tertiary_proteins.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": 7,
      "metadata": {
        "id": "H_-HYyk2NpDA"
      },
      "outputs": [],
      "source": [
        "import seaborn as sns\n",
        "import matplotlib.pyplot as plt\n",
        "import numpy as np\n",
        "import pandas as pd\n",
        "\n",
        "df = pd.read_csv('pc_tertiary.csv')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "x = df[['RMSD', 'F1', 'F2', 'F3', 'F5', 'F6', 'F7', 'F8', 'F9']]\n",
        "y = df['F4']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "model = ElasticNet(alpha=0.04984192943467643, l1_ratio=0.09834971704288592, max_iter=999999999)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "print('Mean Absolute Error:', mae)\n",
        "print('R2 Score:', r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "w3SfjGy9K9ip",
        "outputId": "c8c8bf1a-501c-4f17-eb54-36dfe4aa00f8"
      },
      "execution_count": 90,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 10.010915639161436\n",
            "R2 Score: 0.933944500292958\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from sklearn.linear_model import ElasticNet\n",
        "from hyperopt import hp, fmin, tpe, Trials\n",
        "\n",
        "# Define the search space for hyperparameters\n",
        "space = {\n",
        "    'alpha': hp.loguniform('alpha', -2, 0.1),  # Narrowed down the search space\n",
        "    'l1_ratio': hp.uniform('l1_ratio', 0, 0.15)\n",
        "}\n",
        "\n",
        "# Define the objective function to minimize\n",
        "def objective(params):\n",
        "    clf = ElasticNet(alpha=params['alpha'], l1_ratio=params['l1_ratio'])\n",
        "    clf.fit(x_train, y_train)\n",
        "    mse = clf.score(x_test, y_test)\n",
        "    return -mse  # Note: we're minimizing the negative MSE\n",
        "\n",
        "# Perform Bayesian optimization\n",
        "trials = Trials()\n",
        "best = fmin(objective, space, algo=tpe.suggest, trials=trials, max_evals=100)\n",
        "\n",
        "# Print the best hyperparameters and corresponding MSE\n",
        "print('Best hyperparameters:', best)\n",
        "print('Best MSE:', -trials.best_trial['result']['loss'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "z4IrH83vYp1T",
        "outputId": "9dd6a294-25a6-4234-b5b3-f91562f0ca8a"
      },
      "execution_count": 86,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "  1%|          | 1/100 [00:00<00:48,  2.05trial/s, best loss: -0.9338463439718657]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.767e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  2%|▏         | 2/100 [00:01<00:50,  1.95trial/s, best loss: -0.9338692124803947]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.759e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  3%|▎         | 3/100 [00:01<00:50,  1.90trial/s, best loss: -0.9338913467586138]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.749e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  4%|▍         | 4/100 [00:02<00:52,  1.83trial/s, best loss: -0.9339208509794703]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  5%|▌         | 5/100 [00:02<00:52,  1.80trial/s, best loss: -0.9339208509794703]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.765e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  6%|▌         | 6/100 [00:03<00:52,  1.80trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  7%|▋         | 7/100 [00:05<01:39,  1.07s/trial, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.771e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  8%|▊         | 8/100 [00:07<02:14,  1.47s/trial, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.748e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r  9%|▉         | 9/100 [00:08<01:46,  1.18s/trial, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.749e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 10%|█         | 10/100 [00:08<01:27,  1.03trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 11%|█         | 11/100 [00:09<01:15,  1.17trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.775e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 12%|█▏        | 12/100 [00:09<01:06,  1.32trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.752e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 13%|█▎        | 13/100 [00:10<01:00,  1.43trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.766e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 14%|█▍        | 14/100 [00:10<00:54,  1.57trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.745e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 15%|█▌        | 15/100 [00:11<00:51,  1.66trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.756e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 16%|█▌        | 16/100 [00:11<00:48,  1.73trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.754e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 17%|█▋        | 17/100 [00:12<00:47,  1.75trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 18%|█▊        | 18/100 [00:13<00:46,  1.76trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.737e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 19%|█▉        | 19/100 [00:13<00:46,  1.75trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.763e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 20%|██        | 20/100 [00:14<00:45,  1.78trial/s, best loss: -0.9339227954716919]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.732e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 21%|██        | 21/100 [00:14<00:43,  1.80trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 22%|██▏       | 22/100 [00:15<00:43,  1.78trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 23%|██▎       | 23/100 [00:15<00:43,  1.78trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 24%|██▍       | 24/100 [00:16<00:44,  1.73trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.738e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 25%|██▌       | 25/100 [00:17<00:42,  1.78trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 26%|██▌       | 26/100 [00:17<00:41,  1.78trial/s, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 27%|██▋       | 27/100 [00:20<01:21,  1.12s/trial, best loss: -0.9339252451679778]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.737e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 28%|██▊       | 28/100 [00:21<01:38,  1.37s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 29%|██▉       | 29/100 [00:22<01:19,  1.12s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 30%|███       | 30/100 [00:23<01:06,  1.06trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 31%|███       | 31/100 [00:23<00:56,  1.21trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.735e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 32%|███▏      | 32/100 [00:24<00:50,  1.34trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 33%|███▎      | 33/100 [00:24<00:45,  1.46trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.740e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 34%|███▍      | 34/100 [00:25<00:42,  1.55trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 35%|███▌      | 35/100 [00:25<00:39,  1.63trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.734e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 36%|███▌      | 36/100 [00:26<00:37,  1.69trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.742e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 37%|███▋      | 37/100 [00:26<00:36,  1.72trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 38%|███▊      | 38/100 [00:27<00:35,  1.76trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 39%|███▉      | 39/100 [00:27<00:34,  1.77trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 40%|████      | 40/100 [00:28<00:33,  1.80trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.740e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 41%|████      | 41/100 [00:29<00:32,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 42%|████▏     | 42/100 [00:29<00:31,  1.82trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 43%|████▎     | 43/100 [00:30<00:31,  1.80trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.747e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 44%|████▍     | 44/100 [00:30<00:31,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.780e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 45%|████▌     | 45/100 [00:31<00:30,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 46%|████▌     | 46/100 [00:31<00:29,  1.83trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.732e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 47%|████▋     | 47/100 [00:34<01:02,  1.17s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.758e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 48%|████▊     | 48/100 [00:36<01:11,  1.38s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 49%|████▉     | 49/100 [00:36<00:57,  1.14s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.738e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 50%|█████     | 50/100 [00:37<00:48,  1.03trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 51%|█████     | 51/100 [00:37<00:41,  1.19trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.734e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 52%|█████▏    | 52/100 [00:38<00:36,  1.32trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.746e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 53%|█████▎    | 53/100 [00:39<00:32,  1.45trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.736e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 54%|█████▍    | 54/100 [00:39<00:29,  1.54trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.750e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 55%|█████▌    | 55/100 [00:40<00:27,  1.62trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.771e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 56%|█████▌    | 56/100 [00:40<00:26,  1.65trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 57%|█████▋    | 57/100 [00:41<00:24,  1.74trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.756e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 58%|█████▊    | 58/100 [00:41<00:24,  1.75trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.739e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 59%|█████▉    | 59/100 [00:42<00:23,  1.78trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 60%|██████    | 60/100 [00:42<00:22,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.743e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 61%|██████    | 61/100 [00:43<00:21,  1.80trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 62%|██████▏   | 62/100 [00:44<00:21,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 63%|██████▎   | 63/100 [00:44<00:20,  1.81trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.732e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 64%|██████▍   | 64/100 [00:45<00:19,  1.82trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 65%|██████▌   | 65/100 [00:45<00:19,  1.81trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.736e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 66%|██████▌   | 66/100 [00:47<00:27,  1.22trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 67%|██████▋   | 67/100 [00:50<00:48,  1.46s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 68%|██████▊   | 68/100 [00:50<00:38,  1.20s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 69%|██████▉   | 69/100 [00:51<00:31,  1.02s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 70%|███████   | 70/100 [00:51<00:26,  1.14trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 71%|███████   | 71/100 [00:52<00:22,  1.28trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 72%|███████▏  | 72/100 [00:52<00:19,  1.40trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 73%|███████▎  | 73/100 [00:53<00:18,  1.50trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 74%|███████▍  | 74/100 [00:54<00:16,  1.59trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 75%|███████▌  | 75/100 [00:54<00:15,  1.66trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 76%|███████▌  | 76/100 [00:55<00:14,  1.69trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 77%|███████▋  | 77/100 [00:55<00:13,  1.72trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 78%|███████▊  | 78/100 [00:56<00:12,  1.72trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.734e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 79%|███████▉  | 79/100 [00:56<00:11,  1.76trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 80%|████████  | 80/100 [00:57<00:11,  1.72trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.744e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 81%|████████  | 81/100 [00:57<00:10,  1.76trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.732e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 82%|████████▏ | 82/100 [00:58<00:10,  1.74trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.765e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 83%|████████▎ | 83/100 [00:59<00:09,  1.79trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 84%|████████▍ | 84/100 [00:59<00:09,  1.73trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.780e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 85%|████████▌ | 85/100 [01:00<00:10,  1.50trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 86%|████████▌ | 86/100 [01:02<00:14,  1.01s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.740e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 87%|████████▋ | 87/100 [01:04<00:16,  1.30s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 88%|████████▊ | 88/100 [01:06<00:17,  1.48s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.730e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 89%|████████▉ | 89/100 [01:07<00:15,  1.37s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.734e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 90%|█████████ | 90/100 [01:08<00:13,  1.35s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.735e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 91%|█████████ | 91/100 [01:09<00:11,  1.31s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.761e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 92%|█████████▏| 92/100 [01:11<00:10,  1.30s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.728e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 93%|█████████▎| 93/100 [01:11<00:07,  1.06s/trial, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.729e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 94%|█████████▍| 94/100 [01:12<00:05,  1.09trial/s, best loss: -0.9339260505584175]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.733e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 95%|█████████▌| 95/100 [01:12<00:04,  1.24trial/s, best loss: -0.9339261310835789]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.726e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 96%|█████████▌| 96/100 [01:13<00:02,  1.35trial/s, best loss: -0.9339261310835789]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.727e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 97%|█████████▋| 97/100 [01:13<00:02,  1.44trial/s, best loss: -0.9339261310835789]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.752e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 98%|█████████▊| 98/100 [01:14<00:01,  1.52trial/s, best loss: -0.9339261310835789]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.737e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\r 99%|█████████▉| 99/100 [01:15<00:00,  1.59trial/s, best loss: -0.9339261310835789]"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.731e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "100%|██████████| 100/100 [01:15<00:00,  1.32trial/s, best loss: -0.9339261310835789]\n",
            "Best hyperparameters: {'alpha': 0.135585611226435, 'l1_ratio': 2.656170281849354e-05}\n",
            "Best MSE: 0.9339261310835789\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/sklearn/linear_model/_coordinate_descent.py:628: ConvergenceWarning: Objective did not converge. You might want to increase the number of iterations, check the scale of the features or consider increasing regularisation. Duality gap: 3.751e+06, tolerance: 1.126e+04\n",
            "  model = cd_fast.enet_coordinate_descent(\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.linear_model import ElasticNet\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.metrics import mean_absolute_error, r2_score\n",
        "\n",
        "x = df[['RMSD', 'F1', 'F2', 'F3', 'F5', 'F6', 'F7', 'F8', 'F9']]\n",
        "y = df['F4']\n",
        "\n",
        "x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=42)\n",
        "\n",
        "model = ElasticNet(alpha=0.135585611226435, l1_ratio=2.656170281849354e-05, max_iter=50000000)\n",
        "model.fit(x_train, y_train)\n",
        "\n",
        "y_pred = model.predict(x_test)\n",
        "\n",
        "mae = mean_absolute_error(y_test, y_pred)\n",
        "print('Mean Absolute Error:', mae)\n",
        "print('R2 Score:', r2_score(y_test, y_pred))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h9jwkKkBe3ZM",
        "outputId": "e0d8c3c4-a6e8-4d40-fffb-e9f195ea7926"
      },
      "execution_count": 88,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mean Absolute Error: 10.01063635295334\n",
            "R2 Score: 0.9339229934789638\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "sns.heatmap(df.corr(), annot=True, cmap='Blues')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 452
        },
        "id": "VFpy3-3UIprx",
        "outputId": "b5fc81f5-104a-45f6-8c2f-145efb6d3914"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<Axes: >"
            ]
          },
          "metadata": {},
          "execution_count": 16
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 2 Axes>"
            ],
            "image/png": 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kNq+M/LIKUgq1bDifjZudDe72NvX+DwU3D1Y5F2lpaURGRpo+79ixgzvvvBNnZ+PFfdq0aZw/f755Fd5klOZloVUX4NWqi6nMRmWPW3Ar8qovMLWpqqygMDUWr1adTWUSqRSvyC7kJVn/srdLO35lw8t3s/2Dp7m0cy2GqrpPDzKphFB3O85l1IQiDcC5jBIiPe2t/s4rdA9wJj6vjKcHhvDFpPYsuLUVQyMtOysNIZdKaOPjyJHqiMMVfUcTC+joX/9aKlfTI9iFYDc7TiZbXgHUzc6GfuFurD+dabU+gKKcTEqL8gluX7NwnNLOHt+wNqTHXryuOi1RWlRA/OkjdBw4un6bFu53WnUBJ1Z/Ss975iBT1H27Y2leFtriArxb1+hTqOxxD25Nbj369JUVFKTEmh0jkUrxbt2F3ATLx1TqtCQc3o69uzcqVw8AMqNPYjBUUVaUx59vP8ofr9yHtrgAR59g03E2Kntcg1uZnIDaXGkrz1pt5RnZhfzqtipMjcWgr8SzdY2No3cgKldPi/VWVVZQmBJLeVkJHuEdLNZprDeO/MRoPMI7UJAYjWdkZ7N6vNp0M9Vflp+FTl1gptNGZY9rUCsKEo115idGG8sCa67Vnq26IJFIKEi6bPH8S3LSyYo+YdJ5hZRdv9EmMpxUan7jBiA6u5RQNzuLdYW62RGdXWpWdiGrxl4CdPBxILuknFn9gnhvbCueGxxKZ19Hi/UBKGQS+gS7kFtabjGqeUOQSJtv+w9i1bCIra0tGk3NuPqhQ4fMIhW2traNWkRLp9Oh05kva2yo0iOpDnHezOjUxpuh0sHFrFzp4GLaV+eY0mIMVVWmcdkr2Dq6oM62Lg8gfOA4XAPCUdg5kJcQzblN3+GalU3ACPNQppNShkwqoUhj/kMs0lTg53T9r/71clQwvLUHf17I4Y9zWYS52zGtZwCVegN74i2fvyVc7GyQSyXk17pQ5JdWEOxu+aIFYK+UsXFWFAqZBL0B3t8aw5FEy987tqMPpeV6dl2qmzfSGEqL8gGwc3YxK7dzdqW0sPHnei3O7/sLha1dg5GOlux3BoOBYz8uJrTvGFyDIinNz6pjoy0uMNVd+7u0xYUW6y036at7THGWub6YvZs488cKKsu1OHoFMPjxt5DJjU+wpXmZYDBwcdsvdL1zJqX5ORxf/Smnf/sCrxeXIq22s3VwQXuNtlJaaKuS6rbSFhcglcnr5KPYOrqgVZuf49GV75N+9iAGQxU2Knu6TnmyTp1bX59BeUkRVVVVtBk1leA+Izn9W80QzBWUjjX/Y111O1uyuXJuOnUBilr9RCqTYWPnWKev7FnyPEWpcVRVVhDcZxRtRt9t2leYeAFN3AnkMhnFOvNhVLWuEm9Hy9cRJ1s5agv2TrbG242jUoatjYyRrTzYcCGbdeeyaOftwMw+AXy8N4mYq15nMDDMlQkdvLGVG3O7luxL4h9b1V0MizQJq5yLLl268P3337NgwQL27t1LVlYWQ4cONe2Pi4vDz6/hhCmABQsW8Prrr5uVybx7YuPbyxo5/wh2lRmse2GS6XO/mfNaUA20GjzB9LezXyhSuZzjaz7Db8hdpovojUSKcfhk9ckMABLzNQS62DKstYdVzsX1UqbTc983x1DZyOgZ4srTw8JJK9RwwkL0YlxnH7aez270OyYuHNjBXys+Nn2+4//eajbdDXFuzxbaRg01y+G4ouXK6FRL9ru4vRuo1GloM3yiWfmuj59DUv1UNuCR+TdUQ3CPwfi07oKmuIBLO9dyYMW7DJv9PjIbBfkpcVTpKynNz+bA1wtMWsryssiJPYt3m/pfWXCj6DjhIUL7jmbvZy+hLczj7B/L6TLxcTOb/rMWoNdpyU+6xIVNK7H3sDwkdiPped9zVOg0FKcncH7Dt8Tu+p3IoXdSoS3j/JqP6XPP083+nVfWQjqToWZnrNGBTy3SEeZuR/9QVzPn4khyERezSnG2lTO8lTsP9Qrgg92J9Q7bNq/Q/2bEobmwyrmYN28eY8aMYc2aNWRkZDB9+nR8fWt+EL///jv9+vVroAYjc+fONb3p7QpeA16wRso/hkbmyfDZNU8dVZXGJ21dSSEq55pQoTETPMxiHUp7JyRSaZ2nJq26EFsnV4vHNBa3oFZQpae8KAdb9xrHrlinR19lwFll7nA4q2wotJDM2VgKNJWkFpnnL6QVaekV7FzPEZYpLKugssqAm525Pjd7G/IbyN8wAKnVWeox2aWEuNsxLSqIE8lnzey6BDgT4m7HK+suWKjFMhFdo/ANr8k70FcY/9dlRYU4uLibysuKCvAKDq9z/PWQeuks+Rmp3PrEyxa1pBYY26Il+112zBnyEi/x+3N3mJXrKyvw79ibzuMfMOnTqs31adWFuASEWqxXYdJXWFdfrQiCQmWPQmWPo5c/7iGt+f3FqaSeOUhw90F4RXYk9dQ+Bj/+JrbOriYtNip7ygpqolbakkJcrtFWtZ/stepClNVtZevkSpW+knJNiVn0wqjXxew4WydXFHYOSKRSgqNGcfHP72kzYiq2zm6mOu3djblFTn4h6EoKid76M7aOLuhqtYdOXWiKqFzRolMXYuvkZmbj7G88N6WjK+Ul5nVU6fVUlKnrRGZUrp6oACefIAxVVZz+5TMiBk+gLC8TbUE2B756g8rHJpD464dknNwFwM6X7+TuP3ZaTAoHKNZW4qg0v7U4KuUm+xJdJfoqgym58wqZxTrCPcyjltrKKrSV5eSUlpNwqIwPxrWhi58jx1KLLX634ObB6nUujh8/zrZt2/Dx8WHSpElm+7t06UKvXteOPiiVSrPprMBNOyRikMjNpngZDAZsHV3Jvnwal+ofc4W2jPyky4T1tTxLRiq3wSUggpzLZ/DvGGWsp6qKnJjThPe/pUn6CtMTQCJBbmeep6CvMpCQV0YHXweOpRif6iVAex8Htl3nzAmAyzmldYZVfJ2U5JZYNw5aWWUgOlNNzxBX9sTkmfT1DHbll+Npja5HKgEbWd0njHGdfbiYoSam1thvQyhUdihUNRc3g8GAvbMbSRdOmpwJnaaUjPhougy7tdH1NsTZ3VvwDonEK8jcWbmipVCuM2lpqX7X5Y6HaT/2PtNnbVEe+5bNp8+0F/AMbYOdq4dRn5MrWZdP4RpQrU9TRl7SJSL6j7FYr0xug2tgBFmXTxPQqUZf1qXTRA5soH0Nxu2KE+FTnbNRpa/A0dMPg8FQPZRQhJ2rp6mtChrZVn612iqsuq1cAiKQyOTkXD6Nf2fjQ5Q6OxVNQY7FZNgrdRamxAKg11fUqdNElYGqygpcQ9qQE3PGNC0VIPvyKVP9dm7eKB1dyYk5bXImKrRlFCRfJqSfsZ3dQtpQoSmlMCUWl8AIAHJjz2AwGHANbniF5Sq9HoPBgINXAL2fXgxAUl4JEx5+llNpReh1Glrf+iDtA9zZXU+kMiG/jDZe9vwdl28qa+tlT0K+MSKhN0BSgQZvR/NZO16OijrDpFcjkUiQYMzX+kcQkYsmYfW7Rdq2bUvbtm0t7nv44Rs3/bMx2KsUhAd6mj6H+LvTqZU/BcVlpGQ2T8heIpEQMeg2ov9ajYOnH/Zu3pzf/AO2Tm74dexjstuz9GX8OkYRMcB4kYwcPIFjP36Ea2AErsGtiN39B5XlWoJ7Dzcdoy0uQKsuoCQ3HYDi9CTktirsXDxR2DuSlxhNftIlPCM6IVeqyE+K5sy65bh1GIDcwroEmy7m8Fi/IOJzy4jNK2NMW0+Ucim7q0ORj/ULoqCsgp+rhzhkUgkBzsaZJ3KpBFc7G4JdVWgr9WSpjU/Qf17I5vUxrRjfwYtDSYWEe9gxNNKd5YesX0PipyOpzLu1DRcz1VyonopqayNl4xljAub8W1uToy5n6W7jtMdpUYFczCghtVCDQialb7gbYzp4897WGLN67RUyhrXx5OOdcVZruhqJREK3Ubdz6I8fcfX2x9nTh/2/fYuDizsR3WoidGvefZ6I7v3oNsJ4QyjXaijMSjftL8rJJDspDlt7R5w8vEzlOk0pl47sYfDdjzRKS0v1OztXLzMtcqWxj9h7+GJXnVQpkUhoNWg8F7auxtHTH3t3b85t+gGVsxv+1Y4DwN+fvkRApygiB44DoPWQCRz+4SPcAiNxD27FpV1GfaHV+kpyM0k+sQefNt1QOjihKczj4vZfkNko8K1e+8HRyx//jn04sfZLek55ErmtCoXKAV1JEZXlWorSE7lY3Va+V7XVvqUv49sxivDqtooYPIHjP36ES3Vbxe3+A/1VbWWjsiek9wjO/vE1CjtH5LZ2nFm7DLeQNqabf+aFY+jUhbgGRSJT2uIR3pGYv3/D3sMXfUU5p35dSkWZGltnd0py0jm/YQVVlRXkJ1wkbMA4vNt2Z99nLxG763e82/Yk7eQeClNi6TLpCVM7hw+8jct/rcHew9gPLm5ZZTy3DsZzc/QOxKtNN06t+ZTOEx+nqqqSM2uX4d9lACpnYwQu5fgupDI5Tr7BSOU2FKbEcnHTSvy79DeucyEDh+qE2F2JJdzfI4hbRw7j7IWLPDSmD0q5jINJhQBM6+5HobaSP84bZ+/8HZvP7IEhDItw41xmCT0CnQlyVbGq+joD8FdMHg/2CiA2t4zLOaW083ago48ji/cmAuBuZ0OPACcuZJdSoqvEVWXDyNYelOurOJd17by+ZuGfcmL+pVjlXOzZs6dRdgMHDrwuMU2lW7tgti2vGSNc+Kxx2uz36w/x8Pwfmu17Wg29k8pyLSfWfEqFphT30Hb0f+R1s/nzpbmZlJfWhO4Cuw5AV1LEhS2r0BYX4OwfRv9HXjcL/8Yf2MzFrT+ZPu/+9EUAut/1NCG9hiOVyUk9uZeLW35Cr6/A3s2biEHjselgeUGmQ4mFOCnlTOzii4tKTlK+hnd3xFNUHZ70sFdw9XvmXFU2vDuuZvGice29GNfeiwuZJby5zfj0FZ+nYdHfCUzt5ssdnX3IUZfz/bE09idY77xtv5iDi50NDw8Iwd1eweXsEp5Zc9b09OLtZMvVQ6u2NjKeHxWBp6MSXWUVSXllzN8QzfZaC32NaOeFRALbLpgvNnY99LplMhU6LdtWLEZXVoJ/ZAfufPYds/yIwuwMNOqanI/MhMusWfCc6fOuH43rwrTvP4IxD9eURx/aBUDbPkMapaWl+l1jaTPcqO/Yz59QrinFM6wdgx57w0xfSW4mupIafUHdBqIrKeLcnz8YF9EKCGPQY2+Yhm1kNjbkxp/n8u71VJSVoHR0wTO8PcNmv282FNH73jmc/P0r9ix7DYlEikd4ezxbd+X0r5+b2qrvNdoqoLqtLm5Zha66rfrWaquOEx4CiYTD3y6gqrICr9bd6DLxMdN+mY2CxENbObtuOXp9BSoXDzxbdaYkO42/338KZ/8wQvuOJWbnb5TlZ6GvqMBGZUe7W6cREjUaiVRK93v/j4ubV3Fx0/fYe/rRe8ZLpjUuACKG3kFluZbTv3xWvYhWO6Iefs3s3Lrf83+cWbuM/V+8ikQiqV5Eq+bhTyqVEbPzN0py0sFgwM7Vk9D+t5hFTK5wPK0YB6WMGRPH4eZyD2nqSj7dn4y6etqoq50NVVfZx+dr+OZoKre18+K29l7klJSz7GCK2TDI6XQ1P53MYFRrdyZ19iFLXc5Xh1OIq16Ir7LKQLiHHUMi3LFTyFBrK4nJLeOD3YnXtaaO4J9HYrDiNaZSqdSUjFPfYRKJBL3e+n++qussq4/5p5j99lMtLaFBEq5KgLoZiU+zPFX0ZuChoSEtLaFBEvJ01zZqQeSym/fprrGJvC1F1U3+Bumb/Sa+9I52N7R+1dC3m60uzc6Xr230L8OqyIWrqyuOjo5Mnz6d++67Dw8PjxulSyAQCASClkNMRW0SVmWsZGRk8N5773Hw4EE6duzIgw8+yIEDB3BycsLZ2dm0CQQCgUAg+O9ilXOhUCiYMmUKW7duJTo6mk6dOjFr1iwCAwN5+eWXqay8/imOAoFAIBDcNIgVOpvEdZ91UFAQ8+bNY/v27bRq1Yp3332X4mIx91ggEAgE/wIkkubb/oNcl3Oh0+n48ccfGT58OB06dMDDw4NNmzbh5mb9OyYEAoFAIBD8u7AqofPIkSOsWLGCn3/+mZCQEGbMmMGaNWuEUyEQCASCfxf/0eGM5sIq56JPnz4EBQXx1FNP0b17dwD27dtXx+62225rHnUCgUAgELQE/9HhjObC6hU6k5OTefPNN+vdf73rXAgEAoFAcNMgIhdNwirnoqqq6po2ZWU394JOAoFAIBAIbizN5prpdDoWLVpEWJjltw4KBAKBQPA/g5gt0iSsci50Oh1z586lR48e9O3bl3Xr1gHwzTffEBoaykcffcTs2bNvhE6BQCAQCP45xDoXTcKqYZF58+axbNkyhg8fzoEDB5g0aRIzZszg0KFDLFq0iEmTJiGT3ZyvThcIBAKBQPDPYJVz8csvv7By5Upuu+02zp07R6dOnaisrOT06dOmF5oJBAKBQPA/j7inNQmrnIvU1FTTFNQOHTqgVCqZPXt2szgWN/ObRz96eUlLS2gYh5t7nZFJj01saQn18tTsZS0toWE0N/mqt3LFtW1aCK+oxr3KvqUoU9/cye8pX01paQkty390OKO5sKr19Ho9CkXNxUQul+Pg4NDsogQCgUAgEPzvYlXkwmAwMH36dJRKJQBarZZHH30Ue3t7M7u1a9c2n0KBQCAQCP5pROSiSVjlXEybNs3s87333tusYgQCgUAguCkQORdNwirnYsWKFTdKh0AgEAgEgn8JVi//LRAIBALBvx4xLNIkhHMhEAgEAkFtxLBIkxDOhUAgEAgEtRGRiyYhWk8gEAgEgpuIzz77jJCQEGxtbenduzdHjhyp13bw4MFIJJI62y233GKymT59ep39o0ePvqHnICIXAoFAIBDUpoWGRVavXs2cOXP44osv6N27N4sXL2bUqFFcunQJLy+vOvZr166lvLzc9DkvL4/OnTszadIkM7vRo0ebTcq4sqTEjUI4FwKBQCAQ1KI5X2mh0+nQ6XRmZUql0uINftGiRcycOZMZM2YA8MUXX7Bp0ya++eYbXnzxxTr2bm7mKzT//PPP2NnZ1XEulEolPj4+TT2VRiOGRQQCgUAguIEsWLAAZ2dns23BggV17MrLyzl+/DjDhw83lUmlUoYPH87Bgwcb9V1ff/01U6dOrbO45a5du/Dy8qJ169Y89thj5OXlNe2kroGIXAgEAoFAUIvmjFzMnTuXOXPmmJVZilrk5uai1+vx9vY2K/f29iY6Ovqa33PkyBHOnTvH119/bVY+evRo7rjjDkJDQ4mLi+Oll15izJgxHDx48Ia9yfymdC4MBgMXtqwi8eA2yrWluIe0peukx3H09GvwuLh9m7i8cy1adQHOfqF0ueMR3IJbmfbHH9hCyondFKbGUanTMO6dn1CozN+NsvmNBykryDYrc7KJoNgmtEnn1K9bOLPvH063dkH4ejozefaXbNh1pkl1NoZHbu/F7Lv64e3mwNm4LOYs3sSxi2kWbeUyKc/dN5B7R3fBz8ORyyl5vPL5Nv46EltzHp2DmX1Xf7q19sXXw4nJL/3Ihr3X7vT1MSzSnbFtPXFWyUkp0PL98TTi8zQWbf2dldzR0YcQNxWeDgpWHU9n66VcM5uhEW4MjXTH08H4Dpy0Ii3rzmZzJkN9XfoeuaMPs+8ZYGy/2EzmLNrAsYupFm3lMinP3T+Ye8d2xc/DicvJubyydAt/HY4x2cy8vTczb+9FsK8rABcTsnnnm51sO3T5uvQ1hhbre5P6MfveIXi7O3I2Jp057//OsQvJFm3lMinPzRjOvbf0wM/TmctJObzy6Ub+OljTt6L/eIVgv7ov6fvil33MXmj9KwfuHxDCw0PD8XRScjGtmPm/nuN0cqFF24m9Avjw3q5mZdoKPa3/70/T5w/u6cKk3oFmNrsuZjPt88NWawN4YGgET4xpg5ezLeeTC5m76gQnE/LrtXdS2fDynR25pXsALvYKUvPKeOWnk2w/kwHAc+Pb8/yEDmbHxGQU0/elzdelrzYGg4Glny5h7a+/oFYX06VrN16e9xrBwSH1HlNaWsJnSz5m547t5Ofn0aZtO55/8SU6dOzULJqaRDOmXNQ3BNLcfP3113Ts2JFevXqZlU+dOtX0d8eOHenUqRPh4eHs2rWLYcOG3RAtzToskpKSwgMPPNDkei7v/I24PRvpOulxhj7zAXKlLfu+mIe+orzeY1JO7uXMuuW0HXUXw/5vMc5+oexbNg+tutBko6/Q4dOmG22GT6q3HoB2Y+7hltdXmja1PKjJ52SvUnL2chrPLFjd5Loay8ShHXhv1mje/nYXUQ99wZnYTNZ/eD+eLvYW7V+bOYyHbuvBnMWb6Hrfpyz/4yir37mLzpE143T2tgrOxmbyzKJNTdbXO8iZu7v5su5cFvM2x5BcqOG5IaE4Ki170gqZlJySctaczqBQU2HRJl9TwZrTmczbEsP8LTFcyCzhmYHB+Dtb/8OeOKwj7z01lre/2UHUjM84E5vB+o9m4OlaT/s9MoKHJvRkzqINdL1nMcvXHWH1u/fSuZWvySYtu4hXP99K3xmf0e+Bz9h1PI5f3ruXtqF1E7WaixbpeyO68N4z43l7+Vai7lvEmZh01n/yMJ6ull90+NpjY3no9ijmvP87Xae8x/K1B1i9cAadW/mbbPpP+4iQ0fNN29gnPgdg7fbTVuu7tasfr9zejo+3XObW9/dwMa2Y7x/vjbtD/W95LdZU0OPlbaat32vb69jsupBtZvPktyes1gYwoVcgb0ztwgd/nGfYa9s4n1LImv8bhIej5X5sI5Py63ODCfSw54HPDhA190/mfHuUjAJzR/1iahHtn/7DtN36zo7r0meJFV9/xU+rvueV+a/xw09rUKlUPPbwg3VyDa7mtXmvcPDgAd5+dyG//r6BqL79eOShGWRlZTWbrv8lPDw8kMlkdc4/KyvrmvkSpaWl/Pzzzzz44IPX/J6wsDA8PDyIjY29pu310qzORX5+Pt99912T6jAYDMTuXk+bkZPx69gHZ79Qet49G21xPulnD9V7XMyudYREjSKk93CcfILoNulxZAolSYf/MtlEDhpP6+GTcAtp06AGuVKFrZOraTNImh422rb/Aq8v3cj6v2/8E+MVnprSlxUbjvP9nyeJTszhyQ82oNFWMO2Wbhbt7x7VmYXf72HroRgSMwr4at1Rth68zNNT+5lsth2O4fXlO1i/92KT9Y1u48muuHz2xheQXqzj2yNp6CoNDAq3/Ar5hHwNP5/K4HBSERV6g0WbU2lqzqSryVKXk6ku59czWWgrqwh3t7Na31NT+7Ni/VG+33SC6MRsnlz4BxpdOdNu7W7R/u5RXVn43W62HrxMYnoBX/1+mK0HLvH0Xf1NNn/uj2brwcvEpeYRm5LHa8v+okRTTq/2gRbrbA5apO/dPYgV6w7x/YajRCdk8eSCX41977ZeFu3vHtudhd9uZ+uBiySm5fPVbwfYeuAiT9872GSTW1hKVp7atI3t3564lFz2noizWt9DQ8L4+UAyvxxOISazhJfWnEFTrmdyn/ofJAwGyFHrTFuuuu7Djq6yysymuB4n+Fo8OrI1P+yJ56d9CVxOL+bZlcfQlFdy9wDLEdS7B4TiYq/g/k/2cSQ2l5S8Mg5cyuF8SqGZnb6qiuxirWnLL6n/gc0aDAYDq75fycxHHmPI0OG0at2GtxYsJCc7m5076jphYHzx5Y6/tjH7/56je4+eBAUH89gTTxIYFMwvP//YLLqagqXpnde7NRaFQkH37t3ZsaPG6auqqmLHjh1ERUU1eOwvv/yCTqdr1Du/UlNTycvLw9fX95q214tVwyLr169vcH98fHyTxACU5mWhVRfg1aqLqcxGZY9bcCvyEqMJ7DawzjFVlRUUpsbSevhEU5lEKsUrsgt5SZes1nBpx69Eb1uNytWTwG6DwFD1P7egio1cRtdWvrz/wx5TmcFgYOexOHq1D7B4jMJGjra80qxMU15J345Nj9zURiaVEOKmYsOFmiEoA3AhU02Eh/WOgCUkEugV5IxSLiU2t8yqY23kMrq29uP973fV6DMY2Hk0jl4dLLeHQiFHW25+M9GUV9C3U4hFe6lUwp1DO2Jvq+DwuRSr9N3M2MhldG0TwPvf1lwgDQYDO49cplfHEIvHKGzkaHW1+p6ugr6dLd9MbeQypo7pxpJVu63XJ5PQMdCZpX/VPLUZDLDvUi7dQl3rPc5eKWP/a8OQSiScSy1i4YaLxGSWmNn0iXDn+NsjKSqr4EBMLh9sjKawzDoHw0YmpXOIKx9vqnHgDQbYcyGLHhEeFo8Z3dWPY3G5vHdvd0Z39SdPrWPtoSSW/BlNlaHGEQ/1duTsotvQVug5FpfHW7+eIS3fut+GJdJSU8nNzaF3n76mMkdHRzp26syZ0ycZM/aWOsfo9ZXo9fo6wwVKpZKTJ68v4tOcNGfOhTXMmTOHadOm0aNHD3r16sXixYspLS01zR65//778ff3r5MQ+vXXXzNhwgTc3d3NyktKSnj99de588478fHxIS4ujueff56IiAhGjRp1w87DKudiwoQJSCQSDAbLT43QuH+IpWk5lRXlyG0U6NQFACgdXMz2Kx1cTPvq1FdajKGqCltH8wuDraML6mzL4+P1ET5wHK4B4SjsHMhLiObcpu9wqXCnUNHaqnpaGg9nO+RyGdn5pWbl2QWltA72tHjM9iOxPDWlL/tOJxKfVsCQ7mGMH9gWmbT5HStHpQyZVEKx1vyGUqStxNfJtkl1BzjbMm9kODYyKdrKKj7em0R6cf2hWUt4uFxpP/ObR3Z+Sf3tdziGp6b2Z9+pROLT8hnSI5zxg9rXab/2Yd7s+vJRbBVySjTlTJn7A9GJ2Rbr/F/Ew8W+uu3M81yy89W0DrE8/LP90CWeumcQ+07GEZ+ax5CekYwf0rHevnfb4A64OKj4YeNRq/W52iuQy6Tkqs37RK5aR7i35WGb+OxSnvvxNNHpxTiqbHh4aBhrZ/dnxIJdZBZqAdh9MZstpzNIySsj2MOe58e14bvHenP7on1U1X/JrIObo1FfTrHWrDy7SEuEj5PFY4I9Hejf1p7fDiZx10d7CPN24L37uiOXS/ngj/MAnIjP46nlh4nNVOPtouLZ8e3ZMHcoA17dQmmt36G15ObmAODuYX5jc3d3Jzc319Ih2Ns70LlLV778YimhYWG4u3uw+c+NnDl9isCg5n+g+V9hypQp5OTkMG/ePDIzM+nSpQtbtmwxJXkmJycjrfW7uHTpEvv27WPbtm116pPJZJw5c4bvvvuOwsJC/Pz8GDlyJG+++eYNzQOxyrnw9fVl6dKljB8/3uL+U6dO0b275ZDx1SxYsIDXX38dMHq33t7eyOQ2SOU29Js5zxpJzU6rwRNMfzv7hSKVyzn28ycU2kT+z0UvrOXZJX+y9PnxnP7hKQwGA/HpBaz882S9wyg3KxlqHa9sjsHORkbPIGce7hPIO9vjrHYwrOXZxRtZ+uLtnP5ptrH90vJZuelEnWGUy8m59J72Cc4Ottw+pANfvTKJkU989a9yMKzl2Q9/Z+nLkzn9y4vVbZfHyg1HmDaut0X7abf1ZuvBaDJyi/8RfScSCziRWPNwczw+nx0vD+GevsF8+KcxOrrhRLpp/6UMNRfTi9k3fxhRkR7sv2z5BttcSCUScou1zPn2GFUGA2eSCvBxUTFrTBuTc7HjbKbJ/kJqEcfj8jj5wa1M6BnIqr0JVn3fpo3refO1+abPn36+7Lp0v71gIfNffYkRQwYik8lo07Ydo8fewsUL56+rvuakpSIXALNmzWLWrFkW9+3atatOWevWret96FepVGzdurU55TUKq5yL7t27c/z48Xqdi2tFNa5w9bSc0tJS8vPz+exQOnK5DVWVxhCirqQQlXPN2LuupBBnvzCL9SntnZBIpWhrRTa06kJsneoPczYGt6BWSDAgN2iolFhO5LsZyS0qo7JSj5ebuWYvV3sy8yzPnMgtLGPySz+hVMhxd1KRnqvmrUdHkJBuOWLUFNQ6PfoqA0625l3Q2VZOkfb6xqmvoK8ykF09lpxYoCHMXcXI1h58e9TyLBlL5BZeaT/zJ1kvNwcy8+trv1Imv/hDdfvZkZ5bzFuPjyIhzTzDv6JST3x12clL6XRvG8ATk/vy5MJ1VpzlzUtuYWl12zmalXu5OTbQ90qZ/NwKY9s525OeU8Rbs24lIb3uXPwgH1eG9mrF1OdXWKjp2hSUllOpr6qTHOnhqCRH3TgHtLLKwPnUIoI9678mpOSVkVeiI9jD3irnIl9t1OdZK4Ln5WxLdq1oxhWyCjVU6KvMhkBiMorxdlFhI5NSoa+qc0yxpoK4rBJC64nWNMTgIUPp2LGz6XN5dbJ9Xm4enp410am8vDxat6k/xy0wKIhvvvuBsrIySktL8PT04rn/e4aAgBuXg9RYWtK5+Ddg1aP4c889R9++fevdHxERwd9//33NepRKJU5OTjg5OeHr60v79u1x8Q3GwdMPR58gbB1dyb5ckwFeoS0jP+ky7vUkYkrlNrgERJBzuSZhzVBVRU7MadyDmzacUZiegAHQS+rPIr8ZqajUc/JyBkO61zhkEomEId3DOHK+4aEiXXkl6blq5DIpEwa1Y+O+659qWh/6KgOJ+RraX3VhkwDtfByszo+4FhIk2Misu1BUVOo5eSmdId0jauqRSBjSI5wj5yxPp7yCsf2Kje03uAMbr5H8KpVKUNrcmLnmLUFFpZ6T0akM6RlpKpNIJAzpGcmRs4kNHqsrryQ9p8jYdkM7sXH3uTo2943rRXZBCZv3X19ScYXewNmUIvq1qslfkEigX2sPTiQ0zpGWSqC1n1OdoYur8XGxxdVOUa9DUL++Kk4nFjCwXc1aBxIJDGjrzbFYy07KkdhcQr0dzVasDvdxJLNAY9GxALBXygnxtCer0Dp9YBzSCAoONm3h4RF4eHhy+HDNQk8lJSWcPXOaTp27NlCTETs7Ozw9vSguKuLg/n0MHnJjpkdahaQZt/8gVkUu/P39CQ2tf70He3t7Bg0a1CRBEomEiEG3Ef3Xahw8/bB38+b85h+wdXLDr2Mfk92epS/j1zGKiAG3AhA5eALHfvwI18AIXINbEbv7DyrLtQT3rlnpTFtcgFZdQEmuMXxZnJ6E3FaFnYsnCntH8hKjyU+6hGdEJ+RKFflJ0ZxZt5xSmS8GiU2TzstepSA8sGasPsTfnU6t/CkoLiMls/kjAwBLVh/gq5du53h0OscupjJrUhR2KgUr/zQmSy1/+Q7Sc4uZt8yYzd2zXQB+Ho6cjsnE39OJlx8YglQqYdGP+8zPw78mohTi60qnCB8KijWkZBdZpW9LdA4zowJJyNcQn1fGyNYeKOVS9sQb2+PhqEAKyir45bQxnCuTSvB3Mj5tyqUSXFU2BLnYoq2sMkUqJnX24Uy6mryycmzlMqJCXGjjbc/7f1s/5LDk53189cpEjkencuxCKrOm9MPOVsHKjdXt9+pE0nOKmffFtpr283TmdEw6/p7OvPygMflv0aqapNo3Hh3J1kOXScksxNFOyZSRnRnYNZRxs7+1Wl9jaZG+9+Nuvpp/F8cvpnDsfDKz7hpk7HsbjC9gWv7aXca2+8w4pbln+yD8vJw5fTnN2HYPjzL2vZU7zeqVSCTcP64nqzYdRV/PTbMxLP87ng/v7cKZlEJOJxXywOAw7BQyfjlsdBwX3duFzCItCzcYHeunRkdyMrGQxJxSnFU2PDwsnABXFT8fNNrbKWQ8M6YVm09nkFNsjFbMHd+WxNxS9kTnWK3vi22X+OSh3pxKzOdEfB6PjGyNnVLOT/uMwxefPtSbzMIy3vr1LAAr/o7lwWGRvHN3N77afplwb0eevqUdy7fXrJ/y2pTObDuVTkpuKT6uKp6f0AG9wcDaww07y41BIpFwz33389WyzwkOCsY/IIDPPvkYTy8vhg6ruQbPfGAaQ4eN4K57jDMa9u/bCwYDwaGhpCQn89EHCwkJDWP87Xc0WZOgZbHKuYiMjCQjI8P08pQpU6awZMmSOquJNZVWQ++kslzLiTWfUqEpxT20Hf0feR2ZTU30oDQ3k/LSmvHWwK4D0JUUcWHLKrTFBTj7h9H/kdfNkjzjD2zm4tafTJ93f2pcp737XU8T0ms4Upmc1JN7ubjlJ/T6CuzdvIkYNJ6YnY0Pp9dHt3bBbFv+tOnzwmfvBOD79Yd4eP4PTa7fEr/uPIeHix3zHhyKt5sDZ2IzGf/s92QXGJM8A72dzcKoSoWc+TOHEerrSommnK2HYnjwzd8oKql5sunW2o9tn9SsZbLwyTHG89h8koff+d0qfYeTi3C0lXNHJ2+cbeUkF2h5/+8EU5Knu52N2TCbq0rOW2NrFkUb286Tse08uZhVwoIdxplKTrZyHo4KxEUlR1NRRUqhhvf/TuB8raz+xvDrjrN4uNgzb+ZwvN0cOROTwfg5K8guMNYV6O1CVdXV7WfD/IdHEOpX3X4HL/HgG2vM2s/T1YGvX52Ej7sjRaVazsVmMm72t+w8euPmm7dI3/vrFB4uDsx7ZDTe7k6cuZzG+Ke+NCXIBvq4mvc9pQ3zHx1DqL87JRodW/df5MF5P5q1HcDQXpEE+brx3fr63xLZGDaeTMfdQcGcsa3xdFJyIbWY+z8/bJpe6ueqMkvCdFYpeHdqJzydlBSVVXAupYg7Fu8zzRbRGwy08XPizl6BOKlsyCrSsjc6hw//jKa80nonaN2RFNwdlbwwoQNezracSy5kyqLd5FTnDQW425n9NtLzNUz+cDdv3tWV3W+OJqNAw1d/XWbJnzVRRz9XO5Y9EoWrg4I8tY7DMbmMeXM7eY0cCroWMx6ciUaj4Y3X5qFWF9O1W3eWLltuljSYmpJCYWGNQ1tSombJ4kVkZWbi7OzCsBEjefLp2djYNO1hrjkQwyJNQ2JoTJJENVKplMzMTJNz4ejoyOnTpwkLs5wLYQ0v/XnjVihsKh+9vKSlJTSMg+V1IW4WJj028dpGLcQvn/7c0hIaRvPPJCxeN/Kbd7jQK2pIS0tokDJ18w7/NTcpX01paQkNYnuD15d2vXdVs9VV8MM9zVbX/wr/7ukPAoFAIBAI/nGs8v0srTYmQkcCgUAg+Lch7m1NwyrnwmAwMH36dNMYmlar5dFHH63zate1a61/iZBAIBAIBDcLwrloGlY5F9OmTTP73Jg1zAUCgUAgEPy3sMq5WLHi+hatEQgEAoHgfwoRuGgSNzjfViAQCASC/z3EsEjTELNFBAKBQCAQNCsiciEQCAQCQS1E5KJpCOdCIBAIBIJaCOeiaQjnQiAQCASC2gjfokmInAuBQCAQCATNiohcCAQCgUBQCzEs0jSEcyEQCAQCQS2Ec9E0bhrnIiH3Jn5D4E3+1lFK8ltaQYMcPJne0hLqxz2gpRU0TFF2SytoGIP1rxP/p5Db3DSXN4soVcprG7UgJdrKlpbQILYON/f/97+O+O8IBAKBQFALEbloGsK5EAgEAoGgFsK5aBpitohAIBAIBIJmRUQuBAKBQCCojQhcNAnhXAgEAoFAUAsxLNI0xLCIQCAQCASCZkVELgQCgUAgqIWIXDQN4VwIBAKBQFAL4Vw0DeFcCAQCgUBQG+FbNAmRcyEQCAQCgaBZEZELgUAgEAhqIYZFmoZwLgQCgUAgqIVwLprG/4RzMaK1B+Pae+GskpOcr+HbI2nE5Vl+0VmAsy0Tu/gQ5m6Hp4OClUfT2Hwxp46dq8qGu7v70tnfCaVMSqZax7IDycTnaazW98jtvZh9Vz+83Rw4G5fFnMWbOHYxzaKtXCblufsGcu/oLvh5OHI5JY9XPt/GX0diTTb9Ogcz+67+dGvti6+HE5Nf+pENe6Ot1mUN/bqFM/v+4XRrF4SvpzOTZ3/Jhl1nbuh31sc9fYN4aFAono4KojPUvLHuImdSiiza3tHDn/emdDQr01Xo6fDSX82i5ZGxHZh9Rxe8Xe04m5DHnGV7ORZj+WViW98Zz8CO/nXKNx9N4o43NgHg5aLirelRDO8SiLODgn3nMpizbC9xGZbPz2q9t/dk9tQrfTGTOR9vbrgv3juAe0d3xs/DicspubzyxXazvtg0LTf37+LefkHMHByKp6OSi+lqXv/9Qr397M6e/iyc2smsTFehp92L24z6pRLmjGnF4LaeBLqpUGsrORCTx8JNl8gu1l2XvmkDQ3l0RCSeTkouphbx6poznEoqtGg7qU8QH93fzaxMW6En4ukNps92ShkvjW/PqM6+uNorSM4r5Ztd8fywN9FqbQaDgeVffMqG339FXaKmU+euPDt3HoFBwfUec+etI8jMqPsSwzsmTeX/XnwVgNSUZD5b/AFnTp2gvKKcPlH9mf38S7i5e1itUdCyWJ1zodFo2LdvHxcuXKizT6vVsnLlymYRdoU+IS7c18OP305n8tLGSyQVaHhxeBhOtpb9IoVcQnZJOT+dSKegrMKijb1CxutjIqmsMvDe9nieXR/ND8fSKdHprdY3cWgH3ps1mre/3UXUQ19wJjaT9R/ej6eLvUX712YO46HbejBn8Sa63vcpy/84yup37qJzpE+NPlsFZ2MzeWbRJqv1XC/2KiVnL6fxzILV/9h3WmJsZx9eGteGT/+KZcLiA1xMV/PNQz1ws1fUe4xaU0HUGztN26B3djeLlon9I3jvoX68/dMxop75hTMJuax/41Y8nVUW7ae+s4WQ+1aYtm5P/ESlvoq1+2tukGteHkOotxOT3t5Mn6d/ITlHzZ9v3Yadsul+/sSh7XnviVHVfXEZZ2KzWP/BvQ30xaE8dFt35ny8ma73f8byP46x+u0pZn3x+rXc3L+LW7r48NJtbVmyLZbbPjpAdHox3z7cE3eHhvtZ79d2mLYBb+0y7bNVyGgf4MSnfxnre/zbk4R62vPlA92vS9+47v7Mu7MDH22KZsyCXVxIK+aHJ/s2qK9YU0HXFzebtj6vbDPbP//Ojgxu58VT3x5n8Bs7+HpnHG9N7sSIjtb/v1d99zW//ryK516az1ff/YStSsWcWQ+j09XvSC3/fjXrt+4ybYuXLgdgyPBRAGg0Zcx+4mGQSFjyxTd88fUPVFRU8PzsJ6iq+uffviuRSJpt+y9ilXNx+fJl2rZty8CBA+nYsSODBg0iIyPDtL+oqIgZM2Y0q8Bb2nqyMyaP3XH5pBXp+PpQKuX6KgZHWH4Nenyehh+Pp3MwsZDKKoNFm3EdvMgrLWfZgRTi8srIKSnnbIaa7JJyq/U9NaUvKzYc5/s/TxKdmMOTH2xAo61g2i3dLNrfPaozC7/fw9ZDMSRmFPDVuqNsPXiZp6f2M9lsOxzD68t3sH7vRav1XC/b9l/g9aUbWf93y0QrrvDAwBBWH07ht2NpxGaXMm/teTQVeib2qhsRuIIByFWXm7a86/g/WuKpCZ1ZsfUC3++IJjqlgCeX7kajq2TaiDYW7QtKdGQVakzbsC6BlOkqWbsvDoAIP2d6t/Hhqc93czwmm5i0Qp5auhtbhYzJgyKbrndyFCs2nuD7zaeITsrhyQ83VvfFrhbt7x7ZmYU/7K3pi38cY+uhGJ6e0rfpWm7y38UDA0NZfSiF346mEZtVwiu/XelnAfUe01A/K9FWMm3ZUf48nUlCTimnkgt57fcLdAx0xtfF1mp9Dw8N56f9Saw5lExMppoXfzqFtlzP1L71RwYMBsgp1pm2XLX5jb57mBu/HE7hYEwuqfllrNqfxIW0YrqEuFqlzWAwsObH75n24CMMGDyUiMjWvPr6AnJzstm7a0e9x7m6uuHu4Wna9u/dhX9AIF279wTgzKmTZGak8cprbxMe2YrwyFa88vo7RF84z/Gjh63S2BwI56JpWOVcvPDCC3To0IHs7GwuXbqEo6Mj/fr1Izk5+YaIk0klhLrbcS6jxFRmAM5llBDpafkJqDF0D3AmPq+MpweG8MWk9iy4tRVDIy07Kw1hI5fRtZUvO4/H1egzGNh5LI5e7S1fpBQ2crTllWZlmvJK+nYMsvr7/23YyCS093fiQEyeqcxggAMxeXQNdqn3ODuFjF0vDWLPy4P4fHpXIrwdmq5FLqVrhCc7T6eaadl5KpVerRv3pDdtRFt+2RNDmc74/1bayADQltdEyAwGKK+oom873ybqldG1lR87j8VfVbeBncfjG+iLsrp9Udf0vniz/y5sZBI6BDhxICb3Kn1w4HLuNfvZnpcHs+/VwXwxoxuR1+hnjrZyqqoMqDWVDdpZ0tcxyIW9l2qGcw0G2BudQ7fQ+q9T9koZh94cyZG3R/L1I71p5etotv94fD4jOvng42x0dvq28iDMy549Fy0P89VHeloqeXm59Ojdx1Tm4OhIuw6dOHfmdKPqqKgoZ9ufG7ll/B2mm29FRTkSiQQbRU10RqFUIpVKOXPqhFUaBS2PVc7FgQMHWLBgAR4eHkRERLBhwwZGjRrFgAEDiI+Pv3YF1eh0OoqLi802fUXdp00npQyZVEKRxnx4o0hTgUs9wyKNwctRwfDWHmSqdby7I56/LuUxrWcAA8Os8+A9nO2Qy2Vk55ealWcXlOLj7mjxmO1HYnlqSl/CA9yQSCQM7RHO+IFt67X/L+Fqr0Auk5JbK/KQV6LD01Fp8Zj4nFLm/nKOx749wbM/nUEqkbDmid74OFu2byweTrbIZVKyC8xze7ILNfi42l3z+B6RXnQIcefbbTVP2ZdSC0nOVvPmtD642CuxkUv5vzu7EuDp0Kg6G9TrbIdcLiW7oMSsPDu/FB83yzfB7UfieGpy1FV9May6LzbNObvZfxemfqY272e5JeX197PsUl5cfZZHVhxnzipjP/vlyT6mG3VtFHIpL9zSmg2nMijRWedcuDkokcuk5BRrzfWpdXg5WdYXl6Xm/344yQPLDvPUt8eRSiWse3agWdTk1TVniMlQc2zBaBI+uY3vn4ji5dVnOBybZ7HO+sjPMzplbm7meRBubu7k5eVaOqQOe/7eSUmJmrHjJpjK2nfsjK2tiqVLPkSr0aDRlPHp4vfR6/Xk5dbNm7vhSJpx+w9ilXOh0WiQy2tu6hKJhM8//5xx48YxaNAgLl++3Kh6FixYgLOzs9l2YeM31ilvAlIgMU/D6pMZJOZr2BmTx86YPIa1vvFJQ88u+ZO41DxO//AUxTvn8dHsW1j550mqDJaHcAQNcyqpkHXH07mYruZIfAFPfHeS/NJypvYJbFFd00a25WxCnlnyZ6W+iqnvbCHCz4WMnx8k/9eHGdjRny3Hklrk///sks3EpeZz+vtZFO94lY+eGcvKzS3TF2/238XJpEJ+N/WzfB7/9gT5peXcFVW3n8mlEj65vwtIYN6v5/8RfScSCvjtcAoXUos4FJPHzGWHyVfruKd/qMlmxuAwuoW6Mv3zQ4x9dxdvrj3H21M60b+1Z4N1b/1zI8P79zBtlZXWOUuW2PjHb/Tp2x9PTy9TmaurG2++t4j9e3YzfEBPRg3qQ4laTes27ZBI/vklmcSwSNOw6vG/TZs2HDt2jLZt25qVf/rppwDcdtttjapn7ty5zJkzx6zsoV/qZn0X6/Toqww4q2zMyp1VNhRqr7+DF2gqSS0yfypIK9LSK9jZqnpyi8qorNTj5WY+ROPlak9mntryMYVlTH7pJ5QKOe5OKtJz1bz16AgS0gusO4l/IQWl5VTqq/ColbTm7qAkR924jPvKKgMX0tQEu1//sBlAbrGWSn0VXrUiCl4uKjILLM9UuoKdUs6kARG8ueponX0n43Lo8/QanOwUKORScou17PngTo7HWhearqO3qIzKyiq8XM2jDl5u9mTml9R7zOSXf67VF4c3uS/e7L8LUz9zNO9nHg4Kq/rZ+bRigj3M+8cVx8LfVcW9nx+xOmoBkF+io1JfhaeTeVTEw1HZ6JknlVUGzqUWEVI9fGxrI+WF29rx0JeH2XkuC4CLacW0D3Dm0eER7LtUf2Sg/6AhtO9YMyOrvNwYSc7Pz8XDs8Yxyc/PI7KV5Xykq8nMSOfYkUO88/7Hdfb1jurHL+u3UFhQgEwuw9HRiXEjBzIsYEyjzvvfwmeffcb7779PZmYmnTt35pNPPqFXr14Wbb/99ts6uY5KpRKttuYeZzAYmD9/Pl999RWFhYX069ePzz//nMjIpud61YdV7uDtt9/OTz/9ZHHfp59+yl133YWhEU8aSqUSJycns01mUzcLWl9lICGvjA6+NRdMCdDex4GYnNI69o3lck4pfrXCi75OSnJLLM8uqY+KSj0nL2cwpHtYjT6JhCHdwzhyPrWBI0FXXkl6rhq5TMqEQe3YuO/GTjX9X6BCb7xgR0W4m8okEugb4c7Jeqbg1UYqgVa+DmQ38iZRr5bKKk7G5jCkU00iqUQCQzoHcORSZoPH3tE/HKWNjJ92XarXprisnNxiLeG+znSL8GTj4cQm6tVz8nI6Q7rXPKlKJBKGdLOyLw5sx8Z99etuvJab93dRoTdwLrWYvpHm/Swq0sOqftba19HsZn/FsQjxsOf+L45SWM9stcboO5tcaBZRkEigf2tPTiTkN1pfGz8nsquHVuQyKQq5FEOtJHd9lQGJtOEna3t7ewICg01baFg47u4eHD9Sk2RZWlLChXNn6NCp8zW1bVr/O66ubkT1H1ivjYurK46OThw/coiC/Hz6DxxyzXqbm5aKXKxevZo5c+Ywf/58Tpw4QefOnRk1ahTZ2fU/gDg5OZGRkWHakpKSzPYvXLiQJUuW8MUXX3D48GHs7e0ZNWqUmQPS3FgVuZgyZQovvPBCvfuXLl3K0qVLmyzqajZdzOGxfkHE55YRm1fGmLaeKOVSdscaf2SP9QuioKyCn08aZ63IpBICqsdB5VIJrnY2BLuq0FbqyaoeY/3zQjavj2nF+A5eHEoqJNzDjqGR7iw/1PCFzxJLVh/gq5du53h0OscupjJrUhR2KgUr/zQmIC1/+Q7Sc4uZt2w7AD3bBeDn4cjpmEz8PZ14+YEhSKUSFv24z1SnvUpBuH9N4laIryudInwoKNaQkt086yHUxl6lIDyw5mIW4u9Op1b+FBSXkZL5z0VVvtmTyMIpHTmXWsSZlCKmDwhBpZDx21Hj+ggLp3Ykq0jHh5uNQ3CzhodzKrmQpNwyHFU2zBwUir+ril8OpzRZy5J1p/lq9lCOx+Zw7HI2s8Z3ws5Wzsrtxhve8tnDSM8rZd7KQ2bHTR/Rlg2HEsi34ODc0S+cnCINKTkldAhx44OZ/dlwOIEdJ5tB75qDfDX3do5fSufYxTRmTeqDncqGlX+eNOp96XZjX/zSmNHfs60/fp5O1X3RkZdnDDb2xZ/2N13LTf67+GZPAu9P7cTZlGJOJxcyY2AIdgoZvx4xXgM+uKsTmUVaPvizup+NiOBUUiFJuaU4qWyYOcTYz9YcNtrLpRI+ndaVDgFOPLT8OFIppshIUVkFFXrrhne+3BnHR/d343RSAaeSCnhoSDgqpYzVB43J84undSOzUMu7fxiXBHhmTGtOJOaTmF2Kk50Njw6PJMDNjp/2G28yJdpKDl7O5eU7OqCtOENqfhl9Ij2Y2DuI1387a5U2iUTC5Lvv47uvlxEQFISfXwBfff4JHp5eDBg8zGT31KMPMHDIMCZOucdUVlVVxab1vzPm1vFmQ+xX2LT+d4JDw3BxceX82dMs/mABU+6+n+CQ0Dq2N5qWGs1YtGgRM2fONEUjvvjiCzZt2sQ333zDiy++aPEYiUSCj4/lRHODwcDixYt55ZVXGD9+PAArV67E29ubdevWMXXq1BtyHlY5F5GRkWRkZODlZRwnmzJlCkuWLMHb2/uGiAM4lFiIk1LOxC6+uKjkJOVreHdHPEXVwyIe9gquDpa4qmx4d1xr0+dx7b0Y196LC5klvLnNuN5AfJ6GRX8nMLWbL3d09iFHXc73x9LYn2D9TfTXnefwcLFj3oND8XZz4ExsJuOf/Z7sAmNkJdDb2WzcWKmQM3/mMEJ9XSnRlLP1UAwPvvkbRSU1HmS31n5s++QB0+eFTxpDgt9vPsnD7/xutcbG0K1dMNuWP13znc/eafzO9Yd4eP4PN+Q7LfHn6Uzc7BU8PSqyenGjYh5cfsw07c/PRWX2/3ZS2fDWxA54Oiop0lRwPrWIKZ8eIjb7+iNbV/h1XywezrbMu6cX3q52nInPZfz8jWQXGhdaC/R0qJMTEOnvQr/2ftzy6nqLdfq42fHeg/1Mwyurdl5iwepjTdYK8OvO83i42DPvgSFX9cUfGu6LDw0174tv/W7WF69fy839u9h0ytjPnhkViYeTkotpxcz46qipn/m62Jrpc1bJeWdSBzyclBSXVXAutYhJnxwiNss45OTtbMuIDsbr4KZn+5t9191LD3M4rnERhytsOJ6Gu4OCZ29ti6eTkgupRdz36UHT9FJ/VzuuXvrB2c6GhXd3xdNJSVFZBWdTChn/wR5iMmuGoR7/5igvjm/HJzO642KnIDW/jPfWX+D761hE655pD6LRaFj49muUqNV06tKNDz9ZhlJZExFOS02hqLDQ7Lijhw+SlZnBLePvsFhvcmICX3z6EcVFRfj6+TPtgYeZcs80q/U1B82ZK6HT6eqsAaJUKs3aC6C8vJzjx48zd+5cU5lUKmX48OEcPHiw3vpLSkoIDg6mqqqKbt268c4779C+fXsAEhISyMzMZPjw4SZ7Z2dnevfuzcGDB2+YcyExNGYcoxqpVEpmZqbJuXB0dOT06dOEhYVd48hrc9fKU02u40ax7qu1LS2hYUqsu3D90wQMv7WlJdRLanTjZzm1CEVNy8W44Rj++cWNGotfr6iWltAgOk3Thu5uNKcW3ry/WwAPhxu7wHTkc1uara577A/x+uuvm5XNnz+f1157zawsPT0df39/Dhw4QFRUTf99/vnn2b17N4cP113v4+DBg8TExNCpUyeKior44IMP2LNnD+fPnycgIIADBw7Qr18/0tPT8fWtmfI+efJkJBIJq1ffmIUT/yeW/xYIBAKB4J+kOYdFLE1iqB21uF6ioqLMHJG+ffvStm1bli1bxptvvtks33E9WOVcWEpO+a9OsxEIBALBv5fmvLdZGgKxhIeHBzKZjKysLLPyrKysenMqamNjY0PXrl2JjTWmAVw5LisryyxykZWVRZcuXRp5BtZjlXNhMBiYPn26qZG0Wi2PPvoo9vbmU87Wrr3JhxEEAoFAILjJUCgUdO/enR07djBhwgTAmAS7Y8cOZs2a1ag69Ho9Z8+eZezYsQCEhobi4+PDjh07TM5EcXExhw8f5rHHHrsRpwFY6VxMm2aeWHPvvfc2qxiBQCAQCG4GWiooP2fOHKZNm0aPHj3o1asXixcvprS01DR75P7778ff358FCxYA8MYbb9CnTx8iIiIoLCzk/fffJykpiYceeqj6PCQ888wzvPXWW0RGRhIaGsqrr76Kn5+fyYG5EVjlXKxYseJG6RAIBAKB4KZBeo31P24UU6ZMIScnh3nz5pGZmUmXLl3YsmWLaVZmcnIyUmnNElUFBQXMnDmTzMxMXF1d6d69OwcOHKBdu3Ymm+eff57S0lIefvhhCgsL6d+/P1u2bMHW1vqX6jUWq2aL3EjEbJEmIGaLXDditkgTEbNFrhsxW6Rp3OjZIu1e2nZto0Zy4Z2RzVbX/wpitohAIBAIBLUQcxWahnAuBAKBQCCohZgJ2TT++VfNCQQCgUAg+FcjIhcCgUAgENRCBC6ahnAuBAKBQCCohRgWaRrCuRAIBAKBoBbCuWgaIudCIBAIBAJBs3LTRC7i04paWkK9THpsYktLaJCDJ9NbWkKDpG7f2NIS6uWR1xq3pG5LUVhW3tISGqS4rKKlJdSLp/ONWyCoOaiquimWGKqXLw8ntbSEBnlpWPgNrV8ELprGTeNcCAQCgUBwsyCGRZqGGBYRCAQCgUDQrIjIhUAgEAgEtRCBi6YhnAuBQCAQCGohhkWahhgWEQgEAoFA0KyIyIVAIBAIBLUQgYumIZwLgUAgEAhqIYZFmoYYFhEIBAKBQNCsiMiFQCAQCAS1EIGLpiGcC4FAIBAIaiGGRZqGcC4EAoFAIKiF8C2ahsi5EAgEAoFA0Kz8T0QuJnbz457egbg7KIjJLuHDbbFcyFBbtB3cyoPpfYMIcFUhl0pIKdDw45EUNp/LNtkcnjvI4rGf7Izjh8OpVusbFunO2LaeOKvkpBRo+f54GvF5Gou2/s5K7ujoQ4ibCk8HBauOp7P1Uq6ZzdAIN4ZGuuPpoAAgrUjLurPZnKnnnK3lnr5BPDQoFE9HBdEZat5Yd5EzKZZfHHdHD3/em9LRrExXoafDS381i5bG0q9bOLPvH063dkH4ejozefaXbNh1plm/o3+oC0Mj3HFUykgv1vHbmSySC7X12nf2c2RsGw/c7GzIKS1nw/kcLmaXmvaPbu1BV39HXFQ26KsMpBRp+fNiDkkFNXWOaOVOO297/J1s0RsMzP0zptF6h0W6M6aNsd8lF2j54XgaCfmW+52fk5I7OvkQ4qrCw0HBjyfS2Var393SzpPuAc74Oimp0BuIzS1lzalMMtW6Rmu6FqPbejKhozcuKhsS8zUsP5hMbG6ZRdvhrT0YHOFGkKsKgLjcMlYdS6vX3loGh7sxopU7zrZyUou0/Hwyk8QCy+0H0M3fifHtvXC3tyG7pJy1Z7M4l1li2q+USbm9oxdd/JywV8rILS3n79h89sQXXFtLhBujWnvgbCsnpVDLTyczSKznfwnQPcCJ8R288bC3IUtdzm9nMs20OCplTOzkQzsfB1Q2MmJySvnpZAbZJTUvwnt2cCitvezN6t0dl88Px+u+CNFgMHBq4w/E7N9CuaYUr7B29LnrCZy8/Bs8r+jdGzj3129oigtwCwil1+TH8AxpbWaTHX+Rk+u/IzfxEhKpFNeAMEbMegu5QglAXnIsx9d9Q25SDFKplKAu/eh558wGv7c5EMMiTcNq5+LixYscOnSIqKgo2rRpQ3R0NB9//DE6nY57772XoUOHNqvA4W09eXpYOO9tucz5dDVTe/rz8ZSOTP7yKAUW3shYrK1gxYEkkvI0VOir6B/hziu3tCG/tILDCcYf+ZglB8yO6Rvmxsu3tGZnrYttY+gd5Mzd3Xz59mgacblljGrjwXNDQnl+wyXUOn0de4VMSk5JOUdSCrmnm5/FOvM1Faw5nUmWWocE6B/qyjMDg3l1SwxpRU270I/t7MNL49ow77fznE4uZNqAEL55qAcjF+4lv9TyGzjVmgpGvr/X9NnQAi9ztFcpOXs5jZV/HGT1ooebvf6ufo5MaO/FmjNZJBVoGBTmxqNRgbyzI56S8rr/xxBXFfd392PjxRwuZJbQLcCJB3sH8MGuBDLVxnbMLinnt7NZ5JVWYCOTMCjcWOdb2+Mpra5TJpFwKk1NYr6WPsHOjdbbK8iZqV19+e5oGvF5ZYxs7cGzQ0J5caPlfqeUG/vd0eRC7qqn37XxcmBnTB7xeWXIpBImdvLh2SGhvLTpEuX6pv/T+4W6MqN3AMv2J3M5p5Rb23sxb3QkT/56niJtZR37Dj4O7IsvIDorhQp9Fbd38mH+6EieXnuB/Ca+jbVHgBMTO3nz44kMEvI1DIt046kBwczfGmOx/cLcVTzUO4B157I4k6GmV5ALj/UN5O3t8aQXG3+Tkzp709rLnm+OppJXWkE7bwfu6upLoaaywQeDHoFOTO7sww/H00nI1zA80p1nBobw6ubLFrWEu6uY2SeQtWezOJOupnewM0/0C+LNv+JMWp7oF4zeYOCzfcloKvWMaOXBnEEhzNsSY/a/3BOXzx/nax68yiurLGo899evXNy1nv73z8HB3YdTG7/nr09eZcK8L5DZKCwek3BsN0d/+4o+d83CM6QNF3auY/snrzLhtS9ROboARsdi+6ev0nHUZHpNfgypTEZBajwSiTGoXlaYx7YlLxHSfSC9pzxOhaaMI78uY//3i+CWb+pt0+ZA+BZNw6phkS1bttClSxeeffZZunbtypYtWxg4cCCxsbEkJSUxcuRIdu7c2awC7+oVwB+nM9h4NouEvDLe3RKDtrKKcZ18LNqfSC5i9+U8EvPKSCvUsvpYGrHZJXQJrLlw55dWmG0DW3lwPKmQ9AaeUutjdBtPdsXlsze+gPRiHd8eSUNXaWBQuJtF+4R8DT+fyuBwUhEV9VywT6WpOZOuJktdTqa6nF/PZKGtrCLc3c5qfbV5YGAIqw+n8NuxNGKzS5m39jyaCj0Te9X/BGIActXlpi2v5J9/Dfi2/Rd4felG1v/dvNGKKwyOcONgUhFHkovIUpfzy+lMyvVV9K7nhj8o3JXo7FL+js0nq6SczdG5pBZqGRDqarI5kVbM5Zwy8soqyFSXs+5cNiobGX5OSpPNlku57I4vIKPYOqdxVGtPdsflsy/B2O++O5pGeaWBgWH197vVpzI4nFxEZT397sNdCab6Ugq1LD+cgoe9ghC3pvc7gHEdvPnrUi47Y/JILdSybH8yusoqhrZyt2i/eHciWy7mkJivIa1Ix9J9SUgkEjr5OTZZy/BW7uxLKOBAUiEZah2rTmRQrq+ib4irRfthEe6czyph2+U8MtXlrD+fTXKBlsFX/c7D3O04mFRk+p/vTSggtUhLqJuqQS0jWnmwN76AA4mFZBTr+OF4OuWVVfQLrUdLpAfnM9Vsu5RLplrHH+eySS7UMjTS2I7eDgrCPexYdTydxAINWepyVh1Px0YmpVeQi1ld5foqirWVpk1rwbkwGAxc3LmOTqOnEtQ5CreAUPpP+z/KivJIPn2w3vO6sPN3IvuNJjJqJC6+QUTdNQuZQknsgW0mm6O/fknbIbfRcdRkXP2CcfYOIKT7QGQ2NgCknjuCVCanz5THcfYOwCOkFVF3zSLp5H6Skm7uV8L/17HKuXjjjTd47rnnyMvLY8WKFdx9993MnDmTv/76ix07dvDcc8/x7rvvNps4uVRCGx9HjiTUhBUNwNHEAjr6OzWqjh7BLgS72XEy2XLY383Ohn7hbqw/nWm1PplUQoibivNXhSMNwIVMNREezXNBlkigd7AzSrm0yeFgG5mE9v5OHIjJM5UZDHAgJo+uwS71HmenkLHrpUHseXkQn0/vSoS3Q5N03GzIJBDgbMvlnJohDQNwOaeMEFfLN4YQV5WZPUB0dikh9dxIZBLoG+yCpkJverq8br3V/e5CrX53PktNeDP1OwCVjQyA0vK6UQVrkUslhHvYcSa92FRmAM6kq2nt1bj+pJBLkUklFp/mrUEmkRDkojIbwjIA0VmlhLlb/v+FuauIzioxK7uQVULYVQ5/fF4ZnX0dcbE1BoRbedrh7aDgQq3jzLRIJQS7qriYZf6/vJhdUu/DRJi7igtZ5n3vfGaJSbtcZnzkvvrhxQBUVhmIrNU/ege5sGh8G14bFcHtHb1RyOo+rpfkZaIpLsCvTRdTmUJlj2dIa3LiL1rUqK+sIC85Fr/WNcdIpFL82nQhJyEaAI26kNzES9g6uPDn+//H6hfuZsui58mKPV9TT0UFUpkcibTmViWzMTrnx48ft/jdzYVEImm27b+IVcMi58+fZ+XKlQBMnjyZ++67j4kTJ5r233PPPaxYseKa9eh0OnQ68wtsVWU5Url5eM3Fzga5VFInBJpfWkFwA0/x9koZG2dFoZBJ0Bvg/a0xHEm0PO45tqMPpeV6dl3Kuabu2jgqZcikEoprhXSLtJX4OtlaXd/VBDjbMm9kODYyKdrKKj7em9Tkm5KrvQK5TEpurchDXomO8Fpjr1eIzyll7i/nuJShxtFWzoODQlnzRG/GfriPzCYO0dws2Cvl1Tct8/+jWleJt6PlfuZoK7do76Q0/0m187ZnWg9/bGTGfrL0QIppSOR6udLvag8lFGsr8XVsWr+7ggS4u5sfl3NKmzwUB8b2kkklFGrMNRdqKvB3bpzm+3v6U1BWYeagXA8O1e2nrt1+ukp8nCz/v51s5RTr6ra3s23N//vnU5nc282P925tjb7KQJXBwA/H04lp4KHAQVF9DbFQt4+j0uIxzrbyutq1lTjbGp/2M4t15JWWc0cnb74/loZOb2BEK3fc7GxwVtXoPZxcSH5pBYXaCgKcbbmzkw8+jgo+P5BiVremyHjttHUyj6TYOrmgKbZ8XdWVFGOoqqp7jKMLRVnG+ktyjQ90p/9cRfc7HsQtIJy4wzvYtmQu41/5HCcvf3xad+bob19x7q9faTtkPJXlWk78YbzH5ORYf822hv+oT9BsWJ1zccULk0ql2Nra4uxcEzZ2dHSkqMhyhOBqFixYwOuvv25W5jd0GgHDZ1grxyJlOj33fXMMlY2MniGuPD0snLRCDScsRC/GdfZh6/nsZhlTbk4y1Dpe2RyDnY2MnkHOPNwnkHe2xzXZwbCWU0mFnEoqNH0+kVjIluf6M7VPIIu3xv6jWv4Xic0t4/1dCdgrZEQFuzC9hx8f7UmymMdxM3FfD38CnG15e3tcS0sB4PZO3vQLc2Pepsv1Die2NEMi3Ah1V/HZ/iTyyiqI9LA35lxoK4nOLr12Bc2E3gBLDyQzvYc/H9/eDn2VgYtZJZytlfex96pE07QiHUXaSv5vcCia6NUc/+UzDlTfXIc9Zn6tbi4MVcYhmFb9xxAZNRIA98BwMqNPEXNgG90nzMDVL5j+0+Zw9LflnPjjWyRSKW0Hj8fWyfU/GxH4X8Eq5yIkJISYmBjCw8MBOHjwIEFBQab9ycnJ+Pr6XrOeuXPnMmfOHLOyYR8frmNXWFZBZZUBNzsbs3I3exvyGxj3NwCp1Rn5MdmlhLjbMS0qiBPJZ83sugQ4E+JuxyvrLlxTsyXUOj36KgNOtubN6Gwrp0jbtIQzfZXBlNmdWKAhzF3FyNYefHs07brrLCgtp1JfhYeDeYTI3UFJTiNnBFRWGbiQpibY3XKk43+RUl0l+ioDjrWiDo5KeZ2o1BXU2krL9rWeQMv1BnJLK8gtrSCpIJOXh4XRJ9iZ7TH51633Sr9zrtXvnJqh3wHc292Pzn6OLNgRR4Gm6fWBsb30VQZcVOaaXVQ2FF7jO8Z38OaOTj68tiWGpAZmczSWkur2c6zdfkq5xcRSMEYGakeljO1ttLeRSpjQwYvPD6SYZm2kFekIdLFlZCv3ep2LkvLqa4iFuuvre0Xayrraa/3vkwu0vPFXHCob41BSiU7P3GFhDbZffJ4xwtKpd3/wDCXAxRhR0lca69UWF2DnXJNjoi0uxC0gzGJdSgcnJFIp2lqRDa26EJWTsQ5VdV3OPkFmNs4+gZQW1EQlwnoOIaznEDTFBcgVtiCRcGHH7wQGBtZ7Ls2BcF6ahlU5F4899hh6fc0TV4cOHZDLazr55s2bGzVbRKlU4uTkZLbVHhIB440sOlNNz6uSrCRAz2BXzqY1PjQqlYCNrO6pjuvsw8UMNTHX+VShrzKQmK+h/VU5CBKgnY9Ds02Xq6lXgo2F8VBrqNAbOJ9WTFRETQKdRAJ9I9w5eVV0oiGkEmjl60B2M05PbGn0Bkgt0hLpWeMwSTCOmdc3NTGxQGNmD9Day67B6YNgbG+5tGnLy1zpd+18avU7bwfimtjv7u3uR/cAZxbujCe3tHkcCzD+luNyy+jkW5MrJQE6+TlyKbv+nIQJHb2Z2NWXN7fGNvncrqA3GEgu1NDWy/z/3cbLvt4p5PF5GtrUyg1p6+1guiHLpBLkUim1YypVBkODNyl9lYGkAg1ta11D2no5EJdn+Xzj8zS09Tbve0YtdbVrKqoo0enxclAQ4qriVFr9s1YCXYw5G2XYoHL3xcnLDycvP1x8g1A5uZJx6bTJtlxTRk7iJTzD2lqsSya3wT0owuwYQ1UVGZdO4RnaBgAHd29Uzu4UZ5tP/y/OTsPBzatOnSonV2xsVSQe34PMxoZ+/frVey7Ngci5aBpWRS5GjhxJSEhIvfvfeeedpuqpw09HUpl3axsuZqq5UD0V1dZGysYzxvG6+be2JkddztLdCQBMiwrkYkYJqYUaFDIpfcPdGNPBm/e2mq8fYK+QMayNJx/vbFrYd0t0DjOjAknI15imBCrlUtPc9oejAikoq+CX6oRRmVSCf/VsAblUgqvKhiAXW7SVVaZIxaTOPpxJV5NXVo6tXEZUiAttvO15/+9syyKs4Js9iSyc0pFzqUWcSSli+oAQVAoZv1VHRBZO7UhWkY4PN18GYNbwcE4lF5KUW4ajyoaZg0Lxd1Xxy+GUhr6m2bFXKQgP9DR9DvF3p1MrfwqKy0jJvPY6AtdiV2w+d3fzJaVQQ3KBlkHhrihkUg5XD6Xd082XIk0lGy8an6h2xxXwZP8gBoe7cSGrhG7+TgS6qFh9yvh/VsgkjGjlzrnMEoq1ldgrZAwIdcXZVs6pq3IGXFRy7G1kuKrkSCSY+kZOaXmDQ3VbL+Uws0/dfre3Ovl5Zp9ACjQV/Gqh38nq6Xf39fAjKtiVj/ckoq2sMkVGyir0zTIUseFcFk8ODCE2t5SYnDLGdfBCKZey87IxwfipgSHklZWz6phxnYXbO3kztZsfH+1KILtEZ4p6aCuqLM5qsIbtl/OY3tOfxAINifkahkW6o5BLOVCdmzW9pz+FmgrWVa+PsyM2j2cHhTI80p2zmWp6BjoT7GprWhNCW1nFpZxS7uzoTYW+irzSClp52tMn2MX026+Pvy7n8kCvABLzNcapqK2MWvZX/y8f6OVPgaaS389mGbXE5PLskDBGtHLnbIaankEuhLja8v2xmqhm9wAn1Do9+WXl+DvbMrWrLyfTi03JpZ72CnoFO3M2Q02pTk+Aiy2Tu/hyKbtujo1EIqHt0Amc2fwzjl5+OLp7c3LD99g5uxPUOcpkt/XjuQR17kvbweMAaDf0dvatXIR7cCQewa24+PcfVOp0RESNMNXbYcSdnNr4A67+YbgFhBF3eDtFWakMmvmyqd6LuzbgFdYWudKWjOiTHFv7Dd0nTMfJqXFJ/YKWwSrnIjIykoyMDLy8jF7llClTWLJkCd7e3jdEHMD2izm42Nnw8IAQ3O0VXM4u4Zk1Z01Jnt5OtlRddd2ztZHx/KgIPB2V6CqrSMorY/6GaLZfNE/+GdHOC4kEtl1o2g37cHIRjrZy7ujkjbOtcTGj9/9OMIU03e1sMFy1MISrSs5bY1uZPo9t58nYdp5czCphwY54wBjifDgqEBeVHE1FFSmFGt7/O8FsVsr18ufpTNzsFTw9KhJPRyUX04t5cPkx0/RSPxeV2ToWTiob3prYAU9HJUWaCs6nFjHl00PE/oNjyADd2gWzbfnTps8Ln70TgO/XH+Lh+T80uf6T6WrslTLGtPHESSkjrVjHskMplFTPTHBV2Zi1S2KBhpXH07mlrQe3tvUgp7SCrw+nmta4qDKAl4OSGT2dcVDIKK3Qk1ygZcm+ZJMNwNg2nvQKqslbem5IKACf7ksmtp4nV4AjyUU4KuXc3rGm3324q+F+98aYmn43pq0nY9p6Ep1Vwrs7jf1uWKQHAHOHh5t91/JDKexLaLoDtz+hACdbOXd198NFZUNCnoY3t8aYhhY8HBRUXaV5VBtPbGRSnh9mrmf1iXRWn8xokpZjqcU4KOXc1s4Lp+pFtJbsSzLNRHGr1X7xeRqWH05lfAcvJnTwIruknM8PpJjlQC0/lMrtHb14oFcA9goZ+aUV/HEu+5qLaB1LKcZRmcn4DkYtKYVaPt6TeJUWhVnfi8vTsPxQChM6eHN7R2+yS8r5bH+ymRZnlZzJXXxxUsoo0lZyMKmQjRdqroGVVQbaejkwPNIdpVxKflkFJ1KL2HTBcpJkhxETqdRpOfjjJ5SXleAd3p7hs94wW+NCnZOBrqQmry20xyC0JcWc2vh99SJaYQyf9Qaqq5I82w2dgL6inKO/fkl5mRpX/zBGPPk2Tp41w+u5iZc4vekHKnQanL0Dibp7FuG9hzXYps3BfzTg0GxIDIbGL4kklUrJzMw0OReOjo6cPn2asDDL427W0HvB7ibXcaNoHWx5vvnNwsGTdVfUu5lI3b6xpSXUyyOvzWppCQ1SWPbPryliDcVNXMzqRuLZyFkoLUVV1c2ZmHqF0Hqm5d4svFTL6WxuBi8+cG2jRrLrmb7NVtf/Cv8Ty38LBAKBQPBPIiIXTcOqzDJLySn/1WQVgUAgEAgElrEqcmEwGJg+fTpKpTExTKvV8uijj2Jvb565vHbt2uZTKBAIBALBP4x4cG4aVjkX06ZNM/t87733NqsYgUAgEAhuBoRv0TSsci4as7S3QCAQCASC/zYioVMgEAgEglpIReiiSQjnQiAQCASCWgjfomk0bR1igUAgEAgEzcpnn31GSEgItra29O7dmyNHjtRr+9VXXzFgwABcXV1xdXVl+PDhdeynT59eZ0ny0aNH39BzEM6FQCAQCAS1aKl3i6xevZo5c+Ywf/58Tpw4QefOnRk1ahTZ2ZZXk961axd33XUXf//9NwcPHiQwMJCRI0eSlmb+ksvRo0eTkZFh2n766afrbpvGIJwLgUAgEAhqIZU032YNixYtYubMmcyYMYN27drxxRdfYGdnxzfffGPRftWqVTz++ON06dKFNm3asHz5cqqqqtixY4eZnVKpxMfHx7S5ut7YlaeFcyEQCAQCQS2aM3Kh0+koLi4223S6um+WLi8v5/jx4wwfPtxUJpVKGT58OAcPHmyU7rKyMioqKnBzczMr37VrF15eXrRu3ZrHHnuMvLy8pjXQNRDOhUAgEAgEN5AFCxbg7Oxsti1YsKCOXW5uLnq9vs7LQL29vcnMbPjtuld44YUX8PPzM3NQRo8ezcqVK9mxYwfvvfceu3fvZsyYMej1+qadWAOI2SICgUAgENSiOWeLzJ07lzlz5piVXVnpujl59913+fnnn9m1axe2tjUv7ps6darp744dO9KpUyfCw8PZtWsXw4bdmDfM3jTOxUNDQ1paQr08NXtZS0toGPeAllbQIDfzm0eXvfZpS0toGDvna9u0JFU37smnqQQNHNLSEhokJ/3GhqWbSvqKu1taQosiofm8C6VS2ShnwsPDA5lMRlZWlll5VlYWPj4+DR77wQcf8O6777J9+3Y6derUoG1YWBgeHh7ExsbeMOdCDIsIBAKBQHAToFAo6N69u1ky5pXkzKioqHqPW7hwIW+++SZbtmyhR48e1/ye1NRU8vLy8PX1bRbdlrhpIhcCgUAgENwsWDvLo7mYM2cO06ZNo0ePHvTq1YvFixdTWlrKjBkzALj//vvx9/c35Wy89957zJs3jx9//JGQkBBTboaDgwMODg6UlJTw+uuvc+edd+Lj40NcXBzPP/88ERERjBo16oadh3AuBAKBQCCoRUu9FXXKlCnk5OQwb948MjMz6dKlC1u2bDEleSYnJyOV1gw6fP7555SXlzNx4kSzeubPn89rr72GTCbjzJkzfPfddxQWFuLn58fIkSN58803b0jexxWEcyEQCAQCwU3ErFmzmDXLcq7arl27zD4nJiY2WJdKpWLr1q3NpKzxCOdCIBAIBIJaiHeLNA3hXAgEAoFAUAvxVtSmIWaLCAQCgUAgaFZE5EIgEAgEglqIwEXTEM6FQCAQCAS1aKnZIv8WhHMhEAgEAkEthG/RNETOhUAgEAgEgmZFRC4EAoFAIKiFmC3SNJrFuTAYDDd0fMpgMLB/7UrO7tqMrqwEv8j2jJj+FK4+/vUekxJ9hqN//kJWYgylhfmMf3o+kd37mdl8cP9Ii8cOnPIQvW6Z3Gh9j9zRh9n3DMDbzYGzsZnMWbSBYxdTLdrKZVKeu38w947tip+HE5eTc3ll6Rb+Ohxjspl5e29m3t6LYF9XAC4mZPPONzvZduhyozWZtI3twOw7uuDtasfZhDzmLNvLsZhsi7Zb3xnPwI5123Tz0STueGMTAF4uKt6aHsXwLoE4OyjYdy6DOcv2EpdR1Cg9/UNdGBrhjqNSRnqxjt/OZJFcqK3XvrOfI2PbeOBmZ0NOaTkbzudwMbvUtH90aw+6+jviorJBX2UgpUjLnxdzSCqoqXNEK3faedvj72SL3mBg7p8xlr7quunXLZzZ9w+nW7sgfD2dmTz7SzbsOtOs32GJR+7ozey7r+p3H228Rr8bxL1jrup3n28173cTejHz9t4E+7oA1f1uxd/X1e8AHrmzD7PvGVStL4M5i9Zz7EID+qYN4d4x3fDzvPK72Mxf9Xz3s/cN4s3Hx/Dp6n08t3jjdem7OyqQBweG4umoIDpDzZt/RHM29dr9eGxnHz66uzPbz2fxxMpTpnJ3BwXPjmlF/1buONracCyhgDf/uEhSXpnV2h4cHsmTY9vi5azifEoBL6w8zon4+l905mRnwyuTOnNrj0Bc7RWk5Jby0qoTbD+dDhhvlC/e0ZFJ/ULwcrYls0DDT3sT+OCPc1ZrA+M1eemnS1j76y+o1cV06dqNl+e9RnBwSL3H6PV6Pv/sEzZtXE9ebi6eXl7cNv52Hn70cdP9Iy83l8WLPuDggX2o1Wq6de/Biy+/2mC9NwrhWjSNZhkWUSqVXLx4sTmqssiRTWs4+dc6Rkx/invmL8FGacuv78+lsry83mMqdFq8gsIYfn/9b+R8bMnPZtuoh/4PJBJa9RzQaG0Th3XkvafG8vY3O4ia8RlnYjNY/9EMPF3tLdq/9sgIHprQkzmLNtD1nsUsX3eE1e/eS+dWNS+QScsu4tXPt9J3xmf0e+Azdh2P45f37qVtqFejdQFM7B/Bew/14+2fjhH1zC+cSchl/Ru34umssmg/9Z0thNy3wrR1e+InKvVVrN0fa7JZ8/IYQr2dmPT2Zvo8/QvJOWr+fOs27JTX9lO7+jkyob0XWy7l8sHuRNKKdDwaFYiDQmbRPsRVxf3d/TiUXMQHuxI5m1HCg70D8HFUmGyyS8r57WwWC/9OYMm+JPLLKng0KhD7q+qUSSScSlOzP7GwkS1nHfYqJWcvp/HMgtU3pH5LTBzWkfeeHMvb3+wk6oHPOBObyfpF0/F0qaffPTyCh8b3Ys5HG+l678fGfrfgHjpHXtXvcop59Yut9H1gKf0eXMqu4/H88u49Vvc7o75OvPfUrbz99Xaipn/CmZgM1n/0YAO/i5E8NKEXcxatp+vdH7H890Osfvc+Orfyq2PbvW0AD07ozZmYDKt1XWFMJx/m3tqGz3bEcvuSg0RnqPn6we642SsaPM7f1ZYXbmnN0fj8Ovs+u78rgW4qHv/uJLd/fIC0Qg0rZvZAZWO5f9fH7b2DeOvubiz8/RxDXt3MueRCfn1+CB5OlpdqtpFJWfvCUII87JmxZC+9nt/IM98cIaOgxql5+ta2zBgWwfPfHaPPC5t4ffUpnrylLQ+PbGWVtius+Porflr1Pa/Mf40fflqDSqXisYcfRKfTNXjML6t/Yu7L8/h9w588M/tZvv1mOT+u+h4wOizPPPUEqakpLP5kKat//R1fP38eeXAGZWXWO2iClsUq52LOnDkWN71ez7vvvmv63JwYDAZObP2dPrfdTUT3vngGhTH2kecpKcwj9sT+eo8L69yL/hNnENmjf7029i5uZlvciQMEte2Mi1fj3xT31NT+rFh/lO83nSA6MZsnF/6BRlfOtFu7W7S/e1RXFn63m60HL5OYXsBXvx9m64FLPH1Xjc4/90ez9eBl4lLziE3J47Vlf1GiKadX+8BG6wJ4akJnVmy9wPc7oolOKeDJpbvR6CqZNqKNRfuCEh1ZhRrTNqxLIGW6StbuiwMgws+Z3m18eOrz3RyPySYmrZCnlu7GViFj8qDIa+oZHOHGwaQijiQXkaUu55fTmZTrq+gdbPm14oPCXYnOLuXv2HyySsrZHJ1LaqGWAaGuJpsTacVczikjr6yCTHU5685lo7KR4XfVhXjLpVx2xxeQUVz/ha8pbNt/gdeXbmT93zc+WnGFp6b0Y8WGY3z/5wmiE3N48v0/0Ogq6u93o7uwcOWumn637ghbD16j3315ff0O4Km7+rNi/RG+33S8+nexrvp3YfmNjXeP7sbC7/5m68FLJKbnX/W7MHf07VUKVrw2hcffXUuhWmO1rivMGBDMmiOprD2WTlx2KfN/v4C2Qs+dPeuPhkol8MHUTnzyVywp+ebfHeJhR9dgF15bd4GzqcUk5Jbx2u8XsLWRckuXhl+VXZvHx7Rh5a44ftwbz6X0YuasOEKZrpJ7BoZbtL9nUBiu9gruXbyHwzG5pOSWciA6m/PJhSabXpGebD6Rxl+n00nJLWX90RR2ncugW5i7VdrAeE1e9f1KZj7yGEOGDqdV6za8tWAhOdnZ7Nyxvd7jTp06yeChwxg4aDD+/gGMGDWaqL79OXfW+LtJSkrkzOlTvDzvNTp07ERIaBivzHsNrU7Llj83Wa2zqUgkkmbb/otY5VwsXryYv//+m5MnT5ptBoOBixcvcvLkSU6dOtWsAotyMiktyie4fTdTmdLOHt+wNqTHNl+0pLSogPjTR+g4cHSjj7GRy+ja2o+dx2qe7A0GAzuPxtGrQ5DFYxQKOdryCrMyTXkFfTuFWLSXSiVMGt4Je1sFh8+lWKFNStcIT3aerglDGwyw81QqvVo37mI3bURbftkTQ5muEgBl9ROYtlxvVmd5RRV92zXskMkkEOBsy+WcmiENA3A5p4wQV8uRlBBXlZk9QHR2KSFulu1lEugb7IKmQk/6DXIkbgZM/e5orX53LLb+fmcjR1teaVam0VXSt1OwRXupVMKkYR2r+13ydejzr6vvaCy9Olj+PoVCZkFfBX07h5iVLX52PFsOXOLvq+q2FhuZhPb+ThyIqRlmMBjgQGweXYNc6j3uieHh5JWU8+vRtLr65cZLqa6iyqzO8soquoe41rGvX5uUziFu7D6faVbP7vOZ9IzwsHjMmG4BHI3N5f1pPYn+9Hb2LxjL7HHtzHIGjsTkMLCdN+E+jgC0D3KhdytPtp+xPvqTlppKbm4Ovfv0NZU5OjrSsVNnzpw+We9xXbp05cihQyQmJgBwKTqakyeP03/AQAAqqiPRSkXNg4FUKkWhUHDyxHGrdTYVqaT5tv8iVuVcvPPOO3z55Zd8+OGHDB061FRuY2PDt99+S7t27RpVj06nqxM+qyjXYaOoG/YrLTKGH+2cXczK7ZxdKS0ssEZ+g5zf9xcKW7sGIx218XCxQy6XkZ1fYlaenV9C62BPi8dsPxzDU1P7s+9UIvFp+QzpEc74Qe2RSc39vPZh3uz68lFsFXJKNOVMmfsD0YmWcyUsanOyRS6Tkl1gHk7MLtTQOuDaF7sekV50CHHnsSV/m8oupRaSnK3mzWl9mPXpbkp1FTw1vjMBng74uNo1WJ+9Uo5MKkGtM7+BqHWVeDtaPtbRVm7R3qnWEEw7b3um9fDHRiahWFvJ0gMplF7lAP3baLDfBTXU7/pd1e/CGD+oneV+t+yRmn730iqiE3OaT1+Dv4sB7DuZUPO7GGz+u5g0vBNdWvvT/4FPrdJTG1c7BXKZlLwS82tQnrqcME/LwzbdQ1yY2DOACYsPWNwfn11KWoGG/xvTinlrz6Mp1zO9fwi+Lio86xnOsIS7oxK5TEpOkXkeUk6xllZ+ThaPCfa0Z0Bbb349mMiUD3YR5u3I+9N6YiOXsvB3Y07F4o0XcFTZcPi9W9FXGZBJJbz162l+PZDYaG1XyM019gd3D/Ooh7u7O7m5ufUe98BDD1NSUsKEW8cgk8nQ6/U8+fRsbrn1NgBCQsPw9fVjyeIPeXX+G6hUKr5f+S1ZmZnk5FjXBwUtj1WRixdffJHVq1fz2GOP8eyzz1JRUXHtgyywYMECnJ2dzbbN3y0F4MKBHXw88zbTVqX/Z24S5/ZsoW3UUOSKhsdcm8qzizcSl5rH6Z9mU7z7DT6aM46Vm05QZTCY2V1OzqX3tE8YOPNzvvr9MF+9Mok2IdaPfV8v00a25WxCnlnyZ6W+iqnvbCHCz4WMnx8k/9eHGdjRny3Hkuro/yeJzS3j/V0JfLw3iejsUqb38Ks3j+O/yrMfbyQuJY/TPz5D8a7XG+530z9l4MNf8NW6I3z18kTahFh2CJpV30cbiEvJ5fTP/0fxnrf46P/Gs3LTcZO+AC9n3p89jhnzf0ZXK8Jxo7FXyFg4pSOv/naegjLL17zKKgNPfn+KEA87jr42jFNvDqd3uBu7o3Mw3ODfhlQiIbdYyzNfH+F0YgG/H05m0frzTB9aM1R5e+9gJvUN4eHPDzD41c08/uVBZo1py9T+odesf9PG9fTp0dW0VVZeX/tv3bKZPzdtYMHCD/n5l7W8+c67fLfiG9av+x0wPqQu+vgTkhITGdC3F717dOHokcP0HzAQaQs8/othkaZh9WyRnj17cvz4cZ544gl69OjBqlWrrG68uXPn1snN+OG0MQwY0TUK3/CanAB9tQNTVlSIg0uNp1xWVIBXsOUxSGtJvXSW/IxUbn3iZauOyy0so7JSj5ebg1m5l5sDmfnqeo4pZfKLP6BUyHF3siM9t5i3Hh9FQpp5glhFpZ746rKTl9Lp3jaAJyb35cmF6xqnrVhLpb4Kr1oRBS8XFZkFDSdH2SnlTBoQwZurjtbZdzIuhz5Pr8HJToFCLiW3WMueD+7keGzDUZVSXSX6KgOOtaIOjko5xVrLFyu1ttKyfa1oRrneQG5pBbmlFSQVZPLysDD6BDuzPaZu0t2/gYb7XUm9x0yeu8q83z02ioT0a/S7Nv48MakvT77/R/Poy6tPXymTX/zeqM/ZjvScYt56fLTpd9G1jT/ebo4c/PZJ0zFyuYz+XUJ49M4onAe9QlVV427iBWXlVOqrcHcwjyi4OyrIVddNEg90tyPAzY7Pp3U1lV0Zcjj/zghGf7CPlHwN59OKmfDxQRxs5djIJBSUVrDmid6cSy1ulC6APLWOSn0Vns62ZuWeTrZk1TOrKqtIQ0WlwcxRvJxehI+LChuZlAp9Fa9P7cLijRdYeygJgIupRQR62PPMuHb8vC+hQU2DhwylY8fOps/lFcY2ysvNw9Oz5oEnLy+P1m0s53MBfPThQh548GHGjL0FgMhWrclIT+fr5cu4bcLtALRr34E1a/9ArVZTUVGBm5sb90ydRPv2HRrUeCP4j/oEzcZ1zRZxcHDgu+++Y+7cuQwfPhy9ldEFpVKJk5OT2XZlSEShssPV29+0ufsHY+/sRtKFmrE8naaUjPho/CLaXo/8OpzdvQXvkEi8gqxzVioq9Zy8lM6Q7hGmMolEwpAe4Ry5xji1rryS9Nxi5DIpEwZ3YOPehvNHpFKJKeehcdqqOBmbw5BONQlqEgkM6RzAkUuZDRwJd/QPR2kj46ddl+q1KS4rJ7dYS7ivM90iPNl4OLHBOvUGSC3SEnlV2FkCtPK0I7HAcmJeYoHGzB6gtZcdifkNJ/JJJCCX/nvXhzP1ux41/VUikTCku7X9rn3j+p3CumcQo740hvSo/buI4Mi5pGvry6nWN6QDG/deAODvY7F0v+cjek9bYtqOX0jh562n6D1tSaMdC4AKvYHzacVERbhdpQ+iItw5eVUS5BXic0q5ddF+Jnx80LTtvJjN4fh8Jnx8kMxaQxgl2koKSisIdrejQ4AzOy40fjizQl/F6cR8BrbzNtM2qL0PR2MtDzkcvpxLmLeD2c0w3MeJjIIyKvTGHBCVQl4nSqWvMjRqLQd7eweCgoNNW3h4BB4enhw+fLDmnEtKOHvmNJ06d623Hq1GWycCIZPJLP7vHB0dcXNzIykpkQvnzzF46LBr6hTcXFh11YiPjyc0NNQUqZg6dSr9+/fn+PHjBAdbTtRqKhKJhG6jbufQHz/i6u2Ps6cP+3/7FgcXdyK61axbsebd54no3o9uI8YDUK7VUJiVbtpflJNJdlIctvaOOHnUeNs6TSmXjuxh8N2PXJe+JT/v46tXJnI8OpVjF1KZNaUfdrYKVm48AcDyVyeSnlPMvC+2AdCzXQB+ns6cjknH39OZlx8chlQiYdGqPaY633h0JFsPXSYlsxBHOyVTRnZmYNdQxs3+1jpt607z1eyhHI/N4djlbGaN74SdrZyV26ON2mYPIz2vlHkrD5kdN31EWzYcSiBfXTcp8o5+4eQUaUjJKaFDiBsfzOzPhsMJ7Dh57WTTXbH53N3Nl5RCDckFWgaFu6KQSTmcbFxb4J5uvhRpKtl40Ti+ujuugCf7BzE43I0LWSV083ci0EXF6lNG50ghkzCilTvnMkso1lZir5AxINQVZ1s5p9JrnhZdVHLsbWS4quRIJOBfPQaeU1r/VGZrsFcpCA+sGToI8XenUyt/CorLSMlsvrygq1myej9fvXwnx6PTjP1ucl9jv9tkTHxb/spE0nNr9zsnTsdk4O/pxMsPXOl3e011vvHoSLYevExKVq1+N+db6/X9tI+vXp1k/F2cT2HW1P7Vv4tqffMmk55TxLzPt1brCzTX99Bwo74fdgNQUlbOhfgss+8o1VaQX1xWp7wxrNibxHuTO3AutZgzqUVM6x+MykbG2mPGZM33Jncgq1jHoi0xlFdWEZNlHnEp1hijZ1eXj+7oTX5pOemFWlr7OPDSuLZsP5/N/pj616ewxNLN0Xz2cBSnEvI5EZ/Ho6NaY6eU8+OeeOP+R6LIKCjjzTWnjeeyI4aZI1qx4N7ufPXXZcK8HZl9Wzu+3FazRsiWU2n8320dSM0tIzqtiE7Brjw+ug2rquu0BolEwj333c9Xyz4nOCgY/4AAPvvkYzy9vBg6bLjJbuYD0xg6bAR33XMvAIMGD+GrL7/Ax/f/2Tvv8KiKrwG/u5veew8JIQkk9N6kgzQVlF6kSFMElaYU6SJKUwHpCtIUqQIivfceWioQCOl90zZ1vz8WNmyyCVkSBL/fvD73kZ17Zu7J3Nm9Z845M9eFKt7eBAUGsum39XR7v4e6zuFD/2BtbYOzswuhocEsmP8tbdq2p1nzsufCVRT/q+GMikIn48LHx4fo6GgcHFQP5z59+rB06VK6dev2SpR7RqOuvcnNVnB4/Y9kZ6bj6lODHhO/1ciPSImLJiutcAOcmIch/Dl/kvrzya2rAaj+Vgc6jywsD7p4EgC/Jm1eSrcdx25jZ2XKjBHtcbQx51ZoNN3GrycuWfWj4+5opWGZGxroM3NkByq7WJOelcOhC8EMm/MnqemFsx97azN+md4LJ1tzUjMU3AmL4d1xGzSy78uk29kw7CyNmDGgEY7WJtx6kEC3mfuJS1HN/N3tzYrNZnxcrWhe3YWu0/dqbdPJxoTvhzVXh1e2HA9m/rarZdLnRlQapoYyOlezx8JQRqQ8m9UXI0jPVnm+rI31eV6d8OQsNl6LoqufHe/42RGfkcsvl54Q89R1XaAEBzNDhja0xMxARkZuPo+TFSw9+1gtA9Clmj2NKhUud53URhVnXn5Wt1UQJVHP34PD6z5Xf14wUfVjuWnvRUbO3Fwh1yiKetwNb1c47iZsIC5ZtbrG3dFS494aGugxc8Tz4y6EYXO3a447K1N+md5Tc9yN38DxK/dfQr9b2FmbMmN4BxxtzbkVGkW3cb+W/L0w1GPmqLep7GJT+L2YvU1Dv4rkn1sx2Jga8Nnb3tibGxIYJWf4r9dITFeNG2crY3RwhgBgb27I5HeqYmtmSHxaNn9dj2LFMd37bvelx9iaGzGlRy0cLI248ziZXgtPEC9X9YWbrYnGvY1MyqTnghPMG1CPM/O6EJ2cyepDwfy0v9ArNXnjVab2qMWiIQ2xszAkJjmLDSfCWLj75TbRGjpsBFlZWcyZNYO0NDl169Vnxep1GBoWhpqeRESQ8lzS/eRpX/Pz0p/4du5skpISsXdwoGevPoz65FO1THx8PIsWfPc05GLPO+91Y9THo19Kx/Lyv7rKo6KQKHXINpJKpcTExKiNC3NzcwICAvDy8iq3Imsvle4ufZ18Nm7161ahdGzdXrcGpTJqeNsXC70mVs8q38qDV46J9j1A3hgK3txVOZVavtyE4d8iPko3j8a/TdT6/q9bhVIxesUvrxj6x+0Ka2t935oV1tZ/hf+/gWmBQCAQCASvBZ1sP23LakRcSiAQCAT/3xBPtvKhk3GhVCoZMmSIOq6mUCj4+OOPMTXVzOjftWtXxWkoEAgEAsG/jHgravnQybgYPHiwxueBAwdWqDICgUAgEAj+++hkXKxfv/5V6SEQCAQCwRuDcFyUj1ecbysQCAQCwX8PkU9YPsRqEYFAIBAIBBWK8FwIBAKBQFAE4bgoH8K4EAgEAoGgCGK1SPkQYRGBQCAQCAQVivBcCAQCgUBQBOG4KB/CuBAIBAKBoAhitUj5eGOMi4eJxV/v/caQJX+xzOskNe51a1AqKZkV82rzV8Kb/mKwzNQXy7xO9I1etwYlosh6g8cdoKf/xvz8CrQgcgbKh+g/gUAgEAgEFYownQUCgUAgKIIIi5QPYVwIBAKBQFAEqbAtyoUIiwgEAoFAIKhQhOdCIBAIBIIiCM9F+RDGhUAgEAgERRA5F+VDhEUEAoFAIHiD+Pnnn/H09MTIyIjGjRtz+fLlUuW3b99OtWrVMDIyombNmhw4cEDjvFKpZMaMGTg7O2NsbEz79u0JDQ19lX+CMC4EAoFAICiKVFJxhy5s27aN8ePHM3PmTK5fv07t2rXp2LEjcXHa9zM6f/48/fr1Y9iwYdy4cYPu3bvTvXt37ty5o5ZZsGABS5cuZdWqVVy6dAlTU1M6duyIQqEoTxeVijAuBAKBQCAogkRScUd2djZyuVzjyM7WvnHkkiVLGDFiBEOHDsXf359Vq1ZhYmLCr7/+qlX+p59+olOnTkyaNAk/Pz/mzp1LvXr1WL58OaDyWvz44498/fXXdOvWjVq1arFx40aioqLYs2fPq+o+YVwIBAKBQPAqmT9/PpaWlhrH/Pnzi8nl5ORw7do12rdvry6TSqW0b9+eCxcuaG37woULGvIAHTt2VMs/fPiQmJgYDRlLS0saN25cYpsVgUjoFAgEAoGgCBX5yvUpU6Ywfvx4jTJDQ8NicgkJCeTn5+Po6KhR7ujoSFBQkNa2Y2JitMrHxMSozz8rK0nmVSCMC4FAIBAIilCRbn1DQ0OtxsT/Z0RYRCAQCASCIlRkzkVZsbOzQyaTERsbq1EeGxuLk5OT1jpOTk6lyj/7vy5tVgRvpOdCqVRy7+AWwi8cJkeRga2nH3V7jcbc3qXUevfP/k3I8V0o0pKxdKlMnQ9GYePhqz7/4PxBIq6fIuXJffKys3j3298xMDbT2lZ+Xi4nfphAatRD9I2akCs1L9ff1LxeFcYNak89/0o421vSe9wa9p28Va42X5ZR7zdkXN/mONqYcft+DON/+oergZFaZfVkUiYNbMHATrVxsbMgJCKBr1cd5cjlsArRpZ2PLZ2r2WNprMfjZAWbr0XyMClLq6yLhSEf1HLC09oYOzMDtl6P4nBwgoZMV3976rtZ4mxhSG6+krCEDP68GUNM2su9dXfUB40Z17+Fqq/CYhj/w36uBj7RKqsnkzJpUCsGdq6r6qvHCXy98hBHLhUu+RrRvREj3m+Mh7MVAIEP4/h2/QkOXwx5Kf3Kwusae6N6NmPcwNY42ppzOzSa8Yt2c/VehFZZPZmUSUPaMbBrfVzsLQl5HM/Xy/7myMVgDTkXewu+GdOVt5tVw8TQgPtPEhg1dxvXS7gnpTGohScj21bB3sKQwEg5M3fcIeBxilbZno3cWDywrkaZIjefqhMKl/wtGlCHXo3dNWROBsYxeOUlnXUD+KitN6M7VcXB0oi7ESlM3XKDGw+TSpS3MNZnao+adK3nipWpAU8SM/n69xscu13c9T22SzWm96zF6iMhTP/95kvpVxSlUsmK5UvZtWM7aWly6tStx7QZs/Dw8CyxTkZGOj8v/Ynjx46SlJRINT9/vpw8lRo1a1WITv81DAwMqF+/PseOHaN79+4AFBQUcOzYMcaMGaO1TtOmTTl27BhffPGFuuzIkSM0bdoUgMqVK+Pk5MSxY8eoU6cOAHK5nEuXLvHJJ5+8sr+lXJ6LjIwM1q9fz7Rp01i+fDmJiYkVolTI8Z3cP72fur1G0/aLRegZGnF21Qzyc0t+hXLEjTPc2rMOv479aDfhRyxdKnN29QwUaSlqmfzcbJyq1aNa+14v1OH23vUYWdpUxJ8DgKmxIbdDIvli/rYKa/Nl6Nm2Ot9/2pF5G07SdPhqboXFsnfRQOytTLXKzxrRluHv1Wf8T/9Qd9DPrPvrKtvm9aG2T/kt3kaVLOlb15k9d2KZeTCUiJQsJrapjLmhTKu8oZ6U+PQctgdEk5KVq1WmmoMZx0MTmXs4jIUnHiCTSJjYpjIGMt3jpz3b1eT7sV2Y9+txmn70M7fCYti7ZEjJfTWyA8O7NWL8D/upO/An1u25zLb5A6jt46yWiYyXM33VIZp9tILmw1Zw8toDtn83AL/KDjrrV1Zex9jr2b4233/xHvPWHaHpoB+5FRrF3qUjsLfWbszP+qQzw99vwvhFe6jbZyHrdl1g24Ih1PYtnFBYmRtzfO0YcvMK6P75Our2Xcjkn/aRLNdujJbGO3Vd+Pp9f346GMI7C08TGCln0+jG2JoZlFhHnpVLg2mH1UfzWUeLyZy8F6chM3bDdZ11A+jW0J3ZfWqzaO9d2s8+wt2IFLaNb4mduXbXur5MyvaJrXC3NWHYivM0m/oP4zdcJSaleN/U8bRmUCsv7kakvJRuJbH+l7X8vmUTX8+cxebf/8TY2JhPRg4rcVUEwKwZX3PhwnnmfbeAHbv30bRZc0YNH1pslv06kEokFXbowvjx41m7di2//fYbgYGBfPLJJ2RkZDB06FAABg0axJQpU9Tyn3/+OQcPHmTx4sUEBQUxa9Ysrl69qjZGJBIJX3zxBd988w179+7l9u3bDBo0CBcXF7UB8yrQybjw9/cnKUllOUdERFCjRg3GjRvHkSNHmDlzJv7+/jx8+LBcCimVSsJO7aXa271xqdkES5fKNOw/DoU8iajbF0usF3pyD55NO+LZuD0WTpWo12s0MgNDHl06opbxadWNqu17YeNZrVQdYgKvEhd8g1rvfVSuv+V5Dp+7x+wV+9l74vV4K57xWe+mrN9/nU3/3CToUTxjF+8nS5HL4K51tcr3f7s2Czaf4dDFUMKjk1n711UOXQzl8z7Nyq1Lx6r2nLqfxNmHyUTJs/ntSiQ5eUpaemk36h4mZbHtZjSXHqeSl6/UKrP45EN1exEpCtZdisDO1ABPGxOd9fusT3PW77vKpgPXCQqPZ+zCv8jKzmXwO/W1yvfvVIcFG09y6EII4VHJrN1zmUMXgvm831tqmQPngjh0IYT7TxIJi0hk1pojpGfl0Ki6u9Y2K4LXMfY+69+K9XsusWn/FYIexjL2u52qcfZuQ63y/TvXY8GGYxw6H0R4VBJrd17g0PlAPh/QSi0zYVAbnsSlMGruNq7ei+BRVBLHLoXwMFL3Sc3wNl78cf4x2y9FEBqTztQ/b5GVk0/vJpVKrKNUQnxatvpISCs+2cnOK9CQkZdgBL+Ijzv6svn0A/44G05IlJxJG6+RlZNHvxaVtcr3b1EZa1MDBi8/x+WwRCISM7kQEs/diFQNOVNDPVaObMKE366SklHyZE1XlEolWzZtZMSoT2jTtj2+VavxzfwFxMfFcfxYcSMMQKFQcOzIYcZNmET9Bg2p5OHBJ5+Oxb2SB9v/2Fphur0sryMsAtCnTx8WLVrEjBkzqFOnDjdv3uTgwYPqhMzHjx8THR2tlm/WrBlbt25lzZo11K5dmx07drBnzx5q1Kihlvnyyy8ZO3YsI0eOpGHDhqSnp3Pw4EGMjIwqpK+0oZNxERQURF5eHqDKfnVxceHRo0dcvnyZR48eUatWLaZNm1YuhTISY1GkJePgW0ddpm9sio2HL4nh2rNlC/JySXkShoNvbXWZRCrFwacOiY+CtdYpCUVaMte3LafhgPHIDP5/JeDo68mo6+vC8asP1GVKpZLj1x7QqLqb1joG+jIUOXkaZVnZeTSrWfKPcFmQSSV42hhzLya9UBfgbmwaVex0NwRKwlhf5QXJKPI3vAh9PRl1q7pw/Eph+EepVHL8ahiNamj/2w309bT3VS0PrfJSqYRe7WpiamTApTuPddLvTUZfT0bdaq4cv1IY6lEqlRy/Ekqjmtr7wsBAW9/l0qx24cO0a4vqXA98wpb5H/Lo4CwubBrH0G6NdddPJqGmuyVnnwupKZVwNjiBepWtS6xnaijj3Kx2XJjdnrUjGuLjVNwL08Tblmvz3ub4tDZ807smVib6L6GflNoe1py+Vzh7Vyrh9L04GlSx1VqnYx0Xrt5P5LuB9bj7w3ucmtORz7v6FZs1fzewHkduRXP6nvYNmV6WyCdPSEiIp3GTwkmHubk5NWvV5lbADa118vPzyM/PL5boaGhoyI0bL+fx+f/CmDFjePToEdnZ2Vy6dInGjQvH+cmTJ9mwYYOGfK9evQgODiY7O5s7d+7QpUsXjfMSiYQ5c+YQExODQqHg6NGj+Pr68ip56bDIhQsXmDVrFpaWlgCYmZkxe/Zszp49+8K62jYUyXsa8shOSwbA0MxKo46hmZX6XLH2MuQoCwowMtf8YTAyt0Ih115HG0qlkqtbf6Rys85YV/Ipc73/CnaWJujpSYlLTtcoj0vKwMlGu7v66OX7fNa7KVXcbJBIJLRt4EW3ln442WqXLyvmhjJkUgmpCs0HilyRh6WR7j/I2pAA/eu5EBKfQWSqbjkXdlYm6OnJiEsq2lfpJffVpVA+69ucKm62qr5qWIVurfxxstXM16nu5Uj8kRmknpjN0knd6DN1C0Hh8Trp9yZjZ2VaQt+l4WRrobXO0YvBfNa/JVXc7VR918iHbm1q4mRXKF/Z1YYRHzQl7HEC7322hrU7z7N4QncGdG2gk37WpgboyaQkFMnDSUjLxr6EsMODuAwmbQ1gxNorfLHpBlIJ7Br3Fk5WhTO/U4FxjN98g/7LL/Dd3kCaeNvy2yeNdd6h0cZcpV+8XFO/eLkCB0vtM00Pe1PeaeCGTCqh349nWLLvHp909GX8u35qme6N3KnpYcW8HRXvwUpIUI1fWztN48fW1paEhARtVTA1NaN2nbqsWbWCuLhY8vPz2b/vL24F3CQ+vmKNn5fhde3Q+f8FnRM6n73MRaFQ4OzsrHHO1dWV+PgX/0jOnz+f2bNnAyrr1tHREZmePlI9fZqPmKGrShXG/TP7yMvOolr7nq9NhzeNiUv/YcWX7xGwaQxKJTyISmLjPzcY3EV7GOVN4sMGrrhZGjHv6P1/5XoTf9rPiq/eJ2DrFyiVSlVf/X29WBgl5HECjYcsx9LMiPfb1GDttJ68PWbt/ysDQ1cmLv6LFdN6EfDnl6q+i0xk474rDH63kVpGKpVwPfAJM1f+A0BASBTVqzgx4oMmbPn76ivV73p4MtfDCycq1x4kcWxaGwY082DxAZV3dN/1KPX54Og0AqPknJ3ZjqY+dpwL0f6ArSikEgkJcgUTNlyjQKnk1qNknK2N+bRTVRbtvYeLtTHz+tWl1+JTZOcVlPt6f+/fy9xZM9Wfl69c/VLtzJu/gJnTp9KhTUtkMhnV/Pzp1KUrgffullvH8lKR+1z8L6KzcdGuXTv09PSQy+UEBwdrxHUePXqEra12t93zPL+hSEZGBklJSfx8MQo9PX0K8lQxyuz0FIyfS6jMTk/B0sVLa3uGphZIpFIURTwbirQUjCxKdnMWJS70Fonhweye9IFGuZPiEhkyJ5IMa5RQ879BQmomeXkFOBRJqnOwMSWmyCzz+Tq9p/2BoYEethbGRCWk8c3H7XkYVXaPkDbSsvPJL1BiaaQ5BC2M9EhVvFyc+nkG1nehtos584/dJ/kl4t4JKZnk5eXjYFO0r8xK7quUTHpP2fK0r0yISpDzzScdeRilmeGfm5fPg0hV2Y3gKOpXc+XTXs0Yu/AvnfV8E0lIySih78yJSZSXWKf3pA2qvrM0ISpezjdjuvIwqjCfIiYhjcCHmol+QeFxdG+j28qC5Iwc8vILiiVH2pkbEl/GVUV5BUruPknFw157ci9ARGImienZeNiZ6mRcJKWp9LO30NTP3sKIuFTt74KITc0iL19JgbIwFykkWo6jlbEqzOJpjb2lEUdndlCf15NJaeprz7C23riN3KlR90W0btOWmjULw9A5Tz3PiQmJ2NsXJicnJiZStVrJOW7ulSrx62+byczMJCMjHXt7ByZN+AI3t1eXgyT4d9DJuJg5c6bGZ1NTzS/Wvn37aNGixQvbeX5DEQsLC5ydnbF6pHKFK5VKjMytiQsJwMpVZUzkKjJJehSCV7MuWtuT6ulj5eZNfMgtXGuqlt8oCwqIDw2gyltdy/z31flgJNW7fKj+rEhN5OzqmSQY1CRHalnmdt5UcvPyuRESRZv6ldl3VpW/IpFIaFPPi1W7S3/rXnZOHlEJaejJpHRv6c/OE+WbWeQXKAlPysLfyYzrkaoHjgTwdzTjWEj5Vh0NrO9CfTdLvjt2n4SMlzNUcvPyuREcRZsGVdh3JlCln0RCm/pVWLWz5MRieNZXclVfta7OzuO3S5WXSiUYGryRq8Jfity8fG4ERdKmoQ/7TqnGiUQioU0Db1ZtP1dq3eycPKLin/Zdm5rsPBqgPnfh1kN8Pew15H0q2fM4RjdDNzdfye2IVJr72nH46TJNiQSaV7Xjt9PhZWpDKoGqLhacvFfyqgYnKyOsTQyIk+v2cqjc/AICHiXTws+Rf25EqfVr4efAL8e1LwG/HJrIB00qIZGo8jMAqjiaE5OSRW5+AacD42g5/aBGnZ8+akRYtJxl/wTpZFiAKqRhalpoPCqVSuzs7Ll06QLV/FShmPT0dG7fCqBXn34vbM/ExAQTExPkqalcOHeWL8ZP0kmfV4FwXJQPnX7RPvzwQzw9PZFKtadqLFy4sNwKSSQSvFu9R9CRbZjZu2Bq48jdfzZjZGGDS80marnTK6bhUrMp3i3eAcCndXeubv0Ba3dvrD18CTv1F3k5CjwaF+6nrpAno0hLJj1B9YWVRz1Cz8gYEyt7DEzNMbHWXA6oZ6iKb+ZJTciXli+r1tTYgCruhT+Mnq621PJ1JVmeSYSOP47lYemfF1g75X2uBUdxNTCSMb2aYGKsz8YDqqSrdVPfJypBzow1xwBo6OeKi70FAaExuNqbM21oa6RSCUt+L/0hURYOBcczook7D5OyeJCYydtV7TDUk3Lmoao/RjRxJzkrlx0BqgeATCrB9elsTiaVYG2sTyUrIxR5BcSlq2ZOHzZwoamHNT+dDkeRV6D2jGTm5pNbwgqTEvtq2znWTuvBtaBIrt57wpjezTAxMmDj39dUffV1T1VfrTqs6it/t6d9FY2rvQXTPmqHVCJhyZYz6jbnfPw2hy6EEBGbgrmJIX3erk3LupV5d/yGl+/IF/A6xt7SradYO7Mv1wKfcPXuY8b0bYGJsQEb918BYN2svkTFpTJjhSrE0bB6JVXfhUTh6mDJtBFvq8bZphPqNpdtPcOJX8YwaUhbdh4NoGH1SnzUvQljvt2us37rTjxg8cA63IpIIeBRCh+19sLEQMb2S6rE2iUD6xCTqmDBPpUR/lknH26EpxAen4GlsT4j21XBzdqYPy6o5E0MZHzR2Zd/AqKJl6u8FVO6+RGekMHpIN3DXasOhbBseCMCwpO4/jCJUR18MTHU44+zqtV4y4c3Ijo5i3k7VYbrhhNhDGvnzbx+dVl3LBQvR3O+6OrH2mOqPVYyFHkERWp6jTKz80jKyClW/jJIJBIGfDiItatX4lHJA1c3N35e9hP2Dg60bVf4Gzzio8G0bdeBfgMGAnDu7BlQKvGoXJmIx4/5YdECPCt70e39D0q61L/G/2quREWhk3Hh4+NDdHQ0Dg6qh3CfPn1YunRpsT3Ly4tv2x7k5Si4/udycrMysK3sz1ujZiPTL1yDnpEQQ05G4ZfCvW4LstNTuXdwCwp5MpauXrw1arZGkueD8/8QeOh39edTyycDUL/f53g20nzxS0VTz9+Dw+s+V39eMLEHAJv2XmTkzM2v9NrPs+P4XeysTJnxURscbcy4FRZDt4mbiUvOAMDd0VJjFmNooMfM4W2p7GxNelYOhy6GMuyb3aSml/9VvZcfp2JuqMf7NR2xNFJtorX45EPkT5M8bU30UT6ni7WxHnM6F2Y4d/azp7OfPUGx6Xx3XLUCpp2PHQBT2lfRuNa6ixGcfajbg3THsduqvhreDkcbc26FRtNtwobS+2pEByq7PO2rCyEMm7tdo6/srUz5ZXpPnGzNSc1QcCcshnfHb+D4lVeXF/I6xt6OowHYWZsxY2RHHG3NuRUSRbfP16mTPN0drSkoKNJ3H3emsquNqu/OBzJs5u8afXctMII+X25gzuguTB3WgfCoJCYt+Ys/DmlfjVAa+29EYWtmwPguVbG3MOTeEzmDVl5SLy91sTbmOfWwNDbgu761sLcwJDUzlzsRqXzw41lCn652ylcqqeZiQY9G7lgY6xObquBMUDyLDwSR8xI5Dn9dicDW3JAvu9fAwdKIOxEp9P3htDrJ09XGRKP/opKz6LPkNHP61uHknI7EJGex5mgoyw5oX2H3Khg6bARZWVnMmTWDtDQ5devVZ8XqdRqrQZ5ERJCSUvg9TE9PY+mPS4iNicHS0op2Hd5m7Ofj0NevmKRuwetDolSW3R8mlUqJiYlRGxfm5uYEBATg5aU9F0IXph54dTsUlpcfpi193SqUjrnd69agVPp88vpnISWxbfnr3dTshWSmvljmdaL/6tbJlxeHJq1eLPQayUrXffOvf5PHa3q/bhVKxegVRxK/PVZxBv/UdlVeLPT/jP8/gV6BQCAQCCoIERYpHzoZFxKJRL0U9fkygUAgEAj+PyGMi/Khk3GhVCoZMmSIOoamUCj4+OOPi60a2bVrV8VpKBAIBAKB4D+FTsbF4MGDNT4PHDiwQpURCAQCgeBNQHjly4dOxsX69etflR4CgUAgELwxiLBI+SjXK9cFAoFAIBAIiiJWiwgEAoFAUAQRFSkfwrgQCAQCgaAI4sVl5UOERQQCgUAgEFQownMhEAgEAkERREJn+RDGhUAgEAgERRBRkfIhwiICgUAgEAgqlDfGc6Ene4PNRD2DF8u8TpS6v3Xx30Semfu6VSiZgvzXrUHpvMEvBgMgt/xvx31VSKVv9twpP//NHnsFZX+n5Wvi1T4zpK+4/f/vvDHGhUAgEAgEbwoiLFI+hHEhEAgEAkERREJn+Xiz/YYCgUAgEAj+cwjPhUAgEAgERRCbaJUPYVwIBAKBQFAEYVuUDxEWEQgEAoFAUKEIz4VAIBAIBEUQYZHyIYwLgUAgEAiKIGyL8iHCIgKBQCAQCCoU4bkQCAQCgaAIYuZdPoRxIRAIBAJBESQiLlIuhHEmEAgEAoGgQhGeC4FAIBAIiiD8FuXjjTQulEoldw5s4cGFQ+RmZWBX2Y/6vUdj7uBaar3Q0/sJOr4LhTwZK9fK1Os5CluPqurzV/5YTmzwTRTyJPQMjLCt7EftbkOwcHTXaOfhpaMEn9hDWlwk+kYmWGdbkWxco8TrjurVnHED2+Boa87t0CjGL9zN1XuPtcrqyaRMGtqegV0b4GJvScijeL5evp8jF4LUMkF/fY2Hi02xuqu2n2Xcgl2l9kEx3d5vxLh+zXG0MeP2/VjG//g3VwMjS9btw5YM7FQHFztzQiIS+XrlYY5cDlPLNK/twbh+b1GvqjPOdhb0nrqVfWeCtLb3snTys6d7TUesjPUJT8pi3YXHhCVkapVtX9WO1t42VLI2BuB+QiZbrkaWKK8ro3o0YdyAVqr+C4tm/JK9XL33RKusnkzKpMFtGNi5Hi72FoQ8TuDrFf9w5GKIVvmJH7Zi7ujOLN92lkk/7tddt57NGDew9dNxF834Rbu5ei+iZN2GtGNg1/qqcfc4nq+X/c2Ri8Eaci72FnwzpitvN6uGiaEB958kMGruNq4Hav+bK4Lm9aowblB76vlXwtnekt7j1rDv5K1Xdr1nfPiWByPbemFvbkhglJxZO+8S8DhVq2yPRm4s6l9boyw7N59qkw5qlf+mVw0GNPdgzu67rD8VrrNuw9r5MKZzNRwsjbkbkczkzde4/iCpRHkLE32+7lGLrg3csTY1ICIxg2lbrnP0VrRaxtnamJm969CuljPGBjIexqYzdt0lboaX3G5JKJVKVv68jN07tpOWJqd23XpMnT4TDw/PEuvk5+ezasVyDuzfS2JCAvb2Drzb/X1GjPpEHYLIzMxg6Q+LOXH8GKkpKbi4utFvwIf06tNXZx3Li1iKWj50Cotcv36dhw8fqj9v2rSJ5s2b4+7uzltvvcUff/xRIUoFHd1J6Ol9NOj9Ke3HL0ZmYMSplTPIz80psc7j66e5uXsd1Tv14+1JP2HlWplTK2agSEtRy9i4e9NowBd0nrqSlqPnAEpOrZhBwXOv3Q4+vpvb+zfi174nnaesoPWn36DQsy/xuj071OH7L7oxb90hmn64hFuhUexdNhJ7azOt8rM+6cLw95syfuFu6vb5nnW7zrNtwVBq+xYaTm8N/gHPTjPVR5dPVwKw62hAGXvwqW5ta/D9mE7M23CSpsNXcSsshr2LB2FvZapdtxHtGP5eA8b/+Dd1P1zOur+usO3bftT2cVLLmBoZcDsshi+W/K2TLmWleWVrhjZ2488b0Uz8K5DwpExmdPLB0ki7HVzDyYyzD5KZcSCEKfuCSMzIYWYnH2xM9MutS892tfj+s3eY98tRmg5Zxq3QaPb+MAx76xL6b9TbDO/eiPFL9lK3/w+s232Rbd99SG1fl2Ky9f3cGNa9MbdCo7W0VAbd2tfm+y/eY966IzQd9KNq3C0dUcq468zw95swftEe6vZZyLpdF9i2YIiGblbmxhxfO4bcvAK6f76Oun0XMvmnfSTLs15Kx7JiamzI7ZBIvpi/7ZVe53m61nVmWnc/fjoYyjuLzhIYmcZvHzfG1sygxDryrFwaTj+qPt6afVyr3Ns1HanraUVMysu9jr57o0rM7VeXhX/doe3Mg9yJSGH7xDbYmRtqldeXSdk1qQ3udqYMXX6WxpP/Ztyvl4lOLrxvlib6HJjWntz8AvosPkmzKQeY/scNUjJL/k0tjQ2/ruP3LZuYOmMWG7f+ibGxMZ+OGk52dnbJdX5Zy45tvzN56nR27f2bz8ZP4Len7Txj8YLvOH/2LPPmL2DX3r8Z8OEgvv92LidPaO/rV4mkAo//RXQyLoYOHcr9+/cBWLduHaNGjaJBgwZMmzaNhg0bMmLECH799ddyKaRUKgk59Rf+b/fBtVYTrFwr0/jD8WSlJhF560KJ9YJP7MGrWUe8mnTA0rkSDXp/ip6BIQ8vHlHLVGneCQfvGpjaOmLj7k3Nrh+SmRxPZmIcADmZ6dz+ezONB47Ho0FrzOydsXKtTJa+Y4nX/ax/K9bvucimfVcIehjL2Pk7yFLkMvi9Rlrl+3epz4INRzl0PpDwyCTW7jzPofOBfD6wtVomISWD2MQ09dHlrercj0jgzPX7OvXlZ32asX7fNTYduEFQeDxjF+1T6da1nnbdOtZmwabTHLoYSnh0Mmv3XOHQhRA+79tcLXP4Uiiz1x1j75lAnXQpK+/WcORIcALHQxN5kqJg9bnHZOcV0NbXVqv8j6fCORgYT3hSFpGp2aw4+wiJREItF/Ny6/JZv7dYv/cym/6+RlB4HGMX7CErO4fB7zTQKt+/Uz0W/HaCQxeCCY9KYu3uSxw6H8zn/VpoyJkaG7B+Vh9Gf7eLlLSXe3Crxt0lNu1/Ou6+26m6t+821K5b53os2HCMQ+eDVLrtvKAadwNaqWUmDGrDk7gURs3dxtV7ETyKSuLYpRAeRia+lI5l5fC5e8xesZ+9J169t+IZw1tXZtuFCHZcfkJYbDrTtt8mKyefXo3dS62XkJZdeKQXfzA7Whoyq0d1vth0k7yCgpfSbXSnqmw6dZ+tZx4SHCVnwoYrZOXkMaCll1b5AS29sDIz4MOlZ7gcmkBEQgbng+O5G5Gilvm8qz+RSZmMXXeJ6w+SeJyQwck7MYTHpeusn1KpZOumjYwY+TFt2rbDt2pV5n77PfFxcZw4drTEegE3b9CqTTtatGqNi6sbHd7uRJNmzbl7+/ZzMjd5p1t3GjRqjIurGz169cG3alXu3v73xoagYtDJuAgNDcXHxweAFStW8NNPP/HTTz/x8ccf88MPP7B69WoWL15cLoUyEmNRyJNxrFpHXWZgbIqtR1USwrW73/PzckmOCNOoI5FKcaxah4SH2uvkZSt4eOkopraOGFvbARATdAOlsoDM1EQOzPuYvdMHc/7X75AVaH8A6OvJqFvNjeOXC93eSqWS45dDaFTTU2sdA309FNl5GmVZ2bk0q125xGv07VyP3/Ze0nq+JPT1ZNT1deb4tUKDRKlUcvzqfRpVdytZt5wiuuXk0axmJZ2u/bLoSSVUsTPhVpRcXaYEbkWlUdVB+4y8KAZ6UmRSCWnZ+S8WLgV9PRl1q7py/EphSEipVHL8ShiNanhov7aBrHj/ZefSrLanRtmPE7tx8HwwJ55rW2fdqrly/EqRcXcllEY1S9JNy70tMu66tqjO9cAnbJn/IY8OzuLCpnEM7db4pXR8k9GXSajhZsnZkAR1mVIJ50ISqOdpVWI9EwMZZ2e04dzMtqwZVh8fJ80xKZHAkgF1WHP8AaExuj+0VbpJqe1pw6m7MRq6nbobS0NvO611OtV15WpYIgsGNSBw6fucndeZce/4a7j1O9V15WZ4Er9+2pygZe9zYk4nPmxV5aV0jHzyhISEeBo3baYuMzc3p0atWtwKuFlivdp16nL50gUehau838FBQdy8fp3mLVo+J1OHUyeOExcbi1Kp5MrlizwKD6dJs+YlNfvKkEgq7vhfRCfjwsTEhIQE1RcyMjKSRo00Z+eNGzfWCJuURHZ2NnK5XOPIy1HNAhTyZACMzK006hiZW6GQp2htLydDjrKgQHudtGSNstAzf7NzYk92TupJ9L1rtB79DTI9lQs9IzEGlEoCD2+n7gcjaPbRFHIy03DIvATK4rMQOytT9PRkxCWlaZTHJaXhZKt95nz0YjCfDWhFFXc7JBIJbRv50q1NTZzsLLTKv9e6BlZmxmzef0Xr+ZKwszR5qluGpm7JGSXrdjmMz/o0o4qbjUq3BlXo1tKvRPmKxtxID5lUQkqW5kMwJSsXK+OyhTkGNXQlOTNXw0B5GeysnvWf5kMiLikdJ1vths7RS6F81rcFVdxsVf3X0Jturatr9F+v9rWoU9WV6Su1x+rLpptpCbql4WSrfRwdvRjMZ/1bPjfufIqNu8quNoz4oClhjxN477M1rN15nsUTujOgq3ZPzX8Va1MD9GRSEtI0XfgJadnYW2gPPTyIS+erP24x4pdrjN98E6lEwo7Pm+FkaaSW+bhdFfILlGw4Hf7SutmaG6InkxKXqhlSiUtV4PDctZ7H096Mdxu4I5NI6LvkJIv+usvoztWY0K26WsbD3oyhbXx4EJtGr4UnWX88lPkD69G3ufZJTWkkJMQDYGOr6U20tbUjMSFBWxUAhg4fScfOXXn/3S40rFODfr3ep/+Hg+jyzrtqma+mTserShU6tmtFo7o1+XTUCCZPm0H9Bto9cq8SiURSYcerIikpiQEDBmBhYYGVlRXDhg0jPb1kwzYpKYmxY8dStWpVjI2NqVSpEp999hmpqZq5Rtr+Bl3THnRK6OzcuTMrV65k3bp1tGrVih07dlC7dmGS059//om3t/cL25k/fz6zZ88GVBavo6MjMj19ZHr6tBg1U6c/QFc8GrTGqWodsuTJBB/fxfn139Fu3EJk+gYolUoK8vOo12MkTn6q0EGTwV+yZ9oAjPITS829KCsTF+9mxbTeBGyfjFKp5EFkIhv3XWbwu9pniIPfa8yhC0FEJ5TvYVkm3ZYeYMWX3QjY/JlKt6hkNh64UWIY5U3j/VqONPeyYcbfIeTmK//160/8YR8rJn9AwB8Tnt7bJDb+fU0dRnFzsGThuHd557NfyC7iRXjlui3+ixXTehHw55fPjbsrDH63cIIglUq4HviEmSv/ASAgJIrqVZwY8UETtvx99V/V903jRngKN8JT1J+vPUzmyJRW9G9WiSX/hFDDzYKhLT15Z9HZf103iRQS0hSMW3+FAqWSgPBknK2NGdPFj4V77gAglcLNh0l8s0MVXrj9OBk/V0uGtPXmj3OlTwgP7N/HN7MLf5eXrlj1UnoePvgP/+zfx7ffL6KKtzfBQUEs+v5b7B0ceK/b+wD8sWUTt28F8OPyFTg7u3L92hW+mzcHewcHmjznKRGoGDBgANHR0Rw5coTc3FyGDh3KyJEj2bp1q1b5qKgooqKiWLRoEf7+/jx69IiPP/6YqKgoduzYoSG7fv16OnXqpP5sZWWlk246GRfff/89zZs3p1WrVjRo0IDFixdz8uRJ/Pz8CA4O5uLFi+zevfuF7UyZMoXx48cDkJGRQVJSEqsvRyPT16cgLxcARVoKxpaFKyYUaSlYuWm3sg1MLZBIpRrJm8/qGJlba8oam2JgbIq5gyu2nlXZPbkvT25dwKN+K4wsVLIWToVhACNzSwokBlpDIwkpGeTl5eNgozmzd7AxJyYxrZj8szq9J63H0EAPW0tTouJT+WbMOzyMKh7XruRkTdtGvvT9cr3WtkojITXzqW6ayYcO1qal6JZJ76m/q3SzMCYqIY1vPu7Aw6hkrfIVTZoij/wCJVbGmsPSyliflKzcUut2q+HIB7WcmHUwlEfJ5U9ATEh51n+aXgoHGzNiErXPDBJSMug9edPTe2tCVLycb0Z34mGkKhu/bjVXHG3MubBhrLqOnp6Mt+p48nGPpli2+pqCghcbRYXjrqhu5sQkajdCVeNug6ZuY7pqjLuYhDQCH8Zq1AsKj6N7m1ov1Om/RHJGDnn5BcUSJO3MDYmXl5yQ+Dx5BUruRcrxsDcBoGEVG2zNDDk3s61aRk8mZVo3fz5qVZkWc06Uqd3EtGzy8guKeSkcLI2KeTOeEZuiIC+/gAJl4dgJiZbjZGWMvkxKbn4BsSkKgot480Ki5bzbsPQcE4BWbdpQo1bhGMh96mVOSkzE3t6hUPfEBKpW9SuxnR8XL2To8BF06tIVAB/fqkRHR7F+3Rre6/Y+CoWCZT/9yJKfltGiVWsAfKtWJTgoiE0bfv3XjYs3fROowMBADh48yJUrV2jQQDWBWbZsGV26dGHRokW4uBRPJK9RowY7d+5Uf65SpQrz5s1j4MCB5OXloadX+NtrZWWFk5NTsTbKik795+Liwo0bN2jatCkHDx5EqVRy+fJlDh8+jJubG+fOnaNLly4vbMfQ0BALCwssLCxwdnamevXqWLl4YG7vgoVTJYwsrIkNuamWz83KJPFRMHae1bS2J9PTx9rdm9iQwtUUyoICYoMDsKusvY5KSHU8M2jsvfwBSIsrXHaXnZGGVJlDvtS4WPXcvHxuBD2hTUMfdZlEIqFNQx8u3w4vtQ+yc/KIik9FTyale9ta7D91p5jMh+82Ii45nX/O6Z48mZuXz42QaNrUL0wCk0gktKnvxeW7pS8rzM7JIyohTaVbK3/2n63YpaYlkVeg5H5CJrWcC131EqCWiznBpSSeda/pSM+6zsw9FMb9ClqCmpuXz43gSNo0KPTESSQS2jTw5vKdR6XWVd1buar/2tRg/5l7AJy4Gkb9AT/QePBS9XHtXgR/HLpJ48FLy2RYqHULiiw+7hp4c/m2LrrVZP+pu+pzF249xNdD0zvnU8mexzH/jnH5b5Gbr+TOk1Sa+xTmMEgk0MzXluvPeSdKQyqBqs7mxD01RnZfiaTzgtN0XXhGfcSkKFhz/D6DV13WQbcCAsKTaOlf+KMukUBLf0euhGkPOVwOjaeyg5lGbL+KozkxyZnk5qvCuZdC4/F20pwEVXEyJyJBM2yqDVNTMypV8lAfXlW8sbOz59LFwgT79PR07ty6Ra3adUpsR6HIQiLRfORIpVIKnia+5uXlkZeXi0SqKSOTFcr8m1RkWERbKkBpK2vKwoULF7CyslIbFgDt27dHKpVy6VLZc/RSU1OxsLDQMCwAPv30U+zs7GjUqBG//vorSqVu3mCdPBcPHjygcuXKfPfdd3z33Xc6XaisSCQSfFt1496hbZjbu2Jq68idvzdjbGmDa62markTy6fiVqspPi1V8bqqbbpzafMP2Lj7YOvhS/DJv8jLUVC5cXsA0hNieHz9NE7V6mFoZkFWSiKBR7cj0zfA2V91c8wdXHGt2YTru9bQsM9Y9IyMub3vN3KlZihk2lcrLN16irUz+3EtMIKrdx8zpl8rTIwN2LhP9YOyblY/ouLlzPhZtXSzYfVKuDhYEhASiau9JdNGdkQqlbBko+ZSK4lEwqB3G7Ll7yvk57/cF2vptvOsnfo+14KiuBr4hDG9mqp0O3Bdpdu0D4hKkDNjtSrDu6G/Gy525gSExuBqb8G0j9qodNta6Oo1NTagimuhR8nT2Zpa3k4ky7OIiNO+R4Au7LsTy9iWnoQlZBAan8m7NRww1JNyPEQ1w/6spSeJmTlsuRoFqEIhfeu58MPJh8SlZ6u9HorcAhR55ftBWvr7WdZO78W1oCdcvRvBmL5vYWJkwMb91wBYN6M3UfGpzFh5CICG/u642FsQEBqt6r/h7ZFKJCzZfAqA9Mwc7j3Q9AxkKHJJkmcWK3+hbltPsXZmX64FPlGNu74tVPf2aW7Oull9iYpLZcYKVYijYfVKKt1ConB1sGTaiLdV93ZT4Yx62dYznPhlDJOGtGXn0QAaVq/ER92bMObb7S/XgWXE1NiAKu6FRo2nqy21fF1JlmcS8YoMm3UnH7K4f21uRaQQ8DiVj1p5YmKgx45Lqn1CFg+oTUyqgoX7VfuAjO3ozc3wFMITMrAw1mdkWy9crY3ZdkEln5KZS0qmpnctr6CA+LRsHsS9+AH+PCsOBvPziCbcfJjE9QeJjOpYFRNDPbaeUYUvVoxsQnRyFnO3qyZTvx4PY3h7X+YPqM/aIyF4OZkz7t3qrD1SuIfJqkPB/PN1B8a948+ey4+p52XLoNbejF9fdsPnGRKJhP4fDmLdmlVU8vDE1dWVFcuXYu/gQJt27dVyo4YNoU279vTtPxCAlq3b8MvaVTg7O1PF25ugwEA2b9xA9/d7AGBmZkb9Bg35cfFCjAwNcXZx5drVy+zf+xfjJ03WWc83iedTAZ4xc+ZMZs2a9dJtxsTE4ODgoFGmp6eHjY0NMTExJdTSJCEhgblz5zJy5EiN8jlz5tC2bVtMTEw4fPgwo0ePJj09nc8++6zM+ulkXPj4+BAdHa3+g/r06cPSpUtxdCx5qebLUK19D/JyFFz9Yxk5WRnYe/nT6pM5yPQL16CnJ8SQnV7o5qtUryXZ6ancObBZtYmWmxetPpmjDnXI9PVJeHCXkFN7yc1Mx9DcCvsq1Wk3bqFGImjjgeO5sXstp1fPQiKRYu9dg3iTRqrAphZ2HLmJnZUZM0Z1wtHWglshkXT7bI062c7dyVrDXWloqM/MjztT2dWW9KxsDp0LZNiMraSma7o82zbyoZKzDb/t1f3Lr9bt+B3srEyYMawtjjZm3AqLodvETcQlq37s3B0tNXUz0GPmiHZUdrYmPSuHQxdDGTZ3p4Zu9aq6cHjZR+rPC8Z2BmDTPzcY+e2LQ2Iv4tzDZCyM9OhX3wUrY30eJmYx91AoqQpVjoKdmYGGzh2r2aMvk/JlO83M923Xo9h24+X2kHjGjmO3sLM2ZcbwDjjamnMrNIpu434lLvnpvXW00vA2GBrqMXPU21R2sVH134Vghs3eVuzeVgQ7jgZgZ23GjJEdVbqFRNHt83WF487RWlM3A72n4+6pbucDGTbzdw3drgVG0OfLDcwZ3YWpwzoQHpXEpCV/8cehGxWu//PU8/fg8LrP1Z8XTFQ9bDbtvcjImZtfyTX/vhGNrakB4zv7YmdhSGCknCGrL6uXl7pYG2uMM0tjfeb3qYmdhSHyzFxuR8jp8dN5wmJfblVIaey5/Bg7C0Mmf1ATB0sj7jxOpveik8TLVffK1cZE495GJWXSc+EJ5vWvx+lvOhOdksmaw8H89Hehx/PGwyQGLT3D9F61mditBo8T0pm25To7LpTu6SqJIR8NJysri29mzSAtTU6devX5edVaDA0LQ00REY9JSS40Dr+a+jUrli3l22/mkJykCqn07NWHkZ+MVst8t2gJy35cwtTJk5CnpuLs4sKnn33xWjbRqsg0zOdTAZ7xfF89z+TJk/n+++9LbS8wsPxbAcjlcrp27Yq/v38xI2f69Onqf9etW5eMjAwWLlyok3EhUerg65BKpRrWkrm5OQEBAXh5aV9/rQszDoWWu41XxcKvV75uFUrHqGzLNF8XnYd0e90qlMg/617trLzc5P+7iZ86k1vxhlNF4dSq8+tWoVTkya8+Sbs8RKz79x/oumCi/2rXeO4IKN/k5Hl61nYus2x8fDyJiaXvLePl5cXmzZuZMGECyc8ZcHl5eRgZGbF9+3bef//9EuunpaXRsWNHTExM2L9/P0ZG2lciPePvv//mnXfeQaFQlGgUFeWN3P5bIBAIBIL/Rezt7bG3f/HKxKZNm5KSksK1a9eoX78+AMePH6egoIDGjUven0Yul9OxY0cMDQ3Zu3fvCw0LgJs3b2JtbV1mwwJ0NC60rdkVr6UVCAQCwf833vTVIn5+fnTq1IkRI0awatUqcnNzGTNmDH379lWvFImMjKRdu3Zs3LiRRo0aIZfLefvtt8nMzGTz5s3q5FJQGTUymYx9+/YRGxtLkyZNMDIy4siRI3z77bdMnDhRJ/10Mi6USiVDhgxRWy8KhYKPP/4YU1PN5Y67dun2ci2BQCAQCN4k/gsT5y1btjBmzBjatWuHVCqlR48eLF26VH0+NzeX4OBgMjNVq+iuX7+uXklSdE+qhw8f4unpib6+Pj///DPjxo1DqVTi7e3NkiVLGDFihE666WRcDB48WOPzwIEDdbqYQCAQCAT/Bd580wJsbGxK3DALwNPTU2MJaevWrV+4pLRTp04am2e9LDoZF+vX676Zk0AgEAgEgv8tREKnQCAQCARF+A9ERd5ohHEhEAgEAkERpP+JwMiby5ueECsQCAQCgeA/hvBcCAQCgUBQBBEWKR/CuBAIBAKBoAgSERYpFyIsIhAIBAKBoEIRnguBQCAQCIogwiLl440xLnLydXtX/L+JQ9M2r1uFUtHTf2Nuo1bsLV+8d/3rolLLN/veKrJyXrcKpSKVvrnOz5hT/7xuFUqlXv8+r1uFUgmNqfg3vlYktd3NX2n7YrVI+XhzfxkEAoFAIBD8J3mzp7wCgUAgELwGRFikfAjjQiAQCASCIgjjonwI40IgEAgEgiKIpajlQ+RcCAQCgUAgqFCE50IgEAgEgiJIheOiXAjjQiAQCASCIoiwSPkQYRGBQCAQCAQVivBcCAQCgUBQBLFapHwI40IgEAgEgiKIsEj5EGERgUAgEAgEFYrwXAgEAoFAUASxWqR8CONCIBAIBIIiiLBI+XgjjAulUsm9fzYTfuEwuYoMbD39qNNrNGb2LqXWe3D2b0KP70KRloylS2VqfTAKGw9f9fn83Bxu//ULT26coSAvF8dqdand8xOMzK0ByM6Qc3XzYuRR4eRkyDE0t8K5RmP8uw5C38gEgIQHdzG4tApJRhzk56A0tibfrSn5ni3V1xnUwpORbatgb2FIYKScmTvuEPA4RavOPRu5sXhgXY0yRW4+VSccUH9eNKAOvRq7a8icDIxj8MpLL+7MIgxsXokRrStjb25IYFQas3ff41ZEqlbZHg1dWdC3lkZZdm4+/pMPA6AnlTC+sy+t/exxtzEmTZHH+dBEFvwdTJw8W2fdAFpXsaGDry2WRno8SVXwx40YwpOzSpSv52pBt+oO2JrqE5eew67bsdx57u2NhjIp79d0oI6LBaaGMhIycjgRlsTpB8kvpV//pu4Ma1kZe3MDgqLTmPtXELefaO+/5+lS24kf+tfm6N1YPt14U11ua2bAxM6+vOVri7mRPlcfJjP3r0AeJWa+lH5v8tj78C0PRrb1ejr25MzaeZeAxyWMvUZuLOpfW6MsOzefapMOapX/plcNBjT3YM7uu6w/Fa6zbmWleb0qjBvUnnr+lXC2t6T3uDXsO3nrlV3veT6o60z/hm7YmBoQFpfOD8fuE1jCm0pb+dgyqIk7rlbG6EklPEnJ4vcrkRy6F6eWmdbZly41HDXqXXyYxIQdd19KP6VSyZ+/rebYgd1kpKdTrXpthn8+GWe3SiXW+fO31ezYtFajzMXdgx/X7wQgXZ7Kn7+tJuDaRRLiYrGwtKJh89b0HfIJJmZmL6Wn4PWgk3ExduxYevfuTYsWLSpUibVr1/Lg9H7q9f8CU1tH7v2zhXOrZtB+8gpk+gZa6zy5cYbbe9ZRp9enWHv4cv/UXs6vnkGHKaswNLcC4PaedcTcu0LjIV+hZ2RKwM5VXPp1Pq0+XwCARCJVGROdB2JoZkl6QjQBO1eSk5lGww8nAaBnYERepeYozZ1BZoAk+SH693aAzIB89ya8U9eFr9/3Z9q229x8lMxHrbzYNLoxbb45QWK69tdly7NyafvNCfVnJcVfN3/yXhwTt9xUf87OK9C5X7vWcWLqe35M33GHgMepDG3hwYaRDenw/ekSdUvLyqX996cLdXtONSMDGdXdLFh+JIzAqDQsjfWZ3t2PNR/Vp/uP53XWr4GbBT1rObL1ejQPk7Jo52PDZy08mHkolLTs/GLyXrbGDG/sxp47sdyKTqNRJSs+aebOvKMPiHpq3PSq7UhVB1N+vfKExIxc/B3N6FfXmZSsPG5Fp+mkX+daTkx5pxozd6seioPf8uCXYfXptOgsSRklvwrd1dqIr7pW5cqDpGLnfh5Ul7z8Akb/doN0RR5DWnqyfkQDui4+R1Zu8b+5NN7osVfXmWnd/fj6zzvcfJTCR60q89vHjWn37clSdWv37anCAmVx3QDerulIXU8rYlIUOuulK6bGhtwOiWTjXxfYtmTkK7/eM9pVtWNsay8WHgnjXnQaveu7sKRXDfr9co2UzNxi8nJFHr9djOBRYiZ5BUqaedkwtbMvyZk5XA5PUctdeJDEtwdD1J9z87T3cVn4a9tv/LP7Dz79chYOzq5sW7+SeZPHsuTXPzEwMCyxnrunF9MXrFB/lsoKH0NJifEkJcbz4agvcPPwIiE2mrU/zic5MZ4JMxe8tK4vg1gtUj50Suj8+eefad26Nb6+vnz//ffExMSUWwGlUsnGjRup+nZvXGo2wdKlMg36j0MhTyL69sUS64Wd3INn0454NG6PhVMl6vQajczAkPBLRwDIzcog/NIRanYbjr1Pbazdvanf73OSwgNJCg8CwMDEDK/mXbCu5IOJjQMOvrXxat6FxAf31NexcqtCgXNdlGZOKI1tKHCpT4FtVaTJDwAY3saLP84/ZvulCEJj0pn65y2ycvLp3aRk612phPi0bPWRkFb8xzY7r0BDRp5V/AflRXzUsjLbLkaw80okYbHpfL3zLlm5+fRs5FaybkBCWo76eP5BkK7IY/DqKxwIiOFhfAY3H6cwa/c9arpb4mxlpLN+7X1tOfswmfOPUohOy2bL9Why8gto5mmtVb6dty13Y9M5HJJITFoOe+/G8ThZQesqNmoZL1sTLjxKJSQ+k8TMXM48TOZJqoLKNsY66ze0hQd/Xn7CrqtR3I/LYObueyhy8+nR0LXEOlIJLOpbi2VHwohI0vTAeNqZUNfDill77nH7iZyHCZnM2n0PI30pXes46azfmzz2hreuzLYLEey4/ISw2HSmbb9NVk5+Ma9IURLSsgsPLUaIo6Uhs3pU54tNN8kr0N3o0ZXD5+4xe8V+9p74d7wVz+jTwJV9t2I4cCeW8MRMFh4OIzu3gHeKeB6ecSMildOhiTxKyiIyRcH261Hcj8+gtqulhlxufgFJGbnqIy0776X0UyqVHNj1Ox8MGEbD5q3x8PJhzFdzSE6M58q5k6XWlcr0sLKxUx8Wllbqc5UqezNx1kIaNG2Jk4sbNeo2pO9Ho7l28Qz5+S+n68siqcDjfxGdV4scPnyYLl26sGjRIipVqkS3bt3Yv38/BS/5RX/y5Anx8fHY+9ZRl+kbm2Lt4as2AopSkJdLypMw7H0L3agSqRR7nzokPQoGIOVJGMr8POyrFsqYO7pjbG1fYrtZqYlE3bqAXZUaJeorkUciTXlEgU0V9GUSarpbcjY4QX1eqYSzwQnUq6z9AQlgaijj3Kx2XJjdnrUjGuLjVNzd18Tblmvz3ub4tDZ807smVib6JbanDX2ZhBpuFpwP1dTtfEgCdT2sSqxnYiDj9LTWnJ3emlVD6+HjWLor0txIj4ICJWlZun3xZRIJlayMCYzLKNQPCIrNwMtWuyHgZWtMUKymW/hebDpetibqzw8SM6ntbI6VkWo25GtvgqOZAfditbuTS0JfJqG6qwXnQxML9VPC+bBE6layKrHep+2rkJiew44rkcXOGeipvm7ZuYXfFaUScvIKqF+CQVWafm/22LPkbIimbudCEqjnaVViPRMDGWdntOHczLasGVa/mG4SCSwZUIc1xx8QWkJ44P8DelIJVZ3MufIoRV2mBK4+SqGGi0WZ2qhfyYpK1sbcLBLCq+tuxf7Rjfl9WH0mdvDGwujlIuNx0ZGkJCVSq14jdZmJmRnefjUIuXe71LoxkY8Z1acTYwZ2Y+m3X5MQW/okNTMjHWMTU2SyfzeKL5VIKuz4X0Tnu1WzZk3atWvHwoUL2b17N7/++ivdu3fH0dGRIUOGMHToULy9vUttIzs7m+xslRs7PDxcpYiRqYaMkZkVijTtcfLsDDnKggIMzTV/RI3MrUiPewKAQp6MVKaHgbFZMRlFWopG2ZWNC4m+c5H83Bycqjeibp+xxa5peHIu5KSDsoA877fJd2uMrakBejIpCWma+QYJadlUKeGh/CAug0lbAwiKkmNurM/Itl7sGvcWHeafVLt5TwXGcTAgmojETDzsTPny3Wr89klj3l9yloIyejGt1bppzv4S0nPwcihZt8nbbhMUnYa5kT7DW1dm+9gmdFp4lpjU4i5oAz0pX3Wtyr6b0aTrOAMyM5Qhk0pIU2jWk2fn4WRhorWOhZEe8iLXkSvysHzuB/KPmzEMrOfC9+9UJb9ASYFSyeZrUYQm6JbTYG2i6r/EdM17m5iWg5e9qdY69T2t6NnQrcQQ0YO4DCKTs5jQ2ZcZu+6SlZPPkLc8cbYyxt6iZDeyVv3+E2NPm27a++5BXDpf/XGLwKg0LIz0GNHGix2fN6Pjd6fVY+/jdlXIL1Cy4XR42RT5j2JlrI+eVEJSpuZ3Nykzh0qleOBMDWTs+aQxBjIJ+UpYfCRMw0C5+DCZUyEJRKUqcLUyZlQLTxb3rMGoLTfLfG+fkZKsMrotrW01yi2tbEhJStRWBQAfvxqMnjQLF3cPkhMT2LFpLTPGDWfxum0YmxQfG/LUFHZuXkf7ru/rpqDgtfPSpqC+vj69e/emd+/ePH78mF9//ZUNGzbw3XffkZ9fcux47969TJkyhZwc1RcnMjISd3d3ru3dRKuhE19WnXJRs/twqnXsS3p8FHf3/8btv9ZRp+doDZmcRqMhPwdpyiP0Qg+gNLEDiyY6X+t6eDLXwwuNpmsPkjg2rQ0Dmnmw+IDK67LvepT6fHB0GoFRcs7ObEdTHzvOPTcbrGhuPErhxnM/RtfDkzn8VQv6NXXnh4OhGrJ6UgnLBtUBCcx4yYSwV0Ebbxsq2xrz87lHJGbm4mNnqsq5UOQR9JyXpKIxNZCxoE9Npu+8S7KWmDhAXoGSsZtuMq9nda7MakdefgEXwpI4FRT/r8R33+ixF57CjedyA649TObIlFb0b1aJJf+EUMPNgqEtPXln0dlXpsN/ncycfIb8dh0TAxn1K1kxto0XUakKbjxN4D4WFK+WfZCQyf34DLaPbEhddyuulZAE/Iwzx/5hzQ/fqj9PmffjS+lYt1Fz9b89vHxUxkb/d7hw6ghtO3fX/Hsy0vlu2ue4eXjRa9Col7peefjf9DdUHBXiZ6pUqRKzZs1i5syZHD16tFTZtm3bsmfPHrVxkZOTQ9++fanR9l0NOUV6ClYuXlrbMDS1QCKVkl3Es6FIS8HQQuXNMLKwpiA/j5ysdA3vhSItBaOnCZ/PMLKwxsjCGnNHd/RNzDizbDLVOvTFyLIwlq80UVno+ebOkJOOXthhkt3qkZdfgJ255qzTztyQ+LSyrZ7IK1By90kqHiXMhgEiEjNJTM/Gw860zD/wyRk5T3XTTIi1MzPQTbdIOR52mp6EZ4aFq7UxA1de1tlrAZCenU9+gRLzIm5ZC0M9UhXa25Mr8rAwLCJvVCivL5XQvYYDK89HqFeQRKZm425lxNu+tjoZF8mZqv6zNdO8t7bmBlrzFNxtTXCzMWHl4MLVGM/coXe/7UCnRWeJSMribqSc7j9dwMxID32ZhOSMXP78tDF3nsjLrBs8f3/f5LGnRbcyrirKK1ByL1KOh71q7DWsYoOtmSHnZrZVy+jJpEzr5s9HrSrTYs6Jkpr6z5GSlUtegRIbE83vro2JAUkZJee/KIHIpx6o0LgMPG1N+LCxu9q4KEpUqoLkzFzcrI249rh0nRo0bYlPtcJwcW6u6juQmpyIta2dujw1JQnPKr7F6peEqZk5Lm4exEQ+0SjPyszg2ymfYWxsysTZC9HTew0LG4V1US50yrnw8PBAJpOVeF4ikdChQ4dS2zAzM8PHx4fq1atTvXp16tSpg729PUnPJVHmKjJJfhSCjWc17Urr6WPl5k18SGGSlbKggPjQAGw8qgJg5eaNRKZHfEiAWiYt7glZyfEltqtqSOUfzM8vJYlNWYCkII/cfCW3I1Jp7lv45ZJIoHlVO64/LNvSR6kEqrpYEC8vOfPdycoIaxMD4kqRKUpuvpI7T+Q08yl0W0ok0NTHTsM78ULdnM01lpk+Myw87UwZtOqK1sz1spCvVPI4JQs/h8IHmwSo5mDKg0TtS1EfJGZRrUhIx8/RjAdPl3HKpBL0pNJi6x8KlEokOroGcvNVhlVT70IDUyKBpt623NAyy3sQn8E7S87R/acL6uN4YByXHiTR/acLxcJK6Yo8kjNy8bA1oYabJceeWzJYVv3e7LGXSnMfTd2a+dpy/TnvxAt1e27s7b4SSecFp+m68Iz6iElRsOb4fQavulxm3f4L5BUoCY5Jo8FzuVESoL6HFXeiym6ESiSq/JeSsDczwNJYr8TVO89jbGKKk6u7+nDz8MLKxpbbN66oZTIz0gkLvIOvf80y66jIyiQm+glWzxkomRnpfPPVGPT09Phy7pJSV54I3lx0MgePHTuGjY3NiwV1QCKRMGjQIJauWIWZvQsmNo4E/rMZIwsbnGsWhh3OrpiGc82mVGnxDgDerbtzbesPWLl7P12K+hf5OQo8GrcHVEmhno07cPuvXzAwMUfPyIRbu1Zj41lNbVzE3LtKdloK1pV8kBkakRb9mDv71mNT2Q9TG1VW9oOzfyONy0Zp6gCANPkBeuGnyPd4C4B1Jx6weGAdbkWkEPAohY9ae2FiIGP7JdVUYMnAOsSkKliwT5VE+lknH26EpxAen4GlsT4j21XBzdqYPy6o5E0MZHzR2Zd/AqKJl6tmjFO6+RGekMHp59yaZeHX0w9Z2LcWtyPkBDxOYWhLT0wMZOy4rJolLOpXi5hUBYsOqJamjengzc1HKTxKyMDCWJ8RbSrjam3Mn5dU8npSCcsH16WGmwXD111DKkXtGUnNzCU3X7fA7dGQRIY0dCU8OYvwpCza+dhioCfl/FPX/ZCGrqRk5bLnjurBeywskYmtKtPex5bbMWk0dLfEw9qIzddUrnxFXgHB8Rn0qOlIbn4BiRm5+Nqb0sTDiu0Buq9sWn/mEd/3rsGdJ3JuPVEtRTXWl7HrqipZ8/veNYiVZ7PkYCg5eQWEFkkalT9Ncn2+vFNNR5IycohKUVDVyYyp7/px9G4c50JLjlOXxJs89tadfMji/rVVuj1O5aNWnpgY6LHjUgQAiwfUJiZVwcL9qnDM2I7e3AxPIfzp2BvZ1gtXa2O2XVDJp2TmFjNk8wpUq1oevMpwl7EBVdzt1Z89XW2p5etKsjyTiJiX2zulLGy7Gsm0LlUJiklTLUVt4IqRvpS/78QC8HUXXxLSclh1JhyADxu7ERSTTmSKAn2ZhKZeNnTyd2DRkTAAjPWlfNTMg5MhCSRm5OBqZczoVp48Sc7iUrjuf4dEIqHLB/3YteUXnF3dcXBy5Y8NK7G2tadh89ZquTmTPqFR89Z06t4HgI2rf6RBkxbYOTqTnBjPn7+tRiqV8labjoDKsJj31RiysxWMnTKXrMx0sjJV3x8LS2ukpUxuKxqxiVb50Mm48PHxITo6GgcH1YO2T58+LF26FEdH7cujysqIESPYf/sJN/5cTm5WBraV/Wk2arbGHhcZCTHkZBRa7W51W5CdnkrgwS1ky5OxdPWi2ajZ6g2yQJVLgUTCpQ3zKcjLxaFqPer0/ER9XqZvQPjFQ9zes478/FyMrexwqdkU3/Y91TLKggL0Qg8gyUoCiQylsS15vl3Jd1cZPvtvRGFrZsD4LlWxtzDk3hM5g1ZeUrvOXayNNZKlLI0N+K5vLewtDEnNzOVORCof/HhWnf2er1RSzcWCHo3csTDWJzZVwZmgeBYfCCJHx/0G/r4Zg42pAV909MHu6SZLQ9deUc9UnK2MKHhuLwFLYz2+7VUDOwtD5Jm53HmSSq9lFwl7+nB0tDSiw9OlcH9PfEvjWv1XXOLS/eL7OpTG1SdyzAz1eM/fAYunm2gtPftIvceFjYk+yuf0e5CYxbpLT+hWw4HuNRyIS89h5fkI9R4XAOsuPuH9mg581MgNUwMZSRm5/HUn7qU20frnlqr/PnvbW70R1PBfrz3Xf8Y6J8LZmxsy+Z2q2Jqpwhd/XY9ixbH7OusGb/jYuxGNrakB4zv7qsfekNWX1ctLVbo9P/b0md+npnrs3Y6Q0+On8+qx97qo5+/B4XWfqz8vmNgDgE17LzJy5uZXdt1jwQlYmegzvLkHNqYGhMalM2FHYT6Po7mh5h40+jImdPDGwcyA7LwCHiVlMefvYI49XU2Ur4Qq9qZ0ru6AmZEeCek5XA5PZu3ZRzpPCp7Rrc9gshUKVv/wLZnpaVSrUYep3y3V8DTERj1Bnpqi/pwUH8tP304jTZ6KhaU11WrUZt6yDVhYqX63H4YGERp0B4DPBnXXuN7yzXtxcCp9Y8WK5H90kUeFIVEqS9ipRgtSqZSYmBi1cWFubk5AQABeXtpzI3Rh8oGQFwu9Jn4/GPy6VSgVPf03YqPVEmnftOR9F143J688ebHQa0SR9WKX9etEKn1z330Yc+qf161CqdTr3+d1q1AqK/rWed0qlEptd/NX2v7lBy/eibesNPKyfLHQ/zPe7KeSQCAQCASvAeG4KB86GRcSiaRYYpyuiXICgUAgELzxiEdbudDJuFAqlQwZMgRDQ1VMTaFQ8PHHH2NqqrmUbdeuXRWnoUAgEAgEgv8UOhkXgwcP1vg8cODAClVGIBAIBII3AbFapHzoZFysX7/+VekhEAgEAsEbg4j4l483N9VbIBAIBILXxH/hrahJSUkMGDAACwsLrKysGDZsGOnppS/fbt26tTp/8tnx8ccfa8g8fvyYrl27YmJigoODA5MmTSIvT7edmMVqEYFAIBAI/oMMGDCA6Ohojhw5Qm5uLkOHDmXkyJFs3bq11HojRoxgzpw56s8mJoWveMjPz6dr1644OTlx/vx5oqOjGTRoEPr6+nz77bfamtOKMC4EAoFAICjKGx4WCQwM5ODBg1y5coUGDRoAsGzZMrp06cKiRYtwcSl5wzETExOcnJy0njt8+DD37t3j6NGjODo6UqdOHebOnctXX33FrFmzMDAw0FqvKCIsIhAIBAJBESQV+F92djZyuVzjyM4u20v8SuLChQtYWVmpDQuA9u3bI5VKuXTpUql1t2zZgp2dHTVq1GDKlClkZmZqtFuzZk2Nnbc7duyIXC7n7t2yvwFbGBcCgUAgELxC5s+fj6WlpcYxf/78crX5/G7Zz9DT08PGxoaYmJLfpdS/f382b97MiRMnmDJlCps2bdJY+RkTE1PslR7PPpfWblFEWEQgEAgEgiJU5GqRKVOmMH78eI2yZ/tFFWXy5Ml8//33pbYXGBj40rqMHDlS/e+aNWvi7OxMu3btuH//PlWqVHnpdosijAuBQCAQCIpQkSkXhoaGJRoTRZkwYQJDhgwpVcbLywsnJyfi4uI0yvPy8khKSioxn0IbjRs3BiAsLIwqVarg5OTE5cuXNWRiY1Vv49Wl3TfGuCgo+/vT/nUy0zJfLPQaMTQu26B9XRTo+urQf5H4KN1fdf5v8qa/lC4/P/91q1Aib/qLwa5v3fa6VSgVs8ENXiwkqHDs7e2xt7d/oVzTpk1JSUnh2rVr1K9fH4Djx49TUFCgNhjKws2bNwFwdnZWtztv3jzi4uLUYZcjR45gYWGBv79/mdsVORcCgUAgEBTlDd/ows/Pj06dOjFixAguX77MuXPnGDNmDH379lWvFImMjKRatWpqT8T9+/eZO3cu165dIzw8nL179zJo0CBatmxJrVq1AHj77bfx9/fnww8/JCAggEOHDvH111/z6aefltn7AsK4EAgEAoGgGBW5WuRVsWXLFqpVq0a7du3o0qULb731FmvWrFGfz83NJTg4WL0axMDAgKNHj/L2229TrVo1JkyYQI8ePdi3b5+6jkwmY//+/chkMpo2bcrAgQMZNGiQxr4YZeHN9rkKBAKBQCDQio2NTakbZnl6eqJ8LuXA3d2dU6dOvbBdDw8PDhw4UC7dhHEhEAgEAkERxLtFyocwLgQCgUAgKIKwLcqHMC4EAoFAICiKsC7KhUjoFAgEAoFAUKEIz4VAIBAIBEV4las8/hcQxoVAIBAIBEUQCZ3lQ4RFBAKBQCAQVCjCcyEQCAQCQRGE46J8CONCIBAIBIKiCOuiXIiwiEAgEAgEggrlX/dcbNmyhV9++YX4+HiqVavG9OnT1S9M0UbkzbMEHdxCZlIcpnYuVH9nMI7+hW/rUyqVBB3cyqOLh8nNysCmsh+1e36Cmb2LWiYnI43bu9cQc/cySKS41GpKzfdHoGdoDEBa3BNubV9JWuxjchWZGFnY4FavFVU79kUqK95FBgm3MQvZTo5NNdKr9eejtt582rkaDpZG3H2cwpQt17nxMKnEv8nCWJ9pPWrStb4bVqYGPEnM5Ovfb3D0VjQAk7pV58vuNTTqhEbLaTb1n7J18nMMblmZjzv4YG9hSOCTVKb/eYubj1K0yvZqUokfBtXTKFPk5uP9eeG+8yaGMqZ2q07H2s5YmxrwODGDX08+YPOZ8DLp09rbho5V7bA00iMiRcHvN6IJT8oqUb6+mwXdajhiZ6pPbFoOO2/FcCcmXX3e3FBGz1pO+DuZYawvIzQ+g99vRBOXnqOWmdi6MlUdTDXaPXU/ic3Xol6o77D2Pozt4oeDpTF3I5L5auM1rj8o+U2qFib6fN2rNu80cMfa1ICIhAymbrnO0QDVtaQSCZM/qEmv5p44WBoRk5zF72cesuivOy/UpSgftfVmdKeqqnEXkcLULTdeOO6m9qhJ13quGuPu2O2YYrJju1Rjes9arD4SwvTfb+qsG8Cwdj6M6VxN3XeTN1/j+oNS9DPR5+setej6rO8SM5i25br6ewHgbG3MzN51aFfLGWMDGQ9j0xm77hI3w0tutyQ+qOtM/4Zu2JgaEBaXzg/H7hP43Nh6nlY+tgxq4o6rlTF6UglPUrL4/Uokh+4VvvJ6WmdfutRw1Kh38WESE3bc1Vm3stK8XhXGDWpPPf9KONtb0nvcGvadvPXKrvcMpVLJ5l9WcnDfLjLS0/CvWYdPJ0zF1d2j1HoJ8bGsX/kTVy+dI1uhwNnNnXFTZuNbrXox2WWLvuGfv3YwcuxEuvce+Kr+lBIRq0XKh87GxfLly7l8+TJdunShb9++bNq0ifnz51NQUMAHH3zAnDlz0NPT3uyBAweYP38+s2fPpnbt2vz2228MGzaMgwcPapVPehjItc2L8OsyCKfqDXly/RSX1n9L6/E/YOGsGsRhx3fx4Mx+6vX/HBMbR4L+2cKF1TNp+9XPyPQNALi2ZTEKeTJNP56DMj+fG3/8xM0/f6bBhxMBkMr0cGvYBivXKugbm5Ia9ZCAP5ejVBbg33WQhk5SRTIm4YfItVBdv3sjd+b0rcOkjde49iCRUR18+XNCK5pOOUBCWnaxv0lfJmXHpNYkyBV89PN5opMzcbczJTUzV0Mu8EkqPReeVH/OKygow93R5N36rszoUYMpvwdwIzyZ4W2rsHlsM1rNOkricw/f55Fn5dJq9lH1Z2WRt6XP7FGT5r52fLbhGhGJmbTys2de39rEpig4ouUh9TwN3C3oXduJzdeieJiURXsfW75o6cn0f0JIyy7+6u4qtsaMaOLOrtux3IpKo7GHJZ82r8TcI/eJkqv69tPmHuQrlfx89jFZefl08LVjfCtPZhwMJSe/UPnT95P4627hgyAn78X9+X7jSnzTvx4T1l/h2v0EPu5UjR1ftqHRl/tIkGu/t7u+akuCXMHQpWeISs56em8L+/rzd/wY2s6b0asvEhSZSt3KNiwb0QR5Vg5rDoe8UKdndGvozuw+tZm0SfXAHtnBh23jW9Js6j8ljrvtE1uRIFcwbMV5opOzcLM1RZ5VfBzU8bRmUCsv7kaklFmfonRvVIm5/eoy8bcrXLufyKiOVdk+sQ2Nv9pfon67JrUhXq5g6PKzRCdn4W5rovG9sDTR58C09pwNiqPP4pMkyLPxcjInJVP7WC6NdlXtGNvai4VHwrgXnUbv+i4s6VWDfr9cI6XIdxFArsjjt4sRPErMJK9ASTMvG6Z29iU5M4fL4SlquQsPkvj2YOF9zM1TFmurIjE1NuR2SCQb/7rAtiUjX+m1nmfH1g3s3bmV8VPn4uTsyqZfVjB9wmhWbdqFQQlvzkxLkzNx9BBq1W3InIXLsbSyIerJI8zNLYrJnj99nOC7t7C1e/Grx18VYrVI+dApLPLNN98wdepUMjMzGTduHN9//z3jxo1jwIABDB48mHXr1jF37twS669fv57evXvTo0cPvL29mT17NkZGRuzcuVOr/P0z+3CoVg+fth9g7uiOX+eBWLl68fDs34DKer5/ei9VO/TGuUYTLF0qU6//OBTyJKLvXAQgLTaCuKDr1OkzBhuPqth6+VPz/ZFE3jxDVqpqBmpq64RHo/ZYulbGxMYB5xqNcavfmsQH9zQVUhZgGrqDTPc2FBhaA/Dx21XZfPoBv599SEiUnIkbr5KVk0f/FpW1/k39W1TGytSAQcvOcjksgYjETM4Hxxf7Ic8vKCBOrlAfSSUYA6Uxsm0Vfj/3iD8vPiY0Jo3Jv99EkZNP32Ylzy6USoiXZ6uPog+C+l42bL8UwYXQBJ4kZbLl3CPuRcqp42n9Qn06+Npx5kEy58NTiJZns/laFDl5BTSvrL1uOx877sakcTg4gZi0bP66E8fjFAVtfWwBcDQzoIqdCVuuRRGenEVsWg5brkWhL5PSqJKVRls5+QXIFXnqQ1EG42J052psPHmfrWceEBwlZ/z6y2Rm5zGgZRWt8gNaeWFtasDAH09zKTSBiIQMzgfFcfdxilqmkY89/1yP5EhAFBEJGey9EsHJO9HU87J9oT7P83FHXzaffsAfZ8MJiZIzaeM1snLy6FfKuLM2NWDw8nNcDkskIjGTCyHx3I1I1ZAzNdRj5cgmTPjtKikZuo+5Z4zuVJVNp+6z9cxDgqPkTNhwhaycPAa09NIqP6ClF1ZmBny49AyXn/Vdke/F5139iUzKZOy6S1x/kMTjhAxO3okhPE67t6E0+jRwZd+tGA7ciSU8MZOFh8PIzi3gnSKeh2fciEjldGgij5KyiExRsP16FPfjM6jtaqkhl5tfQFJGrvpIy87TWTddOHzuHrNX7GfviVfvrXiGUqlkz59b6DtoBE1btKGyty8Tps0lMTGeC2dOlFhvx5b12Ds4MX7qHKr618TJxZV6jZrh7OquIZcQH8vKH79j0oxvkZUwURW8+ehkXGzYsIENGzawY8cODh48yLRp0/jpp5+YNm0aU6ZMYfXq1SW+oS0nJ4e7d+/SrFmzwotLpTRr1owbN25orZMcHoS9T22NModq9UgKDwIgMymW7LRk7H0LZfSNTbGu5EtyeDAASeFBqjJ3H7WMvW8dJBIJyY+0zxTT46OIDbqOXRXN0IRxxEmU+mbkONZXXUtPj9qe1py6G6uWUSrh9L1YGnjbaW27U10Xrt5P4PuB9bn7YzdOz+3EF139kBYxkys7mnN7yXtc+b4rK0c2wdXGRGt7JaEvk1CzkhVnguM1dDsTFE+9yjYl1jM1lHFx7ttcnvc2v4xqjK+zucb5aw+S6FDLCSdLIwCa+drh5WDK6cA4bc2pkUkleFgbExhb+CBQAoFx6VSx1f63edkacy82Q6Psbkw6XraqcJaeTNVnuc95KJRAXoESHzvNNhtXsmJJt2rM6ujN+zUdMZCVPi3Rl0mp7WnDqbuF3hilEk7djaFhCfe2cz03roQlsHBwQ4KWv8+5+V0Y966/xr29HBpPS39Hqjip+rV6JSsa+9pruP5fhL5MSm0Pa07fKzru4mhQRbuR0rGOC1fvJ/LdwHrc/eE9Ts3pyOdaxt13A+tx5FY0p++Vfj9fqJ/Wvostse861XXlalgiCwY1IHDp+5yd15lx72j2Xae6rtwMT+LXT5sTtOx9TszpxIettBt6paEnlVDVyZwrz4UHlcDVRynUcCk+i9ZG/UpWVLI25uYTTeOsrrsV+0c35vdh9ZnYwRsLo/9/D8eY6EiSkxKo06CxuszUzJyqfjUJvBtQYr2LZ0/hU9Wfb6dPpN+7bRjzUR8O7tWcWBYUFLDom6/p0W8wHpW9X9nfUBYkFXj8L6LTyI+KiqJBA1W+Q+3atZFKpdSpU0d9vl69ekRFaY9jJycnk5+fj62tLdnZ2WRnq2bE5ubmhIaGYpybg97TMMYzFGkpGJpbaZQZmluRnZYMQLY8WV1WVEbxTCYtGQMzzfNSmQx9E3N1O884vfRLUp/cpyAvF48mHanWqb/6nJ78EYZx10mt/Ym6zNrKAj2ZlHi5QqOduFQF3k7af6Q87M14y8+UnRce0e+H03g5mvH9h/XR05Oy6C9VbPb6g0Q+W3eJsJg0HK2MmditOvumtKXF9INkKMo2E7IxM9SqW0JaNt6OZlrr3I9NY8LmGwRGyrEw1mNUex/2TGxJu7nHiE5RtTP9z1t8378OV+d3Ije/gIICJV9uvcmlsJLzEADMDGTIpBLkRWZyckUeTuba3aiWRnqkKYrLWxrpAxAjzyYxI4cPajmy6Wok2flKOvjaYmOij6Vx4dC+9DiFpIxcUhS5uFka0aOWE07mBqw8H1GivrbmT/svVbP/4uUKfEt4AHnYm9LCz5EdF8Lps+gkXo7mLBzcEH09KQt2q3Iqftx/D3NjfS59/w75BUpkUgnf7Ahgx/nwEnUpio25wdN7q+lVipcr8C5iDD6v21t+Duy8+Ih+P56hsoMZ339YD32ZhEV7VR667o3cqelhRcc5R7W2UVae9V1cavHvhU8J+nnam+HuZ8qOC+H0XXKSyg7mLBzcAD09KQv33Hn6N5gxtI0PKw8F8cO+e9T1smH+wHrk5hXwx7mHZdbPylgfPamEpCLhlKTMHCrZGJdYz9RAxp5PGmMgk5CvhMVHwjQMlIsPkzkVkkBUqgJXK2NGtfBkcc8ajNpyk4JXGx35V0lOTADA2lrTkLWysSE5qeTfgZjoJ/z913be7z2QPh8OJyToDqt+WoCevj7tO78HwPYt65HJZHTr2b/Edv41/letggpCJ+PCycmJe/fuUalSJUJDQ8nPz+fevXtUr65Kxrl79y4ODg4vbOdZ3gWAnZ0dxsbGxBqtpvmAsS/xJ1QcDT+cRG52FvKoh9zdt4Gwk7vxadsD8rMxDd1JRpX3UOqbvrihUpBKJCTIFYzfcJUCpZJbj5JxsjJmTOdqauPi+QS7e09SuXY/kRuL3qF7Q3e2nCn7j6iuXH+YzPWHhQbX1ftJnJzRjgFvVWbR/kAAhrb2ol5la4asvEhkUiaNvW2Z16cWsSkKzj7nJfk3yFfCivOPGdLAlZ/e9ye/QElgbDq3o9M05M48KPybIlOzSVXkMaF1ZexNY4s2WS6e3dsvfrlMgVJJQHgyztYmjOnqpzYu3m/sQa9mnoxceZ7AJynU9LDm2wH1iUnO4o+zr+7ePtNtwoZr6nHnbG3Mp52qsmjvPVysjZnXry69Fp8iuwwho4pGIoWENAXj1l95ru+MGdPFT21cSKVw82ES3+xQhQBuP07Gz9WSIW29dTIuXpbMnHyG/HYdEwMZ9StZMbaNF1GpCm48DS0dCyoc/w8SMrkfn8H2kQ2p627FtedCY/81Thz+m2WLvlF/nv39spdqR1lQgE81f4aM+gyAKr7VePTgPgf+2kH7zu8RGnyPvTu2svSX35G8AQkPIqGzfOhkXAwYMIBBgwbRrVs3jh07xpdffsnEiRNJTExEIpEwb948evbsqbWutbU1MpmMxMREpkyZwvjx4wGYNWsWaWlpmHcdVayOkbkV2WkpGmXZaSkYmqti9IYW1uoyIwsbDRlLV1Vs19Dcmpx0zTYK8vPJzUxTt/MMY2t7jAELp0ooCwoI2P4z3q27I1MkIctOwSzw+ZCPkvQUffLy8nA01kwAc7A0Iq6Ix+AZsSlZqhn/c5mSodFyHK2M0ZdJyc0v/sMuz8rlfmw6lUvwOGgjKT2bvPwC7C2MNMrtzA2J05KMqI28AiV3nqTiaa8yqIz0pXz1nj/D11zi+B3VgzkwUk51N0s+bu9dqnGRnpNPfoESC0PNIWdhpIe8BG9MqiIPc6Pi8qmKwv5+nKxgzpH7GOtLkUklpGfnM6WdF4+SS16B8iAxEwAHM4MSZRLTnvafpWb/2VsYEZtSwr1NzSI3T6lxb0OiUnF67t7O7luHH/ffY9fFR4AqcdfdzpQv3vUvs3GRlJbz9N5qenzsLYyKeQue1y0vv4huz4272p7W2FsacXRmB/V5PZmUpr72DGvrjdvInRp1S+NZ3zkU6TsHy1L0S1GQV+R7ERIt1+i72BQFwVFyjXoh0XLebehetLlSScnKJa9AiY2J5v23MTEgKaN4MuczlEDk03sfGpeBp60JHzZ2VxsXRYlKVZCcmYubtRHXHuuk4htF47daU9W/pvpzbq7K45OcnIjNcwmXKUlJePn4ltiOta097h6aYSx3j8qcO6XylN0NuE5KchKDe3ZWny/Iz2fdz0vYs30LG7brvlpO8PrQybiYPXs2xsbGXLhwgREjRjB58mRq167Nl19+SWZmJu+++26JCZ0GBgZUr16dCxcu0L59ewwNDSkoKODq1asMHDiQMP3iP/TWntWID71FlVbd1GVxITex8awGgImNI4bm1sSHBqiNiVxFJsmPQ/BsrhqgNp7VyM3KICUiDCt3VQwvIewWSqUSa4+SvwhKpZKC/HyUSiX5xnak1v5U47xxxDHy8rO59TCBt2p58fedm4Aqw7iFnyO/HAvV2u7lsAQ+aOKBRFK4EqOKkzkxyVlaDQtQJdl52puyvYSHmjZy85XcfpzCW1XtORQQrdbtrar2bDj1oExtSCVQzcWC409zSvRkUgz0pCiL+HjzC5RIpKVb+fkFSh4lZ+HnaMbNKJVnQQL4OZhxvISQyoPELPwcTTkWWnjez9GMB4nFDYesXFXfOZgZ4GltzF93Ss4ZcLdSub5TFCU/SHLzCwgIT6KlvyMHrj1R6SuBVtWdWHtEe67OpZAEejYtem8tiE7OVN9bYwO9Yg/p/AJlsdyH0sjNLyDgUTIt/Bz550aUWrcWfg78cjxMa53LoYl80KSSpm6O5sQ8NXZPB8bRcrrmqq2fPmpEWLScZf8EldmwUOsXnkRLfycOXI9U69fS35F1R7X33eXQeHoU/V44mhPzXN9dCo3H20kzrFLFyZyIhIyizZVKXoGS4Jg0GnhYcebp2JMA9T2s2Hn9xcuTnyGRqHKbSsLezABLY70SV2b9VzAxMcXEpNBjq1QqsbaxI+DaZar4qH6LMzPSCQ68TdfuvUpsx79mbSIjwjXKIiMe4eDkDEDbju9Qp0ETjfPTJ3xC247v0KFLN/5t3gDnyX8anYyL8PBwJk+ejFRamAfat29f+vbtW6b6Q4cO5auvvqJGjRrUqlWL3377jaysLD744AMWXErk2tYfMLawwf+dwQBUafEuZ3+eStjJ3Tj6NSTyxmlSIsKo00v1oJdIJFRp+R4hR/7E1M4FUxtHAg9uwcjCBucaqkFq7uiOQ7V63PxzObV7jqagII9bu1bjWqcFxpaqmGHEtZNIZXpYOHsg1dMnJSKMwL834lrnLdU+F1J98k01s8iVMtWsbOXRBywb3pibj1K4/iCRUW9XxcRQj9+fzkKXD29MTEom3+y4DcD6E2EMa+fDt/3rsfZoCFUczfm8q7/Gj+6sPrU5fFO1msDJ2pgvu9cgX6lk1yXdpj9rjt/nh0H1CHiUzM1HyQxvUwVjQxnbLqja+XFwPWJSFHz3lyrm/kXnqlwPTyI8LgMLE30+bu+Dm40Jv59TzbLTFXlcCElg2gc1UOTe4klSJk187OjZuBKzd95+oT5HQhL4qJEb4UlZqqWovrYY6Ek59zQU81EjV5Kz8th9W2XMHAtNYGIbLzr42nI7Oo2GlazwtDZi09VIdZv13SxIy84nKTMHV0sj+tZ15kaUnHtPE0ftTQ1o5GHJ7eg0MrLzcbMyoncdZ4LjMohMLd2Ds+KfIH4e2ZSbD5O4/iCRjzuq7u3W0yrjbMWopkQnZzL3T1US2/pjoYzo4Mv8gfVZeyQEL0dzxr3nr7HE9ODNSCa8V4MnCZkERaZSy8Oa0Z2qseV02Qy+Z6w6FMKy4Y0ICE/i+sMkRnXwxcRQT+39WD68EdHJWcx7el82nAhjWDtv5vWry7pjoXg5mvNFVz/WPjWCMxR5BEVqegUys/NIysgpVl4WVhwM5ucRTdR9N+pZ3z0N660Y2YTo5Czmblf13a/Hwxje3pf5A572nZM5496tztojwc/9zcH883UHxr3jz57Lj6nnZcug1t6MX39ZZ/22XY1kWpeqBMWkqZaiNnDFSF/K3089cl938SUhLYdVT/dv+bCxG0Ex6USmKNCXSWjqZUMnfwcWHVEZc8b6Uj5q5sHJkAQSM3JwtTJmdCtPniRncSk8uSQ1yo2psQFV3Au9B56uttTydSVZnklEzKu5rkQioXvvAfzx21pc3Crh6OzKpnU/Y2trT9MWbdRyUz4fSbOWbXm3h+r58H7vgUz4ZAjbNq6jRdu3CQ68wz/7dvLZpOkAWFhaYWFppXEtmZ4e1ja2uFXyfCV/S2kI26J86GRc+Pj4EB0drc6r6NOnD0uXLsXRUfvyraJ06dKFpKQkli5dSnx8PH5+fqxbtw47OzsgkazkeI1Ym01lP+oPnEDgP1sI/HsTpvYuNB46Vb3HBYB32w/Iy1EQsP3np5to+dN05Cz1HhcA9QdM4Nau1ZxbNR2JRPJ0E63CNeFSqYzQ4ztJj48CpRITa3sqv9VVw2NSEnsuR2BrbshX3WvgYGnEnccp9FlySp1s52ZrgvK5WV9UUha9F59ibr+6nJrbiejkLNYeCWHpgSC1jIu1CatHNcXazIDEtGwuhSbQee5RErXsD1Aa+65FYmtmwMR3/LC3MOTek1Q+XH5BvbzU1dqE57fPsDTRZ0H/uthbGJKamcvtiBS6LTpNaExhDsPoX68wuZs/y4bWx8rEgCdJmXy/9x6byrCJ1tUIOeaGMXSr4YDF0020fjodrt7jwsbEQGNfjfuJWay7GEH3Go68X9ORuPQcfj73WL3HBYClsR696zhjYSgjVZHHhUcp7L9XGJ7JK1Di52BGex9bDPWkJGXmcv1JKn/fe3F+yO5Lj7E1N2JKj1pP720yvRaeUCfJutmaaMzoI5My6bngBPMG1OPMvC5EJ2ey+lAwPz3NVwGYvPEqU3vUYtGQhthZGBKTnMWGE2Es3K3bJlp/XVGNuy+fjbuIFPr+cFo97lxtTCh4zsMUlZxFnyWnmdO3DifndCQmOYs1R0NZ9ty4q0j2XH6MnYUhkz+oqe673otOqvuumH5JmfRceIJ5/etx+pvORKdksuZwMD/9Xdh3Nx4mMWjpGab3qs3EbjV4nJDOtC3X2XHhkc76HQtOwMpEn+HNPbAxNSA0Lp0JO+6S/HSPC0dzQ42xaKQvY0IHbxzMDMjOK+BRUhZz/g7mWLAquTFfCVXsTelc3QEzIz0S0nO4HJ7M2rOPNFYzVTT1/D04vO5z9ecFE3sAsGnvRUbO3PzKrtuz/xAUWVksWziX9PQ0qtesy5xFKzT2uIiOiiA1tdDA8fWrwdfzlrBhzVK2/rYGJ2dXRo2dRJu3u74yPQWvD4lSWXZ/p1QqJSYmRm1cmJubExAQgJeX9rXruvDl38EvFnpNrN9x83WrUCqGxtpXW7wpdG79epeUlcbOff/e/gAvg57+m72UMT+/+OZnbwrVari9bhVK5frWba9bhVK5c2jh61ahVKo4lLyypyK4H19y3pauVLF/tbq+ibzZv1wCgUAgELwGxGqR8qHTJloSiaTYEqE3YcmQQCAQCASCNwedPBdKpZIhQ4Zg+DSuplAo+PjjjzE11dz7YdeuXRWnoUAgEAgE/zJi3lw+dDIuBg8erPF54MB//011AoFAIBC8aoRtUT50Mi7Wr1//qvQQCAQCgUDw/wSR0CkQCAQCQVGE66JcCONCIBAIBIIiiNUi5UMYFwKBQCAQFEEkdJYPnZaiCgQCgUAgELwI4bkQCAQCgaAIwnFRPoRxIRAIBAJBEURYpHyIsIhAIBAIBIIK5Y3xXKRnv7kvQIpY2+d1q1Aq6Yq8161Cqay5pPtbK/8totb3f90q/KcpKPt7D/91QmPSX7cKpWI2uMHrVqFUanSc9LpVKJWsG8tf8RWE66I8vDHGhUAgEAgEbwoiLFI+RFhEIBAIBAJBhSI8FwKBQCAQFEE4LsqHMC4EAoFAICiCCIuUDxEWEQgEAoHgP0hSUhIDBgzAwsICKysrhg0bRnp6yYnM4eHhSCQSrcf27dvVctrO//HHHzrpJjwXAoFAIBAU4b/wbpEBAwYQHR3NkSNHyM3NZejQoYwcOZKtW7dqlXd3dyc6OlqjbM2aNSxcuJDOnTtrlK9fv55OnTqpP1tZWemkmzAuBAKBQCAoSgXaFtnZ2WRnZ2uUGRoaYmho+NJtBgYGcvDgQa5cuUKDBqplzcuWLaNLly4sWrQIFxeXYnVkMhlOTk4aZbt376Z3796YmZlplFtZWRWT1QURFhEIBAKBoAiSCjzmz5+PpaWlxjF//vxy6XfhwgWsrKzUhgVA+/btkUqlXLp0qUxtXLt2jZs3bzJs2LBi5z799FPs7Oxo1KgRv/76K0od97QRnguBQCAQCF4hU6ZMYfz48Rpl5fFaAMTExODg4KBRpqenh42NDTExMWVq45dffsHPz49mzZpplM+ZM4e2bdtiYmLC4cOHGT16NOnp6Xz22Wdl1k8YFwKBQCAQFKEiV4voEgKZPHky33//fakygYGB5dYpKyuLrVu3Mn369GLnni+rW7cuGRkZLFy4UBgXAoFAIBCUh9eV0DlhwgSGDBlSqoyXlxdOTk7ExcVplOfl5ZGUlFSmXIkdO3aQmZnJoEGDXijbuHFj5s6dS3Z2dpmNJGFcCAQCgUDwhmBvb4+9vf0L5Zo2bUpKSgrXrl2jfv36ABw/fpyCggIaN278wvq//PIL7733XpmudfPmTaytrXUK5QjjQiAQCASCorzhK1H9/Pzo1KkTI0aMYNWqVeTm5jJmzBj69u2rXikSGRlJu3bt2LhxI40aNVLXDQsL4/Tp0xw4cKBYu/v27SM2NpYmTZpgZGTEkSNH+Pbbb5k4caJO+r2RxkVLL2s6+NhiYaTHk9Rs/gyI5lGyokT5uq7mvOvvgK2JPnHpOey5E8fdWM2NRJzMDehewxEfOxOkEgkxadmsuRhBcpbqjaL96jpTzd4US2M9svMKeJCYxZ47scSm5+isv1KpZMXypezasZ20NDl16tZj2oxZeHh4llgnIyOdn5f+xPFjR0lKSqSanz9fTp5KjZq1dL6+Nn3WrVrOvt07SEtPo1btukycMgP3Sh4l1unxTgdioqOKlX/Qqy8TJqvicU8iHvPzj4u4dfM6Obk5NGn6FuO+nIqNrV2putzcv5nQcwfJycrAwcufJv0+xcLBtdS/IejUPu4c2UmWPBkbt8o06v0J9p5VNWTiHgRyY+9vJIQHI5FKsXbzosOYb9AzUFnbiY/DuLbnVxIehSKVSqlUpzkfN/gWE1PTUvXV9V7m5+ez8udl/L1/L4kJCdg7OPBet/cZ+fFoJE8DuYkJCfy4ZBEXzp8lLS2NevUbMHna9FLbLQtv0thTKpWs/HkZu5/qUrtuPaZOn/nCvlu1YjkHnvWdvQPvdn+fEaM+0Acx5QAAJFhJREFUUfddZmYGS39YzInjx0hNScHF1Y1+Az6kV5++Ouv352+rOXZgNxnp6VSrXpvhn0/G2a1SiXX+/G01Ozat1Shzcffgx/U7AUiXp/Lnb6sJuHaRhLhYLCytaNi8NX2HfIJJkaV+ZdFv8y8rObhvFxnpafjXrMOnE6bi6l7y9xYgIT6W9St/4uqlc2QrFDi7uTNuymx8q1UvJrts0Tf889cORo6dSPfeA3XSryw0r1eFcYPaU8+/Es72lvQet4Z9J29V+HVeBW+4bQHAli1bGDNmDO3atUMqldKjRw+WLl2qPp+bm0twcDCZmZka9X799Vfc3Nx4++23i7Wpr6/Pzz//zLhx41AqlXh7e7NkyRJGjBihk24SpY7rS6Kjo1m5ciVnz54lOjoaqVSKl5cX3bt3Z8iQIchkMp0UeMboXfcAqO9qwaAGLvx+M5rwpCzaettSz9WCWUfCtL6W3cvGmHEtPfnrbhx3YtJo4G7J2752zD/+gGi5al2xnak+X7auzIVHKVyJkKPIy8fZwpCHSVnqNpt7WhGblkNSVi6mBjK6+tnjZmnE9IOhLH7PX6e/5dd1a/h13Rrmfvsdrq5u/LzsJ0JDQ9i990CJbqVJE74gLDSUr2fMwt7egb/372Xzxg3s2nsAR0fHUq/3oleub96wjk3r1/H17G9xdnVl7cplPAgLZfP2vSXqk5ycREF+YX8/uB/GF6OHs2z1euo1aERWViaD+nyAt29Vho/6FIC1K5eRkBDHmg2/I5UWrnJ+/pXrtw9v5/ahP3lr0HjMbJ24uX8TyZHhdJ+xCpm+gVZdHl49xdmNi2nSbwz2ntW4d3wPj66fpfusNRibWwEqw+Lo8unU7Ngbt5qNkcpkJD95gHutpsj09clMSeSvbz7Bs35L/Nt2Jzcrk8s7VlO7SiUW/7hU63Xh5e7lujWr2PTbeuZ++z1VvL25d+cOM76ewpjPxzFg4CCUSiWDBvRFT0+PCZO+wszMjI2/beD82TPs2vs3JiYmJerzIv7tsVfaK9fX/7KWX9etYc48lS4rlv9EWGgIO//6u0Rdflmzis0bNzBn3ndU8fbm7t07zPp6Kp9+9gX9B6piw3NnTefKpUvMmD0XF1dXLpw/x/xv5rDox2W0btNW3daLXrm+548N7Pl9A59+OQsHZ1e2rV/J44f3WfLrnxgYaNfvz99Wc+nMMaYvWKEuk8r0sLC0AuDxwzD+/G01rTu+i5uHFwmx0az9cT6VvLyZMHOBRltmhqXP7bZvWc+fm39h/NS5ODm7sumXFYTfD2XVpl0YlNB/aWlyxn7Uh1p1G9K1ey8srWyIevIIZ1d3nF3dNWTPnz7O1vWrSE1Jpke/wcWMi4p45frbzf1pWtuLG4GP2bZkZIUaF6/6lesJ6aX/ruqCndkbOY9/pei0z8XVq1fx8/PjwIED5ObmEhoaSv369TE1NWXixIm0bNmStLS0cinU1seWc+EpXHyUSkxaDr/fiCYnv4BmHlZa5dt423AvNp2joYnEpOWw/148ESlZtPayVsu85+/A3dh0dt+J40mqgoSMXG5Hp2sYK+fCUwhLzCQpM5eIFAX77sZhY6KPram+TvorlUq2bNrIiFGf0KZte3yrVuOb+QuIj4vj+LGjWusoFAqOHTnMuAmTqN+gIZU8PPjk07G4V/Jg+x/ad1rTRZ8/t25i8LBRtGjdFm+fqkyfPZ+E+DjOnDxWYj1raxts7ezVx7kzJ3F1c6du/YYA3Lp5g5joSL6eNY8qPr5U8fHl69nfEnTvLteuaF9jrVQqCTy+h1qd+lKpdlNs3Crz1uAJZKYm8jjgQom63Du+G5/mnfBp+jZWzpVo2m8MMgNDws4fVstc2bEGvzbvUbNjb6xdPLB0dMOzfktk+qr79+TOZaQyPZr0GY2loxt2nr407TeGo0cO8fjRI63XfZl7CXDz5g1at21Hy1atcXV1o0PHTjRt9hZ3bqt+VB89CudWwE2mzZhFjZq18KzsxdczZqHIVnDwwN8ltvsi3qSxp1Qq2bppIyNGfkybtu3wrVqVud9+T3xcHCdK6buAmzdo1aYdLVq1xsXVjQ5vd6JJs+bcvX37OZmbvNOtOw0aNcbF1Y0evfrgW7Uqd2+X/aGlVCo5sOt3PhgwjIbNW+Ph5cOYr+aQnBjPlXMnS60rlelhZWOnPp4ZFgCVKnszcdZCGjRtiZOLGzXqNqTvR6O5dvEM+fllf1gplUr2/LmFvoNG0LRFGyp7+zJh2lwSE+O5cOZEifV2bFmPvYMT46fOoap/TZxcXKnXqFkxwyIhPpaVP37HpBnfItN7dQ++w+fuMXvFfvae+G94K55HIqm4438RnYyLL774gnHjxnH16lXOnDnDhg0bCAkJ4Y8//uDBgwdkZmby9ddfv7QyMglUsjIiOC5DXaYEguIyqGyjfTZX2caEoOfkAe7FFspLgBpOZsSl5zCmeSW+7+LLpNaVqe1sXqIeBjIJTTysSMjIITkzV6e/IfLJExIS4mncpHDdsLm5OTVr1eZWwA2tdfLz88jPzy82mzM0NOTGjes6Xb8oUZFPSExMoEHjJuoyM3Nz/GvU4s6tgDK1kZubw+ED++na7QO1azo3NweJRIK+QaG3wcDQEKlUyq2b2nVOT4whS56MS7U6hXWMTbH3rEr8A+1Lq/Lzckl8HIZL1cI6EqkUl2p1iH8YBEBWWgoJ4cEYmVlxYOEEtn3Vn4NLviQ27G5hO7m5SGV6SJ7zqMj0Vf194/o1rdd+mXsJUKdOXS5fvEh4+EMAgoOCuHHjGm+1aAlAbo4q1Gb43OxYKpViYGBQoi5l4U0ae2pdmmrqUqNWLW4F3CyxXu06dbl86QKPnuu7m9ev0/xp36lk6nDqxHHiYmNRKpVcuXyRR+HhNGnWvMz6xUVHkpKUSK16hXFoEzMzvP1qEHLvdik1ISbyMaP6dGLMwG4s/fZrEmJL31MgMyMdYxNTZLKyP8RjoiNJTkqgToPCxDxTM3Oq+tUk8G7J39uLZ0/hU9Wfb6dPpN+7bRjzUR8O7t2pIVNQUMCib76mR7/BeFT2LrNO/2tIKvC//0V0Mi6uX7/Ohx9+qP7cv39/rl+/TmxsLNbW1ixYsIAdO3a8sJ3s7GzkcrnGkZ+bg5mhHjKpBHm2poWflp2HhZH2L6aFkR5ppcibG8ow0pfxtq8d92LTWXbuEQFRckY0ccPHTtNgaellzZL3qvFjNz+qO5mx9Owj8nXblIyEhHgAbO1sNcptbW1JSEjQWsfU1IzadeqyZtUK4uJiyc/PZ/++v7gVcJP4+DitdcpKUqLqmjY2mnkQNja2JCZq16cop08cJz09jS7vdleXVa9ZGyMjY1YsXYwiK4usrEyW/7iQ/Px8Ep/2QVGyUpMBMLKw1ig3srAiS56stU52uvz/2rvTuCirho/jv2ER2QVkYhMEZFFcQtHCDXFLVEJLMkPF0FLDyryltM0SRaVc0RA3MJd8LEXNMtRKuS0XtDBQI81dUJR9G5DleTE6MLILMnPX+X4+82KuOXPmP9ds5zrLNVSUl1e/j2EbinIzAci/J/9yP/v9Npz6PsfgGaGY2nbg4Kq55KbfAsDCpRtFuVkkH/qGstL7FBfm8dveaKDyNXvU47yWAEFTXuc5n+GMGulDj25ujB0zivETAhkx8nkA2ts7YGlpxaoVS8nNyeF+SQmbNqzjzu3b3L1bc5aGUKf33sMspmaPZmlLRh377tUpr/OczwhG+w6n59OdGec/mlcmTGT4SF9Fmffe/wgHR0eeG+RFL/cuBE99jTkffEwPj54NzpedlQGAsYlyPuM2pmRnZtR6P6eOnXkj5BPeXxTBlLfnkH47lY/fmUJRYUGN5XNzstm1dQODR4xucDaArAefTZNH8rUxNSWrjny3027y3d6vsbKxZcHSSEaM8mftynAOH9inKPP1tmg0NTXxG/NKozIJQmM0qnEhlUqV/vTkzp07lJaWYmRkBICTkxOZmZn11lPTqVB/272+3vs9jodH2n+k5fHTpUxu5hRz8K8Mkm/n09de+Qfr1PUcFv14mWVHr5KeX8KUXjZoadTd6vxu/z6e9XBXXEpLH2+cbuGicCoqKhji3Z+e7l3YvnULw4aPUJq70BBx3+9ncF8PxeVx81S1f+8unu3dF3PzyrPBmZiYErpkGb/EH2Vwv5485/Us+Xl5uLh2QiLRUMqy7Z0X2PbOC0pzOJpTRXk5AM59fXDyHIpZO0d6jXkdY6kNFx8MnZhY2dE3cBbnfoxl28zR7JwTgIGZBWZmbRXvkeZ6LeN+OMD3333LovCl7Ph6N6Fhi9kcvYl9e2IB+YSpZSsjuHb1Kv169+IZj6dJOHWSvv36o1HP+60qdXrvfb//W3r37K64PG6Wgz8c4MD+bwlb8jnbd+5i/sLFbInZxL69sYoyO7ZtIemPs6xY/QXb/m8Xs0LeY/HC+Zw4/mut9f73xwNMGNlPcSl7zHzuvfrg6TUYOwcnnu7pydywlRTk53H86KFqZQsL8ln8wdvY2DngP3FqnfX+fPA7Xhjqqbg8br6K8nI6OLsyaepbODq74vP8GIb5vsD3e+UHfRdTzrPvm+3Men++4n0v1EwMizRNowbbRo0axbRp0/jss8/Q0dEhNDQULy8vdHV1AUhJScHauu5Z/1DzqVBDDlwmv7iUsvIKjB6Z6GSoo0VuLZMWc2WlGNZR/mGdDyd3PnQ7txjHR3ouZKXlyEpLuFtQwpUThXzu68rTVrUPnwAM8B5Ily7dFNdL7su7vDPuZSj9GGdkZODi6lprPe1sbdm0eSuFhYUUFORjbi4l5D8zsbFpV+t9atLXyxu3Ll0q85TIh3UyM+/Rtsp65szMDJyca8/z0O20VE6fOkHYZyur3faMZx++3vcD2VlZaGppYmhohO/Q/gyy8VHKsiNRvuqkrFSeRZabhZ6xqaIeWW42pjYONT6+joEREg0NZI/0bMjystE1kteh+6AuYwvlWf7GFu0oyKrsCXDo6Y1DT2+KcrPQatUaJBIu/BSLTTv5Pm6u13L50nCCJr+Oz/ARADg5u5CWmsrGDVE8P0p+BNvJrTM7d+8lLy+P+/fvY2pqSsDL/ri5da613kep03vPy9ubzl0rV5c8HPrJzHg0yz1cXDrWWs+KpZ/x6pTXGFZ136WlEr1hHc/7jUYmkxGxcgXLVkbQz2sAAM4uLqT8+SdbYjbxrGfvGuv18OyPk2vlvr3/YF/lZGVgUmV1U052Ju0dnRv8vPUNDLGyseP2rZtK24sKCwib+xa6uvrM/vQztOqZ1/BM3wG4dKr83D7Ml5WVgWnbys9tdmYmDk615zMxM6ednaPStnZ29vxyVD7P5dzZ38jOyiRwTOU/YJaXlbFhzTL2fL2NmK8P1POMBaFhGtW4WLBgAWlpafj6+lJWVoanpydbtmxR3C6RSBr0Zyw1nQpVU7sVZRVwPVuGi1Sfs2nyiaESwEWqz9G/a+4RuZJZiKtUn5+r3N5Rqs+VTPnSm7IKuJZVxFOGyisRpIatyKxjPoVEIh8pq6/nQl/fAH39yiVmFRUVtG1rzsmTx3HtKP8Szc/PJ+mPs/iPHVdnXQB6enro6emRm5PD8V+OMXNW42Zs6+vro19laWVFRQVmZm05c+okzg++1Avy8zmf/Aejx4ytt77v9sViYmKKZ9/+tZZpYyLvATpz6gRZmZn07e+tlMUotTKLrpEJaSlnMW0n/wIsKSrk7tUUXPqPqLFuTS1tzGw7kJZyFtun5T8cFeXlpKUk4uol7yo3MHsKXWMzctOVv+Bz029h7eZRrU7dB0MsF389SCsdHZ717PMgb/O8lrIiWbUeCE1NTcrLq4+xGRrKG6/Xrl3l/Llkgt98u9Z6H6VO771as5w4jotrZZbkP/7A/6U69p2sSNHz9ZCGhgblD3qnSktLKS29rzR3BkBTs7JMTXT19NHVU/5ctDE1I+n3BNp3kC9pLizI59KFZIb6vtjAZw2yokJup92kn9lwxbbCgnwWznkTbW1t3g1dVuvKk6r09PTReySfiWlbzp45haOTq6LelAtJjBjlX2s9nbp049aNq0rbbt24htTCEoCBz43kaY9nlW7/6D/TGfjcSIYM96s357/Jv7XHobk0qnGRnp7OV199RUlJCaWlpdX+orWmNbON9dPFDCZ6WHEtq4hrWUV4dzBDR1OD49eyAQjsYUW2rJS95+TjwT9fyuSd/u0Z1MGU5Nv5eLQzxtZEl22/Vw7fHLqYweReNly6V8hfdwvo9JQBXSwMWfHfqwCY6WnjYWPE+fQC8otLMdHVZqhLW0rKykm+k89LjcgvkUgImDCR9VGR2NnaYW0jXw5oLpUycNBgRbnXggIZOGgI4wLky79+OfZfqKjAzt6eG9evs/zzcNrbO+A3+oUm7U+JRMJLr0xg88YobGxtsbKyYX1kBG3NpfQbMEhR7q1pQfT3HsSYsQGKbeXl5Xy3LxafkX41Hnl9ty8WO3sH2rQx4VzSWVZ8voixr0zErr19rVk6DhzFHwd2YCi1wtDsKX7/dgt6xmbYdvNUlItbORfbbr3pOEDeeOg0cDTHvlyGmZ0Tbe2cufDzXkqLi+ngOURRb+chL5K4fysm1g6Y2jjw98nD5Ny5iddrHyjqvXDkW6QOHdHSaU3an79zevcm/jN7tmJYr6a8j/Naeg3wZv26tVhYWuHYoQN/XrjAls3R+I2u/NE6GHcAExNTLC2tuHgxhfBFYXgPHEzvPn1rfzHroU7vPYlEwisTJrJh3Vps7dpjbW3NF6tXYS6V4l0ly9TJk/AeNJiXX5Fn6T/Am43r12JpaanYd1u/jGHUg31nYGBAD4+erFj6Ga11dLC0subM6VPs37eXWSFzGpVv+Avj2L1tI5bW7ZBaWLMjJhITM3N69hmgKDc/ZDq9+gxg2Ch5Q/zLqBV4PNuPtk9ZkpVxl52bo9DQ0KCv93PAg4bFezMoLpbx5txQigrzKSqUL4k1MjZBo4FL9SUSCaNeCmDH5vVY2djylKU1WzaswczMHM9+3opyc99+nd79B+L7ovwcH6NfGs9/pk/i/77cQL+BQ0m5kMyBb3fxVshHDzK0UVrdAqCppYWJqRk2tu0bvP8aSl+3FY7tKnte2lub0dXZmqzcQm7crnmelfDP0KjGhZOTE2lpaYp/Yhs7diyrVq2qdy18Y5y5lYuBjiYjO5ljpCM/idbqX66T92DZqImeNlWPTy5nFrEp4SbPd5LyvJuUu/klRB2/oTQMcjY1j69+T+M5FzP8u1lwJ6+E9Sdv8HdGEQCl5RU4ttXDu4MZeq00yZOVcvFeIZ8fvVrjuTXq8+rk1ygqKmL+Jx+Tl5eLe/cefBG1Qam35uaNG2RnV3648vPzWLViGXdu38bYuA2DhgzlzbffQVu7cUthaxIQOJmioiLCF35Cfl4eXZ/uztKIKKU8t27eICc7W+l+CSePc+d2GiP8av6RuX71CmtXLyc3JwdLK2sCg15nbEBgnVk6DxlDabGM49sjKCnM5ylHNwbPmK90jou8u2kU5+cortt7eCHLzyVx/5YHJ9FyYPCM+YoeCIBOA0dRdr+EhG/WUVKYh4m1A0PeXIiRuaWizL2rKZz9biv3i4swfqodnq/MIGB83efVf5zXcs4HH7Jm1UrCQj8lMzMDc6mUMf5jmTo9WFHm7t27fB6++MEQhjkjn/dj6rQ36szSEOr03psUNIWioiIWPMjydPcerFm7XinLjRvXyc6qzPLe+x/yRcQqwhbMJytTPqQyxn8sr0+v3DeLP19GxIplvD8n5MF7z4rgt2Y2+iRafmMDKZbJiFoeRmF+Hq6dn+b9xauUehrupN4kNydbcT3z7h1Whn1AXm4ORsYmuHbuxsKIGIzayN+LVy7+ycU/kwF4a+IopcdbvXUfUgurBucb88okZEVFRHwWSn5+Hm5d3Jn/+RdK57hIS71BTk7l/nPu2JkPFy4jZt0qtm9eh4WlNVPfDMF7aM09g09a9052HNxQ2RsXPlveSNyy7wSvz9uqkkwN9W9d5dFcGnUSLQ0NDaW/eTU0NOTs2bM4ONQ8Xt4YD0+ipY6WNfIkWi2tvpNoqVrVk2ipm1lejvUXEmpV10m0VK2+k2ipWn0n0VK15jiJ1pP0pE+ilSurfZitsYxaN25i/j/Bv+8ZC4IgCILwRDWq6SyRSKotXxLLmQRBEIR/GvHL1jSNalxUVFQwadIkxZipTCZj2rRpSqsTAHbv3t18CQVBEAShpYnWRZM0qnERGKg8WW/8+Ob/Fz1BEARBEP63NapxER0d/aRyCIIgCILaEKtFmka9pysLgiAIggqI6YRNI1aLCIIgCILQrETPhSAIgiA8QnRcNI1oXAiCIAjCo0TroklE40IQBEEQHiEmdDaNmHMhCIIgCEKzEj0XgiAIgvAIsVqkiSr+gWQyWcW8efMqZDKZqqNUo87ZKipEvqZQ52wVFSJfU6hztooKkU9QP436V9T/Fbm5uRgbG5OTk4ORkZGq4yhR52wg8jWFOmcDka8p1DkbiHyC+hFzLgRBEARBaFaicSEIgiAIQrMSjQtBEARBEJrVP7JxoaOjw7x58xR/Da9O1DkbiHxNoc7ZQORrCnXOBiKfoH7+kRM6BUEQBEFQnX9kz4UgCIIgCKojGheCIAiCIDQr0bgQBEEQBKFZicaFIAiCIAjNSjQuBEEQBEFoVmrTuJg0aRISiQSJRIK2tjb29va8++67yGQyRZmHt584cULpvsXFxZiZmSGRSDhy5Ihi+9GjRxk4cCCmpqbo6enh5OREYGAgJSUlABw5ckRRp4aGBsbGxri7u/Puu++SlpbWbM+n6uXSpUvEx8fj6+uLlZUVEomEPXv2NOmxmjvfokWL6NmzJ4aGhkilUkaNGkVKSora5IuMjKRr164YGRlhZGSEp6cnBw4cUItsVS1evBiJRMLMmTNbLFt9+T755JNq211dXdUmH8CtW7cYP348ZmZm6Orq0qVLF06fPq3ybO3bt6/xtuDg4BbJVl++srIyPvroI+zt7dHV1cXR0ZHQ0FBackFgXfny8vKYOXMmdnZ26Orq0rt3bxISElosm9Cy1KZxATBs2DDS0tK4fPkyy5cvJyoqinnz5imVadeuHdHR0UrbYmNjMTAwUNp2/vx5hg0bhoeHB/Hx8SQlJREREUGrVq0oKytTKpuSkkJqaioJCQm89957HD58mM6dO5OUlNQsz6fqxd7enoKCArp168aaNWuaVH9T1Zbv6NGjBAcHc+LECQ4dOsT9+/cZOnQoBQUFapHPxsaGxYsXc+bMGU6fPs3AgQPx8/Pj3LlzKs/2UEJCAlFRUXTt2rXFMjU0n5ubm9L2Y8eOqU2+rKws+vTpg7a2NgcOHOD8+fMsXboUExMTlWdLSEhQ2nbo0CEA/P39WyxbXfmWLFlCZGQkq1ev5sKFCyxZsoTw8HAiIiLUIt+UKVM4dOgQW7ZsISkpiaFDhzJ48GBu3brVovmElqFWf7muo6ODhYUFIG9EDB48mEOHDrFkyRJFmcDAQFatWsWKFSvQ1dUFYNOmTQQGBhIaGqood/DgQSwsLAgPD1dsc3R0ZNiwYdUeVyqV0qZNGywsLHB2dsbPzw93d3emT5/epC/eqs+nKh8fH3x8fB673uZSW74ffvhB6XpMTAxSqZQzZ87Qv3//lopXaz5fX1+l6wsXLiQyMpITJ07g5uam0mwA+fn5BAQEsH79ehYsWNAieR5VVz4tLa1ab2spteVbsmRJtQOIqo22llBbNnNzc6XrixcvxtHRES8vr5aKBtSe79dff8XPz48RI0YA0L59e7766itOnTql8nxFRUXs2rWLvXv3Kr5DPvnkE7799lsiIyNV9jkRnhy16rmoKjk5mV9//ZVWrVopbe/Rowft27dn165dAFy/fp34+HgmTJigVM7CwoK0tDTi4+Mb/di6urpMmzaNX375hfT09Md/Ev8QOTk5AJiamqo4SXVlZWXs2LGDgoICPD09VR0HgODgYEaMGMHgwYNVHaVGFy9exMrKCgcHBwICArh+/bqqIyns27cPDw8P/P39kUqluLu7s379elXHqqakpIStW7cSFBSERCJRdRwAevfuzY8//shff/0FwNmzZzl27JhaHMiUlpZSVlZG69atlbbr6uqqpOdMePLUqnGxf/9+DAwMaN26NV26dCE9PZ2QkJBq5YKCgti0aRMgP6oePnx4taMKf39/xo0bh5eXF5aWlowePZrVq1eTm5vboCwPx6GvXr3a5Ofz8NLS3af1aUi+8vJyZs6cSZ8+fejcubPa5EtKSsLAwAAdHR2mTZtGbGwsnTp1Unm2HTt28Ntvv7Fo0aIWy9KYfM888wwxMTH88MMPREZGcuXKFfr160deXp5a5Lt8+TKRkZE4OTkRFxfH9OnTeeutt9i8ebPKs1W1Z88esrOzmTRpUovlqi/fnDlzePnll3F1dUVbWxt3d3dmzpxJQECAyvMZGhri6elJaGgoqamplJWVsXXrVo4fP97k+W2CelKrYRFvb28iIyMpKChg+fLlaGlp8eKLL1YrN378eObMmcPly5eJiYlh1apV1cpoamoSHR3NggUL+Omnnzh58iRhYWEsWbKEU6dOYWlpWWeWh5OgmnJU8vD5PKSvr//YdT0JDckXHBxMcnKySo4u6srn4uJCYmIiOTk5fPPNNwQGBnL06NEWa2DUlO3GjRu8/fbbHDp0qNoRWkurbd9VPYrt2rUrzzzzDHZ2duzcuZPJkyerPF95eTkeHh6EhYUB4O7uTnJyMmvXriUwMFCl2arauHEjPj4+WFlZtUimqmrLt3PnTrZt28b27dtxc3MjMTGRmTNnYmVl1WL7rq58W7ZsISgoCGtrazQ1NenevTvjxo3jzJkzLZZNaDlq1bjQ19enQ4cOgHweRbdu3di4cWO1Lz0zMzNGjhzJ5MmTkclk+Pj41HrkZW1tzYQJE5gwYQKhoaE4Ozuzdu1aPv300zqzXLhwAZCPWzbH81FH9eWbMWMG+/fvJz4+HhsbmxZMJldXvlatWilu69GjBwkJCaxcuZKoqCiVZduzZw/p6el0795dsa2srIz4+HhWr15NcXExmpqaKstXkzZt2uDs7FxtpcuTVls+S0vLag3Ejh07KoZBW0J9++7atWscPnyY3bt3t1imqmrLFxISoui9AOjSpQvXrl1j0aJFLdq4qC2fo6MjR48epaCggNzcXCwtLRk7diwODg4tlk1oOWo1LFKVhoYG77//Ph9++CFFRUXVbg8KCuLIkSNMnDixwV/YJiYmWFpa1rvqoaioiHXr1tG/f/9qwy3/BhUVFcyYMYPY2Fh++umnFp9Q9zjKy8spLi5WaYZBgwaRlJREYmKi4uLh4UFAQACJiYkt1rBojPz8fP7+++96e/JaSp8+faote/7rr7+ws7NTUaLqoqOjkUqliomT6qKwsBANDeWvdE1NTcrLy1WUqGb6+vpYWlqSlZVFXFwcfn5+qo4kPAFq1XPxKH9/f0JCQlizZg2zZ89Wum3YsGHcvXsXIyOjGu8bFRVFYmIio0ePxtHREZlMxpdffsm5c+eqLc1KT09HJpORl5fHmTNnCA8P5969e0/syCQ/P1/pSPHKlSskJiZiamqKra3tE3nMxggODmb79u3s3bsXQ0NDbt++DYCxsbFihY4qzZ07Fx8fH2xtbcnLy2P79u0cOXKEuLg4leYyNDSsNi9FX18fMzOzFp+vUpvZs2fj6+uLnZ0dqampzJs3D01NTcaNG6fqaAC888479O7dm7CwMF566SVOnTrFunXrWLdunaqjAfJGbHR0NIGBgWhpqdfXp6+vLwsXLsTW1hY3Nzd+//13li1bRlBQkKqjARAXF0dFRQUuLi5cunSJkJAQXF1defXVV1UdTXgC1OvT8QgtLS1mzJhBeHg406dPV7pNIpHQtm3bWu/bq1cvjh07xrRp00hNTcXAwAA3Nzf27NlTbemYi4sLEokEAwMDHBwcGDp0KLNmzXpiy/VOnz6Nt7e34vqsWbMA+TLbmJiYJ/KYjfFwvHTAgAFK26Ojo1Uyge1R6enpTJw4kbS0NIyNjenatStxcXEMGTJE1dHU3s2bNxk3bhwZGRmYm5vTt29fTpw4oTY9dD179iQ2Npa5c+cyf/587O3tWbFiRYtPSqzN4cOHuX79utr8YFcVERHBRx99xBtvvEF6ejpWVlZMnTqVjz/+WNXRAPmqs7lz53Lz5k1MTU158cUXWbhwIdra2qqOJjwBkoqWPH2bIAiCIAj/eGo750IQBEEQhP9NonEhCIIgCEKzEo0LQRAEQRCalWhcCIIgCILQrETjQhAEQRCEZiUaF4IgCIIgNCvRuBAEQRAEoVmJxoUgCIIgCM1KNC4EQRAEQWhWonEhCIIgCEKzEo0LQRAEQRCa1f8Dct3tAJUwTj4AAAAASUVORK5CYII=\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "plt.scatter(df['F2'], df['F4'])\n",
        "plt.xlabel('Feature')\n",
        "plt.ylabel('Target')\n",
        "plt.title('Relationship between Feature and Target')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "UKtjPkk_J55D",
        "outputId": "68169a16-338f-4a2a-cdb5-f06831c39903"
      },
      "execution_count": 73,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "corr_matrix = df.corr()\n",
        "print(corr_matrix['F4'].sort_values(ascending=False))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3GJqCuJvSj9G",
        "outputId": "77f0a835-7755-41da-9bd6-722cf87a5edc"
      },
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "F4      1.000000\n",
            "F6      0.938108\n",
            "F1      0.931056\n",
            "F5      0.925707\n",
            "F2      0.793143\n",
            "F8      0.676919\n",
            "F7      0.485185\n",
            "F3      0.031230\n",
            "RMSD   -0.169771\n",
            "F9     -0.891766\n",
            "Name: F4, dtype: float64\n"
          ]
        }
      ]
    }
  ]
}