CNN_with_SkipConnections.ipynb

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  "metadata": {
    "colab": {
      "name": "CNN_with_SkipConnections.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vp4Xh9ydjjrj",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://drive.google.com/file/d/1U2zgA4A7ujloQlAmkyW-v4MjNMisM0SS/view?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SVFrrrogjkUQ",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "DCeQ4A4yhIwv",
        "colab_type": "code",
        "outputId": "894549b9-1514-4c54-9c1e-daf8c746554e",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 260
        }
      },
      "source": [
        "! pip install scikit-plot"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting scikit-plot\n",
            "  Downloading https://files.pythonhosted.org/packages/7c/47/32520e259340c140a4ad27c1b97050dd3254fdc517b1d59974d47037510e/scikit_plot-0.3.7-py3-none-any.whl\n",
            "Requirement already satisfied: scikit-learn>=0.18 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (0.22.2.post1)\n",
            "Requirement already satisfied: scipy>=0.9 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (1.4.1)\n",
            "Requirement already satisfied: matplotlib>=1.4.0 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (3.2.1)\n",
            "Requirement already satisfied: joblib>=0.10 in /usr/local/lib/python3.6/dist-packages (from scikit-plot) (0.15.1)\n",
            "Requirement already satisfied: numpy>=1.11.0 in /usr/local/lib/python3.6/dist-packages (from scikit-learn>=0.18->scikit-plot) (1.18.5)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (1.2.0)\n",
            "Requirement already satisfied: pyparsing!=2.0.4,!=2.1.2,!=2.1.6,>=2.0.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.4.7)\n",
            "Requirement already satisfied: cycler>=0.10 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (0.10.0)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.6/dist-packages (from matplotlib>=1.4.0->scikit-plot) (2.8.1)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.6/dist-packages (from cycler>=0.10->matplotlib>=1.4.0->scikit-plot) (1.12.0)\n",
            "Installing collected packages: scikit-plot\n",
            "Successfully installed scikit-plot-0.3.7\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "MrRG8qrXjoh_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XPpfslJehuCM",
        "colab_type": "code",
        "outputId": "c5038c52-4806-4775-de82-744811e451ef",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 124
        }
      },
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Go to this URL in a browser: https://accounts.google.com/o/oauth2/auth?client_id=947318989803-6bn6qk8qdgf4n4g3pfee6491hc0brc4i.apps.googleusercontent.com&redirect_uri=urn%3aietf%3awg%3aoauth%3a2.0%3aoob&response_type=code&scope=email%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdocs.test%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive%20https%3a%2f%2fwww.googleapis.com%2fauth%2fdrive.photos.readonly%20https%3a%2f%2fwww.googleapis.com%2fauth%2fpeopleapi.readonly\n",
            "\n",
            "Enter your authorization code:\n",
            "··········\n",
            "Mounted at /content/drive\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-0unj6dXjpHE",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "BRYzgrduhvjz",
        "colab_type": "code",
        "outputId": "5d1b5516-319a-4447-aff5-c062763e34c6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 52
        }
      },
      "source": [
        "from zipfile import ZipFile \n",
        "\n",
        "DATA = \"feraligned+ck\"\n",
        "file_name = f\"drive/My Drive/Advanced Networks/datasets/{DATA}.zip\"\n",
        "\n",
        "with ZipFile(file_name, 'r') as zip_:\n",
        "    print('Extracting all the files...') \n",
        "    zip_.extractall()\n",
        "    print('Done!')"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Extracting all the files...\n",
            "Done!\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "o-_7E7SCjr_4",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "R25XGvyBh5Ro",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "import os\n",
        "import cv2\n",
        "import math\n",
        "import joblib\n",
        "import numpy as np\n",
        "\n",
        "import scikitplot\n",
        "import seaborn as sns\n",
        "from matplotlib import pyplot\n",
        "\n",
        "from sklearn.preprocessing import OneHotEncoder\n",
        "from sklearn.metrics import classification_report\n",
        "from sklearn.model_selection import train_test_split\n",
        "\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras import optimizers\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Flatten, Dense, Conv2D, MaxPooling2D, AveragePooling2D\n",
        "from tensorflow.keras.layers import Activation, Dropout, BatchNormalization, Add\n",
        "from tensorflow.keras.initializers import he_normal\n",
        "from tensorflow.keras.callbacks import Callback, EarlyStopping, ReduceLROnPlateau\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "from tensorflow.keras.utils import plot_model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QK8_ErZGh7Yd",
        "colab_type": "code",
        "outputId": "63aa174c-3f84-4275-cbde-8596a907d9f2",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "tf.__version__"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "'2.2.0'"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 5
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "3zxyH5LXjs0d",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "e0wfnRubh9-K",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "BASE_PATH = \"drive/My Drive/FER/Colab/GauravSharma/AdvancedNetworks/\"\n",
        "DATA_PATH = DATA + \"/\"\n",
        "TOP_EMOTIONS = [\"Happy\", \"Neutral\", \"Sadness\"]\n",
        "\n",
        "TRAIN_SIZE = 0.86\n",
        "INPUT_SHAPE = (48,48,1)\n",
        "NUM_CLASSES = len(TOP_EMOTIONS)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "yHpvC5jBiQsm",
        "colab_type": "code",
        "outputId": "36dcad89-4464-4156-d918-ffb0d3e0bec6",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 104
        }
      },
      "source": [
        "total_images = 0\n",
        "for dir_ in os.listdir(DATA_PATH):\n",
        "    if dir_ in TOP_EMOTIONS:\n",
        "        count = 0\n",
        "        for f in os.listdir(DATA_PATH + dir_ + \"/\"):\n",
        "            count += 1\n",
        "        print(f\"{dir_} has {count} number of images\")\n",
        "        total_images += count\n",
        "\n",
        "print(f\"\\ntotal images are {total_images}\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Happy has 2203 number of images\n",
            "Sadness has 1153 number of images\n",
            "Neutral has 1230 number of images\n",
            "\n",
            "total images are 4586\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "hSZ86QTvjxCr",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jZEfInkpiSYa",
        "colab_type": "code",
        "outputId": "d4ce1f01-181c-46d9-9a94-1fcf254e4551",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 86
        }
      },
      "source": [
        "img_arr = []\n",
        "img_label = []\n",
        "label_to_text = {}\n",
        "label = 0\n",
        "\n",
        "for dir_ in os.listdir(DATA_PATH):\n",
        "    if dir_ in TOP_EMOTIONS:\n",
        "        for f in os.listdir(DATA_PATH + dir_ + \"/\"):\n",
        "            img_arr.append(np.expand_dims(cv2.imread(DATA_PATH + dir_ + \"/\" + f, 0), axis=2))\n",
        "            img_label.append(label)\n",
        "        print(f\"loaded {dir_} images to numpy arrays...\")\n",
        "        label_to_text[label] = dir_\n",
        "        label += 1\n",
        "\n",
        "img_arr = np.array(img_arr)\n",
        "img_label = np.array(img_label)\n",
        "img_label = OneHotEncoder(sparse=False).fit_transform(img_label.reshape(-1,1))\n",
        "\n",
        "img_arr.shape, img_label.shape, label_to_text"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "loaded Happy images to numpy arrays...\n",
            "loaded Sadness images to numpy arrays...\n",
            "loaded Neutral images to numpy arrays...\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((4586, 48, 48, 1), (4586, 3), {0: 'Happy', 1: 'Sadness', 2: 'Neutral'})"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SIp9gK38jx4-",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "IGsoz0b6jx10",
        "colab_type": "code",
        "outputId": "06d4d281-74f2-4501-c634-bfc3bff74392",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 419
        }
      },
      "source": [
        "num_emotions = len(TOP_EMOTIONS)\n",
        "fig = pyplot.figure(1, (num_emotions*2, num_emotions*2))\n",
        "\n",
        "idx = 0\n",
        "for k in label_to_text:\n",
        "    sample_indices = np.random.choice(np.where(img_label[:,k]==1)[0], size=4, replace=False)\n",
        "    sample_images = img_arr[sample_indices]\n",
        "    for img in sample_images:\n",
        "        idx += 1\n",
        "        ax = pyplot.subplot(num_emotions,4,idx)\n",
        "        ax.imshow(img.reshape(48,48), cmap='gray')\n",
        "        ax.set_xticks([])\n",
        "        ax.set_yticks([])\n",
        "        ax.set_title(label_to_text[k])\n",
        "        pyplot.tight_layout()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x432 with 11 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "AM5vHXPljxxp",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Af205xmViUVH",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "img_arr = img_arr / 255."
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "4t7sGxz-iWXv",
        "colab_type": "code",
        "outputId": "f32ca881-6613-468e-f271-e633312ab252",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "X_train, X_test, y_train, y_test = train_test_split(img_arr, img_label,\n",
        "                                                    shuffle=True, stratify=img_label,\n",
        "                                                    train_size=TRAIN_SIZE, random_state=42)\n",
        "X_train.shape, X_test.shape, y_train.shape, y_test.shape"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "((3943, 48, 48, 1), (643, 48, 48, 1), (3943, 3), (643, 3))"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UCFydzAOidvN",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "SFJRKZAyF1_U",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def unit_block(X, filters: int, kernel_size: tuple, block_id: int):\n",
        "    X_shortcut = X\n",
        "\n",
        "    X = Conv2D(\n",
        "        filters = filters,\n",
        "        kernel_size = kernel_size,\n",
        "        strides = (1,1),\n",
        "        padding = 'same',\n",
        "        name = f'block{block_id}_conv1',\n",
        "        kernel_initializer = he_normal(seed=42),\n",
        "    )(X)\n",
        "    X = BatchNormalization(axis = 3, name = f'block{block_id}_bn1')(X)\n",
        "    X = Activation('relu', name = f'block{block_id}_actv1')(X)\n",
        "\n",
        "    X = Conv2D(\n",
        "        filters = filters,\n",
        "        kernel_size = kernel_size,\n",
        "        strides = (1,1),\n",
        "        padding = 'same',\n",
        "        name = f'block{block_id}_conv2',\n",
        "        kernel_initializer = he_normal(seed=42),\n",
        "    )(X)\n",
        "    X = BatchNormalization(axis = 3, name = f'block{block_id}_bn2')(X)\n",
        "    X = Activation('relu', name=f'block{block_id}_actv2')(X)\n",
        "\n",
        "    X = Add()([X, X_shortcut])\n",
        "\n",
        "    return X"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "jAISypQAGyPw",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def CNN_with_SkipConnections(in_shape:tuple, out_shape:tuple, show_summary=False):\n",
        "    model_in = Input(shape=in_shape, name=\"in_layer\")\n",
        "\n",
        "    # Block 1\n",
        "    X = unit_block(model_in, filters=32, kernel_size=(3,3), block_id=1)\n",
        "    X = Conv2D(\n",
        "        filters = 64,\n",
        "        kernel_size = (3,3),\n",
        "        strides = (2,2),\n",
        "        padding = 'valid',\n",
        "        name = 'block1_conv3',\n",
        "        kernel_initializer = he_normal(seed=42),\n",
        "    )(X)\n",
        "    X = BatchNormalization(axis = 3, name = 'block1_bn3')(X)\n",
        "    X = Activation('relu', name=\"block1_actv3\")(X)\n",
        "\n",
        "    # Block 2\n",
        "    X = unit_block(X, filters=64, kernel_size=(3,3), block_id=2)\n",
        "    X = Conv2D(\n",
        "        filters = 128,\n",
        "        kernel_size = (3,3),\n",
        "        strides = (2,2),\n",
        "        padding = 'valid',\n",
        "        name = 'block2_conv3',\n",
        "        kernel_initializer = he_normal(seed=42),\n",
        "    )(X)\n",
        "    X = BatchNormalization(axis = 3, name = 'block2_bn3')(X)\n",
        "    X = Activation('relu', name=\"block2_actv3\")(X)\n",
        "\n",
        "    # Block 3\n",
        "    X = unit_block(X, filters=128, kernel_size=(3,3), block_id=3)\n",
        "    X = Conv2D(\n",
        "        filters = 256,\n",
        "        kernel_size = (3,3),\n",
        "        strides = (2,2),\n",
        "        padding = 'valid',\n",
        "        name = 'block3_conv3',\n",
        "        kernel_initializer = he_normal(seed=42),\n",
        "    )(X)\n",
        "    X = BatchNormalization(axis = 3, name = 'block3_bn3')(X)\n",
        "    X = Activation('relu', name=\"block3_actv3\")(X)\n",
        "\n",
        "    #Block 4\n",
        "    X = unit_block(X, filters=256, kernel_size=(3,3), block_id=4)\n",
        "\n",
        "    X = AveragePooling2D()(X)\n",
        "    X = Flatten()(X)\n",
        "\n",
        "    X = Dense(units=256, name=\"dense1\")(X)\n",
        "    X = BatchNormalization(name = 'dense1_bn')(X)\n",
        "    X = Activation('relu', name=\"dense1_actv\")(X)\n",
        "\n",
        "    model_out = Dense(units=out_shape, activation=\"softmax\", name=\"out_layer\")(X)\n",
        "\n",
        "    model = Model(inputs=model_in, outputs=model_out, name=\"CNN_with_SkipConnections\")\n",
        "\n",
        "    if show_summary:\n",
        "        model.summary()\n",
        "    \n",
        "    return model"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6d5tbtPXJT8L",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "iVoBiSuf2PMS",
        "colab_type": "code",
        "outputId": "2acf0055-c597-4c11-acaf-57f22c244225",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model = CNN_with_SkipConnections(INPUT_SHAPE, NUM_CLASSES, show_summary=True)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Model: \"CNN_with_SkipConnections\"\n",
            "__________________________________________________________________________________________________\n",
            "Layer (type)                    Output Shape         Param #     Connected to                     \n",
            "==================================================================================================\n",
            "in_layer (InputLayer)           [(None, 48, 48, 1)]  0                                            \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv1 (Conv2D)           (None, 48, 48, 32)   320         in_layer[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "block1_bn1 (BatchNormalization) (None, 48, 48, 32)   128         block1_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_actv1 (Activation)       (None, 48, 48, 32)   0           block1_bn1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv2 (Conv2D)           (None, 48, 48, 32)   9248        block1_actv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_bn2 (BatchNormalization) (None, 48, 48, 32)   128         block1_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_actv2 (Activation)       (None, 48, 48, 32)   0           block1_bn2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_4 (Add)                     (None, 48, 48, 32)   0           block1_actv2[0][0]               \n",
            "                                                                 in_layer[0][0]                   \n",
            "__________________________________________________________________________________________________\n",
            "block1_conv3 (Conv2D)           (None, 23, 23, 64)   18496       add_4[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block1_bn3 (BatchNormalization) (None, 23, 23, 64)   256         block1_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block1_actv3 (Activation)       (None, 23, 23, 64)   0           block1_bn3[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2_conv1 (Conv2D)           (None, 23, 23, 64)   36928       block1_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_bn1 (BatchNormalization) (None, 23, 23, 64)   256         block2_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_actv1 (Activation)       (None, 23, 23, 64)   0           block2_bn1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block2_conv2 (Conv2D)           (None, 23, 23, 64)   36928       block2_actv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_bn2 (BatchNormalization) (None, 23, 23, 64)   256         block2_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_actv2 (Activation)       (None, 23, 23, 64)   0           block2_bn2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_5 (Add)                     (None, 23, 23, 64)   0           block2_actv2[0][0]               \n",
            "                                                                 block1_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_conv3 (Conv2D)           (None, 11, 11, 128)  73856       add_5[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block2_bn3 (BatchNormalization) (None, 11, 11, 128)  512         block2_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block2_actv3 (Activation)       (None, 11, 11, 128)  0           block2_bn3[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv1 (Conv2D)           (None, 11, 11, 128)  147584      block2_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_bn1 (BatchNormalization) (None, 11, 11, 128)  512         block3_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_actv1 (Activation)       (None, 11, 11, 128)  0           block3_bn1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv2 (Conv2D)           (None, 11, 11, 128)  147584      block3_actv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_bn2 (BatchNormalization) (None, 11, 11, 128)  512         block3_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_actv2 (Activation)       (None, 11, 11, 128)  0           block3_bn2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_6 (Add)                     (None, 11, 11, 128)  0           block3_actv2[0][0]               \n",
            "                                                                 block2_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_conv3 (Conv2D)           (None, 5, 5, 256)    295168      add_6[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "block3_bn3 (BatchNormalization) (None, 5, 5, 256)    1024        block3_conv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block3_actv3 (Activation)       (None, 5, 5, 256)    0           block3_bn3[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4_conv1 (Conv2D)           (None, 5, 5, 256)    590080      block3_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_bn1 (BatchNormalization) (None, 5, 5, 256)    1024        block4_conv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_actv1 (Activation)       (None, 5, 5, 256)    0           block4_bn1[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "block4_conv2 (Conv2D)           (None, 5, 5, 256)    590080      block4_actv1[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_bn2 (BatchNormalization) (None, 5, 5, 256)    1024        block4_conv2[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "block4_actv2 (Activation)       (None, 5, 5, 256)    0           block4_bn2[0][0]                 \n",
            "__________________________________________________________________________________________________\n",
            "add_7 (Add)                     (None, 5, 5, 256)    0           block4_actv2[0][0]               \n",
            "                                                                 block3_actv3[0][0]               \n",
            "__________________________________________________________________________________________________\n",
            "average_pooling2d (AveragePooli (None, 2, 2, 256)    0           add_7[0][0]                      \n",
            "__________________________________________________________________________________________________\n",
            "flatten (Flatten)               (None, 1024)         0           average_pooling2d[0][0]          \n",
            "__________________________________________________________________________________________________\n",
            "dense1 (Dense)                  (None, 256)          262400      flatten[0][0]                    \n",
            "__________________________________________________________________________________________________\n",
            "dense1_bn (BatchNormalization)  (None, 256)          1024        dense1[0][0]                     \n",
            "__________________________________________________________________________________________________\n",
            "dense1_actv (Activation)        (None, 256)          0           dense1_bn[0][0]                  \n",
            "__________________________________________________________________________________________________\n",
            "out_layer (Dense)               (None, 3)            771         dense1_actv[0][0]                \n",
            "==================================================================================================\n",
            "Total params: 2,216,099\n",
            "Trainable params: 2,212,771\n",
            "Non-trainable params: 3,328\n",
            "__________________________________________________________________________________________________\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x-JWXJsm2cmZ",
        "colab_type": "code",
        "outputId": "daec6711-0e10-446a-de10-5f0dd60e16c7",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "plot_model(model, show_shapes=True, show_layer_names=True, expand_nested=True, dpi=50,)"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/png": 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wYEycOBF///vfMXjwYJ6j67veeOMNXLx4EadPn+Y7FJNGe7rEpCgUCowYMQKVlZVwcnLCH//4R6xcuZKSLUdqa2sxd+5cHDp0iOqftWOat2Bft24dKisr+Q6DaKCtrQ0PHjzQ+R9qYWEh8vPzMW7cODz55JN6vbV4TU0NHBwc9DZfXzB58mSEhYV1eO7UqVNIT0/Hjh07eIrKpKWZ5GXAv/32G3bu3Ml3GEQD169fx5dffonVq1frNE9dXR0ee+wxPUXV0RtvvIGPP/7YIHOborKyMuzdu7dL0n3hhRdw4MAB/Pjjj/D39+cpOtNlkknXwsICQ4cO5TsMooGmpibY2trq/Hsz5O9dKBTS31UvxcXFISgoCBMmTKAevBqiE2mEEI2JRCKsXLkS69at4zsUk0NJlxiVkpISzJ49W+d5GGNISUnRQ0Qd1dbWYtOmTWCMYevWrbC2toajoyOSk5O7HVdWVtbh9u5paWlYsmQJdu/e3at1tR1fV1eH6dOno7a2FsDDux/n5eX1as2eTJ8+HXK5HP/5z3/0Ml9/QUmXGBUXFxccPnxY53kKCgr0frKVMYaoqChER0dDIBBg6dKlaGxsRH5+PuLj49Xe0rylpQUpKSnK148cOYLU1FR4eXlh27ZtyMzM7HZdbcc3NDQgJiYGZ8+eVT43Z84c7N27F9euXdNmE3SxefNmvPfee2hpadHLfP0BJV1iVFatWgUfHx+EhYXhxRdfhLu7O1xcXFBSUoI5c+bA1dUVIpEIMpkMcrkcADBx4kSsXr0a+fn5sLW1RXZ2NhYvXowNGzYgPDwc48eP10tsOTk5cHZ27lCBoVAoIJfLYWlpqbaSIikpCcuWLVPedbi1tRW+vr6IjIyEr69vjw3DtR0vFAqxZ88eDB8+vMPzISEhSEpK6u3H7pZEIkFkZCR1I9MAJV1iVOLi4gAAy5Ytw8iRI3HhwgX4+fnh3LlziImJwYwZM1BVVQWZTIZdu3YBANauXQsA8PT0RGRkJADg3XffxZo1a5CRkaG3r9O5ubnw8PDo8Nz+/fsREBCAxMRElWMOHjyIgIAADBo0SPlcYGAgcnNzYWlpidOnT2PmzJlq19R1vCoeHh5drubTxSuvvILz58/j999/19ucfRklXWLULCwsIJVKldf8i8ViWFtbw9/fH7du3eI0lqqqKgiFwg7PSSQSHDt2rEtZVbuNGzdi3LhxEAgEuHTpEtzd3bFhwwbMmzcPjY2NiIiIwObNm9Wuqet4VYRCIaqqqjQa05OkpCQsX76cejP0AiVdYlJaW1vR3NyMzMxMjBgxAgAgEAhQXFyMpqYm3L9/X/nempoatLa26m1tiUSCxsbGDs8FBARgzJgxasfk5eWBMQbGGEaNGoXCwkLU19ejoKAAp06dQkFBQYeY9T1elcbGRkgkEo3G9OTxxx9HUFAQPvvsM73O2xdR0iVGZd68eThz5gwmTJiA9PR0nDp1Ctu3b8eWLVsAADt27IBYLMbVq1eVhxJGjx6NnJwcuLm5ITc3F/Hx8fDw8EBGRgZmzJgBHx8fvcQmk8lw6dKlDs8lJCQgISEBwMMTXNOnT+9xnpUrV8LCwgIJCQnK0qvejtVkfGVlJZ555hn89ttv8PLywj//+U8AwOXLlzFhwoReraWJRYsWITMzE6WlpXqfu09hJigwMJDvEIiGrly5wv7yl7/oNMeJEyfYihUr9BRRVz39XSkUChYREcHu3bun8vWKigo2e/ZsrdbWZaym42NiYlhRUVGP77t58yZbsmSJRnFcuHCBvf766xqN6Wc+71N7usOGDcP69es7PBceHq7Rnk5wcLDKOtHO9Y5c6u3a2dnZeO6552BrawuxWAxvb2+d1/7pp58wePBgiMVihIWFobm5GQDw1ltvQSAQQCAQwMbGBp6enkhNTdV5ve58+OGH+Oyzz5CdnW3QddQRCAT45JNPsGvXLpXlYcnJyVrftUKXsZqMP3r0KCIiIvDkk09qvVZ3nn76aTg4OODEiRMGmb9P4Dvta0PdHsk333zD4uLiOjzX0tLCvL29ez13cXExCw4O7vBcfX09mz9/PnN2dmY1NTWaB6yD3q59/fp15uDgwA4cOMDu3bvH5HI5W758OVMoFDqtv2/fPnb+/Hl2//595u/vz7KyspSvubq6ssrKSiaXy9k//vEP5uDgwDIyMlTOo489XUOjb1AdabOnyxhjdXV1zN/fnzU3NxsgKpPXt/Z0gYd3grWysoKvry/Kyso6vFZTUwN/f3+IxWJMnToV1dXVuHv3LmbOnAmhUAgnJye0tbUp3z969GgsWbJEbb2jKp3nk8vlXdZUVYM6aNAgODk5oby8HEuXLoWjoyMA1bWWZWVlXWpPDxw4gNDQUMydOxdisRgSiQSbN2+GQCBQ+bl7G0N4eDg8PT0hEokgFos7lC61k0gkeOWVV7B+/XqkpaVp8NsifZGdnR1ef/11fPLJJ3yHYpT6XNKNjo7G3bt3IZPJunzdTU1NhY+PD8rLy5Vfh3fu3AmpVIqamhocO3ZMeQfZK1eu4IMPPsD27ds1Wr/zfGlpaV3WVFWD+sUXX2DGjBkYPHgwFixY0O1X9SFDhnSpPb19+zakUqnK96v63JrGcOPGDdjZ2XV7pt7T0xPV1dUabS/SN82bNw9ZWVl6L03rC0yyy1h3xGIxrKysEBAQgKNHj3Z4rbi4GLNnz4ZYLMa0adNw5MgRMMYQFBQEa2trjBs3DtevX0dhYSGioqKURfeaKCoq6jBfampqlzXbPVqDOmvWLLz//vu4fv06vv/+e/ztb3/TaF0XF5cuZ9a7+9yaxFBRUYFPP/20x3aa165d67ZLV25urrLiwBgVFhYadXxce/DggfIqOE0JBAK8//77+Oijj3rsS9Hf9Lmk29raiqamJhw5cgTu7u4dXpNKpcjOzsbkyZORlZUFqVQKxhgOHz6MSZMmgTGGlpYWuLm5IT09HYGBgXB1dYWbm1uv15dKpR3me+KJJ7qsqYpAIMDy5csRGxuLwMBA5R53b4WGhmLs2LHw9/fHyy+/DODhXndoaKjKz93bGIqLi/H3v/8dcXFxKv8BKhQKtLS04MyZM1i/fj02btyoNsZnnnnGqLtSvfbaa93G39+UlJTo1LdaJpNh69atuHLlSodmPf0ev8eUtaPuhEd2djZzdnZmlpaWbNq0aayuro6Fh4czMzMzlpiYyKqrq9nUqVOZjY0Nmzp1KquurmZVVVVs2rRpTCgUsgkTJrDw8HAmFArZjRs3mLe3N3N2dmZ37txhY8aMYQKBgA0dOpRlZmaqja3zfDdv3uyyZkhICBOLxezkyZPMwcGBTZkyhTHGWGtrK5PJZKyhoUE5n6q1S0pKVJ4cPHXqFJs4cSITi8XM2tqaeXl5serqapWfu7cxxMbGMgDKn4SEBMYYY8uXL2dCoZCZmZmxAQMGsKeeeort2LFD7XahE2mmR9sTaY8qKipir776qp4i6hM+71NJlxgvSrqmRx9Jl7GHdcG5ubl6iKhP6HvVC1yRy+XKOtVHf9o7XxH9Y1r0yNVmjCrtfXRTUlJ63UMX0K2PrrZjs7Oz4eTkBJFIhNDQUDQ3N6ust9Znb93urF69mm6D9AhKulpydHRUXhP/6E97qRfRP2165Oqjry57pI9uTExMr3roArr10dVlrFwuR1ZWFsrLy3Hnzh388ssvkMvlOH78OCoqKlBTU4OcnBy999ZVZ/DgwRg+fDh++eUXg65jKijpEt6pqiPurkdubGysyt66PY1RVd/cG5376Pamhy6gWx9dXcaGhIRg7NixaG5uhkKhwLBhw9TWW+uzt2533nvvPU7WMQWUdAnvVNURd9cjNzk5WWVv3Z7GqKpv7o3OfXR76qEL6NYHVx89dPfs2QMnJyeMGTOmQ7VK53prfffWVcfR0RFSqZSTtYwdJV3Cu+LiYvj5+SnriG/cuNGrcVz11u3cR7enHrqAbn1w9dFDd8GCBaitrYVIJFLuYaqqtzZEb111/vKXv2Dr1q2crGXMKOkS3rXXETc0NCjriHvTI7dzb11D9dXt3Ee3px66gG59cHXtoZuUlISKigpYWlrC3NwcVVVVKC4uRlpaGuLi4mBtba18ryF666rj4uICCwsLFBUVcbKesaKkS3gXFRWFvLw8ODo6Ij8/HwsXLuy2R277oYPOvXV7GlNaWqpVb93OfXS16aEL6NZHV9VYdevb29vD3d0dDg4O+PXXX7F06VJs3boVa9euxYABAyAQCJR7yobqravO8uXLaW+Xhzo1nVE9penRd52uIXrrqvu76q6PLpd9cA0xvrveuvqq0+3s5ZdfZnfv3tX7vCaC6nSJaeKyt253fXS56oNriPGG7q2rziuvvIKMjAxO1zQmfa73Aukffv75Z07Xs7e3R0xMTJfn2+9erC0+x8+aNUuntbU1d+5c/PGPf8Sbb77Jy/p8oz1dQginrK2tMWLECJw7d47vUHhBSZcQwrk///nPSE9P5zsMXghY54NUJsDb27tDsToxXvfu3YOtrS0aGhpQUlKiUZtMruXn58PT05PvMIxGfX09hgwZgk8//dQg87/00ks4duxYt1f19UFpJnlMNycnBwqFgu8wSA9u376NpKSkbq/cIsbN0tLSYHN7e3vjv//9r15uoGpKTDLpisVivkMgvXD79m14enoqexYQ8qiQkBAcPHiw3yVdOqZLDKawsLDL3TsIaeft7Y0zZ87wHQbnKOkSg2nvG0CIOo8//jhu377NdxicoqRLDObmzZvd3qiSEH9/f85rrvlGSZcYDGOsv52ZJhoKCAigpEuIPty9exePPfYY32EQI+fo6KjznT1MDSVdYhAXL17EqFGj+A6DmIDHH38cpaWlfIfBGUq6xCAKCwvpAhbSK+PHj8d//vMfvsPgDCVdYhBUuUB669lnn+XkrsTGgpIuMYjLly93uH04IeqMHj0aFy5c4DsMzlDSJQbR1NTU4bYwhKhjYWGBtrY2vsPgDCVdonetra3KW4cT0htCoRAPHjzgOwxOUNIleldUVIQRI0bwHQYxIaNGjcLly5f5DoMTlHSJ3hUWFlK5GNGIu7s7CgsL+Q6DE5R0id5dvHiRKheIRqRSKa5fv853GJygpEv0jsrFiKaGDh2KW7du8R0GJ+hsB9GLK1euoKCgAE899RQqKirg5OTEd0jEhAwZMgTl5eV8h8EJSrpEL6qrq/Haa69BJBJBIBDA3d0dgwYNwu7du+mkGumRubl5vykbo6RL9GL06NF47LHHUFJSAgAoLy+HQCCAVCrlOTJCjAsd0yV6IRKJIBKJlI/t7e2RkJAAMzP6EyO9Y2Zm1i/2dulfBNGbQYMGKf9bKpXi5Zdf5jEaYmocHBxQU1PDdxgGR0mX6I2vry8AQCKRYNu2bTxHQ0yNo6Mjqqqq+A7D4CjpEr2ZPHkyrKys4OXlheeff57vcIiJkUgkkMvlfIdhcJR0id4888wzaG1txfbt2/kOhZig/nJ4QW/VC7GxsSgsLISlpaW+piQmaPjw4Vi1apVWYysrK2FjY9PhhJwxaWxsRG1tLQYPHsx3KEajpKQEiYmJ8PPz03kukUiE+/fv6x6UkdNb0m1oaMDu3bsxZMgQfU1JTFBLSwsGDBig1diEhATIZDK88MILeo5KP86fP4/9+/dj/fr1fIdiNLZu3YrW1la9zCUWi/vF/dLo8ALRK20TLiFisbhf7OlS0iWEGAVKuoQYoZKSEsyePVvneRhjSElJ0UNE/6+2thabNm1CSkoKrK2t4ejoiOTk5G7HlJWVdbitUVpaGpYsWYLdu3f3ak1txmdnZ8PJyQkikQihoaFobm4GAPz0008YPHgwxGIxwsLC0NzcjEOHDnF2/zKxWIz6+npO1uITJV1iUlxcXHD48GGd5ykoKNDr8UPGGKKiohAdHY2YmBg0NjYiPz8f8fHxYIypHNPS0oKUlBTl60eOHEFqaiq8vLywbds2ZGZmdrumtuPlcjmysrJQXl6OO3fu4Ft/YwQAACAASURBVJdfflE+f/z4cVRUVKCmpgY5OTmYM2cO9u7di2vXrmm7aXrNwsJCb8eHjRklXWJSVq1aBR8fHwBAWFgYXnzxRbi7u8PFxQUlJSWYM2cOXF1dIRKJIJPJIJfLMXHiRKxevRr5+fmwtbVFdnY2Fi9ejA0bNiA2NhZlZWUYP368TnHl5OTA2dkZdnZ2AACFQgG5XA5LS0sIBAKVY5KSkrBs2TLlrY1aW1vh6+uLyMhI+Pr6oqmpqds1tR0fEhKCsWPHorm5GQqFAsOGDQMAhIeHw9PTEyKRCGKxWHmFYUhICJKSkjTfKBqipEuIEYqLi1P+97JlyzBy5EhcuHABfn5+OHfuHGJiYjBjxgxUVVVBJpNh165dWLt2LQDA09MTkZGRAIB3330Xa9asQXJyMoYMGaLzV+jc3Fx4eHgoH+/fvx8BAQFITExU+f6DBw8iICCgw6XTgYGByM3NhaWlJU6fPo2ZM2eqXU/X8Xv27IGTkxPGjBnTpSnRjRs3YGdnhzFjxgAAPDw8kJub2/0G0ANKuoSYCAsLC0ilUigUCgAPjw1aW1vD39+fs8bYVVVVEAqFyscSiQTHjh1DWFiYyvdv3LgR48aNg0AgUDZ937BhA+bNm4fGxkZERERg8+bNatfTdfyCBQtQW1sLkUjUYS+2oqICn376KXbu3Kl8TigUcnJ5LiVdQkxUa2srmpubkZmZiREjRkAgEKC4uBhNTU0dzo7X1NTo7R+5RCJBY2Oj8nFAQIByT1GVvLw8MMbAGMOoUaNQWFiI+vp6FBQU4NSpUygoKOj2TL4u45OSklBRUQFLS0uYm5srE2pxcTHS0tIQFxcHa2tr5fsbGxshkUg03SQao6RLiBGaN28ezpw5g4SEBCQmJiI9PR2nTp3C9u3bsWXLFgDAjh07IBaLcfXqVURGRmL06NHIycmBm5sbcnNzER8fDw8PD2RkZCAyMhKlpaXK48TakslkuHTpkvJxQkICEhISADw8wTV9+vQe51i5ciUsLCyQkJAAkUiElStX6jxe1Vh7e3u4u7vDwcEBv/76K5YuXQrg4YUOa9euxYABAyAQCJR7ypcvX8aECRN6tyF00G8amTM9iYqKYqWlpfqajvRDmzZtYidPntRpjhMnTrAVK1boKaKOzp07x9asWaPyNYVCwSIiIti9e/e6vFZRUcFmz56t9bq6jNd1bcYYi4mJYUVFRSpfS0lJYcePH9dp/nY1NTXs9ddf18tcRuxzg+/pDhs2TOVlk+Hh4b3euwgODlZbm1lXV4fp06ejtrZW4xjU6c2cb731FgQCAQQCAaytrTFu3Dj85z//Ufle1k1NqK+vL6Kjo1XO/dZbbwF4WN7k6OgINze3Xn+G3nr096BJDeyjn0lftbP68OGHH+Kzzz5DdnY2p+sKBAJ88skn2LVrV5cSseTkZKxYsULruXUZr+vaR48eRUREBJ588kmt5+it/nJ4weB7ut988w2Li4vr8nxLSwvz9vbu1dzFxcUsODi4y/P19fVs/vz5zNnZmdXU1Kgdry4GVXo7J2OMubq6ssrKSvbgwQO2bNky9uc//1nl+/Lz89XuIf36668sOjq6y/OjR49mAoGA/etf/2KMMfbVV1+xixcv9uozaEKT38OjuvtM2tLHnq4hdben21/pc0+3oaGBhYWF6WUuI2b4PV0A+Pjjj2FlZQVfX1+UlZV1eK2mpgb+/v4Qi8WYOnUqqqurcffuXcycORNCoRBOTk4djvOMHj0aS5YsAfDwrOqePXswfPhwjWNQVeOpbs7e1HE2NTXh2WefBfBwz8/e3h42NjZITExU1oS+8cYbKj/XxYsX4eHhgSFDhuDmzZsAgKCgIMyYMQMLFy5UXjGkbnsFBwdjypQp8Pb2hoODA2xtbZXduoYNGwZLS0tMmTJFZWyPerQGdsWKFco9+aefflrtZ4qNjVWOUxWbuu1MSGfm5ub9Yk+Xk6QbHR2Nu3fvQiaTITU1tcNrqamp8PHxQXl5OTw9PZGamoqdO3dCKpWipqYGx44dU95n68qVK/jggw+06tfaOQZVNZ7qdFfH2X45ZVZWFp5++mkAD6+aqq2txZkzZ/DDDz8oa0I9PDxUfi53d3dcuHAB06dPx9mzZwE8/Lr65Zdf4t69e9i4cWO32+u9997DkCFDcODAAaxduxaBgYGorq5GaGgoNmzYALlcjps3b6K5ublLbI96tAZ28+bNYIwhMjISH3zwgdrPlJycrBynKjZNtjPp3ywsLPrFiTRO7gYsFothZWWFgIAAHD16tMNrxcXFmD17NsRiMaZNm4YjR46AMYagoCDlsdLr16+jsLAQUVFRykJ3fcXQucZTU5WVlXjssceQn5+PmTNnoqCgAP/973/xxhtvoLS0VLmHCQBFRUUdPtejzM3N4erq2iEOiUSCffv24aWXXkJsbCx8fHxUbi8AsLOzg1QqxeTJk3Hx4kVYW1tj+PDhsLW1hZ2dHZycnKBQKHD8+HGVsany2WefwdnZGSEhIb0apy42oHfbub6+Hu+99x4GDhzYbVx8uXv3LmpqanD+/Hm+QzEaZWVliI+P18tcAoFA7SXTfQknSbe1tRVNTU04cuQI3N3dO7wmlUqRnZ2NyZMnIysrC1KpFIwxHD58GJMmTQJjDC0tLXBzc0N6ejoCAwPh6uqq8Uml7mLQhUKhgLm5OczNzdHQ0ICWlhbs27cPCQkJ+MMf/qBs6F1TU4OhQ4d2+FyP1kKq89xzz+H999/HX//6V8ybN0/l9tKEqthU+fe//42srCwcOHAAP/zwAzZv3qzyMz36dVDX2GxsbBAfH0/9dE3I1q1b+Q7B9Ojr6LC6E2nZ2dnM2dmZWVpasmnTprG6ujrGGGPh4eHMzMyMrV27lk2dOpXZ2NiwqVOnsurqalZVVcWmTZvGhEIhmzBhAgsPD2dCoZDduHGDeXt7M2dnZ8YYY3fu3GFjxoxhAoGADR06lGVmZqqMTVUMISEhTCwWs5MnTzIHBwc2ZcoUtXOWlJR0Odn09ttvMwAMADMzM2NPPPEES0lJYYwxtnfvXiYUCtmkSZOYlZUVS09PZxKJhL388ssdPte9e/dYUFAQE4vF7F//+hcbOHAge+6559jbb7/NrKysWFpaGmPsYTnSyy+/zC5evMiqq6u7bK+goCAmFApZVlYWCw4OZjY2Nuybb75hDg4ObPjw4ezrr79m5ubmbM2aNV1iGz58ODMzM2OJiYnK38mmTZvYSy+9pPx8ANjWrVtVfqbXX39dOe69997rEpu67awKnUgzPfo8kcYYY0FBQXqby0h9LmBMP/vzixYtwrp16+jOEURrdOcI07N161Y89dRT8Pf318t8wcHB+O677/Qyl5FK61NXpMnlcuUZ90d/+sMdRslDTIs+udqMUYX66ZLe6FNJ19HRUXk9+qM/jo6OfIdGOKJNn1x99NZl1E+X9FKfSrqkb1JV/wug2z65AoGgS1/dnsaEh4dr3VeX+umS3qKkS4yeqvpfAN32yT1x4kSXvro9jcnIyND6qzT10yW9RUmXGL3i4mL4+fkp639v3LjRq3Fc9tWlfrqktyjpEqPXXv/b0NDQof63pz65nfvq9maMtqifLuktSrrE6EVFRSEvLw+Ojo7Iz8/HwoULAaDHPrmd++r2NGbGjBla99Wlfrqk1/RV8Uv9dImu9HlxhCH66lI/3a7o4giNcdNljBCucd1Xl/rpkt7ipPcCIVz7+eefOV/T3t4eMTExXZ5/tHubNnQZr+vas2bN0mk86Yr2dAkhhEOUdAkhhEN6O7wwZswYLFq0SF/TERNSXl4Oe3v7XrWq7M79+/fx008/wdLSUk+R6VdLSwsaGhoQFBTEdyhGZfLkyXyHYFL01mWM9F9//etfERoaCi8vL75DISaOuowRQgjRK0q6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIUq6hBDCIboxJdFKW1sbPvroIzQ0NCAvLw9lZWWQSCQYN24cwsPD+Q6PEKNFSZdoxdzcHBkZGSgsLAQAnDhxAmZmZjrffZaQvo4OLxCtRUVFYcCAAcrHQ4YMwYIFC/gLiBATQEmXaG3+/PlwdnZWPn788cc7PCaEdEVJl2ht4MCBcHFxAQBYWlrizTff5DkiQowfJV2ik+joaAiFQgwaNIhOoBHSC5R0iU5CQkJga2uLJ598Eo899hjf4RBi9IyqeuGHH35Ac3Mz32EQDQ0ZMgS+vr74/vvv+Q6FaGj48OHw8vLiO4x+xaiS7vvvv4+FCxfyHQbR0EsvvQR7e3ts374dISEhfIej1s6dO7Fo0SK+wzAadXV1OHr0KHbu3Ml3KP2KUSVdZ2dnOhljoq5fv47y8nKj/v19//33Rh0f18rKyrBu3Tq+w+h36JguIYRwiJIuIYRwiJIu4VRJSQlmz56t8zyMMaSkpOghoodqa2uxadMmpKSkwNraGo6OjkhOTu5xXFlZGUaOHKl8nJaWhiVLlmD37t0GG5udnQ0nJyeIRCKEhoaiubkZP/30EwYPHgyxWIywsDA0Nzfj0KFDyMvL6zEOwi1KuoRTLi4uOHz4sM7zFBQUoLKyUg8RPUzgUVFRiI6ORkxMDBobG5Gfn4/4+HgwxtSOa2lpQUpKivI9R44cQWpqKry8vLBt2zZkZmYaZKxcLkdWVhbKy8tx584d/PLLL5DL5Th+/DgqKipQU1ODnJwczJkzB3v37sW1a9e03DLEECjpEk6tWrUKPj4+CAsLw4svvgh3d3e4uLigpKQEc+bMgaurK0QiEWQyGeRyOQBg4sSJWL16NfLz82Fra4vs7GwsXrwYGzZsQHh4OMaPH69TTDk5OXB2doadnR0AQKFQQC6Xw9LSEgKBQO24pKQkLFu2DBYWD89Ht7a2wtfXF5GRkfD19UVTU5NBxoaEhGDs2LFobm6GQqHAsGHDEB4eDk9PT4hEIojFYgwaNEj53qSkJI23CTEcSrqEU+1dyJYtW4aRI0fiwoUL8PPzw7lz5xATE4MZM2agqqoKMpkMu3btAgCsXbsWAODp6YnIyEgAwLvvvos1a9YgIyND56/Qubm58PDwUD7ev38/AgICkJiYqHbMwYMHERAQoExuABAYGIjc3FxYWlri9OnTmDlzpt7HttuzZw+cnJwwZswYSKVS5fM3btyAnZ0dxowZAwDw8PBAbm5u9xuAcIqSLuGVhYUFpFIpFAoFAEAsFsPa2hr+/v64desWJzFUVVVBKBQqH0skEhw7dgxhYWFqx2zcuBHjxo2DQCDApUuX4O7ujg0bNmDevHlobGxEREQENm/erPex7RYsWIDa2lqIRCLlnmxFRQU+/fTTDnW3QqEQVVVVmmwOYmCUdIlRaW1tRXNzMzIzMzFixAgAgEAgQHFxMZqamnD//n3le2tqatDa2qrzmhKJBI2NjcrHAQEByj1FdfLy8sAYA2MMo0aNQmFhIerr61FQUIBTp06hoKCgQ6z6Ggs8PDRRUVEBS0tLmJubo6qqCsXFxUhLS0NcXBysra2V721sbIREItFwixBDoqRLODVv3jycOXMGEyZMQHp6Ok6dOoXt27djy5YtAIAdO3ZALBbj6tWrykMJo0ePRk5ODtzc3JCbm4v4+Hh4eHggIyMDM2bMgI+Pj04xyWQyXLp0Sfk4ISEBCQkJAB6e4Jo+fXqv5lm5ciUsLCyQkJAAkUiElStX9nq8qrHq1re3t4e7uzscHBzw66+/YunSpdi6dSvWrl2LAQMGQCAQKPeUL1++jAkTJvQqfsIRZkQCAwP5DoFoqbi4mL3zzjs6zXHixAm2YsUKPUXUlbq/L4VCwSIiIti9e/e6vFZRUcFmz56t9Zp8j4+JiWFFRUUqXystLWVRUVFaz20IQUFBfIdgaJ+bxJ7usGHDsH79+g7PhYeHa7SHExwcrLI+tK6uDtOnT0dtba1OMbJe1I1qstatW7dgY2ODM2fO9GpNTetf28fqq25WHz788EN89tlnyM7O5nRdgUCATz75BLt27epSIpacnIwVK1ZoPTef448ePYqIiAg8+eSTWq9P9M8kku6WLVtgbm7e4bn09HSN5lCVEBsaGhATE4OzZ8/qFB/Qc92opmvt378fa9aswRdffNGrNTWtf20fq6+6WX34+eefcf/+ffj5+XG+tr29PWJiYrqUiMXFxeH555/Xel4+x8+aNUvncjqifyaRdAHg448/hpWVFXx9fVFWVtbhtZqaGvj7+0MsFmPq1Kmorq7G3bt3MXPmTAiFQjg5OaGtrU35/tGjR2PJkiUQCoXYs2cPhg8f3uP6JSUlsLe3h42NDRITE7vMHxUVhQ0bNiA2NhaDBg2Ck5MTysvLsXTpUjg6Oqpcq6ysTO0/isrKSixcuBAZGRnKms3u1myvf1W1tqr42+tcBQKB8huDqu2oqp6WEKI9o+oy1p3o6Gh88MEHWLVqFVJTU7FmzRrla6mpqfDx8cHhw4exZs0apKamwtzcHFKpFN9++y1+//13mJk9/P/LlStX8MEHH2Du3Lkare/i4oLa2lpcunQJb7/9NhQKRYf5S0pKkJubi/Xr18Pf3x/79+/H4MGDsWDBArV7bkOGDFFZY/rvf/8bY8eOxaBBgzB27FgcOnQIr7zyCnbu3Kl2zdbWVshkMnzxxRcq1+4c/7vvvovJkydj3bp1kMlkarfjsmXLsGfPHhw7dgzz58/HuXPnlLfo6ez27dv45ptvNNquXCovLzfq+LhWU1PToWqDcMNk9nTFYjGsrKwQEBCA8vLyDq8VFxfDz88PYrEY06ZNw40bN1BUVIRZs2bB2tpaWRNZWFiIqKgo2Nvba7z+8ePHMXToUDz11FNgjKmcv92sWbNQUFCA69ev4/vvv9f4mOnevXvxyiuvQCAQICsrS3mRQHdr9rR25/hVUbUd23Wup1Wlra0NjY2NRvvDGOM9BmP6aWpq6vYyZ2IYJrOn29raiqamJhw5cgTu7u4dXpNKpcjOzsbkyZORlZUFqVQKxhgOHz6MSZMmgTGGlpYWuLm5IT09HYGBgXB1dYWbm1uv19+3bx8SEhLwhz/8AatWrYJUKu0yf3vdqIWFBZYvX47Y2FgEBgYq97J7o7GxEQ8ePFD+Y7h58ya8vLxw/fr1btdsJxAIVK7dOX6ga52rqu2oCVdXV7z22msajeHSN998Y9Txca2srAwXLlzgO4z+h5eiCTXUlfRkZ2czZ2dnZmlpyaZNm8bq6upYeHg4MzMzY4mJiay6uppNnTqV2djYsKlTp7Lq6mpWVVXFpk2bxoRCIZswYQILDw9nQqGQ3bhxg3l7ezNnZ2d2584dNmbMGCYQCNjQoUNZZmam2tj27t3LhEIhmzRpErOysmKZmZkd5j979iyTSCTs9ddfZ4wx1traymQyGWtoaGCMMZVrlZSUMG9v7w7ryGQyJpFI2Pnz5xljjH300UfM3NycDR48mJ0/f17tmu3bY9OmTV3WVhV/eno6k0gkDIBynKrtGBISwsRiMTt58iRzcHBgU6ZMUbl99FEyZmhUktgRlYzx4nOTSLrE+FHSNT2UdHlhGnW6XJLL5RAIBF1+2jteEcNhWvTI1WaMKtRPl3CFkm4njo6OyuviH/1pL70ihqNNj1x99NVl1E+XcIiSLjE4VfW/3fXIjY2NVdlbt6cx3dU9d4f66RIuUdIlBtde/1teXg5PT0+kpqZ22yM3OTlZZW/dnsaoq3vuCfXTJVyipEsMrrv63+5w1VuX+ukSLlHSJQbXXv/b0NCgrP/tTY/czr11DdVXl/rpEi5R0iUGFxUVhby8PDg6OiI/Px8LFy7stkdu+6GDzr11expTWlqqVW9d6qdLOMVDnZpaVEdpuvRdp2uI3rrUT7cjqtPlBdXpEuPEZW9d6qdLuGQyvRdI//Lzzz9zul57P93O2u9erC0+x8+aNUuntYlh0J4uIYRwyKj2dCsrK/HVV1/xHQbRglwux+XLl43691daWmrU8XGtrq6O7xD6JQHrfBCLR9999x2am5v5DoNo6Ntvv8X48eMxdOhQvkMhGhoxYgTGjRvHdxhKwcHB+O677/gOw5DSjGpPNzg4mO8QiBbOnz8Pf39/eHl58R0KIUaPjukSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHKOkSQgiHjKqJOTEdCoUCzz77LJqbm/HgwQN8++23YIwhODgY8fHxfIdHiNGipEu0YmZmBkdHRxw7dkz5nFgsxoQJE3iMihDjR4cXiNZiY2Px2GOPKR8PHDgQ06dP5zEiQowfJV2iNX9/fzg4OCgf+/j4wNLSkseICDF+lHSJ1iwsLDB+/HgAgL29PZYuXcpzRIQYP0q6RCexsbEYOHAgBg4ciBdeeIHvcAgxepR0iU4mTJgAS0tLTJo0CWZm9OdESE+MrnohOjoaVVVVfIdBNGBjY4PCwkKEhobyHYpaVVVVkEgkfIdhVAIDA/H666/zHUa/Y3RJt6SkBAcOHOA7DKKBkydP4vjx4/joo4/4DkWtuXPn4uuvv+Y7DKNRVFSEtLQ0Sro8MLqkKxAIYGVlxXcYRAOPP/44zM3Njfr3ZmZmZtTxcY2qTPhDB+EIIYRDlHQJZ0pKSjB79myd52GMISUlRQ8RPVRbW4tNmzYhJSUF1tbWcHR0RHJyco/jysrKMHLkSOXjtLQ0LFmyBLt37zbY2OzsbDg5OUEkEiE0NBTNzc346aefMHjwYIjFYoSFhaG5uRmHDh1CXl5ej3EQ7lHSJZxxcXHB4cOHdZ6noKAAlZWVeojoYQKPiopCdHQ0YmJi0NjYiPz8fMTHx4MxpnZcS0sLUlJSlO85cuQIUlNT4eXlhW3btiEzM9MgY+VyObKyslBeXo47d+7gl19+gVwux/Hjx1FRUYGamhrk5ORgzpw52Lt3L65du6blliGGQkmXcGbVqlXw8fFBWFgYXnzxRbi7u8PFxQUlJSWYM2cOXF1dIRKJIJPJIJfLAQATJ07E6tWrkZ+fD1tbW2RnZ2Px4sXYsGEDwsPDlRdnaCsnJwfOzs6ws7MD8LCRj1wuh6WlJQQCgdpxSUlJWLZsGSwsHp4WaW1tha+vLyIjI+Hr64umpiaDjA0JCcHYsWPR3NwMhUKBYcOGITw8HJ6enhCJRBCLxRg0aJDyvUlJSRpvE2JYlHQJZ+Li4gAAy5Ytw8iRI3HhwgX4+fnh3LlziImJwYwZM1BVVQWZTIZdu3YBANauXQsA8PT0RGRkJADg3XffxZo1a5CRkaHzV+jc3Fx4eHgoH+/fvx8BAQFITExUO+bgwYMICAhQJjfgYflVbm4uLC0tcfr0acycOVPvY9vt2bMHTk5OGDNmDKRSqfL5GzduwM7ODmPGjAEAeHh4IDc3t/sNQDhHSZfwxsLCAlKpFAqFAsDDLmXW1tbw9/fHrVu3OImhqqoKQqFQ+VgikeDYsWMICwtTO2bjxo0YN24cBAIBLl26BHd3d2zYsAHz5s1DY2MjIiIisHnzZr2PbbdgwQLU1tZCJBIp92QrKirw6aefYufOncr3CYVCqnk3QpR0idFobW1Fc3MzMjMzMWLECAAPSwiLi4vR1NSE+/fvK99bU1OD1tZWndeUSCRobGxUPg4ICFDuKaqTl5cHxhgYYxg1ahQKCwtRX1+PgoICnDp1CgUFBR1i1ddY4OGhiYqKClhaWsLc3BxVVVUoLi5GWloa4uLiYG1trXxvY2MjXRBihCjpEs7MmzcPZ86cwYQJE5Ceno5Tp05h+/bt2LJlCwBgx44dEIvFuHr1qvJQwujRo5GTkwM3Nzfk5uYiPj4eHh4eyMjIwIwZM+Dj46NTTDKZDJcuXVI+TkhIQEJCAoCHJ7h626py5cqVsLCwQEJCAkQiEVauXNnr8arGqlvf3t4e7u7ucHBwwK+//oqlS5di69atWLt2LQYMGACBQKDcU758+TL1NzZGzMgEBgbyHQLR0IULF9jq1at1muPEiRNsxYoVeoqoK3V/VwqFgkVERLB79+51ea2iooLNnj1b6zX5Hh8TE8OKiopUvnblyhX2l7/8Reu5DSUoKIjvEAztc5PZ0x02bBjWr1/f4bnw8HCN9nSCg4NV1onW1dVh+vTpqK2t1TlOTaiqr1QnOzsbzz33HGxtbSEWi+Ht7W2w9d966y0IBAIIBALY2NjA09MTqampOq/XnQ8//BCfffYZsrOzDbpOZwKBAJ988gl27drVpUQsOTkZK1as0HpuPscfPXoUERERePLJJ7VenxgI32m/M3V7JN988w2Li4vr8FxLSwvz9vbu9dzFxcUsODi4w3P19fVs/vz5zNnZmdXU1GgesA727dvHzp8/z+7fv8/8/f1ZVlaWyvddv36dOTg4sAMHDrB79+4xuVzOli9fzhQKhcHWd3V1ZZWVlUwul7N//OMfzMHBgWVkZKicRx97uoZG36A6oj1d3pjOni4AfPzxx7CysoKvry/Kyso6vFZTUwN/f3+IxWJMnToV1dXVuHv3LmbOnAmhUAgnJye0tbUp3z969GgsWbIEQqEQe/bswfDhw3tcv/N8crm8y5qqalAHDRoEJycnlJeXY+nSpXB0dAQAlfWVZWVlXWpPDxw4gNDQUMydOxdisRgSiQSbN2+GQCBQ+bl7G4O6+s5HSSQSvPLKK1i/fj3S0tK0+K0RQh5lUkk3Ojoad+/ehUwm6/J1NzU1FT4+PigvL1d+Hd65cyekUilqampw7NgxZb/XK1eu4IMPPsD27ds1Wr/zfGlpaV3WVFWD+sUXX2DGjBkYPHgwFixY0CX2R+srhwwZ0qX29Pbt2x3qMXv63JrGP2ZrvgAAIABJREFU0Lm+UxVPT09UV1drtL0IIV0ZXZex7ojFYlhZWSEgIABHjx7t8FpxcTFmz54NsViMadOm4ciRI2CMISgoCNbW1hg3bhyuX7+OwsJCREVFKYvuNVFUVNRhvtTU1C5rtnu0BnXWrFl4//33cf36dXz//ff429/+pnyfqvrKzlxcXDqcYe/pc2sSQ2/WB4Br165h6NChal8/fvw4CgoKup2DTxcuXEBQUBDfYRiNBw8ewNXVle8w+iWTSrqtra1oamrCkSNH4O7u3uE1qVSK7OxsTJ48GVlZWZBKpWCM4fDhw5g0aRIYY2hpaYGbmxvS09MRGBgIV1dXuLm59Xp9qVTaYb4nnniiy5qqCAQCLF++HLGxsQgMDFTucRcXF+Pvf/874uLilJeEqhIaGoqxY8fC398fL7/8MoCHe92hoaEqP3dvY+hpfYVCgZaWFpw5cwbr16/Hxo0b1cYYEBCA+Ph4ta/zLSgoCN9//z3fYRiNq1evavxNj+gJv8eUu1J3wiM7O5s5OzszS0tLNm3aNFZXV8fCw8OZmZkZS0xMZNXV1Wzq1KnMxsaGTZ06lVVXV7Oqqio2bdo0JhQK2YQJE1h4eDgTCoXsxo0bzNvbmzk7O7M7d+6wMWPGMIFAwIYOHcoyMzPVxtZ5vps3b3ZZMyQkhInFYnby5Enm4ODApkyZwhhjrLW1lclkMtbQ0KCcLzY2lgFQ/iQkJLCSkhKVJwdPnTrFJk6cyMRiMbO2tmZeXl6surpa5efubQyq1meMseXLlzOhUMjMzMzYgAED2FNPPcV27NihdrvQiTTTQyfSePO5gLFuWinxgPZITE9BQQG+/vpr2tM1Ie17uu0XphiL4OBgfPfdd3yHYUhpJnUijStyuVxZp/roT3vnK6J/TIseudqMUYX66RIuUdJVwdHRUXl9/KM/7aVeRP+06ZGrj766jPrpEo5R0iUGpaqOuLseubGxsSp76/Y0RlV9c29QP13CNUq6xKBU1RF31yM3OTlZZW/dnsaoqm/uDeqnS7hGSZcYVHFxMfz8/JR1xDdu3OjVOK5661I/XcI1SrrEoNrriBsaGpR1xL3pkdu5t66h+upSP13CNUq6xKCioqKQl5cHR0dH5OfnY+HChd32yG0/dNC5t25PY0pLS7XqrUv9dAnnOC8N7gEVsZsefV8cYYjeutRPtyO6OII3ptVljPQPXPbWpX66hGsm1XuB9A8///wzp+vZ29sjJiamy/Ptdy/WFp/jZ82apdPaxHBoT5cQQjhESZcQQjhkdIcXbt26RX1PTUxNTY2y7MlYXbt2jf6uHtHQ0ICxY8fyHUa/ZHRJ98yZM3yHQDT017/+FaGhofDy8uI7FEKMHh1eIIQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDlHSJYQQDhldE3NiGtra2nD27FkAQFlZGX7//Xe0tLTAzs4OI0eO5Dk6QowXJV2iFXNzcwQHB6OtrQ0KhQL/8z//g6amJsyfPx/Jycl8h0eI0aLDC0Rrc+fORUVFBe7cuYOKigpYWlpi4cKFfIdFiFGjpEu09s4772DQoEHKxwMHDsTTTz/NY0SEGD9KukRrbm5ucHBwAAAIBALMmTOH54gIMX6UdIlOwsLCYGZmhsGDByMqKorvcAgxepR0iU4WLVoEJycnODk5Yfjw4XyHQ4jR01v1QmtrK9ra2vQ1HTEREokE9vb2CA8PR1NTE9/hEB5YWlpCIBDwHYbJ0FvSfeutt1BaWgpra2t9TUlMhK2tLU6fPo158+bpNE9paSnEYjHs7Oz0FJl+1dfXo6qqCkOHDuU7FKNx9epVJCQkwN/fn+9QTIZe63RTU1MxZMgQfU5JTIBCoYCZme5HqhISEiCTyfDCCy/oISr9O3/+PPbv34/169fzHYrR2Lp1K98hmBw6pkt0po+ES0h/Qf9aCCGEQ5R0iUkpKSnB7NmzdZ6HMYaUlBQ9RPT/amtrsWnTJqSkpMDa2hqOjo49XhJdVlbWoVdFWloalixZgt27d/dqTW3GZ2dnw8nJCSKRCKGhoWhubgYA/PTTTxg8eDDEYjHCwsLQ3NyMQ4cOIS8vr1exkN6hpEtMiouLCw4fPqzzPAUFBaisrNRDRA8xxhAVFYXo6GjExMSgsbER+fn5iI+PB2NM5ZiWlhakpKQoXz9y5AhSU1Ph5eWFbdu2ITMzs9s1tR0vl8uRlZWF8vJy3LlzB7/88ovy+ePHj6OiogI1NTXIycnBnDlzsHfvXly7dk3bTUM6oaRLTMqqVavg4+MD4OGFGS+++CLc3d3h4uKCkpISzJkzB66urhCJRJDJZJDL5Zg4cSJWr16N/Px82NraIjs7G4sXL8aGDRsQGxuLsrIyjB8/Xqe4cnJy4OzsrKy8UCgUkMvl3ZZTJSUlYdmyZbCweHg+u7W1Fb6+voiMjISvr2+PJXjajg8JCcHYsWPR3NwMhUKBYcOGAQDCw8Ph6ekJkUgEsVisvMQ7JOR/2bv3uKjq/H/grwEFhhkRBEQiRAoTNpFVKdF1FUlSvCC2CBWkrgSRlpCp5QOsLEUTEfAuYakbrdd0Y3UVS0FLI3a9ROQlFVC56XARRa7O5/eHP86XgTMwN+YM8H4+HjwezZn5fD7vc6h3hzPv8z6BSExMVP+gEF6UdEmXEhcXx/3zokWLMHjwYPz222/w9vbGpUuXEBUVBT8/P5SXl8PLyws7duxAbGwsAMDd3R1hYWEAgA8++AAxMTFISkqCvb291n9CZ2dnw83NjXu9b98++Pr6IiEhgffzBw8ehK+vr0LviunTpyM7OxsmJib4+eefMWXKFKXraTt+586dsLW1hYeHB5ydnRXeKywshIWFBTw8PAAAbm5uyM7Obv8AEJVR0iVdXq9eveDs7Ay5XA4AkEqlMDMzw8SJE3H79m29xFBeXg6xWMy9tra2xvHjxxEUFMT7+TVr1mDEiBEQiUS4evUqXF1dsWrVKoSGhqKurg4hISFYt26d0vW0HT937lxUVVVBIpEonMWWlZVh8+bN2L59O7dNLBajvLxcncNB2kFJl3Q7TU1NaGhowNGjR/Hss89CJBIhPz8f9fX1ePjwIfe5yspKNDU16WRNa2tr1NXVca99fX25M0U+OTk5YIyBMYYhQ4bgypUrePToEfLy8nDmzBnk5eUpxKrL8YmJiVwrTmNjYy6h5ufnIzU1FXFxcQo3OdXV1cHa2lrdQ0KUoKRLupTQ0FBcuHAB8fHxSEhIQFpaGs6cOYMtW7Zg/fr1AIBt27ZBKpXi+vXrCAsLw9ChQ5GVlQUXFxdkZ2dj9erVcHNzw969exEWFobi4mLuOrGmvLy8cPXqVe51fHw84uPjATz5gmvy5MkdzrFkyRL06tUL8fHxkEgkWLJkidbj+cZaWlrC1dUVVlZWOHfuHBYuXAjgyY0OsbGx6N27N0QiEXemfO3aNYwePVq1A0E6xnQkIiKCFRcX62o60gOtXbuWnT59Wqs5Tp06xRYvXqyjiBRdunSJxcTE8L4nl8tZSEgIe/DgQZv3ysrKWEBAgMbrajNe27UZYywqKorduHGD973k5GR24sQJreZvyd/fX2dzGagvOv1Md+DAgby3TQYHB6t8djFjxgyltZn379/H5MmTUVVVpXYMfPhqFfksWLAAIpEIIpEIZmZmGDFiBP773//yfpa1UxM6atQoREZG8s69YMECAE/Km2xsbODi4qLSPqij5e9BnRrYlvukq9pZXfj000+xdetWZGZm6nVdkUiETZs2YceOHW1KxJKSkrB48WKN59ZmvLZrHzlyBCEhIXjmmWc0noMo6vSku379ehgbG7fZnpaWpvIcyhJWbW0toqKiuAckqhsDH75aRT6bN2+Gk5MT7t27h4qKCkyYMAFbtmzh/Wx7NaHJycltSoo2b96MoUOHYuvWrTh79iyef/55JCUl4d///rdK+6COlr8HdWpgW+6TrmpndeHkyZN4+PAhvL299b62paUloqKi2vw+4+Li8Je//EXjebUZr+3aU6dO1bqcjijSyzXdzz//HKamphg1ahRKSkoU3qusrMTEiRMhlUrh4+ODiooKVFdXY8qUKRCLxbC1tVVoGTl06FDMnz8fwJNvVXfu3KlSH9fWMfDVeAL8tYqq1HHW19fjxRdfBPDkzM/S0hLm5uZISEjgakLnzZvHu1+XL1+Gm5sb7O3tcevWLQCAv78//Pz8EB4ernC2zXe8ZsyYgQkTJmDkyJGwsrJCnz59YG5uDolEgoEDB8LExAQTJkzgja2lljWwixcv5s7kn3/+eaX7FB0dzY3ji03ZcSakp9JL0o2MjER1dTW8vLyQkpKi8F5KSgo8PT1RWloKd3d3pKSkYPv27XB2dkZlZSWOHz/ONVT5448/sGLFCqVnlOrEwFfj2VLLWsX26jibb6fMyMjgng/m4OCAqqoqXLhwAceOHeNqQt3c3Hj3y9XVFb/99hsmT57MnbWLRCLs3r0bDx48wJo1a9o9XsuWLYO9vT0OHDiA2NhYTJ8+HRUVFZg1axZWrVoFmUyGW7duoaGhoU1sLbWsgV23bh0YYwgLC8OKFSuU7lNSUhI3ji+2jo5zS4wx1NTUoLq62iB/ampq0NDQIHgchvRDPZTVp5dHsEulUpiamsLX1xdHjhxReC8/Px8BAQGQSqWYNGkS0tPTwRiDv78/d620oKAAV65cQUREBFforqsYWtd4Avy1isrcu3cPffv2RW5uLqZMmYK8vDycP38e8+bNQ3FxMXeGCQA3btxQ2K+WjI2N4eTkpBCHtbU19uzZg5dffhnR0dHw9PTkPV4AYGFhAWdnZ4wfPx6XL1+GmZkZBg0ahD59+sDCwgK2traQy+U4ceIEb2x8tm7dCjs7OwQGBqo0Tllsyo5za3V1dVi/fj2+/vrrduMSSlVVFUpLS1FcXCx0KAajsLAQw4cPFzqMLkUvSbepqQn19fVIT0+Hq6urwnvOzs7IzMzE+PHjkZGRAWdnZzDGcPjwYYwbNw6MMTQ2NsLFxQVpaWmYPn06nJyc1P5Sqb0YWsrPz8c333yDuLg47vbK9sjlchgbG8PY2Bi1tbVobGzEnj17EB8fj+eeew5Lly4F8OSygKOjo8J+qdLwfcyYMfj444+xfPlyhIaG8h4vdfDFxufs2bPIyMjAgQMHcOzYMaxbt453n1rWuWobm1gsxvLly6mfbheiy366jLGe8QQKXdVBKCsZy8zMZHZ2dszExIRNmjSJ3b9/nzHGWHBwMDMyMmKxsbHMx8eHmZubMx8fH1ZRUcHKy8vZpEmTmFgsZqNHj2bBwcFMLBazwsJCNnLkSGZnZ8cYY+zu3bvMw8ODiUQi5ujoyI4ePcobG18MgYGBTCqVstOnTzMrKys2YcIExhhj0dHRDAD3Ex8fz4qKitjIkSMV5nznnXe4zxgZGbGnn36aJScnM8YY27VrFxOLxWzcuHHM1NSUpaWlMWtrazZt2jSF/Xrw4AHz9/dnUqmU/fTTT6xfv35szJgx7J133mGmpqYsNTWVMfakHGnatGns8uXLrKKios3x8vf3Z2KxmGVkZLAZM2Ywc3Nztn//fmZlZcUGDRrEvv76a2ZsbMxiYmLaxDZo0CBmZGTEEhISuN/J2rVr2csvv6xwHDZs2MC7T7Nnz+bGLVu2rE1syo4zH12UjHWm9krGeipdlow1NjayV155RSdzGbAvRIwpaYGkprfeeguffPIJPTmCaIyeHNH1bNiwAX/605908rie+vp6vPHGG9i3b58OIjNYqd3qjjSZTMZ9497yRyaTCR0a0ROmQZ9cTcbwoX662nn8+LHKpZ1dWbdKujY2Ntz96C1/bGxshA6N6IkmfXJ10VuXUT9drVHSJcRA8NX/Ami3T65IJGrTV7ejMcHBwRrfCED9dLVHSZcQA8FX/wug3T65p06datNXt6Mxe/fu1fhPaeqnq72mpiaVKoa6Okq6xODl5+fD29ubq/8tLCxUaZw+++pSP13t0ZkuIQaiuf63trZWof63oz65rfvqqjJGU9RPV3uUdAkxEBEREcjJyYGNjQ1yc3MRHh4OAB32yW3dV7ejMX5+fhr31aV+utrrKUmX+ukSg6HLmyM6o68u9dNtS5c3R+Tn57OFCxfqZC4D1vn9dAkRgr776lI/Xe3V19fDxMSk09cRWvf/qpD0SCdPntT7ms39dFtr2b1NE9qM13btqVOnajVeHdXV1VzJXXdGZ7qEEIPw4MED9OnTR+gwOp1Oz3QrKip6RJ0d6Rw1NTWoqqrS+u6wzlJZWYlHjx4ZbHxCqKmp0dlcPeVMV2cZcuzYsdzTWEnPVFdXhzt37mj8LLeHDx/ixo0bBvPon9YaGxtRV1eHDz/8UOhQDIquno9HZ7pqeuONN/DGG2/oajrSBdXW1mLOnDnc3V+EqKO6ulrhDrvuiq7pEp0Ri8Wora0VOgzSRfWUM11KukTnWpdMEaKKBw8e9IhrupR0iU45ODjQM8SIRqqrq+lMlxB1DR48GH/88YfQYZAuiM50CdEAJV2iqfv379OZLiHqoqRLNPX48eMeUedPSZfo1DPPPKOXR7sQ0lVR0iU6ZWpq2uFjZghpraec5QKUdEknMDIyglwuFzoM0oWUlZX1iBsjAEq6pBM8/fTTuHPnjtBhkC7k5s2bbZ7V1l1R0iU6R1+mEXX98ccfGDx4sNBh6AUlXaJzlHSJuijpEqIFSrpEXdevX+ceHtrdUdIlOufs7IyCggKhwyBdSE1NDczNzYUOQy8o6RKd6927NxoaGoQOg3QRjx49gkQiEToMvaGkSzqFkZERHj9+LHQYpAu4ePEihg0bJnQYekNJl+iMXC5HQUEBTpw4AcYYgoKCMGLECHrSAmnXhQsXMGLECKHD0JuecQsI0ZsJEybg3r173LOzRCIRPVGEtOt///sf/va3vwkdht7QmS7RGSMjIyQkJMDExITbZmVlhbFjxwoYFTF0JSUlGDBggNBh6A0lXaJTr7zyChwcHLjX5ubmcHd3FzAiYshu374NJycnocPQK0q6ROfWr18Pa2trAIBUKoWZmZnAERFDdfLkSYwfP17oMPSKki7ROV9fX+7spaedxRD1nDp1Ct7e3kKHoVeUdEmn2LBhA8RicY/7D4qo7vHjx5DJZLC3txc6FL3qUtULP/30Ey5fvix0GERFTz/9NKqqqpCamgoAaGpqMuieqY8fP4aRkRFEIpHQoRgMMzMzhIaGdsrcWVlZ+Mtf/tIpcxsyw/0vgMfu3bsxZswYWFlZCR0KUcGCBQtgb2/PXdNduXIlYmNjBY5Kua+//hpjx47FoEGDhA7FYKxdu7bTku6//vUvREZGdsrchqxLJV0AePnll3vcnyPdRWpqKvz9/YUOQ6lffvkF3t7ePeruqI40/5Wia3K5HJcvX4abm1unzG/I6JouIUTveuIXaM0o6RJC9O4f//hHj71TkZIuMShFRUUICAjQeh7GGJKTk3UQ0RNVVVVYu3YtGGPYsGEDzMzMYGNjg6SkpHbHlZSUKDTnTk1Nxfz58/HVV1+ptK4m4zMzM2FrawuJRIJZs2ahoaEBP/zwAwYMGACpVIqgoCCuC9yhQ4eQk5OjUiy68vDhQ1RXV8PR0VGv6xoKSrrEoDg4OODw4cNaz5OXl4d79+7pIKInCTwiIgKRkZEQiURYuHAh6urqkJubi9WrV4MxxjuusbERycnJ3Pvp6elISUnBsGHDsHHjRhw9erTddTUdL5PJkJGRgdLSUty9exc//vgjZDIZTpw4gbKyMlRWViIrKwsAMHPmTOzatQs3b97U9PCo7ZtvvsGsWbP0tp6hoaRLDMrSpUvh6emJoKAgvPTSS3B1dYWDgwOKiooAPEkSTk5OkEgk8PLygkwmw9ixY/Hhhx8iNzcXffr0QWZmJt5++22sWrUK0dHRKCkpwQsvvKBxTFlZWbCzs4OFhQW3TS6XQyaTwcTERGmJWWJiIhYtWsSVyTU1NWHUqFEICwvDqFGjOnxUvabjAwMDMXz4cDQ0NEAul2PgwIEIDg6Gu7s7JBIJpFKpwpN3AwMDkZiYqNYx0ca3337boxrctEZJlxiUuLg4AMCiRYswePBg/Pbbb/D29salS5cAAFFRUfDz80N5eTm8vLywY8cOrgzN3d0dYWFhAIAPPvgAMTExSEpKgr29vVZ/QmdnZ7f5ln3fvn3w9fVFQkIC75iDBw/C19dXIblNnz4d2dnZMDExwc8//4wpU6YoXVPb8Tt37oStrS08PDwUnrJbWFgICwsLeHh4cNvc3NyQnZ2t/ADo0PHjx/HXv/5VoSlST0NJlxi0Xr16wdnZGXK5nNvW3M9h4sSJuH37dqfHUF5eDrFYrLDN2toax48fR1BQEO+YNWvWYMSIERCJRLh69SpcXV2xatUqhIaGoq6uDiEhIVi3bp3SNbUdP3fuXFRVVUEikXBnsWVlZdi8eTO2b9+u8FmxWIzy8nJVD4dWtm/fjvDwcL2sZago6ZIup6mpCQ0NDTh69CieffZZiEQi5Ofno76+Hg8fPuQ+V1lZiaamJq3Xs7a2Rl1dncI2X19fhbPF1nJycsAYA2MMQ4YMwZUrV/Do0SPk5eXhzJkzyMvLU4hVl+MTExNRVlYGExMTGBsbo7y8HPn5+UhNTUVcXFybBkR1dXVcg6LOlJeXB1tbW4Wz956Iki4xKKGhobhw4QJGjx6NtLQ0nDlzBlu2bMH69eu5z2zbtg1SqRTXr19HWFgYhg4diqysLLi4uCA7OxurV6+Gm5sb9u7di7CwMBQXF8PT01PjmLy8vHD16lWFbfHx8YiPjwfw5AuuyZMndzjPkiVL0KtXL8THx0MikWDJkiUqj1VnvKWlJVxdXWFlZYVz585h4cKF2LBhA2JjY9G7d2+IRCKFs+Rr165h9OjRKsWgjdWrV2Px4sWdvo7BY11IREQEKy4ubrPd0dGRffbZZ222BwUFsZEjR6o0t7+/P5sxYwbve1VVVWzSpEmssrJSvYBbkMvlLCkpSen733//PbOzs2MSiYTNmjWL1dfXtzvfrVu3mFgsZufPn1dp3Tt37ijdv47iVXesMtOnT9d6jlOnTrHFixdrPQ+fmJgYdunSpTbb5XI5CwkJYQ8ePOAdV1ZWxgICAjRaU5uxuhjPGGNRUVHsxo0bvO/p4nfGGGPXrl1j8+bN08lcXdwX3eJMd/369TA2Nm6zPS0tTeU5lNV01tbWIioqChcvXtQ4PqDjEiZlJT3K7Nu3DzExMfjyyy9VWlfdUqyW8eqqjEsXPv30U2zduhWZmZl6W1MkEmHTpk3YsWMHb3lYUlKSxmdw2ozVxfgjR44gJCQEzzzzjMZzqGLlypVYunRpp67RZQid9tWh7Ex3//79rG/fvszExIS9+OKL3GcaGxvZyJEjWUVFBXvppZeYRCJhEyZMYOXl5ez+/fvMz8+PmZmZMRsbG3b9+nXubO75559nb7/9tsIao0aNavdM986dO6xv375MLBazdevWtZl/9OjRDACLiopitra2zMbGhpWUlLB3332XWVtbK8wVEBDALl68yIqLi5mnpyfveh988AErKytjtra2rK6ujjHG2qzZ1NTExo4dywAwANxZP9/6reNvHhcVFcWWLFmi9DgyxtisWbOYj48PGzJkCHvqqafYnTt3eGPW1VlTZ1F2ptuT6eJ3dvnyZTZnzhztg+keuseZLgBERkaiuroaXl5eSElJUXgvJSUFnp6eKC0thbu7O1JSUrB9+3Y4OzujsrISx48fh5HRk0Pxxx9/YMWKFdiyZYta6zs4OKCqqgoXLlzAsWPH2sy/bNkyroTpyy+/hJ+fHwYMGIC5c+cqxNuypEdZqdPZs2cxfPhw9O/fH8OHD8ehQ4cAgHefmkunGhsbufF867eOv2XJVXMZF99xBJSXdxECAB999BGWL18udBgGo8t1GVNGKpXC1NQUvr6+OHLkiMJ7+fn5CAgIgFQqxaRJk5Ceng7GGPz9/WFmZoYRI0agoKAAV65cQUREhEbtB0+cOIF58+ahuLgYEyZMwI0bNxTmLy4u5j47depUfPzxxygoKMB3332Hjz76CIDykp7Wdu3ahZSUFLz66qsAnhTqv/rqq23WVIZv/dbx8+E7ji3xlXe1VF1djc2bN7e7b0K6ePEi5HI5zpw5I3QoBkPbUrJz587B0dERzz77rI4i6vq6TdJtampCfX090tPT4erqqvCes7MzMjMzMX78eGRkZMDZ2RmMMRw+fBjjxo0DYwyNjY1wcXFBWloapk+fDicnJ7i4uKi8/p49exAfH4/nnnsOS5cuhbOzc5v5m0uYevXqhffffx/R0dGYPn06jIyMkJ+fj2+++QZxcXHtNvquq6tDTU0Nd23x1q1bGDZsGAoKCtqs2Vwa1Lp0SiQStVm/dfx84/iOozp69+6t1jHVN0tLSwwcOFDt/erOTE1NtRq/cuVK7N69W0fRdBNCXtxQl7JrupmZmczOzo6ZmJiwSZMmsfv37zPGGAsODmZGRkYsNjaW+fj4MHNzc+bj48MqKipYeXk5mzRpEhOLxWz06NEsODiYicViVlhYyEaOHMns7OwYY4zdvXuXeXh4MJFIxBwdHdnRo0d5Y9u1axcTi8Vs3LhxzNTUlB09elRh/osXLzJra2s2e/ZsxhhjTU1NzMvLi9XW1jLGGIuOjuauvQJg8fHxrKioqE31hZeXF7O2tma//vorY4yxzz77jBkbG7MBAwawX3/9VWHNBw8esOvXrzNra2sGgBkZGbG1a9fyrt86/rS0NC7e5uO4bNmyNseRMcYCAwOZVCplp0+fZlZWVmzChAm8x4iu6XY92vzO9u/fzz7//HMdRtMtfNEtki7pGijpdj2a/s4ePXrEXnq87cgWAAAgAElEQVTpJe5/6oTTfb5I0xeZTAaRSNTmRyaTCR1aj8A0aNmoyZjWqLWjetasWYPo6Og2d78RuiNNbTY2NtztmS1/bGxshA6tR9CkZaO2bR4ZtXZUy61bt/Drr79i2rRpOpuzO6GkSwRXWVmJiRMnQiqVwsfHBxUVFbztGgFwLRtFIpFKLR5bjgkODtaoxSO1dlTP4sWL8fnnn+tsvu6Gki4RHF/9L1+7RuD/WjaeOnVKpRaPLcfs3btXoz+lqbWj6tLT0zF48GA899xzOpmvO6KkSwSXn58Pb29vrv63sLBQpXH6avFIrR1VU11djaSkJI3q3HsSSrpEcM31v7W1tVz9r7J2jcD/1Q+r2uKx5RhNUGtH1Xz44YeIiYlp8z8oooiSLhFcREQEcnJyYGNjg9zcXISHh/O2awSg0LJRlRaPLcf4+flp1OKRWjt27Ny5cxCJRPDx8dFqnh5B/2VqmqM63a5Nl3W6ndHikVo7tqXK76yuro699NJLWrU+7UGoTpd0Tfps8UitHdu3YsUKvPPOO7C0tNR4jp6k2/ReID3LyZMn9bqepaUloqKieN9r7sKmCW3G6mL81KlTtRr/66+/4vbt21rH0ZNQ0iWEaOTx48dYsmQJNbRRE11eIIRoJDExEa+//jrs7OyEDqVL6XJnuikpKejTp4/QYRANlJaWKjxgUhM1NTWQSCQ6ikjRxYsX0djYiO+//75T5u+KWvaBbqmgoACnT5/Gv/71Lz1H1PWJGN83Awbq2rVrSv8lIN0fYwwff/wxpkyZAi8vL6HD6RF69+6Nv/zlLwrbHj9+DH9/f2zatIl6D6svtUslXUKampqwYMECjBgxAm+99ZbQ4fRIn332GZycnDB79myhQ+mKUumaLulSevXqhW3btqGkpAQffvih0OH0ONnZ2bh27RolXC3QmS7psjZu3IhLly5h27Zt7T7iiOhGeXk5goODceDAAarJ1Ryd6ZKu691338XUqVMREhKCR48eCR1OtyaXyxEeHo61a9dSwtUSJV3Spc2cORPR0dEIDAzUqlE5aV9MTAwCAwPbfco0UQ0lXdLljR49GvHx8QgKCsL169eFDqfbSUlJAQC8/vrrAkfSPVDSJd3C888/j7S0NLzzzjs4f/680OF0G+np6fjpp5/oNl8doqRLuo2nnnoKe/bswfLly/Gf//xH6HC6vOzsbKSmpuKLL75Q+kgioj5KuqRbsbS0xLfffou0tDTs2LFD6HC6rN9//x0fffQR0tLSYGJiInQ43QolXdLtmJqa4h//+AeuXbuGTz75ROhwupyioiK8++672L17N6RSqdDhdDuUdEm3JBKJ8Pnnn8PKygpRUVGQy+VCh9QlFBcXY/bs2fjiiy+okU0noaRLurWoqCi88MILeO2111BbWyt0OAbt1q1beOONN7B9+3atmpqT9lHSJd1eaGgo3nrrLcycOVMnT73tjvLz8/H3v/8dX3zxBVxcXIQOp1ujpEt6BB8fH8THx+P111/HrVu3hA7HoFy5cgXh4eHYvXs3neHqASVd0mO4u7tj+/btmDt3Li5evCh0OAbh4sWLWLhwIf75z3/CwcFB6HB6BEq6pEcZNGgQDhw4gOXLlyMrK0vocAR16tQpLFu2DHv37oWtra3Q4fQY1JqJ9Dj9+vXD/v37ERYWhpKSErz66qtCh6R3X331FTIyMnDw4EGYm5sLHU6PQme6pEcyMzPD7t27cfr0acTHx3Pbi4uLcenSJQEj072amhrunxlj+OSTT3Dp0iWkpaVRwhUAJV3SYxkbG2PLli2Qy+WIjo5GZWUlJk6ciDlz5ggdms4cO3YML774IhobG1FfX485c+bAzs4OSUlJMDKi//yFQE3MCQGwfft2bNy4EVeuXIFEIsH27du7/GWHmpoaDB06FMXFxfD398eDBw+wdOlS+Pj4CB1aT0ZNzAlhjOHIkSO4fv06Hj9+jOrqasTExKChoUHo0LQSHh6OO3fuoKGhAT/88AP++te/UsI1AJR0SY+3YcMGZGZmor6+nttWUlKChIQEAaPSTlZWFr7//ns0NTUBACorK7FlyxZcvXpV4MgIJV3S40VFReHkyZOYNm0annrqKRgbG6O2thbbtm3D/fv3hQ5PbbW1tXjzzTe5J2mIRCIMGDAAFhYW+OWXXwSOjtA1XUJaKCsrQ2JiIg4ePIji4mK89tprSE1NFTostYSHh2PXrl3o168f+vXrh5kzZyIsLIzuNjMMqZR0DdTGjRuRkZHRI5pHV1ZWQiqVonfv3kKHwmGMobS0FIWFhXB1dYWxsTH69OkjdFgdun//Pi5evAh7e3s4OjpCLBYLHVK3VFdXh4MHD2ry70Qq3RxhoP744w9s3LgRgwYNEjqUTrdkyRK8+eabGDJkiNCh8Dpx4gQuX76MhQsXCh1KhxoaGqjpuB7MnTsXjY2NGo2la7qEdKAr/bVBCdfwUdIlhBA9oqRLuoSioiIEBARoPQ9jDMnJyTqI6InMzEyIRCKYmZkBAKqqqrB27VokJyfDzMwMNjY2SEpKaneOkpISDB48mHudmpqK+fPn46uvvlIpBk3HZ2ZmwtbWFhKJBLNmzeLqeQcMGACpVIqgoCA0NDTg0KFDyMnJaXcuTfZbm9j5xqoz/v79+5g8eTKqqqoAQGEfQ0NDIRKJEBkZ2WEMmqCkS7oEBwcHHD58WOt58vLyuFIqXVmwYAHu3bsHxhgiIiIQGRmJqKgo1NXVITc3F6tXr4ay76sbGxuRnJzMvZ+eno6UlBQMGzYMGzduxNGjR9tdW5vxMpkMGRkZKC0txd27d/Hjjz9CJpPhxIkTKCsrQ2VlJbKysjBz5kzs2rULN2/e5J1Hk/3WNvbWY9UZX1tbi6ioKIX2ni338auvvsKxY8eUrq0tSrqkS1i6dCk8PT0BAEFBQXjppZfg6uoKBwcHFBUVYebMmXBycoJEIoGXlxdkMhnGjh2LDz/8ELm5uejTpw8yMzPx9ttvY9WqVYiOjkZJSQleeOEFrWOztLREnz59kJWVBTs7O1hYWAAA5HI5ZDIZTExMlF4XTkxMxKJFi9Cr15PvtJuamjBq1CiEhYVh1KhRCjds6Hp8YGAghg8fjoaGBsjlcgwcOBDBwcFwd3eHRCKBVCpF//79uc8mJibyzqPJfmsbe+ux6owXi8XYuXNnmy+pm/exd+/enfp8OEq6pEuIi4vj/nnRokUYPHgwfvvtN3h7e+PSpUuIioqCn58fysvL4eXlhR07diA2NhbAk+blYWFhAIAPPvgAMTExSEpKgr29fYd/NqsjOzsbbm5u3Ot9+/bB19dX6Z1tBw8ehK+vL5fYAGD69OnIzs6GiYkJfv75Z0yZMkXpetqOB4CdO3fC1tYWHh4ecHZ25rYXFhbCwsICHh4eAAA3NzdkZ2frZL+1jZ1vrCb73lp7+6hLlHRJl9WrVy84OztzT/qVSqUwMzPDxIkTcfv2bb3HU15erlAXa21tjePHjyMoKIj382vWrMGIESMgEolw9epVuLq6YtWqVQgNDUVdXR1CQkKwbt06petpOx54UvpUVVUFiUTCncmWlZVh8+bN2L59O/c5sVis9Ply6u63trHzjQWg9r631t4+6hIlXdJtNDU1oaGhAUePHsWzzz4LkUiE/Px81NfX4+HDh9znKisruZ4EumRtbY26ujruta+vL3emyCcnJweMMTDGMGTIEFy5cgWPHj1CXl4ezpw5g7y8PIW4dT0+MTERZWVlMDExgbGxMcrLy5Gfn4/U1FTExcVxXw4CT24GsLa21sl+axs731gAau07n/b2UZco6ZIuITQ0FBcuXEB8fDwSEhKQlpaGM2fOYMuWLVi/fj0AYNu2bZBKpbh+/TrCwsIwdOhQZGVlwcXFBdnZ2Vi9ejXc3Nywd+9ehIWFobi4mLtOrI379+/j4cOH8PLyUmgoEx8fzzVIT09Px+TJkzuca8mSJejVqxfi4+MhkUiwZMkSrccrG2tpaQlXV1dYWVnh3LlzWLhwITZs2IDY2Fj07t0bIpGIO1u8du0aRo8ezTtXe/vdWbFrM/7evXv485//jF9++QXDhg3Df/7zH4V9bGxsRFlZmUpraoQRg/Tuu++y/Px8ocPQi8WLF7MrV65oNcepU6fY4sWLdRSRohMnTrDk5GSl6wJgpqamTC6Xs5CQEPbgwYM2nysrK2MBAQEax6DNeG3XZoyxqKgoduPGDd652ttvbdfX53Fr3seQkBAGgL311ltKPztnzhxWXl6uSUhfUNI1UOom3QsXLjALCwt26NAhlba3VlxczFxcXDpc59atW0wsFrPz58/rLAZdJN0JEyYwiUTCTp06pdU8fNpLuq1VVlaypKQkJpfLFbYvW7aM/fjjjxrHoM14bdf+97//zX755Zd251K239qur6/j1nIfVUFJtxvS5Ez3k08+4U1syrY3a2hoYB988AF79tlnO1xj3bp1bOXKleydd97RWQy6SLqdSZ2kS3oGbZIuXdPtYoqKimBpaQlzc3MkJCSgvLwcPj4+MDMzw9q1a7nPKdvOp3XNY3v1q/fu3UN4eDj27t3L1UHqIgZCegpKul2Mg4MDqqqqcOHCBRw7dgwpKSlwd3dHdXU13nvvPe5zyra3xlfzqKx+9ezZsxg+fDj69++P4cOH49ChQ+2upWoMhPQk1Nqxizlx4gTmzZuH4uJiTJgwATdu3MBrr70GExMThbtzlG1vbc2aNfjvf//LvXZ1deVKcFrbtWsXUlJSuAc2yuVyvPrqq1rHcP/+fbz99tuQSqUqHwd9unfvHurr6/H9998LHQoxELdu3dJ4LCXdLmbPnj2Ij4/Hc889h6VLl8Le3h4nTpyAl5cXbt68iT//+c8AoHR7ay3PaNtLuHV1daipqeHudb916xaGDRuGgoICrWPo27cvtm7darD9dL///nv8/vvvXaKfLtGPuXPnajyWLi90MePHj8e8efPw3nvv4ccff4SHhweOHTsGe3t7/O9//+PqIyMiIni3q4KvfnXChAk4duwYcnNzAQC7d+/Gw4cPMXr0aEydOlXnMRDSXdGZbhcze/ZszJ49W2FbYGBgm885OjoqdFFSRfNZ7lNPPaVwyQEAzp07p/A6NjaW620AgHctTWIgpLujM90eQiaTQSQStfmRyWRCh6YTTIM+uZqMMURdpYcvX/9eABr38O2qKOn2EDY2Ntz96i1/bGxshA5NJzTpk9sZvXX1jXWhHr58/Xubt6vbw7cro6RLDFZlZSUmTpwIqVQKHx8fVFRUAEC7fXJFIlGbvrodjQkODtZJX10hdKUevnz9ewFo1MO3K6OkSwxWSkoKPD09UVpaCnd3d6SkpABAu31yT5061aavbkdj9u7d22X/lO1qPXyV9e8F1Ovh25VR0iUGKz8/H97e3pBKpZg0aRIKCwtVGid0X1196mo9fPn69wLq9/DtyijpEoPl7OyMzMxM1NbWIiMjgzsz6qhPbuu+uqqM6aq6Ug9fvv69ADTq4duVUdIlBisiIgI5OTmwsbFBbm4uwsPDAaDDPrmt++p2NMbPz08nfXWF0Fk9fDujjy1f/14AHfbw7Xa07bZDOgf109VMZ/TVNeQuY53Vw1fI/r/NmvvbGiLqMkbI//fpp59i69atyMzMFDoUvRCJRNi0aRN27NjRpkQsKSkJixcv1mhebcbqYvyRI0cQEhKCZ555RuM5DBXdkUa6lZMnTwodgt5ZWloiKiqqzfaWT1BWlzZjdTF+6tSpWo03ZHSmSwghekRJlxBC9IguLxgoS0tLREZGwsTEROhQOl1ZWRlyc3MNdl9ramq07qdbUVGBvn37wtjYWIeREaEUFhbCyEizc1YRa331nRCic5GRkfj4449hb28vdChEWKl0eYEQQvSIki4hhOgRJV1CCNEjSrqEEKJHlHQJIUSPKOkSQogeUdIlhBA9oqRLCCF6REmXEEL0iJIuIYToESVdQgjRI0q6hBCiR5R0CSFEjyjpEkKIHlHSJYQQPaKkSwghekRPjiCkk9TW1uKnn34CABQVFeHMmTPo168f7O3t8fzzzwscHREKPTmCkE4il8thZ2eHmpoabhtjDGvWrOF9ei/pEejJEYR0FiMjI/j5+aG2tpb7sba2RmhoqNChEQFR0iWkEy1cuBA2Njbc64EDB8La2lrAiIjQKOkS0ok8PT3Rt29fAICZmRkiIiIEjogIjZIuIZ1sypQpEIlEsLGxQWBgoNDhEIFR0iWkk82fPx9WVlYYPHgwpFKp0OEQgVHJWBd0584dUNFJ1yGRSGBubo7XXnsNt2/fFjocogZLS0v06dNHp3NSyVgXNHjwYLz88stCh0HUkJOTg759++K5554TOhSlzp07h9GjRwsdhsEoKyvDyJEjsWzZMl1Om0pnul2Qm5sbNm/eLHQYRA2nTp3ChQsXsGjRIqFDUcrf35/+vWrh3LlzyMzM1Pm8dE2XED0QiURCh0AMBCVdQgjRI0q6hBiIoqIiBAQE6GQuxhiSk5N1MhcAVFVVYe3atUhOToaZmRlsbGyQlJTU7piSkhIMHjxYYVtqairmz5+Pr776qsM1W49XdWxmZiZsbW0hkUgwa9YsNDQ04IcffsCAAQMglUoRFBSEhoYGHDp0CDk5OR3GoWuUdAkxEA4ODjh8+LBO5srLy8O9e/d0MhdjDBEREYiMjERUVBTq6uqQm5uL1atXK62iaWxsRHJyssL76enpSElJwbBhw7Bx40YcPXpU6Zqtx6szViaTISMjA6Wlpbh79y5+/PFHyGQynDhxAmVlZaisrERWVhZmzpyJXbt24ebNmxoeGc1Q0iXEQCxduhSenp4AgKCgILz00ktwdXWFg4MDioqKMHPmTDg5OUEikcDLywsymQxjx47Fhx9+iNzcXPTp04f74uftt9/GqlWrEBwcjBdeeEGruLKysmBnZwcLCwsATxr5yGQymJiYKL1WnZiYiEWLFqFXr//7rr6pqQmjRo1CWFgYRo0ahfr6eqVrth6vztjAwEAMHz4cDQ0NkMvlGDhwIIKDg+Hu7g6JRAKpVIr+/ftzn01MTFT7mGiDki4hBiIuLo7750WLFmHw4MH47bff4O3tjUuXLiEqKgp+fn4oLy+Hl5cXduzYgdjYWACAu7s7wsLCuPEffPABYmJisHfvXq3/hM7Ozoabmxv3et++ffD19UVCQgLv5w8ePAhfX18usTWbPn06srOzYWJigp9//hlTpkxRebyqY5vt3LkTtra28PDwgLOzM7e9sLAQFhYW8PDwAPCkEig7O7v9A6BjlHQJMWC9evWCs7Mz5HI5AEAqlcLMzAwTJ07U240W5eXlEIvF3Gtra2scP34cQUFBvJ9fs2YNRowYAZFIhKtXr8LV1RUAsGrVKoSGhqKurg4hISFYt26dyuNVHdts7ty5qKqqgkQi4c5ky8rKsHnzZmzfvp37nFgsRnl5uVrHQ1uUdAnpQpqamtDQ0ICjR4/i2WefhUgkQn5+Purr6/Hw4UOFz1ZWVqKpqUnrNa2trVFXV8e99vX15c4U+eTk5IAxBsYYhgwZgitXrgAAHj16hLy8PJw5cwZ5eXlt4m1vvKpjgSeXJsrKymBiYgJjY2OUl5cjPz8fqampiIuLg5mZGffZuro6vXd9o6RLiIEIDQ3FhQsXEB8fj4SEBKSlpeHMmTPYsmUL1q9fDwDYtm0bpFIprl+/jrCwMAwdOhRZWVlwcXFBdnY2Vq9eDeDJn8179+6Fn58fd51YU15eXrh69Sr3Oj4+HvHx8QCefME1efJkleZZsmQJevXqhfj4eEgkEixZskTl8eqMtbS0hKurK6ysrHDu3DksXLgQGzZsQGxsLHr37g2RSMSdKV+7dk3/d+Ex0uVMnz5d6BCImk6dOsUSEhK0nmPx4sU6iqgtZf9eyeVyFhISwh48eNDmvbKyMhYQEKDxmtqM13ZtxhiLiopiN27c4H3v7NmzLC4uTqv5eXxBZ7rdyMCBA7Fy5UqFbcHBwWqd6cyYMYO3VvT+/fuYPHkyqqqqtI5THXw1l+19dsyYMejTpw+kUilGjhyp9fp89Z0AsGDBAohEIohEIpibm8Pd3R0pKSlar9eeTz/9FFu3bu2UW1PbIxKJsGnTJuzYsaNNiVhSUhIWL16s8dzajNd27SNHjiAkJATPPPOMxnNoRNdpnHQ+ZWck+/fvb/N/5sbGRjZy5EiV587Pz2czZsxQ2Pbo0SM2Z84cZmdnxyorK9UPWAv79+9n58+fZ9XV1WzcuHHshx9+4P1cQUEBs7KyYgcOHGAPHjxgMpmMvf/++0wul2u1/p49e9ivv/7KHj58yCZOnMgyMjK495ycnNi9e/eYTCZj//znP5mVlRXbu3cv7zy6ONPtbPQXlCI60yUq+fzzz2FqaopRo0ahpKRE4b3KykpMnDgRUqkUPj4+qKioQHV1NaZMmQKxWAxbW1s8fvyY+/zQoUMxf/58iMVi7Ny5E4MGDepw/dbzyWSyNmvy1aD2798ftra2KC0tVXjEDV/NZUlJSZva0wMHDmDWrFn429/+BqlUCmtra6xbtw4ikYh3v1WNQVl9Z0vW1tZ49dVXsXLlSqSmpmrwWyM9CXUZ62YiIyOxYsUKLF26FCkpKYiJieHeS0lJgaenJw4fPoyYmBikpKTA2NgYzs7O+Pbbb/H777/DyOjJ/4f/+OMPrFixAn/729/UWn/79u0K86WmprZZc9GiRdi5cyeOHz+OOXPm4NKlS/jyyy+xb98+DBgwAHPnzoW3tzc3586dOzFv3jzMnz8fzs7OMDY2blN7eufOHYV6zJb49lvdGFrXd/Jxd3fHl19+qfT9iooKXL9+XbUDKYBHjx4ZdHz6dufOHa5UT5co6XYzUqkUpqam8PX1xZEjRxTey8/PR0BAAKRSKSZNmoT09HQwxuDv7w8zMzOMGDECBQUFuHLlCiIiIrjCe3XcuHFDYb6UlJQ2azZrWYM6depUfPzxxygoKMB3332Hjz76iPvc3Llz8corr2DVqlVITEzkvY7n4OCg8A17R/utTgx89Z18bt68CUdHR6Xv5+Tk4NGjR+3OIaSioiJs2bJF6DAMBl/vCF2gpNvNNDU1ob6+Hunp6VxRejNnZ2dkZmZi/PjxyMjIgLOzMxhjOHz4MMaNGwfGGBobG+Hi4oK0tDRMnz4dTk5OcHFxUXl9Z2dnhfmefvrpNmvyEYlEeP/99xEdHY3p06dzZ9yJiYl4/fXX0bdvX67mks+sWbMwfPhwTJw4EdOmTQPw5Kx71qxZvPutagz5+fn45ptvEBcXp3BLazO5XI7GxkZcuHABK1euxJo1a5Qem0mTJhl0P93r169zpWmk8/rp0hdpXZCyLzwyMzOZnZ0dMzExYZMmTWL3799nwcHBzMjIiCUkJLCKigrm4+PDzM3NmY+PD6uoqGDl5eVs0qRJTCwWs9GjR7Pg4GAmFotZYWEhGzlyJLOzs2N3795lHh4eTCQSMUdHR3b06FGlsbWe79atW23WDAwMZFKplJ0+fZpZWVmxCRMmMMYYa2pqYl5eXqy2tpab78svv2SWlpbMzMyMeXt7s+LiYlZUVMT75eCZM2fY2LFjmVQqZWZmZmzYsGGsoqKCd79VjSE6OpoB4H7i4+MZY4y9//77TCwWMyMjI9a7d2/2pz/9iW3btk3pcaEv0rqezvoijR7X0wX5+/vju+++EzoMoobMzEycP3/eoM906d8rRc1nurp+XA9VLxCNyGQyrk615Y9MJhM6tG6LadAjV5MxfNTpp6ustlnVmmu+8erUawPAli1bYGdnB1tbW5w9e5bb3vI6LfXTJV2KjY0Nd398y5/mUi+ie5r0yNVFX12mZj9dvt61zdtb97nlwzde1bEAcPXqVWzduhXnz59Heno6KioqALTt0Uv9dAnpofjqiPn65Db3yI2Ojla5t27LMXz1zapQt59ue71rW9dc8+Ebr+pYADh27BjCw8Ph4OAALy8v7otVvh6/1E+XkB6ouY64tLSUu52Yr09uc4/cpKQklXvrthxjb2+v0Z/T6vbTbcZX26ysz60q41UdW1JSgnXr1kEikWDQoEHIzs5W2uOX+ukS0gPl5+fD29ubqyMuLCxUaZy+euuq208XUF7bzNfnVtXxqo61srLCggULUFFRgU8++QQbN25U2uOX+ukS0gM11xHX1tZydcTK+uS27JGram9dbfvqqttPV1nvWr4+t6qOV3UsAIwfPx4///wzGhsb0djYCKlUqrTHL/XTJaQHioiIQE5ODmxsbJCbm4vw8HDePrnNPXKbLx2o0lu35Zji4mKNeuuq209XWe9avj63qo5XdWxzvK6urnB0dMSGDRvw3nvvKd036qdLVEJF7F2Prm+O6IzeutRPVxF1GSOEcPTZW5f66eoW9V4gpAs6efKkXteztLREVFRUm+0tn2CsCW3Ga7v21KlTtRqvKTrTJYQQPaIz3S6opqYGeXl5QodB1FBQUIDS0lKD/r09ePDAoOPTt/z8/E6ZlxredEHLly/XyaO1if78/PPPGDJkCKysrIQORan79++jb9++QodhUCZPnozx48frcspUSrqE6EFkZCQ+/vhj2NvbCx0KERZ1GSOEEH2ipEsIIXpESZcQQvSIki4hhOgRJV1CCNEjSrqEEKJHlHQJIUSPKOkSQogeUdIlhBA9oqRLCCF6REmXEEL0iJIuIYToESVdQgjRI0q6hBCiR5R0CSFEjyjpEkKIHlETc0I6yaNHj/D3v/8dNTU1uHXrFuzt7dG7d2+89tprCAkJETo8IoxUekYaIZ3E3Nwcubm5uHz5MgAgNzcXUqkUCxYsEDgyIiS6vEBIJ3rjjTdgbGzMvba2toavr6+AERGhUdIlpBOFhYXBzs6Oez1q1Cj06kV/YPZklHQJ6UT9+/fHgAEDAABWVlZYuHChwBERoVHSJaSThYeHw9TUFFZWVhgzZplcH34AACAASURBVIzQ4RCBUdIlpJO99tprMDU1hY+PD0QikdDhEIHRxSU9+vbbb3H16lWhwyAC6N+/P8zNzbF69Wqt5qmtrYWpqSmMjAzzfKmpqQmPHz+Gqamp0KEYlDfffBO2trYAKOnq1ddff41FixYpfJtNegYHBwcMHjxY63k2bdoEf39/DBw4UAdR6V5OTg4KCgowa9YsoUMxGCkpKSgpKaGkKxQvLy/69roHGj16tE7m+fbbb/HnP/8ZQ4YM0cl8ulZTUwNAd/vbHRw5ckThtWH+jUIIId0UJV1CupmioiIEBARoPQ9jDMnJyTqI6P9UVVVh7dq1SE5OhpmZGWxsbJCUlMT72R9++AEDBgyAVCpFUFAQGhoakJmZCVtbW0gkEsyaNQsNDQ1K19J2/JYtW2BnZwdbW1ucPXtW4b2SkhLuctGhQ4eQk5Oj8jGgpEtIN+Pg4IDDhw9rPU9eXh7u3bung4ieYIwhIiICkZGRiIqKQl1dHXJzc7F69WrwtYCRyWQ4ceIEysrKUFlZiaysLMhkMmRkZKC0tBR3797Fjz/+qHQ9bcZfvXoVW7duxfnz55Geno6KigruvcbGRiQnJ3Mxz5w5E7t27cLNmzdVOg6UdAnpZpYuXQpPT08AQFBQEF566SW4urrCwcEBRUVFmDlzJpycnCCRSODl5QWZTIaxY8fiww8/RG5uLvr06YPMzEy8/fbbWLVqFaKjo1FSUoIXXnhBq7iysrJgZ2cHCwsLAIBcLodMJoOJiQlvKV1wcDDc3d0hkUgglUrRv39/BAYGYvjw4WhoaIBcLm/3C0Vtxh87dgzh4eFwcHCAl5cXpk2bxr2XmJiIRYsWKXw3ExgYiMTERJWOAyVdQrqZuLg47p8XLVqEwYMH47fffoO3tzcuXbqEqKgo+Pn5oby8HF5eXtixYwdiY2MBAO7u7ggLCwMAfPDBB4iJiUFSUhLs7e3V+hOaT3Z2Ntzc3LjX+/btg6+vLxISEtodV1hYCAsLC3h4eAAAdu7cCVtbW3h4eMDZ2bnDdTUZX1JSgnXr1kEikWDQoEHIzs4GABw8eBC+vr7o37+/wufd3Ny4z3SEki4hPUCvXr3g7OwMuVwOAJBKpTAzM8PEiRNx+/ZtvcRQXl4OsVjMvba2tsbx48cRFBSkdExZWRk2b96M7du3c9vmzp2LqqoqSCSSDs8uNR1vZWWFBQsWoKKiAp988gk2btwIAFizZg1GjBgBkUiEq1evwtXVFQAgFotRXl7e8UEAJV1CeqSmpiY0NDTg6NGjePbZZyESiZCfn4/6+no8fPiQ+1xlZSWampp0sqa1tTXq6uq4176+vtzZJ5/8/HykpqYiLi4OZmZmAJ78aV9WVgYTExMYGxu3m+i0GT9+/Hj8/PPPaGxsRGNjI6RSKYAndciMMTDGMGTIEFy5cgUAUFdXB2tra5WOAyVdQrqZ0NBQXLhwAfHx8UhISEBaWhrOnDmDLVu2YP369QCAbdu2QSqV4vr16wgLC8PQoUORlZUFFxcXZGdnY/Xq1XBzc8PevXsRFhaG4uJi7jqxpry8vBTuyIyPj0d8fDwAID09HZMnT1b4/IYNGxAbG4vevXtDJBJh3bp1sLS0hKurK6ysrHDu3DmugZA24/nGenl5wdXVFY6OjtiwYQPee++9dvft2rVrqtcmM6I3M2fOZI2NjUKHQbqwxYsXsytXrmg1x6lTp9jixYt1FJGiEydOsOTkZN735HI5CwkJYQ8ePGjzXllZGQsICNB4XW3Ga7s2Y4xFRUWxGzdu8L4XExPDLl261PzyCzrTNQADBw7EypUrFbYFBwerdWYxY8YMpbWZ9+/fx+TJk1FVVaVWDHzUqXNcsGABRCIRRCIRzMzMMGLECPz3v//l/SxrpyZ01KhRiIyM5J27+SkMeXl5sLGxgYuLS4f7oK6Wvwt1amBb7pOuamd14dNPP8XWrVuRmZmp13VFIhE2bdqEHTt2tCkRS0pKwuLFizWeW5vx2q595MgRhISE4JlnnlFtgFbpnahF2Znu/v37WVxcnMK2xsZGNnLkSJXnzs/PZzNmzGiz/dGjR2zOnDnMzs6OVVZWKh3PF4Oyz50/f55VV1ezcePGsR9++KHdzzs5ObF79+6xmpoatmjRIvb3v/+d93O5ubksJiaG971z586xyMjINtuHDh3KRCIR++mnnxhjjP3jH/9gly9f7nAf1KXu76JZe/ukKV2c6Xam9s50eyo60zVQn3/+OUxNTTFq1CiUlJQovFdZWYmJEydCKpXCx8cHFRUVqK6uxpQpUyAWi2Fra4vHjx9znx86dCjmz58P4Mm3qjt37sSgQYPUjoGvxlNZnaMqdZz19fV48cUXATw587O0tIS5uTkSEhK4mtB58+bx7tfly5fh5uYGe3t73Lp1CwDg7+8PPz8/hIeHK5xx8x2vGTNmYMKECRg5ciSsrKzQp08fmJubQyKRYODAgTAxMcGECRN4Y2upZQ3s4sWLuTP5559/Xuk+RUdHc+P4YuM7zqT7oqRrICIjI1FdXQ0vLy+kpKQovJeSkgJPT0+UlpbC3d0dKSkp2L59O5ydnVFZWYnjx49zrf7++OMPrFixAlu2bNE6Br4aT4C/zrG9Os7myxEZGRl4/vnnATy5a6qqqgoXLlzAsWPHuJpQNzc33v1ydXXFb7/9hsmTJ+PixYsAnvy5unv3bjx48ABr1qxp93gtW7YM9vb2OHDgAGJjYzF9+nRUVFRg1qxZWLVqFWQyGW7duoWGhoY2sbXUsgZ23bp1YIwhLCwMK1asULpPSUlJ3Di+2JQdZ9I9UbsrAyGVSmFqagpfX982XYny8/MREBAAqVSKSZMmIT09HYwx+Pv7c9dKCwoKcOXKFURERHCF7rqKoXWN59y5c/HKK69g1apVSExM7PB62L1799C3b1/k5uZiypQpyMvLw/nz5zFv3jwUFxdzZ5gAcOPGDYX9asnY2BhOTk5cHMCTMqQ9e/bg5ZdfRnR0NDw9PXmPFwBYWFjA2dkZ48ePx+XLl2FmZoZBgwahT58+sLCwgK2tLeRyOU6cOMEbG5+tW7fCzs4OgYGBKo1TFhvfceZTU1ODTZs2qVyepG83b95ERUWFwm2zPd3//vc/hVpkSroGoqmpCfX19UhPT+cKrps5OzsjMzMT48ePR0ZGBpydncEYw+HDhzFu3DgwxtDY2AgXFxekpaVh+vTpcHJyUvtLpfZiaJaYmIjXX38dffv27bBOsplcLoexsTGMjY1RW1uLxsZG7NmzB/Hx8XjuueewdOlSAE8uCzg6OirsV3N9ZXvGjBmDjz/+GMuXL0doaCjv8VIHX2x8zp49i4yMDBw4cADHjh3DunXrePepZZ2rtrGZmppi3LhxcHJyUmucvmRnZ6OgoAD+/v5Ch2IwSktLFTcIdG25R1L2RVpmZiazs7NjJiYmbNKkSez+/fssODiYGRkZsYSEBFZRUcF8fHyYubk58/HxYRUVFay8vJxNmjSJicViNnr0aBYcHMzEYjErLCxkI0eOZHZ2dowxxu7evcs8PDyYSCRijo6O7OjRo7yx8cUQGBjIpFIpO336NLOysmITJkxgX375JbO0tGRmZmbM29ubFRcXM8YYKyoqavNl0zvvvMMAMADMyMiIPf3009yXLLt27WJisZiNGzeOmZqasrS0NGZtbc2mTZumsF8PHjxg/v7+TCqVsp9++on169ePjRkzhr3zzjvM1NSUpaamMsaelCNNmzaNXb58mfd4+fv7M7FYzDIyMtiMGTOYubk5279/P7OysmKDBg1iX3/9NTM2NmYxMTFtYhs0aBD3u2j+vaxdu5a9/PLL3P4BYBs2bODdp9mzZ3Pjli1b1iY2vuOsDH2R1vW0/iJNxBhPex/SKV555RXs27ePmpgTjS1ZsgRvvvmmwTYx//777/H777/TU49biI2NRVBQEIYNGwYAqfRFWg8jk8m4b9xb/shkMqFDI3rCNOiTq8kYPtRPl6oXehwbGxvu3vGWPzY2NkKHRvREkz65uuity6ifLgBKuoR0C3z1vwDa7ZMrEona9NXtaExwcLDGfXWpn+4TlHQJ6Qb46n8BtNsn99SpU2366nY0Zu/evRr31aV+uk9Q0iWkG8jPz4e3tzdX/1tYWKjSOH321aV+uk9Q0iWkG2iu/62trVWo/+2oT27rvrqqjNEU9dN9gpIuId1AREQEcnJyYGNjg9zcXISHhwNAh31yW/fV7WiMn5+fxn11qZ/u/6ffMuGejfrpEm3p8uaIzuirS/1026IuY4QQAPrvq0v9dJ+gW6MI6aFOnjyp9zUtLS0RFRXVZnvL7m2a0Ga8tmtPnTpVrc/TmS4hhOgRJV1CCNEjurygZ7Gxsbx335DurbGxEb169dL6d3/+/HkkJyejb9++OopMt4qLi1FVVdXm6Sc92enTpxVqkanLmB7dvn0b1dXVQodBBPDpp5/i7bffhq2trdChEAG4uLjA1NQUAFLpTFePHB0dhQ6BCMTKygpDhgyBvb290KEQgdE1XUII0SNKuoQQokeUdAkhRI8o6RJCiB5R0iWEED2ipEsIIXpESZcQQvSIki4hhOgRJV1CCNEjSrqEEKJHlHQJIUSPKOkSQogeUdIlhBA9oqRLCCF6REmXEEL0iJIuIYToESVdQjpJTU0NLCws8PTTT+PQoUPw9PREv379EBMTI3RoRECUdAnpJBKJBJ6enigqKsLdu3dRXFwMExMTzJ49W+jQiIAo6RLSiRYuXAipVMq9tra2xpAhQwSMiAiNki4hnWjq1KmwtrYGAIhEIgQGBgocEREaJV1COlHv3r3h4eEBALC3t0d4eLjAERGhUdIlpJNFR0fD0tISdnZ2ePrpp4UOhwiMki4hnWz8+PEAgDfeeEPgSIghEDHGmNBBENU8fvwYY8aMgZ2dndChEDX9/vvvcHR0RGNjI/r16yd0OEoVFxfjqaeeEjoMg9HQ0ICpU6fi3Xff1dWUqb10NRPpfIwxODg44NtvvxU6FKKmhw8f4vLlyzh27BiWL18udDhK+fv747vvvhM6DIPx66+/Yt++fTqdky4vEKIHLcvGSM9GSZcQQvSIki4hBqSoqAgBAQE6mYsxhuTkZJ3MBQBVVVVYu3YtkpOTYWZmBhsbGyQlJfF+9ocffsCAAQMglUoRFBSEhoYGAEBmZiZsbW0hkUgwa9Ysbrsq41Ud22zLli2ws7ODra0tzp49y20vKSnB4MGDAQCHDh1CTk6OJodDY5R0CTEgDg4OOHz4sE7mysvLw71793QyF2MMERER/4+9O49r6kz3AP4LS0ggUihLdChbxQotlFHwNnQcRQTBKogtQqc4rVNGqnWuUPS6DEyrHUUrKEsrVlxGZ4beQutSqV7FVkE7KuWOG7aVCiIqm4ZVlCXIe//wkgqcQEhCEvD5fj58piR53/c5h8zjyclznoNFixYhJiYGbW1tKC4uxoYNG8D1XbxUKsXx48dRW1uLhoYGFBQUyB/Py8tDTU0N7ty5g++++45zPa7xyo4FgJKSEmzbtg3nz59Hbm4u6uvrAQAymQxpaWnymOfOnYu9e/fi+vXr6u4ipVHSJUSPrFixAt7e3gCA8PBwTJ8+Ha6urrCzs0NlZSXmzp0LR0dHmJmZQSKRQCqVYvLkyVi1ahWKi4sxatQo5OfnAwAWL16M9evXIyIiApMmTVIrroKCAojFYpibmwMAurq6IJVKwefzwePx+rw+IiICHh4eMDMzg0gkgq2tLQAgLCwMEyZMQEdHB7q6uuDg4MC5Htd4ZccCwNGjR7Fw4ULY2dlBIpFg9uzZAICUlBTExcXByOiXGoKwsDCkpKSovG8Gi5IuIXokMTFR/t9xcXEYN24crly5Al9fX1y6dAkxMTGYOXMm6urqIJFIsGvXLiQkJAAAPDw8EBUVJR+/cuVKxMfHIzs7W+2P0IWFhXBzc5P/npOTg4CAAGzevLnfcRUVFTA3N5dflQcAe/bsgY2NDTw9PeHs7Dyo8cqOra6uRnJyMszMzODk5ITCwkLs27cPAQEB8n8Aurm5uaGwsLDfODSJki4hes7IyAjOzs7o6uoC8KgSQiAQwN/fH7du3dJKDHV1dRAKhfLfrayscOzYMYSHhyscU1tbi61bt2L79u09Hl+wYAEaGxthZmbW7xEm13hlx1paWmLJkiWor6/HmjVr8PHHH2Pjxo2YOHEieDweSkpK4OrqCgAQCoWoq6sbcB9oCiVdQoaZzs5OdHR04MiRIxg7dix4PB7Ky8vR3t6OlpaWHq9taGhAZ2en2mtaWVmhra1N/ntAQECPo9feysvLsXPnTiQmJkIgEMgfT0lJQW1tLfh8PgwNDRUmO67xyo4FHl0FeO7cOchkMshkMohEIhQVFYExBsYYxo8fj6tXrwIA2tra5E2JtIGSLiF6ZP78+bhw4QKSkpKwefNmZGVl4fTp08jIyMCWLVsAAJ9++ilEIhFKS0sRFRUFd3d3FBQUwMXFBYWFhdiwYQOARx+bs7OzMXPmTPl5YlVJJBKUlJTIf09KSkJSUhIAIDc3F0FBQT1en56ejoSEBBgbG4PH4yE5ORkAYGFhAVdXV1haWuLs2bNYunSp0uO5xipaXyKRwNXVFfb29khPT8d7772ncNt+/vln+Pj4qL5zBouRYUMmk7G5c+fqOgyiou+//559+OGHas1x8uRJtnz5cg1F1FdwcDDn411dXSwyMpLdu3evz3O1tbUsNDRU5TV1PT4mJoaVlZVxPnfp0iUWHx+v8twcdtCR7gjh4OCAdevW9XgsIiJiUEc4c+bM4awRbWpqQlBQEBobG9WKkQ1QNzrYOsxbt27B1NQUFy5cUGrNwdbAdo/VZO2suj788ENs27ZNXqGgLTweD5988gl27drVp0QsNTUVy5cvV3luXY4/fPgwIiMj8eyzz6q8/mBR0h0htmzZAkNDwx6PZWVlDWoOroTY2tqKmJgYXLx4Ua34gIHrRgdThwk8+gY9Pj4eu3fvVmrNwdbAdo/VZO2suk6cOIGWlhb4+vpqfW0LCwvExMT0KRFLTEzEb37zG5Xn1eX4WbNmqV1ON1iUdEeQjz76CCYmJnjppZdQXV3d47mGhgb4+/tDJBLBz88P9fX1aG5uxiuvvAKhUAgbGxs8fPhQ/np3d3e8++67EAqF2LNnD5ycnAZcv7KyEhYWFjA1NcXmzZv7zB8dHY3169cjNjYWtra2sLGxQU1NDZYuXQpra2vOOszq6mqF/6e4e/cuFi5ciOzsbLS3twNAv2t218Byrc0Vf3edK4/Hk39i4NqPXPW0hChCSXcEWbRoEZqbmyGRSJCZmdnjuczMTHh7e6OmpgYeHh7IzMzE9u3b4ezsjIaGBhw7dgwGBo/eDteuXcPatWuRkZExqPXt7OzQ2NiICxcu4OjRo33mX716NeLj45Gamordu3dj5syZGD16NBYsWCCPt3cd5pgxYzhrTM+cOYMJEybA1tYWEyZMwIEDBwCg3zW7a2AVrd07/u46V5lM1u9+5KqnJUQRau04gohEIpiYmCAgIACHDx/u8Vx5eTlCQ0MhEokQGBiI3NxcMMYQEhICgUCAiRMn4saNG7h69Sqio6PlBfeDcfz4cbz99tuoqqrCtGnTUFZW1mP+qqoq+WtnzZqFDz74ADdu3MChQ4fw/vvvA3hUh/nqq69i/fr1SElJUXiubu/evcjMzMTrr78O4NEVUq+//nq/aw60du/4uXDtx26962m55Obmav1a/8G4cuUKQkJCdB2G3mhubsaLL76o0Tkp6Y4gnZ2daG9vR25urrzwu5uzszPy8/MxdepU5OXlwdnZGYwxHDx4EFOmTAFjDDKZDC4uLsjKykJwcDAcHR3h4uKi9Pqff/45kpKS8Nxzz2HFihVwdnbuM3933aiRkRGWLVuG2NhYBAcHw8DAACkpKXjjjTfw1FNP9VuH2dbWhvv378u/0Ll58yZefPFF3Lhxo981u/F4vD5rc8UP9K1z5dqPgxEcHEz9dIeRoeinSyVjw0h/JWP5+flMLBYzPp/PAgMDWVNTE4uIiGAGBgZs8+bNrL6+nvn5+TFTU1Pm5+fH6uvrWV1dHQsMDGRCoZD5+PiwiIgIJhQKWUVFBfPy8mJisZjduXOHeXp6Mh6Px+zt7dmRI0cUxrd3714mFArZlClTmImJCTty5EiP+S9evMisrKzYm2++yRhjrLOzk0kkEtba2soYY2z37t3MwsKCCQQC5uvry6qqqlhlZSXz8vLqsY5EImFWVlbs8uXLjDHG/vrXvzJDQ0M2evRodvnyZYVrdu+PTZs29VmbK/6srCxmZWXFAMjHce3HsLAwJhKJ2KlTp5ilpSWbNm0a5/7RRMnYUFNUMvakGoqSMbpdzzDS2dmJ8PBwunPEMFVUVER3jhhmuo90e5djqmEnfZFGBkUqlYLH4/X5kUqlug7ticBU6JGryhgu1E9XMyjpkkGxtraWX7/++E932RUZWqr0yNVEX11G/XQ1hpIuIXqAq/6Xq09ud+1wbGys0r11Hx/TX91zf6ifruZQ0iVED3DV/3L1ye2uHU5NTVW6t+7jYxTVPQ+E+ulqDiVdQvRAeXk5fH195fW/FRUVSo3TVm9d6qerOZR0CdED3fW/ra2t8vpfRX1yH68dVra3rrp9damfruZQ0iVED0RHR6OoqAjW1tYoLi7GwoULOfvkdvfI7T51oExv3cfHVFVVqdRbl/rpapAmq37J0KJ+usObpi+OGIreutRPtyfqp0sIkdNmb13qp6s51HuBkGHqxIkTWl2vu59ub4/fwVgVuhw/a9YstdZWBR3pEkKIFlHSJYQQLaLTC8MIj8dDbW0t9TsdYk1NTWhtbcXo0aM1Om97ezsePHigkWv9Hz58iNLSUjz33HOcV4Spqqamht5fj5HJZAgODtbonNRljJDHZGVlYf/+/dixYweefvppXYfTr88++wzHjh3Dzp07YWxsrOtwiHJ20pEuIXh0kcFf/vIXMMaQk5PT5yaf+uiNN97AqFGjEBkZib///e89LkIg+ovO6ZIn3t27d/Haa6/B29sbGzduHBYJt1twcDAWL16M8PBw3Lt3T9fhECXQkS55ov3rX/9CQkICtm7diueff17X4ahk2rRpMDY2xquvvors7Gy9Py3ypKNzuuSJlZmZiby8POzatQtPPfWUrsNR27///W+sXr0aWVlZsLGx0XU4hBvdOYI8edrb2/HOO++gqqoKOTk5IyLhAoCXlxdSUlIQGRmJxsZGXYdDFKCkS54ot2/fxty5c/Haa69hzZo18jsBjxQvvPACkpKS8MYbb9A5Xj01st5xhPSjoKAAUVFR2Lp1K2bMmKHrcIaMp6cn3n//fcyfPx+tra26Dof0Qud0yYjHGEN6ejouXLiAjIwMmJqa6jokrThx4gTS0tKQk5MDExMTXYdDHqFzumRka2lpQWRkJNra2rBnz54nJuECgJ+fH5YsWYIFCxao1cCcaBYlXTJiXbt2DSEhIVi4cCFWrlyp63B0YsaMGZg3bx6io6PR1dWl63AI6PQCGaEOHz6MjIwM7NixA7/61a90HY7O7dq1C9euXcPGjRt1HcqTjk4vkJGFMYaPPvoIX3/9NQ4cOEAJ9/9FRUXByMgIGRkZug7liUdJl4wYzc3N+N3vfgdra2ts27YNfD5f1yHplb/+9a8oLCzE119/retQnmiUdMmIcPnyZYSEhCAuLk5+00bSE4/Hw/bt25GRkYGffvpJ1+E8seicLhn2Pv/8c/zjH//A7t27IRaLdR2O3qutrcUbb7yB/fv3j5ir8YYROqdLhq/Ozk6sXr0a586dw1dffUUJV0lisRiJiYn44x//SBUNOkBJlwxLUqkUYWFhmDhxIlJTU2FkRA3zBuOll15CQECA2jeFJINHSZfovZqaGiQlJcl/P3/+PH73u99h3bp1mDdvng4jG96io6Nx/fp1nD59WtehPFHonC7RezNmzMD333+Pv//972hsbERubi527NgBCwsLXYc27LW0tCA0NBTZ2dmwsrLSdThPAjqnS/TboUOHcP78eTQ1NSE6OhqXLl1CTk4OJVwNEYlE+Oijj/CnP/1J16E8MSjpEr11//59xMbGoq6uDsCjb92PHDlCnbM0zMvLCxMmTMDevXt1HcoTgZIu0VuLFy/GrVu3ejxWXV2N9evX6yiikWv58uXIzs5GZWWlrkMZ8SjpEr1UWFiIvLw8dHZ2wsLCAk5OTvj973+Pr7/+mpLuEDAwMMDmzZvx3nvv6TqUEU/vvki7cuUKOjo6dB0G0aGHDx8iNDQUHR0dmDRpEl5//XW4u7vrOqwRRywWw87Orsdja9aswYsvvohXX31VR1GNeDv1LulOmjQJs2bN0nUYZBDq6+tRUlICHx8fjcwnlUrR0tICR0dH8Hg8jcx5+PBhel89pr6+HkZGRtiyZUuPxzs6OjBr1iwcOHAAIpFIR9GNaDv1rqJ8zJgxWLNmja7DIIPwww8/4J///Kde/93Onz+v1/FpW2lpKWfHMT6fjxUrVmDdunXUBnKI0DldQkgPAQEBqKiooKY4Q4SSLiGkj02bNmH58uXQs7OPIwIlXaI1lZWVCA0NVXsexhjS0tI0ENEjjY2N2LRpE9LS0iAQCGBtbY3U1FSFr//2228xevRoiEQihIeHo6OjA/n5+bCxsYGZmRnmzZun8MtgdcZ2y8jIgFgsho2NDc6cOSN/vLq6GuPGjQMAHDhwAEVFRSrsjUfs7e3x29/+Fjk5OSrPQbhR0iVaY2dnh4MHD6o9zw8//IC7d+9qIKJHCTw6OhqLFi1CTEwM2traUFxcjA0bNig8ypNKpTh+/Dhqa2vR0NCAgoICSKVS5OXloaamBnfu3MF3332n8bEAUFJSgm3btuH8+fPIzc1FfX09AEAmkyEtLU0e89y5c7F3715cv35d5X0TFxeH7du3o6mp8VxdvQAAIABJREFUSeU5SF+UdInWrFixAt7e3ggPD8f06dPh6uoKOzs7VFZWYu7cuXB0dISZmRkkEgmkUikAYPLkyVi1ahWKi4sxatQo5OfnY/HixVi/fj0iIiIwadIktWIqKCiAWCyGubk5AKCrqwtSqRR8Pl9h5URERAQ8PDxgZmYGkUgEW1tbhIWFYcKECejo6EBXVxccHBw0PhYAjh49ioULF8LOzg4SiQSzZ88GAKSkpCAuLq5Ht7WwsDCkpKSoumvA5/MRHx+PdevWqTwH6YuSLtGa7jaCcXFxGDduHK5cuQJfX19cunQJMTExmDlzJurq6iCRSLBr1y4AQEJCAgDAw8NDfkeIlStXIj4+HtnZ2Wp9hAYeXYTh5uYm/z0nJwcBAQHYvHnzgGMrKipgbm4OT09PAMCePXtgY2MDT09PODs7D8nY6upqJCcnw8zMDE5OTigsLMS+ffsQEBAAW1vbHq91c3NDYWHhgNvRn+nTp6OqqgqXL19Wax7yC0q6RGeMjIzg7Owsb6QtEokgEAjg7+/f5/LfoVJXVwehUCj/3crKCseOHUN4eHi/42pra7F161Zs375d/tiCBQvQ2NgIMzOzfo8w1RlraWmJJUuWoL6+HmvWrMHHH3+MjRs3YuLEieDxeCgpKYGrqysAQCgUyvtWqCM5ORl//vOf6Us1DaGkS/RGZ2cnOjo6cOTIEYwdOxbAo/t6lZeXo729HS0tLfLXNjQ0oLOzU+01rays0NbWJv89ICBAfvSpSHl5OXbu3InExEQIBAIAjz7e19bWgs/nw9DQUGGyU2csAEydOhXnzp2DTCaDTCaDSCRCUVERGGNgjGH8+PG4evUqAKCtrU0j7RrHjBmDqVOn4rPPPlN7LgKA6Zng4GBdh0AG6cqVK2zVqlUDvi4iIoIZGBgwAEwkErFTp04xS0tLNm3aNHby5ElmYmLCjI2NWUBAAGtqamKMMXb79m0mFovZM888w9zd3dmMGTNYaWkps7KyYv7+/szLy0upGBW9r/Lz81lMTIz8902bNrFNmzYxxhg7dOgQCwwM7DMmNjaWAZD/JCUlsd27dzMLCwsmEAiYr68vq6qq4hyv7Nj+1l+1ahWzsLBg7u7u7OrVqz2eGz9+vPy/T58+zZYuXcq53deuXWPvvfce53NcOjo62PTp09n9+/eVHkM47aCkS9SmbNLtz8mTJ9ny5cs1FFFfit5XXV1dLDIykt27d6/Pc7W1tSw0NFTlNXU9PiYmhpWVlXE+N9ikyxhjX331FVuzZo3K8RDGGGM7hs3pBQcHhz7fokZERMDb21vpOebMmdOnTpSrblKbmpqaEBQUhMbGxn5fl5+fj5dffhmjRo2CSCSCl5eX2msr2vYlS5aAx+OBx+PB1NQUHh4eyMzMVHu9/nz44YfYtm0b8vPzh3Sd3ng8Hj755BPs2rWrzznL1NRULF++XOW5dTn+8OHDiIyMxLPPPqvy+r2FhITgwoULWjvfPmLpOu33puiI5IsvvmCJiYk9HpPJZEp/vGSMsfLycjZnzpwej33++efs8uXLrKWlhfn7+7O8vLzBB62iBw8esLfeeouJxWLW0NCg8HU3btxglpaW7Msvv2T37t1jUqmULVu2jHV1dam1fn/b7ujoyO7evcukUin77//+b2Zpacmys7M559HEke5Qo09QPalypMsYYxcvXmRRUVFDENETY/gc6QLARx99BBMTE7z00kuorq7u8VxDQwP8/f0hEong5+eH+vp6NDc345VXXoFQKISNjQ0ePnwof727uzveffddzrpJRXrPJ5VK+6zJVYNqa2sLGxsb1NTUYOnSpbC2tgbw6NvlPXv2wMnJSb5GdXV1n9rTL7/8EvPmzcNrr70GkUgEKysrJCcng8fjcW63sjEos+1WVlZ4/fXXsW7dOuzcuVOVPxsZQTw9PcHj8fDvf/9b16EMW3rXZaw/ixYtwtq1a7FixQpkZmYiPj5e/lxmZia8vb1x8OBBxMfHIzMzE4aGhnB2dsb+/fvx448/wsDg0b8x165dw9q1a/Haa6/Jx/eum+Syffv2HvPt3Lmzz5pxcXHYs2cPjh07hrfeeguXLl3C7t27kZOTg9GjR2PBggXw9fVVuMaYMWP61J7evn1bYe0m13YPNgZltt3DwwO7d+9W+PydO3dw9uxZhc/rWmNjo17Hp223b9+GTCZTaey6desQHR2Nr776SsNRPRmGVdIViUQwMTFBQEAADh8+3OO58vJyhIaGQiQSITAwELm5uWCMISQkBAKBABMnTsSNGzdw9epVREdHy4vuAe66SS5lZWU95svMzOyzZrfHa1BnzZqFDz74ADdu3MChQ4fw/vvvD2q77ezsUFJSwvkc13YPJgZlt/369euwt7dX+Hx1dbXWz8cORmNjo17Hp211dXUqf38hFosxadIkfPXVV5gzZ46GIxv5hlXS7ezsRHt7O3Jzc+UF4N2cnZ2Rn5+PqVOnIi8vD87OzmCM4eDBg5gyZQoYY5DJZHBxcUFWVhaCg4Ph6OgIQ0NDfPbZZ0hMTOxxCSUXZ2fnHvM988wzfdbkwuPxsGzZMsTGxiI4OFh+xK2sefPmYcKECfD395df9rl9+3bMmzePc7uVjaG8vLzfbe/q6oJMJsOFCxcG7K/q6emJ1atXD2q7tOns2bN6HZ+2Keqnq6xly5Zh1qxZCAoKgomJiQYjewLo9pxyX/3VU4rFYsbn81lgYCBramqS131u3ryZ1dfXMz8/P2Zqasr8/PxYfX09q6urY4GBgUwoFDIfHx8WERHBhEIhq6ioYF5eXkwsFnPWTSrSe76bN2/2WTMsLKxPDSpjjHV2djKJRMJaW1vl8925c4d5enoyHo/H7O3t2ZEjR1hlZSXnl4OnT59mkydPZiKRiAkEAvbiiy+y+vp6zu1WNgZF275s2TImFAqZgYEBMzY2Zs8//zz79NNPFe4X+iJt+FH1i7TH/fOf/2SpqakaiuiJsUPvbtcTEhKCQ4cO6ToMMgjdd47YsGGDrkNRiN5XPXUf6fa+Xc9gMMYQFBSEffv20a19lLdzWFUvaItUKpXXqT7+0935imgeU6FHripjuFA/XdXweDwsWbIEn3zyyZCuM9JQ0uVgbW0tv5b98Z/uUi+iear0yNVEX11G/XTVEhISgvz8/AEv7iG/oKRLhhRXHXF/PXJjY2M5e+sONIarvlkZ1E9XfbGxsRq9k8dIR0mXDKnuOuKamhr55cT99chNTU3l7K070Biu+mZlUD9d9QUFBeHcuXO4d+/ekK81ElDSJUOqvLwcvr6+8jriiooKpcZpq7cu9dPVjOjo6AFrvckjlHTJkOquI25tbZXXESvTI7d3b92h6qtL/XQ1IzQ0FEePHu2xLwk3SrpkSEVHR6OoqAjW1tYoLi7GwoUL4e7ujoKCAri4uKCwsBAbNmyAm5sbsrOz5acOPv30U4hEIpSWliIqKmrAMVVVVYPqONdNIpH0uNovKSkJSUlJAIDc3FwEBQX1GZOeno6EhAQYGxuDx+MhOTkZFhYWcHV1haWlJc6ePYulS5dyjld2rKL1JRIJXF1dYW9vj/T0dLz33nsKt+3nn3+Gj4/PoPeJKng8HubPn4+srCytrDes6aA4uF9UxD78aPriiKHorUv9dHvSxMURvbW3t7OAgACNzjkCDa8uY+TJoM3eutRPV3P4fD58fHxw6tQpra05HA2r3gvkyXDixAmtrmdhYYGYmJg+j3ffvVhVuhw/a9YstdZW1TvvvIOVK1diypQpOll/OKAjXUKIxvzqV79CZ2cnampqdB2K3tK7I92WlhZqwTfM3LhxAzdv3tTrv1tdXZ1ex6dtlZWVQzb3m2++iaysLCxbtmzI1hjO9K7hzY4dO6jIepjYv38/Xn31VXkLSH1u8dfa2tqjHpcAXl5emDp1qsbn7ezsRHBwMP7nf/5H43OPADv17kh34cKFug6BKKG1tRWFhYWIi4vTdShEzxgZGWH8+PEoLi6Gh4eHrsPRO3ROl6iktLQUY8eO1XUYRE9Rza5ilHSJSq5duyZvI0hIb97e3jh//ryuw9BLlHSJSijpkoG4uroqvLffk4ySLlEJJV0ykODg4B43SiWPUNIlKqmpqYFYLNZ1GESPTZ06FQUFBboOQ+9Q0iUqUdTgm5BufD4ffD4fDx480HUoeoWSLhm0+/fv040IiVJ8fHxw7tw5XYehVyjpkkG7du0aXFxcdB0GGQboFENflHTJoNGXaERZEyZMoNKxXijpkkGjpEuUZWRkBB6Ph66uLl2Hojco6ZJBo6RLBmPcuHG4du2arsPQG5R0yaDduXMH1tbWug6DDBO//vWvceHCBV2HoTco6ZJBMzCgtw1R3q9//WtcvHhR12HoDfp/DxmU5uZmmJub6zoMMoy4ubnR5cCPoaRLBoXO55LBMjIyQmdnp67D0BuUdIlSunvdU9IlqjA0NMTDhw91HYZe0Lsm5kQ/OTg4YNSoUeDxeHBzc0NzczN8fHzw61//WtehkWHA0dERN2/ehLOzs65D0Tk60iVKGT16NH766Sf8+OOP2LdvH2JjY+meY0RpLi4uKC0t1XUYeoGSLlHKpEmTevz+zDPP4N1339VRNGS4cXBwwK1bt3Qdhl6gpEuU4u/vL7/xpEgkwqpVq8Dn83UcFRkuxowZg+rqal2HoRco6RKleHl54emnnwYA2NnZ4e2339ZxRGQ4oaT7C0q6RCmOjo4wMTGBubk51qxZA0NDQ12HRIYRsViMmpoaXYehF6h6gSjN0tISpqamiIiI0HUoZJjh8/lob2/XdRh6Qemkm5CQQB3gn3BdXV147rnnsGzZMpXGNzY2wsLCQsNRaU5LSwsEAgGMjOhYpJupqSnWrVunkbnobiOPKP3u+v7775GRkTGUsRA998orr8DJyUnl8e+88w7+/Oc/ay4gDUtJScGMGTPg6uqq61D0BlWoaJ7SSVcgENDdAp5w6v79zczM9Po9ZGlpCQcHB72OUdsEAoGuQxhx6Is0QohWmJqaorW1Vddh6BwlXaJXKisrERoaqvY8jDGkpaVpIKJHGhsbsWnTJjDGkJ6eDoFAAGtra6SmpnK+/ttvv8Xo0aMhEokQHh6Ojo4O5Ofnw8bGBmZmZpg3bx46OjoUrqfu+IyMDIjFYtjY2ODMmTPyx6urq3v0zjhw4ACKiopU2CODZ2FhgYaGBq2spc8o6RK9Ymdnh4MHD6o9zw8//IC7d+9qIKJHCTw6OhqLFi0Cj8fD0qVL0dbWhuLiYmzYsEHeDOhxUqkUx48fR21tLRoaGlBQUACpVIq8vDzU1NTgzp07+O677xSuqc74kpISbNu2DefPn0dubi7q6+sBADKZDGlpaT3inTt3Lvbu3Yvr16+ruZcGJhQK0dbWNuTr6Dv6mpbolRUrVuDEiRN49tlnUVdXh8rKSty7dw/ff/897OzsMHfuXJw/fx5SqRQeHh74+uuvERoaismTJyMyMhIvv/wycnNz8Ze//AXfffcdWlpasHLlSoSEhKh8RFdQUACxWNyjj3BXVxekUin4fD7nt/KPl9WJRCLY2toiICAAAFBXV4euri44ODgoXFOd8UePHsXChQthZ2cHOzs7+eMpKSmIi4vr849aWFgYUlJS8PHHH/e3G9TG5/P7PTp/UtCRLtEriYmJAIC4uDiMGzcOV65cga+vLy5dugQAiImJwcyZM1FXVweJRIJdu3YhISEBAODh4YGoqCgAwMqVKxEfH4/U1FSMGTNGrY/QhYWFcHNz6/FYTk4OAgICsHnz5n7HVlRUwNzcHJ6engCAPXv2wMbGBp6enkp13FJlfHV1NZKTk2FmZgYnJycUFhZi3759CAgIgK2tbZ/Xu7m5obCwcMBY1GVsbAyZTDbk6+g7SrpErxkZGcHZ2bnH3WRFIhEEAgH8/f210kSlrq4OQqGwx2NWVlY4duwYwsPDFY6rra3F1q1bsX37dvljCxYsQGNjI8zMzJCSktLvuqqOt7S0xJIlS1BfX481a9bg448/xsaNGzFx4kTweDyUlJT0KIsTCoWoq6vrNxZNMDY2piNdUNIlw1BnZyc6Ojpw5MgRjB07FjweD+Xl5Whvb0dLS4v8dQ0NDRq5Y4GVlVWfc5EBAQHyo08u5eXl2LlzJxITE+VlVykpKaitrQWfz4ehoWG/iU6d8VOnTsW5c+cgk8kgk8kgEolQVFQExhgYYxg/fjyuXr0qf31bWxusrKyU3h+q4vP5dKQLSrpEz8yfPx8XLlyAj48PsrKycPr0aWRkZGDLli3y13z66acQiUQoLS1FVFQU3N3dUVBQABcXFxQWFmLDhg1wc3NDdnY2oqKiUFVVBW9vb5Vjkkgkfe7xlZSUhKSkJABAbm4ugoKCejyfnp6OhIQEGBsbg8fjITk5GRYWFnB1dYWlpSXOnj2LpUuXco5Vd7xEIoGrqyvs7e2Rnp6O9957r9/t+/nnn+Hj46PKrhkUOr3w/5iSgoODlX0pIZw08R46efIkW758uQai6Ss+Pp5dunSpz+NdXV0sMjKS3bt3j3NcbW0tCw0NVWlNdcZqYjxjjMXExLCysjLO5zT5//vU1FT2zTffaGy+YWqH2ke6Dg4Ofa7NjoiIGNSRxZw5c/rUZnLVKQ4mBkWampoQFBSExsbGfl+3ZMkS8Hg88Hg8CAQCTJw4Ef/7v//L+VqmoCb0pZdewqJFixTOvWTJEgCPypusra2H5Eqox/8Wg6mB7d4mTdXNasqHH36Ibdu2afWuFTweD5988gl27drFWR6WmpqK5cuXqzS3OmM1Mf7w4cOIjIzEs88+q/IcyqLTC/9P2fSs6F+8L774giUmJvZ4TCaTMS8vL6VTf3l5OZszZ06Pxz7//HN2+fJl1tLSwvz9/VleXp7C8VwxcHnw4AF76623mFgsZg0NDQO+3tHRkd29e5fdv3+fxcXFsT/84Q+crysuLmbx8fF9Hj979ixbtGgR5xh3d3fG4/HYv/71L8YYY//4xz/YTz/9NGBMgzXYv0U3RdukDn3/tKToSPdJpsm/2Y4dO9hXX32lsfmGKfWPdAHgo48+gomJCV566aU+jYobGhrg7+8PkUgEPz8/1NfXo7m5Ga+88gqEQiFsbGx63CXU3d0d7777LiIiIuDh4QEzMzN5neJgYggPD8f06dPh6uoKOzs7VFZWQigUYs+ePT2atlRXV/e5FQ2X9vZ2/Md//AcqKythYWEBU1NTebnQ4sWLsX79erz99tt9tuunn36Cm5sbxowZg5s3b8rnCwkJwcyZM7Fw4cIeR/Fc+2vOnDmYNm0avLy8YGlpiVGjRsHU1BRmZmZwcHAAn8/HtGnTAIAzvm4rVqyAt7c3li9fLj+Kf+GFFzjHdW8Tj8eTHylzxca1nwnhQke6j2gk6S5atAjNzc2QSCTIzMzs8VxmZia8vb1RU1MDDw8PZGZmYvv27XB2dkZDQwOOHTsGA4NHYVy7dg1r167t0c2sd52isjEoqvPsbaAazu7LLvPy8vDCCy/Azs4OjY2NuHDhAo4ePQrgl5pQNze3Ptvl6uqKK1euICgoCBcvXpTPy+Px8Pe//x337t3Dxo0b+91fq1evxpgxY/Dll18iISEBwcHBqK+vx7x587B+/XpIpVLcvHkTHR0dnPF1666BTU5OBmMMUVFRWLt2LQD0Gde9TY//n4QrNmX3MyH0RdojGrkiTSQSwcTEBAEBATh8+HCP58rLyxEaGgqRSITAwEDk5uaCMYaQkBD5udIbN27g6tWriI6Olhe6A9x1ioONgavOczDu3r2Lp556CsXFxXjllVeQmpqK//qv/0JVVZX86LJbWVlZj+06d+4cAMDQ0BCOjo59YrCyssLnn3+OGTNmIDY2Ft7e3pz7CwDMzc3h7OyMqVOn4qeffoJAIICTkxNGjRoFc3Nz2NjYoKurC8ePH8fbb7/NGd/jtm3bBrFYjLCwMABQapyi2ADl9rNUKpWfx9ZHFy9eRGlpqVbKp4aLqqoqjc3F5/Nx//59jc03XGkk6XZ2dqK9vR25ubl9epE6OzsjPz8fU6dORV5eHpydncEYw8GDBzFlyhQwxiCTyeDi4oKsrCwEBwfD0dERhoaG+Oyzz5CYmKhUU+n+YlBHV1cXDA0NYWhoiNbWVvzjH/9AUlISnnvuOaxYsUL+uoaGBtjb2/fZroG8/PLL+OCDD/CXv/wF8+fP59xfg/H5559zxve4M2fOIC8vD19++SWOHj0KNzc3znG961zVjc3CwgKrVq0a1BhtSkpKwuzZs/tcffYki46O1thcPB5P5YOfEUXZs7+KTqjn5+czsVjM+Hw+CwwMZE1NTSwiIoIZGBiwzZs3s/r6eubn58dMTU2Zn58fq6+vZ3V1dSwwMJAJhULm4+PDIiIimFAoZBUVFczLy4uJxWIWGxvLAMh/kpKSFMbGFUNYWBgTiUTs1KlTzNLSkk2bNo3duXOHeXp6Mh6Px+zt7dmRI0dYZWUl5xdNf/rTn+RrGxgYsGeeeYalpaWxvXv3MqFQyKZMmcJMTEzYuXPnWGlpKbOysmKzZ8/usV1+fn5MJBKxf/3rX+zpp59mL7/8snxuExMTtnPnTsbYo5Kk2bNns59++olzf4WEhDChUMjy8vLYnDlzmKmpKfviiy+YpaUlc3JyYv/85z+ZoaEhi4+P7xOfk5OT/G/R/Xd5fL8CYI2NjX3GZWVlMSsrK/n2b9q0iTM2rv082PeQvqAv0vrS5N9s37597G9/+5vG5humdvAY46iB4RASEoJDhw4NTeYnTwR9fw8lJCQgPDwcL774oq5D0Rua/Jvt378fzc3NWLBggUbmG6Z2Dqsr0qRSqfxb98d/pFKprkMjWsJU6JOrypjeqJ8u0ZRhlXStra3l148//mNtba3r0IiWqNInV93euoz66RINGlZJl4xMXPW/kydPxqpVq1BcXIxRo0bJr0B7vH7Y0dERZmZmkEgkkEqlA46JiIhQqia7N1X76fauMw8LC8OECRPQ0dGhVD9dVcc/3k9XIpFg9uzZAH7pp9v7i+nufrpEOyjpEp3jqv/l6pEL/FITffLkSaX66j4+Jjs7W6WP0tRPl2gSJV2ic+Xl5fD19ZXX/1ZUVCg1Tlt9damfLtEkSrpE57rrf1tbW+X1v4p65AK/1A8r21f38TGqoH66RKOULS7T9xpLov8UvYe46n9v377NxGIxe+aZZ5i7uzubMWMGY4zJa6IdHByYiYkJMzY2ZgEBAaypqWnAMf7+/v02/1FUp5ufn89iYmJ6PLZp0ya2adMmxhhjhw4dYoGBgT2e56oz3717N7OwsGACgYD5+vqyqqoqzrGaGL9q1SpmYWHB3N3d2dWrV3s8N378+B6/nz59mi1dupRzn1CdrsbtoKRLtEaT76Gh6KtL/XT7oqSrcZrpMkaItmmzry710yWaRLdgJ8PSiRMntLqehYUFYmJiOJ/r7t6mCnXGamL8rFmz1BpPBo+OdAkhRIso6RJCiBYpfXqh+86r5Mlz7949jBo1Su15bt26pdfvoWvXruHnn3/WyLaOFKWlpboOYcRROumePn2aemE+od58801s3bpV12EQHei+qwvRHKWTLhVPP7n4fD5sbGx0HQYhIwL9M0YIIVpESZcQQrSIki4hhGgRJV1CCNEiSrqEEKJFlHQJIUSLKOkSQogWUdIlhBAtoqRLCCFaREmXEEK0iJIuIYRoESVdQgjRIkq6hBCiRZR0CSFEiyjpEkKIFtGNKQmn9vZ2PHjwAAAgk8nQ0NAAADA1NYWJiYkuQyNkWKMjXcLp/PnzcHR0hJubG4qKiuDm5gZHR0ecP39e16ERMqzRkS7h5OPjA1tbW5SVlckfGzt2LCQSiQ6jImT4oyNdopC/v3+f33k8no6iIWRkoKRLFIqJiYGtrS0AwNbWFn/60590HBEhwx8lXaKQm5sbLCwsAABPP/003N3ddRwRIcMfJV3Sr1dffRUGBgYIDQ3VdSiEjAiUdEm/oqOje/wvIUQ9WqteOHXqFJYvX47Ro0dra0miIU5OToiMjISpqSlMTU11HQ6ntrY2NDU1QSwW6zoUvVFTU4Pk5GRMmTJF16GQx2gt6XZ0dGD+/PlYunSptpYkGtLe3o709HRIJBL89re/1XU4nC5fvoycnBysW7dO16HojfT0dHR0dOg6DNILnV4gA6Ir0AjRHEq6hBCiRZR0icZUVlZqpMqBMYa0tDQNRPSLxsZGbNq0CWlpaRAIBLC2tkZqairna7/99luMHj0aIpEI4eHh6OjoQH5+PmxsbGBmZoZ58+b1+7Fd3fEZGRkQi8WwsbHBmTNnejxXXV2NcePGAQAOHDiAoqIiFfYG0SVKukRj7OzscPDgQbXn+eGHH3D37l0NRPQIYwzR0dFYtGgRYmJi0NbWhuLiYmzYsAGMsT6vl0qlOH78OGpra9HQ0ICCggJIpVLk5eWhpqYGd+7cwXfffadwPXXGl5SUYNu2bTh//jxyc3NRX18vf04mkyEtLU0e89y5c7F3715cv35dzT1EtImSLtGYFStWwNvbGwAQHh6O6dOnw9XVFXZ2dqisrMTcuXPh6OgIMzMzSCQSSKVSTJ48GatWrUJxcTFGjRqF/Px8LF68GOvXr0dsbCyqq6sxadIkteIqKCiAWCyGubk5AKCrqwtSqRR8Pp/zsuaIiAh4eHjAzMwMIpEItra2CAsLw4QJE9DR0YGuri44ODgoXE+d8UePHsXChQthZ2cHiUSC2bNny59LSUlBXFwcjIx++f47LCwMKSkpqu4aogOUdInGJCYmyv87Li4O48aNw5UrV+Dr64tLly4hJiYGM2fORF1dHSQSCXbt2oWEhAQAgIeHB6KiogAAK1euRHx8PFJTUzFmzBi1P0IXFhbCzc1N/ntOTg4CAgKwefPmfsdVVFTA3Nwcnp6eAIA9e/bAxsYGnp6ecHZ2HnBdVcZXV1cRCi4OAAAgAElEQVQjOTkZZmZmcHJyQmFhIQBg3759CAgIkF+W3c3NzU3+GjI8UNIlQ8rIyAjOzs7o6uoCAIhEIggEAvj7++PWrVtaiaGurg5CoVD+u5WVFY4dO4bw8HCFY2pra7F161Zs375d/tiCBQvQ2NgIMzOzAY8uVR1vaWmJJUuWoL6+HmvWrMHHH38MANi4cSMmTpwIHo+HkpISuLq6AgCEQiHq6uoG3glEb1DSJVrV2dmJjo4OHDlyBGPHjgWPx0N5eTna29vR0tIif11DQwM6Ozs1sqaVlRXa2trkvwcEBMiPPrmUl5dj586dSExMhEAgAPDoo31tbS34fD4MDQ37TXTqjJ86dSrOnTsHmUwGmUwGkUgEACgqKgJjDIwxjB8/HlevXgXw6KIQKyurwe0QolOUdInGzJ8/HxcuXEBSUhI2b96MrKwsnD59GhkZGdiyZQsA4NNPP4VIJEJpaSmioqLg7u6OgoICuLi4oLCwEBs2bICbmxuys7MRFRWFqqoq+XliVUkkEpSUlMh/T0pKQlJSEgAgNzcXQUFBPV6fnp6OhIQEGBsbg8fjITk5GRYWFnB1dYWlpSXOnj0rv8hHnfFcYyUSCVxdXWFvb4/09HS89957/W7bzz//DB8fH5X3DdEBpiXHjx9naWlp2lqOaNimTZvYqVOn1Jrj5MmTbPny5RqKqKdLly6x+Ph4zue6urpYZGQku3fvXp/namtrWWhoqMrrqjNe3bUZYywmJoaVlZVxPpeWlsaOHz+u1vyatG/fPva3v/1N12Ho2g69ONJ1cHDoc/lmRETEoI5w5syZ06dGlKteUlVMidrRpqYmBAUFobGxsd/X3bp1C6amprhw4YLSaw62BrZ7rKZqZzXhww8/xLZt25Cfn6/VdXk8Hj755BPs2rWrT4lYamoqli9frvLc6oxXd+3Dhw8jMjISzz77rMpzEO3Ti6S7ZcsWGBoa9ngsKytrUHNwJUSueklVDVQ72traipiYGFy8eHHAuXJychAfH4/du3crveZga2C7x2qqdlYTTpw4gZaWFvj6+mp9bQsLC8TExPQpEUtMTMRvfvMbledVZ7y6a8+aNUvtcjqifXqRdAHgo48+gomJCV566SVUV1fLH29oaIC/vz9EIhH8/PzkxeLNzc145ZVXIBQKYWNjg4cPH8rHuLu749133+Wsl+RSWVkJCwsLmJqaysuIes8fHR2N9evXg8fjwcbGBjU1NVi6dCmsra0BPPoWec+ePXBycpLPq6jG9O7du1i4cCGys7PR3t4uf1zRmrGxsfIaWFtb2z7rc8XfXevK4/Hknxi49iVXPS0hZOjoTdJdtGgRmpubIZFIkJmZKX88MzMT3t7eqKmpgYeHh/y57du3w9nZGQ0NDTh27BgMDB5tyrVr17B27VpkZGTI5+hdL9mbnZ0dGhsbceHCBRw9epRz/tWrVyM+Ph65ubmYOXMmRo8ejQULFvSItTeuGtMzZ85gwoQJsLW1xYQJE3DgwAH5c4rWTE1NldfA7t69u8/6XPF317rKZLJ+9yVXPS0hZOjozd2ARSIRTExMEBAQgMOHD8sfLy8vR2hoKEQiEQIDA5GbmwsAKCsrQ0hICAQCASZOnIgbN27g6tWriI6OlhfcA9z1kr0dP34cb7/9NqqqqjBt2jTO+auqqgA8+kj3wQcf4MaNGzh06BDef//9QW3n3r17kZmZiddffx3Ao6ujuv9b0ZqP41qfK34uivYl0LeetrcHDx5g9erVePrppwe1vdrS3NyMhoYGXL58Wdeh6I3q6mps2LBB12GQXvQm6XZ2dqK9vR25ubnywm8AcHZ2Rn5+PqZOnYq8vDz5lTzOzs44ePAgpkyZAsYYZDIZXFxckJWVheDgYDg6OsLQ0BCfffYZEhMTe1w62dvnn3+OpKQkPPfcc1ixYoXC+RsaGvDw4UMsW7YMsbGxCA4Olh9hK6OtrQ3379+Xf5lz8+ZNvPjii7hx4wacnJwUrvl4vSqPx+uzPlf8QN9aV0X7UhmmpqbYsGED9dMdRtLT03UdAuGirTqJ/krG8vPzmVgsZnw+nwUGBrKmpiYWERHBDAwMWEJCAvPz82OmpqbMz8+P1dfXM8YYq6urY4GBgUwoFDIfHx8WERHBhEIhq6ioYF5eXkwsFrPY2FgGQP6TlJTEuf7evXuZUChkU6ZMYSYmJuzcuXN95r948SKzsrJib775Juvs7GQSiYS1trbK57hz5w7z9PRkPB6P2dvbsyNHjrDKykrm5eUlf41EImFWVlbs8uXLjDHG/vrXvzJDQ0M2evRodvv27X7X7N4fmzZt6rM+V/ylpaXMysqKAZCPq6+v77Mvw8LCmEgkYqdOnWKWlpZs2rRpnPtIEyVjQ6m/krEnFZWM6aUdPMY42iwNgW+++QY//vgj3TlimEpKSqI7Rwwz6enpeP755+Hv76/rUAAA+/fvR3NzMxYsWKDrUHRpp958kaYNUqkUPB6vz49UKtV1aE8EpkKfXFXGcKF+ukRfPFFJ19raWn79+uM/3WVfZGip0idXE711GfXTJXrkiUq6RPMU1VH31yeXx+P16as70JiIiAiVLwSgfrpEn1DSJWpRVEfdX5/ckydP9umrO9CY7OxslT9KUz9dok8o6RK1lJeXw9fXV177W1FRodQ4bfbVpX66RJ9Q0iVq6a79bW1t7VH7O1Cf3N59dZUZoyrqp0v0CSVdopbo6GgUFRXB2toaxcXFWLhwIQAM2Ce3d1/dgcbMnDlT5b661E+X6BVtVQRTP93hTZMXRwxFX13qp9sXXRyhl/Sjny55smi7ry710yX6RG96L5Anx4kTJ7S+Znc/3d4ev4OxKtQZr+7as2bNUms80Q060iWEEC2ipEsIIVqktdML3bWR33zzjbaWJBpy69YtWFhY4JtvvoGJiYmuw+Ekk8nw4MEDhISE6DoUvfHw4UO9bVD0JNNa0p0wYUKP5uRk+JgzZw6++uorXYdByIhApxcIIUSLKOkSQogWUdIlhBAtoqRLCCFaREmXEEK0iJIuIYRoESVdQgjRIkq6hBCiRZR0CSFEiyjpEkKIFlHSJYQQLaKkSwghWkRJlxBCtIiSLiGEaBElXUII0SJKuoQQokV0Y0rCqaysDLm5uQCAO3fuIDU1FQAQHByMsWPH6jI0QoY1SrqEk0AgwIcffoiGhgYAwLlz52BpaYl58+bpODJChjc6vUA42dnZwcHBocdjTk5OsLOz01FEhIwMlHSJQlFRUeDz+QAAY2Nj/OEPf9BxRIQMf5R0iULz58+Hra0tAMDW1hZvvPGGjiMiZPijpEsUsrS0xDPPPAMAcHBwgJWVlY4jImT4o6RL+rV48WKYmJjgnXfe0XUohIwIWq1eaGpqwrlz57S5JFHTU089BWNjY1hYWODYsWO6DocMgkQiwVNPPaXrMEgvWk26P/74IzIyMjBjxgxtLkvU9MILL6CwsFBvKxdu3ryJsrIyTJs2Tdeh6I28vDyYm5vDx8dH16GQXrRepyuRSLBkyRJtL0vUcPfuXYSEhGDixIm6DoXT2bNnkZ+fT++rxzQ3N+s6BKIAndMlhBAtoqRLCCFaREmXaERlZSVCQ0PVnocxhrS0NA1E9IvGxkZs2rQJaWlpEAgEsLa2lveS6O3bb7/F6NGjIRKJEB4ejo6ODuTn58PGxgZmZmaYN28eOjo6FK6l7viMjAyIxWLY2NjgzJkzPZ6rrq7GuHHjAAAHDhxAUVGRCnuD6BolXaIRdnZ2OHjwoNrz/PDDD7h7964GInqEMYbo6GgsWrQIMTExaGtrQ3FxMTZs2ADGWJ/XS6VSHD9+HLW1tWhoaEBBQQGkUiny8vJQU1ODO3fu4LvvvlO4njrjS0pKsG3bNpw/fx65ubmor6+XPyeTyZCWliaPee7cudi7dy+uX7+u5h4i2kZJl2jEihUr4O3tDQAIDw/H9OnT4erqCjs7O1RWVmLu3LlwdHSEmZkZJBIJpFIpJk+ejFWrVqG4uBijRo1Cfn4+Fi9ejPXr1yM2NhbV1dWYNGmSWnEVFBRALBbD3NwcANDV1QWpVAo+nw8ej9fn9REREfDw8ICZmRlEIhFsbW0RFhaGCRMmoKOjA11dXX16Umhq/NGjR7Fw4ULY2dlBIpFg9uzZ8udSUlIQFxcHI6NfvvsOCwtDSkqKqruG6AglXaIRiYmJ8v+Oi4vDuHHjcOXKFfj6+uLSpUuIiYnBzJkzUVdXB4lEgl27diEhIQEA4OHhgaioKADAypUrER8fj9TUVIwZM0btj9CFhYVwc3OT/56Tk4OAgABs3ry533EVFRUwNzeHp6cnAGDPnj2wsbGBp6cnnJ2dB1xXlfHV1dVITk6GmZkZnJycUFhYCADYt28fAgIC5Jdkd3Nzc5O/hgwflHTJkDEyMoKzszO6uroAACKRCAKBAP7+/rh165ZWYqirq4NQKJT/bmVlhWPHjiE8PFzhmNraWmzduhXbt2+XP7ZgwQI0NjbCzMxswKNLVcdbWlpiyZIlqK+vx5o1a/Dxxx8DADZu3IiJEyeCx+OhpKQErq6uAAChUIi6urqBdwLRK5R0idZ0dnaio6MDR44cwdixY8Hj8VBeXo729na0tLTIX9fQ0IDOzk6NrGllZYW2tjb57wEBAfKjTy7l5eXYuXMnEhMTIRAIADz6aF9bWws+nw9DQ8N+E50646dOnYpz585BJpNBJpNBJBIBAIqKisAYA2MM48ePx9WrVwEAbW1t1A9jGKKkSzRi/vz5uHDhApKSkrB582ZkZWXh9OnTyMjIwJYtWwAAn376KUQiEUpLSxEVFQV3d3cUFBTAxcUFhYWF2LBhA9zc3JCdnY2oqChUVVXJzxOrSiKRoKSkRP57UlISkpKSAAC5ubkICgrq8fr09HQkJCTA2NgYPB4PycnJsLCwgKurKywtLXH27FksXbpU7fFcYyUSCVxdXWFvb4/09HS89957/W7bzz//TFecDUdMi86cOcMSExO1uSTRgA8++ID9+9//VmuOkydPsuXLl2soop76e191dXWxyMhIdu/evT7P1dbWstDQUJXXVWe8umszxlhMTAwrKyvjfC4xMZGdOXNGrfk1bd++fexvf/ubrsPQtR16c6Tr4OCAdevW9XgsIiJiUEc6c+bM6VMrylU3qQ2DXTc/Px8vv/wyRo0aBZFIBC8vryGJYcmSJeDxeODxeDA1NYWHhwcyMzPVXmsgH374IbZt24b8/PwhX+txPB4Pn3zyCXbt2tWnRCw1NRXLly9XeW51xqu79uHDhxEZGYlnn31W5TmIbuhN0t2yZQsMDQ17PJaVlTWoObiK6rnqJrVhMOtWVFTg1VdfxbJly1BdXY2KigpMmzaNs45U3Ri2bt0KR0dH3L17F7du3UJ8fDxWrVqFnJwctdYayIkTJ9DS0gJfX98hXYeLhYUFYmJi+pSIJSYm4je/+Y3K86ozXt21Z82apXY5HdENvUm6APDRRx/BxMQEL730Eqqrq+WPNzQ0wN/fHyKRCH5+fvKi8ebmZrzyyisQCoWwsbHBw4cP5WPc3d3x7rvvctZNcuGai2tdrhpUW1tb2NjYoKamBkuXLoW1tTXnuorqTr/88kvMmzcPr732GkQiEaysrJCcnIzGxkbO7VYnhsdZWVnh9ddfx7p167Bz5061/36EkIHp1d2AFy1ahLVr12LFihXIzMxEfHw8ACAzMxPe3t44ePAg4uPjkZmZiVWrVmH79u1wdnbG/v378eOPP8LA4NG/IdeuXcPatWvx2muvyefuXTfZG9dcXOvGxcVhz549OHbsGN566y1cunQJu3fvRk5ODkaPHo0FCxb0OJrrvS5X3ent27c5azcVbbe6MfTm4eGB3bt3K/y7yGQyFBQU4Pbt2wpfo0tXr15FSUkJDh06pOtQ9EZpaalOPlWQgelV0hWJRDAxMUFAQAAOHz4sf7y8vByhoaEQiUQIDAxEbm4uAKCsrAwhISEQCASYOHEibty4gatXryI6OlpeeA9w10321nuu/tYFetagzpo1Cx988AFu3LiBQ4cO4f3331d6XeDRJbSPf8M+0HZrOobr16/D3t5e4fNdXV1oaGjAnTt3+t0OXWloaMD9+/f1Nj5dePDgga5DIAroVdLt7OxEe3s7cnNz5QXgAODs7Iz8/HxMnToVeXl58qNCZ2dnHDx4EFOmTAFjDDKZDC4uLsjKykJwcDAcHR1haGiIzz77DImJiT0uoeyt91wCgUDhur3xeDwsW7YMsbGxCA4OhoGBAcrLy5VaFwDmzZuHCRMmwN/fX37p5/bt22FkZKTU+qrE0NXVBZlMhgsXLmDdunXYuHGjwrlNTEyGRT/dP/7xj7oORW9osn8F0TBt1kr0V9qTn5/PxGIx4/P5LDAwkDU1NbGIiAhmYGDAEhISmJ+fHzM1NWV+fn6svr6eMcZYXV0dCwwMZEKhkPn4+LCIiAgmFApZRUUF8/LyYmKxmMXGxjIA8p+kpCTO9XvPde/ePVZfX99n3bCwMCYSidipU6eYpaUlmzZtGmOMsc7OTiaRSFhraytjjHGuW1lZyby8vDjXP336NJs8eTITiURMIBCwF198kZWVlXFutzoxLFu2jAmFQmZgYMCMjY3Z888/zz799NN+/26aKBkbSlSK2BeVjOmtHTzG1PyKfBC6j0hWr16trSWJBqxZs2ZYHOnS++oXGzZsgK+vr15dPLF//340NzdjwYIFug5Fl3bqVfWCNkilUnmd6uM/UqlU16GNaEyFPrmqjOFC/XSJPnnikq61tbX8OvbHf6ytrXUd2oimSp9cTfTWZdRPl+iZJy7pEs1RVD/dX59cHo/Xp6/uQGMiIiJUvhCA+ukSfUNJl6isu464pqamx+XE/fXJPXnyZJ++ugONyc7OVvmjNPXTJfqGki5RWXl5OXx9feV1xBUVFUqN02ZfXeqnS/QNJV2isu465tbW1h51xAP1ye3dV1eZMaqifrpE31DSJSqLjo5GUVERrK2tUVxcjIULFwLAgH1ye/fVHWjMzJkzVe6rS/10id7RZlUwFbEPT5q8OGIo+upSP92+6OIIvaU//XTJk0HbfXWpny7RN3rVe4GMfCdOnND6mt39dHt7/A7GqlBnvLprz5o1S63xRHfoSJcQQrRI60e6V65cwRdffKHtZYkafvzxR5iamqKsrEzXoXD6+eef8cMPP9D76jFXrlyhfrp6SqtJd9y4cQgKCupRwkP03+3btxEcHKy3fzcHBwfY29vrbXy6EBQUJO/TQPSLVpOutbU1fv/732tzSaIBX375Jf3dCNEQOqdLCCFaREmXEEK0iJIuIYRoESVdQgjRIkq6hBCiRZR0CSFEiyjpEkKIFlHSJYQQLaKkSwghWkRJlxBCtIiSLiGEaBElXUII0SJKuoQQokWUdAkhRIso6RJCiBZR0iWEEC2ipEs4nTlzBi4uLhg3bhwKCwsxbtw4uLi44MyZM7oOjZBhje4GTDh5eXlBJpPh5s2bAIDa2lrY29tj4sSJOo6MkOGNjnQJJxMTE7zwwgs9HvPw8IBAINBRRISMDJR0iUKxsbF46qmnAADm5uaIiYnRcUSEDH+UdIlC06dPh6WlJQDA0tISfn5+Oo6IkOGPki5RyNDQEBKJBADw8ssvw8iIvgIgRF2UdEm/li5dCj6fj//8z//UdSiEjAhKH7osWrQILS0tQxkL0VP/x969R0VVrn8A/w4gzDAjgoDoQQVMCwo0lY6DuQQJwhuIhdA5+Ct/mkRqQmZoBystRQUU0LyRmlZ21DI5kh7FpQ5pKlpeQlNSRFJuMgOomNzf3x/+mBjYA3Nj9qDPZy3Wkj3zvu+zN+PDZubZz7a1tcW6deuwbt06veZRKBSwt7c3UFSGd+/ePYhEInTr1o3vUEyGRCLBxo0b+Q7jsaJx0r116xb+/e9/d2YsxEQVFxfjb3/7m97zvPbaa1i/fr0BIuocn376KV555ZU2VRtPsn/84x98h/DY0Tjpmpubw8bGpjNjISbKUD93CwsLk34NWVlZQSwWm3SMxmZubs53CI8dek+XEEKMiJIuMSlFRUUIDQ3Vex7GGNLS0gwQ0SNVVVVITEwEYwxr1qyBUCiEg4MDUlNTOZ9/5MgR9O7dGxKJBOHh4airq4NMJoOjoyPEYjGmTJmCuro6tevpO379+vVwcnKCo6OjyqXbJSUlGDRokPL7vXv34uzZszocEaIrSrrEpDg7OyMjI0PveS5fvozy8nIDRPQogUdFRSE6OhoCgQBz585FTU0NcnNzsXz5cjDG2oyRy+U4fPgwysrKUFlZiezsbMjlcmRlZaG0tBR37tzBiRMn1K6pz/i8vDxs2LAB586dQ2ZmJioqKgAA9fX1SEtLU4l38uTJ2L59O27cuKHnUSKaosJLYlLi4uJw9OhRDBgwAAqFAkVFRbh//z7OnDkDZ2dnTJ48GefOnYNcLoeXlxd++OEHhIaGYtSoUYiMjMTIkSORmZmJDz/8ECdOnEB1dTUWLFiAkJAQnc/osrOz4eTkpPJeb1NTE+RyOSwtLSEQCNqMiYiIUP5bIpGgV69eCAwMBPCoiqOpqQn9+/dXu6Y+4w8ePIiZM2fC2dkZzs7Oyu0pKSmYN29em19qYWFhSElJwdq1a9s7DMRA6EyXmJSEhAQAwLx58zBo0CBcunQJfn5+uHjxIgAgJiYG48aNg0KhgFQqxZYtW7Bo0SIAj3pDzJgxAwCwYMECxMfHIzU1FX369NHrT+icnBx4eHiobNu9ezcCAwOxatWqdscWFhbCxsYGQ4YMAQBs27YNjo6OGDJkCNzc3DpcW5fxJSUlSE5OhlgshqurK3JycrBnzx4EBgaiV69ebZ7v4eGBnJycDmMhhkFJl5g0CwsLuLm5oampSblNIpFAKBQiICAAt27d6vQYFAoFRCKRyjZ7e3scOnQI4eHhaseVlZVh3bp12LRpk3LbtGnTUFVVBbFYjJSUlHbX1XW8nZ0dZs+ejYqKCixevBhr167FihUrMGzYMAgEAuTl5cHd3V35fJFIBIVC0W4sxHAo6ZIup6GhAXV1dThw4ACeeuopCAQCFBQUoLa2VuUCnsrKSjQ0NOi9nr29PWpqalS2BQYGKs8+uRQUFGDz5s1ISEhQdmZLSUlBWVkZLC0tYW5u3m6i02e8r68vTp8+jfr6etTX10MikeDs2bNgjIExhmeeeQZXr15VPr+mpsakL1p53FDSJSZl6tSpOH/+PHx8fLBjxw4cP34c69evx+rVq5XP2bhxIyQSCa5fv44ZM2bA09MT2dnZGDhwIHJycrB8+XJ4eHhg165dmDFjBoqLi+Ht7a1zTFKpFHl5eSrbkpKSkJSUBADIzMzE2LFjVR5fs2YNFi1ahG7dukEgECA5ORm2trZwd3eHnZ0dTp06hblz53KO1Xe8VCqFu7s7+vXrhzVr1uDdd99td/9+//13+Pj46HJoiC6YhoKDgzV9KiGcDPEaOnbsGJs/f74BomkrPj6eXbx4sc32pqYmFhkZye7fv885rqysjIWGhuq0pj5jDTGeMcZiYmJYfn4+52OG/H+/Z88e9sUXXxhsvi7qc73PdPv374+lS5eqbIuIiNDqzGLSpEltajO56hS1iYGLNnPOnj0bAoEAAoEAQqEQw4YNw88//8z5XKamJnTEiBGIjo5WO/fs2bMBPCpvcnBwwMCBAzvcB221/FloUwPbvE+Gqps1lE8++QQbNmyATCYz2poCgQCfffYZtmzZwlkelpqaivnz5+s0tz5jDTF+//79iIyMxIABA3Seg2hH76S7evXqNpcK7tixQ6s5uBIWV52iNjFw0WbOdevWwcXFBeXl5aioqMCYMWPU9g1QVxOalpbGWU60bt06eHp6YsOGDTh58iSee+45pKam4ocffuhwH7TV8mehTQ1s8z4Zqm7WUI4ePYrq6mr4+fkZdV1bW1vExMRw/jwTEhLw4osv6jSvPmMNMX7ChAl44YUXdB5PtGeQ93RXrlwJKysrjBgxAiUlJSqPVVZWIiAgABKJBP7+/qioqMC9e/cwfvx4iEQiODo6orGxUfl8T09PzJo1CxEREfDy8oJYLFbWKWoTQ3h4OF566SW4u7vD2dkZRUVFnHOWlJRo9KKrra3F3//+dxQVFcHW1hbW1tbKcqG3334by5Ytw/Tp09vs15UrV+Dh4YE+ffoo7zcGACEhIRg3bhxmzpypcsbNdbwmTZqEMWPGYPjw4bCzs0P37t1hbW0NsViM/v37w9LSEmPGjAEAzviaxcXFwdvbG/Pnz1eexTc3d2k9rnmfBAKB8kyZKzau40wIUc8gSTc6Ohr37t2DVCpFenq6ymPp6enw9vZGaWkpvLy8kJ6ejk2bNsHNzQ2VlZU4dOgQzMwehXHt2jUsWbJE5YyydZ2ipjGoq/NsPWdHNZzNl11mZWXhueeeg7OzM6qqqnD+/HkcPHgQwF81oR4eHm32y93dHZcuXcLYsWNx4cIF5bwCgQBffvkl7t+/jxUrVrR7vD744AP06dMH3333HRYtWoTg4GBUVFRgypQpWLZsGeRyOf744w/U1dVxxtesuQY2OTkZjDHMmDEDS5YsAYA245r3qb6+vt3Y2jvOhJC2DHJFmkQigZWVFQIDA7F//36VxwoKChAaGgqJRIKgoCBkZmaCMYaQkBDle6U3b97E1atXERUVpSx0B7jrFLWNoXWdpzZzAkB5eTl69OiB3NxcjB8/HqmpqXj//fdRXFysPLtslp+fr7Jfp0+fBvCoU5OLi4tKrSnwqBRp586dePnllxEbGwtvb2/O4wU86vTl5uYGX19fXLlyBUKhEK6urujevTtsbGzg6OiIpqYmHD58GNOnT+eMr6UNGzbAyckJYWFhAKDROHWxcR1nLmVlZZgyZUo7R5tf+fn5OHPmjPK+cAQqf50ZAtfbM08agyTdhoYG1NbWIjMzU6XoGgDc3ObDX3cAACAASURBVNwgk8ng6+uLrKwsuLm5gTGGjIwMjB49Gowx1NfXY+DAgdixYweCg4Ph4uICc3NzfPPNN0hISNDoNjHtxdCsoKBAqzmBR5d7mpubw9zcHA8fPsRXX32FpKQkPP3004iLi1M+r7KyEv369WuzXx0ZOXIkPv74Y3z44YeYOnUq5/HSxs6dOznja+nkyZPIysrCd999h4MHD8LDw4NzXOs6V31j69WrF77++mutxhjTkiVL8Morr8DLy4vvUExG8y9lQ2hsbKRWkYD+JWMymYw5OTkxS0tLFhQUxO7evcsiIiKYmZkZW7VqFauoqGD+/v7M2tqa+fv7s4qKCqZQKFhQUBATiUTMx8eHRUREMJFIxAoLC9nw4cOZk5MTi42NZQCUX0lJSWpj44ohLCyMSSQS9uOPPzI7Ozs2ZswYzjmLiorY8OHD28w5Z84c5fPMzMxY3759WVpaGtu+fTsTiURs9OjRzMrKip0+fZpdv36d2dvbs4kTJ6rsl7+/P5NIJOynn35iPXv2ZCNHjlTObWVlxTZv3swYe1SSNHHiRHblyhXO4xUSEsJEIhHLyspikyZNYtbW1uzbb79ldnZ2zNXVlX399dfM3NycxcfHt4nP1dVV+bNo/rm0PAYAWFVVVZtxO3bsYPb29sr9T0xM5IyN6zhr+xoyFepKxp5khvyZffPNN+ybb74x2Hxd1OcCxjhqYDiEhIRg3759nZD2yZPC1F9DixYtQnh4OAYPHsx3KCbDkD+zr776CkKh0KTfYjKCzV3qijS5XK781L3ll1wu5zs0YiRMhz65uoxpjfrp6q+hoYHuKI0udhmwg4OD8vrxll8ODg58h0aMRJc+ufr21mXUT9cgGhsbKemiiyVd8njiqv8dNWoUFi5ciNzcXHTv3l15BVrL+mEXFxeIxWJIpVLI5fIOx0REROh0IYCu/XRb14SHhYVh6NChqKur06ifrq7jW/bTlUqlmDhxIoC/+um2TnzN/XQ7W0NDA32QBkq6xARw1f9y9cgF/qqJPnbsmEZ9dVuO2bVrl05/SlM/XcOgtxceoaRLeFdQUAA/Pz9l/W9hYaFG44zVV5f66RoGJd1HKOkS3jXX/z58+FBZ/6uuRy7wV/2wpn11W47RBfXTNQyq0/1/mhaXmXqNJTF96l5DXPW/t2/fZk5OTqxv377M09OTvfzyy4wxpqyJ7t+/P7OysmLdunVjgYGB7O7dux2OCQgI4KzJbqauTlcmk7GYmBiVbYmJiSwxMZExxti+fftYUFCQyuNcNeFbt25ltra2TCgUMj8/P1ZcXMw51hDjFy5cyGxtbZmnpye7evWqymPPPPOMyvfHjx9nc+fO5Twmhvx/v2LFCnbixAmDzddFfU5JlxiNIV9DndFXl/rptmXIn9nSpUvZ6dOnDTZfF6V/P11C+GDMvrrUT9cw6D3dR+gIkC7p6NGjRl2vuZ8ul+bubbrQZ6whxk+YMEGv8dr4888/IRaLjbaeqaIzXUKIUVRXV0MikfAdBu8o6RJCjKK6uprOdKHF2wtXrlxBSEhIZ8ZCTNyff/6Jbt26oVu3bjqNb+43bKqKi4uRk5PTpib3SXblyhWDzfXgwQM604UWSffatWudGQfpAj777DMMGjQIQUFBfIdCuqC6ujqdf2E/TujtBaKxQYMG0S9fQvRESZdojJIuIfqjpEs05uLionFfBEIIN0q6RGPm5uZobGzkOwzSBdGFEX+hpEu0YmFhodENNwlpqbKyEra2tnyHYRIo6RKtuLm54ebNm3yHQbqYoqIi9O3bl+8wTAIlXaIV+jCN6OLWrVuUdP8fJV2iFUq6RBe3b99Gv379+A7DJFDSJVqhpEt0cfv2bTrT/X+UdIlW+vXrh9u3b/MdBuliCgsL270R55OEki7RipmZGZWNEa3dvXsXPXr04DsMk0BJl2jN0tIStbW1fIdBuhAzM0o1zehIEK0NGDAABQUFfIdBuoji4mL06dOH7zBMBiVdojX6MI1o48qVKyq3fH/SUdIlWqOkS7Tx888/Y/jw4XyHYTIo6RKtUdIl2vjll1/w/PPP8x2GyaCkS7Tm7OyMmzdvIicnB9u3b0d+fj7fIRET9uDBA3Tv3p3vMEwGtf0hGrtw4QKmTp2K6upq1NbWYvz48WhoaMDZs2f5Do2YqDt37sDR0ZHvMEwKJV2iscGDB6OhoUGlp66LiwsGDhzIY1TElMlkMowePZrvMEwKvb1ANGZmZoYlS5aoFLn36NGDajCJWseOHYO/vz/fYZgU+t9CtBIREQFnZ2fl956enjxGQ0xdYWEhXF1d+Q7DpFDSJVpLTEyEnZ0dzM3N8dJLL/EdDjFR1M6RGyVdorUJEybAxcUFPXr0gFQq5TscYqIyMjIQHBzMdxgmh5Iu0UlaWhqqq6vxzDPP8B0KMVFZWVkIDAzkOwyTY1LVC4MGDYKHhwffYRANubm5YfLkyQCAuro6lJWVmXSj6vz8fDz11FN8h2Eyamtr4erqik2bNhl8brlcDhsbGwiFQoPP3dWZVNL18PDAvn37+A6DaKixsRHm5uYAgJs3b2L16tVYs2YNz1GpFxISQq+vFkpKSrB48eJOmTsjIwMhISGdMndXR28vEJ01J1xCWtu7dy+9n6sGJV1CiEFdu3YN/fr1g7W1Nd+hmCRKusSoioqKEBoaqvc8jDGkpaUZIKJHqqqqkJiYiLS0NAiFQjg4OCA1NVXt848cOYLevXtDIpEgPDwcdXV1kMlkcHR0hFgsxpQpU1BXV2fwsc3Wr18PJycnODo64uTJk8rtJSUlGDRoEIBHZ5t8XKK9bds2TJs2zejrdhWUdIlROTs7IyMjQ+95Ll++jPLycgNE9CiBR0VFITo6GjExMaipqUFubi6WL18OxhjnGLlcjsOHD6OsrAyVlZXIzs6GXC5HVlYWSktLcefOHZw4ccLgYwEgLy8PGzZswLlz55CZmYmKigoAQH19PdLS0pQxT548Gdu3b8eNGzf0PEKaa+7FQaWE6pnUB2nk8RcXF4ejR49iwIABUCgUKCoqwv3793HmzBnMmTMH586dg1wuh5eXF3744Qc4ODhg1KhRGDVqFCIjIzFy5EhkZmbiww8/xIkTJ3Dt2jXcuHFDrzO67OxsODk5wcbGBgDQ1NQEuVwOS0tLCAQCzjERERHKf0skEvTq1UtZHqVQKNDU1KT2Roz6jAWAgwcPYubMmXB2dla5OjAlJQXz5s1T+aUWFhaGlJQUrF27tqPDYBB79uwxyF8yjzM60yVGlZCQAACYN28eBg0ahEuXLsHPzw8XL15ETEwMxo0bB4VCAalUii1btgAAFi1aBADw8vLCjBkzAAALFixAfHw8du3apfef0Dk5OSqlirt370ZgYCBWrVrV4djCwkLY2NhgyJAhAB79ae3o6IghQ4bAzc2tU8aWlJQgOTkZYrEYrq6uyMnJwZ49exAYGIhevXqpPNfDwwM5OTkd7oehbN++Ha+//rrR1uuKKOkSXllYWMDNzQ1NTU0AHp35CYVCBAQE4NatW0aJQaFQQCQSKb+3t7fHoUOHEB4e3u64srIyrFu3TqXOddq0aaiqqoJYLEZKSkqnjLWzs8Ps2bNRUVGBxYsXY+3atVixYgWGDRsGgUCAvLw85e1xRCIRFApFh8fAEH7++Wd4enpCIpEYZb2uipIuMSkNDQ2oq6vDgQMHlBcyCAQCFBQUoLa2FtXV1crnVlZWoqGhQe817e3tUVNTo/w+MDBQefapTkFBATZv3oyEhATlBQApKSkoKyuDpaUlzM3N1SY7fcYCgK+vL06fPo36+nrU19dDIpHg7NmzYIyBMYZnnnkGV69eBQDU1NTA3t5eq+Ohq7S0NMyaNcsoa3VllHSJUU2dOhXnz5+Hj48PduzYgePHj2P9+vVYvXo1AGDjxo2QSCS4fv268q0ET09PZGdnY+DAgcjJycHy5cvh4eGBXbt2Ydy4cfD29tYrJqlUiry8POX3SUlJSEpKAgBkZmZi7NixbcasWbMGixYtQrdu3SAQCJCcnAxbW1u4u7vDzs4Op06dwty5cznHazpW3fpSqRTu7u7o168f1qxZg3fffVftvv3+++/w8fHR+dho6vr16+jWrRt1FNMEMyHBwcF8h0B0VFBQwN555x295jh27BibP3++gSJqS93rq6mpiUVGRrL79++3eaysrIyFhobqvCbf42NiYlh+fj7nY8XFxSwqKkrnuVuaNm0au3TpkkHmesx93iXOdPv374+lS5eqbIuIiNDqDGfSpEltPlXlqpfUFeugblTbtW7dugVra2ucP39eozW1rX9tHmuoullD+OSTT7BhwwbIZDKjrisQCPDZZ59hy5YtbUrEUlNTMX/+fJ3n5nP8/v37ERkZiQEDBui8viZu3LiBhoYGPPfcc526zuOiSyTd1atXt7nkdMeOHVrNwZUQueolddVR3ai2a+3evRvx8fHYunWrRmtqW//aPNZQdbOGcPToUVRXV8PPz8/oa9va2iImJqZNiVhCQgJefPFFneflc/yECRPwwgsv6Ly2plasWIG4uLhOX+dx0SWSLgCsXLkSVlZWGDFiBEpKSlQeq6ysREBAACQSCfz9/VFRUYF79+5h/PjxEIlEcHR0RGNjo/L5np6emDVrFiIiIuDl5QWxWKysl1SnqKgItra2sLa2xqpVq9rMHxUVhWXLliE2Nha9evWCo6MjSktLMXfuXDg4OHCuVVJSovY/RXl5OWbOnIldu3ahtrYWANpdMy4uDt7e3pxrc8X/9ttvY9myZRAIBMq/GLiOY3h4OF566SW4u7vD2dkZRUVFev0cyePl999/R21tLby8vPgOpcvoMhdHREdHY8mSJYiLi0N6ejri4+OVj6Wnp8Pb2xsZGRmIj49Heno6zM3N4ebmhu+//x6//fab8j5e165dw5IlS/Dqq68qx7eul+Ti7OyMqqoq5OXlYc6cOWhqalKZv6ioCDk5OVi6dCkCAgKwe/du9O7dG9OmTVM5c2u9FleN6cmTJzF06FD06tULQ4cOxd69e/Haa69h06ZNatdsaGiAVCrF1q1bOdduHf+CBQvg6+uLxYsXK68e4jqO8+bNw7Zt23Do0CG88cYbuHjxokpBfktNTU3KXxCmyNTjMzZ93k5r9tFHH2HZsmUGiObJ0WWSrkQigZWVFQIDA7F//36VxwoKChAaGgqJRIKgoCBkZmaCMYaQkBAIhUIMGzYMN2/exNWrVxEVFaUstge46yW5HD58GNOnT0dxcTHGjBmD/Px8lfmLi4uVz50wYQI+/vhj3Lx5E/v27cNHH32k1Vrbt29Heno6XnvtNQCPksVrr73W7podrd06fi5cx7FZ63paLmfOnMHUqVPb3Tc+XblyxaTjM7aamhqV+mRtnTlzBk5OTtSjWEtdJuk2NDSgtrYWmZmZysLvZm5ubpDJZPD19UVWVhbc3NzAGENGRgZGjx4Nxhjq6+sxcOBA7NixA8HBwXBxcYG5uTm++eYbJCQkwMKi/UOxc+dOJCUl4emnn0ZcXBzc3NzazN9cN2phYYH33nsPsbGxCA4OhpmZGQoKCjRaq6amBg8ePFB+oPPHH39g8ODBuHnzZrtrNhMIBG3W5oofaFvnynUctSGVSk2+n+63337LdxgmQ99+up9++qnyqkGiBb7qJrioK+mRyWTMycmJWVpasqCgIHb37l0WERHBzMzM2KpVq1hFRQXz9/dn1tbWzN/fn1VUVDCFQsGCgoKYSCRiPj4+LCIigolEIlZYWMiGDx/OnJycWGxsLAOg/EpKSlIb2/bt25lIJGKjR49mVlZW7MCBAyrzX7hwgdnb27PXX3+dMcZYQ0MDk0ql7OHDh4wxxrlWUVERGz58uMo6UqmU2dvbs19//ZUxxtinn37KzM3NWe/evdmvv/6qds3m45GYmNhmba74d+zYwezt7RkA5Tiu4xgWFsYkEgn78ccfmZ2dHRszZgzn8TFEyVhno5JEVfqUjO3du5d98sknBo7oifB5l0i6xPRR0u16dE26tbW1zN/fnz148KATonrsdY06XWOSy+UQCARtvuRyOd+hPfaYDj1ydRnDhfrpai4lJQXR0dHUpFxHlHRbcXBwUF7D3vKrufSKdB5deuQaoq8uo366Grtz5w6ys7MRFhZmsDmfNJR0Safjqv8dNWoUFi5ciNzcXHTv3h0ymUxZOxwbG4vJkyfDxcUFYrEYUqkUcrm8wzHt1T23R9d+uq3rrsPCwjB06FDU1dV12E9X17GAaj9dqVSKiRMnAvirn27LD2qb++kaSnx8vLK+m+iGki7pdM31v6WlpfDy8kJ6enq7PXJTU1M5e+t2NKZPnz46/TlN/XQ1c+LECVhbW2Po0KEGme9JRUmXdLqCggL4+fkp638LCws1Gmes3rrUT7djdXV1WLx4MZYsWaL3XE86Srqk0zXX/z58+FBZ/6tJj9zWvXU7q68u9dPtWHJyMt566y3Y2trqPdcTj5+qCW5U0tN1tVcyxlX/e/v2bebk5MT69u3LPD092csvv8yuX7+urDs+duwYs7KyYt26dWOBgYHs7t27HY7hqntuqb068JiYGOX3iYmJLDExkTHG2L59+1hQUFCbMVx111u3bmW2trZMKBQyPz8/VlxczDle07Htrb9w4UJma2vLPD092dWrV1Uee+aZZ5T/Pn78OJs7dy7nfmtaMnbz5k32yiuvdPg8ohGq0yWGYeg63c7orUv9dFVpmnTDwsLUzkG0RnW6xDQZs7cu9dNVb9euXRg2bFin9+R9knSZ3gvkyXL06FGjrtfcT7e15rsX64rP8RMmTNBr7Xv37mHTpk3473//q9c8RBWd6RJCOP3rX//Chx9+CCsrK75DeayY1JluY2Mj7t27x3cYRAfV1dWoq6sz6Z9fQ0ODScdnbC0rQFo7e/Ys6urq1LYBJboTsNZvYvEoOjq63RcCMV2NjY2orq5Gjx499Jqnvr4e58+fx/Dhw9vcoklfCoXCaLcj7yr8/f0xffp0lW319fWYMGEC/v3vf9PxMrzNJpV0CQEeNVxfvXo1vvjiC/Tu3ZvvcJ44H330ETw9PTu8OIToZDO9p0tMTmBgINasWYOpU6fi3LlzfIfzRDl37hz++OMPSridiM50icmqqKjAtGnTMGPGDEyaNInvcB57Dx48wKRJk7B792707NmT73AeV3SmS0xXz5498f333+Pw4cN63VaGaOadd97BBx98QAm3k1HSJSbNwsICn332Gezs7BAVFWWQO9iStrZu3YoBAwbgpZde4juUxx4lXdIlxMTE4JVXXsHkyZNx584dvsN5rJw5cwb79+/Hv/71L75DeSLQe7qkS7l06RJiYmKwdu1aPPvss3yH0+UVFRVh2rRp+Pbbb6mDmHHQe7qka/H09MSuXbsQFxeHH374ge9wurT79+/jjTfewMaNGynhGhElXdLlODg4YM+ePdi7dy9WrlzJdzhdUmNjI9588018+umneOqpp/gO54lCSZd0SVZWVtiyZQuEQiHefvtt1NfX8x1SlzJv3jyEhITAx8eH71CeOJR0SZcWExODgIAATJkyBZWVlXyH0yWkpKTA3t4ekZGRfIfyRKKkS7q8V199FUuWLEFYWJjyNjWE25YtW1BYWIiPPvqI71CeWJR0yWNhyJAh+PrrrxEbG2v0XrxdxRdffIGcnByD3pKdaI+SLnls9OnTBxkZGfjyyy+xbt06vsMxKV9++SV++uknbNy4EQKBgO9wnmiUdMljRSgU4osvvkB1dTWio6P1ukvw4+K7775DdnY20tPTYWZG/+X5Rj8B8tgRCARYsGAB/P39ER4ejrt37/IdEm/+/e9/Y9++fZRwTYhJ3TmCEEMKDw9H//79ERYWhvT0dLi5ufEdklGtX78eP//8M7Zu3WrwhvBEd/SrjzzWpFIptm3bhujoaGRnZ6s81tjYyFNUnW/lypX4/fffsWXLFlhY0LmVKaGkSx57zs7O2LNnDzZs2IDt27cDAORyOYYNG/ZY3DPt6tWrePvttwEAjDHMnz8fZmZmSE1NpQ/NTBA1vCFPjKamJnzwwQcAgCNHjuDChQuIjIxUJuKu6u9//zuuXr2Kjz76CFevXoWPjw9mzJjBd1iEGzW8IU8OMzMzrFy5EufOncOlS5fQ2NiIgwcP4tdff+U7NJ19/fXXuH79Ou7fv4+VK1di4MCBlHBNHJ3pkifKhx9+iLVr16pUNAwfPhw///wzj1Hp5s8//8Rzzz2HmzdvKrc5OztDJpNh4MCB/AVG2kNnuuTJUV9fj/z8fPTo0QMikUi5/fr16/jyyy95jEw3sbGxuH37tvJ7S0tLNDQ0YMuWLTxGRTpCZ7rkiVNbW4s9e/Zg7dq1uHXrFkpLS9G3b19cunQJEomE7/A0cu3aNfj5+aG4uBi9evVCz5498T//8z946623YG9vz3d4RL3NlHR5dPfuXezbt4/vMIyGMWZyn6ZXVFQgKysLx48fx4svvoh//vOffIekkcWLF0OhUOCFF15AUFAQnJyc+A7pieHr64v+/fvrOnwzFfDx6Pbt2/j++++7zH90fZSWluLQoUN44403+A5Fxd/+9jdMmzYNb7zxBj7++GNYWVmZ3C+G1iorKzF16lQ899xzJh/r4+bHH3+EUCjUJ+nSFWl8c3d3x5QpU/gOo9Ndv34dBQUFJr2vX3/9NcLDw/kOg5iwmpoaveegD9IIIcSIKOkSQogRUdIlJqWoqAihoaF6z8MYQ1pamgEiAmQyGQQCAYRCIQCgqqoKiYmJSEtLg1AohIODA1JTU9WOP3LkCHr37g2JRILw8HDU1dVBJpPB0dERYrEYU6ZMQV1dncZjm2PSZDzwqPGNk5MTHB0dcfLkSeX2kpISDBo0CACwd+9enD17tsNjoc2+67Pfhhivbr/b2/epU6dCIBAgOjq6w2OhK0q6xKQ4OzsjIyND73kuX76M8vJyA0T0yOzZs1FeXg7GGKKiohAdHY2YmBjU1NQgNzcXy5cvh7pCILlcjsOHD6OsrAyVlZXIzs6GXC5HVlYWSktLcefOHZw4cULjsc3bNRmfl5eHDRs24Ny5c8jMzERFRQWARzXLaWlpypgnT56M7du348aNG2qPgbb7rs9+6zte3X53tO9ffPEFDh48qDYmQ6AP0ohJiYuLw9GjRzFgwAAoFAoUFRXh/v37OHPmDObMmYNz585BLpfDy8sLP/zwAxwcHDBq1CiMGjUKkZGRGDlyJDIzM/Hhhx/ixIkTuHbtGm7cuKHRWVx7bG1t0b17d8hkMjg5OcHGxgbAo34OcrkclpaWaisJIiIilP+WSCTo1asXAgMDAQAKhQJNTU1qPw3nGgsAYWFhGo0/ePAgZs6cCWdnZzg7Oyu3p6SkYN68eSq/4MLCwpCSkoK1a9dyzpWdna3Vvuuz3/qOV7ffmux7Z5ff0ZkuMSkJCQkAHt0ifNCgQbh06RL8/Pxw8eJFxMTEYNy4cVAoFJBKpcorrxYtWgQA8PLyUvYdWLBgAeLj47Fr1y69E25LOTk58PDwUH6/e/duBAYGYtWqVR2OLSwshI2NDYYMGQIA2LZtGxwdHTFkyJAOe/22Hqvp+JKSEiQnJ0MsFsPV1RU5OTnYs2cPAgMDlQm8mYeHB3Jycgy+7/rst67jufYbgM77bkiUdIlJs7CwgJubG5qamgA8OuMRCoUICAjArVu3jB6PQqFQuYTY3t4ehw4d6rDUrKysDOvWrcOmTZuU26ZNm4aqqiqIxeJ2bxbJNVbT8XZ2dpg9ezYqKiqwePFirF27FitWrMCwYcMgEAiQl5cHd3d3AIBIJIJCoTDovuuz3/qM59pvADrvuyFR0iVdSkNDA+rq6nDgwAE89dRTAB7dnqegoAC1tbWorq5WPreystLg90izt7dXqdUMDAxUOfvkUlBQgM2bNyMhIUH5YVxKSgrKyspgaWkJc3Nztf/hucZqM97X1xenT59GfX096uvrIZFIcPbsWTDGwBjDM888o7xtfU1NTbuXEGu77/rst77jufYbgM77bkiUdIlJmTp1Ks6fPw8fHx/s2LEDx48fx/r167F69WoAwMaNGyGRSHD9+nXlWwmenp7Izs7GwIEDkZOTg+XLl8PDwwO7du3CuHHj4O3trXdcd+/eRXV1NaRSKfLy8pTbk5KSkJSUBADIzMzE2LFj24xds2YNFi1ahG7dukEgECA5ORm2trZwd3eHnZ0dTp06hblz53KO5xoLQOPxUqkU7u7u6NevH9asWYN3331X7T7+/vvv8PHxUbsf2u67pvut73h997vlvtfX16OsrKzd5+qNEd5cunSJLVy4kO8wjOLatWvs3Xff1WuOY8eOsfnz5xsooraCg4PVrguAWVlZsaamJhYZGcnu37/f5nllZWUsNDRU5/X5Hh8TE8Py8/PVzmOq+67v2oz9te+RkZEMAHvrrbc4n/fll1+y3bt367PU55R0eaRL0j1//jyzsbFhe/fubXdba2fPnmUAlF8FBQVqn/vHH38wkUjEzp07Z7AYDJF0x4wZw8RiMTt27Jhe86ijLum2VllZyVJTU1lTU5PK9g8++ICdOHFC5/X5HP/DDz+wM2fOdDiPKe67vmu33PeOGCLpUpcxHl2+fBlff/01li9frtW4JUuWYMiQISoXEXBta+nnn39GTU0NRo0a1eH8q1atQk1NDUpLS9WWD2kbw/Xr11XeJjBFISEhT1TXN6K9r776CkKhUJ8eItTE3FQVFRXB1tYW1tbWWLVqFRQKBfz9/SEUCpGYmAgAnNu0UVJSghdeeKHN9vLycsycORO7du1CbW2tcntnxEDIk4YujjBRzs7OqKqqQl5eHubMmYO6ujp4eXnh4MGD+OSTTwAA6enpbba1Z+zYsaivr8fQoUOxc+dOuLq6tqlhPXnyJIYOHYpevXph6NCh2Lt3L1577TW162kTQ35+tB9AxAAAIABJREFUPjZv3qzL4TCKoqIik46P8C8nJwcvv/yyXnNQ0jVRhw8fxvTp01FcXIwxY8YgPz8f//jHP2BpaQkLi0c/Nq5t6gwePBiFhYUQi8VYsWIF1q9fz3lmun37dqSnpysTbVNTk/Lf+sYgFArbFKWbEktLS5OOj/Cv+Wo8fVDSNVE7d+5EUlISnn76acTFxaFPnz44fPgwpFIpbty4geeff55zmzrbt29HQEAAunfvjsbGRlhbW7d5Tk1NDR48eKC8Jv2PP/7A4MGDcfPmTbi6uuodg7OzM0JCQvQ/OJ1k8+bNJh0f4V/LG5rqit7TNVG+vr6YPn063n33XZw4cQJDhgzBwYMH0adPH/zyyy9ISkpCVFRUm23q9O7dG97e3rC1tUVOTg7eeecdFBcXq9SwjhkzBgcPHkRubi4A4Msvv0R1dTV8fHxQVFTEuZ42MRBC6EzXZL3++ut4/fXXVbY1Nzlp6cKFCxrNFxwczHn1Tstbj586dUrlsUWLFin7GrS3nqYxEELoTPexIpfLIRAI2nzJ5XK+QzMIpkOPXF3GmLLmfraMMaxZs6bL9rRt2c8W0Lyf7+OAku5jxMHBQXldecsvBwcHvkMzCF165Bq6ry6fWIt+tgKBAHPnzu2SPW1b97MFNOvn+7igpEt4V1lZiYCAAEgkEvj7+6OiogKjRo3CwoULkZubq+xj+/bbb2PZsmWIjY3F5MmT4eLiArFYDKlUCrlc3uEYdXXJXUXrfraAZj1tvby8IBaLlT1pw8LCMHToUNTV1WnU01bX8S172kqlUkycOBHAX/1sW1e7NPe0fdxR0iW8S09Ph7e3N0pLS+Hl5YX09PR2e+SmpqZy9tbtaEyfPn269J+wrfvZAl2vp626fraAcXva8omSLuFdQUEB/Pz8IJFIEBQUhMLCQo3G8d1b19ha97MFul5PW3X9bAHj9rTlEyVdwjs3NzfIZDI8fPgQWVlZcHNz06hHbuveusbsq8uH1v1sga7X01ZdP1vAuD1t+URJl/AuKioKZ8+ehYODA3JzczFz5sx2e+Q2v3XQurduR2Na1yV3Na372QKG62nbGb2Ade1p+9jTp0cZ0Q/109VdZ/TW1bS1I1/a62fLGL89aQ3Z09aUGaK1I53pki7pk08+wYYNGyCTyfgOxWgEAgE+++wzbNmyhbM8LDU1FfPnz9dpbn3GGmL8/v37ERkZiQEDBug8R1dBV6SRLuno0aN8h8ALW1tbxMTEcD7WfCdlXegz1hDjJ0yYoNf4roTOdAkhxIjoTJdn586dw7p16/gOo9MpFAr89ttvJr2vJSUlJh0f4d+ZM2eUF3noim7Xw6MHDx60ewkm6boWL16MxYsX8x0G6QTPP/88nJycdB2+mZIuIZ1g0qRJ+M9//sN3GMT00D3SCCHEmCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBElXUIIMSJKuoQQYkSUdAkhxIgo6RJCiBFR0iWEECOipEsIIUZESZcQQoyIki4hhBgRJV1CCDEiSrqEEGJElHQJMZArV67AxcUFHh4eOH/+PDw8PODi4oJ9+/bxHRoxIXSPNEIMxMPDAxYWFrh69apyW9++feHr68tjVMTU0JkuIQY0fvx4CAQC5fdubm7o0aMHjxERU0NJlxADmj17Nnr16gUAsLa2xuzZs3mOiJgaSrqEGJC7uztsbW0BAPb29ggJCeE5ImJqKOkSYmBTpkyBmZkZPD09IRKJ+A6HmBhKuoQYWFRUFMzNzTF37ly+QyEmSMAYY3wHQdq6cuUK5s+fD3Nzc75DIVq4d+8eRCIRzp8/jxdeeEHlQzVTUFdXh5qaGtjY2PAdislqamrCnDlzMHbs2M6YfjOVjJmoqqoqjBo1Ch988AHfoRAtLF68GCEhIejfvz8cHBz4DqeNU6dOQSaT0euqHfv27cPt27c7bX56e4GQTmCKCZeYBkq6hBBiRJR0CTGyoqIihIaG6j0PYwxpaWkGiKitqqoqJCYmIi0tDUKhEA4ODkhNTeV87pEjR9C7d29IJBKEh4ejrq4OMpkMjo6OEIvFmDJlCurq6tSupe94AFi/fj2cnJzg6OiIkydPKreXlJRg0KBBAIC9e/fi7NmzOhwNw6KkS4iROTs7IyMjQ+95Ll++jPLycgNEpIoxhqioKERHRyMmJgY1NTXIzc3F8uXLwfW5u1wux+HDh1FWVobKykpkZ2dDLpcjKysLpaWluHPnDk6cOKF2PX3H5+XlYcOGDTh37hwyMzNRUVEBAKivr0daWpoy5smTJ2P79u24ceOGnkdIP5R0CTGyuLg4eHt7AwDCw8Px0ksvwd3dHc7OzigqKsLkyZPh4uICsVgMqVQKuVyOUaNGYeHChcjNzUX37t0hk8nw9ttvY9myZYiNjUVJSQleeOEFg8SXnZ0NJycnZYVDU1MT5HI5LC0tOasxIiIi4OXlBbFYDIlEgl69eiEsLAxDhw5FXV0dmpqa0L9/f7Xr6Tv+4MGDmDlzJpydnSGVSjFx4kQAQEpKCubNmwcLi7/qBcLCwpCSkqLroTEISrqEGFlCQoLy3/PmzcOgQYNw6dIl+Pn54eLFi4iJicG4ceOgUCgglUqxZcsWLFq0CADg5eWFGTNmAAAWLFiA+Ph4pKamok+fPgb70zknJwceHh7K73fv3o3AwECsWrWq3XGFhYWwsbHBkCFDAADbtm2Do6MjhgwZAjc3tw7X1XV8SUkJkpOTIRaL4erqipycHOzZsweBgYHKS7KbeXh4ICcnp8NYOhMlXUJMgIWFBdzc3NDU1AQAkEgkEAqFCAgIwK1bt4wai0KhULmSzt7eHocOHUJ4eLjaMWVlZVi3bh02bdqk3DZt2jRUVVVBLBZ3eHapz3g7OzvMnj0bFRUVWLx4MdauXYsVK1Zg2LBhEAgEyMvLg7u7OwBAJBJBoVB0eAw6EyVdQkxQQ0MD6urqcODAATz11FMQCAQoKChAbW0tqqurlc+rrKxEQ0ODQde2t7dHTU2N8vvAwEDl2SeXgoICbN68GQkJCRAKhQAe/WlfVlYGS0tLmJubt5vo9B3v6+uL06dPo76+HvX19ZBIJDh79iwYY2CM4ZlnnlG226ypqYG9vb1Wx8PQKOkSYmRTp07F+fPnkZSUhFWrVmHHjh04fvw41q9fj9WrVwMANm7cCIlEguvXr2PGjBnw9PREdnY2Bg4ciJycHCxfvhweHh7YtWsXZsyYgeLiYuX7xPqSSqXIy8tTfp+UlISkpCQAQGZmZpsrtdasWYNFixahW7duEAgESE5Ohq2tLdzd3WFnZ4dTp04pL4nWZzzX2OZ43d3d0a9fP6xZswbvvvuu2n37/fff4ePjo/OxMQhGTNLJkydZQkIC32EQLX388cfsl19+0WuOY8eOsfnz5xsoIlWavK6amppYZGQku3//fpvHysrKWGhoqM7r6zNe37UZYywmJobl5+e3+5z//Oc/7PPPP9drnXZ8Tme6XVD//v2xdOlSlW0RERFanelMmjSpTa0oV72kMWhbkymTyTBy5Eh0794dEokEw4cP1zsGrn2fPXs2BAIBBAIBrK2t4eXlhfT0dL3X6sgnn3yCDRs2QCaTdfpaXAQCAT777DNs2bKlTYlYamoq5s+fr/Pc+ozXd+39+/cjMjISAwYM0HkOQ6Ck2wWtXr26TSOcHTt2aDUHV1E9V72kMWhTk1lYWIhXXnkF7733HkpKSlBYWIgxY8Zw1o9qG0PrfV+3bh1cXFxQXl6OW7duIT4+HgsXLsTu3bv1WqsjR48eRXV1Nfz8/Dp1nfbY2toiJiamTYlYQkICXnzxRZ3n1We8vmtPmDDBYGV1+qCk20WtXLkSVlZWGDFiBEpKSpTbKysrERAQAIlEAn9/f2Wh+L179zB+/HiIRCI4OjqisbFROcbT0xOzZs3irJfkwjUX17pcNai9evWCo6MjSktLMXfuXDg4OHDWZKqrO/3uu+8wZcoUvPrqq5BIJLC3t0dycjKqqqo491vTGDrad3t7e7z22mtYunQpNm/erPfPjzy5KOl2UdHR0bh37x6kUqnKn7zp6enw9vZGaWmpyp/DmzZtgpubGyorK3Ho0CGYmT360V+7dg1LlizB+vXrlXO0rpdsjWsurnW5alC3bt2KcePGoXfv3pg2bZoyvtY1merqTm/fvs1Zs6luv7WJQZN99/LyUiZ0QnRBrR27KIlEAisrKwQGBmL//v3K7QUFBQgNDYVEIkFQUBAyMzMBAPn5+QgJCYFQKMSwYcNw8+ZNXL16FVFRUcrCe4C7XrK11nO1ty6gWoM6YcIEfPzxx7h58yb27duHjz76CMCjmsxXXnkFy5YtQ0pKitr37pydnVU+We9ov7WJQZN9v3HjBvr166f28fv37+Pdd9812ZtRVlZWorq6GqdOneI7FJNVWlqK6dOnd9r8lHS7qIaGBtTW1iIzM1NZ+A08uvusTCaDr68vsrKylGeFbm5uyMjIwOjRo8EYQ319PQYOHIgdO3YgODgYLi4uMDc3xzfffIOEhASVSydbaz2XUChUu25rAoEA7733HmJjYxEcHAwzMzOkpKTgn//8J3r06NFhTeaUKVMwdOhQBAQEKC/33LRpEywsLDRaX10MBQUFave9qakJ9fX1OH/+PJYuXYoVK1aonbt79+5ISUlR/jIyNdRPt2P79u3DnTt3Om+BzqqLIPppr7RHJpMxJycnZmlpyYKCgtjdu3dZREQEMzMzY4sWLWL+/v7M2tqa+fv7s4qKCsYYYwqFggUFBTGRSMR8fHxYREQEE4lErLCwkA0fPpw5OTmx2NhYBkD5lZSUxLl+67nu37/PKioq2qwbFhbGJBIJ+/HHH5mdnR0bM2YMY4yxhoYGJpVK2cOHDxljjG3dupXZ2toyoVDI/Pz8WHFxMSsqKmLDhw/nXP/48eNs1KhRTCKRMKFQyAYPHszy8/M591vTGLj2/b333mMikYiZmZmxbt26sWeffZZt3Lix3Z+bIUrGOhOVInass0vG6HY9JorOSLqm5jtH0Jlu19V8pvvmm292xvSb6YM0opZcLlfWqbb8ksvlfIf2WGM69MnVZQwXbfroAsDdu3cxduxYVFVVtbvNGGPV1Zlv3rwZs2bNwhdffAGA/766lHSJWg4ODsrr11t+0a1oOpcufXIN0VuXadlH9+HDh4iJicGFCxfa3cbF0GMB7lrrzMxMpKenY/DgwVi7di0OHDjAe19dSrqEGIG6+un2+uQKBII2fXU7GhMREaHzBQDa9tEViUTYtm0bXF1d293GxdBjAe6+vA0NDRgxYgRmzJiBESNGoLa2FgC/fXUp6RJiBOrqiNvrk3vs2LE2fXU7GrNr1y6d/3TWtY+uqWlZax0cHIycnBxYWlri9OnTGD9+PAB+++pS0iXECAoKCuDn56esIy4sLNRonDH76urSR9fUtK61XrZsGaZOnYqamhpERkYiOTkZAL99dalOlxAjUFfH3FGf3JZ9dZtvsNjRmPZqrNvD1Ue3K+Gqtf7zzz9RXFyM48eP4/Lly+jduzcAfvvq0pkuIUYQFRWFs2fPwsHBAbm5uZg5cyYAdNgnt3Vf3Y7GjBs3Tue+utr20S0vL8fzzz+PM2fOYPDgwfjvf//Luc0YYwHuvrzvv/8+LCwskJSUBLFYjPfffx8Az311O6sCmOiHiti7JkNeHNEZfXXbe111Vh9dvnvocmmvry710yXkCWXsvrqd1UeXzx66XPjuq0vv6RJioo4ePWr0NZv76LbW8g7G2uJrrDoTJkww+JzaoDNdQggxIkq6hBBiRPT2gonq1q0b9uzZQ31Pu5g7d+7g5MmT+PPPP9GzZ0++w2njzz//xIMHD+h11Y7KykpER0d32vzUZYyQTjBp0iT85z//4TsMYnqoyxghhBgTJV1CCDEiSrqEEGJElHQJIcSIKOkSQogRUdIlhBAjoqRLCCFGREmXEEKMiJIuIYQYESVdQggxIkq6hBBiRJR0CSHEiCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIjuHEGIgdy9exfXr18HAFRVVeGXX34BADg7O6N37958hkZMCN05ghADKS0thYeHB8zMzMAYg0AgQGNjIzIyMuDn58d3eMQ00J0jCDGU3r17Y8CAAaioqEBlZSUqKirQs2dPjB49mu/QiAmhpEuIAc2YMQOWlpbK7319fWFmRv/NyF/o1UCIAU2dOhW9evUCANjb2+Odd97hOSJiaijpEmJANjY2cHFxAQD07NkTw4YN4zkiYmoo6RJiYLNnz4alpSXGjRvHdyjEBFHJWCdQKBRoamriOwzCk1GjRsHMzAz/+Mc/UF5eznc4hEf29vZt3tOnkrFO8Oyzz8LHx4fvMAiPfvvtNzz77LM6jz937pxJvzVx7do19O7dG927d+c7FJP1008/4dixY+jTp0/LzZvpTLcTDBw4EFu2bOE7DMKj5jpdXYWEhJj0a2jRokUIDw/H4MGD+Q7FZL311luc2+k9XUI6gT4JlzzeKOkSQogRUdIlpIsqKipCaGio3vMwxpCWlmaAiB6pqqpCYmIiGGNYs2YNhEIhHBwckJqaqnbM3bt3MXbsWFRVVbW7rbPHHjlyBL1794ZEIkF4eDjq6uqUz928eTNmzZqFL774AgCwd+9enD17tt35uVDSJaSLcnZ2RkZGht7zXL582WBVFowxREVFITo6GgKBAHPnzkVNTQ1yc3OxfPlycH1u//DhQ8TExODChQvtbuNi6LFyuRyHDx9GWVkZKisrkZ2dDQDIzMxEeno6Bg8ejLVr1+LAgQOYPHkytm/fjhs3bmh0bJpR0iWki4qLi4O3tzfCw8Px0ksvwd3dHc7OzigqKgIATJ48GS4uLhCLxZBKpZDL5Rg1ahQWLlyI3NxcdO/eHTKZDG+//TaWLVuG2NhYlJSU4IUXXtA5puzsbDg5OcHGxka5rampCXK5HJaWlpzvdYtEImzbtg2urq7tbuNi6LERERHw8vKCWCyGRCJRXl3Y0NCAESNGYMaMGRgxYgRqa2sBAGFhYUhJSWl3ndYo6RLSRSUkJAAA5s2bh0GDBuHSpUvw8/PDxYsXAQAxMTEYN24cFAoFpFIptmzZgkWLFgEAvLy8MGPGDADAggULEB8fj9TUVPTp00enP5mb5eTkwMPDQ2Xb7t27ERgYiFWrVuk8r7EVFhbCxsYGQ4YMAQAEBwcjJycHlpaWOH36NMaPHw8A8PDwQE5OjlZzU9Il5DFhYWEBNzc3lQtzJBIJhEIhAgICcOvWrU6PQaFQQCQSqWyzt7fHoUOHEB4e3unrG0JZWRnWrVuHTZs2KbctW7YMU6dORU1NDSIjI5GcnAzg0dmyQqHQan6q0yXkMdbQ0IC6ujocOHAAgwYNgkAgQEFBAWpra1FdXa18XmVlJRoaGmBhoV9KsLe3R01Njcq2wMBAveY0poKCAnzzzTdISEhQORZ//vkniouLcfz4cVy+fFnZlL6mpgb29vbaLcKIwQUHB/MdAuniNHkNRUREMDMzMwaASSQS9uOPPzI7Ozs2ZswYxhhjx44dY1ZWVqxbt24sMDCQ3b17l92+fZs5OTmxvn37Mk9PT/byyy+z69evM3t7e/b666+zoqIiNnz48A7Xjo+PZxcvXmyzXSaTsZiYGJVtiYmJLDExkTHG2L59+1hQUJDK43fu3GFDhgxhAoGA9evXjx04cIBzmzHGxsbGMgDKr6SkJMYYY+Xl5WzWrFns5ZdfZrNnz2YKhYIxxtjx48fZ3LlzOY9RVFQUKy4ubr35c0q6nYCSLtGXIV5Dx44dY/PnzzdANG2pS7pNTU0sMjKS3b9/n3NcWVkZCw0N1WlNvsa2JyYmhuXn53M+pi7p0nu6RtS/f38sXbpUZVtERAS8vb01nmPSpEltajPbqy3UJAYuMpkMjo6OEIvFmDJlSrtzzp49GwKBAAKBAEKhEMOGDcPPP//M+VympiZ0xIgRiI6OVjv37NmzATwqb3JwcMDAgQM73AdttfxZaFMD27xPhqqbNZRPPvkEGzZsgEwmM9qaAoEAn332GbZs2cJZHpaamor58+frNDdfY9XZv38/IiMjMWDAAO0GGjz1E7VnKd9++y1LSEhQ2VZfX6/Rn3PNCgoK2KRJk1S27dy5k/3666+surqaBQQEsKysLLXjuWJQ97xz586xe/fusdGjR7MjR460+3wXFxdWXl7OHjx4wObNm8f+93//l/N5ubm5LD4+vs32U6dOsejoaM4xnp6eTCAQsJ9++okxxthXX33Frly50uE+aEvbn0UzdfukD1P/a0ndmS75C53pmoiVK1fCysoKI0aMQElJicpjlZWVCAgIgEQigb+/PyoqKnDv3j2MHz8eIpEIjo6OaGxsVD7f09MTs2bNUltbqGkMXHWeYWFhGDp0KOrq6tDU1IT+/ftrXMNZW1uLv//97ygqKoKtrS2sra2V5ULNNaHTp09vs19XrlyBh4cH+vTpgz/++EM5X0hICMaNG4eZM2eqnHFzHa9JkyZhzJgxGD58OOzs7NC9e3dYW1tDLBajf//+sLS0xJgxYwCAM75mzTWw8+fPV57FP/fcc5zjmvdJIBAoz5S5YlNXT0ueLFS9YGTR0dFYsmQJ4uLikJ6ejvj4eOVj6enp8Pb2RkZGBuLj45Geng5zc3O4ubnh+++/x2+//abszXnt2jUsWbIEr776qnJ869pCTWOYN28etm3bhkOHDuGNN97AxYsX4ezsjG3btmH69OmYNWsW3NzcYG5u3m4Np6OjIwBg0KBBiIiIgLOzM6qqqpCXl4c5c+bgvffew4IFC+Dr64sePXpAJBIp96u2thbu7u44cuQI3nzzTVy4cAH9+/cH8OhP1i+//BJDhw7FihUrlH/OcR2vDz74AGvWrMHWrVvx/fff45dffsHWrVsRHR2Nl156CZMmTVL+MmkdX0xMjHJfEhISIJVKkZycjOTkZLz55psYO3YsALQZ17xPixcvhlQqVRubuuPMpaGhwSglXrq6d+8eSktLYWdnx3coJqt1FUczSrpGJpFIYGVlhcDAQOzfv1/lsYKCAoSGhkIikSAoKAiZmZlgjCEkJET5XunNmzdx9epVREVFKQvdAe7aQm1jaF3nOW3aNLzyyitYtmwZUlJSOnxPrLy8HD169EBubi7Gjx+P1NRUvP/++yguLlaeXTbLz89X2a/Tp08DAMzNzeHi4tKmCby9vT127tyJl19+GbGxsfD29uY8XsCjW+a4ubnB19cXV65cgVAohKurK7p37w4bGxs4OjqiqakJhw8fxvTp0znja2nDhg1wcnJCWFgYAGg0Tl1sXMeZS1VVFVasWNHO0ebXhQsXcOfOHe3LpZ4g169f59xOSdfIGhoaUFtbi8zMTLi7u6s85ubmBplMBl9fX2RlZcHNzQ2MMWRkZGD06NFgjKG+vh4DBw7Ejh07EBwcDBcXF5ibm3PWFuoSQ7OUlBT885//RI8ePWBubq5RAXhTUxPMzc1hbm6Ohw8f4quvvkJSUhKefvppxMXFKZ9XWVmJfv36tdmvjowcORIff/wxPvzwQ0ydOpXzeGlj586dnPG1dPLkSWRlZeG7777DwYMH4eHhwTmuuc61mb6xOTg4YN26dVqNMSbqp9sxdf106YO0TqDuQxCZTMacnJyYpaUlCwoKYnfv3lXWWq5atYpVVFQwf39/Zm1tzfz9/VlFRQVTKBQsKCiIiUQi5uPjwyIiIphIJGKFhYVs+PDhzMnJSW1toaYxhIWFtanz3Lp1K7O1tWVCoZD5+fmx4uJitTWcc+bMUa5tZmbG+vbty9LS0tj27duZSCRio0ePZlZWVuz06dPKmtCJEyeq7Je/vz+TSCTsp59+Yj179mQjR45Uzm1lZcU2b97MGHtUkjRx4kR25coVzuMVEhLCRCIRy8rKYpMmTWLW1tbs22+/ZXZ2dszV1ZV9/fXXzNzcnMXHx7eJz9XVVfmzaFkD2/KrqqqqzbgdO3Ywe3t75f4nJiZyxsZ1nLV9DZkK+iCtY+o+SKPb9XSCkJAQ7Nu3j+8wSBdm6q8hOtPt2FtvvYXFixe3uV0PVS88puRyufJT95Zfcrmc79CIkTAd+uTqMqY16qfbPkq6jykHBwcwxtp8OTg48B0aMRJd+uTq21uXUT/dDlHSJaQL4ar/5eqRC0ClfliTvrotx0REROjUV5f66XaMki4hXUhz/W9paSm8vLyQnp7O2SMX+KtP7rFjxzTqq9tyzK5du3T605n66XaMki4hXUhBQQH8/PyU9b+FhYUajTNWX13qp9sxqtMlpAvhqv9V1yMX+Kt+WNO+ui3H6NJbl/rpasBYNWtPElOvsSSmT91riKv+l6tHLmNMWRPdv39/jfrqthwTEBDwf+zde1yUdfo//tdwnIEBoeFkKDil+9ECyaAFXVMkEA9xsDj4FdfcKNbQdfh4qh66ubqCBZqQpyQ8UFFafVaTlVZddZC2ZGvDVEoTBVQQZDibchh5//7gx6w49wyHGe57wOv5ePB4OIf3+33NzXB5zz3Xfd16m/9QP13qp2tSKOkSQxnzPTQQfXWpn24n6qdLCNHCZ19d6qfbMzqmS8gQd/LkSV7Xc3Bw6Nax7X5dVzDuD6HG6jJ79ux+jaM9XUII4RHt6Q6AlpYWnW3dCOmNX3/91aTfQ/X19bh27RpsbGyEDsVkNTU1cd5PDW8GwJo1a3Dnzh2hwyAC+uabbzBp0qR+j29oaICDg4MRIzKu27dvQywWG3zJ9qFu3bp1sLOzu/+uLEq6hAyAiIgIfPnll0KHQUwPdRkjhBA+UdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeUdIlhBAeURNzQoykpKQEK1euRHuD2tSVAAAgAElEQVR7O65cuYLHH38cIpEIK1euxJQpU4QOj5iGLLrWBiFG4uHhgcLCQty8eRMAcPHiRTg5OWHkyJECR0ZMCR1eIMRIrKys4Ofn1+2+Rx99FHK5XKCIiCmipEuIESUlJcHR0REAYG5ujri4OIEjIqaGki4hRhQYGIhhw4YBANzc3PDSSy8JHBExNZR0CTEiMzMzTJ48GQAwfPhwuLq6ChwRMTWUdAkxsqVLl0IsFuOVV14ROhRigqh6wQRdu3YNX3zxhdBhkH5qa2uDVCpFbW0t3n33XaHD0dLR0YH29nZYW1sLHYpJCwoKwlNPPWX0eSnpmqBffvkFZWVleOGFF4QOhfTD/v37sXDhQkyaNEnoUDhdvXoVp06dQnx8vNChmKz//Oc/OH36NCXdh8no0aMRGBgodBikH7777jsEBATg2WefFToUTo888giuXr1K7y891Go1fvrppwGZm47pEkIIjyjpEiKAiooKREZGGjwPYwwZGRlGiOi/GhoakJqaioyMDIjFYjg5OSE9PV3n8xsbGzFjxgw0NDTovY+PsSdOnICbmxukUiliYmLQ1tYGAMjKykJiYiL27t0LADh48CC+++47vfMPFEq6hAjA3d0dhw4dMnie4uJi1NTUGCGiTowxJCQkYNGiRVAoFGhpacH58+exceNGcLVpuXv3LhQKBc6ePav3Pi7GHgsAKpUKx48fR3V1Nerr65Gfn4/c3FxkZmZi/Pjx2Lp1K/Ly8jBnzhxkZ2fj6tWrvdksRkVJlxABrFq1SnPKcExMDJ577jmMHTsW7u7uqKiowJw5c+Dp6QlbW1sEBARApVJh8uTJeOONN3D+/HnY2dlBqVTitddeQ3JyMpKSknDz5k0888wzBsWVn58PV1dX2NvbA+isdFCpVLCysoJIJNJ6vkQiwb59+zBq1Ci993Ex9lgAiI2Nhbe3N2xtbSGVSuHi4gK1Wg1/f3/Ex8fD398fra2tAICoqChs2bJF7zoDgZIuIQJISUnR/HvZsmUYM2YMLly4gMDAQPz4449QKBSYOXMmamtrERAQgN27d2PNmjUAAG9vb03lweuvv47Vq1cjPT0dw4cPN/gjc2FhIcaNG6e5/dlnnyEkJASbN282aF6+lZeXw97eHj4+PggLC0NhYSGsrKxw5swZzJo1CwAwbtw4FBYW8h4bJV1CTISFhQXkcjk6OjoAAFKpFGKxGMHBwbh+/TovMdTW1kIikWhuy2QyHD16FDExMbysbwzV1dXYvn07du3aBQBITk7G/Pnz0dLSgri4OGzatAlA595ybW0t7/FRyRghJkqtVqOtrQ15eXkYM2YMRCIRSktL0draitu3b2ueV19fD7VaDQsLw/+cZTIZWlpaNLdDQkIMnpNPpaWl+OSTT5CSkqLZHnfu3EFlZSUKCgpQXFwMNzc3AEBLSwtkMhnvMdKeLiECmD9/PoqKipCWlobNmzcjJycHBQUF2LFjh+Ystvfffx9SqRQlJSWIj4+Hl5cX8vPzMXr0aBQWFmLjxo0YN24cDhw4gPj4eFRWVmq1luyrgIAAXLp0SXM7LS0NaWlpAIDc3FzMmDGj2/Nramrw1FNP4d///jfGjx+Pr776ivM+PsYCwHvvvYc1a9bA0tISIpEImzZtwsqVK2FhYYG0tDTY2tpi5cqVADpPQpo4caJB26tfGDE5x48fZxkZGUKHQfopNTWVnT592qA5Tp06xVasWGGkiLr78ccf2erVqzkf6+joYHFxcay5uVnrserqahYZGdmvNYUaq49CoWBXrlzhfGwA/wY/oD3dQcbDwwMbNmzodl9sbGyf9nAiIiK0akR11Tf2B+uhdlSpVMLZ2Rm2traIjo7Wu9b169dhY2ODoqKiXq/Z1xrYrrHGqp01hvXr12Pnzp1QKpW8risSibBt2zbs3r1bq0QsPT0dK1as6Ne8Qo3V5ciRI4iLi8Njjz1m1Hl7g5LuIPPuu+/C3Ny82305OTl9moMrIXLVN/ZXT7WjKpUKx44dQ1VVFW7duoWvv/5a53M/++wzrF69Gnv27On1mn2tge0aa6zaWWM4efIkbt++Lcipug4ODlAoFFolYikpKfjd737XrzmFGqvL7NmzDS6v6y9KuoPQO++8A2tra/j7+2uuxwV0fqESHBwMqVSKoKAg1NXVAQCampowa9YsSCQSODs74969e5oxXl5eSExM5Kxv5FJRUQEHBwfY2NhoyogenD8hIQHJyckQiURwdnZGVVUVli5dCicnJwCd9ZETJkxAW1sbOjo64OHhobPGtKamBq+++ioOHDigqa/Ut2ZSUpKmBtbFxUVrfa74u2pdRSKR5hMD17bkqqclpK8o6Q5CixYtQlNTEwICApCZmam5PzMzE35+fqiqqoK3t7fmsV27dkEul6O+vh5Hjx6FmVnnr/3y5ctYt24dduzYoZnj/vpGLu7u7mhoaEBRURH+8Y9/cM7/5ptvYvXq1cjNzcXMmTPh5uaGhQsXdot13759cHZ2ho+PD+RyOWeN6TfffIMJEybAxcUFEyZMwMGDBzWP6VozPT1dUwO7Z88erfW54u+qdW1vb9e7LbnqaQnpKyoZG4SkUimsra0REhKCI0eOaO4vLS1FZGQkpFIpQkNDkZubCwC4cuUKwsPDIRaL8fTTT6OsrAwXL15EQkKCpuAe0K5v5HL8+HG8/PLLqKysxLRp0zjnr6ysBND5EW7t2rUoKyvD4cOH8dZbb2nmWbhwIV544QUkJydjy5YtnMfssrOzkZmZiblz5wLoPDuq69+61rwf1/pc8XPRtS0B7XraB7W2tuLjjz/We9hESFVVVfj555+xceNGoUMxWSUlJXjiiScGZG5KuoOQWq1Ga2srcnNzMXbsWM39crkcSqUSU6dOxbFjxzRXoZXL5Th06BCmTJkCxhja29sxevRo5OTkICwsDJ6enjA3N9eqb+Syf/9+pKWl4Te/+Q1WrVqlc/76+nrcu3cPy5cvR1JSEsLCwjR72Fu2bMG8efMwbNgwmJubcxaot7S04Ndff9V8mXPt2jWMHz8eZWVlGDVqlM411Wq1Zg6RSKS1Plf8ALTG6tqWvWFhYYEJEybo/LQgtJKSEjQ3N1NrRz1sbW11/qdqsIGoiSCG0VeuolQqmaurK7OysmKhoaGssbGRxcbGMjMzM7ZmzRoWFBTEbGxsWFBQEKurq2OMMVZbW8tCQ0OZRCJhEydOZLGxsUwikbDy8nLm6+vLXF1dWVJSEgOg+UlLS+NcPzs7m0kkEjZlyhRmbW3Nzpw5ozX/2bNnmUwmYwsWLGBqtZoFBASwu3fvaubYs2cPc3BwYGKxmAUGBrLKykpWUVHBfH19Nc8JCAhgMpmMnTt3jjHG2F//+ldmbm7O3Nzc2I0bN/Su2bU9UlNTtdbnir+kpITJZDIGQDOurq5Oa1tGRUUxqVTKTp8+zRwdHdm0adM4t5ExSsYGkr6SMdJpIEvGRIxxtA4igvrnP/+Jn376CUuXLhU6FNIPaWlpJt3E/Ny5c/jss8+0Sg/Jfw3g32AWfZFGOKlUKohEIq0flUoldGgPBdaPPrn9GcOF+ukOLEq6hJOTkxMYY1o/XWVfZGD1p0+uMXrrMuqnO+Ao6RLCE1111Pr65IpEIq2+uj2NiY2N7XfhP/XTHXiUdAnhia46an19ck+dOqXVV7enMQcOHOj3R2fqpzvwKOkSwpPS0lIEBgZqan/Ly8t7NY7PvrrUT3fgUZ0uITzRVfvbU5/cB/vq9mZMf3vrUj9dHgxEIRoxDLV2HNx01ely1f4yxtiNGzeYq6srGzFiBPPy8mLTp0/X1A57eHgwa2trZmlpyUJCQlhjY2OPY4KDg7vVPD9IX52uUqlkCoWi22tJTU1ljDF2+PBhFhoa2u35t27dYj4+PkwkErGRI0eyvLw8zvv4GMsY46w3r6mpYYmJiWz69Ols8eLFrLa2ljHGWEFBAVu6dCnndhjIOl1KuiaIku7gZsyTIwairy710+1E/XQJIVr47qtL/XQHHh3TJcSEnTx5kvc1u/rpPuj+Kxj3lVBjdZk9e7bR5+wt2tMlhBAeUdIlhBAe0eEFE/XVV19pzlgiA6uxsRHDhg0z2nxFRUUoKSnBiRMnjDYn0Hnq6/01tP1VW1uLkpIS/OUvfzE8qCHq6tWrBl9ZWRfqMmaCmpubcfnyZaHDeCjs378fFy5cwPr16zX9fk3VRx99hLt37yIhIUHoUB4KI0eOhLOzs7GnzaKkSx5Kt2/fxmuvvQYvLy+sWrWKs6+AKdq2bRsuXbqEjIwMk/9PgnCi1o7k4fPLL78gIiICf/jDH/D6668PmoQLAEuWLIG/vz9efvnlbtd0I4MHHdMlD5Uvv/wSu3fvxscff4zhw4cLHU6/zJ8/H/b29oiLi0N2drZRjvMS/lDSJQ8FtVqNP//5z7h79y7+7//+D5aWlkKHZJDw8HDY29sjNjYWH3/8saYVIzF9lHTJkFdTU4OEhATMmzcP0dHRQodjNIGBgZBIJIiNjcWBAwco8Q4SdEyXDGkFBQWIjY1FcnLykEq4Xfz9/bFx40b8v//3/7p1GyOmi6oXyJCVmZmJb775Btu3b4etra3Q4Qyob7/9Fu+88w4+/fRTOsZr2qh6gQw9zc3NiIuLQ319Pfbt2zfkEy4ATJw4EUuXLsXcuXM1l6MhpomO6ZIh5dKlS1i6dCnWrl2LSZMmCR0Or4KCgqBWq/HSSy/h448/7ncjczKw6LdChoxPP/0UOTk5yM7O1lwd4GEzffp03LlzBwsWLMCHH35IidcE0W+EDHr3l4MdOnTooU80kZGRuHPnDpYsWYL3339f6HDIA+iYLhnUKioqEBYWBj8/P6Snpz/0CbfLvHnz8NRTT2HDhg1Ch0IeQEmXDFqnT59GfHw8tm7dihdffFHocEzOokWLUFtbiw8//FDoUMh9aLeADDqMMbz33ns4e/Ys/va3v8HGxkbokEzW5s2bMW/ePHh4eCAwMFDocAhoT5cMMs3Nzfj9738PANi7dy8l3B6YmZlhz549SElJQVlZmdDhEFDSJYPIzz//jKioKCxZsoTzGl6Em42NDbKyspCQkIBff/1V6HAeenRGGhkUcnJysH//fmRlZcHV1VXocAalr7/+Gjt27EBOTs6gamc5xNAZacS0tLa2oqKiotvt//3f/8X58+dx6NAhSrgGmDx5Mn77299i27ZtQofyUKOkS0zKihUrEBISgtbWVty4cQMRERGYMmUK3n77bZibmwsd3qCnUCjw9ddfo6ioSOhQHlp0eIGYjHPnziE0NBR1dXUICgpCR0cH3n//fcjlcqFDG1Lq6+sRHR2NgwcPws7OTuhwHjZ0eIGYhnv37mHu3LmoqqpCW1sbvv32W8yYMYMS7gBwdHTEn//8ZyxfvlzoUB5KlHSJSVi3bh2uXbumud3Y2IiUlBT6GDxApk6dCnt7exw+fFjoUB46lHSJ4EpKSpCVlaUpZxo2bBg8PT0xdepU+pZ9AG3YsAHvvfceGhoahA7loULHdImgGGMYP348SktL4eTkBD8/P/zpT3/Cs88+S5cY58G3336LDz/8EDt37hQ6lIdFFu9Jd9u2bcjLy6PGJINMVVXVgLRLrK6uRmVlJTw8PPDII4/0e8+2rq4OUqkUVlZWRo5w6Kivr0dubi4cHBy63b948WLMnTsXzz77rECRPVSyeM98FRUVSEtLw5NPPsn30sQA4eHhJn38780338T8+fPpfaVHfHw82tvbte7fuHEjoqKicOTIkUF/leTBgD6/EfKQs7e3x/z587F161ahQ3koUNIlhOD3v/89lEplt7MBycCgpEuMpqKiApGRkQbPwxhDRkaGESLq1NDQgNTUVGRkZEAsFsPJyQnp6el6xzQ2NmLGjBndvtnnum+gx544cQJubm6QSqWIiYlBW1sbACArKwuJiYnYu3evZuzBgwfx3Xff6Z1fF5FIhI0bN+KNN97o13jSe5R0idG4u7vj0KFDBs9TXFyMmpoaI0TUmcATEhKwaNEiKBQKtLS04Pz589i4cSN0fYd89+5dKBQKnD17Vu99fIxVqVQ4fvw4qqurUV9fj/z8fOTm5iIzMxPjx4/H1q1bkZeXBwCYM2cOsrOzcfXq1R63C5cnn3wSrq6u+Oqrr/o1nvQOJV1iNKtWrYKfnx9iYmLw3HPPYezYsXB3d9d8ZJ0zZw48PT1ha2uLgIAAqFQqTJ48GW+88QbOnz8POzs7KJVKvPbaa0hOTkZSUhJu3ryJZ555pt8x5efnw9XVFfb29gCAjo4OqFQqWFlZ6ayUkEgk2LdvH0aNGqX3Pj7GxsbGwtvbG7a2tpBKpXBxcYFarYa/vz/i4+Ph7+/f7ZLrUVFR2LJli9519Fm/fj02b96MlpaWfs9B9KOkS4wmJSUFALBs2TKMGTMGFy5cQGBgIH788UcAnc1WZs6cidraWgQEBGD37t1Ys2YNAMDb2xvx8fEAgNdffx2rV69Geno6hg8f3u+PzABQWFiIcePGaW5/9tlnCAkJwebNm/s9pxDKy8thb28PHx8fhIWFobCwEFZWVjhz5gxmzZqled64ceNQWFjY73VsbGzwxz/+cdBtn8GEki4ZMBYWFpDL5ejo6NDcJ5VKIRaLERwcjOvXrw94DLW1tZBIJJrbMpkMR48eRUxMzICvbSzV1dXYvn07du3aBQBITk7G/Pnz0dLSgri4OGzatEnzXIlEgtraWoPWi46ORlFRUb8PUxD9KOkSXqnVarS1tSEvLw+PP/44RCIRSktL0draitu3b2ueV19fD7VabfB6Mpms20flkJAQ+Pj4GDwvX0pLS5GVlYWUlBSIxWIAwJ07d1BcXIyCggIUFxd3224tLS2QyWQGr/v2229TQ5wBQkmXGM38+fNRVFSEiRMnIicnBwUFBdixYwfeffddzXPef/99SKVSlJSUID4+Hl5eXsjPz8fo0aNRWFiIjRs3Yty4cThw4ADi4+NRWVkJPz+/fscUEBCAS5cuaW6npaUhLS1Nczs3NxczZszoNqampgZPPfUU/v3vf2P8+PH46quvOO/jY+x7772HNWvWwNLSEiKRCJs2bcLKlSthYWGBtLQ02NraYuXKlZo5fvnlF0ycOLHf26vL6NGj4eXlpfmSjhgR49kbb7zBLly4wPeyxEBhYWEGz3Hq1Cm2YsUKI0SjTdf7qqOjg8XFxbHm5mbOcdXV1SwyMrJfawo1Vh+FQsGuXLnC+djLL7/Mbt261eu5fv31VxYSEsLa2tqMFR5h7AOT2dP18PDAhg0btO6PjY3t055ORESEVq2oUqmEs7MzbG1tER0dral15ENv6zOVSiUmTZoEOzs7SKVS+Pr6GmV9rjrPxYsXQyQSQSQSwcbGBt7e3sjMzDTKevqsX78eO3fuhFKpHPC1uohEImzbtg27d+/mLBFLT0/HihUr+jW3UGN1OXLkCOLi4vDYY48ZZT4bGxvMmzdPcyyZGAnfaV7XHsnnn3/OUlJStO5vb29nvr6+vZ6/tLSURUREaM39ww8/sKamJjZlyhR24sSJvgfeD3fu3GEvvfQSc3V1ZfX19TqfV1ZWxhwdHdkXX3zBmpubmUqlYsuXL2cdHR0Gx7B//3527tw5dvv2bRYcHMyOHTvGGGPM09OT1dTUMJVKxT799FPm6OjIDhw4oHMeY+zpDiT6BNWzvu7pMsbYvXv32PTp01ltbe0ARfXQMZ09XQB45513YG1tDX9/f9y8ebPbY/X19QgODoZUKkVQUBDq6urQ1NSEWbNmQSKRwNnZGffu3es2xsvLC4mJiYiKisKECRPQ1taGjo4OeHh4cK7/4HwqlUprTQCcdaguLi5wdnZGVVUVli5dCicnJ866S6660y+++ALR0dF48cUXIZVKIZPJsGnTJohEIs7XrasOlisGrjrP+8lkMsydOxcbNmxAVlZWv35vZOgyMzPD2rVrNeWAxHAmlXQXLVqEpqYmBAQEaH3czczMhJ+fH6qqqjQfh3ft2gW5XI76+nocPXq0W//Vy5cvY926ddixYwcAYN++fXB2doaPj4/OS8A8OF9WVpbWmgB3HeqePXswc+ZMuLm5YeHChTo/rnPVnd64cUNnTFyvW1cdrL4Y7q/z5OLt7a35T4WQ+02aNAk3btzA5cuXhQ5lSDCpprZSqRTW1tYICQnBkSNHuj1WWlqKyMhISKVShIaGIjc3F4wxhIeHQywW4+mnn9Y89+LFi0hISNAU3gPAwoUL8cILLyA5ORlbtmzhPJ525cqVbvNlZmZqrXm/++tQZ8+ejbVr16KsrAyHDx/GW2+91evX7e7u3u0b9p5eN9f6AHTG8GCdJ5erV69i5MiROh+vqqpCeHh4r18T38rKynDmzBm60KIehtRFp6SkYPXq1fj000+NGNHDyaSSrlqtRmtrK3JzczF27Nhuj8nlciiVSkydOhXHjh2DXC4HYwyHDh3ClClTwBjT1DGOHj0aOTk5CAsLg6enJ3JzczFv3jwMGzYM5ubmOovH5XJ5t/lGjBihtaYuIpEIy5cvR1JSEsLCwvp01YPo6GhMmDABwcHBeP755wF07nVHR0dzvu6+xFBaWopPPvkEKSkpWo3jOzo60N7ejqKiImzYsAFvv/22zrnd3Nyon+4g13XGX3889thjcHd3xz//+U8EBwcbMaqHEN9HkXV94aFUKpmrqyuzsrJioaGhrLGxkTHGWGxsLDMzM2Nr1qxhQUFBzMbGhgUFBbG6ujpWW1vLQkNDmUQiYRMnTmTNzc0sPj6eSSQSVl5eznx9fZmrqyvbs2cPc3BwYGKxmAUGBrLKykrO2B6c79q1a1prMsZYVFQUk0ql7PTp08zR0ZFNmzaNMcaYWq1mAQEB7O7du4wxxm7dusV8fHyYSCRiI0eOZHl5eayiooLzi8GCggI2efJkJpVKmVgsZuPHj2d1dXWsrq5OKwZd63PFkJSUxABoftLS0tjy5cuZRCJhZmZmzNLSkj3xxBPs/fff1/t7oy/SBr/+fJF2v8bGRhYaGsru3btnxKgeOh/wfrke2iMZnOjKEYNffHw83n77bTg7O/d7jq1bt+KRRx5BXFycESN7qGSZ1BdpfFGpVJo61ft/VCqV0KENaawffXL7M+ZB1E/XeP74xz8iOzvbKKdoP6weyqTr5OQExpjWj5OTk9ChDWn96ZNraG9dRv10jcrKygpRUVH46KOPBmyNoe6hTLrEeLjqiLl65ALQ9MkViUS96qt7/5jY2Nh+9dWlfrrG94c//AEfffQRr2d2DiWUdIlBuOqIuXrkAv/tk3vq1Kle9dW9f8yBAwf69dGZ+ukan6WlJRYsWIAPP/xwQNcZqijpEoOUlpYiMDBQU0dcXl7eq3F89dWlfroDIy4uDp9++qnWWaCkZ5R0iUG66ojv3r2rqSPW1SMX+G+f3N721b1/TH9QP92BYWlpifDwcBw8eHDA1xpy+C5So3rKwUlXnS5XHfGNGzeYq6srGzFiBPPy8mLTp09njDFWUlLCZDIZ8/DwYNbW1szS0pKFhISwxsbGHscEBwfrbXykr/5boVBobqemprLU1FTN7cOHD7PQ0NBuY7jqq7nu42MsV511TU0NS0xMZNOnT2eLFy/u1oymoKCALV26lHMbGVqn+6Cu1o/GaMz0EPmAki7pFWOeHDEQfXWpn24nY/bT7Y233nqLnTp1yqhzDnGm1WWMPBz47KtL/XQH1qJFi/DBBx/wtt5QYFK9F8jD4eTJk7yu5+DgAIVCwfmYIS0LhRqry+zZs40+Z0+GDx8OxhgqKirg7u7O+/qDEe3pEkIMkpCQQHu7fUBJlxBikKlTp+Jf//qXpsUo0Y/3wwuurq5YsWIFLC0t+V6a9FNjYyPq6+tNup+uSqVCUVERrKyshA7FZNXU1Gi19zQGkUiEqVOnIj8/H9OmTTP6/EMN713GyOBDHbxIT8rLy7FhwwY6zNCzh7PLGOmbK1eu8PqNOBl8PD09cfPmTdy9e1foUEweJV3So5aWlm6n0hLCZdasWTh27JjQYZg8SrpEL8aYzm5chNwvLCwMf//734UOw+RR0iV6VVZW4tFHHxU6DDIIjBw5EteuXaMqhh5Q0iV6Xb58GWPGjBE6DDJI/Pa3v8X3338vdBgmjZIu0YuSLumLmTNn4ujRo0KHYdIo6RK9KOmSvnjmmWfwn//8R+gwTBolXaLX1atXIZfLhQ6DDBKWlpa4d+8eXcpHD0q6RK+2tjZYW1sLHQYZRHx9fVFUVCR0GCaLki7RqaOjA2Zm9BYhffPss8/iX//6l9BhmCz6iyI63bhxAyNGjBA6DDLIPP300z1ebv5hRkmX6ERfopH+cHR05OXimIMVJV2iEyVd0l9isbjbBUHJf1HSJTpR0iX95eXlheLiYqHDMEmUdIlOpaWl8PT0FDoMMgiNGTMGly9fFjoMk0RJl+ikVqupKTjpl9GjR6OkpEToMEwSXZiSdPO3v/0NSqUSvr6++PXXX9HQ0AAHBwehwyKDzOjRo7Fr1y6hwzBJlHRJN62trdi5cycYYxg2bBiefPJJdHR04PLly5BKpUKHRwYJJycn1NXVCR2GSaLDC6SbZ555Bk5OTrh37x7q6upQVVWF8PBwSrikz+hKYNwo6ZJuHn/88W6n/T766KPYuHGjgBGRwUokElFvXQ6UdEk3IpEIw4YNAwBYWFggJiYGjzzyiMBRkcHIycmJTpLgQEmXaPH29gYADB8+HH/5y1+EDYYMWsOHD8fNmzeFDsPkUNIlWoKDg2FhYYE//OEPsLOzEzocMki5ublR0uWgqV6oqqqijwIEAPDII4/A1tYWERERdFbRQ8jNzQ0ymczgeZydnaFSqYwQ0dCiSbopKSlQq9Wa43nk4cUYw7PPPovPP/+8T+N++uknODg4mOyFLBsbG3Hp0iX89re/FToUk3Xjxg2MHz8eK1euNHguBwcHOt7nLt8AACAASURBVCuNQ7c63VWrVmHUqFEChUIGu/feew9PPPEEgoODhQ6F06VLl5CVlUXVGHoUFBTgzJkzRpnLwcEBDQ0NRplrKKFjuoSQAeHg4IDGxkahwzA5lHQJIQOC9nS5UdIlvKqoqEBkZKTB8zDGkJGRYYSI/quhoQGpqanIyMiAWCyGk5MT0tPTdT6/sbERM2bM6JZYuO7jY+yJEyfg5uYGqVSKmJgYzYUhs7KykJiYiL179wIADh48iO+++07v/MZib2+P5uZmXtYaTCjpEl65u7vj0KFDBs9TXFyMmpoaI0TUiTGGhIQELFq0CAqFAi0tLTh//jw2btzIeTrr3bt3oVAoul2Whus+LsYeCwAqlQrHjx9HdXU16uvrkZ+fj9zcXGRmZmL8+PHYunUr8vLyMGfOHGRnZ+Pq1au92SwGsbS0RHt7+4CvM9hQ0iW8WrVqFfz8/AAAMTExeO655zB27Fi4u7ujoqICc+bMgaenJ2xtbREQEACVSoXJkyfjjTfewPnz52FnZwelUonXXnsNycnJSEpKws2bN/HMM88YFFd+fj5cXV1hb28PoPOinCqVClZWVhCJRFrPl0gk2LdvX7cvnrnu42LssQAQGxsLb29v2NraQiqVwsXFBWq1Gv7+/oiPj4e/vz9aW1sBAFFRUdiyZYvedYzBwsICarV6wNcZbCjpEl6lpKRo/r1s2TKMGTMGFy5cQGBgIH788UcoFArMnDkTtbW1CAgIwO7du7FmzRoAnWfKxcfHAwBef/11rF69Gunp6Rg+fLjBH5kLCwsxbtw4ze3PPvsMISEh2Lx5s0Hz8q28vBz29vbw8fFBWFgYCgsLYWVlhTNnzmDWrFkAgHHjxqGwsFDgSB9elHSJ4CwsLCCXyzXNUaRSKcRiMYKDg3H9+nVeYqitrYVEItHclslkOHr0KGJiYnhZ3xiqq6uxfft2TR/b5ORkzJ8/Hy0tLYiLi8OmTZsAdO4t83UiFHUa00b9dInJUavVaGtrQ15eHsaMGQORSITS0lK0trbi9u3bmufV19dDrVbDwsLwt7FMJut2IcWQkBCD5+RTaWkpPvnkE6SkpGi2x507d1BZWYmCggIUFxfDzc0NANDS0mKUM85I/9CeLuHV/PnzUVRUhLS0NGzevBk5OTkoKCjAjh078O677wIA3n//fUilUpSUlCA+Ph5eXl7Iz8/H6NGjUVhYiI0bN2LcuHE4cOAA4uPjUVlZqTlO3F8BAQG4dOmS5nZaWhrS0tIAALm5uZgxY0a359fU1OCpp57Cv//9b4wfPx5fffUV5318jAU6T0xZs2YNLC0tIRKJsGnTJqxcuRIWFhZIS0uDra2t5iyzX375BRMnTjRoe/UW1/Hwhx77//3pT39ipaWljJD+ysjIYMePHzdojlOnTrEVK1YYKaLuLl68qHPujo4OFhcXx5qbm7Ueq66uZpGRkf1aU6ix+igUCnblyhXOx06fPs1SU1ONtlZ4eLjR5hoiPuhxT9fDwwMbNmzQuj82NrZPexcRERFa9ZlKpRLOzs6wtbVFdHS0prawtzHo0pt6x8WLF0MkEkEkEkEsFuPpp5/G999/z/lcpqcm1N/fH4sWLeKce/HixQA6y5ucnJwwevToXr+G3rr/99CXGtj7X5OxameNYf369di5cyeUSiWv64pEImzbtg27d+/WOg6Znp6OFStW9GteocbqcuTIEcTFxeGxxx4z6ryk93pMuu+++y7Mzc217s/JyenTQlxJS6VS4dixY6iqqsKtW7fw9ddf9ykGLr2td9y+fTs8PT1RU1ODuro6TJs2DTt27OB8rr6a0IyMDK2PUNu3b4eXlxd27tyJb775Bk8++STS09Px97//vVevoS/u/z30pQb2/tdkrNpZYzh58iRu376NwMBA3td2cHCAQqHQ+n2mpKTgd7/7Xb/mFGqsLrNnzza4vI4YplfHdN955x1YW1vD399fqz9mfX09goODIZVKERQUhLq6OjQ1NWHWrFmQSCRwdnbGvXv3uo3x8vJCYmIioqKiMGHCBLS1taGjowMeHh69joGrxhPgrmPsTR1na2urpvtURUUFHBwcYGNjg82bN3erCeV6bT///DPGjRuH4cOH49q1awCA8PBwzJw5E6+++mq3PXiu7RUREYFp06bB19cX+fn5iIyMhJ2dHWxsbGBrawsPDw9YWVlh2rRpWrHd7/4a2BUrVmj25J988km9r6lrHFdsurYzIaR/evW176JFi7Bu3TqsWrUKmZmZWLt2reaxzMxM+Pn54dChQ1i9ejUyMzNhbm4OuVyOv/3tb/jpp59gZvbf3H758mWsW7cOL774IgBg3759ePnll5GYmAi5XN7rGJYtW4Z9+/bh6NGjeOmll/Djjz/C3d2dc6y+Ok5nZ2cAwJgxYxAbGwugc8+voaEBly5dwpIlS/D6669j6tSp2LBhA9LS0rq9ttbWVowdOxYnTpzAK6+8grNnz8LDwwMikQgffvghJkyYgLffflvzcY5re7355pt47733sGfPHly7dg1r1qyBjY0N9uzZg0WLFuG5555DREQEJkyYAGdn526xKRQKzWtJSUlBQEAAAGDTpk3YtGkTXnnlFcyYMUPva1Kr1QgICOCMrS/bueuqwaZ6eZ/y8nLcunULP/zwg9ChmKxffvlFayeJGFevkq5UKoW1tTVCQkJw5MiRbo+VlpYiMjISUqkUoaGhyM3NBWMM4eHhmmOlXS5evIiEhARNsTsALFy4EC+88AKSk5OxZcsWncewdMXwYI1nX9XU1GDYsGE4f/48Zs2aheLiYvzwww94+eWXUVlZiWnTpnV7/pUrV7q9tq42eObm5vD09OwWh0wmw/79+zF9+nQkJSXBz8+Pc3sBneepy+VyyOVyfP/997CxsYFYLMaoUaNgZ2cHe3t7ODs74+jRo0hMTOSM7UE7d+6Eq6sroqKicPz4cZ2vqYuu2Hq7ndVqNX744QdUV1fr3+gCqa2tRXl5OQ4fPix0KCbr2rVrend+iOF6lXTVajVaW1uRm5uLsWPHdntMLpdDqVRi6tSpOHbsGORyORhjOHToEKZMmQLGGMRiMQBg9OjRyMnJQVhYGDw9PZGbm4t58+Zh2LBhMDc311uwrS8GQ3R0dMDc3Bzm5ua4e/cu2tvbsX//fqSlpeE3v/kNVq1aBeC/NaFyubzba+vp3PJJkyZh7dq1+POf/4z58+dzbq++4IqNyzfffINjx47hiy++wD/+8Q9s2rRJ52vqYmhsVlZWWLBggcn306XrvulmzH66RIeuOgZdJWNKpZK5uroyKysrFhoayhobGxljjMXGxjIzMzO2Zs0aFhQUxGxsbFhQUBCrq6tjtbW1LDQ0lEkkEjZx4kTW3NzM4uPjmUQiYeXl5czX15e5urqyPXv2MAcHByYWi1lgYCCrrKzkrLHgiiEqKopJpVJ2+vRp5ujoyKZNm8YYY+zWrVvMx8eHiUQiNnLkSJaXl8cqKiqYr69vtzmXLFnCADAAzMzMjI0YMYJlZGQwxhjLzs5mEomETZkyhVlbW7OcnBwmk8nYggULtF5bUFAQk0ql7F//+hd75JFH2KRJk9iSJUuYtbU1y8rKYox1liM9//zz7Oeff2Z1dXVa2ys8PJxJJBJ27NgxxhhjERERzMbGhn3++efM0dGRjRo1in388cfM3NycjRw5sltso0aNYmZmZmzz5s2a30lqaiqbPn265vUBYO+9957O19Q17s0339SKTdd25mKMkrGBpK9kjHSikrEB94GIsc76mKVLl2LZsmV05QjSb4PlyhFdJz0QbV17usa4XA/QWSr65ZdfGmWuISLLpM5IU6lUmm/c7/+hi9s9PFg/+uT2ZwwX6qdL+GBSSdfJyQmMMa0fJycnoUMjPOlPn1xj9NZl1E+X8MSkki4ZmrjqfwHo7ZMrEom0+ur2NMaQ3rrUT5fwhZIuGXBd9b9VVVXw9vZGZmYmAOjtk3vq1Cmtvro9jTGkty710yV8oaRLBlxpaSkCAwM19b/l5eW9GsdnX13qp0v4Qv10yYDTVf/bU5/cB/vq9mZMf3vrUj9dwhfa0yUDLiEhAd999x2cnJxw/vx5vPrqqwDQY5/cB/vq9mZMf3vrUj9dwpuuil3qp0sMZcyTIwairy710+1E/XQF1XM/XUKEwHdfXeqnS/hCx3SJSTp58iTva3b1033Q/Vcw7iuhxuoye/Zso89J+ob2dAkhhEfd9nSLioq0mpQT0ltlZWUAAFtbW2ED0eHatWuorKzEt99+K3QoJqu4uFjoEIY8TdINCwvD999/j4sXLwoZDxFQcXExbG1t+930yN7eHo2Njbxf36y3Ojo68Pjjj5tsfKYiKChI6BCGNE3SDQkJGXS1icS4du3ahREjRtBxP0IGEB3TJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHlHSJYQQHtGFKQleeuklXL58GU1NTbCwsEBycjImTpyIzZs3Cx0aIUMOJV2C9vb2btcNE4lEdBURQgYIHV4gUCgUcHJy0twePnw4Xn31VQEjImTooqRL4O/vD3t7e81tFxcXjBgxQsCICBm6KOkSAMDMmTMBABYWFliwYIHA0RAydFHSJQCAJUuWwMXFBa6urpR0CRlA9EUaAQCMHTsWDg4OcHR0hEwmEzocQoYsoybdjRs3oqmpyZhTEp50dHTA3t4eMpkMb775ptDhcGpsbMSwYcOEDsOkjRgxAosXLxY6DKKHUZPuqVOnsGXLFmNOSXhSU1ODn376CW+99RZsbGyEDofTkiVLsG3bNqHDMGkrV66kpGvijJp0xWIxnnzySWNOSXhy8+ZNPProo3jmmWeEDkUnOzs7en/1wMKCjhiaOvoijRBCeERJl/RJRUUFIiMjDZ6HMYaMjAwjRNSpoaEBqampyMjIgFgshpOTE9LT0/WOaWxsxIwZM9DQ0MB5m6+xJ06cgJubG6RSKWJiYtDW1gYAyMrKQmJiIvbu3QsAOHjwIL777rse5yemjZIu6RN3d3ccOnTI4HmKi4tRU1NjhIg6E3hCQgIWLVoEhUKBlpYWnD9/Hhs3bgRjjHPM3bt3oVAocPbsWc7b+hhzLACoVCocP34c1dXVqK+vR35+PnJzc5GZmYnx48dj69atyMvLw5w5c5CdnY2rV6/2ZrMQE0VJl/TJqlWr4Ofnh5iYGDz33HMYO3Ys3N3dUVFRgTlz5sDT0xO2trYICAiASqUCAEyePBlvvPEGzp8/Dzs7OyiVSrz22mtITk5GbGyswceR8/Pz4erqqjmrrqOjAyqVClZWVhCJRJxjJBIJ9u3bh1GjRnHe1seYYwEgNjYW3t7esLW1hVQqhYuLC9RqNfz9/REfHw9/f3+0trYCAKKioujL6kGOki7pk5SUFADAsmXLMGbMGFy4cAGBgYH48ccfoVAoMHPmTNTW1iIgIAC7d+8GAKxZswYA4O3tjfj4eADA66+/jtWrV+PAgQMGf2QuLCzEuHHjNLc/++wzhISEDLouaeXl5bC3t4ePjw/CwsJQWFgIKysrnDlzBrNmzQIAjBs3DoWFhQJHSgxBSZcYxMLCAnK5HB0dHQAAqVQKsViM4OBgXL9+nZcYamtrIZFINLdlMhmOHj2KmJgYXtY3hurqamzfvh27du0CACQnJ2P+/PloaWlBXFwcNm3aBKBzT7m2tlbIUImBqL6EGJVarUZbWxvy8vIwZswYAJ2tIktLS9Ha2orbt29rnltfXw+1Wm1wmZNMJkNLS4vm9mBrS1laWopPPvkEKSkpmm1x584dVFZWoqCgAMXFxXBzcwMAtLS00BmDgx0zorCwMGNOR3hUWVnJEhISenxebGwsMzMzYwCYVCplp0+fZo6OjmzatGns1KlTzNramllaWrKQkBDW2NjIGGPsxo0bzNXVlY0YMYJ5eXmx6dOns5KSEiaTyVhwcDDz9fXtVYy63l9KpZIpFArN7dTUVJaamsoYY+zw4cMsNDRUa8ytW7eYj48PE4lEbOTIkSw7O7vb7by8PIPG6lr7wbF5eXksKSmJAdD8pKWlsZqaGpaYmMimT5/OFi9ezGpraxljjBUUFLClS5f2eRsJJTw8XOgQTM0HlHQJY6z3SVefU6dOsRUrVhgpIm263l8dHR0sLi6ONTc3az1WXV3NIiMj+7WeIWONMZ6LQqFgV65c0fm4qf0NUtLV8gEvx3Q9PDywYcOGbvfFxsbCz8+v13NERERo1YcqlUo4OzvD1tYW0dHRmvrG/mC9qBvtSy3m9evXYWNjg6Kiol6t2df6166xxqqbNYb169dj586dUCqVvK4rEomwbds27N69W6tELD09HStWrOjXvIaMNcb4Bx05cgRxcXF47LHHjDYn4R8vSffdd9+Fubl5t/tycnL6NAdXQlSpVDh27Biqqqpw69YtfP311/2Osae60b7UYgKd36CvXr0ae/bs6dWafa1/7RprrLpZYzh58iRu376NwMBA3td2cHCAQqHQKhFLSUnB7373u37NachYY4x/0OzZs036NG3SO7xVL7zzzjuwtraGv78/bt682e2x+vp6BAcHQyqVIigoCHV1dWhqasKsWbMgkUjg7OyMe/fuaZ7v5eWFxMREREVFYcKECWhra0NHRwc8PDx0rl9RUQEHBwfY2Nhg8+bNWvMnJCQgOTkZSUlJcHFxgbOzM6qqqrB06VI4OTlx1lfevHlT5x9BTU0NXn31VRw4cEBTY6lvza76VwCc6z8Yf1edq0gk0ozj2o5c9bSEEOHwlnQXLVqEpqYmBAQEIDMzs9tjmZmZ8PPzQ1VVFby9vZGZmYldu3ZBLpejvr4eR48ehZlZZ6iXL1/GunXrsGPHDgDAvn374OzsDB8fH8jlcp3ru7u7o6GhAUVFRfjHP/6hNf+bb76J1atXIz09HXv27MHMmTPh5uaGhQsXasXbZfjw4Zw1pt988w0mTJgAFxcXTJgwAQcPHgQAvWt21b8C4Fz/wfi76lzb29v1bkeuelpCiHB4KxmTSqWwtrZGSEgIjhw50u2x0tJSREZGQiqVIjQ0FLm5uWCMITw8HGKxGE8//TTKyspw8eJFJCQkaIrtAWDhwoV44YUXkJycjC1btug8hnb8+HG8/PLLqKysxLRp03DlypVu81dWVmqeO3v2bKxduxZlZWU4fPgw3nrrrT691uzsbGRmZmLu3LkAOs+Qmjt3rt4178e1/oPxc+Hajl0erKfl8tNPP2HZsmV9eq18KikpMen4TAHV8Jo+3pKuWq1Ga2srcnNzMXbs2G6PyeVyKJVKTJ06FceOHYNcLgdjDIcOHcKUKVPAGEN7eztGjx6NnJwchIWFwdPTE7m5uZg3bx6GDRsGc3NzvW+4/fv3Iy0tDb/5zW+watUqyOVyrfnvrxtdvnw5kpKSEBYWptnL7o2Wlhb8+uuvmi90rl27hvHjx6OsrEzvmvcTiURa6z8YPwCtsVzbsS88PT2RmJjYpzF8OnfunEnHZwp+/vlnoUMgPTFmLYS+OkpXV1dmZWXFQkNDWWNjo6bec/Pmzayuro4FBQUxGxsbFhQUxOrq6lhtbS0LDQ1lEomETZw4kcXGxjKJRMLKy8uZr68vc3V1ZXv27GEODg5MLBazwMBAVllZqTO27OxsJpFI2JQpU5i1tTXLy8vrNv/Zs2eZTCZjCxYsYIwxplarWUBAALt79y5jjLu+sqKiQqvGNCAggMlkMnbu3DnGGGN//etfmbm5OXNzc2Pnzp3TuWbX9uiqL31w/Qfjz8nJYTKZjAHQjOPajlFRUVr1tFyMUTI20EytHMoUmdo2opIxLR+IGNPRhqkfwsPDcfjwYWNNR3h08+ZN/OUvf9GchmqK6P3VM1PbRhEREfjyyy+FDsOUZA253gsqlQoikUjrp6vjFRk4rB89cvszhgv10yWDxZBLuk5OTmCMaf04OTkJHdqQ158eucboq8uony4ZRIZc0iXGx1X/q69HblJSEmdv3Z7G6Kt71of66ZLBhJIu6RFX/a++Hrnp6emcvXV7GqOr7rkn1E+XDCaUdEmPSktLERgYqKn/LS8v79U4vnrrUj9dMphQP13SI67635565ALavXUHqq8u9dMlg4oxC9BMrUaQ9J6+Ol2u+l99PXIXLFjA2Vu3pzFcdc/3o3661E93CKB+uqSTsU+OGIjeutRPl/rpDgH89NMlDx8+e+tSP10ymNAxXTIgTp48yet6Xf10H3R/97a+MmSsMcY/aPbs2UadjwiD9nQJIYRHlHQJIYRHRj28cO3aNSxevNiYUxKetLS04MKFC/jjH/9o8CXRB8qNGzfo/dWDpqYmoUMgPTDqX1deXl63y+qQwSUnJwfDhw9HUFCQ0KGQfrK2thY6BNIDoybdRx991JjTEZ45OjrCyckJI0eOFDoUQoYsOqZLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8oqRLCCE8Ms3rshBePfHEE2hqakJHRwdEIhHu3bsHb29vHD9+XOjQCBlyaE+XYN68eaiqqsLNmzdRWVmJ5uZmxMfHCx0WIUMSJV2CV155Ba6urprbMpkMERERAkZEyNBFSZfAzc0NLi4umttPPvkkJBKJgBERMnRR0iUAOvd2raysYGdnB4VCIXQ4hAxZlHQJACAuLg4uLi545JFHEBwcLHQ4hAxZVL1AAAAODg4YOXIkRo4cCQsLelsQMlCM/tcVHBwMGxsbY09LBlBbWxvq6+vR3t6OiooKhIeHCx2SluvXr2PkyJFCh2HSbGxssH//fqHDID0wetK1sbHB4cOHjT0tGUAlJSXYsWMHUlJSYG1tDZFIJHRIWsLDw+l91QNT/M+SaKPPkURDLBYLHQIhQx59kUYIITyipEt6raKiApGRkQbPwxhDRkaGESLq1NDQgNTUVGRkZEAsFsPJyQnp6el6xzQ2NmLGjBloaGjgvM3X2BMnTsDNzQ1SqRQxMTFoa2sDAGRlZSExMRF79+4FABw8eBDfffddj/MT00dJl/Sau7s7Dh06ZPA8xcXFqKmpMUJEnQk8ISEBixYtgkKhQEtLC86fP4+NGzeCMcY55u7du1AoFDh79iznbX2MORYAVCoVjh8/jurqatTX1yM/Px+5ubnIzMzE+PHjsXXrVuTl5WHOnDnIzs7G1atXe7NZiAmjpEt6bdWqVfDz80NMTAyee+45jB07Fu7u7qioqMCcOXPg6ekJW1tbBAQEQKVSAQAmT56MN954A+fPn4ednR2USiVee+01JCcnIzY2Fs8884xBMeXn58PV1RX29vYAgI6ODqhUKlhZWen8QlAikWDfvn0YNWoU5219jDkWAGJjY+Ht7Q1bW1tIpVK4uLhArVbD398f8fHx8Pf3R2trKwAgKioKW7Zs6XEdYtoo6ZJeS0lJAQAsW7YMY8aMwYULFxAYGIgff/wRCoUCM2fORG1tLQICArB7924AwJo1awAA3t7emiY6r7/+OlavXo0DBw4Y/JG5sLAQ48aN09z+7LPPEBISgs2bNxs0L9/Ky8thb28PHx8fhIWFobCwEFZWVjhz5gxmzZoFABg3bhwKCwsFjpQYipIu6TcLCwvI5XJ0dHQAAKRSKcRiMYKDg3H9+nVeYqitre3WJ0Imk+Ho0aOIiYnhZX1jqK6uxvbt27Fr1y4AQHJyMubPn4+WlhbExcVh06ZNADr3lGtra4UMlRgBlYwRo1Gr1Whra0NeXh7GjBkDABCJRCgtLUVraytu376teW59fT3UarXBZ7/JZDK0tLRoboeEhBg0H99KS0vxySefICUlRbMt7ty5g8rKShQUFKC4uBhubm4AgJaWFshkMiHDJcbAjCwsLMzYU5IBdvnyZfa///u/PT4vNjaWmZmZMQBMKpWy06dPM0dHRzZt2jR26tQpZm1tzSwtLVlISAhrbGxkjDF248YN5urqykaMGMG8vLzY9OnTWUlJCZPJZCw4OJj5+vr2KkZd7yulUskUCoXmdmpqKktNTWWMMXb48GEWGhqqNebWrVvMx8eHiUQiNnLkSJadnd3tdl5enkFjda394Ni8vDyWlJTEAGh+0tLSWE1NDUtMTGTTp09nixcvZrW1tYwxxgoKCtjSpUv7vI2EFB4eLnQIpuYDSrqk10lXn1OnTrEVK1YYKSJtut5XHR0dLC4ujjU3N2s9Vl1dzSIjI/u1niFjjTGei0KhYFeuXNH5uCn+7VHS1fIBb8d0PTw8sGHDhm73xcbGws/Pr9dzREREaNWJKpVKODs7w9bWFtHR0Zo6Rz7oqrHkolQqMWnSJNjZ2UEqlcLX13fA1l+8eDFEIhFEIhFsbGzg7e2NzMxMg9fTZ/369di5cyeUSuWArvMgkUiEbdu2Yffu3VolYunp6VixYkW/5jVkrDHGP+jIkSOIi4vDY489ZrQ5iUCMncZ1/W/7+eefs5SUlG73tbe39/rjJWOMlZaWsoiICK15f/jhB9bU1MSmTJnCTpw40feg+2n//v3s3Llz7Pbt2yw4OJgdO3aM83llZWXM0dGRffHFF6y5uZmpVCq2fPly1tHRMWDre3p6spqaGqZSqdinn37KHB0d2YEDBzjnMcae7kAzxb04U2OK24j2dLXwt6cLAO+88w6sra3h7++Pmzdvdnusvr4ewcHBkEqlCAoKQl1dHZqamjBr1ixIJBI4Ozvj3r17mud7eXkhMTERUVFRmDBhAtra2tDR0QEPDw+d6z84n0ql0lqTqwbVxcUFzs7OqKqqwtKlS+Hk5ASAu8by5s2bWrWnX3zxBaKjo/Hiiy9CKpVCJpNh06ZNEIlEnK+78NK/CAAAIABJREFUtzFwrf8gmUyGuXPnYsOGDcjKyjLk10cIMQJek+6iRYvQ1NSEgIAArY+7mZmZ8PPzQ1VVlebj8K5duyCXy1FfX4+jR4/CzKwz3MuXL2PdunXYsWMHAGDfvn1wdnaGj48P5HK5zvUfnC8rK0trTa4a1D179mDmzJlwc3PDwoULtWK/v8Zy+PDhWrWnN27c0BkX1+vuawz3r6+Lt7c36urqdD5OCOEHryVjUqkU1tbWCAkJwZEjR7o9VlpaisjISEilUoSGhiI3NxeMMYSHh0MsFuPpp59GWVkZLl68iISEBE3RPQAsXLgQL7zwApKTk7Flyxadx9KuXLnSbb7MzEytNbvcX4M6e/ZsrF27FmVlZTh8+DDeeustzfMerLHk4u7ujkuXLnE+xvW6+xJDb9YHgKtXr+rtR3v69GmTbg144cIFk47PFBjr1GoysHhNumq1Gq2trcjNzcXYsWO7PSaXy6FUKjF16lQcO3YMcrkcjDEcOnQIU6ZMAWMM7e3tGD16NHJychAWFgZPT0/k5uZi3rx5GDZsGMzNzfUWj8vl8m7zjRgxQmtNLiKRCMuXL0dSUhLCwsI0e9xcNZZcoqOjMWHCBAQHB+P5558H0LnXHR0dzfm6extDT+t3dHSgvb0dRUVF2LBhA95++22dMU6ZMgXvvvuuzseFRv10e0b/KQ0Sxj5KrK+e0tXVlVlZWbHQ0FDW2NioqfvcvHkzq6urY0FBQczGxoYFBQWxuro6Vltby0JDQ5lEImETJ05ksbGxTCKRsPLycubr68tcXV3Znj17mIODAxOLxSwwMJBVVlbqjO3B+a5du6a1ZlRUlFYNKmOMqdVqFhAQwO7evauZj6vGsqKigvPLwYKCAjZ58mQmlUqZWCxm48ePZ3V1dZyvu7cxcK3PGGPLly9nEomEmZmZMUtLS/bEE0+w999/X+d2oS/ShgZT3Eb0RZqWD0SM6WjF1E+0RzL4dF05gvZ0BzdT3EYRERH48ssvhQ7DlGQNyd4LKpVKU6d6/09X5ytifKwfPXL7M4YL9dMlg8mQTLpOTk5gjGn9dJV6EePrT49cY/TVZdRPlwwyQzLpEuPhqiPW1yM3KSmJs7duT2O46pt7g/rpksGGki7Ri6uOWF+P3PT0dM7euj2N4apv7g3qp0sGG0q6RK/S0lIEBgZq6ojLy8t7NY6v3rrUT5cMNtRPl+jFVUfcU49cQLu37kD11aV+umTQMXYRminWChL99NXpctUR6+uRu2DBAs7euj2N0VXf3IX66VI/3SGC+ukS458cMRC9damfLvXTHSL47TJGHg589talfrpksKFjusToTp48yet6Dg4OUCgUWvd3Xb24PwwZa4zxD5o9e7ZR5yPCoT1dQgjhESVdQgjhkdEPLwwbNoxazA0yarUazc3NePbZZ2FpaQmpVCp0SFpqamrofdWDxx9/XOgQSC8YPel+9NFHxp6S8GTXrl0YMWIEHT8kZADR4QVCCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERJV1CCOERXZiSYO/evWhsbMQPP/wAOzs7XL58Ge7u7oiOjhY6NEKGHEq6BHv37kVBQUG3+xYsWEBJl5ABQIcXCJKSkrpdF00mk3Fe0pwQYjhKugTPP/88ZDKZ5raDgwOefvppASMiZOiipEtgZWUFb29vzW26MCUhA4eSLgEAKBQKODg4wNXVFYmJiUKHQ8iQRV+kEQDAtGnTMGzYMNjY2OB//ud/hA6HkCFLk3RLSkpw48YNIWMhAhszZgweffRRKJVKoUMhAnr88ccx8v9j797DoirX/oF/BxRmYEQQEN0oMKZt2IJukzZYXQoIghqKhWBBbV9JX0RzyMw07S3bgslBwfJEalJpnnayJUlxJ6O0TbLtIaQ0RcTkJMPZ5DzP7w9/TIysGQYYZg1yf65rrqtZs57nudcC7xYz97pn5Ei+w3hsKZPuli1bMHjwYFhZWfEZD+GRq6srLCwscPHixW6Nz8nJga2tLUaNGqXjyHSjoqICFy9ehJ+fH9+hGKyCggI4ODjgrbfe4juUx5bK2wsRERFwcnLiKRTS123ZsgV/+ctf4Ovry3conK5fv46mpiYsX76c71AMVnZ2Ns6fP893GI81+iCNEEL0iJIuIYToESVdoldFRUUICgrq8TyMMSQnJ+sgoj9UV1cjLi4OycnJEAqFsLGxQVJSktr9a2pqEBAQgOrqao3b9DH222+/xbBhwyAWixESEoKmpiYAwK5duxAVFYVPP/0UAHD06FFcuHBB4/ykd1HSJXplb2+PtLS0Hs+Tl5eH8vJyHUT0EGMMixYtQmRkJKRSKRoaGpCbm4sNGzaAMdZh//r6ekilUly+fFnjNi66HgsAcrkcp06dQllZGaqqqnDmzBmkp6cjJSUF48aNw0cffYSMjAzMmTMHqampuHXrljanhfQCSrpEr1auXAl3d3cAQEhICKZOnQpnZ2fY29ujqKgIc+bMgaOjI8zNzeHp6Qm5XI7nnnsOq1atQm5uLgYNGgSZTIbFixcjJiYG0dHRKCkpwdNPP92juM6cOQM7OztYWFgAABQKBeRyOUxMTCAQCDrsLxKJsHfvXpUPnrm2cdH1WAAIDQ2Fm5sbzM3NIRaLMXToULS0tMDDwwMRERHw8PBAY2MjACA4OBibN2/WuA7pPZR0iV7FxsYq/3v58uUYM2YMrl69Ci8vL1y5cgVSqRTTp09HRUUFPD09sXv3bqxduxYA4ObmhoiICADA22+/jTVr1iApKQnDhw/v8Z/MOTk5cHFxUT4/dOgQ/Pz8kJiY2KN59a2wsBAWFhYYP348AgMDkZOTAxMTE5w/fx4zZswAALi4uCAnJ4fnSPsvSrqEdwMGDIBEIoFCoQAAiMViCIVC+Pr64rffftNLDBUVFRCJRMrn1tbWOHnyJEJCQvSyvi6UlZVh69at2LlzJwAgJiYG4eHhaGhoQFhYGBISEgA8vFquqKjgM9R+jW4DJganpaUFTU1NyMjIwJgxYyAQCFBQUIDGxkbcv39fuV9VVRVaWlowYEDPf42tra3R0NCgfN7XbqAoKCjA/v37ERsbqzwfDx48QHFxMbKzs5GXl4dhw4YBABoaGlS6yhH9oitdolfh4eG4dOkS4uPjkZiYiH379iE7Oxvbtm3Dpk2bAAA7duyAWCzGzZs3ERERAVdXV5w5cwajR49GTk4ONmzYABcXFxw8eBAREREoLi5Wvk/cXZ6enrh+/bryeXx8POLj4wEA6enpCAgIUNm/vLwcf/3rX/HDDz9g3Lhx+Oabbzi36WMs8PDGlLVr12LgwIEQCARISEjAW2+9hQEDBiA+Ph7m5ubKu8x+/fVXTJo0qUfni/QA+/9ef/11VlBQwAjpruTkZHbq1KkezZGVlcVWrFiho4hUXbt2Te3cCoWChYWFsbq6ug6vlZWVsaCgoG6tyddYTaRSKcvPz+d87ezZsywuLk5na82aNUtncz0mPun0StfBwQHr16/vsD00NLRLVxezZ8/uUJ8pk8lga2sLc3NzzJ07V1lbqG0MXNTVKz5qyZIlEAgEEAgEEAqFeOqpp/Djjz9y7ss01IR6eHggMjKSc+4lS5YAeFjeZGNjg9GjR2t1DF3R/ufQlRrY9sekq9pZXfjggw+wfft2vTfdEQgE+Pjjj7F79+4OJWJJSUlYsWJFt+bla6w6x48fR1hYmMH2x+gPOk26mzZtgrGxcYft+/bt69JCXElLLpcjMzMTpaWluHfvHr777rsuxcCFq16Ry9atW+Ho6Ijy8nJUVlbC29sb27Zt49xXU01ocnJyh5KirVu3wtXVFdu3b8e5c+cwduxYJCUl4euvv9bqGLqi/c+hKzWw7Y9JV7WzunD69Gncv38fXl5eel/b0tISUqm0w88zNjYWzz77bLfm5GusOjNnzuxxeR3pGa3e0924cSNMTU3h4eGBkpISldeqqqrg6+sLsVgMHx8fVFZWora2FjNmzIBIJIKtrS1aW1tVxri6uiIqKgrBwcGYMGECmpqaoFAo4ODgoHUMXDWeAHe9ojZ1nI2Njfjb3/4G4OGVn6WlJczMzJCYmKhSE8p1bL/88gtcXFwwfPhw3LlzBwAwa9YsTJ8+HQsXLlS52uY6X7Nnz4a3tzcmTpyIM2fOICgoCIMGDYKZmRnMzc3h4OAAExMTeHt7d4itvfY1sCtWrFBeyY8dO1bjMbWN44pN3XkmhHSPVh/7RkZGYt26dVi5ciVSUlLw3nvvKV9LSUmBu7s70tLSsGbNGqSkpMDY2BgSiQRfffUVfv75ZxgZ/ZHbb9y4gXXr1uHFF18EAOzduxcLFixAVFQUJBKJ1jEsX74ce/fuxcmTJ/H3v/8dV65cgb29vXL/9vWKANTWcdra2gJ42Es2NDQUwMMrv+rqaly/fh1Lly7F22+/jSlTpmD9+vWIj49XObbGxkY4Ozvj22+/xWuvvYbLly/DwcEBAoEAn332GSZMmIAPP/xQ+ecc1/lavXo1tmzZgj179uDOnTtYu3YtzMzMsGfPHkRGRmLq1KmYPXs2JkyYAFtbW5XY2n+BZGxsLDw9PQEACQkJSEhIwGuvvYaAgACNx9TS0gJPT0/O2Do7z+21trbixx9/7PA/WUNx9+5dFBYW4uTJk3yHYrCuXr2KlpYWvsN4rGmVdMViMUxNTeHn54fjx4+rvFZQUICgoCCIxWL4+/sjPT0djDHMmjVL+V5pm2vXrmHRokXKYncAmD9/Pl544QXExMRg8+bNat/DUhfDozWeQMd6RU3Ky8sxePBg5ObmYsaMGcjLy8PFixexYMECFBcXw9vbW2X//Px8lWNra4NnbGwMR0dHlTisra1x4MABTJs2DdHR0XB3d+c8XwBgYWEBiUQCiUSCH3/8EWZmZhAKhXBycsKgQYNgYWEBW1tbnDx5ElFRUZyxPWr79u2ws7NDcHAwTp06pfaY2qiLTd15fpRCoUBxcTEGDRqk+aTzpKysDLW1tbh58ybfoRisoqIiKifrZVol3ZaWFjQ2NiI9PR3Ozs4qr0kkEshkMkyZMgWZmZmQSCRgjCEtLQ2TJ08GYwxCoRAAMHr0aOzbtw+BgYFwdHREeno6Xn75ZQwePBjGxsYaC7Y1xdAeV72iJgqFAsbGxjA2NkZ9fT2am5tx4MABxMfH48knn8TKlSsB/FETKpFIVI6tublZ4/zPPPMM3nvvPbz77rsIDw/nPF9dwRUbl3PnziEzMxNHjhzBiRMnkJCQoPaY2vQ0toEDB2LWrFkG3U+3vr5e+QEn6Yj66epBWx2DupIxmUzG7OzsmImJCfP392c1NTWMMcZCQ0OZkZERW7t2LfPx8WFmZmbMx8eHVVZWsoqKCubv789EIhGbNGkSq6urYxEREUwkErHCwkI2ceJEZmdnx/bs2cMsLS2ZUChkXl5erLi4mLPGgiuG4OBgJhaL2dmzZ5mVlRXz9vZmjDEWHR3NACgf8fHxrKioiE2cOFFlzqVLlyr3MTIyYiNGjGDJycmMMcZSU1OZSCRikydPZqampmzfvn3M2tqavfrqqx2OzcfHh4nFYvaf//yHDRkyhD3zzDNs6dKlzNTUlO3atYsx9rAc6fnnn2e//PILq6ys7HC+Zs2axUQiEcvMzGSMMTZ79mxmZmbGDh8+zKysrJiTkxP74osvmLGxMRs5cqRKbE5OTszIyIglJiYqfyZxcXFs2rRpKudhy5Ytao+pbdzq1as7xKbuPHPRRclYb9JUMkYeopKxXveJgLGH9THLli3D8uXL6ZsjSLf1hW+O2LVrl/KmB9JR25Wurr6uZ/bs2fjXv/6lk7keE7sM6o40uVyu/MS9/UMul/MdGtET1o0+ud0Zw4X66RJ9MKika2NjA8ZYh4eNjQ3foRE96U6fXF301mXUT5foiUElXfJ44qr/BaCxT65AIOjQV7ezMT3prUv9dIm+UNIlva6t/re0tBRubm5ISUkBAI19crOysjr01e1sTE9661I/XaIvlHRJrysoKICXl5ey/rewsFCrcfrsq0v9dIm+UD9d0uvU1f921if30b662ozpbm9d6qdL9IWudEmvW7RoES5cuAAbGxvk5uZi4cKFANBpn9xH++pqM6a7vXWpny7Rm7aKXeqnS3pKlzdH9EZfXeqn+xD10+VV5/10CeGDvvvqUj9doi/0ni4xSKdPn9b7mm39dB/V/huMu4qvserMnDlT53Oqo6vvr3vc0JUuIaRX1NbWGmzHOT6p/G/o3//+N4YOHcpXLKSPu3r1KmpqavDgwQO+Q+FUVFSE/Px8HDt2jO9QDNbPP/+s9be0dKaurk55swn5gzLpvvTSS8jLy8O9e/f4jIcYgNbWVnz55ZcIDw/v0jh3d3cIBAKD/R0aMGAA/P39DTY+Q2BjYwMPDw+dzFVXV0dXuhyUSXfSpElURkKUvv76a7z22mt8h0H6MHp7gRu9p0vUevRTfEK6gq50uVHSJZz+9Kc/dfgSUkK6gpIuN0q6hNOYMWNw48YNvsMgfVhtbS19kMaBki7hREmX9BRd6XKjpEs4UdIlPVVZWYkhQ4bwHYbBoaRLOD3xxBP07QKkR4qLi/GnP/2J7zAMDiVdwsnU1FSl1SEhXXXv3j3Y2tryHYbBoaRL1BIIBFAoFHyHQfooxhiMjCjFPIrOCFFrxIgRKCoq4jsMQh4rlHSJWvRhGumu+/fvQywW8x2GQaKkS9SipEu668aNGxg9ejTfYRgkSrpELUq6pLso6apHSZeoJZFIUFBQwHcYpA+6ceOG8stEiSpKukQtExMTNDU18R0G6YN++eUXODs78x2GQaKkSzQyMjJCa2sr32GQPqaqqgpWVlZ8h2GQKOkSjRwcHPDbb78BABobG3mOhvQFv//+O1UuaEDfGkc4HT16FN9//z1ycnIQEBCA+vp6/PnPf0ZmZibfoREDd/nyZYwfP57vMAwWJV3C6fPPP0daWppKI/N58+bxGBHpKy5cuAB3d3e+wzBY9PYC4fTxxx9j+PDhyufm5uaYOnUqjxGRvuK7776jr/7SgJIu4fSnP/0JU6dOVd47P3jwYEyYMIHnqIihUygUaGxspD66GlDSJWpt2rQJw4YNAwCYmZlRxyjSqStXrsDNzY3vMAwaJV2ilo2NDYKCgjBgwADY2NjwHQ7pA/7973/Dx8eH7zAMGiVdolFMTAzEYjH+9re/8R0K6QOysrIwefJkvsMwaHqtXvjyyy/x73//W59LEh1wdHTEzz//jDlz5hj016/U1dVBKBRi4MCBfIdi0OLi4mBtba3zecvKymBjYwMTExOdz/040WvS/f7777FkyRKMHDlSn8uSHnrw4AEaGhoglUrx4Ycf8h2OWomJifDx8aEP/DR45513UFdX1ytJ91//+hcCAwN1Pu/jRu91ukOGDKEPZPooExMTg/7ZmZubw8rKyqBj5JtIJOq1uTMyMvD555/32vyPC3pPlxDSY0VFRbC0tKRSMS1Q0iU6VVRUhKCgoB7PwxhDcnKyDiJ6qLq6GnFxcWCMYcuWLRAKhbCxsUFSUpLaMTU1NQgICEB1dbXGbb099ttvv8WwYcMgFosREhKi0vlt165diIqKwqeffgrg4e3bFy5c0Dh/b0hNTcUrr7yi93X7Ikq6RKfs7e2RlpbW43ny8vJQXl6ug4geJvBFixYhMjISAoEAy5YtQ0NDA3Jzc7FhwwaVW53b1NfXQyqV4vLlyxq3cdH1WLlcjlOnTqGsrAxVVVU4c+YMACA9PR0pKSkYN24cPvroI2RkZGDOnDlITU3FrVu3tDo3upKVlQUvLy+9rtlXUdIlOrVy5UrlffchISGYOnUqnJ2dYW9vj6KiIsyZMweOjo4wNzeHp6cn5HI5nnvuOaxatQq5ubkYNGgQZDIZFi9ejJiYGERHR6OkpARPP/10t2M6c+YM7OzsYGFhodymUCggl8thYmICgUDQYYxIJMLevXvh5OSkcRsXXY8NDQ2Fm5sbzM3NIRaLMXToUABAS0sLPDw8EBERAQ8PD2UXuODgYGzevFnjOrokk8nw7LPPwtjYWG9r9mWUdIlOxcbGKv97+fLlGDNmDK5evQovLy9cuXIFUqkU06dPR0VFBTw9PbF7926sXbsWAODm5oaIiAgAwNtvv401a9YgKSkJw4cP79GfzDk5OXBxcVHZdujQIfj5+SExMbHb8+pbYWEhLCwslB28AgMDkZOTAxMTE5w/fx4zZswAALi4uCAnJ0dvcW3btg2RkZF6W6+vo6RLet2AAQMgkUigUCgAAGKxGEKhEL6+vspevb2poqKiw6f21tbWOHnyJEJCQnp9fV0oKyvD1q1bsXPnTuW2mJgYhIeHo6GhAWFhYUhISADw8Gq5oqJCL3EVFBRg0KBBytvFSeeotSPRu5aWFjQ1NSEjIwNjxoyBQCBAQUEBGhsbcf/+feV+VVVVaGlpwYABPfs1tba2RkNDg8o2Pz+/Hs2pTwUFBdi/fz9iY2NVzsWDBw9QXFyM7Oxs5OXlKRNfQ0NDr9ThcklOTsbixYv1stbjgq50iU6Fh4fj0qVLiI+PR2JiIvbt24fs7Gxs27YNmzZtAgDs2LEDYrEYN2/eREREBFxdXXHmzBmMHj0aOTk52LBhA1xcXHDw4EFERESguLi4R/1ZPT09cf36dZVt8fHxiI+PB/DwA6mAgACV18vLy/HXv/4VP/zwA8aNG4dvvvmGc5s+xm7ZsgVr167FwIEDIRAIlFe0b731FgYMGID4+HiYm5vjrbfeAgD8+uuvemmtWFFRgVu3blHv3K5ievT666+zgoICfS5JdCgwMLDHc2RlZbEVK1boIJqOPvjgA/bDDz902K5QKFhYWBirq6vjHFdWVsaCgoK6tSZfYzWRSqUsPz+f8zVd/htcs2YNO336tE7m6kc+MYgrXQcHB6xfv77D9tDQ0C79X3T27NkdakRlMhlsbW1hbm6OuXPndvvbbVkndaOaaim5/PbbbzAzM8OlS5e0Wrer9a/t49VV7awufPDBB9i+fTtkMpne1hQIBPj444+xe/duzvKwpKQkrFixoltz8zVWnePHjyMsLAyjRo3S6byPqqmpwaVLl+Dt7d2r6zyODCLpbtq0ibPcZN++fV2ahyspyuVyZGZmorS0FPfu3cN3333XrRg7qxtVV0upzqFDh7BmzRrs2bNHq3W7Wv/aPl5d1c7qwunTp3H//n2913RaWlpCKpVylofFxsbi2Wef7da8fI1VZ+bMmT0qr9PW5s2bER0d3evrPI4MIukCwMaNG2FqagoPDw+UlJSovFZVVQVfX1+IxWL4+PigsrIStbW1mDFjBkQiEWxtbTt8TbirqyuioqIQHByMCRMmoKmpCQqFAg4ODpzrt93GaGZmhsTExA7zL1q0SFk3OnToUNja2qK0tBTLli2DjY0NZy2lpvrS8vJyLFy4EAcPHlTWV3IdU1u9qkAgUF71c63/aPzt61zbame5ziPAXU9LiDoVFRW4cOFCn/ow0pAYTNKNjIxEbW0tPD09kZKSovJaSkoK3N3dUVpaCjc3N6SkpGDnzp2QSCSoqqrCyZMnlV8rAwA3btzAunXrsG3bNgDA3r17YWtri/Hjx0MikXCub29vj+rqaly6dAknTpzoMP/q1auVdaN79uzB9OnTMWzYMMyfP18l3va1lOrqS8+dO4cJEyZg6NChmDBhAo4ePQoAnMfUVq/a3NysHM+1/qPxt69zbaud5TqPAHc9LSHq/OMf/8Dq1av5DqPPMpiSMbFYDFNTU/j5+eH48eMqrxUUFCAoKAhisRj+/v5IT08HYwyzZs2CUCjEU089pdz32rVrWLRokbLgHgDmz5+PF154ATExMdi8eTPn+2inTp3CggULUFxcDG9vb+Tn56vMX1xcrNx35syZeO+993D79m0cO3YM//d//weAu5aSS2pqKlJSUpTfrqtQKDBv3rwOa6rDtf6j8XPhOo/tPVpP+6jy8nKEh4drPDY+3bhxA+fOndNbuVRfdPPmzR6NLygowL179/Dcc8/pKKL+x2CSbktLCxobG5Geng5nZ2eV1yQSCWQyGaZMmYLMzExIJBIwxpCWlobJkyeDMQahUAgAGD16NPbt24fAwEA4OjoiPT0dL7/8MgYPHgxjY2O1ReMHDhxAfHw8nnzySaxcuRISiURl/ubmZpW60TfffBPR0dEIDAyEkZGR2lrKRzU0NOD3339XfqBz584djBs3Drdv3+6wZtsxta3bRiAQdFj/0fi5xnGdx66wtrZW/vVgiOLj4zF16lSN/8Pq71atWtWj8WvWrMG7776ro2j6KX3WSqgrV5HJZMzOzo6ZmJgwf39/VlNTwxhjLDQ0lBkZGbG1a9cyHx8fZmZmxnx8fFhlZSWrqKhg/v7+TCQSsUmTJrG6ujoWERHBRCIRKywsZBMnTmR2dnZsz549zNLSkgmFQubl5cWKi4s5Y0tNTWUikYhNnjyZmZqasoyMDJX5L1++zKytrdmrr77KGGOspaWFeXp6svr6esYYY9HR0QyA8hEfH8+KiorYxIkTVdbx9PRk1tbW7KeffmKMMfaPf/yDGRsbs2HDhrGffvqpwzHdvHmTWVtbMwDMyMiIxcXFca7/aPz79u1Txtt2HlevXt3hPDLGWHBwMBOLxezs2bPMysqKeXt7c54jXZSM9SZ1JWPkDz0pGfv+++/Z0qVLdRtQ//OJQSRd0jdQ0u37uvtvsLW1lQUEBLDy8nLdB9W/GEadrj7J5XIIBIIOD7lczndo/QLrRp/c7ox5FPXT7ZlPPvkEs2fPpm+F1oF+l3RtbGzAGOvwoF8m/ehOn9ye9tZl1E+3R6qqqvDVV19h4cKFOpuzP+t3SZfoHlf9L1ePXAAqdcfKzTATAAAgAElEQVTa9NVtPyY0NLRbhf/UT7dn3n33Xbz//vvUL1dHKOmSHuOq/+XqkQv80Sc3KytLq7667cccPHiwW386Uz/d7jt//jyampr00kCnv6CkS3qsoKAAXl5eyvrfwsJCrcbpq68u9dPtnqamJrz77rvYuHFjj+cifzCYOl3Sd3HV/6rrkQv8UT+sbV/d9mO601uX+ul2z/r167F48WJYWVn1eC7Sjj5rJahkrG9TVzJWWVnZof737t27zM7Ojo0YMYK5urqyadOmMcaYsu7YwcGBmZqasoEDBzI/Pz9WU1PT6RhfX98Odc/tqSsZk8lkTCqVqmyLi4tT1jwfO3aM+fv7q7x+7949Nn78eCYQCNjIkSNZRkYG5zZ9jOWqAWeMsfLychYVFcWmTZvGlixZwioqKhhjjGVnZ7Nly5ZxniNt/w3+9NNP7OWXX+50P9JlVKdLtKfLOt3e6KtL/XQf6mk/3ZaWFubv789KS0t1Hhvph3W6xDDos68u9dPtmk2bNuGll16CnZ2dDiMjbeg9XcKL06dP63W9tn66XNp/g3FX8TVWnZkzZ/ZofEFBAb777juD6b/8OKIrXUIIgIc3kURHR2Pz5s2ctctENyjpEkIAPKy3njp1aq9/1U9/p/e3F5YsWQIzMzN9L0t0oKCgAHPnztXJXHV1dRg4cKCyfaUu3L17F+fOnYNYLNbZnI+by5cvY/ny5R22FxUVIS0trUMva6J7Asb1yUIvaW1tVenvSvqv/Px8SKVSJCYm4s9//jPf4fQrpqamKs8VCgXmzJmDjRs3duhlTXRul16TLiHtVVRUYP78+VixYgWmTJnCdzj91saNG2FtbY3XXnuN71D6g130ni7hjbW1NQ4cOICPP/4Y+/fv5zucfunChQu4cuUKJVw9oitdwrvW1la88cYbGDJkCN5//32+w+k35HI5QkNDceTIEbrVV3/oSpfwz9jYGFu2bIGVlRUWLVpE7/vrQWtrKxYuXIiEhARKuHpGSZcYDKlUioCAAAQHB6O2tpbvcB5rb7/9NkJDQzFhwgS+Q+l3KOkSg/LCCy9g1apVeOGFF3D37l2+w3ksffLJJzAzM8O8efP4DqVfoqRLDI6npyd27tyJiIgI/PLLL3yH81jJyMjA2bNnsW7dOr5D6bco6RKD9MQTT2D//v1YuXKlXpri9Ac5OTnYsWMHdu/eTbf58oiSLjFY1tbWOHz4MHbv3o0vvviC73D6tNzcXLzzzjv47LPPYGJiwnc4/RolXWLQhEIh9u7diwsXLlA5WTfl5+dj+fLlOHToECwtLfkOp9+jpEsMnrGxMZKTk2FlZYWFCxeiubmZ75D6jGvXruF///d/8dlnn+nkK3xIz1HSJX2GVCrFjBkzqKRMS3l5eXj99dfx+eefY/jw4XyHQ/4/SrqkT5kzZw7eeecdKinrxI8//og333wTBw4coIRrYCjpkj7Hw8MDKSkpePXVV3H58mW+wzE4bSVhhw4dorcUDBAlXdInjRo1CkeOHME777yDEydO8B2Owfjmm2+QnJyMQ4cOwcLCgu9wCAdKuqTPGjJkCI4ePYr9+/fj888/5zsc3u3atQv79+/Hl19+CZFIxHc4RA1KuqRPMzU1RWpqKm7dutVvS8oYY3j//fdx9epVpKamUh2ugaOkS/o8gUCA9957D1ZWVnjttdeUJWV5eXk4fPgwz9HpVmtrK0pLS5XP79+/j3nz5mHUqFFISkqCkRH9kzZ01E+XPFbS0tLw6aefIi4uDgEBAQCAn3/++bH5c3vt2rU4evQoLl++jHv37uF//ud/sHbtWkyePJnv0Ih26Ot6yONHJpPhlVdewd27d2FiYoKlS5ciMTGR77B67Pr16/Dx8UFlZSVmzJiB2tpafPLJJ3BycuI7NKI9amJOHi+tra147733UFJSAgBoamrCwYMH8dtvv/EcWc8oFAqEhoaiuLgYDQ0NkMlkeOmllyjh9kGUdMlj5auvvsLVq1dVumgVFxcjKiqKx6h6bt26dbh586byeWVlJd555x0UFBTwGBXpDkq65LEyd+5c5OfnY8OGDXjyySdhaWkJxhh++OEHXLhwge/wuuXXX3/FJ598gt9//x0AIBaL4eDgAFdXV+UVPek76D1d8thijCErKwuxsbH46aefYGNjg59//pnvsLpEoVDA2dkZJSUlsLS0xF/+8hcsXboU/v7+VBrWN9EHabr08ssvo66url80iC4sLMTIkSMNtkSpsrISRkZGylaGTU1NuHPnDmxtbTF48GCeo9NecXExysrKMHLkSFhbW/eL3y1DcuPGDXz11VdwcXHR1ZS7BuhqJgI0NzfjyJEjMDU15TuUXhceHo5t27YZ7K2mn3/+OYRCIebOnct3KKQPW716NRQKhU7nNMzLFEIIeUxR0iWEED2ipEt6TVFREYKCgno8D2MMycnJOojoIZlMBoFAAKFQCACorq5GXFwckpOTIRQKYWNjg6SkJLXja2pqEBAQgOrqao3b9DH222+/xbBhwyAWixESEoKmpiYAD5vfREVF4dNPPwUAHD16tNPqja6eh944nu6O1fY8AKrnIjw8HAKBAJGRkRrX0yVKuqTX2NvbIy0trcfz5OXloby8XAcR/WHJkiUoLy8HYwyLFi1CZGQkpFIpGhoakJubiw0bNoDrM+b6+npIpVKVPr5c27joeiwAyOVynDp1CmVlZaiqqsKZM2eQnp6OlJQUjBs3Dh999BEyMjIwZ84cZWMgLl09D4Z2LrQ9DwBUzsWnn36q99aglHRJr1m5ciXc3d0BACEhIZg6dSqcnZ1hb2+PoqIizJkzB46OjjA3N4enpyfkcjmee+45rFq1Crm5uRg0aBBkMhkWL16MmJgYREdHo6SkBE8//XSPY7O0tMSgQYNw5swZ2NnZKT8QVCgUkMvlMDEx4awUEIlE2Lt3r8qdYFzbuOh6LACEhobCzc0N5ubmEIvFGDp0KFpaWuDh4YGIiAh4eHigsbERABAcHIzNmzdzzt/V82Bo56Ir56H9uRg4cCDs7Ow0rqdrlHRJr4mNjVX+9/LlyzFmzBhcvXoVXl5euHLlCqRSKaZPn46Kigp4enpi9+7dWLt2LQDAzc0NERERAIC3334ba9asQVJSEoYPH67TmxxycnJUyoEOHToEPz+/PterobCwEBYWFhg/fjwCAwORk5MDExMTnD9/HjNmzAAAuLi4ICcnh3N8fzoPgOZz0dso6RK9GjBgACQSibIMRywWQygUwtfXl5f+CBUVFSodyKytrXHy5EmEhIToPZbuKisrw9atW7Fz504AQExMDMLDw9HQ0ICwsDAkJCQAeHiFWFFRwTlHfzoPgOZz0duoTpfwqqWlBU1NTcjIyMCYMWMgEAhQUFCAxsZG3L9/X7lfVVUVWlpaMGCAbn9lra2t0dDQoHzu5+en0/l7W0FBAfbv34/Y2FjluXnw4AGKi4uRnZ2NvLw8DBs2DADQ0NCg9jvT+tN5ADSfi17HiM4EBwezhoYGvsPQi7CwMFZTU6Nxn9DQUGZkZMTi4uJYcHAwE4vF7OzZs8zKyop5e3uzrKwsZmpqygYOHMj8/PxYTU0Nu3v3LrOzs2MjRoxgrq6ubNq0aezmzZvM2tqavfrqq6yoqIhNnDix0/g+++wzdujQIc7XsrKy2NKlS1ldXR2TyWRMKpUqX4uLi2NxcXGMMcaOHTvG/P39Vcbeu3ePjR8/ngkEAjZy5EiWkZHBuU0fYxljLDo6mgFQPuLj41l5eTmLiopi06ZNY0uWLGEVFRWMMcays7PZsmXLOOfXdB76wrnoynlofy6amprYiRMn2P/+7/8++mvCGGNs1apV7OrVq5yvddMnlHR1iJJu12RlZbEVK1boKCJVnSVdAMzU1JQpFAoWFhbG6urqOuxXVlbGgoKCurU+X2M1kUqlLD8/n3N+TeehpzEZ8rkICwtjACjp9lVdTbqXLl1iFhYW7OjRo1ptb2/r1q1s6NChzMbGhv3nP//RuM6dO3eYSCRiFy9e1CoGbdbXRdL19vZm5ubmLCsrq0fzcNGUdB9VVVXFkpKSmEKhUNm+evVq9t1333Vrfb7GqvP111+zH374QeP86s5DT2My5HPRGUq6Bq47V7rvv/8+Z3JTt50xxq5du8ZcXV3Z3bt32ffff8/S09M1rpGQkMDWr1/Pli5dqnUMmtZnTDdJtzd1JekSok5vJF2qXtCDoqIiWFpawszMDImJiaioqICPjw+EQiHi4uKU+6nb/qgTJ05g4cKFsLe3h6enJ55//nmN9avl5eVYuHAhDh48qKxV5FpL2/UJId1HSVcP7O3tUV1djUuXLuHEiRNISUmBm5sbamtr8cYbbyj3U7f9USUlJUhISIC5uTmcnJyQk5Ojtn713LlzmDBhAoYOHYoJEybg6NGjatfSdn1CSPdRyZgenDp1CgsWLEBxcTG8vb2Rn5+Pl156CSYmJiolUOq2P8rKygpLlixBdHQ0vvzyS3z00Ufw8PDg3Dc1NRUpKSmYN28egId3Gs2bN49zLW3XBx5eFc+bN0/nJVy6UlxcDOBhi0dCuqu4uBjh4eE6ndMw/8U8Zg4cOID4+Hg8+eSTWLlyJYYPH45Tp07B09MTt27dwl//+lcAULv9UVOmTMHGjRvR3NyM5uZmiMVizv0aGhrw+++/K++dv3PnDsaNG4fbt29zrqXt+sDDuk7qp0sed6tXr9b5nPT2gh5MmTIFCxYswBtvvIHvvvsO48ePx4kTJzB8+HD897//RXx8PABg0aJFnNsf5enpCWdnZ4wcORJbtmzBG2+8geLiYmWfgzbe3t44ceIEcnNzAQCfffYZ7t+/j0mTJmHmzJkd1tJ2fUJI99HX9ejQ3Llz8cUXX9A3RxgAutIlurB69WqEh4dj7NixuppyF13pGjC5XA6BQNDhIZfL+Q5NJ1g3+uR2Z0xfoM+evlz79mSsLnv69geUdA2YjY0NGGMdHjY2NnyHphPd6ZPbG711+cb02NOXa9+ejAV019O3v6CkS3SqqqoKvr6+EIvF8PHxQWVlJWePXADKPrkCgUCrvrrtx4SGhuqkr64h0GdPX659ezIW0F1P3/6Cki7RqZSUFLi7u6O0tBRubm5ISUnh7JEL/NEnNysrS6u+uu3HHDx48LH5U7U/9bLls4+toaCkS3SqoKAAXl5eEIvF8Pf3R2FhoVbj+O6ry6f+1MuWzz62hoLqdIlOSSQSyGQyTJkyBZmZmZBIJGp75AJ/9MnVtq9u+zGGemNGV/WnXra89rE1FLrs5NDfUWtHxiorK5mPjw8zMzNjPj4+rLKykrNHLmNM2SfXwcFBq7667cf4+vpq7Kvblxre6LOnL9f41NTUbo/tai/btj62fQV1GTNwlHS7pzf66valpNsfe/r2FdRljDyWPvjgA2zfvl1ZodDfCAQCfPzxx9i9e3eHErGkpCSsWLGiW/PyNVad48ePIywsDKNGjdLpvH3N4/GmGOnTTp8+zXcIvLO0tIRUKu2wvf03KncVX2PVmTlzps7n7IvoSpcQQvSIrnR1rO0WyMdda2srGhsblUXvhqalpQXNzc0GGx/pG1pbW3U+JyVdHZo0aRIWLFjAdxh6UVVVhcWLF3PeMWUIHjx4AIFAoGzarkvNzc0oKSmBg4ODzucmhmfw4ME6nY+6jBHSRSUlJVi3bh127NjBdyik76EuY4QQok+UdAkhRI8o6RJCiB5R0iWEED2ipEsIIXpESZcQQvSIki4hhOgRJV1CCNEjSrqEEKJHlHQJIUSPKOkSQogeUdIlhBA9oqRLCCF6REmXEEL0iJIuIYToESVdQgjRI2piToiWvvnmG8hkMtTX1+Py5cuYNGkSTExMsHr1apiZmfEdHukbdlHSJURLBw8exPz589HQ0KDc5uTkhIKCAh6jIn0MfXMEIdqaPXs2hg4dqnwuEAgQEhLCY0SkL6KkS4iWhEIhXFxclM/t7OwQGRnJY0SkL6KkS0gXREdHK78d1tbWFhKJhOeISF9DSZeQLvDz84OVlRWMjY3x0ksv8R0O6YMo6RLSBcbGxnj66acxaNAgLFiwgO9wSB80gO8A+qsrV67g119/5TsM0g3jxo3DxYsXcfbsWb5DId00Z84cDBjAT/qjpMuT1NRU2Nvbq3waTgxfcnIyli1bhrlz56qUjhmKAwcOwMfHh36vNNixYwf8/f1hYWHBy/qUdHk0e/ZsjB49mu8wSBccPnwYr776Kt9hqHXp0iX6verEyZMneV2f3tMlhBA9oqRLCCF6REmXEB0rKipCUFBQj+dhjCE5OVkHET1UXV2NuLg4JCcnQygUwsbGBklJSRrH1NTUICAgANXV1Rq39fbYb7/9FsOGDYNYLEZISAiampoAALt27UJUVBQ+/fRT5dijR4/iwoULGufnEyVdQnTM3t4eaWlpPZ4nLy8P5eXlOojoYQJftGgRIiMjIZVK0dDQgNzcXGzYsAHq2q/U19dDKpXi8uXLGrfpY6xcLsepU6dQVlaGqqoqnDlzBunp6UhJScG4cePw0UcfISMjA8DDyoTU1FTcunWr0/PCB0q6hOjYypUr4e7ujpCQEEydOhXOzs6wt7dHUVERgIdJwdHREebm5vD09IRcLsdzzz2HVatWITc3F4MGDYJMJsPixYsRExOD6OholJSU4Omnn+52TGfOnIGdnZ3yE3uFQgG5XA4TExMIBALOMSKRCHv37oWTk5PGbfoYGxoaCjc3N5ibm0MsFmPo0KFoaWmBh4cHIiIi4OHhgcbGRuX+wcHB2Lx5s8Z1+EJJlxAdi42NBQAsX74cY8aMwdWrV+Hl5YUrV64AAKRSKaZPn46Kigp4enpi9+7dWLt2LQDAzc0NERERAIC3334ba9asQVJSEoYPH96jP5lzcnJU+kYcOnQIfn5+SExM7PacfCgsLISFhQXGjx+PwMBA5OTkwMTEBOfPn8eMGTOU+7m4uCAnJ4fHSNWjpEtILxswYAAkEgkUCoVym1gshlAohK+vL3777bdej6GiogIikUj53NraGidPnuxTXdLKysqwdetW7Ny5EwAQExOD8PBwNDQ0ICwsDAkJCcp9RSIRKioq+ApVI6rTJYQHLS0taGpqQkZGBsaMGQOBQICCggI0Njbi/v37yv2qqqrQ0tLS47unrK2tVW7m8PPz69F8+lZQUID9+/cjNjZWeS4ePHiA4uJiZGdnIy8vD8OGDVPu39DQAGtra77C1YwRXrzxxhvsxo0bfIdBuigwMLDTfUJDQ5mRkREDwMRiMTt79iyzsrJi3t7ejDHGsrKymKmpKRs4cCDz8/NjNTU17O7du8zOzo6NGDGCubq6smnTprGbN28ya2tr9uqrr7KioiI2ceLETtdW93slk8mYVCpVPo+Li2NxcXHK58eOHWP+/v4qY+7du8fGjx/PBAIBGzlyJMvIyODcpo+x0dHRDIDyER8fz8rLy1lUVBSbNm0aW7JkCauoqFDOkZ2dzZYtW8Z5jsLCwlhNTU2n57KXfEJJlyeUdPsmbZJuZ7KystiKFSt0EE1H6n6vFAoFCwsLY3V1dZzjysrKWFBQULfW5GusJlKplOXn53O+xnfSpfd0DYyDgwPWr1/fYXtoaCjc3d21nmf27NkdakVlMhlsbW1hbm6OuXPnKmsde1tX1pXJZHjmmWcwaNAgiMViTJw4UScxcNV5LlmyBAKBAAKBAGZmZnBzc0NKSopO1tPkgw8+wPbt2yGTyXp9rTYCgQAff/wxdu/ezVkilpSUhBUrVnRrbr7GqnP8+HGEhYVh1KhROp1XZ/hK9/2duiuSw4cPs9jY2A7bm5ubtfrzsk1BQQGbPXt2h7kvXrzIamtr2eTJk9m3337b9cC7Qdt1b9++zaysrNiRI0dYXV0dk8vl7M0332QKhaLHMRw4cID99NNP7P79+8zX15dlZmYyxhhzdHRk5eXlTC6Xsy+//JJZWVmxgwcPqp1HF1e6vYn+guocXemSDjZu3AhTU1N4eHigpKRE5bWqqir4+vpCLBbDx8cHlZWVqK2txYwZMyASiWBra4vW1laVMa6uroiKikJwcDAmTJiApqYmKBQKODg4cK7/6HxyubzDmgA461CHDh0KW1tblJaWYtmyZbCxseFcl6vu9MiRI5g7dy5efPFFiMViWFtbIyEhAQKBgPO41dXBcsXAVefZnrW1NebNm4f169dj165dPfr5EaIJVS8YoMjISKxbtw4rV65ESkoK3nvvPeVrKSkpcHd3R1paGtasWYOUlBQYGxtDIpHgq6++ws8//wwjoz/+X3rjxg2sW7cOL774IgBg7969WLBgAaKiotR+1czOnTtV5tu1a1eHNVetWoXly5dj7969OHnyJP7+97/jypUr2LNnDw4dOoRhw4Zh/vz58PLy4lzX2Ni4Q93p3bt31cbEddxc69vb26uNAVCt8+Ti5uaGPXv2qP3Z1NbW4vPPP1f7Ot9+/fVXpKWlwc7Oju9QDFZZWRmv69OVrgESi8UwNTWFn58fSktLVV4rKCiAl5cXxGIx/P39UVhYiPz8fMycORNCoRBPPfWU8g6ja9euYdGiRbC0tFSOnz9/Pqqrq2Fubq72jp1H57t9+3aHNdtrX4c6c+ZM5OXl4fbt2zh27JjyfWVt1rW3t0d+fj7na1zHzbU+ALUxPFrnyeXWrVsYOXKk2teNjIwgFAoN9mFkZARTU1Pe4zDkR/uLEj7Qla4BamlpQWNjI9LT0+Hs7KzymkQigUwmw5QpU5CZmQmJRALGGNLS0jB58mQwxiAUCgEAo0ePxr59+xAYGAhHR0ekp6fj5ZdfxuDBg2FsbKy2eFwikajMN2LEiA5rqiMQCPDmm28iOjoagYGBMDIywubNm7Vad+7cuZgwYQJ8fX3x/PPPA3h41T137lzO4+5KDFx1nm0UCgWam5tx6dIlrF+/Hh9++KHaucViMebOnav2db59//33mD59OvXT1eBf//oXvwHw9W5yf6epntLOzo6ZmJgwf39/5Rv+bbWfa9euZT4+PszMzIz5+PiwyspKVlFRwfz9/ZlIJGKTJk1idXV1LCIigolEIlZYWMgmTpzI7Ozs2J49e5ilpSUTCoXMy8uLFRcXc8b26Hx37tzpsCZjjAUHB3PWoba0tDBPT09WX1/PGGOc66qrO83OzmbPPfccE4vFTCgUsnHjxrHKykpWWVnZIQZ163PFwFXn+eabbzKRSMSMjIzYwIED2V/+8he2Y8cOjT83+iCt7+P7gzQBY2paDJFetXz5ckRFRdEVSR8za9YsHDt2jO8w1KLfq86Fh4dj27ZtfH1dzy56T7cfk8vlyjrV9g+5XM53aI8t1s0eud0d15mu9Nj98ccfVX5Pbt++rXZebfflqp/WNHbbtm2ws7ODra0tzp07Z/C9c7lQ0u3HbGxswBjr8LCxseE7tMdWd3vk6rK3bhvWjR672dnZyt+Tzlo0arMvV59cdWOvX7+O7du34+LFi0hPT0dlZaXB987lQkmXEB3gqiPurEeutn11ASjHhYaG9qivbnvd6bGra53VT7d34sQJLFy4EPb29vD09FR+2GrIvXO5UNIlRAfa6ohLS0uVtxN31iNX27667ccdPHhQZ39Od6fHbkBAAExNTeHp6anx7YWu7vto/TTX2JKSEiQkJMDc3BxOTk7KfrmG3DuXCyVdQnRAUx2xJvruq9teV3vsjhs3DoWFhcrvL9u2bZvaubuy76P10+rGWllZYcmSJaisrMT777+Pjz76CIBh987lQkmXEB1oqyOur69X1hF31iMXUO2r+8QTT6gd8+g4XeDqsavuTj0ASE1NRW1tLYyMjNDa2gozM7Me71tQUIBdu3YhNjZWWV+ubuyUKVNw/vx5NDc3o7m5GWKxGICB987lovcqNcIYo3rKvkpdnS5XHXFnPXK17avLGFOO8/X11dj4qCu/V5p67HL1uT127BgbMmQIE4lEzM/Pj8nl8i7ty7UfV/20unUYY2zVqlXM0tKSubq6smvXrjHGNPfO5cJ3nS4lXZ5Q0u2bdHlzRG/01e3K75WmHrtd6XOr7b589M7lwnfSpbcXCOEJH31129PUY7crfW613bdf9s7lQL0XCOHJ6dOn+Q4BlpaWkEqlHba3faOxNrTdtytzamvmzJk6n7O30ZUuIYToEV3p8ujw4cOwtbXlOwzSBSUlJQbd5PzatWv0e9UJde1D9YUa3vAkLy+P9x8+6Z7q6mr885//VLl5gfQtM2bM6PHX2nfTLrrS5cnYsWMxduxYvsMg3VBSUoJz585h1qxZfIdC+iB6T5cQQvSIki4hhOgRJV1CCNEjSrqEEKJHlHQJIUSPKOkSQogeUdIlhBA9oqRLCCF6REmXEEL0iJIuIYToESVdQgjRI0q6hBCiR5R0CSFEjyjpEkKIHlHSJYQQPaKkSwghekTfHEGIlmJiYpCSkoKBAweivr4eZmZmaG1txU8//QSxWMx3eKRv2EVXuoRoKSAgAA8ePEB+fj6Ki4tx8+ZN2NnZUcIlXUJJlxAtTZw4EYMHD1Y+FwqFiIyM5DEi0hdR0iWkC2bOnAmBQAAAsLGxwYsvvshzRKSvoaRLSBe8/vrrGDp0KADgz3/+M721QLqMki4hXTB69GhYWVnB3Nwcr7/+Ot/hkD6Iki4hXRQSEgJjY2NMnz6d71BIHzSA7wAeB2vWrEFubi7fYRA9aWhowODBgxEcHKyzOaurq2FhYQEjI8O8DqqvrwdjDGZmZnyHwpvY2Fi4urr2eB5KujqQm5uLY8eO8R0G0aOysjLY2dnpbL6IiAh8+OGHsLW11dmcunT48GE0NDTglVde4TsUXmzatAlyuVwncxnm/1YJMXC6TLikf6GkSwghekRJl5A+oqioCEFBQT2ehzGG5ORkHUT0h+rqasTFxSE5ORlCoRA2NjZISkri3PfHH3+EQCBQPm7fvt2j/QDg24fZNyQAACAASURBVG+/xbBhwyAWixESEoKmpia147dt2wY7OzvY2tri3LlzAICjR4/iwoULPTkFWqOkS0gfYW9vj7S0tB7Pk5eXh/Lych1E9BBjDIsWLUJkZCSkUikaGhqQm5uLDRs2QF1rl+zsbDDGwBiDk5OT2rm13U8ul+PUqVMoKytDVVUVzpw5wzn++vXr2L59Oy5evIj09HRUVlYCAObMmYPU1FTcunWr2+dBW5R0CekjVq5cCXd3dwAPy9amTp0KZ2dn2Nvbo6ioCHPmzIGjoyPMzc3h6ekJuVyO5557DqtWrUJubi4GDRoEmUyGxYsXIyYmBtHR0SgpKcHTTz/do7jOnDkDOzs7WFhYAAAUCgXkcjlMTEyUd+/1ttDQULi5ucHc3BxisVh5A8ujTpw4gYULF8Le3h6enp54/vnnla8FBwdj8+bNvR4rJV1C+ojY2Fjlfy9fvhxjxozB1atX4eXlhStXrkAqlWL69OmoqKiAp6cndu/ejbVr1wIA3NzcEBERAQB4++23sWbNGiQlJWH48OE9/rM6JycHLi4uyueHDh2Cn58fEhMT1Y4JCAiAqakpPD09Nb5toO1+bQoLC2FhYYHx48dzji8pKUFCQgLMzc3h5OSEnJwc5VgXFxeV572Fki4hfdiAAQMgkUigUCgAAGKxGEKhEL6+vvjtt9/0EkNFRQVEIpHyubW1NU6ePImQkBDO/ceNG4fCwkLU1NQgICAA27Zt69F+bcrKyrB161bs3LlT7XgrKyssWbIElZWVeP/99/HRRx8px4tEIlRUVHT18LuMki4hj5GWlhY0NTUhIyMDTzzxBAQCAQoKCtDY2Ij79+8r96uqqkJLS4tO1rS2tkZDQ4PyuZ+fn/JKk0tqaipqa2thZGSE1tZWtTdcaLsfABQUFGDXrl2IjY2FUChUO37KlCk4f/48mpub0dzcrNI7o6GhAdbW1l09/C6jpEtIHxEeHo5Lly4hPj4eiYmJ2LdvH7Kzs7Ft2zZs2rQJALBjxw6IxWLcvHkTERERcHV1xZkzZzB69Gjk5ORgw4YNcHFxwcGDBxEREYHi4mLl+8Td5enpievXryufx8fHIz4+HgCQnp6OgIAAlf2HDRsGd3d3WFpaIicnB6+//rrW+6mbc8uWLVi7di0GDhwIgUCAhIQEzvGenp5wdnbGyJEjsWXLFrzxxhvKOX799VdMmjSpR+dCK4z0WGBgIN8hkD5uwYIF7N69ez2aIysri61YsUJHEak6dOgQ++yzzzhfUygULCwsjNXV1XV4raysjAUFBXU6v7b7dXXfrpBKpSw/P5/ztcTERJaVlaWLZT6hK91e5ODggPXr16tsCw0N7dKVxezZszlrM2UyGWxtbWFubo65c+eiqalJ6xi4aDsfACxZskRZ+ygUCvHUU0/hxx9/5NyXaagJ9fDw6NAEvG3uJUuWAHhY3mRjY4PRo0d3egxd1f5n0ZUa2LZj0lXdrK588MEH2L59O2QymV7XFQgE+Pjjj7F79+4OJWJJSUlYsWJFp3Nou19X99XW8ePHERYWhlGjRul0Xi6UdHvRpk2bYGxsrLJt3759XZpDXcKSy+XIzMxEaWkp7t27h++++07rGHoyHwBs3boVjo6OKC8vR2VlJby9vdV+yKGpJjQ5OblDSdHWrVvh6uqK7du349y5cxg7diySkpLw9ddfd3oMXdX+Z9GVGti2Y9JV3ayunD59Gvfv34eXl5fe17a0tIRUKu3w84yNjcWzzz7b6Xht9+vqvtqaOXNmj0vntEVJt5dt3LgRpqam8PDwQElJicprVVVV8PX1hVgsho+PDyorK1FbW4sZM2ZAJBLB1tYWra2tyv1dXV0RFRUF4GFN4YQJE9DU1ASFQgEHBwetY+Cq8VQ3nzZ1nI2Njfjb3/4G4OEVo6WlJczMzJCYmKisCV2wYAHncf3yyy9wcXHB8OHDcefOHQDArFmzMH36dCxcuFDlipvrfM2ePRve3t6YOHEirKysMGjQIJiZmcHc3BwODg4wMTGBt7c3Z2ztta+BXbFihfJKfuzYsWqPSSAQKMdwxcZ1ngmhpNvLIiMjUVtbC09PT6SkpKi8lpKSAnd3d5SWlsLNzQ0pKSnYuXMnJBIJqqqqcPLkSWWrvxs3bmDdunUqV5R79+6Fra0txo8fD4lEonUMXDWe6ubTVMfZ9nZEZmYmxo4dC+DhFWN1dTUuXbqEEydOKGtCXVxcOI/L2dkZV69eRUBAAC5fvgzg4Z+rn332Gerq6vDhhx9qPF+rV6/G8OHDceTIEaxduxaBgYGorKzE3LlzERMTA7lcjjt37qCpqalDbO21r4FNSEgAYwwRERFYt26d2mNqbm7WGJu680z6N2rt2MvEYjFMTU3h5+eH48ePq7xWUFCAoKAgiMVi+Pv7Iz09HYwxzJo1S/le6e3bt3Ht2jUsWrRIWejeZv78+XjhhRcQExODzZs3q32fS10Mj9Z4ajtfm/LycgwePBi5ubmYMWMG8vLycPHiRSxYsADFxcXKK0wAyM/PVzmu9oyNjeHo6KiMA3hYhnTgwAFMmzYN0dHRcHd35zxfAGBhYQGJRIIpU6bgl19+gVAohJOTEwYNGgQLCwvY2tpCoVDg1KlTnLFx2b59O+zs7BAcHKzVOHWxcZ1nLpWVlXjllVdgYmKiMS6+FBcXA3jY4rE/Ki0t7fB7212UdHtZS0sLGhsbkZ6eDmdnZ5XXJBIJZDIZpkyZgszMTEgkEjDGkJaWhsmTJ4MxhubmZowePRr79u1DYGAgHB0dMXr0aGzevBkvv/wyBg8eDGNjY41F3ZpiaNOV+dooFAoYGxvD2NgY9fX1aG5uxoEDBxAfH48nn3wSK1euBPDwT++RI0eqHFdbLaUmzzzzDN577z28++67CA8P5zxfXcEVG5dz584hMzMTR44cwYkTJ5CQkMB5TO3rXHsa25AhQ6ifrgFrK8nTCV3UQPR36krGZDIZs7OzYyYmJszf35/V1NSw0NBQZmRkxBITE1llZSXz8fFhZmZmzMfHh1VWVrKKigrm7+/PRCIRmzRpEgsNDWUikYgVFhayiRMnMjs7O8YYY3v27GGWlpZMKBQyLy8vVlxcrHUMwcHBTCwWs7NnzzIrKyvm7e2tdr6ioiI2ceJElTmXLl3KADAAzMjIiI0YMYIlJyczxhhLTU1lIpGITZ48mZmamrJ9+/Yxa2tr9vzzz6scV11dHZs1axYTi8XsP//5DxsyZAh75pln2NKlS5mpqSnbtWsXY+xhOdLzzz/PfvnlF87zNWvWLCYSiVhmZiabPXs2MzMzY4cPH2ZWVlbMycmJffHFF8zY2JitWbOmQ2xOTk7Kn0XbzyUuLo5NmzZNeXwA2JYtWziPqe344+LiOGPjOs/q6KJkrDdpKhnrD3RZMiZgTE0bIKK1WbNm0TdHkB6hb44wbJs2bcJTTz2li8qQXfRB2mNCLper9A5te+jqK0aI4WPd6JPbnTFcqJ+u9ijpPiZsbGyUfUPbP2xsbPgOjehJd/rk6qK3LqN+ul1CSZcQA8ZV/wtAY59cgUDQoa9uZ2NCQ0O7fXMA9dPtGkq6hBgwrvpfABr75GZlZXXoq9vZmIMHD3b7z2vqp9s1lHQJMWAFBQXw8vJS1v8WFhZqNU6ffXWpn27XUNIlxIC11f/W19er1P921if30b662ozpLuqn20W6KDzr76i1I+kpdXW6XPW/jDF29+5dZmdnx0aMGMFcXV3ZtGnT2M2bN5m1tTVzcHBgpqambODAgczPz4/V1NR0OsbX17dDPXZ7mup0ZTIZk0qlyudxcXEsLi6OMcbYsWPHmL+/v8r+x44dY0OGDGEikYj5+fkxuVyu9X7q5oyOjlaprY6Pj1c7ftWqVczS0pK5urqya9euKefIzs5my5Yt4zxGXdbpUtLVAUq6pKd0eXNEb/TVpX661E+XEKKGvvvqUj/drqHeC4Q8Zk6fPq33Ndv66T6qffc2TbTdr6v7amvmzJk6n1MdutIlhBA9oqRLCCF6RG8v6MisWbP4DoHoiUKhQF1dHQYPHqyzOe/evYu///3vGDDAMP9J1tTUQKFQ9Nt+unfv3lV+O0pPUZcxQrqopKQE69atw44dO/gOhfQ91GWMEEL0iZIuIYToESVdQgjRI0q6hBCiR5R0CSFEjyjpEkKIHlHSJYQQPaKkSwghekRJlxBC9IiSLiGE6BElXUII0SNKuoQQokeUdAkhRI8o6RJCiB5R0iWEED2ipEsIIXpkmG3qCTFAt2/fRkVFBeRyOcrLy/Hf//4XAODq6gpTU1OeoyN9BSVdQrT0z3/+E+vWrcPAgQMBADKZDEZGRigtLeU5MtKX0NsLhGjp1VdfhYWFBSorK5UPX19fGBsb8x0a6UMo6RKiJVtbWwwbNkz53MrKClKplMeISF9ESZeQLli4cKHy/VsrKyt4eHjwHBHpayjpEtIFL730EmxtbQEAvr6+EAgEPEdE+hr6II38P/buPaypM9sf+DeAkECgYILBUgUqVuiAjkrH0DqiCAW1IrZI7OhUK5VDtQpeqs4Pz6gdRSuoYBWVakfaYsVepHL0eGkVZMbK6EgVrdKKARUQDRfxwp3394eHjJEEEhKyA6zP8/A8JnkvK4Gu7uysrE10YGdnB1dXVzx69AgLFizgOhzSDVHSNQGMMTx48IDrMIiWIiIiEBsbi4EDB6KmpobrcIgWLC0twefzuQ4DAMBjjDGug+jt7t27B19fX0ilUq5DIVpobm5GYWEhXnrpJQBAZWUlamtr4ezszHFkmuXn58Pb25vrMDjx+PFj9O/fH9u3b+c6FADYTUe6JsLPzw979uzhOgyipZaWFpiZPflI5NixY7h+/Trmz5/PcVSahYSE4Msvv+Q6DE7cunULGzZs4DoMJfogjZBOaE24hOiK/nIIIcSIKOkSYgQlJSUIDQ01yFqMMSQlJRlkLQCorq7Gxo0bkZSUBD6fD7FYjMTERLVjz58/Dx6Pp/wpKirSuK62Y3/88Uc4OTlBKBQiPDwcDQ0N7c5NTk6GRCKBo6Mjzpw5g4MHD+LcuXN6vALGRUmXECNwdnZGRkaGQda6cuUK7t27Z5C1GGOIjIxEVFQUoqOjUVdXh/z8fKxfvx6aPmPPyckBYwyMMbi6ura7vjZjFQoFTpw4gfLyclRVVSE7O1vj3IKCAuzYsQMXLlxAZmYmKisrMXXqVKSmpuLGjRudfRmMipIuIUawbNky+Pj4AADCw8Mxfvx4eHh4wNnZGSUlJZg6dSpcXFxgY2MDqVQKhUKB0aNHY8WKFcjPz4etrS2ysrIAAO+//z7WrVsHmUyGV155Ra+4srOzIZFIYGdnB+DJB4QKhQKWlpZG++KHTCaDt7c3bGxsIBQK0a9fP41jjx49irlz58LZ2RlSqRRvvPEGACAsLAxbtmwxSrz6oqRLiBHExcUp/7148WIMHjwYly9fxtixY3Hx4kVER0djwoQJqKiogFQqxZ49e7By5UoAgLe3NyIiIpTzly9fjtjYWKSnp+v9tjo3Nxeenp7K2wcOHEBgYCA2bdqkcU5wcDCsrKwglUrbPb2g69ji4mLY2dlh2LBhGueWlZUhISEBNjY2cHV1RW5uLgDA09NT+W9TR0mXEI5YWFjAzc0NLS0tAAChUAg+n4+AgADcunXLKDFUVFRAIBAob4tEIhw7dgzh4eFqxw8dOhTFxcW4f/8+goODkZycrHFtXcaWl5dj+/bt2LVrV7tzHRwcMH/+fFRWVmL16tX45JNPAAACgQAVFRU6P38uUNIlxEQ0NTWhoaEBR44cwaBBg8Dj8SCXy1FfX4+HDx+qjK2qqkJTU5Pee4pEItTV1SlvBwYGKo801UlNTUVNTQ3MzMzQ3NwMa2trvcfK5XLs3r0bcXFxym+NaZrr5+eHs2fPorGxEY2NjRAKhQCAuro6iEQinZ8/FyjpEmIEM2fORF5eHuLj47Fp0yakpaUhJycHycnJ2Lx5MwBg586dEAqFuH79OiIiIuDl5YXs7Gy4u7sjNzcX69evB/DkrXR6ejomTJigPE/cWVKpFAUFBcrb8fHxiI+PBwBkZmYiODhYZbyTkxN8fHxgb2+P3NxcZf8JbceqG7d161asXLkSffr0AY/HQ0JCgsZ9pFIpPDw8MGDAAGzduhWLFi0CAPz666/w9fXV67UwGkY4d/fuXTZnzhyuwyCddPToUbZt2za91jh16hRbunSpgSJqa/LkyWrvb2lpYTNmzGAPHjxo81h5eTkLDQ3Van1tx+qypi6io6NZYWGh2sdu3rzJ5s2bZ/A9O+lTOtI1cQMHDsTatWtV7pPJZDod4UyZMqVNjWhWVhYcHR1hY2ODadOmoaGhodMxsg7qRnXd69atW7C2tkZeXp5We+paA9s615C1s/r66KOPsGPHDmWFgrHweDxs27YNe/bsaVMilpiYiKVLl2q1jrZjdVlTW4cPH8aMGTPw4osvGnTdrkJJ18Rt3ry5zeVg0tLSdFpDXUJUKBQ4fvw47ty5g7t37+If//hHp2PsqG5U170OHDiA2NhYfPbZZ1rtqWsNbOtcQ9bO6uvkyZN4+PAhxo4da/S97e3tER0d3aZELC4uDq+99ppWa2g7Vpc1tTVp0iS9S+eMiZJuN/Dxxx/DysoKo0aNQllZmcpjVVVVCAgIgFAohL+/PyorK1FTU4OJEydCIBDA0dERzc3NyvFeXl6YN28ewsLCMHz4cDQ0NKClpQUDBw7UuH9JSQns7e1hbW2NTZs2tVk/MjIS69atQ0xMDPr16wdHR0fcuXMHCxcuhFgsVrtXWVmZxv9Q7t27h7lz5yI9PR319fUA0O6erTWw6vZWF39rnSuPx1O+Y1D3OqqrpyVEX9RlrBuIiorCmjVrsGzZMqSkpCA2Nlb5WEpKCnx8fJCRkYHY2FikpKTA3Nwcbm5u+O677/DLL78om7P89ttvWLNmDd566y0AwN69ezFnzhzMmzcPbm5uGvd3dnZGdXU1CgoK8MEHH6ClpUVl/ZKSEuTm5mLt2rUICAjAgQMH4OTkhNmzZyuP3J7dy9zcXG2N6ZkzZzB8+HD069cPw4cPx8GDBzF9+nTs2rVL455NTU2QSqX47LPP1O79bPzLly+Hn58fVq9erWynqe51XLx4Mfbu3Ytjx45h1qxZuHjxosb2jY8ePTLYt8S6QkNDg0nH15UqKiqUZXmmgJJuNyAUCmFlZYXAwEAcPnxY5TG5XI7Q0FAIhUIEBQUhMzMTjDGEhISAz+djxIgRKCoqwrVr1xAZGaksuAeA2bNn480338S6deuwZcsWjefaTpw4gTlz5qC0tBTjxo1DYWGhyvqlpaXKsZMmTcKqVatQVFSEQ4cO4a9//atOe6WmpiIlJQXTp08H8OQbUtOnT293z472fjZ+ddS9jq2eradV53//939VqgBMzY0bN7BixQquw+DEo0ePYGFhOqnOdCIhGjU1NaG+vh6ZmZnw8PBQeczNzQ1ZWVnw8/PD8ePH4ebmBsYYMjIyMGbMGDDG0NjYCHd3d6SlpWHy5MlwcXFBZmYm/vSnP+G5556Dubl5u4Xl+/fvR3x8PF566SUsW7YMbm5ubdZvrRu1sLDAkiVLEBMTg8mTJ8PMzAxbtmzRaq+6ujo8evRI+YHOzZs3MXToUBQVFbW7Zysej9dmb3XxA23rXNW9jroICwsz+X66vbVfs6n106WSMRPQXslYVlYWk0gkzNLSkgUFBbH79+8zmUzGzMzM2KZNm1hlZSXz9/dn1tbWzN/fn1VWVrKKigoWFBTEBAIB8/X1ZTKZjAkEAlZcXMxGjhzJJBIJ++yzz5i9vT3j8/ls7NixrLS0VGN8qampTCAQsDFjxjArKyt25MgRlfV//vlnJhKJ2DvvvMMYY6ypqYlJpVJWW1vLGGNq9yopKWEjR45U2UcqlTKRSMQuXbrEGGPsb3/7GzM3N2dOTk7s0qVLGvdsfT02btzYZm918aelpTGRSMQAKOepex3DwsKYUChkp0+fZg4ODmzcuHFqXx9DlIx1NU0lY72BqZWM0eV6TMC9e/ewYsWKXnsk0t11lytHHDp0iOswONF6pGsql+uh6gUC4ElZ19P9S1t/FAoF16H1CqwTPXI7M0cd6qdrXJR0CQBALBYre5c+/dNadkW6Vmd65Bqiry6jfrpGR0mXkC6krv5XXZ/c1trhmJgYrXvrPj2nvbrn9lA/XeOjpEtIF2qt/71z5w68vb2RkpKitk9ua4/cxMRErXvrPj2nf//+nXqLTf10jY+SLiFdSC6XY+zYscr63+LiYq3mGau3LvXTNT5KuoR0odb639raWmX9r6Y+uU/XDmvbW1ffvrrUT9f4KOkS0oUiIyNx7tw5iMVi5OfnY+7cuWr75Lb2yG09daBNb92n55SWlnaqty710+WA0UuDSRvUT7d7M/SXI7qity710zWdL0fQkS4hJsaYvXWpn67xUe8FQkzMyZMnjbpfaz/dZz19BeOOaDtWlzW1NWnSJIOv2ZXoSJcQQoyIjnRNxKNHj4x22W1iWPfu3UNVVZVJ//5qa2tNOr6u9Gzjf65RwxsT8PDhQyxfvpzrMHq8yspKFBQUGPxT7vr6+g7LpzojNzcXbm5u7X5DS1tVVVVwcHAwQFTdk5+fn8baYyPbTUmX9Ar/+te/sGrVKnz++edwdHTkOhyt1NTUYO7cuQgODsa7777LdTjEMKjLGOn5/ud//gd/+9vfkJ6e3m0SLgDY2dlh//79KC4uRlRUFBobG7kOiRgAJV3So3355Zf44osv8PXXXyubunQnPB4Pq1evhr+/P6ZMmYK7d+9yHRLREyVd0mMlJSXhX//6F7766ivl10u7q/DwcGzYsAHTpk3Dv//9b67DIXqgc7qkx2GMYc2aNairqzOta2MZgEKhwJw5c5QX+iTdDp3TJT1Lc3MzoqKi4OTk1OMSLvCk2fx3332Ho0ePGuSqEcT4KOmSHqO+vh5//vOfMX78eERFRXEdTpexsLBASkoK6urqEBMT0+6l4YnpoaRLeoTq6mqEhobi3XffNZV6zC63fPlyDBs2DLNmzUJDQwPX4RAtUdIl3V5ZWRlkMhnWrVuHwMBArsMxqnfffRfTp0/H22+/jdraWq7DIVqgrwGTbu3GjRuIjIzEjh07MHjwYK7D4cSkSZOUV9JNS0vrlqVxvQklXdJtnT9/HsuWLcMXX3wBZ2dnrsPhlJ+fHywtLSGTybB//34899xzXIdENKDTC6RbOnnyJD766CMcPHiw1yfcVr6+vli7di3efvttPHjwgOtwiAaUdEm3c/DgQezcuRPp6el0RPeMkSNHYtWqVQgPD1e5lhoxHZR0Sbeyfft2ZGZmYt++fSpXsSX/MWrUKMTGxuJPf/oTfbhmguicLuk2Pv74Y1RVVWHPnj3g8Xhch2PSRo8ejfr6esyePRv79u2Dubk51yGR/0NHusTkNTc3Y968eTA3N8eGDRso4Wpp/PjxePPNN/HBBx9wHQp5CiVdYtIaGhrwzjvv4JVXXjH4BQ17A5lMhkGDBmH9+vVch0L+DyVdYlLu3bun/PfDhw8xffp0zJw5k5p462Hp0qUoLy9Heno616EQUNIlJuTkyZPw9vZGaWkpysvLERoaiqVLl2LChAlch9btbd68Genp6dQW0gRQa0diMry9vXH58mUMHjwYzz//PHbs2AFPT0+uw+oxKisrMW3aNOzfv79bXUGjh6HWjsQ07Nu3D7dv3wYAFBYWoqqqCgMHDuQ4qp6lb9++2Lx5M9577z00NzdzHU6vRUe6hHNNTU3w8PBAYWGh8j4LCwv4+vri5MmTsLCgykZDSk1Nxe3btxEbG8t1KL0RHekS7n3yyScoKytT3nZ0dMSoUaOwevVqSrhdYNasWSgoKMDZs2e5DqVXoiNdwqlHjx5hyJAhqKiogFgsRnBwMP77v/+bTi10serqarz11lvIyMiAra0t1+H0JrtNKumWlZXhu+++4zoMoqPGxkaYm5vDzEz3N05HjhzBTz/9hMDAQPj6+qJPnz4Gj6+lpQXNzc1dsnZ38frrr7dpfXn69Gns27cPO3fu5CiqXmm3Sb13u379On7++WeEhYVxHQrRQWpqKsaMGQM3Nzed54aEhGDhwoVdENV/XL58GdeuXeu1f1dZWVk4f/58m6Q7ZswYfPPNN/jhhx8QEBDAUXS9j0klXQB46aWXEBQUxHUYRAc5OTl49dVXMXToUK5DUcva2hotLS299u+qsrJS42Pr16/H1KlT8dprr1EDISOhD9II6cVsbGywaNEifPTRR1yH0mtQ0iVGUVJSgtDQUL3XYYwZ/NLj1dXV2LhxI5KSksDn8yEWi5GYmKh27Pnz58Hj8ZQ/RUVFGtfVduyPP/4IJycn5SV3GhoaNM5NTk6GRCKBo6Mjzpw5A+BJf+Fz5851+vlPmDABt2/fpm+rGQklXWIUzs7OyMjI0HudK1euqPRn0BdjDJGRkYiKikJ0dDTq6uqQn5+P9evXQ9NnzDk5OWCMgTEGV1fXdtfXZqxCocCJEydQXl6OqqoqZGdnq51bUFCAHTt24MKFC8jMzFSeNpg6dSpSU1Nx48aNTr8OW7ZswYoVK9DY2NjpNYh2KOkSo1i2bBl8fHwAAOHh4Rg/fjw8PDzg7OyMkpISTJ06FS4uLrCxsYFUKoVCocDo0aOxYsUK5Ofnw9bWFllZWXj//fexbt06xMTEoKysDK+88opecWVnZ0MikSgv5tjS0gKFQgFLS0ujtZCUyWTw9vaGjY0NhEIh+vXrp3bc0aNHMXfuXDg7O0MqleKNN95QPhYWFoYtW7Z0OgaxWIyZM2dqPMInhkNJlxhFXFyc8t+LFy/G4MGDcfnyZYwdOxYXL15EdHQ0JkyYgIqKCkilUuzZswcrV64E8KQnQ0REBABg+fLliI2NRWJiIvr376/X22oAyM3NVenvcODAtGZ83gAAIABJREFUAQQGBmLTpk0a5wQHB8PKygpSqbTd0wu6ji0uLoadnR2GDRumdm5ZWRkSEhJgY2MDV1dX5ObmKud6enqq3O6MWbNm4cyZM7h+/bpe65D2UdIlnLGwsICbmxtaWloAAEKhEHw+HwEBAbh165ZRYqioqFD51F4kEuHYsWMIDw9XO37o0KEoLi7G/fv3ERwcjOTkZI1r6zK2vLwc27dvx65duzTOdXBwwPz581FZWYnVq1fjk08+Uc4XCASoqKjQ9em3kZiYiEWLFmk8tUL0R0mXmIympiY0NDTgyJEjGDRoEHg8HuRyOerr61UuslhVVYWmpiaD7CkSiVBXV6e8HRgYqDzSVCc1NRU1NTUwMzNDc3MzrK2t9R4rl8uxe/duxMXFgc/na5zr5+eHs2fPorGxEY2NjRAKhco16urqIBKJdH36bbi4uGDcuHH4+9//rvdaRD1KusQoZs6ciby8PMTHx2PTpk1IS0tDTk4OkpOTsXnzZgDAzp07IRQKcf36dURERMDLywvZ2dlwd3dHbm4u1q9fD09PT6SnpyMiIgKlpaXK88SdJZVKUVBQoLwdHx+P+Ph4AEBmZiaCg4NVxjs5OcHHxwf29vbIzc3FggUL1I7TZezWrVuxcuVK9OnTBzweDwkJCWrnSqVSeHh4YMCAAdi6dSsWLVqkXOPXX3+Fr6+vXq9Fq+joaKSnp+Pu3bsGWY88g5mQ06dPs40bN3IdBtFRbGwsu3jxol5rnDp1ii1dutRAEalq7++qpaWFzZgxgz148KDNY+Xl5Sw0NLTD9bUdp+tYXURHR7PCwkK1j+3bt4/t27dPp/XOnz/P5s6da4jQiKpPu82R7sCBA7F27VqV+2QymU5HOlOmTNFYK1pWVtbma5JdrfV8XXV1dYdjs7Ky8Oqrr8LW1hZCoRAjR47Ue3919aHz589X1oZaW1vD29sbKSkpeu/VkY8++gg7duxAVlZWl+/1NB6Ph23btmHPnj1tzmMmJiZqdV02bcfpOlZbhw8fxowZM/Diiy8abM2RI0fCzMyMOpF1Ba7T/tPaOyL5+uuvWVxcnMp9jY2NbOTIkVqvL5fL2ZQpU9rc39DQwJYvX84GDRqkW8B6ePz4MZs1axaTSCSsqqqq3bFFRUXMwcGBffPNN+zBgwdMoVCwJUuWsJaWFr1i2L9/P7t06RJ7+PAhCwgIYMePH2eMMebi4sLu3bvHFAoF++qrr5iDgwNLT0/XuI4hjnS7Um9/B9WZI13GGKuoqGCvv/46a2pq6oKoeq3uc6QLAB9//DGsrKwwatQolf6rVVVVCAgIgFAohL+/v7JovKamBhMnToRAIICjo6NKt3wvLy/MmzcPwJPC8MWLF7fbu1XdWur2VVeD2q9fPzg6OuLOnTtYuHAhxGIxBAIB9u7dq1Iwr6nu9JtvvsG0adPw1ltvQSgUQiQSISEhAdXV1Wqft7YxdFQfKhKJMH36dKxduxa7d+/W/RdGurW+ffsiNDQUe/bs4TqUHqVbJd2oqCjU1NRAKpWqvOVNSUmBj48P7ty5o/J2eNeuXXBzc0NVVRWOHTumbD3422+/Yc2aNUhOTsa3336LwMBAjQXprdStpW5fdTWon332GSZMmAAnJyfMnj1b49t1TXWnt2/fVtvBS9Pz1jWGZ+tDn+Xt7d1u0xTSc0VGRuLrr7/G/fv3uQ6lxzC5LmPtEQqFsLKyQmBgIA4fPqy8Xy6XIzQ0FEKhEEFBQcjMzATw5FpbISEh4PP5GDFiBIqKinDt2jVERkYqC+83bNiA8+fPK9fy8PDAtWvX2uz97Frt7Quo1qBOmjQJq1atQlFREQ4dOoS//vWvOj1vZ2dnlU/YO3reusTwbH2oOjdu3MCAAQM0Pl5bW4svvvgC/fv31+l5GcuNGzdQUlKirJLobfLy8jBx4sROzTU3N8f/+3//Dxs2bMD69esNHFnv1K2SblNTE+rr65GZmQkPDw/l/W5ubsjKyoKfnx+OHz+uPCp0c3NDRkYGxowZA8YYGhsb4e7ujrS0NEyePBkuLi4qR5aaEq66tfh8vsZ9n8Xj8bBkyRLExMRg8uTJOjf7njZtGoYPH46AgADlVz937doFCwsLrfbXFINcLse+ffsQFxfX5tRKS0sLGhsbkZeXh7Vr12LDhg0a17awsMCQIUMM+kGOIVlYWKC5uVn5P8vepqqqSq/548aNwyeffILi4mK4uLgYKKpejOuzyk9r7wOPrKwsJpFImKWlJQsKCmL3799nMpmMmZmZsZUrVzJ/f39mbW3N/P39WWVlJWPsyQcBQUFBTCAQMF9fXyaTyZhAIGDFxcVs5MiRTCKRqOwxZMgQjbE9u9aDBw9YZWVlm33DwsKYUChkp0+fZg4ODmzcuHGMMcaampqYVCpltbW1jDHG7t69y4YNG8Z4PB4bMGAAO3LkCCspKdH4wWBOTg4bPXo0EwqFjM/ns6FDh7LCwkK1z1vbGGJiYhgA5U98fDxbsmQJEwgEzMzMjPXp04e9/PLLbOfOne3+3uiDNNPW2Q/SnvbLL7+wWbNmGSag3u1Tk7pcT05ODs6ePYsPP/yQ61CIDlauXInw8HCTbWLe2/+uvvrqKwDA22+/rdc6//Vf/4X3338fv//97w0RVm9FVwN+lkKhUOlj2vqjUCi4Dq1HY53ok9uZOer09n662oqNjW33NBPRDiXdZ4jFYmUP06d/xGIx16H1aJ3pk2uI3rqM+ulqbeDAgRCJREZJ8D0ZJV3SZTTVT7fXJ5fH47Xpq9vRHJlM1um+utRPVzexsbFISEjo8n16Mkq6pMtoqiNur0/uqVOn2vTV7WhOenp6p4++qJ+ubp5//nlIJBLk5eV1+V49FSVd0mXkcjnGjh2rrCMuLi7Wap4x++pSP13dLV68mK4woQdKuqTLtNYx19bWqtQRd9Qn99m+utrM6Szqp6s7V1dXNDU14ebNm0bZr6ehpEu6TGRkJM6dOwexWIz8/HzMnTsXADrsk/tsX92O5kyYMKHTfXWpn27nLFq0CFu3bjXafj2K0UuD29Hbi9i7K0N+OaIr+upSP139vxyhzsSJE9nDhw8Nvm4P1726jJGez9h9damfbufJZDKkp6cbdc+eoFv1XiA938mTJ42+p729PaKjo9vc//QVjNuj7Thdx2pr0qRJBl9TG9OmTcO0adMwZ84cTvbvruhIlxDSKQKBAK6urrhy5QrXoXQrlHQJIZ02Z84c7N27l+swuhWTO71w6tQpg11emxheU1NTmzaQeXl5ePz4MRwdHTmKqn23b99GaWlpr+0He+nSJYSEhHTJ2iNGjMCKFSvAGDPaN/i6O5PqMvbgwQNcvnyZ6zBIOz788ENlSRXpPl566aUuq+ONjY1FaGhop7+K3cvsNqkjXVtbW6PWGhLd1NTUwMXFhX5HRMW0adPw1VdfUdLVEp3TJVq7du0ahgwZwnUYxMT8/ve/x8WLF7kOo9ugpEu0dvXqVZXLJBHSys3NDXK5nOswugVKukRrBQUFlHSJWoGBgfjxxx+5DqNboKRLtPbrr79i8ODBXIdBTJC/vz9OnTrFdRjdAiVdorW6ujqVNoiEtLK3t0d1dbXGq22Q/6CkS7TS1NSEPn36cB0GMWHu7u5dfsmgnoCSLtFKYWGh0RuqkO7lD3/4A/71r39xHYbJo6RLtELlYqQjlHS1Q0mXaOXatWtUuUDa5e7ujsLCQq7DMHmUdIlWCgoKVC7gSMizeDwefZCmBUq6RCt379412YY2xHT0798fd+7c4ToMk0ZJl2jFzIz+VEjHPDw8cPXqVa7DMGn0XxLpUHl5Ofr168d1GKQbePnll/HLL79wHYZJo6RLOkSVC0RbgwYNoh4MHaCkSzpElQtEWwMGDMCtW7e4DsOkUdIlaj169Aj5+fmor6+nRjdEa3w+H3V1dVyHYdJMqok5MR3Nzc344x//CFtbWwDA6dOn4enpiejoaPj4+HAcHSHdFx3pErXs7OzQr18/3L59G7dv38a///1vHDt2DGKxmOvQiImzsLCg6xy2g5Iu0ahv377Kf5uZmSE4OBiurq7cBUS6BZFIhIqKCq7DMFmUdIlGT1/z6vnnn0dCQgKH0ZDuQiwWQ6FQcB2GyaKkSzQaN24crKysYGFhgfDwcKrVJVqhI9320QdpRKPf//73sLe3B5/Px+rVq7kOh3QTIpGIjnTbQUe6RCM3Nzc8fPgQUVFRyioGQjrSt29fVFZWch2GydLqSDcwMJAu09JL2dvb4x//+AdCQkL0Wqe8vBwSicRAURleZWUlbG1te+3VMWpqanDixAmDPH9bW1tq8dgOrZKuQCDAoUOHujoWYoLu37+P5557Tu91QkJCTPpvaPHixZg3bx7c3d25DoUT06ZNQ0tLi0HWEgqFePjwoUHW6ono9AJplyESLuldhEIhHj16xHUYJouSLiHEoOhIt32UdInJKCkpQWhoqN7rMMaQlJRkgIieqK6uxsaNG5GUlAQ+nw+xWIzExESN48+fPw8ej6f8KSoq0mscAPz4449wcnKCUChEeHg4GhoaNM5PTk6GRCKBo6Mjzpw5g4MHD+LcuXN6vAK6EQqFePDggdH2624o6RKT4ezsjIyMDL3XuXLlCu7du2eAiJ4k8MjISERFRSE6Ohp1dXXIz8/H+vXr2700TU5ODhhjYIy1+y0+bccpFAqcOHEC5eXlqKqqQnZ2ttr5BQUF2LFjBy5cuIDMzExUVlZi6tSpSE1NNdrl0S0tLdHY2GiUvbojSrrEZCxbtgw+Pj4IDw/H+PHj4eHhAWdnZ5SUlAAApk6dChcXF9jY2EAqlUKhUGD06NFYsWIF8vPzYWtri6ysLLz//vtYt24dYmJiUFZWpvLNOl1lZ2dDIpHAzs4OANDS0gKFQgFLS0vweDyDPG9tyGQyeHt7w8bGBkKhUOMXVY4ePYq5c+fC2dkZUqkUb7zxBgAgLCwMW7ZsMUqs1HuhfZR0icmIi4sD8KSSYPDgwbh8+TLGjh2LixcvAgCio6MxYcIEVFRUQCqVYs+ePVi5ciUAwNvbGxEREQCA5cuXIzY2FomJiejfv79eb61zc3NVLsh54MABBAYGYtOmTe3OCw4OhpWVFaRSabunDbQd16q4uBh2dnYYNmyY2vllZWVISEiAjY0NXF1dkZubCwDw9PRU/rurmZubU9JtByVdYrIsLCzg5uamUsokFArB5/MREBBglGbZFRUVKjXqIpEIx44dQ3h4uMY5Q4cORXFxMe7fv4/g4GAkJyfrNa5VeXk5tm/fjl27dmmc7+DggPnz56OyshKrV6/GJ598AuBJ2aexvpprYWGB5uZmo+zVHVHSJd1KU1MTGhoacOTIEQwaNAg8Hg9yuRz19fUqn5hXVVUZ5GhLJBKpNOUODAxUHmVqkpqaipqaGpiZmaG5uRnW1tZ6jQMAuVyO3bt3Iy4uDnw+X+N8Pz8/nD17Fo2NjWhsbIRQKAQA1NXVQSQS6fr0SRegpEtMxsyZM5GXlwdfX1+kpaUhJycHycnJ2Lx5s3LMzp07IRQKcf36dURERMDLywvZ2dlwd3dHbm4u1q9fD09PT6SnpyMiIgKlpaV6NV2XSqUoKChQ3o6Pj0d8fLzydmZmJoKDg1XmODk5wcfHB/b29sjNzcWCBQv0GgcAW7duxcqVK9GnTx/weDwkJCSonS+VSuHh4YEBAwZg69atWLRoEQDg119/ha+vb6dfB2JATAuTJ0/WZhghGhnib+jUqVNs6dKlBoimrUWLFrHffvutzf0tLS1sxowZ7MGDB2rnlZeXs9DQ0A7XN/Q4XUVHR7PCwkKNj4eFhbG6ujqD7RcSEmKwtXqYT/U60h04cCDWrl2rcp9MJtPpyGLKlCkaazPLysowePBgnWPQpPXcV3V1dbvj5s+fr6x95PP5GDFiBM6fP692LNNQEzpq1ChERUWpXXf+/PkAnpQ2icXiLvvq6dO/C11qYFufk6HqZg3lo48+wo4dO5CVlWW0PXk8HrZt24Y9e/aoLRFLTEzE0qVLO1zH0ON0cfjwYcyYMQMvvviiQdclnaRNatZ0lPL111+zuLg4lfsaGxvZyJEjtU77crmcTZkypc39DQ0NbPny5WzQoEHtzlcXgzqPHz9ms2bNYhKJhFVVVXU43sXFhd27d489evSILV68mL377rtqx+Xn57PY2Ng29//0008sKiqqzf1eXl6Mx+Oxf/7zn4wxxr744gt29erVDuPpDF1/F600PSd9mPq7JU1Hur0FHekajX5HugDw8ccfw8rKCqNGjUJZWZnKY1VVVQgICIBQKIS/vz8qKytRU1ODiRMnQiAQwNHRUeVTTi8vL8ybNw8AsGXLFixevBgWFh335Hk2BnV1ngKBAHv37lUpQNe2hrO+vh5/+MMfUFJSAnt7e1hbWytLhlprQufMmdPmeV29ehWenp7o378/bt68CeBJ45cJEyZg7ty5aGho6PD1mjJlCsaNG4eRI0fCwcEBtra2sLa2ho2NDQYOHAhLS0uMGzcOANTG16q1Bnbp0qXKo/jf/e537T4nHo+nPFJWF5umelpCiGZ6J92oqCjU1NRAKpUiJSVF5bGUlBT4+Pjgzp078Pb2RkpKCnbt2gU3NzdUVVXh2LFjMDN7EsJvv/2GNWvWIDk5Gd9++y0CAwO1vlLBszFoqvN8Vkc1nI6OjrCxscHx48fxu9/9Ds7OzqiurkZeXh6OHj0K4D81oZ6enm2el4eHBy5fvozg4GD8/PPPAJ68Xf3888/x4MEDbNiwocPX6y9/+Qv69++Pb775BitXrsTkyZNRWVmJadOmYd26dVAoFLh58yYaGhrUxteqtQY2ISEBjDFERERgzZo17T6np79VpC42bV9nQsh/6H3lCKFQCCsrKwQGBuLw4cMqj8nlcoSGhkIoFCIoKAiZmZlgjCEkJER5rrSoqAjXrl1DZGSkstB9w4YNKudQPTw8cO3aNZ1jUFfnqYt79+7hueeeQ35+PiZOnIjExER8+OGHKC0tVR5dtiosLFR5XmfPngXwpFDcxcVFJQaRSIT9+/fj9ddfR0xMjPJoUt3rBTy5Mq+bmxv8/Pxw9epV8Pl8uLq6wtbWFnZ2dnB0dERLSwtOnDiBOXPmqI3vaTt27IBEIkFYWJjWczTFBmj3OpeWlurdk7cryeVyXLx4ETY2NlyHwgm5XM51CL2G3km3qakJ9fX1yMzMhIeHh8pjbm5uyMrKgp+fH44fPw43NzcwxpCRkYExY8aAMYbGxka4u7sjLS0NkydPhouLi8rRZ0cJt6MY9NHS0gJzc3OYm5ujtrYWX3zxBeLj4/HSSy9h2bJlynFVVVUYMGBAm+fVnldffRWrVq3Cf//3f2PmzJkA1L9euti/f7/a+J525swZHD9+HN988w2OHj2KhIQEjc/p6TpXfWN7/vnnqZ+uCZs2bRrXIfQe2pz51fQhSFZWFpNIJMzS0pIFBQWx+/fvM5lMxszMzNimTZtYZWUl8/f3Z9bW1szf359VVlayiooKFhQUxAQCAfP19WUymYwJBAJWXFzMRo4cySQSicoeQ4YMaTc2dTGEhYUxoVDITp8+zRwcHNi4cePY3bt32bBhwxiPx2MDBgxgR44cYSUlJWo/aPrggw8YAAaAmZmZsRdeeIElJSWx1NRUJhAI2JgxY5iVlRU7e/Ysu379OhOJROyNN95QeV7+/v5MKBSyf/7zn6xv377s1VdfZR988AGzsrJiu3fvZow9KUd64403lB+kqXu9QkJCmEAgYMePH2dTpkxh1tbW7Ouvv2YODg7M1dWVffnll8zc3JzFxsa2ic/V1VX5u2j9vbQ+r9afrVu3anxOrc9/48aNamNT9zrr+jdkKuiDNPogzUg+5THWTquk/2PqXf+J6TP1vyE60p2GL7/8ElZWVgZZb8qUKfj+++8NslYPs7vbfCNNoVCo9A5t/aGrjvYOrJM9cjs772nUT5cYUrdJumKxWNk39OkfsVjMdWjECDrbI1ff3rqM+ukSA+s2SZf0TOrqfzvqkattX13gPzXHMpmsU311qZ8uMTRKuoRT6up/O+qRq21f3afnpaend+otNvXTJYZGSZdwSi6XY+zYscr63+LiYq3mGauvLvXTJYZGSZdwqrX+t7a2Vln/q02PXG376j47T1fUT5cYnDaFZaZeY0lMn6a/IXX1v7dv32YSiYS98MILzMvLi73++uvK2uF33nmHnTp1illZWbE+ffqwwMBAdv/+fbVzGGPKeQEBAe02/9FUp5uVlcWio6OVtzdu3Mg2btyovH3o0CEWFBSkMufQoUOsb9++TCAQsMDAQKZQKPQaxxhjMTExKvXV8fHxauczxtiKFSuYvb098/LyYteuXWOMMZaTk8MWLlyo8flTna7RfEpJlxiFIf+GuqKvLvXTpaRrJPp3GSPE2IzZV5f66RJD07v3AiHGdvLkSaPuZ29vj+joaLWPtXZv64ihx+li0qRJBl+TdB4d6RJCiBFR0iWEECPS6vRCWVmZSfdCJYb1+PFjMMYM2lu2pKTEpP+G7t69i6tXr6JPnz5ch8KJ27dvG/Ubdr2ZVkmXmmX0Lt999x1qamowe/ZsrkMhpMeh0wuEEGJElHQJIcSIKOkSQogRUdIlhBAjoqRLCCFGREmXEEKMiJIuIYQYESVdQggxIkq6hBBiRJR0CSHEiCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBElXUIIMSK6MCVR+umnn/Do0SNcunQJjx8/xg8//AArKyv88Y9/5Do0QnoMSrpEKS4uDj/88APMzJ68Adq2bRt8fHxw+vRpjiMjpOeg0wtEaeHChbCyssLjx4/x+PFj8Hg8LFiwgOuwCOlRKOkSpfHjx6Nv377K22KxGG+88QaHERHS81DSJUpmZmYYNWqU8raXlxcEAgGHERHS81DSJSqio6MhEolgZ2eHhQsXch0OIT0OfZBGVIwaNQrPPfccmpubERAQwHU4hPQ4Rkm6jx8/RmlpqTG2IgYglUpRXV0NuVzOdShESxKJBLa2tlyHQbRglKT7ww8/YNu2bfDy8jLGdkRPffr0gZ2dHZKTkwEA+fn5eOGFF+Dg4MBxZOopFArcvXsXL7/8MtehcOK3335DeHg4/vznP3MdCtGC0U4vhIeH47333jPWdsSAVq9ejZCQEIwYMYLrUNT66aefkJWVhb/85S9ch8KJr7/+GnV1dVyHQbREH6QRQogRUdIlhBAjoqRLDKKkpAShoaF6r8MYQ1JSkgEi+o/q6mps3LgRSUlJ4PP5EIvFSExMVDv2/Pnz4PF4yp+ioiKN62o79scff4STkxOEQiHCw8PR0NCgcW5ycjIkEgkcHR1x5swZAMDBgwdx7tw5fV4CYkIo6RKDcHZ2RkZGht7rXLlyBffu3TNARE8wxhAZGYmoqChER0ejrq4O+fn5WL9+PRhjaufk5OSAMQbGGFxdXdtdX5uxCoUCJ06cQHl5OaqqqpCdna12bkFBAXbs2IELFy4gMzMTlZWVAICpU6ciNTUVN27c6PTrQEwHJV1iEMuWLYOPjw+AJx+ajh8/Hh4eHnB2dkZJSQmmTp0KFxcX2NjYQCqVQqFQYPTo0VixYgXy8/Nha2uLrKwsvP/++1i3bh1iYmJQVlaGV155Ra+4srOzIZFIYGdnBwBoaWmBQqGApaUleDye3s9bGzKZDN7e3rCxsYFQKES/fv3Ujjt69Cjmzp0LZ2dnSKVSla9gh4WFYcuWLUaJl3QtSrrEIOLi4pT/Xrx4MQYPHozLly9j7NixuHjxIqKjozFhwgRUVFRAKpViz549WLlyJQDA29sbERERAIDly5cjNjYWiYmJ6N+/v95vq3Nzc+Hp6am8feDAAQQGBmLTpk0a5wQHB8PKygpSqbTd0wu6ji0uLoadnR2GDRumdm5ZWRkSEhJgY2MDV1dX5ObmKud6enqq3CbdFyVd0mUsLCzg5uaGlpYWAIBQKASfz0dAQABu3bpllBgqKipU+keIRCIcO3YM4eHhascPHToUxcXFuH//PoKDg5W1yvqOLS8vx/bt27Fr1y6Ncx0cHDB//nxUVlZi9erV+OSTT5TzBQIBKioqdH36xARR0iVG09TUhIaGBhw5cgSDBg0Cj8eDXC5HfX09Hj58qBxXVVWFpqYmg+wpEolUalgDAwOVR5rqpKamoqamBmZmZmhuboa1tbXeY+VyOXbv3o24uDjw+XyNc/38/HD27Fk0NjaisbERQqFQuUZdXR1EIpGuT5+YIEq6xCBmzpyJvLw8xMfHY9OmTUhLS0NOTg6Sk5OxefNmAMDOnTshFApx/fp1REREwMvLC9nZ2XB3d0dubi7Wr18PT09PpKenIyIiAqWlpcrzxJ0llUpRUFCgvB0fH4/4+HgAQGZmJoKDg1XGOzk5wcfHB/b29sjNzcWCBQvUjtNl7NatW7Fy5Ur06dMHPB4PCQkJaudKpVJ4eHhgwIAB2Lp1KxYtWqRc49dff4Wvr69erwUxEcwIvv/+e/bpp58aYyvSBVatWsX+/e9/67XGqVOn2NKlSw0UkaozZ86wuLg4tY+1tLSwGTNmsAcPHrR5rLy8nIWGhna4vrbjdB2ri+joaFZYWKj2sQMHDrDPP//c4HvqIyQkhOsQTNWnnB/pDhw4EGvXrlW5TyaT6XSEM2XKFI01omVlZRg8eLBeMTItakdbz81VV1e3O+7WrVuwtrZGXl6e1nvqUgPb2Xld7aOPPsKOHTuQlZVl1H15PB62bduGPXv2tCkRS0xMxNKlSztcQ9txuo7V1uHDhzFjxgy8+OKLBl2XcIPzpLt582aYm5ur3JeWlqbTGpoSYmNjI5KSkjTWY2qro9rR2tpaREdH4+eff+5wrQMHDiA2NhafffaZ1nvqUgPb2Xld7eTJk3j48CHGjh1r9L3t7e0RHR3dpkQsLi4Or732WofztR2n61j8XcgwAAAgAElEQVRtTZo0Se/SOWI6OE+6APDxxx/DysoKo0aNQllZmfL+qqoqBAQEQCgUwt/fX1ksXlNTg4kTJ0IgEMDR0RHNzc3KOV5eXpg3bx4AYMuWLVi8eDEsLDT39SkpKYG9vT2sra2VZUTPrh8ZGYl169aBx+PB0dERd+7cwcKFCyEWiwE8+WR57969KsXxmmpM7927h7lz5yI9PR319fXK+zXtGRMTo6yB7devX5v9n43/6TrXp2tn1b2W6uppCSFdyySamEdFRWHNmjVYtmwZUlJSEBsbCwBISUmBj48PMjIyEBsbi5SUFKxYsQK7du2Cm5sbvvvuO/zyyy/Kq9f+9ttvWLNmDd566y18++23CAwM1FiI3srZ2RnV1dUoKCjABx98gCVLlrRZv6SkBLm5uZBKpThw4ACcnJwwe/bsdo/a1NWYnjlzBsOHD0e/fv0wfPhwHDx4ENOnTwcAjXuuXbsWTU1NkEql+Oyzz9rs/2z8y5cvh5+fn8o8Ta/l4sWLsXfvXhw7dgyzZs3CxYsX4ezs3Oa5NDc3o7CwEFZWVlr/To1JLpfj7t27uHLlCtehcOLWrVtwdHTkOgyiJZNIukKhEFZWVggMDMThw4eV98vlcoSGhkIoFCIoKAiZmZkAgMLCQoSEhIDP52PEiBEoKirCtWvXEBkZqSy437BhA86fP69cy8PDA9euXWuz94kTJzBnzhyUlpZi3LhxatdvbcA+adIkrFq1CkVFRTh06BD++te/6vQ8U1NTkZKSoky0LS0tyn9r2vNp6vZXF786ml5LoG097bPq6+tx5MgRXLhwQafnaywlJSUoKyvDl19+yXUonLh27RqmTp3KdRhESyaRdJuamlBfX4/MzEx4eHgo73dzc0NWVhb8/Pxw/PhxuLm5Ke/PyMjAmDFjwBhDY2Mj3N3dkZaWhsmTJ8PFxUXlKFNTwgWA/fv3Iz4+Hi+99BKWLVumcf2qqio0NzdjyZIliImJweTJk5VH2Nqoq6vDo0ePlOeXb968iaFDh6KoqAiurq4a93y6XpXH47XZX1386upcNb2W2rC2tsaCBQuon66Jon663YwxaiTaKxnLyspiEomEWVpasqCgIHb//n0mk8mYmZkZW7lyJfP392fW1tbM39+fVVZWMsYYq6ioYEFBQUwgEDBfX18mk8mYQCBgxcXFbOTIkUwikajsMWTIEI2xpaamMoFAwMaMGcOsrKzY2bNn26z/888/M5FIxN555x3W1NTEpFIpq62tVa5x9+5dNmzYMMbj8diAAQPYkSNHWElJCRs5cqRyjFQqZSKRiF26dIkxxtjf/vY3Zm5uzpycnNjt27fb3bP19di4cWOb/Z+NPy0tTe28ysrKNq9lWFgYEwqF7PTp08zBwYGNGzdO7WtkiJKxrtReyVhvQCVj3cqnPMb0/GhfC4cOHcLdu3fpyhHdFF05wrS1Huma0uV6pkyZgu+//57rMEzRbpOoXjAGhUKh0r+09UehUHAdWq/AOtEntzNz1KF+usSU9JqkKxaLlb1Ln/5pLfsiXaszfXIN0VuXUT9dYmJ6TdIlhqepjrq9Prk8Hq9NX92O5shksk5/OYD66RJTQ0mXdFpr7e+dO3fg7e2NlJQUAGi3T+6pU6fa9NXtaE56enqn315TP11iaijpkk6Ty+UYO3assva3uLhYq3nG7KtL/XSJqaGkSzqttfa3trZWpfa3oz65z/bV1WZOZ1E/XWJqKOmSTouMjMS5c+cgFouRn5+PuXPnAkCHfXKf7avb0ZwJEyZ0uq8u9dMlJscY1cDUT7d7M+SXI7qiry7106UvR3Qj3PfTJb2LsfvqUj9dYmpMovcC6T1Onjxp9D1b++k+6+krGLdH23G6jtXWpEmTDL4m4Q4d6RJCiBEZ7Uj35s2bJtsakLSvrKxMY5c2U1BQUIDS0tJe+/cll8vRv39/rsMgWjJK0h0yZAguXLiAQ4cOGWM7oqebN2+isbFRWc5lb2+Pq1ev4tdff+U4MvWamppgbW3dq/++2iuDI6bFaEl39erVxtiKGMB3332HmpoazJ49m+tQCOlx6JwuIYQYESVdQggxIkq6hBBiRJR0CSHEiCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBElXUIIMSJKuoQQYkSUdAkhxIgo6RJCiBFR0iWEECOipEsIIUZESZcQQoyILkxJlGJiYpCXl4cHDx6gpaUFf//73zFkyBCkpKRwHRohPQYlXaJkZmaGnJwclUuV02W/CTEsOr1AlObPn49+/fopb4tEIixcuJDDiAjpeSjpEqVBgwahb9++ytt9+/bF8OHDOYyIkJ6Hki5R8fbbb8Pc3Bw8Hg+TJk3iOhxCehxKukTFe++9B4lEgn79+uH999/nOhxCehz6II2o6N+/PyQSCerq6vDSSy9xHQ4hPY7Rku6NGzfw+eefG2s7ogcnJyfU1tZi9erVePz4Mfh8PszMTPNNUWNjI5qbm8Hn87kOhTMymQyenp5ch0G0ZNSk+/jxY0yfPt1YW5JOGjt2LOrr6+Ho6IjExERMnToVLi4uXIelVm5uLuRyOUJCQrgOhROZmZm4evUqJd1uxKinF1544QWMGDHCmFsSPUkkErz88ssYMmQI16GoVVlZicbGxl77d3Xp0iWuQyA6Ms33jIQQ0kNR0iV6KykpQWhoqN7rMMaQlJRkgIj+o7q6Ghs3bkRSUhL4fD7EYjESExPVjj1//jx4PJ7yp6ioSOO62o798ccf4eTkBKFQiPDwcDQ0NGicm5ycDIlEAkdHR5w5cwYAcPDgQZw7d06fl4CYGEq6RG/Ozs7IyMjQe50rV67g3r17BojoCcYYIiMjERUVhejoaNTV1SE/Px/r169X+arz01q/Bs0Yg6ura7vrazNWoVDgxIkTKC8vR1VVFbKzs9XOLSgowI4dO3DhwgVkZmaisrISADB16lSkpqbixo0bnX4diGmhpEv0tmzZMvj4+AAAwsPDMX78eHh4eMDZ2RklJSXKD+JsbGwglUqhUCgwevRorFixAvn5+bC1tUVWVhbef/99rFu3DjExMSgrK8Mrr7yiV1zZ2dmQSCSws7MDALS0tEChUMDS0hI8Hk/v560NmUwGb29v2NjYQCgUqnzN+mlHjx7F3Llz4ezsDKlUijfeeEP5WFhYGLZs2WKUeEnXo6RL9BYXF6f89+LFizF48GBcvnwZY8eOxcWLFxEdHY0JEyagoqICUqkUe/bswcqVKwEA3t7eiIiIAAAsX74csbGxSExMRP/+/fV+W52bm6vyqf6BAwcQGBiITZs2aZwTHBwMKysrSKXSdk8v6Dq2uLgYdnZ2GDZsmNq5ZWVlSEhIgI2NDVxdXZGbm6uc6+npqXKbdG+UdEmXsLCwgJubG1paWgAAQqEQfD4fAQEBuHXrllFiqKiogEAgUN4WiUQ4duwYwsPD1Y4fOnQoiouLcf/+fQQHByM5OVnj2rqMLS8vx/bt27Fr1y6Ncx0cHDB//nxUVlZi9erV+OSTT5TzBQIBKioqdH36xERR0iVG0dTUhIaGBhw5cgSDBg0Cj8eDXC5HfX09Hj58qBxXVVWFpqYmg+wpEolQV1envB0YGKg80lQnNTUVNTU1MDMzQ3NzM6ytrfUeK5fLsXv3bsTFxSm/wKFurp+fH86ePYvGxkY0NjZCKBQq16irq4NIJNL16RMTRUmX6G3mzJnIy8tDfHw8Nm3ahLS0NOTk5CA5ORmbN28GAOzcuRNCoRDXr19HREQEvLy8kJ2dDXd3d+Tm5mL9+vXw9PREeno6IiIiUFpaqjxP3FlSqRQFBQXK2/Hx8YiPjwfw5EsFwcHBKuOdnJzg4+MDe3t75ObmYsGCBWrH6TJ269atWLlyJfr06QMej4eEhAS1c6VSKTw8PDBgwABs3boVixYtUq7x66+/wtfXV6/XgpgQZiQnTpxgSUlJxtqOGMjSpUvZtWvX9Frj1KlTbOnSpQaKSFV7f1ctLS1sxowZ7MGDB20eKy8vZ6GhoR2ur+04XcfqIjo6mhUWFqp97O9//zv79ttvDb6nvkJCQrgOwVR9ahJHugMHDsTatWtV7pPJZDod6UyZMkVjrWhZWRkGDx6sV4y6UFeb2Z6srCy8+uqrsLW1hVAoxMiRI7skhvnz5ytrQ62treHt7W2US/F89NFH2LFjB7Kysrp8r6fxeDxs27YNe/bsaVMilpiYiKVLl3a4hrbjdB2rrcOHD2PGjBl0BY8exCSS7ubNm2Fubq5yX1pamk5raCqqb2xsRFJSksa6zK6gqTZTneLiYrz55ptYsmQJysrKUFxcjHHjxukdr7oYtm/fDhcXF9y7dw+3bt1CbGwsVqxYgQMHDui1V0dOnjyJhw8fYuzYsV26jzr29vaIjo5uUyIWFxeH1157rcP52o7Tday2Jk2apHfpHDEtJpF0AeDjjz+GlZUVRo0ahbKyMuX9VVVVCAgIgFAohL+/v7JovKamBhMnToRAIICjoyOam5uVc7y8vDBv3jwAwJYtW7B48WJYWGhuM6FuLXX7qqtB7devHxwdHXHnzh0sXLgQYrFYbW2mprrTb775BtOmTcNbb70FoVAIkUiEhIQEVFdXq33e+sTwNJFIhOnTp2Pt2rXYvXt3539xhBCdmEzSjYqKQk1NDaRSqcpb3pSUFPj4+ODOnTsqb4d37doFNzc3VFVV4dixY8rWg7/99hvWrFmD5ORkfPvttwgMDNRYkN5K3Vrq9lVXg/rZZ59hwoQJcHJywuzZs1Vif7o2U1Pd6e3bt+Hm5tbmfk3PW58Y1PH29lYmdEJI1zOZJuZCoRBWVlYIDAzE4cOHlffL5XKEhoZCKBQiKCgImZmZAIDCwkKEhISAz+djxIgRKCoqwrVr1xAZGaksvN+wYQPOnz+vXMvDwwPXrl1rs/eza7W3L6Bagzpp0iSsWrUKRUVFOHToEP76178CaFubqYmzs7PKJ+wdPW9Dx3Djxg0MGDBA4+MPHz7E5s2bVa6dZkqKiopQXV2t8u6oN7ly5Qpmz57NdRhEByaTdJuamlBfX4/MzEx4eHgo73dzc0NWVhb8/Pxw/Phx5VGhm5sbMjIyMGbMGDDG0NjYCHd3d6SlpWHy5MlwcXFRObLUlHDVrcXn8zXu+ywej4clS5YgJiYGkydPhpmZGeRyOfbt24e4uLh2T2sAwLRp0zB8+HAEBAQov/q5a9cuWFhYaLV/Z2JoaWlBY2Mj8vLysHbtWmzYsEHj2q1faOioDwFXfvrpJ9y4cQMzZszgOhROfP/991yHQHRlrDqJ9kp7srKymEQiYZaWliwoKIjdv3+fyWQyZmZmxlauXMn8/f2ZtbU18/f3Z5WVlYwxxioqKlhQUBATCATM19eXyWQyJhAIWHFxMRs5ciSTSCQqewwZMkRjbM+u9eDBA1ZZWdlm37CwMCYUCtnp06eZg4MDGzduHGOMsaamJiaVSlltbS1jjLGYmBgGQPkTHx/PSkpK2MiRI9Xun5OTw0aPHs2EQiHj8/ls6NChrLCwUO3z1ieGJUuWMIFAwMzMzFifPn3Yyy+/zHbu3Nnu780QJWNdqbeXIlLJWLfzKY8x43ys/8MPP+CXX37BwoULjbEdMZAPP/wQ7733nsk2Me/tf1d79+6FnZ0d3nzzTa5DUTFlyhQ6Cldvt8l8kGYMCoVCpY9p649CoeA6tB6NdaJPbmfmqEP9dImp6VVJVywWK3uYPv0jFou5Dq1H60yfXEP01mXUT5eYoF6VdInhaKqfbq9PLo/Ha9NXt6M5Mpms018OoH66xBRR0iWdoqmOuL0+uadOnWrTV7ejOenp6Z1+e039dIkpoqRLOkUul2Ps2LHKOuLi4mKt5hmzry710yWmiJIu6ZTWOuba2lqVOuKO+uQ+21dXmzmdRf10iSmipEs6JTIyEufOnYNYLEZ+fj7mzp0LAB32yX22r25HcyZMmNDpvrrUT5eYJGNVBPf2IvbuypBfjuiKvrrUT5e+HNHNmEY/XdI7GLuvLvXTJabIZHovkJ7v5MmTRt+ztZ/us56+gnF7tB2n61htTZo0yeBrEm7RkS4hhBgRJV1CCDEio55eyMjI6LCInHCvsbERjDFYWloiLy8P1dXVsLW15TostcrLy1FdXd1r/65++eUXREZGch0G0YHRku5rr71mlIsgEv0dP34cDx8+NLnOVUS9/v37cx0C0YHRkq5AIIC7u7uxtiN6uHTpEmpqauj3RUgXoHO6hBBiRJR0CSHEiCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBElXUIIMSJKuoQQYkSUdAkhxIgo6RJCiBFR0iWEECOipEsIIUZESZcQQoyIki4hhBgRJV1CCDEiuhowURo9ejQKCwthZmYGxhhiY2MhFotx8eJFrkMjpMegI12i9O6776K6uhqlpaUoKytDeXk5Zs6cyXVYhPQolHSJUnh4OBwdHZW3nZyc8M4773AYESE9DyVdomRraws3Nzfl7eeffx4SiYTDiAjpeSjpEhULFiyAjY0NLC0t8d5773EdDiE9Dn2QRlSEhIRALBajubkZMpmM63AI6XEo6RIVlpaW8PLyQk1NDZ577jmuwyGkx9Eq6b7yyivo379/V8dCTERFRQUaGxsREhJisDVLSkrg7OxssPUM7e7du3BwcECfPn24DoUTt2/fxtmzZ2Fpacl1KD2eVkm3f//+OHToUFfHQkxEc3MzmpqaYGVlZbA1Q0JCTPpvaPHixZg3bx7c3d25DoUT06ZNA2OM6zB6BTq9QNowNzeHubk512EQ0iNR9QIhhBgRJV1iMkpKShAaGqr3OowxJCUlGSCiJ6qrq7Fx40YkJSWBz+dDLBYjMTFR4/jz58+Dx+Mpf4qKivQaBwA//vgjnJycIBQKER4ejoaGBo3zk5OTIZFI4OjoiDNnzuDgwYM4d+6cHq8AMSRKusRkODs7IyMjQ+91rly5gnv37hkgoicJPDIyElFRUYiOjkZdXR3y8/Oxfv36ds+B5uTkgDEGxhhcXV31HqdQKHDixAmUl5ejqqoK2dnZaucXFBRgx44duHDhAjIzM1FZWYmpU6ciNTUVN27c6OzLQAyIki4xGcuWLYOPjw/Cw8Mxfvx4eHh4wNnZGSUlJQCAqVOnwsXFBTY2NpBKpVAoFBg9ejRWrPj/7N17XFR1/j/w13AfZiBgUDA0wEtBgWZgjeZ6IRDURNwUbHHV1dU0zSHyh5a2aSkaSKAlKkHFliVaqyvqeikFNRU1b+iqqYyg3GKGAUS5yuf3h19mRWaGGWaYM8D7+XjwiBk+n895nwO+O3Pmfd6zFLm5ubCzs0NWVhbmz5+P1atXIyoqCsXFxRgyZEi7Y8rOzoaLiwvs7e0BAE1NTZDJZLCysgKPxzPIfmsjIiICvr6+EAgEEAqF6Nmzp8px+/fvx5w5c+Dm5gaxWIzXX38dADB58mQkJiYaLV6iHiVdYjJiY2MBPKokGDBgAC5fvoxRo0Ypu5xJJBKMHTsWcrkcYrEYaWlpWL58OQDA19cXs2fPBgAsWbIEy5YtQ1JSEnr16qXXS+ucnBx4e3srH2/fvh1BQUFISEjQOC8kJATW1tYQi8UaLxtoO65Zfn4+7O3tMWjQIJXzi4uLsW7dOggEAnh4eCAnJwcA4O3trfyecIuSLjFZFhYW8PT0RFNTk/I5oVAIGxsbBAYG4s6dOx0eg1wuB5/PVz4WiUQ4cOAAwsPD1c4ZOHAg8vPzUVlZiZCQECQnJ+s1rllpaSk2btyILVu2qJ3v6OiIBQsWoLy8HCtWrMDnn38OAODz+ZDL5bruPukAlHRJp9LY2Ij6+nrs27cP/fr1A4/Hg1QqRV1dHaqrq5XjFAoFGhsb9d6eSCRCbW2t8nFQUJDyLFOd9PR0VFVVwczMDA8fPoStra1e4wBAKpUiNTUVsbGxsLGxUTt/5MiROHXqFBoaGtDQ0AChUAgAqK2thUgk0nX3SQegpEtMxrRp03D+/HkMHToUW7duxbFjx5CcnIzPPvtMOWbz5s0QCoW4efMmZs+eDR8fH2RnZ6N///7IycnBmjVr4O3tjYyMDMyePRtFRUXw9/dvd0xisRjXr19XPo6Pj0d8fLzycWZmJkJCQlrMcXV1hb+/PxwcHJCTk4N33nlHr3EAsGHDBixfvhyWlpbg8XhYt26dyvlisRheXl7o06cPNmzYgHfffRcA8Pvvv2Po0KHtPg7EgJgWJkyYoM0wQtQyxN/QkSNH2OLFiw0QTWvvvvsuu3HjRqvnm5qaWGRkJLt3757KeaWlpSwsLKzN9Q09TlcSiYTdunVL7c8nT57MamtrDba90NBQg63VxXyp15nuM888g1WrVrV4LiIiQqczi4kTJ6qtzSwuLsaAAQN0jkEVVXWO6ixYsEBZ+2hjY4OXXnoJZ8+eVTmWqakJfeWVVzBv3jyV6y5YsADAo9ImZ2fnDrv19PHfhS41sM37ZKi6WUP5+OOPsWnTJmRlZRltmzweD1988QXS0tJUloglJSVh8eLFba5j6HG62Lt3LyIjI9G3b1+DrkvaSZvUrO4sZceOHSw2NrbFcw0NDczPz0/rtC+VStnEiRNbPV9fX8+WLFnC+vXrp3G+qhhU2bZtG7t06RKrrq5mgYGB7ODBgxrHu7u7s7KyMnb//n0WHR3N/va3v6kcl5uby5YtW9bq+ZMnT7J58+a1et7Hx4fxeDz266+/MsYY+/bbb9nVq1fbjL89dP1dNFO3T/ow9VdL6s50uws60zUa/c50AeDTTz+FtbU1XnnlFRQXF7f4mUKhQGBgIIRCIQICAlBeXo6qqiqMGzcOfD4fPXr0wMOHD5XjfXx88PbbbwMAEhMTER0dDQuLtttDPBmDqjpPVXWO2tZw1tXV4eWXX0ZhYSEcHBxga2urLBlqrgmdNWtWq/26evUqvL290atXLxQUFAB41Phl7NixmDNnTquzbVXHa+LEiRg9ejT8/Pzg6OgIOzs72NraQiAQ4JlnnoGVlRVGjx4NACrja9ZcA7t48WLlWfwLL7ygcZ94PJ7yTFlVbOrqaQkh6umddOfNm4eqqiqIxWKkpKS0+FlKSgr8/f1RUlICX19fpKSkYMuWLfD09IRCocCBAwdgZvYohBs3bmDlypVITk7GTz/9hKCgILUF4G3FoK7OE2hZ59hWDWePHj0gEAhw8OBBvPDCC3Bzc0NFRQXOnz+P/fv3A/hfTai3t3er/fLy8sLly5cREhKCCxcuAHj0cvWf//wn7t27h7Vr17Z5vN5//3306tULP/74I5YvX44JEyagvLwcU6ZMwerVqyGTyVBQUID6+nqV8TVrroFdt24dGGOYPXs2Vq5cqXGfGhoaNMam6TgTQlTTu8uYUCiEtbU1goKCsHfv3hY/k0qlCAsLg1AoRHBwMDIzM8EYQ2hoqPJa6e3bt3Ht2jXMnTtXWei+du3aFtdQvby8cO3aNZ1jeLLO88k6x7aUlZXhqaeeQm5uLsaNG4ekpCT8v//3/1BUVKQ8u2x269atFvt16tQpAI86drm7u7eoNRWJRNi2bRvGjBmDqKgo5dmkquMFAPb29vD09MTIkSNx9epV2NjYwMPDA3Z2drC3t0ePHj3Q1NSEQ4cOYdasWSrje9ymTZvg4uKCyZMnaz1HXWyqjrMqRUVFBu3Pa2hSqRQXL16EQCDgOhROSKVSrkPoNvROuo2Njairq0NmZia8vLxa/MzT0xNZWVkYOXIkDh48CE9PTzDGsGvXLowYMQKMMTQ0NKB///7YunUrJkyYAHd39xZnn20l3LZiaCaVSvH9998jNjZWq0sWwKNbPpvbHNbU1ODbb79FfHw8nn32WcTExCjHKRQK9OnTp9V+aTJs2DB89NFH+PDDD5Ufc67qeOli27ZtKuN73IkTJ3Dw4EH8+OOP2L9/P9atW6d2nx6vc9U3tqeffpr66ZqwKVOmcB1C96HNlV91b4JkZWUxFxcXZmVlxYKDg1llZSWLiIhgZmZmLCEhgZWXl7OAgABma2vLAgICWHl5OZPL5Sw4OJjx+Xw2dOhQFhERwfh8PsvPz2d+fn7MxcWlxTaee+45jbGpimHy5MlMKBSyo0ePMkdHRzZ69GgWFRXFACi/4uPjWWFhoco3mhYuXKgcZ2Zmxnr37s3Wr1/P0tPTGZ/PZyNGjGDW1tbs1KlT7ObNm0wkErHXX3+9xX4FBAQwoVDIfv31V+bk5MSGDRvGFi5cyKytrVlqaipj7FE50uuvv658I03V8QoNDWV8Pp8dPHiQTZw4kdna2rIdO3YwR0dH5uHhwb777jtmbm7Oli1b1io+Dw8P5e+i+ffy+DEAwDZs2KB2n5r3Py4uTmVsqo6zrn9DpoLeSKM30ozkSx5jbbeLN/Wu/8T0mfrfEJ3pTsF3331nsE8LmThxIv79738bZK0uJrXT3JEmk8la9A5t/pLJZFyHRoyAtbNHbnvnPY766RJD6jRJ19nZWdk39PEvZ2dnrkMjRtDeHrn69tZl1E+XGFinSbqka1JV/9tWj1xt++oC/6s5joiIaFdfXeqnSwyNki7hlKr637Z65GrbV/fxeRkZGe16iU39dImhUdIlnJJKpRg1apSy/jc/P1+recbqq0v9dImhUdIlnGqu/62pqVHW/2rTI1fbvrpPztMV9dPVXWNjI8zNzY2yrU5Jm8IyU6+xJKZP3d+Qqvrfu3fvMhcXF9a7d2/m4+PDxowZo6wdnj59Ojty5AiztrZmlpaWLCgoiFVWVqqcwxhTzgsMDNTY/EddnW5WVhaTSCTKx3FxcSwuLk75ePfu3Sw4OLjFnN27dzMnJyfG5/NZUFAQk8lkeo1jjKmsM1c1nzHGli5dyhwcHJiPjw+7du0aY4yxY8eOsUWLFqndf0PW6dbW1rIpU6YYZK0u6EtKusQoDPk31BF9damfruGSbnV1NXvzzTcNslYXpH+XMcyaISoAACAASURBVEKMzZh9damfru7q6+thaWlplG11Rnr3XiDE2A4fPmzU7Tk4OEAikaj8WXP3trYYepwuxo8fb/A1NamuroadnZ1Rt9mZ0JkuIcSgqqurlW/gkdYo6RJCDKq6urrbtsjUhlaXF2pqaky6FyrpOIWFhXBzc9N7ndLSUpP+GyovL8fVq1e77bXI5tIzQ7h37x6d6WqgVdI9dOhQR8dBTJSpdwcjpoeu6WpGlxeIRjwer8Xn2BHSFrlcbrQbMTojSrpEI3d3d+WHahKiDZlMRt3/NKCkSzQaMGAAbty4wXUYpBORy+WUdDWgpEs0oqRLdEWXFzSjpEs0oqRLdFVeXg4nJyeuwzBZlHSJRh4eHlq3WyQEABoaGrT+xO3uiJIu0cjc3LzdbREJIa1R0iVtsrCwQENDA9dhkE6gpqZGY19gQkmXaIEuMRBtFRQUoE+fPlyHYdIo6ZI20ZtpRFt5eXnw9PTkOgyTRkmXtImSLtHW1atX8fzzz3MdhkmjpEvaREmXaOvq1astPj2ZtEZJl7TpmWee6dBP3CVdR0lJCVxcXLgOw6RR0iVtav60WUI0UfVxRqQ1SrpEK1ZWVqivr+c6DGLCbty4gWeffZbrMEweJV2ilb59+0IqlXIdBjFhOTk5GDJkCNdhmDy6V4+0SS6Xw9raGmlpaaiuroa1tTUSExO5DouYmNOnTyM6OprrMEweJV2i0YQJE5CTk4PGxkZUVlaiqakJgYGBXIdFTFBeXh48PDy4DsPk0eUFotFHH30EHo8HhUKBpqYmAMCoUaO4DYqYnIqKCjg4OIDH43EdismjpEs08vf3h5eXl/Kxg4MDRowYwWFExBT98ssvGD16NNdhdAqUdEmbUlJS4OrqCgAQCoV48cUXOY6ImJqff/4Zr732GtdhdAqUdEmbnnvuOQwePBgAYGtrS5/0SlqRSqXUc0FLlHSJVr744gv06NEDvXr14joUYmLOnTtHr350YDLVCzk5Oco3aohpeuGFF+Dh4YGTJ09yHQrRkZOTE5577rkOWXvHjh2YMmVKh6zdFfGYidy75+vri7/85S9ch0E0qKqqQkVFBZ555pkWz8vlcly5csWk32DbuXMnJk2axHUYnDl58iR2797dIWuPGTMGBw4coMoF7aSazJmup6cn3n//fa7DIO1w5coVfPfddyb9+zt58qRJx9fRQkNDO2Td3377DS+++CIlXB3QNV1CSLulpaVh5syZXIfRqVDSJYS0S01NDQoKCqhpuY4o6RKjKSwsRFhYmN7rMMawfv16A0T0SEVFBeLi4rB+/XrY2NjA2dkZSUlJasefPXsWPB5P+XX79m29xv3yyy9wdXWFUChEeHi4spubuvnJyclwcXFBjx49cOLECezcuRNnzpzR5xC0y/bt2/HGG28YfbudHSVdYjRubm7YtWuX3utcuXIFZWVlBojoUQKfO3cu5s2bB4lEgtraWuTm5mLNmjUa+8MeO3YMjDEwxjT2G9BmnEwmw6FDh1BaWgqFQoHs7Gy1869fv45Nmzbh3LlzyMzMRHl5OSZNmoT09HTk5eW19zC0S0ZGBsLDw426za7AZN5II11fTEwMDh8+jL59+0Iul6OwsBD37t3D6dOnsXDhQpw7dw4ymQy+vr7Ys2cPnJ2dMXz4cAwfPhyRkZEYNmwYMjMz8eGHH+L48eO4ceMG8vLy9DrLy87OhouLC+zt7QEATU1NkMlksLKyMtqbQxEREcrvhUIhevbsqXbs/v37MWfOHLi5ucHNzU35/OTJk5GYmIjPP/+8Q2NtdubMGfj6+kIgEBhle10JnekSo4mNjQUAREdHY8CAAbh8+TJGjRqFixcvQiKRYOzYsZDL5RCLxUhLSwMALF++HMCjksLZs2cDAJYsWYJly5YhIyND75fVOTk5LT7Ta/v27QgKCkJCQoLGeSEhIbC2toZYLFZ72UCXcQCQn58Pe3t7DBo0SO384uJirFu3DgKBAB4eHsjJyQEAeHt7K783hs8++wwLFy402va6Ekq6hDMWFhbw9PRU3hQjFAphY2ODwMBAo30mm1wuB5/PVz4WiUQ4cOCAxpfNAwcORH5+PiorKxESEoLk5GS9xgFAaWkpNm7ciC1btmic7+joiAULFqC8vBwrVqxQntny+XzI5XJdd79dpFIp+Hw++vTpY5TtdTWUdInJaGxsRH19Pfbt24d+/foBAHg8HqRSKerq6lBdXa0cq1Ao0NjYqPc2RSIRamtrlY+DgoJanGmqkp6ejqqqKuVnx9na2uo1TiqVIjU1FbGxsbCxsdE4f+TIkTh16hQaGhrQ0NAAoVAIAKitrYVIJNJ199slISEBixYtMsq2uiRmIiZMmMB1CKSdLl++zJYuXdrmuIiICGZmZsYAMKFQyI4ePcocHR3Z6NGj2ZEjR5i1tTWztLRkQUFBrLKykjHG2N27d5mLiwvr3bs38/HxYWPGjGE3b95kIpGIBQYGMj8/P61iVPf3lZWVxSQSifJxXFwci4uLY4wxtnv3bhYcHNxqzu7du5mTkxPj8/ksKCiIyWQylWO1HRcVFcUAKL/i4+PVzmeMsaVLlzIHBwfm4+PDrl27xhhj7NixY2zRokU677+uCgoKWEREhEHW6qa+pKRL9KZt0tXkyJEjbPHixQaKqDV1f19NTU0sMjKS3bt3r9XPSktLWVhYmFbraztWlzV1IZFI2K1bt9T+3FD/vubPn8/Onz9vkLW6qS9N/vLCM888g1WrVrV4LiIiAv7+/lqvMXHiRLX1ocXFxRgwYIBeMbI26kbV1WGqc+fOHdja2uL8+fNabVPX+tfmuYaqmzWEjz/+GJs2bUJWVpZRt8vj8fDFF18gLS2tVYlYUlISFi9erNU62o7VZU1t7d27F5GRkejbt69B131SQUEBKioqqKOYnkw+6X722WcwNzdv8dzWrVt1WkNdQmxoaMD69es11mNqo626UU11mKps374dy5Ytw1dffaXVNnWtf22ea6i6WUM4fPgwqqurOfkoIAcHB0gkklYlYrGxsXj11Ve1WkPbsbqsqa3x48cb5VN4V61ahaVLl3b4dro6k0+6APDpp5/C2toar7zyCoqLi1v8TKFQIDAwEEKhEAEBASgvL0dVVRXGjRsHPp+PHj164OHDh8rxPj4+ePvttwEAiYmJiI6OhoWF5nLlwsJCODg4wNbWFgkJCa3Wnzt3LlavXo2oqCj07NkTPXr0QElJCRYtWgRnZ2dEREQoaxqb6zCLi4vV/kMpKyvDnDlzkJGRgbq6OgDQuM2YmBj4+/ur3Laq+OfPn4/Vq1eDx+MpXzGoOo7h4eF47bXX4OXlBTc3NxQWFrbvF0g6vatXr6KpqQkDBw7kOpROr1PcHDFv3jysXLkSMTExSElJwbJly5Q/S0lJgb+/P3bt2oVly5YhJSUF5ubm8PT0xL/+9S/897//hZnZo/+33LhxAytXrsQbb7yBn376CUFBQRoL0Zu5ubmhoqIC169fx8KFC9HU1NRi/cLCQuTk5GDVqlUIDAzE9u3b4erqipkzZ7Y4c3uyDlNVjemJEycwePBg9OzZE4MHD8bOnTsxdepUbNmyRe02GxsbIRaL8dVXX6nc9pPxL1myBCNHjsSKFSsgFovVHsfo6Gh88803OHDgAGbMmIGLFy+2KMh/XElJidEvDeiivLzcpOPraM3/826vDz74wKC3XndnnSLpCoVCWFtbIygoCHv37m3xM6lUirCwMAiFQgQHByMzMxOMMYSGhsLGxgYvvfQSbt++jWvXrmHu3LnKYvu1a9fi7NmzynW8vLxw7do1lds/dOgQZs2ahaKiIowePRq3bt1qsX5RUZFy7Pjx4/HRRx/h9u3b2L17N/7xj38AUF2HqUp6ejpSUlIwdepUAI/ukJo6darGbba17SfjV0XVcWz2ZD2tKjKZDOfOndO4b1y6d++eScfX0RoaGto99/Dhw/Dx8WnVR5m0T6dIuo2Njairq0NmZmaLT6YFHvXhzcrKwsiRI3Hw4EF4enqCMYZdu3ZhxIgRYIyhoaEB/fv3x9atWzFhwgS4u7u3OMvUlHABYNu2bYiPj8ezzz6LmJgYeHp6tlq/uW7UwsIC7733HqKiojBhwgSYmZlBKpXi+++/R2xsrMZLGbW1tbh//77yGnNBQQEGDhyI27dva9xmMx6P12rbquIHWte5qjqOuvDx8UF0dLROc4wpKyvLpOPraO09y3/48CHWrl2Ln376ybABdWcclU20oqmO0sXFhVlZWbHg4GBWWVmprPdMSEhg5eXlLCAggNna2rKAgABWXl7O5HI5Cw4OZnw+nw0dOpRFREQwPp/P8vPzmZ+fH3NxcWmxjeeee05jbOnp6YzP57MRI0Ywa2trtm/fvhbrX7hwgYlEIjZ9+nTGGGONjY1MLBazmpoaxpjqOszCwsJWNaZisZiJRCJ26dIlxhhjn3zyCTM3N2eurq7s0qVLarfZfDzi4uJabVtV/Fu3bmUikYgBUM5TdRwnT57cqp5WFUOUjHW07l6S2N7937hxI0tNTTVwNN3alybzcT2hoaEd9nEipGM1f3LEmjVruA5Fre7+99We/ZfL5YiMjMS+ffuUr5qI3lLpSD5GJpO16F/a/CWTybgOrctj7eiR2545qlA/XdXef/99rFy5khKugdHRfIyzs7Oyd+njX82lV6TjtKdHriH66jLqp6vSb7/9BjMzM7zyyisGW5M8QkmXdChV9b/Dhw/H0qVLkZubCzs7O2RlZSlrh6OiojBp0iS4u7tDIBBALBZDJpO1OUdT3bMmptJP98k6bnUe76crFovx+uuvA/hfP11DePjwIT744INWd4ISw6CkSzpUc/1vSUkJfH19kZKSorFHblJSksreum3N6dWrV7teYlM/3daSkpLwl7/8hV7hdRBKuqRDSaVSjBo1Sln/m5+fr9U8Y/XWpX66LeXn5yM7OxvTp0/Xey2iGiVd0qGa639ramqU9b/a9Mh9srduR/XVpX66LUVFRSExMdFol1a6JaNXqanR3esoOzNNdbqq6n819cidPn26yt66bc1RVff8OOqn2/a/r2+++YatW7euzXFEL9RPl+jP0DdHdERvXeqnq/nfV1lZGQsKCmINDQ2GDo20ZPr9dEn3Y8zeutRP95Ho6GisXbu2zY57RH90hInJOXz4sFG319xP90nNn16sDW3H6rKmtsaPH6/X/P/85z/o3bs3XnrpJQNFRDShpEtIN3b//n0kJSWZTDP77sBkkq5cLseBAwe4DoO0Q35+PqRSabt+f01NTUa5zbSsrIz+vlR4//33ERMT06JsjnQsk2l4s3nz5haf8EA6j6amJjx8+BCWlpY6zWOMISUlBRMmTMDTTz/dQdE9UldXB2tr6w7dhinz9PTEuHHjWjx39OhRfP/999i8eTNHUXVLqSaTdEn3VF5ejunTp2PJkiX405/+xHU43UZlZSUmTZqEf//737Czs+M6nO6EuowRbjk5OSEjIwOJiYnYsWMH1+F0G4sWLcLq1asp4XKAki7hnEAgwPbt2/Hzzz/TS10j+Prrr9GvXz8MHTqU61C6JUq6xCRYWFhg8+bNKCkpoY/57kCXLl3Czp07W3y4KzEuSrrEZPB4PKxYsQJPP/00/v73v+vVU4G0VlFRgXfffRdff/01zM3NuQ6n26KkS0zOokWLMHbsWERGRqKmpobrcLqE+vp6zJgxA3FxcQZpjEPaj5IuMUlvvPEG3nrrLYSFhRmkZWF3xhjD/PnzMWvWLPj5+XEdTrdnMjdHEPKkgIAAPPXUU5gyZQrS09PRp08frkPqlD788EO89NJLmDhxItehENCZLjFxfn5+SEtLw/Tp05Gbm8t1OJ1OcyOfBQsWcB0K+T+UdInJ8/T0xLZt2xAdHY1ff/2V63A6jf/85z/IysqizzozMXR5gXQKLi4u2LlzJ2bMmAGFQqH8QEai2vHjx7FlyxZs376dPgXCxNCZLuk0hEIhfvjhB2zfvh1ffvkl1+GYrOPHj2PNmjX47rvvYGVlxXU45AmUdEmnYmVlhfT0dNy8eRMrVqzgOhyTc+zYMaxduxYZGRnKz08jpoWSLul0eDwePv30Uzg6OkIikaCpqYnrkEzC0aNHkZSUhB07dlDCNWGUdEmnJZFIMGTIELz55pstPtG3Ozp48CASExPx3XffUW9cE0dJl3Rq06ZNw5w5cxAREYHKykquw+HErl27kJKSgm3btlHC7QSoeoF0eoGBgXjqqafw5z//Gf/85z/h5ubGdUhGk5qaipMnT+KHH37QuYk84Qad6ZIuYciQIUhJScHMmTNx/fp15fPXrl3rMo1zGhoalN8zxrBixQrcvHkTqamplHA7EUq6pMvo168fvv32WyxYsAAnT55Efn4+RowYgaSkJK5D01tOTg6GDRuGxsZG1NfXY+bMmXB1dcXatWupDreToY/rIV1OZWUl/vrXv+LKlSvIy8uDu7s7Ll26BHt7e65Da5fGxkb4+vpCKpXiz3/+M+rq6jBr1iy9P3qdcII+rod0PZaWligoKMDt27cBAIWFhfjwww+5DUoPn3zyCQoKClBXV4e9e/eif//+lHA7MUq6pEthjGHChAm4cuWKsn63sbERP/30E0pKSjiOTne3b99GamoqHjx4AACoqqpCeno6fvvtN44jI+1FSZd0KTweD99//z1iYmLQt29fCAQCAEBRURHefvttjqPTXWRkJIqKipSPnZycYGtri6NHj3IYFdEHXdMlXVZDQwN27NiBzz77DEVFRbh//z5ycnLg5eXFdWhaaX5TkMfjwcHBAS+//DKio6MhFovpzbPOK5WSrpFduXIFycnJXIdhFPX19WhsbIStrS3XoUAul+PMmTPg8XgIDg5WPq9QKODo6MhhZKrV1dVh+/btcHR0xKBBg9C7d29KtAbm7u6OmJgYY2+Wkq6x7d27F7m5uYiMjOQ6lA53/Phx3LhxA3/729+4DkXp3r17EAgEMDN7dGVt1qxZ+OqrrziOqjWFQgGBQEBdwjrQ/PnzsWfPHmNvNpXuSOOAo6Njt/jomR49eqCsrMyk95XP55tkfKYYU1fT/D9eo2+Xk60SQkg3RUmXcKqwsBBhYWEGWYsxhvXr1xtkraysLPB4PNjY2AAAKioqEBcXh/Xr18PGxgbOzs4a73Q7e/YseDye8qu5Zri943755Re4urpCKBQiPDwc9fX1GucnJyfDxcUFPXr0wIkTJ7Bz506cOXNG4z5zvY/q9rM9+zht2jTweDzMmzdP4z5zgZIu4ZSbmxt27dplkLWuXLmCsrIyg6wFAAsWLEBZWRkYY5g7dy7mzZsHiUSC2tpa5ObmYs2aNdD0lsixY8fAGANjDB4eHnqNk8lkOHToEEpLS6FQKJCdna12/vXr17Fp0yacO3cOmZmZKC8vx6RJk5Ceno68vDyV65vCPmraT1338euvv8b+/fvVbodLlHQJp2JiYuDv7w8ACA8Px2uvvQYvLy+4ubmhsLAQkyZNgru7OwQCAcRiMWQyGYYPH46lS5ciNzcXdnZ2yMrKAvDojZHVq1cjIiICQ4YM0Ts2BwcH2NnZITs7Gy4uLsrbiJuamiCTyWBlZWW0ioKIiAj4+vpCIBBAKBSiZ8+easfu378fc+bMgZubG8RisfLz5CZPnozExESVc0xhHwHt97OtfbS0tISLi4vR4tYFJV3CqdjYWOX30dHRGDBgAC5fvoxRo0bh4sWLkEgkGDt2LORyOcRiMdLS0rB8+XIAgK+vL2bPnq2cv2TJEixbtgwZGRltvpTWRU5ODry9vZWPt2/fjqCgICQkJGicFxISAmtra4jFYo0vqbUdBwD5+fmwt7fHoEGD1M4vLi7GunXrIBAI4OHhgZycHACAt7e38ntT3kdV+2mIfTQVlHSJybGwsICnp6fyNl6hUAgbGxsEBgbizp07Ro9HLpe3aA4uEolw4MABhIeHq50zcOBA5Ofno7KyEiEhIWprs7UdBwClpaXYuHEjtmzZonG+o6MjFixYgPLycqxYsQKff/45gEeVGnK53KT3UdV+GmofTQUlXWLymtsZ7tu3D/369QOPx4NUKkVdXR2qq6tbjFUoFAbvnysSiVp8HFBQUFCLM01V0tPTUVVVBTMzMzx8+FDtDSLajpNKpUhNTUVsbKzyzT1180eOHIlTp06hoaEBDQ0Nys9Lq62thUgkMtl9VLefhtpHU0FJl3Bq2rRpOH/+POLj45GQkICtW7fi2LFjSE5OxmeffQYA2Lx5M4RCIW7evInZs2fDx8cH2dnZ6N+/P3JycrBmzRoAj15aZmRkYOzYscrrxPqorKxEdXU1xGJxi8bo8fHxiI+PBwBkZmYiJCSk1VxXV1f4+/vDwcEBOTk5eOedd1SO1Xbchg0bsHz5clhaWoLH42HdunVq54vFYnh5eaFPnz7YsGED3n33XQDA77//jqFDh6pcX9M+qttPQ++juv1szz42NDSgtLS01fomgRGj2rNnD9u8eTPXYRjFoUOH2Pr16/Va48iRI2zx4sUGiqi1CRMmqN0uAGZtbc2amppYZGQku3fvXqtxpaWlLCwsTKttaTtWlzV1IZFI2K1bt1Sur2kfdYnJVPYxMjKSAWBvvfWW2rHqfvcd7EtKukbWnqR7/vx5Zm9vz3bu3Knxucf98MMPDIDyKzw8XOM2CgoKGJ/PZ+fOnTNYDIZIuqNHj2YCgYAdOXJEr3XU0fYfnkKhYElJSaypqanF8++//z47fvy4VmtoO1aXNbW1Z88edvr0aY3rq9tHXWIylX3UBldJl3ovGNnevXtx9+5dvPXWWzrNW7lyJQYNGtTiRgJVzzXLyMjAmDFj4OjoiDNnzqCyshKBgYFq109ISEBtbS1KSkqUb0roG8PPP/+M//73v1i0aJEuu2pUoaGh2L17N9dhEA5w9LunT44wJYWFhXBwcICtrS0SEhIgl8sREBAAGxsbxMXFAYDK51SJiIhQds/as2ePMuEWFxerrGEtKyvDnDlzkJGRgbq6OuXz+sRACGmNkq4JcXNzQ0VFBc6fP4/9+/cjJSUFvr6+qKqqUr5RoOo5TS5fvoz+/fsrH/fq1atVDeuJEycwePBg9OzZE4MHD8bOnTuVPzNEDISQ/6EuYybk0KFDmDVrFoqKijB69GjcunULb775JqysrGBh8ehXpeo5TdLT01vcgKBuTEpKCqZOnQrg0d1Izd/rG8O//vUvXLx4sc04uXL16tUWN1iQ7sOQt4zrgpKuCdm2bRvi4+Px7LPPIiYmBr169cKhQ4cgFouRl5eHF198UeVz6ty6dQsuLi6wtLRUO6a2thb3799X3l9fUFCAgQMH4vbt2/Dw8NA7huDgYPz9739v/0HpYH/961+xdu1arsMgHJg5cyY3G+bi7bvuTFP1Qnp6OuPz+WzEiBHM2tqa7dixgw0aNIjZ2dmx559/ng0bNowVFBS0ek6dqKgoVl5e3uK5wsJC5ufnp3wsFouZSCRily5dYowx9sknnzBzc3Pm6urK7t69q3J72sZgiOqFjsbRO9jEBFD1QjfR3uqFzoiqF4gpo+oF0i4ymaxFr9HmL5lMxnVoBsHa0SO3PXNMkS79bU25ty1piZJuJ+fs7KzsM/r4l7OzM9ehGUR7euQauq8uF1g7+tuaam9b0hIlXcIJhUKBwMBACIVCBAQEoLy8XGWf3OYeuVFRUVr31n18jrq6ZFNnCv1tDdXblrRESZdwIiUlBf7+/igpKYGvry9SUlJU9slt7pGblJSkdW/dx+eoqkvuDNrT35Z623YOlHQJJ6RSKUaNGgWhUIjg4GDk5+drNY/r3rrGomt/W+pt23lQ0iWc8PT0RFZWFmpqanDw4EF4enqq7ZP7eI9cbXvrdkRfXWPStb8t9bbtPCjpEk7MnTsXZ86cgbOzM3JzczFnzhyVfXKbe+Q2XzrQprfu43OKiooM0lvX2HTt4atPb1t1axqqty15gtFLg7s56qfbfh3RW9dUb44wRA/fjuj1q4vm3ramiqubI+hMl3QaH3/8MTZt2qT89N+ujMfj4YsvvkBaWlqrErGkpCQsXry4zTW0HafrWG3s3bsXkZGR6Nu3r8HW7Cqo9wLpNA4fPsx1CEbl4OAAiUTS6vm2GhjpOk7XsdoYP368QdfrSuhMlxBCjIiSLiGEGBFdXuDAV199hWPHjnEdRodTKBR48OABTp8+zXUoauXn52PatGk6zWlqakJNTQ0EAkEHRUWMoaKigpPtUpcxI2tsbMSDBw+4DoPooaSkBGvXrlXbfIZ0Dubm5lz8jzOVznSNzMLCQnk/Pemc7t+/DysrK/o9knaha7qEEGJElHQJIcSIKOkSQogRUdIlhBAjoqRLCCFGREmXEEKMiJIuIYQYESVdQggxIkq6hBBiRJR0CSHEiCjpEkKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBH10yVES/fv30d9fT0qKytRV1cHhUIBALC3t4e5uTnH0ZHOgj45ghAtrVq1ComJieDz+Xj48CHMzc3x4MEDFBcXw9ramuvwSOeQSpcXCNHSjBkzYG1tjcLCQpSUlKCwsBCvvvoqJVyiE0q6hGipT58+cHZ2Vj5+6qmnEBUVxWFEpDOipEuIDqZPnw5LS0sAgKOjI0aNGsVtQKTToaRLiA5mzpyJnj17AgCGDRtGb6ARnVH1AiE6cHZ2xtNPP40HDx5AIpFwHQ7phOhMlxAdvfXWWzA3N8eQIUO4DoV0QnSm+4Tjx49j0aJF6N27N9ehEBPV2NgIZ2dnTJw4kbMYGhoa8Mcff8DNzY2zGNpy+/ZteHh4cB0Gp3bv3t3qOUq6T2hsbMS0adMQHR3NdSjEhNXX18PKyoqz7d+5cwdr167Fxo0bOYuhLaGhoSqTTncRGhqq8nm6vEBIO3CZcEnnRkmXEEKMiJIuIV1YYWEhwsLC9F6HMYb1/K8IIgAAIABJREFU69cbIKJHKioqEBcXh/Xr18PGxgbOzs5ISkpSO/7s2bPg8XjKr9u3b+s17pdffoGrqyuEQiHCw8NRX1+vcX5ycjJcXFzQo0cPnDhxAjt37sSZM2fate+UdAnpwtzc3LBr1y6917ly5QrKysoMENGjBD537lzMmzcPEokEtbW1yM3NxZo1a6CpFcyxY8fAGANjTOMbdNqMk8lkOHToEEpLS6FQKJCdna12/vXr17Fp0yacO3cOmZmZKC8vx6RJk5Ceno68vDyd95/eSCOkC4uJicHhw4fRt29fyOVyFBYW4t69ezh9+jQWLlyIc+fOQSaTwdfXF3v27IGzszOGDx+O4cOHIzIyEsOGDUNmZiY+/PBDHD9+HDdu3EBeXl67z/IAIDs7Gy4uLrC3twcANDU1QSaTwcrKCjwez1C7rlFERITye6FQqLzhRZX9+/djzpw5cHNza1EtMnnyZCQmJuLzzz/Xadt0pktIFxYbGwsAiI6OxoABA3D58mWMGjUKFy9ehEQiwdixYyGXyyEWi5GWlgYAWL58OQDA19cXs2fPBgAsWbIEy5YtQ0ZGhl4JFwBycnLg7e2tfLx9+3YEBQUhISFB47yQkBBYW1tDLBarvWygyzgAyM/Ph729PQYNGqR2fnFxMdatWweBQAAPDw/k5OQAALy9vZXf64KSLiHdiIWFBTw9PdHU1ATg0VmejY0NAgMDcefOHaPEIJfLwefzlY9FIhEOHDiA8PBwtXMGDhyI/Px8VFZWIiQkBMnJyXqNA4DS0lJs3LgRW7Zs0Tjf0dERCxYsQHl5OVasWKE8s+Xz+ZDL5bruPiVdQrqzxsZG1NfXY9++fejXrx8AgMfjQSqVoq6uDtXV1cqxCoUCjY2Nem9TJBKhtrZW+TgoKKjFmaYq6enpqKqqgpmZGR4+fAhbW1u9xkmlUqSmpiI2NhY2NjYa548cORKnTp1CQ0MDGhoaIBQKAQC1tbUQiUS67j4lXUK6smnTpuH8+fMYOnQotm7dimPHjiE5ORmfffYZAGDz5s0QCoW4efOm8lKCj48PsrOz0b9/f+Tk5GDNmjXw9vZGRkYGxo4dC39/f71iEovFuH79uvJxfHw84uPjAQCZmZkICQlpNcfV1RX+/v5wcHBATk4O3nnnHZVjtR23YcMGLF++HJaWluDxeFi3bp3a+WKxGF5eXujTpw82bNiAd999FwDw+++/Y+jQobofAEZaOHLkCEtISOA6DEI0KigoYG+//bZeaxw5coQtXrzYQBG1NmHCBJXPNzU1scjISHbv3r1WPystLWVhYWFara/tWF3W1IVEImG3bt1S+3M1+/8lnel2UxEREfD3929XHae6GsdmFy5cwFNPPaWxVOnOnTuwtbXF+fPn2xW/PhYsWAAejwczMzP07t27zTdwntR87ID21cGqOn7NMfF4PNja2sLX1xcpKSk6raurjz/+GJs2bUJWVlaHbudJPB4PX3zxBdLS0lqViCUlJWHx4sVaraPtWF3W1NbevXsRGRmJvn376j7Z4Om/k+PqTLe6uppduXLFaNtraGhgfn5+Os1pjnHbtm3s0qVLrLq6mgUGBrKDBw+2GrtixQq2c+dOtWutW7eOrVq1ii1cuFDn2HWNVxV3d3dWWlrKfv/9d+bs7MzKysq0Xrc9x+7xeNQdP3d3d1ZWVsZkMhn74YcfmKOjI8vIyFC5liHOdDuaujPd7oLOdA2gsLAQDg4OsLW1RUJCAnr27IkePXqgpKQEixYtgrOzM2QyGV599VXY29sjMTEREydOxOjRo+Hn54fvv/++xfySkhL4+fnBysoKrq6usLOzA4BWa6gyadIkuLu7QyAQQCwWQyaTQaFQIDAwEEKhEAEBASgvL1f53ONiYmKUZ23h4eF47bXX4OXlBTc3N+VngT0ZY0REBHx9fSEQCFrUOMrlcgQEBMDGxgZxcXEAgOLiYpUtEMvKyjBnzhxkZGSgrq4OALQ6ngB0PqbqjiePx1O+i29hYcH58WsmEokwdepUrFq1CqmpqW39WZJOhpKuDtzc3FBRUYHz589j//79+OqrrzB27Fi4urpi5syZSElJQVpaGsaNG4c7d+7gm2++wdKlS9GrVy/8+OOPreYfPnwYQ4YMQWVlJQYPHoySkhIAaLUGU3GXjqoay5SUFPj7+6OkpET58lTVc49rruMEVNdyqosRaF3jmJKSAl9fX1RVVSnfbOjVq1erus4TJ05g8ODB6NmzJwYPHoydO3cCgFbHkzGG999/X6djqu549uzZEwEBAVixYgUcHBw4P35P8vX1bZXkSedHd6Tp4NChQ5g1axaKioowevRojB8/Hh999BFu376N3bt34x//+AfeeecdJCcnKwvM5XI57O3t4enpiZs3b6JPnz7K+aNHj8YHH3yAr776CqNGjYKvry8AoKCgoNUaj38gYrPHayz379+PxsZGhIWFQSgUIjg4GJmZmWCMtXquLY/XcqqLUVWN461bt/Dmm2/CysoKFhbq/7TS09ORkpKCqVOnAnh0R9LUqVO1Pp4AdDqm33zzjco1ysrKWhxXqVTK6fF7Ul5eHvr06aP25ydPnlTbPtAUXL582aTj62jqbpumpKuDbdu2IT4+Hs8++yxiYmLA4/Hw3nvvISoqChMmTFC+MRMbG4v33nsPNTU1uHr1qtr59+7dw7x587B06dIW23lyjaeeekplPI/XWA4YMAD19fXIysrCyJEjcfDgQXh6eoIx1uo5XaiKUSqV4vvvv0dsbGyL5NqrVy8cOnQIYrEYeXl5ePHFF1utV1tbi/v37yvPNgsKCjBw4EBlw+u2judTTz2Fmzdv6nRMtT2enp6enB4/4NH/gBoaGnD+/HmsWrUKa9euVbv20KFDqZ+uCVP7PxxjXVTuLDS9kZaens74fD4bMWIEs7a2ZqdOnWKNjY1MLBazmpoaxhhjJSUlTCwWM1tbW/bOO++w0NBQxufz2cGDB1vN37hxI7OxsWEAmLW1Nfvkk09UrqEuTmtra2ZpacmCgoJYZWUlKy8vZwEBAczW1pYFBASw8vJylc8xxlhERAQzMzNjAJiZmRmLi4tjkydPZkKhkB09epQ5Ojqy0aNHs7Nnz7aKMSoqigFQfsXHxzPGHr25M2jQIGZnZ8eef/55NmzYMFZYWNjiTSexWMxEIhG7dOkSY4yxTz75hJmbmzNXV1d29+7dNo8nY0znY/rkGsuXL2cAWP/+/ZlcLlfGpu3xaz52CQkJyu/1PX7vvfce4/P5zMzMjFlaWrLnn3+ebd68We3fKb2RZvrUvZFGSfcJxqxe2L59O9uzZw9rbGxkx48fZwEBAVrP7egay2b6xMgFU4u3o+KhpGv6qHrBBAkEAsyZMwcCgQALFy5s8aZMM5lM1qK/Z/PXokWLjFJjqU2MpsTU4jW1eB7H2tEjtz1zVNG1n25bteGGGmeMfrp0TZdD48aNQ1FRkcYxzs7OGnuMdjRtYjQlphavqcXzuPb0yDVEX132f/10U1NTYW9vD4lEguLiYrz44ouQSCQq2zs297/t27cvwsLCkJ2djaCgoA4Zd+zYMQwfPlw59vF+unfu3IFMJsOkSZOwcOFCiEQinW+QoKRLSBejUCgwZcoUnDp1Ci+//DJ+/PFHhIaGqu2RW11djfz8/Fa9dcPCwjTOWbJkCUJDQ3U+42tPP11t+98aehxA/XQJIW1QVVusqUduUlKSyrrvtuaoqsHWRnv76QJt1zYbYhz10yWE6EQqlWLUqFHK2uL8/Hyt5hmrt257+ukC2tU26zuO+ukSQnTWXG9cU1OjrC3Wpkfuk711O6qvbnv66arrf2voccbop0slY0+g1o6kM9BUMqaqtvju3bvMxcWF9e7dm/n4+LAxY8awmzdvMpFIxKZPn66y7rutOU/WYD9JXclYVlYWk0gkysdxcXEsLi6OMcbY7t27WXBwcKs5qmqbVY3VZ1zz9p2cnBifz2dBQUFMJpMxxhhbunQpc3BwYD4+PuzatWuMMcaOHTvGFi1apOv+U53ukyjpks7A0HW6HVH3Tf10qU6XEKKGMXvrdvd+ulQyRgjB4cOHjbo9BwcHSCSSVs/rcvOItmM74oaU8ePHt3sunekSQogRUdIlhBAjossLT3B2dkZWVpbRPzeKdB4NDQ34448/WtydZGwPHz5EdXW1SferLS8vN+n4Olpz7+Qn8diTV7IJIRoVFxdj5cqV2Lx5M9ehkM4nlS4vEEKIEVHSJYQQI6KkSwghRkRJlxBCjIiSLiGEGBElXUIIMSJKuoQQYkSUdAkhxIgo6RJCiBFR0iWEECOipEsIIUZESZcQQoyIki4hhBgRJV1CCDEiSrqEEGJElHQJIcSIqIk5IVrau3cvjh49igcPHuC3337Dn/70J1hZWWHp0qUQCARch0c6h1RKuoRoaevWrZg9ezbq6uqUz/Xp0wcFBQUcRkU6GfrkCEK09cYbb8DFxUX5mMfjITw8nMOISGdESZcQLdnY2OC5555TPnZxccFbb73FYUSkM6KkS4gOoqKiYG9vDwAQiUQYMGAAxxGRzoaSLiE6GDNmDJycnGBubo6pU6dyHQ7phCjpEqIDCwsL+Pn5wc7ODrNnz+Y6HNIJWXAdAOk4FRUVOHr0KNdhdDl+fn64cOECzpw5w3UoXY6LiwteeeUVrsPoUJR0u7AbN27gq6++wuuvv851KCbhjz/+wK+//opJkybptY6zszNCQ0Pxxx9/GCiy/9myZUu3fnPuyy+/RGZmJtdhdChKul2cn58f/v73v3Mdhkm4fv06FAqFSR+P3bt3m3R8HW337t1ch9Dh6JouIYQYESVdQggxIkq6hDyhsLAQYWFheq/DGMP69esNENEjFRUViIuLw/r162FjYwNnZ2ckJSWpHf/LL7/A1dUVQqEQ4eHhqK+v75BxZ8+eBY/HU37dvn0bAJCcnAwXFxf06NEDJ06cwM6dO+nNR1DSJaQVNzc37Nq1S+91rly5grKyMgNE9CiBz507F/PmzYNEIkFtbS1yc3OxZs0aqGufIpPJcOjQIZSWlkKhUCA7O7vDxh07dgyMMTDG4OHhgevXr2PTpk04d+4cMjMzUV5ejkmTJiE9PR15eXn6H5BOjN5II+QJMTExOHz4MPr27Qu5XI7CwkLcu3cPp0+fxsKFC3Hu3DnIZDL4+vpiz549cHZ2xvDhwzF8+HBERkZi2LBhyMzMxIcffojjx4/jxo0byMvL0+ssLzs7Gy4uLsq74ZqamiCTyWBlZQUej6dyTkREhPJ7oVCInj17GmUcAOzfvx9z5syBm5sb3NzclM9PnjwZiYmJ+Pzzz9XO7eroTJeQJ8TGxgIAoqOjMWDAAFy+fBmjRo3CxYsXIZFIMHbsWMjlcojFYqSlpQEAli9fDgDw9fVV3jSxZMkSLFu2DBkZGXq/rM7JyYG3t7fy8fbt2xEUFISEhIQ25+bn58Pe3h6DBg3qsHEhISGwtraGWCzG7du3UVxcjHXr1kEgEMDDwwM5OTkAAG9vb+X33RUlXULaYGFhAU9PTzQ1NQF4dJZnY2ODwMBA3LlzxygxyOVy8Pl85WORSIQDBw602eWstLQUGzduxJYtWzps3MCBA5Gfn4/KykqEhIQgOTkZjo6OWLBgAcrLy7FixQrlmS2fz4dcLm9rd7s0SrqE6KixsRH19fXYt28f+vXrB+BRm0epVIq6ujpUV1crxyoUCjQ2Nuq9TZFIhNraWuXjoKCgNs9IpVIpUlNTERsbCxsbmw4bl56ejqqqKpiZmeHhw4ewtbXFyJEjcerUKTQ0NKChoQFCoRAAUFtbC5FIpO1ud02MdFmnT59mH3/8MddhmIxr166xxYsXtzkuIiKCmZmZMQBMKBSyo0ePMkdHRzZ69Gh25MgRZm1tzSwtLVlQUBCrrKxkjDF29+5d5uLiwnr37s18fHzYmDFj2M2bN5lIJGKBgYHMz89PqxgnTJig8vmsrCwmkUiUj+Pi4lhcXBxjjLHdu3ez4ODgVnOioqIYAOVXfHy8yrH6jGvevpOTE+Pz+SwoKIjJZDLGGGNLly5lDg4OzMfHh127do0xxtixY8fYokWLdN7/LuRLSrpdGCXdlrRNupocOXJE7zU0UZd0mpqaWGRkJLt3716rn5WWlrKwsDCt1td2rC5r6kIikbBbt26p/Xl3SLp0eaEba36jw8bGBv7+/irHsP+rNWUGqDldsGBBi3pOHo+Hs2fPIiIiwijbN4SPP/4YmzZtQlZWllG3y+Px8MUXXyAtLa1ViVhSUhIWL16s1TrajtVlTW3t3bsXkZGR6Nu3r0HX7WyoZKwb27p1KxQKBczMzCAWi1WOaa41NUTN6caNG7F3716cPXsWIpEIJ06cUMZhjO0bwuHDhznbtoODAyQSSavnm6sttKHtWF3W1Nb48eMNvmZnREm3m/rggw9w4cIFWFtb48aNGwAe3Yn1wgsvoL6+Hp988gnee+89zJ8/H8ePH8fq1asBANXV1Vi+fDkmTpyI3NxcrFy5EidPnmxVz2pmZobQ0FC1pVI8Hg+vvvoqALR4o+nJGHbt2tWu7T9eG0qIKaGk203FxsZi3759uHDhgjLpubm5oaKiAtevX8fChQvx3nvvYcmSJRg5ciTEYjFOnTqFVatW4dNPP8W4ceOwb98+jBgxAps3b0Z6ejoOHDiAGTNm4OLFixg3bpzahNujRw8AQHx8fKuXsE/G0N7tq0u6+fn52LFjhwGPpGGVlpaadHwdraamhusQOhwlXaJ06NAhzJo1C0VFRRg9erTacQUFBUhOTlbeENBcd/lkPas6ZWVlcHZ2bncM+mz/4cOHLUqvTE1TU5NJx9fRnrxe3RVR0iVK27ZtQ3x8PJ599lnExMQon2+uNW3+b+/evREbG4v33nsPNTU1uHr1aofGYMjt9+3bF3/9618NFq+h7dixw6Tj62jd4iyfy9oJ0rE0lYwlJiYyHo/HJkyYoKxLHTRoEOPz+WzEiBHM2tqanTp1Sllr+uqrrzKRSMSmT5/OSkpKmFgsZra2tuydd95hkydPblXPWlhY2Ko2dfny5QwA69evH7t7967y+ebtJyQksPT09BYxbN26tV3bV8UQJWMdrRuUTGnUDfb/Sx5j3eB8vps6c+YM9u/fjw8//JDrUEzC9evXkZqaivj4eK5DUSs0NLRbfHqCOt1g/1OpTpcQHbB21Au3Z0576NpvF4CyX0JFRQWAR5UkM2bMgIODA2bOnImHDx9q7LdbXFyMAQMGAAD1y9USJV1CdNCeemFj1BizdvTbrampgUQiwYULF5TPZWZm4sGDB8jLy4NMJsPevXvV9tFtaGhQ3rgCgPrlaomSLiF49GZdYGAghEIhAgICUF5ejuHDh2Pp0qXIzc2FnZ0dsrKyMH/+fKxevRpRUVGYNGkS3N3dIRAIIBaLIZPJ2pxTXFyMIUOGGDz+9vTb5fP5+Oabb+Dh4aF87uTJk5g8eTKcnJwwdepU/Prrr4iIiICvry8EAkGLPrqJiYmIjo6GhcX/3o9v7pdL1KOkSwiAlJQU+Pv7o6SkBL6+vkhJSdHYIzcpKUllb9225vTq1atDXoLr02/3cRUVFbCzswPw6A645ssOQMs+uj/99BOCgoJaNTKnfrlto6RLCB61LRw1ahSEQiGCg4ORn5+v1Twueuuq0t5+u09ydHREVVUVgEdn/05OTgBa99Fdu3YtXnrpJfB4PFy/fh1eXl4AqF+uNijpEgLA09MTWVlZqKmpwcGDB+Hp6alVj9wne+t2dF9dddrTb1eVoUOH4scff0R5eTm2bduGYcOGqeyje+bMGeVnoj333HO4du0aAOqXqw1KuoQAmDt3Ls6cOQNnZ2fk5uZizpw58PHxQXZ2Nvr374+cnBysWbMG3t7eyMjIUF462Lx5M4RCIW7evInZs2e3OaeoqEhtRzV9iMViXL9+Xfk4Pj5eWRqXmZmJkJCQVnPKysrw4osv4vTp0xg4cCD+85//4PXXX4eNjQ08PT3h5OSEcePGYcOGDVi+fDksLS3B4/Gwbt06tXH8/vvvGDp0qMH3r0vhrESYdDjqp9uSoW+O6Ijeuu29OcBQ/Xb11Va/3LZ0h5sj6EyXkHbiqreuKobqt6sP6perHeq9QEg7cdlbVxVD9NvVB/XL1Q6d6RJCiBHRmW4Xd/HiRXz77bdch2ESSkpKcO3aNZM+HkVFRSYdX0d78tJIV0QNb7owhUKBn3/+meswupzKykrs2rULM2bM4DqULqdXr14YPnw412F0pFQ60+3CHB0dMWXKFK7D6HKKi4tx9uxZOrakXeiaLiGEGBElXUIIMSJKuoQQYkSUdAkhxIgo6RJCiBFR0iWEECOipEsIIUZESZcQQoyIki4hhBgRJV1CCDEiSrqEEGJElHQJIcSIKOkSQogRUdIlhBAjoqRLCCFGREmXEEKMiJqYE6Klzz77DCkpKbCwsMCDBw8wcOBAMMaQk5MDW1tbrsMjnQSd6RKipeHDh0Mul+PKlSuQSqXIzc2FQCCghEt0QkmXEC29/PLLsLe3Vz62trbG3LlzOYyIdEaUdAnRwbhx48Dj8QAAPXr0QHh4OMcRkc6Gki4hOliwYAF69uwJABgwYACEQiHHEZHOhpIuITrw8vKCg4MDbG1tsXDhQq7DIZ0QJV1CdDRlyhRYWFhg/PjxXIdCOiEqGevGvv76a+zbt4/rMEyKXC6HSCTSOObBgwdwcHDAtGnTjBTV/1RVVcHW1hYWFp33n+7XX3/drS/LdN7fHNHb+fPnsXr1ari7u3Mdisl444038N1337U5rri4GL169TJCRC29//77+Nvf/oZnn33W6Ns2hDlz5qC+vp7rMDhFSbebs7KygrW1NddhmAwzMzOtjoeHh0fHB6OCubl5p/6dmZnRFU06AoQQYkSUdAnRUWFhIcLCwvRehzGG9evXGyCiRyoqKhAXF4f169fDxsYGzs7OSEpK0jinsrISISEhqKioAAA0NjZixowZcHBwwMyZM/Hw4UP88ssvcHV1hVAoRHh4eIvLA8XFxRgwYIDy8c6dO3HmzBmD7VNXREmXEB25ublh165deq9z5coVlJWVGSCiRwl87ty5mDdvHiQSCWpra5Gbm4s1a9aAMaZyTk1NDSQSCS5cuKB8LjMzEw8ePEBeXh5kMhn27t0LmUyGQ4cOobS0FAqFAtnZ2QCAhoYGrF+/vsX6kyZNQnp6OvLy8gyyX10RJV1CdBQTEwN/f3+Eh4fjtddeg5eXF9zc3FBYWAjgUeJxd3eHQCCAWCyGTCbD8OHDsXTpUuTm5sLOzg5ZWVmYP38+Vq9ejaioKBQXF2PIkCHtjik7OxsuLi7K25Sbmpogk8lgZWWlvIPuSXw+H998802L69MnT57E5MmT4eTkhKlTp+LXX39FREQEfH19IRAIIBQKlTeHJCYmIjo6ulUlxeTJk5GYmNjufenqKOkSoqPY2FgAQHR0NAYMGIDLly9j1KhRuHjxIgBAIpFg7NixkMvlEIvFSEtLw/LlywEAvr6+mD17NgBgyZIlWLZsGZKSktCrVy+9Xpbn5OTA29tb+Xj79u0ICgpCQkKCTutUVFTAzs4OAODg4KC87AAA+fn5sLe3x6BBg/DTTz8hKChImYAf5+3tjZycnHbuSddHSZcQPVlYWMDT0xNNTU3K54RCIWxsbBAYGIg7d+50eAxyuRx8Pl/5WCQS4cCBAzr3hnB0dERVVRUAQKFQwMnJCQBQWlqKjRs3YsuWLQCAtWvX4qWXXgKPx8P169fh5eWlXIPP50Mul+u7S10WJV1COkBjYyPq6+uxb98+9OvXDzweD1KpFHV1daiurlaOUygUaGxs1Ht7IpEItf+/vXsPavJO9wD+DSCQEG4lCCwgYG0FlUtXug3WUUCp4IXiHgrt4Pa4Ul1bZSNIrY6cU9u12MNFkWqgCI6cXWbU2qMj9QYjgux6ZLFixY6XVW7KTUNAtAW55Hf+cHgPmAAJhATk+cxkhlx+7/sktc+8efPkm85O7npQUBC8vb013o6fnx+OHTsGuVyOw4cPY968eaiurkZ2djYSExNhamoKACgvLwdjDIwxzJw5E7du3eK20dnZOewXTCYzarqEaGjVqlWoqKiAn58f8vLyUFpaCqlUit27d3OPyczMhFAoxN27dxEdHY05c+agpKQEM2bMQFlZGXbt2gUPDw8cOXIE0dHRaGhogK+v74hrEovFuH37Nnc9OTkZycnJ3PX8/HwEBwcPWPPo0SP4+Pjgn//8J7y8vHDmzBksX74cpqamcHNzwyuvvIKlS5ciPT0dCQkJmDJlCng8HlJSUoas5c6dO/Dz8xvxc3npMTJpxcTEsOrqan2XMa6sWLFi1Nu4cOECi4+P10I1yuLj49mtW7eUblcoFCwqKoo9efJE5brm5mYWFhY2JjW9SCKRsHv37qm879///d9ZS0uLTuoYpw7QkS4Z1LVr12BpaamV8aj+XpwNVWXDhg3g8Xjg8XgQCATw9PREVlaWVusYK19++SUyMjJQXFyss33yeDzs27cPOTk5KkfE0tLSEB8fP+Z1nDp1ClFRUZg+ffqY72uioq8Bk0H5+PggLi5Oq9tUNRuqyv79+3Hq1ClcuXIFPB4PhYWF+OSTT2BlZTXug8OLior0sl8rKytIJBKV9/VNXIw1Sl4bHh3pEiUtLS0IDAyEqakpkpKSuNtlMhnefvttWFhYYM+ePSrnVIuKijB16lSYm5vj+vXrSmtUzYYON6NqY2OD999/Hzt37kR2drbSNgEMW0tRUZHSGkL0gZouUZKVlQVPT0+0t7cjNjaWuz0nJwdLly7F/fv3cejQIcTGxirNqZ45cwbJycl48uQJvLy8lNaoeuur7oyqp6cn5HK5ym2qmpntX0t5efmwdRCiC3R6gSi5d+8ePvjgAxgbGw/4tlFdXR2kUik36N83i9l/TnXjxo296JGcAAAgAElEQVSIi4vDwYMHkZubq3KNSCQaUV1VVVVwdnYetI6hahEKhTh9+vSwdTQ2NiI0NHRE9elCdXU1fvzxxwmbR1tXV6fvEvSOmi5R4uDggMLCQojFYlRVVcHHxwcA4OTkhMTERGzevBkdHR24efOm0loXFxd8//33OHDgAPLy8pTWWFpaalSLQqFAd3c3KioqsHPnTnz99de4c+eO0jbv3r07ZC2xsbFq1eHg4ICTJ09qVKMuffrpp/joo48wc+ZMfZcyIqtXr9Z3Cfqn3+kJok+DjYzV1dUxb29vZm5uzmbNmsXmzZvHGGOsqamJicViJhAIWExMDAsPD2dCoZBdvHiRWVtbs4CAAJaQkMCMjIyYk5MTKy8vV1rz8OFD5u3tzXg8HnN2dmanT59m9fX1bO7cuQNq2Lx5M+Pz+czAwIBNmTKFzZo1i2VmZqqsgzE2bC0//PCD0hpVtDEyNpYGGxmbKGhkjB3gMUYntyarP//5z4iLi9NbIPd4FBoaSke6Y2j16tXYvXs39/XiSSibPkgjRIvYCDNyR7puMJpk6w6Wl9s3rtd3qampAQBIpVLY2dnB1tYWly5dogxdDVHTJUSLRpqRq89s3cHycgGgtLSUy1hwdXXF7du3kZGRgatXryI/Px9yuZwydDVETZcQNbS2tmLx4sUQCoUIDAyEXC4fNiNX3VxdANy6yMjIUeXqAppn6w6Wl6vK2bNnsXbtWjg6OkIsFmP58uUAKENXE9R0CVFDVlYWfH190dTUxH0lebiMXHVzdfuvO3LkyKjfqo80W7d/Xm6f4OBgmJiYQCwWo6amBo2NjUhJSYGZmRlcXV253FzK0FUfNV1C1FBdXQ1/f38IhUIsWbIEtbW1aq3Tda4uMLJs3RfzcgHAy8sLtbW1XFaGVCqFtbU1NmzYALlcjh07duCbb74BQBm6mqCmS4ga3NzcUFxcjI6ODhQUFMDNzU2tjFx1c3VfXDcammbrqsrLBYDc3Fy0t7fDwMAAvb29EAgEWLhwIS5fvozu7m50d3dzX9KgDF31UdMlRA3r1q1DeXk5RCIRKisrsXbt2mEzcgH1cnUBcOtCQkJGlasLDJ2tqypXd7C8XHt7e/j6+sLKygplZWWIiYmBWCyGu7s7nJ2dkZ6ezn1NnDJ0NaC/GWGib5Snq0ybX44Yi1xddb4cMVS27ljl6g6VodsffTmC8nQJGTP6yNUFhs7WHYtcXcrQ1QxlLxAyRvSVqwsMnq07Frm6lKGrGTrSJYQQHaKmSwghOkSnFyYxIyMjrFu3bsCY0GRXVVU1bJ6uQqHAr7/+qpdM24aGBly/fh0mJiY637c23LhxQ+W34iYTShkjREONjY344osvkJmZqe9SyMRDKWOEEKJL1HQJIUSHqOkSQogOUdMlhBAdoqZLCCE6RE2XEEJ0iJouIYToEDVdQgjRIWq6hBCiQ9R0CSFEh6jpEkKIDlHTJYQQHaKmSwghOkRNlxBCdIiaLiGE6BA1XUII0SH65QhC1FRdXQ25XA6ZTIZHjx7hxx9/BADMmTNnwv6SA9E9+uUIQtSUnJyMxMREmJiYgDEGAwMD9PT0oLGxEUZGdPxC1EK/HEGIuv7whz9AIBCgubkZDx8+RFNTE/z9/anhEo1Q0yVETfb29rCzs+OuW1tbQyKR6LEiMhFR0yVEAx999BGMjY0BAFZWVnj77bf1XBGZaKjpEqKBVatWYerUqQCARYsWTfqfEyeao5NRhGjAwsIC06ZNw6+//oqYmBh9l0MmIGq6etLb24uenh59l0FGYN26ddi2bRtmzpyJZ8+e6bscMgL6HPGjkTE9iYuLw61bt2BmZqbvUoiGent7UV1djRkzZuhsn7du3YK7u7vO9qep8V5ff//85z9RWVkJCwsLfew+m4509Sg9PV2n/+MS7VEoFDAw0N1HIqGhofjuu+90tj9Njff6+lu1apVe908fpBEyArpsuOTlQv9yCCFEh6jpEvKSqK+vR1hY2Ki3wxjD3r17tVDRc21tbUhKSsLevXthamoKkUiEtLS0QR9//vx52NvbQygUIiIiAl1dXbhy5Qp4PB53qampAQBIpVLY2dnB1tYWly5dwvHjx1FeXq612scCNV1CXhKOjo44ceLEqLfz888/49GjR1qo6HkDX7duHdavXw+JRILOzk5UVlZi165dGOwzfJlMhsLCQjQ3N6O1tRUlJSUAgNLSUjDGwBiDq6srbt++jYyMDFy9ehX5+fmQy+VYuXIlcnNzUVVVpZX6xwI1XUJeElu2bIGvry8iIiKwaNEiuLu7w9HREfX19QCAlStXwsXFBWZmZhCLxZDJZJg/fz62bt2KyspKmJubo7i4GB9//DG++uorbNq0CY2NjXjzzTdHXFNJSQns7Oy4SQGFQgGZTAZjY+NBv1gSGRkJT09PmJmZQSgUcl9GedHZs2exdu1aODo6QiwWY/ny5QCA8PBw7NmzZ8Q1jzVquoS8JBITEwE8H0d87bXXcOPGDfj7++Onn34CAEgkEoSEhKClpQVisRg5OTlISEgAAHh6eiI6OhoA8Nlnn2H79u1IS0uDg4PDqN6ul5WVwcPDg7t+9OhRBAUFITU1ddi1tbW1sLCwgLe3NwAgODgYJiYmEIvFqKmpQWNjI1JSUmBmZgZXV1eUlZUBADw8PLi/xyNquoS8pIyMjODm5gaFQsHdJhQKYWpqisWLF+P+/ftjXkNLSwv4fD533cbGBufOnUNERMSQ65qbm7F//358++23AAAvLy/U1tbi8ePHCA4OhlQqhbW1NTZs2AC5XI4dO3bgm2++AQDw+Xy0tLSM3ZMaJWq6hEwiPT096OrqwunTp/Hqq6+Cx+Ohuroaz549w9OnT7nHtba2auUbkzY2Nujs7OSuBwUFcUeug6murkZ2djYSExNhamoKAMjNzUV7ezsMDAzQ29sLgUCAhQsX4vLly+ju7kZ3dzeEQiEAoLOzEzY2NqOufaxQ0yXkJbFq1SpUVFTAz88PeXl5KC0thVQqxe7du7nHZGZmQigU4u7du4iOjsacOXNQUlKCGTNmoKysDLt27YKHhweOHDmC6OhoNDQ0wNfXd8Q1icVi3L59m7uenJyM5ORkAEB+fj6Cg4OV1qSnpyMhIQFTpkwBj8dDSkoK7O3t4evrCysrK5SVlSEmJgZisRju7u5wdnZGeno6YmNjAQB37tyBn5/fiGsec4zoRWxsLPvXv/6l7zLIBLFixYpRb+PChQssPj5eC9UoG6w+hULBoqKi2JMnT5Tua25uZmFhYVqvRSKRsHv37g16f1RUFHv8+LHW96umA3SkO05du3YNlpaWWhkB6q/vnFhbW9ugj5k2bRp27tw54n1s2LCBm6c0NTXFb3/7W1y5ckXlY9kgM6FvvfUW1q9fP+i2N2zYAOD5eJNIJBqTr1NHRkZyR3mazMD2PSdtzc1qy5dffomMjAwUFxfrbJ88Hg/79u1DTk6O0ohYWloa4uPjtbq/U6dOISoqCtOnT9fqdrWJmu445ePjg7i4OK1us6OjAxKJBNeuXRvycbt374ahoeGI97N//364uLjg0aNHkMvlCAgIgFQqVfnYwWZC9+7dq3KkaP/+/ZgzZw4yMjJw6dIlzJ49G2lpafjhhx9GXO9g8vLyuL81mYHte07ampvVlqKiIjx9+hT+/v463a+VlRUkEonSf8/ExESth8AvW7ZsVCNuukBNd5xpaWlBYGAgTE1NkZSUxN0uk8nw9ttvw8LCAnv27FE5i1lUVISpU6fC3Nwc169fV1rD5/Nx6NAhuLq6DlvHf/3Xf8HExARvvfUWGhsbB539VGeO89mzZ/jd734H4PkRo5WVFQQCAVJTU7mZ0DVr1mDp0qXg8/mwtbVFb28vbt68CQ8PDzg4OKCuro7bXmhoKEJCQrB27Vp0dXVxt7e2tmLx4sUQCoUIDAyEXC7Hu+++i4CAAMydOxfW1tYwNzeHQCCAmZkZpk2bBmNjYwQEBKisrb++GVgAiI+P547kZ8+ePehz4vF43BpVtQ32mpKXGzXdcSYrKwuenp5ob2/nPhgAgJycHCxduhT379/HoUOHEBsbqzSLeebMGSQnJ+PJkyfw8vJSWvPi27uhrF+/Hu3t7RCLxcjKyhp09nOoOU5bW1uYmZmhoKAAs2fPBvD8iLGtrQ0VFRU4e/YsNxPq4eEBNzc3tLa24ty5czAwMIC7uztu3LiB4ODgAUfnPB4P//3f/40nT57g66+/HvDa+fr6oqmpCZ6ensjKysK2bdvg4OCAY8eOISEhAStWrIBcLsd7772Hr776CjKZDHV1dejq6lKqrb++GVgASElJAWMM0dHR+OKLLwZ9Tt3d3UPWNthrqopCoUB7e/u4vfT09Oi9BnUvvb29av9/MBYo2nGcuXfvHj744AMYGxsP+JXZuro6SKVSbpi9bw6x/yzmxo0bERcXh4MHDyI3N1flGpFIpFYdQqEQJiYmCAoKwqlTp7jbVc1+DubRo0ewtLREZWUlli5dip9//hlXr17FmjVr0NDQwB1h9j3v0NBQ7hzw5cuXAQCGhoZwcXFR2p+NjQ0OHz6Md955B5s2bYKvry+qq6sRFhYGoVCIJUuWID8/H8DzX3twc3PDwoULcfPmTZiamsLV1RXm5uawsLCAra0tFAoFCgsLVdamSkZGBuzs7BAeHq7WusFqU/c1vX//Pj755JOhX3A9unPnzriur787d+7odf/UdMcZBwcHFBYWQiwWo6qqCj4+PgAAJycnJCYmYvPmzejo6MDNmzeV1rq4uOD777/HgQMHkJeXp7TG0tJS7Tp6enrw7Nkz5OfnjzicWqFQwNDQEIaGhujo6EB3dzcOHz6M5ORkvP7669iyZQuA52+9nZ2dceLECSxYsACMsQFHiYOZN28ePv/8c/zHf/wHVq1aBTc3NxQXF2PhwoUoKCiAm5ubRvWqqk2VS5cuoaCgAMeOHcPZs2eRkpKi8jn1n3MdbW0uLi7429/+ptEaXQoNDR3X9fWn7zxdGhnTk8FGxurq6pi3tzczNzdns2bNYvPmzWOMMdbU1MTEYjETCAQsJiaGhYeHM6FQyC5evMisra1ZQEAAS0hIYEZGRszJyYmVl5crrXn48CHz9vZmPB6POTs7s9OnT6usrbi4mNnZ2TFjY2O2ZMkS9vjxY5X7Y4yx+vp6Nnfu3AHrN27cyAAwAMzAwIA5OTmxvXv3MsYYy83NZXw+ny1YsICZmJiwvLw8ZmNjw5YvX86WLFnC+Hw+8/PzY4GBgUwoFLJ//OMf7JVXXuFeh40bNzITExOWnZ3NGHs+krR8+XJ28+ZNJpfLWWBgIBMIBCwwMJDJ5XIWGhrK+Hw+KygoYO+++y4TCATsu+++Y9bW1szV1ZX97W9/Y4aGhmz79u1Ktbm6ujIDAwOWmprKIiMjmYGBAUtKSmLvvPMO9/wAsPT0dJXPqe/5JyUlqaxtsNdUFW2MjI2l8V5ff/oeGaOf69GTuLg4fPLJJ/TLEUQtoaGhOHnypL7LGNR4r6+/VatWQSqV6u3neuiDtElMJpMNyCjtu8hkMn2XRrSMjSAjdyRrVKE83YGo6U5iIpGIyyftf1H3wzYycYwkI1cbubqM8nSVUNMlZAJTNf87XEauLnN1KU9XGTVdQiYwVfO/w2Xk6jJXl/J0lVHTJWQCq66uhr+/Pzf/W1tbq9Y6XeXqUp6uMmq6hExgffO/HR0d3PyvOhm5usrVpTxdZdR0CZnA1q1bh/LycohEIlRWVmLt2rXDZuQCusvVpTxdFfQyHkwoT5doRJtfPhiLXF3K01Ub5ekSMtnoMleX8nSVUfYCIZNMUVGRTvfXl6f7ov7JbdqybNkyrW9T2+hIlxBCdIiOdPVILpeP+hs/ZHLo6uoa1/9Wxnt9/T179kyv+6fAGz05cuQICgoK9F0GGYFnz56hpqYGM2fO1Nk+5XI5XnnlFZ3tT1Pjvb4X7d+/nxtH07FsarqEaKixsRFffPEFMjMz9V0KmXgoZYwQQnSJmi4hhOgQNV1CCNEharqEEKJD1HQJIUSHqOkSQogOUdMlhBAdoqZLCCE6RE2XEEJ0iJouIYToEDVdQgjRIWq6hBCiQ9R0CSFEh6jpEkKIDlHTJYQQHaKmSwghOkQh5oSo6dixY/jhhx/Q1dWFu3fvYtasWeDz+UhNTYVAINB3eWRiyKbfSCNETQYGBjh27Bh++eUXAEB5eTlmzJhBDZdohE4vEKKm5cuXQyQScdd5PB7ef/99PVZEJiJquoSoydjYGF5eXtx1e3t7fPTRR3qsiExE1HQJ0YBEIoG1tTUAwM7ODi4uLnquiEw01HQJ0UBAQAAsLCxgZGSEP/zhD/ouh0xA1HQJ0YCBgQHmz58PoVCIDz/8UN/lkAmIphcmqObmZuTn5+u7jEnJ1dUVIpEIJ06cUOvxCoUCwPOGPV719PTAyGhitANHR0eEhITou4wRmxivMlFy584d/OMf/8DKlSv1XcqkM3XqVJibm2Pq1KlqPf7KlStoaWnBkiVLxriykent7UVSUhK2bdum71LUsm/fPmq6RD9mzZqF0NBQfZcxKWnyuhsaGuLBgwfj9r9VT08P/vrXv47b+l6UnZ2t7xJGZfy+3yGEkJcQNV1CCNEharqEjAP19fUICwvTyrYYY9i7d69WtgUAbW1tSEpKwt69e2FqagqRSIS0tDSVjz1//jzs7e0hFAoRERGBrq4uAM/Pa/N4PO5SU1MDAJBKpbCzs4OtrS0uXbqE48ePo7y8XGu1j0fUdAkZBxwdHdWehhjOzz//jEePHmllW4wxrFu3DuvXr4dEIkFnZycqKyuxa9cuqMrKkslkKCwsRHNzM1pbW1FSUsLdV1paCsYYGGNwdXXF7du3kZGRgatXryI/Px9yuRwrV65Ebm4uqqqqtFL/eERNl5BxYMuWLfD19QUAREREYNGiRXB3d4ejoyPq6+uxcuVKuLi4wMzMDGKxGDKZDPPnz8fWrVtRWVkJc3NzFBcXAwA+/vhjfPXVV4iMjMSbb745qrpKSkpgZ2cHCwsLAM/H32QyGYyNjcHj8ZQeHxkZCU9PT5iZmUEoFA454XH27FmsXbsWjo6OEIvFWL58OQAgPDwce/bsGVXd4xk1XULGgcTERO7vuLg4vPbaa7hx4wb8/f3x008/QSKRICQkBC0tLRCLxcjJyUFCQgIAwNPTE9HR0dz6zz77DNu3b8eRI0dG/Va9rKwMHh4e3PWjR48iKCgIqampQ66rra2FhYUFvL29uduCg4NhYmICsViMmpoaNDY2IiUlBWZmZnB1dUVZWRkAwMPDg/v7ZURNl5BxysjICG5ubtyXK4RCIUxNTbF48WLcv39fJzW0tLSAz+dz121sbHDu3DlEREQMuqa5uRn79+/Ht99+y93m5eWF2tpaPH78GMHBwZBKpbC2tsaGDRsgl8uxY8cOfPPNNwAAPp+PlpaWsXtSekZNl5AJoqenB11dXTh9+jReffVV8Hg8VFdX49mzZ3j69OmAx7a2tqKnp2fU+7SxsUFnZyd3PSgoaMDR64uqq6uRnZ2NxMREmJqacrfn5uaivb0dBgYG6O3thUAgwMKFC3H58mV0d3eju7sbQqEQANDZ2QkbG5tR1z5eUdMlZBxYtWoVKioqkJycjNTUVOTl5aG0tBRSqRS7d+8GAGRmZkIoFOLu3buIjo7GnDlzUFJSghkzZqCsrAy7du0C8Pzt+ZEjRxASEsKdJx4psViM27dvc9eTk5ORnJwMAMjPz0dwcPCAx6enpyMhIQFTpkwBj8dDSkoKgOcxmL6+vrCyskJZWRliYmIgFovh7u4OZ2dnpKenIzY2FsDzb1v6+fmNqu5xjZEJ6eLFiywpKWnIx1RUVDALCwt2/Phxre23ra2NLVmyhLW2to5qOwqFgqWlpWm8rq6ujvH5fHb16lW1tv/gwQP27rvvjqguTdcO5ocffmCZmZmj2saFCxdYfHz8qGtRpbu7m61cuVLlfQqFgkVFRbEnT54o3dfc3MzCwsK0Xo9EImH37t0b9P4VK1ZofZ86dICOdF9iPj4+iIuL09r2Ojo6IJFIcO3atVFva6RjTUePHsX27dtx8OBBtbav6ShW/7q0OcY1Wl9++SUyMjK4CQVd4fF42LdvH3JycpRGxNLS0hAfH6/V/Z06dQpRUVGYPn26Vrc7rui77ZORGepIVyaTsYCAAGZiYsIEAgE7fvw4e/ToEZs3bx4zNzdnu3fvZu+99x4LDAxkM2fOZL/5zW/YgwcP2Pnz55mtrS0TCoXsp59+UlrT56233hrySPfBgwfM0tKS8fl8lpKSwhhj7PHjxywkJISZmpoykUjE/Pz8GAAGgIlEItbY2MhiYmKYjY0NY4yxhoYG5uvrq7Ttzz77jDU3NzNbW1vW2dnJ3T7U9ufOncsYY8zW1lZpXy/WOn/+fAaASSQS9umnn3Jr5XI5W7RoETMzM2MBAQGspaVF5WuoijaOdMfSUEe64xEd6ZJxJysrC56enmhvb+fOk+Xk5GDp0qW4f/8+Dh06hNjYWKWxpDNnziA5ORlPnjyBl5eX0hqm5g9HOzo6oq2tDRUVFTh79iwA4Ntvv4WbmxtaW1tx7tw5bNu2Ddu3b0d+fj5CQkJgb2+P1atXIysrCwDg4OCgNO506dIlvPHGG5g6dSreeOMNHD9+nLtvsO13d3dzjzl48KDSvl6stW/cKi0tbcAYV1ZWFnx9fdHU1ARPT09kZWWpHO0iZDiUMvYSunfvHj744AMYGxtzGal1dXWQSqXcbGffSE7/saSNGzciLi4OBw8eRG5urso1/X+YcTCFhYVYs2YNGhoaEBAQwNUUGhoKU1NT/Pa3v0VDQwMAYNmyZfj8889RU1ODkydP4j//8z8H3W5ubi6ysrK4H4NUKBTc34Ntvz9V+1JVqyrV1dUICwuDUCjEkiVLBmQZvzjapcrf//53rUwTjAWFQoG6ujrs379f36Wo5cVJjYmGmu5LyMHBAYWFhRCLxaiqqoKPjw+cnJyQmJiIzZs3o6OjAzdv3lRa5+Ligu+//x4HDhxAXl6e0hpLS0u19n/48GEkJyfj9ddfx5YtWwAAbm5uOHHiBBYsWADGGLq7u9Ha2ore3l5s3rwZmzZtwooVKwYN+u7s7MQvv/zCHW3X1dXBy8sLNTU1cHV1HXT7/Rsdj8dT2peqWlWNW7m5uaG4uBgLFy5EQUEB3Nzc1Hot+ohEIsyYMUOjNbrS29sLPp8/but70UQJWx+Ufk9vkJEa6pxuXV0d8/b2Zubm5mzWrFls3rx5rKmpiYnFYiYQCFhMTAwLDw9nQqGQXbx4kVlbW7OAgACWkJDAjIyMmJOTEysvL1da8/DhQ+bt7c14PB5zdnZmp0+fVrn/3Nxcxufz2YIFC5iJiQm7fPkya2lpYUuWLGF8Pp/5+fmxa9euMRsbG/bhhx+ynp4eJhaLWUdHB7eN+vp67nwqY4yJxWJmY2PDrl+/zhhj7C9/+QszNDRk9vb27MGDB4NuHwAzMDDgXqsX9/VirXl5eVxdkZGR3Fq5XM4CAwOZQCBggYGBTC6Xq3wNVaFzuto10c/p8hhT80QdGVdKS0tx+fJlfPrpp/ouhQzj1KlTePDgAf70pz/puxSVenp6EBERgf/5n//RdylqCQ0NxcmTJ/Vdxkhl0wdpZMRkMtmAuL6+i0wm03dpLx02grjGkaxRhaIdtYuaLhkxkUjERfX1v6jzYRvRzEjmmrUR8cgo2lHrqOkSoketra1YvHgxhEIhAgMDIZfLVUY29sU1btq0Se2Yx/5rGhsbRxTzSNGO2kdNlxA9UjX/qyqysf/8sLoxj/3XqJp7VgdFO2ofNV1C9Ki6uhr+/v7c/G9tba1a63QV80jRjtpHTZcQPeqb/+3o6ODmfweLbOw/P6xuzONoIx4p2lH7qOkSokfr1q1DeXk5RCIRKisrsXbtWpWRjX1xjX2nDtSJeey/pqGhYUQxjxTtOAb0Mh5MRk2daEcyPmj7yxHajnmkaEedosAbQiYaXcY8UrSj9k3wLzETMvkUFRXpdH9WVlaQSCRKt/dPYdOWZcuWaX2b4w0d6RJCiA5R0yWEEB2i0wsTWGlpKQwNDfVdBhnGv/71L7S3t+OXX37RdykqKRQK1NfXcz+AOd6N11xidVHK2AT1+PFjVFRU6LuMSamlpQW5ubla/f05oj5ra+shZ4XHuWw60p2gLC0t4e/vr+8yJqXGxkYUFhbS609GhM7pEkKIDlHTJYQQHaKmSwghOkRNlxBCdIiaLiGE6BA1XUII0SFquoQQokPUdAkhRIeo6RJCiA5R0yWEEB2ipksIITpETZcQQnSImi4hhOgQNV1CCNEharqEEKJD1HQJIUSHKMScEDUlJSVBKpXCxMQEnZ2d8PDwQHd3NyorK8Hn8/VdHpkg6EiXEDUFBwfj119/xZ07d1BXV4dbt27hN7/5DTVcohFquoSoycvLC1ZWVtx1Pp+Pjz/+WI8VkYmImi4hGggLCwOPxwMAiEQihIWF6bkiMtFQ0yVEA+vXr4ednR0AYNasWXRqgWiMPkgjRAPTp0+HjY0NfvnlF0gkEn2XQyYgOtIlRENRUVEwMDBAUFCQvkshExAd6U5CQUFB9LZ4BNra2mBmZgaFQgErKyv8/ve/13dJA3R2dqKrqwsWFhb6LmVYCoUC4eHhWL16tb5L0TlqupMQn8/HyZMn9V3GhLNt2zasWrUKs2fPhlwuxyuvvKLvkgYoLi7G1atXERcXp+9ShnX16tVJ+2+QTi8QMgLjreGSiYOaLiGE6BA1XUK0qL6+Xiuzu4wx7N27VwsV/b+2tjYkJSVh7969MDU1hUgkQlpamsrHnj9/Hvb29hAKhYiIiEBXVxeuXLkCHo/HXWpqarjHS6VS2NnZwdbWFpcuXcLx48dRXl6u1fpfFtR0CdEiR0dHnDhxYtTb+fnnn5Rd+oUAAAmQSURBVPHo0SMtVPQcYwzr1q3D+vXrIZFI0NnZicrKSuzatQuMMaXHy2QyFBYWorm5Ga2trSgpKQEAlJaWgjEGxhhcXV0BALdv30ZGRgauXr2K/Px8yOVyrFy5Erm5uaiqqtLac3hZUNMlRIu2bNkCX19fAEBERAQWLVoEd3d3ODo6or6+HitXroSLiwvMzMwgFoshk8kwf/58bN26FZWVlTA3N0dxcTE+/vhjfPXVV9i0aRMaGxvx5ptvjqqukpIS2NnZcZMNCoUCMpkMxsbG3Dfs+ouMjISnpyfMzMwgFAoxderUQbd99uxZrF27Fo6OjhCLxVi+fDkAIDw8HHv27BlV3S8jarqEaFFiYiL3d1xcHF577TXcuHED/v7++OmnnyCRSBASEoKWlhaIxWLk5OQgISEBAODp6Yno6GgAwGeffYbt27cjLS0NDg4Oo36rXlZWBg8PD+760aNHERQUhNTU1CHX1dbWwsLCAt7e3gCeh/6YmJhALBZzpxcaGxuRkpICMzMzuLq6oqysDADg4eHB/U3+HzVdQsaYkZER3NzcoFAoAABCoRCmpqZYvHgx7t+/r5MaWlpaBsxm29jY4Ny5c4iIiBh0TXNzM/bv349vv/0WwPPAn9raWjx+/BjBwcGQSqUAAGtra2zYsAFyuRw7duzAN998A+D5aGJLS8sYPquJiZouITrW09ODrq4unD59Gq+++ip4PB6qq6vx7NkzPH36lHtca2srenp6tLJPGxsbdHZ2cteDgoK4o1dVqqurkZ2djcTERJiamgIAcnNz0d7eDgMDA/T29kIgEAAAFi5ciMuXL6O7uxvd3d0QCoUAnn9Zw8bGRiv1v0yo6RKiRatWrUJFRQWSk5ORmpqKvLw8lJaWQiqVYvfu3QCAzMxMCIVC3L17F9HR0ZgzZw5KSkowY8YMlJWVYdeuXfDw8MCRI0cQHR2NhoYG7jzxSInFYty+fZu7npycjOTkZABAfn4+goODBzw+PT0dCQkJmDJlCng8HlJSUmBvbw9fX19YWVmhrKwMMTEx3Lbd3d3h7OyM9PR0xMbGAgDu3LkDPz+/UdX9UmJk0lmxYoW+S5iQtm7dym7cuDGqbVy4cIHFx8drqSLlbaempqq8T6FQsKioKPbkyROl+5qbm1lYWJjW65FIJOzevXsq7/vxxx/Z559/rvV9TgAH6EiXaIwNM0MaGRk56iOz4WzYsIGbFxUIBPD09ERWVtaY7lMbvvzyS2RkZKC4uFin++XxeNi3bx9ycnKURsTS0tIQHx+v1f2dOnUKUVFRmD59ula3+zKg7AWiseFmSPPy8iAWi8e0hv379+PUqVPcwH5hYSE++eQTWFlZDfnhkL4VFRXpbd9WVlYq4yj7T1xoy7Jly7S+zZcFHekSTmtrKxYvXgyhUIjAwEDI5fJhZ0iHU19fDysrKwgEAqSmpuJ3v/sdzM3NUVlZiTVr1mDatGmQyWR4++23YWFhgT179uDdd99FQEAA5s6di8OHDw87o2pjY4P3338fO3fuRHZ2ttL2VM3LFhUVYerUqTA3N8f169eV1hAyVqjpEk5WVhZ8fX3R1NTEvV0fboZ0OI6Ojmhra0NFRQXOnj2LrKwsiMVieHp6Ys2aNTh48CBycnKwdOlS3L9/H4cOHcLWrVvh4OCAY8eOaTSj6unpCblcrrS92NhYpXnZM2fOIDk5GU+ePIGXl5fSmhffghOiLXR6gXCqq6sRFhYGoVCIJUuWID8/Hz4+PqPaZmFhIdasWYOGhgYEBATAx8cHvb29uH37NoqLi7F9+3YcP34cUqmUa/AtLS2wsLCAm5sb3Nzc1N5XVVUVnJ2dUVdXp7Q9YOC87MaNGxEXF4eDBw8iNzdX5RqRSDRg+48fP8bGjRthbm4+qtdkrLS0tKCjo0Pn54tH4vHjx2N+3n+8oqZLOG5ubiguLsbChQtRUFAANze3YWdIjYyG/id0+PBhJCcn4/XXX8eWLVsAABKJBH/5y1+wYMEC8Hg8ODk5ITExEZs3b0ZHRwdu3rypds0KhQLd3d2oqKjAzp078fXXX+POnTvDbs/FxQXff/89Dhw4gLy8PKUaLC0tldZYWlpi3759mD17ttr16RLl6U4Qeh6fIHow2MiYXC5ngYGBTCAQsMDAQCaXy9mDBw+YnZ0dc3JyYnPmzGHvvPMOu3v3LrOxsWEffvihyu1ERkYyAwMDlpqaynJzcxmfz2cLFixgJiYm7PLly6y3t5d5e3uztrY2xhhjTU1NTCwWM4FAwGJiYlhoaCjj8/msoKCA1dfXs7lz5yrtY/PmzYzP5zMDAwM2ZcoUNmvWLJaZmalye+Hh4UwoFLKLFy8ya2trFhAQwBISEpiRkRFzcnJi5eXlSmtU0cbI2FgaamRsvJnMI2M8xujk1WQTGhqqt6OMZ8+eoba2FkePHuXeyk8U/X85YjyaiEe6O3bs0HcpupZNH6SREZPJZAPyVfsuMpls0DVBQUH4t3/7N/zpT3/SYaXjBxtBTu5I1qhLk4zd4uJi2NrawszMDO+99x66uroAgLJzNURNl4yYSCTislX7X178AKq/ixcvorKyEra2tjqsdPwYSU6utrN1+7ARZOwWFBSgqakJDx8+xN///ncAoOxcDVHTJWSUVM03AxhyxpnH4ynl6g63JjIyctS5uv1pmrEbHh6ON954A11dXVAoFJg2bdqA+2i+WT3UdAkZJVXzzQCGnHG+cOGCUq7ucGuOHDmi1bfxI8nYPXToEGxtbeHt7T1gnI+yc9VHTZeQUaquroa/vz8331xbW6vWOn3k6vY3kozd1atXo62tDWZmZgOObCk7V33UdAkZpb755o6ODm6+GcCwM84v5uqqs0abNM3Y3bNnD5qbm2FsbAxDQ8MBTZayc9VHTZeQUVq3bh3Ky8shEolQWVmJtWvXAsCwObkv5uoOtyYkJESr3+LSNGPXysoK7u7usLa2xv/+7//iz3/+M3cfZeeqj76RRsgoWVtb4/z580q3Ozo6oqmpSel2mUyG4uJinDp1imty6qzRtgULFuDAgQN4+vQphEIhPv30U+6+t956CwcPHhzw+D/+8Y/44x//qHJbx44dU5lgRpTRkS4heqCvXN3+tJWxS9m5mqEjXUL0QJ+5uv1pI2OXsnM1Q0e6hBCiQ9R0CSFEh+j0wiRUVVWF0NBQfZcx4Tx8+BBXr16FiYmJvktR6ddff0VnZ+eEyNPt6OhASEiIvsvQC0oZI4QQ3aGUMUII0SUjAN/puwhCCJkkfvw/2kE4ZNt3Re4AAAAASUVORK5CYII=\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "RWmIkg1zC3dl",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "HUeq7CUIj9L6",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "def step_lr_scheduler(lr_max, lr_min, step_size, num_stairs, steps_per_stair):\n",
        "    lr_list = []\n",
        "    lr = lr_max\n",
        "\n",
        "    for _ in range(num_stairs):\n",
        "        for _ in range(steps_per_stair):\n",
        "            lr_list.append(lr)\n",
        "        if lr < lr_min:\n",
        "            lr = lr_min\n",
        "        else:\n",
        "            lr *= step_size\n",
        "    \n",
        "    print(\"lr_list: \", lr_list)\n",
        "\n",
        "    def lr_scheduler(epoch):\n",
        "        return lr_list[epoch]\n",
        "\n",
        "    return lr_scheduler\n",
        "\n",
        "\n",
        "def cyclic_lr_scheduler(lr_max, lr_min, lr_max_decay, step_size, steps_per_cycle, num_cycles):\n",
        "    lr_list = []\n",
        "    lr = lr_max\n",
        "\n",
        "    for _ in range(num_cycles):\n",
        "        lr = (\n",
        "            lr_min\n",
        "            if lr < lr_min else\n",
        "            lr_max\n",
        "        )\n",
        "\n",
        "        for _ in range(steps_per_cycle):\n",
        "            lr_list.append(lr)\n",
        "            lr *= step_size\n",
        "            if lr < lr_min:\n",
        "                lr = lr_min\n",
        "\n",
        "        lr_max *= lr_max_decay\n",
        "\n",
        "    print(\"lr_list: \", lr_list)\n",
        "\n",
        "    def lr_scheduler(epoch):\n",
        "        return lr_list[epoch]\n",
        "    \n",
        "    return lr_scheduler"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XTrHIC0_j9_G",
        "colab_type": "code",
        "outputId": "9023b6d9-4f87-4bfd-b195-ccfccf50db14",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 302
        }
      },
      "source": [
        "epochs = 30\n",
        "lr_scheduler = step_lr_scheduler(lr_max=1e-3, lr_min=1e-7, step_size=0.2, num_stairs=6, steps_per_stair=5)\n",
        "\n",
        "lrs = []\n",
        "for e in range(epochs):\n",
        "    lrs.append(lr_scheduler(e))\n",
        "\n",
        "pyplot.plot(range(epochs), np.log10(lrs))\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "lr_list:  [0.001, 0.001, 0.001, 0.001, 0.001, 0.0002, 0.0002, 0.0002, 0.0002, 0.0002, 4e-05, 4e-05, 4e-05, 4e-05, 4e-05, 8.000000000000001e-06, 8.000000000000001e-06, 8.000000000000001e-06, 8.000000000000001e-06, 8.000000000000001e-06, 1.6000000000000004e-06, 1.6000000000000004e-06, 1.6000000000000004e-06, 1.6000000000000004e-06, 1.6000000000000004e-06, 3.200000000000001e-07, 3.200000000000001e-07, 3.200000000000001e-07, 3.200000000000001e-07, 3.200000000000001e-07]\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9dbReZcjkaeh",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "Ye06ky4v7ZRn",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "train_datagen = ImageDataGenerator(\n",
        "    rotation_range=8,\n",
        "    width_shift_range=0.08,\n",
        "    height_shift_range=0.08,\n",
        "    shear_range=0.1,\n",
        "    horizontal_flip=True,\n",
        ")\n",
        "\n",
        "early_stopping = EarlyStopping(\n",
        "    monitor='val_accuracy',\n",
        "    min_delta=0.0005,\n",
        "    patience=9,\n",
        "    verbose=1,\n",
        "    restore_best_weights=True,\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "2JqfuDKqADpq",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        "batch_size = 32\n",
        "epochs = 30\n",
        "loss = \"categorical_crossentropy\"\n",
        "optim = optimizers.Adam(0.001)"
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-IrX0jPYkUd_",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7dbExoB7AJX9",
        "colab_type": "code",
        "outputId": "28962525-5dd1-42e5-e44d-0e8c6a0f1b24",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "model.compile(\n",
        "        loss=loss,\n",
        "        optimizer=optim,\n",
        "        metrics=['accuracy']\n",
        ")\n",
        "\n",
        "train_datagen.fit(X_train)\n",
        "history = model.fit(\n",
        "    train_datagen.flow(X_train, y_train, batch_size=batch_size),\n",
        "    validation_data = (X_test, y_test),\n",
        "    steps_per_epoch = len(X_train) / batch_size,\n",
        "    epochs = epochs,\n",
        "    callbacks = [tf.keras.callbacks.LearningRateScheduler(lr_scheduler)],\n",
        ")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Epoch 1/30\n",
            "124/123 [==============================] - 6s 49ms/step - loss: 0.9808 - accuracy: 0.5301 - val_loss: 1.0461 - val_accuracy: 0.5272 - lr: 0.0010\n",
            "Epoch 2/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.7670 - accuracy: 0.6422 - val_loss: 0.9252 - val_accuracy: 0.5848 - lr: 0.0010\n",
            "Epoch 3/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.6898 - accuracy: 0.6873 - val_loss: 0.6697 - val_accuracy: 0.6750 - lr: 0.0010\n",
            "Epoch 4/30\n",
            "124/123 [==============================] - 5s 42ms/step - loss: 0.6313 - accuracy: 0.7192 - val_loss: 0.5948 - val_accuracy: 0.7263 - lr: 0.0010\n",
            "Epoch 5/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.6059 - accuracy: 0.7329 - val_loss: 0.6055 - val_accuracy: 0.7232 - lr: 0.0010\n",
            "Epoch 6/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.5514 - accuracy: 0.7606 - val_loss: 0.5476 - val_accuracy: 0.7605 - lr: 2.0000e-04\n",
            "Epoch 7/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.5343 - accuracy: 0.7619 - val_loss: 0.5270 - val_accuracy: 0.7621 - lr: 2.0000e-04\n",
            "Epoch 8/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.5059 - accuracy: 0.7776 - val_loss: 0.5262 - val_accuracy: 0.7543 - lr: 2.0000e-04\n",
            "Epoch 9/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.5119 - accuracy: 0.7710 - val_loss: 0.4958 - val_accuracy: 0.7745 - lr: 2.0000e-04\n",
            "Epoch 10/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4988 - accuracy: 0.7811 - val_loss: 0.5075 - val_accuracy: 0.7729 - lr: 2.0000e-04\n",
            "Epoch 11/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4727 - accuracy: 0.7956 - val_loss: 0.4788 - val_accuracy: 0.7885 - lr: 4.0000e-05\n",
            "Epoch 12/30\n",
            "124/123 [==============================] - 5s 44ms/step - loss: 0.4507 - accuracy: 0.8136 - val_loss: 0.4753 - val_accuracy: 0.7963 - lr: 4.0000e-05\n",
            "Epoch 13/30\n",
            "124/123 [==============================] - 5s 44ms/step - loss: 0.4585 - accuracy: 0.8007 - val_loss: 0.4767 - val_accuracy: 0.7869 - lr: 4.0000e-05\n",
            "Epoch 14/30\n",
            "124/123 [==============================] - 5s 42ms/step - loss: 0.4496 - accuracy: 0.8144 - val_loss: 0.4796 - val_accuracy: 0.7900 - lr: 4.0000e-05\n",
            "Epoch 15/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4496 - accuracy: 0.8106 - val_loss: 0.4811 - val_accuracy: 0.7776 - lr: 4.0000e-05\n",
            "Epoch 16/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4283 - accuracy: 0.8177 - val_loss: 0.4769 - val_accuracy: 0.7854 - lr: 8.0000e-06\n",
            "Epoch 17/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4362 - accuracy: 0.8177 - val_loss: 0.4782 - val_accuracy: 0.7885 - lr: 8.0000e-06\n",
            "Epoch 18/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4295 - accuracy: 0.8199 - val_loss: 0.4795 - val_accuracy: 0.7838 - lr: 8.0000e-06\n",
            "Epoch 19/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4422 - accuracy: 0.8065 - val_loss: 0.4768 - val_accuracy: 0.7900 - lr: 8.0000e-06\n",
            "Epoch 20/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4289 - accuracy: 0.8156 - val_loss: 0.4782 - val_accuracy: 0.7869 - lr: 8.0000e-06\n",
            "Epoch 21/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4234 - accuracy: 0.8133 - val_loss: 0.4799 - val_accuracy: 0.7854 - lr: 1.6000e-06\n",
            "Epoch 22/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4231 - accuracy: 0.8202 - val_loss: 0.4805 - val_accuracy: 0.7854 - lr: 1.6000e-06\n",
            "Epoch 23/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4205 - accuracy: 0.8197 - val_loss: 0.4805 - val_accuracy: 0.7838 - lr: 1.6000e-06\n",
            "Epoch 24/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4329 - accuracy: 0.8131 - val_loss: 0.4795 - val_accuracy: 0.7838 - lr: 1.6000e-06\n",
            "Epoch 25/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4275 - accuracy: 0.8209 - val_loss: 0.4799 - val_accuracy: 0.7854 - lr: 1.6000e-06\n",
            "Epoch 26/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4270 - accuracy: 0.8207 - val_loss: 0.4797 - val_accuracy: 0.7869 - lr: 3.2000e-07\n",
            "Epoch 27/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4321 - accuracy: 0.8182 - val_loss: 0.4800 - val_accuracy: 0.7869 - lr: 3.2000e-07\n",
            "Epoch 28/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4377 - accuracy: 0.8182 - val_loss: 0.4797 - val_accuracy: 0.7838 - lr: 3.2000e-07\n",
            "Epoch 29/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4255 - accuracy: 0.8146 - val_loss: 0.4805 - val_accuracy: 0.7838 - lr: 3.2000e-07\n",
            "Epoch 30/30\n",
            "124/123 [==============================] - 5s 43ms/step - loss: 0.4256 - accuracy: 0.8151 - val_loss: 0.4807 - val_accuracy: 0.7823 - lr: 3.2000e-07\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "ukLIQggokkfB",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "QD3MM0kqANAd",
        "colab_type": "code",
        "outputId": "aebc8d90-4b1b-44c7-9fdf-f648c08e39bc",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 293
        }
      },
      "source": [
        "sns.set()\n",
        "fig = pyplot.figure(0, (12, 4))\n",
        "\n",
        "ax = pyplot.subplot(1, 2, 1)\n",
        "sns.lineplot(history.epoch, history.history['accuracy'], label='train')\n",
        "sns.lineplot(history.epoch, history.history['val_accuracy'], label='valid')\n",
        "pyplot.title('Accuracy')\n",
        "pyplot.tight_layout()\n",
        "\n",
        "ax = pyplot.subplot(1, 2, 2)\n",
        "sns.lineplot(history.epoch, history.history['loss'], label='train')\n",
        "sns.lineplot(history.epoch, history.history['val_loss'], label='valid')\n",
        "pyplot.title('Loss')\n",
        "pyplot.tight_layout()\n",
        "\n",
        "pyplot.show()"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 864x288 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FAKB8Iggklyu",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "XkUpPiyXHUll",
        "colab_type": "code",
        "outputId": "029d2fbc-009e-4c8a-ccdb-2e0077888dbe",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "label_to_text"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "{0: 'Happy', 1: 'Sadness', 2: 'Neutral'}"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 24
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JDjfZl8nHYOH",
        "colab_type": "code",
        "outputId": "945508da-0e5f-43e9-90d2-43983b39d9e1",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 643
        }
      },
      "source": [
        "yhat_test = np.argmax(model.predict(X_test), axis=1)\n",
        "ytest_ = np.argmax(y_test, axis=1)\n",
        "\n",
        "scikitplot.metrics.plot_confusion_matrix(ytest_, yhat_test, figsize=(7,7))\n",
        "\n",
        "test_accu = np.sum(ytest_ == yhat_test) / len(ytest_) * 100\n",
        "print(f\"test accuracy: {round(test_accu, 4)} %\\n\\n\")\n",
        "\n",
        "print(classification_report(ytest_, yhat_test))"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "test accuracy: 78.2271 %\n",
            "\n",
            "\n",
            "              precision    recall  f1-score   support\n",
            "\n",
            "           0       0.92      0.92      0.92       309\n",
            "           1       0.70      0.52      0.60       162\n",
            "           2       0.64      0.78      0.70       172\n",
            "\n",
            "    accuracy                           0.78       643\n",
            "   macro avg       0.75      0.74      0.74       643\n",
            "weighted avg       0.79      0.78      0.78       643\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 504x504 with 2 Axes>"
            ]
          },
          "metadata": {
            "tags": []
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "7YgXLcy_HaC9",
        "colab_type": "code",
        "outputId": "812ffd9a-0b30-46eb-f29b-0a06d6ab0f50",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 34
        }
      },
      "source": [
        "yhat_train = np.argmax(model.predict(X_train), axis=1)\n",
        "ytrain_ = np.argmax(y_train, axis=1)\n",
        "\n",
        "train_accu = np.sum(ytrain_ == yhat_train) / len(ytrain_) * 100\n",
        "print(f\"train accuracy: {round(train_accu, 4)} %\")"
      ],
      "execution_count": 0,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "train accuracy: 83.2868 %\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9-EueVkfHhvK",
        "colab_type": "code",
        "colab": {}
      },
      "source": [
        ""
      ],
      "execution_count": 0,
      "outputs": []
    }
  ]
}