Submission_Dicoding_Stella_Meilisa.ipynb

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  "metadata": {
    "colab": {
      "name": "Submission Dicoding - Stella Meilisa.ipynb",
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "Oleh : Stella Meilisa\n",
        " 800 XP\n",
        "\n",
        " Bergabung sejak 03 Apr 2022\n",
        "\n",
        " Kota Bekasi, Jawa Barat"
      ],
      "metadata": {
        "id": "VurxmJA5i5hS"
      }
    },
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "1Cp57uSR3sOG"
      },
      "outputs": [],
      "source": [
        "import tensorflow as tf"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#import dataset\n",
        "!wget --no-check-certificate \\\n",
        "  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip \\\n",
        "  -O /tmp/rockpaperscissors.zip"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "5pRO-0Ak5S2D",
        "outputId": "f861a7d3-fc59-4143-918e-3dfc2c576b11"
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      "execution_count": 2,
      "outputs": [
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          "name": "stdout",
          "text": [
            "--2022-05-28 16:06:36--  https://github.com/dicodingacademy/assets/releases/download/release/rockpaperscissors.zip\n",
            "Resolving github.com (github.com)... 140.82.113.3\n",
            "Connecting to github.com (github.com)|140.82.113.3|:443... connected.\n",
            "HTTP request sent, awaiting response... 302 Found\n",
            "Location: https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220528%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220528T160636Z&X-Amz-Expires=300&X-Amz-Signature=bb0b4d3f69418d1d2d895617998deaa090c2cbe3c0a3de523c05a80b7713a77a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream [following]\n",
            "--2022-05-28 16:06:36--  https://objects.githubusercontent.com/github-production-release-asset-2e65be/391417272/7eb836f2-695b-4a46-9c78-b65867166957?X-Amz-Algorithm=AWS4-HMAC-SHA256&X-Amz-Credential=AKIAIWNJYAX4CSVEH53A%2F20220528%2Fus-east-1%2Fs3%2Faws4_request&X-Amz-Date=20220528T160636Z&X-Amz-Expires=300&X-Amz-Signature=bb0b4d3f69418d1d2d895617998deaa090c2cbe3c0a3de523c05a80b7713a77a&X-Amz-SignedHeaders=host&actor_id=0&key_id=0&repo_id=391417272&response-content-disposition=attachment%3B%20filename%3Drockpaperscissors.zip&response-content-type=application%2Foctet-stream\n",
            "Resolving objects.githubusercontent.com (objects.githubusercontent.com)... 185.199.108.133, 185.199.109.133, 185.199.110.133, ...\n",
            "Connecting to objects.githubusercontent.com (objects.githubusercontent.com)|185.199.108.133|:443... connected.\n",
            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 322873683 (308M) [application/octet-stream]\n",
            "Saving to: ‘/tmp/rockpaperscissors.zip’\n",
            "\n",
            "/tmp/rockpapersciss 100%[===================>] 307.92M   113MB/s    in 2.7s    \n",
            "\n",
            "2022-05-28 16:06:39 (113 MB/s) - ‘/tmp/rockpaperscissors.zip’ saved [322873683/322873683]\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#split dataset into train and validation data\n",
        "import zipfile,os\n",
        "local_zip = '/tmp/rockpaperscissors.zip'\n",
        "zip_ref = zipfile.ZipFile(local_zip, 'r')\n",
        "zip_ref.extractall('/tmp')\n",
        "zip_ref.close()\n",
        "\n",
        "#--ratio         the ratio to split. e.g. for train/val/test `.8 .1 .1 --` or for train/val `.8 .2 --`.\n",
        "!pip install split-folders\n",
        "import splitfolders as sf\n",
        "sf.ratio('/tmp/rockpaperscissors/rps-cv-images', '/tmp/rockpaperscissors/data', seed=1, ratio=(.6, .4))\n",
        "\n",
        "base_dir = '/tmp/rockpaperscissors/data'\n",
        "train_dir = os.path.join(base_dir, 'train')\n",
        "validation_dir = os.path.join(base_dir, 'val')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3K26Kdm45-kP",
        "outputId": "5235ead0-47f3-45d2-8fb3-6824324faffa"
      },
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting split-folders\n",
            "  Downloading split_folders-0.5.1-py3-none-any.whl (8.4 kB)\n",
            "Installing collected packages: split-folders\n",
            "Successfully installed split-folders-0.5.1\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "Copying files: 2188 files [00:00, 3298.71 files/s]\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#build imagedatagenerator for train and validation datasets\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator\n",
        "\n",
        "train_datagen=ImageDataGenerator(\n",
        "                  rescale=1./255,\n",
        "                  rotation_range=20,\n",
        "                  horizontal_flip=True,\n",
        "                  validation_split=0.4,\n",
        "                  shear_range = 0.2,\n",
        "                  fill_mode = 'nearest')\n",
        "\n",
        "test_datagen = ImageDataGenerator(\n",
        "                    rescale=1./255)"
      ],
      "metadata": {
        "id": "FL4mrAs-6v9k"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_generator = train_datagen.flow_from_directory(\n",
        "        train_dir,\n",
        "        target_size=(150,150),\n",
        "        batch_size=32,\n",
        "        class_mode='categorical')\n",
        "\n",
        "validation_generator = test_datagen.flow_from_directory(\n",
        "        validation_dir, \n",
        "        target_size=(150, 150),\n",
        "        batch_size=32, \n",
        "        class_mode='categorical')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "ep8lGI_s_8vV",
        "outputId": "7395f4f6-97c6-42de-a673-48b6b338a44c"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1312 images belonging to 3 classes.\n",
            "Found 876 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model = tf.keras.models.Sequential([\n",
        "    tf.keras.layers.Conv2D(32, (3,3), activation='relu', input_shape=(150, 150, 3)),\n",
        "    tf.keras.layers.MaxPooling2D(2, 2),\n",
        "    tf.keras.layers.Conv2D(64, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Conv2D(128, (3,3), activation='relu'),\n",
        "    tf.keras.layers.MaxPooling2D(2,2),\n",
        "    tf.keras.layers.Flatten(),\n",
        "    tf.keras.layers.Dense(512, activation='relu'),\n",
        "    tf.keras.layers.Dense(3, activation='softmax')\n",
        "])\n",
        "\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PySFEcKyGKHG",
        "outputId": "a83399cc-315b-4b3d-8869-3953a7002995"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " conv2d (Conv2D)             (None, 148, 148, 32)      896       \n",
            "                                                                 \n",
            " max_pooling2d (MaxPooling2D  (None, 74, 74, 32)       0         \n",
            " )                                                               \n",
            "                                                                 \n",
            " conv2d_1 (Conv2D)           (None, 72, 72, 64)        18496     \n",
            "                                                                 \n",
            " max_pooling2d_1 (MaxPooling  (None, 36, 36, 64)       0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_2 (Conv2D)           (None, 34, 34, 128)       73856     \n",
            "                                                                 \n",
            " max_pooling2d_2 (MaxPooling  (None, 17, 17, 128)      0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " conv2d_3 (Conv2D)           (None, 15, 15, 128)       147584    \n",
            "                                                                 \n",
            " max_pooling2d_3 (MaxPooling  (None, 7, 7, 128)        0         \n",
            " 2D)                                                             \n",
            "                                                                 \n",
            " flatten (Flatten)           (None, 6272)              0         \n",
            "                                                                 \n",
            " dense (Dense)               (None, 512)               3211776   \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 3)                 1539      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 3,454,147\n",
            "Trainable params: 3,454,147\n",
            "Non-trainable params: 0\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#optimizing with adam optimizer\n",
        "model.compile(loss='categorical_crossentropy',\n",
        "              optimizer=tf.optimizers.Adam(),\n",
        "              metrics=['accuracy'])"
      ],
      "metadata": {
        "id": "3So4BMnMOmB0"
      },
      "execution_count": 7,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "#steps per epoch diperoleh dari (60% dari 2188 image)/batch size\n",
        "#validation step diperoleh dari (40% dari 2188 image)/batchsize\n",
        "model.fit(\n",
        "  train_generator,\n",
        "  steps_per_epoch=41,\n",
        "  epochs=10,\n",
        "  validation_data=validation_generator,\n",
        "  validation_steps=27,\n",
        "  verbose=2\n",
        "  )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-PPamieYOnjl",
        "outputId": "35ec6372-7080-450c-ca9f-70b4350681d1"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/10\n",
            "41/41 - 74s - loss: 0.9777 - accuracy: 0.5229 - val_loss: 0.5084 - val_accuracy: 0.8206 - 74s/epoch - 2s/step\n",
            "Epoch 2/10\n",
            "41/41 - 67s - loss: 0.3497 - accuracy: 0.8735 - val_loss: 0.1579 - val_accuracy: 0.9537 - 67s/epoch - 2s/step\n",
            "Epoch 3/10\n",
            "41/41 - 68s - loss: 0.2508 - accuracy: 0.9200 - val_loss: 0.1249 - val_accuracy: 0.9618 - 68s/epoch - 2s/step\n",
            "Epoch 4/10\n",
            "41/41 - 67s - loss: 0.2084 - accuracy: 0.9345 - val_loss: 0.2208 - val_accuracy: 0.9259 - 67s/epoch - 2s/step\n",
            "Epoch 5/10\n",
            "41/41 - 67s - loss: 0.1538 - accuracy: 0.9550 - val_loss: 0.1482 - val_accuracy: 0.9502 - 67s/epoch - 2s/step\n",
            "Epoch 6/10\n",
            "41/41 - 67s - loss: 0.1493 - accuracy: 0.9505 - val_loss: 0.1022 - val_accuracy: 0.9653 - 67s/epoch - 2s/step\n",
            "Epoch 7/10\n",
            "41/41 - 67s - loss: 0.0974 - accuracy: 0.9718 - val_loss: 0.0939 - val_accuracy: 0.9734 - 67s/epoch - 2s/step\n",
            "Epoch 8/10\n",
            "41/41 - 67s - loss: 0.1005 - accuracy: 0.9665 - val_loss: 0.0986 - val_accuracy: 0.9722 - 67s/epoch - 2s/step\n",
            "Epoch 9/10\n",
            "41/41 - 67s - loss: 0.1098 - accuracy: 0.9611 - val_loss: 0.0863 - val_accuracy: 0.9711 - 67s/epoch - 2s/step\n",
            "Epoch 10/10\n",
            "41/41 - 67s - loss: 0.0711 - accuracy: 0.9756 - val_loss: 0.1343 - val_accuracy: 0.9618 - 67s/epoch - 2s/step\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<keras.callbacks.History at 0x7f33d7b07650>"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "from google.colab import files\n",
        "from tensorflow.keras.preprocessing import image\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "%matplotlib inline\n",
        " \n",
        "uploaded = files.upload()\n",
        " \n",
        "for fn in uploaded.keys():\n",
        " \n",
        "  # predicting images\n",
        "  path = fn\n",
        "  img = image.load_img(path, target_size=(150,150))\n",
        " \n",
        "  imgplot = plt.imshow(img)\n",
        "  x = image.img_to_array(img)\n",
        "  x = np.expand_dims(x, axis=0)\n",
        "  images = np.vstack([x])\n",
        " \n",
        "  classes = model.predict(images, batch_size=10)\n",
        "  output = np.argmax(classes)\n",
        "  print(fn)\n",
        "  if output==0:\n",
        "    print('paper')\n",
        "  elif output==1:\n",
        "    print('rock')\n",
        "  else:\n",
        "    print('scissors')"
      ],
      "metadata": {
        "colab": {
          "resources": {
            "http://localhost:8080/nbextensions/google.colab/files.js": {
              "data": "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              "ok": true,
              "headers": [
                [
                  "content-type",
                  "application/javascript"
                ]
              ],
              "status": 200,
              "status_text": "OK"
            }
          },
          "base_uri": "https://localhost:8080/",
          "height": 359
        },
        "id": "evbD_yj0o6kr",
        "outputId": "55112c6c-cb35-461d-d85d-877776c6bbc0"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.HTML object>"
            ],
            "text/html": [
              "\n",
              "     <input type=\"file\" id=\"files-0ffbc82f-61b2-4db9-9f2b-6c6733ca21a2\" name=\"files[]\" multiple disabled\n",
              "        style=\"border:none\" />\n",
              "     <output id=\"result-0ffbc82f-61b2-4db9-9f2b-6c6733ca21a2\">\n",
              "      Upload widget is only available when the cell has been executed in the\n",
              "      current browser session. Please rerun this cell to enable.\n",
              "      </output>\n",
              "      <script src=\"/nbextensions/google.colab/files.js\"></script> "
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving 00nKV8oHuTGi20gq.png to 00nKV8oHuTGi20gq.png\n",
            "00nKV8oHuTGi20gq.png\n",
            "rock\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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nATWtnxNreSUcdC5LhYZG2C4N8zgK3jSwjbQ9FvRgMxSKa1z2NIrIsS5lEKRhigoyE3Igb6I98hU1Hy/Ad5a6YDtOL0YI+LgnxUJFlsX0myhrzROx9Hs5lzfJvEsSXhx3YHmwkmU4eKeb5tRAjUHwGMrJbQU8wAZoAVLYSMS0oSKRwQG0XwOPvk9jk2ENcpaA1TzsF+iQqCybFM4vJW23KCPJ+LgT13Izg1uTVYABOOQgGGOKcQVhTE3LI5U9pi0TcyPi912ZG+2Ugq5jmgZ4HCLtkT9PY5RRlqVChE5FtxL3NPZHplKMx0wH8kr4mawuRy8+9OwRpq5MehqLGam8CQbgpQgBU4bW5qKTpnB0b5/LB4NN7uxh9ghB8xbH5hoa68vVTzvt8u9zCvgsoa5sE6d/fV1cmSd7bW8FrMSU2wTu66TUZxGOfJDzVrIawqZ3vk7fRXlkxJA+DgRNHgPgyMXLGRkEMBgf7+b84y5G+2vLleoCJPK5zl0y2SH8qkyjG3O8zSYnbPw86zgXGXGa6TROlwkYnUuBJa55b6VxNSNkYKCsqfsc5c3ogyjOmiAt2+MVbE4nMh9k7o30MoQAYsKCFCj9DSkQ/5Wn3k2dtDwh3YlBteOR5jZCOZjI5yS5mXEzKFAwC7NzXQhgnIzQ8Jx+mxsQPzqDJVQRfHGDCD0vp/YFTLX33BzSz/H9qNxJw92ksJHwyRg5w1irnffvhF0fpkwxE+BAg0efUpuK1ss5x6TlYRD9Ga099AP+PmPUtCO5sKJ7k3iq5MiEjAoQBN2bIjCf09yGw9mYFRpFffGQbyog4AUJgRFqaUiIzRmp5iPWTRzWUSNIcexRyVAuSvM1YWOKrjbuDQTwxpaq19jizufUUxPmcE5rR0x4fkb+q3xVDa2oxvbI5zhxHQYn4rzUFrxFAIrDYCqZ8M0pqKnE6UilZJNKysglE1HsZ5jrBGD+lfBkz3NTwExoraEUTgRtTrYOdj8Eo3EK63RGhPcBEzNzZ1DVTT9KP/G8h+9GoxQpCQdfUmzZxy51xBEIicHN62zMrmiDe1q6A2Bhwu4b0qVoWCBVKWjulrI5FR9DpxpWQQlluF9xVUe3EIb7UW6lFyME3IwmqEeW3TaOJaPbKQgo9Exs7gnmNweg+PjMISP/MW1lUJumDR9Wjihfda7NNr836LnW5r31umEF5DEOR04+7xsbchF7nagoM+nOy0Jw1/kwPqbZg+WD9dPZCCrYhj0FAEZNGQiCmVGT0xa64QqGlg7SsXU3YyozJ3LucR46KwOlZwRKTuSkWvLBKiA9B0AYl0g2Dok/Y1rGVBqYN/SEc45ApTsNATTQjI+jObIBXS2h6BkVdHF7xlAM808eHzCMjdos42rYmO9NpgDwgoQAgBknqhS7nd2YY46SyrD5+D2ax2p4ZsRt8I95WBSErceNoBqqiYwHRvs4t3mIFUjPnovwMp8CJQk/Zpb2eAz+zd5HXRFUNFZm4zf87GaJZZrZ7LlkQn0ek2AUs/kpCVd5LwdBT2PEUxsmwclkmplSefYqp3qLw/JddxLKsmCpMJ3CaHXBOhPsDMsG8yTRsCuu6YE9PaD1oQ6fH/swMs9zfosXEzYsQk1hW8+NTx14piPGCJKNBgL3f98W/KcDoWLSPX7p3CWqUbw3aVXjbaoIpm79bQ6EmgWdvFFA4au45c9AOnwUhFoqaqmYBenzjYjzFSktoT1nd9pRY6zaO/LruRGpH5GOxzA0aKCW3W45RpwpcaOMaBf8lKnbHTh+/zCrI4wb7HXEx0qblOj4dsoHyJb50WF9z9H9y0ACjkRpsM0K4hAHBiwQ8GaK9VF5v3PXU2ln7cNJZvCk9VQLet4guvwJCjtt15yH0hYCuIQCmLSflKCVM0PczMk0OejTvpwjYumwrUPksr07QbKGuRJk/39jG9mmJgXG8jQwamiUjRez91N+ieAdMunAgB+EMhWRR7fbspr7MvKZAYyYKAK4JcXKPo5ivuVNSCZsgEQQ6ZmN0Q0m7bbpieLwFMZYh7WBg5YIhFKgpxonYuVjZJj5stnIUAVRG5ALuZOhq+5hiGPnmGZ2KZ1bcZReDBIAMGgwA+ACvykr+imZBrAyJo12q6qhrCgjNsBg0FYYwIBO/B6za3tvtdXNuOm1CY77b6PwyBnYiGFoj9URJ3REKRMzgMb+IKOmgwa7czI3K8Zq+G7LsxMD5ph5l+pk/VCG5v17cyJDYY5YIrjM87ggt+9IbcrCNTrg71jeacyQ0NexCTW20lrmfXf7PdNZ5BuWgbUNcz0e3DWDtSy/D4jvH53VAY5BcGIo4bg5fIcYsv6tg+1SWcswyy1r+mTJRTLnC6fvxkhpFYB4IJwdgE3n/B9r8awjhOEKKeJhdngb70eEYC6gS7BstL4E/Gy6z3+p1b3U3ibtT6VYuz8ikNEzvYedwUzpO6d4A+Wf3rubAsKEMQaZcgONsG85NkEl89h9JzlzWqcwBKHHobs33svdt7mUIvdLHNjMNrVx/FmmCThC8LJ3TArkzT2hWFickfOZhd4/RPtLESTGkb9bRK0JEk7nIwbJDkoUz8xvTt82EiCi7yOiv05Ev0REf4eI/pz+/hUi+j+I6O/p3y+/RWG4yXScO5Gg4UHKUranCSfzWL8xTZKfx58cYs7NL4nhJ7tytzx40Pq+KzfnGscm55AmcsDSg94gnTuQA2l2faBRk05Pb/wewnpmKNeM0/wdxx8ca34Pup3eEbNRzj0sVU+KHkyDG714po8zkhiaFpkAQIOk+JCkcgnFVinIBLQKwkSntlyd+5z5QJbK4wxFKoSi52LEKgXfGsKBNm+ldzEHNgD/CTP/CQD/PID/kIj+BICfAvBzzPyDAH5Ov78x2fl/IOi57dbJBHmRNVT2zY/Qy1Kx+HY2jWGx3ulAu8PG6GSQs9hBJi/4pmg6XloyWzBmTexKnpVKknmcaxycgkPAkcJE0UR2b0FBjk3zWp+nCy/TW28q/k0pd0LbuQO0drCGd3IuouzmUt90BlS1B0NTw4b/9EbUkU2d58yNME2MovLozbRwxMg7YlA0NjuYfRg4ISr141COyaBor+yciWeUFeSEgLyvR7SV0rdtDjDzbwH4Lf38LSL6ZcgdhD8C4E9rtp8F8PMAfvKNBfpZkWPginkGBNanM9jZdmjVASqXlCcOkDRGg0BUPSdgGLSkeRlwQg2olr32BNlPLmWYewyAaCiHi6YlE0xzWGzt0hINWvYoizEG8nSw3LdAYz+1wQBJgA2lDThCHIw4fQcq5KJNPj5TykFJDmt5zJ8989aMEFZl4stsZxNCB42rA0VPWCZkYe2bh7ULSSiV9G7JZR0hyKh/NtHCiah5OTOPu6e9RFdPydyzcc1o1JecsxBwba8joR9az2UJnZlDXCjeTpeKk4YHgeXFm5LSHM+o+z8UnwARfT+AfwbALwD4HhUQAPDbAL7nLcvYaU75PYiPSkyeXRgCQPa9k2i97NwTqAhHAm5PERD3G71F24DErfaftF7sQUjAHNSxk8DOPFVWp0mWBWPPcSYCOcJM5JFA3a7nFPAMbZ2/4/fWm+7Zhwc+PQ/796mUeXXFupHamcpllnMbgbjX0eTv2FhGbIiSS1DmcQuAE+wX8z4jRDl6KyL8ch6hr/v7e1yvV1yvl31H/YDb4q91VjseiAtKyGi0pC3D+56lpkW5+tyEAbMs9Yl/QgReLTWVVdC5+ZxyN19QAhuEYQs5j8PyVumdhQARfQjgfwLwHzPzNyftwrS/YtjeiwtJv3Sy3+y94XvWNqMU35OW5Uj1DBNFpFdgUeTOS29zCeFldgt1cjzF03BkYXo+tjOWlSiIIutLo/gD1BpEFr0ax+cgUeR7m3TTbk713BImh0FS2mhfwn3DcRe7McR+DqWuVDiiDmtHzj86Ao/arcHoR33f9Wc0weIvpyjTsf2GKsf2H+1ZIL/cNFOe053Vk8q5wWJvnd5piZCIVogA+AvM/D/rz/+AiL6uz78O4HeO3mXmn2HmH2LmH6oPi3dyx2DmYIJp83g2z43B0akeABhOJcre95FgnxlMQ1dTFkfI+myA90AEgQx9CCgIb0fqc+J+1zWUmdNUK/s/83UY0hkZ/5ipj5bjRrMFw+9Z2z+X8rhmIQns58zakd7e29dACmU+msc9GRPF81IKlrqgtbYPCPIhZF/hsO8mVGgoNzkqU3NuOY1B0ENv4t08nzFOMfdHzlYXGDQVPvwwo+hBIR80TtK7rA4QgP8ewC8z83+dHv1VAD+mn38MwP/yecvuXQb0/nRGdfg3b16BwGLfw23qI58zB4WZZluKHWWXYU57tdI/q69I3mMsA3gpuqdcbbioTwI6XEr4CUa6EQgdOQAmio5+gGg4MILVgUqDEIhUimgGIjkxR4J+3j568U3mQibO3rsuAb4JYVg7Df4vDt9BDcDVah/qkf6UZALM9eT+H8AmAEDFeb3DJx98iN4bejrx2C6qltL1ExOgR5pVMjrT/PlLAVD4rTlIhJFQnB3R3kk2m0WBWSDYiFQU/UdoIG7wBUzu+q+BZW9zqk9HxY7Oe0Zuv4s58C8A+HcB/N9E9Lf1t/8cwJ8H8JeJ6McB/DqAH3274kKS1lpBAK5t02PuKElKcX75DkNd/svy2o+ggv6ejNKAcfIbY4a5Vl8sNw0aGDL4hWLojsY3NCwNhDRnZm+vZOp9G/tqGZ0Q9/A/Tt0btVE5gKZZ08A0mgu+Y41h+edYjRm1zU7PzLSdO+qgsX1C3FcT+wYi1oKzRjbTjs3pa03XPRsU78YYAFu74luvPkXvbZiXXVvyE4d0KZqSOBBD6qfFBfh4GJpw/wF5aHSsVpMfkDOjMWuFXMjbtClprwMnR+NBymZDtpZupXdZHfi/cAtnAj/8ucvTv0FMhK3Np8xkTZHyJgjOaX7z6vLRMp/l3RO+ce0IUw/HPJkrJkDyisTOr7GbFBVWKVgmmzxBlvI5Q/IBIuqf2e6MZmYmVwSl2scI9fM4DOc6Mqw9jlk4INxnidOQVGobhbDPMtV8MeD9LBMBrW+4bhd/gwevf/Ds0QTT8LvNR6rFlFEa32Ba+NjOZfqRaJqfEW1ijNuPR6f0Ph3NQ/5ugvNWejERg+Ng7yW6/XbodEIa/JLI40AKDgM2Pc4a35lJr5buvQG1+pvmrDlqe2YE95wj5nJuf26RnZ13K+1sRYxOosHnQORwfRZGxQMzoqQdsaZ3dif2HNSZ7X9mOcFgL1gGKQ1ja5qdOam/Xtck7N/gmgCA8Wr7KdEBfUw5hjbs2gbpa52KKX6ibVc0lp6b1hfJL/PhS8kyL4VCOPceqwEK3rTtbyewPd8z2V+GEBAqhjFuaMKSDm2E3MACQBZKWC12CzDQm4eMNdOuNYCcmYEYyKPAD3lH4ajexMvw0/a8fuaSbHPAiLtWQtNdJ5TMkoOjPhwWyh+9jccI3N+UfhSSS1ENGnftrwxMwNOMEqSvh/AF4amP75Y/C+DR0SkjweA0F/L7UA3rUiFv6tOBw2Fpl56fQACrKdN13TuOiZ8QCgG9qf+HCrjbmesmeKwBZYDthrBsF5143tkvf7XOsUZWmuYcT27Wki2GhaQLtugnEN/GLHxX3Qc4DsUh0ubZWPLzeyXtwlixLlTkFzk/gnyCTFhBfQNaawrDf07avQwh4MmAksk+ZQogoLSx+c6DnMX6DbRw/HTyCZDDSi/WUAXmOjnKdSk9IgLJwAPDDSotIZnjNHCh9j734Nay5Pw5mxVTO6JD+3r9Wc4/t5X9uRCiic4QTNkw83FLY2Lza+c2jAgJ47vM6SxHbd8MuzNyzz8Pnr7UlwFdHY8Dpe9mhuzo4qgMnsYtzflRZKQ9k/bKC0fxFiISpCU816FCVBTZ87DpxQgBmxvWE3/kSuXY9+6DQACo+g08esejgcrQHkhry0oMPeSI1QrXx8nuy97Z+I1UyhKoLBh4CwaBwwSI8iNfIQme6Tgwa4w+StFLP0yTmAApSWbYEpVqteQniPHMS6CctPstiDsTE3wso8zbtDSWZwE2dchDWojZzDs253Dw5TEB4E5aqUeXQrWczhrUxNA7AKqjBzu7IKuAYUnR7fJ9x0Y/CtAYCVkgoY2878WU1qjN+1IAACAASURBVNzxEB6zWCnWDozzOMRGgHTntlIAxzV7ZLrTNZFLmb2sP0gvQggY/C1EoFoTFRhkZGViCfetdk3QW6TDE4MnR4vd4T5Lask73cJDhGy1M/MuNgGQNg/edIWA+T0XVAM/ZAEyOdMOlPUR/M/EewT7zcaX/BjyHAmTEBwhlJ/1TCvhySErtn9DpXUay7H82CRzyw+WmXfEQpOG5H4orLK2JPCun/t+307GW5/Hmdp7G8ZTyrEo17fg1oPEELPYrBBbTQGOaf8ovQghYIn5FpSVwYp8Bh2BGbbGGO+dUrshYSTbcJTPNsHh9LpZijNx1sg0kSZP7TyccIozFCx/MDRcSxIwCoepHcNPU9+iKpry5AIyGoD6No7nZe6PQX8mE3JabhrD0UQJ5GYXwdizHII9gAOe2smGFPbBT0fJgoLmXIPZcjCWt3+ncdTeYsy93ROdvikQa3RIj3PiCIt8RL3Jx52R9HKEQNJW4+8KLc0RNdlgdsAEAxDHi0zH3haOgysHXcvsx4K5U0ydigSMzh1FWwxEAJEyru8hpzTZGSIQA0U2QREqmLfJWiDYST0dzbgP+YRhOcWYYU5EIDbMhIP0OSJiF7RZ87UmJ++WMjoFpd6GuJb9oERjJh0b0oElJpQiUXo2h7skE6C9J8BPSNxUPsRbPW0RlrnW26Vsm21CQuGXyC/ElvJw/E4Rqsi78iaHnWpYopH5rE4YrvNbf0ZxSjqXY8RiCEHt1KDACHLwqcVRwGtJiCxUlrYjaNfNKyBO6D5IL0cIqObcHUkVrvzENDIUcoacHpG90zJIL6TBzpBfB6tOEtq1iW/6kGCPxtsoCFI9BOHz2FZqDrtZK1jZU1tcukMIkceuA2kTy6Qtwv6NDSljMh/LXkAcxUhYm2XzUI4lOE69b76sFewQaMWOD8up6jVg7vfw2vX0ZLt7b6o2m3IeSDT5MQ4huqOFg0fquddiLftuXArN9DWMwv4NmseNRFh3HnY+yjJhrnTutPSr1robx+cTHw3hLr0gISDJCNCcUDtz2DQ9gvjj0ZtsIPb8+7XuMd/gvBrqSNkoNEPSBYc1G+qA2ew3JtPKMUjHRLGVNI9HNj0OhNhzASQ536FA8ZYctC9p2t345795nDmPm60g5CGI/vp3H/+Ddtt/DE4f9Mv+Bioz4XGbRnLEZWaeGa7vmfs2m+U4EZc1ZOZSvEoHpQy+qxvV5e64vtGCYxyskOP0IoQAKTPZdso4S95zpK/Sy+IEOGoBKW8vzwdGcAGQ34mQVU4Dae+NS1KWgQJ6ZVwweZXtMasGKIX2F0ak+/SyI9Bs5QyHi96mlJnY/pp5EFqY0vexyiMhMLY/xnAvtxTt2BjoJB7Jtgx6rEwJYjLhmAJs0riEYjzSjAl275jS+mZTvWcAZjm6K1Z12BkV+oqjPo52ZubK43uUUlGSL12HbjTjNJiFVTJpCRITIgCiu8Fk4z8oKxPOONokf1tQvQghAMC1KqukPDwFIe0hGu+2i8s3xnQEDbO+msUrSYSZLzmmdQDK3xkoCxgdm56hh6LUOywVpGU5hhxFzg1Nb7wZJDrsEI1UJwMotsTVUcsytNr61lo7JEhhhHToqB9xJakUSsxi/ZvTLUJXJyxim+seVYU+jQtkDQXIykEpBa11ZZBRoMkIhj1cUpFE0AUFgdLd8bT+1XP3jYGjOVIe947Wba9+PhZmTGx0SYDppkGf+H/txzjh2XSUzQP3JkiQxTzaOa79QJ09OsuBTYUKet+G8TWUWyriNiN7aqbVjfRyhABifX4EeLN2J6AY7NZng/lwG8ZGafJp8B2xTMLERrl1iCmKJSbZGKNS+GbfFLQmrWxXWRtRRuqD4pq1fRYAz2lyI/R5ScrKGcdlz3zkzc1QWPoyIp3Ju+1t26OHHT5TnvXfJ1STNamNrwsthsP3xgnRwZyM7EJgf1Y9uz9Bfg7kOdIPWzEuW8gGR8eje303UtqCOvvrD/O9yeZ3k/JG9tTVYZyfSS9GCDhkwgwpbXWARNofDsAoKG7ZuUYcEQ84IgETIk5OGfbBNJhK7OECjWjLDaWpGiTD1oDQ+1N1oh+5jp73TvDYz9njL1k6SE9ifqO7xN45MBvSU6+n+zFoR2iriECl/VyAzIseTI3hb9/9LkLAxgL+rjGz18E2q/MRYwavi7+T+5v7tudBLcPvvcx5xneP0mC1UgiCPF8Sa/KsKBnm9qaT1n+efVMzrY/pxQiBnLJNY8djATHx7oa7MWp738BBHZPAiAmWyzjMLotJwhBgY5Rgt/fc1MjhYNjVnevPvfcJIxLPt5aT7f3Z5j8qcyarLID0l6ne55NEAI6Cc67bvouAY48BslFQdlSkMbevpyVZaW9vcRGIjH+YZXm5rfduMCSE6qRdyxtO4MkrOXvamWELhiGzVQ6CVc9xcGBCWkdmmymewXRJz2ypMIo6UmLatkMB8Xy/X4wQIJDu+4hDQUTid5XCcg4dQ6JqKw0vD9pj7/kPZwlr/uOBMY+3TXZAaWlXCCS36RgYfAW5NM5EKGUWOfVD4ahMbO8W+6yrwCZsZA1UNFwinFtCBwA6NxSqsKU5N3No7Etuq5kEzHnf/5GgOP7MzMMV3FaWtNsOGx11k619E0gCtrpoW8ffLEKBOGzrGGLWXY3y2ZaLwUBJt+0YdA99bQ9Y0Yy0U0yzNB7WWhVkNvZFBjg1Uw+pKYY0DTVoG1ta1tW2jKaetZLdL2xLuTNacYTm7xc/3t58M/L/ZDLafZkTapzTyxECRHGycpaUShuDEESa5ZQ/M4cvXzlSNByh3yepzLlcLdzlQMo/NmyG/9aO/aB7NoOBvetaeKZuISPThvXQTj3o55BMiMLt8pzPiOmYJmZBNmqbPGa7KEIVclnAMCfEdgRlcxGc5rDruHQ4TTCUqLV88XdmQSZtKCAfv7xu49ntABJmjemAniyd2m0XlIKByl6GUVHI9u5v1KRYIiSZQSzXiXcnlhGB2vhQam8eb/s7LoVOGtDblsfWSry1aB3pxQgBABMSNrgvkr3n5ZWSb6zjtGwSaVjH9lGw33L0/5HGi4EHMnzL76TlwT6aELtuZQ3AvJuW2bEWZViwDjRyzO4NrDduZALitp8ROUy5kn0cW2BF8WXUMELZ9LoLoryc+XmSHDGmfWU7Uo4d+cw+gVk7FpOfAjOmvsYouvxxrH08R73nq9tI+wigMbiwHFEEL0yFCdnL4HSOZHZCt4O6sh9ilIQpj/6v5KhJtjsJyN81P1YagYR5s6C5fQ7ayxICMIhV3AEUYy+eeOlLmd452Enn6nDW1vt37LslY+6ma9nxiIbngAzzfAzIzmaneCdrkl2bFGrGxo/nOWs2EQDYVnrYCbRjfETWNvG3teY34j7nR/G+E/meC2ZG0eg/8C2UwqHhj/qdzRPnXLg50JMACBMsdYq7T7L4TvL6uZEC+dVib0oGoaMh0ie78NURD8PtdT9SPAlFTpN9y5y7SYNp7tn6gOLtKK6IRkTBBp0H4f38vL4QITBrQht+jrm40YfnoPJY8nEKOxGjVFUtZcppZwrswnBH2Jwb4BrPtFyCdq6RE7zNfgc3dfS/oWOMX+hmOw8n3ukjtRecTgEa++l29w0iOvpdqkgCYMrvTM9WB4/S0SadQ6DKsqpxFiUAEN+tL44inXjGMTfHXb60KC+l+/qQ+qWM97lElF/IqpHZYuTsQ8z7MDcTUrvl+c8B1fHLMXoIFADAkFpux430QoQA4DvIKsC6+grSFVwiUAWWGwjAPo/2pth8VPU4c81rwSfkziA5I8CQg8aNHdrOxswMFbZKaV3X9qUtdjwUS3SgUhkTOcQzwhBvuwQ6MW/RH5/H8dwBm/oK4MpyMUmGOSbETDvs4wGQfme32QsVuA9y3lY9aU5mBrf96oSl2BsPNLZTcaXdFpwkpp0eFss6nuYHkOEHdYCaaVx1nLkwkpEQCN9R6ymtDnDmF58jF2+dwJ0Ql4EDsmUrkCPsd2ZsCjJqIaCyXhwr5XYdg1LTiUUUR9C5mCY9YJQTPaV5cmQxj3MSRnIRSQ80p2gvUKdcpkOqbAx0lVIcVd1K73TvgDawEtHfIqL/Vb//ABH9AhH9KhH9JSI6fZ7ywtlys750im4S08eZhSZ0QI2gMrPZFlQjtCx7tUUO+0zT7mSwCwAVSC4g0orEkd13q6NUwFTQJhhkNR/Zmcfdjzp773H8dPwKj3CbhASDDy+zNFPgbVIluf24pnP3YxzyX/Lxcu0PlqAw4xxmiSrU47PN0TbA8EDBXu6RoILPj/Up+VGsnYbAKMaktQ4PwOT4nTXiUUyxlv6l2AYwGE1VBnsbbppJtPuAgCQRp2LJx0zLLfpPFBY/y+nvLAQA/DkAv5y+/1cA/htm/scB/D6AH39zEaIdeu96QUTTm1hvbEF9Ju3hcBIAVtsB9wVssnHM/oWRgLPzad8Trzy+T1mzrewSOvkOgvHHwz5mZrVG77V85J+f3YpnMM0Xy5pH7X3mN9Pqs2+F6Dj/XJ63EYdDm82C+fntuaXDfOr9mOq/0b5kZoR5Yt2V+RMUwkMbh/a+QWjbEva+/fb8+Peh/UlYHWe4/e47CQEi+l4A/xqA/06/E4B/CcBf0Sw/C+DffJuyWttwvV6xXTds24a2yUGSMdjPMzEmgrPPcYvtmJwgimmM4trkUNsjhMOIN62RGH/LdQHpzPton2lmQybu8T9g7uim/Famm3uPmD534kgADD6Eyaw6Yjbpy/FYuqX6lkw/qtNkfJhJAMg6eFA3eLig1B5Njki2HaZwk9C3d2uVc8sy9HZG7x299UAiqdmM7oqq947eGlrf5w0Aw34ugD/rgUqH33FD0CGtRhD5xbcx5wl59ri9mc3cekaGvKtP4L8F8J8C+Ei/fxXAHzCzHQX7G5Cbip9NrTV89q1PwQwsy4LrqwuYGee7E873Z5B6n22ffHQ+nTYEs8A0OXFDJ47RqaVHs4SWz7HN2yCi/N6Z/eRcIoDs5iDS7b6+n37URGKpyf+2TY6grhrpRGXyeruridO5iZ8P+jsTQ/Yn+EEabTJy1Odh7ot5NOz6NPQQmFljDRug2OZERyCZXL1FQAs3BriDmN08ATN6JxQGSOE+2Yai3pEP0BAxwCjooFIdZhcehZOcSkxxkSsDhiklyKjpLkYRpL13jdUS5srw3A8a6dZfSuxmY7+g9ipmCslmLkeANoUdfh2ZWD122SkDpXiAWHHTyBCLDTGB29UviJXLV2ysmyPKwsVb5uYA87NC4NtGAkT0rwP4HWb+m9/m+z9BRH+DiP4Gv2ooZcXpdIfz+R6VKrh1XB6fZIfZoeJPGicp551Fn2ylIPRR8sd79u6RNpu06PCZhseyEWkM7nBUuDMNkrxKZgJ4JIJvNwmqod1vu04MRJ3eMWR0YFvb0Kdf4q9pVACGn50Yk29GtF53W59ZNHA3QeB5Mfh0hn+Q67igJqT7D5L2999n0zCPgCIAyWP1+UM3mQxNONl539S8s3bmmGmrbNceDO20kY85GCltmD/9LcycaOvQpzeYEu96Ddm/QUT/KoA7AB8D+GkAXyKiRdHA9wL4zaOXmflnAPwMACxfv+e70z3u7u6wrAv6taFtGy6Pjzh/8IC6CvzNqwFDWUAKE+cEIXMuY1T9kXIcOqCuJuAw9CjiFbSS6amJd4ZcPZWemLBicZI51Uw2YL4wVZ4eT5xrZRzbke61t/5q03ZC09unApCD0XfLVfb+5IMNOz6NuXUAum7N4cyydvcwqsNmZosNkS23jlyqTVv394EE8XW9x5bzYiiMIIrW2Uc/T+5HOrGp9w2lLBhUhr9ybCSyIhsQ0Kmj1LxRydbvjT4EfVAye5CHz62fBP9ZEUkpSD+5QpuT8IMU+iYBALwDEmDm/4yZv5eZvx/AnwHwfzLzvwPgrwP4tzTbj+EtLiRdlgUfffwx7u7vcVpP+NrXvoav/7Gvg4iwbVdcL3Jh5ZGnGpiIEZNUfMtEdvPLZFKH5xghsWfn45H9q3k90IXo0Nse2UNi2y00dKMjZjfedMIZlAcOVgSCuW/hjFKKEPKEXLLJYUzXe2CvN7kBXEvKl6EPzKmdqU43G7wMg/TmHxiAuZfVWb35ky1+NA7Dbwd1duabNxmNZcicN/URQJGB0UBXYdGTABAB0tRntA9nKrCzMQ/o7ADmM+IG5zezv6QvIk7gJwH8RSL6LwH8LcjNxc+mUgru7+7cIqZCoFrw1e/+bry+PKK1hq1tWMsqLwTuGSSjJ2K/QcYgla+dA/CDPxyG2xclIoi9LDvX9BmrFnJNT/G7r99Gs+Rbh2kCs029iTRGtdnfUmyNl7xMvw0nQ8lhHFI/fD+CvBw2+aypkUyUWfKlz/ldys1NTk0grhHzTpJqOAqob/4FZtH0Xext7qzr96Kx7fdoAmvwfw/7Kd2GbFoPANB0MxazIAqUsIvBAcMVPjKxxGvo76XL70QENJk3uWKAhnn2TUTQMbdzFDul3yB0x3qVBAGkh8+w3Vsgsw6L8Y99TOpJovBD7bWCjpEeqiMCwEwwBR54c/pDEQLM/PMAfl4//xqAP/V53i9UcD6tQO/YegMtFWVd8NXv+hp+53d+G0/XJ7TWsCyLQxzbpUlufbMPEvvsmIZEEDQDnBx+AcPGfeKsv6nDWfmKh4nz+lhOqMEc7pugPwGYpba1T5JoNqI6nGQM7x3j1pSGX8Sgp1Qm2sDOmZ0PH9We9lSG/XYDYcTv7A5DmQuKGIuexpVDbpjiFsGhV5prUFBr8lmed+EYFSAFpi2VSUuELPsk+5UGuvWYTKMzyE5mVqgvcSMyXmyMaod/Mo8sxgzSzUQdjG5XfBUIFxu3VYTZo5GfDMjp0kXqiMNw9UAaFF+1KlT0zMHZfg/fla1Y8UGe/ecQDs4HuJ1eRMQgEdCvF1wvTyjrirWcUHQl4Ctf/Spev36F3/u938dyOg2kbCfTklJbaOhc9tE2X3UAlbQ1GKadR04V2pD37bRZ0/E5meA5diYygIo0JzdTNy0OpTUy7RmDJe2MFljgU/ffVO1w2mg0VXwYQANM0JtRF12RMeFqu/DM8ebv8mHnemvq6degmC4auveO4h73WMZibipQ7AwFb41XVYnQEGcIiIffILce4yVciMYbHBX1ZP9yoINrvwrshgaimVcdLMe/ewvt3ck2N2QDoKK4f0D6YKtDHUAFOqNxj81Awx2NNnc2PwzmDbZpjPmWjW9jQz5GYg4WNDbhdpvyXoQQAGDoEdv1ivV0BpYFtRbUWh3utLaJDNVlNtcGB2Ju8LTnAVCVfLT+Ozpl4FqOEWGYXvaNW3Sf7yJlvOd1DWPQu9+xwEaoqrG4m+YiZ3ye+sDMsRQVGMm19pFmJ4cPx2nnYVZhKAqV/eBUg58mtGRTkraCkn+ErbkGlXtagtMnWl9rVn/IGvFDGJsirpdD9jOosE4XkNqYEou5aEu38l4cFcYhmwbNyw7HbZObrUwwoIhh8EEwo+v16rbsK5vEut4aJKPTmR3J5DEPZ64hG0OLN+ZJ/zNYfhO6OUovRwhoU3vvzo+FaAimaZvE+dsZ90AApsEpmOMH2PYAcNSjjD0MzsEhj5TyhSQlRwNI+Y0xw9a3urQ3biFQIiw73KIrfFYiLtXb7Ofvk7wXxaW9CEeJk5wyYcKc3k1ZkzCYnX/OsM6F5Kape73dok31OTIwjcbBIP5PBbnetjwAJ2Vk5tD20ndGhM9qPvZqgM6oeghNoYrNQlZ8H4IXbTMkc9+1PnSxw7OykMHRemxg03KgjqkLZU72PZttr/mNyjpDDhZVXGnnkXu8mM63024MUGZsNzMNUakQsL/jUvVxehFCgLvAKaIFVBkVFQsXcGt49eoVHp+ecL6/x+X6CAKwnlfQsLCRI8meOXBDJ59RURY1BbLHXiesUk2CpAEERwROPJRr1L34JG4k0y7muHANKZkR35Io0YdbZywl/AAFAeflnEWxp2vZ2/gigAr8FJZ82jEARpPn2dnEQOvNj0nbzQ0H4VmTxQfBss8+jbkhp0bCHKUVtN5k3X9TAdA6sCknbgxq8ht1uK2OFqHUhWO9nFsHkZgIouX1dKEOFGaAZTXgk+Ue53LCiRZ8yk+49g3X3nBpaV8KERZmMU90jkEAtgbUCo+iqmY2EmhTBVMI4AYyc6QXPQKf0dD8xB/0ClADk+4YoIZaLNKzgxfoUeIWxEaoVc+QsMtma1FRvwWtKsPf8uOINdJB1MNN9QxyfRFCgAioywIuBZUZa60gMF6/+hTX60WXZ2SWZhueQGjDgOztJpH8ITQEDiqbdZHSEY5r1LBvo5kEXTWG1SfPLfpMBUA+eUeZjVNZY+ruBbblneyfsLa11lzj2uWsHvGWtDhgdGLOQWkP22GZ5KOpzRvNgzF2wcYe+tmu/8pvRq3hlzDBYGPAvtEGUE04aH44KoiVAd/zCQKhdPF+F2V6U6xFhU/BgoVO+Gh9wFoWVAbqRrjwhqe+4XWHaHpAr3TTceWW4L/6VvQgkc6QSEbuclS4BgD5Or46bvzgWdsNy7SbZ1sGdbTYhZLApjASPWQEJkQAJLoWs1FRbUIsjuTyu28wXV+EEAAAKgWVCojtzDbG4+MjtrZ5iKeFTB68fVjmAHOTFg/2NFiHjNgDzu3qSkR9u1pkk2L/G93IJ8ReaGasMYf1x5twQxs46vEx8P+kEqX/c3khDCx7HiD7UY0KsnbZmEUNbHA4Mbst03EUHhUNJoGyvxK/u384lDYzQF286xWEhSrOZcW5rliogrrc71CYgA5s2BTqs+7EJG+zXWXSrS+57eHskGEw3jJmt7GPBz4fs//Fgb1Im5SZhvnyaeyxfXp4wpyGalZ6tgIx0cON9CKEgNlRy7JqcETBdbvgs88+w5U3NO3Usi5YltGbatc5xRAjYe9U/sBZB2zGZp+pxAYDDboiMAhhPDeiVGjKK8l9+Ym4s18gv2MBSM8FDRdHNkrMWUvwkfOI4pafqR9+qakvNdojnjpOoxxADIwzKYUGbOA4NixVGTsnk+YyZHA0eAC4dfF0CyYQ5qYCqoRKBUutWKjgXM+odRE3HRPKQuBSsIGxllUAYQEas24QAlAX9dgL2zz1TZcEdfu1jTlGpNh0U1gtK3zPREiA9Dn1gzV6UT+bGUiGSplgV0m6v6hotAKb8JKDVfsgUKN8EwJl4oNb6UUIgc6Mp8sTlm3DaV3xW3/wu/j09af4bHsNVpua+xV3Dw+yZFVJbbkDqETxrEM30Rgk1jQruJyyggIzGivZ6YiSxYMbE0AGXcOCbMHyRhonSxsMQBjRIJw51cz7LDf8Vt0QBHWSbmIz3zBfZChsP758r6T2pa130yjhjMXIng1tFq3HyuTcWmTxRX6IA84GsjegbwLxG4MaUDegbITL5YrKBZUJpQG0MUi3k5PC+1oq7nrBuax4WE74Y/UjnGjBWiruz3eyc691oDH6toE6cMYJp+0kfW+Mp6eLNLEUvLq8RgejEfD49Ihrb9jAWE4rzusJtRR0Ar6FJ2zo6IXxrf6EjTs2ZryuG/gq8/1EHRWLjJnHDBQ0Ivfio+jGKCJ4nIba+kwEaiS+kFLi6okUA9EhR7eJryTFKej8dMMvHA5BmfNADmY23lxSwAsRAoA4ea5Ntmh+9vgKnz2+lsAhXXqpVc0Bs52GPhlcs89B+AFXdYCQ8yXhsLPhoswsXw453CVKfN1ly9B8tqR5ROoiiLLQCvRACOZ+fnKtFZS+78XFvDo4wPT5BXXSuVDl0XwwYECDdtIfe2j7qv+os8D5DpTO4iBsjAUFCxFWrrjvFfdlxcfLPb57+RDnsmApFXenO1UO8l6/buDOqFw0spTQC+OiB9AQEx4q4codV+6o1yuuIDQC6rbgg+WMhRbZLQrCRoxSCSst2HrD1ju+RRs23tSH0NCMDTONKAKRsSR3qbjvGXDGpaKrXNm91IFuNo/6GDoZsss0MZqBIuwJkJP5g+456ruVXoQQIBIX0LZt2F4/4fHyhE2hVm8blmXB3cO97KE22zFBySgIiTl2i4AKsTVjPmJaz/0PJ9bNlpruH+r0lQTjH5q16Wi3ZVsx/2ZIoCOcfiN+CcIyJ9+8HGrjaQLrTVpg10OyjThGeEmY6Jj7FWrTe2ALJDLHVhzB2nuDsQCplqcmmtwcZqUzygbc04ITFZxQ8UAnPCxnfHK+xyfLHda6oNSKShVLXSTkt8P3/2+PV40tkZDvZVmwXa64PF3wcHePC3fQdgUQjjU0WW1ZS0VrDSsqToVwWs9YcMKmYeun0vG0XfHUL+j1gmu/YmMJJ2p2IhbB5401/AEEZBKxJeKj5VhbqWBl/K4rIkRyjFnMtW2FjnsqGKxBS2VHp8dBRpJehBAopeDufIfX/Bqffkv2CgAKe1tDLx3rug5KbTxCedI6OhFmLw2DbUbvdEKtEOdbGFCkkd6mQWXMtdSkbScNe1iUq/8uyz8gtN6eEUKIfkyrAZ8n3dxNlzzLqbJAKJ3Ry42t3VKAjzknlGB19d7RupgGvTXwtQFbl/0SW0PtwP16xooFCxWUQni4f8DD+Yzz+Yzz6Q4LSRRkpeqCsi5V6liA+/UO2ybO5KKXmrRrw7ZtuL+/w8KEhRiffPwJvvn4Co+Xq57DJ1F+dVnQHx9BVLAuFXdLxXXbQBfCw11FvV5QtwVtXfHUnnBtV7xqV70ToWvQVAgEQIOkTNipT8JWdOyovIFGNQ+gsQIgFD2Be5xDgxD7CQlBbibrbXp4EUIAkBWBZVkkaku1DTfG6XzCclrhvAs7eEHeCxo9IGAvXQdNNb5FbgUOz+axwiqFY16GaTn/7lUOAsDfSM9vSWGpnuOE6HxIJibEMfymWjpuaxnroNA4lN6zFRJzA5hAPABUikoAH2rrkxFXHg1GML6/yxoT0Bzm08YoG6NcGewxAozSgHNfcOKKc684U8FKoKaxMwAAIABJREFUBWeq+KSs+KCc8UE948Oy4lRXLLWi0uJ9LqgSdQdgLQseH2WvSW+MjRtQF/DdHbg1LLXgg+WEdZW6V1QRGI1BW8e6rPhouQMVwnkrOFFBY8KVCI1WbPWEKzZ8gx9xxQkbNbyqGx77BRfexJ/QxZ5vOpi20s9KvQz1BYAB3XfCmWAa1K+l9KrIwE/IIohQULhv6NMnjSLgDoown9NIL0IIiMRirGuVSxy6dLH3jvuPHlDXRSLnAEiQELnNw2AQJ5yUNPC81i6D2INhOWwzg9CxtmpmgpkO5nCBMq2uXtt5/Vp3BxJxjjcSZGlvcNtv3zKJf+O+QQEuDPM6+9KZGp1hgttyqMVASNleVpJcJne8PGu7bmMehReb3FUb1iWcngkidVRDY52BK4OvXYTA1lG3jnph1AskBqupc6wxPqYz7rGiXjvqQliJ8EArvoIVH9IJH9Z7fEJn3C0nnE8nVCwaLMRyKKveWXheT3jdget1w7VvuHDHsqw4nSq++Y1v4lQEUVwaYbkruK8nPD6+Bm+Mwg2nesbd+oF07cpYsQCo6FhQ+lnmuHb8/mVBZ1kBeV0Zn7ZX+BYeQWC83hqunXEx5tZgIfGlFIArwA2dN10CLAPN6nYFh/xycG33sHETzK0JLSxLMvm6mBREJA5Hcwy//GAhwnW74vXrR/TrhsvjEzoBn3z5E2AhhcoQGzdtlXTYn6ToLPAyZDboFXe4yeBZHpe02bFlzx0ZKCOlS1HKbB8PcCC3JU5Jfg7Km8bmtALCYCzV4tPViz7oeX23pGCepFxykvcZtbDeA4hBWPqtRy1HYkKcTrqSXqFRlQQ9Py9uad4hH7X5W9vUEagSqHWUjfEx7vFAC1YAV2bc1xXndcGpFqy14LxW3N+dcaYFy1KBUnB3dw/uQOuMy+WCWhdHJB9+8BEulyv+4PIHwkgFqHXBsspW9Ov1inpacSLz9BPW9YRShR1OpxMYwOXpCa03LEvF3XKHq3rwSiHcn8XsaMxYqOK0FjzwCYVWfBOf4tPtCX+wPeKKJgKSTIEJc/vx4UTYqQu2z2PMhluPpMuvbDtEC7ruq/Hr3MDgdhX6ZXvxOL0IIQDIbrNt28Quq1UiwGpFL7ODCqk/lP7ODJftfTi0n5lvsIOzeWDvOsaN94zJYkmGA47ZeHPYfVLP0W7GseUiUCaYlxJrxTsmI3MFWV8UMViU207gjOO1D7NWYZdiHvKKwFFirc8iZsWpq06rziAVEt22DjME8rIINyQ/wrJUrMuC02kdZthWiGqpWNcVTUOO5XNTe5rc1j6dTrher2jccL1esS4LttZwuV5xgtDZ+XzGtW0AVYBIjrMjOQVqXVds7Qoiwno6gduGzt3jA5ZlwUKE11vHWhc0ACfacE/36FvB6wujb0/Ity1zZ99W7Ohdkea8lG2O1exf8TnWPRVueumcWznum+mKWP9RuIastYarnjBc18WPUnItPLKL/k0GK2MYwCFSzrwGk0NsEAjT75x+M+bzd0Yo4nnjmcLxG4In6rFSU3+8O0dowZaj9pxoTsbkJz54HvXOQnIfmHTM7btNRZabkU72VWLvehx37yh2VmRCYoUl6KWWAmxSXi2EU5V1+/vzGSsvqFRQICsKS11Qa5UVgGzG9I5OrFGlUubpdMLl8oTt0tG2K5Za0baOtm1opWBZV9S14u50luChLgFAxKLtl1LRtgsKRDAxQZyOeljquqygUnHZnlCWBQzCGauEBNSCR4WvTxvjwqn/YNgt77t5AqkZEOMaQDQhvrysqAJ0zxbiaC4ocq7BjfQihEDvHd+8fIZPL99C6w33Hz5gPZ0EfpoXX4N1gu7cz5/2fMOlppS7yQnF+l1ugVHtZmtofgPRJClNg08Xf+728Wsyk0Dao6ZzZ9SaI/ty0Sa14aflskK8ohuY5LOVSrAQVTFRxkntbEeam/AUT7fUZFuhVVuEaemM7MtqaacbpX6JwzYEYucG6uqdL3YEF4M2YHu6ojc5Bbg/XVE2Rr3Ku0UXP9rjBZ/QAz6mM9arbDiiUvBwvsPX7z7Gx3cP+OjhHl9/+ASnuoCIcF/vcV7PAtcbsNYV57XgwleczuLg27aOdVlAC+HudMZdXfDq9SNOn73C49Mjaj3h7uGE14+vUZmwlgV3yz0+uzyiMeN09wF6Y/B1A3rHiSvoqeOxf4aPP/gITBVbWXDCAuoFxAVfvrvHddvw1K44XcVk2sC4VMJnd0/4vf4p/kH7Jr7x6Tdx7U32ujSSbcbQPRpEDjZ70RurC7mjnEoB2RkEGtQEQPY1XDoWPT+AGaBmR9RVEOsV5tt8AkaklyEEWM5uJ6oC+eoCqjVtRklphsjJUyKoPTa8+DKa8vsAbQ3me1757HjjhkffkgUuFXU4mlwJeDYeHhqNH+F8sh6iT9o2RyTeUxM0FMiD4JBPfu6pVGDvELIYggNEpGUdpQhx1f6rXW9r012PEIOeHMTqMDTN78cLasDQmStOVLCk3hcinJYFp3XFaV1RlwXr6eRCYKEFdVlQ1wXbtflYSgQkOwLIO0zLsuB0PuMBhM4Nl21DbxvAGqOAjrqu8HDeAtRSwdTBKYCN+oZ2vrfB8ONoDVWt6wIuwNIWMBqIJcT3oZzQ6AFbAXq94jM84XW/qr9HDx/hKuPIYjZyh5xbYkhUbEvvLzHHybcUh/A6InCwlpDljXkFXogQYBZCIhSUGgxmXtJjMB3JiZjHmPm8Y44J6mEfd+hFGw5M5+fqo6N2jTadrdHmnAHDrV6TTKktOUPuX/rOyoDZ2593FEbZ+d1guKNAq6M0iqsswOJEXdsnPzgG1T8gRGsHYui4d8ZKCxbIxh+xoGRellrF1l4XLItA/2I+IruSXZFQXCceONgErzWzlIJlXXAmwtNlRUeXncq9g/XeBxGZtlSqB8mUgloKrlDfDovJSoCfBRBQXE5gWghYl6r97ijccULFXTnhA2J8drrH9SKmgZxzoOOFQKfMsWScFVV2KLuvyideiFuHVmcmR8Mez62lFyEEwHINOEji2w93yT3Dpa6/iVBIjmEK+xnOIOaCNaLzyv1f2Je7JnL3I8+OxtaW3wxcPBeh9baJO3uY9HObieZHQyCREtmzrxvx3fBhzNGOzOIRdwvDGkHkTrNuFDnZvXbd3Pl0h4UXFC4e315VAJxOK+7OZ3zw8CAMD8JSFjmaqzVgE2Hx+HjBddvwcHcvTmWlgdzeUqvsMATh4eEB57szttbw2atX7kDctg3rIiKp+5kFBevdHRozWhc7vTVBrNw7cLIlyoZta3ioD1jXBfd0j227AG3DpW2CEgpwLoxPvvQJ2ivC5XVHe2rqtDVtD8AZHRGMFiM/zjfrtb1s26JjMiSy9u1p8J2EABF9CXIF2T+trfwPAPwKgL8E4PsB/D8AfpSZf/9NZdVSQb1gWRa9grqLm4sWlXQWdmr2MRIEzzi1AM2OjDbPuP3rSlRwnwBzLM7YuYMw29eMYF02c6Ys5IxmZTFsTd7i63nQypLsaqixjD4InVjP5+TMMc+8a3k7mw7AUmt4oGfiUROBW3cI2Zj89mATMaE1yMeM/aw8FSzdQpmVufV04LIBfeug1kGXjtIItBFwkQNE7Gahzh2nRrhrCz5Z77Cy8MBKK06t4g4rPi73+BgrPsaCj3DCqRd1DBZtvpoXkFUCroTXr55wOp9QC+F6ueo8FazLCZUaemEstaNdryhUUUrHd3/lu7Btctbhuq44Qfwal6eLnFHYGzoRHtYVvRdsraE/XYWSSG7INtWxgXB5/QgqBR+dFnQ64YkKagfW04oGxpf7Cdd+j28sH+Ab95/gH+Ib+N3tm/hWexRzIx0D1dB0SZHcJ8UAGkUIcXZhya3YZg4IPbH7xSyE+7YW+LbvHdD00wD+N2b+pwD8ScjFpD8F4OeY+QcB/Jx+f2MqkMmupfqe+pF9kCBR+nGXyCcnXtznGm3c4BytIcua6T0cj2eqxzz1WQAM2nRq7wzZnTV9ZWTXRTNG0/vOrVOzomFmDPjagZsk9i/9ztaWEDyBdShKMY3fGbx1Mevs3yY7B0l9A2gdtQMnrjhRxUIijEqRswBWWrCiYuWClQkr9GShroLE+tT1Ci9Sn0zTvRYqqO3YL7PXS5ENaLXIEmOhgof7Byy1AiDxPawrTsuKtVbJRyIEK4mQXWsVE0JNg0pFNzqJ1m1bQ79esXTGiSrOVHFCwR0tuEfFh33Bl+kOX60P+Nr6Mb5r+QgfLw+4r6cYHzWj2PZUdIiXWaKSxvsFnbHTgSaJ1rrGIuT7CG+lbxsJENEnAP5FAP+eVnwBcCGiHwHwpzXbz0KOIv/JtyxTHDsF4KoOJnSEc0tOtgnLFhCWLUMnM7g3+FrqxE3hJRQJOvgVZsZMGlj/w0jv5zIP/A2pFCVMBtDBnbT9OAzoYhxBdGM+cqhsy3JQRqAkRCWcVjf9aOPluCv2U4ww1NMVHUG1jRmogG5i18HlcIRCL9xoG0rTffpNwoVFTugYXzuWXnGPojY+oRQh/FJEGJiTzwomdSy2bcNyPoN06XXbrrDTnJZaHZnAmkwdrV2zPBMa6FJeXWSnITc58qvUqs7ME1AI29Y0/FhuFFrXFddLbOWtml9QXBfmBWFrF5xOJ1S9ccrdQp2xLBUnWnBXgK+sH+NpAbCteLo8YeOmG8f0zIAClJLiLqACqFKYm+YY5u5byt2ss52HNv7PpHcxB34AwO8C+B+J6E8C+JuQa8q/h5l/S/P8NoDvedsCZadUVQ8/+yQLQ0vYrjtOfHSVQVgP/OrsdvnMp88OhTthEMx1g5k7s1wpdlzQzZrCTh/F2O0mmeMqAmBsv0A+9OtWKqX4jr7YVz615bCh2QcRB4A4N5n5pBqJzPZnYLtu7sEWIQQRrDq+pRSx7497LDU2Cchp3OS8AWQkZYIrgq8kqEfrJQn4aY1x5U2XSWXvfduaHyjauWNZFwB3sECaQnIJTgOjlA29s1/mwcz44MMPNPKx4bPPPsW6rCh6JLvdUFQ7Yds2MCTYaD2dUOTIZDB31FpwohXXreG+PuBDZtxf77E9vULb9HwAMlDAgDov5cizgPekJjNILmhtGoHkKyPMvgnpTeldhMAC4J8F8GeZ+ReI6KcxQX9mZrqxNY+IfgLATwDA8qWTa1fRfl3P6vO8atGTlQvTqkCwk2k+HpifBmU20P6NlYJZiOzafmNApG0dz0VnxfsRkORnJDiq4amODOnVDEhnGbKXSLeZm+Jv9mcMAShD/mQfIW/LtngBtyFgNwaVLrEC9ltBchB2gLqcArSWigpgYULVuI0KNRv0XgKDxL5vQwWN95HiPEebw967w/PeBZH4pNsYc5RbSwEti8xEF81aShVTg/00RUGknXE6nWSvPxjtcROh3HTOoe+jgJtUuZaKFUXQS6m4Xq5YSsFdKdi44COsAO5wrR+jd4lL2MAgspBinQA9aIUqlGbNEswzZ2MRaIiY4LElz6R3EQK/AeA3mPkX9PtfgQiBf0BEX2fm3yKirwP4naOXOV1Iev7jH7DZqR1dHRvjaQykUs5DJG3jDGwSrLMRLZfPmmcTrxiRQdJrmGTBXkBQBM94P/Svsb0F20QMPg05be6Iihxp5TnUhoUee61IyDSxX16hDFzQRBMw1MlYlN6jzbK/wCod2216NAeeWZ/NPDCElQwhRyKs1Yu93lGVaXtnoEnUHXXSa8aB0guoNZxqwT0tWDvj1IHaZE5XYiyQjUbcmoToXjbQusKv9GqAH2VAEjwjcfkSlUhdGKWg6NFgHZdtk1OZ9Og6bg3tehE/gF400vUyFL90xM2ZDY3gkZD1jlSWFBUmm++bsPsjF66yQ7IQllNF7UDlglIWXK9POFHFfalAL1iuKz7igi/hu3HtT8DW8JqAThL9SAVqUsmCH10BKnaycUxcUzvNjxRzNKq0Qq5yDtO3LQSY+beJ6O8T0T/JzL8C4IcB/JL++zEAfx5veSEposkCmxAmO5N49Lsue7gSnNtj5VBBJdMaySHiNlRGEwGLTbSQZdVGHa3Rl+kdIFh13zFlmj6/YXfMy7HijRs6iyNKKjZveHWzIJsGwuwlqW92mclsssEEaK41BUSpX6B3VofZHhVJiKz0O18eIlt15TyA9cpoG4M7xUYYPUbe6i+VcNpWLBDmBZOsbTXg8fUTaGWUk0Dpbeu4bg2Xy2uc66qXsdiZgDIGXNKGJfOFVEEA4vUXIVpKzCGRXWRD2C4Nl+sFvTfc39/j7v4eRITrZQOVgtP5jLv7e7x6esTj69d4/foVXr16xPl8wvnujE8+/gTMeptQu2JTpyhWQl0rttbw+luf4qtf+y5QLVi4y8G5/YrL6yf07Ypru+LCGwp3fN9Xvgdf6l/GP/zsG/jNx9/FY5dVCvZNZzKm1Dua0qAJ/l4k6lXm3vwWgfTEeLqNB941TuDPAvgLRHQC8GsA/n2IkvnLRPTjAH4dwI++sRRVyVQy286Nzh7OY7vbnrNC2CEu/gAmD/EIc3nz11swe1qW2+UbmGq2a3P9hrx519YgYhrauXcWwpHCoM2nZBtV7LBSOfUnPcttzh/ZMVM4oCavdJZzORDKBIn9LvBdPP+tNbTa/BIWZjl0ZGuy8adWge6tNdm9WMn3JTBrlKD1GSbrjSkKWme0ywXLUlHKgnXteHp80j0IBa1tAK9uxtkYXK+y3+B0OqHfSZxAKXLNWK3FtzIvy4JSgN4Yr1+9wvn+XvbCXK9o24ZaVo+B2K5yA/G6rlio43ptuFyuoLVgqQvO6xl3fIe+MR4vT4o0dCJaHC3fzelIOUBMxld2f1K6wm3emDSmdxICzPy3AfzQwaMf/rxlBXxGqOKRTxCMbbaa2ouY+UaJDHoEk8NsI+DEQIRhq7LDcmOGA5nEAGaNGU+QJoQAP0M+doEFYwp3uPmiz7xkWQxJNnzAQGtf9ouwvmNefd8yre8FGCI3SaTsnsbTxopTqGkOPsJUYaAnzhDO6uXwb/h0MqM32U3Im+xDkEAcEUy9d11taLhcLlgWBq0nWX1QhrcNStwZtSzaCBEEkicE1daueHx8wocffohlkRDkx8cnnE4rSiF8+umnOJ3OTisFhGvb8OrVa3zw8Ue4u5Njza7XTU8tat6+zozTSZyL18sV3/j9b/hKw/V6xdPlgrulYtUISNouYGbc3Z1xIca1N1wvF/TzAlqA07Liw/oAXBjXi9y54UoNXfcZqN+IhH5sp21ROpfAKcJS4YFDz6WXETEIYKmyTLPxFZ0UXttmmmKaMluyYfdICGgwn/GurkJrDf8/dW8TK0uWpAl9dn7cPSLufTdfZdbUtGpaM0jMjiUCJDaIYQEIaVigESAhQIPYgJDYsRsWLAYJCbFiBQI2NDBiMRIskEYIViChQYgliJ+eabq6uyozX753I8L9/BgLMzvneETczGyKHr3yqpvv3gj/Oe5+zI7ZZ2afdWFrjiVB20MV3c9Mkj7hG66gZ7GJXNiURe8M1L1+O9dji8U+Z/X0JTmKFAPzu+SN+05LMoJ9IhI1tlpyRkWl8fJuLAhrsUZcSAEnBlqOesNf0C2OzNoApBAcBxjKzVzgtNtRYM0UzOq3MgtzTiFp4KO+6qEwIldpIOIJuRYUzki1Ys7SWchXAuWKCvl8OkR48qheGo3WnFCY4ZyQgpq/TtWSaAjG7EsA1m0DGIiesK5XTHHCMs8op1NTNkQO13VDqBXHZcH5fJaqQwek61XyDIjwuq0gIg0PQlZmABMccspwteLl5YRaE9aUcFkvuF6vEhEIDhEOEwVUKuCUMTGDfEB4esG1ZqS1YE2ML8NP8FoO+LoE/EH+hI0zcs1IwVxBIANwxkngNMeNgOqAWILMrJJB3st8/OxLidWkJEcg9pCsKMl+4gfBhT5nzbwUWm5ugrdffYDRmaD9Z81kHiwR6KpIN8fQwC3YumCoCq5WE29mGqMRPDQ90CMaBnQSaqMbB2vdxDDwlr/QrJ/9vbdzjv307vbBcM4b8x33+w6XU5xg77gQCMTUwLeSFSOoFR7dKvDQDHYNcdVCyIWwMqE4alEAkGT3TSHCMaOkjMwMTxNKzsjOwzuP6lmZeCsEmVTLqUrrsO42tRtE8EI9lktBjBNAhJQz5nlGKRmlEEBHSUfmKtT3MYKcwypFAKi5ojCElESf2bzM2By1sKNZOCkl5JyRcgZzxcePH5HShnW9wmskIhhjqD4o7wiuEDw7zD6iIgN+Rjh9AaoTNhai04/1IueuWdOzVfM7RjEszQ/uIApKFtDcuc9dCQybmGSq4axIxXw9ALs1uX04CF//tpnXIGMN6v5FcytUOnZCYBJJo1B3U9zKanXAqhSoD+2hBdABPWCMMqhJ7lwbjhFj3Alrm9x7ZWK8/2Yi3kZH7BxtiIPPPuIGj8Ol2rPR8ACGKrdukgtlQMcIKo/vSi2HWlEKIWdgK4xCJGFFZvgYMU0R8zTJc6kVqK6DkVUq+URna0MTFDjtn3j7fAYdILyVzEhesgJNQE+nI3KWFdR5h21dkXLGtm44PT3DOY9tXQEIe1HOBXGeGjgap0mFP+m9o7kAOWfkKhbU6+srtm3Ful7x8vICC+g2awy1TS0iILiAwBUuTFjijIoJmQtSKXBrwJWEjTvxpYPnlZBL0melc4nkGeWsmZV3Fa19+6yUAKMLpsWC2UoJ1Ze/i+Nx1Zveh+Mq8DChx9TA7cQx//cRsMcspzc9slcYPC71Yp4axtAwDNulKwBy1AtsmH9U/vYu7FjRFFzzx+3e9No/5AuymtPk6f6+7ZpCwtAoyJhZwmfQNFatt7CGrW8lWDFLDkHKjC0VOAaCI8QQ8PT0hON0xCEuOBwmPMcZxyliWRZMYW7KnMzmHZ6t+P9O/WChBhNPRxKOYowIMeLoPXKWAH6MQZ+ng3MepVSxEkB4/fSK4+mpzQvZNyPngpySEJGEgE+fPiH6gBgn4TNMSWjJpyhsRTmjXq/SPyMnpLRhOSzYkpzryy+/xLYmUM3CgASgloxSqwCRYOSSJe/BeUQi0FNErhWpZPzBh19179U5rOsVqWRsNeOySj0Co2BLWUKX4W1R/2yUgKS3jp43dwEzEIpq+6oawYIKAbXzSLmn1vkMBKX9v0wA+b3YjauuKSD53NwLAy1lAM5RHyKrw0CEIknetqTrUfs7E8HdIwiWKTaKoq0WmnUL8AiCShGNqZvxOdi9NmjDxjecvd2n2//tyPXkAT1YwnAa1lOMoGr8HxVaK6BxetPjgy4gJs0TYMmw5YqagWmacfIHvMQZ7+cF7+YDno4HvCwHLHHCvEwI6t+bIu63YPfH0umHfHuP0gGYG9+i+PFBlQYBqNi2Va1ON7RnCzgejy1aMs8TSq1KVRawrgkFCawdidlJboHXtGLvAyYSBRR9gXcB9Zqkh4ZiFw6yOHEpiOxAiMIHQAUBjFQZKEICk4vD0/yECmgGpPRASCXjCy8vvKIil4pCR7Bn1EDYKGHNKz5uZ2xIMncy4Tt8jUfbZ6MEBlt699l+hVNTilz3hyArUDcBpXdbay2t+7TYPquMtgyy/fX35rEDW397MHq6ryH1bLOumfCVahOA0fxvudyDiy/7NVG/ewY0SjEYtTEL2Xns+aiB2c2AvWs0XpgHgHS8hv3NGhlotia365vJaxZECxkqj6BZNnZvQmslaHtgRgiMiSUpCGAs04zn+YiX5YiX40GVwAlPh6MSiwR4djrZTSELlmKBCwl1dqVd1V0AMFgOotws31/SzzNGbj97DsuyaOcrsSou1yvIE6KPogRK0TkmKdmF0QuTnGUcFgRf4b3HZZpw3TZstUiRkSaT5ZzgQJgoAJCiI3IM7yqYRQl4dlhqbPdVOKBQRXEVL3EGQ4qE1pKUBBYg51FiwSVt+NZ9whaykt4w/jf8Lh5tn4USEMHkAdNt67L6f9BCE9bsOO2rp36u90KrxFX56hSdL7A2ZnZe85wLSBJV2+ShZsr2pJx9zoJrSy2RdLfplki3x73RPKupbh3KpXGqUytGaMeMeWfUJ7Ltlrz2jBoPPQhEfhBU6wq0f67e9VW0MSm3+yEVAnNXep2GbTlnBO2NV7L6+7mgpAzOaO8mpYxSRbiadQDCRBFfHJ5w8BELRaAyfMrwWwaXjK/mZ3x1fMbPfvoVvpyOeJpmHJ5OOIYFXkFary6QmPyljf6yJRlHFWote+5p3bSJp1UNdg/cT7NmBxZMU8S6rti2DaVUJTAJOBwCPnz4AHKEeDrhmjZ45xDjhPWacL1esV4Tnt9/gev5LLkEMTbewxAmkLYzcynh/fv3+PbTR1y++xYfvvkAHzxckLE+nU6Y5ojL5do+j9GhqD4NXPH63UdM84Tj6YDqgThNCCFKeFUt4i2tDbBu2aKeUKNH2grIE5x3+O/+2v/yUP4+CyUAQBbaqqmS3CenmcgGeJlpSqRC3FYtA9PG5dZWf27dBkQ23d21bassRSMS4/4Rw2YGDWY5IAJlh7PsJOCY9p4joDc/Ga7fRt1Wpj6ABo3qc7JutZa8Mm4dDDWu+/ao9ve8gzZYLeuuGAFqhgBDTfnaVWOtFTWX3SSy0zrnscwLYgzKdWfRBAXWYsThcBCij3lR3z00rkML98UQlAMggGhrlkwoYn3UIu3HvPMNH2AuCiZWPU6em63WtWqnJ73HUorG8UVpLsvS7jEotZkjwrzMAIDNOVwvFxA5zJOwFRv6LsIpWa4hBuAiuMLxeERZJUuRk9QhlFKQ1Z2otQAkn1/XhBA8TsuMoBZHLRVumloEK4SIlBKYpUFPaQCqcmY03keNXgy9TG+3z0IJmG8ntE2yanZfvO0FjKLGtlaPZj3vhHE3mfUMrObuPf73lsR3RdLPwUr2sBdSGq4z2v0mNs1AaC7CeIru2+98+YZNjOc05dPH1m+ZRgNnUJCqc0gsgNqqLW/domEsEAS/uS7F4v/QeoBSroqWAAAgAElEQVThXy0EIgYCPBbyOCJiRkBkp7kEsq8vwDJFHIL8zNpW3KGX3xJpxWGI0m+Qpe7fJnoIoYO5SkdHkCpU5NLcFrs3bsCR7JNLkTfWnq/ML8vsa81UQmhPI/iAEgqKgo43tppGLjr1uYNv3ZWnELGmCilzkIrGkrN8L0ytgALEjgDvCHPwoMOCnCXiIBmDxsKkrMTMIJb3xACgyVSVNI/A3L97sqy2fRZKAIAkruTSwxvEO8BPBML4wagJgYU+qnqN1G7JxLG/KrG+pbLNjd0d2zW0xn6neobcATMOmBF0QhE6zfdenlj/byr4xvogPXrACKzJKKtCY+bBehANoo6JnJVxp7xG5TB8KudoYSKr2+9ZiPakWn1CJXg4rexT1qDEIG0tjgKE4sAZiIkaLkDV4+QmnDjiyxQwURDBZgG3KAM+O3xBC76giPcu4IUJkZWCnEVJOa+luIdFync3aUybs4QKQ1AaOVfgwtSeY1gWJC0AYinzlzIFcGt5LsVb+v5IinusYDGGiBACkgrePC+tfJi8g4sBkYCZCNfrBSltggNwBWrq8Xh20l/ReQQSJcdR8gRKJlw/XcTpI0aIE7xaSeQKZghuMqPgeIzIxWHbPD5+fIXzBT56BAdEVez5Ko1HnPeYpqivW0rvE3m1zD73PAEyc1/zZIfNVkPx2TUNltCd7aanXfebTbgYoi2HODyg/V4eCNBtPr+NzS7q1P+vymwzPtdeMfg9Njcef9VYcpz9PhzFFhrFoPDG7wEfpJtuSxbS6ETPwtYTcB+w85ZV2KsFBYHvYCwDKDlrHn+G2xg1yQrmq5MIQalwuaKuBa4S3scjFi+WQICDE5ZR1FyRc8IMwhIDng4LjvOESU1wWYEjnAtCNhojpkXoxQGHGivW8wrnbVzyXr2z8Jet6g501ApKNb2Kmcn6npxzmKcJ2TmUEDDNS3uguRT4GAV7Sgm1Cq9AqQUpb4ghYJlnXK9XvHv3AgD4+PED1nVDLYR5NhyGkWvGdJjhp4D5MGNLSQuRXvGrX36L63ZGnAPmecZhmTGFiF4wBgAOJWeAGVPw+FN/6ius64ZtlbwDu6cQIqwJKiGCURF8xOGwgHDSGgwDuO+3z0IJjOEroh5j79/dHSB7qMPaM/1uwS/5bLQo7BpvZsvdyrGdpUUNzOjXF0XAPUvbKPz343/kelBTCEPLbAuP6vcGHrJaBd833vE6YzhyN8T9CNAaprCE/ywPwHLlUaU5Ryla76/pukYe6qpDcF5o4tS8h0UTlAILUMHV4h2nuIb3roFrPoh/HkIYXCzFP7ihOxjrTTqOIua+ZV5WTY32qsC7gEFdI9orRGgnChLGIsnEK12RErX6Ba/XmaapMSuzhhSl8a1cyDkJIVZmzLOAk6Q1EilleOdRQkBxDq70uVQbn4IsDMF7lOBRikMtrnVftvCnzQ4Zl1aoauTC+8/dEhg2eanaTMR86J1aaHvK5ybhI5h4t7H6RmhmuJzfcgLQzGLZW/1g15VFszsIdw1JbDx313wgoT15iFoV2sg7UGsRvw7O+q20/Xm8BwXOrJimo3/96iaAeoLurtzWYnN/LmJCC79dVb6+Wo0rAE3gS6pCD2aAHxuGQ12Rs05k5lafQBCQzX5IfWLvgzYOjYhRsvtijI1PsJYKHyT8lnRy1yrZpdaG3J63M3zAeUHvieDJY9s2HZ9WAFrtBFGLnvjghS0YLGnC1t5ssBJ3Sw0RDocDQpiQc8a6rpimqZVmbykJB4ACnvM8I4SAZfkOBRKmJCKtOpT5Mc8KQG4bwtQBoqL1IjFGENcW2RClIuMTJucZRMC2rQjaX/ENexTAZ6QEmjXAYtpT08nDPm0iEzy0YcObgj+eG6C272jy6wq2y0Tb+08dNUd/+ySxb4Ypku52fN/WFcqQfNSEvwussNUJiYid0YCq5hpp+NOI/8gpBlFJ8RENNJIVVNGQkDXciGEF3GsPTOhKyvDXilgAXzzKukm2YGaUZIlbBLcxFvZYKOBUgIkcIgmrjnUi8tUjMuMIhxMTnuGxuIDJR0xx6iSfBCWb9SDyjYOwaniUbQ44YfApXGUuKLmH3Zf0rvAIgbTARhSOgHpZ92LpkVgU2VHNZTUsQlcOVbpO6NEVqyEuSGlFLrLK9ohMUTdF3lxeV4QpYo4LLkXCiVOY8P7dUXptgvHh229x3hJCDHj3/CTuE0s3JUJoSg15g4xAclEOywFzLMh5w7wskv24ZiTeEKJHnJx0h6q10Z892j4jJTCsXpXBzgAu2u9joNngq9/74YbV6wprK31bFfsZqS3v8omzkJqdzdBxcyGoJx7JlakJ57jKt+Mbgm3jHL4bVnn7wzsvLaYe6BNDuPdRgTo+PH0e1Dj9bKC8u7ThALtHBkBzfiqjlCqIdGJQUeRbw3FFV3dmmWAohOgDZifsPd45BPJw7ECkvf0gghTgEL00HLUmI3FoMuJdgHNB6gLUqpDsT+EFGJ8vOWpWmZn9tQi47NmyATvXQAgBTvGBrZiwOsV5rCKvm/vOedhaVJUotJSCXEvrWtQeMAnoGEMAQ8Bn7xzmWTscb1JCnLeEDOB4kp4KtVaU0xNSSWAwrtcLHISvgJwwKTey0Sv3pqxm8gcPH0ixExJaN1XqOSX4aRaMxf8GpA13P18nd1WNDKh10LPqRC67QLfWW0S4jYV0jGGsrrPdCVaJNu7ffUdW7n1bfdWiEBvSHJI3b8ZMaRBUIFRt7Mo6e+0jMFghj5QAJBy0O6A9NwsRUacHvLFOusm+32qtzS4xso5aBABEZjHIWJhzpKGnEFoJFTaDi0OcAuYwSVTBBwQX4IvTkmepCnWQJp9TkDbhpghiCPDBN7dAYv6+KTzza3PO6pJJOrAIg5B5GH6RkiD01UkJ7TzPaqmJEjCBX9dVGt04CCtQNUo3arkKzg2+NRFc8GAuoKETsrRfM6UE+byiLRjL4YB1W3G5XABAmIVTxruXd62UObwEXNYz1m3F6+srHAHTFBBjaHkHlStSIkyTuEzehxYtixAlCo3kSPJcRd4SphDhwyS9Pd/YPh8lcLOZqd3SaxoIJKtfE06yqMGYZbdfSh86DOMqbB8xt6y0h4eYVeDooZD2kmRTArcDGFfwPqomwEBvIML9u1556Pr+N1ZBP/uDMbfjTW3V/feAmNk22AG4LEXCg/LYuQ2bGc3a8NZIBfJsjOPfk0cjOlDWIOc9fJBeg84qJ0lCvc47OG+rnN/RpNt73gO6Qh5SSt7dS04JGQnblsR/thVXmYGkNVlsjMjzHAFUpJywbZskLoUA7xyysvlIzoJXvFbcik1B0hhm8dlLwXpdEcIkJeD6Xr0TdiIih/W64vX8qoLsNaw3gUmozZ1z2NYrasmoNWKahAPRcgWsY/HLuy9gXJQA8PLyghADCFJ+7Zx0XDqfzyil/AaECAGAR8ESQadm0nffW34HOnefmvu6/NWKgU/fLATjJRzi8u1K3dwfy3ChFom1SIcjKYC5iSQIRDEK/V64bwW138u9kmppy6MLA+wm/u7ioOG5OQyaQ3exB6Ycc80lcG2C9jFBzXuANKmnNR1VMNAXgiuCO1AVDgfH0lw0Fkn0OVDAVBwmJsxMyMqzQHpvEUBU0Mw7+ZHHKxV93nsFPBkGbFruu1UyAoxS0BrWOtI4vYGYbMoqY0trUzayqgNWHt7MfnKg5MSNoP0zd8q+JOMAyHn4AKRt0xThgvPlAufkOeWU1XWI2kNDSrGjVvHNc8QhzSg5oZQM7x3m6QnBOcAHuJnAKaFyQU4ZpZwFE2iZtPI+t20VpeQALhXr+Ywa1HrIG9gRvBc68poTLufXB3NIts9CCZhhvStsAbBbw21Fs/nt+opIwxE7K8B8CTPlb1Z9GoSk72/Hcmtn1TP87tdahgnrfnW/ucHh2oxBGvdgop7mEbZwd0pdJe2+OiwwPjPxVfuzpKbwbvdlXem5MlyBtBCv+pm6RVQdnNKHG5GGZ4eJPQI7BHZYyGMqhMAyuQSLEfIU5ooAQlAlIOa8vv1WhONghhZBiTwVGbeQMLOm50IjD9RdoNH8Yq7IKbWGpqSEpdBzt2sSSSIQ+90zMfYmQmerAjltZJO0RwYhbZt0Ja6sRUUWv5caSAk3SrfieYqoh0VKk3NGLdBOR05YhpxHigFZiVpSFmGPU0B/4UBOG7ybpPdmrUjXKxADgl9Qq9CL1SIE9sUyDt/YPgslAIh8tZZSXmK9JWeNtdJ+P6XY7p1pTRAlOcWQ2mGNxSh05AhcuMGHu213uWFlHei7yFlCDneegWGzMdmK1KvyRqG76U8wKCDCvQJg7uO98/OHWv97PoTxWfSwot1Pcy1UAbACgjWV5gpwlp+calcoFk1g4BAWxBAFtGIp9iJTokTCVwACVzH3zawO3iOGGdNyaH44AJAKrY0vJSncQYUCkwVZmYyZGTk9MHeJ4JxhABkVDO8PYBZeA6nVl3upAKYpIgSPy+Uq5nmtOF/OmOdFw4eiNGqRtOHj6YiPHz9i3Tb89Kc/xXfffYctFxyeTmCW+P8333yDdy8vcm7vAAQcD0dM04zgI7759hucz6/4+uuv8e7dE6YpYNsy3r17Rq0VORd8+PiN3O9ZohHQuRGfjig5AcqZ4H0AHOG6ro1HsTJwWTddP99eUH7dhqT/OoB/SWfY/wphG/4tAL8D4EtIV6J/jqVF2ZubJXAweOgVYH4gmh8mn0Oz9SwDzFb+LuQNAGv33QWXB4E2AR320n/1LxoFjtHNTBov+X3PZ2fKSx8A6p2G6X4f2Kq+830HE/XBNcc8+UeKQL8Re4iHsQ9WR0/oqXu3wgDSvXZsxxQ2v5SbIsfw/PS3Zn1YCq51+S2tVZlEH1JKmtbtFLlHA+vOl0uzCOtgGYzPgJl3LcLMHAcYOWc4L/iD1fcz2YoPReLrDndNaWtgqRXsCHaU4TTKsa7CPSjRA1lxvfc4KOtwTkoCqu5BUMtgihF1nhFiUJBRXAybuNNEOG+vCEqfnsvWyFW//fYD5kn6J4YYsCwHjba4dv0YhRPSIidvbT+G0ObhRkQ/B/CvAfh7mfnvgbA7/tMA/m0A/y4z/90AvgHwl3/M+caYv00qC843gXh4J48werbF6m6VHsavuw5o+bgv4QbFh55Mzk3j/0ahY9woj9th7QdEu99p51XsFNQgUKPQy7D2+/Hw+x57uBdiE0DWld2y+ho+aIpPDJ/2056vFr6Qjs0yPCSDoQVpe5SGbyIQpcCaf8jql5HShi1tohA0LOicUINLCjADSlkm9No9icnuu+fwd1+vXbcOPQvYxlJbrf+t4pa2aKIEjEuxltJcmm3bVFGFhiFZJiGrKyOgXnfzmCtC8JIyfDjAQGnLLpymCXGKWOYF8zxjihFeszEJwLZuSFtGShnrumFdVymNXlPrtkyamRk0CvPW9uu6AwHAgYgSgCOA3wfwDwP4Z/X7/xjAvwng3//BMzXwh8FFYEERRHS8QFdCyw5z6DXwYrLtsur7qv1o+aTRxbfkHclW5HZs7zFnjMKuuqY6jbbbLINHAN7I8/dos0y1Ebfv03ZvjfgHRU93+QmjJTWcx161YQGSyadmeiniHhUGZ7QqQd4KqDi4AqBWTNorVNYV8YM8A44TIk+YtZOwL4TogLk9PwacQzUm5VJRtowcE3Jesa1XxDqjBl0Isryz5BxC7OWzkuknPQmYGTULYYabDsjbBrCE7uz5xRiRFCmPUZJuKlds2li0VMEbain6/uRp5dKxBu88tiIKKaci0Q0vSUhWoZTyink+IPqI+bDg9fVVrQunJcMZl23F0xTVjao4XyRCMC8S32eWHoqVC16evwBzxeVywZfvf4p1XXF+fcVpWiRtu1YQO+mF6By4Fnz7zQe1kAOeno84nQ4yvy18/icRHWDm3yOifwfA7wK4APhvIOb/t9zpeP42gJ//yPPdrZKydSGqbZXklgs9woLyEk0R3ANvO7Oabz4jzdKj/X5OdpIkEnb7Fd58SluZGS37zs49KgbnXSMptkVWIkka4iQa6gbQJ6gCZw2ourEybv9uHWpblliPlnCVwBJsFR+AP9YVshZRxELPlSAMNoopQFb46iQyAK749PoKVxiIGUe3IPgAFydMpwOYfTt/1aIiQCb8lhJW53C5XJBylsShOMH7CQRCBpCT8AIyWdFTbYsFaX1CLRU+SqKS8AWISU9OWp8BUnCUOxCiTU0kRAdmTcEtzS2BugkJwi1YckaME6pGALa1NBzndHon+xQB/pbDQRRVZaS0wgeH4Bas6yq5Ec4LYUtwoErYtg1PTweUPOH19RU5X1VBLEBlzDEgvHtC8KyViwnPzy+iALjicnmFi16jOCITn85nfHr9Dss0IyhXw1vbr9Oa/D2Avwjg7wLwLYD/AsA/+sc4vjckfZl2JuzAz9WFtSmDwQwGmsbWneRT/n4hkcP2+eBy2IOV/MZgb3hAv2A7n7kJ7Xy3pv8b47ADHgI4av3sRnGjXO5wBaiVQ9T83f05BxzBcIEb/xoq9KOvfWdR6Z/blrBiRSjAhViUADPc8zOIlAMA6FyMQEtNzkVIOo0UklyGORKS5iz5IMIo5ds7a+PRexcqsz2rrrkFFmYcHaKWLj7csyXvOO1LUWtFUbrvqvOFVPDteMmLkLoES+3zXtK9C3fOASJB+71zYOoNQZilunJZluYy5ZQkYWiepLEKpNmI1BTIAhi1PoG5YksXXSREgfsQUKu4CjkEIFMLpz7afh134B8B8H8y8x/pA/8vAfyDAL4goqDWwJ8B8HuPDuahIeny89OwNPd/dlNOlkxYXLfb7IOEMDA2GxlP2ADH5vc+ELobX7yZ8qqL+iTYb+YPWxRAkpseWzXUTZa7MwDqHhgAOFoxuK8Jb5GO2rEUrgwfOtLOSoftsFcaRSm8x3vuGZnc+AEsR2A/Zm4hQgIEva8EcgUf8lWQ6adn8E++hA8SRpMx1vbKKmuXoeykki5GpSUvgIa4AM1bcA4uhGYleS+FQW00XLUtl7h0jUtQyUcEnEuYJo0WlLJ77+RI07UrrumKGKK4ZrU0HMAUiSMHF7Q3hoaPS87IKYEhgm5bLQVeCUy2VdKGU0qoWRiGQb3MmfXch2UBIwFE2NYN754PWNcN1+sVp+OCaRLa9E+vV62W9IpHaCk6AafjCUBFyRHkrDipP6/b7ddRAr8L4B8goiPEHfgLAP4nAP8tgH8KEiH45/GjGpJ289kSdip4KJ+RzZktfaMiukmtvuqP3mxlHyb4IBhiosvEbZ1ghyFZB5yO9tPuNLs7HDALSx+u6l37R2NW18I65TaATVefouw5YnEU1YVSBdhj5sOqrs/NrCdrF95Cn4WAQsIDWISOqjIBNaBWbW3FtnI6RNaVvQIBEagexB5P8SjJMtVjfd2wPE8AV1BS7gHnJGRYGZwrCgrKlJG3pJaUDYkaBkBOQo/RlBsBdWhZl7YVISqhjA96n2J9eCUgIKogNwFgkNNQJztUSIUhAoHYwfmCXM0tixLKDBE0yXhyyihZMhGjZv3VKolEOSe5jyHUSYqDuFqEGzBtyGmTXAPVicERuGwg53A8zbhchA15/fQJp8MC74HjIcI7RvDSs/D8WjBPkp15edXKQuXd9L7Ce0I8HlX4I+4o9Ybt18EE/kci+msA/iakK9L/DFnZ/ysAv0NE/5Z+9h/8qPMZ6k4Wy5ZsrZ0Zo8LDavJ3a35Y5TA03zD/mXAvlcC40Nog9N66QJv8jJmEeqGbk9k5703zffhu6P5L+2PfOh/6XQPglsXWBjgccD8sHf1g/dh9NPR/OD8z7ynFBJ6EJ4fZReUKIETnpVCIHY6OEclhoYCfuhklF0wh6IpKGhbMgsADPQqgmEvMCQQSP7pEcQmUXJTIy3icE0qvsbMT0Do02WsuuWgyD5oFB6JWygvqiUWFKxw7xMkjEAHkMJXSrBVHBGdNQL1T3MmsRHFXUCuC95iXGT6LkmjWFHPjSOAqST3ee3hHKDXt3LFtk8xGcQv63F21kWoIATUnycUgEjxB3ZvT6SQul0YiyBg1WfgavBMS1be2X7ch6V8B8FduPv4/APx9f6zzAI0NRxpZMOC4PYhmnlpdp6BaOlFGxqH7sNCeWnxcsU3I605BCLDXKb6gwF9TJjRUDf6Az79TCAyNf1eMOqKH9AbOwO4wNheAoam77Hfn3mcXygrYrkl6P1VcKeHTtxUWrca/5WmooqhQ66pa7F8KbU5xwSFMmMOE07zgME1YwoT3mOHhEUF4z0DeMlAYoRIA6S68afyf4BEAXEsFNH8/rh41FSTnsIUAH6JWBxLidBSeP1eROcGCjhY1AqCCJc9p21bEGLS+g9RtoeYiEEEZnITtqMLh6I+NW9ARtYo/cgSvYKzznaar+p43AADhdMDhsKCWCZfLRTL0SkGpBZOLIBL34vz6isMyY5knfPz0YQhV1lZgRJob4bxHnCZcLudGyHq+nsEsSmWap4ZjvH//Hp8+fsS6XlFKQqCIWoCtZEkkCh4xRLy1fTYZg/vJrJOSRuAMtkQPwo6mtfsHg0nOFbUMFoFcQATolljDjqm353i83yPL4lGizghaju6NFUPp+rwDIB8ZLgTqBUQD98CjvSUJprPKyGfCQCMlwt2VAHPrKbh7JqpgiIXSPcSI03LCHCImH3A8nDD7gNkFhBqFJJQl3ViIQStQK9ImRTlJ03dLKdhqxRSi5Amo0BFlOOcRSoHLBd4HxClK8g2ZMhfuBF0tZKVXRSm+uDWWlXfYks+4Wx8+OOHoC0G6VutzL1pzX1m4+mx/UYDDQqKPqNaKeZ5bnkCrTyCCnyKQgOu6KfU5ScNd0axIKWFZDrher2AuOB5PcI6wbSs+fPstnp4lY/B8PuN0OmBdV+SU8HQ6ah5FwjRN+PT6SaoQNQ+AaAF5wvX1DO89TqeD0MLV+9DyuH02SqCqILDFzUhN1DbHxX4VmXIDcLYX2jc3MgHnh7t31FkTRmhwAewKO5+fhy/69pYr8CgyQAp2Mvjm3H0zfoO3koNs//HsBk7u9zMTmgZzX52FClA1H1WwGAaBmDABmOFxcDO+qBFLnTC7iHfZI7JHdA6nIqLnAQQx4kRfM2NLGZyUKRfUsAnnfKviq0XKjbmyukoiuN57OBJW4ALAUYABM07fPanr1hOF1HKrFaS0YTZ/ai1wdm7nVV9o+LIKTTnBWsJJJ2Wyhcg6ZOsIailAkCdVS21JPNWiFeSwTJMmFUn2HpG2G0+9YtCqAiW7r2K9bnptwhQDcsogJoQptExLZuEKsKrOqlEH8gG1FNRpbrk00YuCLt9THftZKIExt94425tFANzMcHnRcEYWCtzvJNttxl9jnuFHvru9XjdQnstHNP6LLqTdLx9Ocfvdw60LpA6sfdxDX7s9WlLRrVJhNZl2RZg2zjrWJ8h5iQHP2jwFPVmoKQEmuBYOdFjAOCLghRZ8USKOFHHwM56Thy+SArtUbqlWjjRSxgAXBrasSUeStiyRQGk044na6sgstSDQKAKT0wiFJG9VBqqrza+HrvCwv2GWWE8Xdmou67cY06I9CehbWfEKowqXB9fGCpiSUKIPfZRcM7gQKntVCKGDlhrROC4zztcVrCQgRIxaMnIWa9Z7KWgSYtAABCD4VZ68JxzmGefzFeyEoCRtW3vJadv0PkjIQ9QqCS7AHTrTUvBiBZTPvoBI/VHv7sSsMfzK+74P0bGasy10+Oj0LNlwu432QvyW2d/INiAr22Nzn3eKYfTViwJL3o/H9bVbstLCsNob/nAfDhzvp38uE+YeH4COy8An+VtE28PyuRoeMzRDYabWP+AdRRxdxDFMONGMSSnAJLGIUIiRsyoYAK4yKgshSX694PV8BrP284sRhxhwnCNOp6PQiam74oN01pnngyRVOQfyHmDf3BourPkChFKEQUcyy/vzqLXKOVkKi2rhVqLsvXAWbts2dGeS1dnWEcsrMJeLWSIvzhMulwuM4COXhNdvPqGUiqenJ7wql+A0SzNRTw5hmjApT+PlfMHT8xOmacYzWEhNnAB6X3/9NbwThP/0/CwBDjA4VLx7ecH1uuL10ysqJ5xOR+E09AHOixv0h3/wR4gxYJoino4nOGbFYVZ8++mMaZpwPB4ezm/gc1ECEPPMDyGg3fKqL7pP/oox5NGm/AMh/cHtDZd/zHW3azQAb8AYboXvsTJ5/NmY8GKbTWL1FFru/K2VYcDVHhhFP9etPjVXZ+f2dyeX1bSutcIxhNmYTRBu7oAZ1sRFVmk2DxzEUqFn4JgAvp2Z1whFY4xSUuxkdQ3aPdi6/Bg5Z9UW5RI1ik1YWZFvkJjDYz2A7OsA8pK2TJJibsCgvTtre27jY0jrNS49VbzocxhrDuzd2TMpmndgLmPRnufMUZmGinQc1rwBp/cW1LJ4enpCWq9IuUcMGqgLRgzSwei6fmqsSD2K5iSzEDK+6/WqBCYyHsus/Y0oJXZKLYWBbdjMdgyT0CCDu42B3ovAtjtJuD9sP7t3x7fw2s0xt/63Hfam/899350C+RFju88EvBmfPp9xxP1WLIJhlYnDePsFFB9Qoa9VXAOmxiegX4oAicmiHW8EyenvRxRGVQoyS7Lx2ivQqMaDcQoqiYew9qhyaM0zTAmoZQJBzJlV6VCvDmRVXpYmPaYNM1dl+Rnfnz5/M/ODcBoyGHktwoKkCUSGNwjNlyrNhmuIS2rCjfYMlJm5SK2BuLtOGX4IgFcSFQELj8cDPuYE5KSgdVVyVKf1AA7LMqPUFZZwZFRpRIRlmaXCsRZhRpokv4EcIWhU4DdACRCcDyAf1Ld1UBqZFupBMS3uOqDGUPOMe778wDh7ByaMV7xRLmoYAwY2tRWaurDsZJsNbxrNhDvHvNXFa83/SI7JLOlC2hCrjwvDKt3up4cKd9EOBjyUH9+IONtQ9B41+w8m1EXdAgXUCcK7negAACAASURBVAQqDsgFKTH8VhEyYcoOh0w4VODggeirFAiRQ4Q0GyGWZCcrzUVhUMqgJB2lgidMweMQAuYpYJ48YvQIzijInLoKM8K8IEyTAoCWHmW5DeLItMRDfZYMqD/fX6MJUtrOUj03EMO0t2wWSymIywFMpNWJQCoZVEmr8CaUsmLbrvDQNmdOOiZ55zVUuGrlXob3QFBAcl0vatnI4Cog0Q7LiGRRVNMkuf2lRjjvcL1c4dhhng7Y0gpAOCBPhyO2dUXKCdEJFsEADocJIZBkbp4vKCXDERCdx/Q84Xq54Hw+463ts1ACZqJJSMdCPBhWMsC5gHtuPFmlXOvU2/kH0P7mtr8JUI+92391tbSwnZmVu6rEXi4LWHpvVybdjRnvq4+f2wpys4+Oc3R3RLkI1uEeHTRszNzaq7GNsSnEfSjRQDFbAamI0DKz9hOU66IAlCuwSVyfpIEe4OpA5+aaIrYSXy4VLlVkLQ8WrkGPqMw4h2XBFAOi95iVLNN76cAzHU6I04zpsCB4YcwBxB0ScxzIFZqxZ/3+5D2WCahVmnrWXFrEIISgQqqcAIpNGDLvyAGOUXNquj4Er+eq2K4rXPDa8FTy8YPm7RfvkXMRUJGr8gVIim4puQG2PsicrIVRasaaE7hmxDhjlRIFaalWCxwBmQWQ3LYVHz9+xOG4KI2YlQTXZg04pWK7Xq9tofLeIeckKdhpw3RY4L3H8Xh8cw59Fkqgo7vy563/CbMO0PPzG7Cm/zVN/+jfdt4BM+AmxDLRdkVID/bfDYz2hsHon7dzo+/bb5F2bsP4fb9dbu2z+vf3CmZ3zM01qV1nuM/h/FZDT0MVoTW1BAAUVQTNh9Yn3whd0RmNwa2+vmpJspBYCHDX69m9pNoGIeKIUZKCgo9YlgXzYUGYF0zzjBgXoc1iKYhpvPlJrb+BepwBZeUFSqXmz7fvmRtmMIZkDWR2yhbUjA0YwCyWgqlW6Tco9f5Bsw9Z7z+ECFIaN1FYnYU4JcmGZMjYctqEEQgkxxBQq5n2RpEuLsblckGMSnkWpIkrhwBm8f2JrARZ2Z9ZFsWcC7gKOBuKhCOn3xS2YZlkxvM2CIJ9v6MTH0NfFmLq+xsaPm6Ous/c+vWRSb059rejIlgXX7ESlMfAjh2Eba94+kTd36Pas/IlnOZ77+qNzNy/G8vNyKgXEN1aPQTSslqzbMx96G5DG5OZ8rqClprhmRDDjEC+sf9yrZrJaVz/CqxxbcQg/Z5J3TkJhUUl3oxxwrJMOJ5OOumFWGNZDpjmBfPxgBBmEKTcOKUs7EM5g6g2Ig9CL7aSvHmAMlCGQrEsmWJiVZUi7cK4Im/S+sveG7O4ZIaJBO/hSAQzKVNvCAHkYks/jiFqxSFhPh6Rs1gbjgjrdm0twfN5k6jHNKOFBU3Z6vzJpUg6tnfSAWm9ttX7er0CJDkTL++etTuTsBmtmtnozNqoVa2kYsy6LVz6favIZ6EEqP3nvvzH/EEhhHAwhNuNyzK6GT/GxZkLxhZfGJB4HnraDVfTRXcQKNoj6nYINUOd4W2h7gbKfaSA6TbqJ6cajQIVfqfnGguLmNEy3HZ1A3VAP9ojsU+6InIsq0atFZxFeIRMlMAZQBLcwDMwZcKUCXMgHBBwqAFTIUwMTGBMVMUFU9Yd6VBEqNVcOoBYV2nvEBwhEOAdELw06AheBMoFj2le4MMEHyfM8xEgr3hNgSu6+gcPnxnsHFgzD02YDG0HGLGEhhF4RmvgUUqBl6Va3ktWhhSWDsY0gKeexLUoacOyHMCQlXmZZpRScL1c8fz0BE8EdoQYPYiiWhse07SBNoetJhDLcy05IQRVqCSrNxnfAzOgYVELkwu70Iyvv/5VU+ofP37A8XTEtEw4nA44n4VboOT+LGKc1FqqqNXBt8YuvwmU44AuqaYUbkE97tpi8HV39QX3xgNGQWh/DjLUJWfE0e8OF5TYVnsiXcx52Im6hh9Avh0aP3z2veHMG2XSTNjBLbgtILJnNoauGj7QhskKEGocvEJN/9pAPSjbsGeCByEIKtB+pP+gugeVQfbD0m6sknUbAmDgnxaESSccr911tAFpiI30wntD6UdLqlsWYgV4eC9C2R9rx0JC8Kjaut577QZEMu4QPDx71DAuAIRUa8uWvN0MP7JiHVEcsp93DvC++fW1OuRcmyBuKbVSX7C5U9RwKYs+NQ5ArqjavMU5uR8jKAFYwT3pkCzhVElxTtu1zwMFzmstouwhC169zZMZts9GCbAWBAGasAEjRxQBKDX3VVwR4w6CmZrYC5eUVgriNWYP1hu/kdT/7LI8+JzNdIOMj9qrU69FFZTrGohGKdZjH8l7dw1opxAk7t4vCyIFu9BcBWuWanX9oiNJn6HWpzupxGOSCZiE3xoAUIqku1LpvAEoQtm1VCedjiCJmU7Znx13FmBSF8Z8YANq7SlLEpRH9B7BWffeWZiDnExyHwJimDBNE6Z5RpgmXZy1HXiuBngI6Oi8VM8Raay8T3IDXmOMLX5OLmDbVoAlTElOo1DO4/z62uL0AGNbhc+QSfMHiODjhMqMGDyWZcL5fJZWYzGICa+dk2rOkiRUKy6X73A4LHCOcD6/wsdJQEMTRMUBDocDSDNTU0pAKahckHLGshxagdLPfvZTbNuG10+v+ObrX+Ljx4+I0ePnv/1n8cUXRzBXfP31rwDlfci5KiBKqDUrUDkCxPfbZ6MEbtdgS3ZoSZ/cUXtgbDBigjf813z15ivfXMmiCEZwz/0YAK182WrRbXytjsQqHm3cDWgwa8Q0/Ig7AL2D0o/fGBgUUX8WFiaDCuLYH3EXYdFxmeLrisvGy6i5wNW+2pWcURjAZPct7LihysrunO+mrJ20XU6tDBL7v4GTkI6/MUZtDSYx7GmeNU7v2j0YaaiRjHQK935JAebETVwOR2xbQq0VwbcdADgQSRsyr24BOa9hORVOMJblACIhKjkrZbfzHocY1fogOE84no4oKUtUIEvjEOcdlnlu3I7H4xFEQAwR7969k3Cfc5jmRbIsYexDstARtFPQ60fkNSNtCcfDCcxei4Uk0jDPs57vjC1t+MUvfoEvv/wSy+GAw/GA9XLV56TEJcostCwzmMfQ+f322SiBFvveUwSKargFzjB8P6ygtx7EPjKw30eUjFyIuQ7Zdx1c223cV3dbZbv1MJYeo3fO5f342h3ReNqbyIQpMNhEl62Ch3DhYC7DVi67t0ERqqfb7AoT+trTiV3lITsQ7cd+cYpNRG2Vbp2DCJpM1Pwl+bXVDUBdBkhEQbwDDc956TosqbxBiVJ8s9bICRejc1Ldx3oPVccIbdQBfW8+TAgsJrD49X0SGeWYc1JmLZmEYjEZHbmDR6gAk3ROSjnDM7AsUbv/kPrrYkhR0bAs1OLwXtvHEUKMLQ15mRdsWxJyER9AVBSgtaYm0m/RmWIlp+m+Wa/rUIokEJkCTSmAShaew20TheV6VqVrC4CAmTlldak+d0xATXIA4FwVWDIBAYzZ1nq5/9Bi+ra/3Zbs/rdyB7Rkw/HcTZHcaJKba1muPQBY+xyZq6NA3o5jf64G3jcl1X1UMedH4FMUl3mVgLbIIoIzLcoqBOQURJVmFFy5d/dVjIBgCoCRjTaMAVRGcNpSDAQPKVgJyvlQCw/1FDLa3rbAxmzumLoUCgY65yVV3Cn7TmtFrhx8VEEoSLVCsTVkLu3h+DjD+jg4FxGia+W/pcp+TSDIAc6j8RUCYHjAiQJyVOHhQKHAxwW//OUvQZkxL155D43ghECOQc7SjT0YHkwepWYwE0KccD5f1cKY4a+rWDhOGohSzW0aOedbarD3Ac4H5FJxWa/KbSBKwSu3gI8BPkb4UuBAWsIsGYNQF9HDqVftEKPHt19/I/wD8TeATwAAwAJ6OPhWIy6Rq73AEOiBHngk9LI5rUXvufK004xGy8RFVhYhmsQgq7aqADa55bxCP16LVbdhV813O8bRJBsTk/rSOcQcmHf3rYmzHUcYYQfSidr+1MErTkAqBNKeipVUA3pGQmFujUBS2lAwyeSXK7YUYRFUeR/BeVCcUMihXjaMzUKFwptQvWS1Ve04VHNu+ffkJfuvVMbiY+tKLNl+kjgubMtyzhA8kvYGYOaWCMTM2NKmjNrcmm/eWnVWd19Zcg56DX4HlZkJFByenp9Rcsb1csHT8wlgINeMWRuFMAHlurZ3k1LRrkpyrsMi7ErbtuGovxvHIVwAs8PlsiJEhxBkxZ5nCY1O0wHnyyd9RgSv6cGruilPpycclgWX8wXX8wWXT6+YlgnTFNuccpBQ4zQJs/G2bUKE+pZ8vPnN3+HNUPdhjX60U/t1XJQfgR4jfrD/esARDGTbuRT7vx/F++9WdjulnQ/matxvt1l8D62EYQztbDsfYrykPrchKsDNpsfeBeC+4o8/VSvdeAhZNVdn9MNvR0qiMCV27gaBGn9E+VVNUCracMQy66oSc/YCHRV+7s1ExM1QngcFQzsYid4QpO6bfIzn7c8fjSLelIjl/RshJwGNAi6lhJSTUKMVsbK8Ap1m6e2Li6ARDNeUkdXFdJdUFb0mWY2LilkA1oSEuZ/f7sNyK8CS0ARmrNuGdZPaAgOiiQjzPCNaB+g3ts/LEmjbaEaPoNO4SsrfZtzt19+3rYLHSTzjZwRp4kkYsZS2e/+lmb1dYPt/H9/TIz+mn8+Eu3sW+zwB26H5/jtrBbvzjBaDAG0qkPo874TV2Irs791zR3us5ioYjurUCmHnOmmLuQas/jwbnXdtq6I08SgIQdptF+2vAFAbr4XGmsCO7MfNojNadW3MmRKCNgq141qaOHdlZ98RdeqxUorUDZj+hJUdazFTKWopUlOARKrkNMQonxPgPbxW9hE5hECoJck+MPKU2u7TqNINgzCFAHDrCVEdWrLUPM8SMswZ3jmcV6En88sBysMHZkmkKqV8ryXwWSgBAzRKMdZc9XY1UUens7DCQIEVjOJGbeK+xSlgWXrjxGgkE3oOwR7QABtuPijfyLYAS6wrljW9sEnMOiGYuAuPxZupYxZmQtPAQ9/HYqtW0U8GQMky9oYV2w1AZ9seWEDybKVJp4QBHYg9YgVCYaTM4FwA8oiAhgKVELQKb20gQqySHFRZ2XQgxCaeGAVFHjdrqLFW1FxQ0orsHTbnmp8NAub1Fa7McCEihkkq8GpF5opt09ZjpWJbN6lLYMY8H6RmoZSGJchD2ZATwRUP54LQnimfgFGnO5tRVTiJyTG4ZIArgvO4XC4CMjogOMk1yGlF1so+IkJOq8Cl3iM4wrauYEApw/tbqBr6s8XCrBhodMQ50rCe7LNtW2tjnpVGbGMg5RXkgThpeXJmvLz/AjklpO2K43JASgm//OUv8cUXX2CxCMzi4aJHmH+NtGEi+g8B/BMA/pCl5yCI6CcA/jMAfw7A/wXgLzHzNyRL4r8H4B8HcAbwLzDz3/yhawQfsMwzPn76BKDXZ9uT7Ei+2VM9c3C0WZt5bGN31LU+d7uh+4s34BwP6CCh5YvLajRmDnJjthFTfW/i20o4+pto13vkuiiCbquXmnSORIjsM3KS4y+30dDTfl2r8TdrR7nZ9u6B/VObBYBmNWismQkrZ1yvK8pyQHUKIOoKZYrWTE5n6bCq4HwuYFSUnMFKyZ2TtOLOOSHkgFqCEI+4hPW6wkeAUkFyWRRLleON/JeZcblcYP31uBgdmAKaWVDweTnArKGUNuSUpVGHd2Lqm8vmfHNROAkR6rZuKLVbRiklLFHM7qqraUsWAysAKXOmaNr0+ayt0JRL0HkHqkCpWZWe8hvQ3h21eTv2NpymCR7CZBxjwHcfvmlKv1ZZeLx3WGtthVhfffUVUso4Xy64rBe8dy8gIizL20rgx2AC/xHuOwv9GwD+BjP/eQB/Q/8GgH8MwJ/Xn38ZP6YHISRmGkNs5p75jF0ouwDbdgec0WO/3SaQ+cJE8vD2vvmDjUWJNP++udg8+H98o3jUnG5X3g14B07wze99PF0gu2vTLYM9SEo3P3LdURGM/v7+fke/uLbnyczIXJFrxZa0NZcqiv2klesTLAnHNbTfwl3m4/d3WhsWYP5wzQVp29rPer3ier1iva5Yr6u0SVeBzzntMITxeRou4GNs9RjSPGRrDUR6zoFacvrsxU2pyClLeE8VQUm5vZOOD2TFHaReomhCDriCa8G6ba2hiBGVGjOSWLw6/4b33uePRFByln6DrU4hBiyHBTlnbFvCplWRhjcY8UwIES8vIvQpZ5zPZ6yr7Ou/J234B5UAM//3AL6++fgvQpqNQv/9J4fP/xOW7X+AdCP6rR++htWE16ZRe6UYOturmdtscdZRMfTv+oltpb5VIY/883uAsFsaGKzsm9X8+/SIJRW9DVH0a5m1YQfQ/T5m0Ddrw8C7mzuTYXF7lq3l9q11pYrT+OrbPkDz4VOSSV8HRcJAi72PKbCWyeg1R94iFuZv33YD5irve11XbArAFe36k1Lu2AVkDszLokIydPgFGspfq7gM0h6cG0syAKzrugPY7Nkxc2MLjkMYjZwIX3PZLBY/PGsRdJm3MUZManK3hCrDDrzXc9uiAby+vuJ6vTaCkFKlJRvpte09yGKIVoRUi9CThxhbhKPnCci4jscDDsuC4D0+ffwOr6+f/kQwgZ8x8+/r778A8DP9/ecA/tawnzUk/X18z8YatoETPyiw9hsmp2zDtuoauaiY4H0BVoVAug5yBZi0Jp0amGY+/ugOmE/eV10bkyERowIacr9hXAG9vHa/kZrr9ie1cdnkYDXXx+7xO9lnKSayVFYj+QEwUiuooEkuvdNezcyMkos2yJDbIy1vZThJAQbBoWiCD2nHIRlyRsXHuiEVibuXUsEeln7QEnpAPKRhi7/t9XtHQRqBqDVgiqVa7L9WIBeEwKhZlQI2ExWwNmnlZv4GcBUf24S+AoACgtZ3MWg+QggBWyrDii99BmsBQgzIWQQvJ+k1CEcI1rOAgQpJtjFatMq9HbmEa6Fum1T5iQFXGr9AnAKE70Lcj3XbBI9gyXmRRKAK5klwAmZQCC2TsdbS5mpJWToY5awWy1VWd4JkMhYh17lerwoqejhHkpHpPOqfJNswMzPRXT7fD240NCSdvzwIUVVwyGuBZ1ZhUZ55W70wZuU/chMGIR4+MzDuj3FX/XQti6eNW/+Vz3gvtsM+t9kNw9nvvqDdd7vIRRuDrVztw3Yia0YpB7t2IjPj71wn/a9rP3If3gnrz+wcQiH4Isp5XVecyWGjIAVBXpSrIeEVXcDBAJGD86K0yZG0HDPEv6UCsxCaQCIGcEXNZAY5D7SkF/OkWMp/nVpAREobNhR1EeRY617kPFwIfR9AXZLUgMJa1MKRgSNOk5r27cKgJuTcIgX27Bva78S9kFLh2rJGSxFcJEDZiFVziGVQenRBIyG1urbo9ZCtpLLHGJA2ozSTxq3OSS5B5dRcO+eCEJdq5yLCvl7mdvv/qgT+gIh+i5l/X839P9TPfw/Abw/7/aiGpKc/+8LC1Drj2w8fEGNVDetRVb94H3RFe0O4CJCpTA2Uq0CzFsjAtOEAQdSbdA/n7WCbrQp3SsTAnJ2vbxPRTmP0m3r95qtqnJvcw3vp0QJu5wSj+ZbDUxxuXUJrRLryKQ9XtWrB4TFJ667aJnfRmPfkAn56eo+vpidM1WG7XFH/nz/Crz58gz9cf4Hlz/w23j0/4+l0wuKoZf2hMs6v59YUw+nEM6ugamvzAkYugvinlOCZNOKzwleC80XSioP1FZB7NKBumpYWRSIX4PS8PgYsmqwTfBAyECU4PZ6OaolIROhyvuJ6XbU1WO3PD/IslmVBSZuAinnDNE3N7C7VtbZirakpSejPypmPx6PgDykhl4TLVbL/DocDDssBJQupyJdffomck7IyFeEjKAUpi1vkyYGDpEFL7YJXXCBpVqSMXcq3rZsysCwTti2DiHA8HAEnxKcp/f/PMfjXIc1G/yr2TUf/OoB/lYh+B8DfD+DD4Da8uZmA2UsgoraqALqmqn/fVsa2QKvJbjHqxidHu3Pfbi0Hwf6rq0v7Tv1l6+rbQLvhc9l5WPNtTNzDkRjddj22U6vfjgct403Ud1V2ucdba6FtfjPwhlKBKjmL34siKpUFrHaE5+dnPM8n/OmXr/COJwkBPmVEmpE+veL83Xf41de/QkkJjggHLwQhJhQA1G0ojb7Lq6ncfFzeJ9bsx7q3VqCh0GJltqCWJuy98AY2X9gJZ6HBOMIPaFTuCuApoBhiwMzA5XKB86QkqB5VKcEI0Cy9qsSmHbQV1J8bl4Fdv+rzJQDTNOH19RUAMM8LPnz40Ek+D9CaCI/eQFfCu3OckF3GddvEMlEa+W3b4GNApLgDs6dpEtcgZ2QlTAkK/qWUAJbKyW3bFDd5uwvRjwkR/qcA/iEAXxHR34b0HvyrAP5zIvrLAP5vAH9Jd/+vIeHB/x0SIvwXf+j84+adxxzn3q65VMDzvTnfZEh9+ofo2704NDdiNLcHn72vvNTO3Rh5dvvLhO3gIcwxV1tE8IAxdXU3BrVammJrY2tab/in3o3BQn4GVO0MfdVV8qOWDgNkDUfUMiChDISvwOwn/GQ64f38jC/DAacaEBwAiognxsYOMVdcrxvSlvH66RVP2mzUrBx7pKUURNa2ZM76RqCTt97cg+ELbJZVM92gnUw6nuAacCn7tKw9AydJlIwbz9E4Crkd44PD5XrGRFEVCsFVwZ+EOVhWda+uxJhgJu3Uc4syNeLb9haqpGczw7OGU1lKtEvO+kxumKeaiyeszJsqyeyT4GUlA5t0JJZy6j2pSskZpM1H9jkwoui9Fmi9tf2gEmDmf+aNr/7Cg30ZwL/yQ+d8cByoSh710+GEj5/OQijpMrCIed068PSD+soG69TrwOjMs13WSQUCO7Nf3DNdaWkvgAB6Fh16SMyyvGiIlbf97V7INUUwsqCZ992uNdJ8kbUpJ+X6k8SeCotFj3RqPFzv5mEa/lPldwcV+CQcAkgAMoPYIVTGXAk/mZ/w5/wX+Ck94ZRnHKqQiQAAzYRUHY4VuBTgcj7j6z/8FY5w4FLBxwVzkG7FgTxyKsAEUCB4J0Cl+O8GWZqCLfL+BMmTsmNHYO+g5YpgJ4VlzBmpJqC6HnIzTEKrAsmRJjZVafWtfnMpBaVak1UHckrkuV4RonAOMDNCkCat27ZhORzgfMQ8Ldi2626e5pQkj9+R0KVrbr9NqFo2FGtoyoxlnhX0qyjrCjdNoBhUKRlAjBZ2PMwLPn74To5B1RU/Yb1e8O7dO8xzBKHi06dPmOeolY1Vuzk5lFKxLBJOFAJSccxAv4YS+DuxmRZPKYNB2LaEy+WCy+UKTECYPA7Ho2hokNJ0i4n1cHU3IWEHJ6xTD8A43dNAI+7uQyPqIBNcPSdpxIEAw1nGq4qn3ewJ+YwGPoNbY4X6Pm2dbACC/OERupuh7oTFm03p6MCb+9FCYEWSXKgwkKTdFjIDm1BqPWHB++mAP/38Hl/4A44+InrXKgjBYqrGacbTs8fL8blhKJPzyGnF+fWK3/vV38K2rgAILz95Dx+cttP26J4Pq1kaEKYFYVoQF/03RvhpBrmg92b9J9R98h4RsSH1gSJitNCYawq5r8VAzgnn1wumaUIuFdd1E3Neew6+e3lBrUUSlZTxKHhRKilJm/BpmRGCYBDZOhArXRcXKYbatg0FVashASIr+c04n1ccTwtqLchpw7at2NIKck7aiRutPsS8N/r842HBlqToZ5oE3YeXFmbBe4TjATkliT4AIFqxrqtQoC0TSmEQeRwOB3z8+EkiJvHeBbXts1ACQBdSMXcivM/Y0gbSNuWlFHkYD+5F3HmD4Gyj/h23nd64dk+EabH3JqCDAmEWQeQex7UwkSmj8fO7y43aASb2Aw4BBfSorxKPDiWzTAwl59oVApsis1x7QbrJlIQm2XCtCMFhjtJl2A9ViLJSA4YoOufgYsQ8TcrQ4xCYkDYpKY4xYruuqCzVeZYwJLFybqP3JNVtBrQJ5XgvtnFq7vLNM3HOwbNHoc4SLKu/EJHk3Jt/sLoYBtxakpKFDxlo+RDGuhxCkOdG3Ue33AerK4Cey6ubwAa66viKZi9OwTXrxOaV04hKcV67EUn2pDE9WJMUY5TyXvo8bhYl0IVpvV4xTbF3MdKae+FsFKWYc9ZwquQ9CB7CKOUzLyCySQuSFOLDQVI/tw+bhk9I0j/jdO/5M9T/sVXn5mbrPjjGNsGxF9Ie+hv9O12hCTC24Q4b2MF7rKKL0uBLgnar9hhCpPFgKKioCoQhJuduxR+OtOiEse6a8NqkKTmr99GJQ8yHYCgGEyZ4Grz1sdBo9KO9w+xdC5FN5FCmgHmKKGlD3kRpT9MkyUKary+5GXJ2IxHpimBQAGT+rtTUt3ek0RnHY+ZdtxLIOeS8NWYie0pO2YOu1yuytRVzwu9v861qHcJO+QPwamUwc2uO4kj4/J0KINq9MUKcpE9hrZjj3BKLgL7A+OARawRnNITf8IGx+lEq/sZcEsb/S93bxNi2LOlBX2TmWmtX1bnnnHvfT7t53aZpywhjxAAhM2SAhAwS6gkDhhgmSEZMkEANYw8QEkgIiREIWQIZZniABEZCYkKLAZKxzYRuaNnd7n7d997zU1V7r7UyM4JBRGTm2lV17vN97dbppXferaq99vrNjIz44osvPIW4XlYwn3Ay0pQuPkovXk4LRBiPj48IIWGeTVSkFDBTK1B6bvtsjEAwq0sIePvqFV7f3uL2dMIfvvsDcKmYJBwKiMIw2ZkNHAwBvUc9dD+nh9JRsHw4OzTWH+mbR0PSJyCZzCC1jIXt0Cd0jwNwMBB2HGqhi2IGbBx+guEM3qHIlq0mp2mHEzaVIa+XGeMcgYp+FgYVRsiWHjQgMBWtz68CxDDhdU348RrwioAlCkJgspXcqgAAIABJREFUgCtiESTLGkykYFUKATOZESAyGmqEYMLt7R1evdmaEUjThJgSKDpbU59VWm6sOy8Q0tRYdb5q27QxghQZEhLt/VWrvfduPUpn1pU4tvJjgRbt1MooXG0SeJm0SaBP5kaHYLG3xvY+8YPXSPjvAkwTGt25Fl2Qar4gl4o4KfZRRXBedwgiQAlMG1r7GCIsNyfw5Wwt2kwHkpTc5Ck/f48pRtzd3CJRAAcCJuD+4wftbjQlFVstWVOni3IOFJBUSTHmgnffPOCLu1fY9w1ny1g8t30WRgDwnLO6ZZEi0pTw1dsZl+2My3bGaqBKULRJV1dfWT2l9iQjQM1dfy4U6Fz9pxMWdriDHxE6lu98Qb2E55SA/Ztd3qvRdgcXwNNSh1qHdkVyuKzQ/4rasiKj+89AFdScwYURirIGwUBgzRd7SXEilcuKAgX4SPXvmMjYiIRI2oE42irtysHaCFSzNmTnn4xp1917avfrfILZ3l9M6lKHpJ7BNC8IaUZTlzJXqHVrtnccRKw2v4NcXmPf9QYs908w2bKgVZAhYFtXi9uN8pwSRPR63YNyYRP1tAK4FoACYpoG/X7l5nsoUHJR4wJtYaY1Dxlgxnq5IKWEadbGpDESpnnC5XLBaZkQDOl3PcyUJguV9Jk/Pj5o+JUS5sl4GVAjxaTW3bMCHgmxVV2llLDvimUsn3vzEYLmvFPQB0JQV/Xu9g4fHz+CUfH+4wfMNzdIlJ64Nr0Kqy3N8Jcl468YPj78+Hw6z4/df9arbRT/Z+xGuyFYADoAjiE67Vm6N9GulFoc6fuP3onHpTbrwOJKZtSOyaweQCnFjABQ9qwy4ky9qaYQQlS3UzXwGZUYbPJb0qTJNMU3roxa7+6hhRopxwKcquupQV/lY4yYpqVV87nEmMbXEct8AmKCUFCjZbF/jAHFCn6UJMVtYgdAXXrLt/dMTi8N99jZQdPdmnpM0wSCYhDqqhP2XdF5anUQGnYww/ZJ7TNF83VyeRyeUlKtwaygNteKGIDtskKWuXkvIRDmecLHjx+xmBqQti3zsMsVj7SHwIcP7xFTwrLMmJfZOnfrtUutyrQ18NmHb8lFJcXmBY+PZ4RAmKbP3AgA/gC0ljumhEm0qOjt2zdAEHz77h1qKSp/DUdFO7D28uYr9Cdn7GET6TGk/u4TczjOmDq8znG3A/n/eYxnHouvVhYSfGo7YAHS/ZKxgErcFRZRbr3TYauFCyzWHqwaUEoIk2sVWsUcVxQETIEgEo4eELnSsjQjAsCYbtb8gtAnmHte5FVzbhzMnZ9NcThEhJh0MlpH6hSTTT5BqRUppv58J0Xqt21Fiqndswt++LOqbjgoYEoJ1UC9aZoauUnMO9CwRsE1tvLhZVbCmoukaLqxNkgIIk9EPl0JSQ2TXnvJBWmaUJmxXlbEKRiwLHj16pXSIJhxujnZkNDn6qXItRbj/ismcXt71/CeeZ4RoByBddswz6qqvAHY9g0pJixzMIxADc9L2+dhBGzuVK4QQrNqQsFcqVljsNJ7wXUEfTgA7CUd5uL1JDOij628BwehhQdP/6Z5X1eowXDu8SvmIrvnEXp6sHEEMBovBiQoABcMhCPq3x9TE8PPzab5Bdhl+wQXJoDJmoxSQ/oDS8MHlkSqP9MhcwgqSEzpFwRi1pWfTaVX1P0IIIvJq6HPmq4NGOrkARAJXGorORYQ1bUOMQHG8z+EUWYsQwCmQAbqHQFBCFTBl1UYJJDyELQOzB6eDAaUqAN6tksnGtm7hCJBPBCztBBIcYOyV6sxgLntyg+otSKCUHYtVy41IwUCpQhhIG8basngacJCsyNQ2rjES7gZiMk4FFzNvVcMYpknYz8G0DJrW7bi+IbxI6CchBSCyZdfrG4i4HRzY9Ll/xALiP7oNmmxoMfRYkVEGk8uZgS4ueNet3SdJ8bwGw2I8WH7LgcC1x7A+CUZfveAwriCg2E5og1HIZPDSn78vz4xh2uk8b/jDfn5WuihBgDGCIQMwiYMCw2AxAouigikWgl3BIQjvOhCWBWGyFKLWsfkbchra0Kq1XWxyW2NjzZaRmGkYqvKrv8LPczx9+9GJEaTNffQxBqpRBg5SEU6PBwkoVZ16oZPQyoAISCiS5d5qvHaeVOGn41GW424aksyN3YAEKLH8toUpVitQGGTC6cIYcLl/IjAOgpiDEbqUWPomSqP9AIJaq7q8tt7S1PHP2LSpidesOVhi1cdEgLmacaWcnvey+kE2jbjcTy/fUZGQB/clCYQ6eBY10dkZLAw3r59jT/8w280ldKgtv7d48zok3RYc/ven9Bgf846+Mo95n0P6b4Bj7j+tnsNHsv3K+y1+w13sIlfveJH0JXNRhyErv6LDmy5WRndfKlVew0Oj0Zd3ILMG7IApQAbReA1MENZaZUrptNJ04vFjIbFyKWq7v1uJbzTnDBN3kDEdfUmLMvJ3OWKIJNKr9kknKbJ6v09dcvmbTkTFG3FZRZUi9mTPX8twlZvw2nBnCv2bYWwmNuvK0akgDhPmKAe1fl8xhymppLkcubz6aRVjhZebJfHVml4eXxENU7GnCYUSzfyNGNdL1YQVHE6aZqwkpPONOOzrat2ZE6eDeAGoOYta8pRzMCRdlsWCHLrMaDPJkag7LsRsgil6DOstWLNynjMpeDj/SNOy4JlTuDb5ZmxrttnYQRceSaaKqoSTdSNzSVjXS/4+PFjm4BAX3n71gip45HbYHL6r26jriAwGhAWpyBfmZnh9yOA2E3MOKkb6Ofag0+jkqtrha2UASTem66dXCcSX0mcUS/nfQJsjmm3o40BQY9VhVGIEQQNB8g5Q2KEBOoqvhb6sPQGKDoJnZevkzNYEw6yZ6CiHNKut7MdqXkHStnV/oFoqz35S2ugl9+4x90emzsgTGSKPTE0kLAVYg0dmf25LMuCGKlJzCtmIk/UimXwCtZ103BmSqpWxLVhLtXamLvACajrEbqHmOYZUgu2vWBZTgpGQgVJIFqPEK37c2VGLgW3tzrB2apCHeysLrpiRoLafWkLNO+Y3ADcT2yfheQ4C6ssUykOeTcnu9SCbd9xPp91ALV0ztXa28AodPcXdv+Ccc8WQwMvTe6OAxy+eTjAsDUggp6Z71cTUfoq/XSjg9E6GgmPc8fzDn+Q4W9wSMI8IWsuGqTfgqYUa9Pq82Ope8998LQHKEcAUhyRN/FOM0Y+MR3o1Yvm4e89dTiGRl6M4yFDmzwD9jLa5VYG7AbXHr/H+mP40caEMSgBVyPq7Vt89a2mFNRSrjrTICLIxjkAupyZMPfSXkFLX7bn3+J2kzGHdAERezY57/0dQBdEjeOzjh/qJeRu9NXL6yIt3nTFpfM0AhpL1//o9QT+SLdSK959fI+Kgtev34BhTDeK2EvGZVvxeP+Ir358QvSSSCGNd0nrqUf8rG+xTSRX29F1I/RADLr6dsky/7NaXqIZGgqwYhAOSOo3cSwYGjQMhBAk+PTBJ7MABIi4SjEPM9UO5p810GpY2p0y7M+EBcEGU9LWIYAQYg1gqQoLQFRdF/qPobG0KDKmAp9RbIVSNSTVQgAggkpKKpKawSU3NeJIBHBFSjOmGBEhjcxE0IIiTx9WLqDW2zCAmeDiKc78Yyhjj8wToKhpQ+GIddsGz9DeFTRWX5bFXPpe0i2iDU4lRIjEtkor3FHhwG02NxvuFQ3AMHOFcACZTLhnfLLVFURLkepY0WuZksqKaaFP0TUuBpS643SaQDThcjkjLIuO930Hi5GdSkXOJ1w3dhG7r33T64shYt0uQFHyEdQ5wRRT01d0bcbnts/CCADAw/kRW96QprnVpec94+tvv0GpBW9+8JWmS8zSdont4+ooh5/6qthepsWEGpdisBpPonk8aSA6egretWj8G8OUbfS47ioDMKJJAFEntDRNguEKHF3wq3Vaa7Rjjs6Ir8yuKAMzUm31CTB8BQiRITUYxiDIzNi5IrOCXSmpWn3OGZmBKEkr66piMjAgD8JgKdjXFfu+o+RsLECbwFGJXsE4HyVrDj3d3CibcFbG3zLPiHFGjFNzgQFVFfJQkCiamKemIqVa2+5o3ZFtJfTCqmCqyDEoSIkEXNaLyqyVijQr3iRCjaCjKb3SsAoRwWaYAgtQa/aHjTRPQAgowkAMShhixjzNphe4Y9sfcXNzY4o+gtPpBBb1LsbaF5ZqTFdYkVNtRkf3FwAB9/f3jThU9q0Zr1p3ECUQCJf1gpubGw0DigqTAPr8ksmXf/aegANH21Zx/6hVT8Kai837DiFgXpZj3v4a1tVP0AIJXz0G0G7c6/jDMSzo3xt/H36T/nsn9Fxbois3/WfcqC+Iw9ev6wyghqxlL8YbQTNCOqmc+OIYiu7rmg1eccgm3cVsxKHhc8OhISCV6a4uB64u8RRTQ9pD0GwOBd3XswFTUjpxakVD9s+owyFQ66eoz+Hq/TZX3VtruPdsz8bB22Y+u97DGIKxAXJEZgDMgJZsdQQwZqVNwlG41vkFZVAVAnnzk4Hrb3UJ1QDQYF5UGLIkYK0KhDBipMYvEPGmK2rEnY8wT9QXlCE88kXAz5O5mOcweEBDZ+fnts/GCLje+tdff90saS1aOhpSxGT97HxTAG+cGN1FHmPpjuL3aXRtQw6svPa9MSzwE9HB7VCrfaxj8PgTuAJcnsziZ5+E/U/aqs/iMamj+89YlcEg+GAgYhujwVLxPO5uRqC7uW3CE2udhYG1EqLKoNlg8m5BdTAgTpoBtADMmXhcubUiP51OTXpMwa1eZESBECkh8DGLA6hyL6Ao/7ruID7qOQCGD3jsz9KEUftT1QevBqyAqmCa4sE+55JRKyPFhGyFVyEewdYYtX15MeJRSgnCgvV8NqBxhoiuwvqMivETqGMFrbiIsW4ZtRQsy+RvX1f5AdfJeQcgrdeh10KkdGz7zlYina0LsZKqcpPuf2JUh+3zMAKBcPvmDqd6g7KpvPVeMiIRXn/1xhD2CqHU5hElt/dqsXWO2kp3WOp99nkUD43PGqBGbTU5TlJ3SYf4u5+97dUzCdROJwYSMdGBlw7RzjMUFGMwgmof+ObW9oYgtuqZYagmUyUEEA9Gr3LjBJD0QiyqggRrUDlHpFBVYKQCpe6oOeNxLzgtC8qeEYvg5m5CIiUalTVjkwBOwDQRQt7hrEUuFQkBYUq4Pc2aA48B05QAVjXdKQXcvbrBvJwwnxbc3N4hRKXXAs4P0OcWiJQ31WoBpE1kx0OmmDRMLJuSjWzTnoY6Bmq1Vd3ITOu6ohZGLYLEs7HvGJd97ePEnnuMBBHN07uXAeAQkzuvf7tcTHKBEMNkWQJGilMz2GFSirNwaY1PYhRD97U0nhKQMyOS3sf5fMbN7Y1WF9YMzhXZRFkpdEB2zUVVkUiJSw8P58YdWJZZPcDIWM8XxJgwz595ihBQKxsotNgMEPMCQps0faL3reXGg380Wu/jyq6f0nGyDwagG/0jE/GYftPPDjyBcTTJ0QhdX0l35ciuhlShpNkpP0a33uRH8Gt1t7cZl9E8mTdBpioEICHgJkSkhRCZkIpgx4TpsmptwTwfogjPwQO2ejZ3undzYhYkc+lb/7yO0rR7bD0IqKcy9Rl25Lw9dfN+uoFWDKdhL+YxCmCluNbaW6Tx78WEVGotLW9fSkHedXKziLWHe4rHuGtfvDgIQfEQOTYEJdN2LMaRiMstnPnH1AlKLVxptzNkVfyfu/Oxk9pKLtbMpWdJxN9/c/EZRNy8Lk0hawhN9oy4Mkqtil/9SQgHtIxWGVLejjpGc/EIwOD+NTswLvR46im7G9RfiK2iz4CAR15+7AfFVcz9zOYROwGNCAPLCDg/ocetgwEw69OOzwACN9NxSF96M5Zh/Hr8OTL9+zlM7VcINxTxNp5wN58wCWGqwBZXnD8W7PkCqaLgoRs4ZjChMwoNgHRhEmPrAkSISYUtPBcOy9SIVR667Ft7JiLqKcBDFTLPQZ9JcHfY9gN1Xj4AkFGQL5cH0GzegeEFXBnEhJrVAGx5AyyG3/ZNa+5Zm6SmoRJRcV4dJCyaIRDRcmeCYgZsHYw8ro9pQj1r85DTcoL2bejjxF9dQDwMTDbdDJ/81QlHooZlmiYVF40R6WZpVY3+/Px9lFKNeam/R2vKcrlcej1DZRMvIXDML47fz8II+KY8gKgWEDaggrv9x02kx1AuPOFLYjBNsREM6cw+26+t9v0vP1PY/uSa/XquCCoDCOeNQ1wN9ngf0sQynz354Gg0j+QK1HC2nH+hHYq0cOQunPDl9Bq384JJCHPVjjzzVnGp0rkZ4mShztFX8KyCOYAcRDTJdCA+MWyjCjOaYVAMog3moINda/ijhUmDA+UYDTR93D2BbuR0VedG2imlKDK+ZbTW5bZ6E2BdfB/h/Sxubm+bEIm/B5cL88zGermoxkK7LLIJqLyAaZ4RQsB6uWjbsBDw6tWtHc/eu4F/JLpqa1s1RrQiKSIVBammQzjPs2UJ1EB45SLgRCJBqRnbtjZVpGmKIKBxNbZ1a8+6Fk0pcvk5JMfp+Yak/zGAfwXADuC3APwlEXlvn/06gH8Tqu7x74jI//Rd5zhuPkFtBW314T5ppe3mqSF3iXW//rm7EfoxdzBpcO1HL0Lan/rnfVAfjYZfE/qeakhErBGoxouOUTy3qje0wlmFA25xzAdIP64AEG6upLDiAZ2GoOebQPiSZ3x5eoW34Qa/QHc4YUYCIUJQ0oz1jnHJCV9/+w1mVExBVGUoRlAUVdsNDBGVrHJPQM9b2+rI0glcXNlWL8BltgBYO3MvI56sHJgQEAyI9NCDUbI2JMl7UXFUNx6ERo7hqpPJtfo9W1FzUTfdMZVkXp1Ie67CFfu6do8LsPDHVInbmNJcveZFxncBwIRAAUGtYgQrxrquLfSJKbRmJCzcKjk9xBBR3KLW0sqHBYw0BTNwO0TIag4iCjs2ESxsyag14/5BwcBSK87WHdlH5XndtEx/9Hyutp/FE/ivAfznAP7q8Le/AeDXRaQQ0X8E4NcB/PtE9E8C+NcA/HkA/wiA/4WI/nFpPb6f38ZUl8b4jFYdIL5IejB9/V20ST2uyh5vj578oU+hHavFoOMkapkE6UYgPDUCfi6vUDiev5/EPZZn7/24V/+tHWwwNOb9sE02ByAD9yIiEVXCmSXiKyz4hfkLfEU3+IVywiJT4wkgAvk24FITHn/6LZJUJGIkiqBUQBGIiCCqKlgaBJTMoLJ6CC6/zTL1cEsF7nRlGuN9u8cYI+I0tZXqkHq0lbvkjJIr9t3EUWJEmibN21sbrloKyqY8hXXbWoYDbDJcIggxaR+LoE3joocWUG0B79Xob4LImIQhWvim5BwSDM1Qehjmlk8BTOUrrOuKGDTcICQlUNk4qsYXEOtQpMZcOyLfxlvEEMBgxCmCqiDvWSlQYUIAev8CIvOStFnrh/sHUCBUFqzmRXjYtm2baRT8HKXEIvK/EdGvXP3tfx5+/Q0A/6r9/GsA/pqIbAD+PyL6TQB/AcD//l3nUffSN33gDM/H2wShYXKgx87fz5GHAU/dfXUZLJ3E6jn4EAkG8Dw3mb8rBeNbkCHsCArWvPxdOfy3GwVpWvq+kd+/7ROKGoG3yyu8Or3CDRbMQcUoqbK63wDickJCwC/88Id4ePcOedvb/bQGpEyqOiQAMx2MlVRGzRVSNLXIgQHEVv/RQrVghY0WcwfThFShTouJ2dKOlbVHYLG+E2bMSVTII+es3Ysvq+W/dSK7YApBS5WlFOzrhpiSCdcmgFg1DSiAK2Pbt9aUhFmbmNZakUwnECAscdFJRwCX3cZAAHMGrFVa5bU9FROMg5h+orYZt79X1Qhg0WYhMaClDEfi2ZRmYJoxzRUP54tOdq5YHy+Y5glxShZqmFDqNOHh4RGlVCAlMBtRzZqXxpSwzC/jWn8UmMC/AeC/s59/AjUKvnlD0u/cGngHDPPZ6bp9v3HS9OKS6/09zsPw81Glp0mFUQ85XrquPsHkyXGO+/aX+Smj0DIadtPOZjwekwDqCLJA04Uj8aVdH+vKIuJurwtqmIgGRSRJRszRGJNZECYtr33z+jX2h0fkzVqBhwRBl/pyurJr+jXP7RqtuXoejl8IixkSgfckpNbIVLMObCpBmgfXFVOVmJSjsO87csnavXhXOW7PWPRW8Vpu6/c/vhMF9VSKu7EbTcQkxoDKvTLU255HoyR7zUpAsl6G3HsZWlwerMQy7xscjyqlNB6E6jKqDgZxx7IIhCkpvbcUxY1UNkyfoYqBmBFM2hp9yztYBNu2NUxkWzcUZsRhMSRTHKbwFIsat5/LCBDRfwgloP833+O7rSFpfDv73673MY/LUy6DDNgAmI3uc8fMeixve7QH+1JoAXNOffPTtF1blPCyVe3fPRqCw0p+XORVW4SuDcrT47UVuIGDaM9HDzvgF3B3Gy0+jzHBmYNUGVEIhIhXr17h4eZGjUDrGixDmCSHid/uQfqjEPOqmpFyo8YAPL9tsTFVBrWmKvrdapVztQ5t6YOmjLky9pyx51078ubcXWN7KsFW7hAiwkQNoA3WPLUZV4NV2J5fiCpcQ9wXg90FOOg4TrRKEgDzQZ3IjYAYGcnLl70bkBt8IjJiWffu1Dgk5G3V49EEUF/4/L5I9HnUnLFZwdGWd23rXgou+w4BMMfUAVjLNui5/yFIjhPRvw4FDP8F6cvX92pIOv/SnWJyBChV02if6BffB6EfoF3H1cp8hQN4+mmcVPaz9dfA2GDEr8FQR5iqhX7J5MBHNZyfeRM5NBx60iqanWCSWpgw3mOwSSRgnVRsKyl3Gi2J6TCTFrnk8wVrmHETCRwWhNMJEwETK1ocKyFUwfT6Kyy/BNy/+QJ/7/d+D1wZGTvSpEJu5EIi5tJrPMzGs+qrsIhV1w3+gRbaBEQSBbqgxVAFuU1IASHnYqnhAHjqEMDOBXvJ2NYV54/ngxfkxn9ZTge+gW/zaYHnz514xGZwdjMiRITStAV1HCyNiKTGpNZizVZNR5ECIMVKdqOdQxWQ5levLaVZINb92GP4agOue4yqrBRSQqzaJn3LjGQKVpWLjj/RDFAWq/koBY/rqtW124rf+enXmJcTlnnGaxuj6v0IXr9+NQikPr99LyNARH8RwL8H4J8XkfPw0V8H8N8S0X8CBQb/LID/42c76tEt9oseXX4GVEOfCMxFXVsa6gnaoXwJf7oSHyC7Q1yNPvHd/W+rhzu+nX/fwMcrwOi57eXQwAaFZQcEaMbAj9fvP0Cdruuwwf/krjqjQgd25Yp9PWNPQJ5m3OBOUfkQIJW0IScRQk24vVPNu4/391gfL5YuU6mslIw3D1+1ayOfBOc1kLr47pn0e1CDQdDvUa0AWS+CMeyrPUwYx2stFXXPyOuGdVtV1TdFdfmDqVRPziQdvysAqyteCjcjEAbQkhsmE+2d6mTzXgi+2ouElod3r4tLbhqFzLFxKiBoFXsuiELmcZAEZS9Wvjq/hhsR+rnrC17WFTe3S/Ncez2HgEEIacI0M+abE5iBYnUeAQRmzZrcm9T4dbHauH3fhqS/DmAB8DdsgP+GiPxbIvJ3iOi/B/B/24j9y9+VGWgv7PDzOLFe/oZ65p/YAc9P0OMZdDs4CtSDgm6SBkKOud40GJrn3P/rye8sv2fTDN9xPzrPj6QgtUPSPvP/mHi4rmBlR5ZkCk3Wk4FIVz6j3McYEeYZ4Bt8cXeHuqmSTTXZsSihPRPnCWgsTlZgNBrvfsVHwEvaIA42MY5wgu7TQh37Wy3OAdCUYG82SiZkYqrBIoAQJHh8QtZ+rxd7jR6hv58WappbMuJD/oyJqKkK+70pY4+G99zDUbVv1CZ6v78eXI5FR96Gzz2KYPRpZT3qKs7VcBELhTyVGWLCfFqQd722XIqqdrOKkggNXuML2/dtSPpffmL/vwLgr3zXcZ/fjiPjemIFoMVW8TvAjna4drD+H+9s7kOXrvY/mKQ2KY+DXIb/ew7LOH73ydf7Ca8mA3QsH46jkYiBiRZWuAT3aOGlX1SjkAYG1hhxKRe8ygVziIC3sYI+02lKAAlSAH784x8j7wVyL9jyroq/VvTjGRx1NbXzrg90bjhAH+hE1IBAR7MrVVCorcT12oEDlOfvf9+3DXnPjYzjDMRo/QeC6fCTteSClWr7GFHijYt9aJfeYsw/N6LKWQiGyzgtt9o7cIAR1taLG3DoxrC9JCjXwXUYuGZ7F9wIXYGU679tFwBoACMM8yq1gEJqwi73H+/h6b7z+Wxt1Sp2QaMM393dIU8Ved/x/v17fHF714R6WCKkWv+JF7bPhDFIw7+rGv42qORqBX1qBFrWoE14Bp4BREZiTS+xRZ99PpaJtFDHwgsCrGEuDaq0L6cOX7zVq9vSKGSI27ir2ur1qgoNhQSAEWH8AJDmkaMNMgGiBFQw1gC8i8CdCHivgBQ8flvwo9u3+FN3b5Gg7arTFFCrIKQFhAlhjphSwvnxjK+//gYP5wdsuUBIZayBAIoR+8oIpFyB+DBpA5N5Rjwtja+/7QXTpKtzrABHaBVgKSphJp3t2A1mAFgpwLlUlH2H2KTVTjsCiEqRT9b0NKUI7+XHDOs6pRMqF+1+TAHG1y9ArdpYJQVbTfW8RITTaYaTcXL2ld+apEJQy468lVYwREQtVSm2IBRTJ2I2xSG4/FloaVIgoBj7r7LKjJVasa4b1o+q1XC5XPCwXrDtGdu+Y6tsALdqPDZBFkLrp4jlhA+lYs87zpczCNocdko/H1noj3V7OpmeOu89PfZ8Sk6e+eb4nXauJ+fwtax/0xdXwrW7/+n7OOTxBxcWdpaXMxQt6m9XKb6juYxC/m1qg2LMgjAzMhjnSKCqXYbWvCOcH3FLE7Z0Ak0nMIXGrAtRgbFpXhC/IKSo3Wu2vCLXglx00IFMyw9KBRZRUstuQh/TlBBqRSjqPocdzJU+AAAgAElEQVQYEIZSZWLRIhtmeLGkr3QCamGChgKuMEVtxffFwON6ZydKexqDZ0TUJOzbs3YX3FJ3+vhkMMJdC9ENvL/LMABsY6p2TFG6J9Kwg1pbIBkjH46nx1G3f6/KgXh8vOB+fcS279i3DR/XTbMA+44iulioF1OR3KOLBqbaopC5ogiAGMEMVMEB/7rePjsjoJvPjCvuP17wqq+2UdKqocbNO7gWKPVNV3uF8Du92KEFQUfhNQHkZTtPr6ij5dLxhRY3Hj+7/k6/WI+lj3LqMhxmxBiaCpJNrCKMB3u7qQaUwng8X/CIhHOYkV5PyIBKfPmkIgKlGUQJMU4QETye73G+nLGVMoiE6DUp2UfDDtXRB9IU2zOnGBCtS2+pBYETqAYwBa1us3tT6rF6gQIzDpYapJAQSIuXELgBdjVv8M5IQt2DDEF6H4SgHZPY0o4hRoSo+8ZBEFS9wdDSbjGwZRQUfHaNvtOyNAxC9h4yuO4FiAwU7MCx11koB8IGkw40e++MWjPO64rL5YJ3797h/eWC3ZqWPlolYClF68tCQHRAN+p7FwpGljIhVmH1kKZXTVpslEu/3j4jI9BBljFL8FJ+8yXm3vVnz6Ki13H48H3xFBhdmYrnkMThGN+dJdBYngag6ukBn/lrex6+F0EoAFLVmAUYs+7aiOh/mBm5ZJwvGcu84L7e4w/XimW5A1m3n7gs6raSElrmZcEyz5hTxJ5XfPh4j3cfPqDW3PoSphSRrX4g52ytzQXbOpQDBzJhDgDQdFhSuQHtpWirrzIU1QAA0KapNpl01VPvxlWJUoqQqA1KWAbiF9QwantzI1g1g9VVeUJUrMA1EKLpGwgRJqgsumdi1NXX1V2Zez1U874DALDt2kHblX5BMHKWXmPOBSGIeTQBtWaVZysZ63rGw/mM8+WM9+8/4H7ftJKSgB3h8GZrLahFw9h1WxtP4e7mFlNS451zRs4FW17x5s0blT27/AkwAq2IxrTavfGIT5gjugp0IzH+/jxA178POGnICUdkK+lIJgG0WMfBZAE15V47WNtvJDC9iAt4OnFEJ/0aYJPBCCbuBfRrtglOHSwjAye0GGYIi8zP8eNUYVwAMAkkCW5Em1ugMuLDt/hyvsXr6QTEoKnAEDAHgpgM90QnfPnVD7CcTliWBR/vPyBnDQ2mKbXuv/u+mzehBk7/lrFkLeyZ5oxyYtQKxFQQgqsPDV2FKML7TfSGKQKRqsVFJpghBHAlA+bcqDqP/zg6vL5BXxcB0us7vK5BQUCN39lUiZxR6aure3KlVmRTAa6iz5dNiLU2vr7Yz0AxgLHUiizaGBYGom7rhsf1jHXfcL484rztWPcd51pwgRhlPqD4uDL5NWarsyhVxyczkgAlbIi5aA3B0JWZL2dU61vw0vZ5GIGGCwo0A6rKOOodGLV0ANK6uzVuRybWiwU7mkPrx6JwKH/1opZ+beSX1myMQI/ReHGidQaHSXqY0DpoaTimXovm2D01rl8zVMIHn+jLbtXSZAPaeuiJsKkX2aQxeEAAZDAuVPEYGQ+L4NVWsbJO4vP738cv336JePsWHIA5BEwxgmLAhC4Y+oPTj/Bmf4O3r9/i7//+38e7Dx+wfviAeZ5M5Ucr8oJlAkoprR5+SRP22w3zacGeM0q2ldBEObR7jhKDUpwRY0KcVHwUgOrwcZfVDqIq0Z2jEKxngqodNyMQ9FocSddx5G3Q1f2P9r7c7XdVXpFjwxMdInqt1Yp9dpP8KgbyCXsNotO8tYFMMVyjSEGWHmLkknF+vODb80fcr2fcny/YqhqPAkENhvtoYQI0oRNQmVCLoFi4BxBIBFEq7utF7y3AjJ3+e3//0fpSfvbZAd2EpUlei6gqDUUXnvBikpfd7dETeA6YexIuHML059EGGkqJVdEXHYhzgwS0phttZX/mmq4vt018d4RFC5Xguvnm6jbj5pZAzK11rf5BL0JcY160qQjXiiqCEgn1VCFrRbxkyH3AF3XCbTV1oJQQ0QVI1Z3XdttxOWGaZjxelBdGIjhfHrSJ50lQ94JSAZGCOUyoewFRxU4FmSumdcf0uGO93VSxGNGUcxJi0Nbkyw1hmgiIDL0hfTpl25WHQIRa/R0GhJAsby+YlgUsTq0FalYNfk0FJjOQBUAvDW9Ua7YemFLt3RmGYN5bMG0FFqU1O8h3WdfB6FtGwDIBpRYVMjFCEQdAKGDfNzyeH/F4OePj/T3e5xXnuqMCuFBGRkWdrFbDrjlNNv6DIM4JQAJJwoSTMktN9DW7x0MRVSoCCIkicAogjggyQduVPt0+GyMw0kFvbm4QY0IVAVsbMh/4LwGDHi1cT7nnDAYPK8khNPDv4IXjjOnEwW3X4ep/97DGd7s60mh4DNxrakceVvi9EIbQo988EVoxTzvkYOi6CtDVfZuxYVEu/uWy4lEmvLq9RRTtVzDN84HJJmbcQgi4u7vVSr59x7qdEVPEDGBPqZXltqIZIhSoSImugCqlrZLsEfM0I8WKFBkhqqgshQAqEdJQ+E5G0uJylQsXU+iF3Xep6mq7ERCRpqoTzEVgPiruUgMTR7JPr3s4kJfsdeeczUCgofz+z5uWwO65ZU5KBgMoEFwuF/13viC7WrN5b+4ZxYjmBSvkQ81LjtG5FfZumIEoYCIgJfVshBB8vgBWpYimcfHc9tkYgcaeAlSZdpqRmbFlWC7V3bqng1u3nq93Flj7RHra50WGoR5iuKBnPjQX3ZSoDtf+7D11FOBZ0PJwzmcM2GGfHks0PKJbhdEcDcU7V/cqIqii6+xWMx63FR854PX+BSIEAdZ4pBbU6pRZ88CIWofbsmd8vP+gJBuqSGmHFDElH43RySx2zsqZV/ZhaUYAAkgSQAiTufxUKkKoVpKsIZG3+lJPS8DBmXhoLblKzvDCO89MeO8KMf12Fm5CrZ2l525zz8CovOVgAIaJrvUN1TwCtKxBNV6D6gRop6LKDA7Wx0EYWRgPlzMe1wvO+4qdCwoYTOr+wyXYAqwMWzMbvlDAOjz3cQQQa3iEoHqIAjW2ATAvoZqq7p8AIyAieHV3hzev3yBE4P27d3hcV/zox78AwSts24avv/4G0b14AmrLlQHqxukLY/Wm244ENxqEDjqixYe6G+E4XcxwPJmWBEJAoIhqXZLCVeoPGJSOAjWWXRtoPBooi1UxfL25APZzByLQMxZica+Cg5UAjpY1cE18MxK+pkaLkT/OwMcl4C5veLgUfHtZ8eGe8KfnV/jBfKurVq2Ylx03xLiLSeXeQkCYT3j1dsHN7VvMacb7+4+4f3hAyQXMJ3huf88bStGVN+SijUmngFoqKDIo6OS8vYmIiVC4AkXThFyhbb3FVtTKLcYV617kApwIvXhHJzOacAiC9gysZbcXLkhBabba4MQzE+Y5gKzAS1BRrdAIAFvp8rohV9H8fd616AgKL61ltzEkKJxRUbBLwXnV9nk7VWxU8f58j4sUbHPFzhllJtSogA9ZmbFIBQeBkAqEdmEWQtByUx0BEa3QrDIgFgIsFKwlWkTEpOnLNk6f3z4LI5BSxN3dCSEK9j2jWG704f4eX7x+jWnI6cIn8OAa9yIbL1clm64jgNgfoH7PrOwQhnRX3cG5p0VN+jVfd+lJ7ECk6C0ARInt2kQGebNntgOGIdIMgdjPR7bAcA8woU9/PtRBIRia3u/FpFiIsUbrOiQZW8lYsWNDwlJ2lDIhxoC8bqgLlGYsmm6LMSKeIm5ub7HtG8q+A69et3RfLRnrumHPOy7bii3r+wQsJYeEZMas1oI9Z0zLrF4EGAUFsA48pVTUzE12jEVr/FOyNmI2kVmsUSeRhRL6DJmovUuySU7Q+5jmWT01IeScm/yWqhPpgkKi+ooOILNk5JKx7Zs2Ph3AWFiFp4ZaBVtWT0sCYa+Mh7rizDu2KNgDIzNBUjCAF5pBbR6Jr/aD/qSb/zYUResC0AuUNIOlUvPuKMZJhU5/rgKiP44thqDiCSRNH45Z20e/fv26N1m0gf7Eox/DdP9RXsIPeppI97tmHL4E5plhMEPjybhx38M35Xk84klR0afCEzsmRIwQI80ItuM0/KBZSPTUKntgYLlv+42AHIEcGdnTXijIIaOwgmrVKLslxu6PBDGBjIh5mTHPE5Z5QgqW2iMCl4IpTtj2DQiqGjCq6oICQtSVuQqDLPfOlUEe/bO62qVUcNE0X2HdL8aIWtPQ/de+JaoaJBysxkBXSucQEEIziETWPLX1OUQznv6OHJh1bQWIIv4eZoA6Y1KSaQmIXmc2I7DnjDBFFK7Y8o4dFRmCakAhxdCyPI29CCuGIg1//N2O+phjSODvP5hwiVLagdbS0he7F9G0z8QI+IthEctnEhACLpeLPfCnk7IZebQQDgD6Q3jhPL6NrL7DNkzew4S1EwoLCveXE1yquk83jEzFT03y7zIAT/Z5bnfSOJisOSsCNQkEgYUf7LegIVG/JaUKb9uKLBNyMIWblJv7uFufwHmeEYKKkoDQugrVkhGCpvRYxHoTJiz1hNOrO9yuZwUg7x8UWBNNA5+WyfLyjG3bAEQrjAKK96LMFajAnjPWfUUIZOq62s7MjcC0zMh1RyHCbGpK1CjF9i9ERILRhQPWdYW7fvu+Y8sKQKdRlAPKWsw5m4eqoVZKCbtwb+8lSv4pteCSLzifV2RmCDG2tWCvO4q1LteaAmCaT5AIINrYHxrRNtDTjMCTNc9mv9pU7zhF3RMwvYsAr/zEM6Ft3z4TIwCgFgQIvrx7hZ9+/Q32dcPrL96AWGu4b6eIdS9w5y4Q20MFZHTzA4FadVcYUnf0nC05bI76Pp9S7F7AYc1XkQNF4wEl6r/wwP2a+orj5xitezTas7VZo+7GH9qd+f36cYJqGDIJRMNLWJm8klqS3VsFQmbEQLgEQT5VlLxi4QjOQK0zeI+4EcZNCrgRVrHMmCAeN3MFpYS7129wur1rtFXmgrJZo9KiOfW7u1fIOWN7e0GpjHXbcV5XvL9/xJx2zNNsIppsgJh1EaoV+7bj8aJIeinFmp1oSDKlGcuyYF5m1UUwinAIRgmO0ejd2unpoDlCLt6iYCZF0tDNtP/zrlyH8/kRLKRluRA81BWZKyoqLlwhqGBU5FpxuZyxlYyzZCBp74SNKy6yYl8E+zSDoc1hU4BWLGo1GkSjfYjVMIiVQAv0verg8WFFFqqEtviNFbUVgJhmAlNArVXvS14ORT8bI+DqrVNIKrlEpr22bxpPQ6wYo9dfO6ImEtrkHEtr2rHt90+y+q620RAMIfpwwRYeNAzB4QSXQuvnxifDjeeWd/dSht9fQHcNL9RzWrjkDDN3MUFQsEmAwGanGKhBUAAELnioGbdlx03ZcRNVmHOGNCactoA3Qwg1tvOyAMsCBjXl4RhEJ1VWzXwRlbiapgQBI112gBIu6wU5FwgL5nkBS2gCJdXSe+tlxeP5Ypkhc3ktRVhqQaxRW67XjEATyPkH0cMT84bcUtpLEnu/Qp6es2doXqDX4m95R2X1UKsI1qreQKmMrapcOEvFWjac9w1bzdhUSAiFGBcqWCOjTkCZtU08BfV2GFXfso8zWKhrGYLm648YlIGPcE6ETezrsd4GhIU8Gl189p6AaIOEWlFkx81pwTwtgBR8+KA11FLJmi0G0NA2y1d4tnRWgDWU8B0cJGlx8/G8Xg70JCrwXOzwQacuE1rWXXqcjtABSjKrLYOBuD7+cwVS9m075pEZeV38dKwp0M7DgYKm2Ljvp1VzSmg5XAMBTIzzDDyed5wz4bKvyNNN4xRoY03LgZNTm3WAqWsdkchKW1NEioZLhKI1ArUiIkAkIQRgmhbc3rzCZb3gfD5jW3csp4rFmi5yFWUh7hmP5wtyEcQ04bRMeP3mDvM0YYpJQwVlPSCX3bgJAAUgTlrfwOhCteRiIzBWH3rqT4uVLK0HQa3Z5MG06EoXgaBxfVZwMJuBLLXicT0rfhEEaYm47Bs2yTjHApwmcFDJ9kCk7lpQOXf3KQWe0bKxTP0x+wLmgyhQcNvQWJLkPr++7FYxSKQhUbeEz2+fhREQ0XJUzU8XFKt6u6xnlLICAKa4aD17eFoXfQDwpR/TzOJL3vmwHXe4rgd4KXZvCTsxjOBJyDGoCuM5KjMNF/70r/pue57+yX6Dy6EiGgSOAVJ16MQQwUHz1x1QeuoNKbGmaslwzq2lF9cKidKUadhk2BVrYMMU9P5jiAggVI6Y51n3Y9XKb+FPEBAHTAC+/PJLLMuC3XoGOPYjItg3VRN2gY2UEpblhGU5qYZASFiWYEQkFwjpPP1kVGGELjLqLcTUlLKRe6wwqHTvbNs2bJsW4AS/nqwMwEu+tCzFeV9bh2B2FB8KZkvUJispAWy1GDH6JB1X9v4elT/RB4BYKDi+Khbnu+CoiTG4gxTCgSJ8AJBf2D4LI8CV8fB4AYSRJqd5AgCBwmRAW7JOtJ5yO7rUZKAI2rN2P8H2MJ9eIwgH8WzPAWjp7vpoCBwRsGMPKRs/SV+9h/Ohv4R23k8YFb+XdvXSvtn+ru6gjCOoXbIAuqSweSWaVNfdRAzEJHDsR3SZpXNivKeCVFe85h0LzzhZyqsaup9iOmRdXHS0V2tbkXWwCTDbs7OwhKUiAIgg3CwzAgHF0oNpUn3/y/nSJjOR6hOclhm3NyfMU7JuPDrYk0QjDQWtpoRY2nFDrAUhapszFrL+igqUsZBR043zz4QqxYQ+d5z3DXsuyPuO+/2CXLRG4r5c4M1ZL9UWLWHUqPdYibFCQJOAo3I3OGo4LkbYEHf54ela9GV9nK/+PgcrcPAWbYB0N99Ch2B5q+aBdq/ype2zMAIlF3z7zXukacJXX30FAiMSY5lv1JrbikdwVhqjcjCvVGGVpqgqXU+gdX+xlz3WH7TtsCry8U+E5loFs7QtIsBgOKijsHFoMQbTlSegd4UlaquGPCE82X9tFKjzP6SGyLwUkVZL7tfZ+hFGgmv116jgEBGBmBFSAhMjW389qpZJCAHvbgQPUnBfP+KLcoupJiy4Q7G0114KljSj0ZJhjUO56CkdVWUghoRAVjocI0rNQBDsu7rmIQhOc8DN6VUj7oSgIiaXxwetfhMFCu9uT7i7vcHrL24RU3eLQYyYCFESgikEgRilbChVW2/FmHA63apXYgar1t5LsZrZimHCJW/YS0auBe/WMy6bKvu8vzygMKOy4EM5oxhjkEJCFb3OTIyQImoiXGZS74mk+etCADt9OVCbwAXSvIiG3/jgecZrc1xLbMK3BceajbRzehGRgdV9QXl++yyMgAhwc/cab96+xevbU6vi+vhwj3XL2oMNShSpopVqmFJLjaCt4HgxPUiGO6ic9REpdbdQIB1PALr+/XFvW4ntuH76FgroY/c89MtbNyKa/77yLuzBkMt4DkSSKgWzLINPSANoJNa0A5BQQBJAgbVwz1OJQ8pQU6qsqDIJHgNhLTv2fUOpBTW4wq2tnJbvjxQb9sTUDQBBz+OQR5gCAiUkpwYbkStNu72LiBQTUpwwTzN+6Zd+GfMf/BTbviPEiI/393h42LFtj/jiiy8aVZhIacMxBlBU7oK+M0fDCRWMbbtAKwhdyl6NK6xZiDCraEqtyFyx7zv2bce2b7hsKwoztj1rya9s8HaQe91Ak0L9eSEIlCZcVS7ATX8fJG0y9+wSja3tviNkJfOwVM/RhEvssxAC2IqraBiYbW6IfMoGPJNzuj450X9FRH9ARH/7mc/+XSISIvqh/U5E9J8R0W8S0f9FRP/Mdx3ft2XWvPPX33yN9+/f4Xw+ayFRii2V5hVyrujSfFP7ryVV2qqkH/nvfqZPPY6Xt+e+5XlqeJw2hBGgHkCIhyst4+DeGvV7e/GyhoF0dSVi9zyGKWMY4so6zmcgC4eewxccKGNANQNMC0C4XawRZbi1v/LnzjyGUsdH7ZGLy3MHqwoNpo/XopqgVaOn0wmn04JlmRFjxM3NCSFEnM9nXC4XwwG8U3IvCurmEIdnXa3KrjSVXl3/BX0sFSvmKaVi33ID/0ot2Hf/vbTqQiJCSBEhRVAyEpLRf3WFforhtDoFR/zg7/+KpNTuoX8vuKfgN2g41/Vb1NT58Tn4AT+VFftOIwBtSPoXr/9IRL8M4F8E8HeHP/9L0F4DfxbaXei/+BmODyLCaVkwTxN+93d+B7//ez/F+/cfcHt7q6gvXIKZmwR1s24e319NJI39RtqvDJOwu/3PrL/6GY6TVivrjvtIM0DyzMt0qu4Iyo1X5+ftk1ue7Hd1smGCH4yb+CA73IB2Fw4+iNxlpmFA9uevE1alyve8Y903bCV3JeEGvBVrF85NEderDkesgIf3Algj0hibMYpO9hkuOBhz9HTSRhpEhC9evcIyz0ro2TbkfbeefrX9E2vO2gygTRq/tmwaB6UUpSJz/1eqNjfZjRB02Va9933HXrTZ6ZZ3DVGcEhaVoBSmBIpauOPPWQ3+WJxEbTKKgUZj6lafv9UxwPCCBiSH9u56uGDHNDox2ff6ejQeeDA+n1Dn/l4NSW37T6ENSP6H4W+/BuCvir6N3yCit0T0iyLye586x+1yg6/mW1zev8MtZvz45kd4c/slaAfiXkFrRj1fwJghAGY5QWoBIepAZwestPCiEXda37nj5G8o+fiwRqVh6au7OuS68sT2Qg2AHNqgk8lzAQliCjbtsdsEczpvj9PUZx6FhtUY9IIYEWrsPQs0bL+j1LX/HkPU2oUgkBmohL7yiuIoHKzQSJNo6rabS18A/I6ccZ8LPj5E3MYJb4JgSaI6gKKZgps5tUYmKUzmimo5cluXrcMOoPeTkgqRVDCWmwUlV9RckQ1pjzFhXmZ89eWX2G5u8M23f4hcMl7dnvDF3T+Kh8sZzBXnx3tMy4xSMogI67ZhmVWUBCEiWpl1FsZ2uWgoNSWgAq5KDGY8cMWlVpwvF9yvK9acse0ZH+oFa81Y846cCLIQMBHqPAFJb0uSNR41QxyCxv5FgBqsOxNZQRMxCngQKpUet3twPwbuoY9ZboYEraWbP1uBVi3GAJAYM0yAlso1TGyMTJ7bvm8Hol8D8Lsi8jev3IyfAPh7w+/ekPSTRoCIkKKCSDe3N9jzjoeHRyxvTs31E2Fj0GnBTK7FeOHeONO2YSV9GYWnw+fXBKP2vbbQ6vST47cH1xv9mbdjPHdeQZO48skyGgt3pw38ufYU1LsY/+YX5F6Rud4m2Cm2YniTEWeokQCtLgYGSNpSwmCskTAHZbzlXFBiRkwJmTdAdLXLWbRDj3fVhVdo1ubyCtDq+QVXXlmIOnECqy1lVvZdzpitSehpuYHQ2lz2btyVf+CTI4J6bT5qu55ieXwEAdXQNAJZVIzjwgWPteDh8QH32461ZOw14wEbdjDyLCgTjN4LULLJSwIJ3pGJdF76/OZhLNpzbUQg+Dhxj9F2C/19jX/39zl+FyBLCR5Ho55Cn3w4HMAe+SfCgX9gI0BEtwD+A2go8L03GhqS3vzgFjFqddjptODysCJnxg/ww97sAebm2QDjWlXEsq+rGH8Ehkn+iQcw7uc/t9iayKw9hqwAhsHsN/PycQ9pHbEJJ8csQ8MFfD/7v2v68nMnasbClx74+/bqwu4minkFJIRiA9TPTdZ8Q4SRA2EP2tZK4+mCNBXkmhveUWxSxxCHexQocdVFMZTNqfUGXYNPRFoZcLACH0fvSy5IxvdflhOqMFZeTS1Xy3eFrjv4SOMjVFALFVmkcaaoVmtiUls4ceGMS8l4PD/inAu2WrEjYw0FJQl4CigzWnovha6ToI/PnzcpGWk0/P7M++rQx8PV7/639nZ9BbcT+eLSgD+iZuxsBB0GzneN9+vt+3gCfwbAPwbAvYBfAvB/EtFfwPdsSPr2V38gIUTEqM043737FkDEr9KvttiXKGCaZjCL9pWXAoqENHTmaaazAYO+smLQrQeOq3gYr8k+w2AI9ADCcvTv27FVjsp56/7+PoXHjivieETguWl+vQeOk+7K8D3dvLoMVmqrK0kIAVG0Abk5tgATRJTOWipjXVecwwWzBCAQZovnFQiLiHbPU5jb9STNOepFmTKxUmU75qET2BSd7ZiKM7LJk1fEEHB3dwdV1FFQ8Xw+a6y+59YJmMwgtOIk14wkXWErxKi4xQqB9oYvvC8bHmtGyRkrayiESTUGaQJ4CqikJCaKAfCOem3yHefriEn0eP8aDHxuLKDhBuOC1mN9e55uhECWEvaxP1zWOHaHMfCp7R/YCIjI3wLw43Z4ot8G8M+KyNdE9NcB/NtE9NcA/HMAPnwXHgDohKmTIKQZX/6pH+Lm9VsAETQBcV4Qa0XGhiz2wtYV8dUMiski4dp0RsepFdBX2LFd1PiQnoYM1FYlVb71pNLRI+jswCF0YIE228SV14ArI4L+ndGrIAd5wnEn/387r1OO3dAEqMS2CqsYCCQaQ1JU9LqS9i5CZgAVYY6gCkglcGSgFuVRcACHgCzAx5rxeP8Ry5oRSsG+aAhAMSBvQLUVbKWzrsjQ3gxK4VUg0iXcVFDZ1HcEWq3ncuBwyS4C54IiFRy0Cmq5OSEtE+bTCWleVKmnaLnzvuuk/nD/EZ73TNPcvQUiUwS2rMeesZaK1QDFB1RcBCgx4vFWkJNAAlAm7ZYkBHAM5jERhCZ/TcYFuPbUlKclJnPEYBNNFRCb16XZyyEEQycMobv/6soAlDzOIDXSPrZH3GDgpoTBQKljYHHfH3VDUhF5qRfh/wjgXwbwmwDOAP7Sdx0fAISUW00Ucfv6DnevJx1EQRDnCSFPkH3XWu2asZcdt+EEomBCEWxG9FhG7CRRYLS4/ZPhCvxu7VdpQJc5Al1SjLpq8DiBfUHu3sNTd/658MSPfzTWnl04kpf8TAoSdU+Hr6x+K5ghaqCjqnRbrToPqxNBRy4xULl9r1bgLBXnxzNusmAKEZlm5WdIREqTMqAAAA/8SURBVCn9OVSY/DUIE2nXYOdweCGPd+ZxVxqpIqbZ4mEzsHZPDEJMjMCEtMwIiIgpAUSqfVCKeoNVAGRsuYCtucZUFakXAIWssEmsKGgvWEWwmmG4RMEWgEIB20mwz4ZdpE7okTYHVbnJjbGEihGLaf8x5qIaAW2GAoEJLNuktwIhfzeCXmuiD9IXBRjnwE9BbVyO40jGCwjXY4tapetL2/dtSDp+/ivDzwLgL3/XMZ89ji1deg/d7ZmXCdsesa0b5pBM3lkM5QWChMGLerq6t1jbJyf0+OOZn/xs8ZwSTJxZE/r3pL8Wb27a47nhkOPE/oRH1og+/aJ1MrDgkNkRVqR7AOJAsGYpNgYaiqQfeswPiHXvJQQO4JwHgUu7paAMRRGgBmANwPvLihQEaZpwO99CIIi1IlS0tFxFabdY0oRYVOWnlbgSDG/QCREAxJQwzYKYnCbu/9jKgrQiUHLnFExT0s+rUoVPpwUpTQjzDe7v77Guq2kTmLaBmLahMLLhSpkIJQIVOulz0vvGTKAkSjYaJL0ExeLwPj7E/fRhvDz3Hr1T04gDuJJ0CJ3qrDv55B4m8aH+eQwd3ST4eYYCOLdKbVwGgPhT9UOfB2Owb11QUidcwLZuuFy0Rn2+SSBStdzxYfc4fDQAz0TlMjxAGr2B48M+5FqH76o1NTS8VhvY1KSpDowtvxZ/x+HpW2ikIm+s8fSsz3zHYvsRy/BBOJ4Xut/YEBO+/pNVAJojo9eWdPWSquIUZvO86/DDwyPupluUmBACYYbG3ixaeOSgZwq7FhOFYLRtNCPg1xSgHkLKmhYEwdJ31vGHYE00CtK8ICUtInIwMMYIqgWeL48pmkJ11P2EVdIsFzDvTVVJRO8rkDZtDWYAVZHX43nzrK7SqqM7Ru19dk9PH+UI9Ol4UUxA32wIqhswFHgO+1vdhS08h7E82hsbmyRj2Olu6tXOV+Pmpe2zMAKcKx5+/z0QgZu7G0xz0JfBBY8/fY/Hdx+RH87AjxYlhuSKmos+Z1NnAeBWYDiyPD/P0R9VQ2j7NzC+XBz+juNUNaPSLfMRkHmSGTDzPL5cJyW5wSBzD2XwM/vK47/IcWBcPc8WRjwZENL45eQDpx1DfU8KWrvg+N7jCaAiWEPBab8gkZZqL+hqxFkbAoAATEHBwEjB+hPa8zHXORhQpgxCLQF2DwVil2RGIJeCqQrSlDBNCQRqxCV3hNXAGR5CQBElAWVmrFKQUZEjYyfVntwTISdBIcGW1BOgIKhRQ1JAdQbcbRehVtHHljFyo9DHjPSQzowDuWc1xnIU0CoDh3+jQfFJTf7/g7c2vkcaLL8rED0ZDG6AQqeKP7d9FkZg/7Dh//1f/xbkTvDn/qk/j5sfJqRTxMPDGT/9jf8HH376HtNpAf/TJ0gA+CFjn8/AMiPMM+oUGgAVpSvr6dgTXz7b3wJo5PlARUuCvcPQusmME6h7g9JWS49jvUy1UVVNBCOilz23OoRxYAyxGg3nYEPoY+h96Fp7clDjjpsJ0XoA5xZQ93bq4MZW0ckvpK4+JKiiLVwjQF1HSqQFSmY/fvurikkEtyx4/PYdkobCWMRcTTOLQfTfIqIxfIiYTJrM2YVAN7oqOmpGgQgpaXZoSgtS8rQjI5kgyTwnhDS3lVO4gKSCuAI1Y9vOeLic8QcfPoCZsUNwHwUUAvIMnG8U/V8nwTbp6i9G8AHQ8/6uxRgEIXpfFweX3ftQLyy4go84m1Q9LIvLQCma1qEakOr1G1eeQI/53GBSG49u/4nIjJS/rwgX0jHeke3cteXGBUZ+Hkzgj2MjAV7F16AI/Obf/C1Mr2YIBO9+92vgQjhNN1i/OePj+wfQjdYSlD03qzdFTVF5Xfn1ynhc5dHAuzFgOIAr7qINK6ly5dEsswAt562y4SPt76lL9sTVP4QmL/tq47W3vey7umj0c/UBqZ95TNo+7QuL9txrqxCQYgRBkKVoupPtM1FdlnMEtlNEZTWgVLzYilCqqBoxCyQrTTrUilgIrU23FR75Ncf2D5jThFwrQihYkvR6h0gIOSPFgG2KmObFAMdgxCJVK/5wvuDjwyMetxUP+w6JQJkI+RRVVHUC8mSCpBFNgenw7NytJum1/QcQDmBK7doOgjDQ5qgWJ9g+/pl5FoPbNmIO6n9d+ZMNUMZwncA1uOchLXFXuGzjyTAtiHoN8UkWrG+fhRFgFqyPK0CCwgysQJWKx8dH3NBtQ8PznhEmtZS1VoSqGuuRuQlAXG+NsDFOHHfbD9bBaEdXE/LJIYd99fiHP4+fPH+zT6xBXx2vr/+ZXQcOQ/+uX8gYjrQjuXvZxtHRu+mEE8DJO03lergNIUAMddcenKSxOw0xLingpt6Mp1Q1ZJDq3ovt6IZULRcA7UsoSbkCwchjAFACIdWo75wiQogoosU9257x8PiouFEpekFWzCN2IxK0ZLefe7TnNuHaA/IJdAwHe0p4XCD8K20ZbuSno1dOw6EHT9AvRK4Mg18TeqjaQ9brwQO7XkEfCv0KvmM0AvhMjECtFV9//TVwEfziT36CsERkLoh3C/hsqaVZhSA4awUXF0aNASFqDzjXm/e32yjA/pSPC/1hrX6aupNhFnkM6qW64znsYDJYcutXSPb3g5Xww495zO6xHy6QoIbPWZJarIoWK3rTEw/ru0MPV6+7wgxoOJ/YiipaM9A+Fm3OUXmoRe+D37NNsKZgsGeievsAxJuL92euhoAbTuHPkEJo7dKKlHbfG0VtsZ0ipinaMwDiri24FXSM2FkLfrZtx/uHR+TKqERINwvE8vy6qjtjsR5SsX1CD8+PzBNok7bH/U26fJyM/ZE2QDsEah77ONeDqTB5cdH4yj2mV+M+QsvdUBFZ5qcNIWkrlOtPOA7xdJx9irr2mRgBkoA35Ye45Rl/mn4Fv/23/y7OHzf8Ez/5czgvj5BbwfyLM3777/wWtvWjXvVbgF8t4Nc3uP0zPwJNFYjAcmPsNYKuADI+uP+/vfMLvayq4vjnu+9vRkwtNUXmIR0nKvApBwkf1JcgTNQSX5Qgox4LlOhhwBdfLepNEENBRAuihHkJLAl7UtJxdDTLfxkk44xNxMw085v7u+esHtba5+zf1etvGJ1zLt79hcM5d997z15nnb3XXnvttddy905NjM51NxCOpq7KZsleGIOSkmekgXghzmMP6S2I7Mml37alfgVioiwfesEg6KZwmWiJbs+6hSqXb5nCq0TKDrS5kwpa74y0sC2knHJ696irc79W8hE44v/7vLT1sGTmefO6BpQjFyf4x/ZZdGwjtblzgRp1D5La8GZrDYsQXs7KhDUeA3ISPW9iyZ+paTwPtYnUGudgTGhYk6Gm8Xu1YNN+KdLA59KfSWycfy5TtcySsbEtVjTwoB0ptICNlP8Ja3J7S5tcm7Gm9QisYUhTaruBIst9AduK/uVu1rkzt7GvR6whD3JCMQ/vBI96AR+qo7NC3o5omeTweZlvyfndthbBW4g2Sm4kNOWIBrhFExT+EV3dC7AUQgCEzcRsveXokf9y6viU9pTBFM9gow2ma1OaYzOYtp2vTzvdoJkJuzxcO6N5dEa/QuOHPphjtrD3L6OQ2uTvoifGubWy2/VTi976G5XZ4iXGvoqi0s1a6qaX2Vn3N2kJ1n3T3TE/zyZxn42GZcMtyjNBKWsy/e3UdZhsKXcD2mxCv9Y/6StTmbd9ktxvofUO1u1naAWTREKR4MeYWDhRTsIGYeY+S/JNQUkWYdP9Oaztx0LDNQQmRpMSDYlGRjNpu+mJmWgV+wcKlbxMyZWnhVnjMWX+9C8jC4Qu5VwWyvHRLCI4JehXKtS1h3yTcqWgtOj376fUAsqeHbYJ6w3a2czgTSK3k/k2Z8W9F+sCSyEEJNE0LSdPbHC4OcT6qQYssb4+5djxE6xvnMTaDZr1GR4jGzgO1sw8RFTsJ3dXLH/YbiWtt9Dkph+/mN+KmyV1fCh0x6zOKqLpeCGh5nlBHji7LvoRknceuaZMa7e6EY3IRBde2t/1/Au1uXNvpOwfp182zA0+C5kmP6ug23SUBCRaRfCQ7Clpec981GZRXZtrU3js9Vb07G9AigG3LZ4PICXamY/4TCLhaDHPFjlUXCn8DAsnmM7jMG4nWRbjm/jSG8wyT3o1vJvylCHhyk06uceXHbkrinYXX3djgPIkrkdvPyCPL8wL8DxW5bqy92L/hyyi5u8eGsr8M4dWuwhavN12OEh6H/gf8O+xaSlwCZWerbBsNFV6PhpXmNml84VLIQQAJD1vZteMTUdGpWdrLBtNlZ4zw+mEF6uoqPgUowqBiooVxzIJgYfGJmAOlZ6tsWw0VXrOAEtjE6ioqBgHy6QJVFRUjIDRhYCkGyX9XZ6wZM9INHxB0p8k/VXSq5LujvL7JL0raX8cNw1I0zuSDkS9z0fZxZL+IOmNOF80EC1fKXiwX9JRSfcMzR99SCKcRTyR44wS4XxMen4m6W9R55OSLozynZJOFrx68JOm54yxOarLsAceku4tYBewHXgJuGoEOnYAu+P6AuB14CrgPuAnI/HmHeCSubKfAnvieg9w/0jv7D3giqH5A9wA7AZe2YoneJi73+OeNdcCzw1EzzeAtbi+v6BnZ/m7ZTrG1gS+BrxpZm+b2RT4NZ7AZFCY2UEz2xfXx4DX8HwJy4ZvAY/G9aPAt0eg4evAW2b2z6ErNrM/A/+ZK17Eky4Rjpk9C1woacfZpsfMnjKzWXx8Fo+4vdQYWwgsSlYyGiTtBK4GnouiH4Vq98hQ6nfAgKckvSDP0QBwmfXRm98DLhuQnow7gF8Vn8fiT8YinixD2/o+ro1kXCnpRUnPSLp+YFoWYmwhsFSQdD7wW+AeMzuK51L8IvBVPIvSzwck5zoz243nd/yhpBvKL811zEGXdiRtB24FfhNFY/LnAxiDJ4sg6V58p8vjUXQQuNzMrgZ+DDwh6bNj0VdibCFw2slKzjYkbcMFwONm9jsAMztkZo357ptf4tOXQWBm78b5MPBk1H0oq7RxPjwUPYFvAvvM7FDQNhp/CiziyWhtS9L3gJuB74RgwsxOmdmRuH4Bt4V9eQh6tsLYQuAvwJckXRmjzB3A3qGJkG+1ehh4zcx+UZSXc8jbgA+kZz9L9Jwn6YJ8jRubXsF5c1f87C42J4MdAndSTAXG4s8cFvFkL/DdWCW4ltNMhPNxIelGPFHvrWZ2oii/VNIkrnfhmbvfPtv0nBbGtkziVtzXccl470g0XIerkS8D++O4CXgMOBDle4EdA9GzC18peQl4NfMF+DzwNPAG8Efg4gF5dB5wBPhcUTYof3ABdBCP//kv4AeLeIKvCjwQ7eoAniVrCHrexG0RuR09GL+9Pd7lfmAfcMsYbf3DjuoxWFGx4hh7OlBRUTEyqhCoqFhxVCFQUbHiqEKgomLFUYVARcWKowqBiooVRxUCFRUrjioEKipWHP8Hn3U82SPbp0QAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}