4_1_training_performance_reports.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "uo9tTfNALroY",
        "OTssUvLYMAz0",
        "-KJX2yFkMasg"
      ],
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/LouisVanLangendonck/UPC-AML-ArchitectureClassif/blob/main/training_performance_reports.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Training Performance Evaluation"
      ],
      "metadata": {
        "id": "b4_xWrSvLfUe"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Specify here the MAIN DIRECTORY of the project"
      ],
      "metadata": {
        "id": "crZMcX6RLyrr"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "project_dir = '/content/drive/MyDrive/FIB-2022-2023/aml'"
      ],
      "metadata": {
        "id": "5SlrcMWALxma"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Imports"
      ],
      "metadata": {
        "id": "uo9tTfNALroY"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i1qXd-xeIsVp",
        "outputId": "d2627338-6288-4a3f-9239-6ca6dce5d885"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "import os\n",
        "import matplotlib.pylab as plt\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import keras\n",
        "import pandas as pd\n",
        "import seaborn as sns\n",
        "\n",
        "from google.colab import drive\n",
        "drive.mount('/content/drive', force_remount = True)"
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Function Definition"
      ],
      "metadata": {
        "id": "OTssUvLYMAz0"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def model_training_report(model_path, plots=False):\n",
        "    model_name = model_path.split('/')[-1]\n",
        "    model_family = model_path.split('/')[-2]\n",
        "\n",
        "    history = pd.read_csv(model_path + '/history.csv')\n",
        "    epochs = history['epoch'] \n",
        "    acc = history['acc']\n",
        "    weighted_f1 = history['f1_score']\n",
        "    val_acc = history['val_acc']\n",
        "    val_weighted_f1 = history['val_f1_score']\n",
        "    loss = history['loss']\n",
        "    val_loss = history['val_loss']\n",
        "    #print('epoch with lowest val loss {}'.format(val_loss.idxmin()))\n",
        "\n",
        "    if plots:\n",
        "\n",
        "      fig, axs = plt.subplots(1, 2, figsize=(15, 7))\n",
        "      fig.suptitle(\"Training and Validation curves for {}: {}\".format(model_family, model_name), fontsize=16)\n",
        "\n",
        "      axs[0].plot(epochs, weighted_f1, 'b-', label = 'Training', markersize=10)\n",
        "      axs[0].plot(epochs, val_weighted_f1, 'r-', label = 'Validation ', markersize=10)\n",
        "      axs[0].set_title('Training and Validation weighted f1-score')\n",
        "      axs[0].axvline(x=val_loss.idxmin(), color='black', ls='--', lw=1.5, label='Early Stopping / Optimal Model')\n",
        "      axs[0].legend(loc=2, prop={'size': 16})\n",
        "      axs[0].tick_params(axis='both', which='major', labelsize=16)\n",
        "      axs[0].set_xlabel('Epochs', fontsize=18)\n",
        "      axs[0].set_ylabel('weighted f1-score', fontsize=16)\n",
        "      \n",
        "      axs[1].plot(epochs, loss, 'g-', label='Training', markersize=10)\n",
        "      axs[1].plot(epochs, val_loss, 'y-', label='Validation', markersize=10)\n",
        "      axs[1].axvline(x=val_loss.idxmin(), color='black', ls='--', lw=1.5, label='Early Stopping / Optimal Model')\n",
        "      axs[1].set_title('Training and Validation loss')\n",
        "      axs[1].legend(loc=2, prop={'size': 16})\n",
        "      axs[1].tick_params(axis='both', which='major', labelsize=16)\n",
        "      axs[1].set_xlabel('Epochs', fontsize=18)\n",
        "      axs[1].set_ylabel('Categorical Crossentropy Loss', fontsize=16)\n",
        "      fig.show()\n",
        "    \n",
        "    return val_weighted_f1[val_loss.idxmin()], val_acc[val_loss.idxmin()]"
      ],
      "metadata": {
        "id": "lFKDdDwJI2I1"
      },
      "execution_count": 9,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Evaluation of all MLP models"
      ],
      "metadata": {
        "id": "-KJX2yFkMasg"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "Model evaluation loop for all models. There is the option to filter on which loss function is used at the top (with 'CEL' being Cross-entropy Loss, 'FL' being Focal Loss and 'WCEL' being weighter Cross-entropy Loss)"
      ],
      "metadata": {
        "id": "X2703im2MKej"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "model_type = 'CEL' #Choose from: 'all', 'CEL', 'FL' and 'WCEL'\n",
        "\n",
        "\n",
        "for model_list in os.listdir(os.path.join(project_dir,'models/mlp_models_selection/')):\n",
        "  if 'feat+' in model_list:\n",
        "    saved_model_path = os.path.join(project_dir,'models/mlp_models_selection/')+model_list\n",
        "    print('\\033[1m' + '--------------------------------------------------------')\n",
        "    print('Training Report for {}'.format(model_list))\n",
        "    max_f1 = dict()\n",
        "    max_acc = dict()\n",
        "\n",
        "    print('--------------------------------------------------------'+ '\\033[0m')\n",
        "    for model_str in os.listdir(saved_model_path):\n",
        "      if model_type == 'FL':\n",
        "        if 'focal' in model_str:\n",
        "          max_f1[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[0]\n",
        "          max_acc[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[1]\n",
        "      elif model_type == 'CEL':\n",
        "        if 'no_sample_weights' in model_str:\n",
        "          max_f1[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[0]\n",
        "          max_acc[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[1]\n",
        "      elif model_type == 'WCEL':\n",
        "        if (not 'no_sample_weights' in model_str) and (not 'focal' in model_str):\n",
        "          max_f1[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[0]\n",
        "          max_acc[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[1]\n",
        "      else:\n",
        "          max_f1[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[0]\n",
        "          max_acc[model_str] = model_training_report(os.path.join(saved_model_path, model_str))[1]\n",
        "    \n",
        "    if max_f1:\n",
        "      max_key = max(max_f1, key=max_f1.get)\n",
        "      max_val = max_f1[max_key]\n",
        "\n",
        "      max_key_acc = max(max_acc, key=max_acc.get)\n",
        "      max_val_acc = max_acc[max_key_acc]\n",
        "\n",
        "      print('\\033[1m' + 'for {}: {} is the model in this family with the highest validation macro f1 of {} (at minimum validation loss) and validation accuracy {}'.format(model_list,max_key, max_val, max_val_acc) + '\\033[0m')\n",
        "      print('\\n') "
      ],
      "metadata": {
        "id": "Iyw2zBqhJwqs",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0bc1f019-ae8a-4eee-d47b-aeb6dac006ec"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_efficientnetb7\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_efficientnetb7: model_512_0_SGD_lr:0.01_no_sample_weights is the model in this family with the highest validation macro f1 of 0.2497615218162536 (at minimum validation loss) and validation accuracy 0.3098820745944977\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_inception_resnet_v2+vgg19+xception\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_inception_resnet_v2+vgg19+xception: model_512_0_SGD_lr:0.01_no_sample_weights is the model in this family with the highest validation macro f1 of 0.5608643293380737 (at minimum validation loss) and validation accuracy 0.5896705985069275\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_xception\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_xception: model_512_0_SGD_lr:0.01_no_sample_weights is the model in this family with the highest validation macro f1 of 0.5426589250564575 (at minimum validation loss) and validation accuracy 0.5673037767410278\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_inception_resnet_v2+vgg19+xception+SIFT\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_inception_resnet_v2+vgg19+xception+SIFT: model_256_256_adam_lr:0.001_no_sample_weights is the model in this family with the highest validation macro f1 of 0.5890365839004517 (at minimum validation loss) and validation accuracy 0.6128507256507874\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_inception_resnet_v2+vgg19+xception+SIFT+efficientnetb7\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_inception_resnet_v2+vgg19+xception+SIFT+efficientnetb7: model_256_256_adam_lr:0.001_no_sample_weights is the model in this family with the highest validation macro f1 of 0.594926118850708 (at minimum validation loss) and validation accuracy 0.6087840795516968\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_SIFT\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_SIFT: model_512_0_SGD_lr:0.01_no_sample_weights is the model in this family with the highest validation macro f1 of 0.3818376660346985 (at minimum validation loss) and validation accuracy 0.4282228648662567\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_inception_resnet_v2\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_inception_resnet_v2: model_512_0_SGD_lr:0.01_no_sample_weights is the model in this family with the highest validation macro f1 of 0.5393762588500977 (at minimum validation loss) and validation accuracy 0.5628304481506348\u001b[0m\n",
            "\n",
            "\n",
            "\u001b[1m--------------------------------------------------------\n",
            "Training Report for feat+mlp_vgg19\n",
            "--------------------------------------------------------\u001b[0m\n",
            "\u001b[1mfor feat+mlp_vgg19: model_256_256_adam_lr:0.001_no_sample_weights is the model in this family with the highest validation macro f1 of 0.4996239840984344 (at minimum validation loss) and validation accuracy 0.5302968621253967\u001b[0m\n",
            "\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_training_report('/content/drive/MyDrive/FIB-2022-2023/aml/models/transfer_learning/try_one', plots=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 523
        },
        "id": "mRsgqsLuLao_",
        "outputId": "bdcef3bf-e162-4e01-fb63-29e2e4241e46"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6896275281906128"
            ]
          },
          "metadata": {},
          "execution_count": 4
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1080x504 with 2 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA5EAAAHpCAYAAAAbPLg6AAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4yLjIsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+WH4yJAAAgAElEQVR4nOzdd3wUZRrA8d+TSkICSQgYeiiKoBQ9UMCCp8AhIModYqNZsdfTEwUEUcSCqNhQRERALKcoRQ8bYAGRsyugHtKkBAgB0kh77493Nmw2u9ndJJsCz/fzySfZmXdm3imZd59533lfMcaglFJKKaWUUkoFIqy6M6CUUkoppZRSqvbQIFIppZRSSimlVMA0iFRKKaWUUkopFTANIpVSSimllFJKBUyDSKWUUkoppZRSAdMgUimllFJKKaVUwDSIVKoGExETwM+mCm5jlLOe1HIsO7ui26/J/O2fiHRxjt1dZaSZJCJFItIqwG2WOh8isklEZlc0v2Us10VEJohIkpd5RkQmBLtOFRgRqSsir4pImnOsnwjhtkaJyBWhWn9lEZEeIvKViGQ5x6RLFW03oP+z6lST8igiZzn3Df0uqdRRKKK6M6CUKlMPj8/vAN8DE9ymHargNpY429lRjmUnAU9WcPu1ljHmOxH5ARgOPOI5X0QEGAZ8Zoz5owKbGgwcqMDy/nQB7gPmAuke83oA20K47aPdDcAlwBXAr5Tv/zBQo7Dl/qwQbqMyvATkAOcB2djjoqxQ3wuCcRb2vvEAUFS9WVFKVTUNIpWqwYwxq90/i8ghYI/ndI804YAYYwoC3MZuYHc58/e/8ix3hHkFmCoiJxljvvWYdyaQiv2SVW5e1ltlyrrWaisRiTbGVPThS2VpD2w3xsypjJVV1r5V1zFyarXaAQ8aYz6ppHXWpPNdQrB5q857QUWISCRQYIwx1Z0XpVTl0CYIStVyTnOvB0XkbhH5A8gDOopIHRGZJiI/iUimiOwUkUUicrzH8r6aT84VkYtFZJ3TrGytiJzusWyJ5pMikuqsa7SI3C8iO0Qkw9luM49lY0XkORHZ6+TvHRHp6Sw/ys8+t3WaAP4hIjkistFZV6KX/G0TkZNE5DMRyRaR30TkWi/rPEdEvhGRXBH5n4iM9nfsHfOAAmxtpKcR2BqVNwM9Hz72t1QTtkDzKyITnXQHRGSPiHwiIt3d5o8CXnY+/iaHm0mnOvNLNWcVkX4isso59vtFZKGItPNIs1xEPheR3s72s519H+xvf53lWznneKeIHHLO8ZNu85eLyHJ/x8rt+j5TRN4UkQzgKxG5U0TyRKSBl3X8IiLvun2OFZGHnestz/l9r7g14xOROBGZLiJbnPymichHZZ1fETHY2sHmbsf9LGdeO+d/IsM5zqtFpJ/H8hOcZU4Ukf+ISCbwho9tLQd6Aae5bWt5WcfImddNRN5y/o9yRGSDiEwWkRjP9QdyvkXkOGe/0pxrd4uzzQjnWizEfjcZJx7N9UWkl4h8LCIHxd6T/iMiJ/rIx3ki8q3YB2/X+zoH/jjX4TwR2e2c1++87FOw96MeIvKliOQAj0hw901f13d3J58HRGS7iDwlInU8lm0tIkudc5MmIlNF5Bopx+sMYu8J9zkf813XlDPPtT/Xi8gjIrId22LmZGf6+V7W5zo24QFuX0TkNud6zHOO2dMiUs8jnRGRB0TkZuf8HBSRFSJygpd1/t35P8t2jv+bItIimOOi1NFEg0iljgyjgAHAP53f24FoIB5bCzYAuA6oA6wSkZQA1nkGcAcwDrgICAcWi0hCAMuOAdpim+jdgm0SOdcjzQvO/MewTbQ2YAOyQDQBtgK3An8D7gfOAZZ6SVsPmO9s/3zga+A5EfmrK4GItHeWzQEuBu5x1n2Ov4wYY3YB/wEucf8C5HyBGwK8Y4w5QMXPR7Eg89sUmObs+yggDVgpIh2d+Us4XFN6IfZc+WzeLDaQWQJkYq+L64ATgc9FpKlH8jbY5s6PA3931vmmiLT1s3+tgDXYmtzxQD9gIpBc1nJ+zAP+wJ6Tu7HXRLizD+7b/gu2dnCO8zkCe36vcvblXGAm9v/iUbdFpwFDnXz2AUYD3wFl/b/0cNa9k8PH/RsRaQJ8DnQGbnTWmwEsEZFzvaznXWAFMMjJhzfXA98CP7htyzO48jxGAC2c/bgWex6exP7fvkxpgZzvJdhr8jrs/+7d2AAjzJnnelD1kpPHwQAiMgD4GHvdDQMuxf4/fSYizT3ycRzwFDDd2cbHPo5JmZz1foU9D7dhj+83wL9FZJBb0mDuR/WBBcBr2Gtpvtu8QO6bvrwK/A973J/DNpMe47YvUcCHQCfssR8FtALu9bLfrocTqWVsbyb2HIE9Z65ryt292HNxDfY8/oK9/5Z44OWUKUOBmcaYQn876ngQe519iG32/IizT0uk9Duaw7D33FuAy7HX9LvO/7YrD9cC/3byOMTJ44nAChGJDzBPSh1djDH6oz/6U0t+gE3AXI9pBhs0xvhZNhyIBQ4Ct7lNH+WsI9VjO/uARLdpXZ10l7pNmw1scvuc6qRZ7rHtfzrTmzif22HfobnLI91TTrpRQR6XCOwXGQOc5JE/A/zVbVo0sBd4wW3aPGAPUNdtWnNsre6mALZ/obOdc92mXexM+1slnY/ZFc2vs80IbMD+pJdttvWyjAEmuH1eC/wGRLhNawXkA4+7TVvuTDvWbVojbE3TPX6O5xxssNCkjDTLPa8zH8fKtW/TvKT9EFjlMe0J7LUf7Xwe7ix/pke6e53j3cj5/JP7/gdx7c71PGfYBysF7ufDOXcbgG/cpk1w8nZLgNtaDnzuZbrPY+SRTpzrZxj2/7dBMOcb+xDAAIPK2EaE5zXnTP8d+NhjWj3n/+AJj3wUAV3KcS48r52XsE39G3ik+xD4zs8+lHU/Ot8jfSoB3Df9XN8TPZZdDPzq9vkaJ90pHufze0rfb8Y7119LP8fLdf1F+Nifb7CvVnhea4Xu6wZudrbXLMDzlIR98DDbY/owz+vL+fwbEOk2bYgzvafzOQ7YD8zyWF8r7P/4rcFeS/qjP0fDj9ZEKnVk+MAYk+M5UUSGiu3lMANbSGdhC8x2nmm9WGWM2ef2+UfndyDNezyfwHsueyr2C8ybHuneCmDdiEiUiNwjIuudJmH5wGfObM99yzbGfOr6YOz7R79Scj96AEuNMVlu6bYCXwSSH+A9bODh3qR1BDa4/8gt3xU5H+4Czq/Y5oWfisheZ5v52NqBYLeJiNQFTgZeN27v3BrbadAX2OaS7n4zxvzmli4NWxPq7xrqCyw2xmwPNo9leMfLtDlAd1dNmVMzcQnwhjn8nlo/YDPwpdgmlxFOumVAJOBqGvw1MMq5LrsG2izPhzOB1caY310TjK2heQ3o4tlkz8e+lUep9YhIPbFNef+H/eKej631EuBYj+T+zvdeYCMwRUSuFhHP5b1y0rUB5nmcg2xgFfZ4udtkjPkukHX70Q97L9vvsd3/AJ1d5yHI+1E+NsDzxt99syxLvCzrvlx3YIsxZo1rgjHGYGvfSjDG3G+MiTDGbA5gu2VZ6GzD3QJsrfrVbtNGA0uMMYF24NUdiKJ0Le0C7D3O8z70oTEm3+2z53HtgX0g4Xl9bQXWU/r6UkqhzVmVOlKUanooIucBrwPrsE2/TgW6YZ+s1/FM70WJXjrdvlQHvSyHe5B1LdvY+Z3mkW5XAOsGeAj7FHwutpnSKdhmXN7yt4/SDnmka+xj2wHlxzk2rwMXiEi8iByDDYTmOl/+K+N8uAsovyJyMvaLaSZwJfbLVzds7UOw2wRIxAYP3pq67sTWELjzvA6g9LH3pgGV3yOstzy/jQ3kXcF/X2ztmXsnN42Altgv/+4/ri/jrncqbwJmYJsifg2kiX0HNrYceU3ykd+d2OOf6DG9snp09bael7FNWZ/CNtPthm0qCaXPY5nn2wko+mBrsx8CfhX7/uB1fvLVyPn9EqXPw0AOn4Oy9qM8GmEfBnlu09WM2bXdYO5Hu43vJpv+7ptl8bZstNvnxpS+30Lg99zyKHUejDG52GvqCidYOwPoADwfxHpd95kS63cebO3F/33I87i6rq+PKH2uO1L6+lJKob2zKnWk8Nbj3cXA78aYUa4JYnvIKzUWYDVwFf6NsO9guRwT4PIXA3OMMcW9nopIXAXz423bgeYHbC+t12KbSiVgmx+6ByOVeT4Cze8/sE/m/+7+JF5shx8Z5djuPuy15u0dzhS8BxHlsQf73lxZcrG1B558Hc9S/yPGmCwReQe4DNtJyDBgozHGvUZ3L/YaHepjvZucdWVi30EbIyItsdfBFGxzuH/52RdP6fg+xobSD0a8/f+XR4n1OO/1no9tWureqVFHzwUD3oAxG4ERIiIcfufzWRHZZIx538die53fY3Cr2XeT57mZ8ubPy3Y/Ax72Md9VUx7M/aiy8hasHdhgzVMw97hg+drX54DbsdfWYOz/0H+CWK/rPpMC/Oya6NQeNiD4+5Dr+hrlvj43B4Ncn1JHBa2JVOrIFYsNINwNxwY31W0N9gvGhR7TPT/7Eot9Suzu8grkZxXQ32muCRR3qnFaoCswdiiMX7HHeATwX2OM+xeSyjwfgeY3Fvv+kXFLdzalm8e5nszHUAan+ex/gQs9OhFqCfTEvo9WGZYBA0WkcRlpNgPHOR2GuPJxJrazlWDMAdqIyN+ACyjdRO4D7PummcaYtV5+9niu0Biz2RgzFdts7kTP+QFYgW1mm+qa4Bzvi4Bvje2oqTwO4ecce4jGXp+e/2ujyrn9Ysb6DhtMQNnHaQM20DjBxzn4oaL58eEDbEc0P/vYruv/prLvR6GwGmghIqe4JjiB/D8qsM6A7huejB0aahlwJ/Zhy4vGmGDGmVyNfXBwscf0i7CVI8uDyQ/wJTZQbOvjPG8Icn1KHRW0JlKpI9cH2OaV07Dv4HTFNrkrTw1UpTLGrBeR+cAkpye9/wJnY3vZA/8DV38AjBSRH7EdbvwdG8SU1wPYAHaZiDyKfd9mAsE39XrFWZdgO4vwzHNlnY9A8/sBtsfI2SLyMvZdyHHAnx7pfnF+3yAir2C/EP9gjPGs4cFZfgm2p95nse90TsR2TDG1HPvizX1Af+x7iJOx57gp0M8YM8xJswDbWcgssUMetMIGJPuD3NbH2Bqll7Bfhl/1mD8PGxB8LCJTsU2Bo7Dv6A0CLjDGZIvIKuy7sT9imw/3wta0vRJkfsD2sDoK+FBE7sMOLn899vwNKMf6XH4BrheRi7A9eR4s6wuyMWa/iKwG7hCRHdga4ivwX0vslYh0wvbe+jr2nIZj97MA8DkmpDHGiMgN2B41o7DDmOzB1qL1xL7r93h58uTHeOwDr5Ui8jQ2kE3EBrytjTFXOOkq+34UCrOxNeJvi8i92Gb0V3G4aXTxPVdExmP3vY2f9yJd9407ROR9oNAYszbA/DyL7VU4n8O9vAbEGJPu/C+OEZEsbJP99tj74ueUfj/U3/oOiMidwDMi0hB4H3sfaYr9P15ujJlf1jqUOhppTaRSR64Xsd2gXwQswn4pP4/gv2SHyjXALOAubIceJ3D4XSt/ebwJ+4X9QewX0nhshyjlYoxZhz0+sc76pmC/7AY7NMCr2Fq/fGwnKO4q7XwEml9jzH+wwexp2MD1Cmwt6e8e6b7HBqHnYb+EfY0dtsDbtj/ABjIJ2C/zz2Pf8zy9sjrCMcZswr6/uRr7vtn72EA1zS3Np9jmw6dij+fl2OaoQQXlTg3IfOwXxlXundk48/Oxwza8iL1ml2IDy5HYGgxXoL0S2+R1HvZL7BBsr7tPEiTnOJ6ObVr3HLbDqSRggHP8y+th7DUyE3uOZwSwzCXYhzzPYAORndihEspjJ7AFG+y/h/0faQIMNMb8t6wFjTFLsR2c1MXm/z/YYR1SsDXzlc4YswX7sOd7YDK2V9bnsIGFe9BbqfejUHAeCPXFDvHyPPbhxlbseYWS96EwbIAvfla7GBsMXo89B18HkaUl2CGK3jV2mKRg3Yu9js518nE3tlXBgCBrNQEwxszAPhRqh72PL8XeEyOwQ9wopTxI6Y6zlFKqeojIP7FfDFOdL3BKKaVCREQWA+2NMW2qeLt9sE1aextjyjWOp1KqemlzVqVUtRCRgdhmYd9hm1KdgR0X7Q0NIJVSqnKJyO3Ypta/YWtLL8S2KvDXO25l5qEN0BrbZPsbDSCVqr00iFRKVZeD2I5M7sY2UfsTO4zAfdWZKaXUkcV577qs13dMGcNuHEkOAbdhO9YKx3ZYdJUxJqh3EitoHLbZ+ffYpvUl6LlSqvbQ5qxKKaWUOmI5HS+NLCPJCmPMWVWTG1UWEZlA2Q8SNxtjUqsmN0qpsmgQqZRSSqkjljNUSnIZScrspVZVHRFpgo9OvRyHjDE/VlV+lFK+aRCplFJKKaWUUipgOsSHUkoppZRSSqmAaRCplFJKKaWUUipgGkQqpZRSSimllAqYBpFKKaWUUkoppQKmQaRSSimllFJKqYBpEKmUUkoppZRSKmAaRCqllFJKKaWUCpgGkUoppZRSSimlAqZBpFJKKaWUUkqpgGkQqZRSSimllFIqYBpEKqWUUkoppZQKmAaRSimllFJKKaUCpkGkUkoppZRSSqmAaRCplFJKKaWUUipgGkQqpZRSSimllAqYBpFKKaWUUkoppQKmQaRSSimllFJKqYBpEKmOKCLyvoiMrOy01UlENolI7xCsd7mIXOX8fZmILAskbTm200JEMkUkvLx5VUopVb20fA1qvbW+fBURIyJtK3u96sihQaSqds4N0PVTJCI5bp8vC2ZdxphzjTGvVHbamkhE7haRlV6mJ4tInoicGOi6jDHzjDF9KylfJQplY8wWY0ycMaawMtavlFIqMFq+lo+Wr0r5p0GkqnbODTDOGBMHbAHOc5s2z5VORCKqL5c10lygp4i08ph+MfCjMeanasjTUUOvR6VUTafla7lp+aqUHxpEqhpLRM4SkW0i8i8R2Qm8LCKJIrJYRHaLyD7n72Zuy7g3IRklIp+LyGNO2j9E5Nxypm0lIitF5KCIfCQiz4jIXB/5DiSPk0TkC2d9y0Qk2W3+cBHZLCJ7ReReX8fHGLMN+AQY7jFrBDDHXz488jxKRD53+9xHRNaLyH4ReRoQt3ltROQTJ397RGSeiCQ4814FWgCLnCfdd4lIqtMsJsJJ00RE3hORdBH5XUSudlv3BBF5Q0TmOMfmZxHp6usYiMiTIrJVRA6IyH9F5Ay3eeEico+I/M9Z139FpLkz7wQR+dDJwy4RuceZPltEHnBbx1kiss3t8ybnevwByBKRCOeJtWsbv4jIYI88Xi0i69zmnywid4rIvz3SPSUiT/raV6WUqixavmr56q989diH+s5yu53jN1ZEwpx5bUVkhbM/e0TkdWe6iMg0EUkTW0b/KEHU4KqaT4NIVdOlAElAS+Aa7DX7svO5BZADPF3G8qcCG4Bk4BHgJRGRcqSdD6wBGgATKF2wuAskj5cClwONgCjgnwAi0gF4zll/E2d7XgsmxyvueRGRdkAXJ7/BHivXOpKBt4Gx2GPxP+A09yTAQ07+2gPNsccEY8xwSj7tfsTLJhYA25zlhwCTReRst/mDnDQJwHt+8vy1s79Jzj6/KSJ1nHm3A5cA/YF6wBVAtojEAx8BHzh5aAt8XNYx8XAJMABIMMYUYI/PGUB9YCIwV0QaA4jIhdhjM8LJwyBgL/Ypdz+3LwcR2Cfcc4LIh1JKVYSWr1q++s2zYzq2jGsN9MKWaZc78yYBy4BE7PGc7kzvC5wJHOcsOxRb/qkjhTFGf/SnxvwAm4Dezt9nAXlAnTLSdwH2uX1eDlzl/D0K+N1tXixggJRg0mILiAIg1m3+XGBugPvkLY9j3T5fD3zg/D0eWOA2r65zDHr7WHcscADo6Xx+EHi3nMfqc+fvEcBqt3SCLZSu8rHeC4BvvZ1D53OqcywjsAViIRDvNv8hYLbz9wTgI7d5HYCcIK6ffUBn5+8NwPle0lzinl+PebOBB9w+nwVs89i3K/zk4TvXdoH/ALf4SPc+cLXz90Dgl6r4H9Mf/dGfo/MHLV+1fA2ifHXW2xYId45TB7d5o4Hlzt9zgBeAZh7Lnw38CnQHwqr7+tefyv/RmkhV0+02xuS6PohIrIjMcJpTHABWAgniu2eyna4/jDHZzp9xQaZtAqS7TQPY6ivDAeZxp9vf2W55auK+bmNMFmU8uXPy9CYwwnmqexlObVY5jpWLZx6M+2cROUZEFojIn85652KfqAbCdSwPuk3bDDR1++x5bOqIj/d1ROSfYpuK7heRDOzTTldemmOf8nryNT1QJc69iIwQke9EJMPJw4kB5AHsU+5hzt/DgFcrkCellAqWlq9avvosX90kA5HOuryt9y5sMLzGaSJ7hbNvn2BrOp8B0kTkBRGpF+C+qFpAg0hV0xmPz3cA7YBTjTH1sE0lwO2dghDYASSJSKzbtOZlpK9IHne4r9vZZgM/y7yCbSbSB4gHFlUwH555EEru72TseenorHeYxzo9z5m77dhjGe82rQXwp588lSL2/ce7sPueaIxJAPa75WUr0MbLoluxTXK8ycI+fXZJ8ZKmeP9EpCXwInAj0MDJw08B5AFgIdDJeUdkIDDPRzqllAoFLV+1fA3EHiAf23S31HqNMTuNMVcbY5pgayifFWdoEGPMU8aYv2BrPY8D7qxgXlQNokGkqm3ise8eZIhIEnBfqDdojNkMrAUmiEiUiPQAzgtRHt8CBorI6SISBdyP///Tz4AMbHOSBcaYvArmYwlwgoj83XlCeTMlg6l4IBPYLyJNKV0o7MJHkGaM2Qp8CTwkInVEpBNwJfZpa7Disc2gdgMRIjIe+96hy0xgkogc67zg30lEGgCLgcYicquIRItIvIic6izzHdBfRJJEJAW41U8e6mIL9d0AInI5tibSPQ//FJG/OHlo6wSeODUAb+G8D2SM2VKOY6CUUpVFy9fSjtby1X29hcAbwINOedkS2+fAXLDv/svhToX2YcvEIhHpJiKnikgk9gFtLlBUkbyomkWDSFXbPAHEYJ+MrcZ2jlIVLgN6YJu+PAC8DhzykbbceTTG/AzcgA0sdmBvyNv8LGOwTWxaUrJjlnLlwxizB7gQmILd32OBL9ySTAROxtb6LcF2EuDuIWCs07zzn142cQn2PY7twDvAfcaYjwLJm4f/YPfpV2zTmlxKNoN6HFvwLcO+1/ISEOM09emD/aKyE/gN+KuzzKvA99j3TpZhz7NPxphfgKnAKmzh3hG3Y2WMeRP7Hs184CC29jHJbRWvOMtoU1alVHXT8rX0Mkdr+erpJmwguBH4HHsMZznzugFfiUgmtrOeW4wxG7EPdV/EHufN2P19tBLyomoIsf8fSqlgiO3Cer0xJuRPatWRS0RaAOuxnVEcqO78KKVUddPyVanaQWsilQqA0yyjjYiEiUg/4HxsrZJS5SJ2jK3bsU2kNIBUSh2VtHxVqnby1yOTUspKwTYraYBt/nKdMebb6s2Sqq1EpC62+etmoF81Z0cppaqTlq9K1ULanFUppZRSSimlVMC0OatSSimllFJKqYBpc1YvkpOTTWpqanVnQymlVIj997//3WOMaVjd+agttHxUSqmjR1llpAaRXqSmprJ27dpyL79hwwYA2rVrV1lZUkopFQIisrm681CbVLR8BC0jlVKqtiirjNQgMgRGjx4NwPLly6s3I0oppVQNo2WkUkrVfvpOpFJKKaWUUkqpgGkQqZRSSimllFIqYBpEKqWUUkoppZQKmAaRSimllFJKKaUCph3rhMDYsWOrOwtKKaVUjaRlpFJK1X4aRJbTgQMHSEtLIz8/v9S8pk2bArBu3bqqzpZSSvkVGRlJo0aNqFevXnVnRR2BcnNz2b17N7m5uRQUFJSar2WkUqom0zIyMBpElsOBAwfYtWsXTZs2JSYmBhEpMT87OxuA2NjY6sieUkr5ZIwhJyeHP//8E0ALSVWp9u/fz65du2jYsCEpKSlERERoGamUqjW0jAycvhNZDmlpaTRt2pTY2NhShSPA1q1b2bp1azXkTCmlyiYixMbG0rRpU9LS0qo7O+oIs2fPHpo1a0ZiYiKRkZFaRiqlahUtIwOnQWQ55OfnExMTU93ZUEqpcouJifHaHF+pisjLy9PyUSlV62kZ6Z8GkeXk7emqUkrVFnoPU6Gi15ZSqrbT+5h/GkQqpZRSSimllAqYBpFKKaWUUkoppQKmQWQING3atLgL85pORPz+pKamVmgbs2fPRkTYtGlT0MuOGjWqwttXSilVc2gZWVJtLCMnTJigzf2UOsrpEB8hEBcXV91ZCNiqVatKfB48eDCdO3dmwoQJxdOio6MrtI0BAwawatUqGjduHPSy48aN45Zbbik9wxgoLIQIvYSVUqo20TKypEDLyOz8bKLDowkPCy+e5rOMVEqpENNv4CGQmZkJ1I6Csnv37iU+R0dHk5ycXGq6u8LCQowxRAQYwDVs2JCGDRuWK39t2rTxPmPfPvjjD+jUCSIjy7XuI4YxUFCgx0EpVStoGVlSIGVkfmE+6/esJ6FOAq0SWhXXAvosI5VSKsS0OWsI/Pnnn8WDlB4JRIR7772XKVOm0KpVK6Kiovjxxx/Jzc3ltttu48QTTyQuLo6UlBTOO+881q9fX2J5b011UlNTGTZsGAsWLKB9+/bUrVuXrl278vnnn5dY1rOpzqZNmxARZjz/POOfe47GzZuTkJDAeeedx7Zt20osm52dzXXXXUeDBg2Ii4tj8ODBfPnll4gIs2fPLnOff//9d4YPH06rVq2IiYmhdevWXHfddezbt69U2hUrVtCnTx/q169P3bp16dy5My+99FKJNC+++CInn3wyMTExJCYm0qtXL7788ssy8xCwPXvgxx9tzaxSStVwWkYGX0Ym1Evg8gGX88mKT9ibs7c4nc8ycsYMxo8fT+PGjUNSRnpz4MABbrzxRpo0aUJ0dDTt2rVj2rRpGGOK02RmZnLTTTfRokULoqOjadSoEb179y5xTJ588knat29fXF527dqVd955J+j8KKVCS2siVUBmz55N69ateeyxx6hbty5NmjTh0LhImdkAACAASURBVKFDHDx4kLFjx9K4cWPS09N59tln6dGjB+vWrSMlJaXMdX722Wds2LCBSZMmUadOHcaNG8fAgQPZtGkTCQkJZS770DPP0LNjR2Y9/TRpWVnccccdDBs2jOXLlxenueaaa3jzzTeZMGECXbt25eOPP+ayyy4LaH+3b99O8+bNeeKJJ0hMTGTjxo1MnjyZ/v37l2je9O677/KPf/yD0047jRkzZpCcnMzPP//M5s2bi9P885//ZOrUqVx55ZVMnDiRsLAwVq9ezZYtW+jZs2dA+SlTVhYUFUFeHuj4bEopVeWqqoy8feTtHPvVsZzS5hRiIn3f7x966CF69uzJrFmzSEtLq/Qy0lNRUREDBgzgm2++4f7776djx44sWbKE22+/nd27dzN58mQAbrvtNt577z0mT57Msccey969e/niiy/IyMgAYN68edxxxx2MHz+eM844g5ycHH744QfS09PLlS+lVOhoEFlJbr0VvvvO/p2d3RyA2NiqzUOXLvDEE6FZtzGGZcuWlRpEeubMmcV/FxYW8re//Y1jjjmG1157jdtuu63MdR44cIDvvvuOxMREAFJSUujWrRtLly7l0ksvLXPZ1JQU5j/wAKSmQnIyu3fv5s4772T79u00adKEDRs2MH/+fKZMmcJdd90FQJ8+fcjOzmb69Ol+9/fMM8/kzDPPLP7cs2dP2rZtyxlnnMG3337LSSedhDGGW265hS5duvDpp58SFmYr9nv37l283O+//860adO47bbbePzxx4unDxgwwG8eApaTY3/n52sQqVQtJiLNgH8BXYHOQAzQyhizyc9yxwE3AH8FWgMHga+BccaY772kvxq4A2gFbAKmGWOer7Qd8eLWD27lu522kMzOzgYgdlXVFpJdUrrwRL/QFJJVWUZ++cmXJCclc3zy8T6XTU1NZf78+cWfK7uM9LR06VI+//xzXn75ZUaNGgVA3759ycrKYurUqdx+++0kJyezatUqLrvsMq688sriZQcPHlz896pVq+jUqRPjx48vnta/f/+g86OUCj1tzqoC0q9fv1KFI8Abb7zBqaeeSkJCAhEREdStW5fMzEw2bNjgd509evQoLhwBOnbsCMCWLVv8Ltv/tNPsHwUFXpf96quvMMZw4YUXllhuyJAhftcNkJeXx+TJkzn++OOJiYkhMjKSM844A6B43zZs2MDmzZu56qqrigNITx999BFFRUVcc801AW03aMaUDCKVUrVZW2AosA/4LIjl+mIDyFeA84DrgYbAahH5i3tCJ4CcAfwb6Ae8CTwrItdVOPdHsaosIw/tPUROQQ5bD2z1uaxn4FXZZaSnlStXEhYWVuoB8LBhw8jLyytuwdOtWzdmz57N5MmTWbt2LYUer2F069aN7777jptuuomPPvqo+IGDUqrm0ZrISuJeA7hhg72xt2vXrppyU/m89Rq3aNEiLrroIkaOHMl9991HcnIyYWFh9O/fn9zcXL/rTEpKKvHZ1cNdQMvWrw8ixYGT57I7duwAoFGjRiWWO+aYY/yuG2DMmDFMnz6d8ePH07NnT+Lj49m2bRt///vfi7exd699L6VZs2Y+1xNImgrJy7NNWUGDSKVqv5XGmGMAROQqbHAYiAXAM8bt5TMR+QRby3gLMMKZFgE8CLxqjLnXSfqpiDQBJonITGNMSG4k7jWArgBKy8iy+SojKYSUuBR2Zu7kUOGhoJatrDLSU3p6OklJSURFRZWY7mqy62qOOn36dFJSUpg1axb33nsvSUlJjBgxggcffJDY2FhGjBhBbm4uL730Es8++yyRkZH079+fxx9/XIf7UqqG0SAyBJo3b17dWah03saDWrBgAW3bti3xAn5+fn7VvLsQHW17I3VqIj25CvS0tDRatWpVPH3Xrl0BrX7BggWMGDGCsWPHFk9z9SjokpycDFBmBxHuaULyhclVCwkaRCpVyxljisq53B4v0/aLyK+A+4CMPbA1lHM9kr8KXA6cDnxanjwEQ8vIipeRTeKbkJmXSWZeJgbjfwEPFS0jPSUlJZGenk5eXl6JQHLnzp3F88H2yPvQQw/x0EMPsXnzZt566y3uvvtuoqKiePjhhxERRo8ezejRo9m3bx/Lli3jjjvu4KKLLuKrr74qV96UUqGhzVlDIDY2ltiqfiGyGmRnZ5fqwvzVV18t1TylUrnW7QoifQROp5xyCiLCm2++WWK652dfsrOzifQYMuPll18u8fm4444jNTWVmTNnluh9zl3v3r0JCwvjhRdeCGi7QXMFkRERGkQqpYqJSBJwIrDObfIJzu+fPJL/7PzuEOp8gZaRlVFGhkmYHeoDoaCogKIgnz9UtIz01KtXL4qKikotP2/ePKKioujRo0epZVq2bMkdd9xBx44d+eknz0sSEhMTueiiixg6dKjX+Uqp6qU1kSFw4MABAOrVq1fNOQmtfv36sXDhQm677TYGDhzI2rVrmT59ut+eVSvE9X5EnTplBk7HH388l156KePGjaOoqIi//OUvfPLJJyxatAjA5zuMLv369eOVV16hY8eOtG3blrfffrvUkBwiwhNPPMHf//53zj77bK699loaNmzIunXrSEtLY+LEibRp06a4U52DBw8yaNAgwsPDWbNmDccffzwXXXRRxY5HTg5ERZUZUCuljkrTAQHce5JxtXH0HKso3WN+CSJyDXANQIsWLSqcMS0jK6eMjI6IJi4qDmMM2w5so0X9wM9NRctIT+eeey6nn3461157Lbt37+aEE05g6dKlzJw5kzFjxhS3yunRoweDBg2iY8eOxMXFsWLFCr7//ntGjhwJ2B5j4+Pj6dGjB40aNeLXX3/l1VdfpW/fQFt2K6WqigaRIeB61+BILyCvvvpqtm7dyqxZs5gxYwbdunVj0aJFJXpaq3SuINJVE+nenNPDCy+8QHx8PI888gh5eXmcffbZPPPMMwwcOJD69euXuZnp06djjOHee+1rQ/379+e1117jlFNOKZHu/PPP58MPP2TSpEnFvc21adOGW2+9tTjNY489Rtu2bXn22Wd55ZVXqFu3Lp06daqcQjEnx/bIKgIBvGMTFGMgPR0SEiA8vHLXrZQKGREZA1wKXGmM+b2i6zPGvAC8ANC1a9fg20560DKy8srIqPAowiWctKw06kUFdzwrUkZ6CgsLY8mSJdxzzz08/PDD7N27l9TUVB5//PES5eGZZ57JG2+8wZQpUygoKKB169ZMmzaNm2++GYDTTjuNl19+mVdffZX9+/fTpEkThg0bxsSJE4PKj1Iq9MRXM7yjWdeuXc3atWt9zl+3bh3t27f3Of9I7DSgxtiwwTZp7dABtm2DXbvg5JNtEBWAxx57jLvuuotNmzZVyhP1alVUBN9+C8ccY//euxdOOqny1p+bCz/9BM2agZ/xzFTt5O9edjQQkf8aY7pWdz48OR3rvEgAQ3x4LHct8Bww1hjzoMe864BngSbGmB1u0xsBu4AbjTHPlLX+ipaPoGVkZSsyRazfs55DBYfo0LAD0RHR5V7XEVVGKlVBWkaWXUZqTaSqPYqKICsLnGYxRETY2rLCQvu3h8WLF/PTTz/RpUsXwsLC+Oyzz3jssccYOnTokVE4Hjpk9z8mxv5dWGiPUZDNkHzKy7O/PToUUkrVTCIyHBskTvUMIB2udx9PAHa4TXe9C/lLCLOnQiRMwmid2Jpfdv/CHxl/cFyD4wgT/+XAEV9GKqVCSoNIVXvk5NggKS7OfnZ1fFNQ4DWIjI+PZ+HChUyZMoWsrCyaNm3KzTfffOQ0i3E15Y2JKTnMR3T5n0KX4HrHMjPTBqsB1vYqpaqeiAwGXgZmGmP+6SPZKmAPcBnwkdv0Ydj3Ir8IaSZVyNSJqENqQiob921k+8HtNKvnf1ipI76MVEqFlAaRqvZw1Yi5gkhX4OhjmI9evXqxevXqKshYNXEFkXXqHK41DEUQWVBgm7Z6GUhbeWGMbWZdv74eMxU0EXGN9v4X5/e5IrIb2G2MWeGkKQBeMcZc6Xw+E3gN+B6YLSLd3VZ5yBjzLYAxJl9ExgHPisif2EDybOAK4CZjTF6Id0+FUFJMEgcOHWBn5k7io+KpX6fs9xqP+DJSKRVSGkSGQMuWLas7C0emgwdtgOQag8pVE3m09kqak2MDyLCw0ByLPLfvk5mZGhAFKjfXvq+bkwNu468pFSDPMRaedX6vAM5y/g53flzOBqKBkyldm7gZSHV9MMY8LyIGuAO4E9iCfRfyWaqIlpGh07xec7Lysvgj4w86NOxAVHiU/4WUUqocNIgMgTp16lR3Fo48xthAxr3HOD81kUe83FwbREJogsj8fLv+wkJ77Bs2rLx1H8n2OaMnZGRoM2AVNGOM3wvGM40xZgIwIYhtzABmBJu3yqJlZOiEh4XTOrE16/asY+O+jRyXdFzQw3UopVQg9M4SAhkZGWRkZFR3No4shw7ZYNHVlBUOB5FHY01kUZENIl0DdoeqJjIy0h5z7VwncBkZtna4sNDWniulStAyMrRiImNoldCKzLxMNmZsRHvhV0qFggaRIbBr1y527dpV3dk4sni+Dwn2i3p4+NFZE+kaE9L1RF/EBnyVXRMZFWWP+aFDJZu3Ku/y8uxYpsccY8+JflFWqhQtI0MvMSaRFvVbkJGbweb9mzWQVEpVOg0iVe2QmWlrHj2bQVV24FRbuPfM6hIRUXnHwhi7LldNJGhtZCBcQWODBlCv3uEmrUopVcUa1W1E47jG7Mnew47MHf4XUEqpIFR5ECkizUXkLRHZLyIHRORtEQl4QCIRaS8ib4rIHhHJEZENInKLR5oGIvKkiGx00vwhIk+LiL7UVVsdPGiDGc/3yyIijs6ayJwceyzcg+rKDKgLCmzwExVlm8yGhWkQGYiMDHtO6tSBxERbM+kK+JVSqoo1iW9Ccmwy2w9uJy0rrbqzo5Q6glRpxzoiEgt8AhwCRgIGeAD4VEQ6GWOy/Czf1Vl+OXAVsB84FohzSyPAe8BxwHhgHXYg5fuBriLSw2i7jtolP982p/TWsUtEhJ13tHH1zOoeVEdFVV7A4gpGIyPtNvS9SP8KCuzDjmOOsZ9dnUBlZBx+d1UppaqQiNCyfkvyC/PZsn8LkWGRJMYkVne2lFJHgKquibwaaA1cYIxZaIx5FxgEtARGl7WgiIQBc4CPjTGDnOU/Nca8YIx53C3psUBP4F5jzHPGmOVO1+VjgVOxwaVyXHDBBSQmJnLIRyB28OBB6taty6hRowJeZ2pqaon0s2fPRkTYtGlTmctt2rQJEWH27NklZ3h7H9LFqX174oknePvtt0vNnjBhAnIk9o6Zk1N6yA1XrWxlPCNxvf/o6rAnLs6+63c01voGav9+e+wTEuxnV1NgV2+tSqlap1aUkX6ICIvmLOLL/3zJxn0bOXjocIdf1VVGeh4DpVTtU9VB5CBgtTHmd9cEY8wf2HGtzvez7FlAe+BxP+lcgyId8Jju6uEi5PvcqlUrWtWS8eFGjhxJRkYGixcv9jr/rbfeIjs7m5EjR5Z7GwMGDGDVqlU0bty4fCvIzLS1Yd5qc5zAyVcQedVVV7Fq1arybbemKiy0QZ5nEBkZaYOYygj0XDWRrjE5XQF8VpmNBY5uGRn2HNSte3haQoIN+I/G2nKlfNAysqQKl5EBeOrJp1j90WqiI6L5Pf13cvJtq5UjsoxUSlWJqg4iTwB+8jL9Z2yT07Kc7vyuIyKrRSRfRNJE5CkRcf82/TOwEhgnIl1FJE5ETsE2bX3fGLOuojvhT1RUFFFRtWOA3wEDBtCgQQPmzJnjdf6cOXNo0aIFZ511Vrm30bBhQ7p37050dHT5VuB6H9LbWFeumjIfmjVrRvfu3cu33ZrKW6c6ULnDfLjW4RpGxRUYaZNW74qKbE1kQkLJJsauWkntpVWpYlpGllThMjJAYRLGsUnHEiZh/Lr3V/IK8o7MMlIpVSWqOohMAry17UoH/DXSb+L8fh1YBvQBHsG+Gznflch537E/sAH4GjgIfAVsBP5RgbwHLD09nfT09KrYVIVFRUVxySWX8P7777N3794S87Zs2cKKFSsYPnw4IsKyZcvo378/jRs3JjY2lhNPPJGpU6dSWFhY5ja8NdXJzs7m+uuvp0GDBsTFxTFo0CC2bdtWatmvV69myE030eyss4iJiaFdu3bcc8895LgCqYgIUgcNYvPmzcybNw8RQUSKm8l4a6pz4MABbrzxRpo0aUJ0dDTt2rVj2rRpJbpAX758OSLCe++9x4033khycjLJyckMGzYsoPHNFixYwNlnn03Dhg2Ji4vjpJNO4pVXXimVrqCggIcffpgOHTpQp04dGjZsSL9+/Vi/fn1xmt27d3P99dfTvHlzoqOjad6uHcPHj+eQZ1BdmUGka4xI1zbCw20gqUGkdwcP2kDSFTS61Kljg30NIpUqpmVkSRUqI7/+miFDhtCsWTPvZSS26airjKwTWYcujbsw7pZx/Jr+K+PvG18tZaQ3a9asoXfv3sTFxVG3bl3OOecc1qxZU2p/+/TpQ4MGDYiJiaF169Zcf/31xfN37tzJyJEji/PeuHFjBg4cSFqadiqkVGWr0o51Ksj1jXmuMWa88/dyEQkHpohIe7daxheB7sC12I512gMTgbdE5DxjTJHnykXkGuAagBYtAu4s1qvdu3cDkJSUVKH1VJWRI0fy9NNPs2DBAm644Ybi6XPnzsUYw4gRIwDYuHEj55xzDjfddBN16tRh7dq1TJgwgd27dzNlypSgtjl69Ghef/117rvvPrp168aHH37IpZdeWirdlt9+o8txxzHqmmuIT0nh559/5v7772fjxo0sWLAAIiN559FH6X/HHXTu0oUJEyYA9smuN0VFRQwYMIBvvvmG+++/n44dO7JkyRJuv/12du/ezeTJk0ukv+WWWxg4cCDz589nw4YN3HXXXYSHh3sNCN1t3LiRIUOGcPfddxMWFsbKlSu56qqryMnJ4dprry1Od/HFF7Nw4UJuvfVWevfuTW5uLitXrmTHjh0cf/zx7Nu3j549e5Kens7YsWPp1KkTab/8wruLF5MHlHhuXdk1kZ61vHFxkJZmgyVvtcJHs3377DGJjy89LyEBduywzYwjatMtV6nQ0DLSv4DLyC1b6NKlC6NGjSI+Pr50GQm888479O/fn86dOxeXkTH1YjhUcIj0nJLBfFWVkZ5++OEHevXqRYcOHYqD6ilTptCrVy9Wr15N586dyczM5G9/+xunnHIKs2fPJj4+nk2bNvHll18Wr2f48OFs3ryZRx99lObNm7Nr1y4+/vhjsrOzg8qPUsq/qv5Gsw/vNY6+aijduR4BfugxfRkwBTgJWCciA4BLgN7GmI+dNCtFZKOT9jzgXc+VG2NeAF4A6Nq1a/A9k9x6K3z3HQDNXTerqu6RsUsXeOKJoBfr2rUrHTp0YM6cOSUKyFdffZXu3btz3HG2LyL34McYwxlnnEFeXh6PPfYYkydPJizAwGLDhg3Mnz+fBx98kLvvvhuAvn37kpmZyfPPP18i7T/OOQfat4eTTsKEhXHaaadRr149RowYwTPPPEOD2FhOateO6KgokpOT/TbLWbp0KZ9//jkvv/xycW1l3759ycrKYurUqdx+++0kJycXpz/zzDOZPn16cboNGzYwc+bM4kLOl3vuuaf476KiIs466yx27NjBc889V3wcP/nkE/7973/z5JNPcvPNNxenv+CCC4r/njZtGhs3bmTt2rWcdNJJdmKzZlzSu7cdh9BdZddEejY3i4uDXbtsBzveOjkKFdfT75raQZIxtilr/freg2tXEJmRAW7XllJHk99+u5XMTFtG5uTYMvLbb6u2jIyL68Kxxx5hZeQ/DjewMsaULiMbNOCkk04iOjq6VBmZnpNOXmFe8bIiUmVlpKf777+f6OhoPv74YxKcFh19+vQhNTWViRMn8vbbb7N+/Xr27dvHI488QqdOnYqXde+gZ9WqVUyePJnLLruseNqFF14YcD6UUoGr6uqEn7HvRXrqAPwSwLJlcdUudnR+f+0x39Umor2f9RyVRo4cyZo1a/j1118B26xk/fr1JToL2LFjB6NHj6Zly5ZERUURGRnJ2LFjycjICKqpyFdffUVRURFDhw4tMf3iiy8ulfbAjh3867nnaHPccURHRxMZGcnw4cMxxvDbb78drtkJsEfSlStXEhYWVuqJ7rBhw8jLyyvVwcCAAQNKfO7YsSOHDh1i165dZW7nt99+45JLLqFp06ZERkYSGRnJzJkz2bBhQ3GaZcuWISJcffXVPtezbNkyunXrdjiABO89s4JtchoWVnk1kd6CSKj6Jq1btsD69ZXT62woZGXZ4+XZlNUlNtYeS23SqlStVWPLyAMH+Ne//kWbNm28l5FlSIpJol60fRj5R8YfFBYVVlkZ6WnlypUMHDiwOIAEqFevHoMGDWLFihUAHHvssSQkJDB69Gjmzp3L1q1bS62nW7duPProozz55JP8+OOP6IhuSoVOVddEvgc8JiKtjTEbAUQkFTgNuNvPsu9jx5f8G7DIbXo/5/da5/dO5/cpwEdu6U51fv9Znoz75VYDuNUJFNq1axeSTYXCsGHDGDNmDHPmzOGBBx5gzpw5REdHc9FFFwG2Nm3QoEFs376dCRMmcPzxxxMTE8PChQt58MEHyc3NDXhbO3bsAOAY13h6Ds/PFBVx+V138dHXX3P/pEl06dKFunXrsmbNGm644Qa7zSCbB6anp5OUlFSqU4eUlJTi+e48m1u5Oj4oa38zMzPp06cPsbGxTJkyhTZt2hAVFcVzzz3HrFmzitPt3buXpKQkYrwFhG5pOnfufHhCfr5tFulrGWfIkwopKrLb8GzOGhlp3/GryiCyoAD27LEBZHZ2yZ5Pa4qMDFtL6hoX0pOIDTD37LE964aHV23+lKoB3GsAN2gZWaaAy0jg8ssv56OPPuL+++/3Xkb6ERdlHw6m56STlZfFrt27Ql5GepOenu61d9qUlBT2OcMk1a9fn08//ZRJkyZx/fXXc/DgQU444QQmTpxYXCP7+uuvM3HiRB555BFuvfVWGjduzLXXXsvYsWMDrglWSgWmqv+jXgQ2Ae+KyPkiMgjbtHQrMMOVSERaikiBiLjefcQYsxd4CLhWRCaLSG8RuRvb6+orbsOGvA1sB+aIyHUi8lcRuQ47xuRW4J3Q72bt06RJE/r06cPcuXPJy8vj9ddf57zzziMx0bY+/t///sfatWt5+OGHufrqqznjjDPo2rUr4eX4QuwqKDyfVHp+zt23j3dXrODOG2/klltuoVevXnTt2rVk0CViA8kAnzYmJSWRnp5OnmscRMfOnTuL51fUqlWr2Lx5My+88ALDhw+nZ8+edO3alQKPoTeSk5NJT08v0QGCp+TkZP780+25h6+eWV0qI4h0Le+t59u4OBtEVtXT3b17D2+rJnbEYYx9HzI+vuwHGgkJNjg/eNB3GqVUjVUjy8jcXN59913uvPNO32VkgNo1sAG9iTGkp6eTe6hkEFiZZaQ3SUlJxdvw3K7rGAN06dKFf//736Snp7Nq1SratGnD0KFD+ekn2/F/o0aNeOaZZ/jzzz9Zv349o0aN4r777mPGjBml1q2UqpgqDSKNMVnA2cCvwKvAPOAP4GxjjHv1hgDhXvJ3P3AXMBRYClwHPAoUtwc0xhzAdqrzvpPW9XsR0MNjOyHRunVrWrduHerNVLqRI0eyefNmxowZw549e0o003G9lB7pFljk5+czb968oLdz6qmnEhYWxhtvvFFiuqsTAJdD6ekUFhYS6fH+XamBliMjiY6KKjMYc+nVqxdFRUW8+eabJabPmzePqKgoevToEfiO+ODtWO3bt4933y35Km7fvn0xxjBz5kyf6+rbty9r1qzh+++/txOqMoj01gV/XJytHQzyKXO5GAO7d9vax4QEG0TWtKZJubl2DEhfTVld4uJsDaQ2aVVKy0g/Ai4jDx2yZaTHA79SZSS2hrCsMjI+Op4ODTvw115/paioiCdeeoKsvMPjAldmGelNr169WLp0KQfdHrQdPHiQRYsWeR0+JSIigu7duzNp0iSKiopYt6706G3t2rVj8uTJJCYmFgeZSqnKU+VdBRpjtuBnqA1jzCZsIOk53QCPOz9lLb8VuLL8uawYzxt6bXHBBRdQr149pk2bRqNGjejXr1/xvPbt29OyZUvuvfdewsPDiYyMZNq0aeXaTrt27bj00ksZP348RUVFdOvWjWXLlrF06dIS6eqL0L1TJ6Y+8QSNmzUjOTmZWbNmlayZA4iIoEPr1nz22WcsXryYlJQUkpOTSU1NLbXtc889l9NPP51rr72W3bt3c8IJJ7B06VJmzpzJmDFjSnQYUF49e/akXr163HDDDUycOJGsrCweeOABkpOT2b9/f3G6v/71r/zjH//g9ttvZ+vWrZx99tnk5+ezcuVKBgwYwFlnncVtt93G/Pnz6d27N2PHjqVjw4bs2baNd6dN4/nnnyfeszfQyggiXbW0vmoiwdZGluNpd1CysmyQlpp6OAA7cMB3s9Hq4AoK/QWRYWE23xkZNhCuqZ0EKVUFtIwsW8BlZP36dO/enalTp9K4cWPfZSTQoUMHv2VkeFg4Iy8cyYtPvcikuyaRtjuNbp278dXyryq1jPRm3LhxLF68mHPOOYd//etfiAgPP/ww2dnZjB9vG6UtXryYF154gQsuuIBWrVqRlZXFU089RXx8PD169GD//v307t2byy67jOOPP57IyEjeffdd9u3bR9++fUOSb6WOZtpAPAT27NnDnj17qjsbQYuJiWHo0KEYY7j00kuJcGueFxUVxcKFC0lJSWHEiBHccMMNnHnmmcU9xwVrxowZXHnllTz22GMMHjy4uDe6YsZAZiavPf00f/nLX7jhhhsYNWoUKSkpPPnkkyVXFhnJQzffTLt27Rg6dCjdunUr7sbcU1hYAao++QAAIABJREFUGEuWLGHkyJE8/PDDDBgwgCVLlvD444/z4IMPlmtfPDVs2JB33nmHwsJChgwZwpgxY7jqqqsYNmxYqbQLFixgwoQJLFy4kEGDBnHFFVfw888/FzdnSkhI4IsvvmDw4MFMmTKFfiNHcse0aURERHgfrDsy0jab9DMuWZnKqomMjrbbqIr3InfvtsFXYqINwMLDbfPWmiQjw9aUBjJwekKCrcXVsTbVUU7LSP/8lpGO1157zX8ZCTz00EMBl5EfvP8BI0eOZO5zcxkxdAQLFy1kyqNTKq2M9KZTp04sX76cevXqMXLkSIYPH05cXBwrVqwo7hfg2GOPJSYmhkmTJnHuuedy+eWXExERwYcffkizZs2oU6cOJ598Mi+++CJDhgxh8ODBrFq1innz5nH++eeHLO9KHa1Ee64qrWvXrmbt2rU+569bt4727X138lobOw2ocXJz4aefoGVL8DHmY7EtW2xw4d6D6ZHIGPj2WztMhK+xTPfsgU2b4MQTbSc45bFtmx3K4+STvdeY/e9/tpObjh1Lz6ssBQXw/fd2X1u2tNM2b7bnuXPnmtE5TV4e/PADNG0KXjqEKKWw0A4D1KgRNG8e+vwFwN+9zCtjYO1a6Nr1iKhRFZH/GmO6Vnc+aouKlo+gZWRtkpGTwab9mygsKqRJfBNS4lKCGrpDqdqsXGXkEaasMlJrIlXN5KqtCWQ8wogI+wW9qMh/2sqWm1uxWr9g5OXZfSwrOKyMsSJdY0T6+qIQF2ffA/TonKhSud5/dH+AkJRk97+mvFcYaFNWl/BwO7anq0lrbbVoEZxyCrz4YnXnRCkVYgkxCZzQ8AQS6iTw58E/Wb9nPbn5VfBOvFKqxtMgUtVMmZk2OAykNs0VOHn0fhpy2dnw88+2Zq4quDqziS1jgO7KCCLz872/D+kS6vEiXR3qxMaW3Ne4OBvc1pQmrRkZtnlvMDW+CQk2AK+KjolCxdUR1Pjx2jRXqaNAZHgkrRNb0zqxNbkFuazfu56i6nhoq5SqUTSIVDXTwYM2aAik2YzrvZSKdigTjKIi+OMPG/AcOGA7gQk1V896VVETWVYQGRtr31UMVQCRlWX31bMZswg0aGCPd1Wea28KC+01mpAQXJNOV62lM+5ZrbNjByxdCn372ibPU6dWd46UUlVAREiKSaJ1YmsKigo4kHegurOklKpmGkSqmic/39bWBNKUFaqnJnLrVhvotGljmyk6g0OHVE6OrYkrazzCiAgb1JQ3yDLGLltWRzEih8eLDIU9e2yQ6m08Mte06h4zcv9+e6zcxi8LSGSk7YinpjTJDdacOTaAnj4dLrwQHn0UvIztppQ6MsVHxxMu4WTk1tJ7mFKq0mgQGQJt27albdu21Z2N2iuY9yHhcFBVVUFkRoZtbnnMMTaIaNTITgtgnMoKycnx33RSxB6P8gaRrndL/XXBHxdnm/NW9jEvKLABYlKS985zYmJsTWh1N2nNyLDHuW7d4JdNTLTHLpTvlIaCMTBrFpx+Ohx3HEyebPfBRy+PSvmiZWTtFSZh1K9Tn4zcDLRjRqWObhpEhkB4eDjhNaH3yNoqM9MGQ2W9++euMppwBiovz/Z+Ghtre+UEG0yGhYW2NtIYG0QGMjZjRcaKdC0XSBAJld+MNz3dBrFl9cjboIENwkIdtPtSVGRrIoNtyuriatJa22ojv/gCfv0VrnSG4G3bFq67zr4j6WWgb6V80TKydkusk0hBUQGZefpOtFJHMw0iQyAtLY20tLTqzkbt5XofMizAyzMszH6ZD3VNpDH2PciiImjd+nD+IiJs0JOeHroOUw4dstuvqiDS37iHrhq4ymzS6qtDHU/V3aT14EFbYxtor6ye6tSxP7UtiJw1y/5fDhlyeNq4cfZaKOdYeOropGVk7VYvuh4iwr7cWvput1KqUmgQGQL79u1jX23tOKO6FRbaWqZAm7KCDSArEjgFaudOG0C0aFG6Wekxx9h8hOr9MFetW6BBZHkDalcTS381keHhNniozCDSVbuYnFx2DV9kpB0qY+/e6hkqIyPDPkCoV6/860hIsNdSVfcoXF4HD8Ibb8DFF5f830xOhjFj4L33YOXK6sufqlW0jKzdwsPCqR+tTVqVOtppEKlqFlfzyGCCSLC1gaH8Qp6VBdu32/fZGjQoPT8qyn6h3rs3NO+6BdIzq4sroP4/e/cd33S5PXD887R0UkqBUqgyikwRB1oF5CIbRLY4WAIqguO60HvV6wC3iAu9oKAyVRB/CoIsQQXHBQc4cDBlliHQyiqjtOf3x5OUtE3apG2SjvN+vfIK/eY7TtLQk5NnFSa5e9sSCWcm1ymuqd737/c8oU5u1arZ1znQS0yI2CKycmXvW8rdiYuz5zp0qPhi86c5c+z/gZtuyvvY3XdDrVpw//3BWatVKRVwcZFxnMo8RXpGerBDUUoFiRaRqmTxdVIdp6JMJlOQzEz4809bnNWt67mVrGZNWxj4ozXy+HG7JqE344iKMkY0I8New5sCKSbGPt/0YvgQkZl5ZkKd/GafdYqLszEGeoKd9HT7GhW2K6tTxYr291RaurROmQJNmkDLlnkfi4qCp56C77+HDz4IfGxKqYCLi7B/A3WWVqXKLy0iFdOmTcMY4/YWV9QPy7m0a9eOdu3aed7hyBE7Hs6LYikrK4upU6dy2WWXUeXSS6l4ySXUr1+f/v37891332XvN2/ePF566aXCB71jhx2TWK9e/gVORIRtITtwoMACzhjDGF9mtfR2Uh3wqoh87rnnaNiwYd4HTp3K0Qr5/fff069fP2rUqEFERARJSUncfvvtpKSknCn0fWwNnDZtGlOmTMm5MTWVaR9/jKlXj23bthV8ktBQ2yqcllbk1i/n+7+g6w4bNgwTE0Ot7t3JqlQpz+OPP/549v+b0wW1ihtjC9FDh/KN39vY3F/Cx/eYJ3/8Af/7n51Qx9MXKIMHwwUX2K6tJ08W/ZpKlSAlKkf6IEeOrFKFihUrFluOrBBagUrhlfw2LrLY/n4VksccmUu+ObKQ3OZIipYPisKnHGkMtWrVIstNXvMpRxZzbO4E+z1WFmgRqbJ98MEHrFq1Ksdt+fLlgQsgK8t2mfOyFfL+++/nlltu4YorruDdV19l3osvMureezlw4ADffvtt9n5FKiIPHrS3s84CN4VDHomJ9nns25fvbqtWrWL48OHexZCVZSfsKcYict68efTu3TvvAxkZ2cfPnDmTVq1acfDgQcaPH8+yZct46KGHWLp0Kc2bN+eXP/6w3WuLo4jcv5/unTqx6n//IzEx0bsTVa1qWzAD2CU0OiqKPQcO8MVXX+V5bMaMGVTy5j3iFBdnf7dHjhRjhH4wdaot2m+4wfM+oaF2zcitW+H11wMXm1IBFPQc6aMcOfLdd5k3bx6jRo0qthwZFxnHidMnOJFR/BPK+ZQj/cBjjnRRYI785ZdCXdtTEdm9e3dWrVrlfY4MgujoaPbs2cMXX3yR5zGfc6Qq8bzoN6Z81bhx42CHUCgXXXSR39buOnnyJBEREfnvdPy4/VDtRRF5/PhxJkyYwJ133skLL7xgu5Du2kXn5s2545//dPstWCGCtq2QMTG2OPRGZKRtIdu/33Zv9dBy2dJdt0BPnDO+FlMRuWfPHr777jv7uuV26hRUrsz69eu55ZZb6NOnD3PmzCHE0b31iiuu4JprrqFFixZcc801/LZwIWFHj9purYVZ7gLsFwfp6VRv3JjqCQneHxcba5/rwYP2Nfe3zEyqVKpEk+RkZs6cSceOHbMf+vrrr9m6dStDhgxh+vTp3p2vUiXbJTctzY6xLIkyMmD6dOjRw04elZ8uXeztySdh2LCid/lVZZbmyLy8ypE+yJMjHTp37swdd9xRLDmySmQVdh7eSdqJNBLDirew8SlHFrN8c6SD1znyt98IK2iiOi9Vr16d6vktf1UCVKlShSZNmhRPjlQlnrZEKq/t37+fkSNH0qhRI6Kjo6lduzYDBw7M021jzJgxGGP49ddf6dq1KzExMVx33XV5zrd3717Cw8MZP3683eAyHnLMmDFER0d7nMHv2LFjnDp1ipo1a9oNzmLN0UXC+Qd92LBhTJ8+nZSUlOxuFElJSdnn2bBhA3379iUuLo6oqChatmzJkiVLbFH055/2+bzzDiYkhHXr1tG+fXuio6NJTEzksccey5GIV6xYgTGGD7/9lmGPPkqV+HhiY2MZNGgQB3ON3cvdjcL5mm3atInu3bsTExND3bp1eeKJJ8hyTjbkKCLXrl1LmzZtiIqKonbt2jzzzDOMHj0a4yziCigiP/74Y6pXr87ll1+e8wGR7JbI8ePHk5mZyWuvvZb9WjpVq1aNZ555hk2bNvHRihX2NT9xgqSkJAYPHsybb75JgwYNiIyM5OKLL87xjWS7du1YuXIl33zzTfbvo13HjmAM0xYsyNMtxXnOmTNn0rhxY6KiomjTpg2bNm3iWHo6I59/nmrJydSoUYP77rsvRxeZEydOcO+999KsWTNiYmKoWbMmPXv2ZP369W5flwI5JkwaMnQoH374IekuY0FnzJhBmzZtcry3nDIyMnjkkUdISkoiPDycpKQkHnnkETIyM23xeOgQiPDnn3/SvXt3oqOjqV69OnfffTcnPXQNnTx5MhdeeCGRkZHEx8dz8803k+qPJU8WLYK//jqzNmRBnn/eFsXPPlv8sShVwvk9R+Y6h085Mpci50ggvEI40WHRPPXEUxhjvM+RH37IsGHDqFKlSvHkyFwFcYE5sgAec6QLr3PkRx9lby90jnR0b3bXdTPfHHnsGCNHjqRatWqByZEOQ4YMKZ4cmeszTInMkeWcFpF+sHfvXvb6a6kHP8rMzOT06dM5bq5/nFNTU4mMjOTZZ59lyZIljBs3jk2bNtG6dWtOuFkfsXfv3rRt25b58+dz77335nm8Zs2a9OnTh8mTJ9sNjmUTMkNDefvtt7nuuuuo4qGFKT4+nnr16vHCCy/wxhtvsMP5euf6o/Poo49y1VVXUb169ezuR3PnzgVg9+7d/OMf/+Dnn3/mv//9L3PmzCEuLo7u3buz+J13bAtZ3brZBWqfPn3o1KkT8+bNY+DAgTz55JM88cQTeWK754EHMBERzHr6aZ5+8knmz5/PNa5r6+Wjb9++dOjQgXnz5tGnTx9Gjx7N9BkzbCtfRAQHDhygY8eOpKamMn36dF577TWWLl3KtGnTzpwkJMR2L/RQRM6bN4+ePXvmSXzZ+4eF8dlnn5GcnOyx20z37t0JCQnhc2eXKMcXACtWrOCll17i6aefZvbs2URERNCtWzc2bNgAwMSJE2nevDkXXHCB/X18/TUT77/fdk31MA72yy+/ZOLEiYwdO5bp06ezZcsW+vXrx6BBg6gUH8/sp59mxODBvPTSS2feS9hv9o8cOcIjjzzCwoULef311zlx4gStWrUq3P/PU6fAGPr174+IMG/ePMAm4g8++IAhQ4a4PWzo0KE899xzDBkyhE8++YRhw4YxduxYhg4daltQMzI4lZZG586d+fHHH5kwYQLTpk1j69atPPXUU3nO9+CDD3LHHXfQqVMn5s+fz7hx41iyZAndunUjMzMz585FHTM6ZYptUe/Wzbv9L7wQhgyB8eNh+/bCX1eVaZojLZ9zpEscPufIHTvc7lfoHLl4MWBbIzOybO7wOkfecw/GGGbNmsXTTz9d9Bzp0rLlVY4sgMcc6cLrHPn55zm2+5wjV61i4sSJ+cabb46sVInZs2czYsQI/+dIh379+hVfjnQ4deqUf3KkKhoR0Vuu2yWXXCL5+f33391ub9u2rbRt21YuvfRSufTSS6Vt27YyYcIEERE5duxY9uOut6lTp4qIyP79+90+Pnv2bBER2bFjh9vH58+fLyIi69evzzfm/EydOlUAt7fu3bt7PO706dOyY8cOAeSjjz7K3j569GgB5JVXXvH4Gjl98cUXAsiXX36Zve3jjz8WQFatWpVv3KtWrZK6detmx3pW9epy0+DB8u233+bYb+jQoXL22WfnOf6+++6T0NBQ2bRpU47n1KhBA2neuLHI1q05ns+zzz6b4/jhw4dLTEyMpKWl5XguXbt2FTlyROT770X27JF33nlHAFm+fHn2sYCMHj06z2s2ZcqUHNdo1qyZdG7dWuTXX0VE5KGHHpLw8HDZuXNn9j7p6emSkJAg9r+zw7p1Ii7Py+nQoUMSHh4uCxYsyPuCHj1qY05Lk8jISOnfv3/efVzUqFFDunXrJvLTTyJ//il169aVsLAw2bFjR/Y+hw8flipVqsjgwYOzt7Vt21Zat25tf/jrL3vNI0ey34dbHa+7iEjdunWlSpUq8vfff2dvGz9+vABy8803i2Rl2ef6xx/SvHlzadeuncd4T58+LceOHZOYmBh56aWXsre7u24eJ07I0O7d5eyaNUVE5IYbbrC/ZxF5//33JSoqSg4dOpT9e8zIyBARkXXr1uX5XYuIPPnkkwLIz2vWiPzwg0weOzbPez4zM1OaNm2aI7atW7dKSEiIPP744znO9/XXXwsgc+fOzd4GyOhbbhHZvt3j0/L0t0xERHbvFgkNFXngAc/7uLNjh0hkpIjL77wkA36QEpB3SsutsPlRRHOkU9By5FlnyU033VT0HNmokTRv3lxERNJPpcsto27xPUe6KHKO7Nw5+2evc6QH+eZIFz7lSIdC5UgXhcqRLvyaIyXn+6jYcuTPP4uIyOTJk/2TI3NdN7d8c2Q5kV+O1JZIlW3u3Ll8//33OW6vvPJKjn1ef/11LrzwQmJiYqhQoQJ16tQByP4WzVXfvn0LvGa7du1o2rQpkyZNyt42adIkLrjgggLHRLRs2ZINGzawePFi7rvnHpISE5k+axatWrVixowZBV77yy+/pGXLljnGuIQaw4BOnfhp40YO5xqnlru7Uf/+/Tl69Ci//vpr3v1iYuyYt337uLZfP0JCQli1alWBMXXv3j3Hz82aNWNHSkp2V9bVq1fTsmVLatWqlb1PVFRUnuOy14rMZdGiRYSHh9OpU6e8F3dpifRJTEz25DAtW7akdu3a2Q9VqlQpezIAtw4csONIK1b0ePpWrVpR2eV30aRJEwC6du1qW2irVYOjR2nSsCE7d+7MceycOXNo0aIFcXFxVKhQgYoVK3L06FG371ePROyEMcZkt5YOGTKE5cuXs3fvXmbMmEHv3r2JjY3Nc+iXX34JwODBg3Nsd/688ptvICaGVatWUbt27Rzv+ZCQkDzvuWXLlpGVlcWgQYNytIa0aNGCSpUqZV8vu/UxJMR2Ry1MN56ZM+3ERTfe6NtxtWvDPffAO+/Ajz/6fl2lSqhSnSPvu4+kpCSmT59etBwZGsqAAQP46aefOHz4MFFhUVQIsb11fMqRLq699tqi5UiXVlavc6QH+ebIYuBzjvRCvjnSRZMmTfyTI90othy5ciVA8edIVSx0Yp1itGLFCuBMsnCdPCA6Ojr7cXfi4+Pzfbx27dr5Pl4cExU0a9Ys30kDXnvtNe666y5GjRrFuHHjqFKlCllZWbRs2dJtVx1vZxC77bbbuP/++xk/fjxHjx5lyZIl/Pe///Xq2IiICK688kqu7NIF1q5l44kTtL/+ekaNGuWx64RTamoqzZs3z7nx5ElqxsUhIqQdPkysS1ehGrkmFnH+nHu8S/Z+iYmwcSPhhw9TpUoVr6b8rlq1as7nFx7OiZMns4vIPXv20KxZszzH5Y6NsDDbHTeXefPm0bVrVyIjI/Ne3DHmj7AwatWqle+U2ceOHWP//v02GcbE2G6T7uJwbHP73NPTbYy1a+c7KU/u7lrhjiVIsrdXrQopKYSL5HgfLliwgOuvv56hQ4cyevRo4uPjCQkJ4aqrrnL7fvVozx7bXddlwqcOHTqQmJjIyy+/zNKlS5k/f77bQ51jMHL/X3COU0pNTYXKldmzbx813EwqlPv1/OuvvwA8/j/NHlfkHF9cubIt0Ldts0vnuPu9uyMCb78NrVtDYf62PPggvPkm/OtfsGxZ4SddKmeMMbWAB4Bk4EIgCqgnItu8OHYU0N5xbE3gcREZ42a/acDQ3NuB8SJyT2Fj94bmyJwCmiOvvBKAjRs30r59+8LnSOzfLxEhLS2N2NhYIivYvyvVqlfLsV+BOdIhPDy88DkyIiLHa+t1jvQg3xzpwqccWUAcHnOklwrMkS7b/ZIj3SjWHIn9vXp67Vx5nSNVsdAiUnlt9uzZdOzYkRdffDF729atWz3u7+0g9iFDhvDQQw8xbdo00tLSiI6OZtCgQb4FFxICISE0qluX66+/npdffpm//vqLhHxm+6xatWrefv8nT7L34EGMMXn+AO/bt49zzjknx88AZ599dp79ANsSWbEip3buJC0tLc9+XnEOgncUkYmJidl/JN1e0ykszBaFcmbW1FOnTrF48WImTJjg/louLZEdO3bk7bffZs+ePW4/6CxcuJCsrCw6dOhwprjKysobhyM2t8/9wIEzLYlFERFhX+tcA+xnz55NgwYNcoyFycjI8G1w/dGjsHu3LVRdZk4MCQlh0KBBjBs3joSEBLp06eL2cOcHnr1791K/fv3s7c73XdWqVSE2lsT4eH5buzbP8blfz2qO1+rTTz91OxbK+TiHD9v7iAioXx9+/x22bIEmTbxag5X//Q82brTFYGFUrgyPPQZ33w1Ll4LjA6wqUAPgOmAN8BXg/o3l3i3AYWAecGsB++4HeuXatseHayk3SnSOdGjUqFHRciT275drjnQWkZt2bCL5vOTs/QrMkQ6nTp0qfI7Mxesc6UaBOdKFTzmygDg85kg/K5Yc6UGx5kjs7/W3337Lc3yhc6QqFtqd1Q+cM2qVNenp6Xmmqp46dWqRz+ucnW3SpElMmTKFAQMGuO324CojIyPvN0qOLpzr168nKioqu3tHREQEx48fz3OOtm3bsnr16hzfJmamp/P+smU0v+iiPDHMmTMnx8+zZ88mJiaG888/3/1+xkBiIh8sWkRWVhatWrXK9zm55RwE7vhWtGXLlqxatYpdu3Zl73L8+HEWLlyY87iwMFtAugwi//zzz0lPT6dHjx7ur+VcI9IY7r77bkJCQrjzzjvzzHyXmprKf/7zHxo0aMDVV19tW7hCQkCE1atX5+guc+TIERYuXJjjuWf/PpxLc3hYBsUnzjUjRbI3paenUyHXuWfOnOn9wPrTp+0MvRERdoKlXG666SZ69uzJI488QqiHwuyKK64A7HvF1bvvvgvYrmpERdHqoovYmZLC6tWrs/fJysrK857r3LkzISEh7Nixg+Tk5Dy3evXq2R2dRWRICISHwznn2CV0tm/P8Rp5NGWK/XLg2msL3teTW2+1Bey//pXjfajy9aWI1BCRq4APfDz2PBFpAdzpxb6nRGR1rlvAZkLSHOmbYsuRDkXKkZmZvP/++zRv3jw7hrAQ+5zff//9HMcXmCMdPvjgg8LnyFy8zpFuFJgjXfiUI134lCP9rMg5sgDFliOxXXZ37txZfDlSFQttifSDRo0aBTuEQvnpp584cOBAnu3JyclUqFCBK6+8krFjx/LMM89w2WWX8fnnn/N///d/xXLt22+/PXvMx623FvQlOhw6dIikpCSuv/56OnXqRK1atTj400/MXrKExYsX8+9//zt7za2mTZuSmprK66+/TnJyMpGRkZx//vnce++9TJs2jc6dO/P4448TGxvLxBdfZOOOHSx0Mxvam2++SVZWFpdeeilLly7lrbfeYsyYMTnGIgD89ttv3HjjjfTv35+NGzbw8NixtLv0Ujrm+kbSK84/5o7nMmrUKF5//XW6du3K6NGjiYiI4KWXXiIiIiLnhzLXZT4cSWLevHm0bduWOE/r9506ZQsO4Nxzz2XSpEkMHz6cjh07cuutt5KYmMj69et5/vnn+fvvv1m2bNmZD0wxMZCVRY0aNejSpQtjxowhIiKCsWPHcuzYMR599NHsyzRt2pSJEyfy/uLF1G/dmkoZGUXvalalii3aXZL5lVdeybx587j33nvp0aMHP/zwA6+99prn5+9KxK4ReuqUx9a7Ro0aZc8+50mzZs0YMGAAY8aM4fTp01x++eWsWrWKJ598kgEDBmR/uBo6cCDPTZ3K1VdfzTPPPENCQgJvvPEGh53FoEP9+vV54IEH+Oc//8mGDRto27YtkZGR7Ny5k2XLljF8+HDat2ljuwq7io2Fs86yraoxMZDfepxHjsD770P//l6t2epReLhd6uO66+CNN+COOwp/rhLOGHM5UFVEPnH8XA34L9AMWAo8ICIFfjITkUJPpVuUYwNNc6TviiVHHjzI7Nmzi5YjJ05k48aNOYoyZ+6ZNWMWlSMq0+KyFt7nyI0befjhh2nXrl2OdQULy+sc6UaBOdKFzznSwacc+f771K9fn0qVKvllbdUi5UgvFGuOdMzgWiw5sn37Ynl+Cp2d1d2tKLPPlUb5zTwHyP79+0XEznB26623Snx8vMTExEj37t3lzz//9DiLmnP2LVe5Z55z1ahRI0lOTvYq5pMnT8rzzz8vnTt3lrPPPlvCwsKkUsWK0vLCC2XSpEmSlZWVve/Ro0elf//+EhcXJ4DUrVs3+7H169dL7969JTY2ViIiIqTFhRfK4jfeyHEt5/NZt26dtGvXTiIjI6VGjRryyCOPSGZmZvZ+zpnnPvzwQxk6dKhUrlxZYmJiZEC/frJ/2TKRgwez9/X2NRvat6/UPeusHNvWrFkjrVu3loiICDnrrLPkiSeekLvuukvi4uLO7HTokJ319NAhERHJysqSxMREee211zy/qL/+mmdG11WrVkmfPn0kPj5ewsLCpE6dOjJy5Mgcs8uJiEhKitRNTJRBAwbIm2++Keecc46Eh4fLRRddJJ8tXWrj2LtXZOtW2bNypXRr3VpioqMFyH4/eJp5btCgQTku5Xydly1blvO1uvpqOTshQcQIYaTWAAAgAElEQVTxO8nMzJSHH35YEhMTJSoqSq644gpZu3at1K1bV4YOHZp9nNuZ5/bvt6/f7t1nzu9hBkNX7n6PJ0+elIcffljq1KkjFSpUkDp16sjDDz8sp06dOnPggQOyZe5c6dali0RFRUl8fLzcdddd8sYbb7idFW/GjBnSokULiY6OlooVK0qTJk3kjjvusDMSHjwo8v33eWeey8oS2bBB5Icf7Ey84uFv2dtvi4DIN9/k+1y9kpUl0qWLSFSUyG+/Ff18fkAxzM4KfAmMdvl5CnAI+BA4BjxaiHMOd/wNTvLxuAqO48Z4eHwacAo4AJwGNmLHYYZ6c/7ylh9FylCOrFRJWrZsWbQc2aKFLF68OMe1nM9n1mez5B9X/MP3HDlgQPZr6OR1jhw6NEe8Il7myFy8ypFueJ0j5Uw+y5MjP/ssx3579uyRbt26SUxMTPHmyFw5rEg50g2/5kgR2bJli3Tr1q3oOdIhT450oyz+PfNVfjky6AVbSbwVNUmmpKRISkpKvvuonNavXy/GGHnrrbcKf5KtW+1yE0Xxyy8imzfn2JRfwnfl6Q939jIUv/1m/+2Ln37KXmrEk9OnT8sFF1wgHTp0OLMxPd0WQQcOiIhNdECOP6B5rF0rsm2bb/E5HTpki8h+/eyyHdu3i6xfL/LjjzYO5+3HH+327dtFjh0r3LU8SUuz13CZ6rxQjh8XWbPGxunr76uwTp3KU7QW2tat9nfpLvaMDJGff7a3jAz3f8suv1ykcePie+579ohUry5ywQX2tS1hiqmI3A90d/w7DDs28SbHz/cAfxTinP4qIu/BdnntAFwFvAlkAW/lc84RwA/AD3Xq1Mn39fTmQ5fmSN8VS470E2eO/H7n97IldYvH/TzmSD9zmyNz8SpHFpG7gk+VbFpE5p8jtTurHxxxLHegCrZr1y42b97M6NGjSUxMZODAgYU/mXNZC5HCzQYpYrsveli8udCMsQu2b9tmx6rl6trjUUaGveWaJe7RRx+lQYMG1K1bl4MHD/LWW2/xyy+/sGjRojM7uXZnxY4TsX8LPMjKsl1nHd1ZfeZcouPYMTvuLiTETgYUF2fvnbcKFfw3U2dsrD3/wYPev8a5ZWXZcZDGQL16gZtVNCzMvj6HD9tZfYviyBE70ZC72CtUsOMU16+3y5bkfk+sX28n1Xn++eJ77jVrwtSp0KMHPPAAjB9fPOctWWKwhSPAZUBF4BPHz2uBOsEIyh0ReSXXpkXGmKPAPcaYsSKyyc0xk4HJAMnJyV4Mqs2f5kjvFWuO9LPKEZU5dOIQWZJFiAnelBte5chcCsyRSqk8dGIdFVRvvfUWHTp0YN++fbz33ntEOWYhLRTnAPHCDgp3zmbqMgtnsala1RZo27dnr6lYIOcU27leE2MMTzzxBN26deOGG24gLS2NefPm0a1btzM7hYbaIsDNWpFuuSzvUSihofbYSpXg/POheXM491xISoIaNWyB55i0x29CQuwXAGlpduxfViGGiO3ebccTJiUVvqAurMqV7WywRZnU4MQJO0ttfpNuVKxol1U5dOjMBDxOU6fa3+UNNxQ+Bne6d4e77oJXXwUvJrgohVKwS3IAdAN+FRHnFJFVgHS3R5Ucsxz3yfnupQKuWHOkn1WJrEKmZHL05NGgxuFVjlRKFZnRb17ySk5Olh9++MHj43/88Qfnnnuux8fdrYGlAuDgQdu6ct55eQovrxw+bJc1aNzYFkPF7dgx28p18qQtrM4+2xY+nvz1l53c5YILClfQ/PKLnRjFZVkSj44cgQ0boFGj/AuQku70aVuop6XZLwPq1PG+VdL5+69e3e1srH7nvH7DhoVvSXW+Z5o1y39NSBHYupU/Nm7k3LAw6NjRfuFQuza0bAkFTIZQKCdOQIsWdt3Nn38ueotrMTHGrBGRIhVPxpgngbuBT7FdREeLyDjHY48DnUXkch/PORzb1dSrdSJdjqsAZOBhnUgPx1wGfAsMEJHZ+e1b1PwImiPLqqysLH7a9xPVoqpRNy4If0OVKmbe/D0r6/LLkdoSqcoOZyuac21FXzlb/vzREgm2BahpU1uk7NsHf/yRdxZNV8ePn2nhKwxn915vuKwRWao5u2s6Z3/ctMmuj+hsafUkI8N+AREZCbVq+T9Od2Ji7JcKhw4V/hxHjtgvHAp6DxtjC+WwMBgwAFJSYPFi+7686abCXz8/kZEwa5ZtbR02rHAtxSXXGGAsEAE8B7zk8tiF+L5cR6ANwo6j/D7YgajSKyQkhNiIWP4+8bd2DVWqHNAxkX7gaT0c5WfO7qzeFk65nTxpP1z7s5AKDbUf3uPi7BjJP/6wSy/UrJm3q+fx47ZFtbBdQMPCzhTGBSlqd9aSJjbWtkjv22e7qB46ZF/nhIS8rb8i9ndx+rRtBQzW/9+QEFtI5u5i6i0Re6xzuZOChIbaLzTS0+0yHHFxtoXcn12+mjaFl1+2a0i+8gqMGuW/awWQ2OU7nvbwWB9fzmWMucbxz0sc992MMfuB/SKy0rHPaWC6iNzsclwykMSZL4ebupxrkYikG2PqAjOB2cBmbNHbFxgGTBKRLb7EWliaI8uuKpFV+PvE3xzLOEZMeBGWCFJKlXhaRBaSiHhcc6hBgwYBjkYBRW+JPHnStuAEYjKVypXtB+rt220r0KFDdiIXZwuSiC0iq1Yt/DXCwmyrjzcyMmwRU5Y+3IWE2C6TVavaLp67dtkuz3Xq5OyuvH+/ff1r14bo6ODFC/Z9sXPnmfeiL9LT7XhKL7tii4h9j7z1lm2NBPj3v/3/RcKIEbBkCTz4ILRrBxdf7N/rBYAxJh6IFpEdLttG4lgnUhzrR3opd6ulc9HalUA7x79DHTdX/wSGuvx8reMGUA/YBhwBUrFLetTAzsq6HrjL5TpFll9+BM2RZVnlyMoYDH+f+FuLSFWqaWt6wbQ7ayGEhYVx/PjxYIehcnO2RBa1iAyUsDDb9TIpyRYAv/0GBw7YAjIjwxYERZlEISzMvhbedBs8dcr/E98ES0SEbWFs0MC+phs22K6rGRm2UN+50xZvCQnBjvTMeNTCtEY6j/FyTOvx48ftQtj9+8Odd9pusP7qyurKGFu4JiTY4vXYMf9f0/+mAA86fzDGPAq8DgwEPjbGXO/tiUTEeLi1y7XPsFzHDcvn2G2OfVJFpI+I1BWRSBGJFpGLReS/IlIs/YvDw8M1P5ZjFUIqUCmiEmnH0/RDuCrVsnOk8kiLyEJISEggJSWF9PR0t38kd+3axa5du4IQWTlnjC0kC9OdVcQWkflNRuIPxkB8vO16WbGi7Va5ZcuZGVyLWkSCd69HRkbgZyMNtLg4+zonJkJqKvz6K2zebFtfk5JKRgEdGWl/b4UtIqOjC2xJFBHS09NJSUkhwVk4jx9vW2sDNdFJtWowc6Yds3r33YG5pn8lA5+5/Hwr8IyIVAMmAGWj364X4uPj2bVrF6mpqWRkZGiOLIfiIuM4mXmSE6e9HE6hVAniNkcqt7Q7ayHEOr7p3717NxluPqDv3bsX0LWwguLgQfthOr8Ja9zJzLQzW54+7X0X0OImYuPYvPnMtsjIwncxPX7ctmz+8UfBLawpKXafsjXZiWdhYbaQPHHCtoi5vubBduSIHcvpHKPrjaws26IaG2t/3wUICwujRo0a2X/LMMaOhwyk9u3hoYfgmWega1e49tqCjym5qgL7AIwxzYCawHTHY/OAIUGKK+AqV65MREQE+/fv5+DBg5x20zNEc2TZlpmVyYHDB/h5389UjizkTNNKBVGeHKnc0iKykGJjYz2+uW677TYAVqxYEcCIFAC33WY/UH/5pW/HffWVnVBkyRK47DL/xOatdevsOn2ZmfbfhbVmjX1Oc+dCn3zm9hCx6zreeSeMG1f465U2IraQrFYt2JHkNHu27eb57bfevxeXLLG/66VL4XKfVpIIrjFj4LPP7DjJFi3seNXS6SDgnNa3A7BbRDY5fg6jnPX6iYyMpHbt2h4f1xxZ9o14ewQnM0+yZsSaYIeilPKTgCc2Y0xtY8z/GWMOGWMOG2M+MsZ4/cnBGHOuMeYDY8wBY8xxY8wGY0ye/lDGmLONMVOMMXuNMSeNMVuNMc8W77NRJU5Cgp0oxVfOlqiSMOHD+efDjz/aIrAonOvwOb719ygtzbZ6nX120a5X2hhT8gpIgE6dbGyffur9McuW2ZbkNm38F5c/hIXBe+/ZL0wGDbL3pdNyYIwx5p/AfdjWR6cmwPagRKVUkPRt0pe1e9ay/W996ytVVgW0iDTGRAOfY5PqUOAGoCHwhTGmohfHJ2MXRI4AhmMXdX6RXLPUGWOSgO+ARthZ57pg1/Eq5IwrqtRISLDdUn21ZYvtNlpSWkKMKfoYxYQEe549e/LfLyXF3pe3IrKkio+3M5YuXer9McuXQ+vWRRtDGyznnAMTJ8LXX8PTblfJKA3+DewEngW2AI+7PDYI+DoYQSkVLH3P7QvA3PVzgxyJUspfAt2d9RbgHKCxiGwGMMb8AmwCRpJzgeYcjDEhwAzgMxHp6/LQF252fwNIAdqLiHPQ4sqih++dWsFarFzZwik11U4U48usWps328lVytJMXBUq2HUAC2qJ3L3b3p91lv9jUt7p2hXGjrXjewsak7FvH/zyCzxbijtaDB5si+bHH7ctsaWpSy4gIvuAzh4e7gToDCMuNEeWfQ2qNqBZQjPmrp/LPS3vCXY4Sik/CHQR2QtY7SwgAURkqzHmG6A3+RSR2PWxzsUWmx4ZY+oDXYEhLgVkQL3zzjvBuKyCM8s0HDhwpjunN7ZsscttlDU1a2pLZGnUpYudcOaLL6B37/z3Xb7c3nfq5P+4/GnCBDs+cuxY+PjjYEdTKMYujtgUO9FOKvC7iBRiqt2yTXNk+dC3SV+e+vIpGr3WiEoRlYiNiCU2IpZK4e7/XS26Gl3qdyE8tIzPFK5UGRHoIvI8wN2ng984syiyJ/9w3EcaY1YDlwBpwGzgARFxLkzV2nF/3BizDLgCSAcWAPeKyMEixK9KuurV7f1ff/lWRG7eDAMH+iemYEpM9L6I9OX1Uv7VqhXExNjWOW+KyKpV7eRIpVlsLFx9NUyZYmcWLmVdc40xw4GngOoum/8yxjwiIm8HKSylgua25Ns4mH6Q1BOpHD55mCMnj7D97+0cOXUk++eTmSdzHNMsoRlv9XyLFrVaBClqpZS3Al1EVsUWfrmlAlUKONbZ1+594L/YhZ2TgSeA2kDfXPtNAWZix6g0cNw3NcZcVlyLKntyzz2268Yrr7ziz8sod5wtkb6Mi0xNhb//LpstkYmJ8Ntv+e+ze7cdh1fQMiAqcMLD7RIYBU2uI2In1enYsfBLwZQkvXrZFsnPP4fu3YMdjdeMMYOAydi1It8B9mKX+RgETDbGpIvIrCCGWKJojiwfEislMqH7hHz3OZV5iiMnbVG5ds9a7ll6D63ebsXdLe7mqQ5PUTG8wOkylFJBUpqW+HBOAvSOiDzm+PcKY0wo8Jwx5lwR+cNlvxUicofj358bYw5hWy27Aotzn9wYMwIYAVCniJOr/PTTT0U6XhVBYYrIkjQza3GrWdOOiczKghAP82ilpGhX1pKoSxdYsCD/rtbr19vfX2nvyurUti1UqgTz55eqIhI7sc67InJDru3TjTEzgQcALSIdNEcqp/DQcKpFV6NadDXqValH5/qdeWj5Q7zy7SvM2zCPST0m0aV+l2CHqZRyI9BLfKThvsXRUwulK2c31GW5tju/qm/u4345iMhkEUkWkeTq1au720WVBlpE5pSYCKdP29ZWT3bv1kl1SqIujg9Oy3L/KXPhHA/Z2dOcLqVMRISdVGjBAvvFR+nRGNsC6c47jseVUgWIjYhlQvcJfHXjV0SERtD1na4MnTeUg+k6EkmpkibQReRv2HGRuTUFfvfi2Pw4P3F4u58qi+Li7KykvqwVuWWLva9Xzz8xBVPNmvY+v3GR2hJZMjVsCHXr5r/Ux7JltpWyLL13e/Wy79e1a4MdiS+OAJ6mHK3leFwp5aV/1PkHP936E4+0eYT31r3HuRPOZfavsxGRYIemlHIIdBE5H2hpjDnHucGxpmNrx2P5WQycxHZHdXWl4/4Hx/1q7HgUT/t971PEqnQxxve1Ijdvhlq1St1EHl5xTpbjqYjMyLBLRGhLZMljjG2V+/xz+3vKLSMDVqwoO11Zna66yna9nl9QSihRFgPPGGPauG40xrTCTraTZwiFUip/kRUiebLDk6wZsYakuCQGfDiAXrN7sevwrmCHppQi8EXkm8A24GNjTG9jTC/sbK07gUnOnYwxdY0xp40xzrGPOGZVfRa41RjzjDGmkzHmQeAxYLpz2RAROY2ddKe7MeYNY0wXY8ztwERgBfC5v59ko0aNaNSokb8vozzxtYgsq8t7wJki0tNakfv22clZtCWyZOrSxa4V+d13eR/77js4cqTsdGV1qlYNWrcubUXkv4FD2HH6O4wx3xpjtgNfA4cdjysHzZHKFxfUuIBVN6/ipS4v8fnWz2k6oSkTv59Iln/nSFRKFSCgE+uIyDFjTAfgZezMqQY7m909InLUZVcDhJK3yH0C2y3oduB+YA8wDngy13WmG2OysJMZ3Iid/fUd4CEJQF+IyZMn+/sSKj/Vq/veEtmjh//iCaaCurPqGpElW4cOtlVu6VJbWLlatsy2VrZvH5zY/KlXL/jXv2DHDijiRGeBICJ7jTEXATcBbbDj/LcBK4FpIpIexPBKHM2RylehIaHc2+pe+jTpw4hPRnDHojs4cfoEo1qNCnZoSpVbgW6JRER2iEg/EYkVkUoi0kdEtuXaZ5uIGBEZk2u7iMhLItJARMJFpK6IPCYiefp6ichMEWkmIhEikigid+YqVFVZ5UtL5NGjtjWurLZExsTYm6eWyN277b12Zy2ZqlSByy5zv9TH8uWQnGzXiCxrevWy9wsWBDcOH4hIuoj8V0SuF5HOjvuJQENjzKvBjk+psqBelXp8OvhTrqh7BRO+n6CtkUoFUcCLyPJgxIgRjBgxIthhlF++FJHOSXXK4sysTjVraktkada1K3z/fc4Zdg8fhtWry15XVqdGjeytdHVp9aQBcEeBe5UjmiNVURhjuPWSW/kz7U+W/7k82OEoVW5pEekHGzduZOPGjcEOo/xKSIBjxyDdix5k5aGITEz0XETu3g1hYRAfH9iYlPe6dLHLXXzuMpx7xQrIzCx7k+q46tULvvjCFsyqTNEcqYrq6nOvJj46nklrJhW8s1LKL7SIVGWPc61Ib5b5cK4RWVa7s4JtifTUnTUlxRaZIfqnoMS67DKoXDnnUh/Ll0N0NFx+efDi8reePe0MtO668iqlyrWIChEMu3AYH6//mD1H8lnCSinlN/rJUZU9ziLSmy6tmzfbiXhiY/0bUzDl1xKZkqLjIUu6ChWgY0dbTDnnBVu2DK64AiIighubP11+uR3vWTa6tCqlitmIS0aQKZlM+XFKsENRqlzSIlKVPb4UkWV5eQ+nxES7FMSxY3kf271bx0OWBl262JlKN26EXbtg/fqy3ZUVbPHcvTssWgSnTwc7GqVUCdOwWkM61OvAm2vfJDMrM9jhKFXuaBHpBxdddBEXXXRRsMMov6pXt/fetkSW5fGQcGaZD3ddWlNStIgsDbp0sfdLl9qurFB2J9Vx1bMnHDwIq1YFO5I8jDE7HWtC5nsD3gh2rCWN5khVXEZcPILth7bz6Rbt9q5UoAV0ncjy4pVXXgl2COWbty2RJ0/Czp3loyUSbJdW1+d69KidtES7s5Z89erZLzs+/dSOj0xIgGbNgh2V/3Xtaid+mj8f2rQJdjS5fQb4fd3hskhzpCoufc/tS/Xo6kxeO5luDbsFOxylyhUtIlXZU7GinXSkoCJy61Y7xqy8tkQ614jUlsjSoWtXmDrVvr87dy4fkyHFxkL79na9yHHjgh1NDiIyLNgxKFXehYeGc+NFN/LiqhfZfWQ3Z1XSL0WVCpRy8Ckk8AYPHszgwYODHUb55s1akc7lPcpTS6Qr5xqR2hJZOnTpYpet2b+/fHRlderZEzZssDdVJmiOVMXplktuIVMyeXvt28EORalyRYtIP9i1axe7du0KdhjlmzdFpHN5j7LeEhkfD6Gh2hJZ2rVrZyebgbI/qY6rnj3t/YIFwY1DFRvNkao4NajagE7ndNIJdpQKMC0iVdmUkFDwOpFbttjucvHxgYkpWEJCoEYNbYks7WJj7bjAZs2gVq1gRxM4devChRdqEamU8mjkJSPZeXgnSzYvCXYoSpUbWkSqssnblsj69cGYwMQUTO7Wity9GypVsjdVOrz7LixcGOwoAq9nT/j6aztTq1JK5dK7cW9qVKzBpDWTgh2KUuWGFpGqbHIWkZLP5InlYXkPp5o183Zn1eU9Sp/ERKhTJ9hRBF6vXpCVZdeMVEqpXMJCw7ip+U0s3LSQnYd2BjscpcoFLSL9oFWrVrRq1SrYYZRv1atDRgYcOuT+8dOnYdu2sj+pjpO7lsiUFO3KqkqHSy6x7+ES2qXVmPLQnaH4aI5U/nDLxbcgIrz9o06wo1Qg6BIffvDss88GOwTlulZkXFzex3futEVmeWmJTEy0Y0QzM+0kO2C7s15xRXDjUsobISHQowfMnm3Xd42ICHZEuW03xrwJvC0iu4MdTEmnOVL5Q70q9ehSvwtvrX2LR654hAoh+hFXKX/SlkhVNrkWke6Ul+U9nGrWtN0Bna9HVpYtIrU7qyotevWCI0dg5cpgR+LO58CDwDZjzEfGmC7BDkip8mjkJSNJOZLCok3a9V0pf9Mi0g/69etHv379gh1G+VZQEVlelvdwyr1W5MGDtiVWu7Oq0qJjR4iKKpFdWkVkGHAWcD/QCFhijNlijHnAGFM9qMGVQJojlb/0aNSDxJhEJq+ZHOxQlCrztIj0g4MHD3JQZxEMLmcR6WmZjy1bIDKy/BRRNWvae+fkOs7lPbQlUpUWUVHQuTPMn5//hFlBIiKHRORVEWkGtAX+B4wBdhpjZhtj2gUzvpJEc6Tyl7DQMG5ufjOLNy9mx6EdwQ5HqTJNi0hVNjnXfsyvJfKcc+xYq/Igd0ukrhGpSqNevWDHDvjll2BHUpBvgLnAT0A40BP4zBjznTHm3KBGplQZN/zi4YgIb619K9ihKFWmlZNP0KrcCQ+HKlXyHxNZXrqyQt6WyN2OuT+0JVKVJj162HVdS2CXVgBjTG1jzBPADmAO8DfQG6gEXAlEAdPzOb6WMeY1Y8wqY0y6MUaMMUleXnuUMWaBMWaP47gx+ezbxxjzozHmhDFmuzHmEWNMqNdPVKkSrG5cXbo17MbbP77N6azTwQ5HqTJLi0hVdlWv7r6IFLEtkeVlUh2wXXfj4nK2RBpzprhUqjSoUQMuu8x2aS1BjDE9jTGfAH8CtwOzgEYi0k1EFohIlogsA0YBF+VzqgbAdUAa8JWPYdwCJADzCoi1K/Ah8D3QDRgPPAI84+P1lCqxRl4ykt1HdvPJxk+CHYpSZZbOf+wHHTt2DHYICuy4SHdF5J49cPx4+WqJhJxrRe7ebV+fsLDgxqSUr3r1gocftu/hktMd+2NsUTYcmC0iJz3stwV4N5/zfCkiNQCMMcMBX2Z5PU9EsowxFYBb89nvOeBrERnh+PkLY0wM8Igx5mUR2evDNQtFc6Tyt6saXsXZlc5m0ppJ9GnSJ9jhKFUmaUukHzz66KM8+uijwQ5DeSoiy9vyHk41a+acWEe7sqrSqFcve79wYXDjyClZRFqIyPR8CkhE5E8RuTGfx7MKG4A3xxpjamNbQt/J9dBMIAzbMul3miOVv1UIqcDNzW9m6ealbPt7W7DDUapM0iJSlV2eisjytryHk2tLZEpKSWrFUcp7550HSUklqkuriKx1/tsYc5Yx5lJjTEn8D3ae4/5X140ishVIB5oGPCKl/GT4xcMxxvDmmjeDHYpSZZIWkX7QrVs3unULyBe6Kj8JCXY9xMzMnNu3bIEKFaBu3eDEFSyJibYlUsR2BdSWSFUaGWNbI5cvh2PHgh1NNmPMEGPMVmAnsBq7tMdWY8zgIIfmqqrjPs3NY2kuj+dgjBlhjPnBGPPDfk/LJvlAc6QKhNqVa3NVw6uY8tMUMjIzgh2OUmWOFpF+cPz4cY4fPx7sMFRCgi2Ycq9HtnmzLSArlLMhwTVr2rGgBw7Y9TO1iFSlVa9ecOKELSRLAGPMP4FpwCbsBDe9HPebgenGmDuCF13RichkEUkWkeTq1asX+XyaI1WgjLxkJHuP7mX+hpLTc0GpskKLSFV2JSTY+9xdWsvb8h5OzrUi1zp63ml3VlVatWkDsbElaamP+4BpItJFRKaIyELHfWfseMP7gxyfk7MFsoqbx6oAqQGMRSm/69agG+dUOYc7Ft3Bb3/9FuxwlCpTtIhUZZenIrK8Le/h5FzOY80ae68tkaq0Cg+HAQMgOjrYkTjVBGZ7eOw9oEYAY8mP81P0ea4bHWtRRgO/BzgepfwqNCSUhQMXEmJCaD+9Pb/s+yXYISlVZmgRqcouZ7cr1yIyNRX+/rt8t0Q6i0htiVSl2RtvwKuvBjsKp3WAp2+mGpJrIptgEZEdwM/AoFwPDQYygMUBD0opP2sS34SVw1YSHhpO++nt+XHPj0U+58nTJxm1dBTX/9/1iEgxRKlU6VPOBoUFRo8ePYIdggL3LZHOmVnLY0tk7iJSWyKVKi53A7ONMQeAj0Qk0xgTCvQD/gX09/ZExphrHP+8xHHfzRizH9gvIisd+5wGpovIzS7HJQNJnPlyuKnLuRaJSLrj3/8BPjHGTAJmAc2BR4DxgVgjEjRHqsBrWK0hK4etpMOMDnSY0YFPB3/KpWdfWqhz/Zn2J9d9cB1r9thc+kDrB7g48eLiDFepUkGLSD+4//6SMvylnKtaFUJC3BeR5bElMi4OIiJg+3Z7X9XtRIxKKd/NAZGWInQAACAASURBVGKxXVozjTFp2DGGocBRYI4xxrmviEh+U0N/kOvniY77lUA7x79DHTdX/wSGuvx8reMGUA/Y5rj4IkdxORoYBuwDngGeziemYqU5UgVD/ar1bSE5vQOdZnZi6eCltKzV0qdzzFs/j2HzhgEwtfdURiwYwax1s7SIVOWSFpGq7AoJsV1aXYvILVvsfb16wYkpmIyx4yK3b7ddWc98qFVKFc1nQLH0aRORAv9juttHRIZhi0JvrvER8JGvsSlV2iXFJbFy2EraT29Pl5ldWDRoEf+o848CjzuVeYoHlz/Iy6tfJvmsZOZcM4d6Verx0R8fMevXWYztPJYQoyPEVPmiRaQftGvXDoAVK1YENQ6F7dLquq7Z5s1QqxZERQUvpmByFpHalVWpYuMo4JSXNEeqYKpduTYrh62k44yOXPnOlSwcuJC2SW097r/j0A6u++A6vk35ljsvu5NxnccRUSECgIHnD2TBxgV8tf2rfM+hVFmkX5uosi0hIW9LZHnsyurkHBepk+oopZQqp86OPZsVw1ZQN64u3d7txmd/fuZ2v4UbF9J8UnN+3/87c66Zw6vdXs0uIAF6NupJdFg0s36dFajQlSoxtIhUZVvuIrK8Lu/h5CwitSVSqWJljDnfGPN/xpj9xpjTjvs5xpjzgx2bUiqvmjE1+WLoFzSo2oAes3qwdPPS7MdOZ53mweUP0mNWD+pUrsOaEWu49rxr85yjYnhF+jTpwwe/f8CpzFOBDF+poNMiUpVtrmMijx6FffvKd0ukc61IbYlUqtgYYy4FvgXaA58A4xz3HYDVxphL8jlcKRUkCRUT+Hzo5zSJb0Kv2b34ZOMnpBxOof309oz9ZiwjLh7B/276Hw2rNfR4joHNBpJ6PJVlW5YFMHKlgi/gRaQxprbj29pDxpjDxpiPjDF1fDj+XGPMB8aYA8aY48aYDcaYu/PZv78xRowxu4rnGahSJSEBDh+GEyfOTKqjLZHaEqlU8XoWuxZkkojcKCIPiciN2FlRf3U8rpQqgeKj4/lsyGdcUOMCrn7/ai5840J+3PMj7/R9h0k9JxEVlv8cCp3rd6ZqVFXe+/W9AEWsVMkQ0Il1jDHRwOfASexU5AI8BXxhjLlARI4VcHyy4/gVwHDgEHYh5xgP+8cBrwABWfvK6brrrgvk5VR+nGtF7t9/pogszy2RtWvb+7r5rTCglPJRS+AGETniulFEjhhjxgLTgxNWyaQ5UpU0VaOqsvyG5fSc1ZNDJw/x/jXv0yS+iVfHhoeGc23Ta5n5y0yOnTpGxfCKfo5WqZIh0LOz3gKcAzQWkc0AxphfgE3ASOAlTwcaY0KAGcBnItLX5aEv8rne88DPwB6gU9FC997tt98eqEupgjiLyL/+OrNGZHluiezSBRYsgFatgh2JUmVJQct7FMvyH2WF5khVElWOrMzKYSsBMD4ugTXw/IFMWjOJBRsX0L9Zf3+Ep1SJE+jurL2A1c4CEkBEtgLfAL0LOLYdcC75FJqujDGtgcHAHYWKtAjS09NJT08P9GWVO7mLyOrVITY2uDEFU0gI9Oiha0QqVby+Bf5jjKnkutEYUxF4AFgdlKhKKM2RqqQyxvhcQAL8o84/qBVbi/fWaZdWVX4Euog8Dzs+JLffgKYFHOtcDTbSGLPaGJNhjPnLGPOqMSZHh3VjTBgwGRjnWrAGylVXXcVVV10V6Msqd3J3Zy3PXVmVUv7yH2x+226MmWGMGWuMmQ5sA5oBDwczuJJGc6Qqa0JMCP3P68+SzUtIPZ4a7HCUCohAF5FVgTQ321OBKgUc65xO8n3gU6AztrvqcCD3Vz8PABH4MJmBMWaEMeYHY8wP+10Xp1elW+6WyPLclVUp5Rci8h3QAjtmvyswCrgSO9yipYh8H8TwlFIBMPD8gWRkZfDh7x8GOxSlAqI0LfHhjPUdEXlMRFaIyAvA40AfY8y5AMaYBthvff8pIie8PbmITBaRZBFJrl69erEHr4IkJgYiI2HnTnvTlkilVDEyxoQ7ZggXEblGRGqISJjj/joRWRfsGJVS/ndRzYtoXK2xztKqyo1AF5FpuG9x9NRC6eqg4z73QjyfOu6bO+5fxX4bvNoYE+eYoTUcMI6f85+rWZUtxthxkN9+CyLaEqmUKlYicgp4DpvHlFLllDGGgecPZOW2laQcTgl2OEr5XaCLyN+w40Zyawr87sWx+clyOddV2KLUeRuA7Q6bhq7XVf4kJMDatfbf2hKplCp+f2BnHldKlWMDmg1AEN7/7f1gh6KU3wV6iY/5wAvGmHNE5E8AY0wS0Bp4sIBjF2PXl+wKLHDZfqXj/gfHfX8gMtexDwKXANcCuwoZu9eGDRvm70soXyQkQEaG/be2RCqlit9jwHhjzBrtvlowzZGqrGpYrSHJZyXz3rr3GNVqVKHPc/TUUWLC3S6BrlSJEegi8k3gn8DHxphHsGtnPQnsBCY5dzLG1AW2AE+IyBMAInLQGPMs8Kgx5jC2y2oyNnlPd87CKiJ5plI3xgwDTorICv89tTM0QZYwzsl1YmMhPj64sSilyqIHgBjgR2PMNuzaxK5rQ4qItA1GYCWR5khVlg1sNpBRn45i48GNNKrWyOfj31zzJrcvup1PB39K+3rt/RChUsUjoN1ZReQY0AHYCMwE3gW2Ah1E5KjLrgYIdRPfE8C/geuARcBtwDjgFv9G7psDBw5w4MCBYIehnJxFZIMGuj6iUsofMrFDMr7Cfil62rHNecvyfGj5ozlSlWXXN7seg2HWulk+H7t612ruWHQHp7NO89w3z/khOqWKT6BbIhGRHUC/AvbZhi0kc28X4CXHzZdrDvNl/6K65pprAFixYkUgL6s8cRaR2pVVKeUHItIu2DGUJpojVVl2VqWzaJfUjvd+fY/H2j6G8fLL631H93HNnGuoFVuLfuf244VVL7Bu3zrOr3G+nyNWqnBK0xIfShWOa0ukUkoVM2PMEGNMNQ+PVTXGDAl0TEqp4Bl4/kA2HtzI2j1rvdo/IzOD6/7vOlKPpzL3+rk81OYhosOieWm1T20mSgWUFpGq7HOu+6ktkUop/5gKePoDU8/xuFKqnOh3bj/CQsKY9at3XVr/vezffLn9Syb3nMyFNS+kalRVbrzoRt795V32HNnj52iVKhwtIlXZd+GF0LgxtGkT7EiUUmVTfv3VKmLHSCqlyokqUVXo1rAbs36dRWZWZr77vrfuPV759hXuuuwuBl8wOHv7PS3v4XTWaf773X/9Ha5ShRLwMZFKBdxZZ8H69cGOQilVhhhjLgIudtnU0xjTLNduUdhlpzYFLDClVIkwsNlA5m+Yz1c7vqJdUju3+/yy7xeGzx9OmzpteKHLCzkea1C1AX2a9OGNNW/wnzb/oWJ4xQBErZT3tIj0g9tuuy3YISillPKv3sBox78FeNjDfgeBmwMSUSmhOVKVBz0b96RiWEVmrZvltohMO55G3/f7UiWqCnOunUNYaFiefUa1GsXc9XOZ/vN0br/09gBErZT3jJ3wVLlKTk6WH374IdhhKKWU8jNjzBoRSS7EcZWBOGxX1j+Bq4Efc+12EtgnZSjRan5UynuDPxrMok2L2Hv/XsJDw7O3Z2Zl0nNWT5b/uZyVw1bSqnYrt8eLCC3fbknq8VTW37Ge0JDQQIWuFJB/jtQxkX6wc+dOdu7cGewwlFJK+YmIHBKR7Y4lqeoBixw/u972lqUCsrhojlTlxcDzB5J2Io2lm5fm2P74ysdZvHkxr3Z71WMBCWCM4b5W97E5dTMLNi7wd7hK+USLSD+44YYbuOGGG4IdhlJKqQBwFIynAIwxCcaYOrlvwY6xJNEcqcqLzud0plpUtRyztM7fMJ8nv3ySGy+6kZGXjCzwHFefezV1K9flxVUv+jNUpXymRaRSSilVBMaYWGPMVGNMOrAH2OrmppQqZ8JCw7i26bV8vOFjjp46ysaDG7lh7g1ckngJE7tPxJj8Jna2KoRU4J6W9/D1jq/5LuW7AEStlHe0iFRKKaWKZgJwPfA2cBtwk5ubUqocGnj+QNIz0nlv3Xv0fb8v4aHhfHT9R0RWiPT6HDc3v5nKEZW1NVKVKDo7q1JKKVU0VwL/EpEJwQ5EKVWytK7Tmlqxtbh94e0IwqeDP6VOZd96uFeKqMSIS0bw4qoX2fb3NpLikvwTrFI+8Kol0hjznjFGV2pXSiml3NsQ7ACUUiVPiAlhQLMBZEomz3V8jo7ndCzUee5qcRchJoTxq8cXc4RKFY63LZEtgeuNMeuBScAMEfnbf2GVbvfdd1+wQ1BKKRU4s4GewPJgB1IaaI5U5c1jbR/j8tqX07tx70Kfo1ZsLa4/73re+vEtRrcbTVxkXDFGqJTvvF4n0hjTFRgJ9AAygDnAJBFZ7b/wgkPXwVJKqZItKwtCimFUf2HXicx1jp7AK8BKYBGQmnsfEfm8KNcoKTQ/KhU8P+75kYsnX8y4zuO4//L7gx2OKgfyy5Fej4kUkaXAUmNMTeAW4GZgiDHmF2zr5DsicrQ4Ai7tNmywvZoaN24c5EiUUqpsGj4cwsLgjTfAiwkO/e1jx309YJjLdgGM415XCXfQHKlU4TRPbE77pPaM/3Y8d7e4m7DQsGCHpMoxn7/HdSye/CRwOfAVcCEwEdhtjBlnjKlYzDGWOiNHjmTkyILX/lFKKeW7detg2jSIjS0RBSRAew+3Di73ykFzpFKFd1+r+9h1eBcf/P5BsENR5ZzPs7MaYzoAtwK9gaPAy8AH2PEgdwHnAP2KMUallFIq20MP2QLyoYeCHYklIiuDHYNSqnzo1rAbTeKb8OKqFxnQbIBXa00q5Q/ezs5azRhzvzFmI7AM22XnVuBsEblPRFaLyMPYbq5X+i9cpZRS5dnKlbBwoS0gq1YNdjQ5GWPijTE9jDFDjTFVHdsijTG6JrNSqliEmBBGtRzF2j1rWbldv79SweNtYksBngC+AVqKyKUiMlVETuTabz3wV3EGqJRSSgGIwAMPwNlnw113BTuaM4w1DtgFzAemAEmOhz8GHvbyPLWMMa8ZY1YZY9KNMWKMSSroOMexIcaYh4wx24wxJ4wxPxtj8vQKMsascJw39+0eb66jlAq+wRcMpnp0dV5c9WKwQ1HlmLdF5H+wrY43isj3nnYSkZ9EpF7xhKaUUkqdMXcufPstPP44RP0/e/ceZ3O1PnD888wwMxiUa4QylISSplIhiZFbkY5SOrrRqXSqX1fdTuSUJKfogm6ky0F3t9yvlUSU1HEXIsbdDOb6/P5YezLGjNlj9t7fPTPP+/Xarz3z/a7v9/vsc2J59lrrWWW8juYY/YF+uC9bL8EV08kyCVfV3B/1gR7AXlzNgYJ4DngWeA3oACwGJopIx1za/gxcmuP13wI+zxjjkTKly3DPRfcwec1k/rfrf16HY0oov9ZEquqwYAdSnDz11FNeh2CMMcVKejo88QQ0bAi9e3sdzXHuBAaq6gsikrMK6zqgnp/3WaCq1QFE5E4gwZ+LRKQa8DAwWFWH+g7PFZH6wGDctiPZHfRyey7rI40pvHsuuofBiwbzn+/+w6guo7wOx5RAfiWRIvIfoIqq3pLLuXHAn6r6SKCDK6ratm3rdQjGGFOsvPcerF7tRiNLFbgkXNCdjhv5y00q4FfVclXNPMnntweigA9yHP8AeFdE6qrqxpO8d8BZH2lM4VUrV42/n/933v/5fe6+6G6aVGtCZITtJGRCx9/prNcAM/I4Nx3oGphwiocVK1awYsUKr8MwxhjPJQVg9+BDh+Bf/4JLL4Vrry38/YLgD6BxHufOB4KdwDUCUnCjntmt8r2fm+P4BSKyX0TSRORnEbkjyPEdw/pIYwLj/y79PzI1kwtGXUDFwRVp+V5L7p92P2NXjOWXnb+QnpnudYimGPP3+9zTgc15nNvqO298HnjA1SeYN2+et4EYY4yHduyAs86CG26A0aNPfk/HV1+F7dth/Piw2Rcyp4nAMyLyI0dHJFVEzgYeAkYH+fmVgH2qqjmO78l2PssC4ENgDXAK8HfgbRGpoaqDcru5iPQF+gLUqVOn0MFaH2lMYJxT5Rx+u/c3Fvy+gB+3/8iy7ct4e/nbHFpyCICYUjGcX/18LqxxIc1qNKNZjWZUKVvF7/vXKF+DUhHhN/XDhAd//8vYi1vwn1st4fq4/SKNMcaYv0ybBgcPwttvu+04Xnyx4PfYvdtd16ULtGwZ+BgD5FngMlyC9rvv2ESgNvAtbl1iWFDVZ3Ic+lJEPgeeFJFXVPW4/lxVR+NLhOPj43MmqsYYD8WdGkfcqXHc2vRWADIyM1izew3Lti/7K7Ec9/M43lj6RoHv3aNRD8ZfPz7AEZviwt8kchbwlIhMVtUdWQdFpDqucuvMYARnjDGm6Jo6FWrWhK5dYcgQqFwZHn20YPd44QWXiD7/fHBiDARVPSwirYGbcOsT1wG7cRVTP1TVYM8p2wucIiKSYzQyawRyTy7XZPcxbllKE+C7IMRnjAmRyIhIGlZtSMOqDel1Xi8AMjWT9XvWs/zP5RxMOejXfT797VOmrJlCakYqUZFRwQzZFFH+JpFPAz8Aa0VkMkensHYGjgBWas0YY8xf0tJgxgz4299gxAjYs8ft8Vi5Mtzh5wq833931/buDY3zWnEYJlQ1Axjne4XaKiAaVwU2+7rIrLWQv/p5HxtlNKYYipAIzqp8FmdVPsvva6qWq8q0ddP4dsu3tD6zdfCCM0WWX4V1VHUTcBHwBXAl8IDv/XPg4nCq+maMMcZ7334L+/dDx44QEQFjx8LVV0PfvvDZZ/7d41//cmsgBwwIbqyFJSJVRKROjmN3icgIEfF3j8jC+BpIA27OcbwX8IsfffTNwGFgZRBiM8YUQa3PbE2piFLMWJ9XXU1T0vm9WtaXSP49eKEUH8+H87wrY4wJgalToXRpuOoq93tUFHzyCbRrBz17uvWSbdrkff3KlfD++/DQQ1C7dmhiLoR3cTN07gEQkaeBAbhppveIyE2q6tfCIhG53vfjhb73DiKSCCSq6nxfm3RgrKreAaCqO0VkGNBfRA4CPwI3AG1w1dWz7t0SeBz4DNgEVAR6+9o8rqrJJ/fxC8b6SGPCX4XoClxa61Kmr5/O81fZn1lzPCu5FASXXXaZ1yEYY4ynpk51hXAqVDh6rFw5mDwZrrjCbdUxdy7Ex+d+/RNPuGv79w9NvIUUD4zN9vs/gOdV9SkRGQ78H+BvdYqJOX7PqoYxH2jt+znS98ruSVyRu/uB04DVQA9VnZytzXbcDKSBQBXc6OXPwE2q+rGf8RWa9ZHGFA3t67XnqblPkZicSNVyVb0Ox4QZv5NIEakG9AQaADE5TmvWN6IGvv32W8A6SmNMybR5M/zyCwwdevy5SpVg+nS4/HLo0AEWLoRzzjm2zYIFLtkcPNi1LwIqATsARKQxLonLSiq/oACzeFQ1301McmvjW5M5yPfK67p1QAd/YwkW6yONKRoS6iXw1NynmLVhFj2b9PQ6HBNm/EoiRaQBrmJbKaAcsAvXaUbipuvsD1aARdETTzwB2B5YxpiSado0996pU+7na9aEmTNdIpmQAIsWQdb2g6quAM/pp8M//xmaeANgN1DL93MbYJuqrvX9Xho/6w+UFNZHGlM0NKvRjEplKjF9/XRLIs1x/O3YXsJVZ60OCO6bzDLAncAhoFtQojPGGFPkTJkCdetCgwZ5t6lf341I7t/vEsnERHf8iy9g8WJXTKdMmdDEGwCzgGdFpB/wEG70Mcs5HN070hhjiozIiEjaxbVjxvoZHLt7kDH+J5EX4dZlpGRdp6rpqvou8BrwSjCCM8YYU7QcOQKzZ7uqrJLPxMymTd201d9/d+337nVrIRs2dNt6FCGPAluAF4D1uKI6WW4GFnkRlDHGFFZCvQS2J21nVeIqr0MxYcbfNZGxwB5VzRSR/bgF+Vl+wO0jaYwxpoRbsAAOHXJJoT9atoSJE6FrVzjvPNi6FT7/HEoVobJvqroDaJfH6ba4/ZSNMabISaiXAMD0ddNpXC3MN+w1IeXvSOQmXKEAcBXf/pbtXGdgn78PFJHaIvKJiOwXkQMi8lnO/bXyub6hiEwUkV0iclhEVovI/dnOny0ir4rIzyKSJCLbReQrETnf32cYY4w5OVOnQkwMXHml/9d07gzvvecSyEsvdZVbizoROVdEugOxqprqdTzGGHMyalWoxblVz2XGBtsv0hzL3+96Z+K+ZZ0IDAP+KyItgHTceo9/+3MTESkLzMFNi+0NKK6S3FwROS+/PapEJN53/Tzcesz9wFm4kdIsCcCVuMp4PwKn4KYaLRaRFqq6zJ9YC+OVV2x2rzGmZJoyxe3/WND1jLfcAmecAWedlf802HAjIq8BpVT1H77fr8Nt6REJHBCRdqr6g5cxhhPrI40pWhLiEhi5bCSH0w5TpnTRWaxugsvfJLI/EA2gqhNE5DBuI+OywKvAW37epw8QBzTwlRpHRH4G1gJ34RLUXIlIBPA+MFtVsxfymZuj6X+B1zXbCmARmYMbTb2fApRaP1lNmzYN9iOMMSbsrF0L69bBAw+c3PWtWgU2nhDqwLHrIAcAk4FngJeBf+Fm7RisjzSmqEmol8Ar37/Cws0L/5reaky+01lFJBI32hiddUxVJ6lqL1W9TlVHq/8lm64BFmclkL57bQS+AfKbwNQaaMgJEk3f/XbljEdV9wNrgNP9jLNQZs2axaxZs0LxKGOMCRtTp7r3Dp7vRBhyNXBfVCIitYBGwAuquhIYjitOZ3ysjzSmaLnizCuIioxixnqb0mqO8mdNpAJLgQsC8LxGwC+5HF8FnJvPtS187zEislhE0kRkp4gMF5ETjq2LSCWgMfBbgSM+CYMGDWLQoDz3ezbGmGJp6lQ45xyIi/M6kpA7xNFlFVcAB3D9JkASUN6LoMKV9ZHGFC1lS5elZZ2WTF8/3etQTBjJN4lU1Uxc6fJyAXheJWBvLsf3AKfmc21N3/t4YAZujeYQ3NrIj/K5dgRuf0tbiGGMMUGQnAzz5kGnTl5H4okfgXtFpDFwLzDT13cC1AW2exaZMcYEQPt67fll5y9sO7jN61BMmPC3Ouso4AERiQpmMPnIivUDVX1GVeep6lDc2pOuItIwt4tEpD9wE9Av+zTaXNr1FZGlIrI0MWvXa2OMMX6ZPRtSU/3f2qOYeRJoDvwENACey3auK7DEi6CMMSZQstZCzlw/0+NITLjwt7BOeaAesEFEvsZ9q5p93aGq6r/8uM9ech9xzGuEMrvdvvec//XOAAbjptseM11VRP4BPA88parvnujmqjoaGA0QHx/v7xpPY4wxuKmssbHQokX+bYsbVf3Bt1XVOcBaVT2Q7fRoXPE4Y4wpsppUb0L1ctWZvn46vZv29jocEwb8TSKfyPbz7bmcV1z1ufyswq2LzOlc4Fc/rj2RzOy/iMgtwBvAy6rq1xYkxhhjCk7VJZHt2kGUl/NVPOTbouq4LaRUdYoH4RhjTEBFSAQJ9RKYtm4amZpJhPg7mdEUV379F6CqEfm8Iv183ldAcxH5q+yCiJwJXO47dyLTcPtLts9x/Grfe1YRA0SkG/Ae8LaqPuxnbAEzatQoRo0aFerHGmOMJ1atgi1bSux6SABEpImIfCIiiSKS7nuf4FsnabKxPtKYoimhXgK7Du1ixZ8rvA7FhAF/RyID5S2gH/CliDyFG8F8Dle4568eRUTOANYDA1V1IICq7haRF4CnReQAMAeIx+3DNTbbvpOtgI9xa1PGiEjzbM9PUdXlQf6MNGjQINiPMMaYsDHFN9ZWArf2AEBELgLmA4dxX4j+CZwGdAE6iUgrVT1ulLKksj7SmKKpXVw7AKavm06zGs08jsZ4LaRJpKomi0gb4D/AOFzF1NnAA6qalK2pAJEcP1I6EDgI3AM8jFub+RLHFjFog9vTshlu/8nsfgfODMRnOZFJkyYB0KVLl2A/yhhjPDd1KjRtCjVr5t+2mHoBt33VVap6MOugiJQHZvnO2w7dPtZHGlM0VY+tTtPTmjJjwwz6t+zvdTjGY34lkSKSybGFdI7j75RWVd0MdM+nzSZcIpnzuALDfK+8rn0WeNafWILl5ZdfBqyDNMYUf/v2wTffwGOPeR2Jp5oDt2RPIAFU9aCIvAiM9Sas8GR9pDFFV0JcAv9Z/B+SUpOIjYrN/wJTbPk7EjmQ45PIyrhvVqOBMQGMyRhjTBExcyZkZJTYrT2y5FfR2yp+G2OKhYR6CQz5dgjzNs2j89mdvQ7HeMivJNI3unccEYkEJgH7AxiTMcaYEEpMhC++gNtug1IFXOQwZQpUqgTNm+ffthj7HnhCRGblmM5aDngMWOxZZMYYE0At6rSgTKkyzFg/w5LIEq5QayJVNUNE3gBeA14JTEjGGGNCRRVuvtmNKC5cCGPGQISfldszM2HaNGjfHiL9rdFdPD0BzAN+F5HJuPX6pwEdgbJAa88iM8aYAIouFU3rM1szY/0Mr0MxHgvEJi/RQKUA3McYY0yIjRrlEsg2bWDcOLjvPpdY+uPHH2HnzhI/lRVVXQJcgqsa3h74P9z2U3OB5qr6g4fhGWNMQCXUS2D17tX8vu93r0MxHvK3sE6dXA5HAY2BwWTbo9HAuHHjvA7BGGPytXEjPPwwtG0LM2a44jgvvQQVKsALL+R//dSpIOJGIksqEYkC7gZmq+r1XsdTFFgfaUzR1r6e+0t/xvoZ9Lmwj8fRGK/4O511E7kXBhDcfo73Biqg4qB27dpeh2CMMSeUmenWQEZGwjvvuGTwxRfhwAEYPBgqVoTHHz/xPaZOhUsugapVQxNzOFLVVBEZjBuBNH6wPtKYou2cKudQq0Itpq+fbklkCeZvEnk7xyeRR3D7Lv6gqhkBjaqIGz9+PAA33HCDx5EYY0zuXnsN5s93CWQd31wTEXj9dTh4EPr3dyOS99yT+/WJibBkCQwYaE5tKgAAIABJREFUELqYw9hvQBywwOtAigLrI40p2kSEhLgEPvvfZ6RnplMqIqTbzpsw4W911jFBjqNYefPNNwHrII0x4WnNGjfK2LGjG43MLjLSFddJSoJ774Xy5eGWW46/x9dfu7WTJX09pM8zwKsiskxVV3odTLizPtKYoq99/fa8u+Jdlm5bSvNaJbs8d0nl75rIs4Eaqjo/l3OtgO2qujbQwRljjAmsjAy49VaIjoa33nKjjzmVLg3jx0OnTi7JjI2Fbt2ObTN1KlSvDhdcEJKww91jQCywXEQ24aqzZp+9o6p6hReBGWNMMFxV9yoEYfq66ZZEllD+Vmd9BeiSx7nOwH8CE44xxphgGjYMvvsORoyAmjXzbhcTA19+CfHxcOONroJrlvR0mD4dOnTwfzuQYi4D+BVYCGwB0n3Hsl6Z3oVmjDGBV7lsZeJrxjNjg231UVL5O4k5HhiZx7kFQO/AhGOMMSZYfv0Vnn4aunZ1e0PmJzbWjTheeaW7ZuZMuOwyWLwY9u51I5UGVLW11zEYY0yota/XnhcWvcC+I/s4JeaUQt9PVUk8lMjWA1vZsn8LWw5sQRDubHYn0aWiAxCxCSR/k8jyuEI6uUkDKgYmHGOMMcGQnu6mscbGwsiRuU9jzU2lSm77j5Yt3frHefNcYhkZCe3aBTNiY4wx4SyhXgKDFg5izsY5XNfwOr+u2X9kPwt+X8CWA1vYsn8LWw8eTRj/OPAHKRkpx13z267feK3ja4EO3xSSv0nkBuAqILcx6za4LUCMzyeffOJ1CMYYc4wXX4QffoAJE9xaxoKoXh1mzYIWLSAhwSWiLVq4bUBKKhG5AJgF3K6qX+bR5lrgXeAKVf0llPGFM+sjjSkemtdqTvmo8sxYPyPfJPLXxF95bclrvP/T+ySnJQNQOqI0p1c4ndoVanPJ6ZdQu2FtalWoRe2Ktaldwf085JshDFs8jJZ1WnJDYyvGFU78TSLfB54Tkc3A26qaIiLRwJ3AA8CzQYqvSKpSpYrXIRhjzF9++sltxdGjB/ztbyd3jzp1XCLZsiVs3Aj/+EdgYyyC+gE/5ZVAAqjqlyKyDPgn0DdkkYU56yONKR5KR5amTd02TF8/HVVFckxxycjMYPKayYxYMoLZG2cTHRlNzyY9ua3pbZxd+WyqlatGhJx4Yf3gtoNZ/Mdi7px0J01Pa0qDKg2C+ZFMAfhbEmEo8BUwAkgWkZ1Asu/3r4AXgxNe0TRmzBjGjBnjdRjGGENqKvTuDaee6vaALIyzz3ZTW/1dU1nMXQmM86Pdh7iZPMbH+khjio+Eegls2reJdXvW/XVsz+E9vPTNS9QfUZ+u47uyevdqnm/zPFse3MJ7175HqzNacVrsafkmkOAS1fHXjyc6MprrJ17PobRDwfw4pgD83ScyA7heRNoA7YDKwC5ghqrOC154RVNW53jrrbd6Gocxxgwa5EYiv/gCAjEAdP758Pnnhb9PMVATWJdvK7cc5PQgx1KkWB9pTPHRvl57AGasn8Hh9MOM+H4EH678kMPph2l1RiteavcSXc/pSqkIfyc/Hq9WhVp8eN2HdPiwA/2m9uPda98NVPimEAr0/6iqzgHmBCkWY4wxAbRsGTz/PNxyC1x7rdfRFDuHcXtD5ieWvAvTGWNMkVavUj3iTo3j8dmPk5SaRJlSZbi5yc30u7gf5592fsCe075+e55q9RTPLXiOlnVactsFtwXs3ubk+DWdVUQ6i0i/PM7dKyIdAxuWMcaYwkhJcdNYq1eHV1/1OppiaRX+TVNtC1hRHWNMsXV709upWb4mQ9oOYev/beWta94KaAKZ5V9X/Is2ddtwz9R7+HnHzwG/vykYf9dEPg2Uy+NcGd95Y4wxYeDwYZdArloFb7/t1kOagPsIuFtELs6rgYg0B+7CrYvMl4jUEpERIvKdiBwSERWRM/28NkJE+ovIJhE5IiI/iUj3PNr2EZH/iUiKiKwWESuTZIw5aU+2epLV/VbzyOWPUKlMpaA9JzIiko+u+4hTYk7hbxP/xoGUA0F7lsmfv0nkOcCPeZxbATQMTDjGGGMK448/4IorYPx4eOEF6NDB64iKrdHAMmC+iLwqIm1F5Czfq62IvArMBZb62vqjPtAD2AssLGA8z+Eqpb8GdAAWAxNzzhQSkT7AKOBT4GpgIvCGiNxdwOcZY0zIVY+tzn+7/5d1e9bRd1JfVNXrkEosf9dERpD32o/yQOnAhFM8TJ061esQjDEl0PffQ7ducPCgK6Rj6yCDR1XTReRqYDhwN27Lj+wygTHAA77idP5YoKrVAUTkTiDBn4tEpBrwMDBYVYf6Ds8VkfrAYGCqr10p4N/AOFV9Mlu7mrhtvN5W1TQ/Yz1p1kcaYwrjijOv4N9t/k3/2f1pWacl9158r9chlUj+jkT+BORV0P1mwCYmZ1O2bFnKli3rdRjGmBLk/ffdCGSZMvDdd5ZAhoKqHlLVO4E6QC+gv+/VC6ijqn1UNbkA98s8yVDaA1HABzmOfwA0EZG6vt8vBarm0m4crup6i5N8foFYH2mMKaxHL3+Ujmd15MHpD/LDHz94HU6J5G8S+TJwnYhMFJEEETlXRNqJyESgG/BS8EIset544w3eeOMNr8MwxpQA6enw0ENuDeTll8OSJdC4sddRlSyq+qeqfqyqQ3yvj1V1ewhDaASkcPyWI6t87+dmawfHF/rJ2S6orI80xhRWhETwftf3qVG+Bj0+6cHew3u9DqnE8SuJVNXPgftx33ZOA1YC032//1NVPwtahEXQhAkTmDBhgtdhGGOKuX37oHNnGDYM7rsPvv4aKlf2OirjgUrAPj1+cdCebOezv+f811bOdscQkb4islREliYmJhY6WOsjjTGBULlsZSZcP4E/DvzBrV/eausjQ8zfkUhUdQRuw+ROwC24Bfk1gV9ExHb9NMaYEFq9Gi65BObMgdGjYfhwKG2r000QqOpoVY1X1fiqVat6HY4xxvzlklqXMDRhKF+t/oqXv3vZ63BKFL+TSABVPaiqXwNLcGsnVgJzcNXkjDHGhMC0aXDxxbB3r0si+/TxOiLjsb3AKSIiOY5njSzuydYOIOemLznbGWNMkXHfxffRvWF3Hp/1OAt+X+B1OCWG30mkiFT0TWn5BlgNPInrkO7BjUgaY4wJIlV46SXo1Ani4mDpUmgRklIoJsytAqKBejmOZ61x/DVbOzi6NjKvdsYYU2SICO9c8w71K9Xn+gnX8/u+370OyVMZGcns3j2NtLR9QX3OCZNI3+bFHUVkPLAdGAmcAbzua/KAqo5SVdvt0xhjguyf/4RHH4Xrr4dFi6BOHa8jMmHiayCN46uo9wJ+UdWNvt+/A3bl0W4P8E0wgzTGmGCpGFORL2/8kpSMFLqO78qhtENeh+SZgwd/ZOXKjhw4ENy/0vPcJ1JEXgZuAqoBR4DPgbHALKACx++JZXzmzZvndQjGmGLmnXfgtdfgwQfh5ZfhuImLplgQket9P17oe+8gIolAoqrO97VJB8aq6h0AqrpTRIYB/UXkIPAjcAPQBrgm696qmiYiTwNviMgfuP68DXA7cJ+qpgb/E1ofaYwJjgZVGvBx94/p/FFnbv/ydj7u/jHHz/Iv/pKSVgAQG3tBUJ+TZxIJPAgobpPiW1V1d9YJEbHyR8YYEyLLlsG990K7dm46awnsE8OOiGTi+kh/qKqeqL/NbmKO37P2wpgPtPb9HOl7ZfckkISrpH4abtlJD1WdnCOQkb4+/CHgEWAz0E9Vbc8NY0yR1/GsjgxuO5jHZj3G+dXPp3/L/l6HFHJJScspXboaUVE1gvqcE3Vq7wB/w1VjXS0i/wXeV9UlQY2oGBg6dCgADz/8sMeRGGOKut27oXt3qF4dPvoIInOmDsYrA/E/ifSbqub7FUFubVQ1Axjke+V3/Shg1EkFGADWRxpjgumRyx7hpx0/8eScJ2lcrTFdGnTxOqSQSkpaTmzsBUEfhc0ziVTVPiJyH9AN6A3cBdwtImtwU1ttNDIPkye7L36tgzTGFEZGBtx8M2zf7tZAVqnidUQmi6o+63UMRZX1kcaYYBIR3u7yNv/b9T9u/uxmvr/zexpWbeh1WCGRmZlKcvIqate+OujPOmFhHVU9oqofq+rVQB2gP5ABPA4IMFhEeolITNAjNcaYEmbgQJg+HUaMgIsu8joaY4wxpmgoU7oMX9zwBWVLl+Wa/17D3sN787+oGEhOXoVqGrGxTYP+LH/XaKCq24EhwBARiceNTt4IvA+M4Ph9p4wxxpykKVNcEnnbbbYPZFEgIlFAB6ABkPOLVVXV50IflTHGlFy1K9bm0x6fcuXYK7nx0xuZctMUSkX4nfoUSUlJy4HgF9WBAiSR2anqUmCpiPwf0Bn4e0CjMsaYEmzDBujVCy64AF5/3QrphDsRqQksAs7ELfXI+n8s+7IPSyKNMSbELq9zOW90eoM+k/rw+KzHGZow1OuQgiopaTmRkbGUKVM/6M864XTW/Khqmqp+rqrd/L1GRGqLyCcisl9EDojIZyLi925nItJQRCaKyC4ROSwiq0Xk/hxtIkSkv4hsEpEjIvKTiHQvyGcrjDJlylCmTJlQPc4YU4wcPuwK6YjAJ5+A/VVSJLwEJOKWfQhwCRAH/BtY5/vZ+FgfaYwJpTub3Um/i/rx8ncvM+6ncV6HE1QHDy6nXLnzESlUiueXkI7pikhZYA6QgpsOq7hKcnNF5DxVTc7n+njf9fOAO4H9wFlAbI6mzwEP40qeL8NNu50oIp1VdWrAPlAepk2bFuxHGGNCKCkJHngArr8erg7iWnVVuPtu+OknN501zlKPoqIlrs/Z5vs9U1U3Ac+ISCQwHLjWo9jCjvWRxphQG9Z+GKsSV9FnUh/OqXIOF51e/AoNqGaSnPwTp512a0ieF/w09Vh9cN/IdlXVL1T1S9xGyGfgqr/mSVxK/T4wW1Wv8V0/V1VHq+qwbO2q4Trzwao61NfmLmAuMDhIn8sYU4x9+SW88w506AC33AK7dgXnOaNHw9ix8Mwz7lmmyKgMbFPVTCCZY2sEzOHo/o7GGGM8UDqyNBP+NoEa5WvQdXxXth/c7nVIAXf48DoyMpJCsh4SQp9EXgMsVtV1WQdUdSPwDfl/S9saaAgMy6ddeyAK+CDH8Q+AJiJStyABn4znnnuO556z5S/GFBeTJ0O1ai65Gz8eGjaEDz90I4eBsmQJ/POfLnl85pnA3deExFYgawOW9UBCtnMXA0dCHlEYsz7SGOOFKmWr8OWNX7L/yH6um3AdKekpXocUUKEsqgOhTyIbAb/kcnwVcG4+17bwvceIyGIRSRORnSIyXESyL65ohJsuuy7H9at87/k9p9Bmz57N7Nmzg/0YY0wIpKXB119Dp04wYAD8+CPUq+cK33TsCL//XvhnJCa6qbI1a8IHH0BEqP9mNoU1F7jC9/Mo4GERmSEiU3DLKz7xLLIwZH2kMcYr51U/j7Fdx7J462Iem/WY1+EE1MGDyxEpTblyjULyvFD/U6USkNtGLXvIf4uQmr738cAMoB1uy5E7gY9yPGOf6nFjBHuynTfGGL98+y3s2wddurjfGzeGb76B4cNh4UJo1Mj9nJFxcvfPyICbboKdO+HTT6GS/Q1VFD0FvAmgqm8C9wNlgRq4fuoh70IzxhiTXfdzu9Pvon4M/34432z+JqD3VlXSMtICek9/JSUtp1y5RkRERIXkeUXp++6sWD9Q1WdUdZ6qDgUGAF1FpGFhbi4ifUVkqYgsTUxMLHSwxpjiYfJkiIqCtm2PHouMhPvug1WroFUruP9+uPxy+CW3eRa5yMiAtWvhs8+gd2+YNQvefBOaNQvOZzDBpaq7VHVNtt9HqGoLVW2mqk+oqk1nNcaYMPJC2xeoU7EOd3x1B0fSA/NXdHpmOu0/aE+rMa3I1MyA3NNfqkpS0vKQTWWF0CeRe8l9xDGvEcrsdvveZ+Y4PsP3nvW/2l7gFJHjdlbL+n5/D7nwFeiJV9X4qlWr5hOKMaakmDwZWreG8uWPP3fGGa6K6ocfwvr1Lgl85hlIybbMIjER5syBV16BO+6Aiy+GChXg7LPdVh4ffQQPPgi33Rayj2QCTESqisjZeZw7W0Sq5HbOGGOMN2KjYhndZTSrd69m4PyBAbnnwPkDmblhJou3LuaTX0O7iiE1dTtpaYkhTSJDusUHbl1ibhN1zwV+9ePaE8lK+VcB0UA9jl0XmbUWMr/nFFrlypWD/QhjTAisWwf/+x/cc0/ebUTcdNSEBJcMPvccTJgAtWvDypWwY8fRtlWrwnnnQd++0KSJezVqBGXLBv+zmKB6A/cFZW5Vxh/EVW/tEdKIwpj1kcaYcJBQL4Hbmt7GkG+GcP2519OsxslPB5q9YTaDFgzi7+f/naXblvLsvGfp3rA7kRGRAYw4b6EuqgOhTyK/AoaKSJyqbgAQkTOBy4HH87l2Gq5gTntgUrbjWbu2LfW9fw2kATfjprpm6QX84qsGG1SffvppsB9hjAmByZPde6dO+betUgXGjXMFdx57DPbudZVWzzvvaMJYvXpw4zWeaQHcm8e5GcBrIYwl7FkfaYwJFy8nvMy0ddO446s7WHLnEkpHli7wPXYk7aDX571oUKUBb3R8g6lrp9Ljkx6MXzWem5rcFISoj3fwYFYSeX5IngehTyLfAvoBX4rIU4DiKtdtwVW0A0BEzsCVSR+oqgMBVHW3iLwAPC0iB3B7b8UDzwBjs7YNUdWdIjIM6C8iB4EfgRuANrgtRowxxi+TJ8O550JcnP/XtG/vXqZEORXYn8e5A7iRSGOMMWHm1DKn8manN+k2vhtDvhnCk62eLND1mZrJLZ/fwr4j+5jRawblosrR/dzuNKnWhAHzB9CjUQ9KRQQ/3UpKWk6ZMvUpVSqXtTdBEtI1kaqajEvm1gDjgA+BjUAbVU3K1lSAyFziGwg8ipsWNBW4G3gJ6JOj3ZPAIFyFvOm4kc4eqjo5kJ8nL/3796d///6heJQxJkgOHID586FzZ68jMUXAVuCSPM5dAhS/Xa0LwfpIY0w46XpOV3o06sHABQP5NbFgq95eXPQiMzfM5NWrX6VJ9SYAREgEA1oPYM3uNXy08qN87hAYoS6qA6EfiURVNwPd82mzCZdI5jyuwDDf60TXZ+CSyEEnHWghfPfdd1481hgTQDNmQHr60a09jDmBT3CzX35S1SlZB0WkE26pxpueRRaGrI80xoSbER1GMHvDbO746g4W3bbIr7WM32z+hqfnPs0NjW6gT7Njx7O6ntOVC067gIHzB9Kzcc+Tmibrr7S0fRw5spEaNXKOqQVXUdriwxhjQmbyZLdnY/PmXkdiioCBwErgKxH5Q0SWiMgfuDoAKzl2fb4xxpgwU61cNYZ3GM7irYsZsWREvu33HN5Dz097csYpZzC6y2hybgohIgxoPYD1e9cz7udxwQobgKSkFUBoi+qAJZHGGHOcjAy3dUeHDlAq5PM1TFGjqoeAK3BLKxYA+4D5wB3AFb7zxhhjwljPxj3pfHZnnpj9BOv3rM+znapy25e38WfSn4y/fjwVoivk2q7z2Z2JrxnPcwueIzUjNVhh/1WZtXx5SyKNMcZTS5bArl22HtL4T1XTVPVdVe2pqgmqepOqjlHVdK9jM8YYkz8RYWSnkZSOLE2fSX1wq+iON/z74Xy1+iuGtBtCfM34E95vYOuBbNq3iTErxgQpapdERkXVICoqtCXgLYkMglq1alGrVi2vwzDGnKTJkyEy0qqsGhMM1kcaY8LV6RVOZ2i7oczdNJe3fnzruPNLty3lkZmPcE2Da7j/kvvzvd/V9a+mea3mDFowiJT0lGCE7ElRHQDJK8suyeLj43Xp0qX5NzTGFEvnnw+nngrz5nkdiQk2EVmmqnl/lZz3dRuAbqr6k4hsxG1ZlRdV1XonHWQYsf7RGFPcqSptx7Xlhz9+4Nd7f6VWBfel1/4j+2k2uhlpGWms+McKKpWp5Nf9Zq6fScIHCbze8XXuueiegMaakXGYhQvLU6fO48TFBb6e6In6SBuJNMaYbDZvhp9/tqqsJl/zcXtAZv18otcCLwI0xhhTcCLCW13eIkMz+Mfkf6CqqCp3Tb6L3/f9zsfdP/Y7gQRoG9eWFnVa8O+F/+ZI+pGAxpqcvArICPl6SPBgi4+S4IEHHgDglVde8TgSY0xBTfFt0GDrIc2JqOpt2X6+1cNQihzrI40x4S7u1Dj+3ebfPDj9QT5a+RGH0g4xftV4nm/zPJfXubxA98paG9nm/TaMXjaaf17yz4DFmVVUx4vprJZEBsGKFSu8DsEYc5ImTYL69eHss72OxBQFIhIFLAYeV9UZXsdTFFgfacJVRsZhQImMLOt1KCYM3HfxfUxYNYH7pt3H4fTDtItrx2MtHjupe11Z90pan9maFxa9wJ3N7qRs6cD8N5aUtJzIyIrExNQNyP0KwqazGmOMT3IyzJnjRiFzbPlkTK5UNRWoC1gVVmOKuP/971ZWrerhdRgmTERGRPLONe+QnJZMxeiKjOs2jgg5+dRpQOsB/Jn0JyOXjgxYjK6oTtPj9qkMBUsijTHGZ/ZsSEmxqaymwGYCCV4HYYwpnOTkVSQn/+x1GCaMNKzakNl/n838W+dTPbZwW2i0OqMVbePaMnjRYJJTkwsdm2oGSUk/e7IeEiyJNMaYv0yeDBUqQMuWXkdiipgRQE8RGSoiLUSknojEZX95HaAxJn+pqdtISfmDzEybWGCOalGnBQ2qNAjIvQa0HkDioURe/+H1Qt/r0KE1ZGYeIja2aQAiKzhbExkEZ9tiKmNCIjMTNm501VRXrnQvERg7FsqUKdi9VF0S2b49REUFJ15TbM33vf8f8GAebSJDFEvYsz7ShKOMjCOkp+8FXDIZE1PH44hMcXRZ7cu4uv7VDPlmCHfH30356PInfS8vi+qAJZFBMXr0aK9DMKbY2bXraKK4cqVLHFetcusYwSWPdevChg1Qvjy8807B7r98OWzfblNZzUm5Lf8mJov1kSYcpab++dfPR45stiTSBM2A1gO45O1LGLFkBE+0fOKk75OUtByRaMqWbRjA6PxnSaQxJqwNHQrDhrkEL0vlynDeeXDHHdCkifu5USMoVw6efhoGDYLLL4fbb/f/OZMmuUS0Q4fAfwZTvKnqWK9jMMYUTmrq0U4mJWWzh5GY4u7i0y+m89mdGfrtUO696F4qxlQ8qfscPLiccuUaExFROsAR+seSyCDo27cvYN+2GlNYY8fCI4/AVVfBQw+5hLFJEzjttLyrpz77LHz3Hdx7LzRrBk39XCoweTI0bw5VqwYsfFPCiCuPdy5QCdgD/Kqq6m1U4cf6SBOOUlO3/fXzkSOWRJrgGtB6ABeOvpBXv3+VZ654psDXqypJScupWvW6IETnH0sig2DNmjVeh2BMkTdvHvTp4xLIadOgtJ9ftEVGwkcfwQUXwPXXw7JlUDGfL/m2b4elS+Hf/y502KaEEpE7gUFA9q8hdorIU6pawMnVxZv1kSYcpaS4kUiRUjYSaYKuWY1mdD2nKy99+xLdG3anUbVGBbo+JWUL6el7PFsPCVad1RgThtasgeuug3r14JNP/E8gs1SrBhMmwKZNcNttrmjOiUyd6t5tPaQ5GSJyMzAaWAncDnT0va8ERotITw/DM8b4ITV1OyKlKFv2XBuJNCEx/OrhxEbF0umjTuxI2lGga70uqgOWRBpjwszu3dCpE5QqBVOmwCmnnNx9Lr8chgyBzz93aypPZPJkqF3bTZU15iQ8Cnyoqu1UdayqTve9JwAfAY95HJ8xJh+pqdsoXbo6MTFn2EikCYnaFWszqeckdibv5Nr/XsvhtMN+X3vw4HJAiI09L3gB5sOSSGNM2EhJgW7dYMsW+OILiCvk7noPPuhGNB97DBYtyr3NkSMwYwZ06ZL3Oktj8tEA+CCPcx/4zvtFRGqLyCcisl9EDojIZyLiV5lIEanru3afiCSLyFwRic+l3SYR0VxeXf2N05jiJiVlO9HRNYiOrkNKyhavwzElRHzNeD687kOW/LGE3l/0JlMz/bouKWkFZcs2IDKyXJAjzJslkUHQtGlTmvpbzcMYA7gpp336wMKFMGYMXHZZ4e8pAu++67b+6NEDduQyW2TePDh0yKaymkI5CNTK41wt3/l8iUhZYA5wDtAbuAU4C5grIif8l4KIVAYWAY2Bu4Abfafmikhu9d+nA5fmeM3PpV3AWR9pwlFq6naiomoSE1OH9PR9pKcf8DokU0J0a9iNF9u+yMRfJ/L0nKf9uiYpabmnU1nBCusExSuvvOJ1CMYUOYMGwbhx8NxzcOON+bf3V8WKbl1l8+Zw001u1DEy27bvkydD2bJw5ZWBe6YpcaYBz4vIGlVdmHVQRC7FFduZ5ud9+gBxQANVXee7x8/AWlxieKKJ2XcD1YFWqrred+0cYAMwAOiRo/0uVV3sZ1wBZX2kCUepqdupUOFSoqPdwH9KyhZKlSpYsRNjTtbDlz3M2j1reX7R85xV+SxubXprnm3T0naTkrKZ2Nh+oQswFzYSaYzx3McfwzPPwC23wJNPBv7+558Pb7wBc+a4LUCyqLoksm1biIkJ/HNNifEosB+YJyKbReR7EfkdNzJ4wHfeH9cAi7MSSABV3Qh8A1ybz7XNgbVZCaTv2mRgIdBZROxLY2PykJmZSlpaItHRNYiJcUmkFdcxoSQivN7xddrGtaXvpL7M2zQvz7ZJSSsAb4vqgCWRQdGrVy969erldRjGFAnffusqqLZqBW+9Fbx1ibfdBrff7kY8s6qxrloFv/9uU1lN4ajqn0BT4H7gO1ziuBi4D7hAVf0tu9cI+CWX46tw+0+eSAaQmsvxFKAMUC/H8S4ickhEUkRkcSjXQ1ofacJNaqr7IxoVVTPbSKQlkSa0SkeWZuLfJlK/Un2uG38dq3etzrWdK6oDsbHeLguwJDIItm6lb188AAAgAElEQVTdytatW70Ow5iwt2EDXHutq4z62WcQHR3c5732mhuV7NXLJY+TJ7vjnToF97mm+FPVQ6r6mqre4KvSeoOqvqGqhwpwm0rA3lyO7wFOzefa1cBZvrWRAIhIBHBxtntnmYRLcNsDNwNHgM9FJNfMTkT6ishSEVmamJjo1wc5EesjTbhJTXV7REZF1SA6ugYQaSORxhOnxJzClJumUCqiFJ0+6sSuQ7uOa5OUtJzo6FpERVXxIMKjLIk0xnhi3z6XvGVmuq08KlfO/5rCKlPGrY/MyIDrr3eJ64UXQs2awX+2MUE2Etenvy8i9USkBjAcqOs7/1fJP1W9T1XfV9WFqvoJcBWwFHghtxur6mhVjVfV+KpVqwb3UxjjgZSUbQBER9dAJJLo6Fo2Emk8U/fUunx545dsPbCVbuO7kZKecsz5cCiqA5ZEGmMCZNMmWLoUfvsNNm+GPXvclh2qx7dNS3NJ3Pr1LpE7++zQxVm/vqv+unQp/PCDTWU1hSciG0VkQx6vdSKyTERGi0jjfG61l9xHHPMaofyLqm7AjSpeCKwDtuEqrv7H12T7Ca7NACYCtXzJpzElSvaRSICYmDo2Emk8dWntSxnbdSyLNi/ijq/uQH3/mMrIOMShQ6vDIom0hfbGmELbtw/OOw8O5rKRQWQkxMZCuXLuFRvrkstff3XJ3BVXhDxcunWDhx6Cl19202mNKaT5wJVANeBbYAeuUurlwJ/A70AX4BYRuUpVv83jPqtw6yJzOhf4Nb8gVPVTEfkCOBtIVdX1IvImsEVV/f0XcS5f+xhTvLkkMoLSpasBEB1dhwMHvvE2KFPi3dD4BtbtWcdTc5/irEpn8a/W/yIp6Wcg05LI4urSSy/1OgRjQmrcOJdAjhwJFSpAcvLRV1JS7j/ffTf07u1dzEOGuGI7jayCuym8hUAz4BJfkR0AfKN603FbfNwCzMZtt9Euj/t8BQwVkTjfyCIiciYuGX3cn0B8o4q/+a6tCdwAvHSia3yVW28ANmePP1isjzThJiVlG1FR1YiIcP8sjompQ2LieFQzEInM52pjgueJlk+wds9anp3/LPUr1ad1Zbd/afny3ieRornNNSvh4uPjdenSpV6HYUyRoAqNG7tRxiVLvI7GmIIRkWWqGl/Ie6wBnvCtL8x5rgfwvKrWF5GewEhVrZjHfcoBPwGHgadwo4LPAeWB81Q1ydfuDGA9MFBVB/qOlQaG4EZFD+BGNPv72l2lqqm+dj1x24VMBbbgRkzvBVoAPVX1vyf6rNY/muLo5587kZq6nfj4HwH444+RrF17N5deupXo6NM9js6UdKkZqSSMS+CbLd8wsnl9zorZTssWe5FglbPP5kR9pK2JNMYUyqJFbmrq3Xd7HYkxnqmN20ojN0eArH+F/gFE5XUT376ObYA1wDjgQ2Aj0CYrgfQRIJJj+3AFzgJG4UY+HwDeBdpnJZA+G3HTbl8CZuAK8qQAV+eXQBpTXKWmbicq6miFNdsr0oSTqMgovrjxC+684E7Sj6xm2e4D3Dv1XrYe8LbKtU1nDYLu3bsD8Omnn3ociTHB9+abULEi3HCD15EY45nfgIdEZIaq/pVMikgM8LDvPEBN3HrJPPnWLnbPp80mXCKZ/Vg6kG+ZKFVdjEtUPWN9pAk3qanbKV/+wr9+P3avSJt+bbx3SswpvN5xOAsXvstvKefw9vdv887yd+jTrA/9W/Tn9AqhHzG3kcgg2L17N7t37/Y6DGOCbudOt2VG795QtqzX0RjjmUeB5sBmEXlPRF4UkfdwBXUuAR7xtbsMN/pXolkfGV7S0naTkvKH12F4JjMzndTUHX9VZgUbiTTh6dCh1aim0u38R1l731p6n9+bUctGETc8jvum3se2g9tCGo8lkcaYkzZmjNuu4667vI7EGO+o6ixcYZ3ZQCvgPt/7LKCpqs72tfunqvb1LFBjcrF2bT9+/rmj12F4Ji1tJ6DHJJGlSlUgMrKC7RVpwkpS0nIAYmMv4IxTzmB0l9Gs6beGv5/3d0YuG0ncq3HcP+1+th/Mc0engLIk0hhzUjIzYdQot0XHued6HY0x3lLVX1X1JlWtp6plfe83q+pv+V9tjHeSk38jOXklGRnJXofiiaw9IqOjax5z3O0VucWLkIzJVVLSciIiylC2bIO/jtU9tS5vXfMWq/ut5uYmN/P6D68TNzyOB75+gD+Tglts25JIY8xJmTkTNmyAf/zD60iMCQ8iEiEijUXkCl+lVWPCmqpy5MhGQElKWul1OJ5ISXFTALOPRIJbF2kjkSacJCUtp1y583Lddibu1DjeufYdVvdbzY2Nb+S1Ja+xckdw/0xbYZ0guOqqq7wOwZigGzkSqlaFbt28jsQY74nIvcC/gCq4SqkXAT+KyBfAHFUd7mV84cT6yPCRnr6PjAy371xS0goqVmzucUShlzUSmTOJjImpw4ED33sRkjHHUVWSklZQrdqNJ2xXr1I93rv2PQa0HkDtCrWDGlPIk0gRqQ38B7fZsuDWjDzgq0iX37V5bWp5gaquyNauMvAM0AWoAfwJTAEGqGpi4T5B/p5++ulgP8IYT23dCpMmwSOPQHS019EY4y0R6QO8ittSYwYwIdvphbhqq5ZE+lgfGT7cKKSTtd6qpDmaRJ52zPHo6Dqkp+8mIyOZyEibWGC8deTIJtLT9xEbe4Ff7etUrBPkiEI8nVVEygJzgHOA3sAtuH2t5hZg6s8YXL3l7K812Z4hwFfATbh9sDr43m8EJkkoduY0Jkzt3QsDBrj3wnj7bbcmsk+fwMRlTBH3f8DLvqI5n+c49z+gwfGXGOO9I0c2AVCqVCWSklacuHExlZKyndKlqxIRUfqY40crtNq6SOO9gweXARAb29TjSI4K9UhkHyAOaKCq6wBE5GdgLXAXMMyPe/zh2+cqL2fhyqjfpaqjfcfmiUgm8CZwNrD6JOP3S4cOHQCYNm1aMB9jTIG99BK88AKsWgXjx8PJfKWSng5vvQXt20NcXOBjNKYIqgtMz+NcMnBKCGMJe9ZHho+sJLJKlWvYufO/ZGamExFRslY6paZuO24qKxy7V2S5cueEOixjjrFr16eUKlUprJLIUBfWuQZYnJVAAqjqRuAb4NoAPSPK934gx/F9vvegf+bDhw9z+PDhYD/GmAJJTnbrGKtUgYkT4f33T+4+kyfDtm1WUMeYbHYBZ+ZxrgFQcjfhy4X1keHjyJGNREZW4JRTriQz8wiHD6/1OqSQS03dnmsSaXtFmnCRnn6AXbu+oFq1G4iIiMr/ghAJdRLZCPgll+OrAH83CbhbRFJE5JCIzBGRlrncawHwtIjEi0isiFyMWyM5zcqtm5JqzBg3jfXzz6FVK+jXz1VXLag334RataBTp4CHaExRNRl4RkSyj82riFQBHgS+8CYsY07syJFNxMSc+dc6q5I4pTUlZftx23sAREXVBCKsQqvxXGLiZ2RmHqF69Vu8DuUYoU4iKwG5rcbaA5zqx/UfAPcAbYG+QGVgjoi0zmqgqgp0xE1Z/QE4CHwPbMAVN8iViPQVkaUisjQxMei1d4wJqYwM+M9/oHlzaNECxo2DyEjo1ctNT/XX+vUwY4ZbC1mqZM14MuZEngJScF+SzsJVZx0O/AZkAAO9C82YvB0+vJGYmLqULXsOIlElLolUzSA19c9cRyIjIkoTHV3TRiKN53bsGEdMTD0qVAiv6slFap9IVb1FVcer6kJV/QBoAWwDBuVo+hbQHPgHcIXvPR74RERy/cyqOlpV41U1vmrVqsH7EMZ44KuvXAL4f//nfq9Tx40ofvcdPP+8//cZPdoln3fcEZw4jSmKVHUXro95ASgNrMfVHHgNuFRV93sYnjG5cntEupHIiIjSlCvXuMQlkWlpu4CMXJNIsL0ijfeOHNnKvn1zqV69F+FWGzTUYwl7yX3EMa8RyhNS1YMiMgX465+0ItIJ6Am0VdXZvsMLRGQDrvR6F+DLgj6rIDp37hzM2xtTYMOGwZlnHrunY8+eMGUKDBwICQlulPJEUlLg3Xfh2mvh9NODGq4xRY6qHgSe873MCVgfGR7S0naTmZlMTMyZgKv6uHv3JFQ17P6xGiwpKW57j9yms0LWXpE/hDIkY46xc+dHgFK9ei+vQzlOqJPIVbh1kTmdC/xaiPtm3z+yie8955/6Jb73hgQ5iXz44YeDeXtjCmTJEli0yE1nzTkF9fXX3blevWD5cihfPu/7fPop7NplBXWMycn3JWU3Vf0pl3ONga9U1WoZ+1gfGR6y9ogsU6Yu4JLIP/98l9TUP4mOzn1krrg5ukfkiUYiP0M1kzwmshkTNKrKjh3jqFDhUsqWre91OMcJ9Z+Ir4Dm2YsPiMiZwOW+cwUiIhWAzhxNEAH+9L1fnKP5Jb53q5JnSpSXX4aKFXOfglqxolsfuWEDPPDAie8zciTUqwdXXRWcOI0pws4EovM4FwOcEbpQjPFP1vYe2UcioWQV10lN3QbknUTGxNRBNZXU1J2hDMsYAJKSfiI5+ZewHIWE0CeRbwGbgC9F5FoRuQY3KrgFGJXVSETOEJF0EXkm27GHReQtEblJRFqLSG/c1iCnAU9me8ZnuHWS74vI3SJypYjcDbzve07OjaADrnXr1rRu3TrYjzEmX5s2wSefQN++eY8ytmwJjz/upqp+9lnubVatgoUL3ShkhH0Za0xuNI/j8RzdYspgfWS4OD6JPA+ApKTlHkUUekens+Y9Euna2bpIE3o7dnyASGmqVbvB61ByFdJ/DqpqMtAGWAOMAz4ENgJtVDUpW1MBInPEtxo37XU4MBMY5ru2haouzPaMA7iiOtOAR7O9T8IVOMj+HGOKteHDXdJ3330nbvfss3Dhha7q6rZtx58fORKiouDWW4MRpTFFj4g8KCKbRWQzLoGclPV7tlci8DrwtbfRGnO8I0c2UqrUqZQqVRGAUqUqEhMTV8JGIrdTqlQlIiJyn0hge0X+P3t3Hh9VeS5w/PdM9m1CkkkIQtgUQUHA3aLWDesObbXaulR7rXq1tdqrXuvSXmutba1Wa1ut2latWotVy+K+oFZRbN0QUJFVAiQkM9m3mSTz3j/ODAxhksy+5fl+PvMJOfOec55DgJdn3uVJL319mfNffGP6aWj4G+XlJ5OTU5HscIJK+Cb9xpjNDFFqw9dmE1YiGXhsCVYiGMo9agnYbEepkai1Ff70JzjzTKipGbptbi489hgccICVKL7wws4Rx85O+Otf4RvfAIcj7mErlS42AP7N284H3gMG1odyY633/1MC41IqJP6dWQMVF88eYUnktkGnsgLk5Vmdp45Epr66uodYs+a7zJjxFA7H/GSHE7Xm5lfxeOpSdiorJCGJVEolxp/+BO3tO8t6DGfqVGsX1//+b2sE079G8u9/h7Y23VBHqUDGmEX4Nmnz7WR5szFmY1KDUioMPT0bKSzcd5djxcWzcTr/SV9fO9nZQ+y0liHc7rohNxHKzi7DZivC7a5NYFQqXA0NC1iz5kLAS13dgxmRRG7f/ihZWaVUVKTubta6ukmpDNTbC7/9LRx1lDVNNVQXXwzz5llrJFeutI7dey/MmAGHHx6fWJVKd8aY72gCqdJJYI3IQNbmOobOzpVJiSvRPJ46cnODl/cA6wOi/PzxOp01hTmdi/j003MpLT2cMWMuoqnpefr62pIdVlT6+ztpbHyaqqozycrKT3Y4g9KRyDg488wzkx2CGuGeegpqa60SHuEQsUYw99sPzj7bSiDffx9+/3vrPaVUcCKSC5wETMXakTWQMcZo/Ugf7SOTr7e3Aa+3Z5Ak0tqhtbR0ThIiSxxjjC+JHLqciVXmQ5PIVNTU9CKrV59JcfEB7LffM3R2rqau7gGczsVUV6fuNNDhNDb+E6+3k9Gjz0t2KEPSJDIOLrvssmSHoEYwY6yyHnvvDaecEv75lZXw4INw8slw2mlQWGjVkVRKBSciewBvYZX6MOxc0x+4Y6smkT7aRyZfd7c1cJ6fP2mX43l548jOrhgR6yJ7e10Y0ztsTcz8/PE4nSNnx9p00dLyL1at+hqFhfswc+bzZGfbsdsPJS+vhsbGBWmdRG7f/ih5eRMoLU3tKWA6nTUOurq66OrqSnYYaoR680147z344Q8jL8dx0knw/e9DSwucc45VT1IpNahfY22qMx4rgTwUmAz8HFjn+7Xy0T4y+QaW9/ATkRGzuY7HY5X3GGo6K1gjkb29DfT3dyciLBWCtrZ3WbnyFPLzJzBr1kvk5JQDIGKjsvIbNDW9SG9velZWcrvraG5+mdGjz0UktdO01I4uTZ188smcfPLJyQ5DjVC/+Q1UVMC3vx3ddW67DX7yE+ullBrSkcAdWDWKAbzGmE3GmJ8AT2KVplI+2kcm32BJJFhTWjs7V+L19iU2qATbmUQOPxIJ4HZviXtManjt7R/x8ccnkpMzmlmzXiU3t2qX96uqzsKYXlyuRUmKMDoNDY8D3pTeldVPk0ilMsjatbB4MVx6qTUNNRoFBfDTn8K4cbGJTakMVgFsM8Z4gU6gLOC9pcDRyQhKqcH09GwkJ8dBdnbxbu8VF8/G6+2hu/vzJESWOG639ZnPcNNZ8/L8SaSui0y2zs5P+Pjj48nKKmH27FfJy9t9FLmk5GDy8ibQ0LAgCRFGb/v2RygpOYiiomnJDmVYmkQqlUHuvBNycuB730t2JEqNKFsAfxXV9cBXAt47BOhJeERKDSHYzqx+OzfXyex1gOGOROoOrcnV1bWOFSvmIpLNrFmvkp8/IWg7EaGq6kyam1+mt7cpJvfesuVunM5nYnKtoXR0rKKj46OU31DHT5NIpTKEywUPPWRtglNdnexolBpRXgOO8v36PuBqEXlJRJ7F2lDnyaRFplQQPT0bd9tUx6+wcCoieRm/LtLjqSMrq5SsrKGn7eTljQVERyKTqKfnC1asOA6v18OsWa9QWDhlyPbWlNY+nM5/Rn3v7u71rFv3QzZvvjXqaw1n+/ZHgSyqqr4Z93vFgiaRSmWIP/4RurutDXWUUgl1I3AvgDHmXuAKoBAYA9wGXJW80JTalTFeenq+GHQk0mbLoahoRsYnkW73tmGnsgLYbHnk5lbrSGSSuN3b+Oij4+jra2XWrJcpKpo+7DnFxQeQnz+ZhoYnor7/li13AV7a29+nvz9+k0qM8dLQ8Bjl5Sfsts4zVWmJjzi44IILkh2CGmHcbquW4wknwIwZyY5GqZHFGOMEnAHf/w74XfIiSm3aRyaXx1OPMZ5BRyLBmtLqci3CGINkaJHgUGpE+o20WpHGGGprbyc3dzTV1VHu0heF3t4WVqyYS2/vdmbOfJmSkv1DOs8/pXXz5l/j8TjJzXUMf1LQ+7uoq/vLjp9/R8f7cSu70dLyBm73FiZP/nVcrh8POhIZBxdccIF2kiqhHn8c6uvhKh3vUCohRMQmIqeJyKAf24jIfiJyWiLjSgfaRyZXT4+/RuTEQdsUF8+mt9eJx7Nt0Dbpzkoihy7v4ZefP35EjURu2vR/bNjwv6xZcxFdXeuSFkdj4wK6uj5l+vSnKS09LKxzKyvPAvpxOp+O+P7btv0Rr7eLadMeBKC19e2IrzWc7dsfISurBIdjXtzuEWuaRMaB0+nE6XQO31ClvaVLYf365MZgjFXWY7/9YO7c5Mai1AhyLvA41m6sg2kHHheRbyUmpPSgfWRyDVXew2/n5jqZOaXVGIPHUxfSdFbYORJpjIlzZMlXW3sHX3zxM6qqvonNlsu6dVcmLZbW1mXk5FRRVhb+f26Ki2dRUDAl4imt/f09bNnyO8rLT6Ss7FgKCvairS0+SWR/fxeNjU9SWXn6sGt0U4kmkXFwxhlncMYZZyQ7DBVnbW1wyilw4YXJjeOll2DlSvif/4EMnXWkVCo6F3jQGLNxsAbGmE3An4HzExVUOtA+MrlCSyJnApmbRPb1teD19oQ8nTU/fzxebw+9vZn94ce2bQ+wfv3VVFaeyT77PMrEiTfR1PQsTueSpMTT2rqM0tLDI5pSLSJUVp5JS8treDwNYZ+/ffuj9PZup6bmGgDs9jm0ti6LywcJTudi+vvb02ZXVj9NIpWK0OLF0NMDb7wBH3+cnBi8Xrj+eqipgW/pWIdSiXQA8FII7V4BDopzLEqFrLt7Izk5o8nKKhi0TXa2nfz8PTM2iQy1vIdfXl4NkNm1IhsaFvD555dQXn4S++zzCCJZjB37AwoL92HduiviuqlMMG53PT09G6Jag1hVdRbgpbHxqbDOM8bLli13UFy8P6NGHQNAaenh9PY20t0d++ln27c/Sl7eOEaNOjrm144nTSKVitCCBTBmDBQUWJvaJMPjj8MHH8DPfw55ecmJQakRqgRoDqFds6+tUilhqBqRgYqLZ2d8EhmsWH0wO2tF1sYtpmRyuZ7j00/PpbT0CKZPfxKbLRewduqdMuV39PRspLY2sRu+tLUtA8BujzyJLCqaQWHhNBobw5vS6nI9R1fXZ9TUXL1jFNRun+OLK7ZTWj2eBpqaXqCq6hxE0istS69olUoRzc3w4otw9tlwzjnw6KPQFJuatiHr6bFGIfff34pBKZVQTiB4tetdjSdg51alkq2nZyMFBYPvzOpXXDyb7u519PW1JyCqxHK7rQ2Dwtmd1Tov80YiW1reYPXq0ykqmsl++y3ZbU1eWdlxVFZ+g82bb6W7e1PC4mptXYbNlk9JyQERX8Oa0nqWb+fT+pDPq629nby8Giorv7HjWFHRvmRlldLauizieIJpaPg70M/o0efG9LqJoEmkUhFYuBB6e+Gss+Dyy636jH/+c2Jj+N3vYPNm+PWvwaZ/k5VKtLcIba3jBb62IRGRGhF5UkRaRaRNRJ4WkfEhnjvJd26LiHSKyGsisttUWt/OsteJyCYR6RGRFSJyeqgxqvRlTD9u9+aQRiL95RQ6OlbEOarEC3c6a06OA5stP+N2aG1re4+VK08jP38SM2e+SHZ2adB2e+55O2Bj/frEbQHf2rqMkpKDd4yKRqqq6huAobHxyZDat7X9h9bWNxg37kpstpwdx0VslJZ+KeYjkdu3P0px8WyKi9OvPpv+1zMOLr30Ui699NJkh6HiaMECmDwZDjoIZs6Eo46Ce+6B/v7E3N/lsqawnnwyHHdcYu6plNrFXcBxInKniOz2vxwRyRGRu4BjgTtDuaCIFAJLgWlYCep5wBTgNREpGubcCqxkdQZwCfBN31uvicg+A5r/DLgJ+D1wErAc+IeInBxKnNHSPjJ53O5tGNM3ZI1Iv0zeodXjqSMrq5js7NBmmotIxtWK7Oz8hI8/PpGcnApmzXp5yFqK+fnjmTDhBpzOp2lqCmUpeHT6+7vo6PggJjUZi4qmU1g4PeQprbW1d5CVZWfMmO/u9p7dPofOztX09rZEHRdAV9ca2tv/k3Yb6vhlJzuATHTWWWclOwQVR04nvPIKXHPNzt1QL78czjgDnnkG5s+Pfwy33ALt7XDbbfG/l1Jqd8aYd0TkKuAO4BwReQn4wvf2BOB4oAK4yhizPMTLXgRMBqYaY9YBiMjHwFqsxPA3Q5x7KTAa+LIxZr3v3KXABuCnwJm+Y1XA1cAvjTG3+859TUT2An4JPBdirBHTPjJ5QqkR6Zebuwc5OY6MTCLd7rqQRyH9MqlWZHf3RlasOB6bLYdZs14hL2/ssOfU1FxFff2DrF17OQcfvDLqEcKhtLf/B2P6oloPGaiq6iw2bfo/3O6tQz5rd/cmGhv/QU3NVWRn23d7v7R0DmBoa1tORcWJUce1ffvfABtVVem5M6KORMZBbW0ttbWZufhawdNPWyOOgf8Pmj/f2iH17rvjf//16+EPf4D/+i+YPj3+91NKBWeMuQs4BvgP8DXgOt/ra8B7wDHGmN+Gccl5wHJ/Aum7x0ZgGTDcx1OHAWv9CaTv3E7gTeBUEfF/aHwCkAs8OuD8R4H9RGT4IaooaR+ZPKGU9/ATkYzdXMfj2RZ2EpkpI5Fu9zZWrJiL19vNzJkvU1CwZ0jn2Wx57LXX3XR3f86WLXfFNUb/ukMraYuetbZx+CmtW7bchYiNsWN/EPT9kpJDAFvMprQ6nYsoLT085HqlqUaTyDg477zzOO+89ByaVsNbsAD23htmzdp5LDsbLr0Uli6F1avje//rroOcHLj55vjeRyk1PGPMv4wxp2DtwFrte9mNMacYY94M83LTgVVBjq8G9h3m3H7AE+S4GygA/P9TnO47tm5AO/+/XMPdJ2raRybPziQylD2hrCmtnZ2r8Hp74xhV4nk8kY1Eejx1eL3uOEUVf729Llas+Aq9vQ3MnPlC2OvwKipOoqJiHps23YzbvTVOUVpJZGHhPuTklMfkekVF0ygqmklDw+BTWnt7m6mr+xNVVd8iP39c0DbZ2SUUF8+itTX6JLKnp5bOzhVUVJwa9bWSRZNIpcJQXw+vv26NQg6sfXvRRVaZjXiW+1i+HP7xD7j6aqu8iFIqNRhjvMaYBt8r0tXR5QQvG9IElA1z7hpgim9tJGBtoAMcEnBt/9cWs3vF7KYB7XYQkYtF5D0Rea+xsXGYMFQq6+nZSG7uHthsodWEKi6ejTFuurrWxDmyxDHG4HbXhVzew2/nDq3xS57iyRjDqlVfpbt7HTNmLMZuP2T4k4LYa687MaaP9euviXGEFmO8tLW9HZP1kIGqqs6ire3tQcu0bNt2H15vJzU1Q28eZLfPoa1tOV5vX1TxuFzPAlBRcVpU10kmTSKVCsOTT4LXu+tUVj+Hwyr58de/Qkts1lzvwhgreRw92lqPqZRSAf6I1af/VUT2FJExwN2Af3qqN9ILG2PuN8YcZIw5qLKyMgahqmSxakSGPmM5EzfX6e9vx+vtjGgkEoj7usjm5qWsWnUGHs/2mF63q+sTWlvfYs89f0VZ2TERX6egYDLjx19LQ8PjNDe/HrsAfbq6PqWvryVm6yH9/Kc44J0AACAASURBVOU6Ghv/sdt7Xq+brVvvpqzseIqLZ+32fqDS0jl4vZ10dq6MKh6Xawn5+ZMpLJwW1XWSSZNIpcKwYIG1DnGwtYiXXw5dXfDgg7G/98KFsGyZNY21uDj211dKJV0zwUccBxuh3MEYswE4BzgQa6rqNuBL7NwZti7gHqNEBs6l2DECmeCKtyqRurs3hrQe0q+gYCoieSmVRBrjZcuW30eczIVb3sMv3rUivd4+Nm78MStWzMXpfIrt2wcuW46O07kQgMrKM6O+1vjxPyI/fyLr1l0e86nOO9dDxjaJLCycQnHx/kGntG7f/jc8njpqaq4e9jr+uKJZF9nf30lz86tUVJzG7v8Upw9NIpUK0ZYt8NZbwUch/fbfHw4/3JrSGstyH729cO21sM8+1oY6SqmMtBprzeJA+wKfDHeyMeYpYKyv/V7GmAOBYqDWGOP/n+9qII+dayQD70Eo91Hpyevtw+3eElYSabNlU1y8X0olkZs23cS6dZezZUtkO9m53VYSGf50VmudXDxGInt6almx4hi++OIWqqvPp6hoPxobn4rpPZzOhdjth8VkE5esrAL23PNOOjtXsW3bPTGIbqfW1mXk5FRSULBXTK8L1pTW9vZ36e7etOOYMYba2tspKppJWdnxw14jL288ubl77Eh2I9Hc/CrGuHE40ncqK2gSGRdXXXUVV12VuIKsKjH+4ZsBMdzu9JdfDhs2wPPPx+7e998Pa9daJT2ytTCPUplqMXCYiEz2HxCRicDhvveGZYzpN8Z8aoxZLyJ7AGcB9wY0eQHoxRq1DHQusMq3G2xcaR+ZHG73FqCfgoLwNuD179C6+zLaxGtsfIovvvgZILS2vhXRNTyebUD4I5FZWQXk5FTFfCTS6VzMe+9Zv8fTpj3CtGkP+tbvvYPbvS0m9+jpqaW9/T0cjq/G5HoADsd8yspOYOPGn8R06m1r6zJKSw+PywhdsCmtTU0v0NX1CTU1V4d0TxGhtHROVJvruFzPkJVlp7T0yIivkQo0iYyD0047jdNOS+9PF9TuFiyA2bOtnVmH8vWvwx57wO9+F5v7trbCTTfB0UfDKafE5ppKqZT0ALAJWCQi80VkHrAIqAXu8zcSkQki0iciPwk4liMid4rIV0XkWBG5HKvMyGqsWpYAGGMasOpNXici/yMiR4vIvcCxWOVJ4k77yOQIp0ZkoOLi2fT1uZK+oUxHx0o+/fR87PbDGDfuSjo63qe/vyvs60Q6nRViWyvS63Wzdu0VrFo1n/z8CRx44PtUV58LgMPxdQCczn/G5F4u12LfdWOXRIoIU6bcjdfbzYYNP4rJNT2e7fT0rI/5eki/goLJlJQcRGPjzimttbW3k5s7lqqq0OvX2u2H43Z/EdHfCWO8uFzPUF5+QlxrbSaCJpFxsGbNGtasyZydzBRs2gTvvjv8KCRY5TcuvRReegk++yz6e//qV+B0wu23774jrFIqc/jqOh4LfA48AjwGbASONcZ0BDQVIItd+3ADTMFKNp8HrgT+ApxgjBlY+uMG4BbgCuBFrJHOM40xz8T6mYLRPjI5wqkRGWjn5jofxjii0PX2uli1aj7Z2aVMn/40ZWVzMaaPtrZ/h30tt7sOmy2f7OzSsM+NVa3Irq61fPDBl9i69W7Gjv0BBxzwDoWFOz+hLirah8LCfWI2pdXpXEhh4TQKC6fG5Hp+hYV7U1NzFfX1D9Ha+k7U14vXeshAlZVn0d7+Ht3dG2hv/4CWlqWMG3dFWAmdv35lJKORHR0f4vHUpXVpDz9NIuPgkksu4ZJLLkl2GCqGnvB9aHVmiOvRL74YcnOjL/dRWwt33gnnnAMHHhjdtZRSqc8Ys9kYc7oxxm6MKTHGfNUYs2lAm03GGDHG3BRwrM8Yc6oxZrQxJs8Ys6cx5kZjzG5DNb4pr7cYYyb42s40xgxdhTuGtI9MDiuJtJGXVxPWeUVFMwFJ2rpIr7eP1avPxO3eyowZT5OXNwa7/UtYU1rDLcXqrxG5R0TTJfPyaujp2RzV1N76+kd5//0D6On5ghkzFjFlym+DllyprDydlpY38HicEd8LrPqHLS2vx3QUMtD48TeQmzuWdet+GPW1WluXIZJHSckBMYgsuKoqa0prQ8MT1NbeQVZWCXvscXFY1yguno3Nlh/R5jpO5xJAKC8/OexzU40mkUqFYMECOPhgmDx5+LYAVVXWqOXDD0NbW+T3/fGPrdIeP/955NdQSimleno2kpc3NuwpdNnZJRQU7JW0JHLDhmtoaVnK1Kn3Y7cfCkBOThlFRTMiWhfp8WyLaCorWNNZvd5O+vqG3Cw5qL6+Dj799AI+++w8iotnc9BBH+FwzBu0vTWl1YvLtSiiWP1crmcxpi9uSWR2djHjx19Le/u7dHSsiOpara3LsNsPDrmOaSTy8ydgtx9GXd39NDQsYMyYi8IelbbZcikpOSSikUiX6xns9i+Rm+sI+9xUo0mkUsNYtw4++CC0qayBLr8cOjrgoYciu++KFVbNyR/8ACZMiOwaSimlFIRfIzKQf3OdRKuvf5gtW+5i7NgrqK4+f5f3SkuPpK3t7bCLvrvddRHvULqzzEfwgvWD6e7eyPvvH8T27X9lwoQfM2vWa+TnDz0iXFw8m/z8STQ2Ph1RrH5O50Jyc8dQUnJwVNcZyujRZyOSQ339wxFfo7+/m46OD+K2HjJQZeWZO9YIjxt3RUTXKC2dQ0fHB2Gty3W7t9HR8T4VFZmxJlyTSKWGsWCB9TXUqax+Bx8Mhx1mTWn1RlDm+5proKwMrr8+/HOVUkqpQD094dWIDFRcPJueng309bXGNqghtLX9mzVrLmHUqGPZc8/bd3u/tPQI+vs76Oz8OKzr+qezRiI/30oiw91cZ8OGa/F4tjJr1qtMmnQzNtvw26yLCA7H12lufjni3/f+/m6aml7A4ZiPSPz+y5+TU0FFxWls3/5YxHUj29v/gzG9cV0P6VdZeQYgVFWdueNnGi67fQ7G9NHe/l7I57hczwJkxHpI0CRSqWEtWGDVfqwJbxkJYI1Grl0LL74Y+jnGwGOPwcsvW9NZR40K/75KKaWUn9frwe3eGlUSCdDREV7CFim3u45Vq75GXt4eTJ/+RNCky18eIZwprf39nfT3t0U8nXXnSGToSWRn5yc0Nj7J2LE/oKzsmLDuV1l5Osb07kg+wtXc/CpebycOx9ciOj8c1dUX0NvbQFPTCxGdv3NTnTmxDCuo/PwaZs16hb32iqzWKOBblxve5jou1xLy8ydSVBSsHHD60SQyDm688UZuvPHGZIehYuDTT2HlyvCnsvqdcQZUV4dW7qO/36pFefDBcO65MGMGXHZZZPdVSqlUpX1k+LzeXpzOJXR0rIrofGv6pYlqOiuQkCmtXq+b1atPp6+vhRkzFpKTUxG0XX7+OPLyJoS1uY7bbZX3iHQ6a25uFSK5YY1EfvHFz7HZChk3LvyNZ+z2Q8nN3SPiXVqdzoVkZdkZNeroiM4PR3n5ieTkVFFf/1BE57e2LqOwcNqgP+9YKys7Nqp1ibm5DgoKptLWtiyk9v393TQ3v0JFxalxqYGZDAlPIkWkRkSeFJFWEWkTkadFJKSxZBExg7xmB2k7VkT+IiL1IuIWkY0i8ovYP9Hu5s6dy9y5cxNxKxVnCxZYZTXOOCOy83Nz4ZJL4PnnrRHJYHp64L77YNo0a8psWxvcfz/85z/W+UoplUm0jwyfMb188slZ1NXdN3zjILq7I6sR6ZebO4acnMq4J5HGGD7//Hu0tb3DtGkPU1w8c8j2paVH0Nr6Vsi7pUZTIxJAxNrdNtSRyK6utTQ0/J2xYy+LKGERseFwfI2mpufDrolpTD8u12IqKk5JSD1Cmy2H0aPPweVaQm+vK6xzjfHS1vZ2QtZDxlJp6RxaW98O6c9fS8tSvN7ujFkPCQlOIkWkEFgKTAPOB87Dqmv1mogUhXiZh4AvDXh9PuA+E4F/A3sDPwC+AtwEhLf6OkIfffQRH32UnF3MVOwYYyWRRx0FYyLrbwAriczOhj/8YdfjLS1w663Wpjn//d/W+scnn7RGPy+6CPLzo4tfKaVSkfaR4cvKKqSs7HiczkURlZfw14gsKIhsJFJEErK5zrZt91Bf/2fGj7+BqqrhP70dNepIPJ56urvXh3T9nUlkZGsiwVoXGepI5ObNt2Kz5VFTc1XE96us/Dpeb3fY00RbW9+ht7cxbruyBlNdfQHG9LJ9++NhndfV9Rl9fc0JWQ8ZS6Wlh9PX10R39+fDtnW5niErq5hRo45KQGSJkeiRyIuAycBXjTELjTGLgHnABCDUolFbjTHLB7wGfjzzR2ArcIwx5gljzBvGmIeNMT+O2ZMM4corr+TKK69MxK1UHK1cCZ99FvlUVr8xY6wRxgcfhPZ22LoVrr7aWmN5ww1wwAGwdCm8+y6cfjpkZcUmfqWUSkXaR0bG4ZiP210bUSJn7USZRW7u2IjvX1w8m87OVRFvnDKc5ubXWbfuSioqTmPSpJtDOqe09Agg9HWRbvc2IPLprNa540Maiezu3kB9/SOMGXMxubmjI75faemXyc6uCHtKq9P5T0RyKS8/MeJ7h6u4eCbFxfuHPaV153rI9Eoi7XZr/aY//sEYY3C5nqGs7CtxLV+SaIlOIucBy40x6/wHjDEbgWXA/FjcQET2BE4AfmeMic+/dGpEWLDASuhOPz36a11+uTVN9fjjYdIkuOsumDcPPvrImup6zDHWtFmllFIqGGtHR8HpDL9uoFXeoyakXUEHU1w8G2M8dHV9GvE1gnG7t1FbewerV59BQcFe7LPPoyHvJFpYuA/Z2WUhJ5EeTx0iuWRnl0ccb37+eNzubcMm05s3/xKRbMaP/9+I7wVgs2XjcMzH5XoGr9cd0jnGGJzOhZSVHUd2tj2q+4eruvp8OjreD2v9bmvrMnJyKikomBLHyGKvsHCq78/f0JvrdHSswO3ekjG7svolOomcDgT7U7Ua2DfEa1zqW+PYJSJLReTIAe/7P8boFpGXfW2bReSvIpKY1boq7fmnsh57LFRWRn+9Qw+FOXPg44+t6a1r11o7sM6aFf21lVJKZb7c3Crs9jm4XIvDPjeaGpF+sdxcp6+vnfr6h1mx4njeeWcc69dfTWHhFGbMWBxW0iNi862LDG1zHau8x5ioNjaxdmj14vFsG7RNT89m6usfYsyYC8nLi3zqrF9l5en097fR3PxqSO07O1fR07MhoVNZ/aqqzkYkm+3bQ68Z2da2DLt9TtptOCNiw26fQ1vb0Emky7UEECoqTk5MYAmS6CSyHGgOcrwJKAvh/EeBy4C5wMVABbBURI4OaOP/2/oXrLWSJwHXAqcAL0o8C+WojPHBB7B+ffRTWf1E4IUXoK7O2ql1UnR9uVJKqRHI4ZhPR8eHYdcpjKZGpF9Bwd7YbPkRJ5Feby8u13N88sm3ePvt0Xz22QV0d29gwoQfc8ghn3PAAe9QWBj+SFRp6RF0d3+Ox9MwbFu3e1tUU1khtFqRmzffBsD48ddGdS+/srLjyMqy09j4dEjtnc6FWEnLvJjcPxy5uZVUVJxKff0jeL3Db0Xi8Wynu3td2k1l9SstnUNX16f09jYN2sbleoaSkkOimtacitIqoTLGnGeMWWCMedMY8yhwBLANuCWgmf+ZXjfGfM8Ys9QYcz9W8nkg1lTX3YjIxSLynoi819jYGM/HUGng73+3NsP5WgxLK5WUQGlp7K6nlFJqZHE4rKTA6Qx9NLK/vwePpy7qJNJmy6aoaL+wkkhjDG1t/2bt2h/wzjtjWbnyFJqaXqK6+jvsv//bHHroOiZN+mlEyaPfznqRw5da8I9ERmO4WpFu9zbq6v5EdfX5EReyH8hmy6Oi4lSczoUhJWZO50Ls9i+Rl1cdk/uHy6oZuZ3m5uGLZPungqZrEulfF9nW9k7Q993uetrb/43DkTm7svpFPjk+Ms0EH3EcbIRySMaYdhF5Frgw4LB/X+GXBzR/yfd1f+D5INe6H7gf4KCDDgp/67MAt956azSnqyQzBp54Ar7yFSiPfNmEUkqpILSPjFxh4VQKCqbici1i3Ljvh3SOP9mJdjorWFNa6+r+wjvvTEAkC5EsIGvHrwd+7/E00NOzHpE8HI55jB59LuXlJ8a05ERJyQHYbPm0tr5JZeXQn/x6PHWMGnVMVPfLz68BBh+JrK39Ncb0MX78dVHdZ6DKytNpaPgbra1vUlY2+DP09Gymo+MDJk++Lab3D0d5+Unk5Dior3+IiopThmzb2roMkTxKSg5MUHSxZbcfAmTR2vp20GdtanoOIOPWQ0Lik8jVWOsiB9oX+CSK6wYmfauHaeuN4j4hmTNnTrxvoeJo+XLYvBl+9rNkR6KUUplH+8joOBzz2bLlN/T2tpCTM2rY9tbOrJHXiAw0duz3McaLtW+hF2P6d7ygf8D3XoqKHEyYcB2VlWeQnR2fqTg2Wx4lJYcMu7lOf38PfX3NUU9nzcoqIju7IuhIpMeznW3b7mP06HMpKJgc1X0GKi8/AZutgMbGp4ZMIv0bLyVjPaSfzZZLVdU5bNt2L729TeTkDP6JfFvbMkpKDkrbXUuzsgopKdl/0JFwl2sJeXk1FBUNXfM0HSU6iVwM3C4ik40xG2BHTcfDgR+FezERsQOnYtWE9FsO1OPboTXguH+P4/+EHXWY3n7bGprXjjI9LVgAubkwPyb7BSullAqkfWR0HI751NbeRlPTC4we/c1h2/trRMZmJHIm06b9KerrxFpp6ZFs3vxL+vo6yM4uDtpmZ43I6JJIGLxWZG3tHXi9biZMuD7qewyUlVVEeflJOJ1PM2XK3YPuYOt0LqSwcN+opgjHQnX1BWzd+lsaGv7O2LGXBW3T399Ne/v7jBv3wwRHF1t2+xzq6h7A6+3FZsvZcby/v4emppeprv522m0aFIpEr4l8ANgELBKR+SIyD1gE1AL3+RuJyAQR6RORnwQcu1pEHhCRs0XkaBE5H6s0SDVwg7+dMaYPKyE9RUT+KCJfEZHLgHuA14Gl8X7I66+/nuuvj/0/ICr++vvhH/+Ak07S9YtKKRUP2kdGx24/lJycSlyu0Ep9dHdvRCQn6hG4VGbVi+ynvf3dQdvEMonMy6vZbSTS43Gydes9VFWdRWHh3lHfI5jKyq/j8dTR1hb8OXt7XbS0vJHUUUi/kpLZFBXNHLJmZHv7exjTm7brIf1KS+fg9XbT0bFil+MtLa/j9XZSUZF56yEhwUmkMaYTOBZr19RHgMeAjcCxxpiOgKYCZA2Ibw3WtNe7sdY7/sZ37hHGmF32djbGPAx8G2vjnSXAj7F2dj3NGBPVekeV2Z5+GrZtg/POS3YkSiml1O5EsqioOA2X6zm8Xs+w7Xt6NpGXN963XjEzlZbOAWxDTmn1J5GxKLlhjUTW7nJsy5a78Hq7mDDhhkHOil5FxamI5NDY+FTQ912uZ4H+lEgiwRqNbG//D52dwVes+aeA+jenSVd2u5UEt7XtOqXV5XoGm60w6nW4qSrhu7MaYzYbY043xtiNMSXGmK8aYzYNaLPJGCPGmJsCji0xxhxujHEYY3KMMRXGmHnGmH8PvIev/SPGmBnGmDxjzBhjzOUDElWldmEM3Hor7L03fDU1/v1VSimlduNwzKe/v42WljeGbdvTs4mCgsyuK5Wdbae4eCYtLYPXi3S7rbqOsRmJHE9/fyt9fa0A9PY2s3Xr76isPJ2iomBbf8RGdnYpZWVzcTqfJtiYiNO5kNzcsSmzSc3o0ecgkk19ffCakW1tyygomEpuriPBkcVWfv448vJqduw0C9bOxC7XEsrKjicrKz+J0cVPWpX4UCqeXngBPvoIfvQjyMrcD2yVUkqlubKyudhsBbhcw5f6iEWNyHRQWnoEbW3L8Xp7g77v8dQhkk1OTvQJy85akdZo5Natd9Pf38aECTdGfe3hVFaeTk/Pxt1KrfT3d9HU9AIOx/xB10smWm5uFeXlJ7N9++41I43x0tr6dtpPZfWz2+fQ1rYziezsXIXbvTkjS3v4pcafMqVSwK23Qk0NnHNOsiNRSimlBpeVVUhZ2VdwOhcFHZHy6+/vore3ISab6qS60tIj8Xo7B61jadWIrI5JghVYK7Kvr40tW+6iomI+xcWzor72cCoq5gE2nM6ndzne3PwKXm83DkcMC1zHQHX1+Xg8dTQ371p5r6trDX19TRmTRJaWHo7bvWXHhksu1xIAystPTmZYcZXo3VlHhLvuuivZIagwvfkmvPUW3H23tTOrUkqp+NA+MjYcjnm4XIvo6PiIkpL9g7bp6fkCiE15j1Rnba4Dra1vYbcfvNv7bve2mExlhcCRyM10dPyBvr4WJk78cUyuPZzc3EpGjTqKxsanmDRpZy0yp3MhWVmljBp1VELiCFVFxalkZ1dQX/8wFRUn7TjuXw+ZOUmkta6ztfVt8vPH43I9Q0nJwRm9oZWORMbB7NmzmT17drLDUGH4xS+gshIuvDDZkSilVGbTPjI2rOLlsqMuYDCxrBGZ6vLy9iA/f/Kgm+tYI5Gx+Q+9NaKZTVfXJ9TW3kF5+UkJXYdYWXk6XV2f0tn5KQBebx9O52IqKk7dpcREKrDZchk9+myczoX09jbvON7WtoycHAcFBfHZyTbRiopmYrMV0tb2Nh5PA21ty31/RzOXJpFx8Morr/DKK68kOwwVog8/hOefhx/+EAoLkx2NUkplNu0jYyM3twq7fc6QpT5iWSMyHZSWHkFr65tBp/jGMokUySIvbxx1dQ/Q1+diwoTEjEL6+Xdf9U9pbWtbRl+fK2V2ZR2ouvp8jHHT0LBgx7HW1mXY7XMypn6izZaD3X4ora3LaGp6HjAZW9rDT5PIOLjlllu45ZZbkh2GCtEvfgF2O1wWvBauUkqpGNI+MnYcjvl0dHwUtPA9WCORInnk5o5OcGTJUVp6BL29jXR3r93luNfrobfXGZPyHn55eePxensoK5tLaemXYnbd0O49Frv9SztKfTidCxHJo7z8hITGEari4gMoKpqxo2akx9NAd/fajJnK6me3z6GjYwUNDX8nN3csxcWZPeNCk0g1oq1ZA08+Cd//PpSWJjsapZRSKnQOx3wAnM7gu7T29GwiP39iyuzWGW+lpUcC0Nq6a6kPj6ceiE15Dz//ushEj0L6ORxfp6PjQ7q7N+J0LqSsbC7Z2SVJiWU4IuKrGfkunZ2f7SiFkWlJpLUusp+mphd8NT0zY5R1MCPjXxWlBvGrX0FeHlxxRbIjUUoppcJTWLg3BQVTB53S6k8iR4rCwqlkZ1fsti7S46kDYptEVld/hwkTfsyoUV+O2TXDUVn5dQA2bfo/eno2pexUVr+qqnOALLZvf5i2tmWI5FJcnBr1LGPFbt85Ip3p6yFBk0g1gm3eDI88AhddBFVVyY5GKaWUCp/DMZ+Wltfp7W3Z7b3u7pFRI9JPRHzrIndNIt1uK4mM5XTWsrJjmTTp5phdL1wFBZMpLp7N9u2PAJLy9Qjz8qopLz+R+vq/0tLyL0pKDiIrKz/ZYcVUTk4ZhYX7YrMVUFZ2XLLDiTtNItWIdccd1terr05uHEoppVSkHI75GNPn28xjp76+dvr6XBQUjIxNdfxGjTqS7u51uN31O455PNuA2I5EpgKH43TAWouXDuteq6svwOPZRnv7vzNuKqtfTc01TJx4E1lZBckOJe60TmQc3HfffckOQQ2joQEeeADOOw/Gj092NEopNXJoHxlbdvuh5ORU4XItZvTob+04PpJqRAYKrBdZVXUG4J/OaiM3N7OmHVVWns6mTT+hsvL0ZIcSEofjNLKzy+jra87YJHLMmAuSHULCaBIZB1OnTk12CGoYv/0t9PTAtdcmOxKllBpZtI+MLZEsKipOpbHxSbxeDzZbLjCyakQGKi4+AJutYJck0u2uIze3CpGsJEcXW0VF+3DggR9QVDQ92aGExGbLo6rqbLZtuxe7fU6yw1FR0umscbBkyRKWLFmS7DDUIFpb4fe/hzPOAP2/jFJKJZb2kbHncMynv7+NlpY3dhwbaTUi/ax6fYftskOrVSMydushU0lJyWxstpxkhxGyyZN/zuzZr5GbW5nsUFSUNImMgzvuuIM7/AvuVMq55x5oa4Prrkt2JEopNfJoHxl7ZWVzsdkKcDp37tLa07MRm62QnJyR95/10tIj6Oj4iL6+dsBaE5lp6yHTVXZ2adJ2tFWxpUmkGlG6uuDOO+Gkk2D//ZMdjVJKKRW9rKxCysq+gsu1GGMMEFgjMrNr1QVj1Yv00tb2DmBNZ83L0yRSqVjSJFKNKH/+MzQ26iikUkqpzOJwzMftrqWj4yNg5NWIDGS3HwbYaG19C6+3j97ehoydzqpUsmgSqUYMjwd+/Ws44gg48shkR6OUUkrFTkXFKYDsmNLa0zOyakQGys4uobh4f1pb36K3dztgdDqrUjGmSaQaMf72N6itheuvT3YkSikVnIjUiMiTItIqIm0i8rSIhFSISETGi8jDIrJZRLpF5HMRuUVEiga0e11ETJDXlfF5KpUIublV2O1zcLkW0dvbQl9fy4jbVCdQaekRtLUtp6dnM4BOZ1UqxrTERxw88sgjyQ5BDdDfD7/8JcyeDSeemOxolFJqdyJSCCwF3MD5gAFuAV4TkZnGmM4hzi0CXgFygB8Dm4GDgZ8CU4CzBpzyMXDJgGObon+K4WkfGT8Ox3w2bPhfWlv/BYy88h6BSkuPYOvW3+JyPQugI5FKxZgmkXFQU1OT7BDUAP/8J6xZA088ASNwjwGlVHq4CJgMTDXGrAMQkY+BtVgJ32+GOPdwrGTxBGPMS75jr4lIOXC1iBQaY7oC2rcbY5bH/AlCoH1k/PiTyC1b7gagoGBkj0QCNDY+AaBrIpWKMZ3OGgcLFixgwYIFyQ5D+fT2wq23wt57w9e/nuxolFJqUPOA5f4EEsAYsxFY+dpH9wAAEqxJREFUBswf5txc39e2AcdbsPr6lPn4TPvI+Cks3JvCwmm0tLwKjOyRyLy8agoK9qK7ey0g5OaOTnZISmUUTSLj4N577+Xee+9NdhgjljHwySdw990wbx5UVMCHH1o7smZlJTs6pZQa1HRgVZDjq4F9hzn3FawRy1+JyL4iUiwixwJXAH8MMhV2f9+6y14R+VhELow6+hBpHxlfFRXzAMjKKiY7uzzJ0SSXVeoDcnIc2Gw5SY5Gqcyi01lVyvjzn2HhQpg8edfXpElQWDj0uVu3wiuvwKuvWl/r6qzje+0FZ59t1YWcNy/+z6CUUlEoB5qDHG8CyoY60RjTIyJHAE9hJZ1+fwK+P6D5v4DHgM+BUcC3gT+JyBhjzC0Dry0iFwMXA4wfH9IePyqJHI751NbeRn7+pBFZIzJQaekR1Nc/qFNZlYoDTSJVyujpgc2b4fXXoaNj1/eqq3dPLouK4F//gpdfhs8+s9pVVsJxx8HcudbXiRMT/RRKKZV4IpIPLACqgPOwNtY5BPgJ0Adc6m9rjPnJgNMXicg/gRtE5C5jzC7/Ahtj7gfuBzjooINM3B5CxYTdfig5OaMpKNgr2aEknX9dpO7MqlTsaRKpUsb3vme9jAGnEzZssF4bN+789ZtvWqU6vF7rnMJC+PKX4bvftRLH/fYDm07SVkqlp2aCjzgONkIZ6ELgaGAvY8x637F/iUgrcL+I/NEYs2KI8x8HvgrsB7wTVtQqpYhkMWvWi2Rl2ZMdStIVFEwhL288BQVTkh2KUhlHk0iVckSsEcXKSjj00N3f93isEcvmZpg5E/LyEh+jUkrFwWqsdZED7Qt8Msy5+wHNAQmk3799X/cBhkoi/XSkMQMUF89KdggpQUQ44IB3ycoqTnYoSmUcTSLj4Mknn0x2CBktN9da66iUUhlmMXC7iEw2xmwAEJGJWOU7fjTMufVAmYjsFbi7K+D/KG7rMOefA3QDK8MNOlzaR6pEysurTnYISmUknfgXBw6HA4fDkewwlFJKpZcHgE1YaxTni8g8YBFQC9znbyQiE0SkT0QC1zY+BLQDz4nI+SJyjIhcA9wOvI9VJgQROVJEnhWRC0XkOBH5uogswiov8tMgu7jGnPaRSimV/nQkMg4eeughAC644IKkxqGUUip9GGM6fWU57gQewart+Cpw5YDNbgTIIuCDYGPMJhE5DLgJuAVwYCWf9wM/N8b4VpJT5zvvZl+bXuBj4GxjzOPxe7qdtI9USqn0p0lkHGgHqZRSKhLGmM3A6cO02YSVSA48/glw5jDnrgNOiiLEqGkfqZRS6U+nsyqllFJKKaWUCpkmkUoppZRSSimlQqZJpFJKKaWUUkqpkGkSqZRSSimllFIqZLqxThw899xzyQ5BKaWUSknaRyqlVPpL+EikiNSIyJMi0ioibSLytIiMD/FcM8hr9hDnfNPXZkvsnmJohYWFFBYWJup2SimlVNrQPlIppdJfQkciRaQQWAq4gfMBg1XP6jURmRlikeOHCCi67PP5IPcbBdwF1EcacyTuueceAC677LJE3lYppZRKedpHKqVU+kv0dNaLgMnAVF+tKkTkY2AtcAnwmxCusdUYszzE+90GrMAqrjw3/HAj88QTTwDaQSqllFIDaR+plFLpL9HTWecBy/0JJIAxZiOwDJgfyxuJyOHAucD3YnldpZRSSimllBrJEp1ETgdWBTm+Gtg3xGtcKiJuEekSkaUicuTABiKSA9wP/DowYVVKKaWUUkopFZ1EJ5HlQHOQ401AWQjnPwpchjU19WKgAlgqIkcPaHctkAf8IuJIlVJKKaWUUkrtJq1KfBhjzgv49k0RWYQ1snkLcASAiOwF3AB8zRjTE+q1ReRirMSU8eND2ixWKaWUUkoppUacRCeRzQQfcRxshHJIxph2EXkWuDDg8N1YO8Au9+3OCpALiO97tzGmO8i17seaAouINIrIF+HGM5CIRHuJWHAAzmQHESeZ/Gygz5fu9PnSw4RkB5BO3n//fWcs+kdIiT4yU/4MD0afL31l8rOBPl86GbSPTHQSuRprXeRA+wKfRHFdM+BaEwielDYDvwWuHPJixlRGEUtKEZH3jDEHJTuOeMjkZwN9vnSnz6cykfaP6UOfL31l8rOBPl+mSHQSuRi4XUQmG2M2AIjIROBw4EfhXkxE7MCpwL8DDn8TyB/Q9EfAgcA3gC1hR62UUkoppZRSCkh8EvkA8H1gkYjciDWC+DOgFrjP30hEJgDrgZuNMTf7jl0NTAVeA7ZhjTZeDVQD5/jPDVZDUkQuwJrG+no8HkoppZRSSimlRoqEJpHGmE4RORa4E3gEEOBV4EpjTEdAUwGy2HX32DXA13yvUqANq77khcaYwJFItav7kx1AHGXys4E+X7rT51MqtWX6n2F9vvSVyc8G+nwZQYwxw7dSSimllFJKKaVIfJ1IpZRSSimllFJpTJPINCYi40TkdyLyjoh0iYjxbVQ0sF2+iPxaROpEpNvX/suJjzg8YTyfGeQ1O/FRh0ZEzhCRp0TkC9/PZI2I/EJESga0KxORP4mIU0Q6ReQVEdkvWXGHKpTnE5GJQ/zsRg11/WQTkRNEZKmI1IuIW0S2iMgTIrLvgHY1IvKkiLSKSJuIPC0iKV+INpTnE5GjB/nZtSQzdqX8MrmPzOT+ETK7j9T+cUc77R/TXKI31lGxtRdwJvA+8CbwlUHa/Rk4BbgG2AB8D3hRRL5kjPkoEYFGKNTnA3iIgM2ZfD6PT1gxcTWwGbgea8fg/YGbgGNEZI4xxisiAiwBJgKXY5WouQ54TURmG2NSeafhYZ8voO0vsHZuDtSeiCCjUI715/IeoBEYj7UL9HIR2c8Y84WIFGLVrHUD52NtJHYL1s9vpjGmMzmhh2TY5wto+wPgPwHf9yUsSqWGlsl9ZCb3j5DZfaT2j9o/ZgZjjL7S9AXYAn79Xay/hBMHtJnlO/6dgGPZWBsVLU72M0T7fL73DHBLsuMN89kqgxz7tu9ZjvV9P9/3/TEBbUqBJuDuZD9DDJ5vou/77yY73hg981Tf81zl+/4KoB/YK6DNJKxO5H+SHW8Mnu9o3/dzkx2bvvQV7JXJfWQm94++uDO2j9T+UfvHTHnpdNY0Znb9tGow84BeYEHAeX3A34ETRCQvTuFFLcTnS0vGmMYgh/2fVo31fZ0HbDPGvBZwXivWJ6/z4xthdEJ8vkzj8n31f9I4D1hujFnnb2CM2Yi1q3RK//wGMfD5lEppmdxHZnL/CJndR2r/CGj/mBE0icx804GNxpiuAcdXA7lYU2IywaW+ueldvrnqRyY7oAgc5fv6qe/rdGBVkHargfEiUpyQqGJn4PP5/UJE+nzrIhan+nqWQCKSJSK5IjIFa7pYPfC47+2hfn77BjmecoZ5Pr/HRKRfRFwi8rd0WNOiVICR0EdmQv8Imd1Hav9o0f4xjeiayMxXjrVOYKCmgPfT3aPAM8A2YALWupalInK8Meb1ZAYWKhEZC9wMvGKMec93uBzYFKS5/2dXBnQEeT/lDPJ8bqx/eF/CWlcwDWuNyNsicogxZmBnmoreBQ70/Xod1lSkBt/3Q/3dK0tAbLEw1PO1AncAb2DV7d0f6+f3jojsH9BOqVSW6X1k2vePkNl9pPaPu9D+MY1oEqnSnjHmvIBv3xSRRVifcN0CHJGcqELn+7R0EdY0iO8kOZyYG+z5jDF1wH8HNH1TRF7A+iTyBuDcRMYZofMAOzAZa7OEl0XkCGPMpqRGFTuDPp8x5kPgw4C2b4jIv4B/Y20mcGPCo1VK7SLd+0fI7D5S+8e0NuL7R53OmvmaCf6pjv/T1aYg76U1Y0w78CxwcLJjGY6IFGCt35gMnGB23U1uuJ9dsE/xUsowz7cbY0wt8BZp8LMDMMZ8aox51xjzOHAcUIy1SxsM/fNL+Z8dDPt8wdp/gLXrY1r8/JRihPWR6dQ/Qmb3kdo/av+Y7jSJzHyrgUm+7ZQD7Qt4sIbgM5VJdgBDEZEc4EngIOBkY8zKAU1WY60bGGhfYLMxJqWn6YTwfENJ6Z9dMMaYFqy/T/41VEP9/D5JVFyxEuT5hmwe53CUipWR2kem/N/RTO4jtX/U/jETaBKZ+ZYAOcA3/AdEJBs4C3jJGONOVmDxIiJ24FSsaQMpSURswGPAscBXjTHLgzRbDIwVkaMCzrMDp7F73aiUEuLzBTtvPNYUq5T92Q1GREZjrVtZ7zu0GDhMRCYHtJkIHE6K//yCCfJ8wdochLXVedr9/NSINaL6yHToHyGz+0jtHwHtHzOCrolMcyJyhu+X/sW9J4lII9BojHnDGPOhiCwA7vJ98rURuBSrHs85iY84PMM9n4hcjfWX8jV2bhxwNVBNaj/fH7D+0/JzoFNEDgt4b4tvWsti4B3gURG5hp2FlAW4LcHxhmvY5xORO7A+yHoHa+OAqVjP5/Wdl7JE5J/AB8DHWIvm9wZ+iLWu5Q5fsweA7wOLRORGrE8ffwbUsnvh75QSyvOJyGNY/558ALRgbRxwHbAVuDvxUSu1u0zuIzO4f4TM7iO1f9T+MTMku1ClvqJ7Yf3FC/Z6PaBNAfAbrO2He7B2lDo62bHH4vmwPnFcBjixan25sDqWQ5Id+zDPtWmIZ7spoF058BesdTldwKvArGTHH4vnA/4LqzZWs+9nVw/8DZia7PhDeL5rgfexOocurMLk97F7IfPxwFNYHU07sHBgm1R8hfJ8WB3ix1i70PVidf73A2OSHb++9OV/ZXIfman9oy/2jO0jtX/c0U77xzR/ie9hlVJKKaWUUkqpYemaSKWUUkoppZRSIdMkUimllFJKKaVUyDSJVEoppZRSSikVMk0ilVJKKaWUUkqFTJNIpZRSSimllFIh0yRSKaWUUkoppVTINIlUSkVMRF4XkU3JjkMppZRKJdo/qkynSaRSKUZEjhYRM8SrL9kxKqWUUomm/aNSqSM72QEopQb1OPBckOPeRAeilFJKpRDtH5VKMk0ilUpdHxhjHk12EEoppVSK0f5RqSTT6axKpSkRmeibvnOTiHxLRD4WkR4R2ew7ttuHRCIyU0T+KSIuX9tPROR/RSQrSNtqEblbRDaIiFtEGkTkZRE5PkjbPUTkcRFpFpEuEXlRRPYe0CbfF9caX5sWEVkpIr+O7e+MUkqpkUz7R6XiT0cilUpdhSLiCHLcY4xpC/h+HjAZ+ANQ7/v+/4AJwHf8jUTkIOANoDeg7WnAr4BZwDkBbScCy4DRwF+B94Ai4DBgLvBywP2LgH8By4HrgUnAFcAiEZlhjOn3tfsD8F++6/0G69+fKcCxIf+OKKWUUto/KpV0YoxJdgxKqQAicjTw2hBNnjXGnOrryDZirQE52Bjzge98AZ4Gvgp8yRiz3Hd8GXAocIAx5uOAtguAbwBzjTGv+o4/B5wEnGiMeXFAfDZjjNf369eBo4BrjTG3BbS5Brgt8HwRaQKWG2NOjux3Riml1Eim/aNSqUOnsyqVuu4Hjg/yumFAu5f9HSSAsT4Z8ndYXwMQkSpgDrDY30EGtP35gLblwInACwM7SN85Azcu8AJ3Dzi21Pd1SsCxVmC6iMwY5HmVUkqpUGj/qFSS6XRWpVLXWmPMKyG0+zTIsU98Xyf7vk7yfV09yPnegLZ7AQJ8GGKc24wxPQOOuXxfKwKOXQk8AqwUkQ1YnyYvAZYE6XiVUkqpwWj/qFSS6UikUipa/UO8J/5fGGMWAROB87A+iT0OWAi8LiK58QxQKaWUSgLtH1XG0iRSqfS3T5Bj+/q+bvB93ej7Oj1I22lY/xb4264DDDA7VgH6GWOajDGPGmMuwvpk9zbgSGB+rO+llFJqxNP+Uak40SRSqfR3vIgc4P/GtxnA//q+XQhgjGkA3gZOC1xz4Wt7ne/bf/raNgHPAyeJyNyBN/OdExYRyRKRUYHHfOtN/FOCysO9plJKKTUM7R+VihNdE6lU6jpARM4d5L2FAb9eASwVkT8AdVifWs4FHjHGvBPQ7gqsLczf9LWtB04FTgD+5t95zuf7WJ3q8yLyMPA+UIC1e90m4Nown6UEqBORxVgdYwPWOpRLgWastR9KKaVUKLR/VCrJNIlUKnV9y/cKZgrQ5/v1YmAN1iemU7E6oJ/5XjsYY94TkTn/394dmyAQBFEA/QVYlpFFWIA9CF5kJFwtdmIDmlmC6RqsgVw0CnKK78XHctnns8xOkiHJJn1/1Tk98A6Tby+PvVnbJKsk6/QwO6W/iveqW5Ixfc5jmWSRHujHJPvW2vWNMwH4T/IRZmZPJPyopz1YQ2ttN+vPAMCXkI/weWYiAQAAKFMiAQAAKFMiAQAAKDMTCQAAQJmbSAAAAMqUSAAAAMqUSAAAAMqUSAAAAMqUSAAAAMqUSAAAAMru1QRy2JT4BjYAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}