
              "      <td>10.290162</td>\n",
              "      <td>1089.084278</td>\n",
              "      <td>2.006903</td>\n",
              "      <td>0.298464</td>\n",
              "      <td>29.701860</td>\n",
              "      <td>2</td>\n",
              "      <td>True</td>\n",
              "      <td>9</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>CatBoost_BAG_L1</td>\n",
              "      <td>0.873856</td>\n",
              "      <td>0.716791</td>\n",
              "      <td>1.000510</td>\n",
              "      <td>0.080181</td>\n",
              "      <td>214.527202</td>\n",
              "      <td>1.000510</td>\n",
              "      <td>0.080181</td>\n",
              "      <td>214.527202</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>4</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>15</th>\n",
              "      <td>LightGBMXT_BAG_L1</td>\n",
              "      <td>0.851222</td>\n",
              "      <td>0.691207</td>\n",
              "      <td>24.027397</td>\n",
              "      <td>2.232585</td>\n",
              "      <td>159.429058</td>\n",
              "      <td>24.027397</td>\n",
              "      <td>2.232585</td>\n",
              "      <td>159.429058</td>\n",
              "      <td>1</td>\n",
              "      <td>True</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "                     model  score_test  ...  can_infer  fit_order\n",
              "0     ExtraTreesMSE_BAG_L1    0.951816  ...       True          5\n",
              "1   RandomForestMSE_BAG_L1    0.950733  ...       True          3\n",
              "2     LightGBMLarge_BAG_L1    0.941654  ...       True          7\n",
              "3      WeightedEnsemble_L2    0.933433  ...       True          8\n",
              "4   RandomForestMSE_BAG_L2    0.928190  ...       True         11\n",
              "5           XGBoost_BAG_L2    0.924183  ...       True         14\n",
              "6     ExtraTreesMSE_BAG_L2    0.922771  ...       True         13\n",
              "7           XGBoost_BAG_L1    0.918588  ...       True          6\n",
              "8      WeightedEnsemble_L3    0.917171  ...       True         16\n",
              "9     LightGBMLarge_BAG_L2    0.910860  ...       True         15\n",
              "10         LightGBM_BAG_L2    0.910333  ...       True         10\n",
              "11         LightGBM_BAG_L1    0.909696  ...       True          2\n",
              "12         CatBoost_BAG_L2    0.904196  ...       True         12\n",
              "13       LightGBMXT_BAG_L2    0.890435  ...       True          9\n",
              "14         CatBoost_BAG_L1    0.873856  ...       True          4\n",
              "15       LightGBMXT_BAG_L1    0.851222  ...       True          1\n",
              "\n",
              "[16 rows x 12 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 26
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "luS0JzoK9bCO",
        "outputId": "21bfc6d8-8f4b-45a8-bac4-8238a3aea9fd"
      },
      "source": [
        "predictor = TabularPredictor.load(save_path) \n",
        "y_pred = predictor.predict(test_data,  model= predictor.get_model_best(), as_pandas=True)\n",
        "subx = pd.DataFrame()\n",
        "subx['Price'] = y_pred\n",
        "subx.to_csv('out2.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Predictions:  \n",
            " 0       15303.309570\n",
            "1       18110.746094\n",
            "2        7216.854980\n",
            "3        4291.127930\n",
            "4        4139.128906\n",
            "            ...     \n",
            "8240     7292.358887\n",
            "8241    18826.531250\n",
            "8242     1103.352905\n",
            "8243    46510.843750\n",
            "8244     1708.250000\n",
            "Name: Price, Length: 8245, dtype: float32\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "wu2sMP4cP0SN"
      },
      "source": [
        "predictor = TabularPredictor.load(save_path) \n",
        "y_pred = predictor.predict(test_data,  model= 'RandomForestMSE_BAG_L2', as_pandas=True)\n",
        "subx = pd.DataFrame()\n",
        "subx['Price'] = y_pred\n",
        "subx.to_csv('out3.csv', index=False)"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "n9RBK-vvRVXl"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "aPKXLx8oRVZ3"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}