210101_Legoset_classif_CNN.ipynb

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  "metadata": {
    "colab": {
      "name": "ImagePreprocessing.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyPLFe5FThetWsgnp7kCxijj"
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
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    "accelerator": "GPU"
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  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "sZSpmoPA430j"
      },
      "source": [
        "# Legoset classification with Keras and Tensorflow\n",
        "\n",
        "This notebook is based on an in-class exercise at [Propulsion Academy](https://propulsion.academy/).\n",
        "\n",
        "The fine-tuning of the network was further inspired by these articles:\n",
        "- https://medium.com/@kenneth.ca95/a-guide-to-transfer-learning-with-keras-using-resnet50-a81a4a28084b\n",
        "- https://towardsdatascience.com/learning-rate-schedules-and-adaptive-learning-rate-methods-for-deep-learning-2c8f433990d1\n"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "t6MII7PCpH29"
      },
      "source": [
        "## Import libraries"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dedr_ttmpNgD",
        "outputId": "5519720a-8df7-44c0-ac5b-ea5fb9073fb1"
      },
      "source": [
        "import numpy as np\n",
        "import pandas as pd\n",
        "import matplotlib.pyplot as plt\n",
        "plt.rcParams.update({'font.size': 14})\n",
        "import seaborn as sns\n",
        "sns.set_palette(\"muted\")\n",
        "\n",
        "from datetime import datetime\n",
        "from sklearn import metrics\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "from sklearn.utils.class_weight import compute_class_weight\n",
        "\n",
        "!pip install -U tensorboard_plugin_profile\n",
        "import tensorflow as tf\n",
        "from tensorflow import keras\n",
        "from tensorflow.keras import layers\n",
        "from tensorflow.keras.models import Model, Sequential\n",
        "from tensorflow.keras.optimizers import Adam, Nadam\n",
        "\n",
        "import os\n",
        "import PIL\n",
        "\n",
        "from google.colab import drive"
      ],
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already up-to-date: tensorboard_plugin_profile in /usr/local/lib/python3.6/dist-packages (2.4.0)\n",
            "Requirement already satisfied, skipping upgrade: werkzeug>=0.11.15 in /usr/local/lib/python3.6/dist-packages (from tensorboard_plugin_profile) (1.0.1)\n",
            "Requirement already satisfied, skipping upgrade: protobuf>=3.6.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard_plugin_profile) (3.12.4)\n",
            "Requirement already satisfied, skipping upgrade: setuptools>=41.0.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard_plugin_profile) (51.0.0)\n",
            "Requirement already satisfied, skipping upgrade: six>=1.10.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard_plugin_profile) (1.15.0)\n",
            "Requirement already satisfied, skipping upgrade: gviz-api>=1.9.0 in /usr/local/lib/python3.6/dist-packages (from tensorboard_plugin_profile) (1.9.0)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wl8rG-146eEp",
        "outputId": "f50285e7-32ce-42b8-a752-295d8c15c6d6"
      },
      "source": [
        "#drive.flush_and_unmount()\n",
        "drive.mount('/gdrive', force_remount=True)"
      ],
      "execution_count": 2,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Mounted at /gdrive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 53
        },
        "id": "Gs_oVMKJ6pFf",
        "outputId": "37a33c27-a9e6-4ee4-c5e1-65b71d55448e"
      },
      "source": [
        "# locate data in the gdrive\n",
        "project_dir = \"/gdrive/MyDrive/Colab Notebooks/Legosets_classif\"\n",
        "\n",
        "images_dir = project_dir + '/images'\n",
        "%cd {project_dir} \n",
        "%pwd"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/gdrive/MyDrive/Colab Notebooks/Legosets_classif\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/gdrive/MyDrive/Colab Notebooks/Legosets_classif'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 3
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "Ei_4k7hfILri"
      },
      "source": [
        "## Import the data frame with the image names\n",
        "\n",
        "The data and images used here were scraped from ads on [Ricardo](https://www.ricardo.ch/). The corresponding code is available in one of my other repos: https://github.com/Alessine/ricardo_webscraping."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "0WBV5L5e65Qp"
      },
      "source": [
        "legosets_raw = pd.read_csv(\"lego_sets.csv\", index_col = 0)"
      ],
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "LiAeo2Opq_jW"
      },
      "source": [
        "legosets_df = legosets_raw.copy()"
      ],
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hqgqoxEB_RbI"
      },
      "source": [
        "# Basic cleaning of the data set\n",
        "legosets_df = legosets_df.drop_duplicates(subset = \"image_url\")\n",
        "legosets_df = legosets_df.dropna(axis = 0, subset = [\"title\"])"
      ],
      "execution_count": 6,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        },
        "id": "ZHLZiezJA5B5",
        "outputId": "fe82567a-182a-4899-d9c1-75809cea8897"
      },
      "source": [
        "legosets_df.head()"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
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              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>line</th>\n",
              "      <th>title</th>\n",
              "      <th>image_url</th>\n",
              "      <th>scraped_at</th>\n",
              "      <th>image_name</th>\n",
              "      <th>next_bid_auction</th>\n",
              "      <th>price_buy_now</th>\n",
              "      <th>ends_on</th>\n",
              "      <th>time_remaining</th>\n",
              "      <th>sale_type</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>starwars</td>\n",
              "      <td>Lego Star Wars Sammlung</td>\n",
              "      <td>https://img.ricardostatic.ch/t_1800x1350/pl/11...</td>\n",
              "      <td>2020-12-27 15:13:55</td>\n",
              "      <td>0_legosets.jpg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>330.0</td>\n",
              "      <td>2020-12-29 10:25:00</td>\n",
              "      <td>1 days 19:11:05</td>\n",
              "      <td>buy_now</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>starwars</td>\n",
              "      <td>Lego Star Wars 75080 AAT</td>\n",
              "      <td>https://img.ricardostatic.ch/t_1800x1350/pl/11...</td>\n",
              "      <td>2020-12-27 15:13:58</td>\n",
              "      <td>1_legosets.jpg</td>\n",
              "      <td>NaN</td>\n",
              "      <td>59.0</td>\n",
              "      <td>2021-01-03 14:40:00</td>\n",
              "      <td>6 days 23:26:02</td>\n",
              "      <td>buy_now</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>starwars</td>\n",
              "      <td>Lego SW Minifigur Biggs Darklighter</td>\n",
              "      <td>https://img.ricardostatic.ch/t_1800x1350/pl/11...</td>\n",
              "      <td>2020-12-27 15:14:02</td>\n",
              "      <td>2_legosets.jpg</td>\n",
              "      <td>2.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-12-27 20:30:00</td>\n",
              "      <td>0 days 05:15:58</td>\n",
              "      <td>auction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>starwars</td>\n",
              "      <td>Lego SW Minifigur Obi Wan Kenobi</td>\n",
              "      <td>https://img.ricardostatic.ch/t_1800x1350/pl/11...</td>\n",
              "      <td>2020-12-27 15:14:06</td>\n",
              "      <td>3_legosets.jpg</td>\n",
              "      <td>1.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-12-27 20:28:00</td>\n",
              "      <td>0 days 05:13:54</td>\n",
              "      <td>auction</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>starwars</td>\n",
              "      <td>Star Wars Battle Pack</td>\n",
              "      <td>https://img.ricardostatic.ch/t_1800x1350/pl/11...</td>\n",
              "      <td>2020-12-27 15:14:10</td>\n",
              "      <td>4_legosets.jpg</td>\n",
              "      <td>8.0</td>\n",
              "      <td>NaN</td>\n",
              "      <td>2020-12-27 16:55:00</td>\n",
              "      <td>0 days 01:40:50</td>\n",
              "      <td>auction</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "       line                                title  ...   time_remaining sale_type\n",
              "0  starwars              Lego Star Wars Sammlung  ...  1 days 19:11:05   buy_now\n",
              "1  starwars             Lego Star Wars 75080 AAT  ...  6 days 23:26:02   buy_now\n",
              "2  starwars  Lego SW Minifigur Biggs Darklighter  ...  0 days 05:15:58   auction\n",
              "3  starwars     Lego SW Minifigur Obi Wan Kenobi  ...  0 days 05:13:54   auction\n",
              "4  starwars                Star Wars Battle Pack  ...  0 days 01:40:50   auction\n",
              "\n",
              "[5 rows x 10 columns]"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 7
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "FnOtf6P-86ay",
        "outputId": "4be14d36-cc50-4e76-c5a0-14930e9e650c"
      },
      "source": [
        "len(legosets_df), legosets_df.value_counts(\"line\")"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(1051, line\n",
              " duplo       356\n",
              " starwars    354\n",
              " technic     341\n",
              " dtype: int64)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "zFYjxKyeIgIB"
      },
      "source": [
        "## Split the data into train, validation and test sets"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "YwFQg9NR-Ria",
        "outputId": "8f985ece-7bf4-491b-80e5-b25302a3029a"
      },
      "source": [
        "train_all, test = train_test_split(legosets_df, test_size=0.1)\n",
        "train, val = train_test_split(train_all, test_size=0.1)\n",
        "train.shape, val.shape, test.shape"
      ],
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((850, 10), (95, 10), (106, 10))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 404
        },
        "id": "rQJjBvaplSaq",
        "outputId": "656058ab-aa71-4d15-ea2c-76ed7410e739"
      },
      "source": [
        "fig, axs = plt.subplots(1, 3, figsize = (15, 6), sharey = True)\n",
        "\n",
        "sns.countplot(data = train.sort_values(\"line\"), x = \"line\", ax = axs[0])\n",
        "sns.countplot(data = val.sort_values(\"line\"), x = \"line\", ax = axs[1])\n",
        "sns.countplot(data = test.sort_values(\"line\"), x = \"line\", ax = axs[2])\n",
        "\n",
        "axs[0].set_title(\"Training Set\")\n",
        "axs[1].set_title(\"Validation Set\")\n",
        "axs[2].set_title(\"Test Set\");"
      ],
      "execution_count": 10,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 1080x432 with 3 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "O9Ck-cAVCy0K",
        "outputId": "21c74239-46a9-45d0-b453-5571151b4f80"
      },
      "source": [
        "x_col = 'image_name' # column in the df with local paths to images\n",
        "y_col = \"line\" # column in the df with the names of the classes\n",
        "\n",
        "print(\"'{}' column contains {} unique classes\".format(y_col, train[y_col].unique().shape[0]))"
      ],
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "'line' column contains 3 unique classes\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "yEQTmfxkIqDS"
      },
      "source": [
        "## Image preprocessing and augmentation\n",
        "\n",
        "Image augmentation can help prevent overfitting and improve the results. However, it should not distort the images so much that the classification is no longer possible. The simple options that I have chosen already showed a big improvement in the accuracy of the classifications.\n",
        "\n",
        "Additionally, the images also need to be scaled to the right input size and the pixels to values between 0 and 1."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-z0ZEktoCD-Y",
        "outputId": "51b6a9c9-fbaa-45cd-917b-6e9df4da9019"
      },
      "source": [
        "IMG_SIZE = 224\n",
        "batch_size = 50\n",
        "\n",
        "#need to pass the class names to the .flow_from_dataframe method\n",
        "train_classes = train[y_col].unique()\n",
        "train_classes = np.sort(train_classes).tolist()\n",
        "\n",
        "datagen_train = tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "                      rescale=1./255,\n",
        "                      width_shift_range=0.2,\n",
        "                      height_shift_range=0.2,\n",
        "                      #zoom_range=[0.9, 1.1],\n",
        "                      brightness_range=(0.9,1.1)\n",
        "                      )\n",
        "\n",
        "train_generator = datagen_train.flow_from_dataframe(\n",
        "    dataframe = train,\n",
        "    directory = images_dir,\n",
        "    shuffle = True,\n",
        "    x_col = x_col,\n",
        "    y_col = y_col,\n",
        "    classes = train_classes,\n",
        "    class_mode = 'sparse',       # Sparse because all the labels are saved in the same column.\n",
        "    target_size = (IMG_SIZE, IMG_SIZE), \n",
        "    batch_size = batch_size)"
      ],
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 850 validated image filenames belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "gWqNGzm9tCYD",
        "outputId": "dfce50a6-832c-4cc3-d84e-301f9d930782"
      },
      "source": [
        "train_classes"
      ],
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['duplo', 'starwars', 'technic']"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 13
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 179
        },
        "id": "CwPx5cLHj6Nk",
        "outputId": "44b7f236-2837-43a1-dce6-f99662d8042f"
      },
      "source": [
        "# Plot 5 images:\n",
        "sample_training_images, _ = next(train_generator)\n",
        "fig, axs = plt.subplots(1, 5, figsize = (15, 5))\n",
        "for i in range(0, 5):\n",
        "  axs[i].imshow(sample_training_images[i])\n",
        "  axs[i].set_xticks([])\n",
        "  axs[i].set_yticks([]);"
      ],
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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tA5a1YloFXG+e69GjR48ePXq8JHpi1eNTj48M6LqyJX4b2re1burZDSyCeOL+O3EHNYfCBU1VRoIzKyz3L2refrLiBx+c8sHpklUlrKqayd4uJycrGh8wyqKtJwkB3+7UVtYV+zua2fwE0wxZLgOV2qEyCaouGOeglcbohEQbTJrjvaf2HpONaSRjtijAwvliyf/wf34LI4L1MeSuqVbcPNohTRMa69DOYpSmUJr9G7cZXl7gpycElVHUGWa0h7VDvns38IOHH/DHf3afV/Yc//6XD/jdL99kMkpjUxmBdlWYbzeeWJXCd94/5dtvXfDorOadJ5e8c++MsmporNDUoHXCIDUkQ8XucMRsueKfff0HrKpLfv/zx3z22jEPLzwr51ASQA35o689xezNuJgtWRaeOjvmj/6q5PvvP+Dv/lbJP/qDW+wMUv7k62/wT//1mzw4cSxKoSSDkFIsznG+IZeKfVVy59oBv/Ert/m937zD48uC7z+seOu9FW+/f5eysp9cR+zRo0ePHj16fKrRE6sen2qIxHdAfBTCCzxVmzyeORYKrE9pXErjFZel4wf3z/n6dz4AM+b00nL30TnTwtOgaVwJpPjgeHy6YpDvIBIYjjVKBRKdorShKEtSExgNM+aJkBiF2dGsvML7BPFCqKFWBmsDKkRyIspQliXGGHQumGRIXTesZhVhoHE2gE5xklAVjuXjGlENQcd1XykZaapxrmCiA5/5xVd55+4jlFLMZxesHEyGGXsKGjnkW++f8viDdzgafoVf/uIdskRAZdR4guo25Bjy5sM5/83/+C95+zFYUrSyaNEoSdjdVZydXbKzd4RKDNPzFcun73DneEg6GlPOFN95Z86fffcpIZngh7ew9QqrKoq5w8waXHNKvVjh3BH7xzd4e37G0z+9z7fvLvn8q/v8q794n+++c0E+2CPYAp3WVKuSfSy7k11UInz2M69z7dYuzgX+rz9/j7cfzbh/3mCVwXvHcvHspvI9evTo0aNHjx4vRk+senzq8VGbEHTvHLm6LfnVbde34YPnbOX55ptPeO/RgrcePOXdk5qTOTTVGXqc4LzDW4f3QO3IM8NkkFFWS2yiQRSLYDFmhBiDdRayIdo0PD1bsH90G4dmVVlU2ZCUl6SDMcpniBpQB0doKopFQZqlNI0nMYY8TfAkiHcUxZJhkhEMWJVwMS+oi4LVtEKnhp3dEbspKPFMkhEhTQnJiMerBjc4YNEIRZpS2Ib5omT2ZMbhERxf3yH4iu8/rVG7M24cpIx29xBtEJp2u3PFrKo5K1bM6oA0U27uGLzZY6V20Br2Jwll6SmWJbY54WA3IR/uMZ07LmrHBQ5fC1m9xIec2iUEv2SiczLJqEPCOB1SOsvZ2RSta1wqfOOtJ3z7/VNmU/BqD1cXpH6FFsN4d8Bre4Ek3eGDk4o3Hnl+cPKEqlixXNWsmhjCmWUrJM2YFdUn3Bt79OjRo0ePHp9W9MSqR4/tPcK3ONSLSBWAVTkPF4H/4/99m/dOoSRh1YzwVQ2rKc5bgjI4rwiiGYwmDAdDvK0oy4ZhUmOyAc20BO3Id0fk6YA0TXBNw7yxrGpFEEXpB0zPzxmoAp01OL/DctlQO6GaT6mKFUfXrpHnOQTPoiiRRKEGOwwPFfP5Eu8sJI5EGyoSRuMxTgKojIV3LIpLmpNTcpOjjKERwfuGyWRIpgOjbMysUawGjmRVkV8OKKox/+u/mHHnLy+5uX/KteOMW3d2eO3OATuDFO0TqoXh2uiQBw+eUg33afaGZCpjTMoqNJTOE2zBMDVcXMBSD/ne05KigUG2Q+JKprNLXD7m1vE+51OwjefVwyNcbanHeyRiUalmPpsxvZyxMsLgYJckSclSAcnYMQmH2R5FZTGTMXMCp09rFtbgZ+dIvYy7FKqU3KQcjDISq1kFw5nvF1n16NGjR48ePV4OPbHq8enHS4QCInEnv/ULen/IjnAqGPbGQ9J8zKqYsbQl6ATvLIOdfZLUUNQWEYUPgqgEr4Q6CDobsSwqTOMpLh7jV5ekk12Gu8co5ckHCifC+ckTtM7RowOyQYYxOU4UdV1TVkvK2iEmY3x0CMOcRgQtjtwk+GqO9QEXAjoxpKnGI9jgsd5izIBV5ahmC7TxpHmCwZANRlhnGSQpOhnT1BVuVaAzsNWCTAKLumHeLPFNiXMNJ088d3cLhvcSJt97SGKE8VDTsMOyseztHbF/6LkgYbZSmMSRqYrd3R3yLKecTxnlKSOdcjabMZ/PqOrAjVf32MsH2KlDJ4ZGYLI7pFwEvBj2jvepasdyMaO2NaOBYWAGnM1LLi5W1GPNcGeCdgHrUmpZEMqnWCl5WirKRuGCQyio64YsH3Gwf4BrHL4uWFaKWuq462GPHj169OjRo8dLoCdWPT7d6LYG/2FoF1itL3uBp2rzLiooKviLb7/HvBK0yTGhQRtN5TxBG+qgiG+RgkCgcQ1NoxAl6CQFLdRNg8qHgCObHIBOSBIY5IbFasnB7ojVcsVorEnSEYtFQVNZfDlD4xAnmEGOzlJK6xgPc1IdX7rrvadpGpwoAmBM3GRDlGYwzLG2wYeAFc1AGbRJSTQgChcCVVFCpQlNw+5gQFVYqjrglMY3Ba5ecH2iWV6ecTktgOuMD7/IaOcaJ6dPmVVwuoLSOUQtIRuyn0BxuWRxVrIcT3gyOyVNU/J0yMVFwd5gwP7ta1zevctIW5ytqVxguL+HGe2T5jnzyylJknAxnTFbrdjZ2WGws4ttGnxd4p1jNEppKs/ZxTnz6SXXDo8Y7uwyvVixmq0YLubMm5ygDMZbglEk2YjJaEK9XFKXS3xxSSAh2x0gwdGjR48ePXr06PEy6IlVj7/x6Hb+W7+n6kN4WAiBAJSN519+4w1OLhJSPcAh1I3FWhvfX6U0SZoQvEcnBoWntjV1XZOYlKauCSLkk118nqPSIdY5humAoBOs0iyaJSafoNMhCQGlNcORwUz2mAxTymLFcrWiwuK8xjY1y9ICHlc3iFKICWRpxnw2xQZNOhwzHA0o53O8OOp8B+ctVWlRODJTM0kNi2WFs5H8ndYV1oMXwVcr/OyEYZ4xkTFBBZLRgKKqefuDu5xeXrBazBiMc7Iw4JX9MUuVsags3p2xvHwPOy2p3BHejMGO8JUmNZpVA5ezknR8iFpc4KYzssE+PhsyXxUs5gu889y6cRNjDLPFgtl8gVquMNpwtL/PwkOewFDXIAFfVUxPH1Gff0CmHPNVhR+OuTYSJhmUjeHB0pOlQ+aLJcrX+LpEPKTDFNHDlpT26NHj4+JlX7AtIj/8XYPh+ZMSX3v+8cqDXIn4DlvvLvypoH3Px/btN+t9Ny+Vf2HSZ+YsQvsS+NDNXyG+P5AQP9bx7rENpL1PCCBtOUIImxcytrvjhtD9da0d2vfLy5XWD136F+yi2+PThZ/WcPl5widErH54U7+kfO1eKfohP170/RPG+sWnrcei+2xfBPtS6T/O8b8mbC8Z6vGj4MXeKqCdZRzD/UN2bY0r4c37FzgxKGPQSYLWGtO+9korD0rhArgARgSl4mzlLQSvKVZLlosFp2eBwWhC0BNynVLNL+DyEUj0OCnRVLYhUQuq6QmZahheux3DBKuGejUnyxJCUCQmwWiPq5akOqAArWP3ri5PCFLjlMbawGA05PrRLlocynsuTi+oLdQhUIaArSu0tSSuRpUzdg9ukeUDdtMhTglns4KLZaBAoUvP05P7OHXAYF4xGGoSUdy4PkFdOyJwiheLckvsdIZJc/auX8cJXC5K0izF1yvqoqBeZSR5xu1rBzy5mFHXDdPZlMlkh+vH1yDAyckJy+WSy8spo9GAQZ4xGWYYDWGQURcFF5fnjHTNlz57g4PJmGt7A/LxIe8+Kbj77TcYmrgDYGMV6Izhzj4YaHzCcDj4SXa8Hj1+rrGtVD+7RnWbLCgVvemE0CrkrbKvZE2AdJffhynqoiB4BAWirrwaI97JA2r9W9p7KiUE7/HOE4Lg1/eRDSnZIi6bOl2dWTdVe2bGbdWFcOW8XJ1VwubF81maooeG+WKF92FNZJ6r7jOk5ep3CG0se2g3D+rOd+XYjm73AZxSqADiHUp5lPcoUWgFiRKUVmilsD4AnkQpGmMxKu5Ym2rBhBiVIXhsHzb9NwK9bvnR+JGJlUgceN4LyigImhA8zgveBfyWEUokILgta1EkK957RAQngdpZtNMkCsJaAAjOx8GqOwuJD3gCOAfaIESl0ROQ4LHWkiYpwdOurQlXLFsdYYqSXNZkSmeGUbKHSQfkwzH5cMCTB++xunxCtbjENSUh+Gi5kViHtYejswLBtmHoOXnrQkDH6QLQbfoozMKW1JW2fFEwSnu/DbpJIBAIEnC+IQS/mbi6W/uo1MdMw5X5SSl1RTD74Ami2jUl8YbqZRnxzzRevAHFNp5VBq4mb/vdVtslypMkit2R4dXXbvLB00usGHQ+iGuZqoKyKlAExuMhZjDCezBJiiNgbYlYR20D3gdEHCo4nAjFqkApBQLNssI3Hq/WZki8t9Si8VpzcOOYLM2xDoJ1KA2hKQloLA3T81k744IeTMjTnNoBzZKBLrGhBJ3jbIOtK7x4nlysqCSndI7ga5rlnFBXYGucEjIxrLzh0czixZCNUhbVjMLGEX5nf8z+uOHyYs7QLxmHhEb2eXo5ISQ3Mcd7jK0ibVbMZxc0dcnD+/cwRlMwpCoS9OySkASWvmSoU84uT8mTCTdv3OLi8oKz0xOWs0uuHx9zfHRAUIqT8wtm0yngCASSJEUnCShDFoQb45q/9euvMB6NOVnBn37zA57MHEfXroMxVHWNyoYQAquyQJmGSbZH9WkYAj16/ITxfCj1i85vERaRuJvolQtbj0zYeqfgJgUBB6g4F0okLiFskZhunkdQSrVEL8p70YLXcdfWgIukpCUjncbwLHGJaTtDq2pLGH1A24ROtGw8Qe1kHESQAIJC60jsgnf8wmuvoQVu3rjBv/nWd5ktVvEF61farNVj1uUJG92gPeagfUF9N8/71qfXKWIBG7q82s/gt1UglASUCEqESoFqyapRCkJAK43SAaOE1GgSrdEiKPF47yMz/lToDD16/Hj4eMRKBKX0+gWqWhskpOAdzloQQ2kdi8piGodSm4GqtMI711qqZMuVDaGuUSQYYxAlqCCIko2lKni8CErrVk+MAit4j0Lw0lpKnCMEUGKAQFABE0AFad3XkYiIEOUxW9YtpUiyYQyX2tvj4PgO1+98jsvTe5w/usvs5AHz6Qn4BsEjEglWCD62y9pysyFbXbhC8B7XCnWRrTCGVukV9YxgJro7Yt4KL1HIaa0RJfgQcC4QlIobI4QKH+KLTJ+zaHX5aokGPBGUELcC79pRQCsVyy8C3reE4pO1Tfz0LB0fLezjZhUffl1ACEEIPmC04XgkTJ+UHB9lHIw8i8WcorDMVwWNrdF40iTBVuDsihqHtQatBowGGdcPd5jNGrLccPb4CScnUxwSJ0JZYbXClxekDaRaoY0CpVAqBT3EJTmJzgBFsZxhixWDNMUkGSpJSdIMkwwolys8gdJBcX6GI3AwzvjM8SFvFimn8xrvFU/OljR1xbKyuKaBqgRbEMoFRqUkWU7wBcloH2U0Z/MZko0YaoPYmsPJgFVYgRkwzibcPtpnd6C5fX2Pu1PN9x7V2CCoZMIwNZRzx/DwNnXTUK4WNLYh+AbbNAB4Y6h1QnF5yerklHR8hCQZ1w+PGAxzphcn3Ht4n9Qk7O7ucrg35trBLstVwcVsQVXMaIqCyf51BvUFYeV4+/4Jq+Ypbz2teTIV8uGEui7wVUnjLM416CAYXyF1QTKcUJT1J9QHe/T4m4p2DhK1mdKf9cisT2zm5W1x3M2t0qVtCZeso0xo51IFPkT9gfi7m4uV1lcMitpogg/4oNsiRp3Eh9AadGNcnIhqDWBAsHH+Fr0ulxLBuaiDKFEbL9wWAaIlfoSASaJ51TYV4zQBb6nni5bcdRsnbbePPPP54gietT1XpFUtNvkIgr8SBdS2ue+ejRB9d0QdDFmHCG6Il41NrRRCsw7hjB+xfv1mP59+9NT5o/GxPVZKm2idCB5RCq1TLHUUBiHgPZSNXROqzq0lxdoAACAASURBVN2tlI/iLVgkRI8JRKeSNtLq8u2ifxMFlFIBpaM1yvtWPEoroKX19HiPDSDKEwJorWicA6UwmNaCpQhetURoE3O8dvsTSZr3sSxpmpHnOePdCQfXbnHt5meZnj3g/NH7XJw8pJyf4aol3tdrYU9nyNoEOa9jlr34VhgpHH6dxreCW3fxy+sJIy6Yj/WMgt4HISiF92CDx3lAqeidaBzOW/w6/jkQ/CaMIZKltkw+sBZ9SuG9a0OxQ/v+IcEHj/FqbTn75PDTolYfRxRsWUdfEI4R+6nmzuEO33/jkqJaYfyManpKkVxntHPIvhFeO0g5PtjlfFHyte99F+dWDA+ukx8cgYdBAgslnM5KhjuHJCuHtzaS9o5epzlaNezlgdt7KXkqNOkOS9nlcqEY6hHJKEOUYaFTnIcmQKYzsmxIYhIev/8WXgQ9GKHTFOMco4MdVuk+o6CorMUGxWK5pKlL/GKGtgXWC+iE8e41zGiCpBkUU5JsiDYOv6rY2d3BOYf4mkmiWBWOh+crTrWwtz9g31n2bh/y/sVjnFIszx7iqpLx8euIVngCST4gyzM0gUXpcA5kkLA/NuT5iLPHb7DnL9kbTzidX1CUJc4tOdidMNYDZheXzOYzzqZTtNbcvHmdqlhxcXJOCBZbZujVGctFw181MHM5C59SO8EWFcM8JxNHfXaJciWH149Rq4LBcJ+Fyaj6vSt69PhIPOvhgY3Xam00lI1m381LnZxdz1PtPNrNnUp1x3w7R65n3Hi8k5YtmZA22kLaSI9tF9f2PZVSMWLGxLw2hEuwzsX3ChIjCjpdJbI+E8lT6wGjNUq61qhrlCaEgPMuRpGsyxorFvUMi1KancmYm9eP2RuPePLkCc51837nadtuy60yxBK37ejXx7p0sS2eSd+mDWvvV3cirNOvCW2Ifn886+fRcVfVpu+MxEpFg2+aJAwGeXxePyLOVosfOW2Pnxz6UMCPxsckVtHzFIWojQNZa7TWuLWnhhgnTMCv3f3di1Y3Fg3VWnCUUoiLwiz4SBhcYtBGoZXGGI0yquUbDqUEwaFbeSLaoFRAnMIBXnkkeLQAQfAEVCu4NyKkteaE1gtBa2miM5xFUqiNJt0dMhjl7BwecXjjdS5P7jN9+gGnD99jMX2KbyoItvX+xPtoHb1Kmu2gZo+TTbjemvQgWPEx7K5bPLrl1ZK2fF6BaycH72PIJUEILrAqVlgbcF3ysGXbelGYm0SySmt92sS5x3CM+Gy2JrC/YbjyjNgyprYTvvOeg1EKoji5mDJOhfNQMhikhHyAzpd8/ouH3DzY5Qd3H5N7x4AM7YeUtWGQWX7317/IX735kP/vex/gc8+t2zepGofVngaQYMA7qqrEpopwtMPCVkwvF1TeUwehnF0yCCM8CispdXDQGiFsWaHFMxgPyIYTnM4xScIvXxswX624e1GjfU2qx4SqpqpWeNugVpeYUJNNrlEnA3ySUdUOXIXWQ5J0SFVd4EVjXaBcLhhjSUKDbTTZzoRBCmeFxRjDN9++x5v3zsjGe9y5fsDjd39AWS7RScpyegEosjRjf2dEVVtGwxGJHpGrAilrxnbJv/elVxjdusX//W5gXtaszh5y8chx/Rd/FSeK5ewCbwzWBbQRlicPWV6ccu3Gdc4f30O7QFV4UhxlsLhQxnUe+ZCDyTXqasneICFPUnaOJlw8LVg6T2N+Nvr/2sN9RVHdaI2hC0hqQ346b/NGBrSHt/VD2VLdwtb1V6q8bSGXjfCUbTnKluRvT8coo/V31h76tX4ZPf5daJJsPBJhK/TrSp50YWCt1+JDH03YRAOs22r7eyxU6DwSodu9s62LdB6ReBNR0eq/aavNc+ga1nmH1uqK0vnDSie8aP3Oh6HzPmzq9JH4qWg+6xZ8rk4dcYpQV46/6Pq11tB6RNaRIKH1rKz74dqa2f682m86D5GorXt2URrbZK5Nq0SRpCl100ADWhua2qJFEKMZ7Byhbn+OoqrJLh+jyst4f5PS1CW4miwb4L2Lu75KS9ak0wti6FxVljR1iRLNxXTGJBFWVY3dsmOu16fJhjh2Ve5CEjf9Z2tkyLZvb5NX29Jtu3dpPeEFu552JHXzPDqyGr9Ho7dCa0WSJGRZxiDPGI9HJEnyXH4vi/cf3v+R0/bo8bOEj0ms4mjTWuFcnHACAiYhKAVuawJYW6IANjHDQQR8iPG8BGjDA51rJyUlIDkmGLSOYYFpkHW8clyAr/EhplcEVEgQo1B4lBcaa1FaI2KJ4YsmetgIMWTPQ1CtNUpHZ46WTmhtQvY6pcEkilGSR4K1f8jq+msc3f4s50/ucvb4HYrpGXWxBBpCANuSk42lbkOSfPCtktNOGhInbR880k4CwbfCUUDQoKKXLXquHNZ6Aprghca6uH22j3HOcWFwJ1Y3k/d6EglhQ6q2wwjasnof2rBA38Zkf3J4Wb3gJ40XLbB+Ft2iYogk/GCUgAjL0nJweEQqmkfNiBPnOF0K/+Jbj8nME5I048u//Rl2tWLVjHnrpKZYLvjed7+Pb2rSakpROVQ6wGEwWcZQEkZ5TjYeUviK6eKS906WYDIMBzg8TmpcWOJX4CXBOk+QQFPHrdTNZIjOMsbXX6exnrryVIVlb3dCNsh4bFfYqkYEchXIE8XKeiQdIskEnw0JxsSd/6wlsZaSBOUt8+klIWm3Iq+WjFXNYVrzSCVoY7iW1zTTiuVc4/yK3FXUjcUl++we3eKsCTTKk6QJzXIJOM6fTlksl2RpjhFhlHoOU/jKb7zG73/5dZ54jXrvCbiGJFRk3rNYLBCBcnFKaR0HN1/HWUs5vyAxhjzPUa7GSkoZPMpV7OWeLM1w2oAWiovHBKXBKA5u3OCyKDldOZq6Zv9wB/xP3mXVKVIiEq3YEleRRvkgBNcpRcL5+Rnf+DffYDQa043onZ1dDvZ2ONjdZzweo5TGuYambsiyHKM01rq41qOVCSKC85YoxDtNNYA4vPMo1ZZJaZR4kEjitQa8jXLSRCObD46mtmviFJXkNsIgCM5ZtE5QOlr9fRdypHTrHYjrdAOBbs1M8NGg4ayNsl9Fj7q1YB0QGkQCidYsFwsePHrEqiiZXs7jOppubW5rzfcukirR8X5Gaw7296nriuVixt/7e/8OeT6gXAUuLpcMU+HJk4dczBw+QFVVLYH05IOUz33+NW4d7aCMJh1kWw8zhs+vQ8S35oUru7ptP//2GqVUJKgS4pz1sypAgY4OhbCRoc8a9Tri/8JNGCQQgnrB8Takvg2Ljzl0/7A2QnbPtU1JXBKwxTHXO985CLShfZv7ORdNw9LEuVq1hNoYQ7qzRz2aIHuvsv/Vf8Cd4+ucfO2fUnznz0jvfA75hd8mW14Q3v0zsnKKAGVZAQHnLEmatKRQkRiD2VfMZlOaYsXp+YzVfMajiznO+/VaMGAduqjbY+qZz2eJoYSoy8hWxYWN8dR7j9abXU59a1zojAmbvNS6bbTWKNFordDaxI0/Wi6bJmZNrOLGQfmV/P+mYWNk2hizOlzl/LIm99vnt2XBi4wN3XKFTj/dWLeeMZ6/QIV5XnJcNcpti6GN0Wwr37ZMcW3gxkD1HK5ktOUZDR0xv1qE7Wpu66ldPTojVOdQ6K7bLljw20bEsDHkXMk8OnGUdE6ebrlNWJ/viq+13hhvuiqJoJVGBHzrBPI/JKLrYxGrTnnXWqO1wTkbyYoYtE5wrnlBOEBX4a6zdQ+U1svVWg9FaKxgG481bl1ZSysQkPX6LB+5BsYLygrKOLQTgu+UkYC1HhGHKINyAVGtpWrjOV/fQ7Y6+balp9tTSNrJQisww4QsP2KwO2Fy7Sa71+4wffIB54/uspw/xdULCC56NkLYWJvaUbde19WSrGiJk87OivJxRArR7b7e5NT7djmqYEOA4BBRVHVFVS7Xna5r60jUNot1YWO56kISWD+bcPW5hbi99stul/uzjG1r34fhhSEs60NhLXy2cuRwMsQkCe/evceXPvdFpIGyHrGYNdi64XxpcD4wzh1meJ1rNyeMJEXvBv71t/+KNx/P2TEltlrg0xzvPau6RIeAVhYvmpW4uNOS92RAtVqwWC0RYhidrUvKJF4bxGMSw3AwZDAcUjnP4nJOPZ+iRWF0ilaK7z+4ZDzOaXzD3GvS0JA5y06ekyY5YeeYYDRKFFk7ArQEjK9JXUMaSubVknw0weDRvuJ4L+ULN4ZM8bzr4jqwplzgE4VdLci1ZrYquH+WIYVGpRl1OzbqaoWyCsFifElSVxgBXzsObg34nd/8ZW5ev869+5c0ZUEzLzicDLm9u8Nbi5qqLgnVnFA3JMozn08RFciGI5ZFTa4V+5OE7NAwGE949c4NLpaGb731PsvlgjQbUhUlO4e7XFjP5WWJLZYYk+G9Wa+d+ElDiawVrRC6pei0YzpKBwJcnM/54z/+f+LieFGkScre7h47kzGEwO5ODNdcrZZx7YQ27O7ssihW5PmQ4AVrPYPhkLIsonLrFSZJ6BbyEyDLMhKTkKQDCA7nNEFytNnB+yWDXHF8tMuNm/vMpie88/Y7FJXFhwwJDpPUKDyutizKAmOSuGYWCK2vxyMQNEhKZe1m46IQQ75FAo21eK8QEnwAT4rKjwjNBbY8RemEuiyZz1dUtWOxrCEYlDL4aMpDJO6cGeMZOk9Fzhc+8wqL1ZLrNweYbMhsOuPhoxmLueXWrqJZzPn6N06596SgaRxKHNiaNIUb19/jaG/A7/7O5/j13/oltG4n8K3/osK/CS3bVo63ZfCzsjxeo37G5fHVDRW6snYbVHUekm2PFTwreze6wZYjqtNs6AhTN5lK+zW0oe8bD+NmLVa8b7v2WrVLClqPpUiIa8bXRBfqugG98RaZw9sMf+fvM77+Gvb0nGUTKAuF/NIfoPWIcnCIOf5VvviFV6m/f8j5v/pDaBqMCzjnyNIkLndQGtAkScLB/h7Xrl/n0f0PWCwWnM2WrMpq0y+UwmgdSV2akaZJNIgYg7UNIJHwKCHN2vC7Tj8MgSzPWw+cxOtFsNZCiBtQBLpNkwSjFVme0zQ1zjl8dI+u21BpTZqkLYHaIk7ekSSGwWCAUpok0UhLJH+e8IlF5QRomppEpQBRNxaB9bgNhBAjk1QbOto0DWtvo2hMG/0FAWdtfB4+xLZVit29PRKTMByO0FoTXAyPjRFMcSlMkifRMN8ayDsS7sNmeUgsn2vljEKZSJo7OeSdxzkb9W4DaZrS2Ia6KJleTjmbDNnNYx+sQvTYJzrZGAFUjKSyzmGtbfX2aBQcjcex7CFgbdSThUhWZosFVePYHQ7JBhlVXZFmKWenZyyXS548ecK1gwO8dS1RijtqXlycrwnYYrlEKU1VVZRlSSuScM5xfP2YNE0ZTyYc7B8QguPp0xM62RWCZzgccnw9Bdx6PAYfSJOEo2tHFHXJ2++8Q/CeYf7hOwZ/LGLlnKUoVozGk7VVw7u4M4+0izm929qI4YpV5So754oXv7VweahtwDQNSilscIDHK4VBo1uFP4SA0lHd1QiqiQtK0ar1ZEWrStfZQuisPK3Rq5PI2+OiZcSiPOIF17Jlr2J5t0OHlRYGg4w8z5jsHnBw41UOX/kss6f3uXj8HqvLp5SrKcHVQOzUtB43aReU+uA7fx8u+JadqzV50uLjutLIktDrAWFxLoBoxAtNU+O9pRu4IHFAdINka5LuPFpxwId1E2yz/Wctqp8kPunNMD5pPLs9MLBlaQnExcCxAyVGExDuPbhkZ3LOo5OakI1QIfDKzX1CMsCjePTgEe/cy/jBvRlHOxN2BwO8GbMop3i3oKgLslShg+Zwd5eD28c4nVJXAe8VtijIJzvkWqFmZxRPHzAyGTYbkqWa4W5OGOywQmLcv1csVyW1dzR1Q7NakQ1ydo8OGUx2WNRLzueWaanwekC1nFItLwnaoIb7NMHgrCXgEAveLWF1wWA04fD4iHpxRmUdNw6OGA4UYW65fftVbt0+5BUsb3xgOSkUjXVUeKTRmHJOgqUIhmByqBw+TRgmA0xiKBZTsiz2/s/cuc2tvSEpDZ99dcKrd66xMx6zenOKLaCua8JogE5T6sU5lfVkOGT3kLqc0VQNO5MhSg+QVJNnGV+6M+ZLn7/FwbVrLNUuf/S1t1hUJSoZU9YWbTQHO2MeXxS41YJdLdgkI0mGvND89xPphypuMtPtEMZ6CkZQBNzGBKgMq1WJiFCWllVR8/DRE3xwaBTBRaWKLYUeo1E6RdAolaJ1SgjSTuwKpXQMT24t4EoSlBKMUSgxiKQENSDJjuPEbhcM8we89uoR5+dPmc/OaazgGeDtY5RUeGtxjcW1gjh4j1IaH+Lifx+EEBQ+GHwQbHsNtpXjPlrnPELwCagEVIrKL3GNJ7gZgqIqzuPiez1AJIlj1mtEDKJSkBRJEkJwSPBxq/2QUPgUDPydv/MV0nSXr791j6fnc7TyjLMJr9za4ze+NOL9s1Nq16BDQ6pLqrrgwcMlZydTvvCFO+2T6qyvMdJASyTFQZ4PQdvGi2RvaM2QPw94vvyBq+F/cY56kZwNskkjbBPJrj27c61XZovMxYOd9XZjUHTOgWrXVAWH8rotTrzO+43lN5JeHb2ZSiF7x+jPfhVuf4H86DpNNkY5z8X5JZ/7vd9kfusXOL/7iPHOmLfev8frn/8dRnvHXPzxPyEUHxC8g5DQ2JpgwOhI8rz3HF+7xmgw5uHjBzx8eJ/SWkxrUU+znPFohIgizwdorRjkA5IkYTweUVYVSWIwScJ4PCbPc/IsJ82yqJ+NRoxGY4piRV03GJOAgLVNVOxNNBhJq4QdHx9TliVFsUKp6M2rqxpjNPlgwCCLRC1NU0KAsizw1q6N7HVdY5uGslxgf554Vef5+QQQCNimxlNQ10KiFEoCmkgaXF3RNBVpPowOg7phejllNI7KeZqmkfx0HhulaaylrmuMURSrgp3JhDzPGNSDteHeu27XbIUNDXVVE0IgTVOcbaK8tA2KaKgTAec8ZVUBcUwYSQh6s+9Ba43Gec9Am7XsrZcFh/v7nJ6esf/aDZRqImnxHpoGMDR1jXce21iqssI7F4lWu1bxC5//AtY2UaL50HqaA9oY0iQjHyTUVYn3jjzLWBUrppcz9vb2oj4vwmRnB9VGGgyGA85OT7lx6xZnp2fUVc1gNGSxXFE1DTvjSRuN4XDW8cGju0x2dlpDQsN8NuXg8JDDw0MupjOOr1/nyeOn7O8foSTKj9lsxnA4ZFms2D84YHp5yWAwoG4+fGOrj0WsbFMzm56S5Xnczrhlns7arbB4tbYW8Vwo2pYFTkVPDdK6r1siVP//5L3Hk2RJfuf3cfenQkekrsqqalEtp0dgMBiIwQDYxUItjaQZzYgLeaDxQOOFfwnNeOZpbQ80ntYolsZd7i45AJYcYDGLUcBM93RPd2mZMvRTrnjw9yIzW4DoZRM9Q7hZVUaGeBH5wp/77/f7ip+1qNoRqQilfDC6UAqvGs+agHU3lTAPOEQUEqrwvuC9wqFQhODXi9Z0wCNlMIMQ3uKcoqUu+iaj987hpcD5kMjhWyJjoAs27ISN61CqFHG6RW80ZGvvJpNrLzM9fsD503usz4/R5QLrSmQramgCpWAhEXRRraVsCBnkBTRPcO+TsWyqTC4EINZhJMRCYXWOM5bWaGKzsbXUkUvI8NVESVy8houk4nJS/HdpXKEDcpkk2QQ27aYtPEZ1qSOBjBSdzoDruxP+6r1HqEEPqSIeLxXC5iRY9rZ3yfOck3nNaQ2LpKQyNV1n2R6PWU5nTLqKNIE7D96nLM5Jd24yGe7x6vUdHj97jEkyhDNUJwsmYsWrOxnrXhfrO9Db4two1uuSWle4qsRZj/GeSCniJEWkKbWKWOYlzni0hrIWmHqJys+pl+cImRHVgjiNcOUcqx1xMiBTBuVLrN/FOEu+XiHihEWlWa5WDJOEmezwkyncOcrRlWTpUpSr0ZWnN7mGqhVyfsZ8KXDjm+iiBBGTRQkiyYiTPAgIZYJF8MpLO7xxMOFgJ2VnFOGEYbYq0VaBijgrLNXzOevVDJkNcVEPFw/I8xzrBGUUM45iVvmUcax49XrKS/sj0uGYf/Wjpzw4npNlHarCQBRxeOuQMs/R65yhX/LizV0eLiXe66DV/FsffpMzXRSlNosniFay70nTmDRLWa0LhA9U6ciHddO5QJUWDR3NC9hYgzUTvLEEaI1Am+RKolSjg5WicSQNNKCW1gyWSFjwa6RQWAdFXnLv7kOkcAgZo4QMaIGtsb5u6HweEYVWE5amckrY7JGhOCeFxXvR2EeDF4H22GLHAN4bhJfgDN5YcALVvYkhJkt71IsnOBcYC0omeCGBCCkSPAovYpxMgn7GaLyIWKwKXr45QKiY7/34iMfPNd/61reJE8GPD/b5+hf2yLIIEUU4HYGXaKNRMkUqgdZzal1dSsXbyqFvQYDN+Lj19eO0SeHVnxY1/XwKWB9fh7uMMrXPaxkAYvM6IZq9l3YfshuU7nKStvm9aX0SJq3bHEsI39QMm/PbPt81dHoc2HCcNn5pP533wcApUEZj6O8Q90ZIGaG1oZydcf7gLtnhbWSWMbp2QDdOOXn/PrUpsINDtm//HqiU0//hv8WeP8NrjbU6zO80JHzrPGe1WjMeT7gZ38I6i9YG7z1ZltHrdRkOxyHJcw4lQ19CKSVSRsSxZzAcBF2flPR6veY8hop7r9djMBiwvb3NYjEHBEma4PFNcjbk2dNnSKlIkpitrW3iWFJVVWhqLxWuQa/SLAtIlLEURXmBQEiFJCBipq4xxjT/LJ/X/PvUoy1cfwbDGUNVV/jKIKMekQwtgowOLtJlWXB8fMTBtUNc7CmL/COFbCEEppkH3jvqqm6S4S61rrHWNAmcRitJXZe0Vv/GGLQ26Fqzztdsb2+h6xrvHVZrNqYlzlNrjXOeKJJUVY33Df1NCOIkJsnS4PbdoEC61lR1KOAf7O3x3rtvo53DWINQqkE5PcI48nXOuihQSlKVRSiMNeiS1pqdvX1W8znL1RxtDWmasqpqpFSMJxOyNOP+o4cURcGNG4ecz+aMxkMm4xHz6YwoifEOjAvrg/OBxZAlKVobhBTUdU2SpnSyDqrxZwBI0pjrh4dUZcnZ2TG7e7sc3rpBpCKKouT05Iyt7R2EcGRZxnq9oNfrYa1FKkWapnS7XeI45uzsjDRJPnE+fLrEymhOj57R6QwYjLaJYkUkI5wIGWiwg4aLipG7UsHy/uIxZ1sjh6ang5AEqzuPU7K5gJOwuF7g3GESexBOEKjDAXL0yNDHx4dkCydwLiQjxlgMMakME7apYYbryjm8cNDwu513SN/onD60+bnw8TfLhmj+k1KQdWO6nYjhuM/k2nW2r7/B9NkDzp7+hOX0GVQ51hRhYd8Iu5vKXSMilY3NqkM2vbmaJzoJjaW8sxZvQ+DiiTDGNA5EnotkqTEOaTJN35y0K4jMxywolzd719CQ/i6Mj3L+rz5+YcISWgoIr4milMkgoT8eMxyNiCWU+ZxBv8t0YcA5hDMMRluMtzTXr3U5na44WxXUtaXXTRiMO2SxJPI1b7xyyP2f/pTqpGRdVqzHnpdGGUwfcffZGb1en3I15aUbN7n5wgF/eRbxcBVRnaxxAqqiwJua4aCH1paIBKkiVLdDdzhiqR1FraGq0KsZvq4wVYGwOb1el/HWNdalRtcLMl8SRdAbOl650WfUGfLjo+D+WVclvcEQKwSurBhkGecy5dk04c7zNSrpYpVDVmtMbin6u/Q6Y9zpIyIp8dJhgmAQJ1LSdESCwaxWVDLleFnz7PScX7jeZZBIYgwrq1hajez1yCLHYj4nVylRU1mb64hJnJKmHc7OZpzlgnEqiDB0egP2xhFWRHz3nSP+1Z/9gNNqgC8L6rxkfOsV1oVmsayIzJIvXe9w+/V9ijsrjk5ON4WQv90RTIJCZWWzCoTLOSwMiHbz1RqjTXOdO75QWX6tP0LhUHGCkKrZNMMLrW00JkIQRQl/tJjyfieiJT5LKfj6177EL3/9S8TqUv8fAsUjuKQJfvhXH3Btd5e9gx1A8Cd/+hPe/ck9fuWX3uDN119AiIC4TmdT/sW/+Gecni7x3oXWEdYHap5zoUpLoAU4a3G4BlBQBGF9e+21yYlCyjicE+/BG2y1DEm5GaCGE4Zbr7N+/B3Wpw8BExALETUJlQKZ4EWEiNNAoYwdMkqYrQt+53d/h9OTc85OT3jrtZf46Ts7PLz3Y947eo/TJ9f52q/+Jjs72yyfr8CGYpizFV54RuMRB9d2gwOsuJJFbQppgosC1mUt0hW9xaUMzHlH29LjZ3+0CVN7ux0hUb6cWV78/W2y/lFs+JOYEyGOu9q3keZ4oZhw+fXNMUSLUAXkL9Cx3Gaxbylu2uhAa0lDuwopVXDLrQqUUvT2rjO+eQsVS+ZnC04/eI/jB4/Zu/0C/d1dSHrsf/33iKenPP9n/whdrBEiUJYEokGKQnIVNQjUzZsvkK9KOp2A4AshSOKY/qDPYrFgOBwSNfQmbYKOu65qev0uRV5grSWJExA0SZanLCuSJCFJYrQOWlqPR2tNVVYURY6QUJaS5XJJliX0O12WqyVZlobmyQjEYt6YijnqukJrjdWNVVlDAzO1RusK6/SnnzKf6/B4+9kgwdJ7TF3jEEQIkjQiSSJ0XbJcztG6ZrVaUdclIKiqckObvEwV3N3bQwhBVRboMrAQWlOcNIuD+7PTWCtxVgeE0arGldJjjKaqAoJYNwlvVVYkcYxSCl1XG32pbmiizsN6vUIK6Ha6OOfodDosl6vN2uSsxdQFQkIcJ0F67OUVZFko0bC5EozWGBfM5mIlyLKUWhtOngfdOR7iKG7ogo5ineMqTRGH5N1Yw2q9JIrCOZwvZmxtTwCo6oput4dxjlgl5Ktjjo9PmM9m9Ad9Sl7G5QAAIABJREFUlJTsbk+Ik4TlchnwbylZLlZcOzhgARRliRKSutZUXrNYzOn2ehRFwWAwIM9XzBeLDWqWpSlRHHN0fEQn63Dj5k3ufPDBJ86HT6exspb56RGD3ogkTVHRMGxakUJosan0O+8uuey1i2NbEb1YJJ1vk4yG9iKCuYS2gshapDXEIg6ceuHwMlRAnXVEUYwxJnCNYYNEhSa84dgK1aA8Fqk81oCSAdbcsPN8oGpsnANdSwlshK1No+FNMnV59RdXfuClQCaCcdKl1+0w2tlia/8a0+ePWRzdYTl9RrmeUfsi0BKQSOdDtS7UlpHOAbYxmAiUIOccUsRBV+Nc6HckFDhHVZfYhqt6ef/58EYdRKcXaqEPm1qEKiBXdrfLNML/v48rwlHCudgsGG0Lai+wIqYqSvJCc/vGPn/+4+e8/eCEZLhNZCzbPUhVRVGDSnqczRaUTjPILP20w8F2xqKeUlrH+3fucuv6AffuvMPO+Ku8enOXTqqo45h3FmvuPDnmhWGH+3dPWOfnZN0uN6/vsXcwpj5ZklcF3mqsM8QNFcYhkHGKFzFZp4uQkFcmBOBFgV1NsatzFEGMGQ2GZJMRpY8ovUZ7xWS4S5LG2ChlazzhtRt93n18j1pKjLVkUiFNja9rkq1t5gvN8dEMV67IkpRSl6S6oKsUxpQU3jIcjCAvMfUSE/fQuqaqYoRM6KRdfL2gWJxRxtv84MeP2Usgiq4TxxlOSnpJgq3n6LIkFo4ki+j3d5gXFu0gUx5rNQKL95JFXlGul0zSHoXsc//+Kd/6zh2mqwIZJSxXU9Kox2jQYz4/wdclt7cEX35lj2TSgajEVst/B7TgM5uQCCmD2U5LGxaNI1pb3LEGmrWwnbcvLub8wWxK2rACBKKhBRPWG2jcSgUKD/0eD9MulTQgFDtbY37t62/y23/v6/T6XaQAKaOQgMiAZnkhObh+wOHBHvvXtvDA0+MT3nn7Hb74pVf5g3/vGwEhILAcJpMO//gf/3fMF+ehcby1CBHWZk8IakOQ68CHhhRSBMJjoEM3jzkHKBxRSI6co+mhEZAKCaZeU1vJ9ivfwJQ55XqKEJJ0sAeAri1epaAUMsnCOislIk6pihX/y//6LymXZ/yD3/99VBzz8utf4oUbe0hnSLKMe09OSDKNUkNck6h5GaPtmsnWHi+8cGuzR4QWG+GbCevHBd3m45KqjWGF96EweFGB+/90qn22oy3wtbfDT9FWIT/m+a4x6NhYLl0pxobbF0W+NghtT8ulfcvLK68T4mIPdK7Z8Nt39e13Eq6RVgsGhLkexQihELrAPPoRtlyjlGBnvEMyu8/5nz+jNIqo1ly7ucverQOktZh1gUg7pK99jc6//ZeYh+8Rx0kw0CJ873Vdh0r9akmapQyGAwbDIft7ezjvMEZTliVKKbIsoyor8kZzs9m3naMsio1RVZkXJEmyMc3yvsQ5T5omeB9MOACiKMJ7T1mWSCXRVWiVo5RiGkWhUOAcWmuyNMVbG7SeSm5YNNaGhsptuxtrDNZUhIrPz89cdc5TlsVndCxHksRIFZFlXaraUOTrDSsoSWJGw2FDi7PUukY2WjrvHGVd4awjy9KA7jQoUbfbCawu5zZFgSROEAKWqyVKCeI4QgDb21vkec7Ozg5VWZKvc6q6YrkK0oUkjgPFW4qmnUAoNKg4whqL9Z7JZBLWQyTGzCgJyXNZ1ZTLJaPBFovliuVqRS+LiCK1Qb96vV6gH9oG1GiSPSEFw+EQhCLtpKyWa5y1FGXBepUHeqIz9LtDirLaFBbiOKHWJhQXBkM6nS5lWQIwGA45Pj2l0wnmKVEUMRqNKMsSawxRGmOdQ8kIYy1pnKDrmqPj40BPJOw7nV6XoihI04y6WjGfTbl+7YCqrhkOh8ym041OETxxFDMYDBiORuzu7H7ifPhUiZX3ntXslOMkJU5DM9Is6+JF0FiFjb+llrSOMxts6FJyRYPANFSJxhYd71A+9KGSOqBKbffv0DvL42WDeDWV09DxWzYCPt8skuBl6EtlXUgsRJt4ePBegm80YU2lV/hGjyRlq4LC+BCEXGq/sRmbvwM2dLv2d48gjgWjSYfB4EV2Dq4xO32J8+cPOHnyU1bTp+TLM4wum62n4UJKRbu9yCbR8cIivQRhcQKMB2M8MpbgHKYMnFbv2yroRxGYAIq1lAff6NA++t22iNeHX/+Zjc+JIfA3oTVePV9Xq80hZwnJ7KIQ/NM/+ynzNUTRiPX5GT+aTTFRn+Fkm6PVjH4no6s83pVBCKoFcxszX1l6SY53ksJCaiS3b7/I8bO7nJ+tuHn9gOFAoNIRD3604OT4iL19ya//+i9z+ugO13bH/OIXDumOOuw+EVjOOZ7VrF2E8xFSJqxKT9ZNUElM7S2mMNS1oVrNcbpASIeKI+JsiEh6JHHEsqoRxgAKLxPmWpH1ekjvuHPnEd5NePjoGf2DXZwHY2qSqqIyEdNkm3rlYfmUHTlDyxEuTYk7iq6IWdVLVH+AVwM6kWY6fUS0+wamrtFVjMoSrOqQ9rdI5k/RqzMW3ZT/4/vPePL8mP/0P/glOr2U1BhiZ7FYElExiFNm6wrrI9JUkSrNoixx9Yr+cERnvMO6PGK1XPPBLOPte894vCqIVUyxeMJk2KE73GN+8gzv12Qu5ys3x9y8scUjrTjNa8o65/OktQj4aFonWhJguKbPj09ImqBJCYWME+RiTuzchmwVXL0inJKhTYWxeCFJnONXXr7NP/GeulmKd3bG/Pbf+wZZJ2hDEB6l2sAsJNbOG954/SX6vR5np8eMhkN+65tf5V//0XewukYC59M5VVVx7XCX3/v93+Zbf3yHt9/9Pt6dBSRMqQ3vXarWnCEU2jzh810wcG1DCwzojbAFXtqwhssMGQ1xViNcSayGAfnKrjG59WWev//nuM4E0+kz2jpAOs35yTlxkhJn3aYpd+MYZVKePnifX//t32M02aUsa6Rb89XXRnTSGBl36GQRp2cL4niCVgnexQgX47QgzsakaYY1JjgeytCHUIQC8UeKjZdvbyrDrfNVs8G07lafZhb+bBGxPvnT+MsbaPO750Prb5OgffScXeznLUtWXErONkjVJu/aZGIX7+XDF+N8SBaChsgHtDiOiYRBnD+gPAsJv/Ue//RttjPB3GRoZ7k+jKiyCfNnP2D5p13EeIvxm19meOtV1MGrVHfexrcumUDSmMKwXIZkUQh29/fAw/bODkkScXJyghCCIi8QUlBWBfk638Q9aZpgbegV6py94vbnvG8KLZ48L/C+GxIfFxrWy0YTpaRC1zW6rkP/KSVZao1q+nvVVUWZhOPIVqPW0AQ9QXsi2uTZupAYy8tB0c/+cM6yWi0+k2MJIRqtWxpMQwTgDHhHkgQa9fb2BCEluq6wxtDpZNR1Sb5eIlVgC0ilUHGEa7z3jTYoJUmSiNWiRHhHXZekWcxqtUAIwXg0RDuNsRrnA5BQVjXaGs5Oz7DDIWkcMRoMiOMIJYPTYwBAII1TbF01bq2q0Q9pKl2Tpn0WqxXGWGbnUw7feJFYxcznC9JoEGLuphhkrcU2c8IaQ1nkOOtRsWI8GmA9pFnMnXtHbI9GGGsZTyasViusc6RxHBJ/AbPFjKos6XZ79Hs90izF2mCYYpoiw2w6Q8lQfPDO8eYX3uS7f/FdkiRrtGaSfm9IWa6JVUi8hJBsbW1hjeF8NqMsz4mThK3JmCxLODo6otI11ga3V+ccN27cpCoqYqXoZhn5ek2apty6dfMT58OnbhBclAWz6SlZp0/S6aJ2rhFJiWqqms7bzSL4YYTq8mjvDgnJhQbL2ED/0BJiJYicQzoXEjRBqFK2RhTigk4XNiULzoWmwcbgVYTwCrXJ6EA7kNahIodFBo795uqg5RlcocI1L72SW11OEv3VNZtmvQYhkJGgM8yIuocMdraZHFxndvSM6bP3OT9+SJ3PMbrCOds0D26PG1A/2WRt2llq59DG4X1MpKIGwWqcGDd/h//4c9582LZo92EqSvgDw0/nL5ycfv7HVQrKxw3/oW93oyNpFgnRVFwFEbNVwXfvLhl0Buiz97A6x0YdesMORsZYmdEdjClWc6piwd72mB0lKUREZQRlXpDFEosk62xzfHrC/rXr/NVfvc1WR7C//xrd0Yjbe4YPHk25+3TGrrvGS7tb3Lixz5lJOJ8LjuYl52tN0ulT5xW5FVS1JuskqDhGOEdpaur5ClNVSF8zSLvQ6eBNiVMp2kmKokAoQSeKsd6FRsJZRpRl6PUZVuU8OBWITkyVV6goZWt3j6oW1LnjpLD4XBNRcLjTpxoO0Z0ea93Dl0t6ScmyznDa05ERsV9SVktk1MPXGpcm1DJD4ej3hhSLU/K0Qz6vMbrix/emvPpCRGlA20BflMLjTdNoW1jSXoeqzHFGECtHr9vFCrA+YVVZfnRvyoPna4zo4Iolk04EaZfVYoUTniTW7PUMLx0OGY363D02FEai0sHnU4Ft2brN/1IIgr9Ns9hYh7OW2jpG2xN6vS6t81wt1RWqskARxSl0UkxDh1NlEdYobVGLGaLXC3JXEZKYvCpJ0ohKa37wg7eZzRf8zj/4TaIo4v/89neYL+fsbm3xa7/6dY5PjkmShK2tPt1uEsyLnGN6fsaD+4/Y2RtjvCMZ3KS/nbJa/QSzukunp4LuC4O1GqWy0LvNBtvswNAJxTbvdBNwt+UgHRAxr4JBAAkq7eFdha+WSKfpZD22v/wNZidHaCuw6wWMdzi4ediwOyNQCu0ETihUlJCw4D/8rd/ltbfe4OS0QsiIG9sx77/3LjhPWdbMl3OyyQ1QEpEOwBqcrZEqRiRjvHdYF4xFhLVhzZUBK/TQoFZXkZmL26JhU7SpwIUr46dr1v45BLceLtudhxFKAFdNKD7uerJX7r/Ktvjwfa7Zby/RKZuHXItcNkv3Zf0UXHVF2zA1Gs2haPQw3ntEY6oSCYfUJS3nXwqIheDVZMpSxcy04Jae80Tv82gd40QEjyXTD77P3q/+DqUPYntrDELIjWbKaE3UJEJlUVDkBUVZNNomT1EE2qFMg9ap2+mia01dlyEpUiGID5b/gjy/0OukaYrRwVQgSVKqokDrBu3qdrG2pKpK0jRtitKevFhTVmWI45Rs6+DUzobrj9ZhTjTGMyEpoWkd0zKUggbs58du3VrLcrn8TI4VR3GDKCrKaoXWhv5wjIrUxlWuJrhOVlWI95SSTcGqWddl0DgpqahNTRxHjCcjlFLESczz4+cc7O8xHA6o67Jxfg7rYhzFgaZpDD6KwrqDQGuDs45bL7/AerUIgEQiUZHCaUscxSjV0rxtY8hwIddBgJIRMlGUVUXSSUk7WUNJ9cFiXwrW6xW11gG1Ewmm1sGkwnukC8eWSlHkBTvb2yRKUVQVaWoDAopntVqyzkuyfndDbUySmNFohBSSWpsGYRQ8e/aMqqwwJrgrzuYz/CPQWtMf9FmtVrx8+xpPnjyl281wPugLT05PUUpydn5OWVRIKdjd22W5WBAnCUmSkCYJcTdmna+pqoqiKJien7O3u0dV1ZRlyWg0ZrX65IbWnzqxcs6yXs05fvYQFQUb5+FoC7Gximyd57iyAMLVGEVcCnjDtRoMGowH6YK7YG0dwmhEpC4QHB8gaHcp2QrHEA2v32CtQIgY74LdYxSF7BwRNxtyoNSF6CLQSVpKwObzbRbgS5teUyr+sEypzcc297efybfUQ0ESCeJBh6z7IqOda2ztvcD46B5nR/eYPb9PsZpibR0MMgCLJBBf2iqmRLjW2c9gnETXZsOF9s5eSq4ufV8+bM7CN/btPpSBP9wzIVhyujYj5OM3wJ/P8f8cZoiLebS5q4Uhwz/f3FARGB/hdI23JdcP9yiiMcPhhJwOtojQPmEw2mHS76CLFWfHlmgckWWCne2M+0c5M2up0iFRT7GfKnZ6HZRZUlSGyHj2h5KfRBHTKiapILp1yJ8+9dRnni9cj5gt55yWihcPtjkYrHn7g6dkcZc4SrDGIoqAGKS2Znc8QHa2qUtJ7QyV8ThbIYSkNiXDbIiKFCpRdAZ9tLcsV0tsXrFQjl53zGh7wPGjx4wP9hhv7XP//iOSToL0hpWesZvW7O4csE4STvI1dWG4NkpJUkc5s0TjXaI6JitWlMUUPxpDoxfMVUKxniO1JU17LFeG8XjImc/40YlH9teclwaZJSQmIa8MeQ5ZHFPXJbKTorVDooikxBqP11OEcSzrkh+981M6/S3MquSFScrhtet8+/v3qLRktLvF3jDm66/vc+3Wi2T9Ced3zyi0pJv2kZ/TdbAJHJseNNIJcIFipa2lrmuKsmQwGjCejJHyEV4I8oYapACjBIqIlTKsXUWcjsk6HbywRMYQJRJb54hOxOGNQ/q9Ed0s49v/15/z23//G3R7Xe49eMg//+f/O1988zV29nb4kz/5M/7i+9/jm7/yNX7567/Aw4dPiKKYg2v7bO30gm0vgt2dCXu7W0RS8ZMPPgBzxN7Y4cwe63JKXpwFmh/BCtq5Cu9jHEmTRLTGOk3G5zSB068IjScCncN5i3BTvNHE2RgnJK7OKfMFZX9CMjwgqhZ0Oz12toYc7o3pKsfzkxV51TRtjRJ6vT5f/MKAneExev6Eoyc1q8rztVd3eePGkCwWlEXFH3/733I0mxOnGUS9ICj3KdgOOurisQ0FzV/0FfQeZKj8++CdcKUguOlL5JriF7JBb1pU5UIL9LM9PvwZwz5y9aNfJF0bKl/7o1lnQ/PoBpndVAI/Xu+7SbhEg+/6S+/QaK7C4cNP5z+aXF2s7nKDYKVpEnpm0rquCUZxoLZmZsFEGQ47CdJrTp2mrzw1UFqBXM85/4tvsbt/g2WcQb3aIEttPKF1zWS8RVmWnJ2cUBRrjo+fE6kIrXXQtOiQkOkm2LU2iOqD7XpwjIsb7Ux7Hqx1TKczpAo9s9q5FUXRhg7prcVKNnbYLcJgrCaKIoQIFDVnPFIJsA4ZKaxrY7nANrpoCRFiB9ca4/ycDF1rnj5+8pkcazAYYL3nxRcTyqKkquuQJEWh0NXpdIgJ/c3KogDvUUqEXoKGBuGGSKUh/mvQI+cM2tToWrO9PQnfJw3SlMRYZ8mLnLzI2dnaxlvXWKmHXktZFmhyKlJIpRpDH1BKAMGgwpgKmjlZ1xVxkiCFDAiPlGTdTkDZXHjfKA4OfkmSkhdrqrrCeodxlo7MiJKUuqzQdQ0NKlbXNZ1ujyhOuHk45uT0BCrPcjlnOl0w2Ro38yigUnGsSJKI58+fByfEThchPEVRkqUZUgj29/dQUUCs1vkaIWBrezs4/mnL+dkpSsJ4NGa+mDMej4mSOJh8aB0sHQg9DIuy5trhISpJqeqQPCVpys7uLs5ZXn7lFZIo5vnzZ1hnKfL1X1tE+NSJFYCzhtVyysnT0HlbCEvW6V3pjdSOy5W2tuN3u3BeHi1lsO134Xyg1LeOJypOiHxYpFvo228obZcSoku3nA9/YKAM2OaGCPbm3qE2dABwziAscKlpIN43/aMunEXU38DP4TKaZa70zQragazbIT28TncyYbh3i9nODWbHD5k+v0dVLLC2DjC/D1b24JAWjAvnzQuQMgYTBJPtZiKg4ZWKj3ye8PNqhbQVpSPchlbxyVz4vyNjo6u6tPE2jo1eOOIsYmvY5/R0wfboGkk5pxaScrViXpXsTbaIhGada2bzgsP9fV4eGx49f85M93kyhdgZtBMsagW9PXpizqPnD9mf9Hh8toT1kOs7GbV9hsyuY5OY3At++rSkjEtiNyeRFXWZsFjXvHV7m2ePn1AoRW0FxfIMP39G2htw49oWq/mK+VJQ14A3Dfc5CPbTpIdxnjTJGAx6FMU5+WxGmVc4rdGTDl5l5OUUIwVZ1mF2doapcrZ391nUHlEtiBOH7HR58vg5KwORKXnz+nW6sWddGJ75GCl7dBNF6U2gLEQCjMZKGeB8m5JKyepkyrruIgb7fOdhTWEqZkWEVh1QHZQoQCV4Kamrku29AWlvwOLZA1yVs57NGXYSOnECUiHTDBmn9AeevYMRSX9EMlzRUQleefr9hDUD/uL9BeNuzXvvHePyKdrmqM/JwCU4+clN5Q5C+GeMoSxLyrrG2Kay3mzeQghMkiFVAi8Inr81ZJTGPHGCOpHsjwY8XikO1TbrWDBwkmd/eYTNLf/Ff/af8MUvfnGzFiZJihDwhS+8xnf/4keUVcFg2OeXfukXef/uHX7lV36Jbq/LN7/5DZbLJd1uh29+8+uAxTpNmmUkaShu7W1v8V/9l/8+67Liv/5v/iecOWQxXeFcgZChGGdNTRQnOBRhw4+xrsb5EkGMF8EpTjbCWNFA+wqJx2D0AutqRGxRboKrlhQP7pHUJ2xtjbh544Du1hhvCw72+pwvNYVzSCLiNCPLYpRfspxN+YUvv8l3fvRj5rMl5XVFlkEiIwaDLi/cusHy7gndtEMpMnQTWA66KelQh4BmU4xrvjcX6OYOgxcyUAKlgCaButz4sgn/aYNXmp40nwqF+lxzsLYa9Um/t+MCeWoLW62Gpx2tYy/QJKsf3teaeAGCBKEFrz5U0N0UzpoK6Idp35sYQDYouJTEWaeJVcLzMul4IzlBxJ6+ssSuRLk5DsUNccxu4nlsd3lo+wghsKs5i7SPNgZpQk8jrTVKagQCKyWr1Zo4jpCN7mq9Xm0oek4pjDas12viOKaqysZ6O6Be6/V648TXara899S6RkBjt31B7fLekecrsk5Gka8xOjT3Fd5TNwGw946yzENAHymiprG1AIy+cPtrctFg9w2hBQPgf840VrWuefz48WdyrMPDG9R1zeHh9bafO7PpOTt7e0Gr1tAnrTGNWUgWYkRrsUazKgq6veA455ynrktUu/43MV0ALgzGhL2h0+ngnKUocvL1GjMcIkRAbWqr0UaH/oNJxDpfoxuk1Dk2c6fb7bFcFU1/NEIM7zxeBL3Ver2i0+uzXM3J1znalFjvqGpPXZugsdMmJNUxdLJOmANSYoE0jpprzqGiiPPzc5ariLIsiZUM7Q4EDZPKcePaNQqjQ+uAsiLLOnS7HZSUaK0ZDUdNUhQcipGqQWkNq+WKqtZoXTOejLHacP3gGsv1ivVqxWg0Yms8wRrDZDxmsneNH//wLzHGEkcR0/Nznjw95uYLt1jNpyRJSq/b5fTkhE6acvfuXXStiVRoV/DX9bj8d0qsIMCoi+UU+ewBFs/O3gHdSMHGme9yRahdQNvGdHCBVrUXYmOZKsBKQdTc31b93GbTaYffUOBaDnDb1O/ycQMlwDXuSn6TmOGCSFM6j5MO2SRt3rtNE0EJNNaDtJU368KeeOmtLn+kj0ksLx5rHxKAUJJuv0uSHtIfDBnv3aC/dY3584dMzx5hqxVWl00LS4/GYb1DuwDNeh/hdAnOfuTNfHO7pUOI9uw2n89fEuriCSJ30ZwX4fAb/dlnK9z/We5j9XG0x7DpQvvNeSE4my558uSU50cr9KTPycmazm6fSGjmzx6jT58Sx5LxtReQImE2L8knHXZuHpI4w5Onp+Q2phMrdG1456cPOZgonp3M2bl2g/cfHMFAcOt6h5tbXR6eGY6mFa9tC750Y5fv3Dnl8aM5rx5OOJueM52ecf/xmpdfeZ0fvnefxXJG4gt6rMhUxpdfv8WDu/c4vrsgSjMGoxFeSNaLBUoqClNjvCOjT11r7GxK/fwp0jhUmjFfSdbuhLyo6A97zKbnRFFCFMX0+wPOn5+iqgW2P+DpSnE+L1DKMEksnUHKi7sjpq7i+HFNjQRrGHYgywTniyVUKgTKmSAZjvBmTX9kWBQlxJqzVcT33jvCyj4mGmO8xBDRHY1ZL2c4BZ1EUFcWpVd4q6mNYqUTvIzIel1EnJBri+gOeXuVkK4sYusWUgi0rbm3qnn4kxl+dcKr+32ePT6hms0xSXrREPNveVwxNXCB5myMoa6qxu3JNtrKoLlQTUX6xDvO8NwZ95D/8R/yJz/8t3zzN/8jhv0BUgqud3f44J0/4/DGbebWcZr89+y8A8V6TRILVKyQItlQrA72r9Hvj7h39z6vv/E6HtgabfG1r34VKRRSakaTIUIIYuF4+Ogxuv4ai8WawWhIJ03Y399FCM/jZ0dYvUZrQW/nV6mW9zDlM5T0GGfo91JmC413Ecio8aVoaICqD2i8L2mlHB6DkBHey9Aaw5VgzjBLEB1467UOz2RGf5Ay7jpKneO7AyyCTndI7nWg0cQx3Uzx9O67/MM//ArzxRLX2WY/S3j66B7s7TA9LTk42EUQmqyOBl2ky6iNY2t7QKIE1foOzm0BUUMlF5t9L4ApHo8F2fZputgwWr1P+yzXFAIDNe1nd938+PFxwfXHbZY0BdZwHsRFhLV5/JOKfB+h+fmWBXO1gNgmdh91FrxkFNIeybV7oSSKsyvxi/GQUbAlVngvQcWIao5ymt2oohQpD/xe+DtESNBW58eobp96NWsoq2C8RklFkiSslvOAqtkBpq5ZLpbEcRQSnMatrihyiqJtpuqo6zq4A2p9yQnONIZboa9lp9NB65okSYMpmNEYH9g8dZmHz6ED2uWswRpLHCms1eH8O4skAiWoaoMgxDyRaplCF02WEUHTLlSDBv4cFWWzLOO11177TI41Gg45PjkJTX6jCF0ZFqslgeUSERDHoCmFQNkUBAZYXVfUVQUCJlsToii4YnupwjrvwlrQ7/eoqwKj9aY5s7Ea7y4auButsU2hoGr2CpulzOdz6qpkNBgCIZm21tIfDKhqTVWWITGvqlCsiyOSJGa9FoxGI7y3FEVBsVrT7fZ4dP8+g3SXNL7QVwEYa0jiZKPpp4m3nXeslkvKsuD8PEcKwWQ8JE1T3nj9NU5PTsnXa7rdARAMOk5OTtjZ2WE+XzAcDjDW0sm6FGUJhJwgJIrBBEkKwcH+Puv1CmPNQVloAAAgAElEQVRDwqm1RgjBzZs3MTage9ZYDvb3WRU5Z2fn7O4EOr13nlduv8wqz0mzDAj9wNI05eT4hOPjYzpZh1deeYXTs1PSJP3E+fCpE6ugOwoLmdGa5ew8GEMYzc7WDtmku3n8IlgVGxTnIn71V4L7y/w6IZqanRcBrmvthUWb1FwYNXjnrgYhTeXFN6hUWxH01qJlcE8TSjRNLe1m+ZZCIbBIJTdW8O3iEVz1LpKkxihwQwH88Pjw0iI+VHD0l24IqYi7XcbxIZ3+iK3dQwbHhyxPnzA/eUxdzILFprVo48BJrPAYoKoLTGMXLxGNNuoiKJPygsgkL010fKBIiGaDbymWIeuSbOTx/udnkfzrxt9EK/bJz2m3bgEiYTrVTBcFg0GCFIbnszmTtE+mp8j8mO29CbVPOXp8h16nx7WdfbzqcrZcBbOSWCEcyGqFt447Dx5xL5GM/Yp1oZmfL6jMMXcejXj91a9wvvhLVrXinfee8qu/fEhizpivI2azmi+/esi/+eHb3C/gK1874MX9Me+dP2W7D68fHuC729y+tcN2Znn7p9/j+uE2Sy+ZLUuMdeh8TW/UY7QzwRGxWq3Q8ymiXtBREZ2sQ+6AusbrkvHOFstZTr4u6UwmrCqDXs8Z+Aptx9y/8xDZGVKaiu7eNZ6sYoywDEdb7M2PKWwXaXfZGiRMq4JpcQRaoXoOF6W4LMMITzpQxMUZenGELgVTN2fvxQn59DGyypFREihj1hE5w/rZfSoyduIaE2es6gSSEV5A7iVCh4KOrTW5jUk8WCmJfNiUtE9JvCSSQ+5MNSurUCqlk3Ux8efVcsBvrmPrHFrX6KoKGy6h3uNCYx62tiZEUUxda06c42wE8y/tM4peJZ506Iy+wvHZOS+++CJJIvnF3/jPidEQpextz3j/H73D0dExHkcch81CSIHzgu9+94e8++77/OZv/ALGhmrrfDbj/oP79McD/viPvs3J6TF/8A9/h9dev829e/eoNMxmOd//3rv8xm8Fd0GEbAwpSor1FMUE/G5DB3yEUpJ1ud5oXgLvXgARQkRNkUei4j64wLP3PricGRsICSpSRLFkOBBsZadEfsL+dkacSKSvKBeWuvLcun2TYVlTEZzfpFKMxinf/PJvcHhryHj7gIP7d7jVGzKOh+jKsljMOD9fgPd0spitrQ5C91jnNS/cGPHBT5/g18d4P8J7tUmohG8t1i8QqVbHgxANEkfD7LjoYdiyJS4ClJ/lslQYvoGLrmqqPu7+K6/iAmq6+PXyU8N1ILjs8ndll73EyLiiG750QC/8xqmyfU6LFG7YCc0xVZQE97b2OM13VIg+0pzjki5SLxCtXbarKdQ2C581aI5HekcncWy/+jofnDxutOPhva21lGWJc444jtEmo641dV0hVUgQ8zwEhS1qHUUKY/xGV1WUBa2bYZIkgWrYxDN1VSEkeGOpGz16QLYl1gZ6YVuwxgdduq41IQlQIDzOGLQLAbrHNyYsF3FQe3olwanU28YT+lIh8md9JHHMjZuHn9mxEKFhrfeefJ0zn03R+jr40LeqyHMWywXOabrdNFD6mqJ2msSBjmkCHbPX7RDq/wZrQw8rKQN10Nng6pp1OliTUJZBk1eUJcITqHRKXVCMmyRLa02lK6yLgmGLc+E7Ew7vLMJ7qrKkrCrG4wnOhkTJ+YC4xlHMan7OrRdu8fjBw2DuIxszFhf+rYuCStfB4VsphIBaa0SkSbKILMsYjcZYozE69E1ztmm7YT11Ffp8Su/YGo2Ioojj4xOGwyFSShaLBbPZjPFohDWGvf19FstlKCYYzWw+ZblcMRwO6XS7OG9x3nF6doazwcAljiLKoiRfr3jzrTfY3tlhNZ8irMU7zfnZMb1ul9msZHdnh6IoWS5XvPLqq4yGI7xzzOcLXn75pU+cD58qsZJSEEVxUyXRm4t4vZwTRQm9NIFx5xNeHbD40PjvQ5C+bOtVDdTcLBhOCJyTIbBwFonavLalyrTP3yRQDkR0mSbYBFXOEbXBSjMJnHAI1yRZjWYrWJsLnA2CTKEuqAyXKX6NGSHOh8bBmzXlY1Cry7+2r2+Pt9kMhCRJOojJAVG3x3j7OoPxdc6P75PPj8hXM7xveK4yRggwVjc6g5bWd3XzDnq0izfcfLTNxgJSeJz1Gzv2TYUPEB/xDvx/Oz6fBffTOhx+NADweEIy2u30GQ1S+r0OLx3u8p07z3FSYaqKyFekbsHhaMCJGTCrHQ/Pl2Q9Q085RDxhbrdw/hmYCkyNNJok65PFI6zxJL5irVe8/6DklhswGnSZPplypBQ/fO89trZ7PJop7j2b89brA4appypr7t75CV+4/RLL820O9gb88pdf4if3j3n+9BHv/OAHlIsZs+OIqexSlxpnDP1+l/HWmKouqXSNrkq8t3R7Gd1+j6J2iDimyldEUcRyNkUQ+lGMu0NOzhb4csF42KdWMUIvcZHG+BjR2eXRzPD2ux/wa1/9KlmccL5c47VjL+vhVgVKaKRZoXSKXghseoAjxtuSybDHfDojsZLt3R1cscKePWFre4wYjalrQy+OqUpJvVxS25zd633ifp/p85R1vgYRijHeWWQkEbokEgtEkmIMmKpEdjr0e30wFcu8hG4XMb4JasGt6wOePog/u4n4KYaSQWcaNtvQlyRschdzuV0vq7IMhgxesE47LJMeX/nD3+XUTRhkM3b3tzk6OUEKz+nZFME50/MZb731JkqlYCRx1uXtt99HqghtgiuSdZ7/+Z/+b6zzNctVxbe//T0e3n9MURQ8ePiUvJL8mz9/m/nslFdfeZ293T3efOMtfvz2Hc7Pcv7Hf/ItJlu7vPHm7WAdHfVQwuPQwQJedRHdA3S5Io4tJu7h6yOEq0EkpL0XsPE2EZqqOsfkx8io3+hODQJFv99nvV6TdhNGgz69Xo9+v8+LN/fppDHrCuraklc52qQUOkfXjoOdGEtMZUPAMBnGHO5FvPfeu7z5xYxHb/9rdt+6jcbiHSRpj/Ggi1CKzmiX0/GAJO9RlDGjrqfOC24OQwP6MAL9ZfNtidAPSVxej33Tb9C3dAa70QQ1h9isxu5T4P2fXwrWMjsuKoltMbUNxi8XW69ulh/ao/ikdftij2r4g1cf/chrwmdpi6Atj2WDh/lNrRGaGCJKIwQOISNwHiVhP67ouRXeGmR1jrIV0gcNpLCGvqq4leW8v+pSNzpl6yzn07MGbWo0c7Zt2N30tBKiEcE71utVY2oQelYZYzYaqtZmOo7jkHSZgFpIFYEINCtf26bJskfJOKAZtQtOc0piNERxuO1scDoUEpyxtLprZ0wI3gFvg4GXkoILLXp7IhtbdyFwQiCaZuQ/68n/5aGiiK3J5DM5lvMOgSRNUqq6Ik6CmYTROhguNPGqFAIVBZdAj0dKRZ7nLOYLpJL0h33wPtDbjMU3PUqdsbTCEGuDOY7RwZK9rmuMDRTxJIpIkgTVFNXDe4b3zbKMwWAQ3CabMoI2IXlvI2+tDWVV0u120aagKkuqumqe4ynWS7JJn+n5OdcnXfDRpvhjnSXP88YBcYfFfI7J12He+xBXJ0lKJ8uwNmJWFZiy4PHT53Q6HQaDHmVZkFcl3V6P2nmOj4+5ceMmUqoNeyQw2FxDVw/JlrOOJA3915RUwcCFoO0SQlLmBTs7O7S031prhsMBQkacnp/x8O4HvHDrFv1+DykFtdbUVU1da7q9LkYHVHi1WnHv3j2SJKbWn9y37VMlVkJIsixF61DdaBcxZwyuLrC2bHRBMVeJb9Byqi8djXYRFlytULmGUxoSYYFxoDb6XQGXm5IJ0TT1bRb0Df0C2kXV+4B44QLH3TmH8A5H6JtlvUV4hyQI8GLZpmRttVFdSYban5f/nsuypvb+dlNx7Vm4dP/mtS37od1fpSRLU2K1RdbtMdrZZ3F+xNnRfc7OnrNezBEyxWhLXVYNDNp22wkLm5KygeVpDDBEc/z28mlQv/Y7FO1HuZRIXbr/78K4XO388BANIRM0h3tb3N7t81cfHLG/vYMjxqEYjEPw389i9g66vDzaQckRhTU8OTnl3oMltUgY7m0z2NpDpT3Ozo9xiw7OKvLBTVx3j9H4MXMjWFSOd9/5EaPtXTAr4u0XMGnCwXaXBydHVNmA0sDB4S3u/t/UvdmzJEd25vdz99hyX+5etxZUAagCqtEr0S0uIjk2NNI0xuGQHJKSjfQm/QN6kuldD/Ogd5keRiYzSQ+akZlGRtNGGjWa6W729Mruxo5CodZbdfebe+zurgePyMyqBtjEEGoQblZ1782MjIyMiDzu53zf+b4Hh5zPx8wI0YPL3F2UtD8coUsYGsHR8YwwKJgcHlGECg9Fa3OP9uaQeZygjStqFIsZIi2QjSbtravMTyfOVDhdsHXlOrnOmU0T/MAFMFkWhCYjbG6gjU+js8F4PKHX6XJyMSHODJ6NOJ6MORvnZNMFeVYy8/qk5hDyjL2OprPhcf9CU84WFMmMvNQEW112+gPCMCQtcs7OxzQHe9ggwlMNul2PydjD623i+dtEcULY84k6HvIkhiLHx6JMCTpFm4Amlg1fEzUbJHGJlYYz6ZMjwA/J8SlS6LfbZJFga9jhzPtsEquaNlLqsmpGthi7qqI7fQeLNHDz5Zf57vffZjyeUAqfk5nljR8mXPmlgCxN+ea//iavvPaFJdI1Hk95fHDI5vYmzfGcrhGcn51zcXLoREB8xfGha9JdjM/Y3Yr4zre/zfbWJsdPn3Bld8D7777Lv/zTb3Hrxet89Uu3eHT/PvPxmIcPH3JwPOWtdw/ZGjYIPUGWuF6SRw/vM5vPHX1Zn6MaV1GqQVYWqGYHr7lLEL1HMr6HMQHR5lcxsomhpKMM8dFb+MLSaQ2Zje+DFXgCXr22Qa8VIqSPFzZpthts9AOsaDBdTEnTgtFcMy1KCiF4fDjnxkuXuHJZkSQljTCgF1l6nYxQbvPdb36fO29+n0d336QRRc5rRmteu32bK5f26Pd6RO0Yv7uJtiHNSDHoBfzmr/wSjTBbi7MOibHSUTmd2T0OfVoWBd3fxtZJSR3Jq/+MQX9ulqviI37/GJRpbaynSsAaPXDtlcuXPr/flccky8R1fVO73LxO7OpC7McduTWVJYl1xtuR8rgezGjmZ2AylDFrtHqLxODrmGsccMB1CsJlkTNOXLFKm6pHqWKB6NIsF5x10pXlGXmRuXlY2AplUA49sxalPPLCUpRFRRdzPX1RI0LkUGTOfJZatAKqonGJ1rJCmxRGl1hrKUqBEu4YRFU09pRaoqPOI6tKBqWzdKFSJ60X06YucBvj0LbP0ZpBAEr9O3fDPDOUEJSBpixzhITAV2xtb6LkyrxXl4VTX1QKT0mUBCUk7VaLi/NzykxT5BpCQRA4obUSS55mrpBeup4mcNYNQRAA4ElFs9kkiZ1vVe2f14oaCG0wpkQGAUo59FMKgfAkZWmX9HJX5LFIYRkMB2itieM5RVGQzOMqFBmUlEuBk1pYqUZ+tdaVeW8T33MJV5IVTsXXWAIvoNSW89NTRvMZrSiiLEq2dnfxfQ9dGSK3Wi2HjpkFvuexvbPN8ckZo9GETqfFrduvMBmNmY4njM/PGfT6xElMUbi2hqvXXmA8Hrt7vCiQUtHpdmi2XL9UjU5b20QbQ7qY0e10q35qS6fVxiII/YiLizE7O1scPnmC1q73+OqVK0glieP4Y++HT04FFBJfSlTgOadl6RMon1arSaPhlHTAmTWuqGRV1Ug49b31uFeLVaxXsuqm0RrGrAOi1hqlvEpAQjzzmhr2VMvESizp2jWypA2O/ywrJKc+MruSuV3S9qrgUns+1W9VMTiw1i1S6seqPHE5ngnUtqIOrhDT5f5XNMYqwAkwSBAS5fs0O328MCRodQh725weH1CUhjQvXaM0VUJUJUL153LiF26/pkosn495y/MnLM+PlSHjpzf+LsfcZ3sBn3sOAIkSkijwaDY90kVOPC/dJIMTJFkEAWEY0Wt7bA8Um902kYq5uLzHnZ1t7jye8P6HH9Do7TF88QuEzTb3Dg5IywQjG8y1x9bmNmePT5j5l+m2t7j9yhXQcw4nF5zJDW5c3+DKYM7dmeLOwwtuXd1iexeOxxPeeHCK8UN0avjmOydsBSWXt9sM2k3SImORJHjW0rp0nWCww3yhSYoCzwvIkxlZHNMQilKEXMwLEiMopgmdfoc0jpGNFrpc0O6EDjrPY5phAFim5xcoGRJJyVavxYOzc6xosNFp8fj0nMUkR6cZjW6bpLTkaUzfy3lhq83e5QHa07x75z6tRkQqW8RBH+t7jBZT0mSOFZKYgCDcZDLLEWGODJpYPJIsRuBxZyJpFCC9gIGvXHIlJM3Ax0rNZiPil6522ei3sAoyo/nWh4b3Tg3WD/CUIk+mJHkTL4p4cHzuEstf9LAsUXSjq14q6ni4QgLqm7Pb7aKUq4JjDKm1PD0q2MlKBr0hW9u79LtDJqMRs+mM0A/YGG5wdnxA+KO7XIt6/HiR8h/+ye/ieR4f3r/PlX/4OzSbEW+++T6vvvoib75zF13k/JN/8gd4vkdRlPzT//p/YGNjyO/9o99yfV5S8u/98peYzAr+2X//L/mDf/jr3L59nYptzMnpE7JsAbagjA8IGpcQKkKalHQyQcyOKDwogxdpDm+C34VsgsH5G7Y7fdBz2v1LDDs+UXHMS9sB/aZkmpaUykc2PPxAORqOTTBW4vkKYwvSOCWxYM9ntIcLblzucbkfsNFvovOYp4dP+bff+hbf++53ODk9pihzZFVVBsnDxx86VO7Wa3zl17fZ6knmuWSRWvx2l27HYkmxlVWHxS5ja02HW17Hus+1vp52ZTlSUwHd6+yy1/XzZH+x7C1b/v38Fitk76P7cp5Hoj7qPVgWZlcJ1tr/q4yrfgXAMhFYUi6rndXJULGIsdpgq2ppkhU8pEXkD/B1unad3F4L4ROrPg/zHrmRVbJiUUIxGPSYV3Q8XbUVrD5ThZApSVEY8jxDSUleUQCllBRl7haltXeVdL15ZVXcxlqKNMOqsvJPcxLtuV21AnieAmy1wDdLVMwUuftu1epm0ok0CdxaS1oJqkKpjAUhEdZU56lql7BOz1kK6577vNQAcOffyYv/7YcQzoNKSoUAGo0W21vbBGGFTllBXlErnTS9i9dJMidN4ypRUmAdLVMErqBnSqfQ2241K0DAGdtaDHleUJYOvfKUhx/4mKIgDALyJCGJF0ShTy1SJqVXSe+XCOuADlNqBx5U62YLNBuNlW+stWR5ynw2d8m21URRWMV053dWf34sbG1vo5RiNpu5oqC1FKVBKcNkMiZJC6yUlIVGe4Z4EbN/5QpgmWUZge8TNhqUpcaTkka3w9nJEaenFxyfnHD7lZtsbm3x+NFjdw9bQ6fbJSty0izD8xTn5+ecnp45FKzbdoWF0lSok8ZTio2NDZI0JfJD9nZ2GE/GxElCmmb0qkTNWkF/MKAoSsaTOTuNBkpJ2u02p2enhGHwsffDJ0SsBFEjRMkGyvcdTOwpojCi1WrR7XYq0zu7RuxbvdY9Xo+6QrcyTXSL+aqpd53jzKrfYJ3qVu14SQt0xmey8gmpgqapPKmqSc2IlY9IXcVy+zVLgQyHTyikcE2H9fG5yWE9MK4mDLv2zPMIXL2trZI7oKo4OwENYwy2dBUth0Ct87vB833a3T5ho8FguMNkOmY0OieZjdFFQZFnVUB0b1wfi6jfZ+1K1OdPrh2gqZQt6kT3I2exT2F8JjFXfPIk8WcMki1gXXASaIosJUkKpouMEuUSduXMQKX0aSmPJFf86N6M29faNDsRMjjn9S/fgnjGG/cfMVlkNLstTB7jN5sYLPcfPKZj5oRKcGoViBbj+YKvfPE2J9/+HmnS5Fv/71/ia4tpXCWVAW+9+Q7dQRcPnzwtUbpAxzNMbhA++K0GV1+4xCQb0W72WARdZH+Ts/NzbCnxmk1MkZHPp4g8QUURqREUSUm6iGkGIVGzjc4Mi1kMukSnc7yoSZ7PaQ5bHJ2eI/weyXTB5taQi9NTWlYx0hmJDsmenBN2O+RZwvZgn9F4iswXXGkbbu4PuHR1yGZvxoMHObE/xA8aWO2TlJpkGuPpAj+NscJyUeSkIkQWEQ3PKVpqbYn8iIkImGcQhDj1ImGdKW6jgVSaNIwI+11+5bVtPD8nNZpxmXD3+JBCNfHDgCgtsckEmkPmhSLJPoPECovVTnKXNRGDVVJVF0QESEfHdh4qAu1Z/EGH1762Q2/YYDqy7F/a4Mm9N2j1BmSLEzavdLl29Rb33/kzNk/HTLOIJ7Nz4iTl8uVd9vf22N3bJgh8vvzlLzAcdvnz/+c7NALJpUt7ZHlOp9Pm3//V1zHa0G23abTaTEYzoqhFoyXY6AYM+g38QBKnOY8fP8HifHKsMRhTMDv5IV60jy5GSEqsNpisJNy4gfBCdHKELWaEUZemp/HyIyRjNmTOl65v0CtnlMWYMJJcvRxiAp+z2ON8UrC9fY27D55ihEeaSbLcIKyTG97rWwb+mKNHp7x3es5Ll3uI9JT33/o+b7z1V0wuzpb+gRVohPIE52fH+IFPiqSxsU/oe4xTzfGkxKoQyfhZ2wtbkfis64lYsjxExQ1YZ12YlS0G9Wvr5MrYZxgHf4Pb5zMabuZ5NgF8Bovi2Vly/e/1fXzUB1jftl4TuP2s5rt1GuKz+12nIdb9RfU/AZW6v7vm6AxhNZhK687C01iw3+vQ4bhCH1f7l75PZgMeZy1KC6JSF0QXnB89QucZdQuEMZXZ9fKUVH0u9RqlEtYCRwNWlcBGnroFrFIetpLTFgLwnDiFfaZvypkcW1PP5y6G1LLoUnpY46TSlRAY42KclAJtq54sIaqFvFj+ba1xvVZSoCrUyiVXrh3Cq9cgn6PxSdsEPm7keY6paJ61tHmz2SQvSyoQkLJwdD0pWIo9ZFnK6dkpSZLTbDaWxYI8y/A8lxSVZekoe0I682ocY6H2dPJ85dav2iAqLzJrDWVZIKwliiKarSZR1EAKwWQ8WibGTvhEO6VSWxdy3H3TbDah6vXTZQlSki5iurrEC/zqPZyxdi2k1B86i43zs1OSOCGMIiaj0TLB6/S6TGcL0iSlHUWUZcnDBw9otpqEge8QtNmMqBE5EQzrVsPXX7iGwCkDTsZjkjim3+2RJClpmrK5uYU1Ds2NwtCZNVfWBMZqSut8wzY2hkzGY8rS0RY9z8P3fMaTMb70KPICrWeMxmO6vR5lUbBYxOzu7lEa55t18OSAK1euMJt+vAfaJ0qsPE8x3OijpEIIifRkpZoROcQqip5ZxK6kVNcnDYMwgJA4pUXrYMXlN7JOuKx73kJpnOmiqOiA9SLCIUwuKdJaO3dwxFJ5cFn4s65WWBvY1RSN9Xe0QmARTlrdsgxyspI3XaJga2uanzeWx1C9SS2yZ4y7WVYJnQukbp6oq2jG9YfUByodN7fRauEHAVEY4HmCs6MjRucnpIuZowbYCkWpJ+3nkKpnlZDc5VHV+TCCJdS8un6f//HzEquPXLQ+uwVUqKa0xlHnQomIFNZ3qMnFxSlxklLqLgaPDx+P+Omhx2CzTzE+40cfJIThU1545ToLXVDECeP5hGG3SWNjgyeLE55enNJRmr0ru1yWJeN8ylt3Zrxyuce1S7t8cP8h89EZftAm2m4jogEmHLK1u0NjLjmZTUjPD8FarvRaBM0Oo0RxeLrAqgZB1CMRbaYXC0xh8HwfyhyTLpDZjNCTlEVB2O24/qMyJRxukccFQviUeYZNFqTpjObOHkZZSjyMdPRg60lU1GAxnaGsxvcC8KCwTlJWNRoI5WGzCS2heXG7ze7uFt3tXeYqYOuFFzmcKjylKBcTjC0IhGZjOIA8oCMTRvGMc+l8OWw2IUsyVKOD1+0grCVJCnLtmsKtsmS2JM8EvidJC8vxOCXNY9q+IpKSnZ6PzGfYKECFIZ3IJ07OUX5I4QckWfn/wx3584epzUyteBYJFzWi7wpQdeyUFSRupSUrBf1vfofH3/4WV/c0H3z3T3lpz/DB45xW1zA8u81T+xfc8Fs0Fg0ufMl8MeNb3/4u/8l//Edcu36tovUINjcGWGvwleLq/j7K8zh5/JQo8tkctjk+mWJwVDfPC1DCyaamccKjR0+4du0q81nMf/fP/jn37j/m7DzG4KjKUi4o5vfQMgCrEcLRRmT2CG+akKSaMAjZbaRc22lwe+cmvUjQ6XSZ5XPk7IztrYTNPUvQbTKaRegHClSLw6MjSu2EfbLckGYFzXaTW5evsv/SJh8cTDl/+pRidIQdlZw9eptHBx8yT+cV0uQYEK4nwmdnZ4dbr77K7/6jP6K98zKlajHLDeNFSVZY8vkMW1RULFaFuCVbQKyKWrBKH2rehKgShVVe9awg0yfB+z+rTpdnQ+fzx/AcpaPe5OPAqmUOVVUvnwWdnt39cp5cO7t2tXGNYdXz+M8oBFbvI4RLYkWeoqYjTHfoCpXG4imLF19gpY8xOda6hbHyJBjwihlKbCMMiNqeo7SMj566Akn1Tk6JW0P9sYwljhfOezAIsGi8SlzLGI2SnkO7tK5U/3RVKHZcCSNBSUWWJYDF89SyOOqQAtfKIC1YK7GFa+a3QoJ0PeRC1GbJ7iIaC1RFc6wrAlQlb6R0qIVDXlwcUsJWzxvn2fY5Gp/WEkdJSbyYs4hj+v0BQRhggTzLEVJS5gVZlqKLAhsGGK1RVd9Qo9HE8wKiyMdSYk2FMAmXJDWbDTzPSe6XWMq8wA8Ctz4VkiIvKIV2c55xKFJZFjSbTZSQTsBBa/IsodlouXuJ6n7HIfJSSUdnrNbRtvq+OFjLcuPGDQ4PD0nSFKtLSp1XPYAKz/MorDMD9pXr8ZJCkmc5URTxwo0bxPMJwmpajYgsTrn9xS8yGV8gZlNm0yl7e5dIshhtDKkYptkAACAASURBVEeHJww3NvA8SaPVIIxCwtAlWWmScnT4lF5vgPI8RJpR5hkLXRIETkmw02px+eplykKTJrFLYi1MJxNHq9eaLE8dmqs8lJLcfOllHj8+cMUDKyrjYwcSXbl6mXfeec95z/X6ZKlD8IryU+qxqrXzlVL4ykMFPmElR+h5nnMLX5c9X2YUbnEv12eOn7mhP4qCtYKDSu0yTlPTLKzjh9Y80xr5UVI9s0iW9UIEgTSVK3s1adXNgOtmxQ4pWu1PPoeo1Sou68Pa5/ytnnv+mTrbcrKkMtVzSBm2kpSvqR9r6Nzz50NKQdRosbtzmV53wHi8zeT0jPPzY5J4QpmnaFvRHSXLz8z6+1cHYanYKtX7e1UFCmtX5oyf1viMgu7Pe9flImdZFXb3g628ZOwa0iqsQSqPqN1CC42xCm00WzsbkIwojCWTHvMyIS7g4eMD9vb2uXPwLpnXJ/Gvce3KPsXJI/aCAcmVK6QG5HjE3MQk4zEbnYhfeXGPnz5e8PC85OnJBa+98iL3332TUGh6gxb72yEPxiV0dzlPLJaQIsvJFwleGHLj0oAP5z53Ho1IF5bpQnJ6fkrYTikyD78/RPgBxWJBEU9ohz6eUhREYAVFPGXYimhtD8jOExZJjskzyOcoX9HuRKhOhC4K/GabeJaysTUk0QVeq8N8es5WZ4AMA9J+hzjO2BpuoI3FpCm9EK7u7xG0ejwYxfzZW2NO54KGsCRZiilmtFoh23tbGBRnM8Mrl4ZcNfC9+wmlAF9Cmc+Jmm18z6OlDOU8RhuL0JYiW1AWKSpI0UIjow6dRpdFntHsDvBQ9Ds+gS1JS41BEUpByyYU2YLCiM+mBrte91hDyWWFRtcU39rLW0nBcNhnNptjjOWtk1N+/1885ssE+Lag6wtapeUbXgPZDRD9C7YvbaJ6W/gXKf52WFWrPYRUhGFAkiQYowmjEF3CrZuvcuvmVc7OThldjNm/tEOn1+LkbIEQEmtLopYC41ztzy/O+eEP3+D2a6/Q6bR59Qtf4idvHaGtjxXOp1AYi7U5UkRgc4RwtBZpSsziCYNAcfv6C3z99g67QcBoPMIzAU9+/GOGN17gS7/1Jww27lIyI89yGmLCcEPR7G1w79EHzGaGeQHGBHQ6bX79N75M0N/lh3fmpJngN15/BW/uU8wnfOtA8OVXv8LF5IzHhw8rE08QUrIx3OLV21/iP/8v/wsK67NIJXEhmCeGojDEi4wijSm1xlq1xGLqtf26t6AQcnl5V2yMVREOVmp1bhuznOw/f+Ovi7x2heqtoz9UCn5WrBJEu8aAsc9iYMu3seZjcrd1dOujEIrVgsRU1fHQFLQuDsnDNlkQgDYEvmJSROSqxR4nKJuBFeSF4lTvYQOfUHgkVdKBECQXZ5RZskLFqsOTrC62EJAkSSWVnbP8Xls3A+lavMAYp4KqFFiNsVSolEOeqArUxoCuHVisxemgV+dOO8qeQ7uc2AKmEiVTjg6GFssDFfW5thZU5Wm1pkKshEvvFAZPgCfM56rHyvLsd+1vM5zcvWQxXxAGIdq4ROdidEHf9vGU8yuT0onllGVJGHh4lbz+cDggCDynclqYZesLQqCUoxM6UYacw8NDdvf2nLpeUTJdzLGUKBm4NXAYOo8l65RSyzznYnRBEPgEXoCUzjw6rXryVH1tXTrnDHa1W5uWRYGqaKn1l8tYjee5PjApHDWxyAuMdaqCRpd4nsel/UskScKl/Us8up+QJgviZOHELTY3GY/OaTVbnJ2fOSNqC3ES0+500Lokyws6vQ66dOthKZ0a5mBjSK+3wf379/ADH7AuEfL8ypfM0u12ef/995HAcDgkTTPieEG708EYQ7xYEIWhK0RIwcnJCaenp0tj5DAIGY/H9Hp9Tk6O6XTaxLElTVOm0ylBENButz/2fvjEiVU7ivD9AOX7+BUd0KuaJeUaDcDaFZ949V2rQ2KVzlgQNZq1rInYygQS6rKOtbgAUhrwDNbqymvKNeMZ6xo7jVEIYxz0CpURsEYSgFkZA0uhnJpf1ctEhUa596kCfWUU7ODV1edySj7rn2clfSzEz8b2esIUuD4rbasKpHaB0j1Qqb8YV01yppBUwhqrysES6q+qDUo5oYvNjS26rS79QY/x5IzR2RlpEpMXqTODXVbw1mhudUW0pkcIwFi0BWVBi9VrPu/j54XOmpmhqotlq3urflJLJ0lrEBhbOk66HTCblphkgWwHdHtDLt1+hfHpYwrV4tKVFm8dHPHWBwWysclOU3I/Sbh/ckL3yhZRs0thFQczi99sUbYlL1+5QjEa8/K1Pq9c38HvaR5+8w0S26DUGS/dfIl0Puart69w48ot/vyNY+6cLphcAMpSji9QRpCNRrx7uEnWaPLiZouw0eQHPxacL2YUqoHX2QACyiQnX4zxigR/uAlBE1HCYnJGZHL63QGi2Wcxz9HjM5qU7F3u0e51yPtt0AGz0zPSzDLY2EBRMJlPSQtBd9Cj1WxwPBlBovFVk/ZgyHQRY2xJp98nbu5z/8mCt48PeTQWCG1JyxzPluwPmyhPcX50TEpApgWTjQbtVkROhi4tXtil0cpp+AH5LEF7Pg0/wGKcl4dSNLyIwJfMU0e5SUXAWRbQRuH7Hs2+T7/XYzLX2GYTYyCwmiSe0Bg0XHz4BQ9X1zDLxfkzAjMCF4+WZusucvqexFoXQSd+gExTOmWGBBpl9fIihiIln2koAxhKClyCurm5gRcGLglQkrI06LIkikIW84S3336LV29dpdPu0nutRxAE3Hz5OqNxRlGUeD7EaUyr0SBLY77+jduu2VobAs/n8v42UrheJWeYK90CD4vQczexYgmk4daNHba6Db7x5S/S9zye/Ktvcq8VIve26XsC21AMrl/Ba/aZxddo93J8JrQ7Pq3wgnmSsLW1QWozDp+MSPMFv/nrL+N1hzw+h3ZTodKcL171YbHBh/cWFDrHSstv/tpv8ujJY77zvW+zWEyRQuL5PhubWwgh8aRECkhzlxo0Gz5xPMOasrL1tag6CaAqWmGpRSmqR7FL9oPb0piawYBL5t1kVL3WUuu0/o1voM9sPIvFPfv4sysB14YlfuZ4RTUX10+vHv/Zfbu7pp7H6uxpvZhpl9utXrSOWj1/rIKWMYjZOZ4KKDauUErFaFZSFD4dadluCaYMEEg8m3O3aJN7ilLmCM/1b+VpQnx2iH0OHVh91LUPbQ0Ch1DV6GTVOYGpzIWFcXO2tqUDsSv6vrF6eS6FqiXP3TpBIrBGowuNqNZnVrh1xdI2B1Ghcu68m2rxv7zvLEtT7rpNQ1iLEiDRVWIlCaTCF/aZFoPPx/h0jldI5ZT4pKNwZ4khDAPCwHcJjnKokEMSc5RS6NI4NCbwkRaMFkjPQ4oSa6EocqS0ZFlK4LWrZapLaJpRhK8URZZVJtMSX0kWeeoocMMNRufny2uYLBboMkRuu/kszXKMdolTnmaUuTMUttaymC8IwmDZMuP5HrPZ3ClSKw8hJWHQqHr4HJssz3P8wGexWCwBkG63Q5zEzGczyrKsLNI0jUbE00ePKLKSdrvDdDbj/PzC3ZPWcOXaDabTCYdPnvDg/gM2NjdZJAnNZoM4gV6nx2Qy5fzsgk67hVLQaG5ydHReqRmWLGZzoqBBI4ooypI0TWg2GjSjBnGaEAQhSZI6P7EqBr3wwnXSLF3SOX1PLePw5saAHz14yN7eDsPNzaVP4ceNT5xYNVuuOowSeEI6/6fKTV4iViZPgKv3SgeNr1VZV0BIlVQ5TNwFhIqWVn/xja1FIlytxxjrZE4Fy6zek3K1EKYKMtXzywAuHCKjqiOrg0fd2FcfzRK5qhIsrSuYW7JGJXDnuz6xDuGiouTAMs6vHU/9CZaqRHUiZw1WWzC1kZquvLPsMgFyMvKrpKr+WUsvSyld8/hwSKPVpNcdsFjMGI8uWMwmpPEC5JoC0TrtzToUTwqwUjqlRF2hZ/LTtfv7zOb7n/chnj8wUTV0Gq8qLHoYoJQai8LTinSWM54dYmWO9jwKPKJQ4nsKq3xaoWC/3+DpgwtORgu2ey0eTkfEeZ9JYVikhvl8wZO5Il1AqSRzu+C1/T0Gu11MYRj4KXtdy8Ei4u4o5xtfuEWQPuWFvR22WyG/+/UdFt++z/HUp7w4wKYFtpijMDyZC8L4lNbLEX/vN36Ft+6cEmZ9tBcRdHfQeUoxPcPEE7xmC9XoklnFIp3Q6TbZCkL8To+LwidLSyI7Z3fo8dorX+TyzT3+7+89Ip5q4oWh1BKTp5yOz1HtAaSn9Df2OD89QwYeZXxBuHeTWWaZJzkKS9m6wk8PBfeOp0xKQ55kEBe02hFbgx756ISTSYIOu5Q2wfdDtFXMU0Ne5iAUst1DFg1m43O0UIigjfSdUWLgS1eUEAZCRdNzstt//uYRP31keWkr5NUXdvGDBo1OHxEvsMKjVBEtqZDxnDw3TkjmFz7cDWmrRnGJK9zYZRxxAU5Vv/tBwI0Xb3D3w8cUVpNGEWLqzCntc/sVpSYyGdEsQUuPcHsDP4zY8BTnx4ccPj2m1+tggUWS02w3ODh4wv17D5gvJqhAcvfuAzY2Buzs7DI6O2Q6HhNFm4wvZvhbkv39ff74j/9w+b0r8oLFIl8W0GqKo1tAF0jpko7Qg9/9nV/jD37vdxh0O6AN6XzBk7v3Gb/5JpEtufLVr/Cl3/57WOWxWMyYTeZsIuh22wgrUGh0OqcZNugOhrQWDbbCnJ39PY4mbnHYakS8cmuHhl8QVzRyYzSLxYIsz7m0t88Xv/AlfvyTv8JYw8XoHOEpjk/PSTOIc48i2CLVitOZJksKeg2BLwXSgBE1q8IlBs/Sz6rm/7X7ap1NsFrvr+J8zSb4m989fxcqYo70to4IPTvER/zmhl17iaiSAUffN8t6a13BrNGg5cxdMw6WG9b7qQsU4mfea/mQEHjCEhiDp4Hzp8hCIzqbhMrjtp3TMTMCP+DNoktTxNwMFa0iJbMtlCfwlCRJYyYHd8kWk2c+f6XDi1wmfgJfSUxZOHpd1XftjlwsT4xbBqwVR+3qfAgqar+s1yyOQSHrgnGFnlmjsUK67Y2TThfCd/OctY6ZUxVmatDN4pTflHDqm1K4nixY/e4p8AT40uJRWc98ToY1hixJP5V9SaUqw1qPsiydBUTDZ2dnizx3Zs5FXlRJiRN8qtefjYZro8nzwlFMBUsvVTCcnZ7gSUmhnZ+plGrpQ+X7HmGz4T6HtSRJijaWRqtJvJhTZClKSspSo3VCkedo48AIrS2z2ZwyL1jEc0xFoU/TFKEEvuehPImnJGWZ4fk+RZ6hi4QwiNBCsjUcuMRJlyijSGKH3EdhwMl8QZamHBwckKcpoS/QWUaS5MRJRl4aVKdN4LuErNFs4CtJFs+ZTKYMhgOSLMViiedzBlvbeMpjsVg43YF2hzxLGfTazCdTdFnQ6/W5uDgjaraWkvZpklStQoonT5/ghyFFWhLHczY2hmBhZ3eHhw+fgISjoyN6/d6yQBP4PvFiwXAwZDFPUL5gNpmwvbP9sffDJ0qslJR0mqFrqFw+6poZwVUxVlGuXryv0pW6olpDO0pU6ECV2qwmoBVCVPcOLCthgmWvlIMxpasULL/wdWJkqLKoipMsq+fc/pa+EsuAJZbvr21l4AiVIVsNs6+Mg9eVC5eoVF1oBLzn1mNCuJhprUNGjJRu2qlesKR8VFKvokawKtftVUVzJW8pK4EPIQTG6aDiee6GazYatNsd5tMZk4lztc6ShKLIV4gVbvpziaBbqNUTupTCeWR8iuPvas/WMwufKvF1kGGJFiWUirJQ4BsCMja6BePxA6Z6TrN/hSKe8Oabh7xnfLr9NvtFRiMYshilXOSGpydjfv31V7l79j1OC8294zFf399CxHfYNCnn05SktUUWNnjjwyMuzo54/eUbdCKPq1t9Hl6MOZy2uX9S8uqgD1rji5yWaOBJgRYlyeKM0osoC0EQNigP30Z1+nz4pAfhDK/R4ovXmrwzCZkXILIMm8WEnkuoC2NIk4wwCGlsbTCKZyRHI6RaUGQxG70hl65usvfSNd64c8iju/fJ5iWy2aHVaiF0gh82SEtohgFZEpPpEmYJnswYbgXM04QijjGzgocPTpnnmsxTyFDQQDDc2kA0FCfHBxQ5qGaXMolRRYLobXMxTWh22ggsZZ6Q6C6+AZ1NkCLH94do0yLOQuI8cEtYT5EUPk3PIwg1qYZ7xyWPns74qw9KPE+SWDd5pMYgvAZe2GZbxhyW+WfEXnUxSogKSa6hcIMrIEmzbLYXwqKUYnNj6Hw+sgJLQOn75OQubohqwVQtNj2hsZMJViqSyOf00SP6X/oC52XJwcEx7V6L8WjGw/uPefHla1yMY5Ae41nKu3ff4H/6H/83vvLl2/zhP/5dtDU8OjgiKy0HT8548viMy1d3K4Nfi5Ieb7/9Hv/zP/8zLu9v8+jJmEWSgzUYk7t4I0qULfnlr3+ZP/nDf4AuNT/4wQ+5tL8PUvCN3/tt8l/+KqrbptnvYhG8994d4mRBr9sFX9DpNCjyC8an52TjmItTn0Re4Wtf/xq3b7S4cyTJMkNZTGmKBa+9MsBmC0qtidMUP/TZu7SLwYLRDPpDtjd3idOYRTLjm//mX/OTt+9xcjql1e7xjd/6I4rBbS7mFp3nvHRzSLuVYkjcwnedcl1f1bX5zdmPiGpecPNKHdeNrZfdbi6oqeGfr/HRPIHlHFCfn3ruZA1lqsSvlmhUnVjw/Dmt/16rXD6TedXJVv30uiBWfTyr7dsYLmFpVYfvY7g8P+GGmRM2W1w3Mb7KSNIB72QXXBEJN9OSXbvgp2rIJAjZC+BscsbpYuQq4djVfFJ9jaVYHZOvJFqJSjV4ZZriergEq6OrD9YiRSXFZV3V3Fba78JajC6R0qnGIap+c1xyJISpkjEqAYvSxQzr+slrpo4U7nhkVYiWGKzWrierWu9InFS4Q8sFgapMdtYSwL/rw2LX+t/+dkMpJz0dhhGnZ2d0u23AOvl1HG3SaIPneU4eXzifsyQukErgB16FUEFRumKP8hQW2NvdQylFXuRIKdna3AScb5SSctmCY7QmyzLm0xmtna3lzb9EmoBFkjgJciHRRpPmBXnVM6TLgnanQycKicJo+R11atyKMAwxpsRkGVeuvkAyPWMwGCKFIEtTtNaEYYivnW5AnueUuUPngijEFhl+ENBuNDF2SqTcOsEPfLzA5/L+JY6PDjm/OOPsfMTNl150CpkIFosFm7uSfq/HPFmw2evz8s2bfPfffofdrSGe77G7t8ejh4+wQBiE9Lo9Dg8PnX2R73ynpJJ02m1+9N5P2N/fW56/eBHTHwxptluMz05cr3kYopRiMplSliVXrlzmzgd32dzerZLjTwmxUkrSa0VLD4O6ILKsvonVd8oiKg6wqTj4dQB1iZXB4mh4tUFu/Zx0QbV6TApQ0qKkM1+Usk7SjBOiqPT0l4bBYoWOYY2j6FmNJWC9Z2uVVNUEjqq5VTlE7GcaXJd/1gnYs9Wu53MGXVO+62oZlcz6EmLX2MrAVAioiZPuu7DGq37+OExND3GTrTMorKmU1TtJ17AaNBr0fJ9mq+G4oaMxs9mIdDGnXKMXIt0HFIBYNs0DheZTHZ/J4uB5laqfHeseVm7yVVgKDAUXiSCeGR4+OGew3eRSV/G1W5e51LuHPyvpNnqcTh+SFVPGRZeF1+XhGby+3+all69wmC6Yz+cUIuIrX3iVf/XTp6RpRFJIXnr5JdT9+zTGghOjaViYJ5r7uWac3GN/c4BXePQjwUU85933n3JoRvzh3/8ig67knWPN+VwjyhQThOgix2u2kCbHz0bITp8Hj8754P4puxsNZOgjQgU6w+YxQhcIz8NKQT6fUqYF/UuX0fiM5hYzXaDyJ9Ds0b9ymUs3bnHn4Igf/PBdssk5JssIei12r+wxOTuGMkZnOeHmHkkyR4QCL13Q2dxEpGPKeYmfLpCzC+I4JhruYoVkq9tE2pIsScmtwEqfUhpsOmajIWlvDckafUazlFnhCiue1ZTWCQsMej6X2oZuzyMTlsNZwenMcb5NAbYQFMLDClCiJFAWPI+Z1m4BYi2+VCR5RmYkMR4vDDucjwvnufQLH7aqIYklDXA9jkAVM+r71lqMLV0xBMjDkHd3dzDxlGmcOtl2C7mRlFX8Sckoihmd3Gd0UtIsXiVOEp4+PeJ8fI5QPkdPT3jr/Q8pS8u9h4f8N//tv2C+mHF6NuP7P3qHew+OyHPND374PkHYYD5P0bmlP+wgKtpfGPZ4cnDE8YVm14/QNsQKg6ga3p0EdE5pMn7jV7/G+HzEu+/ewfd9xudjTs5Ouf3KyzT6bQ4fPOBSeJP5IuYnP32Tew8e8uort/j93/810HfQ5WN8f0g8OWM8DRgVmtdfv0muPeJcE09HsHhKxCmjkxdphiFJnDAajdA654uvvcL56Wi5IPE8RRg2aIQNWo2I44cfMkkkyWzOX/6f/wv7X/+PmJctpkdP4dIAJTpgajlq6VCEqkjFRyKfqwRgPdbXCIUQLqlyhbaftcv4uPGZSF7/zMHZ5Ty5/tiyfkU1By//WKZRa1uvA0/Pfyi7RGPWFh1rP1fndnWMzz5WC2opJB1peUkaWhjy6nD2BNwKNHs2ZjSKSYxg2IPrek6UT9AZDDzJzd2IveQIUst2FPFjMt6wllGdVFHr/FZzTJUgCYET/alL47ZeTdmKIVn7Jz63D7HCO61xCZhUjpZVF2olajnfmmphjKmEKnDJ0hLFsyyl5albGky13qougpQOpRK4QrYnrPsnBZ4wFWplPrl/z2c5lgn7pzCEYD5fMJ3NnC+UiTBagzVOyVEItM5RVT+bNYW79tYs+5eEcLL7RpdIJar+qIK8KGh4ainQJn2nyIeRWAm6yJ8pzC/iBbrok6Yps8mMNEkYDodYDHlRoI3F810hJ0tTFlVvbpYX+HlBXyl8zyfLU4IowhGqKj9XA5OLCxqtJqPTwqG8ynNAhwVdalptJ5CR587kervTIWpEXJwe4/lOha/TaWNQxNMJUrrX1eWVoigRCEajEZOpQ32jMKTIc6bTEUEYksQx7753x3lnlSWRtcymU1rtNvN4znQ6YTadoivj4Pl8RhIndLse8+mUr3zlq+gyYzwZ0+30GI8ndLo9To+OKEuNFJU1gdbEiwWXLu0jJOzs7LC9vcuHd6dMp9OPvR0+IWIF3aZX5SxVW+hS0c8p1AgrKwTFBSdFSE3xq0H7Zd5DldLUlZ0KLTA4rq6q1EpkpXzje/4aOlW92rpeADwBeMuwKYTrE5JU9D/jsuilXwjuOlpRuWZb4479mcDLSizDuEAsZRXuBBgjlsmVdCI7yypkXbipFXXA5XDG2GU/lV1OwqyV1uwy4C3niTrRs5WKoDFL361lJWwN9kfUxyWQShBGLmOPwojeoM98OmU2nVQGcFkFO1NRAldnQH7K/SV/N/GqjxpuYit0wF9+/wit2rx394KLb7/Bf/aPX6cXpPzx37/FKB7T37rKX76R8+H9hEF/m6ezmJ880mh1yrVOF1HcQ/pNfvT2Pb786nW2W2ecZyXvH4yY9T0u5oZmZ4OuGnDz+g5v//gNznLDuZLMDiY0fcXNm1c5u/+QO+/e4cAkfHCwx9k85LvHOVmuyU8PkV6LTuQamv285LxzHRkM2Q8DWjLlXHu8Pe2Tl1NENiWfnRPogtbGFoW1JLMZzbBFmcZk0ynedESrHVGUPbpbO7x06zqj0Rnf/85PKWcnDEOPOIzo7l8i8wJm2mMeJ7RbHRqbl4lHx2QXBwwbAbI1YPr0FG0CGmXM7rYibG1yUXp0+xuMzh6TJHOUF1EGATYrCOyCva7P9u4uqrfH48MxWVZCpOh22kwmI4pS43s+rXbA668Oub6/jfCbPBwpvvfOEXGc0m1G2CJhsshY6ABdGdoZYyjLGN+3FL7HbLpAtrcRns8sF4ShYNiNOOcXn1jVviJ10V/Wa0fpkOtlcKnDoHCLJ6rJ7xz4p3jo5gAaAo9KiEU6PxthXXztd7t87Zd+iWa3y/l8hjaW+Tzmp29+SGFKuu0e3d4AY0pKbXn/g4f4vo82lpPTESenU5ArWWeXHEjEkxDfb2Ms+OoUi4dQLQ6enlULCX9ZWBJSAYoXrl5mc9jj7bffIwoDXv/6VxBC4gfSNShL8KMQrOXBw8fcuPEiV65c5Tvf+wEXFx3a5s8pjlOePnyZx6c5T84zinLGxZO3OMu7pLZJK7/g6p7P7CLj6dMndNtdTs/GPHryiHk8pdkKaERbvP3OXdI8ozccEscpk8mY8XiOKQuaYQOkRzwfM733b4j962SzCSYTUEaOC1V7WNlahbVOmtbZDhUa8QwNsIr4S/GiKjH7nEAAz3+mZx/7iO2pc6H1mWGVQNaJ1fPzxtptv0rUbP3fs8Ouveb5vQkEVhqaEm4hGEhDoV3bc6BgSwl6niIKPXYaIEpLu+XTDARbSclxbpiVgo3FmC+ECuELIk/ibzb43jTmh3NNvC58tH4dZSV/rnWlQrx+QlbFTbGGRq0saerlgmPSOJaF27fEqfwqUe+HpRiVEG5+V6IuKIOj5lbzvrFIr95H5UuFcG0eVXFHCYsv3U9POvqrjyVQlluX27x4efOv7T3568Z/9X+8/+/0ur/N0PrTie9FUZDnGUkc42TIc4qyILA+RmvKyqfMVz66KEh0uhRbU1KSpglZlhFGLWoZdF0apDAcHDzm8v5lTk5OGA6HFFlGq9lEVpLneVKQlwVSQKvVoNVskqQJcZwwnkzI04ytrU1AMpstyPOM4aCHEIIsy8iybFlkT7OMOEkJwwSDwWh335WlptVqEyPQec7m3h463UNUyFddli6KgkajyWw2xVcejWYTz/PAWrQ1lNqZbksp2NnZRGA4PT6myDKmsznWQuAHXfevygAAIABJREFUtFptsixHAFma0el20EWBMYbRxch5ugnBYDB0YlVCYk3JcGPI44NH5HlOs9nCM7aSuJc0oqhaO5eMRiOePn3MzZdfdNcp8JlMxmxt7WCMKyIkSYzyPDzfZ7GYI5Si3+9z584HhIFPp9v52Pvhk/VYCUHkK7c4sVRqPlUdpeIzCuFgT1sZyzlVPVUlMgpjyupvV6XRthKZqCedKlNZSgorb2n66ynnKyWWGf6zaVDd8C0q4QmLQQiv2q9LrOpUoe50crK/LHnLTglOugpD9ZeqqonLCdFtArjFjLWV14lSLKVLwaFxFso1RMQYhzqZUldBtZpQjUswRbUzWwVDa3RFC1inLq6mIyuqJLeahYSV1T4dxO16z1wQ9XwP5bUIw5B2u8V0NmYxn5PM56R55gJrtWeLra7Lpzc+H8sDC2gy4Qz5zkcJRwUk0uM8gUcnCwZ2zheuN1B+j2Z3j363y//VarHd6ZO+94SnseX+ScKl4YBOD87OR4xzTZZLbDKhTCUjscFWMOTqXsF7d49IOk38wzNe2N+lGaccjOcUvmRuLO8+vODWsMfrX/8a7929z1z7vHlvTKwlxBPydM5wf0imG5S5ZqFLdBBREpJJgQqb4HUpZxo7OnHNy2VJq+Ej/DblYkoY9dkYbjA6fYw1vqubNkMG7SG/9MWb9KKAv/iLb5NPpgy7Idd3etDb4s25wJyekc5GWKvp7F0mKw2T+ZwonxP0NziZl8i8JAoD2r2Qa9cuU0YbyDzk7OSEeDKG9tBV6Gdjmr7H1mADz1OczizF4oJkDkEZklkPYUpKoyFbUEQRs1KBarDf8mj2NhglcxJjGJc+VyLJr371ZcbTBWdzzSguiZOMyTTh9GJKlliE9inTEtUCTyiUFyEjRSA3kfIXX4Nd/54sq8przzrvdYGtkX8DnU6bXrdLnp8DEFtHEZJCktcLMwE+TsVLeR5WhUwKzRdeeZWnh08Yj48ZLUZ0N9os5nO0yJFKsljM8XxodjxMWmLQGFs4MZd8lRQ4LN0Dm5EVJZ4KmBdjQBAEbYRx1BsjRc0RwOgcYTUvv3SDKAgxWnNpfwc/8AgaDV689SK+VExHI8IoYnpx7pqTFwtazRb/4D/4bSZn3+Pxo1O0+DoHT054/0nB3eMFNy71iXwJZw94+doLjC4WfPDeATeuXyHVlv/9f/1T5rMZaRrj+ZJvfec7TKcJd+8fkBUQJwW6dDLFpoJWlN+gMBK/2SLxPmRr32Nnt0uv5yBFY1zBr1audTS/6louf66ox/W88oyv0nKuMM9s9ze/fz7bSPssQ6Caq+pK5hpqtXy+zierRMA9tMSrqt+e88C0FU2WlWLgqkDJsoy7Oqj1/a6e2ZBwQ2iGwmJMrQwoCLF4SpBYy0le4ElBpAQXpSW2BiMl7Y4gEK7wYYQg9BRB4HFZBvynNyTzByN+Os+W1+N54oSqmvVtdc+szt0qtVwhXizXPVTF3DprEjgbECHrmr9ZvlZS0QwrJT9pqySpQqxEpQooq4KLsP8fdW8WJNmVn/f9zjl3zT1r66reG1tjmwFnIYfDTaaokE2FSUskZcp2KMIO2ZItRzjsd9lhOfzsCD8p/OAHK+yww5bJsGgNqeG+ArMAswCDAdBAL+it9sqszLzrWfxw7s2sbsxwPKMWwTkBdFVWZd28mXnznPP9v+//fdaDLOedLKXwbQ1KeRML1YAr7wLoiBR0Ism1nR6ffG6TTue7h6b+ZRxKPZnisZKS4WDEwf4RYRCQJilKSqIwpKpqiiJndjql3+9T1yFHR/v0+n163R5FUXD//gMALly8SNQwUlm2oJMmTCYTLl+8xO6DB5zb3ERJRVGWpE2QrrGWsCEegkGACiOKvCAIA69eUtIXsJpjpmmEwOek1ZVeMndesujf87z0xg5SeSdCZ70zptaaOA7YOrfJ7oN9ThdztDGUZUVVloRRyMHBIUVRsDbq0+12mE6mhFG4lD0bq8myjKooicKAVoq6OD31xkzAtSuXuHf3Ljs7O8znC05OJoR5xua5bc/aKcm1q1e4dftDFosFo9HIm1ss5gyHg6XvwHyxaCSMlizLiOKYwXDIZP+Ip595hvF4jNE149GYG++/T5p2OH/hMrsPd71dfJxQFiVCCOI4xllNv+fjpSaT6Xe9Hr5vYNWJQ+rGytEK1+h5V5PBKnHDV12ts55CbCuly/v7IZwg5IxkQPgKSctaCZzXA7c9VE3FJhSr5mC5XIj8QmacQRIixar36vG53DbW663hjbc5971NvpIlkNbLC41oQZen3qUQSCcQspn4hD+vlt1aDuvBic+IWBq/NgvuGeau+abNtXr06+rkl4tuC9w8ZXimbLeSGEghMa7NvFj9vXMOpSRp2vF61+6QRfeU+XxOnvlqRtuT9sMChZ7EWDJ++Pw025boAsUf/Ok3ubCzQS0Ed+4eYroVnVTRTdZIRUU1n5CMtnGh4vrVHQ6+dYeTheb2/gnnRjF7u/sUpSC+fIHL1zZ4+NoN6miDg8M5v/jzn2f/8Le5OTnifl1xlJ3wylNbDLcSvvHBPdLhNtOiw5c+OGItNETxmN1Ty8QEqPyEcnFKNFyjQlCjqOua0iniYZc6rzk4XkB3iE0EUbaLK6Zo40iHa4j189hsgi4s0WjEcaaRpoNQgv5oE9Ubk251sBH84Z98haPJglHHcnk75dKFPoeFYXrz2/R7AxJXIK3hdG+XME6Qi2M2I4fSM2Z7B0T9MUI4dsYJ9xeKXFv2pyfo+Zyw26XIThEYNvspSRIzKxyFLhGBwApLVVoEEZUNvTwvVl7OmPQw4Zj39x0//lQHYxR39qacFpZcC+bzKReGO7ywM0ZFKVoG6NpwfHLK4cmCD3cPef8w43YYsR8IEBJXKt7b08wC38f5F38xrr5IIdHu0aqqOLOZOyv3OXfhCqML11FK+WJMM8+URUmWZWR5QZnPyfMFFJqFNXDviAt7h2yM13j6YpesziDtILVAH9whO8iJ9Cmf+ysvoAc7OLpIaSgP3uNkOuPVNz7gzRv3MNZiHAihENYw2rnKueeu8O0vvUadL9g4d4HNC9e4+ebraL0AKTHGop3EIDk9nfLGG9/g5vs3ufbUZT64eZtev89w0CUNE6aTGZODA4IwYTQckiQJ+7sHvPX2O0TVHue6z9LfOs/r773Jt+4eYoWi/8w5LuycZ3Nzg8nJMfOTQ9I04De+8C/Y2z2k34kIlKSXxARhwO1bNzg5WXA6zyjyEkzt5298c7+1Dl074jBEzGE6v8Hi9qukaZ9Pbf4S9qW/5nte3Ao4tRvlx5mpx8HVn/f9x6Kg/gHGioF6/Kf2O96rXRF9d+/jUsePwsOzkOisW+APCiQ3JbyiBB3nqIxrwLMHEGkD/o+MRWj/4EKCyGoEEAiBchBJuFMY0kAyDBWfHK8TW8unzm3x99Mh//WbNzkuq0feb9EoTJx12MZ86xFWsn2iSyRmm31RKwZsWyX85kWKVu3THntVeJVilUHl/3MNGKPZRLd7D99PLp0FJ72lulA+o6oFVBKiQPmvUhBJ6CSKp84PeP5qnygS2CfUs/QXNZ6UFFBrTVXX5HnO5tYa3W4H56w3TrAWY2vSTszJ8SFpHC573ZWSFGXF4fEBa6M1dF2hmmvFWN0UazwpEEUxAs8yTSYTep0OSkrKWqPCAKM1VVnSCcOGofTFtyRN6fV75HlGVVco6ajCqCnEO0IV4nDEiaSTJqRxQlY0jnk0UtJEeit1a6grSy8MmEymSBORKEFdV0tZ4WyRUdUlnSRAa0Oe5d7MI5KEiWIw6DGZzDg8OOT45ARtDEZr3wuGRGMoixxrDHleoLUhThJUqEjjmIPDE8rKK60uXjzPyfExZVFA7Kjygo31DWanU8qypN/rgYDj4+NlcLBzlkuXznN8csq9ImfY75PEMWmSYrQmW8x9q0KSkOcV/f6AWldIIajKgjL3mXFHR8ff9Xr4voCVEIIgUASBbBiqdtEQq4qQaB32/FTpwYjXKj4SxeGa3zUbWn8I2cjaVENl+z9QeBtQ0SBVcWaCOjtc0y9lrQEV+uOdqYbRVmScI5Cevg5DX44RwrNU1loEBmElNJlYomGvcG0jqn9w0ehj216IVaXONRft4wuN1zg74asIbYZVW5VurdbP3t8/r8cW4dbIwnop5OpxRdPE6HXjFrMEYo/IT6w3HJFWkqQJYRjQ7Q3IsozF7JQsm3nrTftkJp3ls/kevU5/KYYQTb+bobRwvCioCSidQZuco8kRa1FMvxtDFEMY0hv0ee+1t0mTmOfWU6hO0dEap1XAixd2+Ob79zE6YDLP+elPP8fN27u8m02ZTOGtb73Fc9ef5dYfvYGWMbq/wbsf7vL8Mxe4ttnj/Q9vEaxdojPY5mT/JvP9B6TnAqKNIdViQi0cIkiQMsEZQ1kUBElC3Emp50dkswOESglVQX1yD5n0sbNjTLkgczGqFrhaEAvDyfSYUI0QHUXQ67K9uU7aKfjql25wvLeHkxlJmrCxdhHiNQyGnU3HaPM8gczJjw85WcwIqRmd32LcVQgRsBXNSWJFJMAF8MGN26i1SyyMQuQ1CZDGMZ1eB+ccB6cLqniAigLqqsLanFjVBEGHhVVIGUEQIWoNWjMzknfvz7l/ohnWM27tzdDGYesSKSyhrOgnEd2eIE4CAhWhtyPycszpYp0Pp4Y//NaUL743AVtjbMWDmSYYnalq/EVegrDsFz1zWdKy9O1t2Syc4LhwfpvwG+/z3rvfoK1WC+dlzqbWWFNTaY3VGmetNyIapmyPOgxjCeUp4eQWn//Ri8SXrxCqPu5be3z1/XfoZ4f89as9ws9+BtH7NKGsqD7MmOoOh4d7vHsjQ+MIzsqs7YTxCy8wvvM2s7sH2PKAtes/w+nsHicffAuNQSlH5DxLELqcixd2ONg7YD6dcf7CNotZxoN7D3jumafZOLeBmU6xKE6LnA/v3uPaU8/wjbe+yTObAhFJJouMWWmQruLF517k537ur9JJugyCLljDBzd/j9t3bpOEcHlD8vzVNYpacJIZsspgjSMJoQodTluskrjab7itM0t3Ooefg4WzTWHPUNeZf93b9e87MFL+fVuxU4+H1Z5lraDNUvwBGKuPC4iJlmH5aAjvnzdaNsqDhkaZcRZmPQKi2n+X6ONRqmq58IpH4JxfslcKlwHwlHMMrUU30lrnnwKRgFT6qJOzT2OZ6wTUzhdmcyuYaYMqNQcCiruHfOb8BuFiwSd6CT+zNeBfPpxQGLOS2YvGat9ahLW+3YG2haDN7xLLFos2JFw0158/hGuAEkuZXnu7LbwIaMKpVwVmIf0TanYtS5DmGa1WceOavM4GVElHICVRIIkCRSAFsRIkkeLK+QHPXx3R7wisNTzhLcO/1uEAY54UsKo5PT3FGEPUsDNaG2qhcc47KIZRwMO9B6ytjTg5PqHb7VPVJdZZwiAgiHxs0WQ6IUkSqqpEiB5B4M0ptra2kFIyX8ypqoo8L7z2y1rCNETrGussuiqRUpBluW8BCT2LaKyPvhDC72uLLPfzmACjLUEULqV9QRgsg9+t8axqXixwzlFVhuxkgrOWQIUILEopAhVQFQXd8bBpI9DUpT+nIAiWjGpZLBiPx+RlSV5UxGGAFmJpMAFweHgADuI4ZjZbMFof4XCEUUhVVoRxSBBIDvb3CMOYsqoIQp8LNp/P0bXx6x7esfHqtWuUhXdIrCoPAvMsY3Nzi1p7eWIYBsxnp3R7fQbDPoeHJxRZxtrakIP9fS5duoQxhm63i5SKQX/0Xa+H7xtYxWGwBE1Lapo2v0A0ekf/kW3DNY21ECi/abVuKV1zDUCxjf4XWEonhFCNPWgzUbTBvM43Zi6rNqqlx/1kZK0jDMSyIrTsvmyAUegK1lLL+kCQdnuMty8jZUBZG45Pc45PcyptEcb4yb2VFZ6VAjrwMsPWlQ/axcSH7608jWBFs7ZDCuntz5VsHJ+WrzAOuZTxtS/Skr2yq0V3+fOWCXMWRyMraBwxWn10e65nF3J7hklDCOI4IghDkjSlX/SZL+bUVfUDVwO/05BPiHb/1zHEY98H1jB3Ke/dn1ALQVVWuNpR1iUVXWoR8fZexQd3vkEoQ4rZgsKAurTJuO+bJfdPIn7s6QucO3eRmwclk9Lwwf0Zl69e4M7b94jCLn/w2ldZWz8HdYEu98jUNqTneGcv48WnrhOJmN3JjOwUlDNsnNtGD9bRRY4pNdpYhAwIpKIuZjhhibp9yqoxqFARcdohyPcxCAoDLoyI0xRdnFBpQ7/fpascc2EpI8FguEaSwivXN3n/rfc5ebBHaE8Zr0smheBrt2rSnkFujYi2h9zNBb0kIT63QVAbEIpKwp6rECjk1hbh/B5aG24eleTTQwISVNgnsoYwDBBxglYJ2lhcLLxjnF7QCw2jXsiVi0NENOB3360wKkCGPciO0XmGjEPmRnFn94itcZ+jhYMyR1YF49GmX7TilCBOieMEJS1BaFCRIUhApnDzWNK9M2VxfEwxO8AOBkQme2IVze93WNduMD+6Mfe3TdPT6T/unTRBmIzje7d81p60oPycEeD7fNpoDKUcQin68QbV4oQ3v/Y662tjUJqXJjOiwV3CwZD7+w94eHCAmGfkt94jvbqGSy5gSbBByORwRqUl3TSiqmq09k531tUUB/fJHt7k6md/km/v3aaaH3Lr9T/i2k/8DFTHnDy8jzZeAtVupp9+9im2zm3zJ1/8Iod/9Gdcfv46WT/lta++wU/8+Ge5c/MOUdrl4svPYx3cvnWLn/u5f4PNgeSbr3+Zixe3+Suf/xHu7k6JOn36gz7vvXuDbjfhja9/jW+/8zaxcvzI5RH9bkinVzIYD3gwqbm351UMWSYIAt9gXjYZRLJVNTTvgzbaB/46L2MPAsnm1gbOGZx7NCne2tZyvO0l9u+ftat+mdYNcAWoVl9/ECngx6M0cI99XX3/OJMlz/zWg4kGGInV363+8js9xkfHKuPq8ccXj/ypANaE46XAMcSDKt9TIRobdMcgAJ+G9wh8e+ycW9ZsJVq0wN2DEyhKPnN+nXNpyn/1meuob97kN+/sU7WmXy2QFNLL8ITwcXktgGrBU/tvy2A3j3eWzGrZrxZ4NU0SSxDnt2XOAycBoonFWQlr3DLsVwrP1knpCJX0DJb0crlACaJAkoSSNAxJIsnVC2OeuzKgG0MUBs189MOErFYOy/+qwxpHWVY+r6kBVYFSPvw5DKi1Js8yLl+4SBgopBTcvXeHWu+QpAm9Xt/nYwJV7W3ZoygkUGp5/XW6naYvz++sA6WoigIVht71r4nl8Y6CId1ez1u041gsFlhjGA4HqCBANjETiCZwXgiCwIOpqq4JohBnHEVeUpcVUmUeqAUBVlse7j5gcjplrZ8SKMeg32cO5HlBN+lw4dIFjo4OyRc5AL1eD6UcRT5nscgYDjeYTKd88pVP+naA+Yy18Yj7D/dw1jIcDnD4ay+MY1QQeRm5tk1mVkQcp3S7NZtb29y9fYtO4oOZcZYoDlGBQgKdTg9r4d69B5zb2kBrTYCj2+1gjGEw6FOVOcPRCDfxoHaRZ+i6otfvUWvN2vomZSMNDOOQ9z/4gN7gSQErvImEbfT6rWeNVJIAgaf07XImVc2kI5Vcsi5SicYGvWVjGqZJtE57LVhr5HhitaC5dsIQq/4BeHSSwbnGqEKjnEQ0/VzCaboiZ6eXMhp1iHt9wqjbVIcFUajYXuvRS0PuH5xSas9sCds6+Inlhb+SHz4y4/nfWdM4qIhHMh1WOutG+SzaD/SqgunvtVpkWwOL5SLjWIIrrwJoFl1rG6bNeetVAU1bVsOAna18+9dVnFmDPC7072UUBijZJUwSz/w9wREE4RM93v/f8T2netEGUq6Wc2kNWWGYFJKyNOw/mBIZQ7keYkRI6SL+5O373DvUvLjdI7Q1c224f7zg8uUL7L65R63h9sMTXrx6hQdH36QuM77xzZu4ckIviKhKhwpGHJ7OSdKU1FoKc0Rttjigw9c/OOTKYMzL21u8+uZNiiJnc2sbEwSY6TGVkYRJB5UmXi9dZCS9DlG3y2JygDQ1jM5DMmCsMnbzMS4/QXT71OkGUXmKqzKitQ2KCiQJgaihMFw6v8bB7j3efO9b5PN9Lg8EFzfHfPso4kT1yUpBuVdiYkEeDKhnc5z2eRFCKlQYAZ6pNsYxdx0EhlRZkk6KCiHuSHRhMFISSOVDC3WF1DkbUc3aSHF1p8sz185x9foFdic1r94+ohKCIOpRV3u4qEL1QkKlsFKyyGvy2iLrjFTBeNRvGrElzimcC0B6ExuBQTmBDWP256fkWiAayZeoasJa493tPoZxptiyip5Y9e2cvaNfyCuKMgfp54YwDPmxz32GyWTKu99+188HQnDlyiWs1UynU175kVf44P2b3Ll9xwcuxopet+D6ScZgPaKYWfr9DQLzAFtlmGxKdnqPKDnPg3tz/vCP3+HW7XtUZUG3O0BIhdGOk+khSjnmN9/i5X/n3+fkxvM8ePstTu/coH7hJf69v/f3+af/4//AycmRJxgQHB0dM5svuHLtCuWVp/mTP3qNSVmhLl3gmReeZT6foeKIWlt2H+5x4eJ5Bv0OnW5KVZQ8/eLniMIeO5c061tzti48xb27d8E4ggA2BiN+9sd+EmUnnEumjNfmXH66Q9hNEbcEs1wwz7zmy2e+mKbHyS5ZBNvOu8KDHoVAOMHm1gaf/OTLH2Gg2g24Ma7ZyHpwJdt15AxDZc+U+h/fnHrnxO9jw/ox7W3PlBJXDEm7Rp25z/KFaX+xRC0fLeM9CspWR3kcrNGufe3fiFXvFW71EB3gReVYFxZj3TICxeFQEoQVpA3j84jkpDmAwW9fVgZaj5wC2jr2FgVzJzgHDOcLfun8Gl99eMzDsj7z2kh0rZFBm6m5erLLXrP2uXp6awm02j5u0bQjtGBPinZ/4XurWnaqZbokTT+W9PDM90utDLba40q8LDBoAFUoBaFSRGFAGgcMkojLF0ZcvzJCCu1ZcefnWKU+pvnyBxjOOXTDkPyrDq0t1li6nQRwLLI5m5tbVHXdSLMd3U5K2O8RRzFhGDCbTABHGEb0ej163d6yNyhQEm20D2lv7r++tsZiMSdJfA6WChS9fp9S1xR1RV2VzOZz1tfWQEBVlUtzDikUQejzZtsivRC+VccBKgyIkxhjNWVVkXZSpBTeilxrRlFMr9NlNp9T1AXaGPrDEfcf7HJpc+j7u5p4nrqu2Nvb966DTYZXVVWESlJVliAwnBwfMc8y9vYfYCpNGIUUZc50espw4EFmVVWUDfirao21htPZnNFoxL37DxmNRvQ6HaaTE7TWlEVBd9AjTTrL/CutNbP5KZ1OjyiKCKMIbXy7D04znZyQdDocHB5yfmebRbYgy2bkeUEYKqI4YjFfkKQJi8Wc8XiMNpo4Sf7cOfkH6Mxud+SymSRXE9OyYXR52y80QcNXWweeB5IrelqAMavGzVX1SiCkWh7DXwCNWliAiEI/CTTHccLb2UjlJypnDAiDdAHCGVI3Y7sb0U+HRFHqq5DCNKxbO5FCLw05v9Hn/tGMqtYowmU1CCxWtjVWQcBZGSC0oZe01WapGgC0akIWQiCdA+1dAW3D7bUgZsltOdlUQP2rbq1v93bOVxja9lS5BFhiaVzhLXqNX79wS+C3Wuz8e9Fa1C+lLcKft1SCQAjsE95UfhxSQCeaXpTvMdpKn2/CFxhCFIIQg5ARO1sp5XFGvqhQzmJdwDyrONApNkqJpWW/tpzmgk89c413P9gjc4Z3b37IWqIYiIrZ7JAHkylG5yTrFwmThKoO6PYjlK7YXh/QHXd498Cy0DW5gbfffUgvNlzZ3OLgRBLFfbKqIJ+fEASOsD/ACEFVLMA5Or01qqJGL44IsfR6fer5ESdOo4xmuLHOTCZILEVR0+v2KBczykVOQYfuIKU8PeKdtw9wacJkVhKY0DuzBQFx2qEqc45npyA7BN0OUWiR5YwwEGirfGMzmrzMKaYzrFNUYUwUSsIg5KlPvEJRKqazBXUlSVJFXs6gXNCLQ9YG8NT5MS9f2eLT17dZ2xlj05hUnLKzWXP3IENaUN0ulc2oTcxiOuG93Zpr43WK2hLYChH3GIRgq5rf/fodZkXNi0+f5zMvbhO5CkeOwzGbW779wV2qQpMGPaJOH2cqqo/LPNg1m53H5Uxn2IvW2GBFYDtfrRMegH3+xz/Hf/YP/x5a1/yjf/Tfc/fOXZSS/PIv/006/R7/2z/9X/n7/+A/4n/6J/8zb3ztm0wmJ6Q727x90uVBZhjKfWKdIWPHBMEb7874/Kcd0h5jq5gP93Jeff0D7tx7gCkyMiEJogStLUJ6cHrrrW8QDTb4T//L/4J/8o//Mfdu3+TWq3/Iz3/6WbY315meHNEuIK99+Sv81he+yE/+9OeYCMODQZfgww9Jv/ktLhYFi16CSWK+/sZb7L16wrPXn+Jv/uK/zelkwvw0Z21tg6PjQ9585y51nXFdpty9fY9rV65SzhesZTk73QHBxefo9EquP7NLd6vLfObYyR+yvz/l+ATS2K9rpilWecLJSydpMsFo9ttSKaIwJFLKV3ItCHE269AtN+DWPu4E2MzljVGREB9lqFbvsfv+5s6/+Gl2ybKsTsA99vszoIt2f7D62eNHa8d33rqcBWCurTl+j+EfrwuMsaAb1Uzz26BpKRbCmzP4ZyQ46zS45OTc6nhnCbH2s1oaw73pKU9vb4IxXLOCz6YBv1tVFEsMJZaf1bMKF19abd9v/7Nl4ZgGJEl/jUrRslmiud26+oozRJfvjxLCfy7V0uDCh4qoJqIhEL7nKpCCIJCNjbojUsozVYGiEwX00pBnrq7x3LVNImkx1vdK1tYDC6ofrh6rJ1U7LrKcoshI0ogoUJS69GxLqOj2OizmGRvxA7AZAAAgAElEQVQb69Ta5zJtbKzR6/a8RA5YWxsTRzHGWPq9HlVdYZwlKwuSNCEKI46OjzFaM+oN0V2NUhLrnDcicpa6rnnw4CH9Xo9YCBYzbywRRgFpmpBnC4z2bFgYRqSdDov5gjAKfV90EOCMV0JZZwmbvXVRFKggpNftUtWVv60CLl+8xFe//IBrl85BXVNr7YOQa40sSqyDjc1NptMpWZ4tWbMk2iQrM3Yf7hKwQxAoOt0OOEun2yVbZBwDQahIOh0kUNYVSZpSVRXgqOoKXVfIQHG0f0BR1/T6PayBLJsxXFtnsZjT6XQ4OZnw1LWn0XWFkD5zK05ihPGfLxWFPosT6KYdqqrm/M4Od+8+wBjL0fGUja2AMEpwQhBEIVtbm+wfPKEeK2howwYsWOFlbe0Eac5Mb+1EK5eLTLPQWC9Z86CqAVeqBVXNH7vWyUQ2MjoJIkAKgbE0oAuEVI12U3qwIDzyFlIihdel4hyhnTNUM8JgEwBdFX5hlJJiMSNMOjjnqdWsKHFYenHAcV3Q1CVX7I/2jJwTEuvLPKg25byZ8N2ZGXfVvNy8ZtbLBTnbJ2XNSupnW6vdMxU9zoIzfz9jfHN6C2yb2fKRhdg6vAmFs7jGzGL5/vjud4QEhfLWmstKuD/gD2qb+pdunDUU+U7DwZmLz7/VQhIpRzc0RMrRSQVxL6QTGxLpSENFPwJT5kyymLVByq29BfuTgpMczm+v8faHJ4g04Nqz1xDlCe/fukdQzTAqxghJp9NHhx1yPUGfHjPuJFxZHzPaSvn9L3+dSq0R9MfM5rtcjkMuP/8s797+ED2f46wm2ViHJMUsSpyB8cUroAT1wQFBXsBoi8BNMNkhxsVcGtTsR0OisIM7nVJbqBWUk0NcPCZyNUQjiv0PKOeHiOEmlH5vUQZ9SrHOaGOdITUnk2OqyiFjUIFGxCGdTodaW4SxBFiiuqA6eQ8ZJkRrF5EyxQUJE9NhkRVUlcEgmM4zAmfYiuZc3ox54alNfvKl57m4vU2/nyJCgaamKwSqLnDakmuQViNMjnVrVIuSu0cKqTOsDEgHAwrjSJXmqB7z228fMM1KvnFnwloy4/K5NZTyafGhNvQSSaArrAsJkg7kNbn1+vCPYzTC3uXtsxvrs/2S7XA4Lpw/z1vR25R5xc65c/yz/+vXePHF5/nxH/9RTo6OieKQyekpm9tbXLl6jQf374FwbJ3b5PLly2xsrHF0fMi90wVff7DLdDIhm0+o84KXNmO2j0JeenlMuTglSWN2TyaUVYWQAYPhiOlsRlHkywKFMZb9999kUC/4qZ/4HL/28D7zw33ee+st/urP/hXufniPLMv8c8I3GJvasLG5zqWiYqMqmXYTvn3rDl0s587vcOHSDuNzm9Rlznw2JS8LZosZSgrSTpf7D+6RFwsePrzP5UuXfTyGELiNPsVkwe5v/g4v/fIvkEWXGfVDunLGsD9jbTAlDh1SGFQgG6bJYo1/ndvm/aYGSNBUliUOXWu0NkTuo31T7dt01rhgpQRogbHA2kclgWeB1ePH/J7jY2Ss2hP4zlDp7E/dmZ839zlbWH1stL17y2LuY8d8TJfxyNcVueQIcRi7AlUtWGlLiEFDSbpmbW2U/36/IFidA8v4x0cezjmogbopviqpGMTwq5fXOLnl+EpeYprH9hI7tZSEtm+xbJm2BlSJM/us1V5q9VxbkCWlB1VnpwYhffFUNYDMZ1E5H4zagCklBEr66ztQiiBQhEoQKggVxAqSSLI+7PDCczs8c3WDEIOu62Yfpahr/cj+4odhGGOYzWZP5FieHdJEUUgUhcRJtPzMxnGMrjVhFFCVimy+YDQaE6yHzPOsUQl5xz0pFVIqjPaGDv76UJSlt3KPo8hH6KhW9gZCSLT1JMFisaAsCpxz9Ho9ZosFEkEURWhdkxc5NPE6KvBRFs7BfD4niIa+t8oaz9rrrCnYWYoip5+mKOFZySCKuHb+Gu/duEGn36eanyKAQAUEQcDGxjrzxYJut8tiPqeoS+I49oHMAkbjIZdFiK1K5vM5SRrjtCVJIvLFnJPJCS+88ALzxRznLHEUEQQBTms6nZhLF89TliWBirl69Qp3HzxkvvAg0TTZU1lRsNFdo9vr8nD3IcfHE+IoIooirDHEUYyuLafHx3Q7PWptSJvzXcwXxElMFCVsb58jjCOmJ8fkWUGnkxAEIaPh8LteD98XsGqnwmW16cxkb6FJDl+lNYjlfVuQJT1F71r9r28MFtI1/JcHR7iVxE9JtdpEOJCBBBU0dLVCBoHPZxGeRZNB6B9ZSoLAp4iLfBcZxkhRossMGUgsCuoMo4HZjDDtoMIIaz0LFScxKquw1njpHAKDRdpGuicEqJAlc0dbffJa5fZ8V0uMn02tsY08wjUg1VcI/CLrM0tMY2rhmTrTLLi+OdRY05hc2IadanqtaGpojb26a/8VnqFCSL/JaJwLbRtg2TZvCpYMoLeOf0wD8QTGD4V5BWeWamOQpkJiKBaG0FQMuxFJpEhCwcWNEe/tT1hkNZcunOPNu++RGcXdk5zt9TXkjV3yeIv5/JTnrl3g4MFtEhFy1NkhHQz8ImcrinwOTnFcJ9w8djx9TvGZaxu89t4RVXyRZHyJD041L+70eOrZy0xe/QphmiKHY2xtcJUhjPqYzoA8P6bSBaEcwfoO290Tjk2MmWqqOKEQMSFQ5QVxlKJnR0QqJDOC9VGXhbBEcYLJYt+eKCwqkBxXknoWMReaSFVE6YigE2OQLGqL1pLJSUUgHNIZzyhVFVe2+0SdPkcuIq8tTgQcTwp0kRPoAsoZwhku9lNe2hrzuVcu8/Lzl7iwOUZECQ6IpEO7EClSZFWAFQRJisqhzBaEVqOc43BaElIj4i7GSXS1IAwFr98+5KCSVEQ8XNTsH5QsdMGVzYg0CoiDmvWuoJ4fI7tbuCBBcOx71D6GS3b1Wf5o7f/xz9Cyx8J61yZjffHr1dde5T/4u7/Kyy+/yHPXr9Prduj1+/z6//1rDPr/Lj/1U5/n1q1bnNvZgtA3HSexIk5TvvLVr1GXJbWxZHmNcIpvnSr+zy++wy+vP0uxqHnr2w+ojJdwWwfD4YjJ7NRnCiKW+U1Hu3v87m/+Nj//N/46v/mF36SuZvze7/4+/91/+9/wqU+9wle/8jrWOeq64rUvf5m/9bd+gY5UbG1vo63huU9/ine+/g32f+cPmGyf42f+47/L+s4OgYDKGIIgoD/oc+vObe7e30VKSbfTo9/r0km73Ll3n0Gnw/bakIO7e1z5xItsnd+hLCVVKQkiTTeV9OKC0JTo0vcUaO3NLIQQKNXkblm/dvmw1Ha+FCC94sD3B5+BF00x7dHcqpUEvFlEAbUEUWdlnmdv/7DYrZ+9OlfQqV2HvvNfPHJFNyjoI/c9A2o+CqIedRRcZkAt7+dvSKArhHczbtgpgQdVESAClhlNS6V+yyS5FSPVtia0PViyZTDbcxNeMhhIiRQOqzUvj8b83adC7t14wMPaUmm9lFoBj7KSYmW65Y0kJKqVAzdIa0VWNdcgFiVWKqL2i5Rt75T/PxBN31Sz1isFgfTASkpBqCShlERKEAUQB4puErK5lvLZV65y8cI6nTjEam/BvWTMhPTgonYf6/X3/QxrLdli/kSOpbXG1DWyGzaAOfAMjTUUWU5dVVRVidMefKogIIkTFosFutJIIQmiEG28dDBNU3BeylZXFbP5nOnJlPFwSLQ2RkiBqTV1XYGSGOP3htr4vV/d/F3UGF9oY0iShGyxoCwLBmmPXqdDWZTkeUE2XzBwK09v2xjOhWGEwOdTTWcTdO0L+nWREUjfb6qbfCkpVeO655pgX5gv5j7mSCq2z+0AmrJc0OmknC40i7ygKCqsdURBQK+riKNtTqcTjo+PqMqK3tqIzmjE6cz3/Y/6HaSELM9JOiF5PiNfzCkrTRRFjEZDQBInMVXpjTwWizlXr17mYP+IbjehKEvCSJGkMWkao7Uly04Zj8YshKCqKqRwXLxwnrK8xf3dPXStuRSllKUmSWKOT+5+1+vhB9K6LDftzQ3bpJKcUffS6qWXVSixcqFBrnqUlh44Z/oIsGIJZmiP0QATpXzatEQ2FZj2NBQqUJ4FUxIpFUEQElBRL445tQlFMafTLdi6GNIZBsxOFkhjUUFKaUDFBhWlvgLQZFAZXYP0KF2cNUV13gACYxGoJTA5OxF70KWXDNTKyc8sjyEA66QHcK0ZhXNYq1f3g2bhXlUyRfPV38+/vksoJFiFD6vGMMStmDBr21Bhv0gICeh2QWqb4394gNC/8mjeNtEYgbB8jSTGggxDLILZ4gilNvw1JiXdXp/F/CH3ZqdEO0MSqSic4/bdBwRjz5ZMdcV779/kpz/5LBvnr3GaVSyiHWIZoBczAqO5tNHD6ZT9UvPe7oLJac1PfPI616Zv886tXerBOVy/x1fvHfBM19FNOhRpSl1oXFFS1zXdzoDFbEFZlDgbEAzH9JREOclsrkmc5aFdRyhBnc194SHuEOopRnWIZYBa38FND5Ghw3Y7BHFAnAyIojHECbUI0CImm5aEQYALFFEosUaitU+Gr51GqQAZR/SShPFok9oJiiNNVteoLIO6IJHgsHSSkLXU8VPXO3z6+ctcvXSJ7XGKiDogmshvF3H7oObL3z7ygYr61HvhhCEag5nuofIJM3pYO6e/FXCw+4Ber8tkXvLefk5V5JRFzoXLF7lzFPDlN27wd37yMpM85/xGnyR2uDqnqgpMFBMKCKipvh+m4Ilekg7pHIZWAuzPo+37EU2TpDdC8Fu+9Y11Ot0uVVlw99593vzmm3SShN5gxCxbkJVeG2+tYTaf8/kf/1E6H37IF774e4RScuHiDkoqrly+QhiEWKu5c+cWx4eHzPIFr33zfW7v/i9gFfv7R8zLJscPx63bt7yjk1gVmkSzQfztL/4Og3Gf51+4zle/8jWm01N+/df/OZ/+9Cd4//0bHJ9MSeOQbLHgS19+g1/523+Tz/8n/yFf/j/+GXuvfpn9b73FsVS88olPIMOQ+3fvEcmA7mhAXVcMR30u1DscHR+TJjFHh4dcuHCeIAo43j8kBHqDAU/9tZ8lGPWbbBbN0e6cnfM1or4DiyNsrcmynKKocRaUDLw0q5mHXSOdDqQgDALfQ+wsWmu0qZdMlGjZA7FiIR43H2kLbf7nppnL7XLefTzD6lGToz9/fEyX7JnVvJlS2+feStBhuY4/sra4tpjglkd5pIfpDLu0Ok7zXdtXtborOLeUdiNcK+xAIliz3vXPCQ+iWqe/WHl/rRbANQbFtKzRkk0Sq32JPQMAPQO2ejztBFYGWFNS1hVxFPDJtT4/s7HGr+0eUTpLGIZeTOFasLZ8BkuA5MN5VyzVirFqwZVYgq2VLqPpfV+aY7RFAP8zJZyvRQufv+SBgGesQgVRIEhDSSeW9NOAi+d6XNoZsjGKiQJJURbezCKJG+c6iRaGkAgpf3hYK2edD8d9AkNrQzdNOLexSVlVGGWotO+p+/DkhPF4Da0rX7SPIlQQsCyBCEcYhqRpynR6isRv7NM0Ja9KpFLEUYQTvpCutUY2gDwIfBRR7Wpv3BZHTWuN/0QNB0OiMPQKJefIi4JA+Tk6iiIGwwFJmiCC2BtjNMWhJE5wOMIg9IYXSnk2ttYYY8lmpxRFQV4UVFWFqWriJGIwGjOZTnnwYJd+v0+/18WTAg4Z+AJSVdVkeY7RtXcytJajw2PG4yG9fhepQqI8pq5rVBCQxgkP7t5jY/sCW+e2yKbHZIsFlbFo3cOiee76c3x49z5pmjTFthGlteTzU+Ig4PDgmH7Pm7IFoaKsKm9kpRSHB4cIFTIejXzrUhAQxxFaG3Z3H1LkOaPBgGtPP8Pd27dYWxs3phdPiLGCVZXOh+02U4/1s5NbQi7hN6lCoMSqImM5G8jmFx/pBKbh1EVjXW6F80YRYlXPkkIilEAK5StOzSIH+MwF1QQBN03Cbe/V8f5D9OSUyfQU5wRhMuX+wZTOaJ/BYJOkD2EqiV1AqPzFJaTEWI0KQ6jrprNrxZwtJzO85a61DmEcVjSyxSUgbDdAzXLhHIhmgXQr1m+luT/biyWb203mln9aTdWU5ne+B8CJM46BzTHaqGOMXTJaLbBqm2f960Tjyvi4hOXJSwE/Npj2vR7YnblLo/twDgyKWoTUwlHU/jWUQYBQIRrBrXu7zGtJsDil3BljrQRd4WTFaOM8W8cTTo6mHNRzXn3jHSqRUouExGmchrwW9AcbbF9OeXarx5++8Tb353scuE3e2l/w4isvcbj/+xwVd1HhOVTluPH+Q3Re0n1xC7uoqLMCJKTdCLfIvSW1AUaK827B3n5FNauRo5RAG5zVVNkp/cEYmQ6oraaaTxDDMcfaYWdT7GRCKixSxNTpCG1D5KJAzmtMpHH5jFJkuG6XcpGB0TjrP8vOaVycIqMxKu7w4emCrCjJFyWmKjG2ZmNtSLVYUEZ9eqN1fu6T67x0oQthzLePHZ1hQk9bAjQFHW7sF/zWq7d488MJx/MMZRe46Yyk3ycZDsnmU7qhoQwkIgxQeoErc1R/wMG0ZG8u0LXBVTWBTLjxcMIiGZJlll/7l9/iF/+tlxFK0E0iZnWF7PbpdVN0pT+ei7b9sNOy2WeZkOYChY8UPz772U9x89ZdXnv1Naxx7Jw/z/bONr/xG7/Fb/3271BXNd1OyvHkmNf+7HUe3N1lfWPE0e4hBsuNGzeJ44Q4itjb32Nv7yF55quEuiooCsHpLCeMIsqiQNeNYFFAVVdeUXAWFDQ9RlmW8eqfvMY//M//AWVR8PWvv8mXv/JV/trP/TS/+As/z5/+8Z+B0UxP5/y//+ILvPD8M2wczKm++hbJ0xdZXLnA1ZdeJr24w8HhMd1OhywvQAnibkKaJmxurPPKJ14mUO/Q7ya88bWv0+/3SYOAT/3Iy/QunCMZjwkC35sbhRFFNcOV7yGyG5hCUBQhVe1b/AMlcVIsw+v9nOpzfZT0Yq0l+yS8S6Ofj9tNr1xuUh6Xbj7eN9XO+21PVwsqHrFd/74kqR8bsvLrdbsfaHCPOIuKvsupOVhe92KZy/I423WGisFD+jMPzJlD+O/F6nd+xXOEzfUqgVD4HKfGO+sx1qt56DOgyh+t3QO0eZFnn99S68D+5JR3dve5vjkgCCRZkRNGCVup8ptcxNJG21q3fKxlUbM97+b/di2WUi73PUJ45kksn6s/19YR0IOqlZuxonH9E60ToVw6/nnZnyAMJGms6McBW33J9lrCeBwx6IbYpngQhIq6yFHKy2FrbZoA2BXo/GEYxhrm89MnczDn2N662OTYSZy16LokUAHOWKz29t9BEGCMIQxDwjAgSRPyoqDWGlWULGZzoihCSIEKA1xZUJa+eFqXFXVaEYYBeV56dZGpsfg5KYljxuMxMlBk8znZImPYHxIGAXmWU9c1WZaTxCG61uQ2RxtNEIckPYfTGoE3dggCDz6iKGRjc9OHxAchQtW4RmFghUBKb1una01d1VgZEEQx6xsbqCauqCxK8iIjK3KyRYawUJUVw0GXKs+ZaZ9X1e12OZ0t2D88IolTet2UOE0p84JyPqcqC+oqRArYOb/N3tGEtNNjdjolb1i98XiIlILK1H4/5jwOSNOEsixYWx9htGmKGoJARXS7XZJOj7oyTKYnWOPZ2F6vjwoUa+sjhAjYP9inNJ4VrKqS8Wjzu14O37croJfzsaSdl4uoW1WsHG4Jupahuc4t3ZCgWYAb/tzhmjwoL6XzgnPvLobwUC1ofo8USCVQMlhOQkFjI+ytxWkmJL871mXBLCso84qqNshgTnRUEPdmrG0UbGw7+mNF1yVsb3QxQlHW4IxqMiAasOi0l4EIX+ZyWIRTtAG91hmsqVHKO98J4VkzsbR+98+JBiT5bICWlWoqfQ04dY1EsOmcPjO5+9daOu8+dNY0w1jjGwCc79fSSwmKbGxQ7UrK4NrN2YpJE1LgjP8q7KML+5MaH59A4Hs/8lK+4EA4iRESKQyVDaCuIQiIooAgFohQYAPYGm+SJhCHlk/8yAvcu/sl9uo5cRwSWMFLn3yaW7/3NbSG+w/3ob9J3N9AaEM+P8WIiEpE3DvKuLre41MvPM3Bl75JUWc8OJyylih+4ic+xde+9jp19YDFTFNnFcPNHXRW4soKXWbEaUJY1fRDQ17PSQc7hMpismMmx1OUrClLRawPKJVCCU1v0GOe5+TzKUo6RKeHzRagNd0kJFGOrKyoszlJkhC4nFgoAm2wsmSQwPpW2Eg/JEHUwREiZMAkrxHdPrvHE+8ytMiJ6pKttQEilIRJl7LWWA15oQnTMd/e09w+2uNokrH3YJ3Pv3COvcMjPtgr+cM3bnPsRhzPC8q6JlCSJHZsrY+wSQ0DxYVOwp1ccOcoY3Hi0CKkLkNuHlQc5UNsfkJETLnIueVO0Z0Bf3yjpJAxZVHTE4Ze2qOalf7TqGIiUZJ/HBsF55Y9mG2Tx+oz226+VmxGe1vgWBuPUFJiasM779xkc/M849E6v/JLv4Kx3qK9rKA/HPGl118nzxYUZc5gOORkcsrp5C51XXEyOSHPFsRB02PaGAlZ64iimLIo0UY3lVDZzFHukfNppAhYa3j//fe58d57/Orf/iWG3RHbOxskUcgv/o1/k6vnt/jn/8+/4HgyZW/3AXmZUXYUn/w7f4vX733Ii59+iUuXL3F0eMRJ09fViWO+8L//Bj/z138WeeUipjYMx0N+8qc+T17U7B+dcOHcNld3zrHV6TIejUiSxAe+N5bEg/6Mcvq7HD+suH0/YbpQhEmfMJ9gtEEbA8LLpM5ueAPVFL0aZuXSxUuN9BE8CFaNy98KTbTvVxsk//h72LJV/rZtZISNmcHSsv0v//Bsj1yuTWc/Pe3r9fjwMwgNc7Niis4c8TsWOMTZb86Cn8cKgh54+G7pyELsP1Io4RkaceYQHwFXHznXM8dsb6+Is+Uv52XNlz64hat3uL4+9EUnY9mQEGhNGKqlDHAp58W7RraOfwiWUtKlyUXzve+nAiXbz74Hee1zaM24VPN6eAdLi5ICJUUjCZSE0tuop3FAqByRFHQj6EeWfizoJpI4lGCbLLyqXOY0tW6VQkh0Xfu9QyB/SK5UEM5559wnMILQb/jv37/HYDTwLR044iheZlIZq1FKcTrzeVdSBoQqIDcOU9ecLDJOTk4Yr68RxxH3PrxLEIUcHR1yfmcH4yy1Mcymp74nCyiKjDTtgLUoJTFGI5BU2pJlBYtFTlVVREmMdY60kxKFIQ5BXhRorRn0ukwXC5QUhDJAG0NRllhrfWhvGIFSaGe9c6AxWKMxVjMer6O1paxK6rLk7Te/xdrmmGefukaeZdRGE8YRTnib9CjtYKuG5dLWs2tSNDbyBilinnn2OsdHxygF2hjmeUWaJmSLjMnkiPXx0BMISGrr6PeHTE8mjNfXvbrIOhaTUw6OjpmcTnn++nOsr68RhCGLRYYQgtFggLGWsigoipzT+YKrV59mOBpSlTl1mTMaDZnN5tx/cI/x2jqd3oj+hcvcePc90k7KYLT+3a+H7/cCOls5fdytyD02GS43Bs3vlk6BtBOAn0nPTmC+OiOXk4WQsrE3l5zd55+VVpydDoXwbk1tXoOuKrJcMzlZUNYG53LCxBCfarLCYUSIkyFR1GdRWdJugiurpexuWaFyDud0Ex7rq0bO+uZN3zPme5taQAUO6zTYZgI9e+7WvwKtrMe2EsHW0IL/j7v3epYsu878fnsfn/7m9XVv2S7TvhqER2OGDiTBmOEMyKGGiJjRgyImQi96VCj0D+hB5kX6CyTNw0ihmQiRIDFDA4Dwhmhfha7u8lXXu/R57N5bD/uczFsNgECTPWxAO6K7bubNPOfkyX3XWt9a3/qWld+tQJapmphP31eqqlZp3PR7gZCZO2etZ9njyqHPaQdmzkkXYla90toazA82Zf8Lms067RXtHAGMFDiiQAgXT+c0ag3y4gDXFxjXELgu159a5Stv9VCpT3/QxyFDxCOSYoHH9x5w5eklHKMZTsY0mi2cVhsjHOI0I01G1NtNyEdMhoq/+vYNPvrSczz11NO8+/ABySDl3WzIx188w9Vzi+QnxzzUOW6tid/wmB4fgXBxHUFQpGRpzOVLm3jETGULNY3Z6U0w6TFho0aep3g+xNMBgReSDnsoJdDpgHp3mSKL0cmERnsJURRM0wmFienKjDMtWF9psLbSItUhqsg5tx6xcWaBVlRHuj6xt8juyOHN+0cMb+9y0B+ick2RTXCKmHqzjVdvMR73GY0OSbIxJs7J3IA3Ho2YjPscTIaMBgX9/SPuH/rcvv+IvYM90jwlbQo7gd0PaLSatFsRe9t7SApW2k3One/iHp7w6DAnqHVQjiQep9x6NMK0GrhFSqLtrCu3KXHSMT84yHjxTI2FGpi8IIgUTjIFUZBLH1ekHwqt6nRgKapcu5kLHsxeJyq6mQ1ix+MpN2/enM0JBDtoUQjYevwY1/MIAp+D/ROOTw4Zj4bEpXjEydEJT197Bt9PUEoR+D4Yg6NT4twmxaw9UaV4TmFBgqgCTD2z/5Xdqa7RYNDK8M2vf4v/8l99kT/4579Lf3DCc89ew/Ncnn7maf7iL74KGIoiJxnHsHGGg96AkwePWdlcZTge4wchSVYgPZfVzVUunT/LG3/yH3nx936H1fNnSbKMySRhOk35/G9/DmE0Z5a7SK0IXGuTbYCjKIqCbHzC3oOUV9+M6KctJkWBMYWtSAl7T12vnCMzq4JYvpjn2wSaVppPf/ZlvCCwHAHzpO2cVxysT1NKnbLB7wVWZX9KSSubg2n9Htv+s9aHE9rOqzlPgv+f+NpTv39CmOXJ//0t55ofh7IKNb+GJ+OBanCxrwUhisCAloLAlSDU6SLwfM3zAk8sLQAcZMkCee9VVoaC8iAAACAASURBVHvFCBinBbd291lv1VkOI9Ii4flFj9Vtjzib987NE6enKptVxaysUD0JquSsd8pWouwHmPV4lfdFYGZKgRWd0Cl7yFzHQQo7Ekdqhc4KcA1RzacdOLTqEs8TCGHBmOe5VrylSJlmqe3F8j1czyurs3ZfW4rbL8cS0s7v/CBWVKvhOg5FkYOx9rHqfbLKf4I0S3GkBVfaGNvHIx0ajQZ5nhPHU9IkK0GXZGlxEY2hXqvj+77tvQpDdDnoNy8KVKEQ2J4qV7gsLy3RaDSYTCaWupdlaOOAFAyHQ6IoQkjrS6s95bmeFXsrS5+qsFTGMjpkNJownkwJOm10CUSllIS+z0KnQxL3MNqUND+r2pdmKYVW1Gs1lNY4jqDIcs6srtM7OsQUmmazSZYb+oOBFZTBJuEa9Tr9fo8iz5FlNVfbCgu1RofjwZQ0V0TtRU5GMd16hFKai1cukSYxaMNk3KPZaqKBMAxJspSiUBweHBInCYvdLo5r24Uc12WhWSfLUvb2DnAdGI+HrK6t4nsejUaTMAzpdDrs7x9x7drTZHnGaPzThU/e31+BmBtOqa0rPU0jm2dOT5XQS6chpHU4yrHGxKVS1TEYxzqdkgY6PxanZbA1IEu6nTWmFsDMidJCnpowLgwqS3j0aIcsTpnEOUlm+5TczBDkmkJpfD/CdUNqtQUacY7jWL67UkUpxW6tpDba0v+MsdQ7TjfM2gqTRJYATJXGpgRFpzixBl1+BttrpbWmsJJQaFVA2QtFKcGuVEkRUboc6svMmlcAyYpeCEwJjHR5fqO1zVIDQs8pf7Nrru4tltYipEAoO33dZst+QYHQ+1w/TzOtOeUmrRALFFqgihxTZORFgZBuyVs31IzH+QXNpdUO3/3Bbd69LRiO+7TVlKlTY3u7R+hnuK6HH0W4nTNk0sNMJ+giw6/VaHbb9I4nZMInaJzhtbfu8NyV8wTKThs/ciRvbB/ym9deYnT3TVKmTI4SzPQE35XEWY4vbLNxFvncvbPH8lKXhVbI0e6AkVGEsiAbHYHTQYQSNT7B92qc9CfIzgpBZxmJjxhPyU1OTAMTdMBrYrIBTX/EC1faPHNhkY2liLDWJgoDGg2foB4RCI9+5vOdByPevH/It28e0Bvl6OkI4gmBkUTdNkVQZ683wS0KiuEQmY3x6xHNpTYPHu4y6B2jvZBpqjA+9F99jf5gRLS0TJpbo9jqdul226g843gcE8vQZlFPEp5Zm+KoY2p+iFMPiFLBaLCFyj3agSDwFhkMpgwyQUsFYCxFYzUSLIQuUjVpeSMORZNCGJRwEAVl1fgfdplT/54OPn/apVR213UdFhcW2Xq4Y7PRLlx7+gqbZzf5yle+xo0bb5EkQ27ceJO8pDgjSp6+1rz15ut0u0scHOwR1UKMKRjHE7RxZgBOa8Vo1J8HzbNcmkVYAmEbqZXC9VwKZZv0W40Guzs7tJ0CPwi5e3RslfR8D4NhfXmFCxcv8Whnj1azzZ27D/nen36Z3/r8bzOUDrdu3WbzzFkajRqbm+tIB17+3d/hW9rwpf/nT/jcf/EFBr0et99+l8/82q9y8fwm0+m0pHjWOdrbprO+ga5mmADjQZ27j64x4ZDHJ1OmqUKVvkoIMZvhA8zoWlHoUwvtsMpJHGOMIAptcKa09RGVTm5F1ZxVI06DZVMF03NgZftzKn9jZkmz98sc+DCSAcCTlaOSjva3ScX/pOd/LJb4Ke/l9LHFXEev6uSeX0eFiCUNCX5ZyrEzJp+8UT+xQlW9vfyfNjaL7s56sN5z/aeOUwBHqebGzj6fPL9JTUoWi4KWKxG5ns0YsiDczN5vTh3MOR3T8KQ9ELKkBgKcik+qz1IR82QZxxhj+3M0Cl0IHKPBhTBw8CTUXJeFustqt8bCQp1a6OBITRC6tuInLV0Qt0w+lvTUqFazYEHrmXz4L8MKfJ8LF85+IMeyoy7sHKvQ80iNKclMhiSJbQAvJXmR4vtBOcg3p15vMp3av/PxaMp0mmKUjeemWYzAodNZwGjNQrNF5AcEnh1f7Th2b3iex2QywXVdNtfXbW+S45HnBf3+gHanhSMl02lMVKsB9j1ZnpBmGVmWo7TG9YMSFILjetae+SGu32Ft4yrtdpvjnfs45ogsHuI6Gi/yOTqYsNbtcHxwQK0W0m63yIuMNEnI0hQjBH4QkcUTxsM+O7sHrC42GE9GBL6FIOPJFG0M62vLxNMxjhAoIXCkpFWvc9gfcnjY59HxlO/88A3SrEC6Do6AaxfWubjS4GTQZ3VlFc93iZOEay++wI9uvs3W7i6dcjaWMYaLFy8hhCDPUsIoIk4StM5ptlogHB4+uE8QWLq567kEge33ko6g0WnzxutvsrF5hvbCwk/dD++bCijkfJih7TWqMjv2z3hGODvtVOYlqrnKkc3vocs5CtaBaEwliFNma2aqgoZSLKcCTpXAgkEKZ94gK8uKmNFsPbrD1779Pa4/fZG8sBkCS5l3cFVOnqUMR32ifpt4eURclmiNlHzt61/n2WtX6C50LG4r6XzVDAnMk6Ibp2kb9j6Ud8zY6e6iNKK6rFCJ90jripL+V1WnZg71lOgExoKjijb4RBWMuQM+fX2Vo56DsCr7WTl7Zk7elEGFpQbKuQzSB7Q+LIP78wQm9vsrv0uBpQMahTYFXrULpYOU4CHwXGg0fC6uNLjRqBFPUlZWF1FHU6LIsH0S887NQ9LmKs2lNWStQTwZk42G4DdothYZDyfkcYbxXHwZMJ7G3Hz9+5x96jluvPMQUTTYPkj4fnHMQl5j/2RIfniMX48Ilp+i5hpG21uErRWc+gKkU/onU1pS8dy1c4yWIkZHdU4Ot8lUiBv4nN3cRKQ54xMru+o5AVmsCEKfOJ2QqTGu7+GELZTnMXUdBl6DVAbowiP0QhpRjZrrEcmA44nPd97t8X9/7zb744zhKMVMY+T0mKbv0F27SE87JGmKKazqWpZM6LTbbJxZ4mhvi8HJCX6nC7mm4QmiVoNB/xAZNUgnCscLWVhdJDcO/fGUbDKkyBVFbsjSmEJlPN6N6QQCPwjJkKisoF43FG4L4ToUOgRG+EIg8Mgz8GWIwUH6ipo2bHR9bj0cIZsGLUMKLX8uUP6fa7030JtXqKpnzAzw2IcGxxGW+uMIvv7Vb/DuO3d45oXnGA4GfOrTn8KVgnv3HnFydPJEddxxJVkWc3y8D0ahipwkjSkUSGltR1VVsVWXsqpTJl9mgyfRJU3JUgeFMJxdX+UPvvB7ONJl+8t/ztLFp8gnKYNen1C3KQ72CF3J8vIyZzbPc3jU4//9ky/xwvPP0rl8iW/96Z9x0uuxsbHOxuYFPFcijSRXBevnzvPXr9/gP/3lX+MXBa1Wk0LnCAFh4BGFEbrIiZotdFFggLzIKfKMk96QxupFutEG6tFrIDWYAqN1GdDax8JxkcLgGc3GYg0hJEe9FImxA6jLHmFjrLQ7Uj7RV2RFiU4DCYHRP161Og20Zo/L/35Z6FWU+/OJ/qf3Oqu/9znsSZ4AHKfOL8qglvf0atWEIRQGTwqkI8tjOPPrNafomLzHX80eGAplKPWqTlWITmG4U9eT5Yq7hwNcpXhhcwXha9ZdlxsqJZ/FSHN/LE5tnKolwPoegZZ2dIouqwOW1iftfqtmbwkxA2xGK1Sh0MomWIUpQBW40uA7ksiXLER1NpcDOnUftKYWCUIP6qHHwkID6dgkrxBQFDmu8ghrEaqw6nBpbCW5fd/HaEWe/vKIV3i+z+bm5gdyrMFgQG/Qp9lo4nseWlumipQOBwf7rK6t2/4rbRUELb1YIh1JUlLyfN+zvVllJQojbPFCK/I8Zzwe02w2kVKSl6IPYOPworRtQRCUANoO5p1Mp3S6bRCCc2fPoowqQZ0qRS4ESZqwcfYsWZIxGQxBOvjNLn53jfbFZ2i3VghD229kzl9Hjo7Ze+3rGCPI0owkiWm3zqDzjJXVkLAeMRr2SaYxUkpa7Q7D0QjfsUDfD3wMkCUpjhsgq1jecVBaM+j3qXpU4zhhojTffeM2N7eOOOpNSOME6Thl+4vhtWnG1k6NP1pfZ2FxiUH/mCAMmI7HVhXQkchSfMN1XUbDIWfOnCH1XIwxDPoDWu02u9uPCIIaS4tLKJVb4Cpt1dERgvFwwM7uEatrq3i+z9Hh4U/dD+8PWAnwXa+soJxSBhSgjLaqSSU3RGlTAq+yOVNUlRU5q6zIkruujX2tNpR9TbIMKqqs3xxsuWVG0ABGKlxZASo551Ybq6L04NE9jgYjCp1SlEZMS4MyOUYrW45NUtIkJklikukI6bn0xzH/7t//Bf/9f3uZtcAnS7KZXa2qbzOTbcRMdKPihxttqjqQpcgYbM9EiWBMqSZosLZfGlW+t5T/qEBm5VCNtoCLCnTpU07ZfgfaXgyqsM9V4HLmpO3NPTUbpfws+klQpssZB1rPj/1BrQ8nSP079IpJgSgEaZbjOsL26Y0nND07s8zDxYkUXtBkrTWh3mwwPjnh6rULbA/2afs5D4sYaQSiVke5Efm4TzoakMdTwvoCWnqYVOHlE5qRJs/H0FoiGU7orjY4O+7waPeQqL3I/e0+zUtnOXupjpsPcJsLbAULnOu4uONj9kdTApHiBwLlhByPNMM7fcLQZ2PzMuub62w97lFrd0BKXKdguQjoDzOO97YRXkScJ7T0mPObdZAjHo0zUjekZyLeOCzYXA1ZTAxhpggKg3QDtnYzvvS9W7xy/4RHvZjcGBgd4RSalc0L1Nsttg5PSHODk08pxkNcR3L+0ga11gJbt29RZDF+dwnPjWjKBQbDbSYYUtEBrWgFAYtrbXLfZRor8sSg4xw1HkChIBmjSNjZq1EsLZA7YIwHQtBtewxVDVmMGfaPUJMhRB0K4aPzKYXSbI/q3O4F+EbRWWqB3MUxCi0jomaHyX+eTfkz148H3RVN+MdeOQsGrXJoQVQLKdKMQgrCIOLpy9eQrsFBogrFF77w++zt7HDv/gP29/cYDgZgCmvTy5F8dq6NA661GWmaWmVUIVhYWGBQiaS855qtfbQiRbXQ5+Jmhy/+s8/Sagn+/Fu3WHywhzoeMhEOPxoNiRs1Gm1DmN9jsH1ArX2BO49OaLeavPzyp0njCStLSyx0O7x0/Tk78NL1kEIyyRP+/GtfYzSdcO7aZS6c3WR/b4ednW3WV5YJAo/pZIwfRjiBj1YFWdkoLXTOzvZdrl5aw/U8PvGxl3jn7mPu3buP5zrWFhtDkeeoImdzuc7mQojnO/QnCRsrNZTTYmPjDGHgoMt+LPs9VGIj5nQ8DjAfaK/no0j0E8mu00kw5kmvn1G9+UVYWuuZo55R9EtgUFEc7W8rBeEn1xN9Z8z3vqWxzyXI56yYU7T2WcKhepGY+b0qGeELS0myvs32rVW9ccYIJA5SWnBT3fvT59HaWLELSugzLzSVydfq81UvAGUM49xw63hEs1bn4nLEJxeW+crRhLiwCYBZ/PBEcnSevK4Su4h5sloI2+fuSGlnelYiK7M5mbYCavuubawVeg6RZyXV66FHt+6zUHdp1z1WFyNCz3YGBb5AqwQp61bEpxz8KqWlumWpJIpCMFDkBVkcowtFFEWzvrFfhmWMIS8+mIHGBsjzHCHKmVF5zngwpF6rl0kpheeGVpXRtXY4LXLcOKFQOVIIQs+FQpNMY9JagDaaPFdIR+K4Hse9HgvdDr5v9QWyPJuN7Gk1m4AFDkmSMBmPMdXjNCOsRbTaLabTKXlmGTha2556hGBxeZXROEPWF2ktrNG58Az+whLKSHa2dnnnR6+ysXmWC5eeogianPuNNaLhQ1qNA46FRzqZ4gjBysoiaQH94oRWu21BY54ReB6FKkiznMuXL9E73LWsN50jgE6rxfrmGipPy349iTaaUVZwd3vA997ZYjyOSeOEXq9HGARWij7PWVhcBAFf+tYN3HqLZy+eYdA/5ORwn2YtYvPCBUaDIWligV6WpRgMB4eH1OsNut0uRZ7jhwGB73D37j0wEIQunXabeq2GMRpV5Dz39BV6wym7B4e4fwvt9X1TAV3XwRgH7ZhTDgEcI6nIDgqDcCrqmS1ram2nfBtMCZ7AEQ5qdmibfZkFCYaSqlJlRE8ZZ8eCGAeJFK6lAsxq9qUxN3B8cESRpDiuJM0MylR9S4KiMLYioTWFKsiylEJplDZ841uv0Fra4Gtf/RqX/6s/mh27qlCdNqIwF+ioFACr56pJF6cDJACh5xlmZgBqPrvKUgRL+XWjZ8dSWpciGHoGkLSxTZJCCkyhsQLNGmMEaMeqAAEKNT9/CVqrbJlWVb9cde1mdnm//OvnUCqa+2JMOePE0YaT/gnTccLgpA+BQygdnMAnrDcImi2kDGi1akhpyNMETzoY16cWGMLIBREh3AijNPm0Rz4Z4te61FsRjslJ8yltL+NM02cr6ZFEywSLl9jaGXH16iZF+g6jdIAK64yzjAvdJlxc4ySGaaJJJgln1lfZurUD0wmxEvg1iS9rFHgUWca7j3d4+uIGQctm0E4GE2qtkJWVJZZaNZrtAOX4jKcj1twlzi0GLHQWULdOuH8yodZt0ksm/Gj7hGDVx685qALeeLjDV169z/2JJJEhOo0x6YS6U1BfXiNx65ycJEzjBJllOEXOQrNFu9vCEwXpaMoEiWy18f0WgVEcHR3ghj5SSUIzYHlzHafR5Gg8Qh0npNpDGQcTJ7jJECdPKLKcuq+Ikxr3+pqsmIDUuMIKK0g3wExPkPmUrMjw/BAc1864Uyl3twecTGIiPSWqR1Bo8mGfLHBQefGhVawMZTLkFMCq1mm7C8yy1bVaxBe/+Ef823/7f3Hr7Zt0Oy1WV5b4i7/4MkHocfnKVXwvpNFsMpoMGU9GSAmNRmSz2yU4MNpYGl3ZF2SMmYEqsMMklVLzCq9h9t6Ke3/14hqf+cgmF8+28Vy4e++Qj338ozQ++zJvfue73Lz5Lm/cf8inPv4c52sB5861UdLBb4X84NVDfvXXf51aGNBZaLGyusxbN9/h/sNtFhfbaGMY9kdMxlOOkpRao8bB4QGT0Ygg8GimLXb39tg4s4YQgiIrGA0GRI2QwA8AgRvWWd54hlhNmY52ObtSJ/Qv4ErB2++8WzZ2K1xXsrkQcP1CHWEE3a7HwkoLEzZ4tJvieDVkSWX/iZUZY54QU3gCOJTubJ7sKu+3PjUaugKrP6Nn6cNeMzpjlQ0U83mXlEnH6gPPqZHzilP52/nP5tQTGls9ZA5aBDapOUsuCoPRtmz0JEuj9HNgK5KyuufV9ShUOY5JC4FQpqTYibIPad6XXI0pkUrZ5Kq0iWBZAbDyuFUFy5z6XFNleDyccGVtgZZXUOSKZKY6Ou+VrJZVG5wjtbnPnle5rJiWPbEU5hQQtMIcriPsjCrHsfLoEpo1j07doxk5tGse7XpA4EmiwKfdCnHdso/LEShlQZTv+zMwJ6QgzzIcR+J5nqURG9ubmCSTWcvDL8MyRpNnH5Dcep6WveySLE0ZDIYoZStDURTiuS7CaCI/AAmx0rYrX1u7WZSKfbMEiuWczqjLvufRajTsucrBwcII8ryg0Iq8nItWFAWe60FJJ5SOw+7BPvVWk52dPVzXscqOvmcLH46P11okbyzTvPwUgQjIRQB+SIEgGY35yy/9J175wXe5cuUKH/nEJ+j1+vyj3/pNamtXqLUfMJ4mnBydEDUikiRlPE0IwoDVtXW2t7ZKkRM4vX+zvMAXAgpNVAtAOLiOg8oFSEOuChJteOPBkG+8eoNJbAUvJpMJRZ4yylKgnJeIjdse7R7w7/74a3z2E89x7WwXD02r3eFwb5+jw0OCIMB1HZTSFHnOU5eu8ODBA8LQ4+DwgNXVVRwhuXrlMo4fMRra3jEpHfb2DllaWmRvd5def0it3mBv/+Cn7of3rwpYSn1KaQNwbebO15SgR2iNMmZGm9MCS1EpDcjcuNp/LTd43pxZne0JQ0sZIlcAC1FFwrbCwKn+LimRBuqB7W+5u9VnsVmbHa0aZladM9cKZQzSdXFrEe9uPWI4POBk56AMIKyhtjRDbStrqrwWUfHBS4AkyuyTmVeYfuw+zsCfnjlOgwVN+hRoeqI6RQWyymxUFXCVM61MBbqq+zu7h2I2X2We8SsflMDsyW94zv+bfxcfzPqwpFglP8/nKGGwMEhT4BlJI4roBh6DuseVC2dwpcv5C8ssr3cIwgY6zekudWnWAuJmHWOslG7UCDl7boO9HjhaME3GpKM+jgxwu+sYNDIdQzJmqW040/FYdn1uHR1xoiLyk4xIp7zw/It89duvQxDweK/PdK/PR1+6SP/2fdxkQJJrFq+t0m0dMtYxBW30aIpwU2StQ+BAolzefjikVQtoLzY4c8bn6PiY7e1tCulDrYUfRoSNOkdo0lTibKUcTxVpnGD6GSrweWs3hrCNCRTD3S3+7PvvMPYbZDKiyMa4cUwURXQ3n2KU5MTjMXkyRUx66LzAW10lrIUkR4fsH/cx7VUcv8Fis0YgJMf7u8hmRCokrVrEYnsD5WiOeiekmUanE0yaorVLqBPOLAakJ32OCfCaC0ymOUW7ht55gNKazmKHzIuYOAI5GBLU66SZxosijFAYk+BkR6TeKgcjTVAkFHtH+DplNEgJN9YweB96duHJ5nZsoF5GlnPbUiafpMNCp02rWafRaLF5fpMv/ut/yWuvvcG3vv1Nnrp8mdt37vLsxed4uLNFZ7VLLW1Yu5PlVlZdKYosJS9VmypqcqVmZ0xJqa6AxGzEhKXerS82+Oe//VGeOtthMhnzg9f2+I3P/z7Pr1vwc/mZK1z/xEf4P/73/5MLFy5Rr0dk0wFrS3UWFrs82hnx3NUp9ZMB6u5D5EvPolTBc89c5Zvf+T5f+rP/yNLiIqudRWqew5VLl2g26pyMBjzc2qLZaPBUGHHSH9DttMmVYn/nHosl77/TXaDRqCNdl83zV3nrrVdQqcf+vbe4dvkKL3/8WfIs4eY7txFIrj//PJ/7zHXu3b7FxXMbPP2MZmljjdF4SvjaHXb3clSel77HJu0Qlr1hqwxi3rRZLm106SOe3F76FJB+r994X6DqQ9qys54hoKLhWdhjxSVmu9WAPPX7KqF3mirJrDpbiV3N9z1U86MqKawKdM2pLULAbPRXGRekHqhyOK7N3ArULOlJtZ0x2laaMAWOI2dJBSlttcE3AteTONWsMqXQeTF3neXHqLxOJfCyP5pwZzDFC1wa0nCIPVd1vdUNqWTq7d/WHKRVsF0KY5k6xu4LKeyQX4PBEdYOSGOIAhffkXiOIHChGUqakcdqN6JTF9R8h8DzqEUWHLmeQ7tVw3Ed4jhGa2UFFhwH3/eRZU+PNuX8J2NV73zfLUUbFHn2wajs/UMspRT9fv8DOZYjJa7rWlGcLLPjJzCzezYaDQm9wNIEC5uIdx2npAdqO5DcaFzXKfebrTZlSrG7u0u306Gz0MHzXLRSKGN/r0taX5ql+P4pIY7SP0S1iP54iDHQ6w9o1CI7QNcPIGrT3bxC+/zTuK0O4yTm9dducO/2fV7+x59l89wGOou5f/tdhicn3HzrBvfu3SXPCjbOn6fzseuEi+sUyo4zEUKSxAmj4Yh6I+To6JgkznBdUVY8rSy7pfrZeNrzPJaXlxmNJ4zHY1zXwfUc9o4HvHLngL+59ZjxNAGs8mIQhoxHw9IeWGabH/h22LZ02Ds85k/+/Ft8+sULfOalaxhdkJRzs+JpzOLyEpPxiMFggOMGuJ6H43kEQVjG4gqlUna3doknE6LQRwhYXV3B933ywnDhwnkoxZx+2nqfVEDb0Ku1DUKldDDKonROGTinaqgUttdISKt4Z6VBrbOx9EAxQ7A2/SNmNDttTinkYA2rlLZjVBuLxqu+LgHWkZ2+VgzLSx02zizwo1sPkb7LxuoKrXpAIwpoBRFuVMd4Aa5fIy0MhycTdvoPGO5tcf18l0+/dLnkYOcYFNLI2YatKItaGCsLT5W1K6oLnl33qb1uDaQR6NJYGmMBjFKqyv+eStdVjlbPVAorGeaq18pQVsBms6rK6dlGYExugx6hSiXC00uC0GU2VZ/CUxYUVmCRJ+7q3299GP7eJvT+9s9gqpTgjIZRgJRsdrv869//LF/+1k28zhKR79FqL1ELJEU2ZTRI8ajhBRHTqMNxoVCixkksOeon5KkHOiGZjgmVQi8tkjfryDwGlRO6mtV2wEa3zupih8tnA/7kRz3GacT9x3tMkwnnV5vc3jtELpzhsJdw+/4eqQnwjM3CP9rLuXR+jdfuHkPYwcOFfEAxlQxjB8eJyESdYS4ZHaecXwnoLnTJ4i3ibMJ4GmJMyAhBoQx115APJ6jpFKdIyPoJpr3MULR5e8fQaNQAwzjTZDVBNkzwC2hEEYUbsDvUZEmMMz1BxSMEitXlNVqtkN3795CZwl9oU0jY2DiLJwW9oyOIWhgNqyuL+O2Ao74inUzJkxyV5Zg0wZ2c4GqIApfFWoNRLEkETLxFcPpE+YQkjHAmJzTyIXGxSWEkQZGgVURhBNKLyAwEOqEeOIy1/VvOjUOWJhjp2oHdRhM6c0rOP+iqsv7MbcTPF2wbtnceo9CsnlnHDUK+8s1vkxU5z11/EcdTLC3VuHPnLa5cXOTy5cVy7pLHcJxw3B8zncTkScJ0PGI8HJPGSTlcOSnpI3pGsbJBne3xCFyXz3zyJb7w+V+hHmS8+fp9rjz9MZ5+4TxXn3ue1197FWUK8jThzu4un/vN32Tz7Cb9/oDxYEyjXmdnf4fbb99n3Q8Jf/Q2bz14QPPtX+Ha5z/H1tEJH7t+nclozOHRMUYKJnHCRz9ynWk85d53H1KkKUdxwr37D7h85RJ+o0a7VufenYeMpxO2tnd46frzrC7XGQ+2GY8LLl66hu95nAzGKByy0RbPX9ugNxhy4dJVPvYrL9IRBn8jpIQYoQAAIABJREFUoLW0zH6cslTfYLF+wsVz+xTZCIQufVJlN4VNdsnqOxIIWdl0ZslBa+araoVlGZgn2AJm9j2/H2D1YfYF2vOfAkKnN3JVgCn385y+Vz1/OlFQIR0bZyAESlWUQCvRLKQg9H2yZFqCuvm98zzXzq47xTY5Tc+b936XlTVB5YyxJTKLvUxhfa8jLCNHSRfXMfO+EEHp/+fnr5Y+9TEQgmmu+OHdLTa6HZZdh4cif/IdZfVJlJcxq0bNDmH3ihTgl2DPGIUUwoppCCulLqVAak2r5tKIHAIXIk/QqXvUA59a5LHSDakFLhiD50o8z0GpHM/3CMPQ3r/y7z3Pc3zfszQ/aRMHGEOWZniuh+dZhcwsK6s2vyRLKcVg+MHMsWrWG1YC3RRoZVCZrQq50sEoxXgaQ0ugR32rpujaexaEARLI04QszWbJ8yRN6S4skOQZK0uLhL5P4lby/LZHMMtzgsDHdV1qUYTShiTLUNqK8EjXod1uM80tuyBqdTBeSHT2aeobl9kbZBwYycGjHTqLCUppvvONb7K/vcNHfuU66XTCw70T9k+OSUsFw2Q6JWo0GI2GZEoj2itov4aRBqMVRhjC0CfLMuIkozCCWhAwGg4xRuO5LiBwPd/2+7kug3FCluc4xkG6Dvu9KV/+/jvc3x0yHI/J84JavWbHiCg7nqhKOCAFeZ4R1es0O11u3XiTKAz53lsPUMbh08+fo9ls8dTVa+xtbVGrRaRJQjydUrQzWu02WZagtGWvhWGE5wXs7B7QXuiSFQZHaHzPs+cVmtGwT6u9QLfb+an74X33WDmuBGUzRkrpWUAqypJ29UJL3Ztn54RjAZXQlhYgy3kH2lTZFzHLhjhCzlQEoaQBVpRAaUGYMHOAIbVGC40jZNmvZec7XL18nsd7J9y+f8hoNOXt4X1L51haoL3fI/AjpHCJ6nWMcdna32Y0mbCytMJ//W/+Z8b791B5gdDalt3LSYK2H8pej0PpDOV7kttlcGRmpXsxS1pq1GxGAKiS4jcHUXMwpSmUsrLup6gW896q+QlnjnoGIsr5WNX9F+LUkEkzc9yVP/lJQdsvOKX/A1tzBSrs9wtoWeAZwdULHb5z9wxff/0BtcgnamhevhYRFlMmU0iTBGU0sajRSwompsbhUcwoEdSCBuNhH1dNWG8HjBc6HOqCbDrBpFOanqDTilheaNBsNxHUeOGczw8fTBm3mhzs7/Lp564yHo3YjlPqtWVe+f4NvCDC6SwTNNpsHx3xwlMtHB2TqRxlFEWSInKBSlPcqIaHj/JDPLfB/f0Yk09Z7izR8iTZ/YcMdh4iWkt4tYgg9JGBB94SFPs4yZg8zUh9l9644LtvvsuS49NutNg7PELECqfWINUNtBbE4xEmHSGHx0Shx+bFS9SbHbZv3SWOc/zlJeJhjO/DcDImTjOyJKXT9mmELXLjsrM/Iik0FDnFaITJYpwiRidTtJYoPLRusrLcRQ0SJtpBdJag9wDR3qDr9FlfELyjBI5XgOdRFAV4IU4QIbMUioxCODieRzw6JAhgZSlklECcRXasgjDvjZX+wdZcvOBUcHrqp/f2X9mfwfVdzl/a5JnQ49xiF6/m841vfJ2N88u8/NGLuPIKJ9s/YrUlESYnKxQYn/sPJxy0JLlcRNQ7PD7oMxgk7O6fMDjpM+73GU+mZElMkWZIIcusvg1+/+nv/S5/+C/+Cb3jQ2688QrrKx0unlEwzrjx2htcfuopHj98SL/fZ3mpS1YoXnn1Fc6fu8j5zU1ufO+H3N/aIpIOrh/gnVnhYr/Hre/8DU63TbG6jO+v808//zkePt7ijdfeZDKd8lwYolRBq9EgjWOmSca9Bw9xA4/LV55CF5p2s0F/MKA3GPL9736dT7+4TDw8oLFymeXFLodHPVbXnsKtSUbjhOmoz6//6q9y9vxTJAd77O8cMcoLkqMB6y9c45tfv82v/6MVFuo5a0shTuiXDIVSEAE7XHjmEARWGEhUNZZTtroCqbNqy/y7r6jgP8k+/4zN8/fZen/ndfqzVdS16jOJqhpUPn0qVPixVfnX6le28VwgpSEIApSyCUHXkSwutOn1NNPxhAqIWWqsmYGU8uKoBz6tNuRZThIbyAVoDyMVUmgcDAWWojVrnzJlJVGWvjdN8VxQ5cgVjO34Lkk3NsdcnbL886hmdhoEk6ygN5iy1qrjxQlP5DtL4AQGh0oRsKJPSRxRsYbAcxwCz0EYCwQdKQl9B2kLcXiOptMMaNU8GjWPeugSeZbFIx3wXZfuQgs7hcDg+TbmStOUer1OFLUYjcbEcYzrSrIsxw+CkvpnyMq5VVmaYowhDAOEwFayfonWB9W3aKCUnC8rScbgSUmeZSRJgiosyK/6coo8J81Sul53ntAV4LgCx5Wzio7ruSx0Ojiug+M6NsA3VkI8ThIcKXFcl2wyscBXgOf5FFlOq9XG8wMmaU4WtLlw/VMEi+uEC8u8fuMdvvblP2fnwV2KPOHM5gYf+eSnePH55/jq7gFxHDPoD3j73cf0+iPyLENiK0yfevllPvGZT+L5HtHZ86xdfgZ5cgc/CPA8j8WlFfqDHpNpzHAyIMzEDB8URcFCo06aehTjPgZDrgyFhjNr69x6uMd/+OoPebw/IkkSDg8PEVISRiFZlpFnGVGthu+5eL6HF9iqk9LQ7w9I0xQBTKcR3371Hfr9Pv/qC79DnCT0BwP8wCOMIgb9Pk8167x9613q9Tp+ECIdh1wVZEXO9evPs7VzyPbWNpub66RZius4uK5DfzAkDOtlEuYnr/c9dKCc9WudCPPqUxWo29h/3mtV0ckqMQQ7T7KURBUCx5jZFPB5pspuMlmlbIQo+aKnUl5UDkgBLrYvyeCUoG64e4+kt4+U2GHC2OO1GzUclXK4PcQLQgbjmEIZXOkgAwfPC/i1X/8NsrzMVJlKz1+jDAikpZaVWcbKSWgjkMaZh0DGICtaR1lZq9LPldKLVgqt85Juo23VqZplZUruvS4pBtqAVlQSvNVxMLYSqJUor9ICMImduq5LoGZ5u/Y11pHba6nkhTkl/DHLqM5Mxge1PiSH/zOMZ0Uhnb0eD+VoHFcxHo24s3PMxET0RwU/vJeTFikbDRiONQuexldjzNGEg50j4sLFuIooWEYXU1xRsNIUvHh1g3fyGpOjKaiUcf+Y9mJIvdXBr9X5wVbM27sHvHRxgxoJEykIFtfZmxquP3eVg1fvIgOXIFA4ToZnYlzZxKQ++TSlKQQKbQf5OSF6OCRIT2hTBzkmnfoQNEn8FsrU2B7kNP2ClW6XYrLNNO6hXc3x+IQsSXDqq7QbTa6tBGRejRv9KYlbYxoXHOQ51194gfR7f43Oe+QqRXkdHFcg+ruYZIgfRCysn0OHbbaOBkw9F9Fqkk1jvPEA4fn0lIGwRuT5yCRhr7dD7jcsuM1j8mmMSEaIbIROx2i3jmitMk3H7I8KnjnbYFVIdo9TYq+OaazhNOucX7vISk1wYysgdAymuUw+7OHX20ySHEdPyYFYBzSlhGSEDJpcWF9id5BzdGKlXpX04EPoGagy/j9esTBPVJ7f23cjBCw0PP7wH52jLcfEJ33y+iK8sEHUWWbv/h0moxFn6zYY8sIAQUKWKcLihGvdGt2lACMnFJsBuWzx1uMG271z3Ll/xP7hMcPegEn/hHyakCY5RamCevbcOcLQZzSecuHqVbLhO9y9e4dzF66wdag53tnj+OEWW1vbPPeR6wzTmKhR5/DRDiff/B4bRU7P97l1sAeLy2QC1ldXuXJ2g+ToiKuf+QQL3QVee/1NtDI4rsuzzz3N8fEJaZYyHk+ZTGKuPX2V45MTuu0WoeOTxQndxQXOrC2xuuhwsv0a2WiPYdqgXTtvJf3DCCkd+qOc5vJVml1I4pSDV1+j3mzgnNvAHB2wc/se7cE6uVAMjnfw1D7t2jnSICjllsvqFGUlrwQaFRXZ+s35LEdm7AQAMVP5mlPB1cwXvB/b+WHWq2z8VLFJqqqURVqm9PUzRt/M6s7vQUkKqRh/9n26sD7a9QmDkCzPkdLB9xz6/T55lpXMi/Koovr5NLKC2qLLwlWXovAZ9AxFooEQXzgErksyGZP1Y2Re0TjBwcE4CqE0KMUgdWhJg1NSPAohUEZgjIerAZFbFosBd5Z3LmOA8rvuS0tJrHuCQptZotjeO4OW4BqJW1afRDnU18H2VDnCIKWmHjqEQYAxOa4Q1EKXwHNAaxq+Q7PuEIYu9ZpPo15DYnCFpttuWPDlCprN0FbiVIHnuUgpmU6neF6LVrtFpTJo5akzgjB4wu4YZUiTxFbNPPdJOtov+MrznN2d7Q/kWM1Wk2ajgUCSJRlpkhLUAsJayOryMgcHh0gDNT8kyzPSLCM2MVmSgLFV2TzLEY6tXvmug++4RF5APJqQJLGdH1ioGQXQD0LSoiDObOVTqYLOwgLj0ZSg3qSxcobO1Rf4rd/4Q2SzS9Cwyn4HhxO08jl/6SqiKDja32J/e4e//OM/5Xf+2e/x/LNP0zvq8+xzz3Ll8iYf+8RH6W8/5O6d+9RqDZZWVjFIjo6OAcHHf+ef8Pir/55iemRtKQ5GCdqtFkeHfQaDAevra0zGYwqliadTesMxTd+2qayeWWHnsM/rd7b5479+jYfbhxSFjbtrtRppljEaWMXuWq1mJeNLA2HnKwrGwyHTyYR2q4Xne0zjGCHgzXd32HzlNp/6yBXW1s9wsL/L2toaCMGo16PTalJvdRn0+iXwCnAdh4P9Xd584yZhEOL7LmfOLKO0wndtBbI/6BM16j91P/ydprlZZR1tx3pjgYzSFlTZ3itKSp+YAQDHsUPkKuU8UYEreUqS/VQqqyp9GyEQjrQKa6Va4NxUarRRSOPgWHKg3aRCY7Ixb956QCNq8+zFVYbTOqbEKLXAp1FvgPQ4PBnwcPeQLLeCEbrQePUG/9P/8r/y3/ybL9JqNemNJhRZjNFVr1ipqlcFOwaEFuXjSrWnBCelApE2lYEtexK0mSkTSm1maj5G2zlUVUn4icx0iY00GmmsKqMwpsx6ldUu+2pbKdRlRap08KKU+zQzekJ5j7UdbGwrgGoOXGffxP//16kkqk07Co1xLd0jkIbID0EVbO2O6fVH+PmQKKizHhkaXk6xfw+KKV57iW6rATpFGw/Cgsvnlrl4+Tyv/M02kXJQOiOXmlq9TtRcoHAiXt3aY3visHpywuWNRQav3WK6sMjdGGoZPHNukbd/9CaLSwHLS2skiaSX5ASNJcbTDBcwSYzbaFIEDfCmNI3hTBjjh4LC0wwmE/ayHOXV0b6glxSMp1OEF+L7Pm6txkLH5+TRIcPjMbW1Tc6urjFVgkngcGs4RQoP5Ut2h2M+8vHrpAe3OO5nbCmJ4wvCpoOigDBkMJoyFi6T8Rg9GeEXOU3X4CxKTOHTm8SYwiE2MZN8TLTUpVAxosjR0xiRJBiT4FDgSEPm+Mj6Aq4fMErGHI5yVhoRralLjCFvn8XXR/RyaKkQGdRA5CivBmaIjOogBa4W5EKg3DaFKhAafDckz22ApI1C6KIMdD+c/V8F1+8Nk+08q/moBvvcvOq8c/8uqxsZYUuSTfrsPt5isV5Hyil5f4+8d8zbDyZcvHyRp57eJIgaZNMcnB7CKNoNQZJMiGoBUgo6FzzGGy67Fy7y5oMu97ZHbG/v0z86on88IEsTMJqv/NVf8rGPXOX69Zd46403aIZn4c5D9n50g/vv3CfzDKOsIJcODySsPvsMB2/d4op2mPzg+zwuUorlDbwzqxwcH9Jo1zBZwuUrZ3l4811u/s0rrF17mjROWVzqstBts7d7QBJPOX/+LGmWMpxMkFLwmU9+goVOm9FoZPuvtrbYuvEuwusjV+ts9QrubI/ZHr/Fr/3jl/HcgqPjHt3FRYwWNOt1ssEEs9/Db3V4++2bFEcnnL92mc2XnuWcK/Hdm5AfY4oNO9xSaJRmxtSobCvGoEtBhconVj5wJpdc0q6fAFan5lq9XyrghwWtbG5OzlkaFYCsknXVv8IWgOyvSstbMV7eU2WqQFKl0qi0PUYtqtHpNNnZfgiqnB8288kW0jqyAqVVzJEhfJv9/sqtCfvjAiViaq5kbaFue2AWJJ6xasNND1pRQKPm4mUZ+WjK3W3BsH6GugdFllAkU0yeEyhJTZcUPWmrZq5USHIcg02yYitPgzzjqFC0IodCacuuQ5YJPiuKIYXElRLXMbgSfM8OppaO7WiUjiDyHTqtGoEvUXlGM/JYWohwsXMyo8BDOhLPdeh0mriei8pSXAnNRg2jCwyaKApRyp0Ns7UiAVPanTbtTpvpZDKjBcaTmKge4ZXq0Glu1UIHgyFRLfpbldJ+0Vae5+zv734gx9K6IApDpHAsBVVrAtelFoY4QjCZTIjCAMdxkIWNmb2ylUYKgS5sL3+z0ZzFyr2TExAGlVvlPM93cT0How1ZnpEXdv5UlWg3QiD9OrrV5PJvXyVaWqe5tE5vMOSVb/yAa89e4eLli0RhQBT63L97m/t3biN0TuDbauTbN9/m45/5BD/44Zssra7yzHPXeOHF/477b9/kf/sf/kfi0YhH9x5wtHfIt7/5DVSW8wf/8vfpXHqa4vFN2p0Ox/0hJ70BKysLBKGP0Q5FKe9uqaIax3HBEUyGPcap4ubWmG+88jZ37j1mXI49EtLBDwLqtTpZZuditVotWq0WcRKTJAnjyRSDwHFcms0G09GIYb9XssFgHAT8hz/9K4aTmI8+d44gjGY9k71+n3OXLjGaZCAk/cGE5aVlpumUXm/Ab/z25zC54vHjhziOb2N1x2FxeZH9/UOiKPqp++HvULGyQ7akNOVc3mqulTWAxoDjzA2poVTuKBFmSa0u5b+ZZbIq2oDNaInyoZmBlCqgELPqVmW8DUJoJI7N/JTykZnw2OkVXDlnGzKD4P8j782fpD/u+75X9/ece3b2vp77wk2CBAkSkEhJVGS6RMmSbCWWy0k5dlIuV/JfpCpV+SH/g0tlOXJZUlSSIlGyJF4gQQDE+QB47nPv3Zmd83t3d37o78wuGFElUghJJV31oDCzs7Mz8+3pz/U+PPrHA2qVKs1mg3Znnlx5gOTa1UtU5xbZPjji7OVrXHviaW7e22fl3BMUaKrVkH4WYbuKajbjN6fUnj7yPrCj/9mkyJxAIuwkSEPpJzD18IIS+mGdfD+SPJXSFUw7oJbfBihlP/9THe1pQTotVE8HZl12QGeqTGaqOHWqS8p0wvXxClf8NK/THTgryV/ga02hHJZrFf75z5zn9199xPU7A6ICjlOFKjyevbZBpQnJXkw1KBBuyGLT5xPXzrO41mHnwYgsPeCZTz7BrQPDRBkwBUalNBYXacwvM7+0SXc0okhjUB7v3Rny1OUqi/MOd7s93Ll17h7GdHSK4zSo1lusdNrce3yAKxUjv05RjPAqNfKdA4IwRAY1dFhDyRbzC4KFumFuqYPjuryzG3Fv55jhqIrxQ5ygijQF8XgIlSpFe5mzT7Q4ePwYrQuysM3S0gL9+7epPX4IpoFuVDjoHiAjxVNnLuLXjrl365jAbVCphbQrbZSGO7vbmCJGpWOqqk+n4bOxNMfm+XP04gpvXn+A41eZREPwApRKkfEIp8jRk8JyNTsdy2+YjCBNkUkfp96GQrE9KMCvstYU9LsFKRFpt8tuS5ClLkImGKNxTEFmqugwoJIn6ESSjUZQm0MVxyi3wiTKeff+BBPUMdGQQCdE4/yHTGg/3j0JJwWTOGWV8Lct6VU4vHuf/ckemdLEeAx2t8l9h0rN5VPPPcHO1j7jcZ/D7QcsLnaQUtCoZNYUO1dIYeFSWsOwO6R3PMHzQj617HOp0+DeWp2b2wtsPTpgfNwjGg052NlhcnBAE8mZKOLgldcZfXALXwsacURaD3GfehKxuo4jHbb+yzdob20jq3XcLMEYhbO3QyAl6xuLtHr7VOZbRFLiXLuGUpp33vwGVy5dYGV5hdu37rK6tkyeWU5rNQgQUvD6995BG8ELn/4E6WTC+P5N9r79XYLMpf3sE2TNZTbnXHRwxH/5+jcRQvLiZ17giSefwgskWZrRaDQI6yGjRo1Xfvf3WFyY50u/9c8IlxYIqyF+4FP1zvPog5DtY02zY8AxVvJalAJM5kSRTuui5PA4s2mkmF1XSsTHifKchXxrzE8Qivr3WaKMfWY2nZqiNaY9OxvDyh3ONPYbKI1nmeHqrBGqg+ta25Y0Tcp4NiIMXBwEeVm4SMkMxmeNw+zrsXwogScCpPBwvAqxLtiLXAg3wWlw/+FNjMlwvKblxRQxZ/0RrWrGWrvKJ9Y8Li6u8UY65tb8M3j1OZSBPM0hj6nqjKqQhNJBJRHR4JgiHeEWY6RUeAgaXo3VzYu47gR/711WdUGhFEwbogI7uTJWMMNxJVJoKp4g9K2AhMH6ylV8D9eVzLVq1CoujjC4jmSuXaMWeKgiJ89yPN9FSkGtXiUIK2AUeZLiOuB5FSh1mYNSvlprTeBbjkxRFIRhQKPZYDwak6WZVaFTBZVKZSa0oLXG81yyNCVLsx/PJvsYlus4zLVaH8tzhUEAgOM4xFGCUgVSSvu55lahtihOlJ7B+oLlWU6RF1ZZVFuoa61WnWkPaKNolFLqWtvvgi6noK7rkiUpaINbn8Nf3MRsfpJqtUXN9ZBScrS/z+/+zn/mzW9/h3/z7/41ly6dR1Bw++YNdh4/JI0jpACVK/wgACkphODw8JA/+cM/otmqs3l+k80LF/niz/8sv/fbv82DO7d49Vuv8ubrb+BLh70vvMzZC0+h8gG+p7l9+wHDwYha1cP3PQrlUBQFcRzjSMlkHHHu/AX2dx7THU545/4Wr90+pHt8TJamM0VstCBJErSGRr1GkaXs7e6SpCntOeuNFU1igmqFjc0V+kdH5IUVT5mm5qooGA6HfO2V13n48BFf+tzTNFtNgjDE832KNGVn6zHtuUUqlVoJaw25fHmJyXjCzs4OcRQx35lDSoc0S5FSUhRZiaj7AXvrh9o9ovSlmqrwzOopg3Sw2vP6owFjOpVXUHpGnAoW4mSaImfdrClUwszggUZZiJMsXcintQpgN+QUU422BY8RZIVmMJ4gpCYMXKSESrBQdhM1fqXCM898lpvX32Y4HhEGAf/0N/5rFtY22No95ItfeJH182dwyMkGfaStpewGNxqQttsgZWmIaeEOlO9zJodeFoOW+zWVgjWYUqziZFKlLfRjpgw4/RJO4Y7252iQJZQQVcIUjbES61PYgTmJYtPEbKowNLtmGMsBllMoZ1mYielIsQyI/79ap6CoxsI9aq7hk2eb1Gtn+YuKx+9/+32GhaDZWePtuzusr7bYrC9w5YlrVIqM5y6vsbnRYWG5RbaxhCM3kfV5/vyDWygR4soRES5usMLEa3Dv8JBmWGfO1xzHKUq5fHDrAc89tcp4eIdRSWr98MEH6MY6RXURFXg0WlWSwyHjbAldFLiFS4UUM+6iOxVMtUaRj6jXJUtz0Gm7zM21OXd+hQc7Oa+9N2J3kBGZmCwvMI5LMjrmuN7BcwqWF+a4vd3lte0eZ6SgGB2wavbZG2bgbdBZrtNsVYncAK/qEDgjsnSMrw2RdNisavaIGI4NVR1xaVmwsrmMDhcgaKKUoL60yHE/JtMCT3vIJCfIImQ+AeGQ+23CzjqyyFC4CNEnj/tkQYiotcgGO2z3Qs7Mh9TVkEL7UKuQ9vo8XqrjDA8R2uAHDQrPpaY1cjREK5B5QehJhMoIa/Mkk4iJcqj6VUS6he8q4vzkO/3jXtNz8iMwwL+hqDKn0WTA2uZZzL1H7BxMkKHE1TmFKDgcpMzJJvfub5PEEcudFkLkpMkEx/VwHRcESCNxXauYGqcxrWYd1/Xoj2K6xwc06zWeXQo5u7zMjcU57m8dkuzssFZELN57n/GN6+y/+T57d3ao5ArXd2gHAd1RitMdULgBxeM95vt9Fuo1ZOBjxoL2pWvciTK28pRrgeTCmavc2D6kUVukurqI/PANnrtcY2XZodH0OXN2je2tfQaDPsvLi2xsrNMdDeh1B7z59nu0Ki7rKmbw9lvsbXW58qVfZPmZJ/nOH/85RisufvFlvvDy59na2kaiefjwEWE1pFqtopQhisYsri9y+YsvEboB1bUlwrCCG3j4vkOWd3j/dpvI86lpgxQl/9bImSw32DN06jUjxFTJqoSSzxANwnZzT8no25+dMqz9IQr8n1Av4KSZV94UUIoilRyyaYw+VVLBiXre9BHTwgxjIWjSKIIwJFX2c5oC/dqeQYQOx5ltvDqO5Tu5nLJmmTU3BZ7jWyCmyLi0GfAgapH6VwkXnyTOUlTSw69cwGmfJ+l/iOAttISoUCR5lY32s/jhTQwu185vcvbKFZwgIDcOR70Rj7Z3iJIcZQxZnlJkKZlWGKlxjGKUphzWmjxxZpGlt3bw1RilFXleMBWCcYREGI3nO3iuiysNrgv1wCcIAsCQZQm1SoDvW35Jo1mlEvj4roPrS0LfxcHynZSyXllG5Ujt44UBgeNSFBlKFYBV/nMcWyRZPpAtBLpHXTqdOfzAx/U8siwjmkxIEhuTgjCw8DRlOePTadU/FG624zg023Mfy3N5nhX38Dy3lFnXdj+W3/MkihEVyNKEPMvw/QClDbv7uzjSwSiDKgqq1QqVMKBQ+cw70BhNmliVQcdx8D2XXGlUnhNHMc2FVRaeeonq+nkmmWLUt0bCQejRaDZo1qtMxiOKvMAF+v2Y/e1t0mGf0GVGMYmjMbVmg/Zch40zZxiNxyhTsL+9x6vf+i5BY56Lly7RPrPKxatnWV5pEfg+c/NtgoV5osOzjI9uWshcxUVrQ7vd5N6DRwSdJo1GgyiKiJOEJM3ZPRzwzeu7fLAzIDeSolBkuS3oLfJLIxwHx3OoNRskaYIGPNdFlYbKqlBUq3Vac/Pp6eRwAAAgAElEQVQUSUo2GaOmZ1A5u5FSEqcpe/2IP/32e7w4GfH05XOkWcbO9g7zcy3G8YS9/T0qocOZzXUmkwlJmnPu3BlGUcz29g7nzm5iXFAKWq0OUfKD+YQ/tNz69PQTp9ios3G9ECANugCLsz4phJACrcqOnLEwh9MmitOplHWtF7PnndYIU76VOf1YbJFjiaYFGGEhgwacEodqsIlCWuJQtTJEcY5Xi5lfP8PL6+vcfvsNwloDnaQIrbly9Qrfe/9DXr+5z+eurhJNxvZFcDIpOyk8phMhgXTck6hmwCiNUfmsS6mn8qlYZSOjivJwOwkCH5Vb1/b3ykLrRJFLY5QCcwJ90KYAIewI1GiktvwpU76+aVE1U/QS1j9h6mFF+XMhSuNncxqU+f/t9TcmLeXgVEqBwqFerfALLy9xHI35v165STzs4zbneHwwZjJRLC9dou0pYpMgcfG0oBpKgqDJezt9Hh1FaOWSjPtk/Rjtb/JwEHA0OOLFJxuc31jhce8eKljFSw4Y7I+49uRV3nrrPZLhBCe2ynlH0SJFFy6uX+L+zut4fozOMsaJRrg1iiRCqwLpB8hqg2EcESxXCByf0AlYnp9jruMReQMuJJp3bz7k3o1H4LuY+iZprjG1OqJQOEKx2+9xaAqeqHboBPO4y4qeW1DkNba7E/aPYxp5xMb6BsKXVmAij1nuGM6NJxyNcjbn57lyaYOBaPDu4wh5OGAYR0SpIJrkmMwgRIHRA1zdo92U1Ot1jmKHUZ7TbDQYHm7hC4PxAtI0xXgOvtciH455KCS6UBQmh3qbIOqjohg3OiaZDCnCRcTCEqLfJR8OkKGHqPg4QR2Vzlkolxqh/Sbac/BbS6BjvGJAZtT/c2/8v76+HwJmk+6/ycvodJMJBLmGuN7kMAjwKiFRlOOGIXGq8Y2HCOqMDvo4esyVK+dwHHAdDykdVFGQ5Rm9oz5pKhiNhyy0O9QbVaqNkKXFJkr6ZIWhmeTUlg1rE4dmFBI/3uPRN17BKEn3aEIvTqkKgVNIJAWLtZCi2+Po9n083yNpNeg1WzidNuPQ46EfsldvcXFzk539h8xV57jwi88TZxnKaNpzdZpVg9AR23dfp9PZ5Mknn+DGjRt0j/rs7+/z1NUrDIcRWzffhw9fJzeKlfNXGK6kjIBX/v1/ZLC3R+vqJfqHR5ydb7Oy0ObO3dskmaKaZ9SrVZqNOm+8/gaf/ORznH/2KZLREM/38EMf17c+KNEoY5RUSEQp2V0WUwZtxRamTUVzEiNm/kNWGuXUBFJ+5Hp/5B9mFgN/mpcN8SWSA3MiQCFOoH5SyFksOpkmUSr82rgqpRWJMsZKhzvS+lu2Gh65UyGPczqhYX5hjS8+0eGrrx5SQyCkgzhVoqmyWTkVbhJCUPFzjHGJ44hqYwP3/BdQao3a0iWKShM93CdPxhTBGnJliVU3Ys59TEBBlsQoKlTn1lkLK/zcXMKauYPIIJc+g6pmt92ntrDE8uYZlBuSaZfjKOfN6zcZFxq30uDO4z2c0W02FjyafgspBXGckud2Qu+U8ttSCgLft2p/0iaToWehfVHsEPo+1WrFSsB7LvNzbYLQQ+UpUwn6qhuSRLZBWxQFaRKjjYWVZUlKnudIaXA9hzRNy8LNbjUHYScieUYURWRZhkUkmfIssubAShWzvEJKUVIKfrx770ddQkh8P/hYnsubeX1J8jynKApMKYWujSFJEyphaAvpIqfVbFMow9HhEc1GgzzPyushyFU+E7sJggBVelTZ5otFKBVa2yJEeFQufYraxgUGozF/+dW/5K3X3uAffeVXufb006RpwrOf+CRf+9OvctztoZRiNBgwOtjiTM2wtrpErz9i/6iPX6/wwouf4clnnuTs+XMoI/A8l/3dAz784BabGxt85b/717QWFnnv3et8+M7bhEHAYDDiuU8/z+aZy+TJHs88+wyFyunt7dFsNBhPxnRaNWphlfFkQl4oHm7t88ev3uLGw11aS6tU3ICj/X2rLDlrJILRGr8SYoBWq0W9UZ/5LIZhSLVao7O4QhCEBEEwo9NMD0xTTvkqVWsjcNSP+OtXb+B7PlfPrlCv11hcXKJ75y4LC4sI6WI0pGlCJazx4N496o0Gm5ubuK6H77uMxxF5VpDk0Q/cDz9UYTXFCE+dyaeJ55RgO1O+c2xBJN0SEgHI6aTk+758s4AjBUbImYypsd66FvI2hcVgTv5eCY2bbmYjLLlzCsNzXMnG6hIOVvK9KDRxqrj7eJfhKMa5u82drS7/w7/7n7n47IuMB0csraywvrFOUghEZY08TzEqteIQ2EmYHcXLsiI+eQ3Mbk/Nge3LVrMAaYtJDUijZqqGU/VYg551fmbQvSkkZKYMVcIFZ9Uts642pgwm5uT3p5K2MwLt6e63+ujpZ4yawRm11jYIfsw11U/zeTs1kdS67IgKiXE8lPG4f3jIn71+k6XlOs88fYkb9/d4fDhETMAIzVjWGCUx+WIbJ2xwr18gKw6tqiE1gnv7KeM4h7ggGcVURUbIAePYcCw9Xru1yy98Yp3NhR639iZU/JDHhyPOdRY5u97h/b37NCuaZiulZzK6kUtjYqjOr5EPhyjj4EwO0LVF8mSCSAu8MIDAo5soCsdHCQctBY5fkEZVXnl3n/Obqwjp0um0yZM+mUnJ8oJ+ZKFLEoXIY7K8ybHyuHz2E4TFA0zRYKibOE5BFo/Z7w0pCvt9b1d8WtWAYyNZv3CNtULRatQYKcHhsGB0HKN1QpJPyAoDSYZIBzhOwULTcGZlgdWVDs1ajcE44Ot3hxSJw9zSCt29bWvwK0ANu5j2OhUpGQ4Sy69QQ9xKFdms4uU51GrWSFgkuF6B6PWo1iskcYysWtl4iUAXCa4ek3tttGOQ1RZqnOKafMoS+fEvO1o/uWm+/zt8cv9pOFmBpjsZMchz6v4CQadJOhxSUwpTKKI4pt2s0mzVycsETmsr2Vwoxc5+15pb5uAEFR4fDgj9FM9xmZ+fI0Ayicdk4wnD791B3u8TG2OhR8pHpDmeUsx5knFh6BcFNb9C54kLBGdWab79Pt39PrEnkGfWydbXMXnC3b96heXnX+DatctMVucp2k2ysts+Gg5RVNnv9ViZA+HWaHcsfv/qtatsPd7hL7/2dZIsZXN1DdcvWHAEi089j1i9yMXdPe5981UC4fHF//Y3yep13vizvyLaPWLu08/idppMkoxOZ5NatUZrrsGnPvUJstzgeh7PPv8MTuAjHQelDFmcMB4MuX84wWnXuabsue7I6aTEzBRYZ3zb8rpNxZxm5zsAitNQwJPrLGZn/g+nXvbj37NSQMUXpT+Vbai6UlqZ+TLGSyFn79mVlgtdIvdnRYUtrBy0LvAcm2tIIamHDkGtiqgVXJxzqLaXePrqU+wOc27duYE2KXLW6BRIHLQqZs1fhKAeapQyxImi1x1QjG6hiy7j8SMcP6QaBEivBk6EEB6jiSBPY0KhmKv7jKIYWWkw5wmqaY8H93bYGSQ0ayHtRkCnSKh2j8gHt8m0lcRuGJcXXQiaFWqrLf6MOeYe/BU1GdNqNPAdlyRwmEwmlnfjurM95EpJEPjl5yJxMASBTxi4YDS1ilWjFBKSaIzrVu15daopa6dIBt/3cRwXz3EYT8ZkWYIBXMfFdZ3ZHvU8z/oCIQirFVzHwfd8PM8lzwuCwLfnhZRUqiFFXlAU6lRR9Q9Hbt1xJa1W/WN6NuvRpLUmS1MbP8v813MdNjc2SNIUVVhrG6UUaZLQPTqiWW/guVOzZWum6zouSZIgEVTCClEcURS2kM2UptCWp3Ttxc/TvHAN15HsPtrm9Ve+w8H2Dt/5q69xuH9A7/iYR/fu0jvY5ztf/yaf/fyL+GSEaR/jCo4Pu+QlcuniU09x7vJF8lwxnkR88PZ75LnipS+8xMXL52jPzXHhyWf54P27jAYxwgj2H91n+95dbr33Hr/+3/wzLi5eIBSC8WhMT+wRjcY4jo/BYTSKiJOMXpzz9vcecvPhPqPxhLllwei4Rzoe27OCaWpt+Wf1Wg3P9zHKwlCPez3SLKPZblFvNsmyhDQekyURDpp2s0qn00Zpzf5hD9eVVOt1/DBg0D8mS1z+4pXrGAOfeuYye3u7rK2scnv0wDYRinxGM7p48QJZbk3E799/wOJSG8dxcVwX72/Z6z80x8oGclnixu2s6OQLZQsbx4KcMQY0UwPg8gsvThv/lupJpyZe9vkNTgkDnDqcg0IIS+C0Ha9pQSPK/8c+VlhRjGq1yfrGCk4eMZqkvHN7m16vT4E94B2VsPX4Af/b//q/8I//8S/z5JPXkGGA9H26/Yhbtx/zW195gXjQI8tz6zswRcmVHCZRFlgnnCVdFopy9jkJMSXwlt1mcQoKYU5zqU4rQmm0UuUBOeU7lQpR5UWwmhhWldBOtGYjppMJoJQzXtVJoXYS9M0s+E8vj5Vol1Mvj499/bSWVicQQKb8NCFBeNx+dMDD7hAxt8Y3r/dZmt+mNSeJJ4ok69MbToiiOq21s+wcR4xHkoNKyr3tHT791AaVsMGr72+B0fgqY5xp5lfqXDgn2R31eTQO6cUhb3z4kKcvXuDundfoV1v4rQpxWvD0sy+Qjnostn1WFud4fUexnWjStMD1qphigFtfpD1+xLFKiI0mSFNq9RoFDhMk/Tims9AkE6B0lTiuMkw93ry9g+dIls+cR4y3SNIR99UccV6w1JmnuZQx3DrEraSMk4KHrmRR1sgiwX7UpR4G1HyX+soGSZSRpQnjyYTB3kN2lzZpVCVhMeE4D0hzzWTUp2JiBqMRRZGSxzEVVxB4OWcXOjxxrsP5zQ5zc3WEoyBzUUGD79wZMnHb1JYkw4MtPMdFFClSpWRBC3HURYQOvk4wowp6oYHqD1GtDotSkKYTiqjHSiukJwxuIZCygl9MyJNdtK4QeB6FFyKFi6sze765PkL++MnYFgV4apJc/vejBdVpDs/JowA2l1Y43v6A+PCAdHBEKKHjWQ/CqHfE4sIcGFOS4B3SvEALwdbOAX/xrffxJCzOtagvr1Ob3+Co32U82Gf39hHN4xG745jJWBMd9ogiTafTYm6lSX7UJe1OcIxgYWGONenRG45YO7fC/NkFjkzBruOgL5+ncukK3voy3XHM0c4hc8srfPbzz5Orgma7Rr3ZYDgcUqiCbveITmeByAj+6Gtf59ozn6U2uc/m2U2yJCWOYs5srLO1s8Pu/h7PvPCzbJ5ZZP2Jp3nvjTfp373J3MY8Z156mdbcAq/+zu9x+M57HLgB9977gM9/9lOcX11hPJ7w8PE2ODDXafOdb79BvLbK6toybjrCrTVQuSKZJNy+t8POQPG5586XjcCyaVZy4aZn8fdPGE84ric/s40xO81Rasp5nXZtT3iwP8z++XEvV8JS1UFoMxPwCDyJ55XeM+W5qtW02LRqttbvEjx3Kl9urMhCDq7n2sTfq1IJmmRRQuAGCFHh0uIZnnz+n/PEl/97/vPv/O9cf/XP8CQYXVjj1XJqhYEsz/Bch0YNVKFIM81kHCOTI5peTCAfo0aG8+sNRvt9thPHJoL5Q8ZFwnxV4tckH965y6CyTqVZgcLn3qTFn90c4Ac5YRDhk/Nrl316qeC12/ss1D1WOw0qQjFX84nGA3S+ylwFFoI6jgRhCqqBi+dULfTPcQiD0FqtFAVFUViOmeMgjPUR9FwXhbKQPWGV+ISQSGOhYlJKpGM5PLgSz/NQynJ1BIIwDBmNRhg0RW45UbZgKpvcCCqVCn4QoJXC9VyC0HKw8jwvJ1V2kh9WQvI8R5fTFPkTUFH9UZdAWAj0x7CsKiil2W+B61oD4MlkjFKKMAyQUpbm6vZ3DLZ41kpRFPZ6amXwPduAcKVDXkynX9NzQJKkKcYInLCFXDwDrkuhNONel+7BAVmacv/OLbZ3HlNoiCdjllcW0KNj/tN/+D/48q98hU/9wpfoH+7Q6/VYXF2n0V7k4jPP8/jRHrc/+JD3332HR7duMdeZ4/KVi/zmb/0GnisYTgzzix3u3oThcITBeqBtPXzA1//6ayz8+i8zXz2m++Axk8hK9V+5co3xqM/x+JgPt/pcf9RlrxdxeNQljiLm5np2upen+I7N+6doK4EkTxIcBEWeMzjuE0URYBgeH6OVsqqZjuCZpy4zWKhyYXOBlz73Kaq1Nv/pD/6E928/psgzCtdh0B+U9YPkq99+jyQ3fPa5i4yjhElkhZC0NkwmEzzP8hjv3r2L41cIKjWk9HEce1YdHf1gc+kfaVedyKfCSVd1inwuseQCzMxu/WTSo8rx/Gk8gOVenTyLmg5jjCnFzaVVwDtVVJ0uxqQQOMKZFWvGWFnK3a0HLLVC8jhBUFCvhswvdKjX69QbcxTC58MPP+A//Pa/Z2VlmX/1P/5bls9e4NFeny998XkW6j6T4Rhd4ogdpzQNLP+2LtSJuuEUvqOnxZOcBU5RQhVMOfUSZRE2Vf3TmlkRZW9PJ08nxdeM1DxNsMpp57TDKYREK9u9MmBfKB9VEJsqFM5MhAGELnHrJ8HeBnb44bqkP63LdlD/1kdMkyCMbb0aTY6giBV//soNXtmKcVpN9o9SHh5GtKtVLlxokI8j7u9IdoYpxw/vU+vMk7pVBuMcneTs9e+ytLLI9ljjG4POBrSDgo2Gy+baOheEYPBun8SB/UnISlbjiSfP8O77D4hqy+z0J9Tiuzzz1BNAgi9iFoOUrahOdzCCychK3foJRX0BmYNX5Ih4lzy1U6qRljw4SDm/rugrl3v3Rui8YKXu8XAkSYqCULusLq3AsEdwLHAch/1RQiNoUHV2SKMhsdth7+iY+ZWQ0IsReUY/HTLGQTg+vqMJgzrSr+I36uTCpTecoPpdhBeR5bC40KK1UEcGNTKVkAyOqJoJsfZwmg083+F4nHFmJaQIDRUKPtdYwHMc/upugVtr0ejkjA73wA1Jh8c4VYXbapNnQ9xGk3gSIVWLsF5HSx/RblIZ5/hFxEK7zuPtHn7Fx/gVdDpARylBxUfWG7huYJsnpbxxLkuI109inZpQfd/dTGfW8FEY4BSdHTghn37yMt9++zq+7zJfCfjUk09QrVTZ3jvg0dYBT106QyUIGIwGvPbuDZ598iq9cUw3yglcl83WBpefeYnC+LSufgKRjrn+H3+XnfceILVh4dLTfC8N2Tm4zWfPnedoOIDjjGamqTYCrvziSxR7O4Tbh0Sug6989Oo6Y1Ol2u4QtBr0Dw6J9o/pHx7zha/8V5w9t4kQhv39Q97/4ANWV1ZpzbUYl0ng/e0jvnd9j1Zth8I/sHFCGJYXl/nyl3+Je48e8Ae//4esb6xzrr3E8WjMmStXMHlKY36ehbPneO2rf8HDu3eI2k2qq+tUleb+X3wd9+dfprKxzI2bN4miMZ954XmUsolOHCeYeEBd+sRxzM72Hm+9fZ3D3pCllZXyioDWEqSxvofydPFUgqrFKYTGqSkWUPKp7KF+YlliSqh2Cd/4O++dH39l5UrBQkUgmDYTDZ4Hvi8wxhY6WhksMMJyJ1xXWsKCENYHqayyHMchz6yanesHyOoyjjdPHBh2BhkHsU80qvLMccHVi8v8xr/8nwiLPo9vvwlKEgY+CKzQQqHJc6uq5/kF2iiUFkS55Tx7rsB3UkS9Qs1VZCR40gUj0EVuvYKFwBEug36KRkAjpxtP+PatXQ4GKVY+paDtpHB+iXuP+7x59xCpFY7JafjwS5+9xHx1ETk3z0rQYqNmjUqzLCUIQvs3pAQjrEKr66KNYjAYMqVCVIIQ17NmsNKTVuo8TXAchzAMSdMUKT08z6Z1mc5wpYPneSXKxuCUtyuVkNHYQlzBHnPWtNYqzlUq4axBoAqFEzgEQTCLla7rzryZHMfF6Nw+/h/OwAqDoVDFx/JcU6SLUoUVk3DcUh49B2F5+I1GnaPDI5RSZfFlp4V5nhNNYozSqFxROIW9Zo60UyylSY1tnktpRS6UMTRaHerteUyhuPdwn62DIUkUU+QZSRIjPY+nP/1JXvrCy+S9Q773l3/Bh3cf8Gj7gM9/6csUpqDfGxBPYnYebvOnf/SnHO7vc7S/z+DogPFwQFFkvPXaa6yf2STwfaTMeHj/Lu987zWGR4dIY3Bdh7m5Fvs7e8TGJa/OE6WWaxhFEWGzwp37D3nUm/DGjT1GcW59vNKUPMvY3dpmdXWFVrNJFo/RheUc5lojHQ+MlfSPJxOyNJ7BJLVSTEajmRfu3DNXqImCYb/HztYjzp33OL+xRJJr7uwMONo/JM9zXM+lUDlxUvDXr10nzlJe/uQ1nnvuOY6P+xz1jnEd+5077nWp12ssr54hyQoe3r/D0uIcYRjQ6XR+4H74EQurj/T4y/u+31C2nCjpKWzPJizSCJsYlHhr601lC7ATeJqFzlnctF3yFNxOOg5T/+UZD0s4M9iEdARkIy6sNfFch+VOnUb94mw6JqXHpade4FM/90tsPX7MV//kT3Ck4PHjh8Rxwffefot/8etfQOucfBLZLoywRsDCGAsxKN+/UGBOKejNhCNOsvVZJ8jWkxIhTtSjtLbv9UTA4nQydVJsTfHnU9Nk+OgZNhPJYIYZ5CNk6OlrKIs7Zn/HdhitsnRZIGK5W+Ljjs8/oYHV36VAnDYLwHqMaOWj85RRqullLY62I1ThIKlwmBWkyy2W5hss6QIpcvb6I0ZdxdzqRfBqKOPw9t3byEcJbq1FxdFk2YROKNiYr9FotFitu/y8qvLV9w4ZKsFbjyJ+9fknUdGA97YOmOQL3BjsM443CRcCRG+b+VaIcxQT5VUqAnQ8wRUHjDrr+CKn7gV4w13yNMKEdRzHYzfK6cdw+3jCG/f2WGnWEUUMwwlJs8Ne4VJhDs/VBNEWx+Yspl6hFlSZ77TZPzxG15sYt6A/ydBRTNNzOU598vEAqQsKzydxYwokrusjfUnVa1FdaaClINt5wOHeNtKvsnT+CVzHUF9fI969QXYwRAuPh0cTZLfPExstHD+EsMGaq7ncNHzNGPrjjGZzgYYURIN9RFGQDY9xOqtQb2NEim/G5HGM02iinIDcMVw95zLvFQzGGa2KR+TUcYTApCOEdAmEJs0lmhxjFJlJCLRAJ7nlMv4E1kcEK6aTaso2gdXo+b5VTsSN5s13PuDnPz1P6PuMByOy4x6vTnIWV5ZxgGefvMDaUos8z0mTlDSD0Thmc22BduuAheVzNJavYZwKt27cYmVthcvnzlCr1ujlBZ4D5uAOLzzzGd5sVvjOmx/w0tIiy502XhSxvFwl6e7w+MFD0gyC5TM0P/t53EaNWhAwPB6S3XvE8dYOz3/hZS66LsaBXq+L63qEYYWzZzcxxiYo9XqTVquFVnc46h7z3T/9c178tV+x56fS6NGAN66/z7mrl/jMZ15ge2uLjc11Go069WaDp17+GXZ2djClyuTqz/0Mcjii0IZgFHP//n0qvWPOrrTZ2FgjyzK6Rz3OnNnA4NHrHlOVOWI44v7Dx9z48DavvPEWudIgzQy6xczr0MaFk2NnOpk6FRaMmcWFGe/1VCPNPkajZ/f/EHvnh99uf+/lSMFiKwQs9yYIAhxX4LlO2ahwyJOEvCiQGDzPtZ1fAcpowjCAEt5kDKjACjkYBKraQrlLOLLJuIhIhc9gN+eNN98nz47RzZT1uTpZq0aSJVQrIUJaw1ZlKLkuGUVhmEQphTak2kqXp1mKwqBMwt1Rj0AJjOMghC7tTKxYlMRhob3GcOksfb1DYjRbR0fEcYn6QLO6UsMJfEapIkoyjCrQRYquVxjpClpVuTaXsewIAs/BdSo0GjWKIreJtH3R5EoTBB5ClIVOkuL7VrxiPBqTey6hb99jpVq1ctzCmrfmeUatViFNM3sOo9HG4HgeSZzYaZ6Q1Or1Mu+w//I8t8mk4yAEZQLqlYJhJ00ez/PJs8JKfSv73qslf8UYPbvvH8ISgPMxNY+n0ySMIFcF1WpoaSwGqpWQXBVsbT22giNlMRVWKjiu5WYVRTH7nksprQG7UjjSJSW1hbfrkOcF0nFJ4oQ4idGqwBUBD+49xsiAer3BMJ1AkbJ5dpNf/83foL04T3x8jNKGq8bh2ic/SZblbD/a4eHdB9z88EMGxz10kZPEEf2jA8ajY8u5TeDt11/n05/7HJeuniMax+zcf0B0dIjIE6RrAbij4Zjz1+bJFaj5TVprF1FpxHjYY3LU4917h3y40ydKcqaWAvPz8xzs7jEaDfE8h0athkCjplNUR+L6VfI8t/s+z1ClPYEpBy0YQxpF9A6O+Ma3XmN5ro4sYta6Y7Tc4/5un92jCePRiNGgT7VapbO4gFtOVtNc8Z237jAaJ/zqP1rA81xE4SKFrVGCMMR1A3a2thinGbVWC22sbVG1+jH7WJ1eopw8zWTRjYVE2EBQcnxmQf+UnDqnlHuEfawsfZYkNnGQUp6C/DGFSoMuP1Rp4XYW36tmmxKVEI97ZLGin8QooyxWuzQYdh3wfRcHw8baCv/kn3yFVruDV6/y4PEOWZqwsriEQ0YS+qUkqj2oDbqcSgmQEsnUB+qkyLOFUDm1m02zTox6Z2lS+b6EtGpcp3H5hlNF2uzdl8996t4Zjo8SjiK0NSI0J9cFQDjW6G9a7St9WtTipKtqO6bT4vDvuzv+YS5jXBzlg5tx/sIi73SP2dk7onDrmLBNKEPu7qVsyYKzrRWaawFBR9E9zuk+2qK5OE9/uMOo18Ot12k3BOgCTypajQDRmOf6gwMmiw02luZ46hx88+GESd7j2x/GPH35CkeT22xNDMqrcfvWDS6ITYx2cCaG0GREpoZ0PCphFVGMiGWI4zrUFwN8OWA/GZD5IcnwiMbaGm9vpdyIQkaqikxhvdkm3+4jdEFcBDzanlDPxlTMgF4yoahVGYwT6kYgiglZFoEj2OmOEJMRtUYbmbkIIzCTPsYNMIO0bmQAACAASURBVLJAi4A0aCGSmNzxGXuSSqOCqrSphCHCDzgexxQKvDAnlCGuGjMcpYykYrHpcTxRNHxoeQV5Ifn27R6TgSYzDn3HpVlv4hcj8nFEKByy8ZBgYQEtAmTdQUYxmdIIV5IrD8eMubixgDKSzvwCrzwuKHJwHYHrVfHJ6I0LZEfgg31PaW4Jns5P5ktwOpmenikfETTQ0+AyOxlAWGLzJ154kSi6Th5nOAUoJRj2hyzNNfjUp5/B9SSTOOX4eMTD3S773ZjLZwtU4eK4ayyuPMN4knPv9n02V9eYn+9Yjsq1J5i7e4ug5M0Ut2+zRpXt5SWKfEzgCqh7HE8ytl67TdSs8/K/+hdUNjZx6zX++q+/QTYYMz44Yu3ceYaLKabq0262mOvM4YU+/eNjXOng+x7dbo/FxSWEI7l16zaT0YArVy/Cjdv0btxkOBrwsy+/RP/6e7zz3dd49bU32Nzc5HOf+xy9Xo8/ef99NjZWqdfbNFs1DtJ9Ll27ir5yhf3dPdIsR2cKf3WJ9+/eo7m2zNrKKu+88y4L8/NUwwpJFhMGa3gy5P6DR3zv7ff45rdf5+HjbRYX5ynyDK19xFTICVnGuFOIhWngEifF1UdhgJwqoE4jF6a3Tzyv/q5758e9pISKJ20y6ft4nlUvc1yJ69hEMZXWwsJzJJ57IlihVEEYemXzUNoiLM/JswI3rNJqG0b5mAO9yNm5BkkQ4kZ90mGPe9++x/jgFRqLKWeW5lCuoD8c0GjUCALPcrqBJEk4OOwyiWO0Bq1chBdiRUQUpsjxdM44k5jQgFAnqYkQtJvLVJRHxfMxCipI2jUP4Wi0hrwoWOtUqTXa+NUxQljrFGME0uS0RQI6QT1+E7+VIB1/xm3KswTXtVMrlWlcR2CMwvdD6lIy1ppCKURmId7GGHzXwvt832eU50hppxsIU0LFLG/NaEOhFb5rf56lCb7ng3GpVKrEcWQbGK5LXuR4wsXzPLQ25FmOH1iJd4oC4VkeUKUSWosXbYsvpRSB55fNgY9nAvTjWeJvlcz+4dbU29SKszmy/PzLpoGKNYPhgPnOPK7rUagCx3Jm0FqTJNZyQqmCvHDwXI88y2fGwEJaJdE0SzHSJU4S8qN9Wt1DFhtN2u060oHnPvEUqttGYqiuLOEEAcYIKu05nvzcy4yHEVsPd7j14U32t7eZjEbEsZ1yZWnMeNAnGg9QKscKrOV0d7b54K232Dyzyv7OPtt3bmCGh3gi4+zaIhefuMyNR30ajSa1Wg3lh6w++TzR3gP29nd578EOHzzqMphMUIUmrFQoioLJZEKW5xij6XW7TMZjGo06fqNlaTRSgtIM+n2yLJnBT93Sh0qVaoZGQxxFHB52Udrgey7f+nAf9e5jjg6PSNLc8t6ALI6ZDIc0Wi2klORpRpEXvHNrm+Hoj/mnv/xznD97lje++x3qtQrVSpXcZFSrARevPcHB/h7Dfo9GvY4jfzDs9UfgWE0Tc7uZPnqI26hRpvKA7fYYLPpMlAo/02RA6FJWfVacYX/fYjyYmanPYINloBJlp6HkMCGtpK2UEiEhGhzTO+ozGCcopSlyjTwFBxNSlN0EQx5HFrqhNCbJWGk3+Tf/8jdx0PholldXOTrqkkVRmdBMHaUsZMyc6nhMpdKh7Cgb60kx+2zK37ef46mJUtlpPs2FsrFYlpO9YtZZEqefzUyFFqb3nUAPT6ZdJz+fqi5OC8GTa3q6rLLQSo0+mRJ+XOunvFCbfl4SH6TDsQ5pra5w4UxOmsxxd3uXwWSCH9YImg1S5XP7AJr+PJ5OCSoDNtdjtnfvkg4HVKWi3goAQRpPqLuGleV59lOfW9vHRIVPd7xPxVN4xRBVCB6rISZtUKlWEMNjTNCi3o5BK84//RyHD7ZZbEI/UhQyRNbb6MEEWUTlVDQhd1zqgaInDQaPeDBkhyWiNEcKyDAkgYNTd3BUhGsqZKnCeFVay6tMhopekpJVXPz5NRYdw85YY/Iaqcgp9BhnOEQkAqpNm5RIiSwSjAO5q2F0CK5P7ldBRZArcuEicQilwpM+bq4JlGAiHQaJBqFZaFa4/igh6dX40gXN/mEfYQQyizBU0UoyThWN9gp+MCGLEtQkpuju4rXmUZ5DTbqM4zGhX0UnBXdGXc4vtrm81kY7deTeEKkLUu1QdRShm+P4NYTv4GgIlSQtEmQg4CfIGTiZWumPjCGmhZTNyU+dwWWjxAurXJhf4fGtNt4kIs5THM9ludnAkYZCZXztu9e5vztgFMdcO7fG1l6f9653+dznf4324gr7e7v84R/8Mb/8K1+m3WmT54r1q5eZvDGPGyfs394iHhWEOVxdmOfN/R6Xzy/QyxychWXanznHpeeeJLx0CceUEPAoRvXHfOaln6G5scz6uM98p82771zHCzwW60tUwir1ep22UbiOx82bt1lZW6bVahL4HteuXOTOYRdvcMzQc+h2uyxfvMjZbpe33n2PDz/8kOFozIuffZGnn3qG3/uj/5Pjoy5n1teIooRas02rM8+VK5e5f+8h1y5f5As/90UWVld4+HiPdlsRVqrEUcLu3iGNZo0kTdg77PHa2+/x53/9DQ6PehgBru/aREqrGRrAMSfiSja2lc2tWbPrZJ3mWik15cudQhmUoUGfiit/x53zo2y3v9eSQNUzhIGFsU1FKcLQx5EGx3WQTZ+iKGxXuGykTvnB0wmWLch8JnHKvreG35qjPXlMdLRPdtClWtlAJzX8fMKDD26zvX+P862I4fkXMIsdFvQ+2iiCike71UJpOwnwXJ9efwhY2FuWCQoj0SrB9yFPJrg+JK5EOAIKhcbgCEHgOsx31mHgUxOa9ZUO81XFv335Cl6rTWZgb/+IihrjalhbqPPZp87hCI/xZEjDDNicC7geJZypOlT8AKVyXN96r1UqIUYX1oS0VAWM43gmgV6pVhiNJ6WEty6LUYXneSCsuIHlfTiEYUCRKxzpEIQBWhVkWUqe5aRJQhrHNBoNHNdFSpdqtUo0iU5RAcBxHRysup2lQNjRi6c0BFbQpdl0GQyGszwmSVJc18ELvR/73vtR15Qy8nEsrewZPRnHCGOV6JQqIBeowoqSRZOIhflFmo0mcZKU3l8JqlLFTM8QYRFM0pFIR5JkZUGQZWRZjjECN7DmzFG/x3B3h7mN83zyhacAeOn5q7z7rW/gVWvMX7xGpVojSxXHxwPu3XvIowePONrfI09S8jQmS1PyPCVPU8aDPuN+D51nmDwniSYorXCF5K3XXucTn/00viiYHD5CqpGduKcTfub5a5y/5qI766SZlVJfbrY5LgS7/YhXP7jPOM447vZJkogwrACGeDLBdV2q9RqT8YjxeIzWmqVgCbRmMirv+7/Je7MnSa4zy+937/U1PCIyI3KtrKwNhSrsAAHuBJvdzWFvM9KoNWqN9djI5kGjVz3pr5HpSWMmySSzGWl61OtwaZIAQQAEsRSAKqBQe+W+RMbi4dv1e/VwPTKz2M3uJoVuktb3JbOiIj0jPTz8fuc75zun1se1bxj6eMLJZbWpSfPcSbeMIUtTBkB3vocMS9LRkN29PZTy6fd7nDuzxJ079xkNBlRliZKSLMvwg4D5xQUebBs++OgWly+eY36+jxWiYWYriiLnw/ffYWt7l8V+nygKaX9qjFWzkc/YkJn87/TX07NWx5V08+2JLXvTtRMwm0U67TSorISZw9LxPBbH+8Wss8Wsw2eaZ0oQRpMO9hgMxyjlURtN3eSMKCURxnVappMh+WRImedk6YhiMqI918dDsNBrU449siojmevRW1xib/MhdVU3MzgWS81xWrpw0jljm4R1a47jOizmBDg2UkVjT2alrDFNFoR5ZFM9Npo4taHOGCbXcnMJ1jOwZ06xW7PnCXECso7/z8zeK042cmZMmjvJs3fO2E95g/4FSQF/1mWFYELOd24ccncQYZI+fkfz7JMRB/sjtnaGVOMUL0oQosO0rBgPj1jut7l04QJJb5fN2/eZDA6RssTzJJNiSivxWVjoci9XGGN5cJAShx2yXLNQ7HAo5vBEwPZBxheeWAQ75cEO2PZF7hcTwsOc3kKX/aNdqAWljOgvrlLWE8j2EXXE/s4tpFRcPjNPYSH3FtDbt8mnlk7YYhguUBcpw3qXZD4kOxjBVCEDhY4ipp5lLkqZTHOqKGY/lcz7baSeUtUS2QrxO/Pke3uIfIrpLhHPL5KPGtbKKETQwp9keOkRRdFB1j5dpTHWZ1i2mIgAFfgkQpOICpKISvoo62Yw3vvkgN66x858zH/84S2ev3CG1YWEzSNLXlbUwFB6xDKmVjXCKyGvMJMRXmTJNrfRkU9pJZENmAQLvP7JEUmg2M4CxlNNKCUq6SHLHVqRIqBNJsGzBk1NGNXoyv7CZgaOTWzMifnMozLhmWz35EN1/BxjGY/G9KTgTG+essrJreVwY4d2t0Xc65JWHpPCIFDs3jsgbC/y3HNfYX39PNOs5Mbb19jZP+CtH7/LE09fdUO8b77F3s1tWsCFSxcZjDLe3xnh7e/RCmI+SiWtC+d54ne+TnJ+FeW57BsjnUVtsrBAHbVYuLROu9PC8yWj4YjFpT7WGna2dhkNR5yZdyDq7NoZsmlGNk1JkpjlhQWMgPEzT/H+K6+yZgzX334L/0tf4eu//Q3G0ym3bt1m6+FD/mjr/+aZlz7H41/7BvffeAWM4d7DTQ4GH6E8j2996zuUpeaVfp/LVx5nNDqiNz/Pzo7LqToajRiOM/oLfT65eZuHD7b5zvdfY/fgoNkD3R5U1w4UzbIMa2rXEJtFup9SXMAJmPrrQNQj87D2RAL4dwmG/kUv35NcOr9MXbtcpCRJmE5Ter02qjGVMramLARh6Ab5lVLoSoMA3/fwPbefKiFBBaQXfpPp+Rc4997/zN5rHzF8eJvSvovxAnJbU5mCRGt6aoFs4YvUl57g2Q//F+IopJ208HwPaUBKj2xaoGvdvA432lVLZ6ARxwHSVqQS6sJiKo3SJTPVfTuO2R8csKhWadmMhUiAUPjllOpgipBwxtO0WhKqMU8tK55Zv0jsh6TplHI6dO5+hceVcz4tEZKmLshUQAM0Bb7vWKwiz5v93zGZgR8QhpoyLxpxjxuub7fbrlntKbIsw/M8gtBrrksHvKynKCsHrHzPR7YkUnko38PzPHTlZqSqqnCZoECW5cRhhFSiiXmp8TyPoihcOSZdxlan02mYFle/lJVG/CoRVrh67NM5jvuwFoXLNgrCAKUUk/GYdrtNWZXu2pbeqaa4oBVH6FqfqA6autBYQ20MuqpQyiPLC7Isp4Wgsm4OUdclb3//W3Qfe4L55WX3GlodnvjabyGFhx967OwNuf3JQx5sbDA6OiKfTqkrTV1VlEVJWZZURUE+nZCPj7DlhCodk45GVEWBEZK81WZ3c5Nrb7/H57/8OT775S/x6l8cUEzGZKUmqyWXn3mOKunxH/6vf09VVfzLf/2H9J75Avff+IBRaSm1pipKal2TjkfHcsf53jxxkgDOPbE338Nay2Q8ZpqmjqWyBowl9D0WuglgqHVNO4kJfMV4kmOsu8cUWc6Io+P7ZRwnSE/hhwFI6ZoUaUo6GmNN7WIhmpElXVvefv8WT185x3B0xNJiF79prAaBj++FPP+7v83Ww02U7/3E6NOj62cMCMaBigZAOZODWdEusNYgpQeI48elVJjacsK4zlin0+6ApwBX8xwzUwkKBxxEo/V1P0sj+zvFszS0XJWlDEdjykIfb2If3dvBmIqnr5zHaM3121t8dHcfYwTz/T7TLAUEa77CDzyivZC45Sjw+f48MgiIOx2qwcEpNmj2twiOFY0CrHEdkNNSj9nrtMczYSd5VPCoDGT2M8dlkz0JGgYQ1rjuBrOOpnGbuGiyP2bJ9fzk8dxJOw2EZ+dOyRMmcmau8TdeNT/v+kUwVoLGs/9vWo2xh3UCVUxBmVV89OCIv7w2Qvk5g+GEVtTGnzvD5eWLFJND7t29S2XHtHt98AQTLfjw1h6hPqLdbdNbXGB/UrHS9dg4rOgnEWcWJaIU3N6RFLXlg3c+4vzlx+m2JelozHS6hBeEbB1lXHniCqPD6xyMCsT8AhubBdGZFr7nI0yAMQKdTbFIzHiXcVlQjw7p9Hqc7UlkabmTewStHtPREe3lAGNSCiuotg9pLS0w3+owGo4gnmNceSgVM6dSPFFSVV2mhRtcljqnzkpkdAZlPSrf0flhnVHGi8jJCCkjRD7Ctzn+4hn03haegkRMuNiLiEO4dlAxHOxTxjFC+Hj+GBOecfilMkS+Yg+FKgyv391lb5wxyQesdyy+9bk7rDF+G1srcqOI4zamLqAuoRhTjA9oMSU0LbKhpOqewfe7PBzn/PCjfVSrh/FCalvjCUEv8Yl8RVn4gI+lQIuCfmCY6uAXY17xd6ihj1kR20itT4EsXVvGo4yeVLQ9QdyZY6xrxCjl/vW7iLk5vHJKRxnEVPCZK89y9ZkvMJiUvPLv/g+q2uIrePHq4zz37NMUkwn33nmfjf/8CvPjjLOXV9DCsvHgITtjw1K3y+ML8zz1jd9k4YnL2DggDGPnaFXV5Lrix2+/h+8JFpd6lLrgzt0dDg4PWDuzhpSK/nyfKIqpqyk3/vTP8BcUj33ln7C4tMS9+/e48vgVqrLm4PCA9fPrPHjsMrd2dghLw/bmJr2Feb788lcAuHXzJroq+eC997jk+VBqCmMYTjIQijyvKCsN1nI0HHLv/n2SdsLG1jba1LSTFlvbe3TnehRFwbvvfMh7H9xg5+DgVLPJ0mq1nJSsnsXc0+xzpimeThirn3QChBNw1fyLmcx8Nkc7s2Cfvde/zEspN8BeliVpmhLHIdZU+L5jr6qqcqxV46jl5osNnu+5+RHlNv+ZBEp5Pqa7TNZdJ08WyaYVtqygrghbFWErZHJYEWCRdY0WCUU0T14bIpx0SgpJ3bwHURQ1US4u39GhFkunm9BueWityYylRlMWNTGVcxk24EnD7s5DgnZISYwxIe/spPz59YdUpUYJWI4Vv/f8KkHgcXtQUlcDrK4QOuPsSo92f5lAK1piiFKyCaKunTGEmjnxumvC830smjzPAAjDyM3lCJBKujkbKcmzzAX32vK4sJdCogKPsqwoyxLPUwRB6NjB2plRKKUo8gIbWpeP1eQEWesylqx1phhRHJ9cu01/1snWMmwYOdOMOHbug1WFsuq4ef6rsj4tgy5rLbpypgxgGrAMQRCQFxll5QwpFtfOcLS/T1U5d8DF5SWmaeaMS4zLegNBpR3Y8sPI/V+WUZmaoqrwhKIsKiqtufPRh6z8+C2+8I3fZnAw4MHDHYyumY7HPPfCM4yHQ7a2tiny4nhe7tSLxuqaIk0phvtUw110llJMxpiyAgSYmmw8IBv3+PCdd3jhS1/gv/g3/z1XX3iRo719wsAjunQFnSzy7rvX+e5//hadJOLll7/KY1euEC6exchrREkbKfcacsGZekSdLnHSQkrB/NwcptPG6JrRcEjRAHb3Op3DaBJ6+MoSeCFTOyX0BEmvw6VLF/nww5uYJjMwTycIoDs/z1x/3hExSrE3GGNQznilLrDipD5XSmGBnYMjDg5HLC8s8eDhBufWVvF9n07SptI1g91t9nZ3WVxZwfwN1/nPzFgdQ59T7JH9K4+fbAIzc4bm2afA02z26uQ4x1+FRTYslGNTrCv4m3kqV024DUcqxSnPC/I8ZThOHepNU6rKomqNEApZW3Jt2ToY0jM+r7zyXS6dWyMvSwaTgl//jYT5/jLTdMzDuxv0l1bpL6wS+oJub4EinZDnGbPcLTdTJhCzcEghEEo60z1mXUansz4+hY2trsuKmgV+zvK5Tm2k1lLXM1vThiUzxnXdZi6BMzB0SiI5O/XH81kzcPXXbMzGzgKEOZaqOBDmqP9PPZPiF1IbiGOHrp+27AwVz86jNSTC8vRqwscPK7aGGhm2OdKSosrxspzH+j2euepx787H1HqKXxYYoZDz86Sl5XD7LkGc4C+usrDcIcj7LMcFSRzz5GLMw52Ad/YEvpYMdrZ47IWnOXrrfUZ7O7CYcHM7JRtu8OtffoJvvXKN8cgn77a59eCQ4f4IG3fxTE2RTYkwCH2ELyr6/YgrZxO+9twad9N57n3vDllZE7c7ZLVm3h8xknOIZJlifxvV7yGrHGFqdO8Cg7IiaS0gh7t4+Zgi6pNKn7U1n527m9iyoLKCutXFYCnH+6juGVQ8B0WGtAZhSozvI8MAU5UoW7DUjsHzQXqE5YhcWUCRWoEJPGcQU03QuWBiu2xPPAZDTattyXRFguGJJYkShlvDlDqcp5I+xngk/RWM2kdkYxLP44X1iwgpub0zYaM4og4DrN/l1nhCPEpRtsRKNw/Q9kusDahRDWAtCH2IpKLOmpC5f+BlsY/cB/46d0CEk1I3mayzh7BAnteUI1hp+wS2RpiCxJMUnsBqQX444MXFOS6urpPpVboLZ7l7d5ODmzfpnT1H0E34+LUfcfjRJzwYTTn8029ibt/mUuLTmfNBG/YmNUNa9Nqw/OLzTNoByy8+TavTIU2nDaiQWFtx+/Ztbt+9x+VLF7hw8QKtVkIYBqyurGCwbGxssr5uuPb+e9T1mNIf8+JXX2Djk1t0H3uGpaVFqqrk4sWLtJKY7e1tfu93/wkbWxt85StfokhT3n/vGq//8A0uPX6ZFz//EhsPHlCWJRvvvsWduw+xwsNodz8MfJft007c0H3g+Tz71FMMh4dsbe8yHAxpJwlBX/Dhh9d5/cdvczSZuKJVqmOt//x8F8RJjiM0jUbbqCdmgo2fUB7MitVH1AjGBYnak1s2M4OLX3ZQBa5Yn9l6z3W7SCUpC5dHJaQ6lq/NivPZNa21boDVbD7LmVlIT1AFMak3x3brMtnq04StmhaCfWmZ+qBaKdJkpAs9Rt48bVk7Z0EjmKYZnpIUZYEQiiD0iYLQNRdxw/PGWkwtybOaPHNyo6qsEVahFIRC4tWCQFpMVZAejVhdf4ZIVbx/e5MHgynGCoxV1CJASMWeDvmjt++Q5xqdZ3jVkN//2nMUXQ8tQlqBAFuTtEKOjsZ4Sh5nSVVVdarBaqnqCl95VFWJlNLJn5rMKM/zyPOMdjP3FAQBfuBT5AW+7/Kv6tq4gXJrm4a3s2evayc7G49GzHXnHeiUiqLM0EY38TgzNscBpaqsjs1isE6aFrdiJ/tU0jUczF8NMP9lX5/aq23UQS7M113vRVFSm1lcjgu4Heztkk4mzGq+Y0CNRCiJ8jyq0oGmulEt6bKkNoayLFGeT2UyDo+cGUWZT7n5o9d54vnPsLWzz3e//T0moxFFOuFoMODlX/81zp5d5pNb94HGGEO5uUdRl1TDXdLte+h86BoXWjsLeE8ijYtF0FlKMR1x79Yn3L5xk8++/AXOP/UMR+V1htOUP//um6STnAcP7nPxwgUuXL5Et98jbnd57nOf59XvfZ/h/t7xiIsrWuXx3+hGeBS21kzTlLIojq8jKQWhJ+kkEcLWTmLcSHR9JYgDj5XFeQ6X++zuDygrVy9n2dQFy/f7BFHoSB8kURwRTUPKsjh17t29SXkeCEmla1ZWltnb3SQvSicX7PvUtSafVJxZP8udW/c4d/78T70cfuYZq2O2SDiv+dObxwxIuHXKQl3I4wvYdVhp4pwERjaD2PYkPHGWvzRjV6x1A8HGnlQRQp5iY4RqTpAlnUyZTjPKqqTTabO5fUAQ+Fy6cBZfQssInrp0Dm2g20koi5S9vQEfPdgnjkPCpM365ce5c/MGpjYsr56nF7uAsm5vgWJrw9maK9G8ZuXYNHsCRE78JE4KohNjCxcUjDg9rOxu6nV9KuCXGZPVsFbGNF7vzmpTgBuyt5b6VPE3CwY8lvc10iCYuVBx/BVOZIyzTsJJBhdN4OI/tiWwwiP0Bb/x9CILSZ/vXtvmlXsZlZHE1QGm0tx+MGZxbo7zT79ErxVx45132NzbwJMVfrdPHM8zqWoKAsalpt/v4ZWbSCsJrOalx9bYH0047C9SppuYgzFri/Nsb31AlbWgu8rO7h4b7T0eO/8Y1z6+h7WGwe4O06MpLPfR3hwyTpiPJVGxTSfucH5lnhevXGDxzDne/PE2UkIQRZS2Bumx3JakwxwTzBHnmygyFld6bDSdLuNJ6rhHGA1ID3fAm6PyFVXg0WpHDEcHBPMroCxSpxTDCa14im330KJEGB85nRKqeYwXUmY5sQjpRj5bacWkhEQarPKJZEXlz6OVwqPAMyklcxgFhcmpKokpNPe3KoI4YLmriQOFX5ekkxTR7qKFwNaKbneJylT4okuvv8Bix6Md+4w+HjAph9jAJw+cW121/xAvcpLGOgStBdpYVFXhYVC2op1EyAl/Kyj/+1qni/XTj53kGzVAqmmszFzlAI6GY7auXWft8RhbVWhTYvwAAXRij1Y4x/zlz5HEZxGqy9t/9k16ayusf+2rVEdjbv3Ftzm6fpOe1RQ/eI2V+Rb9ToCi5s7IsL2/S2oli+fP89nf+DWWn3uSrcN9KlNTViVCSepG8lwVJek0Y3N7j9EkY23tPI9datHptDk4OKAsS1ZXlmm1WiTtNr4X0XvmSTbf3mVnV7P6zGepjM/O7g7txEmP0smAx9djlq+0mU6G3L+3wRuvv85gcMT47ff48stf4TO/889541vfZPPWh0wLDRRIa1BC0ooiPF+RFSVVmaOUz2g0YnFxESkVSZIQBQohPd5+9yHjSdqoEh4FuE8+9QSe77t9qSlWRdM1FVac3GPlyfsnhfwrxzkGXseBwCcA6+dhq34Rta37nLh9aJZlpHWNKDVKuuJJ6/oRefkMWJVlSRzHRJGbHfE9n1FhOChqsqTPu0//IWb1n+HlNUobRKlJbYU/HnL75ju89+ATOnnMV02NEuD7AZPJxLmo1RohCqRQG8NK8wAAIABJREFU+EGAsZZOrFiYl2wcFYwnkk6iCMMAXVuqwrFEvrQkvocwilBBoCTt1hxJyxIoi8XD83xqa7GNw2DoC7YHmlQLylpSGsVab4kKj/0q5smLK3RbYyIFdWnodNqMR0PH6kk3IwWzmboaJSW1qd38HZAkCfsHh3Q7HVeUK0mR57Q7HZf9pZ05la40nu83Tn3gByG6qhxgUhZd6UZFEzSgSxGEAs9XGFtTlSWuOHVzU2HowoW1rqhN7aScOIc6D1fb+L6PrkzD2PxqLCFEE6D8/3+5c+8aB57noaQijmOmWY6SksD3WVxcpCjcOfWUQinR5I4pN8vkOzBW6hLpKSpjEJXLMksnKZXWUBQus2qaUZY1utY8uPUh773xQ5790susr5/hxrVDijzjnR+/xfLqMo8/fpnhaMzGwxJraurpiHx/i3TnHtPDXURZUGQTlPSRnsC3CucY2RAaFsp0RJlN+OCtt3nimac42NvnT/7Tn1OkY+I4oipLDg92+W//1b/mN37n6wStEIDPfflLXL5ylTd3tkHMYhUkRZ45b4QGbJVlQTYeU5XlcWNBCEscSLqtkMiTmBpCz8UwCHz8phlzuLfDlQur9Ofa3Lj1gLJyMvoyyxgdDZnvS/zARwrBcm+O9cU2127cJEkSgiBgOJySTad4gY/nhyTtFqPxEe1Ol1bcIp2MKfKCIAwQSrGwsMA0LZB/w7XzM0sBHWtkmuJ7xlLNXPEcnXwSHCyOwRGczFidfO+Kd4N9BKAJwamfZUbY/A0OLq4LZY1lPB4xGU9csF6lieOYpUXJYDikHScoqejGAUVZ0fIc27S2OM/K8jJVXfMn//5/5xt/8N8xHU/Y2XjAmXOPkSwsIJUi6nRppxPGoyMQs0wtjmV2TkF3kj7+6DlqtkfhgJIx9vhnaObVTvKrbCMpbPS2TYd0Nv0kTpDbI0sK2bB7j0oKkY1cpQFVqsm0cAJT2chYXCEkT7GK5legU/qprtl7ikRLj25seGk9ZGX+IouLu7z+8T7bex5TJUh9j4Nxxc7RATIbs9Tv8livxcaDHYpijJ9nWAKkMewd5Fx99hKT2wNMeYSM51lut/nK+Q5/dCPD6yxy984Wjz37JIt726STQ4q6T9xe4s3XrxEmCb7QBOOUfLhLVykCdcSRbVFbQRS3COaXaEURJTV7mcf7b97nvbtD6tpp5GXcIpyfp7MYo7duIloxdFYw1HjtLmEnoc72iXt9dvcP8PHB1vjZACEiJsISdNuIgwFWO/CBLtBBQD4ZEK3OE8p5TK2p0wmi3cf6bYJEIAPA8zEejo0OQqyMwUwwtDDSoOoKg2VfB1gvRJkcbUuysaJSmnyc8eBQEIYdSu0h6ymm8BFRRFUKct8naC9RVCEfPBzy2cfnWVuMWd0e8tHB0CW3hy28Vgt8ic0PiRNYXDzPvf0CoTywTs8dS0snivFF8bckoP19rVmD6q8W4O7rqUZWowmeSaqNNfhBwNzSInF9gKKmEpZxVjLIJP6Zx8kWrzLR8+hBRX9Z8dIf/FcMtvb5+Ht/ycarr8PBkARY6rQ4t9hFKkuqJTeGKYdW4rfbLF+5wgv/7LdYXD9LZQ2tVovBYIgVAuU5eVdVVfhCuSzAWvPGm2/xznvXeP6Zp1hdWWHtzBIvfuYZtna26C/0Obd+jqOjIfHjMYOHO3zmc48xzqaEYcC59fN8fPMjIq/is090mUtyjqaaBw8e8Nprbzo3QU8x1464ceND8mnN7t4h43SCappEwrP0fMWVs11EK+GtG/eo65rB4QFvvvEmq6tnWFlZIQxrNg8P2No+YP9g2PSz7CNvj8U684AZCJInzcFZQwvbjNrO9kfAcNqIpFEiALMIDGOa8PdjINc8/Zf8VnysQhGCPM+PQ4GDIKQoCpRxBhLVTIVh7THIquu6yfbxXFdeGQY6Yip8qrBFJs+jYw26RtSG6fAID8G0NSbb22O4s40QjimvCk1qc3RlODqaMs01813HELm9T9GKLecWLZvlBF0bpNYI69z9/AbMLLU0V/saW1vmI0HPzHPh3FVu7X7C8uIKS4ttnghdnEVRlvS8EoHlvY9vYaoSaWokml5njtLvEi+e5Wx3gM6GEDuL7VYckWfTY1mk7/sNg+cMJGxtKYuKdidp9nZBt9uhLArCKGjyviqqsnTXIjSzNzWhUu6aNBZdarzAx1rjgmylU9h4nkdRlhgMSZLg+YqWSRjkJRKBkp77DPs+QSAxRlJUFZaC2jhJZSSihrXykEajjPqVYa1MXTMZjz+dYxlLrQ15WdDr9YhaMb1+H3F0hO8HHA52CYLAERLWAbFsmmFMje8rwihASME0m1LXNa1O2xmcKDdaU2ntXEyRTKYZRaFJp4UD46bmR9/+JmfWL/Bb3/h1lDV8fP0GaTrl+9/5LgZ44YWr6HzC9U/eYfejt9H5EWWlqXXlak1jkAr3QfYkxghMU5cKBLrIKKYT7t3+hJs3PubshfMsLvXpXFzn2aef4H/7X/8dB7vbvPP221x+6gnWz5+jtoYwSfjnf/AHfHLjOrbSzqkTSCcTlK8IIyd1HB0N3Txu3WSjSUkcSOYSH18KAk+B70KX/VBhrUIJB7LmOx2kkDxz9Ty9fpc33/mYaV6ChWyagoBevw+eIvAVv/al51lYXsRawxe/+Hl++MY13nn/Bp5UrC4tMNdpkWdT1lbPkKYpURwf3+CqIqfMMxb6Pb79nb/8qdfDzwHXZyDAPgKUYLbhN4wNztffNCyWM3pw3bzTAMnAsaWxNSdgSkrpnJeUk8jNpHOnf98xOJtpp7Umm+YY6z70dV3jS1DCUglFWhQcHA7wvACwHBwMWOjP4UkPawo2NrYIwgDfaB6/fJWHD++yv7PByvoFfD9EBY7ubzSKToJqayQuFV00fzvihOJ15+VY2wGN/vtk0Nw0zlKnJEfWYm19woDNHubR/VUI4bplVmGEc56RP/EkY2aAzc2rzeJFXedbYtEnrwvRPMcdQyJOn+5f2fV3vs83f6syBi08aqlQASzM+fyXLy3xmfWIP3njNjf2KrbwqG1NnWumMuTIStaXejzfWWRz4y674z2slYh8wjg13B9UrHYWGE93aLdCQr/gyQs+b9yxfJSGBAE82M957oWvcf+d19nId0mWltBehpmO6HdjloIu+co8/cUuFy+f4/v3Lbfv7jPSmjBe5EFREBWGsSq5ee++mzNEUeQZYeSTa8NIJCwtL7Bxf4dy9Ty+MFTj1IUb375Be2mBQSUwKnCd1HSfovCwogOtNl6gqEZ7+J0YUxZEQUCmS8psiG8stqogCRnqKarVJowihsWA/WlJ5c2hVIpu9RG1xAiJEZ7r4lswNmQ/byH8CGkMns3xVUyr38HmI6xMyIgpZUld5YjCZcKpsEVRAn4LKy2H2vLwIOXF8wnri21ubR9gJym+ignCNvX8Gq3sLhcXFGsLCZ/s5c3gtkVRE8pZzgx/P7OGf8uyPyGXco8dc/4nTzwlQxOn/i+MWiyduYAZ7CBtSa2hKgUbQ4tYWWJVxUwne8yvXEJFTlr36v/5H5h+9BFBmtFVkr6n6EiB9QJuHo7YK2rSWkNvnou/9mXWnn6S9soSVkkO9/aQ0g0eK8/J7CSCbDRi8nCTm6/+gGKcstCfQ0URP3jjLcqy4uWvfJHtnX0eu7SOlJJ33nmX/tICSWeVzrkOMmrRKku++92/5MXPvEDIEaE+JO5abm8O+MsfbxC2Fji7vsbR4T5xHBElCbfvPYDtfXwM0hqkkIS+JPAkZyPNk+qAu0VJvxszLWuG04I0nXL37n02N7fxfEVe5GSZy00x9lE5qLWNqqKuT9j/2W3T1MdF8CO34uMe2gnIcvvhoyHuZiaBn+0PM7n8z1Ss/sMXtqZh3WYgwQGlmrJ0tulGuXknrbWTIVnrHHWtxdZOKSGkQlhBpWtKGeDHc2jpdJVCzMKXcU5naUptjJsJEgolJbqsGI4mFNXYmWHYGmENtfaoNUwGFYNNTWoM2o54NsqIfM8xBtInM4KskBR1wXpHs96xeFIRGg+TCkRgGQ0ndMwCn39siZe7PVpRxCQdMzncQ1Lz/HqfCyuSqNVxxhM648mzfW7sbqK9A9L2CF90CaIIqRyrMVt1k48ppSQMQ4osJ0laxxJUrTWtKEYJSVlWBEGINQ6Y5nmO77twYKmcmYUfhQSeR5kXKOske1YIRN0ojUztGLHcNcOD0EcpRRAGGN3oXmpBWVUIJZGeRytwM3Cep9w8YG1AgVQCz3fM1i99F6BZuq45PBh8KsfyfR8/CAiCkM581xnVWcfeVmXFZDQiDCOC0NWefhAwmYybObmAuV6X0WiMrnQj+3Ofm0npZNV5qZmkGbLQlKWm0gatXV0trCE93OX7f/LH/Nf/9n/giy9/gbzI2HiwwWg44LXvvoIAXnrxaeLikG/f+4BsrLFGHzfUfTXLbhIYrYkCD2Ogrg11U0Pq6Yjx4JCPrn3AE888w7/6N3+I50kWugkf/PhH7D68y97mBq9/7/tsXrrE4eEhX/zayzzxwvO8+KWv8Oo3v4kbjTGsrp9jdOSysqwweEpRKYWpnX9AEvlEniX0BIHv4TWZtlKAJyX4HhaLBKLAJ5sWLPb7rK0uEYYhr7xxjXTqYgfKwpm3CGBz74gf/OgGaVFweDRhMM6xRhCEIUIY+p2Q0BOO5dUl77//IVevPsbcXIdWq4XWIwb7ewgv5NLFiz/1evg57NZrZi5+BsHJDMKMvpt978CVPcV9iEav64BTA6CsxYgTh8HTIbiikRwaCUKoY1mcQDXZIDhjCwRSSPKqYJSOqJubvKMZDRLDQicBYUiiZbR29q57BwMGkxyrNUVVYrRhcSmhG1i6yRxGazbvf8z6xauErQ6155G0E9Lx4FhLTxN47IYPT6SA7usJe+XO3exFN586a3AJw815EzSyPY7PkzvOSedgdhy315rZgZvmpznpcHJqcz7GdbM5LqedtnWjSW9YMzcXIJoi1zSs1s96hfySLcvfYUxGIKyC43HEmsAWGKxznZOCQHo8frbLv/3dF3lvY8B/enOHuwdDjtKSMArQFu7e3WZeV6ysrRHHAQ/u3qTIRozHBdfvdZhfU+xkXRYKRUtWyEjzjRcXOPxgzME44XB7QDvwePGFZ5m+c42jjZv0WpZeO+HsYsJzV8+ztLxMb3mFhbkE8UHO9v0HZMMBXudFxsWQo2nOUI+YTEsiUWF1QZmlqFAgojkGB2POLq9wuLHHZDREJl1Ge9sEZy8QJh12H27i9ZfRxv1dVX4EVYCdKNLSYkxOQI62AX67R713H9npouspNmwjpYEwgbqkFh6FF1DrAYOpIQ09ImXRXo9wMqBUCVpJBB7a1Mgypcg1ntJURYanS4riiHTkI+MFWn5I4ofolmFaTMlGY0xhMUIivADV7lBmhtyrGdaKykvoz2vO9sbcOxoj4jmU8onaXR5fXefzVxfw4w6GMb7V2BICr2KunaB8jRfIR+IU/iHXaVB1ArJ+en3d8FvuZ4WgMB5ZKfGlR1bDTqaprSQcbGFaHourF/j4kzvcffg9Lq2dpTgaM5rW9L0QYWomRcVGmtFK5tnNakpbk8cxT3/5Czz2+Zc4GBxQ1Zok8JFKMR6nzHV7RGHkmPHRiHvf/x77t2/yWKJo5x6Hc/McKcUDNmm12/zgh2/wmoBza2e4e3+Tl7/yJS5dPEdZagaHu0RnYw4GBywvLzPe+4TI7gDwYLjNx5spByPL9sfX+frLn6Pf6zFJp1z/+BbD4YQo8Om223SSGGkt/Uiw1hasmpLVUNC9dJH9D7fRkxGJsexnlqmumJQ5unb7ldamUWmIE/leI0WfmQGYRgJobTP/i8DQZDHamSSbY3UDTaNRntx4T4EojkFVPdtfcDktP9Nl+IuQAjbNxOpUptJslkwp5eZ/isLJ1GR0PDtmLBgryfKKomqsxI2maEmCOKFEUjfHwFqsMCjpZlGsNlitEdJrMptqBsMpYZWRxDGtlgNcZVVTjVNGBwUPNwUPowUqAZ7ReNIQYPFxe6dPTiJysrJmZ6potyJa0sebGqaTIQdFwdZHd+j15ojHAzzfd93+WjMwhiuLEa0kod3uUBlBMR0zLqaEteRMW7uMSeFcxoy1JO2EsiiRUjAZp+RFQRD4VGXpZqf8gFo7BUsSdlAqxPMyptnEnecm8Nc59zXskXQ27drU7vqTkslkQhQFjdOhy2/Ksgzf94hbzohomk5pd9vMzc0zHo2PHYuVUkihjnOyZCSbUYXaMSlFgfQUQRj/IvpQvxTLGIP0FCtryyStFkVVMp6MGY5G1JVmMhxTJzVhFCKhmZ1zznxSSeIkpihLytIBJ4MgKwt29g6c/T+SvND4tRuJqaoK08xd+YFAGMvdD9/llT/+f/nNf/Hf8MWvfpn33rnGrRu3GB4d8Z2/+BYvfu5zvPRrv0Wrv8Lrf/YfuXv9PYpsjFUGZSVKeUgp0FVJHARU2rgRAlzD3mZTdDbhzvWP2N7c5vKTl53xCvCbX3uZ9958g8nRAe/+6Ee88eoP0FVFK074zd/7Oitn1/GjFt1uByUM46NDBkdH6CKnnbRotVp4UYguCuosJfQgDJyteqBkQ544AO97LprBhSULDkdDlAo4OBrRSQJeevoxhsMx7330AKF8ojjC1DXDoylZGDAtcjzlGhAPtg4QFrTVhErQb8fYSiNjR6JcfeopAinxvBhjLFmeg5BcefIxpAp+6vXwMwOrR61f7TEgOs1WHSfKH8OpGXtz4pDkMIbbhEQjMcTOANaJXLDxWmo+sDOb9ZlTzaljYfGUh/J8NxCnnAZYeYpWK2ZmoKGkxIgaTyn6va7TfpcV1bCk35tDSY9sNCCZW+RoOEQFHvc++YC53hIy8F0omLurNJulwAqOrVlPCp+TNubpYWVjrQOLZiYJdDI/Y00jozy1iZ9eM/mPaSQUxjQASFNTH89fGWMRsmEVGwt30cgVT1uyY2czWI1Dkjj5Hf84VwN6H6lK3LXtCdswewLVivjCxTkWey3+6Aeb/NnDfXJlCeNFok6Lo51b7F57D6kzbFUSJBUENfl4wE6xwEGes9LStMIpkW5xYQG+tBby/3wkUMrn4btvErz4AlGYkO/tMz8f8uyVFR5f63Pp3ApLK6tESRtfwNkEOkmAMZLJdABhhJJdijxF1AWmmiK9mpaqMZnGJj751ibb45DVi2dJHzzkUCfEYp5qsEm1egV79wamaCNDN9CMH6GMIC0MSTsmVpoi7GOKKd2FPqOtFDsRkJyjilt4YYsgy6lEG1NVEIRIFbJ/OCULM+rpFOsJF1AdJNDM/lgqlJkgSbG0iNGURYbSGdV4Fy0UR+MxrTCiPdel13bHL2vcnIwU6LqmtlCJkO3ccP1QcibusrYwZOtojyw9Aj8gFpY8XMQLY4Su0F5AVCom+7vIBcWZ/iohE7TMmYns/qGvxUeNK5pHT7FXx98f/4g9uUcAIvKQXkhWGEa1ZCp81i5eonv+WQjbHNy8zo9e/TF3dgfUn32JF77+G7z3ze9RDY/Y2D8gEZJNJKtFTmttiRLB088/zZUXnubM2ipr59fxAp80y/jhm2/xg9feZG1tnX/5+/+Udivm7puvEhYjzqz2GR5MqXYf8vSTT3HbwL32Azw/oMwLpBLkVcV3X/khVigGgxFnzy5z9uwZhuMxla44d26N+x98G9H2OJj6bB0GPP3MZ+gtpmxs7PDi88/x4M4dbt/fcvbE1HS8kMAWLPV8+knAU0uKs15G4vfptjXvH+7ywlpMKxuSjyM+OvR5+6ggLZ0yQhvruvQCN0fzk7dFazBGP6JGmJkUmWPDItu8l+7+OzNiAokVFjhxkD15n5sgdztTcbgO789Ssf4izC6EwA3fK0UURRSNFKeu66bYnzFZTZEg3aB/3WRBmhryoqCq3dzIRFaEvk/W0IEz1zvRgFUpBB4WiQWpoNZUWcHB9hiRj4mjgjDwiUIH8LSuSHfGTKM22bnL1NKj0DUYJy8UtaW2NYoa6gpVa7akYt6fZyWOWZ3s8XBvm57dZ3I4phrHlMqnkj7aSrSBTGs8P6QzN4cIAmohscqnNiEL/ZJeLMCEVFWBrmLiVuxAkXUGBWEYIZVHUeT4KmjAKRhp8Oo5/OxxQtnBRh8ShgXTadaElNoTttPimlRKOZmhEPiB35hqaUDiBT5gkJ4LA3ZOlE5hJIREeYK5+TmGR0PcnqiwCKrK1SdSuPfVMbk1UkqyaU5dW6Io+oWw/D/PUkox15v7VI7ljNQ8sI69bbfbWGMYj8aYuibLMoIodPUnltrUbh6t6bN7nk+rlTCa7FIUFWVdMy1yxuMJomEDZzlmxrjXLpWzd1fK5elpY3j9e99GhhHf+Be/T/TFFr4fc/fWHXa2t/nRG29wsLfPi194kT/4H/8nfvSX/5l3X/0OhxsPKLMpYFGexPd9F1WgoNJZY6zWAOliynQy4fp71zhzbo1ut0U6LSmMQgpBOZ0yHg2ZTFKEkBweHFLVNcLzEQh6vQXiyOPOJzfRRUmR5RR5TqsVk3Q6dOY6EAeousRTzWfd81yjqqlthZQoJLquZ2Irzp5d58HmQxYX5qmsJGp1WVxaIk0zirwgyzI3vwUEUUi326XT7TILKK91AYGi24k5OjpiQc2TFSmLy2fYuP+A2tZUOsb3fIyFna1t4NMKCD5VmJ+2ieUUEHJfT4p7py+XGOsMJ07sLWeg7LS8z5wyeZjNaMmGjXFDljMNu7HyJ+R2Am0tH97ZYeP+Ji9cfQxbZ46hOX4SLhE79MAagib5PIxjwiDECIMfKIajAZeXz7C7t03cCrj7yYcsnTnP+eQ5GmKjYczEMXNmBC4jy84c/lxnclasH/89x21ne+q1N4CpdrkaJwV+Uzw1mRa2AUDCnFjGngz6zX6naMCXOf6dZmYv+xNrBoJnW/HMLGPWYf3HC7I4LnKEEHhWOwrak1R1ifAVrZZFTzPOLsxzc+MOo2lJe/EMybmr+PeuMXmwSeB7JKpgP0pQ6QGH6QKX4oiNvQH9tiYQPl5Q8LnLPd7eOeT+viIwPvdu3mX9bA85EqyfnefSmTbnlmJ6iaDlaUKmeDLg6TMJ6wsJk8Aw8hS6qtHGIKsCW06piilr5y+AzhimFYUeIaKE/c3bLF1+hrVnX2T3/Vt43UX88RYiHUDokY4OaZ+5SHaQEilBXkvqKqeqDXORZFwD2ZB6omnJHD0tMKqPTkJU1EUf3Sdsz1NODgilT3Gwz5bW6PkuosqOh6lF1EN6PlBQywDZStBBSCUUXSqMnrLcCyhUxL5U1NaQTcfouiAMInzPR/kSW5SY0pKOwA8CtJUMTcJbD6e8uGhZ6Scsbe7wcLKLjSJEFLA18bi3O6UjITWG2mTU+RAru8wlAaZQVJ9OduTPcfE9KgX8SXD1VwCW+IlPtzXUUpFaj4FJsAvn8ONFepefojjYp/jgNez+FomBlXaHa6+8xsHlfZ586XlGN2+xXxSMJ1M6CzGtuYQvfOEzrD52kYUXnuX+vTsc3LnO6pPPk2UZZV7w9o/fZ2t7H6Rie2ebOZ0RmimtxR433vyAWx/co9aGrhTEUtLvzeFHMXmR89RTV3nu+ef50Q9e45233iObTPn+q0PiKOH8ubM89cxV4rhD/8zzXL9xDRHOI2XAfGeJVjjPs09e5bVX3+DWvYdU2pkXJIHH48tdLl70kUVBq5Xw1c8us33jAZefO0O685CrrYKR3+Hp3jzf/NMNnuquEAQBP9w8wAQhaZ6jT0VXiKYZdrx7NU2xuslGkladigUR1My2xSYn8JHmotsTT4PiE+dAx4RZ61qBJ8YWP8Pl8wu4bVvLcfC81jXjyQRrXNiysYa6dHNUVVW5WZG6Rtc1M2ePonC5UVZIrPAoSksowaPGGheaO9s7rXY5jlYXCF0f1x9ZrhkMp9TTDClyV5Qpj7rSVEVFlBuqcAnheVjlI5VPneZIXSEDHz9sY9OUoN3H+h6ZlFRRgK40cyjGVcaF1YCk1yIMFciKvMhJc80oK5nmoEWIKkcEVpB0fETcYnD/kLOtVTx7GSskWmuqqiLQAVYa5whYWJe1IwAihLX4foypLKqaZ0E9x/RByXgwQZ1bwDuT48mCutZEURttNGnq3JCRglYcMzfXZRYP44cBo6MhQlqCoOVqp9pQFgWeZ47rsqrS+J5q5qpCiqykKp1bYW0cQztTG1lrkZ4lDAIsiiKvAf0rA6yklESt1qdyrLqum7gll5fqGgpOVpmOxrO+97HhmxDCNWwE6NpQjidI5VMZ0NaSphOyokTXLtfNGoGpa2ot0ZbGldqgdYExbn5QCp8yS3n1z/+YsJXw1d/+Lb74lc/TmZsj/PA6D+7e48b162xtbfLEU0/z0td+h8/9+m9x48dv8v4bP2D7zsdQpniIxvjEgY6yqrFSuQZAOqAsJ3z84Yc8+ZlnmeteYePBNh9+fJcyn1BmGQQRpq74zOe+yAtffIkw8EnimMHBLtN0xPxCn9X1dbaMdg6ApmY8GjOdpMStiCiKCMOAKAypMQhhXRPFczOslZVIJajw0LXEl4qN/SMOpzVbw10m2X2y1OV+5XmObeY9Z94EuqqcBNEYtNboskAJSxwEBL4gacck7TZFecBCv0e7lXD71m3m+jGi0oyOBsz1FlheO/tTr4ef2xLlWJ422/BnHf+GthLM8pA42fxtjbHi+INprG1ozgZIccJ8zR7DGqyQbsOaPU/IY4A1uyHUxnLt2nV+9P9x92Y/kmX3nd/nnHO32JdcqzKrsrburt7JZpMUJZEUqZFkzZgyDNnw2zzYGMDwg/1k/yGGYcBPBuyHsWHY2kbSSMKMqJHEnd3svaprr6zKPTJjvXGXs/jh3MjMJimpSbVJygcoZFZmZGRG3OX8fr/v9sb7HB9PWGl3aCXeDcS5RTK2p/ZIKQiYQT1ZAAAgAElEQVSkJAjUqV2xUsoLNh3oPKPT7dPpdjkZjnAW3vn+N+mub9Bf6vsLp3JNWSBpokLTFoGe1bvEwinJVI4xxnoqw1lTdNakLixpbYVGGe07cqkFTtoKml/s9ufF65xmYp36WpzbXE/f29Nj54/XgnLp+cCer7rIrHD2x6Bm/8j185ikLqg8f9/6ca9z8X4JPF01wId/IiQTEfEf3j3iO/d2acUNXnnmKrfubDM9zInCi0hRstoKiZOEjbWEMq0zOpwyns7Yev4qg0cz0rmgFc0JXUSrlvOrz/b4vdExrneJkCG95R7d1k2WooxaEqGEF8diDdJpUHXWeglNMePu/glurUGAAGOw+RxTzkGXaCtor1zEql3K6R5Z+wXi1csc7R6Sqg5ST7CyQdhZI5jsoJa3yPb2sNMRca2DGe1Qj9rkMqAoC2zUQk7nYEvyLKO7tk6jsDQ7sJ0N0ckStDoYV2DSIcV4jCgNoreMrCfEIsKGEU5k6EIQOIMoU0zhmJQBtOoEUlA4TRLkXN9a4dC1OdjRIAVRLaEoC/LxEBHGCCkIJRgUWggMhkA5jIUyCnl4NEa2JXG9BaMx5BlZGKG05f2dKav1OqLICa1DBjkyAq8DDwgxvpj7GS/HeRTjfK7Rj2quFiYJH2m2gJ1BiptZ2ldfYdza5GB3l/jDtwkOHtJwPk0+3j6gt9InS2rcv/U+T/Z2eGbjMq7bYXVrk5vXLzIYHmEiwcFwn+P3BObebdoNyX6Y0N64wsnxkEa9xpe++EWOjwfM85IrmxcZPL3Dzq0PmDzdoyYspRK8+Zd/Tf7iSyRJjePRCWWeo43h8sZlxs8O2NvdY3v7LqU54OBQsLO3z+0Hj/jtf/ZZDo8HHAwtz97oEgYCZ2AwGPKXv/cnvPvebTbWlplNpzQCyY2W4pdf7vHpf/Y5RntPeXz7Md97Z0BahIi9gh/87Zhm05AsScyRQsfriNExW9rywBlqUcS2VRzPNQbPctB2Efvh3++klhCEIcaaikXhkE6ACPyGQpXTKKkYCJYz3rW/11aOFz/URPuGzVaPcg729w79PvQxz8Usyz6xc/HjLv+KzzS6C4dbY7R3r8sLSq2RgXdA81Rz/7iiLJEyONv/kJTaIVTgnW9ttT9V1enC3MnqDKvzin0RUJga88JhsrKyrvfaEWMsVhtC7RCJr0+kdTA4Quw+RmRzWFqlvHiF/NFdykYN1V8m7q/gZlPMZEZDCrqtgEZXokVJluYIVWKNIgpguQ2mKQgjRRxCGDjqNUutoRnMLReutAkDgRUBptDM0zmBUlA1omma0m5HFR1LURYFOpV01U1EvoqdOLInT/nw9hvIDw2v/BcvoMqnzO5sE12/guj1MKbwTWCvAwJUEJDNswpNkT6axhh0aZDKgZAU2pIVPmMpCALS4QQpBLUkwmiDDANvIa4UKoy8Dsx5jaGUCoFEBdLr4KTwCP8/kXmsMYbpZPaJPFdSi0nTlCSpgfO5bdPplCgMOc5zTFXTeYaQRwa11l77IxTGgiky0vmcLMvIi5JSm1OXxSCwp9eMl8dVEhjlkaJQCZQAbTU6T/nrP/p9snTGl7/2L3jt9U+x1O+jVMD2w4cMT0743re/w/aDx1y9cYNrL77Gc5/+LDpPefj2Gzy6/Tbz+ZTZcMbxYMC4agxNRXfsdNq0ux3KvMRaR6NZ982JlOgiR+YZ1mhuPv8Sly9fRirBwf4uk5MBY+co8ozPfO5z1GsJj9U9ZtMJ6WyKLgumsxmz2QwB1GoJURITBZ6GKgNFIATClQhZNfq2wLmcfPfI0yMLTV5oTKUjlEIglKfGLtwra/UaYRRRZhm69KYzQRyRRCGBlP51lJp6vc5sMiIKE2pJwvHxMdIYn3llHXdu3f47z4efqLFyLBqEhVaqoq1ZdwZcVRqrxUkAUHmrg5N+ozlFpDhtvHxDUDVkpxqlc+iV/8Lp5iOEOkWsrLU8ePiIP/2Lf0epSyyaw5MhT0sodOGtS5WHs4UQKCEIpGCl12Kp06KWxEhXmTU4mI7H1OsdVlYusLf3lFJrjg92SCcT+v3+6fux4NQvcgrO/mbLAu3wcKFDCh/iK4ynQCwoePYUeToTMJ+fUgprscJfRIsQW/97ZEVFqXjU1lXmHlSPO0O9TpFEd4ZMnaFUC8v8s7uh8HHUBPLvhjp/mrXQiP3s18e507vTj2ehnvhGHACFEP7CtvmMt957yghFOk7ZrPV49eVn+PCD98ge/gClT1hNHFevX+DKpT76wPLd+TJBlPDh0Zyr/RWGk0ParZQoD1GiYLOzwoV4xuPYEijNh3snfOq565SH75FbPz0qNJSFIYkUVikSFfDKM5d5+6nGJjWWo4RxNmaWzXBlQRgG5GbKTKywtnkVc/8+J7MZor9Jefh9ONxhrZuwv7vNydYLSFsgVUy312F8uEeycpl5kaKzCWLpWcIQpq4BxYCk0UI0u4wDw1LTcfNqiHs04V4OsrNEmaW4so6aH9OMIoowonCabhwysIogCHEqQKFRSiDKnKwAKQICl2EE9DsR/X6D/VmdpC1xpcEaQyQtWZZRFAZXFhTzMSIMEdZgTULpNNKWhJ02U6n4YDdFmwb1XoNcB+jS0YwMR2WNwW5KcbJPr5MwEwV2NkEqhxKKpQiemp+HfbBHpAXnIxd+DFIFP+ajf4ajUcolGXJ7Z8LK5piN7ID64Q5YQxlEhGFEYmByOKS/1CWMYh4NB7xx5zY3rl5nJgRby6tc6DUYzybE3WX6tTYqiun3urhun9IYTFnw2qsvc2lrizAMGI+H5McDHnznu4wODjEawkCgc0c5nbPUrZOLnNHI0WjUWOp2uXvrNlEU8Uu//Es4s8Ph0Zvcu1cDEfDd73yTz32mxvraSxSZ5PqNa+AcOzv7/Ov/6/e5++ARWMd8PKSZ1PnsjS75ZMqVV1+mtrqFnA753p0Dnn3tOkE34fZf3+L2jqGmJHt3BtSd4tXnb3Lt6ojJnuGLRBxYycAGjAtDUzq6Dcmjsa0QTIcTjsuXLtPvdb1jq3M4JxFKcUpelwIlvW3x2VhLnKJQ2hic0RVd26BLja5o3qXW1YTVG7t89/tvce/+g9Ph2T+0RuNPxunsJ12BUsxmKdZ41kYYeMvlLPNFlxDqHHVf4pzBWMd8nlGr1b3LnS6xQqG19QNYDNZWg1FbIVVGY7XGaoPOC5y1GBRWBZRaYPTZtSNO40tEFdHi91BzdIB4ch+XpxROIK1F4LB5ynx6AqNjolpCPp+zmY544crLZHaPIBJMhiH37g7RRoMJ6a8oNrYCxiPHZJyRRCFSGlqtgnYDmq7B8oUeLpAEUiJlDYe/B1priWKFlO2K1hUiw4D8SNKcPUcgVtHplHQ45fhwh+HsAWsrNfIH7zDZuUXblqhBTN6IUTJmnmWEWVzRWefeRGY+Jwy965x1Hh0UCKLI0xidDUiCOlJJGlGDPK/yP9XCtc1r5JQKKzqWJIi8K2E6S2GR6flPBKlaLGsd83T+iTxXrVbj+OSElWXF/t4e3V6X2WRKnMRo7dFaU2nShJDEiSCMEqbzHGn9NTKdZ0ymU7QxlIXBOk+PjQEVenv/RZZYmReUxpDEcdWgL8bWgrmBeTrlr//0j5mMJ3zpn/9znnn+Gq1um3feeItb773PyfExT3a22Tvc491332ZjY4ONzQ02X/o8r3zltxE4pqMRg8NDppMRCz1koEKWV9for63hlGI8yVhZ7XLlxhbyt77Gk0ePeOsH3ycUglwXfsBhHbOTE3SeIZ1jOjjk23/1ddr9Hu1+j6W1VQaHBwwOD9B57pE5ZxmPJzCenOo3FxlcwOIyrurks/1QBUFlwBJ7WmpVz529T/5n8nmKM5ooCirDFYsKFGmaejOtKt5ocLBPs9Xm8uVNPrx7n0arQSAgm6U0G7UfdyoAPw1idbrJy48gJue+zcL1yE/tFkjKRwuEBdLjf8Y3AqcomPihzysU7PwSzp1SLwYnQ/6X//X/4ODoyBtTKMlsNkeo2DsQKVhZ6lEUhT9RkwhdGPZOxjzaO2Z9ucd6v00tiXBOkRY5s2zO6uYVdg/3QSmQysOW1YZwRr1zlTX6gvLn/z4fjChZGHYI4YODHTm+odJ+Mnl+Wnm6cf5w4eQ3aF/pVw2YNThxlgviqpsmxv5Ik3aqgauee3GcfJai8LxGc2YxAt7dyXzMjfwXe/0QfPfj1insev6x4rTxt9JRiAhcgHSCehTy0vUOT9KCwbHgzoMHrHZrvP7Zz/Hk3W+RDx1rvQZXVxPWuwlFEnB7qtFZzvbjXTpbNUodc1LmNEWAC0P6NcOlXsb2ZMaMkJMi5oMnAz61tMHOZMxGs0TrgiLPsDVBggEluHBpC9GdUtiS8WhOkU6gmKBMBiKiKDRubpm0OiSdJcTJDMo5UadPOTpm9bk1jo8mZMMxsr8CZU4jipGuID98zHLdsZ9mOOnLwtQ4pE6xtSU0EaIsmcqSSW6o1xP0JEXKANloI11Jv97j2eWYe5OYgStRZUYWNImMRTQTEluQE2GLlBBFqBySHCUNzXaT1EQMhzOmsxClYqIoREhDLawjyhKRTUmnA5zJkGWOrPcJ4gauLClPxgT1mOHxmEBB3KhTCyzTdIROVrAqwSmLDCI60nBYFKh0QC0oSJKEX766xAff/PkYrp9Srd152vUPDV44Nw44dZbzF/u1Z59n9XBKefSU5uNdamgyaziZFehQYcqA3oUlng6GZIMT+msXaF+4wJO9PUQSMx6O+Ks33+dXX3uBjZdusnbpEioM+N5bb7N26TlImmitiaOY/f1DXn7lFfr9Fs6ts/vuW8xnOXlqyXKBLhxGKGQc0wwKXtqoEQSbaCdJ4pg33noTKS1Pnu6wsdFjlnYodEGjGXNx4yJxeJV2a40XX1xnMp1weDhm/+iQpN4kVAqE15R0gwKN5s68xXtPLdPwCTfqjk5NsvnC8yBKpt2nHNZLVluSL16RbA8zLiylPNPtM+8O2SxC/vVbBTmCXqR4rgOrKzHpg5zdifZNknUEQcBkOmcyTXHOO4wBfsBY5VEJIdDG64is0VWD4PcKIaiaKv9RW3PKVsgqqpwx/pju7O4zm+cfG/FfIJw/y7UoqpM4wQF5URLHEUVREFXfC5yoXlO1V1b35iiK8YYdBiUVRaHRSoOxlMZrJ60xWK2hLHGln/TrPMcZr3OzzqF1hQosdMj4W7oxrmJ7VFo355iPT0hkgIjrnp0RRUjlp9u6lNisYLqzTXLxErkoOElHYGdkxRxcnfHoiLJ0YDWtdoCzEaOjgEcPThBopDQ8ezOk3wyohREGSy2O/J4qBFlWkmUZKghIpzOklEzHM5JaHTLD/vdLhJhig5Rmv0U2OiAtH/Dy6z3W2znDp/dQ04ywI1ndjDkOxxybHggoixIVhP7cwlFqzxZSSmAtyDggSRKsc3S7XdLZ3GcqBRVVSpfU4pgo9oU8wrveKRVWbs1e8yOkpKEkRV6c2sX/U1pSSeqNT4YKWKvVSOIYKRVZPvHFe56RxF5L6pzXnznrQHngQUpvnR4Zy2SWMktT3/RKQVnmICStdgshBPm8IAr9+2+qyAJnHVEQ+MFk1XwkoSDSlnFhKHXOm3/zdfa2H/Ol3/4X3Hz1Vb74lV/h0rUt7nxwm0cPHjIcjhgOh4xHY+7evUuS1GjU6zRbTbr9Hq12h6jeJQhDhJBkacbDx7u8+cY7PHm6zYWLG/z6b/8Wn/6Vz/PZL32e46Mj1v7wD2k2O/zKb36VWiNiPEqZDSfIapBCkTPTBfP5hFazw9rFDdbXL5LEEePhkHQ6I8/nmKqPWFzL1hicMZ5cXTHXFq9bBR5xDeOYMAqJw4havU5F5wLr0GVBns19pAEOpXwPE4aSl5+/wfPPbBG6knk6Z2mpj7WONE0xuuTk+Ig4qTEZz3CmZHV1nW67/XeeDz9hY7Uo+BdNjueDL7Z3IU8JgGerQrI8p1ycGlssaHROWE+hwDv+OM5rtc41WIunO2cEISv4azyZMByOEUKSxIqk1eTZ61cZj0akecFoNmf/8JBup0OoFM1aDRdDzViK0nJ/54Cj4yEX13qsL/WpacNsMqLZ6y46RR96KNTpwVzw4c83QYs/8pSx4bwu7COUv6pQ8tkdZ+LnRWO1KJpOm1YBOO3F/hUyZaxHvCwLc4qz43HWe1XHpEIET50WF2jg2UPALdwYzwo0637UZvgfu35umq1/YJAm3I+nKXpapESryKd+WwsERErxa596hseDnG/PU2zR4PHjbXqdFtdf+wLDe29yoeloNWLiwLG5HLNUN9x/vINuLLM3VriowYPHc/pblqg0SJXx5ZsdElXwVzsxuVUMxnNul9A0J2x1lrkUGazOkcRIfGP+4dMDMhRW57giR88nmCKnFkfe2MHGlDpmZ6xYamwSTJ6Q7m3TXF6mCJcYqQ4r1x2PHj5C1a9RJg0KYal1l5nuPGbjWouaWuVJFlICymRYZylkHaHaBGbCeDbnw+MEREIoC8p8gm30iOoxvdYq/SXJ/bLE6gBD4i3Z5zliSWCGxyT1BjNKVNIgkBJhBLrUTF2TW/sZuwdjijwkUAkkMSoOCJM6YRwhVQvTW6HUOaYooEgpBcRhQD6doLMpsZIEkSKfzzFFSU5I6CSBUIRRTKPbJZYj1pqKZzfbrPag2wp5duka/8+/+WQ23p9kLajLpwHfP+bfYtkKhfYB5dXPW4eVAUOnSJSm0A4XRgwKmMoEjKUbtEjrJTkSPTxheHBEVyhevPkctpbwS69/mt39A964fY8L16+jQoW1hquvfwbT6lEWBUdHA6y1rKyssL+3T6/XRgrJ+vWbdNa2GDwdkxWOpNWhfeUyr33+szw63qXdr7NkCm7efIm33n6bra1LHBzsEQSSw6Mpuoi5+dw17ty9TyBj3n/3EcPjgmdvXOPP/uzrfPv7bxNEiqtbW6QrK2zv7CCU40o/4IXXv4h9OCJI97m8tkot2uDVz18hXOkzfLzH8STnqNZmuX3CVz/XJ29s8b1vP6IWN6DR4P17B+SyRaffYMkZXm4Naa2HPJ06SgTDVKNLGBwNeOONdxASrBFVHoz2LncLw4lzBYC/vZyxMT56vN0pi36hrfL7jg9aPjg88ojDx0QEfh7IgauwuVq9xnA4JoqjU9MKB6Spd6DzuhDDQlfsnLekFlJ4py7nyEoDAnQp0FacFpJSG4Q2GK3ReYHNMpwtMNbnUCm7oF26j9QZ4BuqUyt7BLXLzxIEEbIscMUMGQeoMEJtXsPlGcIaRBQQ1+vMRcH9gydc6XkGzM7uFG3A2QjwhlTptGR/b1JR5Sqqp43IhwbWElQQkk5TnPAW1llekmc5YRyTpXMv8EdRZhpzWMcdOUpxSNCsU0YC63bpbxwxGY2ww4z1rS5iqw0mJ6hn9JUm253TaqxBs44IYl8IzzMS56UOZZ4ThIGvZwKFLTVJrUYQhd4OvFZHyQAnGmBchTD6c9BYCCJ1WgsVZUmSJIRRglQh08kEIWRFufqZn34/1RJAEHwyrBznLFEYU6/VmE7GNJvNinZZ+kgAB2EU+kFDHJ/ew4uyQFvHbDYjL8tTjeYiuqBer5PNc9+EK4U0GqMX4t9FwWmJk8QbukhBjCHUiqOJJtcF2/c+5Pf+twNe+uwX+KWvfpXrN65w5eoWT7Z3uH/3Pk8eP2F4dMxsnpKPhozHI9jzuahOCpwUlb+B8uhbnpOOh2SzCXvb26hA8au//hXWN5ZZ39jgX/43/zXWQZlrjg9H3L/3lKOjA5IkIstLjDEIC7osOZrNGY1OaLSa1Os1ms0mtUaNIssp8gxT+gBxUyFTwlXimkriEQQBgVIEYeTt7uOYIApRnPUJs9mMdDbDlCVKCpTyMiCPgglefPYKr718ha997T/l3TffZDL0tMI4ionCEPAUwigEq0N6Sz2KImc8Hv+d58NPaF7BKUrkrD2l1p1+2/hpjHWcNiC+agckni5xDunxUL3w5vRUDYmQpyedqPRQ500V/HlUFR143ZT/LYJrW5f5z373a/z+H/wxK8t9QmXpA0vaofM5j7cPqCcJe9kxEkjTgrX1JZbaCSKIebA3ZG8w4cJyh8HBLt2l1SqfQ1AUxrv56Mr73y4EyAsbdPcRwMPflM6aCeNstWEqrNWcvuDTauhsEm2M565KzkTstuq6FwHC4DM9pPO/XTtPf5B28fslVJbp5/sZa11FsfCkFSmkN93Au2HZytbeGsvPKR3157cWCF81DThDSiWhmSFFjBEBoSu5Wtd87dWLnIwe815ao7l6mYcPHxAGgudvvsro4dtkhSHLMwgVL64otm8LRF5wZ3vAvpwRTo64tCxpyg6D8ZSGKHn1mQ128ynfP7RYG/Dg9gcIM+VGP+RiKyGMQggUuZXcOzjhzQf75CIiKCyuzLH5nDgIqUU1AmOZlCUKQWYUgYxZ3bjA/t27DMdTZP8yJ9s7tFqaeq1OcTSAS12sLVEqYmmpx9JqjydHGhPGYEqkzjEuREQRovAmGfPpMdvJZZxQxLYktAWldcQi4OT4kLLZxEYC6RIKZXCzKSKU2NBi8iH1RsTUFtgwxpiCxFpKKzjRDUSY0OwmBCdjymJOOcnQRUxRlAS1GKylubxGXhYU6YRyNsPqDKMSwjjEZikykAgH3Vab+eSYPPM0C60NVpeELqezVOO1l5Z59vISqysx9dARuYRG8rOfwi40JCBO7d5/uLE6j1j5UVcVkl7NvTJr2CsD6qLOHMimJYMprK0vI072uffoASdBn/rWFpO85OnBE54eHPGyK+m+eJNev8fB0RFLK2sMTk5YWu6TxDGdpT7j6YTpxNs9X9y4SLvfY3B0gjUW44x3IGv16F2/yfrFC/SuXKZz5TLzUnO51+PWvTv8zu/8x/S7y6wuL3Hn3l2UFEynM8Cx1O/ijOXVF58njELSyRytLX/+5/+e/cND72A1npJ1mnzp177EH/zRnyJdSdJu8oUv3KS3OmD33/9bnqo9xmGLp7t7mOy7PoOnJXhlqcZnLoW0l5vMu5vcuJKRD0vu7xtOgjaXXr7J0izleOeQJ9Mal+eai23L0soa72xPuL87RmvNZDTBD/Qr7ZRYuOH6IyMEvlCtjqvgjCq+QLROXVkX5I+KXmCFF1kPToaoIPABoh+TSv1zaayc31s0hrzIWbwrMvA26daaylXOv1d+b1coAU4YoijCWotCVnlMEZmF0kJZGqzRyLLEFQWmKCjTjPl4SJ75PdkYg5ILIubib6qGkYJKvXBWiBYqIbr6KnrvLlFZQwmLldDY2MA5RyBACoNC0KqFlCJDhCFIzY2bDa5ca5Kmc0YnENcEYWipN7zoXwiJtBAoSSQFy5vrJEnMcDql0KUP0xWKIIqRQpHUOsQ4irwgPZpTPq6x3r9GfnTC6PiELJ2yP34XmRyg5znJSkxyKaG7FSER6Jnh4INdygczOusz6HexrQ5mkQ8mJEVRUG80EEqiy5LpZEqzKmbzquB01gcIR1HkG9equJeVFsy5ynpdKrTTZOmceqNJEIfgBGmaEtp/Il1VtX4IBvipVyAl7VbTI31hSL1eJ8/mTIsCENVgyl/TURyT5TnzXFPoEp0XpPOsAg0WeVK+WVjQhkvtGwwhotOaUgqfRIqzJKGvN5MkBOFoOYVyBbujnNI65pMxb/ztX3Hr3Xd44bXP8NJrr3Hp+haXrmwyOplwPDjh4OCAo6MBk9GEfJ6fochBQKBioiQmDGK0znny8A7TY4W1lod37nL56lVW1vogIcvm7DzZ4e4H95imBdksY7D7lHYc0k4ShtOUrMjBeUOaMtWMspSJlIRJQlJLUEFArV6vApL9oL/U3v1aACg/sFFSEgVhlZ8IpS4pioxynpNnGXmeUxYFOEcQKIIoRAp/3JNI8tKNTX7rS69RFBkfvvcuT7afgC3p9aaoth/4aG1RMkCKjP7KMvl0xuB4QL32CVIBncND8giEqypvIdEe7vATKgu2spKVQlWpzovtf+FwR1UR+DysUz2V8AX+ogEzxt+MrXPIRcMl/f+9sNzSqDe4eGGF3/zqr3Lj6iZKSd69fY/lXotASeIoIhCWTrvO5Y115nnBZDyl1qzzdP8Ia6BWl3QbdVQgeXJwwmw2o9btYBzMspxZlvPm977LeDLjysU+rnI7xIGpGiWlVCWaPWsEbdWILprSBafemB+1HLPnECu7oDOcPtdHqYbO+ucUYqFxs5Vt6gIB84jTR10YzzMqHefvgbbiCp6+JtzPRbj/i7KkkF4TJ7z2zhEw0HV2dk4IJFzsN3lutcZvvrLO8eQxDycxNZVx78O7LHU/Q71zgcP0mLAZQQkXWwEirFOc7GGCPpsvXKLYdRwXhoPDOR8MUi6trxHNNBcaAa3jOeVkzHz/MVE98LlOUYCIG5SywXCu+Ivvf8ijk4wSCLXFZCnClFgnmGaaTqfDLM+xxYhat0uWpRwUY7QtsNMTVLOHSo+Z5Zb+xjUOHj1CFSk1YRk9fcSFfo1SJRzP5+hYIKzBGYdVdWSRUpzskDRiRD5BAkYG1XmuiQCyktk0Y5rWKF2IFAG5lAjt0LGgpjQuyNGhQ0lFGdaIhPMujHGN3CpMIQlVjbBhwHnHogKHyXK0zhACtDMgApJ6m0AE5PMZSIVBo6KQNJ8RlpI4DFlq15nrKa5MMUIQqBCCiHq/zrXLXfrdBmEUY6Xi6UhR6p89HVbgqb0gqgwj39wbq72lvPbFa1lqf6/QHiFxeH3JIsPmOQKkk2RVon0jkoSF10QY47j6+c/w7XsP2N7ZZaRLYicYPdlm6zOf8uGiccJknrN/eMQzz95gnufMpinj8YRWq8HFS5t0lju0s5I7dx6wttr3RZk2XPvyV8i0Jmq3KXTBk5093n7rHcIwRCjH0cEOymmEKxgeHfGFz32Wd959lzwvqNXqBIHyH6OAg/0D3v7BW9z54D1WL+sb0d8AACAASURBVF7ga1/9Vb7zrW/y3u37qE6Pa1/4AvJgh5de66LcAS9vBNSvLdNaj5juae5Oe+x/d4f1rRYrmy1++VqLzSvrFErxZNohrTU5evsh335iycs6K8/Cf/RygwfvFrz3WBB2uvzGFy+ze5LiGgc83B9VzApZaYK9aYNjEW3hj+JCp+mZC4uB4dm9fPG5ONc8C0QVSibBacrcO8gpefrE/+BynzDT4GP+Vqx1lLogThKKvMA4i7CSvCgIgpD5vPCh2/hOR0lfmDnjaWTWOspS+/NaWZ9H6SzGWkxZIrXG5AUmz9FFRpnNKcqyYn8YQlcSYymcq4a14nTo6ZGAan8zJXq0T+eVGseDQ2g3sD547HSY65zFWYFUgrUgJpQSKR1KOKaTCfV6wlJf0GorrC3Aam6+qFBBRBBG6ByEy2nXagSJN9AylXauXm8gZEBcq+MI6S2vUZqS+XTGW7ff5u5b3+PG1V8iLi0uL9A2ZTgb4IoZnXbMpVfX6G0kWATj/Ry9m5PvSYQBFSukO2Je1JBRh6RCOouiQGtDGITkxYwwCE4DrsMgAOuYTsaEoQ8itlSBz8IbACwYNkpKnHSVVMCRZXOipIYMQhwwS+fnKDu/2EsIPuIs/Y9ZSkrq9RrWWYIg8NlelSaoVq8hpMJoXTVIJfl0xmiaUhYlxvnGweeRWZA+lNkbvSl06XVZWutTpEUpRVHqSl/oc1mVksRxhKhQ0UaMH3KaAIdA5wWjgwO++af/lne+/U0uP3eTF19/nWvP3ODqtQ2u3rhMUZZk85J06vVRRmuP9jpBGEUV1dYwffUFTJETRQGNdotuv8/x4TFPt5/w/ltv8+jufYrSsH7pEsPdXdLhgDhUNBoNmo2Yw5MR4+msiimqooG0piwK0snktKEPwtD/CwJkEKAqIzu/fH+QFt60RWtNnmWUuvSmHwu2GdVxrvoOKQW1JOaZrXU+/eIValFAHDYZHBzS7/fZfvSANE3pdLoEQchkMiQMvIHFwe4e167fQKnzf8ePrp+4sRISnPGe9r6oB4c+pTE4t8jmEDgkCz9yn9/kcxQWdD8P1VVan4XIt8q7WDQui5vcwj3wPEwqKsBnqd/lf/jv/hW1eh3nYDiaMBxOuf9kj1Jrmo0Gays9TOkheCUlK6tLnkM5Ten1+9TqiuPhnFBKxsYwGw/Yeu4FfvlLv8H/+D/9z9x78IB252/4ype/zL/6L/8lURRWnHhzupEJ4Q/k4oaF8I2ldRZdFlUDdMrvO9dEVfQ767OsZJUr4SEo4c+GH9E7WS9GNZ5aYZ0F6VDn7mnnqUTAaXNqxdmE1B+r8z/kQNpT2dH/L9bHeC1e8ifO/8+/fwKsqHOiE/73v7zPnVv3eH6rz2efv04zMiwFUz69prlzmFKEPeLlHu8+2Of6asTVlas8HB4QpBmBcKggoAhAFiVhlNDbWOdgljNMBQdTx3R7yHK3j9AJUbbPaPcRQRBwqRdx7UKLsB5DUGOch/zZG/f4izcek4YdJCXSeNtQ6yyWgGazwcVLVwgHhwyKjKawzIoCLQNaGxvw+DH5aAe3ehGz/xgzHVJfXyY72SWbjRHDfZY3r9FqJJhyghBjbGEpjYS4i7QZ2IyCGjKQhC4ntB45LYoCkU2pNWsQLHF/MCRPlomkZCYNGImINfHgEBV5dCWJauQqAQpwGl0UaDPBBRriCJE0CZAU6YxAKFxhkNkUXWTM0wSR1AlqHcKkCQT+3hQEREqCjtGzlDIvaPea9IuCwXQALYmIGhjX4mDiKG0NTYfC1tg/GvKH37jFYJL+f3NO/j1rNpvzrW++4YcrRlc6EVtpdfy9xVpLWWryvCDPc0xZ+qbLnA2u1HPLbEYFuigJGk2UVIynUxrSp9Efvv82k0lGGYUoKajPcq6srvBMt8OHt26ztr7G7t4+EwxFXnB4cERZaC5urHPx0gXqrSZFUZKlGVEYcefuA7Yub2CNg1YDkxYcHB8jgGI2QeZj0uNjlpd73H/v+zySCcNpTljr8m/+7Js8enCH3YOnFR3ZB5i3mxENndOq1enFkmS6z4cPHnL1M59nfPsJB3mD6MpLtII6ut7g8W7AfFayG2wwjC4yamXojTEbzQ7ffuf7XKklLI2W2b815YPxEkeTGXE9wsUvcs/cwQY9HjwuGIsmKn6Wk7UC3VnlKN9kOt9jfatFGNzziHZlUFHdoE/vGYt1xs7g3HDsbC2+dp6hcaorBpxw1OsR+ahgOpl+7PPHmp99Y+WsI8tzb5pQelcu5yxhFCOVIi8yvx+qACpa3kJGUBrfPIZBiKkokNoaylz7+5nVfmBZBTIb7XVWQudI4TDCa4nWtaZfOB5owb6C0nmjEVE1TYvBopQCmc+IsjFSlyihcLJCAKphpXXeDbZmSsrdp9TcHInC4fjwvQwpDGHoqDUEV66H5FmNxw+HCGGoNRL6/YRmwwcbB0mIDCVLqyuUWhPGCaCwTtDqdtHWMM/mOOE42j1mMh7z+MHbPNN9njCJeDh7woeTkn4zoBzNefOtPT6TrHG0O2G2nbISR7TW20zDOXk8YiXpMSvGFDYhCiOiKCZOklNqWhCE3nhiPieMvLW1L8q8O50QgrLURHGEs46yYtAI/Ndr9aaf4gcKbX3zp1RIUqtTr/9TorksGu9//JpXTpzGaOIkYnC4h5ABjXqDmy/cpNSGweCI6cnUBwNry3ye+fe1avr9sN3iXFDVvmf3BwForYEEbd1p/Is1kjzPieOAKA4Ig6BCuUowBUExpRk1SYUi16XXOEnByVHO+OSED3/wAxrtNteeeYYLW1fora+xtLJKp90hTCKCwIfzUtXa/nq0FP2Y+WzO4OCAe7dusf/kKdv37jMbj70xjxDEScLsZMD9d7+LyabEkaLXrjFPZyTLHfalYzjLvJ27O8OavQ26wVLg0rRiZFT6SLkYSsGiTluEsZ95O/AjH/1/HJ1WjWeubrC+3KddD2g3W4AgCCXWwEsvv8zR4SHWQp5nxHGMczCZTen3+2xe3mI8TTk5GdLvdf/O8+GnCAh25zYEr6ty9hy1D4lv5JzvwIQA4UN1PYKyoK7IirYiKlRqEbRrkJbKTt1xGrTovKvPwnHPt54+iFEgqdcS33Q5x6svPEtear7+N9/GaM1wPGE0HiEcHIxTAiVZ6nZYX+lWFAKJQtGs10kUFPMAyjlloXnu+RcoSj9lSLOMv/7Gt/jKl7/IzZvP+EwsJX3oIRWzz/qNwFhTWauHOGvQWqOEF1ovNuEfemdxzovzzl/upyfbgh5UNa4ChTH6lK9OhTh9RINRdUcL5z8lpW9Sq67KmTNjCyE8XVF60/WPTFI/sfWLyhJwcNrsLk5QqJoqmBPzrQ+f8uffvo+yEc8+v86f/4f3eOZCiNFTLjQNn7lccGuvycwqXD7nb7/7DvNnn6Ne0xwPjrlxscWFFtx2awRmzs7BkKvrKzy6fwsR9onbHcp5yp33HtBot7m6uUFHzZkPNS9cbtPoJJRKYq3ke3eO+cNv3OLweI5b6RKaEqenGJ1hdYlSDWxQY6oNN24+z+AHH5ANj1EqxhBCnLBy+QpPHm+TmVXaaxc43ntIf/M6ZTahPNlja3OZF25s0u32EGKP2FkMIXNtUb1l3PyIeqdJYQtvxUsAeYp1BsoUyJnYkERJjmeCSBqkShE4QgtieoIQY+JuwPikIBJ48bQtyY0hHe5BbR0hC1wR4KIuoUoImgFKSuQ8xaRQb9TRKHQ6oywtJqqhwsiHlYrQxy3QQIYpbj5inFuiRp/mbJvZLMDVWyBCnoymPJ5J2k2Fmjvev33Irb3pzyXLKssy7ty6szglPZ13obusTtczWuC5c9cB1heFzhruPnjCy69uMkrqHGtFIg1xHU+pkCFRo8GFpM308BgZxzQOjugcDZj/8Z8w6y+hy5LdnT3W15d5eP8hURxzeWuTzSubhFHIZDIlm2cUecGVK5f53htvU0sG1JK4QrfmpNMpSVjQEUdc7AoOckE3shSDx9zaHlFoS621yt4oJk1zar0+xmrywQmrDcEXPvsS77/7AaETXLu0yTffv8dkfJtfe+ZVuq8+y2A3RR8oNjc+h2tPOVJ9Howc1C6RThS4I9bW67RWtpjVO4zMMe9sT2lOtnkv79LfvEJ7+RrtesB/9anLPLJX+bO7Ez6wc0qxSt4sYKZI38nJj+f8zqsdWs0mKg7Pw/++0Tr99KPN1Q9/7cd976y58oXpwpg9CBTddtMH6X7M82fv4PhjPvITXEJgtEFFPgDX08806SxDW0uhHaUBhw8MRvgiyctFfFFjpH/9cRITCYUunP96NcAUlW26MBZMiTD56QDQGsOSg4sR9BF8o3DsC4/8ykpztZhNSqGoL/VRYYxrNCBQqMDzEoSxFS1eIp1FTkck42NqtTlCKeq1LtZmTCYZOGh1JFwXPHlUcLhXxaPIkmwKV645ar06KxsXEPUYFSUU1qC1JQwSSm2Yz2ck9RZSSGbTGeW09MyXPCPLZqwsrTEcZ6S1JkHoiNyY/b053/nbfdxco+Y53Zf6bL7SY0nGpIVlNswRkSUVKWE7IC9L6vUa8ywny+aoIATnMNpQFLmnSIUhjXqdeTZHG0tS842Ywz8uTmJwkM5yECFxHCPwKIY3r8Br6PhYc8xfiOWsJZ9/MtEEutKcWecH7XnuA25NFFKPGqiqVp3O5hTaEIQRC3eCQCmUCtClR7lUKJHKm2tY6zV8xpgqu0oiF1RahA+std6MwWqNrdzznPXZsdoaoihgPp0zz0pUEKGC8IwRNtPMZxOGh/sE3/0Ocb1Bs9Ol1elRb7WqkOoKwTWWoixx+CYvS+eMjg4Zj4Y4a1Cq+t14szerS46ePuJ4bxuFIVIhoRKIJEbnU3r1mEgFTLOcSZpRVrXsgiklKsr06fHyVKpTuu9CsuH46Dn3wx4NAEEouXF5lddfeY6rWxfp95bZfvyEw6NjojhieXmJST5l+8k2UVRjMknp9booGdBqNjg8PGZtbR3rHG9+7zu8+OILmL+HGfBTuAKe+/TcpA0EQSBOkZYFpfmMluC5vmcudVVDIBRnhhfO9w2Bf6tcNT1a8AY9NF2JDQW+eHLe9n0B6QaB4j//3f8Eqw0nhwc8frJHWhgm8wyrDaPJDCcEg9GUu4+fgjUUIiQJJUWac23rIgIYHg/4+r/7E26/d5ud3T3iuIEUnlbzf//BH/HfX/1vqdfDU0TNVg3V4vUpVW2NC9t0/KbirdZ/uECywDnbyMXjq6BfaysEqdJMATihOaOSACj0ObfCBe1ksaxzPmVeLI6Lb7gWzdZiGeupmFJKPulb5M/Lbv0f+q2nr/Ks6z/7KT9u5417B8xyi8tz3nh7n5uXW3zn/Q+4djFC1Zt8abNNVxZ8/RGIICZqb/K9997h0vIyly72OBqkLNczDqYd8tYKA12jXwQUVvD00TbNpQusrK3Rv9BicvCYu4M9LmyucfPmdbpixNwUlLMR394t+T/fmHM0mhF2VglkhM3muFmKSFOkMQgzwegah2lJnBlefPE5bt19gouaBK1lZkVBo7lEoz3FZUOSlRWmcYf58IRms0GjfpHPvXiRzdUa7+wM0EFM6Ap0maMwBK5E5ylJu42dTkDGmEJTnBwR1utItwjkUwgL8/mc2fgYqVoQxNRaK1AecqGhUEHA6DhAJ00Sa2FyAirBOU13qUc2nZOnKSqdU0YJJA3iJCKJI2y8isXRCAOmg2NPjchn5NkMEQZe8I8DFaFUjJUJg3FKu6VY7zV4MJp7qoOyTIaGt97b42K9g5LQ7fZxYvyJG7h83OU3qGoAUk01T11DfwT9qLRVVZPlByeK4TxnqAW7s5ykluCUYJYbepFEhSHJ1hbhzhFrFm4/fkxDBrhpyux4wNazgkejIUtLHdbXlonikOvPXOXC5gbGGoZHQ6azGboovT04kloSe/RMl0ymU4aDY3o1TdMdU6sL1HqTEE0cKkyakWc5gZSY9BjsBbRRvPobv05ABre+hSxyLn321xBas7u9zQ8eH2GERBdz/sN3vk+2/jp116KearIc5tMx+XyOUxdpt5aoR5bR4T7HxycQr/LMlSuMyx6PHv8Ntz4c8qXffZnuUpubz12j3H2C3R7zQpKx9PomUT2h0VhiODyk3uhx+xvfYLrZJtc+06VVryMr++7zyp4f11Sd//w8O+DHueBWBxjnLAGCKIxwaNTHZwKexm78LJdzXoA/Go0XWx7WONI8w6HIS0tZGJyr9MXVPqSNpSwtpc0B74TmBJjY70XGUD2ZwxnjrwFjcdogyoKKMgPWEgWwVA9JUsHx1HFgKm1zVcEaKxBGAwZadWxoCdYvIRE0GnUEDpNOyeYznPOmGGmtQYlFipw4CQlESLsVgXUUhUZVrJo8l6fH2BgIJEQuZvPqFlGrQWZKnClpNNvkuUagQAakkykqSrCUjA7GDHf8AFigkCJAhjEzCtYurhEpmE7qiHSXw6OUmlJc2WqgOhoXS/JjxfDxMY2lOskLF6kVDmsLbwsvE2q1OkVZVjopQZZnFZJyzPLKKsY5wijCZroyFPH3oCAKEZUJl7aWyWSGkIpavUYcJVjjnQiDUBGF0UcHDr/Aa1F/fRLLlN7J0livwW80GiAkg6MBWaFxzg/9ozhCG0sYSh8tYB0iqrRUWqOUgqqm9O51XqPorydT1ZVn1EGprKe/lSXpvKDRaNCoNwhUgBCKxtIKqYmQyqBNyiydEycJYRh7a/LKwt1Zi9El09GQyWTM0cEBSoX+uYs5zhoEDhkkxPWGpwUah8lTnC1Pi8oFJR1tmE5POHz6gGJy7LWURjI8GRJHIdZolHPUQ0kc1WjUIk4mKfMsxy4cqhcmbI6P3F8/UkdW780i/Jdz99TF46WU1JOE55+5yvpyh7WVJdYvXOLx4ycY501zlFS02i2Wej3mac6D+x+i9SpSSVqtFoPBkKPDAe1ul9c++zrZbPZj5TyL9VMEBJ9RG6rXdfoCABa0wwWIsvgRS5WcjjdMQCzyLAxSBqcXoxSL99OhFo/5f8l7zydLr/y+73PCk27u3NOTMRhgEBbA7mK5u1qSS9GUirRkl+yyVRYty+G1q/zCVX7jP8ih5LJLpoqkWGQxmgEbASwyJvdMx3v7xied4BfnubcbWMLcpSBh1zxVU93Tfe/T3ec5zzm/8A0+qOEhG9NF0fx855BSrzhay3+RlhR1yfWdNXqxZjxfcDIaI7Rmvig5Hs+pjcMisF7w8OAUgUdJybh6gKtL/uX/9VvMa8/Tg2Os9yipw4bnPO+88w5vvv0mr3/1q2CBBq7Q1I1X1QTnQwcOQCmBMWaFtXfWN7CeC3LrYSIbLHYzJU0XSiJWC+hckdCukqTQLv4UZp+l7Gx4v112+4RfNrgwS1VBHzyEzrlxgr8r6hUrwrk/V588f5Ah1p7XvnSbHzyY8eThKR/ff8St268SbV1hODlkXZYMlOS1S44z0+YHjwt8nCKyLY5GY27c2ENogRrnXG1Pedu0cTbjaDhFp21SOWXxZJ/D2jLYvcL2zTs8/vh97t1/wnQy5cXbV2BWUdczjs7G3Hv/EXYwwK9vovJgUFqZCm8NSRwjzAJjCnI0j89yLrclmbScTUZ0BjsYMsa1JVvfYPLwPYZCkKztUZ89Iu12aLXatFOLtwvePywwKg0eUtUcbwpiv8BUC4zpYCsLURthDarOkT4JYaYXKAui9mAssh6CF7jS4dfatM0V7tzIMOUUkca8P41RdkE1O4PuLu3eAKkVrW4XKSX5+ART5khj8HWFEw6btNCJwglH0u7g53OcrVE4vLUYFwi/Om0Tq4hWawAmopcattIWc5FzaAqsjkC1uft4ylvbh7xysx+q5KbC/8Th7Oe5IAMSYPXsS7k6LD4dmMO5UutF/iSAbveZO4nH4ExJ7YMNxCDWiLrmBz94m6PTMe7gkFpYPIJISqqyoO0sVy9foWtrdi/t8MztZ9jY2mSR58zGE+q8oigLTF1TlRWzRcGbb77Jyy89j8Dz5NED6vkIsoof3H/Ereu7pJGm24mpSkNdhWLRaDwjTmp0VJFKze3Y8I2vvcK/fPevOBrO6SxmIFr0Wxn/87/4J9TTE/71X7zFduZ44E55dwqtOMIuHjN0T3nhhS8xP5zjnWQ2PePk5AlXL62zd3kblWSIsyHClxzPF0gxYz42HBx2uLa5y+kH7zHY3WDv+i3mXiKEpZX28d7w+os7bGxsciAS7j065OT0AElTEIPl7vzjt/Kv6UoF/8Owt346sLsIHVRKoZd+8PInX4dfhHhFEF0Kkuc0nOiiqKispTKeqnYIcQ5Tco4gj06YQUEZVAIBlGbcyTF1zVKAwjnXcJQNWEtdVVgTuIO2oQ5IGeBR/VRwrfZktaTS8rxgWUMynzE4vIdNUmLh2ZjM8c4g4wSSlDJSRE13QQpItCDCIaWikyUkSc7tO5LFTFNVMVIaksjz/J0206liNi2ZTT272xGdJGY4ntK1BqE0obgcug5VWZFkbSIdkaYpdaWpRgZbhXhAy4SknVFpy9Qu2NQbOFdyMC/xnV1scYwgp7Qpa3uXOf3whPn+CC8l3NhF6Bbje49gKNi5dYfSn6Fanna7g/eeNImJoxjjbENJCMUw7yBrd7C2BkLcZazF1TVaKbIsC35GWkNDZYjjGGsd8/mCOqp/ZoEpnx5LUsbnMax1+MYsvCgDikMI0FHCaDTCGEuSZGRpFmCfzmKqGhlFIa70Hh0p6sogZFNQcR5rXSiq+MDPRzTxsxNNjChCDOxCIbOua8oy8Ola67vMM8H4+ITj4yFpmtLpJEynU0anQ9J2h16v28jAh1DWNjQbJwXeGkyV4+p6xddXqkE1eUHwobMrqKL3Fu8gX4yp5meY2YRyckakIDQBQnJYlm6FpgoFI0+iBL0sIdWhUZKXJVUNtXWY5rrefxI2fXH8+LkYhlSBB1tUlh++e49+u8UzNxXT2ZTbz73IyckBcZKACN2/Rw/vMxoFDqVSkqLIkVLRH/QaZUSwtef45JSt7a3PXA9/K/GKi4nAKoT3hMluUgApJEtzRO/PO1gggnlw4ymhlA4PNYF0jQflAjba4UE4BArV3LxlUhVuUsNJUvoTFUEhBNPplDSOGPRaJGkU1MM6bYx16P1jHh4N6aYJSaSYzPPgl+Edo9kibH4nZ5SmRurws6UQWFehUFSlocxneFutbqRvWrYXb+x5N+q8g3TOy/LNQlWrFmhwqrdNctkIUXiP9IHTtpzw5d8vpGxklpsDG7Wqaq8SLBfgm06seoINdUs0reVGxfE8lWi6Wz/OCfj5HOL/k2S4elWznv+6CpaTkq/eTPj+85c4OTxlsRjxb/7w+7z07DrjM01XleTdjCjTfOuGZFEq3rxf0d7cwI1bvHn/lG987XkqQA5H6MqAhJOnT0Earl/bY7L/hKPjRzwq54wGV9i7+hxXI8uTR/d469GIo26bs80tNgfwzMYj3rM90qnBugprC7wWEAdzSWE8xho6OqKqHY9P5lzZ3mL84UfMh0ckg10KU5MoRdbtcTaeotJ1kv4GC+EY4TkpLKKdMJMaREXtwNY5DomSgiRLQ2joHVF7DeVypPJ4pRBShwrYbEJd5mRxyiBpc+wz6lxTaIGuCnwrAdWnm2ZQVch6TC0qnJZ0kjW8SgBL2u4GiNH8jGI+p6pyKgGqLsCk5FFEGqd04ghf1xT5PEBkkSitsHVFVVtUHJNiacuCS+spXtZMTxaUrkWSpkymOW/fv8d2/xmKRYVYVte/iCHOO7x/HQdnpQq4+t4SNs35XgMoaWmnUbiWr4kiwaysiOOE+/cfc3RywpYNIh9aBkNVKSWVdQiluXnjMjdu3SROU4bDM/LpjKooQ0JV1ZRVRVGV5HnB5vqA6XTOo4f3KA8+RFjPx4sgKf3DD4eYouTapTUiHLN5galL5nlFWVmuXJ7z3K020/vvkb/yAv3tPvNiyl9+93v88//4l7i99i3mm8/w4f19fvmrES90InxR868elvze3buUVcHmK8/yJ3/055zNSr7xS/8hb7zxp7zy0g3irMOjRx/yzLNf4uDBiI8+/BDnDA8f7mPRdPrrzHt9juuU9cvPI3obdIRgPp/R3+zS7rSQt54HU1O/9y6b2+uU1Yx8PvvMAuOPeTE2lVPnHEoti4oXIJzNfVsaxofkSzXvX97jn2zpfBEdK4A4jqjrwI121lDVNbVxlLVnsagQQmFd49nlzv3ZAtq/4WFLRe1q5qLEmqoJLm1jqBy8rHxdU5kKZ879fJy1qyJIrCRbieRSKyaPNcYGawptgEXF7vQIOVf40VNsbcCFsxStKbTCCoH0gkRKEjxpUSA6EZNJjfVTpHL0+gGhA4pIRWzveDa2NFUBVelJlSCTnrjXClLyUYKUmiIviZOU2jjqukI1Kn3axXz4/buYyiAc9NsDpBK8+fBtUDA+PcXZiiyJqOMWR8UarnIc3p9wlD1B2lmAfa31cP1dKuu5+8cfcCl+mWTjGezaB5BNGnEZw2g0pt1pY+sgZT1fLNBaEycJSRJTFAW1KRFaBqQLArwgSSLKypCkQc7dWovWEVGSUJmasiy/kLX3tx6f07NirUHKwDcWVQjmrVmKC1mquiKKEpI4aYygJe1WRtHwi6RSCBP2CykkWikqWxP0BYJYhanroIjdFAtWxuLGUJvQ4Qr7i8G3tylVl3xyxJOnT9nf32dzc5PLV65wdnbG0dERRfGAfr9Prz9gsLZO1u6cJ1VSgDeBz+jdaoMLBrzLopDF+aDY6a2lLnLKfMbx031S5em0YiLhsM3rV/Fk03W5COlTEpJIIGx4FjtZF6U186JkUdZMZjm1sas94/zWfSrBaq4vRLAF6Pf6TKZTEIJFCW9/dMCl3W2ydMjG1iVGozMy7Yjj0Lkzdc329jYHh485Pj5mb29vpYMwGp2ysbnFfHHGOu6kggAAIABJREFUxuYW48n4M9fD3yKxChOyrJL6C3yhc6+kpo2CIHCHlq9ddlyWExuAFOHl51LBePASlhxcpSBos4UAWMpAgKVJDi4qLcgG2matDc7hUZAKjZOEXqeN9Z7kZATes9lvc2Wzz6KqGM8KitpyNpvjhWKt12E4neOQbG6s8+WXv8Tv/v4fIAXcef45nrt9e5VgXjw4lyaPFw9VY8wKFmmtOZ/H5aHszmGAy8Jk0MFoDBXdsh16zrXw3gWSOOeKikvU5HkC29wzwXnHyvnmTkiWNRvRJHFL6fhQfQj35f8PqdXfuHleXHg/9l5QwtGJPb/ywhZvv5XwxG0zPzpieBJz69oljoZ30a05WTUgyyx3djQf3rfUXhJ3dlmw4N1HU1599g4H5T7p9JhyVLE4PUDv3MRkfV76hR344bucTQ/xw4pTf4PNQYfLN5/jdD7h43fe5aCVsHN1m/bmsyTHJbIocCwgX+BKg0Bj4hb9zQ1UbhDzKTLKKOM2otXl2VtXeOfjA6KkTdrNqEpD1NslLo9YDJ/iNneoZTAn/WjsOBOCmdEoP6MGjKmRnQ2cjsl6AxbTOQoHKsK7GUa4UDmTUZD9L2Y4Y0i7bW6stzg9CUluJS0OycHCczxSzM0cN5mSxUFyta4rChtRU5LEgjiSUJRIb2l1epQiwhanyMlT7CKlam9iUoi1JooSsijBW0tRV2gV4fBYK/C2RPopu4M2/UygRMRJIXlcWBKRU9Zn1JOI2jpqUyDFF2eQHZ7/AMXxwn/Cr+6T3nUXigEiBOVCSBweIywd7ZBYCiOCZLW3zKVjEYPIFHEUkcUJJRDVBuMErTTD99pcvX2F7atXcc5x+OQpVV5gjKUqa6qqxFgTZIPznKIoyVot7j96yMHhR+zqmtJJtI5wQBZnLGoYTSouDTIWiylSCLJUs9bSfPmK5IePah48OeRLp4ccncz443eP+We/eoubz93i6OQYMxrx1nff4Dd+7R+w/1e/x+njJ/iFZH6WcndsuPvhb7GxucvW3g1OT06YzuYcny7wKmM8GYHoEsddnnvmMrV19LsdOuubrK+vIXTKWqfH/OiQzsYanXaHdislb2SnjXFYY9BRRpxm3Lh5k4/ef4+6rj953y4gJz79NeCCz8+SYdGYBV84O5e84mUBTTbk9p988fwUr/2cRlBH1EgZfHusscGTLtHkVdlAyZr1KSU0a1gq2RQkBTiP1EGYRhBU9HwjXOG8b/jLTezQJFTLLds7hxVBDVM5TzsR3NpuMQJKozHW0spSfFnSaSekcUSsImpnqMoaZxztVofposTWHozH5EVQZFOKKVCcWPyxAQ9J5MliTxILksiiRUkUCbQMXGopHU4JLt28RtZqkxc1VobkMV/kKBUBIWAu8xzqmtHBEA+0sh5R1GJRltw7eMi8ZVhb76PijMWi4tEH9/FeYtc6XMmmmMUUqLFS0t3aw+gElUc8f/PbdNrX2L/3AzqZpRNF1MagtSZrZY3CXIsoqlfFGtkYNKdphjYReZGHzpZxeKnCs9xu40UwDq6qGqkkcRTh0iwY234+6Lp/L+PzqkF4QteqkRtAa41zQZRlqe4slYDaB7NlqZBCEuuAlNJKESkViq9KorxCCIMxFVon9PpdTuqKIIHe8C39uZK0MZb5vEBHCWtXrzETHRaTBYWpOR2NKKuKo8NDBHB0fNyod8YkSczp6TGHB4cM1tfo9fpIpVFaB+60Wvo9hT2oLBahm+kctlxQLCbMJmO8qYKghgBf53gZoaQnSyS1aZBR3iKlCgJ4+KCnIAOrXwoBWmNkFRI2E2Lc4DulmjlxQChOLXv+jiV6S1xQXiVA3dM0YAq8x1QVeVUyzEv+4Lvv8B/9/W/Q7qR8+1e+zdH+fawxJO02Wsd0ej3i7BnGwyOkkk2zw5NEEcJ5dra3OJtMaGXJZ66HnyqxCvwft2qhnkPNzg/3c9f3JmgXyx7Jsl3YrIjlAeKXi/ucXxQEJEUjq+4aOXO9SgW8t0Fo4YJZr5PnkopSSDqdToDfSVacoTiOgk+DDhXcNNEMBh16ziPliH67h5BB6SbrdPjOOx8zni3odNr85m/+57zxve+xWOS8/pXXGAwGqwrkMtBZYi7PieVulWhxAabnlqRQGxIu4YPi0ZK050UgAgsREi0ngtriKpFbovolq2qCbBK3ZVIVqp/Bh2AJGZIibOzOgloubrfsBJ7z40A01/M/T3vkZ46/OTxezugyq70gHuI8MTnetnh+K+X1Fy7x+2885dKVy6gI0v4Gx8N7dPOSWBbEcZtLGxGvbQs+mKTMhQUdcTwzvPHuhPHRkHx8xiw/oxu3kTLj8cSyt93mzgu3uPvehEjMOJvc48kko725x5WdHjsvPsvdwxFvvXWfSKXIq5cQuUHXDpcv8HnwAKqlQG/s8NVL1/nud3/IbDYm3V5n//SMG9s9NtqnFKNHDLZfZKw7TM5ysrU1ONhHLOZEjbLmw6M5T48OsPEAKWOcX2BdQRpleHKcT6jLI4S3VFWOdvPgNzNdgAKSDJ0f4pQiSzQ6y6ilwQ/WkVZR5JIffFiwvncZ688Q5SFZOkdaD4sJi1pQVrDoJGSJI8k0sUso5jU+UaisTeYs2hVUxSmFMdgoxipJpARKQ5I0EAsRU2mFtjV7uuLGpS06KXRdxsx7zKOSxWJMT5Y8u7vBzrpkHKXgzBcCBVw+jyw/+rCH+U99/TP5AcI38BJF6RXGlgFSYg2pt8R4VOTZ2doktop0MkXXnraSSFthncV1+ww2N5lOJkzPJpRFSVUZrPFB6r0qqU0FUjGZTDkbzzg4Pubp2RP6LUFhJVYJrHB46xmPF1zfW+fB40M0nlnpcF7wrVcvI63h+x89wSeXeO3OFX779/6Qe0clZe1RRw9457tv8fH+Pjdu32I+n9FraX77NOFwmqGFJRncRqmSupqye/VZrj77KvX0kBdffgWpPVYIRmdjJtO3+fYv/wPW1r5Nt9Ulrzylg9FwiLcWzg7JW5LRzgbD06NgWKuCqqtH4p1nZ2eLb33rW2jpGR4fcXx8vCqgfeIWNPvppxOt848XOaxBAewiNzZAe5by3399J/1nbThvyNIYn1viKCJZy8irmspM0JGjNgZnPcaDVEHV0uNRSq5UdPHBPsQa25yTrik8Ln+K+FRiJaBRy6xdjEeH4mmiuHl5wLaWzEqDE5Kt9QFVVSOEI0s1eEeSxURaM5tOSZMUpSWSIGUtvWc6nuExRFoT6YjaVMynjkgIhDPYqgYE03lB7DSJ8mhRs7bRo9ttM6sryCsQGlMZirKg3eqghMJ6HxI3qTl4+ISqKJBC0Mp6xGnGLB8Tm4LRtGCkJb21PnVtWcwLut0BZB2ipMLYGlMbnM4Q61dIsi3U8CqbuzFnJ/c4evoD1n/hpdD1a7qiUkrm8wVxHK/WYl0ZZEdhnaU2AapW5AWtdkIUx0ilmxhBMp3M6HS6SBlhrCeLNVGsQ/Lw8zKEQEV/CzbMXzOkMUitQiEc0DqiMnaVnGgfY+qaeT7He0jiBCkFwoXGgFJyZT8gpcQpF7qZRqC0QEeaJNZNwWGpRL3sfEuct+g4Y/3Kc9TtXSbHQ8qypNVq8cJLL6OVZv/hQ57s7+O8p9VucevWLdYGa0wmE06HQ9Y21nHGs//4CdP5jChJ2N7aImsHvzPjLE8efogtipBI4YgUYQ0jiCJFlMQEE/AKZxVKemQkEHVAXclY4T2NNkHIJ5ABuYbwaBWUwp3zoeONDM+ilEipGjVyCGwisWpAKB2EqkKo7Wm12yRJwnw+ByCJY6TWGO84OJnwO3/yff7+33uVX799h5PDxygRROwOj05QUSioCalYLBa0sjZCCcrRmP0nD8labQ6Oh4EG8Rnjp+9YXejG2GXgfTG58qGT5Hyj2NcEqYHHcm5wuzSuE+ICNr2pTIkmK1VNUuQVK4y19wohHEIrvPBgw3tlI+PuEVgsWSsjrwxVZXAelFz6jCwPtiAwEScRzgvaWUav21oFML3tDa5MCqYffMiXXrhNHEukEty8eY1vffPrAOdkw+ZvX3aslodqELU4n/wA5/Or5CpUKm3gnzXBEAiMNQ280IaFx1Lwo4FAetsYNIf5Uk2V5NOH77LaqUSQZQ8JZqgcOu9W3anlnERar+6n/TF593/78cXFBn/TZt/AWr1YvfZiSul8ipOOLPL86pdv8f6DGU+ezmhrxY8ezbi1fZ2D/ffpRBlZock6KV97/RJ7J4I//N4RQ+tpr28z+uhtVOzo97swPUUknjj1FEXJGz96xOs3B2xvrNNiwZ6HSVlzOH3EvbzNxvYlXnphk3tC8nQ4Q5U12tYUVY4t55hyhtIKdMLIxIhWj5de/wp/9f/8Gbo4BN3maCjYvXqZBx99xGJ0QtRaQwiJSlv0BhvM5gsG7YxiPubs5IAo6aG3engv0KYkEgKkozg5pNXZw9matoZcWIgiRJwgbdgXIh2Ras/MeKzz5NZjhcSpCGEE1hqciZFaIusIHWva7ZR2qaicpoqOafkFZtwnjzeoW4K1zoBOx1AXBfU4xw8GaOHZEyXODDmtIqZVxIwgXBFFEZGyiNghcGSq5MvP77C70SaJJRaF1w7yY4xN2F6/w0u3L9Hf7vJ4BEodfUHVV8FSHe6TanHLQFt+ouP94yO8rrKOp+MFvahCWEssPJkCb2tiY9HDET0pKKXCCkMmBUoKKlPz9oN9srffpT8YBK5MXVNWIbnCe2azOcYYFmXJZHTGcHjCzOb0uh02uimxrSnnC2q3oMorSmNYFFM6rZjTcU5HO16+2mFYzNnfn7G710GoiD96a0S0lvHV17/K44f71NEAlba58vJrLE4ec/XqZZ6cTIizhF995Q5vH3uoWkyPTvCl5uBozPaVnFe+9BKz6SmHhw+xpqYsC6Dko3e/w6/+wqt4Y3lvuM/cOOqkRgoP8wnZvIsYn4WevpSBBB5wmXgPpo45OTzg7GzEdDr9BOTv02MpqLS8Z8vPl/foHCre3PUl0IPl6wOP4ZPv+9kcSim2NtYbfkeN0hFraz2SoiIvDPO8RCqJsS7AfURIrEIlXCBECCQ9EufqxmYgmGQDLAWiIAg6BYiGxDX2Lx54KiJE4bnuYRBHrA0GRMqRGoPQgk4rwfuU2WzG2lo/+DJphRQerTr0+4NwDhMk4521DPoZcaLRWmGtJY4VSkTEcRzOybpGRxEISZIE7zdrarJul3arzfHhPomBJIlAQBQnlGWFUgGSJ0WATt9/+x7FbEEkEvJiyiQfMl+cIUxB19ecHo+Y5hX5YoGUgvWNAbEQrKWSsjAsFobO9gad7svo2QvUhaco9pmcHdC7vkHUTpjnc5I0RjXBfhRFzdx6yrLANx2QJE2p6wqpFVmrBSJ0Y+IkCobE3pK1gjhDkiY4ZynLqvHESj63LtC/6xFFEZd2L30u1zo+OWGxWCCFRGlNWVYIEYr5QgpiD8ZakjilrmuWxtVSCLRS+CZGa7Wy1deFkIE24iwSTZqGJFgKj7VNAU4GsReHYOuZ59EbN3jw8CF333+fqiwQwtPr9Xj5tddIoogHH39MHMfceeEOO3uXAUHW77Nz5QrOWvYfPSFKYp67/iLr62uoJmnpDgYMNrb44M2EB++9iRQOJQRSemTTAKDxslVKrIQ2oqb5gBDY2q6aEUs/Lo9rbJNc8JyNolCglSJQZIQmySJUZDEm2IlUVbmi00gh0FqTtjKiOEGraNVhM013Nk1TtA6Q4NHxEK0UdxclEui0Opw8vc+1S9tA2MesqSnyHGdqyqIKZt5K0W63iGJNu5XxzW9+k3t3H3zmevipO1bLtuby/58+4P2qTblk7cjQNsatEFdB8rtJJiQsN08pVIMIaEitTTZuTZMY+fOqrZQgpEe4kCyE3XVpBxiuWTtLXtZ4axl0W+T5nDhOSZOIONJIJZv3NpMhJZGWeCHY2bnM//Sf/gv+/E//mL/3C19GK8Wl3W3+q//yNxkMequbZ+15VTLgW9Wq6gjnB+LFzpV3S/+qJWzwfH4D8Z8mAW2m+gJ/K1y0gfJ5j1ieKhfGMglbKTDiGzJ8CHoDBv9cBGNpDeA5P8DEvwsc4Be04f4k1d4VjPLHvwMuArlAOMe1TsorV1u8c1AxnFiUn3BtY4CLB5xOSlIdM7U1j50hqeaIyQm1VLjeBnk+pAPcuLnH/vgJuagpygXIHmZ0nzfHj/jlb34Fhu+R+orLA8EVF/HRVHA0nhJZy2D3GqfqlMX+Y6JuhKsXeJOTaiirHG8l0zrjLz9+yNdeusZzz13h3jsPUH3NTDi67S5Xru1y78EjOhuCfn9AtZiRrW2Sm2PK0SH56DG+rJHtLSwgbAl5QaYzCko4m1LHOTGGtXZGLcFFLWTLUI5LJA5haqSpsC5hOMnJtAfZXVL28c6SaoXG46RCRIq4NaBNC2UVsU1p24JqmjMphxRVxqmp0WlEN0pRcUw+KxGdFsNEs9eLuKIWjPOaJ4uaRVlSzRIWSmGSHJ1o9rY0z14ZsN5OSJoDr5N5rg8u02oldFoxWaaZEvYAIdIv0M/tPAD/ROAuaCwBPvn1T5J6A29nOst5+GjKazd6aBFgVMdFjXSOtN/nqquwdPigrFFlSeocWmmMEuzcuU272+NsPKYsqyATXNVUJvBdhqMzprMZs+kUNR+jzZSph53dXfq9PsOjY4raU1pLlCiqumRRVuz0Y/aynL1rl3n05JTRfMFrr13nnY/OeOP+MfMio+tnXK9Lbjxzg3SzxcPJFArPbP8J3/qVX+Tw/bf49vNr/O6fvs3/+mCHmc/piZJ0Y5etzW2qYsHxyHDwNKdcOHqdGGc9WSdjkAqmf/hbzJ+e8VFvnedf/wY6jvB4pmVOvZjBbIaWmiTLMKZC+FDB9whEAcOzIGfe7nQoiuLHoH/L+/BpZarl5+diI/58jyf4OonVeyVB2El+glfwk62cf/8brZSSNEka49guRVGiZOAeI9wqcXLNeRY+OOIoDgmOCpXsUPAPxPkQUH56bsP5KVwgIfimcGudZeI9ZlFTLRx3ehovPFJJOnGGjIKkON7T7baDaatoPDK9xTRds06nhXeWJInxzlGVJaKByGdZiyhSpGlCq90mn+dYG69+s7Tdpr8+ACGCzDyCKO0wneQsdAi0vXcUeUmnMwg+RM6QpW06SpKoCm81RTHjcHgfQTDXigys1YaT8RTdShD40BmYDnGtmtwqxmMLUjG7n9HassymT7FmxvDoKbe/dp1aOpTWRFFjVmyXQlquQdqErsh8vsB5T7vdxkaWJIOqKqnmC6yzyCgG46jrGt0UYrWOGQ5PSZOYSC+7KD/7Q0oZEsfPYWRZxnw+x2HRUlMbE5Ie70niGBDMFwuiKIh+1HVNXdckaYqKomCArQSR1tgGBSWlwFiLti7s+SL4qNFAh0WzZyAienvPMbj+JT746D6PHjxEScjiIBZT5jkIwZ2XX2Z0OqQsC1qtNlmrSxQn1NZSLOacnhwwL0peef3rtLo9To6eMD07Q3hPPp+RJCnf+of/iFaiePze98NZ7jxeNmbT3qIEJDrYsiAgjhTWBtPtWjQeclI2IhmCJY90GcRKJYOdAgKvFM4JyrKkLAPfUggRxDa8p6pKvPdkWUaWtRFKNcEv1Kamrg1RHNPptMkXebAFsIa8NsyloKgK6tpw6/IaG2sDen1Ju91iPp/xwp0X+PCD97l6/SbD0+NALdKK8egMrTT3Pv6Q05PJZ66Hn97Hqul0hAD83FdqqT63TKxWkL8L1VeQzaKgaQc6sGIl9+ipAvxCytCN8h6hNMIFfpAQwcAvUIVCsuAjH/wmnECr8PsEMQsPwnN0NmE4mqJ1xKWtAcYUZIni1ef26GTJajMXUiKUBBkk27d297h9+zm2uylVXWK953/8H/57Op3OKukJ4hkNnK6pLiz/1iXvCU8j2dxA8dxyThpPAkkjfBE6UGHaGuiDlHh7MWCy1KYm0MsU0p9XNJcHd8jklwdRA5sgqL0IfwGO6JaGasvfKfw9wRwxKDL+nRmfmVSBwKNsINg/eDqjncW8fmuXP7lf86MnNf3ZXT54b8gvfOVlzp7c58nc8TCvefPQs6ch7mf0ipz58RO4+iXGT37E+OSIvVeeYfjwMcOzY0bdDv3eJvP9d3n37R/xyp1rTI8/Zqej2Y0V6z3J3TG88eCMShSU9QxRLrCtDJfPUK5EU2F9RTWbIdZgWFne/Og+33jhNq0k4v7TEypTMp1JdvpdlD+iHB+z1suo8gm11XS6KeNH+2g7Z3dnj2GUgXdE3mKtR6UDFA6dpOBK+rGg1Urxdag6x3FGxRRvq9BtFWHN13VNXieoJCNJU4ppjscTCdDesTAWB+QLTVmDilO0iDGJRMYTBsWY8TSnKAyYLkZLZGedlvcoUbPIJ3y8sMzX17m67ri+WzLP4XQCjyclk/mUtbjLK1evspElrHUbQRsJPRNw8QFOkFAJSWkIht5VwRfUslrBo5erchWQi4YTyScLW8vvXyx0hbNYInHUVcW8sFS1Z/PKdXaf+wqd0xkf/OAtdrZ6dDOJnRXcLyUHZYE/m7L++AlVVVCUNdPFgsl0wXRRMhqPKYuS+WJBrCUdYaHWnOUF4/KUj55OsV4zmxusiYnjlF6subm9zp1nBox8n7cPDXH7Ps882+WNDz1/dpgx0Q7biaGbcPPZZ/gv/tl/Rn/QxQvJYj7HrknY/yGX5BF//MYT/vX+GnW2QVcqMr2OquZMCk11aijtIYm2qKiDdY4vv/QSajHi+s1nUTu7fHz8Z/T7a8QKxtMJnXabkYuYziuYzNna3KS/sRU8Z7zl8aMH1KZGKcHx0TF5UZzzeJOEPM9/LMFa7vFC8GNfX350S1IGYoWWwPsL7z8v2v0sD+vOucVpGqScAfJ8jhCeJI5Y5FVTjQ9wfqmCWu4qPhAyWF9C2JObCniYm0ZJWCx51Z+UXXbOYZykdnBSOx4vataMwSqJijS1CV2CoixXAgKtVmuFsmm12qFLg6C2NshZS0mUxNi6WnWMZ7MZk8mEjY3zZ7MowjWtccxnOSpSlGVFVTkWRcV4OKLXeAKZ2qAjzeHhMZ1uF4Hg6O59Tu8/oB/XZC3BaO4oqxyQJEmKcRbtLGs4qjhGr62R5xXPppayjGh3bsPZIY8OCtK773MpCgl6OZmRDCDpR+Fnm4rZbN50C5aoGs98NiOKY4q8JEkTyqIkTVLSLMN6h45irAmwRa1joigBqgDRMkFNME0zhP9xk+yf5SGlIEk+myfz04w4CZLp1jrSTDGbToHQTQkq0OH5KMsyFPNplBWBJlcK6CKtVpQQ5/wnbG+sDYUYKUJR31mL0Al7L7zK3su/xNFwyNlkSqvVYv/4iNn4DOdq2u0OG0rS3tnl1a99nTf+7E94+623+ZJKufPKa3x09y779+9SFjnP3nmRG8+/zHQ6YdM5pLHM5xOMqXl8/x57N5/ll//xP+UP8hmL4328DbY/ZVninSCKFQmeshLBH1aH2EC5hqajznlQukF7LS1NVloMwlN7hREicHmNvYDScBRlSZamtFot5vM5i8UCLyRZ1kLHEZPxJOwzWpO0UrJOB1ObEIuY0EkTAuazOR8/eIzxluvP3GTLeZSExWLGZDJmOBrDvYeMx2d00ohut0OcxKEDmWhe+eprn7kefrrEKvSKV0TSJadoScNdVkpDV+v8bef48aUobZBrNA3/x9pzQra3FqkDltRJB84ihcYQSK9ShA0PY1FaY73BCk+koHYWpaOAy/ZgSxs2iyzj0ZMTTscTnr+2h7eWm5fWqWrHZDbn4OiMnc0Bs/mcbqdFHMX0+n2SJF2pF2op6fXaK04SKwGOZcU4qP0pFbpTy0THuyU/apmIQiODhBABTiiXnKgVVt/T4Be5WLl2OKSWBCqUhKaLh/cXDt6mCidFs3kuu2ANN8v5oHHpw2GoGkXFpSjGebIFn3tQ+XNRyvKf+MwjmDnJ//b799k/GvP1V7q88+5jXhhU3N0vibKEJ4+f8r2kzd7uDm+++zbzaJ3K93iYO5RYsLu1yd27+5gkItt7lkNb0B/ssRd1WLx7D1UcM+5eZm37Kk/379Nptbm+u8OkmrKdKda04/mtmJNC8+FpgZrMEG1N7gyyrlAYXLUgixSxW1CMjzGbfY4mkrcfTrl57RLX22v86O2HzEYjyBN6a33OzsbMj/dRdUG5qGn1N9CdLuvS0t69zOk4RmNxpqZ2Epu0iOycdGsTZ2rWI4VIOxgj8dMxiRSkMkBbnZIQx0gD7SQENw4duBHWIJxHi9Bxdc5jvWIydhjl0MLgNGBKpNeIZItuC6J5yWJ4zMLVqE4P3+3QkprYSaazGR/XKdOZ4rmdDpe2U3a2ay4vHKfDKWvdhNvbMYOOpt9OkZFqYESW0nqsE1QiIiel9JZMV7R19YV1Wc+V4M47HNAUlmTzjPpzq4blvmFMMP6RQuKjGJ1oTic586Lm0rVnaOuUG9/6NdZuvkZ3Nkf0NqiGQ97++AFWttnc6fF7/+r/ZOPoCT94w9EarDGel5yeLRhNciLdIbca4yW5UdRVMFW1HgofU5p1St/FyBibaoRX9GPPi7dge7fkvriK2HqeX//FLYoPf5/f/u4BB/Eal+8orklJksT0fUH+5IQPfvQOW1cv4REspnO++0c/5NHjRxSbz/NotsHa9ia7SoVus60phOK4KKGYcfdg3BDpDa8/v8PLz3cpv/On1K02+tZtnvmlb7H/4XtoW7O2NuDk9JTT8ZhBmiKkYjg6Y//omJdfeZVYRaRZi9nJEfPxGfl0wpODIzrtFnuXr/Lii3d4442/ZDqZBoTaKsFaik6cdxWX58Ly/1zAWCAFkgCPrxouAXzSl+UnWzz/NivvbzcC2iIE9M6FgoqpTQjGpcIJg9YCYRuovxEQa9IkQsgQlKoooqwsi7wK1XCzpBOEvylzanwbAAAgAElEQVTAnho/TEEDnz/nZnuliDopwsUsWhmZCxrStsgpqop2u4W1lvligbXBT7LTyYh0SlWWKK2ZTkNQFjUd7XyRI/FNgqGx1pHECUpptNZMppMAOVKa/f19tnYuNYR3h/cVp6cj6rKiyIfUjYdUHMUURU6300UIQf7khNnRCZHyXNsy7KynvP+wZlEGP6k0y7BzT2wrdJ5j0gzyOXGr5tHMoRZzrl7+KlmU0NtaQ+sQxE5nI/q3BsS9bFXZzxcLjAmcsZA8BpNfuTRmjmOm0xllWbG1vUXayrDG0Ol2GI/HQVBEQhJnVHUVOjPCU1U1kZJh3n5OEqu6NhwfH30u15rNzhVCAZTUIT72nro2gYqBaOCXYX/I85zFYka71w11dBEgmpigwihVgCLrC4a+IQRvujs64tKtV9l75ZeRUYvZ/lPSVsbh4zFno1MW0yllkTMejpDAYGOb3tomURRjrKHdH5D1+sgoxgvB5s4uz770CippE1c1Dw8OefLwAdbWtFot1tbWePTBO9z+J/+UX/z1/4Tv/e7/zmw0RMpAwEEEbpl3viHmCCKl8SLkCrWBZdwrhECr8Cq3SpwaoQmvqFF4HyTQrbWURTDkXgp9VFVFq5URRRHz+ZzajNFRRLffYzqdBnglrPzXqrJkMZ831JywnyZJwmR8xv6B5k/eeBv5lee4vrdJksSMRse8+MorPLx3n42tHebjIQjo9frgQ9x+797Hn7kefjooIE2iQBBFOPeXIOBwG16VX1XhgIZPFKrCSzPalQwFoScQNmYhJVZYMHU4gIQMyZQ0TVYbJl8o1dwYhZIKpSNcA5HR2oQqhJJsrveJtaIoK25cWqcsLU8PT6iN49bldbSOOJlOg9GdDtd7cnBCqz0nLxerjpxbwhtFqJyt/oYGLbeqOjYJjm1IosvNOyScrunSBdWzJfcqnAnnh4N1QVJWBEwkS7GK4DPRzFjABza/30XvpfCznA9QiXN/gfMqkie4cQco0Xn1ALEU//Crq/2dGYJVshv+Y3ECDBrnJd99/4D/5S/uQeVw8TFnI0M1ekLPGua+i9Bdnt69z/FZRV1pnp6N6W6lVAp6cZu1zS7t+/cwx48wu89SJgkfPJ7z4u1nuXQLzIMnjBcHZLtXMEpweDjizrMvMTtbUNSGLJMMVMkreylPD3NOphOyQUbtoTYeUxoUjs1um3Yv5dAOmZqEnIxHhwtOh3P6UUU7M4zPDPO5YGO9S6ed8OjeI1qtLsqWqGIIHpJWRF6W6FqT+ZyZqai9R+kIdTZHbPQhX9BdjykjHTrI0yNc1iPLQnX0VMbIrE9sPBEFQi4hJA5sMBQUIsjSaq1xKMZnR/ikQ2kEKlGIYoSZHWFb66j+JaKsi0pG+HyMmS1wdp1Ztkaa7pCka9TjA46PCyaLNlfzLi9eHXBt0/PS1Q4b6322up408whRI2n8OITEaIG1kuEi489/eJ+5bNGJDDvbHT5wX8BzcKHDsXx2Lz7DgmBjIpHQVD+dczz33HOsrQ9wLvjuVcYyiGLKxZQ6X/D8r/9jrNIYFfP44IDx8BTR6tJ1cDM+4OmDpzzTs/x3/+gfMn/rDT443Oc7D8dYEuLSks7HrGczZBzR27uMQfH+sGLkU6RMkemApLNOOT0jJ6YSiht9wW/cmvOl621Oqsu8/uLLXLri8NU+8ddfIl5XOBfhXEQkPd1ul+Hjp/zo//gu948fUuev4KwlN4LDeZeP4tc4GqYYIh4+PkXLKEBCGj7q0m7Ce4P28PXnd/jnv/E6WwPFB5MJYmOTjStXmD6w7DvP2YN9kk6QGX789JDHZ0NsO+XSzg5nkwm//Vv/N9s7Gwx6A6qyxBc5d65v88Jz15gsDMPTEXu7u/y3/81/ze/8zr/h44/vruDdYoUjbXhYfDLnWd7XJXrgouHkOTx8yalzP9N1Kd0od9Ek+EpJrPGNYWxAuSACuX+Rl6RpTJxERFGEVoo4EgwGfbyQ7O3k5Ok2fx4pjFDNum/iBAh+lipwreWqpCuIBynPfP0abTOgt9bFRoLR2Sh0eJ3FGEOapgGKZAxFUdBuJ5TlHDzEOkZEmroyZFngCi3mljRtMRqNiSIdIFRZi6IoAnqFgKZZ5AuMqanqilS1qMqSoiipK8t8lmPqJT8kWLRUVUUSjYIVyuFJ4NdEmo1OzPalCB3DD98PNhGRVEFspZjjq4Ly5IhWu0tGxkFhMYsxSh/zjV/7D+iut3AeqjxnWp5y7fINamuJtAp8LrEMIkJcJmXgoFhriWTgfUWRDiiDPCdOMqTSaKXpdKGuKgQq7DFKY4wljjOUrBiNhpydTX5uQgdjDMfHJ5/Ltayxq3jNNzSPQKcQGBM625EMzndLbr5snpVlUVzJEF866VA+FGaiWLOE1KwUuZ3Dobj84je48pVvQxxgrWmrHWDap0PGJyeYsgDvqQQ8ffyInSvXufPll7h2+wV2r1zmuZdfJUoSnr1zh7IouHT5Kht7V5kvFhz86DEPP/6A6dkJpjbMkhhT5HQ7bZ48uMeLv/CLzEbHfPDnv4PyjUVQUzjyCJJFia0tWmmcCHGwUiFZxIdnJoqjJpYOtkwBzOUwIsL6gB4ztaFcLILwCmAQaB0RJwm1MbTabYqyCoUOpcnznG63S1VVq++XZcl8NoEG9RbuUTgzpVLkecHD/UP+sK741lfusLnWwnrY3Ha02ikvvPAS33vjr6jqmtlsSqQjqAQ7l6595nr4KaGAwdwv/GJ+daNNo9pjnQktc09YZE2AYL1fmf6uxuoBb3DXCMQFb5bgs3RujBmSk0aaVYBs2ohKqLBpN4syeEbEoZprHbGWaBnjfYxvBW19vCKJFceTnKPhGYNOi26nhRKKRVVSW5jPpjgvqG29EnKQUhIoWaGNHqR25ScPvAvVRdfYxl9UCzTG8AkeExf8PGhcwAQImoS1If4Jca5uJ5tNkab71TSOQ6euqQg4F8iFSw6YbyCJvsm0lnywJfzELwNIQdO9+sKK9Z/7OIeifsbwnqCk1mx0gBeN4bK13P34MXMf4ZzjOx8+5Wsv7PFnb34PpMTFbaL2ADma0YkVMmrz6PgIOwiVwKIsqbznmVtXuPd4yGhyBr0+QxNz72jCS7uXEa7g4w8fMx+ltNoDcut558GQF67f4nT4EYl29ERBledMTk6p84K4k+GVorYe5QLueHOtT6+jiEzNfjXGOoEVmtG8or/b4mvf/Cbf+Yu/4vjghLMz2FjvEmtJXlS0tEDWMzYSxe4g5TAXMB9jVEmNACXPE29jUdbQarcovUUJTazON06t5KpuomSEMxXGhufbeg/e4RAYB8Y68jwHb8iS4I80s1CZFlHcJmv1qIoxNh9Ba4NWfx3T7VLMzvCLHFM58v+XvPd6kiy78/s+x1yTrjKzXLtqP94bDIAZYLHY5YKLXS61XFI2QkHxVX+DnvSgV4X4oiClR1GipKBEUhvk0qzFgnCzY3pMj2sz7aqqu1zaa4/Rw7mZ1TNLkMBqSADiieiu7kp3895zz/mZr7EmuLcPTmOzKcV0n9t3crJ5yfxMl+cvrtBOI7yoqZxH1hAjQ/eXIP1ufMSbH9/nR+/fZ+xjWrqgQ1D8+lmNBvm3PIaH+VSLwDtA/TzdTpdHH3mEEydOkM0z9g72SZxnlku6/QEXTlmcinBC8v7bb3N/dx+pJY9ubmCuf8D5T9/gSVuxaSNeefmb3Hj3jzBJn6K1wYvDhIuy5sHVmwwTiFTKxpNPM3jmRV7/v36X0eSQlrcMLj3B+m9+k/f/p/+eK/mQ/PwT/NJLfbqywrSGPPPYRdZXSlpqhI0147zP6rmXmM4yfA1lkbM7rbi5n7EvNPfvbpPPZjy91UfEPU6teFZPrVIpRSk73NzJSfqneOOtd6nLDFcb8AYIG/2LzzzK33xlgPrgO7S/9Vs88Vd+k5u3bjGazrh/cEhvuEav0yNJW0ghMXHKu1evsrp1lhObG9y//4Br12/S7rQ4v3WWrdMnmIx3yGf7lJXFxGsUheXq1ausrg2DJLBSDXfq+BqFCyWWduufhfvJhmuwgJUfIwd8U0BbRKo/140AEarJQi4gQBpvDVEckZfZcl9SUqIjtYTfhq9ekyRder0uOtJ0OzET3UZFAidFI9Ec4oHwRyFkEIzgoaRLJwkrGx1WvGdlMMQ4S54HkZUoiYmThFaa4p2jdEFxUAiozZiTpzWpOMB7SAc9lLBMJyWnVUxWVrQ6KVrHWG/JiowiL4miwCnTUYSzlrTdIcvmIdFQEdYU5HlFWZqQUM5zrLVNciXIfImWErd7hDKOtBPT68X0OpoXn4yo6pIPPjGUlSSSCcONS0xG2/TdnLVEkJcKE0hrpCuStKOXCdPR/R1WTrQYbPQxwjaxlKPVbjeCCI44Th4q8Ia0VWu9hJ7OZ3MGaxu0u93QhdUR+XyOKYNljJKKJEoQHpSKGA7Xwnf7BUmsFt/xixhSSrRUaNmIKSRxUFlsGgBCSkxtGrudIL0ulKTXW0E0yblpPE6lkAgl8D6YMjsfYIvWO4QKnmibj7zMhVd+HaN0QJAB3f4KprZMj46wdUWsVYDd4jBVhfUwPHWKb/7279BOW8wmY4osZ2PzBK+89nXWN08Ttbrk5R3u3/2U0f4utgqeW7a0HO7VrG2uk2VzjNQ88fVfI9u9xXg3dG7q2iK1Qicx42lOWdYIQRAAEoEvplSIl60JIhcgUTqlrOpG9U9hG+NwIQK/TwiIdWA6ei+XKqFSKKIoodMNMFul1TKujaIIoRRaa6rZDCU8aRy4Xc5LnA0+Y1IpqrLG1JadBxN+cOUTXnn+Mqd1wv7+HsYaPvnkE65dv86F8yeJVyNCx0tysL/3Y+fDTyleQdPeDNnpYiNwi0B8gQV3C6+rY4jb56W7Q+bIspK1aHMK+VB11vulyp4QYknqg+N+l3zIfGyBPYUgUZlEkiJzSylXIaEdR+HzcAx6XS6fDjC+OIrAw6DX4b1rt3FeI2RQCVkqEjpHVS108489BJbwO9GQbgnJiRSflaMP8uphYiwUBBcVjtCxaqRQG76WbZQDA5SQ5XNdkxwt1Kpcw9F6GHailKLZvZbn5+FkaXHthGy8cjh+/5DI/YKsjv9ORkgCIm8pidk6f4rNqxG7Lmc3z7hzOObZZx/jrbc/JlczKjmk19+gziecv3iJR8qanaP7RIN1rGxzZ7/k+fOniJI2r1+5Tqk1Jlnn6GDEe5nnqc0uK2LG0eE22fpZ3OAEH+1NKcpDnrl8kf1qh8qELtrZU+vcV5qJAy8jrIxR6QqtYYyJEnQMJ6MCkUlkLRjZFkjN7tShtydceuwJ8vm7zKcT/P6YRErKqqZwNd2o5oUnVhm01ti5AU7OGj6kRUUxXipUEhbBYQSddovbexWxjIhbbayURMIjVISmIj/agWRAvICkLjsuAdbqAOtBCUEsKrbWU6Q13D06ZFQ7clYR8RptneDrjHq6hynmuN46rdWz+HSCmU+ppyNyZRGdNknUo78WUxQT7ucw2y7ZmdzjK1PDsxcHrAqB1AbvNMpJrK6onONgpnjz2i551MWWBfM55GX5MwlmxWd4qWF9+fxxLIJviQDp2TyxwZmtLaqqoihL1tfX0VGCywr03j1m1+/x5p2bzEXMg3yOFBFKCu58/AHrO/foyZLu6ipRr09+80NiX7N+5gTfGJ6iffMD0smIkyt9TnQl3jo2TqzSaWseze7xYF6ynijWunPOnNH0eMAL8gHdL71M/sQlPvjun2Dvf8x05yaXvvGr7N1xDC9cps4yalMR6zYylqwMhnTabZ5+/nneufQ0d2/fodeOeO5sSTuCB3O4Me6SV8HU/YWtAYfjA+zgDrFOuPreFaq8pChyXn3+Rf7WX7nExZNDev3nMUkLm1X0ej1+/4//hCRtEcctjHUkBFjIi199ib3ZhAd7B3zvB6+T5QEe5p3j8ccf59VXXmLnVh+7/wHCG0znHG98uM+VK1dRSjbmqGGjEQ1HeLn+fub6/vnYcyFSIUSAeC9Uqxf7opQ/+UT8WUCxAlw+GADHURADkUoRx6Ejlc8zhAr+k3EUB+n+BjJvjKWqa/IipyM7lEVJrktoUDG+4TuhJE4GYRmpJF6qZYcMDzKOAnqlIMDuIk232yGbZzgBrTRZKuEVZYGSgjSNMXmJr8doNSPptth6BCYPCno9xejjMSeeOsPB0SqTnRh8jXOOtNUKQZ+SZFnRKEhKpFBUZUWe18znRVA485L9/YNgH+Mc1gTOTV3XpEqi5zXaQzfVRAqyyYy45Xj1uS7eVVz9aI6pC3yVc+7kU2STe2ytn2R3dIj3h0RxwqlzZ0k7abCTkRV1uc/wuRNYYYl0hBAOgcTiiHTUiK4cI2yiRrii1QrwqgBVK5jPpgillrGVVhEGu1yPojgKxrQ2QA2TJP33Pvf+osNZSzb/YhKrOEnQrdaSQ6SVxshQWAePNSagvLxbxr0LbygpBYLQJQwx2cK3KYiN1MaE+M85EIqzz32dcy/+KlbFXPnB94h0zNMvPI/WEWWeUc7HdNKoScoaVVEZM9g4Sbe/ynB1k+nRIW//8Ic4a/jmb/wWJ8+cZ3XjJEJF3LpxnYPtO8HKIo2QQgTajfNks3nA9NSW7soar/21/5K3fu/vcf/WdfKiQkWaKIkYj+cN10mQxAprAyfMexrnqdDNC+dLoJSj9IrMemwDg1RSEEeCSOiGsx0SsbIOcu46Cv50g8Fg2bSYzae02y3iJCFWoSMex5o0lqF4ZlyIpaSgMsdeeM55siLn5j2Dc57XvqQZDgZorYgjSSttEXbb0NxJo5jR+AsSr1iQ80L7chGMu+OkybNMEvyi8r9InuCzHaum68OyKheqdw1KjeX2s2gxhjdoBCDc8cbUdM5QC2EMixSCJOqwuj6kNIIsL/G+Xna7hFIUZYbSbdYGXaRUyw4YlWFzY4PJpAiAxdDqASEaVRJAiqXK32IzXEA5FpualKFb9LBhsBRyaXbojFl+h4fP3eJGfBhTK4RvuF6haxeUjI65FVKKhvQrl3DAxVAikHEX3Kkm5Q0Y68+oDS4CAZaf/8Vv0D+bkutPqgr40CsojeL2/ZJPd8ckK2s8f9qw+94+1szZ3Yt57uVH+eT6Lp8ejhGdAT5do6qP2M1rXnjxCfZ//ztkI0O8cYpZ7bl6/RZMp8S2oJhPaK9KummHaTZnkic88dTjvPPhDe6P76PFFh7JtesfstZ/hDNbp/jko/dYGQ44feIEGxunuX44YpKXiMFJpHD4QcLubIx3NesdQb/tMbpgks9Qqk1tE25sH3HoJwipWOkNyKYjKEraaZu6Kuj3O5zeHHB04JhlhgiL1C1EnaGiCCclcadHmY/ptxSxVsyKAqUMUkfUTqK8xSBQziJMgUwceI33x7A17yzeh+qWEyLIsmIZtATDdkIaGQ7mOXexpP0N6rlk7lJ6Kx20K8iKOTrtUcWaJNlA5zV1mZPNC8ooJm63Ue11tBY45fh0Kpm/t4cWnhcu9aFd4r0loY0zYTG/fu+IcaUxqMaUFOqi/onmzr+LsSDLL5G6/5p7cSlSISXb97b5P/7+/45zjslshpLBg+nJXofnbcb0zh0+3R8zQtN99DIqSkljTbx7Fz0f013rYqUkLyvk7V0KFcHpS9y5cZtBkdNxhl63Q7SSkk3neCnZu/IWUivawwGdQYf5NOPw2nV8VbN/MONv/93/ja//9b/KbHuP2bWPuPb2m9x+44esbG6y/sLXOfPSq/TX+kGSeKGm6hzeWc6fP0O332J8dMh7n7xPv5dw8+59DkwX41rs3N8FYDgc8Oi5NeIoYrP3DBfOX2B7e5ff+MpXmd++wa3qJI8+s0bbgY4iVlb6bKyvcTg6QmuNFBFHR4fkaYym5IlHz4NO2T84YDQeMzrc4+tf+yovvfQcUisGGxdIWzkriWVuEyb1WW7cvMPR0aiB+zWEIFhCxY//cXwNj+HbzU7nF+qyn9smm2v/885bEQ2U3DiLbpTNBAZjauIkJq4NtXWNyEWAvEMw+HTek2V5Eyg5nLGUtlyiP7wM52/hR+m0RimNkzqIWjSFCCEFUku8hKqu0AIqY2h12sFUtSnYGlOHBERITFXhreL2R2Mee9zT63uqeca1t/YZbHRYWevQ35JMqxohS6RXWOupTU1ZBfW8wPkwVKVFCks2L/FeUlvHLCsos4KyrIPCoZKBl9SgTczhHFk6vPIIWzGbzKlTR1R5ej3NKy/GFNWcG9cc+fyISMVsbjyNTleYZtsIPO1umzMXzyOEIFIVUecugycVrZP9wA3DU1UVUSMMZm0oNjtXLzl/WkckSUJV1bTSFKWC+uH4aIR3gnZjCuycR0cR1lTHqsJSh/Vcapyrf+wc+Xkbzrmlz9H/5+GD+p9zFq2jAFf1Dlv7JTxZisAZ8oT4MNBYjrlYZV2FJKOZ51L5BkEUEuAaxeqlFzn38q8Rt1d47523+e6//Oec3trihVe+RGdlBR0pFI4kjtBa4b3COVjdPMNTz79Mv79KNpvy3ptvcufax9jacOvShzz/SydptVKipEU+mVBnM9JGOXsJ1/eeNE1ptzts375F3Eo5d+EcZ1/4OuV8jtvbBeHptBMG/R7z2RwPxImmrsK6UBuzjKP9Qt0aj/GCSe4oa4dt5NiF8KRxhFfgncRZ39zvULtFw/rYgbTIMrLZDGtqVvp9dBQTRxE2DslVpBWmdlBUIQZfxOUyaDuMRiOiONgi1D96j5XeCqc2m2LfM89w9b0rxJGm1U7xXlDX1Y+dDj+1KqCxhnC1jw9sqerRBO7Nf45/PsQZWIpUNJPxz8EewhlvCn+NpLgMikmqkZs0YWUOm5NsOF3Og2wSAik4PDzCOIjbbXIPzukmfxOkSYf724e0W4ZYx8GcrdHVX48jTqwPeebJy8G12VRICea4URVc4/1nk48F32mxGYSF3B3L7BIy6kX3a7H9+qYr5wlJEMsOoEcphaktXjUBFArEQrDis9dl0VlbJE/OBMMz0/AOrA8O3wv/K7u8TotEbIFhD35Xnw8E/n89/LEIiQAKG/PP/uwu/893PibSCcOW4rHL5/juR57x/UPGusVbH96lM+wj7xzgzQ7z1bPUOiUbj7HS0x8M2RvnaK3Ji5yDueHJrQsoeY/qaI46uEXdX0Vaw837lo2nLnCqtOx8uE01PqA7PIFY6fPW2++yP9oivz9GbI8xMidqtdm6cBGpNVV5nr3RmAdHOyS0uF9FbOdzOqkjjjzkExyhsmqjiLGBuDKst1PSwYCjak4xm7C+lvLY+dP0ow4TXcLsHitOkycr+CIkTlYIagTOW9ZWUuJIUXtFJEFFMaIOi3rpA5Y/0QlW6tC5loqFUTjOhEUxisLvnUWjSCLP6qCDSiPKg5qua1Pbgogaf3SLsj3AtIf4JKYoJ7i9+7i0hRqu0kk71FVNURTM5obId0msQmkJcY+CmpHp8tatkvZKwsVNxTAp8SbioIab96dUXlJnE4QtsbZA6+pnfgf869Tk4HPFAh9UP40pm861pKwM2BJpK9qnT9L3t3lCFpiqpv7kCsZYOjpiI9VoYSkygVo/xf3tI/SD67QGq7z78S12/vhP+M2NProrGE2mHN13FMaTfXCDeJ5RV+Co8VJTj6ccvf4meeWYy4Q7t+7zd/7236G10kbGio7S9CdHbG4XnJl8nw9vbnP+3Fm0CgUz7x0GydxY7o/HqNmEqMhopSnzUrJzx3Lx5Uf4+NqNJal7sd7+8i99jTRNeOSRyyipMUXJ7v4D3nzjLbq7u1y6eBEdBxhHu5XyvT99nzhNaHe7VGXN2voaj1w8Tbo/Zm9/RKfTYTqb4T1cufI2H1+7xulTp0mjlLHpkEYzYukZdgRnzpxlMp6Gc++b1bSxGGmuHIsAgqawtrDlWOwBnxeyCKINC5uShwVKfk5HE6CkacJ8lkNTQZ7P5ygVIQWYuqaqLbVxGOebIEYE7xjrqCtHLkuqsmKuomZvEmilsdLiFlBLJfE6wquo4WCHoqoSkiROcLNw3q0LEuOdTmcpWlHkOUprBv0B1hbMszlnz6cc7oY1bPVEG2EdcUtQjCtOnOoSxRBFmiyfI2Qakgsd+EVKatJWG09JWVSUZREKxA6m84KqrhHGMrQSXxl8XVF7i1eSqqpRo4LIhYS6riz7e3O6PUm7LVCqpD0Q/PJrqwifcf16wWS6A3is38CYAiFguLZBq5Ui9YSks8P9G2+x9tSXSNaGGGOCWavzeBnmHZ5GDS8kXIKg3Cqa4nFRlsRxHOTAkzRcxywjbbWDR5l4CG3jw1y31gZft5/lHPwph5SSdqv1hbyXjiJMXSGkRCdhXtcTiyCo/xnnQhfahYygDkoOga/TILu88yitl7DXhzs83gsuvfyrPPurf52420NFmvevXOH+7Vt0kxjvLMPhgM5KjySNSYQLvqTOIoTmqS99na1HHsdZw/tvvckH77xJMZ9SFwWv/+kfcuriBU6d2SKKFavDHi2t8Sp0q4QPx6OUZuviI2ycOcff+5//Lt1Wi7/2N/8WZ1/4OtV8zPxPfw9nKuIoYjjosXd/H6V0SDht0aghGhAepYPhOsJjvWBSeeaVIy9L5tmc1eEqSkviKCJKgjGws0GdWEpFYUNXUOrQFJHek2dzvLOUec60gQPv7e6ipUOq4CkWRwGBY6zDOAFKNGiDgqosg7qrVAih+IPvv8vXXnqcXqdHK025+eltTp7aIGnE4SL149Onv6Dt9KKMuvhxzAFYBPYPPeszm8JSLW+RMBFagQ9vMIvOi29aOUup1SYBUFLihGjIf8ewNkzgXhkLBwdjZvMMhEaoKCgNSonQEU4KstqyujkMmGLvwQdYQqQkcZoQpWk4fiEaMuJDCaM4ToiOYXONwW+TaBqz8IkIUIbjhejH+M8gsLZ+iIy7kK4PpGzhBdbZphIYVFeO32ch+S4+46n30cYAACAASURBVDNmnTv22Wq+CwuYJQ9v5MfKUwL58K//wxkP5Zn3Dgv+wQ9u8+49wxOnu0hf8/716/RbsOsF06NDbiQdvvTkE5y89YA8mzLPxtTtNtYV7B4V9KVGVBV2XpEoSdXeYG5SHnvkLJN3rtEubzMtIlQSMa8NP/hkh+cfeZLLc8+dBxOqqqY/PMNodsjeg30evfwcH77/LlnxAKdj6lbKubNnWT+5ikgi5rN9Lly8yPbREZVZJ5/O8KMpjhwhKnydIqI+qtVF2pLt27eItKTTTqnyI6qsYiWS9FTEsGsZJgWD/gY3MxO4EUqjqALHss5Z7bVCBUhqNI5IpQjn6eiEeS0IXsVtjNR4V2FV07VyFm/q4A00m1J1+hgg0glJmpBEKd4k7EuBUykUY2ZH+2ghYX7EzCki26LlPbK/QpWPqfcKXHtAr52SRF0meYnNR5RIRLtH5KHVjblxf87uwRFJK+bJrZgvP75CL+7yYALbBxlUmnJyhI41uJwzQ8Xuz4gwoKRaqv6FefnZ4xAPrX0P36dSSJRq1kMnieYZcv8BiXfMrMQicbUhjjSFr7mdh0JZ5GqSG/fYnhcM4xI/3SUyIx6LJC7PuWdhWuTkWISOycYZxdEIc+sBQ1Gyd/cuKokZzA1ucJqqmPAfne/xw9Ehc1HTxTPEEmUz1pIuT5iczs330e//iMjMENYgWy3Uy9/gcOsSa5ublK9/h+TqD4iqiqo94LUnv8LFl1/i+Reex7tgtuqwXHn7PZ5/5klWhkMOjsZUVYn1lo2TJ/nKq6/yz3/vn7KxuUF3pc/+wQFlnuOd49q165R1jfIKqSVvvB7R7qxQFBWD1VWcc7RbLZxzJBZ6UYKMNStnnqGc71OVOVLOefmFF+h3OkyyjP39fXZ3dj53jRZQzgB/UQ/th0tuKw/7Wy1e+1lPoJ9V9/QnGr4h5UcqFCKNQyqBqetGHMGTFcF3xnlPXQVCexRFTWEz7PHWeOZZQRm3WUQWQgYokrAeoXWAFMYxtU5AKaQOHBbvbIBk9XpMp1O6/ZUgcd0IRpRliY8jVBTCnlaSorVkuFlz+el1MAKdtBDK8MhLa1SlQPUU3kGZ5VgDUjukVCil8V7z5ts3MJVHCke3mzDod/ACirKkrEqiSFHcG7O+MyexkqNRxmMnh1gpmFWC2UyCUMyd4GhikC4gXbxTRLHDUtDpa179yiqII65fL5nO9iiKI7x3KKV59KnHiZOC9uAAM7vLbH9EahWqrhFSEscJ1lqsd0i3oE4cd0aVVlhjSZMUgWQ+nxLHEc46snlGnIIrg+mzdw7jLZGOKcsCY+plgbmuTMMr/8UYX6SP1dITrek2xUkSujzWoLSmyvPj0q33xHFEXUdN50pSlCXOWuI0Dd1XWMLUEIoTj77Eo1//HeLeylJ1e+vcBdJ2m6vvXuHTax/z9Etf4slnX+STH36H+cF9dKSIVMrpR57hK9/6NjqKePetd/jgyptMj/ZwVU5dZDy4c4s3/+gPOH/pUVYGQ86cO89gc4N8dD8oSzddhd5glYuPP0ueVTy4d5ebswmPPPs8w7UNzr3wNa6/+zbV0V20EPR7PVqdViNIEdaypdAdCxsJ8A4yI8iqgIbLpjPKoqCMM6JuhySOSXSIaY011FWwabDGI3RAwEghmE3GeH+MGKvKkulkglCSTruFjoNoTdzM9awsiZMUoZMGQhnsMxZwXeccn97ZxZuaTtom1YooTrEOdBSjI01efIE+VotgPhDZ/3zllEUQj0A1cMDF6xavXbTvWDSamnSiecslRyk82Hhm2UZuPZLgmsebjlZYdAP3K7TfS+Z5TpEHN/FQARBIFZN2ehhhcU7Q6a/T6q40js0S5Ry4mu5Km9XN0xwdjZnMa9JUNxPMLsXjFgIbNDeAoNHjb4wLF5h5b+1Snn4xFpDBJT8NwvE7w6KX5bE44UD5hnMhkGJR828+zzmORReO/7+IBS3N+QPE8uOPM6aw18vPEKZD9WRxXd0XnFv9HAcGgqbA7Nne3ube0RQTd9kdFZx/4gSffPQh5WjESlswmY4YzTf4dPuQ1772At/74++hzZxxkdLttanHe9y7+zHGx+R06KyvI7stRpnh0VN9Ll/corr3Lr7aZU9uISPFYR3z4c6cZy5cBHGfT+8eIk5skqxsUky22ZvOWT97iU8/vgq2ZnT3Y/LDe3ROXWZ44ixb6+ucPnsOF/XYOxpTyJgiVhSjEc5kxFUJukZ32jggm47QEUROI82UEyvrDNsxnU7CRq/DUy88z8qgw/z9e8xLRawdUmaMfUFMQTsOcNKKKMx3q3GuDPLJpSAWDhlFeCKML0EphFcI56itI0ISK41SEqIYpxQiTvBKcn+aU/kersyo8ozaeFqdFVbqI2yaEne62MkUJ9u0BzGmKMmKMdPZPklvQL+/QV3k5HlOns2oS0mp21yfWJxOqLKctz7cJxvf4bUXnuXWruFgUkORk8/GdFZXiSWc6i4nxb/38eMSqc8//hlImQhctlarjSCn7Q0ntebuvT3mueVHRw7VinAalFUEZdOmK17OYZKB9RRVzZnhKk+f7vFgNOX785zCaGqf4qTC1xL3r94iUoK1NEbWgYyMF+gHI4QUPLO5wUunTvLVy+eY3fmAo2sfIVxNp5Ny4cVXMNtj9O4OIpvi6gxjCvCWwlRsPPIYd+/d49T5C/D+j/De0PI1h3u3qR9sc2de8NFH16jKim/9+q8xOdhjZ2ebWTbn/asf8+mtW+R56Bw8eLDPzr1tTpw8RZbNuXnjU3Yf3McLSbc/ZLqzG+BgzmBKyfjgiLTTZ6XfDxXkSNOOFbtvv0EyHyFObxEJ2XikbHLycp/szm10S7D56l/ie2++yf7eXlMAe+gCPXTdllzkBeyaJcv1M89ZQOKF+DlPqqBJBj1aKaJYIwRUtaGVpqHqLTxpEoVOqm8Uc/EYG8jmcRw6QM4G6e5Khsq+VBIpFV64oAasFUJriCNklATPSmmWpqNlWaK8J0kTojimrmq0VERxRLvVwhP4VzKOabc7aCXI52N2iyliVNHd6LHxWBe0w1awffWAsy8kRLpDq6WZZzWdXkpde37wxg5X379OO5aksaLbSXj66QuoSARfKyuoqpp6nLG9PWarN6BLRF9qdg7nTMdTEpmwsXWK25MDDooZXniGLvAYPTlpT1M7z8qG4LVX13FuxO3beTCuVpLB2horQ0/SuUfsDzjc2aGzeYbC1MR1hY6TpddaVZdYa9FKhUTLBp7bbJYHyFQdeEBlWaF00XQhA9TSGJhOCgaDXjAXVhpTzzE2KC2CR2tF7X426+VfZATvxfjf/sSfYDhryYocTYwQkkhHDfKnuZY2CIdJpRtdALG8X4JipMXWromJA57IWYs1nvWLz3P5679FOhg0XlahG/wr3/42VBn/y//4P/DDP/h9Lj/+BE889wL+v/qveed7f0JdFVx+/BkuP/9lesM13r/yNh+9c4WDnTvYfI6tMrTweGP46MrrvPWvnuDE1haPPfc8X/ut3+HP/tk/pJiNMMIQtXo8/vJrPPbil/nuH/0xh4d7dHorQT3ZWTr9dR7/pW9z93u/i7QZUZqCEMzmOcN+i6US+JISGdazeQ1H8xpjF9/XIHzwVktbKWkrQimHswYtoqDm15w3LxWtdhtha6oiI9ISJaA2LnRes3koCgtBq9VCa9AK2q021gucE+g4RTjBrJ6gpKTX79PudgEw1vLp9j5/+P0r/I3f+hVefOUVtu/eptNJSZOYTu/He6D91IlVMPMNG8GidfzwTwgQPuebgH9RfVtsDi6cVClUCMiaybfoR4XWo8c5uayqLNymjzepBjrS/F5JFRTanKAuaqa5YTTNKYqSSAXde4HEyRJrHaWXOCE53N+hVWSsb55muLFFp9un3U5ZXV1lbesyb77+OjYv2FQdlDpOCBeJyFIAotlBXSNK4Zyh8QQOCRgPaTv54yBpmXz5hRHiYgMN3byHDRAflkVfwisJkEMWwhn++PWLBNd7dwww8R6pdAMZbM7r4vMWFYRwyz8kzPHFjZ/z2AAI3//cqVUurHfYuyWYHE754KOSJy+c589ef0BkAZXgx9vciyxbw00uXbrA7U+u4YXAzdqUdz7BlDmdfou0U5N5QwsopeGtWzlPn+5xa3/ACTNibIbk0ZBKr7A7LhGTGTIv0cWYctwi7Z1EJxEP9vZpD1YhTRgyZTMtsLJg70FMnldIm+GVZNAfkrQiZnlOlrfYiWLMZILN5ygv8bHFHh0QRQ6RepLIsL6a8sz5DdY6CpG0ub07ZyJ61KVk7cQms7zk/t5tVDtGGkcn1ug4QUYJQtZUgMIgvCOJFLJh3kdaIawmrxWytggVdC9l0kWS4oixtUFah1eSce3xM8etwzlGtbAmJGFxqklUgeqs0V05jdCKEkm9dwdhPFFrjX7awmRzyiwn6VlkkqKTFmVt8bM98plhZiQrgzbCC0qbcO/BAfnc8PGdEdNSIOqKKIJI1Jzsp8SRWRZP/r2Pz+V0/8ZEa9FFx9PudgLP6O5dohs3aCcC5w1VrDjod5l6SxdLLDVCxERpQ7jGk6ad0D3wsJOX3B3nsDLAr64h0ojhcJ1hv4+MIqq6DkFZU7ixwgdYZ23ZufohH9++D7tHfOuRx+k98jJ3CsXBnduc6axwdPMOs+0DxKQmmc8xzlDj0B6inQNWPv6Ia3du8/6HH3BuzyJKiStqDvdv8gfj/5Xe089wZusM77/zHg/29ijnU373d3+Pazc/5ehwHNZo65llGRAQB//oH/1jDrd3iOMYHcWIOGKcFVRVhdaa8+cuc2HrNHdv3uDmh9fYXF3Fm5p+t8fJQZ/9quDKP/4nJJtnMC7ArC+c32Jt2GeyvU19/T2qtMXZ81usf7LOwd7+Uk0W3NKn5fPXT4gFhVcgJQQQxKIA2YhgEB77aabOz2JEsUJJQRQp6qqirqtwvrXGxSFRKitDVTdEdBH407YOnEYdeYQMMEHrQqKGaOIKGf4I2YhWaI1IEhByeZ6FUhhrMI3vkgPa7TZ1VRGnCUJKIq2XnlQeT5K2uHOtIo4dGysg0ph8WnLtrRHDtVXM2GGtQKIo6xytE6JIM5pmfP/Nj3F1hR50GHZXiVoJ04ll80SXbq9Hp+vJZxlZHFNIyf7RjNODFlootAyJZ3clZXPQZ2c+ZW5AFE1Z1XlqanpeUlmLYU5/IPnqV4Yopbl5Y4IUggsX1hmcOQI3phzXjA/nRI8+gooUR4cHrG5sNklvUGSsypKyKAM0s1F4jqKIIiuWYiJ5VpJlNTqK6A8HgW8lFWVVYk0rqLl5A4IlKkcpiVDwbxPg/XkazjWKtF/A8C7wBFdWooAsspYojnBWHMeIInSnvA8cw7quabc7SPVQcRuwLvCtqsrQO3GOp775V2mtnuDo8AhrPb2VFUDw6Y3r3LpxHa01b3//T9k8fYbf+I//U1762q/wxAuvUpk68Jqqio8/uMq1jz7i/t3bZLMxti5ItAQV0ImuKrjynT9i68JlXvmVb/Ebv/Ofcfr0Oa7+2Y8QwnPi7FnOP/osO9v3ee/tN3jx5S/z6//Jf87JM+fQUVBAvPzsK8TVlNtv/CFxHERQ8mx+jPYiwEadDTw842CUGfLKhq6+lLTbbcqioKorptMpcTRACQm+MVpXQXxGeomKItIk5nB3Dy0EWiuclECAGzvvybMchEDrNWzcoZYSoyp0S5E4j5CKMlt0qxpBEflQLG8M1+/c4/f+5Af8pV/6KqfPnmWl38c5Q/vf0O38qRKrBURBSFiqczcJgJeLalv4/SLxUSLIh4cul1rW5QQCtTTBbCBzC7CflKE7syRwexa+UZ4AB7ALKXNjGxX8oEhknKcyhoPxlFlRo6SkHUdhgZcC62GWG2pnmYzHzCZjjvbusX39Qzr9VYabm1x6/GlOXXyKosyx1jPKCnppkK5ccqaWG2OjWNi0wIOcum+SxyAPG248t/zpfKhMhIKlRTY33kLH3zfnZWn6646l6BdJUOh0LW7YBbQy3OBumTAR8LGEBMsBwoZuY1BqXJilyYfUFkVz7M1m9gWOn2eKwMNjvd/mtae3uP5gh/nYcOfWLptrA05trHHjwQxhNUJ4KtXl6p0DvvbMYzy49RbCO/aOPFQzUi05P4CnLmtuG8V7I4lrpYzGBXluSbpDqqMplzpzrts2MooRNuNgPObxM+toM+b6vZskXGZleBInEg6MokoHSJOzMUyJWgkrpuCjbMr0aM7k1g1ubp6g2xtyYrBJvz0E2aLu9RmNR8yKAjeu0WXJyqCPTQ1bw5it1S4nTgzQsWZUOj64PePWqKTX67C5OqA3WGf77i7ZwX2SVouTWxt04ha1igOhWYBxnkQ6klgjcBTW05IRZVZhLIi6wrkyiNM4gZUR4/GMjgqBkXeea/cyuu2EXAxxlWkoFgGmm8SKQqSoOCWrMiocHW3A5FRlGxWn+GSI1BVZURNFGhnFJBraLUnlfOBC1AUOR2EFse6zd1BwkAkcCmELkkSjfclGKyVN/oJI6S9gfAZS/Tn48Gee95nfC86cOUNvpU+nfUR9MOK9uuTc2S1amyfY2N5m+uCAQ+/odrrgPaWQJL0uCMX6+hqbJzYAT35/D28sQng2NzdY8GKni06MTD57bIBuIFacPsWsqaz+w3/5x0sYdBStsGtXicuSB3t3KTNDpBOyHJxOOdM/y4lbd3lm9/9ETOYwybFeYEtLjSHPDQ/iA0Q2w5ma4foQYw0vvvQy/81/+9/hvAhmrQTFyqcefZT1Uyeo65rDoz0O7m7jZjNcnPDVL/8KreGQIs+ojeHbv/6X2du7T1HM2bt7m6TKWEs0G8Mh50+eYf38aVbPPkJVG0ajMUmacOnyRQa9FcS5R+CrX8NIwYPpiLIsiKIYVxbLTuLiWn3mei2KajwEg18UE5vnSKmWz/lFWD+NrSmyHIHE1DXW1IGI7h1KikCqjyyyDBDUhXqwcZ6iMngERWUxiV9yrBedPCkCBcArgVcSF8V4QDWPRToiimOooDI1/f6QOInZ3g68pKIsGbbbtKMomEeLAOtRWnLn0zFyK+XUs1AVJQ/u5swORlx6bEDa1kynY6RsI1DEccLR+AFZUeGto7Ag2qtcfP7LpPUc4e9SVDndlQFllqOsZ2uYcgJJu5XQiiVnVjuousALC8WcjtaUQiKsQ5YBdWKcp6xr2mXELHdMpwVpy7K5FnHvbhAVWF93KD9CWEs5yYjSFdRKn6qqMaLG1jV1FbhY1tqgkGgdUaRBgDEVVVlRVRWxTvBCEkVp8ONqJXhrsALiJG7EXixaRXgPaZqEpDUO6msLG5lflOG9py7LL+S9jLUUeUm34ynzYqn67IxZGjEbx0Mq0a6REpcoJXHeUdY1Le+pTB34e0mHx77+V+ieOMd0MucP/uk/IU1iXv3GrzDPMn74nT/m6rtvESctynzKH/3uP2A6GvHKr/0mlx9/kkRJ6qrg1vXr3PzoA/a3bzHZu4ebT4kJ4lGBgmKRUrC3fYvv/4t/wubZi5w6d5bnXvslHnvxFebTGfPplBuffMjr3/0uOzc/YfOll+j1B+gkXRbpayc4+exrzB/cpRjt0esPyItyKWLncBjnKMqKKEkZlZasajQDCLF1r9sljjSj0RH5bMYkinDdHpFOglS78Dgs0sNwdZViOsbXFUmkEDSiOd6jhAzqf95TzHOO5FGIUVothA+y7c4abFVSZlnTBawZjY5AClqtdrBWyAsSk/Due9dY6XR54enLFKUhmxyxtr76Y+fDTxk5BE3+haQ6C06OD8H8grqzEKiwDZRPNslV6IUGaJsUcqnEZhep1oI35Y8rH0KocNlcmHzhRRFCaYR0RDrIlyIEsYqQOuLgYES7leI85KVhlJe4eUmiFJ1OG9OorJRFwEA76zD5IbPZhNHBNtiSlY2zTMcTklbKwWSO8G2SWBDrkDQaY5Yb5meNHRuopJDNBukayJ9dcHGDv1RDFlUEny9PwDobaxtuFUucrYSlxPqST/ZQ4CU47n5JKZHIQJptulYLnwOafzfFk890uELC6FnkZM4HsYv/EEesJb/83HmuXJtyNZ9wOMm5fzjl8a2L3Bt9hDsa0Tv3BDNbc1R4dmclr3zzm7x95X1m5R6qH7PRb/Hi4xs8errHJdHm3nsVB3WM0jU7RwXDVsxdelyOHaeUZsdWxNJg2wmHZc7WIxcYlzX3Dz7FRZdZWVmjmE1RcZu5aXNQSM6u9VhHcKsSzKxho5dSVDMO7+0znuwTr6yzovvE7R6rnZRoOmV6OMYmKSJK6K1oOl0H7S5T2UVVgr3DEZlt0+2nTI5mHO3d5ORwwJPPvMz84B6jwwNW+wE+e1QaBKAFaOkRdU2adPAUFCiqqIWlwNUZrowhSdAGitkE2U6JlGCl0yKra2bTiv1RTe0SfKzwbo6paigzhDXU9FBxlzob45xBVFM6Cax2YrJiRiE8e6VHJylRpMizjMRDV3vWW5I7Ux8w0tM9RJRibcH5k6d4MHEUTqJw2KrAWWhHkrPDpNkQfzZzcMGdWnT94fNJ1PF42EpB65iyMiStNue+9Wtce+8KG488gixyvpnCqU5M+urXmNWGcVFyZ3uHytR4HEVdYK1BKc1g0Gd//5B793YZjyas9PusbawuN1HvFkqwy/Y5k3FGnueMDw9YG6w2gVgIguuywuYF051dlIS0M+TsMMGUFbv5DvNWi1/9rb/KO//g71OM90lqR+Q9MyEQnZi1rTOcSrt847d/h9Vnn8N5R22C6Sum5rUvfwkZxWxsrHP93Q/odlvEUcwnH3/M1rkzCODiU09yf2+PTqfDxScf49LFC5iqZjAcoJTAs8o3fvkbXL36AVdv3qSbtkBotJWoXo/B5jr5ZExW5BgB3ROn2Dx9CqkUWieURcYn3/kTDo5mbKyuYmzdCDsd86WW1+84F26KbMfruZRNZdcdr/U/1zDqZtR1uRRxMsYAjk4nBaGwRlGUBXEU0e8pyrLGuMA/pikUFmWFcYJZVmJjg1BB5U8ZHeINL/Ba45SGSKNabZRS1DTeWZEgSlJkrtE6xliLEIo0bRNFMVoVAb0iBEmconVICM5f7rC2OaQ3iInSCO0UT75wmsP9AtlVCOkaeK0M3EfneLC3T20ddW3JK4Mxlla7w+wgRwHdVkorTXkwy8mNYb3bYi3tkM0rtvePWO91WF3p0BkOODiYMNACoxQT4xA14ME6KCpLXpakHcVsYvA+Z5aXJNqT9Hr01iuUiBAIxtMcv3KK0WRGWdWk7TbjoyNQkigKYilVWVNXFZmzKC2pG46UtQ7iUPVnwQWUgiiOqOua2ayi0+mQZRmdToc4jmi3W5g4wjm/hAzKX7CYwX9BiaCta+q6ojZ1YxQd5OyFFCivQEucCcp4Ukpm2TwUEgR4IUInt6owxgazYW9J18/RPf0oQmiuffg+77/xffJsys0P3iFJWty7e4eyrPjLf+O/QPiSf/F//33e+N4fsn7uIhcuXwIPn964wY2PP+Jwb5eDezcpRwdEWOIGNiekwEgBEjw1dz66wp/94T/llW//Nt1uH+8t77z9Fu/92Y+4e+sm+zv3KGaHfPDW61x67Ele/bVv47Xmwe42K70+w401Lr/6Lfbf+S7De/fZefCA2jS+tIs1zQuKWjCeBxGJhzUYrLfUVYUSEuMN0/GEMi9IWy3SVgupwzxO4hTlPSbPaCUL8+uAFpNIYudoJzHTvMQ4RzafByixFCipccYym05wxjRqjTFVFQpB46NDbFWDgCzLg0XD6ipvvPMRX/nKq1y/dY2Orun2vtCOlWwW/iAy4UQQZhAEdQ3vFmIT4TWuaflLJF4EQ1+WQX1IpjTB3hbCBvPZTklYUKWK8E27juDewuIvLyzOO5QM6iCdVLI+6NBKNPPCUhhLZTyuNkyzHBUF74X9UUU7DfyqNIrQgJCeIsuo6gpjalwuUMB4ltFraWoVqm5S8JCgRUjsHk6wHk6mpBQN9HExiRaQSrc06AO37L4tcfg0iZBgKU2/4Ft5wuerz3PXmo08bFgBBklzHAs/L+MM0oum4yWXxxsqJ4vj+wUoj/6k4yeJSR56jvew3lZ87clNbnx6m9W1Vc5tDnj32j1cMcNnB0xHexS1Je32eP/afR4/00NEXZQccem04uzJIRtrPXTaZqub8pXTM37/wxFVR7KXgTA5/Y3T7M8O6K4LqoMZkXKkccq0qDnMDU88/Rz5D37AbP9TROtR1jsJmZFUtWBnf87q+pBJrhht38PbGjZPckKV9LOciZ+TTw0HPiNqdeilLfrtNqlUmJUWpiip6oIobjHKHXWq2ZlNOTzKOTjo0D/Rp9/rk40Mt25+Sm+lw/nzF9h69DIdUZGh2DkY4XSQjKXIELZCyTbGC6yUFD4CkaGwwWBYCxLvqUyONp44SoliTaoEReYoyhw9s6QrHuFqTF0Q11PiVg8j28yPJsTtHKSGfMbGqubCiS6uhlL2eHvHUOso3BNJUPhTqUa3LUbESA/T8SGdtdP024pIWG5PKgwxqfdUWLqdFTa6jtVewjyvfyZQQLHozCMao2qxLFYJRKP8t7BjOF6Dojgiz3N2HuwTScmlZ5+ia0vGScLe9jZy9xAbB1PTWmmML9jY3AABcRzT7f2/5L3Xk23Zfd/3WWGns0/o7tPp5jA5YkCEAkGCBCkG0SzKkk2XLdGSWC6r/OBHv/kfcZXLT5aqLPtJpWBLBCESQiSRZgaTMBc3972388lnpxX8sPbpvkMJMiFOEUB5Pc1Undt9ep911vr9ft/UORvQZFkSMm6cYzSe4oVguLmBEG2QhghuUavzQiDIO11iJdl98Xm+8IufZ3trO9AqWoZB3TqKxVIFi+35nFgpcAadZ2xfucK1SzvIYsl8NiPr5Ni6IY41UsJsNuPefMr9732foqrIsozZfIZSzhcnnQAAIABJREFUktc/+wsMN7e4f+cO2zevsLuzjY5jvvpPfsDJaMxnP/MppJDc39vjlddeZefCDjvbWwihsLbBS8e1jauh+LRwUlvqGD7/8otc6XeZPn5Id72HjDS99QFxHHN0dIjAonTUfm4i2L+/8DyvvfwyX/m3X2K2mJ9NZFevWX1eK7rBiqGw4oqHz3Ul8D6nBf4EO+ivugV/4uW9I4sT5otlKM6FJ4qCI19ZGnQ3BJ3WtcWjyPOMomyomgZ79jd7GuOojaepHFEcIXRw3kODxwRnR2eQTQppjogzmqhASg1NQRxHoVBEsFwWLJYlk+kcqTTHxyP2j2dIFWENJKlkY7iG8o+RYsyg3+HgyNLrpigVI3PJeOapbxXYcp08zYmSBC8EWxubSHUP1zik0izmEz5485tc2dnAiBpBB9sYlA9D0VMct8cLkjpolXpZh3HVsDids380YWOzj5aSEwTCBCMs4SDVUJsa4yRpqrGuAhz9jufqq+v0d/MQ/1IaDk+WRBsdrAu22FJJkiTBuGAq0ckDDXrhPGVZkyUpWZ6hlKKua5q6IU27TNp8njiOWzRKU5YVRRGotVmW0TQmfLdpTUuUammBPz/mFd57nP14GitjbDAd8cGOXiuNoT6zt1dS4tpqW6w0F+1yzlHXwbnRWkvThHrt2U98jqTTxXvPo0d7zCdTlrMR7x0c0hsMMF7yi7/2m/z67/4eWhq+99U/Zbh7idc/+RmkkOztPeD+7R8xn4w4fXyf6dETpDNEsSaOQ2C5kCEqaJUxaaqCt77xFXavv8Dzb3yaKFa89+4PePftN9ne3OCLv/1bfPOP/w3z8Yhv/emXGAyHeCH40y9/iVdeeY0v/PbvsLm+y+aV54jefBvTGIqiIk/jlmansF4zXRpMm+W2WCxI05Q4iqjrCucCVVcqhTGW5TIM7IbbWyQqDbEZgy7z8QlSWLIswRrXovsBIKiqijiOWoZafXbfSCnp5N1AhPGh/hZKM1hfRyjBYj7HtQ6XK26dbd1NtdZ848+/zec+8SKJL9roqf/w+k/QWIn2zQMIhGupZN4jBQipWlSltfYUov2DV7Q/dY7G+Keodf6c+seKAdAu7wVSRkRKtzQUHRyz2uIjUrodsgiqsiBLFNsbfRrjmBUN40VFZSSlCfSDrJMzGU+olgsmyxJrGzpJRhJFdPOU6WLB3oP7OOsQwlI5R11ZvI3IkoAqRVoR6yDSlSqkaq9g8NUlIQmIk/cB1VrZ0htnVlI+IAi/vbAg/Ud/BitK4Lm1+7mWzbV0SwiX8TlSGBCowHd33rep6x7cKnDYB0f7lRMH4fK2ps27kjKE1n3MReVPyy74//O3Pq19ADSCWAk+dXPIv+4nPDRD6vkxG+t9urtrSNGwP3pA3L1Cs6g4to7pZMxrWxmPnKDf67C21iFSHukdSPjcC5vcOdjjw6IBEmY+4oXdXe69PyVenrJWGualJtnu40XEnffeRd54lhsvvsr9H7zJ8uiA/NIVZFMzG50gUs2dY8t4OseWS7qbF5gUljSueW57gMz6FFZwfyb54MlhyJdJFLpckHVSkixjfjRnVnapreOHj+9y4fo1GpkymzxhPJuS5F36/TW6OqIcH/D2977HcGvIMzev8qSIeTSx+OkcryLqZoGuF0AHS9zSfHWbIK8QIkZriTMNyjfkXuOSHCtilF+gnUPSID3Us3n4d0g62uHTjLqeYZcFvr+NLypy4XjpYs76IEfFGTPf4dtP9rFSktgGZIlwUyLXZ2IMPl5DOovQikYmDJSh8PBkWoCKiYyi7PbQdcUrV7YDZcAK9E9hAutbBOPpJcUqyPucHggfRbHquuH27dtYQvPy5nffZNCJePzW2xSnY+I4wXnL8VtvUm9u0TiBUDpkn0jJfLbAtLl73gdns16vS6/XI06iFVGbVUjiygELgh31F7/4q1y5vEsaJxwfHeFEcHxN0oRhf8DB0RFRFJFlKbFSDLbXKaswCe/3uzjruP7iczTWc3J8iHVh0DAc5Oz/6Dv40wdIYzg+6fBk3FDWFVma0cm7TKdTnuwfMZtMMN5yMp0wGU/4xGuvcvfBfT54/4eMRmOKquTG9Wss5lMa0yBFoOloFWGtIUkSBoMB9x48Io5idi5ss9MfYO7fZWd7E68UTWPQWpGmKY2twtntAQSD9R4XL+6yvjEI1uAtEiXOJrbnlO6VFnalhz13kV2d5z/jFutPLw9pmmKMxVjDxvoaVVWTJgnehn2r13osFiV2XtDPU7TUuAUYU53tdmMMxgmc0AwHXaaJpG4AQtSKdB6vNSKOcFGMiKJgcKElwteUVRkollWJEJrFomQ2ryirgvuPTnEEK/TJtMA4SxJJXrgGWRJzeLDk0sUaqeYkUYz3Yfi5s9Hw/HOCul6AFBgn6K+t0ckTrPV08w5SwYN7PyJXl3jt5SsIKajKitMnhzg8p4uG48OSi1pzZXsDLz1H84LGFqzlOZ1uhx1bcdzUjAEsZ8ySSMB8Hga9SeIRCjr9mMvPbpDmKaZyPLi9T7Z9iag/wLYItgW0FigRBVdXpVlfz4mimKKI6PY6OOeIoojlMlAE66phfX0NpSLmizlKh6YpiiKaomFlrS6lJNJxCGsWMnzWWTDK+HlZ3vsz2/O/6qqrGnzL6DINpvJnDCLwwZFRKLTWeCDN0jONjjGGujY0TrCsKkzjMF7idMgOi5OYV157la/963/B6cEMmoqpLfjkL/0Gv/O3/ys63TWcb/j87/4+b/zCZxju7vLo0UMe3LlDuZgzO37M9NEdIlsiIkEaS+JEc5a65wWuRRuNccwOH/HdL/8rti5c4OLNZ7ly4SLfnIz5zC/+En/r7/1DkrTDH/2f/zv79+/wr/7pP6Y2luOjI2aHB1x79gX6b3ySbOcqRoZaeb5YkiW6HfbDrDRMqqAlK5ZzyuWCpipCBIC3KKmI0oQ4TTF1yJda3TwA/UEfWy3wzZIkkkjAWEfcorJaJAHgkZJuFuM9LMomuFxOZ2A9WZ6Rdzph33sH0tNfG+Dx1GV1ZiwHog1sViAFH9y+w9/67V/l7vtvkiU//mz+yRArRJuXdD4plTpGevdUUR8eWPh+CWTbMJ1RpVeioLMLRZ3RBqVokTC1uoDCIajEKh64pR8KhUEiBXhEm0gdzBeK9oHGUUQUSVSUgNQsK4euI5zQRFFM1skDDbB9gNY6GmBpBXsHp2QP79Ff20DaOjSLxjLzFu9juiKEiArvz56Hfaohov0zvTN4vwoO9ggfUCfVNkG002Eh3TmtT4jQ0EmB8KAQIYtAqTPff9eahzytrfDtBHTl2KikPHvcqzA0KWUQrLa0Tdd+2QWtJq5t5gT+KXj/41v/ARbTX8v6y5QmT781L0BSM0ggjTvMTc3dR/tk3Zws7nP96iVOT98BEbR8cZPQ2dlh+9IW1fwU4xtiL4h9m7TuKrqZ4rWbGQ++N8WkPZZmyZOTMf2tHSb7t3l9oLjrFQd1isrWSGSPez96nxsvvUp3uM3haMz4SLI8eYLDUqVDHjcdVJyQD3NKNKYoUGXNC+uKCwMNaZfZvYK0WrIzMJweLClNw3wSgatJhcQ9+woCR7NouPtkTn99yHNvvMzJ/bscHj1hMZ8Tdzps7l6jngWdyuJkzJUXnmd44RLV0fc53X/CMorpdFOkMygvsSLCYvFK44RC4xGmbmnAgBL4JMbKEPynvCVWwW2tMhZnBR7DMtmkF6e400O6g52QX2HmPD9UbPQy+r0OcRQxX2hK1UXLCNwcrCeVjrUuPJpoVEeBnZEoRQIoB5O5xfkMbSuaeomUKYOOZW2QoDUQyZ8qteWjTdQ5nffcvfN8YiZa4WusY05GI2oEvazD5nPPMH3rbYrpBJPn5Ikm1TBYX0NmeWBY+aCbrZsG3Z5jzsN88ZCHD/bodDoMBgOiZxKyND6jEYds1vA/QsD2ziaDfh+lFOt2A2uDCD/WETqOWN9YRwoVXOAiTdNUaB2hdZiIa63xDpo6BGXmeY9IKR492SdlyfXLCc5F3DmOKAiUbiklk8mEXj/HNI6NzU28szSmYTKe8PJLL6O05NZ7HyCl5NKlSzx+8oRuJ8RpSNUWQ3iscQwGA15+6UW+/9a7XL58mUF/Ax1HZMZjjQlhtTIMyqqqwtjmrGEK56jk+OSEKApGCucsvo9OqWnvrpVm9pxW+RRd8OmX/yQN1k+hFwu5XCukM8ZZg2lqvEtxPjjHCREm0F3niYzHU7EoS4QkMDp80Kp473AyYjhI8bGg0AQpAAqvfXAQjRQilviWHSNjidQZIIjb7KXGWBySurZUxuGRVLXDNI7GeKrGEMcZSmompxKzUBwdxByPC9YHsDVc4/jkgPVBhzTLWCwXGOM4nFZ85/0nVCJmexgzny/oJIq00+X6q58gzQ3e1ZwcHDM+HrWMG896ItAOGlcRxznDPOPJaIEUnn4ScznP+WAxo3SWkQXpwjmFBImnMRAlHgUMLw5Z391A6IhlCce+x86Fi6R5h7KqEUKSJhHGNOS9XotMSHQUkaQZdVMSxzHWhMYiSRKWiyVKS5IkQ0qFWCwZnU4YDteRUpJ3OtSNoaqqgD4IUHGMlIqqacLA+eeosXLOMVvMP5afZRpDkmUkaQpwhl6tDBGcCTomt3JdjKMzI7imaSir4NgYBvQgowyholZPDzeee4Xrz7zA0f3bSK2ReIbb26RZF4QgUim/8bv/OQIYjU95dP9BCMydjDh9fAflSmIdjBmiWBNHGmdDzpZVMuxRKVEKGtOw96P3+cGffZ31rV0+8/kvcPBkj9c+9Vm0TvnVv/l7/PDt73L3nbc5PniCB6rFDGc22xpVtcOQAB94xJnXAMLRWIvzgYWxamydtdR1RRxplFZ08hwhFXEcArWlCs6e3V6PXqfD0ck+WnkirVAIcC44BbbU9HhFUVWCTqKxHso6GC4tFsF9M89z0k4nvMv2fF1bW2c+mVBV1VlckVIyaLalpKkbnjw+YDKe0tld/7H74SdDrIRA6hjf6oDOig4vEWJlLyvQ6qlMDu+htVMP7jFRy4IIjdCqoI8ieTZ9bV2vg8ZHw6pxcHJFd5PE4nzCV9UNWmoa2+B8SL72hMlrohV5llBT09EaVEJjHFpLpEzPiwSxek8SnWTMFwt0lKCTBKU0EklhGwQhgyAWkvlySaQ1cRSxCvQFMKbBO4tqaXtKKLxvzighHrDOIFvkL/wd7qwx0iszDxsmlx4BLUwspWhNKs4/lhUNMNAT+UiT61YaKiFw1gYBsFtp4oL2zTnXcmBbm3e/ogj+nExLP47V6h98O6VQviETcOPiFm8fjij8kNH4lFnjeP35q3T6e0ysQ0QpzXJBVRluPRnx+vM3uX/rDtaAN7bNfVqg44bXrqb84D3Jh9bhoy6n8xFRMaMZTfCdLjeHffx0xLgQpFefpXj8IQ/f+wGmauiJErWocComHe4gkhyhInSUUVczytrgFnPW+w3rccKg1+Gd+49494N9kqRLnnbp7TQ0ZOyXcQg/dfDg8T694QbXn3+N45NTjk+PKaqIZ2/eZGun4PaP7jA52efIrBN3+wwv9amXc/YnFak5Zu3mkOEzW4wmnthWWF8QY4Mbp/dn+8oWc0xdkmQDhAQrBU4KzIrOZi1pkoQBibO4YobHogbPMB+PsaqHz/qwOGaolrxw4QKDPKbTScFa9g5OsKpPjAVp8caylimULJlVOUnmkG6BEAopLCaKuXeywCebVMf3aajQcpvL1zL6nUBrjpxC/RQlA0/nA0KLThHOKCESQnisRQro9/sI4dFaMhxu8Nqrn+DylcucnBxxqyxZ66TYLAulfN2gnKDbz6mrGkfQeQq10oW2dGChyNMOr7/+Mu+/f4vjoxMuXb7QMhPCwVlXNViLFZJ7P7zN5mcGjMYTymVJkqSMxyPSJCO3hqIsidpGyts4CLbrJVtbm4xOx6RZhyQOYujT0SkX0wRjDWWxJBIaIWOUtJTLKZOxxktJkqQsl0u8tzSNR6uSpqmZHh1x64e3kCheffVl/vzP/pytnR22trbo9/pcuHAZbwzWO+oiaGTiKEIqxRc+/zn++Mv/jtl0zsHhMU0nZrZYEk+mEEVnn41AnCP7IrATnLXs7x+wXM6pQ3VxTrd+6owHcTb0erqR+rlBqP7iEtDUzZm1tzGGNIloqoI0iUnimLKqSNKIprE4HFmi6XfTQAlyLTNFSEDiYsXcJ1iniKTASBMchZVEaYVQGqmDaY2UEh3FyCgKduxSoZVgWRYsipJFVSNUDEJhTIMN802s8yRRTBRpRgvBYG2d09k8DF3GCrOc4auEppI462gqx2Q+4/6RZ3/a4fZsjZeHgq2+Jc16dF/+FZ7/3X/I4sOv8d6f/DMOH+8zOi1Q1pFa2NzI6SuNVppFY+jkCRcUpElM0xhcXRNrFb6P1jJzoE2LUMUeHYXhSZRFbD+zi0oiKplgBx1e+40bWO+Zz5bEaUKSptRNw3Q8QeqIXr+P0kHc38kzjo72iWNNkiRBs5LEDAZ9ppM5pycPGG5ugpA4K5jNlqxv9PFekmqNsy7kVQmJbQvjPM9DftxZjMDP/jrXAv7Vl/OOPO8Qtw1TUzehidHRU74DgTIIgm6nxwmnLIuSsm5onMPiaRoHQhHpFBXFaKWYTeccn4544fVP8uF3vkZTzBACytmExhkiEajVUiomk1OePH6MMwZRLagP7iCXp3SziJKQ06SFQEsRZDrOn+05CJIdpxVNOeedb/xbrlx/lpc+83n+m//+f8QjscKxsbXD537tb3J49xazyRTnIcLz2V/6VZ55+SVQ0HiLjjRCCBoLjQ3sMu0DOEMTYhbyTs7CefCOSEsipYIF/lNZtZ1uFy8gyXMuX7tGebyPMjVJokOdIUWrMwyMuRXYYUwTrO1FS1cXnmXZ4IwJtEChyDpZCCte1dBKE8cR3ltME2itrqUs4jymtpRVzUsvvcJi+THlWAkRUJTgZPI0b1yeNSWsNEAtX9y5czRFQGisntIRrahoUoakdY9HSwEqNGNOtMWukIh28/jQbeB9sFpPdBAAimByFx6sCE560oO2llhHNIqQ4m4LtAKD5NxxqTXPaCcM+MCtDNqjNg1btQ0SniyKieNghyuFRakAkSulQucM+DZA2BHev/fBMVAggrjWGPAOJ1vHI+/CF0KqlkLoV6XVmZ7LWoeQq43S8mKtPbPxXF3esn3enlb3e/acJc4/5VzYwrOByikRqBZFW9EM//+zwr7yCG+ROBIt+cLr17h3WvP+Xk3tN6hszaOTJZu7Vzi8e4yUKUQZxbJifxrz4uV18n6fo9mMTpohXY1vLDSe9XyNX/30VR588yEu2aWaQzmeIBu4NfJciSxbWjIrTjC9AYP1HZajPexyxOZahO5tUtstUB5pKzQVVbnE1QV+uWQjMlwZ5sSDTd56MOZ77+3hszXivM+o0rywZdmKGi6VgnER0U+63DmdMTma4zEMB2tkesjJfMF7D0/YHeS8+toL3Ltzl4f7h8yLCpv1SPM1nILp6ITR8RN6gyGyP2BjuMa4eIgp5pj6EUvTYH2DFg7blHgXI1Lf0nhbF0wXtAQIhUeH1zVLVDPHJx1EccpsNqez+zxLW6OKJb0L60S9deIk7N1FA4fTBu0t1eQQGRtiDL1OQLZ90iGRDtFY0rzPvFoGXUvaQ9JQLCeIRLGdLdld75Nqh4w0zquP2xjzL7VW322tQ+ZJWVRIL9BIlBAkjSHVmmM8NYo0ifkHf/j3Wd/ocXJ8wsZwg8npgp0LWzw+OODWpYssjo4gTZlXDZdf+wR+sEbtQ0aQbW2Xwxm40h0I+v2cyxcv8MUvfo5iWTCaTjk9OUFLiWwMdVly/OgxrqyQwL3vfI/pf/37/Mrv/Q7dPAcXRMFaK4SSRHEchMB44liHCzlWwTnKWdI0oawrqrohz3OaOrg95XmXjY1P4uopwhdsLu6hj49RUUKUxGxtbWNMTZJAWVbQVLjHj3g+jVnuPeQbp6fUxrI+XOcXf/GzjE4nvPX9t/jy4T4CT1mWFGXJ5nAdhKTX6xDHmiiOmU4nnDyeUZ2OkMen55Nm30ZoeNFqjgNnvzGGo+NDHjxYsJZ3/31KdSAdhPuhLSgCxfujjAcIQ0d3psX62V4C6Ha7Z/foYrGgqWvW1wakWdD91HVNJ02pawPSBvSqk2AdTOYl3vhWauBBRkyI0YQBq5MyNP6AVyoEA7fNlJSh0fLO0JiGyAvKqkZJ1SKLNVIr6qZpdRXhngwooyNS0M0bnhzus3HlCn6xIB9ucPT4MesbHu9qRuMx9+6M2HtyQhXvMlr0MLLLraMFy77mrt1gubfJSx+e8urmczx+POLBrceUBwsSIFVgo4oE1erQfTAP8KDlkkROEZ0kULQij2kEjfGcLF24i3zQoXs8cSdi4/ImC6uoZYfB1iXiTsbo9BSpJXGUoHREWVX0e33KoiLv+kBrdg4hJZtbm8ERs2nOCtEojhFS0O12WcyXNMYihORg/yg0YVlMnCSUJrhdzmYz0uxcl5kkCWn647N9fuaWCFToj2NJ4UjThJVjc9OEor4xDdbZ8F1XPjjxecGyKmg82MZQ1k1rWhHqZYTDmQbhPFpHFMWCd9/6Hvc/eBehVPg9zjE5OmI2HtHrryGQFMWCk+NjnDUI32BO7pL6MVkUMiVXWnulBEI4IqVw1oZhnQvDMi8FxoI1ltnxE777lX/D1qWrDHev4FXLEnMgRUQaxZg0Dowsr9gYDkOUhRBUVbAwj3SEsz4wUHw4N6VSCDwI6PV6eFMjvCWOw90gWyABGb6noo1ZuHj5Mmt5wmTU0O3EwVxFy9UHgAeiSKG8xzoFuDM5knNVQK6so6wNtjEsFzOEhKyTndPavQnU8NYko7GhVl7M5sRxQrfbwbrg9Pjg4d6P3Q8/IRUQpNbQGi48LaD2XoZpsAy8+tXUE0IhpVo3ngCgnCcvB1aLbEXZnKE+AoFb2bG3Y2NnQ8evW76jaimDSilqY6mb+nx66F1wEHGBU1o2Jdigh9IymFU0wmGswJw5L7W6rxYxstYStYnuUihsXeOlxNSOKUs6aUyvE5NECi09SZqGw9qHC3M1iXfOhsN/hSI5x8qEAiFCIJwICddKKExj0FqDPJ8IWWdRbSK3QJyZfayCilduf1IEeNnJp0wvaNG/1euUQNhzGqFtG7OgzZJnPGqH4S/n/PCXW/6n1ar9JTmILcMK6YI1vReOF3Yj/vBvPMc//rfv8v0fFdSzOUcHBZsba+TaMRofEg+vIKKMwiXcPXW8cPEq99/9Hp08IU4bTC3wsaCrcz75Ss73n0x463FBmmSIfJ3pomI0b5BdifUxiTKMHtylcQXbGxlba5r1fsSpXINFTBYBwmPKGqoFfnHKeqy5NhzQ5Bt881HDk6lB9i+SiZTSeGbLEcdxBLHkwjDn5lZCnva4fDnl/umC9+7dYXKcs33xJju9IfujEx4eHrFYJmxeuohMEh7ffcBofkzUGZD01lBRSrZ1jfH0lPr4h8y6fV588Qob65rje7domhIVReTZGpU1OH8+pcd7hGkopmOipgoTLQGuKhD1HBHl0N/BNVOywToIQ708RVnJqRiyX8Vse0NTGfYmlmmjSX3JYnZE0xuS0NDtZjyaRqS9DpGfsyhB9ASmXDAqIR+kDLRFSY/sDLi4I9gdZsRRCCa1kUOIn86eVUpx5eIVzHgCy4qBn3Mdx05ZkNenTOOU/yt6lv5wlzSL2dwakHe6vPPWh+zsXuFrX/82v/wrn2TvwT797YuYyRgrYWdziOn3IFLBdoxWNSVClSe9xCnByXjE9RvXUEJw984Dtre38VIwn885vXMXOZ6Ff/vUd6sU8M/+yf+BHvR46bVXwEuOj48ZDAakacp8sSBLFJmoUN2MuU2wTpB3uyGzrAm0Oi0V89mcb3z167z83HNYb7m/p9h78ADrDLYumC8NQtTkPRemwK4NiveCW2++y/4P3qEHbHRz7jWWzuVLbGwMiaOIR3t7PNrb4/T0lGVRBAEzgk6ek3VSrl66yBd++XN869vfozE1J4fHHJeGnbrBldXZnccqm8qHwFvnGgSC8WjMfDqll3YQIlyx4Vr6C1mBWFb2t16cq+rO7dlX2VcfRbV+FpeUkjSNUUoxnU7bgl3S63WRUlMUJVpJvBDEkQYEZKJFUWBZNTTWIGhrgSjHxl2kiMLEWRIOZ9cOB4VAaBmkCEqhFWTWE9Ga6cjgFJgkCVJqFoslK1OQqq6w1gc6rXMYY3jyZEJ+4TmGu1vM9h1R3mG8cGR+gaBPuTRsbm4wOllQ7J0wGVekPmJNGlLd5WCy4OD+Xb79Lx4QP5dxabOHm+3ywZM7uMbgtKC0jryTYXUwshLCBm15VdORnovdNRLjEcaEwXVtWVSWkYfUgm4cSSS49MI1apnh4nXy9U3iTk7Z1ERZRuY83sFisUQIQW1qVBRjmiawiXSCtY4kySiWBU1tcRoEEq0j8rzDdLLAWs98Pg/NMrBYlvQGPQDiKEIImM9nJElClCTtHvj5cgX03mOa+mP5WVEcoXRoVEQ7jfPO01RBSrJyXqxMxeHRKRvDIcZa6rJiMp2cmV9AuB9NtaQplzhruXhxB9PUFGXFb/3+H/Dhn3+F05ND3vjCF9m5dBElJWVZcXpyirOWyHuag1vI5T7dVDOJNHjQkQxB7viWuSFajVyotVf6eq011oUA73sfvMO3/uT/4df/iz8g6/bRSrP/5IDbP/oQ7xxxFOFaGvDtH77DK5//VTqdnTCsWhaYxmAJyGDIZ1vlvhLOTWeJIo2WAalyzrIsa4SHKEnP/AE2NoZcv3SJ8d4tUuXpZIH5ptoGSADehUgHSXC0lDLUy2kcYVyI/EgiRW0Cxa+uaqyfIKQgzTIgnGNSiJbaDKqx7dnuQt6jt5wcH7HdT3nhhRd/7H74yamAK1tuzikzlgphAAAgAElEQVRo5xeBOntzAWHxKCWQfoVogXHurMk4G8YJf3bpPF3IW2uDFsM7cLLV+q4aJx824urlHrx1AaUSEq+C8NQ7RaQkeRrhK6iNQeLRQoAOKJtvwIvWcEIGsRrOhywBHawZrQ1WmSuHLillcDSaNgy6adBkqKBX0lqjRNjAUoT37Vrr1/C3Qm1CsrmSEm8sZ44d7UTTWbN65GfNXqCRuLNnu/oMAhp4NkJFikDtWf0A7zzCh00tJdBqE6wJRO7QoLYmFwAiNLP6Yx6U/kVB/l/LOn9U/5HXhBfIlg9s26beK0h8yfWdhN/81CUej+Y8KRNOjh6Qr22xNRxSjI/xzRwGV9G+4cFxSaYSRnPD3knFsNulimLirIeONSQZn33tJrf2PgyDAx1jVYSpDSdlitKCrtbY5WOW0wN2Lm9w6cIWo7LmcOrQGrxQVLXHzsc0iwkDueRCbwsfd/nR2DAzEqc7YBrkssDUBcXygEe2x74Ekfd4ua9w2rCRaYbrW3RTyTsPpuzdvcfW9g7DJGM9izmcT5hXE7bXNnj1jR5Hj/aYzUqm4xOII+LhDvFWgsSxnE24/cO7bF+4xI2XX+X04DG6rIg6HczolBiHdA0oFWxmF0cslsuQQ5PExIA0hlgpfL5FuraF8esIY1gulvjFHJ9vgMiw1vFwpjBVwTv7FWObI+oxNCXoCN+AJGFSOFQe4YzCdvpQF3TdnHm6RZR2UPWEVOUI1Wc7rcgThZJRa98codVf/571HhpjOLl9m7+TVTyrDlHNjE7aIb20wVJeZj91JI8y/ts//AM6vZTx6BQpYtY3hiAUz79wkze/fwtrZ8huj7Kx2MbjWdK1AWlBSKTSKAHLlQufc5R1TafbR0qF85ZFWXJ0coJSmisXLuDv77GkZQ2073k1mlrOF3z/rbe5/uwz7O5eQCnVpt5rOp0Mv3jE6M0vk1y8QnzpDfRgh8lkSpIGXcx8Pg8C+rU+670+dz78IReefY4ojllf3wiW3E1KYWckaUJVFcRRgtCS5XLJ0ZMDHt27T7axRqo1i+mMC/0evWtXWVtbwznL1vYmk+mE6XyBqOo2/ygiihJ0FDNfFHQ6OZGOODo65vrVy9STEU+ePCHOMtTKRr79+7UE5wzl9JhiNkLVJbubG+09F06Vs2vqKVpne5H9e02TVCrcXbYtGH62eyrgvBlczOeBzq5UoP8VgRIqpSCKNCqKEUKyLGqyVKHLkuWyopNG1KaVFwiBVzHSgXsqbiD8njBZf9o4RXqH2HsX38uhCfROKRXzeQgnjaKIejzHmuBEBuCsC98BF0wHuv2UPEuZHB5RO8f44IDlskD3JGVhOT6cY12EqRoWh8f0lxO2kyGVTFk0Eco0bM7vUN2ZcuRTHu09YmttG50k1I1BOOh0Ita2ekhT44zHu7aQ9J7SOZba0klTZFmHPEzAdBRlY5kIT24FOtJkm5vI/hbd9U2iToZrn0mcpkgvWExnYYAcaaz3GGtYLkNWlq0CHVcIQSfvUy7n1FVNHAU6YhzHbG6lnJ5O6YkezgaHttFozObWkCRp6V2NpZt38dYGiYEK6GBd/xxs1qeW+JgoCVmaEsUxKtLBNdE4TFPjmnBnqygK+7lpWBQFfQ9lVVNWFWVlsC0Dy7cFmLWGu++/yeYzL9JbX+fVNz7BG7/wKZ559jkef/rT3Lt3j5c+8wWiOLg/FkVowrT3LI7vI6cPyWOoRNCzSiGCHbwPrCcpVw2VQKq29m7r7xUCj/KYcsa73/p3XH/xdV7+9OfQWcpiPuX++29jvSXv9airEtM0LMajs1yw8ekJ0/FxQN6ECKyqtgZ1ba4hQmBNMEKREnSkMFZgbUXdLOlGMVpKunmXl198kc1M0iQa4yP6vZyqpVo75wPSZtqzgaC9ArDGoCNFZmOc9cSRJzOeWhhq43CNYT6dBYOKNA2DDqVChEySopOMyWRKmmZopYi0JNOS5569QRynP3Y//CfhoKtAX+CMgrbqLFebI7hYtY2Bs2eH4wrlUjIIhqUUYXrUfphnThwiFP9hICjPEBmPb80dAjXQt774TV3jnD1vOlZ9hXQIJFkSg3JMZnVAdiQIFxLDpbVnHFMhg8DT+2Ajb4w5Q7CapkFKf0750xIjBFFZ0cuTllMfuvMkFggfhLVhYzVIacArrAlWpQHxtGcZECt9Dy0H31rbVizte2NF7fMfaVFU++ytD8/5aW2Ga8NXA73QhQmFDOig8x7ZBgivtG60NvCrDfvxlpU/w4duu3HPihyxoodCJCwvXt7gkze3OTmtWFvbYdMtOBQxSW+XajnF1hN0N8dXhlvvfEhxPAGheHanw+WtmGXnAt+/t0Smiq2NhJ10wuNxQF2ljknzAcQJSlnqpiKNJFIrlErYdwPuL0oiXyNExdJHWFfhmoKYgs7aBmO9Rt2k+FghvMXVNTQVul5iZqcM8oT1XpdlY3h8NGY90Qw3U3qEaekbNy/QX9vk2+/eY+/xLZLuOrsXdtleHzKbLzk5GaHWejzz+meZTcfcef9dxgd7zIxFb26g8jW6nQHL5Yw7j47ZuXaVy88NEXXJ6eERdX2Ecguy6JhYS6wTbc+rgyMRMZUtQEriaIAUjpOjx2RrG5iqwMzHeKHp9NfoqppMCt66PWa2bDgqI+IsQbomaCJxrHU71I3FOI92FaYqiLQKGRlRQtrto6OIclrgZYytalzVEMvgPKqlRGh5dkD/dS8hBFeE4cWuZeP3fp2ZHfKkSdj9xKscTC3+wZ+g/vkxOhI8frBH2tFY55ktF+ztPUJFBaejh+AMeX8NJzR4S1UW7H3/bbaeuUF/axiS6oXGK4WXEqkUnTj6CLpeFEVrMADlbIotlsj2OwIrBCb8h0RQWcvXvvXnfPYXPslyuWRjY0gny5gvJ7x8WXLzN17CI3hwOiVSl/DOUSyLUKQ5i8dSLCtevXGDP/m//yVfefgEFSUksaLbH3D90kUWRc2iCMGnTdOgo4iqLPnG179JUxTsPPsyFy/uokYzjvYP+M3f+iK37+2B8/zK5z/Hyy88z2QyxRoTCl8ZKCNJHNHr5nz1q1/n+vWrPHvzBm9+45vUdU11eMT1mzdI4oQkiRmNxgzWBlzc3WHQ7zF/8g6q8Pza6zv4bMhhs8GdH91lMhm31Jdzk4qPmBWtnALbKZpvozW8Pz+DvPsZPjshFEytiVSSJCgZEccBwYp0hHWWuqrpRFEofCKFlBohM4puhRWKovIoFZxro9M9ll/552R/+x+B7p3daa7lbCAknsDA8MayObnDpZMCpQu88NR1Q1UZnPNBRwRnpltCynB+OtHStGryPGZ5Oqf2NT6S7D85IVcN2cAzmklGkxGzeUl5PGc5r1gzFT1XUssUXwikd0jlEUmH+WyX8bxmMXtMZRoEkMeSXp6RJQpb1kE/7Vw7SPX0NnK6ax2sVERRSd1UaC1RGpbLitI6Jg5evHaJ3o2bdNY3QSmKqqLT6aJ1RFVXgSqYRCAFSZZxeHQEUuCKhjjJ0DoGEZr3NNUs5tPWMCTUW40xZFlCkkRtgxCMR5bFkmJRoFQehsPt+VBVNWUTXiNFQLN+XpYQ5wypv+qKk+QsmsMTmFHKhZrLWoeKBJ08o6grojjGOkdVVZRVhTEWgcTjaEwTvvdKcf/973Hjjc+hsw7PPfdCSzG0XHj+E2zdfAljAkKTZQqlg9HS6OEH+MN36WhLFUkgoptleB9cY4VwZ9llq15KCAGy/YYJgdIS6lATSgnz0yO++aV/ydbFy1y5eZNLly/TX1vj0u6Qz/zaf8Z73/0Wdz94i0/90m8w3NrBWsujvQdUizlSCGpjQu2sxVlNupK3CEBpHXoEGSF8g5SKJFJnn831G9fY2chJmhFZHFG7mDxNmC/mQSPbGEwDtAwrIWQAOkRoUIUQJEkczJQM2MgTxRpRmfbfNswnU5IoQmmN0hGeqKXjS7RehWMLdjb6xMpx7+5t4uRjyrEC0FoFOI/V9C3Y7/oz6+5zpORMQiUlEoH17kw/YJ09K/DPGgfRIkjetXRBcI3Dy3ONUJgSeoSzCBVhvcG6YLjwdHK9oG1cXGgElYBYhYDfsgqvVe17VEohfNAqBa990WZOBZqJFC5MKaVogyTl2QZRKlDnGtNS7lT4o40RaBHSpj0GhA95Hc7ipT+7MK0NQby2RcGcDcLegPKBwaHa6WiYagT3RGPPzULcKlBYnmurQk5V++nIlnMpfDutkCghcUqhRPjdHn8WfKxUiw7+pJvj53WtChpom1qPbPeZBBSOjQS+8OIFvv3+Ecd2gw4j1vvrdPJLPPjgB0zLA1y6jV7OOHp4D2Etsk7oZTvMkk2+8t4p794+YpClvLBTMsxmHJ8knC4qoqxD0ulilcI2S5q6xFQVadKhlH32xx6j+iiW2KahKiaocoHEk62ts+isU8uMyAtcXQZEslpgFzOEcgy6CdtXb9KJFKe37xBHHR7OoY5KsjWFd0uEg2JWk+ddrl7IuHPvHg/mpwy3LtLr9Cg9HJzMOF54Yu0YbvdJWHI0m9JMFEpE+DRF9raIpWQ6N0wLS5KpYLIhJYlw9FVJZROsiFA6IZYZ1li8dVRNSGQX6TrV4hRU0FyZcoFrZujeNjrJ0WrJqJA8mTiWi4bCeKSfoZoFWa9H4xvyTofT2QSlOkgzZbmckqQdhG1otCZOOtjGBoQ7yohEweb6IDw7ERperfVH9TF/nVsSiF2J++Vf4e7VX2Y+K9m5sMvSKz74+j9l7QdvUk42+NMv/yl5mnDt2hX+6M1v8ebbH5DEjpdeusGlKxf49Kdf5+TRAT/8+lcR01O8VvTiiK3NbUgUwVU1FAYhU++jIcRCSJqmIUlSwFNMppiqCjrYdoDVAuWBRitCUfG1r3+Lez+6wz/4w7/PpUuXsXVN2kl5/91vY0fvk3QyfL/DZm+BtRbrHGvra1SjiqZqkEIyuHiBv/OP/gf+l//1f0NJD0jSNGPv1m2ORiP6OzskaUJRFjhnGI1HrA83mAiwSnE0nrI4PqE2hihJ6Pdyvvtnf8Y3/vhLdPMcvYrvEECrE16tV196kcvXrnLj+nWOHz7mG9/+Drlx/N2/93d55ZUX0UKyv7/P+nBImsZ0spxmep3x3a/SzSTGRtyabmCM5/333sGs3K98i8qs7tCnPu8wRGxZCwikDCZDZ0yNn/G1YnUA1E2FbCnlxhiUDM5aq9D5KFKUZYWxnjSJSGqDbO9O5z3maI/6O18i/bX/EgZZaKBcyF70or2cbI3wBmlrLnYlPVli6prGWLwNd2TwUgi/u5tlaG2pmmBW4iuDRzJfmnDPJgdQGHwhWe84cJ5F6ShFg/cFde2oSnBNyPD01lKUUxIBsQAVKbxNuPPgCcuiZDEtqKyh7wVJDPVyyf6BJfNBCN+0zVUiYeviOsmwg1cK57oUyyXGQGUMVV2zmC5RSDrPXGXt2jVkFFHXJd62+T0rA4A4wtQNZVnSGEeed6nqmqSTYhpDFCfUddUOhiOMDWycsiqJoig8e2uJk5jxaEre65FmGUkacu2qqoa2ZvKtLsv74CoohfgINfhnfQkRqKIfx0rTNDC6tGKVKh8nMXVVkyQpuv09xrmzkFuHpypDKHDQ4beDcC+QwlOPDvnwa3/EYOsiWX8jnNUebFVQLBY8eviQwaDPjeeuMeh3OLn/Dv7kPTLZQJLgrSWOJfWgw2JRksYRSrozREr4gPau6mq8C7RaL5DChFxYCd5bxkeP2X/8mN2rN8l7ff67/+l/xjjH5s4uL33qszx68JBLV6+TZhkPbt/m3gc/pK6a4EhtLHVdB38DR/Ae8OH5Sx2dMdUaC84qojQLRjRCsDYccvXiFnlzinAVg17K2MzpZAnOB4qfVoICMDaEW0spUDoMYYSSWGdJkoTYW4xw1K7BOUE3S5j54FwbxQkQ6ngpQzA5hHM4arVf/V7Ky89eJo4kdVnyHyO9/sSNlZDB5S7Q4QR4Sat15iw9vh1hntP99NmGUW04pBAB+fHIoF86416eG194G3hxwreOd3/BXtjaJkzKvMO4BrzHOROQGR+ymlqMB+fDA48jSaQlVePCxgJiJaldKKa1ioIzSoteOBuyb0xjg5tZ06CjOIhxrSWPQx6A9466bkLegwsTgTiK0N6jonAh0NqlewctmIXzDu0lWH9+CanzrAjdOnVJyUcs3Vf3rICzZyc4p1eG5qvVvwGyRQCtcxgXtCOBY9s+a85w6FAstc/w47zOf5aP3POmivP5QDjrUN4SCbixEXNjmHK4bNhbVOT9iu1eTrG1zvL0CC89s4OH0JTknZRLww4+yvnjH0547+Ec4SOcKXhz/JhBHgSTynmWZorDE2c5tqqoiiVFWWGyDrVJ8ErSoWZeg1tWmPEhHkuUppRZHxelJNKTmAWVtTjTYCeHJDTsbK3T7XSZNDJoSHp9RsuS/s4u07piXNTEkWFSLLj1YMbUdlkfDHjuykXu3X/AwYPb9Nc26Qw2ybt9llXDdDEl3+py/Y038B/cZ//wFGM8dZYSZQN0nBJHCuUVzaygZytuXFhDGY3O1zkdO3TapSjqEA4rPaaY470lTvqUVUXjoTNYo6xr6qpAxDFZt491glltGO97FosaV8xJO+sk0kGzoKKH0JrJoqasQacx1fgxVelJk5ymqpBxRqIjbNGg4hSNYzP5f7l7syXLrvPO77fW2vOZc86sqqwqFAYCIEiAg0hTEiWZ7aZbakluKdpShMMdvvIbOHxhX/gB/AAOhx3hjrAv2m23uy1ZrYkWQUoEBYATRhaAKtSUWTlnnmmPa/DF2udUqUMTZJigvC6AKKAyc+fZe6/1fd9/qlgfxUh8IGmgpDdd+IQK2mo6p9yB8+017r78R6yPIr7/yhHD3hrFg7vcOhX0nGHy2negmvPdyvH+8BkaIfnSF1/kN37jH3JwcEI3i3jreMLoxrPM9+4jTI5KI1QoaFo3UBAo0WYBLShui/3PGsIw5PLlS8ymUxhPiQIJWj8aYrnWIKfdeyazGQ/295lfnPPuG29Q5gXD4YiD/T3ieIV7pwHl/TlPvhTSb0xr8+unk0GUQlF6i/NehjWO8ckpSSfhy1/5Cv/h13+Zw/09/tW/+T+Qyp9H1hjG4zn7+wd0Ol0OHz7k/OyM07qhnM9JOx1++MprfP7aDvf39zjaOyJfW/GOUe1Zg1jQyxT9XsaPX3mFSFY8cWWd1ZWMfi8giR3F9JCQq2Dh8vaQSlu0bsiLgkqHyGSFg4NbSCWIA8fu1Us8uHeXi4vzx0Ke/XTao1WLwxJYDsvaf7hFw/XRoi8+Gcq1Z3QkSYIxhqKYk6Yd8mmO1posy1o9hG+qnFTUTY0uK6/TsF40vjiLtG3QJ/eYvfMazWjf65SNQ2mLNDWqmKOO9+kU9xl2Gq6vJWAtMkjpZqGfiocdnIVOXhAEgrKsKesKYyyz6cxTgyLFrMi9JhuNlP4eJImfqM/nkjAQKOUtohvjz+I4VMRxwv6pt25OFMRRgAwCSqP9vZM+RgYpQIV0egk1jsnMIIUgTCJipVjtZAxW+2zc2CHKEqyQjCdT0rSDCgNUGDKbztnY2mS02iPuD32ga6CYnZwQlZWnmSNAShrjHc2iNPAMHOvIsoyL8ZSO8MV9WTZEkWU4HFCWxRJttK32OutkzOY5ZVWy1luj1+tSVRVFmZMkCVnmre0X4dVlUdDJOt497e/JUkrR6/c+lu+VJMlyuD+dTcGANb72iuKEII6oG41sNfTT6ZS6btC1xmrrWVi0dRc+m8nohg/fepXVq8/wzFf+A8IkYj6f8uDePR7c/ZAP3n2HXqfLl/Ov8uRTn+LpG0+THN9kdnIPETTenVoIOp0EYzRVpYiiGFH6Pd4ag7EG0TLHwIMESnlEWEoIw5S13V2+9Mv/lKc//RlOjw/odjMGaxtY4dliSW/EE88OEU5zdnzIO99/lZP7dxDaeAfJllm22NOUtdBorApb+YxsB3wepZctVdUag20axscPWdvw2ufRzhpnJ6cEgTeJ8+ZAHth5XKcmWiwhbh03pZTEUYi1UEWGptaEQeiNcKSPINCmYTadEQQBaZYShqGvv/EmN5ujjNVRjzjyQ43gr9HK/J0tUR7nOCMEWrfFvfPF+rKpWlDQXDvlsa5FklrUBrHgCy4PdCkfD8J1OGMx2CU6I4V6jI/q+dLGeBqeb8K8lbPvwOXy8JLCa7ZDJagNYGX781rqz4IH3vrhLxqBpvY3JmgnQ57/7PVRURBirEZo/I1yHmYHR1PXaBzSKMLA02SEeCTy1NogWote39d4aHexHg+Yw3kHxEXmlv/c/eZNa8sOXgwYSImfPbP8rK0xBO2kSeJ/1oKGsvha2zaIznio1i2oKx/X+oQ6q7+N8HvpCNj+WbgFkOoQWBAB3dDySy9c5mBymwfjLayVnB/OGF2+RnTwXeTsFOHOSLuSnSuXYbjJ739QcpiXOJGijeOs1LhCMTVdKlui5zO0mRIkHerKu/vlszmyM0D1V7EqJpSOspij8wozHSNsheykMBhBEhEpS6wcTVFhi4Imn9CPaq5sDvjMM6sMhxt8680589k5TzzzJPf2jnl4VtPbSrmoG6gND8ZzRDYiqeB8PKafpVy5eoWH+3tMjvcoy5r+5g790DHNNWfnJbO55dLlq3T6Xc73H1DWOUXuECbDuhTpQgLbkFDw7O4QwZDbE4XLvc4qCr29P9UcaacEQYrOS5p8TP/SE9QOirwAC9naBlGcUBvLVDeYvEEVEyJlqeMEIUqwDUY76qLg/lTTGQ5InEOYEhF0abRFBREqSX02HAKnYhI348UnVxlkDimt14W21LdPCigwuuZ6abj257/Hldff4GF/l9+r1rkaHLE+PaVbHLPb1Fx2NUGo+ZEbcpg8zbNf/Bw///NfpNNJeP21H/HkU5fZ3t6i+2u/xdtv3+S56z2++91XmJclMvRFvg/KEeBaaqoKaBrdMg4sSRxxcnLKbDpjdX2d6WhEPZ54W3zjw86NaF9vKTHGFwpra2ucnpywtr7OtatXkQI6vZTeYIXZZMbmzo7PETSabm8AzqF1hcPSyTIkgg/efIvo9JQ7t2Y8vLfHzTfe5iu/9HMYHMIYxhcXGGP59re/Q9PUKKGo6obT/SOUFD5AVWtO7zzAfvh9Pn+0z+ZUe+dNZbwzmwg840JZgiTjanyFQNdcnt5DXvT4R1/Z4Ve++lsoqej0QvTx9/xnlaScTxQ62kKpCOs0towYDoYMegn7JxNmcsClK5eZTC6W+gncgvK9aIL8OefPO0/FFsLvPItN0znzt9ZafRImQaI1irCthkLXGpk6ep0MKRVhGC2bmqoqSLOOL3S0b1iSSCFFyxbAT+yrswfk/+K/Jdx5CokF54ehwjkC48XwgW0Y9TKG4SqNDhgMfZj1ZDplPJ/RTSJGw5RO2MdYTd2UGG3QuksYeiSsKOYenXaC2XxOWdbUdY2utadGtROE2jlErEE1BIGltCUqEAQ4otDn5ZRuQXNqz27ZTryzhCowxEFAPROYRpMo7zo62hhx7bnrdFYHqCgmSGLWjME6yHp9r7lrKZZ+YwqxrkGogLTTxVg/nG0aS12XdPt9ZtM5ZVWTJElb9HtUqaoqul2PYnkHY0kUReR57imYbeCyEzBaGWG0W+ZWxUlMWVUIISmKiigKfQ6W8JRMrcO/V+YVUvph48exwiQin01J05hqXpC2+jMpFXmRkwYSISVRFPtmCgCHMc4jrHjHa6MNTniNojUWdM273/lDRluXuPaZFynyCa+9+irTi1NMVXAym/KtP/h9JmcXfPqFz/DkL/4Wk713Of7gBxR7d5BWk6S6tR93qDCmqr1hh15oy2VrGNcO2aQSdPpDNnZv8Kmf+SrXn/8cYZzxzg+/z6t/9k2efPppPvPFf4/RxiZx0sEah5SO44f7vPanL3Pz9ZepZqdEC31yKzsRbeZcGEiENTSNIUpTz0hb1FttTWoaj3I1dcNbdYku17m21WdtMyOOvWY3TmLqqvK27ggabcjzctlXuNYVsCjKJbssCkPS2GFsjQxD0jjAtrFHTd1QNzVlWZHnBd1ul16/i5RwaXOFbqIoi4L1lSHCeVOLv2p99MaqneZ7+p+H9SwLi3W3/PeCJrjUrUjhcyhgGT7rnMVhscYXtkr5JkIICbL9upZiKB6bojpsm0Xhi2HTOn7QUto8k9Aui2QpFQLhs6MAb21v2i285WQ6QRCES4g7iDzHNAgCtDH+oXN+2oN1SKWQQqJ1Q6CCZdMD4Nrws6VLiQNjfXZA2KJRQmuk8wHAtt2MwR9QSzYl/vvYxQsgRIvQuUcNV0tdciwMMoQXswrvMrjMt3IGbVphNO4v0H4EbZMqASuWh7r8uL2mP5EitYWe/oa1UPy49v56mLxFGDEoZ5FRyOdujPjgaMS9NyWnVY2rImQN/eEaZnZOb33NNwC9Hd46spyeX5ANN8BZmqqhLHOsgXk5xc5OUM2cznCFBoGr5jTTMS5MYXULwpigMdRNSVVMEUVDr5PhVIyIQ0g7KAeyabBVRTMfo6cz1vuC3e0Ozz65zRee26bXW0OGc17+/k10NWX38jpv3j7mzkGO2+pwrgv2zwpE3GU4HHF+XnPvw3fo9ztcv3GVg3v7nJwdQRTT78bEoSAKFKUWPHhwQBxLdm88SV4WHO8fIPUpZR5hgi5hYBgNKgZZSuEyTo4tYdbDuoCkmZC7hqYpCFRIE4WI4gKJwDqJdhqpQfVWUYMN8qZGNQVae3g/iyWNiAmUBWlx0qNMbnZOoyImlSW0JcoEJMMOVWmIogyR9HCm8RPGpmZrGLO50iNQ/l02ARhpPL3xEyLEplHMlemE6Xfepi4dbxEzj1Jemr/HU+VDZBpzEUrExhXsS9eZ//ghX3nhc3z5H3yFYbfH++9/yLXr1wik4IUvPs+Hd/d47/2an/361/jsV77M/oCgZmAAACAASURBVP4+v/u7v4PRDUo5hAoQqDbYUmOsn7o3uqauK6qyIAxDstEKl3/mC1AV5IdHTA9PMHXNbDxBttSgNEnpdbu88MIL1HXN+Pyc9957l/ksZ2NjnW6vT38Ycnx0RNWUDIcjyqqkrCqUCgmDmCCMabTh3e//gLXpMYlQlONzDt58iw+vbHHv7j6f+8LnmE6n3PrgFufn5z4zBocMAzSCxjnKaU4URkylZLx6Axd0GAxrzNkRw9UeoRDk5xeEcUTaTXBImnJKXjekyYDBaAuVzJnnU0QgqBqFcYKyKomtIEtXuKhqpFL0eh0Kt0F5eshKELK9NsLOYg6GA+I4ocjz9u4udMS+sRViAfd5JsaSlgyeXiRave1P82rPKGMMRnuX2aqsiOOYLM38QE95+l9Te8pxGASILGY2y4kUxKFk0M0oK4MVGlM4yqO7CKXAGbI4Yn3YI0sloQqJA4FrNL3gjHSWk3RSAglRoIiCgPPTM7KtDoP+ALI+2jRYnZIXJUqFSOUzKmfzGc464jjh5PSE+XxOlqzS1N5JzxmvgWkay6A/pxoWdKMIrQTiNKc8nkNRoLEEcUAqA3zGnPNmD8agpzl27h3j4pYCKOYFCIkyNaONETYI22GmI8lSGm0I4wgpQ7TTGOfopKmvEbTAaEsQRlRFgVSKOIqp6xohFKLNmzLGLItq2aK70+mUbreLNppHrpUC3dQttc9itCGKEipbk89KojCkaTzyFUURxjRLVovWmjAMKfLCD8J+mqkpjy0BS13U/9slW4gkUCF1VZGkGaPVVcaTMU2jqZuGNMkQSlJrTRwoXMtSstaC9A2HtV7yYZ0lL2Zkqs/k6D6v/tt/SWe0zs6VHZ548hq3b1boQGJ0Q1HkvPbKn3JydMRnPv95di6/wLXNG6wf3eP4gx+hDu4g5TlVbQniDtm8ptEVxhb+WWqzn8IoIcz6rO8+xdXnPsfWk88Td3scHxzwg5e/yXtvvE45HfPDk30e3LnF53/ua3z6818mCARnR4d8/5U/5cO3f8Tp/Q+IhUa0emUPMjQ4EyNwy8GlLirPgIs9DW/RO9R1O9jQmrIsMbphPp9z76DHWamIkswzAkIN1st0QOFcGyYslXcAF96IwiLQxrYaKkmYKgIibIuSNU2Nc97avtvpUVUVTdPQND6GZHXY49f+0deQpuHk6KHXzLXN71+1PnJj5VjwxAN/ICiBtMJfZPtALD4gX6guZboeUpMS+RcaL69B8t/P34bH4TwcfgMAcI5AqlZrtHAX8ZuHMIuez1uZS9FCi3jusbXtbFC0rn3KYMyCjuf1VioMEUHgXVIcWOupeUKKVm/ltUxR6DNYPC9WLZEfg78u5zy1xSw0aO1L1xiN0f6TEAoCIRduu22zY1uLyEd0keVLS+vqJ8Rj1ryLdsBfK4upsWsNPpZTT9p8EJ9PIAIP+frA0bbpsBJsa2LBwqVRfqxl5U+tSsC1U177l5lXOCQOicWKgG4i+MoLV/mTW2/ysJTgJHpakW3tcHAvZy0dUIUJh6dTzsdjAmGxepW60ZRFQWM0aRggxvskUQaDKxjbYGdnNMUMEYQko3VkkHhEoCmw+RQzmxFlPbqbmwgMVVlincDoBtPUVLMzZHnOzihhd7vL5z59haefuML2aooUAS8+tQnA7/75bVQ34Or2iDsPT9k/q0gCiXZwdPd93OUb9LpdZp0BZwf7pCJk99oVXPCQ8fkD5kUIYYLtb5JEilpJzg6PmY2PWVsbcW0U0pOah5M5s6omjQKe2t0gTDvcO9bMTYCUIaqaoWuDK+eEGIzq4III46C/ukleNxg0gWjNZBaaFFPj6hKhc+JYUWvIhCGLY2ZxH1uVrPcEp42EpINozqm1wRi/1xgDSmuU9IMPSc3mekIUaiQB0oUIG2JdQEmI/pt78o99CSEI0i7fjwZ8c77BAxlQCUm3nEJX8OeXnmH01a/zx2+8yWe/8DWe//QzUP/3nI3POT48IotDrlzZYWW9YXJxQV2UbKyOGPY73Lp5h5vvfkBeG+4/mDOdX3Bjd4M40jhplwHiUjgWPkBGG+IkQSlFYzXpcAiuS7w6YvDMM5ii5PjBAyJtmU+n+HDFmm9842U6nYRPfepp3n3nx7z00ufY2NyiaTRZFHJ+fkIWKyJl6XdjinxOPhvT73SoiwsuznMKCR/MNde6Chsk9Dc36A37qDCgKAq0bggDuZy6LoZbdWtk5JyjNiWv/uiH/Np/81+zs7FK1kl554c/5Nr2JULg1e9+h8H2KuubG4zPzrEaTk4uKLee5tRs8uZ7b/P2G2/xg++9hjGWje0dnnjqU+xeu8ov/OLTDKsGKQSdboetzS3ulMeMp8dsrq+xrjTbWyPurowoyqLVbrb7tljQusXys16YXAjAST9YtFb/PdBYOYTzUSNG+xyyqqrQWlMUeas/CbztN6CkoCprlBKkaUQURQxWVqlrOD07x1iHIKC2lnSYkZclvSxl1I9JAp8d2UljjA6xjSNNJFU1x9TgIouzJZd31kmjmjQOMdKSqpggDEhmc7S2lFVDGIWkrkPQaip63S5JErG6skJZlC2zpCaJvU15U5aEQUS330cAZ6dT5uOCw72HxElEPOwQxbHXwlY1xUwzPTyhl8SEgSS/GKMbjXQSGku/l3L5hesQRa3IPsY6P6CQqhX4Sz/8reqaoK6pGk2SxiC9TXXTaqrCMCIIQxCCwWjE2ckZ1jqUAq01aZIihaVui8ZFODBAGEVt5pJFyJA4DoniDBmEVHVNXhQEgZd0JElC07jWtj0icL5e0MYSfUyapZ/IEoIg/HjMK4RoTcTwLC0hBU3TUJQl1jlMkVNXPpw6au/1gna1kMBIKTzbqfH0vLquSawBCQe33uJP/uU/5+d/8z/hq7/wVdZGI27d/DGz8ZgynFMUBT9++032H+5z46mnefa551nZeIrrl57B5hcc73/I6cFDIgFrx0feDbKsME4SZENUt8fa5V02Ll8j6gwpq5rT42PuvPkOD+/d5fzoAWka04lXqBvNtLWIF8DJw/v86JVv8eC9d5jt30Y2FWEaIAWegls7yqKEYae1M4c4UCgJxXxOaKxHrtoM1bquCUNvflNXFfl8TlUW5EXF2bRi0O+wuTpkY5ASoXzdKgPiRJE1HgmzC0BGhqSdDtpYahSFteTaYoWkafRy/11Q0VUbULygNa+t9Hnx2Ws89/R1Dh8esX//PlVZ0u93lnrRv2x9dI2VeJTz5JuWtsQXi9wkeIRasXSPWuiJaCmBS9MFJ1pqmj8EjbFL2G6JZEnVIiyL0ERPh/MNl5/COGvQ1neYKLB6kQAusHbRWLS0F8zyoHI8giARouWV+sbDCZ8xJa0P7/VZDz69XCmJNgZr1ZLSIRbNnPAvhVKqFe/pFqHzbZ+0vlgnlMsXbBEALERrhyn8dS8aRX+xLZqHa1GsBVLYQoHt57yAVT1Kt8iqcTStgFoiUYFs+bzeil20k1EHSxpm8LHD+p9McfC3yoBZbHDLAmYxQXYtqukpKErA5aHiS09v8Mc/PKQxMXk1prmYUlQBD2xIPdVMzk4I44i4M6SqS8qyRpuGbppSXRySSkm3P2DSCJrZBa4YQ9hBrGwjkg6pFei6JJ+cexMK4XBZzBxL2HKRbZFjTIk0FaqZsDUIefZKl+ee3ua5J7dYGcTEgY8OWE0sn7k65O7ROn9+84CN7V06QcLFbEqSKLZXVylnU/bu32ZtbZvtnRsETvDg7l3yRjMarVJO55TjI4yMKHRIvz8kilIGGztMLg64d+c2L25FXNsasDVQTGtJfzTgyZ117pxb9i7OQPaxRtOc7qODDoGuSOKAadQjiBTpYB0RJ+i8QNcN0miUrnFlRaikp57Zmm6kWw1mjKhz5rOGWmUoV9DvSWaFQkYxojFYJbGNQTYlTVlSn9SEnRQRJnRTwagvUFgkEUj/IlU2Zv/IMCt+8poBKSXpcIXX5yU2dJAGYBydvGDwm79G+vxXGG5d5TOd6+yudDh/5Q/Rr93k3iDjN/7jf8LKyoB8lnP/zh4qCPi//+QPOR/PeLD3gO+9+SHvv/8B/fVtTo8OqMbHPP/kFVaGA6b5BVVd+z1cBUjpc0JUy2UHMKZBSYFr91onBWE35cpzzyCA+WxOXlcE0lEUM8I4YO/wiGme80d/+Dv8yj/+VaKkz+HhAbdv/oifeX6bfpMgzw9JrKEcn1LPBChF2N2ks7vFykvPkvTXuXpll3htDbM68jS88wvefusttrc36fd7JHHE+cWYqqqWVHHnwGD9eaCgcpZht8PPfuklTv+X/wEzOeeFsiQ4VEihCIVFZSnbKMJvH6CjhKeeeoYk7fHu7QOOT885mhj+01/8Glcu7SBlRCcLfRNnPRY13LmBOCu4mF6ga8MoXuXaE9d4eHiIafRyD/9340r8oLDdfVq9hve5+OmHAKx1zKbTVkfdGlQEIUEYet2NEATOG0QJwGpDqAI/SAwkQRoilKKuNf145F10lSROYzpZj253h6KYoxtNVVdEcUSSSpxL0HVAGAatYYZBYnCmYtCNCWTotT+1pigLAgRxkuCKCq1zRp0OszxHKs9WGa6uUMxndDodwjAkiWMaXbf67JB8ltPteNRGhSGdXoaUAdtXVqkqn9s3GA6X+ZU4y8X5mKyTMuh3mU2mFHnhhxRVxcpohe6wi1OeSqlbRClsGobDEY01WO1NEcIw9Ghap4eD1rTCD36tsyAlSvrPQQURWUtHCwI/qa/rBqTzqLwxKBVQty51fuovubi4IE29E5pzjqzTpSorZtNpG+AqCMMQh/PoFJ5i2NSaQAnSNP3E6NMfdUkpydK/2tnto65KVYRhgJC02hxfs9a1d5y1CLJuRpyELLNcpXsshL7dB/CDftk6X+uWbnn7R9+hNIZf/c/+cz770osMhn1uvvMuFycnhLMpxTxnPrngh6//OQ8+vMXWziWuXLvO6so6yaXnuXH9RaJAsVs3S20VQuBkQGMsZVExmYw5uPsW+w8fUsxLkiRlfWeHbj+jnJxRzi6I0y4vfPHnuPb0sxzv3eWNV17m8N4HlKcPoJoQSG/6JaUjVHLZA7jWvE5JQRJK0lBhjEZXJQBhmi4HVPCIxaYCRVmUzGZzyrJkPptxejbmfpaShBBHkigKW/qkQWufQ1oZP3jWRlDV4HJ/ttW68ciWkKgw8A6lwufkLsCfOOnQ73b4wgtP8cTOKmcnR5ydnxElKadnFwx6nZYh8Zevj45YWR+yt6jkFy5GXvwol9bhUgbtxtJiVS2S5f/kWsqJ55U6Z5Zhv9BC1tbrfcBnurR1Lto1aGNpFm4mViPMIi6XlrrlKXXW+tRmKfFufAtgcllEP2oMEZ6tGLSp0AKDFN4XTkifxeWsR89w4JyfTjTa0OgGkBiDz8dyygeMGq93WjRei6+X+BBanMCpNki41UstUDdtHjtMhW8ohfC/k0ftFg2Tb3QXGjS3dHnxX6+EbPn5gjBSS8pG0+gWvl7eWa/FWtA5rcN+7NP6n84C4fFsGbegBDr/TC0opos0GimgG8M/+Mwl7uxPeXAoODiecHp2wmC0wnReU00v6PX6yGTAuIYmnyClZNDt0uQTAlMh1q9Tmoa0OUPrHBclBKMd6nSVVDlEVdDkU8r5lEgp+utr6CRGKoXVGlPm2HyKriakoWVnAM9s9/jic5e5urvOWj9udX0WgSOyBaux42svDAlcw/duXbAZSk6ylKLOmRWazctXmb73IQcPH+CcYbR9CRkqDvceUpeW1UvXOT+wNPkUk59y3tREvVWyTo/B6iUaUbM+VIxGfRIagiRiZX2LQbfHw9kpmIZIaC6m5zjdoF1BksSYKMOFGVkqiOOEspxjijGygbDXJ8syjNa4xiDqijQQDNKYh6dTRJKi8wll2SD6CYNY0jiHCzzCYgWIJCV2moA5BTnFVDAtA9L+gJWdmEGUEYoAEUXUoWJcG+7vnfPW7YLy48mP/EiraRpu336fr37154iTGCkUe3sHXEpzeoMebvoe4o0/YPv173O5Kwj27nPKNpWG1197g0tXtnn11R/w9tsfcjbOcVmfcT5D5+c8//nPEfXvc3z3NZxpEEZzsL/Pta1LiDynlpKD81NUFhNGMa7WYCx1VRKEEmGEp2kb7SlSQhAGAcr5aZ9R0ODodTr0NnvMipJv/dkrdDt9BskZ926/yhe+8uv0+x0+fPtb2Pk9rIuY1zUOQTMrqZ0hTDu8ctrw6tGcT+1eZtBfo7c+ZBon/Ku37iPPzrk4Pubk9IzRygrPPf8sm+vrfPOb36Isy8caFD8AVAj29x5ybXeXo6NjElPywb09xg/u0s26gPA5PkmMcZDECagzavMHfPa3fxshBFd3d4mSlOFwxPHxKZGAze1tirIiz3M6nYzzizNM1bASrzE5eA8hHMOuYWd7jau7u9z64NYy1H6xFkPFR0yPdi+yLavisf/+07qsMTRNQxhGjCcToiBAqZDKVH6452A2nfspsTWcHp9QFAXgSOIE6xyjtRWEECSJpGl83ESvm1HMJnS6vmBXoSQLUwbDkc9O0pq5m5MmKUoFREmEEN6AJM9r776nLUEUEllvlBKG3g1vMBjQH/ZBCgLlS6EoiclnGaFSrK2ttoZPGqN9xpMzjiTNSNIEZy1hFBNFMVIqyiJnns+o65y1tTXPggkUcRITBgqHZXNnyzsX1g1VXTPo91unZc8dkWFAUVXIKMZYt5y2B6Fv/ELjA1XrqiaJIlo/YKaznDDKCKOIWPqh9XB1lfHZOUVZEQYeAWh045E33RCEMUqK1ilYoVSAEJKwlUQgvV11b9BvQ2hLlJRYY1FhSNX4IOjRKPbhuNZQVcUn+Rh+pOWspSrKj+V7WWepqhLnLFEYEAYKpSRxFKG1pTGWrHVWtNYQyADRAg20gSM4y3Q2RylFHIVtjI4HEaI4RrmKm698A+UsX/+tf8ZTzzzHcGWFH7/zLieHJ8wnY/LJlCKfcX52ysnxET9+9x2ytMNgtMJw6EPk4zhBhF5rX9cNeZ4znk2ZTWfMywIhFcPRiKvXb9Dt97G6IZ9PmI9XSNOEp559ljTNuPPum7zz+itcHB6QX+wT6imdWGBKXycFUhAFEiW927SPLvLmNWEgyEKFkgnzoqIsvIFKnKTeul4ptNYYY4gTr9Fs6pqmrplMZ4j5nIsLhZLKM9CCYGmJLgArFvUbLVjTstycHyxEcbR0/RVt7S8FntESCFaGfZ67cYXnru8QCIfVNS++9CIX51NuvX/Tawz/mmiBj66xEt4sYmEN/ihrynfZViwK07bxEr6V8s2SeMwwwS0fHO+m0rQNS5tzpRTGLLKdWE71tGnhOzw8ZoxPiLYtNZD2QLLGsBgFCOm1MliBwtP8sJ6Ot0SBlJ/O0jZ9QRAQBZKyakAorF1ow3yDY1oUqtaanoz952GN3xzbFk7iOec+RM9P8byez5Pt6qZBG+cNM1oNl30MvVpATwt9F/hufyGCttYLekOpUIFqp4EL6p/yNEtrgNbS3dBSA3woKG1TvED2HG45mX40dvr/AxnwoxYlbWO1oAgKr7laWHkYBFd6li/cWOPWh29TFjmD1QF1VUBe0R+uECV9ZrMKO50jY+hmCXEcksWr1IHEOEteVAgtqVSXpDcgTPuEEtAVeTGmmE2I44Tu2ho2iQiNRTW1L4hdTT07oRdaVtOIa5sZn3pilUubq/Q7GVJFgEJY422KrUNYy0oS8LPPbnJ2cYc7RzOuXb7CLI95cHhGlQVc3r3M/Q9vc3BwB223WV3ZQRvB6dEB2sHq9lWmFyfIfErdXDCbWqZ1QdCJuL7R4+qGpJtFdIKQrJfQ64YkUcSN7T7jWcmP7sw4yseopENiCkQ2oFAdhIpQrqSuHbouiVyBFAkiTbFCETlN1dQ+9DhwqCDEBhFBFGGLhjBLcNQMeynnc02jErAVofKZOpku2FyJmJQNJxc+KNzWM1azmE7o7VlLIs7OQt5/eM7eWck4D72r1094ObwjVDHLCSPFrVsfkBeGZ+sZ8f/6PxJT0BxdcN3GTGXIgRPsb61TN/CNb3yHtY7EuYIbcYNRY+6dPqS3dRnV63Hv3VfpD1a4OIrRpadqIQWdTsqt196nG8Uc3rvP5eefI5/OqYqcaT5HBgHWeJpyVZZY3aDrCnAo45DWoquGk5NTOjvbpGlGt9f17pbasbu7y2xi+PaffZe5HrJ75TrHZzn3ugFRVDKfF8zzirOzCZPJmFmRc1v3ODQhZTkmn2tUEEE24CgeMprPmeqGTreLUoqd7Q0+eO8DJtNp26g81ow4MM7xz//nf8HZ+QUvfvbT1HmB+fLXObn1AVuffp4szainF2SdLidHh2ytb1HjmFYVvd6AOOvxy4MB1vkJ9NXdXc5uv0cZh2TrG3R6XdIkodENTVUThEN2Vi4jhKMyoNYDur1Vqqpm78EDz0jgUVMFLetBLpgUDukW9suWj0IF/CR2WQdIFdE0lul0RhJFLbvDIYWirCrOzy8wuqHX63l2i67JshQlvXDcOkOapkRR7Cf6yjdBVekL1iAMl06dSZL63dgaAgyuKelkHcIkoqo1YRgRh5Y4CtGNRgjXNkN+dBY0DZ2sT6ACkjghSb0dOcpTj5u6IUpTf97jKPI5KgjorwyZXkwIoxBjLVEUkKYZs1kOlWRlZY28mCOkJOn4KAmhFiZZgvPzc9/QdTtMxhNkGHrTKOmtp1UQsLa1RdMYbxQRhjghiYSnuzZVjbUVYRx4BoyFMEoJIh9EHES+ApFSoY0mimPmszmD/sAXr+11lHlFGJX0ut2lZltISX/Qp24aohZ1rOsa8AYc1nozhrqpCURImiQY3TCbzZZRLY1uflrnp/+fLoGgasNx41anttALCSGQrUmINgarDVGWtKiUaOUpkiAUKElLS7VEUUgUhmhbLWvoUDree/1bmLrmF/7pP+P6s8/yxS99iTt37nJ0eMzsbMxkcs5sckGRFzR1zXQ2YTw+5157nT4/rjWfEwKEQoYhvV6fjY0thiurDIYj0qzra11T08lSbjz5FFmnQzEd84OXv8G9d3+EqwtCPcPmZ6RpSBCHNEnUMryUl1IIh273siDwTteBlMRhCELj4ohaF9R57gODhSDuZEuL/7ppltcbRRGNqWkaQ1XVCNcsUf/FHimlR/2EUEuKuFKKIAwZDPrLoZZ9DDloMR8CFbA66vPMjSusdBSmqQjisPVmUJRliZAB5xcTAvVX77R/B8QKFo2TaxuBhQDQtlN+0+p7PBolHiv2BUbr5cu9sE9fOAiCR18WtpXwyGLctjeGVl9Fm9sgsB7WFLR0O4MzrcOKM97Zi4UJhKe+hYEiDAN0sxAEi/ZaH324AFob4ijACtB4cZun0zlvvW41gQvQWrfWrcHyVFvkXAnhm0vrTCtCbql94jGaSlUThd7imVYfZhauZQuDDudaMwzlNW4tVOzaomFB39Otm5dS/rN4dONouaRBe120CJf/n4+bjywa3497SPrTOnUVwi3t9X3XzRIdXPArhTAILI0IaZwhEiXXNxKmTUG4tkGqC6rpnHTjMk4KJvM5+SwnjRNIBU1+jHIDbDLECImaHxLagCbuIpMu88rQiyE0OZOyQJc5cRwiww4iyvy11BWyKdC6wNUzklAz7AVc2ki4trvOysYqYSoxaCod4aQktv6g09ZvNoGAtY7m3//sCt/4wYQ79z/g2aefpqwMe2cTwkHE7o2r7D3Y5+z8Al07VldXUEHAwYN9stU1OmvbyFlMZ3ZE0syZVxUO2Nwe0OvGdBLodzp0uhlJEqKkZnUk+cwz23x47yaj1L8nWbrCWHgRaSIMRivqZoyrZ/TjkNzEqLBD4CB0NaYp/e8tBLqIcVGfVAlKFVMS07eW2BkmpoNMMqQ1SJUgbMlKFy5tZmyaHjtrkovxBSow7O6sI5OAC5Nw527Nw5M5FzplVjiqev5Ig/gTfR4FWTfh/Vs3aW7WSBUiZca8KDkqa050h/dMl1lnnXK4wj1pMStb/PyXPsPa7CZ9FXHjucsc/vAm914uKMeHjE+PuPHSV8jzGXffe5dL15/kwXtveQfFBw+4mE4YbWyRnxyjkpSjgxMGwyG9JOH2rdsEcUqv32NtdQWhBWVpqKZTvx+JiHI2Q9c1YZQgRMDG+habW2uoQJCXDcPhkCC8wQcn7/K//e//mqtXbjC5uM+fvTZDhhBGQ8IoxhpDWWnywnJ6/D5p6pj0Ivb2GyazEgGsbK2jOwlzIdnZ3uaFzzzPd7/xTU7vP8Bq3e69/j1eGh45x+HJMf/md36ft956l//o13+Zp770RXZf+ixBIEmVw/7Rm0xvHnO1m9EpzwhVjM1S5nc/oPfUs2R1yv7eHgLH+toq1X6f87feZvQLa6gwAW1RVhGEGdZZZtoXniqIEKbgiau7/JPf+E1+9//81xwdHjGbz5d74sK2Gudazaxd6lz9M/ETfww/0jLWcjQxbK8PWF93VEVBURTLbKumLun3Mn+GKUmaJkjZIwhDb36QxIxGK1hrCeOYMIwIg8Aj9M43l0pJkiRBRSnR6rPIwBHpEte5wDYlKoSTwwPOz89YX1ulrjVKwmgwIM9nnn7XGi1EUYSU7aQ7DAmjCNHS9ZM0Izdzb1AFOOv1SMY6Op0Op8cnjC/GrG6s4RCUdUW33+NiPGal32Nty5sVCQlhHDGUkuOHD0mjiH6/72l9ccRwdcR87oPLi6LAOf/3kywhimWrr1JI4R3PRNsEllVFiLdyb4nq9Pp9HtzbY/dqCkKgWsmGDAPiNOHs7JQoir2TWl17ZPHiwuuu2mDgIArJul2q83MQgiiKWoTFEqiYfJ6zYMMIAZ2sQ1nmHB+dsLIy8kU78qdYTP0Xl5CCKI0+lu8VqJCz8Xnr2qxbd0zrreyFQoYBZZETxRG9bgfh/LuthCKQliD0OalB6O+dg+dc1gAAIABJREFUs6aV1wh00xCFEUoK4khRNg1vffeb7O894Bd/47f5/Fd/ieeef57dqzMO9g44PT3j4uKCIs+pioKqLv0QrWkb30WTFsZEUUKSdUh7XXr9Ed1unzhLkcpnzoVBQJJEJElEVVbcfvcNbr7+Z5zfv43Esj4IyDopMk+IQkkUQN3E5EXROus+MkOrG00Yxv73DHyMgSPAOkcaBVAbmqZiPDZ0rCXtZERpQtLJaKqauiw9WuoUcQIms9B+zqYNuX50P0TrRhoiW1RLKslgOKBpWtS2rv8CU0Aqweqwx8/+zEus9js8uPMBs/mMOBpijeHk6Ij79x+we+UKd26/64eSf9Xz8FEfINGG/TrHstteLE+7aD9I12ZAIVtEqQ2AwGdYLQt3t0CjFiIh4ZPRjcE64wublnronA/YRfiHzvdZvhh21i3RmSDw2qaAwFuHKz/9ovFIUhRIUucNKCo0ooVihfR28Aaf3bIUpzlPJbFYhGqBMAFBGKDayaiQbQaB8A5OSnpapKeXeZTsEfrWGnQ4bw9vcJhWy+NYTCjlcvBjWiROtNS9xefvv9cizXqRB+PRCawlUD4f7PHD+1EGVkvLbKmDj26jf+EXv+PHuT6p4uBv+rmL5nJBA8Qt/ptHCpUDQ0AtFI6Kw4uGN28d8/bdM0Q9IwpHhFHEUETURlCNJ5RNTZAEhKmknJ0h0AhnaIopsp7RDxq6/R6npeUwb+hkAwLhqKs51kGUZgjdkKYRympcU1HnE8r52Ls3Sc16N+XKZsaV9YyVXkKoFGVjQDQoU+BkQ23N8jYKpI9SE4aNlYwvP3uZo4sPeP/+MVcu7eBMweF0zsqgy9WnnuX44Jjj/T1MU7GyvsWNpwdcTM85Ptrj0uYG4cYuYZ4TzicMEs0ok4ShIo0C0jQhiSLCQCCkJXaSQVfxzO6QYj4hFoawl9KMy/b+KBrrcHpOJxIo1UW4zEP9TuNsjTQ5WajZWhlwMjeIIEHYeauPSEmlJgjBqpgoCFG21TQazdowppsIQpVxeRRhd9dwcUJ/2GFvOuX9vQmnVYK1irycettvXS0mST/R5Sd7AdpoP8hpHFGS8b1glbeCy6jBiPsnh0TRgM/vbPHbn7NsfPpFkkaz/42bPPHVX6Hs7vBGfZ+75zOqskHbmve//6esX7nCzsaIi/u3GWQddre32d/b43f+r9/jxU8/z5t37iHSjKgsuNS/wsHDPerGcj45o6jq9jkSXJyf0e0EyLBHWTvC1ZRBJ0UG3oG12+mQRDGXL13BAeOLM5CC0/MZRWn48XvvMxqFGBx1pTmfXBAnCQLRohKS0eqIuqkoa9jYXKGo9miahno+p5KC4XDE5tYWd+/c42D/AGE94+FRU/UXhzkCwenZGecXF0xmU/6r//K/YGtrh/sP7vDWm2/S/+53EGenpGnsjQSiDr31DbLOgMOsz9k459btu2xvrnP48CFbTz1Damou3vgeD967SRX1qdIOTVMRBSHdTofpdEzdGHrKsd5JuPZL/5Bf/fVf4/79+3z75W9zdHTUohnSnxHthXtDi0fMj4+is/okxlcCSX+4RpopImr0QmNsDVEUtjbfmTd+ain/Xo8liLIUA3S6XeqmRqlgSe9RgSLNMprGn4Flpdk/Kvjs536J3uoKUjiGWiNsianOcffvE03OSVNFWmqyLKSbOuZ334VAYcqStJdiyzlWK0TSIQkzVBj6/bcoMDYnCEOE867AeTFDm4owjBEy8bQ8KQhC70bWaI0KArZ2tgmUpy2B9ToxKdvfw6MEQkqC0BeTQioQCm0tSgVtEbgwlvL6k0W2WlXXBFHk/07TtAwZ1zoXhzS6oT8YcH5xQb/Xx+A86yaKSdKUpq4x1lCWZWuL7we3RV4Qxd7UI2ibzJWVFZ/zE8U+I7RucA46vT5FnuPaZstoTRCEDIb9JaXKI1w/nQPUf3eJdqD+MX2z1hTNo7NhHIPR3oQl8GHBqh2aS7zWsGm0dx9+jB0khUAqkEos0VIl2qigEKyTNBoEloNb7/Jv/6f/jv3bt/nS1/8xl65d4/qN61y6fJnDwxPyoiDPc5pG09RV23wsnKt9XlQUeuOYOM1I4pQwjtr3T5HGKVJAVc45uHubH3/vFe6880Pq+ZhIWVZ6ISudCOEcSaS85igQlGXTOrxCliVM84Ky1m2eVIYKLEEkCbVCtCbYTeglNrZu0KYhn4wxuiEzhihNUCogzTKMMR7dtgb1mAWAa0GMBfLqWu2QbOVJQRi0wxq/F9nWZXXxNUoJNlc6fOrGLjd2t6nKnCeeuE5Tz7HGMp/NGKxt8sSNa/S6fW7+uP5rgYKP3FhZ8xg1TzxCsBa6qAWNyr/0skUDvOe7QrUHR/sk4h6jEvrMKdd2/LQiNxAYvaBKtD2W72KwVmOcZokqsPDLFy1K5Z3yjDWINqdKOAicIItEq8XyGVzyMc67NRqCYNmsYC1KBv7Ak/7AM8ai8NfirEEGAYtexFrrbR4Xwb3CQ65GmxbBEo9lVLVufdYhTJv63P79RzQRt9T46DYnJFhsCM61P3eBDEqs0z5AkNZoRPjrNca0E9BFo/rIwdG11EDTZi+INqzx78ke+deuvwtStjgofLsll83WpNL8/ivv8cev3+F8XDI9OyUeRjRBgq0NZn6KLWtP7Ysc5cUekVCEo22Mdqh6wkjlXFtNuXZ1lbM64Js3z3BBjK0m6Hruc1qyDlE2IBLQVHNsNUdPTsgCwcrqCKtrVkcR3Y4gCiMgZjxpOL6wrA9TOmmLzjqDbIcNgfIWDQKH0471vuILn7rKH799xmR8zjO728w+POR4qhlGDiUhdCX5+QwnFZur6yRBxLgs2Lv3IevXniTtDol1wY3NjJUsJIskUaQIA5/a7p9IRywV/cjwpRdWWUsFti7IXcDp+Izjs5p0sIktC2JXsTLoc5yHyKgLOJxtaHRFIiue3BkQRyEPilZ4aqDEkcYBaQilgCBKCITACi/+FaZkrTuiH1s/gQsFDwq483BO97zhojBMqozSKJqqxNbefnmREP+TXp7eLEBIVBChnWK0dZX79+7S7/XpdiKCiUFUBxzeO+J0KLj89C63XnmXD9+8IL6xR9lT/N7L7/Dg8LTdGwVNlXN4+31+5vMvcXm0wpNPPc3TTz/Jt15+mR+//z7TvGT10iWsc8znJQ8fHtA0jkuXr3B/bx8hA+q6ITWaYRRyfj5h/v+Q915Ptl33nd9nhZ1P6tO5bw4IFyAJCEwiKZEaxdLIVXaNZx7s8rv9ZP8dLpdrHlx+sZ/GLqvKVo1m5JFVskaJGooiwQCQyMDFzbf7dg4n7bCCH9Y+py81pIaQOARZXlUoXFx0nz5n995r/X6/byqnqCTGeM+qWmMw6FFVDXXTcLY7Zmf3CTeuXybJc8q6Ik1ToixmebDEYNClLEsODo7Y++AuxoQhT15kgUIiw6GvtGZtdYXtnd1231R0u11e/MQt+r0uf/pv/4KoG7QD/s69RUbifD1NCRSt/uTO3fv89//DP+eXf/mL/PZv/SoP0oLv5Jc5PJFc37rK6ckp3jqavYob93b48iuf49733uHfff1v+PIvfYmtgwM6/SEbL32a8XtvgdTcPigZXL0SXGsdJGnC7PgIbE0vkTz4yz+iUSnP/uZvcnJyyvPPP8/BwcG5lXqYSJ5rbtvPMGc//EwvIQixOyXT8Zi6roMJS5YTx0GzMyc5pmlKWZY4Z9A6CU52zqEijSlbLV9Ln3LeE8cJSnnqpmZndxfT/yS66IJKw7WKPE4U6GyNC0svUI/GfPNf/QtuXbrB5me/jJCOaPMLeG+phA1mLB6El/jY460gQ4IdcfD4Dn50RG/FEcWKNE6o1RHCBGvqeLDKWrZOPR2TdhKcNYi6oq5LGhukAKJuQlhw67wbRRHdfg/T6nmssS2zRJNlGdYY0jQOJBwbnEuNqTg6OmZ5dZU0TYmTgB7JltI0Hk/IkhQvVXutNJ1eL9QYUgbRf1uDpHmH2bREtBbRSinqqiGKE5q6IUnThQZmQUqVknJaEiVh4h9c0jKch6aaEeoMh9ZRsGUvZ22z+R9hIvsfaXnvFy6V/9CVpilJmoZBtvMLtpHSkiTPA3LT6ny0UjS1wdugf9at3bn1Dq0CshKuogNvSZKYSEswMrCtlCDSUJUNk6MnfPVf/S5vfe81Pvcrv85nf+krDDc3uXTlEgDTcoazjqqsqeom1OiiNXRTQUIS6YgkjtFato2IojY15XjCk7sf8OEb3+XBe99jenpAVc7o5Zq1bkIWOZStsS7U6oHGGBNHQYunpG7Bl1BrW+dafDXkxiodEOJ5PIJzDi/j4OBnLLPxCGcb0qaD1JokSciKnCiKQsZV05zTp1ugRimJ1hFzEwpr5z4LAdQZTyY4M0e3gh4u0YqrF1d55uo6K4MenUQTiYxiZcj9Ox9ircfYCmcanIHb77/HyvI6xv5oAfbfIyA4PHpujrbYefMwd5XzbRZTa48cTMiDdmeBBLTICXIxlRMq6KkCVzucMU1jcK41uzD2XI+1mN61zREe6+bWiXLhHOjbmyhwsf3C2U+ooA9LlMA7iVGaunEgW/5pSx30hM1PtVxK1XKaAzTeaszi0B2H9x42JNmKPp0LG5kXLgRaSoEzJhwyC6RpHp4cHqqmsS2Nz4cHbGH4QXhg5TlKJURw/ZPzG6ttbEOP1GZ9ufb3AYvgYyHm7o2h8RWtgNUzRyFb18ePYVr/cax5ltccNw3oYGt1IgSljFGmxDeOv/r+iK+//oiDu3ewXhP1LiOQ1OWUupwgmhLdSVEamtNT+jpBdZYYuRjMhOXUcXOg+dTNdW5c36SO+9we3eGDh2f42RjXzFhOLFm3z7T2mGoaxK+TMTGGra0NiuEWUdaF5owphkOnqE4jlI44Ozujd1Byca2DFI5C1WgBeZYQqTYEWwT7Xdc4Lg4SPnsx5pvv36W2V7i4scnth084PT2lFzkubAw5erJNMz5g20J/MGD98rMcbN/hyZ33KQZDullMrQqcTnEEGquzBpwKjY0HjSMVFp1Z8msrlI3hpPLsTmq2j+8gylMSV7HcTUAoVNJtdVcWa2b4+pS1YcTyIOFk5hk1DqmD2UyUpOArVroR20cnUGuEPncTHRQxnSwmjoEoY+Ql3737hEPbYdDR4GBWlTjrW1MNwAkieR6H8NNccRxz89omF7a6dHua7d0Z3//+hygz4suf+zwP799jrC2mdpTJgK9/UPLaoz/jye4J+6eG7/w/bzMu3+CD+zvUNrj3iblGM414+OA+L12/xtHjRzyk5uJKhBKXGZ9UbGURYxRZ0aGqKvKi4ODoCICj/X2yOqfTKTBVg3SK2FccHh4xKyuKTo+qDGGiR0cNDx9tk+URp8cd7r3xNl/80ufoFjkqjkiTlP0neyAETW0YDpeIk1CAK6UwJpwbAdzw7O7uBgpZnnHp8gVeeflltBZ87Wtf5+GjbdIkJk7SRfYI/PCByvzvqqrm+2++xd179xmdHvPpV17m+le+RP/4lF6vT3c6pTs64vDRY6YPHzB59IDh8pC1tTWuX7/OrJwSRTFxr8/6Jz9NerDLwWtvMn6yi4ojrGk4bupABZSSUylRxRJFLJlNxzjvWVlfYzBY4vDosD0/5YLSMo8KmbvJ/qzSqOdLCEEchxiTLMuCxseYYBmdZvS6QV9RliVaRwEd0a0+aTqBBUVdLPTK3gf3OXwwdAkNf8XNz32SRHjMk11QgrhX0CQxOJgeH/D2//4/4//m/+XuYJNZp+DSy59HdTcx1hEFiAAlFdPaMq0NwjlIz0jTlKWbNxi4q3gZ8jERig466LOVJIoG5Ci8N+hmRjWZEFtHDnhb4YUIobvCUJ/tMT28i/IjJuMpWIvWnk6WMXdANo1BRZqqqik6RRhqCkESx6RpiCDQWpPGKcYGxAkRrLwjHaEIjZuOQmMGQevknFz8XqRS9IdLTM7OWkZM0JupSHOwt0+cxhSdDs46tIbGhvibsiyRStI0TdvkOtI0YTwaUeSBQjeZTIlbPV3T1GRp9vPSVzE3UftJLGPtor6SUrYDb0EUaYbDAbYxlJMpzgUNVl3VKIJFu5AgtcIbv9AQhlFYKP6dqRBJ1Mo/RIiXEA6ERSqHnU148Oa3GO3c5d1v/CU3P/Vpnn3ps6xsbdEfDsnyLnlWhLqvJYbNB4bOiwWyb+qa2fiY08M9tu/d5v77b7F7/wOmp8d4aylSTRw5tpZSstjTVBVOCZSOENITaRlo1VlCUaatttASaYXwYJrgnKqEbE0nPHiLEIo0icNwvzHEShJryaw22LJkVJZIFVG2z0OSpIuhkxC0IEALKyiFaOM3kji4bHoEuDDM8C5EQmgdkKxhL0MLx+W1PheXByghiLVgNjHMJlM6RZe6mpFlCZPxGKFitra2mE2DA+OPWn+vxsq5Od2OBZVs0ezMmywZ7Avnz1jQY7mWx3w+jZs3MdjWMKD9BhvCqlDtzxPCY71tURbV6occwoekaOkkXtIaSMinuJPte/CtWYZgoVVCe7yQKBcocdbb1m05NDpS6sV7F8K2OqxgJSlarZZq7TKFOKf7LRwJReuu16JxtPD/3Fln0R4KgfOCqmlaQw8W19S3tEndNmMSifE2iPPm6JyYc/HPm6rwudufPY+qsm7Bi1sENPvW2IDwgAfEyswxrY9+e/xdd87Pdm0QGir+/YLME3FYR3zrjcf86z/5Po8f7ZAmOV4XmChmVo1xsxOEsySDJZQQjA92ibVG99dD3kc9ZjlpuLoc8dK1JT7x/CWWV/s0KuXqiubdd/fJ6lN6WczNVYUbaG4/OkVVE0ztGOiazZUeRSo5Ho3ZOWyIYugUBWe1p2MzhNJMxjPyWcPIGpq6JIsMeRLRKzyxdmjpyZVEYnHC4+oRlzqeB0spD3e22djSrGSS3VOLjWP00hYFmvLkiHK6z6m39JbX2LjyDOP9XY6OnpCvb7AzkujEU3QTYmOwTYVtN1p8QMkCXh2RpRoVCbxsePnaOtNxyYc7e6QJ5EXCw2ODSjVSeLANzpT0UsuVjWXSPGFW1ngdBihlVeNVROKmXFjKGE88O4d7nE40eX+ZhIalXhYcvbRm5FK2TypqmSGs42R0hpYhbBMrFsGCifAMM8Ox/ulXCcNezn/1m8+QZWdMxyPWO0u8/t07gOO1115jf2+PsizJ2+yPUz3k3UdPqGvJzCgevXWPurGLeAWlwsGlkXz5i5+iyPskKmihZNIhV5o8jfHpEWd3H5KsboK2TMyIbrcXQtAjRZ4mCCHZfPZ5dh4+5vTkBBFFbK2tU87K4IhnHWkcMZpOmVUln/vsS6wtdblzf5uvfe1VlBSsb66xvNynqWccHB1zcjZha3O9pVbRDoLaIVGAjbHWstTrU3S6rK6tEGvJq69+h/ff+xDnPdNZyWR67kj2tLve0/9+egkpGU0m/N7v/xvuP3jMf/vf/Td84xvfwnlHN4sYvPEOn9aKRoB4/x02P/MFtjY3SdOEbhHx4O4HnJ6sMHtwD3c2Ie0twSzY1Os8BSyRFEgU08mY+sYtjlREvbtHXhR89zvfDVofIRfnj2jtiZ1vqeHt55AfIfpCfAyVrRSSLE0QokFFmlxrJuMJeR4mzHkRsqK896ERaBurpglBs1GsgEC9k1K2OgiDkBJTNwghqWYldeMwLubku6/y3u//b0RpzrO/+tt0v/xrCFGz+8b/Tfn1P2RrPEZFOR/+3u+higGrN58NeiQBeydT7k4N9/b2Odjb5cjFfGr1Nmv6TUw1QTgBwuHdjFwL4kQhZE6ve5kLw+voaAOd30R0lhHFRZpa8/BgxOx0zPNXl0mKGCUtaq1BFq9jHvwNg6UlZuMJDs9sVtHtdvCuRkkd3Cjj4CJXNw1FkjCZTMmLgqr6wam4bzV4wZo9UPHa/9GigoLxbEae5wuRvheCSCZ4AWkUcqaiKAoZXlnG6GxEkmUkUchXMsYu9OLTSaBFeoJ8QUeavCgoyxlxFDN3EYy1pqyqwDb6OVo/qRrHe0+kzllNxhqEU8SRppyMcSbIYoJxg2prRYtEtDVfGAQKAlJrF0ZpAUwI9eTcxjyww+bfG+vAtJidnvD+a69y9903+O5f/RlrFy6xeeka65du0F/foN8fkBR5cLOTQbvVVA3j8Rknx0cc7mxzurfN0c59Dvcf0ZQVWoBS0MkSLq9m4GOSWBArhfVlAFDa9xVFmkgJOlmCNY6yqRBCUuQ5Z2clprHt868xxgXHbOWQAuJIYn3rJI4gkbo1RPHM6hrTlNTVjGqqqZM80GujQL0UkW4pnaHvsD7osOpWbxu4hjXONAExk4JOEpMnKc9dv8LoZJ9MhQikKIlpqhnO1nR6K8Q64snOBGdhdHbExUvX6HT73D46ZGl55UfeD38vu3UIcDbtlE20v+i5dOdp+1jnRItAyads1M9vjnkzAE/TC8PfW+eQzDOzDODmJPTzKZ53eAPSBZpceG9moW+SLRqDmGuVAgfT2XBjxrK1WUQSYgfaJgMWjccc0VGtmHVujDE36HBz+FfJRejj/PM1TRO4oE5grA13qQvoXLA4lQvqinWhk54LfkMT61rL16DJ8iIcsrZ1ZLTO4r1saZAtetgihotA4bbBWvyezqGZc9qmbMmE7ftH/PxA+v/QNdczzD+ulxZvBZDSYNg/NfyfX3vI1159h0f3HpDlXUQ6oKwMfrSHdA2Rqkm7y4iiw3T3kDTrkHYyKmHIhGUtnnBzLeHWc1vcvL7O+voSKsvQFn7p1gW++63XyBPYWCl45mJBFcc8fFxzNisZRJZnLvXJI0UtMs5Gksm4xArFpJyC90hmWOcwzZQiUhzUKVm6TOQNqrGkdbA4jVyDcDWmmaI1JN7irGfYLxiVE46Pj0i1YrVfMGk8h6UiHWygnadfHmGqI473LcO1FbL+MunsiL2dxwih8VGB3pXc2kzIohKtw+bpohgtDXiJROGlI8UgYgtdwRdf3GRlpcBYw92diooG5R0ag2sMifDc2FpmaVDgrOF0ahAqQQpP7UOWy9ZywbBQXFkfMBkfsH02Y3R6TGeQsdItqIRm+8yxP51wWil00iNxU6aTEqvjRTZeLDRdVbPZETx7IeXRmz99yEp7w+6jR1hxxrvv3aHJnqV2BuMsDx48QCpFkgSL59PTU2inyrOyoqoNVR0ydOZc+TiKuHZplcvrPX7tV36F3f0Jr33vTSKpUSfH2HvvkV+6zpOdbdTyGqdeEjWndBOJsQ1Lw0F7jwl6q6t8+Gib0dmI9+7cYTwtQXjSJAmTSKXp9rrsHR7SX+pTNQ2PnhywtDzk/oNH7O3v82B7h98c9FFaEycReZ7ivKUsK2bTEmNCuG+SaPr9LuPxhCLP6XZzhIDtx495ff+Au3cftoj9D0d1fhwnPSEEZdXwzW+/xh/+0b/lFz/3Cqtrazx59Ji7b/Z5/PAh1z/xEjevPYeNEq5fu8bZ2Rn9zjplNcOdHvDhW2/REY6LX/ktchWsvHUUDAlQCu8c+87T39gklgpiwfHRCd/4xneYjs9YXh4SRbrdi11b7LVU+ZbO9ZHuwo9j25aCSAuaWY1wnrqqgi5hMDjXkLVnsrVBw3x2dtZaS8dESYKOdXuuifbrguNtMFaIA/tEaJzTZEXCSGpSZzjZvs3BWzFXlh5xo/8BJyueMzSxO2Jjo+bgnd8l6f0TZN6DZJk/f3DG63vH/Gdbd/n81RP+j0c3+L0PenTtM1zcuAh4riwl3OpDoRuUqjHmiG98/6sMh98hcgWXVtZZW76Az36H7x+u8MHjkg++fY/PPHPMr37xGhsrWSg0sz4NoOOYxo7odjsAjMfTcN86g3EGZWA0GiOVYjIZk2Qh2N1aHzRdrZFHyNcEqxSirRuqug7slqCpaBHfecMao6MwEI7jhLKcopVCtkYeKtJ4D7NZiY5ipFGL50kpFVza2mGv947ZbEaSpjRNTVVWzN3wQpCyDTXOz/wENSwpghnKT2IpJTFRGH7rtqHVWpMIufjdNLUhzbJW43aeU7cIqG1lHyGMeq6Z10hZo5WgadG1EIge6rhIApGg1qCUw9aGZtKw//AD9h7d4f3Xv0mUpGRFgY4T0jQnTrKArjmPMXWINzEmuBo7G+zFffA46Pcy1pcH5Imm20lx1uFMHWifSmGdRQFxFAUwQgSzi0lZoV3Il1pbSzk+mzIZTVoWVxi4WmfBK7z0OK8ChdqHxkoIgpu2EmglKBtD1dhgEV9OwteooEWUkUbraBGlsEAMWxad0jLo1LwjTWO8tSz3MjZWl4PeWBmaOmRbFSrH40hSze7eNlevXQc22dt9gvaS2WxMYxxpktDpdn/k/fCRGyspA/1svvUvqHZ+zt2cB9oKPLaddMypGQLn5w9pK6BrmykIU7o5lVCYQDUMc7u5AUNrguFF21wEqohom7A51vn0gerawGEIpg/OhyZLSIkznPOQpUJJQeODBirQ8ALFUIh2Q/N2wdXUQraf06NEsMc0i5Df87BgLQMVMIhVZYtoac5DIsP7nFMJjbEI72hqh9LqfKIy/1z+3G3QzbtxQfvn87yw8PPn9D7ZXvBwiYyxCzpiaKbmDo/nVIyfB/rJj7v+QwXWgjrU/uO8wwmFFSm3n8z442++yR99+wH72/tkUY+4u0JjPIKGxhrSROPzBKsHIAydQQ9rBULU5EwY5HBzKeOTN1Z59vlLDJeXieIUF8VIP+PqaoevvHIDX88Y5hH9boKVio3co2rNMxtL9BKBTvo82a04m8xIdYwl5Mc4ZNvkB0v9WsCT0xniuAJfoZQnSRRxrEkIm3Je9OjnHRpfUU5GAaq3I07Hh9isYHlpicgq9k5Lal+Rd1eQWtOJjyjLGU05wwtYXr/A6PiEw9Mz6ObUOzXWeJLrGZH2RNIgUUjVBnaLBqkEAotWkMSCpU7EzXid3WnJqx88QegcJBhbo6z3oDLrAAAgAElEQVSnn8CVjVXiyDKpS07HDVL1w/4QRchmzMagR5EqLq4MyJOUO09O2R9DEUtkknLnqGZ/Ak5llAZmswpnJM7HUAuEhFRZ+pHnwnLMs5sZV1d14Lb/lNfMWj44m9BVDVle8Orrb2OqpNUIgVJh2OMFNNZiqoqqqoK1uXMLRoCWgkgprly8wOdfeYELheHg3bc5PprB6QnTgwPGNGT9Lis3nmeUdRhsbnJwdMrdex/y6M6HbG0lGGdpxmMmkylv7u5yNBnjrGc0Plsk3Btrgz5AKibVjKqqyWvD0emY0dkIrRVlXWOsYzSa8id/+u9YWurx4ovP0tQHvPfebaqqpq7qls4HURxx69Yz7O3uUZYVWkUkScLG5gYPHm4vdDxah4J8oVX6IetH7WULamBd8y9//9/wne+8zn/5X/wTvPPcL4Y8Ss6IrzzDy7/wGU6fPMZaw/HpjNWVJZx3zKZTNq5e4v63v4HefgA66Fas90wmE8bjMeV0ysnpMXmRMej32dxYZ3x2Rp7nfPmXv8DBwR6PHz1uIzLA+HkmYeBs/zAU/WdxNeUULSBL05AP1DZRc5TKtBPksiyZzYLDXFEUoQ5Qqo1xCd9jjAk6Seeo62rhDryydZnBcIXYe9afe5mqLtk/KRkc/DWPT7cZOsOV37pBJ+5wVh7S2azYPXgTc09yVk25l32Cvz54lmsbA35BvU7HPOD5bpfb0xc4ObjAtcEternkpUs5zy9paEoQBukqUvEpZM/z52/c4f3H76Afvc6F9ZeJ4jFFusKFKyscHp7w1W/d51c+vcbW5hKuRRyUCkhPKLw1IgsMl9ksmEfMyilJHOGsCwG8zuOkbdGKed1BK8wPw2fbBFt5ay1KK4QK1yuOY+q6DqiWMQsLfx3FmLoKNNtWeyKEoNPpMitnzGYlURQjRdsgRBHWhHwyFWkgvHfVBusez47RUjGeTul3+8RxQqazH2ug8TOxxEdDgv+uVTcNtrEL9hIeOt0uZRtpIXVwbIZQCwaqc6hdpRQURY6xBgcto6llCCiFUmEeb1rAQUnZut4Feq3znkhBpMGaIFVR3lLWFcaUuJFjfJKEKKB2v5Qi/HuOhikZctayJAztBp0UaNgcFqyt9KnqCmdqirxgMipRT1m2x23GmvCBvaWiGNlqrmKlifKcolswHk8wtkGI0PxoFSKBQt0KiQfvNNC+HxvcsGUrr4lkAErqxmKcD7W9cFjT0DBDKonRmqg11Qu0WlBJTJKlJEnMYNBD4Lm6tcHG6pD1lSErgz737n64QFuVlMSR5mDvkMFgCWsNjx8/QMmYcjZlpdOnqpqQdfgj1kdrrNrmKSBR8zK0zZoStOJbsTCQcC40AoEGB7ToE7Qcch+oeJ5A9QMWkGdjmkXTNEeoQmMgF9SQINoKieNzV5D5u5qL2eaueFIE/dNclSWFROgQ4OetQBEoetKBCf7oWBEmEdA2Pu1GH4ShOiBrPqRDL+gXYh6WHN6IcMFqXWvZhu66sNk9tfnMnQO9czgbOnTvW1qjP7dZl+3PR4jguDjXWrUuS3Mkao5oKS1bhHDeOJzTYs5DksPvDGht3l0bpsz8Sv4E1892cXCOtGbMvOab7z7hD/58m7ceHlKT0OkPkTKhsmEjS2JN3OtAHFMpjRca6S1WSqRtSIVhmEmeWYVPP3uB61fXWF7uMjWev3x9mySKWe5Izk6P2VjpYKeeLBJMmwZPw2duDJnOKpQUSJFS+x4Hk132S3C6RRStRUsTChIkTVOhvcJKh/IhLNNYh6+grAy2doHzfDJByTHlyTZZErGytITCk9kRs9GUAzxLK5v08pT93W28jlgebqKShuXUMJnOMCpCqz69YRdlpsyMxLqE9/csSjT8wpWcJK6InMe1mRZCqVAcKIUSDm0tSSRIjODhozPGTQgUlG5GbT2dyPDsxR7LnZTG1jwcQdkoVBThrUc4QSf2DItgSBPHkn7R4eJKysnUcv/Q8t7uFKdzvIg4PjijLGekWYazc7dST64Fm7ni6mrE1a2CtZ6mn4dB0k99SbiymTE7m/Lc5z/Jv3v7O7hJoBMLBQKNkHGYcgowzqKjhDgO1EkhQ3OlpUJJ+O1f/Qr2cI+HX/s+/vAIH2d0kYylYrZ1lfd2dhHVjI1nbvFk+zHXrl9nUlYIC3v37qGylFd+8Rf5+te+jpSStNfnw9sfQNu8iXbvqmwFHoqiQGtNWVbs7R0yHo3AO5q6NRoCRuMJo/GE3b2DMOluzMLues4qr+smCJC94OR0jPfQ6RR8/guf5/HjHaqyIknisM+3Goe/q7n6Yes8+0QymUx44813+F/+13/Bf/Lbv8l//k//Kb4x3L57nz/9y68yXOrz7LPX+d4bb/PeB7e5euUS3gmM1FBZDh4+oLOxFc4C7yjSiDwZ4IY9Ll7aDHuzAOE9Rbfg1ieeZ3l1mTTT7O3utdRNgVJBW+TsfPD2s71vQjgfcSVShaLRGIPWupUKSEajM6QS4Z7RiqZxZFkI8a7qIEI3zTnTxLVsi6qqghjdQ1VW9NavszTo8vj3fpeVB++xv7+LfeFzeAE6kuwdTdnaqJlMKmbRkMnJIbgp7vARaVXxpu5xLG6xUZ8wK8+IE8PEGGQaUaynqEjQS2IupJ4Yj4gSvIuQKuf69U8zs47u2rOM6t+g4/fZ7J5y2f4hp72rvFq8yHp2j29/2OXlWx3WmghPjZNBh5LmOWdHxwyW+nSyoGmSbcRKYzRSaaSAcjYjzQusC02WaUyw3E7TMPizFiUVVliMNURx1Lr9xYthrK88s9mMqM1Okkqhk5jU54xPTtBJjHMBxbLO4tpnKOipAiVNEOJZpuWMrG1A5rVFnKYkWYgY0Ekc9qGm4fRs8nODWCml6A/6P5HXGp2NqMoK70NorRCCXrdHFJUcHx+1msFQF0et0RktsJCmCXkWbPTDvW/xUpKmSZsD12r3RaCpewlJHFHHhkgHPWKahIbXKEHjg9140oIEjXH4psI6T+MdVodooTRNFghxVmQ0Tc1SZ8iw12Gw1KGcTOnmCd1ORnNcIgn777jVFlkXWFxFntPtdKjLKVISDCGioDOVAvKioNftsf/kYJGBG2mPsxKv2kZQCSIkzunF3hFFHuk8cSyIjKIUEGtNoy3WeYzz7eczC6O4WHq0DC6KOgomLktLHdZXl0nTlKvXroL3aFcxHHRpqpLh8irD4RABbch5BAgirXiy85il4TKdosN4fMZ4NCYvJjR1xenJ8Y+8Hz5SYzWnuc0nonKOmjDPDZkbS4QNVEqJI1wEiWwRknCjWNwi7Hb+eoLWxc75hS4jNFUhbMx7i/AhWWZuTR7OsDYY+Glefds4yLneSbbNiJJz8CaYC9rAbUUGrZV0HoPAtMJ71zZOyLmRRPh67xxJGhMp2X6OQFMUXiJFa79uw4RJ+rYIXujPzhGhuVPfwibScz7NEGGD89bhEAtKxQ9mn9AeXnMuvgrNFA5jgzOjc6FAbK/O4pqEJjhAr/OmGWhdac6RxJ/U+rgmWT/utHf+dcY6Xn97n//rj9/n3YOaSsYoG5yXnDckVHS0oOjGDAcb7Iwsj08ahLD4qgZvyGXDamK4ttbh0zeWuHXjIstLXXSS8eqbe/z5m8cUkaKbS8r6jHo6JcIy7BUcHB0zHY+4tjkkUQbf1MSZxVTHJJyQyJjjOmgNlDAk0iCUxusE4zwJgto6pAtBj0KAUGEa3pgST6tjqBqmpxPGTc3xzg5rw4ybm12MTNifGqrpEb1sCdMr2H/ykDSDru7QFTOSZEaDwNkGgySKQugx1lE6eLAzZqBmbC118S5wxpVWSML0yQuFNy7oqKTk+HTMe3d2MfRJlEU2JbETXFhR3NgoyGNB3ShGpQARM49g0DjWejGrPU2RKIzUreFEcEx8b3efnb0R/Z6hnJZMz8YQJaBE61boQRgGseSFCwOurcUMOjGdVJPGTw1MfopLWMu1lSXyG6u8f3dE1SiQYT+I44is02V9fZVuFhNHIGQISZyMZ4wnJWVlmNXgnCA+O2H67b9Gjka44xEpAj3sofuriKjgyrO36O484fjkhOXlIVI6rKm5fGmLfrcgjhy9fo9OniGdw5mGxjvyNKEgbinL0BjLdDbDWk9d1Zh2yr6/vx8smJ1fTM6fXlUVXLnidrqutW51HgZjLEXeAb8brks7tMqyjF6vizGGpeES29vbeO/pdDqcnJz8QHP1t7VWT/95jqSkSUKkQ6DqdFpy7/4j/vUf/DHP33qB55+7wYf373Pt0hWyPOXD27fZ392lNobNzXW0ijkdTTiZVmTTKX40WiAzvmUpzDMH2x+KUkGDUZYVDx5tszLoLgwbEME9z7nzJjQUZT++yP7jQLekFCRRiGIpJ9NFxktV1WRZYIUkaRoYHD4UZHEcAwGhaoxZ2IjPTQCsc1RtsWmdoyobjo8fcj3a5crv/DrN9kuM33+fS1/8DUTPMDt6ne3xARtDGOQPSaYzvnevZu8ooZtr+psv0Bn+p1w8TXiwd4+/ip8hzW/xvYMlptUhl69eotAaZ+GtvYZPrmv6CYuGeNY47pxZrEjItGIl75CKN1kp36EbVfx1fZmOv8ul4iYPHn0I5XusJiWimpKpJJhDxDHGOKwtWxQvuPglaYYpK5yAJEuYTaZkeUFtm0DtcwbVNkhREowjrLOLQnBOnUzTIOyPWr2iB5QIWhwHxEmG5QRMqDuyPMdYR1HEGNMs6hDrLFoHhFhrTW3NYid07f2V5znj0RjXNNRlRZYkFHnx89JXIaWiKDo/kdc6PTnFGhNMJlSQbuzt7aKUDsZr1rau1y2zyJgF6JBnKVmWBudsKbEuNNtpFGOcC5RSLfFuvjd4klhTp5o4keB1G9UgULUBFeiCQigkAk2Qlag2hzUvMuq6pt8vCMBIQ5GnVKVn2I1ZWcroL/U5sQ15GoFp8I2ByGNNAy1NsDGGOIqII83y8hI7j6eBeic8WggipdFS0Ot2WFtb4dHDx0HmokKTGPZpgRVzi3SLjwHvkBLSVFGWDVGkiSOFNU2QL0QJ1rtAO0VgfTDKkULSLdI2nFwhtSSKY7qdnAvrQ9Ik5tLmGnGScuf9N9FKMp1NKYqctbV1dp88JMvmuixHlsUcH+zxwosvcvfOhzQnR1RVRVOX5FnCkyePfuT98JEaq0AbCwWNa00rQoDc/LASCztbKc5djZyzGBuQnrlpgniKDjjfRF3L6w1ht649aNqmpO2Gwmc2LdkwNDRztGqBEongmqNUsHQUlgUVL8CHImzwroUMhQQl8VKQSIX20LhAtQnMw1BUz3nHIaA33FCizZ2aOzkpYYONptQIHww3wOK9fkp31lqaQ6uDcj8QQyoJ3NtwHeUCYZpTB3/Ata+lB4X/Ds1jgLjnxh2ty58715nNa4w2dSxsBDagVPLpBvX/Jxqrv12IWGt4+GSXo0kNwgZaalUBjq4Ys5rVXFzrcfXSMptbW3ztzSc8OTgg5EcZFCWrccOtjQ4v3driuSsbrKwUpImmMoLbew0PDhsKXaPyjEjFZEKRRJq61jRJzOGxgzONLCWZ0BTtQOLmZspgPOO4MjSuwRuDszUzqzmZTRCzKcJFIeSPCOlapLMMbmmJJPAFbPjt9le3kM6AnbI00FxZSZBaMywdT04nzGpJf9DBmx5nu7dRnQu8sFGQdyIqozkraw5GNae1RKgI4Su8dFRmilJdlFcIp9rnDPTC6TLcX7VUlFbx5oM9ZpUgTsoWva7pa8211YhuRxFFIkgsdYbWNVYE50HpazaGGb1cBwRRJ0jhsE6AFfTyGFmdoCrLcpYR+RQjEoQ3JMoRSQPecmOty42tiNUiI0viNrD76YyRn94qlIJ7j/mgHPHuXoRQKVsXB1y9tMylC1021zK2BhGRLekkEd5bzk6nnJ4qSlNgRYQTKQf3TskPE9TBIb6asry1xuDKNe6IlJNxxTPPv8jS+jKHpydcurTJpaFjIx0gBxuoOGbn/jtkRjOuFePTM6qmZFZVeO9ZHgxYySTDIue4ttRVw/bBMdOywbSB7cY0wc54fg3/FpgkpSRLUxpj2NjYYG9vj06noN/vcXBwyNHRMXfu3GUymS6+pyxLPrz9YQiVTVLAkyQpUJHnGZPJZKFfmO+3wOLPWqmQI+QcTRMauW63S69TMK2mGOuYzkoe7ezwP/7z/4lXXvoEzz9/kz/7i69SNw13795DR4qv/KOvYIxlb2+bs9MReycjOgeHJMhWIxaK3pBtoxdDvrA8TVMjhOSdt98lVkETG4ZgvjVJUjjpWktm2Z63P9sVq9ZhGDlHD+fN1YKK31psJknShs96qqoKNDZaCrY9jwFxLa0VETIjvRd8/f1d9rf+jKvDDpevXOfic18i1ileedLikzxzYcZ0dsSDE8M33zrgNL2GW38ZGa+Rd4bEDPjtR3/C8P1XceNdShS/5r7H43zIWvc3MEtDGmKeTDxuryGVhk4kWS4S7h4Zdk7GFKclXT+juz9jN6uxxT8mdheIk8u88/gSWRKzvTflwcEBn7pheTbrQB2QpbmmWXgYj8YICVpoEB4dBSQjiiPKqiHxnqWlJSbTKWU5Q0fBgMLWboHuTqfTRTPaNM1Cr6N0xHQyIc3SlkoV8rFkpOj2lzg+OMRYQ90GJus23+f4+Jil4TLz2m4+9JWuvUdbp1VjDHEc0+12mU6neOVorCOJ4vPn/Wd+zbMr/+ErBAM32CaYiQQUPzAiRNBjEElJXZuQYWUMSgb0KksTYq3CkF4plA+ulXka0RhHlmZkSQqe1jE67ATGBsRKJKHeaxpHJMNzlsSSFLWg4zrniJOYqoJ+r2Ay9awshfyx6WxGkcX0i4ylbkGRR6wsdZmdnCKsRzhLGgc5TDWZUNc1cRKTZjFSQm1qzFN1JXj6vQJz4oIjcRqzurxKv9ulrs6CPri9So1xYdDg5vu1BxeM8YLWzBNFrbFLFQytsjwGTxt7FKiRdROa0uGwQ5YmZEm26D+EEiwPuyRxjCScNXc/eBvnLFoKFI6VtTX2959gGkNdV0gl0ZFiMpkyK6fULcusriq0VkTx382O+Og5Vj6E0ar2EGhvoXCZRJiSQpgwzZsw6VtHOjlHRc7TjuebriDAd96d66kCHcIubtynp4++bcSECDft3KQBD964RZceoKnAVZ+LMEXrHkirTUFYvGvRJdGaSmgdPpUSCKnxLQUyvEZwIMHbMIFXiqZu2k+hkNIvpg9PP7bz+25OXQmGEQuZ1A+0MfNGdU49nH/fU6+ycEvEzr8+6LTmHes8nBnc4lrMfyeB2himh8649jB3C8qln286P8Gz/OPTCfx4P3d+ZSOtuXRlCfmNO0QmxZYlSniUcGwVgpsbOc9dX+XSxWX6wxzLJb77xkOm1qFdQy7GPLPR59MvbHLj6hrLw4I00wjhmE0r9vaPSFVwqTk7rhjtP0CUYzrdAcQZUjjWlpcRxTKu4xhVI06rGQpJ4zxWa3IMkZBkQqCkZtLAo4MTRO5Y7nhkAjoSTI0N4XxWYJ1AigTjJMaFcEonHOgYlCLONZ3EopWjk8cMujlvPSqZGkG32yVuhkT+hIvLOUVe4AQ0UrN3mvH6vSkTp1tkFfKo5PLGJooghlY6XNdYn38NIngF7p7U3DsuUVpTlSNkqtB41jqOK2uhIY20QnpHLAzN7IzGGqIoJ43g8voSaQxJnIDWSCzGgfWeK8OU6uY6IulwNGoYVRVaSgplGRaSfpZSJIrnrw1Y7cfkOiGJFUoJpFwQi3+qK5GC4viM57ausvXiKyxffMAzl1KGg4bxyR7d3LK5tUpVx0Q65vhoxuPdfTwpsYwRowpTNmx2hoyOp6QqQnf7mOdfZuXzX+Jsd58lY3nhE5/A2kAzmkzOWF4bMOgu0SRrvPadv6DQR6S9FYZLL/Dma9/DSEe3U+CMZSnXfHYouXFhhX3vccdj3kgFD0YV+5OK42lFP4WTsmFmHCGz3PE0cpSlKV/55V/m7v27VHWYzGutggav/brd3X16vd5Tw6UQQipaAXTIQ3IkSYK1hjQN4b6z2ezfQ8iVUiRJTJrGNCYEVlZVxWg0oqkbjAsCZkFgAdy9d5/HOztsP9mhbhriOKFuLOPZlPFojF1Z5uT4hCd7+5yMx0z39pBlFcZV4ny/mzd1T8dkSCXI0pzd3V2cabh+5XKbfeiD4VNreCS1wjmJ8B/BFfBjyAiQoj1TnSfPc7z31HVNlmVMZzPwntmsxJqGfr9PHEecnBy3QcARiHAWO+uwPliEK6VIsxTThLPSRzFbL/0Ofvk53j++w+NyG1++TTM6RtJghUHkCT5e5my2yezClymWr6CyAd4FQ4Hlx+9y5dv/kv6Te4EBIyTWK86E4snOG4z+8X/N7OWv4KOE08py7CwSx+Mzjz454ebD9xgefo8OD1FOwBf/Ge/WX2Y6UYx2Dti5m5B3M175yhXK7gvIlQR1+jeIow9D/k5VL859pTVxFAXzCSlAyFB01yY48TlH3TSkacrpaITvOITQVHUdkNYsCxRKa0iS4PZXVVVrbCNaLY8JGZ0yWFB7IEkzuoMeVVnSGNMac7kQhFpNKIrO4vWaFsUKTo0GRYjqkE8hsU3r9ia8p3TlT/3e+/su78OA4yexpGw1aW0Oq1S6HSy01XFbn8p5plLLPlKRIk5i4igiioL2XrrwWlEU4QkB21ESYa2latrhdwx5lgWNlbS4CMqqxjQKYwJimWUpxjTEcYdZWRLHEVorup0c72ryLFDeZrMJRZYyXBqghQkOsbZBCdtmyYbl5jpW74l1BD581rKcoqMMHcWo1oyj2+txeDICQDhHr9dlMFxi++ExQgjiKNTXxjjKusa20UjKGJyjbTyD1knrEOUgujnGWrqdDAiawcZYcIGd5axh0O20QeRRu2fHWGdY6vfo97rMZiWdIqPfHwSGhFLcu3+P6zefC0OM0XH4jTkfgrebmuPjI+KWcl7XDdYYrNbE0Y9unz4aFbDV+0Ao4dsTaCECtNada3cIIWS0WIxUCu8srjV68CIUVyHeSiwarIUTX9tYzKdcTsxphk9NIn1AXOYBwN57AjtQ4HDzsKbw5udT8tY0QioWCJhpLDpWi4MwvCcTBHqtRaxonV7mRg9SCpw5z9iiPUgFoXjQOlCvlJTtQRm0TB4WFuy07oPtB281UIHqiPNYUwdRr/fnN/hTlD0h+IEDXLQUzNCXnh/i80YWcW5UEQKR59o2sbC5nDtSCT7ORugnuX6c4jhYpAjh8dYhveD6xSHXNlY4OjhEM8Nbz2oHPnl9mVtXlrhyaZVONyNJNLcud/jsJy/yre/foZNX3Lq0zJdfusL1C8sMBn2yeO60JKiaiknTMBwWIbF8ZtFmmepUcnw6pbFjTDPmyc4TdNZjuLFBN9HEgJUepbt0O8tU5ZhqNqauLbJx4GtWuoo8TtkaJKx0M6IkpREW4zo0LsY4SVk7Ds5KTsug62tmlsY2NL6hk2i6RUScaIpOzsWowMcz3rm7T1k2rG2ucik3rAxislyikwgVpaytZhgx5e37B8xcgjSG6ysJK0UoDHUkSJOIuHXucV7QOIHDclY2fP/OHqNaItwUV1mEgl6quHk5p5dFpFFEHCmMErx4eYmj0wm397bRxSr9gWS1UGSRIEnCweQQ4ASJs1xaKejmKa/d3edkWqI0dOKGyysxF5cjhp2UYTeiX2QUcRImWFIiZdjTPg7UtmksB7Xmyq1fp+hofuH4+8zOam4fTIAKt9zjQrSB8glvvrPNwe4p79wZs3LhItf7S0RP3iGyFpNn7ByccnV1Bf3iZ1h64WVK4MneAc898wzTyZimbtBKE+mEO49LnnthibWlPs/euMH771te+IVf4q23PuB7b7xBpGNWOh2WYnhlU/PC0OFEycZwwOPTbf7ZJ3Lee+z4k8eQmQmfWC+YdNd4eDLjgweHlNYvNDQQCsuNzXXSPOLd925TFAVZloUh11Pap4tbm7w3nuA5ZzS8/NKn+OY3vx0O8W4HYwxVVf5Q6h/MLedT8jRqQ08lE1lijGE0HjOeTIAf3PO899Rlxbe+9TqbW2u8+IlbRMmM2VSFKX04f9nZ2eXs5JQYj5y0f99y/n+YRipQBMM+vXewj/Seq5cvk8Qaa/xi7100YVIS6fjHvhM/DpQVQsMbab1gQKRJskAJhRDBbEQIppMpVSmpqoqoDbvtRAlxnodzV+k2mylQ3CpZ45yltHDr5S+wtLpF79kX2Tk45vHJGcfRGOdt0ChFKXE6oDsoGER7ZOzg5AlVdYHqpCH56u/i7r3HtAlUvCROUEBezShOzrDvvIp46UvUOsLb4A/slaRzfMj6u3/AVvVNxIVHyBWLMmC4wmfSV0gGXT54cIezK4qT6jHxTg1Ln6exKbMKVMs4yPKM0+OTNjNKM55MiWNN3YTBbBRplGrpgtpRjcd0ig5JnGJNCFRWMhhJNMbhhQShsC7k380LSq01SZJRltOgJ3V2YdQVJTFd0aVpGjIdrrMUAvy5Jt3YYKIlICCuUjCbBYpnkoTYBedb1DnPmYzHxEnyc1UzCAH6J/SsaK2IdGimnA2Mrbqqg1tmHAfXRGsCKukDY0pIT5onpFlMHCnyLKNpGlCSNMuYzKY470niiEgrfBpjjCGLE9I4DrWbEJg40Katt6RxQiMMWazp5Cl1rej3+5ycjIiT0AxrJUjiiE4n7LXjM0GnSFhdXWJ0coiWikjI1iQpyEp86zKrpCCOomCvrhSmDrrtWAtsOywNPyM4b4tYopVnsLrKYGnIo3v3FnFNQgisdoCjsW0t31LBZzNPEkWkSYxzgXLYzQsmkzGdPENJGQLhkYwnZ0ipKGcz4ihEhyRJRGoUURwDjixJ0FrhbUOsJZ1On+noAJkmLass0GTHp8ckUYwkBJLXdc3xwR4XNtc52NnFCcN0MgmN8N8RxfLRc6zs3MUjpFEy2jUAACAASURBVID71vjA2vAQznn0zs3NFdrv80HvY1p4eR7eO+fvzV30rGt9AOdNFJa5yyAtx37ugPd0g+Xn1uyypRQEr7RAKVigXq1GqQ3Ycq3uY54Kbxev61qbyzDhUe2EyViD1LoFwCxRFCaMVgAuNJIhhFgsqIne0VI83OK9zpvIuZW6nzeA4hy5E7BAyeZ8v7mBhphfVHHuKji3UV+Yc0ixyAILf9MiWP6cauFDCQrz4GQhgplI+5ryo5n8/hjrY6II/Ac2+7/9roSrGcSOLzy/zp07J8wqxZWVhFuXcm7d/P/ae7Mmy677yu+3pzPcMW9mZWXWPGEiBpJNstmCRLZEdnfY/aAIhyPs7+Fv4jeHXx2OsB8cLasHt8RWq5u0KJICSAggQQpDTSjUXDnd8Ux7bz/sfU5mSRQFCHCXYN8VgYiqQua995y7z97/Yf3X2uLMqQmT4SDQ04RjK/f8/rdfYWJKzm6mXNyZcOl0n1EvQyUJSrbrKnR8Cy9YNQmuKfBKko9OY/rbjLxF1AVPHt2lsTWNhLoquffwAfO9B5hUY/IeSZ7TH/TJ8h6jrW20EBSrGdVqgXMrSufJjSKRnjQT9PKUPB9itEEkklVtaZyiqj2P9+fMlgv2plOunBtyZqLJ8pQ0MTRC8RVjMHXN2788ZEt7rp3bZpw09AcZaS9BKM3Iav7JC5uMTM1bt49IcVzamZAYE2gOSZD91m3HGEBIahT39g+59fAAYyFNPcgg/3t6ojizPSBPNKnRmESROLi000Mml9j40HM4q3nu9AbDTNDLs+BVRVQhFR6nPVmiMZXg3qMjCpdzdmK4OBZc2M4Y9wMne5DlJJFuoOLBICKd9lksWW9yelf/MU9u3eag+pj71Qxp+linODvZ5MKVC0yXDdPZgh+/c58r51/kyitbKDPg1GSD6QcfIo4OufPxI9Rkh8k/+30mFy9T1w2/+su3+N6/+df8YDhmdnTIxsYGw40Rw8GAay9c5P1/c4Pf+85vYcyIM1e/zM0Pr3Pvoxucu3CWezfe45svbfHSuQ2Kgz2accLND2acFhXPv7LLjljimj63bIObNAxwvPq7X+Vu2fC//Ksf8/H9Jzjnurmsqqr46V/+jOeuXiHLMra2NjAm6eh8QoSA5dpzl7lx8ybegzaGZVFw5blrvPfedQSWwaDH0XTGo4cVVbWiKMqn7qfWijxN6eUpiVEILxAyBEJ/nc5xsjMW/4HaBmXYnZ1d7t27T57l3XxwXVv29/bZP5rS1wodq9SuEwAK7w8Eg8p2PxZhnXoPu2fOhjMiqudaFz5TKxQA4D9FZf1ZBbcmBkVVUZJl6YnkMOtUG7M0ReuQVCVJQpIkrIowk6d0KCK2M8DL1RKlwloxJmO1nPLyKKOfh3m47TyhLPqQnaERmtb6pHE1tr5Fc/CHpOI9honENt9k/kbFhff/jLECJxPSJIvjBGHgfWJAfPgTkhs/Z/Xq6xQqFGo3Dva5+sG/ZXv4n5hefsiTfMJetUnpc15Nr9GXQ6RouPiVXX7+458yUZ671z8gv/QKjPtYH9Z6kiSdFHeapWRZFimpVZDhB4pVgUTi8LgiUCFXMnTzg4JgL8ZdniRJEFG6uyxLUqU7cQDvPXmeUzdVoP/TSoEHWqmIHcHWPqdqRStEEAxxMcZrPccEMkqqt6IkIZax3sZkULFYLOLc3BcFx53xz4q2EFCsVngfZgsNQWZfyjAbW05LFsWSPMtJkqCkN+jnpEkSk4Rg9lxVdehmJgYH9IVEJxqcp1Iq3O/oRRU6jjookVaW2tR4FGmiGfdzllKQZQlaBxW+PEvpD3ooKcizDAGkqWbY77M5mbA4OujYU8YE0RgtQ6dfKR08y+J3nJiEum5ivO46oSchwFtLU1cIkyG8YzweMRqOUDrMnGmlkEKiVGReNU1I1q0LAhZ1YKnkvTQWYELRAG/JstDdM8bQ6/WpmzLI3TcVSgmyzJBmKUZpjFIgQqfQu6CXMJ/PQhd4L8xv9fpDDg/36fUGPIlFsTRNEEqSpSlPHj9md3cXpSR12XB0dESWZb9xn/3UiZU9If3pYnephRAyUgDp/A/aQ0KJpPPoaNX7IimN1gOr/X8huy9x1sbgv6W3Hc9TtfNBHXwrFx5UA7WU2MYGFbI2+SK8aUfJiDNXrZxjS2qUCFoWUCtLDuFQb5M5Fbmr+FDhDgWf8Fld4yCaCSsRZCzbuarW46r9Sjp1v1gxCvNrx3fHRm5nl+XT1j9tyLfiPRDxJoTRN4+1xyTDljopZCt20aoL+ng/fDQP9l1Hr3tC/r+AvzPOiK3LaLqHNwys43de3mV/XrB/dMQ/vnaaSVIxmWyQ5xmpVjGx9WhZ8NLpnIv/4lX6pkEgyLQKB33gtxLlRxj3DF+9vMkbt2r2Csu8rFBWg8nJkgznQPc3GfZTaiHwUpJLSZoZmnrB6uCI5mjGXCi8UORnzmLyPlmWo5MMisdUdkmqJJkS5IlmlGmGPUGaSmSi8V53s5FXtzZomLCqGvq9hJ4OG5z3nsoKUteQXOjTLAa8eGmb7VFCpixpFmaxnANnFVmvYXBtiFcOZR0vXN5i1DMM8wylI001PMLgwtNrhaJ0QQVrbAyjfsadwyUKyZXdnFEqQoJoBFoLhJcI32BOKbaHV1kVnlODlFNj1VVdPR4lPMJ6cA6vBT1leenCFvfnltcuDDgzEIzyhDzVqDTDaImSsWspxXF3WDybAFWnPZKjKQ/uvsONDUG5PeDl8xsURw7vS2rr+elbH9A4yHrncNlF3n3nl3z15Rd48NbPKG7dYtnLOf+7/4xya5d890ygAj15wPxnP+Jiz3CQpZgi4+KVy5gs48zpTbL0kKlI+N73/jNnd89z9uxZ/v0f/CtOb47YvnCRy6cM3/32l7lwdsCjmx/R7/dZzm/y2otHbD9/kUfvPiZb9flOf49LO0MeHHqO5vtcPXOF7/zO6/zRn/6Qh0/2uut0ruGvfvVX4GFn5zRZdoblYskjb7lfB6U4qRSTSxfpDQfURYEQMHPwJB8zPHcOMd3DuprRsMfBvqEoyqeod1IK0jQh72UkqUF5EUgUMvjMiKr6tQfkscF82EP39g549PBRVISVSB24+9Y22LJkNl+Qbm4ExSJCobFFEyXHT7522Nsd/+1/8/sUiyWrxSIKdgTattGhi9EZuX8KSuqz6hnkaQbCU62WQV6/KLr4QJ4ocJpoLLtYLCiKQANqrUOM1qHqLSXVaoXS7QydYFY4EimwjcVozebGGKkzllPNvLQoH1TT6uYxe/f/N9z8zyAtSIyk/+YvOf22Y+CK8P35IDoV9kGHxDPMNEzvUf/r/4n+eAN/5QXc/oKLH/4x53be5MEk483Fd7n55GUc53E+4aDIOH/3Hba149q3vs5wLNk9dY4H//H7bN76gGx7iAQSnaBFMI8ejIaUZdmpwJkkCTTAxpLniixPEVJSFCuct5RVRc8kPH74CJPkDEejsDYiwyRJEpbLBVVJV7Rw1mG0ZNDvUzc1OCgoSNMMKRU60wxHYw4PDmiaphPBaGfiZ7MpJknwkZaV5TlKGzySoqrQUhODpTAnriSrZRGtdb4ocYPH+0+nIvq3wTmLsxZtDKvlitl0xnC8wSjLSEyKFBLb+CBKkuaYLCHrZRgdKJ94F9TsdIwRCYXtRGsGg2Eo7lhHkwb2hjIKL3PEqsAKQWoUdRWtVbxAa0mSGeqmIksUvVRiEkOvlzKZjDlwnl5m6GU9njx8gPCWpqpiRyp02IyUnWWQjuJXSZqS1XX4zDKOsAiP0pCmCdI1YV26hjQJnc5qtWLQGzDZ3CDJgnlwliR457AtWy1ev3UCMHEG1TMejliqJUJCv9dHS4mQljwPCevGeMT08Cic/VmKVoI8NWRZSpYFi5KmacBZvA2frVoesX1qwp3bH0CxJO/1mE0Pef75l7h3Jxhdp3mCSQzD4YjZdIqSisnmJnc+njGdzZhsbnYCMb8On1JuHUCEiZ12gu4Eb7wdYHNRAao9DFT05zgp8d0mAuHgsCFhiIoptglS6i2Vot38OqpepNeBC4le7PZgg5qKip0ZoUWUK2+7Y9HIVwRVEocDr2IVUZ6gDRKV/TxetJ+RrvPRdp+kFHHYNnx+1Q4nS491FQIZTc/csYysbaJCm+/eK9ADXdfAEzFBCglXUK/pOl1KxK6WfDpR9aFj57yPnbqYiwrfJW/H3bRjOmF7aAshI1UwfKhOHONzxbM58v/ObT4G/K3BovASrGaYlfzz3zpLXe3SF8FMN1U6GDDH7qJH46VHi4rBUELYb6J4i0N6i4tKLV54hqnmX37tKjs7S+4fFBwuPcvDPQ5mCx7PCw7KEnxDUwlK62lE5OJvbJPKTXqndqiqirqylFVNowNtpFpWCO+Qiykyk2SRGpenCb08o9/LSBMDKgl9YN9aGISndNwLnTWNDevGixB8GIXZMPzTr12m30sxEhLhMVLgfBB5sVKgEJBYfvu1sxgUG/0m0K6UCjONMZgMz114Tyng7GTAl89vYJKE2aJCU7I1UJwb9xkmCZkxpEagtMf7UDnVsqFvBHojj0O/FtFu8rGzbQQQbQuGOXz1yoSXrGSrr0iVJ0/DYeelROFipyouBuHjnz/v9f/J4BZT/P2bSCWZ7++xmUJx5x4vf+15btx6wL/9D7/gaH/JhdPXOJ+fZnn7I7amBzRv/Ij7Tx7SbO2SvfAlNr/6DX75V+8zWSzZpOLRr36Cf3SPl0/vsLe5zXz3HGVZgahpasH77815+dWv8Wf/9/c5vHOXP39yn1E/47EacqmZ8dJLX+LdD28w1Ns8uHef+aLhuasKvTWh2fkdkvl7zN77BbcWkgFXOPvbX4H9GU8Kzb/4r77BvPT84b/7912hKuw9cP3D68ynU37r9W8y6OeMRgMeP9lnsVjg05T76ZB05wz2zm0QsFgu+OHbP6fZ3+PFrRGr1YLZdMF8sXgqqVJKkWcZeZ5iTBBxCWIQoqNrn+xQ/Y1u1QnMFwum0wWXr1xkejQlSVLquuThg3toozk1Gsd9xD3FIjimxh+j7eSUZclqucJVTaRphUqutQ7r6rhP0zENnlnG9AnQeToqhUkMi/kiWIRITVVVwby8aaiqin6/T5JI6rrCWk+apuR53t1/7xwymuIaY5BCUlc12899k4Xvs5mEbtiyqtirwgGqhUficL7m43vfY37wBgNWQZr6rmD33RI1K5FJnAFwPs6zxbhCBNrfyCjKj9/l/v/+P5L99/8D/fkhG/I/cn+k+NPZ73J79ipKbXBmaxvhHNI7+levcnFL45uS1XyOHC44f/aI84c/Rt4YQ17TNDYEqTqc27PZjOFwiBBEYS/RMYEeP3rC1ulTOA9KpxSloy8VW6dO0Qp9tfFEK/IhhGQ6mzFqTUtFYNi0xue2aTDCIGVNlgXKtNI6dp88WRYMx4ui6F7bJKGr17jAmNFGo7QPQhoyCmER5sKE1GztnEF87iyX/3chxOezx2sVhCUG/T6reTCeV3LBaGMSKLBaxYKBQKrQJRoNBpjEdI0Cj6exDQJHksS5YifpZSmNVdiiZCUEaaKDWmPjKMsCkyi0EKRKk6igzqshiOJkCaNeTrFKSZKUtNdjMhljm4ZUawb9YGQd1KfD/JTRwV9KiEgNJdDyqib4cAkhuriWOI6TpSmrosIWdRA2UYqt7QnL5SokfMKzMdkkzXOq2gZqo5cYIWhqHawWhEDjEUKxWC7BByNe29QgLEmisDbBNwVGSrSS9DJDf5CDc6Q6GG73koTMaDJtqG2DB5xvkDIUFZq6pj8aBEVOEWZBL5zbRUtJ1TQUFUzEGCEEaZZyeGjZPzzg0uXLPHjwIFDOnQP1t6/1T9mxChtQJzgh6AJx52LS0qp+xURKxuq3s20CdMIXyh9L8Dp/3E7sVJ3wnffTcVJA174O7yziUFQIXMMMWKxESd9GW12S0FKRvAifQUrd0Qq990itO456K/OuukHE8Nqi7VB5QISDNLTVw+fWRiKsJqhPRyO+trMkg/+Vj6+vlH6qmxYolMeJj0B2Sn7ShzmdtuvU3Y824WuvA+IMV3soOzwOJVTXDRPixDVEilo7z9Z2ucTnXrF/Frwq/k4qYPeDXYBVY6UG69k0Hi81HoOkxogSvMZ70728Ex5FNPU0BuFs108Nf46JvAcFbA80/+RqimtyVqVhuUiZLRe8dfMRP3n3AKE8440e02XJ0cqxahR4TS0VlRPUGLwGnUhwFXiLdBaFxxUlg6QfBj5Tw7iXMMoT+lmgHiAtVkDQsxRYLxHeoyRI71A+FC2cBSMcIhEYnZKn4T0VAgPhen2DFzJ0hZUmdRl9YVEoUqNQoqWgtjMvns55wFk0lq0e/N5XL3Bnf8mf/+VtsiTj2rkhk15KzxiMNmjtUdLj0YBEiiaI0QjRqWZKYY9XVyy06CBDiMhTjLHBqFgnCBklXxGhcEK4rtCxbem0zw7ew3JRcGpnk6/kpxknjmY249aP3mRR5zR2wKXLr9Gnx6M3f4o63GPH1+zXDdXps1x5/Z+ydekST/YPeeMv3mCysUF2/zG3336L2dER+eZZTvUFudniwd4+G6NNTJKxWpX8pz/5v6gOH5Cngt6gx88+uIXaLPjKqTN867Ud7t/dp3YF1x848sNHmP6KZnSJVaMYPv8ql27uIXoVkyvnufjN/5rewT7m3j1+/MO/QMsw4N5SoMPFBhrSw0ePuXvvPjs7pxgM+jHQltii5sd/9Cf4vTCLJBCwmPHwz/8zzXzBq9/5FqWQ3Lz1EfP5IjAPRPDf6WUZg/4AbRQeG77vVjDIe1ITxDKq6qSKYPxQ7Yc7sWe99/4HnDu3S55nPH/tGocH+9y795CirECBVK0SWGyJxddw7mT362QC57h5/RanNjdJUkNjLS6q5wZWQRmPXIm3fMJ97NnAecdyuQqy0U2g76dpSJZmsylCCJLE0DQV8/mcPM/CuRjnTdIk7WTqw6xO6Gz1+j2qsmQ2W5Bde459a7Arx6Ks2JvXLGvPtKpxtmLc64GvOZz+AkNBVdVIl6LfPEAdQSOC8qIkJjLiuFgby5kIBGPlWXz4JuUf/M+c+8Yu82sFP1x8m9uzb3Bu+xSnxgN6mUF4j6Zmd/w8I7sP1Yx0NOFHP/kR/ct7ZFtzuJehL5wJ1hQ+KOcJqRgOx/R6YfZpPl/gnCfLMmQcTQgm2bGgjGO1XDHeGLEqSwShC2q9R+lAwxsMBqwWC8qyCsInXaorEEqioiFsGxuFbVgGcZQmFNOsteRZznyxCJ0tZdA6wUgZaYJgadDaUEXPNeJsDNLgSbCfQmTlWcNay2w2/1xeq6qqQGWtLcVqRV1VLJZLnLU0ztI3GSbRjMYjksREqfc+2uiuwNPGiFrp+DMSqQSDwYD5bEaSJBitgrqdkBglGeQ5IqpDJyaIodRYEpPQ7/WQQJ5n9LMMk2WkeU6eZWgdWFppYkiSNAitORfjJRETa0OzKsD7ILnfNHjvKKsS7+OIiz/R8ADaU1gAqTbM7AJnHU1dc/r0DqONDZYHD3GE2THvIU2S4GXXMUUcaWqw1tPrZVhbBX9OJcmzlGJZkhqDVoG+v7U5YTGdotOE5WLRUb/TLENWVaAXNhYpAwUX7+n3eoFCWzXUVc1oNKJuGvI8w9mSuq5Io9+Y957VYkWWDTFJymw2parqrjv86/DpxCuIVcb2gPBRaCEmAUqpbnC1TSZc/LKU0qFSHavkgYImI4+8Ca1U7xBeYV1zIhGKX1qX0Imn/p8kGPB6FzZpH34YXKB8eOG6mNnHLDxkMO2/1SA03geKR6gaxapnlIVvO1SiVUI8QYGUUnWccClCktdSG4WI799+dh8avEFrPmxuTeSWSunBxSRUeEQ7D+ZbJS3RVQ+8jB5fsROGJ/YAWirfie/Mu5jLxsqSaD1VfEdXkVJ13RoXu3vdAPQXpav/G/CJZw7iNTthQRYYPLIRQfaYCvBYQtdECBeZmz4y4uOvewGiFTqRgQIYoiJUtCBIxYKJaGiEo2dgtCHYGPTp9c5ybpxQVQ3bWwMW8wUH04b9peDBUcXevOHhwYpaK1aNp6g9uApjJNJ5jAchBdvjPkmaMOz3GA1zBv1e2AQEobv21D2J3V5ilURIhAh2B8rKQJ0SUVil41A7vJfgErTw6JigpCJ0jIX0JDL4WQT6aXvIH2/exjeAJ9WejYGnrlI2xzljnfLcTs64JwOXOg3D8EKEwooWRFf28DmltPH1w989YXBYqFg8wKOEwWvTrobgNUeQtA0XZGNCJcP8Df5ZsQA7jLKU1eyQl7aH9FNJ6QX7pWO48xIbL36JeQE3f/B9FnfvMLCW/X6PcrjBuZde4vkzm4jNDW7f/pjf/c53eeUrr/HBv/s/eDgt6L/6dXqXn+fjuzc4ezFjfmBZruBPvv8faJYf8VuvvcDm+DR7ZcWb790Dk5DZFTtbA/be+h712LB/mPHg0YxvDxR2YRi+/DVmdkC/N2brdMapDDa/cYalL9jcOo1Jc65/cIebDwrk8DzN/B6i6yyFtVg3DW+99Q5Xr17i2uXLONuE/bgpEHdvdj8vW1+2psEj2H+yx6PHjzk6mgLhsFda0u/3GGQ90iR0aJ0AEfe29hxIjGHQy5lGO5C2AHBc3Dv5jQiODo94srfPSy88R1msePL4CcuipF4V1HhMlAsPa92dSM8DA6G1zwimljVnTu+wOZngnaeuLNaFM9PaOsxXuGNJXxVncz4JnoXgindBCbdu6qDaGDuGgd4Yhs+DMXBkpVgbRKNMgo8MgKosUdp0Z5hSUfTHee4/mWLPS7z1HO2tsM5R1Y7Hy5I3P7rPzmTA13tDFo3jSf0yWbXPUN5goRPO/84VNvZKnpQ5q2VOs1/injyg7zQiz9GDBCc8tvFY11A5w7Jy2NUtstP7/Ei8ykfLf8S13XOc2coitcHGmS74aGp5XCp80+NXzTl+Vd/nW5tHHGzfo9I/4PTo26Rliq3DmnbOsTycImUwh22aQOtfLVeYxHA0m5LmKUmaBIGJRFMVK/DDONcSjLOda308HcYkDEcjlvM5JtGh4BR/pqkbtEnQOvxuXdWI2A3USlNUNVormsaxrFbxzw12VaJECLwBhFY41yCEZjabk/f6mLQHSBKhKK3ENv/wbQFatDHa5/JaApq6ZLkoWK6CMmJT1RTLFaKuGY2GeG9J0gStQ2KVaI0y0dcx7m/t3B3eI6VHSN0Ji5joGaWkQGlBojRNI9AiwXtPL0tZzQsEkGdJ8B4UQS49TxOEDjNWrmnQkQWlTfg3IRzCO9LEhCTL+25m0Ub/tDb+LusarSQegVbBmNg1DU0VDIFV9Kky2uBqGzqeUnB6Z4fxaMzj+x9RN5bRYIC1jr4IOUDZBAE1rTz9PGVVlDS2openLGwdkqWNnMd3V2Rah7kza9nZ2ebj5YLMGLxtUFJFCwcXk7aSuiixfUvWy1mtVkgkWZZTlUvm0ymPHz9ka3uXs2fOcev6e7iqQWTBc9A5x+HhAVInDIZDZvNpaK78Gl/GFn8vufW2IxXTuSDg0FiMMcetdU9Q6nOtCl2osmito7x3zHZd01EzpJA0Td21F11MHFrXahlnorruUmeWG9oBNrb0cWGDVOJY8z8s3hj4xSdK+NYXynefOXTJXDdbFao/IVH0Ll5y+3oxSPT22MAx2I0EbX3nHQoVvQyiEIcA723Hjw7tIheDcReFK0Iw2/lIiGMRizaDloLQ/YuXf+ybECueLqpDiWN2fksdwPvYdQuJm7U2bMS0ohviWMr+i7FH/kZ8ksTqbzwiot14g2g9bXevXT8Qv1N/4ndPdFK7mxcT3vinkHu70EVWgpDhg9Cesxsp24MzNHUJroHNBOstq6biaJkxXTV8/EAyLwX70yWHsyUPZo6jsqERIWnf3crZPhWc0yfDPoN+hkmS4+5mpBcdB46/bnOIASy+K0CEmDReRZuISYHiWEAl0H9D0CpPMGvb9RlmmEA6ojCLR6sgvXpq5Pi9r5ynamrObCT0s0BT0bFq1KpXtv/Fks4xBVCEnxGEmkp7DRC6UaGL67uP/9ShGgP8NtB/1vA+OM7f2dvj0pUUaknhUtSZ19hrtrh/6wF379zDOAGnz+IkDFLN1eef49ILz6OEoGwcV69dC0wBqZnnm1z+l/8dZZLz5p/9CRviiGlzyP7tJ1T5gtz0eOHFK+xOMu7vz/jhz2/SeIlOwjD1jRs3+Npvb9Lcv0txmFOvGg56GX0/4kKSspt43OE9eucdw1Ovs2pW/OKNN9l96XVWzYD3y4u83YywuyvkB38AuO5gam03lqslt29/xP6jJ8wWC9LUBIGhuG95fNzTwmpKk5T33vuQumrCa3mPVopBP9gBtGIK+JhUnSwmAEIGSqh3nsrV9PJeMK41CYdHR8xmixN7R0j+3nzjZ9RFqJ6+8ZM3me4fspwtEImkWRUYbRgOBh3rQEqFdQ6tBLZuovxvoMx9+eVX8c6yXCw7w8tEq+6ZC0lVnOPU+hMnTJ/XQP6nQnx+GntsjtzeOxUr8C4yRDzQNJaqbkizPAj8KEUrsxTkv1U3AtBYy8G8xNcwcCGR8EDtGn519z5vX/+QL12+zCDv8dGjezzcF2wnK9JEYKVFbBZ8vDT88ew5HqgX2LjS47tXfkX/Y0OVnKF+/QXyK7sUyznlfM7RsuT7v3ifwfAxpzcLbq++w6Uzr3BuawC+6daRURDE2A0zPUQkguFzI3YGW4xPH3HkP2K/t+Jg+kteSV6O61eGofg8Yz6fIwQMhwOc85RFULXsD/pBJVC0c4KKXi+hqRuUSVjOF4w3J1FoKhSCrbOkeUZVFjjnMUmg+CVtQUlKqqpC64TVaglpGuwNlI5xQCgIF0XJYDCgKCqUNMzmS0bjBIdHNg6PQmvD5NQZnJNIZSjLFfgmzIO7Z0Of/vvgE5NZPuFrOQdVWVFVYojMIgAADAVJREFUwU/MWkdZVGRaM5/N4kMddAK0VsF+CABBmqZUZR1Eq+pgG5EmBkToUEGI50J3K8wipmlKWRgSHSTFExOo9945kphEDfo5ZVnSdxleSkbDAXXZoKXAO0uWaHr9PkpYnK/IUhNVtmMM0PnShiaIMUlI/mLMiIpjPzY8q8KHZ1wqhXThvLWuwdYrdra22No8xQcO6rrBaI2ScZ60qhBU3T4yyHPqpqapK7ZPnaJpapQWbGyMme3to7UiS8KYwObmJvc/uhtidg+2qsMIQmQuSCmpm6ebNXVTsbEx4eG9BZWvOTg44PmXvoR1DWUZ5m6rqkQKSZalFEWBwHPxwgWmh0c0tXuKOffX8ekMgmPlObSSjwkzIraZPR4RH1B5IghzzuNthYQgZy3oAvq2mkc8iDyum8cITZlj2qG1tjMSa4PDNrtHiTCs7jwgj4PA9mftcVehzcbbJAIRFcBistZy5ByEGRlrEZ2hcBzC7XjOkULiPVIprG1obed8NFHuqune4WwdrkfKEBz6oF/YSskHekT7uMWuoPdIH6rxxM9gRbjHLYWvDTzCwjmW3D0p23u8CfiY8IaDTAgfKYhEFSb/uW04XxR0FFHnngoKug5nRLdm2jg/Fui65Jz25333e2FWLnRBQl4TulcID9IjnMPIsBassljhwgyhF3gn6SvJxEDTczw/GVA0nlVlWdWWu/sL7h8VHC4dy0XFKxe3uXY2yIcPEhOrYyc/T/hMQegBomX7U2snPhJ4eUIkxhEvtF2cLb0p/LtHRHPpeM+6ROXkOnRtUyxcOsEwOPEKkQvOJx4ldTdE3XYPjhP8GGBLH+0a2mfm+L08wbU9dKOPiwRdQtU98nEHE7QvHr+58Oz79rt+BpDCo3NJ+QSaoqYwAw7FCMMQ7t7k8fU9bt1/zOT0LmdeeIXG1jz3pec599or7O8dUTtP7iXbO9ssZgumR1NOXXuRmx+8z/Uf/Z+cyy2DTHM4m9JYh17d4msXLqIlvHf7Pu9cv0dpQZmgAual5NTlV9j46quMtn/OOz+5ySSd88LXz7N9poc2luk7f8zdd27zkAXNCxe43pzm9uEuH75zj1mp+Xi/R7U85MXzQ96/KUMU0t7z9j57mM0WbE+2uHL1Mj9/95eUZYWOxSVnFd/6+hXefv8O81WNUB4jU1KToFSDtA6f9OkPekH2u7ufAnyruuc6kRMBCBUUJXORszEZMx6O6PX73L59h7IM3ZeThZn5Ys6P/+IN3n7n5yGQlCEhkE6SOs9gq89rr7zMzqltvIfBYMB0PmMwyBFAUawoigrvYofCJOS5Z1UWKK+D2qw23RmnYuDrPkXA+mmELj4vtN1EFVXLnPWx0i3JsgxtNNY2pDrh6PAQIQQm0t5MkuCsoywrlArqau35LhBUVc1g5xL981dQ3mFjgv3owT1++of/K/Oq4aN6zpPljMV8SXm4x3CyoDECUQOi4AfTF/nB7HVmB0vSRPKd18+zqo+wgwtM3ZjJ5ArqjEA5UIsV9x40ZL7m7fkYkZzn3NYQI21UHCXITxNmu5SQlN6R6YTtjRxBn1Q46nqXwpXM5wueG5akMg3WKUoxGo9jDOTjrLaNc3s1o9EG06NDer0cKRRN3WB6CXVTY9KE6fSIXr9HkgSpaOd9kOnGo5RmtSrQysbAWGKSJCRqkX7aNDWrGL+oaEpbFBVCBPGLsiqpGwfK44WkqMO1IQRSa6z3SKMRPuzfVe2YHU2RuGPF4i8AfHffPjtCASvGYbGZ0NQNR9Mjkl7GarlES0mDQzU1/UGPpqnj/HvoztpI/YXgP0a8v0IGYQhrbfD5E7IbrTFKR48lHea3BBipyfO0S8K01jjd4ICmKKmqmixN8LbC1jWDXp9qNQ1FrLYz7nmKDRU8nzR1XZMmKavVKqhve0dd1/RywWDQp1jOjwsidVDdw0NTLOj1MrZ3drDOM52tcDuBJRA6cSaOsoT9OU0T9CKMKWxubLCczZDek5njIo1WEgX0+z16/T40QTm1KIogFuJC108KhbOO1WrVqTQWxYqzZ89x6/qH9PoZy8WC5SIUOuq6pq5rpNGBitnrczibI4RnOBxw/sIFHty/w2jc/1vXw6cWrwjxYCA6aKm6m+G9Q3jZBfwQjeSEpPV2ColZoNE5awmpxQlKlWi/1JCQwHFXoI1t2+H09uAJgZOLCVug/1jrowlwrFJ3FCZx/BrEwFC2QR+xohQ3Ou8xUerVe4d3LXVIdElZCBSjsS6+66y1wZmKKomtvDsnukDd/JkI9yB4QohO2bCL9+JQro1UQxfdz8PLhGA5JHGxMxiDaH8y0I+BxHGnL1YHuwCSGAjTqRcK0SaGnyf+YW+67Xxa+Evb6RK03l9CHAuqnPitmGz4p/8N4sPiuj96EbyypPA4bOw6eqRoK9MWJRxWShofzHstNVKEA0sJR+IVqRH0EoV1ku3BmBfdBqsKbO2ZjCXjpMSohEx4guiz6z5XN1zcletiAkm7nv7adYg2wfTHlyWOk7DuddvnU5z8Tzz9WvGNhBRIH4zDtQzKh07L4HYu49yUfHqOs30e3Ml7Cyfe96RIgDj+3ixdAtUmySc7Vl1yFt/Ac6xY+qzgCbOZShoKhqhzX2FzdJa96+9Sf3ybwRzO5j2OFkd8eA/OnD/Duw8P2PySp5ISlQh2dk+xWpVBdY9Q+PnlG3/KWMwZjUYY0UcvCrSaIZoCJR0HZcrb1x9R1CHh8N6HoXspmS1q3r+7YFBu8M6NOTPveWQTrjz3PCthuC4m/LTs80c3TvHRB9s89GOclgz0ir67yTXxmJ3Nfe588A460zRFUGITCJw4XnVGKX779X/Eyy+/yEe3bvO4rBEudNiNlnx9t+TW9YalCEPWWigaHOcGKS9OBL9Y9LBRoKXzRY9770l7DrozIAQvENgQVVVR1zWrougW+F9PsL33LOPhLZwLlCvvcaVjNp9T1Q3D4RApJP1+n/PnzqGV4ODogCd1zaCvKYqauZ2TJES57AonIFWmO0cSk9I0LnwHnyZgfQbbrPeeoiiY9DcplyuUDr49WZIH7x5xbPqrjaEsC7ROAUFdVYH1EQM42zR4obtr9h5Gk20Gm5uU3iIV1M7xwY0Pmd16G9OUzB7fwH/juxwhwHmKbEDTW6F9GKR/JHex6SZXUsn9YslwmGMmBYdG4RAc7O0znAyolaEsBVZkLOyYO4tLfHl3QmYiLd+HQrH1HolH4dFCYKVHYqnKGaeSgnpumBcTVtUcZBmC2iYE8a1a8mKxjHSoQIFqmoamtiRJ8LtyNnhOWRuMglVUjBuNhyGpzzKwAqVCN+pkIuW8R7fqzfZ4ptHa4EdkmwYAIRSOcA+KZRES4MaS94eYtIdOM6SUaJ2gTXgOnHOsVouwfxpDv99jHqm46jcopf1DhHOfjyqg9yf2GxdYT945yuUq0OkagU7auM5hTKDiVdaG8QuOmw0yKo861wAKLQVpaqibMN+mYzd3sVicaAqEgrySEqE9aZJE1bp2nYY5prqsYpIkcEia2pJpyaIO9MCQ2MhgQ2RDnInzOB+KCvVqRd7rU65WnS5Cy4JJEs1ibtHKgI8JU5bjXcP86ICqLtjc2sKYnMWywrlwH9I0CfNeRkeaqyBRKghUAKnS9LOMqi5oioLRYMB8cRTyCR8SyfF4zNHe40A/tk1giQlBkiaYMhQN6qqMZ46nWM7YGI+pbUXdKBaLOQ8f3Of8xYugFLPlCqkNQnt0kmDrmsP9fcYbE86e3eX2reu/cU8Wn2bDFkI8Bm7/fRffGv+/xyXv/fZ/yTdcr9k1PiPWa3aNLxrWa3aNLyL+i67b9Zpd43PAr12znyqxWmONNdZYY4011lhjjTXWWONv4oujjbnGGmusscYaa6yxxhprrPEPFOvEao011lhjjTXWWGONNdZY4zNinVitscYaa6yxxhprrLHGGmt8RqwTqzXWWGONNdZYY4011lhjjc+IdWK1xhprrLHGGmusscYaa6zxGbFOrNZYY4011lhjjTXWWGONNT4j1onVGmusscYaa6yxxhprrLHGZ8Q6sVpjjTXWWGONNdZYY4011viMWCdWa6yxxhprrLHGGmusscYanxH/Dz9D5DHafR9mAAAAAElFTkSuQmCC\n",
            "text/plain": [
              "<Figure size 1080x360 with 5 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "lF3OdwbxJ5ul"
      },
      "source": [
        "The validation and test sets only need to be rescaled."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "08tGR_AkPoxK",
        "outputId": "5d929b83-ec5b-4cf4-eb2f-478704f4b08c"
      },
      "source": [
        "datagen_val=tf.keras.preprocessing.image.ImageDataGenerator(\n",
        "                      rescale=1./255\n",
        "                      )\n",
        "\n",
        "val_generator=datagen_val.flow_from_dataframe(\n",
        "    dataframe=val,\n",
        "    directory=images_dir, \n",
        "    x_col=x_col,\n",
        "    y_col=y_col,\n",
        "    class_mode='sparse',\n",
        "    classes=train_classes,\n",
        "    shuffle=False,\n",
        "    target_size=(IMG_SIZE, IMG_SIZE), \n",
        "    batch_size=batch_size)\n",
        "\n",
        "test_generator = datagen_val.flow_from_dataframe(\n",
        "    dataframe=test,\n",
        "    directory=images_dir, \n",
        "    x_col=x_col,\n",
        "    y_col=y_col,\n",
        "    class_mode='sparse',\n",
        "    classes=train_classes,\n",
        "    shuffle=False,\n",
        "    target_size=(IMG_SIZE, IMG_SIZE), \n",
        "    batch_size=batch_size)"
      ],
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Found 95 validated image filenames belonging to 3 classes.\n",
            "Found 106 validated image filenames belonging to 3 classes.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "HTAdaCpSKxRE"
      },
      "source": [
        "## Defining other model inputs"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "z0_gKy_7Po_a"
      },
      "source": [
        "# The number of steps depends on the number of images and the batch size. It needs to be rounded to integers.\n",
        "steps_train = round(train_generator.n / batch_size)\n",
        "steps_val = round(val_generator.n / batch_size)\n",
        "steps_test = round(test_generator.n / batch_size)"
      ],
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "aM9Eni6MROsd",
        "outputId": "0ee70d1f-7ef9-47e7-96d3-14bc257a1d9d"
      },
      "source": [
        "train_generator.n, steps_train"
      ],
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(850, 17)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "HgNY6CxCRenM",
        "outputId": "e2854287-0eca-4c7e-a293-fcde9610278d"
      },
      "source": [
        "# This creates lists of all the class labels in the training and the test set.\n",
        "\n",
        "cls_train = train_generator.classes\n",
        "cls_test = test_generator.classes\n",
        "len(cls_train)"
      ],
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "850"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 18
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "eRjVmtzwRepY",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4c7201a4-8d1c-43e4-fd5c-26d3364d73e4"
      },
      "source": [
        "# Calculate the class weights based on the labels in the training set.\n",
        "\n",
        "class_weight = compute_class_weight(class_weight='balanced',\n",
        "                                    classes=np.unique(cls_train),\n",
        "                                    y=cls_train)\n",
        "class_weight"
      ],
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array([0.99067599, 1.02657005, 0.9837963 ])"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 19
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "4KthZVdgWH-C",
        "outputId": "052e4ff2-50cd-4a3b-eeae-f42044b46af5"
      },
      "source": [
        "# Turn into dictionary\n",
        "class_weight_dict = {i : class_weight[i] for i in range(0, len(class_weight))}\n",
        "class_weight_dict"
      ],
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 0.9906759906759907, 1: 1.0265700483091786, 2: 0.9837962962962963}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 20
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "hnExiLOhM--g"
      },
      "source": [
        "## Building the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "fdapC5PSRere",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "5dc719ad-3e33-4200-800b-1d38f48f8b90"
      },
      "source": [
        "# Load the ResNet50 model without the top layers.\n",
        "model_name = 'resnet50_v01'\n",
        "model = tf.keras.applications.resnet50.ResNet50(input_shape = (224,224,3),\n",
        "                                              include_top = False,\n",
        "                                              weights = 'imagenet',\n",
        "                                                    )"
      ],
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/resnet/resnet50_weights_tf_dim_ordering_tf_kernels_notop.h5\n",
            "94773248/94765736 [==============================] - 1s 0us/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PLNIqY8dZUjm",
        "outputId": "6307c95b-69cd-45d5-d76d-3a14e2f2dd36"
      },
      "source": [
        "# This is what the structure looks like.\n",
        "model.summary()"
      ],
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"resnet50\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "input_1 (InputLayer)            [(None, 224, 224, 3) 0                                            \n",
            "__________________________________________________________________________________________________\n",
            "conv1_pad (ZeroPadding2D)       (None, 230, 230, 3)  0           input_1[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "conv1_conv (Conv2D)             (None, 112, 112, 64) 9472        conv1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv1_bn (BatchNormalization)   (None, 112, 112, 64) 256         conv1_conv[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv1_relu (Activation)         (None, 112, 112, 64) 0           conv1_bn[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pad (ZeroPadding2D)       (None, 114, 114, 64) 0           conv1_relu[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "pool1_pool (MaxPooling2D)       (None, 56, 56, 64)   0           pool1_pad[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_conv (Conv2D)    (None, 56, 56, 64)   4160        pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_1_relu (Activation (None, 56, 56, 64)   0           conv2_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_2_relu (Activation (None, 56, 56, 64)   0           conv2_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_conv (Conv2D)    (None, 56, 56, 256)  16640       pool1_pool[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_0_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_add (Add)          (None, 56, 56, 256)  0           conv2_block1_0_bn[0][0]          \n",
            "                                                                 conv2_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block1_out (Activation)   (None, 56, 56, 256)  0           conv2_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_1_relu (Activation (None, 56, 56, 64)   0           conv2_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_2_relu (Activation (None, 56, 56, 64)   0           conv2_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_add (Add)          (None, 56, 56, 256)  0           conv2_block1_out[0][0]           \n",
            "                                                                 conv2_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block2_out (Activation)   (None, 56, 56, 256)  0           conv2_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_conv (Conv2D)    (None, 56, 56, 64)   16448       conv2_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_1_relu (Activation (None, 56, 56, 64)   0           conv2_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_conv (Conv2D)    (None, 56, 56, 64)   36928       conv2_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_bn (BatchNormali (None, 56, 56, 64)   256         conv2_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_2_relu (Activation (None, 56, 56, 64)   0           conv2_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_conv (Conv2D)    (None, 56, 56, 256)  16640       conv2_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_3_bn (BatchNormali (None, 56, 56, 256)  1024        conv2_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_add (Add)          (None, 56, 56, 256)  0           conv2_block2_out[0][0]           \n",
            "                                                                 conv2_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv2_block3_out (Activation)   (None, 56, 56, 256)  0           conv2_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_conv (Conv2D)    (None, 28, 28, 128)  32896       conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_1_relu (Activation (None, 28, 28, 128)  0           conv3_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_2_relu (Activation (None, 28, 28, 128)  0           conv3_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_conv (Conv2D)    (None, 28, 28, 512)  131584      conv2_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_0_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_add (Add)          (None, 28, 28, 512)  0           conv3_block1_0_bn[0][0]          \n",
            "                                                                 conv3_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block1_out (Activation)   (None, 28, 28, 512)  0           conv3_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_1_relu (Activation (None, 28, 28, 128)  0           conv3_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_2_relu (Activation (None, 28, 28, 128)  0           conv3_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_add (Add)          (None, 28, 28, 512)  0           conv3_block1_out[0][0]           \n",
            "                                                                 conv3_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block2_out (Activation)   (None, 28, 28, 512)  0           conv3_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_1_relu (Activation (None, 28, 28, 128)  0           conv3_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_2_relu (Activation (None, 28, 28, 128)  0           conv3_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_add (Add)          (None, 28, 28, 512)  0           conv3_block2_out[0][0]           \n",
            "                                                                 conv3_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block3_out (Activation)   (None, 28, 28, 512)  0           conv3_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_conv (Conv2D)    (None, 28, 28, 128)  65664       conv3_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_1_relu (Activation (None, 28, 28, 128)  0           conv3_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_conv (Conv2D)    (None, 28, 28, 128)  147584      conv3_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_bn (BatchNormali (None, 28, 28, 128)  512         conv3_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_2_relu (Activation (None, 28, 28, 128)  0           conv3_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_conv (Conv2D)    (None, 28, 28, 512)  66048       conv3_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_3_bn (BatchNormali (None, 28, 28, 512)  2048        conv3_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_add (Add)          (None, 28, 28, 512)  0           conv3_block3_out[0][0]           \n",
            "                                                                 conv3_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv3_block4_out (Activation)   (None, 28, 28, 512)  0           conv3_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_conv (Conv2D)    (None, 14, 14, 256)  131328      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_1_relu (Activation (None, 14, 14, 256)  0           conv4_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_2_relu (Activation (None, 14, 14, 256)  0           conv4_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_conv (Conv2D)    (None, 14, 14, 1024) 525312      conv3_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_0_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_0_bn[0][0]          \n",
            "                                                                 conv4_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block1_out (Activation)   (None, 14, 14, 1024) 0           conv4_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_1_relu (Activation (None, 14, 14, 256)  0           conv4_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_2_relu (Activation (None, 14, 14, 256)  0           conv4_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_add (Add)          (None, 14, 14, 1024) 0           conv4_block1_out[0][0]           \n",
            "                                                                 conv4_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block2_out (Activation)   (None, 14, 14, 1024) 0           conv4_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_1_relu (Activation (None, 14, 14, 256)  0           conv4_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_2_relu (Activation (None, 14, 14, 256)  0           conv4_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_add (Add)          (None, 14, 14, 1024) 0           conv4_block2_out[0][0]           \n",
            "                                                                 conv4_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block3_out (Activation)   (None, 14, 14, 1024) 0           conv4_block3_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block3_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_1_relu (Activation (None, 14, 14, 256)  0           conv4_block4_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block4_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block4_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_2_relu (Activation (None, 14, 14, 256)  0           conv4_block4_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block4_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block4_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_add (Add)          (None, 14, 14, 1024) 0           conv4_block3_out[0][0]           \n",
            "                                                                 conv4_block4_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block4_out (Activation)   (None, 14, 14, 1024) 0           conv4_block4_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block4_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_1_relu (Activation (None, 14, 14, 256)  0           conv4_block5_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block5_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block5_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_2_relu (Activation (None, 14, 14, 256)  0           conv4_block5_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block5_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block5_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_add (Add)          (None, 14, 14, 1024) 0           conv4_block4_out[0][0]           \n",
            "                                                                 conv4_block5_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block5_out (Activation)   (None, 14, 14, 1024) 0           conv4_block5_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_conv (Conv2D)    (None, 14, 14, 256)  262400      conv4_block5_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_1_relu (Activation (None, 14, 14, 256)  0           conv4_block6_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_conv (Conv2D)    (None, 14, 14, 256)  590080      conv4_block6_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_bn (BatchNormali (None, 14, 14, 256)  1024        conv4_block6_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_2_relu (Activation (None, 14, 14, 256)  0           conv4_block6_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_conv (Conv2D)    (None, 14, 14, 1024) 263168      conv4_block6_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_3_bn (BatchNormali (None, 14, 14, 1024) 4096        conv4_block6_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_add (Add)          (None, 14, 14, 1024) 0           conv4_block5_out[0][0]           \n",
            "                                                                 conv4_block6_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv4_block6_out (Activation)   (None, 14, 14, 1024) 0           conv4_block6_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_conv (Conv2D)    (None, 7, 7, 512)    524800      conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_1_relu (Activation (None, 7, 7, 512)    0           conv5_block1_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block1_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block1_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_2_relu (Activation (None, 7, 7, 512)    0           conv5_block1_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_conv (Conv2D)    (None, 7, 7, 2048)   2099200     conv4_block6_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block1_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_0_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_0_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block1_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_0_bn[0][0]          \n",
            "                                                                 conv5_block1_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block1_out (Activation)   (None, 7, 7, 2048)   0           conv5_block1_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block1_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_1_relu (Activation (None, 7, 7, 512)    0           conv5_block2_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block2_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block2_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_2_relu (Activation (None, 7, 7, 512)    0           conv5_block2_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block2_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block2_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_add (Add)          (None, 7, 7, 2048)   0           conv5_block1_out[0][0]           \n",
            "                                                                 conv5_block2_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block2_out (Activation)   (None, 7, 7, 2048)   0           conv5_block2_add[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_conv (Conv2D)    (None, 7, 7, 512)    1049088     conv5_block2_out[0][0]           \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_1_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_1_relu (Activation (None, 7, 7, 512)    0           conv5_block3_1_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_conv (Conv2D)    (None, 7, 7, 512)    2359808     conv5_block3_1_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_bn (BatchNormali (None, 7, 7, 512)    2048        conv5_block3_2_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_2_relu (Activation (None, 7, 7, 512)    0           conv5_block3_2_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_conv (Conv2D)    (None, 7, 7, 2048)   1050624     conv5_block3_2_relu[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_3_bn (BatchNormali (None, 7, 7, 2048)   8192        conv5_block3_3_conv[0][0]        \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_add (Add)          (None, 7, 7, 2048)   0           conv5_block2_out[0][0]           \n",
            "                                                                 conv5_block3_3_bn[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "conv5_block3_out (Activation)   (None, 7, 7, 2048)   0           conv5_block3_add[0][0]           \n",
            "==================================================================================================\n",
            "Total params: 23,587,712\n",
            "Trainable params: 23,534,592\n",
            "Non-trainable params: 53,120\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "iups3Vw2NQ-d"
      },
      "source": [
        "In order to apply the ResNet50 model to this specific data set, I added a couple of new layers as the top. The final output now consists of three probabilities, corresponding to the classes of the lego sets."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "nJg0Sj0wRetg"
      },
      "source": [
        "# Create a new model with resnet\n",
        "\n",
        "last_conv_layer = model.get_layer('conv5_block3_out')\n",
        "conv_model = Model(inputs=model.input,\n",
        "                   outputs=last_conv_layer.output)\n",
        "\n",
        "new_model = Sequential()\n",
        "\n",
        "new_model.add(conv_model)\n",
        "\n",
        "new_model.add(tf.keras.layers.Dense(256, activation='relu'))\n",
        "new_model.add(tf.keras.layers.Dropout(0.5))\n",
        "new_model.add(tf.keras.layers.BatchNormalization())\n",
        "new_model.add(tf.keras.layers.Dense(128, activation='relu'))\n",
        "new_model.add(tf.keras.layers.Dropout(0.5))\n",
        "new_model.add(tf.keras.layers.GlobalAveragePooling2D())\n",
        "new_model.add(tf.keras.layers.Dense(3, activation='softmax'))   # Softmax activation function returns probabilities."
      ],
      "execution_count": 23,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IgpObOM-S0Nf"
      },
      "source": [
        "# Make the model trainable and define from what layers onwards\n",
        "conv_model.trainable = True\n",
        "\n",
        "fine_tune_at = 143  # This means only the last block will be trainable.\n",
        "\n",
        "# Freeze all the layers before the `fine_tune_at` layer\n",
        "for layer in conv_model.layers[:fine_tune_at]:\n",
        "  layer.trainable =  False"
      ],
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "GlZ48OM5OBrT"
      },
      "source": [
        "## Compiling the model\n",
        "\n",
        "The learning rate should be low, so the weights from the ResNet50 model change only slowly during training."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fRd9bGTHS0Rl",
        "outputId": "ceee943a-8605-4025-9fcf-4abd62b74554"
      },
      "source": [
        "base_learning_rate = 2e-5\n",
        "optimizer = Nadam(lr=base_learning_rate/5)\n",
        "loss = 'sparse_categorical_crossentropy'\n",
        "lst_metrics = ['sparse_categorical_accuracy']\n",
        "\n",
        "new_model.compile(optimizer=optimizer, loss=loss, metrics=lst_metrics)\n",
        "new_model.summary()"
      ],
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            "Layer (type)                 Output Shape              Param #   \n",
            "=================================================================\n",
            "model (Functional)           (None, 7, 7, 2048)        23587712  \n",
            "_________________________________________________________________\n",
            "dense (Dense)                (None, 7, 7, 256)         524544    \n",
            "_________________________________________________________________\n",
            "dropout (Dropout)            (None, 7, 7, 256)         0         \n",
            "_________________________________________________________________\n",
            "batch_normalization (BatchNo (None, 7, 7, 256)         1024      \n",
            "_________________________________________________________________\n",
            "dense_1 (Dense)              (None, 7, 7, 128)         32896     \n",
            "_________________________________________________________________\n",
            "dropout_1 (Dropout)          (None, 7, 7, 128)         0         \n",
            "_________________________________________________________________\n",
            "global_average_pooling2d (Gl (None, 128)               0         \n",
            "_________________________________________________________________\n",
            "dense_2 (Dense)              (None, 3)                 387       \n",
            "=================================================================\n",
            "Total params: 24,146,563\n",
            "Trainable params: 15,534,339\n",
            "Non-trainable params: 8,612,224\n",
            "_________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "WLwjsGfAOSzz"
      },
      "source": [
        "## Defining callbacks"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "cz-f8eqJS0Vr"
      },
      "source": [
        "# Use Tensorboard\n",
        "logs = \"logs/\" + datetime.now().strftime(\"%Y%m%d-%H%M%S\")\n",
        "\n",
        "tboard_callback = tf.keras.callbacks.TensorBoard(log_dir = logs,\n",
        "                                                 histogram_freq = 1,\n",
        "                                                 profile_batch = '500,520')\n",
        "\n",
        "# Can use this at a later point."
      ],
      "execution_count": 29,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "safgW37MSYxo"
      },
      "source": [
        "# Early stopping callback\n",
        "early_stopping = tf.keras.callbacks.EarlyStopping(\n",
        "    monitor = 'val_loss', min_delta = 0.005, patience = 10, verbose = 1,\n",
        "    mode = 'auto', baseline = None, restore_best_weights = True\n",
        ")"
      ],
      "execution_count": 30,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "39HAHHqiM0Bj"
      },
      "source": [
        "# Learning rate scheduler\n",
        "def scheduler(epoch, lr):\n",
        "  if epoch < 20:\n",
        "    return lr\n",
        "  else:\n",
        "    return lr * tf.math.exp(-0.1)\n",
        "\n",
        "lr_schedule = tf.keras.callbacks.LearningRateScheduler(scheduler)\n",
        "\n",
        "# I tried this, but with the Adam or Nadam optimizer it does not yield better results."
      ],
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "bfHpcqdrOvH9"
      },
      "source": [
        "## Training the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yfGuugxoThpB",
        "outputId": "33c744c3-0b20-431b-fd35-69e4f6e1c1a7"
      },
      "source": [
        "# A high number of epochs is fine, since training stops once the loss stops to decrease.\n",
        "epochs = 120\n",
        "history = new_model.fit(train_generator,\n",
        "                    epochs = epochs,\n",
        "                    steps_per_epoch = steps_train,\n",
        "                    class_weight = class_weight_dict,\n",
        "                    validation_data = val_generator,\n",
        "                    callbacks = [\n",
        "                                 tboard_callback, \n",
        "                                 early_stopping,\n",
        "                          #       lr_schedule\n",
        "                                 ])"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/120\n",
            "17/17 [==============================] - 278s 16s/step - loss: 1.1660 - sparse_categorical_accuracy: 0.3535 - val_loss: 1.3534 - val_sparse_categorical_accuracy: 0.4000\n",
            "Epoch 2/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.1211 - sparse_categorical_accuracy: 0.3903 - val_loss: 1.2919 - val_sparse_categorical_accuracy: 0.4000\n",
            "Epoch 3/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.0618 - sparse_categorical_accuracy: 0.4386 - val_loss: 1.2505 - val_sparse_categorical_accuracy: 0.3579\n",
            "Epoch 4/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.0321 - sparse_categorical_accuracy: 0.4518 - val_loss: 1.2199 - val_sparse_categorical_accuracy: 0.2737\n",
            "Epoch 5/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.0345 - sparse_categorical_accuracy: 0.4534 - val_loss: 1.1991 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 6/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.9982 - sparse_categorical_accuracy: 0.4606 - val_loss: 1.1824 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 7/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 1.0042 - sparse_categorical_accuracy: 0.4828 - val_loss: 1.1737 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 8/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.9756 - sparse_categorical_accuracy: 0.4989 - val_loss: 1.1716 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 9/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.9753 - sparse_categorical_accuracy: 0.4970 - val_loss: 1.1747 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 10/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.9614 - sparse_categorical_accuracy: 0.5232 - val_loss: 1.1764 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 11/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.9396 - sparse_categorical_accuracy: 0.5692 - val_loss: 1.1715 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 12/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.9270 - sparse_categorical_accuracy: 0.5878 - val_loss: 1.1653 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 13/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.9196 - sparse_categorical_accuracy: 0.5936 - val_loss: 1.1559 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 14/120\n",
            "17/17 [==============================] - 40s 2s/step - loss: 0.9026 - sparse_categorical_accuracy: 0.5788 - val_loss: 1.1388 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 15/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8858 - sparse_categorical_accuracy: 0.6045 - val_loss: 1.1216 - val_sparse_categorical_accuracy: 0.2526\n",
            "Epoch 16/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8881 - sparse_categorical_accuracy: 0.6193 - val_loss: 1.1041 - val_sparse_categorical_accuracy: 0.3684\n",
            "Epoch 17/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8748 - sparse_categorical_accuracy: 0.6187 - val_loss: 1.0852 - val_sparse_categorical_accuracy: 0.3895\n",
            "Epoch 18/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8657 - sparse_categorical_accuracy: 0.6188 - val_loss: 1.0627 - val_sparse_categorical_accuracy: 0.4000\n",
            "Epoch 19/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8466 - sparse_categorical_accuracy: 0.6607 - val_loss: 1.0390 - val_sparse_categorical_accuracy: 0.4316\n",
            "Epoch 20/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8269 - sparse_categorical_accuracy: 0.6774 - val_loss: 1.0176 - val_sparse_categorical_accuracy: 0.4526\n",
            "Epoch 21/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8359 - sparse_categorical_accuracy: 0.6537 - val_loss: 0.9983 - val_sparse_categorical_accuracy: 0.4947\n",
            "Epoch 22/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.8157 - sparse_categorical_accuracy: 0.6675 - val_loss: 0.9774 - val_sparse_categorical_accuracy: 0.5158\n",
            "Epoch 23/120\n",
            "17/17 [==============================] - 40s 2s/step - loss: 0.7952 - sparse_categorical_accuracy: 0.6872 - val_loss: 0.9580 - val_sparse_categorical_accuracy: 0.5263\n",
            "Epoch 24/120\n",
            "17/17 [==============================] - 40s 2s/step - loss: 0.7870 - sparse_categorical_accuracy: 0.6706 - val_loss: 0.9341 - val_sparse_categorical_accuracy: 0.5368\n",
            "Epoch 25/120\n",
            "17/17 [==============================] - 40s 2s/step - loss: 0.8040 - sparse_categorical_accuracy: 0.6676 - val_loss: 0.9163 - val_sparse_categorical_accuracy: 0.5579\n",
            "Epoch 26/120\n",
            "17/17 [==============================] - 40s 2s/step - loss: 0.7855 - sparse_categorical_accuracy: 0.6755 - val_loss: 0.9031 - val_sparse_categorical_accuracy: 0.5579\n",
            "Epoch 27/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7697 - sparse_categorical_accuracy: 0.6720 - val_loss: 0.8754 - val_sparse_categorical_accuracy: 0.5789\n",
            "Epoch 28/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7774 - sparse_categorical_accuracy: 0.6765 - val_loss: 0.8664 - val_sparse_categorical_accuracy: 0.5895\n",
            "Epoch 29/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7618 - sparse_categorical_accuracy: 0.6810 - val_loss: 0.8467 - val_sparse_categorical_accuracy: 0.6421\n",
            "Epoch 30/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7599 - sparse_categorical_accuracy: 0.6853 - val_loss: 0.8378 - val_sparse_categorical_accuracy: 0.6526\n",
            "Epoch 31/120\n",
            "17/17 [==============================] - 42s 2s/step - loss: 0.7414 - sparse_categorical_accuracy: 0.6988 - val_loss: 0.8305 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 32/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7249 - sparse_categorical_accuracy: 0.7096 - val_loss: 0.8283 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 33/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7210 - sparse_categorical_accuracy: 0.7182 - val_loss: 0.8103 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 34/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7083 - sparse_categorical_accuracy: 0.7255 - val_loss: 0.8083 - val_sparse_categorical_accuracy: 0.6421\n",
            "Epoch 35/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6943 - sparse_categorical_accuracy: 0.7364 - val_loss: 0.7969 - val_sparse_categorical_accuracy: 0.6316\n",
            "Epoch 36/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.7168 - sparse_categorical_accuracy: 0.7050 - val_loss: 0.7866 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 37/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6886 - sparse_categorical_accuracy: 0.7189 - val_loss: 0.7806 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 38/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6852 - sparse_categorical_accuracy: 0.7320 - val_loss: 0.7670 - val_sparse_categorical_accuracy: 0.6842\n",
            "Epoch 39/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6854 - sparse_categorical_accuracy: 0.7302 - val_loss: 0.7817 - val_sparse_categorical_accuracy: 0.6526\n",
            "Epoch 40/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6645 - sparse_categorical_accuracy: 0.7443 - val_loss: 0.7901 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 41/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6703 - sparse_categorical_accuracy: 0.7334 - val_loss: 0.7761 - val_sparse_categorical_accuracy: 0.6211\n",
            "Epoch 42/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6573 - sparse_categorical_accuracy: 0.7536 - val_loss: 0.7632 - val_sparse_categorical_accuracy: 0.6211\n",
            "Epoch 43/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6504 - sparse_categorical_accuracy: 0.7578 - val_loss: 0.7510 - val_sparse_categorical_accuracy: 0.6526\n",
            "Epoch 44/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6399 - sparse_categorical_accuracy: 0.7197 - val_loss: 0.7404 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 45/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6737 - sparse_categorical_accuracy: 0.7377 - val_loss: 0.7275 - val_sparse_categorical_accuracy: 0.6737\n",
            "Epoch 46/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6464 - sparse_categorical_accuracy: 0.7597 - val_loss: 0.7278 - val_sparse_categorical_accuracy: 0.6632\n",
            "Epoch 47/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6119 - sparse_categorical_accuracy: 0.7709 - val_loss: 0.7209 - val_sparse_categorical_accuracy: 0.6526\n",
            "Epoch 48/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6183 - sparse_categorical_accuracy: 0.7679 - val_loss: 0.7153 - val_sparse_categorical_accuracy: 0.6526\n",
            "Epoch 49/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6213 - sparse_categorical_accuracy: 0.7489 - val_loss: 0.7098 - val_sparse_categorical_accuracy: 0.6842\n",
            "Epoch 50/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6127 - sparse_categorical_accuracy: 0.7693 - val_loss: 0.6975 - val_sparse_categorical_accuracy: 0.6842\n",
            "Epoch 51/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6652 - sparse_categorical_accuracy: 0.7059 - val_loss: 0.6904 - val_sparse_categorical_accuracy: 0.7158\n",
            "Epoch 52/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.6214 - sparse_categorical_accuracy: 0.7526 - val_loss: 0.6833 - val_sparse_categorical_accuracy: 0.6947\n",
            "Epoch 53/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5695 - sparse_categorical_accuracy: 0.7812 - val_loss: 0.6801 - val_sparse_categorical_accuracy: 0.6842\n",
            "Epoch 54/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.6103 - sparse_categorical_accuracy: 0.7538 - val_loss: 0.6731 - val_sparse_categorical_accuracy: 0.6737\n",
            "Epoch 55/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.6188 - sparse_categorical_accuracy: 0.7523 - val_loss: 0.6736 - val_sparse_categorical_accuracy: 0.6947\n",
            "Epoch 56/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5570 - sparse_categorical_accuracy: 0.7872 - val_loss: 0.6771 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 57/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5721 - sparse_categorical_accuracy: 0.7775 - val_loss: 0.6814 - val_sparse_categorical_accuracy: 0.6737\n",
            "Epoch 58/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5544 - sparse_categorical_accuracy: 0.8094 - val_loss: 0.6412 - val_sparse_categorical_accuracy: 0.6947\n",
            "Epoch 59/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5683 - sparse_categorical_accuracy: 0.7928 - val_loss: 0.6440 - val_sparse_categorical_accuracy: 0.6947\n",
            "Epoch 60/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5325 - sparse_categorical_accuracy: 0.8076 - val_loss: 0.6265 - val_sparse_categorical_accuracy: 0.7263\n",
            "Epoch 61/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5138 - sparse_categorical_accuracy: 0.8183 - val_loss: 0.6363 - val_sparse_categorical_accuracy: 0.6737\n",
            "Epoch 62/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5626 - sparse_categorical_accuracy: 0.7899 - val_loss: 0.6648 - val_sparse_categorical_accuracy: 0.6947\n",
            "Epoch 63/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5068 - sparse_categorical_accuracy: 0.8190 - val_loss: 0.6368 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 64/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5263 - sparse_categorical_accuracy: 0.8008 - val_loss: 0.6288 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 65/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5133 - sparse_categorical_accuracy: 0.8250 - val_loss: 0.6328 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 66/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5119 - sparse_categorical_accuracy: 0.8117 - val_loss: 0.6484 - val_sparse_categorical_accuracy: 0.6842\n",
            "Epoch 67/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5199 - sparse_categorical_accuracy: 0.8206 - val_loss: 0.6268 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 68/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4879 - sparse_categorical_accuracy: 0.8230 - val_loss: 0.6263 - val_sparse_categorical_accuracy: 0.7053\n",
            "Epoch 69/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4925 - sparse_categorical_accuracy: 0.8100 - val_loss: 0.6300 - val_sparse_categorical_accuracy: 0.7263\n",
            "Epoch 70/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4762 - sparse_categorical_accuracy: 0.8300 - val_loss: 0.5941 - val_sparse_categorical_accuracy: 0.7263\n",
            "Epoch 71/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5239 - sparse_categorical_accuracy: 0.8006 - val_loss: 0.5828 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 72/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.5331 - sparse_categorical_accuracy: 0.8090 - val_loss: 0.5820 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 73/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4948 - sparse_categorical_accuracy: 0.8094 - val_loss: 0.5850 - val_sparse_categorical_accuracy: 0.7368\n",
            "Epoch 74/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4798 - sparse_categorical_accuracy: 0.8279 - val_loss: 0.5886 - val_sparse_categorical_accuracy: 0.7368\n",
            "Epoch 75/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5206 - sparse_categorical_accuracy: 0.7978 - val_loss: 0.5698 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 76/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.5194 - sparse_categorical_accuracy: 0.8005 - val_loss: 0.5724 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 77/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4764 - sparse_categorical_accuracy: 0.8420 - val_loss: 0.5827 - val_sparse_categorical_accuracy: 0.7263\n",
            "Epoch 78/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4840 - sparse_categorical_accuracy: 0.8320 - val_loss: 0.5626 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 79/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4666 - sparse_categorical_accuracy: 0.8470 - val_loss: 0.5739 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 80/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4548 - sparse_categorical_accuracy: 0.8395 - val_loss: 0.5766 - val_sparse_categorical_accuracy: 0.7368\n",
            "Epoch 81/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4792 - sparse_categorical_accuracy: 0.8258 - val_loss: 0.5870 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 82/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4425 - sparse_categorical_accuracy: 0.8394 - val_loss: 0.5557 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 83/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4345 - sparse_categorical_accuracy: 0.8446 - val_loss: 0.5727 - val_sparse_categorical_accuracy: 0.7263\n",
            "Epoch 84/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4819 - sparse_categorical_accuracy: 0.8482 - val_loss: 0.5751 - val_sparse_categorical_accuracy: 0.7368\n",
            "Epoch 85/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4428 - sparse_categorical_accuracy: 0.8470 - val_loss: 0.5355 - val_sparse_categorical_accuracy: 0.7474\n",
            "Epoch 86/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.4671 - sparse_categorical_accuracy: 0.8191 - val_loss: 0.5658 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 87/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.4660 - sparse_categorical_accuracy: 0.8258 - val_loss: 0.5486 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 88/120\n",
            "17/17 [==============================] - 39s 2s/step - loss: 0.4413 - sparse_categorical_accuracy: 0.8526 - val_loss: 0.5509 - val_sparse_categorical_accuracy: 0.7684\n",
            "Epoch 89/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4471 - sparse_categorical_accuracy: 0.8285 - val_loss: 0.5500 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 90/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4769 - sparse_categorical_accuracy: 0.8279 - val_loss: 0.5746 - val_sparse_categorical_accuracy: 0.7684\n",
            "Epoch 91/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4069 - sparse_categorical_accuracy: 0.8587 - val_loss: 0.5635 - val_sparse_categorical_accuracy: 0.7684\n",
            "Epoch 92/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4217 - sparse_categorical_accuracy: 0.8545 - val_loss: 0.5638 - val_sparse_categorical_accuracy: 0.7579\n",
            "Epoch 93/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4162 - sparse_categorical_accuracy: 0.8433 - val_loss: 0.5681 - val_sparse_categorical_accuracy: 0.7789\n",
            "Epoch 94/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.4461 - sparse_categorical_accuracy: 0.8525 - val_loss: 0.5548 - val_sparse_categorical_accuracy: 0.7684\n",
            "Epoch 95/120\n",
            "17/17 [==============================] - 38s 2s/step - loss: 0.3976 - sparse_categorical_accuracy: 0.8630 - val_loss: 0.5383 - val_sparse_categorical_accuracy: 0.7684\n",
            "Restoring model weights from the end of the best epoch.\n",
            "Epoch 00095: early stopping\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 489
        },
        "id": "xmqKU6KgYqIf",
        "outputId": "a5f6f217-95b8-47c0-d3f4-8f1fbbb68cfb"
      },
      "source": [
        "pd.DataFrame(history.history).plot(figsize = (12, 8));"
      ],
      "execution_count": 33,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x576 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "0ALLA3wDADd3",
        "outputId": "a1b53467-fbda-440b-9f63-0a480afed0f9"
      },
      "source": [
        "new_model.save(f\"{project_dir}/{datetime.now().strftime('%Y%m%d-%H%M%S')}_resnet50_model\")"
      ],
      "execution_count": 34,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "INFO:tensorflow:Assets written to: /gdrive/MyDrive/Colab Notebooks/Legosets_classif/20210101-193443_resnet50_model/assets\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "U_WVzav9gq8M"
      },
      "source": [
        "#new_model = tf.keras.models.load_model(f\"{project_dir}/20201230-205011_resnet50_model\")"
      ],
      "execution_count": 35,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "LHzuH01HPGXF"
      },
      "source": [
        "## Testing the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ia1vHRnxcI2X",
        "outputId": "f2992aae-5775-4941-93b3-df64fa6134ca"
      },
      "source": [
        "test_history = new_model.predict(\n",
        "                        test_generator,\n",
        "                        steps=steps_test,\n",
        "                        callbacks=None,\n",
        "                        max_queue_size=10,\n",
        "                        workers=-1,\n",
        "                        use_multiprocessing=False,\n",
        "                        verbose=1)"
      ],
      "execution_count": 36,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "2/2 [==============================] - 17s 15s/step\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "XnK9YJRI-pPK"
      },
      "source": [
        "## Evaluating the model"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "id": "BrUVkYGheTgR",
        "outputId": "d936b1ab-221d-4c1f-c578-eab5bbaf8bc8"
      },
      "source": [
        "# Results data frame\n",
        "df_test_results = pd.DataFrame()\n",
        "df_test_results['y_true'] = test_generator.labels[:100]\n",
        "df_test_results['y_pred'] = test_history.argmax(axis = -1)\n",
        "df_test_results.head()"
      ],
      "execution_count": 37,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/html": [
              "<div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>y_true</th>\n",
              "      <th>y_pred</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>0</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>1</td>\n",
              "      <td>1</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>1</td>\n",
              "      <td>0</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2</td>\n",
              "      <td>2</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>"
            ],
            "text/plain": [
              "   y_true  y_pred\n",
              "0       0       0\n",
              "1       1       1\n",
              "2       1       1\n",
              "3       1       0\n",
              "4       2       2"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 37
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Gz8kMbdO1VRv",
        "outputId": "60e635b5-6b6f-4a8c-d3bf-e12bb1b5d49c"
      },
      "source": [
        "# Print the classification report\n",
        "print(classification_report(df_test_results['y_true'], \n",
        "                            df_test_results['y_pred'],\n",
        "                            target_names = train_classes))"
      ],
      "execution_count": 38,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "              precision    recall  f1-score   support\n",
            "\n",
            "       duplo       0.81      0.76      0.78        33\n",
            "    starwars       0.81      0.74      0.77        39\n",
            "     technic       0.67      0.79      0.72        28\n",
            "\n",
            "    accuracy                           0.76       100\n",
            "   macro avg       0.76      0.76      0.76       100\n",
            "weighted avg       0.77      0.76      0.76       100\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 614
        },
        "id": "fTkim-FAXvH6",
        "outputId": "a7bf5674-45ad-463c-dc9f-53c244b2893f"
      },
      "source": [
        "# Confusion matrix\n",
        "labels = np.sort(df_test_results['y_true'].unique())\n",
        "conf_mat = metrics.confusion_matrix(df_test_results['y_true'], \n",
        "                                    df_test_results['y_pred'],\n",
        "                                    labels = labels)\n",
        "\n",
        "df_conf = pd.DataFrame(conf_mat, columns = train_classes, index = train_classes)\n",
        "\n",
        "fig, ax = plt.subplots(figsize=(14,10))\n",
        "sns.set(font_scale=1.2)\n",
        "sns.heatmap(df_conf, annot = True, ax = ax, cmap = \"GnBu\")\n",
        "ax.set_xlabel(\"Predicted Class\")\n",
        "ax.set_ylabel(\"True Class\")\n",
        "plt.show()"
      ],
      "execution_count": 39,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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SpJoasJWBUaesUnUI0oC17E7nVh2CNGA9fM3eVYcgqZ84m5AkSZKk2hiwlQFJkiSpL1kZkCRJklQbVgYkSZIkoIbLDFgZkCRJkurKZECSJEmqKYcJSZIkSUDUcJyQlQFJkiSppqwMSJIkSTi1qCRJkqQasTIgSZIkATUsDFgZkCRJkurKyoAkSZKEPQOSJEmSasTKgCRJkoTrDEiSJEmqESsDkiRJEs4mJEmSJKlGrAxIkiRJOJuQJEmSpBqxMiBJkiRhZUCSJElSjZgMSJIkSTXlMCFJkiQJqOGaY1YGJEmSpLqyMiBJkiQBEanqEEpnZUCSJEmqKSsDkiRJElDDmUWtDEiSJEl1ZWVAkiRJAgbZMyBJkiSpLqwMSJIkSUANWwasDEiSJEl1ZWVAkiRJwp4BSZIkSTViZUCSJEnCdQYkSZIk1YiVAUmSJAkrA5IkSZJqxGRAkiRJqimHCUmSJEk4tagkSZKkGrEyIEmSJAE17B+2MiBJkiTVlZUBSZIkCXsGJEmSJNWIlQFJkiQJFx2TJEmSVCNWBiRJkiQg7BmQJEmSVBdWBiRJkiTq+S15HX9mSZIkSVgZkCRJkgB7BiRJkiTViJUBSZIkCRjkOgOSJEmS6qKtZCAi9oqInZq2T4qI5yPi+ohYvv/CkyRJktRf2q0MnDzzSURsDBwPnAXMD5ze92FJkiRJ5YpIpT06Rbs9A6sAefH8k8AfUko/iIi/ANf3S2SSJEmS+lW7ycBkYLHi+fbARcXz15v2S5IkSQNWHRuI200GbgNOj4iRwCbAnsX+tYDn+iMwSZIkSf2r3Z6Bw4G3aSQBh6aUxhT7P4bDhCRJkvQuEKTSHp2ircpASul54OMt9h/d5xFJkiRJKkVbyUBEDAdIKY0ttt8HfAb4Z0rpiv4LT5IkSSpH1LBnoN1hQr+hqAxExNLArTRmFTo/Ir7ST7FJkiRJ6kftJgPrA6OK53sCj6eU1gM+BxzSH4FJkiRJZRoUqbRHp2g3GVgImFA83wG4png+Glipr4OSJEmS1P/aTQYeA/aIiJWAnYC/FPuXBcb3R2CSJElSmSLKe3SKdpOBbwGnAk8Do1JKdxb7PwLc2w9xSZIkSepn7U4telVErAyMAO5vOnQDcGV/BCZJkiSVqZPG8pel3RWISSm9BLzUZd+dczhdkiRJUodrOxmIiLVozCS0MrBA87GU0hf6OC5JkiSpVB00lL807S46tguN4UD3Au8H7gZWBxYEbuu36CRJkiT1m3YbiE8BvpVS2gyYAnwWWJVGz8At/RKZJEmSpH7VbjKQAb8unk8FFk4pTaaRJBzdH4FJkiRJZXJq0Tl7ExhSPP83sEbxfDCwRF8HJUmSJKn/tdtAfCewBfAw8Cfg9IjYAPgkcEc/xSZJkiSVxqlF5+wYYNHi+cnAYsCngH8VxyRJkiT1gyzL9gYOAzYAFsvzPLocT8BkYHrT7s3yPH+wu2u3u+jYk03PJwJfbOd1kiRJ0kDRSWP5uxgHnAssBPx8Dud8LM/zW3p64bbXGZAkSZJUvjzPrwfIsmybvr72HJOBiHgTaGvgVEpp8T6LSJIkSarAoPY++vaJLMuGAcNaHBqf5/n4XlzyiizLFgCeAc7L8/yn7bxobpWBw3sRhCRJkqTuHQ18s8X+b9Ho0e2JHYDbafQMbAdcnmXZ4DzPz+vuhXNMBlJKl/YwCEmSJGnAKrln4Ezgkhb7e1wVyPP8xqbN67IsO4PGIsG9TwYAImI4cCjw45TSG12ODQWOBM5NKb3a06AlSZKkuiqGAvVmOFA7ZgBtpTbdNRAfBazaNREASCm9HhFr0ihxnNjjEPWu9Mb4N7jigt9y92338ObrE1hy6SXYY//d2fET21cdmtRRvvyZzdh9y3VYa+WliAgefuplvv+/t/HXu5+Ydc58g4Kj9tqM/T+2ISsvN4znX36dc668kwv+cE+FkUud77677+f4w05i2RHLcvEfLqw6HA0g0aHrDGRZNh8wP7BAsT1zMeC3gQ1pLCT8AI0kYFsaU/+f0s61u0sGPk4jIZiTi4CzMBkQMGniZE489BSWHL4EXz7lCIYvN5xxr45jxvQZVYcmdZytN1qNS/98L/94dAwTp0zl87tszFXf24edjr6UOx56DoATP78NX9h1Yw4//Y888MRLbLreipxzzK68PXU6F//p3op/AqkzvfbKOE47+Uw23nRDXnj231WHI/WVzwIXN21PKv7clsb6Xz8AVgKm0Wgg/kae5+e3c+HukoHVgSfmcvxJYLV2bqR3v6svu5Ypk6dw/OnHMf8C8wOwzIjhFUcldaZP/L/LZ9s+4YIb2PEDq7P7lmvPSgb2+8gGnPWbUVwzMgfg6X+PZ5O1V+Br+21pMiC1MGPGDH5w0ul8/NM78/aUqSYD6rFBHbrOQJ7nl9C6v2Cma3t77UHdHJ9KI8uYkxVpZCBti4itI+JDTdsHRMTIiLggIhad22vV2UbdfBfrbJBx8Zm/4MBdvsgRn/kKl559GVMmT6k6NKnjRcDiiyzIW5Onztq34AKDmfz27P+LnTRlGqssN4yVlx1adohSx7v8Z78mCPbaf8+qQ5EGjO6SgdHAJ+dy/FNAT7+eOhNYDiAiMuACGmOcNgN+2MNrqYO89MJL3HHzXUx6axJfP+1YPnfYPtx+wx2c9922prmVau24fbdk6KJDuOjaf8za95c7H+dLe3yQ9VZbBoAPrLMCn/vYhgAsv9RilcQpdar773mAP135Z756yjFEBy8jq84WkUp7dIruhgn9BPhNRDwPnJNSmg4QEYNprENwJLB3D++5BvBg8fxTwF9TSl8qqgVXAl/s4fXUIWbMSCw2dFEOO/FQBg8eDOvAtGnTOe34MznwKwew2FALP1IrB+++CcftuwV7nvArXnjlzVn7jz3nOs4+Zhfu/OnBJODfr7zJpf93L1/ddwtmpM75h0Sq2uvjX+cHJ57OV755FEsuvUTV4UgDylyTgZTSVRFxKvAj4NsRMbN/YHVgEeCHKaUre3jPGcB8xfPtgd8Xz18ElprbCyPiYOBggJPOOIFPH7BHD2+t/rTEUsNYZvnhjUSgsNJqKwIw9sWxJgNSC0fvtRnfOGBr9jzhV9w8+qnZjo17czL7fetK5h88iGWGLcKYV9/koN02AeCpMeOqCFfqSE8//iyvjn2Nk778n8lT0oxESomdP7Q7X/3Wl9n2o9tUF6AGjO6GzLwbdVcZIKV0QkRcDexL41v9AP4GXJ5SuqsX97wbODEi/gpsSfHhHlgVmGunT0rpQuBCgIfGjfZrsQ6zzoZr89A/Hmb6tOnMN7iR773w7BgAllneRmKpqxM/vw1H7PkhPvH1Kxh5/zNzPG/qtBmzKgZ7bfdebrv/GV55fWJZYUodL1tvTc7/1Tmz7fvj7/7Enbfdzbd/fDLDl1u6osikztdtMgBQfOjvzQf/Vo4GLgd2B/4npTSz2vBp4I4+uocqsPu+u3L7jaO48IcX8fF9dmbcq+P5xdmXsc3OW7Lo4lYFpGY/PGwnDvz4+9n/21fx2LOvsOwSiwAw6e1pvPFWo+n+/dkIVlluKPc+9iLLDFuEo/balPXXWJbtj7ykwsilzjNkoSGsusYqs+0busQwBs8//zv2S5pdW8lAX4mIQTSGCW2eUprQ5fCxwPQy41HfWnXNVTjhjK/xy3Ov4NjPfZ1hSw5ls+03Ze+DnNVB6urwPTcF4Dff+cxs+//3uvs4+NRrAFhw/vk4fv+tec+IJXh76nRGPvAs2x5+Mf986uXS45WkOuikxt6yRCqxCS0a7f1TgHVTSo/Py7UcJiT13gf2+GPVIUgD1sPX9HTeDEnNVltsrY6d7umk0SNL+3x5ysZbdMTvodTKQEopRUQODAfmKRmQJEmS+lIdG4ir+JmPA06LiA3DiYAlSZKkyvSoMhARS9OYVvS+lFJvl5X9DTAE+AcwLSJmu05KafFeXleSJEnqtTr2DLSVDETEYsDPgT2BBKwJPBkR5wMvppRO7sE9D+9pkJIkSZL6XruVgVOBFYCNgZFN+/8I/A9wcrs3TCld2u65kiRJUlnqOH693WRgN+CTKaX7Yvb6ySPAe3p784hYDligeV9K6dneXk+SJElS+9pNBpYAXm2xfzF6uDZARAwFzgL2oksiUJivJ9eTJEmS+sKgGvYMtDub0N00qgMzzfxNHQLc3sN7ngZsAHwCmAz8F/BV4HngM3N5nSRJkqQ+1G5l4Hjg+ohYr3jNMcXzDwJb9fCeHwP2SSndFhHTgX+klH4dEf+mkVz8rofXkyRJkuZZHXsG2qoMpJRuBzanMaznCWB7YAywWUppdA/vOQx4pnj+OrBU8fyO4h6SJEmSStD2OgMppQeB/fvgnk/QaDp+lkYD8t4RcRewB/BaH1xfkiRJ6rE69gy0u87AknM7nlLqyYf4S4D1gVuA79OYnvRwGlWKo3pwHUmSJEnzoN3KwCv8p2m4lbZnAEop/ajp+U0RsTawCfBYUX2QJEmSShc1bBpoNxnYtsv2/MBGwBeBb/TkhhExOKU0beZ2sa6AawtIkiRJJWsrGUgp/a3F7hsi4kngv4HLe3DP8RFxO41hQrcAdzUnB5IkSZLK0e46A3NyHz2fWvQTwJ00phi9CRgXEX+JiOMjwtmEJEmSVIko8dEp2p5NqKuIWBQ4GniuJ69LKd0A3FBcYyEa04nuC5xMo/fAFYglSZKkErQ7m9CbzN5AHMDCwFs0Psj3SEQsQ6MPYRtgO2Al4O80hg1JkiRJpXNq0Tk7vMv2DGAscGdKaVxPbhgRDwOr0BgqdAtwMDAqpTSlJ9eRJEmSNG+6TQYiYjCwCPCHlNKYPrjnYsB0YBIwEZgAvN0H15UkSZJ6rZPG8pel2wbiYqafH9KYTnSepZRWojEt6VXABsDvgdci4pqI+HJf3EOSJElS99odJjQKeD/wTF/cNKX0BPBERFwCfBA4CNgP2AX40VxeKkmSJPULewbm7KfAaRGxMvAPGo3Ds6SURrd7w4j4II3G4W2BDwMLAqOB07GBWJIkSSrNXJOBiLiIxvShMxcVO6PFaYmeTQc6ErgH+BtwJjAypfTW3F8iSZIk9a869gx0VxnYH/h/wGp9cbOiGfmTNGYheqUvrilJkiSpd7pLBgIgpdRXvQLTIuJKYG3AZECSJEkdI2rYM9DtbELMvthYX7gfWKOPrylJkiSph9ppIH4xYu4jqFJKPekZOBk4PSK+Setm5Nd6cC1JkiSpT7TzLfm7TTvJwMHA+D6855+KP69i9qpD0PNmZEmSJEm91E4ycG1K6eU+vOe2fXgtSZIkqU/UsWegu2Sgz38jKaW/9fU1JUmSJPVcW7MJ9YeIGAGsDCzQvD+ldGt/3VOSJEnSf8w1GUgp9XkfRZEEXA5sRaPyMLNXYCZ7BiRJklS6OjYQV/EznwlMB9YFJgJbAp8GHgE+WkE8kiRJUi2100Dc17YGdkkpPRqNLo2xKaW/R8QU4NvAXyuISZIkSTVXxwbiKioDC/Gf1YdfA5Ypnj8MrF9BPJIkSVItVVEZeBRYG3gauA84NCKeAw4DXqggHkmSJKmWPQNVJAM/BpYrnp8CXAfsA0wB9q8gHkmSJKmWSk8GUkqXNT0fHRGr0qgUPJtSemVOr5MkSZL6kz0DJYiIkyJi4ZnbKaWJKaXRwFsRcVLZ8UiSJEl1VcXQqG8Ci7bYv3BxTJIkSSpdlPjoFFUkA10XGZtpIxqzC0mSJEkqQWk9AxHxJo0kIAFPxuyDsuYDhgDnlxWPJEmS1GxQDXsGymwgPpxGVeAi4ATg9aZjbwNPp5TuKDEeSZIkqdZKSwZSSpcCRMQiwK0ppQeL7R1pTCn6z4i4K6U0vayYJEmSpJmikwbzl6SKnoHPAusBRMRKwB+AJWksOvadCuKRJEmSaqmKZGBtYHTxfE/grpTSzjSShH0qiEeSJEliEKm0R6eoIhmYj0aPAMD2wP8Vz58Alq0gHkmSJKmWqkgGHgK+GBFb0kgGriv2rwC4ArEkSZJUkjJnE5rpazT6BI4FLp3ZSAzsBtxVQTySJElSLRuIS08GUkq3RsRwYPGU0rimQxcAE8uOR5IkSaqrKioDFNOHjuuy7+kqYpEkSZKgsSBW3VTRM46JLeMAAB3JSURBVCBJkiSpA1RSGZAkSZI6zaDonCk/y2JlQJIkSaopKwOSJEkS9gxIkiRJqhErA5IkSRL2DEiSJEmqESsDkiRJEvYMSJIkSaoRKwOSJEkSEPYMSJIkSaoLKwOSJEkS9fyWvI4/syRJkiRMBiRJkqTacpiQJEmSBETUb3JRKwOSJElSTVkZkCRJknDRMUmSJEk1YmVAkiRJwp4BSZIkSTViZUCSJEnCngFJkiRJNWJlQJIkSQKihrUBkwFJkiSpg2VZtjdwGLABsFie59Hl+EbAT4ANgVeA0/I8P6udaztMSJIkSQIiynv00DjgXODorgeyLFscuA64HlgS2As4OcuyPdu5sMmAJEmS1MHyPL8+z/MrgCdbHN4DmA58O8/zyXmejwJ+CnypnWs7TEiSJEkCBpXYM5Bl2TBgWItD4/M8H9+DS20A3Jvn+YymffcAB7XzYisDkiRJUvmOBp5q8XjHUKBuLA50TR7GF/u7ZWVAkiRJoldj+efFmcAlLfb3pCoA8AawbJd9w4r93TIZkCRJkkpWDAXq6Qf/Vu4H9sqybFDTUKH3A/e182KTAUmSJKmDZVk2HzA/sECxPaQ49DZwFXAqcEKWZT+g0UNwEHBoO9e2Z0CSJEmisehYWf/10GeBSTSmD6V4PgnYKs/zN4CPAjvTqDRcCXwrz/PftnNhKwOSJElSB8vz/BJa9xfMPH4vsFlvrj1gk4E1Fl+96hCkAevuq3atOgRpwNr5vOeqDkEa0B45bq2qQ5ijkhuIO4LDhCRJkqSaGrCVAUmSJKkv9WIs/4BnZUCSJEmqKSsDkiRJEvYMSJIkSaoRKwOSJEkS9gxIkiRJqhErA5IkSRL1/Ja8jj+zJEmSJKwMSJIkSQBEDacTsjIgSZIk1ZSVAUmSJAlqOJeQlQFJkiSptkwGJEmSpJpymJAkSZKEDcSSJEmSasTKgCRJkoQNxJIkSZJqxMqAJEmShD0DkiRJkmrEyoAkSZKEPQOSJEmSasTKgCRJkgREDWsDVgYkSZKkmrIyIEmSJAGD6lcYsDIgSZIk1ZWVAUmSJAl7BiRJkiTViJUBSZIkCajhAsRWBiRJkqS6MhmQJEmSasphQpIkSRI2EEuSJEmqESsDkiRJEjYQS5IkSaoRKwOSJEkS9gxIkiRJqhErA5IkSRL2DEiSJEmqESsDkiRJEvYMSJIkSaoRKwOSJEkS9fyWvI4/syRJkiSsDEiSJEkARA2nE7IyIEmSJNWUlQFJkiQJwNmEJEmSJNWFyYAkSZJUUw4TkiRJkqjjICErA5IkSVJtWRmQJEmScGpRSZIkSTViZUCSJEkC6tg1YGVAkiRJqikrA5IkSRJ1rAtYGZAkSZJqy8qAJEmSBEQNawNWBiRJkqSasjIgSZIkAbjOgCRJkqS6sDIgSZIk4WxCkiRJkmrEyoAkSZIE1LE2YGVAkiRJqimTAUmSJKmmHCYkSZIk4aJjkiRJkmrEyoAkSZJELdccszIgSZIk1ZWVAUmSJAlwalFJkiRJtWFlQJIkScLZhCRJkiTViJUBSZIkiTp2DFgZkCRJkmrLyoAkSZIEtVxowMqAJEmSVFNWBiRJkiScTUiSJElSjVgZkCRJkrAyIEmSJKlGTAYkSZKkmjIZkCRJkmrKngFJkiSpg2VZdjJwIjCpafe1eZ7vM6/XNhlQn7rtb3/n7DPP5cknn2b48KXZZ7+9+NwB+1YdljQgvDH+Da644Lfcfds9vPn6BJZcegn22H93dvzE9lWHJnWUL3xwZXZccxlWW2phAnjslbc4/46nGPnUa7PO2eN9y7Pbesuz1tKLsMDgQTz92kQuuedZ/vjwS9UFro4Xnb3o2G15nm/T1xc1GVCf+edDD3P0EcfyuQP24/unfYcHH/gn3/nW9xkyZAh77f2pqsOTOtqkiZM58dBTWHL4Enz5lCMYvtxwxr06jhnTZ1QdmtRxPrTyElz14BgefPENJk+dzp7rr8B5n9qAz10xmntfeH3WOTc9NpbTbnmM1ydPY/s1h/P9nddj+ozEnx99ueKfQOocJgPqM/976eWs9951OeqYwwB4z+qr8cTjT3Lxz35hMiB14+rLrmXK5Ckcf/pxzL/A/AAsM2J4xVFJnemQ390/2/Zpf3ucLVZbkh3XGj4rGfjanx6e7ZxL7n6WD6w0jI9my5oMaC7KqwxkWTYMGNbi0Pg8z8e32L9JlmVjgYnA34ET8jx/al7jKL2BOCLWi4j1W+xfPyLWLTse9Z37Rj/A5ltsNtu+D2+xKWPG/JuXXrQsK83NqJvvYp0NMi4+8xccuMsXOeIzX+HSsy9jyuQpVYcmdbwAFl1wMJOmzr2StviCg5k0dXo5QUndOxp4qsXj6Bbn/g5YD1gG2AyYBtyQZdmi8xpEFZWBC4GfAA902b8ucDiwRekRqU+MHfsKSy+91Gz7liq2x459lWWXW7aKsKQB4aUXXuLF519is20/yNdPO5ZxY8fxs9MvYdzYcRx9yuFVhyd1tEM2W5XFFhzMb+57YY7nfHzd5Vh/xFC+d9NjJUamgabkjoEzgUta7H9HVSDP84eaNsdkWXYg8DqwOfCXeQmiimRgfeCuFvvvBt5XciyS1BFmzEgsNnRRDjvxUAYPHgzrwLRp0znt+DM58CsHsNjQef7yR3pX2mfDFTh401U57Kr7eWlC60radmsszSkfWZsT//wID7/0ZskRSq0VQ4FaDQdqRyoe85y/VLHOwHRgaIv9S9DNDxQRB0fEPRFxz89/ekl/xKZ5MHz40rzyyquz7Xv11deKY0u1eomkwhJLDWPESss3EoHCSqutCMDYF8dWFZbU0T7/gZU5dps1Oeyq+7njmXEtz9l57WU5Y7f38s3rH+Wah18sOUINNFHifz2RZdlnsiwbXjxfBvgpMBa4fV5/5iqSgb8BJ0TEfDN3RMRg4ATg1rm9MKV0YUppk5TSJgcedED/Rqke23Dj9bnj76Nm23f7yDsYMWJ5hwhJ3Vhnw7X59/MvMX3af8Yzv/DsGACWWd5GYqmrI7Z4D4d9eDUOvfK+OSYCn15/BN/deR2+/n8PmwhooNsXeDjLsonAvcAQYIc8z+e51FXFMKHjgJHA4xExsti3BbAosFUF8aiP7Pe5/2L/fQ/k7DPPZdfddubBBx7iist+y7Ffa9UHI6nZ7vvuyu03juLCH17Ex/fZmXGvjucXZ1/GNjtvyaKLO0RIavb17dZkrw1W4NhrH+Kp1yay9CILADB56nQmvN1IqPffZCWO3WYNvv3Xf3H3c+NnnTN1+gxenzytstjV4Tp0nYE8z3frr2tHSqm/rj3nm0YsT6NZeMNi173AuSmlMe1eY/L018sPXN269W8jOfvMc3nqyWdYeuml+K/PfsZFxzrQ4288UXUIauGBux/il+dewbNPPMewJYey2fabsvdBe7LgkAWrDk1NPv3T1t9CqzyPHNd6Ib7fPziG4//8CAA3HLI5Kwxd6B3n3PXsOPb/1eh+jU9z98hx23fmJ27gsTceLu3z5ZqLr9sRv4dKkoG+YDIg9Z7JgNR7JgPSvOnkZODxEpOBNTokGShlmFBEbAzcl1KaUTyfo5SS6bokSZJUgrJ6Bu4BlgNeLp7PaSqkBMzXYr8kSZLUr3o6y8+7QVnJwGo0pj+a+VySJElSxUpJBlJKz7R6LkmSJHUOKwOliIiFacwktAxd1jpIKV1VRUySJElS3ZSeDETEDsAVQKslae0ZkCRJkkpSxQrEPwb+BKyYUhrU5WEiIEmSpEpElPfoFFUME1oV2K0nC4xJkiRJ6ntVVAb+DmQV3FeSJEmaiyjx0RmqqAycD5wWESOAB4GpzQdddEySJEkqRxXJwO+KPy9sccwGYkmSJFXCRcfK4aJjkiRJUgcoPRlw0TFJkiR1IisDJYmIFYGtaL3o2BlVxCRJkiTVTRWLju0LXARMA8bS6BOYKQEmA5IkSSpf/QoDlVQGTgFOB05MKU2v4P6SJEmSqCYZWBb4mYmAJEmSOkkdewaqWHTs/4APVXBfSZIkSU1KqQxExB5Nm38FTo2I9Wi96NhVZcQkSZIkNatjZaCsYUK/a7Hv+Bb7XHRMkiRJKkkpyUBKqYrhSJIkSVL76lcYqKRnQJIkSVIHKD0ZiIiLI+IrLfYfExE/KzseSZIkqa6qqAx8DLipxf6bgJ1LjkWSJEkCGg3EZf3XKapIBoYBE1rsfwtYsuRYJEmSpNqqIhn4F60rALsAj5cciyRJkgTUszJQxQrEpwPnR8Qy/Ge40PbA0cBhFcQjSZIk1VLpyUBK6dKIGAJ8A/h6sfsF4JiU0sVlxyNJkiRBLWcWraQyQErpAuCCiBhebI+tIg5JkiSpzipJBgAiYhNgdeCPxfYiwJSU0rSqYpIkSVKNRf1qA6UnAxGxLHA18EEgAWsCTwJnAJOBo8qOSZIkSaqjKmYT+hHwErAUMLFp/2+BnSqIR5IkSXI2oZJsD2yfUhoXs5dingBWriAeSZIkqZaqSAYWAt5usX84jWFCkiRJUuk65/v68lQxTOhW4ICm7RQR8wFfA26sIB5JkiSplqqoDBwH/C0iPgAsSGMRsvWAocCHK4hHkiRJquVsQlVUBiYAGwC3A38BhtBoHt4ImFpBPJIkSVItVVEZeApYPqX0zeadEbEU8DwwXwUxSZIkqeY6aZafslRRGQga6wt0tSg2EEuSJEmlKa0yEBFnFU8T8L2IaF5jYD4ai5DdV1Y8kiRJUt2VOUzofcWfAazD7NOLvg2MBk4rMR5JkiRplvoNEioxGUgpbQsQERcDR6WU3ijr3pIkSZLeqfQG4pTS58u+pyRJktQdG4glSZIk1UYVU4tKkiRJnad+hQErA5IkSVJdWRmQJEmSsGdAkiRJUo1YGZAkSZKwMiBJkiSpRkwGJEmSpJoyGZAkSZJqyp4BSZIkCYiwZ0CSJElSTVgZkCRJknA2IUmSJEk1YjIgSZIk1ZTDhCRJkiSo4SAhKwOSJElSbVkZkCRJkgCcWlSSJElSXVgZkCRJknBqUUmSJEk1YmVAkiRJwtmEJEmSJNWIlQFJkiQJewYkSZIk1YiVAUmSJAlcZ0CSJElSfVgZkCRJknA2IUmSJEk1YmVAkiRJwtmEJEmSJNWIyYAkSZJUUw4TkiRJksCpRSVJkiTVh5UBSZIkCacWlSRJklQjVgYkSZIknFpUkiRJUo1YGZAkSZKwMiBJkiSpRqwMSJIkSdDR0wllWTYI+A5wILAIMBI4JM/zZ+blulYGJEmSpM53HLAPsBWwHPAscG2RJPSalQFJkiSJju8ZOBQ4Nc/zHCDLsuOAl4AtgFt7e1GTAUmSJKlkWZYNA4a1ODQ+z/PxXc4dCqwC3DNzX57n47MsexzYkDomA0PmG9rRqVvdRcTBKaULq45Drb13iY2rDkHd8D3UuR45ruoI1B3fP+qtkj9fngx8s8X+bxXHmi1e/Dm+y/7xTcd6xZ4B9ZeDqw5AGuB8D0m95/tHA8GZwGotHme2OPeN4s+hXfYPazrWKwO2MiBJkiQNVMVQoK7f9M/p3NezLHsG2IRiqFAxdGh14L55icNkQJIkSep85wNfzbLsJuAF4FTgXzSmGO01kwH1F8dqSvPG95DUe75/9G70AxrDhEbyn3UGdsvzfMa8XDRSSn0QmyRJkqSBxgZiSZIkqaZMBtStiPhjRFzSx9d8OiKO7ctrSpLqKyJSROw5j9dYtbjOJn0Vl9TpTAYkqZci4oCImFB1HNJAExG3RMQ5VcfRwnPA8szj7CzSQGIDsSR1gIiYP6U0tZ/vMYhGr9j0/ryPNFAV740Xq45DKpOVAc0mIhaOiEsiYkJEvBQRx3c5/o7hPV2/4SnOOTkifllc58XuhgRFxMoR8fuIeLN4XBURK/btTyf1TkRsFRGjir/Pr0fEXRFxOHAxsEgxrCBFxMnF+ftFxN3F3+WXI+K3EbFC0/W2Kc7fubjW28BHImJqRGzadN5zEfFo0/YOEfFWRCxQbB8TEQ8U+16IiJ9FxLCm8w8oYt45Ih4C3gbWiYj3RcSNEfFGcfz+iNi2v3+PEkAx7HRr4LCm986qEbFuRPyp6X1zRUQs1+W1+0fEgxExpfg36tIul1+yeL+9FRFPRsR+Ta+dOQToUxHx14iYGBEPR8SOLc7ZpGnf2hFxTfHenxARd0TE+/rntyOVz2RAXZ0G7Ah8Ctge2AjYqhfXOQZ4BNiYxlLb342IPVqdWHxbeTWwLLBt8RgB/CEiylwWXHqHiBhM4+/nSGAD4EM0Voe8DTgamEhjWMHyNN4/AAvQ+Hu/AbArsDRwRYvLnwp8A1gbuBP4B7BNcd81aKwsuUrTB6JtgDtSSm8X2zOKGNYD/gv4IHB2l3sMAU4EDgHWBZ4BLgf+XZy/IY1l7ye3/UuR5s1RwB00kumZ752pwK3AQzT+Xu4ALApcXfwbQUQcAlxQvG59YOfi/GYn0Xi/bgD8GrgoIlbucs7/AGcV59wN/CoiFm0VaESMoPHeTzT+bdwY+AkwX+9+dKkDpZR8+CClBI3/8U4B9u2ybzxwSbH9NHBsl9fdApzTtP008Ncu5/wMGNnlnGOL5zsC04FVm46/h8YHnR2q/r34qPcDWJLGB4GtWxw7AJjQxjXWLq6xYrG9TbH9qS7nfR+4vnj+38Cfi/fXPsW+kcA35nKfjxbv4UFN8SXg/V3OewPYv+rfrY/6Plr8u3EKcGOXc5Yo/v5+sNh+Hvj+XK6ZgO81bQ+mkazvV2yvWpxzSNM5KxT7tuhyzibF9v/QSKAXqPp35sNHfz2sDKjZ6jS+0bxj5o6U0gTgwV5c644W2+vO4dx1gDEppaeb7vskMGYur5FKkVJ6DbgEuL4YwnBMi28aZxMRG0fE1RHxTES8SbF0PND1dfd02b4F+HBEzE8jYbi52LdNRCwMfKDYnnmf7YrhDs8X97mKxnu4eWjFNN7ZDHkG8LOIuCkiToiItef280gleD+wVTEMZ0I0GvOfK46tHhHL0PjgfmM313lg5pOU0jRgLLDMnM6h8e8MLc6ZaSMaX2S9PYfj0oBnMqCemgF0Hbozfz/ez1XxVLmU0udpDA+6FdgNyCPiI63OjYhFgOtpfCP5WRof4D9aHF6gy+lvddkeCSxYvGZr/pMMbAtsTuOD/V3FfVYB/kRjON6naXyY+kKL+0xJXRqGU0on00i0/1Bc94GI+AJSdQbR+Pu8YZfHmsAfe3Cdrk34iXd+1pl1Tkpp5r8xfh5SbfmXX82eoPE/yeYGxkWA9zadM5bG+M6Zx4fQGALR1aYtth+Zw30fAUZExKpN130Pjb6Bh9uOXupHKaX7U0qnppS2ofEBfX8aDbldxw6vTaNH4PiU0q0ppUeZ87eOXe8xgUbfwEHA4sBoYBSwErAvs/cLbELjQ/+XU0p3pJT+ReM90+7P81hK6ayU0i7Az2kMS5LK0vW9M5pG78szKaXHuzzeTCm9DLxAo5etTPcCW8xs2pfejUwGNEvxQeTnwKkRsWNErAdcxOz/w74J2LeYDWXm8VZT1G4aEV+PiDUj4iDgc8CP5nDrG2iUbS+LiE2KWRwuo/GPw0198sNJvRQRq0XE9yNi84hYpZh1Z30aierTwJDi/bJ0MZTnWRrj9g+PiPdExC7At3twy1uA/9/e/cdaXddxHH++zFhCZC2jMVdgCpZoQghkQcuSBYqLKNsI7IfRD1asMHKjQrFER91ss5FKynAFC7PMHzglQNCKaksCUeOKdVlC/KjNBC8Q4bs/3p9rp8O994Be7ol7Xo/tu8M538/n832fu3su3/f5vD/f71TgkYg4FBH7ycXFU6koEQKeIv+Gf7nEOJlcTFzr/ZwkaUH5DA+UNAoYjRNv614twMjyO3gKuSj3ZGCZpFHls3OhpIWS+pY+88jf95mSBksaKukrxzjOH5Br5+6QNELSGZImSxp6jI9r1m2cDFi1WWRpwl3lcRNZGtHmevIE/W5gBVnWsL6dcW4gT5jWA9cCV0XEne0dsEzTfpCcdXiobDuAiRVTuGb10goMBn4KNAO3k8nq/Ij4DXAzeaWg3cCVEbGbnDWYSJ5gX01eXetIrSET7DWdvRYRG8mrslxRjjON/PzWcohcmLkY2Ex+1tcdZYxmL1cTOTvwBPnZ6QW8myxFfQB4nEwQDpSNiLgJ+AI5c7aptBtyLIOMiG3kFfV6kf83rQdmkCV7Zj2CfK5lXU1SC3mViKZabc3MzMysfjwzYGZmZmbWoJwMmJmZmZk1KJcJmZmZmZk1KM8MmJmZmZk1KCcDZmZmZmYNysmAmZmZmVmDcjJgZnYUJH1EUlQ8/6SkvXWK5T5Ji7tgnLmSNnVBSGZmdpxxMmBmxz1JiyVF2Q5K+rOkJkl9uuHwy4C3HGljSS2SjuTmYF1CaZqkdZL2SHpO0qOSrpT0mu6Kw8zM/j+dWO8AzMy6yErgMuCVwBjgVqAPML26oaQTgUNdcYfriNgH7Hu54xxDPwI+DFxH3rF4F3nX1i+Wfy+uW2RmZlZ3nhkws57iQETsiIi/RsRSYAkwEf5bBlNKep4GDgB9JJ0saaGkXeVb87WSzqscVNLHJW2V1CrpPuCNVfsPKxOSdJGk30naJ+kfku6V9CpJa4ABwHfaZjIq+ryrHL9V0jZJN1V+cy+pd5kB2Stpp6Sv1fqBSPooMAWYEhHfiojfR0RLRCyPiPHALzroN0LSCkl/LzMJv5J0flWbz0lqlrS/tHuwJFlIOkfSqtJ3r6QNki6oFa+ZmXU/JwNm1lPtI2cJ2pwGfAy4FDiXTAiWA6cCE4BhwMPAakn9ASSNIr85XwgMBe4FvtnZQSWNA+4BfgkMBy4A1pJ/bycBz5Qx+pcNSecAK0q/c0u7ocCiiqGbgLHkt/zvL/G+p8bPYArQHBE/b29nRDzbQb++5IzCGGAk8EfgfkmvL/GeBywArgHOLPE8UNF/KfC30ncoMBfYXyNWMzOrA5cJmVmPI2kkeeK/quLlXsBlEbGztHkfeaL6hlLqAzBH0iVkudG3ybKaVRExr+xvljQC+HQnh58D3BkR36h4bWN5bJV0CNgTETsq9n8VWBYR3614D9OB9ZL6Aa3lmJdHxINl/6fIxKIzg4A/1WhzmIhYXflc0gwyCRkP/Bh4M/A8cE9E7AG2AhsqugwAmiKi7dhbjjYGMzPrHp4ZMLOeYlwpSdkPrCO/5Z9Rsf+ZtkSgGA70BnaXfntLuc/ZwOmlzdvKWJWqn1cbxv8mIUdiODC1Ko5fl32nl61X5bEjYi/wWI1xdZRxZCepn6RbShnQP4E9QD8yCYCc9dgK/EXSEkmfkNS3YogbgFslrZb0dUlvfSlxmJnZseeZATPrKR4GPgscBLZHxMGq/c9XPT8B2EmWwlR7ruvD69QJ5ILn77Wzbxsw+CWO20wmNEfrdnJtxEyghSypWkUmJETEHknvIMuUxgKzgeskjYiI7RExV9IScibhA8DVkj4fEYsOP5SZmdWTZwbMrKdojYgtEbG1nUSgPY+SJ7wvlH6V267S5kngnVX9qp9XW0/W0HfkX8Ar2ollSDtxbCklTE+TSc6Lxy6XTT27RixLgUGSJrW3U9JrO+g3Gvh+WWj8ODkz0L+yQUT8OyJWR8Rs4O3klZsmVOx/KiJujIiLgduAaTViNTOzOnAyYGaNaiVZinO3pPGSTpN0vqRrJLXNFtwIXChptqRBkj4DfKjGuPOASyVdK+ksSUMkzZTUu+xvAcZIOlXSKeW1+cBISTdLGibpDEkTJN0CL5YE3QbMlzRW0hBycXF1UlHtDvI+CEskzSlXCRogaZyk5ZSrLbWjmSxbOquskfgJmcQAUGL7Uol1ALk+oy/wpKSTJC2Q9F5JA8si7NHAEzViNTOzOnAyYGYNqdxj4CJgNfBDYDN58nwmsL20+S25cHc6uQh4EnllnM7GvZ9MGMaTswRrySsKvVCaXAW8ify2f3fps5EsuRlY2m8ArifLmNrMAh4C7iqPm8jSqFrvcTK5EHpC6fdYGXst8LMOul4OvBr4A5kILCKTmDbPkonESnKB8ixgWkQ8AhwCXkdehWlziXcdcEVnsZqZWX2oC+65Y2ZmZmZmxyHPDJiZmZmZNSgnA2ZmZmZmDcrJgJmZmZlZg3IyYGZmZmbWoJwMmJmZmZk1KCcDZmZmZmYNysmAmZmZmVmDcjJgZmZmZtagnAyYmZmZmTWo/wAjI4bc8f+gCQAAAABJRU5ErkJggg==\n",
            "text/plain": [
              "<Figure size 1008x720 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "w4T73BNiPOKo"
      },
      "source": [
        "## Showing some of the predictions"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "quTWRL1UEPFG",
        "outputId": "229ae9be-e956-41f6-8a1c-52e7496d1d7e"
      },
      "source": [
        "input_shape = (224, 224)\n",
        "row = test.iloc[2,:]\n",
        "\n",
        "image_path = os.path.join(images_dir, row['image_name'])\n",
        "\n",
        "img = PIL.Image.open(image_path)\n",
        "img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n",
        "\n",
        "# Plot the image.\n",
        "plt.imshow(img_resized)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.show()\n",
        "\n",
        "# Print the prediction and the true class.\n",
        "print(f\"Probabilities: {test_history[2].round(3)}\")\n",
        "print(f\"Predicted class: {train_classes[df_test_results['y_pred'][2]]}\")\n",
        "print('\\ntrue class:', train_classes[df_test_results['y_true'][2]])"
      ],
      "execution_count": 40,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Probabilities: [0.003 0.82  0.177]\n",
            "Predicted class: starwars\n",
            "\n",
            "true class: starwars\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "hPxJrCR5EPQv",
        "outputId": "9c6665a2-dfdb-493a-9eed-b13ba573b29f"
      },
      "source": [
        "input_shape = (224, 224)\n",
        "row = test.iloc[30,:]\n",
        "\n",
        "image_path = os.path.join(images_dir, row['image_name'])\n",
        "\n",
        "img = PIL.Image.open(image_path)\n",
        "img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n",
        "\n",
        "# Plot the image.\n",
        "plt.imshow(img_resized)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.show()\n",
        "\n",
        "# Print the prediction and the true class.\n",
        "print(f\"Probabilities: {test_history[30].round(3)}\")\n",
        "print(f\"Predicted class: {train_classes[df_test_results['y_pred'][30]]}\")\n",
        "print('\\ntrue class:', train_classes[df_test_results['y_true'][30]])"
      ],
      "execution_count": 41,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Probabilities: [0.009 0.604 0.387]\n",
            "Predicted class: starwars\n",
            "\n",
            "true class: starwars\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 317
        },
        "id": "ch8Xv0jSDEqr",
        "outputId": "5cecd012-79ec-41c5-88b4-3d09416807bc"
      },
      "source": [
        "input_shape = (224, 224)\n",
        "row = test.iloc[81,:]\n",
        "\n",
        "image_path = os.path.join(images_dir, row['image_name'])\n",
        "\n",
        "img = PIL.Image.open(image_path)\n",
        "img_resized = img.resize(input_shape, PIL.Image.LANCZOS)\n",
        "\n",
        "# Plot the image.\n",
        "plt.imshow(img_resized)\n",
        "plt.xticks([])\n",
        "plt.yticks([])\n",
        "plt.show()\n",
        "\n",
        "# Print the prediction and the true class.\n",
        "print(f\"Probabilities: {test_history[81].round(3)}\")\n",
        "print(f\"Predicted class: {train_classes[df_test_results['y_pred'][81]]}\")\n",
        "print('\\ntrue class:', train_classes[df_test_results['y_true'][81]])"
      ],
      "execution_count": 42,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Probabilities: [0.111 0.281 0.608]\n",
            "Predicted class: technic\n",
            "\n",
            "true class: duplo\n"
          ],
          "name": "stdout"
        }
      ]
    }
  ]
}