Breast_cancer_classification_binary.ipynb

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  "cells": [
    {
      "cell_type": "code",
      "execution_count": 1,
      "metadata": {
        "id": "_XEEm0du79HH",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4708c45d-1977-478c-9f97-b8fb8330a49e"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/drive\n"
          ]
        }
      ],
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/drive')"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# General dependencies\n",
        "import os\n",
        "import random\n",
        "import pathlib\n",
        "import zipfile\n",
        "import numpy as np\n",
        "import tensorflow as tf\n",
        "import tensorflow_hub as hub\n",
        "from tensorflow import keras\n",
        "import matplotlib.pyplot as plt\n",
        "import matplotlib.image as mpimg\n",
        "from sklearn.utils import shuffle\n",
        "from sklearn.preprocessing import LabelEncoder, OneHotEncoder\n",
        "from tensorflow.keras.preprocessing.image import ImageDataGenerator"
      ],
      "metadata": {
        "id": "u4ULTKqv88M3"
      },
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get helper_functions.py script from course GitHub\n",
        "!wget https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
        "\n",
        "# Import helper functions we're going to use\n",
        "from helper_functions import create_tensorboard_callback, plot_loss_curves, unzip_data, walk_through_dir"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "dDAx6B0Vz81m",
        "outputId": "134e66f9-1ab4-44fc-8bbd-143c5b83c010"
      },
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      "outputs": [
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            "--2023-10-14 18:17:17--  https://raw.githubusercontent.com/mrdbourke/tensorflow-deep-learning/main/extras/helper_functions.py\n",
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            "HTTP request sent, awaiting response... 200 OK\n",
            "Length: 10246 (10K) [text/plain]\n",
            "Saving to: ‘helper_functions.py’\n",
            "\n",
            "helper_functions.py 100%[===================>]  10.01K  --.-KB/s    in 0s      \n",
            "\n",
            "2023-10-14 18:17:17 (112 MB/s) - ‘helper_functions.py’ saved [10246/10246]\n",
            "\n"
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        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Obtain the paths to the training and the test sets\n",
        "training_set_path = \"/content/drive/MyDrive/Project_4/calc_train_cropped\"\n",
        "test_set_path = \"/content/drive/MyDrive/Project_4/calc_test_cropped\"\n",
        "\n",
        "# # Trying example dataset\n",
        "# !wget https://storage.googleapis.com/ztm_tf_course/food_vision/10_food_classes_all_data.zip\n",
        "# unzip_data(\"10_food_classes_all_data.zip\")\n",
        "# training_set_path = \"10_food_classes_all_data/train/\"\n",
        "# test_set_path = \"10_food_classes_all_data/test/\"\n",
        "\n",
        "efn_net_url = \"https://tfhub.dev/tensorflow/efficientnet/b0/feature-vector/1\"\n",
        "res_net_url = \"https://tfhub.dev/tensorflow/resnet_50/feature_vector/1\""
      ],
      "metadata": {
        "id": "0sjCee2r88z5"
      },
      "execution_count": 4,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Get the class names programmetically\n",
        "data_dir = pathlib.Path(training_set_path)\n",
        "# Created a list of class_names from the sub-directories\n",
        "class_names = np.array(sorted([item.name for item in data_dir.glob(\"*\")]))\n",
        "#class_names = class_names[1:]\n",
        "print(class_names)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "RLYXMYY3-Eqh",
        "outputId": "d56b9ad1-6fbb-4724-e753-247991c2d67a"
      },
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "['BENIGN' 'MALIGNANT']\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Walk through a directory\n",
        "walk_through_dir(data_dir)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "iksvfyak0072",
        "outputId": "b7c5ae41-9cbc-4c57-8c26-751c13159472"
      },
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "There are 2 directories and 0 images in '/content/drive/MyDrive/Project_4/calc_train_cropped'.\n",
            "There are 0 directories and 553 images in '/content/drive/MyDrive/Project_4/calc_train_cropped/MALIGNANT'.\n",
            "There are 0 directories and 528 images in '/content/drive/MyDrive/Project_4/calc_train_cropped/BENIGN'.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Walk through a directory\n",
        "# for dirpath, dirnames, filenames in os.walk(training_set_path):\n",
        "#     print(f\"{len(dirnames)} directories and {len(filenames)} images in '{dirpath}'.\")"
      ],
      "metadata": {
        "id": "hybsttURr1S8"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Visualize random signal-set\n",
        "def getANDview_random_img(target_dir, target_class):\n",
        "  # Setup the target directory (we will view images from here)\n",
        "  target_folder = target_dir + target_class\n",
        "  # Get a random img file path\n",
        "  random_img_name = random.sample(os.listdir(target_folder),1) # this will output as a list\n",
        "  print(random_img_name)\n",
        "  # Read the image the plot it using matplotlib\n",
        "  img = mpimg.imread(target_folder + \"/\" + random_img_name[0])\n",
        "  plt.imshow(img, cmap='gray')\n",
        "  plt.title(target_class)\n",
        "  plt.axis(\"off\")\n",
        "  print(f\"Img Shape: {img.shape}\")\n",
        "  return img"
      ],
      "metadata": {
        "id": "7MwNCgTB-gVz"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Test the getANDview_random_img\n",
        "# curr_img = getANDview_random_img(target_dir = training_set_path + \"/\", target_class = \"MALIGNANT\")\n",
        "# rows, cols = curr_img.shape\n",
        "# print(f\"Current img shape = {curr_img.shape}\")"
      ],
      "metadata": {
        "id": "IhsgvXLP-8f2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# tf.constant(curr_img)"
      ],
      "metadata": {
        "id": "m1rzIqfVDhic"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Data preprocessing for transfer learning\n",
        "IMG_SIZE = (200, 200) # define image size\n",
        "train_data = tf.keras.preprocessing.image_dataset_from_directory(directory = training_set_path,\n",
        "                                                                 image_size=IMG_SIZE,\n",
        "                                                                 label_mode=\"binary\", # what type are the labels?\n",
        "                                                                 batch_size=32) # batch_size is 32 by default, this is generally a good number\n",
        "test_data = tf.keras.preprocessing.image_dataset_from_directory(directory = test_set_path,\n",
        "                                                                image_size=IMG_SIZE,\n",
        "                                                                label_mode=\"binary\",\n",
        "                                                                batch_size=32\n",
        "                                                                )"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "p5pUCkze1bwZ",
        "outputId": "f626f87d-fb74-461d-d705-aefcaa4bd301"
      },
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Found 1081 files belonging to 2 classes.\n",
            "Found 259 files belonging to 2 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check out the class names of our dataset\n",
        "train_data.class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "KlUTJUgs1_rv",
        "outputId": "752c4f92-35b8-4048-cc5d-7875db58c400"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BENIGN', 'MALIGNANT']"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "test_data.class_names"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "hKPRtn-02FoN",
        "outputId": "5a6a3c32-c94c-440f-8e0c-ba23744c87f8"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "['BENIGN', 'MALIGNANT']"
            ]
          },
          "metadata": {},
          "execution_count": 9
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# See an example batch of data\n",
        "for images, labels in train_data.take(1):\n",
        "  print(images, labels)"
      ],
      "metadata": {
        "id": "MhpMfMtn2PYL"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from tensorflow.keras import layers\n",
        "data_augmentation = keras.Sequential([\n",
        "  layers.RandomFlip(\"horizontal\"),\n",
        "  layers.RandomRotation(0.1),\n",
        "  layers.RandomHeight(0.1),\n",
        "  layers.RandomWidth(0.1),\n",
        "  layers.RandomZoom(0.1),\n",
        "  # preprocessing.Rescaling(1./255) # keep for ResNet50V2, remove for EfficientNetV2B0\n",
        "], name =\"data_augmentation\")\n",
        "\n",
        "# View a random image\n",
        "target_class = random.choice(train_data.class_names) # choose a random class\n",
        "target_dir = training_set_path+ \"/\" + target_class # create the target directory\n",
        "random_image = random.choice(os.listdir(target_dir)) # choose a random image from target directory\n",
        "random_image_path = target_dir + \"/\" + random_image # create the choosen random image path\n",
        "img = mpimg.imread(random_image_path) # read in the chosen target image\n",
        "plt.imshow(img,cmap='gray') # plot the target image\n",
        "# plt.imshow(img) # plot the target image\n",
        "plt.title(f\"Original random image from class: {target_class}\")\n",
        "plt.axis(False); # turn off the axes\n",
        "\n",
        "# Add a channel dimension to the grayscale image\n",
        "img = img[..., tf.newaxis]\n",
        "\n",
        "# Augment the image\n",
        "augmented_img = data_augmentation(tf.expand_dims(img, axis=0)) # data augmentation model requires shape (None, height, width, 3)\n",
        "plt.figure()\n",
        "plt.imshow(tf.squeeze(augmented_img)/255., cmap='gray') # requires normalization after augmentation\n",
        "# plt.imshow(tf.squeeze(augmented_img)/255.)\n",
        "plt.title(f\"Augmented random image from class: {target_class}\")\n",
        "plt.axis(False);"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 839
        },
        "id": "VzTvLIG-8m0J",
        "outputId": "20387f2f-d827-446c-c56e-50d381d82dc1"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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vnz9/+umnz1NeV7b8yI/8yMI151Mvu+yy+sxnPlPf8R3fUcMw1Pve976F+4/V39///d+vffv2jdpaXV2tJz7xiaPnPvWpT9X73//+Ou+88+qa17zm/Po3f/M313d/93fX7//+7y+0/UM/9EOjOr/t276thmGoRz/60fPrp556an3DN3zDlRqDZeXRj350zWaz+f+3u93tFtqh/WzH+vz85z9fX/ziF+vOd77ziAn/4R/+YVVVPf7xjx89mzoahqHe8IY31H3uc58ahmFkk+eee2598Ytf/LLSJJQHPvCB83Ru1dU/Jr/9279dOzs79fSnP31hHcDjkWV1dXW+lrSzs1Of+9zn6siRI/Vt3/ZtI/2ceuqpddlll41SX1lOPfXU+uAHP1gf+chHlt5z4xvfuIZhuErb8d/whjfUW9/61nrLW95SF154Yd385jevBz7wgfXud7974d7HPvax9da3vnXh55a3vGVbN9EMG4yuTPn3f//3qqqRT6Wcc845dfrpp89/XvrSl7Z13OUud6m73vWuX3E0c5UShtvb2/W6172u7nrXu9ZFF100v367292uXvSiF9Uf//Ef193vfverJMAnPvGJqqp2d9qZZ57ZTrIb3ehGo/8BnMwtcv3zn/98VdXcsO52t7u1spx88smj/zc2NkaTtKrqtNNOm9d3tPLRj360hmGopz3tafW0pz2tvefiiy+u61//+vWJT3yiDWm/4Ru+4ZjtUPbt21c3uMENFq7/0z/9Uz396U+vN73pTQtyf/GLXxz9f2X6+4lPfKKud73rLRhvysq4dn34xm/8xvqjP/qjhc0J3bhubGyMFlS5/tnPfnah3itbror9pM7e/OY317Oe9ax6//vfX5ubm/PrdpKf+MQnamVlpf7Tf/pPo2fTxi+55JL6whe+UK985StHKUmXiy+++Er2arFk+1f3mHzsYx+rlZWVpc70aOVVr3pVvehFL6oPf/jDdfjw4fl19/Hxj398/cZv/EZ9z/d8T13/+tevu9/97vWQhzyk7nGPe8zveeYzn1n3u9/96uY3v3mdddZZdY973KMe8YhH1Dd/8zdfZZlc7nKXu4x08qAHPahudrOb1ROf+MSFtP/Nbnaz+q7v+q4rXfcpp5xST37yk+sZz3hGve9976vTTjvtmM9c4xrXqKqqL33pSwuf/eIv/mJdeuml9elPf/qYGxDOP//8Ovvss+sVr3jFl709/iqBzNvf/vb61Kc+Va973evqda973cLnr33ta+cgs4yZfDnRTpbV1dWrdH34/y/espD+6le/er4w55KLdMvquzKFtp7ylKfUueee297z1dz2vb6+vsAOt7e367u/+7vrc5/7XP3UT/1U3eIWt6gTTzyxPvnJT9Z55523sLHgK+nvV6N07R9rTL9a7Sy77nb+5E/+pO573/vWXe5yl3rZy15W17ve9Wr//v114YUX1q//+q9fZTnQ/8Mf/vB2TauqviLn95XuCqr6/25MjlZe85rX1HnnnVf3v//967/+1/9a1772tWt1dbWe85zn1Mc+9rH5fde+9rXr/e9/f/3RH/1R/cEf/EH9wR/8QV144YX1yEc+sl71qldV1RVg8LGPfax+53d+p97ylrfUL/3SL9XP/dzP1Ste8YpR1PaVlpNOOqlud7vb1e/8zu98VXZ6PulJT6qf+7mfqwsuuOCoa3WUU045pa53vevV3/3d3y18BqG9Moe873KXu9Q555xTz3/+8xeyHFe2XCWQee1rX1vXvva12/DqjW98Y/3Wb/1WveIVr6gDBw7M0TZ3HcGmKOzP/uhHP7pQZ3ftKyk3velNq+oKY7wqTOJoZRmY3uQmN6mqqv379x+zrTPOOKMN3//hH/7hK5Ltb//2b+sf//Ef61WvetVoof9o6YRjFbZDfulLXxpFMykr49r14cMf/nBd61rXOu62WL/hDW+ojY2N+qM/+qPR9ugLL7xwdN8ZZ5xROzs7ddFFF9XNbnaz+fW059NPP72ucY1r1Pb29lfNHo9Wru4xuelNb1o7Ozv1oQ99aGFb79HK61//+rrJTW5Sb3zjG0fzrTs2sba2Vve5z33qPve5T+3s7NTjH//4+sVf/MV62tOeNid117zmNesHfuAH6gd+4AfqS1/6Ut3lLnep888//6sKMlU1X8j/0pe+9BXrlWjm/PPPX0pIstzrXveqX/qlX6r3vOc9o1T8VS3nn39+nXPOOfWLv/iLX9bzV3pN5uDBg/XGN76x7n3ve9eDHvSghZ8nPOEJdemll863555xxhm1urq6sOPkZS972ej/r//6r6+zzjqrfu3Xfm0U2r3rXe+qv/3bv/2yOrWsnHvuuXXyySfXs5/97FHITbnkkkuucp0nnHBCVS2C6bWvfe35wHRbId3WPe95z/rzP//zes973jP6vNsaflUKjNMMcxiGq7RrLcs973nPOnLkyGhr4/b2dr3kJS8Z3Xe9612vbn3rW9erXvWqkW7+7u/+rt7ylre0h9H+o5fV1dWazWajaPzjH/94/fZv//boPiLXtPXU0erqaj3wgQ+sN7zhDS3j/HLs8Wjl6h6T+9///rWyslLPfOYzF6Loo0VBnR3/xV/8Rf3Zn/3Z6L5M162srMwjQVKbec9JJ51UZ5555ij1+dXYwvy5z32u3v3ud9d1r3vduva1r/1l1+Py5Cc/uU499dR65jOfeaXu/8mf/Mk64YQT6gd/8Afr05/+9MLnVzbyPPvss+c7+w4dOnSVZK66CpHMm970prr00kvrvve9b/v57W9/+zr99NPrta99bT30oQ+tU045pR784AfXS17ykprNZnXTm9603vzmN7c55mc/+9l1v/vdr+54xzvWD/zAD9TnP//5+oVf+IU666yz2pzil1tOPvnkevnLX16PeMQj6lu/9Vvr+77v++r000+vf/qnf6rf+73fqzve8Y71C7/wC1epzgMHDtQtb3nL+l//63/VzW9+87rmNa9ZZ511Vp111ln10pe+tO50pzvVrW51q3rMYx5TN7nJTerTn/50/dmf/Vn9y7/8S33gAx+oqiuM4dWvfnXd4x73qCc96Ul14okn1itf+co644wzFl77cFXKLW5xi7rpTW9aT3nKU+qTn/xknXzyyfWGN7zhSq0pLSv3uc996o53vGM99alPrY9//ON1y1vest74xjcurO9UVb3gBS+o7/me76k73OEO9ehHP7oOHjxYL3nJS+qUU075srZkXt3lXve6V/3sz/5s3eMe96j//J//c1188cX10pe+tM4888zRON32tretBz7wgfXiF7+4PvvZz9btb3/7ete73lX/+I//WFXj6Pe5z31uveMd76jb3e529ZjHPKZuectb1uc+97n667/+63rb297WnvP6SsrVOSZnnnlm/fRP/3T9zM/8TN35zneuBzzgAbW+vl5/+Zd/WV//9V9fz3nOc9rn7n3ve9cb3/jG+t7v/d66173uVRdddFG94hWvqFve8pYj//BDP/RD9bnPfa7udre71Q1ucIP6xCc+US95yUvq1re+9Xxr9y1vecs655xz6ra3vW1d85rXrL/6q7+q17/+9fWEJzxhXg9bmB/1qEdd6cX/17/+9XXSSSfVMAz1r//6r/XLv/zL9fnPf75e8YpXLGQ7/vqv/7pe85rXLNRx05vetO5whzssbeOUU06pJz3pSVd6A8DNbnaz+vVf//V62MMeVt/wDd8wP/E/DENddNFF9eu//uu1srLSruVmecYznjHf7HWVy5Xdhnaf+9xn2NjYGC677LKl95x33nnD/v3759sxL7nkkuGBD3zgcMIJJwynnXba8LjHPW74u7/7u4WtrsMwDK973euGW9ziFsP6+vpw1llnDW9605uGBz7wgcMtbnGL+T3d1r1hWNy+R2HL6l/+5V8u3H/uuecOp5xyyrCxsTHc9KY3Hc4777zhr/7qr+b3POpRjxpOPPHEhT6yxc/l3e9+93Db2952WFtbW9ii+rGPfWx45CMfOVz3utcd9u/fP1z/+tcf7n3vew+vf/3rR3X8zd/8zXD22WcPGxsbw/Wvf/3hZ37mZ4Zf/uVfvtJbmDtZh2EYPvShDw3f9V3fNZx00knDta51reExj3nM8IEPfGBhDK5Kfz/72c8Oj3jEI4aTTz55OOWUU4ZHPOIRw/ve9752XN/2trcNd7zjHYcDBw4MJ5988nCf+9xn+NCHPtS2cckll1ypfnXbdruybAtz2sNVaf+Xf/mXh5vd7GbD+vr6cItb3GK48MILWx1ddtllw4/+6I8O17zmNYeTTjppuP/97z/8wz/8w1BVo+2/wzAMn/70p4cf/dEfHW54wxsO+/fvH6573esO3/md3zm88pWvPGYfj7aFOecJ5eock2EYhl/5lV8ZbnOb2wzr6+vDaaedNpx99tnzIxHU5S3MOzs7w7Of/ezhjDPOGNbX14fb3OY2w5vf/ObhUY961OiIw+tf//rh7ne/+3Dta197WFtbG250oxsNj3vc44ZPfepT83ue9axnDd/+7d8+nHrqqcOBAweGW9ziFsP/+B//Y9ja2lrQ35e7hfnEE08c7nCHOwy/8Ru/Mbr3WFuY3d4yfX7+85+fH2042hZml49+9KPDj/zIjwxnnnnmsLGxMe/3D//wDw/vf//72/7kuCNTVV3lLcyzYfgardZ9Fcqtb33rOv3007+iNYS9slf+o5T3v//9dZvb3KZe85rXXOXt6Xtlrxyv5T/E2wMPHz68cNr1ne98Z33gAx/Ye+nkXjkuS3eu4MUvfnGtrKzUXe5yl6tBor2yV66e8uW/WOerWD75yU/Wd33Xd9XDH/7w+vqv//r68Ic/XK94xSvqute97pe9bW6v7JWrszz/+c+v9773vXXXu9619u3bN99S+9jHPrZ9V9Re2StTLf8h0mVf/OIX67GPfWz96Z/+aV1yySV14okn1nd+53fWc5/73Pm2472yV46n8ta3vrUuuOCC+tCHPlRf+tKX6kY3ulE94hGPqJ/+6Z/+il6auFf2yvFW/kOAzF7ZK3tlr+yVaZb/EGsye2Wv7JW9slemWfZAZq/slb2yV/bK16xMLjl8t7vdbf4Or52dnRqGYXS6fzab1eHDh+c72ra3t2sYhtre3p7/XXXFe8z27ds3/97ura2t+Q64YRhqGIba2dmp7e3tOnLkyLwuf9f3MAy1srJSq6urtW/fvlpZWamVlZX5c8jnn3379tX6+vr8s52dndrZ2anV1dXRu8mOHDlSs9ls/tzhw4drZ2dn3v7q6uqoXr4ZkYJ8Pk09DENtbW3N9bSyslL79u2bt83/ebjM7VA3sg7DML/f1124hozuO3Vsb2/X4cOH57J035FeVfNxRO+rq6vzsWGc0Cd9c9sU9Il8Hof9+/fXxsZGra+vz9s9cuTIXC7bBn2pqrnsvoac2QfLgh2l/dim6R9l//79o7HymGEftGV5Z7NZ7d+/v6pq1GeeZyzcp/3798/txf3Y2tqaz721tbX5uOzbt29eN7pA1vX19bl+GTuPH+NG/zzX+b22tjb/bBiG2tjYWLhmebe3t+vyyy+vra2t+RxCl8zHffv21eHDh+djY7tCD8iODRw5cqQOHz48bwd9I+cwDLW5uVlra2vzcd3a2jrqd+scj2VyIMPAUTBeDJp77HTs+DAGO18DDPXYIfoHR4KT96Tjedqx8/WkRWZkTAO103F/7FCYDLSzf//+udEDCEzqlZWVuXM2cFnmBBI7d9/b/Z/j4764jSNHjozqN+B5LHCS6Bn9bW9v1+bm5txpoVsDr3W0b9+++SvkNzc35/Zw+PDh2tramusZ55TtGlCwjwT31dXVkf6wGzs665W6DPIe552dnTkAJvDwvO3ddsI15MZRG9AAZvTpMQWIPEaM2TLQ5hm/qt9zybaEk7ZMBmD6QhvWI/8nAbEd5tzG5jc3N+vgwYNzufbv3z/XGX23DrJOk07PeWS33R06dGhun9YjtjrFr8eeHMhU7TLrzsExAR0leJAzasDxwKKoByfuumw0Bg47Sf5PkKHYKdgQ3Y6fA1C6vpo54uyoi+f5LIHQ/UkZcCp2kDmhuzHBCbhOnidSsW7s5OxYOnaOM6MORww5LtYf9a6urs7bM7jbyXSRk+2o20PTkQzutY1YDyYH9J0xwzGmHjpykMX23jng1E+Oo4laXu9swX3ymCz7WnEDFbZrsLX9UJejR4qBJvWbujpy5Mg84jJA+03TR9NrZ+tcNwmiTeTufASfT61MrkfJKrrPv5ptuc00xmUGyGdH+9xMKtv8cvqQ7SXgHS0F5Xa7qOaqyuSI0s9fmXqXXevq8WepC/e9A45sq9NJynRldHI0XR1N5s75JIlZ9vmVbd/9W2YvXR+P1s6VtQ0DSvYnQS91caz6j/a57for9Rl53zKbORqoH80nHK9lcpEMIadZS9VuesT3+X+nAngWZrVv3755NMNnjg5gPjA2p346ZpjRj42btFZVzZmWmSCpDOrhd4bxjsaGYaiDBw/O73Fu+vLLL5+zKCIB6nCKwmtLCaRmZJS8J9Me6Xx2dnbmdSOnX6efufsERWQ0E2Xsct3B6yf0fWtrax7J0C7jx9gio/uLHXEvn3Xrbdgm8lhu6spxJD3mqGb//v2jiMRrOMscmMfdERzj7jqwa6cAU1+eX45Asm7u83qR01UeX1JJ+/fvH6XNcs2Tcc3zRgYMR7LWh9tdWVmZr6utrKyMUn8Z7XfAdiwdkTL0s055swbDOg7/T61MDmS2t7dHi2/k2qtqNOlyMlQtT/PY8XkBfBiGef6eRcE0NteRC7fpZO0ouudcWFvx816ENBilk3e9bod7LUe3JpCL/wkYuU6Anryu4fuoyylIp4PsKLw+YieGvGtrayNHnQvHTGKIAw7cqTnsx6SDdnD6Bt9OrtlsNt9cYvnt7KiX1NDKysrcuaILb2TAkbMIzbjT/2XrgFyzrpK5sy63tbU1XyRncTv1n4CUGwW85gIxWF9fn6cgvW5looUM1qP14H7QFrZo4uV0pOtx29Y36yAQDUdq7k+m7Tw3nLr2XNja2mrTdzzjVHUS36mUyfXIkYUNzs6zqlrHaePyJM1IIY3X9VTVgjPpSjL63MHjkou3PG9gcL7bMiWr9X2AkZ26F139XAKtnWaC87J0Br/tSHLydUDvKM4AZ1ByPZY3mbrH1s7HbeOE/KwdQo596odxWpYu8ri7Dufr+TzXA9M+7KBcr9dJMvLit50x170WhdNPMHfbltv9YXxoyxsliGxs09a96+J/R01pz67XJC/HNO3EOnJ/+CESceTr8U2bcL8dNR/LH2QfplYmCTJpoFWLaxBm01xPR+Hn/DcOOllMFrN5A1WCEe1m9NE5KD5Lg3W6Ltm35XGaJrd0dtFXggCTJ6MrOzE7t3QgyeS668v6m87KjDqfT2dlgLHsltvglc4TAHax0zpaP90P65jnDL7oFvm5xv1ZV/7t/hqw/Dw65PMEzy4Vlbo92nUDrG3V82F7e3sh9cmz1rkjEvpAlGXA4Vn3L/XNHLMeidT46bIN/G2gh5h6J6j17+gZmdBB+pSOqE2pTA5kPHmIEKrG+V/vXuEZO2d2mtggM71APfysr68v7FCys/AzbrNqnM7IiMaRiVM0pHsy3cBztOE6zP6YqMkAM1ffAUNGIF7Hya2syOctrdRjPdkxUZcdnZ2hHagjs0w3dtGE12qQy8/4XoMw4+x7/GPASZkT2DNN5Tp8pisjnS7CTAYM8zbouX92lAkklpHP0BPyJQmjTT/ffX7o0KGF9JJBx38TOfh+p7nynIxJh529bTu3vHveONVNPxhzE8MEOo+J76EdEyCv93hednN1amVyIOOFc/8PAHhi4UQNQMlsuT/3/XMwEMPLjQBdFJWpkNlsNtqT37EsO8qMipz/5f90vukMmZhMPINtplyqFs8X2Lkh49bW1jw3b+D1fRyAze23dnoGr9zKy4E1y4W83OvDtSm7x8GAQsmv4zZwrK+vL9hFMmz6jL3gvHyAMB1cstiVlZXRBhODqW10ZWXxPAX1OYpB3zhmr1fh8LpoErmRx8VzwnPENmog9Xk07AMbzIjeY4NtAXiWlb+7iBSgoS0W9tEJdTr66dZskYWx4JkEMtuKIxvGLImM557BB315PkylTA5kqnadsJk7hsGA++Cfy8rKynwh0wZnAKlaXLfpdvhwPaOBdHbJKPOcQBdOd9GFoxn0QDs4GOvE4JK6y3y9nWuCUIKjdwHlxE3H1ekA3XYpRt9Dfwy6ebg2D30mkHb98ef+3/11PWbwTk9BILooJm3U+jBrt70tS62gq+7NE+nAM4Xb2Yt15jUFZwn4O9m/60md5fhmwY7yXJD7aUDOccVJd3rKSJnNHZbN0SB1spPPEYjrT1Jp4PG1BNEEKss4tTI5kOlYEdeYYGYPXYjfObRucuc9Obm57knnvzM/6+J1jaP11fJ5gnlBu0vT0M9s3xGSJ6H1l+kef57yZiRkfS3bGms27MXXTJelk8yIj/4YTB3BGWBsI8iS9pNpL+qnbttBl271jrAO5OyQcl2rS5Ulk/YGB+slQZCyzLYsi/vRAXA+Y51kOshj1JEm981gSF1pP1nXbHZFZsDkKEkC/U5g7/pvu0mgZl44Pc4YIK8zEdzT7fLMtdSplcmBTNVu6iInlwfeLBLnvIw5p7OhHI2hdMXARrHDrNrNgdt5peEZoPxsTh4DgaMd5Mx7M7T3+oUnlc/xpEO0I+cerymZOXaAxPglmCQjzI0dGW3lGKVsOU5EXl5LWsbg0YsBGX06bcc76HD+Ttcm43Wqj9Qg+skdcLbXtK0uMksCkA6c+9GfHSXtGTAzC5ARftbtseX+jtglWfFPLvbnGNEGZ+RsX0612b46oER+tqnblinItLa2No/0nKJENtZ2M/uR0e/RfMYUyuRAxnvwMUAcQhdqM9ieWEx6DD9fEjmbzeb57mShyfTN1nMyV9XofWJVNX8xILJ0O6hgbMjulznSv5WVlflaAk7Q7PvIkSOj90nZEaZjyWhm2c6jTK1llGGdH2vHlMfC+k2g8hh63caTO8fOzhOHS2qEvjkqJcWXUZX7hq0AFjs7O/P3qHmdZFn6x+m+YRjmawrplHgm351n3XeRT7eJhTNDjDW2x7v/8nwOsrhu24LXk1I3tgf0zvqI09MGQ6/HYPNe/7FuPefzVUlJbKwjRz/o1NGgSYbtz0Qgo1XbSYK8/3fporuplMmBTNX4rAwTzCBilp0FI3b4m0aa95g5GUzSUCnIBLj5DcAGEApOGVBLh1hVozfC2rnaKdE/7s8Uio2/y9VbdvSXDi4jCUrH1jICS4DOMySAMUSCezIVgo743BGJoyXKMAzzF4UiV9ab+sSOkukCDgYAP+d+eit4Fym6TUcnCb7YoRed0YWddgK91348praLdHzd2Rfr3rKaaCVZyLYAGrcNyLC2lWkr6siIlzFARkgB4OKI3pG4dWNdG2Swvzw/tLq6Ot/cAgH1+Foej6lJ5FTLJEGmYwpVY8aeRpUlGY8nRtXu2529gGfjzL/dhqOCZLZOvZgBmbmlU/Vun0yZZDogU2pm8gZT69ATMlMd9CW3G/vZ1J0dkevM1GHX30wLpa7shOzUfc3OBlm8W8spEPefOvy3o8MuQuoiUTv9ZWm4tMmMzlPXHUPuQNC6MHFCD34VyrLUrdvsoqaMdpJ45fWMsm2LuU3Z/Wb+GbSTzDg9aeBzPblZwBGn9er55HStrxv40aefBUx9X47z1MpkQYbfNu50YL6X0k1mG693wMCS0ml6EiYgdE4iHYXr6tJDnix2al74TePPfmZe3o6uauxY09Fkf7yWYbDtGFoyWjvGjukmYLpeO3YzRjtp71RC5g6Mlu0eSiDroiGzXuuTiLR7vT91eA2MZ3JnYerOJMWyu95Mc3XFzhVZvKUdxu70kSMz+uz0Yeoo5XfayWtLm5ubC6zf9XCf28bmnWp0v7LQdkbfCSBkChj3jrBiFy7cS/s+kGl/YPtd9tmUyuRAxg7YRk0Ym+w0o4l0dM6zYjxMKB9aSxZlhsQPxpthsoHGE9vOE0Mkl+1++mBYF6nxPw7XTsT9TlmYBHa6zjN7QTWdU25TtkPhbI0nO4dLKZmyMTjZ8XWO307GzNgpHjNVp1S4ZufjFCuy+bQ67bAm4efRDQ4nSQq6N3NPFk2/lgF9vr/L/eKeJA3Wj+3TQOZorCMELo7onX6jkOrMtJMd7KFDh+ZAmylZt2Og8Vzy3LTN5boN9+X3xqBn65/2fYAXn5Akrvub1Bn/s06aNu0sxNTK5ECmqkYTet++fXXo0KHRugcT006U4ugl10o6BsffmSbgmWTHZo9m4S55qMtv4QWoMqUAALhtL8TaaXQOJ5mc//a1nDSWj3vy7bMdE0yGajBblh5ZtviMDgz6TlWZHXcpxXQWXHMKMOu1vC62AzZ1+FtY0Q1rDbzxOcGSvhusMirP8cLBr6xcsQnCTnZzc3N0GNa69g/F4JLjZ1mdjuo2c1SNgaFzpklCiAL44rhc00IP6NIpKPfJEZp1lGTQRKRz+jm/DbqWJW2Gdj0XnBFhnDyn6MuUyiRBxpFM1e4OLozdKSG/giMnhwHArN0Ozp/hYLuw18zS15iodnj+3P3gf78kMtNi/L+9vT3a6ZK5+W6yVY0X23GGuVDp+3d2dhbeNGuH4uvpFHMtwVFmsm+Ph09fM7Gdskv95li6f8mM3S+iM6+VmCCYODiySyeVDtLpzHSIBkDrIgEoyY5BApkz+sm3DFNXB7Z81qWo/FymoV0cReQ4eD5x3W8kyDSTbZ5+c29ulEndZWSY4NDtljQRdYSUIO/+dzowucmXz6Z9+veUyiRBxsVrMB5gA5FTU51TcjrCaYFk/2bK3LNMJqcouDabzRYmJbL4JyMEO62q3dSdt91aF5YjwZWJ1wGCHRbXacfy2en5HVj0heJ+JKi4P6kLHCippc4JU28u6roe2jHLTDDwvdmGU0zI5fqcDnSariMPdnxZaMekxs8biLx7yeNuJ9gBhfvjzzPdaJ06Al1GjgzWtimcL4TB8ntd0cV1uGTq0RkG6zDH0HPOcltH2Sfksh9wCtdby7ELnzOzPN652kXFUymTA5l0OskWbYQ2PLMTHJhBxcbPs3YoVcvf/dUxR6dyXIZhmEcGTB5C6G4nV1WN+oOh+yCY+5Cs3amGYRhGZ3bcH6/1dIzQDNkOyedxEqg60PTnubDqsSVySWZOnw1yBs0cB1iqSUeXIkrgsvM0mKZzxLHYMaOjBHfbTbL8zrbcb+zEAAPJ8PfaeLcWKTynxfxer06mtD2niigZWVhW26C/MMz97dbnHCXnddLH6NdzrptjabfI10XdLl2Uaf05Y2J95+aLlMc67aKh471MDmQcYlddYTwnnnhinXDCCXX48OE6dOjQ3CjMKqt2HQjGz9bKZJBMYLNFrl+ZLbddisLG6xw0JQHGzsJGnc7Q7Nqh/8rKFYc18/UtKTdfyEb/mIzc272HzPfyd/52GpL2nHry/ZbHz+Cgus0OBhrSMNxnp2sQd3sJgoyRmWm+J81OBmdH+9SZNtJFYB7bZU4v1ylyq67Hrjusa9vNFJAL31JpMmA77sYcMLKuTOA4k8R6lO3TqVbXmwv6R3Pa1q3XLLNOz0k2fgAKBs/MOORY8tvkjnsy24C9+UW1yOrfUyqTA5k81LWzszOf5Ga+NgSzTTO5qn7XlZ0CDtvRCRNq2aYC6lo2sXP3DJM5J5MjNC8ymvHxDGBkR8Z1nEKy5EyD0M+q8bua7GysI0/y1GfKtqyks7V8XnMyAC2rL1MUbhvdZT0GmIykXHwALwHWY2cwyPRVApvbpi4K9zhVtL29Pfr6XgO3HeKxQMvRqudHRmn+PFPEfqt1pqh2dnbqwIEDC3VmWs66yXmQczRt1W1nBE893RZkj43BFLvJSJmtzjk+tGEAhZBtbm7W9vb2fHOGx2IPZI6Tkg7AeWUX59NhME7DUIhoqvpzLen8/VymkKrGJ6A9ebjHoXs68CwZXXSMy8/bSR9rodzyuR6zwGSztGknSXs5qZMVdoDM33aU6JZ6/Syy4lgyUnJd6UjtiJ0achtm1XYe7p/HwGBSVaMt7NTd6TDHI8E/bTCj56P9ZHRkJ+yUU8f8u40SJjeWIQHpWM60i1K6+gwyOf4JQDlPM9pPspn3VC2+rSKjLeQFiNCTU6gePxPCjshNrUwSZKoWdwklC8v0SzKedNbUaXaekzFBx07R7Nn35v9OLTiqSnDLqCKLd17legCfd2zWawZ2OClvgqcnTPYV3ae+M+VmZ2sdJlilo0xHDECwTdgbEzJSNSCiE5yA9YH8RHzJzP3OMerPsyHZx+xP6pbIyVGCnVL2m7FwNLWMEFh3juD97jWPX7a/DAgyGrbecr3HY0BxlNjNV9tX9o/+A+TZT4o3eKBfy+YMQwKINypYP5Cb7Jv14jHDZqa4ZTnL5ECGNIr/39raGhmZWb/PLXBPpmCqxhPNdezs7MzPifj9YWlwDs+7tSBPHD/fOUWfJM+XEsK0HY0h48GDB+vAgQM1m83muXocDMae25ozr+zrnbPJaInJ5Hosl/WTbNVpGtYXnA50iifPf3SpT5iqv9jOfSStmmlOn4RHPqdl+QHM7IhMNCAmGWX6/hxPy2ln6miIPL/tGZ10KTxHMO6//89UHsVM3OSiixQSyEwWvKOTe51JwL7T5jY3N+c2ztqpbdDRPLL44CT/u63sq3WX0bbTrfTXUZ5l9ZywnF0K0PJPrUwOZHxQkQlhg8nDiVU1f1tuMlQmEgbke9IheSL7fju7LuVRtcve8vwHchgAPWkpbp//M+3m9A+L34T0TFY+c8RhcGDy2eFlzton1u1UPOlwnAYS2qbP1osnM3L5jbzU5fUhrxd5AXdnZ2f+lmEKAJPgwG+v2dmZ2nGbffvtAR4z24DtAJCHGKR8BqpM/6BDACztIvvJcwnS1jvjnpEn9XnnImCQfWQM0FF+KybyOVL1XKMABGwSOHTo0OgVNGtra6O5kfrtSFO2nbbj+YVsfn1UbghizHPeGow83pYtbSXHbAplciBTdfTwlGt2Wl2qIvPdGFrV4jf4+bX/WV9GLFzL60x8Qv0uneDIgD65GETtVOkLEVAXaVA82boJm+mX1Jedk2VP/eY4ZX2dU/VaShanWawvs8tk2x5f2l1dXZ1/J4kBNdcKcPzWV+4Kc7rITjf7nqmt3JTgPrufqWPr3Qyd5yAd2JoBwQ447cr3pG0vu0b7Zv+2kRw7t2tyYgKGbgBT10+f3VfPcT9Dn/zb9yCD7c3tQFQ89z0W1ON+e8ea+7m1tTUazy71fbyXyYFMOmhf74qNn8IkSBZvp5XOpNtlRTGYmdnk51WLZ1m8QGxm7n5xbzotR1/Ia0fnyd3tuqMY1HyvwQhdLQPYBP50At1n3iKcqa+uTvSXuqM4SjFLByzSoVGHozEDgqM+otvOsVMsT0eC0k5cDASwaO8WM/i7vwmQjoA6W+2KHbXlRs4ch84Wco3OO/USJJC9AzGnJ5Mw5HM5530fcnhjiIlFRzJ9n0mBbSn76HqTgJEGRm/DMIx2B06lTBJksmQIbMMxQ6kar2n4GTu9ZKQ8l20T/SRLrlrMy1I3qQVHHX63kSMKilmXmbt3SvmAnI0+z9sYOMzU01miUw7Odak1M/lkzNmPHB9vkeY6bSXYWA9HAzZ0QUrPn+d7puw8cw3NUatBMJ1Tl75CD+5DOh+/d8zvgUsANRvv0jUZIZlkeByot1snWVnZfSGr6yHqJgXWnZcyaAPmyHr55ZfP9Wb9uF3rm/eY8Yx3DmJ7uZswU58ZtdIWenEfvGEmx7qL5C13vkiTuebI0cWp2SmWyYFMl65hsjOhPIHJf/M/htid1K1aTEk5HK7aNVqznVyrsYPmGS/Wm1VX7TpXy0hazeBGjpo3HANo6fzs7H0QMZ2ymXIChPVh58REzDTV0VIreZ/ryBdTdrv1DJRpA/72R0DfaTX07B1vOzs7o0XlLoXh8TEpARQ9lgZYt0sdabO+lp+53hwDRwSs0TgS7hxjOk70arv2Cz6Rz+NOPzo9AQB8FTWOfGNjY64v7JbDwbZXkx12YxlwLS/69UI/YGhwdvoRG/OhUfqYuqU+y+YvKjNIUVjvNeDmepfl7+o43svkQMbOp2rxMCUGZ0PLHDjPmSl644DfwJsO384Oh2W23jl6n/6lHtrJKKOq33mFfF36yW3t7Cxu10xZ0Q+y5ERwNOe0HNec97cuOnlcOudHMdg5bcd9bsuOyUDFvbzqhkmP/MjN/3ZOvIaeehIwq3ajC6fT0qa2t3cP4XkckuXaCfnFisnQM/VnO7aeugVwM3/uT/v1+PoZ5GVclq0l0Lajnqoa7Wjkf8DeazdOvR0+fHj0vyNFkzvvsEs9G8DoS7cBwkCQdmkSgX5WV1cX3lvmrflOq5kouf4uCzOFMjmQqeoX8s2Yq8Z723OdxCkHG1vmYH1Yb21tbbSxgN/JWriO8ecaDO14bYQJ6sgp8/IYuIGISZeTh88dvdhxZUTjyZCOy5PbTDfXFNzusnRX9tvP5NjYoXuiw3idKrQ+GAd0iB6tA5c8F5NRCDLZ4ZrAoEPrJ/XqfnU6IzrtdJL2bX2gC/SREa2JgG1nmb3bMafjPFZx+zh9g3iCnv9njCw/8uV4JdkCnJxay/lpUmSilSndBL+q3W9R7QgHz1hXjhiRYS9ddhyWLk/qgcQpY/j5bimMK/PmaWTe2plpItr2TzKqXGTOvHmydNfDBHX47fSIIx0br9mq8+E8a2eTEYcnMKC2f//+WltbW4hmmExeq0nn7EIfcIpVi4v9yO71K09wnKrP06QToX1SMyYYju7yTdIeJ9eb7NmR57Fs1KksO6kcb6/PGdRdF3JTr1NcJlRpl7nw7ajZPx3ZWEYWGPdlTB19EtEhu0kAsq2trc11vX///lE04PW5ZaDgeZFp0gQbPvMaFPXZjo8WSSZZ4D7bKPe675nGnFKZHMjgYMz6q8YOBAN13tZOKE89Yyjsyc8J5MjGOXM7KhhZgopDe5xfx1I3NjaqancR9PDhwwtnCPbt21dbW1vzH/qaayj0gXUHnCoyONWUE8sLzMifOW1vUiDt5NSGU1/WC86RlFWmudAF/bVzRt8HDx5s9W7HRVtm5tSdKUf+3tjYGAGgAcZ2Qv/4PxkqbeXmjbQpfhwtGcitG/52umZZROh0FDrzmNhRm33zLPemQ6cYfHGoBhOeWV9fHwE8tgSgui0Xg16+fdkpKtt72oDnsKPaBH4yCJAo625n54o0Ned2On9iuzVw2Xa8sQX9TK1MDmQyTeMUSlWNQASjSONwCM8zjnJcj9l7x+IzJWLHyKTI3DCTwpPGaTEfhvPkRWb6YQblvwETA4zz7maLXCeqAAgNxHYigJXTHV20kCk52vNLLy1rtwnD/TZge8z4ScftN+Hu379/tJW0Y51eS+BalwJzutLghTNxFMF9OEz66TcMeENBglrqwfa3srIyP++Tzs02kWk0ExKnQ5264tmjpXlsjxkxWIduhzUav4XDhxWZc7YZ2zVjmXM1U6PpyBOcmHtdZOFo++DBg3OAcf+sD9uB/Y2Jpt9QMsUyOZAxu8l0QBeWmhlmCJ0pAl+neIJ61xmfdUBjp+v2urCevuBEzDKTueKc8rU4ljHXC5L1VtWCI8mIKIHDW27tUD3BXJKxm02bCWdxf1Kn6dSW5e5p00xzWeqK/jImdnZOlziCccrHr/9xyjZJjNuibx4vLzRzb24Zzug860y9ZorOgAG5cBoIJ+1NCFmse/fFjj2fty4z/ZbrGn6GH4OodYo95pkyftOeU40G6IwAuYf7TWjsY9xWpqo91+2Ppl4mBzLJGnJydYOeTrNqcaGaNA7FE9ihto3ai5Y843SVc7jck+knJmUaZDpoIhzveElWDTNEBwBTphbtxDmfYAcPiKAz0m7O3VufTl+5r10azylH59w9Kb0Wlrpx3r4DKhy39ciWV0e3/hygoF3bgdM6XmuCPSfbT8dVVXN9GsStnwSO7e3dV884jZk24rRkV/KzJDJ2omkftJ2A7b75ua2trVEfHdV3+qnajYqt2yQQHifbmWVFZwamLDkXSFtnhJc6cvrb27ydbsySxDMBc2plciBz2WWXzQcYgyS3WjXO0VaNnZfZlNkLBuXIyLn5ql1DYbI7vUHpIqucLLnbzBOVa4cPH66tra3RFsmdnd1XXSTTo89mxwcPHmwjMvpEWqmqT8Vx/8bGxig1591a6ILJ3+0Ucv+Qk88zxWc983euQbCJI1l4rgmYDADsOMEkIiYQywgJPzm+GSnyVgD/0I+MPByd2E497plO9Tj5DBTb3K0/99d9pn9OX3b2mONhXeTZJT/viIk5wTEAk0HPuUz/mtBQl8/g5HqKn/FZnEyRowfAk344dWoQczTrOrAZ+mjdsbY7DMPonN5Uy+RAJvObGIuNg0H3JDIY5SJ25vqZnLlLan19ffS2YM5j5JqEnaNTBXa6s9ls9NLEzLnnZGO3DvemUz106FDr3LodXN3Gh5QJ3Vx66aWjr4d2Ssl/O93itSeKnSW64I3R9JO0FTJ7l5lTfanrbMt97Zgv8vo+byGmDWTwbrZMg3izRzp0r1nlLkGzWkeltrt8m4T1bfZeVaMzKYyd307uMXLaaHV1df6NsgcPHqy1tbXRS2h5ZmNjY2G9yhFzvoQ1idD29vboq8BtB9Rhxw0h4RlnLawzE720hQTBToceM0cqjigNeJaVkpGexwViSuns9HgvkwOZLEwo/59pL5hs7qbCyDI1VrX45VMUJikLuDyXIbKdov/PHTfOGec3QdqYE7AcfVSN37zMc2wPtR4MYjZ4t5OpqtxKSj9IO/gsQbeA751sXssBzNCziYJ/LBMlI0BP6i5qTUeUIIPDTLB0vzMisP6xQxMJ5DSzzlRVl+dPB2Y76mR2v6kjU4P8TofoFBI6WwbYzCPGuVvbcfQBIXSWwbJjk7aXJFldJJXRkPtlUDboGSg8Z5z+YsHfpNNyp14gJNYttmo5vZ6Zc2MKZXIg4wlCMRh4ncJO3L/T2JkodoJOd3nyUrfZ6bKJYPbl/zN0tww28HSW2eeciK7H91XtbgFOh2znnWmb1B1/M4HN+J3qQOZ8LoEIXSQAOerLTQp+PiMm2Lm3YPN5OiOv+zhS4jMAemdnd3edSUCujbi9dDz+P8cm60kbtkyO+rqS0bBthOds5/Tfm2kyVei6LaNlMSFxtJB95G+Dk+eD7YR+22bSyRvYkcn24yjKJCbHzRkNR/k5r5el3jtyuYwcTLFMrocdy4IxpQE5L7vMIfNeIp5PNmmm6ollRmdH25VjGatZlNmZJzb9gP0ti7BI7djZ0mZVjVIWdtoUO1Gcr1MxdsjWU6aSdnYWz3p0TA6nnM6FNnDwBs8EGG+NpQ1vKc5DspY/SYZtZmVlZf6mh3yG4qjTejAQeEch4+Tt4x4fk5l0UEkCrKsuykp9Y2Osa3nzSW5gcSoqd7o5jcmPtyXznjLPD99rsGHumbwkyHjNlT5YRtuBI2XGx/rL4vHPA64ZTTvyqVr8Xh70Q9tOkVI6AD/ey+RAxvlO/uebK3MRrwtzM/1DHUxcp5d4WzKFA5AYfi5yG8Box+m6NDpPNrMwO5GMRJwSc9Szvr4+ej0NZyiQAYfpL4Ey80Y33avInXarGqcszBQpdiz0i757LSmZNvU5hWInknZg4HA0y+K703JeM/AYAEZ+0abJBn3D0RIVoSsDgaMVOzQ/S5RkcDLBwDZtVzhAR2geC/9Ghwnavu75wfib5WPnrsPg0PXXDtk242jZ0Wj+LNts4U0d6JL5zvj6jQkZ4UOSIDK2CeaGxxiSRr+so0xDdkBWNX6hK2e00BEAP6UyOZBJgLBD8jbUqvHksmPmsF5VzR1QRgYJAEdjiTnBMudsx5SMc9lCYDp/5CC14Q0IwzDepsszTolQp/+mOEpjUvMsk8r9y1Px6CvrdMqCkmtJ2WenfPJ5y5As2hPf22EhIQAwIGw98IzTRZlygryYUHCqnf5mX5A506rIQDG5SL042nI/7eDTPk1YPJaOTjMl2aWSTNK6VFRGfVm/5fT9FJ7zQdUuws+UWvYt5Qaw094NXDm3mDuZ0nPhWUeUTuPRln2QCYTtd0plciDjVIlTC44OusjChlE13qqY+W5vT3a9XUoi00MGNIwUZ5fpi24SGJA8uTBop4EyKsr0SzrndNCum5JMlTa4FwZoPXq3kdvwRE8nSH9yXJ2u69KPy1KTGaXg9Ogf56C69FTWwTMZnaXTsdN0v/2ZQcY6tj1m5ON+WA+O3KoWv/cmo4IEmbQrRwqW37uzPJ8SPAzYBhPa6QiU5fIW54xKrSeiVttgpqmTSNkHAHyWLcfS6U63nXbR2Z7H3c86WmbeTvH0/+RAhijEzMxO0BGAjcNO2LtkbOQ2rFxArxqzO7N8f871XF/hGU8UnsnPkwX6cxwxujCI5kThf15yyQSx00pwOJojTV0kW3dfiOBSPxQfcjOYAbJeB3CfumvpzBzRcY8P22Zd6UxsKyY0aU/ufzde3hzCMwYWR9dmzhkhUpevI18SgOxjnmex7hi3dKxm7J09eK4xxmkjmXFw3QYx5M7+YptOj5sQJgGznG6DwhwxWehApJOX55eVJALU7wiJ/6dYJgcy/rKpYdh9BX/H4DG+3BUEs62qeajuVBZAlU47gQiDstHiNHyfD9fhnJyb9t8UOzqnSrx+5HM2ZuuuF0cHyDjf7rQXfbUu6Xsy6ZzA/O+oBTmSFLh/RH9e8GXMfJ7D+nfkQP0GpgR3r4PBiBOcTUyQLfvqSNUL324n19yciqJeAAVH6GvpPG17JiCO3h3JdhGun61afCW/U1RdKrOry/aJvk844YQFezBAcB922H29RUYlGeklGFhX3tACwaEwR/M4Aff7b2zI5+uWtQnI0k/PTRNQj8kUgWZyINMdxnRk4kVaG2lOPowuGUvV4pkIG0uX8nK6yLtfqsYH8uywkqkbHDyhMmXH8/kc/XD+184znbCdq9t1qi91hmOlLwbhLmXmMycpN/clCPBcpnbMzHNsLB+y2WEmC3UKMB2wUz3WLf0EMH1S3kCSGye6dJlBwg4We+ki4fzN+GU0ZV1i13xmx5sL6thPbmSgzm6dgR/aB0AYkwQG21aup2SUln13tOZ6c02GzzzXeHuGsxPW8+bmZh06dKgOHTo02liTxSSRNjpQT3+SGY2plcmBjE8xV9XIuXoimDGkY0oHlZPBf9vR5UI6jNSTwHJ1KRTq9CKuc9+0YRDyOkNVjdinHaX72U1wR2D8drrE/XHbmYvPPllHrjtBJBlytxCfYG+d+HMzRjNLQM3bvC077a6srIx07EjW8vGZowwiFKJCy8fY81z2ib6arTsS9P1d5JKAgR48Rq7L4+UvETMp6PSdpZtzlrU7C4RM1o8dc5feS/vqUpy2pS4qsRzeKs68o3+8vunQoUPzjTRpN+kz6APtJMnBrpwazczA1MrkQOZoDJXrFDvanMA870inSwv4f34yFE5HmfcjiyeHQ2qnjLJkKsF14uC8IQAHa8eT+jEoObVhoEgHYeduXebk82fHaj93PWWf3b9ON3bUHle2aWcU0slE22avCYAuMH1syynKHNe0T5wejsckISPSbqNB6s42TB8TJNCdI+lOh76nWy882nzLMXJK2DZlgF4GKrmZIO0n5ebvnLvoOg83exz4XibAJe0wU3e2KfeV306B5nptl+6bSpkcyFQtAkVVvzBnY01jsKNm+y/FE8CT1ECU6S7fj5Elu3Q6K1MzOQH9yhraXeaMbfiW0wv+REuW0xseaNMHIzOCyzat93QeHiffY0Zrh5+68zMG2Uzd8Xc6YK5zlsXflNnl600CvIZFm6TG/PJDxtTj4WjBTs2p27QX2rGz7KIYZCc69NmarBf5/VaGlZWVEXBRZ9q6gc/98NoS9uXocVkUbTu2/okafIiSej2GBgnPafqw7HVJjK3BzvKzXuvD2Jm2687FUDJSyYh5yikyl8mBTJ6CdsgKc7ETcFqFQc9T71W7u2E8Ofy3z58kw0mgY+JVLb4SJPPtmbLDQHl+2fuUKEyg/DInwNRREnrIfHi2mSxyWQ7cjsBs1rpy/zMycHSWazy+xy9JzNQEkWCmiqjPOrD+kRNHx/hTVy6K40Qzned7Mlq1I0x225GP3OjBZ27PMq6u7u405CsbHEl67JOYpb3lOCUAenOLnS5j4HXIlZWV0QadjG5tu0kUuE7d6NftW7+eP/xGJwl67iPkkjUY68zkj78zInOf+dxgaJLknY5dNHm8l8mBjA2mm7w23GQuXMsQPVl51+ayXSEGGBsWJZ2Jn+vC8844uxz4/v37F85x2Jnkq0D43UUhrgOGnGkcO4FcWLf8dkx5zW1mNGFZuijBgIZj83km+ufIL50C7fPG5RwDOzbqy8N3TsWsrq6O3ozN4VhHXwbqLoXmKGpZBJDFKUs7Twr6cUTt+nI9xs7X52acijXYsGkmHS46JNpyv1OvmZKzfpLkZHTisc4o0f1y5EMfcntxjon/t12aPNiWPCYGGwPV1MvkQKZq8bS4na0BIdM8XLPD8mcZnaSDcvHkzzSIP3eqxzLmfU7L2DnlRGOyesE7+8ekNkh50lpuy2ggWCZv1pG66HSV9/iadd452Mz3Z/TjqAknmSkfp9ayfwYZk48kJdmHnZ3d7deAsm3GqSc7nexnkiT62C3Mc7/HLfWb0bify76k/XSR6TKSxDMGtKPpPMcx26saO3W3ucym/HmCVD6bunTkuCxDkLaxTPfWTdZp2TodTqFMDmSSwTmaodho8tClf7u+qsXtwml8yaJt2L63m+TUaVnT8VNPOkv+NhjAyJeBG6zTabtk1tkvf9alrrIffJ4OizozzdDptQMWj1GX504HnEBlx8d9dvqOgvx89oPrPjhqkPM1M1Y+y4g6x8Q5/9Qd99up2e7cZqZkLUdHClx/OkGTmy595KilGzOiGPedenxIOu3CYMzf3euLvMbSgUgCnnWfaVLvJMvoxFFb6s46oj5kc2G8png2xmVyIJOLyxgkrJafZG12thhp5qkpBgI7YDv4zBWnc8LQM33XgZ3XKpCNdMxsNhu928nsmPsdqmf/DFQs1CI3Mjv1kUzME92nwM10c52MYp2l47EuPJbp+D1Ortdj72ednumIQud8OkZLXbzQkHUhH8jk/n379s11YtKRETYbTLxm4H50qaIu3bKsX7nDa5lDxD68ExH5rXNHHzzTgRnP5CYNrrHxwuNIP9FZ6tTAwpy3Dq0jA5b75/5DyPg8z814PPjc6zVeM6R4TibwmlBaX92GoeO9TA5kOrbGZGESpcPPNAepDjsc6liWZ7Uj9i6ozC13f6cMuXiO43LeNx1r7sYahmEus+9J4PBJ9HQQXcTilwseS+/Un18F0EU6ZnUdE7UzhTAgX7eRwECOM/SaUtX4Tc+OGLxmwHWfurde/M6zDhxTn97SnLuxPF4ZSWY6xfV3dTlKy/bQVUaCOe4mCN6FxvOeS14TAVQtvwHFY5BkxzL4byKLjkRllsKRJG36fstj+U3IiGasR8uTaTqDt+cHOjYIWT4T3Ko9kDkuih0x/zttkuyG62kwmaJwaiQP6fk3z2cbGRVlCq0z2HT6Zuh+LiOgrCeBYWVlZR615ITL5/PdWsjliedn/QwTnM8dAXapg9RJd08CVObYEwTQiyPcJAt2vtiIHZmZMH203qybtAM/C8hQJ3r1zsVcwE4HfCx7MIPvwNx99DOuz/pEb93mjC5CzrlEvX7LNff73py3rtNt2SE7xZU/7kf+T7G9+MhApihzvtqOrTcDukvOq9S758ceyBwHJXfLOM1QtZga4xlHE5ly84R3KswTxBOuanEBNvP+yGLmyXN8lsUOzkbvyZUTlfrtJJaBUtde9segZuC0M+Vasmyn+hxp5OL8Mpk6VrnsQCL3r66ujs6m4Jj8ElGDSvY3UyDLoj9HVZbBu+lsV464XDe2lVtb+dz97eyhSx/SRtZnXScYuo3cKeYx4xwL7TjFZBvwGSRHDd2Yum3PtQSJBBmn7/yT0Z3H1/87RZZt5drJ0WT27/wsIzSuO4U4tTI5kMmIgeIw1uzc7KRq/C4xPve2TC84d2FwpuOqFndBZX6eiW1D9oTmfwNDRibI7n55ITXTE3wxlidenvXBSfh8hp1otp8g5P7bmWR6kLrN5pdFkx4v7u3A3vow6KRj8IsZMwrK0+B2mPkWBp9VInLiS+1si/v37x/VldGZGTzjnGSCvmKjpIOwPdsv95ohZ+SXa4AJrknGkoRZRwY63+N022w2m2+xT9uxjE575rgti5h4zm15LTbnOzJyJmbZuo93m6U9MFb2DZYxIzZ+02deZZNrQFMpkwQZs7JuQY77qsaLq16oxnh4Qd7hw4drbW1tftrb79zygcjO+bnY6BxVHS0Ud9rF/bLcPEebGK4nN44Jdr+zszP/FsF01PydqYRl6Tnfl2ya65bP6zRMUvqVKSNKgn+mfVZXV0cLvzinfCuyowvksk3gkLv1Ef73WNN3FoLRrw950rf9+/cvpERMZDqQrtoFkryekSO2BcPP0jlnP3/gwIH5PSYd1kPnUPmb9ZgEjYx63ab1gC78rkF06zkFmLj+lZXd94J5TTVlcSTLswaZzFzs7OzU5ubmyFekHpal12wjmWlgPHPuTq1MDmTs9Fy61EfnOElJmB3yw2tVlhW/4ZiSeWVPXpyQd7vNZrPRu7WWTWSnLCg2VmRf5gxSPgDPGyRSP0zkTIWhczsYt5HvfXI06SiGOux8PPl4zvo8VsnIizZtJ5liSUadjr9bV8k20wF2ttf1IR25n01W3xEl7kvASuBwe5lusnMHsLALwDIjRNsI5AWZlm2icX9NvgCVzc3NkX4zxeVIOscvddYBN3UBMP4Ka8uGPWc6twNJ/3ZmwbqmuE73Z2plciBTtThx01lyLSesJ433ySf7S0fdtWtHizHl5Kc41555ZT/vtmHklq0LyV0yIsq+pG7M1pEtAQpQxHmztZN7uZaOAAedbNeglWsQKacB4WgTNMcuJ3U6gnwmdeN0op1utpl2YydpwLAT7MYjo7duXJ1m7bZcV9WCLVtmAwhA76iLiMBEw32ybg1mHs8cI4Of9eHsAKDkdRyK52z+Tv26j3zu7cqZDnNx+i3nj+VInWd/c95PMTXWlUmCTFfMmNOBV9UobYUxdCy+arwg6ZKhcDId51zN1P086wMuufuJNFEusqYsCa6ZF6ckQ2WbrcHMAOXnSB+S/iL95vaJAJ3L74Db7WSKA512xWwV4E2HTRv82GFxT6bNMgLBhkwUvIkkASJTjx3AdOnajJC41/I4Ss0Ixfqz4+siU0fHtg/Ijt9akDveXGenL+vdzt0Rv+0g06p+IWsHGNSRQJZ22pEMzz/OyDgC6ea8379GvTlOtkN0Z0CznvZA5jguaVRcY1Bz0dbFOXpPADMpmDnFIEDp2DHXzZaTjXuym6HaMC0fn2WEQX7a5zvotzc+ODdt9mn5ch+/J1K+IdfgZEA0yCSDR2bqdD9YKE+dAbIJVl0Ky5PZQJqpI4odkXc3+W3cTgu5fTvPqt0vKWPs0GfakuXOdGGujWRky73d++how/rhGa8tup6MdnL8t7a22u31XjPLOYGT5jOnFGk7AZxNExlh5/jkZ7ljkD51xMXyLUuFdyTPdTltaXDODUaej46GqeNo8h3PZXIgAwOi+IV+VeNFTDNfBrhLg+UOpu51Fvxv1miQGIZhxMqqdtmrgSQdjj/P4vu6Fw7iIHzPsrQSz7kt9OYtqr6HfvgczjAMI32bEXoMEhhcmPDJxFNW6srPcqtuFm86oJ8GyDyLQV1my13aDVntFDc3NxfGPOWyjq0DnrNztsy+L+3btmkA91gQhTrKcDGIWxZHIn4dDHXkK2KQ14QqQWiZjvy35XfdTkX7jdxuj7ExkLmOLLa71Iv1muQgd6BmSjsjdoqjqKmVyYFM7tzJRWdAYtlg2vF7YiZjztxwXsPojzZ5vHXU13jev2lnWQSQDq9q/GZit59MzOBhJ0kd3tq6jC3nGoXv6fLYtJPXu1SXnZzvS/3gROwc7AR8L8DifD/6d38dbXqcXJJouE9EOpl2XMasUyfWh9tKXXbkCHn47bM9RKFd9NTZiG3bQOaIO9NFlt8Rh+uhnWV9cHS9srIySiVbRtuDN9J44w6bd452HsaF+jPlalDPdJ3tZxlJSsLlz4+2seh4LZPrkbc+YnDOJ1eNAcC7nCgOnW1AaRBMoDTWLipwWeYwuhCf3127XZqMfrtO35/yeZLTx05eO/5lznZZjhw5XLIPdjjovct3u/CcneJsNlt4f1VufbWTzHMsOIkkBrYV+mqg5F6e5748++D0mxm4de12nIJyvx2Bc42xZu3RjtDRC+mcPLNj4uMIqos0LQvXuvnhcegcur+vJ6O5rAe5E1ise9s1IENqlL/9WqFc7LctZP35N+Ocm1XSRpLAmWzQ9wTjKZVJgkzVOGfO93hw3ROxapxGSuadIOMceZdeYrLlRPf9meNNMMqcvIGzY3spO7I6IqMurg/DMJpwyG/H0LWXjDy32SK/++dnDWrL9IAOneZBfgOox4rC+ZQcbzsmttfyuR30MAzzheAEajsUUkbun1+dYnByOtSMHidjZ2d56PvGxsaCfRBV2onmel3an+0FILOTNUkxiOZuNa872N7QV2YP+NxjktEFY4I9AYZsJEFmz0HbCLaQ62SQjq2trVGqDzmRy2kuz18DcJeSdroxswCpC5OzZWOzBzLHQcnwHmNwqqA72UvxJF+WButYqA2ONu0Q/SxA41DcBp+G65x8twZhVsTkyO3XTmcxofP1Kn4pJNdSbgOL2zcLzUgydZlRUReRVNV8K7RlcRrJjjnZOPXOZrPRl5AhQ7d2QVm2/dbpMzth5MHxZqrLxGZZ+ov7lxGUjJBwmpubm7W1tVWbm5sjRpwEyTIaBH0vkY3rSQJkcMkxTUaf+qBfnKeynvkffXltxeNkm800M/PDYOs6EqC6bct+/Y11ma/Wsf0sAwZH6MjBNdtgrmlNrUwOZOxc0gH5jbId66gav6W1C/HNULsUDvd0bWRUkWkHG6NZlaOHlNXMGNk9gZiQmcrKVAqTyDvAqvpoLSMts2Y74i5l4wnmugxaXbsd6OM0vCDsdl0vZy5yzAzCuUbmVKodS6YvM1Vip5btHD58eGFx2um5lMm2Y1343s6heqx4hj7mLkPLTZsGSp43OUhbTOAioqR9Zw5y/cZzk3u6udnNN0e/uQbpv8kGeI0swajTG3UwdzwuSWRzHDtZDDQuCV5TKpMDGU+Gql2WlIPtVJIjjmUD7fxqVS08B+tz2xkO5+TsUkwGGLOz7vnO2XCP0yh8TskJ7XdxpQ6Shbl/Xc6attPJdwCRfTHQOEWD87Mz9DZQy2Q92YGiU0eeHkf64o0e/jtf05KRQEYvOb5Vu442CYOvGWSXgZXXddxPduTRj4wWkRuA6d5BhsPM4ojM40pbBhkiEevc5KIjEAakzr6yv069otuMbCwz0VPOT9unyUq+eSJ1kl8DkDbovnWfGWi6qHZKZXIgk7l+nFWG41WL6zPJTMyCfBALI85wGKeeDrtjl26rY/3eep2RRLewi3z00ZsdEmCcBmMB2MCTKUfrNjdKZKrAjMygkAzY6YJ0XLTjfLxZeN7fbaMFGLjHjt2Ri/uC3v3ak45x2skbxNwnt2NdpYN1nRl9u/Cc20AHdtCZzqXYeTr1lvISVaBzL1g7GnKxDNZ3VY3e/efo2ovm1LG2tjaKMihJTGwnTkGmzTl6dv+Rjy3P9DltJAE6bdY7UHMNJ8eXPgK6mYql7K3JHAfFOW1PRrPVXBcww+V5DA9ntbm5WVVjR5QO06w7i1lS1a6RcqrZn1FIr1SNz/t4/SIdD8/xDMUbIryoyv2ug/w8urNeeFGo28ooqmOEtI3eDQB2bMiSkYOjEtrFsXds3KmQTA2x/sVkz7GhDjaM4BwNZnZm9CGjXWTMqNSsPx259eT6u1Sqx8EECns2GeFMTHfQl+I0GrJxmNSbRro1uSRK6MJrRwcPHpxf39jYmIMubdKODxA7gqH/duQGfMDE7VddsX6FHJ4X6+vrCwCfxXMto3GvX9l+HbHyItocOxOETB1OrUwOZLriXGmXejKguGTe1iw0mSPtdGmTLGZXLmaSlqGq5kzQTsDymiUTUbE9N50rkYLrP5rM6GoYhjnYuv38O9MpBpeO5VnGdFo4kG5dhLror9sww+RZr3NZv+jckSvPOyrw+Gcka7bMvXa6nYPJiMVym8QQzTrVxNcIcA874rjfzo3/M3KxjtFFRps5Vqurq6ONKc4QZEHn1pcdckdqkiBYBrY52x4N4H4FDuQAIMgFfp7xIn9GfwlsmVrL6MX2sbOzM9K5+5rR89TLZEFmmfP0pM9rON9MleAQbECemFXj8wydM82dNF53sJNyjpeSu8CS3doxe1uowapj6hmJJVPzNU9Cvwol0xeuE3m6lA56Sn2ZjaNz70TKPuckRj929gYgf05ZxmbNRv21yX4miYkdjtM16MTrgxlNp9NMO7I+kc/berNO7nGdgBW2mGBheTNFbJ2krVtWA68jFQMEcls+j3FHwpxmMiC7fxn9+eAlfbadOcI0gbHO6XeCw7KotrOHtDUTJqfIumjqeC+TAxk76XTgywyBzz3Y+b4nTz4z2HSOLn7G6TE/7zSH01/OabNFdTab1fr6+kK6ilROXrNzZXuqAQEn7ijPTjvTaDn5zGq5xykY7odxp+7NAl18LcfT9eKs3IdMq+3s7MyjQINxLhInADqqTNswCDqKTfnt2Le3t0fvMYPJW960oQS8jDzSUbr9dOAZZft599lybm1tjWwo5fRvP5/nVJB/Y2OjJX85L91Ot43e96Ru6K9Jjp15Ehr/ZmzyfrfltnNzBc8kSct5Q9YEUJ8isLhMDmS8iF21+JqZZcWT0qDC3zCvXH/B0LrXQTjNQvju4lSbC4fTmCBM9kxxOZ3n7+dYWVl8/Ybz3LPZbM7MPXGrasQMM7UCWFXtLrZzUBBZndahPsuBjtfW1kbgQIrKzoJ+GdCTUTv6o35PYo+TgTuvp6NB33Z4yaAzJeaFYJ4BYIdh95CnIxZvvEAO1oJ4PlO5ju5MShibnZ3dd6Y58qWNJF4eY+q0jPkbu8uxZbx82BV5q3bXQEyubPMJnoBdjrWfzfU0R/IdIHWEwvPU8wBbMogmmfFaqdPJngOO8Gx3ECTG29HRlMrkeuS0DP/nW5OdfnHqwkzeTNj/G4COlnJyvfyfk9IMPRmeF/SdO/bis0EmjbNrL6M599Xte9LYsbpO6sito6wFcD2dmOUxOKALJpqjLEDcOW6nLi0rsjgKw1Fb13Zox9rtxTU7Oy84JwBZNwYb5M5Feo8/45ss3/Xu27dv7sxyrcEMOaO0jPA655qRlEmXdxY6QsioD8fpsfB96dzTbvnMGy6wy2UZA0clAK7JAD9+PtvP1BvyLwMhz0nsKOdht/aCbiAUEAJ80NTK5ECmavH1GFXjFIwjCjOydPZOaVUtni9J9ksx8+lCcjuNnGSZ57fzXZbuc7/dHwOgnYTBh79XVna3QS/TW0ZvXT/NEhNUlpWcwHbs7nvm0w1K7ofvydfF02ff1zkQp2mSAGT6zu0ZOKt2oxv3ybKaiScrd5sJSmtra/PoD8af7D4jMdqkrm7RfplNUw/PpePtGL/TQct04+LnHUV08y3/x1YYG56dzWZLD+L6msE8I5DUhQlHRsGe75klQAe+3tnW1MokQQYjyJCVlJeLJ67Zjg0+3+/ltZmqxYV0nuscZzqMZKLJiJOBZaifzNwy+T6DFe1YLy6uK9MUuS3WfV4WxWW04r66z8tk82edrtIpZ1/9OZFNsnCPn6Nh0pV8btBIAsHntoe0Nzv7jIywiWVRl/Vsvcxms3lkw+cZxXhcTRQoBgWPWerbdSVIMef8VQAmJm4r/87sQVWN3hLtKMbz03Uk+UD/liPHwrI7UsvUdkbk3UaWrDtlS79i/R6NPB7vZXIgY2P2/8vYUDqoqloYfEcXZvp2zhntZPtO8XiiuP3MyQJoPjOC0zNAcY+f97oDfWCdB5kI70kn4Xy9SJ6HLxPw7HDNpN3XBCg7FHS8tra2MHbpxEnDUK/HxUzWzNQRiRlopiNzzJ0CoV0ihqoaRUhO1xlkvI7ndKXBz/32WNI+ztVrJTzPmK2trS043WMx4oyozLINjglSjoByLZAxQgcGCK8XuU1kMWgzHn5rwGw2G4GO5w56YSOK7aAD+m6thjSlx8x2lWTP63v2Cd7lZ73Qnv0BPsT2s7cmcxyUE044YYEpXXbZZQvGbSbrHLjDXA9+x77MdjA0TqlXLf/CMW9JZnIkMzIIGWBwikwe/0+/cFQ4q4wGcis2uvA20AzjkcmyetKhk3SiTCwDRrLMqv7UfhcFJDvuSIL7lhEf9bjOBCrX4fU7yrIIISOI1dXVOTDRvlNijgi5p/sWS+skZeN66i7PhnRRZf7OtJrPszAnWKROp4jN+cwStoLzr9olCoBGOmfL4Gv79++fbxzIsbP9r6+v18rK7jefGpSRM0kSY+qDql7T8Q7Bql1b5X+vT5lAzmazkb5MsnzQ18VEZiplciCTjh1D6ZxF1XhHkKMWOyWz3KNdo5gZLguDu3xtTnSuZ5rAYJKT3WG4naHP2RjMmOA++2IQc8nJ2bG1jgU6FUSfkGlZOsHOLdmg2/WOOTuFdF4JOrThNJ7l9PhknVxLB27AyP5U7UaO/szgbd0Y8IkeMvXJtdzE4tSQASTtP20w9e+xNiGxnTr64ms16GdHPNy2Ac6AkOOyzD7cx8wKuA+OXGkjU72OVvws7XjeWAavE1J3RjYmA+4zn3ueLSNNx3OZHMh0eeRkp743HTr3mH3YSBJ8utLlf6tq5KgsZ+5K8X0GQBt9GqZTYPTbC5OdQ3ckkWzWIGkdelHc/fXkyOjPAGBn4D7YadspHU3Xy8DMkZSfXQac/m2nznXSNglcdorca7kZFzu3DlRTPzmutOHddgmEOWZuwwQGmxqGK7ZUHzp0aN6Ot72njjpCYTuyQyUla8Lj8agaR9OZBs1UKu120ZbHs5sXmaZzGo7i+ejnbdcdAc31VIpBxPd5njvCdvSyly47Doq/oKxqnMJIsKCkgXEA0sbHJDdTzzbsWD3ZlzFMOxSe8/55s+ZkOJn2c1swL++6wtDdb3+ODOkkk+EzcZ1685eqob/UbRbYpPWb+nYdTgFap0Qy3vmHM3CUQ5+8cSOdsdMl1ruZLzL4O03MvpMNIwv9c5tuz1EkdWTqBd0awPyZwcSREe1mBHX55ZfXF7/4xVpdveKL0einbYX0k6NPp5xMYNA5UTTjg63ZboZhGL04E2fsSND6H4ZhfrYrI+MEXkf46GiZ3edmB9ucbbmL3PKAL3X4dTb5cl1HXRkhmoRMqUwOZAwYGM/6+vooLZRM28yjqkbGwTW/IRanWjXeNmq21OWZc+Lk+6WqFr8S1kyYkuw2ZUqH67bsUIdhmLNXPvNEwzF0rM8Thf4Y9KxjgzttZ3Tpn2EYRnlv9Olv8kSXPgxH+34tEHXZeadj2draGm33dT9wvE5zWWaPKe3RfjpNGD7vHeOFla6TMTOAcII+gdmO1OPtMXDKz2Bhm/fmgpxH3Vkfp3zQbW6bzi/eO3To0FxG2Pvll18+73NG6QZdf2Zw8X2ug7Nx7g99NPnyPMhUlued11kdrW1ubs5lyPeU0T5y4U9ms9l8c0IXfefmlymUyYGMnRf/m7WY/TiM9ZoMRmMnnobe5U5zUjtFYSaYk6Vql3H6VR5M3mX9dN/oHxPTUZdBxo6ednFg7hMOA30kWBgEub8bg4wozPgN9LRhBtjpxykjO6GqXRa5zPFY/wbALlWVaSDkS5B1vzM6tr3wGQvYa2trIwB3fRsbG3P7A8gMthk9GDAy8nHdtpV9+/bVaaedVgcOHGidOfo0kUCPXszGCecifUbJ2Kd36zFWAJhlN8gkkDoKIGrqwNPZAPchyZTnotOPCcT0FTszKGX6z+Pl9pMA2zZzHk2lTA5kHJFkXjRzx56oVeNzHmagGbnY8JaVNKbumj9LmXOhmeerFsGS38gGw8/Q22y6c0QJWm4/U4RmfL4vU12UdPpO46RzMwFI9p1tuC2cl6MBfy20ZeHZLtJJnXcL5slCDVx22gm+dsBdhORIh/HyDi7kZ3yd2vJ4W6ZMG3HtwIEDC/qxfTqCzvHF1gwYjGe+ITojK587Qi/drrvOXjpA6SIc2wr1ODqzLpHF26/zPtvEMkB3lGdboY+53tLNkymWyYFMLsaRuvDbWM1EbEQ2AofUVeNFXe7voqaqHkwwYjPtoy3ydQZo9tg5ZTu1dNzcZ8fkSII27SxpLyOcbHeZLtKJdAvzCZYUM0E7YqcV0rF4fJHJ0RHtJLj68KP7Y0JhvSC711TsTDy+HivICj9dOtIAmTq3U6Xu1dXVOVkya87orGPvudPQdmRCkxGTx8w7yhgXg4zTTYzD5ubmwiYQnjX4ZumIxbHIHvcT3Xtrd0azJqBZDFJpE/QhfY+JGLr2GHGPS/4/hTI5kKladNCEuAaXrpjd+B4zSQzVqai8z99nwfXMuZqlJWi4bRwT7eZCJAXGa+aXax9+R9LW1tbCgcKsE6dglm2Gab15wrk+fvL1Ljnh7LyR3cw1d9jZEXlyp57tGPkhZ+/UF/cCqpkuyfFKG7Kz4d719fXRuCGz9ZDRI9edfsERuq719fV5SjaZfNqjI4kkGgmO/t/AZF143LtUGWtN9KMDB9u6/zYwZhoLGZHd6x6MObpc1mcO1ToN7KjHfUybdmrMduMMiXVLPV47zI1JuV7ZAdzxXiYHMjgvDAemZ5DJiUVJhmTHgoF6N5UN0o4igYT6kp15ciRTrhrvcOkcqe9dX18fraH4bzNyyuWXX14nnnjiwqnqzql4QjkNQnHKpUt3dIDcpXmcxrIOl0URs9ls9MZfg7nHNOtKR5opjS5S5HPGkp1RCbwGQ8jBbLa75pBpIMtpeUxUNjc35zaA/dpZeR0BeZPA5KI+adNs3443o1HsA6Dzm8K9oJ5zcG1tbf7/+vr6yFbsmNOWkDVTUFxnbDM1Zl1W7X6hm0la6pxxdf22e9tmkjs/5zFwlgRZV1ZW5mSPz9kIMAzD3mHM46F0bCvTPXb4meqxI+D+qjEj61gjjMppjMxjp5NzvrsDDjM3nmUSOiKDQXqHFA7BcqYenGLr1oAoCXzuD3J26SL6ZjBJpu56MpWFnJ1DtlwdOPl5g3umjVxXOh2XLuKwPv28WXLVbgTodR8cP3Xk63vSmTvKMrh06byOKCQQJRi7dIw9QcipVNuez6UYCOiv9ZhOOCOLnLsZKbsO24mBhmjLQOV7rTeezbUV+wLkN2HtUlyuI6NYE99sd4plkiBjh2dAYVJkqod7KZ4YBplcZ0nm4/WDqt0IpGr3LbXJnjGyYxlYpqnsvB2Od8yMSe0UEnXSt9QHn5v1dnl5T5CcxFmfwSajiWTMGXF64dpO0GPXsVhHF5bB9dJHO6jUi9tB38tSZvzY3hzZGBiIxDon5+g5gQ9921EZZOyo3Z770UVqTi1tbW21aULr0GswOe7oJ99GkcXXLK/nmMHb8nfpTP/krrkEIevB4+joqiNraSPuQ5fySvJrfXTzYkplciDj3VNV48FdXV2t9fX1pSE4xYuz3v5sJulJgEMASLzH3uH5vn37RqGxHSXFz/tasrR07EwKOwTOf1Ttfpmb+8wXhyVzs8PC2VN39t2L5uTUc5GT6xmtZaosGZ0ZfDJIs+wEIDt77+CiTbNfH7JMEpCRndfDHAknaycFif5zgT3X1rwZhGJni/4cmbgvCTK5ZkDpos1lLJwDybkVmc8yik4gd2oP+/OakgGiqpbOWQMDunXkmlvac8x4xvV1ZKnLIHhOeNcZ9dou8lwO8vK363Dqe2dnZ5Qi21uTOQ6KnVnVIsv1fnfviEmj98ROZsT9eZ7DaQCes2M/cuTIwqJoTvJkrcmYuvMjnjg4LDNdM8plEVM6IPrBRGSybmxsLI0MnKYzE3RfMuVAHekk3G/kyLcNe4yoh2ddp0GIcaLeI0eOzBerO5uxfjY3N0drctYR99s+HNF6vOiXF6DdtscAMPAZk4zgGFv3ESJgnXcRpNcWc7OBdYpNOaK3nD4k61Qa9bEuYvndBn3gflJMOR4mNLZPX3ehvSQjHcm0HiASlhHSM5vtfg06Gwls77QDwTBQ507AYRjmB1WnWiYHMk5LYegZZqejtoOr2t2pxZmLqnHIbGOvqnnKCkPmftIITO58zm3b6P2almT3yGE27b4le6K+TGUk6+K3dz/hTJDduXfLb2cDu0SPTmVwT6ZP3DdvCHC92ZbHK5lrpkO4J52KHQd6c/1mq/666g6Qrcd0dHa6Jip28AnWjsTs4Le2tuZOzY7efUibQNZlUQtjkOfBIFIJ+tZvnjFysR15g0Zurfb6BDJ5Z6YjHMAqI0j3x2PvyM5j6h2nyMH93Nul+Rx5WU/ootM9MiTQu26T2amVyYGMma0dRgJAOqwsBhyzOU8OR0Zp2Ha6fp72fV8auJm4++E6qsYv00vjdOqEus1EncbISC0ZcJeTzmJZO6bpZzMKMGD42RzHdJpdRJdbnj2W1j3PW86UzyWZOfqHsHi8DBYea8uB3aXDBCwMzrTJJgAfZsxUnMHEpMjAZ90RJbmflpXIrdNf1pcRguvK9tN2O1DweqZ1lbstqcPtdPaS+jRwOALzPLR8Gdla5xlRd/3mM0f6nT6nViYHMjaWzjkly+2MjtI5ha692Ww2T7ckK/MOLjuDNEAbXzpQG7lZkeskBcTfyJLFL1gkTeQNAbmDKs+bOJKy3hJk3D8zd5z4spSawRAHnimljpHDPDPF0tVtHSVDTedBITWXkYvtLcHNQJZ126El0Bn0fVAQkHH0hZw8nxGZi+XyVuPOibrfeUDR0fIw7H4fk6NQR+fpfGl/Z2d363PqiLaTCPjwZzdXcg45GjSBsF7dNv31GBi0+R/bdOqyIy0ewySRqU/b4ZTK5ECmapymYQG2aryo3k14Psdozep5dYYZpHdtHThwoKqu2JPv12xUjVMSNqLOsTApcsNBTnI7bCYQ20cNcIDIxsbG/LXugOKBAwdGLzLsoh0v5O/fv3/+DDryuhQyO53jye3oitQHeqJPnUPif5wr+qLkCyetV9eb5MNjThs4CgO5bcG7+NwX+siLVHN3HH/zen07y3zpZzpmdpltbm7W5ubmaC3Hjt19SABydOV0Y+cQXawr69QbGvbt2zcHIuR1G7yPj7a4F5ti44DXPFyf5bTeuujaKVfPZ49lzv1Dhw6N5qj1SBvIQ/0Uy9e9ddnAz/1OT5scuh9TKpMDmYMHD1bV7oS57LLLqqpGYOFQ207GRosxMAEYfL+exIbEdk+zt1zkNEBh9LA1g4bfjIwBO+XhKCZTBHw+m81qY2Njfi+7e5jcJ554Yh04cGDEsBzlOSXE8yx2UnZ2dubAZuaXa2AZweUrfpDLul5bWxu1lc7LOfPcAUXb3s1kh7V///45EbBO7Ig88R2tsi6FLdmGvKljNpvND93ZtrqXSwLSftasGYDB9kwMfD/FIJDgmsDi1E7qqqrma4q5M8zkJlNh1Ou/LUcCgsHI/cp56gOpyyJgk4iM6LknU2O2ZxM6g5TntKNG/An9sOzoj35sbm6O7DWjaWSYWpkcyOD4MP50IlXjr3U1y8wwG5blXS9miDY4Tn9n+Mxzy3LSbi8Nj/qd2qi6wkg5OZ2REZ93L+NbXV2dv45kY2NjzkANDp5kZoPWn9u0vJ60HcBkGsjs2s/YOSVAdX0yaDtFaOdoILaM1rvByWlEA5sjM+9aol7/uL9JEngOJ+6SKRVHO/TXNoWt2r5TX50jsxw4WCJx7Mhg54jUtuCvmDbxsg3n5x5bSq5hJekxETCJ66LblLHLCCQ543MAl3syAqJ4tx+74UwWPY88JthjAuVUy+RAJieC2Ruf89tGaOblNBSTmuLJDQvt1j7cVsdOMFgzQjsJpzgy3PeWW6dmPCGqxhsDnOoBpEi3+HP653RSgmXKlRMqJ306W0r3v8esq2eZI0c+Ox1PajNc+guA893xlsURC/dbPjs0ig9WOgVmYHMUdjQAhXHbVgxMsGIIS7d2lnbXpW3StklHJXi6faeUE0QMLk4jUT/3EgFbr5kidrTuKMS7vjItmdET7VqnBm/bRRI25jcvMrUcbiOjIs/nBNpM5dJ2+qkplUmCTLdI6eL3IxGGVy0aYy4CbmxszJ/LV1XYoA1oMGJHPTZGHASfs92YyeLoBnnX1tbmh0o9Id0+Wz0djTGRmDS588yREG05cvOktgNwislskUmU71Fz/5c57s6R24GQv2e7eUZhFCa/IxCPDfaQbBUg5zl0y5j5dS/03U4LnXon3TAM83HIF7V2KS3rxzbrdBFOyo7X4+CUa77x2XJz+LKzaafK7PRpP9Of2LNTTIwZ9nf48OF5OjcB3m0mObD9EJFb99at55vnkWVLx56RNn4AvZvcZKTI++ySNBqoTUjZMu6x9VbvqZTJgQx5UiapGakNFYeOAeU3+VXtTtiqxde6eBHQaZlcF0mmnczNKYmUBacOi2LSYOCOIpwSZJIgR6ZT7ADtIKrGL9pEJjt0pyw8aamXib4stZCTL9eduJ5jhq5x8Nvb26MzExR0YhZJnb7uHW84PduNgRDd8U2IMH1koe/oC1lhwWbv3vbsjQ/LGLLHwFGmwdL2VrVLElgTSvC3jRhAWBPhs8svv3xOrBxpZHSy7LQ7n6E3bI43X0AIASDqMwCga8bAC+vYieef56qjSEfBuYEkU1eZaq6q0eFtt+HNJmxwyMxJEibPXd/jbMqUyuRAJtc2qnYP0jk6MCPFYXiCOYz15MLQHPLaoeLI7MDTIdhJmKVSv9eUkCNzyXbY3fqLgSB3olGH5aiq2tjYmK/VcE/HMu2kV1ZWFhbAcQae7JYRB4EMdszI42+GtK7N2mHmTj1lusZRTII9MjuH7vUKR5pcS1tjbBzZ0hfvlvJY2OGlM3L04UgiU2B+V1jau23RDtvRWqazDLKOZpg7AJJlt6weG6JsnP6BAwdG6zyATIIWIJREibqYUzh8dOAoxrIk0fPYeD51aSqDENFRptb8jNfMMiJPEHFqLlPtOdemUCYHMk5RVI1f38FPpqt4hh8bDxPZjhBmWTXeH2/H6+dJEdgwzW7SiaXRdxPAabxM6VTtTmb3i11VgAdtMwnYQWZn3EUkft7sq3PGGQEuy2tTACQYar7ni98AczrudOAJMHY6mY7qyIIjSOvDAIiT5F47YIDTzh4n5xRZjr/7kPpBB7a3HH/u8etm0tFmG11Kl0giSYDnQuoTx2+QXrbRJW06ozRHlV5o539vj+YZ/03pxiXTXb7XOsQOmeM5Jk6hJdhnmg+7Qo4kiHsgcxyUw4cPL3wrH2kAtsWabZiB5U4yCn87DZLvnWJieZJ6bcJ1dYbENSZN1a4D79J4BkrXzWcGGfctw3ZHOI7CcBjJ2Oin02z5hWQAcEaCdmh23p6gPn8Bu3a0yUSt6t8yYBBwOjKJhbdLz2azuV3AlnEEBjp/tfMw7J7/yPHidy7IOy3lBfZuPDq9e6w9TmbKjhAzukMGdAFAoAvkc9tJCLI+ikmPvxyPMTBJSwefKVGeQ89OSUIwPBcTVABDA+Ky0q3hZZRjUpfz15EXslln2GxGpd24TjFVVjVBkMlB9Asj2ZFz0kknjQbUi+NmnVWLWyExelJCfM5kyhSIHbtZqMFja2trZKhOvdhR+fCb/8ZQnd+nrrW1tdrY2JinMMwM0QeybW9v16FDh+ZRD+szdjzpNK2PLi3oiU87vmY9+z5Y7cbGRgtETst5QRYHk2/rTfCxY3OKNDcBIE8enHT0Ygfjc1mMiQu5e8vtKJjDnLTVAUmmgRzNOhKkDzhk1rBcPFaskyQg5Du5IBi06SglnTHy2jYBaOyGugHXPMuWrz5KfbpudOpIwelB2xqyMA8c2TqCzkjMcxH9kW71eGdmIkEae3M0s2yn6vFcJgcydmJVi19Ba4aaqZuq8Y6aqnGkAACx2MeCpsNltwNjdo65W1T0ZOtSJPQj87tMKufn7YgcuWXu3NGZ030ZwsO8M63jyMcOvmr3AJ/7lPpcliqwg+8cNI6PnUXeNecUjtlnEgUcHE7d31rJb48resMBGlzQo6MPO0d/XYHtinZy44XthbHKb4uknq5/6A59enx9It19cCrHRATH6bGkryY56JU+oCMDh+WiDi+mu49OizkaznUW2y/1A6SZQnXEYWfvTRFOveZ4WocUzqrZpi1j1e5rnNKeUheeR1MrkwMZmAsFYzHjyHQAKaBuotqJmFWa6WRqo2p33QBwYDJ0IbzTUo6C/OOIJtvKBetMf7kPAJLZHs9RN6CHs+CwHY7F6aoEAjtI6jOL7NKFZuAJttnXJAtm4mahTFj6mqlH6nA0mADvVFjm2juCwvUsmVKznpxOIdpwtAkQ4oTQZ9cP7reDc2rTPzlu1rejI+skI/yuGMxsB97d6Kg101VdfQkWlsFbfg286Ah7c/TbjZ3TvR4ny2tbhsR1C/22YYNMkjvGyGTrWPo4HsvkQCaNPyegJw6Mx0bnswA8b8fpSCA3CfgZp7WSoaRx2+Bg2F60T5DxpgPqw4h5xvdjuLB2ctp2xtTHpCBlx//p+ADmZLmdkzVYMCmtF6d4vBbk9YOuPtbfcqw9LpbT7VunbtOple6gLaDv6Cz7aEBAJ50ODFxe9/H40R/vhHR/q2rkQC1PvkfPr0BJVm0w6RxusnoDmQEPAENWIiJHaNRpHUHcHJE73Wg7w9nzt+t0cdTr8TPB6iId1+uxcnRtQLaeLKfXaHnG44aNky7vovcplMmBTMc4uU504a2aZm75jJmXHYfTJlW7qSynCTwZ/by/vIniyAhmY2eWxs/EzZ1VdspHY23OV/uenHCccTBw4bTtuJ0uAADt/FlYdp9544DldhTmej0uuSMJGez0nUOvqpFehmEYpR6HYaiDBw8uMEgDqNNwthe/KJT20RnPOxp1hOAxy/Qo7ynz4cmMVJGRa+6/Zcm63T4RTjr7jOptf6R86ZvHqYu8vUkC3a+trc2/qCtTgZaDNhKsTVLc/s7OTq2vry/sQvMYMfe9y6wDaGyNtaq1tbU6cuRIra+vj8iCo0XPXerzPE49O/3mlO/UyuRAJtNlFE8usxIzZrNG7seAya0bIBLQeJ66L7/88hEjtSzcZ8PyafBDhw6N1lOcsqAOR1g56Q4cODB6KSbyMrmQp0uTeVH3hBNOGAFPsuAEAnYSOarA6Vmv/O2FfmQxQFpPVbtOiTpzhxasmbE5cuTIfDHdNmDH5LZpyye3k+UmKFqH6ID76KOjQr5dNCPmBFBkM6CbKRu4TJo6cEE3Trc5gnPqyTu8unWzJGXWB2OU71Jz9M3//trm2Ww22jWWsvM5Nmw7ThBKp5+p5iR43XzkOT/L9n7mt6N9r3PlxhKDEm+ZcCRmgpDzaQplciDTMTd+ZxrADsCGZ6Zo525jdl18bkNO5+W6Mz9ftWhcmeJzX7ozMHYaLIqvra3NJ1zK4UVbM3Y+S71Zvoz0ukmbqS87EzN5A2iOoYHBb0bgHn5bPxnhocOu//6BkXo8HYF6gZjPTE4sN/3POjMd5yjS8nsnE4CX/XFEaX0gj9tzW5ScDxkRolcTCz/nkpGwbSPZu+3BDD/X1ryWwfMQPTt03iSeKc4kjrZVr0UalFwgK96pl3ZkPTlVnWTTY+xnPRZTLpMEmaqxI/SEAQjM+p024DMbhuu04zMTTZDht9MrCXC0T4GVZRtOn+HA/Ll/cFI+BZ/M3TJkBMF1M9x0KulI7DQzNeSfZMHpYN2O26ZffO60ifPibsdyWFanQrAHmLXH3cCHE7K8dvLI4PSMwcklQSYB1lHcMhBPwMdBWoedk+d/F+vIKcFM21gvy0DGf7v9bu3OEYhLRoYGm3yFTPaN+1l7ssOnfx2xSfm8wcXrqtbvvn37RtGZn8l+dkTAdmR76bIwx3uZHMhQkqFlnjWdW+5SSRaYAOJ8s3O7GJqjIwPHshy5jc3nI8ymuM9rAdln98cLqVVjJuk0oSdrd66na4OCs/AbiNGhI74OrHieNp0uYuI6gmHc+N+Tn7Z9ENTfVup7aCsdsRmux81OjWf9ShPr0hGa7YViR+2oIcEx2b/1avlsZx5rZPdvj48ducmNnSoEhM8BVdJWAFwCaW6YSKZPZJBbmK1PbCrXjZCF/mxubo4A3W1jI6uru9ulk1Bifwms6fD5DH10tppkzpsiPOc8rrPZbJSG23tB5nFQMoc8DLuL7eTnM7LA+Lm/C6HtROy03Y4ne3fIiklph8L9GDupLsvIrjAcI3Unu3Qd9JdJS57cEx6jttwJevSNCUedGTVQnx0ii7wrKyujA4hVi+dBEoQSdBIIvHZmZ2CQtIP12lA69GEYRl99kGshTr0gs6Mf2oEcUD9rYt6h51Qebft/6nZUmdED3+RI8XiZxHBOCxkZa+wE+/SaXUaL1m2m1Hg2rxvUu0h8NpvNv4gtbcbtemccdee5t83NzdE8AzAo9LtLy2YEn8TA5AG9mSw6Q5KbaFyw5QQm5GWNzuM+pTI5kElw8GG9qsVXxFSN89NmKp60OB0bgd+ii8GZeTsMJkJxW1Xj70u34/SaRuZ6c82hS8tkZIQz8IIsffd6SYKW00DpCNAr8ro+3hjghfrUfU4oRyUmAV2EkKm27LPTRnn6HSeXaSgTDcvh3VS06/NDFNuBxw45c30NwpDRToI7Ds5rE8ie0V4XfSKb5fC6Dm3hoH1fVS2Qiq6kPXdrcFW7ZMuRNm0kuOe6Rb7JgDFxAdx5xkSji/ysLwM7bZggOKq07XXz0HpcNiYmmvZFUyuTAxkzH7NAM8dkNF1xWi3BKENijNdAkI6cOnOimul58ntXSxdd8GxOstwR5HRHtwiZUVlO7Az3DUaWgc9o39/d7klqp5u/O3C2/lw8xn7Wzs312NH4OffdtpFg4DqIYliQ9xjYqTkNmE4qna+ftf753/V6wbmzY9eJ3Gn3aTe2k9RLt9bkcTNZsI1kAVQ8b4jkeNbRt+duzoFjzV8/4/pyJ5z74THxvGf80tbSRvIMTe4YSzJh4LsyfTpey+RApqpf3Fw2qWw4fh7nwPMd6/Z3YGBATkmlY/aLOz3BSCs5rWCZ07jtKBPQqMuRlXfb5OQ38BksMzow6838tO/hy76cPvBkA2TcXjqmrNMTmufsfDymCTJ20nbQdrgwX39GfV6j8Jd64SStKxaD7dSdJkkWnNEmci5z0gkQmdJcphcDbLJ4r0Otrq6O1jO8E9Jj0DlgCuPTbUP2mgS6TAJo3dJWphiRx/WmjDnniTzzbFvaOf9nVOpxJLIzmbO8BlG/TaLTnevzGE+pTA5kcLJ25sk0uS8dPgVDsMPKnKvTPTZsA4WLPzO7xCn7S9MsK3X57EfHvLyl2c4Wp7Xs+0fcb29m4DOeN5N2/d4NBWB68pATt7xmdk6BGRiS8TtNQptO72RqyWPJbzsQOzJ+5zb2ZQ6bz5EZ0LRjdjTjPltW/+S4ZpsGB7/Ictkztm2z+IwS+I1O2faeW31pG8JCn0xqDCJVNdIxhxh3dnbmBzP5LM8yJTFyCtw2bxtPUDP45Td+YqeO1BydZJ9NApEx3zRucOW6synWjcd7GIbRi2r31mSOk5KRhCeznawnYqZPuB8DsuPMtqoWt//yjA3YIMM11i+8gyw3GbDwb/aZ7HFZ9JT/5w4fpy28fpWM3s7QacE8a2OHiL5ya6yjLDt6rpkoILNTT9YfbRtEuldzIO/Gxsb8NR4ZlVDseL1T0LbEOGY04XUD20KCAE7HXxcAKDl95DUKxhjd5NZxy4lDxFnzipnsAyWJQ9XuV4xTHEk5JZo20pG2PBnvqJn7bTfoodv+zPh4TQd9eGuzNwk4IjJp8eeum3oNRh6PTH2lDrHDBHrLa5KV7U6pTA5kMtWCs3BKwDuQcAxpNGaIuQmA3+loklUaWLxg72I5iVjMpoh0cqE/DTJ3Ylk+2un6l89kWqRLRyWT5F4vIqPzBHrGKHc2GXBhjQaUTJ8lwCSRcJ8z0sDBOxJ1uoMo1cQi1/qy/9TjqKArjtao29tWsU+PTxIEgxefGyytazvFrk7r1DpzCivrZGz8Oe0YQFwsA/dbRzzr8c0+OrrN6Bf9U1cXEXdAmqTROvA8gLi4Hymf1x8pEDwDIsWEjns78Drey6RBBufibz1Mtm4nn+yrarwOYlZJW5RM7wAwdg4+X1BVC8bqewwy3kfvyMgTjHo7p+D+cf8yY/bkdljvaM+Ozp9X7bI397Gr244U/cKO+d2BHXpytGkggx1bP6kzR1E58YfhirU224kZsqM5OzHqyi9wQwcea9rxT0aZaRv+3aXIsE2ncdKmE+DsbJHTTjOjWkd4Jgl5Oj+jn7T31AWfeQt4bl6x3roINvuKTAYWR5nHKh5D6zXPS2Fv+Yzl8DWeX1aO9tnxWiYHMvm219XV1XkO28brw1kdM/aE8yliilNoVYuLjgYMOw8DiBmdWTCTezbb/Upkg1zey5oOBk8qgWKHmSBjZ4TMnhR24vQTR5XOir5491M6si71iGxOTdiZdI7V93ncMorlmUwTOWfvSLKqRmsMBoKsIx2b+4ecPh9F1AxZsNPGNk1CrFcDjA/rUhJobb/oMlNEGd1kpOE+p73wvF8Ua7uyzk3M0inzudNotlU7dOo1kexAM+ew05BOE/vHdkPx//Q3D91SMqpPwpp6Rq4kOcsi4OO5TA5kkuFX1YKzg3Hi/JkEnlRV4x0juSvH7B5m7B0wdv5Ol1XVKCKyw7bz4TOcvicZ7Tov7hfvecK4XeeI7Si51zl25/cBH4Nu7kriHuuq0z3OkXw/dXojQj5vOT0umUqxDViXGUnMZrM5cJvxGhjtYPhyKoiG++0IK+0g7ch2Y8dom+LFnDzniCIdIPoyY8cG+CzJgCP8nZ3dRXj64HHIKIs+m/AkAPoZ+uDoyvWbbEHkTAKwJ4CVcbPNZVTPs0nIrHtsOIGT0oGyiWZGYQY0t2OQtd3YlvETUwQXyuRApjMqHGXHvjFIOyM7nnQ4voZhJbPC2SxLwSWzoSSTynppk/7kjrKqsRPwjrKqcT6Z+q0HA4zlceTEs52z7SaKdeK++DPf69/duCY7NmPOOnMh2MUOxOm0HCtkzpSZHbsBJl/L46ggma5ZPHq0XRhcOmeaenPJPucznS06yuh0b8BLO3I7uc6BHF5b7NaJUrfuR6bF0HfuynTU7XGhMLe7SD/9g/2I121z7hosHZ1lXamvbjymCDaTBJmcKN3iXrJjDMFbk21MTIx0qMtApmNPywzIEUeuVaTT8r0Gs2X1+VkDrVMHTiWYBXd9WjbBMnK03vhtx+Fr1mVX0rGaEJhB+36DkYsdqu8xiOU4obuU1/8DMPl2ATtz76SyHDjQXLBOfVoPti3rwGkuEyXb+rJyNJLgOtCXoyv+p2Q/c76hE0cB1n+SRG8UAAxpL9dZu8jMMiXoL4uIrX/G2O0kuHnM+Tu39Gedvn9Z+8d7mRzIOD3E4PndRTgMG3ZGKvv376/Nzc0FplU1/q4Np0e8CGgHnyG6016evKy9kA7owIrrTl+l8wYcfFYIB+fF6qqap9jcD6cFrUvX37HYdIw5WawHO0cmb+7IypSex8lj4kmajtslo7lca0hZDQ5Zj6NGn4PJF0emXJ0ePXZdJOcxSEefuqVvTgVhpx1w5m+vB/pakox81mOzuro66jvj6/str+2ItbAsCTJO0e3s7IyiGUeFOXfQmbMB7kOmYX3PbDYbrdU5YqJk2t0+xWOVdu7XE13ZjQnHU5lcj2xUFL/gsFsbYTJReGkdZxgwNowmnTyf+beBLpm4/6YuzsLkq0hms93v1qCunPi04xw+oJeH2BJYqM8TzovUfsFiOq/sH8WT1ekRCnrPvHo6hZSd9gwAZr2w1JzsaR8rKyvzvnRnHnwQ0M+hO49B1u2xcPTSrQ8BTOjE+qFPjlozukmn6Z/cIoxc1mGXMjOAZhRL2draGjnlBCc7Y0cN/L29vV1bW1tz57qysjJ/QanHws4efXhcDHze8JLrlyaUqY8EdK57w0Y31vkNu/kcPsNvcsA288iEATl91xTK5EDG7MjXqsbMxJ8tm4yORnL3SNbhexxhcK8nhOt3Hh9DJGWWk2NZmikdREZBGZ34eRb7YVArKyvtm5mdd7YMnf6ccusYsu+jzwnYy6IAinWbztLOlgVjj4PTVu6jHRlfD+xoxf9zQj0XdDPK4nraFgWwRx8+Te/xzAgi9UM9GTF6PGwvvpb1pR15zYLFedp0FIzu6LvXNrmG3g0WmcZy3dYT74vzq2EMZLabzi7pk+3RfbAsqT/64bTobLZ78j9t1fpCv5m+s0xdFDuVMnmQYaLYSWIwXnTFCPjbz2JcGE7uDnPInSDmOigYt5mi6820hAGKOqnH7fgn88xmqAYfrwW5LkdwLmaOHTg74uKawcYT2GwX2bwmltHLMqebpcuPu77sj3XUgZrByZGqiYBtwbZmOzEzzwhqWV8y4nD/u4jIQOvF7XTCvmaQyT53hCbJg9PFnY47fXp9kGg933TRRbkZOSSxMHiYEBnwnd7ztRyHjNDTLzj97nUql1yHqRrPW78bzzJNqUwOZDJF0jl4L456Ima6xhMsd4uZ3WS6zEa8LErKFBzy2MlSEkDs4KjPawOWI52xNxJQt+XEYZGOsXyelExcACm3Xic7S1aKDn2Qz1GcF9DtALONdIzpDD3u3JOf5bqH5SSdmesiWexs3Kadn+/JyJD2uvReOu8ce9uFU2V2wKmvfJZr9C+JjfWPrLSZ55uwB4+1iQMkpWrXCROlGDSXRbFXpizzAzs7OwtzwBs2jpauS31Rck4uG1PPFz87xXUYl8n1LhliGur29nYdOnRogXXbyWZJpmOjwhFtbGyM9rt3hpeGn7KlASfzNCDYgVgOA577ZPbvPjgP7XbtTPN8iBkuQOFzE+mQuzQB+fN0cF3UVTV+i7LrNaunTeuTvLidMu05qvXYr66uzsfSOsVh7OzsLtZ6fKyDLurMsd/e3h6di1lWuhSL23b72AIkJqPOLmIyycjoNg9beru3x4HPDh8+XAcPHpzrzk7UByE9n4hiDDKQHOr1WFGvt0Rnes07BtE17dkmO3tJPWXdOzu7h1A9zrmzkHsNuglA6DnteEplciCTjptJiEE5PVM1Pi/jaCXZS4bozstmxOKJkIyzSwHYiFkfWQYMdspdP5P1GizsiJEj12gMbBkpVO06Cj9XtTtheBYnvLOzM/pWT+vWEzpfKkmdPAOY8R0uWZ/Xd5CZ+tO5My4G/jxTZOdl51E1/mK8ZP04TJzcxsbGyIla1owOKOinA1GvWRnU3JduLQi5nerK9TX66DRpRibeVJF26FP7tj/LwmesVVCs+46YZLqLcenSXEeOHJl/hv6tF0d63E/dnrM5R60f69TzH11iM/5KA9sc92Wabw9kjoPCWQ9Ppqoxo8xdUl6nMIvmOTsRG0iuHVBnrm9kFJQpCcAqryfILDN0PreTcYqGZ/idE8gRDcaejNjMOEsCLffmBgCnX3zdunRJZ2ZH5zF1NJV9YyztLDKNltGUZYFMYDcJagYhywyxMdBlVIs86VhsA12U4wiW3/QpbdH/Z1ScRMGRYtqDdUIE24GN+2R92nnz21F3yuwoljG2DB7PJG2MG2/coO4O/OgDwNSNg+3MfoPiuk1GDSK5Vky9+cbwPZA5Dgo5fQY59+h7iyPX0+A6BoPTNAPKKKCqPzeSn+UaTreIns7Fk6nLNXeA4j6mc/fzrtsOASfqVFFXZ7I4HHn3jB0a1+xMPWEz7dA5Tn/GRHaEmTpMmcxe/b8jvXS42Yd8CSKAaHBL3aeeHCVhE7kLKu20S6N6PI5WklTRtnXJfRlxegeZQYXP9+/fP3Ke3OPXEZlg0bYjCh9irNolbiZsXod0XyAGGRFZTs+5BAbqtz2nfKljxrxLkzoSTNIIeXE9UyuTA5n9+/eP0mG56yb/T0fmw5XLctlmjWZcHaPvWCr3MRG8EHk0JpNOgbaZ2L7P9XfO3Wkrf27n5wnTfZ6pQE8e1kK6t85mPww0efbDsjJ+y8Ajx8SA4tfTQxaIUMzy0YnTZ+6TbcCpKsbDTgUH4hc/poMycHQpIacFuS93Yfm3Iz5HDtRr/SdL5xnvlLLDpn3WkRz1mDSgQ+veL6MchqE2NjZG47a2trbwhmNHotSTkVhGPLYJ5E0gZOw9J5JkAf62Ga9HuQ1nD7rUp/9Gp8jAOpbtY2plciCDc7Wxes8815isPiAF25rNZqOvEM68t9munZ7Zs1M4ls3Ry9HSRWav3boJ8rKWtLGxMaqDw53U5f36XPPWYTN3567NVs0O2QyQGwJ8wBOdJBN3P6oW02lOSfG5n3O9BsNkuwYQ98PRGv10Own82ITHgXRPRgIcMuRLwrxLirYzLZT9taNxpJNpMevQoGob5H/bbT6LDezs7My/ViLXqHiGucI110vhYKVfKMkXp5F6c8YBObs0s22W6ND24/HsSi74ow/mLv4hN5Tk9784QrM/ccFusDmv19AO9SKzSfHKysr8jR9TKpMDmVx4dCrDzMjnUbJ06yA+hJYOkjo9KRyW27HnhPBEo25PPBu+nX3uqjFQzGazOnDgwKgur1dkKizrtdHzjA9s0hfn6rtopVvX6qIkM0nrtdsRRJ9yXQJ5cwwcyZm5OjVEP+wQMirxe6twnrmWlykiM9QkCtZB2tRVYbQZlZjtp67pOyU3NBg8vK2X5yzXsn5Qn/WNc02AMwmoGn9Hj4GrW8tJwpFy0Y+cj/6f+3Pd1u0iv/Vk28oDoe4bxek66nZKtUurTqlMDmRyC3PVeCts1ThVluw6SwKCr3mCOJ1iA3YbTuPYcfKM2ZydX+4EcxRjo+3y/XYg+eVS3f78DnS7NJGBDXk9uQ1QPsVuveb/6Wh93frKtEQydDuBBBkDuCMh9Otn3F8/ax25D9SZzsy6WFa69k1S0il2js768rZYrvMM/ebejGiTMB3L+flzR2Toj98JiAbxY6VUPebL5mpGXR0IObVpuzBgWmb3zzbhiDHbSnmS5Jh05vPd/DveyyRBxgPlswMMZH4vhNlMVX/KOgtGD8s9Fkh58pMuSCdtp2dHaKaFA/CXi9n5cQ//O8XnvmXaDPncr5xk1odBxo7fjsoM0m07UuomqAGlA2WYsfuCPHyeqSUcPesk3TgZ0F036Z5k38ucq+ui30l87NjSCSXYILP1awcFgbBeM9VE3e6H6+e53G7Os5mapF/o3fbWnQmx3NhEl1Lm3mVATr3OQjiCSEDP1KDXiUzeLLvltm9I8FwGRpY5iWzWldFP2skUyuRAJtc2bDwYxubm5sgBsc2R4hwt9fGuKrNBih1sskXaIN/qSex1IGT0hHK0YqfPPV3qDfk5cIoR86Zap/FwXna+CUY54fK3Jykl1yEMdrmGkw4gwSFz8LSVqTcDsOUnTUPdfvknurIOca6kuvz9I44y0xl163b79+8f3UOqxww3yYEJiGUxANrBUq/z/wYr94vo1TqzLE7n2nFSD6fyvVuKSKpqN1W9tbW1sOmE9Qz/+B15/sI7j4lTTznnsjjSNYniBbTuV5Ik+mcSxOf+4sDV1dX5QcwOHE000LV9TxKQbl1zamVyIJODxIRPoMn0jieXtyvyORsBmEQspJOjr9rdfmpHbueYaTvawGidvycnzlpIZ4RmwfmqDLP9jLLMYO34V1dX54u2BqyMagwuTEBPbGSzM059dhFiRoJOVTEpfXo/QcZO07JmeoyvVUD3FMYWMHJ6MVk842F78QFUjw/F+vLnlt+pGPTn19fnTi/rNuu1vWVEa7sxAACuWYdBO9M8tne/QNLjkPOSNznzk+dZHD3mDr0u/e0dg9aFdWadduTJ/7Nxxv3ns9XV3bcrOzrPNURHxN7ckKm2jCynViYHMukMOufT5WN9v+tJZ5jsj/sz/eZ2PdE9QTEunxLPKMdbbykGIrdlR28Dr9rdAGG5su92cFzvojJHMjnZPeHdx4xQsl1HO5nu6eRMmZ32SUA0wVhdXZ0f0vN9TvcANv5qYqeoPA6OQk1iqAtyQn+WrWOlzUEQvCMpHRO6ySjaNmIdJRhbloyY0/4tuyMCt4ndJuHJ8cYmrBdvQqDPOPJuC3jnkDPitA1U7RImg7bT6Zkt8Fdv2LasE489z3msTDy9sYb6/OaDJGpTKZMDmXQEyd6qxrs9cmcJ1/28WRUlAcopM09IP2PWmQ4AwyM14nRGyuMJgLPJsDvldETQsSj3wU7bDirrRFedc3UUZ4beRStOKdgB8jltmkFap3Zyrpff3qlHFOO0EcWAkS9stOMyQJuUGEwNaj5Y6L5lyi9TKx4P98msnL+9q6zTr0HGdbDNHZvzLkrrFJnZcotcTi25f2brHr8kEv4xeXH9tAGAeWMCbWILjqw8Nt5Kbbt26jltzREZNpy7UtPmOmJp+zH4GBSp35H1VMpkQcYM2o7X75VyDtxGmdEIxuVvsMvUhdlyRjHpMLyugKwGC85XUFynZTYzgnnjhMlD54JrOiKDwPb2dm1ubo4mUkYN9NXg7BShSzo0O1Ozee5xisZnGDI9Y2DvUh+ZTkK2ffv2jb59NFMsBqR0yOnQ7DTRh6MJdMX39XS59rQ56rfMbpNiJ8vGk3S82ITTptaPiQm2RCTtti0jUaCjDMtnQPaYOE2Efv1ma+7JOWsQq6pR5IM83iThKNTrnZ5rBl305DrRU9qB+2Xwy7S0yRR12r843WgdIVfOoSmUSYKMXysCM0umWDWerDZWf56TzKkCL9AyQTFGct0YJPdU7aYVmNBOWdjQ+NvfZpks33U6PVRV840Gmdbxm3/tUMyurJtOb/TfunA0ZWBwH90P/s4JZmZr2SiOkNyW9eC20PX6+nodOHBg/s2N2W+cCmPpdQL0jT3loVNHqSYZpOWsj2TymYrBDsy2DZJO9SVp8GYQy2eC4B/agNT4mzqxtUwRuT6TKafTfNDZpAAZbX9e+/I8gUx5HLNvtJvgSd2uz5Gb11Tob6a8ckHeNuz0KHL4Xup2X5MsdBFobkKaQpkkyOT/dlA2SKccqhYPxCWoeAI7uvBkTiDgmtvxZMjX0ANOybwyLZMLp8nIvUXa/bJDzfRTtpWpgNz95kjPzyUgdPrz89Znskqz34z8XGjTDjFJhHPsjAHOxH22Xu0AkKPb/WSHQb+czvR1iiOglKEDXGwDkCGFmxtVDEjcY6KFXg8fPjxfd0I+nLDnhAHNcmWkyzPeVcVzbJZxBOG6AZsuxWYwMbgZLHPOURxh8L8/Q07fk587wvX71zKVafKQYEwf0ZPBxfOli3iP9zI5kPFETaOpGn9BFYX77LwcmufuKv92uwYGG24yHBhWF4mwUGyAY6Iwmex0YXuwJt/nviX4JrO1DB2L7iaNn3G9OWlwpp581pnHytFYslwzWYC0avz6eXSbcmd6M/XgZ9gBhhyp/278LTt2xDjaBrzYTL95Btltn3xu0mDi4JdVpgyuO9OKgKXTx0R4tr10onbWXdqHMXefPEb8371UlAjaY+bsgNNpnUzHKpmeMkCZMHYF+RJkfF7LNpNjmH+bnKQdTK1MDmTszBxiwzKIEpY5pfxtJmMmnTldAxIl0wzeoeTnKDg4nJxTQjZgnjdAdAyK1I6fc3t2eJ4Yvj8dZ9X4IGqmdpIxZj1duiOdhycwIOP7zRpJTeIEPHHTYXmMXD+A5QjIfTDLp7+ZRjKIpGNMW+j04zF0+7RlNp/9cbTuCJzSAS/6crRhObjWOWZH4I5AMv2HrCZpyEPKNlPS6WxzjDv9JfnwOgvXDZYG0CRT2GbaLf32zjdSzyYNXuPyXOuA2OOUMkypTA5kcmsqL/xLh+CUhs+j4MQyz+/FVTtbftuI7bCd682FSh/MNCg6D52fZ2rMRto5eIMjfxt4PTnToZgNIw/ghWPxVzXzvBdI6XdGK/StA69lzI/7mdTedUS9OW78toN0+gP5MgWWfXeEkWCVwOfDf+6Hv2XSzNz179u3bx5R4Mio1/n9BPV0aHaM7ndu7/XmEI+DU4W2Pew5gdh27rFP5070xa4qR2euL8kBf9MW9+a5HAMeKVLbnutG//xkJMNGH9sowMKB7i7VBYhadxkZeh67/isblR1PZXIggxGbfWYaK6MVnK4X89LIPcmTJTpczknje5mcTnnAxJz68nN2QsjD6X36ZEbmsN3gRPupJ+pmEvhUuxlnRmte5Gex0jus/LkjRoOXI6AEGMDQfUIuM2k7oNlsNj93YIB2SgknlGNoh5xgnYvpGdHyP7pzOssbAwxEZv12cGbgBrq0mxyDtIPsm9m3iYV3KSaZgJU7tYX8thmDpSN/A7udKzvU0JV3Uuba2jLC51RXl17MqN7zB7nxBeiNfhI92vl7I4PnZI655c45jCz2MfY/Ge1OpUwOZKrGC9iZoln2d9Xi19NiYOm0u/aIXpKVZBrCDiQXMpcxriyWz87ZKbRk0Zal00OyxZww1mfW6VDfMlWNFztdb+onmbkju4xsDArWq513l77J8Ta7zDqtPzPR1Jv1we/UfY45cnlbr0uOU244cVRk8HIkQUnwNqBBapIU5Zg7rer1r3SmaU+2Qzv9lZWVeYYhD2+SUgYY+TvTamlHlhvylqk7R2yAIzrz2sqRI0fmZ6kMMt64kOm4JHLInPbsuep2c+ynVCYJMlXjxfVkN5nuqhofujSzS4ZZtbjrKvO3liF3WqUz7BigUx9ux7+dowdcYLidbI4IXL/7BoPjusEi9ZQ6qNpNaaXjTrDsADDrouRETYBKUHYUkeQgmXqmO12n9ZCps06PPNM59NSPf3jOenV/l53h2dzcHI2p7cj6yfGzDZImtl6casuxc/SUdm8SQd+TuNjeNzY2RmA3DMNoy39uaPD4dnbS2aX1062TWUbmjtvj/myPcckUV0e6OpLp7dN55miKZZIgk+zPk4WFdRuEIxHYHe/wSmCgPqflXMz2YD6dY88JxrO+7pQPBut8NnVlBJBs1CkLH5RzJMD/liFf8Ni1hT6cFoJl+yCcAd5pwS5SSgCx8+V5M1TrHpnoLyBsfXKmJ9NmWY9/DBzoDAbOKXiDa4JSOmTkyLSOx8ppwYywvF5guQym2Udv30Zu689O0DJ4wTtTcrSXESuf5UYF5oejSOR09iDfx+Y+un7PS+TIrdBe4/QPAI8szgR00XDaBH4kX/bZkUjPZa/nuM/YwNTK5EAGcMBQ/M1znuAwl87ROJJJI67aXc/wBKV0aSLy0CyyLjvDkhM3QdITcGtra7Su46iDye3nDIo4jc3NzRHjwoHkrizL4jpo02A0DMP8GxB5zrttcM6ZgrGuuv7YORqYHck5lWN9pSPP3LqjFvc1I5cuavJmEU7Y2xmyUGwiQ+4fW/RXguOA/UZg2691ameZwJgRMvK7f13xZ8i4zHkaeJMYcI83ZyArz/hzkxTrBUeekXHOTZdMzXl+dREffezIpPvpc1AdGDiCsf6c/kvbcnTltqZUJgcyuXPKQGBHxe8u0shn0jCy+NkMtz1ZEpQcsbgeOzen0JxaSbkzHeZ+2hmZ7dmBWWYmHs7McufupGThyWg7kDG4mtVaDxklJthntOpiR2R94tT8jMHeDtBpGWTLaNFj7DHgOTtog4xlcaqM5z1+nc2mPtxH2uKeTNnRdo551eI3QLr/rpP20vHneHn9wf12G+5PRs3++vAEbz9r+bg/7TbryTHvomdvn8eOabdLcVkPSdyqak5MGfOMaJmXUyuTAxmH5WbjVX24XFXtBHa+lns6h+ZJlzlwAMZv4sV5dmmnrv7M87p0jtTAgiG7bdebbaRT6iK1jBCWnbMw4PmV6QZ3p16SZcMaEzi7tKBl828+8y4iy5i24DSiwdyydczZoOczIRkZJUu1Q3M00NVrG02gdD2uz0CEs7c9WVfcg065N99IYXZetfgdQK6Hv70G0UVK1ON2knA55Z3z1f1JoLJMBvocI0eE7ldGjiZZ+ZaFtI2MaN2eo6JMiU+tTA5kcrDNWtI4ud+TmWv5WTr0rh4XDNLfS4HhehttRh5V4zWBrm7LliDXRWW58Oq+JrvPHVudM7K8XqT1Z55APlxq1uw8f1WNwCidRjo3O69sl+fyniQRGRVYT/S9iwatb8tmgtMBZKZI/f46+pSAYaLUOU3/3UVS2IkB1MWAQsntun4zcOqYdGBGGciOzGwtxx7oN+3w9nFkQr8diUjQPVqUxrVlabWch46YPIZER4wbW/t5vZP7m/ODttxuklb60BG7471MHmSqFkPxbu3DjiQdqp/JtFC2a0DLLz4yS3SxPHzG+R7LklukvaZghuUcsAErzwGRWvRkT1DpmHwyczvrXMhMHbnYOXjDBZsTZrPZqA92cP5tubqXVFrfXhNjLDtGmu11EYn/T0BB312Ky2kSzqE4lZM2Znmz//ztNSlKRjwJ1Iyv9QPDztQnf6MrCIJJgovtptshyOYLywAhRGdsIHF040iAuebzLda35ff8dBsGLgAk08gmGE6zda/0yTNHXM91TpMpg7jPDE2lTA5kcpBs4LlwmJFAlnTy3eRPA+NvDNaOyKmrzkkMwzB6a3R3+r97r1knfzrQ2Ww2X5hGLwmqZu9d5JaprnREnshV4/dVoQtPNDtPM3un4zLV6XSHQbkDxRxb6x8mzSRPNuodiLmhwBFI6s9gb72g/yQzTun5NS+MofVggpL2458cS5dlQO9x9AsuV1b6r5FmzLo5gs1lROvxx7a9wcHyot9MteW4Mlbdelu3Puj/ucfjZ1v3tWwfoCBF6nodWTqKSjLGs1P8DhmXyYGMGZ9TBZk7ztRIslIcmg3HTDFPxmddngyZJ+4AzUbJ5GJyptHnxM56XHCiTP48t+O/nVrrinVkWZcBdkYRWZfXXSwvvzNVhsPr5Okcrf+3U18GzNaD719Z2f3KCOSAYOSY297SaXW72LArn8jv0rspO6W71tmz7+/G1J8ZCLmHMWds0JNTdBScfaYbq8bnu7iXNpHBMjmKWkYEPU+7YwWpK687ZiSckaf16M8hSSzm83cXxbtN69ntJCBOqUwSZHLLcgKOJ54ZcjqqjmnayVSNFywTYKjXbCyZbk5et5spBDsfG2hGGJkic+os0wBdmsdypV4sZzpsT9wEb+syxyYjBtqwvnkmd/5xr1OByM//yVz92yzbEVQX4Xmba64LmO3amWWEl06UfnaH8qw/j4VlchSM7B34Zv+7MXMdTmd5nKquWF/J9KxtzlG09Y8OeC2SI0TqoS3G02NOZEXx3LE98Jn1kBFgApazDhlt+jmepV9+9ZHbtt4o6ZeSLE4RYKomCDKdEXXsvmrMgjw5nKfmM5wAbDoNpEtNLGsz5bXDMIBhpOkk8/BaGnP23RPR/aI/XY46J1eCIff4tS/O23MmyEBGPSkT1+10DH6+Hxlzl48jVMC1Y6q044OJ6CMBNtvLw4wZkXh8HJVY546I0h6QlfG1zLlu43NKjGsCup0f95kEeCw6lk99BnanW2ez3a9FcGqUMbGu/D8vAOW7bGx36I46E8yXRQr8cA7J+qT/BmqDSo47wOa1ky4LQJouo28TFx9KZlz9f7fNemplciCztbU1crI2bhenshzmAjwZ8nuSejdMsrQup141dvye/Lk4nA7UTC8dRkYD/M8aCHKZEeZibeakl0VkjowouRPM/UyZPQYdK3ddnqzWgZ2w890mCwlOOANvckDH9CudRwd0jqiQKaMJp3zSyZvA+LrX8agv0yeADA64O/Tp4sg8iY/HNceJ1KpB07rlGZyrgZ05kWt+Phdim7ed43gBYNtE9qtLc/tYAG3ztw/+0jb3IqPf1EwE5cPTHlMDHrowOLnv3dj5ZbzuR5LWKZXJgczm5uYCu8wF+ExDVPVrJTmJ09ky6eyw3G7H2O0gOyfm1FBGZR2Lph2K7/GErLrifVGAqjcYuI6sy9tunXagHTsRChOLkikupxlSZnSWu93cj9xmmukaT9iM+Aw6jnogI0ezDctnuTKV5nE8FthmyojrOHfkw4nzbZY5DiZCCfpuz/pwX3iefuRz1kPKn+DKvdlnb1u3TrAf7yo00ObcTMdsUPXntscs2HISLANh/m/C6nnK/36fWUbGKVsSlimXyYFMRjJVNQ+j+TujiqMxCKcXuhRZLmRyPfPlmU5Jpum6zYrtlO3AOoe4DKCQDcYGY803y3K/SxcF2tm4Tqe87CCWRQaeXL5m5+riLa+WOZ10RmHePorMdgiOcMywkyBYJvpiPXfRXzoyL5bbNhKkM2rDyQE09CWLIyCPpfuPPUEGvGjt9PGyOtPpJ2jwt209x81zh/qcGss0o0Gw6zMA5uxCB7q5vTiL7dVjlyBj8oXsJk4daeOeLquS9jSlMjmQSacJS8rzGzlhzNTTudgBdSzQAGIjScbta8kqHRGQioNt+40BKTPtUq/TR5abe6gLx4JM6ARZ02H4Pre9uro6j5Bg2V1k4ZKg7PFwG3Y0Bq5k/QY0b5Xu2KQdvttGRsYBJ0jJaNLnbGDhAG6+jiT7TTTZbV9NuVN2A2fnUK1rE6tME9mpOzVsfXcAaP13ZA3ZSZ3xe3t7e24bTqtxlox1MtuNQaiq5hFcblvP9alcJ2ReeF2tS31ZdzmOmX1IAHNdBimepy9+tyIyOl2391qZ46B0YOJFPK7lT+bLbTA4hs7h2WnYYXi7qg3ehwtXV1dHqRvq9iTKV7JQjyegd6HRv46Fu12eYxHWbSej8oRMR51RmeWs2j0PYaeY8mVkk2zROlmWwvRzBktHDdzDZx4rcuu04c0VHfvFkUJqIAXWNdGBv2ANZ++xBXCWpXUcjfKcv0acqNSv7U9HB8s3+3Z0XHWFw/e6SMqJ/AZxiIA3kdgBO8VnIPQ9zLGMRvkyPI+fU4weT+YuYJXPpT3lWHegbZtBB9ZPRpkpEzr2vNzY2JjLlmnaqZZJgkzuhukG0QDjhXAmj9dx/PbUqrEBun6H00ycLrx3+9RnJmQAwRg7B5/g40mC08t0DkBH8Qnvzc3NUX8y9eMUDn0YhmG+DubJmy/E9OJ7Tn73s0sxmt0mABoA7Dh5Pp2ZU58pH/dzaNIO2+DMNctv/XgsncpJB8k9PtzpL8+imGnnmJhc5NpZ6tH2bZ25TrYum0QlaPM3/3v+WDb0ymYF6qOvBgZAO/vnrx1wf7BBp58AdjIBOecBvSSh/nHU4ogP3TCnM2thm3DdfB289Yy+jpZam1KZHMjgNNLp2YktewWLWZ7ZdQcWaewYDwYM+6naBSIcXNXiK/SdEvE1s2raoSAn9bnfZr8854iNvlle2vT/BrF08AnC/N9FLpbXjtFMFCdhZ7xsAnYgaIfd6W4Zs7VOk5Q4ultGVmwLqaNMp6ZzwXn5f7b2Gowc6bk/OUaZ5nL7SV58jyN423reb/1bx04PYnuO8By9+HODTG5ZRqcG6NS9AS3Xhriec5X54bc/5Dimbm1jBoq0X+unKx5LdLIXyRyHxY7JEQfXHJpjWDbuqn4nWTofUgGONKrGB/z4zYTymoHTG5ad+2kbhkdZFpVZPk92O9SDBw8u5N2zzny2i7h8b7JUP2fWnA4eGbx7yqmjLmpgciJfrpskyHTMPw/VGnxph/rs3EhRVfXRrVkyfchXrNAWqRJ/x5Dt1OmlqvErjGg/HWHqr4vODRoGdp5JwKLYSVtHyAxxsc3TJuuAtJNb9JcRPfREiipBzH2zDj22zFOTDc9NUo/uRweYyOr/cy0Tma2jBHTSx57XuT45tTI5kDFLrlpc4/DiOsZkQ0t2510rBicfsrKhe+LnziIzJ7dlJ8wz3uFk5+X2co2Iz+w4OF1dtcvSDTp25Gtra7W+vr4QZeV6VU54O06cqdm667IDoG7qzLQK60Vu22/CzbUsxpNr/t/3oTtsw/JnJJMsnn7hEP1cRnTWEQ7IbbnvTs8k2VlfXx8BJCDp8fcie0ZI6GBtbW1ef0ZKyAaQuk84yzzA6LlgQOI5v8VgfX19Ic3sFNShQ4cWXjhZVfM+7ezsjEDespvc8GV+pGdzcwX2l7ruXsRJcZTSRdDDMMw3L/jQpj+nnnz3oHcLVtV8zWZKZZIg47QFjiZDZDMxCsaDgTlU5xrOyswzowGAIVM9nhB2xCxwmkXRBwOBJwdtZAqAkguRdvoZbRg4sg3rk+J7qd8OAodp0INp+nemccw++R4exsD6gBSwo8uplWOd/zHjTt0kQNjRZwrMdmU9m6Bkesx6x45YUM6ILBfp8zP65BRPrqN4fNDnskicsfKuN4+7oxT+92+DbRK4qt0U6tbW1nzbNLJaJuzIRIEx8ziiFxON2Ww2ivrRDf32uHv+d+Pvz6jHBIp6nYJMm3IbyI1uvNMNXUMEplYmBzL56nGnV9KJmtV5sdDhvid/plRwcBhdLjZ6bcH1mr0iXzofM6UuJ2w2TFnGoFMX6dQo1o3rozi/7XQY99qRZ/spE/3HMdmJ+347M+QzyBB5MfakjXIXlOXL/7mWEVonr0HZP8jsPnXreLkukVFWlyb0WgOAYqeHzrvS2YHH1hGV02c5HimzwYV6TDTcVz9rvZuUAW7YNjK4znzWY5upOus71ysd6WXUUrV7eDvr8dhn1OyMSc4Nkw2PHXqyrSwbx+O5TBZkHLmkMeYbADxRYF9Oy1RdMfikcqqqTjjhhHk4zgRxe0yQzPE6oqBtnKN3GeWCNCVBw33tQnn/Tha/bOcbxQ4oJ103kXAAOdn8tyc66RRHZO5b1uEoCP1415oXk2mjA81cd2B8nLJK0MkUoJ+1jtCFty13UVNGAgZd66crriOjnOwP9zhVmszbfXPqpnOymWrNRX8/myTIY0t9fuWK9bnsDBelmx8QkHTYjjYyqsz1IfqRwM/fFPTA8znXV1ZW5lGJ04fI5e3t1tMUX/s/OZDJhcKqmqdf+NnY2BhFMBkVkG9PxubIo2rxi8Uy2qDtBAY7f9oljbC+vr7w3eZOcRk8+dwLiGZ0Tnt5Bw3PeVGaCWrZnYboohrqA8Bop9sSnik4s1f3kXGhbkDFMnXpoI4ppzO1E+hO9aNfnAcsc2VlZeF9YWagOztXbP/GPkhD7du3rzY2NkY2cuTIkflWcezVsuUhxowKO/ClHjtndGeWvLW1NTqXZefpNBlt+a0AkK/ZbDZfN5jNxi+AXFnZPWzoSLJj+fQVnR06dGjhtSxV46/hzj7RPvflepLtNBfvqZ/n/RYFy2HQsD2zoWF1dXdLsyNy9GYShJzo69ChQ/M3lCDf5uZmTa1MDmSS/eXi/OHDh2tzc3M+yXKSO8JgwR3GYadsYDLzNOPDGNMIDQBmhmY2BhMDCvlsQMHg4H76Go4PuauumCybm5vtm4Up3bqA9eS3K3APu4fMyL3I3i1uUzcTlue8ESHZtM/DMIZ56M9M0wy/ix68ZmEAcRRoWTMStXOZzWajNyvYngAj6jJQmck7mjJpwl7s/LGprLPbPOIUkuUnSu8IhndpbW5u1uHDh0cbSihOCTmVd/jw4br00ksX3oqwtbVVm5ubo+yD55fH2LJVjckk88NA6ajE5MAZBRMzv/TTpC3fEIAcgIcLkTTtmPRklsERjKOlJHNTKJMDmQMHDoyApdsLX7V47gKG4UnulIVTCpnDpq0Mp9222ZXZUDp4O85MoViO3IabjN8LqAaITKVxj9m2HRrM0E4A+Zy2MWjSDnq0vg00POu27JRx/NaJT5+nLOlcku17/cMRFXYCE8bhOoIAOLnuOuwg7JicvkJGO2HrN+2JYqeYqRuDvwHEEbkB09EZY8HfmTbiB11bV0Te7gPyOeVsZ2pg3NzcnGcXaB977dJVLk5PW++dDXF/AoxtlevWP88Bhhkx55hajwlGjqycEszozvVOrUwOZMxcYZSeDP6savEQokEDA/L1dG42jGTmyOMJ1MmbjJC6bIyeUExWTxYcssNup1qcbnBKwuzYjN2fWwY7Re7xjj2nu3B8OXk8CfnfuqE/XhtDdkDGqRpPeAO1WbvTL3ay7j/XzKzTyWWxHuh3OmaDrftqWU1sMirNtSbaTWe2jAg4zcszTlNmdOfxsoy2PxMut92BPCBNCtAEyDsrDepVi1mJYRjmaWXadzoNWQzMXusDYHKHHLpye9ZbpnM7ouKINKMl1h29ppvEJKOlKZXJgUymm7a3txdC+wSFTGOk48cAPRlhc+lsPYkJ+fncxgfzJt1Vtbu9mNAd1md26rSaC8aKEz5w4MA854s+7KCRD0duJuuJnkwNOd1eOk6cCs8z+buJZRDk9TSsZayvr8/bdB4cHebrOTxmtgODMfV7vGHZl19++Xy9xPLnmSiPYdqEd4wdPnx4tE7AcwZ5fufORNeZTt0O0P1ibJIsGKySVLlNbJzxNgjT71y3S13Tv/yeFVJaRHq0xf+54L29vV2HDh2a68Jg26Wlq/pFc0dq3plIGpa5RJoVfSC/1w2ZKyacJjm2EQOcN7jY9hPs8u+plMmBTKZNmBRe7HQarGM7NhI7WE9878en3nwvkiMO74xyMZvCefity0zw9fX1Wl9fH7211g7LC8owRNpNkKFdHKCdFDrwegYTDlbnxWFHNk6xOD3pA2rWL3LQBxzA2tpaHThwYO5UmNDeaNG14/U1dOr0YqahADMzfh92pDAWXWRQNSYhtOuFb6fPDLreTUgduf3a/fTiPmsZmdKij93uq0ztecyclk2GzpgA+sm2He048qXv+/btm7N4y+eFfwNIpnq9Aw279Dg4JWgC5N/I4Rdd+gvaMv3IWJhEZmbDfXVaLiPgTDVa784mTDFVVjVBkMGpwZrMrFZXV0fhdrK4HPyqcUrH6x78b5bXyZK7arpUiR0U6RzaYbdOvoImUxw+0IVD8k4cwIGNA95M4EiDiVy1uIaxvb09jwCogzf3JtPPPHWmsDKdQt+9C80Tj4lqnRNJZkTjCW7nY3Bweozxz40dTkd5oRxd5qK9QdbpEjPx9fX1hSginVKmHol0rVc7QUfrXSrUbdl+DChOs9lR25ZJWTIeFNtc7iw0oXPk4e8zctREMSl0lE1byE27lhPbdCoyz89xb0camE/WhUEUOQxitu8cT+vfoIYtMRdtj1MqkwMZpwU86exknGrojMGgYYeQ13zdTsgTNo0rF/tcrxm5HZMjqHye4pQKzohnV1dXR0zeTJWJ6hSaI7l0rCz4WgcGIjNL9ysjATuCTBt5zHJyZ19z/NIGcpzt9OxY6EemwBxRWBfow2w3d87BwjsQQUc5FlWLbxa2s8trlqlLtbjvBs6sy2Bpu3SE1LWRAOz73IbnSWf7HsucM27L4Oe5tiw9mDpOvZhkedy6krJi413q13PBvgi78fXU59TK5ECGU+BmrDs7u7th0tiS2RKhVI0XHnHCnjDkZ21YmRYiLWLHYucJA8WA+dwOuHMOVePXhzjF4slNv8hXAyBehIQ1ur+wOWTy9tBMnzmd5Py8gdIpMuvFuoGl2rl0ztYM1Hrw5D6aIyJNY1DzuHfpUYOLQdfprRNPPHEUjWFD6eRskxkFOULmeSJZ/rcNJilJYkPJ9hN0ndLJ6BzZfCYlgdgEDt0xBkTH2I116L57Q0kHDMhnECGazfRrggWfo3PvXjMh85xwlGM74bkELdc5m83mYGI7W1nZfU8aOvG8Y5ynVCYHMj7MWLXrQLe3t+dvIE5m5RdkVo3PCuA4bcwAmZ/xoU6Myju+ci3Gax4GB2Ty+g3OnrWKdN44ACaoc/dmWe4b/coFWn9RlHfF4cDsRJ2KyLQRxZOYfjmCSGdiAE5w9bu3VlZW5gcd3b77l2QiZUrmSTvJPquqDh48OAK3XFCezXbX/xII7Yy71/hnpJbAaqdsZ+pncj3CY2R5PI48B2BW7X5LI3VnutKL4G7bW5U3NjZGm20cGecaKdHxpZdeOm8HgoNODaB23KTdmE9uy9u3GSePp4kMEScyeg7wOel2j6vr9E/aM/3NyN7zwWM3tTI5kPEXTg3DMHIOHnAKztTsM3O3yRJZi/C6RhoTE92G7nCfOpalOMz8WcgHDM1E6QO/7SATDPJV9O4Xu9DsvHBKmToiCvG723iuS7kAFt12bbNXTsk7rdKxW+TzxM0UBfJaPwYss10fRqyqkcNMZ24CQXRLu/7SN+Tl4KLByAvZtgtskVPfdt7U523b7ktGHo6SuK87v8O6iEmKZbWN5GcmNbY72zW2gd2yrue+MxcOHTo01wH6t9xJomyTtn+vrzlCNtkzychUbu5Sy/Sw9c48yK3Ytq3MdKSP8JzZA5njoHCan5In/u2Y8h471I4FY/D++lvuzUjETjfZMcURQjrCqnF+Nh1eTjIfQPN1t52MKR2UGSe6SodifVheR3/J2Oys7FANGunUcOReuHfbZvvpTLnuie3/XZcjKbN+dJxRBjJ6K68XvA1WLma51I0z7ba+ZzTEZwaozm4ypePPDdDuM88xBzze6eBNLGyb2VfkzQjTEbPf1uxt9NgDbeSWZfrmfhpsKI5YPX5pL51jtx4TxDM97dL930W2Cd6WeWplkiBj5wwLJBqoGoNGsjGuZ2qC394Gyb2Ocpx7tdNKgEGOTC/Rjh1y1mnHY0bkevmMwuQiH22GaLmcH3Y05r4io5mg2+MZpwTchp1U9tVA031/SDoXM9AEU6d3qhZP+RuE+dxjkfcZDNmlZ0eT6y/pSDweyG5Ha9kTTHOHnOV1vRmFZz8ddfC3bcckYZndogc797QB9y3HbBiG+ZkT9x074RxN7vi0/VCfx8+2mms52D067EqCSqaaTcT8f7bnOZ/9Zl7wvNdwu/GbQpkkyFQt7kyxw+Jzr4tgfDgFn/rGWQAwTre5vqp+qzLFhkdKw8yd32tra/O1HNo3o8y0ke/h/1yj8bupHMFUjV/X7h0zXrA3aFhmr1t5yzJt5G4xt2e9ZkqO70b3GHbbOx2lGFAMQAac/JIonKxfuWJn7siFyIr0En2wvaHrzc3N0RsYhuGKTR7WEwvXpDGdPqFOdMNajkE5o7JuXcb9xL5xuBSTgWT5JlFpe8naHWl4a3LqCV3lgUiTBG9PTyBL+6HMZrP5QWanI72ehL2hL2TNiK/TQRIOt2/5LRv69hxLYtuN55TKJEHGJ+jZnePBs9GsrFyRQ8YJm4l6Mnk9xIwzI56jpVg88Z1Gq6pRGu7AgQPzRe7uTQN2LFXjaI1+maU6ivPrWrgvd4WZ8eUBUr87zCBlh44uqN9RFAW5PBlpxxEADtOpFy+eZ2SHXqyj3JiQ42DGarDAKbHG4Pt2dnbm3+aIowCEGDsv2DOOZvcrKyvzw6C2TxMOg47HPFm0iUXq0ynJHLcuWk8b8+Fb6xfd+60G3pFo0uLImfm0sbFRhw4dmtuZ+8k3aXr8iB6dWl2W9kU+AMV1JPgzNtSda5mOvtJuktw4EvTcTxBztMs3wFYtvkpnCmVyIJNfX4rxkvPNvG/V7uIkxeDkyWFGXDV+zxVtVY0nbJfb9TW3aSaHzD6EaWBzfXyWzggn2ZUuBZJpFz+fQOl7AQE7n4zguvb92vlMy2SkhgPLlFKyP2RxO+kwclMGTDbJhZlqruu4btuEHZ8jlCQ61mlGbI5EkDP76nbssKzzjPzsgJ1KpE5vT/e4GRw6J2kd5MYHR1DdTi9HUUlUDDwZOVI/+jMhzPlnW9rZ2f2KAZeMAK2/BArPY7dN3x298HdG0UmOTG6mViYHMulkzArtNJLROaXD/2bb3OcQPA0lJ3qG1r6WjicdGobuE9ZmTQlc3EOxY0rnk0BgFpgLpZ44CQTui52hCzJn+sZg1QFSOobst8cqU1uuh+t8lmDhbdpmrcnc/ZnbS8ebKSvq7orZbYJMFq89MWap6zw5nlFPbsqwTrq1AADZYGJ9UNz3Zam07hn31+8zs21azx1pSrkdDWeURlvYJG10+na9nW9wHw1kR5sDScQyE8G9UyuTAxmnDXJiYTgdczZ7mc1mo+/vZiIS3ZAi6FI1VYsRS0YzmXZzqG7mw71MMEdOjpL8t50hffY6hWU2sOWkcX1mwK4HIDYYVu06OKffMn+eazUGbeTOtI77lQCUDiXHBieWRAFW2+2icz3YRYKTIxEvVmc+PsExo7FsM8fKQJtA6uewFY+b7WrZ2p63/Dqa8Fqexzud5DKHDkhZh9keW5iTNJgAZNrQ/bce0D02liRuZWV3/YfrtnvbVIIluncEhXx+NglPRnZc68jhMkJyPJfJgUy+RytZem4vxsEYeEhTeRKlwfAG5WSsXfrHE9QpoqorJgX77LsXWNpRuLguf87kxHnY6HmOieYIbzabjdZrqC/f8+YtqekQ7LT8g6yZXuhSIen8PE6W347KrDdfVJnRBw7ETs5Oh2uWxZ8lqGWd2X/ANuW3/jrW7HHb2tqan2OiDtu05XRbXWRnJp5fD+zX5HSRVb7/yzZh0uEIAZn8FdtpO3a8jvANFrThcXOfKJ4P6Ic2MxrvojPbtsfCKVUfYcBfeO0n9QPRYuOHCZ7JWDfPp1AmBzKZHjIjcdhatbi9s2rsYJJlcq8XzbPtZEg5icwizVowOm8w8IQzAGYqLyfEysrKwnbtjOj8IkwmgXfPeV3Bu28caVTVKCowaHtCO2pxsc6dakJXdkYZFTmCsy4zYnD96JEdSHYq2T8TlIzgbFuOMNPJui5HhZlGM+NOmxuGYfQqFBMknnW0YPvz+NqhZ1/Ri38sX4JtjiX6M4C6HWcDbPtOaeVXJ0MIvYZKSXJhOUysss+MNXqxfuwXHLF4rLro07a4s7MzX8RPAmFAOlYKemplciCTIa6vdUZpx50TvWOZuY6QIJTnTLLYUKnHzArG47bsDDrGaDmYWGm4eTo+HYeBzAyPZ62LTFtYhpxA1r8BxHLkmkA+b7aXE9OOJOVaxpbzx3JUjb8LyGk+Awz/0+eq8QJ5ko3sk+vIMbGuvWPLdpf9wY4zKujGy3KnbbkNHKdtNoG2G1O3sSzFlUCWuknnzL3ImATP0XoCjGUACLu3H/gZg1FGPp771oVtMyNKj4fnGjJNuUwSZLI4HeRrMCW/x6mbGBiTnb5zxJ3h27By8qTDzYntSWD5cwcTbZsl2gl0mx6c18+JnUwtZeSeDhCcrvGEymjA9XmLb+eUs+SGDsudTN796aJR6xq5SSNmutUExCXb9QYIg32SiXSA/rENEnEauJ06yj4ZZLq0XYK2U1gunWNfBjKu2+OfW3E7UHEUmZE//exsyREsxQ69IwNel/SLbTOiMsGgTeukA0P+z/Ney9aqKNiKP1tG4I7nMjmQscPDQDtWnCGzt7JiiFU1muwAC5OA+ty2DdBRkieZ87jclzIySQ0Qy5wd/URW6mEtxNEXjop+5iHV7i2/nrgZGWRq0azeaTJPcv43MGYU1aU8rJtMNzoictRgvWfkhN4AdB/SNHjQB69JUD+6zJeJGkjNji1r/qA/b+V1CtL12znbQTsay6jCAJXbaQ1CHUHKNFbOK6egac86MsDSpjfPAPAZZaROHSU4xZU7F9MunZIEvAATfqjX6yaeYxn5+sdvK0DOHHdsDrJLX9KOp1YmBzJVPWuqqgUniAFicFXjvHs6/qqxszTj8yYAO1hPjtlsNj/Nn4v2pMnMAj0RunelZUknihy0Y8fKZEmQoX76D2jb+bm93ApqfeF8E6gcXWTklhPOzt6TnHHI6CXTm24TGVNu77hyZOjIld+WJ6M9O2UDj8dyWZRoWVPHjkz9k+nOjNiWRSAmI7YZO2U2neTaXkZI7lcSuXTUni8+h4aNmSjxOeCTEYEBm51p7rtBkfoToC37ysrKfP5B/jyX3a+0daeJvemnGxNnEWgzZZ9amRzI2Bnxv6/lZ5403lFlI+9y2xgjE57inWpmfNQDuBg4/L9BxhOXNmmjYz92JF0fnQpMh27gSQfoYkfv+tOJ2XF580WCdjoiA5IZa9dWypB5dbPqvDftg75Zt06lUZffgmznYEJiWfjMbeGErAfad6SZqawuQsvNB8ia56syyvWW7rQjO033yfdZr07juv+Wj88zqvd6qNv1OAH6PkSZNtARSn6n3hzJ0ybj7q34XYrLOshUmnXIOHfpU8tv2Y/V5vFaJgkymeP0xE7nbGbh6959la9WMfvmPI0ZrycPTgOA8e4tA4/fieYtjZQEyWSqnfPnd4KMJ4CdfDcBXDJiMRB4IuWuJjtbpzKoK8E+I5iUa9kPDiPry9RR6tayIJv7wOeZ3uI+p6sScF0S4LsIED1ltNp97j77tUAeW/rfte96kJcx8nOpp+xTEoZcH/SakncudrvRujUQR9RExgby1Kefp7+ZMk7Q9P8AgwG2i4o9NsuyDGl/uZbLb0dWUyuTBBkXJmFGJXzWPWPnWTU+54JBra2tzb+ciXRL97UCDrn9vSIZ3QBWmT92u1WLBo0BZ8on++IJT8noxlFZx6SRJ0HO74UiTZepBBxyym9Z+G2QMICnM0BOO9tc5zGT7Jy89ZXAlruvMnpJ8sLBRTNm67CLbHy/deE1ly6i7OTxfd0aSG4Y6PTi74IhesiIxg7bciTJ8dgYiK0f68Ky2CZoz1EkbUIGDUaO1lLn2BXpcsvokms69Nu6M3ha177m+90XAJPn0NFeJHMcFO9EsXEn2LjYoZodedHezqSLTDL0dTrMxp/nXEiV2cGbzVEPcnZOw2E+cjjlln10LtzOywDh+p0WyQnkVA36z/UVM2Y73YyykCHHEp17cTaZPLKaSWc0g4wGQcuR0UCCQLLvfJeaD5VmSpN6cl0rASedrB25nZJTdrPZbPSV2B5v+uSvik5gTfkcAaTN2G5MXJJcuE+pD4+L7ci2knMUYpdjy7NJBNOmbIvMQewo11mSgBhoPF6k1LFVkxvayUyBI7Kqmn9RW0bXUyqTAxkv6Ds9k06vi2z4zF8R67USs21+58TxwmxOOoqNMO/huu8BZOxcqxY3GxgIHA0ZnDr5vA7gdIQntB25J2E6ZgOXUzHJcDud+B47uNyZ5nroc+4sS4aab0GwI+0iBerEDtK5JgPP9JnbtJPJaMmypo46h+ot1ukk3Sfbmeuxvv1sjoP1YdbPdYNUbkhATor76DpNApcVRyIeA89lrluf7k+SBKcD817Lk4DZAajHy6CagGwAzBSj+9NlD473MkmQ8VbU/CY/g4sNrnN8BplcZ8h1Au73WosNNo0tt4HaCXQTx8XOlImTfUjH6e9GJwrzPeks7cT8Q92UnKjI5zoMTLTVtWt9ck/36vNugjulYaDx/Z74bsNjlGOaMvm+XKNhAZnPUvYE6tTlMvDtUmbJstMurVvX06WjKAYk2xb6ynYc1VrGBO4EGWzVO/wyeuzGwmNmh+3+edddyuF7PCbL5r/nb5f2tB5NsjJt56io043lmWI0M0mQ8RZggwyTx2slTjNhvFVXGBWfe93FjoLnMDqDjJlTsijvzXeaw6zGziXZMIzZbWUaKZk3xRGU6810kZlWTqosnhgGjIxw0gH6GUdHudbgkoDrCbxsIleNU2/pNJc51IxWDGLuh6Mp17u1tTVa6M2DlQm8SRJY03EaNRk4cvkcl3WS9VnH1O+o3zu4bM8ZCVEnkZ71YR0b3JDB2QZvMfbWYcaFqC3nne3J6xnp3Gez2cKXknmNh/lnkpPPd+TEUavH0cQVnRpMbYOMK4RvGIYRQZ5KmRzI8MVRgAsLmUweO3gG2dGHD+Y5XZWGQ1s2Wu+cqdqNhDD+ffv21fr6+lw2O76q8Vuiq/qFWUdJPMMk8X12EDs7O7W+vj6fVPxsbm7OHdPOzk5tbm6OjNyL5i52Kl6PqhozeAPnsTYU4PT4287PzNf6zIjPuuuKiQFy0l8veJuMuF/Wgx2fx4AXp/K/GXp+pz3OKUE1WW6mlJAZe84xsgPv7McO3LokkvFaRka31O8oxgTBNpsRIptfNjY26ktf+tJoLYT20ql7rSOJ4MrKyvw9dBkBmVwZfDOTgY3ld8yYHFqv/oF85totchiAAVPkSoJJ4VzSlMrkQGZjY2PuWFdWVmp9fX1uWF6cTtDwiyl9T05yOwi+PhbDsVNwxEQ7BhhPhqrFNFGmdLiHfnVOJaOGPEgHK0am3FDAG6ydtvPz1J1poIxSPKHscC0vz9nBZHSTuWvkY3Jbzx7z7vncdeZ+m9EagOiznQfA5BQiYz8M47cR5BhCgNz3jiln9On2nbKyE01bSLm79BS6y2jMbfH10F16p3PCXRRKlGXbYxOHAaizkU7ebgOL23U0i410UR12kTvN0p4pnnfdfMdGAeZc+zTx8I/nk4nNVMr0elTj18EbFDAmn7aHDRkQCIVtQBgR17pT+MmSLEemXjwRuE49ZoZ21DmJXAyCyNCli2CrntDJ0pHDDo1n0VdGX10qLGVctqhJHekEO9AgzVFVozF0e9l+rhGl8/dnRBzeKZZjl8zeDjijinT26ayTtVvWBONsMx1nOndHj9ZFEpQkODlmGSEbIOzA0166ceRajlknDzrxRgdH8bPZbBQNGai5ZiLjev15RuxOEWZd1OP+O6rOSC5TjiYBjC1+yW1MqUwSZMwIKXaMfncQjN8TchmbdFoNNu3PO6PLktGKJ6TTLja2ZIuZhnAdZledTszAuadLTfleM8fcbu3IKoGO+pxLd7/ToWb6hP5ab8jTRQHum593PzrgTLbP2onbTcefzgK50zF1IGO2nGy7Y+Vd+13/fL917Wc6EFsGMNaJ7cWRCXValmXj5nHAAXcRLP872uDZZRtW+B/AoP+WMfVnEGcud3Vbl9mmyULOeT/L78w4ZOTSjcHxXiYHMnaWVTVfn3E6zACCsTMZ7Yi4D1CyYeSOM7M8O/+OnaTjSWbsiWKn6MgrIwnqJfVncHB/vbiP7OkEMp9vx2Ggpf2ciLwDKvvoYkeXwJA6svM20FQtsvzcBUR6K8+V8JtoNElFgjIRTn5OX9Kh2IFl/akb1+foJsEmU4D0l9/ue6bj7NCxgRwTgONoIEVb/voB/2as3T+Dk3Xj75BJHWLH2KG3g+cbOLoNJvl/RubWY6a9kBWyt4zoeE2W5xwldWlMAzp97LIAUyqTA5lMhXRpAqeSMp2FwWxtbY0G36kwG5QBBeCxUVftGhzt2lGTDvDbAkjf0Re+tMqpPU9O2nNkgmP1pIOlO+3EYTDK5ubmwi4o6nWqJM+uOA2Vi6G0l0DC856AFByjxy/Tad42nM6uc86ZpkSfhw8frs3NzVE/DTLozfr0gi62kG9dQI8JQPyNjCY2Tp+4ba8DuX7uyWgRfXdRnf+3XMvSmZn6pe6tra1RSqiLuOkPsnQHVk0Ku8iPzEG3RrrsLFoehEZHRGFE2PQZEsr11I+fod1c2/Xcpt+OfnLcXTelmyPHe5kcyHgBEYfNIPqrec0YbUSUNPJMhXV5XZgJhmTm6yhpZ2f3dHTuAnO0YMa9LP+L40hm5xTCzs4VO8ccLeGcOAXubcd2iu6/+2VnnbvCUn8dO7MTTGZZ1R8s9MHYTDUm+GQUZlae5MPpoEx50D+niAxCKW9GgwCHAReZsu8JgNTJGHV6SlmtN35yC62fw05sTxllJQCgYxMv2z1O3brhege0juAzC+ExyGesowRYy2ZwqKr5m4/ZQeqNKiZqrJHRP/pie/Ec9K5Ry+ax8dGBzHhkOm8qZXIg0534T8PMYiO3U3FYu8zJVy0uttv5en3FzoW60wl2ueN09q4jJ5cdRUY2VYtfe2CgsS46gKGudMqZFrBTs/N3JJn3LwOMdLwZLeS9jtY63aSzdarSk71LQ0IAbCfZTxdHmDzn7yzyOHpsXJ91bzmzJElKkoLsTn3mb0dTjoA7PSO7nbMJHJ/7d+qT0gFE6sYkIXVMHenYM0Limn+6bx312FIvuw+t7+yzMxmdPUCUsr+pg6mVSYKMjT8PWxokbKBOVa2s7B4Cq1pcH+hYYTIqrnd52bzPIb8dSUZL/J+vFUkZKTgVb03lx9cBGjvR3DyAXD7rQbsGb7N1b331GCCb36vlCA6nbMefgNaBMSkkb8NOndjp2rm7D9TDZ45eXE86k24nFmwZJ2QwTKfesdllNmdddtGLo+OMUnPNxGsV1gkkKXVs0Mh1SZOlTJFVjXcEur8mdMvmmH8jo+e61yP9k3qCrNBWEgpHnVwzYeyyC7zkNgmo6ybV7TWozj6nViYHMqurq7W+vr7A5hnM9fX1BcaSbDrXbBxyO1XAhMGYuxSaD905GsDInOPtwKwL4zc3NxeMvAM3T2IzsQSY3AhAyQnj9RyDYk4UHEuycTvm1An68HoVwJPOCzlow04ynaLlsgyZbiMd4kgo9YANHC3qcr129gYSj6fHl3GERKTjT5bvawZvHGj3rHXL/dzH2zJMXgARnksHbhvOfppg+ZAqxQCT6Sf6mLp0fxJgcPYQly5dmluKPUe9MYj1JpOS1IvbhITYdyQYs+5jopbR9R7IHAfFRsrfLBx68iUzZnJnpONJiwPqnKfZGI7Gi99Vu+yWyWL2noDSgZLlpC5HJZ0OvN4ym81GO4ucKjCbtxNGV2bdjibMjGm7WwfJMVo2VgYtj4Hrz7ZpC0dhB4hTdprCDt6Aa/kTkL3Aa4eTkYz14x1IZsYZuWRaybqoWvwKBjtV67SLYrIuO/UOhJCJ6NIONAEu7c1OmPs5EGwCRv0mELlRxP30cxlJ8RlysOHF0T6RVJ5ro+9+C4hJgu04f3v+Ve3unPNB4W4ue84fixRNpUwWZOxgYDCc0M/FaYfdVePvzHCx0XgbZZe2WWYsmcZJUFvmENKZ2XC7lJx3Nplp2hnYIVDsvHJSOxJxVMdndgjuW9ZFPXb6nYPNaCnHwrLh3D0mlIzIloEe42MHhL5pk9/p4O0sOgLjewyiZrSuK9+q4NROkhzLkdGO03LZ59S1nTdy+PyIi8fLc8f35m4wO++MSlKHWRf6Mvj7Hn/uiJK0bI5XzosEbdebc9Qk1bZ15MiRpfqyzj1uPpO17LnjvUwSZNKYHZFkKoF7M+pwYWJ7fYAtldleF/7aWRog0nizvarFhfbsqx2Ln+H7Q+yE7eQNXm4/w/3cmWQgxBl6d06uq9iBMJmI8iy76+2ihKrF1Ff3f0aWfJZy+7mMjtIpO6LItjLqcdrEdtGx1iwmCF4TyijX+sm+Wa5k/Cm7bSgj0NR9F0H4uje4ePyZczhh5PLOLSLsBMm0d/cz043WTerF8xaZnfZMEDRh4nP6klFyymg9p84yW5Bzr+vzFMrkQCYZlSeMmRkDjjHgIDM1g0H5vWdra2vzA4cYcJ4ryV0mNrpkmz71a6CiXTsfM15kXltbm78XjQm/tbVVm5ubC2ku6rXTp2TfDTZu186CyQZb9MSx0weQmMBOOaALr7V4MtJGgneyYcbBUVwC37J0ix0t+vMYdszfkQuy5YKyx448P212pCFTo/Td26YPHz5chw4dWjpu/m3dYAN85rUL68vjl5GhbTzX9NAL7wt0m0QUtJVrkPTLa0AGpA4ckxw5S+G+5maCJBD2Aa4vo1PWNRnPJFLWG2PJOTTG0P3j+dzuPbUyOZDZ2NgYMX9/tXHV7vqG88wd0/ELEikZVps5+h5HTMt2l5lx5gExZMrJkKy5aveFjYTqOzs7c4Dx+Qr6bOA0q/eag/u9LIXnyYccfO6J5Gdcr/WWrDqZYgKknZd1lY7V7RtY7DiXpavcPzPPjBDSkXoDgesBUDvGTPs4Ls5x2Ma69TeDhd8CQR/Z7ZSRUa5Ldv0xCeK3Qcckw2Qh++JFdb6e2rrkGrpwiq1LI2X07n4zdh4PA6LtwLIaWDO7gI45o0Xf0z94XjL2W1tbtbW1NSKNBhpvQJhymRzIrK2tjb6ga21tbbR/vUvreHLaiSSA+P8uncM9lC6cTkdOBJROnL8zhWMHRcHgeTMAqTIzR347vZUsinrNGu2E7BQNeMlm7WizT568BpU82+LP8rU/jhoy1WE2zhgYHDL68PjwGU7AO5HS6drZUb8jpUwJWgceSxwxuk3d2JYSKBPEcg3IjpPPbU+OmnMXWdp6x7A7+7euGC8cLZ9T7Kyrdr9uIaNOzxGDBLLSlvWaEZn1jm/gudRT6t91+f8uKq7anY+5KSXHnnsyezG1MjmQSccJo6OYFWJUZok2wgzdM9LpQMQl2XvV7qu8k8n7bzsj5/KXGfUwDPOJzMTNLcqwWe4z66atw4cPL5xa7lIMXerBDtxj0LFHt5kAmhM7J7MjmQTEZIRd+sLrR52TpJhV44TRG3VhE8jmtb6qGkUk6ML9YUypswOyLLTpyDz7A3nJ8cpxs3NLnS4DFeTnXs+BbMOpT2zYfTSx6Bh9R6SqxrsDOxn9vOdLAlDVeIu2+4fMvifBK8eTNvxjG8xxctTjcZhamRzIOAWQrOhoA1+16EhZ7KsaMxAfHuRe/6Yus7B0sMmo3W6yTSaYnYLvr9pNm2VUwXUORpKeAHjTaZJ2W/biyARbg6CL0xMGBo+PJ9nRFsXph9tfdr7HUWjW4X7acXB//lheUpEGLLfjKAunub6+Pm8DuTsnbF12DitL6rRzTN4w4nG2Hq0LxjQJGiXBnPszI2C92f63t7dHXwzHO/LsXD0+3nJuIuJUWPcW9GVvU7YdUI612Yd7Pa4JFh0ZMlHyy2KRD70eOXKk/aLAqZXJgUzVotNPJ5n/m1FlOF21uPWXiYAB7du3b+60kyliXMmofG9eS0CsGp8mT6Dhc6fJaBdDZhMATsR94H8WuzkwllGdU2xm0t4UQV9xGD7AaceAE8lF42TktJHbtqtqYbMBz+Q6mFNCZt/+jHU0SAR14RiI+tDtMAzzcafPfnWPHaHHLvu4srIyT3MawLuNGciZLNs694+vYeud7VuurtgmKfQNeXONkz4nOHjsMq0NwaL/dtzYCalvzqNYJtru1tz4yZ2lXdSW60s8Z3LAfdmW7cqpXrdvssqXwmWEOqUyWZCxUWS04jSI1yu6VE2Gx3zu/51XTznSmLscbObaLavBrap3DPTDaTE+B3R4vQmFiMWTmWd9kh5degOF9WUn4sjL+snoh/b5f1ke2n32ZKckE2dcM2pBF1Xjt/MaRF23t6pmNEw7gA7OjjHAYbhfBg6PH9d4eSnjb9kzUmOcsGNk29zcHAG2nTw/+RXD3mlpZ5vrExQ7zly3yfWRzc3N0TjawTL+CRC5qcDEw+TEKTen60wYrCvAn/YdbbnfmaZ0GtDzkmv5lnB/ls84AjLZS2BfBvTHc5kkyHTFDta7vjJF5pLsrcutdrncLlTPtIpBJp/LBVo7mExB8eN3dvm5TCv5BZOWLyO1qt18v1MQREcpN5876rBefU8yawNxyuT67fAN0DhcM/0EZ2T32DuFlRFPglBnR1U1385Ou+woYu3LsuDstra2RoCQb+vuohL/ACDe0NI9myBj3dqpu8/WLzbgujsShC65lmen3DcXj2vOQ88v7jE40ObR5lr2vavHhDEzDl1U7WfTFmzbXRrcm2Yo3tXZRVVTKJMDmWTFGX34t6/73nS8GTbbUSd4eOJ0daZcHQjRjzwN7D4mEyJi8RoH97FW0706Ixmy2/N2aus2361lnXg7th1FOofOmXqCoU90CXu27oncHDWYKZrNWya3A9Bwr+XtwI7PAd719fXa2NiYv01ic3OzLrvssoW0EanMYRjq0KFDC+9Ho/502o6GrbcOEGyz+Uxne53D5ToyZLSdzD1/e3wplhW9OqrB1nx2BhDsXmnjuZVE0Klcg7L7m4CaqdOs0/Vxf+54TDvx2KXfsS6d7lxZWRltxplKmRzIZC7bTjIniB2hWQvP+f40vnxzsqMLM5ZkdJlas7NPNmcj7tgtz3o9JkEGGY4cOTI/JOe0Ui4Em1Wb6bNukGmOZGbIY/3SPzvy7EumPVgjQl9EKT7rY7kNFC7JutP5ObVCXQYkj1OevVhbW6sDBw7UxsZGbWxszNeyDhw4UJdeemlddtlldfDgwdra2hqtQfhtDJlm6taZMt3ktSXrwnZr0E4iY4DzBg/GMudBx/JdL3Jbv/4m2qrxIdVhGG9zd5RneaijS48lUeD3zs747I3nDs97t6lTmgZ09IXMPmfk+7vI0Wk27svt9k4HZp1TK5MDma4kgHT5UKeqyLl70G2kGQ3xeTKj3OmVLLiq2hQXC80wYwOIo5iq8TZI7yTDKVaN13m8YMzkSTn9Y0DwOgo64y0D1qPv7SaN0zTWiZ1mrknYQR5tDKrGk9W6sjO0Y82USRcZmAzAOA0yJ5xwwnzL8sbGxuggJGOI83NdjJMdqSMv5DLQzmaz0SYPj7kjGfqSuszIJdNL7rvr6VJlGX3yvL/JElCxTXk9yMTQQAWpoZAidPTlhfQucnNkYRm71JhLjrvBl3mTc9z6r6qFMUyQdtu5U3VKZXIgY0aSTDqLjSZDdOfuqxZfrdLV5zrT6GAwycQySklZDSJ2JBQ74XTQZoHcZzCpqtHages00zLbc112ME6tpWO3bjLNaHbrqMu70ty/3L7KBDbopAO186H/SSDSDtC99ZB2kM48nTegZhLgXXLL8u8eDxyy7dJjaTnzQGtXp+vN9tMZGxQ65+cx7EiYGX0+489M8Py/7dCgBgFDJ9hN3k/9rHGmPhJk3W+DFvIaLJaBWlcnZVmU7XTZHsgcB8W7i8wiKAyojYEUDCDjyWfHmumcjg3yeRcNcJ8nLU7Q0ZXTYLkVtMuP0ybFk9wyOmrKrczLJgVM06Vjh9zvLb4JXl1qICd1pvtoh0jOeevckOB2/INcXTrNfbFsCZYGHsbfkQQAwCaAAwcOVFXNt6h6q7fHrwNhO0qcpNfTAK+MKtxGppR8n9tKIOA7ZZYRtY5gHc1ZLnPmaRPL5DV4VO1+749JIbISETuli/7sByzH0Rx7EgqnNR15dcCZfeqIYcoyRYCpmiDIYEyeoFW7A+tDdWn8HVt1MbvyiX+zZ6852NF1Tj+dK5/5dTDdoTWedeTThejdeZGMnDpH6omJPvjd5Y/JqXcOJgHf7VDM9Dt2CPv3oq77hd69vdqEwTaxLCJNZ+MUjiMIp3AOHTo0PzfEjjLSZvv27auTTjppniqzzRjwEvQcmfFD+rSzzwRSp9v43ONoh5jvO+P6MmKRBCejlWURD88bEHKTjM+vdO3m/x3QGXQ8Z3iuu38ZEFhWACxt23OsA+/c+Zc79TodLUupHc9lciDD4ivG4PMdLCRW9WdQuO6Jsozl29idNnAdmfe3k8AZsijM9ardnWJeLKbYYXZpFe/G8cszqZ9nvL6SzMpAtbKyMkrT4RxgrWzH5Tn0610yqQM7duTKrdY8h4x2Svx29AeL5f5Mo9lJHG1bLvX6a5P57fUBxunSSy+tyy+/vFZWVuqkk06qa1zjGnXiiSfWgQMH5jIg29ra2ig6NGCls0tykuNn+6rafYGjozX6lv02eOXr6zsQt66tX8tmp5wv43SbHu8ueklmvwx0mOMJOICnx9/9QH/exJERE3Pep/U7EOnAyePiNSnGxOlTkzL01O0mPd7L5ECGwWcAGeg8GFW16+xxKAYFPu/q7kLsNPTOMA1MtJuhva97S7GdtM/RpNPhB4ad0YplTVZoR53RkFMxBlM7yi6NZ50mqCO3+5BOwfqibqf67MSde6c+r1vY6eRGB2TFSXpMlqVBbD8GOjtO7y7LenH0joaR3eNo3XlM6avXp7ye5XFOQHZf+LtbxKbY9uxA00naiXs34NEIhMfG9oYePG+tI4N+Rt+2AYM0PxmVMNcysk0g9PgnQeuew06SuNqucyPQ1MrkQKYrdvpOZzFpDSZmQOmEKF1qINMGyaScJqhaTMm5HZ//6NpbFhl0wOd601Flv9z/7CvOBQbq+twe0U1GE8jZRSHLQMbRk9OB2bfuGdoAuPncsmb7Bu0cb+vH29ftmPiKBUcql112WR06dGgUDXbglo4LhwkIEdF6A4Gdc76HqwOLjPYcgXOfAaqL8qwHQNBnl3Ca3rJMsa14AwN99mtauMdjazukPup03zMi7KIz+wKvA1r3vj+jExMYbMQ/SVrTX7hunu/m4xTK5EAmWYIdjNmPWTgHCKtqwbiSRWNcTAo7iIx00rgS0DBGM6yMPJLN0kcDGbJa5mxvNpuNXvVBPZaVNIMdmHfZGWAywrCTdCogd4JZj/yNzHYU1gnAmCXBlbY9mZcBsP/vHHO2k4CAjP59+PDhOnjw4FxPR44cqUOHDs3Xa8yWvUbjem0HdmomJ/nGgKrx1zPbTvzb0QfFqVHbBe3ZPmxvJh+klXDUPkTpMaBPpFJ9JgugNFDkmgb3ZfTlLzvL+UVdHkvr6WhkE5ugHr9hYTbbPTtjkDdZ6sDFekxSs4z4He9lciDjKMDpg0zDJHvFKTqKyfuqxhHICSecMJpQngDLmImdPhMJ5+hcrSdVlztOx5c70VZXV+fsl777xZl8jnPz4rKB2Xl9nJ03JlSND9AhIxM/Hfb+/fsXDrK6/1zzRgjrzEwynYiBkc+7FF4yTezG+Xj6Ykdix5f6pxw6dKgOHjw4T6P5fJP7m9EiJR1frtmkXVl3mT7N9RA2JGALtjnv5ktyYkef8yLtNOVKp59HAyyfx3NlZWV+1shjQd9y/JY5Z/pLH/3i18wmIGuuayGj7cOys96W36OUxMiRI/8jV5dWm0qZHMh06Rty5mYZfN5NeurpwnunR3CkZjduu2N91G1HYuBwu2Y9yYicbkIWP99NOtq3zAAMC8DuixmeUy1ViymeBKbOIQHIfvNzgmpOfiZx1eJbb9Mppf7Ra+coMx3lCMV9YvInsC0bJ5Mc7w40aFrvGf3ZXnCsvu4FfgNjLlxninD//v11wgknzIkRKbiDBw/Of3hTt2U4WlSIfGzhzrnl4v7aHh2JZXSW65Hd5gvAyNGEn/HGBsbG60roLKPhjIgdeS/rH68WIkp1ijSjwY4wWDdTKpMEmTRU7zbDuDoD6qKbql3nlozULNBOymzSJUNlp+tyYTRls0O1nLn2Q0nAqhp/QVOmIzLiQE+568t6YeKYcVs3ZvodkHdAzAT0RKS4HrP1Lp1jJpoRakYCTvlVLe5QtMNdxoIB5dy1l47fY2DASqeeY+znvK6S+k7At554QwHOkNQWY2iQ3dnZmQMH/9sJZhTpuqwrA4lTv12kkLLbHjJqMTBkqi93R1rfjiI6YkO92d+03bQFnjOBS1CkLyYvttmplsmBzNHYkJkk1+xImKw2QLNOjMT58UxBVS3umU8GSDG7Mtuy0zOA5G+XZSDjieNUnp/xRPFrO5DRTNt64XnvnLIeqRvdZApoWVrG8iX4JXB26wR26nbUGYl4LKkLJ+ExMOs0iFkXvmbnlXL5/mTFy0Amd3H5u+Yt19GclVOivNDT+tnc3Bw5x2W7xyw/xTJzbzp+AIfPnNI2yFKP5yFAlfMrx67TpcEmtxMjW+78s26zDwk+aWuOkgBeiscTUE95url9vJfJgQzvh6LMZuNcdNXigneG/9vb2wvfsNcZqaMkH/I8lvNxmszAwrXNzc0Flm6nk2kmrvlMDPLxWbfTx5M5HQc6Qk/UBajakXCqvWo3WiJ9x/379u2bp46WMcz/X3tvuBtHrqTbUpKtkrp7z37/hxxggG7bkmyp7o/GKq1cFemec4CNe5wQAUFSVSYZDAbji/jIZNoh4Hys74kGojhqN43ka6m/tBv6dnbH81XNKNwW8vPTcbTTpX3anrI0PmcsqcPHA1EHuq0DZQz4nnsaKEBd/vbbb+v79+/r+fl5PT4+bl6iVXrLc8HF/5t+nbJedGuQRl7Po1KA0zqe55fpSM+Z0muMBcDiZ9Fs7x2fZuvYp79/eXm5rDmiX68B1SYKMOjpaOVwILPW9UuunIp7gttR2Fm/vf29g8cHVcL9cq9PJcYx4LAdLa+1NpQYn2O05vDP5/PlIEUmXCeOZfQkKcB4olL6FLSdrh1VD81ca23kbTbXRXt+e2IatO7u7i6UjR20HS2T2CcJ2Al4ktqZ3ty8P+3taNTRph+MvLu7W09PT2utdfncctJ3L4xP2SnjRxZk3e9lkmutK3C1w6V+O9HaOHLXKXqzC+38+PFj/fnnn+vl5WV9+/ZtPTw8bOzm06dP6+Hh4dIHbMPROvOldRvkvG7UeWh9UBf6q+5sUx6/tu9rCKgcYNiOkYUdesxZHtgugFLcJnI5eEDn2Cs24rlZH4Es3dBwtHI4kJl2tJQ+a3SG4dpBOgrHaJvVlPueUmx+Y+jd1VNaB5mYlHXSbo+/iXzX2nLk1YXvQU5H7tzXNuwEXSYasBw0febYfke6vgZQsSObKC+falBAKQ1n51460k6jmx4slx2Co2VnpaUIsQ87IztB67TZNG3/zIZtTx47B1MeY8vMVuqXl5f15cuXy98ENn77KffWMVp3yGBaCD0WTKzbUmyud3K0/tzZNf9bN50LfF5bpbQ/vc521w0g9gfozWPtrevWqYNfg/oHyPwCpdyqjXHPwMz7FmiIxnGSrvdnjqHO3Q63D9N1Hck7dXCedXh2JjhQy+fvLbO/L/3RaxtdeXdcddCIt7o2ZVgHZKfNWHlXlWUncyyVuBf1Ws5mNciwBzKVseNq2b0BZK3tWk7HawKriVLpj3Xp66CmPH6UabfgWu+0sjedMA/2nDNzZZLHnxU8JyqWYpsweBg4HPXv2arlKXW21+6eTLSF/tAxWa6LQcaMCFkNGVP12LH9GcD+6uVwIONoGIP1hOQaO3FHfo1g1/p7InI+lQ2QgnOwE267Tp8NMqYFMOYuFnLfNJlKa01A50iL+5wBGPAm5+YIq86bek3hNVqnH1Mk3OJJik47gYmsAQfqngCuzs+6BVimtxGic6JY0xqu23LVUTljY5yoE3kaGFSvtTdTrVy71vV7ifx8je/1eEPlWDcem5ubm0s230zMGTNjMN3v38jgfhrcPE4OmpwB8x1Awhh6jk0ZO/9PdFyZBHTv7e2s0XUedG454PBcdz+mAGYKEI5SDgcynz59uqyXYJhrbWk0jMbZi8HGNIQzEDspp7vsvW8UY8eD8XHkiqNa0zp18Oa6G+nyWSNbZGgk7UnB3zjTtbZOs6Ds9rvOYyAwlehJjN7rWC27S7MwZ1UGK19DvdadI3iD1+vr6+YwT+rxOgRg1izBkfekY8tzf39/RYvSFxyZZUf+Lrqb/mwmRKbdccI2pqzJD4ry+efPn9fpdNoAM5sfALYJaKeAwbpwdkLWxekT3ItT9j0+ULR2Y4eNzbH13EEc9B8y2S67+O52nFl0XtoW8SM+o81jZVthTJs92q6bKR+hHA5kzCljBOVj7aDrhPixA3JUs9Y7T42RMNF7ejJtYcjs4ploLK6BxnA0tda8kG+n2UjawGcH6mgNg0ce684Tj7Ysr2XAWTXyRi89PsUTi7YB32Y6dqQ4Hve9dJKBxmPjgyO7U8nRPG0/Pz9vQAY78ZjjePeoIjt19McrsOm76yxV1QV16vIY2HabIe6BPCDmHYEdN5wj9uc1QgDDjr8PtU5AhJw+68x25GCrB57SnwYo3njTLIlrbEtTluuxw2f0NQjODJHRC/8ed/piEHNAafshGLVMRyuHAxkvuK61Ns7BBmXg2aM7+N0JZaOhXj9xj/Nh4tvZlUbb478pppvqtNxOo+gW2l9rXVFyfD/9X8eKTJaxFJDB2rrFSZlCoA1HfaZK0MdEezTT6318jp5KX3IPYMK1Hb/X19crx2idVA47IF9DwFL6qVEzfS69R33WvyncOk3rqs62mYHBfLrGEbn72LZqR54ztvfuokPPtE19dboT4HajR4Mg68F2577zuwGC7YQAEBoU4HRQ20ykwYyv8YadI5fDgQyD6YntXWITyJgy4DpnL43y1lobsKD4HRd8b6OdshHaBLTWeqdvHK3vOW47WIMp9VYvpPVNzR35W347dF/rvx3FIUvXiRxNcxyPJ2rr9fVek9kDXb7zODb7aBtti+/JzHqt17SsV9dhegdQRUdkYQYoO+9mAfTBwOP2AQXTfR4HbMr2V8qpoADINIOibm+5NrjaybYP02906X6zSE4f7fSb+Vj/XQPsKdk4cmeUHjsDJ3/Tvn0GO/DsO2AD+J8567HAxr2W5gyn/TlaORzI1Ng96dkhYurE1MVaW653MhYbe0EG5+2sxVGno14mRl9KxsNcpsJYcDTAIGszMvqGQ2My++ViOO6Xl5fNdtOm9aUg1lqbk5wtC9d5G6dBC2fLhH97e+fLp+cTzufzhbbwwnzppBb3z1vPcQYeF9NRHl9k9YN1DhCwCx/2WeeAbg2Q5/P54qiwx0+fPl0otLXWJsuz80Y2DmI0Nds1wcnxMsZE486w0AUBGXYN7dugwnLtZeTNqgrQXqNwPdzv8a6j7nykP2utjT7f3t7W09PT1VqSt3xb3malznKRlzr8bJtpU9uBN4zQjt8hdXPzfi7fkcvheug0nr/9hjs+9/EpjrQxPK8jlBu2I7ahOfrFodhwieIBuIJPqQW+44n0tmeKwA8RWm76iXNDDkeMLp54gIOdMFGzIzLL5F1R1HV/f79Op9PVWVi8e8XcPzIY/PxgYSkRKKiOC/Uw0XHM6OF0Ol1AwjSagdEBgrMAnKKdEHaG7TiTpt8GAzt/29W0lb677JplAsgAgh2jnafXEafg6/Hx8bJW8/z8vHn192Qj1OO1CXTh3W44ftuV+0/A43UPPwjtLMjgSbvTaQDYP5sH0CO2O2W2pi2533OuGY99R9cHbcP2SdTvcZh8wJHK4UCmpQPXNNiOnWKjmrh4c/yNcG20TBwbnY3dztLZUx1ci3ew1EFSV0GVs6pw5l5HoZgi8SR2NtNddFxrntmLpqa7fOqwH5w0FeZtsNUV+qdN7xC0rhzJ47hLdzL+6NCBBGPAWgxtdJ2l9lI6yoFII+866o6dgXLvp1mY17UM/PSXDLqZDk/6/+tf/1qn02m9vv59tNFff/21/vzzz40NN/J2W5bXY4eM7qev4To7b+8MpDjbts3bgXtrPnWZTnQG4rGyLZM9uy7bs4MD24EDNvRSm7HePd8t79HKIUHGgNC/cbR2Zk27MSobr2khbxlldwjG4oVdzoFqalxHboP1k/EFSGdZfdbGjhkqyn3yWWzc3x1ze5x6H4xscbTvCN7fN4pzNuXo1hloozyuRV6cpu8xcAAePvtrAgEDlAMI79pChwZ/73bCFkwBMVbtt7O/ZtGNsOvU+M7O2DoyAHWtiXvs/O/u/j7i5+Hh4fIqANshmZ8z8tKUE208AX/72/F0Vkp/u3PSmepa60rfnsvUY5prAg1ToPzvB1xrh50j7ovnQLP8aQ6ZHu3uuaOUw4IMDmat7Q4pTzY7NafcjSoMIiwkwvU+Pz9fKDBoAa+pPD8/b+iKOpKHh4eNjHW4U9/YBm3H2Six1BsOzuDE+WyTYVNHHampBtNGXG/Q833OVDzpqcfHxTTicx+p02sljGWzLJyoX86GXr0bELm8fsV4Igd6g1qC4unmDjY1ED1jG/S7z1PQN4+v7bH9wlEBsOjWgQDX4zjRYzOku7u79fj4uH7//ff1+++/r9PptKHMGP8vX75szjgrePCZHWpPraBAk661NhQac6EBl0HGgMRYQkdaltJnyAcF1wDNdkUW5/7RBweVztitD75D/xMNPO1wqz6PVA4HMg8PDxvH8dtvv621ttGWI9I6n9IwGCRZCT+Ai42UhVIvKJs2MF211jaKI5p6e3u74qLXut5KaarE1JXXBtZamwP7eDBw2h2Fjkz/mWtGT6bz+jS7HTLrLc4iqJf++EFE6qawjuO1CLIq2vAajzO5RqB2cl6HaduNqqnTWY3tzM6Na3omm8+78jgaGDlMEr0xju439xhsGJ9v376tp6enDVj6+RbG32PbtaTHx8f1X//1X+uPP/642NYff/yx/vjjj/Xnn3+uP//8c/3111+XDMd2Y5qqNNTEHtzd3W0CJfT//Px8WVssjebsqY7d66e+BttEd4x/6TQCBgeRzcbLUhT0608mNsXAx3y8vb1dX758uQRmBJ1HKocDGYMJINEJ7gzGP6bIbByltIhoMEYvUjuKmWgtG7e38AJQpWigYHykhvtirtlrCLe3t5f6uiHB2ZL75vPZTD+g14nmoH07MPPr1GGH2Yjc9BiROVEs/Hij89vb2/X09LQ5laHZHI4Jx4vzNagw1gXfidbBNrxeULrVWRlrHe536+Y++tftyMjrrIXvyAS6C6yZuwMRZ5XI9/vvv68//vhjPTw8XF6LQWbB//T569evV+PMdz4x2/10dmvbcsZR4MAJdxOJs0MDJePjeeWs1vp2pm8asMxB6S37iq617M1LBwne1Qowd0NAqcgjlMOBzFo/f6hqre3W23LHvsaLx3Ume8XUhSNgG7wNy07XgGHQojjjmtpFZi8mdpePr51SfXRlpz39dAJ6LYOJ3gyxcnY8vFYwjU3vw7H5OwOLgwQ7ftN7jsjd364zrLV1irRfkPD33iFFW4743R71NZBwxmg9OGL+/v375mQLU5juW0EK6s/rMY+PjxvHyE4x1iG9Q886cBDgIKeyTrbjueo+4HTt3L2GUaq0gVEDIMs6ZUuWx+t1zFHPGVPU1at103FAHsuPbiba+gjlcCDjCNp/20H6Wr7z/3ZQjbq7LZH7vReelLvbGtfapu91So0Az+f3o0Ycgdo5GkidkXAvxVSMddM1l0bb7YOfo3A//DnO1W0VtPnt3WOmHKwbMkfuKWi5XcYJ3dhR4/D9rAjy4RBM/U2Ow21a3uk77MVA8/r6ur5+/brRcbNCF4MM+ve4U//pdNpsWChY2xnSHw595G2Z//rXv9bDw8PmevT18vKynp+f19evXzf6sxM1zViHydqY54WzHvThjNmA+7MAy7qeqCwDkPUJ4Ds7po1JDoOey6RbwGza2rzW2oCbZZts4FcvhwMZv5r29vb9RWNQCkRjjnr6Y4PF0XYt4fHxca31bmB+rqAvLrLBYog4AygBH8rIBLH8XqjmfpcauNv2My3ohhd1+eVhfFfn3IibSff29nYBtLXe17qgWNp3R3x1zOjWtNbeeVie7I5STTNSDAJeM7K+WKs6n9/ft+Ks4+bmZj0+Pm6ABTnsCD0+zoj8Qiz0g77oszNet1GaC/3gsMlQHx8fNwEOa4bWuXUH6DLGgJXXMAEE1my8q9K2zjokcpfaI9viPDgCAmf0nXMOrJxVOfrnGm+HZ4wMELZt/7Z8ptvIkAnKsEXaaGDROrA7fIW/5zs+s71hw0crhwMZR5f+7UGv4XnNgjowaNMEGD2Ou8fOOMNhkk7RpA2Ue1yHHSan4hrs9nanIA8T2BOCNv30sykKT0oDiteCPPntzL3WgU5ZI7Cz8I+pBuowkHsiMnlNLfIderNTtl69xjY5cSb66+vrenp62uyiop/Wp9utvZnyeH19Xd++fbvIx7XIYIBxgNGFf+zQuxShuD59+nRx9P671KjHrlkO13QzgNeCOk7YZE8DcMbJFnoHfW3Ph88CvM0gsAd+cy2fMccsO3ZLW+iNwMcvEltru8uN4rU+j6911VOjGxTRnunz9m3KzI9WDgcypcUwpNIyjUS8s8lO1Qv4fGZHbyqjbTeipu6JXnG0VmDk/hriNAnclvtaymqvTGstlrdZkeu3g8XhUp8zKjt5t9M+4aDspD2O0y4qrqt+m6FZp3UYFBwFmZkdrvtFXQZC024/Cww6RsjsKN/Rua8BRJHV1M+U2XZ8DcIGZTtLTpvwmHO96UADuDevMI5kdD5l3BlMM1BH/aXAnP24r80sO3eoqwECGRQy+D6Dq3VoWrKy8OOMeKJgCVxMa3+AzC9QnHEQbdlAmcAuGM1a7xFJAcWL+E7RXYd/bKyUAkkdg6+3M6whd1HakwJZuK/UQifiVPbAEn14otSBmTKybryrhkjb9ZsCNKBPkTVttx2cRmmr8uIFRo+/M1GfnTYt7HsszufzZtuznVb70uyO/rgNO3Y74+raduF7mhXalmwHtTfsn/ZKp/Z6+uz54P46YDKFBgVVqrYgbuddnRWcrDfL2HEzODvbNoDRr0nPyFIwtX3Tnm3ZxQFZ6z1aORzIsKjqgnPCKPwQIt/5LYgUO1ufOYaBmFpZa/sqWyarjRuDsjPyiQC06UVzUyQ465ub90Mc7bTW2vLOzgjMeU/Ru+vxGgA73RyVmnJrxoLM05bQAo91aXrSFCUOgedbfD06LdgVtArIfM6zOKU4TEHhCPnbUTzZGf1l/YxrTesgs7Md6w75ac868rZm7KwbMOibHa+pQWTnJWS0z2L+w8PD+ve//71+/Phx0TUPZz4+Pq7n5+fLeW+mDtEb2Yzpq9oE13/69Gn99ttvl2dC7LQ9H5wtNVNw1lWn7zb7oCYBjkGG+e1gretKpmwpzs5ZuzJjYEqu8nq8pizrSOVwIMOzHo5iPSlwZDZgUzSeIACT011HvRTagTdfa8vV26E4cqNuOyZnH46quJ5SmsPPxABmTAwviHIfD3RS11prPT09XeiMCRz9FLfpAOgQR6RTMU3RSBDgxWF4QdmT3UePdG2I/pmCdBTqLMdy3t6+n+1mqoWxMoVpQHJ95/N5ffnyZT0+Pq7T6XQBMJ820DZpx9E59sT3bcfRvvtiB8luMQOw++8M8uXlZX358mXd3f394DKnlHNyBfbJOtDpdLq8ihwH7rHEDrAPAhY2K9zc3FzWk6qXsgk4bdsc7dZpNzBpFr6nM8bUGamBhfW619fXzQ5FdOoxwe6Y1w2kLI//ti38E539K5bDgYwdD4PYnVkU0wr8v9b14X04WTKIprVuy8ZqAHNUaYO047LROzq3YeLwSt/ZsE2JmMJzRGgnhizsGDJP3rUH0w/92zI7qzB9R1s/o21ct52E1ybcRukgR/g4pd5L6fj7h7E2ONs2GA+Dp2UyTUTmScAwOT/u4/NSTXZo1r2Dmbu7u8umlNol9da2fvz4sb5+/br++7//e621Ls/L+E2pZJNkMwZkAhxH7ujDtKVBe63rl/T5iBiDvOtyZsP84nv6Ps0xl/oAftwnZ0UEPeiebd8OjJCh6zQOBqnX42rZm4kdpRwOZJg0FGcka223nXoSetJ2zcY0CvzzFH3aEay1royb65CLust5O222k+41NkqfLOC2Tc38TGfWke91MeBaFkf6FEff1tdeW9ZNwdW/GQPf50lqcCh9Vt00yOiaQAMHy4LzsrMz4LhubLB0i+v0+HQ9xTI5sOi4Vv+03czNcq/1t+08PT2t//mf/1nn83n9/vvv6+HhYT08PKzHx8eL7uz4HYA0AygNZJm6jsJ9pvQ8ru6LMxhswEEfv7uJZNKN52EzMY+57dUBRo8LmvTbfky+weDLWBytHA5knp+fNwPsiU4BKOyUu1MJA+Iz3tfhrKi/bUQtpWpopwbpqIr7+A2f7M8tgx2OI0jrohEbn6MnT+SpMAn87EABhzo9eR2RmybE+SKbM4Q60EmHUAxeO0Ifdk4OIHBOdih2BNbpBNIGDbfjrAx5+Bw5+dv24rGs861tAdrOZJ2RWO/OMJxNETR5QwPU0Pfv39dff/11WY/597//fXlA05s39oIq68B0qLNWB2rN3HzWXbO8bn5wHx3QOBApVei5MunU31l32JFBEj3WXmh7WtBv4OKsq7Z9lHI4kGnK3gjeAOMIZa33SeLJyD2eQJ6Ya20jGL91r5EcsngxeeKkbZClovjOD0Hy3XT8CN+vtQUZ2uLhurXW1e6o9h3ZmJReQC13PnHSU+Z3c3OzkcFOh2JH4QXZ0iLIZPm96aGUV52Y6+m9Bszak2mS2kQpHfRBW/TDcqGDvZ1Jb2/vb3x0RG7dGFjYyOBsnu+9WQHw/fr163p+fl7fvn1b3759Ww8PD5d++NRxP8HvsaLfzjR4tqb6ambogGTK5j1uBlWDhilEgMu6KND4+SK35Ye3vQmktB/FfqFzj/leMGOcTW0frRwSZFqckvc5DztxJmcX1R151ngavdlJuJ06WJyM63FEP/VhrW0a77ot61rr8lBc7zd96N1yBlTksePnx28ULQVhIOL/ZjamRSy/I387lAK6++UMCmfB5+gHx+KNFT6J12PJvbTptRiud5TqHW/eeWabQba3t7/XE/yWU0f6dYx+vTJ6d3+RFZtkPc1OykEKctgZA57UYye61t9z6c8//7w8VHo+bx9kZLG9W8SdATIuXdfyiRDIauBYa/s6A9NaHXODtXdv9TrAxMDO+HvDiEGI75xBoVO34/ngzMeZcNdeOj63t++nQRypHA5k7IQd/fd/Jlm3OFKHo+e1tkeirLWd4KVnbDiNyD2xGrm6+LMpupnksbx+4r4RvyeuM4rSP5bF1INBqVGh5W0W5kjaO3Pc34JyA4AJ+OzcCtKmd5jo5LC/TAAASvFJREFUva7c/xRpu5j2KTi0nglMrTccZ/Xr10o0m7T9ICvUUYOZlgYntG/dWHdE8wUBdGgZu1vTwFBb9fyhTGuKdsh7duLvbFcOVpyhOgvmHtfle9F5KW1n6QUKCvpwgFUdofefjdmvXg4HMuZo7TgpODsPsA1tra0zIBqZjN2OzdE79621zawMcPzfxcMW6m+kVFlMv/G56ayfLRhPbVrmZixeqGx/7KCReTr5uFGj+4a+yRAYQ/P4Xs+yM2+EaGfmbK6BSI8EMf3l6wokdkY4OP9f0K7DLeAxjjhvTjx2XxyNGyhcD3qkDe63c5sCA6/5ecdY2+BeMpk6Tmzd9JTtqdkAbU3BRcfOupsCoPaN+icbb1buuU9xANE6er/ni22A650p1gb3gppfvRwOZMxV4zzqFL3Q60jRBmOqx9QG1AWlUdPe7hvarMP7p4JTwXBLHzWi5sfrCcjQV0Gbnmudjrq435PCEZ4XhLnWFEkdM9fQlnUBePCQpNeZ6tC6ecAOhjbZsOFMyBmV6anuWvJ4QrG5j35Vttv0WBmAp/UC5CE4ur19f2bHNueTjwsm3mHVtTDbs58x8jh4A8Raa0Nneu3S9KrHsBnRBATowHboQKy0G7TRBDBlDPzTuXhz8/6eG9voXkbsYMF+wnOkc6zn+1kfvtcMCeOC7VIKhkcohwOZteZ3ezD4zUga+TkSsVHVweJUWKNoOu9JRwRjh8S1TbspbgtHXiOnDmdnGD9O2v33GVPI4Cf4DYRTxtLoea33BzUnyqiZg6Pi9qEZQBdXmaCe9ETS3XFn+qibKWjDcjImHgueg+CZCI+Vo3zLa/Ba6z2r9nqKnQpyACDV00Rj1ZkbZAo03ZRBP2pfdoq8sXLKCmw3BVKDUtdoGCeuo35nOw5UHP03cDF4sibmIIU6/KBoM/dmdAaEtdbm5HbutS1SLJuDG05VQL/OFg3SyOnMaW9X569cDgkyjXTW2jqetbaHLtbp+XPX2TUaTwooji5MGkAm6oq6myFwf4HP/fLn1OdJP+nCjqhRoCcV93mhv8BtisfOf49aaDbUMetn3Fdd0d+J/vE9FFNhBvsGB3ZEnz9/Xo+Pj5sHBJ2Zlm4pGPsar7nUuXdc19pubphsjp9mGf7bukA2v2mS70zt2OlTSj/2cwce7bfrJvPje2dLtFcbKHjQR2fiDlAaCHj8DTbI7DGZijP9Ul97fS6rMGXpDqKw4ynQPEo5JMi4YICO1m38jWL4vLSEo8CpeMJ4gtfZUxwdFkgKgOX7vUtnyrxeX1+vzkjqdVzr1H2t/Sf70eXETztDY4J7stnp+7vqr7qxA2n2+TO99u8CTNff7Chpi91jZCHYTaPWUoJ1cM1W3WYp1Mpd3bsYaLzbqwGMM0D/VMYCWWVzkGQqz+M1gT36RxYzCdUZ/9eWnLGZwvLOPwM37XANcpOBWrfuYwMT24UX7BscNqizjpuBI1PHYArMjlIOBzJO1W08LKSu9f78g1N5P1gIWNjRdivrWvMT4o4wibbqeHyI4J7TblTmbbee8JRShHZsneSmrJyNmFryZHHU6fq5z31zRGZAKfA5gq0sjQr5rKBpqqpZiddSerjlxLP3Mzsuj0VpEeqeMl8ibt9ruzufzxe79JjQdzLk7rpyFtZMz/bZjRV85yfrHVXTB35Yy7A8zLFu9TVQ1kbQg+vj2RPry8FTx8cH1NIOD143k/T7n8hIm4W5T6Yjnbl43tAHy2Q7dL/3glGDqIHUpf8foRwOZL59+7aZhJ6ga223+jLIXkz2JHNEwsuk6sTJkMhgbEg2NkfClBqj6TRf411jdigTRbPW9XZst20HbMdmYDFweFHdkWy3qtrpOIq0ozfIcU239DpAqNOlDvRCtuG+8pnl7W43P0jqflAv7ZvGoY+WsYc4uk0DdMfcTtnPbrh9O7hu3fYaVIMU66mbOhwITaBsm9oDL4+1s6YGBP7tYMqL/a67p0dUTgcT7rOzdAdQpg0dVHpsPK5+IBMgdmDiNTjqsC/wg9WWaQoIGghMAHikcjiQsSMo5eXo0BGr1x1s5K6zL28iCjVVUXqgxZOWie3U2VGcjdg7vOz8pno9Ea0PT2jfY6NuxsVnFFML/vGzE/SNUmB1AYDokzOl83m7e8t1TvWZTuHIlEbUjoJLTxkkfP8UpU924kzA/fbPtI3WNM+UQRnEqdeA4EzZDvDu7u7qzazYm4MF6ga8qL9Rdvtgp1m9lOYqRU1m3kCvmyI65pbd424b7FjZtnqf/3Y/nJE0uOn4rnXNItRWPPca0DnTLZV7lHI4kFnr+nh0G1jpj7Xmc6K4dq21e+0Umbsd7qU4WueaGlajNAPmHsiYwnFU7jr2HDOR1F6W1YVig5MjVf9vB2zd2Ok4I3BAUCCnzVIS1lfpIdMf1tFUr6m8CUDc146d60O2RrjNpL0JwMBgvXRcpzF2n5pB1Zbt1BzQOKpu5uhxcXuup3psQOCApuDpl4Kxhdlj7TF3MQAa9KZxsq6cFbley++FfdsXc8Dj03Gy7vfGjlJ79jj0aKgjlMP1iNTYzmd6EtqTqpOlDtug1Oi/0aojIV/jaz3Zeu1a1460Mjbj8P9TNtKoezJ6Z0v89jMAnbCl2uxo2wdTYt7i2mPdp/74s8mx078CjR1bo9PqxBE015hmdCZh2say8tvgQYSNLNYh15lKnBbu7TiRt0Bcp22H5fsnm+sYFvjJlPjOa3fW15SN22apC72TLVLX8/PzRedT8GVAtuy0MwWTvrfgad1yvYM6BwNek6w+Xaefr2nG4/9tV57TU3B0hHI4kGGwzb+vNT8wRrraN9oR2ZYn7gR0ZO5dWWutK6fnNj3JnUlwvycP7VInP6bauMbOiLb8fRf2cU60e3u7fY3utN3XkZ1l8eR1H5oN2tnYKdJe73ehbQOc+3E+ny+bO9yW7+X/yXGYCuI6L3bzHY4Hm3Ef+Nzg5Ejfsrm+UivYqTNlrmkGjc0zxs00PQ7tr53fBDSWe7IR7q1d87nvR047Y+uz63xTNtXnX0wBch1zygfRTvMDfRi47T9sL9aV++Pr3H/PBQcdtO+1NvukD5D5BcoU5bk4cmlk55S/tAffY3Q+Rp2fUjo2oL1IdK214Yz9wGQzJtqjDTsX2nOm1P6a/8UZmuv3bjjuKTj6qBoi9aenp7XW+5EonpCOlKnTTqHrGL2uTpjvPN7IWcC0U+PzOlBnvR4XR5e3t9dP/FPcP2d8jL3l9nNUAMKejdoB2dl6/csnIJcCsnzOftvPKauxbieqy+O1lxn1O+Tv7kZKgXmt7aI4c8QnFvdebNffvb29XU716Lg5s3YA5eDN83fK1DpmHce1tpSzn2eafMMUXP3q5XAgs9Z28O1M/Bnl7W27u8z3MCkclVB/KZu13qOdbqndk9HO2Ebs+m9u3o/F6E4x3+d+d3I3ivaE6zpAM0FH+37egOLsws65evH1rRu5+L4ZXIsXrv27mZl1Mp38PNmGx8dAY8Czfhx5T/RNQccPwa71/uCl68Mm0akPcbUjs7OyY6wdTJF0A6vaOjIZoOpUa2duk+K/naE4ancWV/mQjYDIWVoDqAKhAWsaM/pc516bQObayTT/mzG57dLOrqf6PFI5HMhMDrf78QsynkjOYEyhrPX+gFYpMGcs3WniNv1503VPmDp9H7vhiLn0Vw11cqSOrpsx+SnqZnK0Z2dlp2Fd/mzSTE6o/HtBzZ+1jjrUAmHvKffdCV9nz/3OvEyPTFlI5bL8kyO1Hu0gTQm5/+b0a3u1MwNao/kGG2tdP4TofnlcXYd1bEfqLKyZFnZtOZzlTTRT55ztchoHP4ANJT5RsR3HKTgs6+H5amCZ+gkIlik4Kqi0HA5kakR9Jma6fq13R+3zqAwEt7e3m7diEm1ysB/bbYlaoKa6SEp9tGkKAVrGRntzc82zQwF4PafrHD4Mk4m6F+1RavyNHh2VU3/BxIDttgrsUzRXJ2i6hGvcpjMLT2audabj88F8+KLbpq0+62LHyXM4nE/lDNh6qoNEzgmoPK6T05myROvQ2aEdsmmgKeuw7Vsfvn6ilae23SeuYT5167FtlLZ7wrafO3HpdmTsE0rM8667PplrBjfG0wHlFGS9vr5ebSrifvTuB8FLwXItdLJByXN+8he/ejkcyNzf318cMAYEd83AlqNvpGRKo4vrU8RUALAjWev62Z1OUkfgXcBtVO2so9HiWmvzvScTaz0GElNU7msdEvIwAZxVIbMj++p0ehGTnaDpkIKa66xu0ZsB1jo1nz8dKYT+7GxwBJRmBtZxuXZsC3keHh4uQYgz5el5lIlmxL4ASORxFts6ka/OqtcYhG3fPsHA496AokyB7ceZvh1/bdQO2mNRub243/nnDTfWC3X2FQ7TnHK7Hm8HrGYwDDK2UdtOsy58Cvq1fnrI7tHK4UAGZw0337UWO96JFrHRF5Q6QT3puM8vcLq93R466eNHHCU6Q/CE65rNRF8gk4GMdv09hm7n5FcIeJI5inMEurdTyjqoY3cEZx16LKjHDg1QQTdTZF1ALPggcwHTAOj1DANE75mAx/ot2Dm7srPqqycMqgaZOkFnJA16nM05O/PYsWXYunaxk/RBmpye7Ay149G5MWWptjsHJNYNNoo+zShYZ9aPgZ25XlD0eDQbm4I+xoXSjM8vajudTps3pE725QDI8tim2v6RyuFAZq3tIm8Hr9QIRunsBqPme0dhNh4Djek1T+ICGNEM9xl4Sk9NGQ+llFGzJYonCFka37vfXIOcdSZ2ujh+/5525NQptbBhoDIX6PqZnZv76czGeqqTcl9Mb05rLQYKyzJle46aO1btB59D33Sn2TQWti/XV7mo23J0i3Dvt/PDAWJXjPPUj9qXbbZZfvUxRe8Ffz6zvblO12V7KgXYflIqHzbrtUsHTh4vA6ZtZ+rTz+xgbzyPUg4HMq+vr5sIwQaBM6kTMBA0SnOm4OzChnNzc3OhoywHda+1TfvdXlN5A4EdS6kCR4bIUf759vb9neGmupzl8EPb3o1DPf5NX+mbQYZ2SqPZqRb4m3H0iedp0tXhUr+do6/pMy3US/u8M8bvfaG0zjoUyzfRUc3Q3Aev6VXv6IcfKOBmrM0orTcHAVAytpu9n9KUXWc0lUlg5Ix9b8OGP/N4dDMK/bEeDHZey2kGaAqSvnr7OWNgOyODakZlHd/e3l4O3uyuP/rkOTKNz7Rtme88pkcrhwMZ859N89faZgCmIrgHR+cJaxrk5uZms9e9kXUdiR1go1/LyvUc1LfWO49uZ+xioGi2gGyO7H777be11t9A8/z8vB4eHjYcs+tqpobMPvCx1BW6ZiLS/tvb+3EZ9NWT1321c+51r6/vL9UygKBfxg157bTQl7NU12891+GWKkOH0Ep25m7HwLC3AG2HybhZNkDw/v5+vby8rOfn5/Xt27e11jsljONt3wsY7oP7ibymLwEly2fg4jPmAuPCWJju+vTp0+WJfuzJTpd7kNuL82tts4sGKnwPref1rgYbLqVEm62utS6gwmnbzlbXWpfP/cZRzw2CFn6ckXU97gNkfqFikDANVdqpmQKGj5H73erl25mArccLgxSDWrONtbbPqfC91wlsoAUp/y5nPdVPNHh/f3+R05HWFIECeM3snH35HgNCqT/r0/fW2U0Uix2+r6Fdrin9N7XTtkyzuG89idf1Mo52Oh6Dn2UwdqzNUKcsDb36oMa9DMF9tWwEMM4064zJcBn30lUt/sz68e4w6u7ro0sr+SHjiXZifnbsLSPZRxfgAa3SkbZ/j0ODvbe3t/Xw8LDJ6pq5rHW9CaGBn2V0VtxNHEcrhwQZl3LGjlBrBHainujevmgKwQ7DjrHUm6N8G9HkpPc46lIkjU65dqqb317ob6pvB0xxptdoz5shmtlYPkfJdpjcY+ql/eX7tmGaxtdN4MT1jFczWv/ve+wofb3BibqR0U+WYxttxxkj+muW4T54A4XHo6DIfa6n9uz1BMaw29prUw2mpkDHoNW2m13blhxwEQB5flpX/JTW9ZZ1Pm82WrtgDG2vZQoK9mZDajMNWBkTZy72DdjIXsB4tHI4kLEDK0fqSMFRtdPsKcuhYDCTM64BY0ymATxRLYNldjFYOfKBpim1VD3UeTcrKVVn5zBFjdWlnYU/a0RfHTkTsIzuK21VF1NW4QywmYtpm7X+dqzT+WxczwK5n4lArspHP2w3U5BjmS3b1E/3q7r3Z901199T1ta1E8a5u7P26pvktQ0XbBpwGRzteKe67aC9OcaZHL8NdrZpA4f70iyWcaK47am/HQ9T0p3jreN8Pl/Z5MRUHKkcDmRKjxUs+N9GV2qqDtHGZErIFNla18esF7B+/PhxWYRs251wpg9cvCXSkSlPNCOHjZxr4cXXen/vOQXH2igNELaT6JqVI0va5jmdKdMp1VE9o5d+X30V0EpF0ma3Fpt2Qhdw631Whr5Pjq7UBltbscHT6bR5RsjO1VnYlMUZOF9eXq5oVescWbpDrk7eMhf4Jge3RzN251XHleyLdm9ubjbrT4yrdVlqzhkc9tQArw8k0zZBo7eLF0SaBRu0GQMHCLU1ZHQf1ro+68zbm6m72dMHyPxixTSGJ60NdEqfKTZogGGiqqYJ6N1jk8NDPn+Hc+dvTzYbuNsiK5k4fztcdIHjbGTHRPEErePEYTnymxbJadP3MXmcKRItr7Wl7Lo4XsfgzMnZi4GV31Omyf/IdTqdLt/5+ykoMciYbsKR0re3t7fLe1EMbo2Mm7XazvxjvXphnTG10+dzb1XGqTuT4TpnL+4LdU/9RVY7RK5HhwbGBip22tWxqTTPW29omLaI18F7F5rbd1vU5YzGYFw61Fkqn9n2mp16UxB9tw3Xput/jlYOBzI/K6VaPJk7uBiIAWWqy/d3z/xEaUy7jOyQcBKmYezMO0FrpC5MfH7bAZU/NjhRZ+m2Uhn9jMmHXJaXnTbWs2Vxxrc3RgUd9NIsZaJzHAy0LjuFtbbgZwdjxzXpcDrKp+PtvvI/AcNENznbsS1Vl1OZ7nEkbSc3UVvto2XrZ3baBQ2+d7HNmlJt3w3cZhQKds5KJhD3757Rx/i53dKcfidSQQyZYQymMfBYoJ+3t7fL8USUBnlHKIcDmb1I1EZRg1prXsyzkbWOyXg82VzXXlsAgFN+p9FrXW+fbtptWo4yRVbOIhy971EBdvjtR9dg2r/zefsUNnqxg3Jb0+fT+Pm6gjsAUxpwAhhK62uGa/3aUU1OGYBFHmc3brdU2evr6xXINHvkM9tE++KssZSY++Pvpv6XzjNw7OmstBJtuZ9+VmmtdXk9RMe5Y1PGAT23T1MA4ICpwGN5yxZMwE6b1u9e8LMX2Hiu+GQQ13XEcjiQ6UKsnQQ/fosg3zkSokzRB2U6f4rrbKSurw7HjqHpvbMJMps6DK4tncHnNn7v7jKw4ZhZk7CenPrb4VEf/TUlw99+hsQvhZuAfnJ4Ho8pwud/nIzP3Oo6hCe567XD9e+Wm5ubywGo05qJ5UcOtom3bVOc5/P7VlmfxABI267qCCmmvmzzdb6T7gq43I887i+6Zj3LduETLDwe1jM/tpHavG2o492gh8DF6x0AUDOirr8wr6zXadt05w/bugvi0N3NjCawsQ2439OYHKUcEmT8hLajy7XenRbG4gjFBsZWSP/vCe/dOJ7E3mK61tZh2mg96ez8+wKlnio8RemOUPmciY/TeHp6ulqU5El30wf39/eXvptXd3uOMicqsVE58kx0h3Vk4G8WYJ06WpzAlDp9XWXvWWpeKPZE91Z120odoce39mRAqZPtdS62W/er359Opw33X7ncl9a/lymutX0SHmfabNcBnJ0/pQvqXjexHNBGABw7IR3pA2g3NzebB1Etj+eMMznbo8cCu6itdSzcHwMeoFuAwD+YeWjQttb7+4QoH3TZL1Km1BcnVsPrRC/VZAdXGm6ta8fi72qsjp59bbMd6nX0asc0rU3UOLnPJxCv9c5HU+x8cdo3NzeXnXB7O6ksa0sPgcRZOfuqY+0GhkkvBndHy0SwpT7cPxc7S5yLQaZO0/q3zBMNgr6a2VKn9eh6rK9J181q3D7fN5Op02wgRdmjjKDy+Mx96FEtrtuBkfUyBVwOAgCJsg/YK3PQwGPdII8DBQOISwOFyU4KcC5c3zFv2x4jbyHfk21vTv3K5ZAgY+N3mWiTvYgFw5oitRq1I2+3U4BZa7tdsVFdIyrkaPRKaXTevjg7YgJ4EpjusNw4c+ugbUyRLzIBAKYg7Pyskz2qcq+Ytinl9uPHj6uz6TpGOHOPj/vRseX7yZbQjZ1Q9eQdXufz+zt9qjPf0/+n/vMbvTUCJxt1W1OU7jY9XhPoewwdqHhnnEHDWeDk0N02rILLZPe2yc5zj3P7baoZnbU9PyvH/OTa1l+qtf3r/1O/9mQ9WjkcyEwgwOcTyPi+td4Xe+04msXYabSOrp80Cupk8YSZJtn0PAUT3H2bftf5leYzBdPIt31yZlSHWt6ZbdluyxHoNOkmR91iBzZFpntO1BPZDsf8vJ1hd5vVsbsdr1fVmb29vV0WuO0Qp+yRayx39WOARS4HN752yhj8XeeG6TAD41rXx+VQb9eopkzPlHTXTizf7e37dumfgavtxGPrbMiy/FNg6a3+7lcZCs8907RTYOTxL0B5LPi7NObRyuFAZq3tNuFGqXbunoh1TqU7qKtRx83N9qw0IkjaKU1gg280Snusp3jCeUfRzyIgMggmrCdEaTLa9eTrus3k8O2gDFyUPi3v355gjIPHYy87844r8+neNk4b1OGspfVCBb29vV3k9dlddR6mGW0vfS221+kY/7ZrObv112PpNgxIPUqlY2hH7Kyii+sdY64zTdVIfJLR6ww3NzebQIR6+xyMs2vvCuRzHzrZ4Akdeldmz0qbAsA6+WktpaBUQMYW2dhSCrJ+43zevsiOMXBA1ud4jlYOCTI1cAymJ8o2em5kTulmgkZQ/u322a1lh7MXddYx2GnU6TTS4pqeQkxdLJK63+4bz3d4u/Ra71Gbo/VmG40kuQ89IFsXpqsD/285G5E7e/DOuCk7KF00tfn29rZZfGUciFLrwO0kuN8gg3zYGnKiS9uO+1jHj8OlrgksmlXappst2hHbFt2Xbg0+nU5XW5lN8Ra4qWPa4UVpm3XG3ZDh9t2nZv3OJidgnHwCp1I0APOcLVjXLhso1qcwhgUT7vPD3hM4HqEcEmTWul4z6ATZ27VSR4Bx+3pztV6MrEMzpbDWupqwjnQnR+t+4Dj20n87FwOPJ0azIDurAphBYy/aM7D6tyfmWmsTtfa6OvJp51GdBnXvgYxBr/dRZ9cNDDrWv/vt7HCiQ/13Of86Fdddh2a78nZX66OgX93sZaE4O2e7Hg9nxHxu4PBmgGaKzVAtq0vtuzs8naES5JQ29jztOHl8PV8NaNaH6+u4TYWA1ePhH48DfTL9Snl7+/tB024/P1o5JMjY+fqzPUc+ZSN8vta6pO84ZO43bVNQ2ysGl/LBa+0vMk//N6X35Ha/umZgkPKWa6717q9Jt814+NzylNOe6DDroQ66wN8ddAaZ0joTiJQedZnW3ErVmPq0E2ymUwBoW27HDth1lGLt2NshWe++ZqIdPXYFSJcCh6+xAz6f31/cVYfua0xrVQfU70NLCYCsuwn8PUbT3DXwGWhch6/1WFSHP5OlWXPvmcaIeqybD5D5RYtT172U1QbR6N2f942VNgwck+/3URSmJ9jK2Ik1/XZWYOfpkwWot1QJmxCoq5OSvw1UzoJwSmttQbpg3f5Zj76Gz01FNVJ3gdIwpz1FjP6/Y2qabi9bY1yn52AoPLuBbnxED332Nm1vHmh0XFqlwG8dOoCxI6cf1MGDgub+7diQoZsvXLeP/ql+PZZ22B5z9FIQ4fruSLM+/AS8x8bjZ+Bu9uA+2yYa7EwZXoEYu+c5simYQx7GmutdKifzsbb/s63SRyiHAxkG0v+XKmrabSPDMXTxurROI3e3xfe+z5F8o66Cx7RA3/sKEp48Nn7roQDrB9ps+HZgjeBwCN0q7AwKioVinTSL8/d9GVzBwmPkMfBOtgYFDQTcL4+733Zqys6Rtg+R9EI2fW6Ea3kN3s4kGKdGy7aL0+m0mykasH1fMx7+L9i5nZ5tZxltrwURO3TGwxkdZcpmXD9rMox17WQPHAp01IueCoYGjEnfnNjw8PBwAV7biHfBuf+2B8DR/fr8+fP6/v371UPOHaujlcOBDIZiR8LCvSkOX9+/cVh9l73rxzB+ZhR2bo5O7ThtqF74pDSKpw5kcZS0JwP3TNlFM7ly6Xb23oH148ePTdRpislA4IzODoAyZY5TllKw8HjYcdqp3N7eXj1RPWWVOA4W69tWHS5/Aw6m69xHR9bYidc6JifOPV73c8biNcUpe/M9U2DlcbE8Hgf+75jy/1424SyPttBDTwsoiFdX6KDOeKIx0Q02aX34/46ny3Sfx3oKCp2REtQwH0yD2U48X7BVZ52V6wjlkCAzOate0+JJRjRTSqEOoHU5m6jz8HV17I5wK0cj3DqytbYZVNsqfYUxexso/XJbrtMOkB057eNEv9lRAloGs+q+gFvwdKRnXbte66HZFX1wFA2wmMaYHJIzU/exeprsYYrEG03bBriugNAs2eBR27NjNOXmtvzUvvXUetEZR6jYpkrLASDQuVzjA0OdKVPs1N13lwZJLvSZ8TPV2MBtokMb7Nh+p/W3SZYu7tf+arue87TZbPYI5XAgM6XNe8UGMS0eu3jrqB2QKZRGdK2jzolig+dvU0BdOHW/JofzT/oxZQVo0pZpMCZJo7u1rhfL219Han5nTeX3RDQ/Pa0teA1jrf1X71YvBfBy/H5eohFyQQs5G4kX/HEYdhrNCibKpJmYx6CAx33+XdDaC26cSbeuUnG+5v7+fqzTu8+8uQBwMkBN42QK0X1Grsrk8XBQQL8dMEy2WZBp0IccrHUVJLorrfpwPdgMdVXnHbOjlUOCTNcivKBpI3Z06x0iplG4joMk13qnXDjGBCPxceaOJh1Jmho4nU6XupCd9Q73p2VaLJ8ovbW2EZoXHlnEdvSEc+hpxsiB3nyKdXfteKKYkrKc0AnIZdD2OFgGywr16YdWmcSNBg1IHj8K40uk3dOQ0VuDCzt8qBKvCQGu3iGHvN5ogIwGyjpYy1fw5B63ZVn9oi/bhNfobKtuy5mYgyk7SubV8/Pzxt7Rc98j5AzKoIAsBv5uZ2/UbxlK91K/35Tp+W6bdbu2D1NbbqssB/0ieHR/Lb+fa6ottU9HKocEGX57oCcag+9qGGtdG5rXS+qk7GyoC0fu6/jdU2odiZmKwoEYtBo5N7V3W5an0TOHX9roS4VYd6VPAIopY6jDcF88RnZke/c347G+mdT018exuxjITMPZqU6ZRV/nYD3ZOVAK8n7I1WsJduKmWAAgBz3O4BiPKXu1XTB2BjHTVwVP9xnAcia51nYDjU/43ptjbhc5ef0B4+Tgxus4bLKofvu/gxnbpTOhZkf8uD/oj7+9Q266d8pUJju23iyH5wQA2PE8UjkcyGDEE11BsaGYPqA0Qpom0DShqGvPCP3bwOI6vbvGxmhnU8fVSNQTyRGe7zclZgfK387qCqR+Utp9tl6r6ym76zjY8a+1paVMH9V5Ogjo1ljrxRx7x8Xy1S4A1NIplm2qz/1vRum6qjfryONWXU7Rr3VrmwE4uKbROXWjP2/nbuBhx9k+FDjt7Fnbmeo1MPZ712M9eJyRy3pD1w4wXPbstfXazmsrBqfW2d/U2aBhys6OVA4HMkSDOKVpTaPRV3e2+N7y6JNTqtOuY7fhuS1k4X7TG+7LWts1kIKJIyI7jNZj50zfSk+4r9YR7dJGgaY68T3TRO7konQh2dF5eXRHo15c5j5kqsMtj851lm8KPqbvKXsP+3UcnF1O11cm2wB97jUF0zqtBjLc5/WTOlDXTamzRc+mlfnMfXH25Dq6U6xz0DqzPh38WIcGMuq23RSY7Qd+1m9s0J9bDvezeu78b6CFzB8g8wsVO03+d/o/ZTFrXUeyfrOjOdPeN7UPyPk4lbWuFwUbAftQQGcxBYg6KDtkrjE4Qj98//79KkK/vX0//dbPlSAr4Eadp9Pp4tQrZ7n0KVpnjazOstkidfjZGfTU9rj38+fPl2cR9iZsQYffdbbOks7n95dpTfLS1hQIQJvasf4s+3D/qdPPLrkfk/Oc5LPuvZ5VW/gZ/ef+VGY/pNwx8v8vLy9Xa53uR0+XXmt7dA39Yty8rmfdeqNJg8jqjnYdGFbGAqhBvbIV4JvlFWgt3xEBZq0DggyO0Eaw1hx9NlplkFkkZuCbCWGkpUv6NG95XV/raM0ODqfPtRMP3clJGzgzFlu9ToOzpi8GKP423dBIzY4U3fhtl2u9Z1uWr8AyUQONvq23nopAfYC473eGQLm9vV0PDw+Xv7neesMpebyboU4RqwHWFNzUr7XW5kVwzYyn9Qc70u5+5DPrivu8LR05ABZ+/Mpg264pLd87Rdpu2w+ncr2fQXLQ9fb2th4eHjbrLgAMtuJgx/31OtX5fL48ZY+eX15erpiIjjfjxX0OJgsa1GHq0jpGZ9zTbcy2tykzLn12RKA5HMj0iXmiL08QR0N1os1aDApenGThshFLQcSOzc7JoFegK51juVvq8Hw97ZARsI10rfct0tZDIy2iVEppnolfpk4DgiNodNSDIr1BwY7AwG59eMusMxlTgA00kNUP7RlcqJfMru/EoVhnlcug1O3g2I5pIjs36rCeubdBDvp2hlFZ7Ozq4GojHgf3sZF8SzMyAz5UtefVFNQBMq7LANr+OMMrkJi1sC/wvbZ35gZjDEj4e1PLe3aLzjwunscFGvubKRM9UjkcyJR28ORe6zr6s6Ms5z8ZCEYxPfsx8dp12r7OTtgRfAHIjrAToE7MpY7eu3hwyt2VNlEEPg7dUa0zrSnb6uGVdXJ7Dxu2nurSfbWjMU1WTr3j3azANuD3t9RZ1GYaJTfytgMvYHic2+9pEbuyMiboyO/DMRA5Wm9G4nnhMSIT6fjUkdrGLJMzE9PXlc86KEPgsTc4OJsw1UV73rZdsKp9e14UyLi+88xZrv2Nd4qaavX8aH+ntbyjlcOBTCe6jdpUT+msqZ61ttEKhlKqhtLUnO8LNBNt04cbqY/JyrpKJzptQpV18ljuZngAjWm6OkEDC+15+zN6dXTmrGJve2YBxBEdfev1lt8T3eP8/Px85fi91uRx87pVHRoy+YFVO7cpivaYlHZxhtzgZbIZZ3Z29gWH2rszB0fz3sRiAJna9dpG6U/04CzPmSp2ZkCz7vsM25S5NyvzWJaKdhvomnXDAoPb5V4zCbYpKDvqtF1MgZiDF9OSzfSbXU8geLRyOJAptWGKxo6TQW7E42ipYPD8/Ly+f/9+iZj9HSBgp+pIfq0tnbLW+4KjQQO6xnWwk8uLz677fD5v1jzu7+/XH3/8senT09PT1e4rR2OAzMvLy4WzL/309rZ99ofJ6AMDkdlRXqmeOhADm8fDTtkUGo4X+XH6fm4H/ZUuod/Pz8+X60yNOXtgrO/u7i6L1hO90bWgPUrEQGVHXJuk7b0AZsrWCrrUTVDAk/pc12ecHEDQ/7u77Vl1zbYdyNAeY+/NAJ8+fVqPj48X+zV48z9rKc3COkfRiwMYZzc/fvxY9/f3l+snYOq8xAb4eXt7W6fT6QJWyIf9OWtrQMPhmqfT6UJP22Y6zqWcP0DmFygvLy9XA1Uayzu1SosARqWGmMB+EK3cvCNTO2/a4m9PMCb1/f39Op/Pl8nWekyheHcWE9IP+GHkjoTdL0r5ZRaDcTZugwn48vKyeVNjKbMJJLqmYLqgGWOzANq3c7azNUXWDRrOOhxZ46xcvxfLva5mOf0WQ/cN+ZuxAtYdA2SzDToqrj11LGpfzf4aTXttqOBkvd/f31/s0HMGx97TC+xsPU62DT5zVN/naBqkTLSeAzLqtE5KY00bXdzmXjbJWHndjvqb/RrouqOymfmUwRZMj1oOBzJ2aI1qa5x1dh70Rqd2LlPW4xS4ZYpOSs34b1MAE3A5wwAw/VBiHXHXnKjHJw848rOjpw3q8bZsOxf+nxZau36xN9kmHTm6bRbUet1vR4k4NTtl19XgowDg7Nhj4mzqfD5fHDSykA0ylntvIC2gGmScTTdzMji5L7U3Ozzfh15q67YJgirqQMd2wAWVjmd1Td2magkO3Z9u2+/6i8feAaKvKYDXbvZ078092JHHy6Ay1TOBzGSHHf+JxvzVy+FApusOa22dGMZS3tj0QQ25abyBxk+CN4pufUzIGqSPkqnj5G9PHkdM3Gcnf3d3/XIkvzKXqJXokjWL6q7OolE4bTtq//Tp03p5ebl89zOQqY7rICy/23Pf1lpXZ9OZkvHzDqVIDOAGUGyBSNWZmmXEMeKIABTGqLubmg39jE6cHDX1mPb0PR6bAqx3FSI/NmF76jt4HIh0HtB2Qa/yOxNGpp4/N2WXE43UrKX2w5bm6s9ZfbMm7nedXsTn+4n58JzkOuazqTXbscG1QYTn4FHK4UBmigTquH4WfbgOv3yJlw1RX42M+7rQ5/pr2NM9Bq4JaPw/k9sO2pOcY+w94Rx9O3OhLu+ycdRquRzVrfW+QcFO2xGmDwZ03zsxkQ2qAmf08PCwobIAzdfX182pwHvcfscAGaeo3yBhJ93TFJoRN0uoo8ap+oVVk76m7NUL69ZDMzw7cdbX3B9nL58/f16n0+kKwB0gUHDcjBNrK2778+fPGzumDuQHQJzJsXay1t9z7evXr1djhg1/+/btMg9pB/2birRNWYeAC3LQdnVJvZ1r1b9B3Vm+qXD0bruzjTCH+v3RyuFAptHA5MTN4zvawMH5HSM2uNJszoK8NtEohXvM0bs00q+zwilMtIPbbR/LXyOrnb4X6emj9fX58+f1+Pi41vo7yv327dvVicNc30jdzr/PH7T0nR8Amh10HYezFdo3kDs7qQOqzu3QTYdSr7MiHCQ6bZbE2hUUGjr2m0gt6xR183uiPwtkXOsAwPPAztVO1/XRBk63UX0zUq7DZtx3Z36eA9CJ2I8PnAWkaj8GiI6Z2QdvVrHdWyYK9dquqMs/DqackdiurSuP1RQkus/2E36t9wfI/ALFUbUNrZSEd3DZ+TL5oA0wNK51XabZzMHacdl49jKo0m12cETnzQ6c/ltuT0Bz2nd3d+v5+fkq6+mOqkajXjg2dYSumSQuExhOk8fBwETNlTrwOovlsFNAn4yxT/y1A6rjcTbnDLHZoe2n42xqznRVqVPLbkqmFNNepl09lY51wNCo2XUCkgYzso5u8Z9ktH3bhvjf+jQN58DOdmtHjnwGFmdsFG8lP51OG/mtEwcEa12f6lF90ucChUv9QutoADOB1V6dRyqHA5kaD0bpSUbkYIfjd7xDK6y1NeJmGBQ7vhpJM5LJILsAb+dU/pjvnaK/vLxcjBc5p4ljJ+yNAs1IKrv7znHtfO9JU2dUioDPyaxMPRj0HWk6YvdY1SEbILse1QX93ufffYC07VuHdaw/o7B8PzpBr916bdn2PrOenFU4I6vd1YYdaNjGm5XbLu04LdcUtfPT+Ug7fa0Fdtu5RfsUZ+UTiHo+Wh5ncF6bmqjVUmYt/zSnqcNj0iDSmfFHJvMLldJKrKs46mjk7UzA1JkXy7333hlAHRz3NutwhuHI08CE4XXBEBkckVGXqRBHsHYoTPT7+/vNOgf1MFG8O4oC9Wbn0vdt2Dkhh+uG/0enPHvS7b2uy5Gfx4++7mUynaQGXz8L47EiWwTAkJvfDlLctz2HYJuYaCjrkbGy3ktXuU1+M6bWC/23YzfA+G/oJdtKg5MCPe0UFJvl1PHTv/aLOh1UGQSdfXh+NWvns6enp3V7e3tZV3L/qde7/dxP2vC87XNlZQbsWwxYE/B5/qMrghr+b8B5lHK4Hp1Op7XW2jgcZykYq6MdO2QKRlCjw1kTFflsrx5nYeNbawswBr06NNo1oPnhyJeXl/X8/Hyhglwvddzc3Fzt9WdR/8ePH+vr168X0LHjqQ6cCay1ffDQoOK/eUCzUb8dpSctuuN3+4uz8TqT67LjLYXBxHXWQfFnzih4cLVgPe0Mc7GD8DXI3cBjorCcBTlbAgDsjJy19rULU3DC316/aGbiqL/Bw1prs6bo4MoAii01M+I7aLBmJ97lyKkS0F8EF3x2Pp8vz/UY5LDn2rAz92Y61hOf/fjx4+phTPTjeVob9TxgPQ79+EFq2+0RgcXlcL2beGRKU2xKJ5QjLUdiTAKeiveOKNdVemiK3Aw2OA5H5KWzuI6dPUw8R0bU7S2i/qz9BjStmzpRy2mnSX8ctdFWszoDMuDjs62IMPl/otJM4ZQu8bhX91NgUXsoreesyM7xZ8WROroxZUexs7cOka+ZZIOQjg12we/qz9c5y/HxSsjvjBZ9NILnvru7uyuauTSQx992zNxBHwCKT2EoDeh5VD35f7+czWMy2Wnb4h6PVUG/DIY3tHS8fEwTevGpIGYuXOfRyuFAxsZuo1prm16XWsLJwRVzbdc4+oQ59fZBsTqHtu97DTB1Is6mvMZQvrnOxJPG60WOVA2g1OsMoXWampkyPctZ50kk562d3dlmfTuz8MSvs7ADmCaoM58pmypFY7ntYLpA3SymdRqk/NtO3vVYl7Vb9OX+d33FoD0BjXVukLPjxjmbImOX3JSFeU1lKtZfX/Vg0LYNONBxkAcw1V4K7u6X1+L2QKn2tFdKfe3Zhre6M6fcN+urweXPdPkrl8OBjPfwm1YwJWRj5XNnMkRQntAYs2kTt1GnYxquDsKTs1TXBBLUx29v7zX1QHpOf3F2jS651zJZNvetnL0X1Q1EOA2uqRMrreUHKF3sNLz92BPYTps6vVGDCd4DGemv6wagOgb8jc7P5/NmHaOgRr177aErzsSys570ad3d3r5vf6aPDkT6u1kwMhScHGTQnsd7D+BbL2Mx6RD98qwTurfe6U/X6NC3x9V99RxCT54jXANwGXQakBgcPB70ze+4oXgMO18sBzZssLY+q+OjlcOBDBGzAcCTCIPpOownJxPBxsm9NlxHidThSPb19XVzIqypDeo04NlxeRL6c2c4Bgo4dtMgPkzQBySutX11seWib/TnfH4/u6pRcycN9Z5Op0v/DRoGolJGRH3OdBjPgpF1aV0TYDgDquN0m1NmyDV90h0KcspK63BdHh4ersDKTsX3NKK2/D1/zPbhc73cR4Mbh2QiB0EQz0CVFkO/BSXbJ9dyGKSfv3IwxTNFDtbsgJHFDhpb6hqg+zhl6959aN3xvwMLO3nWYAx8tkXAzv33uDrjMl1nOylATVnURybzixTvIGnq3ayi0Y8N0JmNnYijlha4apwzdSNTQcMREwYJb9s2CmDUwbWO4MhwXLedisFlrXenzjMl9NlRX+/xJPeE8qkB5qytP+uR0uuqczsT2gVYXl5eNtSOA4AuenMvEbR3G9KuHbrBslvcfU8dRjdTUJePozHN2qCFgpOuw2oE7D46G+cz7wrsPWu9U83OhL1+4gjfwQp1mp6bovNuPTcomW2gLQdyzuSsY8vFDkHmGHOOwNOgxn3WZbMlB3GeT+0b7TkYsA044LF90pefBSlHKIcDGQPJWtdP09uRrbXlRYlqp50idnB7kacNtI7RE7jRip2tsxu37fb9v+Xwtd7F43amzMjF8hlQzNm3j3xmQHQfTV2UPvI4NML3DzIYdJCx48vnBhuchnXiaJ16rR/q9pqX658cQtt1hmiqsxQs+pucdKP2aczspEvnue7aK78dVExA5zEt8FPHz4KzKcCYghWKsxYDjXXhrIR1VK6zI0fnznKpu2OIft0XZ4aW1/32WlfHzzqrvitDAfQI5XAg4+gRh91B72RxtLHW9WnFGKgd/eQsG0U3gnXdBbEa3FS3sy07WdMMpQT8lL4dJ8WTwk7ITofr/JtCv8pFtz07XOuQYsdTZ+OyF8G/vb2t+/v7q2NkLHfHpGDTPiI/awN2PrRbuyoYle5rpuT7DAa1Af53v/23nZxBpjbmv+087fQYO9ovaJQGLbC2LoOp6530Zzv3G1mdLUx2gx48H7DH7obsPG6w47oalFnnU0bj+q0nt8d1puA6/45UDgcyfpJ3rXU16NODXM5k1tru899b5F1rXkB01mOntndtf5p9UGqodih+34jXejw5b29vr4BuAsI6YzuWtdbmoU4A3BQBbXty+TkF6izfzgGkjQDtHD1prT9Ti5bVgOQF2EbqfvcIk90T306c9qFSqLfrGVA1HYc6L2eXHlfTcb7WTtXFx/9M42f5KR17A4bbnoCuusGuzAa0/QJXsxt+oD+532uj3vLLtbaHaUfd3d3d1Vqf12m6s7JZvn2D7dzZELStA9ae6v36+nqxtcr5ATK/UPGg1sl7MdyOyQaOcWB4nrxMfjuiUjelecw5l4Yji7KTubm52Sycf/nyZd3dvT+QRnTHIr8X+gumXrw+nU4bGmGiT9a6Pq3AVF8zIU/KHtOOLD1NoQ7Ak36trdP2RLZ+DVaAHfKxAcKlJ+ISiPjZHAca/KAnjx9j11125/N5o2/LT/uOXB3V8hkyek3FIMQ1fv7C11X/tstmG65zjwaawIHPfQ6cs4Hz+e+TnE1f0SceYqbvnme3t7eX52TYPGL9eO7SJ+zGevDbYwEYdMbWe+Rphmmw5QFaZ9xe1K+dovdmcwYy9IqcBCteS/wAmV+gEAG5lDrw56UmMDYfM+Inf31d6/wnTpY6fP2UKU3yrbXdEul7C144htIBdjSN2qjH24/dP/fXUXYnUCeWd+mgA/fZcpSqcKRoQEIm2pgyCevMUbV1aydEfe6rAYbiPrrfBnyPlReiG7nWRuy03I/aU8eswO9AwtdMGZD14jGZgjSubX17AV3bY6x6vetvxmhZGgBVr1Bw0+vRySwBst5Lm2Q9tnG+ByQN3rZvSncyTrp2BodeDLRHKocDGS8yrzVPZgPFXuTQLIWnkpst2OB8jIvboj5+l5awweFgG/U70sK4u8DpSTOBgQGwke5a8wTfW+OgzgKBHbB1tdb17rRmfZRG2qXZ2oZ32k2g0fEggpw2R+xRmKafTCGZsittRDTvQKCFPpXim7Icy2gKzLbinz54Wap4CmysV7fH9ZbL4+ix//Tp0+YoHdqzPfp+68L25WeZ3IbHxdf7u65dQisjR3VV8Oh3fjzA85DSvpri9HXWnW23We+RyuFABsqEwpbcta6fuK+TrbF50OsM4Y259+7u76P0nWFgkBggaTIRkyN26q3joA8+QqZA4clpkKHYETaCok1TQ57Ypo2YbM3qXKAaul20E7JZ2t4Edxvlzd2Gv+tzEc0UHcW6GMSo59OnT5tM1jJ6DA1GOFjoHb73kSKmkaCemjViV7a/m5uby0O3OD6f8zZlax736ryOvVmhMwhkmUDGIGzn72yx2Wr1yefU6YzX13c9z/WgB9Oe5/P2jLGu/dUOOq94xsj2yLqtbfDm5ubq0NGJMbBtNhCagP9XL4cDmclxlMLxhKtD36Mx+lT/lEHY0VCPz1KyQfpdJzVmR+MGutvb2w1XbbCw8a71HrmVCkF2L4YSbXliGSj9GTIZPKnDawbU6XWPOi3rs1kF1zSSbcTf8fS9zQJrDwaqx8fHTeTPsyzerchYmNqpTTgrscNwUIBeePrfQNIM2f1Df9xL+4ChI20DL4GC6+Baj0nfdmnbnvTasafNrntS7HSnw0bdfwc3PjLH1Kkp3Gk+FlAcXNjJG0AZZ1OlALlt3sDsMTdFWVYAnfhUEuyI0mzvCOVwINOUE8Pluzo3O4y15ie5p4i7kZ1Tahc//Ed7peIwvkbce3U00u8itDOkctaOku107PwNshz/X+dSimSa7BQAb4o8/X+d9FToL7w5kTr1FeQtr7Ma76RDRm/yWOt6px51eQydxVBsG/f39+t0Om0eqJzsqQDha+uQ7+/vN5mLAwFnGaaMChSmA/25gRk53I6DCfpqHZsmtB26r4xV7bn277YsqwGgmYDrsz16TBuY0VazRdsFY+z7GWeA34X7PA7YajOZZpJHK4cDGaIgF2cqHtBmLFOEZgNthIeR975yx54snaSOyOtgmSBOve2EKI3+J0dv+dCT+9Hrp0nhekzPGWQMPMg28eZ2WqYMJ4dUuUontX7L5e9cr2X1gZAt1a0zKTtiOzHLZeqksrj+tbbUkq83ZTRlmQUYBxjWo6P2PbpwyhKmQKV1Wh+eE1Nf3UbH3E6/+io4FhgKnLU12m6dyGanX3ZiGjt0ubcOWL/QPvr7PXs9QjkcyPToCowRY+mW3zrmAoyjay8YTusXNbDuHOvEdluOAik4/wIKDscOs+12krNRwM4QEKnTKZA2erPsjT4ti0GxzpQJ7GtMDU368XeuZ2qjfaAuU3nI0CxrkmV6Bba/h37kmmlRvrLRHgEG9zEuBo3p9GqPd0//5dq26QNUp+CB8TZAMVcmmy9l2r72WorrQt69jNz0V0HA84B6m+Hs6d7X2H7QcfvBPbatgoLt//b2mt4t5ep5txfo/OrlcCDz+fPn9fLyconAnOo66qhh8VlBoFGXjbxZx1rvNEUXeNe6Bo0p6sIIe02zkE6iOq63t7fNgYi+3k7XawIFGf+NMzDQ2DFPAOH6vLWccfGaEf2annGAsnp6errUzcuq6qR4FbWjTEei8PoGAEDGDmSi/0yPuS7a5nucPs8mGXDoT9shCGgGR32t245pirbP5/PVbihoS29Q2Ntx5myBcd/LjnjTqbMqlwYJ1MUDuN6c4+sYm2mRn/Hl+1LQbttB2URHAe6u11RXd6o5Q5qejSroOYi0LvmeOvcywF+9HA5kfv/997XWe1T1xx9/bCLXtdbmaWLz+XzfiN2RC59Rmlpzbc8fs2OyfJPD5l6iR+ov5VYaxsbMvdRfJ8R9U8RWCsMTs9uRq4/WzzXOMCcwspPHITriZqOEH4y1DO6v11dch/tWIJroGzuQrrXtZa7+HxlwhIA+Y8nL7wo4FGc36NTrW1Ng4eIgqLQl8ntcyII8PhMlNWXjU7DD/fQFOfwQph1u54KL6272djqdLrs9ndViM93K30zEduwg7Pn5+bJIb2aC4odivUvQYFN61e0gY8fxaOVwIHN7e3uJckuVrPVuGDWoRvFMqol6oNRB702wtbZbVl2/+W/qIjJca11F+62z33kSOTJqpMpn1oWdDH2eQGfSq51enbd1Xx1VX3aWpUYaWbtfBhMDjEHItF/X0yqD6/Y6i2mmbin3mPSnNM5eNkMbdUjOYgqKpa327KJOzW8qpX91eD2xwfXVLjyH6iwZS+oB6L0lvn1Gxw1o3D+De7MgdOYTISybgzQDaf1BAzrmice9tmRb9k8DowYHHyDzi5RypeXbTZeYUnCE09SbiNJG5ei2E2uK7m34k4NzdIccew+w2VDdvieKo8hOmtItXO/jYayzqX91ak75GQNHsF2/2XO0Hifz8Tin0g1tq+8Lsm46VtWNqcg6kGa7HZOW8vMTrWPwd9sdK9srEbOLnaTr9fj8E8g4G0QeH2VkmUx9VYZmP9ah7cSZdI+DMfDQlpmBtmcAcH/8YOgEMJ0rE9jYni3DNC86dv5x36agteN2lHI4kAFAKI7EHBmZUvFkdgZjZ+9zp8xPU6a1Ctoszz1FSd4uy7UTEDh6ttNw22RzyO0FU2TxxGWCwZE7unN/DXB8Zzm5tpSKQd4T2LKY2rCsdgr39/eX9Tbq6rqLHUIdiLM26/X29nbjcJHRwLfW+zlZzuYK6o5wTcvRjm2B87yQhd8GPuoAXHgo0GCMfurcvXnA4OI1rGa+/uE5Lsa09xgQCzbWv3VIvd2IYobBc7C0c7MFBxzVH3X5oEpstnY76QDfYH/RrMR9qk8oIDMm+BKfp3bkcjiQ8aL7Wu+HI671PrExoMnIuA4H7TTddEujNK6ZgGatbcRtw3Mkxmc80Y1D+/Hjx2YffrOBt7e3y8N5lt/0kqNo5HEdLJp3UwHXGuCmhWd+A85+un1vEtlJME6NHO1UeD4EQLCu0IujWH7jALgHZ2356qhKszULs5OhYF8+YsZ9xWYAfj+pD2ghl+0IMOorgBtt2548vrTZyNtgYGBl/Jw9OIMziLp//Hbm5jqbATX7Zb7R5x7R7zlV3RvIvDZmEHe2a5tp9u25Y4rb9ThL9fiXEvXvtd432vAZAabrOFo5HMistd3autbaTCAbfiecjcFGSHFET3TDNY5Q19o6gIn7pXiiewIYYLxbbq1rw6yhv739vajstSnkpw9M6Pa/jssT/ObmZrNNd9ILawaOog1syE2Eauc4OXomZCkm777r2LtPzcT4qRylYrzTi9+OqrEjBzHuuzNIxhGH5cV7r7N4swOy+LXaBl366bGwfuqQvW7hMbF+Cgj0yRmsx6zZoiN32tpbdLcszhycNXguESTUHl2Xx7jzjwwQsC2wun8eg+rW9uXAyNRfqdg9AOfzSSdHKocDGRs+pZH5Wtd8OX/X4dlJeJJ0UXkvApkmIgXD8zV1ThiljbELnTZs2nRKb6fhCWb9eIIim/VlR1U9klnQNk6C7xstdkvsnt5Ky5hysf5cyoNbD/TB9fE39GKdiwGItg2AdvbUsbezrVvbXU93zbntPhfjfu9l072+jpl+PD8/X9l67dz1dg3TOu2aUjP/Uq5u0zbWoMtjWwD1uOx9Rl3orZRxd0xWVwZa6rX89Ac6zfeX1uz/zZA/QOYXKVOaanqjA4the4LZQbEA6knVFHrvb0/qTt7SMN5tY+777e3tsuPMZ1RRr6kg5HOmVsP15DW4eD0E+RsZlmJCN6Y/iMgpnsTITd89VnaMgJEjb0/OKWCg3r4LBEdup+9rz+e/NzyYurINWG5TW9MRQn4uqycJGKCsj4KJv5t++N4U6949LpMTNfWIjNav6SOPawMHjw3fU/fLy8smMzQwtT1+2wk7SKPQtmX2+PB5AYSx9b3dXm99IXfbxt6nzRoFUgIwB0mdc27zaOVwIONJAT22Z8iUUjte8MSRevHTqa+/nxxLDcsyAipN8+18ud6TxxElddepMDnthKAkTJ/5kMy1rl8sxv3WK7rxxohJN76/1EDPl2sUi3zw8ujEPL374v64PjsX/ibLwMF8/fp1AzA4TMbT4+atyBMwNJgxHebz6bCzp6en9fr6uk6n01rrnbNvNIzuGo2jY8bOVOsETB1Xg6U3yRSYTee5TWem/vEBsOjEWZCj/Dp9+nd7e3s5860bdAz6zL27u7v18PBwySjcfk/S8HxCZjt+5EInyG8qDVszfea6qufPnz+v5+fn8VQS2/DRyuFApin2lCaXduGhOEeGpXTqCNd6f3e4HagfGFxrXfGvjpJxUt5IYLBDzumtk2ttHx7zg2AAoikYt0m2xEOpODM7qGZAzlysG+QofVSQMfj0PSHIUIC2PnytnfR0coABprqa1jZwlHVcHi+/lbFBBHq18zLlw2I+i9DUge2V8judTlfjQJvN6KjLgGE9Nou1Q6evznz37jEta3mcCRD540yRzfppJuE6m5FMtLAB3fq3DXFNN7vYNuzYpwDNQaQZhe7884/nD3beXadtt8/aHbEcDmTsYBw1lY+28Tha5p5Gr2utq8ngRWlPzlJMnbSV17/5uxHV+XweXy3s/rgwSWzYnuimOSadObKz3qa+ONItX27HQB0/oxiqk4nO4zu3S/+ob4rg3Q8HGjc37wv4pcAo7oMzHevMADPd34zP7ZO1Fayo36BXx+k+NgNBZw5k6iQbOOxRknsU8zQuXnsyleof26EzGM9bdARQVS63WwdunZcid6n92Vd0rMxemJLme7cFALvPDghp64Mu+wVLwcKDXAfqyMxZj+vpLjIM3s4bY1prCzJ1fJ5YfP6z4p0/ZCFrXRs+Bo48zia43k6LDKoT32tFpdL895RxWL627/unyV5HUgqhGRxODuD1uBUgrWt2eVkGO8u1tqcs1IlNWa6dXx2jdeHr7cjs9H3aw5QxFLBr3x5f/jat40jcjt2bN+hzwcPrGxTr3d91zXIviHBd2GPr67wqCHh8OmbNZqijGd1k12ttAxfLZbBpKYi7rrIXrXcKUH71cjiQMadsPtQA0olJMR1D4XsiROqfIhkXA4MpPPPSjYiYyOb9XeooGuHbERYskYM27+/v18PDw2V9ZVqcpB+N2E2PNOvj/z5ciexdi2nkTB+8HdROBXmghtjR5bF2tO9MgL47U3AddRhT5M7npUhoxzZh21lrraenp3V7e3t5pqnH30yBkEszANtlF7ytYztnryuY9rWdeg5RrJtm8BMoOGhjjWSSyfbpQnvWT6lWZLbNdA3WevJca2kQ5sCJ9SDs2eNv3fqaXkcb3an28TDmL1jKZTdqbGEy+MRiZys2DG/F5N5GZY4UPVG85rPW/DQw3+P0GiGfz+erZ2bsuGnfi7V+8JDXOL+9va2Hh4cNF9/IzAu9nnh9qx998WS2nKUSSz26IIcX1g0GXpj2eNqxO4s1iPO3MxHGpI6XLM4gbfCifT8P4wCB6+tMG7lODm2yn8kOLJedq7MhdO3gg7axi46FAdnAObXbrNM22yDLNrbW2mzgoFiP1hfftQ8GhlJvvt9j4h2FzSA8R3k2iXlkWfdA3BtPbL8GH59GvjcHjlYOBzKmjTwxmh5P9+19P6X6/P2zOn9Wz//m2kn2qb3pu1IMdiKVp3KVktlrb7qmYFO64/+0WP69KHRPrp8VR60uzkD+T+Sb9Pp/U/7JTv83cvxv7LKOuP/3mrW2JzlPtuN79+Sjnv+NXGvt2/Heff/U9j9dV1qtPuSfSqnBfudAYbr2/2bs/18vhwMZiikUKJZp8J1RNCvh+zq4RnKtjzpwZETRBYMu+hF1OQrvuompJOpw6TZg98+yW67K275Pui3d4UVv9G2nZN30b0qpIOvdVIWzvkn/Hnc7XstmushZztRXrmnxuNa5T6BuOSxvo+rWVdBoOxPduDeOvc/Ujqlgrq1zZ0z3HPeUtTVQcN3VZ8fN9U1j5KxhDzC5t2sk0xyfKFFnYnugwG/PLWf8XouzzHt2d6RySJCxo+3EWet6UuAomlb7+1IKUFFrbRe5MWJz9efzdgeb12jcFnVjoD6ZFqdBGu9SOsUAS98AMKiVtdbmpVWmG9CXF6vt+EyzUW9pGyZUJ7dpKDtCry2U7poOzyxFRV2W23KYQmxfeXEWffUDlchSMGGsb27ej9rxeNO2waHrL9RLXZbL1xUUnU14jNiwYKpssnnGF/qUH6icCSBbl+m0KQty/xg367Ng7Gv3nHvHlH64T11XI0DxScxTtldb9zql5aDOqQ5Tqj5w1WPSIJL5TpkC11+93JyPDqMf5aN8lI/yUf5/K8eDzY/yUT7KR/ko/8+UD5D5KB/lo3yUj/IfKx8g81E+ykf5KB/lP1Y+QOajfJSP8lE+yn+sfIDMR/koH+WjfJT/WPkAmY/yUT7KR/ko/7HyATIf5aN8lI/yUf5j5QNkPspH+Sgf5aP8x8oHyHyUj/JRPspH+Y+V/w/OtWiVjho4YgAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Setup input shape and base model, freezing the base model layers\n",
        "input_shape = (200, 200, 3)\n",
        "\n",
        "def create_tf_model(input_shape: tuple[int, int, int], # = input_shape,\n",
        "                    output_shape: int, # = 3,\n",
        "                    learning_rate: float, # = 0.001,\n",
        "                    training: bool) -> tf.keras.Model: #  = False)\n",
        "\n",
        "    # 1. Create base model with tf.keras.applications\n",
        "    base_model = tf.keras.applications.efficientnet_v2.EfficientNetV2B0(include_top=False)  #ResNet50V2(include_top=False) #ConvNeXtXLarge(include_top=False) #. Xception(include_top=False) # #\n",
        "    # base_model = tf.keras.applications.EfficientNetV2L(include_top=False, input_shape=input_shape, weights='imagenet')\n",
        "\n",
        "    # 2. Freeze the base model (so the pre-learned patterns remain)\n",
        "    base_model.trainable = training\n",
        "\n",
        "\n",
        "    # 3. Create inputs into the base model\n",
        "    inputs = tf.keras.layers.Input(shape=input_shape, name=\"input_layer\")\n",
        "\n",
        "    # Add in data augmentation Sequential model as a layer\n",
        "    x = data_augmentation(inputs)\n",
        "\n",
        "    # 5. Pass the inputs to the base_model (note: using tf.keras.applications, EfficientNetV2 inputs don't have to be normalized)\n",
        "    x = base_model(x, training=training)\n",
        "\n",
        "    # Check data shape after passing it to base_model\n",
        "    print(f\"Shape after base_model: {x.shape}\")\n",
        "\n",
        "    # 6. Average pool the outputs of the base model (aggregate all the most important information, reduce number of computations)\n",
        "    x = tf.keras.layers.GlobalAveragePooling2D(name=\"global_average_pooling_layer\")(x)\n",
        "    print(f\"After GlobalAveragePooling2D(): {x.shape}\")\n",
        "\n",
        "    x = layers.Dense(128, activation='relu')(x)\n",
        "    x = layers.Dropout(0.2)(x)\n",
        "    # 7. Create the output activation layer\n",
        "    outputs = tf.keras.layers.Dense(output_shape, activation=\"sigmoid\", name=\"output_layer\")(x)\n",
        "\n",
        "    # 8. Combine the inputs with the outputs into a model\n",
        "    model = tf.keras.Model(inputs, outputs)\n",
        "\n",
        "    # 9. Compile the model\n",
        "    model.compile(loss='binary_crossentropy',\n",
        "                  optimizer=tf.keras.optimizers.Adam(learning_rate=learning_rate),\n",
        "                  metrics=[\"accuracy\"])\n",
        "    return model"
      ],
      "metadata": {
        "id": "_M1agDr73HF0"
      },
      "execution_count": 12,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create a model for transfer learning (THE ORIGINAL MODEL)\n",
        "model_0 = create_tf_model(input_shape, 1, 1e-4, False)\n",
        "model_0.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fuHgapRM8hUP",
        "outputId": "a907f2cf-9157-4f1c-ee17-0378f980f875"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Downloading data from https://storage.googleapis.com/tensorflow/keras-applications/efficientnet_v2/efficientnetv2-b0_notop.h5\n",
            "24274472/24274472 [==============================] - 2s 0us/step\n",
            "Shape after base_model: (None, None, None, 1280)\n",
            "After GlobalAveragePooling2D(): (None, 1280)\n",
            "Model: \"model\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 200, 200, 3)]     0         \n",
            "                                                                 \n",
            " data_augmentation (Sequent  (None, None, None, None   0         \n",
            " ial)                        )                                   \n",
            "                                                                 \n",
            " efficientnetv2-b0 (Functio  (None, None, None, 1280   5919312   \n",
            " nal)                        )                                   \n",
            "                                                                 \n",
            " global_average_pooling_lay  (None, 1280)              0         \n",
            " er (GlobalAveragePooling2D                                      \n",
            " )                                                               \n",
            "                                                                 \n",
            " dense (Dense)               (None, 128)               163968    \n",
            "                                                                 \n",
            " dropout (Dropout)           (None, 128)               0         \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6083409 (23.21 MB)\n",
            "Trainable params: 164097 (641.00 KB)\n",
            "Non-trainable params: 5919312 (22.58 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # 10. Fit the model (we use less steps for validation so it's faster)\n",
        "# history_0 = model_0.fit(train_data, epochs=5, steps_per_epoch=len(train_data),\n",
        "#                         validation_data=test_data,\n",
        "#                         # Go through less of the validation data so epochs are faster (we want faster experiments!)\n",
        "#                         validation_steps=int(0.25 * len(test_data)),\n",
        "#                         # Track our model's training logs for visualization later\n",
        "#                         callbacks=[create_tensorboard_callback(\"transfer_learning\", \"feature_extract_step\")])\n",
        "# # Check out our model's training curves\n",
        "# plot_loss_curves(history_0)"
      ],
      "metadata": {
        "id": "5tmqYFli8wMr"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Setup checkpoint path\n",
        "# checkpoint_path = \"/content/drive/MyDrive/Project_4/checkpoint.ckpt\" # note: remember saving directly to Colab is temporary\n",
        "\n",
        "# # Create a ModelCheckpoint callback that saves the model's weights only\n",
        "# checkpoint_callback = tf.keras.callbacks.ModelCheckpoint(filepath=checkpoint_path,\n",
        "#                                                          save_weights_only=True, # set to False to save the entire model\n",
        "#                                                          save_best_only=True, # save only the best model weights instead of a model every epoch\n",
        "#                                                          save_freq=\"epoch\", # save every epoch\n",
        "#                                                          verbose=1)"
      ],
      "metadata": {
        "id": "WBKNptq-N76E"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Fit the model saving checkpoints every epoch\n",
        "history_0 = model_0.fit(train_data,\n",
        "                        epochs=20,\n",
        "                        steps_per_epoch=len(train_data),\n",
        "                        validation_data = test_data,\n",
        "                        validation_steps = int(0.25 * len(test_data)) #, # do less steps per validation (quicker)\n",
        "                        # callbacks=[create_tensorboard_callback(\"transfer_learning\", \"data_aug\"), checkpoint_callback]\n",
        "                        )\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "CdfJy6K5PWqG",
        "outputId": "6ee6e194-6e86-4f25-96c2-56c00c66de47"
      },
      "execution_count": 14,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "34/34 [==============================] - 547s 13s/step - loss: 0.6892 - accuracy: 0.5458 - val_loss: 0.6744 - val_accuracy: 0.5781\n",
            "Epoch 2/20\n",
            "34/34 [==============================] - 14s 378ms/step - loss: 0.6745 - accuracy: 0.5745 - val_loss: 0.6701 - val_accuracy: 0.6562\n",
            "Epoch 3/20\n",
            "34/34 [==============================] - 13s 359ms/step - loss: 0.6692 - accuracy: 0.5865 - val_loss: 0.6770 - val_accuracy: 0.6094\n",
            "Epoch 4/20\n",
            "34/34 [==============================] - 11s 303ms/step - loss: 0.6505 - accuracy: 0.6096 - val_loss: 0.6700 - val_accuracy: 0.5312\n",
            "Epoch 5/20\n",
            "34/34 [==============================] - 10s 255ms/step - loss: 0.6493 - accuracy: 0.6272 - val_loss: 0.6504 - val_accuracy: 0.5781\n",
            "Epoch 6/20\n",
            "34/34 [==============================] - 11s 249ms/step - loss: 0.6474 - accuracy: 0.6105 - val_loss: 0.7135 - val_accuracy: 0.5312\n",
            "Epoch 7/20\n",
            "34/34 [==============================] - 10s 258ms/step - loss: 0.6454 - accuracy: 0.6031 - val_loss: 0.7052 - val_accuracy: 0.5156\n",
            "Epoch 8/20\n",
            "34/34 [==============================] - 8s 192ms/step - loss: 0.6380 - accuracy: 0.6272 - val_loss: 0.6571 - val_accuracy: 0.5625\n",
            "Epoch 9/20\n",
            "34/34 [==============================] - 10s 265ms/step - loss: 0.6398 - accuracy: 0.6105 - val_loss: 0.6776 - val_accuracy: 0.5625\n",
            "Epoch 10/20\n",
            "34/34 [==============================] - 9s 221ms/step - loss: 0.6348 - accuracy: 0.6281 - val_loss: 0.6532 - val_accuracy: 0.5781\n",
            "Epoch 11/20\n",
            "34/34 [==============================] - 7s 187ms/step - loss: 0.6258 - accuracy: 0.6392 - val_loss: 0.6406 - val_accuracy: 0.5625\n",
            "Epoch 12/20\n",
            "34/34 [==============================] - 9s 212ms/step - loss: 0.6332 - accuracy: 0.6244 - val_loss: 0.6663 - val_accuracy: 0.5469\n",
            "Epoch 13/20\n",
            "34/34 [==============================] - 9s 222ms/step - loss: 0.6170 - accuracy: 0.6420 - val_loss: 0.6880 - val_accuracy: 0.5312\n",
            "Epoch 14/20\n",
            "34/34 [==============================] - 7s 162ms/step - loss: 0.6255 - accuracy: 0.6309 - val_loss: 0.6372 - val_accuracy: 0.6562\n",
            "Epoch 15/20\n",
            "34/34 [==============================] - 10s 262ms/step - loss: 0.6132 - accuracy: 0.6596 - val_loss: 0.6476 - val_accuracy: 0.5469\n",
            "Epoch 16/20\n",
            "34/34 [==============================] - 9s 241ms/step - loss: 0.6156 - accuracy: 0.6438 - val_loss: 0.6630 - val_accuracy: 0.5625\n",
            "Epoch 17/20\n",
            "34/34 [==============================] - 7s 170ms/step - loss: 0.6175 - accuracy: 0.6494 - val_loss: 0.6685 - val_accuracy: 0.5312\n",
            "Epoch 18/20\n",
            "34/34 [==============================] - 8s 219ms/step - loss: 0.6176 - accuracy: 0.6438 - val_loss: 0.6008 - val_accuracy: 0.6406\n",
            "Epoch 19/20\n",
            "34/34 [==============================] - 7s 168ms/step - loss: 0.6196 - accuracy: 0.6318 - val_loss: 0.6063 - val_accuracy: 0.6562\n",
            "Epoch 20/20\n",
            "34/34 [==============================] - 8s 180ms/step - loss: 0.6143 - accuracy: 0.6457 - val_loss: 0.6288 - val_accuracy: 0.6250\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate on the test data **USING TRANSFER LEARNING AND NO FINE TUNING\n",
        "results_data_aug = model_0.evaluate(test_data)\n",
        "results_data_aug\n",
        "# Plot model loss curves\n",
        "plot_loss_curves(history_0)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "rhqtqu_HQMC4",
        "outputId": "5a601803-956b-4824-d5a6-b8cd201371fd"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 2s 115ms/step - loss: 0.6674 - accuracy: 0.5869\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.save('/content/drive/MyDrive/Project_4/model_0_EfficientNetV2B0_onlyTF_B-M.h5')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "7doz_CUeN_Nb",
        "outputId": "2c49041d-b1e6-430a-ab24-b5a93663efce"
      },
      "execution_count": 30,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py:3000: UserWarning: You are saving your model as an HDF5 file via `model.save()`. This file format is considered legacy. We recommend using instead the native Keras format, e.g. `model.save('my_model.keras')`.\n",
            "  saving_api.save_model(\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Load in saved model weights and evaluate model\n",
        "# model_0.load_weights(checkpoint_path)\n",
        "# loaded_weights_model_results = model_0.evaluate(test_data)\n",
        "# # If the results from our native model and the loaded weights are the same, this should output True\n",
        "# results_data_aug == loaded_weights_model_results"
      ],
      "metadata": {
        "id": "tHue9g73SGmp"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Check the difference between the two results (small values)\n",
        "# print(np.array(results_data_aug) - np.array(loaded_weights_model_results))"
      ],
      "metadata": {
        "id": "_mwLzu7lTmPg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Access the base_model layers of model_2\n",
        "# model_0_base_model = model_0.layers[2]\n",
        "# model_0_base_model.name\n",
        "# # How many layers are trainable in our model_2_base_model?\n",
        "# print(len(model_0_base_model.trainable_variables)) # layer at index 2 is the EfficientNetV2B0 layer (the base model)\n",
        "# # Check which layers are tuneable (trainable)\n",
        "# for layer_number, layer in enumerate(model_0_base_model.layers):\n",
        "#   print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "id": "w59PsXJyUb30"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Layers in loaded model\n",
        "model_0.layers"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "nyEEjZuL3Y05",
        "outputId": "c4b55417-2348-4bc1-97cb-d2de2eeb2759"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[<keras.src.engine.input_layer.InputLayer at 0x7b5a817e8580>,\n",
              " <keras.src.engine.sequential.Sequential at 0x7b5a7c0e8430>,\n",
              " <keras.src.engine.functional.Functional at 0x7b5a61d33f70>,\n",
              " <keras.src.layers.pooling.global_average_pooling2d.GlobalAveragePooling2D at 0x7b5a620d76a0>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7b5a7c130670>,\n",
              " <keras.src.layers.regularization.dropout.Dropout at 0x7b5a7c095d20>,\n",
              " <keras.src.layers.core.dense.Dense at 0x7b5a7c094850>]"
            ]
          },
          "metadata": {},
          "execution_count": 16
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "for layer_number, layer in enumerate(model_0.layers):\n",
        "  print(f\"Layer number: {layer_number} | Layer name: {layer.name} | Layer type: {layer} | Trainable? {layer.trainable}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "-BVxBN533wWJ",
        "outputId": "609bb16c-7b2b-4ae2-c6d1-3d26a30aa934"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Layer number: 0 | Layer name: input_layer | Layer type: <keras.src.engine.input_layer.InputLayer object at 0x7b5a817e8580> | Trainable? True\n",
            "Layer number: 1 | Layer name: data_augmentation | Layer type: <keras.src.engine.sequential.Sequential object at 0x7b5a7c0e8430> | Trainable? True\n",
            "Layer number: 2 | Layer name: efficientnetv2-b0 | Layer type: <keras.src.engine.functional.Functional object at 0x7b5a61d33f70> | Trainable? False\n",
            "Layer number: 3 | Layer name: global_average_pooling_layer | Layer type: <keras.src.layers.pooling.global_average_pooling2d.GlobalAveragePooling2D object at 0x7b5a620d76a0> | Trainable? True\n",
            "Layer number: 4 | Layer name: dense | Layer type: <keras.src.layers.core.dense.Dense object at 0x7b5a7c130670> | Trainable? True\n",
            "Layer number: 5 | Layer name: dropout | Layer type: <keras.src.layers.regularization.dropout.Dropout object at 0x7b5a7c095d20> | Trainable? True\n",
            "Layer number: 6 | Layer name: output_layer | Layer type: <keras.src.layers.core.dense.Dense object at 0x7b5a7c094850> | Trainable? True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# # Access the base_model layers of model_0\n",
        "# model_0_base_model = model_0.layers[2]\n",
        "# model_0_base_model.name"
      ],
      "metadata": {
        "id": "UITLxvmS4ewv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# How many layers are trainable in our model_2_base_model?\n",
        "# print(len(model_0_base_model.trainable_variables)) # layer at index 2 is the EfficientNetV2B0 layer (the base model)"
      ],
      "metadata": {
        "id": "BkQwXTha4emc"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# # Check which layers are tuneable (trainable)\n",
        "# for layer_number, layer in enumerate(model_0_base_model.layers):\n",
        "#   print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "id": "u4RNXn_Y4rrP"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Create another same exact model\n",
        "model_1 = create_tf_model(input_shape, 1, 1e-4, False)\n",
        "model_1.summary()\n",
        "\n",
        "# from tensorflow.keras.models import clone_model\n",
        "# model_1 = clone_model(model_0)\n",
        "# model_1.set_weights(model_0.get_weights())\n",
        "# model_1.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])\n",
        "\n",
        "print(model_1.layers[2].name)\n",
        "# Count the number of trainable variables before unfreezing in model_1\n",
        "print(\"Number of trainable layers in model_1: \", len(model_1.layers[2].trainable_variables))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Zm4AfBcX5sS4",
        "outputId": "f9f09f9c-d47a-4c92-cf28-f479b4154b5b"
      },
      "execution_count": 18,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Shape after base_model: (None, None, None, 1280)\n",
            "After GlobalAveragePooling2D(): (None, 1280)\n",
            "Model: \"model_1\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " input_layer (InputLayer)    [(None, 200, 200, 3)]     0         \n",
            "                                                                 \n",
            " data_augmentation (Sequent  (None, None, None, None   0         \n",
            " ial)                        )                                   \n",
            "                                                                 \n",
            " efficientnetv2-b0 (Functio  (None, None, None, 1280   5919312   \n",
            " nal)                        )                                   \n",
            "                                                                 \n",
            " global_average_pooling_lay  (None, 1280)              0         \n",
            " er (GlobalAveragePooling2D                                      \n",
            " )                                                               \n",
            "                                                                 \n",
            " dense_1 (Dense)             (None, 128)               163968    \n",
            "                                                                 \n",
            " dropout_1 (Dropout)         (None, 128)               0         \n",
            "                                                                 \n",
            " output_layer (Dense)        (None, 1)                 129       \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 6083409 (23.21 MB)\n",
            "Trainable params: 164097 (641.00 KB)\n",
            "Non-trainable params: 5919312 (22.58 MB)\n",
            "_________________________________________________________________\n",
            "efficientnetv2-b0\n",
            "Number of trainable layers in model_1:  0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In model_0, make the last 'counts' of layers trainable\n",
        "model_0.layers[2].trainable = True\n",
        "# Freeze all layers except for the last few layers\n",
        "last_layers = 10\n",
        "for layer in model_0.layers[2].layers[:-last_layers]:\n",
        "  layer.trainable = False\n",
        "\n",
        "# Check the number of trainable variables after unfreezing\n",
        "print(f\"Number of trainable variables in model_0 after unfreezing the last {last_layers} layers: {len(model_0.layers[2].trainable_variables)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "D2CMgXZq4-i0",
        "outputId": "d710fee1-65e6-4843-f947-c889ccde783c"
      },
      "execution_count": 19,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of trainable variables in model_0 after unfreezing the last 10 layers: 10\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# In model_1, make all the layers trainable\n",
        "model_1.layers[2].trainable = True\n",
        "print(f\"Number of trainable variables after unfreezing all layers: {len(model_1.layers[2].trainable_variables)}\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "chwXT5SV0vzt",
        "outputId": "631236c4-3323-4802-d62b-ab21b9c01601"
      },
      "execution_count": 20,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Number of trainable variables after unfreezing all layers: 241\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Recompile the whole model (always recompile after any adjustments to a model)\n",
        "model_0.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # lr is 10x lower than before for fine-tuning\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "ZzBhRbbC6kfe"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.compile(loss=\"binary_crossentropy\",\n",
        "                optimizer=tf.keras.optimizers.Adam(learning_rate=0.0001), # lr is 10x lower than before for fine-tuning\n",
        "                metrics=[\"accuracy\"])"
      ],
      "metadata": {
        "id": "4MuhNHYsm3EY"
      },
      "execution_count": 22,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Check which layers are tuneable (trainable) in model_0\n",
        "for layer_number, layer in enumerate(model_0.layers[2].layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "PVL4OSWk7DTq",
        "outputId": "636bc905-bc4d-4670-b4b7-0b3b64212387"
      },
      "execution_count": 21,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_1 False\n",
            "1 rescaling False\n",
            "2 normalization False\n",
            "3 stem_conv False\n",
            "4 stem_bn False\n",
            "5 stem_activation False\n",
            "6 block1a_project_conv False\n",
            "7 block1a_project_bn False\n",
            "8 block1a_project_activation False\n",
            "9 block2a_expand_conv False\n",
            "10 block2a_expand_bn False\n",
            "11 block2a_expand_activation False\n",
            "12 block2a_project_conv False\n",
            "13 block2a_project_bn False\n",
            "14 block2b_expand_conv False\n",
            "15 block2b_expand_bn False\n",
            "16 block2b_expand_activation False\n",
            "17 block2b_project_conv False\n",
            "18 block2b_project_bn False\n",
            "19 block2b_drop False\n",
            "20 block2b_add False\n",
            "21 block3a_expand_conv False\n",
            "22 block3a_expand_bn False\n",
            "23 block3a_expand_activation False\n",
            "24 block3a_project_conv False\n",
            "25 block3a_project_bn False\n",
            "26 block3b_expand_conv False\n",
            "27 block3b_expand_bn False\n",
            "28 block3b_expand_activation False\n",
            "29 block3b_project_conv False\n",
            "30 block3b_project_bn False\n",
            "31 block3b_drop False\n",
            "32 block3b_add False\n",
            "33 block4a_expand_conv False\n",
            "34 block4a_expand_bn False\n",
            "35 block4a_expand_activation False\n",
            "36 block4a_dwconv2 False\n",
            "37 block4a_bn False\n",
            "38 block4a_activation False\n",
            "39 block4a_se_squeeze False\n",
            "40 block4a_se_reshape False\n",
            "41 block4a_se_reduce False\n",
            "42 block4a_se_expand False\n",
            "43 block4a_se_excite False\n",
            "44 block4a_project_conv False\n",
            "45 block4a_project_bn False\n",
            "46 block4b_expand_conv False\n",
            "47 block4b_expand_bn False\n",
            "48 block4b_expand_activation False\n",
            "49 block4b_dwconv2 False\n",
            "50 block4b_bn False\n",
            "51 block4b_activation False\n",
            "52 block4b_se_squeeze False\n",
            "53 block4b_se_reshape False\n",
            "54 block4b_se_reduce False\n",
            "55 block4b_se_expand False\n",
            "56 block4b_se_excite False\n",
            "57 block4b_project_conv False\n",
            "58 block4b_project_bn False\n",
            "59 block4b_drop False\n",
            "60 block4b_add False\n",
            "61 block4c_expand_conv False\n",
            "62 block4c_expand_bn False\n",
            "63 block4c_expand_activation False\n",
            "64 block4c_dwconv2 False\n",
            "65 block4c_bn False\n",
            "66 block4c_activation False\n",
            "67 block4c_se_squeeze False\n",
            "68 block4c_se_reshape False\n",
            "69 block4c_se_reduce False\n",
            "70 block4c_se_expand False\n",
            "71 block4c_se_excite False\n",
            "72 block4c_project_conv False\n",
            "73 block4c_project_bn False\n",
            "74 block4c_drop False\n",
            "75 block4c_add False\n",
            "76 block5a_expand_conv False\n",
            "77 block5a_expand_bn False\n",
            "78 block5a_expand_activation False\n",
            "79 block5a_dwconv2 False\n",
            "80 block5a_bn False\n",
            "81 block5a_activation False\n",
            "82 block5a_se_squeeze False\n",
            "83 block5a_se_reshape False\n",
            "84 block5a_se_reduce False\n",
            "85 block5a_se_expand False\n",
            "86 block5a_se_excite False\n",
            "87 block5a_project_conv False\n",
            "88 block5a_project_bn False\n",
            "89 block5b_expand_conv False\n",
            "90 block5b_expand_bn False\n",
            "91 block5b_expand_activation False\n",
            "92 block5b_dwconv2 False\n",
            "93 block5b_bn False\n",
            "94 block5b_activation False\n",
            "95 block5b_se_squeeze False\n",
            "96 block5b_se_reshape False\n",
            "97 block5b_se_reduce False\n",
            "98 block5b_se_expand False\n",
            "99 block5b_se_excite False\n",
            "100 block5b_project_conv False\n",
            "101 block5b_project_bn False\n",
            "102 block5b_drop False\n",
            "103 block5b_add False\n",
            "104 block5c_expand_conv False\n",
            "105 block5c_expand_bn False\n",
            "106 block5c_expand_activation False\n",
            "107 block5c_dwconv2 False\n",
            "108 block5c_bn False\n",
            "109 block5c_activation False\n",
            "110 block5c_se_squeeze False\n",
            "111 block5c_se_reshape False\n",
            "112 block5c_se_reduce False\n",
            "113 block5c_se_expand False\n",
            "114 block5c_se_excite False\n",
            "115 block5c_project_conv False\n",
            "116 block5c_project_bn False\n",
            "117 block5c_drop False\n",
            "118 block5c_add False\n",
            "119 block5d_expand_conv False\n",
            "120 block5d_expand_bn False\n",
            "121 block5d_expand_activation False\n",
            "122 block5d_dwconv2 False\n",
            "123 block5d_bn False\n",
            "124 block5d_activation False\n",
            "125 block5d_se_squeeze False\n",
            "126 block5d_se_reshape False\n",
            "127 block5d_se_reduce False\n",
            "128 block5d_se_expand False\n",
            "129 block5d_se_excite False\n",
            "130 block5d_project_conv False\n",
            "131 block5d_project_bn False\n",
            "132 block5d_drop False\n",
            "133 block5d_add False\n",
            "134 block5e_expand_conv False\n",
            "135 block5e_expand_bn False\n",
            "136 block5e_expand_activation False\n",
            "137 block5e_dwconv2 False\n",
            "138 block5e_bn False\n",
            "139 block5e_activation False\n",
            "140 block5e_se_squeeze False\n",
            "141 block5e_se_reshape False\n",
            "142 block5e_se_reduce False\n",
            "143 block5e_se_expand False\n",
            "144 block5e_se_excite False\n",
            "145 block5e_project_conv False\n",
            "146 block5e_project_bn False\n",
            "147 block5e_drop False\n",
            "148 block5e_add False\n",
            "149 block6a_expand_conv False\n",
            "150 block6a_expand_bn False\n",
            "151 block6a_expand_activation False\n",
            "152 block6a_dwconv2 False\n",
            "153 block6a_bn False\n",
            "154 block6a_activation False\n",
            "155 block6a_se_squeeze False\n",
            "156 block6a_se_reshape False\n",
            "157 block6a_se_reduce False\n",
            "158 block6a_se_expand False\n",
            "159 block6a_se_excite False\n",
            "160 block6a_project_conv False\n",
            "161 block6a_project_bn False\n",
            "162 block6b_expand_conv False\n",
            "163 block6b_expand_bn False\n",
            "164 block6b_expand_activation False\n",
            "165 block6b_dwconv2 False\n",
            "166 block6b_bn False\n",
            "167 block6b_activation False\n",
            "168 block6b_se_squeeze False\n",
            "169 block6b_se_reshape False\n",
            "170 block6b_se_reduce False\n",
            "171 block6b_se_expand False\n",
            "172 block6b_se_excite False\n",
            "173 block6b_project_conv False\n",
            "174 block6b_project_bn False\n",
            "175 block6b_drop False\n",
            "176 block6b_add False\n",
            "177 block6c_expand_conv False\n",
            "178 block6c_expand_bn False\n",
            "179 block6c_expand_activation False\n",
            "180 block6c_dwconv2 False\n",
            "181 block6c_bn False\n",
            "182 block6c_activation False\n",
            "183 block6c_se_squeeze False\n",
            "184 block6c_se_reshape False\n",
            "185 block6c_se_reduce False\n",
            "186 block6c_se_expand False\n",
            "187 block6c_se_excite False\n",
            "188 block6c_project_conv False\n",
            "189 block6c_project_bn False\n",
            "190 block6c_drop False\n",
            "191 block6c_add False\n",
            "192 block6d_expand_conv False\n",
            "193 block6d_expand_bn False\n",
            "194 block6d_expand_activation False\n",
            "195 block6d_dwconv2 False\n",
            "196 block6d_bn False\n",
            "197 block6d_activation False\n",
            "198 block6d_se_squeeze False\n",
            "199 block6d_se_reshape False\n",
            "200 block6d_se_reduce False\n",
            "201 block6d_se_expand False\n",
            "202 block6d_se_excite False\n",
            "203 block6d_project_conv False\n",
            "204 block6d_project_bn False\n",
            "205 block6d_drop False\n",
            "206 block6d_add False\n",
            "207 block6e_expand_conv False\n",
            "208 block6e_expand_bn False\n",
            "209 block6e_expand_activation False\n",
            "210 block6e_dwconv2 False\n",
            "211 block6e_bn False\n",
            "212 block6e_activation False\n",
            "213 block6e_se_squeeze False\n",
            "214 block6e_se_reshape False\n",
            "215 block6e_se_reduce False\n",
            "216 block6e_se_expand False\n",
            "217 block6e_se_excite False\n",
            "218 block6e_project_conv False\n",
            "219 block6e_project_bn False\n",
            "220 block6e_drop False\n",
            "221 block6e_add False\n",
            "222 block6f_expand_conv False\n",
            "223 block6f_expand_bn False\n",
            "224 block6f_expand_activation False\n",
            "225 block6f_dwconv2 False\n",
            "226 block6f_bn False\n",
            "227 block6f_activation False\n",
            "228 block6f_se_squeeze False\n",
            "229 block6f_se_reshape False\n",
            "230 block6f_se_reduce False\n",
            "231 block6f_se_expand False\n",
            "232 block6f_se_excite False\n",
            "233 block6f_project_conv False\n",
            "234 block6f_project_bn False\n",
            "235 block6f_drop False\n",
            "236 block6f_add False\n",
            "237 block6g_expand_conv False\n",
            "238 block6g_expand_bn False\n",
            "239 block6g_expand_activation False\n",
            "240 block6g_dwconv2 False\n",
            "241 block6g_bn False\n",
            "242 block6g_activation False\n",
            "243 block6g_se_squeeze False\n",
            "244 block6g_se_reshape False\n",
            "245 block6g_se_reduce False\n",
            "246 block6g_se_expand False\n",
            "247 block6g_se_excite False\n",
            "248 block6g_project_conv False\n",
            "249 block6g_project_bn False\n",
            "250 block6g_drop False\n",
            "251 block6g_add False\n",
            "252 block6h_expand_conv False\n",
            "253 block6h_expand_bn False\n",
            "254 block6h_expand_activation False\n",
            "255 block6h_dwconv2 False\n",
            "256 block6h_bn False\n",
            "257 block6h_activation False\n",
            "258 block6h_se_squeeze False\n",
            "259 block6h_se_reshape False\n",
            "260 block6h_se_reduce True\n",
            "261 block6h_se_expand True\n",
            "262 block6h_se_excite True\n",
            "263 block6h_project_conv True\n",
            "264 block6h_project_bn True\n",
            "265 block6h_drop True\n",
            "266 block6h_add True\n",
            "267 top_conv True\n",
            "268 top_bn True\n",
            "269 top_activation True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Check which layers are tuneable (trainable) in model_1\n",
        "for layer_number, layer in enumerate(model_1.layers[2].layers):\n",
        "  print(layer_number, layer.name, layer.trainable)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "EA9bI6Yf-eos",
        "outputId": "f23edc0c-f5c7-45fc-9c2e-3034fef34a02"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0 input_2 True\n",
            "1 rescaling_1 True\n",
            "2 normalization_1 True\n",
            "3 stem_conv True\n",
            "4 stem_bn True\n",
            "5 stem_activation True\n",
            "6 block1a_project_conv True\n",
            "7 block1a_project_bn True\n",
            "8 block1a_project_activation True\n",
            "9 block2a_expand_conv True\n",
            "10 block2a_expand_bn True\n",
            "11 block2a_expand_activation True\n",
            "12 block2a_project_conv True\n",
            "13 block2a_project_bn True\n",
            "14 block2b_expand_conv True\n",
            "15 block2b_expand_bn True\n",
            "16 block2b_expand_activation True\n",
            "17 block2b_project_conv True\n",
            "18 block2b_project_bn True\n",
            "19 block2b_drop True\n",
            "20 block2b_add True\n",
            "21 block3a_expand_conv True\n",
            "22 block3a_expand_bn True\n",
            "23 block3a_expand_activation True\n",
            "24 block3a_project_conv True\n",
            "25 block3a_project_bn True\n",
            "26 block3b_expand_conv True\n",
            "27 block3b_expand_bn True\n",
            "28 block3b_expand_activation True\n",
            "29 block3b_project_conv True\n",
            "30 block3b_project_bn True\n",
            "31 block3b_drop True\n",
            "32 block3b_add True\n",
            "33 block4a_expand_conv True\n",
            "34 block4a_expand_bn True\n",
            "35 block4a_expand_activation True\n",
            "36 block4a_dwconv2 True\n",
            "37 block4a_bn True\n",
            "38 block4a_activation True\n",
            "39 block4a_se_squeeze True\n",
            "40 block4a_se_reshape True\n",
            "41 block4a_se_reduce True\n",
            "42 block4a_se_expand True\n",
            "43 block4a_se_excite True\n",
            "44 block4a_project_conv True\n",
            "45 block4a_project_bn True\n",
            "46 block4b_expand_conv True\n",
            "47 block4b_expand_bn True\n",
            "48 block4b_expand_activation True\n",
            "49 block4b_dwconv2 True\n",
            "50 block4b_bn True\n",
            "51 block4b_activation True\n",
            "52 block4b_se_squeeze True\n",
            "53 block4b_se_reshape True\n",
            "54 block4b_se_reduce True\n",
            "55 block4b_se_expand True\n",
            "56 block4b_se_excite True\n",
            "57 block4b_project_conv True\n",
            "58 block4b_project_bn True\n",
            "59 block4b_drop True\n",
            "60 block4b_add True\n",
            "61 block4c_expand_conv True\n",
            "62 block4c_expand_bn True\n",
            "63 block4c_expand_activation True\n",
            "64 block4c_dwconv2 True\n",
            "65 block4c_bn True\n",
            "66 block4c_activation True\n",
            "67 block4c_se_squeeze True\n",
            "68 block4c_se_reshape True\n",
            "69 block4c_se_reduce True\n",
            "70 block4c_se_expand True\n",
            "71 block4c_se_excite True\n",
            "72 block4c_project_conv True\n",
            "73 block4c_project_bn True\n",
            "74 block4c_drop True\n",
            "75 block4c_add True\n",
            "76 block5a_expand_conv True\n",
            "77 block5a_expand_bn True\n",
            "78 block5a_expand_activation True\n",
            "79 block5a_dwconv2 True\n",
            "80 block5a_bn True\n",
            "81 block5a_activation True\n",
            "82 block5a_se_squeeze True\n",
            "83 block5a_se_reshape True\n",
            "84 block5a_se_reduce True\n",
            "85 block5a_se_expand True\n",
            "86 block5a_se_excite True\n",
            "87 block5a_project_conv True\n",
            "88 block5a_project_bn True\n",
            "89 block5b_expand_conv True\n",
            "90 block5b_expand_bn True\n",
            "91 block5b_expand_activation True\n",
            "92 block5b_dwconv2 True\n",
            "93 block5b_bn True\n",
            "94 block5b_activation True\n",
            "95 block5b_se_squeeze True\n",
            "96 block5b_se_reshape True\n",
            "97 block5b_se_reduce True\n",
            "98 block5b_se_expand True\n",
            "99 block5b_se_excite True\n",
            "100 block5b_project_conv True\n",
            "101 block5b_project_bn True\n",
            "102 block5b_drop True\n",
            "103 block5b_add True\n",
            "104 block5c_expand_conv True\n",
            "105 block5c_expand_bn True\n",
            "106 block5c_expand_activation True\n",
            "107 block5c_dwconv2 True\n",
            "108 block5c_bn True\n",
            "109 block5c_activation True\n",
            "110 block5c_se_squeeze True\n",
            "111 block5c_se_reshape True\n",
            "112 block5c_se_reduce True\n",
            "113 block5c_se_expand True\n",
            "114 block5c_se_excite True\n",
            "115 block5c_project_conv True\n",
            "116 block5c_project_bn True\n",
            "117 block5c_drop True\n",
            "118 block5c_add True\n",
            "119 block5d_expand_conv True\n",
            "120 block5d_expand_bn True\n",
            "121 block5d_expand_activation True\n",
            "122 block5d_dwconv2 True\n",
            "123 block5d_bn True\n",
            "124 block5d_activation True\n",
            "125 block5d_se_squeeze True\n",
            "126 block5d_se_reshape True\n",
            "127 block5d_se_reduce True\n",
            "128 block5d_se_expand True\n",
            "129 block5d_se_excite True\n",
            "130 block5d_project_conv True\n",
            "131 block5d_project_bn True\n",
            "132 block5d_drop True\n",
            "133 block5d_add True\n",
            "134 block5e_expand_conv True\n",
            "135 block5e_expand_bn True\n",
            "136 block5e_expand_activation True\n",
            "137 block5e_dwconv2 True\n",
            "138 block5e_bn True\n",
            "139 block5e_activation True\n",
            "140 block5e_se_squeeze True\n",
            "141 block5e_se_reshape True\n",
            "142 block5e_se_reduce True\n",
            "143 block5e_se_expand True\n",
            "144 block5e_se_excite True\n",
            "145 block5e_project_conv True\n",
            "146 block5e_project_bn True\n",
            "147 block5e_drop True\n",
            "148 block5e_add True\n",
            "149 block6a_expand_conv True\n",
            "150 block6a_expand_bn True\n",
            "151 block6a_expand_activation True\n",
            "152 block6a_dwconv2 True\n",
            "153 block6a_bn True\n",
            "154 block6a_activation True\n",
            "155 block6a_se_squeeze True\n",
            "156 block6a_se_reshape True\n",
            "157 block6a_se_reduce True\n",
            "158 block6a_se_expand True\n",
            "159 block6a_se_excite True\n",
            "160 block6a_project_conv True\n",
            "161 block6a_project_bn True\n",
            "162 block6b_expand_conv True\n",
            "163 block6b_expand_bn True\n",
            "164 block6b_expand_activation True\n",
            "165 block6b_dwconv2 True\n",
            "166 block6b_bn True\n",
            "167 block6b_activation True\n",
            "168 block6b_se_squeeze True\n",
            "169 block6b_se_reshape True\n",
            "170 block6b_se_reduce True\n",
            "171 block6b_se_expand True\n",
            "172 block6b_se_excite True\n",
            "173 block6b_project_conv True\n",
            "174 block6b_project_bn True\n",
            "175 block6b_drop True\n",
            "176 block6b_add True\n",
            "177 block6c_expand_conv True\n",
            "178 block6c_expand_bn True\n",
            "179 block6c_expand_activation True\n",
            "180 block6c_dwconv2 True\n",
            "181 block6c_bn True\n",
            "182 block6c_activation True\n",
            "183 block6c_se_squeeze True\n",
            "184 block6c_se_reshape True\n",
            "185 block6c_se_reduce True\n",
            "186 block6c_se_expand True\n",
            "187 block6c_se_excite True\n",
            "188 block6c_project_conv True\n",
            "189 block6c_project_bn True\n",
            "190 block6c_drop True\n",
            "191 block6c_add True\n",
            "192 block6d_expand_conv True\n",
            "193 block6d_expand_bn True\n",
            "194 block6d_expand_activation True\n",
            "195 block6d_dwconv2 True\n",
            "196 block6d_bn True\n",
            "197 block6d_activation True\n",
            "198 block6d_se_squeeze True\n",
            "199 block6d_se_reshape True\n",
            "200 block6d_se_reduce True\n",
            "201 block6d_se_expand True\n",
            "202 block6d_se_excite True\n",
            "203 block6d_project_conv True\n",
            "204 block6d_project_bn True\n",
            "205 block6d_drop True\n",
            "206 block6d_add True\n",
            "207 block6e_expand_conv True\n",
            "208 block6e_expand_bn True\n",
            "209 block6e_expand_activation True\n",
            "210 block6e_dwconv2 True\n",
            "211 block6e_bn True\n",
            "212 block6e_activation True\n",
            "213 block6e_se_squeeze True\n",
            "214 block6e_se_reshape True\n",
            "215 block6e_se_reduce True\n",
            "216 block6e_se_expand True\n",
            "217 block6e_se_excite True\n",
            "218 block6e_project_conv True\n",
            "219 block6e_project_bn True\n",
            "220 block6e_drop True\n",
            "221 block6e_add True\n",
            "222 block6f_expand_conv True\n",
            "223 block6f_expand_bn True\n",
            "224 block6f_expand_activation True\n",
            "225 block6f_dwconv2 True\n",
            "226 block6f_bn True\n",
            "227 block6f_activation True\n",
            "228 block6f_se_squeeze True\n",
            "229 block6f_se_reshape True\n",
            "230 block6f_se_reduce True\n",
            "231 block6f_se_expand True\n",
            "232 block6f_se_excite True\n",
            "233 block6f_project_conv True\n",
            "234 block6f_project_bn True\n",
            "235 block6f_drop True\n",
            "236 block6f_add True\n",
            "237 block6g_expand_conv True\n",
            "238 block6g_expand_bn True\n",
            "239 block6g_expand_activation True\n",
            "240 block6g_dwconv2 True\n",
            "241 block6g_bn True\n",
            "242 block6g_activation True\n",
            "243 block6g_se_squeeze True\n",
            "244 block6g_se_reshape True\n",
            "245 block6g_se_reduce True\n",
            "246 block6g_se_expand True\n",
            "247 block6g_se_excite True\n",
            "248 block6g_project_conv True\n",
            "249 block6g_project_bn True\n",
            "250 block6g_drop True\n",
            "251 block6g_add True\n",
            "252 block6h_expand_conv True\n",
            "253 block6h_expand_bn True\n",
            "254 block6h_expand_activation True\n",
            "255 block6h_dwconv2 True\n",
            "256 block6h_bn True\n",
            "257 block6h_activation True\n",
            "258 block6h_se_squeeze True\n",
            "259 block6h_se_reshape True\n",
            "260 block6h_se_reduce True\n",
            "261 block6h_se_expand True\n",
            "262 block6h_se_excite True\n",
            "263 block6h_project_conv True\n",
            "264 block6h_project_bn True\n",
            "265 block6h_drop True\n",
            "266 block6h_add True\n",
            "267 top_conv True\n",
            "268 top_bn True\n",
            "269 top_activation True\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Fine tune for another 15 epochs\n",
        "fine_tune_epochs = 40\n",
        "\n",
        "# Refit the model (same as model_0 except with more trainable layers)\n",
        "history_finetune_0 = model_0.fit(train_data,\n",
        "                                          epochs=fine_tune_epochs,\n",
        "                                          validation_data=test_data,\n",
        "                                          initial_epoch=history_0.epoch[-1], # start from previous last epoch\n",
        "                                          validation_steps=int(0.25 * len(test_data)) #,\n",
        "                                          # callbacks=[create_tensorboard_callback(\"transfer_learning\", \"fine_tune_0\")]\n",
        "                                 ) # name experiment appropriately"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "8BFv4azz7XmB",
        "outputId": "29e40d20-c321-4d43-b4cf-6e7d9d35c8c7"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 20/40\n",
            "34/34 [==============================] - 21s 245ms/step - loss: 0.6183 - accuracy: 0.6244 - val_loss: 0.6621 - val_accuracy: 0.5469\n",
            "Epoch 21/40\n",
            "34/34 [==============================] - 8s 169ms/step - loss: 0.6020 - accuracy: 0.6679 - val_loss: 0.7335 - val_accuracy: 0.5312\n",
            "Epoch 22/40\n",
            "34/34 [==============================] - 7s 187ms/step - loss: 0.5996 - accuracy: 0.6586 - val_loss: 0.6060 - val_accuracy: 0.7188\n",
            "Epoch 23/40\n",
            "34/34 [==============================] - 7s 168ms/step - loss: 0.6048 - accuracy: 0.6522 - val_loss: 0.6350 - val_accuracy: 0.6250\n",
            "Epoch 24/40\n",
            "34/34 [==============================] - 8s 193ms/step - loss: 0.6006 - accuracy: 0.6494 - val_loss: 0.7066 - val_accuracy: 0.5781\n",
            "Epoch 25/40\n",
            "34/34 [==============================] - 7s 168ms/step - loss: 0.5915 - accuracy: 0.6707 - val_loss: 0.6173 - val_accuracy: 0.6250\n",
            "Epoch 26/40\n",
            "34/34 [==============================] - 7s 153ms/step - loss: 0.5904 - accuracy: 0.6623 - val_loss: 0.7222 - val_accuracy: 0.5469\n",
            "Epoch 27/40\n",
            "34/34 [==============================] - 8s 215ms/step - loss: 0.5985 - accuracy: 0.6586 - val_loss: 0.6946 - val_accuracy: 0.5312\n",
            "Epoch 28/40\n",
            "34/34 [==============================] - 7s 176ms/step - loss: 0.5895 - accuracy: 0.6781 - val_loss: 0.6459 - val_accuracy: 0.6250\n",
            "Epoch 29/40\n",
            "34/34 [==============================] - 8s 199ms/step - loss: 0.5890 - accuracy: 0.6577 - val_loss: 0.7789 - val_accuracy: 0.4531\n",
            "Epoch 30/40\n",
            "34/34 [==============================] - 7s 167ms/step - loss: 0.5827 - accuracy: 0.6873 - val_loss: 0.6046 - val_accuracy: 0.6719\n",
            "Epoch 31/40\n",
            "34/34 [==============================] - 8s 205ms/step - loss: 0.5802 - accuracy: 0.6716 - val_loss: 0.6540 - val_accuracy: 0.5781\n",
            "Epoch 32/40\n",
            "34/34 [==============================] - 6s 151ms/step - loss: 0.5880 - accuracy: 0.6744 - val_loss: 0.7084 - val_accuracy: 0.5781\n",
            "Epoch 33/40\n",
            "34/34 [==============================] - 8s 210ms/step - loss: 0.5730 - accuracy: 0.6642 - val_loss: 0.6357 - val_accuracy: 0.6406\n",
            "Epoch 34/40\n",
            "34/34 [==============================] - 7s 174ms/step - loss: 0.5784 - accuracy: 0.6744 - val_loss: 0.7061 - val_accuracy: 0.5781\n",
            "Epoch 35/40\n",
            "34/34 [==============================] - 6s 144ms/step - loss: 0.5649 - accuracy: 0.6901 - val_loss: 0.6298 - val_accuracy: 0.6094\n",
            "Epoch 36/40\n",
            "34/34 [==============================] - 7s 184ms/step - loss: 0.5814 - accuracy: 0.6818 - val_loss: 0.6731 - val_accuracy: 0.6562\n",
            "Epoch 37/40\n",
            "34/34 [==============================] - 6s 141ms/step - loss: 0.5543 - accuracy: 0.7031 - val_loss: 0.6010 - val_accuracy: 0.6406\n",
            "Epoch 38/40\n",
            "34/34 [==============================] - 8s 193ms/step - loss: 0.5524 - accuracy: 0.7049 - val_loss: 0.7730 - val_accuracy: 0.5156\n",
            "Epoch 39/40\n",
            "34/34 [==============================] - 7s 165ms/step - loss: 0.5587 - accuracy: 0.7021 - val_loss: 0.7245 - val_accuracy: 0.5312\n",
            "Epoch 40/40\n",
            "34/34 [==============================] - 7s 170ms/step - loss: 0.5532 - accuracy: 0.7077 - val_loss: 0.7426 - val_accuracy: 0.5469\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data\n",
        "results_tf_ft_0 = model_0.evaluate(test_data)\n",
        "results_tf_ft_0\n",
        "# Plot model loss curves\n",
        "plot_loss_curves(history_finetune_0)"
      ],
      "metadata": {
        "id": "AieMc4XNAeQl",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "outputId": "b3755fa5-9ca8-4cd5-f46d-14f85c583bd8"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 1s 46ms/step - loss: 0.7036 - accuracy: 0.5946\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_0.save('/content/drive/MyDrive/Project_4/model_0_EfficientNetV2B0_TF-FT-10L_B-M.h5')"
      ],
      "metadata": {
        "id": "3RGeyxBLTCl-"
      },
      "execution_count": 31,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Refit the model (same as model_0 except with more trainable layers)\n",
        "history_finetune_1 = model_1.fit(train_data,\n",
        "                                          epochs=fine_tune_epochs,\n",
        "                                          validation_data=test_data,\n",
        "                                          initial_epoch=history_0.epoch[-1], # start from previous last epoch\n",
        "                                          validation_steps=int(0.25 * len(test_data)) #,\n",
        "                                          # callbacks=[create_tensorboard_callback(\"transfer_learning\", \"fine_tune_0\")]\n",
        "                                 ) # name experiment appropriately"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yEMB3CnO4tHC",
        "outputId": "0af8c2a5-fd99-4345-c2a9-8901fd2c5854"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 20/40\n",
            "34/34 [==============================] - 82s 1s/step - loss: 0.6945 - accuracy: 0.5375 - val_loss: 0.6493 - val_accuracy: 0.6250\n",
            "Epoch 21/40\n",
            "34/34 [==============================] - 26s 715ms/step - loss: 0.6928 - accuracy: 0.5430 - val_loss: 0.6738 - val_accuracy: 0.5781\n",
            "Epoch 22/40\n",
            "34/34 [==============================] - 23s 628ms/step - loss: 0.6487 - accuracy: 0.5967 - val_loss: 0.6868 - val_accuracy: 0.5469\n",
            "Epoch 23/40\n",
            "34/34 [==============================] - 20s 553ms/step - loss: 0.6374 - accuracy: 0.6152 - val_loss: 0.6892 - val_accuracy: 0.5938\n",
            "Epoch 24/40\n",
            "34/34 [==============================] - 20s 566ms/step - loss: 0.6144 - accuracy: 0.6475 - val_loss: 0.6483 - val_accuracy: 0.6250\n",
            "Epoch 25/40\n",
            "34/34 [==============================] - 18s 511ms/step - loss: 0.5849 - accuracy: 0.6512 - val_loss: 0.7003 - val_accuracy: 0.5938\n",
            "Epoch 26/40\n",
            "34/34 [==============================] - 17s 454ms/step - loss: 0.5748 - accuracy: 0.6799 - val_loss: 0.7769 - val_accuracy: 0.5312\n",
            "Epoch 27/40\n",
            "34/34 [==============================] - 17s 479ms/step - loss: 0.5511 - accuracy: 0.6957 - val_loss: 0.6144 - val_accuracy: 0.5781\n",
            "Epoch 28/40\n",
            "34/34 [==============================] - 14s 379ms/step - loss: 0.5657 - accuracy: 0.6698 - val_loss: 0.6162 - val_accuracy: 0.6250\n",
            "Epoch 29/40\n",
            "34/34 [==============================] - 14s 372ms/step - loss: 0.5313 - accuracy: 0.6984 - val_loss: 0.7931 - val_accuracy: 0.4375\n",
            "Epoch 30/40\n",
            "34/34 [==============================] - 15s 418ms/step - loss: 0.4974 - accuracy: 0.7317 - val_loss: 0.7748 - val_accuracy: 0.5625\n",
            "Epoch 31/40\n",
            "34/34 [==============================] - 16s 449ms/step - loss: 0.4936 - accuracy: 0.7271 - val_loss: 0.8248 - val_accuracy: 0.6406\n",
            "Epoch 32/40\n",
            "34/34 [==============================] - 16s 409ms/step - loss: 0.4530 - accuracy: 0.7687 - val_loss: 0.7666 - val_accuracy: 0.6250\n",
            "Epoch 33/40\n",
            "34/34 [==============================] - 14s 361ms/step - loss: 0.4787 - accuracy: 0.7549 - val_loss: 0.6905 - val_accuracy: 0.6406\n",
            "Epoch 34/40\n",
            "34/34 [==============================] - 14s 387ms/step - loss: 0.4283 - accuracy: 0.7826 - val_loss: 1.0021 - val_accuracy: 0.5781\n",
            "Epoch 35/40\n",
            "34/34 [==============================] - 13s 361ms/step - loss: 0.4002 - accuracy: 0.7974 - val_loss: 0.8439 - val_accuracy: 0.5781\n",
            "Epoch 36/40\n",
            "34/34 [==============================] - 13s 357ms/step - loss: 0.3827 - accuracy: 0.8076 - val_loss: 1.1460 - val_accuracy: 0.5312\n",
            "Epoch 37/40\n",
            "34/34 [==============================] - 13s 352ms/step - loss: 0.3374 - accuracy: 0.8363 - val_loss: 1.0555 - val_accuracy: 0.6250\n",
            "Epoch 38/40\n",
            "34/34 [==============================] - 13s 363ms/step - loss: 0.3766 - accuracy: 0.8205 - val_loss: 1.0598 - val_accuracy: 0.6250\n",
            "Epoch 39/40\n",
            "34/34 [==============================] - 12s 324ms/step - loss: 0.3107 - accuracy: 0.8520 - val_loss: 1.0513 - val_accuracy: 0.5781\n",
            "Epoch 40/40\n",
            "34/34 [==============================] - 14s 372ms/step - loss: 0.3038 - accuracy: 0.8649 - val_loss: 0.8043 - val_accuracy: 0.6719\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Evaluate the model on the test data\n",
        "results_tf_ft_1 = model_1.evaluate(test_data)\n",
        "results_tf_ft_1\n",
        "# Plot model loss curves\n",
        "plot_loss_curves(history_finetune_1)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 945
        },
        "id": "G4Y14CqAJU-Y",
        "outputId": "691be22a-1fc5-450c-88bf-a1ec894dbe4e"
      },
      "execution_count": 26,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "9/9 [==============================] - 2s 54ms/step - loss: 1.1112 - accuracy: 0.5869\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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uDh48WKhbTAgh9GI0akQcjePzv0+x/UxB63iPsAAevLkBNzUKqBQfnOwuGXF3duTwa/11O3d5/XdWzLRp01i7di3vvfcejRo1wt3dnREjRtxwhWJnZ+dCPxsMhhITh6L2L223U2kMHjyYunXr8vnnnxMSEoLRaKRly5am68gvkV6cG91fVLxFDVD97+/33Xff5aOPPuLDDz+kVatWeHp68uSTT5Y6LlBdNW3btiU6OppFixbRu3dv6tate8PHCSGEtWTm5PHj7mgW/HOaU1fHgjg7GrijTS0e6FGfZsE+OkdYmN0lIwaDoVRdJXpzcXExlbIvyebNmxk/frypeyQtLY0zZ85YObrCfH19CQoKYseOHdx8882AalHYvXs3bdu2veHjL126xLFjx/j888/p0aMHAJs2bSq0T+vWrfniiy9ITEwssnWkdevWREREMGHChCLPERgYWGjA7YkTJ8jIuHH/5+bNmxkyZAj33nsvoAarHj9+nObNmwMQFhaGu7s7ERERPPDAA0Ueo1WrVnTs2JHPP/+cZcuWMWfOnBueVwghrCEhLYuvt5zl661nSUxXH6q83ZwY06Uu47vVo6avm84RFq3yv2vbqXr16rFt2zbOnDmDl5dXsa0WYWFhrFixgsGDB2MwGJgxY0apu0Ys6bHHHmPmzJk0atSIpk2bMnv2bC5fvlyq5j1/f3+qV6/OZ599RnBwMFFRUTz33HOF9hk9ejRvvvkmQ4cOZebMmQQHB7Nnzx5CQkIIDw/n5Zdfpk+fPjRs2JBRo0aRm5vL6tWrefbZZwE1q2XOnDmEh4eTl5fHs88+e11rT1HCwsL44Ycf+Pfff/H392fWrFnExsaakhE3NzeeffZZnnnmGVxcXOjevTvx8fEcOnSI+++/33ScBx54gClTpuDp6Vlolo8QQlSEyPg0vvjnNCt2R5OVq94javu7M7F7fe7uFIqXa+V+u7e72TS2Ytq0aTg6OtK8eXMCAwOLHQMya9Ys/P396datG4MHD6Z///60b9++gqOFZ599ltGjRzN27FjCw8Px8vKif//+uLndOMt2cHDg22+/ZdeuXbRs2ZKnnnqKd999t9A+Li4u/Pnnn9SoUYNBgwbRqlUr3nrrLdOKtr169WL58uWsXLmStm3b0rt370IzXt5//31CQ0Pp0aMH99xzD9OmTStV7ZUXX3yR9u3b079/f3r16kXNmjWvm648Y8YMnn76aV566SWaNWvGyJEjrxt/M3r0aJycnBg9enSpfidCCFFemqax7dQlHvhyB33e38g326PIyjXSprYvc+5px4ZpvZh4U/1Kn4gAGDRLDgywkpSUFHx9fUlOTsbHp3A/V2ZmJqdPn6Z+/fryJlCBjEYjzZo14+677+b111/XOxzdnTlzhoYNG7Jjx45SJYvydyuEKKvcPCO/H7zI5/+cYn90sun2vs2CmNSjPp3rV6sUg1Kh5Pfva1X+dElUCmfPnuXPP/+kZ8+eZGVlMWfOHE6fPs0999yjd2i6ysnJ4dKlS7z44ot07dpVl1YrIUTVkJaVy3c7zrFw02nOJ10BwNXJgeEdanP/TfVpGOilc4RlJ8mIKBUHBwcWL17MtGnT0DSNli1bsm7dOpo1a0ZUVJRpjEVRDh8+TJ06dSow2oqzefNmbrnlFho3bswPP/ygdzhCCDv1b2QCk5fsJvmKmiVYzdOFseF1ua9rXap7ueocXflJMiJKJTQ0lM2bNxd5X0hICHv37i32sSEhIVaKSn+9evWy6BRoIYT4rwPRyUz6cifp2XnUD/DkgR71Gd6+Nm4WKCdRWUgyIsrNycmpULVUIYQQlnE6IZ3xi7aTnp1HeIPqLJ7YCVcn+0lC8slsGiGEEKISikvJ5L4F27iUnk2LEB8+G9vBLhMRkGRECCGEqHSSr+QwduF2oi9foW51DxZP6Iy3241rJ9kqSUaEEEKISiQzJ49JX+7k6MVUAr1d+XpiFwK9bX+QakkkGRFCCCEqidw8I499s4ftZxLxdnXiywmdqVP9xgUcbZ0kI0IIIUQloGkaz/90gLWHY3FxcuCLcR1pHlK5FrSzFklGbFi9evX48MMP9Q5DCCGEBbz7xzG+3xmNgwHmjG5HlwbV9Q6pwkgyIoQQQuhswabTfLIhEoCZd7aiX4uaOkdUsSQZEbrIy8vTZfVhIYSobH7ec57XfzsMwP/1b8LITvZZsbokkozo5LPPPiMkJOS6N+QhQ4YwceJEIiMjGTJkCEFBQXh5edGpUyfWrVtX5vPNmjWLVq1a4enpSWhoKI888ghpaWmF9tm8eTO9evXCw8MDf39/+vfvz+XLlwG1MN4777xDo0aNcHV1pU6dOrzxxhsAbNiwAYPBQFJSkulYe/fuxWAwcObMGQAWL16Mn58fK1eupHnz5ri6uhIVFcWOHTu49dZbCQgIwNfXl549e7J79+5CcSUlJfHQQw8RFBSEm5sbLVu25LfffiM9PR0fH5/ryrD//PPPeHp6kpqaWubflxBCVIT1x+KYtnwfABO71+eRXg11jkgf9peMaBpkp+vzZUZZ8LvuuotLly6xfv16022JiYmsWbOGMWPGkJaWxqBBg4iIiGDPnj0MGDCAwYMHExUVVaZfi4ODAx9//DGHDh3iyy+/5K+//uKZZ54x3b9371769OlD8+bN2bJlC5s2bWLw4MHk5eUBMH36dN566y1mzJjB4cOHWbZsGUFBQWbFkJGRwdtvv80XX3zBoUOHqFGjBqmpqYwbN45NmzaxdetWwsLCGDRokCmRMBqNDBw4kM2bN7NkyRIOHz7MW2+9haOjI56enowaNYpFixYVOs+iRYsYMWIE3t7eZfpdCSHsW2xKJh+uO87+6CRd49gddZlHluwm16gxtG0IL97WrNKstlvR7K8cfE4GvKnTWijPXwAXz1Lt6u/vz8CBA1m2bBl9+vQB4IcffiAgIIBbbrkFBwcH2rRpY9r/9ddf56effmLlypVMmTLF7NCefPJJ03a9evX43//+x8MPP8wnn3wCwDvvvEPHjh1NPwO0aNECgNTUVD766CPmzJnDuHHjAGjYsCE33XSTWTHk5OTwySefFLqu3r17F9rns88+w8/Pj40bN3L77bezbt06tm/fzpEjR2jcuDEADRo0MO3/wAMP0K1bN2JiYggODiYuLo7Vq1eXqxVJCGG/LiRdYfTnWzl7KYMP151gcJsQnunfhNBqFTt99mRcKhMX7+BKTh49Gwfyzog2ODhUzUQE7LFlxIaMGTOGH3/8kaysLACWLl3KqFGjcHBwIC0tjWnTptGsWTP8/Pzw8vLiyJEjZW4ZWbduHX369KFWrVp4e3tz3333cenSJTIyMoCClpGiHDlyhKysrGLvLy0XFxdat25d6LbY2FgmTZpEWFgYvr6++Pj4kJaWZrrOvXv3Urt2bVMi8l+dO3emRYsWfPnllwAsWbKEunXrcvPNN5crViGE/bk2EfHzcMZggF/3XaD3+xt4/bfDXE7PrrA47luwnaSMHNqG+jHv3va4OFXtt2P7axlx9lAtFHqd2wyDBw9G0zRWrVpFp06d+Oeff/jggw8AmDZtGmvXruW9996jUaNGuLu7M2LECLKzzX+xnDlzhttvv53JkyfzxhtvUK1aNTZt2sT9999PdnY2Hh4euLu7F/v4ku4D1QUEFFq9Nicnp8jj/LcJcty4cVy6dImPPvqIunXr4urqSnh4uOk6b3RuUK0jc+fO5bnnnmPRokVMmDChyjZ1CiGKdm0iUqeaB98+2JXE9Gze+v0om04msGDTab7feY4ptzRiXLd6VlsR93J6Nvct2EZMciaNanixaHwnPFzs763YXPaXihkMqqtEjy8z3wDd3Ny48847Wbp0Kd988w1NmjShffv2gBpMOn78eIYNG0arVq2oWbOmaTCouXbt2oXRaOT999+na9euNG7cmAsXCidsrVu3JiIiosjHh4WF4e7uXuz9gYGBAMTExJhu27t3b6li27x5M48//jiDBg2iRYsWuLq6kpCQUCiu6Ohojh8/Xuwx7r33Xs6ePcvHH3/M4cOHTV1JQggBRSciIX7utKzly9f3d+bLiZ1pWtOb1MxcZv5+lD7vb2TF7miMxtKPAyyNjOxcJizeQWR8OsG+bnw1sTP+ni4WPYetsr9kxMaMGTOGVatWsXDhQsaMGWO6PSwsjBUrVrB371727dvHPffcU+apsI0aNSInJ4fZs2dz6tQpvv76a+bPn19on+nTp7Njxw4eeeQR9u/fz9GjR5k3bx4JCQm4ubnx7LPP8swzz/DVV18RGRnJ1q1bWbBggen4oaGhvPLKK5w4cYJVq1bx/vvvlyq2sLAwvv76a44cOcK2bdsYM2ZModaQnj17cvPNNzN8+HDWrl3L6dOn+f3331mzZo1pH39/f+68807+7//+j379+lG7du0y/Z6EEPanuEQkn8FgoGfjQFY93oP37mpDsK8b55OuMPX7fQyes4lNJxJKOHrp5eQZmbxkN3vPJeHn4cxXEzsXiqOqk2REZ71796ZatWocO3aMe+65x3T7rFmz8Pf3p1u3bgwePJj+/fubWk3M1aZNG2bNmsXbb79Ny5YtWbp0KTNnziy0T+PGjfnzzz/Zt28fnTt3Jjw8nF9++QUnJ9V8OGPGDJ5++mleeuklmjVrxsiRI4mLiwPA2dmZb775hqNHj9K6dWvefvtt/ve//5UqtgULFnD58mXat2/Pfffdx+OPP06NGjUK7fPjjz/SqVMnRo8eTfPmzXnmmWdMs3zy5Xc5TZw4sUy/IyGE/blRInItRwcDIzrUZv20XjwzoAnerk4cupDCvQu2MW7hdo7EpJQ5DqNR4/+W72Pj8XjcnR1ZMK4TYUEy2+9aBk0zYz6qTlJSUvD19SU5ORkfn8J1+jMzMzl9+jT169fHzc1NpwiF3r7++mueeuopLly4gItL5W/2lL9bIazLnESkKInp2cz+6wRLtp4lJ0/DYIDh7WvzdL/GBPuW/jiapvH6b0dYuPk0Tg4GPh/XkVua1LjxA+1ESe/f15KWEWHTMjIyiIyM5K233uKhhx6yiURECGFd5U1EAKp5uvDy4Basm9qT21oHo2nww65oer27gXfWHCUl8/pB+kWZtzGShZtPA/DuXa2rVCJiDklG7MDSpUvx8vIq8iu/Voi9euedd2jatCk1a9Zk+vTpeocjhNCZJRKRa9Wt7snce9rz0yPd6FyvGlm5Rj7ZEEmvdzewePNpsnOLH8v33Y4o3llzDIAXb2vGsHYynq040k1jB1JTU4mNjS3yPmdnZ+rWrVvBEYkbkb9bISzP0onIf2maxrojcbz1+xEi49MBqFfdg2cGNGVgy5qFSgr8cegik5fswqjB5F4NeXZAU4vFYUtK200jk5vtgLe3t5Q+F0JUadZOREDNvLm1eRC3NAnku53n+GDtCc5cyuCRpbtpV8eP5wc1o1O9amw7dYnHvtmDUYO7O9bmmf5NLBqHPZJkRAghhE2riETkWk6ODozpUpehbWvx+T+n+OzvU+yJSuKu+Vvo07QG288kkp1rpG+zIN4c1kqKMJaC3YwZsYHeJiFMylozRghRWEUnItfydHXiyb6N2TCtF/d0qYOjg4GIo3GkZubSuV415tzTDidHu3mbtSqbbxlxdnbGYDAQHx9PYGCgZKCiUtM0jezsbOLj43FwcJDZP0KUg56JyLVq+Ljx5rBWTOxejw/WnSArJ4/3725rtZLy9sjmB7ACpKWlER0dLa0jwmZ4eHgQHBwsyYgQZVRZEhFRsio1gNXLy4uwsLAiF2cTorJxdHTEyclJWvGEKCNJROyPXSQjoP7BOzpKk5gQQtgzSUTsk4ysEUIIYRMkEbFfkowIIYSo9CQRsW+SjAghhKjUJBGxf5KMCCGEqLQkEakaJBkRQghRKUkiUnXYzWwaIYQQtk/TNPaeS+KXvRdYue8CienZkohUAZKMCCGE0F1kfBq/7DnPL/sucPZShun2BoGeLLm/iyQidk6SESGEELqITcnk130X+GXvBQ6cTzbd7u7sSP8WQQxpV4ubGgXgLOu72L0yJSNz587l3Xff5eLFi7Rp04bZs2fTuXPnYvf/8MMPmTdvHlFRUQQEBDBixAhmzpyJm5tbmQMXQghhe1Iyc1hz8CK/7D3PlshLGK+u4uHoYODmsACGtqvFrc2D8HCRz8pVidnP9nfffcfUqVOZP38+Xbp04cMPP6R///4cO3aMGjVqXLf/smXLeO6551i4cCHdunXj+PHjjB8/HoPBwKxZsyxyEUIIISqvrNw8NhyL55e951l3JI7s3IJVqzvU9Wdo2xAGtQqmuperjlEKPZm9UF6XLl3o1KkTc+bMAdRS6KGhoTz22GM899xz1+0/ZcoUjhw5QkREhOm2p59+mm3btrFp06ZSnbO0C+0IIYSoHIxGjW2nE/ll73lWH4ghJTPXdF+jGl4MbRvCkLa1CK3moWOUwtqsslBednY2u3btYvr06abbHBwc6Nu3L1u2bCnyMd26dWPJkiVs376dzp07c+rUKVavXs19991X7HmysrLIysoqdDFCCCEqN03TOByTwsqrM2FikjNN99X0ceOOtiHc0SaEFiE+slCkKMSsZCQhIYG8vDyCgoIK3R4UFMTRo0eLfMw999xDQkICN910E5qmkZuby8MPP8zzzz9f7HlmzpzJq6++ak5oQgghdHIuMYOV+y7w857znIhLM93u7ebEoJbBDGkXQpf61XF0kAREFM3qI4Q2bNjAm2++ySeffEKXLl04efIkTzzxBK+//jozZswo8jHTp09n6tSppp9TUlIIDQ21dqhCCCHMcOxiKh+sPc6aQxdNt7k4OtC7aQ2GtguhV5MauDnLaurixsxKRgICAnB0dCQ2NrbQ7bGxsdSsWbPIx8yYMYP77ruPBx54AIBWrVqRnp7Ogw8+yAsvvICDw/VTtlxdXXF1lYFMQghRGZ1OSOfDdcdZue8CmgYGA4Q3qM7QtrXo37Imvu7OeocobIxZyYiLiwsdOnQgIiKCoUOHAmoAa0REBFOmTCnyMRkZGdclHI6OKlM2c+ysEEIIHZ1LzGD2Xyf4cfd58q7Oyb2tVTBP9g0jLMhb5+iELTO7m2bq1KmMGzeOjh070rlzZz788EPS09OZMGECAGPHjqVWrVrMnDkTgMGDBzNr1izatWtn6qaZMWMGgwcPNiUlQgghKq/YlEzm/HWSb3dEkZOnkpA+TWvw1K2NaVnLV+fohD0wOxkZOXIk8fHxvPTSS1y8eJG2bduyZs0a06DWqKioQi0hL774IgaDgRdffJHz588TGBjI4MGDeeONNyx3FUIIISwuIS2L+Rsi+XrrWbKu1ga5qVEAU/s1pn0df52jE/bE7DojepA6I0IIUXGSM3L47J9IFm0+Q0Z2HgAd6/rzdL8mhDesrnN0wpZYpc6IEEII+5WWlcuiTaf57J9TpF4tUta6ti9P92vCzWEBUhtEWI0kI0IIUcVdyc7j661nmLchkssZOQA0CfJmar/G9GseJEmIsDpJRoQQoorKys3j2+3nmLP+JPGpqup1gwBPnry1Mbe3CsZBipSJCiLJiBBCVDE5eUZ+3BXNxxEnuHC1ZHttf3ee6BPGsHa1cHK8vv6TENYkyYgQQlQReUaNlfvO8+G6E5y9lAFAkI8rj/UO4+6Oobg4SRIi9CHJiBBCVAFrD8fyzpqjprVjqnu6MLlXQ+7tWldKtgvdSTIihBB27tONkcz8XS1m6uPmxEM9GzK+Wz08XeUtQFQO8pcohBB2StM0Zv91kllrjwMwvls9nrq1sawdIyodSUaEEMIOaZrGe38eY+76SACevrUxj/UJ0zkqIYomyYgQQtgZTdP436ojLNh0GoAXBjVj0s0NdI5KiOJJMiKEEHbEaNR4aeVBlmyNAuC1IS0YG15P36CEuAFJRoQQwk7kGTWmr9jP9zujMRhg5rBWjOpcR++whLghSUaEEMIO5OYZmbZ8Hz/vvYCDAd6/uw3D2tXWOywhSkWSESGEsHHZuUae/G4Pqw9cxMnBwEej2nFb62C9wxKi1CQZEUIIG5aVm8ejS3ez7kgcLo4OzB3TnlubB+kdlhBmkWRECCFs1JXsPB5asou/j8fj6uTAp/d1oFeTGnqHJYTZZCECIYQwg6ZprNx3gU0nEtA0Tbc40rNymbh4B38fj8fd2ZFF4ztJIiJslrSMCCGEGb7eepaXfjkEQOvavky5pRF9mwXh4GCosBhSMnOYuGgHO89exsvViUUTOtGpXrUKO78QliYtI0IIUUqR8Wm8ufoIAE4OBvZHJ/Pg17sY+NE/rNx3gTyj9VtKkjNyuO+Lbew8exkfNyeWPNBFEhFh8yQZEUKIUsjNMzL1+31k5hjpERbA1uf78OgtDfFydeJYbCqPf7OHW2dtZPnOc+TkGa0Sw6W0LEZ/vpV90cn4ezizbFJX2ob6WeVcQlQkg6Znp2cppaSk4OvrS3JyMj4+PnqHI4Sogj6OOMGstcfxdnPiz6duJtjXHVAtFV9uOcPCzadJysgBoJafO5N7NWREh9q4OTta5PxxqZmM+XwbJ+LSCPByZekDXWhS09sixxbCWkr7/i3JiBBC3MCB6GSGfbKZXKPGhyPbMrRdrev2ScvKZenWs3z+zykS0rIBCPJx5cGbGzK6cygeLmUfoheTfIUxn2/jVEI6QT6uLJvUlYaBXmU+nhAVRZIRIYSwgMycPG6fvYmTcWnc1iqYOfe0w2AofrBqZk4e3+04x/yNkcQkZwJQ3dOFiTfVZ2x4XbzdnM06/7nEDO75YivnEq9Qy8+dZZO6ULe6Z7muSYiKIsmIEEJYwOu/HWbBptMEervyx5M3U83TpVSPy841smJ3NJ9siCQqMQMAHzcnxnevz8Tu9fDzuPFxziSkc8/nW7mQnEnd6h4sm9SVWn7u5boeISqSJCNCCFFO/0YmcM/n2wBYOL4jvZuaX9k0N8/Ir/svMOevk0TGpwPg6eLIveF1eeCmBgR6uxb5uJNxqdzz+TbiUrNoGOjJskldCfJxK/vFCKEDSUaEEKIcUjJzGPjhP5xPusLoznWYeWerch3PaNRYc+gis/86yZGYFABcnRwY3bkOD/VsYBoQC3AkJoV7v9jGpfRsmtb05uv7uxSbtAhRmUkyIoQQ5TBt+T5+2BVNnWoe/P5EDzxdLVMjUtM0/joax+y/TrL3XBIAzo4GRnQIZXLPhiRfyeG+hdtIysihZS0fvp7YBf9Sdg0JUdlIMiKEEGX0x6GLPPT1LgwGWP5QOB2tUFRM0zT+jbzE7L9OsPVUIgCODgZcHB24kpNH21A/vpzYGV938wa8ClGZlPb9W8rBCyHENRLSsnh+xQEAHrq5oVUSEQCDwUD3RgF0bxTAzjOJzFl/kg3H4rlizKNzvWosnNAJLwu1xghR2clfuhBCXKVpGtNXHDCN1Xjq1rAKOW/HetVYPKEzB6KT2RedxJ3ta5WrLokQtkb+2oUQ4qofdkWz9nAszo4GPhjZFlcny1RPLa1WtX1pVdu3Qs8pRGUga9MIIQSquNirvx4GYOqtTWgWLOPThKgokowIIao8o1Fj2vJ9pGXl0rGuPw/e3EDvkISoUiQZEUJUeQs3n2bb6UQ8XBx5/+42ODoUX+5dCGF5kowIIaq047GpvPPHMQBevK25rPsihA4kGRFCVFnZuUae+m4v2blGbmkSyOjOoXqHJESVJMmIEKLKmv3XCQ5dSMHPw5m3h7cucTVeIYT1SDIihKiSdkddZu76kwC8MbQVNWQROiF0I8mIEKLKycjO5env92HUYGjbEG5rHax3SEJUaZKMCCGqnLd+P8rphHRq+rjx6h0t9Q5HiCpPkhEhRJXy9/F4vtpyFoB372qNr4csRCeE3iQZEUJUGckZOTzzw34AxoXXpUdYoM4RCSFAkhEhRBXy0sqDXEzJpEGAJ88NbKZ3OEKIqyQZEUJUCb/tv8Avey/g6GBg1si2uLtU7CJ4QojiSTIihLB7cSmZvPjzQQAe7dWQtqF++gYkhCjESe8AhBBVl9Go8efhWJwcDIQFeVHb38Pi68JomsYzP+4nKSOHlrV8eKxPmEWPL4QoP0lGhBC6efuPo3y68ZTpZ1cnB+oHeBIW5E2jQC8a1fAiLMiLetU9cXEqW0Pusu1RbDgWj4uTAx/c3RZnR2kQFqKykWRECKGLZduiTIlI4yAvzlzKICvXyNGLqRy9mFpoX0cHA3WredCwhhdhNVSS0qiGFw0DvfB0Lf7f2JmEdP732xEAnunfhLAgb+tdkBCizCQZEUJUuI3H45nxixrD8VTfxjzRN4w8o8a5xAxOxqVxMj6NE7Hqe2RcGmlZuZxKSOdUQjprD8cWOlYtP3ca1vCiUaBqRWl0ddvH3Zmnl+/jSk4eXRtUY2L3+npcqhCiFCQZEUJUqCMxKTy6dDd5Ro0729fi8T6NANX6US/Ak3oBnvQlyLS/pmnEpmRxIi5VJSpxaZyIU0nKpfRszidd4XzSFf4+Hl/oPN5uTqRm5uLl6sR7d7XBwcJjUYQQliPJiBCiwsSmZDJx8Q7SsnLp2qAab91545VyDQYDNX3dqOnrdl2Rssvp2QWtKFdbVE7GpnIhOZPUzFwAXrmjBbX9Pax2TUKI8pNkRAhRIdKzcrn/yx3EJGfSINCTT+/tWOZBqfn8PV3o5FmNTvWqXXeuyPg08owa7er4l+scQgjrk2RECGF1eUaNJ77dw8HzKVT3dGHx+M5WXRPG09WJ1rX9rHZ8IYRlyRw3IYTVvf7bYdYdicPFyYHPxnakTnXpNhFCFJBkRAhhVYs2n2bxv2cA+ODutnSoK90mQojCJBkRQljNusOxvP7bYQCeHdCU21oH6xyREKIykmRECGEVB6KTeeybPRg1GN05lId7NtA7JCFEJSXJiBDC4i4kXeH+L3dwJSePHmEBvDak5Q2n8Aohqi5JRoQQFpWamcPExTuIS82iSZA3c8e0l/VghBAlkv8QQgiLyckz8uiyPRy9mEqgtysLJ3TCx816U3iFEPZBkhEhhEVomsbLKw/x9/F43J0dWTiuE7X83PUOSwhhAyQZEUJYxGd/n2LZtigMBvhoVFta1fbVOyQhhI2QZEQIUW6rD8Qw8/ejAMy4rTn9WtTUOSIhhC2RZEQIUS57oi7z1Hd7ARgXXpcJ3evpGo8QwvZIMiKEKLNziRk88OVOsnKN9Glag5cGt5ApvEIIs0kyIoQok+SMHMYv2s6l9GxahPjw8eh2ODpIIiKEMJ8kI0IIs2XnGnl4yS4i49MJ9nVj4fhOeLrKIuBCiLIpUzIyd+5c6tWrh5ubG126dGH79u3F7turVy8MBsN1X7fddluZgxZC6EfTNJ7/6QBbTl3C08WRBeM6EeTjpndYQggbZnYy8t133zF16lRefvlldu/eTZs2bejfvz9xcXFF7r9ixQpiYmJMXwcPHsTR0ZG77rqr3MELISrenL9O8sOuaBwdDMwd057mIT56hySEsHFmJyOzZs1i0qRJTJgwgebNmzN//nw8PDxYuHBhkftXq1aNmjVrmr7Wrl2Lh4eHJCNC2KBf9p7n/bXHAXj1jhb0alJD54iEEPbArGQkOzubXbt20bdv34IDODjQt29ftmzZUqpjLFiwgFGjRuHp6WlepEIIXW0/ncj/Ld8PwIM3N+DernV1jkgIYS/MGnGWkJBAXl4eQUFBhW4PCgri6NGjN3z89u3bOXjwIAsWLChxv6ysLLKyskw/p6SkmBOmEMLCTiek8+DXO8nOMzKgRU2eG9BU75CEEHakQmfTLFiwgFatWtG5c+cS95s5cya+vr6mr9DQ0AqKUAjxX9GXMxi7cBtJGTm0CfXjg5FtcZApvEIICzIrGQkICMDR0ZHY2NhCt8fGxlKzZsnln9PT0/n222+5//77b3ie6dOnk5ycbPo6d+6cOWEKUekZjZreIZRK9OUMRn++lXOJV6hb3YMvxnbE3cVR77CEEHbGrGTExcWFDh06EBERYbrNaDQSERFBeHh4iY9dvnw5WVlZ3HvvvTc8j6urKz4+PoW+hLB1mqbx+4EYer+/gR7vrOfg+WS9QyrRtYlIveoefPtgVwK9XfUOSwhhh8yuUjR16lTGjRtHx44d6dy5Mx9++CHp6elMmDABgLFjx1KrVi1mzpxZ6HELFixg6NChVK9e3TKRC2FDdp5J5I3VR9gTlWS6bdRnW/nsvg50axSgX2DF+G8i8s2DXQn2ddc7LCGEnTI7GRk5ciTx8fG89NJLXLx4kbZt27JmzRrToNaoqCgcHAo3uBw7doxNmzbx559/WiZqIWxEZHwa76w5yh+HVNemu7Mjk3rUZ/uZRLaeSmT8oh3MGtmG21uH6BxpAUlEhBAVzaBpWqXvvE5JScHX15fk5GTpshE2IT41i48ijvPN9nPkGTUcDDCyUyhP9W1MDR83MnPymPr9XlYfuIjBAC/f3pzx3evrHbYkIkIIiyrt+7csJiGEBWVk5/LFP6f5dGMk6dl5APRtVoNnBzQlLMjbtJ+bsyOzR7enuuchvt56lld+PUx8WhbT+jXRbdVbSUSEEHqRZEQIC8jNM/LDrmhmrT1OXKqqkdO6ti/TBzYjvGHR46QcHQy8NqQFNbxdeX/tceaujyQ+NYs3h7XCybFi17CUREQIoSdJRoQoB03TWH8sjrd+P8rx2DQAQqu580z/ptzWKviG9TgMBgOP9Qkj0NuV5386wPc7o7mUls2ce9pX2BRaSUSEEHqTZESIMtofncSbq4+w9VQiAH4ezjzWO4x7u9bB1cm8RGJU5zpU93JlyrLdRByNY8wXW1kwrhP+ni7WCN1EEhEhRGUgA1iFMNO5xAze/eMYK/ddAMDFyYEJ3evxSK9G+Lo7l+vYO88kcv+XO0m+kkOjGl58ObEztfyskxxIIiKEsLbSvn9LMiJEKSVlZDP7r5N8veUs2XlGDAYY1q4WT/drYtGE4XhsKuMWbicmOZOaPm58ObEzTWp63/iBZoi+nMGoz7YSfVkSESGE9UgyIoSFZObk8eW/Z5i7/iQpmbkA3NQogOcGNqVlLV+rnPNC0hXGLtzOybg0fNycWDC+E53qVbPIsSUREUJUFElGhCgno1Hjl33nee+P45xPugJA05rePD+oGTc3DrT6+ZMysrn/y53sOnsZVycHZo9uR78WJa8BdSOSiAghKpIkI0KUw4nYVJ78bi+HLqQAEOzrxtP9mjCsXS0cK3DF2ivZeTz2zW7WHYnDwQBvDGvF6M51ynQsSUSEEBWttO/fFVvMQAgbcDw2lVGfbeXQhRS8XZ14ZkAT1k/rxYgOtSs0EQFwd3Fk/r0dGNkxFKMG01cc4OOIE5j7GUISESFEZSZTe4W4xvHYVEZ/tpVL6dm0rOXD4gmdCfDSd6VaJ0cH3hreiho+rsz+6+TVwmqZvHpHy1IlR5KICCEqO2kZEeKq/yYiS+7vonsiks9gMPB0vya8NqQFBgMs2RrFlGW7yczJK/FxkogIIWyBJCNCUHQi4udh3YJjZTE2vB5zRrfHxdGB3w9eZNzC7SRfySlyX0lEhBC2QpIRUeXZSiKS77bWwSye2AlvVye2nU5k5KdbiE3JLLSPJCJCCFsiyYio0mwtEcnXrWEA3z7UlUBvV45eTOXOT/4lMl6tjSOJiBDCLAdXwNHVkJ2hWwiSjIgqy1YTkXwtQnxZMbkb9QM8OZ90hRHz/uX3AzGSiAghSk/TIOI1+HY0nFynWxiSjIgqydYTkXyh1Tz44eFwWtf25XJGDpOX7pZERAhRevHH4PJpcHSBhr11C0OSEVHl2Esikq+6lyvfTOpqqgoriYgQotSOrVbf6/cEVy/dwpA6I6JKsbdEJJ+nqxMLxnXknxPxtK/jbxfXJISoAPnJSNNBuoYhyYioMo5dTOWez+0vEcnn7OhA76ZBeochhLAVqbEQvVNtNx6gayjSTSOqBHtPRIQQwmwn/gA0CGkHPiG6hiLJiLB7kogIIUQRjl7tomlym75xIMmIsHOSiAghRBGyM+DUerXdZKC+sSDJiLBjkogIIUQxTm2A3EzwrQNBLfSORpIRYZ8kERFCiBIcW6W+Nx0Ehhuv/m1tkowIuyOJiBBClMCYB8fWqO1K0EUDkowIOyOJiBBC3MD5XZCRAK6+ULe73tEAkowIOyKJiBBClMLRq100YbeCo7O+sVwlyYiwC5KICCFEKR37XX2vJF00IMmIsAOSiAghRCldioSEY+DgBI366h2NiZSDFzZL0zQOXUhh3MLtkogIIURp5K9FU+8mcPfTNZRrSTIiKrXkKzmcS8wg+nIG5xKvcO5yBucSMzh3+QrRlzPIzDECSCIihBClYeqi0XdhvP+SZEToKjMnj+jLKsmIvppkqGQjg6hLGaRk5pb4eIMBujcMYM497SQREUKIkmQkQtQWta3zwnj/JcmIsDpN09h59jKn49MLtWycS8wgLjXrho+v5ulCqL87tat5EOrvQWg196vfPQjxc8PVybECrkIIIWzc8T9AM0JQS/Cvq3c0hUgyIqzKaNR47Ns9rNofU+w+ni6OhFbzoPZ/Eo3Qau7U9vfAy1X+TIUQotzyx4tUsi4akGREWNn/Vh1h1f4YnB0NdG1QXSUZ/0k6/D2cMVSCcsRCCGG3cjLhZITarkRTevNJMiKs5ot/TrFw82kA3rurDUPa1tI5IiGEqKLObIKcdPAOhuC2ekdzHakzIqzi130X+N+qIwBMH9hUEhEhhNBT/sJ4TQaCQ+V76698EQmbtyXyEk9/vw+A8d3q8eDNDXSOSAghqjBNq7RTevNJMiIs6nhsKg9+vZPsPCMDWtRkxu3NZTyI0Mehn+G7+yByvd6RlE52BvwzC767F5LP6x2NsCcxeyE1Bpw9oV4PvaMpkowZERZzMTmTcQu3k5qZS8e6/nw4qi2ODpKICB3kZsOqqZBxCY6shIa94dbXoGYrvSO7njEP9i6D9W9C6gV1m6svDJ2rb1zCfhy9OoumUR9wdtM3lmJIy4iwiJTMHMYv2k5MciYNAj35fGxH3Jyl/ofQycl1KhFx9gQHZ4j8C+b3gJ8ehqRzekenaJqq+zD/Jlg5RSUiXjXVffu/g5QL+sYn7Ecl76IBSUaEBWTnGnn4610cvZhKoLcrX07ojL+nVEMVOtr3jfrecQJM2QEthwOaun12B/jzRbhyWb/4zu+CLwfDsrsh7jC4+UG//8ET+6BONzDmwLb5+sUn7EdSFMQeAIMDhPXTO5piSTIiysVo1Pi/H/bxb+QlPF0cWTS+E6HVPPQOS1RlGYlwfI3abjMaqtWHEQth0nrVX56XBf/Oho/awuaPVf2FipJ4CpaPh897w5l/wNEVuj0OT+yFbo+pJvTuT6h9dy6CzJSKi03Yp/xWkTrh4Fld31hKIMmIKJd3/jjGL3sv4ORgYN69HWhZy1fvkERVd+gnyMuGoFZQs2XB7bXaw7hf4Z7lUKM5ZCbB2hkwpyPs+xaMRuvFlJ4Aq5+BOZ1VfBhUovTYLuj3Orj7F+wb1g8CmkBWCuxabL2YRNVgqrpa+QqdXUuSEVFmX205w/yNkQDMvLMVNzcO1DkiIVCJBUCbUdffZzBA437w8CYYMhe8QyD5HPz0EHx2c0GFSkvJzoC/31WtMNs/Vd0vDfvAw//AsPngF3r9YxwcoPvjanvrPDUYV4iyyExWxc6gUo8XAUlGRBmtOXiRl1ceAuDpWxtzV8ci/qkKUdEuRUL0dtU/3uqu4vdzcIR298Lju6HPy+DqAxcPwJI74ashELOvfHHk5cKuL2F2e/jrf5CdCjVbw30/w30rbjyrp9VdajBr6gU4+EP5YhFV14m1YMxVLW3VG+odTYkkGRFm23U2kSe+3YOmwejOdZjSu5HeIQmh5LeKNOwD3kE33t/ZHXpMVQNHuz6qZt6c2gCf3gw/ToLLZ807f35xqfnd4dfHVW0Hvzpw5xfw4EZoeEvpjuPkCl0nq+3NH6vjCmEu0yyayt1FA5KMCDNFxqdx/5c7yco10rdZDV4f0kKKmonKwWiE/SV00ZTEoxoMeBMe21nQonLgezWe5I8X1KDYG4neCYtvg29GQfxRNQ6k/5swZSe0vsv8EtwdJ4CLN8QfUZ9whTBHXk7B300l76IBSUaEGeJSVVGzpIwc2oT68fHodjg5yp+QqCTObVXTGF19oOltZTuGfz0Y/gU8uAHq36wGwm6ZAx+3hc0fFT3z5lIkfD8OvugDZzeDkxt0fxIe3wvhj6pWjrJw84WO49X2vx+X7Rii6jq7GbKSwSMAanfUO5obkncSUSppWblMXLyD6MtXqFvdgwXjOuLhIgV8RSWSX1uk+R2q+6U8QtrB2JUw5kcIaqkGAq59SdUo2fuNqpqaFg+r/w/mdobDPwMGaDtGzZC59VVw9yvnBQFdJoODk5oGfH5X+Y8nqg5TF80ANUaqkqva7yZRW9Vo9Ts/Bycp0lWcnDwjjyzdzcHzKVT3dOHLCZ0J8DLj015qrJpe1mKYZf5Bi6LlZqk35EZ9wbe23tFUrJwrai0aUFNmLcFggLC+apzH/u/grzcgJRp+fhg2fQAp5yE7Te3b6Fbo+0rhqcSW4FtLdRvt+0aNHbn7S8sev6rKTFZ1XLLTrXP8wCbQaoR1jl0amnbNlN7K30UDVTkZyclUi2ilx0FwGzWITVxH0zSeX3GAv4/H4+7syILxnagX4Fn6A6TFw6IBqtjT7q9g3Epw9bZewFXZny/C9s+g5QgYsUDvaCrWsdWqLodvHVXB1JIcHKHtPSqZ3vapWswu4Zi6L7itWvOmQU/LnvNa3R5TyciRlep1VE1WwS63f2bB5g+tew43Xwi71brnKE7cYdVl6eQGDXrpE4OZqm4y4uymig399BBsfEdlsX519I6q0vlg3QmW74rGwQBz7mlH21C/0j84M0VNlUw8pX6+sFutSHrP92XvRxdFu7AHdnyhtmMP6huLHky1RUaaP1C0tJzd4aYnof1Y2LUI/OtD86HWO1++oBaq5eXkWtgyF25737rnqwqO/qa+N70dfEIse+z4Y3B6I6yeBo9sLX+XYVnkL4zX4BZwMePDo46qbjIC0Hok7P4azm6C35+F0d/oHVGl8s32KD6OOAHA/4a2ok+zUkyVzJeTCd/eAxf3qwFUA96CX59Q0yZXPKjKc9tAP6ZNMObBb1NBu1pBNPGUuq2q/H5TYwuKlbU2cxZNWXhUgx5PW/881+r+hEpG9iyBXtPBM6Biz29P4o/DpZNqGvfQeeDmY9njZ6WpcUSXz6gWmN4vWPb4pWEjVVevVbUHsBoM6lOGg5N68vKzScFfR2N58Wf1Cfux3o24p4sZrUZ5ufDj/WrQnYs33Pujmto4aon6B3D4Z/WpQWonWMauRarVydVHrXWSlw1JZtbHsGUHfwAtD2p3ggA7rXlT7yY1qDY3E7Z/rnc0ti3/jbp+D8snIgCuXurDF6iuoISTlj9HSVJi1P8DDNB4QMWeuxyqdjICUKOp6pMF1TpirQFNNmTfuSQeXbqHPKPGiA61mXpr49I/WNPgtydVM6ijC4xeBiFt1X0Ne8OdnwEG2LkQNsy0QvRVTFocrHtNbfeeAdWvvhlX9D9APeXPojG3togtMRgKFtDb/pkqMy/KxjTLxIoDO5sNVl1redmwamrFfvA6fvX6ancsXeG/SkKSEYCb/08NfEuOUutIVGFnL6UzcfEOruTkcXPjQGbe2cq8omYRr8Ker1U57hELVa2Ga7W8E257T21vfFsNCBRl9+cMVUsguA10ur+gZSDhuL5xVZSLB1UZdwdnaHGn3tFYV7M7VB2UK4mwd6ne0dimtHg4t01tW7MLw2CAQe+qAaSnN8LBH613rv+yoaqr15JkBNQAn4Fvq+1/Z0PcUX3j0cmltCzGLdzOpfRsWoT48MmY9jibU9Ts3zlqyiPA7R+qTwdF6fQA9Hpebf/+DByQtTfK5PQ/VyuOGuC2D9QYkeph6r5LJ3QNrcLkV1xtMkCN5bBnDo4QPkVt/ztbdYcK85z4A9DUOkHWnv5erT70mKa2/3heTSe2tqw0OLVRbdvIlN58kozkazpIPXnGXFj1dJUbz3AuMYMJi3dw5lIGtfzcWTS+E16uZoxv3vsN/Hl1oFafl6HDuJL37/kMdH5Qbf/0EJxYV7bAq6rcbPV3CqpseO0OajvgajJSFbpp8nJh//dq21K1RSq7tmPAo7oaE3Rkpd7R2J6K6KK5VvfHVddpWiysf9P654v8C/Ky1EyvwKbWP58FSTJyrQFvgZO7ml2z/zu9o6kQeUaNhZtO0//Dv9kfnYyfhzNfTuxMDR+30h/k2Br45VG1HT4Fbnrqxo8xGGDA26omhjEXvr8Pzm0v20VURVvmqFoXnoHQ56WC2wOqUMvI6Q3qn7x7NdU/XxW4eBQk8Zs/qnIfmsol54p6swb14bMiOLnCoKvd0ts/gwt7rXu+a5MtG1szTJKRa/nXVZ/YQS2OdeWyvvFY2fHYVIbP+5fXfjtMRnYenetX46dHutOohlfpD3J2Cywfp2YztBkNt75e+heBg4OaWtewD+RkwNK7IO5I2S6mKrl8VtXGAej3P7UgW778bpq0WFXnxZ7l1xZpNaJqVVDuNEl9aIrZq2asidI5tVH9n/GppbppKkrDW6DlcDX1ftVUNe3eGox5cHyN2rax8SIgycj1wqdAQBPISICI1/WOxiqycvP4YO1xbvv4H/aeS8Lb1Yk3hrXk20ldqW9OddWLB2HZSDXdsPEAuGO2+QWgnFxg5NdqWmZmEnx9p6ocKIq35jnIvQJ1b1K1cq7l5gNeV0fQ23PrSGYKHLlauMqeZ9EUxbM6tLtXbW+WBfRK7draGxXdatD/TTX1/vwu2G2lkv7ntqnBzW5+UCfcOuewIklG/svJpaDC4c6FEG1fi1PtjrrM7R9v4qOIE+TkafRtFsTaqT0Z06UuDg5mvEATT6vqqlnJ6g//rsXg6Fy2oFw8VVXWwKaQegG+HgbpCWU7lr07ulr9U3VwUn+nRf1TzW8dSbDjZOTISpWQBTSGkPZ6R1Pxwh9VM9ZOroXYQ3pHU/kZjde0GugwsNO7JvR+UW2ve0XN6rG0/GSrcX9wtL16ppKMFKV+j6uVHDVY9ZT1mtUqUHpWLq+sPMTwef9yIi6NAC8X5tzTjs/HdqCmrxnjQ0BVvPx6mOoKCGoJo78tf8ljj2pw7wrwDVXVEZcMh6zU8h3T3mSnq1o4oGrj1ChmgJppeq8dJyOm8u+jbK5v3CKq1VdTfUHNrBElu7Bb/b9y8VYF5PTQ8X7VPZSZDGtnWP74NjqlN58kI8Xp97pa6ChmH+yw7UXHNh6Pp98Hf7P43zNoGtzZvhZrn+rJ7a1DzKshAuqFtGQ4XD6tah7c+6PlVuL1rQX3/axmC8TsVeXkczItc2x78Pe7qhaObx1VG6c49j69Nynq6lgJA7S6W+9o9NP9cfX9wHJIjtY3lsouv9WgUR/91sVydILbPwAMqlDfmU2WO/a1Je4b9rHccSuQJCPF8apRMEvhr9ch9aK+8ZTB5fRspn63l3ELt3M+6Qq1/Nz5cmJnZt3dFn/PMgz4y7kC34yG2APgWQPu+0k1P1pSQCOV4Lh4wem/YcUDdtEyVW5xRws+AQ98u+TFrwKuVsy11+m9+TPd6vcAv1B9Y9FTrQ5Qr4eajbZ1nt7RVG75rQZNb9M3jtodocN4tb3qaTVF3xJMJe5vtk6J+wogyUhJOkxQ/dFZKWp5dhuhaRq/7rtA31kbWbHnPAYDTOhejz+fupmejQPLdtC8XPhhIpzdrAZi3fuj9ZYyD2kHo5apcvJHfoXfnqraUxg1Tf3jMuaq/u4bTUvM76ZJjFR95fZE067poqkitUVKkl8iftdiuJKkZySVV+JpiDsMBkdo1FfvaKDvy2rx0PijsHWuZY5p4100UMZkZO7cudSrVw83Nze6dOnC9u0l14dISkri0UcfJTg4GFdXVxo3bszq1TawKJ2DI9w+Sw0UO7BcrThbycUkX2HSVzt57Js9XErPpnGQFz9O7sbLg1vgaU4Rs2tpmlpx99hqVd549LcQbOWpcQ16wvAv1O9+95fw1/+se77KbP93qvaNk3vBAlwl8aurErncTEg+Z/34KtL5Xao52tmj+Aq/VUmjvlCjOWSnqQUTxfXy36jrdqscVXrd/dWUfFBT9Ms7e7CiStxbmdnJyHfffcfUqVN5+eWX2b17N23atKF///7ExcUVuX92dja33norZ86c4YcffuDYsWN8/vnn1KpVq9zBV4iQdqp8OVxtVsvSN55iGI0aS7edpd+sv1l3JA5nRwNP9g3jt8d60L6O/40PUJK1L8HeJeqTxYhFUK+7ZYK+keZDrvaxAv+8VzWboq9cLmiV6/mMqoVzIw6OBa1W9jaINX9RvGaDwdVb31gqA4MBul0dO7J1XqX9/6Qr05TeSlQevc0oqNtd1T35/bnyHSu/xH1wG+uXuLcis5ORWbNmMWnSJCZMmEDz5s2ZP38+Hh4eLFy4sMj9Fy5cSGJiIj///DPdu3enXr169OzZkzZt2pQ7+ApzywtqjMSlk/Bv5ZvXfyo+jVGfb+WFnw6SmpVL21A/Vj3egyf7NsbFqZw9cZs/KrjmO2ZXXOXCfB3Gq9VoQdXX2Fc1KuOaRLwO6fGq9k3+uiSlkb96rz0NYs3NKlhwrKrVFilJy+HgHaJmi+SXxxdKRiKc/VdtNxmgbyzXMhjU1HwHJzi2qqD1piwqusS9lZj1TpWdnc2uXbvo27eg383BwYG+ffuyZcuWIh+zcuVKwsPDefTRRwkKCqJly5a8+eab5OUVPygxKyuLlJSUQl+6cvdTRWsA/n5P9UFWAjl5Rj7ZcJIBH/3D9tOJuDs78tLtzflxcjcaB1ngU+OeJapVBFRl1XZjyn/MsujxNHR9RG3/8ggc/1OfOCpa9C5V6wbUPy5zqowG2GGtkRN/qpYi72Co31PvaCoPJxcIv/r6+Pdj+xsnVB4n16nq0IHNrDfGraxqNCv4gLH6GcjOMP8Y15a4t+EuGjAzGUlISCAvL4+goKBCtwcFBXHxYtGzTU6dOsUPP/xAXl4eq1evZsaMGbz//vv873/FjwGYOXMmvr6+pq/Q0EowYr7VCDVSOTdTrTSr84DKg+eTGTJnM++sOUZ2rpEeYQH8+dTNTLypPo7mFC8rztFVsPIxtd39iYJphHowGKDfG6raqDEXvh8LUVv1i6ciGPNUjRs0VfOmfg/zHp8/o8aeWkbyB662vlt1RYkC7cepgeUJx6822wugoIumolt0S6vnM6q2UnKUmrpvLlOJ+9oVW+LeCqw+m8ZoNFKjRg0+++wzOnTowMiRI3nhhReYP39+sY+ZPn06ycnJpq9z5yrBIDyDAQa9r+Zxn/gTjv6mSxiZOXnM/P0IQ+Zu5nBMCn4ezrx/Vxu+mtiZ0GoeljnJmU2wfIJaS6HdvdD3VcsctzwcHGDIXAjrrypvLrvbvitP7ligaty4+aqaN+aqbmer96ZfguNX32RbSxfNddx8oONEtb35I31jqSxyswpWA6+sXRgunmqqPqip+/HHzHu8niXuLcysZCQgIABHR0diY2ML3R4bG0vNmkXXmwgODqZx48Y4OhZ8kmnWrBkXL14kO7voOdaurq74+PgU+qoUAhsXTKX7/VnISqvwEJ79cT+fbjxFnlHj9tbBrH2qJ8M71Da/eFlxYvapWiJ5WdD0drj9o8rzR+7orMrOh3ZVxde+vhMun9E7KstLvahq24CqdeNVw/xj5E/vTb1gH5VsD60AY44apBfUXO9oKqcuD6sPS1Fb4NwOvaPR35lNkJ2q1mqqzEsGNBmk1vYy5qhJEqVtdS9U4t62u2jAzGTExcWFDh06EBERYbrNaDQSERFBeHjRC/N0796dkydPYrymH/P48eMEBwfj4mKDK23ePE1NnUw5DxvfrtBT749O4pe9FzAYYP697ZlzT3sCvS1YTfBS5NUy7ClqEbbhCyrfGgcuHnDPt1CjBaRdvFqWvuiZXDbrzxfVcxDSXtW6KQt3f1XLANTAa1uXP4tGaosUzycY2lxdOPFfaR0xDexsPMD8BTwrksEAA99RU/fP/FP6QciFStyb2Y1bCZn9DE2dOpXPP/+cL7/8kiNHjjB58mTS09OZMEH90xw7dizTp0837T958mQSExN54oknOH78OKtWreLNN9/k0UcftdxVVCRndxh0tW9v6ycQe7jCTv3OGtWEN6xdLQa0DLbswTNT1MJ36fFQsxWMXgbOZq5ZU1Hc/VXRNb86kHhKddnYS5XWUxtUTRuDg6pxU56xEQF20lUTf1zVFzE4QssRekdTueVP8z3ym+0/7+WhabY1y8S/LvS8usTDny+UroBdfhdNWF/zBrdXUmYnIyNHjuS9997jpZdeom3btuzdu5c1a9aYBrVGRUURExNj2j80NJQ//viDHTt20Lp1ax5//HGeeOIJnnuunHOr9dS4v+rCMObCqqkVMnp904kENp1MwMXRgaf6Nrb8Cda/obo8/OqoBevcfC1/DkvyCVbr2Lj5woU9uo3hsajcLFg1TW13ekDVuCkPe5neu//qwNWwW8GrjBWEq4rAJtB4IKDBljl6R6Ofi/shJVoVx2tgIzOvwh9TA8/T4wu6aUtiS8lWKZSp7WrKlCmcPXuWrKwstm3bRpcuXUz3bdiwgcWLFxfaPzw8nK1bt5KZmUlkZCTPP/98oTEkNmng2+Dsqfpn85uQrcRo1Hh7zVEAxnStY7mBqvku7IXtn6ntwR+XbYyCHqo3hM4Pqe3NH+k+w6nc/v1YJQ6eNQqWGy8P0xo1NpyMGI0FtWWktkjp5M9827vM/rowSyv/jbph7/KvKF5RnFzUFH5QA9jP7yp+32tL3IfdWjHxWVkl7kir5HxrQ6+ry7mvnaGK61jJ6oMxHDifjJerE1NuaWTZgxuNqnVHM6riSQ1vsezxra3zg6pE/fldBcWNbFHiaVXDBlRNG0u0TAXYweq9ZzepT7iuvlc/8YsbqhMOtTqqQej5HzKqmmtnmdiS+jdfXYlag9+mFt/9fG2Je/dyVtiuJCQZKY+uj6hiOhmXYN0rVjlFTp6R9/5QY0Um9WhAdS8LL3+9e7F6I3fxLijsZku8AqHtPWq7ElbHLRVNU7VrcjOv/jOy0LiI/Om9l2x4wbz82iIth1XeMUyVjcFQMOtv++e6zPrTVXK0mhWIQZUCsDX931DJd8zegqKH/1UZS9yXkyQj5eHorAYZglrMzQrT6b7bcY4zlzII8HLhgR71LXvwtHhYd7WGSO8Xwbvo6dmVXvgUwKCmucUd1Tsa8x39TdWucXBWtWwsNZXav64qN52ToWZ/2ZrsDDj8i9qWWTTmaXqbqjiamaQqKVcl+a0GoZ1tc4yRVw3oc3UJjIjXILVwKY3CJe5trOWnBJKMlFfdbtD2apn0356CvFyLHfpKdh4fRagm9sd6h5V91d3irH1J/bOq2bpgMUBbVL1hwQqu/87WNxZzZaUVLJTV/QlVy8ZSHJ3B/2oCa4tdNUdXqdVo/etBaJcb7i6u4eAI3a5WUN4y16L/lyo9exjY2XGiGsCelVKwUGa+/BL3NZpDNQt/QNWRJCOWcOtr4OYHsQdgx+cWO+zCzaeJT80itJo7ozvXsdhxATizGfYtAwxqZdzKVk/EXPnN0vu/g5QL+sZijo1vqzERfnVVDRtLs+XpvdfWFqkshfdsSZvRqtZMchQc/lnvaCpGZgqc/ltt23Iy4uAIt80CDHDge1X2PZ+tjoe5AUlGLMEzAPq+orb/egNSYkrcvTSSMrKZvzESgKdvbVL+1XevlZejBq2CWhW3dkfLHVsvtTtCnW6qiuG24pcaqFRiD6taNaBq11hj1L+tDmJNiYFT69V265H6xmKrnN1VVVaAzR/a/myz0oiMUP8DqjUs+Nu3VbXaQ6f71faqp9XUf1socV9GkoxYSvtxULuTKj/8x/Qb738Dn2yIJDUzl2bBPtzRJsQCAV5jy1yIP6o+NfV92bLH1lN+68jOReoTUmWmaVdr1OSqmjWNrTTQzrRGjY0lIweWqxledcLtqim6wnW6X9XauHhAFdSzd/ldNE0H2UdrWu8Zaqr/pROqC9pWStyXgSQjluLgoJrVDA5w6Cc4GXHjxxTjQtIVFv97BoBnBjTBwRKr8OZLiiooY9/vdbuZFgZAWD8IaKL6WXct1juaku1dpmrUOF+zUJY1mFpGbKibRtOu6aKR2iLl4lEN2o9V2/a+gF5eTsFiivbSauDup2bXgFrVN3+qdmUvcV8G9nU1egtuXVCEa/U0yMks02E+XHec7FwjXepXo1djC48GXzNdza6o293+Zig4OBQUfNo6D3KLXohRdxmJqjYNqFo1vrWtd678lpHkc5Cdbr3zWNLFA6qgk6MrNB+qdzS2r+sjqjjWqfUQs1/vaKwnaqsakO9eDWp31jsay2l1l1p7JjfzmoXx7CTZuoaNj1qshG55XrWMJJ6CTbPUz2Y4EZvKD7uiAXh2YFPLrcYLcGyNmkbq4KQq/dlDM+Z/tboLIl5Xq9Ue/KGgBkllEvGqqk0T2Ey9UViTZ3XV+nXlsqo3EtzauuezhPzaIk0HqU+Gonz860KLYer1sOJBqNHU8udw9oBez6nlJPRy7cJ4tj4g/1oGg2p1n3d1TJwtlbg3gx09Y5WEmw8MeBN+mAgb31Eluc0oYvXen8cwatC/RRDt61iwCyU7A36/uhBT+KNQo5nljl2ZOLlC18mw7mXY/HHlm4lxbkdBF9Lts9T0W2urHgbR21W/c2VPRvJy1ewBgNbSRWMx3R9XyUj8EfVlDUlRMO5XfV5vmgbHVqltO5tlAqgp/90fh3/eV+PLbKXEvRkkGbGGFneqJsPtn8FPD6lPd4363vBhu6Mu88ehWBwM8H/9m1g2pn/eU/8sfEOh57OWPXZl03GCKq0efwROrIXG/fSOSMnLVbVoQNWmqdutYs4b0FglI7YwvTfyL7VQmEcANOqjdzT2I7gNjP1FrYBsacZcVZzrzD9q4HHruy1/jhuJP6oW+nR0VevR2KNbXlCDVuuE6x2JVUgyYg0GAwx4WzXFH/wRvrsPxq6E0E7FPkTTNN7+XVUPHdGhNo1qeFsunvhjqpUA1GBJF0/LHbsycvOFjuPV6PPNH1WeZGTH56oWjZufqk1TUQJsaPXe/IGrre6qmFajqqRBL/VlDTkZaqXZP55XA8krunstv/ZGg57g6lWx564oDo7Q7Ha9o7AaGcBqLQ4OMHQ+NOyjXqjL7iqxVPmG4/FsO52Ii5MDT/a1YBVOTVNz1I05qi/VDgc+FanLZDU25uwmiC5h9cuKkhKjatCAqknjGVBx57aV6b1XklTVVZBZNLam2+OqBS49XiUlFe2ofRYCq0okGbEmJxcY+bVaQfPKZfh6mOoq+Q+jUeOdNWoxvPHd6hHiZ8H+wAPLVfOpk7tqFalM4yesybfW1dUvgX8rwZTGP55X9QFqd1I1aSrStdN7K3Phq8O/qJVmA5upbgVhO5xcYNDVVad3LIDzuyvu3KmxcH6n2paVnW2WJCPW5uIJY5ar+hepF1RCkp5QaJeV+y5wJCYFbzcnHunV0HLnvpKk3gRBlRr3r2e5Y9uC/LU5jvyqZpLoJfIvOLRC1aC5bVbF1wfwr6+mdmanQWr5qwNbTf4smjajqk7SbE8a9Lz6AUBTY6OMeRVz3vzpriHtwSe4Ys4pLE6SkYrgUQ3u+0kNHr10EpaOgKxUALJzjby/VrWKPNyzIX4eLpY771//U82mAY1VM2pVE9Rc9V9rRlV1Vg85maqbDFQNGj1mszi5qOmdUHm7ahJPQ9S/gEGfAZDCMvr9D1x9IWYv7FxYMee0h4XxhCQjFca3lkpIPKrDhT3w7T2Qm8U326M4l3iFGt6uTOxuwbLX53fDji/U9m3vqzekqig/Cdu79LoWqQqx+SNVc8Y72OyaMxYVcHUcUmUdxLr/6nTeBr3Ax8LLH4iK4x0Efa4W9It4XXWhWFN2esEaRjJexKZJMlKRAsJgzA/g4gWn/yZ3+f3MjVCDWh/vE4a7i6NlzmPMuzqFVFPNpvVvtsxxbVG9m1TzbW5mQSnlinIpUtUFAOj/pqpBo5fqV2fUVMbpvYXKv9tZVeCqqONECG4LWcnw54vWPdepDeq17VcHglpY91zCqiQZqWi12sOopeDogtOxX3ky61PqV/dgZKdQy51j50LVTOrqW7CuQVVlMBSUiN/+ecWVRNc0WP1/akBmg1tUBUw9VebVe89th8un1To9djx1scpwcITbPwAMqoDd6b+td678Kb1N7GRhvCpMkhE9NOhF6qB5GDUD9zj9xWe1f8fZ0UJPRWqsah4F1VzqVcMyx7Vlze5Qg3evJMKepRVzzsO/qOXMHV0qR+n9yjy9N79VpPkQ+6+BU1XUaq9WDAY1Zsoa60QZ89QSFyBdNHZAkhGdzLrQjBdyJwIQduxTtbCbJfz5omoeDWmnmkuF+qQWPkVtb5mjKqFaU1aqWpAQ4KanoLoFZ0iVVX7LSFIU5FzRN5Zr5WSqmUYgtUXsTe8Z4BkICcfh348tf/zonZCRoFqA63a3/PFFhZJkRAfnEjNYujWKb/L6cKbNVHXjmudg33flO/Dpv6+u63F1YSUHC41BsQdtx6jBw0ln4cgv1j3X+plqGrd/fbhpqnXPVVqegeqfNpoaUFtZnFoPmcngU0utTCrsh7sf9LvaTfz3u6pcuyXld9GE3SrVeu2AJCM6+GDdcbLzjNzUKIB6Q19S1UIBfnkEjv9ZtoPmZhdMIe10v2omFQVcPKDzg2p788fWK/518QBsm6+2B70Hzm7WOY+5DIaCsvCVqavm7Gb1PezWiq+/Iqyv9d0qyczNhN+ftezr7phUXbUn8uqvYEcvpvDTnvMAPDOgiXqT6P8mtB6pFpz6fqxaZM9c/36smkM9a6jmUXG9TpNUJdqYvdYZVGc0wm9TQctT4x/Cbrw4YoWqjNN7z21X30O76huHsA6DQY2ZcnBWxcnyE4jySjip/t85OKlEVtg8SUYq2LtrjqFpcFurYFrX9lM3OjjAkLmqQFfuFVh2N8QeKv1BL59RzaCgZs9U9CJVtsKzOrS7V21bow97z9dqdVwXLxjwluWPX16VbXpvTqaquQMQ2lnfWIT1BDYpqIb8+7OWmdF2/Gqhs3o3qYUxhc2TZKQC7TiTSMTROBwdDDzd7z+L4Tk6w11fQmgX1Yf+9Z1w+eyND6pp6gWem6maQ1vdZZ3g7UX4o6os+8l1cPGg5Y6bfgnWvay2b3m+chbuqmzTe2P2Ql62Gs9SrYHe0Qhruvn/wLcOJJ+DjW+X/3hHr5nSK+yCJCMVRNM03vpdFTgb2SmUBoFFLHPt4gH3fAc1mkPaRfh6KKTFl3zgY6tV86eDsxq0qvcU0squWn3VhQLw72zLHXfdS2oxxKCWqux7ZXTt9N7KsGBefndkaBf5u7V3Lh4w6B21vWUuxB0p+7HSL8G5q387Ml7EbkgyUkHWHYlj19nLuDk78ESfsOJ3dPeHe39UnyIST8GSOyEzpeh9s9NVqwiowl6BjYveTxSWXyL+4A+QHF3+40VthT1L1PZts8DRqfzHtIZqDQADZKVAWpze0VwzXqSLvnGIitFkIDS5TY2NW/V02RPiE3+q9aaCWqnKq8IuSDJSAfKMGu/+oVpFJnavT5DPDWZY+ITA2J/BIwAu7lfr2ORkXr/fxrdVs6dfHegxzfKB26ta7VWXljG3/PVd8nKult4H2o+FOpX4jdXZreCft95dNZoG57ap7ToyeLXKGPgWOHuoWVT5xe7MJbNo7JIkIxXgpz3nOR6bhq+7Mw/1LGUBrOoNVQuJizec+Qd+vL9wsa64IwUr0Q58VzWDitLr/oT6vmsxXEkq+3G2zYe4w+BeDfq+aonIrCt/Ro3e03svRaqCVY6uENxG31hExfGrAz2fUdt/zoCMRPMen5MJJyPUtiQjdkWSESvLzMnjg7XHAXikV0N83c0ozhPSFkYvUyXFj/4Gvz2pPlFqmmrmNOaqZs8mA6wSu11r1FeNzclOK/tS58nRqsAZwK2vgUc1y8VnLaZBrDrPqMnv86/VHpxc9Y1FVKyuj0JgU5WMRrxm3mPP/AM56WoV7JB21olP6EKSEStbsvUs55OuEOzrxrhu9cw/QP2bYcRCNQNkz9cQ8apq3jy7WTV3DrTAyPSqyGAoGDuybT7kZpl/jDXT1T/G0C6qwqstqF5JCp/ld9HIlN6qx8lFja0C1TIZvbP0j722i0YGPdsVSUasKCUzh7nr1SfQJ/uG4eZcxvLszQbD7R+q7U0fFIxR6Pks+Flwtd+qpuVwVYY8LRb2m1mK/8RaOLISDI5XS+/byEspv2Uk4bi+cUTlJyMyXqRKqtcd2owGNPX/rDTrRRmNcOxqfRGZ0mt3bOQ/qG36/O9TXM7IoWGgJ8Pb1y7fwTqMgz5X61jkZqpmzq6PlD/IqszJBbpeLcX/72z1z640cq7A6qsDhrtOhpotrROfNeRP7006W7bWIEvISISEY2pbZtJUXbe+rgqWXdwPO7648f4xeyE1RhUVrH+z1cMTFUuSESuJS83ki39OA/B//Zvi5GiBX/VNT0Gv56FaQ1Wx1cml/Mes6tqPUwvIJRxX9VpK459Zquqtdwj0es6q4Vmcd031z1wzQuJpfWLIn9JbPUxVxRVVk1dgwQesv/4HqRdL3j+/VaRhbxlnZIckGbGS2REnuZKTR7s6fvRvEWSZgxoM0OtZeHw31O5omWNWdW4+0HGC2i5NifiEk7D5Q7U98C1w9bZaaFZhMOhfidU0XkRaRaq8DhOgVgfIToU/ni9532NSddWeSTJiAclXctgSeYkv/jnFU9/t5dZZG1myTZVyf3ZAUwwy0Kpy6/KwmrEUtaXgU3tRNA1WP61KmDe6FZrdUXExWtK1lVj1YKovIslIlefgcLVytAMc/BEi1xe93+WzEHtQ7de4f8XGKCpEJS0VWXnFp2Zx8EIyhy+kcPB8MocupBCVmFHkvqM7h9K1gTRDV3o+wWqp8z1LYPNHMGpp0fsd/BFObQAnN1Xa2laTTD2n9+Zmw/ldalsGrwpQJQw6TYLtn6qxWJP/vb4bJr8LtU64bUyhF2aTZKQYmqZxPukKB8+ncPhCMgcvpHDoQjKxKUUP+qvt706LEB9ahvjSopb6XuNGlVZF5dHtcZWMHF2lumICGhW+PzO5oBm5x9O2vbCbaXqvDjNqLu5XA7Dd/QuSIiF6vwCHf1YJ8uaPCgqj5Tu6Sn2XQmd2S5IRVLn20wnpHLqgWjoOXUjm4PkUkq/kXLevwQANAjxpWcvXlHw0D/HBz0MGk9q0wCbQeKBamnzLbBj8UeH717+ppgBXa1hQvdVWBfxnwbyKbOG5dryIrbYsCctz84X+b6pK03+/B61GFCT8V5JUXSWQ8SJ2rEonI2/9fpQdZxI5EpNCRnbedfc7OxoIq+FNy1o+tAjxpWUtH5rW9MHTtUr/2uxX9ydUMrL3G7jlBfCqoW6/sBe2f6a2b3vf9kfyV7u6JEFmEmRcAs+Aijv3tSv1CnGtlsNh91dweiOsfgbGLFcJ68l1qtp0QBO1TIawS1X6XXXnmUR2nb0MgJuzA82DC5KOFiG+hAV54epUxkJlwvbU6Qq1O0H0Dtj2KfSZAcY8WDVVTYVtORwa3qJ3lOXn4qFWhU6OUq0jFZWMyOJ4oiQGgxrMOi8cTl4tKth8iCyMV0VU6WTkgR4NuLdrHi1CfGgQ6IWjgzQbV2kGg2od+e5eVYTppqfgwPdqwKWLN/R7Q+8ILSegkUpGLp2AuuEVc86ks6qry8FZ1hURRQtopF6Df78Lvz+nVtc+sU7d1/Q2fWMTVlWlk5EBLWvqHYKobJoMUt0YiZGwaVZBZcjeL6pZN/aiehhE/lWx03vzS8AHtwFn94o7r7AtPZ6GA8tVYcFvRkFWMngGqnokwm5JnREhruXgCN2mqO1/3lezaGq2hk4P6BuXpQXoUGskf6Ve6aIRJXF2h0Hvqe38br3G/dVrU9gtSUaE+K82o9UnMQAMcPsH4GhnjYj503srsgprlFReFaUUdqtaIDRfE+misXeSjAjxX87uBdN3Oz1gn6X381tGLp+BvOunsFtcZjLEHVbbkoyI0hjwlpry6xkIDXrpHY2wMjv7uCeEhYRPgfo9IciGVuQ1h3cIOHtCTrpKSKxdgCx6B6CBfz3wttBaTcK++daGR7cDBjUDTNg1aRkRoigGAwS3Vmtn2CMHh4KaDRUxbsTURSPjRYQZvGtK8lpF2Ol/WiHEDVXk6r2mwavSRSOEuJ4kI0JUVabVe628Rk1eLkTL4nhCiOJJMiJEVWWa3mvl1XtjD6qxKa6+ENjUuucSQtgkSUaEqKoqanqvaXG8TvY7BkcIUS7yn0GIqio/Gcm4BBmJ1juPaXE86aIRQhRNkhEhqipXL/CppbYvWbGr5tx29T20s/XOIYSwaZKMCFGV5beOWGt6b3I0pESDwdE+i8cJISxCkhEhqrIAK8+oye+iqdkKXDytcw4hhM2TZESIqix/eq+1umnyu2hkcTwhRAkkGRGiKguwcjdNfrEzGS8ihCiBJCNCVGUBjdX3xFOqOJklZaXBxYNqW2bSCCFKIMmIEFWZT21wcgdjDiSdteyxz+8ELQ98Q8G3lmWPLYSwK5KMCFGVXbtgnqXHjZim9Mp6NEKIkkkyIkRVZ63pvaZiZ5KMCCFKJsmIEFWdNab3GvMgeofalpV6hRA3IMmIEFWdNab3xh2BrBRw8YIaLSx3XCGEXZJkRIiqztQyYsFumvzF8Wp3BEcnyx1XCGGXJBkRoqrLHzOSHgeZyZY5pmmlXumiEULcWJmSkblz51KvXj3c3Nzo0qUL27dvL3bfxYsXYzAYCn25ubmVOWAhhIW5+YBXTbWdYKGuGhm8KoQwg9nJyHfffcfUqVN5+eWX2b17N23atKF///7ExcUV+xgfHx9iYmJMX2fPWriegRCifPK7ai5ZoKsm9aKqWWJwgNqdyn88IYTdMzsZmTVrFpMmTWLChAk0b96c+fPn4+HhwcKFC4t9jMFgoGbNmqavoKCgcgUthLAw0/ReC8yoye+iqdFCtboIIcQNmJWMZGdns2vXLvr27VtwAAcH+vbty5YtW4p9XFpaGnXr1iU0NJQhQ4Zw6NChEs+TlZVFSkpKoS8hhBVZchBrVP54EVmPRghROmYlIwkJCeTl5V3XshEUFMTFixeLfEyTJk1YuHAhv/zyC0uWLMFoNNKtWzeio6OLPc/MmTPx9fU1fYWGhpoTphDCXPlr1Fhiem/+4niyUq8QopSsPpsmPDycsWPH0rZtW3r27MmKFSsIDAzk008/LfYx06dPJzk52fR17tw5a4cpRNWW301zKVIVLCur7AyI2ae2ZfCqEKKUzCoAEBAQgKOjI7GxsYVuj42NpWbNmqU6hrOzM+3atePkyeI/gbm6uuLq6mpOaEKI8vCrA46ukJcFyefAv17ZjnNhDxhzwTtYHVMIIUrBrJYRFxcXOnToQEREhOk2o9FIREQE4eHhpTpGXl4eBw4cIDg42LxIhRDW4+AI1Rqo7fJM783vogntDAZD+eMSQlQJZnfTTJ06lc8//5wvv/ySI0eOMHnyZNLT05kwYQIAY8eOZfr06ab9X3vtNf78809OnTrF7t27uffeezl79iwPPPCA5a5CCFF+ARaYUWMavCrjRYQQpWd2neaRI0cSHx/PSy+9xMWLF2nbti1r1qwxDWqNiorCwaEgx7l8+TKTJk3i4sWL+Pv706FDB/7991+aN29uuasQQpRf9XLWGjEaIfpqAURZHE8IYQaDpmma3kHcSEpKCr6+viQnJ+PjI3ULhLCKvd/Azw9DvR4w/jfzHx9/DOZ2Bid3mH4OHJ0tH6MQwqaU9v1b1qYRQigB5Vy9N78EfK0OkogIIcwiyYgQQsmf3psaA1mp5j8+v/KqdNEIIcwkyYgQQnH3A89AtV2W1pFzMnhVCFE2kowIIQpUL2NZ+PSEggSmdkfLxiSEsHuSjAghCpim95qZjOS3igQ2BY9qlo1JCGH3JBkRQhQwrVFjZjKSP3hVSsALIcpAkhEhRAFTN42ZY0bO5dcXkfEiQgjzSTIihChw7fReo7F0j8nNUmvSgLSMCCHKRJIRIUQBv7rg4Ay5VyDlfOkec2GvWmDPI6BgfRshhDCDJCNCiAKOTlCtvtou7Ro1+Yvj1ekqi+MJIcpEkhEhRGHVzazEmj9eRLpohBBlJMmIEKKwADNqjWiazKQRQpSbJCNCiMICzFi9N/EUZCSAoyuEtLVqWEII+yXJiBCiMHOm9+a3ioS0AydX68UkhLBrkowIIQrLbxlJiYbs9JL3lcXxhBAWIMmIEKIwj2rgfrWk+40GsZoWx5NkRAhRdpKMCCGuV5pBrBmJEH9UbUsyIoQoB0lGhBDXK8303uidV/dtBJ4B1o9JCGG3JBkRQlyvNC0j+cXOQmU9GiFE+UgyIoS4Xmmm90bljxfpbP14hBB2TZIRIcT1rp3eq2nX35+XA+d3qW1ZqVcIUU6SjAghrudfDwyOkJMOKReuv//ifrWYnrt/QeIihBBlJMmIEOJ6Ti4qIYGiu2qirpnS6yD/RoQQ5SP/RYQQRStpEKtp8KqMFxFClJ8kI0KIogUUM71X065pGZHxIkKI8pNkRAhRtOrFtIwkRUHaRXBwglrtKz4uIYTdkWRECFG04qb35peAD24Dzu4VG5MQwi5JMiKEKFp+y0jSOci5UnB7lBQ7E0JYliQjQoiieQaAmy+gwaXIgttlpV4hhIVJMiKEKJrBcM0aNVe7ajJTIPaQ2pbF8YQQFiLJiBCieAGN1feEqzNqoncAGvjVBe+auoUlhLAvkowIIYoX0Eh9z28ZMXXRyHgRIYTlSDIihCjef6f3nrum8qoQQliIJCNCiOJdW/gsLxeid6qfpWVECGFBkowIIYpXrQEYHCArBU6th+w0cPWBwKZ6RyaEsCOSjAghiufkCn511Paer9X32p3AwVG/mIQQdkeSESFEyfJn1Bxdrb5LF40QwsIkGRFClCx/EKsxR32XwatCCAuTZEQIUbL86b0ABkeo1UG/WIQQdkmSESFEyfJbRgBqtgRXL/1iEULYJUlGhBAlC7gmGZHF8YQQViDJiBCiZF5B4OKttmVxPCGEFUgyIoQomcEAXR5SA1cb9dU7GiGEHXLSOwAhhA3oM0PvCIQQdkxaRoQQQgihK0lGhBBCCKErSUaEEEIIoStJRoQQQgihK0lGhBBCCKErSUaEEEIIoStJRoQQQgihK0lGhBBCCKErSUaEEEIIoStJRoQQQgihK0lGhBBCCKErSUaEEEIIoStJRoQQQgihK0lGhBBCCKErJ70DKA1N0wBISUnRORIhhBBClFb++3b++3hxbCIZSU1NBSA0NFTnSIQQQghhrtTUVHx9fYu936DdKF2pBIxGIxcuXMDb2xuDwWCx46akpBAaGsq5c+fw8fGx2HErE3u/Rrk+22fv1yjXZ/vs/RqteX2appGamkpISAgODsWPDLGJlhEHBwdq165tteP7+PjY5R/Ytez9GuX6bJ+9X6Ncn+2z92u01vWV1CKSTwawCiGEEEJXkowIIYQQQldVOhlxdXXl5ZdfxtXVVe9QrMber1Guz/bZ+zXK9dk+e7/GynB9NjGAVQghhBD2q0q3jAghhBBCf5KMCCGEEEJXkowIIYQQQleSjAghhBBCV3afjMydO5d69erh5uZGly5d2L59e4n7L1++nKZNm+Lm5karVq1YvXp1BUVqvpkzZ9KpUye8vb2pUaMGQ4cO5dixYyU+ZvHixRgMhkJfbm5uFRSxeV555ZXrYm3atGmJj7Gl5w+gXr16112jwWDg0UcfLXL/yv78/f333wwePJiQkBAMBgM///xzofs1TeOll14iODgYd3d3+vbty4kTJ254XHNfx9ZS0vXl5OTw7LPP0qpVKzw9PQkJCWHs2LFcuHChxGOW5e/cmm70HI4fP/66eAcMGHDD49rCcwgU+Xo0GAy8++67xR6zMj2HpXlfyMzM5NFHH6V69ep4eXkxfPhwYmNjSzxuWV+7pWXXych3333H1KlTefnll9m9ezdt2rShf//+xMXFFbn/v//+y+jRo7n//vvZs2cPQ4cOZejQoRw8eLCCIy+djRs38uijj7J161bWrl1LTk4O/fr1Iz09vcTH+fj4EBMTY/o6e/ZsBUVsvhYtWhSKddOmTcXua2vPH8COHTsKXd/atWsBuOuuu4p9TGV+/tLT02nTpg1z584t8v533nmHjz/+mPnz57Nt2zY8PT3p378/mZmZxR7T3NexNZV0fRkZGezevZsZM2awe/duVqxYwbFjx7jjjjtueFxz/s6t7UbPIcCAAQMKxfvNN9+UeExbeQ6BQtcVExPDwoULMRgMDB8+vMTjVpbnsDTvC0899RS//vory5cvZ+PGjVy4cIE777yzxOOW5bVrFs2Ode7cWXv00UdNP+fl5WkhISHazJkzi9z/7rvv1m677bZCt3Xp0kV76KGHrBqnpcTFxWmAtnHjxmL3WbRokebr61txQZXDyy+/rLVp06bU+9v686dpmvbEE09oDRs21IxGY5H329LzB2g//fST6Wej0ajVrFlTe/fdd023JSUlaa6urto333xT7HHMfR1XlP9eX1G2b9+uAdrZs2eL3cfcv/OKVNQ1jhs3ThsyZIhZx7Hl53DIkCFa7969S9ynMj+H/31fSEpK0pydnbXly5eb9jly5IgGaFu2bCnyGGV97ZrDbltGsrOz2bVrF3379jXd5uDgQN++fdmyZUuRj9myZUuh/QH69+9f7P6VTXJyMgDVqlUrcb+0tDTq1q1LaGgoQ4YM4dChQxURXpmcOHGCkJAQGjRowJgxY4iKiip2X1t//rKzs1myZAkTJ04scUFIW3r+rnX69GkuXrxY6Dny9fWlS5cuxT5HZXkdVybJyckYDAb8/PxK3M+cv/PKYMOGDdSoUYMmTZowefJkLl26VOy+tvwcxsbGsmrVKu6///4b7ltZn8P/vi/s2rWLnJycQs9H06ZNqVOnTrHPR1leu+ay22QkISGBvLw8goKCCt0eFBTExYsXi3zMxYsXzdq/MjEajTz55JN0796dli1bFrtfkyZNWLhwIb/88gtLlizBaDTSrVs3oqOjKzDa0unSpQuLFy9mzZo1zJs3j9OnT9OjRw9SU1OL3N+Wnz+An3/+maSkJMaPH1/sPrb0/P1X/vNgznNUltdxZZGZmcmzzz7L6NGjS1x8zNy/c70NGDCAr776ioiICN5++202btzIwIEDycvLK3J/W34Ov/zyS7y9vW/YhVFZn8Oi3hcuXryIi4vLdQnyjd4b8/cp7WPMZROr9oobe/TRRzl48OAN+ynDw8MJDw83/dytWzeaNWvGp59+yuuvv27tMM0ycOBA03br1q3p0qULdevW5fvvvy/VJxVbs2DBAgYOHEhISEix+9jS81eV5eTkcPfdd6NpGvPmzStxX1v7Ox81apRpu1WrVrRu3ZqGDRuyYcMG+vTpo2Nklrdw4ULGjBlzw0HilfU5LO37QmVgty0jAQEBODo6XjdCODY2lpo1axb5mJo1a5q1f2UxZcoUfvvtN9avX0/t2rXNeqyzszPt2rXj5MmTVorOcvz8/GjcuHGxsdrq8wdw9uxZ1q1bxwMPPGDW42zp+ct/Hsx5jsryOtZbfiJy9uxZ1q5da/aS7Df6O69sGjRoQEBAQLHx2uJzCPDPP/9w7Ngxs1+TUDmew+LeF2rWrEl2djZJSUmF9r/Re2P+PqV9jLnsNhlxcXGhQ4cOREREmG4zGo1EREQU+mR5rfDw8EL7A6xdu7bY/fWmaRpTpkzhp59+4q+//qJ+/fpmHyMvL48DBw4QHBxshQgtKy0tjcjIyGJjtbXn71qLFi2iRo0a3HbbbWY9zpaev/r161OzZs1Cz1FKSgrbtm0r9jkqy+tYT/mJyIkTJ1i3bh3Vq1c3+xg3+juvbKKjo7l06VKx8drac5hvwYIFdOjQgTZt2pj9WD2fwxu9L3To0AFnZ+dCz8exY8eIiooq9vkoy2u3LIHbrW+//VZzdXXVFi9erB0+fFh78MEHNT8/P+3ixYuapmnafffdpz333HOm/Tdv3qw5OTlp7733nnbkyBHt5Zdf1pydnbUDBw7odQklmjx5subr66tt2LBBi4mJMX1lZGSY9vnvNb766qvaH3/8oUVGRmq7du3SRo0apbm5uWmHDh3S4xJK9PTTT2sbNmzQTp8+rW3evFnr27evFhAQoMXFxWmaZvvPX768vDytTp062rPPPnvdfbb2/KWmpmp79uzR9uzZowHarFmztD179phmk7z11luan5+f9ssvv2j79+/XhgwZotWvX1+7cuWK6Ri9e/fWZs+ebfr5Rq/jynJ92dnZ2h133KHVrl1b27t3b6HXZFZWVrHXd6O/84pW0jWmpqZq06ZN07Zs2aKdPn1aW7dunda+fXstLCxMy8zMNB3DVp/DfMnJyZqHh4c2b968Io9RmZ/D0rwvPPzww1qdOnW0v/76S9u5c6cWHh6uhYeHFzpOkyZNtBUrVph+Ls1rtzzsOhnRNE2bPXu2VqdOHc3FxUXr3LmztnXrVtN9PXv21MaNG1do/++//15r3Lix5uLiorVo0UJbtWpVBUdcekCRX4sWLTLt899rfPLJJ02/j6CgIG3QoEHa7t27Kz74Uhg5cqQWHBysubi4aLVq1dJGjhypnTx50nS/rT9/+f744w8N0I4dO3bdfbb2/K1fv77Iv8n8azAajdqMGTO0oKAgzdXVVevTp8911123bl3t5ZdfLnRbSa/jilTS9Z0+fbrY1+T69etNx/jv9d3o77yilXSNGRkZWr9+/bTAwEDN2dlZq1u3rjZp0qTrkgpbfQ7zffrpp5q7u7uWlJRU5DEq83NYmveFK1euaI888ojm7++veXh4aMOGDdNiYmKuO861jynNa7c8DFdPKoQQQgihC7sdMyKEEEII2yDJiBBCCCF0JcmIEEIIIXQlyYgQQgghdCXJiBBCCCF0JcmIEEIIIXQlyYgQQgghdCXJiBDCJhgMBn7++We9wxBCWIEkI0KIGxo/fjwGg+G6rwEDBugdmhDCDjjpHYAQwjYMGDCARYsWFbrN1dVVp2iEEPZEWkaEEKXi6upKzZo1C335+/sDqgtl3rx5DBw4EHd3dxo0aMAPP/xQ6PEHDhygd+/euLu7U716dR588EHS0tIK7bNw4UJatGiBq6srwcHBTJkypdD9CQkJDBs2DA8PD8LCwli5cqXpvsuXLzNmzBgCAwNxd3cnLCzsuuRJCFE5STIihLCIGTNmMHz4cPbt28eYMWMYNWoUR44cASA9PZ3+/fvj7+/Pjh07WL58OevWrSuUbMybN49HH32UBx98kAMHDrBy5UoaNWpU6Byvvvoqd999N/v372fQoEGMGTOGxMRE0/kPHz7M77//zpEjR5g3bx4BAQEV9wsQQpSdxZbcE0LYrXHjxmmOjo6ap6dnoa833nhD0zS1wufDDz9c6DFdunTRJk+erGmapn322Weav7+/lpaWZrp/1apVmoODg2nF15CQEO2FF14oNgZAe/HFF00/p6WlaYD2+++/a5qmaYMHD9YmTJhgmQsWQlQoGTMihCiVW265hXnz5hW6rVq1aqbt8PDwQveFh4ezd+9eAI4cOUKbNm3w9PQ03d+9e3eMRiPHjh3DYDBw4cIF+vTpU2IMrVu3Nm17enri4+NDXFwcAJMnT2b48OHs3r2bfv36MXToULp161amaxVCVCxJRoQQpeLp6Xldt4mluLu7l2o/Z2fnQj8bDAaMRiMAAwcO5OzZs6xevZq1a9fSp08fHn30Ud577z2LxyuEsCwZMyKEsIitW7de93OzZs0AaNasGfv27SM9Pd10/+bNm3FwcKBJkyZ4e3tTr149IiIiyhVDYGAg48aNY8mSJXz44Yd89tln5TqeEKJiSMuIEKJUsrKyuHjxYqHbnJycTINEly9fTseOHbnppptYunQp27dvZ8GCBQCMGTOGl19+mXHjxvHKK68QHx/PY489xn333UdQUBAAr7zyCg8//DA1atRg4MCBpKamsnnzZh577LFSxffSSy/RoUMHWrRoQVZWFr/99pspGRJCVG6SjAghSmXNmjUEBwcXuq1JkyYcPXoUUDNdvv32Wx555BGCg4P55ptvaN68OQAeHh788ccfPPHEE3Tq1AkPDw+GDx/OrFmzTMcaN24cmZmZfPDBB0ybNo2AgABGjBhR6vhcXFyYPn06Z86cwd3dnR49evDtt99a4MqFENZm0DRN0zsIIYRtMxgM/PTTTwwdOlTvUIQQNkjGjAghhBBCV5KMCCGEEEJXMmZECFFu0tsrhCgPaRkRQgghhK4kGRFCCCGEriQZEUIIIYSuJBkRQgghhK4kGRFCCCGEriQZEUIIIYSuJBkRQgghhK4kGRFCCCGEriQZEUIIIYSu/h99u2T6J6dbeQAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "model_1.save('/content/drive/MyDrive/Project_4/model_1_EfficientNetV2B0_TF-FT-ALL_B-M.h5')"
      ],
      "metadata": {
        "id": "A7nyR_HgTRAk"
      },
      "execution_count": 32,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def compare_historys(original_history, new_history, initial_epochs=5):\n",
        "    \"\"\"\n",
        "    Compares two model history objects.\n",
        "    \"\"\"\n",
        "    # Get original history measurements\n",
        "    acc = original_history.history[\"accuracy\"]\n",
        "    loss = original_history.history[\"loss\"]\n",
        "\n",
        "    print(len(acc))\n",
        "\n",
        "    val_acc = original_history.history[\"val_accuracy\"]\n",
        "    val_loss = original_history.history[\"val_loss\"]\n",
        "\n",
        "    # Combine original history with new history\n",
        "    total_acc = acc + new_history.history[\"accuracy\"]\n",
        "    total_loss = loss + new_history.history[\"loss\"]\n",
        "\n",
        "    total_val_acc = val_acc + new_history.history[\"val_accuracy\"]\n",
        "    total_val_loss = val_loss + new_history.history[\"val_loss\"]\n",
        "\n",
        "    print(len(total_acc))\n",
        "    print(total_acc)\n",
        "\n",
        "    # Make plots\n",
        "    plt.figure(figsize=(8, 8))\n",
        "    plt.subplot(2, 1, 1)\n",
        "    plt.plot(total_acc, label='Training Accuracy')\n",
        "    plt.plot(total_val_acc, label='Validation Accuracy')\n",
        "    plt.plot([initial_epochs-1, initial_epochs-1],\n",
        "              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs\n",
        "    plt.legend(loc='lower right')\n",
        "    plt.title('Training and Validation Accuracy')\n",
        "\n",
        "    plt.subplot(2, 1, 2)\n",
        "    plt.plot(total_loss, label='Training Loss')\n",
        "    plt.plot(total_val_loss, label='Validation Loss')\n",
        "    plt.plot([initial_epochs-1, initial_epochs-1],\n",
        "              plt.ylim(), label='Start Fine Tuning') # reshift plot around epochs\n",
        "    plt.legend(loc='upper right')\n",
        "    plt.title('Training and Validation Loss')\n",
        "    plt.xlabel('epoch')\n",
        "    plt.show()"
      ],
      "metadata": {
        "id": "dWQr-po3zGap"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare model_0 to model_0 fine tuned\n",
        "compare_historys(original_history=history_0,\n",
        "                 new_history=history_finetune_0,\n",
        "                 initial_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "nrV6ScCHzL7Z",
        "outputId": "9fb21af9-5f88-4afa-9a12-7c00585cb7e7"
      },
      "execution_count": 28,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "41\n",
            "[0.5457909107208252, 0.5744680762290955, 0.586493968963623, 0.6096207499504089, 0.6271970272064209, 0.610545814037323, 0.6031452417373657, 0.6271970272064209, 0.610545814037323, 0.628122091293335, 0.6392229199409485, 0.6244218349456787, 0.6419981718063354, 0.6308973431587219, 0.6595744490623474, 0.6438482999801636, 0.649398684501648, 0.6438482999801636, 0.631822407245636, 0.6456984281539917, 0.6244218349456787, 0.6679000854492188, 0.6586493849754333, 0.6521739363670349, 0.649398684501648, 0.6706752777099609, 0.6623497009277344, 0.6586493849754333, 0.6780758500099182, 0.6577243208885193, 0.6873265504837036, 0.671600341796875, 0.674375593662262, 0.6641998291015625, 0.674375593662262, 0.6901017427444458, 0.6817761063575745, 0.7030526995658875, 0.7049028873443604, 0.7021276354789734, 0.7076780796051025]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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/4cOH4+vry+eff86aNWsYOXIk3t7e5c59x44dbN++3eo59+3bF09PTz755JNi43344YclttVoNCWsjD/++COXLl0qtk6p3WlJea1Bgwah0+n49NNPi63/4IMPUKlUFscTV8SiRYto0KABDz74IHfccUexv6eeegp/f3+jm3/UqFEYDAZeffXVEuMo73/48OGo1WpmzpxZwopp/hnFxsYWiycG+Oqrr8q0oJZGaZ/7J598UmKMUaNGsX//fn7++ecy560wYcIE1q9fz4cffkhISIjdPmeJpCogLagSSRWmW7du1KhRg0mTJhnbcC5cuNCpbtCKeOWVV1i/fj3du3dn6tSpRqHTsmXLCttsNm3alNjYWJ566ikuXbpEYGAgP/30k0WxjGUxdOhQunfvzn//+1/i4uJo3rw5y5cvtzo+09/fn+HDhxvjUK8v/TNkyBCWL1/OiBEjGDx4MGfPnmX27Nk0b96czMxMq46l1HOdNWsWQ4YMYdCgQezdu5c1a9aUsDQOGTKEmTNnMmXKFLp168bBgwdZvHhxMcsrCFEWHBzM7NmzCQgIwM/Pjy5dupQaXzl06FD69OnDCy+8QFxcHG3atGH9+vX88ssvPP7448USomzl8uXL/PHHHyUSsRS0Wi39+/fnxx9/5OOPP6ZPnz5MmDCBjz/+mJMnTzJgwAD0ej1bt26lT58+TJs2jYYNG/LCCy/w2muv0bNnT0aOHIlWq2XXrl1ER0cb64ned999PPjgg4waNYp+/fqxf/9+1q1bV+KzLY8hQ4awcOFCgoKCaN68Odu3b2fjxo0lymo9/fTTLFu2jDvvvJN77rmHDh06kJKSwsqVK5k9ezZt2rQxbnv33XfzzDPP8PPPPzN16lSXN1CQSJyJtKBKJFWYkJAQfvvtN6KionjxxRd599136devH2+//barp2akQ4cOrFmzhho1avDSSy8xZ84cZs6cya233lrM4lganp6e/Prrr7Rt25ZZs2bx6quv0qhRIxYsWGDzfNRqNStXrmTcuHEsWrSIF154gZiYGObPn2/1WIoojYqK4pZbbin22uTJk3nzzTfZv38/jz76KOvWrWPRokXG+pzW8vrrr/Pqq6+yd+9enn76aU6fPs369etLdDF6/vnnefLJJ1m3bh2PPfYYe/bsYdWqVdSuXbvYdp6ensyfPx+NRsODDz7I2LFj2bJlS6nHVj6zxx9/nN9++43HH3+cI0eO8M477/D+++/b9H6u57vvvkOv1xcLk7ieoUOHkpycbLS+f/vtt7zzzjucPXuWp59+mjfffJOcnJxi9VxnzpzJ3LlzycnJ4YUXXmDGjBmcO3eOW2+91bjN/fffz7PPPsuff/7Jk08+ydmzZ9mwYYNVHaI++ugjJk6cyOLFi3nyySeJj49n48aNJZLx/P392bp1K1OnTmX16tU8+uijfP755zRp0sRY9F8hIiLCWKt1woQJFs9FIqkOqAzuZGqRSCQ3DMOHD+fw4cOcPHnS1VORSNyWESNGcPDgQYtitiWS6oS0oEokEodzfVvSkydPsnr1anr37u2aCUkkVYD4+HhWrVolraeSGxJpQZVIJA4nKiqKyZMn06BBA86dO8cXX3xBXl4ee/fuLVHbUyK50Tl79ix///0333zzDbt27eL06dNERka6eloSiVORSVISicThDBgwgKVLl5KQkIBWq6Vr1668+eabUpxKJKWwZcsWpkyZQp06dZg/f74Up5IbEmlBlUgkEolEIpG4FTIGVSKRSCQSiUTiVkiBKpFIJBKJRCJxK6pFDKper+fy5csEBARY1b5PIpFIJBKJROIcDAYDGRkZREdHo1aXbyOtFgL18uXLJYpQSyQSiUQikUjcjwsXLpRoTHE91UKgBgQEAOINBwYGung2EolEIpFIJJLrSU9Pp3bt2kbdVh7VQqAqbv3AwEApUCUSiUQikUjcGEvCMWWSlEQikUgkEonErZACVSKRSCQSiUTiVkiBKpFIJBKJRCJxK6RAlUgkEolEIpG4FVKgSiQSiUQikUjcCilQJRKJRCKRSCRuhRSoEolEIpFIJBK3olrUQZVInIHBYCCnMAcAHw8f2VZXIpFIJBIHIS2oEomF5BTm0GVJF7os6WIUqhKJRCKRSOyPFKgSiUQikUgkErdCClSJRCKRSCQSiVshBapEIpFIJBKJxK2QAlUikUgkEolE4lZIgSqRSCQSiUQicSukQJVIJBKJRCKRuBVSoEokEolEIpFI3AopUCUSiUQikUgkboUUqBKJRCKRSCQSt0IKVIlEIpFIJBKJWyEFqkQikUgEBoOrZyCRSCSAFKgSiUQiAUg4BO/EwvbPXD0TiUQikQJVIpFIJEDcVshOhoM/unomEolEIgWqRCKRSICsq+LxyjHQ6107F4lEcsMjBapEIpFITAK1MAdS41w6FYlEIpECVSKRSCSQlWRavnLUdfOQSCQSpECVSCQSCZgsqABXjrhuHhKJRIIUqBKJRCIByLxiWpYWVIlE4mKkQJVIJBJJcRd/orSgSiQS1yIFqkQikdzo5GdBQZbpefJJKMx33XwkEskNjxSoEolEcqOjWE81WtAGgr4Qkk+5dk4SicThnEzM4P0NJ8jJ17l6KiXwcPUEJBKJROJiFIHqFwZBMXBhh0iUimju2nlJJBK7E5eUxW8HLvPr/niOJ2YA0CQigMGto1w8s+JIgSqRSCQ3OkoGv18ohDcrEqgyUUoiqS5cTs1h1YF4fj1wmQMX04zrPTUqejUOJyxA68LZlY4UqBKJRHKjowhU/3AIbyGWpUCVSOyOXm8gKTOPS6k5xKflcjk1h8upucSn5XC56LnBAI0j/GkcEUCTyAAaRwTQOMKfAG9Pq451JSOXNQcT+HX/Zf49d824XqNW0S02hKFtounfPJIgX+vGdRY2CdTPPvuMd955h4SEBNq0acMnn3xC586dS922d+/ebNmypcT6QYMGsWrVKgAMBgMvv/wyX3/9NampqXTv3p0vvviCRo0a2TI9iUQikViD0YIaJiyoAFcOu24+EkkVJy4pi62nkricmkN8qkl8JqbnUqAzVLh/UmYe204nF1sXE+wjhGtkAE0ihHBtGO6Pt6fGuE1qdj5rDyXw64HLbD+djL7oUCoVdK5Xk6FtohnYMpIQf/ezmF6P1QL1+++/Z/r06cyePZsuXbrw4Ycf0r9/f44fP054eHiJ7ZcvX05+vikbNDk5mTZt2nDnnXca17399tt8/PHHzJ8/n/r16/PSSy/Rv39/jhw5gre3t41vTSKRSCQWYYxBDTUJ1GtxIrvfy89l05JIqiJHLqcz4vO/ySvUl/q6WgURgd5EBXkTFexDdJA30cE+RAX5EB3sjd4AJxIzOJGQwfHEDE4kZpCYLqyul1Jz+OP41WJj1Qvxo3FEALmFOv46mUSh3iSA29YOZmibaAa3iiIyqGrpKasF6vvvv8/999/PlClTAJg9ezarVq1i7ty5/Pe//y2xfc2aNYs9/+677/D19TUKVIPBwIcffsiLL77IsGHDAFiwYAERERGsWLGCMWPGWP2mJBKJRGIF5hZUv1DwC4esK3D1GMR0cO3cJJIqRHpuAQ8t3k1eoZ6mkQF0qV9TiNAiIRoV7ENEgBYPTflFlNrWDi72PC27gBNXMjieIATr8SLxmppdwJmkLM4kmcrENY8KZGibaIa0jqJ2TV9HvE2nYJVAzc/PZ/fu3Tz33HPGdWq1mr59+7J9+3aLxpgzZw5jxozBz0/clZ89e5aEhAT69u1r3CYoKIguXbqwffv2UgVqXl4eeXl5xufp6enWvA2JRCKRmGMuUEFYUc9eEXGoUqBKJBZhMBj4708HiEvOJjrIm6X330QNPy+7jB3k60mnejXpVM9k9DMYDFzNzONEQibHEzMo0Onp2yyChuH+djmmq7FKoCYlJaHT6YiIiCi2PiIigmPHjlW4/86dOzl06BBz5swxrktISDCOcf2YymvXM2vWLF599VVrpi6RSCSSsjB38QOEN4ezW2SilERiBfO2xbH6YAKeGhWfjmtvN3FaFiqVivAAb8IDvOnRKNShx3IFTi3UP2fOHFq1alVmQpWlPPfcc6SlpRn/Lly4YKcZSiQSyQ1I1hXxqFhQlfqniTJRSiKxhL3nr/HmanFD99zAZrSvU8PFM6r6WCVQQ0ND0Wg0JCYmFlufmJhIZGRkuftmZWXx3Xffce+99xZbr+xnzZharZbAwMBifxKJRCKxAb2+eKF+EBZUkBZUicQCrmXlM23JXgp0Bga2jGRK93qunlK1wCqB6uXlRYcOHdi0aZNxnV6vZ9OmTXTt2rXcfX/88Ufy8vIYP358sfX169cnMjKy2Jjp6ens2LGjwjElEolEUklyU8FQ1ObQt8hNGNZEPGYmQHaKS6YlkVQF9HoD03/Yx6XUHOqF+PLWHa1RqVSunla1wGoX//Tp0/n666+ZP38+R48eZerUqWRlZRmz+idOnFgsiUphzpw5DB8+nJCQkGLrVSoVjz/+OK+//jorV67k4MGDTJw4kejoaIYPH27bu5JIJBKJZSgJUt5B4FEUM6cNgOA6YllaUSWSMpn952n+OH4VLw81n41rT6CVxfQlZWN1manRo0dz9epVZsyYQUJCAm3btmXt2rXGJKfz58+jVhfXvcePH+evv/5i/fr1pY75zDPPkJWVxQMPPEBqaio9evRg7dq1sgaqRCKROJrrM/gVwptD6nm4cgTqdXf+vCQSN+efM8m8u+44ADNvb0GL6CAXz6h6YVMnqWnTpjFt2rRSX9u8eXOJdU2aNMFgKLtzgkqlYubMmcycOdOW6UgkEonEVsoUqM3gxFppQZVISuFKRi6PLN2L3gAj28UwulNtV0+p2uHULH6JRCKRuBnXJ0gphLcQj1eOOHc+EokDKNTpyzWUWYNOb+Cxpfu4mpFHo3B/Xh/RUsadOgApUCUSieRGpjwLKgiBaqcLu0TiCv6NS6Hzm5vo+/4Wft1/Gb2+ct/nDzeeYPuZZHy9NHwxvj2+XjY5oyUVIAWqRCKR3MiUJVBDG4FKA7lpkBHv/HlJJHZg++lkJs7dSUpWPqevZvHI0r0M+ngr6w8n2GRR3Xz8Cp/8fgqAWSNb0TA8wN5TlhQhBapEIpHcyBgF6nWdaDy0ENJQLEs3f5Xg1JUM3lp7jIS0XFdPxS3YevIqU+btJDtfR89GoTzetxEBWg+OJWTwwMLdDPvsbzYfv2KxUL2cmsMT3+8DYFyXOgxrG+PA2UukQJVIJJIbmbJiUMHMzS8TpdydAp2eBxbs5ovNp7nry+1cvJbt6im5lD+OXeHe+f+SW6DnlqbhfD2xI4/3bczWZ/vwcJ9YfL00HLiYxuRvd3Hn7O1sP51c7ngFOj3TluzhWnYBLWMCeWlIcye9kxsXKVAlEonkRqYsFz/IjlJViO92XeBMUhYA51OyGf3lP5xLznLxrFzDusMJPLDwX/IL9dzWPILZ4zvg7akBINjXi6f7N+XPZ/pwX4/6aD3U/HvuGmO//oe7v/6H3eeulTrmW2uOsed8KgHeHnx+t2k8ieOQAlUikUhuZDLLEagRRQI18bDz5iOxmsy8Qj7aeAKAaX0a0iDUj0upOdz15XZOX8108eycy6oD8Ty8eA8FOgODW0fx2bj2eHmUlDqh/lpeHNKcP5/pw8SudfHUqNh2OplRX2xjyrc7OXgxzbjt2kMJfPPXWQDeuaMNdUJ8nfZ+bmSkQJVIJJIblcI8yCu6EF8fgwomC+rV46DXOW9eEqv46s8zJGXmUz/Uj8f6NuK7/9xEo3B/EtPzGP3lP5xIzHD1FJ3Cz3sv8sjSPRTqDYxsF8NHo9viqSlf5kQEejNzWEv+eKo3ozvWRqNW8cfxqwz99C/+s/Bffj+WyNPL9gNwX4/6DGgZ6Yy3IkEKVIlEIrlxUeJP1R7gHVzy9Rr1wMMbCnPgWpwTJyaxlCvpuXz95xkAnunfBE+NmvAAb7574CaaRQWSlJnHmK/+4cjldBfP1LH8sOsC03/Yj94Ad3WsxTt3tsGjAnFqTq0avrx1R2s2Te/FiHYxqFSw7nAi98z7l4zcQtrXCebZgU0d+A4k1yMFqkQikdyoKPGnvqGgLuVyoNZAWBOxLONQ3ZIPNp4kp0BHuzrBxax7If5alt7fhVYxQaRk5TP26384cDHVdRN1IIv+OcczPx3AYIDxN9XhfyNbo1HbVji/XqgfH4xuy7rHb2ZQK/F5hvh58end7Su0xkrsi/y0JRKJ5EalvAx+BZko5bacupLB97vOA/D8oGYluhkF+3qx6L4utKsTTFpOAeO+3sGe86UnAVVVvv37LC+uOATAlO71eG1YS9Q2ilNzGkcE8Pm4Dmx9pg/rnriZ6GCfSo8psQ4pUCUSieRGRbGg+pcnUM06Skms4t+4FOb9fZYCnd4h4/9vzXH0BriteQSd6tUsdZsgH08W3tuFzvVqkpFXyIRvdrDzbIpD5uNsvtxymld/Fd/L//RqwIwhze3ecrR2TV9C/bV2HVNiGVKgSiQSyY1KeSWmFMJbiEcpUK1i97kU7v56B6/8eoQ3Vtnf+rzzbAobjyaiUat4ZkD5sZH+Wg/m3dOJbrEhZOXrmDR3J9tOJdl9Ts7k400nmbXmGACP3tqI/w5oandxKnEtUqBKJBLJjYpFArXIgpp8SmT9Syrk4rVsHliwm/wiy+m8bXEs33PRbuMbDAbeXC1E75hOtWkY7l/hPr5eHsyd3ImbG4eRU6BjyrxdbDlx1W5zchYGg4H31h/n/Q2irNZTtzVmer/GUpxWQ6RAlUgkkhsVYwxqKSWmFAKjQRsE+kIhUiXlkpVXyH3z/yU5K5/mUYE8cHMDAJ5bfpBDl9Iq2Nsy1hxKYN+FVHy9NDzWt5HF+3l7avhqQgdubRpOXqGe++f/y8YjiTbNISdfx8GLaWw8kkh6boFNY1iLXm/gf2uO8cnv4nv4/KCmTLvF8vcvqVp4uHoCEolEInERllhQVSphRb3wj0iUimjhnLlVQfR6A49/v49jCRmE+mv5ZlJHIgO9OZmYwR/Hr/Kfhbv59ZEe1PTzsvkY+YV63l4rXNsP3NyA8ABvq/b39tTwxfgOPLp0L2sPJ/Dgot18enc7BrSMKnX7Ap2es0lZHE/I4ERihvHxXEo2Sgv7sAAtLw9tzuBWUQ6zZJ5IzOD55Qf5t6jT08tDmzOle32HHEviHkiBKpFIJDcqlghUMBOoMg61PN5Zf5wNRxLx8lDz1cQOxszvD8e0Y9infxGXnM0jS/cwf0pnq2p0mrN053nikrMJ9ddyf88GxV80GMT/1D+83DG8PNR8enc7nvhhP7/uv8zDS/by/l162tYONgnRxExOJGRwJimTAp2h1HFq+Hri5aEmMT2PaUv28lOTi7w2vCW1ativ01JugY5Pfz/Fl3+epkBnwM9Lwyu3t+DOjrXtdgyJeyIFqkQikdyoWOLiB7crNZWdX4iHWl1qC0tXsXzPRb7YfBqAt0e1pn2dGsbXgnw8+XJCR0Z8/jd/n0rm7XXHeX5QM6uPkZFbwEebTgLweN9G+Gmvu4T/8Qb8+Q6M+wka9S13LA+Nmg9Ht8VTo2L5nks89t2+Mrf113rQOMKfJpEBNI4IoElEAI0iAgj19yJfp+fzP07zxebT/HH8Kv3e/5Pp/RozpXs9m0W4wrZTSTz/80HikrMB6NssgpnDWsiSTzcIUqBKJBLJjYjBAFlXxHJFFtSIIoGaeNixc7KAuKQsRn2xDZ3BwIO9YpnYtS6+Xq69lO0+d43//nQQgIf7xDK8XUyJbZpEBvDOHW14eMkevvrzDK1ighjaJtqq43y55QwpWfk0CPNjdKdSLIiX94rHM39UKFABNGoV797RBm9PDUt2nEfroaZRhL9RhDaOCKBxZADRQd5luu61Hhqe6NeYoW2ief7ng+w8m8Ibq4+yYt8lZo1sRetawVa9R4CUrHxeX3WE5XsuARARqOXV21vQv0WkTIa6gZACVSKRSG5E8tJBly+WfSuwoIYVWftSz0FeJmgrzhp3BLkFOh5avIfkLDHv/605xjdbz/Jwn1jGdq6Dt6fG6XO6eC2b/yz8l3ydnv4tIniyX5Mytx3cOooDlxrw5ZYzPLPsAI0i/GkaGWjRcRLScvnmL9HS9NkBTUvvapRdVN/UCku3Wq3izRGteKJvY2r6edncgalhuD/f3X8TP+6+wJurj3H4cjrDP/ubSd3q8eRtTfC/3tpbCgaDgZ/2XOKNVUe4ll2ASgUTbqrLU/2bEOjtadO8JFUX9/GPSCQSiaTSJKTl8syy/fyy7xIGQ+mxg4DJve/lD14VxAz6hYB/hFi+etw+E7WBV389zJH4dEL8vJg5rAW1a/qQlJnHq78eoc+7m1m84xz5hY4pil8aSsZ+UmY+zaICef+uthV2MXqmf1N6Ngolp0DHAwt2k5ZtWQb8BxtOkFugp2PdGtzWPKL0jXKsF6gKYQFam8WpglqtYnSnOmyc3othbaPRG+Dbv+O47f0tbKigWsDZpCzGfbODp37cz7XsAppGBvDT1G7MHNZSitMbFClQJRKJpBrx7vrj/PDvRR77bh+jvtjGvguppW9oTJCqwHqq4OKOUsv3XGTpzguoVPDRmHZM7FqP35/szZsjWhEV5E18Wi4v/HyIW9/fzLLdFyl0UPcmBb3ewBPXZeyXiAktBY1axcdj2lGrhg/nU7J57Pu96PTl3EggMth/3H0BgOdKaWlqJKeojWnGZdOyCwgL0PLRmHbMv6cztWv6cDktl/sX/MvURbtJTM8ttm1+oZ5PNp2k/4d/su10Mt6eap4d0JRfH+lRLI5XcuMhBapEIpFUE7LzC1lzMB4AL42aPedTGf7Z30z/fh8JacWFgcUZ/AouTJQ6kZjBCz+LfuuP3dqIHo2EqPbUqLm7Sx3+eKo3rwxtTqi/lgspOTz1435u++BPVu6/jL4C8Wcr764/zvojiXhp1Hw5oQMxViTu1PDz4ssJHfD2VLP5+FU+KCo6XxZvrTmG3gADW0bSoW4Zok1XCLlmdVavHLN4Po6iV+Mw1j/eiwd7xaJRq1hzKIG+721h4fY49HoD/8alMPjjrby34QT5hXp6Ngpl/eO9mNo7tvQQBskNhfwGSCQSSTVh7aEEsvJ11Knpy5/P9GFU+1oALN97iT7vbubjTSfJLdCJjY0CtfySREZcZEHNyivkocV7yCnQ0aNhKI+UUpjd21PD5O712fpMH54b2JQavp6cScri0aV7GfjRVtYdTig/3MFKft57kc+LMvbfuqNV2aKxHFpEB/G/ka0B+PSPU6w9lFDqdttPJ7Pp2BU81Cqe7l92fCu5qcWfX3F9QhuAj5eG/w5syq/TetCmdjAZeYW89MthbnlvM3fM3s7JK5mE+Hnx4ei2LLinM3VC7FeiSlK1kQJVIpFIqgk/FbXTHNW+FpFB3rx3Vxt+ebg7HerWIKdAx/sbTnDLu5tZuf8yhkxrXfyKBdV5AtVgMPDCzwc5dSWTiEAtH45pW26cpI+Xhv/0iuXPZ/owvV9jArQeHE/M4D8Ld3P7p3/zx/ErlRaqe85f49mijP2Hescyol0tm8ca3i6Ge4qKzT/5wz5OXcko9rpeb2DWGmGxHtu5Dg3CyklOUxKkFNykJJhC8+hAlk/txqu3t8Bf62EsHXVXx1pserIXw9vFyAx9STFkFr9EIpFUAy6l5rDtdDIAI9ubyhy1qR3Msge78tuBeP635hiXUnN4dOlePGocZBBY7uIPayoeMxMhK1kkTjmYJTvPs2LfZTRqFZ/e3Z5Qf61F+wV4e/LorY2Y1LUeX289w9y/z3LwUhpTvt1F4wh/bmoQQoe6NehQtwYxwT4WC6NLqTk8sGA3+YV6bmsewVO3lWPRtJDnBjXl8OU0dpxN4YGFu1nxcHdjUtCqg/EcuJiGn5eGR2+toKVnjnsLVBDxt5O61eO2FhEs3XmBHg1D6Vy/pqunJXFTpECVSCSSasDPey5iMECX+jWpXbO4m1SlUjG0TTT9mkfw9Z9n+HzzafSZV0EDP5/Io2vHXCKDKmiZqfWH4Lqi1NTVo+DXw4HvBg5dSuPVlcJa+0z/JnSqZ72QCfL15Kn+TZjSvR6zt5xmwfZznEjM5ERiJgu2nwMgMtDbKFY71K1B8+jAUuMfTRn7eTSLCuSD0RVn7FuCp0bNZ+PaM/STvzhzNYvp3+/nqwkdKNDreXudiCP9T69YwgIqEOeKBdXTDwqyhKXbYBCtat2MqCAfpvdr7OppSNwcKVAlEold2HY6CX+th02FuSWVQ6kfCXBHh7Jdzt6eGh65tRF3dqxN5pezIBt+v2Dg+Xc381DvWO6/uUH5tUTDmwuBeuUo1HOcQE3LKeChxXvI1+np2yycB25uUPFO5RDir+WFwc15sFcs/5xJYfe5a+w+l8Lhy+kkpOey6mA8q4qSy7w91bSpFUzHekKwtq9Tg0BvT574fh9H49MJ9feyOGPfUkL9tcwe34E7v9zOxqOJfPrHKfy1HlxIySEsQMt9PS3oOa9YUGPaw7ltIos/MxECIu02T4nEmUiBKpFIKs2uuBTu/noHahW8e2cbRra3PS5PYj17zqdyNikLH08NA1tFVbh9ZJA3+OVCNtQIjyYnQcd7G07w3a4LPNg7luFtowkorfZkeDM4scahcagGg4Fnlu3nfEo2tWr48N6dbe0Wmxjir2Vw6ygGtxafUU6+jv0XU4sEq/hLyylgx9kUdpw1ucyVMlYiY7+jVRn7ltKmdjCvD2/JM8sO8MHGE/gW3ShM79fYsk5ZigU1IApCYiHphPg/SYEqqaJIgSqRSCqFXm/gtd+EYNEb4Mkf91OoM3BXaa0YJQ5BSY4a2DLSoo49gDGL/9WxvemYUIP/rT7KpdQcXlpxiFmrj3J7m2jGdq5D61pBJoGoJEolOk6gzvnrLOsOi/JNn49rT5Cv44q0+3hpuKlBCDc1EPG0er2B01cz2X3uGv8WCdazSVnEF5Xo+t8o2zL2LeWujrU5cDGVRf+cJytfR8Nwf+4sxyJeDMWC6ltT3EgknRD/p9hbHDZficSRSIHqTPR62PU11O0Gka1cPRuJpCQGA+z8WvRet9CF+8v+Sxy4mIa/1oMBLSNZtvsiz/x0gDydngk31XXwhJ3I5X1wYSd0nAIa9+lsk1ug49f9lwEYZamY0RUaLW4qv3BubxNGv2YRLN15nsU7znH6ahbf7brAd7su0DwqkLu71GFY22gCjKWmjjokvnH3uWv8b42Iu3xpSDOnh4uo1SoaRQTQKCKAMZ3rAJCcmcfuc9fw03rQvaGFFQ8qwYwhLTiRkMmucym8MLgZHpbWA1UsqD41wacGHPnFLROlqhx7F4E2AJoPc/VMbjikQHUmJ9bAmmegZiw8ststg9clNzjx+2DN0xBUB544WOHmOfk63l4rWl8+1CeWqb1iCfT2ZO7fZ3lpxSHyC/Xc28OC+Dl35+RG+H4cFOZCXhrc/LSrZ2Rkw5FEMnILiQ7ypmsDCzPrs5MBA6ASFjeENfGeHvWZ0r0eu+KusXTneVYdjOdIfDovrjjEm6uPMqJVGK+rPFDlpUH6ZQiKKfcw1pCSlc+0JXso1BsY2iaa8W5ycxPir+W2Fs5zk3t5qFl0XxcS03NLJLuVi7kF1b+otq2Lun5VG+IPwC8Pi+W+r0CPJ1w6nRsNWQfVmVzYIR5TTsPlva6di0RSGkmnxGPaecjLrHDzr7eeIT4tl5hgH+7pXh+VSsVLQ5rxYK9YAF777QhfFBU0r7IcWwVLxwhxCvD3Jy5tI3k9int/ZPtalmeVK0X6fUNAXTwpSqVS0bl+TT4Y3ZYdz93KS0OaExvmR3a+jsW7EzipE33gf9+6mYxcy/rIV4Reb+Dx7/cRn5ZLg1A/Zo1sdUPXxPTyUFsnTgGyi76TPjUgvIVYvnpMeO4ktnFomWl54yuw+X/CcyBxCjYJ1M8++4x69erh7e1Nly5d2LlzZ7nbp6am8vDDDxMVFYVWq6Vx48asXr3a+Porr7yCSqUq9te0aVNbpubeXNpjWj70k+vmIZGURWqcaTnlTLmbJqbnGsXnfwc2NWZ/q1Qqnh3QhMeK6ja+tfYYH208addOPk7j0HL4YSLoC6D5cBGDmZcG2z9z9cwAuJKey58nhNg0r31aIRa2Oa3h58W9PeqzcXovfvhPV4a3jeYUIrZ4+/a/6PLmJv770wEOXEy1ZfpGPt98ij9PXMXbU83n49tbHkcrMWFuQa1ZHzRaKMgWVRck1mMwiN8/QMN+4nHzLCFUq+K5rApi9Vng+++/Z/r06cyePZsuXbrw4Ycf0r9/f44fP054eMmWefn5+fTr14/w8HCWLVtGTEwM586dIzg4uNh2LVq0YOPGjaaJeVSzE5ReD/H7Tc8P/wz9XgO1NGJL3IhrZhez5FMQ1brMTd9dd5ycAh3t6wQzpHXxzHGVSsUT/Rrj5aHmnXXH+WDjCfJ1Op66rUnVsYztWwq/PAQGPbQeA8M+E2E634+Hf76ALg9a3oXJQfy89xJ6A7SvE1x+l6HryUoSjxbOX7Gqdq5fk5wNveHvf+jok8DXmTpjrGqovxeNIwJoHBFAk8iAomX/0qsBmLHtdBLvF/Wif21YS5pGBlr+PiQmzGNQ1RoIawIJB0Qcas1qEGbjbC7ugrQL4OUPoxfCv9/Cuufg7w+FN2XA/2SYnoOxWgW+//773H///UyZMgWA2bNns2rVKubOnct///vfEtvPnTuXlJQUtm3bhqenOFHVq1ev5EQ8PIiMrMblMJJPQV46ePiIBIv0S3DhH5EwJZG4C+bWluSyXfOHLqWxrMi1/OKQ5mWKzof7NMRLo+aN1Uf57I/T5BfqeX5QM/cXqf9+C789ARig/UQY8pG4mWw6BKLailjdvz6A/m+4bIqi9qn4H9zRwcqKCYoF1b+kUaEifGq1BOC2sGR+GNeVJTvOsfpQAkmZ+SRlJhu7WSnEBPvQOMKfxpEBNCkSsA3D/fH21HAlPZdHl+5DbxAtL+/sKCs/2ITBUNyCCsLan3AArhyGpoNcN7eqysEi937TweDpA10fAg8vWPUk7JgNhXkw+H1pZHIgVgnU/Px8du/ezXPPPWdcp1ar6du3L9u3by91n5UrV9K1a1cefvhhfvnlF8LCwrj77rt59tln0WhMsU8nT54kOjoab29vunbtyqxZs6hTp06pY+bl5ZGXl2d8np6ebs3bcA2Xi9z7UW2gZgPYv0S4+aVAlbgT11tQS8FgMPDGqqMYDHB7m2ja1ym/7M79NzfAy0PNyysP8/XWs+QX6nl5aItKdeG5eC2bNQcTyMovpF/zCJpHBdpP9O74UiQzAnR+AAa8ZboIqVRwy0uweBTs+ga6ToPAiuuOOoJDl9I5kZiJl4faWNfTYix08ZdKUakp1dXjdK4bROf67ZiVr+PklQyOJ2RwIjGD44mZnEjIICE9l0upOVxKzeGP41eNQ6hVUDfED53eQFJmHk0jA3j19pbWz0UiKMgGXb5Y9lEEqlnFBYl16HXCywnQcpRpfaf7wMMbfpkGu78VInXYpyXiuCX2wSqBmpSUhE6nIyIiotj6iIgIjh07Vuo+Z86c4ffff2fcuHGsXr2aU6dO8dBDD1FQUMDLL78MQJcuXZg3bx5NmjQhPj6eV199lZ49e3Lo0CECAgJKjDlr1ixeffVVa6buepT405j20PBWIVAPrxAXP001C2eQVE10hZB20fS8DIG64Ugi288ko/VQ88wAy3qRT+pWD0+NmhdWHGT+9nPk6/S8MbyVVSL1SlHHn1/3X2bP+VTj+g83nqRBmB9DW0cztE0UDcNLnjMs5q8PYaM4L9HtUeg3s6Qbr+GtUKcrnN8OW9+Fwe/ZfrxKoFhPb2seQZCPlWWvjALVhhCFGvWEJ6gwB67FQUgsPl4aWtcKLlEWKi27gBPmwjUhg+OJGaRmF3A2KQsAf60Hn49rj4+XvMjbjOLe13iBl59YVmrWSoFqPXF/QdYVkXDWoE/x19qNFyJ1+QPiOl6YCyO/cqvSc9UFhysjvV5PeHg4X331FRqNhg4dOnDp0iXeeecdo0AdOHCgcfvWrVvTpUsX6tatyw8//MC9995bYsznnnuO6dOnG5+np6dTu7abu4YUC2p0e6jfS2TPZifB2S3igieRuJr0S2DQmZ6XIlDzC/XMKqpTeV/P+tSqYXmm8d1d6uDloeaZZftZuvMC+YUG3r6jNZpyRGpKVj5rDyXw6/7L/HM22ZiboFKJnvNBPp5sPn6VM1ez+GjTST7adJKmkQEMbRPN0NbR1AmxcH4GA2x5Gza/KZ73ehZ6P1d6jJlKBbe8CPMGw+75QsjWcG5JpPxCPb/sE61NLa59ao4xBtUGC6oS3xi/T5QxCoktc9MgX0861atJp3o1jesMBgNXM/M4kZDJ6auZtK9Tw7r4WUlJFPe+Tw3TdzaiSKAmnYDCfOGelliGkr3f7PbSP7dWd4ibgWX3wOHlwnp9x1zw0Dp3ntUcqwRqaGgoGo2GxMTEYusTExPLjB+NiorC09OzmDu/WbNmJCQkkJ+fj5dXyX9+cHAwjRs35tSp0i04Wq0WrbYKfRF0BZBQVFMypr2402o+DP6dK7IEpUCVuANK/GlgLUi/CLmpwjLjaxIXC/85x9mkLEL9tUzt3dDqQ9zRoRaeGhXTf9jPT3suUqDT8/5dbYoVI0/PLWD94UR+3X+Zv08lUag3ZcyKhKxoBreOIiLQG4CM3AI2Hk3kt/3x/HnyKscSMjiWcJx31h2nTe1ghha1towKKqM9pcEAm14VMaUAt86Ank+W/0bq9RCWlTN/CGE7vOys/rScAg5eTKNbbEilwhrM+f3YFa5lFxAeoKWnLcXjK+PiB2Gdi98nrHPNhlq1q0qlIjzAm/AAb3o0cm2SWbXBPEFKITAGtIEi9yHltMnlLymfwnw4slIsm7v3r6f57TBmMXw/AY79Bt+NE8lUnvZvg3ujYlV0r5eXFx06dGDTpk3GdXq9nk2bNtG1a9dS9+nevTunTp1Cb1aL7cSJE0RFRZUqTgEyMzM5ffo0UVGuie2yO1eOCDeAd5CIPwVoeYd4PPqriGORSFzNtTjxGNZEiFQoZkVNzc7n400nAXjqtsY2lwIa1jaGT8e2w0OtYuX+yzyydC9pOQX8uv8yDyz4l46vbeSpH/ez5cRVCvUGWkQH8t+BTdn6TB+WP9Sde3rUN4pTgABvT0a0q8WcyZ3Y9UJf3hrVih4NQ1GrYP+FVF5fdZSus37nrtnbWbA9jqsZZr83gwHWPmcSp/3frFicKtzyknjcvwSSTpa6ycnEDAZ/vJXxc3Yw8zf7FU1X3Psj2sVY3mnInEoLVCW+URaCdwuuT5ACYUmV/yfrOfOHuDn3j6i4m17j/nD39yLk5dQGWHIX5Gc5ZZo3Alaf2aZPn87XX3/N/PnzOXr0KFOnTiUrK8uY1T9x4sRiSVRTp04lJSWFxx57jBMnTrBq1SrefPNNHn74YeM2Tz31FFu2bCEuLo5t27YxYsQINBoNY8eOtcNbdAOU+NPodib3S52uEBAlaiqe2lj2vhKJs1ASpGrUNbltzQTqR5tOkpZTQNPIgEpnWw9sFcXs8R3w0qhZcyiBdjPX88jSvaw/kki+Tk/DcH+e6NuYTU/2YtWjPXmwV6xFhcuDfb0Y3akOi+7rwo7n+zJzWAs6F7mXd8alMOOXw3R5cyNTvt3JheRMkam/4wux8+D3oOvD5Yx+HbU6QJNBogzV5lklXv7rZBIjv9jGxWs5AMzbFseqA/GWj18GyZl5/HHsCmCjex+sLjNVAiW+MVEKH7cg28zFb44iUOX/yXKU7P0WIyxLfortA+N/EuWozv4Ji0ZBbhVI3K4CWG0CGT16NFevXmXGjBkkJCTQtm1b1q5da0ycOn/+PGqzsgu1a9dm3bp1PPHEE7Ru3ZqYmBgee+wxnn32WeM2Fy9eZOzYsSQnJxMWFkaPHj34559/CAuz8e7e3TCPP1VQq6HFSPjnM5HN33Swa+YmqbakZRcQ4O1huVtZcfEH1wVUIj66SKCevprJwu3i9RcHNy83btRS+jaP4KuJHfjPwt3kFeqpU9OXoW2iGNommiYRAZXOyg8L0DKxaz0mdq3H5dQcVhclWO2/mMaW44ns/uQNarMZUMHtn0D7CdYfpM/zcHy1+A33mA6RIhP9u53neXHFIQr1BjrVq0GzqEAWbD/Hsz8doHl0IPVD/Wx+Xyv3X6ZQb6BVTBCNI2xICMvPgoIiK09lLajJp4QHSMbeuRals5m5BRVkopS15GeL3zOU796/nnrdYcIKIU7Pb4eFw4Vovf6GQWIVNvnopk2bxrRp00p9bfPmzSXWde3alX/++afM8b777jtbplF1uFTU1jSmffH1rUYJgXp8jbhoeNl+0ZJIzFl1IJ7Hv99L3RA/PrirLa1qBVW8k7kFVVMUflMkUGetPkah3sCtTcPtGjfYu0k465+4mYzcQlpE27FU1HVEB/twX88G3NezAacTrnH520n0zNtCoUHNinovMbT13dgksSJbiRvNw8vhjzfRj17MO+uPGztsDWsbLRLBVCqOxWewMy6FqYt2s+Lh7sbOW9aybLdS+9RW62mRe9/DW1h9bCEwWoQs5aaJ8IZIWSLKpZQWgwrSxW8tJ9dDfiYE1YFanazbt3YnmLRSiNNLu2H+UJjwC/iFOGSqNwKywqyjyc82nRyirxOo0e1FyZaCbCFSJRI7sPXkVR7/fi8FOgOnrmQy4vO/+WjjSQp0FfTkNreghhQlQCWfZtupJDYeTUSjVvHcIPsnWtQN8aNlTJDTivfH7n6dnnlb0Kk8mFbwKE8db8KoL7YRl2Rj7Fjv50ClhuOrePfbJUZx+uitjfhwdFu0Hho8NGo+ubsdof5eHEvI4OVfDtt0qGMJ6Ry+nI6nRsXtbaJtm695Br+tn7lKJa1z7kRpMahg+h9di3Ov2Mjk0/BpJ9izwNUzKY6Svd9ypG2/jei2MHmV+G0lHBT1kvUVnHclZSIFqqNJOChK9/hHCKuDOSqVyY2g9PyVVEv+OZPMtCV7+PtUkkOPs+9CKv9ZuJsCnYGBLSMZ1CqSQr2BDzaeYNQX2zh1JbP0HQtyILOoOkeNesYYVEPKGV7/TYip8V3q0DC8GpQDKirArRn5JaMnPkwNX08OXUpnyCd/sXL/ZevHC2tMbvO7ALgp7gs8NSreu7MN0/s1Lia6IwK9+WhMO1Qq+P7fC0ZLqDX8VLTPLU3DqeFnY9mgytRANUda59yHsiyofqHgFw4Y4Opxp0+rTPbMF+Wvtn3i6pmYyE2HE+vFsjXu/euJaAGTV4PaEy7vFRVRJDYhBaqjMY8/Le2OTMnmP7UBclKdNi2Jc8gt0PH6b0cY+/U//HYgnklzdxrrV9qbU1cymPztTrLzdfRsFMqHY9ry2d3t+WhMWwK9PThwMY3BH29l7l9n0ZuVbgIg9bx41AaKuKngOqD2QFWQTUrCOQK9PXi8b2OHzNupZKdAdlErzsYD6NM0nNWP9aRzvZpk5hXy6NK9PLf8ILkFuvLHMeNkYgYTTvUm36DhZs1BVgxRlZm81L1hKE8UfY4vrjjI8YQMi49TqNPz814hoEe1t9G9D5XP4FeQFlT3oSwLKrhnR6mzf4rHpBOQXvnEQbtwfDXo8iC0sQjdqQxhjcU5FIp355NYhRSojsa8g1RpRDSHsGai0O+x35w3L4nDOXgxjSGf/MU3f53FYIDGEf4U6g08/v0+5v191q7HupSaw4Q5O0nNLqBN7WBmj++A1kODSqViWNsY1j1xMz0bhZJXqGfmb0cY980OLl7LNg2glJgKritupDSe6INF8fn66gQevbWR7RY7dyLljHgMiAKtsAZHBfmw5P4uPHJLQ1QqWLrzPMM/+5tTVyoWj3+fEpn6u9ICWe3ZD4AWRz/G2FGgFKb1aUjPRqHkFuiZung3WXmFFk39z5NXScrMI8TPiz5Nwy3ap1SMArUSY4CZ8LEtXEFiR8qyoILZjYSbWLpzrkH8ftPzuK2um4s5SvZ+y1G2h76YozTvUM6tEquRAtXRlJbBfz1GN/9Pjp+PxOEU6PR8tPEkIz7/m1NXMgn11zJ3ckfWPnYzk7rWxWCAV349wvvrj2MoR8hYSnJmHhPm7CA+LZeG4f58O7kTftfVKI0K8mHBPZ15bXhLfDw1bD+TzIAPt/LjvxfEHMwTpIo4axB1iDv5JzOxa71Kz9MtUMpmhRRvMuChUfPkbU1YeE8XQv21HEvIYOgnf5frhv9+13kmzd1JRm4hHevWoNd9b4vEo/Pb4PSmMvdTq1V8OLotkYHenLmaxXPLD1r0Pfhpt7C83942Gk9bap8qVLbElIIifFLPQ57llmCJA7DIguomAvXcNlGaTeHsFtfNRSErWdQ/hcq5980pusE3xvZLrEYKVEeSk2q6IEa3K3u7liPF45ktkHnV4dOSOI5TVzIZ9cU2Pth4gkK9gUGtIln/xM3c0jQCtVrFK7e3YHo/4eL9+PdTvLDiELrr3e1WkJlXyJR5uzhzNYvoIG8W3NOZmmVYOlUqFRNuqsvqx3rSvk4wmXmFPL3sAA8s3E3OFZHYo5xUL6fmsCVZZP6PrJuLl0c1OVUYBWrp7Tl7NApl9WM96BYbQk6Bjqd+3M+TP+wnO99k5dTrDby19hjP/nSQQr2B29tEs+i+LtSIrAud7hMb/f56uVbUEH8tn40zNStYtON8udNOyy5gwxERI1wp9z7Yz8XvWxP8izoIulN8442GrlBUU4AKLKhu4uI/UyRIaxb9Bs/8We5vxSkcXQn6QohsDaGN7DOm0YIqBaqtVJOrjpsSv088Btctv9RESKwQsAYdHFnhjJlJ7Ixeb2DuX2cZ/PFWDlxMI9Dbg4+KYkDNBaNKpeLRWxvx+vCWqFSwZMd5Hlm6h7xCy2MeFfIKdTyw4F8OXEyjpp8XC+7tQnRwxW326of68eOD3XhmQBM8NSo2HElk++4iS3/RSfXttcc4pRPioy5uEiNmD8qwoJoTHuDNwnu7ML1fY9Qq0bVp6Cd/cSwhndwCHY8s3WvK1L+lIR+NaWsqGdX9cfD0E8kRx1aVO5UOdWvy7ICmALz26xEOXkwrc9tfD1wmX6enaWQALaIDLX+/pWEvgQruZ527EclNNS2XVnczXHzHyIg3hQK4EiX+9OanQO0Baedd7wZXvJf2sp6CtKDaASlQHUlF8afmyGz+Ksul1BzGfSNaWeYV6unZKJT1T/RiWNuYMksnjb+pLp+ObY+nRsXqgwlM+XYXmRbGIgLo9AYeW7qPbaeT8fPSMG9KJ6sy7DVqFQ/1bsgvD/egaWQAEXphnfvmkI6tJ6+yYt9l4gxCoKrMuklVeZKLLMU1S7egKmjU4kZiyf03ERGo5fTVLIZ9+jfDPv2bVQfjTZn6tzUp/j/2D4ObporlP96osMTMfT3rc1vzCPJ1eh5aspu07IJStzOvfVrpclz2cvGD+1nnbkSUIv3aINCUUtpcG2BK2Ll6zHnzKo3MK3C16LvSeADEdBTLimh1BenxEPeXWFa8mfZAWlArjRSojsSS+FOFFkU/jPPbIM0xWd4S+2IwGFi2+yIDPviT7WeS8fHU8Nrwliy4pzORQd4V7j+4dRTfTu6Mn5eGbaeTGfvVPyRl5lW4n8Fg4MUVB1l7OAEvjZqvJnakda1gm95D8+hAfpnWnVhPkdn+3SkNE+bsBKBpi6KwlNRzoCtdOFUpDAaTQC3HgmrOTQ1CWP1oT3o1DiOvUM/xxAyCfDxZcE+XstuMdpsmxMKVI6KAfzmoVCreubMNtWv6cCElh6eW7S8Rj3r6aib7LqSiUYuEt0ojLajVC8Uq6ltO1yJ3SZRShGhkKxEi0qBX8fWu4MgKwAC1u5iEvD0IriceMxNEGT+J1UiB6kjK6iBVGkExUKebWK7goiZxPclZ+TywcDdP/bifjLxC2tcJZvVjPZlwU12rLFw9GoWy9IGbqOnnxcFLadw1ezsXUrLL3eeddcdZuvMCahV8NKYt3RtWzhKmLUjHWyfqo6qL7vp9PDU8MLg7ePqK2KzU8mMkqwQZCaLFp0otar1aSIi/lm8nd+KlIc3p1zyC5Q91o2tsOSE7PjWg+yNi+Y83RYxgOQT5ePL53R3w0qjZcCSRb7YWr/Cg1D7t1TiMsIBKthTV64sX6q8sivCRvd5dh5IgVV5bTeVGwtX/JyUhqn6RMK1/c9F6F8ahmmfv2xPfmuBV1Io49YJ9x75BkALVUWReKSrQq4KoNpbto7gXZDa/2zPs07/YcCQRT42KZwY04ccHu9ncX711rWCWPdiVmGAfziRlccfsbWXWx/xm6xk+L4p/fGNEKwa2irL5PRhRXFB+4fz8WF9eGNSMOZM6Ehnsa3KFVwc3v/IeguuCh3Uls9RqFff2qM/XEzsSG2ZBKEWXB8E3BFJOw/6lFW7eqlYQM4YKsfe/tcf4N06IDp3ewM97hUel0slRINzBhqJ4Z187tGAMayIes66YhK/EuZRXYkrBXUIxFEupIlBrdRKVL7KuuCb84FocXPpX3LQ2H27fsVUqk5tfxqHahBSojkKJPw1rImKALKH5cFBpRIKF4op0ESlZ+Ra5m11NgU7P+eTyLY6OICWrgKaRAfzycA8e6t0QjbpycYENwvz5aWo3Gkf4k5iex52ztxlFisKy3Rd5fZW4wDzdvwljO9vJHaUkKNSoi5/Wg/tvbkA3xSobUg0FqoXu/UqhDYAe08XylregsOLf0rgudbi9TTQ6vYFpS/aSnJnH9tPJxKflEuTjya3NKlm3FEzufe9gq0V6qWj9TdZoV4ufG5XySkwpmIdiuMpSee2cONeoNFC3q1jnoYU6N4llV7j5lZyPej0hIML+4yuJUq5OAquiSIHqKKyJP1XwDzPF5LgwWergxTRufvsPer71BzvOJLtsHhWh0xsY980Obn7nDz77w/ECauF2013wvT3q8cu07jSvbEa1GZFB3vzwn660rxNMem4h4+fs4PdjInlp45FEnv3pAAD39ajPQ73LT/KxCuXuXjmZmqOIOSlQrafTvaIhQNoFi3qOq1QqZo1sRWyYHwnpuTz+/T5++Fe4Boe2iTJVCqgM9ow/VXAX69yNiiUW1NDGQhjmpopQF1egFOSP6VDcaFPfhXGojsjeN0cW668UUqA6Cmsy+M1xcdH+01czmfTtTjLzCskp0HHPvF3sPnfNJXOpiDl/nWHnWXFyfmfdcb7ZesZhx/p570VmrTG5oJ68rQlaDzsIhusI9vVi8X030adJGLkFeu5fsJu31h7j4SV70OkNjGwfw/ODmlU+k9ucUor0G6lWAlVJkLKjuC8PTx9RSgfgz3cgv2JLv5/Wgy/Gd8DbU83Wk0ms3G+H1qbmOESguiBR6uqJqhEXnZEIF/917DEssaB6aE2/ZVclSin1TxUjjIIiUOO2gt76cns2c+UYJB4CtSc0G+qYY8hSU5VCClRHYDDYZkEFaDoENF6iFIeTA9rj03KYOGcnKVn5tIoJonvDELLydUyeu5P9F1KdOpeKOHUlg3fXnwCgR5E7+vVVR5m/Lc7ux/r9WCJP/XjA7uOWhY+Xhq8mdmREuxh0egNfbD5NXqGevs3CeWtUa9SVDCcoQbkWVMXF79qQE7uQ4mSBCtBuosgMzkyEQ8ss2qVxRABvDDf1Am8Q5kfb2sH2mY8SJ+rvAAvq5b32G7M80i7ClzfD3IEVlvFyOd+Ph29uFWLIUVhiQQWzGwkXWLoNBrP405uLvxbVBrSBotmAeQtUR6MkIze8tXxxXxlkqalKIQWqI0g9D9nJ4s4ssqV1+/oEQ0PR09vSC5o9uJaVz4Q5O7mUmkODUD/mTenENxM70bl+TTLyCpk4dyeHL5ddSNyZFOr0PPnjAfIL9fRuEsbCezsbXd4vrzzM0p32s6zsikth6iJhvby9jR0SkizEU6PmvTvbcG+P+gB0rl+TT+9uX7kWl2VhiQU1/ZJFFkC3RVcIKUXZ8c5y8YOI82x1p1i+sMPi3UZ1qMXdXUSM8UQrK0OUiyMsqPV6iiST+H3OcWUe+gkKc0QSqjtb9gvzTYaKpBOOO45SB7UikeXKUlNJJ0W5JY0WanUu/prGA+p2F8vOcvMbDI7L3jdHWlArhRSojkA5KUW0EK4VazHP5ndCQHtWUbvMU1cyiQz0ZuF9XQjx1+LjpWHu5E60rxNMWk4BE+bsLDO73Jl8+ecZ9l9IJcDbg/+NbI1KpeLp/k24r0jMPf/zwXJ7qFvK0fh07pm3i7xCPbc0Def1EVbebFQStVrFS0Oas/WZPiy9/yb7xCBej15vcpWWVnrJt6apfE2K40IoHE7aedAXiAtkoJ3c5ZaieFEuWWdhfGN4SzZO78WkbvXsNxdHCNSACCFSwTmx8+bhT8q51h1JPiVKtIGwoDsKowW1nDJT4NqatUp5qTpdwLOUGtHOrocav194VDx8oMkgxx1HqauamyZan0usQgpUR2Br/KlCk4Gi/uS1OIefgPMKdTy4aDf7LqQS7OvJwns7E2PWLtNf68G8ezrTulYQKVn5jPtmB6evZjp0TuVxLCGdDzcKa8QrQ1sYC+KrVCpeGNyMiV3rYjDAM8v2G+P3bOF8cjYT5+4kI7eQjnVr8Nnd7fFQu+bnUrumb6WrBJRJZiLo8kQCRVnCrTrEoZrHnzr7/6icB64ehfwsi3dTqVQ0DPe3b7yxUaDaoYuUOc6KnU86VdwNfMmNBaq5EHSkQLUkBhXMLKjHnB8acX15qetR3P7ntwvLs6NRvqeN+4tKFI5C62+6GZRWVKuRAtURKLFY1safKnj5CZEKcNBxJ3yd3sD0H/az9WQSvl4avp3ciUYRJUtiBXp7suCezjSPCiQpM4+7v/6Hc8mWX2jtRYFOz1M/7qdAZ6Bvs3BGti/eVUelUvHK0BaM6VQbvQGe+H4faw9Z30f+SkYu4+fs4GpGHk0jA5gzuRM+Xg6wXroDiks2KKb0NolQTQSqksHvxPhThcBo8I8Egx7inRfLXCr2LNJvTrOhIqQp8ZBj4y0VYeFRdBPtzhZUZwhUg8HyGNSa9UXN0cIcSI1zzHxKQ683ZfCXJVDDmoFvKBRki7qkjp6PYul3pHtfIVjGodqKFKj2Rq+Hy/vEsq0WVDD9cA4vd8jdrsFgYMYvh1h1QPQVnz2+A+3qlO0iCvb1YuG9nY11Ou/+egcXrzk3JvGLzac5dCmdIB9P3hzRqlTLklqt4s0RrRhZlGD0yNK9bDpq+cUhLaeAiXN2cj4lmzo1fVlwT2eCfDzt+Tbci/ISpBSqQ6KUs0tMXY9yLnC1oHKEix+E9a7hrWLZUZ3wDAZTXH63aeIx4aD7tuE1T0bKvOKYYxRkCw8IVGxBVWtMjRWcmSiVeFDEyXoFQHS7MuamhvpFYSKOdvNf3Cnil70CoNFtjj0WyFJTlUAKVHuTfBLyM4SLPrSJ7eM07Cv6eWfEC7eHnflgwwkW7ziPSgUfjG7LzY0rvmCF+GtZdF8XGoT6cSk1h7u/3kF8mnN6DB++nMbHm04CMHNYC8IDy+51r1arePuO1gxpHUWBzsDURXv488TVCo+Rk6/jvvm7OJaQQai/loX3di73ONWC8hKkFKpDNylXC1RjHKqrBaqDLKhguqk+uMwxsfOJh0SykUYLXaeBdxAU5rq+v3xZOMOCqlhP1Z7gZYGr2hWJUorgrNutbC8NOK8eqmKFbzak9HhYeyMTpWxGClR7o1yAotqU/2OsCA+tqTabnbP5v/37LB//Li7YM4e1ZEjraIv3DQ/wZsn9N1E3xJfzKdmM+3oHV9Jz7Tq/68kv1PPUjwco1Bvo3yKC29tUPF8PjZoPRrelf4sI8nV67l/wL9tOl92KsUCnZ9qSPeyKu0aAtwcL7ulM3RDbWpdWKYwW1Hplb1MtXPxFCV41XeDiB4gpshy50oJamAd5RZU47B2DCiLZxMNHJJ84olyQIiwa9RPVThRrnKtFf2nkZxW3mDnKgmoef2pJrLKSKOXMEoaK4Ly+/un1KHGoF3Y6rmKIrhAO/yyWneHeB1lqqhJIgWpvbK1/WhpKNv+RX+zmxlqx9xKv/ipOTtP7NWbCTeVYzsogMkiIVKV3/LhvdpDswLaon/5xiqPx6dT08+KNMlz7peGpUfPJ2Pbc0jScvEI99877l13XtQ8F0OsNPLvsAJuOXUHroWbOpE527RDl1lhkQW0gHnNSTBabqkRBjujmBK63oKacMZUFcjaKe1/tIVqd2hutv0g6AfsnSxkMpjFb3SEeo90kbKI0lL7y6qLwoMwrjklMUr5LFcWfKji765euAM5tE8vX1z+9npoNRKKmvsAhXkNAxMJmXRWfV4PejjnG9UgLqs1IgWpvKpvBb079XiJwPDvZVKajEvxx7ApP/SgsG5O71eORW2y/WMcE+7D0/puIDPTm5JVMxn2zg9Rs+2dfHrqUZmxj+tqwloT6W1e2y8tDzefj2tOzUSg5BTqmfLuLvedNAsFgMPD6qqMs33sJjVrF5+Pa07m+g4o2uyOWxKBq/SGgyGpdFUtNpZwFDCJkxhGWQ0vwrWkq4+WsgvbXYx5/as/KAOYYs/ntHDt/8V9RDs3TDxoViWDlHGtl+S6noAjAWh3Fo75AtBm1N5aWmFJQLKjJJ52TLX9pD+RnCkEY3qL8bVUqx5ebUm5ymg8DjZNyC5Sb/9TzTikbWZ2QAtWeFOaLoH0oOxjcGjQe0GK4WK5kNv+/cSlMXbybQr2B4W2jmTGkeaXL19QJ8WXJ/V0IC9ByLCGDCXN2kpZjv4SFvEId03/Yh05vYHDrKAa3tq1Qvrenhq8mdKRrgxAyi5oOHLokXJ2fbz7N3L9FAfd37mjNrc0i7DZ/t0dXIArwQ+k1UM0JqcJxqOYZ/I4SZpbg6jhUY/ypA0V6o9tE8kn6RZGMYi8UYdF0EHj5imXl87xyxP2aSCgu9Ki2JvHoiDhUS0tMKQTGiK5N+kLn/JaN5aV6WlbeTbGyOkKgFubB0ZVi2VnufYCg2qKRRWGuY8uNVUOkQLUnV46IjErvYJNbtLIoP6Rjv0GBbbGeSsH53ALReemdO9vYrV1mgzB/ltzXhZp+Xhy8lMbkb3eSmVdol7E/2niSE4mZhPp78dqwyhXJ9/HS8M2kjnSsW4OM3ELGz9nB22uP8c664wC8NKQ5I+3V77yqkHZBlD7y8AH/8PK3rcpxqK5OkFIwZvK7gQXVUXh6i+QTsJ+bX68zixu8w7Q+MBr8I8CgMxkG3AUlCSm8mZgjOEacZCsufgstqCqVcwv2K56/sspLXY/S8CF+n/0L25/+XRTMD4gSCVvOQuNpqjEt41CtQgpUe2KMP21nP0tN7ZvEXW9eOpzaaPXuSsH59NxCOtStwRfjOti9XWajiAAW3duFIB9P9p5PZfw3O9h9rnKxivsupDJ7iyhr9PrwVtT086r0PP20Hnw7pRNtageTml3A55vF+NP6NDS2FL2hUJI4gutU/H2t0gJVKdLvYoHqcguqIlAruBmpLMYSeT+LpJTKcu5v0SbTOwhibzGtV6ncNw5VcfGHNzfd/DkiUcpaC6oyJ3B8HGpBjkh4AssFalCM+J0a9OL/bk+UG6YWI0TJLWdSQ8ah2oIUqPbEnvGnCmq1+EGB1dn86bkFTJwrCs43iQhg7iTHFZxvHh3Ionu7EODtwb4LqYz6YjujvtjGusMJ6PXWxd3kFuh48od96A0wrG00A1pG2m2eAd6eLJjSmRZFSVBjO9fhydsa2238KoUlCVIKVdnFn2LWRcqVRLURrr6My5CR4PzjO6qL1PU06C1iDrOumgq0VwZjWaDbweO6G9UYF4v+0shOEYIaILypgy2oFhbpN8dZpaYu7BAexYBo6357jig3lZ8Nx1aLZWe69xWMxfrjnH/sKowUqPaksh2kykIRqKd+tyrx4JVfDhOXnE1MsA8L7u1MkK9jg8Jb1Qrit0d6MLpjbbw0anafu8Z/Fu6m7/tbWLLjPLkFOovG+WDDCU5fzSIsQMurt1cQWG8DQb6e/DS1Gyse7s6bI1rat5VkVcKSBCkFowX1TNUL9HdlFylztP6m2siuEFSOrIFqjsZTJKFA5d38ugJRxQRM2fvmuKMFVbFMBtcBbYBjBapNFlQnufiN8ac3W+dRdEQc6u5voSALatSHmA72G9dSZKkpm5AC1V7kZ5tOTPa0oIKwvHh4ixqGFmZR/3bgMsv3XkKtgo/HtiXCSQXn64b48dYdrfnr2T481DuWQG8PziRl8fzPB+nx1h98+vvJcrP9d59L4aut4j3OGtGKYN/Ku/ZLw9tTQ9vawTeuOAXrLKjBdUGlESd5V1j/bCUn1WQ5dFUNVHNc2VHKGTGoCoqV6uhKkZxiK6f/EKWU/MJN8YnmKMmoyafsH7NoK8b40yJLpeLiz3AXC2qRQL0WJ+q1OgpL659ej/J/vnLEPmEReZmw9X2x3OMJ1yRKylJTNiEFqr1IOCCC9f0jRfC+PdF4QmQrsWzBhS0hLZcXfj4EwEO9G9KhrvPLJoUHevPMgKZse+5WXhzcjOggb5Iy83h3/Qm6/e93Xll5mAspxTNvc/J1PPXjAQwGGNW+Fn2b30AZ9a7AGguqh5dJyFYlN7/i3vePAG83qG3ryuLyzhSodbuJZJTcNJGcYivGuMHhpccN+oWYvr/x+2w/jj0xxp8WCUF3s6D6hZrikJV6rfYmN930HS/txqI8/EIgouh6Zw8r6o7ZkJ0kEpfb3l358WxBWlBtQgpUe+GI+FNzLEyw0OsNPPXjftJyCmgVE8RjfRs5Zj4W4q/14L6eDdjyTB8+HN2WZlGBZOfrmLctjt7vbubRpXuNJZ/eWXecs0lZRARqmTG0uUvnfUNgtKDWs2z7qpgo5S4JUgrmFlRnh0o4o8yUglpjFjtvo5u/IAeOrRLLLUtx7yu4WxxqWRZURyRJ2WJBBTM3v4MSpc5tEwabmg0guLb1+9urHmpOKmz7WCz3fs55tU+vR7mJSr9ot6Y7NwJSoNoLe3aQKg0LXYPztsXx16kkvD1Fq097Z+zbiqdGzfB2Max+tAcL7+1Mz0ah6PQGVu6/zJBP/uLO2dv4dpuoR/rWqNYE+bjoRHKjkJcprApgmYsfqqhAdZP4U4WIlqK7UM415yZMGAzOtaCCWYm81bbVKT25HvIzRB3JWp3K3s6d4lANhuIlpsBxFlS9TliowToLKkBEUWy/owSqefypLdgrDnX7p+IzCmvmmuQoBf8I0GhFdYK0i66bRxXDJvXy2WefUa9ePby9venSpQs7d5ZfkDk1NZWHH36YqKgotFotjRs3ZvXq1ZUa0+0wWlDtUKC/NJSTcPyBMku3nEjM4H9rhcvm+UHNaBju75i5VAKVSkXPRmEsvLcLvz3Sg2Fto9GoVeyKu4bBAGM61aZ3EweXwZGY3PvewaJ8jyUotX0Vq2RVwF1qoCp4aCGyqKavMwVVXjroimK/ndVNK6aDsBwVZMGJtdbvX6wsUDmXKnfqKJURLwSRSgOhRdVB/IuqkOSk2Ld7U04qUGSFt7QOqoIinhMP228+5hgFqpXxpwp1uorP8NpZ0YHJFrKS4J8vxHKf551fWsoctVqWmrIBqwXq999/z/Tp03n55ZfZs2cPbdq0oX///ly5Urr7Ij8/n379+hEXF8eyZcs4fvw4X3/9NTExMTaP6XbkpJpi3aIcJFBDGooOIIU5cLXkXW9+oZ7Hv9tHfqGeXo3DmHCThVYxF9IyJoiPxrRjy9O9ua9HfUa2j+GFwc1cPa0bA2sSpBSqpAXVzVz84Jp6qJlF1lOvAPD0cc4xVSqz1qdWuvnzMuDEOrFcWva+OVFtAJVwnzrCjW4NivU0pKG4GQEhHtUeYlmxYtsDJf5UG2i969qRtVCzkiCxqHGCtfGnCt6Bpmx7W62of30g2qxGtYFmQ20bw54EyzhUa7FaoL7//vvcf//9TJkyhebNmzN79mx8fX2ZO3duqdvPnTuXlJQUVqxYQffu3alXrx69evWiTZs2No+Zl5dHenp6sT+XopSXCq4rArwdgVpddCKm1Avb+xtOcCQ+nRq+nrxzR+sqlZ1eq4YvLw5pzvt3tSXAW7r2nYI1CVIKisi7dtY+BdgdjcFgEqjukMGv4IqOUs6qgXo9ikA9ucHkjraEY6tFa8iQhhDZuvxttQEQ5sLyXeZcnyAF4tytJCXZ081vjD+10noKps8rM8E0jr1Qat+GtwD/SoSTVMbNnx4Pu74Ry7e85NoWxwrSgmo1VgnU/Px8du/eTd++fU0DqNX07duX7du3l7rPypUr6dq1Kw8//DARERG0bNmSN998E51OZ/OYs2bNIigoyPhXu7YNQdj25LKDE6QUyohD3XEmmS//FBfiWSNbEe6kklKSKowtFtTAGFHuTF8IaTa63ZxJ5hURw4gKarpRpzBjzOQ+EUfoDJwdf6oQ0QLCmoqC7UrCkyUoFteWoywTF+4Sh2reQcocRyRK5RS1ObU2/hSEqA+uI5btbUWtbPypgrlAtTahcOu74gan9k3QsG/F2zsDWazfaqwSqElJSeh0OiIiipf/iYiIICGh9NqIZ86cYdmyZeh0OlavXs1LL73Ee++9x+uvv27zmM899xxpaWnGvwsXLljzNuzPJQcnSCmU4hpMzy1g+g/7MRjgjg61GNAyyrFzkFQPbLGgqtUmS2RViENVQhGC65jcre5AWBPw9BOxmUknnHNMVwlUW9z82SlwepNYtjSxxV0y+ZWYzvDrQpUckSiluPitzeBXcFRHKVvrn15P7c4isSgj3rqwomvnYPd8sXzLi+5hPQVZasoGHJ7irdfrCQ8P56uvvqJDhw6MHj2aF154gdmzZ9s8plarJTAwsNifS1Fcdc6yoF45AgW5ALyy8jCXUnOoVcOHl2VpJomlWFtiSqEqtTx1twQpBbWm3HAdh6CUmKqMy9VWFJF5+g/TPMrj6EphpY9oZXJFV4S5BdVVnc70Orh6XCw7w4JaGRc/OCYONe2S+N2p1KIWbmXw9IE6XcTy2S2W77flbdAXiJa79W2MgXUEsli/1VglUENDQ9FoNCQmFr8LTExMJDKy9H7pUVFRNG7cGI3GlEHXrFkzEhISyM/Pt2lMtyIjEdIvASrTRcdRBNUG31Bx8k44yOqD8SzfI7pFfTBaxm9KLMRgMJ0krRaoVShRyl0FKji/o5SrLKggbmqi2oq6mErb0vIwuvdHWn6MyKLyXdnJtmd9V5ZrcSKJ1cO7ZEiJIy2otrj4wTEWVMV6Gt3O8uog5aG4+c9YKFCTTsL+JWL5lpcqf3x7olhQs646toNXNcIqgerl5UWHDh3YtGmTcZ1er2fTpk107dq11H26d+/OqVOn0Jv1kD9x4gRRUVF4eXnZNKZboVxgwpqIuB5HolIZL2zpZ3bw/M8iU3Jq71g61XN+tyhJFSU7RWS3grjpsYYqZUF1wwx+BWd3lHKlQAUzN//y8rfLSICzW4vvYwkeWlNtT1fFoSqWyLAmJUsaOUKg2lqkX8FYrP+I/azO9oo/VVDKVMVtBTMNUSabZ4lao40HQq2O9pmDvfCpAdoi0e6qm6gqhtUu/unTp/P1118zf/58jh49ytSpU8nKymLKlCkATJw4keeee864/dSpU0lJSeGxxx7jxIkTrFq1ijfffJOHH37Y4jHdmku7xaOj408Vio5zYMcfpGYX0DImkMdubeycY0uqB0qQfkAUeFqZUGe0oFaBGFSl9Ju7FOk3R7GgJh6yb23MsnBmF6nSULpKnfsb0i+Xvd3hFYBBFOa3JoEPXB+HWlaCFJi5+N3IghraSNQazU0TcZ6VxWCofP3T64luB17+IiEs8VD52yYcMlnf+zxvn+PbGxmHahUe1u4wevRorl69yowZM0hISKBt27asXbvWmOR0/vx51GZFlWvXrs26det44oknaN26NTExMTz22GM8++yzFo/p1ji6xen1FB0nMvMIWg81H45ui5eHe3SLklQRUuPEozUJUgqKQE27IFpROqumprXodZByRiy7owW1Rn1hUVEuvI4+f7jaghpcWxRfP78dDv8MXR8ufTvz7H1riW4PzHVu+S5zru8gZY47WlA9tOK3kXRczD0wunLzSTkjatFqvKB2l8qNpaDxhLrd4eQ6IX6jyik59seb4rHFiPK3cyU16kLCARmHaiFWC1SAadOmMW3atFJf27x5c4l1Xbt25Z9//rF5TLfFYHB8i9PrOOPZmAZAA1U8L99Wm4bhDg4rkFQ/bCkxpeAbImLLctMg5SxEuGliXtoF0TlJ4wVBtVw9m5KoVMI6dPp3cQ6p7gIVhOg8v12I0NIE6rVzcHEnoDJZXK3BGNe7T7iDy+s+5QiMArU8C+oVcd2wR2a5scyUjUlSIMR00nFh/a1sOSYlkalWZ/DyrdxY5tS/uUigboFuZWiEi7vh+CqRnNXbTa2nIIv1W4k0vVWGa3HiJKH2NLUvdCD5hXoe+fUiFw2hqFUGxtZKdvgxJdUQW0pMKahUVSNRSplbzQaubXFYHsaycQ62+OkKTe5gVwrU5sOEgLi022TdNudwUXxqvR4QYEOCbGgT8PQVtW+TT1ZurtZSmGf6zpVnQS3INsV/V5bKWlDBFLdrj0x+e8efKijjndsGuoLSt/lDlK2k9RgIc+OQNyUpVdZCtQgpUCuDYj2NaOGUOosfbjzB4cvpHFWJmDqVq1xZkqpNZSyoUEUEqhsnSCk4K5M/u+hGVqW2vSSRPfAPN8UmlpYsVRn3PoDGw/nluxSST4nqKtog0dDierT+IpYS7FdqqrIxqGAS00r9VlvR603JbZWtf3o9ES2FCM/PLD18I+5v4YlQe0CvZ+x7bHsjS01ZhRSolcGJ8ae74lKYvUVcdOu07CFWurpriqRqYmuJKYWqkChlLDHlhglSCooF9eoxx5adySoSRL4hrrcml5XNf/UEJBwUIqP5MNvHd1VHKfMWp2W57+2ZKJWfLTolQeUsqEo4wtXjletqdvUoZCeJBhT2DndTq031TK+vh2owwO9F1tP2E92rY1xpmCdJuapebxVCCtTKoNzNOTj+9HxyNk98vw+9AUa1r0WT9kV3qI52DUqqH3odpBZ1XrPFxQ/CbQ5ubkF14xqoCoFRopKCQQ/x+x13HHeIP1VoNkSERF05XNytrFhPY2+pnEXQVZn85SVIKdgzUUqxnqo9KlfesEY9Ube1MKdybmelTmndruDhZfs4ZVFWPdTTm+D8NtFxqudT9j+uvVHay+ZnmGKIJWUiBaqt6HUiGB8cakFdfTCewR9v5eI10S3qldubQ3Rb8WLaecs6s0gkCumXRZcVtaftWbvSxW8/SmlfbHdcXWLKHJ8a0KifWFZEqcFg5t6/o3LjK/VlEw46p3yXQnklphTs2U3KPP60MglXao2pW1dl4lAdFX+qoISGXNgpqodAcetpp/sgqJTQCnfD08d0oyLjUCtEClRbSToheml7+orgfDuTW6DjxRUHeWjxHjLyCulQtwbf/6er6BblHQQhjcSGru49LalaKO79oFq2u3sVt3l2EuSk2mVadqUwz1QI290FakyRoHKkS9qdLKhg5ub/SYiMhIMiqcnDG5oMrNzYNRuAdzDo8uzfY748lBhOZ1tQK2NtVqhsy1NdoahvC/arf3o9IQ0hIFr8Xy/sFOuOrRJeTE8/6PGEY47rCJTQKhmHWiFSoNqKIgyj2ojgfDty5momIz7fxqJ/xEV2au9YvnvgJmKCzWpOOrtVoqR6UNkEKRAuRf+iLOsUN4xDTTkLGMArwH1EWVk4xYKqCNRwxx3DGhoPAA8fkckfvw8OLRPrG90G3oGVG1sp3wXOOzfmZZrERrkC1Y4xqPbI4Fcw7yhlC/H7IS9d3BhEtqr8fEpDpTJZZ8/+KZKy/nhDPL/pQfB389+5ObLUlMVIgWorDqp/+su+Swz95C+OxqcT4ufF/Hs68+yApnhqrvtXOePCJql+VKbElDnunChlniBlj3qTjkQRU9fOmkSHvTEKVDdw8YPIaFcspQeXmRKmWlXSva/g7DjUq8fFo194+Z+x0YJqBxe/sQaqPQSqUmrKRoF6drN4rNfDsUl4RoG6RZQku3JEVE3o9ojjjukIashMfkuRAtVW7JzBn5Ov49llB3jsu31k5eu4qUFNVj/Wk16Ny7gzNLegymxAiaXYw4IKJje/O8ahVoUEKQXfmqKrFDiuA5IxBtWNrEyKm3/XN6Kpgpe/sKDaA2Mmv5OSSBVhV1HTCke4+O1RNkyxoCafEuEx1mLv9qZloQjUS3vg99fEcrdHXFs6zRaMFtQ4l06jKiAFqi0U5pv6AisWkEpwMjGDYZ/9xff/XkClgsdubcTi+24iIrCcPumRrUQGZ9ZVSLtY6TlIbhDsZkGVAtVuODpcx91iUEF0LdIGmkolNR1sv7a5yud55agox+RoLEmQApOLP8MeLv4iC6o9xFlgtLBE6gut/z0X5sH5oi6R9q5/ej3BtUWMsUEnxJ1viHDvVzVqSBe/pdg3ePJGIfGQaKPoHWwquWMDBoOBH3dfZMYvh8gt0BMWoOWj0W3p1tACV5ynj7jzTTgoLmzBtW2eR6mc2SJOBLG32Hfc6kJ2CgTZsZ2fszBaUCtZL9CdM/mrSga/QnR7kTDkqLJx7ihQPb2h2VDYt1g8r2z2vjmB0SJGOjNB9D2vc5P9xi4NS0pMgcmCmnVVVIGpjDvcnklSKpWY+4V/YMvbENrI8n0zr4ibDP8ICHVCB6f6N5u6kPV4onIltlyFYhxIu+CalrxVCClQbcEYf9rO5hi3rLxCXlpxiOV7LwHQs1Eo79/VlrAAKzpSRbcXAvXSnsoVt76ejERYNFLUZ3zsgP3Fb1XF/I534XCYuNL2Uk2uoCAXMuLFcqVd/GYxqPbqLW4vlMQtdy7Sb47DLahuVGbKnJajhED1qQkNett37Jj2cHy1ODc6TaBWYEH1CwNU4sY/O6VyiT32TJIC4ZG78A8cWWHb/g16O+cc0KAP7J4nbkA63uv44zmCwBhQaYSRKyO+apTHchFSoNpCZBu46SEIa2rT7kcupzNtyR7OJGWhUauY3q8xU3vFolZb+QOPaQ975tv/wnZkhXD3gAhG7/6YfceviiSdhIUjIbToJ5N8Cr4dBJNWmoovuztpFwCDKMviG1K5sWrUE60z8zNFTJ0tvdMdQW66KcavqgjUqDbis8yIh/R4UcDfXuRnif7v4F4WVBDemds/EZY3exd3jy4SqI7O5M9KNn3fwiooN6jxFL+77CSxT2UEqj0tqAA9p4OXn6nGqDV4aKHzA/aZR0U0ux0G/A/qdgOvKujBAlH1J6iWCLdKPScFajlIgWoLtTuJPxtYsuM8r/x6mPxCPZGB3nxydzs61bPxJGNMBthnX1eBUjRbWb7RBWriEVgwDLKvQmiRNTm4jsi8VkRqJUI9nIZ5glRlrR0e2qLPIE5YUd1FoCrWU78wUS+4KuDlJ252rxwRgipwsP3GVtz7Hj7iOO6ESiXaUzoCpb6sozP5rxbFnwbXsczd7B9hEqi0tP249ragBkZDv1ftM5YjUavhpqmunkXlqVFXiNNr54TYlpSKDH5wIqsPxvP8zwfJL9RzS9NwVj/W03ZxCiJuyMNb1KCzVz3K1PNwYQegEm6I+P2Q5IZxhs4ifj/MGyz6mSvlWAAmrBBu7rQLQqRePeGyKVpMapx4rGyClII7xqFWtfhTBUeVjcs0iz91pzAMR6N8nimnHdtMwpgg1aL87RTs1U3K3hZUiXORxfotQgpUJ5FboOPN1eJkNqV7PeZM6khNv0q6tTSeENlaLNvrwqbUJKzbHWL7FK37qeztqzMX/4X5Q8XFILo9jPvB9FpAJExeDWHNhGt23iBTNxl3xV4lphTcUqCa1UCtSjiqo5S71UB1Fr41TSLAkeWmLE2QUrBHqSm9ziS67WVBlTgXWazfIqRAdRLztsVx8VoOkYHePN2/CSp7WTPsnWBh7Ik90qwl4bIbr9bque2wYDjkpkHtLjBxRcmSLgERMHmVuEnIuiosrZf3uWCyFmKvElMKNZVSU25UrL+qlZhSMK/dac/fmjtm8DuLaDufG0vD0hJTCvawoOamAUXfkapWA1QiUG6eZC3UcpEC1QkkZebx6e/iwvl0/yb4etkx9NeersGkk6Isi0oDzYeL2oQaLSSdcH/roD05s1lUMcjPgHo9YfzysuMZ/UJEDGpMR9HdZf7tcGGXU6drMXa3oLphLdSq6uKPaAkaL/EdunbWfuMqArUqtYK0F47uKGUwuMaCqsSfegXYP7lM4hwUI4F08ZeLFKhO4IMNJ8jMK6RVTBAj2tk5Y085CSccAF1B5cZS3PuxfYTw8g6CRv2KXltWubGrCic3wOK7ROZz7K0w7kfRmrE8fGrAhJ+hTlfISxMlqOL+dsp0rUI5GSp375VFEYHXzgq3o6sxGKquQPXwEiIV7Cuo3LGLlLNwdEep9MvCmqnSWF471B4C1Rh/Kq2nVRbFSJB+2bbuXTcIUqA6mBOJGSzdeR6AFwc3s76UVEXUjDV1ZFHcTbZgMJhEqOLaN18+9FP1d/Mf/Q2WjgVdHjQZBGOXWt7dxjsQxv8k2v3lZ8KiUXD6D8fO1xpy00z9u+3l4g+qJSzsuvyiElYuJitJ3CCgqnwjAlcQ4wBBdSO7+JXyXemX7NO96XqU821IQ1HVwhLs4eK3dwa/xPn4hYGnL2CQnSDLQQpUB/P6qqPoDTCgRSRdGlSy9mRpqNUQ3VYsVybWKvGQcOVrtMK1r9B4gKibmXoeLu2u1FTdmkM/wQ8TQV8gwhvuWmD5RUfByw/u/l70FC/MgSWj4cQ6h0zXahT3vm9IxRZhS1FrTOW13MHNr8whqLboVFTVcEQm/40sULX+EFpUm9QRcajWuvfBzIKaYPtxZQZ/1UelMtXPlnGoZSIFqgPZfPwKf564iqdGxX8H2lbU3yLscWFTkqMa9Sseb+nlC00HFd+murFvCfx0n+jw0noMjJojKiTYgqcPjF4ETYcIS+x34+Dor/adry3YO0FKIcSNEqWqaga/gmJBjd9vv5AJd+0i5SwcGYeqWFAjLCwxBSKxEoRHoyDXtuNKC2r1QMahVogUqA6iUKfnjVXiBDa5Wz3qhTqwSHZlM/kNBpP4bFVKT2yjm3+5e8Qa2pN/v4UVU0Vb1/YTYfgXotNHZfDQwp3zxOemL4AfJsFBF8fw2jtBSsGdEqWqaga/Qmhj4a0oyIKrx+0z5o1sQQXRjhrcx4LqHSyS4UDUVrYFJVRHWlCrNsZMfilQy0IKVAexdNcFTl7JpIavJ9NusTCA3lYUC2riEdta1V38V7jwPf2gUf+Sr8feKqyqmQlwblvl5upO/DMbfntcLHd+AIZ8ZL9uXBpPGPk1tLlbWGZ/ug/2LrbP2LbgMAuqG9VCreoCVa2xT7iOgl4vuhbBjStQzS2o9oyh1+tMNxGWlpgC4do1uvltFajSglotqCEtqBUhW506gPTcAj7YIDoLPd63MUE+NrqLLSWolrgAZV2FhINQu7N1+yvW06aDSu9v7OEleiDvXSi2rd+z8nO2Fzu/hq3vCxFoDQaDyYLR7RHo95r9O+2oNTDsM2FR3f0t/PKQSGbrdK99j2MJDrOgVlKg5qbBb9NFMktlrdcpZ4rPqSoS3Q7O/S0EVbvxlRsr55rwDICIPb4RiWgJak8h6lLP2a+CxbU4EWfu4W39mP7hIqnQ1kx+o4tfZvFXaYzF+uNcOg13RlpQHcBnv58iJSuf2DA/7u5Sx/EHVKlsj0PV6+BwUXmplqW49xUUN/+RXypfzspe6Argjzch47I42Vvzp4jTm59xjDhVUKthyAfQpah/9KrpsP1zxxyrPOxdYkpBEYOpF6yPqctOgQXDRPWIgz/Age9tn4deb1ZiqorGoIJ9G28o7n2fGrbHVFd1PLQQ6YDyXYp7P6yJuBG1hsqWmpJJUtUDxVggXfxlIi2oduZ8cjbf/h0HwAuDm+GpcdI9QEx7OLnO+gvbub/FidI7CGJvKXu7+jeDX7gQdmc2m+qjupIzm8XJ2i9MFNO3VmT61BDWZ0ejUsGAWeJi+feHsO45YX3p+aTjjw3CWnzNQS5+vzBR5iwvXVgCwi1MBsxKEp26Eg8K66lBD1v+B63utK34ePpFkZSm9jRlx1ZFlBvNhEOiPqK1lSTMUW7CblT3vkJ0O1G66/Ie0SHPHljbQcqcypaayi6KQZUu/qqNci7OSYG8DNAGuHY+boi0oNqZt9YeI1+np2ejUPo0CXfegW21oCru/Wa3ly8M1BpoMbz4Pq5GmUeLERDVGiJbWffnDHGqoFJB31eg9/Pi+aaZwvrrjNqymVeEIEYlSjDZE5XK+kSpjAT4dpAQp/4RcN8m8Zh6HvYusG0eyrFr1rfeouVO1KgnhIe+QJR+qww3eoKUgvHcaMf6ssYEKVsEqr0sqNLFX6XxDjSFaUgraqlIgWpHdsWlsOpgPGqVsJ6qHOU2Lg3FNZh8sqhXswUU5guXPZSevX89ipv/6G+2JWPZk4JcMQ8o3ljAnVGpoPez0PdV8XzLW7BhhuNFquLeD4xxTGtEa+JQ0y7CtwMh6biYz+TV4rt789Pi9T/fte27VVU7SF2PSmXKPK+sS/pGLzGlYCzftc9+VUhcakGVSVLVBllqqlykQLUTer2B138Td9WjO9WmaWSgcyfgFwpBRa7Ny/ss2+fMZpFI4Rcues5XRK3OwgKXnyFagrqSk+vFPAJriXlVJXo8DgPeEsvbPoY1z4oYSkfhqAQphZoWWlCvxQlxmnJGuOGnrIbQIkHZfqL4bmXEw6451s+hqtdANcdeHaWMFlQnenLckdAmomtPfiYknaz8eIV5pu+bNSWmFCpjQS3IKfKGIGNQqwMyDrVcpEC1E7/sv8T+i2n4eWmY3q+JayYRY2XNP6OLfLhlblG1WrjTzfd1FcrxW460X2koZ3LTgzDkQ0AFO78U5a4cJVJT48SjveNPFYwW1HKK9SedgrkDhRu/ZixMWVM8YctDC72eFct/vS9isqyhqpeYMsdeHaWki1+g8RBtT8E+yWdJJ0FfCNogCIy2fv/KCFTFeqr2ELHfkqqNcg6UFtRSqYJXdvcjJ1/H22tFTbyH+jQkLKASiQ2VwZoLW0EOHFNc5Ba49xUUd/qJtdaLCHuRl2FqIVpV3Pul0XGKKK2kUsOe+aJhgK7Q/sdxtAW1ohjUK0eF5TTjsrBmTVldevxvm7FCvGYnw47Z1s2hurj4wWRBTToOeZm2jyNd/Cbs2UbW6N5vZlv1D3MXv7XhPcYaqDUcV3lE4jyCpQW1PGwSqJ999hn16tXD29ubLl26sHPnzjK3nTdvHiqVqtift3fxPtmTJ08usc2AAQNsmZpL+GbrGeLTcokJ9uHeHvVdNxFrXIMn1wuXV1BtqNXJ8mNEtREioDAXjq+xbZ6V5fga4eaqGWuyjFRV2o6FUd+ASgMHvoOf7rV/GS9HFelXUARq1hXITS/+WvwBmDdYvBbREiavgoDI0sfReECfoiSyvz8xdcypiMJ803usDgI1IBICokVlg/j9to8jLagm7Fm+y5YOUuYoFtTCXMvzBRRk/Gn1ooashVoeVgvU77//nunTp/Pyyy+zZ88e2rRpQ//+/blypeyA78DAQOLj441/586VvFsYMGBAsW2WLl1q7dRcQmJ6Ll9sEdabZwc2xdvThRnEUW0BVVER6Kvlb2uri1ylMmt96iI3v3lb1upgRWg5Cu5aIEokHVkhWqMW5tlvfKMFtZ79xjTHO8gU55hi5ua/uBvmDxEW0eh2MOlX8K9ALLUYKRJP8tJg26eWHf9anBBzXv6mi39Vxx6CSgpUE0riWcJBcUNTGRQLakQL2/b39BHhAWB9opSsgVq9CK4nHlPPOaeiSxXDaoH6/vvvc//99zNlyhSaN2/O7Nmz8fX1Ze7cuWXuo1KpiIyMNP5FRJS8iGi12mLb1KhRNUpovLf+ONn5OtrVCWZo6yjXTsY7EEKL2qqWd2GrrItc2efUJtMdvbPIThHHBSFmqgvNhsDYpaIzzfFV8N3d9qmUoCsUmfPgOBc/lIxDPbddFOHPTYPaXWDiL5ZdVNVq6POCWP7ni4pvtMCsxFSD6nHDAvbJ5De6+KVApWYD8A4GXT5cOVy5sSprQQUzN7+VcajSglq9CK4NqKAg2/R7lRixSqDm5+eze/du+vbtaxpAraZv375s3769zP0yMzOpW7cutWvXZtiwYRw+XPIEsXnzZsLDw2nSpAlTp04lOTm5zPHy8vJIT08v9ucKDl9O48fd4uL/4uDmzi0rVRaWxFodWy3cSyENIbK19ccIawIRrUStxqO/2jZPWzn6qzhuREvLi8JXFRr1g7t/EBnHpzbC4jsrF4MIooC9QQcaLfiX4Vq3B+ZxqGe2wKKRospCvZ6iiYJ3kOVjNR0sBFpBlmhsUBHVKUFKobIW1IJc0TwBZAwq2K98V16mKZwkrDIC1cZEKVkDtXrhoYWAIsOWTJQqgVUCNSkpCZ1OV8ICGhERQUJCQqn7NGnShLlz5/LLL7+waNEi9Ho93bp14+LFi8ZtBgwYwIIFC9i0aRNvvfUWW7ZsYeDAgeh0pdesmzVrFkFBQca/2rXtXHzcAgwGA6//dhSDAYa2iaZDXTc5YVhyYTO69yvhIlc6sjjbzW+cexVOjiqPBr2EoPMKgLitsGiU9XFq5hg7SNV2bLUDRaAeXgFL7hIWgdhbhODW+ls3lkoFt7wolnd+DemXy9++OgpURUxdi7PNS5FdZI1Re1p3c1CdsUfYxNVj4tE/AvxCbB/H1lqosotU9UPGoZaJw7P4u3btysSJE2nbti29evVi+fLlhIWF8eWXXxq3GTNmDLfffjutWrVi+PDh/Pbbb+zatYvNmzeXOuZzzz1HWlqa8e/ChQuOfhsl2Hj0CtvPJOPloeaZ/i4qK1Ua5hbU0mJaslPgdJGLvDJt/5R947ZCho0dUawlI1Ecz/z41ZG6XYVL3DsILvwjWoLaGkrh6AQpBUUcXj0qrPONB8LY78DL17bxYm+FOl1F+9I/3y1/2+qUwa/gU0O4pcG2eqjm8afu4NlxB+zRUcoe7n2ohAW1SKDKGNTqg6uL9WdegX9mu2UMrFUCNTQ0FI1GQ2Ji8R9VYmIikZGWuQ89PT1p164dp06VXdS7QYMGhIaGlrmNVqslMDCw2J8zyS/U8+ZqESh/b4/61K5p40XYEUS2EjXyspNEstT1HF0pavhFtBKuelupUU9k/xv0IrHHGRxZIY4X09FxCT/uQq0OIqnIp6aw+My/3bYYJUeXmFIwF4fNh4mkr8r0kVep4JaXxPKe+eVbF1KqoUAFk6DaMdv6pDkldle6900oFtSrR00x+NZSmQ5S5thqQc2RMajVDuVa5opSU+mXRdvptc/C9s+cf/wKsEqgenl50aFDBzZt2mRcp9fr2bRpE127drVoDJ1Ox8GDB4mKKjuh6OLFiyQnJ5e7jStZ9M85ziZlEervxUO93axzjae36eRZWqyVMQPeDi5yZ2fzm2fv3whEtRE1Q/3CRd/6eYNFH3trcJYFNawpdLwHuj8Go+bap6Vqve7QoI+4odrydunb5GWK7lMAIQ0qf0x3ovMDInb45Hrrk+ZkBn9JAqNFWJNBD9+Nsy1+3tUWVGOSlJuElEkqTw0XWVBTz4v61MknRbnJpoOce3wLsNrFP336dL7++mvmz5/P0aNHmTp1KllZWUyZMgWAiRMn8txzzxm3nzlzJuvXr+fMmTPs2bOH8ePHc+7cOe677z5AJFA9/fTT/PPPP8TFxbFp0yaGDRtGw4YN6d+/v53epn1pVSuIljGBTO/XhABvT1dPpyRlxVplJMDZIhe5PTLgmw8HVHBhh/iyO5LU8+I4qIqOe4MQ3kx0XQqIFvFv3w4yZeVbgrMsqCoVDPkA+s0U9UzthWJF3b8Urp4o+bpiPfUNrX4X7TpdYJxZ0tySuyA/y7J9pUAtnRGzxblPXyDKuR1cZt3+RguqjSWmFIwCVZaZuuEJdkEMavJp0dnvWhzUqC+uMTXd7wbfaoE6evRo3n33XWbMmEHbtm3Zt28fa9euNSZOnT9/nvj4eOP2165d4/7776dZs2YMGjSI9PR0tm3bRvPmwsqn0Wg4cOAAt99+O40bN+bee++lQ4cObN26Fa3WRR2ZKqBTvZqsfLgHozs5PznLIsrK5D+8AjAI17w9BEtgFNTrIZYPLa/8eOWhjF+vhzjujURoQ2FJDa4jBNm3Ay0/mSl35VU1JKJWB2gySFi9Ns8q+boxQcrNPBn2okFvGP+TSJo7+ycsHFmyGUJpKAK1orqzNxoaT9EYo81YUd3ip/tg72LL9s1KNlk8KxMeBbLMlMSEci1Ouwj60hPD7crV48LQkX4RQhsLcRrsnlrGpiSpadOmce7cOfLy8tixYwddunQxvrZ582bmzZtnfP7BBx8Yt01ISGDVqlW0a9fO+LqPjw/r1q3jypUr5OfnExcXx1dffVVqrVR3Qq1WoVG7afKBYkGN31+8v7t59r69cJabv7pn71dETbO73NTz4gSTVHYcNwD52aYLoKNd/I5EqYt6eLkotG5OdUyQup663WDiClPS3MLhFXfZkjVQy0atgWGfQ4fJgAF+eQh2zal4P8W9H1zX+soU16N0U8tOslyU6PWQmyqWpQW1+hAQJapt6Ash/ZJjj5VwSFw7MhOEF2DyKrc2+Dg8i1/iAsKagYePqIOoWJiunYOLOwEVtBhuv2M1HyaSshIOQNJJ+41rTtJJMb7aA5rd7phjVAWCagmRGtpEnMjmDYIrx8reXgm70AZWbfd3ZEtTSMofbxZ/rbpbUBVqdTQlzV3aDfOHlp80J1385aNWw5APocuD4vmq6bD98/L3sVeCFIBvCKjUwjNgafJjbqrYHqQFtTqh1pgsmI5MlLq8t6izX5LIb5j8m8mS76ZIgVod0XhAVFEBfiUO9bCZi7ysXui24FtT1LsEx1lRlXEb9Klc7cHqQECkuOuNaCmso/MGiX73pWGeIFXVSw31eV5c0I+vhov/mtbfCBZUhag24n/vFy4syfMGl13iTQrUilGpYMD/oPvj4vm652Dre2Vvb68EKRCiRPnfZFqY+KhYzb387ZOEKHEfHF1q6vwOUQkm55oI8Zu4skpY4aVAra5cH4fqyAx4cze/vWupGQw3XvZ+RfiHCWtadDvR537+EGFVux5nJUg5g9BGIm4Q4PfXxaPBIDJQ4cYQqAARzUU8spI0N28QpJXiFjS6+GWZqXJRqaDvK9C7KLF300xhpS/tPGZPCypYX2pKxp9WX4zF+h0gUM9uhYUjhEe1bneY8DP4BNv/OA5ACtTqinkm/9UTwuLiKBd5k0Gih3zSiZIxgpUl8ZAYV6MVx5EIfGuKYv61u4hOU/OHwfl/im/jrBJTzqLXsyJW68wfEPeXuGArXbbcMAPVYYQ2EiI1qI4Icfh2YPELm8EgLajWoFJB7/8KoQqw5S3YMKO4SDUYTAI1wl4C1cpSU7LNafVFSWK1twX11CZYfIdoG92gN4xbBtoA+x7DgUiBWl1RLKgJB+HAd2I59lbHmPW9A6HRbWLZ3m5+pQxM49vEcSQmvINEW9R6PUXf+4UjRaa3gpLpXx0sqCDeR/uJYvn3103W06Da4Onjunm5gpr1hUit2UBc1L4daAp3yE0TZZRAlN+SWEaPJ4TLH2Dbx7DmWVOSafplyEsTN/khjexzPGsFqrSgVl+CHWBBPb4Glo4Rnf0a9Yex39ve2c9FSIFaXanZALRB4su5o6itrCMz4I1u/uX2c/MbDKbyUvasPFCd0PqLfvext4i75MV3wsmN4rWqXmKqNG5+Wljrz283fa9vJOupOcG1YfJqUSom/ZIQqVeOmdz72kDRuENiOTdNFfV8AXZ+Cb89LkSqYj0NaWi/+E9rXfyyBmr1xejij7PPeIdXwPfjQZcPzYbC6EVV8lwgBWp1Ra2G6LZiOT9TXNQd2SmicX8RvJ92vngSS2W4uEuM5+VvstBKSuLlK/reNx4obki+GwvHVpnuxquLix9ESZROosmHMfHvRok/LY3AKCFSzZPmTv8uXpPxp7bR8R4Y/oVIytszH1ZMFVVEwD4JUgrSgipRCK4nHjMTrOsYVxoHfoBlU0TZqpZ3wB3zqmxSnRSo1RklDhWEgHRk7ImnDzQdLJYPWdmdpSyUcIEmg6qca8LpeGjhrgWi7JcuH76fIILiQRT4r070eAI8/UzPb2SBCqakuai2ImluzdNivYw/tZ22d4uC/iqNCJHa8pZYb68EKZAWVIkJ35rCEAOQesH2cfYsgOUPiHJkbcfDyK/s29nPyUiBWp2JNhOozihwrxzj8M9QmFe5sfQ6MQ7I7H1L8fCCUXOh9WjRJQdESaLqJu79QoUrVuFGF6ggLnCTVkKtzqZ1UqBWjpaj4K75IjGvMFeskxZUiSNQqSpfamrn17DyEcAAHe+F2z8R5cyqMFKgVmdqdxbZ7z41neMib9BHJGVkJsJ3d1fOVRH3lxjHO1iMK7EMjYdwT7abIJ5HVLJnuLvS7RHRfEDtUX3fo7V4B8GE5VC3qP3wjRqba0+aDYUxS8R5VKURVmp7YRSollpQi+qgSgtq9aQycajbPoHVT4nlmx6Gwe+JML8qTtW1/UoqJiAS7lkrXAfOyHL28II75orMwVMbYcldIjbSy6/ifa9Hce83v73Kxs+4DLUGhn4s3P3VVbz5BMN9m4RVKSjG1bNxH7QBMP4nEYdar4erZ1M9aHwbPLhVhE/Ys2e5IlDz0kVb4oo8HTnSglqtsdWCuuUd+KOoNnSP6XDrjKrfmKWIqi+xJeUT0x7CGjvveA16iQukV4AoebRwJOSmWzdGYT4cXSmWZfa+bajV0KgfBEa7eiaOIyQWandy9SzcD8+ihEhZls1+hDWBut3sO6Y2QLSkBsiywIqaXWRBrcptiyVlo1RbsbTUlMEAm14zidM+L0Lfl6uNOAUpUCWOoG43mLhCuBwv/AMLh5vcU5Zw5g+xvV+4tAJJJJLqiUplXaKULNRfvalhhQXVYID1L8LWd8Xzfq9Br6cdNzcXIQWqxDHU6igyi31qijac84ea6jNWhOLebzGiygd5SyQSSZkobv6MhPK3K8iFgmyxLF381ZNgC2NQ9XoRb7r9U/F84DvQ/VGHTs1VSIEqcRxRbWDyKmEJTTgI8wZDRgUZqwU5ooYnyOx9iURSvTFaUCs4LyrWU5VGeKYk1Q+lHGBuGuSklr6NXge/PgK7vgFUItegywPOmqHTkQJV4lgimouWjAHRcPWYKCSedqns7U+sE40FgupALRlfKJFIqjGWZvIbS0zVqFYxhhIztP6m1sSlufl1hfDzf2DvItFEYsSX0GGSc+foZKRAlTie0EZCpAbVgeRToiVjWYHginu/5Qh5IpZIJNUbS2uhyiL9NwbGUlPXXR8L80V3qIM/itJ6d8yFNqOdPz8nIwWqxDnUrC9Eas0G4u7w24GQfLr4NrnpcHK9WJbZ+xKJpLpjaZKULNJ/Y1BaqamCXPhhgqhso/GCuxaK/IwbAClQJc4juLboGx7aGNIvCZF65Zjp9eOrRceWkEYQ2cp185RIJBJnIC2oEnOut6DmZ8N3Y+HEWvDwhrFLRQm5GwQpUCXOJTBKiNSIluKkPG+QSKACM/f+KOnel0gk1R+rY1ClQK3WKLVQU89BXqZodnP6d/D0g3E/QsO+Lp2es5ECVeJ8/MNECaqotqI7y7whovPU6d/F6y1HuXR6EolE4hQCzCyoBkPZ2xnbnMoaqNUaxcV/9RgsGglxW0XTmwnLof7Nrp2bC5ACVeIafGvCpJVQqzPkpsKiUaAvFK59Z3a+kkgkElfhFyYe9QXlNzORFtQbA2Ox/vNwYQd4B8OkX6DOTS6dlquQAlXiOryDxJ1hXbNuUdJ6KpFIbhQ8tKbWpeW5+WUM6o1BUG1RQgrANwQm/wYxHVw7JxciBarEtWgDRGxN0yEQGANtxrp6RhKJROI8LEmUkhbUGwONp8jQrxkrcjVu8GRhD1dPQCLByxfGLBYxWDI5SiKR3Ej4h4uYw3ItqEoMqhSo1Z475sprYRHSgipxH+QPUiKR3GgYLagJZW+TIy2oNxTyWghIgSqRSCQSieuoyMWv15ssqD4yi19y4yBd/BKJRCKROBmdTkdBQQEE1AX/2pCbD7m5JTfMTQO/GLGs9it9G4nEjfD09ESj0VR6HClQJRKJRCJxEgaDgYSEBFJTU8UKv/bQ/T3RKejs2ZI76ArE6yo1XLjk1LlKJLYSHBxMZGQkqkqEK0iBKpFIJBKJk1DEaXh4OL6+vqjysyBNDRothNQvuUN+NqQWgtoTQkt5XSJxIwwGA9nZ2Vy5IpL+oqKibB5LClSJRCKRSJyATqczitOQkBCx0gPIUoFaD97epeyVBx4q8PAs43WJxL3w8fEB4MqVK4SHh9vs7pdJUhKJRCKROIGCggIAfH19TSvVnuJRXwgGfcmd9Lqi7aQ9SVJ1UL7jynfeFqRAlUgkEonEiRSLy1NrgKLn+sKSGyvr1JVPOpFInEVlYk8VbBKon332GfXq1cPb25suXbqwc+fOMredN28eKpWq2J/3dW4Kg8HAjBkziIqKwsfHh759+3Ly5ElbpiaRSCQSSdVBpTJZR3XlCVRpQZXcWFgtUL///numT5/Oyy+/zJ49e2jTpg39+/c3BsSWRmBgIPHx8ca/c+fOFXv97bff5uOPP2b27Nns2LEDPz8/+vfvT64spyGRSCSS6o6mSHzqS3GHVlMXf7169fjwww8t3n7z5s2oVCpT9QNJtcdqgfr+++9z//33M2XKFJo3b87s2bPx9fVl7ty5Ze6jUqmIjIw0/kVERBhfMxgMfPjhh7z44osMGzaM1q1bs2DBAi5fvsyKFStselMSiUQikVQZlDhUXWkC1bUu/us9oNf/vfLKKzaNu2vXLh544AGLt+/WrRvx8fEEBQXZdDxbaNq0KVqtloSEcrp8SRyGVQI1Pz+f3bt307dvX9MAajV9+/Zl+/btZe6XmZlJ3bp1qV27NsOGDePw4cPG186ePUtCQkKxMYOCgujSpUuZY+bl5ZGenl7sTyKRSCSSKonGLFHqelzs4jf3fn744YclPKJPPfWUcVuDwUBhYSnvoRTCwsKKJ4tVgJeXV6XralrDX3/9RU5ODnfccQfz5893yjHLozLJRlUVqwRqUlISOp2umAUUICIiosw7jCZNmjB37lx++eUXFi1ahF6vp1u3bly8eBHAuJ81Y86aNYugoCDjX+3ata15GxKJRCKRuByDwUB2fiHZhSqyC/Rk5+SJ5+Z/eQXitUJVydcq8WcwGCyao7n3MygoqJhH9NixYwQEBLBmzRo6dOiAVqvlr7/+4vTp0wwbNoyIiAj8/f3p1KkTGzduLDbu9S5+lUrFN998w4gRI/D19aVRo0asXLnS+Pr1Lv558+YRHBzMunXraNasGf7+/gwYMID4+HjjPoWFhTz66KMEBwcTEhLCs88+y6RJkxg+fHiF73vOnDncfffdTJgwoVQP8cWLFxk7diw1a9bEz8+Pjh07smPHDuPrv/76K506dcLb25vQ0FBGjBhR7L1e7yEODg5m3rx5AMTFxaFSqfj+++/p1asX3t7eLF68mOTkZMaOHUtMTAy+vr60atWKpUuXFhtHr9fz9ttv07BhQ7RaLXXq1OGNN94A4JZbbmHatGnFtr969SpeXl5s2rSpws/E2Tj8lqxr16507drV+Lxbt240a9aML7/8ktdee82mMZ977jmmT59ufJ6eni5FqkQikUiqFDkFOprPWGe2JgE4VMbW9nUzH5nZH18v+0iA//73v7z77rs0aNCAGjVqcOHCBQYNGsQbb7yBVqtlwYIFDB06lOPHj1OnTp0yx3n11Vd5++23eeedd/jkk08YN24c586do2bNmqVun52dzbvvvsvChQtRq9WMHz+ep556isWLFwPw1ltvsXjxYr799luaNWvGRx99xIoVK+jTp0+57ycjI4Mff/yRHTt20LRpU9LS0ti6dSs9e/YEhFe4V69exMTEsHLlSiIjI9mzZw96vSgTtmrVKkaMGMELL7zAggULyM/PZ/Xq1TZ9ru+99x7t2rXD29ub3NxcOnTowLPPPktgYCCrVq1iwoQJxMbG0rlzZ0Doo6+//poPPviAHj16EB8fz7FjxwC47777mDZtGu+99x5arRaARYsWERMTwy233GL1/ByNVd/O0NBQNBoNiYmJxdYnJiYSGRlp0Rienp60a9eOU6dOARj3S0xMLNZxIDExkbZt25Y6hlarNX64EolEIpFIXMfMmTPp16+f8XnNmjVp06aN8flrr73Gzz//zMqVK0tY8MyZPHkyY8eOBeDNN9/k448/ZufOnQwYMKDU7QsKCpg9ezaxsbEATJs2jZkzZxpf/+STT3juueeM1stPP/3UIqH43Xff0ahRI1q0aAHAmDFjmDNnjlGgLlmyhKtXr7Jr1y6jeG7YsKFx/zfeeIMxY8bw6quvGteZfx6W8vjjjzNy5Mhi68xDKh555BHWrVvHDz/8QOfOncnIyOCjjz7i008/ZdKkSQDExsbSo0cPAEaOHMm0adP45ZdfuOuuuwBhiZ48ebLTQieswSqB6uXlRYcOHdi0aZPRRK7X69m0aVO5XzpzdDodBw8eZNCgQQDUr1+fyMhINm3aZBSk6enp7Nixg6lTp1ozPYlEIpFIqgw+nhqOzOwPeVmQcgo0XhDezLSBQQ8JB8VyRAu7xqH6eNov6apjx47FnmdmZvLKK6+watUq4uPjKSwsJCcnh/Pnz5c7TuvWrY3Lfn5+BAYGllshyNfX1yhOQbTVVLZPS0sjMTHRaFkE0Gg0dOjQwWjpLIu5c+cyfvx44/Px48fTq1cvPvnkEwICAti3bx/t2rUr07K7b98+7r///nKPYQnXf646nY4333yTH374gUuXLpGfn09eXp4xlvfo0aPk5eVx6623ljqet7e3MWThrrvuYs+ePRw6dKhYKIU7YfW3ffr06UyaNImOHTvSuXNnPvzwQ7KyspgyZQoAEydOJCYmhlmzZgHizuqmm26iYcOGpKam8s4773Du3Dnuu+8+QMRiPP7447z++us0atSI+vXr89JLLxEdHW1RnIhEIpFIJFURlUol3OxqLXiqQaUHc7e7Ll+sB9BqRc1UN8TPz6/Y86eeeooNGzbw7rvv/p+9M4+P4fzj+GeySTb3IYkchEQEQQRBirppHE1RWmeJs1Vpq2hV1dmWFlVFlV8RtFWqRbVuqSjqJm5B5HDkIOS+s/P748nMZpNNsrvZK/F9v1772tnZZ2aenUx2P/M90bhxY1haWmLo0KEoKCiodD9mZmYKrzmOq1RMKhuvamxtRdy8eRNnzpzBuXPnMGvWLHF9cXExtm/fjkmTJomtPCuiqveVzVNZElTZ87ps2TJ89913WLlyJfz9/WFtbY1p06aJ57Wq4wLMzd+6dWs8fPgQ4eHh6NmzJxo2bFjldoZA7TJTw4YNw/LlyzFv3jy0bt0aUVFROHjwoJjklJCQoBCk/Pz5c0yaNAl+fn7o378/MjIy8N9//6F58+bimI8//hjvvfceJk+ejPbt2yMrKwsHDx4sV9CfIAiCIGodQpkpXiavewoo1kA1UnGqjFOnTiE0NBSDBw+Gv78/3NzcEBcXp9c52Nvbw9XVFefPnxfXFRcX49KlS5Vut3HjRnTt2hVXrlxBVFSU+Jg+fTo2btwIgFl6o6Ki8OzZM6X7aNWqVaVJRy4uLgo66e7du8jJyanyM506dQoDBw7E6NGjERAQgEaNGuHOnTvi+76+vrC0tKz02P7+/mjXrh1+/PFHbNu2DePHj6/yuIZCI39BWFhYhS79yMhIhdfffvstvv3220r3x3EcFi1apBA7QhAEQRAvBCYSgDMpEaiF8pqnQokprma1OfX19cWuXbsQEhICjuMwd+7cKt3quuC9997DkiVL0LhxYzRr1gyrV6/G8+fPK4y3LCwsxE8//YRFixahZcuWCu9NnDgRK1aswI0bNzBixAgsXrwYgwYNwpIlS+Du7o7Lly/Dw8MDHTt2xPz589GrVy/4+Phg+PDhKCoqwv79+0WLbM+ePbFmzRp07NgRxcXFmDVrVjlrsDJ8fX3x+++/47///oOjoyNWrFiB5ORk0eBnYWGBWbNm4eOPP4a5uTk6d+6MJ0+e4MaNG5gwYYLCZwkLC4O1tbVCdQFjQ6NWpwRBEARBaBGxWH+pOqI1tIvUihUr4OjoiE6dOiEkJATBwcFo27at3ucxa9YsjBgxAmPGjEHHjh1hY2OD4ODgCr2ze/fuRWpqqlLR5ufnBz8/P2zcuBHm5uY4fPgw6tati/79+8Pf3x9fffUVJBJ2I9G9e3fs3LkTe/fuRevWrdGzZ0+FlvDffPMNPD090aVLF4wcORIzZ85UqSbsZ599hrZt2yI4OBjdu3eHm5tbuVDIuXPnYsaMGZg3bx78/PwwbNiwcnG8I0aMgKmpKUaMGGHUnmqOr27AhhGQkZEBe3t7pKenw87OztDTIWopOYU5CNoWBAA4O/IsrMxULzJNEASRl5eH2NhYeHt7lxcGT+8ABdmAoxdg6cjWZT8F0h8AUjvAyafc/gj1kMlk8PPzw5tvvqlxmcvaQFxcHHx8fHD+/Hmd3ThUdK2ro9dq1m0ZQRAEQdRGlFpQDdtFqqYTHx+Pw4cPo1u3bsjPz8eaNWsQGxuLkSNHGnpqBqGwsBCpqan47LPP8NJLLxnEqq0O5OInCIIgCEMjKRGhslLZ3DXUxW8smJiYYPPmzWjfvj06d+6Ma9eu4ejRo/Dz86t641rIqVOn4O7ujvPnz2PdunWGnk6V0FVPEARBEIZGsKAqCFTBglqzkqSMBU9PT5w6dcrQ0zAaunfvXu0yXPqELKgEQRAEYWgEKym5+AkCAAlUgiAIgjA8EmUWVHLxEy8uJFAJgiAIwtBUmiRFLn7ixYMEKkEQBEEYmtJJUkKcIE8WVOLFhQQqQRAEQRia0iJUVsREKllQiRcYEqgEQRAEYWg4E7lIlRXJracAWVCJFxISqARBEARhDIiZ/IXyBCnOhD1qON27d8e0adPE115eXli5cmWl23Achz179lT72NraD6Ffav5VTxAEQRC1AbEWapHcvc8Z1r0fEhKCvn37Kn3vxIkT4DgOV69eVXu/58+fx+TJk6s7PQUWLFiA1q1bl1ufmJiIfv36afVYFZGbm4s6derA2dkZ+fn5ejlmbYUEKkEQBEEYA6VLTRlJiakJEybgyJEjePjwYbn3wsPD0a5dO7Rq1Urt/bq4uMDKykobU6wSNzc3SKVSvRzrjz/+QIsWLdCsWTODW215nkdRUVHVA40UEqgEQRAEYQh4HijIlj+KC4DCXCAvgz0Kc9m60mO09VCxo9Crr74KFxcXbN68WWF9VlYWdu7ciQkTJiA1NRUjRoxAvXr1YGVlBX9/f/z666+V7resi//u3bvo2rUrLCws0Lx5cxw5cqTcNrNmzUKTJk1gZWWFRo0aYe7cuSgsZHVjN2/ejIULF+LKlSvgOA4cx4lzLuviv3btGnr27AlLS0s4OTlh8uTJyMrKEt8PDQ3FoEGDsHz5cri7u8PJyQlTp04Vj1UZGzduxOjRozF69Ghs3Lix3Ps3btzAq6++Cjs7O9ja2qJLly6IiYkR39+0aRNatGgBqVQKd3d3hIWFAQDi4uLAcRyioqLEsWlpaeA4DpGRkQCAyMhIcByHAwcOIDAwEFKpFCdPnkRMTAwGDhwIV1dX2NjYoH379jh69KjCvPLz8zFr1ix4enpCKpWicePG2LhxI3ieR+PGjbF8+XKF8VFRUeA4Dvfu3avynGgKRV4TBEEQhCEozAEWexjm2J8+BsytqxxmamqKMWPGYPPmzZgzZw44jgMA7Ny5E8XFxRgxYgSysrIQGBiIWbNmwc7ODvv27cNbb70FHx8fdOjQocpjyGQyvP7663B1dcXZs2eRnp6uEK8qYGtri82bN8PDwwPXrl3DpEmTYGtri48//hjDhg3D9evXcfDgQVF82dvbl9tHdnY2goOD0bFjR5w/fx4pKSmYOHEiwsLCFET4sWPH4O7ujmPHjuHevXsYNmwYWrdujUmTJlX4OWJiYnD69Gns2rULPM/jww8/RHx8PBo2bAgAePToEbp27Yru3bvjn3/+gZ2dHU6dOiVaOX/44QdMnz4dX331Ffr164f09HSNWrV+8sknWL58ORo1agRHR0c8ePAA/fv3x5dffgmpVIqtW7ciJCQE0dHRaNCgAQBgzJgxOH36NFatWoWAgADExsbi6dOn4DgO48ePR3h4OGbOnCkeIzw8HF27dkXjxo3Vnp+qkEAlCIIgCKJCxo8fj2XLluH48ePo3r07ACZQhgwZAnt7e9jb2yuIl/feew+HDh3Cb7/9ppJAPXr0KG7fvo1Dhw7Bw4MJ9sWLF5eLG/3ss8/EZS8vL8ycORPbt2/Hxx9/DEtLS9jY2MDU1BRubm4VHmvbtm3Iy8vD1q1bYW3NBPqaNWsQEhKCr7/+Gq6urgAAR0dHrFmzBhKJBM2aNcOAAQMQERFRqUDdtGkT+vXrB0dHRwBAcHAwwsPDsWDBAgDA999/D3t7e2zfvh1mZiyco0mTJuL2X3zxBWbMmIEPPvhAXNe+ffsqz19ZFi1ahD59+oiv69Spg4CAAPH1559/jt27d2Pv3r0ICwvDnTt38Ntvv+HIkSPo3bs3AKBRo0bi+NDQUMybNw/nzp1Dhw4dUFhYiG3btpWzqmobEqgEQRAEYQjMrJglUyA/C3gWA5haMOtmTipgUxewddfNsVWkWbNm6NSpEzZt2oTu3bvj3r17OHHiBBYtWgQAKC4uxuLFi/Hbb7/h0aNHKCgoQH5+vsoxprdu3YKnp6coTgGgY8eO5cbt2LEDq1atQkxMDLKyslBUVAQ7OzuVP4dwrICAAFGcAkDnzp0hk8kQHR0tCtQWLVpAIpEnqLm7u+PatWsV7re4uBhbtmzBd999J64bPXo0Zs6ciXnz5sHExARRUVHo0qWLKE5Lk5KSgsePH6NXr15qfR5ltGvXTuF1VlYWFixYgH379iExMRFFRUXIzc1FQkICAOaul0gk6Natm9L9eXh4YMCAAdi0aRM6dOiAv/76C/n5+XjjjTeqPdfKoBhUgiAIgjAEHMeEqPCwsAPMLFlilMScLUvtFMdo61HiqleVCRMm4I8//kBmZibCw8Ph4+MjCpply5bhu+++w6xZs3Ds2DFERUUhODgYBQUFWjtVp0+fxqhRo9C/f3/8/fffuHz5MubMmaPVY5SmrIjkOA4ymazC8YcOHcKjR48wbNgwmJqawtTUFMOHD0d8fDwiIiIAAJaWlhVuX9l7AGBiwuQaXyp2uKKY2NLiGwBmzpyJ3bt3Y/HixThx4gSioqLg7+8vnruqjg0AEydOxPbt25Gbm4vw8HAMGzZM50luJFAJgiAIwhgQsvj5YpbJDxg8i1/gzTffhImJCbZt24atW7di/PjxYjzqqVOnMHDgQIwePRoBAQFo1KgR7ty5o/K+/fz88ODBAyQmJorrzpw5ozDmv//+Q8OGDTFnzhy0a9cOvr6+iI+PVxhjbm6O4uJiVIafnx+uXLmC7Oxscd2pU6dgYmKCpk2bqjznsmzcuBHDhw9HVFSUwmP48OFislSrVq1w4sQJpcLS1tYWXl5eopgti4uLCwAonKPSCVOVcerUKYSGhmLw4MHw9/eHm5sb4uLixPf9/f0hk8lw/PjxCvfRv39/WFtb44cffsDBgwcxfvx4lY5dHUigEgRBEIQxwEkAlFg2C/PYs5G0ObWxscGwYcMwe/ZsJCYmIjQ0VHzP19cXR44cwX///Ydbt27h7bffRnJyssr77t27N5o0aYKxY8fiypUrOHHiBObMmaMwxtfXFwkJCdi+fTtiYmKwatUq7N69W2GMl5cXYmNjERUVhadPnyqtQzpq1ChYWFhg7NixuH79Oo4dO4b33nsPb731lujeV5cnT57gr7/+wtixY9GyZUuFx5gxY7Bnzx48e/YMYWFhyMjIwPDhw3HhwgXcvXsXP/30E6KjowGwOq7ffPMNVq1ahbt37+LSpUtYvXo1AGblfOmll/DVV1/h1q1bOH78uEJMbmX4+vpi165diIqKwpUrVzBy5EgFa7CXlxfGjh2L8ePHY8+ePYiNjUVkZCR+++03cYxEIkFoaChmz54NX19fpSEY2oYEKkEQBEEYAxynaEUFjMaCCjA3//PnzxEcHKwQL/rZZ5+hbdu2CA4ORvfu3eHm5oZBgwapvF8TExPs3r0bubm56NChAyZOnIgvv/xSYcxrr72GDz/8EGFhYWjdujX+++8/zJ07V2HMkCFD0LdvX/To0QMuLi5KS11ZWVnh0KFDePbsGdq3b4+hQ4eiV69eWLNmjXonoxRCwpWy+NFevXrB0tISP//8M5ycnPDPP/8gKysL3bp1Q2BgIH788UcxnGDs2LFYuXIl1q5dixYtWuDVV1/F3bt3xX1t2rQJRUVFCAwMxLRp0/DFF1+oNL8VK1bA0dERnTp1QkhICIKDg9G2bVuFMT/88AOGDh2Kd999F82aNcOkSZMUrMwA+/sXFBRg3Lhx6p4ijeB4XsViaEZMRkYG7O3tkZ6ernbANEGoSk5hDoK2BQEAzo48Cys1kgwIgiDy8vIQGxsLb29vWFhYKB/0JJqVnxKo68eSpgjCwJw4cQK9evXCgwcPqrQ2V3Stq6PXjOfWjCAIgiBedEzKZHgbkQWVeDHJz8/HkydPsGDBArzxxhsah0KoC7n4CYIgCMJYkJQRpJxxxKASLy6//vorGjZsiLS0NCxdulRvxyWBShAEQRDGQmkLqomp2uWgCELbhIaGori4GBcvXkS9evX0dlwSqARBEARhLJS2oJL1lHiBIYFKEARBEMZCWQsqQbygkEAlCIIgCGNBUlqgkgWVeHEhgUoQBEEQxkJpqylZUIkXGBKoBEEQBGEskIufIACQQCUIgiAI48HERJ4cRS5+4gVGI4H6/fffw8vLCxYWFggKCsK5c+dU2m779u3gOK5cC7TQ0FBwHKfw6Nu3ryZTIwiCIIiajWA5JQuqxkRGRoLjOKSlpRl6KtXCy8sLK1euNPQ0DILaAnXHjh2YPn065s+fj0uXLiEgIADBwcFISUmpdLu4uDjMnDkTXbp0Ufp+3759kZiYKD6U9dAlCIIgiFqPqZQ9S8wNO48Snjx5gilTpqBBgwaQSqVwc3NDcHAwTp06JY7hOA579uzRyvHi4uLAcRyioqJUGlf2MXr0aHTq1AmJiYmwt7fXypyUoezYpR8LFiyo9jHOnz+PyZMnV3+yNRC1b89WrFiBSZMmYdy4cQCAdevWYd++fdi0aRM++eQTpdsUFxdj1KhRWLhwIU6cOKH0jka46AmCIAjihca+PlCQDUhtDT0TAMCQIUNQUFCALVu2oFGjRkhOTkZERARSU1O1fqyCggK1tzl69ChatGghvra0tIS5ubnONUViYqK4vGPHDsybNw/R0dHiOhsbm2ofw8XFpdr7qKmoZUEtKCjAxYsX0bt3b/kOTEzQu3dvnD59usLtFi1ahLp162LChAkVjomMjETdunXRtGlTTJkypdILPz8/HxkZGQoPgiAIgqhJ8DyPnMKc8g++GDlmFsgpylX+vhYePM+rNMe0tDScOHECX3/9NXr06IGGDRuiQ4cOmD17Nl577TUAzA0NAIMHDwbHceLrmJgYDBw4EK6urrCxsUH79u1x9OhRhf17eXnh888/x5gxY2BnZ4fJkyfD29sbANCmTRtwHIfu3btXOkcnJye4ubmJD3t7+3Iu/s2bN8PBwQGHDh2Cn58fbGxsRM9taTZs2AA/Pz9YWFigWbNmWLt2bYXHLXtMjuPE1+vWrcPLL7+sMH7lypXiuQFYeOOgQYOwfPlyuLu7w8nJCVOnTkVhYaHC+Snt4uc4Dhs2bMDgwYNhZWUFX19f7N27V+E4e/fuha+vLywsLNCjRw9s2bKlRoY7qGVBffr0KYqLi+Hq6qqw3tXVFbdv31a6zcmTJ7Fx48ZKTfV9+/bF66+/Dm9vb8TExODTTz9Fv379cPr0aUgk5YPElyxZgoULF6ozdYIgCIIwKnKLchG0Lcggxz478iyszKyqHGdjYwMbGxvs2bMHL730EqRSabkx58+fR926dREeHo6+ffuKv9tZWVno378/vvzyS0ilUmzduhUhISGIjo5GgwYNxO2XL1+OefPmYf78+QCAqVOnokOHDqJl1NxcO6EOOTk5WL58OX766SeYmJhg9OjRmDlzJn755RcAwC+//IJ58+ZhzZo1aNOmDS5fvoxJkybB2toaY8eO1cocynLs2DG4u7vj2LFjuHfvHoYNG4bWrVtj0qRJFW6zcOFCLF26FMuWLcPq1asxatQoxMfHo06dOoiNjcXQoUPxwQcfYOLEibh8+TJmzpypk7nrGp1m8WdmZuKtt97Cjz/+CGdn5wrHDR8+HK+99hr8/f0xaNAg/P333zh//jwiIyOVjp89ezbS09PFx4MHD3T0CQiCIAjixcXU1BSbN2/Gli1b4ODggM6dO+PTTz/F1atXxTGCG9rBwQFubm7i64CAALz99tto2bIlfH198fnnn8PHx6ecxa9nz56YMWMGfHx84OPjI24vWEbr1KlT6Rw7deokCmkbGxtcvnxZ6bjCwkKsW7cO7dq1Q9u2bREWFoaIiAjx/fnz5+Obb74RDWavv/46PvzwQ6xfv179E6cijo6OWLNmDZo1a4ZXX30VAwYMUJiTMkJDQzFixAg0btwYixcvRlZWlpisvn79ejRt2hTLli1D06ZNMXz4cISGhups/rpELQuqs7MzJBIJkpOTFdYnJycrjfWIiYlBXFwcQkJCxHUymYwd2NQU0dHR8PHxKbddo0aN4OzsjHv37qFXr17l3pdKpUrv4giCIAiipmBpaomzI88a7NiqMmTIEAwYMAAnTpzAmTNncODAASxduhQbNmyoVPxkZWVhwYIF2LdvHxITE1FUVITc3FwkJCQojGvXrp2mHwMAi//08/MTX3t6eioNO7SyslLQHO7u7mKCd3Z2NmJiYjBhwgQF62VRUZFOE61atGih4Cl2d3fHtWvXKt2mVatW4rK1tTXs7OzEzxEdHY327dsrjO/QoYMWZ6w/1BKo5ubmCAwMREREhFgqSiaTISIiAmFhYeXGN2vWrNyJ/uyzz5CZmYnvvvsOnp6eSo/z8OFDpKamwt3dXZ3pEQRBEESNgeM4ldzsxoCFhQX69OmDPn36YO7cuZg4cSLmz59fqUCdOXMmjhw5guXLl6Nx48awtLTE0KFDyyVCWVtbV2tunp6eaNy4cZXjzMzMFF5zHCfG4mZlZQEAfvzxRwQFKYZdKAs1rAoTE5Nycb6lY0srm5NgyKsITbapiaidxT99+nSMHTsW7dq1Q4cOHbBy5UpkZ2eLWf1jxoxBvXr1sGTJElhYWKBly5YK2zs4OACAuD4rKwsLFy7EkCFD4ObmhpiYGHz88cdo3LgxgoODq/nxCIIgCILQNs2bN1coK2VmZobi4mKFMadOnUJoaCgGDx4MgP3ex8XFVblvIea07P50iaurKzw8PHD//n2MGjWq2vtzcXFBUlISeJ4Hx3EAUGXZLG3QtGlT7N+/X2Hd+fPndX5cXaC2QB02bBiePHmCefPmISkpCa1bt8bBgwfFxKmEhASYmKge2iqRSHD16lVs2bIFaWlp8PDwwCuvvILPP/+c3PgEQRAEYUBSU1PxxhtvYPz48WjVqhVsbW1x4cIFLF26FAMHDhTHeXl5ISIiAp07d4ZUKoWjoyN8fX2xa9cuhISEgOM4zJ07VyVLX926dWFpaYmDBw+ifv36sLCw0KmbXWDhwoV4//33YW9vj759+yI/Px8XLlzA8+fPMX36dLX21b17dzx58gRLly7F0KFDcfDgQRw4cAB2dnY6mj3j7bffxooVKzBr1ixMmDABUVFR2Lx5MwCIQrmmoFGSVFhYGOLj45Gfn4+zZ88qmMMjIyPFk6GMzZs3K9x1WVpa4tChQ0hJSUFBQQHi4uLwv//9r1ylAIIgCIIg9IuNjQ2CgoLw7bffomvXrmjZsiXmzp2LSZMmYc2aNeK4b775BkeOHIGnpyfatGkDgNVNd3R0RKdOnRASEoLg4GC0bdu2ymOamppi1apVWL9+PTw8PBSEsC6ZOHEiNmzYgPDwcPj7+6Nbt27YvHmzWPZKHfz8/LB27Vp8//33CAgIwLlz5/SSTe/t7Y3ff/8du3btQqtWrfDDDz9gzpw5AFDjjH4cr2oxNCMmIyMD9vb2SE9P1/ndCfHiklOYI5aEUbVEC0EQhEBeXh5iY2Ph7e0NCwsLQ0+HeEH48ssvsW7dOr1WPKroWldHr1GjX4IgCIIgiFrC2rVr0b59ezg5OeHUqVNYtmyZ0kR2Y4cEKkEQBEEQRC3h7t27+OKLL/Ds2TM0aNAAM2bMwOzZsw09LbUhFz9BqAjP88gtygXAagjWtIBzgiAMC7n4iRcFcvEThB6pSTULCYIgCKImo9NWpwRBEARBKFILHJcEUSnauMZJoBIEQRCEHhA6AOXk5Bh4JgShW4RrvGzXK3UgFz9BEARB6AGJRAIHBwexb7qVlRXFshO1Cp7nkZOTg5SUFDg4OGjUJlaABCpBEARB6Ak3NzcAEEUqQdRGHBwcxGtdU0igEgRBEISe4DgO7u7uqFu3LgoLCw09HYLQOmZmZtWynAqQQCUIgiAIPSORSLTyI04QtRVKkiIIgiAIgiCMChKoBEEQBEEQhFFBApUgCIIgCIIwKmpFDKpQEDYjI8PAMyEIgiAIgiCUIeg0VQr51wqBmpmZCQDw9PQ08EwIgiAIgiCIysjMzIS9vX2lYzi+FvRck8lkePz4MWxtbfVW9DgjIwOenp548OAB7Ozs9HLMmgCdl4qhc6McOi/KofNSMXRulEPnpWLo3ChH3+eF53lkZmbCw8MDJiaVR5nWCguqiYkJ6tevb5Bj29nZ0cWuBDovFUPnRjl0XpRD56Vi6Nwoh85LxdC5UY4+z0tVllMBSpIiCIIgCIIgjAoSqARBEARBEIRRQQJVQ6RSKebPnw+pVGroqRgVdF4qhs6Ncui8KIfOS8XQuVEOnZeKoXOjHGM+L7UiSYogCIIgCIKoPZAFlSAIgiAIgjAqSKASBEEQBEEQRgUJVIIgCIIgCMKoIIFKEARBEARBGBUkUDXg+++/h5eXFywsLBAUFIRz584ZekoGZ8GCBeA4TuHRrFkzQ09L7/z7778ICQmBh4cHOI7Dnj17FN7neR7z5s2Du7s7LC0t0bt3b9y9e9cwk9UzVZ2b0NDQctdQ3759DTNZPbJkyRK0b98etra2qFu3LgYNGoTo6GiFMXl5eZg6dSqcnJxgY2ODIUOGIDk52UAz1g+qnJfu3buXu2beeecdA81Yf/zwww9o1aqVWFy9Y8eOOHDggPj+i3i9AFWflxf1einLV199BY7jMG3aNHGdMV4zJFDVZMeOHZg+fTrmz5+PS5cuISAgAMHBwUhJSTH01AxOixYtkJiYKD5Onjxp6CnpnezsbAQEBOD7779X+v7SpUuxatUqrFu3DmfPnoW1tTWCg4ORl5en55nqn6rODQD07dtX4Rr69ddf9ThDw3D8+HFMnToVZ86cwZEjR1BYWIhXXnkF2dnZ4pgPP/wQf/31F3bu3Injx4/j8ePHeP311w04a92jynkBgEmTJilcM0uXLjXQjPVH/fr18dVXX+HixYu4cOECevbsiYEDB+LGjRsAXszrBaj6vAAv5vVSmvPnz2P9+vVo1aqVwnqjvGZ4Qi06dOjAT506VXxdXFzMe3h48EuWLDHgrAzP/Pnz+YCAAENPw6gAwO/evVt8LZPJeDc3N37ZsmXiurS0NF4qlfK//vqrAWZoOMqeG57n+bFjx/IDBw40yHyMiZSUFB4Af/z4cZ7n2TViZmbG79y5Uxxz69YtHgB/+vRpQ01T75Q9LzzP8926deM/+OADw03KiHB0dOQ3bNhA10sZhPPC83S9ZGZm8r6+vvyRI0cUzoWxXjNkQVWDgoICXLx4Eb179xbXmZiYoHfv3jh9+rQBZ2Yc3L17Fx4eHmjUqBFGjRqFhIQEQ0/JqIiNjUVSUpLC9WNvb4+goCC6fkqIjIxE3bp10bRpU0yZMgWpqamGnpLeSU9PBwDUqVMHAHDx4kUUFhYqXDfNmjVDgwYNXqjrpux5Efjll1/g7OyMli1bYvbs2cjJyTHE9AxGcXExtm/fjuzsbHTs2JGulxLKnheBF/l6mTp1KgYMGKBwbQDG+x1jarAj10CePn2K4uJiuLq6Kqx3dXXF7du3DTQr4yAoKAibN29G06ZNkZiYiIULF6JLly64fv06bG1tDT09oyApKQkAlF4/wnsvMn379sXrr78Ob29vxMTE4NNPP0W/fv1w+vRpSCQSQ09PL8hkMkybNg2dO3dGy5YtAbDrxtzcHA4ODgpjX6TrRtl5AYCRI0eiYcOG8PDwwNWrVzFr1ixER0dj165dBpytfrh27Ro6duyIvLw82NjYYPfu3WjevDmioqJe6OulovMCvNjXy/bt23Hp0iWcP3++3HvG+h1DApXQCv369ROXW7VqhaCgIDRs2BC//fYbJkyYYMCZETWF4cOHi8v+/v5o1aoVfHx8EBkZiV69ehlwZvpj6tSpuH79+gsZv10ZFZ2XyZMni8v+/v5wd3dHr169EBMTAx8fH31PU680bdoUUVFRSE9Px++//46xY8fi+PHjhp6WwanovDRv3vyFvV4ePHiADz74AEeOHIGFhYWhp6My5OJXA2dnZ0gkknKZbcnJyXBzczPQrIwTBwcHNGnSBPfu3TP0VIwG4Rqh60c1GjVqBGdn5xfmGgoLC8Pff/+NY8eOoX79+uJ6Nzc3FBQUIC0tTWH8i3LdVHRelBEUFAQAL8Q1Y25ujsaNGyMwMBBLlixBQEAAvvvuuxf+eqnovCjjRbleLl68iJSUFLRt2xampqYwNTXF8ePHsWrVKpiamsLV1dUorxkSqGpgbm6OwMBAREREiOtkMhkiIiIUYlwIICsrCzExMXB3dzf0VIwGb29vuLm5KVw/GRkZOHv2LF0/Snj48CFSU1Nr/TXE8zzCwsKwe/du/PPPP/D29lZ4PzAwEGZmZgrXTXR0NBISEmr1dVPVeVFGVFQUANT6a0YZMpkM+fn5L+z1UhHCeVHGi3K99OrVC9euXUNUVJT4aNeuHUaNGiUuG+U1Y7D0rBrK9u3bealUym/evJm/efMmP3nyZN7BwYFPSkoy9NQMyowZM/jIyEg+NjaWP3XqFN+7d2/e2dmZT0lJMfTU9EpmZiZ/+fJl/vLlyzwAfsWKFfzly5f5+Ph4nud5/quvvuIdHBz4P//8k7969So/cOBA3tvbm8/NzTXwzHVPZecmMzOTnzlzJn/69Gk+NjaWP3r0KN+2bVve19eXz8vLM/TUdcqUKVN4e3t7PjIykk9MTBQfOTk54ph33nmHb9CgAf/PP//wFy5c4Dt27Mh37NjRgLPWPVWdl3v37vGLFi3iL1y4wMfGxvJ//vkn36hRI75r164Gnrnu+eSTT/jjx4/zsbGx/NWrV/lPPvmE5ziOP3z4MM/zL+b1wvOVn5cX+XpRRtmKBsZ4zZBA1YDVq1fzDRo04M3NzfkOHTrwZ86cMfSUDM6wYcN4d3d33tzcnK9Xrx4/bNgw/t69e4aelt45duwYD6DcY+zYsTzPs1JTc+fO5V1dXXmpVMr36tWLj46ONuyk9URl5yYnJ4d/5ZVXeBcXF97MzIxv2LAhP2nSpBfixk/ZOQHAh4eHi2Nyc3P5d999l3d0dOStrKz4wYMH84mJiYabtB6o6rwkJCTwXbt25evUqcNLpVK+cePG/EcffcSnp6cbduJ6YPz48XzDhg15c3Nz3sXFhe/Vq5coTnn+xbxeeL7y8/IiXy/KKCtQjfGa4Xie5/VnryUIgiAIgiCIyqEYVIIgCIIgCMKoIIFKEARBEARBGBUkUAmCIAiCIAijggQqQRAEQRAEYVSQQCUIgiAIgiCMChKoBEEQBEEQhFFBApUgCIIgCIIwKkigEgRBEARBEEYFCVSCIAiCIAjCqCCBShAEQRAEQRgVJFAJgiAIgiAIo4IEKkEQBEEQBGFUkEAlCEIjQkND4eXlpdG2CxYsAMdx2p2QkREXFweO47B582a9H5vjOCxYsEB8vXnzZnAch7i4uCq39fLyQmhoqFbnU51rhSCIFxMSqARRy+A4TqVHZGSkoaf6wvP++++D4zjcu3evwjFz5swBx3G4evWqHmemPo8fP8aCBQsQFRVl6KmICDcJy5cvN/RUCIJQE1NDT4AgCO3y008/KbzeunUrjhw5Um69n59ftY7z448/QiaTabTtZ599hk8++aRax68NjBo1CqtXr8a2bdswb948pWN+/fVX+Pv7o1WrVhof56233sLw4cMhlUo13kdVPH78GAsXLoSXlxdat26t8F51rhWCIF5MSKASRC1j9OjRCq/PnDmDI0eOlFtflpycHFhZWal8HDMzM43mBwCmpqYwNaWvn6CgIDRu3Bi//vqrUoF6+vRpxMbG4quvvqrWcSQSCSQSSbX2UR2qc60QBPFiQi5+gngB6d69O1q2bImLFy+ia9eusLKywqeffgoA+PPPPzFgwAB4eHhAKpXCx8cHn3/+OYqLixX2UTausLQ79X//+x98fHwglUrRvn17nD9/XmFbZTGoHMchLCwMe/bsQcuWLSGVStGiRQscPHiw3PwjIyPRrl07WFhYwMfHB+vXr1c5rvXEiRN444030KBBA0ilUnh6euLDDz9Ebm5uuc9nY2ODR48eYdCgQbCxsYGLiwtmzpxZ7lykpaUhNDQU9vb2cHBwwNixY5GWllblXABmRb19+zYuXbpU7r1t27aB4ziMGDECBQUFmDdvHgIDA2Fvbw9ra2t06dIFx44dq/IYymJQeZ7HF198gfr168PKygo9evTAjRs3ym377NkzzJw5E/7+/rCxsYGdnR369euHK1euiGMiIyPRvn17AMC4cePEMBIh/lZZDGp2djZmzJgBT09PSKVSNG3aFMuXLwfP8wrj1LkuNCUlJQUTJkyAq6srLCwsEBAQgC1btpQbt337dgQGBsLW1hZ2dnbw9/fHd999J75fWFiIhQsXwtfXFxYWFnBycsLLL7+MI0eOaG2uBPGiQCYMgnhBSU1NRb9+/TB8+HCMHj0arq6uAJiYsbGxwfTp02FjY4N//vkH8+bNQ0ZGBpYtW1blfrdt24bMzEy8/fbb4DgOS5cuxeuvv4779+9XaUk7efIkdu3ahXfffRe2trZYtWoVhgwZgoSEBDg5OQEALl++jL59+8Ld3R0LFy5EcXExFi1aBBcXF5U+986dO5GTk4MpU6bAyckJ586dw+rVq/Hw4UPs3LlTYWxxcTGCg4MRFBSE5cuX4+jRo/jmm2/g4+ODKVOmAGBCb+DAgTh58iTeeecd+Pn5Yffu3Rg7dqxK8xk1ahQWLlyIbdu2oW3btgrH/u2339ClSxc0aNAAT58+xYYNGzBixAhMmjQJmZmZ2LhxI4KDg3Hu3LlybvWqmDdvHr744gv0798f/fv3x6VLl/DKK6+goKBAYdz9+/exZ88evPHGG/D29kZycjLWr1+Pbt264ebNm/Dw8ICfnx8WLVqEefPmYfLkyejSpQsAoFOnTkqPzfM8XnvtNRw7dgwTJkxA69atcejQIXz00Ud49OgRvv32W4XxqlwXmpKbm4vu3bvj3r17CAsLg7e3N3bu3InQ0FCkpaXhgw8+AAAcOXIEI0aMQK9evfD1118DAG7duoVTp06JYxYsWIAlS5Zg4sSJ6NChAzIyMnDhwgVcunQJffr0qdY8CeKFgycIolYzdepUvuy/erdu3XgA/Lp168qNz8nJKbfu7bff5q2srPi8vDxx3dixY/mGDRuKr2NjY3kAvJOTE//s2TNx/Z9//skD4P/66y9x3fz588vNCQBvbm7O37t3T1x35coVHgC/evVqcV1ISAhvZWXFP3r0SFx39+5d3tTUtNw+laHs8y1ZsoTnOI6Pj49X+HwA+EWLFimMbdOmDR8YGCi+3rNnDw+AX7p0qbiuqKiI79KlCw+ADw8Pr3JO7du35+vXr88XFxeL6w4ePMgD4NevXy/uMz8/X2G758+f866urvz48eMV1gPg58+fL74ODw/nAfCxsbE8z/N8SkoKb25uzg8YMICXyWTiuE8//ZQHwI8dO1Zcl5eXpzAvnmd/a6lUqnBuzp8/X+HnLXutCOfsiy++UBg3dOhQnuM4hWtA1etCGcI1uWzZsgrHrFy5kgfA//zzz+K6goICvmPHjryNjQ2fkZHB8zzPf/DBB7ydnR1fVFRU4b4CAgL4AQMGVDongiBUg1z8BPGCIpVKMW7cuHLrLS0txeXMzEw8ffoUXbp0QU5ODm7fvl3lfocNGwZHR0fxtWBNu3//fpXb9u7dGz4+PuLrVq1awc7OTty2uLgYR48exaBBg+Dh4SGOa9y4Mfr161fl/gHFz5ednY2nT5+iU6dO4Hkely9fLjf+nXfeUXjdpUsXhc+yf/9+mJqaihZVgMV8vvfeeyrNB2Bxww8fPsS///4rrtu2bRvMzc3xxhtviPs0NzcHAMhkMjx79gxFRUVo166d0vCAyjh69CgKCgrw3nvvKYRFTJs2rdxYqVQKExP2U1FcXIzU1FTY2NigadOmah9XYP/+/ZBIJHj//fcV1s+YMQM8z+PAgQMK66u6LqrD/v374ebmhhEjRojrzMzM8P777yMrKwvHjx8HADg4OCA7O7tSd72DgwNu3LiBu3fvVnteBPGiQwKVIF5Q6tWrJwqe0ty4cQODBw+Gvb097Ozs4OLiIiZYpaenV7nfBg0aKLwWxOrz58/V3lbYXtg2JSUFubm5aNy4cblxytYpIyEhAaGhoahTp44YV9qtWzcA5T+fhYVFudCB0vMBgPj4eLi7u8PGxkZhXNOmTVWaDwAMHz4cEokE27ZtAwDk5eVh9+7d6Nevn4LY37JlC1q1aiXGN7q4uGDfvn0q/V1KEx8fDwDw9fVVWO/i4qJwPICJ4W+//Ra+vr6QSqVwdnaGi4sLrl69qvZxSx/fw8MDtra2CuuFyhLC/ASqui6qQ3x8PHx9fUURXtFc3n33XTRp0gT9+vVD/fr1MX78+HJxsIsWLUJaWhqaNGkCf39/fPTRR0ZfHowgjBUSqATxglLakiiQlpaGbt264cqVK1i0aBH++usvHDlyRIy5U6VUUEXZ4nyZ5Bdtb6sKxcXF6NOnD/bt24dZs2Zhz549OHLkiJjMU/bz6SvzvW7duujTpw/++OMPFBYW4q+//kJmZiZGjRoljvn5558RGhoKHx8fbNy4EQcPHsSRI0fQs2dPnZZwWrx4MaZPn46uXbvi559/xqFDh3DkyBG0aNFCb6WjdH1dqELdunURFRWFvXv3ivGz/fr1U4g17tq1K2JiYrBp0ya0bNkSGzZsQNu2bbFhwwa9zZMgaguUJEUQhEhkZCRSU1Oxa9cudO3aVVwfGxtrwFnJqVu3LiwsLJQWtq+s2L3AtWvXcOfOHWzZsgVjxowR11cny7phw4aIiIhAVlaWghU1Ojparf2MGjUKBw8exIEDB7Bt2zbY2dkhJCREfP/3339Ho0aNsGvXLgW3/Pz58zWaMwDcvXsXjRo1Etc/efKknFXy999/R48ePbBx40aF9WlpaXB2dhZfq9MZrGHDhjh69CgyMzMVrKhCCIkwP33QsGFDXL16FTKZTMGKqmwu5ubmCAkJQUhICGQyGd59912sX78ec+fOFS34derUwbhx4zBu3DhkZWWha9euWLBgASZOnKi3z0QQtQGyoBIEISJYqkpbpgoKCrB27VpDTUkBiUSC3r17Y8+ePXj8+LG4/t69e+XiFivaHlD8fDzPK5QKUpf+/fujqKgIP/zwg7iuuLgYq1evVms/gwYNgpWVFdauXYsDBw7g9ddfh4WFRaVzP3v2LE6fPq32nHv37g0zMzOsXr1aYX8rV64sN1YikZSzVO7cuROPHj1SWGdtbQ0AKpXX6t+/P4qLi7FmzRqF9d9++y04jlM5nlgb9O/fH0lJSdixY4e4rqioCKtXr4aNjY0Y/pGamqqwnYmJidg8IT8/X+kYGxsbNG7cWHyfIAjVIQsqQRAinTp1gqOjI8aOHSu24fzpp5/06kqtigULFuDw4cPo3LkzpkyZIgqdli1bVtlms1mzZvDx8cHMmTPx6NEj2NnZ4Y8//qhWLGNISAg6d+6MTz75BHFxcWjevDl27dqldnymjY0NBg0aJMahlnbvA8Crr76KXbt2YfDgwRgwYABiY2Oxbt06NG/eHFlZWWodS6jnumTJErz66qvo378/Ll++jAMHDihYRYXjLlq0COPGjUOnTp1w7do1/PLLLwqWVwDw8fGBg4MD1q1bB1tbW1hbWyMoKAje3t7ljh8SEoIePXpgzpw5iIuLQ0BAAA4fPow///wT06ZNU0iI0gYRERHIy8srt37QoEGYPHky1q9fj9DQUFy8eBFeXl74/fffcerUKaxcuVK08E6cOBHPnj1Dz549Ub9+fcTHx2P16tVo3bq1GK/avHlzdO/eHYGBgahTpw4uXLiA33//HWFhYVr9PATxIkAClSAIEScnJ/z999+YMWMGPvvsMzg6OmL06NHo1asXgoODDT09AEBgYCAOHDiAmTNnYu7cufD09MSiRYtw69atKqsMmJmZ4a+//sL777+PJUuWwMLCAoMHD0ZYWBgCAgI0mo+JiQn27t2LadOm4eeffwbHcXjttdfwzTffoE2bNmrta9SoUdi2bRvc3d3Rs2dPhfdCQ0ORlJSE9evX49ChQ2jevDl+/vln7Ny5E5GRkWrP+4svvoCFhQXWrVuHY8eOISgoCIcPH8aAAQMUxn366afIzs7Gtm3bsGPHDrRt2xb79u0r16rWzMwMW7ZswezZs/HOO++gqKgI4eHhSgWqcM7mzZuHHTt2IDw8HF5eXli2bBlmzJih9mepioMHDyot7O/l5YWWLVsiMjISn3zyCbZs2YKMjAw0bdoU4eHhCA0NFceOHj0a//vf/7B27VqkpaXBzc0Nw4YNw4IFC8TQgPfffx979+7F4cOHkZ+fj4YNG+KLL77ARx99pPXPRBC1HY43JtMIQRCEhgwaNIhK/BAEQdQSKAaVIIgaR9m2pHfv3sX+/fvRvXt3w0yIIAiC0CpkQSUIosbh7u6O0NBQNGrUCPHx8fjhhx+Qn5+Py5cvl6vtSRAEQdQ8KAaVIIgaR9++ffHrr78iKSkJUqkUHTt2xOLFi0mcEgRB1BLIgkoQBEEQBEEYFRSDShAEQRAEQRgVJFAJgiAIgiAIo0KjGNTvv/8ey5YtQ1JSEgICArB69Wp06NChwvErV67EDz/8gISEBDg7O2Po0KFiDUJN91kamUyGx48fw9bWVq12ewRBEARBEIR+4HkemZmZ8PDwUGgtXNFgtdi+fTtvbm7Ob9q0ib9x4wY/adIk3sHBgU9OTlY6/pdffuGlUin/yy+/8LGxsfyhQ4d4d3d3/sMPP9R4n2V58OABD4Ae9KAHPehBD3rQgx5G/njw4EGV2k7tJKmgoCC0b99e7KEsk8ng6emJ9957r1xnEQAICwvDrVu3EBERIa6bMWMGzp49i5MnT2q0z/z8fIXexunp6WjQoAEePHgAOzs7dT4OQRAEQRAEoQcyMjLg6emJtLQ02NvbVzpWLRd/QUEBLl68iNmzZ4vrTExM0Lt3b5w+fVrpNp06dcLPP/+Mc+fOoUOHDrh//z7279+Pt956S+N9LlmyBAsXLiy33s7OjgQqQRAEQRCEEaNKOKZaSVJPnz5FcXExXF1dFda7uroiKSlJ6TYjR47EokWL8PLLL8PMzAw+Pj7o3r07Pv30U433OXv2bKSnp4uPBw8eqPMxCIIgCIIgCCNG51n8kZGRWLx4MdauXYtLly5h165d2LdvHz7//HON9ymVSkVrKVlNCYIgCIIgahdqufidnZ0hkUiQnJyssD45ORlubm5Kt5k7dy7eeustTJw4EQDg7++P7OxsTJ48GXPmzNFonwRBEARBEETtRS2Bam5ujsDAQERERGDQoEEAWEJTREQEwsLClG6Tk5NTrpSARCIBAPA8r9E+CYIgCIJg8DyPoqIiFBcXG3oqBAGJRAJTU9Nql/1Uuw7q9OnTMXbsWLRr1w4dOnTAypUrkZ2djXHjxgEAxowZg3r16mHJkiUAgJCQEKxYsQJt2rRBUFAQ7t27h7lz5yIkJEQUqlXtkyAIgiCI8hQUFCAxMRE5OTmGngpBiFhZWcHd3R3m5uYa70NtgTps2DA8efIE8+bNQ1JSElq3bo2DBw+KSU4JCQkKFtPPPvsMHMfhs88+w6NHj+Di4oKQkBB8+eWXKu+TIAiCIAhFZDIZYmNjIZFI4OHhAXNzc2pWQxgUnudRUFCAJ0+eIDY2Fr6+vlUX5K8AteugGiMZGRmwt7dHeno6JUwRBEEQLwR5eXmIjY1Fw4YNYWVlZejpEIRITk4O4uPj4e3trdA1VB29plGrU4J4EeF5HrlFuQAAS1NLslQQBGEUaGqhIghdoY1rkq5qglCR3KJcBG0LQtC2IFGoEgRBEAShfUigEgRBEARBEEYFCVSCIAiCIGo8Xl5eWLlypcrjIyMjwXEc0tLSdDYnQnNIoBIEQRAEoTc4jqv0sWDBAo32e/78eUyePFnl8Z06dUJiYiLs7e01Op6qkBDWDEqSIgiCIAhCbyQmJorLO3bswLx58xAdHS2us7GxEZd5nkdxcTFMTauWKy4uLmrNw9zcnDpWGjFkQSUIgiCIWgLP88gpKNL7Q52KlW5ubuLD3t4eHMeJr2/fvg1bW1scOHAAgYGBkEqlOHnyJGJiYjBw4EC4urrCxsYG7du3x9GjRxX2W9bFz3EcNmzYgMGDB8PKygq+vr7Yu3ev+H5Zy+bmzZvh4OCAQ4cOwc/PDzY2Nujbt6+CoC4qKsL7778PBwcHODk5YdasWRg7dqzYCVMTnj9/jjFjxsDR0RFWVlbo168f7t69K74fHx+PkJAQODo6wtraGi1atMD+/fvFbUeNGgUXFxdYWlrC19cX4eHhGs/FmCALKkEQBEHUEnILi9F83iG9H/fmomBYmWtPUnzyySdYvnw5GjVqBEdHRzx48AD9+/fHl19+CalUiq1btyIkJATR0dFo0KBBhftZuHAhli5dimXLlmH16tUYNWoU4uPjUadOHaXjc3JysHz5cvz0008wMTHB6NGjMXPmTPzyyy8AgK+//hq//PILwsPD4efnh++++w579uxBjx49NP6soaGhuHv3Lvbu3Qs7OzvMmjUL/fv3x82bN2FmZoapU6eioKAA//77L6ytrXHz5k3Ryjx37lzcvHkTBw4cgLOzM+7du4fc3NpRZYYEKkEQBEEQRsWiRYvQp08f8XWdOnUQEBAgvv7888+xe/du7N27F2FhYRXuJzQ0FCNGjAAALF68GKtWrcK5c+fQt29fpeMLCwuxbt06+Pj4AADCwsKwaNEi8f3Vq1dj9uzZGDx4MABgzZo1ojVTEwRheurUKXTq1AkA8Msvv8DT0xN79uzBG2+8gYSEBAwZMgT+/v4AgEaNGonbJyQkoE2bNmjXrh0AZkWuLZBAJQiCIIhagqWZBDcXBRvkuNpEEFwCWVlZWLBgAfbt24fExEQUFRUhNzcXCQkJle6nVatW4rK1tTXs7OyQkpJS4XgrKytRnAKAu7u7OD49PR3Jycno0KGD+L5EIkFgYCBkMplan0/g1q1bMDU1RVBQkLjOyckJTZs2xa1btwAA77//PqZMmYLDhw+jd+/eGDJkiPi5pkyZgiFDhuDSpUt45ZVXMGjQIFHo1nQoBpUgCIIgagkcx8HK3FTvD2131rO2tlZ4PXPmTOzevRuLFy/GiRMnEBUVBX9/fxQUFFS6HzMzs3LnpzIxqWy8oTvCT5w4Effv38dbb72Fa9euoV27dli9ejUAoF+/foiPj8eHH36Ix48fo1evXpg5c6ZB56stSKASBEEQBGHUnDp1CqGhoRg8eDD8/f3h5uaGuLg4vc7B3t4erq6uOH/+vLiuuLgYly5d0niffn5+KCoqwtmzZ8V1qampiI6ORvPmzcV1np6eeOedd7Br1y7MmDEDP/74o/iei4sLxo4di59//hkrV67E//73P43nY0yQi58gCIIgjA2eB7RslazJ+Pr6YteuXQgJCQHHcZg7d67GbvXq8N5772HJkiVo3LgxmjVrhtWrV+P58+cqWZCvXbsGW1tb8TXHcQgICMDAgQMxadIkrF+/Hra2tvjkk09Qr149DBw4EAAwbdo09OvXD02aNMHz589x7Ngx+Pn5AQDmzZuHwMBAtGjRAvn5+fj777/F92o6JFAJgiAIwpg4vgw4tx6YcBio06jq8S8AK1aswPjx49GpUyc4Oztj1qxZyMjI0Ps8Zs2ahaSkJIwZMwYSiQSTJ09GcHAwJJKqY3C7du2q8FoikaCoqAjh4eH44IMP8Oqrr6KgoABdu3bF/v37xXCD4uJiTJ06FQ8fPoSdnR369u2Lb7/9FgCr5Tp79mzExcXB0tISXbp0wfbt27X/wQ0Axxs6uEILZGRkwN7eHunp6bCzszP0dIhaSk5hDoK2sUD2syPPwsrMysAzIgiiVrLuZSDpGjDoB6D1yAqH5eXlITY2Ft7e3rCwsNDjBAkBmUwGPz8/vPnmm/j8888NPR2joaJrUx29RhZUgiAIgjAmMkoKw+ekGnYeRDni4+Nx+PBhdOvWDfn5+VizZg1iY2MxcmTFNxKEZlCSFEEQBEEYC0UFQM5Ttpz91LBzIcphYmKCzZs3o3379ujcuTOuXbuGo0eP1pq4T2OCLKgEQRAEYSxkJcmXyYJqdHh6euLUqVOGnsYLAVlQCYIgCMJYyJD3fUfOM8PNgyAMDAlUgiAIgjAWMksLVLKgvnCkPwRS7wEGKKFlbJBAJQiCIAhjQUGgUgzqCwXPs7jj/Ez2eMEhgUoQBEEQxgJZUF9ceBmAksqf+fqv8WpskEAlCIIgCGOhdAxqbhpQXGSwqRB6Rlbqb52fwSyqLzAkUAmCIAjCWChtQQUP5KUZaiaEviktUIsLgKJ8w83FCCCBShAEQRDGgoJABdVCrYTu3btj2rRp4msvLy+sXLmy0m04jsOePXuqfWxt7UcBWRlr+Qvu5ieBShAEQRDGQmZJHVQT1oe9NsahhoSEoG/fvkrfO3HiBDiOw9WrV9Xe7/nz5zF58uTqTk+BBQsWoHXr1uXWJyYmol+/flo9VlmBujk8HA4ODto9Rg2CBCpBEARBGAN5GUBBFlt2acaea6FAnTBhAo4cOYKHDx+Wey88PBzt2rVDq1at1N6vi4sLrKystDHFKnFzc4NUKtXuTgWBalbyGcjFTxAEQRCEwRGsp1I7wKEBW1ZXoPI8UJCt/4caCT2vvvoqXFxcsHnzZoX1WVlZ2LlzJyZMmIDU1FSMGDEC9erVg5WVFfz9/fHrr79Wut+yLv67d++ia9eusLCwQPPmzXHkyJFy28yaNQtNmjSBlZUVGjVqhLlz56KwsBAAsHnzZixcuBBXrlwBx3HgOE6cc1kX/7Vr19CzZ09YWlrCyckJkydPRlZWlvh+aGgoBg0ahOXLl8Pd3R1OTk6YOnWqeCwAcoFqbg1IzMEy+is+rwkJCRg4cCBsbGxgZ2eHN998E8nJyeL7V65cQY8ePWBraws7OzsEBgbiwoULAID4+HiEhITA0dER1tbWaNGiBfbv31/p+dU31OqUIAiCIIyBzMfs2dYdsKrDltWthVqYAyz20O68VOHTx0xYqYCpqSnGjBmDzZs3Y86cOeA4DgCwc+dOFBcXY8SIEcjKykJgYCBmzZoFOzs77Nu3D2+99RZ8fHzQoUOHKo8hk8nw+uuvw9XVFWfPnkV6erpCvKqAra0tNm/eDA8PD1y7dg2TJk2Cra0tPv74YwwbNgzXr1/HwYMHcfToUQCAvb19uX1kZ2cjODgYHTt2xPnz55GSkoKJEyciLCxMQYQfO3YM7u7uOHbsGO7du4dhw4ahdevWmDRpUsmkSwSqiSm7SQEq1KcymUwUp8ePH0dRURGmTp2KYcOGITIyEgAwatQotGnTBj/88AMkEgmioqJgZsZCR6ZOnYqCggL8+++/sLa2xs2bN2FjY1PledUnJFAJgiAIwhgQSkzZugFWTmy5lrY7HT9+PJYtW4bjx4+je/fuAJh7f8iQIbC3t4e9vT1mzpwpjn/vvfdw6NAh/PbbbyoJ1KNHj+L27ds4dOgQPDyYYF+8eHG5uNHPPvtMXPby8sLMmTOxfft2fPzxx7C0tISNjQ1MTU3h5uZW4bG2bduGvLw8bN26FdbWTKSvWbMGISEh+Prrr+Hq6goAcHR0xJo1ayCRSNCsWTMMGDAAERERcoFaXEqgmlqW7J1n1ukSES8QERGBa9euITY2Fp6engCArVu3okWLFjh//jzat2+PhIQEfPTRR2jWjIWL+Pr6itsnJCRgyJAh8Pf3BwA0atSoynOqb0igEgRBEIQxIGTw23mUEqhquvjNrJg1U9+YqRf72axZM3Tq1AmbNm1C9+7dce/ePZw4cQKLFi0CABQXF2Px4sX47bff8OjRIxQUFCA/P1/lGNNbt27B09NTFKcA0LFjx3LjduzYgVWrViEmJgZZWVkoKiqCnZ2dWp/l1q1bCAgIEMUpAHTu3BkymQzR0dGiQG3RogUkEok4xt3dHdeuXZPvSFbMnk1MAakNAA4Az2JRzSyUfj5BnAJA8+bN4eDggFu3bqF9+/aYPn06Jk6ciJ9++gm9e/fGG2+8AR8fHwDA+++/jylTpuDw4cPo3bs3hgwZolHcry6hGFSCIAiCMAYEgWrrDlg7s2V1BSrHMVe7vh9lLHyqMGHCBPzxxx/IzMxEeHg4fHx80K1bNwDAsmXL8N1332HWrFk4duwYoqKiEBwcjIKCArWPUxGnT5/GqFGj0L9/f/z999+4fPky5syZo9VjlEZwrwtwHAeZTCZfUdrFbyIBTEuSsDQsN7VgwQLcuHEDAwYMwD///IPmzZtj9+7dAICJEyfi/v37eOutt3Dt2jW0a9cOq1ev1ug4uoIEKkEQBEEYA6UFqmBBrcV1UN98802YmJhg27Zt2Lp1K8aPHy/Go546dQoDBw7E6NGjERAQgEaNGuHOnTsq79vPzw8PHjxAYqK8ruyZM2cUxvz3339o2LAh5syZg3bt2sHX1xfx8fEKY8zNzVFcXFzlsa5cuYLs7Gxx3alTp2BiYoKmTZuqPGcFgQrIraZKBKrw+R48eCCuu3nzJtLS0tC8eXNxXZMmTfDhhx/i8OHDeP311xEeHi6+5+npiXfeeQe7du3CjBkz8OOPP6o+Vz1AApUgCIIgjAEhBtXOvdbHoAKAjY0Nhg0bhtmzZyMxMRGhoaHie76+vjhy5Aj+++8/3Lp1C2+//bZChnpV9O7dG02aNMHYsWNx5coVnDhxAnPmzFEY4+vri4SEBGzfvh0xMTFYtWqVaGEU8PLyQmxsLKKiovD06VPk55cv/TRq1ChYWFhg7NixuH79Oo4dO4b33nsPb731lujerxJeBvClXPwAYGaJ4mIZoi5eRNSli4iKikJUVBRu3bqF3r17w9/fH6NGjcKlS5dw7tw5jBkzBt26dUO7du2Qm5uLsLAwREZGIj4+HqdOncL58+fh5+cHAJg2bRoOHTqE2NhYXLp0CceOHRPfMxZIoBIEQRCEMSCUmVLI4q99dVBLM2HCBDx//hzBwcEK8aKfffYZ2rZti+DgYHTv3h1ubm4YNGiQyvs1MTHB7t27kZubiw4dOmDixIn48ssvFca89tpr+PDDDxEWFobWrVvjv//+w9y5cxXGDBkyBH379kWPHj3g4uKitNSVlZUVDh06hGfPnqF9+/YYOnQoevXqhTVr1qh+ImSlrLQmJXGqJmbIys5Bm+DhaBPYDm3atEGbNm0QEhICjuPw559/wtHREV27dkXv3r3RqFEj7NixAwAgkUiQmpqKMWPGoEmTJnjzzTfRr18/LFy4EACL8Z06dSr8/PzQt29fNGnSBGvXrlV9vnqA43k1ipcZKRkZGbC3t0d6errawc0EoSo5hTkI2hYEADg78iys1EwKIAiCqBCZDPjChbl5P7wJSG2Br0oSYOYkAWaW5TbJy8tDbGwsvL29YWFhUe59ogZRmAs8uc2sp27+8vVpCewmxdoZsPeseHsjo6JrUx29RhZUgiAIgjA0OU9LYhA5wKYuE6i1uN0pUYay8acCQj3UPM0SpWoyJFAJgiAIwtBklJSGsqkLSMxYVrympaaImkeFAtUWAAcUFwBFeXqfliEhgUoQBEEQhiazVJF+ARKoLw6iQJUorjeRyDt05WXqd04GhgQqQRAEQRgaUaCWalNqLZSaIoFa66nIggoAFiVufg3rodZUNBKo33//Pby8vGBhYYGgoCCcO3euwrHdu3cHx3HlHgMGDBDHhIaGlnu/b9++mkyNIAiCIGoepUtMCZAF9cWhuBKBKsSh5meyZLoXBLVbne7YsQPTp0/HunXrEBQUhJUrVyI4OBjR0dGoW7duufG7du1S6MqQmpqKgIAAvPHGGwrj+vbtq1BAViqVqjs1giAIgqiZlC7SL0AC9cWhMguqqQVLmJMVAgVZcotqLUdtC+qKFSswadIkjBs3Ds2bN8e6detgZWWFTZs2KR1fp04duLm5iY8jR47AysqqnECVSqUK4xwdHTX7RARBEARR01AqUDVsd0rUPCoTqBz3Qrr51RKoBQUFuHjxInr37i3fgYkJevfujdOnT6u0j40bN2L48OGwtrZWWB8ZGYm6deuiadOmmDJlClJTK/6HzM/PR0ZGhsKDIAiCIGospYv0C4gW1Nrb7pQoQVami1RZXsByU2oJ1KdPn6K4uLhc6y5XV1ckJSVVuf25c+dw/fp1TJw4UWF93759sXXrVkRERODrr7/G8ePH0a9fvwr73y5ZsgT29vbiw9Oz5hSvJQiCIIhyCGWmFGJQhW5StbfdKVFCZRZUoFS5qXygqHy71dqIXrP4N27cCH9/f3To0EFh/fDhw/Haa6/B398fgwYNwt9//43z588jMjJS6X5mz56N9PR08fHgwQM9zJ4gCIIgdEBRPpBbIkIpBtVoiYyMBMdxSEtL0+6Oeb5qgVq63FQ13fxeXl5YuXJltfahD9QSqM7OzpBIJEhOTlZYn5ycDDc3twq2YmRnZ2P79u2YMGFClcdp1KgRnJ2dce/ePaXvS6VS2NnZKTwIgiAIokYixJ9KpIBlqfwL69obg/rkyRNMmTIFDRo0EHNQgoODcerUKXEMx3HYs2ePVo4XFxcHjuMQFRWl0riyj9GjR6NTp05ITEyEvb29VuYkwhcDYF3nOTOp0uNzHAfOpQkWfLOu2m7+8+fPY/LkyVqYuG5RK4vf3NwcgYGBiIiIwKBBgwAAMpkMERERCAsLq3TbnTt3Ij8/H6NHj67yOA8fPkRqairc3d2rHEsQBEEQNRox/tSNJcQIlLag8rziezWcIUOGoKCgAFu2bEGjRo2QnJyMiIiISvNPNKV0JSFVOXr0KFq0aCG+trS0hLm5eZXGOI0Q4k85EyQmJoqrd+zYgXnz5iE6OpqtKMyBTV4ikJ/Fyk2ZaOYEd3Fxqe6M9YLan2769On48ccfsWXLFty6dQtTpkxBdnY2xo0bBwAYM2YMZs+eXW67jRs3YtCgQXByclJYn5WVhY8++ghnzpxBXFwcIiIiMHDgQDRu3BjBwcEafiyCIAiCqCGI8aceiustS2JQZUVAXrpKu+J5HjmFOXp/8Dyv8sdNS0vDiRMn8PXXX6NHjx5o2LAhOnTogNmzZ+O1114DwNzQADB48GBwHCe+jomJwcCBA+Hq6gobGxu0b98eR48eVdi/l5cXPv/8c4wZMwZ2dnaYPHkyvL29AQBt2rQBx3Ho3r17pXN0cnJSqCxkb29fzsW/efNmODg44NChQ/Dz84ONjQ369u2rIDIBYMOGDfDz84OFhQWaNWuGtWvXKh6slHu/7DE5jhNfr9uwBS8PngBAxspNAVi5cqV4bgBWV37QoEFYvnw53N3d4eTkhKlTp6KwsFDh/JR28XMchw0bNmDw4MGwsrKCr68v9u7dqzDFvXv3wtfXFxYWFujRowe2bNmim3CHUqhdB3XYsGF48uQJ5s2bh6SkJLRu3RoHDx4UE6cSEhJgUkbVR0dH4+TJkzh8+HC5/UkkEly9ehVbtmxBWloaPDw88Morr+Dzzz+nWqgEQRBE7UdZiSkAMLMAzG2YGMlJBSwdqtxVblEugrYFaX+OVXB25FlYmVmpNNbGxgY2NjbYs2cPXnrpJaW/9efPn0fdunURHh6Ovn37QiJhLUCzsrLQv39/fPnll5BKpdi6dStCQkIQHR2NBg0aiNsvX74c8+bNw/z58wEAU6dORYcOHUTLqLm5uRY+NZCTk4Ply5fjp59+gomJCUaPHo2ZM2fil19+AQD88ssvmDdvHtasWYM2bdrg8uXLmDRpEqytrTF27Fi2k6riTwU4DuBK9FV+RoX1UI8dOwZ3d3ccO3YM9+7dw7Bhw9C6dWtMmjSp/OCSG4uFCxdi6dKlWLZsGVavXo1Ro0YhPj4ederUQWxsLIYOHYoPPvgAEydOxOXLlzFz5ky1z5W6qC1QASAsLKxCl76yxKamTZtWeHdlaWmJQ4cOaTINgiAIgqj5VCRQAebmL8himfxOPvqdl44wNTXF5s2bMWnSJKxbtw5t27ZFt27dMHz4cLRq1QqA3A3t4OCg4FYPCAhAQECA+Przzz/H7t27sXfvXgVd0rNnT8yYMUN8LQhcwTJaFZ06dVIwtp04cULpuMLCQqxbtw4+PuxvExYWhkWLFonvz58/H9988w1ef/11AIC3tzdu3ryJ9evXqy9QAYBjn6OyRClHR0esWbMGEokEzZo1w4ABAxAREaFcoGYy633o6BEYMWIEAGDx4sVYtWoVzp07h759+2L9+vVo2rQpli1bBoBpuuvXr+PLL7+ser7VQCOBShAEQRCEllDW5lTAyglIi1e5FqqlqSXOjjyrxcmphqWppVrjhwwZggEDBuDEiRM4c+YMDhw4gKVLl2LDhg0IDQ2tcLusrCwsWLAA+/btQ2JiIoqKipCbm4uEhASFce3atdPkY4js2LEDfn5+4mtPT0+l9d6trKxEcQoA7u7uSElJAcCSw2NiYjBhwgQFcVhUVKSYaFVZm9OyCKK5qOJyUy1atBAFuTCna9eulR+Ymw5ksbm2atlcXG1tbQ07Ozvxc0RHR6N9+/YKm5atxqQLSKASBEEQhCFRVqRfQM1SUxzHqexqNzQWFhbo06cP+vTpg7lz52LixImYP39+pQJ15syZOHLkCJYvX47GjRvD0tISQ4cOLZcIVbYZkLp4enqicePGVY4zMzNTeM1xnOgxzspicaI//vgjgoIUwy5KC0hVLagmJibMI29uDRRkA/kZCrGllc1JJpMpDiouYjc+wjbWDlVvo2dIoBIEQRCEISlxs2pDoNZkmjdvrlBWyszMrFzDnlOnTiE0NBSDBw8GwERgXFxclfsWYk4ragCkC1xdXeHh4YH79+9j1KhRFQ8UBaqk4jFgYQ9JSUngzW3BFWQDeRlVls2qkNxUVt5KhZuZpk2bYv/+/Qrrzp8/r9lx1UCvhfoJgiAIgigFzyuWmSpLLayFmpqaip49e+Lnn3/G1atXERsbi507d2Lp0qUYOHCgOM7LywsRERFISkrC8+fPAQC+vr7YtWsXoqKicOXKFYwcOVIlS1/dunVhaWmJgwcPIjk5GenpqlVFqC4LFy7EkiVLsGrVKty5cwfXrl1DeHg4VqxYIR+kogW1e/fuePLkCZau2YCYuAf4fv0GHDhwQP1JyYqB4kIWz+roXeXwt99+G7dv38asWbNw584d/Pbbb9i8eTMAZmnVFSRQCYIgCMJQ5KUDhTlsWakFtaTUVHbtEag2NjYICgrCt99+i65du6Jly5aYO3cuJk2ahDVr1ojjvvnmGxw5cgSenp5o06YNAGDFihVwdHREp06dEBISguDgYLRt27bKY5qammLVqlVYv349PDw8FISwLpk4cSI2bNiA8PBw+Pv7o1u3bti8ebNY9gqAygLVz88Pa9euxffrfkRAn+E4d/k6Zk6rvAZ9OXKelTQGAODoBZhWXc3A29sbv//+O3bt2oVWrVrhhx9+wJw5cwBAp9WWOF6d4mVGSkZGBuzt7ZGenk5dpQidkVOYI5ZvUaekCkEQRIWk3AbWBgEW9sAnCeXfv7gZ+OsDoEk/YOR2hbfy8vIQGxsLb29vWFhY6Ge+hPZJvgEUFwBOvoDURrVtnsez9rjWdQH7eqptU5gHPI0GeBlg41q+7q4afPnll1i3bl2FreYrujbV0WsUg0oQBEEQhkKMP61ALLxAMagvLEInKVWy+AUs7JhAzc8AoIJAlRUDz2OZODW3UW6tr4S1a9eiffv2cHJywqlTp7Bs2bIqO4hWFxKoBEEQBGEoKisxBQBWtS8GlSgFL5O73NURqFJb9lyUBxQVVO6q53kg/SEba2LGXPtqxo7evXsXX3zxBZ49e4YGDRpgxowZSruGahMSqARBELWJrCfA6dVA27G1prB7raayIv1AKQuqanVQiRqGEH8KVJnFr4CJKWBmDRSyclMwda54bE4qs7YCTJxKzCoeWwHffvstvv32W7W3qw6UJEUQBFGbOPc/4NR37EEYP6oK1Lx0lnlN1C5Ku/fVzYgXWp3mVdxVCgU5zHoKsDASVWNcjQASqARBELWJx5fZc5qShBvC+KisxBQAWDoAKBEuuc+VDqkFuc4vLuq0OS2LtESgFmSyUAFl+34eC4BnY23qajxNddHGNUkClSAIojaReIU9Zzw27DwI1RD+ThVlVJtI5KWmysShCh2DcnJydDU7QtcIVnFNBKqZJduOl7HOUqXheXaTWlwASMwBh4bqW2irgXBNlu1qpQ4Ug0oQBFFbyEwCslNKlhMNOxdCNaqyoALMzZ+TCmQrxqFKJBI4ODiIPdOtrKx0Wjid0AG5eUARD5hwQF6e+ttzVkBROpDxDLAtJQazU4HsNAAcYOMOFBaxh47heR45OTlISUmBg4ODYktXNSGBShAEUVsQrKcAS5zIy5DHqRHGh6wYyEpmyxWVmQIqLTXl5saErSBSiRpGXjp7SHOB5xrEGBfksAQ6STpgW2JJL8oHslIA8IClI5CdrNUpq4KDg4N4bWoKCVSCIIjaQuJVxdeZiSRQjZnsJ6zEEGcCWLtUPK4SgcpxHNzd3VG3bl0UFlISVY0j8mvg+k6g3USg+Tvqb5+bBmwcB0AGjP0bkEiBHaOYJ8X3FeCVL/Xq2geYW786llMBEqgEQRC1hcQoxdcZjwGXpgaZCqECQvypjSsgqeTnWBSozyocIpFItCIKCD2TcR/IegBYWLCHuli4AQ6uwMNzQPw/wK2/gOSLrCtV34WA1FL7c9YTlCRFEARRW0gqsaCal5SSoUQp46aqElMCVAu19iJYxa0rqWNaFY17s+cj84GYfwBTS+DNrfJi/jUUEqgEQRC1gdzn8tJSPj3YMwlU40ZtgUrdpGodwt9U+Btrgm+JQM0vqYf66reAa/PqzcsIIIFKEARRGxDiTx0aAnVLfpwySaAaNVW1ORUggVp70YZAdW8jj2FuOwZoPaL68zICKAaVIAiiNiC4991byWtqkgVVEZ5nxcs1aPWoE1QpMQXI3b/Z5OKvVfC8dlz8JibAwO+BhDNAt4+1MzcjgAQqQRBEbUAoMeUeANjVY8sZjww3H2NDJgP+1w0ozAUmRxpHy0fBwl1ZiSmgVKH+ipOkiBpIXrq8k1R1LKgA0CSYPWoR5OInCIKoDQgufrcAeUwjWVDlZCYyK3PqXeD8j4aeDUNVCyq5+Gsnwt/T3BYwlRp2LkYICVSCIIiaTkE28PQOW3YPkLv4c1KBQg2609RGnsfJl0+tAvKzDDYVkaranAoIArUolxVmJ2oHYvxpHcPOw0ghgUoQBFHTSb4BgGf1NG1dWfcY05L6h9TylFFaoOY+A85vMNhUALBQg7w0tlyVBdXchhVgB6jUVG1CiCmuTvxpLYYEKkEQRE1HiD91a8WeOU6eGU5ufkZaPHu2rsue/zOwFVW4cTC1BCwcKh/LceTmr40INxvVjT+tpZBAJQiCqOmUTpASEBKlyILKECyoQZMBR28m9AxpRRXiT+3cVWtFSQK19iG6+MmCqgwSqARBEDUdUaC2kq8TS01RJj8AuUCt4yMvxWNIK6pg2a6qSL+AddXtTokahuDipxhUpZBAJQiCqMkUFQApt9hyaQsqZfIr8rzExe/oBfi/KbeiXthomPmo2kVKQLCgUi3U2oNws0ExqEohgUpol8JcusMnCH3y5BYgKwQs7FkXKQGxFioJVBTkAFklLnVHL0BiKreinvqOVUHQN6qWmBIgF3/tg2JQK4UEKqFdNgUD3zQFIhZRORSC0Adi/dNWirGM1E1KTloCe5basQoHgKIV1RCxqKqWmBIggVr7oBjUSiGBSmiPjMcsFq64ADjxDfB9B+DW36ydG0EQukFZghRAArU0Qga/Y0O5iJeYAl0/YsunVunfikoWVILKTFUKCVRCezy8wJ5t3QF7TyD9AbBjFLDtTeDZfcPOjSBqK0klFtSKBGpWElBcpN85GRtCgpSjl+L6VsNKrKhPgfN6jkVVtc2pAAnU2ocQDkcufqWQQCW0x8Pz7LlJMDD1LNBlBmBiBtw9DHz/EnBsMYtRJQhCO8iKgaRrbNmtleJ71i6AiSnAy4DsFP3PzZgQEqRKx+gCZayoeoxF5XmyoL7oFOUDBZlsmQSqUkigEtpDsKDWbw+YWwO95gHvngEa9QCK84HjXwPfBwHRBw07T4KoLaTGAIU5rNi7s6/ieyYSyuQXqMiCCpRYUb30a0XNfQ4UlbSgVTeLnwRq7UD4O5qYsgRHohwkUAntUFwIPL7Mluu3l693bgy8tRt4cyvLKk6LB34dBmwbrth6kCAI9RHc+24tmSAtiyhQX/BaqKJA9S7/niGsqIL11LIOYGah2jZCnGLOM0Am08281CX+P+Brb+Da74aeSc0ju1QGvyqNGl5ASKAS2iH5BlCUC0jtAacylhyOA5oPBKaeAzpPY3eMdw4wa2rk10BhnkGmTBA1nsQo9lw2/lRATJQy0m5SuWm6///necUkKWWUtqJe2KTb+QCl4k9VtJ4CTMwCAF8M5KVpfUoacXEzkPsMuPKroWdS86ASU1VCApXQDkL8af1AwKSCy0pqA/RZCEz5D/DuylxckYuBtS8Bd4/ob64EUVsoXWJKGWItVCO0oGY8Blb6A7+9pdvj5KQCBVkAOJa8qQyJmX6tqMINg50aAtXUnJXJAoyj1jTPA7En2LJwHeqa4kLgyDwg9l/9HE+XUIJUlZBAJbRD6fjTqnBpCozZCwwNZxaE57HAL0OBnaGUbUwQqsLzFZeYErAz4hjUuJNAfgYQc4wJD10huPdt3St3pwtW1OwnureiqpsgJSC0xDSGONRn9+WW4OwUIDNZ98e8+Se7gTgwS/fH0jXZZEGtChKohHYQLagqCFSAuf1bvg6EnQc6vcfc/jd2A9H7dDdHgqhNpD9grl4TU6Cun/Ixgos/0whd/IK4lhXKs+x1QWUJUqWRmAFdZrLlU9/pttGIuiWmBISC7sYgUMtaMZP0YEUV8hyeRNf80DDhb0g1UCuEBCpRfXKeAc9i2HK9QPW2ldoCr3wBdHibvSZXP0GohiDw6voBplLlY4zZxS/MHwBS7+ruOKoKVAAIGM5KUenaiqqxBVXI5H+q3floQtwJxdf6EKjCNcMXA0+jdX88XUIxqFWikUD9/vvv4eXlBQsLCwQFBeHcuXMVju3evTs4jiv3GDBggDiG53nMmzcP7u7usLS0RO/evXH3rg6/sAjt8ugie3ZqLHdBqYtvb/Z87yh1niIIVRDjTytw7wOK3aSM6f+K5xUFzdM7ujuWKFArSJAqjUIs6krdWVHVbXMqYCylpkrHn/oGs2ddx6HyvOIxkm/o9ni6htqcVonaAnXHjh2YPn065s+fj0uXLiEgIADBwcFISVFeCHrXrl1ITEwUH9evX4dEIsEbb7whjlm6dClWrVqFdevW4ezZs7C2tkZwcDDy8mq4Cf9FQV33vjIadgbMrJgrMvm6duZFELWZquJPAcCmxEJXXGB4UVOatAQgL13++qkODRJiBr+XauP1YUWt6TGoT++yuFOJFGg3nq3TtQX1eRyQX+qaSarhvxPZgkDV0KjzAqC2QF2xYgUmTZqEcePGoXnz5li3bh2srKywaZPyf+Q6derAzc1NfBw5cgRWVlaiQOV5HitXrsRnn32GgQMHolWrVti6dSseP36MPXv2VOvDEXpCFKjtNN+HqRTw7saW7x6u/pwIorYjtjitIIMfYJnf1nXZsjElSpUVM7oUqOq4+IESK6oQi7pS+1bU4iJ5Zy91Y1BL10I1JHEl8aeeHeSGiWf3gfxM3R2zdEgIUPMNGRSDWiVqCdSCggJcvHgRvXv3lu/AxAS9e/fG6dOnVdrHxo0bMXz4cFhbWwMAYmNjkZSUpLBPe3t7BAUFVbjP/Px8ZGRkKDwIAyGTAQ9LXPzVsaACgG8f9nz3aPX2QxC1nayUksQnDnBtWfnY0m5+Y0EQG0J5LF3FoBYXAukP2XLZNqeVETACcGjArKgXw7U7p+wU1n6Wk7B2tOoguPizDRyDKrj3vbsC1k7yWGddWjXLegySrxtX2Iq6iDGoJFArQi2B+vTpUxQXF8PV1VVhvaurK5KSkqrc/ty5c7h+/TomTpworhO2U2efS5Ysgb29vfjw9Kygth2he1LvMreLqSVQt0X19iUI1AdnWQFvgiCUI8TiOTVm9YUrQxSoRpQoJcy/5evsOSdVN1bB9IdMDJpaADauVY8XKB2LenKldq2oQg1UW7eKa0ZXhDHEoPI8KxEGAF5d2LObP3tOuqa74woCtdUwgDNh5yBLeWih0SOTUR1UFdBrFv/GjRvh7++PDh06VGs/s2fPRnp6uvh48OCBlmZIqI3g3q/XlrUMrA4ODQCXZixD8/6x6s+NIGorYgepStz7AsZYakpw8TfoCNjVZ8u6cPML7n2HhuqLQdGKmqJdK6rwd1Cni5SAMQjUlFvM+mdmJa/aIljCk65UvF114Hn5Nd/gJaCOD1tO1qEg1iV5aex3DiCBWglq/cc6OztDIpEgOVmxIG9ycjLc3CoP9s7Ozsb27dsxYcIEhfXCdursUyqVws7OTuFBGAhtxJ+WpnFJqAeVmyKIihHjTytJkBIwNhd/2fAE58ZsvS4y+atqcVoZurKiZpayoKqLMdRBFcpLeQaxGGdA9xbUjEfsM5uYMk+dW0lYS03N5Besp1I7+TkkyqGWQDU3N0dgYCAiIiLEdTKZDBEREejYsWOl2+7cuRP5+fkYPXq0wnpvb2+4ubkp7DMjIwNnz56tcp+EEaBOBylVENz8944yNwhBEOUpG8NZGbZG5uIvG57g3IS91kUcqroJUmVRsKJu1s6cNC0xBcgzvvMzgKIC7cxHXYQC/d5d5OsES37KLd10BROudxc/1g3MtSScrMYKVKqBqgpqu/inT5+OH3/8EVu2bMGtW7cwZcoUZGdnY9y4cQCAMWPGYPbs2eW227hxIwYNGgQnJ8U/CMdxmDZtGr744gvs3bsX165dw5gxY+Dh4YFBgwZp9qkI/ZCfCaTcZMv1tGRBbdARMLcBspL1U/iZIGoauWly4aWWBdVIXPyCG1gQNU6+7FmXLn5NBWrp7lInv9VO9yJNS0wBgIUDi78EgFwDZPLLZED8KbbsVUqgOjQEpPasnNmT29o/btkEKdcabkGlNqcqoXbQ4LBhw/DkyRPMmzcPSUlJaN26NQ4ePCgmOSUkJMCkTKxPdHQ0Tp48icOHlZcP+vjjj5GdnY3JkycjLS0NL7/8Mg4ePAgLi0r6JhOG5/FlloBg7ynv+V1dhHJT0fuAe0cAj9ba2S9B1BYEN6q9p2o1FMVuUkbi4hcbDJQIVGddCtQSF786GfxlaT0SiFzCXPNxJ+ReHk3RtM0pwOJoLeswC1xOqmYitzqk3ABynwNm1oBHG/l6jmNu/viT7PoUXP7aopxALbGgPolmluSa5ianElMqoVGSVFhYGOLj45Gfn4+zZ88iKChIfC8yMhKbN29WGN+0aVPwPI8+fZT/Y3Mch0WLFiEpKQl5eXk4evQomjRposnUCH2i7fhTAbHcFMWhEkQ51Ik/BeQ3jwWZQJ4RlOQrKzYEgfo8Vvvu4epaUAFmRW1S0i3pnhZK4FXHggrIRY0hSk0J5aUadmTnpTSCRVwXHaXKXjP2nsxiKyvUbRcyXUEufpXQaxY/UcvQdvypgCBQH543fEFqgjA21Ik/BQBza8DCni0b2oqal86EKCAXG7YeOWgx8gAAbmBJREFUzCInK5ILSq0cK0PuBtckSao02kzeFEItNIlBBQybyS8kSJV27wvoKlEqM1meVCckR3FczY5DpRJTKkECldAMntdOi1Nl2NcH6jZn4QMx/2h33wRR00lU04IKyN38mQYWqEIhd7v68vAEExPAqaRskDatYUIGv5UTILWt3r68u7EM8mcxrGOSphRky9t1alJmCjBcu1NZMRBXEn/qrUygCqWmrmm3gL5wQ+bchN1sCYgCtQaWmqIYVJUggaoBBUUyzN1zHfGp2YaeiuFIi2ddVkzMVLfkqINgsdCGS40gagsFOcDTaLasSg1UAWMpNVXWVSsgZPJrMw5VG+59AQs7wPMltnwvovKxlSG4982sNRfNogVVz96lpKtMXEvtADclN0cuTQGJORujTUt4RddMTS41RTGoKkECVQOWHLiFn87EY/TGs0hK10JWZ01EcO+7+bOyH9rG9xX2fPcIlZvSlMLcmt0KkChPyk3mWbB2Uc8CJ4w1tEAV42fLiGtdJEqVLtKvDXy14OYXaqDauTM3tSaItVD1HIMqxp92Ut6URWIG1PVjy9p08wsF+ssmzNbkTH6KQVUJEqgaMKW7D7ycrPDgWS7e2ngWz7MNVI/OkOjKvS/Q4CXA3Jb9IwtfUITqPDgHLPUB/nrf0DMhtEnp+FN1BI6xZPKXzeAXEASqNmuhChn82rCgAnKvTtwJzctNZVSji5SAoWJQxfamL1c8RoxD1WKiVEUhLS7NAHCsJGHWE+0dTx8IfzsrsqBWBglUDahra4GfJwbBzc4Cd1OyMDb8HDLzdFCc2JjRtUCVmAE+3dkyZfOrR1E+8GcYUJgNXPudvSZqBxW5O6vCGFz8hXnyGpllLahiLVQtxqBq08UPMIudrTtQmAMk/KfZPsQSUzVMoBYXAfEln1lZgpSA4PrXlgU15xmQnlCy7zKlq6Q2QB1vtpx8XTvH0xfZgoufLKiVQQJVQ+o7WuHniR1Qx9ocVx+mY9LWC8grLDb0tPRDYZ78rlbbJaZK01joKkUCVS1OfiuPUyzMYdZUonZQkYu8KoxBoKbcYP3HrZzkFl0Bp5J2p7nP5T/e1UUUqFpy8XMc0LgXW9Y0DrW6JaYAwwjUxCusTJmFfeU1TrVdakq4IavTSF6JojQ10c1fmMuMBwC5+KuABGo1aFzXFlvGdYCN1BRn7j9D2LZLKCx+AeIlk66y+nNWztqzTihDLDd1QXs/WrWdlNvAv8vZsn0D9kyVEGoHxYXyH2JNLaiGzOIv7d4vG55gbsVqWwLasaLKZEBaieVNm99R1S03VZ02pwKC1U2f34lxJe1NG74MmEgqHufaAgDHrjNt1GkVwrsqut5rokAVbixMzFjCGVEhJFCriX99e2wc2w5SUxMcvZWCmTuvQCar5Ykppd37mgb6q4KdR8kXEE8iSxVkMhZzKisEfIOBHp+y9fePGXZehHZ4Es1aSUrtAAcv9bYVBFFOqnbadWpCVdZfbcahZiUBxfkAJ2ElrbRFox5sn0+j5QJYHbRtQdVXEqSQIKWsvFRppLbM2gloJw61qpCWmlhqSow/ddLt72ctgASqFghq5IQfRreFqQmHP6MeY/7eG+Brc/a0rjpIKUPsKqW8TS5RioubgAdnAXMbYMA3gE8Ptv5xFDU8qA2ICVL+rHaoOlg4AKaWbNlQVtSqxIY241AF9759feUZ55pi6SCPu9ekBF512pwKCAK1OJ/VVdU1xYVAwhm2XFn8qYAQAqANN794zbSu4FglFtQn0drvQqYrBMsylZiqEhKoWqJnM1esGNYaHAf8dCYeyw9HG3pKukNXHaSUIcShxkSwQtGEcjIeA0cWsOWecwEHT2alqdsCAA/cjzTg5AitoG6L09JwnGHjUIuL5G5YZTU0gVKlpu5V/3jazuAvjVhuSk2ByvNyC6pdNZKkzKwA05LSfvqIQ310icVMWtZhDVSqwr1Uwf7qkJcub4pQ0TVv34BVeykuAFK1cN3oA9GCWsew86gBkEDVIq8FeOCLQeyO7vtjMfjfvzEGnpEOyEgE0h8A4IB6bXV/PM8OrOdyTirw+LLuj1dT2f8RS2Ko1w7oMEm+XrCiUohEzUfdFqdlEQVqonbmow6pd4GiPGbdF1zAZXHWgQVVWwlSpRHiUGOPA0VqlBjMecaEFADYVMPFz3H6rYUqxJ96dVbNci92lKqmBVUQuPYNKhZzJiaAa4lorilxqFRiSmVIoGqZUUENMatvMwDA4v238es5DeKUjJlHJdbTus2r3z5QFajcVNXc3Avc/pu1YnxtlWISgyhQj1HR/pqMTCb/wdbEggqUEqiPtDMndRDcva4tKxY5Qjep53HqCT9lpOnQguoWwBolFGQBD86ovp1QpN/KGTA1r94cxHanegjdEeufdlVtvCBQn96tXgiC6N6v4oZMiEPVZnMAXUJtTlWGBKoOmNLdB+90Y72lP919DX9fNXBxbG2iz/hTgcYUh1ohuWnMegoAnT+Qf1kLNOjE2g9mPKw5LjCiPM/uM0FkaiEXcupiSBe/KvVbbd2ZhZUvBp7HVu942q6BWhoTE81aMWdqoUi/gL5KTRXlAwln2XJVCVICtq6AjSsAHki+qfmxH0ex54riTwVqWiY/tTlVGRKoOmJW36YYGdQAPA98uCMKx6JTDD0l7aDP+FMB4cfg8WXtdQxJjWHlmPKztLM/Q3F0ActYruMDdP24/PvmVkCDjmyZ3Pw1l6QSgefaQvOkH6H2qCGSpFSp38px8nqo1W15KrY59arefiqisQZxqKXbnFYXQdzoWqA+uggU5TKLsUsz1bcTO0pd0fzYqjalqHEClSyoqkICVUdwHIfPB7ZESIAHCot5TPn5Is7FauCOKcoHzv0IrGoDbAmRd/MwBMVF8jhQfQpUO/eSLzyeJUtVl4Ic4JehwD+fA0fmVX9/hiL+P+BiOFsO+Q4ws1A+zqcne46hclM1lurGnwKGs6DyfMUtTsuijTjUwjy5GNRVnWafngA41nxA1fMptjmtRvypgCButFFrtDKE8lJeL6tXEsmtmolSBdnya8CjdeVjhRjUzMc1o1qJMEcSqFVCAlWHSEw4rHgzAD2auiCvUIYJm8/j+qN01TYuLUz3z2Quvth/gfB+wE+DgYcXdTt5ZaTcZJ2JpHaauxk1xfcV9qyNONSIRfLs0IvhQMqt6u9T3xTlA399wJbbvFW5+02IQ407UXNKsRCKVNSPXB0E17K+BerzOCA/nRUmr8oKJ3yvVCccRahPam6ru0xpqzpAvUC2rKqbXxslpsTj68nFHycIVBXd+wLVLTWVdB0Az65Zm7qVj5Xaym9EakLLU4pBVRkSqJqQ8ww4v0GlL3oziQnWjgpEB+86yMwvwthN53DjcSUitSif7VsQphmP2Bda36+BwHEsESbmH2BDT2DbcO21lFMFIf60XqD6dRiri7bKTcWdBM7+wJbrNgd4GXD4s+rPT9+c+IZZGKzrAq98XvlYV3+WmFGQJf8bEjUHnlc9YaQyBBd/VjLzhugLwb3v2rzq5CDRxV8NC2rpDH5dFkIXajSrLFC1UKRfQEyS0qFALcyTt0lWV6AKN1IpNzW71lR17wvUJDc/xaCqDAlUTbhzCNg3A1jhB/zYk4mFJ9EVZklbmkuwcWw7tKxnh9TsAgxYdRJvrPsPOy88QHZ+yT9vaWG6b4ZcmPZfDnwQBbz0DhCyEgi7ALQeBXAmwJ0DwPouwI639GMFNET8qUD99qwXc+5zFhelCflZwJ9T2XLbMcCwn5lV595R9WsaGpKUW8CJFWy539eApWPl401MgEbd2TLFodY8Mh4Buc9YB6O6LaoeXxHWLuwGl5cxkaovVHXvA3IL6tO7mled0GUGf2mEONSYSNVEmDbanAqIZaZ06NJ+eI41A7BxlYdeqIqjN0t4K8rTrDOY2gJV6Chl5BZUmYz9LwNUZkoFSKBqgtQWqN+BLT+6yFzG33cA1rRjMY0PzrELsRS2FmbYMq4D+jR3hQkHnI97jo9+v4rOXx7A7v8tRMGKAOXCtMMkwFQq31Edb2DQWmDqOaDlUAAccGsvsLYj8MdE7RS5rojSLU71jcRUHkupaTb/0QXMumJXH3jlS8DJBwh6m713eI5+rUqaIpMx176sEGjSF2gxWLXtKA615iIIPJdmFccZq4KJiWHc/Oo0GHDyAcABeWmax1fqMoO/NB5tWPH6/HQm5qpCtKBqM4tfhzGoYnmpLupbok1M5FZNTbx8mgrUJC0L1MJcILw/8MekqseqQl4au0EEqFC/CpBA1QS/V4GJR4AZ0cCrK5n7WWLO4qZOfQds7AOsaAb8NY1Z5oryAQBONlL8OKYd/vukF2b18cZ7dv9iH/cBBj9eAfOcRDzhnHCyySw8GX+mvDAti7MvMHQjMOU/wC8EAA9c28mE8p6p8i9pbZHzTH4nrM8SU6WpThzq/ePA+R/Z8sA1gIUdW+76EfuReXIbuLRFO/PUJRc2KrYzVfWHQ2x7eolZoYmag7o/1pVhiFqo6szfzJJ1QQM0s7wBpTL4dVCkvzQmEqBxL7ZclZu/uBDILqlAUlPKTAkJUqqWlyqL2FFKTYFamAc8KfEIquvif3Jbu4aGexFA/Cng2m/asVYLN10W9qzGN1EpJFCrg60b0G4cMPp34KMYYGg4s2pK7ZgL7WI48MsQYKkPsHMccO13IPsp3O78gilX3sCMgnWox6Ui3dQZi2Tj8HLuNxh9NQAdl53C5K0XEHErGUXFssrn4Nqcuarf/pdZ1PhiIOpnYHUgE8jpWvohenSJPdfxMdydn+BSS4wCMtVwUeZnAn+GseV24+ViDWC9tbvPZsvHFrP2esZK+iPg6EK23Gs+6zOuKnYezALHy1iyHVFzUKVEk6oIAjVTT92kMpNLwgm48jV6K8Kpmpn8umxzWhax3FQVN81ZyQB4FlKkjeQYYR+5z3XTArogR+4xUzf+VEAsNaWmQE25AciK2GcU4qarwtGbtYAtypMnwGqDW3/Jl6vbGQugElNqQgJVW1jYAS1fZ1bNj2KA0X8wMWTjxlpQ3tgF/DEBWOYD7JvOCqfbugP9lsF+1g1M+3QZ5g8ORICnA4pkPA7fTMaELRfQ6at/8PXB27j/pIp6ne4BwMgdwISjQKMe7B/8YjiLaT2+tPqfz5DufQGbuvKizeqUmzo8F0hPABwaAH2UJBS1G8di33KesnhiY4TnWdJcQSb7G7SfoP4+GlHb0xqJNi2otnq2oAo/6s6+gLm1atuUjkNVF57Xn4sfAHxKLKhJVyu/aS5dYkobCaaCkYCXsWYd2ubBWRZGZFev4ta0VVG61JQ68cTi9d5adQ+RiQlLegWAZC11lCouZHke4ry0IVCpzak6kEDVBabm7M761W+B6beAiRHAyx/Kv3hLhCnejwKCJgNmFrCzMMPIoAb4c2pnHJrWFRNe9kYda3OkZObjh8gY9PzmOD754yr4qv7RPdsDY/YAoftZF6HifODYl8CN3dX7TIboIKUMIXNW1TjUexHyWqED1wJSm/JjJGbAK1+w5TM/aD88Qhvc/BOI3s8sMCFl2pmqihiH+k/taHuanwnkZRh6Frol+6lcTApuzOqg71qoiVHsWZ36rc7VKNaf84zdxAHshlTX2LiodtOszS5SAPvOsrBny7pw84vlpdSsf1qaun4sKS/3OZD+UPXtNL0hc9NyJn/cCUWPmjYsqFRiSi1IoOoaExMm6novAMLOAzPuAB9cFYWpMpq62WLuq81xZnYvrBvdFj2ausCEA7aff4CfzsSrdlyvzsC4/UwYA9Vz98tkwCMDZvCXRohDjfmn6lijvHRg7/tsucPblcdS+b7CMt2LC1gylTGR+xw4UNIl6uVp8sLU6uLVmQnctATtusH0Cc+z2Lg/JrLQme8CakZxbk0Rfqzr+MjjpquDKFD15OLXpH6rWAtVA4GaFseebd2rl1CmDqqUm8rUYpF+AV3GoYoF+jV07wMsh0Koe6uOuNNUoGq71NStv9mzcKOjTQuqNQlUVSCBqm9sXauuBViCuakJ+rZ0R/i4Dpj7KhMlX/x9S/Vi/xwH9JjDsk3z0oA9U8pVF1CJ1HtM7Jlaqh5HpivqBbKySnnpVdf0PDSHhVI4egO951c+luOA4MWsfNeN3UDCGe3Nubocmc9i2Jx8gS4zNd+PuTXQ4CW2XNPc/JnJwMlvgdVtgS2vsoTA4nxWsuXBWUPPTndoM/4UkMf06dvFr878hRjU53FigqnK6CtBqjRiual/Ko4H1WaJKQFdCdT8LJZMCWieICUgxqGq6HYvLpQLTLUFqlBqSgsCVSYDbu9jy91msefUuyw2tzqILn4SqKpAArWGENrJC32au6KgWIb3fr2MrHwVMxUlZsDrG1gAeexxeZF6dRCEoEcbw2cemkjkcV/3KklMuHMYuPwTAI6V5VIl/s21BevKBACHPtVMzGubhDPy6gKVtTNVFSFB7H5k9fajD2TF7O+4fRSrOXx0AbP8mtsAgaHykAUhga82os34U0DeBz4zUfdhHrlpcsGojovf1o11geJlwLNY9Y6pz/hTgXrtStVoruBa1GaRfgGxFqqWS009OMNyGOwbVP88Cn93Va2PT24zL5bUXv1jCzGo6Q+qX6nk0QUgK4ldh/5vsFqwvKz64pdiUNWCBGoNgeM4LBvaCh72Foh9mo3Pdl+rOh5VwLkxEPwlWz66QP1/MmOJPxWoKg419znwV4lr/6V3gYadVN93jzlMAD26CFz/o3rzrC48zxK8AKDNaOairy5ColTsv8Zb9zUtgVVUWOkPbHsDuP03q05RvwMw8HtW3i3kO6Bpfzb+8WXDzleXqFPkXhVs3ABwTATouk2mYDWz91Sv8gfHlYpDVTOTX58Z/AISU/n/VUU3zdpscyqgKwtqdctLlUYsNaWiBfVxlHw7dWNfLR2YqAaA5JvqbVuWW3vZc5NgFqogJnxdqd5+KQZVLUig1iAcrMyxemQbSEw47Il6jJ0X1Qg8DxwHNOnHfpj+mMhqzamKITtIKcOnFwCOfekpi6U7OJtZiJwaAz3VbGNq6wp0mc6Wjy6ovkunOtzexwqAm1oCPbTUjtU9oKS4eIbmHbl0QVEBcGMP8NNgYGUr4PjXzA1tWQd4aSrw7hlWe7jNaHmim0db9vz4cu1I+ipLXgbwLIYta8uCamou722uaze/OgX6y6JpHGrpNqf6RLxprkigllhQ7bSUJAWUaneq5RjsOC3EnwoIcaHpCarNU/AYeLTW8HhacPPzvDz+1C+EPburaQmuCGpzqhYkUGsYgQ3rYHof9uU978/ruJucqdqGHAe8tpq1O0y5ybpfqUJ+FqtLBxiPQLVxYeEGQPnEhNv7gSu/sljSQT8A5lbq7/+ld5nVJ+MhcOb76s9XE4qL5H+jju9q74fNRAI06saWjSEOledZ29YVfsDOsSVz4gHvbsDQTcCM20DfxSwjuCyuLViWcM5T9bKEawpC20a7etr9QdNXN6nqWH/FWqhqClR9tTktixCH+viy8g5YGVrO4gd0Y0HNy5BbMb1erv7+LB3k8cCqWFFLl5jSBFGgVqPUVPIN4HksIJHK/66iBVVLApUsqCpBArUGMqWbD7r4OiOvUIawbZeRW6BioWYbF+YiBZjwUqXt5ePLLPbGrr527/6ri5g5W8pikfMM+HsaW+4YBnh20GzfZpas6gIAnPhWvaYA2uLKNuBpNEsI6/yBdvctxG7eN4K2p/8uAyIWMpFp4wZ0mcHKr43dC7QcUnk3NTMLedzZ41oYhyr8WGvLvS+gr0QpUWxoMH9nDQRqcRGQ9oAt61ug2roBrv4A+PI3fvmZ8tJX2oxBFW5aNG0Jq4yE0yycxtFL3tGruqiaKCUrlo/R1GOgDQvq7RLraeNecm+NcA0n32SJXJpCAlUtSKDWQExMOKx4szVcbKWITs7Eor/V+GdsEgy0KynyvmdK1W4XMf40ULPJ6gqx3NQx+RfGgY9ZtrtzExZLWh1aDmEW48Js4B8lxf11SWEucGwJW+4yU17vUFsI8XIPLxi2c9alraxGL8Dq0H54A+g1D6jjrfo+BEt6bYxDvXOQPddrq9396qPUVGGuPH60Oi7+p3dVD9/IeMjElURaEmurZ3xLrG1lvTqCe9/cFpDaau94urCgCl3mtOHeFxD+/lVZH5/eBYpyWQ5AHR/NjiWI4ZRbmnfYErpHNXtVvs7Bi3WILM7XvMNZQQ5QWBIyRi5+lSCBWkNxsZVi5bDW4Djg13MPsPeKGu66V75gLrTMROCvDyr/ATC2+FMBjzbyWMoH54Cbe1npIc4EGLSu+tnuQtkpALj8s+pB/trg3P9YUoVdfaD9RO3v38GT/f35YnlChL65c4jV5gWYCO/0Hks2UZd6peJQaxNP75ZUWuCAVsO0u287Pbj4k2+y68vKWTO3dp1GADggP13ew74qhAQphwba6dakLoI7+F6EYgUQoQaqtj1QuhCoQvypd1ft7VNVC6roMfDX/O9XpxFgasGEoCYNV57dZ6E1nARo2k++3sRE/jk0jUMV/k4ScybCiSohgVqD6dzYGWE9WLbrp7uuIe5ptmobmlsBQ35k8Xu39rKYTWXwvHG0OFWGiUT+g3DlV+DvkoYEnadpz9rr2YFZUsGzslP6SMTJfS5vt9rjU90VGxfKTRkiDvXhRWBnKBMwrUepn8hWmtIW1NqUKHVhE3tuEqz9hB99uPiTSrn3NelEZGYh/9yqWqwMUWKqNJ5BzEqa81TeQQtQbHOqTUSBqqUkqdw0ufjSpgVVCFF5Es0s6xUhnLPqJASaSOTx6poYFYTkKK/O5StPVDcOVSgHZuWseXeuFwwSqDWcD3r5ooNXHWTlF+G9Xy8jv0hFt4ZHGyaAAGD/R8rrDaYlANkpTMhqK4tYmwhxqJd/Yv/8Ln5A90+0e4xe85nLMPbfyjvFaIuTK5nb3cUPCBiuu+OUbnuqT57eY6WjCnOAxn1YuajqfFm7+LG/T146S2yoDRRkA5d/Ycu6sKALLv5MHbr4tRE/q26ilKEy+AUkZvIExNLfFWIXKS2WmALkArUgU/2GBsqI/w8Az6qfaNPaa+fB5soXM9d7RVQ3QUqgOh2lhPhTv9fKv1fdTP5sij9VFxKoNRxTiQm+G9EajlZmuPYoHV8fiFZ9487TgAadgIIsYPfb5etiCtZTN3+WOGRsCOWmAOaSGfxD5Uk1muDYkGXRAyyZR5dkPAbOrmPLvecza4Cu8HqZ3Xg8j1W/GLqmZKUAP7/OXF0ebYA3Nle/8YOpubwHd20p2H/9D+badmgob0qhTQShlP5Id1ZnTVqclqV0HKoqGCqDvzTK2p7qos0pwGLTuZLvCG24+bVZXqo0HFfKzV+BuJPJtHPNAJoL1MwkeVe6ZgPKvy9aUK9p9n9DbU7VhgRqLcDd3hLL32D/1JtOxeLITRWzzk0kwOB1LPj7wVnWSrI0xhp/KmDtJG/d2WWG3N2rbV6ezspz6bp/feQSoCgPaNARaNJXt8eS2rLC94B+svnzM4FfhjIR4egNjNwpz5CtLh61KA6V54FzP7Ll9hN0E0spWMcKs1kMt7YpLmKl7IBqCtSSYv2q1kI1RJvTsghhRw/Py13vumhzCjDhp804VCEeXRvlpcpSVUep57HMEmxqIb8x0RRNS00JrU3rtVP+t3JpyuJH89M1i28VXfwkUFWFBGotoZefKya+zLKfZ+68gkdplcT6lMaxIdB/OVuOXMLiAwWMNf60NIN+AAb/T94vWRdY2FW/KkBVPLnDkrEAoPdC/cQoiW5+HQvU4kLgtzHMhWflDIz+g5U80xZiHGqU9vZpKB5dZFYmiRRoPVo3xzC3Biwc2LIuMvmf3mE3Wua27GZEU0QLag2JQQUA+/os7ISXyW/8xDanOijTpy2BmvNMLui0bUEFFK2PyhDiT11bapYsWRpBoKYlqFelRMje93tV+fsSs1LxrRq4+anNqdqQQK1FfNy3GQLq2yM9txAf/HoZRcUq9pJv9SZLBuKLgV2TWHH+onz5P6GxtDhVRh1vIGBY9b/UqqLNW4BLM93t/59F7EetaX+gQZDujlMaIVEq9rjmJVmqgueBve+xWFczK2DUb4CThiVkKkIQqIlRitnTNZHzG9hzy9d16wrUZaKUGH/asnoWYCEGNS2h6s53+ZlyAWCoGFQB31LZ/EApF78OBKq2aqHGnWTPzk1ZNz1tI9YRva78u0aMP9VCroNVHfn1XVnMa2lyn8tDHJqFVDyuKktwZVCbU7UhgVqLMDc1weoRbWErNcWF+Of49qiKlgeOAwZ8w/6pn8UAh+ewf8DiAvbPVB0rSG1BYsriQgWe3tPevh+cZ3fvnAmrA6ovPNqwOLa8dN25xyMWlXT2kgBvbgXq6aCernMTJn4LsoBULf5d9E12KnB9F1tuP0m3x9JlqanqtDgtjU1dQGrPbtyqCq8RSkxZOmq/brC6iOWmjjIxpos2pwLaancq1D/11oH1FGCJV6aWLDlS2d9SmwIVKOXmv67a+DuHAFkRs34LoSXKULWmqzIoBlVtNBKo33//Pby8vGBhYYGgoCCcO3eu0vFpaWmYOnUq3N3dIZVK0aRJE+zfv198f8GCBeA4TuHRrJkOrVW1mAZOVvhqCLvLWxsZgxN3VawhaOnI4lHBARc3M3c/wNz7VBKD4d1NvvzrcCDldvX3yfPA0QVsOWCk8paeusJEIv9MusjmP/s/4OQKtvzaankCibaRmMotGzW5o1TUz6wQuHtr7RfnL4tYrF8HArU6LU5Lw3Gqx6Eag3tfoEFHwMyaNQ25HwnICgFwgI0OLJPacvHHlFh7hSYe2sZEIheNghgV4Hl5eI5Ha+0cTzhWkooCVXTvV2I9BapnQaUuUmqjtkDdsWMHpk+fjvnz5+PSpUsICAhAcHAwUlJSlI4vKChAnz59EBcXh99//x3R0dH48ccfUa9ePYVxLVq0QGJiovg4efKkZp+IwIBW7hgZ1AA8D3y4IwopmVW4xwS8u7KC6YD8C8uY3fuGJPMxsCmYNQmoDveOAvEnWcxhj9namZs66CoO9eafrLMXwOqcthml3f2XpaYX7JfJgPMb2XL7ibq/KRRcoJlaFqgyWSkLqhZatKoah2oMGfwCplJ5ofvLP7Fna5fqV6xQhjYE6rP77GFiqt0C/WURroey1se0BCAvDTAxYxZMbaBOJn9Bjjwco6L4U3G/LQBwQFYSq0qiDhSDqjZqB+6tWLECkyZNwrhx4wAA69atw759+7Bp0yZ88kn5GpSbNm3Cs2fP8N9//8HMjP2Denl5lZ+IqSnc3AzQnq6WMu/V5rgU/xy3kzIxYNVJeDlZwdlGCicb85JnKVxKLTvbmMNGagqu52dMrAgB88acIGVI6gWyKgdbXmOu6yavqL8PmUxuPQ2azBIs9I0Qh/rwHIvj00Yrxvj/gD8mAeBZW90uM6u/z6qo6S1PYyKYyLKwL2kOoWNsdeTiT4tjlQEk5tqJ2XYqsaBWFVJjDBn8pfHtDdw5IM8M13aJKQFB7ORUIwZVEGeeQSwhVFdU1FFKsKi6Nmdl47SBIFBTbrLv2cpioWMiWItVhwZVW/2lNuyaTL3LrKhCvLEqUAyq2qglUAsKCnDx4kXMni239JiYmKB37944ffq00m327t2Ljh07YurUqfjzzz/h4uKCkSNHYtasWZBI5HUe7969Cw8PD1hYWKBjx45YsmQJGjRooHSf+fn5yM+XFybOyNBBqZQajoWZBGtGtsWQH/7Dk8x8PMmsupCzuakJXGykaG0xBd9y0yDjTPFrnCM8cpPg5WSNhk5WsDDTYW3OmsTIHcCud4B7R5i7f9Ba9QvrX9vJYqSk9qyUlSFw9GLtAZ/dZ4kSpdv7aULKLXY+ivNZL+v+y/QTIiImSl1lZY50nTSnbYTkqNajWac3XSMmSWlZoAquz7rNtWMxVNWCakwufkAeh1pcwJ61XWJKQBsWVCG8p7EOau6Wxq0kfjPxKnPrC98L2o4/BZiIlJizuPS0uJLWuRUguvdfU+27yr0VE6hJV1QXqLJilogFyBPbiCpR61v86dOnKC4uhqurYiyNq6srbt9WHo93//59/PPPPxg1ahT279+Pe/fu4d1330VhYSHmz2dJJ0FBQdi8eTOaNm2KxMRELFy4EF26dMH169dha1veorNkyRIsXKjjoum1gMZ1bfDvRz1wKykDqVkFeJqVj9SsfDzJKkBqVj6eZuXjaclydkExCopkeJSWi0dwwC1uMUwgw73DDwE8FPfpbm+Bhk5WJYLVGl5OVvByZuLVyryGiYLqYGYFjPgV+HMqcHUHa3SQ/UQeIlEVRfnAP1+w5ZenlW+rp08a9WACNeaf6gnU9EfAz0NY0pVnEDBkg26bDZSmjg8ra1SQCTyNlseg1QSex7MkDQBoN14/x9RVDKo23fsA4FySyZ96T1HUlOW5Ebn4ATYPJ1957KzOLKjVTJIqKpAnSOmiKURp6vqxRNCcpyxxTEga04VAlZgyC37SVebmr0igFhUAdw6y5WZVuPcF3FqxZhrqxKHmPgdQUtzf0oDf9TUMnSsKmUyGunXr4n//+x8kEgkCAwPx6NEjLFu2TBSo/frJfxRbtWqFoKAgNGzYEL/99hsmTJhQbp+zZ8/G9Olyi1NGRgY8PT11/VFqJPZWZnipUdUuhdyC4hLBmi+K2cdpuYhNzUF8ajZin2YjM68Iiel5SEzPw5n75b8Q69pKRUurl7M1vJ2t4eVkDS/nWipeJWbAoHUsvuz0GuDwZywuqc+iqu/EL2wC0hOYqzXoHf3MtyJ8egIXNlYvUepZLLOcZjxiVq8R2/XbfczEhCVYxJ1gHaVqkkC9GA6AZzcKlWUQaxNBHOQ+Y/3RtfW30kaL09LUacRETX4GSzpSJvR4vlQMqpG4+AGWFCgKVCO1oD44y6yM1i7a+5tVhLkV+254cpsJRzt39rcTaqBWt8VpWdz85QK1ouSnuBPshtq6LuDZQbX9VhRLWxmCe9/CoeZ5dwyIWmfK2dkZEokEycmKnYqSk5MrjB91d3eHmZmZgjvfz88PSUlJKCgogLl5+ZgTBwcHNGnSBPfuKY87kkqlkEq13NLyBcfSXALPOlbwrKPcvcjzPNJyChGXms0eT5lwjSsRsM9zCpGSmY+UzHyciysvXl3tmHhlgtUa3s5MxHo5WdfssAETE+CVL9gX/NH5wH+r2I9FyKqKv4jyMoB/l7Hl7p/ox6VbGd5dWBmo1HssYcFBeWhNhdz6C9gzlXVYsXVnhfgNYRH2aMN+cB5fBtq+pf/ja0JhHnBpK1tuP1F/x7VwYF6AwhxmRdVWbVqxXWVr7ezPVMriSp/HspanygRqVjJrDMCZAPZGZKho3As4s5Yt66LEFKBYB7UyC3NFCC1ZfXrqpmtZWdz8mUBNvAo0CWY1YrOfsO8fbd9UqlJq6vbf7LlZf9W9PUKowrP77LtclbhdscQUuffVQS2Bam5ujsDAQERERGDQoEEAmIU0IiICYWFhSrfp3Lkztm3bBplMBpOSf4A7d+7A3d1dqTgFgKysLMTExOCtt2rIj8wLAMdxcLQ2h6O1Odo0cCz3fnoZ8RpXYnUVxGtyRj6SM/JxNra8eHW3t4CXkzVa1rPD+Je94W6vR8ubNuA45qa3dgb2vg9E/cK+kIaGKxef/61m7zv56q5bkDpY2LNqDQ/OsgS5wLGqbVdUAByZB5z9gb32DAKGbjJMshdQMxOlbv7JrgW7erpvb1sajmNu/tR7TCRoQ6BmJgHZKUwoalNsODcpEah3lNfpFOJP7errJlNeUxq+zGp/FuXqpkg/IHcXywpZkqO6SU5CtZbGKsZSVhe3Viz2XrA+ChZ3l2ba97hUVWpKJpMnsVVVXqo01k7s/zXjEbPONuxY9TbU5lQj1LY1T58+HWPHjkW7du3QoUMHrFy5EtnZ2WJW/5gxY1CvXj0sWcLqaE6ZMgVr1qzBBx98gPfeew93797F4sWL8f7774v7nDlzJkJCQtCwYUM8fvwY8+fPh0QiwYgRI7T0MQldY29lhgArBwR4OpR7Ly2nAHGpOYh7ykQrE7FsOaNU2MDp+6nYejoeoZ28MKW7DxystJTRqS/ajGZfQDtDWVzTT4OBkdtZjVmBrBTg9Pdsuddc43H3+PRkAvW+igL1eTzw+zjWmhMAOr3PmgwYUiAIAjX5OhPP2soI1iVCclTgOP1fC4JA1VYcqiA2nHy16xVw9gXuHqq4CYOYIGVE7n0AMLMAus5ktVAbdtLNMcyt5JbwnFT1BGpmsjyjXlf1T8tS1j2ui/hTASGT/3ks644otVF8/+F5Zn2X2gNeapbXcmvFBGrSVRUFKpWY0gS1vxGHDRuGJ0+eYN68eUhKSkLr1q1x8OBBMXEqISFBtJQCgKenJw4dOoQPP/wQrVq1Qr169fDBBx9g1ix57/SHDx9ixIgRSE1NhYuLC15++WWcOXMGLi5a7NdNGAwHK3O0tjJH6zLiled5PC+xvMY+ycaOCw9wLvYZ1v97H9vOJeCdbj4Y19mrZsWvNu0HjPkT2PYm8OAMsKkf8NYueVLK8aVAYTYrU+X3mmHnWppGPVhzhvuRLOO0MnfX7f3AnndY7JaFA2vwUN3sf23g6MXmk5cGpNyQC1ZjJfEKK+9lYgq0HaP/4wtxkdpqdyq697UcyygkSlWUyW9sCVKl6TqTPXSJlROQXiJQ66jR9U+IOXdvDdjo6bdWiHN9Hse+P3QpUK2dARs3VrM05RbgWaZk4q297LlJsPo3s+6tWBkxVROlsgWBSglS6qDRL39YWFiFLv3IyMhy6zp27IgzZ85UuL/t27drMg2ihsNxHOpYm6OOtTnaNnDE623rIfLOEyw9GI1biRlYdigam/+Lwwe9fDGsvSfMJDWkM2+Dl4BxB4GfXwee3AI2vgKM3sVE38VwNqb3QuPq0FUvEJDasWzTxCvKOxkVFwIRC1mIgrDNG5vVj1nVFRzHROn9Y8zNb+wCVSjM7/eabvqfV4WYyZ+onf0l6UhsOAkCtYJuUsZqQdUXVk5A+gP1E6WE+FNdl5cqjVUdFoqR8ZC53nUpUAHm5s9KYl6V0gKV5+Xxp1UV51eGILSFa74qKAZVI2rILz7xIsBxHHo0rYt9772M74a3hmcdSzzJzMdne66jz4rj+OvKY8hkvKGnqRquzYEJh1k9vvQHrOvU3vdYv+fGfXTX81pTJKW6yCjL5k9/CIT3l4vTl95lItxYxKlATekolZvGYvEA/SZHlcZORxZUbWeDC7VQ0xJYxYGyiAJVDethbUKTTH5Zcan6p3qKPxUQCvbH/FNy7XGAW0vdHKuiRKnk6+y6MbXQ7PMLXoKU2yycqCooBlUjSKASRoeJCYeBreshYnp3LHytBZxtzBGXmoP3fr2M174/iX/vPAHP1wCh6tAAGH8I8GjLyvnEnwLAAb3nG3pmyhG6St2PVFx/9wiwrgtzR0vtgWE/A32XGGeMZ01JlLqyncUNuvjpLj6xKrRZCzX3ubzUkyBAtIW1M0vkA88yp8tiTG1ODYEmAjUxin0nmdvqv1ugIO6itrFnp8ba6WCnDOFaLNvy9FaJ9dSnF2Burf5+7T1ZOJGskHnJqoJiUDWiBgX3ES8a5qYmGNvJC0MD62PjyVj879/7uP4oA2M2nUPHRk6Y1a9ZubjWsmTlF+HBsxz2eJ6LB89y8PB5Dh48y0VWfhG6N3XB623roW0DR3C6cLlbOwNj/wJ+e4tZDAJGaP8HXFsIiRIJZ1hSgakFcOwL4OS3bL17a+bSVyfOTd8IAjXllnbre2oTnpcnR7WfYLhQD20KVCHZxqGB9uPsOI5ZUR+eZ3GopSsEFObJ528sbU71TelSU6pyr8R62qib/hMbhe+/zJK/m0dr3R1LtKDeUCzDJXaPUiN7vzQcx4R27L/Mc1BViAK1OdUIEqiE0WMtNcX7vXwx+qWG+P7YPfx0Oh6n76di0Pen0LeFGyZ19UZWfnGJCM3Bw2e5ePCcidLnOYWV7vuXswn45WwCGtSxwqA29fB6m3rwctbgjroypDbAyN9YAfl6gdrdtzap04j9yKfFA9d/B67sABL+Y++1nwQEf8nqUhozdvVYTdrsJyzGrWxihDEQ+y8r4G5uA7QaZrh5CO1Os5JZfHF1hIqu3PsCTr4lArVMJn/6AwA8YGb94sb3id2k1LCgGiL+VKDsNaKr+FOAXTcmZqzRQ1oCi1NOjWFJlJyEJUhpiluJQFWlYL/Q6cuaBKo6kEAlagx1rM0x99XmGP+yN749cge7Lj3EwRtJOHgjqdLtHK3MWBMCRyvUr2MJT0fWkEDG8/jrymMcvJ6EhGc5WBVxF6si7qJNAwe83qYeXm3lAUdrLbmxJWZAgyDt7EtXcBxz81/cDPz1AVtnbgu8tgpo+bpBp6YyQqLU3cPMzW+MAlWwngYMV79upTaxcmY/3rJCJlKrU79W18kuFWXyl87gN6akQ30iuvhVbHeam8bEPqD79qbKcGjAQjby0tlrXQpUU3NWYzX5GrOiOjaUJ0d5d6metV+Yd1WZ/DxPMagaQgKVqHHUc7DE8jcCMLlrI3xzOBrnYp/B1c7i/+3dd3xUVf7/8dfMJJkkpJHeE0JJCBJKgFBEuqCuC1ZUVBRERdxFsOL+FMXdxa+srIuygljACoKFVWy0RKVJQm+BQCC0VEjvmfv74zKTBJKQMpkZks/z8cgjkzt37py5Xsk7557zOQR3dCa4o9OlMOpkWhnLRV//ZT4i0pe/T6jkl4MZfL37LL8fy2J3Wi6703KZ9/0hhkf6clufIEZG+VrwE1pR55FqQAXw6wl3rzDfKkOWEti3OqDamvxz1cXB+125jLNFabVqAfm8NLVdLQmo6a3cg2oMqDmXzeS/mKp+b68z+KHpY1BTE0CpUnsXrXHeNBr1Ojn5m/pzay+x6tejOqBG3Vw9/jSqGbP3azK2O+OAWvS/vpW4KorVlc5AxqA2kQRUcc3q5ufK0gf6tfg4zg52TOgTxIQ+QWTml/K/vef4ZvdZDp7LZ/2hDNYfysDN0Y6xPa9cQavN6XojRN6szoge9ZJtjuG8GlueKJW0Qg0HYUPUSg/W5hZYHVCbq7y4umez1XpQL83kzz5WeyyhaQZ/eOu877XAGHqKGzkGNcXCq0fVxRhQO4aDk0frvlfNmfz559XJntDygOrdVV0prLxQnbzn3aXu/YzjT+0cmzchqx2TWfxC1ODr5sgjQyNY99eh/DLrBh4f1pkAd0fySytZnVhdjmfdvvPXRiWBprJ3gnu/gHH/vDbDKVRPushOVid72Yqqiure6f5W7j01Mq4R35KAmr4fFIM69tfV3zztulzHTuqYwfJCdUlVo/Y+gx+a1oOqKDUCqhVu7xt1HnnpuwXaUDOgJl+6exE8oPraby6trsZyqg3UQ615e7+9DkNpJgmoQtSjm58rL9wUxZbnR/L5I3Hc1ifQ9Nyza/bxyIpEzufVUZfRzPJKKtpmGG4trv7qKkmKoXETGCzlyDq1aHgHX4hq5uxhczNOlGpJLdTED9Xv4de33i9gO4fqEFpzHKqxB7W9zuCH6oBakgtVlQ3vm31ULZKv06u9+NbSdTRM3wo3/r3138tYNSDnOOz7Un3cnOL8dTGWzGpoHKpxbLCMP20yCahCXIVWq2FwF2/+cVt1eSgHnZaNRzIZs/BXPttxqlUWEDieVciMz3bR69Vf+OvKPdfOIgW2wBZv8xsnR8VOtp0assZSUwXNXE3q4snqBQcG/9UsTarX5eNQFcW2lzm1FCfj0CNFXea3IcbZ+2GDwcG5NVt1dX49LNMGF1+1dx8FTu9Qt7X09r6RaUWpBgKqlJhqNgmoQjTDV9MH0SfUg8KySv72zQHuXbad1Owisxz7fF4JL3y1jxv//Svr9qvB4bu951jwS7JZjt8uBNlYQM1KVsfcabQQ+5C1W1OtpbVQt76tjqntPLLu5XHNyTST/1JALbmolg8C21vRzJJ0dtUh9Wq1UG1h/Kk11Kyd69vDfBM/a/ag1neXS5Y5bTYJqEI0Q2dfF9Y8PpiX/xSNk72OHakXGPfWryxNOE5llaFZx7xYVM4/1h1i2IJ4Vu48TZVBYXR3P54bFwnAu/HH+XLnaXN+jLbL1npQd36gfo+8uWWz5c3NtQXLnRZkwK5P1MfXzzZfm+rjdVlANd7ed/Gzfm+gtTVmHGpFyaXV7LDu+FNr8KuxlGpzi/PXxTdaHRtdnF3/XQgpMdVsMotfiGbSaTVMub4TY6L9mPP1fn5PyWb+j0dYt/88/3dHDN0DGlfjsqiskg9+T2XZrycoKFPHkA3o5Mnz46KIDVN7RkorDCzaeIwXv9lPcEcnBneRv8YbFHApoOakqPUWHd2t15ayQtj7hfrYViZHGZl6UM83XCqnLtsXQ1WZOuEk/PrWaV9NNWfyg0yQqsnZS73WGwqop7ao5Y7cgtTaoO1JrYBqptv7oE4k9e6mLnd6fl/1/081yTKnzSY9qEK0UIinM59MHcAbd8bg5mjHvjN53Pr27yz8JZmyyqp6X1dWWcXyLakMW7CZheuPUlBWSY9AN5Y/3J9Vjw40hVOAWaO78udegVQaFB7/NImUTBuanW6LOnhV3/Y9t8eqTWH/l+qtaM/O0Gm4ddtyOVd/QKMW62/KSkQlF6t7hYc+bZnZycZb/Hmn1dJWMkGqWmN6UI239zuPbH+zyUMGgNZOvb1fM6yaQ8BVxqEWGQOqmZcAbgckoAphBhqNhrv7hbBh9jDG9vCj0qCwaFMKtyz6naRTF2vtW2VQ+CrpDCP/lcAr3x0iu7CccC9n3r63D989eT3DI33RXPYLRKPR8MadMcSGdSS/tJIpy3dyoajckh/x2hN4aUykNW/zlxVAwgL1cf+pTeuhtASdvTqJBJp2m/+PZWrJJ7/rWrZcZFM4e10aa6nAheNSA7UmU0BtYAxqex1/CuqY08d+hQe+MX84N06UOl9PqSkZg9psNvavpRDXNl83R5Y+0I93J/XF20VPSmYhdy7ZyqvfHaSorJJfDqZz039+5enVezmbW4Kfm55/3taT9bOHcWuvQLTa+v/xdLTX8d4DsYR4OpF2oZhHP06ktKL+Htp2zxbGoW6eDwXn1Dqe/aZYrx0NaepEqfIi2P6u+vj6WZbrjdNoao9DlRn81a623GnuabUusEYLEcMs1y5b4tcDXP3Mf9yr9aDKGNRmk4AqRCu4qWcAG2bfwB19g1EU+GjLSfr9fQOPfpLE0YxC3J3smXNTFAnPjuC+uFDsdY37X9HLRc9HD/XH1dGOxFMXef6rfWavkXrwXB7ZhWVmPaZVWDugpu+HHUvUxzf/y3YXPjDWQi1oZEBNWgElF8AzAnrc1nrtqkvNcaimHlS5xX/VW/zHL/WeBvevUZZKmIWxzmpumjr05XIyBrXZJKAK0Uo8nB148+5erJgygCAPJ0oqqnCy1zFjRGd+fW4Ejw3rjKO9rsnH7eLrypL7Y7HTali75xz/2Xjs6i9qhPS8Up74LIlbFv3OmIUJ7Eqr4x/ba4lx2c3cU9XjwCzFYIDvZ6slmKLHq4XJbZVrE1aTqixTS0sBDHlKXU3HkozjULOOqGNRQXpQ4eoB1TT+tJ3N3rcEp47V493T99d+rqqyOrRKD2qTSUAVopUN6+bDL7Nu4O17+5Dw3HCeHRuFu5N9i445pIs3f5+gDvZ/a8Mxvt3d/JWAKqsMfPh7KqPejOeH/eoykheLK7hv2XY2HMpoUTutyslDnZgEcN7Cvai7P1HX/HZwgXGvW/a9m6opt/j3rlR7Wl0DoNc9rduuuhgDauqvYKgEnUN1wG7PjOMb66qDWlUJJxLUx+1x/Kkl+NezopSpR1Ujk6SaQQKqEBbQQW/Hrb0C8XV1NNsx7xkQymPDIgB4bs0+Ek/WM/6sAXtO5zJ+8RbmfX+IovIqeod48NX0QQyP9KG0wsCjnyTy2Y5TZmuzxQVZYaJUUQ5smKs+Hj6n7tIztsS03OlVAmpVJfz+b/Xx4L+Anb5121UX4y1+47g+9xDL9+LaoobGoJ5NhLI8tacvsLdFm9VuGO/WXD4O1XidOnWU67QZJKAKcQ17fmwU43r4U15l4NFPkjiV07jVrPJKKnjp2wPc9t8tHDyXj5ujHf+47Tq+nj6Y2DBPlj3Yj7v7BWNQ4G/fHGDhL8lmH+tqEaZxqHss954bXlZ7Tvyug7jHLfe+zdXYHtRD38LFVHDyhL6TW71ZdeoYrpYLqvmzqO6dq+sWv3F5084jJSS1lvp6UGWZ0xaRgCrENUyr1fDvib2JCXbnQlE5Dy/fSV5xRb37K4rC2j1nGfVmAp9sP4WiwO19gtj49HAmxYWZqgjY67T83x0x/HWUekt10aYUnluzj4pmrpJlNZaeKJW2HXZ/qj6+ZaG6DKWtqxlQ6/sjRFHgt4Xq44HTQe9imbZdTmevVkQwkglSKmMAqihSV4yqScaftj7jTP7so7XPv5SYahEJqEJc45wcdLz/YD8C3R05kVXE458mUV55ZZBMzS7igQ/+YObKPWQXlhHh04HPH4lj4cTe+LheebtWo9Ewe0w35t/eE60GViedYdrHiRRdWu3qmuAfA2jUGp8FzR9PW1ZZRUpmIQfP5dXfk1xVAd/PUh/3eQBC45r9fhZlHMNZUaSuulWXoz9D5kF1TO2AaZZrW12M41BBelCN9G6gvTSuvWYvalFO9R9n7W15U0tyDVBn6StVkHmoeruUmGqRa+DPeyHE1fi6OfLBQ/25892tbDuRw0vfHuD1O3qi0Wgoq6zi3fjj/Df+OOWVBhzstDw5oguPDYtAb3f1W373DgjFx0XPk1/sIj45i3uXbefDh/rj7WKFMYhNpXcBn0h11ve53RA5rt5di8srOZVTfOmriJM5xaRdKOJkdjHn8kpMnYuhns7cGRvMHbHBBHnUKB21Y4n6y8nJE8bMa+UPZkYOzuoYuZKL6nriTh61n1cU+O1N9XH/qdYvU+TdFZIvPZaAqtJo1BBUmK4GVPdgdfuJzYCiDjdx9bdqE9s0jUbtRT2+Sb3NHxSrbjeOCZaA2iwSUIVoI7oHuPHOfX2ZumInqxJP08mnAz2D3Hnp2wOcyFbHpg7t6s1r468j3LtDk449OtqPL6YNZOqKRPadyeOOd7ey4uEBTT6OuSiKQnZhOReLyzEoCgYD6ndFwaBcemxQH3dy64FP1hHSDmzhpCYWg6KQX1pJmjGE5hRzMqeIzIKGa786O6hhPu1CMQvXH+XfG45yfRdv7owNZmxIJY6b56s7jnm1RTN2T18oJv5oFvFHMjmaWcBLt0RzY49WDheugWpAzT8Lvt1rP3dqi1qRQKeHgTNatx2N4VWjB1WWOa1WM6AaGcefSu9p6/O/FFBrTpSSMagtIgFViDZkRJQvc2/twdz/HeT1H4+Ytvu46nn5T9H8KSbgimVUG6tPaEfWPD6IyR/9wamcYm5/dysfPtSf3iEeZmp9beWVBs7mlpB2oZi0nCLSLqi9m2kX1K/i8satovWgzoV59nBsz29M3TmwwX3dnewJ93ImzKsDYZe+G3/2dnGgpKKKnw6k82XiabafuMBvx7L57Vg27zn+hxsposg3Fufek2jKGS6rrGJn6kU2J2cSn5zJ8azaE93+8sVuVj02qNXOM6COQ808WPdEKWPvaZ/7W2clnqYyzuQH6UGtyfhHkbHmr8Eg408tKaCOiVIyBrVFJKAK0cZMHhxOanYRy7eeRKOByYPCmX1jN9wcW1Z7FSDCx4Wvpw9hyvKd7D+bx73vbWfxpD6MjGpecCmrrOJ4ZhGp2UWculDE6Roh9FxuCYYGCgdoNGqg1Gk0aLUatBrQajTqlxZ1u0bDBUMPKIY+dql093JFp9PgZK8j1NMYQp0JvxRIPZwdGmyvs4Mdt/cN5va+waTlFLNm1xnO/rGWGyt2UKloueP0XRj+8zt39wthQp+geodBGHtJE5Iz2Xo8p1bY1mk1xIZ2ZFikD3+kXiDhaBaPrEhk7ZNDag8pMCfTRKnztbef3aX2Cml0MOSvrfPeTeXXQx1G4ep/5XCE9swYgoyhKOMAFGWCfQcIbfgPM2EG/pdKTWUcBEOVWjFBxqC2iARUIdqgl/4UTZ9QD7r4utAj0N2sx/Zx1bPy0YE88dkuEo5mMe3jJP4x4TruGRBa72sUReFsbglHzheQnFHAkfQCktPzOZFVRGUDKdTRXkuopzOhnh0I9VTDZKinM6FezgR3dGrUGFoq4uCfc/BUcvnx4Yjq8XktFOrlzOzhISiHPoWL8JvnnaRmhVOWUcjf1x3m9R+PMCLKl7tig7m+qze7TuUSn5xJ/NEsUjILax3L11XPsG4+DI/05fqu3qaFHCYPruTOd7dyJL2Aqct3svrxQbia4Q+NK5gC6mULPvx+aeZ+z7tsp7dS7wIz96hF+kW1y1eTMi5v2mmodWrWtjeeEeokwvJCdSle3yhZ5rSFJKAK0QbptBrG9w5qteN30Nvx/uR+zPl6P2uSzvDC1/tJzy9l5qiu5JdWknwpgB5OLyA5vYCj6QUU1DP739XRjq6+LoR5dSDE05mwGkHUx1Xf7CEJJvZO4BsNGfvViVJmCqgA/LYQzcWT4BrIiMcX8ofBke/3nWN14hn2nM5l/aEM1texGpdOq6FvqAfDI30ZHulDdIBbnZ/TRW/HBw/1Z8LiLRxJL+CvX+xm2YP9sNOZuQBLXbVQs5Lh8Hfq4+tnmff9WsrRvH90tQmXB1Tj7X1ZPcoytFp1Mtrp7eo4VN+o6uEWsopUs0hAFUI0i71Oy4I7Ywhwd+TtTSm8teEYn2w7RU5ReT37a+js40KkvytR/m5E+bsS6e9KgLtjy0Po1QT1qQ6o3W81zzGzU2DLW+rjcfNB74o7MCkujElxYRzLKGB10hm+3nWW7MIyfFz1DK+jl/SqTfdw4v0H+zHxvW1sTs7i7+sO88qfe5jnMxgZA2pBjVv8v7+lfo/6k/rLVtg2U0DNhrICtSYvqAX6hWUExKgB9fxe9a6DjEFtEQmoQohm02g0PH1jJP7ujrz07QFTOA10dyQqwO1SGFUDaSfvDjjYWan0cmAf2PWx+Qr2Kwqsmw1V5WoPVfT4K3bp6ufKizd359mxkWTklxLo7mRaCKGpeoV48O+7ezP9s10s33qSTt4dmDw4vIUfogbXy27xXzwF+1apj4fONt/7iNZTc7nT1N/AUKEuauDV2brtak+MK0ql71Nv9VddqgwiY1CbRQKqEKLFJsWFcUNXH9LzS+nm59ro3kGLqbmilKKoM6xa4sBXkJqgll66eUGDx7PXaQnu6Nyy9wNu6hnAc+MieeOnZF797iChXs6MiPRt8XGB6h7UkotQXgxb31aLjkcMr67pKGxbzVv8xvGnUl7KsmrO5DeWmLJzAgfrlOO71slKUkIIswjxdKZ/uKfthVNQx6DqHNQAdvFky45Vmgc/v6g+Hvq0OjnCQqYP68xdscEYFPjL57s5kp5vngM7uquzvUG9PbnrY/Xx0KfNc3zR+moGVFP9Uxl/alE+3dUVvUpz4fwedZvc3m82CahCiLbPTq+WJ4KW3+bf9A8ozADPznD9Uy1uWlNoNBr+cVtPBkZ4UlhWydTliWQWlJrjwNW9qOtfUm9NBveH8KEtP7awDGNALcxQ/wjT2st/P0uzc6ger318s/pdJkg1mwRUIUT7ENhX/d6SgHpuD+xcpj6+5V9WKd/jYKdlyf2xRHh34GxuCdM+TqK0onGLFjTILUD9fman+n3o0y0fCiEs5/JxjqED1ZJcwrKM9VBNAVV6UJtLAqoQon2oOQ61OQxV6sQoxQA9brfq7GgPZwc+eKg/Hs727D2dy9Nf7sXQ0KoGjeFWoyyZbw/oOrZlxxOWZe+o1uE0kvGn1mEch5qXpn6XCVLNJgFVCNE+GAPq+b3qMpBNUZQDG+bC2SRwcIWx/zR/+5qok3cHlt4fi71Ow7r953lzfXLLDmi8xQ/qzH2t/Hq45tS8nSzjT63DOJPfSMagNpv8CySEaB98otQZtWX5cOH41fc3GOBEAqyZAguj1JntACP/Vn073MriIryYf7v6C3Hx5uOsSTrT/IO5h6jfO3aC6AmNfpmiKJzKKSKvpKL57y3Mw3g72cVPLRovLM//OqDG0BgZg9psUmZKCNE+6OwuFdLeod7m9+5a936FmbDnM3Um+4UT1dsDekH/adDnfsu0t5HujA3mZHYR72xOYc7X+wju6MTAiGbcVux5F2Qegl73qufqKrIKyvh291lWJ53maEYhdloNcRGejO7ux+jufoR4try0lmgi4+3kzqNk/LC16F3Vyh7GP4JlDGqzSUAVQrQfgX2qA2rM3dXbDQY4sQmSVkDyD2C4tCyrgyv0vBNiJ1cPEbBBs8d0IzW7iHX7z/P4p0l888QQOnk3sfai3kWt6dqAiioDm45ksjrxDJuTM6m6NO7VTquh0qCwJSWHLSk5vPrdIaL8XdWwGu1HTJB7sxcpEE0QMQxObLa5P6LaHf+eNQKqjEFtrmbd4l+8eDHh4eE4OjoSFxfHH3/80eD+ubm5zJgxg4CAAPR6Pd26deOHH35o0TGFEKLJLp8olX8OEhbAf3rBp3fA4f+p4TSoH/z5HXj6CNz6lk2HUwCtVsObd/eid4gHucUVTFm+k91pFykurzTL8Y+k5/Pa94cY+M+NPPZJEhsOZ1BlUOgd4sE/bruOpJfGEP/McP7fLd0ZGOGJTqvhSHoB72xOYcLiLcTN38icr/ex8XCGeSoOiLoN/gu8eB7Ch1i7Je1bQI1xqDIGtdma3IO6atUqZs+ezZIlS4iLi+Ott95i7NixJCcn4+t75aom5eXljBkzBl9fX9asWUNQUBCnTp3Cw8Oj2ccUQohmMQXUPfDFvXD0J3VWPqjF6mPuUXtL/cy81r0FONrrWPZgPyYs3kJqdhG3/XcrGg2EejoT6acuORvpry4/G+7ljJ2u4f6J3OJy/rf3HKsTz7D/bJ5pu4+rntv7BHFnbDBd/VxN292d7HlkaASPDI0gt7ic+OQs1h/KIOFoFlkFZXzxx2m++OM0TvY6hnb1ZnS0HyOjfPF2sXyprjbNzsHaLRDGUlMgPagtoFEUpUm1SeLi4ujfvz/vvPMOAAaDgZCQEP7yl7/wwgsvXLH/kiVLWLBgAUeOHMHevu4VZpp6zMvl5+fj7u5OXl4ebm5uTfk4QjRacUUxcZ/HAbDjvh0428sYu2uOoQpeD1XXyTYKHayG0ujxYO9kvbaZSUpmIf9Yd4j9Z/PILiyvcx8HOy1dfV2I9HMl0l/9ivJ3w8dVz2/HsliddIb1BzMor1LDu71Ow6goP+7qF8ywbj5XDbc1lVVWsePEBTYczmDDoQzO5VUvLKDRwNhof+aN74Gvm2PLPrgQtqIwE/51aYz7c6kyUaqGpuS1JgXU8vJynJ2dWbNmDRMmTDBtnzx5Mrm5uaxdu/aK19x88814enri7OzM2rVr8fHx4b777uP5559Hp9M165hlZWWUlZXV+sAhISESUEWrkoDaRmx4BQ58Bd3/DH0fBJ9Ia7eo1WQXlpGcXsCR9AKS0/NJzijkaHoBJfXcZnfQaU2hFCDK35W7+4UwvncgXmbo6VQUhUPn89lwKJMNhzNMPbNujna88uce3NYnCI1M7hFtQfzrUFkKo1+xdktsSlMCapNu8WdnZ1NVVYWfn1+t7X5+fhw5cqTO15w4cYJNmzYxadIkfvjhB1JSUnjiiSeoqKhg7ty5zTrm/PnzefXVV5vSdCGEUI1+pd380vB20ePdRc+QLtXj4AwGhdMXiy+F1oJLATafkznFlFcZ8HC2Z3yvQO7qF0KPQDezBkaNRkOPQHd6BLozc3RXDp/P57k1+9h/No/ZX+5l3b7z/PP2nvjZUG9qXnEFi+NTyCks5+kbuxHoce33sgsLGH71u7+iYa0+i99gMODr68t7772HTqcjNjaWs2fPsmDBAubOndusY86ZM4fZs2ebfjb2oAohhGiYVqshzKsDYV4dGNvD37S9tKKKs7klBHd0Qm+ns0hbuge48fUTg3nv1xO8teEoG49kMmZhAnNv7cHtfa3bm1plUFi18zQLfj7CxWK1xuvPB9N58ebu3DsgRHp6hWhlTQqo3t7e6HQ6MjIyam3PyMjA39+/ztcEBARgb2+PTlf9D1737t1JT0+nvLy8WcfU6/Xo9TKwXgghzMXRXkdnH8uv3W6v0zJjRBdGd/fj2TV72Xcmj6dX7+WH/dbrTU08eYG5/zvIwXP5AHTzc6GD3o7dabm8+M1+vtt7jv+7I4ZQLxnmI0RraVKZKQcHB2JjY9m4caNpm8FgYOPGjQwaNKjO1wwZMoSUlBQMNZYWPHr0KAEBATg4ODTrmEIIIdqWSH9Xvp4+mGfHRuKg05p6U9cknaGJc3mbLSO/lFmr9nDnkm0cPJePq6Mdc2+N5oe/DmXN44N56U/RONpr2XYih7Fv/cqHv6eaasEKIcyryXVQZ8+ezbJly1ixYgWHDx9m+vTpFBUV8fDDDwPw4IMPMmfOHNP+06dP58KFC8ycOZOjR4+ybt06/vnPfzJjxoxGH1MIIUTbZ3epN/X7v15Pr2B38ksreWb1XqauSCS9xux/cyurrOLd+OOM+Fc83+w+i0YD9/QPIf6Z4Tw8pBN2Oi06rYap13fi56duYFCEFyUVVcz7/hB3L91GSmbh1d9ECNEkTR6DOnHiRLKysnj55ZdJT0+nd+/e/PTTT6ZJTmlpaWi11bk3JCSEn3/+mVmzZhETE0NQUBAzZ87k+eefb/QxhRBCtB/d/Fz5avpglv2Wyr/XH2XTkUzG/DuBl/8UzZ2xwWYd/7n5SCbzvj9EanYRAH1CPXj1zz2ICfaoc/8wrw589kgcX+xMY/4PR0g6dZGbF/3GU6O78ujQiCaV4BJC1K/JdVBtkdRBFZYgZaaEsLxjGQU8s3ove8+oJalGRPow//YY/N1bNjb1ZHYR874/xKYjmYC6AMEL46K4rU9Qo5dlPZdbwovf7Cc+OQuAnkHuvHFnDN0D5PeQEHVptTqotkoCqrAECahCWEdllcHUm1peZcDV0Y5JcWH4uurp2MEeD2cHPJ0d6OjsgEcHe1z1dvX2shaVVfLO5hQ++C2V8ioD9joNU4Z04smRXXB1rHsxmYYoisLXu84y7/tD5JVUYKfV8MSILjw5ogsOdtKbKkRNElCFaAUSUIWwrmMZBTyzZh97T+c2uJ+dVoOHswMdne3V0Opsj2cHB1wd7fjf3nNk5KsLvdzQzYe5t0abpXpBZkEpL317gJ8PqhVpIv1ceePOGHqFeLT42EK0FRJQhWgFElCFsL7KKgNrks5w6Hw+F4rKyS2u4GJxOReLyrlYXFHvKlk1hXo689Kfohnd3des41kVReGH/em8vPYAOUXlaDVwV2wIQR2dcLTX4mSvQ2+vw9Feh5O9Dkd7LY72OhztdDg5aNHbqc852mtxdrBD18ihBkJcK1ptJSkhhBDCmux0Wu4ZEFrv86UVVZcCawW5xeVcKFaDa+6lABvi6cS9A0JxtDf/YgQajYZbYgIY1NmLV787yNo951iVeLpZx3J20HFnbDBTr+9EmFcHM7fUfBRFkUULRKuQHlQhGkl6UIUQTZFwNIuE5CxKKqoorfVlMG0rqzRQUl5FaWX1czVpNDCuhz/Tboigb2hHK32SK+UUlvGfjcf4etdZpg/vzIwRXazdJHENkB5UIYQQwsqGdfNhWDefJr1GURTKKg0knbrIst9OEJ+cxY8H0vnxQDr9wjryyNAIxkT7We32f1llFSu2nuTtTSkUlFYCsODnZDo46HhoSCertEm0TRJQhRBCCBuh0WhwtNcxpIs3Q7p4k5xewPu/neDbPWdJPHWRxFNJhHs5M3VoBHf2DcbJwfxDFeqiKAo/Hkhn/o+HOX2hBIDoADd6h3rw+Y40XvnuEB7ODkzoE2SR9oi2TwKqEEIIYaMi/V1ZcFcvnh0byYptJ/l0exonc4p56dsDLPwlmQcGhvHAoHB8XPWt1oY9p3P5+/eHSDx1EQBfVz3Pjo3k9r7BaDXgoNOyfOtJnlm9F3cne0ZE+bZaW0T7IWNQhWgkGYMqhLC2orJKViee5v3fUzlzUe3JdLDTckffIKZeH0EX35aXzDI6m1vCgp+O8O2ecwA42mt57IbOPDYsAmeH6v4tg0Fh1pd7WLvnHI72Wj6dGke/cE+ztUO0HVJmSohWIAFVCGErKqsM/Hwwg/d+PW5aZQtgeKQPAyO8iPJ3JTrADR9XfZNn2ReWVbIk/jjLfjtBWaU6aeuOvsE8Ozay3hW8KqoMTPs4kfjkLNwc7Vj12CBZUUtcQQKqEK1AAqoQwtYoisLOk+qEqg2HM7j8N7pnBwei/F3pHuBm+t7F16XOMltVBoXViaf51y9HyS5UFzMY0MmTl26Jpmew+1XbUlJexQMf7CDx1EV8XPV89fhgQr2u7X8nUzILcHKwI8jDydpNaRMkoArRCiSgCiFs2fGsQn46kM7h8/kcPp9PanYRhjp+w+u0Gjr7dCDK342oADW0KorCGz8lcyS9AIAwL2fm3NSdsT38mtQDm1dcwcT3tnEkvYBQT2fWTB+Er2vdva627rdjWTz00U48nOzZ/Oxw3JqxFK6oTQKqEK1AAqoQ4lpSWlHF0YwCjpwv4HB6/qXgWkBeSUW9r3FztOOvo7ry4KBwHOy0zXrfzPxS7liyldMXSojyd2XVY4Nwd7q2wt3xrEImLN5iKqU1e0w3/jqqq5Vbde2TOqhCCCFEO+doryMm2IOYYA/TNkVRSM8vrRFaCzhyPp/MgjJu6xPEzFFd6djBoUXv6+vmyKdT47jjXbUn9ZEVO/l4SpxZSmKdvlCMr5sevV3rldfKLS7nkRWJFJRW4uemJyO/jGW/nWDy4PBrLmhfyySgCiGEEO2ERqMhwN2JAHenVi0HFebVgY+nDGDie9vYefIiMz7fxdIHYrHXNb1XNq+kgv/tOcvKnac5eC6f7gFufDp1AF4u5i+tVVFl4InPdpGaXUSQhxPfzBjM/e/v4GhGIR/8nsrsMd3M/p6ibs3rvxdCCCGEaEB0oBsfPtQfvZ2WTUcyeW7NPgx1DYqtg6IobDuew1MrdzPgHxt4ae1BDp7LB+Dw+XzuXbadrIIys7f51e8OsvV4Ds4OOt6f3A9fV0dmjVZD6Ye/p5JbXG729xR1k4AqhBBCiFbRP9yTd+/vi51Wwze7zzLv+0M0NPUlI7+UxZtTGP6veO5dtp1v95yjrNJAlL8rL/8pmq+fGIyfm56jGYXc8942MvNLzdbWjy8thKDRwH/u6WMqkzW2hz/dA9woLKtk2W8nzPZ+omESUIUQQgjRakZG+fGvu3oBsHzrSd7ZlFLr+YoqAz8fTGfq8p0Mmr+RBT8ncyqnGBe9HfcOCGXtjCH8OHMoU67vRN/Qjqx6dBCB7o4czypi4nvbOZ9X0uI2/nYsi1e/OwTAc2OjGBPtZ3pOq9Uwa7Q6QeqjLSe5UCS9qJYgY1CFEEII0aom9AniYnE5r353iDfXH8WjgwODO3vxZeJpvko6a6q7CtA/vCMT+4dyc0//WitWGYV7d2DVY4O4573tpGYXMXHpdj6fFkdwx+ZVVjmeVcgTn+2iyqBwe98gHh8WccU+Y6L9uC7IjQNn81n663Hm3NS9We8lGk/KTAnRSFJmSgghWmbh+qMs2njsiu3eLg7c0TeYu/qFNHq51rO5Jdz73nbSLhQT5OHEykcHEuLZtH+Xc4vLue2/W0nNLiI2rCOfT4urt0LApiMZTFmeiJO9jl+fG4GPq/knabV1TclrcotfCCGEEBYxa3RXHhgYBoBWAyOjfFn6QCzb5oxizs3dGx1OAYI8nFj12EA6eXfgbG4JE5du42R2UaNfX1FlYMbn1TP2lz4Q22D5qhGRvvQK8aCkooqlCccb/T6ieSSgCiGEEMIiNBoNr/65B59Pi2PrC6P48KH+jO3h36zyUwAB7k6senQgnX06cC6vlInvbeN4VmGjXvvqdwfZklI9Y9/7KmWrNBqNqczUJ9tPmXWClriSBFQhhBBCWIxWq2FwZ2/83c2zBKqvmyMrHx1ENz8XMvLLuOe97RzLKGjwNfXN2L+aG7p6ExvWkbJKA/+Nl17U1iQBVQghhBDXNB9XPV9MG0iUvytZBWpIPZKeX+e+Dc3Yv5qavaif/5FmlgoCom4SUIUQQghxzfNyUUPqdUFu5BSVc+972zl4Lq/WPo2ZsX81gzt7MaCTJ+WVBv67WXpRW4sEVCGEEEK0CR07OPDZ1IH0CnbnYnEF9y3bwf4zakjNLS7nkRWJFJRWEhvWkfm390Sj0TT5PWr2oq7cmcbZXOlFbQ0SUIUQQgjRZrg72/PJI3H0DfUgr6SC+97fzs6TF5o0Y/9qBkZ4MbizFxVVyhULDwjzkIAqhBBCiDbFzdGej6fGMSDck4LSSu5euq1JM/YbY9alXtTViac5faG4xccTtUlAFUIIIUSb46K3Y/mU/gyM8ERRaPKM/avpH+7J0K7eVBoU3t505eIDomUkoAohhBCiTXJ2sOOjhwbwl5Fd+O99fZs0Y78xjL2oX+0626RFAsTVSUAVQgghRJvl5KDj6RsjualngNmP3Te0IyMifagyKCySXlSzkoAqhBBCCNFMxl7Ub3efbfQqVuLqJKAKIYQQQjRTTLAHo7v7YVBg0UbpRTUXCahCCCGEEC3w1OiuAPxv77mrLrPaGiqrDJSUV1n8fVuTBFQhhBBCiBa4LsidcT38URR4y4K9qPmlFSxJOM7g1zfRY+5PTF2+k42HM6gyKBZrQ2uxs3YDhBBCCCGudU+N6cpPB9NZt+88fxmZT5S/ecpZ1SUjv5QPt6Ty+fY0CsoqTds3Hslk45FMAtwdmdg/hIn9Qwhwd2q1drQmCahCCCGEEC0U5e/GLTEBrNt3nrfWH2PJA7Fmf4+UzEKW/XqCb3afpbzKAEAXXxceuyGCmGAP1iSdZk3SGc7nlfLWhmMs2niMkVG+3BcXyrBuvui0TV/a1VokoAohhBBCmMFTo7ryw/7z/HQwnQNn87guyN0sx006dZGlCcdZfzgD5dLd+35hHXl8WGdGRvmivRQ8/3ZLNE/fGMnPB9P5fEcaO1IvsOFwJhsOZxLo7sjE/qFM7B+Cv7ujWdrVmjSKolzzAxXy8/Nxd3cnLy8PN7fW61IX7VtxRTFxn8cBsOO+HTjbO1u5RUIIIWzNzJW7WbvnHH1DPbhnQChhns6Ee3fA11WPRtP4HkyDQWFzciZLE07wx8kLpu1jov147IYI+oV7XvUYKZmFrPwjjTW7zpBbXAGAVgMjo/yYFBfKDd18LNqr2pS8JgFViEaSgCqEEOJqjmcVMmZhApfPU3K01xLm2YEwL+dLX+rjcK8OBLg7YqdT562XVxr4395zvPfrcY5mqHVV7XUabusTxKM3RNDF17XJbSqtqOLng+l8tiONP1Krw26QhxMT+4dwdz/L9Ko2Ja816xb/4sWLWbBgAenp6fTq1Yu3336bAQMG1Lnv8uXLefjhh2tt0+v1lJaWmn5+6KGHWLFiRa19xo4dy08//dSc5gkhhBBCWEVnHxeWPdiPzcmZnMop5lROMWdzSyitMJCcUUByHWWo7LQaQjydCfV05mhGAefz1IzkordjUlwoDw/p1KIA6WivY3zvIMb3DiIls5Av/kjjq11nOJtbwsL1R3HR2zHl+k7NPn5raHJAXbVqFbNnz2bJkiXExcXx1ltvMXbsWJKTk/H19a3zNW5ubiQnJ5t+rquLe9y4cXz00Uemn/V6fVObJoQQQghhdaO6+zGqu5/p54oqA2cvlnAyp4i0C8WczC4m7UIRJ3OKSbtQTHmlgdTsIlKziwDwcdUzZUgn7osLxd3J3qxt6+Lrwkt/iubZsZH8dCCd1Umnub1vkFnfwxyaHFAXLlzItGnTTL2iS5YsYd26dXz44Ye88MILdb5Go9Hg7+/f4HH1ev1V9xFCCCGEuNbY67SEe3cg3LvDFc8ZDArp+aWczCniVE4xzg46xvbwx9Fe16ptcrTXMaFPEBP62F44hSYW6i8vLycpKYnRo0dXH0CrZfTo0Wzbtq3e1xUWFhIWFkZISAjjx4/n4MGDV+wTHx+Pr68vkZGRTJ8+nZycnHqPV1ZWRn5+fq0vIYQQQohrjVarIdDDicGdvbl3QCjjewe1eji9FjQpoGZnZ1NVVYWfn1+t7X5+fqSnp9f5msjISD788EPWrl3Lp59+isFgYPDgwZw5c8a0z7hx4/j444/ZuHEj//d//0dCQgI33XQTVVV1L9s1f/583N3dTV8hISFN+RhCCCGEEMKGtXod1EGDBjFo0CDTz4MHD6Z79+4sXbqU1157DYB77rnH9HzPnj2JiYmhc+fOxMfHM2rUqCuOOWfOHGbPnm36OT8/X0KqEEIIIUQb0aQeVG9vb3Q6HRkZGbW2Z2RkNHr8qL29PX369CElJaXefSIiIvD29q53H71ej5ubW60vIYQQQgjRNjQpoDo4OBAbG8vGjRtN2wwGAxs3bqzVS9qQqqoq9u/fT0BAQL37nDlzhpycnAb3EUIIIYQQbVOTAirA7NmzWbZsGStWrODw4cNMnz6doqIi06z+Bx98kDlz5pj2nzdvHr/88gsnTpxg165d3H///Zw6dYpHHnkEUCdQPfvss2zfvp2TJ0+yceNGxo8fT5cuXRg7dqyZPqYQQgghhLhWNHkM6sSJE8nKyuLll18mPT2d3r1789NPP5kmTqWlpaHVVufeixcvMm3aNNLT0+nYsSOxsbFs3bqV6OhoAHQ6Hfv27WPFihXk5uYSGBjIjTfeyGuvvSa1UIUQQggh2iFZ6lSIRpKlToUQQojma0pea/ItfiGEEEIIIVqTBFQhhBBCCGFTJKAKIYQQQgib0uqF+oVoK5zsnNhx3w7TYyGEEEK0DgmoQjSSRqORiVFCCCGEBcgtfiGEEEIIYVMkoAohhBBCCJsiAVUIIYQQQtgUCahCCCGEEMKmSEAVQgghhBA2RQKqEEIIIYSwKRJQhRBCCCGETZGAKoQQQgghbIoEVCGEEEIIYVPaxEpSiqIAkJ+fb+WWCCGEEEKIuhhzmjG3NaRNBNSCggIAQkJCrNwSIYQQQgjRkIKCAtzd3RvcR6M0JsbaOIPBwLlz53B1dUWj0VjkPfPz8wkJCeH06dO4ublZ5D2vBXJe6ifnpm5yXuom56V+cm7qJuelfnJu6mbp86IoCgUFBQQGBqLVNjzKtE30oGq1WoKDg63y3m5ubnKx10HOS/3k3NRNzkvd5LzUT85N3eS81E/OTd0seV6u1nNqJJOkhBBCCCGETZGAKoQQQgghbIoE1GbS6/XMnTsXvV5v7abYFDkv9ZNzUzc5L3WT81I/OTd1k/NSPzk3dbPl89ImJkkJIYQQQoi2Q3pQhRBCCCGETZGAKoQQQgghbIoEVCGEEEIIYVMkoAohhBBCCJsiAbUZFi9eTHh4OI6OjsTFxfHHH39Yu0lW98orr6DRaGp9RUVFWbtZFvfrr79y6623EhgYiEaj4dtvv631vKIovPzyywQEBODk5MTo0aM5duyYdRprYVc7Nw899NAV19C4ceOs01gLmj9/Pv3798fV1RVfX18mTJhAcnJyrX1KS0uZMWMGXl5euLi4cMcdd5CRkWGlFltGY87L8OHDr7hmHn/8cSu12HLeffddYmJiTMXVBw0axI8//mh6vj1eL3D189Jer5fLvf7662g0Gp566inTNlu8ZiSgNtGqVauYPXs2c+fOZdeuXfTq1YuxY8eSmZlp7aZZXY8ePTh//rzp6/fff7d2kyyuqKiIXr16sXjx4jqff+ONN1i0aBFLlixhx44ddOjQgbFjx1JaWmrhllre1c4NwLhx42pdQ1988YUFW2gdCQkJzJgxg+3bt7N+/XoqKiq48cYbKSoqMu0za9YsvvvuO1avXk1CQgLnzp3j9ttvt2KrW19jzgvAtGnTal0zb7zxhpVabDnBwcG8/vrrJCUlkZiYyMiRIxk/fjwHDx4E2uf1Alc/L9A+r5eadu7cydKlS4mJiam13SavGUU0yYABA5QZM2aYfq6qqlICAwOV+fPnW7FV1jd37lylV69e1m6GTQGUb775xvSzwWBQ/P39lQULFpi25ebmKnq9Xvniiy+s0ELrufzcKIqiTJ48WRk/frxV2mNLMjMzFUBJSEhQFEW9Ruzt7ZXVq1eb9jl8+LACKNu2bbNWMy3u8vOiKIoybNgwZebMmdZrlA3p2LGj8v7778v1chnjeVEUuV4KCgqUrl27KuvXr691Lmz1mpEe1CYoLy8nKSmJ0aNHm7ZptVpGjx7Ntm3brNgy23Ds2DECAwOJiIhg0qRJpKWlWbtJNiU1NZX09PRa14+7uztxcXFy/VwSHx+Pr68vkZGRTJ8+nZycHGs3yeLy8vIA8PT0BCApKYmKiopa101UVBShoaHt6rq5/LwYffbZZ3h7e3PdddcxZ84ciouLrdE8q6mqqmLlypUUFRUxaNAguV4uufy8GLXn62XGjBnccsstta4NsN1/Y+ys9s7XoOzsbKqqqvDz86u13c/PjyNHjlipVbYhLi6O5cuXExkZyfnz53n11VcZOnQoBw4cwNXV1drNswnp6ekAdV4/xufas3HjxnH77bfTqVMnjh8/zosvvshNN93Etm3b0Ol01m6eRRgMBp566imGDBnCddddB6jXjYODAx4eHrX2bU/XTV3nBeC+++4jLCyMwMBA9u3bx/PPP09ycjJff/21FVtrGfv372fQoEGUlpbi4uLCN998Q3R0NHv27GnX10t95wXa9/WycuVKdu3axc6dO694zlb/jZGAKszipptuMj2OiYkhLi6OsLAwvvzyS6ZOnWrFlolrxT333GN63LNnT2JiYujcuTPx8fGMGjXKii2znBkzZnDgwIF2OX67IfWdl0cffdT0uGfPngQEBDBq1CiOHz9O586dLd1Mi4qMjGTPnj3k5eWxZs0aJk+eTEJCgrWbZXX1nZfo6Oh2e72cPn2amTNnsn79ehwdHa3dnEaTW/xN4O3tjU6nu2JmW0ZGBv7+/lZqlW3y8PCgW7dupKSkWLspNsN4jcj10zgRERF4e3u3m2voySef5Pvvv2fz5s0EBwebtvv7+1NeXk5ubm6t/dvLdVPfealLXFwcQLu4ZhwcHOjSpQuxsbHMnz+fXr168Z///KfdXy/1nZe6tJfrJSkpiczMTPr27YudnR12dnYkJCSwaNEi7Ozs8PPzs8lrRgJqEzg4OBAbG8vGjRtN2wwGAxs3bqw1xkVAYWEhx48fJyAgwNpNsRmdOnXC39+/1vWTn5/Pjh075Pqpw5kzZ8jJyWnz15CiKDz55JN88803bNq0iU6dOtV6PjY2Fnt7+1rXTXJyMmlpaW36urnaeanLnj17ANr8NVMXg8FAWVlZu71e6mM8L3VpL9fLqFGj2L9/P3v27DF99evXj0mTJpke2+Q1Y7XpWdeolStXKnq9Xlm+fLly6NAh5dFHH1U8PDyU9PR0azfNqp5++mklPj5eSU1NVbZs2aKMHj1a8fb2VjIzM63dNIsqKChQdu/erezevVsBlIULFyq7d+9WTp06pSiKorz++uuKh4eHsnbtWmXfvn3K+PHjlU6dOiklJSVWbnnra+jcFBQUKM8884yybds2JTU1VdmwYYPSt29fpWvXrkppaam1m96qpk+frri7uyvx8fHK+fPnTV/FxcWmfR5//HElNDRU2bRpk5KYmKgMGjRIGTRokBVb3fqudl5SUlKUefPmKYmJiUpqaqqydu1aJSIiQrnhhhus3PLW98ILLygJCQlKamqqsm/fPuWFF15QNBqN8ssvvyiK0j6vF0Vp+Ly05+ulLpdXNLDFa0YCajO8/fbbSmhoqOLg4KAMGDBA2b59u7WbZHUTJ05UAgICFAcHByUoKEiZOHGikpKSYu1mWdzmzZsV4IqvyZMnK4qilpp66aWXFD8/P0Wv1yujRo1SkpOTrdtoC2no3BQXFys33nij4uPjo9jb2ythYWHKtGnT2sUffnWdE0D56KOPTPuUlJQoTzzxhNKxY0fF2dlZue2225Tz589br9EWcLXzkpaWptxwww2Kp6enotfrlS5duijPPvuskpeXZ92GW8CUKVOUsLAwxcHBQfHx8VFGjRplCqeK0j6vF0Vp+Ly05+ulLpcHVFu8ZjSKoiiW668VQgghhBCiYTIGVQghhBBC2BQJqEIIIYQQwqZIQBVCCCGEEDZFAqoQQgghhLApElCFEEIIIYRNkYAqhBBCCCFsigRUIYQQQghhUySgCiGEEEIImyIBVQgh2pD4+Hg0Gg25ubnWbooQQjSbBFQhhBBCCGFTJKAKIYQQQgibIgFVCCHMyGAwMH/+fDp16oSTkxO9evVizZo1QPXt93Xr1hETE4OjoyMDBw7kwIEDtY7x1Vdf0aNHD/R6PeHh4bz55pu1ni8rK+P5558nJCQEvV5Ply5d+OCDD2rtk5SURL9+/XB2dmbw4MEkJye37gcXQggzkoAqhBBmNH/+fD7++GOWLFnCwYMHmTVrFvfffz8JCQmmfZ599lnefPNNdu7ciY+PD7feeisVFRWAGizvvvtu7rnnHvbv388rr7zCSy+9xPLly02vf/DBB/niiy9YtGgRhw8fZunSpbi4uNRqx9/+9jfefPNNEhMTsbOzY8qUKRb5/EIIYQ4aRVEUazdCCCHagrKyMjw9PdmwYQODBg0ybX/kkUcoLi7m0UcfZcSIEaxcuZKJEycCcOHCBYKDg1m+fDl33303kyZNIisri19++cX0+ueee45169Zx8OBBjh49SmRkJOvXr2f06NFXtCE+Pp4RI0awYcMGRo0aBcAPP/zALbfcQklJCY6Ojq18FoQQouWkB1UIIcwkJSWF4uJixowZg4uLi+nr448/5vjx46b9aoZXT09PIiMjOXz4MACHDx9myJAhtY47ZMgQjh07RlVVFXv27EGn0zFs2LAG2xITE2N6HBAQAEBmZmaLP6MQQliCnbUbIIQQbUVhYSEA69atIygoqNZzer2+VkhtLicnp0btZ29vb3qs0WgAdXysEEJcC6QHVQghzCQ6Ohq9Xk9aWhpdunSp9RUSEmLab/v27abHFy9e5OjRo3Tv3h2A7t27s2XLllrH3bJlC926dUOn09GzZ08MBkOtMa1CCNHWSA+qEEKYiaurK8888wyzZs3CYDBw/fXXk5eXx5YtW3BzcyMsLAyAefPm4eXlhZ+fH3/729/w9vZmwoQJADz99NP079+f1157jYkTJ7Jt2zbeeecd/vvf/wIQHh7O5MmTmTJlCosWLaJXr16cOnWKzMxM7r77bmt9dCGEMCsJqEIIYUavvfYaPj4+zJ8/nxMnTuDh4UHfvn158cUXTbfYX3/9dWbOnMmxY8fo3bs33333HQ4ODgD07duXL7/8kpdffpnXXnuNgIAA5s2bx0MPPWR6j3fffZcXX3yRJ554gpycHEJDQ3nxxRet8XGFEKJVyCx+IYSwEOMM+4sXL+Lh4WHt5gghhM2SMahCCCGEEMKmSEAVQgghhBA2RW7xCyGEEEIImyI9qEIIIYQQwqZIQBVCCCGEEDZFAqoQQgghhLApElCFEEIIIYRNkYAqhBBCCCFsigRUIYQQQghhUySgCiGEEEIImyIBVQghhBBC2JT/D7Z7l6KfWSs+AAAAAElFTkSuQmCC\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Compare model_0 to model_1 fine tuned\n",
        "compare_historys(original_history=history_0,\n",
        "                 new_history=history_finetune_1,\n",
        "                 initial_epochs=10)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 793
        },
        "id": "hiZg7v_U3zya",
        "outputId": "18a2ceb0-1300-434f-93b8-654e042e5d2c"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "20\n",
            "41\n",
            "[0.5457909107208252, 0.5744680762290955, 0.586493968963623, 0.6096207499504089, 0.6271970272064209, 0.610545814037323, 0.6031452417373657, 0.6271970272064209, 0.610545814037323, 0.628122091293335, 0.6392229199409485, 0.6244218349456787, 0.6419981718063354, 0.6308973431587219, 0.6595744490623474, 0.6438482999801636, 0.649398684501648, 0.6438482999801636, 0.631822407245636, 0.6456984281539917, 0.5374653339385986, 0.543015718460083, 0.5966697335243225, 0.6151711344718933, 0.6475485563278198, 0.6512488722801208, 0.6799259781837463, 0.695652186870575, 0.6697502136230469, 0.6984273791313171, 0.7317298650741577, 0.7271045446395874, 0.7687326669692993, 0.7548565864562988, 0.782608687877655, 0.7974098324775696, 0.807585597038269, 0.8362627029418945, 0.8205365538597107, 0.8519889116287231, 0.8649398684501648]\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 800x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Data preprocessing\n",
        "\n",
        "IMG_SIZE = 224\n",
        "BATCH_SIZE = 32\n",
        "\n",
        "datagen = ImageDataGenerator(\n",
        "    rescale = 1./255,\n",
        "    rotation_range=20,\n",
        "    width_shift_range=0.2,\n",
        "    height_shift_range=0.2,\n",
        "    horizontal_flip=True,\n",
        "    validation_split=0.2\n",
        ")\n",
        "\n",
        "test_datagen = ImageDataGenerator(rescale=1./255)\n",
        "\n",
        "print(\"Training images: \")\n",
        "train_generator = datagen.flow_from_directory(\n",
        "    training_set_path,\n",
        "    target_size=(IMG_SIZE, IMG_SIZE),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical',\n",
        "    subset='training'\n",
        ")\n",
        "\n",
        "print(\"Validation images: \")\n",
        "validation_generator = datagen.flow_from_directory(\n",
        "    training_set_path,\n",
        "    target_size=(IMG_SIZE, IMG_SIZE),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical',\n",
        "    subset='validation',\n",
        "    seed = 42\n",
        ")\n",
        "\n",
        "print(\"Testing images: \")\n",
        "test_generator = datagen.flow_from_directory(\n",
        "    test_set_path,\n",
        "    target_size=(IMG_SIZE, IMG_SIZE),\n",
        "    batch_size=BATCH_SIZE,\n",
        "    class_mode='categorical',\n",
        "    seed = 42\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "cyWYAMKy2WNk",
        "outputId": "f4ed5efa-8d2b-4789-aaaf-e4ad0e78adb5"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Training images: \n",
            "Found 582 images belonging to 3 classes.\n",
            "Validation images: \n",
            "Found 144 images belonging to 3 classes.\n",
            "Testing images: \n",
            "Found 271 images belonging to 3 classes.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Create TensorBoard callback (to track experiments)\n",
        "import datetime\n",
        "def create_tensorboard_callback(dir_name, exp_name):\n",
        "    log_dir = dir_name + \"/\" + exp_name +\"/\" + datetime.datetime.now().strftime(\"%Y%M%D-%H%M%S\")\n",
        "    tensorboard_callback = tf.keras.callbacks.TensorBoard(log_dir = log_dir)\n",
        "    print(f\"Saving TensorBoard log files to : {log_dir}\")\n",
        "    return tensorboard_callback"
      ],
      "metadata": {
        "id": "sfue95JefknE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# install the EfficientNet package\n",
        "!pip install -U efficientnet"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WnVcFLTwGzON",
        "outputId": "1d809233-c182-459a-a534-9785fe9c6379"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Collecting efficientnet\n",
            "  Downloading efficientnet-1.1.1-py3-none-any.whl (18 kB)\n",
            "Collecting keras-applications<=1.0.8,>=1.0.7 (from efficientnet)\n",
            "  Downloading Keras_Applications-1.0.8-py3-none-any.whl (50 kB)\n",
            "\u001b[?25l     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m0.0/50.7 kB\u001b[0m \u001b[31m?\u001b[0m eta \u001b[36m-:--:--\u001b[0m\r\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m50.7/50.7 kB\u001b[0m \u001b[31m2.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25hRequirement already satisfied: scikit-image in /usr/local/lib/python3.10/dist-packages (from efficientnet) (0.19.3)\n",
            "Requirement already satisfied: numpy>=1.9.1 in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (1.23.5)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras-applications<=1.0.8,>=1.0.7->efficientnet) (3.9.0)\n",
            "Requirement already satisfied: scipy>=1.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.11.3)\n",
            "Requirement already satisfied: networkx>=2.2 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (3.1)\n",
            "Requirement already satisfied: pillow!=7.1.0,!=7.1.1,!=8.3.0,>=6.1.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (9.4.0)\n",
            "Requirement already satisfied: imageio>=2.4.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2.31.5)\n",
            "Requirement already satisfied: tifffile>=2019.7.26 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (2023.9.26)\n",
            "Requirement already satisfied: PyWavelets>=1.1.1 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (1.4.1)\n",
            "Requirement already satisfied: packaging>=20.0 in /usr/local/lib/python3.10/dist-packages (from scikit-image->efficientnet) (23.2)\n",
            "Installing collected packages: keras-applications, efficientnet\n",
            "Successfully installed efficientnet-1.1.1 keras-applications-1.0.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# import efficientnet.tfkeras as efn\n",
        "# from tensorflow.keras.applications import Xception\n",
        "from tensorflow.keras.models import Sequential\n",
        "from tensorflow.keras.layers import Dense, GlobalAveragePooling2D\n",
        "\n",
        "# Define a model from the model url\n",
        "def create_model(model_url, num_classes, set_trainable):\n",
        "    ftr_extr_layer = hub.KerasLayer(model_url,\n",
        "                                    trainable = set_trainable, #True, #False,\n",
        "                                    name = \"ftr_extr_layer\",\n",
        "                                    input_shape = (IMG_SIZE,IMG_SIZE,3))\n",
        "    model = Sequential([\n",
        "        ftr_extr_layer,\n",
        "        # GlobalAveragePooling2D(),\n",
        "        Dense(num_classes, activation='softmax')  # 3 classes: BENIGN, MALIGNANT, BENIGN_NO_CALL\n",
        "    ])\n",
        "    return model\n",
        "\n",
        "# base_model = efn.EfficientNetB7(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False, weights='imagenet')\n",
        "# base_model = Xception(input_shape=(IMG_SIZE, IMG_SIZE, 3), include_top=False, weights='imagenet')\n",
        "# base_model.trainable = False  # Freeze layers\n",
        "\n",
        "# model = Sequential([\n",
        "#     base_model,\n",
        "#     GlobalAveragePooling2D(),\n",
        "#     Dense(3, activation='softmax')  # 3 classes: BENIGN, MALIGNANT, BENIGN_NO_CALL\n",
        "# ])\n",
        "\n",
        "model = create_model(res_net_url, num_classes = 3, set_trainable = True) # efn_net_url\n",
        "model.summary()\n",
        "model.compile(optimizer='adam', loss='categorical_crossentropy', metrics=['accuracy'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "vmRfebfyHCmo",
        "outputId": "594b36df-db79-40fe-e7d3-0cf583b95412"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Model: \"sequential\"\n",
            "_________________________________________________________________\n",
            " Layer (type)                Output Shape              Param #   \n",
            "=================================================================\n",
            " ftr_extr_layer (KerasLayer  (None, 2048)              23561152  \n",
            " )                                                               \n",
            "                                                                 \n",
            " dense (Dense)               (None, 3)                 6147      \n",
            "                                                                 \n",
            "=================================================================\n",
            "Total params: 23567299 (89.90 MB)\n",
            "Trainable params: 6147 (24.01 KB)\n",
            "Non-trainable params: 23561152 (89.88 MB)\n",
            "_________________________________________________________________\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history = model.fit(\n",
        "    train_generator,\n",
        "    steps_per_epoch = len(train_generator), #.samples // BATCH_SIZE,\n",
        "    # steps_per_epoch=train_generator.samples // BATCH_SIZE,\n",
        "    validation_data = validation_generator,\n",
        "    validation_steps = len(validation_generator), #.samples // BATCH_SIZE,\n",
        "    # validation_steps=validation_generator.samples // BATCH_SIZE,\n",
        "    callbacks = [create_tensorboard_callback(dir_name = \"tensorflow_hub\",\n",
        "                                             exp_name=\"resnet50\")],\n",
        "    epochs=20\n",
        ")\n",
        "\n",
        "# Accuracy\n",
        "plt.plot(history.history['accuracy'], label='train_accuracy')\n",
        "plt.plot(history.history['val_accuracy'], label='val_accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.legend()\n",
        "plt.show()\n",
        "\n",
        "# Loss\n",
        "plt.plot(history.history['loss'], label='train_loss')\n",
        "plt.plot(history.history['val_loss'], label='val_loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 760
        },
        "id": "4Wd_jbgvHW7p",
        "outputId": "f1b453fd-903f-47c1-888a-a79486fffb22"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Saving TensorBoard log files to : tensorflow_hub/resnet50/20233610/10/23-193608\n",
            "Epoch 1/20\n",
            "19/19 [==============================] - 74s 4s/step - loss: 0.9629 - accuracy: 0.5309 - val_loss: 0.9672 - val_accuracy: 0.4792\n",
            "Epoch 2/20\n",
            "19/19 [==============================] - 75s 4s/step - loss: 0.9594 - accuracy: 0.5275 - val_loss: 0.9677 - val_accuracy: 0.5139\n",
            "Epoch 3/20\n",
            "19/19 [==============================] - 76s 4s/step - loss: 0.8952 - accuracy: 0.5344 - val_loss: 0.9714 - val_accuracy: 0.4931\n",
            "Epoch 4/20\n",
            "19/19 [==============================] - 81s 4s/step - loss: 0.8770 - accuracy: 0.5378 - val_loss: 0.9641 - val_accuracy: 0.4306\n",
            "Epoch 5/20\n",
            "19/19 [==============================] - 74s 4s/step - loss: 0.8329 - accuracy: 0.5722 - val_loss: 0.8455 - val_accuracy: 0.5139\n",
            "Epoch 6/20\n",
            "19/19 [==============================] - 87s 5s/step - loss: 0.8287 - accuracy: 0.5773 - val_loss: 1.0386 - val_accuracy: 0.4792\n",
            "Epoch 7/20\n",
            "19/19 [==============================] - 86s 5s/step - loss: 0.8309 - accuracy: 0.5790 - val_loss: 0.8563 - val_accuracy: 0.5069\n",
            "Epoch 8/20\n",
            "19/19 [==============================] - 85s 5s/step - loss: 0.8158 - accuracy: 0.5928 - val_loss: 0.9482 - val_accuracy: 0.5625\n",
            "Epoch 9/20\n",
            "17/19 [=========================>....] - ETA: 6s - loss: 0.8360 - accuracy: 0.5869"
          ]
        },
        {
          "output_type": "error",
          "ename": "KeyboardInterrupt",
          "evalue": "ignored",
          "traceback": [
            "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
            "\u001b[0;32m<ipython-input-15-c5b05660089c>\u001b[0m in \u001b[0;36m<cell line: 1>\u001b[0;34m()\u001b[0m\n\u001b[0;32m----> 1\u001b[0;31m history = model.fit(\n\u001b[0m\u001b[1;32m      2\u001b[0m     \u001b[0mtrain_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      3\u001b[0m     \u001b[0msteps_per_epoch\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mlen\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mtrain_generator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;31m#.samples // BATCH_SIZE,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      4\u001b[0m     \u001b[0;31m# steps_per_epoch=train_generator.samples // BATCH_SIZE,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m     \u001b[0mvalidation_data\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mvalidation_generator\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/utils/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m     63\u001b[0m         \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     64\u001b[0m         \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 65\u001b[0;31m             \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m     66\u001b[0m         \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     67\u001b[0m             \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/keras/src/engine/training.py\u001b[0m in \u001b[0;36mfit\u001b[0;34m(self, x, y, batch_size, epochs, verbose, callbacks, validation_split, validation_data, shuffle, class_weight, sample_weight, initial_epoch, steps_per_epoch, validation_steps, validation_batch_size, validation_freq, max_queue_size, workers, use_multiprocessing)\u001b[0m\n\u001b[1;32m   1740\u001b[0m                         ):\n\u001b[1;32m   1741\u001b[0m                             \u001b[0mcallbacks\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mon_train_batch_begin\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mstep\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1742\u001b[0;31m                             \u001b[0mtmp_logs\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtrain_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0miterator\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1743\u001b[0m                             \u001b[0;32mif\u001b[0m \u001b[0mdata_handler\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mshould_sync\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1744\u001b[0m                                 \u001b[0mcontext\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0masync_wait\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/util/traceback_utils.py\u001b[0m in \u001b[0;36merror_handler\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m    148\u001b[0m     \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    149\u001b[0m     \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 150\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    151\u001b[0m     \u001b[0;32mexcept\u001b[0m \u001b[0mException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    152\u001b[0m       \u001b[0mfiltered_tb\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0m_process_traceback_frames\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0me\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m__traceback__\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    823\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    824\u001b[0m       \u001b[0;32mwith\u001b[0m \u001b[0mOptionalXlaContext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_jit_compile\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 825\u001b[0;31m         \u001b[0mresult\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_call\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    826\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    827\u001b[0m       \u001b[0mnew_tracing_count\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexperimental_get_tracing_count\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/polymorphic_function.py\u001b[0m in \u001b[0;36m_call\u001b[0;34m(self, *args, **kwds)\u001b[0m\n\u001b[1;32m    855\u001b[0m       \u001b[0;31m# In this case we have created variables on the first call, so we run the\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    856\u001b[0m       \u001b[0;31m# defunned version which is guaranteed to never create variables.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 857\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_no_variable_creation_fn\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0;34m**\u001b[0m\u001b[0mkwds\u001b[0m\u001b[0;34m)\u001b[0m  \u001b[0;31m# pylint: disable=not-callable\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m    858\u001b[0m     \u001b[0;32melif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_variable_creation_fn\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mnot\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    859\u001b[0m       \u001b[0;31m# Release the lock early so that multiple threads can perform the call\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/tracing_compiler.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m    146\u001b[0m       (concrete_function,\n\u001b[1;32m    147\u001b[0m        filtered_flat_args) = self._maybe_define_function(args, kwargs)\n\u001b[0;32m--> 148\u001b[0;31m     return concrete_function._call_flat(\n\u001b[0m\u001b[1;32m    149\u001b[0m         filtered_flat_args, captured_inputs=concrete_function.captured_inputs)  # pylint: disable=protected-access\n\u001b[1;32m    150\u001b[0m \u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/monomorphic_function.py\u001b[0m in \u001b[0;36m_call_flat\u001b[0;34m(self, args, captured_inputs)\u001b[0m\n\u001b[1;32m   1347\u001b[0m         and executing_eagerly):\n\u001b[1;32m   1348\u001b[0m       \u001b[0;31m# No tape is watching; skip to running the function.\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1349\u001b[0;31m       \u001b[0;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_build_call_outputs\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_inference_function\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m*\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m   1350\u001b[0m     forward_backward = self._select_forward_and_backward_functions(\n\u001b[1;32m   1351\u001b[0m         \u001b[0margs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/polymorphic_function/atomic_function.py\u001b[0m in \u001b[0;36m__call__\u001b[0;34m(self, *args)\u001b[0m\n\u001b[1;32m    194\u001b[0m         \u001b[0;32mwith\u001b[0m \u001b[0mrecord\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mstop_recording\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    195\u001b[0m           \u001b[0;32mif\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_bound_context\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mexecuting_eagerly\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m--> 196\u001b[0;31m             outputs = self._bound_context.call_function(\n\u001b[0m\u001b[1;32m    197\u001b[0m                 \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mname\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m    198\u001b[0m                 \u001b[0mlist\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0margs\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/context.py\u001b[0m in \u001b[0;36mcall_function\u001b[0;34m(self, name, tensor_inputs, num_outputs)\u001b[0m\n\u001b[1;32m   1455\u001b[0m     \u001b[0mcancellation_context\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcancellation\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcontext\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1456\u001b[0m     \u001b[0;32mif\u001b[0m \u001b[0mcancellation_context\u001b[0m \u001b[0;32mis\u001b[0m \u001b[0;32mNone\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m-> 1457\u001b[0;31m       outputs = execute.execute(\n\u001b[0m\u001b[1;32m   1458\u001b[0m           \u001b[0mname\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdecode\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"utf-8\"\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m   1459\u001b[0m           \u001b[0mnum_outputs\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mnum_outputs\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;32m/usr/local/lib/python3.10/dist-packages/tensorflow/python/eager/execute.py\u001b[0m in \u001b[0;36mquick_execute\u001b[0;34m(op_name, num_outputs, inputs, attrs, ctx, name)\u001b[0m\n\u001b[1;32m     51\u001b[0m   \u001b[0;32mtry\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m     52\u001b[0m     \u001b[0mctx\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mensure_initialized\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m---> 53\u001b[0;31m     tensors = pywrap_tfe.TFE_Py_Execute(ctx._handle, device_name, op_name,\n\u001b[0m\u001b[1;32m     54\u001b[0m                                         inputs, attrs, num_outputs)\n\u001b[1;32m     55\u001b[0m   \u001b[0;32mexcept\u001b[0m \u001b[0mcore\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0m_NotOkStatusException\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
            "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 887
        },
        "id": "llEqo_puHaob",
        "outputId": "23ea07d8-b1fb-464f-c454-9286f70ce81d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 640x480 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    }
  ]
}