2_3D_Unet_BasicImplementation.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": [],
      "collapsed_sections": [
        "tBQ1_4QRBanC"
      ]
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "LswqcHZKjB34",
        "outputId": "bdbaf156-2dad-41b6-92c1-00690060d450",
        "collapsed": true
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Requirement already satisfied: tensorflow==2.16.0rc0 in /usr/local/lib/python3.10/dist-packages (2.16.0rc0)\n",
            "Requirement already satisfied: absl-py>=1.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.4.0)\n",
            "Requirement already satisfied: astunparse>=1.6.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.6.3)\n",
            "Requirement already satisfied: flatbuffers>=23.5.26 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (24.3.25)\n",
            "Requirement already satisfied: gast!=0.5.0,!=0.5.1,!=0.5.2,>=0.2.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (0.6.0)\n",
            "Requirement already satisfied: google-pasta>=0.1.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (0.2.0)\n",
            "Requirement already satisfied: h5py>=3.10.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (3.11.0)\n",
            "Requirement already satisfied: libclang>=13.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (18.1.1)\n",
            "Requirement already satisfied: ml-dtypes~=0.3.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (0.3.2)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (3.3.0)\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (24.1)\n",
            "Requirement already satisfied: protobuf!=4.21.0,!=4.21.1,!=4.21.2,!=4.21.3,!=4.21.4,!=4.21.5,<5.0.0dev,>=3.20.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (3.20.3)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (2.31.0)\n",
            "Requirement already satisfied: setuptools in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (67.7.2)\n",
            "Requirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.16.0)\n",
            "Requirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (2.4.0)\n",
            "Requirement already satisfied: typing-extensions>=3.6.6 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (4.12.2)\n",
            "Requirement already satisfied: wrapt>=1.11.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.14.1)\n",
            "Requirement already satisfied: grpcio<2.0,>=1.24.3 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.64.1)\n",
            "Requirement already satisfied: tensorboard<2.17,>=2.16 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (2.16.2)\n",
            "Requirement already satisfied: keras>=3.0.0 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (3.3.3)\n",
            "Requirement already satisfied: tensorflow-io-gcs-filesystem>=0.23.1 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (0.37.0)\n",
            "Requirement already satisfied: numpy<2.0.0,>=1.23.5 in /usr/local/lib/python3.10/dist-packages (from tensorflow==2.16.0rc0) (1.25.2)\n",
            "Requirement already satisfied: wheel<1.0,>=0.23.0 in /usr/local/lib/python3.10/dist-packages (from astunparse>=1.6.0->tensorflow==2.16.0rc0) (0.43.0)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras>=3.0.0->tensorflow==2.16.0rc0) (13.7.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras>=3.0.0->tensorflow==2.16.0rc0) (0.0.8)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras>=3.0.0->tensorflow==2.16.0rc0) (0.11.0)\n",
            "Requirement already satisfied: charset-normalizer<4,>=2 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.16.0rc0) (3.3.2)\n",
            "Requirement already satisfied: idna<4,>=2.5 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.16.0rc0) (3.7)\n",
            "Requirement already satisfied: urllib3<3,>=1.21.1 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.16.0rc0) (2.0.7)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.10/dist-packages (from requests<3,>=2.21.0->tensorflow==2.16.0rc0) (2024.6.2)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.0rc0) (3.6)\n",
            "Requirement already satisfied: tensorboard-data-server<0.8.0,>=0.7.0 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.0rc0) (0.7.2)\n",
            "Requirement already satisfied: werkzeug>=1.0.1 in /usr/local/lib/python3.10/dist-packages (from tensorboard<2.17,>=2.16->tensorflow==2.16.0rc0) (3.0.3)\n",
            "Requirement already satisfied: MarkupSafe>=2.1.1 in /usr/local/lib/python3.10/dist-packages (from werkzeug>=1.0.1->tensorboard<2.17,>=2.16->tensorflow==2.16.0rc0) (2.1.5)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.0.0->tensorflow==2.16.0rc0) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras>=3.0.0->tensorflow==2.16.0rc0) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras>=3.0.0->tensorflow==2.16.0rc0) (0.1.2)\n",
            "Requirement already satisfied: segmentation_models_3D in /usr/local/lib/python3.10/dist-packages (1.1.1)\n",
            "Requirement already satisfied: keras==3.3.3 in /usr/local/lib/python3.10/dist-packages (from segmentation_models_3D) (3.3.3)\n",
            "Requirement already satisfied: classification-models-3D==1.1.0 in /usr/local/lib/python3.10/dist-packages (from segmentation_models_3D) (1.1.0)\n",
            "Requirement already satisfied: absl-py in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (1.4.0)\n",
            "Requirement already satisfied: numpy in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (1.25.2)\n",
            "Requirement already satisfied: rich in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (13.7.1)\n",
            "Requirement already satisfied: namex in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (0.0.8)\n",
            "Requirement already satisfied: h5py in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (3.11.0)\n",
            "Requirement already satisfied: optree in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (0.11.0)\n",
            "Requirement already satisfied: ml-dtypes in /usr/local/lib/python3.10/dist-packages (from keras==3.3.3->segmentation_models_3D) (0.3.2)\n",
            "Requirement already satisfied: typing-extensions>=4.0.0 in /usr/local/lib/python3.10/dist-packages (from optree->keras==3.3.3->segmentation_models_3D) (4.12.2)\n",
            "Requirement already satisfied: markdown-it-py>=2.2.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.3.3->segmentation_models_3D) (3.0.0)\n",
            "Requirement already satisfied: pygments<3.0.0,>=2.13.0 in /usr/local/lib/python3.10/dist-packages (from rich->keras==3.3.3->segmentation_models_3D) (2.16.1)\n",
            "Requirement already satisfied: mdurl~=0.1 in /usr/local/lib/python3.10/dist-packages (from markdown-it-py>=2.2.0->rich->keras==3.3.3->segmentation_models_3D) (0.1.2)\n"
          ]
        }
      ],
      "source": [
        "!pip install tensorflow==2.16.0rc0\n",
        "!pip install segmentation_models_3D\n",
        "!PYTHONHASHSEED=0"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Import other modules\n",
        "from matplotlib import pyplot as plt\n",
        "import zipfile\n",
        "from shutil import copyfile\n",
        "from time import time\n",
        "import numpy as np\n",
        "import random as python_random\n",
        "import os\n",
        "\n",
        "# Import TensorFlow/Keras\n",
        "import keras\n",
        "import tensorflow as tf\n",
        "from tensorflow.keras.models import Model\n",
        "from tensorflow.keras.layers import Input, Conv3D, Conv3DTranspose, MaxPooling3D, concatenate, Dropout, Activation, BatchNormalization, GroupNormalization\n",
        "from tensorflow.keras.callbacks import EarlyStopping, ModelCheckpoint\n",
        "import tensorflow.keras.backend as K\n"
      ],
      "metadata": {
        "id": "wB-JtclbrOf9"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Xl2V3ylttL7Q",
        "outputId": "d32117ac-7cfa-4748-8303-68e516b10ac4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Percorso del file ZIP su Google Drive\n",
        "# drive_zip_path = '/content/gdrive/MyDrive/DL_PROJECT/'\n",
        "# Local path\n",
        "local_zip_path = '/content/gdrive/MyDrive/DL_PROJECT/Data/128.zip'\n"
      ],
      "metadata": {
        "id": "oyodx57FeZz3"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "local_extract_path = '/content/Training_Data'\n",
        "os.makedirs(local_extract_path, exist_ok=True)\n"
      ],
      "metadata": {
        "id": "zAsDezqde1FS"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# Decomprimere il file ZIP nella directory locale\n",
        "with zipfile.ZipFile(local_zip_path, 'r') as zip_ref:\n",
        "    zip_ref.extractall(local_extract_path)\n",
        "\n",
        "print(\"Decompressione completata.\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "tgNmT0DDe9sa",
        "outputId": "48ef5e8c-81a5-4080-a32b-1ffc0982e8eb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Decompressione completata.\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Defining Data Generator"
      ],
      "metadata": {
        "id": "WCrkgdfU-Dar"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def load_img(img_dir, img_list):\n",
        "    images=[]\n",
        "    for i, image_name in enumerate(img_list):\n",
        "        if (image_name.split('.')[1] == 'npy'):\n",
        "\n",
        "            image = np.load(img_dir + image_name)\n",
        "\n",
        "            images.append(image)\n",
        "\n",
        "    images = np.array(images)\n",
        "\n",
        "    return images"
      ],
      "metadata": {
        "id": "oKr1In0G-MpE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def image_loader(img_dir, img_list, mask_dir, mask_list, batch_size):\n",
        "\n",
        "    L = len(img_list)\n",
        "\n",
        "    while True:\n",
        "\n",
        "        batch_start = 0\n",
        "        batch_end = batch_size\n",
        "\n",
        "        while batch_start < L:\n",
        "\n",
        "            limit = min(L, batch_end)\n",
        "\n",
        "            img = load_img(img_dir, img_list[batch_start:limit])\n",
        "            mask = load_img(mask_dir, mask_list[batch_start:limit])\n",
        "\n",
        "            yield(img, mask)\n",
        "\n",
        "        batch_start += batch_size\n",
        "        batch_end += batch_size\n"
      ],
      "metadata": {
        "id": "DfH_Y1wJ-Rd5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Testing data generator"
      ],
      "metadata": {
        "id": "tBQ1_4QRBanC"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from matplotlib import pyplot as plt\n",
        "import random"
      ],
      "metadata": {
        "id": "ntODfJZgBaCC"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_dir = \"/content/Training_Data/X_train/\"\n",
        "train_mask_dir = \"/content/Training_Data/Y_train/\"\n",
        "train_img_list=os.listdir(train_img_dir)\n",
        "train_mask_list = os.listdir(train_mask_dir)"
      ],
      "metadata": {
        "id": "Yc6OAuyuBZ9W"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4"
      ],
      "metadata": {
        "id": "LAG0BX2pBZ3C"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_datagen = image_loader(train_img_dir, train_img_list, train_mask_dir, train_mask_list, batch_size)"
      ],
      "metadata": {
        "id": "0fJLgmyIBZvu"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img, mask = train_img_datagen.__next__()"
      ],
      "metadata": {
        "id": "M9EgecfsBZgB"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "img_num = random.randint(0,img.shape[0]-1)\n",
        "test_img=img[img_num]\n",
        "test_mask=mask[img_num]\n",
        "test_mask=np.argmax(test_mask, axis=3)\n",
        "\n",
        "n_slice=random.randint(0, test_mask.shape[2])\n",
        "plt.figure(figsize=(12, 8))\n",
        "\n",
        "plt.subplot(221)\n",
        "plt.imshow(test_img[:,:,n_slice, 0], cmap='gray')\n",
        "plt.title('Image flair')\n",
        "plt.subplot(222)\n",
        "plt.imshow(test_img[:,:,n_slice, 1], cmap='gray')\n",
        "plt.title('Image t1ce')\n",
        "plt.subplot(223)\n",
        "plt.imshow(test_img[:,:,n_slice, 2], cmap='gray')\n",
        "plt.title('Image t2')\n",
        "plt.subplot(224)\n",
        "plt.imshow(test_mask[:,:,n_slice])\n",
        "plt.title('Mask')\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 699
        },
        "id": "m2iFAiZxGrAv",
        "outputId": "e83ab947-2c75-403a-9778-e03516652031"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 4 Axes>"
            ],
            "image/png": "iVBORw0KGgoAAAANSUhEUgAAA1MAAAKqCAYAAADfUgT5AAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjcuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/bCgiHAAAACXBIWXMAAA9hAAAPYQGoP6dpAAEAAElEQVR4nOy9e7Dl2VXft865j/O45z769vRMz1szIzEjCQnZQsgjCgFCWFLFIUSKcRJiQIHYSWlkLJmULScgCz/kxHEBRiJ22ZRw2VKJYGPHhArEhZCFKRFkYbAMEcgghJjRjGamp7vv69zXOfmj/fmdz2/dfXpGMz3T3aO9qm7de8/5/fZvP9da3+9ae/860+l0GlWqVKlSpUqVKlWqVKlS5UuS7tWuQJUqVapUqVKlSpUqVapcj1LBVJUqVapUqVKlSpUqVao8DalgqkqVKlWqVKlSpUqVKlWehlQwVaVKlSpVqlSpUqVKlSpPQyqYqlKlSpUqVapUqVKlSpWnIRVMValSpUqVKlWqVKlSpcrTkAqmqlSpUqVKlSpVqlSpUuVpSAVTVapUqVKlSpUqVapUqfI0pIKpKlWqVKlSpUqVKlWqVHkaUsFUlSqXkUceeST+i//iv4jTp09Hp9OJH/7hH46PfvSj0el04qMf/eiXXN5f+St/JTqdzpWvaJUqVapUqVKlSpXnXCqYqvK05Cd+4iei0+nEv/k3/+ZqV+VZlXe84x3x8z//8/Gud70r/tE/+kfxxje+8WpXqUqVKlWqVHlW5cvBxj/00EPxV/7KX4lf//VfP/Hdb//2b8c73vGOeM1rXhP9fj86nU78/u///nNexyrXh1QwVaXKZeQjH/lI/Gf/2X8W3/d93xf/zX/z38R99933jMr7n//n/zn29vauUO2qVKlSpUqVKk9HHnrooXjPe95TBFMf//jH4+/8nb8TW1tb8eIXv/i5r1yV60oqmKpS5TLyxS9+MTY2Nq5YeYuLi9Hv9y97zWQyifF4fMWeWaVKlSpVqlR56vIt3/Itcf78+fjUpz4V3/7t3361q1PlGpcKpqpcMfmu7/quGI1G8Qd/8AfxJ/7En4jRaBS33nprvP/974+IiE996lPxute9LlZWVuLOO++MD33oQ637z507F9/3fd8XL3vZy2I0GsXa2lq86U1vit/4jd848azPfe5z8S3f8i2xsrISN954Y5OOV9rL9P/+v/9vvPGNb4z19fUYDofx9V//9fHLv/zLl20LKQ7T6TTe//73R6fTuexep1/6pV+KP/kn/2Tccccd0ev14vbbb493vOMdJ6JQpT1TnU4nHnjggfjgBz8YL33pS6PX68XP/dzPXbZ+VapUqVKlynMpzycb/9GPfjRe9apXRUTEW9/61sbG/8RP/ERERGxubsbq6upT6pfJZBI/8iM/Ei972cui3+/HmTNn4o1vfOOJFMl//I//cbzyla+MwWAQm5ub8V/+l/9lfP7zn39Kz6hybcvi1a5AleeXHB8fx5ve9KZ47WtfG//r//q/xgc/+MF44IEHYmVlJf6n/+l/im//9m+PN7/5zfF3/+7fje/4ju+I+++/P+66666IiPi93/u9+Of//J/Hn/yTfzLuuuuueOSRR+Lv/b2/F1//9V8fv/VbvxW33HJLRETs7OzE6173uvjCF74Q3/u93xtnz56ND33oQ/GLv/iLJ+rzkY98JN70pjfFK1/5ynj3u98d3W43PvCBD8TrXve6+KVf+qX4mq/5mmI7Xvva18Y/+kf/KP70n/7T8c3f/M3xHd/xHZdt90/91E/F7u5u/A//w/8Qp0+fjl/91V+NH/3RH40//MM/jJ/6qZ960n77yEc+Ev/H//F/xAMPPBA33HBDvOAFL3jSe6pUqVKlSpXnUp4vNv7FL35x/OAP/mD8wA/8QPyZP/Nn4uu+7usiIuI1r3nNl9wn3/3d3x0/8RM/EW9605vie77ne+Lo6Ch+6Zd+KX7lV34lvvqrvzoiIv76X//r8f3f//3xbd/2bfE93/M98eijj8aP/uiPxmtf+9r4t//2317RDJgqV0GmVao8DfnABz4wjYjpJz7xieaz7/zO75xGxPRv/I2/0Xz2xBNPTAeDwbTT6Uw//OEPN59/+tOfnkbE9N3vfnfz2Xg8nh4fH7ee89nPfnba6/WmP/iDP9h89rf/9t+eRsT0n//zf958tre3N73vvvumETH9xV/8xel0Op1OJpPpi170oukb3vCG6WQyaa7d3d2d3nXXXdNv/uZvftJ2RsT0bW97W+uzX/zFX2w9hzKzvPe97512Op3p5z73ueazd7/73dO87CJi2u12p7/5m7/5pPWpUqVKlSpVnm35crDxn/jEJ6YRMf3ABz5w2ev+1t/6W9OImH72s5898d1HPvKRaURM/9yf+3MnvqNOv//7vz9dWFiY/vW//tdb33/qU5+aLi4unvi8yvUnNc2vyhWX7/me72n+3tjYiHvvvTdWVlbi277t25rP77333tjY2Ijf+73faz7r9XrR7V6aksfHx/H444/HaDSKe++9N37t136tue7nfu7n4tZbb41v+ZZvaT7r9/vx3/13/12rHr/+678en/nMZ+K//q//63j88cfjsccei8ceeyx2dnbim77pm+JjH/tYTCaTK9LmwWDQ/L2zsxOPPfZYvOY1r4npdBr/9t/+2ye9/+u//uvjJS95yRWpS5UqVapUqfJsyZejjZ8n//Sf/tPodDrx7ne/+8R3pPT/9E//dEwmk/i2b/u2po6PPfZYnD17Nl70ohcVI25Vri+paX5VrqiQL2xZX1+P22677cReofX19XjiiSea/8k7/rEf+7H47Gc/G8fHx813p0+fbv7+3Oc+F/fcc8+J8l74whe2/v/MZz4TERHf+Z3fObe+Fy5ciFOnTj3F1s2XP/iDP4gf+IEfiH/xL/5Fq00848mENIgqVapUqVLlWpUvVxs/T373d383brnlltjc3Jx7zWc+85mYTqfxohe9qPj90tLSs1W9Ks+RVDBV5YrKwsLCl/T5dDpt/v4bf+NvxPd///fHf/vf/rfxV//qX43Nzc3odrvx5//8n39a7BL3/K2/9bfiFa94RfGa0Wj0JZeb5fj4OL75m785zp07F3/xL/7FuO+++2JlZSUefPDB+K7v+q6nVHdHtqpUqVKlSpVrUb4cbfwzlclkEp1OJ/7v//v/LvbTtVDHKs9MKpiqcs3IP/kn/yS+8Ru/MX78x3+89fn58+fjhhtuaP6/884747d+67diOp22mKv/8B/+Q+u+e+65JyIi1tbW4vWvf/2zVu9PfepT8Tu/8zvxD//hP2wdVPEv/+W/fNaeWaVKlSpVqlxPcq3Z+Mud0PtU5Z577omf//mfj3Pnzs2NTt1zzz0xnU7jrrvuiq/4iq94xs+scu1J3TNV5ZqRhYWFFosVcemUvAcffLD12Rve8IZ48MEH41/8i3/RfDYej+Pv//2/37rula98Zdxzzz3xv/1v/1tsb2+feN6jjz56xeod0WbgptNp/MiP/MgVKb9KlSpVqlS53uVas/ErKysRcQnMPV15y1veEtPpNN7znvec+I62vvnNb46FhYV4z3vec6L90+k0Hn/88af9/CrXhtTIVJVrRv7En/gT8YM/+IPx1re+NV7zmtfEpz71qfjgBz8Yd999d+u6P/tn/2y8733vi//qv/qv4nu/93vj5ptvjg9+8IPNy3Bhm7rdbvyDf/AP4k1velO89KUvjbe+9a1x6623xoMPPhi/+Iu/GGtra/EzP/Mzz7je9913X9xzzz3xfd/3ffHggw/G2tpa/NN/+k9P7J2qUqVKlSpVvlzlWrPx99xzT2xsbMTf/bt/N1ZXV2NlZSVe/epXx1133RUXLlyIH/3RH42IaN5Z9b73vS82NjZiY2MjHnjggYiI+MZv/Mb403/6T8ff+Tt/Jz7zmc/EG9/4xphMJvFLv/RL8Y3f+I3xwAMPxD333BN/7a/9tXjXu94Vv//7vx/f+q3fGqurq/HZz342/tk/+2fxZ/7Mn4nv+77vu+L9XeW5kwqmqlwz8pf/8l+OnZ2d+NCHPhQ/+ZM/GX/0j/7R+Nmf/dn4S3/pL7WuG41G8ZGPfCTe/va3x4/8yI/EaDSK7/iO74jXvOY18Za3vKVRuBER3/AN3xAf//jH46/+1b8a73vf+2J7ezvOnj0br371q+PP/tk/e0XqvbS0FD/zMz8Tf+7P/bl473vfG/1+P/7z//w/jwceeCC+6qu+6oo8o0qVKlWqVLme5Vqz8UtLS/EP/+E/jHe9613x3//3/30cHR3FBz7wgbjrrrviiSeeiO///u9vXf+3//bfjohLaYiAqYiID3zgA/Hyl788fvzHfzz+x//xf4z19fX46q/+6tY7q/7SX/pL8RVf8RXxQz/0Q00U6/bbb48//sf/eOvUwirXp3SmOeZYpcp1Kj/8wz8c73jHO+IP//AP49Zbb73a1alSpUqVKlWqXCGpNr7KtSoVTFW5LmVvb691At54PI4/8kf+SBwfH8fv/M7vXMWaValSpUqVKlWeiVQbX+V6kprmV+W6lDe/+c1xxx13xCte8Yq4cOFC/ON//I/j05/+dHzwgx+82lWrUqVKlSpVqjwDqTa+yvUkFUxVuS7lDW94Q/yDf/AP4oMf/GAcHx/HS17ykvjwhz8cf+pP/amrXbUqVapUqVKlyjOQauOrXE9S0/yqVKlSpUqVKlWqVKlS5WnIVX3P1Pvf//54wQteEP1+P1796lfHr/7qr17N6lSpUqVKlSpVrrBUW1+lSpXns1w1MPWTP/mT8c53vjPe/e53x6/92q/FV33VV8Ub3vCG+OIXv3i1qlSlSpUqVapUuYJSbX2VKlWe73LV0vxe/epXx6te9ap43/veFxERk8kkbr/99nj7299+4p0DWSaTSTz00EOxurravLytSpUqVa6ETKfT2NrailtuuSW63asavK9S5bqXauurVKlyLcqVtPVX5QCKg4OD+OQnPxnvete7ms+63W68/vWvj49//OMnrt/f34/9/f3m/wcffDBe8pKXPCd1rVKlypenfP7zn4/bbrvtalejSpXrVqqtr1KlyrUuV8LWXxUw9dhjj8Xx8XHcdNNNrc9vuumm+PSnP33i+ve+973NG6O/XKXT6cQtt9wSq6ursbKyEoPBIJaWlqLX68Wjjz4a/+7f/btYXFyMF77whbGyshK9Xi8WFxdjMBhEr9eLlZWVGI1Gsbi4GIuLi9HtdqPb7Uan02kYP/4/Pj5ufiaTSRwfH8fBwUFEXHpjeLfbbcqZTqfh4CbfdTqdWFy8NL2Ojo4i4tJ7Iw4PD+P8+fNx7ty55r7hcBi33XZb9Hq9WFhYiE6nEysrK009O51Oc+3x8XEcHR3FdDptroVRcP0nk0lERPO70+nEZDKJJ554InZ2duJjH/tY/OZv/uazPWxVrmNZXV292lWoUuW6lmrrv3RZWlqKr/u6r4s777yzsdNHR0dxcHAQX/jCF+Jf/+t/Hb1eL77ma74mRqNR7OzsxGQyicPDw5hMJnHq1Km46aabYnFxMXq9XnQ6nVhYWGjK7nQ6Lf+g3+9HxCWWHnuKHfdvfAF+23fAzu7t7cXR0VHs7u7GeDyOvb292NnZiW63G8vLy9Hr9WJzczOWlpZiNBrFwsJCU/7h4WEcHx+3fIgcLeDzw8PDODw8bPyP4+Pj1rOPjo5ia2srdnd342Mf+1j8f//f//ccjmCV602uhK2/Lo5Gf9e73hXvfOc7m/8vXrwYt99++1Ws0XMjVlaLi4uxvr4eN9xwQ6yvr8doNIp+vx8rKyuxvLwcn/70p2N5eTluvvnmWF9fj8FgEAsLC83fBlEoJP9ERANOjo6Omh/Ay87OTkTMwNTy8nIsLCzEZDKJ6XTaKFjqyjWAmMlkEt1uNw4ODuLg4CD29vYaZTkajeLMmTPR6/WadlP+8fFxozAnk0kr1YO6YBiWlpZiYWEhut1u89v1nUwm8YUvfCEuXrwYn/rUp1qKupTtWg+6/PKWmlZUpcpzK1+uth6wExHR6/Xirrvuipe97GWNHTs8PIz9/f3o9/vxb/7Nv4nBYBB33XVXnDp1Ki5evNjY1aOjo1hcXIzj4+NYWFhofkyCdrvd6Pf7sbS0FCsrKzEcDlv2lZ9er9ey9fYH9vf3G/IyIloEJmRnt9ttQA7AbjAYxMbGRiwvLzd+CeXQhoWFhVheXm7qGzGzxe4PgBdAj88AUxcvXoy9vb1YW1tr9a+Fcql7lS9PuRK2/qqAqRtuuCEWFhbikUceaX3+yCOPxNmzZ09c3+v1Gkf7y0VGo1Fz+tFwOIxerxe33nprrK+vx9LSUiwtLUVENIpyOBxGp9OJjY2NWFxcjPF43ACiw8PDiIiWogRwREQDWLiGclFSCwsLjbJFYIayANr8LCvETqfTgECe1+/3i2N8dHTUYsHMmMFG0UZ/77bxDOowHo9jOp3GvffeG8PhMPb392Nvby8ODg6aPjs4OIjDw8N4/PHHm4hclSpVqlT50qTa+ieXM2fOxP3339/Y+eXl5bjnnntiZWWlsWfHx8fR7/fjhS98Ybz5zW+OTqcTm5ubDeF4cHDQgKHBYBCrq6sxHA7j1KlTjT2cTqdN9AoQExEtQjRiZqf5TdqlbW1EtOwsZeA3dLvdGA6Hsbi42AJ02Ho+txML+eloFX6Oo1C0EzKX71yHiIh+vx/dbjf+yB/5I3HTTTc1dXN9sf+/8Ru/EU888cRzMNpVnq9yVcDU8vJyvPKVr4xf+IVfiG/91m+NiEuT+xd+4RfigQceuBpVuuZkMBjEC17wglhdXY3Nzc3o9/tx5syZWFlZaSI9gIm1tbW48847YzKZxM7OThwdHcVjjz3WMEmHh4eN8jFoQukAWhCUYcQMVDmcPplM4uDgoAhyHBUycDOgWV5ejn6/39RleXk5lpeXmxQElL7TDa2kuWZ/f78VEUNcZ1IMUdw84/bbb48zZ87E1tZWw2BduHAhDg4OYnt7O8bjccP4ValSpUqVL12qrX9yWV9fj1e/+tWxubnZpL5BcNpuTyaTGAwGcebMmTg8PIzHHnssxuNx4wc4orS2thaj0ShuuOGGFoDY2tpq7DZikBIRzfc8H5sPmCILxH5CBjLYWcrHL8DWQ7hGRAucRUQDvrKP4i0FtJV68Rzuj4gmuvXCF74w7rjjjqbetJWMm/Pnz8fv/u7vVjBV5RnJVUvze+c73xnf+Z3fGV/91V8dX/M1XxM//MM/HDs7O/HWt771alXpqsrp06fjhhtuaNibU6dOxd133x2DwSCGw2GjaHZ3dxswBVAB5Eyn0wawkOYHy4NyAgihJCNmrI9zlK0Ive8Ipgi2zGmCXEMZDqF7L9bCwkIMBoPmvn6/38rHjogGrPEZ9bOCdV39vaNX1NeKljRC7hkOh3F4eBg33HBDo2DH43H0+/3Y2dlplDj13dvbi/Pnz8fR0VHs7e3VdMAqVapUmSPV1rflzjvvjDvvvLMBFzfccEMMBoMmEySiDU6cLRIRTfr8aDSKpaWlJm0OewlYyRkllIc9g2y0rc/p9E7T9w/feRuCy+U6fBKiTIuLiyf2XJkkjWjvu6a+gCCEZ/g+rrVfBLHa6/VaGT0R0QDDbrcbL33pS+PGG29s/A3vw+52u7G1tRW/93u/V8zGqVIl4iqCqT/1p/5UPProo/EDP/AD8fDDD8crXvGK+Lmf+7kTG1W/XOTs2bPx0pe+NFZWVuLMmTMxHA6bTaQRl5TruXPnms2mk8mkifBERGu/0sLCQoxGoyYiZTGLBaNTUo4R0QIRVkooO+dYOwXArJEVLMp+cXExRqNRU65TELiW/G/aauWaD55AUKxcY9Ysg03asbq62rBoAL69vb3Y39+Pzc3N2N3dbbGEnU4nvvjFL8ZnP/vZ2N3djf39/RORsSpVqlSpckmqrW/LV3zFV8TrX//6Jh3PGRQmFB3pwbZh0wAv/X4/Dg8PYzgcxng8jsPDw1hZWYl+v99cQ1lEiBYXF5t0PzJADFwcucKOm9SkvIhogb2IGTFLVgy2e2FhobG52FNHn9geULLtJjJ9EBXll0hW2ko0jz5z9szh4WGzReKrv/qrY2dnp/E76Cfq9vnPfz4efPDBCqaqzJWregDFAw888GUZ6ieveXFxscmTvuWWW+L06dMxHA5jZWUllpaW4vDw8EQIe2lpqVEkEXECMJXYqIh26l4GWER2zPIgOWKVI1UGMDA6OWKE5L1MeQ9UxGz/Vq63AVIu13U16DHD5rxws230o+sGe7W6utraf0aKAsDv4OAgzpw50/SdhfLZs0YU6+joKLa3t1tplShtgG6VKlWqPJ/ky9XWLy4uxi233BLD4TAiLtlnSFIDlvzD5/4ewf5jAw1ETOyZyOR/R4IuZ/NLBzKYVM2nAZfaEjHLHsmHVfhaHx6VwRkyD1yR6eJoFEJdvW/L/gft55ANiF4AIPfhi7385S+P3d3dYt1M3JL9Mx6P4w//8A9bWwU2NjZiZWWl2V5Q5fkj18Vpfs83WVhYiBtvvDFGo1Hcfvvtcfr06bj11lvjlltuaUV6tra2WmF1TsPxaXte3AY3pfC+lQrRn5w+lxVkLsvK2il1XIfygZ3yvT5OnYMtSFmIiNbxrr7HG1h5riXnUlOOQWCuN2DISp9Ti7wJ+uDgIB5//PHY2dmJU6dOxfr6etx0001x++23N5G6hYWFZrMrho4DOnZ2duLChQtx8eLFeOihh2J7ezt+93d/N8bjcVOfwWAQg8Egdnd3Y3t7++lPrCpVqlSpcs3IYDCI1772tXH77bc3+4x8ih3ZHAYEtm/ZZh4fH8d4PG6Iuslk0mRWQNzlPc4mHw1K8h5qrsNu+3UojhpRJz7LZVIe/xsgeUsCvgMpeLksxP1Du8bj8Ym+8iEUEbM9U0Tq6F9sP/XjiHiewTYA9nFDdt97770tsIaf0u/3m9OCj46OYjwexxNPPBEPP/xw/JN/8k/isccea/r+BS94Qdx1113xmc98Jv79v//3T2tOVbk2pYKp50AyaFhaWoq1tbVYW1uLjY2N2NjYaE6+ybnJDmXnaEwOuUfMUvYcSXIOc0R7j5H/RwxCsrIy+LDy98ETvt4/XJtZL++tcj1z/Z1n7c98TwaDLtuGgzQEj1F+f5VTAg8PD6PX6zXpE7TVAMw570SjOL1oOBzGZDKJ7e3t5v0XAEfKRHFTR59UVKVKlSpVrm1ZXl6OlZWVxv6srq7GaDSK4XDY2svkaIYlR3L8jieTjs60wPZiPwx8TDQitunZ9puAvVxUq3QgVLbHeR+z0/D8ChMEoOU65ohXbg97sXi294SV9oznFELqgA2mPeyvWlxcbLJR+O39ZhlMEd1iuwBRSPyfG264ITY3N+PMmTNx6623NnWAtK2ZKdevVDD1HMhoNIrbbrutUQz9fj9e9KIXxfr6etx4443NexCIaHBCj480j5jt/QEIWFmVBOaL6/f39xul64hSllLOMkrOp+JZIVqROcXNwAjl5vQ6mDkUFDncuS48x+F8wAj7lnLaQAZe3rdlcEZf8e6Mvb29WFhYiNXV1WZPGumXBj7uF57nvV3uw4ODg9ja2oqdnZ144QtfGNvb2/Hoo4/G7u5unD9/PnZ3d2N5eTlWV1ebiNd4PI6HHnqo5mlXqVKlynUgN954Y7zyla9sSLbl5eU4c+ZMLC8vN4DKJGQGC96bbOeeV3bkFPXpdNo4/mwBMADIp9E6iySiTMhmIIUAXNir7b3TpML7lSxEzfjhHtpINMh233YcX8UgiHKd4giY8h7tiGgRnt5KgG+Cz9Lv95s93D4ynnEh44RDsThF2NswfLIxvsPi4mK86U1vavy5iIjNzc1YXV2N22+/PV71qlc18+Lzn/98/ORP/mScO3fuykzEKs+5VDD1LIjftcQhBxsbGw2Q6PV6DWM1GAyi1+udiMAYhFjp+roni1jkPGvuR1EReXkycapeKQ3QdbQiLuVdu8z8Y1arVHf6Mx9sUTphyO+2ys9w/9EfMHx5H5WBk1MOfXqR60bfeo8XRoEI1hNPPBG9Xq9J0eQ9V95DhgEYDodNOqYBZJUqVapUuboC6MHurK+vx+bmZpP+jS7PNiGTmDmCgw+Rv59HnjqixTOyj/BU7HO2afnZrl/OAKFMHxx1fHzcSs/HvuWoFraZNEXv6XK0qdQXzsJxnR2NygDRZHTEbDsBdcv91ul0TvghOcLn+uAznDp1qnkH2HQ6jdXV1RgMBtHv92Ntba15cfLBwUGsra01xCmpjDVSdf1IBVNXWDqdTtx9991x8803x+rqaqyvrzdv/cYZ7nRmx5p2OpdeiudFnyMbBgYwVLBUXsBWLPxvEGUFAjgASFB2BhSUwR4fG4aI9gt/rUxzKD5H13wf5VgR5w2uKCgbE+eGe68VCtjtcEQtIhomjLYDdCjb/Q/gjZi9uJD+cLtKypW/qX+324319fWmfjs7O9HpdJoXLJ45c6a5dzKZxD333BOHh4fNyYG/+7u/G48++ujTnJ1VqlSpUuVKSLfbja/6qq+KF73oRc0eG5xkbNDCwkKsra213q9ksb1fWFiI4XDYpJc5syRiBhB6vV7rhfbY7pKNzSfN8r9tpe2UiUnslVPZiUxhD7Pt54X3+fQ/IjcGid6HTaQHMIX9JXoUEU0dvB3i8PAw9vf3G//GkShsPuXiXxwcHMTe3l6r//b29qLT6TT3Avo8RmSp9Hq9mEwmzf51Im8GhwcHB7G7u9scROHxGwwGrX4+ODiI1dXV+E//0/+0iSQeHR3Fv/pX/yr+w3/4D89kilZ5DqWCqSsoKIGNjY24+eabG+d4aWkpBoNBwzZ4X07EzBF3+D1i5oRzrUPKPs6Ua0tgiu94Tv7/qbBVVsp5/1be42QA43bk1IIcYcvvlMpSYoMAJDBXOa2wBG6c5gBQRGGbbcubajlK1fU1+8X/845Jd7TSxshpGevr63H69OkT904mk7hw4ULs7e3FQw891GrTk0Unq1SpUqXKlRXsxU033RT33HNP9Pv9xkl2ZoNT9nyYhO2ngVfe85TtcfYJss3xgUvz0vV4LrastM+Z57hu+W/K4XfOFKEvKMcp/X4edhvgmMvmGtrnyFsp08T70ngeaX0AK6cL5q0IpeiffS38DXwyg18AIPWwr+DMk+z3LC8vx1133dX0zcHBQfzGb/xGy7/LvlaVa0sqmLpCsry8HC972cvihhtuiNtvv715ER8v40NBcKqMlSWL3WDA4XBv0iTCYWBFhMaRKkdivKAjZjnDu7u7rfJL6QM5vG4GjvdVZIWWlTTK3gwUACa3LUe0LpcuaODIBtC8+RXQYiDp6zOYyptjMVber2ZQSJn85IMtGHuUbEQ0c+Luu++Om266qcml54ALJBuYXq8Xm5ubTTSr0+nEeDyOra2tYv9UqVKlSpUrK71eL1796lfHzTffHPfdd1/cdtttDVAiOmNQ0u/3WyCE7wxWAFGj0ehECl3E7EAjCMGIaNlOR3lK5KgzLiJOHjV+dHTURHj8niV8FJO52CX7E9kW5pR3g4F88JOjaNTX78J0hKrT6TTvjTIZ6vaOx+MWSby/v9/4HAZVPN/97XQ/7wFHTMiyV4s+dD9TNoeLOSuF+tN31I33hD3xxBOxt7cX99xzT3OU+nA4jM9+9rPxiU98Yi5hW+XqSgVTV0gWFxfj7rvvjrvuuivW19djOBy2cn9ZdAY5nU6n2dSY2RcWbc4tNsPhQxQc3SqxSkRWzBxxL/ndTgGMKJ/G5yNDzbA4zc1slcWbTEssFZLB5jww5VP3ut1uszHUhszHtLuPMsNmNiv3u/srnzZk1ililgboOjotgTotLCzELbfcEoeHh43C5Ghb9yMKlvSH0WjUHFJBW7a3t2uEqkqVKlWeA1laWoqXvOQlcd9998Xm5maMRqNW9AU7hK3giG5naJCdgh0dDoetU+UQE3kRccK+myjN0Q7EGSr5x+SjAUe3223qhN1ydoqPdM/RL+pHG7HT9j343/5AtvV5jxOEov2anM1COyKiySThRbwmQynPBCrtMQFaiuz5YJCImc33tfhLHFoFWO73+62UQECiI2W7u7uxs7MTZ8+ejVtuuSU2Nzdjc3MzFhcX45Of/GQFU9eoVDD1NGVlZSVuvvnm6Pf7sbGxEYPBIM6ePRv9fj8mk0snzEW0N31GzBgh9jw5n5cFHtF+Z0JOqTPzA2jgHsrIbIqjYygJ8nOtRHD2KY965LREKyezPRk4ug/M/viHMt3mDAxdRmbXUMg+XYd25BMLzagh1AOA676OiFZaAGl5OWqYN8Fmxsz9QlkZDKL4XT++52TBO++8MwaDQezs7DQRKvoAI0J6AW90r1KlSpUqT09OnToV9957b5M5MBwO44477ojV1dXmdD6f9oZjbbvs/0tRGxzk8XjcSjUDCGT7mAlGAxUceUexImY20RGYfDJwBhLYvOyLOGpm++a90JQHeehT/bJN5pAGE570VUS0bDGgysAHn8T9QL9l++3yvU/cPofbgD/k6J8jUvhwtJf3WHH4BiAq+w1kxywuLraI9PX19eYlwpPJpX3Vu7u7MZ1O48UvfnFMJpOm/SsrKzGdTuM3f/M3617qqywVTD1NWV1djZe85CWxsbERd999d5OeRRRkb2+vyGqgHBy+z/uffHgC7FFEtIAGCzCH+nOKnYEJCpawuRVfPvkmC4ofZYXysMIivcF50m4vbbZS6fV6LSCAAsl7wWyQXK4jRmx+NTvkQyVgDm2IiLLxExGtFEqAsY8n9+l8GFOzY4TsnYJpQ4Ph8KlNjDmHkWSDu7m52dT7zJkz8dBDD8VDDz3UvMOKTbUR0Toso4KpKlWqVHn6cvr06fiGb/iG5qQ+9kDnQw7YD20bbTts+5UjIo5AmdwECJQAlYXPAQc8A1uA+FTZxcXF2N/fP7FXKWJGgJZS+kq/EROF1IFDK+gz0tx49sHBQZNimNPx/ULihYWF5jrbXu+ZxpfAJ/Hn9o28/cEAy+DM0UODNac7Ys8PDg6aa4+Pj5t64lPQdsaKI9EBXJS5uLgYp06diuFwGNvb27G/vx9PPPFEnD9/PiIiXvayl8XS0lKsrq5Gr9eLG2+8MSaTSZw7d66CqassFUw9RRkOh02Idnl5OW688cY4ffp0jEaj5pQbFps3GXqvUo44RESzcK3IuMd5vQYUJWUKs+LffpbBjBVHxEzBUh9vlKU8nHquzSFtP2veJk7qbRaG+wGC/kGBZmDo/sDg0F4fX46Roq1W1K6bUwHcntJ4eYy4l/+d/+zvHZ2i3QaE9ImBL59nQM1RqsPhsGWUYL+4JuKSAi+xivRtlSpVqlRpy+bmZnMiX7fbjdtvv715MWs+KS7ipP1zBkYmMZ1eZztQ2ieVy86RomxbbKedkZEjYyXfwb6F6wvw4DcghHoAGijX7cx19meZgLR/w4/7x+/twt/yAVS27bb9pf3cHgf3t8sv7XFzNCr/UH9n6rgN86J6bqP7GyDG4RQ+RZn5Qrun02nceeedTbmkPx4dHcX29nZ8/vOfr6mBz4FUMPUUZXNzM86ePRurq6tx5syZWFtbizvuuKM5jMGhehgI5zl74jtylJWylSXinOuIkxs+cbopw1EurskpBmZ9fEgGYW3A4OHhYZPLzW8AjBUkZft5mZVzRCvngLv9BiUoEbfFaX8ul+sc+cqbdg1avc8JZpH7nUrAONloAOAogzx5wvseIwPTbrfb2oDK2KGAfdpTCWStrKzEaDRq8qq73W5sb283YMrK+9SpU9Hv9xugSJmHh4d1n1WVKlWqJOl0Lr3a5Cu/8iubqAr7VL33xTYoZ4Ngo8iKyNkotnPY4eFw2PoeO8L/CLbOGRmOolBnMj6c+sb9zj6BWCMCZkBEeYPBoInGcSz4ZHJpPy+ZD9me80OkyMCS59KGiBmp7HY5DR6gs7KyEoPBoMkoyVkr+C/u4xxNsq9DvQAp7HFCMtGbgZn9OTJL9vf3G18C0OfIk/uXdtIH3jeH72K/cnl5udUODtu4//7742u/9muj1+vF8vJyjMfj2NnZid/5nd+Jn/7pn27eaVnl2ZMKpgrS6cwOZWCBcuIaChXAkSMnOd3LjBDX+DfP84L1tY5KZEbF15XYMTMiViBcY2BR6oPLiYGMWaHSvaWycm40v2Fj3K/zyrFRcJty1Il+yMAPRQWg2d/fbxm+nG/utqPgfHiIgXCO3Nlw2KC5D/O9JUPtuvV6vRgMBjEajSIiWoaBOi4tLcV4PI79/f2WccUoOi2ySpUqVb6cZGFhoXl5Kvbj1KlTjcPOPqlM4JXsju1Fju7wrE5nlhqPZFuR/YhsB3MdcjRnnh12yjnOPen5PoXWfo/bkIFSjnTlPsl1NlDKY2B/gG0IuS9sG21/S3bTNjlHrZy9QlvdvpLYrro9/u2DKCgXItz7u/w3kv0h9yVgm1TJXK+IGQHs0wh7vV6srq7GLbfcEhcvXoxz587VjJRnUSqYKsjS0lLcdtttsbKy0oAmAJTfbJ4XH/m/LPC87yXiZIiYMkqpVw4de6FYYbL/hmt4HukIWbkghJFRTERFMojg2pw+CJvlk/loo8PhDrs7BY7n5X1VnCDkVAbvwXIkDzHLk4FURPvEvfxM9lrt7++3XtxnA0iaAz+0HaUfEQ1YsSHz+JmJ5DqPi1MMM0ilHo4u9nq92NjYiNFoFKdOnWr1N+wVLw184okn4sKFC7GzsxMXLlxolOzh4WE89thjrf1gVapUqfLlIsPhMF7zmtfEDTfc0NjMfr/fpPX5vU8R5T1B6GOTqkhOUVtcXGwOFIiIlv2IiIbcsh2BYJwHYLCnPjjKkRTKMGmLzt/d3Y29vb0T+34cmcon8hkYlQjVDMSwcRyugS8RcQkEZLttsOn+IRqWQS3+D3+zF2s8HrdeZcIWDSJcjkzZBhrYmHTOr1/Z3t5ubRHgIAoOhlhcXIyVlZXGN6EO7FljL1YmhXkRMeWORqO48cYbYzgcxqlTp5oxmk4vvWz46Ogozp07F7u7u7G9vd3aJ33jjTfGt37rt8ajjz4aP/MzPxPnzp37UpZHlS9BKpiSmPEfjUYxGo1ObDR1xKoU/s1KxmxQxEnmyWCqxE7wd452zGOJSp/589zevJBzHXN6gP93uwBNWTHOqxPPMMiMmB104b5zm3M95z3H11j5upxSPQ2aSiwkwhg7CuR87Czuq3mRKfdNTtv0XICB9ClJvoeNuRHRbDL2uyzoZww999YIVZUqVb4cZGFhIQaDQfOi9DNnzjTEacTMF0DHZruF2GZAXnKdyTCeaV8i7y3yfZeLBFmybUNyVCun3AMyAFE5bX1ehKb0/Kd6rX0IR/pcr9wO+wEmYjMpnUljR6xyhM17j+al8WffyfcBvOzb+QARP8fvq6LceRlGPDsT66T3ESV1uh9bLnZ2dhpAZ1lcXIzNzc04Pj6OtbW15qCquofqyksFU5LhcBi33XZbjEajuOuuu2I0GsUNN9wQKysrrYVMVGJra6uJBKAYWEgsrqOjo1YUi0VFRAkxmHBecVbKMB9cg/L3yTZ5kecT8hDnOUfMUhMcYaOt3M8zfAKQWbrMElEuzj9K8eDgoGH0YJzoFysj+jQDJIvZI54HS2dQkkEL15Iud+rUqaZsK0j+Z3w8Nigvp+PllDx+8qEYub5cBzPliCOpJswfs14YcU5TxDByYMrS0lKcOnUq9vb2GuZqd3c3Dg8Pm5MAH3rooZpXXaVKlS8LufHGG+Obvumb4tSpU3HbbbfFYDBodDjZFvl1GQYCPvIawOWMjsnk0pHWEFj4Cez14T2U+bAmAzKeye8cKTFgMjGLrfPJedgzojbb29txeHjY2nubs2e8ZydnuWAPfUJdJn+pT8504RoiUDs7Ow3h5/RGR7awmQAGH4YREQ1IcMYMfQMIyodXuN+9b4nPGXvv3aKNziTxMzNJbuLc0UPPE0e1fBQ+z7RPh43n89XV1Yi4dNjU0dFRXLhwIba2tmJnZycuXrzY1GVjYyPe9KY3xcWLF+OjH/1oPPjgg091qVR5ilLB1H8UFOTGxkasrq7G+vp6jEaj5sQ+FCwOKaFyRw18FCqKF4WUIw+OYHE99UDMlEREywnPACj/z30GBL7Xig9Fwv+AKECPHfZ8ih33OUc8AwXXzekCpfb6FJzLsTcl5og+tvJ5siiR0ycdpQHg5bHI/zviaEPg/iw9v8Ru+p6S4fJzzZwCSJlnbhtle5+fU0o6nUvvMnn88cdbJzP5WTViVaVKleeDYB9WV1fjrrvuilOnTsXq6mpDEJK25chCFkcYfHqeDxfwy9ezzSSbINvt/IyS/SpFq+bZSQMZH0CEL8NBCePxuEnt8/Nz1k0p6uYU+3k2stR/9Ad1yafgZoLUthBQ4jbb9uW0Q49T3oud/S8+s+10JNFjYHkqtj/7NDkzJe//ylEsnuO2MW4Q2c5k2dnZaerEOyq3t7fjk5/8ZCslNPdvlacnFUxFxNraWtx4440xGo3illtuidFoFDfffHOsrKxEr9drLThOtfGbznG6nTcdES3l5Ze6RbRfOOtFiGKB8YiI1ov2MiCyY54XnpVWVoYuL6ctkm9MBAMg5fdW5NRGs0o5fxqWDGVG35EvHRENkOE+JO+RchszcCXP2KkAiPsNoBURDROU98HZgGQG0ArL79lyGp3TObw51H1XqhvlEuFyxDEiTrzvajKZNO+XwmD6ZcWA+el0GsPhsHm3xalTp5r6Hh0dNZEr9llhZPf29uL8+fMVUFWpUuW6l1tvvTVe9rKXxdmzZ+OOO+5oTodzNgmpb9m+RUTroAZAExEn9kaxF4bMivF43HKCiao488P7nZEMaHzinu0Q9oUoGZJT1/zDPmHslveGs7cL++r3TFFn9gi7nyJmByE4KoUtcl9m0jK/VJc65/4AfGGzMxiDVDSJSxnUN18X0X7vZ6fTaSKI3hfnlH6idezD8ufe7sBvMkvYi+dncp9PFd7f329FP0ejUSwvL8fq6mrjgzqaiI/oLJSbbrqpiUZi68fjcbzxjW+MJ554oolSXrx4Mc6fPx/nzp2Lz372szUF8GlKBVNxKUR60003xWg0am3q93t6ImasFg4uAAKm30yBHfKIdoSGa32AgYEPysYAzHm3FnJ0KaMUxSmxDplxcRkYFA5lcI5uPjyB+hgEmlXh5+joqFHSDl2TNpEjX+6j/M4rgykDEFLiUEyU4R/nW0fMNnu6rvlwEYyAjROfUy/mQgY03v9F/1/ufVauA2AqjyMKGzDqwzqYgzzHkcB8tCqGbDKZNPnUFy5ciO3t7dje3o6LFy9Gt9uNCxcuVDBVpUqV6142Nzfj5S9/eZw+fTpuuOGGBjzg7AN+fKiA3xvkKJMJ0QwmIma63BkXEbN9waR2ca1/I35Zbz61DkHH27F3/XJmgwleH/bgdEWTmLb5+DvYDUeSchQIf8npgyUwlSMy86JFBpfuE8BCKSKVI0HuT1+T+x3x53l/N3bah2iYQHabANAcM+8onElsxoXj5MmY4iQ/9vBz7D5tNaDudrtxcHAQGxsbzX6qw8PD2NraisPDw3jFK17REOX7+/vx8MMPx8MPPxxLS0vxuc99roKppykVTEU0+08Gg0Hr1B5yWL3YHfVgMfX7/WaB8Z0d95wSGHEybG3nnVxgLxbErI2debMofo4XNc+x4jZgYDHDzMG+kGsM88Y7ENyGrABdrlMeuCdHTkrvgTJ4cvutsNznAEAzT4AZ6meWqZTLTTlmk1w3M25Ob3C7+J3zsh3Od5343Ayl+4dr3N/UzRFSlKDf5ZXHKBsR3qnBSUWDwSD29/dja2srLly4EKurq009mBPb29v1iNUqVapcl5L1PLbch/IsLi42+5Oxh9axuSyfEgvphg7HbmKzsl2IaL+j0WU7+uCoTLYHGYT4SO7pdNrYCEdrHM3wK1+yL2Hy1HYVYjliRuTmk29L7SQljbryt0FSJmRLIMUgMCKakxddH0spTc822N/Zz8s+Ez/0XwZFBlmcTgxQNZAdj8cNoLU9pQ4AKHwvCFoiYvbf7GPkutMno9EoJpNJsy0AMMX49/v9Jitpa2srDg4O4tFHH22CBVUuLxVMxQxM8WJaO4+EoHEkWegsJkd1xuPxiUMLDKZYTFZOAJjM6kScZKmycmDhONKDArTDDlAiUjGPjWGRA0oiolnAfvt7BjxmelzPEtgy6IiIJs0hGw7Kph+sAM2YmW1ivHimmSQ+B0TB8OSIi8Ef84E2U/ccVfOY0lbG0RFBp2v4WlIUrKj5cXqJjTZzkLYxxhHRGuN86IjZURQ213W73djY2Ijj4+O4cOFCrK+vx4ULFxqHgsMrSA+oUqVKletJDA5IUUOXQlDB+rN3ivsiouXI2xHnXu/X9THY+TCHkqNvEpRnGnjhJPvoa57vqA22Gn8AEGZyklREIlm8QxNn3dkRiCNzOXXd0ZrL2R7ssvuV/jcIwLbaltFWk9uOyjjTJ4My+1y57w1EPQ45swMwZxucs1UoAwBNOp7HnOje7u5ua386/WGSfjAYNKmBBlSMb2leOpLKWC0sLJx4GfHe3l7s7+83L6Xe3NyM1dXV2NnZiQcffDC2trZia2urgqmnKM97MLWwsNCceoaSPH/+fOzt7cWNN94YN954Y5w5cyY2Nzdb+b2AKOfiWmlFRGvBR0QTsckgJTMuEe0Ur8xUzYvycI1ZEm+qdCoZhqLEqjmlLoexO51L73qywFYZHJSiHaV8bysit4O68lk2Jlm58VlWhFwTEc34ZqXp/ndqowGcP6NfMij0+GUp1SvXg0gWzFwGYDZg/s04Olffxpz257RNt9/GttS/rg99ORqNGqfi4OAger1e84b3vb29JrXR44CRMbDzHKcunL5UpUqVKldClpeX4/Tp062Xm37hC1+ICxcuxB133BG33357vOAFL4jTp0+3Xnlitt8EnO1Dji6h93xiqp16Sz7gKYv1biYIEe8lcv0i2imC/G0SFzBlh902KGLmcJu4NEELecx9jtDl9Dr7KLnN3k9k+2p7ZrEv5B+e6+t4Jpk9Bsr2C+xjOIJD/XJ6oH9y/5iQtE21T1Yir3l23i9Feik/ef+3wW6O+OU6uGy31X4CJxnz3dbWViwvL8fu7m70er245557mlMp3W+lCGG28Xy3t7cXW1tbJ8b2+SbPezC1vLwcN998c3P09eLiYvzmb/5m7O3txX333Rdf+7Vf28p7hS24ePFiRJRP2XHEJGLmxOOEexGjGPM+mRwhQGAWMuPC8/ICQtl5nwxRGk4fLDELfg7lmklbW1trFqAd9Mx+UJbf0cFiMhsW0QaFKBvYH6cyuI9hYEqAJrNh+VpYPNgkK2vamkPzVsrZMLleWfxcysype1mh5lQNb2J2xOj4+LhhiGD2fOAH5ftAEcr3WHgMMmCnXhjVwWAQq6urDXO1v78f58+fj/F4HL1eL3Z2dppIFW0jl/vo6Ch2dnYiIhq2kxQMjPu5c+eaaGGVKlWqPFNZWVmJl7/85bG+vh4333xzdLvd+H/+n/8nLly4EK961aviW7/1W1uH86ArecEq9taZAqRocaKvX2mCzSTTwWnwCM42+1wj2iSb0/LR06RSHxwcNPqYKJmdVZO4OZLkPV4GVdgL9HF29v23U9XY79Pv91vgjKwI+qVk6xFncThKlm1mvo86+yfbNMaS8YqY2VfvXQYE0B/YVNpNqmMuNwMkxo7+LwHEDMJKvgl1gFzM2VDLy8uN/9jptLNl6EfPNZ5hgMYpvdhb3pXKgWkc1T8YDGI6ncbu7m4z5+68884mkmYfFkKVekZEE4XzSdDsydre3n7e2/rnLZhCwa2srMTGxkYMh8Mm+nTzzTfH4uKll5n1+/3WonaaH2IQgVwuWpE/y040n1G274uIRvHaeXeEh2sNhBArpRy58HN5jqNZWfHlKIbBldtnsBAxY+JyP2WxAkQ5+LkRcYINy5Ek1w2lSb8ZoHENfZOjZFnZWVC2OQpjmdfODKaoZ45uOh/a7aLdKMZStC2Dy/w7y7x7rJz9NyCTVBYil+R7W7yx1n1uB4JxZ59WftFglSpVqjxVgSjd3NyMM2fONClLS0tLcffdd0en04k77rgjNjY2ImKWEuUoUEQ7QmQ9Zbtn3WbAkR3trF9tb7LtQWxD/F0mDTO5WoqCINa92Wa7zHlRGH5yxKYkpXZb7E+YbJ3nq2Q/IpflUwbnEZb4dO5L1z+3hbHOZXi+cJ9teunZvsZbA6iv93M7UsVnlOfsI+riU3p5lp/N3GTOGkjSf7bxPANfzEenT6fT5tAM6sYWF4ApQK/T6TQgi3qMRqPY3NyM/f391nHtzzd53oKp9fX1eOELXxirq6tx++23R7/fj42Njej1enH33Xc3kQAzI6D2iPapNyDxDKb4n4mTlYPZi7xozeYvLMxeguu6ZPCAYrCCMBuVw/osGL94MJ+s4+eyr8t1Go/HrefkqJxZGvrT9clK04oT1g72y8qAxepIGOzcYDBoKXXGCMNmxcd4Ugfva2OMrETcRwauTrMo7WvLzKKNQzYKtIt68D+n9sDqUL+jo6PY3d1t3lxOpMcHpNDfPLMUETMDmiNt/h52ifYyH3gmoXuO1qUO3hhtsIhCdzprr9eLM2fOxHg8jnPnzs2N+FWpUqXK5eTWW2+NN7zhDc0rTrD1y8vLcd9990Wn04nV1dUYjUbN5nqyUCKiyVyAsccmYRtsDyOiBag4HICIhh1s7+vJTjFlcxiAMzKm02lTBxxsCDdHpiJmKXu2pZnoLRFv1KXkhHu/lcEibff2h9JhGFzn5+Y2RpwEKd6fPG+/lUFBBhyMo8GlfaXcL/7Ovpw/NyDKh0TYzrqfbG8ZG9pFvfv9fpPVQTaUX1yMf8rrTIieEgXKADuTsAQTOp1OnD59unmhLyf2coJxBvaO7nlu0C6P/3A4bIFTfK8nnngi9vb2mn15w+Ewzp49G48++mj8u3/375635OnzDkzh1K+ursapU6eazXX9fr8JV7MRz6lSmRnJyigzPna0s3Iwu/RkKHwey4Pw7NLfTucya5HbZBDwZM/j+sxC5UgM1z0ZU+V7MzuWHf3s/AMcPQ6ZCTIYLgFWyjD4y6zMU+kT6m9AktlGlLDLM5Now2IwZhZnXl3oN0cTS5HSJxuLfH2OTPkzz3vGBIA6mVzadL23t3cCQPl5Lh9jZ8cgIprUQAxZlSpVqjyZ9Pv9WFlZiRtuuKF5T+T6+nqTtrS8vNy8K9K6uuT44yjaebTONYNfigiUIjvZPpZ0c+l7l2kbZcc5R5lKUSnr3vyTn4U4pS1HdBzJw3dylC9iZuMuB6ZsXy5Xp2yf3Gb7ZbmtpTaXJPd9vi5Hp3I9PB6ZKPYz5tWx5FPhqwDA5mWs5Gwbxi4/21k/nLyYyWDPMUembPcBihDurJc8DynDfg+g8PDwMFZXV5v3XT3fCNTnHZi67bbb4kUvelFsbm7GC17wguj1erGystJanD7unAMjcuqY9wTZSfYCtHNvcTQlM//85JQxK2wmvb/3MyPaQAXWxM+i7hiFEkDy+zX4n0UDO+FozXQ6bZRoqT37+/sngAS/2fuTAR79m51xcrB9dCgn/+3v78fCwkKTdkYZ8w5E8Jj4mf7f6WbU0aFuG4Vs2AAHZvVgLLmP9z1Mp9PWixUjook6wfZ1OrNoIoeacPy+WU+fMMkY+bfbkvsg/87ROAPEHDEdjUbNptWVlZXY3t6OnZ2dE1FO9y9zCwaXOXZwcBA33HBD7O7uxuc+97l6clCVKlWekrzyla+M17/+9U1EqtfrxerqaivCgf1CN7E/xVEGTlflUAqiAREznckrN7BjzobAxgDG+IGZx6cwQCJNy85qCQDYTlKWs0siTh75XQIQzkJxloAdX5x2R+g6nU5rH09Okcy2O4OpEuGao0vz0iR9n4li2yynpNHPRG8YY0fWkAya7RNhnwwODCZ9zDy2Ou+r8js1p9NLJ+dhSw1GMzgjGLCwsBAbGxuxvr4eq6urjQ9LBC7XxYQ0AClH+Kjn5uZmK7pIdsnS0lITeGCeI/QhB1Dt7u42ZbA/2qCYo9Y5oKXf78fZs2djc3Mzzp07F7/6q7/6vDuU4nkDpphEa2trcfbs2djY2Gj2SLHY/PZrh6i535IjKBk82LksMR9mY+YxEBEn84izAuH5Fjv0jsjkMrm3xJIZQLLg7OQ6RB4xUzrZCPCMyzFABqK5Xu4PKzWfaONFz3OPj49PgBIfM0+dcsSOtpeAXYkl8+ceT5SVwZyNEkfN5pRLt505Rcid8rOBi5gpM49xKb/a/ZvnUGb6KKc0P11GNmKAbML8GGqAX47UUn/Woo/gReHSZ35/2bz5VKVKlS9fwUbdeOON8eIXv7ilW9gEn6M9tvuZWItop0hDJKKDnI5twGSAFHH5vdK2KY6qUI7tsnVyzgx4Mv8g6/csl4sE+XRDRy8mk0sHTPAy2UxElsAUfWyAVMrQcBuyn5P7jHszmUs/IU7HdH+UMj/y/Y4Cud6234DpPBZEawzEsu+QXwKdgbMJWU6J9KEpjk5RF0cGGTvXMRP6AL3cXzy70+k0ZCmSAS/bDTzuXA/AJBus1+s1WWHT6bQ5QZDo1PPF3j8vwFSn04n77rsvbrvttjh79mzceuutTXSHfT8Ow+Lgci/KwoyIc0a73W7s7u42OdYsrlJak6NMXiRWTGb7mWCWrCQzq4N4QVAfvzsjIpoT4vLBAAZXLFSfcAdbMR6PW4ocpeScciuNkoLjN8fVUh5j471ZlMfpMbQDZzuinb+bwagNEePMmOe2Z0OXlb7LAVjCNlIe+7h4Lv/zhnF+E0nqdrvNy6FhgLyvi+hbNqKkpsKQModczzw/SsbAUSzPO/oAxswMmfdxMacYg7W1tVhZWYmVlZXm5KDj4+PWseeO1A2Hwyai6Hrv7+9Hv9+P8Xgcjz32WIzH47h48WKz5qpUqVKl2+3G/fffH/fdd1+88IUvPPEuwFKqlSML6G472uhM7IUdUTvZthfOEMn2mQyNnO7E9f7bzn6Ovpi8csSBDArKwrdA9wJ6HMHAOfez+By7hl73PiIiPfk9Va4Lbc4ANdtV2mu7ZYBj/4x77V8YTFBHl5VPs6WPsl2F/LPPAljgVLss7GnCDvodZN7nlTN68GtoG/X0XHOkDVuZD3jgZEciXezh8xjTD8x3t9PznH6hTfxMJrNTmfFJcgSTuuzu7jan/F64cCEODw8bP4fIJn3JqwqOjo5ic3Mzbr755lhYWIiLFy/G5z//+dja2opHHnkkzp8/f6Lfryd5XoCpbrcbN910U7z4xS9u8qaR4+PjE/mZdsD5DePExAFYMKldhhe9AVeJXXEdI6KlrEtsSY6KWEqRlBL74cW5tLTUHBzAPZmVYqH4GFbq6GhQbqcZHJ5fYtAQKx36FKcbZUefWAGasbHkccztA1z6mpzOaaPIs7LRcV+jCD0HHKHzyxmXlpZid3c3tra2mrKcw+wx4jmehy4XEGdWy8YtzxMrV8Y4R4qyIqY9bhP9bjCOskTZUweng6D0+Y49DIAptx/DsLy83MyHixcvxng8rmCqSpUqjSwsLMQ999wTX/u1XxvD4bDRp+jUzHDPi5hkEMM1tqHzokLZJqDvHfUyEDARmSXbrpwC53IiZoAvIlrRiohZajx2O2djuB7U1WUYtGW/Ir9aI3/P8dvZFjmSksEUR3N7HLDbmZh15MfPoN/z3mjfa5sGWWyQQZshNkmpz+OML0F53idmUEob8GPs43m8MvDnc6JRXOM2ul3um5wlxd/2B30ffeltFJ5rJthNcgOmOFqfLReAy6Ojo1a6LH8DUKfTaWxsbMTCwkJsb2/HwsJCPProo7G1tVXB1NWUxcXFuPfee+PUqVNx5513NsqVhX05Bj9PvAwEImbOcUQ0bATOn53uiPZmfRSTwZYnd8SMLXP6Vr6GOpiV8d4WFoQBCk4yTrfT5DKbZqWZ78lRH9qYF5fB1DxQmBWJ31Gxu7vbUp5ELQwUKS+H1rOSMDuEMw9QyayZ60J9UJZ5zxHzyAbTe5tgk2yQqCd7oayoAHnMObOIjD1sFmzh6upqE9EyK2ax4+B28nN4eBjj8TgiLkWXvDY8TyOiiS5hfKbTaQPIvR8KBgpARDoIB1OYXfWG2qzYFxcXY319PVZWVmIyuXRaIBvMeV+FjTpr5vm4kbVKlSptWV5ejle96lVx8803x7333hsrKysxGAyaU2Cz3c6Cfs/kV0R7TzLX7e7uNgQWku2bIzuOOJDlwnWZGDNo8n5U/9hGk+mQnXgLESSiSNgpO/MGNa4Tz8dO2b5OJpdOwMU2mlTFV/BpvflZ2fZmf8zRO/YrUe8SSehUsjwegAPIS58IbCLTJGjO6sBGZvB6dHTU2HJsHG0spQbSB/QpNs/9niOkRLNyel8JRHmvFz6FyW/PnQzqKccp93nLhOd7Jm0BUX6PFb4Bz3NaoE867PV6cXx8HMvLyzEej2NpaSkuXLgQZ86ciYcffjh2d3eblwSzNuj/J554opmH16Jc12BqaWkpXvrSl8bdd9/dLESUGQMQMVOWBgdMkCwGLTigKKqsBLy5FWHRGuxEnGQhWBD85DREysosgVP7zNR43xMTkQXmv3OEiedY6ZlhM1uUF6f7KzN8uU+9GFFMgCkrgcPDw0aRGNRYwQKQDDyoBwuXKJbBQTZWuc8BGplBtNF12qA3maKU+G02iRNwHLqfTCYNkJz3LBua1dXVxnhzRHrJ4Hg8sxKljTYgJaKBzyeTSWsMnNro6C1Ht9rweIOqWTXWqZlI5u9oNIqIiOFw2KQ1DAaD2NraiosXLzbpoJTjNJMqVao8f6XX68XXfd3Xxctf/vImdajf7594TQaSM0EgenIGSkT74AaTarYvvjZiBjSys4u+i2gDKTuytrPoWfsQrr/333Adjit2jDLs9+QshpztkAlQdCn1xokFTGHDaAv7Y31EuiM9BlfOiqCe1AUbAyHJPmMDMut3t8eAIjvdx8fHDflo8OAX8jqSxbPYJkF/5a0IzCMfUEZ5RKX8TIObnPZp/8P22kfSG6gZJDkSh20/OjpqPZuxgOjNNp76evxM6Pu3fTjKIZ205Bs7XdRppKurq9HtduPMmTMxmUyaw6duvvnm+OIXvxiPP/54fPGLX2zSBiFWx+Nx7OzsVDD1bMl9993XhAwjZg6zF3VE+VjoiHaaWA6Bk/fMhGePiydljiTl6IyZ/iye9F5QpYiO/6aueWGYdSJCwgLl2nzCUY7A5EiUr+V/R2YcWWOxuN05cgQw5WhM0vg8FvztwySsSKxkfT3PMaizETR45B6XTX9kxZHv4b6cJ+0IHVGd/f39JnIC+5PHzgqqZDQAD8wx+tCMltvB/LBBdBqM+ze3P4N9kwYlsMzzHK01qDWT5c89Jygvs3gASNIll5eXG8aY9k8ml95hwSld2SGpUqXK80Puv//+uOWWW5qTRCFlMmlWir7YZtq+Wodyj3WLyzJxil5FZ/lQJgMjE5fWidhG61TbWZOb6FqiTq4b95YySjJxnInlEqHqgxsi4oQvhd33HjQDAfwBR4Ns2zweEe13XSHUvRR943v3oZ17ZwwxFpmwoy08G5+OvXfZT6BfTBTmPnEdPJcMXN2f2PNMliP4Pc6EycS32+A+yUfWY/tdL4Sx8pgaVGW/NLcP4t5p//Y5nSWUSQf6ZzgcxuLiYtx4442tbQC8D+7w8DC2t7ebfV07Oztx7ty5azL9/4qDqfe+973x0z/90/HpT386BoNBvOY1r4n/5X/5X+Lee+9trhmPx/EX/sJfiA9/+MOxv78fb3jDG+LHfuzH4qabbvqSnvX617++YaZyyDxipqAyc52VFWCAa3FiPYEWFxcb5twhehid0iSKiJZi4RrK8wLJCsRAwIaChYSDW0rtgo0osSGOqJnR4fmOXnnDI4sf5gaWgGjZ6upq630e7hcWDwBqb28v9vb2ToA+t5NUTTvglJWVJuIUSpQjyjIDAhtW+hol5kigy4choQ8dircRmUwmsbW11bwkj+dRF+YDKZk+gccpExHRzEMfSW+w6khhPpCEskrA228t59kWA3DPlxzt7HZnpw/5PtIImcPU0WNMP2DEWYu8oPDs2bOxtrYWu7u7sb293SjYg4ODOHfuXLPP6vj4uPm/SpUqz408l7b+rW99a9x0002xtLTUvDsKyRkGjjiU9FjWYdY/1ud2frNujbikw/b29or7ifidoxI4yc6SyOST7SwO/P7+fit12nXIh034GhNVEbNI12AwaGUIYK+d3m8ymu0T3upgIhn/CcLQYMckp+0RERPX1dE096XFfgpZKNhB9uz4MCT7M/QBR3aPRqNYWFhoolgm5Whf3i5BvTKZaSDGZz5e3v6p/QKyLAzyiZD6xDzqRBs6nU5TPiQj6fD4tBz8VBLuNYGQTxtkvmfASB92Op0mQsX8dESt1+s1P14D/D516lRERKytrTWHWZw7dy62t7fjkUceifF4HE888UQcHh7GDTfcEOPxOH7913/9ywNM/at/9a/ibW97W7zqVa+Ko6Oj+Mt/+S/HH//jfzx+67d+K1ZWViIi4h3veEf87M/+bPzUT/1UrK+vxwMPPBBvfvOb45d/+Ze/pGfh7Bo9WxGYlbJ4oSN50ZrdN6jyZ/6utOj5DPT/ZNeZyS99npU0ZUecPIwhR9BKz80G6HLt8vO4bzKZNAp2cXGx2WMDW0JUhoXj8LgVoZkV90muw7xoXKkvEYerI2b7ffJJOAZ9ud0lVsZ1cKTGAAGlDIvKmHiOYqjMMGUQzd+Ak8ysOoKXGSUbVJyCyzF/SAZhHgszSzlK67mTSQAzijzDBtakAsbR0U5SKDGSjkgNh8MT7F3+cZ57lSpVnpk8l7Yextp7MZBMSmbbUfrh+nnMu8tDJ+WISOn6bD8i2qcF+lpf53rl/7N9QvfmyJqfRRlOsYuYgR3bHNsMl2EwhU0jTdy2i/Kw6Y7YuC60IQM9Z6EYgM3rY/qSa0hvo05OX4toHzqVy7TtyfbWfZ+jNO5T2y1sPgczODJJXUvRr/w8/+2oY8nW07/MCX8GUZqBsf+3LwSY8ry37bTfx14pExgQ5Ph2jkrlbRsW7uv3+03QYm9vL5aWllp7prvdbmxubjZ7zr1GKZfMoPyMZ1uuOJj6uZ/7udb/P/ETPxE33nhjfPKTn4zXvva1ceHChfjxH//x+NCHPhSve93rIiLiAx/4QLz4xS+OX/mVX4k/9sf+2Jf0PDNLdhitSPjJzridTKcKsiD535M6O6gR7ReeZoc8T/B5Mi+aRTl8zkL0j/O8kRztyJMZ9sttzE64+9SMgifu9vZ2s2lwf3+/eXHi8fFxwyjQfsoYDAYxGAyaayeTSbN3KrNuRH88nvnlutQ5nxbItTBBDpu7f2lLVuDZcNpYUD7ROdggWBpAJsrBUTgEZqgEfD1WHnPaur293Sg9XnTrdcD/GCvmEWlxZnYymGY+8jznfXt9eU7QhznKmRkuj0Huf+ruHP2VlZVYW1tr5jJKnOPTH3300RiPxzEajVqgOR+8cnh4GI899lgzd6pUqfLM5Lm09aPRKEajUWsjf0SbgLRdciQ/O7UW2/WI8mEUJkJtHyg728qcEcN7BE2GZZtukozrIqL1IvccEaKN6GCDEgMI28y1tbXWHpxsE9HhtMNE8Pb2djz22GONnRgMBnH69Onm6Gunk1v32o8xkYeDzMEOjpqtr6+3MobsPLv9+/v7jY/AYSRra2uNP5D3LNHHtDPPkwwus70ycDKwc5Sz07kUKVpcXIxTp041dfNeamxZRDtNswSm8utQIBnxKZhHS0tLLdAFAAJYrKysNFE4+oZrd3Z2mr3sbiM/TrXzez+pd6cze38VWUr9fr/1ShS3ycQm85TsL/zC4XDYHARDhGp/fz9e8YpXNGmv7GODVGULye/93u8959GrZ33P1IULFyIiYnNzMyIiPvnJT8bh4WG8/vWvb66577774o477oiPf/zjRQXLICIXL15s/i6x3yXJn2cWwso0X5fDrBkYldglM2SZ9ch1MUAp1dEOa65vRuZmHHId7ZSXnusy533mZ1n5cwrOdHrppWwAGCtBH6Axjx00I8KC5XqDvnkyj4HKQNgsIUo8tyuDY9L7SkycwXhOleCaUj1LdbVkBpC+zGVeLmrnv/PzmKc2egbQjhR5/mSnII9jbmtpjXh+z1uPPMvt7XQ6TSrg7u5uY/Cd629ng715pAZllrFKlSrPXJ5NW+9N7UhJp2Sicd5PxPwItu0E+pGIQikS7/8z2IpoE0WZIL1cWSW/IdfRUYdcB3QnYMHAzXK5KJDtpbMtMqCwfciEb/aFSjY+A0H+znXMfg/le6zoDwPd0tiXQHf2c/I+tJLNsE/CM5gvjE+2wXkuXW5+lCIw7IV2m+dFvOb5nyXy2EDSnzk6lrN6SlFIzzVHwjKJWpqzzAH2RJLKT3kQ8fv7+81BKMvLy3F0dNTaduJARj4w5NmQZxVMTSaT+PN//s/H137t18ZXfuVXRkTEww8/HMvLy7GxsdG69qabboqHH364WM573/veeM973lMs35OMQTFjUJqgfpN3RPvYbQadQQDxOo8aKTmwXljZuet2Z/tLqFtJidEejsB25CMimrBnfl5ENPuWcj39bPYP4Sz7nRTU2W1zOhkREfYM8Wbr4+PjOH/+fCwvLzd9yxGlHO/NC15RykRIKBtQNpnMjmP1oqXO5N/CsJD+YfYu7/OxsuV59ClK14vNzBzAodfrNXXgc/ZBoYg5JIGxgK2hbTA4BiQOq5td9fixNxDFzF4nThfy/POx62ZZM5ieTqfNCYYZ4HjNUBan9/Fss3eeP1mZRswAq5WbWTSeZYNgZhcHizJPnz4dx8fHDcMGu5kNx/HxcTOXer1e61QgH/lbpUqVpy/Ptq1H11uHOUPAOsp+gfeTZic/AwT2KWX94+iMIy8GBBGzw6G4x3tJ+cxlZ+fYdtB60joNv+Do6Ch2dnZiMpm0fIoMcJwNYf/GES+Thdw3nc4OI9jd3W18JgOBvKcYsf6lDfYzvPcLe2UbCCGL7TSQNjClTbbpEdGyJ6VDDzz22GrawfuTDByIoHlsmCfYR/uI2Otut9vYYvqXF/KS0uZ5QJv88mS3kT1QjJHnKG3D9/EcKEXW7Cdzai7RTdcp70PG/h8fHzf+B3YXXxn/AF/UfoCJYfcl/ZtT+ihnOr1EINsn7Ha7zb4t+3DHx8dx8803x8HBQTz++OOxu7sbX/jCF+LRRx89MVevpDyrYOptb3tb/Pt//+/jX//rf/2MynnXu94V73znO5v/L168GLfffnsLPDEw3m8yj+3OeaV2tPP1eaLPiyJZMnuS77fjWmLq/XfpWZkVoB0lZghxqgOf5yhWiV3zM308KBtYAS0sBCZ9xMxAOCSNMjDjY/aEz4lq+W3u02n71CC/XNd9kBkaK1OnftIHmSHKZeWoDN+xwZI5yFgTTXN+vw1nNuzu+wxm/F2um6NhXOd57LnK/c6hziREiT3MLKedBV87j7hwXczQ5bbntef25HlpgoH3UNG2XD5tXVxcbNIcnApYwVSVKs9cnm1bXwJDGUxFREs/5d85OpEjDegKHDLbi4iTe6DRe9lGG5xkPWgdaxufHfXLZWFwvQ8B8oEZJT1cilpwb7b3uX98HDhiu5nLLNkRRzZsz2mrMzqIVNlWZ8Bn0Jifn21l9oW4NteFz+ely5kcz/3Eve4bg1LsrY9mz5FWPs8+iT/zQSh+5jy/z9d5PDLIJwXRc5Zy3Z8GRb4/E8F5HHJ5pUiV54rXM/1Uaht19yFl0+m02c4wmVw6EOOJJ54oRgKvpDxrYOqBBx6I/+v/+r/iYx/7WNx2223N52fPno2Dg4M4f/58i7F65JFH4uzZs8WyOA0ky+Hh4YkN9WYlslNpBt3XTKezI6hhW5icMCTLy8vNC8ecM0o5pYXMs3HqIqLJL0Ux4XC77jkqkZUD97gtETPWgHrb4TV7kyMK5OTSLiIQLPjj4+MmlxbFSmSMie6Ni34eYJXycV5hN1x/FDFskJUVjMvi4mKTf0xEajgcNoySI0RZmVNOVlZ+qa4jUc7Pt/Lj2eQuHx9fOk1uf38/dnd34+DgoMn5BXDm8Hg2ZLA8mWVD/GI86p2ZVdrEM62QaI/f4QXLacPmlAuEfqD+nNRnxVZyKiJmR7nTxzagOTrse8ihz8Yw9wtOj9k4xpm5gHHsdDrNPrbxeBznz58/EWFzilGVKlWeXJ4LWw8hl3U6tj0f6Yy+xiawb6mk/0ukEN/hCOMA5mgB1/Gcfr8fa2trLZtvosn6fx5JZjDF83IEw2Xhu5j0s47lO8rJejhHtNCJ2JadnZ3Y2dmJixcvxs7OTvR6vWYPDM/Y3t5ukXeur6Ns/mw8Hrfsog8toG5k55godn/ZZmfCzj4Z9tbluG4GTN5vRnvwgei/TqfTRJA8T5iTCP1sf8ZzlZQ06uD+wYfxC+/9XjXs6mQyaUhu+tN+n4Gq73E7eLcj6XLsoWLPIM83oRoRzXtBDw8P4+LFiy3b7QwwPrNf6f7JxEieuwZVznbJ69D7t5aXl5v91oeHh9Hr9ZrtKJwceCUB1RUHU9PpNN7+9rfHP/tn/yw++tGPxl133dX6/pWvfGUsLS3FL/zCL8Rb3vKWiIj47d/+7fiDP/iDuP/++7+kZ2UEbCfUC8cLYh6jkUPYDBRMApvp7Hxyb/47s1G5ztSFa6xAXU5G0l7gGaXbeHjiW2nmenvTpJkYH/iQ9zhlRdjv9xtA4Wd48pvxIxzu7xAWLeNnIRpBNAgg4+M36ZOsJN3vmS00U0OdaX9m7fieF0ZSJx8NalZtZWWluQdlaoDrdvp5pfnk+ek20qcZmOX7YXCsEAEiVsD5WFjPN7OxPCezUl5TrmOOyPK55y/jZGfBxtJORol9xAhzj/+njaQMcEoQ65m5ggGrUqXK5eW5tPXWNbY1GUwh1oXca1KsFKnIhKhtcil6k+0vYGBlZaVxoDMA4hno83lRIf+23iyVxWcQir4W3V0CkPm6EskM2bq3t9ccNBURzb5obAj2LY9DTuNyfbGb9HHO5DBIKRFriPuxZD/9XI99CfAxp0xIuh+wJ/YN89xx+z1WzEeDB4hvnguh5+go1zsTximPXOc5W2p7HmdHu0ijhXDEb7Jf5CPPGQfSSPf39+PixYutvrS/nQ/O8Bxzf5gE9Vx0YCRnuOS+pp87nU7Tt+vr69HpXHo9z/b2diwsLMTW1tYJcvaZyBUHU29729viQx/6UPyf/+f/Gaurq01u9Pr6enNKy3d/93fHO9/5ztjc3Iy1tbV4+9vfHvfff//TOskPyeFSR0h8vHLEyZf7Wonw2wqYa5zLycCbzfLE8AQwW2Mlnx11QElEO/XJ7JRD7Shwrnd988ZJL073AwwAUT4iF0S2HAXjiG/S/XA+efcEe4H44TlcwxHqXpAlw+YxgP0BwKBQDCyywTEwMYPD9yhV+iDnihM9s8IwW2cwyYt5P//5z8d4PG6BWIegAWkezwxOaBPAzAaNt8Y7asgxt267lXpWFBgC+oj5YwVGv1OO5y99srW11ShXDCj1o+9t/PIJiJ7bLpf+8VihhK1M3R6vZa9NvkcWFhaaU55WVlbi+Pg41tbWGrZ1e3u7WQPHx5f2BlapUmW+PJe23q9BsbMXES19E9F2KHGKM1HKnhUTRrb56BI+Q9d6nxC6LyJadhT7Me8lu/Y7MlnpyJejRiXSMxOYJuiy3uU56Gx0ej5MwOCK5xkoAtg8FiZgvT/KdUTss0CG2k6Y8KOPPM623Yj9twwIPScYp0wMG2TYpkCMMqY7Ozsxnc72gNv+2ta73/P+aI8TNo9MD/qOk43pG/d3Jk6z7c9A0r6DD2LyXjr7Cn5fGG1lLtpfsD1mDQDIPK4GVKV0vHyqn+cvY4TviDjlF8lgFlI0k9vUn1fyHB0dxWOPPRZXQq44mPrf//f/PSIivuEbvqH1+Qc+8IH4ru/6roiI+KEf+qHodrvxlre8pfUivy9VDIiYvF4gEe1QYM63LTmcESfzWbkO0GAEHREtdgVHLoejkXnOfl5wKHPKQlFYWXghZvbfjIAjGJ7QWWkApCjXxgfhJWuEgb/4xS82Idz8cjgrXJSSD68gXc5O+HA4bIEF2CEfMe4wvJWmHXX6zmlc7r/s4FsplY7tdAQG8DAej+Pxxx+PnZ2dePDBB2N/fz/Onj3bhO0dyep0Ok2kCoXFCx8ZI7cH4E6bYPyy4UGJA/Y9X70OEJRcBmEZTLleNkQR0Sg2jB4GlTIxNDmsPw9MATy9Pm3kWAMGShaPLyDPa9sslZ+zvr4e29vb8cQTT8QjjzzSGJ2jo6MKpqpUeRJ5Lm29T/OEzGPN++hlJB/cYB8AEDMYDFrR+ayTImZADSfMji3lUg/AFGlUPiTK4ghIBlH2V2xznA5n/ZiJXOrkKA82IttF+ikTUtkvsE7HEbdtNwDEid3b22vZfcpg7ypAw2PrMcp+DGNi8EJbbCeznchgwwCrFC1zRCxidtjX4eFh66htSDeApIGUbZ63A3S73WYbgO24T5nFZplEtB/K5/bJ7BtmQAXIh6TlXu8Lc4QR3xAwhc3Evmd/0OMImMKHZPzcz/1+v2mr2+x25jHKB8blMXbghH5g7UG8uF4m5K9pMJUVR0n6/X68//3vj/e///3P6FksLC+SzEY4DcrX+f/s4OWFFTEDTAY5Ee0TcuzQZac+M1CeCP7f92VH2P/jAFsZM5nsfHtxeREYTHoymhHKLIeVDIsAJsKK2NdnkMhvmEWn/ZHj2u/3TxghR8ts9AApHEiQ+8zXAYTcLiswPqONHgezneyN2t/fb06GAyT1+/0YDAYxnU5jZ2cnFhYWYmdn54SCNxOV2THnTmcDRwpAZogy60m9Mxtm8RzP4IV6Ok2zxIKxHkpjjZLkh7IN9lyPPM/dphyhcj08VqXy6QdHM2HofA8EQX0XVZUqTy7Ppa2PaGcrYENI03UatOtnZ215ebmVxsv3nEKGbSClH6IvM+r+XSKmTHyV+irrooiTx6c7GoVDSB0h4byfxY68iTZ0ptO3I05mRUS0fZCIGfE2HA6bPSi8y8mkMlGPiFmkC2BjoETdTLS5LOyPAWUefxOn84hw7BWSSUX7jTkqRftNQDvC6Pt4nsc0R9fs72FzHI3zHOBv7/9m3tJHeY7R3y4PctV+LhGeiGjeGWVgXZqrgC5st+vtPvM44iMB3Nx2E/QZ+LEGHOHF52bNMw8zOc6PSQ4DPM+B8Xgce3t7zSnGV/IAqmf9PVPPpthxQzyBcHJhV3xfHlDKYJHk8DnHMxO18cJmEWSF5fqgjLNYgSCOSGVA40ns/UXcw2SGuXPqghVRdqRRQE61MrCyco+YMVoLCwutF7KR+mZghfGD4RiPx61QdsSllzL2+/245ZZbYjQatRSI+9j1hq3gWh/G4P6fTqfN8e1s1HSbsmFx9Mrtx9He3d2Nc+fOxcHBQZN3iwJdXV2NtbW1OD6+9NLiDDpQaignjzHKy6Fx2uNDTzBMZpkywPRvK/881xhTnpfBlAGS2UL3E32Vr8kKmvHnWQbezKkSWDITW1L6eR2aWc19z5yCIV1eXm7G7MKFC82LIKtUqXJtCXqLk2Rh+XEsiQREtEEDOpP9ko5qHR0dtYixw8PDGI1GMRwOY2VlJTqdWTq1CT3Ett2+QiZ2/T335YyHiGjpT2zp3t5ek05+cHAQFy9ejOPjSwciRMwO7eCwrAxUsJVk15RIWttZ6sd32HfsH/3G//Sxo2eOhtledbvdJlozGAxaNh7AaP+Jccop7TkSw7MjZpEup9x7TlCOHXFsEfUl1d59hk0z4DMYdIpbJn6pD06+wSB2zZGZ7LuZiMW2MU/cLkhl5o/n087OTkMcMJepL0Kd+MxHubufAJkRs9RAQBxzw3ORsaDcnP3jMiEL6H/AJXXwXIZczsCOKNnq6mosLy83h07s7u7G1tZW7OzsxOLiYgOqroRc12DKTqOdbCNVR0SygsvI1sAqM0sIg5gnuB26iFmEaR57l8GBn2UH22U4UuIF42cCZFBqPtXPQMplmkFAchTMzjgT3+FZt8PAzuAW5Qrj1+v1GuA0HA5bb8xmkdsguC4erxKLxbUsNNgllEmOBllQlDnX3syTjaoVLe2wkvchGNTXZbp/PY89dzw2nhelfvc1ue+oU55vVsqlfqQ8rsdgeq66XgZy/tv3l8bM5WQGEkBmMaPI/xicvJ5L/UfZGDpSUKpUqXJtSU7rMRhhHTsdqXR/jgj5cx+8FNE+zc+kG99xb9bTWdeU7K3r77b5npyC5udSVkT7kI0cJTNxZf8oA8LcT5msNSCiT+iXiDhBYvK96+a0dPssmRRzO0t+S/bbGBscY549HA5P9HPuv9JeJpfhSIz7wd+5HQbF9t2cCp/nrOsSMZvfOZPIEVLK8PvOsj9U8k9dnuvobJwMOnObfZgEpPDBwUGzJ4kxMbjPe7Ozn0P9HYV1Jpj9SOpg8pV+51lEsdbW1pooM8EQ2sQJlFdKrmsw5Ynpl9BFtCcnA5aVjvcTmcUuAQ7KOzw8jJ2dnVbEaTQatQCFF9U88SLODIaNRkQblPA/gMmnxB0dHcWjjz7aMA9OLfTGUxYO/YHzzz05l5YyImZKgUUcEc2R8V6Que+5jrayX2p9fb0BJ0tLS02aH2CKH0BsBhBejBlkkVvviBQvIDRDhyHlxXU+9MLME22Cgcrj2Ol0GtaUPnTePJE8FjSMDoohKw0r97wfzXPNgKBEGnAN9clKuWSoPXYYAedwW7nm+Z5BkpU8ZeTUypKTYEPGPIfIsKHxmsipg5kc8VqD7WXM19bWmuOcq1Spcm2J94qgPzlNdW9vrxWtR4dnG4azZfIIvUlUywI5yR4Ssl0QbJnTsLJO5Dn2PeZFWUxWkuqU9666LZ3O7BhoDojimQYbTj10poH1I/3E9/lUW0ejbLOcIkmbvf+aqATRJfrJUSwDw5yWZ8ITB9rZIti18+fPt/T86dOnm7R7Mj4yyPB+JfdZab4Q0aQM/CX7VdinXHdesFxKacyHmzAOGbAuLy/HaDRqkbQ7OzvNYWDO3siZTga3jD1+zGRy6dAL/FmTllxLNpaPa6e++Iz9fr/1Amb8OJfL3Ka/qDPRYQcJmF+cnIzPwtYK+1dkUg0Gg+j3+3HTTTfFcDiM06dPx2AwaObs1tZWnDt3Li5evBgR0doH90zlugZTdqhzNCriZD4z4u+zmAnhf9/ne7LjyvOeDEiVIgiXk8z220k2ywSohDnIStrK0225HDuW65DrbOVgRiv3gRkplCuHSrCo2XPkN59boWSQkeuUwayVbU7po78yQ+fwfWl8I2bAxiDVCsr9CYAqsV42wPl5Oe0uM10ZOHhO5DF6svnlvnsq113us9Kc8bybJ6X5lQFhRLRAZ16ProPnSKk8OzKMN3PyqfRXlSpVnjvJepA1W4rGlNh5BDt0fHzcAA+AF+U78vV0dME8PVeyJf4utzXfk8Vtt068nM7Nfk2OqORn2iaWyvMJtTj52EHvjXKKV7Zjbs/lJNsZg8BsC0v1tW+WfaDL+YjUC+Dh8nwfQAFwYX/Ffgh1Y3uAIy6Xs/EmrJ0un8VllPyYDOo9Du7Xkj32fGNumIwAXOMH5uwiBzzsp/m1JKWABusWkA8Q5cdz1fMtR0V7vV4MBoPmfZMmdZ+pXNdgam9vr1nMDIoniU/eYQC98Mza2Hm1grHzzeD4rH8UR0S0TtZz1IOBttIy++DrWHgZ7Fj5+bucsuUFC8szHo9PbKL14vI7mqbTWQQvAw8zW1aMTHDYEde30+k04V9erku+LtEpvsOZJQKY9+Z44bv9tAlmiN+ASy9Ih535DIWAMsgKkvGjD0sAGiecPH7K5x6fQMW84X1Zq6urJ5gbFEsGVW4rLCG584x7NhDMUSsYgwnnulMHR+KyoTaj6OdQB3/uuZDZxlzf7BjwmaOguT5WzNybr0WhZmbUz0I5b2xs1D1TVapcY8LeCDPunMiXT9izvnSqNnqQ90WSQZBPVXXUBduAvaKsnAlhB3PeYVOXc14R2yqeS1kcskEf5BSqDACy02rdneuNWLe6PvQlRCkvd11cXGxsLP0XMdtrk+1WxCwixX3oZzu2jrBkYIeO55nMDaIgKysrLRvPsxl/nk35EdEaT4MY/MOFhYUYjUatOZl9ICIlvBgWf8agyi8qBtCvrq62xpUyyTDif/YTZdKYOYff5BMYPR+cAkfqGy+yza+OyemD7jvbUft/nGKcf2yXs59BfXd3d5u0O/upBkiML7aa73mZsjOcGDuiWAZYa2trzX28N+1KyHUNpkoI1pKdyszYOKTrz7ywc8QrL247kplZ8GeuS65rdt7t0Jbum8dW5eeYAfHiyvfkPVNZmV6OoXDdKIeJbrDGfhRvliUitbCw0FKAOLd5bHkeQI8654iKlYDbCFuSw9k810e+lvrVytz5zV7ovB8qA7BSvyBmbrJhdfpEKXKUQc28eZYBYKmdVtI2spkxNMDme5eXo4j5WSYqMrPscS6NbamP5gFIpMTE5f7ieTmKWKVKlasv3hMS0Y7u40hZfyG2VdgB/veBRT61LUd88vaAbK+5x75FyTbz2+2Yd72jFVn/2g/JTi33lvRhiSy+XDRoXr34jn7BBkbMDgkh08TkNfcZsJTs4TxfLvs9BhQRbbKb+TBP7OTnskp2kfKzL5mvc//iV2QQ7fdXRcyyLeZtU6HNOVJFf5f6Jvedy7EtLN1jv6u0lvJ9/OY9qfQBYDn7kv4xmQs4z36u1zp9Op1eOngM0Acx7ffK0d8meCEpeG/plTy597oGU5l9QBhAmJyIWfg+yzxlkdPDyBm1I+7JENE+0Sw7h9kxzU5c3iCaF0DEbMETqjToIsKzubnZCh9nI8Lz/JMNhdk3O8UGTFkp28jRDw6t5ncx8BlHrXr/kBUFkjdrAtIyy4YCh52hTTjJ+fAJxtgAye2iTU51cARsd3c3Op1OrK6uxsLCQsNM+SQhmBwML+wUpzJlAMOBHAY9GPo8p/r9fnPqFOPCKTh+cWIppZFnYRDtUHgduc/pY8ooORb8ne9jHOcBw8uRInkztNNLsoOSy3buv9MNMLxeJyUAV6VKlasr2S6w7hcWFprIgffV4qjntW295JeLTiaz1044whIRTQYFpFmn0yk6vt5kH3HSjqBjrRNdvwywrM+cJcP1OeqGWNejz0r/25/huSXHnOvoG96x6D7DqXValU+Zdf2zXbI+zm2hnxlPp41hA6gj4+A97LSZMtyX9lVy+9mDxvg5Zc2pdi7f8zKDAO4lIoYfQJ/lewD7zDnqwvOYh35nJREmv2/SbWSMOLXy6OioOeFyHnnNcygj+3fY0IjZicPsS+cEPc9L/Av8J0eEHY30O83IbCLKh4+H73NwcBBra2utNT0ajZp7ffo2c5fyaprffxRPXqSkwOZJicFhIuVjzj3B7HDZgcyhdiQzIPm5KPMcgZjHGBDRQYl5I+TKykoLRBoQWIFmoDGPvbNCB0wZlLivqbPTH7OR4X+Urd8QTrn0kYEn/ey+9MkxZo/4jIVvg5E3P/qN9e5nM6HuK77DqPByV55B+bk/qBt9eXR01ISgAWAOw+e5jRKind78ubKy0qo3Si0rSQuOhY2sgbL7Pc9p978dlNKz8jrI88VRrNIzbRD4PhsqrvMcyOvV84lrfUhLTomoUqXKtSOZsMv2E+fYaUR5v6p/ozuPj4+bA5QiogXMuBbnznrIWQbUyQ5nRDsF3ToxRwb4zNcazGTSks9Ngl5O5gEk2/rLRXEQ2z5sjA8NMDBAN5sYzhEpO9AeW+pnYrmUEm5i22CGLJF59TcBXuorl4+tNqGagavrbT/KP/MiVJ1Op3nnk/0x2u925nEzoCTVLe+lss9GmyEcMrmeSU6DKPcL4NIZPTyL38wRHz0OKex0v1xfA7Z+v99KsfWBYKxttpfYH4LAJw0YsOf5lOt+JeS6B1PZ+fJidAjPEyJPmnmMuMXOdd5E52vyZMwKzE4bYue1xGzNEyaHlRJ5sHlfi6N4XpC5X/gNwCu1x+12znGerLxM1+9eIPyP4qB8szwsJpQx/ZBPC8zvFHF/ud9KERO3HQBmRWtHvd/vt0564jrSFSOiWegoCysKfhztnE6nTWia47ipj3Pl3a+wWbC00+k0hsNhbG5uNsqHthFxcf+5fE6nstMxmcxO4OFa911OgcgpHIxNSewAPBXCwfMOJs6Swb7LcOR4Xj2Ye2YKfW+VKlWuLcm2rrRHNuJk2jzkYrbHEe0TAv2Cz3lkGveiuzNZY72Z9afJUOs69I/r7vRF9LUJQZdXInpNGmbwYAfZ0TLKmAe+8liYyHT031kYOapmEhop+T1IJn7zXKA9Hm/AD2PkdE4DLwMu2kp/mbTzeGWyPreV0x65x2ByOp3G3t5es/eJ94dR35zRQ3aL/SHqRB1ol8l4z4vSeFIfiFcDPUcpPe75REnq7HHg2a6vUz1zv9qvwcYvLy+feD2O92hlAIjvxrvQjAM8vy2UyX6xKyXXNZjKDlUeqKxo7Pjn9CoDjJJDzvUwV5cDI1aqWQl7Aec6WmE4OmLJhgJgB3DCoSaVLIee3Xf58xw1oHwDi8ymcb+VAgwVZ/2zGPjbBzmYRex0ZocywCw4DOw+MGtCXTJjibM8mUyakHouB+UBy8bc8Pj1+/3mnRX00TzDjfLe39+Pg4OD1qbRHMlDYQDWshHJAI/N0hyFSnjbL+DzPLcBYH6a0TFAtXJyPzqFww5GVrZ5bOZJiUllDcISwXb6nsx+zZOc+ltiIe1IZKY3A7QqVapcG4LeZb2S0uT0vqx/bB9tNyLadsTOq32DeU5qxIxI5MAhE7mWfH+28xm0GUA4NR7fxJGkDEqst7FDJfuKriPjwXbD/tC8CI8d4U6n0yLgPF7uy/xjf6mUYVTyobKvYp+N57D3xs68iUD3c+47E2wRcQLIeh5SL6epOTWv0+k0+4acQgr439/fb/wF5p9/+2W1ttPuz9K8dP/kNlpMYOdxMNihXQAP1oznAP2SwZSJ6jy3qAM+Zj5O36/HyZG7iHY2icdmOp022UaZLIiIJiKN/3Wl5LoGU5YMBPJEKl2bFVH+2wuX37BIpQXu33ZkcdBKjprLdhqbF77bFTFLVTOqt7HAMYXVMGiyEsr9lhWOAaP3L3mReUHlReG0vvx5Vtj8sIeHdhv4ul60K7fP92WFDNixQoo4CX65NqdtGESZMfKYzGNAUfKelz59iP1fZpwyMMoMKM8lBzrPnTyWVsCZreE7g8k8r/g/r43sDJTEoKU09y0ZAGWQzGd57Ogvs4UuP+e4l+qDcatSpcq1I9aNEW2nEsIHyQ54xExP2QGzXsgpZ9n+mkjyc1x+9gvQeSYZ7URmP4F2RszsiXVkBnF2LufpNOqZU++5Pu8n5VmUk51pAIF1ruuA+O8cxaJO2E4c5ty3+AyuPzbXJxsaTPG8fCKun1ny0zKgyIAFkJRBW0S0DpTgOkfH/Dza4khcTnMzIMmghbo59dDzJ/9N+fYLnDJnn8mZP/Z5PPdyhMjX5NS9EulcWo8Gq0SmMqlrKZGdXJe3VGT/wEDYJ0ZfCbmuwZQ7yqeeOWxoxWaW39GHkvL1wrGCLTmqZlXyZPOCNHPlsqgTDrEngoEL9fPL4zxBrNR4Vs6JZtHktljp+H5HLLjf16O0+I6okqNQACQ2FJZAFIuHTYZW3J70bqPzY1EyETPlhmRgOJ1OWxtmS4Aron30LP1MWBwlwH6l3d3dpi9KRm0ymTT9xP2k1K2ursZgMGj15+7ubkwmk+bZtM+sYsQlVvTcuXOxtLQUu7u7TZ6wnQazmswV6pIdBFg2R3g8b93P3Mt8srHKypM+yMrUY+TrciptNtRWvgbWOZrnMqmnDb/XADqgvrS3SpVrS9BL2eaxzvPrDPJrLhyFzvrZL4P3NWbWiTBkpxIxeZedVDIssC92xHNaFTorgzeDhXz4TgZILs+6G/Dkax2Ro635fT+kUhpIZXJvnvOb/S7XhXvxEwxo+dzv/XNbqavtlFPt6G8f+U2ZFvs51DUTiz5a22MBeGDPM4cbdDqd5kAwxthAuNu9dJiHozYmBKg7bbd/Rx9yDf1SIiupq9uFv+RtC/hD+NGewy7DoNv2E7uKT+UXTdvXdn8zt3JUGL/KpLH9hhLZgdj/8KFfXtNch42fR/4+HbmuwRQAJaNyOj9HdDIDUPpxOZeT0sTNdeDv7AxzP85fVkIGPlZqmeEoTbDc5twWgwbECiwiTiiOXCc7pq6bj5y0cs2A1MrUiykbKPdz6R5HkexEs5hddzM+bmMeR/eD+9gMixUK5eb75xmciJmhB3Q6v9psWzaMNpDsQXsyY5r/LoEI+sUprm6XGaY8b3yNweq8dZbL8BgjrJUcfcpScmbMVpsds7NkHZFBn6OgVapUuTYkZ1dknR/RJmjYu5J1ov2Cy+l+7J8BWwZT/MZZzmnQTv3KUabssFrfZluObiY1m+szSeh7S2Cq5DjmKFdJJ7qvDYjs5JbAhvsuE77+bV+h5LRnsOBrSIPDhlnXZ98hE8tu95PZQcbT4n7xuxCz3S8J4+m5VdrKkMvIfqqjcdkG2g+mHR4Xr6csGfx4nLM9Nlj2PCGlMUe9DLJcdgmIuz/xEW3TbavtC7Fn3D5/tv05mvpM5boGUxFxQtGZ4fHEyovRk32eosn35omcARD/Oyrizz0xHe0oMfFuAxOSCAxl+IdrHf6m/BI7xXPMLHAP6XheBNQTpUGdAC6DwSBGo1H0er3mmHCey8KhfCI+ME9M/qy4cioEynMwGLQWNMqMfUosZAMYg1rq4PmDuFzX38ySw+Sec9xjw0dfcd/CwuxgDtpeMirUK+f+0ibncPNcpxRYgVh5uj+8PmAHiQIaNDtSk+ec15mBGvdktsl9mctjjMhlLil7A38D6XyKIvsF3FYDv4hZhJB6UO8amapS5doS9Kd1FKeFeXM8v9mnap2KbslOKnoBu8R3lG89bDuSU7WyveZQDH7nZ2JD+LEes7PJ4UKZ9KQcO5bWg2637Zr1pFOs8IuIEnEt9pQIDEI/OBuCuvAiZKI1jA1tscPrMc5AxqCW39STvcK2e+5v7JhTBB0ZoYySH0dbaHtEtFLpO51OkzWzv78f4/G4ZZtt27vd7olsIEhnxuz4+LjJRjH4s43KADMiTvhhJpJz6ioZW1xncJn9RBPAJngR1t/x8XFrP7ojdfSL5yU/zIl5BLBJeuqB30R/4e95/nCMOu8tdYaTf9AN2Y96JnJdg6kS2xwxy6fmf6evRZT3R2V0ntlpBtbgyN+V7vPnua5ZrCSzA+kFXPrJbXF9cz3yPb6vxIa4L0s/rh8Lv8Re+DorCf/goD+ZGChEzAyZQYCVb0moi4Eybc39kfu41P+576yA8v/kBNMPNqb0eWYM898GGu4HM1G+lnttnADfuW9NIvjzbMT5nRmvvBad0nC5sXAf5nVWWjOuY04DyuCK55TKcD9mtqtKlSrXtmRdiHNpJ9E6eJ4/4GtwOhFHNKxbS86oy0RK+qvUBt+bdX0+OAMpEcf5mfN8Bl/nex3lcR+4je6rXJ4Bocv1s02I5TqW/KyIGQhyNJLfub98TbaV2RfL35ck9+s8fy6Pn+2j62N/yGT2vHEq1cf21uNfitzk+zx3ue+p+Jbu43ntzJGqXE8Eu5/31btv2B7iU4gj4oRPwVx0pDa3JffPk63LL0WuazBlxBkx20BOZxJFsWPnhe/0qjzZs3hiZmVa2vyaQ84+ISaDJhxVoiWedJ3OLJLk41hLE76k3LzwzdIYvFEHR6Zgp/jObIA/B0TxImHvqYEx4mcwGES/34/BYNC8Swl2yie5ZUBlZiUimn1JtC2/0JZrzcwgjrQBLjCcOOOUZVBY6uccgXKOdI6c0Vb2SmW2dGFhdoQrQMupiuTdl8bUTJSVItFPn6roFxHChgF2MEB2NnIkiL7LP8xxM5peKzBhnjdWpAZ6lMG6yiyVx9SnJTEGzF3WHOOYc+vz/KAtV/K41CpVqjxz8ZplTWN3zLhDohAZQdALZAlg46yHfDgS+hf9ZN1BeQAznFL0PGl/2BLbGuyjfQ079BxEBCGUSUn3AXVCrzqq5bQqO6v8tl9CBB/9jh72Kbqk02UfxqSloyy8sBWb4Gdnks+gJKe6IX6enXM+s412lNLf5b7MWT4R0ex9sv+VoyfYnDy2JZBK5IR9aIhfD4P9dz9CQrJHm/Lob/sCBgeMPX9j95nvjrjRr9ybj2LPcy77xc5+ciSVOUWUzD4Kz8LmQyzzSh8OoHDmDplIfu7h4WEsLy+fAGbM0b29vVZdjQWob45sPVO5rsGUO9IOV97UWZIc6blcJIPvS+XNi0Tlz3EgDZTMsFDvvIgzAMqKIbMHGWRZCfDb3+X/54E1G7OM6HNEyn2cjZV/cuTGbSj9bWBn5ZqjEvQxxiwrUeqOEaKPnaZgAFECU/6hLPrCINMG1gdauL18ZnbTwMz1m8dWAQI8TwFJDunb0Nj5sND+DIioW0mxcl+pLOpnFvLJxPd5TLxebdCcFsq452cxliVHxvW/kmxVlSpVnrnktWq9me1zJpn4LGIGEHImwDy9ipgIQr+XbNW8z/ycbIvcNtsF2x9nK5gAMiFaagOfu3xH57CnXJN9FNukTC5mJz5i5ieUHPDcdn/u35cT91WOdBiY5flAv9k/sW9Ae0qp67leJsnzuOW6lvxD+2S2rfa9chklO5X/n0dy5nk4737a4T6xrbQ/wG8D4txPTzbe+EiQIoCpwWAQS0tLMRgMGqAFuWBgXurX7KPkwIL706DySsh1DaZg8fNE9n6NiJPHdF4uHO/BQMyalKI+KDcGrzRAnpRM0lyXkrFwDrHr4gmUnd1Op9NEiWxE2E/ldht8ui/zgrSTbyVDtGMwGLTy0R2JgX0YDofNouEFiXnBAbIMKGEi6ENHHCIujbejCeRqO3LkxUe9McSMmZk96lJSfORqY8wp04wZ7FW3222YFjMs9DsRSRMAjpj2+/0TucnOv6Z8xrfT6bQiOgDPbrfb2kPk+Rgx25NAuVn5luYfLFI2oHkuem55zXBfxEyx2vCX5jmf07/5lK1swOnfEmDPJw4hdc9UlSrXlpgs8kmxg8Eg9vf3Y3t7OyLahw0sLS219oCiI4laoafIjvC7fNCj6FSTrf7fOh/dRaYEWRa2oYuLiw0j7s9zdCWfwOo9p0ThbZ9t45wyT5+h++g3R5LsZNqO+R1UmXyOmAGxyWTSejegyyJawP0GpS7bUSKL7YXH1+OUiXDGAn/EtsH9FdHe3x4RJ/aFEZHDfrivqEOv12sBmuzTuS4A0hIxja/i0xQzIWAgTZu5h7m9t7fXmhvD4bDpJ/un/M1asV/DOHn/mcc/29jpdNrcs7q6GsPhsCFriVIeHx+3InSdTqfxCTc2NmJlZaXZL+0oE34JdcnbQ+yTuN/QFcwt1lr2ca+UXNdgKqNuo00jfX7niEZpAZuxyk4k982LdPh/K5P8veuen4/YIfWA+2+zUn4ODj+HSGTQl9kzymJylvYe+XquxVHFuDlywjU+3S+/2dp1MsPDmNnBZbwMdqxMHfbGaLLAM+tG+Y6oWXE5taE0XgBIjKONXd4vhjJwmkQ2InnfmIEN941GoxNGhzahUDx3/Ruw4M2XtMeAMkdK8xzJiseRRRsHz13P5zzPHSWzsp/HcOV1boYpGxyvD7NtLg8whuPAtdnAVqlS5eoL+sK6lzXNQRS2P7aPduCdelXKqEA/ONpdsuGUbZ1pZw6AkQFABhLoboOWTOpaV6OfTEb5Gju26FR+O2PC4Ia2RLRT9iaT2es5sg63v8XLgam764XNsi224/tUIoMI17rfkXn2qQQ8HZmzmLTlObR7nm/orQLzfEyuwU/i+HaTojm9MrfRwMv9xxgA/p3SxljnfUtuC/2En2KbXuqjXBf6ivnkdpBWy/+21fiPkOscMOHDp/hx3b0dBQBKfxl88oyST1ZqwzOV6xpMeWGiKDwpM6vhe/g+T1grODtq+Ro7ZB6czLR7Tw33RrRzr7Ozl0FeBhosFDvhOPOUB8qnHZ7IWaHwLIeZPTkjZm8CJ6LkU15Q4tQRcDUajU6cbJP7ye1jrHBuMXrUbTAYtBxf9ln5iFEUSFZeZpccLXKfYogwgPMWYY5IoTyJwsH4HB8ftzZTIk4J5G/6G+NGFMmh7eXl5RgOhy3FyRjAKnk+ZiaHceV5Bq70u5UiewwMXm1cSsA+A5msZHPUFqPue/LJR9nhcXnUyfPbhtPGzM4Xc2Yyae+R8n6DKlWqXDtiZ9721MSl9YIBiO0s+mJ/f7/R1XbYIP1w2Gwrbb9NVJnkwx/p9/stR4/vItqgiucaTFHvvO8H3RcRrei8dbHJ5Pz6DECST4HL9UecAm67Qv/7fp9YyPU5u8d61WRgPnHxcpGq7DcYfGb74ZNwc19n4GW/MINinpPfkekoEZ/5JcOeszzbPirzw+XTLqIqztYoRYiY7wcHB82zDbB8Gi92LhOoy8vLja/G2jHwzkR9p9NpTrr0GOU9cextp22TyaQB3RAfKysrTbaSCXafheD5hD9jAnfetcfHx3HhwoUT/iXt6vV6V3R/9HUPpiKi1cF8bqbcE8FisGRlUULEXJ8ZmqxgItonlHD8petixVJizzOoMoMAM0V4lgXHoQYsLg4doN4Ghz66NaN4t8mRBhZq3vdD/7gd3kDo0LEd3czYUU8WDBtBDRiXl5db/ZjBFGW4Lx0y9m/qhtCfHmezGhkcZFDMfYCp/f391ol9ZhEpO28Ipe5OOXS6IGBqMpk0R/8CpBgj+r/T6cTOzk6TqnE5lrW0jrzpmrnqCJgBOfOS/jUgzUrZIIx+zobNkVGPs0GX1x/tcb8xv+lfHAvm0cLC7C31ef5dyTzqKlWqPHMx0Wdblp1ZE3wGEbYJrHMcKX7nlDynnGeCxQDMP9Sl07m0uT4iWilw9hGoh3WYwQH6NqdOY6ciZinPPqzCDr+de5fnFGfaRz9QH4BEthluryP7GfSVbLuBDGVn5942jbE04Mx+nDM6rMP9PPt5Hgf63OI55mPEM4hxqqjnVqlMk+fYI5PABlPUz4ekZHvrOe9yM/AymKK+BmTT6bTZguG1k8vNWSeTyaQFphYWFmI0GrWyk6gzPpjnC2TxcDhs7aGn7UdHR81R8R4nE7q0yWvZhPjR0VHs7Ow0h1V4Gwp72PNLnJ+JXNdgyosjR5v8XRYvLpeF4JhGRMOmW6llYRLkBeWJnBWtgQAD6/Cm2+KJnCe6Ix9WAlagJZBpMJX7LitG9wvl2UChHHDA+THDkRdBzsXOjJaVUk6Do+/cDxhAK72S5GflPs2CUs6RJS9aKwn6DRDp6GROT8zvXvJ4d7uzdDPPV/an2cBbSRrU+hklA0zfeTw9NhnoZHKi1Lc5Kszn/L5cXyP0revL/fS522Oji6K0EvdPbocBN33qsqtUqXL1xbqadWqbh05G12byLoMu/qbsTOQBgPyd73WZTpWLmEWGcrnUFQBhAObvKYPf7Nvy9/YnkNKBGxlUYc/8LqC8L6bUPzD6GSgZHNmePxUxkHRfYvtsL3iGUw4jZtsyDDJMFlvX2wa4zGzjPO4G1QZ9tJ3n0m7PBf/2CYC5rfRd9g3tQyH8nee/o4iun8fDkVTu63a7cXBwEPv7+y076ihR9qltOz1PDBhtVzMINCHL7/39/RZh4UhpjlKaePac7nRmr0TxNRD7nJTJtexHv1Jy3YOpyw12nlzI5Y5TR/JnDo37XiuQrGBQWtQhT9ZOp9NEcYbDYXNAAWwYSi7nXlMGACzXyfViEhnQ2Gi4/3y0NSlkVuAGaHmDLIaM306r43ozUiXHn4VhRsz3OwIVMVvogBanpPF9Pk7cygmnPI8h7crKmDJom5Ukh0PkiBhAmefRj56Xjlpx3dLSUtMWA2BSJm0sCJ3Tp7TL8wNwwcZQXppn4J3Xhvstg1zPizy/fV9Oj8nsnpm9HIGlTF7+l406z2X+WbweqSvz3al83GdFy5qtUqXKtSHWnSWAAoHi14jglNvGZb3jsriWF/UuLy+3otfZxkfMHOIMhuyoew8M9owsCx82gGAHsUEmPbNes+NM6reBAMQm91EWWQt+yWvEydfH5L53FMZ2qVQn23u3DSmlU2eSzD5CRDR2NTvy+AeUO5lMYnd3t/UM9ucgEJnU0T6QAQ+ZDPZpEGwuPh2pez4K3DbHafsmmG3vvP88g2gDCqfD2/fJkdkMdpgjXDMej2N3d7c1vu5nk5i26/hJue/sIwJYsKlOZXS9d3Z2TqwB1hZEAM8GANlvMDFB/SaTSeN78ToegCPz4UpmoVzXYCqi/S4cJDu/2aHLi9sTOTNMdg6ZUE+VebECxAnkmY62lMBYboMntJ1WAw76w+WWokNcx7NyKDXf7z7Jf9s4cNAEJwmyIG28MkPHd+5fg2AMQq6HpdQHNrJWKvnEQjMsEW22KLc3K3ADDTOgLs+AzsrRc446l1gfK1ynP2AYyPu3AS31j8e8ZDDz/LeT4FRE9xttLwEl/0RES6nnPs2Mor9zX+Z5b/F9BuLuQ4NWA2mPB3WtUqXKtSOs4ey4W9APrG3b/xyhsnOYSddsh227kaynM4EbcfJABJeV7886lOttN3O51sO2lU51JsLj7IWS81nyabKdtJ0r3V+yae5zRyNy/2QfxUAsl+vPab+Pd897s+a1MY+P22qfIkemPA+z/XAb8/jYdpVIeT9zns+VM28A3SUwRVnub4Ng+pT9TwAXE/o8J/tKzs7JgNQpsMw9SIlsvw02S9FfxtZrgP6A0Dfw4r6cngiBbB/gcj7l05Hr3mvwQQAoi8w0IQ4LWlgwnhhG5EyIEigrOZXzFLAF4JFZsoho1R/JC8aTmt+dTqc5m59ojZ+RFZ5ZmoiIvb29ht2CwXJuNfVwVInox2g0ivX19QZMZWc24uTRpexjgYGDqSMqRlSHqIEVeAa3KDIfOOFn57EgzcHKjrnEvexLot+8cDNj442qmZEaj8dNWw1UGTvvTcqHL/AcFLnziolg8b9zuH2v52UGTRngGLx5TvqERI+FlafBrCNzWal7TnNfZtoQzwOL+z4TJzYUboePI0Zy6kaOUFepUuXqS06Ls9238816HgwGjS0r7QfKeiBi5gdYt+PImXyMaKcKE+GwX5EBEc4l32UQZTtl/YP+s43J2RLoXe8pdhZNtk3+PpOJth0+PZD2OY3K4+H92f7t7JUMErJtxlfJdhny0PXnGR57732jDTkFD/FYeF7wHf3e6/Wak3S5hiyJXKbHKhOtZOt4P5dfLEt/2zfIgMM+AHaf9DhOs+x220e1O1WQuZEjUzs7O3F0dBTLy8uxsrLSjNM8sgIgRdYLmVNeA/ZdSVX1WJjg9Do1ce6oJ+vH857X8WSynTVBdPLw8DC2t7ebNvnI9RKgfrpy3YOpjOBLSDN/Bsq1w8VCycogL9ySo5WBk537HC2xY5lZBj8/Aw/XLzuzdvatjEvtKTEpCJMLEObInPsLpUH0aTAYNOmKGbzRdupQYuVYOGYNDFr43+LPM/tUisCUxreklM2AZAaH751ON2/OIf7uyZz0zHRFtHO4aRMKgTQUzwnPr9LzSvXNLBqSmUfP5dI1Nmz5bwPf/JPZvLwubegyCJwnrpfTEUp9Qds9zlWqVLm2xOSm178dLOsS26us17kvl8/vbCNLdto6DpCRbRG/7exlAou/M8k1T0/ndvu7HOFxNoHBz5NFpdwPfp7Z/mzDc51KdrHUP6W+8uduUybCS3o9+wwZ2NBfuR+ycL2jKra1pTk4zyco9U9pjLOdjJgRhJ3O7PCIfH22/6V14rH3nivvT8OfcCaJ+9L/X44sN/leimB6LWfJ/UJ/zPOlSj6iiW/8NdfJ/Xql5LoHU2YxLFnJ8DcdXspJ9WIwEGASO9fVz7CjbcXrF5DltDyfXuIFQT0MLGADeDeBnXy/GNeOICx8zr91tMB7qagbEbqI9vHRMIFEvlZXV2N5ebl5QRvMS8Rs8ucTeWALaDP15eQWGyQDs7wYfUBBNkJ5EfIbA5CVnQ0Pz+cFkDA2ORWz2+02B4tQ1jw2BxYHJofv8zyj7My4eswjook4rqysNICX8DXMzsbGRgO0DJJydMnP8/zj2syk+r0O0+m0ie65nw8ODlpKlnXBPTBJZoHpJ44kzqmfBtuwj3n9Wrg+5+Q7CkX5pT0GV3JTapUqVZ65mMzxfl3bQ75jHVvPZXKH3yXH1/t+c0Q8Ayn/4JRiL9A31pmIMxfsfOa0pZID6fZkIhDdhm0gw4R9tSaOTOhS/5JQV+v67F/5AKZMvGYQmkFjfo7r6PHMtpMyAQZci10h28VRj3mHSXjskOPj49je3o6dnZ1YWlqK1dXViJidzpj3b+WIj8fO/XN8fNxEVezbGNxg7+wLMo8udwod+5Rsv7M/ib9CvR0RzH5bJjaph6NwjAdrkv6mDawj+0D2bd0W/L5s+7nWwNo+NJJ9ejLAHEnFN9jZ2Ymtra1iPz4dua7BVAZAESdP67kcO2Ln2GXOe87l6pGRclao3odhpVdy/jOCLtU9Ky5/l5mYHDnL9eA7FgOTfzKZNCl2BoiEv9kn5WPZmfx2cktOb6kdVkQ51O17DHSzs57HxI7+PNYog2lfN485mjcupXbm9pfq6baXDEx2AGw8UA4G+4DQeQxaBnK5L3I9PYe53+A3M1Gl33mt5n41q8zY5b4yk5RJiiwZuJckz4l59atSpcrVFdsGpy1lPWW7ho3gc+uErOP9t21Qvj4iTpRjvVHSO1kHW2xTTNTOs0G+b56P4CyCTNKW9GHuh2wHKCNHRvy9f2zrSnYvf5+fmfvK9rxknyjTfkNpvA30uKdUr4j2+6mwd4Ad26I8tgbzduyznzoPMLuuztjpdDoN4PEeMe7z3M8plbTH7TKRXBrHDEzmif0xz9nc3uwDuX9KY57nTQbo89ZU9meyf2hyYB6B8HTkugZT7EWxmBX3xnYvan8X0Z6IMFF5sDJg8WdZiET45X9+Rp4kTNbskOaDC/wSNxYjbIjBW2mB+NlebK4DjAdRq+Pj4zh9+nTTVreDCIlZEtoBU0TbfRKiF6nbRp5vbkeO0GXF7jGgfLebv0tzwMrHdeFnMBjEdDptvW/EYkVrkENdDSbMnuaoiufGPLYHsHTx4sWYTCYngNTh4WHs7e01IBjw4z5zP+XwtiNUEe13d2Vxn5tFMjvreeY5y08OsZtgcN9yDXPJEUYkl20QTt9FRGvfBONg4wtLRtSxSpUq14aUIvvW93nvMRkb/oxrHZ3PYl3Gc11GjsBku4reKjn92cmLOLlvKAM7/1AuWSfZpmX7535xlonti20T+1oGg0FEzPaF7e/vt/a2ZL+BZ/O8EnHs/sp+mXW89w1xyltENJk3BomOwqHH/bkJ3wwkDYTsiOe9vY5ocApcPkrevgxZRMzVTAjzfsvBYBArKyut1+cYuDDX2A/lo8uXl5djb2+viULha+G7Ubfsa2XCemVlpTnt2ZEjExAZ6Hjeeo9zBquee/Y3eXdUPteALBfmIP7zwsKllxtHROtUP57lU4ldP/sjx8fHzSsAvO+PswGulFzXYMrovARyrEC8YDIi96TJ4Cziqe3TsCPI5OEnLzZPiIz8MwiKmO1XsqKiTPrA186rZwnQ+Tu33YycQaaNTI5sua52UvPBBaXIoQ9/yI61yyz1T170ObLkn9wPuS88VtSJ1LMM0PycDA6zYszX0cdZGWdj6nZPp7OjcokIcj3GznOBfmZO2HBlMFUy6nlcs4F2uTbkHhcbuNyHmc21ssxrxMYyz3XqWgKmGDyPl6+hnj7FqKQDqlSpcvXEusTEnPWD17QPT5hMJiec2pIDlr+zTXTWhe/Jej+Ta7kN/vFnJfH91pOOVkC4cY11b0SbBM62zoSVU6gzeMMB9UEeJdubx6Dk4LpOEdEix3w9er50SBTPyfbI9txjx/8+ots20qA6R4rsTzlClYED9x4fH7fAXZ53gAQADPe4X7KNPDo6ar2ug5fRk0FEv+S54n6b50+yFWU4HBaJSP/mXo9Z9hEQr1f6n/H0uJoYN3D1nMqpnl7/JXLV/cBx7fv7+81hHSYSamTqPwooPztKWcFaKdjxspTC61k8yDjb/t+Ax4vedWHSk3drNiE7wVnx5RxRt4n6knOaT/Pjh7xh143+sxKyM2tnGCYExI+Cdbn5t08jykqYts6b1LkcFgbK36DAJw6ZcXDfGgRSD7fX9fLpN16snhu0LRtHFFVpwee2u34R0bTLzKffbeV5sLy8HGtra82zut1L+7l2d3db97g/GVODwRIzauDoOZbTUFHsZoy4hrq6j+0I5ef4eTly1elcip4yJhg26uQ2Iaw5A1mvRQNar5MqVapcO+IMBe/HcLTDet0ZGiboIJy8JyXfg84ywVQCIY7SWG+g07AbJnft4Ge/hc8NFByBKkV3ImZgwu8IzA7w5aJSfs+UHVvvR6OO3lPtemQyzsSpwUhJv9re4UtgV913tlE+FCLv+6WPnBHBc9jT6x/Xxy/X9Rxj3mGDeJ77Gjto4g/7zXYJ6uT5kVPVIQDxeWw7+Y55XHrnEnUzkei5RRSKqJS3ajjLqCQlMEkULvvDJhiYmz75j3HlOo8xc4B5Tf+73w3EmEM5coyuyL4y+wl9ouIzlesaTIHMzaJkBt0Lks2YCwuzEz4iTm7sjDi5N2aek4Z4AaEwMphicdjhZMLQhhwxoy48z/tWmCh2dL151qDOiiNvXrRD7bp6wXDvaDSKxcXF5qV//HQ6neZAi7wRFAVsVoDn0R47xfkalPbS0lLrePg83nxHWDy30c60HfzMZjoFsmTwLDmtkvFmnD0vc0iZ5+aUt+woUF8zPNRrcXGxObp1PB5HxCydzW2ax4SWGEbXJbOGfO+1kpWw22qWzoDVz/F68Lymf2xsTJ7kSFhmNzORkfsSQ0UdSsa1SpUqV1+83rPTZIcd/WIwZB0HSZZ1tgmrUkQh+wY5OoQ+sf7I0bOI+el81s+I7Tr6MbP2/LYPYbttMjmz+eja/AoW9DL+kvsgg7jsfJfIaL6zL8G1OLzo/MPDw8aPMNBlTJxa5ghLrrcJMvsHgClvv3Cd7Hj7kCk+89ww0LI/Q3m2PfSTszoARL6OfqI8+5SOyAAmmcuOEGXgbX+YeY7v7CgZaX5eM9n2e6wMaE1m2N7yfIMpz3myfuwHM6c9Jq5DXh9EHA8PD5s0wnyEf9YRzIXsJz4TedbB1N/8m38z3vWud8X3fu/3xg//8A9HxKW9Tn/hL/yF+PCHPxz7+/vxhje8IX7sx34sbrrppi+pbJgeJp5Rc0S0EDQgKjM2EScVgb9DnF5np8tOaGa2qQ+TpOSoZcVI+V4IzpV2nq7r70Vmh9uMe2bYLFZQpXRC6ug+MkvjN1s73SwDVreNMbJQ17yIvSD5zkDGbXV9PUY2so7S8f4H6mJwZSNUMtolg00ZVubcT2TNQJFn28jTDurEqX55rDqdTvOSZD7HGFGvHAmin81k5XEugSw/t9QfVoAeBzNqJWDoHOYM9Bh3yvFnNtxW4LTB9cvkB5+ZqTJ7XaVKlS9Nnk1bn0FASUyAZpuQ3zsIkemy8kvmnV3i39gogyrqyDMyqRMx20+UnUM7naW22VGmHo5soFtzue4L+0W2DbTRNgIQRXn0RymdkPLQrdQpAx6usz/APdhFBCJ4Mpk0e6V88rDblbdRRESLcM59AsHsvVn2pWyLM3B1lMh+DGWYvHbbsj/mMc/+ooGb56Xn2sLCQgyHw8Z/cVucKp9JZ9fTZEMmWO3XUC/Xm/HCV3TGkQEg1+O7TKfTpi3ZZ7Yf4YiT+8U+o0Gvs5XYz5b9BOpHdJJrd3d340rJswqmPvGJT8Tf+3t/L17+8pe3Pn/HO94RP/uzPxs/9VM/Fevr6/HAAw/Em9/85vjlX/7lL6l8wBQTrNOZhbrpSC96FCCTOw8gf2dhcThykR23zHQz4ZgQTBpHhfyszC6wWK0QMgPmSc9z/OI6L2yzBvOEPgFgmPXhRbruKy9gl+GwbGbszKLZEFAvlEFuO8bD6RksTEdqAFmMRTaQ+QeGhgVJ39OHGBcfrMDYGSxlkJmZGurP28Y9PjlqxTxhPk8mlzZakitN/zDmo9Eojo8vHbea0zNIwzQL5nk4j/UpgUSuw2DOO5qUOcf1HhvST/mctZuBE3WDrSQ9wN/zHK+9bKS8Xn10v8fMKR0RUQ+gqFLlS5Rn29Zb7+dIk3VTTnOz7VteXo7Dw8OGPIPU4p5+v99y7Ow4o6PQN9gVgIcJGXQivgkCmMptQEqAyLbK5fhFp3yXQR394JTE7PzbzmJf2dTv51u/OiKH+LApE262D3nrA305GAyacrEHBr+AqU6n09TRxK/Hy/fY6aau+Yhu+oj6ul/px5xpY6DE9/w4G8cg2ZJBeQZLtu30e7/fb4EiIjo5A8nH+dOnzFH8O7/o1v6g55l9W/cBdTNoxudkLjpdj8/xHxnHTLLz27bZa8d14TCJHNHET7J4rkynlw7A2Nraau65LtL8tre349u//dvj7//9vx9/7a/9tebzCxcuxI//+I/Hhz70oXjd614XEREf+MAH4sUvfnH8yq/8SvyxP/bHnvIznAqU0TOfIfMmdkR7L1T+PitUTwRHHzyJMkueUTb1MXPuyZcZDwMpt5drPQGZ0IS6vX8pszgZAGTWJDNmKJuSciiVZUBhoT8cSfNiNVNkRWew5GiE85zdd1aAGfi6D83Y5fG9nOFze/157kN/l42t+8htncfCltgat8kRMITycx6310wGhDbItNVRMxsDt5HrM8gqMaZZPG6uU15TpXWdo2L+nz7JUVvqUYqCVqlS5anJc2HrrYMzk1367fuyzUU3+eRTvvc92CdHq53aZT1nfe/IlOvu7AcTPtQb3Y/YruaUMvSw65v1IOXZDmVSkXpl+xlxkiS2P2Kd7Dq5Db4GMJqjdgBMiDGucdvyPfgJpWybDHZcJ9t519nEru3q5exInicZNNvmep7YZvrZbpv9gdxuj4vraPIYoIgfwLOoowGlQTL+neczkv0Rj6nTSEt1LPkyuXyPS+5bAzcAUybWDUS9dgymGVfWfSY7nqk8a2DqbW97W/wn/8l/Eq9//etbCvaTn/xkHB4exutf//rms/vuuy/uuOOO+PjHP/4lKVgAA5KVYkQ74pMnb8RM0ZnhyffilPslo1Y4bN7LkSAUxcHBQWxvbzdsiReNy7ODi8LyBEVKissLi2cYZAyHwxYrkBexjVSOvHkhkpuaDRrXWPkSmjfbBuBzqBXmhfa6fzFAXgxcS/nj8bhhKfJx8kSO8h4e7zejbYwJjJkVg086skL34RQZSJXGl35xZIfIGGwLZZtR4dn0m+clbeIodyJfe3t7DSPkNhmU5IisAQaGNQOmeWyV10Teq2UGNOfo2xmwoSn1O995czf3GTh3OrP9VTCNGTAy/h7rDNaqVKlyeXkubH1EW4dmRy/v70H86hB0EjqTiACpQegGohOZ7Mu6kAiBCZpc34jZUdomW7GREIM5dQkdxv8w6ch0Om2iFaQ4ZVvjfkG/+fApyjGxaT3s7JSIOGHzPQbORrF9ysQybbIt4gW2fG/9jN22nWIMc7m0Mx9LboIyRyocXTEw8HyhbPpnHjlsn4PU+8FgcAJU2WYaeAK08j56z7cMhplLg8EglpaWot/vN1FT5gBZHfQrR46zX4ryOYa95AtmUN7pXIoWEnVy/7gfAFweI9fda8X30e6Dg4PY2tpq9kPxvDzfGYf8LPwlZHFxMTY2NpprOXb9SsizAqY+/OEPx6/92q/FJz7xiRPfPfzww7G8vBwbGxutz2+66aZ4+OGHi+VxrCFy8eLFiDiJbu0Q0ll54kecPDJ63vceYK7PgrOe62NlYkBHPUsRgNwOT7isqDOTbvbLCsSLtsSEUNcSALDzOc/BfLLPSwDWLIIVGgvSfem0zdwn3oTpn9xv/uEewvhWlgZsViCuX5aSEfWYlPrS15i1udycsOG1UjY7l5WL+9Fz2HPRys5GLN+X50X+LEtem6Xr8ti4/7mntH48Tw2msqHPrFmeh7nPcz9WqVLl8vJc2Xqv4wymst7zd2bMs0NuB5xnWAymshON3bevQNlZn1unZHtIOTki5b8hP/PeGr57sv2juW8MWGijo1L52oiT79TMfkm2Wdnxtz/gMbDkcSv1Y+n6bC+wBzzH+j+XRd/6PvraUcc8Ru6fbJ/c/3yfwVTEybRMk7fT6fSyh0dRpsnUeVki7o9OZ0Z2c6/bmG385cbNbaSd2Zdwn9rPyvUr+fEAJ9L6IM3nkaBZvHaQxcXF5p2mpTF9JnLFwdTnP//5+N7v/d74l//yX14x1Pfe97433vOe95z4HDQOs+A9IhGzhcRktwLLHW0n1cJgOTc6TxRvBCxJpzNjyflhQoDqvSeL+ua0PNczO/tE6YhWwD74FBWYpqOjo1hYuLSJ0Q5xDuWjaOelEeSFZofdwCiDOU4AtKDwYFF4Biwh4ojX9vZ2TKfT5v0BdvId/uYa1wkh4lgygjmNgjZS/mQyaXJuvfHTEUPu4XM7BXt7e03dsrLPIGs4HDYsF33uMl2u9/9YmHdW6r1er9X/NqzMkwxc8piVFG1uvxlOzwuvBZ5rBen1SJ0iZpuMDSopMzO4nhvciz7geaw1Uk2qVKlyeXkubf3+/n7zolJeUGqbiG7KWQW7u7uNXwCRZjuJ7S2RLY6M2AnudGYvfXUkAdtKpAi9RtTF+o86W0+XyDZnM2Bz8gEROP/zUsPn6dHj4+PY3d1t7FC+PjuoOXWcOlBX91MpNRD9ng/4QWfbdvio61xuCSza5lM3Mj3wIVwv5o+JWaKHzBOPN3bV/eTtHTli5LQ75qwBDdHKiGhOGOQ7fBKPR/a9+N+RL9rt7Cf7bd5nxZyyz+qUwAyKeKZttPcP2hegL1mniOeAU209T/AN9/b24sKFCzEej+Pxxx9vwNTx8cn3eLmPbMcj4oRvx8uSue6aPoDik5/8ZHzxi1+MP/pH/2jz2fHxcXzsYx+L973vffHzP//zcXBwEOfPn28xVo888kicPXu2WOa73vWueOc739n8f/Hixbj99tuLzp1Dp5YSqzAPIWeUjtLzoJSQMOXkKBSfUw87nyUn3mXx/BxN8t8GjV70mTUykJvHLrhMynUbSu1zWRkQoIi82KyUMjjACDG2paNbbZQAYI5oGYzyXL813gycQcw8psiSmaWcIsi9pTIy25ZBUJ4b7hfKyakL1MPzoDSPXLf8nccvpyvQrjzuHo+S5LnHC7Tntc8KPEfPSgwTfZvTbvI69XrIc9vl+zmldlapUqUtz6WtZx1b95VsapZsp7MdzfqvdG/Jnucy7YuUyLdcDmWhu7M/4jqg53x4hYlU2znXyfYgM/+2OQaZ8/T8PMn63P2W/891yCQjz892sVRe9v38mcEbQNo+T6kMp5dnX8hl0rfcA6jIp/c5Zc+/7fdgk22rnInh8TBIc/3zYQ/Uz/bS9py65Of43sutJ64p9SXjal8lg3j7KnnOuC6MH/6fDwVxmdn/zMRvJkgAtqypK3ly7xUHU9/0Td8Un/rUp1qfvfWtb4377rsv/uJf/Itx++23x9LSUvzCL/xCvOUtb4mIiN/+7d+OP/iDP4j777+/WCa5nVkcgTITxGd58jsX1crGzrdPeSnd48GA3TAaZtHkzXnc7+O3zSjkdC3KjZjt58oT0Yoys16kSjgMnN8vZYe/NPHyNXZecwTPSsubdfN4GYyaHSQ64/x17vH7FSLakR4iPNkgIFxHZCorCsK+7lePGc/rdrvNy+1QSEdHR7GzsxPT6bTFmFmstFCm3jxJm2HP9vf3GwXtOvkUI7edMXLbvI8rAw6L7+GHdnjss8G0sfJ89nOoqxk494nBrNuS2+d22Lj6ze8uhz6EIcvPswHzWuaa7JRUqVKlLM+lrUfXWOdhbyy2n47qlMpxWdb33LeyshKrq6vNvdgRO4W2RSZzOp1Le4Ei2pkw2XH1yavWr9kvIPsFHwQ7a6YfWzkej2Nvb6+JrNlHybot+zsW2+p8H9G8DE5pD7oXXyiTiBn4mcjyWLCnxy+GzaApg8W8L8rRKO9b49nU36Ao94MBBGMyGAxaBznkuUc0qGSneKazJphT3kvtcjM4sw/KoQyIx5y/iW56PvqHOca8KQGrElHrlFf6OyKa/VsG7dk3M7ltcDiZTJq64HMx//P6dlvIWrIfwprJY3WlidMrDqZWV1fjK7/yK1ufraysxOnTp5vPv/u7vzve+c53xubmZqytrcXb3/72uP/++7/kDakZ6c4DAkh2Kv19ZgEi2qFjJhGb/RiYiPYiyPfkFDc7rSxgRwByXfg/T+iSg+s25X1JtJ/vMntvZsGS2Q36JTM3LCaPiVMh8hh5kec9Tznqwg/KwuChBKZcd7/DqJQGsbS01ABPK3v/ZnycnkE6wO7ubms8Hbmir8wsRrQNDnXzARqZ0TFZkN/w7bF3n9jIZKDuPsxgJyvHyylUg/lskKmLN/ZGtI8qhnDIayMDKoMdA/HMQOX+ysfZ+1qv53nORJUqVebLc2nrI9ppwZ1Op/XKB+xt1g9Ojfbat04rsfDYZhNtOJm+z0521smlfTIAHxxpl8192SHPGQJ8l+2rdTVAk77KRKrriLhv+D/rVvft5fS+bWlO9crEmYGX/RDsII5+Juwy8LIddJ2pr48QNyHINaVIjdtBXwFgSJPr9Xqtlxd77HzgB4Q4v72XyM/z2Li+BgvMS56b/ZtsI+cR9h5T95VPLHS9/Hfud7ff/V2KSJXmGvfnoIQBru91e7g2H0lvf9snF86bv89EnvWX9pbkh37oh6Lb7cZb3vKW1ov8vlRxZIMQuJ3vzJJ4kXkxMNFy2VxnIc8YJshKEwH9OjxJVCYj6+w05oGeTqete+28en9YRth8DhvhdyUh+/v7RcWRlTZtzEx//pu+ZUxoK8yCx4Mc5tLEtzJmIe7v78f29nYDYGw42JjousAC4cz7VCOUmReZWTGf5ORF6/1Vjpi4nzL7Q33MkAJg/DZw95/noAEwY+ZIlucOSic7ClaWlJuVXI56ZXEZOVoUMTvFKbeBzZ6ZQXZ7aYOdAsrM4NuEhecu/Q97BRtF35fWqfddVKlS5crLlbL1BwcHTdTeJ7yREo7T5UgAOt16BttjkGC7ZDKPU2ddTok8ys67T/W1beQkXJfnKErE7ECKTOp1Op2m3dleUr4j99mG5/c3muXP92OfnEmS22pCkzpl4Gg/xvY6+yr8XwJPmaTMDnEmDy2c1pgJa/sVzIucLseccnpl3h/mOZZBFN8RUbMfgK1jfIfDYYvQtng8/T9gjHaNRqOmL0q2lnvtl+UsKbfBZeexMjme+9xzAr+MtuOTl+7Bl3Gfj0ajWFpaanxo5qV9Uq9L9zt14Deg10R1qf7PRJ4TMPXRj3609X+/34/3v//98f73v/8ZletJYETt8L9TkTKY4gVnDJ4d6SxmMlCITGgUOIoWBe9QvM/vd5kRswWSHXRPfju7zu/lmHKnhdHuiEtGCGBH1APlgHLOCtIshsPTVno8x0rK7EapnwG57HEqGSQbh6zgt7e3Y39/Py5cuNDqD8orRV9YgPOORofNwOAAcjx2+ThZNhkvLCzE2tpa67lELQnVu5/YpE2d8oZKsyfuO8ac8YEh3dvba4x+t9s9cbw8UjpGlvludi8bds9Fj6/nLPOc/71hmPobvHpPHONcAqY2XjZEDtHnNqFgTbBYDKbMZuUUmypVqjw9ebZsvQ8RyKnzJqog7myT/WN/wNkPl8teMFFV0hO2WXbo/JJwyh+Pxy2H1HbfYqDnz0rObP6JmNlo60T7FLk8A0ieW3J83W8lEGgfINuiUvTN/zu64H0tGRRaj9tvMYiwsw257XoAkrFFdsINwHq9XpOuSf/6vUcG144AUc/8ug7s37yDjlw+fW27a5/Iz3OWFL5N6ZAvz9ts9wxWieh6n14mW0v153OPCXXMW3F4bkSbOGUecLw8/p2BqAkUjyfjzHPsszrFM6+XKyFXJTJ1pcQgw3ubPEFwHD0JM8tgRt/sT14k2bH0oFpRsWisuDyorn92CvkcmU5nqWqeABl9m33jdy4TpskKz0rJz/G7pPIERBnQ306nQokhOSJQYtOok9tlQAQrMR6Pm6iM+xfGzwaWevkdCBGzVMy8F2t5eblRmu77iGiMuBnM8XjcGEv309HRUYsJoz5OGcFIjcfj5rvFxcXo9/sxGAxah24wTzEIk8mkSSuwIkGhMvdcf5RTVnA2tlxn4sHj57GzkfUa8bwzc0ZfZCaU3wZxXsOZ2HDaCs9mHnsu09YMjEppN4gPMJnH7FWpUuXqCPo/oh2RMhiBCHM0I5ND6P1sr2yfL+c4liIy/HZ51iM5YhRx8sCGrI+4nh90tZ9dckppA851trWuP7aHsu3XRMRchz/X00I7DE58upyvmZcNVOpXA6NSamIJTDlVDODk6GN2tl1HslC8h4fn5a0bjIujHvbJXE8+dwqf7RJ+gUEHvoP7wcDA9XNWloF4iSSkLwxMckodv5kvGYyX5m5+Ni8S9v3cS914hrcyMCcAprTP5DP7qnxqn4X7BoNB04fojMPDw+Ieuacr1zWYouM96RiwiJMHKzgtKmKmlM0gkQ7mfEwrZySj44hoLVRHZ1i0lMVnefBB7zlSkMEUC6jk9HlR5f9pW17EnmA8D1CQjU1mRDjKkoVBqkUJjJYYDu9L6nQ6DQNhQEAkaGdnpzlggHsnk0ns7u7G4eFhK/3Ne5CsaGEu2bC5v78f4/E4er1erK+vNxEkMxm0bTweN4oOsOm0DaJDTh+jnjZwtIsIGOmma2trTd/5yPxut9soAqJnvV4vlpeXW4qLuZVBiJU8KS75yFmDZYMa1pEBKfPRIKakbKmTT/Kj/7wGvIb53N8jZpXdZrOazFPX30oftnFxcbF5oR+6gbZeyRN+qlSp8sxlPB63dBh22y/NNYGDTrBTaoLNxKijINZjTktyZH2e0+aIFPo9or23FF3o/U52pLNfQJkGUSZ+EBOyPkSIdkPc5S0D/DiaQp1wgLNTnUlY2wZntEDOmcCzP5AJcLc7gynK47jtElgx4Ol0Os3+ojz2ADz7ZR4H2p4PGHNbsBuMr6Nq2G0DapO9ERHD4bAVReU3Y2Ff0oeyeK+9IzHMWfwB0mLpb5MO9DNbPwBR0+m0BcI9Nl4LLsd+pPsZ34b/KcvEabbVmaiIuGSPB4NBM3+4j/4i4wmdQNYP9aFv8ZeoI6RsaS/h05XrGkzZYffic6fbUWdie1GXyoxoo/E8kBY7n564/r40eTK77onG51lp54lsJ5Ey8+bIfr/f2hvmRcWEYkHmyWqwxbNZGGYKrCToZz8HRWKg436hHNrjMTQzRT1R8o5UZFYjGwvq51QOh+Tdds+TbIhzPTMzV2JN3XceuwwaSswPhp9n5r7GATCjmKMyTo3jGtqbjVeOQnl9oXAxGHzuED2fM14GOBgZ6shPNtpIiSzI7KwP7KANnjt5DniOYHhyn+d6VKlS5eqKSdCIk4cN8EP6tVn2UpTD/1tcbkR773TJriDWpyXdblttB9S6ERvDtUhuo21K/p31cL7f7SzZ9uzIWodmIpVy7Hhn/WxiO5eb/SD3n7MnXI6BKPflyJF9iEywZxvrutjv8d6sefbZfcGzss2jTv7fgLQ0RtTFf0e009Txw7ydxT5RJhGync9zwIQw5bvckn3O92X/1Rkv88TzN5edfRLGMq9bCBH61b6WM4QMBk3GXym5rsGUkXleoA7xg/gjorVfw9GniLZzmiejJUeXGODM8Odr+N9sh5VHdoqzUrUDSputABwGHw6Hsby8HKurq619PBGz6ISfl51ml8ezqSfC5kCzXTCGBhr0T94r5chidsJ5HnVZWlpqGAqYKZiVra2thg0Zj8cnjIeVgdP9zAjR33a2M4ABiDF3sgEw2Mxj56hKVsZe4B5f30NZAD9Y2DznubZkOOlbz1mnfnY6nWatZOPNvMjOx3Q6jd3d3Zaiggli3EhH5DmMA3u9PEb0C+3nB1CJ+GWaTjvN5XkOEnHyBvZ5c6VKlSrXjqAHItqp46xXZwpgw5eWllqZAlmHZYeW3+hxRwts3yJmqdrcx4/riXC/HW/bbmzZ8fFxcyiRncDszHtPjElaR82sS63nEDvOtCcfrY2/4Kgc/Yodwp4QicDGZ0LPfk3uMz532r33QDsDxJlDBj7cY1+CdHj8hMlk0kQLqRNttP3L++i5N0ek+v1+6xAKfKzsfxpkIhmseW5hx+i/fIDG8fFxE2nMEVMDZM89+yQmCBgXgCDjydH67Mv2eOf56+0Lnnf5EJg8Bz1meV66D/K2CY8BPiIHXdlXM2DCP8xgPO8reyZyXYMpS4k5yN9HtJE5Cz6DJ1/P3wYSGbCVWKDskFl5mA1wylNWQF4AJZbddcynBEWcfMeRlWeJ+crMgpmpLDn6ZEEResLzuZVkiTHJrIP7mjQERx5YzIA6wFRm2fIYWonm9lrJ+d48jpZcfjaG7ofczswK5fZbrKzMVLke8+69nMyrm+d2KSpLW00AGOBb+WVmucQe5rXEb4Om0vxiDefIMH1WWjNep/PmX5UqVa6+eF2X1niO8MzT2Vm3RcxsWXbqrBOyfrT+QTLoKekb6zeAUH6u/QDb/qyb7Cj6f57l55Tqb0ee60r2zVkb88SZGKXrMtHltmWH2/2dwSdt4/7Sc1xm9rew+9gpZ+zYcbe/5FRLgyQ7/dznaKj9o1wvP5f6eBw9dzKJQBqfgbt9hxKgyv3l8XBfcb3JSIhbz9UM8hnXbE895iYsEd/PmJhk9bzNES6TKfZ5fY/J2FJE83JRsy9VrnswZQe2NHnmKTh/5yNQ/b4Al+cTApnUnDZiBscMhiNf3qfkxeQJCzOU31ngxVVyTCNmihk2BkYuYsYSuA7cw2/qkNOmMiBAmbDfyKfPcY8P8YiIE8wO93gs8olztJPv+v1+rK+vn2Bj6Ofd3d146KGHmlP/Ll682ETWMnD0XHDOMnXwvPDcyqmP7h8YFu/lsbLKTIvLWF5ebnJ6zbZl5tDRQVI4rVhh7UoAy0rO483f3MP8zAApK1j/T/SJMhmziGiYVrOaDtvT77CzntfMEepilhBwxf8cTkJdvC7yvgevPTahZuepSpUq144QMcDhys63iTA74I4WRLSJlZxubp1rJ8uAxY53Jsq8H4S68F22EdQvO9Cuu51vs/L2BQ4PD5s9w3xu+0FKNuI0726320qLx5Z6D1lEey+4CWj7WWR3DAaDE05ujpLlaATbEHh3oyODPhWv1C+lurBXyj5Mt9tt9ik58uWXthsUIdhrjzGvaOGZPqzCrwJxH9AOXmRrMEubeeVLBt3Ug/E7PDyMixcvRsTMhq6srDTZIPQzdch7gE2wG1RA7ntskExQ5Lk4nc5OVaaelOl93RkkUTevEQID9pPwbbvdbuPveE88ZTPf8Q13d3ebyOTR0VH0+/0mosjhFVdKrmswlRVYZl+4Jk+CeWXlezOD5GtZxGZ3rJSoW2bTnsxhuxzbYrYjM14sHBYIkRoDPJRaRuPz2Ib8DNcvsyeIgR7PyXUwE4gCz3XIeb9e8BgWPuMgBi++UpQv92Wpj32tDePlrvG1iIF3nj/ZKLjc3Kc5cpbr6/qU5rDHr8RWzVsP+Xm5XvlZgJ0MMuetCf9YqXt+l/rdDKH72fe4z2zQc1v53wxxBVJVqlx7kqMMSNYT+Trro3m6MuuGrPf8t/WE9YrtputbKoPfpcyJbF9KNieneJtUcrmOrmTdV+rLkj52W3xCm/tunlOeIxW2zZZM3vpwCKfzeWwdqXL5Oepjv8X7dbm+lLbmPnG0iTr5dR+U5fHOY8q4GuzTr/Zf8/7d0jwHHOWxKEVCS/Jka+Ny5RhI+/o873IfZMI2ty1f6y0B9iVyG3g+fen16S0CAKm8R6rkBz4Tua7B1N7eXoPCc2cb2XqQ8p4fL6Ic9rMjb+XiiR0xe/mtoxjci5NvEBAxS8vLub9WEFZEjpyg2OxA+qVzx8fHcf78+djZ2Yn9/f1YWVlpjt1m75EdetdveXm5iTq5r3A46VOzDTYqRBg6nU5zAh1tNpgiilIK+XossxNOHc2EWDH2+/04PDxsooZmyhgPDEBENLnH3G+l62utpLMRZY6U9jBZGdPHMCxWtjA0x8fHMRgMmqPQGVfvXeM3KY3MESt9z8OszLLBtRHPEVTKtsH2JmnP4dJegRwVys8GDPMcGDozrZ4TVp4YI9YTdWRPoNe+x5y2MoeZz7TlSrJVVapUeebiQ4LygTe2ZehbMjSIVFsf2Jm1/nQauXWer8GmIZwKGzHbj21/w+Akv+sp6zY/J39m8MAJZgsLC82pbUdHR0103j4N9o86ZJ+I5zm9rd/vN9fYQYUwc50zeCW7gpN1eY4BC/refpX9oMPDw9jb22vVK0eQPK60gTJ5n+Pe3l5r7xtzg76KiBNlZPBHPzMW0+m0ySJx6hi2hEwM+0P2B2wLmSPs5eFez1WeSx8yBth66s/rYTJIyv3s77M/471uOXsEu8rfOfJEn9l/zQc+0P/8dhv9qhrv//JYsM7oWwIGfjZ97BdPe77imxLh2tnZmaNxvnS5rsFUfpN5RDsc6oGNKLPwXqAlpsZ/52fZQc/f5b/nsQzORc3OcL4XhVhC+U5tmk6njWJlQyET1UrJ4AxlicGib3OKhPvICjkrh1K/WhFnQMICMiuVAQdtZcHk8WUxYUTtqBt4ZrCRX+DHdflkJf9dYgFZpFZe80BWJgEMUjiwgf63A0D9MfSep5nBcd/NY6w8npmpyt9ZMZo5MnuU14HrYeDs/mYcMytYYsn433Mgl++5UmqvxXXyWqxSpcq1IznSkwmPrH9L0YzsaFpvUgblmGTydSVg5iiBo9wRJ/fi4nxebmuC/0cMiPg7gzXb7QwObVNzZgplOKUuon3CbvaxDDpyWSX7kUk/19sg0SnY9LnJPf7OpzV67Kk/aXN24PkuH1yWyTq3I/sLPsSJe3IGRLZ1lGl/xz+liAzP5DuPm/0BQF/ekmCS3OOTx8p2M782wPY1r7l59tX95VRaj6GPJM+kAusJfzN/Tnk+GZpns26dvpoJe0BhRD2AopHJZNJ6EZdRtifuvFQfO4dmUTqdGcOSQZad6fyCX9/DJM0AIzuMBho4l1bo1NMAzM6s06aoF2AKtmRnZyeGw2Hs7+/H0tJS7O7uxuLiYgyHw1b7zAw5bdALkv4jcoXxcH+a2bNBgkmgrW6L0+K8KAAN3h8F8+QTkMbjcSwuLsb6+nqTF2sxcDIYHQwGRTAe0U7VsyK3gSkxWU7v5H0VVjCwbuQ4j0aj6Pf7LZZxe3s7FhcXW/nD/Pa88O9svJgj9HMGLv6N5DnvNcIzPC42VjzXc9r9aqCJwMh5/jD/cwpvjjq5/qw9PxPJJ/3xrLy3it+lMqpUqXL1JDtuGTThXDsCYWLQQIn9nRGzo7ZN6NlRdcTGOgT7he5FN0ecBEImhRwt4BmZWPRPSQdzLbYCW4cPsru7Gzs7O807ILGZkIwALQNDi4FFJm5L44JdpV+y0wz4oG8hdHNUBbFfQf1yhIp6ZrAaEU2kB3+D53vrQAbK7l+Tddk3iWhHXBhTwC12CiAH0c5cBNA5Vc9+p8fAfq3nTES05nLJ1zWZbfuX5xOf5X3DJtpNetoPzmuCMp3BYgCGv8oYZ9/Tfcxadp15RyXzxZklJhQYI0fD3L9Oz7yStv66BlNMerM+KMeI9uDyf8TJPTGOCvCeijxRLAyCF4MXvMOxPAMnkAMRssJloaOQHVlzRAhmKzuvZsMmk0ns7e21joKmTxYWFhpQRf/RL04z5DMrPuoJui8xKZ60OYLh/FXfb4WVX5SbD1jwkaKMAdcsLi42GzH7/X5zjw0V9XB/ojg9ZzK7FFFmsbKh8fyj32wUUbbu236/H6urq7G9vd2AVF5SSf/llNRMCnhe83dJSft7K2O30XM+953b6B8rZQMxP8/PtWLLa8GGIBMhLjcrcpS1DWMmMLiGfiVlpkqVKte+2MkkitLtdhtiKqeD2baYcUeP2KGyjslkjYESOtxgKusiruV/62MTVrbbBnSle20r7aw6fbDb7bbAiYGk+y8DzwwqzepnAgzxZ5DR2FZS1e1rkUqdT76D+GXMrI9to532nu0O44uvlYGTxzfbG77zuBmwOQJon9FAmv6l7zhaHBJ9NBq1iEuAnudACdS6bdmGGii5PswZr4NMtLo/vCYygHMbXUa+1n2a9yrZ/7Nv7vL8POar1zKAHd8RYGVf2GJixO0zKZ+DK89UrmswFTGbcJnFnqfw8uTMQCwv/ogoLlqLHcPsrFMW9cxhxZKDTtpZXmBWpk7xshJmMZt153oAB/XPIVLnpNJvPn3NdXAY1dEOtyM7qeSXZ0VnMEtOK4wWC5J8ZMaJ+pup8L4Z9mTRJqcE5AiFw+MGUSXlBQNFe31NRDR19Oc+mcjKeHd3N8bjcWPUANOZbbWi8ck0rndJ0ZWMB/PFSs7G2qygo1qOJmKM6YPMWnkd5t/zDJDraAXqdUD/2LHI42IWygbF89pj779LDkOVKlWuvhC9RjfhWBtEGVzxvwFMRJtAzY6UdSjPsmPK/SYIIUi9wT1nWVi3ZgIy621+W7/l60p1WVi4dKLbYDCIiJnNhxzOgg0wmHS0LAPMbENKdcjOv20DvkQJdGJPMjGeSUTfY8ec59H/EJL4KPk0u9wPlFcCuo4uZdtXsrmU5VRA6pQJb4No9y2S/YvSfM1/50wil1+yb56LvsY2lnEt2Xpfm+0r95QAGr7aPKDONSbyAY7cZx+IMry27dtRd7Z9OFp7peR5Aaa8wBA76EhOYbLywNH1iS84rxFttjyjdL536J+y/ELYiFneJ5IPnTDjYkXuxcQkch0olzbjkNMPACkrSZQNE95Ah7b4BX3uF+4tsSTU1Zv/JpNJA6asRPghgoSx4bhUR8LG43F0Op1m8ygLh/1h4/G4OVAiH7HqqI7njeeHmSn3O20HWC4sLDSAt5QqkVPRmJekFDJW29vbMZlMWi//88vxfKwvdc0bXJHMFNkgZiVsxYdzwt97e3vNXMEgZLET4bVhRZZTITK4pi99Lc/igI4MajlC3WsbEMUc5jPWnttqo5rnoMe9SpUq15aMx+NGr0G0DQaD5qAJWGz0Zz5xDZmnO/ltvW1iE6fLwAMdiO0k9QhCELtjW+Q9wTzTDmPWPxlMzSNpFxYWYjgctvZI7e3txfnz5+cSbehYl+E6cV3exG+gkUEffYMOdlQsO+DeBuA0M+4BJNv+5agc99t5BkyZoMWncv09nj7S2z5eJmQXFhYa38SppCaVTepRLw6LygCbvijNwxIwMoGa5wl9n8V2Lc8vk5uuk/1ERyczQWkfLgNvz6PSuuK5BtOI1x73+aAqDgDx2rL/aN+H6K2P4GebC2VeCXlegKmIciQk4vLHH3rQS05jRu08B/FE8WKiLp1Op5kQpfv9DNfDCs7o3mVYiVEXfmcAiYOZ67a3t9dKafTCsGJwWzOTQ70N/jKYYoLv7++fSLvLTI0XBW0xIHQbHVInP9ynwLlfiMTkBe85kJW++8XtLLE53oNkhos+cvriyspKTCaTJvLmnGaDs8yKuc3U3eNv8JbnvA2cAY7nJErNgMTszuUYLj73T15T7o+s+EqkiE8XMjikLvPYZV8zrw4l45LrUaVKlWtDTCxClPD+Qf5Hf0XM9EN+WedTWd/WG5kgzOChpH+y3UbnOuXbjqX1eMmxtl2y3suRH/5eXl6O1dXVxr64jj79LDvtPD/rZa7PYMR1tDPuelKmAWO2+S4DO4W9zNkLJT/NfW8bSn3zYRXuS+/Hpk/pD8hcE8h5rkGiZxIa+8l7reZFiuaBq+y3ZnuVbXD2qezDeGxdXrbtJb+JMcnzyDbUz87+1ZNJ7hv3kX0et5c5wfzg+3l+sQF69pOuZIr/dQ2mHFb0MYkRs/cD5IXvCcuPEXFmy+1UMsDOofWCYGJ4UXlB+/mID7HIKJ4Jm/e15AgYzNl0Om0UtxeDFYsdVSIolM9pOlY+oH/n97rvS2CVemXWYG9vr9kcaoXt8KtfyJYjjVaohPGtxABrLDLX1/vbsnicfW+O2PFdSQH4BKROZ8aeUlcW8tLSUqyurka3243d3d04OjpqIjEwrDZoROHcpw6Pu042ENPptJUigXHgpYLc4xflwrrZWDhlMiuekmI3mC85CICZeQwSf3c6s+P3c5TM/eNn5nmYo8C5nvnv4+PjJte9SpUq146gc4lW9Pv9WFlZafaimLWeTqfNOmbvcNZJdtSzQ+voUz6kxie8GkwZGHifVSYh8S+syyNmZF/2Kaz7fF9ENAdPmHSKiBiNRjEcDmM8Hkev14v9/f3mRbPj8bjJbrD+xD5hR91GbKEBbbarCHVxJgD20FkW9Bm2zGQfIMQgLWfCZMBhhzkfKmVb6UiSXwGDH0bkgjnFGB0eHsbOzk4r2wi7bd+DeUcZHjuDzDy3PIc8p9w3maxEsIf4TZ5LJnntC1B+RDvNnn7yi4/5PAcssv3O2SjZ18pAJ7c7E5sQydTJUWdnnZDO6hcAZwDJPZ4/EPBXSq5rMFVC+/6/NHBZAfJZKa84l1dCvhYWqRUr12Y2LLPsJVCS0/3m1cnllaINXsSdTvt9WFzr55tlspOeFWG+z84z9zLZUUiOPKHQUNQR0Tj3XOf+8qLn3nl9zr0ZCCM54pj7NINlR/Y8ZlmsxDCcBnUeEysrA/U816yY6MvMsrhsA/jcBpicDAYz00hbS2zVPNYs31MaR4v70r9drvs990fp+jxv/flTkdI6rFKlytUVp+fw2zrQ6xt74JQwPo+YrXHv4SmV4dQjAwnrLetqS9av1vfWX3k/T3aW/dt22NeXog9EVfr9fnS73cbOYvtxvCnT2xscpXEdbM+yHTGJlvuOMkq6O7c3A4nso5XAhMcs96WfgRh82I+hjU9mVzNocXQr7/8tEY6lOvkaBwlK95TutQ+Z6599i9K9+f9sX0v2tlS/eeXbfyzN7/zc7Gfne/Bjs3/CPMz+Wg5U8F2NTP1HyRv0Ik5uoswdHXFygzzXZyTuhe2QoBeeyzPKdd3MJlk8yXu9Xus7L/JSJMSMiJl5numTfhwWtVLwkdQR0eSQguznGQ6DFy9ch1QjZmHU3d3d5thvG4+Dg4PY3d1tLYisjBDAWKdz8qW6Xhz0F/t+fL/LLUU7DOislDC6ROhKzj9jhKHP5du4O/UTpcAPUSB+XG/+n0wmTZoLbbGxwYCyn8Bj5T16Dp97vmIsMqObI120i98GzT6VkL6hT3NKSCYyDPwYZ7O4VoKO2lIHs6PU2+OUwTfPnE6nV3RDapUqVa6MbGxsxOnTp5sIh/fyTqfTxpZZ/6PDJpNLBwBwTcQsCm8WHkGfOE3LkQvrdGya9ZWzUNCx2C3runzgU6fTaR0HbQDi6EQmlvxaiEwYAnp6vV4cHh7GhQsXGv3sfWjLy8tNRIe6cLhHrjdts/PswwGm09meZ/taPjSL+tnBpb9cfrbBtlG2X7635JvQb9gHIkiMAX1Idg716XYvnbZr++M9ZOyf9nyjPvMA42RSPugi+1cGHr6G8iOiFa3BtpcIWMbG89397nrSv7azttfuU5P93trgcc9ZRNTX48r8wPdw+awt71GjrzOh7tRafDbGMdc5Z7M9U7muwRSSF97Tud8yDzU/lXJwHA288sLy9aVIhBULiyNHRGCJ7My6/RkkoEwsWXF5AfFsM1n8dn29KFFuGawyaYlMcb0jFzmiYeVtJikrJQNNg0p/5nY/1XHkt4GlP2Ms3d/589KzMuvi+hmwuy8yaDPzyv3+7brY8HuM5wEKz88c3cnMF31jhmieUaAMfpsRykQB453ngH9nmcfI0Y7SGPC3nYLcB1WqVLn6sry8fGLjv/WQdUzWPehKE5oGKICfUoQKyT6B9Rx2yQ4bz8h/+3+3gftsa6xbLXZY83Ntp5yqBsEGwMH5z/3h9vrgqQx6sv70lorpdNqMk32PUuQo69zsv9i+5+vyvdlGZH/Kc6EUkSrtTc82MCJOgCofLHK5+VOaO47o2Tfwc7nHkS/vEzIw8xjZZ8zl+3PPAc9p1xXxWJT85gzOsu/CZwZeJr09Zvkzj0Emc/0Zf+MnG3S7TPtbV0KuazCFw8WkJKqSHUQ62Se2wNh70rjDs2LJjDrPRxgcQr++zxMgO4gsWEcBqAuTzc7ydDpjz11/H7/tZ/rghohoTVzESgTWhs2WPMt1NYhyW5wza+E9Eihw8o4dHici43INGFkYJQVDX+cT3Pi8pNyI2BmkOYIXMdsvRoTH84HfjBMMh6Mp9At1pf/JB8cA+bhzM1uuSz410Ep1HruSI0m53pSD+LRHmNt8bWbHMMyl9Ez61HOea7Khc5thkLNjkZ2d7ACgpC3MEfoKydFc55ZXMFWlyrUlm5ubcerUqUZHZQKKbALWLocR+R2FPgm12+22Ti2NiOaoZO47Orr08lXEescphNnBRVdllhyxE4dd4XsiU47m5Ei7U8gtdpb5jogF711cWlqK/f39eOyxx5oTcnmnIdEZDvUAeKFjyYbIhBO+lwlbE6a0I39XAsXZXuY22W74UIlM5NlZznaDfuQAE8bK2Tjs7WY/joEM9+Pr2dEfDAYtO2ifkzr4NGY/00AGQEofkuWxs7PT1IkxWVhYiLW1tej1es3x+JlknkcU5n7LmTD2W3w6o20mbTegnkwmzdrDj3J9vLfdxIb98Ol02pDwpT3tmYR3fd3mHCmmTjs7O7GzsxNXSq5rMGWlWkKqEeUokwFWxElGw5POE8Dl2znLIdgclSopH8ryJPCPlY2VNw6jJ44Vhp3DrFj9zFwfsyb5mEna6zajfNxmMzXucx9iUTpVr3QPz/R45n7M42aw6ggP9fX4Wom5fMpB6bnPsmSwTP/k9rm/XVa+32DK9cm/Ha3MisT1xMEo5a/bwGdmj88ysCixYiYRDJSQUmQws5R5LVKWQ/xcl/tgXt/485xb7e98LXP6yfZPVqlS5bmVfr/f2vBv4s76BD3v6IPTqHO0gWvRkUtLSy39RiZFKeKfbdjl7KrF5WQSp3RQVQYheS9Tyc5TF+pA/QeDQSsah9MLsehtAbbV9mkykLE9yfWgLiUybx6Ycr/m9mUfbR7xlX2mLNzrF8PmOcE8cznZJ3H/GmjYJ/PzPFfpl5z1kn1Y+v/w8DD29vZaID+fZsmhJH5unocle287bj/Pcy/3e8k/4Sf7uPbT3X6vG/94vE0muE2lvst1yfVwW/FHr5Rc12Aq4mTalJUlCgQHlUnV6/Wi1+u1lC0vCDN4gqFgYcwL49uZj2gfG45TmJkRFp6vcwTJk6TE9pCDync4zt5U6smTJyrlGYQhfp5PhuEzAysbKLMbeWz4H5bQ7Fy32z2xZ4xreCYKIz/L/eJ+I1oEwEPMbrhNuV8sHpt5oDwfdHG5KFo2TrCotA1jZyNFeQbXKL4cETQoyMqGucCcptx8DHluq8Fyltwu1y33EzIvAuRnE8208swROsu88aMf7EC4rmanXU6VKlWuDTGYsf63/badwW7z2gx+873tL2Vkhw37XyLx0KPex1ECaS6P+pkssn6PaL8ixM48bfS1pQMe+NvOprNqVldXG3JpfX09tra2Ymtrq9lHFTHT5/ThdDptQJZtULZv2SHOdcqEZiYyPaZ2crHlfp5tm8fEz6Ke9r/sVDv7wnPK9bDv5NN5GSPKsR0tgcnsE+W5PQ+AHB8fx87OTnMSMqf7EpFaXV1tTgju9XrR7/dbGVrzbK/9gvz5ZDJpQJkjqBkwci3+dc6Qoc7Y8cXFS+92or/xfcgww79j3TDe1Mu+KH3md39yPf5a9lW8jgBSFUxJSmwRispOf8TMaSa8y6CV3sbMb0L+PhnN0Q8DLL5jsc1jA0pKpxRly4suX2vGnR8mEWV44mE8Sk5nCUB40mcFaWUPgOO+3O8u0/2djZLHkFC2lYEXeClilYEhLCYKlc8ymPJc8vjbSJb6KbMg1M3P4ft5zCj3OI0gIk6EtF0f+jIimn5yf2MgaJPbx7Nzn7ifSsaQMckOh42iAQppm65//pk3z2lnVnY+9t9MaB6XPJ6ZBHGaYTaYVapUufaklM2RnW+vZ3SHDz7KqYAlxzk7+3YQs30xyWVdFNHWtY7w8FyiX7ZbfJfBnYGZn5n1Vv67RD71+/2mnYPBIJaXl2N5eTl2d3fj/PnzLb8C8swHa2S7mf0ag7dSfbKD6z73WDoNzj5dfmYuK9uYiDZIcPo9kciI9qES2eZ77A2msi2kzhbfM08Y03zACGD28PAwdnd3mzkMCFxcXGzGEBDFARr2rZylRH+Uxo1nm1zM9jVnimRSw/OFtYePQj3dR+53zxlnPbluHj/8Rl754jaZZLZfx5rOvsyVkOcFmMrOaXbScEydj0yYlIG0gvKCYcCYRGZ5kNLipR6ZdTDwK7XFzilsTFa6TFrAjMvLoIHvjo4uvWOCdwehaP0btsOAkQmJUs57h3LfZ6XpxcbempLCy6wa+euMw8LCQsuxdl28wL04UAy5X9zf1K8klJs3eGamyb8N0OdJZjvZU+Z5R9sz42qnwjnhEbN9VTl1IRs4g7TcH5kIsJIEqPMDK+T9Xvm9HRgY15NxKL1ng373uNAPHpeINoDyfHdbXK4B7+VSQKpUqXJtiW2d09ANmEz44MRtbW01+hUGnNQoH2iR0wMzSZn1YtatESffJWm7kslVvkev+YRat9F7sijXp7Nxb/YTDNCsS/kbh9ynnaHDOWGXk4IdCTNJixjgOXWP67KeNuB1X+YMBwMxPjOosQ01cHS9bK/z/nH7cwYdtn2U43rYzpSAOZ8b3M8j2BlLk9bMQd4JtrOzE+PxODqdTuMXjUajWF5ejlOnTkW/32/tc/PeJdcbgtOfGVTmvXgZxOe5T10BJ5yQaP/VpDs+g8cKMEXAwhlLuW93d3dPzDHmJ3uwOAsBP8Tj4nGgHTlq90zkugZTjj5lZ5wOpIMXFhaafGGceu7jmoi2g21kn8ObfmauExOyFP3IDJg/zwuKSZWdeUKjKJj8bEdeHNrkKFScbdC8nW+XgbJ3fbnWLIcnbMlBJxKF0fM4OfWK8lEa3W63BcBsnHKECWOQw7aMa0kBUr+Ss829OazMPQbeHss8Lp6XMEaE6vkew+86EKqn/33ARQYTtI36MkaUT1/5xXWUYRbHQMng0HvhskKKmB1dfnBwEOPxuOX42FDxm3HPgC4r0VynvIfNCh4FS3klooExs4NSpUqVa19sixw5IUWa6BO2hlcyXLx4Mfb29ppyBoNBo9M5UMEnz2UHjM/sL0ScJGj5cfTJtt4EVsTMmcVmmjiyE4uuiji5d8g203UwG2897jLsOHMfhCvgE1udTzuk/pRnBxdfy9GEEpFJH5WISdrt52VfKEczbE9sz93P9j8oix/uz/1UIuqzf5P713bJNieDqexH0c/4bHt7e7G7u9uMSa/Xi+FwGP1+P06dOhXLy8tx4403NtEoH87C4WEmA0zMuv4mpgEjtvmXA1MRs+ytixcvtg55cR96TDkQhfazvrLf7mdMJpMGrDlrh3FzhCpitk0k97t9m5wq+kzlugZTMCx5gpbSt2CprCgd5nWZOVJhhZedUD+bwYFBsaLgHso2w+LvI04qKi+QHMnJkqM+OLAsrBKDAztC5IF3eDhn2w6wUxlpC+2nv6xkWJA5upeBl/vBfZHHk2tQOqVTHHNfmpmbTqcn8p/df7QpK1Mrd0tm/cwkGvTx5vT8BnozcQZBrodZMxuRzKyVolAGnRlcGphkI20gc7mcb9d33m8UpNdSTq0t1SWzre7ny41DaUy9diOi1edO1ynNtypVqlw9sTOITdrf32/AFABgb2+vYfWJtGDTcDDzHotMriF2Ah2Vj5iBKTt/Wae67OyQ2j5HnDw4p1SvTPQ63cw63yfxZX/B9TIR1e/3mxPhcKZxXp1pkJ9VIk8dhSn5Yxb7QB6TnFHCdTnFcV6b3E8Gto68REQTfYNk9PuQckTQdsPlG8RSt0zQ5r9zeqj7mLk7Ho+bz9gPNRgMotfrNel93O8o0GQyab6zPfPpjLnvSgRCJla5zoS1X3tDv8wjZmlb3g9uUMmYl/wshOcDHiH0TUgbzHkcvZYhf6+UXNdgirC9FwgOI4sYpXt4eBiPP/54c+R3p9OJ9fX1ExvPPQieXHYwc8oS4qgQg5hPR4mYDbaVjFkqrqFO3oeTGY8sefH6xa4+Nt1thcUDmHAt9e92u83xqYA66mZWKitVl8ECt8PPdRkEGYxaGdiBnk6nsbe31wJH/i4bJj5jIdFOlKaNQjaAbit9kJWRDb0ZHfbnLS4uxmg0ahaxwQlG3+2kDzxHMkDIIXNHejAQZm9ypJS+zgAwK6Ic4SkBmWx0XI7HwWsps6nOcXb/ex67nIiZYcvpowaTpZRGs5boDH9WpUqVa0dY3zidnGhGJJzPODp6d3e3IQk7nUtHVo9Go+Ygo6dykA1OKi+Xd+YDNm8wGMTS0lL0er2GLMtEmH0H7sVe2ZZabzrKxfc5auVDi+zUc8CWU+OzX2AwxDu81tfX4/DwMFZWVmI8HscXv/jF5mXH/GBPAKX8jTgjwW11+1wPfuMv0IcIbcsRhtyvtJ//6R+DAkdr6MudnZ2YTqdNdM/XZ4AxTwwuI6IVycx2x1E6+4kmCc6fP99EBg8PD6Pf78doNIrhcBgbGxvR7/cb35U14VTWyWQSg8EgIqL1cmteQO2+47meZ/go7gPmin0BSAtepcPz6Xd+2y7v7+83fhf+CT5VKQKW/fHsY9mHYL77M+rk18/w997eXj0aHckOsNn5EtvkUKbzRCNOHn+dGaMMXBwVyyxTZmxy/m8pIlNy/P1/Zujt0FuywsnXZAVtxWEnnvKz0cmn1ZTajdBGFBgT3c93Pd1uFkyp3i4/90OJpSpJZq5cFg51joRYuWSwnVmsnNqQGZ8MTLLjT9nOV3cfmGHNOfPuH0etSnMsp855THJ7vSZyP2YlnZ+Zx4JrPe75+Y54mg00wEYyQPOaKUkJeM1bm1WqVLm64iwJn8aVfzstDqLJv/Nat45Ah1COHa8SsMFhRx+V9nLyv+0SkvVM1sER7Vd6ZB2ZI2HUKfsItkX5ma4XzyIFi8Mq7CjndjjakaMtlE+KegaVtq/zfBmuneencY1Jv2xjXd9M1rnv3Icud55faEBSGt/SWJfI8pJvwDhyaMPKykpzyIRfpuy6ZX/Ivpzr6f9LdSiltZrYzb5abn9eU5eLNJXWYrbtpbWa28HzvfZ9L+DL757zVporIdc1mPL7c6wsvVeIxdDtdmNjYyM6nU4zKeloFDSSHaqcS8sEAbg5EsbATaezaEketDwp7PixoPjOTjaOM5/nU8mycsj1J6SZARcMlycgC3k4HEbEDESRouayMVb0KWXQ/rW1tRY7wBGfTHD3S95wywk11MGAkvxY+sXphHkvFgqAfsljko3lPKXpMfN+IqdI8vycwkaaac5dZg7nthpMwKZ6fJgXpf1+1D2TBjbIRG3d324jfcQYGdjRh7Sdsc3ERXZebCy4fp7yNLCkfE4zYu8D37PvzGuyBCAtjl5lYFWlSpVrRwyWxuNxk9rHe3c4gAI7zn5g0qEcmUcvEr1hvaPDAWtEpGw3IGrQ4ei//5+9d4uxLbvK+8euqlNVu27ndLdN2w0msRQnISiKoxBZwTwExZJD/kQmiRJZ4sEBCUcgghxLITiyQ7AACxIhxzwYwQMxCkSKFIGSByxFRMpFscxFgJJAIMEGc+mLu8+pU1V7133v/8Pht/ZvfTV3dbf70H3KrCGVqmrvteaa1zHG940x58Ju5V7QtPWUgaATvUfKdh89ZluJnXckxTqSupMZgi3HJzFAc6o8kaEv+ZIvuRLpsxPqwzvcn0ToTAZyLD1txS9w5kRmaSAGqzs7O7WystLpefs7jshwX9XVVEH6yKCJvuM5Lof+we+gPMbX2TDYa2wZfkM69fYt0wY7Q2g0GnXHnt+5c6du3759xdYbMPqAMMacOWp7nSDE/i/z3WAKP2Nra6ubI5Tnk/jol6rF3m37p5TldFuilrbVjmSSeUY9KZc5m9Es+o356Syi2ezBoXOnp6fdmmZ+Piy50WCqqh/pyUiRlVLVYsLR0VXtU1j8+zpJpqtVr3Tik7losVFmTqh/MiXJmOS9nsRWGr7uOjbIDrkjCKlsWoxPiseHRZWKJSNPHpdlLJMZtwQDHn+Px7L9V606IDakGRJPpbyMDbPBWNY/jqqkOOKa11s5G5zn/GzNCRMO7oO8jvbnXF/WZgMV939rnrTmk/vFn2U0y2JnpVXPVr97vQwgapBBHk3B2XXUKAnTqv6BN5B8L0V3VvWzB/ycdFytF519YqKV65Bk1vms9TvFtstlmmyz/sKZHo1G3bYG2x/rzrQDlGdH1/1E2YAHgzk71Ti2BioZMUuh/zL6YBtim2vb6/5O+0dfua6ZSWJ7l7Zgme1wnVrXpO2i3CQ0W8Q/wAOClTTETNN3W3Nu2WfE12y1h2cbMBuIMWZ+Hs9YZq9bPgL3ttZCiu9Jv65FUPi+nI9e4wB29//DkhsNpi4uLmpzc/NK1CY7kReFGdmaUUd5thxRg5fLy8te5MGslgFZVX/fDUjfnzvf1QoB4T6Xd3Bw0LvHANLtpW+oNwsJhZxRiqyTEXuCHSvcTBmgTBYyUazM7a5anFZHe2Bn6HuzTrPZrNffjt4kwETZu6+9yMfjcddGs1qtFAYbEJ5jkEC9XU/abgBs9gTmhHK51tGsBED0EWPkqKgNhseR346QuZ70n49l9xjaWPEdQJI15zoxt1rHspo0oHx/7wiU11FGPauqm5fsocj9Ym4zkkbEa83z+8VA9SCDDPLayP7+/hUwhW5kX4h1q/2Ai4uL3l5g73NKcs3OG5FwZyCkE042Apvhq/okWtVCZ5vVz4g9p6gZ3KSzj/2x7TXzvrq62kt7tJ9A1L6qb7vpSw6ccIYHdR6Px12dfGoiY3B2dtY5/NgqAzuyCdzGqurq6H3v1NP7iRyx41AGopO2xfaxRqMHkR2iKfQVbfPpuLY91/k07nPG3YSc7YZ9LPo5I155Si5zYnd3t1ZXV7tj0Dc2Nq4A9HyWyWrKZw7lvrkEWpwayDHs9gvxmbG32GHmxmy2OOyCyG4Cfvo7/VHa7vLtD6d/R9k822PNs+gb9vfRp/Sj63F6eto76fOVyo0GU1ZeLWRe1T+QwpM/GZmMHHjAk8lIpov78vmt6EqLaW9FNVyXRNGU6XSCFmNhMGYnPSMZyWwZmLQ2J9KvLZYgwWWyXu4f90WLpUgHO/fDUUYuutZYcC0GjUXrhdiKHro/qEcqVRsIvk/2Cck9TlbgBseMSc4J91sCKRsCykhg5nqahU1pRQRtVJw64GhazolkhVqMlO9ZBnhyvdmJyP5LQ5Zluk3uF4/bIIMM8ugIp66Z/LLeMPHCdTiSjqzjD/h9PC0W3HbHji/OWIKgqn7U3CRay0HP6Iz1eurqqnaUi+ttH+102lnFqSVlzaSn62si1QSZU7EMNgE0OMMGOHaO/UzaY1+CPuE762z0POV5PxtjSqqnfSxSwU0m2h9J22Tx2LTGj3vSlpk4pRz3L/cRBeI7+gzi0Mf2t/YHJ/Hb8sXss7TsrucIJ2M6vc9rKzNwPGfth3jt5HOSUM01xpr2ARPp93K/bX76Bf6dOsL987Bt/Y0GU0w6R6ZaSgxnkdPfGCyOVU3FZZDCd35BrMvN6FBVfyE6klNVvVPDqvpHotootNhzKzLngOczea4BHSf2uJ9gEgwi/CwbDupFuJkT6iyewBgeL4rWpOfvVq447aCuvGi5lbOcQCTbQy63gYgBt5Ud91pRmfHz6Xjj8bg3pxxdguWzkatavLSXPiX/3HMi28DYtd5wzj0Zpckf5iz97ZRNH/WbIMQyGo16ue5WhGaTbNA9NlbO9BtGkbFiXtoxYk1zmInZK68RA2o7UR4j6uY5nc7JIIMM8ujI+fl5j/BC32FP82RTE5Tz+bw7bY99wOib+XyRQYFOdKTeehM2ez6fd9H5qj75SN3m88X+HNhv+yroHU5dQ1+RocHvjGBVVY+V59qLi4srmQgGHvQf97OXbG1trcbjce+YaN7FhFO9t7fXXT8ej7sIxmz2YB8K+ntjY6NOT0+7QxPW19d7JKj9A8aUvuN7O/7YfUfjbIcBcFWLaETVwkc7ODjowLNtPDYLP6blu9lZRzKLAduTjnkCaOaYAQljY4IXP4CIFOK9xd4b5fomYU19Wv4p9QBAcaodpAHlMoe41/vl8UWw5waKnnPeo8b89BpIcaohoN7PtA/BT8vfMGlgH9v7zrjmYcmNBlMemEztqeqj1Nls8e6Jqv6g23mi483mtBgwyjAzzv2eWGZfrBSoV8vhtTJJ9iMZk2WsCc9l4fj0t2S0qGsqBCszhL6GOWlFZ7jXzjb1bbFuZlC4Pw0VKWmuH4COBeX8eIvnRyrLZOc8F6iDWU4zWown88VzwWmJBi0GAIwHudFm8Qz6rGh9pLv73KysGVeLjX6G5Om3nAMtRsuHt9BmHB3uzUiknRfKY27RFsCx2WTG1srQgMlEgNeMmS7IA79DZD6fd84Yjojn4iCDDPLoCKQU+tcsfsvJrKqePvb+k83Nze5764uMHlRdTdcHAKQT7giAGX+TimdnZ51Dib7xcerc41MEbbMzRTAdWPoiiTu+x3GfzWYduegUal+LYGd92NPh4WFni6mn0wO9qd9jkLbFBxiYnPO19Cm2wtEK19P/MwaAWJ9+l/VKvwCb63r7d0ZUXFcTnC7T42owBcBgbJ2KasLUxEASxp47y+yW7bjLNODxiY2e977/4uKiS6nlh37xGDurKTOb7AP7nhxH1kCS8Ema4wt5bmVUyv6D/YHr/OcvRP5IwNTv//7v1z/5J/+kfvZnf7am02n9qT/1p+rHf/zH66u+6quq6kHnffd3f3f92I/9WO3v79fb3/72+vjHP15vectbXtZz6JwXA1E4juxvQZxT7HISEBlcOURsNozvGbxk7CmLCc2Ecr61FwSMTrIdfp4d/MwttnIwKDDTUNVPO/OkTrYNZ5fTYlhcfOcxyCgFkorQz+AkG0942uucZyssp5q5HrCVTukAjDgC5La6Xj5tz2NPXzkimKdAtuYmfcf37NuyEnX6oZVxCwDb4beBNTjDACUblmvGDI8BkYFKCuPk/20wWuyuDZrJBL73/CEnfzwe9yJ0bvsyRsmEh1ldkyR2uAC7Jycn3dp6mCf8DDLIF7O8Wrbe5Bp62AckJJGUNs1+AIJTCcniaD3XEQlHp6ITeXmq9TTPwe6lk2vdCJiifOrGi3Ihfu0nEOmhLlUL3Uqbk71H77sO/M59VAAm9xmkE2ViR6uqt9/25OSk28+0trZWp6enXXlV/XcKYod8irDHquUYk5XC83D+3T4fUlK1IMbyIAfXyT4gn5kQtE+0zOYsyw4ykKa+XMPc3djYqO3t7VpbW6utra2eb2Ff0xEot5XPryNODfDsM5LWZzDlaBBzysDu7OysJpNJR2QzHgl6aDPRwgSArXnhe03GUzZrfXt7u1t7Kysr3T4+E/f2dRw580l/DzMqVfVHAKbu3btXb3/72+trv/Zr62d/9mfr9a9/ff3f//t/67HHHuuu+cEf/MH62Mc+Vp/4xCfqzW9+c33oQx+qd77znfVrv/ZrHWv0UsROZ04qgxlCrCisRKzpYFFOon0WPKleTjHwRDEDnsjcTrSVv69tKZWq6pgLmCLC7RcXF10KI5PKbBaLwUrek5PUvTQm9JkXMuCkqr8QaIsXQW6sdCTQTJ/r735MA0mftFK70khm+9l0DHvnMXX/X1xc1NHRUU8RuY0oOzv3VXVlcTqKwt/0c25cdRpCsmQGOgb5nv/ufxvXVOQZCeUet5FrDIj8TOa3jXcyvAb3vjcjhwkcGbuzs7M6PT3tsV+egx63NCJmm+yA4RR4bpiN8wspW3vIBhlkkL68mrbeui+zI0wIsrZJw0MvscHeBJ8BRitKhS6dzxcvdeXera2t2tjY6EUMKBMyLslJp2uhx7CZgKg8JhtdxgtX2URvhh696iiLiTxHRexIW7+urq52z6AfIArxO2gv9xwdHdVkMukc7NnsQfbP6upqTafTbmxsn9DBkH22DwZV9uMMhFvktiOA9EdVddGzzc3N3pH5PIuxZ97wbPuGnidV/QOT3NeOMLnv6G98QvskpE3yAl6inQYG5+fnPZvpv23/7TNU9dP+bGc93w2knFrHGgC4OOrG/CSqZj85o01kE1G/PDSi9ZM+BONLXVZXV2tra6u2t7e758zn8zo6Our5nO6fDB5sbm529X+YgOqhg6kf+IEfqDe96U314z/+491nb37zm7u/5/N5ffSjH60PfvCD9a53vauqqn7iJ36innzyyfqZn/mZeve73/2Sn5XMghG0fzzpuK/1O51hf4dTy+QBHKRDTRtbQCxZcy/G1rMTIHqhmE0APFVVx1653kb8qahY+ChelJvBQ9aH7zOlwMbOTIif75S+jERl1NBsh+vODwCsxcpULd6DwLyA/clF7agI7JtBYVX1DJYjN362jWTev0xcjueWGU8bcf5OhUofmVlzmWaQEI+3wY8N6nXtsLHM9IMEt/6d5If3HuY4e1xcfs7LXF+t5/I3/ZUgOVmsQQYZZLm8mrZ+Op129h6H2DaE317z1oWw12QecA3lWEeYEccGmezinkwv4nPK5nvKs73Af7Dj7OiYnciqfpp01WLPim2XIzI8C31JRoltPsAHwJjRBdsY23H2XAGIAGZJQpk8NRHp6FgCJPsOFpx4+sd20eQt9a2qDiBAzBnQ2Abgf5hgtl9jvyd9jZwbTlcEDHl+GUhtbGx0z7M9tG1yVorrYD/B/h91sr+b5EHa12yTganfT5bPz0yXVr0dKTVQTl/DgNOEPz6myyDlk3s52bG1xuzjeJy5Lvf9vxJ56GDqP/yH/1DvfOc76+/+3b9b/+W//Jf60i/90vq2b/u2+pZv+ZaqqvrsZz9bzzzzTL3jHe/o7rl9+3a97W1vq0996lMvG0zBWDj3mN8Mih2wqn4ObTIhdu4T0WYoEmWE8+0FngiZMv2C34w00CYmJRMpU/ccknV6kiMFiBdO5kLDymQdUHq5D83gDyVMSpadUZRzGhErFyuDzA9mMVsJUCe+u7y87NiwPDxhbW2tTk5OesqR9ITHHnusdxiFU0Z48/v29nbHEFX1Nx9znCbRKwwefZ/gxeOQLIyNaKv/HW0yG0Q/OKUl+9uGF8PpOWVQa0IgnQDnPdO/XnPsQXDEkmesrq5eeSWA5x91RDkCqFvgBzFjbDCUknPajhPXOy0m5/gggwxyvbyatv7evXsdc09mhvWsHWvbc9Yz5KOBRmYVtNa+WXVsgm0pz0on2nbXEQITVRaABVsR/DJ1R5RGo1FNp9NaW1ur3d3dng7L1LeVlf5Lfy8vL+vo6KguLi66iBJtxA7yf9VVUpe/NzY26vHHH6+Li4va3d2ts7OzeuGFF7rDDLA3Pkade3kWbbbd8gtdscWO3FHvjNYwDvkKFh+9XlW1s7NTOzs7HRi0/SDKiS3LiBm/KbdqAcIzMmq7jq2kPFL7xuNxjcfjXraGfSKuTdvENe7PFrHK/57vno9JrNOH1N0nIzo90uSrSVDWEeM4n8+7Pk0/j2cwB0ajURc1tr/hKKbX6XQ6rel0eiUDzIAacYoqvqnXsbdpvFJ56GDqM5/5TH384x+v97///fVP/+k/rV/4hV+o7/iO76j19fV6z3veU88880xVVT355JO9+5588snuuxScWOTg4KCqqhedIRzp9LtkoauuHkZgJ7yqz3Ib2S+bsKSO4ZBbMXGfy2Ph+FQ/BjcjJH4edfc1AICqxULjmhaozDayCNKIJNBE0jCwWB0RaU1+ykFJMWZVfYVqNigjFC7H/Ua9DMbMmHA9cwEAaIALeIH9tAJw/xhgm1WBwaFMP9/jmUYqxQydlabHJOctn3lszFC2DLf/dz/RLtfFAkhKcOP6eY7l/flM2sl4eBxdng1bjjt1aNXJfee1QTmt+tvwDTLIIMvl1bT1mbZk/VtVPZBj3WWyBD2Z6csZDajqb163Dk273IpEmaDyb9tkk1W2XUjaCL5fWVnpHEBvW6APkkjFHvkzk5bW0S0d3vptXcvela2trc7nAbxWLfaq2Y7jdziKkH1G//K/97X5oATqgx1JUtH+AAdSrK2tdYAMm84eIIOBlk/oettWOSLDPa15SGplZlDkONhncaTG0rKFtDWvSx8pfULX32uLfXF50EtLkix2NBIQ6ePe+aF+uTXA88191do3t8xuL/MHkyh+GPLQwdRsNquv+qqvqu///u+vqqq/+Bf/Yv2v//W/6kd+5EfqPe95zxdU5kc+8pH6nu/5niuf37t3rz7/+c/X8fFxPfnkk/XEE0/UnTt36s6dO10UJBcXTAIT2+HRqrrilPHDqTVVC0YbZmFvb68XlYC19wS2IoHdIapFJAVWykbDjmWe1sMGfRiQFhPSqgMT3QAgDYMjQJ6A1MfgyA6pnX76wMzF+vp6HR4e1vHxcbfni+sNWNIAVF1V5pyAh7QYCtp3dHRUKysrNZlMemMOCIZNAxgxruvr6/X444/Xzs5ObW5udvm2zrWez+dd/3NUrOuOEcxUOKcEWrG1TsBxFGplZaVTBChnp0xgbFM52dDTN7CRnrueN1y3srI4RtbsG/3AXCCNw4xPzj/as76+Xqenp3V0dNTNE+ZBEgdpUAwYW0AXaRk6xKCe+wYwNcggL01eTVt/cbF4j9DJyUlHmiZ4SEDiyANHgCdbnzrReghbl/rHhzGhc1Pn5RHPtt/z+YP9J9gcpxu53gnWTJhRH7/w3gz8sqwPfhyZoz0m5ZK8dN2o08bGRhf9OT8/r1u3bnVpV5DdRMN8tDZ9SL8AEldXV+vk5KR3qEZV/0RmAyv3t1MkaZf7EF/DWTw494BBgyqAYRKj7h/PO8pdW1vr9vfg5+ErcnCJ5xh2On1Dxs0vSHb0KgMFtJ0MlvQX7RulP4wf6jqdnp7WvXv3ujl8+/bt2t3drZ2dnSYhm0CKSN/m5mZtb2/X+vp6r1+Yn4w9dc82Mj+Yjxx2wgEXtu/eAzgajbp16X6qekBEsOfvYclDB1NvfOMb68/9uT/X++wrvuIr6t//+39fVVVveMMbqqrq2WefrTe+8Y3dNc8++2y99a1vbZb5gQ98oN7//vd3/x8cHNSb3vSmbqM65+VzmkymFnkgkoWysrFcx9C7zKq6Mkm9ydVKyD9M8GTbWs/KCEc6uQY4mXO8zDlMJ7NVp1bbfY3r1brOdUQxwvwAtHz8peviSJfZH9fLzrP73d+Z+aKfvah45vn5eReZshLi/SSUYcMEc1m1YAXNmlmRWzEjrdOjqvppJ9yXKZetfrfRt2PBNQmYWuUkg0U7TAQ4/973tKQVZXO/Z1R0Pp9f2Ujt+nleV9WVPm1JXpPEQtZ1kEEGeXF5NW192ienBltXpd1lrdvR9Kl6SWQlqdOyMVWL9CSX74ORbAucvYGjyjVpR6lPq14JHHleay+QwaL7LgEkdXBExVkP6XukPuZzCLbcIuD0fO4zGPGzcryITGFnbZta6ee5hz3ngO3fxcWDExUBHm6DwRTjSXktu2gfA18FSdDGb/szLUnwmrYp22QiNrdvpJ3LvvQ1aZO9VjIS5DKyPPoHcJmv5vGP15h9K899f896oq0mSp2ZlSSI+7vld71Seehg6u1vf3v9xm/8Ru+z3/zN36w/8Sf+RFU92KD6hje8oX7u536uU6gHBwf16U9/ur71W7+1WebGxkbvmE3k8PCwVlcfnM5xdHRUJycndXR0VPv7+7W1tVVPPPFEVV1VUFX9sChRGCJQXnR2bI1+5/N5lx/8mc98pncMNSf9cAKOJxOTHLaG+5hQMGgGIV4oRNxQgCgC6gaTk9E4O+dMViYeES7yVmGVvNmTKBZ96I2nGTUxQKxaOM6EeXd3d+vLvuzLumgQudBejGaYvEAvLy+7cD0L6+joqGO9zBDN5/Mu2pcpEZ4Prqf7hROhOIqT3OEWQ+Tc3KrqXlrohZ4OPG3nx4pnPB53TJ33TyGOQtF3KLoEnQn03afUjXnhCCz1hUXyXjPKYi6Y4XHfsFYSzLh/Aa6sQfoNQ0SZ7gPPe7N7aZhpo8cIVqvq6js4eNYggwxyvbyath59SPTfhxtUtQ+bQebzeaef/MLZvNZOov/Gtpn8I3rAT+4hsc2h3pwC3NqLjF5z1MAAxI4futCEnp3iBBp8Pp8/YPXt82DXsJuuSzqx6EkDUtrm6NitW7dqe3u70/FbW1tdBgKk5eXlZe804Uz7sk9joIQd4Zm2ae5PrkFMrNshJ2oxnU67ujuak31YtSA73V9ra2u1s7NTW1tbNR6P686dO7W+vl47OzudH+iIIT9+TpLznr8tYF+1eDULnxFY4Mh1fDfqSZs9J22vqSNZKH6R8+HhYZeut7a21s13+xgmKIgu+jUCCaSTtKC9Xk9eg6PRqJd9leQKQDJ9IJ5lEuVhy0MHU//oH/2j+uqv/ur6/u///vp7f+/v1c///M/Xj/7oj9aP/uiPVtWDTnzf+95X3/u931tvectbuuNSn3rqqfqGb/iGl/UsR6AAAWaL9vb2OqVjhcQAMomuS5PzwsmNdoCB+/fvd5stmQjz+bx3KIZTjAAE5+fn3W8zOHYkW04ei8HXmQlCWuwG9aEeyeqbrXJOc7IoCZRa36Wx4loOLUAh0n4vREd4DArsQCNOK/O48fnl5eKo2WRUvGi9j21lZaVLJzk9Pe0UJoaHBc4YAQzybesGFNSfMc1Nl05boS5mmVwGYsCcCiLZojQ8aZCvY2kTtDOvrEj9GX+nouT7HEcbLhscl+F7MmWyxYy5H2hvzh3qZDbrOsZwkEEGeSCvpq1PXYPju8zWIE6lxt5wCqzL5lrbvpYOt52xI819Bl5+NQU2jbq27HTVIsMhAVKCKTvYtvOUb6LQNscHWQD4zs7OOkCYfd76bV8q+8okmtuAPSYLxBEK+y+p9+2zZFuYC07jol2+L21Sy1Z6vjgKlHbbIMq+JL/JugGkkjaYkRmTki1Q7zFIMpJ6VV3dH814rqws9tZV9V847DLcLwYuWVeTAgYy3OvPKdd95cBAgtOWMFeZlwAl+sf23j53zgX7MpRJv2RU9JXKQwdTf/kv/+X66Z/+6frABz5QH/7wh+vNb35zffSjH61v/MZv7K75zu/8zppMJvXe97639vf362u+5mvqk5/85Mt670RVdS8Po6Nns1kdHBzUvXv3ant7u6bTaTcpqvpsvh1pD34CGOfnZi4q0RqAk0+gwUE+Ojqqqr5Cms1mHbuGOMJBuXbKvaAceuU9UCglXvxm9otJjYKDXfN7JGi3pTUpXV8WEfuEOGWIfjCAM+tjYIqTPh6Pr0RQbAgvLi6690agrIkYVVXv5XBWvIx1sn0GIChLL07GiXFM551nEa3hGtpzfHzc25NGv1ctFLzfhWIF6g2aBhvMP/qf35lWyJw2ODXo9lpwvjbKk7xj6kkb6Td+yPnnmTBwnu+ZRmtm1gbJ4+H9Ty1AaMUKc5aGwgAPUPz444936wBjY8U6Go2uvIdskEEGacuraes/97nPdWx5OtYZ/fdv25NkpNMWmFBFL6Sjb9vs37ZZSUolodTKusB+UZdMdbYttU1tkVJm351+TuTi8vKyt0eG50NkYb+XiaOCrqN1OX2HX+TjySFSE0i4ndgjH/9uopTsGZOL6XMYwOY4mKC1b5VEn0lW7BJRJvrv9u3btbOzU7u7u/X444/XxsZG3b59u/NRnObIWDhCljYoDyejP5hv9mG9jxB/0MQm9sx90oq4+dkrKyt1eHjYu45+oN2sF4Mw5i71oE0G1GRB4VMkccBzPP/soyUwxo6TrUQ/OdrpPXL2W+1jPgx56GCqqurrv/7r6+u//uuXfj8ajerDH/5wffjDH35Fzzk9Pe0tNibVZDKp09PTbgHkS2nJ/SQkyiAx2AYQgJ7j4+PexteNjY3a3d2t9fX17ohSDi5g8NnTZfamanGsOo5jKk6Hulk4zmumLNpqFoC6+NhNJhdRFRbc8fFxzWazbpE4r9cbWOlbGwTqi0Pq9xJULZxmFh7tcp0MTqgbwMSL1KDHZdHfABnqhaK1srfizPJaDrvb7IXM/K1apMiZZaPuVihV1YFBl/vYY4/1XpRopsrKNNk0Px8lZkObqSJm+FIZt8Lu7o90VMxKeZ/AbLY4Kj37yUCVOUW5KGDGzfPCef/J0iK0g+/SqTDQs1Knr92nBpKDDDLIi8urZevv3r1bjz32WI/AXFtb66IqSOrzJB5bkWmz+pBEXJvkGbrDNozn8hyAUTrHrmNVP62Q/+1UtiLzWXfqZSfTESNnZjjzBuCET1NV3WES2UdJMHtfDvemjseupB/gyJ5fEJvRGPo6U8v9Ghr3h/uLMct3PPk7bJn3uSEJWhlv/CRS1vCRtre3u8MZOLSKAy3YsuGIYwJ6A2vb4ZYdZZ+XbTeg2VszVlZWOv/D6adJNuQzGScDbftcBqvpp5mgdYTPc9pbHwA1eY37xr89N/xjApmMLerEc7yWKS9Pv3yl8kcCpl4tYcKurKx0IIK0MSZyRpCq2kdRJuPPAHOe/eHhYad4Njc3azwe1+tf//q6detWbW1t9ViRPP8eBescVfbebG1tdROMyWcx+5AsDuU7F9f7l3DqKZPDOpy7bGdyd3e3ewcC4emqq+f9p+JE4fHcBH3UkdxZA7sMD1ctXn6L0qRPqcP29naXKmngA0N0dHRU9+/fr9HoQYTIi8mbVM1uWtFaQZs94TlcY8V1enraA6QJklFQnnNV1Tn6q6ur3bzyOyjM5LjMBOgGHjgFzMMWw2XQm2xVKtxUZmbJmGdcY2Yxy0C5JYOYBi/724ywHQzXn34nzQEQyXxfWVnp5vXOzk6PYXbbljksgwwyyGsn6EDsB38TrXK0pqofmUI3+t1CZtKxzURObt26VWdnZ12knb0vBkYmbbBL6E70skkjp3xZ0q44+pKAJlOaneJlws12lc8y3d2OMnrc+58pl/tdl4zmVC0iEHZiTZTZWV9fX+8dzmGHnDpRZ/Yy4cfcv3+/2/NKH7lPs06uL2VzD/4F9xnAOBLDuJFJkkQ1J0jbbifgSCI4I1+ub4JXxsLRMJPC7JvG1+F5SQw4i8aRVINxfAsIcgICRH62trZ6vliuBZ6b5DRlUkfmGf4w89W+Q2tOMO8JnJycnNTx8XHt7+/35jOBjr29va4sZwFxmt90Oq2HJTcaTDG5qxYvZDPbULVYFF4sLASUMk4wjqgZLVLYtra26ujoqHN4d3d366mnnupyZL1g2AvDhEIpMJkIs1dV7e3t9ZS8Q/JVCwbHbJPzb4kikTbhAzTm83l3BDlO5mQyqRdeeKFbgI4uPPHEE7Wzs9MtBiI/NlD+G4Bg9G9jBQtFe87Ozrr+owwr7CyftDaAIsqPlECOlWXB7ezs1Jd8yZfUrVu3us2pBhGpnHJDsFkRG+hlecL0txXk1tZWNydQDjYknpfuU5yFnZ2dzklAkfOdlXAyUzbiOAeAKsTvRDNbylh5LDzGTiN0WzG+7hezeFmGwZTTGDEUKPIM67sPbYRoU1V1pMTh4WHXBkAuR69DFHBATDLPjuQNMsggj44Y2GxtbXUH/HhfsvUC4hQrdIyjJNhTfjtjBX3BoUmpL1JvWufiQ6Brqbcj91VXD7vJbAl0IXVf5lzyGWVYl2c2DnbVAAh/CL1sUs62MZ18BPvs6yiHcTE5W7XY856RDZOddpwvLy/r8PCwuy/BRUZsDKYs1M31w8+hbd4jTaoeh0ns7Oz0bPGdO3fq9u3bV8A9YCEBnklpbKVBicGs/T7aa/DB2B4dHdXR0VG3Jrw36PDwsIugGaRn2qttOffzyh62WBD9MvmZJDtz0fPRzzKJyef8OCvK4+r+AExPJpPuBb77+/vdc1jPvIDZ/ea2TSaTbhvOw5AbDabu3LnTLRjvT/Fmef/fmqycUw9gMQiqWoQmWUg+zYSJ5VS7qqvHkTs3k0XMRDEIw5FLFioZFjuUjp5wfVX1oiIYCU7fwwhsbGx0R8pTP+dDO/rg1CiLneKMHDi1imupK4ATQET5PkUGZWq2xHV64okn6vLysm7fvl0XFxf1+OOP197eXp2fn3fhdR+X78VpNsgsEvOC76wg3OdOb6CtGUo26+aokI0LTgK/fXSq520CzpYRdrQKww6LlAwkBtoRH//4YIxUkMxvlCzP8ftU2LuXLJMVa5aXBttrMMFURhFp72Qyqf39/a7/fAjJvXv36ujoqFZXVzvG02VQN6dnDjLIIK+9QDr6paetKITtvMkbSzLo3GPdzb3p2Jo04lr0Lp+3bCE62yQR9bV+TUn74Ta4LRbrVWwa9i6JIt9rnZvANCNR7mOXQ5udbWOiGHBGvfxCX0cRrfszSyEjSABmR1eq+i+ZT6KQ653Jk/bFh0kQmQJgGQCR8eDPTZaaoLO/4fGmzZ4v9j88ThDT2Lvj4+OaTCbdKZVVi33Xfo7ne2scTcZio/EX7Oe53ynD/gm+gMfee6TcP631Ql2YD06/Jbp6dnbWbb3hf5MbnPCM70tWFGsCsv1hyo0GU0899VS32GBevI/F6WdV/TxRh/u435OWybO5uVkbGxu1tbXVPZdFCkK+f/9+XVxcdIvNC4aFTLiXCJIVho9pZtG2FlUKAKOqeiCQ55AmxrUslL29vTo7O6v79+/X2dlZ3b17t8vzph8BAhgvL4ZUuF5UWW/Xnb4x67G/v9+FZTc2Nmpvb682Nja6ExIRHHYrub29vd7bxP1s9pNZOZBPa0bMc8NRSRvTVLQcXe420z/0n/PKSdVz1AaQvrr64Oh1Xm4Hm9J6S3qmKyAGN8xloqqkQhrY8bJc+hsDwZpZX1+v4+PjOj4+rtXV1S7N1HsLLi4uajqddozY0dFRbW1t1XQ67VJgDaroK5QZBrDFvtKnfn9KRj+TSSQd4e7du/X7v//7tb29XXfu3OmYtfPz8y4V4PDwsLa3tzsj6DnAtYMMMsijI69//etrZ2enF9U2OWWbg6DDq/oRn1ZqOZF26yo7j97PaZuMvrdN9OsgWtkErmfLN+G3iVNH+/F50KFOp7O/gwMK8cu97h8/y5kUVYsUalKxWoB1Wao8dcMOkj7p9HPSKp25YlKbOjjyxjh5DKhnEuEIfprHivuclk8fO+MHX8OEuu35aDSqnZ2d2tvb68hK+sg22aSrbZ+FvmzNmYzUOL1tMpnU3bt3u0yj+XzegQfmPv3rsaLdjnCyFQSfCbIVoEj9aauJkrOYaAABAABJREFUT/xcAI59Jve7QZZTGu0PAKIIVvj38fFxZ+8dcaW/q6rbSwmxTjQb8bp5WHKjwVQi9kTcVgBcX3U1h7ZqAZAow4rUipGykz1x9IbvDT5YEFzviJLbkQ5+i7FAsh1mQRw69XdVizD79vZ2lx9OWtpsNuu9I8nPsvOa9axaHCtq1o0x8GeUAygBQGIsMnLBfSxmnony8f8oDSte2gSw9Ti0Ij6wjH62x5a/bbxIG2PucB9KCMXBXPGcaY2t56wZHdcLMQNnRhKF43mEcVxZWemik76X/jLj6rQVgykbv5yDAJIEok7fSMbY88NzxukBnt/Mufl83nvVAHMBo03dUOhcg5HneSZlBhlkkEdHHLHPqLQdtNZ+E/SO7aFBg+1wEoGp662vXQc/y+XaL6jqvzPIPgXfuR4u3/bHvoPvS0JxPp93QMMZJjjRy6J89nfsM7lcrjPoyzFJkjXTtVZXV7v0NdtVi/2zqqsvgaePKc9+jsfJGUEmNXd3d3vpgthlR2TymHNS7ynbWUCtPsg50gIV7ueWbeR/vvfBXz5o4/z8vHeyseel/SXq5j7LoATnAThQADDKuVzVz2pyW7H3nttuI2Pqec89+Bn+O/3Y9KVdL0e4PIcNxB+W3Ggw5ZNoqqo3MYyWEzh5MfMzHo+voGPuSWYglTJ7tUDx3odkx855yQCZzH31xHC0jJBrKkvviTHAyEMILISu9/b2ajab1e7ubo+1sZKtWpxE54gACsgbSR0ZtFGp6jNi3gf1ute9rgNK8/m8Y1ycnjafP9i/RVonfbO/v98zWNPptJd2VlVdagj3bG5udiCA6xwBs5PuvWPOhSa649/kUTOPmGtE9WzoabuVNZ8ls8b8SPBKeZ4TzDmifoeHhzWZTLp7AVcrKyt19+7dbq8e+9gwvES1YLr8vpTLy8uaTCZdnjYvYMxU0MlkUquri3eLeMNpph7Sh/QxzGO+wiBlPl8cELO/v99troa5c1QNEMjzV1ZWev3uvh8iU4MM8mgJL7NvEZrWISa1LDhjzubgECj0PBEMgxJ0Fw60bbwJJ2yDr4HAI02MOhukkdGA75IZMiZZDTqSTEzCL8k+6sQBAjs7O739o9hfgyO/EqPq6rt6nN6YABC/iueSouUMEXy33E/jsh1pqapufE0cY3NM9uGPraysdFsGnCHE+HCIGM+mPRmB2djYqMcee6z72wCIeWmgSb0gKx01s7102j31xma5DsxV7/chvc/bNJxRwv3U10Q//q/XkcEf2yQef/zx3jzCttMO6uY5YmA7n8974C4J0iTNyZKhLCJSTvPD5/OrZaqqOyiOuWB/n/Xq/eIt8P5K5EaDKYMJO5t856iVAVVGa1rha8QoOB1dpMXiZDQASWVnJZIsgUORTCxPbAO9fC794kiLn+/JhNI0w+ODJ9xvLKBM0Wr1afbHMtbQC9obJw12qUMqhWwv93HkOICVsr3JGCBlh9obGBmbnZ2dK+/OcgogIAqAlHVZFplC2WdOcivq15p3rc+q+rnR7m9C5/Q7YITxY1zZC0WKIEbKoCqNqccWxef2QCI4rYO5a3bSzKNTTqmjQRWgj4iUT250BM0G3+uIMjIKneTDIIMM8tqK9WLaDOvk3B/dKsc6FT1p1jrZa4MDdMWL6Qjud9TCYCMzEwyQbD8dkXE7cbbN6Gfkyn3ge6v6YMEgLO9rRVX8OyWjElmGfQS+SxBmcMq40E9+HQpEHeNXVT27Qns58p3vbJMgERH7Ko6EZjpgEsXZRvdf2mEks1LsM6XvaBCCHbQd5RrfZwLeczf9Mvt1tM3kcUZPASoGu25/+oPXzZW8DmDpZ3n7gv1114v2e1zcHymtuf5K5caDKS+enCieZHmfGWizP/zPRHWH20F1uXbWjYLNeHtimkkhN5Xn44iaMaNNCSKoA4uFusOy5eTie0/WqkUKhcGnxay9F5FPYjFTZtaoqrqIAafnkcubzn1V9VhDbzjGObbion8IQe/t7XV9bGBKpIIIm8PkjIOVmcGFAVNGJ8nfxaB7jDIi6rE3iDI45Hc69/7tee/PmDfMpdFo1J0MyImOzjsmh/38/LxjvaoW4A9pzfdcP4xRVdV0Ou0BEgwSY0l7iZ45/J658PQXho3jTAn3m60COCWD63QBjw9rwd+5bwcZZJBHR+x42vnnO047hYVPB9Y6O1P+0CU+gMgOOTYLPYTuI9JtYFC1SInjfoT/fYgVehNdRV3N1CfowbZBfrXITJOx+CKrq6u1tbXVRW18wAIn0Vl/2lmmXlWLfdX2QXDaeS62wbbCL5ZNP8Rl2YbzGYTvMpvpjBL8K76D6PS8gcDktTbpIzqC4XsRgJ3rieALet5m+1x/ymZssGme+7SJecopdth2gF5mCjmrifGlrRDK7C/nObdu3er2UucLnO3r2T9Nu0l9lwGqBDP2Ge1DOsqU/jzrkc8yndDluix8OIPXhyE3GkxVLQdNRsutgUtwQKcbUCXIajEtWQ/SCMwqtcQsVOu5fr4nabJidvDdtnRm/Vyzce4ft9Nt9f8Ghk5lzHBt9g0sUiqFzO9lQflku7W1By+qs9Nf1d/8CkhJBUfd1tfX6/LywZH0KBwWqY2fJVlO6k//cuy2gSPXeGxtmIkEpsHwOLTqkmCqNYcN5Hmm64ySd5oGKZX0hU83yrTSZYwQACwVH0bVUSDKJc3SaTfJkGLwcTg4eRIQC8BmLCmf8XBdTISkAvc897obZJBBHg2xrrMush414dUiEVOXWz9TVjrW1n2OlrQIrWS6W8y3nel0qlv3uH1OT7KucoQ/bbDtOM6nIxZZF4OGdEr9u9U//G+9ap/I9qkF/lqOt324tOm23flZOvdOlwMgZ1qkn+U9eq355LFJvzPr1eo3t83ty3babvkZtvdElZL0TjuW9o/xwR8AaBgMe+tGtsMEhW2+gXVrnTAebrPnRVXfn142L/i+BcCyjh4TymTNPCy50WDKC5DoAuKJbmfOk9OKKPN4U4G2nssEoEwrczMjHlCYLAMLf+9ojUP8uTCYGJ58tMfMSB4k4X6hvxwuBrTkZt9WJKS1sBzNMZtkY5aGsKquvGthPl+8rwvA5T7KvlhZWamDg4Ou7T5YgGsol4UE+wTr6DQ8Pw/A4XcgcMrj3t5ej/3ymLsuKCgrENrsdDnqmL/N5jkSy1hcXj44DpTTGX0susESJ9rQZ9vb271jX32KIGwp9wNg3E+7u7td7r3Hg/sODw+7Pjg9Pe0ZC+a4XyDs7/mffp/P5738dvrXxpqUAKdp2NDb4CAv5gANMsggr62cnJxcedceeoF9Fta96FOna6cNRTegGw0o0EH4FbxWhDqg93gnFZEq233raesVHFDqYH2zubnZ00/ev+zUe+wt/oLTtLBfROttg32v06YAa9aX9psMeIheWPgfG4fN4MTXdLqxS/P5Yt+rT5HzCcctXwVSMAEpttLC+7289znBpI9y5x6yKbBPLdCURHOK/QDbziS1k6y0z+YTAN12p/rZ77Lv5qgq/gbPdmSK94v6xORbt251foEJi6wLe6g5PZlxdXucPuiIoA+1wH7zkmBOyfYhWpSX8yLnrLNNVldXuwyiDJJ4v/wrlS8aMFXVPuXMYoc/y1nGRiczlWX5+XbazHa5bCt515nfBmeUtWyRWjk7QmHFVbVQdPP5vLeYXSeHSJMFS1bPz2nVC6VkZ9ZMSYK7VG6j0aj3rgMrABQTzzGg9IES7J9JBoR72Bhq5UObUbgYKQOhNLgAT45NTUfc6SAsXOpAnRyOd5SUseUH44cBNFC0MZpMJr2X6xkI2zBQf041srNAG8/Pz3vGjvqjqFdXHxymsrW11WNNDcI4KpVDTBhv+ptNyjYI7kcrcUeeaLsZOp5JOo4NvxmslnjNDTLIII+O+JRNR5OtF7xuucaHG9hmGHhZr6eTBkgxMFpZWRxeQz18rX0Fk7cJhKzLKce2umqRgp9txE7b7tMXvgbba4IqWX+3fxn4y+cmmLIza1uGPbJNM+DlmrOzsy5tDT3eAi/4BU4PTxI0iV4yUthHnYDRTj7lYZ/ypD6PTQtQtcTj7bmSYMA+EOKx8TPts/Jsz2vAI9slsKtVi9forK6udrYSW398fNyL3PnUR/+dgHx19cEBYMwR+3q291tbW3Xr1q3uADbGjjHl/Y8m+H1aIXPYa8ZzMKN6tBWQ6AOwquoK8H4lcqPBVItFziiQnXkvMgMKR7ashA1YHK3Jk28MZvy3y8Nx5H9PkBaQSmfZv2EkAAKWXPg+lc+KHcU0n8+71CkzYCgWAyZHU5iQdoJt3FgQVXXlVDhH76hzno4HGDDjR/SB/7PfyDXmOitYX+t+Qsm6/zidB+XGKU9mCWHx8jAJlAOKjH5jbqDomEdmqVAeVdUDVWZarWxfjBnjHUsYft6Xtra21kXSGGuUHLn8KD8zn7u7u73nAbyI0qUhmM0ehPxPTk7qmWee6TGW4/G4bt++3b0vrAVymMeZ5sI7JHhvBm9nN8tKH9qAZ3TVxsKHsFxeXtbv/d7v1SCDDPJoyO3bt7sTU9HDGxsbPT1m54m1j/ON3iKKxd9VC1/AJJEBQVVd+d+EIZ9zuJFfDjqbzTp/gftMBi4DX0n+2X6gY7FP3idme+3rM2sGkpTn2D8yaKROCYZ8INdsNut8CN4PyV6eyWRSR0dHvXRu22LuNRlou8czne7l6B9jnv4c9ayqzldxxgpjgf32fOB5Z2dnNR6POzvmF9xzTYIsp9d7jGznqZ/T/w22DA7tA15eXnan+OHjYK+x6z7MygdfpU9Ie8lE2tzc7IhPfBRsuzOkHPlkrlJenv7odgDGiHRllJXnsa7X19c734++YhxN0kN0GKgy1lXV8yvH43HvvatE5R6WfNGBKSs/Kwk+a91nJO09Jwl0CPmalTFLkIDKA86E834d2CSUIPdlGh7PMdq2Uve1/J0h1lQw8/kidQw2iHo6zJxgypEcs1S0M6NwsA0cWZ4bV2mrw784yqSuEUbOQyToM++bcXgXpVDVT2XE0KFI2E9FfjCpb7u7uz3DaiVgY+d+py92d3c79iX3SAHWUCAGuMyBjCoZCPjaZYKR4mh0Nhpvb2/XxsZG3b59uxtDDPLq6uKYdq5nrlxeXtbt27d7qXS8G8z5544uAdCph99SzibXzc3N7nh852nbYNLPGDOMJ8a3qnpHnttYmX323GdtkMrASxc3NzeHo9EHGeQRk93d3drZ2amq6qVa+9QxE3YGU+gdnDY7hXZ0ba/RXyaznKWBLsIh5Te6EztVVZ2TmZEpfAkTSdSz5QQ7mmGnmza5vhn1cqTewMlEa9oY1zejIdzHPRB3zz//fE0mk87p51RYp2vZD6tapCOm/TYg5XNsDX6KScgs16Qu9h0CrmpB4mbWQ9Xi9SL5Shv7OAmsve3DYNvgyq+qMUlOPU1GQxoYDNKn+AfMw52dnc6XsT9m/9ckv8fYkUqn4tu3s03OCCb+sMGmwTvlkHLo+6mXD0KB2OVU4QRVjpqxTq/bCsL4cXIjfTqZTK7gh1ciNxpMeWLYqfUkr6ori6zqapqdo0EZFbJzlgyL2aPcDNcaqHToHMFCaTgMWbXI//QE5HeGizMCY0aLRcLnLFArLEKhjjBYwVuxJXtF9Ojg4KBbAORNO0Ut+86Lzy/XhR2CXfQJbla0ubnVY5pplVaEjnL5u3yfAf1ONLCq/2JeC0oD0GBWxtEefuchD5TpdA/aY0NjxWjFPZ/PO8UKmOGdIpubm7W7u9uBKvq5ZbzpG7cPRpf5ikFDGbfYTH8Ou1f1YG8Auez0gVnHXCuO8FYt3u1BeqUjyxmBQqwrRqNR95b4zc3NjvWGoRtkkEEeHUkQYWcNXYttsC3AkbZe4z08jlKgA71n2gSRowRVC13s9xU6LZ0yq/on1KIzU8dnO3EYk6xFxxkQ2dewTQdAmIjFbuH4Oq3bdTfAoyz7DM4WOT8/r/v379fJyUndvXu3ptNpHRwcdKndXGsCtKp/0p2jHLTDdpm+MDBJwcY72peZEin0T+sEPewa40dGhOdHzscWGE1COsW+oAlAAHwCXQNo5uvm5mYHpuyzeMxN+NOXOVe5N3/TNvs3DgrYznsPOv3jfVdZLvPdfYqPsra21vl9zCNnBxEN5hpOjmZ93rlzp8bj8RVy+/LyssbjcR0fHzfn0hciNxpMmb02AMoQYipUAw4vUE9OyrICwGHL6I+dPyvGTNXiGp7N/0xUJgWRIuc0w4ZlRMTAJuvte3HkrXgxOBgCWDL6iMltZXd2dnYlj9jKYDKZ1O/+7u/W8fFxPf/8873+Y0H5HisXL6bWWBv00V82mMnutfrewAhlwItsURKj0ahjpljYGcJGErADmiaTScfqsPhZzBxHDlNiAE90CMbVAMHjTTs9Z82eAdROTk66FzNubW3V7u5ura+v1/b2dlVVb19YAntANWUCdL2ZuWqRTuh5z/88xw4JkbG9vb2OkUr200bW42gWEaeBvuKADNctmTnWAvvc6BvAGS91HmSQQR4dSdIIpw7CDEBDShn2AN2GfXfEqKqf1m3dgpPGM9DlVf2sDnQfJGgrdYtrd3d3u2h+lofYAV1fX++ianbqucfgwbYY2wUYoJ0mBzlcwS8ub0VcMupT1X+lBO1/5plnajqd1jPPPFOTyaT29/drOp32DpNwvQ0YvSfHjnpGziz24VLnZ1qawSi2xYQt13v8sX2kH3rbAPPJc8MRKPuKLVLdfqJBo9tkv8H9zzPY/0V7IFC9p8l95j5tbdFw2SYSHL2132uy3CcBsi8KsG7/02sYXyVBlYMRjz32WJfFYh+Fe4hMO0X03r17PX//DW94Q/eeUPy48Xjc9Qf7vB6G3GgwZQSfjr2/z0nFb09kBotBbYEdhIXF91knBGWU95u1MHPCs1qAwp87EpZiNgdl1GJoDLRQAA6dGlT6evqOPnfa3mw2q6Ojoy4UzWJnEZu5o6zc80VdrWDsZLdYuRzjHId0zCnXTJz7rqW4UY7ux2XX0uYcdxSODZQBt50D0gvc/y2waIeBtmIUAG/ejJqMbgsgIma3PA5WeB5j+pA6tepu0Oj+ymvSeOdcZywc6apaRBy9tm2guRZwSa55MouDDDLIoyMGILaP1j/5kw50OrH8nRENxHbKBKR17rKMCBO0qUer+sew25ewvW4Rgq4b7XBdW/3CdTwvI3vuj5aNy75zVsdkMqnT09Muld+OrNuIpP3FOXYbW3a9Rdx6fJF8XoIt/By3C3t7eXnZ7XfL8TU4zjq5rRk1a/me6Zu4jCTqc3wN9qmjn5+gxP1gn8nRvqwHv20zTbS7/z0GOYcMygxw0+fwGCaZsLKyeP9WjrFfn0O/eMvHaDTqMnIcsaN9rIOHJTcaTJldSMc4wUvV1dP/fB8pbmavvUA8Oa1Ml03eqv5+JSsYl5+AphX6dzsSiOXL8dKR9OZ6n8Ji9gPnkrQnfrxIHHkAXFxePtgQ6ZfxnZyc1P7+fs/BN8CgjclM0C7qCvCC/XDKQkb8EuzZeFkpWJEwbzxWBghe9Dw/Uy8sLHyPNQYMRcy9zv+FLeU+wIGVYws8JqhkHjuffGdnp9cmA+UW6Pc8ciST+UIEj3nN2JCK6XlL/+Xx7FZu1J/+8DjCyHm92ajznO3t7W7ewsjyMmDWBsYaRvqJJ57oonTj8biXWss8G2SQQR4d8Z6pqoVOYL3aBlmntAR9Z8cf8gniJQ8jYL8Pz8M+ed8mOgn9mgcbYTdx7Gz/cFbtRNtO2b4Z2CTg49m0D52HzvaBHexfSVLNhLLT69DPZ2dndXBwUMfHx/XCCy90EQHsP7aGqIHbXlW9ctwe+iHBatUivRuhffxNmwxQuZey0u+x3eOk2dls1qWpMdZEB32gVGaTmIgejUa9iKLnRoucZ3zwfQyokyT1Hinmuf0M5oNT6un7jKA6U8htsn9hW+j9faydJLhdXw7HSJBp38r9Qx9z387OTs3n8+6kYHwsrwfXlwND7NuR5mffgTnpw9kehtxoMIXYafZnfN5iAlrKyI60/172vKr+8aMt1O1ntH7sHGfZrXpnuSy41vd57zLw0WINWlEv91GygPmeiFxYLpvTWlBAbrfZQSI5SDr92b/L6mvne1lf5jX53Ytdg+T8wSglELISyjITPLXauWw+VC2AoQ8KwTAuizZlW7PO/ox6mPHzva6LQXTVIsKaSjvbn33TWgtmmVD0BkOMhXPGq6pLD3D+N/e82PgOMsggr77YfqSdT2fZUYgEJ75vmVh3t56XP4htOoDCUTHrbmxiRq9axG/qQOu/vM7p7q17XsyGZP3Q9450cVofB034oADvjXYbDVoNanJM0jb6u2Xj0ZJWuR7X7Cu30TYrf/J5Lf/Rc3BZlC/73n3e8g1bz221OedL2vllc7lVZtph2/5WFKzl7+X8vk5yfJl/mWqbETW3gf1Q+K+ZRnidX/9K5UaDKU9YT0CEwcdhT5bA+4G8tyeVSIZcEQYfhiX31VC2mZBcTACPPCqdCen2JeiiDY6qZB/gLM5ms17usp3tfM8PYXvYDZcHC5fsP0yRN786Ora6ulp37typ7e3t2t7erp2dnR6jyLsoYMvM/p2cnPRyyH1ohMeXe8xMwdy4P+xge4wsywCYHfRlIN176+bzee9a2Mz5fN7l7iY44Braxm9OjLKhsuLJlEmzZm4XLM58vniJ9DIj7jamgXAfsY/K+fDz+byOjo56Lw0mEtpyNFw3M2IrKyu9fVUtw4rBpgynF8A0Pv7447W6utqdIuix85rMfhxkkEFeWzFwQOwkwrjDutuZdgZL1dVIhk+Gu7y87PYBWSe00q4oo2Un0CE8CzvrtKT19fWOdads7wO2A5/Oq209tjhPzCUSZefXz3c/Unf7E1knTtZ94YUX6rnnnqvj4+O6d+9e934i1zf3oDmVDkCVtteElsdqNBpdIVarFv6bfSKPPe1whCpPPKQfIXedwsg9zB8iPbQhxdEf9uJwSrFBRYvUtP9F3+f7lZylw2fpt3L0N33DCYBuO/Wx/+b+cj/6RdTz+byLqvoVKuwtz0ga2S2OeHndsk4SaKWf7mijfWmXg29BHXyMOm1zpO6PAlTdaK/BSsBsy0thLnxfsiQt5uQ6VN2KlpgpSsbB1+CAtoCSmYDr6u92J9vjOjJJc3ImMGAytpgKtyvfUu56u44sqvF4XDs7O90PzyA9gPqjbL3Iki1rjb37zG1p9eMydii/z+s8jo6MXDdGCT7ou2WskBUsyjyvcflZRgsItlgqp5Ak88s1rn+ye1ybz2Fe8QwfhpLKsNUez1Wek3M0I7LOtbaRde51Hv+eqZ4vxpwNMsggr41Y3ywjTu0gtz7zD2XaNicwy+8pM+uVDplJ0Kr+u4YSFNkpdz0NTGwD/dxWnX0SsB102/rWnmzq2/ocHQ7JOp1O6+joqE5OTrpDJkivzH6xI51ZJmmn6INlUan0a1r+ib9b5gf6GY5g5D5eAAVbQDJClcA691klWMo+yQyWbI/toAFOzuckQ3O+2Mf059fZ8uv8ZspYFlXNNrqt6SPmGnNUz8/yT9p4X+fDsexDAur9fEjvhyU3GkydnJx0yL8V1oXpwOl3riVOPguIidHaL5GDDTOR+b0exKrqlIwnQb7s1VGcnGxmH6iHfzxJMy3PdeB7kHq+eDaf7f1gvLSOMP7BwUHHcFRVt/8JYRPgbDbrnNg7d+7UrVu36vbt290pQiwGxs8574A5xhAlzAkyGAw7+VZ+7p/cg+a+SAXgaAZjtMwYtSJZjBH9wE++VBH26vj4uGNUvAk3jeBoNOrNZfrfQKs19mYCHTny/PI+Ns93twlhzPyuDH9Hu+lPGx+/XBD2jGfhuNgJsfGCKfS45vGvCO/G8jqFnaVPmV8GkwZjL2ZIBhlkkFdfyKCoqo5oYv2SQWEny05kS5c5a6Nq4R/wbio7Ykjayqr+qXe+B12E3vI7gHgeEbAkl5JItN0yEJjNZt1LXE9PT7v9IOhIIgk+WTbtmp3i+XxxUiGZHhx3/sILL9S9e/fq6Oio7t6922WWtPwPpNVXLXBoe2QxIHD/O7qTBwwleHN0gmvdj1kPAxYTgkSr2BeGL4MT731T6Y8azDhzwpKgyn0CAYv9x37RTuzr7du3a3t7uweafTJhRj3x85jj3qM2Go1qa2urB5Zms1nvBDzmHDa+qrqDnXxUO6djEsFkL5/B1urqaucPm+RnnvpFwvgzXo/4uvQjeoF78tTgTIl9pXKjwZTZ64zItBgeTz6kFTFaJp5Q3veRLJFBkDfCougdTm0xHVXtdyJ40vE/TidMSLYfo+P644h6s2oLyKEkXDaHDVCWlQgvv0V5oWw4EpYF5j4HkNKOTBV0mD1TH6hTMjZua6bz5TWeP2ZZEkxV9fNvvdfLc5HPvcGYfqYcv4TPB5+0WCwbEzOQyUotYxRRlq25ZkciWb2WuL8NymizARHzk2eg3H10a5IPOR5W+IBq5rwP0TAgNijluRydnn3T2tfHzyCDDPJoiR1QO5xOKbMtNuBI3VrVf6k3+hhdk74Cv/PVFNZVaXusF82oO1PBr59AkuRL4GiCNJ1s2mQHFNtsvdmKIhi0mUA9PDyso6OjunfvXt29e7eOj49rOp12pFRKi2ikLx0JSf/juiwBvjOx7FRKX4/Ny7Lss7VeMeL//bejTdhsj3kS27TNdta2Gn/G/dWy/7TFz0/i2H3ho78R/BD6ZBnJ4Oe5Pbw2gPuIQOaawQ8AsPudaj4Mgz418HEfehxzbtqH8N4o921ul4E8ANglGeF6vVK50WDKURkvWDu8nsiJzGFgGCDYI8r2AkNp2cnGKXQuqpWz3+vUUsooyRZz5onlCZ8LLnOfzTIRcbgumuIfP4s68t4OFKsdYPoUUHT79u3uXURVi71SHNFNhIUNq9xPjvH5+XmXNuDoV1VdyctmTOxQ+0SkXJgtyahGKjHXgboz7rkPr3U6I7/zXQ1ZP57Fc5zr3QK6aZSyrSgq/27tp0qllixlMqLLAKnnJHXzOyiIEJmR5Vk2otSFexyFm81mPWBqEOg6sQYQWLFl/YkY3C1jWQcZZJDXRnyscVVdsZuOxNjRdbYDOhP9yl4PHEaTpTwr9QU6wlGRtbXFe4l4pvU9dSe92O8loszUPZmOZycYPcmPN+ijJ3nO1tZWFyUz0LQupFwcZfbYHBwcdO+QOjw8rMPDw5pMJl1/YouS1OJ3RgrS/2DfbpLgtmsef+tsdLzvTYLzOl/ApxVTHv2DfXI7saG8suTs7Kw2Nzd7ZKL3hyG2U+nPGfi6zd6H7nb4hESABGPPybS8uzLtZwIWrxH7uulD85n71FEsjzlrYTwed3sBsfsJClm/zAHPd4A/a5nfRFf5nv4ysGMN8DnRbHx2nsWcfJj7o280mEpGn4mQ7IudvYx6XF5e9gCH7zVrwj1+uRwLnN92QNNJhzG30mwBGK6vurrpdRnoskIxaDMyB837uFQWvZ1j14GDIViYZkMMplZWHrxAjQgU48KC3Nraqlu3bnUONi/1y3IvLx8cte4DCxiT09PTOjo66hlDKygvmFRaOWdybrQcbBtFyqdPDMKoQxp7MynMKQMsK6tUmvwNyHLdaCN9nEbK9aIffYiF0zuqqle+GTwrXDOhni/ZhwbxHtsEU3aAMlJGv3sPAPfYMOY6cp1a6Zp+boJk95nn+CCDDPJoiI9lzmg6ugoHbDQadWnBgClnATgVkGsBHCYeTdQ4Em//ABbeRBGZG5TjtPrWARBVC53EcyAlKde61HtQqxYp5dTJ6eU41yaU5vN5dwiSdR46F8D0wgsv1PHxcT377LN1eHjYS+my/kwwQL8b/OEkG9hQB/qVtmIzKR8BKPp/iw+i8r1JOuOQu965HSLtCsS0wTPpbvS9X0BPm3Le0L4kIPltkMuYeVxpj9NQV1ZWuhfQO83QZdFu+jnnsaNqOabUn3udYtcCUxsbG71sEPqvFWUjy4YUQXwPyiFNn/Xt9E6vUacnEtjAN+detxV/ZQBTfygtZzmZEDuO5JWSuubT35icma9ZdTUV0GxRThQ/12LlQ5mevAlo8n7u4Xc+Y3W1/14el+t3HuCscg/Xe7HzmU/OowznU5uZ4HQ0DBrXXFxc1OHhYVVVd9rQ0dFR3b9/v2eseKbZGhYDp7/lfpwEpJkPTr87ymQDZKe7lT9rZsuAxX1voJJgyv1fVVdOdLzuOQZfvo7n0P4EPR4b6mqQYlDifjcbawObYMqKK4Gp+9sRKeaH2aFMWaDv+YxrnKJKXRizlNYcdtTPTgDf+zq3YZBBBnl0xPtdWo6u9QZi3YRO4uQ76zwiU5B+rXQ4JB1gPxf/Ab1oJt3RIkflM9Jl/Z/OOP2AJDnlNCvvkUqnmfJXVhan5p6dndXx8XGdnp7W/fv36+zsrO7evdtlkWADeF7al1Y/eRwcnfA1JtrsSGc2B+XkM+zrMD9w0E0KXle/qn4UzXODcbDvc3R01IFoyGTmQqa8Z0TNc9Rz0+PSmndJEtIG+5z2x2g/v122szsAHiYfqbc/Sx/V6aWsp/Rj+PF2A/pnc3OzF0HCH/fzaLO3TLhvktDmWZ4XBujpM/i6hyE3GkylWAFZcVi5ebJOJpPu6ObpdNpTgGaTrPC8MA1O7KjDwPhaQJsnNuIJd13buDdTsZwTC5PvyAOGJBkR9xN9x2/u4cjydKidV35xcdFteiRK5cMiSN07PDzsnQbkhW1F6gWTYMoLDyWPUvAbsWE6uPbo6KgXhWFD4vr6erewbWRz3pjt8WlPXOdrPI5m5jAcmUvPWGbkimd7zJmXvNPL88hjjZJcXV3t5XlbUaYycZTIDgo/AOoWyUB5ZjiJ5JJu4pxlOyGp+AzivKGaVJQWiMu2GJxxnL+djwTyy8oZZJBBXnthzwN21uQROtbEi3XT2tpad9z58fFxHRwc9ADQzs5Ora09eEkoLxQ3qEqxrjbhY+d5NFqkKxEdMkio6uuo9FXs1PMM2mQCzzqQPkLnmkiyrapavMicFHuyRabTaT3//PN1enpaBwcHvUiUyTfX3b+TqLPfYlLP5TmDA8fZ6Vn0q9vs8XbfsyWBz1rkmv0YO/POgkA8xtjg6XRaq6urdX5+3vkO9kEy4uHnMh4mTvmNf+pn53xmfnEwGH1JO2zrvbXCNhpfhT4kPZbfTrnzjyOYpIMCqAx+aBtrCLDOuHofFwDV/e8xd90SBPJM+xQJ7nNeeky8beVhyI0GUy0Eb6HDEzTYaWWPVG6kNAr3omwx6TiOLt8LhcFNBp/vPTFol397wO1wIssidNlPZkT8PNeXe9nbNJ1Om6kFZqpIz7t//3733eXlZR0eHl7ZD+UjVHMsUBY2YltbW13qoE/mm81mXc6y9y3l/Sjwra2t3ph6oTqS5r5Lo2xA6fcnGIRn/5u1sdHIyGem39nAopisIDM32yzNdZKG3EAcyYhRphpSjpVa1gNJ1rFVV68l358g0+swo0ruu2Vy3fO85q7TKYMMMshrI0mCtHSdbRv6cjabdSlY/I19q3qQMYFOxtbZuazqEzHoPmxMRjHSqcTWODOB+3DosefeP5r7SxCnL1tXJWihP/jM33FiIS/enUwmdXh4WMfHx3V4eNidDuh+bkX/3O9IOqhJfmHH7XQ7U8SEH6DK9pZxMriz/ueEQT7zdQbfVQtQ6VdlJNGW6WVpywALkMeZdpn9Y+ef8pdFwzynW9GrjEZ57jEf7DMYqBtceZ9S+iEtgtXzM+cDfQyY8vzjma5vttdzKvuw1Qdui8vic1/fasvDki86MNVinbmGyUQ+so+15F7nclb1X8jbWnBmOXLgKZvJZyNgFO5olVkBFrpfnFa1ULhOpfKGStctU8XcH44kedITsTs8PKznn3++S4+g71opkCcnJ3VwcND9f3l52UWDjo+PrzjkgCPGwsrTDEmeCONrudfKJMfD5VLeyspKl3JolsSA2uWyjyvD2ZTPsxlP30vf0WbXyeOeypdn0X6OA6W+x8fHnXJIhZbAg/+zr0ajxYErHhvn5htkZt+YaeMZNgDJdBFdSkWIgvcphS2A6lC/DaTXTpbta9IpMBhkPJIdHGSQQV57wUHDHtoOZ4QbXVBVvZeLbm5u1mQyqdFo1L2eAtJvPp93L6ona4E9R3bibL+wh2Q3+BQ1dAk2zodO2E65TKLoJiBpJ3WhTGxCOo/e1+pImf2ay8sHh0tMp9O6e/du3b17tyaTSZfWd+/evR457LYbULmfbX/soDrlENvFXhgigmSetEAM+2XS9rfIQ+53tGY0Gl3Zl01WCz5E1eIwDK41WHB2RALlqqrj4+Nu3m1ubnZzwXYqwWZGVwDbVQv7atCToMCRKK8B5qO3sxBF43ofmAaQIqKWh6px6BcRKE5zNtHOM+h/rstMIaLDzEd8AeZQkpvZ7oz4pf9vP3tZ1MnPBvw+LLnRYIpJnex41dVFVrV4c7WVAYAlUwgSNJkBoMxkaezEOSzccvCSMTFblXnRWQZghTalE9ti2/MzyuVeL3AmmY9bNWOGospwPhMd5sinyfC528Ab4K0kWdRO7UMJOzrTAlOMB+NXVVfKzdOecNCz37nXfexQslkynk0bveANBpLZyhTHjA7xTBvgTFMwiG9FQV0Gc82g0Bu7vVaSkXL93eeWrKvnmctMybZZ3CetZ5qZakUGs3zGs8VeDTLIII+mZKaI9dXa2lqPnPTppZka5M3xm5ubV06co0wcMx9dbj1ZddWutljzZTqLdqS+9P2QTy4/y/Vvg7P0B7DhnMo7mUy69L7JZFLHx8edowzwSCLWOjTbaXGmC5kf2GCnPXLKL+DQQI3nO8XLfoLFc4H5YDvoFH2n3TMHfA3vQ3JU0Fk5ttcZeeFa78W3r2ngwJzOdtim53zJeeXrcw75eoAOa8FEqjNl0n56LGxDvTaYK64Pbcs14a0Q2Q8GqBkJbInXqsV4oHVv+lBDZOoPhcnsQUzHKSeZGXcz/aQAeN8J6Wscy40kwHKe7Gi0yFGljskQ4cB5EZiFqOrvRyH0yiJmsfvlqQY17pPWhGKiEpVg79F0Ou2Yqf39/V40hvoTQmchonDIS3c6HnWCXdre3u7YO5Qr7BR9yCKDzfOLDr1vyIslIyceozytzsb17Oyslz7glA7GFeXvY1KvM5aO2rTAckt8D8DNIA0mzcoL42BFSHSSvUmMHfPh7Oysi8gR2dre3u4ZK+aQlSJ1dERwdXW1l7JJGz033UfLTtDjGTZey/rH13O/c+1NhGT/X15e9pi0+XyR3kB/J8M9yCCDPBrCUdS2t+jB09PTDnjASKMrOckPpnw8Htf29nadnZ3V3t5e9x4lEywI+0LQMQZVOIqbm5u1vb3dRSOSoMEG8Tc2yKl6dlJ5LlEF9DXRA9sWP6vlzFJvUuxPTk7q6aef7iJS0+m0O/LcfYcu5XnYHiSB39raWneIh20pn4/H49rY2OjAk18qS13pJ0eQKMe638SjfSTbu7yWegJymBcmgFsOtkE131FWC9BiY7wvij7xqYrz+SJ6Y8kolgF0i7zNNFDK5X77UswH5vTFxUUXtaUtl5eXXeSIuYC/57mJP7y/v999zvtETajTXkdpWZtOqcW/JEpJX/nExfTrqYv9BYNV5hfXO1hgoj/H4JXIjQZT1yFXI9R0jhLJMwhWgCgp5+e2gJoVq9lxg7tkFqoWebKtnFPXMxcU91YtIm3uA0cBkt0ws2cFTBRqOp3W8fFxF4513VJhOYrH/44Gub6AVgyZFe7e3l4HBNxWAymDSYd6s27u3xynFuCyJPMHaGP8aatZj2WAynOopXSXzUcv/Mx/57vceJvGIw2B2+fyYFydppdzdVkfef602stcyO+TBbJys7HyuC7rD89lj1mLTbNRBNSZIBgiU4MM8miLowMGNax5p385S4B7+BzdxdrP9C70ke1n6lHrQDvMtr3Wj9ZB6Y9QfzuBiAkebwdAEvy1bBHPJerEC3jZH0VUKvWq2+q6+8dEVh4rT2bJ1tZWd/jQ3t5e95nb4mwXQBtgytEU6uS6uY601/3XAiQZ7fMYM2f43mAqD/PgOtutPB3a0SuPD3O2RXonYduStG9uv+2fbSP94bmWYD5T/NOW0h8GQq3oqtvLvHAqI/sWaUuCrqyr53L+b1+A61tZKtk3JjAehtxoMMVRium0VvUXSobozUDAauXC86A7CmTFwuKEKTMrxHVsckUcImZhJ2hxhID60l6DMVgftycnjB1vJjURDEK1L7zwQk2n07p//34HpIzYM2LHM8h75v0GKFTvSeP/1dXV7jCJzc3NLr3PURO3dT5fbJIlvzkXliMvsEwczGB2CGOCsqBOPokHxUk/cY/724rX13rMHC2squ4kO48t84Wyj46O6uTkpGMhcw54rvn9DbBJfl5GxqzonWpBFM59miwPfWfngrYzdykXYAYTfHh4WCsrD07K8tqA5fQ+vGQa3Teug9ceRpa6WTEzN3w6k9uB4zUej7u5mmBtkEEGeXTEEfKquqLX0OU+MRQbApNOihFr/M6dO3V5uTgoiUi7SRd0HdkQJv343K9ruHXrVpeJ4f2X1pWpc21j7IDOZrOejiJ7xnrNkRQcUUdfIEh/7/d+ryaTSf3Wb/1Wd0of19hHMWhA15K54sOg6AeeeefOnZ4TPB6Pu71kqWN5P1Om/affVXU1TRGx3eB/oj70Nc4+ZC79ahCE35Ekou2PU0YtLTDjDJLV1dUuGudTlu0H+Fn2K2kP/gw/rQhaAnrsInu1eQZ95ewggB/rhNObM0OE73mtzenpaedPsk+LCKT728/nWvu+kPbMF6LHHnuvlSROuManFePfjEaLTBn3W5KqD0tuNJhyxCAjNFVXjwM12KlasF1mDhz9sFLDATTD5fdI2LHLN2fzbE/2VlpVCxQirrtRex5SYcBlYOVFbLBwcXHRHTiBU8/CzUgbxsVOLWH7W7dudY464WMULywVi257e7t2d3e7MWyFYg1YCAsbzMAmVi3SPd3nMEMwL96AOZ/Puzo61OvFycKzcvO4mKmrWigmnuP55PTBlZWVLs2Be32caY5zzuc8sMTgzdFYGxuDTtZDAn+nCnCtHQ/GyvPUisj9zklQdl64BgN2cnJS0+m0t44yd9tzlnnizcDur0zTgSjwOnA/8TzWoRnYAUwNMsijJclI+2/0ote/STJ0mVPibQchgnDssIsQMk7zMxFmW2HWHD1mEtffZz1dl7QpPIs6pFPt/si2+tjzg4ODOjw8rLt373YHblQttj24P0kV4zfPQlez15nn3bp1q3Z2dnop80SjOMiDttKn83n/haruU4Me95mlFQXJKJO3KHCN+9d2L4m8HAfub81J28ncIw6QxJ8yoLYdMyHosXQGk/sir82Dr+zrcE2eCOy5yLiQ5pfADnvKqYWARfw6AJRtePaXbb37/vLystvLmPMl1236HdkO+y/2DxwEsB/7SIOpy8vL+uf//J/Xv/k3/6aeeeaZeuqpp+rv//2/Xx/84Ad7Df/u7/7u+rEf+7Ha39+vt7/97fXxj3+83vKWt7zs55mlXvY9v/23lUnVYqB4d49zSZlUsCZV1YEG7s0JbzFidtgah9mMhwc4WRJPJhbfdDrtgJGVL3VuObwcUf7CCy9075NoRaTcD0TAcFDv3LlTW1tb9fjjj9fu7m4vCphHcbIvhbS98/Pzunfv3hVw5rqaFWSccnHZ0eYHQ8gRpY4+wVRyz+rqageSGaeMeiRYSgOa88lKiAVt5W5jAfgk5/7g4KCqqjspp6oPFDGmVsQZ2bRBSYLBypG5gfJMcEVZbg/j6OdU1ZV3ilVVl0PP3HQaDWW5Tu6v3GScQMnj6Xx2yqbeuRZZvwaxzBMzoK01PMggg/Tl1bT16SwlkEinCJtKtGJra6uXnuQ2rKysdJHy8/Pz2tjYuHKSKaAtyVZ0FREaH+iTesRR9txbCtFlvTabzXqn4SWph353uhVt4uW7zzzzTB0dHdXnPve5Oj4+7k4JRDg90I49+6HJOMFBJgsFQIW+hDBsgSIfxIDd9inK6XTTz0k05xjTL/Sr55vtFmXYJ7Izb4LR/lJr/NLHzNRM+yXcO5lManV1tTsUBRIX8OD6c499DhOnztBwPT0v+Yz2eVyJJlKmfSVsdB44hrA28EPW19e7PVI7OztX5ovnIoDQfkVrHEyie52nz+X20rfcQxYT17SybVi/gP2HJQ8dTP3AD/xAffzjH69PfOIT9ZVf+ZX1i7/4i/VN3/RNdfv27fqO7/iOqqr6wR/8wfrYxz5Wn/jEJ+rNb35zfehDH6p3vvOd9Wu/9mvdBHup0mINlqHNdCzNSNjhNZNgpG1HFEd9Pp/X8fFxL986kbMRuQ9nmM8XBzkYMPm0GTvjtI2FzkECPHc0WhxdnvuWErgcHx/X5z//+S69zFEFK0WHcmHwRqNRfdmXfVndvn279vb2am9vrzMQ3GMlaMOzurraRSVaCtYKgXq77W4rLGEaPiI9sCWMq8sjRZQ225nP+WVl3GK3/D9CvznikeCM/mDD5Wy2OBqXPHauwfigzMxULgPw/DbTRN0oC2X2YiAi2S+3F0AKGEexOqoLm+WIpg0Fvz0OrTmAOGpJ35qcSNbJRsTj4TmbYH2QQQZZLq+mrW8xynauLLZBScwQgbIOwHYAqpwaBFCBZEQ35as7ABgW6y3sIb4GNopUZ9vbqsXmeqfCmbDEflQtjvWmnwBpk8mk/uAP/qCOjo7qmWee6e2Fdn84i8MZJQCn3d3d7gAJbBX6klSytN+2j2TNmEhzKhbXZUTE/cY1fE46Wfa354P73P3mlHHKwz+zDchDkzLDKQn6bAfOPaCSPoVETdIcm5ekJ98ZSKWfksStbSfjlacPAtoBVD6gI9Pg8BHxSYhO+kARv8bG9WsFCLDRfmWA56/bkPOA/qAc5jFz2b5m+mmUZQLkYclDB1P/43/8j3rXu95V/9//9/9VVdWf/JN/sv7tv/239fM///NV9aBRH/3oR+uDH/xgvetd76qqqp/4iZ+oJ598sn7mZ36m3v3ud7/kZ6EEHFKvWkxKL8KW0+vJynVV/QgQEQqDkvl83jni1KMFpMy4+8fgrRUOTeWZn7u+LNyMrmQbbThg4Y6Pj7tcWIem2Sjod2RwcAQAZnd3t0vfc3oDZdjYmZ1CKVhhWFG53slMGAxWVa89jBHPYVE67dHjwliaZcyx8XhTJ49rKr1kyOhzFjg/rj9GnHsMKBxdMchGQbaciRajlm3xXLUhtgHLOerPM4xPNBcDuba21ilY7rVhop6tvzOilGs3wbbnjse1dW2OnetnA7uMjBlkkEEW8mra+nznHJKkiZ1fO6Be83ag0MGkAcFm26EjqmACz2QZ5FASpX42ZbXS2qlHOv9m7w007Ovw7Pl83hFxR0dHdffu3bp3714dHh7WZDLpnchne4HN5ghzTth1+v7Ozk63l8Vkbaaa+W9HH+xDtQCu70+yzp+npJ1Lp90kuP9mTHy9bR1ji2+z7OCJlq1dVi7zCXvPCY0GP05Dn88XWUAui7Jt8zwnPIeS+AUwUSYpex5Hys3tIhsbG935AYB85jw2P7dzOFrp8fEcz2wR+p7rbcfpK+riMmlDZpR5LDKY4a0AD0MeOpj66q/+6vrRH/3R+s3f/M3603/6T9ev/uqv1n//7/+9fuiHfqiqqj772c/WM888U+94xzu6e27fvl1ve9vb6lOf+lRTwfKSMIR0KN5cXtV3kqquRmK4JidZMgr8bWVqVAyzhRPu8pNFMlonRYrBvA51p/Ofn/McRyesbFoo3qCDU3wODg46QOpr2TjK/iY2mK6vr9cTTzzRhfi5HtbFRqrFJOZiZ8woh/51O1pspKMS/Hb6G/eMRou8b7N6lEu/+ah1O9SOZlnpOfJHefSJwSuMFP2DAnB0zONF2TZWPurX8wZFzDzL+duaS0QzfU06H+4Dl0E76S+DX4wja3FjY6P29va6NEqnN2b9sg5m8SwGTVaeBrNmFelHs5XMt0xJYSw8ZwcZZJDr5dW09XlEduqpBBwmNln/6ANssfU0duL09LRnP4hWTafTLtXNusn7cvzCU+8NSh/De2TR3fY3WsQcbUviuGoRFSKN7969e/X000/XvXv36oUXXuiIU0g4E6AAKE7b29jYqMcee6w7OIk2cx26H92OLfVpi1X9dH+Tg/7cfkE6zFVXfTok/YD8jvnBOGV2UdUCLFAW45jRCrIq7H/wHMY5xy3HmnJoP/1DvTIS43LIFKKuzpgyEez76ce0s9kO3i3m9ERni7gvHU2zvTbhvru72+2d977BZVEqfAnWIevI217cRq7heZRnsJ6+fm6BAOThUz1MW//QwdR3fdd31cHBQf3ZP/tnu4nyfd/3ffWN3/iNVVX1zDPPVFXVk08+2bvvySef7L5L+chHPlLf8z3fc+VzT1yUXy68VMD54wmTn/GMZP6XhbTz/Ue5ETXrQXkuu1V3T5Kqai4+sxxVi8XkcOj6+noHnvL5o9GoU56cwLOxsdGFcnn7uhf2Mqfd9Wpd24ri5Uk5lhwj78ni88vLy55yMgPTkqyvr3P/Gly16tOag/x2nbnGjFnOV5/85xPvqqpTZjZWHseWo9ECtNn+BLO+zmmJbqPbyhzjpCnGIcsy6DED5/paEuzxdxq0FjuZ35ngSMY06znIIIO8NHk1bf0y0nOZzURw5Kv60WfEQMCnjHGdN9qbHE17ljqXZ1ct/ALXGecv071a0RSTirYtbuNsNutexMvrTSCl2KPjPUImA4kmEIHCKfYpgmb8qSuObqaWud/dxvTRsh2pyyELDdLyvrSv2KOW/qfOkJbuO9s2yqB/AIImerMueS3i5xhY4MzbR8u2OZMnyWDbMM+PVn8gtusG9dQBwtckLn3CPKEcE6uAbPbEk7rnKBNt89rNfqTeSeQiGQxJv4y56n5ireW8w2fMOfhK5KGDqX/37/5d/eRP/mT91E/9VH3lV35l/cqv/Eq9733vq6eeeqre8573fEFlfuADH6j3v//93f8HBwf1pje9qbdxHJbAyjMXrVmBRPoeqPl83kub4zcOfJ70x+CjfJjErU2SdmJZKBkJ8ff+n2cRrgVYrK6u9g6IgHVw5MW5rE5bcPmvf/3r6/bt2x2QskLFILjvzK5VXWUHbRiYvGyOpQ1E+TIHHeE5Zjkc5YM5vLy87Bbz6elpx8SlAnSbkxVKZz2dfpdh5TMaLV5K671zHnv2KLmtGcnc3t6uvb29Oj09rb29vTo7O6uDg4NeKmCCKeru/nJKgY/4RQl6DubftJ8xop1meRgPngtrmc6E+8LrhXbkwSs+fSijynZC/D0/SQ64vY72eR2kU0W9HqaCHWSQL1Z5NW19ixXHnqA/W046NoUIA9H5+XyxD4p72Q+ETiGigw5dXV1ssucz6lF1FayhZyApKZfjpyE47Qj71ECe4/0g2G+eh46dzWZdWt8LL7xQ9+7dq/Pz8y71CufTp9nSHshT9kbduXOni7RxH/2a9oMDnrLtRLvwgayrbR8op+oqAYYvZbH9oy62Rfal7LslmDEha/LVWQ2OoHGNbWgCrlZqYPqP1JNTlJ3RQpt8AFYeIoHvk3vinX7naGf2Kbb98vKyS/00AOLwFe5nrRDRcZ9z387OTq2trXVrw/5mktuMs3+yfxwpy7FKcsT+Bamp9mNGo352kgldRwofhjx0MPWP//E/ru/6ru/qQvh//s//+fqd3/md+shHPlLvec976g1veENVVT377LP1xje+sbvv2Wefrbe+9a3NMn1coiVZczM8+T3X0NEtNtz3MCFz4eRnKDeDFpRbRg+oA/JSHbhsj5WZr2HCmdFhcnONHdjHH3+8zs7Ouno/8cQTtbe311tcfmGun+0c6ByDbGOCEdc5gRO/0zlOZZxsE/1hxZl9ZEDivkxWx1G91hh5sdrJvy7Ckde3GBrqDttjoJh9lOAnFVMrfO022Bjld/7fP/R9MpFp0KqWH7me9Xd/Zh0SMLXak/flfDSoRxmbqHgp0a5BBhnkqryatj5ZaT5LktJ2w/YidZdZbdub1IH8zmi2JX0K32dCMVOQXHY+k/riJBu8pe3wNSZn7ZMYTM1msw5ksQ+K0+XY/+LDhlL/2u7ibxh0eWwcmTLgoW3U32OSY+hrDLwov1U3E6UtO0edTMjz7LQJbq/rRz0chQGML/NBl/l5LXu7bK4t+9/9kPO+qh/Vov+8ljwXvY1gPl+k1iWYYq54vrhufpajql4vy2z7i62NFvhmvXkNZGaYo7zL/LUvRB46mOLdMRYz0m9+85vrDW94Q/3cz/1cp1APDg7q05/+dH3rt37ry36eHWAv0hyIqkU4PlPVGJRMvwJ8mBVjcMxccOQkyLwVjmfgiHi1ojf0EyFWT9yqq0dltvqCCMFo9IDZ2N7e7hTl9vZ2xzacn5/Xn/kzf6aqqra3t3t5xd6omPVHYY9GC8Rf1TdemYZIv3mC+5hO9nHl4rDh8GESvDDOzjH1oN5Wbi3BEPAss3aOMrl8h/FtwNw/zik3y8nYo3wzFcTjCWM4n89rZ2fnytzOtAQ+80Eonn8GD3l6jaOGnkcG0gYeVdWdUOTxzLVGm+lLg0K3lXGwIXP/ZzpoGpuWrvHapnzSV21QWGcGffTjIIMMcr28mrbeURzEuix1IjoEnehXYKAbUxf6HYFVi0gT1xoQsdck9RA6bHV18WLbVuQl9W5G6X1Kb9pT6zXsim0E9tUEEr6K9wZBtJIpkXtfk8yy7nSUIPt5Npv1nOzWiWlc65Pklu1t8nhSF58ClxELHzJAX7tvHVVhTvF+TdutFvHn8uhvA1DGhLKczdECgMxrAwFnPlkM5uw/0i7mF+Oc2VXuJ88h+7SMPdsNkDyBsFVfrxmyVzxfqEOCKvtZrCHPWa7lPq9T+oMx4IW/Jg3sM3F6NX6wX2z8SuWhg6m/+Tf/Zn3f931fffmXf3l95Vd+Zf3yL/9y/dAP/VB98zd/c1U96Kj3ve999b3f+731lre8pTsu9amnnqpv+IZveNnPSwSf6NzSCiGbkeCeZL/8txF9Aq8WCnY9W3VLFmMZc8H3Vf2XrmYbc+GgMMbjcW1tbfUmGAsGMGUD5M2KRvGOSHmSGywlm5TA1XXNyFSLqUjQy2eEtc0wJEPmcc1+yjE0qE6HnHs8b9zG69goP99gM5VyaxzpHxsVg6Xs87w/o1R+ZgsE0e5W33gsXG/PxxZJgVPgPsq65rpb1n+5jsye8r3HyO1I9i3LHmSQQV66vJq2vhWZSiLPn/G5yRk+c5l8ZluWgs5IfW0d6jo5KuNntOqZdsN2NtOYWvXK/3Gw/W5HiCTbRqc8Zwq/bWGrD/xj8DAaLd4rlSAznWWn4dmG5RjnmLl/csxac8T9YnFqnonaTEejXO7xb+piIJXEoH0Ik/H+3GR6grYkOqlX2lK3seXX5nxkDrjPXQf7mpCrOWcz1TD9sJzrOS9ezO4umwce/9QFrTXBsxzQYH1l374Seehg6od/+IfrQx/6UH3bt31bPffcc/XUU0/VP/gH/6D+2T/7Z9013/md31mTyaTe+9731v7+fn3N13xNffKTn3zZ75iyckr2IBdz1dVT1HyPF4h/e4IzGc3Yt4BFLkIWEZ8ZnFAXMy2tfjADZdTu71GIKysrNR6Pu7pysAGfUQcQOmj96Oioe+mZ+9HMik+Xox35nickF6IjXz4IYz6fd5EpmCOe3Vqg5JAfHh42wZTrzjMchk6ARXt92p4XH+3zfPE+IPo5jWceiY4SgNGlX/gfZcaBH65bpss5AoVygB2kXfS/WdQ0TC1Dk87DMoDr9WSA1fpN/Z1yAoOZJAdiY5JzoOX0+HMbBp5FRI2y6BezbNdFMwcZZJCFvJq2/uLiovcepwQeeXy1naWWX4Du9j3pgBJd8udJ2qSDaXtnnW8AZt1lBzFtqffAGOwkyUfUjL0r4/G4O001D2uybXBEomUXs88SQALaHEHLqIGflXvNz87OOlB3cXHROwnOp7XZAT4+Pu4iLrkvKX3BLKNq4YvZr+P7i4uLbl4aYDHW3qudoMe+hQFwpv0RQUGYY5RbVd2epqwj88L22X1q2+h56uiT7frOzk7PpzCZTJ3YM+85jB2nHc6MAVyurKxc2bNtct0ANeeefZUWQHTbWr6V68Rv9oCzn562PtJHo+/u7tZHP/rR+uhHP7r0mtFoVB/+8Ifrwx/+8Ct+XqJvd3QrJc4MlUFODpqvS8akNRGuU0SAl1Y9mJxeEBnydwqSnT/KzwW0srLYwOpQuE8kcjh2Mpl0k+z4+Li3SOlTK3kzLo5m+T0WKBXut7JxCDo3KGY/Zf+77wBk6Wx7nGh/i83xeCUYp245Rq6TxfvluIe2tNhJFnlV9fqCsUvFl+luaZw9Np5v7rMEK+4D7rHkZ8uYpJyj7h8bMZ5jAJx9699ezy12jt92MPzsTJPwHON/jLgNzgCmBhnkxeXVtPXXETp2Mp2Sbx1s3Wcd45/UsVX9dybyTN/LZy39haSebpVlPWkn0NF06+6WnwOwAeRYl5m45H+DIovJLfslmaptUIKubpFzvt5OfW6J8LuJ8oW+2KJ8p2drXuR4t6KSrg8AwP3Jc3wtPhGgys9y3+Zz3B+OBFYtjkgHTM3nD1LRkrzNsfG8MbDPU4w9TlmeI3PuU3xQxmFtba17HxV+nbcxpM/Dc5bN+5YvbT/H0iIyWn5ICxTaPwVI2U9dVtYXKjfaa8iT3zzBzRZ4oDywOPWw+q19IgZdTCKe5QmRjj7Paw2y64rM5w9OWbHyc9ty4hlAGoAweSiHiXN6etpFcnBmp9NpB6bIKecEN/ZDJUhMkMnfKIS8psXEceS3nVYUlBcNZRmU2XAl88gC9/PdjgQTBtNe7PQ3Su/k5KTOzs46heR2M6em02lPgbhuzMVWbnQaMp7nfWHZJ+4XgyhYwTzFx4rQxo8+tHiethShJSOYVqqZr+3+z/VI3nIaCvqJfHqvIwOoZEuT7WoZPj5nDnJaFlHTQQYZ5NERv+/Hn+V7+NATRJ3Rr61DFbzOrZ+sOy2pf8zYpx4yEYVOMpHmSJqjHY6MpB7OumR2xNraWu3s7PT2nOLf5DHcJuusS3GkW+Dn1q1bvZPerPO9X8mOPaflJbE3n887Z5160Z8GCehngAZRBn5MmKb/Qf+6nwFDRF7sU3n82OqQZVCn1jjnHLAQdYK4s/+E/2Z75q0Atltp21r+GPM9bb3nXAugM+98bR4Owj1OaWT8aYPno8V91gLctD1Pr3TfJVmRBEO+LxL9cHp62oFU3/8w5UaDKSZ1i8Hx4klQghJwhKRq8RbwltihT8mBSXRs5ZyMga/PvTStHNr8neVwsqBzmP1slOPl5WUdHR11zqz3QuVhBH5mtpEfFo8ntMGr25PvN1hZWRxI4ZQx+t2/XSfqmRsdWZBVizeOu18oz4vOBo7yOLCA1AIrg9zkSrl5GAT1HY0WL76DfUOh55y5detWB94MMj1/UvG2FAjX4XSYfXN5nmPJjLovEzwlOMl6mAVtEQ/c49QQQCwOiI2s+9n1tJMA82fjskwBm4DAeB4dHfVeGjrIIIO89mKyiJ+zs7MuJcpkUVV1+tckX9ViA7ztbVV1TjbPqmpHw1qRAe9vMqHj9Dzr+dSBBlN50AVsOjaTMvBhKJ+ME0d7IFBti12XrEdGOaiL37/FMfMp1r8JDOjvFphyO9JnquqnI2LLqAsvtLWdSMCbPz4Uw4Q19xoI2F7ah+NzbLPHP0G5QZCBaYugzD6jrzxm6ctm37aAUvoo7guPN2uIuqRvwb0GVAaEjFGC9PTFW8C6RcDjs2UQY1lGWBLJlEGwpAWO0699JXKjwZQnTtXCmTMTYgWb7Atl0Om+1/t/vADMXFlpuD787cWdC7Kqvy/Lk8m5xHxmhsoTxvVxvSjX6WRW7naYk53yQqdNyVq5HXaaW33k63LvDN/jwFo5uE6+FnGqIr/52wxgC2jnwRFelCgVmBHYPSvLFCsvnuUx45n0PXnirQWNckY5+ZqWYWq9S8tMaCvdNfcNGlh4LnlsXb8sz32JYU8x42aD0SIJfE06PlV9ps91yXns8ck553qhbMmtHmSQQR4tQZ+jH9H3JsKwPYApAwOcaO/XSsBkAjOZ+yQQ88fEjm1r6qJ0KnFMrdMdcXP6IeCB+lAe93mvGHbGto3/W+9zTN+IegNgDHSShHNGjf0VR3HseBtUsq+b8bR/wf+2Q4yjv7OdTHtqf4j2J9HLWPAZBG+LHET8WW7N4CX2eU/aKdqU/WKQmH9ne1v+SPpP9nPcRwk+XKbLtQ9hX8F+gp/ZIuTTD/fa45ke99w/6Eis1w1lAJq4J31mYwKDxYclNx5MsUjtUOcEztQ8jsUkjYrBqlo4mU4p8H6jlkPNpGCSZn5qThwrpQRNLcCRC9/h2gRctJ/DD3KBWrFlKNnpAdnOjPi57Tj+yyIorp+BnRcQrIFZKzN0NiKUl4diOLRNWUR4MBKtU2kSTCXQakXYPMdGo1GnPPnfyvn09LRrPwDX7c++XVlZ6VI+UexWfpRDWfly3RyfFBv3jDqhtKyAMxVxGbB0/b0ueY7ZtpbxNlD3OkjjUVVXInZp8FgLBra0hfq5HqRy8nuQQQZ5tIQIMoRHkmbWFaenpz0AA5gCSFmnei+lHVeTh1VXSU2ebRBjnZl2H3G5mRLlzAY7kXaqfS3lGQg4isU9vLsr7YztmnVsto3n8FyDOzvgnBrIcedpk2wTvFdoNHrwyhM/w2Sb67a+vn6F9KMPaCv+TALlHHPaxfYO+gOAZ8ec+ns+Vi0OlbANdrqpy2iBswQ+LsNjnkDA42i7Z9CUZLrrSBsMpFrRVM8bS+s1P/xvH4r6t3zgFtDlN+n/GVHyHLb/e3l52b1SgPWf85P+wm9KIvqVyI0GU1ZWdpiq+sy3GQwGCwVFaN8nv+REt7Obzjfi56aDmdcZJHjicz+SAK9Vv8xHdnTE92Qd/H3e44WU4Mj30O95Up5Zl/ztcXCdnRPtOvr6FIeAEyyxYAxyW/XLfrGiaSmwVt09hslG2bAaaLQYz2wjRtXGNw1oS4m6n2308lo/0/XigJJkl67rN/eFP+N5afQs1MNgy/3idrT63/cgBt82tNQRo74ssjfIIIM8OmLdYfAAqGrtlWixzjhQBjAtm1nVJkG5Dn3qyHg6bNY11l9J+KU9TxtvW2FH1aSr22A7iqQuT7tlvW79C5nX0rn5dws44mdZUr+3bLztiH/yfVno75az7z5ZFq1qPSvbu8wGZXkQu7bT2R/LfBz/pI/gz7FpPmkw+99jb6CU47mszW53gtrsuxZh4Pc3eoxzTrrvTE6YDGC++xnU1QCMMtL/8H2+5uLiottH9bDkRoMpKwiiTVWLSQA6NftuJDsej2s8Hnd7pc7Ozmo6nV5xmpnYsBcv5sh5P1CmFrWAjCeGI0O54RZx6J/nEGmDlcnogdtBHwAkWWTexJvh08xXt2Nqtn80GnWbSrP/W0qaujjtkmd6gZmBMSh2GRsbG71DHrxPiBxpgF+2h3p7jBwpoT7OK+aaqv7Jfzjp1NFleh5Z4SQoZh6x2DmiNI2Gw99JACQjhaSioe2OwBKlaaXQZFk2Vq3UkVSICbBbEdfsH7fL17jPzNbxfjU/x9dh+FqRqAFMDTLIoyVnZ2e9QwPQ74Ajov/oafZSIZubm91pd+gHl5UOIr+dloxY72NT7MSaZKzqp04jrci4Je236+LDAebzRXaLwRticFlVvWiHQWECUUs69mnTDGb9XP4HYHDPdeXYp3I9qcPm5mbn643H496LVxPs5EnGHGLiZyew4XP601kpbl+LHMU55/nU136LScqcf7Q9I4O2aePxuEciUKc8ghyfgP9bZLLBf46zgSrzhrrmfban/La9J/vLmV3p93GPU3f5yRdC83zvfcP3d1tYIx5n+uPs7KwODg5qMpnUw5IbDaYsLSctkbQnSuYWJzr35M9DDDKycJ2kw+fPXffr2pSKiMXWipTYQbUTucxBTOXrv/nu5TiXBj12sFnQy1iZBAlmUvgcxZ/Mlu9x2T6MINu2rL3LvlsGJFpiMOO2ZYTMjGCOY0Zz3G983+oDPrPxT/bQ9WwZXsC4AWTV1Zxn38//dmAMnmyws92ZQrFsbbX6O8FWznWXbwULQXB6etop72XM2SCDDPLayjJblvrPDqV1pp3iJHdakZe0p8ue73tsl60brVddhm14ptynPURvpo56MZ34UvqMzxIwLLvfz82ykiCjTVlXAwyX4/vSziToc7phqw9a9V1ms9PXcPlpr5f1naOG/tzf53xyG1uAM/smf7Kvci3Q126D/RBnrCzzSXNcKDPHxD51bhWwL+n2ea20xtjkNGDK5VVd3c+1zM+bz+e9VGHer/qw5MaDKTop2ZTRaNQ7dtOLEiYL5n91dfFeg/X19bq8fPDyr/l83iHXjY2NLu8aFt9REAusQOt4yqr+yYC5AKl7/s/Et6POT7Jg8/m8d5qRWRgmcMthpS5pWFyn1iL2OPgeco6534s7f657iaNZBQxPS/G6/SgLmBy/u8H1NXNDH/p5ri9j2lKojqp4r5MVVaaVmNnJumWaQCrzTE2zMc/N1+TLLwMLs9msF51hPcHuUhfvPazqR4bNtFJfj1kCQwOojEARZU0mzQaBeWonxMDZG1jNyvLd5eVl3b9/vw4PD5t9Msgggzx60gIF2Ebv8cl3InHNyspKl/HB0d25LwWxbeY5edAAuoxIuLM4eA7X4Deg26iDCStOkPUJenyfr+ewHbHNpg5uMzbAGR/peBO1SUDH7wRb7h+enXYpU+/93AREFxcXvVNzDUDoL5zf6XTavYQ9SVmXTz+19pcxhlWL/T+0NfelOwKZ+/Ho0zwsJOcS7bDQ754XjnRS15wnWZ5Bm30yyltZ6b902PbVew5bYJDyqxbp8/QHdWLOJVFBmWT0MBdcpzzTgOfio/MKH/c7vjpt8h5EyrIQxZ5Op92JvQcHB91Lfh+G3HgwhXhy58JKp5ZrGXRAUlX/WHIrHueo2hlcBkyWicFQ1XLU7zZdxxa9FNZpGXvl5yczsIwtoX4ZKXmxNicz4s8NVlqKKPuLOht8eFwMnu2Me3Fnf1kJZdtyfF3P/H0dAHBkx0aqNYa+LpWyxy3nkxmyFgD2sz3v8n7SZ7Is19tioGvyoNUvrTXgur2YeIyzfI9ngime55OybLxb62yQQQZ5NCV1Y9XVaEBrXaeNtH4mFcnXXMd0p47nmUkiub6pz+0Yp81t2Yls0zI7bT3cimq1fICsJ+W17J3tShLKWYe0i7R9WdmtOlb195GbBL3uOdm+lASI7qvcj+bIiO9v+S0tPyE/93MNGrN+ntMuL8cny2/NuZxf1/VHfmYbb1/D9jYDBF4n/s2a8zzNOcfzKNPkhte4t3y4XINlTuzlPZ5DZEoymy0OH2AQcwJ7UvlAAfI4ebkfZfEuoJ2dnY6FIL3PCtQThjzZPDnEp5DkyTVIhkStpLifckHxyUKkouWeqjYQoG9yr4jZF/Ybea+R65LOs9vk0+t4phm1fJ4PC/A7h3wvgNafMdZO07q8vOyBFr9ngHJzH5sXXAqsVmtO+V5+Z+QLlpE5wJyqehDt5KWGTj3znGUPHH1ugO8+yjGAaaXfEyB6nLOfrBSJ2lZdjdQZtFb1T2Ci/VzjE/uYw7mXznWvWkSXuKblGGQk8uTkpFOck8mkt0Zy7Xl9ei4NR6MPMsijJZk1gK1Dj8F+m73G3sKA+6AkgydObuN767EkaasWkXv0o08HpjxHxhFHnEaj/mFCtmew8rTb99GuqoXuQ3dz7Xg87tkNnjGbLTI0IJMMUtCjeYJhHjNtJxcyamtr6wphiCQZZ1/CwAodje1yObb93MseabJa0kbbL2DMk4ClzGX1XVtb66J61DMJRsT7dE042rdz3yXYy6PeE5QgLT+Sv1skpn2rqoXdY02k3+lyWvutmUv0L3PKe+TT93aAwOvvscceq42NjRqPx73XqeA7AZjdD9PptIsC2u9jbtmPdzQaIHVwcNBlnw1g6g/lxRB0Ou5O1fJkqFo48WwYZKG/WF6uJ09Vf1Knc49SbJWTC8jOJgs/N00ambeAwjJWib+dIuV+ssOeCtnXus4826DHAIP+sFLzZsZlTI8ZL56Tp+zl99Q1lQQGx4DXz+R/KxQ78S+F3crfbm+ygxmlszLzPEKpUIdlCjaVexoP901rnjLnDLINdHP8UmGazEhmzM5LGpo0BAZnXJM/GUmz0YVxOjo66q0RDFBGQd0PHoNBBhnk0ZBkmqv6Nt1rNwGX9Y7to7/zNeiYtHcW25M8gCl1q38vi56kvcbv8H6QTPOHlOKeJFPTHvB96k/ud7/Sp26rdS2fQTTbB8i2WW+nY24xWDRI9v3uK8jefB9U1hPxlodss+vpPmjZi2XRzjzYI9uegCVT+XNM0g6Z4Ew/MoGh/+cz96X7O9thac0j6uL0Pv9OMOXnmEBYW1ur7e3t3jx2f7fmb1V1pAnRJQIPgHHAPf20urrakcT4Bi7jYcmNBlOp7MzsGIwkW2JWi8ElQpURJEdl8hmZPsRiSiDH9fP5vKbT6RVnMgEKZeQJd8435blM3OPj495EJPfa+4tyIRo0MBGrFvmlVQuWxkDJ7fOCTyff7UoQYQNUVT0A5v6CpXKfpRKgjbBxzl8nL573QJFjm4bNipfvmAM+2SfBdYJoRwQBF8uUmVk+K273D3WgfjamrkeyaaPRqBtHlJwdCPYKGrh4/tu5YO8hc8KRo3RguJ+xMAvrvvI8SlCb/ZQKljFuKXmfSGgDaLbXY5xr2+zqIIMM8miIdbZ1d9XCvuamd/QQ0QvegeRIzzIglA68Hf3ZbNbpWPZtmHSqeqBvHIXi2vQP7Hyja9Hd2Gsk69Tan+NrWu3J7BnrfdufBK48I+0U1ybpl+NmEjCjAR4H/k/AaduD3mYcNzc3uwjJfD7vRd9y7rgv+c42B7tqO5lAxXbP9TWxnT8eA9cpv2fuuFx/1wLh+TvtKe1iL7Xb6oiqBV/DJC7+Jj8GMvZlkuh0mf6f506n05pOp73Tsh1lxue1DzOfz7vsE/ZC4S9RZgJ7xrkVNX4YcqPBFGJQZQXDQHBNVX8fCosRh5MBSqYK4d4WY2AnuOU022mt6iP+XADXsSfJelVVL5pgIMCznUaWbbLgCBuIOrLG/63IyItJsiUZrfFi5PoW29UqF2YjNxO7jwAENl5eZF7oBjLJzDj6kuJ2uSzfm/Mio2kttjSfkQq2xaaNRqNOyRHidvlEYElLgHmE7fPGYQ52SEfAm1aZJ/yMx+MeU0e6rI16Kl+vLxs1+srzgH7hkBOD2da6xQi6D5kT/syvBBhkkEEeDbHT3rIFGaE26bKxsVGbm5s1Ho+rqh9FaIEzrqE829yM2jgV3gy7dV++oBdG36AvX22CQ+n6LCNoaa+vM/FG/6BP00nncxPIy3RgC1ClPbU+5h58ChPAlAPx63L9LI+BQSdpatSba30Ak+9J25l23UDDfXB8fNyLWraOgedZSZQuc9gN7uzoJ0DmmdSdtLccd1+/jJxk3hlIXF5e9lITs06U5ZQ+XprNC3LTriJuT6s/AMWcroff4fdcmpBOP5BXm5ycnNRkMunmRZLQOe+Zj9eNzxciNx5M5QCivPjbkhOtajH5/d6JqsXkQVCODnfnYiTnNTfKuVwjfdex5bAn6GFiOLXA9eJZVhjed5LSYr1QDK10xGWCY2zxOBARSEDFb0dxYCccKeQZOLqpzJwKwUJx3jsLMdk6Pz+BCAsxc5vdX1VXx9ERQABdKigbOHLObbCtRAH5rX11qXhtnA1Kqqo3nvP5vGcgHOmib/1OB0dvqGPVQlk6tG9F5xQQwuyO/GQqB3Vz/SmzZWw9flb+9P3GxkZXN9c7HQDawlx62IzVIIMM8sqkxfpX9SPMJmOcWbEMcGCvW1F+n0zGNek7YOtbLym1o2YglVES7F2+x6fqgc4+OzvrbIBJ2+wb+xm2M2xpoB+oK/XCRtgGZ3/ZJrrP0d1I2lf3l+tiYDQajbq9w+l0mzQzqPCz2efLmEMIJlHZGhOPlW1ZknpZl/R1TJ66POZSqw/to2Y/JSD3OCPXkfLpwzi6Y9+O+ea+cD3cdwQcSK1jX7KJASSBNWsEwc8zyCV67DoxL03Szmaz2t7erqqFjzEajXqnMSbpQT0c8cv2Pgy58WAK8SRtOb9VC4bCHWvn1jm1KDC/FGx9fb17yacnNtewiPLt67BOfJ9REAad+pjpSJDFZ1bOfp4dd+pyfn7eU9JWSvyfBooXHC4DYon4WwYOxsOAzmOSC39lZaXG43GvXXaASd3LXGOPK5KL9zpFmC/eNRtDv9uQtQxm9kGCKV/PXLABTgPDc7P/reBpYwJTs1hO3eO+i4uLDkzxXToCp6enXeqoI3VWdvQnkVHKov+pw3w+7/KUSbsh8tUiJmwAcq7mtV53zMfV1dVOOZsJddpvjpefM6T5DTLIoyW2ibafrFkcRJM5trcm7aoWeisBxWg06vRZOt/cV1UdA47egV1HRqNRL4UqiSv0qqNX2CLbJKevJbG0zKa5X7DjOJ5OyaIv7WymLbH98mdpw1pRsqwPvhn1pg/5DRgyOHRaYlU/pc57dtwP9HUrJb81nsyFjIZkanyrLgjP45neg0ybDZbZfmBwnD6A09WZC/y2f+P77UtxyJrXyHw+7/kDtn8ev7THRIGOj4/r+Pi4N5f4Td8ClCiDgx8od21trcbjcde+tbW12tzc7EASdcgIpv0yfAfmSb4s2WuDshhPz+OHKTceTGVHp7Puvxlwoh44+Sg+RwhSuN4Rj6r2vqCWUsmIFNJShC0knXUhhzTzP1NJZF/lNf4s2ZVldciFmCFTO+UsnhwfAzHahFLjM4MkFJCVbYKarEMqBdrZUiDZP/7ce6+yfVzP3Gkp72TIkDRerneWk4rSBtdj5PamAjfgTuPgOmKoNzY2rrCpNubuHzsjVu6UbeOb6Stuo9thMIUwl1r9RBtYG14jVYvonAEk97hvHraSHWSQQV6Z+KQzBB3o1CfIQ0crqvoALMmgTAvjlK8EQdSDMlIP2plLm5C2MQksAzuXmcStxREAl+2//U4tyk5bQn3Sd/B3PM9tS/ubPgjC+PiZjAljZvvua20T/CzanRko+Bv8n/2Z9tbXUKb9Gj+Xa3LcW3aKz7F73vfN9T4lmufluCbgoVwHANJe2c4a0BloGCQjnuee70T/SPEzeEo/azab9faZMf5sn8GnsG9AxMtAnb5PwtkgmwAHp2m6r1injpxlOdcFC74Q+aIBU2Z9WiCCRXt5eVkHBwd1fHzcDcrm5mYvVzknb9ViULzgM4fTC9sOJQAu62Qnlvo7cpV7kwB8Dp2azbGCcHjZp9/lYrES4Nn8ZsKB8h3dskLLqBPKE4bCR5d6cbiufA67x0JxNIVF3IrWoFjdbsrx//7byrnlRDMezunN9MdUalYyrb1l/rylHFNaAIP/yS1ORUFZmbpKW1yGDaTnO3sMcFgSFLrtWT8DL/cXBpP9U5mO67mZipTrrLQTcOa9HFF8enpao9HimPnNzc2OtfNastMzyCCDPDqCs+X1uba21qXJcajBZDKps7Ozmk6nNZ/P67HHHutFHXDCkMvLyzo6Our+xlafnZ3V1tZW3b59u3ePiRkyJdL2+uAil9simbAH6J8EQwY2dvbR/3aI7Tvg7+RpZUlupq6130GdffCVo/a2t3asPU60jwiEAQZAl1epJFgzwDP5ZbvK/9STrITRaNQdYJBOf5Ks/p/6pi9oIMVzWiRu2jJ8REcy8SPJGPJ4MH/oQ9so5qH7yS93bgHby8vLbg5gz/NglJa/bN/u4uKiJpNJ3b9/v6t3i0gm+snR5dl3HIHOvqjV1dXOL57PH2w98EEx+B0ZJXbmCc8cj8c9H5Gxx+fPzCwk/fJXIjcaTLXY8paTxcCenp52k8uKooXuk6loMeithUm9ln3v7/i/5cBlmTybxc4kd4g7AVO2Jcs1Sqfv8j6XlWHSF4uyZZSIMltOsPu4JVYseZ2VSI6TQ/WtZ5vpWsYsOR3Okgxkq+0Jlvnb3+V9fk6Ol5/N97CaWbf8qXrgBCwLn/MMA+hsSzJwLfIBZd7qL76n3q6bxyHZRmRZGm/OSRtkg7aM5rWktfYGGWSQ107Oz8879tkgBGeL9PuWY5s23N9VLfQae0D8wnL0nVPUWjakqr+XiHLRZSZKl6UZ5/+UmSRVXp9614SoI+4tW5V2uWULqGtGt5bp0JYudpuuA43XSerlZffaJ+S+VlTO9j8PBXF/O2JiEr3VnpZ/dZ2YCM/5mmNJ27Kd2Uetvk3fsOUTtfwq+5reV+bn2a9xep0DDyZlW+OWn7XGyf2SbXZfZL+15taysl6J3Ggwtb6+3p0aZobELPP5+Xk9//zzdX5+3iHrnZ2d2tzcrJ2dnY55clgTpekDJ+h8Ii5VV3NUnTblBYh4gVPffEGoU9tyg75Dl7D7Z2dnvWfTB+m4uw6ZrsbfPNfKnu/ssNM35OC6PRZQPxOWvTL+LKM0Vt4sCBZ0vmTV7U2j4nQM2mxD6EXEZ85TTyXh/OwE8dm/XO95SF5wRvfsGNhY0j73lxUC/c94uS7MYeaFGTvaB1uVBi/3F3me5FzNaJyNAf2EYkxQTv38w9jnOvL6Ys7lcawoXeaKCZadnZ1evfNo2zSGA5gaZJBHS+7du9fTMVXVvSdmMpnU888/3zusAV3rvcpmqNEn8/liP+f9+/d7KUqc9rmy8mAvb5J2gDf8Bd53ZN05Gi2i847I+yjy1GPWXbaZ6QDmXqHc9+O9NaRXkS3i/TdpA1qHQdhBbZ2gx/0tfwO71LL5qXvR97Z3aYshSV0nymdc/UzG30K7OJmOI7aJ2jBG/nGKmoGSQafL91g5O8j9j620H2kfwj4CY45fQJQpfcwEZTzTL6bmM9vvHJPLy8s6Pj7uTsvjKHT3/crK4jUovH4GP421ybMZV3wj/CKvR4P79AN9ejCfJekA8HNUkv6n3w2ghzS/PxRPVjqOfGkmBAoWReaQMGlbuSiqrk5KxExR6zNPhuuu41nJZPhaMxb+bhljcR0rYmCUUZNlkvcng+E6ZTtdfjItWWYyBFbi+ZvyMXLZpmQhMKCub6vdLSe6pThb8yOVqP+2IWoxJDCWaYRakvcmmM+oZ6uNXJMsUc7bnC+tMb+4uOgBVM9ps3wpnqs5JskgGlz7Oa353GLpWnMERZpGZ5BBBnk0BeCUYIr9TaTzoFucUlb14tFm6+oWI566Pu2WCZ1WdMA2O/U1Yr3na9CxLV3c8gvyulZ0K/Vhy/7lM01U0bcP67AeAyf/dltfyhimT2ICOG2NHXPmj517v2rFxF7W2dKqf9qxrFu23X2ez2iVw++8vjX+L9aHLbuc4DfL97wC4CVxYf/JwIixSaKadtp/Sl+q1Uf+zH7ei43bw5AbDaaee+652t/f75y3qgc505PJpFOoVYs856eeeqpu3bpVjz/+eG1vb3eTyy82zYiGB9y5qpYWQ8NCRXmymP2dBzxDzIjLhAGg3Pl83su3dV2SVTJ7AvuWSra1oZYy88Q7TnRhA+BoNOoiH9wHwwWbghPrBWO2gXvcH44MmmWsWiiWPCq9BezcNy1Q24qicA05vOyd8rjBOjr83VrIs9liY2Yeg9sCzqn4sz/y0BEiVFYkjqhVPWBaNzY2mvPEaYwthm0+X4Tt/WyMq/cgwTq5r91mWF63z23OtZBG0XMsxYDdrK8N5snJSU/ZJ8DPCNwggwzy2srv//7v1+HhYbfPAn3KqaS8T+qxxx7rOXB+oW46sNYza2trtb293dnYqgf6kn0t7DWlHB9ilfuTiVAZhKA/M+sBHWTbaQCBbfNPa89tVT97gbp4/1b6Dug6n/jmcl0Xl5HZFenMWqdmFod9oclk0tkTgIztxDICzXZ8Pr9KqvpYe75PQo7oCH4M7WAeceqsn+3jt912+08GwFV9u92KWGb/ZludkWESME+KpG6MJ2U6Gmn/zu3heQBIi/1fQJLt5draWm1tbfXWFofC7Ozs9KJZ9kcgRugzn+zHi5jv3LnTI2YzIuX150jTMiCX6YmM78OSGw2mjo+PexvYZrNZTafTTumiGBmk7e3t2traqr29vdra2upNPIcuq65GlrxA0gFLJW1nOh10K4d04uyA+nM/w8/Oay2edGbF7LjnhtCq618YbPEGShbDMpCZoWS+c1+1GL5cuAn0EjTmePm+ZGdaANjlWlBEjgZiNFosX0swjH5uKzyfaR85X8xuGpi6Le5Xf56GL8V1yLrT3vzcfUeUjee3wGuyVCZCKDPH3ff4OPgkHnI+pGQKTLbDBMgggwzy6MjBwUFnq8g2Ya/U5uZmR+ptb2/3nP2qPnNusZ4iXcnOpNPdHPWyzneqkJ3bqvaBTkl2uY4pqcOzjPQN+DsBlb+3neU3GQa2gZYWIZnX2i9Jm22AYaBke5F+l/u41Vb/JJhyCmP2T/ZtHqiQESmDYexuS7I+eaKgU9Ja9TbY9N60qsVBK5m+3yL3DbhMCuT4pp9KvZZJ+gVup1MH7ZcB5HwwRO67MtB2uy4uLjoSm7ll3yujVC0fxwECz6Wclw9LbjSY4q3H8/mDHGkQ7crKSrcnir0iq6urtbW11Sk6FLLf0ZRKxwskF6kBRDIVubhRGiwUrq1aLF4zUHk6nqMTOJ8JvLiXFEfatWwBmamjzsucUAMBTsqhfA71cNTDitGT2HVyn1lSEWRkwxEY95HFwDfL8ak6rif96zZbmbHwNjY2epEwPz+dcQOKVh2sgBL4tIyVy6E/k7WiTxlT5otziVsgxEbDz3W5eUKPn2Om1fXlO9YgZee4eS7yHevT/csPdXE7kolKUJ5zzeuqlYM/yCCDPBpydHTU7fNkjW9ubtbrX//62traqieeeKJu3bpV4/G4qq6m1/nddnag0J929pN0yld7LCOksG2QuDyfPVnoQcowQ5+EmPWhCSrab7HDyPU8z/oYcf3xj9y+3Itl0jVJx1aUxnVqkXNcm+R0Ov0tP8E2mc8zY4L3I3LPxsbGlZfWpxNufyGJS+/BxV9zlMX2NAEX3+X7Pg0quS59MoQoVKsOVf2TpjNjxW32nvucA/YpHPFhfnAiM8/w2ObfjI3f80j7iUbiNxJFdIS3qmpra6uOjo66NtO3zJfRaNSduklZtuP2vXKeXRcAeCVyo8GUN9FzVCk/4/G47ty5070gzE4Wk4YfTywrJTukyRB5glMmigx03XLYcxH7XT5OC6BMi51lJkVGkngmEyzRtxd+KqsEKVYQpKiZUXI9MRRcbzFIJJJoZzuVkQUjmADJCjEZOysmt92skwGMx8d1SAff/d0CVO5/90v2dzKnBrHJfiaTQn0MTK8DliYCzGxhQFsgxmNmBmhZKqrrbEbOYIr1YmXn+1sRYeqcDNTl5YONsZ6jNrJJcizrl6pFBNfvzhjS/AYZ5NGS6XRaVQ90M8Tp9vZ27e3t1e7ubr3uda/rMlCqFofMeC9MRo7Qhbwqgd9m0AElL9XpGo1GV/ZhO3LF/9Zr6UinpD/SAhwJxBLsLCMv0dmp71NnosdNuraYfdrQOv3WdpG20C9Vi1d5JBjh+Qk0IaGrFvbYZJiBkh1q92eSlR63zASxc26CPMcpgWVVdZFU5qFf2ZJ1SLtlAMZ1+Fo+5CuzMgwkDMqdhm877fv97jUiY/aXPCc8RhlpAnRybgFr0ts7iDCzVufzeU2n0+51CLn9xMQ6gMzrKdN3E+Tjy+Vrbl6p3Ggw9dhjj3WdtLu726FnJgBKLZkWT5pc4OmcmUXwQDCYCU7MmOTkTsU6Go16SsAL2pPCEQOek0qn5XjznGXMEIbCL0JNh5p705H24sboAG6tkFy3dOKXOe/0MQ61wZsXegtEZEQkmbMWK+V7GQePUSqb635yjCjHdcu0jxwb7m+BNX67DxlHR1qyPQbdaQQ9PxNctQxrttlz1WDW5TsCTPucRmNj6T4jrxwjAPmRQAsBTLX2SVB38uSph08XMrM3yCCDPBpy+/btqnqg28bjcW1sbNTt27dre3u7bt261Z1qi4ObqVomsVKvZ7qUdRPPtN6xQ4qt4r7ZbNbt2Tbw4HsTebbXrXS8BET8nwy925K6m2ut0/yMrJPtUgtcpF3IPTZ2+O3I28Zk5gR1ov/c5vSn0m5hW9wWR+p4preDpO/HGFctTsvz2OCzGCBk+zxm+ChVC5DHGJOSip1iTtlm5hzwWPGdX/fjyA71Rdie4H5L22gfEVKdurGm7HOlffQ8hUzIPUvp1xK14mA4TlU8Pj6u6XRas9msDg8Pe21OoEbfAKa894+6+j1rBlHLiItXIjcaTD3xxBPdBOS4c3cmIIoJnRPCkygXNeKFk6AklY7L8qKtWixWb0KEqXc0i/uZxDjLia4zKpYTPHNQqaMNg5mxjIi4T1isLHzYPL/3I5kM7rO0DIb3rDFGKGkbOfqT1AgrmnyRndtMWoXfQWJA01qsjBUMiMPBjtS47xNwUA8rEsrIdLzWEePZdwYiOc+YQzk/nedvEEu9Xfc00NmuFvB1GU5NbeXfsxbztCQ/m9/cbyDFGB4dHXVAijC/15TBFONG2ymflwrmPGyBvkEGGeS1l9u3b3d6YHt7uzY3N2t3d7d77QEHRGBL2f+UDhN6waDKez6q+oCgavEqibSdVf3XfVQ90CWkJ1Em3yejXrUgLDP6gI1bln1CveyEZ9SNfqEttknoTD6nPbYhSazZsb+8vOwiSVzr+93WtI22NS0C2YDFB1K5H7DfEGHUAcLWfoxTwTwPsk6AZPpvNpt1B1Fk1MX9nv4X9pjyGGPSUMmUchQ0iVgk/VX7jLPZrDtMCWlFBE1Yer57vHgmNhnbCxhLEtPzIUnU9Mc8lpTHffb7zs/Pa39/v55//vmaTCa1v7/f89E8L9JPT1/WYMr19vafhw2objSYWllZ6R0yUdU+AQdJx/k6aQ2U7/NiNfhIx7kFUsx4tZ65jM1KZO76Lau7n9eahClZRwOWjLblu4hyIyuf8+NcWzuyvq5VHwMY9wviBea/qxZK0oA0c4bdj1b+VnI2gga2fOe+yjplf9MGnuV3MrltOQf5LOvA362IiuetjXUydB6nBFzZB1n+MkPA/wnEsm5+Vt7r/sMROjk56V7See/eva4sl2fDjJhU8dzNcR9kkEEeLcG585q2Y2sn3kRiknGplx09Sr1KJNwb6JfprCQks07WZTicqW8pk9/WyzzDv1v1cF1whls+kOtZ1SfRnIFT1c/QwM74GdzXsr/pJ1AHg8K0N62IVY5N9pntHPV3HRyhsw203a3q74fPSFS2v9XnOR7UxYC9BZ6SfEyy0r+zr7Jvk5g1QM0xcjvS1iaQIdsrs1DcR9lfXgOeK5ThyNmtW7e6dMjRaNTtr/L7RdM3dp85yyt9RUjWzc3N7t20D1tuPJgaj8c9Zsin6+QkycgDg57OXjrAOONV1b2bqurqS+iSuRiNFvnTKE9C0lbOLUfSn6GMWeAt0MX/ubh4hnN00ylOlqFV5ubmZs3n844J8XGmZgKSXaLNuanRBo96eaz4232cR4umk53KwuFv6u2X3TnNzM+iL/Jleh7zBFC0NU/c8bVO20wF2bonQacNgVNYKK8VUfF8cJ6w+zWBlPs/DcPq6mqPvVsGgq2IW8Y/532Wg6L1D8fWnp6e1traWh0dHdXh4WFNp9Pufp/QSfudX57pga3I7HVkwyCDDPLqi1+Im2AKJ8m6emNjo2cXWmsdXWt7Z53H0esXFxd1cnJSVVdfmVJVnbPc2g+TtmM+n3c+REao+M3n/BiEpW4yGOSZ2JEWmOB7gzlsMv1CX6cu9PYGA4ok3JLYPjk56aVXUcbJyUnPR0sA4gMpnPmS/UH7vL/N2SnpO1gyRZMojI8TX3YiYs4bA1K+o3zmI+UboJA5QT8mKMCmOXhgP8A2PuvgetAmRxQ9L7Ncl8HBbtnuqj5ZwPh773QCF0e68odXC2xtbXV+28HBQW9/WstvzjZj4+kPzlTY2tqq3d3dzgdv+S5fqNxoMNWaNF5UXJP3mFFxOUh28DIWhP9bLEHLiczPsw6pnMz4IJ4grbJb/1/HXuSzW9ENFoTTCqw8ljmf103UdLSpT9bZLMZLKavluOfYvlgUotXfLxb9o2w/0+AkmSj/bWNHn6YyTsXdAsWuc86fliJcNs/djmyf2bLWs7PsHM9l/ZbAatl8dPoBRmFlZeXKHHGZKO4kMLjnpazbQQYZ5LWTBBnY8GU6KvUpkmQR9/i3dUTaitxvBSnXson5Y5uQdXH9+c42MEGNxc+wTUid6n7kb4g4yjVJl36DMxtaoI7nZ8Sr1b783+020YXPYYCxrL88niYesx7LfEKuyy0GBuEt8ec5Jz0uLb+vZYPTbrdI+pzTeX/Oh/Rh7Zvkd/mbvqRPfGBJPmuZb+t5k0EI14nTks/OzjoCn/5wdDF9BvsLrb4z2UGf5GnBr1RuPJiiQ7wAZ7NZL5SOZKSgtTB9vRWjj9/MyZeHShiwpTOeC9L7chzdcBsRHwfN4srwqcttATF/ZyVqJdZaIC3lnuyG+43rsi+pM3m1jFH2E/X2Oxd4ppkvL5RUAGYorIxSlhk3G5o0wg6Dp9Fi4fuZqWANTomYuC4ZRXU/Ml94OZ7nPeDBQs63JXPmESskA3fmpUGJx95zrcWSmkFLY0J9EzDiNFH+2tpa92wYPX7MMtH/eeKi2U2XC1NsgzHIIIM8OsJpYOhzNsWjQ9HF6EtShcbjcW/Pi6M23o9je+hIv+32ysoiE4ZIQ0YCljm/VdW0JelzUH9nTvjH9g+hLNh32wPvdfb13MO+U9KfMtJlyYie9x27T7HrPlnRdaC99sPw2agXNuH4+Liqqnv5K/faKU5fh7p7W4FfXpy2oaq67AX22vHi3vn8QYQS4Gyg471Ttm3YygQROd4Jsk3ye17xDKJLiG31sswK97XtuEGjwQopdS1/xCl+9KGf6X7weFLv9K3cB8wl5jC2/uDgoF544YW6vHxwgm/Wq9VO/8bP3Nzc7L1H7vz8vA4ODnoHVLxSudFgqqp6aDWdQH4n+uf6lixjClr3ppO8TFKZtZ6x7LssxwvxxcTO/3VsRD6jVS/+Rhnlom1FSbKfkiVpgRgrlHy+DSJ1TYWVoCYd/XxejmFeu6w9vn7ZmLXYk5Ykm+h7W7m9btd15fr6bEe2xco+rzV49U9r3l5Xl2wb5beem2JWumqRTpJMaN7jeeC8+0wToA4vFnEbZJBBXn2xviAtzQ6d/QDETlrq/GTRl9l9nuOUL8hApzIZeGWdl7Uln5e2N+32dZkgJhuXXYd+S2c929/qHz8DsW9lO+xr+bylb1v1z8Or/Ns2zzbf+8Jsl/x87qHOCVwStCRIznHLPklb2PJx3Gcep4yi+fq8p9VvrbFxvZb5v2nP8yfLwuZ6jDxX0hb7ntzb6Lb5OSbbvaWG52Uww+CtFYXOrQJV1UW+JpNJl777MORGgyn2w9CR+XI5QExOpnTKHLps5RbbeW85555MrX1NnAZEub4PpsDRhBaIseIyoMj6tBaCn7G6utrLP+caLzrKTGc1DUxuBl72vgorMUeL5vN5zyC1lC9Mm4+6T8fdL+I1q5gbJb24/d4j+i3ZJD9jPl/s/bKCpW2ZJ888czlEsygPIbrivnG9XCeikxl19TUoDZhbs6z8jRLxO5k81zyvWoYiFWgL2GIorARtEFOS4UIB4jQ5tY/I1ObmZncYhevLeHrNJphzfakjm14HGWSQR0fSwb24uKijo6PORhA9aNmqqur2VHkPjG0SNsaRH2xO1ULvoZPG43Fv39Hx8XEXDcuIVDq8to2Uix3kM+8Btt3CDtm38b4fbBH6zDaxamGTvH+m6oHudblcY8DliH6SotTXdhdblqfWpV6mbePxuNsr42iWdTd94vRK7x22UL980SyREiIhJtDc7xn1sc13290Wfqf99umSCWIN+CnffoLnlEmD0ah/DDqfuz3uu8yW8atGXO/W3LXdHY/H3THmBujsq6J/fKquMz8YE+Yy9TVY4+TDs7OzWl9f767L6Ca6gWexzrmOU6c5kn46ndb+/n4999xz9Su/8isDmLJ4IKraIfR0vr2x059zD7+vi17ZcbPCc1lcm+DNzzXzYgaM71NZZlsSPC1zVCnfz2y12ddnJCn7iuuyXckqGexRJm1Nh90ph/4Mxd+KyBiAIV50foZD0dyXCntZH1rx0ZdWAC3QQT2WMTN8hzFzv7ofbIipR4a0XR7t8zzht5WS70+j0ZLs82VsWeu5tCv7NwGbCY5M6eQ7s12I50WCekuSIVzLZull4z/IIIO8NmIdyLrGoSN6nkAJ/Wu9cJ2tTnt+HSCyE29n1fowdSNkYDq7tguOQthmoA8BBfZtUp9RP2fmmCDjGuvSBHnuJ2cxtPrG3xkg0L4ko7mOsj126+vrvX1Szibwc2wfIHFbWRw81zo9wZLHoxXVy/8pM8tfZnNcZh6I4vmZflLa6JaP4nRHg6JWXXOeGHgluLdttZ/j+eB3rdkmp/325yaBsbmWlo33eLlfPWb8Tp/EOmE+f5DqP51O6/79+/X5z3++S2t8GPKywdR//a//tf7Fv/gX9Uu/9Ev19NNP10//9E/XN3zDN3Tfz+fz+u7v/u76sR/7sdrf36+3v/3t9fGPf7ze8pa3dNfcvXu3/uE//If1H//jf6yVlZX6O3/n79S/+lf/qntnxEuVzc3NHttftXyCIHSqkXMLLDD5HYlwWDOVnZ3Q1qlqNgLUk992cA2mHGHz4ndfpzK281lVPYWU4NJ95nZYmbYiFPP5AybObEFGqNyXFi9W9xV1YEzMkrieZqsQysjoSwscsvA5jrw1TtexJ4xbjpHHPpWWx8DPcH08rzyeNh78b2DCHCV0XVW9/PCM8sASMkaUmyfgJctrA4mkcs0x5u9lBIXnghUrY+W2E02dzR6cwrO/v18nJyd179692t/f7/ZT0E7PaZ7l9o1Go+6UpATVgwwyyKNl65944ok6PDys1dXV7tUI6Dx0OREHfttO46g72oSOMiHre1r6Km38MsLGLDx6jmhK691I6GwiDS7T0QI7tY6aVS1eXppt4r75/MEG/6rF3qDV1f7prOjzjPAZJNlppw9te9HlaS+9V9vXIGQhAIw8Dvncquo9I8EM5Rmc8Nv9gT1MUGpQwLW27WkXXUaW1yoz51QCl7T72QbaBlDwvmu300DYhO98Pq/pdNq9fyvff8rx4UR6vEfbdedZXjessyQ8k8DAT2tFUD2Hl4FTrmFtEYFy3dADv/Ebv1Gf+9zn6vT0tANUy054/ELlZYOpyWRSf+Ev/IX65m/+5vrbf/tvX/n+B3/wB+tjH/tYfeITn6g3v/nN9aEPfaje+c531q/92q91Df3Gb/zGevrpp+s//af/VOfn5/VN3/RN9d73vrd+6qd+6mXVZX19vVModljp/Hy5WNXVvTdmM1pOrMu7vLzsHcOe4fyqfm6vwYm/53dLsaSSqqoess7FynPypaytiVm1POJmUJaKCZbIbB8bgjOdbmVlpQv3ZrojvzOUnm1uMUHuszz6HkmFTlm+t6qdMmkQlf1iZeN+ahmbnG8JCmFsEJeR/Z9tNANlQMVvjGIyNBluhw1sgT6DcrNIjiQiaRxoaxoL7vdnni8eP/dxpnIw70jxmU6n3W+njroMz4Hsc2+4HWSQQfryKNn6nZ2dbu0DpLDxVQ+IVR/i5GO4Dajs4KG3fXgRdiAPNKpaOH8JLNKmtmxDVXWArmoBkMzi2x8h/ciOuu2O7St+Th7WhH61nuU7QCfivkwijfvcH2nnHHGyz+T7ncpIfxiM4A/YD6P+EGTuj0y5XzYWVf1sjWWAxu1LIrIFuHxvvtvUdWqBqfRx0gfgd7aFMpgvtMPgnJ/M8uF6xsagovXi+9ls1oFvnmmf0O10vQE2JoyZg9kmxtR+pH1G+8nuK5dj4ti+stv89NNP16/+6q/WH6W8bDD1dV/3dfV1X/d1ze/m83l99KMfrQ9+8IP1rne9q6qqfuInfqKefPLJ+pmf+Zl697vfXb/+679en/zkJ+sXfuEX6qu+6quqquqHf/iH62/8jb9R//Jf/st66qmnXnJdvBnNyLbFTFiM0HOwYGVaixpF6Htpt8XfpxLLa1wnJAFeMmjUx+yTmbVWm1O5Wzm2GI2WwkiFWdV/7xaCIWsBmVa7LF54fheHUyWzD93HBtIGs8lwpMKpWswnrmm9qNEKsbXYbXgpC0UFg0r/WFLJJAjNz6wAXWfqjfHyXHZ7rbh5vhnbTDf1nLTh8Ji0ok4eGwNL18XXeGxQ+BgRHKnT09M6OjrqUvLMHHoO2UFyf/u9Z4MMMkhbHiVbz/7Ii4uLjsSz3kVHsDfDeyX4O0mmquqcv6qF/mqRpOnkpc7nmS7XmSVV1ZE36B+nMGHfcIq5hrKoXwK8tKv0hZ1Yg0ATkulD5P4bnpl2NvvCbc4+c524hjb7NMUkiLNdaU+yrR4Xb6mwP+M+y75NUtPXAVIZm8wYSvK0Vf/sj9Z3EJHu86r+C+iZc35XFfOH++hXZ8/Qz/gN7Js+PT3tiHHqAGgiWJHjb9Cex8jTltls1oGy7FPK8B4uiBL7aj6IAvCPnwIxMZvNunVuspc5/lu/9Vu1v79fTz/99NK+f1jyUPdMffazn61nnnmm3vGOd3Sf3b59u972trfVpz71qXr3u99dn/rUp+rOnTudcq2qesc73lErKyv16U9/uv7W3/pbV8o9PT3tHWF4cHBQVf2jLzOsXNV/2a2FgffEMlNkBc3vFqp2SJ2FZSc1HVmXWXU1hcuSEQhPMr43aHB0pbWfJBdnKj2DAO8r4nv6N9MfmOhcx7NajJFlmeJFabQiHow3bUuj1/qNU26j0gKefOf6+xhus0KwNAbwHie3PzfT5vzKuvpvl9kChrlZlGuJtph9zLIyeuj+Zpz9Hfd5/tE/rrfHrKpveDzGKFqL59zKymIzM+XApHESDwbAQM39YoaPMufzeVfGIIMM8oXJq23rNzc3OxDl1CSTLY6+s+F8PB53YCrtLfrI0SLbBkva4nw9hPdQ5ZHRSDqzfk5m0VC+y/VPi4hEso32j05PT5vkIIcgcf98vjjowoCE7xLAuDzbXNeBstH7jImBJCn1jpLlb/Q4/2f/INb/tp0Jwl1v+oO5QEpmRkdcvv0ezxd/1gKdaRf5O+0iINzRIbc/iUnPc7fbc8DE5NnZWQ9UOSq1tbXVA2X2BXkuLxJ2202Isy7pS/tFrqNf5GswxdxkLfA/7bx161YXQbN/dHZ2Vr/+679en/nMZ+rVkIcKpp555pmqqnryySd7nz/55JPdd88880x9yZd8Sb8Sa2v1+OOPd9ekfOQjH6nv+Z7vufJ5RgNyMpkBr+rvreG3HegENrkQcjFZ8loWY7Lveb1BghXOMuWA+HNHwCgznezrlG9KS+m02m7Qk/VMcEa5VsQuwwrX0SOD1GWMIdc47cJ91GKOXH6ynKmYUNjuG6dRuj2kBJjxy5S6nLcGr55HBmA8z4wlfUfdsj6AQG9azrnostx2SxIBOQ89RkhGAFNsMH2dx5nrbDBQ3CjPW7duXUlTSKEs+u66dTzIIIO8uLzatr6qOqdpPB53Th2Aifc/bW1tday104StyxNM8bezIPwZfydxk36HdajLzeg8go2gLWnzk1TMVGnrRq7nOaRFO1vHRGnL2U/QY/LM+jnv8/9Zv7S3AC3bI/eDbUALwLgfl33nFM8Es+5794N/tzJpsm3Um36nv5MQzfcXZp2oi/vIPhdzg5OY7Ve5rdmn1DPJV//O+ebxJfLFKZmcqtdKbSXjJk9mxl/Jvs32ArB5jxVkK2t9Z2en5vMHkTKIiNPT0w5UcfKfo23PPPNMHR0d1dHR0dJxfNhyI07z+8AHPlDvf//7u/8PDg7qTW96U1VddfJRZJ64LBqzDVYwLRBmseKyoGwoP9kYgwSfMlS1UAgwAz4MwxECnsNvK0nXq6Vw3Q/pwOeio05c6wVD31VVb8E4CuZQNGNg5sH96HZmSNrg0vVPtsb9w29HGO2MtwyYn5d94s2dtBlAg+KwkaH+HieYHo819ySrSb6/2SorHbcJ9o7wt/Pwc764jihHjx1skOuysbHRe5Gy11OyXPRfrhvXnbLdh+k8UCc2i5rdvLi46AwGLNXZ2VnnSB0cHHTj5nXtlBnGi/zw6wzlIIMM8trJMls/n897DPjGxka3zjc3N+v27dudU+WX6uJsOfKQ5J6dVv84FdhMvHUdetEv2bW/UXU1LQzdZpY/U8ph4n2iaYIT61fqZj+mqq5EerAztou2o7Z7rchLknG2mVWLFDCnxyEtss++Vu5nc3/Yr6FOBoAm3Ej54jP7S9b/rg92LMlezxOe7c8RxtxAeHV1tYvsUJcEMYy9y6GtkH+8WDn9R/tF6fdh/3wP9rCVUUOZjCPRq8PDw7q4uKitra2OuGRu83NxcdF79xo+G9/bP8nDuLx+RqNR11an7L7uda+rjY2Nunv3bneC58XFRe3t7dX29naNx+O6fft2nZ6e1mQyqcPDw/rFX/zFeuGFF17VVP6HCqbe8IY3VFXVs88+W2984xu7z5999tl661vf2l3z3HPP9e67uLiou3fvdven8DbqlthxZOIs2yv1UqQFpPhtAOLn+9nUxfeziDICY+WUzuiyurV+sp6t51LXVAa5sHl+a3Nn1VWAZKfZxoP6tkCb27Ksn9PhbY1nSzEZAFAe4+MxMgDN65yO14oG+vMch0wtsJKlzMylz/Ip18A0xzzZLtfLdW4BLM+/7Nscl4zcJvB2/VqSbfSm4zSIOe7ux5zflhYApc0Y0JbxGGSQQb4webVtfYIGUpFWVhapRDhgfJ8HTqTeRd+YZCUKwG8cXWyCtw60IvWOerWiOvy09q629LBBmSX1tNvjz7OOeZ9tvm17y09IO2q7btDj6IefkwRbEnHz+eIwEMaoFaFq+QfMBfshbpuBmcUgzO0xaZjEadYh/YS0/xk9TJ/F5Ox1kv2ewD5JY2+LyDbT12xX4LNsB2DJgDTtchICrDuTx9TPkV/q6fq3/Ij0sbjPZV5cXNTx8XE9//zzde/eve606VdTHiqYevOb31xveMMb6ud+7uc6hXpwcFCf/vSn61u/9Vurquqv/JW/Uvv7+/VLv/RL9Zf+0l+qqqr//J//c81ms3rb2972sp+Z5/bDStm5Ak23wvBIghwvfCa8T0VDUKzJBuUmfSt+7sMZnc1mHao3G5bRmGWKrKqd8si1CfZcL+51BMTMhcWKwREp9q7Q5jx6lr/pT0dxXH/3Fc9Otsrj5fcc5HMcLaTulME8SdCQJ/Y5ZY86msGyouJvzzGffkMdaL8BK8/3mOWxt47w0MfMmXwO/eXIjOeE252RJ8r1eJ2cnHSOitvKEag2DAm+PIdcfuaiJ5inrW4bPxwD63Sc8/PzOjk56bXx1q1bdX5+XoeHh0uN6SCDDPLy5dW29aQ5VS1OGnXWAnqFyBTRfh/TnNkb6BUi1kS8YcdPTk5qMpn0QAo6iaOjYeyxObbpSCuN26DQ9ip9Fds47BaAsWoRybCOBGhWPYhwzefzDpSYVLLNddYFfes9TXaoSbc2cWeH2v7DMqfbbSV6uL6+3js9LgHwfL7YD9wiI2n/8fFx9xnjZmBqwp02EPXMOWDAwz6dJHFtK6kb/WPQzXeUQTnuR/t3SZw6zT0zerJO7H+yX+L0R9vYjCq6HvQ3exmx+7Th9PS0Dg8Pu2f6wArICe+PTp/Qc4FnY7fxEeyLuq+ZlycnJ90hE//tv/23Ojo66s2BV0teNpg6Ojqq//f//l/3/2c/+9n6lV/5lXr88cfry7/8y+t973tffe/3fm+95S1v6Y5Lfeqpp7r3U3zFV3xF/fW//tfrW77lW+pHfuRH6vz8vL7927+93v3ud7+s030QJpoRM9Jy1Pi8JWYbWgw5fyeQybBw65pcfK3rmeTLELqvz78Nol7svhdr/3Xsvb9b1l8JAK6rk5XRi9Ut/28ptVZ7qEMqqmXRmtaz8jrXN0Fea3wsPNuKepm0+mZZfyYYebH9QTnHl7GRnpMtFjYZ2OskI3d81hp3G8EkC1ptMXO6LF1kkEEGeWnyKNl6p9tZH+FkOlLv9PNles36zREp0qqc7mfdQh2cXuVj1KvapNUyO+R7LC2CqnVtAiNLEqkmTFvP5tr0AxJ0ZUqbx8Xj4/LTPzDJ7Xc9WU+7j9xv2YaM7LS2OvDc6/y4FI+Z7daL2RLAkf05pHVAh+3fMvtp4JcZIQkYPXfcToPblh3O57l/vP3C9/I9YKqqOlDl1xekz5VzuDUOrfnMuvYR6ESleFXKZDK5dnz+qORlg6lf/MVfrK/92q/t/ie/+T3veU/963/9r+s7v/M7azKZ1Hvf+97a39+vr/mar6lPfvKTvWNDf/Inf7K+/du/vf7aX/trtbLy4EV+H/vYx77gRthBtgK0M5kL31EBxBOCSeuX//mHQXQdKBtGwsopGSHXA4Vc1Z/EKK7WhEcxOP/b9c4Fj+GxbG5u9lgBKzenY1FXnmPlbcNBGziymntgaahfKnXXDWDs62mP+5syrNTpp4wmJavE5zyHjY9pJHLBW3kkYGEvG2J2bplyt2J0FMzzkPo415tr82XC/Eyn054iZT66XS4r55WfDfNTtchxJyfdc8vz3vPFBr0F1A3Ocs6iNL0XjnpxbD73GVhzL6zsiwG8QQYZ5Ko8SraeF/bapo/H4y7yNB6Pe9F6v+emamEvbOf4jJQgfvNj+4L+IvptQFW1eHWKHT0z9AniDE6q+gRYC1QQZWuVyz2OcFQtMgHsd2A7bBMMluxroLe9/ylto4k+617bB9tG23HsrX0q9Hw6/t7HTN3xsfjhGZn14vryN88gMuK202/pk7m/uN7fG+jR/slk0vM1icIZmHoe5bywX2CSwO9XHY1GXcSKAxjcv8z79LUQfAleO7CystK9rBdh3nPYS1XV1tZWraysdO9942d1dbUrh7pmNhd+nsmKnEf269gLxx5J2/Wjo6Pa399/1fdIpbxsMPVX/+pfXYrkqx501Ic//OH68Ic/vPSaxx9//GW/tK8l2enJprcARVX7vUeInUB+e29Ki53g80xxogznmi6r83XSYrr8uf/P/WLZB1YAXlRWqAZTCRwzwtdSWMv2W1ncH9cxdC12r7XwWmLDZUXyYp8n+MlnZJ2SeXMdE0zRnx67nFOpUDynPb42PL6G3GXPXdfPSmpZH9toZP29RjAg/J+MWba11Y/LInNmVGmPCYa8rwWCBxlkkC9MHiVbD7hJwqrqqo3OiEbVQjd47wf6DSeQd0Dx07Iv1sN2fA1WUpLooV72UWyTTA45HS339bSiOW5z/p9+CeWYZPR1fk7re19jm2IgxPUGTEmwpdjO89w8wMtEm/v5pfSBf9s3q6pe+mi21embbr/bapsHcDQoZ5764AbKyrFYZjNzDKlDK9rJ+FEu917ni7bm/fn5eUdS2Lewr2hS3iDRmWOt8ci+a9WD/nck2hHlg4ODLiX3tZIbcZrfMplMJh0yz0iTFWy+eIwz76vaoAYWwhtaXS6S+ap+702L8Ud5e0KbJbECpR78NlDCobQSzwmZkR4DTBYe7AP3nJycdHnTuZBhG7mWqEgLaDriZUDmhWNA6/o6fSIji1YO7hePDf/Tbvcjn6dizL5MSeDMb7fBx48zzo6AMqfc354bVgwGdNTd/Z0pbMxlPmes8nk2+Iwb/Z1A1SAw3yNmlson8KURms/nPeYvQWzuCcx9bAaBtBd2KkkPr6mVlZWOaX6xVMdBBhnk0ZfpdNrpafTcxsZGZyewiUmUJkCBfUfnXl5e1mQy6fZWnp6e1snJSXeimCMh1mM+Jc9ZDlULEODMgaqrBwaQUtgi8AzAsNlpz2zrR6PRFV8Fh932xkdpu3w7806XrKpeJAm9TVTI9ea7dJz9v+0gGRCU4/o7cpGOtE8ktI3lWa3+TnAIOGcusDfIEU32wrmf6Neq6qKiHrPc8009sJP4ldj5BLIeV/rL84k6eu8Ucy/9tgQkzCP7i1XVlYW9pF8Muvwcr4uqqt3d3Ss+WYK1PI2QeZhRW4gMUveOj4/r6OioptNp7+Th2WzWpfQ999xz9ZnPfKaLkL1WcqPBFJ2XSsdhQYtBynXpRy0UnJLsDoqVI5tb97iOfpaVK5/ldzyTCW5WytECMzko4BbT4IXt+rk/vDAyFF21ONnPfUq6Q4KfloFz1MGKHMDodiL+PxdtiylKFsfsZUvZuH1ptFpsVaZpOMJng+V6+LPsT588R335rpUuSB+ZuSQdhP9b72Ey4+k62fFwGoLBPoaHF/8ayNA/TonAILoMty3BpiXBs4+rb13P2PJi30EGGeTmCy8ttdNtG7jMxiU5BGGFc+qI1MnJSZ2cnNR0Oq3T09Pu5b/2AdKhtuOcDrujVtQnHV7XizJejt0DTNnJtZ7Ncv2KioyMocfTNtDX9ofSfiZo4ZmtiJbJVfcXz8J2ATa5Bqe+RRBaloEp2/kEw3k/EZiMYnpuQba3gBb+C/3tg50S0CS48Vw2gKfe6XPhm6S/4jJaZVOGD5zi4AZSBUkn5Jj1s7Oz7jPayKEh+B345PY77NuakM56cR8nafqFwkmun56edil+zz333GtOmt5oMLWzs9NNZiZtOld2Ks164Ag7UsT1/m1lnQ4uks4v4VBv2ssjVTNyxeSxwmXSWqk5ouJ80wRTKCHKynB4K/LjVAqUNH2VCjP72pGH3IOVIMD9SnkwIbSTuvu6TFnj+a4j92VZbnuLObOizXRNGwmMSs4zt8k/qbjyesbKY+I2eK4ZPNjg5ZgsU1JWNm5vyxAmwDUbaGfEoX7agEKlzQ7zZ4qB64JkH+T9LZKDdYeD9FoyVIMMMsjDlfF43IEA23QTP7mP044rjvnZ2Vm3N4pMjKOjo+5UstPT004Po9c4Idg2mbL9bp102k2E2Sm2LalaZCYk+Krq+yr8Tx2SuLNuTV3Pc5eBwEz5d7YB/lWrbxMYta7hf4T6npycdHbEGTFEXrgP38T1SuLPbU0Ax+dZ3uXl5ZX3LPIMIpO+J8EUpLltrOtmwScyyZ1EZvYdP35ZLn7V0dFRz1fFDvMc/2R0j3FLEsIkqNuc9tr9zJhlZgrf469meq7nPgRtHgIznU5rOp12JIdP2Tw7O6unn366nnvuue40wddabjSY2tvb6yb85uZml45UVd3GeU8UBtppflVXJ4YnaUYNrMhZFChLO3t5wo/TBTzprHBPT09778dI5znD3NzndqQyzqM57UTjSBv8GGSx4FuAINkx96PrZuV2HXPIosVA0kfUxQ475Xshm81L0Ed7eQ79YsNjhZjOvI1ytiOVkpUYbcq+y3mHIrFCRlHnXLRhNElggM7vHBcLfdwCqmmAmHewdYeHh50hMrhxm11vruGZrfa0+sVtsfHOseG6tbW1jlkeZJBBvnjEYApBf2G7rCsS2OC0n56e1vHxcZ2dndXBwUG338JpfthpQATOvW0AAphqRa8MhGwX0FkmIHEiT05Oem2kbMq0s8t12Om0N5ZWpMM+S9oKk6kGpEk0pn+AJEHnvwEHHM5AZDD9J+4z2PRhBtTV9jrBFdfRR/gJEJikWSKk0tne5KtHeBaZGa5LggfbeANT+1a+1rY4/Rj6HzDFHAPY8V36IGQQ5Zjn8w2mqE9Gllr9mn7IMr+q5WOZ/KeugKplYOr+/fs1nU7r6aefrt/5nd9pzvfXQm40mEoFYGXSWlCO3pg9Sja/qn+E5Wg06iJH6+vrHWhrOdjpiCLpVPv5VhxWYGyGTBbOTqSdSxaz2aqsp5/Nb+/v8T3uEzvf2ffZViuHZK0MdjK6ZYXr8crrUmlaqdt4+ndrTLJf2NC6jE3LfnP5Hk+Xn/MujZ1BkI2wlXTWxY5Cy3jauHC9HY2MNJmJMoBqvaOFfuI+NqayJvJ9Eq6/I3qpXF3v09PTDkB7TbMmyOlGyZ6cnHRs8xCRGmSQLz4BTJl0amWjJAFlf8DOJQw3eytx6Ofzefe+o/F4XBsbG907kFwe5XOCX9Ui7d260nUxoWebi28BoZp+DG1C73oPOLKMkEr7jB72nnHsUhKDtgVE/TMalvt1s//pd9tBbI1JutFodOXwB0ACQBjnPn2YBAgIzwWIMR6kjdHfrX6kDbZBSQQy3vldy5+0nXW9/awWAPW42e4naYl/YiI8y+MaAzP7WE5btO+Sc8WRVtfVgQL7aI64OmOF51pYm8fHxzWZTHprk/+ff/75Ojw87ADloyI3GkzZoU5WJtPa+MzRA0d6cAYNtBj82WzWOW8GPyl2gDNXOheZFRVOYlWfyTeYqqo6Pj6+orCdEoeizPB1MmYWHGJvpF0Wsm1FLRL0WElk+xMcLTM83Gtl4wWaysH9wH2ttrpst9E/9LnBpY/JNWClbq5rRkxgWahvS1EyD7nfdbcCbDGuy9qHtPqY+e42Oo2Eftja2urVl7ox/2GRiPTiXHgcEmSaucqx4m9eopnKmefCJk+n006pTqfTLrV1kEEG+eISUvqxbXYmrU9sP61LcQK9N2oymdTp6WkdHBx0G+RHoweH3Ny+fbu2trZqe3u7S/NzuegxR0rOzs56voTtnEHF8fFx7/UhFxcXnc7jEADrakBUVXVkLjo6dWnV8q0JaZvsC7X2T1MegJFn4dw6OwFd7dQ37x/KCBI2mLIBic6kWVlZqa2trd6YE8FIgtkEIYCAKB8OOe1hDlBekqP+n/FcXV2t8Xjcs9tEtxijqv5BSPaXnKVj39RbKfIH8RzHv3MZlAvZ4IO/6H/aTV0S9Dra5+f7gDf+98uMsw9Ho1H3ImvG0wCcOmQ0i/vPzs7q8PCwDg8Pa39/v4skn56e1t27d+vw8LA+97nP1fPPP3+tD/RayI0GUxYGK5mCVCzJ1ljJVPXzYwEagA0mWkYSuC/BgBW5Q8B8798GRwmwuI40BZ8443a2gE3WCTHjkP3Cda3ITzIJy1gUP691TX6e12TfeWxakm3jM99rAZQl8OC3mRP6MaOCy+qRAISfZfu3Ugx+r+vvl9IXy8T9nOPka5JlNLCrWrCspNmkQU8Q7rJzjvIZTgJrLdcWChyGMfPeBxlkkC8usdNpm3CdPU9dbRtpxtwpZDjlOINOrwc04Hwa7KSO8vOoV6ZKGeARkUpdxv1ESOw4t5xuP5e2+XMDG6f7uR8pl/qZXOQ5LX+HNuF8J1GWACD3RtOXSZq6be47npP7emzbnDrmSCD7tfg87ayfw/dpVzNbZZlv4GvoE/oPn5X70x66v/Mzl29g7uhU+pm0yc+xv5GRRMDP6elpz75TpgFSRgYpM+uY9XeqJwTpZDKpyWTSHSR1cnJS9+/f71JxH0V7f6PBlAeWCWTEyx4PR5cMuLx4MwXJ59cfHx93CnZ1dbU2NjZ6oWfYggRwLGQ+S0ahaoHQfRT7xsZGL+RKRGoymdTR0VFtbm526Qebm5u9NnoBWenRfucjm6mwgXI7UMb0jQ0Hi8sLCGeYey1WBJkXzL1mRGgXCp1NwCmtqBnt8RGkrr/FANy/fXJcRjWdyui2pbFz1ClPRGyBAO71ONkImiFMJe2yKYtrEwgm6GkZkxbAtTJECXKvT7HMsL7Hu2V0TFKYzfIPzzw/P6979+7V0dFRHR4e1nQ6feRYqkEGGeThCfYXm0CqGntt0EsJhGx/DTD8TilS7LCrOzs7defOnZ4tdjSICEzuKbK+NSHrzAz8hJWVxesbTk9Pa39/v5d6hf6jTqPRg1PTcJZN0DmjhP/pm+Pj4xqNRr2Mi9Fo1EV6KMc6Fh1OCtzp6WnPZlctjrY2kOFvO/VVV/fHeqxom+2CIyEGGLZRjCvjBxhze/JEONLBfTy+waojj3629w25/OPj45rP5719c/gXtv/Mv6o2AW8w4mu8F9pkv31DyvaeoySf/eyWTWbc6JfT09Ou39jXdnn54GW5W1tbXcaK595oNOpOvsx9/QbvuQUEUIuffe/evXr++edrOp3W3bt36+TkpO7evVvHx8f12c9+tg4PDx/ZVP4bDaaWsUHLIiGt+xGzBQw+E8+sVYsRqrqa+5nOXauerd9cSx0AQUzu8/Pz3kEbLi+BVEZCWve8FEmg4t/XsSh5L98vY3BaUQxHThIQtJixZe16KfMimT3+T6VT1U8jzb5pyXURLaejZGQ1n5Gs4DL2KQ36MmnNZerh5+Z3GBSzfWZfWxtWlz3b4LyqunSeZMpQxqenp93mVCLGgwwyyBevWCeySR79g/OG42k7XXV1f2ZmBCRj3rJpBi84sxk1TxuzjLjyc4lK4Qx770tV9bJTfF/Lria55qiNU+vSyXbd3M8uL+1OVT+lvWWz3A+t/kiCzX2ewNTfZT9wfZLBHreMvmQ/+z4A8DJwzDNbKfgZxWr5Jct8Ff7GpvnkPxOM7ldHaB194/sc55wzCXSzPgA0QI/XQGusPS6ZIeTP+A2RwR4pHzjhdFw+f5RT+b9owFQqPoCQGe7cJMk9MDjIbDbrlBuLZWtrq3Z3d3svrzUbVdV2IIkuWGmYHUglvrKyyHslzPn5z3++t1FyZ2en25C7rD+cNki5GAAUNP3i02por/uUa7ONfgaSp//MZrOOpXB+bEvx87+VoNkvfuhTO9quv/sy97fBKDK2rhf15k3afA472HpRYyrwTEHJ/vP8S6Vrp4Ax4Zlm+Zx6Qn3cfsr0nEEJmlF0vc2ktVJkuAZW1mPjk3YYK+oHs5uKNR0gR4cZH9aBj0Q9ODio+/fv1+c+97mO1R1kkEG+uGVzc7PLwnCmwHz+4MCIvb297tAI7yVy5MnsuHWzSRts//HxcWfb0WNra2u1vb3d04Hc4wwO9Ca2h/dhAprQ9efn593JZHfv3u21CZ24tbXV7ZPCjjoLxs4wPgV605kx7DPyacEGGpC2VVcJvRahRr+wjyidaz6jPxy1su2h/ib90qczyLvO5/Hx6isrK7Wzs1Pz+bw2Nja6o85pJ1FJspU8z9gntbGx0fkIl5cPTh9MP7MFEA2o7OfZt+EzxoY+mM1m3b45Zyv5pb+j0ah7l6dPJcxoWwJ7p8/n2NPHrA/WGECKdzbu7e3V3t5ejcfjun37di9qy1ga9DCXbdOp78XFRbcvan9/v0vn43nskfrt3/7t7vNHWW40mEKWMf7p8LaAFJM4U64yTG02qvX8ZHEsyeDwWTrbBmig9ZOTky5PFIVuRbWsL/J36zPqbBZmWX+2WCrXfxkAy2iTFaSvSwXrhd5S1L7WRsj1c724j4XtZ6YRzFxo6uMcbwOhVj+3WKhkrVpj0RqjvJ662KlI1sz3Zh/k8zyXlo29jUCL6TMYo38xZL7O/Ux/8juVP/MH54CX9PHm8wFIDTLIHw/BsUxntqpqY2OjNjc3e9Ei698k3ZC0VWmDMxJgMAIR5M3+1pMtG2kgUVVdlN1ElOtXtUibd5npMPuzjLxwnaMsvg9bkvV0HyXx6e/SNrZ8Ldphh9t2OiMj6Z9xv30US9ptk8QrKytdqj9zKEGeCVBfm9GVzEbx/i73e+4d9u+cb/6bZxv821/wPr0kYnPM06fx/5mKSBm+xr4V6fxkaF2XQmvgbIDpFF3mPQevHB8fd3YdwHV6etpFpI6Ojh7piBRyo8GUN1faucu8zGURFyawN+yl0mXyVS022+f+FSuqTCGgjJyodkhR1J7AnGrCRDs/P+/2SfFjBsrtom30UQu4tSRZHzMrXhjJEnkxWfmQS5yK3s9LBU1fUu+qfj4zdWA8fO91aQbOeV9dXe3YH9efMmCkKI/oCwIjN5st9mS5j3IeZPQsx4E2w5phpM0WMuZbW1tdbvLKykoXsalanC7kecX8tTPi9jqnO+vn8fHcaq0Vs1gGaJTPaXvsaYD5c/QqTwIk8kQKwOHhYT3zzDNduusggwzyx0O2t7e7d+lgZ1q2lD1CVQ/0CKQLbPvm5mbt7e3V+vp656C1GHwiYVtbW51+snOK7vPrGAwssDV8zh4mR6iOjo7q/v37nd62o0ykbW9vr7a3t3uEEzrUh19YR1u/o1MdiapaEJH2J/IY8gQu6Hz3g1+xUdW3yS2fyP6U+x1fw/bHe2+IxNgeZbYD++fm88XLcwGK2EX8EWyosx+cIsd4cSQ8bXO97R8g+FrZfwBiA6EEvpRFnZhXZ2dn3f697e3tms/nvdOn3b8+KI3xNhg3cLbvQlm5FuhfAD/9i19qv7aV+cV4M7cSTN27d69OTk7q6Oioi+Cen5/X3bt363d+53e6626C3Ggw5UnrRWjxAszUpRYL1UrfctqWAYEnnJ/la1qRET5vMTc8B8eUDYG5adDAy2CuFXnIvmmxJS0g4g2DCEoqWQ/q4f6ysrEidr0y6uQUBpShIzHZ577ebcpnOWSefZ8AxC/V43ueY5DQYjDNGPnzZKLcP1bAnns2EBh0jLxZPjYIIzneNvDZT55H/tx1TgPqupqxM5HheXh5edmLrmIYfIjKyspKl0JLPVGwMFQHBwf1/PPPL13rgwwyyBenoCtMaBpIoXdxfNFNdgJxZjc3NzuyD/2aTqHTq1p2J0kkgy3bK+pu2zGbzTrbjgNpBxiCaWdnp0vza9Uv097S3iSp1YpU2MFfWVnpEcstP8F/cz+f2x7aPrXs0TJJW5PPsS+B3eS3bY99C/tjflcX/1dVNw98/TLfkLa1/J2cC3zOGOCH2K8yKOQ5SfAD8rCV6Vd4XOwTZaaPnwd45fuWz8ozAJxra2vdUfPso8q55vYyv3woGSSx0/y8VYCD30h9vSlyo8FUVfUmeFU7OmNFtew7FDWftRxkQI5DjqlAqEOy+umIVvWPQWdSs4hYlFbKsPkoeIeBUwHlgnZbUXKtieoFR7+4v5DsTyvQZG4MPvk8FbtBVTJilAGgtGK14vG12Ta/aA6jQbtoG/3syFfVIucdheR3idAPmQJgJbYMzOb1Vpq0E/aR951ULRilZLUMmDzWjkrZiLrN2RfJLlFP1x1Q5LXBWiB/GyZqOp12UWAigwDSVMY7Ozu1trbW9TspAJQ5yCCD/PES9g2Z3W5FFMg8GI/Htbq62qUnJcGZthKdvrm52dnZ0WjUpVzx3jue4ZNLk5k3yKpabBmwjc9XOnA/6Yq3b99ugin6AVKwJdbv1JMybIvsZPvvVuZBi6S0/WEc+GnZopZNoR6OJlkSXLXAGfdj4wFG2Br7CYw/Nn57e7tms1mNx+Ne2QlW6fsEtR5v+32eF+5DR/A8j5nLZCVhKynH+wDpB7fFpCjZLdhX+5WuD3Ob9jqlzwdAeI9ZHkTheUOfQmj4hMO1tbW6f/9+feYzn+kitKPRqB5//PFuP/rZ2Vndu3evXnjhhZpOpzeONP2iAVNVV529lhi4+D4mZCs61QIfGQlIVG+mpgWmKNMK0ZMZNg1AVdXPDbdishJ0KlwqLNeXOlpcRyQjR8uiclbICabMICWQ8v0pqTgcsfE4WVGgOMxGVi3AVJbl8ltKgbJR0AlCcs4ZeFB2ApiMdPI8pzkwH73xGeeA9yzkPG3lrQOm/OJHj4PH12Wm0WutK462tTFBUaKoMRAnJyedQjbRQLqLAeHrXve6Go/HXd8fHh7WwcHBlecPMsggfzyECDaReJxjnLDJZFKrq6vdC0O3t7e7VLjU/ekkoxuxr0TBqhbE1cnJSZcOhy2zDbB+51k+/ME6GYfXpBh1og67u7u1t7fXeykw/YBNaEk62QaXVdWlPKbD7x/sgKNAGQmp6p+M5ygFz7dvkhEwi/vPaec8w+X5GSk+ZIR+dz/Z5lMeWSgGZD7QibqShm6bST3sf/h02STVqYOfwxzmN2lugFMiqGmLk+g3mML3YV476ki9mNOOzOLnAJpIR+U389WHrUBCeOwgTllHjP0f/MEf1Kc//eluW8LGxka99a1vrde//vXdc/f39+t3f/d3m+P7qMuNBlOZr+tJZqc22XUDqpZj32Lo+dxlpMO8TAGwAM3qtJ6bz2ah7+zs1Gw26yJSTHozRzwv29xielA4jpzx7Kyj22HFyvP9XTJRy1gmSwIvA4QMo1u5uZ9bAKAFzlqpbPTDaDTqTvxxn5iR4l4ryBaQ8t8ZMaOMZNgo14rT7UBBEq3BMDG3WuxXGrNkzbIv/JkNqesIAEL5G4C535hjjlYxb1zX3Pc2m826d4FwzbA/apBB/niLXwlCdIoDaZz2R9QmIyN2/HHcbAer+vtV0Dno0Nx3mgChZc9xsieTSeegOq3P9sO61D9OAax6YCd9SEKLvTeh6vq5PY6QmCjMyJV/05dps6vqil9Cvzq1LcGnfZS0QwYs7tuMnLk8j01VXUnpt3/UeibPWllZ6UXa6CfXN33K3H5hsME8MXGJ7TOoAmCzv5jyt7a2unJ8L3OBe7mfuhoQZvvxd9iTTZk+GMJRK4PJlBZAns/n9fzzz9fR0VE3LrxLDbm8vKzPf/7zHagjC+Wmyo0GUyhXT0pLRmk8mdJpbjEvvtasCCicTZFVfVbfSsn51yibZQ54OsPe5zMajbpUL1gF59W6vHTq01nleNYEOyhAL3wrKoMoyjFz1gKlPJO+dq57lm9wlPu1HP3wvb4ehsQhbu5N4GWmy7nDTp9EQcJWJuu1rM+r+iDcEbRW//ie1ibOqgdsz+XlZR0fH9f5+Xnt7u7Wzs5OMzWDdrkv8nSilARSjpIl8IHdNIhKIE4Z6bS82Lsi5vN5HRwcNMHeIIMM8sdTjo+PO/1OxPvw8LDu3r3bi8JwYASvwKha2GafCur9GegmdBZ7OLAHpPUB0vKn6urhDNh6Ntn7XVLWn9guUrPyc35XLWw5ETL20WbGg0EUvoOzREwIopttIxADmCSq+Yz2rq+vd/oe3+Di4qLXdwY3aR/dd4wl37XIWb+ehPqwL4eyfFATAIkxzTYaYPgACMYRsIhfkZG2VrsA97SHPXrU0ZEpoqtnZ2fdoST4Ybdv3+7KSSKSOTyZTLrTn32wiv0g6oSPMZ1OewAMIJc+AD6Qo2gIgDnHcjab1W//9m/XZz/72d61rsvFxUV99rOfveID31S50WAqnVOj45ZDugxVZ5TKDmqyW5STTE+rXskkpEPdip7wuReOD61w+WZO3O7W5LwOwLk8num6+V4Dy2V92wIJZkjyvmSd/DsVuf82a+T+aEXEULrus9Z8WBYFs8zn/beIv5g4VcBtSPCMcc55mM+j71tRU8RtAxy6fXmdyzGTmz/UJeczRh8jBNvkPQ2t+bpMbrJSHWSQQR6uWB9gHzc2Nmpra6sjNjmcBwCRmRtV/Wi9AZGJVV9nvYgeM3m1LLpiSR3r5wCgWjZrNptdiXxllIlr3U+2qUloZuTpOoLNZWYkzfof8JQkaMvBziwGg7tsR6v/lvUBgMW2Mu2oQZr9K/dhZnX4cwM46k2ZGZXKNrTIVD6n34gUAbzdPy27bDDcArotkhPAO5/Pu33IlEH01OPqFFXvcebn7Oysnn322d48wfZDAL+UufXFIDcaTJ2cnPSiInnqToY3rQD5nonHhCGClM6/F0O+WJXr8mSYZJ6sRLg2JxMMi09ASUeXNAUkgWMu5lSGsCzULdkGWBScY/oOBqpqwQx582MqZyu6PGzD4vud5+twdSom+iFBnf+HxTT4S7avFbXBYPp4dj8XZtTzyJtRE3ymIrch4rmMc+tEHOrL5+vr693G5EzZdL8bvLQMZhICButOPyCS2QJT/O90vslkUs8///zSY+MHGWSQQV6OeC+K3/M0Ho87fcheqarq0pQceYLMwrZxeiiHTVjnJojx3ikiLtQFx9wkl3UxttypYlVXT/RFsA2kNnKCm20JdoKDA7iv5VQDdriGZ9Ov2Ejsnn0TfhuMWe/Tv7lnir6pWqTIAbicLuZolJ13px4m+KRM/me88N2wVx5P+ou5QxmACafHUS7+F3v0EqSxJ2h3d7cboyQtmRNnZ2e98Xa0lAMf9vf3u9+0n35Jnwp/Mk+DJHpqItN1ubxcnKx7eHjYgSfXh/5nDPH7MgpL1PV//+//3XvnI8/745aef6PBVCvC0QIWLWXVuj/Tz1oO4LIoSdbpumfm5y3mjMXh75ikLYcUYLOsn5Kp4J4Ectl/Wcdl7XupkowP9bABcn387ExV8EbIZBUNoFrzhHb5Hgv9f12EyqDF7OYyAMi1yYgZZOecMwNl5YRi5F7/nf2Y7JTrbrDO/y7TQInn8o4uK1WftGggNsgggwzySiXtAI6xU8gyfYtrHO3HyfYpY5kqh07jOp7p6Dr6N22uAQeyLAUs/ZUkf90Wgyn7KS1fpAVClvkfWe98bsuG+W9HNRKMUIckc13XVrp8y5a7/tgsA0vb3tyfbnGUyvf4eVWLSJcjkQZwLb+z1ZfUw2Co5VflWOR45f/uh1Y9cp5QF4AS6YEmPLH3nlOcqsd+fe7lOUSgDKb+uMqNBlMcf+rBzwnkSe7IA98x2YlOwPQ4bG2BRfDmzzyy286xwZkXoRWzFasjGGYM5vN597m/8z4xM19eVL6HBeOoE/nXzgl3+N1toq9z0dnwpBKjrTZYuaEz+5ByqDN1NGPEWLWULyxVplBgcDNKST0p18AtFZb3lOUeqs3NzVpbW+tyrz2HYJM8pq73MpnNZnVwcNB7h8X29nZv7Okn/qbMFhBcFkkz2PaJfGavLi4u6nd/93drMpn07ql6wPDu7u4OR5gPMsggD1VsLzPaPxqNur0fVdXpf+zE5uZmt0fq8vKy09+np6e1trbW7dGczR680mF/f7875IeT88bjcQe+5vN5L3JgxxYdiU3D7q2vr3cnmfoHO5LvEcR59b5vogNE0nhG65AK2m5fpGoRaXC/+roEGQkEENtm9q/5ZDifOgwQxVaTUQIZ6uwW9qHjl7l/qxa2zXvYGBP8GsCP0/L4nLLYf8ecop0ufzQa1WQy6SJv2DxOihyPx11klPuJAALG7dOtrq52h0mMRqOu74kGjsfjWllZqclk0s1h+yuQB96vz5xibiPMgUwHPD8/773fzFsA6N/RaFRbW1t1enpa//N//s9uX6L76Pbt2/WlX/qldXx8PNj6P5QbDaa8AdTO8UuNmuQkwPlssfVIhnsT+ef1ZjqSlcgULCtOPk9WBgWQrFGrrq1+YGG5HaQ0oJgMHlr3u51WfMsWVUZjXE+X4Xrns2m7UzNbY0X/WXG6bIOjZfVtMUN+hhVLpti5/mYxc7xtwFqAx0DHwAalDBh2O1NaqZ9mQ91W+pFyaJvTCG1UWqfuzOfz2tjY6AHtQQYZZJBXKkk+WqxbcQ6xt0lAVi1Su2/dutURVI48cQjFfD7vnRjsaFQrOpURBz+vqh+tcWQFEGOQ2LKBmeJnXY0dST2fQMgEZurorPdL8aMc7TCYAgikzU+fwXVynVt2y2NI//m1H+6HZeLon/1H7wX3eNAOp1Py/NYBJGnrvZ3AYDfbZVDpCKTb7XmD7+M+bvkoo9Go8/Poe2x6y+fyGFxcXNTBwUHdu3fvSj9CRpBSOcgNB1NV1XuZmdkJJAFPS4FU9ffasEhQEn5/wcnJSXey0Pb2dq2srHRMEU5niymijhmpMgjkt1mHdLztoFfVtYq8BQQSJPIcMzde+M4HT/HiTvbISpuFSWQJhsph5ZZycTogx3W63lkngxyuy2u8r877wbLc7LOsF6cXcZSoozEbGxtdGN3j59Q4+tb1dQQPtos5wzyHCSOXvjVGHv/RaHE8qxk6GC8isDl/cBSoz3Q6raeffrpjtVpydnZW+/v7vfk5yCCDDPJKhdPO0GcmlHA+AUi2u0R4bHfRT4AprjdxRHlEjJytYSILsIYTTATBdjjL9b6gzc3Nzk44gkF9iDgQLbC9dSQlbT5/U2d8Cj/bGTr2k/JU5CQKaT++kffLYi94PlGUdNZtX6mT0y6dWVJ19TUoXMsziArlgQzuG++F42Q9R2Zms1l3eIJB5ebmZs9GTyaTOjk56aJb7IfyM034OorKc4hCYk/xkRhrA37mugmFBKKUC/HqKCr2HJ/EJ1kSgSWSuLW1VQcHB/XLv/zLdXR0tPT9joeHh/WZz3xmSOeX3GgwZTRvRsBIPFn4lpPnRcDCZBGwoK04mECkEric1pGdLJ6se0uslBP4+G+zEVaaLUXiZ/raVH4oFhue69ipFlhrPTdZo2QBW89KdsnpE7k5kvsNIJalvFmZJ5h9qYKiMzvpzbUrKyvdfHH90zBk9Iw2tl7IiBMBkCLcn2mtngO0KZk11o2VoEkF19dgdn9//9rcaBT+IIMMMsjDFB/641QudCe2ICMuACHS+7B3jvDYmc4Ml4wSGJA4K8HXpi3BJ+DAIO/x8vYC/+S9tiUv1e5yjfsF4IZTbfDm9rmslh+Q+8fcd/ZBrou0GQhybd6DzfdY01/UzSAwyefskySMIQaTgLWtdB8BSjjkIQ9ZaLU15wa+nV8onSR5poFSt/T9sj+dTokfaVCP70ob8TccFZvP5/UHf/AHS4FUVXWnDg6ykBsNpngnkNklJmlV+yWxVf1Fa2eWxUuZKGlLK4Li7xz2zcgU0lJ4vsbskRcX97bSt/iOtji1IKMqNi6p+LJ+boMXeyq1ZXXiWqeMAUa82N1v/Ha00Mra5WZdAIbLjAp1ze+t0GxUbEjdDr+02HV3pMcGuepBHjbjasXnscHQZsQVR8CnR5EvnW9mp30G/Yw5dea5kAF+1wTRVxi4+/fvdxG4QQYZZJBXW6ybTYah99ifg47D/hKJsFNZ1U8TM4EJyDg+Pu50ptPLnYbl3y3bY3tsR599T7wPC4BIFMdgi7L9/kP0vsFg2sU8QMOOeJJ5aTcMiiyZPs+15+fnPRuR/sPm5mavX3K/maNpjjRm/yUBbL/G77/0fDHoTZLbL292pM7ksolJxgqilHeRJfDxS4rzkBMiet67dHJy0nvfk8UgaGVlpTtZcGNjo+bzeXcf4MaEtecDhC9jZF9mOp3WyclJTafT7kW7y7JPBlkuX1RgynnSLMyWLAs1I+lYezNnVT+yYElAlM+jbP/OKJAVQEYYsrwWGHTkjevyTemE0HHuOR50WXTGzzOrkbIsomXFRrofqZEsWgPgluHzeJpJrFoAAUBDAiXK4LfBtAUlbaXse6iTUxN9WlRV9fY2mX3keU4XcJoBPz4W38KR9DYwAGIbCDORnv+U77niNBVvCkbJTyaTOjw8rKeffroZ0R1kkEEGeTUEvWU9jP7KFKqqxWFMpPkts1fWmZkmRSTJoKUFrBy9ch2pt18lUbU4KY4X2kLi+gXBdsgpn/adn5/3npv+jO1zZi1kdMN90CIikxR2GqUP2jg8PKzj4+POoR+Px52dR7DrPojDdtlkafosjoTxP/eT3pf+nqMvCWadJgrAsc+XkUdIy6rqAA0HM/k1NgamBlTUFSDld0qx98in4qb/BADFl6DvSdkjbS+JefeDT++jrfggZ2dn9fnPf77+z//5P0N2yRcoNx5MbWxsNNkHvq+q3oKoqqbSQcxGVV1la/xsKznE0YhUSv6d6XipMP1MvxcB5e50RLe/JQYQZnl8+lFufs1QsvflmDkymKO/3GaHz8kN5jNYKOqFtBg+QG22JRnL7G/akP1u0OQ6tqKJGaVymSgjFCH7wlobhf3squqMNSkqyRRa0hDCqrrvkn200nRUCvFcmE6ndX5+XkdHR12uNO+9WDavBhlkkEFeDXHEKe2xySETW7ZNvh8x2ZhpdoAp9pKQVp0gxrbBtqlqYU/Q044wLQNC/sygLclA2yITyibTeA59lFE5nGnAjd/Zmb6L/89+ns1m3SnHHKV9eHjYRd5495fluqiigVHWxf1ARM92DNtoghFxVgj9gl/j7Cauxd8C+BjwEBXKPnHdWj9JhjJ3PY5OxeRdY64PUTXGGLvuKJjXjbNpiGIB6DhkYn9/vw4PDwdb/wrkRoOpW7du1c7OTq2srHShSia7JzdObDq3XGtHlt+8Y8BipWfl6u+r+mDAjq7rAlvlULfBH5PfypT2sS/HbM6yF6QZ+FQtFJ/vQZnlQnSbE1zSDkfQMtqWRu/4+Lim02ltbW31DA0MSyvCQjlcu7Ky0rFdpNRRj3y27/eYGzxaqaGoMrUz9xoxt87Pz+v4+LhTUJeXl92GZbNVBjYeZ+fLZ6pGSvYxhv7/Z+/P4zQryzNx/DrvvtXSVdX7Cs2+yyK2uAIRiEoUEo06H5cYNTNgVPLNEPRnFJIRYxJNjKjzSRycSSA6OFETnMigbEFAWUSQpWmgoRu6q7ura3337fz+aK5T17nrvNUL3V31Fs/1+dSnqs57znOe7T3nvp7rvu+HK6naB4R1ddDxYzkkg6VSCZVKBePj4ygWixgdHcWuXbvcw9XBwWHOERXoznc0F6M0KYGSHZsgwb6rSJw0+UE6nUYmkwk2A85ms5EEh+9pNaStqz//5/tAF0XVgFdCqGqXjdtSpYPts5vC8jpduNT78By6mWnabfWKUHvJ9pmqgZVKJViIazabyOVyQVk9PT0h8qdZaDXJAt+j7KNEIhEk3dD6aBuUfGisl44ViYcSIapMHBu7wbx1+aT6o8nIeH+WE+UVFOUxogRH70myw0XnarUauH4qiQKmt1mhyyHPI4FVzxf+TVJYrVZRr9eDBdQdO3Zgy5YtM0izw/6hq8nUxMQEgGl3P/1y6xeQBqRdNbLEQY1aNbb5vyog1lCPWslRg55fJHUFmE1NIfgCsHXVVaYoZcU+AO3KkpapZEnLiIIljKpIaRkshy8TXVnh3yQdLENfPFErfHoPjheJr+1PS8S07UqK+NKxhNuuFuqc0pcVf+fzefi+j56enlAGKbtqZlepbN/a+nL8SaTZJiW++tLWdvP7QCODLxsSaiaVqNfrmJiYQL1eR7FYDDbhcw9WBweH+YByuYxMJhNS/AGEDHQg7OGh7wq1AaxiYQ1fVYJ0MUoJjB4Dwsl3+Nzn9fqe5P34O+q9qe/2qB9bD00qYO2NqAXFTu93bVfUOXZxWBdKo+KgSV6YsEEXKUkqPM8LFo11wdDaMaqmaRmKqIVgAMHiM+us56m9p2Xo+53lsC3VajXobxLvqPFlW9hOki/tN+1ztY/sIrxd7Od9tE85j6JcCcvlMnbs2IFSqRSQKMZPVSoV58Z/ENDVZGrTpk1YvHgxcrkcBgcH0dPTE6zQ8CEDTD+UdEO4dns6halOJE5OnssJZ4lPLBYLVlh4D7typhPf/q9uifxsNuOVL4KoB69++VgXdQG07g3qymDvoS4I9gvPevIePM56ceWNvrskOXb37GQyiXq9HmxQSHlbyQzButsHiY4V76t9q8RBV/PYDm7GyLZwJ2+tt/aZJVeqLiWTSQwNDSGZTKK3txfpdDrkFsCVJlXYtP+0nnY8uPI2MjISqF8kU0NDQ8GKlPYH681+0Wx+fIhWKhVMTEzgwQcfxNTU1AzXUkekHBwc5gu2b98Oz/MCtYgqCt/hXKHnuyefzwefW9VK3a2V0OgCG417Gs48j9tR6JYpuqgHTBM7vns1ZkpJEZ/dUaSHn+sP66BxM6o88G++U+Px+Az3P5v0QV321JDX89kftA+0XSyDi4nMuNhu74nF5p6ETL+t73G+H3m+viNZJ022wXspSeE4so84F2jfed6elPKxWCwgF5aMWG8bQskJCUm1WsXIyAharVawTUk8Hg/Sp7PeJEi0R9kvGhbA86h6MS27KmysB+0MxlSzD+nlw7gxephUq1UUi8VgS4Hdu3fjvvvuC7Yu0bnmiNTBQVeTqWKxGPjjFgqFYEKrwW+hqztWTdLVHKtOqGqiUEKh5QDTCQ2iVJJOqkcU1OC29476MsymLimh0DpredpX1qjWFT27eqLnaz9atz+unCiZ0XpF9ZOOxWztsytz9jOdG/ZHH9havq2LHucDm+nKdR+rTmNsVya1XF3ps/7OSqb4ANaXf9R46typVquYmppCrVbDxMQEJicng4evg4ODw3zFjh070NPTAwBBxjt9zpHwcEGO/6sSYVf17TNaiVWUMsF7qdFv1Ro9L+p9aj0obPlRqoSWx3MAhAxzVTWYzEIXIaPCEaLez2wP323ar1E/LEeTLUSRSdpbtq3AdDIJ217t7yiXSNuveg/tZyVb9h0ZVZb2Cdtl+6jRaAQx3CRO1o5S8sl7qDKlC5e27ew33YpHPVF0LtCm0uRRk5OTGBkZCUIRRkdH3bv+EKOrydTWrVvRaDSQz+dRrVbR29uL3t5e9Pf3hx6qmnUFCLsGcHJrEKZVD3iNdT3Th4Z+KfTLqe4BXDmxX177JbEkoF6vB59bg5wrM3o9P7fnqjsd26ifqy9wVGCmrpDxS8wsMrw+qg/0QUKVpl6vo6+vD729vUFd1CVO66lt0XYQHENdndJx0rHQ41pPrpzpi4cPQusWwOvj8Th6e3uDFUvP84KHmT4EdX7omOiGj57nhVwiuPEvyysWi6hUKkF5u3fvDnz5+/r6Ap9362ICAKVSCY1GA8899xweffTR0AvYPVwdHBzmO26++Wacd955WLVqFVasWIGBgQF4nod0Oh28V4BwYijNwquLXAQN5Ww2CyCcEIjuW4z94XtP34dqcPN/u7ip7y+qUBqnHeU6aPcCouucetq0Wi1MTEwE7wz7nmF9GE/Dd5tdTNbkVsxSZz1LMplMqP+UPNBWYPpz3buKpIPvGCpEmjiJ19COUeVJ+4gKEK+x7pjWHtL3vpIZ2hnquWLfz6poqi2QSqVCG/rSa4kxSCS37CNL8u39tK/ZLxzPZrOJiYmJkAtePp8PbY3i+34Qsz0+Ph7ErVUqFTz99NN4+OGHQwS7VCod+BfQYa+I7f2UMO666y68/e1vx4oVK+B5Hn7wgx8EnzUaDVx55ZU4+eSTkc/nsWLFCrz//e/Htm3bQmWMjo7ife97X0B8PvzhD6NYLO535ZnQgH6g5XI5SEoAzCRAhP0iKjoZ4rq6YaVyPdce04dblAIURRb0ngBCDy97D/1fj1viF7USY1fi9EHbafXKHrf3sETHXg9M+x7rg0nPsckW9ga7yqPH9Xena1lPrkDZeRN1jA9offAC0y4BGqxq3Sz1nkq09SVtE2RoClcGxXKliffiuQxi1Z9SqYTx8XHs2rULIyMjQfYeJ/E7ODhEYT6963fu3Indu3djfHwcpVJpRjIAfc7SYFWXtE6KCp+9qlzobyVnSnqUsETBvnOsR4T+rcdYXxtXY39oIKu7of7YY53K0vpaNcSWHfUeVSJg91Sy5FDbGqUQsbzZ6mlJaFQf2v9Jauy9LOGKapdVtWz9NN4pap7p52oPWFJl/+dCJ8mRplDXrHxTU1OYmJjA6OgoxsbGMD4+Hvy/Y8cO7Ny5M3jf2zAUh4OL/VamSqUSTj31VPze7/0eLrnkktBn5XIZDz30ED772c/i1FNPxdjYGD7xiU/g4osvxgMPPBCc9773vQ/bt2/HrbfeikajgQ996EP46Ec/ihtvvHG/6tJutzE2NoapqamQusN4nP7+fnieh2w2O0P1iMViwT4IOpGteqFqlT4QuVcT/2d99MvBL5SqHTzG+6iEzLLsg1pXgViGEkbWR9UyPky4OqRt6hRgynrbFwgwvfpm/W2VCKhvuV7LlahsNjtjs7pKpRLai0nrwnbow1o/Ux/1qJdQJ5cK9osGEEfdezaoWsV9Nrj6U6vVQpmhmNq003iqmsTz6YPOByofoFNTU4G6Vy6Xkc/nMTo6ilQqhUKhgEajgS1btgQpatnOdrvtyJODg8M+Yz696+v1Oh544AE8+eSTOOuss3D00Uejv78fixYtmhFPwlghdY8iMdJsaXw3Mzss7QTN7GcX0riIppur871EDxh9L/KdbIkO60P7g4oJY7QYB0uoG2I6nUYymQxSXPPdo897+46hXUBVTNNjcwFaVQzaDXxX8l1rEzQwhqy3tzdQbmKxWLCwnUwmUalUQm6ZtBfULtCN52kjADNdIzV2jDYH62s9b9gHdI/nZ6w/MNMlz+71pLFS7GNL5pjEIZVKoVqtBm6oUYvknFMkRUwMUa1WMTY2FhDYZrOJZ599FmNjYzj22GNx9NFHI51OY3x8PLArSqUS7rvvPoyNjQWqIueae9cffuw3mbroootw0UUXRX7W19eHW2+9NXTsa1/7Gl796ldjy5YtWLNmDZ544gn8+Mc/xv33348zzzwTAPB3f/d3+M3f/E381V/9FVasWLFf9eEu1JVKBblcLqQS8GGiLmQKPmSt7Kpkx6o1KkVrNjwAIbIGhI1yu6Khx60KoiQsauWGD3SrbNjy+SDU/+2KF+sZpQTZlSS7Kmfrbx+WhE3WoBvEkujpuGk7OIYcR6sk8f4cO0tw7cNTSV6nc60iyXOj1Dl+xtWjyclJVKvVYH8SZqCyD1Ulw3Y8NV2s3bWcySPYp3wop1KpgKQ+88wzmJqagoODg8OBYj6969vtNrZt24Z4PI6VK1dicHAweEdmMhn09PSEyA+flwACQqXGsr7vGBukz90obw0lJ6pa8d2r91QCpqqPTZqgpE3tDsYUt1qtEBHhe4/HeUzvwx/2m767+a7RNNwMI+D51l2f7+h0Oh1aqGT9+Z4DEBAq9gfvYxd5lRiRlCpBIqzypP1K28261OnCLD016D7fyStJlSS1cdQbRI+rd5LaMiST1nbh3+w3XegulUqB94iGjuzevRsvvvgiBgYGsGzZMlSrVdRqtcCFdWJiAo8//jh27ty5b18ih0OKQx4zNTExAc/z0N/fDwC499570d/fHzxcAeD8889HLBbDz3/+c7zzne/c73t4noeBgQGsXbs2cH/i5E4mk+jv7w9iSoBwYCRBo1YffCxbgyv1XCVLlqCoka+Bj3a1gPfTL6YSN658qN+vdTXUh6WShqj76XUsT/eVIFiWTbqhcUR8cNmHmj7AtY65XC4ohw83rkAx5khX9NiPdrzYF/ytK3+8RtvL/tA2K8my6lQUudIXo44N60NX07GxMVQqlWC/h0wmg1qtFihHUe4JXAll+a1WCzt27MDY2Bi2bt2KYrEYECq++LgSVSqVMDU1FfiVMwjVwcHB4XDicLzraYBWKpVAuacXCuOb7J5RJB8kUXbBkgtYjJmix4ou1uliJX9HrfyrlwGf78y4xme4bsbKeC16FXBBMGpxk/ekYa97LFmXPd37iu9rTcyhLnz6W9+ztFs0+QHbSIUsFosFBIKqmnr7qEpIpYvXEWyDLm4rCeFCKT1JdJFZ96Zin9PbRxdtWaaSVvXa4LUca36u92Y/8V2t8ePVajWoGwkllUdL3AAE72266k9OTmLTpk1BOe12O9j6Z9u2bTM2VqbtdCAusw6HBoeUTFWrVVx55ZV4z3veg97eXgDA8PAwlixZEq5EIoGBgQEMDw9HlkN/UWJycnLGOX19fVi+fDm2b9+O3bt3B5nLcrlckIKbAZAqiTLTjN0DodPDFpg28LkCYx9kaozb1RQgTHCsckSQVFhfYyAcw2NXtCwRiyJvBB+QqpyQnNAVQTeeVVcAXscVJT4s7UtGCRUTJDApiK4W8t6d3O6iXPKUTFnVUfsnSm6350WpUXaMdJy0TN/3US6XMTU1FQSCkkzlcjm0Wi3kcjnk8/nQihyvV3dTAJiamsLu3bsxMjKC7du3o1wuz6g33TMcHBwc5hqH813PoP9qtYrx8XHkcjk0Gg309PSgt7cXvu8HBjXJlLq76+Ijn8ftdjvYBDgqM6smIiCivDTsO4J14H1JKFgW1Z5cLheKA+tEpqi0WKVLF/mULPA6EgJV0dTtUN0f2UZuQK+u/WwbSQdd2Gnc873OWF6+73kPu5iodeGitS4c876atIPqle/7M9Kd6yKubuarC83qYaPt1f7TPmTdbUIqXQDVOcs+SSaTQYp+3pttnZycxPj4eCj73tatWyOTROzatQu7du2acdxhfuGQkalGo4F3vetd8H0f3/jGN15WWddeey2uvvrqjp/7vo9t27ah3W4Hk5RIpVLBhn8rV65ENpsNyeuqVulvlqurF7p6ooa0Pvh0RYPlWZJEckZXQSsLW/WDD/nZyJQldIRKyvY8dVsj8bKBmipH68vDyuX6IFLSqX3KFxtX4AAEK2j6ILVSu9ZDy9V2qITPPrCSv/ZN1EOdZdsXpz3HyvQkUsViMVjRZDpSvkCAPSu3VDg5BzzPw/DwcLDSCuwxTCYmJoLd5B0cHBzmKw73u354eDj0nmaMaTqdxqZNm1AoFHDKKadgYGAArdaefZfUBctmauP7Q9/t+v6x7xFdILTvDv2fZfBdozFKjONhPZhtj5n5VJXRhUwAQeZCq6qomyHbpYRFiZEmQ2Cb9P2vhEm9XNRFUhU72lJ0tyyXy4EnBV3RmdVPla+ovmbdqcKpbWRd5u3iro6RjoMqbpYoKemMIrAajxaLxQI1kXFR27dvx86dO7Fo0SIsW7YMyWQySE9PtzyO5/DwMMbGxlCtVkN9xLhoh+7FISFTfLg+//zzuO2224KVKgBYtmzZDB/PZrOJ0dFRLFu2LLK8q666CldccUXw/+TkJFavXh387/s+nnnmGTz77LPB/wrP84LUnYODgxgYGAjtpk6p2LruAdMGNT+jjzBXRbQOdE/Th5/9guiKFTD9wLMrSQACwqWpRnkfDcRUEqarMHwJWDcH1sMqMOoqp+eoa4E+aJW4aFtYJ96P6h/jiNgH9Xo9CJTUQF4N1lXMRqYABKuJfHBGKY5sg7oUaJl6jZJiJV+q+vGhODY2hlKphCVLliCfz+OZZ57B9u3bgyBcXRmkQkc/80ceeQTPPffcjHZGPdwdHBwc5gvm4l3//PPPY8uWLaHrdFGPbv1HHXUU2u02enp6gvcJ3zP2HWzJlJZLDwz+HfUeYhn6vKaawh+qECQYNM6pYNBNm+8u2hCatIIeMXyvqkeKfgZMp4kHEFoQjvpfCYYSF12kZPm6sMw+oI2yaNEi9PT0BLYR34uMCeJ4sM60fSwpYlu5HQjbpTFm6nppF4zt/yxf+zPKpTFqAdcm9CgUCojH44G74mOPPYZHHnkEa9asCRZT6bGTzWaDedZqtfDAAw/g6aefDuqt89q967sbB51M8eG6adMm3H777RgcHAx9vmHDBoyPj+PBBx/EGWecAQC47bbb0G63cfbZZ0eWSTeo2TDbZOSDZ2xsLEg5yZUgGrXcL0iJjaa75BeRKw62fGBmynE+cKxRDkRnjLPugZakqMufPjC0DrqJLK+zLwBdTdI22OBbGy+lREJXW3i9dQewq0MEH0RclYnF9mT/0QxGukrGl05Uxj9V5nQVz94/aozsqhzLU5cAXZ2zrgck4fozOTkZJImg/F8sFmcEj3KMgD3+0y5tqYODQzdhPr7rgT1bpmzevBnVajWIpeL7fMmSJVi/fn3onUrVij9KEDT7HRDOxKoGu74T7Xtc38OaREmNeb5n9B3Hz0lMrHs40Wq1gu0yqFrRo8WSIrVndNFX349R73nt+6j3PG0lTTahC678LJFIBO1h3VgnLlCzj/SdaMMkWA/tD7UHqNTpmOnciVqw1cVWqojFYjGUnpzzAtiTrp+upu32nuy6u3btCpE9m5VxamrKvesXKPabTBWLxYBZA8DmzZvx8MMPY2BgAMuXL8dv//Zv46GHHsLNN9+MVqsV+EYPDAwglUrh+OOPx4UXXoiPfOQj+OY3v4lGo4HLL78cv/u7v7vfmfz2B/V6HZs2bZrxgPA8D0cffTRe97rXBe5X/GJYX1luoGbVJX2w67EoFzx94PK4xgPN5qrGh4ySB7s6ls1mg5WuZDIZ+GEDmKHWqB+v7/uBWsfsOkoKWGeSNF5vV3y0vbaf+aBKJBLI5/NB+VRv0uk0Fi1aFEqkwLKZDtY+2O2qllXWogihdRPgahsf8pwHuVwuCBLmA9QSJLrjlctlFIvFgLCzHvzMvmT1t5P3HRwc5hu69V1fLpdxyy23hBQovpNe+9rX4gMf+ECQirvd3rNlBLOq8X3JdyS9CmhI813J5zndC1VVUTdC/c3NbzUhA1Nu893DzWltBld6MUQt7lUqFVQqFYyMjGBycjKoQzabDWLAWDe1Oazqwjari5/1gFEi4/t+YBPpu5q/rdskt+uYnJwMXDM1ix/fw7qQy/9ZN7XHNM5KSR7bEPW+Jcli31LxYj1JBkmipqamsGPHDjQaDRSLxZDdd//992Pbtm3B2HE/J72n/s1FfYeFif0mUw888ADe/OY3B/9Tkv/ABz6Az3/+8/jXf/1XAMBpp50Wuu7222/Hm970JgDADTfcgMsvvxznnXceYrEYLr30Unz1q189wCbsOzoZrVNTUxgdHQ0SVQDhAE0+dFS218BSwq4YWVhDXgmV/q+EhA9jkjwgvMeTJS7q48wvr272xodUVF1ZPz4c1f2R91DJX1eU9Hp92US1UQkj+5jktaenJ5DJ9cFHeV/Ls0qg9q/WlX0R1W4lXHzRTU5OBqQpmUxi0aJFyOVywQOcrn3lchmTk5OhlzAf9oRdYXNwcHDoBnTru55GfhR27dqFLVu2IJ/PBy6JlUolSGqh7tjxeBzZbHbGXpB6H+sVAUS/j+y7iooMCQ5tCS4ksh5KHLR8vntbrdYM7whNXEWiZz1NrEKldg3PVU8V2151gaOCpmUC08kg7PuQrnua4l1VM+tu16nPWbbWRcF6W7tLY7l2794dSsxBwquxXsAeO4WZ81hXZvDVPnVJoV658PwudNScnJxEX1/fQSsvnU6jUCigr68PRx11VEia5UORvs6MQaL6wyx/fAjRdYArWFF+yEB4jyeFEjg+BJkRhg8E3SuDdWS96L7IskdGRgJpmSswXP1ith7+5sN0cnIyWK1jm/v6+kKpPjWAVQNplWTy4UhCxsyKfFFt374dv/rVr4IVqXQ6jaOOOgq9vb1YsWIFent7MTU1FaT+jnKR1AcrV6miSJT2lT64rXK4a9cu3H333cE9E4kETj75ZKxcuTJ4eDL7TqlUwvDw8Kwxdw7di4mJiVAMiIODw+HFwX7X5/N5DAwMYMWKFXjzm9+MbDYbvE9JpoDp2KvFixcjn89j2bJlIVKl3hwkHbrvkypSUUQMmHZl5wId1ZtGoxEkKaBBn8vl0NvbG7IBSqUSGo0GxsfHAy+JUqkU2CHc/4m/o1QtHkun04E9wyRdjPexi6G66KrxWyRCwJ4F6kqlghdffBFbtmwJCEwymcTg4CDS6TT6+/uRTqcDDxB16aeNwLgkEiaSNksGlcTZBVMlvLTNqtUqhoeHccsttwQpyJPJJDZs2ICjjz46UPto4+zcuRN33313sMej7/vBWDl0Pw7Gu/6Q7zPVDeCKTrPZxJIlS4LVCV3N4UONX0auqJDw0PCOUmKsWkNYdYnQVSf+rw9ne26UjMyHdKlUCu1FwAQNXBFT1UvdBorFYvAg0Qeo1t2uQulDUOvHB6zK/vpw0wBTKmK6mqZE1cK6Odp4r9lUIXVz4PWNRgMTExNBSt54PI7x8XH09PQEe0NUq9XAj9r5QDs4ODh0B0qlUpAIYWRkBIVCIXiH0OWOoEFNjwVg5p6MfIcBM13L7DvfqlU8j7YDPTDocqfvME1mxfdWpVIJsslSSVHvGdoVJCDWdY8hBLppri7M0gbSLIFsE9tuFyV1kVhjsnTBkcQxk8kEtgjL5v+0U5SgWpuDf5PI2bh0Wy+GFZBMjY2NYdu2bYFrXiKRwOjoKCYnJ4MYZ5bHBBpR25Q4OACOTIVQLpfx1FNPIZ1OY8mSJaHN5exeRrFYDBMTE4jH48FmgVxhyWQygfqj/rwkOzbgE5iZzlzldxuIaX2T+aBqNpsYHx8P+WJzdSuVSgWqUhQpYxsVhUIBg4ODwX5JJBv64OIDV18SfOiz/nTdY6Ybpq/niprK7Nu2bcOuXbvwwgsvBMHIUatrnerPl4V9CLPeqhS2221s3boVzz77bPCyqdVqM6T7Z555Btu2bQsRaZta1sHBwcGhO7B792785Cc/QaFQwNFHHx3aUJ2YmJhAtVpFOp3GyMgIkslk4KFB8pFKpYIMgfRQoYu6jRmmggWEFyP1vcR3W6FQCPYrqtfrKJVKmJycnKFmtVotTE1NoV6vB8oaSRGP6Ttf3QxjsVhoby5LpNgezRpo6209bliHsbGxwMNF48xIahgnHo/HUSqVQh4lfH9rrBf70oY6sI1Kai2Zou32+OOP45FHHgn6ulKphPZ1arVaePjhh/HMM8/MyHys5MrBIQqOTAkajQZGR0eD4EiVrVVeVuiKEoDAF9hK/+pfzP8t9MtrgzB5jA8xLZPn1Gq1wD2gVCqFiBtTw1vfYZXO+ZuxY5lMJth8lu3gPhiEqkFK9PRByzL4wKRixlWidrsdkM6pqalQnyxatAiLFi0KfL/5oFdCpS58zWYztHpIBY73YYp3jsPIyEiQUj8Kvu8HxM/BwcHBoftRrVbx3HPPIZ/PY2hoCAACF3QAwSJmtVpFIpFAuVwOMtBywTQWiwWJkdLpdPDu53vPkikiipDYdz8TZGjihWKxGCwM0sVOFSomT9Dr7KIt7Rm+i/leVRVNY6f0HWvd5Qh1w2NoAOOKSZzUFY/vYNo4JIBcCKX91Gq1gkyMSqa0Lqp4McGWdcVkvwwPD+Pxxx+fNR5rx44d2LFjx8udXg6vQDgyFYFms4mRkZHAbQ/ADBIDTMv6fDBRjSJxWLp0KVauXBmQM6pXwLT7GY18qh00/tVlzyoxXKnh53xYqEROX3BVkDRQUx/KGjzKlSMSxNHRUeRyuUB9447tvLcSG5vSlC8cPgR37tyJUqmE7du344UXXkClUgnc6ZjxzvYvN78lMc1kMhgaGgr63Pd97N69O1CTlNhlMpkgXS8VPJJN3mN0dPQgzhwHBwcHh25BvV7H008/HRCpKI8HKjZ8p9i9jmKxGI4//nicdtppQVr3KA8KTYgATLvlq+eEnpvJZJDL5QL3Pf4wyQEVsShXOfXEUHLFsqkMVavVgDAyRoiqFgkPY7PU/U7jurmQ22w2MTExEWxKDAA7duzA448/Hkopr/YCMG3fqIdLPB7HwMAAzjzzzCB2q9Fo4PHHH8fIyEjIhbHZbKK/vx9HHXVUaNH3qaeewtjYWNAPw8PDHYmUg8PLhSNTEWi32wdFiahWq8jlcigUCgGZoguAytSaBcb6/lo1SR9INu7IPjj54FPoNby3StpsP7DnYT0+Ph48jFRpY934gLUujXQhICGq1WoYHR0NXPh0k9rZYJWqQqEA3/dDL5LNmzdjbGxsxrW9vb1Ys2ZNKB3tCy+8EIohc3BwcHB4ZaLRaOCFF1542eX4vh9sEqzhAQoSHs3mZ9/FJBPMIphOp5HNZgNPEyoxJFv0BiFBUi8T2gQkKXxna6IGqkjcCoXETBU6Ztol4dTFXi5Sjo+Po16vY3x8HLVaLVhQHhsbw1NPPXVAJGb16tVYu3ZtYEfUajU8+OCDeOqpp2acu2bNGgwNDQVxWJVKBY8++uhBGVsHh32BI1OHEOPj49i8eTPy+TzGx8eRTCYDhcf6Vdu0pEpyVB3j76hYHZIsXqsZBTUTnrosqiJFaCCq+k3T79v3fWzZsgUTExPBuYsXL8aqVatCq2LFYhHNZhM7duxAqVTC6OgopqamAjXqQMB0pronGDcqjDp3dHQ05H7osu84ODg4OBxMbNmyBXfeeScWL16M448/PnD9U/WKBIM/wLQ7oSo3wHT8siZI0sVRusoDCDxMPG/PXla0J0iueB8AoYXaeDweKG3M0AvsScjFOKFms4knnngCu3btChYwFy9ejNWrVwckkEmbqtUqnnnmmcAuSCQSeOGFFw5YDZqamsKjjz4atKnZbEYumvLcJ554IljQ5Ya7Dg6HC45MHUKMjIxg9+7dyGQy6O/vRyqVQl9fH5LJJHp6eoLEDvzNzWHpx1yv10MPIpsW1PpD82+SH7r/abYhG9MEhOO3+EDXINR4PB5yLWi1Wti0aVMo1uiEE07AkiVLgj2juEpVLpfxxBNPBHK7vd/+ol6vB5tDRmVCVFSrVWzbtu2A7+Xg4ODg4LA3PPXUU9i0aROOOeaYYKN3phqnoqNhACRWfNfbjLDq/qdkSuOcy+VySGmiOz7VLFW81KUfQHB//iZZUbuAsdc/+clP8MQTTwR1O+uss3DRRReh3W4HLoDc5Pahhx4KvXNfzrt+fHwc9957b+hYp4RPY2Nj+MUvfjEjxsvB4XDBkalDDD74mGwBmCYkiUQCxWIxkOy518OiRYtmxEhpOnGV2DXJhPox894AQqtUSsZ4jn3osIxSqYSRkRGk0+kgRSz3gygWi6EH29TUFJ577rkg5ooPWGbBORRZ79zD0sHBwcFhrsH36NTUFJ599tlQFlpmBubm7kywMDg4iDVr1szI2Mvy4vE4KpVKsLch39+apAJA4D3C+01MTGD79u0h20C9VIDpWGemRadKRUVJM+VNTk6G6jc6Ooqnnnoq8AphEgzW9WC+6/enLEegHOYSbtPewwSbKYerVszEw8+WLVuG448/PiBWTFrBlSPdvVv9ovmwLRaLaDQamJycDJVL97p9HW6tq62/Jq8g+GC2JE1JnoNDN8Ft2uvgMLfotne9ZuJj7NPpp5+Ovr6+IIaHC6VnnnkmLrnkktB7E0AoqdXo6GjgQke3PsZG0+1ucHAw2LrE8zzceeeduOWWW2aQC3sP+7f9rXHdSqZ0UdW+7zXplYNDt+AVu2lvNxrm+uDRTWrpc0wwuw7TgDOxg5IpKj9Um0iwNBGF7lNlA0/3tb6s675AfbsdHBYCuvE54+CwkNBt30Ebw9tut1Eul5FMJlEsFkP7Gk1NTaFUKiGZTM7YcJbv/HK5HChTdPu3cdS0A3jM7pV4sGH33HRw6HYcjOdMVypTL7zwAlavXj3X1XBwcFjA2Lp1K1atWjXX1XBweMXCvesdHBwONQ7Gu74ryVS73cbGjRtxwgknYOvWrQvOFWdychKrV692besiLNR2Aa+8tjH2YcWKFZEpjh0cHA4P3Lu+e+Ha1n1YqO0CDv27vivd/GKxGFauXAlgz15CC23QCde27sNCbRfwympbN8VpODgsVLh3fffDta37sFDbBRy6d71bdnVwcHBwcHBwcHBwcDgAODLl4ODg4ODg4ODg4OBwAOhaMpVOp/G5z30O6XR6rqty0OHa1n1YqO0CXNscHBzmDgv5O+ra1p1YqG1bqO0CDn3bujIBhYODg4ODg4ODg4ODw1yja5UpBwcHBwcHBwcHBweHuYQjUw4ODg4ODg4ODg4ODgcAR6YcHBwcHBwcHBwcHBwOAI5MOTg4ODg4ODg4ODg4HAC6lkxdd911WLduHTKZDM4++2z84he/mOsq7ReuvfZanHXWWejp6cGSJUvwjne8Axs3bgyd86Y3vQme54V+/uAP/mCOarzv+PznPz+j3scdd1zwebVaxWWXXYbBwUEUCgVceuml2LFjxxzWeN+xbt26GW3zPA+XXXYZgO4Zs7vuugtvf/vbsWLFCniehx/84Aehz33fx5/+6Z9i+fLlyGazOP/887Fp06bQOaOjo3jf+96H3t5e9Pf348Mf/jCKxeJhbEU0Zmtbo9HAlVdeiZNPPhn5fB4rVqzA+9//fmzbti1URtQ4f/GLXzzMLXFwcHDv+vkL967vjjFbqO/7+fSu70oy9d3vfhdXXHEFPve5z+Ghhx7CqaeeigsuuAA7d+6c66rtM+68805cdtlluO+++3Drrbei0WjgLW95C0qlUui8j3zkI9i+fXvw86UvfWmOarx/OPHEE0P1vvvuu4PPPvWpT+Hf/u3fcNNNN+HOO+/Etm3bcMkll8xhbfcd999/f6hdt956KwDgd37nd4JzumHMSqUSTj31VFx33XWRn3/pS1/CV7/6VXzzm9/Ez3/+c+TzeVxwwQWoVqvBOe973/vw2GOP4dZbb8XNN9+Mu+66Cx/96EcPVxM6Yra2lctlPPTQQ/jsZz+Lhx56CP/yL/+CjRs34uKLL55x7jXXXBMax49//OOHo/oODg4vwb3r5z/cu37+j9lCfd/Pq3e934V49atf7V922WXB/61Wy1+xYoV/7bXXzmGtXh527tzpA/DvvPPO4Ngb3/hG/xOf+MTcVeoA8bnPfc4/9dRTIz8bHx/3k8mkf9NNNwXHnnjiCR+Af++99x6mGh48fOITn/DXr1/vt9tt3/e7c8wA+N///veD/9vttr9s2TL/L//yL4Nj4+Pjfjqd9v/5n//Z933ff/zxx30A/v333x+c8+///u++53n+iy++eNjqvjfYtkXhF7/4hQ/Af/7554Nja9eu9b/yla8c2so5ODjMCveun99w7/pPzG2lDgAL9X0/1+/6rlOm6vU6HnzwQZx//vnBsVgshvPPPx/33nvvHNbs5WFiYgIAMDAwEDp+ww03YGhoCCeddBKuuuoqlMvluajefmPTpk1YsWIFjjzySLzvfe/Dli1bAAAPPvggGo1GaPyOO+44rFmzpuvGr16v45/+6Z/we7/3e/A8LzjerWNGbN68GcPDw6Ex6uvrw9lnnx2M0b333ov+/n6ceeaZwTnnn38+YrEYfv7znx/2Or8cTExMwPM89Pf3h45/8YtfxODgIF71qlfhL//yL9FsNuemgg4Or0C4d313vDfcu777xkzxSnrfH8p3feIg1fGwYWRkBK1WC0uXLg0dX7p0KZ588sk5qtXLQ7vdxic/+Umcc845OOmkk4Lj733ve7F27VqsWLECjzzyCK688kps3LgR//Iv/zKHtd07zj77bHz729/Gsccei+3bt+Pqq6/G61//evz617/G8PAwUqnUjMm8dOlSDA8Pz02FDxA/+MEPMD4+jg9+8IPBsW4dMwXHIeo7xs+Gh4exZMmS0OeJRAIDAwNdNY7VahVXXnkl3vOe96C3tzc4/od/+Ic4/fTTMTAwgHvuuQdXXXUVtm/fji9/+ctzWFsHh1cO3Lt+/r833Lu++8bM4pXyvj/U7/quI1MLEZdddhl+/etfh3yNAYT8UU8++WQsX74c5513Hp555hmsX7/+cFdzn3HRRRcFf59yyik4++yzsXbtWvzv//2/kc1m57BmBxff+ta3cNFFF2HFihXBsW4ds1ciGo0G3vWud8H3fXzjG98IfXbFFVcEf59yyilIpVL42Mc+hmuvvRbpdPpwV9XBwWEBwL3ruxPuXd/dOBzv+q5z8xsaGkI8Hp+REWbHjh1YtmzZHNXqwHH55Zfj5ptvxu23345Vq1bNeu7ZZ58NAHj66acPR9UOGvr7+3HMMcfg6aefxrJly1Cv1zE+Ph46p9vG7/nnn8dPfvIT/P7v//6s53XjmHEcZvuOLVu2bEYQeLPZxOjoaFeMIx+uzz//PG699dbQSlUUzj77bDSbTTz33HOHp4IODq9wuHd9d703APeuB7pvzBb6+/5wveu7jkylUimcccYZ+OlPfxoca7fb+OlPf4oNGzbMYc32D77v4/LLL8f3v/993HbbbTjiiCP2es3DDz8MAFi+fPkhrt3BRbFYxDPPPIPly5fjjDPOQDKZDI3fxo0bsWXLlq4av+uvvx5LlizBW9/61lnP68YxO+KII7Bs2bLQGE1OTuLnP/95MEYbNmzA+Pg4HnzwweCc2267De12O3ipzFfw4bpp0yb85Cc/weDg4F6vefjhhxGLxWa4Ojg4OBwauHd9d703APeuB7pvzBby+/6wvutfdgqLOcB3vvMdP51O+9/+9rf9xx9/3P/oRz/q9/f3+8PDw3NdtX3Gf/7P/9nv6+vz77jjDn/79u3BT7lc9n3f959++mn/mmuu8R944AF/8+bN/g9/+EP/yCOP9N/whjfMcc33jj/6oz/y77jjDn/z5s3+z372M//888/3h4aG/J07d/q+7/t/8Ad/4K9Zs8a/7bbb/AceeMDfsGGDv2HDhjmu9b6j1Wr5a9as8a+88srQ8W4as6mpKf+Xv/yl/8tf/tIH4H/5y1/2f/nLXwZZbr74xS/6/f39/g9/+EP/kUce8X/rt37LP+KII/xKpRKUceGFF/qvetWr/J///Of+3Xff7R999NH+e97znrlqUoDZ2lav1/2LL77YX7Vqlf/www+Hvnu1Ws33fd+/5557/K985Sv+ww8/7D/zzDP+P/3TP/mLFy/23//+989xyxwcXllw7/r5Dfeu744xW6jv+/n0ru9KMuX7vv93f/d3/po1a/xUKuW/+tWv9u+77765rtJ+AUDkz/XXX+/7vu9v2bLFf8Mb3uAPDAz46XTaP+qoo/w//uM/9icmJua24vuAd7/73f7y5cv9VCrlr1y50n/3u9/tP/3008HnlUrF/y//5b/4ixYt8nO5nP/Od77T3759+xzWeP9wyy23+AD8jRs3ho5305jdfvvtkfPvAx/4gO/7e9Klfvazn/WXLl3qp9Np/7zzzpvR3t27d/vvec97/EKh4Pf29vof+tCH/KmpqTloTRiztW3z5s0dv3u333677/u+/+CDD/pnn32239fX52cyGf/444/3v/CFL/jVanVuG+bg8AqEe9fPX7h3fXeM2UJ938+nd73n+76/f1qWg4ODg4ODg4ODg4ODQ9fFTDk4ODg4ODg4ODg4OMwHODLl4ODg4ODg4ODg4OBwAHBkysHBwcHBwcHBwcHB4QDgyJSDg4ODg4ODg4ODg8MBwJEpBwcHBwcHBwcHBweHA4AjUw4ODg4ODg4ODg4ODgcAR6YcHBwcHBwcHBwcHBwOAI5MOTg4ODg4ODg4ODg4HAAcmXJwcHBwcHBwcHBwcDgAODLl4ODg4ODg4ODg4OBwAHBkysHBwcHBwcHBwcHB4QDgyJSDg4ODg4ODg4ODg8MBwJEpBwcHBwcHBweHVxzuuOMOeJ6H733ve3NdFYcuhiNTDocE3/72t+F5Hh544IG5rsohw7Zt2/D5z38eDz/88IzP/uVf/gXvfve7ceSRRyKXy+HYY4/FH/3RH2F8fPyw19PBwcHBwWE+graC53m4++67Z3zu+z5Wr14Nz/Pwtre9bQ5q6OCwdzgy5eBwgNi2bRuuvvrqSDL10Y9+FE888QT+03/6T/jqV7+KCy+8EF/72tewYcMGVCqVw19ZBwcHBweHeYpMJoMbb7xxxvE777wTL7zwAtLp9BzUysFh35CY6wo4OCxEfO9738Ob3vSm0LEzzjgDH/jAB3DDDTfg93//9+emYg4ODg4ODvMMv/mbv4mbbroJX/3qV5FITJumN954I8444wyMjIzMYe0cHGaHU6YcDhs++MEPolAoYMuWLXjb296GQqGAlStX4rrrrgMAPProozj33HORz+exdu3aGatUo6Oj+P/+v/8PJ598MgqFAnp7e3HRRRfhV7/61Yx7Pf/887j44ouRz+exZMkSfOpTn8Itt9wCz/Nwxx13hM79+c9/jgsvvBB9fX3I5XJ44xvfiJ/97GeztuWOO+7AWWedBQD40Ic+FLgpfPvb3waAGUQKAN75zncCAJ544ol96S4HBwcHB4dXBN7znvdg9+7duPXWW4Nj9Xod3/ve9/De9753xvl/9Vd/hde+9rUYHBxENpvFGWecERn3dOutt+J1r3sd+vv7USgUcOyxx+LTn/70rHWp1Wp429vehr6+Ptxzzz0vv3EOCx6OTDkcVrRaLVx00UVYvXo1vvSlL2HdunW4/PLL8e1vfxsXXnghzjzzTPzFX/wFenp68P73vx+bN28Orn322Wfxgx/8AG9729vw5S9/GX/8x3+MRx99FG984xuxbdu24LxSqYRzzz0XP/nJT/CHf/iH+MxnPoN77rkHV1555Yz63HbbbXjDG96AyclJfO5zn8MXvvAFjI+P49xzz8UvfvGLju04/vjjcc011wDY49L3j//4j/jHf/xHvOENb+h4zfDwMABgaGhov/vNwcHBwcFhoWLdunXYsGED/vmf/zk49u///u+YmJjA7/7u7844/2//9m/xqle9Ctdccw2+8IUvIJFI4Hd+53fwox/9KDjnsccew9ve9jbUajVcc801+Ou//mtcfPHFsy6WVioVvP3tb8c999yDn/zkJ3jta197cBvqsDDhOzgcAlx//fU+AP/+++8Pjn3gAx/wAfhf+MIXgmNjY2N+Npv1Pc/zv/Od7wTHn3zySR+A/7nPfS44Vq1W/VarFbrP5s2b/XQ67V9zzTXBsb/+67/2Afg/+MEPgmOVSsU/7rjjfAD+7bff7vu+77fbbf/oo4/2L7jgAr/dbgfnlstl/4gjjvB/4zd+Y9Y23n///T4A//rrr9+nPvnwhz/sx+Nx/6mnntqn8x0cHBwcHBYy1Fb42te+5vf09Pjlctn3fd//nd/5Hf/Nb36z7/u+v3btWv+tb31rcB3PIer1un/SSSf55557bnDsK1/5ig/A37VrV8f733777T4A/6abbvKnpqb8N77xjf7Q0JD/y1/+8iC20mGhwylTDocdGi/U39+PY489Fvl8Hu9617uC48ceeyz6+/vx7LPPBsfS6TRisT1TttVqYffu3YFs/9BDDwXn/fjHP8bKlStx8cUXB8cymQw+8pGPhOrx8MMPY9OmTXjve9+L3bt3Y2RkBCMjIyiVSjjvvPNw1113od1uH5Q233jjjfjWt76FP/qjP8LRRx99UMp0cHBwcHBYKHjXu96FSqWCm2++GVNTU7j55psjXfwAIJvNBn+PjY1hYmICr3/960O2QH9/PwDghz/84V7f5RMTE3jLW96CJ598EnfccQdOO+20l90eh1cOXAIKh8OKTCaDxYsXh4719fVh1apV8DxvxvGxsbHg/3a7jb/927/F17/+dWzevBmtViv4bHBwMPj7+eefx/r162eUd9RRR4X+37RpEwDgAx/4QMf6TkxMYNGiRfvYumj8x3/8Bz784Q/jggsuwH/7b//tZZXl4ODg4OCwELF48WKcf/75uPHGG1Eul9FqtfDbv/3bkefefPPN+PM//3M8/PDDqNVqwXF977/73e/GP/zDP+D3f//38Sd/8ic477zzcMkll+C3f/u3g4VZ4pOf/CSq1Sp++ctf4sQTTzw0DXRYsHBkyuGwIh6P79dx3/eDv7/whS/gs5/9LH7v934Pf/Znf4aBgQHEYjF88pOfPCAFidf85V/+ZcdVqEKhsN/lKn71q1/h4osvxkknnYTvfe97oSxFDg4ODg4ODtN473vfi4985CMYHh7GRRddFKhLiv/4j//AxRdfjDe84Q34+te/juXLlyOZTOL6668PJa7KZrO46667cPvtt+NHP/oRfvzjH+O73/0uzj33XPy///f/QnbHb/3Wb+E73/kOvvjFL+J//a//NYNsOTjMBmfZOXQNvve97+HNb34zvvWtb4WOj4+Ph5I6rF27Fo8//jh83w+tUj399NOh69avXw8A6O3txfnnn7/f9bHKl8UzzzyDCy+8EEuWLMH//b//92UTMwcHBwcHh4WMd77znfjYxz6G++67D9/97ncjz/k//+f/IJPJ4JZbbgntP3X99dfPODcWi+G8887Deeedhy9/+cv4whe+gM985jO4/fbbQ+/9d7zjHXjLW96CD37wg+jp6cE3vvGNg984hwULR70dugbxeDykVAHATTfdhBdffDF07IILLsCLL76If/3Xfw2OVatV/P3f/33ovDPOOAPr16/HX/3VX6FYLM64365du2atTz6fB7CHzFkMDw/jLW95C2KxGG655ZYZro0ODg4ODg4OYRQKBXzjG9/A5z//ebz97W+PPCcej8PzvJCr/3PPPYcf/OAHofNGR0dnXEsvFHUNJN7//vfjq1/9Kr75zW9GZv91cOgEp0w5dA3e9ra34ZprrsGHPvQhvPa1r8Wjjz6KG264AUceeWTovI997GP42te+hve85z34xCc+geXLl+OGG25AJpMBMK0oxWIx/MM//AMuuuginHjiifjQhz6ElStX4sUXX8Ttt9+O3t5e/Nu//VvH+qxfvx79/f345je/iZ6eHuTzeZx99tk44ogjcOGFF+LZZ5/Ff/2v/xV333037r777uC6pUuX4jd+4zcOQQ85ODg4ODh0N2aLYwaAt771rfjyl7+MCy+8EO9973uxc+dOXHfddTjqqKPwyCOPBOddc801uOuuu/DWt74Va9euxc6dO/H1r38dq1atwute97rIsi+//HJMTk7iM5/5DPr6+va6J5WDA+DIlEMX4dOf/jRKpRJuvPFGfPe738Xpp5+OH/3oR/iTP/mT0HmFQgG33XYbPv7xj+Nv//ZvUSgU8P73vx+vfe1rcemllwakCtizue69996LP/uzP8PXvvY1FItFLFu2DGeffTY+9rGPzVqfZDKJ//k//yeuuuoq/MEf/AGazSauv/56HHHEEcFGwl/60pdmXPfGN77RkSkHBwcHB4cDwLnnnotvfetb+OIXv4hPfvKTOOKII/AXf/EXeO6550Jk6uKLL8Zzzz2H//E//gdGRkYwNDSEN77xjbj66qvR19fXsfxPf/rTmJiYCAjVZZdddjia5dDF8HzrN+XgsEDxN3/zN/jUpz6FF154AStXrpzr6jg4ODg4ODg4OHQ5HJlyWJCoVCqhfSiq1Spe9apXodVq4amnnprDmjk4ODg4ODg4OCwUODc/hwWJSy65BGvWrMFpp52GiYkJ/NM//ROefPJJ3HDDDXNdNQcHBwcHBwcHhwUCR6YcFiQuuOAC/MM//ANuuOEGtFotnHDCCfjOd76Dd7/73XNdNQcHBwcHBwcHhwUC5+bn4ODg4ODg4ODg4OBwAJjTfaauu+46rFu3DplMBmeffTZ+8YtfzGV1HBwcHBwcHA4y3LvewcFhIWPOyNR3v/tdXHHFFfjc5z6Hhx56CKeeeiouuOAC7Ny5c66q5ODg4ODg4HAQ4d71Dg4OCx1z5uZ39tln46yzzsLXvvY1AEC73cbq1avx8Y9/fMa+QRbtdhvbtm1DT09PsAGrg4ODw8GA7/uYmprCihUrEIvNqXjv4ND1cO96BweH+YiD+a6fkwQU9XodDz74IK666qrgWCwWw/nnn4977713xvm1Wg21Wi34/8UXX8QJJ5xwWOrq4ODwysTWrVuxatWqua6Gg0PXwr3rHRwc5jsOxrt+TsjUyMgIWq0Wli5dGjq+dOlSPPnkkzPOv/baa3H11VcfrurNWySTSSQS4SHzfR/tdhv1eh0AkEqlAobteV6wmnfuuefi7W9/OxqNBkqlEhqNBsrlMlqtFiqVCprNJjKZDFKpFOLxOJLJJFqtFprNJnzfD36azSba7TZqtRparVbw43neDGYfj8fheR6y2SySySRisRg8z0M8Hkc6nYbv+2i1WqjX6xgfH4fv+8E5iUQCsVgMsVgM8XgcKqC22+2g7Wyn/t9ut9FqtYLz2u02qtUqGo0GxsbGUKlUsHHjRmzbtu2gjo/DwkJPT89cV8HBoatxsN71r8NvIoHkIaung4PDKw9NNHA3/u9Bedd3RWr0q666CldccUXw/+TkJFavXj2HNTr8SCaTOOOMM7By5cqAuJA07NixAw888ABSqRROO+009PT0IJPJBKQlkUhg2bJlqNfrqNfrqFaraDabqNfraLVaAXlS0pNIJAKyQ8KmJMXzPLRaraB+SqZIvHgtCVej0QiO+b4fXKO/0+n0DGKmpCgKJFFKxFgGyWUmkwnKbLVaKJfLjkw5zArnVuTgcHjR6V2fQBIJz5EpBweHg4iX1ugPxrt+TsjU0NAQ4vE4duzYETq+Y8cOLFu2bMb56XQa6XT6cFVv3oDkANhDppYuXYp169YFio0qR8lkEul0GsuWLcPAwEBAjLLZLFKpFBKJBGq1WkCoSKZ835+hdhFW+VGli+AxkhgSJSVeJFB04aCSRhIXj8eRy+UCBcrzvBnqkypTVonSz9hnVMCSySSSySR6e3uRTCaRyWQAAH19fUF9O0HJooODg4PD/sG96x0cHF4JmBMylUqlcMYZZ+CnP/0p3vGOdwDYozr89Kc/xeWXXz4XVZp36O/vx4knnhgoK4lEAkuXLkU8Hg8IA4lAT08PTjzxRHieh0ajgYmJCQB7+pTnt9ttNJvN4IeueTzP931UKhXUajUkEolAoVIXO9/30Wg00Gw2g3OJRCIRkDJeQzJCd79EIoF8Ph+6Pz/TNlH5UvIWj8cRj8cB7CFM6nqoULLXarUC975YLBZq06te9SoMDg6i2WwGLos8L5VKoVar4YEHHsDu3bsP+tg6ODg4vBLg3vUODg6vBMyZm98VV1yBD3zgAzjzzDPx6le/Gn/zN3+DUqmED33oQ3NVpXmFfD6P4447DoVCIXBPUzJAguT7PnK5HNauXYtmsxnEQmUyGXieh1QqFXLNU1c7Qt3yAASKTSKRCAgMz+M9arUaqtVq8BlVIBI/nk+iFI/HgzgojbMi2Bb+aNwX25tMJkMEq9lszug3jZtim+LxOKrVKlqtFrLZLGKxGNavX49169ahUqmgVCoFBJEqWbFYxMaNGx2ZcnBwcHgZcO96BweHhY45I1Pvfve7sWvXLvzpn/4phoeHcdppp+HHP/7xjEDVVwpWrFiBFStWBGSir68PqVQqpNqokkSQvORyuSCZAzBNvHitxjyRXPFcJTAkT+qup79JblKpFACElCWbMIL1pOLUaDSC+jebzRn347n8zeuazWZIkVOCxz6w/aLlAECj0UC73cbk5CSSyWTQHsaN6bXpdBrxeBwbNmzA8ccfHxxPpVKhxBy7d+/GQw89FCKVDg4ODg7TcO96BweHhY4522fq5WBychJ9fX1zXY2Dite85jV4zWteExAeTaSgioxVc0iOqtVqEJcEAJlMJiBZmUwG9Xo9UKX4mySCCg+TVZA0kDRpDJQSMboRUonS+mkCCv6Q0PBHVSybPMK2UZUvEiuC92W5muCCUCKo5FSzFDabTaTTaSxZsiToA21PoVBALpdDo9FApVLBE088geuuuw5jY2OHYko4zDEmJibQ29s719VwcHjFgu/6N+G3XAIKBweHg4qm38Ad+OFBedd3RTa/hYZ4PI7FixcHyRBisRj6+/sDdYRqj1WlLDkgEaJLnhIIZvMjAeE1BOOONE5Jy+T56k5IgqQudPzc1lOVLf1RRUqJGGHbrIqZja+iQmUTUtjygGm3P+1b/Vwz/TGGiooT08GXy2Vks1m02+2AkK5duxaDg4NBuZqaXq8larUaXnjhhVC82dDQEAqFAiYmJhwxc3BwcHBwcHDoIjgyNQdIp9M4+eSTQwkl4vF4kPyBBnkUkWLMEYlJrVYLZcXjb8YnacwSCQ2AQPHi3zadebPZRDKZDAiKVZlIEhqNRkDmdG8oxmjxnEajEahjmUwmRPbUBVD7Q4lWVDr1dDodSfaioG6EmiVRP2c/FItFAEClUgnSqNfr9SArIuvWarWwYcMGANP7ew0MDCf281EAAQAASURBVCCXywV9XavVUC6Xg3uPjIzg+9//fkCmYrEYjjrqKBx77LH49a9/jYceemjWDIMODg4ODg4ODg7zB45MHQakUink8/mAFGWz2SB1uW5mCyAw1K16oud0yolPgqRkipnzlAx1Ihy2fFV/VGXSfZ3U5U4VLv5vSQzJiMY52Rgw1t9mEuR5VMRs+br/Fo+RkOkx/V8TVvB/9g/vqe6I2i7dv4r9TcKl2Qo1xiuRSGBoaCg0TlSmFi9ejLVr1wbumeVyGVu2bAli2xwcHBwcHBwcHOYXHJk6DBgcHMSpp54aSgqRz+cBIMhSR8Oa+0VR0QEQit1RWKPf931kMpkQSZuYmEC5XA7c0jQ9usZKqSLEe6XTaRQKhSAWq9lsolwuhxJCKFnQfaXYNtaPJFLTobNcuy8UVa54PB6UqW6I9Xo9tLeVxkFZYprJZAL3PqplmngiKr06gBARooqm/c1zmIiDZVUqlWAfL+7lxXg2nvOGN7wBnueht7cXqVQqqP/SpUtxzjnnYPHixTjmmGPw1FNP4eqrr8b27dtf7hR0cHBwcHBwcHA4BHBk6hBAkxfEYjH09fWhr68vpEJFufFpRjxgmozYxA6EKjqaIMIqW/zcZuZTMsX7WZfCRCIRJJqIypbHOvEzJYFKzFiWJqtoNpszYpa0/fYeei+rTNl+0v+VwNk+tEody9U6qcukKmlWYVPlT2PLeB+Synw+j0Qigd7eXqTT6YCocpPlnp4e9Pb2ore3F/l8HoVCIXBpnJycDMVbOTg4ODg4ODg4zB0cmTrIiMViOO6447B27VpkMhnkcjmkUinkcrlAFQGmVSWqUkqugLCLGokA45SoemhiB34+OjqKRCKB/v7+QHHKZrNB7A9JlxIj3o+ETo8xsQUJTKVSCSWpqNfrocQXjBNiXwB7kmGkUin09PQgl8uF6q97RSmRYllUd7TPLInR9pD8qQqlCpKSGsZyUTEi+dFxoKuktlFjvWz2QPYh1UH+sP7sH8aQeZ6HarUaIkiZTAa/+tWvUK1Wceqpp+JVr3oVTjnlFORyOfzzP/8zHnjggf2YkQ4ODg4ODg4ODocKjkwdRNBVb9GiRVi+fDlyuVzgJqcZ8vg/3c8skbJxQFahsT88TsM8mUyi2WwGChWVFXsNgBnqWCdoLJJCiYvneaH4LEvOmFxDSc5sbVWyqOqX9oXtLy1L+0WVJXtM+9XGb2m2Q6sizhZ/pu1m+Uwewg2EOSfoGsi+KxaLGBsbQzwex8DAAAqFAo477jj09PRg8eLFQR8CCJXj4ODg4ODg4OBweOHI1EFCKpXCKaecgsWLF4cMXrtBrk3rrQa7dS+zUBVESQEQTg+uqgeVEcZjMSEFMK3wMBOd1oPxP7t27QKAwHWPe0XpHlgKKj2qGDHjX7FYDPa5UmVN68A2kdRQxWo0GqH4KiWI+re2gf2uCSRYp2q1Gqor+93GjSk0Rswmx1BiqGPJsdBEFzabIOPJdE8txlJls1mk02lMTEygWq3izDPPxOrVq4Nxffzxx3Hbbbc5QuXg4ODg4ODgMAdwZOogIZFIYPXq1Vi3bl2IFGkWPCLKYI/KWmeVKpstT89RpYKuaZoVTxUwEjlNIqEpxllf7rkEhLPhaRySVW9U9aHSQhVOEzHwGGO8NJZK28wySKbY17xWyVMnQqN1JxkkydF+VIWwU/ZEG+vG8m0cFn+z7lo/7UObcbDVagVJSJj8IpFIoFKpoNFoYO3atVi/fn2QXr7dbuPOO+90ZMrBwcHBwcHBYQ7gyNQBor+/H0cddVRgCDNOSY14qkDqJkYCwmQUVmkCpgmRNeytWxsw0xWOClKxWAzUF/5omnMFr1VXM3VLBKYVNMZ4RbnF8Vy7gTDbrO6N2h5VkCzJpHoEdE6vru6EVANZLgmXEi8dC3URtDFb2na9bxS5sn2pBJrqnSao0P6hsqabGvNazQyobUylUkin00gmk9iwYUOQJIQqn+/7ePjhhzE8PAwHBwcHBwcHB4dDA0emDhD9/f04/fTTkc1mAYRdvZQkMGOduohFkSklMJpMwWb/I3gvEiS6zdFQLxaLAYnQ+CzWVYkQyQcTI/BHE09oogy64am6ou6BvI/uzcT7sj9UgdJ+IukhNCteFKnROmnfaP/zmCVdJFNso/axdf2LIlO2PJJZVfYABOTGummy3lTmSJii1EwmDyFBV/fN17zmNfA8L1Cz+vv70Wg0MDw87MiUg4ODg4ODg8MhhCNT+4iBgQH09vYGBvDixYtDbltR7mG+74diiqISHgDThr4qJXTR4996HmGTNmgZmiTBuoB1SibBOB2NCaJKxs9IAtmGTvWIap8iKuGETaihJFPJBQkXj0e5HFpXS0sgrarWaQxt/W19VTWz9Wb8E/tLx0b7j3/ruXpvjmG73UalUkGtVguIFNvueXuSj8Tj8SDBxeLFi3HSSSehUCigUCgELodjY2PYuHGjcw10cHBwcHBwcHiZcGRqH+B5HlavXo1jjz02IBpUjagS0PVKY5B4raoSukcSXbMs0dB7qOGtChUJhSouVG+03upCp3s5AdOKFJHNZhGLxQJlirFFjN1RJUaTR7BuJH2EEhrWJ4r8WSgZoYubkh8qNNpeKmfqgsh7a3lUupTgsJ+1fzoRKKvCqYqoY91qtVCv14OYJ9t37H+tm6p/qpppwozJyUlMTU0F849t1vrS1fPII4/ESSedhCOPPBKrVq1CrVZDuVzGI488gueffx7FYnFG3zs4ODg4ODg4OOw7HJmKAFNS0z0LAHp6ekIkR7Px2eQONt5J42QI/dsSL5s4gX9rogVVOfQ+UbAKjFWEVK2hOxsJm7q4zaYikbgp0YiCJY5ap6jPO7nU2bYo0bTqj5IvkiktrxNsnWw8VVS8GKF9xustebXtVsJr54q6DfI8kndVqOy8isfjQTp2xl+l02msWbMG4+Pj2LVrV0C+HBwcHBwcHBwc9g+OTEUgl8vhNa95DYaGhkJpvgkavjRm1T1OXc54jNdr/I8mJ6D7HAkJXbpqtdoM49jG9rBeSrT0f1t/pt1meVSu6JLoeV6gprDOSjxYN0umNNGDttESHVVc2JeqUKl6oy6Plkhqsgrta02BzvoyK6EtM8otU8mZqmb6WRQp1nHwvHBKesZDVSqVkMLIGCeb6VHHi/OBiTgYG5XL5ZDL5UJzh+S/Wq0Gqmez2cTw8DBGR0eD+ieTSbz1rW/Frl278G//9m/YvXv3vn0xHBwcHBwcHBwcQnBkSpBIJJDL5dDX14fe3l709PTM2JfIphPX1OP7EhukrmAW9lqr/HRSMqg+aBmdlCEtOyoVumaN0/pHISquqFNK8U710XvNphRZNWq2dum99qaWaSKIKHe+TmqckkEb56XxT6o0KaHV621MXCwWm5FNkWAMHrP5aTITm2IdQKAw2jTw6XQa2WwWfX19aDQaKJfLLobKwcHBwaFr4KXTiGUzgGdsB7+NdrEE373THA4THJkSDAwM4JxzzkFvby8GBgaQTCaDrHYkLFQ/EolEyJi1igWNdFVD1GjnMRqwNLzVENa9l6KICrPH8f8oRMUlqaFOZUqJVKFQQC6XmxHjo+TC1kdVOE3zbUkbz+Hf6XQ6KJu/NXGFwu5FReXLuvFZNU/VICWbzWYTiUQC7XY7GFMlVVGEtxMJrtfroSQZJDtKVDlWth4ce6pZHFe2hfMvHo8jn88D2ON2ms/nAxKlZM3Om3q9jkajgWq1inK5HIr3Ouecc1Aul3HPPfdg+/btM9rm4ODg4OAwHxFbuwqTJw/BN1wqXvfR84staG532WwdDg8cmcJ0bEkul8OSJUtQKBRCcSjcWJbn2Y1wOxEZq4hYQ9wmMiAxIKzCM5s6E6VIdYoLUnKkdVMikMlkAhKibnyqotj72wQXUfXqVD8lnVHXzKZyaf9ZKBFVFSpKrdJ+taTOkmXbB1Zt5D1V+YuKk7N1pEug3lPJIOddKpUKJTyxcW9aPyXPSnAZG1goFJDP55HJZILrlAA7xcrBwcHBYa7hJRKALA63ezKoLorNIFOJKtCTTh3m2jm8kuHIFIDFixfj6KOPRk9PD3zfR61WA4AQeVI1hceBMAkh6PKncTSqUKj6pIYyr7Xky8blEMy6p2qMlsNr9f5q2NvYJe4zlUgkQrFcNMLVvZHtIJisggqI3of1U/Jp92yiakM3NrqmMUaLsWnaHyxLY6o0Fko3VLZkirFUNv4sajzZF+wn/a3lAtMxZYxD03gnVeNU2dTxrVaraDabgZo0NTUVZDRst9vIZrPB9c1mMzJ7Idtt+y6ZTAauq4whY/+cc845qNfrwUbAExMTGB8fx/DwMB577LEZc8/BwcHBweFwwUskgNOOQ3VJNjjWKMSBzl7/Dg6HDY5MASgUCli9ejXS6XRgANN1T5MR0BDXvaOUqNCQVyXFJoiwyRsU1oWPhjqv13uynuoKBkyTKVV8opImRKkyGjdFcqPGuhIvrZPWVRNoaCrzqH5S10YtT90E6R6XTCaDviQJ0nrbZBesI13grOqlmwNbdUzJqdYzKhZM+8+6EAIIkSmCZFWzJrJ/mPSj0WigVqsFe0bxHnavL15r5yrJvE0DT9dV9lu1WkUsFsOaNWuQSqWQy+WQSqUwMjKC4eFh+L6PjRs3zpq50MHBwcHB4ZAiHkd1SRZTq2Y3Wz0fgHtFORxmODKF6YB8Deq3G6hqqnObkU5Jhk1mQMOW2fGAaQXExiDp31RmLAnolPDAEhwlcayjzaDHz1OpVFAnKkbtdjswunmeGuhaf9aJJIr3oKukJkPg71QqFSqPpIAkjqobySvd2+xYANMZ+0iAtW/0byVtVHt4vbr/2T7SfowiUqoGkRyxz3lvVTn5Y5U1toOueDpump2Q9VbyGIvFkMlkQqnSqdRp/YE9Sls2m0W73Q4WBqjWFYvFIM4qm81i5cqVeN3rXhcQu2q1iueee87tUeXg4ODgMK+QqAK9z1aQmKrBHx2f6+o4vILgyBSmN25NJpNBHIpmYaNRqpuvWpVJ3fWUNJBUpNPpUKpxbopLRMU3MQObzfamiIofAjrHZ7Ec/vZ9PzDs+bcSII3fAfaQnKh4H1WeeNxmOFQliEqT3UyX55E0kHhEZUvkMZI4dZG0/aKETd3vtH9trJHei+VbUPlRIhePx0OufbxWU6VbtYuKmFUu2a+sq1UjNXmFxlSpGsV+I1Hj4oHeg+6F6v6YyWSwdOlSLFmyBPV6HePj45iYmMDOnTsdmXJwcHBwmFeI13wkn9yK1ojb7sPh8GLBk6lMJoNVq1YFMSO+72Pr1q0YGxvDsmXLgh/u2WMzugHTRrbupUSoe5UlOvYz7i2k2ddoWAMz1RSN+bGKFMsnCYpKjkBjO0pNiSJyliyomqMKBxUsVeJ4XFUaXmPJoFWArBJIlz0luCRntn6WgFgXxijXP44p93mKGnPtZ5uIQYmvkhXWWVU7JdYkUoSNQ9K+piKXTCYDMsV+t1kUSTbtXOJ9SbCYjVKzH/LcXC43w7WS92Q98/k8EokETjjhBKxatWpG7J2qZlFJMKi2NhoNVCoVR8gcHBwcHPYNrRayW6aQKOc6nhIvN+BXqoexUg4Oe7DgyVRPTw9OP/109PX1IZvNwvd93HrrrRgbG8ORRx6Js88+OzDYgZnxQDRcuaoPhNUIVSU6uQVq/A4VGSoqCo3Z4fme5wVkwn5OAhLlwmeVMksCY7FYiBxoeVEb71Kt0DI1BTc/930/SHdOWJKicVIsT0kDSSawhwxzbDguqi6pQqixU+qCyLbpuGSz2UiSqa6GWq7eW8tSt06SKZJYJojQNmpcHfuZ1yqZymQyQSr0WCwWJOOYmpoK4pw4BnRNZT+pWyXnD/uXKiBdSNlPJGLs/8nJySCRCMe2t7cX8XgcQ0ND8H0/lGSj2WyiVquhUqmEFEiqhcViEY1GA5OTkygWi9i1axdKpZKLv3JwcHBw2Cv8ZhP+Yxsx0z8kjLZ7pzjMARYsmcpmsxgYGMCiRYuQyWQCIgMAq1atAgAsWbIkUCeUCHVKuGAzt0UldVAXMRq81n3PXkvY+0SpOFGfR5Vt42ns56pMaUY5XmvLj3Kz075g/TopaNpHNiGDKhyaUMHuK8XjUSocz2GdNNGFlmUVKltXq7bomCpIFPTeVllk3XSDZ3udVRbtnKNbHvuMP5asa7+oKqSqoZ0XVtFTMpdKpZDNZgOSxDaRwJJUWgWOZRIknvwOsu4st1arYXJy0pEqBwcHB4fZ4d4TDvMUC5ZMLV68GBs2bEAulwv2jaIy8NrXvjZEiNSoVTWCxn2UysRrowx7TSygsSzAdPY1W3ZUGTbBRVRSjKhyeW8bP8N2Rbkpsnw9r5P7otaJ9bDufvpbiQLVGCZ9sAk7eA2PayyS9rn2u/ZhlDFvSQcwnTZclT5NghGVPIL9RbKhSpReo/WgMsS5oNeqwkblU7P5aQxYpVIJUqZTudP4Pqtssa5sF+ca5wOVqlqtFlLj6C6Yz+eRTqdRrVYxOTkZpG3XvlVyqOnjmRpex4abDefzeTSbTSxatAhLly7Frl278NhjjwX3d3BwcHBwcHDoJiw4MpXNZpHL5TAwMICenp4gRknVALr10XUqCp2UJn5mf1v3QEKv1/P3ZyWeRnIUYbFlqXplVYgoUtQJlkh1UtesysX6kiTwPLoCqjse70PCYu+t+1DNdk/tY57DzzWNuoIEQ8mUVaSs8kgyyLgjVfS07fytyp0qUFqnThkWWQeqQrVaLVTnKGg/8562f3TOdFLddL5xEcK6f9q+t/1EBStqs2e6MQJ7kqwMDAygWq2iWCy6/awcHBwcHBwcugoLjkytX78ep59+OnK5HPr7+0MptWkENhqN0F5IVrnR1N9c+U+lUkilUiGDW12erAKjG6pa5cXGJNHQVqLEcwnP84IyNZ24JSY2rskSKmA6NTvbHEUOgXDcl90wV1Ud23b+r+qEQt3uWCZJgyaFYL90IjtWHWNbNWsi3eQ4B6Ky7EURAtbTqjssk+qQxl5RHdINh5PJZJA5z84FqkT80X2g6vU62u124AJHBYmb6vI+UaqdTS5B1UjHi31H11cdM6piJFMDAwNoNpsol8uBQmVjvRjvRte9YrEYStpBNz9uP5DP59HX14fBwUGsWLECY2NjuOeeezA5OQkHBwcHBwcHh27BgiFTTDvd19eHxYsXB0asKkpRmd9oFCq5sEY6f9v4FnUNJCy5sveyK/sKLcueo2TLHidsnJSNb5qtfp3uyXLZXqtCRZGdqB+ti8Zx2eNUEUkuSBi0jlHKka0T76lJQwAE7nhKQqNSntu6WVWH6pImvFD1Sl3rolSyKNXPEiOSEZa5L4pmVP9oH1riqC6Cto91PpHokQhHETlVFG1MliX9dHdMpVKBSlUoFEKp33XfMgcHBwcHBweH+YgFQaZisRhOPPFErFu3DkuWLEE+nw/IFBBOfKD/q5uXdS+yaopmilMlQY0+jZPhKj9jg6jEWMOSZWuclUKNY6s60eVMkxowjobQvYM07bW2X41mJQaWQGi9bR9q+/mbxMUSUgAhRSiZTKK3tzdQepiggGRC29But4PYHU2wQNc7xjBZUmHd6VSNtKTRKkfq8mhjr6rVaqifqtVqSP1iOSSJtu06r+wcisfjgarFuWYzAkYRYNt29iVTrZPI8Led6+x/qmP8bmgiFxt7x7b09fUFc6zZbGJiYiLYL4xjbffB6u3tRS6Xw3nnnYdKpYKRkREUi0Vs2bIFO3fuhIODg4ODg4PDfEXXkykSlaGhIaxbtw7ZbDYwGrmvVNQKuapRwOyxTnqOjSXSVXwAM0iSKhe2vE6wioiWbxUN/Z+qgVXQrGJilRnty6jYL6taWCNeiZW9nxIzrbeqMfF4PBg3kiluImvJHDDtcqf7b6m7pI65kjCSkqjMhBaq7mniBjsfbJutC6mSOx3TqPglew6AgEQpMYtKWqKwZbIP7PUal0YlKZFIhNQ8bm5sXRKB6SQeSsLpCkvy3m7vSUah7dHvEF3/UqkU1qxZg3q9jmQyiampKYyOjmJ0dHTGXFM45crBwcHBwcFhLtHVZCqZTOLUU0/F4sWLsXbt2kDd4Cq4KlFA2KXJxkZZly+rSOg5QDghhZIlu8pviYQetyQsyh2OnwHT2dN4T5upT921LDGiWqD1J+y+RHTpUkJUrVZDfcnfuteTjeHifahGZLNZZDIZZDKZYJPYRqOBZDKJvr6+gDj4vh9sfqvtYF1o0HN/JQsSbPZvPB4P4rdsRkSrOilsSnOtB/dYsskeWH/2oW4IzH5iXfR8HmeiCRIa1oEZADOZTNCfrJfWW8eOUBXJLihoHJj2PcvSzIKZTCakGtpFCnXR4+9cLodcLhf0r43Xi8fjQVxbOp1GOp3G0UcfjXa7jXXr1qFYLKJcLqNUKqFer6NUKgV1qtfr2Lx5M0ql0ow54ODg4ODg4OBwONDVZCqRSGD9+vU44ogjkMvlArc+Gq42BbglGFb1sIqQVaBYXpRqBYRjijSexpKuKDXC/m1d/qLia9TVMCrWyrbVruIrCVRQvVEly6au5nU0qvVc9hNjbGjg5/N55PN55HI59PT0oNlsolqtIpFIoKenJ6RIkSzoGFqVi8SZxMO6E2r72E9Rqk5UX+oxTWLCPtSkCwBCKhnroPFfVsnUsvgZVSDtQyo5LCedTs9IiW7bYwljPB4PXDvtfGccl5KcWCwWKLtUAHlvgu6rdmFClUCSP/aF53lBggurNMZiscCNsKenJ+TOOD4+jt27d6NcLmP37t1oNpuoVCoolUrYvn27I1MODg4ODg4Oc4aZy/ovE9deey3OOuss9PT0YMmSJXjHO96BjRs3hs6pVqu47LLLMDg4iEKhgEsvvRQ7duzY73u9+tWvxvLly4M9cTQuBUDIyNOkAFFxMkA4PkndlCzxUbcvNWjVSKa7F39bImB/NA6Lbk+pVCpIS61Gs5ZvSUy1WkWlUkGlUkG5XA6pCITeT41uJUjaV9qPrGM6nUY2mw0MXrprMe08Y3KsGyKJAdWParWKUqmEqakpTE1NhRJE+P6euKtqtRrEImkqe40H0vFlvzN2iSRKY5W0XZZAkaRxPmm/VqtV1Gq14L7aHsZ8KWlU9cwSD1WXtH8SiUSg3pE8kajZRQGFVcfYL1QHSWjsmFuVSa9XAqekz7p+2r5gW+y42DqxXox5azQamJqawvj4OCYnJ1EqlYKYq2QyiUwmg0KhgMWLF2P16tV49atfjXPOOQdLlizZ+wPDwcHhoOFwvusdHBwc5jMOujJ155134rLLLsNZZ52FZrOJT3/603jLW96Cxx9/PNi481Of+hR+9KMf4aabbkJfXx8uv/xyXHLJJfjZz362X/d63eteh0WLFoWIjRIXq9Yo2VHDVImSjU8CMMN41aQPGqvDe9nYJN24Nyr2yRrKNFyplNRqtVBbNMBfyaEml7BETdtsVS6rikUllaBSQpJE8qqGNA1kGytlXSapVJGgsN+pTuhmtY1GI0jJzbFVQ11dG0ma7F5MqvJFEYao/tKkIZpUQvuJCg4JkCow/J+JHLQPtV80RbyNI1J1TdORRyUp0TG1bnisp52D2h/WzZT11Wx76sqpY6HfMbthMBUs1k37kH1F8LNyuQzf95HJZIIYLP4uFAqIx+PI5/OIx+NYu3Zt4P7nklU4OBw+HM53vYODg8N8xkEnUz/+8Y9D/3/729/GkiVL8OCDD+INb3gDJiYm8K1vfQs33ngjzj33XADA9ddfj+OPPx733XcfXvOa1+zzvWjYq8JiM8YBMw096/JGA7YTopQkIJyYwRqVUURMjdYo9z415NXIjIrfUpVMyYDdY4j15Lm2XewXPdeSL+0DKg/ZbBapVArNZjNQargXkr2O96CRTJTLZVSr1UCNU3WvXq8H5FD7zBIe7VseUyWOLmRRCpntJ57LPrWun+rGSfAcm2SBrorMRKh9Ydtgx9/e0xJIuhbqebowoPPSzh+ea783tn9J/Eic+LeNNdT2EJwjurDAenRqp7aH17Jes40/y1m1alUQ10fSxkUIuo5OTk523KTbwcFh/3A43/XzHfHBAWCgH16xjObwDiDCfd7BwWHh4pDHTE1MTAAABgYGAAAPPvggGo0Gzj///OCc4447DmvWrMG9994b+YDlRqAEN/ZMpVIzkgMACJIPKGhAep43IzEFjXkaf2q8aZC/NYCtYaYkTRUE6+al97ZxMzzfGqdKULQczcamhrZ1TdR7sCxN1Q0giGdR1zNex3bTha+/vz9IIlGv1zE6Oorh4eGAmOpvJploNBqYnJzE5OQkxsbGAvc5urXRiG+328GYM728GtIkXkpY1ZVP1RYmN2Ab7JhYZUvb2mg0QgRP1UIla3RHbLfb6OnpCdWP7oY6/1QlUoKvmQZZvqZGB6Y3HY4iI9om/ub9dNNeq6jxXPahnWdU1thu1kHvw81/dX5xHOmmx/Oi0rHrXOP+cOqqqG6m+sNNgM844wwcd9xxAUksl8uYmJhAuVzGjh07UCwWsXHjRhSLxcj7Ojg4vDwcynf9fEd7zXKMntqLwgt1pEZG4Tfqe7/IwcFhweCQkql2u41PfvKTOOecc3DSSScBAIaHhwNjXLF06VIMDw9HlnPttdfi6quvnvVevlkJolHMv/W3ns/YFWuYaht4Po196wKnZVpFwN63U92t2sVjnVb/9TNep0kA9gVR6lEnhYcqION4stlsEEtDcmDLJoGw6bC1/3gv7pmlCRF001YlTrZPo1zF7NgoopQaHQfdf4vH9bf2MdujSo5VHi2UiGlfWVdOkjQes+6JUdB5r/fTfufY8rftC3VhtXMs6ntGdHKh1L7Vn06ESu9nlTBVYLV8dXllX3L+ZbNZtFot9Pb2htpVq9VmJFZxcHDYfxzOd/28RNxDOwk0C3Fkly2BX6mgNToOtFt7vdShC+F5iPf3w8tMJ2XyS2W0JifhpdOI9ffBo9dHu4326Lgj2Asch5RMXXbZZfj1r3+Nu++++2WVc9VVV+GKK64I/p+cnMTq1asDgzvKOObquxqo1rizsT1Rhi9jkHgN1Srf37Mpr6od6hqmCoe6PdEVzroZEjxHXaFIRmwsEBA2zDW+yG7sapNl8L5q1Np2cJUwkUhgcHAQmUwGK1asCFz84vE4KpUKGo0GKpVKEFtDcsWkDOl0OrRxrSatoAqmCggVHSVTVh0k9G9NxGGNb+sWZvdN0mQenfZk4nxjHJSOHfvYJi6x4Phybmm/6FjSbVBXabUMJWAkoACCPlWVj2nEOReoKqkKpP2iKeGtqyXnt/aLVa10LFkHVXhtlkrrjquKmJJjjonv+yiVSiHla2pqKhTbRrUZAHp7e5HP55HNZoO5WqvVsG3bNhdn5eBwEHCo3/XdgspAHPU3rEJmrIXsfzyJ9tTUXFfJ4RDASyRRP+0IlJangmM9myvw7nsE8SWLMXH2SrRSLy1S13303fcCmi+8OFfVdTgMOGRk6vLLL8fNN9+Mu+66C6tWrQqOL1u2DPV6HePj46EVqx07dmDZsmWRZXH/GYtOygE/o6Gmgf88h65h+hkQVhbUGCSs8kBlS8uPUqJodO6LSkXVoNPn1oWPBnWUa19Uf+0NSrhYF2ZT41houuxsNotCoYDe3l4A4f2Z1M1MDXVLiDrVTcuJ6hM7NkqkovpNxysKllzrWEQpQ9b9k/3H82277LixDI2NsvXQc2YbQ+uaae+p3xOrUFnlSF1TtRyWrXVRQmUVVFuXKEQphJ2uUyWQCqESYWZyVPdfzrtcLhfKJKgJLpQsOjg47DsOx7t+ThGLI5bNABHvH79SgS+eIO0E0E54SNRi8OIHPVmyw1zD8xDLZuHlsmgUEqgXpt9RzXwCmb5e+D051AsxNDNAO+UhVvfR09+D2ERPcK5fr8OPWCh16F4cdDLl+z4+/vGP4/vf/z7uuOMOHHHEEaHPzzjjDCSTSfz0pz/FpZdeCgDYuHEjtmzZgg0bNuzXvfL5fEgN4Iq0upVxhVuNJKZjZowQgJBxRvcqXWnnQ14NPDVebXwHP1fXOQAhNyN1X7KqiypSvCfVGtZbM9zx3lR8qAZZ5cKOFduuUHLG/mQaa81kGIvFMDQ0hEWLFmH16tU48cQTA0OX8VAaY8N72rTrAIKYKRrLTCmupI71YmIB9p3G1Fi3MvYlFUzOlygStLf+Yb09zwtUOI4p+5tEs16vhzbntWqgton30rTiPE4lyG7Uy2u1DVQEfd8PkYzZXC0bjUZwrc5r/b5odkqdy5ZAttttpFKp4J6sn7pA8vvGemrMn7adCjDboftk6TjTzZRjUiqVUCwW0Wq1MDk5iVgsFux1xVg2zTjZ09ODarUaJEMZHx/vuBjh4OAwjcP5rp9LJJYuRun01WiljSt720fh17vQenrzHNXM4XAjViigftYxqC1KoF4Iz4fS8hQa5x6HVtJDOwG0sh4mj2sAcR/wFiF/TN+eE32g8MwE/EeenIMWOBwqHHQyddlll+HGG2/ED3/4Q/T09AS+0X19fchms+jr68OHP/xhXHHFFRgYGEBvby8+/vGPY8OGDfud3UezfimZoKHG42p4AuHNXXU1PMo4V3JkVQPCKmNRCpj+rzEdWkbU/7Z+6opm44qilAxbb4uodijxse6B2p8AQskEaIST9BWLxWBjXk3XzaQMVplRI14TNChhVtgxsqRQ+1uJm6qE1oWsU79EjSH7lS5lJJ2WtNjyVMmx9+N84dxmPdVFTlUjVWBt8gr2jxKWKDXXqq2soypyClWvtG5Uufi3JkRRAqRjbMtjf+q48DsZpRxxzEiUarVaMF/oRskEGDqX2+02CoUCAIQ2/eX5Dg4Os+NwvusPK2JxeDF5FxdyKA8l0DKCmdcG8oUsPCbxaQJ8hXstAG23KNP18Dx46paeyaA6mEB10cwF2GYWaGanj7cTQKq/hnS6gcqSRQBi8NoAfCC7M4N98xNy6BYcdDL1jW98AwDwpje9KXT8+uuvxwc/+EEAwFe+8hXEYjFceumlqNVquOCCC/D1r399v+9l3a+suqNZ4NQIteqRXs+YE8Zb0ADUzGvcG4nXULFKp9NBnJY1QKmGaLyQ7/uBamYD7a3BrgYloeoWP9M01JYIWWJnXdaiymeby+VyEENF0hCLxZDNZkOqkhI+to3EKUrhYTa5Wq0WUrOYqVHVGruhL8dE49GogtGQ5jhqhkIqN0q+qM7o/FFFj6REjXJ7Ldui6pvGbpH82P2V7BhxvrIfOP+sa51m4tO28p5KZEjg7LxSlcyqPmwvs2PaOc2+4HksT93vmMWR8U9KQO2cZD9pIgkluLrYYfuO92a2QCC8xxXbxnP4Xe/v70e9XkelUsHk5CTi8Tjq9bqLpXJw2AsO57v+cCJ+9BGoru0P/m/mYmhHWUoeMLW+B6klpyEx1cDin+8OUqJ71TpalerhqbDDIUNi5QpUj10GP/7SezLhoZHfP/fNZLyF4voqqssTyG5NILvLkeyFiEPi5rc3ZDIZXHfddbjuuute1r3UiOoUT0IDUt2TohI5sBxNF67l0tgjSSBoyKtxq2qR/a0kh7BuTGr42rbY31ovNTS5zxHbov2l16oqYePDlEAyKQaJJA3oQqGAfD4fUmK0Lvpb70m1QJMcNJtNlEqlIPMajXVLUC15JtiH3PNKCQ7br/dXF0kSnKi5pHNFyTDHVePBlEBZVZRjS2PeEhpVFWeLH1IFUl3kdP7pOZZA8zq9X1TfqhqmqiShRMqSTI1j0u+IKnBRWTR1jiixZV+TkHGes82cRzoWOvY6/7gYwA2iqZSm02lMTEygWq06MuXgsBccznf9YYPnobG4gIl1yb2e6ntAZTCGymAMA4830Xpik9tfaoHB7ytgYl0KfhzYJymJwy/nJuNtLBmaRKsdw8TYILBrz9zxPM/NlwWEQ77P1KEEDdlO7khAOK5J/9ZECBorRUOyUCiEYo8qlUqQLayTy5YSLwCR5IQxKmq82zTU1u2JbbWxKtbQVYNYFQ4be2VdpVj/TgqEJZO8pt1uo1gszohJ42fsK3Xps2PD/tf4KgBBVsJqtRqoGySpVk0pFouo1WqoVCqoVCqh/qHCpZsCW3dP7XMAwb5QllSwHCbiYFtZVrvdDvbF0jgxdfuz8Ui8lgSB51gyH6UkqRsc5zCJAkmDuqzq/ZT86NzimPEzlsM66dxg3ZTIsW6a+VDnfNS8U+IJIHAL1TpYF0HGeFnlSdtnE6Dk8/lA7eRc4B5oPT09SCQSAZl65plnZsxXBwcHhyhUl6SRfc0pSOwuovX0cy4l+iFGYt0aNJYv2ut5nu8j8dwONId3ILFqJZqrBhEfLUWPkechftQRaA71oLI8g/LiGGr9HqpL2mgVWhhYMYFUovO4joz1oL0zg3auhaEVEyjEW/D9aAa21/nyUl1agwUkXhxFc+sLe22rw9yiq8kUV7ut0WVjMqJ+a7yJprImqejp6UE6nUY+n0cqlcLY2FjofEINdDVOVelRdcrGmkSRKZarao+qS1FuYWqQal3sCr1CY1aA6VgpdaWz5EnVnFgsFqh0ei77hCSL6au1vZYoMmW8ZmGr1+sh9UJdvzgOzWYT4+PjmJqaQqlUQrVaRSaTQT6fD5QpbjQMICBrWg7nDpUU3XuIhAyYTjSRSqUCNY71JwHgRsPq3heldFlSyDqpi5sl3TovVO1T5YtzRYm0JirR+c8MjUqobNKKTgqSggRF56Bm1iPJU5c9fq6LD+wzumvqPLcLDNovdm6yLqwXXf96enqCz3zfD8asUChgaGgIqVQKk5OTgfrq4ODgsC8oD8VQHsqhd0sK6c1b4TsydejgeaivGsDY8Vl04CrTp7aAxeVFwPAOtJYtwu6T8+jZmo4cIy8eR/WIAUysS2L8eB+Dx4yg4PkoADhm0U781ap/x5J4vuO9bpgaxNc3vxEnLNqBL624FbvaPv7rc5dguNQz49y9zRfWZXJtEoNtH3Bkat6j68kUMDOhgMK6M/E6S0hUoQIQ7J9Eo3NychKTk5MhI5DGoXXH0zgbNRSti5+uuLOuSrRYvrolquGtcUDZbDYUn6TlKrlUhULJj9bHqg9KrtSoVkIAIGSosjy771KUa5u6zSkx7qSSKTn1fR/ZbBaJRCJIzZ7JZJDL5UIbRipRIgnj2Oq9dVxtO3m9xjyROHC+MIufJYtKmi0x0bLo1qgqkJII66bIeaJzjnNY98xSEqtzXwmYdS/Ueltl0yqlbBvnAevPODTrRmjVU7ab48dEECR3JGSc8zr3tR918YCupJlMJnDr073n9Dp+p3t7e0OZDR0cHBz2FY1CDJnjj0Ss+dIzptmC/8J2tMvlua3YQoDnIbFqJfzePKqLUnslUnuuAWpL8siccAwqQ1kAEWP0EvxEDPWe6Xd/zPMxlCvh+N5hrM/sRM7rvKUKAKxP7sQblz6N9ZmdSHsJxLH3TXptG7xEAvHVK9HuyU7XJbYvDXWYa3Q1mVJ1yZIAILzXVJQyoMRF0zA3m01MTEwgHo+jWCwiFothcnIy+NvGifBeapjSQGS8iMZ46LlKMtSoJMHwfX/G5qU0BjVBQzabDVbbW61WkElP22pJiBIz9ot18dKkEdqn9lrWQ937NIOfXh8VN0O3OSUFGpumn1kiNjAwgFgsht7eXhQKBaRSqaA/GA8zMjKCdruNcrkcEB7GyWQymVDSEavksb/o/qfGNgnWxMREkLKdqgjjeJSsaF+qm572u43hUsLPudBoNFCr1YL66xhSGdMkG1Ro0ul0pOJo3fh4XOeyugLqOCih05gmz9uTrpzp/Du5HNIFU7ceYGrzWq0WpDvXOKdcLheqk9aD5bRaLSSTyYBY9/T0hBLLKJmrVqvwPA+LFy+O3CjZwcHBYW+oLoqheta0+1my7GOgVHFk6iDAS6VQPmEZplYl9y1+CYAfAybXpTC5dhDw9pAXO0YhmNwSx/cO4/+35B7kvBSSXib6mpdwVtrDq5Y8iBhiSHopoLV3MmXhZbOYOmUpykPxGXVxmN/oejJFWPXJutGRAOjKuFVgCCUC/ExX4nkOf6JcoNR9LMp9z9Zb/+a9bF1tXJWth1WCuJpv+4bXaeY/Gp/2c9tXNn24Var0R/vWqlJKdBWzpaVmmZoJUN24mFmQJJduZqoYdXKl1PbYvuV9NTZME1CogmJdOBU8rmRW72EVEZImtsXzvMBlUuPx2IdsmxLvqH5XWMXI9kvU3FYCxj6wZJ+f6d5XUWVZBU/rot8vm1hCxyiqf1Xh4zyx90yn0zNUKjt3HRwcXgHwPMQHB+DlcygXDlyZ9j0AcSDWANKTbSRLbfj1xl6vc9g3+HFvTzKIDmgnPDR695CoTojVgdSUP50sIqqcXAvHLNr5kiKVQnIvqhQAxL0Y4sKA0h5wbO8O5BJ1bC/3olibuRl1KxNDfNVyoPnS4mY2jWYmFrTR84FmPonM6unNsP1qDa3doy4ub56hq8mUjS8hYbJxQzRSNQ25TVutRhoVHgCBcU53IRs3ZGM5VIXQ1X6tKxFl6JLIaSyUujop2VBCxIQH5XIZjUYDlUolSE2tblFq8LIsvZdVlKyrGcux+/5QuWGdVLmKyqrHvuHY8H5UMFR1sxn/yuUyYrE9SUJisRjy+TwKhQIKhUIwRvV6HdVqNVCMJicnQ0qNZgrUjIVaJ7ZLyQvjiXbt2hWaW+wPuhzqeJJkcaNfVZc0fbfGi7Fvi8ViiKBWq1XU63VkMhmkUqkghov9T2WQLn6WfFuXO85hzmlNd27j1ez3jvPHkimCYwQgmJe6UbaqQ+x/nX+q6mWz2Rmxapbo63eQSiNVWyW/HPNMJoNFixahXC5jcnISjUYD1WrVKVMODq8weIkkaqesQ3FlCu2D4OWbKvnou28r2uMTLkX6YUQrC1ROrCCb76wKFXfk0f9oArFmBzblAQMrJvBXq/4dOS++V0WqE5bHc/iToXuwq+3jz7ddhCdqy2acUxmIof66FaF7t1LT//oeMLkmheKK1cGx7EgT2bvKaMv+iA5zj64mU9Yw1OOd1CL7owqQKiVR8Tq6wh5Vvr3Xvh7TMu2qvK2DtlUNZXWh0mvs9VH/R92j0z15P/YFoUkQOrVVr7fopGp1UklU3bF/q1FcqVSCDHtUd1Sp66TUdOoD3oeqnxrwrJcqVNqG2WBJtqa4JzkkyWdiDt3YN0phiio3yu1SP7MKlY5zp77R+uq1dFfUPlAXUsK6nyo5IoEHEMrQpyolSZmeq26z2v/2+0HSrvGMexsrBweHBQLPQ6xQgJfLopmPo5k9iGXHYqENXx1mRyyTgZfNwq/Vwm6RMkbc76kT/BiQSLVQyMxcEMsmG8gn63iinAK8CNPXA+q9HppZ4OjeyVmTTewL4l4Mi+I5AGWkY9Eqkh/HXudcO4kQwW9lYkDM+QDON3Q1maJCovFPUca3qiQkS4yR4vU0jpnIgcarZjuzMSR2I1irNBHWSLSIInZRsVG6CSsNYgbZ237RFNCq2lmXPa2TDei3BEdVv6gkDTRs2bdUSNTgZx/yXiQL3LRX90hSpYa/qTioAc/4sHK5jHQ6jVKphKmpKdTr9UDZ4blUKTQZhBrzwLQboY4LFTb2m3Wt4/WMm6JhnkqlkMlkQu59mnVOVU3WRdP0s3+Y4IJp4KmQVSoVlEqlGQoSx0jHXNPLV6tVxGIx5HK5gJTxeiUc6rYX5WbKayyZ4jn8n981rZdu7mzrTSWPCSPY3lwuF9TL9/1Qf6dSqchEGvzekEyzLZo0g8pXOp0O4vccHBwWNmKFAmobjkWtP4FG7uAtotQLHsZeuwrJUhv5+59Da4fbt26vOGYdJo/pQ/6FCvCLxwI3toM1Rq8eeh7vX3Qvrk68DU/94ljAeF+2kx56zxvGx4+4HSentwE4mMzaYaGjq8mUEgSNb7IxGjTieA0/t6oUDTka7TyHmcWI2VawOylBUWpS1PkAQsarJWdKEGlkqqKldZwtzkvLttd1ckXkfdXg1WOa5MISM+sSqMes2yXbaRUdqwzyvowrAhBk1iuVSoFCxWui1KJOYxilKmkMnmZa1PpRAdO4Hk0soWMQRUpJTJTYqeseSaqWpW6hs0HL5OJBlJLZaU52glXG9Lher4qTfqZ9qN9BzZapCwQ8Zuc3gCDdu5Zt55YuCvBHF0Y6bSXg4ODQ5fA8eKlpPyovl0WtP4Fq/8FVo9sJoNbnoZWKo5DLwpMFGr/RdPEuALxkKpSprtWTQWUghuRUCplsBv5LNteBjJHn+UjG2oh5PsqNJBrNOHriVZyYyuKI/G48lgd8kyWvlQbOGNqK3+0Zw7wmUh7gpZLwkin4zYbb+HeeoKutBpspDwgHoasiFYvFkE6nQwqWdQGiWgAgyJBm1QMtN4p4AJixSq/lqkFn3fl4jPUvvyR1qwFJVYYKhrqDKbSeNJqt8mKNRl3Vj8VigdGubVTD1Lq4WUOa7dB66tix3uqqZ8dD1SPrBqiEhnFIsVgMlUolUG9YJ40Zs65szJ7I/y0psa5u2hYbg8WYHrqkUUmj2kliFEUobbyRVVLj8TgGBgYAAPl8Hvl8PshEpwRfyRvnAtUujW0iQWHZVtXRuljFTOuo48b/2R4qR6og27ZpLB/JoY65nRP63WXfWtdAnYNR85z15JxhlsN0Ou3IlIPDAkVi1UqUT1gWuIv5ce+gKlIWrRQwedoyxI9fsueAD+SeGUXrqVf2puCxTAat049FvX+a2NZ797wPKosT8N90QpAg4kDGKB1v4R0rH8aRqZ34k19dAv/XvfiPwlFoDT6O9y26D8l3t1Brh5/zSa+Fd/XfD2B+eybUemKIbTgKyWITqYefQWt8Yq6r5IAuJ1OqyPBHDTMNkNdkFOpWZQ13fqZuZZ63ZwNZBqbPtkqvZfFvS8bsuTbGhseZGpyGuqpkdsNTlmHroPdVI1dVE0KNXKuMETym6klU+6P6Q8uKGjtLpjqVZ4+pGxuAkDub9r91yWSb7X5hVl2LUvGsOxzvQVczzSSXSCQCYqrjYF3g1B1R3QRZNstNJBKhxBOcC5yrOr9ZV5IoJZhab+2fTrAqMK+3LpF6DpWhqAQsnZRBS6Ct257ej3O10/xRMmVVTy1P22+Jv4ODwwKA58HvzWNqVXLWjHAHE34cKC+OgXmuPR9Ij728WJx5j714SAB70pxXlmVQHpr5zmnkPDRy+2GaeuG/Pc9HOt7E6dnncHqqimYjjp7tPnYW9yRDOiWVwSlLHu1Q2PwmUsAeBa24Io5UMYb0kxkAjkzNB3Q1mbJGj5ILGzPC7Gl2ddwaZVGpubkCXigUQhu+AphRDjBtvOpKvVWftJ5RChswrd6o6yEJIWGNVq1zlBEZpcYRutmrxpXouVHXW5XNuvNZVzb+b10XrUGsqbDpyqZ9T2KsBJl9ovspUX2KUl2oGKkKad36VG1hH6p6ofE9+Xw+IEDANLFTUkGFR5Uk9oMlCzxOxYT7aHEOcw8nJeVKQKL2O+NCgyZb4XH2AcvSOjLGTutp28Bx1Puoq51m8uOP7rvVqf26vYF1TbRjp+OlCqqdw3q9ztu9LZY4ODh0FxLr1qC+agDVRal93qPIYf/gJVPwjj0Szf69Z79rpuKo519+EoXqoIfK8tb0mPY08btH/wrrMiN4oHwk7iqmkMnWMXZSBq8Z2vGy7+fg0AldT6ZmW01XJULjTjR7l7pk2VV3JSFUHDRmheTDuoipEadkSpUHXcn3vOnU1GrkqWKihrGmE1ejVeuiLnBRSoJtI8uzRrK6RFnDlK6AVlWxbn9W8bGGrEIJlbqhkTiwfwFEpn0HwnEzJEB2s2Stj6p8Wl6UIsJz9Ccej6NQKCCZTKK/vx+ZTCYgTdVqFVNTU6Hx1wx0lshblUfnHzeezWazoXK0PTpGVnXS+DQSSJ1HqspaNZH1jHLzs26FWqb+cJ7aucr7WfVKE5BoXfVcHSsluLbuUeqmzjl1J5ztXAcHh+5DY/kijB2f3bMPlMMhgZdMoHxEL4rLD18Gw0afj8EjxxCP7Xn3rChM4MMD92AgFsN/3fYbeHT3cuRSDeTW78ar+rYg7rkseA6HBl1NpqJWnRVKLNRgs+RAjUBrMOoqu8Ya+b4/Q3Gx11mFRutkSQSNQT2u5MMaxjyf2c6iSGVU+/ZWF6oT1hXS9q2mqLbkxP7PcpTcKqlU8sRjGqvGflZixz6L2g9JVRQ9j3VSUmT7zioUPMe6FapCQoOfrn0k2wBQrVaDv9lnnE+WhJMYqOrJ9mWzWaTT6eAemi1R+5dlqkrJ/tH90Xj/RqMREFadA7yeRMgqRXYOaZ353VASa4l61LywihJVrFarFVIh9b42Y6WqmlFuf/p9VuiCh1OmHBwWADwPiTWr0O4voDyYnhdEyveA6lAGuVOPD455pSraz20NEi50G2KZDLy1q9DuyaBxENSmTvBjQHWxh2ZebJgjSrhg1RNIenveV0PJKfTFPKS9BF7ftxErMuP41fgqDJd6Dlm99gbWZSBVwk3PnQ3AEbqFiK4nU7oZp41TAqYz/qmKA0wbfWpU0whjeWr0awC9bsqq91bSpXEfaswpoWD9aHQSVkGxhqwlC1EGoBqkSjT0N6Fud5bA8Hy9RgkMj9MNj30fNRb8PyomxSoyUSnoLflRZTEej4f22tKEH9Zgt+TZttO6e+kxtpHuhlRZGo1GUAdN0a3EhIkuEonEDDIFzCRb/CyRSCCfzyOXywUxU9xvShWVTklONHZQ3VhZX9/3A5LG/rIbRMfj8VD8VpRqx/ryXP0+kBixLP3uqTuq7/tBu7jBL8ti6nISV44H+04JlXVP1PJrtVqobepmaOvq4ODQnfDicVSOXoLJdfPLta+0PI7Ssv7g/9yuFvLbd3YtmfIKeUycOoRar3dI+9mPe6isrWNo2WRw7IJVT+Czix9CAtPv8bi3JybtfT07UStswxf8GIZLxx66iu0FuVgK7+vZiWJhK/5l4DTM60yBDgeMriZTnZIgRKk+qoDYz3Wl3q5wa2yH3isq/kav0x/9DIiOF1EDVT8nrDuejb1S6Or+bP2ix6LULy1D1Rytj5LHqDgyVQK0vKh6a/9blU/PtwqWptImWbL1tHPAjoOtj1WfVKWKGhNLBm35nQivXmvrbYkBiZaSOT2P9dGMfRo/pf0Qpeh2cm9T4mNdCFVNisViM1w9ST5JIEmkbH/oPCQxisfjSKfTwRinUqkQidLvlI13s98/vYf9PtvvlnPzc3BYIIh58OeZEOB7CJGOZiYGb80KJKbKaO3YBb9Rn7O6zYb44AC83pkKT7sni1YKh6efPSARb2Fproil2Ukcn92GBOKR7ntxL4Y0Ejgmsx27hgo4MrXrMFQwGnEvhqQfh+e5d8tCRdeTKY0zIdTo5Sq2KlDWqGc8led5weq7xthovIeudqtCpav6/Hw2A1vjSNrtdpBCm3W2rnB6LYP69Rw1EHlcjXnWwaoICrvKz9gaVT9oxLJ/6/V6cA4NaqoHbAfTlGvyASUpVmlg+dw/SpUzJQHsf6oqSq5ZH/5vU9zrHLKxTHq91tW6P2q/aeY+3QSXZeuY8ceqNVZBYdt0A2a69lFdobKj9dT+posc+4jbAzA5i6pWSjB0/DjvNY24KrTqCmj7VzdItm6eUbFTdlypWrFsqnuWDOq8sYqunqvtpPqmSq8qiQ4ODg6HGvUeDyNnDyFVbKP3zjpau+bO6O8Iz0PzmNUYPyYX8RnQTs48fChx3tCTeH/fk0gijriX6nhe3Ivhtwvb8FuFrch4CQCHuaIOrxh0NZmKUpQ6wa5UqyFPqBqjboBRCoSuxqsqYGHVlU4/9tyoMixxsvXS31ZFma1vOtWZ5Wi/8X/db0qJXFTslqJTPEonJUbro4axJbVqtGv9VPmz80T7W/vLEm1bd+1rW1dtjxrt6gpqx7eTkqVlknxodkBbjlUrVR1Sd8kogm9JhMahWbU2apyilFf2kZJ5/dzOWauOKpliPeweUDpvrEKnfcA5ofdTWFXLwcGh+5EoNZGeSKCZ8dCap1mv/die/aiaGQ8Y6EO83UJ7sjhvFKp4by+QTqOaT8ybPszFauiL7Zu7XC7WmWwdTsQ9D4P9RYytyiFW9xCvAbEGkKj4wZ5a+wM/Bvh9PYg3GvNqvrxS0dVkisYlgBmGFzBzvyAaaroPET/nD1ffqYroeZoFEJjpTqcGrhIuVZC0bmpUqgKmdeK56k6nxmqUEmDva12qOhnh+nmr1UIikUA2mw3KBRCoHVzV1zGgimEJRtReQxrzxXNIjqik8J42vTlTg/N3NpsNEYVKpYJisRhk09P7UAGyBEJTy9uYKe0Xtk/jn6LcGhuNBur1eih1uSVjHDuNr9L9qHR8pqamUC6XUa1WQ0kjqMYwxkjnH9uay+WQzWZnkA2dJ4xPUgXPJnuwMWqa4VKTW1B1s/MskUiEyK3GSmlfsnyN4yLhpcpsvwuW0Oo1OufpakgVmP/zvGQyGbk9goODQ3fBbzYRf3gT+jdmUD95LcaOnidMoAOaGQ+jZw4hUR1E3wPb0Hx+61xXCV4yhcar1qO0PL2H7DkcMNJeEv/j+H/E80cvwv/ZfSb+4/kjUd+ZQ9/jccSa+8+m5uN8eSWjq8mUVWeiPt/bKnOU+qBKhapfndSHKFij1ZIVS7q0HlaZmC0+yt5T62nr3UnxskqB/k0Dmi5odPmaDaoWav01xkeTSUSpbDYex7r6sU40vkluaNxHuZYBYcVlNti+ilIfoxRRdRlU975OZfK3jhnP1XnJchqNRkD0ta62f9lW3/eDTX5V+bLxhjrfbEKSKEVK2xCl6tgx1X4icdb5p4qUxkxxHDt9B2eD/V5ZF0D9bdvj4ODQ/WiXSkCphMTUciSqabTjh98lbV/hx4BG3kMr5cEv5BDL5eDX63OTmMLz4KVSiOVyqPQk9ySXmEcot9MYa5WR9hLzRnnaFxyfyuH4VA07mxvx3OAANjfjqPflEK95SJR9ePuRSDY0X1LzdFK/gtDVZEqNWVVy9HNVhXgeV/RpmEaRDxp4Sq5Yvgb4AzPTM/O4Gqw07Li/jxq2ljRZpYhlqCtblDuXJSQ2U511c7LxMZYYUIlRFykqU9ZQVmOcK/s8x27cqoYyz9P4IbrGqdGvqqMle9ZQV2WHqeNtjJlm47PER2N5qIipmsX6a2ycxlh5nodarYZ6vR46R8tg3T3PC1Ke1+v1oA2qmNmxtgopz6capIpXPB5HT08PFi9eHJxTrVYxOTkZUnSUACsJtuRV541V0XiOkmD2ie4FpoROx5P3ouKoyhsJMvvQum8qQbfkUlUp7VPWWWPHHBwcFh5iT2/F0Egf6qsGMHZsFv7h2wppv+HHgYkTFyFxZB/yT46gtenZw16HWC6HxlnHot6fQK13fnWW73u4deR4PFJchdf3bcT7enZ23f5Rv5F7DuuP2Ilda3qx6ZSluHPkGDz/f49AatwlqOhWdD2Zmi1eglDSA0y7V6nRr+TJqjlWtWGZNuheiZ0atawrDXEby2QNPpsMgHWyKdlZH6uCaRu1nzrd07pBRilkSkytAa1lRBmzSgjoPqhjpkRMjXAgvM+RjoeObZTiZttmXfhsHJPWm/PCJj+IUjXsvCA5Z2IOuitqGdp3Gvdl93Oy6hTbY+e7KqjW5Y1kMJvNBi6KVnGNIuPsKzuPtM/VddXOLxtLaN1j7bzV/lQCaq+xKp/9Ttvxj7pGP+f865RAw8HBofvRGp8AxieQzKYRa2XR9g5T9rkDgB8Dqv0evL44sttzQEzIjN8G9uKh8rIRi8PLZlBZnER10fzspOFiD7ZP9WJpehLtnh2YX3Rv71ieKGB5AgCKQL6IFclx/HnuCCSKHmKtA4ih8rw98+RwzA+HSHQ1mcpkMjPiOggab1FxL2qwKQFiOVRKbJwRDVc1vpVMqMGs5/J6VUisisVzotpilTPdx4pQtUDL17qqEW//VxVO72fVMj3GdlP9sWTR9oXGyURlI6SiozFs9p6MY4vH44HyUy6XgxibRCIRGjtVWyzpUGUqqq2sk7o4Rs0f/VtVRKpIqkxp/7P9uq+S3lOVFvafzhuqnOw/u0ea9rOOFbP9qVLJH8ZiWXKihMMSfx0/nbMkcvr90MQgmklP78V22aQi+h3RBYxOboe6AGDH1RIqR6IcHF4BGB7B4ANxNBdlMbE+g9Y89xArrs0j3XsavJeeVcmxKvzHnj5kyQbiixahecJaVAoJNHLz85nY9j3s2t6H5K4kfpY+Eu3FDwFdR6fCeE3mebz5rQ/h8bFl2PkfK5Ddse+EyI8DpWMHkVzZh/Szu1zs1Byhq8lUKpUK9q9R5YBQw9MqGmpwRbkIquHM//mbn9G4p8GoxAGYuR+UEhslC9a4i4KWrcTFEirr6tWpbDWEreFs45VYbhTUSLb9q3+zrdYdj65t7ENLNnk926BKBGNveF4mk0E6nQ6RQuuCaNU1O0ZRZErTh6tKxnT2JA1KfkgCNK25JlPQ+6pKxj7UzaitYsh+Yvs5flHJRyxJ1rT3JKU6jp1UIa2LVah4T11Y0I2WdS5o3/NvG8fG7xfvrfPGzjH9Hum8UwKt39coRS1K7XVwcFh4aI2NAWNjSK1eBW/d6rmuzqzwPaAyGENlcJrxFbYlkHsyBv8Q7SvuFfKYODKL5jzfVzYxmkTPZmDbkX1zXZWDgvXJAr6+8j48PFTDbz/yCWR37EdscAwoLo/D8+MYmuwFnj+EFXXoiK4mU2qoWlceNY5obNl9ftQ4t2qQXmt/eFxX1IGZmwirwci6KYHj32pk67VaLtuohqA9x5JGJSNR7oPaH6o40OjW69W4t/ez/WxJoipwSowsKbSGrRrUBJUkkhxrhGtyhqg4NxIXxn4xbo7QMVLSoISHZauhbueDtoGuf1Gp7aOu1X6z8zIqW6JVVzUWjf00OTm9a3y5XA6pX1pXtkndDu3+V5aoKyllfazbnMaWaf/qfmxWAdSx1zbrZzbWyS526Dk29ksVPy7KkKA6ODg4zDfUe2JIv+pYxBpmLzzfR2zLzgPeoyo+NIj22mWo9abRnqdWodf2kdqexEhxENkxDweUT9zB4RBhnn5t9g1UA4DplXpr8AHTWc2sSxew95VpddOyahYzyBFKbpTA6P1ISqxBrCRIY1WU6NCgj7ouitywHvpbXQ1VcVF1SV2zotLBkyDYrHLWCFVSoC5lmlrdjkOUWqdGvud5obgkVdAajQZqtRqSyWSgGEXVSfds0kQRSsK0/zR5hs4tkjGqK3ZcOe9I1iwhZfk05kl8rBsl662EWBNgqPKlypKO1fj4eHBP1kdJHO9nCaoleVp//d+6beq8U5c9XZTQtPL6vxJ+9rf9Pqj6psqVTdmvc1A3LNZ5pmqpfXY4ODg4zBfUezyMnhSxcW4bWFxrAQe64e+SQew+tRd+fI8iNh/htYDCFuyJD3LeAw7zDF1NpoCZrj5qhHGV2br1WCOdUPc2qxRZxSeKhOk91GVKj/M+UWVrXdRdy7o26Qq9XquGoYKfR6lXQJi8KTmxddK6ch8lSwqjztWy7F5aavgDM7PX2frxHEsSeZ4du72NIV3jFFbVjOpbJbBab3V7tPFg1qWQdWi1WqEU4Lb+nRQ7Ow9U6dPzVL3h+STMnJ/aFkuueZ3OEauMsZ+i5jbLpNujtot/R7lbWrJjx5ELAtpH1sVTxzFqTzC9lvtPucx+Dg4O8xVRZMfzgPqSPNLHrD+gMutLCns2gZ2nRCoEeV03dmbxqW2vxyn5rXh/7+auSpPusLDQ9WRK40VU9VAyZTfzVUNYM/qp25Eas/bHuvxZ41Y/p5Gs6bZtuUA4a51VtaLuEY/HQ8H8mipa76MqFFfqLUECZroMKgni59ontVotZHTazXAtWAcqH5ZsqvpklRv2h62r9rG63ZEkab8pUeAYMuU5XbwYv6VKpF6rRjgJB9U5HXc16i0RZj+w/jrnopRM9o91KbX103bZWLJ6vR4k9mB7c7lcqD21Wi0Uy8TvCL9TWk+WrZvsqtJjy2CbtU9YF6tm6XdPx9mCdWUyjaisfpwDusGyJXCsD9tG9crBwcGhW+DHgIkjUvDWLjng6+drdsPZ0PdEHD97+nT8+0kn44K3fAVHODLlMEdYMGRKDX0N1rer2+pSpJu7qgFHg44GbpRx1UmJ0XtGKVydEHUe66rB/dpmew7LUfJhSRGvtyqYvbdVCixBsmVqprfZCFun/oyqx2yKlx6PUmhIqqJc0joRndmUF6uG6Vjr/VTd1LK17vvSbiC8wbDWxx6LcjFkW/WeWle6Slo3QoLjZMlN1DxU4mbrGtW3tt26oKDjqHVRdbOTkqX9on1iE1xYpdS2MWruOjg4OMxn+HHM6/2zDgViTR+xJpAYS+CfJ87AqtRuAEAm1sCbstuwJJ6f4xoeXrQKKaSWLwv+96s1tMbHnVvkYUBXkykaTfxRYy/KbYnXpFKp4DiVLK7MazyJdQOz6lWUCxx/W6PPGttRRqUG7XueF5kl0BrjatDqKr9Vtay6om5cqnCpOqH3tmTUklVtv4WNp4kyVi1BjOqrTkqdqhx2411CyYHtE5ajCuZs5FXLYNuq1WpA5K16Y5VRQo18zlGbsCGqPG2HVYx4TBUwVe6Yfn1qagqx2PQGubavdZ5YcqZzw8aZ2Wt0ry5eY1UhlhflXkciy3rzXJu0RdvPzzVuzvf9IOmIVXQteSuXy5Hj5eDg4OAw/1DY4uGG75wXqGuNgo8/uvhf8Qf9L85txQ4jfA+YOCKD+Mp1wbHcjgaSd/8afq02dxV7haCryZRibwpQ1Mq/NditCtJJCdBjUfewrnFaN6vMWOJlY6xI2qJW7DvVSYlCp3ZbFS0qrkmv5d+q5FlVQA1b2x+23lZdmk0NUKXQqkQW2lf2flF936mcTuqX/q3GPcvXts82ZkSUqmN/s91UVDUDpf6oq532R9R8I5Gw2SyjiGwnZU3nTpSLqlVD+dls80IRlTyk0wIG667zUcfJLmxw7NSVkf2lMZYODg4ODvMbsbqPzMj0//Gqh821xdjZegp9sRTSXnLuKrcPKLfreL7ZxCO1NfCaBx641koBrdT09anJOFKe5/IeHgZ0NZlijAsVJTWY+GMNTlUT9DyNveLvWCwWbAysLk96nSUVavBZN7koMqAkBpipBHUy+KlIWKPdGqu8h93jSO8dBdtnRJQSpcY3EE7aYNunZajRa6FKHe+h+ygRrVYrOK7EJsog1nqy7qockYzYmCaruqgSxt+qYNr2qKHOdnD8rCufVZlIdtLpdBDrlEqlArWJGQVVYWSGQZtKX+MJ2T5C56fGoTHGyhIqS8oqlUqI0FnSk06nA0XYxrep6mbng/aNjadjvaMIJMtWtVL7Rb/DiUQCyWQyUMAqlcqMuePg4ODg0B2INYAfbT4RO2s9+E+L78F52fm9QHZ3NY+P3fkBJHYlkdvu0r53Iw55yOEXv/hFeJ6HT37yk8GxarWKyy67DIODgygUCrj00kuxY8eO/S5bU1zTWNL0551Wvu1KNdA5eQSVABpcNlW4RSeipfeN+ulUxyhEGZtR7oP6mbbR3seeM5vLV1RdopSWgxHEr/2mdbDJGCzZ09TrNlU2PyMJ0Ix1vF4Jd1Q7LemwP1GqjiW6UW5xUUoTgFC5nIf62+4Xpe2wpNaOkz0/qk62PRqHxL5iqnx+B+2PLV/vqePKH5Jc2697mydsJ8e22WyiXq+H6lev14N62bnC+js4OOwfDuW73sFhf+C1gdJ4Fo/tXoZNtWUYa5VRO1Q7HR8E7Gr1IrMlhZ7ngUT1IBIpD/BSKXgdQg0cDh4OaQ/ff//9+O///b/jlFNOCR3/1Kc+hR/96Ee46aab0NfXh8svvxyXXHIJfvazn+1X+dxclCv0hDW+Ox2zMTSE53lBBjBNHc2VcZupTO8b9XcUCbExSUA4Y5u6G0Vdz+O+P71JbpS7Fw3GTuVEEQQ1vO01qmqpohdF5JglTxGloGl/dRoP1o/l6d+dCAvPs+21bdb2aKydKopad6t82bmgfan1YwyczSpn+1jraFPJNxqNGVkTWYa2RUmbznclZHY8tO5WwVUCZUm2XZgAZm4czKx7qVQKqVQqmLe2/iRS/E7bRREbF6bxUKo88bvbarWCeEi7qEGSZ9O1d/q+OTg4RONQv+sdHPYHsaaP7DMpTGwbwpeeexu+VGjgN094DH+74l7EvfmXtnB1cjdaxxcxvjiLwuY4klMHh1BV++PwzjkWqckG4g9vQrtUOijlOszEISNTxWIR73vf+/D3f//3+PM///Pg+MTEBL71rW/hxhtvxLnnngsAuP7663H88cfjvvvuw2te85p9vodNqWwx23Fg5n5P1uVIDXbrPmiNx05QlzKikyJljXYaezYJQadyLeGgkasbsUYZ+QSNdIUa6NpnarxH9THrbQmruu9F9YPG1ti2ads7YV9cCLVdWje7oTNjiniOGvY2DknJlVX57L2iFCH7mZIUbT8TMZAk61xlwoWodgIIqTzZbBa+vyclutbDurNqf9p2KdFUBcwSO15Dlzr+bzcMZhupSHleeMNkhVXPdKsDTeTSbDaD5CC2b6ioAdPuvXZ8HBwcZsfheNc7OOwPvBaQ3fWSXbElBiCNu/qORHvFPZiPCQ/7Y1UctXQEzycXofViHw5WhFczC0ytTiA9EUf/xgzgyNQhwyEjU5dddhne+ta34vzzzw89YB988EE0Gg2cf/75wbHjjjsOa9aswb333rvfZEoNJ11t19/WENMVdhqFqhTxGiU0US5hurIe5SoW9b+NjaKxbesMzCRXUfVXw3c28kgjmrCuiLyPuknxN/uWhq4ar1FQ0qQGOuvN+CLrhqbqh5aj91FyYhMx2DYqWIa6p9k+16x3vBd/8x46fnqO1k9VFv62/aN1sW3WMbSxX1FZFpWA6hipS6qOATCdOU9jBS1xTyaTobgou9+UziGN3+OYaD1JWqwbpI6fzhebWdJm5+T3UtVijZPkeUrs2HabZILqFeHc/Bwc9h2H411/sBBfugSttUtR6U2h7TyfXlEob+rHaxPvwfKeSbx+cBNWJ0fxtvx2FGKZw3L/ht/C1btOw/978bgZn5WqKVS2FxAvxZA7BCG7zYyH+slrkZhajtjTW9Eanzj4N3mF45A8Tr7zne/goYcewv333z/js+HhYaRSKfT394eOL126FMPDw5Hl1Wq1kLEzOTkJAEEMDF2XrNGrsRrWBUtjMhh4rsYhf9s4GyVhdmXdJllQRBEDLScqGYRVCICwmqZkSo1ZqwrwumQyOUNxUAOV0BX+RqMR9JWqIGq8q0qmhip/qyGvxILGsIKkjQaxxsBpeQBC7ln8X8mDKkaWIKk7mPabEgLtZ7osKomIIq9RRIokn2WSWCgxsam+SepsAg/NwmeVL7aHsX1MWqHfCxIVfp9swgtVZrkXGwmLEjAlkWynVQNZb1X7dKNfJqQgrIJKKJlif9hU6fyuW3WWbdT5ogsk/L/ZbAbt4Pg4ODjMjsP1rj9oGOzH6EkFR6RegejbBLQ3DeKpVYN44bQ+HDe4E6/LbkXhMHn9NfwW/vcTp6Pn9tyMzzIv/ezBwU8+0UoDY0enkaimMTTSBzgyddBx0B8pW7duxSc+8QnceuutyGQODuO/9tprcfXVV884TsPWrohbpSeK2NAwVINPoQY8y1clxLp5dSJChFUAiKjU1CyTZaihqGWrOgUgZODTYLUkTI1bvZak0hr0NKr5t4XtZyUgNLCtq5+Okc2CF0VQtGwlRfZcrWMn5YjkgYa7Gu+sE/s7yrXM3pvxP5yLVgniuNhx43FLhO0Gs1HKl5Izjg3Ls+drX2g72E92zzRbd51POodtHdl3NqYJCKtrugChc1Dnh42VsmSM392oBRPdrJfnsGwl+ari8b4kU53UTQcHh2kcznf9y0V86RJgsB+1ZT3BXkQOr1D4HnzfQxuH1p272K7i57U8ptpZAECpnUajmNrLVYcW7ThQXzWAZDYNDI+gNTY2p/VZSDjoZOrBBx/Ezp07cfrppwfHWq0W7rrrLnzta1/DLbfcgnq9jvHx8dCK1Y4dO7Bs2bKIEoGrrroKV1xxRfD/5OQkVq9eHXINAsKbiEbtlURY1zIFDUub1UtX35XIqMFHIzSqTup6pXXgPXkvddviCjsD6C2sokCVTt0HlXCyPlQaLCm0q/+e5yGdTgcqR5Sx34nosD90TNhu62YZhSiSZomUGv2qTmqcmFVc1NhmXVQd0v7WmDX2txLKeDyOTCYDz/MCZTNqDlhFTeeoKiTpdDrUJmA6aQWPUU0l0VeFVPtUy1AXR45rVIIVnTf80XHU1OZaftQ48nOqoUqW+EOFzS5MKJnVuasb8VarVbTb7SAzn93k2C48qNrKMi355vi6faYcHPaOw/muf7lorV2K0ZMK8GNwZMrhsGBHq4n/vv1N2FbsAwC0fA/J3XMribaTwNixWcRaWQw+EAccmTpoOOgje9555+HRRx8NHfvQhz6E4447DldeeSVWr16NZDKJn/70p7j00ksBABs3bsSWLVuwYcOGyDLT6TTS6XTkZ6qyaIA7YVUOe1wNPS0vajVfyRPPta5yncgBoddGna/lWZe8qLKijgPhbHesv5I33reTqmbd1aJUBkUU2bPlKhlSsqT102ui1BFeb1UlO85KuGxdtM+tMqLXkLRYtzZ7rZapdbWuZjatvu6bRGKjZFv70SpEUYoRr+2Uvl9Vmqh+2Vt/aF1tv1ty1el7Z5Vkq2ba8pQM8Xq7UBKlEuv3LEpxpYplF0g6jbWDg0MYh/td/3Lgx2POtW8ewvOB1JSPeO3Q76vUzHqoFzzEa8D4aB6P+0vx94WzcUR6Fy7IPYvliQI2N4rY1U5jdbyG5YnCPpX7QrOIba00drV68Fx9MdovsfUX6ovw2I5lqJb3LEL6bQ/pytzvIeXHgbYHNBdlkVq9qvOJzSZao2Pwxe3WoTMO+uOlp6cHJ510UuhYPp/H4OBgcPzDH/4wrrjiCgwMDKC3txcf//jHsWHDhgMKSNU9poDph7EaRWosAdNGoLoa0filwkDSYFe8gXAWQWuM2hTZhCVISqaiyBzRyQ3RtktdtaIM2yhXOhqVts6qgqTT6UAtsGVbKKm1pFDJkpKIKPdKuv8pObAkB+ickY8Kg7o62vTwqkjZNtn72b+jjHirsGg/kyjlcrkgk57nTacLZ0pvulRqGn5LiHUTXUt47VirSqbxQVpGVFybVWvoymiVOlXJLGxyDVWjlJBbMqm/+aMJMKrVaqh/otQoddHV77KOPeeGJa+WBDs4OETjcL/rHRYg2kDfk5PAM1sP+a1aJx+J0RNzSI/5SP4yDT+exv+5942oL/IRe8dNeHfPdtwwcSbuHlmP311xPz7Yu3PvZfpt/LB4PP51+6nY9OISZJ/IwHvJJPTaQKIOFMREjNfnx2a8fgyYWJ+Bt66z6pustNH3M6C5PTq+0SGMOVmr+cpXvoJYLIZLL70UtVoNF1xwAb7+9a/vdzm60k5YxUNXpJV4WKgrmVVHolbR9R6d7t2pzns7x95jNuMuKglEVHlRbQPCCoDGimgyB2vUq/E6G7lSI1r/VtWgUx0tUdT2dSJB2h6SBatadGq7VR6VaHS6n/YZSZzeR6+zG9Da/laXN9vuqLqqK54tTwnKbGM0m+JpSZyWp8f2lXhE3YvtVFfCTmNqv4O6MNLpOruowXmrY6Exe7ZsBweHl4eD9a53WLjw4x5iiQTa5fIhVUHikzUki1m0kx5aaR++ByRKgOd7uG/qKCxLTOCBsbXYMroIj/WvxJbcs+jxYlgUn04Y0fLb2N2uoEpPHR94sbYIE7UM/GIC6TEfXpesxbX2Fr7lxeD3FhCv9KFdqsBv1A9LvboVnj+bVT9PMTk5ib6+PnzmM58JAl9pIGkMiTXa1ehkwgU13lutFiqVSuhcGmCMt7BGvhrr9Xo9lPFPP4/qZjWKbcC9XhOVhpz1JywB0wxsmr1MVRolAvyfdbGpo8vlcqAYUa2gMRqPx0NZ32zslQUz3KnqxrpRAUulUkGyBeu+pv9r21WNVGKkc0Dro26W6mLXaDRC49BJ+YkiMJqwRH80e6TWO4roM2bP9/0ZmeUymUzQ9zb2jHFQts/t3I36jnSKhbP3UaKrLrbsD+uKqS6CSpajromKe9I6NZvN4DvG/tGNhbU9UWScc5dxgEpeWVa9XkelUsFnP/tZTExMoLe3Fw4ODnMDvuvfhN9Cwjvw3Xf8Dadi98kzs6g5zC08H0iUfSRqPnqenED7108esnvFcjnEenvQWrUYu0/pCdw+2wkP1cVAK+MjUfIQawKlY2s46+jn8PpFT+MP+p9F0tvzvtzZKuEvd70Oz5cHgnLHajlM1dLY8dwAFj0S7xoytTd4bSBZ2jM2vb8cRnPz83NdpYOOpt/AHfjhQXnXd7UXsbooRRm7UQZWlJKlhmGUKtVJmYoy3O319v72M/u3ltdJbVJD1e59tTflbTZlze5LpOQmyuhXZUQ/U6VB7x3VZmvAR8EqMFZ1UjJls8VZdUL7QPtKXQItSVAXPhtPo8kfohS1KAUpah5Y1Yf9p3ty6f0tkbdldDpu54HOM46dna82Hky/X3bxQPuC5/C3fte0jXZu2Dlqy9Zr9XxL7HSMOG/Uvc+Sqdn60MHBofvgJRJAPA4/5r7T8xG+BzTyHpo5D/nCoc101y6X0S6XES/kEK8XAHjw40C84aOwFYAP+HEffgyo7k5i08BiLEqV8ULhMWRemj5bW2k8NrEc2ydnGt5ea2HNMT8G1Hs8NLMeCj1ZeOk0/EYTaLsETVHoajIFzNxcV2OQaIjZ+CmSsEaj0XFPGTX6rLGoq+w00oCZhnYUNH7DGoW23nrMuppRLbOxR1EEgHvnWIJh1a4oQ5tlMpMbDVIqRslkMjBOPc8L4llsm2arWyKRQK1WCx2PIpiaEVF/2x+NobP91in+TBUjHROFEnbromeTIvAcS/R5flTf87eSQmbz4/lRcT7WTU7JmyqNVLxYP52DSiRVqbFp21UN4vzTNtr2sv8ZF2ZjBbUv7ffMxvKxb7TPVOlS4sW56Xle0If8DrBs7UP+bRccHBwcuhSxOHDysaguz6He67Y7cNgDf8cIBu/x0RooYOyEHvgesOiJIuITFUydMIjy4hiywzFUiwO47clF+H99JyMIhGp7SI7FEa/PJE75MuC1F967w48DU8f0IbHyFOSeGUXrqWfmukrzEl1PptTw0xVvfkZjqt1uB8HyvKbRaKBer0ca5OoCpiv01vjiucBMBUB/a31n+5/l2Pvyt34WRd7UoKayQQNWU71H3Xc2I5KEUYkj76OGv1VdOpEqNaaZfKGTWyCv0euiDHgiKr4rStFR0qp9q0SJ52l/K6FhGdxENqrOWm9LaFlfJbK6SbIlTuqOF5XUhESX813Jt1VfbfnqbsrfqjwR6i4apf4pLJFk2VbtskSK59q08lbR0vHhGJEcZjKZoD9UFdO27U1ddnBw6E54MQ/1xVlMrZpfZo63j48Xf2EJHfMG7akptKemkCgvRezoPenyE9tG0RregeTqPnhDKaQnfKQneMVL7/rQeLxy3hG+B1QGYvAWxZAeywP6nnfvygDz6ymzn1ADWI1Wa4Ba9zNdve7kRsXruV+RGlhRxpwa9VFGmf6v6giNXj3PuitZ41kNU81Wp78VLEMzuFmjVcvnsagNi9l+KhzNZjPUZ1Fuh6o88LeOjbp66V5GWncqIRxfzb5mz6UBrdC+s+oGf+veVFpvSwB4TMc8m80G52m7rFKjCg/rxTmjxMSSwChoPJJC1RdVpYAwUdT+0uv086i+si6Itn1WBVbCGjUmnY7r/FOSpHNT1Sarkun1Cl144f15TS6XiySFDg4ODgcD+eEWsttKHT/34zFMHZFHrc89hw4l2lNF9D86DsSA9sQk/FYL2WdGkBqbmRK92ZvGxLo02gcettf18D2gvCKLzDmnBscSUzX4jz/jklOgy8kUyYGujFuXPmsYqXGmbkt2/yQ1SK0xqKvYwLQRp25V+hMFukhZg5Pl6f1sogg1/nSfHHtPJUJKQniNrRsNTEsCrbrCumifWSLJY1HKgxIHmzVOY1lIuqz6yDZYEsA66ua9rKOtE//W8knWrAqihrcFz81ms0Ef0sjXdPJKinXMYrHYDMIetTCgY9GpDhZsu5KpKJVT69aJ+Ci0fvp90s+jCHqUix7PUeVOXfB83w/NX6ZpV1VM1SYdP5vsQuuk84KLKolEIkhb7+Dg4HCw4flAZlcV/oOPdT4nnUZq6BTU+rraPJv3aJdKgEl40dz8PLB55rnp1asQW7X6FU2mAKA8FEN5KBv8n9uVRuHZtCNT6HIypcY5DTslSVYhsuoCy1B3KN1jh7CEQo9pOfY667akhqp1HdN7KayyocRJy1H1isaiJSt6riU2Wq9OMUVR/RHVZqsKKYmw7mZRLmJRBrX2nbocdiKrUYkU7HioWmXjr3hdpziaTgTLjkdUNjzeU4mNKj02419Ue6zCaOcJgBD5jCI9do5rmwnWLaofbfydJdwklVHXaN/qPGQ77HeVxFRJEK/Rfaj4WafYKqsA2nNmWwBxcHCY//CSKcTWrkS759DGSnk+kB1pIz02HXfd6EmgtDSOeB3IDzcQq898liR2TKA546ig1UJ2yxQS5ensg7VFSVSGYgvW9c/3gOpQBrlTj0dsvIjmlhfmlQuZXyyh75ky2qmX3stxD6VlSTRzHrK720iPNhb8GDnMjq4mU1FxMJqhiwaeNcBUpeBxGnE2XsbGhOiKPI1fTQShZUcZ3J2SHEQZgzZ7nQb+241ICRIRppBWlyb+2BTa2g9WUbAk0ipJamiTCLB8rRPbzpTu/IkiU9bwtcY9Y6w0bbrtAzvWlkjxPFUzLZlkX0a1vVN/6N82FbqdD6qu8RpLAmxa+yhjn/fgZtW8R7PZRKPRCC0UsI1Wie0Eq2TaftQ+tmoh3WT1+6Xzy8ZO8T5UighNu6/tUyKl59gU+nQFjCKmukDRqX8dHBy6B14mjeJJi1EeigOH0rD1gcKzk2g/Mq1upI5Zj/LQYiTLbWQefBat0bEZlzX3QhL8ZhP+Yxuhb7bCqcejMth3aNszxygtj6O0rB99m3NIvrgdfnNWynlY0Robg3fveDAmsWwWrTediGIugfyWEvyHnkDhlGMX/Bg5dEZXkyl1w7KwRr5VT/hbFQP9reda1zc19u3xKEQpG0CYUGk7rAuWrTfL7KS02RgmTTxhs7PxmK2/Gs02JkmPRbXLEqROLmPqkmXLiiI/eo0lcjT0aWCrIqK/Z1MklDTsy7gCYaWE9Ysau04qlxIU2xf7M8ei4vssIWa5StyjXP2IqFgmq85apVPv00mV1ftF9ZGN69LvhvY326yLBqynVVL1R++rxAwAarUa6nXnsuDg0M3wYx78vSfXPSB4bSAz3kai0kZssoy2vrdKFeSHm0gWm/CrtQNXV8x1sckyCtvzaGZjqPbHDlnb5hK+B8ADGoU40utWwytV0Nyxa/6k4lZPjnod6ZEq/FgWsYkyWu1WMEY+3ykpD9X+WLCf1UJCogJkxltIj9aBDhmxX2no6mFWtyLrsqRGMt2hdGXbuljZH1W61DVNy+O9OkFjorhCz+uoImhb9qW9el8qF1qOxrEAezYSrlargcHJ2BDGh1BJ0vghtlfv2ckIZvvtBsi2zhqfpmNHVz1V99jvWictz6oTNPLZx2xzLBYLJbRQQqPuZBxPu1kvr+ukILXb05vrkrCyL5kynvfhHNB4HY17UwVS62CJu5JoS5CoQqkyyLoyXo7zgJtTs36sN+usY8u/2Ubbd7rZrnWxtfWwrog6JkqI9Xuq30dmTWSq9VqtFuovjgfjq3QxhHOFmx6zXpx/wB4yVa1W4eDg4BCFWAPofWQE/pYX0ayHDcnmtmFkR3bved7Uagftns3ntiK7bRje2lWov2YxWumDVvS8Q3kojuo5y5Db1UT2zqk9sU3zDH6zCe+XG5GLx9B+6d3BMSJiQ4Novn416j0LT6rK7Woi87Mn4dfrB3WedzO6mkwRuhJvg8+BmWm1O6k9WpYagkoQ7G+WMZtCFqVM2TKjlLPZ7mmhxqrGnljioSoVjVdbrlUatM+ijGUau1F9pgZx1D1sQgRVeHjMEju9Vse0UxyWEsGoOWH73o5jJ5WMZVt3Nat4Konh33RVZLmapS5qrGdTkDohqr+0L20/RylqRNRiRaf+sQTKzgvtu05zmn3K+kaphrYO9vtEF01LUKOInq2zg4ODAwDEmkCy7IPZsON1H16pglbUoku7hXb1ECgpL5WbKFWQnmyjmfXQyO7ZdHahwY8DrTjQSseAefws9ht1+Mqlzdh7pTLSEy147elBamY9tA7t3sSHBV6zDb9SmVeumHONriZTXC23xITGkzUYGcMS5V5m3bCsu5IqL1ZZYHyHrrDzRxUXVSFs0DyhdYuKT4pSTlgH9oGtTzweR6lUQqVSCY4lk8ngJyqWx6ZCV+UBQBCTZd252GdK3DKZTFCm7ntFRUTrHDXGVD+sqkgFRkkkXbwymUxQbpSyxrZHqZLafywjKn09sEdhZB9pn/N/xq81m01Uq9VQHBHVINbVLgBouXpcSYHG+OiGylEkSceL2Qdtv3OcoghlFMngfTQLI8vRflCCw/HUe2s9qS6x7HQ6jUwmEyiBto/1+6YEjMcs+beqsypynBcODg4OAJAZa6P3F1vhcwW+1UZrampO6tLasQs9d5SBwX6MvXop6oWFp3osFLQmJpG75ynk+J7zYqicsQ6Ta7ra7HbogK4eVSVOnVa4O6ksnRSpqLItouKUOp0bZZSyfDWK+VlUHFRU/exqujW+E4kEms1mKFEDP6Mxb41QrZ9tqx5XtWs2RClvtq5W8bIgOVJlkf9rzJftj6jsfPbe1tCPcjmz1/F8dUXT+5E823G292DbiCjlJkopipprnKtWsbPjFKXMaXlWrZntO2V/R80HW7b2dadyoxYMrPKphCiKbNq+tAQ16r4khJY4Ozg4dBnabcRrbcRre+JVDlS98Vp7VKlEuYX27lG054H7r9+oozVWRzzmIVFdgnbipWedh5fVVodDgHYLrfGJ6f9jccRrq9HNZnesuecn3mjDb8+fbIvzAd07qhGwLkTWoFZC0cldyKpCauxzJZ2kh8qLXXXXGJWoPZ2s21UnohLlihWLxULZ/LQ8/V/jdbLZLFKpFNLpNBqNBiqVSsiFjipTVHY8a/izr3SfLB63sS5c4bfKlrZf99BifaIMY+0L1pOqkxrPanCzXCUJvEaVLh1PVSGj4sa0bE3IoAoH54ItI5VKBe2kYqikLooIRLWf7VK3QUuelFRFkUVVdrRdep7WXb8jtu+0/E5uivaeiUQCqVQqFENm+5rXNZvNIFaqU8wdjzWN2wHP0TJtvTotsDg4OHQf2pUq8g9uQa6QQ/GkxSgtOTCGURhuIv/4TvjlClrzLC6kPVlE78+3AsmXTLhYDMUTlqC4wrEph0MDzwd6Xmgg+9RO+MUSWvMlMcg8QVeTqU4r3IQaufrbkqhOaoA9ruXY1N4sJ+oaGntRik4UeeukINAo3FvshxqtdGWjcVyv12es+qubWCfwPrPFhSlYX223radViqKUmqjfdCmzxM/2lTXUeU4U6dB7qHuYbeP+qIa2PZq2X93Y9Br9fDZYgmRJmJZpCa/9W9U/6/IYBSVqbKfuC2bP7XRt1MJGp3byuxb1/QKm46+0/+z3hvW131teP5sa5+Dg0CVot9Ac3gEvkUBi3QC8l1Kk7+v+P54PwAeSxSaaz2058Ix8hxB+o47mi9umD8TiSK4ZCMXnADgoWf+8g7xTxN7qxP732j4QYW90NfyZ/dlNmRmTk/U93wmHGehqMhWLTWdrA6LdoTpdB0xnoANmpn2OUkhU7bDKVdQ9achRhdDy1Tjk51qeJX+qsCiBImxcFkEVK51OI5lMotFoIJ1Oo9VqoVwuA5jpiqaKidZX+46qk+7vo2oGz6H6ZF3qbH9ZFUX7mtANcHXMbL/5vh+QRjtOul+TGvYsT9U1S6b4uRrjVIWYVdEqO2rM05BXIq7to+pGN0yWH0XwlZhZEqxZA22dbJwS76tEzqpqJLpWldSMi52Iq43jsrFvnD/az0pGVQHupC4pUY1KkGLdQ9lOWybba9UtBweH7oPfaiG7cQfSOwsor+ndJ9XG84HCC01kX5xCbHRqr3tCzRu0W0g/vQOLR3uCQ/WhPCaOSL0s1z+vBfQ/W0Nyd/kgVBJo9WQwflR21myEmdE2ep6ZQmyyPO8UwZeFQzRGDvMDXU2mPG86DbRdhVeoa1CUimMNfKDz/lSqWlhDHpi5ETAQzjJI49MqMbYcratVIJQA8HN+Zt2kWF+6xjUaDSQSCdTrdTQajSCgXw1M1pNklW2xbny2foSti6oee1MutF1qVKvBbO8bRSy5z5T93BIYJZ9KVKPmUpSiwfspwdZ+Zxp6HZdarTYjOYKti8ZuaV+oqsX/baY71sUSD6vk2H7X+a3kybp4alstEY9S40j49McSniiSre3RBQeti9YfwIz+0/KilOEoxTVKfXVwcOgy+D6aW18AtgLp/KkoLc/tkzqV2VlG+5En0W1PgeYLLwIvTP+fPmY9vLVLXpahHmsBqRfG0dr07MuvIIDk8mWIr12HVrrzQKQmmvB//dSCXNQ6FGPkMD/Q1WRKM4hZNyU18KIIku/7oVVxGp80tPWLrMfVuFOo4aiuSbyGsC56vLZT+7QubBOPaxlaltaRypQlSolEAplMBslkMqTW1Gq1UPIKW28lNkqWeG8SCyV9jG+y7mbajk4kS8mNnqt10L6wClmUm1lU37G+UW5wNuFFp/paMmuJnMYLqdqj/UaiRXXKKoKqytm6RMV4KYHQGLUo0mphibslmJqN0o4t71Gv14Pvj60T+8DuwRbVr7bsKFJrr1EyBgCpVCrog07ZGa266uDg0P1Ibh/DQGz6+dIoJFBcmYTXBgov1JGoiAfEjnEsCDN+bBKLniyg2ZPE1Mok2vuRpDRe39MvyWIDGJvY+wX7iHaxhL6niminO5ueiV1TM7wcHOYOXhvID7eQHqsjsX1sYXw3DgG6mkyRFNBYpwGr8UFAOEsYMO2m1Gg0Qi5Es32B1Yi1hjp/a0Y3lmVVEEt+7HEtU8lUJxKn5SnxUDeyRqMxI9U5N+0lgeKmsvwNIHChVGNW76tqAI1UdcvitalUagYxswZrp76PUhOYWtv2nbrQqfFux4nGvSqESg51DrF9WpYl5BoTZkk976ttYOIFvYfOSV0IsKqqKkhWPbV9qElILKmy5Cyqj9h2m8CBLpSsc6c4MiVbNi6KWSaj1OJORC9q3tj5YclXq9VCtVoNjQ83VOZ5NqHLbETNwcGh+9B8bgs8ifXIrV6F8pLViLV8ZH/9AprDO6bPnYsKHgK0du2Ct2vX/7+9dw+y9K7LxJ/Tfa7dPT3XZC4kIYmCICK4INmoq1BMLaKFQV0VNltmwSWKsILZ0pgtA+JPjaKFFKwF5daWYonuaq0G1yrZigmXVUIgJIECQpiE3Ejmkrn07dz7nPf3x/i8/bxPf9+emTCZ6TP5PFVdp/u87/u9nz6f5/t8Pp8vWnv3oHPR5RjXTj/BzvRgbVzOJq0ZLy8Dd38ZG7UkaNTmQmUMzH5jAaOvfv2C+Ww8E5hoMqUuYEo03O1MlSpgzU0ppezwNWUgsk43PN3A07iaU7kjaRt1d95jjHzn3w10VUX4NwmPGo5q4Gtb1NXJVR5VNNT9zNvBMjXroZIT7+tGv+vYqiKkfdPxoOGsKkdqrpT4uMugu7fp+Cih8vggbWMKHCd3W0utm7I517HzcXUVSxU7VQZ1vbBNvj7dTU/rd3Lobpd+j8LV4izLMBwO1ylVqXqcUPnnKtUP/3zoOJGwkmx63FbsigYCFzaybhdzTwwwNRoj2wQpz59JZL0+5p4YYDhXRXfn9IbxSsS4WsHokoswPTeD7PDRkyQocNZRWW5jy+MDrM5No7tjGuPzaJHXOhmax0eA7SVOjTJUltrnp1EThIkmU8Ba6mQ1JNXdTuNZNJ10yoinQVqprAXOO1QxohHHnXpNEqGuW4QG4NO49XbyPrbLDXC+74ZzyiBlP6gYuVGsrndKSlPuj0oW3DVL3fmYbMDJE8eV4+IxQfq3jiGJ2UbEgu3t9/vo9/vr0qanDiB2pNpUljxB1SNfG9o2/+GY+qHPPk5alqtOeqAs29LtdvN5q1QqaDQamJo6edjt9PQ06vV6roa56rYR3BUutZad2OqacEIDIFeDqZjqHHv8o67p6enpwgaFzlGKTHEM9TPW7/fz9UdljC6tTL2eZRl6F7hxFQg82zE6dhy1/3eSIIyGg/PcmmcWo4UFVP/5y6jPz2H0yueh2zh1+rhRAzj23VswPZjDzs9kQaaeIaweOoz60eNo7bkYgx+8JD837HygeXyE1v/7GrLe+qQfq6vD89CiycJEk6mUsabuRWp4KSFJKUYppUDr0fsU6q7FenyH3Y1NVXxS7Um1Q+O2WJYbst6+VJKLFDSWR43X1Piqq1tZrI73gyRCje8ytyqWq4Z8mSua16OqncZVefne79QYbeR6xnJ03jxGL6UmegyP90ef1zbqmtK6dKNA54Bk3Ukun9UxKYtB0j6klCkdG3++7DOmY6FzpWeMlSnCZUqvl+194Fj4HPvcqTtiKFOBwAWOLEN2gZOoHP/S16zbQ+PEKrKpKoYzUxsqVFkFyKonf8mmJyh396Qhn5sumidGmB5OYzBXecYUqsoYqLUzTA/Xf4/WF062I7sAE3+cC0w0mdK4C4+XUYKjqaJThnPKwPVYEzcC+ZyWvxF5Iqge0JDUNrgRWubGp+WzPN7L8VBlgG2jmuQuUnoelBK8wWCAXq+Xjx2z+1ENGo/H6PV6ucLgbn5+9pDek1LSnPiqAphSlFKqDjMUUqFsNBrIsrV072WucARVC66vMjc2riVmDXS1ydvKtinxYV2uoOhY6aHG7CPnYzgcFhQeZmxsNpuoVqtoNBq5KsVEI+piWEbElWTo2DA+zGOxvE9cO3xG+6NluUsmx89dJtlPbZu7APr4cY51TbPvOqdU9IbDYT4unNNAIBC4UDDudlG/62totJrovOJKLF8y0ebfBYXR8QW0/t/XMDu/BYtXX4rujmeGwE4Nge1fPA48eXjdtWwwxDiI1NPGRH+aUoH06m6nhIrXnEgA6TTLHqvB+3xH2937ytqpahSf1R1+J2kOEgqNddHdfa2HxqOWlYqF8b81iYcSzCxbi2+hga9Ki6sXZePA6x5rlVI1nEimxjRFWrwc/9E6N2qnl6FzlyLW3jZfZ14WoWPhRCEFd4dUMqIEx5NOaJ9O1Wfvv76fiuvaaBNBCYz2zT+XqqLqGk59NnztOIlKETQf97L2l81pIBAITDSyDON2G5V+H7X2Kmodcd2vVjCuIU8dXxmfzOg33c9QWQ2l/hnHeHTSlXI8RrX9HNSaiVAEm6MzAeez2jsZ/7S6cPYyNAZOYqLJFGNC1LDkTr3uRKvaoYZSKgubxjR5ggJ91TTWrIeplwkauDQmqWIBRZc6JX4KdzFTQsV6+/9yqJ1eU9VHDVRe8+xs7BOfUxWEu/YrKyvIsiwfFyde/GHZVEh0PDTLXspVr8y4VeLj5Jlg3c1mc937HHe6t6UUKW0Dx1T7omqMKnyekEJJkf/NMafyMRgMCter1Wqu/LGNnlyCMUc6n61Wq3BMgMb7cbx1Ph0p4pZSgAiWwX64QjUen8ykqQlBtEwt10k7P4ej0aiwfnTN61j65oGua61HoS6DXPOM8+OcBAKBwIWIbHUVtS89gp0PzeTvDS/bheMvnAH+hV9N94Ed957A1IkljI8dP08tffZh3O1h5u5HMNuor7vmc3QmqHYz7LjnGCoLyxgdP3EWWhpwTDSZchcx3VlW5URf1eVMY3NohCmpcsPYy1eXK6DoFnU6CgDLTyk0KWUm1R7tD41Rvmp/9TlVmFL1uhpHI5MuUJXKyXTnnuEOWG+oeuyVGr2nA70vFU/jipimYPc5ONWc8B7+0K2NBMoPx02tDX3f59dj2Fzl0bWoioxvFuj6Y6pvHWtfg6yHiR+cIKniqWPpc7WRgqrXfCPD7+fa8OQiSuLL1jrLUvKmY+xjnmp/aq5ON74wEAgEJh2jEyeAE2tGdXXLDKZWZ8B/udODDJWnjmP18JHz1MJnKcYjjJ56KnmJczQGgKmNFarK+ORP/mw/A44cw2oQ42cME02mxuMxOp0OBoNBYbfe3e90V15dgDzznypTjD1ivIZnAtQ4KVV/NNkCgKTRSmhbWI6qZe525P2jcZzKrMZ2OHGqVquFOB22Qw1QljUzM4NqtZrv1q+urqLX6xUy/jFboPaJhjlfPQYmpcIRzFanrod63pWTZgDr5ibl4uaKD6EKkB6Yq89wXjTDoaoyfF7VIk2nznr0b1dI9bw0zoHH/6nxrzFhs7OzhSQTOheuDDmR0Wf8M6Cug/wMqYqk61pVVK5JXc+qhiqZU9Ll61HjC9kOZqB0wu9zqnPNNcXPOs9V0zq0r2VJMAKBQOCCxMEj2PXZDODBxsNVZItL57dNgSL+ZY5Wd85i4fkzGK0Xr3LMPDXC3IOLADfb+0OMViK9+TOJiSZTNIy63W5SKUiRHTXM1FCkMcf3aPiqyyDJS2pnm+XTWKVBWabAlKkk4/F4nbriaoga2YSTI1Ws1KhUY5HGpSff4DUaryRUTO6gRBVY73qnrlcpJcrVDTXKnRRqQgJVMFIujFqXG+6nMpKVIFMZ8fa6OqT3KJnTOr09Xif7qARMx4t167pTslCr1da5UyoZ0fndCKqOsS26Dj01P9utbUmtdb5PsujzUDYuqlCxrtTYla0xwokjybJ+jlQ9LetHIBAIXKgYLSwCEUezqcE5ql16CaaubG14CHN9cRWjr34dudQYeMYx0WRqZWWlYOCrkU6o0eXufmqop+7X52jQMiZLjTTulrtbFQ3ulHHmmcVobLoqpWqC1qEKhb46oaR7mqpfNGDdIE0Z3Wxbs9ksZPLrdru5atftdgtnO7FP7oJIxU/7T4NZyZGrearoAMiVKs6LjplmrOMZSxp34+NHUN1k/Wyjkm+dWyV2fEbjlpRo+DpSYqBlj0ajXAXk+Hk7lXjx1ZXF1JzqODlp1vWgChf7T5LJs5j0Ho9pchdY3YxwBbZsTF191DFQVVnHn8/5ZocSWt9k0Oe0zeHuFwgEAoHNiGxxCdu+soysUR48VT28iNUgUucUE02mer1eniAhdQ6NGuQpUjU9PZ2n0U7t7PN3YM1YHA6H6PV6ecppNQoZV+SxHUq83NWI7oTahpQa5WRNCZg+owqJ/vB+Ps+26ZjwdzW6ec/MzAzG4zHq9XohiQFd/0iSlERolsMsW0teQZKnCRFI1AhVxpyg6Biqm5YaxcPhMD+UVcfMVSyP9WLdwFqChVqtto6kOoFRUutuhk4SXCnUtpBMkVz6euZ46A/77YRay3WyxTo8zkqJMOeHnzPOVaPRKKwPJyJKqDStOdcLCT7HSpOG6OeQc6YETFVGveZ91Pv1/RTx1M0P/x8SCAQCgcBmwWhpCbj3KxveEwnOzz0mmkypYQ2sxQnRDYzw+BRCs7Qp2UkpQw4avmrYpdQxdwfzdqh6VNZH30VncglXsbRsjW1RNUp39l1BcaVHDX2tH1iLZVLjmNdVrVESo66Vej6Yz4u6bbE8jQGjEkhCpuqkjoESOp57pHXouCnxc6XE3Sn11eczFYOUmlONS9I6vE4ntR6LxXtT9XscEutVEkIyqs9wPDi33CRQ1UyVJnfV87XIdju5ZMydkyCPA0wpqRq/xveVbOnY8ZmNFC8lf3HOVCAQCAQCgdPFxJMpqkOqSgBFAxgoulOpax1/PEEBja2U4gWsJRxQkpJyXSKo3Gh7aJSOx+OcXDjU4Hb3MN+V55ikjET2heXVajXU6/VCHzTbG1A8d8qNT2bz0zEHiiqXtkeVMXdRo6qi5EsNaKbZpntlo9HI54DX+/1+4TmOlapoOo5KXlx1IpSQKhFMEU0lDYqUIqQqlhJKH0sSR5JPrVvj5lZXV/MDe7Vtqui4mx+vMzujt3k4HObjrpkcOS6sgwcCs34dF50LrheWT0WZ/XZ3PKq23mcl0Dqnmrq+zHVXPweu9LLMXq+HXq+HQCAQCAQCgdPBRJMp3ZFWA9qNQzWidedaz5LSstxNyusjPCYoFeeixrOW4UZtKlZDFQBgzYhVY9L77LEhhBMDGuNK0JRMqnIGFJNVcKxJApvNZkHlUJevSqWSK1cav6U/nvWOxKter+evJEw0xOnmRyNfFRY35l2Vc/LH1zIlSefb63Ck1CwnCl6HusVp2/mskzgdJ33P59PvczLFurkm2H7OpZ8LltpooJusj4evR7aTmwp0c1Wy7qRT269jpeubny0fJ/8M+pjqJgL7TaVaE28EAoFAIBAIbISJthrUdYkGkO7Mu6uUH6qrSO2Ou8LjrmGsX12O1HBjuzRLG+9NpV4ndHdeobFZw+EQ1Wq1EKvEZ9k2JWJUcDg+4/E4mTjCx4d9YtwQjX7tV7PZxGAwQK/Xw3g8zkkP26/JNfg3lcAUmatUKmi1WpiZmcHWrVsxPz+PXq+HlZUVdDodHDlyBP1+H4uLi3lSBCUBOp/NZjM/i0nHSRU/jrcfaMs5UkLgWRpT11PrhIqJupRyLKm4kFT5vTpHWq5nx6PKlGqfkg3OzXg8Rq/Xy9vHzQD9YRv0PZJZqpnaJ12rugZ5Dz+DnU4nPwhajxNQ0uiugRqbxXKVAOn/AHUXdDXR0817fGPZ/4hAIBAIBAIBxzNiNTzxxBP4D//hP2Dnzp1otVp48YtfjLvvvju/nmUZ3vWud2Hv3r1otVrYv38/Dhw4cMb1qAHqO9FqQHrAvhpMqgr5eVIekK475Cn1qqwdahC6EqV1qGGdCuina5SqBl6n1u279ald/pSboPfTy6dhTDLHZBxUkry/jL1J/XjCAh0DdaWs1WqYnZ3F7OwsGo0GarVaPib8UWLqcTSEls/7U2N4KoPayVLZWG30rCtS7jaYWjuaEl/dL31NpeaVcJVK26WEhc/7WtJ7y+rUMdH1m4pxOp1x8s/FRvAx0Tb4Otf/Fa70BQKBjXGuvusDgUBgM+OsK1MnTpzA93//9+NVr3oV/uEf/gEXXXQRDhw4gO3bt+f3vPe978UHPvABfOQjH8EVV1yBm2++Ga95zWvw1a9+tZDZ61RYXFzM42hoZKWUFioMfJ9GnZ6ZxJ1u3lOmCqkS4WqSxqxo/ep6RyWFCRFo9LGdZUQLWJ9+WtUAjYUCiqoFlQVCY0g0AQTjWvS6JvVg+dpH3ks3vMFggOXlZayurmJlZSUvfzQaodPpFJILpEgB3+t0OqjVark72Pbt23HllVei3+9jy5YtWFhYwPHjx/Nsc1RlqEC2Wi00Gg1s2bIlH7vRaITl5eWCmlWv19FoNPI06ilXOCfI6pLJceBc6HymkorovDJteEoZ0Rg6fUYJpis4To40jkvXAttH5YnrUWOHVNVlOY1Go0A+SW5UeWS5WtZoNMrjkPyQZx07J2tcO5oEg/Ps2R9JSJ088fOun2nNEKnjq+sxEAhsjHP5XR8IBAKbGWedTP3e7/0eLr30UvzJn/xJ/t4VV1yR/55lGd7//vfj13/913HNNdcAAP7sz/4Mu3fvxq233oo3vOENp12XpkT33ehU7Ii2QeM+SBrUNUqNSUVZXJYjVZ/vjqeMdFcsgLV4J+8jy+CzNIzVqObvGvzvRmNKfXODks+o+5nHVRE8B0rd2TjWdDX0ODJPDjAen4zHWl5eRqvVwuzsbE42Z2Zm8tTnngRD3fiU0LBcV6T0GSKlTHBcN4KrPymkFB2dq5RbGp9zksDneJ+7TJb1j0RR548kyNeH9lvHNqWoKsH2fqraRYVYNwNSipCqUvqTOtjYPzeqqhFcI6kYL1WNA4HAqXEuv+sDgUBgM+Osk6m/+7u/w2te8xr81E/9FD71qU/hOc95Dn7xF38Rb3nLWwAADz/8MA4dOoT9+/fnz2zduhVXXXUV7rzzzjP6B7t9+3bU6/XCLjl3/NU4499qWKuRliIuQNpNz3fstR53HfI6uLPu5EUNUP9RRYnKkbvq6e9UkLyP2idXBlRp8DaxXK2HRErTdDMeptlsYnZ2FuPxOD+faGFhAYPBAJ1OB91ud934qmKnLnoAcPDgQdTrdezduxdHjhxBs9nEli1bMBqNsGPHjlyJowrGPqvSwvKpljFxBTPGNRoN1Ot1zM/Po1arodVq5eoQiYq6u6UUIBIDKkcKJ8GafIHzqmeW6dhrSnigmBVSyYITaCcHZa6bqc0CulAqidf5Ztv7/X5OaldXV/PMfnTpUwU3FY/GnWn9TKnC6+3kGtM+agZKrnuNm2OfvP+q1mlq/dFohH6/j0AgsDHO5Xd9IBAIbGacdTL1jW98Ax/60Idwww034L/+1/+Kz3/+8/ilX/ol1Ot1XHfddTh06BAAYPfu3YXndu/enV9z9Pv9goGztLQEAGi1WnmWOEKNK+7g0+h0l0A3GPk8ocZtCrqTXhZnQTKjxpqSJBp1bLO79Sn0XCwG/6vSxWeUTOmhqU4G2C8ax6p6eH+YaIAGrLrEaapzktvxeIxWq1VIgd3pdPJ5VAWNblwaO8S+aeY+EqnLL788n/8sy7CwsJAnUlB1hfFlWZah1+thNBqh2+0WiBbjrUajERqNBrIsy1N9qzqkZCrlwueqDeGqqSabSM2rZk10pUTrd6Kt93jdLNPHh3PnroZOYvi7pnRXxcxVJ22ruiCq4sWkIOrOx/p9A4N1KWlVtVPHX9e7E0Z1jWQZdHt0VSsQCGyMc/ldHwgEApsZZ51MjcdjvPzlL8fv/M7vAAC+53u+B1/+8pfx4Q9/GNddd93TKvOWW27Be97znnXvX3nllXmGN8bpOFEB1ieJUMNJXYmANWOO7mNqeGlyAzXyXGXSuBA11IA11zctRw8JVZVL1Q4lGGwjSZpC26uGLt/jTrwSKj6jmdloZBIaE6RtokHMNmvbeY7VcDhEq9XKy9EYGJJXlkE1SBOGsKzl5WWMRiMcPXo0Jx/NZhOtVitXtlZXV9Hv99HpdPI+s3wlVRwXxlxRRavVauh0OnmdShx0bpU0KnwNcI48di/LssIZUr5eUgqpjosTbl3POg+pw5mdLKtamTougGRTU9PrelYCybJrtVph7fkmAkmsQmMdCX52uDb1fSeaOh5lRFHVVCZM4RrjvXHOVCBwapzL7/pAIBDYzDjrZGrv3r34zu/8zsJ7L3zhC/G///f/BgDs2bMHAHD48GHs3bs3v+fw4cN46Utfmizzpptuwg033JD/vbS0hEsvvRSXXHJJvsPV6/XWqR5Odvi7u12lFCo3KDWmR3e+dXdclR/ClQBNca074aoO0dhTRUHrVsLkBqnW6wqJkit3SSQ5YyIKde1SA5flDAaD5JlRmkSBLlmj0QjNZjM/Z6rX6+Wkzse9VquhWq1iZmYmdxtk6vV2u43xeIyFhQXU63XMzMyg0Wig0Wjk9wDI79W5cTc8nXcmUWC6+X6/j6mpk4cDqwFPw5uugUosuE74TLPZzAnFYDDIMx+qqsW/Wa6uS0LjlZwM+DyTUHIeVMV0NdJJt6usOnaqcvpmhbuduqueum36546ugOou626yquZ5v8vGStuQaq9uGHCtc61yXgKBwMY4l9/1gUAgsJlx1snU93//9+OBBx4ovPf1r38dz33ucwGcDFDds2cPbr/99vwf6tLSEu666y689a1vTZZJg9mxdetWtNttDIfDPKMbU3UD6+NB1HCkq5ESGUKNMSCd3cvdk7SO1PvuHkZFhwafZkRTdYPPkgCxblUgUnA3PXXh43MaQ6b9pAGsBrmfI0Qi4QkfnBhqXYyRYVwVSc9wOMznt9Vq5bE3JC6qWNDVj/PMcaBxrq596h6m7dTx8P5kWYZOp4Pp6elCrB1fSbaoXihhZ19pqKu6qUTBs9e5+52uNycYugZ8nTJroypHHs/H310x8syW7vLpmw8A8nlSgkP1SslOqn9UJ0m63T0xNRb6mfONDELPdGObdI7cDVT7zPEKZSoQODXO5Xd9IBAIbGacdTL1y7/8y/i+7/s+/M7v/A5++qd/Gp/73Ofwx3/8x/jjP/5jACcNuXe+8534rd/6LTzvec/L06Xu27cPr3/968+orl27dqHb7eauXW5oqcFFg9ENPP1J7XS7QUdoOU5q3B3PVQVgLVbKd81ZdqWylobcjUu6SJHkaDs0UYGqZDSyfXceALrdLobDYX4YsBrimqRBjWrGOKnS4eSTJIQxSPPz85iZmcnHqdfr5ent2+02AORESV3rdBxbrRa2b9+eqwckPDSO+/1+7pbGdjmZYpyWEgPN9ueZHdUVzDPJ6fxynEkEnTRpP/g+x8tVGx1Td33TNajlkhTws8Dr3kYtz2PrVC3UdapKKtcaU9DrWV+dTqdA3NzdVMvgJoKqQnxGn1VlS1Vgzpe2t9FoFM468/J0vPX/AtXIarWaK5yBQKAc5/K7PhAIBDYzzjqZ+t7v/V787d/+LW666Sb85m/+Jq644gq8//3vx7XXXpvf86u/+qtot9u4/vrrsbCwgB/4gR/Axz/+8TM+d+Lo0aNYWFjA8vIy2u127uanBETJlCoWSqbUgFXDjX/7jjbr4I8/Q+PejWAlH+4C5TvwJDKe0ECf9/eANeOXRqWeqUPjvNlsYjxey3DH5/iqRCalGHhb3ZVR3QZd2QDWSES1Ws3PeOJ7zKhHMqjkZzgc5m5/JCrT09N5Nj53OfN5ZFvclTM1timXTfaR/VGyQwLF862azWaBJHhiBSWh3mYnbNp+vu/ECzipFGmMlruDsr26Jp3IaXv1FViLc6I7YavVQrPZLMSDpcYrlT3TXft8fJTspJJxKHRO1X1Qz5PTezkOLJNri5/bIFOBwKlxLr/rA4FAYDOjkpVZKJsYS0tL2Lp1Kz7ykY9gcXER7XY7zxTnCg+wZlB7zAcNNjdydSfdE064wQgUE0SMRqPcYKUbFOtSw5XlkMzovQDWGaj6o/Wk1Kzp6WnMz8+jXq9jbm4OzWYTc3Nz2LJlCxqNBubn59Hr9fDYY4+h2+3i6NGjhSQMSkJVAdOxpKHqyoW7bVHZSalwNMz1vfn5eTQaDWzfvh0zMzN5OUtLSzhy5Ajq9Tp27NiRG8Orq6s4cOAAjh49iieeeAJHjhxBp9NBp9MpEA0a/SyPCpbD3fGYRCLLsgIRYXtJ4ubm5lCr1bB9+/Y8y6SSGV2PJA+pFOC87i6YbBNJsipovH9mZgZZluWuk3qeFtuvcVq6Ht09T8kYXRsPHz6Mfr+Po0ePYnV1Fd/xHd+B3bt35+RXiRPL4jh3Op086ch4fDLT45YtW/L4OFXySPK1/TqXnCdCN0pckVJXQM4J54rodrtot9v5OK6uruK///f/jsXFRczPz69bI4FA4NyA3/WvxDWoViKWMRAInD2sZkN8Eh87K9/1Z12ZOpcgkep2u3nMlGah01clSCm3IRqvHsOiz9DIpNGnqpQbw7rjrgqYp4HW3ftUnJPeS0NRs6R5DI0mDSAZogsfiUu32y380HjV5BSqvmiMjfZR69YYGx8Pj03SpBOsiySh0WjkhFDd4xhnRZfDSqWyTnFIuXbxWhlSypCrK1qmu+tpxkH+eFp1vupa8xgorU+JTIrEKynT5+iiyc+CK59KaDW7nquvKRWI5JJunzqu6irr5SoRKlPjPL5Ly9DPqyuHPj/6d0p1pvsfP2v+f0HTtQcCgUAgEAicDiaaTD300EO5G4+eFcRMa2rE0dhMqQDAmmGvqcvVFY7laUyGxm3QGNPYGv4+GAzyg2JZN5MT0GXKiZaSQjdy1V3OFSMa6sy2R1JCEkL0+30cP358XZwZgNyVjkqXpmDXGB5V8fiqLo6u8ukPyyL52LZtW8G9r9/vYzAYoNlsotFooNVq4fLLL8+Vr9XVVRw9erRAqFQZoaKmCRI4r6zbXf6c9FBJ8XThJH+qPrZaLdRqNczMzORulATr8SxxrlxxHtSoV3c0XnfywbaeOHECg8EAx44dy10iOe+sW4mZqql6TdcD+16v17Fz506MRqM8Tmpubi5fx51OJ89cqJ8rJf4km/ycqQrKGCySZJ1Ld1PV9vmZXfyspNK3c864nvl/g/c1m03s3Lkz3PwCgUAgEAicNiaaTPGg1pSxrkaxq0NESpFwtUl37VPuRv6sqimp3W9CM46xLoXW5S6CCo8noWHKgHr2n65abAuTPtBY1X5TgdB2pnbrU/Fi6u7n/VNSyjZrjBaA3HVNEyOsrq4WSKzOr8bhaLtU/VE1yedL1Q53HVPXPk8GQaKmWQf5u6YF17pTag/nh2PoLp18zwmXPq8KqBIFPdeKc5pyZ01tMOjY6HpWslqv1/N6tQwdS11Tqlz6WPtnUdvB+1Xt87GkO6aST61Py/M4Lq4XkmLPXBkIBAKBQCBQhom2GnjIqhpYapxrIL+61Gmcibrs8ZVG5tTU1LqdexqSGn8yNzdX6lLIuCCNffHYFVW1dKeerxqn4woVlRknea4Q0NjXZ6lW8J5er5crcb1eD41GI8++l3Kf1HGkkarjTKjLobpwMc6GLohZlmFpaQmrq6tYXl5Gr9fLU6Nv3boVu3fvzuOpAGB5eRmDwQDLy8vodrsAkKsxrVar4HbGMfS0u+qeqESJfdaYL/adsVJUopipsFar5fWrse/KYyqGSuOklPjpOtDyCLpGtlotzM/PY3V1FTMzM+j3+4XMfCTXVOyUzChZo6pKhZd94poF1mIEVRHmZ5EEi3PLbJuMx3MlkJ8Bql8A8nazbFXLmGRGN0qY+EPP1RoMBnmWSsbPdTqdAjGu1WqYnZ3Nf5+fn8dznvOcUKYCgUAgEAicNiaaTKmR6YqS79orMeE1JVMEDWvudPMZJ1Sq+rBM3e1WF0M32PU5j01K7air4ctrqsykoO5vbLf+0IjVNml5rIfufqra+fjytSymh25bSiqV8GqyhGPHjqHX62FhYSE/72lqago7duzAeDzOkyxMTU3lsUFM6Q6suZIxsx3VOF0L7vbnZIrEVlOc69xojBTJEzMMKvHSOStTFnXsXB07HeimgPa51+theXk5H1fNoudz7ySZY0DCyOQdmhAkyzKsrKyg1+vlGw8k33TdZH1UGzXtPqExZpq4hOOQUj9dUSIRYzIQzgPnlWSKmyAkjPV6Pa+/VqvlSVqCTAUCgUAgEDhdTDSZUpcvvipJUfUHKLqsKYFRo1JJEI1KVxCAohLkxIK/04hVhYBtoWGnBroTEm2LG5UkUzRUXWnQg1xdiVA1QZUljTdjXTxY1cfK5wEoHv6rbdDyVEVksgQqDqPRKI+9mZ2dxezsbF5vtVrFwsJCnnBE42c4lswKxzaORqOCKyjrd5WHRr+TKRIOz7qn16mI6FlUesiwqnKuUDkRTbnd6bipcqbEmMY/Y5jm5+cxHo/x1FNPYWVlBU899VQeH0fiQwWJbWD7mNK9Uqmg1WrlJEndHbmmdu7cmcfUUXVycj87O1tQ4lzN0nGgasj4Qiql7kLIcrjOKpW1LJYadzc9PV0gRuzHzp07MTMzg127dmHbtm15n1qtFnbt2pWfeRYIBAKBQCBwKkw0mQLWn2WjsS3uhubJJfSeMjcqJQQkPan4EoUSLg2mp4HPNmhdNHJVSaOx6TE3VLpUSXKVSfus7n00epWscUefr5oSXVOjK6HyvrIvShac/PF+fZ91raysFAxspnQnGOM1NTWFTqeTp9RmzA4JgpLH1dXV3D2xTMHzeSQJV+LifVZSwTFT9zFN/OHkW1VLva7kKKVgqWLDpA+8Zzgc5nU2m808dTzVnoWFhVy94/EBquwoOWH9zWYzJyt65hfHp1qt4uKLL8aWLVvyddjpdLCyslKI92MfPbHIYDDIiS4/i6pQ8fPAvnKOOZ8kWbr+ffw1ZTuAPEnI3r17sXXrVlxyySXYvXt33q96vY7Z2VksLy9vuFYCgUAgEAgEiIkmUzSedLdejSk1rFNxKK44ZVmWuwelgtdT7luKjUgWn1e1g8qFGtL6nMdzEanYGk8EoHE/hKozVMzU3Y67+96HlMHvxC3l+sffNXZNSQAVCk3h7QREySbfo7pBoqfKmLpz6niUuSWm3O7UjVNJlY6/ugCSuGgbSApJFrhWPPGIE2h1ZdPr7qLI9rBsngPFuCO+8jynbdu25aofgMLBviQwxHA4xMrKCrIsy9UuxodVKhW0221k2cnzrBqNRj4XjE9i+1Qd5hj1er087snBsSHhbLfbuRsh1yo3A0jEtP1TU1N59kftCxOtLC8vo1qtYjgcYnZ2NneFbDab+QHE7HsgEAgEAoHA6WCiyZS7YAFrKa81q1rq1bPzuTufHoiq9QFFNcyNdd7n5M2VC/6dyvLnbnIpMsWddKo3bIvGCBFqpNMAZRpr7T+D8VN1usLC+zRxhioe+owTNB03PchV3TS1T2w3n2V8lI6TEjUlICQJSgyVRCjZceJGpURVJqp9NN7pEseySKJYnl5LqZg6n55sQeGEju1RMnX06NFcNWJijJmZmfyQ5qmpKXS73XwMSe6VHGrKcLpUMskFifmxY8cKh/wq2aGixPJ4gDHBw3u5dtVd02OjGJNFFYvEiIcAj8fj3HWPqhPJFOdJ7+XcHD9+HLVaDcvLyzh+/Djm5+exY8cOzMzM5CQuEAgEAoFA4HQw0WSqLJhfd/v5nqpRSqQ0Ax3vpXHrdaVcr9RAd6VKn6XSpYqDG9zefje8tV/sj7uvpdrh5ZD4kGgwiYJmRtR2psaY0OQGHgvk5ESztPFHCRCAAjn284BIvFT14Xg4YeMYDYfDAplxtTLlVscxZDmMyVIS7Mqljm0ZWJ+Pj68tJ/YcG1+/APLECVmWYefOnXlfO51OXg/PHKvX62i1WhiNRvk4aKY+9lHVQqpAJLI+5xrnpe6hrmbS3ZXzqfOsqpOOAzNLqlLon1+6LY7H4zx+SxPH8B4dM66jbreLdrudJ+1oNpvodDp5ZshAIBAIBAKBU2GiyVRZEgga156xTI0+NaI1AQTjizQzmsYoqXGrsTOu3KTgzzvxSylXQDHznZIbGrpKJtylUXf7lUBUKhVs2bIFU1NT2LJlS8Fdray+FFEg0VHCyLaosc22aqICGtcAcqWHddKQXllZwfLyMvr9fu5+5e3QmB51a3Q1B8A68qBGtipPlUolT77BNjUajZyAKqHVZ9UV0t0+XXHjWCgpU1LpcIUxyzLMz8/j0ksvRbPZxLZt29Dr9fDFL34Ri4uL+drlnDDNvap7VHaownE8er0eut0usizD8vIyRqMRtm/fjunp6dylz11U+X6KTHW7XfT7/ZxEq5rIdUHVjOOgqdF5v5N3dRnkHNEdscx1MstOHguwuLiIqampvC+MOStzQwwEAoFAIBBwTDSZUgXH3exS7wPrz/jR92l8uYGsBEVVDSVANAxVcWIby9zulFTxXi3PCZHHy2h/vXz93Y1wJw+EJ47QslUF0nZoljqv34mTnifFdtCdkooG6yFJ0/ioVF3sgxNBzinbyr6RII3H40J8nM6nP0eyTYLONmh2vLK5SI3z6UDH2MmZkp7hcIherwfg5DlhvV4PnU4HnU4nJwmeiEHXs88voeuQ5IvEnXOiUBUytR5T7e/1eoVXVVl9jabWqq4HdfP0NmjyD2Dt2IB+v49ut5vXrUQ+EAgEAoFA4HQw0WSKaaiBogucxkupi1eZC5bvXANrBqAeVqv16AGlej6OGoV8lgapunW5e5u62fnZV2qI8rqrVqqyKXHyM6/oLkcFBlhzp3K4AUy3MCUPjL1yRWY8PnlI6urqan6ALI19j1dSdY/jqGdI9fv9vAx183NCkMq8qK5kTJXua4d/NxqNAqFz8sH5UrVHiZa6iCrJ4zU+64ky3JVPiRTbSFc4VbdWV1exuLiIbreLer2OLVu2oN/v47HHHsPKykpedqvVyt37tL9KCN2tc2pqCq1Wq3Do9OHDh1GpVHLyQ/dQTQ7C+eH5ZAT7zc9nv9/H0tJS3hfWp8k8SLQJ/TxTzdL+cD5IjAi6LqqrIesZDof5WmC82akyPwYCgUAgEAgQE02m3GXOd9qVvABpVcrd4PweGsRqoGmacHUl85gY31U/lXrmLoR6r77qdXcrVGJIUqHuha7s8F4lm66YaRtT8UmuoFGR0lgnPVyXhCOlWLAsVaTUnczd8jaCzr/OM+daSRnTqPt4KLEiUdB1osa3E6MyxTClVPma9Dn3uVfCoWoMU49ruvTUOBO63lVd5X26DlXVS21MOIHUuvUeJrrwM7W0DP3c+bOpDQc966tM2XX4uPj6CgQCgUAgEDgVJppMaSpvNZzUdYzvEWo8qVubum55HTy/SN1/UimuNbUz26RGn6s8TqQIkjU+yzguVbeoiFAZoHGq97AtNLRpBNdqtbzNAPKkAGxDvV4vxCGpG532RVUlqhurq6u5arCysoLhcIjl5eV87NSVUWNfVGXIsqwQX0NCpePkRNbbRYOY5dKlTw851j6pyyLnxc+b4tri/TTelUSTQGjb3H1Sx1XXHX9n23S9MGsj+8aEEpqkg+POOeYZWJxLHyP9jKjiqgoP14OuWU2jTuLMOKj5+fk8PktdJdle9k0VOtbvqfmdaDUaDczPz2NmZgbNZrPgjunnfbH80WiUJ5SgGsdMh7Ozs3mWQiqjutYCgUAgEAgEToWJJlO+c64Kgrv2nc7udEodoMHM2JON1IXUbri6gylcgUqpWWpYar+8Tq0v1Y/UM9pWlqvkIvWqdXnyD72m8U48KLbf7+fGtNet/dPylRi5esHXFKFieZx/1lemGClUjdI6fKxU9UvNg7qk+Zh5O7TvrhIS7CdJr46hJlTR9pC4KBlU8phyb1UFU+tOqaz+uVFC5OnqncD6vLnCq2Oh95D8VCqVQrwayZQqdpoCnusbWNsgoVtfKrFLIBAIBAKBwOlgosmUx9q4gccdf2bn8jOdgLQrFhUIGol6ppLH+7BcVVncoE6REDcqGTOlLlLuXldGksru01fdbVd3NHWd8lc+q+1nzBGfZd8ZGzUYDPJYKR64yixpc3NzaDabBaVGzycqG1MlMEqONGMjnwGKWQSdTKnrmBr5SmxYN0GDW8dPSY8a9R4b5HOfWm+6Fl2VdJdDJU1M8c1+quucri2P4SK0rVquKjRlZekaVtdJ32TQZ6kOslwqpNqe1AaIrlO+T2WKc61kyudyZmamoH5SmarX6/kBv+y/HmQdCAQCgUAgcCpMNJnSXXdXqVLZwdxQUgKiRhzdluhOxbpouOuuPu8HUKhPy+fzqd1+Gt0s3wmilqN9TKlDTri8fTRWNS4GWHMrTNVNsFyOoQb8A8jd8Rivo+55LLvZbGJ2djbP6qd9URUqpcDpe2yfJ+JQRUv/pvuaK0M+Dqm+uvKpxEPVHn3dSAVN/e1rUlUa3xzQNaqbA1yzTuadSDmp1rWsxNATl/B+LZNrJbUWFbpu+bnSeXCymFL5lMxpW9lfuuq54jUej3M3VG6Q8IBfuu8q0dpo/gKBQCAQCAQcE02mgPXqjxpYfE2RLBrDvuvt6gx3/Llbr+DfqTOB3C3MXao8hsWRcnXjc2p4Up1wEuKuV/4ejWFtqxM8ttfVCG2jJpZgBj49H0jdqCqVSk6kNCZK58oVqtR4+/hpH/i7qkZ+XeeBfVJyxntJFqgY+llcVId07bCNnBdVmZy0cwy9LyRGOmfuAqkql6pKHHf/URc6LYNkIqVWOjT2zpVQ9kM/O5VKpXAgr6q9Os6qKJbNUZmyx365e2Hq86dzo2nj9X9CkKlAIBAIBAJngokmUyn1RhUoNdyAoitPyhXLd/HdXVBjQDSZgbqSsS3c8VdDV0HDMKWMEO7m5gau76gT+nsqUYYa2NonfYYuWU5S1P1M3c3o4kcipf3jQblKvphem+13MuWqnp5tpHOTWgPsp6o0TnydsDmRUsKprpckCnyW6badUHFsPPOjzo8TC11vfqYWz0YinGh4inMnUk7eWa6ru9omH6OU+uTrSzcj2C7OMzMh8nkm0HAy5fXwHn1WP6dMsuEuv/rZZIIMuqOybY1GY53qGQgEAoFAIHC6mHgypTvpqhgoqUqpNnyPRhbJip4XBBRd95RA6E65u585GShruxu7+qOxKmwzsEYSytzhXKlz1zQdK82EmBobdZUjnJB2Oh30er1ccaLRzLZ6e7RNwFqWPXdB0/6Ox2PUajXMzMwUCJQqMzS6VVFjXYSSJVdsHFSVSGrUhU4VDY4t15GSSCVfOs5OpvTVlZGUy5y/z/Y44XLCofc7eS2ry11adf3oBoYrrtoPJV5KVlVN8zWn9ysZTJFdHeuNPne6eULXQPZHlb9AIBAIBAKB08XEkylXDADk7m+6K67EQ9+jQczdbZbFw2nV/YfGn7qmuVuTx3Q4lAzQBU9dyJRM6f1ehu/2u6Hq2dTYbnXFovufxrGkVDQ1ukmi2u12QZGi4sTnOTdKENkW7b8ebMz2esps3ufpwVNqnKY+V+j8sC0690q0CJITlk0lheTbibCuQ86tJq8g0dL+6fw60UoRUQUJKJUXni3F+fO5dndAVTbd7TW17tgWplp3lVZdFpVc0Z1Q++QbGqpS8rmyOCig6IKpY+3l61g72WL56iYbadEDgUAgEAicCSaeTAFFY79MqdGYESUR6qrHVMo0/miYqkKjRqa7D6ZUMLaBRqU+qwa+ki81BjfaKdcy2A5XgFR9IjQDnrZP/1biqcoMSaoqUSmlTFUWHT/erxn1dN5UNXE3PldJUn1WpFzUXKnS62yTj5mvM2CNtNHtUUmJuyqmlD//2+fD11iKUCtJJsllWWybn4XFMpX0kASpcub1qcqobVAiQiWPhBEouqqqOyT7pG1J9TVFJjUOTZUpbaMqhCzXXRKr1Wp+TpsqsBu53gYCgUAgEAgoJp5MpdyDVDWgkQcAzWYz381m6m6SAyZEUOWBu+msi6+emU8VB1Ue9Fl1tVL3JDViVUljP3i/GvQACkqcvs/2kDCxnhQxU/LgKo+qBqPRKD8QttPpFP5Wo15Jk5JHKg80+Pv9fiGzohrbngmQ7VEFSA9CBoruezoGijLVo+weVSqVGPR6vTyGq1qt5mdn8TrHksqmzq8qmto+JRPqtqrXfa71LK+VlRWsrq6i2+2iUqlgbm4ud4tsNBq5aqNrq9FoFOIAddxUsdN1z7ZxrlXd5bOaZAJAIUEKP1Mcfz1MeDweFw7FBtbHsel4NBoNTE9Przu7LKV8qeqkLorNZhP1ej2fk9Ho5AG/vnYCgUAgEAgEyjDxZIpIxfb4fapS0bXODVs1/HWnWg017vI7OfGd97K2qmHKtrr7m/fBy9K4j5RLoT+rykTqemosqXrQaGfqc76vLlRugGoclMfHaHuVgKlhrqTV1S72x5Uj76+OL+dFSVmZC50qhkrKVX2kCkPSqoRaCSHLK4tB07ErU6FUbVKC7eqmxsKVuS4qyfUxSKmpqlq5kuefAZbrfdVx0/K9zynlMQXvL1HmmqjJQfiqZSgZLFsTgUAgEAgEAilMNJlS4qLGa4roAMgNexpwGi/B69wd5zlI3D2nUqEGKnf23Y1Ik1coaVADmOVorAmvUzHxeBo1QNWQVLVDXZbYLzUeffzUqFZ1qN/vo9/vo9vtYjgcFhQpdd1yg13HnmPZ7/fztlDR4EHK2k62aXp6GnNzcwU1hUqixrO5myPLV3dMj7lJuQ86NK6KbSSUHGjmPlV4OP/9fh+NRgOzs7OFctyFUufSCYKqYtpmKqxclyRxAHK1xjPqaR8IJ2mq/jUajYIbIutWRcpJvLpBsi69R+ekLPmIQwmkZldUd0JVbLnBwPc0bk3VMf8sK+EPBAKBQCAQOB1MNJlSeOxNWSyNx864Uc3r7hZGw03dBrWsVHluRHKX3NtVRv54jc+XqW1so9bpak5qLLzN6rI3HA5zUsVXuuh5e1VdSKkQfj6XttGVCCoqdMGi0T4YDAoEyZOO6HhpzFKj0ciJB+tOxV3pmPgcuRueP6s/mqiDhEbj8liHq4QbkTtf07qWgLUzlvi8Hmac6p+D7dFrHF9d72Vj5vFXp1JAUz9lZWubXY1MrTdVjXUDgySK5C01PjqHgUAgEAgEAqeDiSZTVHSAYlIFj91h3IamStc4iampKczMzOQxFaqW8H4qNYQarjTu6/V6njVN26jPqCGnxiENYDfMeR+wZqy6cemqkxICrY/qht6TZVmuHHU6HfT7fbTbbXQ6nZxEMR5H41p0d1/JFNW9VPyMtoVjpvPGMaxWq9i1axdmZ2fRbDbRaDTQ6XSwvLycq2PuEujjyfJUvQHWVBW6KfphskpSfKyYsVDBOrUfGp+mhj6zKfLeFElzBUoVFV3XqVddK6p2qjJDUHnVYwGU3DQaDTSbzYI6p2nMSU603yThOgdUidgWz16pcJWSbeEzbO/U1FQhWYuSqtQGhh6azDWsZ4Gpksu4vkAgEAgEAoHTwUSTKSB9eKgrVIQbTmqAarpnGttKqFLuPzTQ2Q41Tn2nP+XiB6yRQCoBmnqddatBq+3WMj1Dn9alMTsp0Ajudrvodrtot9tot9uFWKler5e76BGewIFt5FiRVLmywfbSlUxJX6vVwvT0NLZt24Zt27Zhbm4Os7Oz6Ha7WFxcRL/fx8LCQt5mHWdNWMAyG41GXp8Sa6ZzJ0HyMXXXt1T6fb6WqUo6X0p6lEy5OqMudbpWuPbKYpXYToWWo58Pda1zIqpkV91NvV4SJP/c6Rzr+CgZ0vTxG6mEOvb1er0wZtoGvu/ZDL0tHBN1HeV65hinlMtAIBAIBAKBMkw0mXIFgK9q9LlB6S5m6jrG7Gy6Q63xISRPagxzl50JCUi89Hd3HVOCobv6fvZPKoGAtp33uMLhxI310HjVbGyj0QiLi4sYDodYWlpCt9vNVTgdI+2PExglHpqUgv1W5a5er6PVauWkqVarFQiFKhbdbhcACqSO86DZEf2crzJiROVRFRtmuuOamZqaygmeYzAY5HFESip1XDx1OhUt3pcivO4qqG32OCtd+x6PpypWqj4lmaoQqTKlypP2UdU81s2ylZRoXTo++llRtZCEjXOjGxaM/VI3Rn4Gdb1o/JW7AXosIsem0Wjk61w/54FAIBAIBAJngoknUynlw3fuNascUIybUTLF1yxbO/S31+vlh6GyLO6SAyg8q7vdruA4MXI1gQYqjVZ33fN+uyujuw66aqCg8cjkEgsLC+j1elheXka32y0QOo6dKnYpUqBuVu5+R6O52Wyi2Wxiy5Yt2LZtW7KNSjjpWuguWSRSNKw1xTaVB1coNDkEjXoA+dxyLKenp3Oix/WhZFrbooohjXIdY9ZHlzoSJ1WDqKjofPo4KFnlNU9uwrFUlzd1V/RNBSatIHivpjJXwqSKoyc7oTuou/2x3areKYHVNnAdK6HhWuacuVsoyZWuIx8noKj+sq86rlQpdb4DgUAgEAgETgcTTaaUiOhPakdfDVk1OhnHQsPRVQ0a3UoMeDaNJ6vQnXolaZp9zNvgCgh34l3NUng/ldApsUq5TJEoDQYDLC4uYjAYYHl5GYPBICeOakymXAO1Tc1mE61WK6+XZVPZqFQqaLVaqFar+blHrVaroP4o0VTiRgNbSYG6UqqxrYSOdbsy58Qq1R9VmXwNVSqVXLl0ZYrri/FnPrfsz/T0dE48ABSInBIibQvHSNvK57R+VZ50XHWd8H7e5/OrJD7loshy+burw7zXE6G4WqpzU6/XC/OecnN0N0RX3FLuhj5/XjaVaKB4RljETAUCgUAgEDhdTDSZ0gNp6ZoHFAmA7oorWVIi5cYxDS1Nh04jlwoL426oSqihRgOTyRTUdY2kjS5M7rKXIlpsM92dAORKGBMiuFsT+6h9oiHKVOfHjh1Dr9fDyspKIShfSeSpdulbrRZ27NiRG6Y82JfzUamcPES2Xq9jZmamMCbuZpllWUGN4vysrq4WsstxDDz2SA/71cyLqZiylHLJ9cF1pCoR54xkShN8jMdjtFqtXE3TNnA9aL0ke6rkuCua3qdr3RUmttOv6e/qbupKkY4F3+cz2g8F+55S6TgemojCyZGSJM5rihiSJKfUJldHPZ5Nx5yqtLdH47B4n6+NQCAQCAQCgY2Q9iP7FjAajXDzzTfjiiuuQKvVwrd927fh//v//r91BOdd73oX9u7di1arhf379+PAgQPfUr1qfKXcmtRNiEZxo9HIXY74U6vV1v00Go2cEGmguxrqGgOiZWsmMu2/x3qxLO2Duysq/DklUKoG6PtUn9rtdu7ipwQqBe1jvV5Hs9nEzMwMZmZm8kx7VOsajUaeBY6/a//VZc5jr3zO3OhmH7Tv6mbHH5al5BFYc210gqRqjsZh8R6dL1Ve/Fm+KnlW8quKDWPAmERDVUZXclTV9LXD+3T96k+KlKva5OvQP6PaX33Ox0fXrpetGxE+p/qck0D/cWifuKmg69k/NyR4HHu919dMmXtsIBBYw/n6rg8EAoHNhrOuTP3e7/0ePvShD+EjH/kIXvSiF+Huu+/Gm970JmzduhW/9Eu/BAB473vfiw984AP4yEc+giuuuAI333wzXvOa1+CrX/0qms3madelxrLuSvvutWYl051vGlQ0sIBiOmaN4fB7p6amcpWF6dDdbU1JFBWKsoOF3Z3NEzk4WWJ9dDn0GB7fqe/1euh2u1heXsaRI0cwGAzyg3hZv/aXapDu4k9NnTz/SRNHzM/P5257zWYTo9Eod9tiCnNV5dhWjXtKuajxfZKCFAFg39zQp6ukjguVkmazWSBAJMdA0eXO3fxYlqf+9nZz/FOup8w+yHZyjBuNRq6SUEXRZCfeRyUAnuBD14omk/A+ubucxm2RyGuMlH/G+CzfoyKlrnqcP2Zt5GaDZvRLqUsbESnWzblSMpplWf555Ni466GTVqJWq60j64FAoBzn8rs+EAgENjPOOpn6zGc+g2uuuQY/+qM/CgC4/PLL8Zd/+Zf43Oc+B+CkofL+978fv/7rv45rrrkGAPBnf/Zn2L17N2699Va84Q1v+JbboO58/Nuvp5QQoHh2E/8Giuf96FlAbvBpeWW76izH403cpU6NVqAYpE+S4ga2Bta7uxh37weDQf67p4snPOC/Xq/nhj+NfY99cfLnbmTqCpYaB58zLdeVPC0zdV3nT+OFCI6fEyYdz41UwTIjX/vvBN/VxjKlR59NtT1Vj95TNlb+LF9PlzykyIbOPwmLqzyuuKbawvt9/lIKWqrNPneuhKZUOW2LkssylTYQCKxhM3zXBwKBwGbAWSdT3/d934c//uM/xte//nU8//nPxxe/+EX80z/9E973vvcBAB5++GEcOnQI+/fvz5/ZunUrrrrqKtx5553Jf7B+YO7S0hKAtNsSocYikTKYNUMcUExqAazFh9Ddj5nZqHilyBRJiO+GK9EZDAaFZ3UnXUHjjoRI1RY9C4jlU/ly4tVut7G8vIzl5WUsLS2h3+9jZWWlkCxAD9JtNpuF15mZGUxPT+d/a/s0fbz2VbMTAsgPNSY50znh/Loix+dTBr8nDXFDnHOk86v3s9yyRAsey5ZaU54SPOWqp0qjjkdKhdH04ToeOq7uZqh16DO87kRZXesU2n8lvLqmXAEksWZmPo2vmppaOzes0WgUFCkF/1aVlfWpkkukNi90vvx/giqCWZatcwFVhbIs6UsgECjiXH7XBwKBwGbGWSdTv/Zrv4alpSW84AUvyAP7f/u3fxvXXnstAODQoUMAgN27dxee2717d37Nccstt+A973nPuvfLdtRpMKsawPtpMNLITKkhTsyANVKlcTVlrkhuJJfd63V7oL8rG0pWNNV7qr28l4TBkzO4oqX91zO3NA6MrmQbxX+lyKD2tUwZ2IgQp8hNGZxQab9SpEvr80QR3o5TKY2pvukzqfZvND5lZeuzKQXQ3fJSSG0ClJXNz3GqDBIzxofRXZb1kxx6/FtZm/icq7YpNVCfKVOUylQ577uT00AgsDHO5Xd9IBAIbGacdTL1V3/1V/joRz+Kv/iLv8CLXvQi3HfffXjnO9+Jffv24brrrntaZd5000244YYb8r+XlpZw6aWXrjP+XH3Q+B8lFjSkPB4kZVDRkNNdax7c6sa5qgaaoELb59navA/6nioLWr7Wxxgetp+KBmNJer1enniCWQdnZmYwGo3yuCKqBUy0oQkMNGmHuvVpPBXH02OONNW09pNKAVWNjYx6NcA9TX2K1PmYuvumG9fqDqZt8Ho8UYK3190FPYmGuhW6mqXnNpEkM0sjy3QXUE9UomOoh9MC693vdN5cOVXiQhLEMlXJA9bi2lqtVh7/oJ8JrnnWq58HdZvlevDPp7bFVTidG1VH/XOp/xd0bpzIK9GNBBSBwKlxLr/rA4FAYDPjrJOpX/mVX8Gv/dqv5RL+i1/8Yjz66KO45ZZbcN1112HPnj0AgMOHD2Pv3r35c4cPH8ZLX/rSZJnMDOcoUw/01V3PNAbFM8oRaiy7eqUuRWUxT0p2aJi5gabq0EYKm/bVjV0lebxfCdloNMrjozRzmcevUFUgmXIiQLXKjVmPDSMxcTWQ72tCBKYGTylJHGd1Z/O5dUXG1Ssdw9S9KWWibOxTxnyqzSlw7LhelACpO6SWp4lAvB1lbU6pYbq+WHeqrBQZVNKoaew5n8DaOqIyxXJTGwJan24wKNneSFHT9cZ7dD50/lOEWfumc+jPbaQ+BgKBNZzL7/pAIBDYzDjrZKrT6awzhjTQ/4orrsCePXtw++235/9Ql5aWcNddd+Gtb33rGdVFY8rjQVTJ8DgKNbyonKih5ucRuToBrBmfGsPkioUbt2pA6j2peB3NLqf3ptJ26+9lrmLqjkU1SMeK8Syqoviz2n+Pa2JblXARSl5Zr9blhivrplrhcSwplaZMuUkpGU6ylXh43JyXr211Auv3qrHP8qhoah99rNk2HfvUXDNDniovSvRJWPU5b6e+ejZCJS2Mb3Plh8S7Wq0WzmjSNum8pRRFhRMljw2rVCq5kul9Zcp5Xev6WfYNFZ0H3WTxeKpAIJDGufyuDwQCgc2Ms06mXve61+G3f/u3cdlll+FFL3oR7r33Xrzvfe/Dm9/8ZgAnjat3vvOd+K3f+i0873nPy9Ol7tu3D69//evPqC5VC/w9GnTq4sRDYFM752U73SRcelaSK2K8/1Rkyu9NqSmuPJX11xUAf14JhhqYWoanO2dbmAJc4Yf4plyylHSpAsC2UAFzd0c+z3JVJdFxYF0av5MaoxSRcbhSpOPrKqK7t6UUHk0eom5lqTI1UYT2QcmOK1PeD3WR9M8BFUASEu2bw9UmflZYRxnhBdbIlI4Nx7QswYX2yde4bjjoxoUSSCfsg8Egr380GuVt0vT9Ou7j8dph206wfNMgEAiU41x+1wcCgcBmxlknUx/84Adx88034xd/8Rdx5MgR7Nu3Dz//8z+Pd73rXfk9v/qrv4p2u43rr78eCwsL+IEf+AF8/OMff9rnTlQqlYILGd8D1g719OtAMU7KDzjVslO7+mWETMvmvb7Dru2j8UaVQjOvqftXqh36t5MKVxpS8WGs28/D8n5WKmtndXH335UZNbydIOmY6OG4nnVNx1PPm1IlK8uyvC2qkDnBdRKQimlywsX7XYnRMdXndJw9QYOSC8Y/ObHi2Uap+fW15W1wcp9S55g8RM8gY/0ks6k1kRpPf+UYKolUtc+f1XnyM9FI4PgZVEVJx1XjzcpcFglXe1M/LEfn3z/HgUAgjfPxXR8IBAKbEZXMLd4JwNLSErZu3Yp3v/vduX8144LcwN7IgPJYIIWqDE4wSEw0kQKNQC1HCUwqjTqwZghqggiPIaHhzEOCy1QGoBgjplObMnTLSI9n/KN7FVBUpJxAuducG/fMDkgjn65uWq6mxc6yLH9GjV4a1JqS3fuUmjN3NXNyo4qd95XPpEik3sv6NS28JglhevmpqalCintff07+CN7bbDbRbDYLBFxj4jhnVKc8ZTlJEA9WVkWHbdO05DpOHEsmKdF1wwOtfZ16gg91k+VniOPCtvActBSUzFYqFXS7XQyHw3WJU3SNEdy44Bpwct/tdnHjjTdicXER8/PzyfoDgcAzD37XvxLXoFqpne/mBAKBCwir2RCfxMfOynf9WVemziV891kzk+l1N9D1vBs1chW6c+710Qj2GBKgaKSnXPZSxiX/Vjcyd9Nz1SJFGvRedV3Sayxf//Yx0/pdvdE4M73m5EmNbyVtbthOTU2tG0cnfT5e2hY+r3Eveg/f8wx3bJ/OvZJEHUNVxnydsH6+shyuMZajREUJeoqQ6hrwsVCiogksuC51XrRdOme+XlTFUzLOvpSpf14e28brPk7+firujONQRqKUrCqZ9c9HimQSKTWMZZXVGwgEAoFAIJDCRJMpVX6U4GjgvRqb3AmnMqLGLYP5gTWjfzwe57vj6pqkpEqNTdZL17SUIaokTUkE62Gsh5OrlLHI39UgVnc8rU8NZhImPXyXzzCuTI17xuewHELVCVcyeK+SDD/riuOufXL3Np8TJRNsv8fAKJl19ckJNeNwuD60XJZHRVAVnRTJ1vHW5Ayu4rF8EiZ1aeO4qPucjgnXbqVSyceSBNWVSAD53CgpdEKjbdA50M0KJTuuyqnCo5n/dH3q+Wysh+Ppbpu6+aHQa9pmTcCh60XVTlWnqWh65krvayAQCAQCgcCpMNFkSgmJkqkyZUqNQ93Vp8GvBiUNLo21KtulV2wUb+FGqz+nCgiAgtENFJUkNd7PNMZDCV2Z2uWKQ8oN0o1sJSzAehdJV31SxCTlhpeC1svXlHLiKhrfSxGLVB0aQ6SEz5VBvVcVPRrtrgD5mkuNrULHw/vqbXalLKX8uVrr68DVIm2b3qv1lbXHy3AiRiKjSBGpsj6n3k+pnDpnqZg6/4wHAoFAIBAInAoTTab6/T6AtV1o/XE3Od6n9+thoWq8jcdjdLvdQlyFGlpu/LN8Gs3qfqbkSQ1cN7wZG6QGngfpKnlUwqgGuxNJNQ6VvFB107aooU8lgYqUqhtsi8Ljfnw8qfCQvPJ9PZzW3QnLXBjZP71XCbOrblSDnIykSIQqVPyhYuRqmRrkGk/EezjnjMFiG5yI8XffFNA6OL6p9noSCG4CUClUVzZ3ifVYMl3LVOz0Gb2nTCH1vml73bXWx1NVLX2Ov7NsXUOqPus9vu61nayPc6Z91DUZCAQCgUAgsBEmmkwBSBqH7vJVBo8xUXcrGmjA+kNGHa4abNTWFHHxe/i+1+sEhtd8l92VBb2X19xgdRVH+5bqs5aRIgcah0QjWsmYu5Kl3MhSdSr0udR9ZWOgpFbboH9rObqWlAyk5jPVTgDJOUrdr2vQ18bpKHXeN9blGwK+OaCbBqkxS6Fs/Mvg41y2hsrUOm2LblJspExtNC9OFKlIq5twIBAIBAKBwEaYaDLlrjk0iDTJgZIS3b0H1uKKNLkAM8cBRYNNlRfdjQeKachTbWNZqhxpPQrGwGjsD+/T9irpUANUy280GgU3Lk/24Iasj2WlUinEnajbYyrLYYqMqRKk7pUp1UwVCz7rqqHW5+c0pdwh+TvVJiptvE/b48Y232emSHXFTGXg4/xRLXI3Ml83hCo0etC09kHXsY6fro/UXPh7Hj9EpNzeBoNBoXw/HFgJnH82vG+agdHJs6tvfN9JTYpopZQsJ2m6HvSV5Wt2zna7jV6vh0AgEAgEAoHTwUSTKaA8/Tmhu+76HlA8w0YNZRrDaqRWKpWCuxDrVmNQyZq2J2Xcl5GOsp321L0bKRU0TLU83q8Hs+oYuuugqnOq9qXG2RWOVJuVIOpcqAG90WGvXmYq81qZquFKjLfbn1XDWxOdpNSqlNKXImepceMYOFFxUnI6qlTZ/VpnitCl4tOcHKfWq9d3qnWr6yrVJ88EeKrsejqfqf6m2uNt4fzS7bff7+fuw4FAIBAIBAKnwsSTKTW8qPbwzBjGPjA2inAjS41/VTnUZUqzjqnSwmvAmnsggIKBp5nc+Ly7KfkOvbazLBjf1YmUSqHPaOZDHzt1A9MYFDV8tU4qPbzGLHRqeGsiDRIluq+xHFdbtF2p+dH2l7l++TizfbomnBR5cghtk8aQpZJMeFs3moMyQuWEV2O8nOh7v71c77/HFSlp9VcdE3eXTR1srYoXx0X7w1d3w9Wx4ivX1EakV9cfXzlnvu50DHRchsNhIa6SRKrb7eL48eOhTAUCgUAgEDhtTDSZUuPRzz/SdNE0nBRqQKsR7WSKZSghKouXodHpu/osL6XYqDHPujVRhBvfajjTMPXMaCm1RPtdlkzBlSk1pJXgueGuhIVwAqCGrsayqdFdptyVKVUp8qVzQeNfx8EPHdZx8TXgRELXBMmjrhcF691IKfP7ve+apMHVT/3d52QjVYxkVteyk30n6mVt1TWiRE/HTTcEuPZ0/XnbNoKqhNwgUbdK31BwVU3dZTXRx3A4xGAwQL/fR7vdDmUqEAgEAoHAaWOiyRTPcwLW3MiANaKUMtz1mmcW8915VU7UOHW3MI0pSRnRHrPFct0Qd8VJ+wMUlS99Tp/Xe1PJCEgM+ByJkt7rboBqiGp2P2+HKnZqTLuSp2qcu3Y5yUrBiZy2wd/XcsoUIy1X43R8DFwtUVLgxINzVdYWbY+SNP7tZDeVdc+VKidaSmJdVXKSlhpbPejYSZWvcY2pc0VTn0kpTt5Gr0/XlMZ8aQzfYDBIEnzGt2n9/X5/3WZLv99Hr9fDYDCIbH6BQCAQCAROGxNNpqrVKmq1WsEIJlFRFYQoIxaqoviOvpZVds3VEJadgu7Ul7lMpdQooEim3CUw1Uf9XQ1MTZhBY5Nlra6uFlKN6/PuCpdShlKEztNWu2ujq4B8Vg12JwypNvn7OqZOIrw8NdTZd01HniJTlcpakgRPa89DmFMKoJOllBuokqssy3Lj3xWYlKJTRua9fFe8dI5SBN2VL1WoOH5MnuKfNSeyKYVTXSdTayqVwIRKlRIk1sex4gYAk5n0+/2Cm+rq6mquTA0Gg0I5gUAgEAgEAhthoslUmYHm6o3u4LsxzOtUpvSnWq2uM141tqcsyxwJS4pAeIKJ1G68G7BOrpwoaDv0gFiWyWfKCJ67ZG3kgqb9VNVPCUdqTFJjpfW7uuXj4Ya+E45TtTcFJ4OucrlitFG57rqm959KDfL2OMkomxe9V5OFKCEF1kihKq3+vCpKvM/HWe/V/pHQKJlyQuLxdEpy1ZVUyb2OXYowKxn1sVBXXD47GAxyMqXzpZsUnk0xEAgEAoFAYCNMNJlS1SK1c63GlhpZajSTONXrdUxNTaHZbGJqagqNRgOVysnDdKnUaPmsP2XgpggcX6lY8JomzXAlrYwcsm49U4sGsBMTj41xlcWVJB8/HesyBUrVOFW+XIXTOdO5czK5kSqRUnZSc+BlqXroRneZiqXujx5jx/I1EYKOkZIhJVEcL5IIwg34lOrnffM1n1qjANBoNFCv1wtzw3JJjPr9fp6MYXV1FfV6HfV6PVd/eS9VMlWAGYNEdzmNaXIFkp83J4j8rPFz4zFqKaJK1Wk8Xjtk2zMv6meq1+vlKpbGjil51dT5gUAgEAgEAqfCRJMpYP0uPrA+lqgshbYad0poaCSmzlICyg1fbUOKsGhbVW1x9yt3q2JZbvTz9zLS5Ma/G/op1YfjVqYI0BhPxb3w/jL1JqXIkGgp4VK4AqVugak+aZv0le1Vd9CU+lOmwLGM1JliqXFI9dvbq/Nf1vdUHU7mfezVdc/LSa3hlPLHe8rWrBJuJ/V6r65lJX0pZVHdLEmolLRtNDZlZF+VNN0ocCVOX0+VCCMQCAQCgUCAmGgy5emYgbWzamg06449/6YaxINteVgvXY308F5VNPQ8Gq3Xd8yBtex4apipwe/GPN0KVVVI7eyzb1REaGxSeVAFIEWI1CB2I1gNfs9Ux/dU4WDgPw93JVKEi++rajUajfJgfycVfNUYJ6pD6gbGetyQd7LAeBk/BJj90PlzEq7GPcdDf3fXT+0D15JCiYbGwLlLId/Xcj1miOOxEfFQcqX99zHTdrCu4XC4Lq5K154TLCeMrE/XOj9nVKLYdj3YV91sa7VaPoapDQf9jHBe+DnVe/m5VYVN1Sz2wQ8LDgQCgUAgECjDRJMpN5r1fSJlOALr0yarEV1mjCsp8/JYpioFKWO1rB9uXDvZUuM69Tfv8/6xLDWG3W3QVaGUurbRGHqbfDxS/fUxSal7nmghNWdaj4+/Xkv13ZHqQ5l6l3rW1cKU+uJ99LqcRAHFNZxSmbxMr7NMKUyVUzYGqWec2Dmp5X2p8XPXW+0fibb2RxVBV3KB4jlwwNqY+dr2z5j2xT+DgUAgEAgEAqfCRJOp0Wi0LmBcSY+qEWpIq3sf42JUjVDFJJWVTUkG1Qd3RVPXNXehcmOXCg+QJlHaxrKkADQGNQHF1NRUrmKxHvaRyhyVDY6djyX70Ov1MDU1VXDRcqM2ZYQryVBDVeciRWhVDfOy2dcUadAYmxQ8TbbOh7vwaexNilCUESK2X9cYx1Ofc3dMHTOSDY09Yvunp6fXqUMsj/ekxsvVG1eZWIaqYTrnrnZybamy40qqxpSlFECdbycxXPceA6V9pWJVrVYxHA7zM6MYH6XxW0r2nJyz/fp5CQQCgUAgEDgVJppMle0i03XnVMpQyhhW18EsyzAYDHK3Ixp/apCfqjxtkxrNeshwmWriiR1SxrerR96WlLJUZtSqO5ZCnyMRA9an3E4ZxF6Ok8uUEqJlbnRPGVyVU7j6qG1PxaRtVG+ZMpnqw6nW30Yq3kaqGFUcT9SwUdmnUhBJ5MpUNdarLnL6edPEEP6MtlldYF0JBtbc/UjYUmRZ50/dKTkmWmdKCXXlMhAIBAKBQOBMMNFkijvOwPoAd6CY8YtGHg1o/j4cDgsqEsvijnq3282NOnclYh1AkSyp6sLy1PjjLj+vpQhOyihWA1DPQNLyWW6/3wewptSpwuPuUKoWuQGcioPxg1lT/U4RMx6yzNgz7ZPDY800tsbbyNiXVGIKvtLQ1pgajotm22P/eG+WZeuy8al7G8ExdYJAtUQJsSqZquRomZ4oQ1VFJSN8X+PZvDyOtxNHVWsIJ9ap9ecufnzO1T4dU50Ljf3TzyMzaHoyiEqlgm63WzhE2MG6GV9VqVQwHA7zz7Gnjdc1wPu0/4FAIBAIBAKng4kmUzQQPdZBCYArNEpkVGlZXV0tkBOWx0B2NRxpOKeMrpQ7ocZ/uMrEa2pwlsVBpYxeGqskjLzHjW5Vkzx1eZkLVKpOddkrc+/z8VAjmgaxqwZ+f0op8Gfc6CaU+BDsc8ol010N/RwlVxXLyK+6inKMdKxIzvSAX1ccyxKWpPqsJMHbos+QdGl/2C4lTakfh5K71Dh4G5zc6TVN8KEKqfY7tUGinxclc0pWV1dXcwKtxBVYI6okmazDXWgDgUAgEAgEToWJJlMrKys5seHuMpUXZgHjjj2wPnMbcOqkCq1WC/V6PS9LM9K50aUGN40/NdadaPE+GrM09D1bnRvH7rqkP+6mWKlUcuOd9dNgpVGpWc+U1FGJ07Z5n2mQnoqs8dXJgI47263t1zgm7XvKdc3bn5objg8VC1X4yuCxVK7Y6Di5u5mqK36+FOee69VVQyWWuk7YP4850jF2aDnuLqdrPqUqeXv5WdPPnK9VzpVmY9Rx5EaFZt3TrJC6ptW9VrNW6hrQc9pUERwOh/kZWixPz3rj2Gm5gUAgEAgEAqeLiSZT7XY7J0rdbheDwSBPd67GrSZw0IQUAAqqEbDeDY+vdB9S5cZJiaoLaqiRYGjdagRrggsajCwPKKb+VkLhCS70RxNSqAuWGtV0c3JlRhUCNbCV6KmqofEvHtPi5Efbp/fxXi3TlRl3sdTy9Xm/pvPpRFQVoxTZZp+8P05GlIRov5RsqKGuipG6/jmZUMLgZFHHTsv2sXWXPy1TiVNKpdO1p0kaSKQ0njClZqX6pDF8mnyl1+sBKKbDZz/YNh6qrRsEwFpiDiV+XOODwaCgXKsapWs8pcYGAoFAIBAIbISJJlODwaCgOKjLnhp4emYUjUmPsaFx5mRAFYHUT8qAV9ckLdMNyTKFIKXoEG5Ak/TQfU7P7NE+OiED1s59KtuNVxLi73PcPENdipjqexwbN7qdJPiYKml117aUGuXqUWpMnSTwfY1F4jg52Sirx/utLqEaK8Rn1YVNVS0dt5Qap0TJNwC8z2VKlSqOWpeT9NSzOh5lcBXNCSIzcfracSVMy2OcG+OrlHhpZsJ+v492u43hcIher1cgT+yjxsK5whcIBAKBQCBwOph4MkWlhLvlqkbR2KrX6zlxoAuQJh9Q1zQ3pngv30+RDycqqkBx59sN25RrlbpVuXrjJIrPalZAbRd341PGMMslgVSDVJWmlJqihif/dlWLcONe1YEyMqXjrEkDUr97qu4U6dyIRAHF9OeuijkJ0cOdOVdlMUs+R0qUlRTqeiTpd4WI86vtcXc8J6xl6p7PFd3bdO0qCS9TabxuXaOnWgMkUk7Y+LuuXT/fjePJDZNGo1Egav1+H/1+PydRq6ur6Ha7haQnbHO9Xs8PDtZ59bUZCAQCgUAgUIaJJlO6s0xjiG5+JFBqaBF0CVJD1xUigoRB41pIYFI758BaDIYbxXqPG56uDKTKLTPyXJUoa5OOG99LKWL6rBrU2kY1yPVcIzVIlexou07HjcqVqtQYeh9T/fd7UmoP/9akHU6UnEhulFmO9+tPmeq40dw60UjNUaqPuuY1Rs4JvM6VE0efe1WSarUahsNhMr4r1SZVTIE111pN/e9kTmP+SKyV6FOV5rzwmcFggMFgUHBjdXBMdB70vUAgEAgEAoHTxUSTqWazmRt0MzMzOYlhXAUJDXe2/ZBeNY7dyFb3PzekgbWYjFRWt1artc6wVONQy6fRqq6Bw+Fw3T1lCoy3zd3j+JwmSdC2pspj3d5+Vyo4Pp6yXHf+U4qJupL5OKnxqwSRZZYZ7RxD74eTSSV4rkJx3DU2CCgeHstx8PanjHN141PCrsk1ePC0K37qDqr9SM2bKkk+bjo2qpCxTh8PV4BYvrabY6JnQJUddOvrl4qtr0uOO9tJpU7HcDgcotPpYDweY2VlpbAueExC2aG76u6q8+BrJuXWGggEAoFAIFCGiSZT6nJH489d5tRw3yg2KKUYqdGuBqzukCtOpbikytNnXZFKkSm/36GKykZKlr6661sZWXGVSMmgk5xUm70NqTHQ11Td/DvVnzJCxd9TdWn/U2ORuv904OXpezpurhSVjYu2X//21zKFUVP0l/2cagyd0Pm8ECmizL9TB1Hre+5mqMTTyY7HdunhwakNCCezfJ8bAlRZA4FAIBAIBE4XE205NBqN3MhSw1RVAXUFUmNMrysR0917oOgyxZ1vxrhwNzxFLNStieWkCB133X13n7FeSuq4c1+miClSZEDdpvT+VHnefm1XmSLj2eY2ao/Wpc96CnAnxm7Eq8Gs866HtbrS40oM++9kx9ugZP1URBXAunXIazTYNTmF10c3QlXJPJZOx1XVJs24yDlj251Q6bPAmgtelmV5lj0dE53zssQrLAdAwf2Wa8rH2V1UW61WIZaMz/R6PWRZln/uXL2jK6KWzXkbjUbo9XoYjUZYWVnJ+5ZlGbZu3Yq5uTk0Gg1s3bo1VygDgUAgEAgEToWJJlNqaKu7nhpr6loGFJUH3dUmNlKC3PhOxZm4O1xKsdEyU7+rulZmtLuLl99XpvqUlVVGJFLKhEPVDFdG9Nqp1K8zhSsl+vdGbdK2eP98vlL1eDk+7xv1TYkT6y5TQbVuIjWWrNOzWOo6T7Vd+5CKnfL1mVIEVVnT9uk9SkZ9zFWZ0r4omecznv5c25ha307+1D2TbXW3xrL0+IFAIBAIBAIpTDSZqlarhQNSgfXn5tAwUyMzRT40W5y+kowxw9hwOMx3tdVIK4NfY2ZAN9g0wYUae3zGjX8/B4tw45k/qcx+bFutVlunzJAYeLxMiiykCOOpyJKqbSxD69e/fayUOOt8OlFWN1Alne5O5gpHSgFSBc5JKomGJzHgGtQzz1SdLCP6Tha0TiovJPWq6vE+PTuMz/ia0uempqZy5UjHX+/TZ6mgKhGp1WqFrJd8n+oxMxXqRoS329Vd9pX3jEYjNJvNvA1U67iOtDzdkNA1NzU1hVarhWazmY/Jli1bMDMzk6togUAgEAgEAqeLiSZTuttNuBuYu+6lYlPKVCqgaHRq0olT7fYrVOXx3X+9x4mBGrFqBKuhnFJdtE4tN+Xq5gZsihj5uKSUPzdcy6DtdyIBrFeyUlnWUkTUlTTvXyopA8vS8lNkuiwpQWq98P2UmlUWY6RlOcFzeLlKIDVlPNvgKimh9WRZtm5TQtvkBzJ7WUpu3VWVCSbKMmuW9TH1+VJXS/0sOvn0LJveVvaVZTGRDYloKFOBQCAQCAROFxNNppRwKBFQsqO77aoQuDsU7/Ggdt1FV7exqali+nNgbXecu+aaqSyl5qSQUg505x4oHgDrRDBFKhgzQhcmj0ty1cvVMT6jUBdLVSjKXBSVtGn/3BhW4koi4wQy1Q4tV8dCn9Mx1bYwTkjHK1UPyQHv2cjodnUvRaK1Lb7mWIanH/c6dL2yHB4LoCD50JipskQRijKFUMeJ46Lt55lvupHha7NM2dV1WK1WCyqUKlCMX9T5VFWRffS1q5kMSax6vR6WlpbwxBNPoN/vJ2Y0EAgEAoFAYD0mnkw5EVCjUgPudYfekwm48uOqUIps0H2LrwBy9z9NJqFkjuV7HwglMKoI6HUnCFpOyv2M/fW+pYij1qOGacrli/c5yVTSqi5XGqui/Vb3QiVYqhAoWVC4S2eqjx7P4/OYgh8GnBp3nwMfG64xddF0ZcbvT7kzKtHya7rWSZYrlUruUke4ipNaQ2W/p9aGj1OtViuQPq7bsng+Jd9O5nzdptaouggy0YgmluF92g6Wp66/tVotd3EdDAZYXFzE1772tULijUAgEAgEAoGNcManVH7605/G6173Ouzbtw+VSgW33npr4XqWZXjXu96FvXv3otVqYf/+/Thw4EDhnuPHj+Paa6/F/Pw8tm3bhp/7uZ/Lz405E9CIHA6H+e61H7QKFNUV3eF2cuWp1UmUaCzyVe9lPW6g052oXq/nz5Fk0JjTc69SKpEa8bzfM/HxPRqG9Xod9Xo9r89TSbv7k8eupMiTk0glKdVqFfV6PY+NUcXPoWddsUx9nu5W7At/NNOizoOPF8tjmUoqXFXUeeIzHD+Om8axOXSMnLT6+6yfB8r2+330er3CevW4La5rxujp+tYfkg32mapU2XpxJY9jwc9Gv99Ht9tFt9tFp9NBv9/PP1+uIupYck61nbp+WCezVLKdOt9cA5wDJT+65vjZ0jivVJt07Ln+tM5jx47hgQcewIEDB3DgwAE88cQTyUN+A4FnIzbTd30gEAhsZpyxMtVut/GSl7wEb37zm/ETP/ET666/973vxQc+8AF85CMfwRVXXIGbb74Zr3nNa/DVr341Dx6/9tprcfDgQdx2220YDod405vehOuvvx5/8Rd/cUZtYYzG6upqbjwxpTSvq2LAXWzer4TFyQyv0xilykXFaSNXNhp4NHAJNa7VxZAKRioGRMtzxUiNZd/l1+D8FHRcnAgqUfB4GVc2lLAp8eOzSjRUCeQYk1zR3YrjTMLrxjTHI8sy9Pv9gtqihFXbr/3RdcE26Bieaqx0LDgeqfdUKeErU33rYcBad4rQ9vv9UgWNa0DHRceU0DlUlzhd+2xTr9crkDd323SlzjcClPApadZ1rH0lmdV2DwaDAhnjNSpd/Fyl1E79jOj4s14lzE899RS+/vWvJ8c2EHi2YzN91wcCgcBmxhmTqde+9rV47Wtfm7yWZRne//7349d//ddxzTXXAAD+7M/+DLt378att96KN7zhDbj//vvx8Y9/HJ///Ofx8pe/HADwwQ9+ED/yIz+CP/iDP8C+fftOuy262+2Ki5OklPKTgmcu47OuuDjxUJKh6pWW66573j7fVS8zov2aG/vaLv9djVp9z93j1M2OY6zGsRrXngCA9zkRU7JJw7hMKSEhUdVMjWc1nMvi31heyvVP46SU8LiKA6wRUyeGCh9/de9zwsVyUmvK+1eGVOyXqlpOmFKuizpn6jZHQkSiqgfmuvqmz7tapuPi2TK9fo4zgJxM8TWljvq8E3yPyhdJLIlUpVLBiRMn0O/3sby8XDq+gcCzHZvpuz4QCAQ2M85qzNTDDz+MQ4cOYf/+/fl7W7duxVVXXYU777wTb3jDG3DnnXdi27Zt+T9XANi/fz+mpqZw11134cd//MfXldvv9wtB4UtLSwCQG03qVqRGvAbIq0rlbm3qpqQB7MBasDp3zZlYQsvR+lI79mpoUh1zZYb9AdYTAQ2yV6jR6sYqXdw8TkjP0nHCRUViPB7nCh+NU5anCpUnjvBEC3pdXfXYRzXy2Y9Go1GYG7q60dBXqIsj2+vKmRr/anx7shBVSlLzx4NcOQ9UHF2tUQLlKhDb6OSyjOAqNB25r1EnFLyXrpdcv1mWFQ4DZn84zuPxGP1+Pz8GQOfTCQzXpraFY6nt13XibqD+ueNnuNPpFEidKne6jrQtrK9areaugjMzM3m7uNYHgwEeeeQRPPnkkxsqt4FAoBzn+rs+EAgENjPOOGZqIxw6dAgAsHv37sL7u3fvzq8dOnQIF198ceF6tVrFjh078nsct9xyC7Zu3Zr/XHrppQBQMBRThpXvxNNo3Siux3ftNXbFd+zdNS9FUMqUMDW6U3EoKfcqf57tcyJVVre7zKX6rYRJ30sZwd4WfU0pf258p8htqk2peUqNjfbXE1v4+KRc3byeVD+9rNR174v/7TFNqiz5j89L2Xicamx9PL2cMnXMf1wpVCKkLn6agS/1+XBVSz9TqTWh41fmekj3v5mZGczNzWF+fh5btmzJSeXy8jIWFhbQ7XZP6QYbCATKca6/6wOBQGAzYyKy+d1000244YYb8r+XlpZw6aWX5q48DO6ngcRYD6CY8a3ZbOaB7prAwONh1EBst9t5pjR3cXKDTFUsuhiVxSVp4gElLJpOndcZL+KqhMcLqSGsJIv9ctc8KgHsl6tCbqi6kU13OdbDctlnN9jVuKbikIr5Unc9lqMET2OEdJyorvBedbVUdzM+q2tDlSTPwMj31PVRlUJV47zdTqQqlUqpyqfj4nOmh/U6GUqRgqmpqYKyw/c0hbkTbeCkoqlqFcfMiZ+2h/evrq6i1+vlype61vkGh46Pz5+79mlb+VlgFj6OD5OX7Nq1C7t27cLs7Cy2b9+OXq+Hw4cP48SJE7j//vtx9OjRSH0eCGxSlH3XBwKBwGbGWSVTe/bsAQAcPnwYe/fuzd8/fPgwXvrSl+b3HDlypPDc6uoqjh8/nj/vaDQaaDQa697XWB499yeVlU8NaHVTIpQ8AMVkD3rmkRIKTW5Q5t7Ge4nUrr9ec2UgZYT6jr1eUwNd4WWnynXjXtW8FMnRsnUcfFwJJ4LaBneJSyGlqvg86pikSIPWo23WpAWptmsb9Hqq/LLx1bo2ijnz9qfGNVW2zhnH2F1HN5rDlLrr5evvrM9VVlfIyuYz9bnQMdJnU20u6wMz/XEDoNfrodPpoN1uo9PpnLKcQCCwMc71d30gEAhsZpxVMnXFFVdgz549uP322/N/qEtLS7jrrrvw1re+FQBw9dVXY2FhAV/4whfwspe9DABwxx13YDwe46qrrjqj+nyHX2OZNMZDM8Zt5B7GMoA1cqSZxRi7w2fcPU9jTFQpSrlQsYyUq1XKparM/cszuCnpcTLmiQp4zbPZOZlS49oNfI1V4rOeXprEU13AtG+qBnGOnGikiBTLdULIclVJcgN/I1fPlNukq1zunqnw2CLGVzlh9zFKkTLGIKn6o4qMPu/rUduihNjboH+TjGj/lNxRQUq5iSqR0kyMSnB8HVHJ1bnr9/v5503j2ZSwacp4/s1ne71evg4PHTqEO+64A0tLSxF/EQicJZzr7/pAIBDYzDhjMrWysoIHH3ww//vhhx/Gfffdhx07duCyyy7DO9/5TvzWb/0Wnve85+XpUvft24fXv/71AIAXvvCF+OEf/mG85S1vwYc//GEMh0O8/e1vxxve8IYzzu6jioIad+qK5IatJzwo26UvM9z5u8ei6LNK6FyhYHvLdutZfkq50vZq+8pif5zEuSqhbUkZ2d43bVtqrGhAlyk8OmZqdPMeug0q6SgjHz4WZdfcVUxdyHRNlLXXx0JJBVBMO857qGLyfSceZcpdqg8cC00VrnO2UblKkE5Vn65P/cykynUVyvvndZV9npzw0wWxWq0W0rK7cueugK54Ms6x1+theXkZhw4dirNtAoEzxGb6rg8EAoHNjDMmU3fffTde9apX5X/Tv/m6667Dn/7pn+JXf/VX0W63cf3112NhYQE/8AM/gI9//OP5uRMA8NGPfhRvf/vb8epXvxpTU1P4yZ/8SXzgAx8448YvLS3lMR6+U6/ufPV6PY/fSBmKjLPKsgy9Xi9/X9UozcTn6c097TSf5996D+v1eCuN1SqDKwwKdzH0WCQ3grUfNM69X3zlvWoAb3S4acq4duKjxInjoeTOs7ZpOXrWl5NUT1yg/aE6qX+nCImrISlCxHvLiI0nJknNiZIAXld31ZRCmSLPOu5KRJ2MUMXjetfnUmRbM//p+0qgWB8/WyRCmkGvLMsfY7GcYPGzQVWZY7i6uppnGqQyxbbVarU8JnJqagq9Xg/Hjx/H0aNH4yDeQOBpYDN91wcCgcBmRiXbaGt8k2JpaQlbt27Fm9/85tyFSg9ApTHOQ1xbrVZu6HlMCI1FGmYkT04+9MwbT+zg6dM1U5vWqUa5Kw1q0LoioEqKEyReV9Kn46EGOsuhoatGPJEy2PVaSmUiyhQkJXo0ilVB4LPumkYCTAKkfWW7lXDRjTDl8qbrQudOy1VirXPsySrYbl0LNOK1v9qXSmUtiUS9Xi+oTdpOJdXeH1cVtU3aZ3V7TbkC6jpxgse+azY+Tcuu5TNLXr1eR71eLyh/TETBfvFePs86+LwS/jIyNRgMsLKygm63iyeeeCJXnsbjMbZt24bZ2VnMz89jfn4eKysrOHbsGI4cOYL77rsv3yQ5EywuLmJ+fv6MnwsEAmcH/K5/Ja5BtVI79QOBQCBwmljNhvgkPnZWvusnIptfGRhbASA3vmj46g491QOPl9Kzhmi40khWo1MN2xTUCFSipu3Rc4n0OY3xUWMeKE8m4a55rEuNZfbJsxBqualzuNQAdhc5hxrhPh4p6P1USVJjSnLp7VFFx8dSlTO/xr9Tqo6SFFfd/H0nNera52TYY47YRhIJHe8ywsox0DlKjbG2j+On/dZxdbLpSmAq9b+f76WukUqQ9X2qWjo+/vlTlcpf/fOg6pbOA+tpNBqo1+vodDpYWVnB8ePH8eSTT6Ldbq9rfyAQCAQCgcDZwsSTKQWNLFWJaDBrMLwayWowkkypIuAqRVnSAtbhMS1si6oraigSemaVu7G50alt8WyGNNL18F3tO4B8bFzBcNVJSaWrLXzWXeBShCZFjAi6aSr50Ln0/jrp473abo/NUePdlUkdVyUl7tbGejnP2v/UK5EaVx17hytVbKcmbwDS54spOVPCqs+oC6PWQWKv850iU+56yc+QthMokimgqCL6/Ki7qc8r6+/3++tIVpZleXn1eh2NRgPHjx/H8ePHceTIEXzjG9/Y0G02EAgEAoFA4FvFRJMpPS/KlQE3lnW3XQ05va6ERonN6upq4ZwlBw1YN4Rd0eC9KWNWjT413lW5UKLGPrM/agjzmVQsl7uHaR/cUHXVRNuXOh+K5aQMWI+LcVLh7fHy9HwkbYeTJ1VzvH7ti8a/6Vwrmd3IbdHHxgmvqzL6u6tMCiVG7A/L1TWmmQp1jp046r26MaBkyJUqJTV6X0rhSc2rluft8b91rfn8sq16WLa6AOomQL/fx2g0yonU0tJSqUIaCAQCgUAgcLYw0WRqdna24MbnRpsaororn1I4nFBRoVIDlLvuZUa/vhJaN/9WQ5w79q5+eFllKhl/9BBi9pHtc6M/lQpdx4BtLku8oOVupEw5UmqSHhLrJEyJA/vLuvm+JjEgUXEXO3Vj4xhrAgONE9J61VjXsVZSznvcXVGVKj2EOUWmtJ9l5bgKpTFHLN/XFOvp9Xr5eh4Oh3mqci03VTefT6VKZ33+WfL4Kj1qwD+Xut5T60bdb/l7v9/PNze0/SsrKxgOh/jmN7+Jb37zmxuuw0AgEAgEAoGzhYkmUyk1Q1WpVOwNkE6b7Tvy+t5oNFoXA6Kv/F0JxummYFeXKt2BZ/sVvKauWSkXuZQKAKypd6pusR4fI09yQRLhY1im0pQZshqL5gqiJ4Rw9abMZcvnLDXfruK4a1xKeVH1hC6TbAfbr8qNt1UTQ+g8+Px5O7wsJaxlY5Ca5zKCX1ZOaj2nxlCTnPj93i+SzrKNDD1zLPW5Yls55sPhMI9r1PXPxBTdbjdc+wKBQCAQCJwzTDSZUmOWcAKju9huYDuRSbk66e64KgApA5ftYRbBsvN9Ui5Y/X4/d1WisejQOBFNAEBjFViLh6rVausOauX9w+Ew2QY9j4vjo6RHk1ykEkd4kgH2m8azu5u5C6OSUCcXTghcVeR7bJsqUsBJlUONdVXv9FwiV6CUDHniCCo8KRKnygnVIHdJVSLBfqgSU6lU8jTDzBbo8Wyqcmm2PJ1XzRroSleK0DqJ8nHl2DabzUJMHlVCLZ+vPOjXSaWvpamptaMM2GYe3tvr9bC4uFgYp36/j+FwiCeffDLSoAcCgUAgEDjnmGgypYTAFQB3eXIDH1gzXnXnO0Wo3JD3mBR1C3OD0f/m84THajmRY7naJi+bhmdKddHxcDdCJTKukvg46fi5gqRIjdXpuFulEjdomWXKnsJJla8NfSZF0tyN0OvfqG/uAlfW55Qa5XU4UUytHVdhWUZKcUyNmd/L/qfqcuWP11ShKlMZXUHSa2Vrg/cxXlFTtfOzwp9ut4vBYIB+v4/BYJAY8UAgEAgEAoFnDhNNpmq1Wn4ODXf5K5VKrgpRnWk2mwXVg0YZjTCqEupq1+l0CrvcbrxS/eHvwNrZTpolj4pCWVIMVaRUQVhdXcXS0hIA5OdkKdlimXqWFLBmlPNcnXq9nitl1Wo1V0oUesAq28ayqCjQYHa1S41/JxRKZlIGtbtpEjT0VYnRdjmpK8u8qOqgnh+mZfBZKiKct9RcDQaDXGWq1+sFNU/7zvI4T1QL2X8qYO42mVIjPVZKXTWduFHN8r6lNhn4u8Y06SvP7dJxJfHnkQTD4RD1eh3NZjP/jDHrIGPX+BzHn+3iPap4KmnKsgztdhu9Xg+9Xg+dTqfwOaW73yOPPIKVlZV1mT0DgUAgEAgEzgUmmkypq5263vF3GtMkAkDxLCFVpDyLH9/TMt3gVKNeCYAbrG7IKlwRU4OTpIdGvrZXx8B3/PVeks2UupBSRdzAL1NkPPbFiY7e6+6VPl+ptqTG6lTqlBIHHQMtN6XKeZ88oYa61qmrn1/3enxeUnOgz6TgLoSs29eTq5H6bEphTa1nfd7bo39zvbJferCurnVN1+9llH0WVJEdDAb5hkev18s3TVZXV/P32+022u12cuwCgUAgEAgEnmlMNJkiUVIlQFUYxl54DImqFDSea7UaVldX82u8l+oWCZm+enpwJW1AMT4JKJIrNdCVYLghOx6P0W63C3U7gdTDgbWvLEvP8WFWN/6t5IFlsRySSW0LX5008nn9m/frmHKuOKaMOWKc2EbnY7mhr4cOK8FQRYZ9dAUpFfdVRrh1vdXr9YL62O/3k6SQKpYSNP5wXabiw9gOX7PtdjtJoHRdODieVHFcjdI50r/d/ZB11Ov1PHaJpEYJTqPRKIylrjfOiSupxGg0yg/YZfkaP8a2DYdDdLtdPPHEEznJCgQCgUAgEDhfmGgy5cRCyZESA1cqnMTo/Qo3Vj3+ShWr0zHYU6pHmSKhUPetVEyLGuop1zolBpqcQQmIKzPq6uaETOtPtcf75+qKklGm3WYfdX60XU44Ui6CSn7UAPc5JHkrU9F8zLQdOufaN3XlY1s4NzqmbIte41j5nLoi5m31/mucEl+V7LDulPLHZzxFurZRD7zWcnVjoV6vF/pEtzyOlR4+7OuLBJQufequyDrpZriwsBCufYFAIBAIBM47Jp5M+Rk7aoxqDBJQNFj9EFjGNs3MzORuRDTw3YCnYa5kjK9KaFLGMn+nYehB9YzxarVauPjiizEajXDs2LHc5Y+7+1TfVDnzGBhXGNy9jKRAFS8lCrzmLl76qgZ9ykhOuZcB6xMTaEZBEi5to5bHujk/TpS0fTyLyc8I0+x+bIsa/aqWpcamLBuez7G6+vl5WO6Op8/qeCsZ1eedgGk7NL5Of7TNSsh0nJ3IsTxPAsHzqxj712g0coLDMpTQce2miD/LUDVR19ZoNMLKygq++c1v5mdNBQKBQCAQCJxvXDBkSlUhN/jdGNfseGqw8Xe6c/lzep+TCHfl0veV0NA4Jnng73TFarVaqNfrBTKVZRk6nU6+y6+uUiRSGszvRr8TF2+3Ps/n1KB1lUtVPVW63N3PXcjc2Nc5Ypkai5UCY2q0TlV/GL+j97sbpZJpnReWyz6qyyH7RfWMbpRKyHRd6TiNx+M87s0VLtavsVv6rCaByLKsoCy5MqXP6o/OJ8mlzr27ehKa/IJ91g0Axi3RNa/RaKzLQKlujymVz8cJQO5KqX2iG+ChQ4ci/XkgEAgEAoFNg4kmUxq7AhTTjBPuysb3+OrKiO6+a+yOGuxu/PJV1QZ9xomIgs/RWB4Oh2i323mcDNuq5xSpO1wK/n5KuXDXQFdYdMzcLVBdGb2OjeBugU5Q+er9c0KkhMlVo1TckxNEHXfGbGmd2l8nzmVjrteUqBFK8geDwbrzmbQ//L1snTrJZ7/1TKfx+GT2QSU/7LOeB1UGbYcScvZP1UC2hQSL/QWQu/Vptk1VE7vdLg4fPpyTsKmpKVx88cVotVp5PxYXF7GwsIDl5eUNxz8QCAQCgUDgXGOiyRRTo/tOvsZSuOqS2vmnMqQGqMbLlKUfJ5iogbv8nkacz2jMkhME3sNU6f1+P1fHKpWTadiVJKrq4C6EChrFSiJoxJfFgJURJSVDejirEyl3F9uIaOkY6ysTF6RIEEml9kdJLxM/pIx7V0aoGvFgWL03NZ6p9rg65WuNz6jboMYa6VpTIqV163rl35rYQesbDAY5MefaZswU50WTi+j86PEBfJ9EX5NIZFmWp4dXJY9Z9zgHTOvPtihpr1arWF5exte+9rX8M8vkHNu3b0e320Wv18NTTz2FgwcPrhuTQCAQCAQCgfONiSZTrs6k1BqSDo1x0vgTGuY8g0kJgLuluYuUu5kBWGdAq5LlykfK5YsEQI1tgs8pvC2uvikxUPc7VXPcUHe3uFMpYH5d41lcxTsdBYv36pwpnAT4fanx0H7rPZx/utApmdko8QXL4lrRuUkpUzoeGh/mcX28x/8meSpba9onX8PqMqfrDFiLKfN+KElyd0HdbOC4uvLKjYVGo1FQ9lZWVtDr9fLsgMePHy+cczYajbC0tJRvLAwGA3S73XUEPxAIBAKBQGAzYKLJ1HA4zI01JQ9OqnSXneAOOxWfWq2W7+CrIU0DUwPoaQjTSHVXOTU6WZcam2r8ahkA0Ov1cPz4cQDAU089hWq1iu3bt6Ner6PRaORKHNvCcpSYAcWsbLyXBq4TLk1dThcwKmse+8L6nBxq/5TAck6A4iG6Ph+qUAHrY+Dczc/7oT+qcKXmQstnfBxjetQtTg+TVQKshEWvAWtngqVIjipUnKuy8dB1SrVJyTwVHCXtSi453qzDszF65kO2lXPEuWUyFm4mUPnlZ8XVUo5ts9lEtVrF1q1bUa1WsbS0hE6ng6NHj+LRRx8trCMl36urq3j00UeTRDYQCAQCgUBgs2GiyRRQVCH4t77P3z22Clivmvhzel3JwUZtKFOIvH1lZfAZummpIubGuZblhEJVIxr76ualRNEVjrJ2lilRKSKlSpeOZUrtUjJS1gZXf3Qs3PXOSavWU6Y++pj6Pan2sa/exrL6Cc3q52vKn/E+aZwgr7krZUo5ZRk+zz5HPm9+npT3S11ZmV1yNBqh0+kUCOPKykp+LhQzU5bB4x4DgUAgEAgENismmkxxF11VAqpNakhSvaLh1+l0Cime1R3Ks7zR8FOXMDVO1ShmmnK2SctnuTRINXaKYNvr9Xoh9TbjXur1+ro62CY9lBVYn0lPY7oU7tbFcWUqcSUr2l4lmZq8I0XkeJ8b6qpEpZQfvU9f3dBWAqOERl3qPAMgn1N1yOvz2CyNUVLyCCAfL1V0Um551WoVs7Oz+VxrPBHbmeo/x5mZ7mq1Wl4n+8J55ivHgp+LlIqkdes4cR0OBgOsrKwkSXK9XkeWZWi1Wmg2m5iZmcG2bdtw6NAh3HHHHWi32/lnjuMWZ0MFAoFAIBC4kDDRZIpQQ5IGsru+eaIFPqduYSwDWK86AMXkDWUZ3k5HJUupPqk2liVv0H6lFJOyv8vq3khZc3KSil1JtcGJE7A+sUOKyCi5SyVI0Hq8bak26O96j8azlbmSuduct5991PaSQJUpTCxPVSl110u13f8uG49U+8vWJsmVpuonaed7TJTBZ7kmqVYRmhyFbo7tdhsrKyvJdgUCgUAgEAhcKJhoMrW8vLyOhHD3Xw1hdzGj4eixML5Dr7EttVoNMzMzBZVpMBjkZICH6Kq6k4qpcTjp879VhdAYGHUVc3IHFF3dqEzQ4Pd7VcVg/5QQuOtaSk1T9zmqb3yW7VOlCECuspWpZT5OTh5TGfD0XmC9Qkejn3OlMUEem6Xjw1eSEK6XbrebKzlUQfUAZSZaYFuy7OSZYUx4olkMVVnTufMzrdRd0QmyKkiMs9J+aMINHROSqHa7jcFgkKtSzWYTu3btQrVaxczMDDqdDj796U/j8OHDeRlcjxdffDGuvPJKHDt2LA7VDQQCgUAg8KzARJMp7pCnkhYAKBjfwPpze/i77uB77I8qNH6wLY1aJU6qdqi65QrBRuqFEhtX1zZSkVIE4lSp2L1+d0tzMpJSgPSwWj6nY6R/ewa4lLqm6pRClSp1H/T587FRsuj90Cx1Og6ufnH8VdECUCCOJCskXUp8fX3putWEGjpuqbZ521PkmPd47FFKmdS6mHyj3+/nmxIA8oOk5+bmUKlUsLKygqNHjybr3LZtG5aWliL7XiAQCAQCgWcFJppMUVkoM7oJVTBobFMZIPSgXicA3LGnAtFsNpPt0UxtfE6NZzXsaXQDa+dUab8U3hbeq+qUGuSMeeL4ZFmWZ39Tl0ZVWDRmKpWpj+PKupkcQ4mUKnv6PJ9zoqOxbeynx+Wo8a/Z7LQvSmyVAFNRZNv4vhJcjhv7wjlMkUWWyTWnCRdYN1Wuer2erzFVOLW/ui6d5LDNVIxYt64l7SNfsyzL4+c0TtAJLctlQohOp5P3gUoo18xwOMTS0hI++9nPYmVlBceOHUuu/6WlJRw4cGCdIhYIBAKBQCBwoWLiyZTGd7iywnv4qoRBjXAlACkyRZep8XiMVquVEyFXhmgUaxpqVTA0Rohqlqo5jjIVIaWwuasgXzXNN7DmqpZy3XO1RkmNuxNqOm13vVNixevaF7q/OTHS+dSylWx5X1KKk7s9qqqYioMj4dKkISllRc+h0rOiOB58ZVk+x54ARMfdx1LHUeOWdIzVRVCz/Clx18+Gq1DD4RD9fj93deQa5Rphe0ejERYXF/HFL34Ry8vL68aF6HQ66HQ6pdcDgUAgEAgELjRMNJmi+xFBY1DjoxjTw+tKmIAi4aFhPTU1hbm5ucLZROr25wqDGqAp0sCyPe7Fy+WZR81ms+CiRaVE1R1Vp5wUsT4ABXKgJEeNa095nRobVXbcrTB18KzexzK9brbHM+RpWUpA2XdXrZSwakIIHW8lQpwTP3tKibYnKVFXPsZ6ZVmGmZkZZNnJLHXj8TgnJ1SIuB6VAPqa4FhrRj+Oi2ZIVOKkMXSpPuhcOcklkVKSxrI0Q2a/38fi4iIWFhawvLyMwWCwbo4DgUAgEAgEns2YeDJFVYHGobo30Y1MD1JV41WNZF5jume6fBHtdhu9Xm+d25TGxqgCAqCwu0+4ssTXqampPCV6q9XC6upqvstPlyyNDXN1KuUW5/2kce8qmR++6kqTjo/GLanSo2OVUqw0hscJkroj6nOqDvr8aT38XRUlziGJNAkMSdVgMCiQO3V/VLc+HVtClZtWqwUAubvc4cOH0el0Cu52mirfY6o8o2CKUGkbmeiEhE7Tt6s7psZ2OYly90FuIgDISdpgMECv18OhQ4fwla985ZRnQwUCgUAgEAg8GzHRZErjk2i0qtKj7nZKBFJKykZGeooc+N9OkE4HThr447FNVAvcnausvd4m/q0qhRITd8MrA419JTiqUnl6eU27rQRB3RFJDNWdUF/dzc/7eKr2ptwoOcZlbogEx0nPqFLyMxqN0O/3kWUZer1engFyMBgU1Eo9E0znztumqqkriakzorjePWOkq63eX73Hk3qQaC4tLeHEiRNYWFhYRyYDgUAgEAgEAicx0WSKyg2N1Vqtlmf4U9cxVWKmp6fXHS7rwfnAGhnhjrwaq6oS8W9VadTgJmjMqstWKq6F7VTXMB7Wy7TbZenEgaISxXI9uQVJqBI4TeueOhTXFShX5/Q9jq+6qgEozA2TSVBZZN1UEzWDocNVMCVbrvRQrXSyWla2zpUTGlesxuMxut0uxuNxrky12210u91c4eF8MUW6rjVNTKHrQ8eNsUy9Xi9/pl6vY2ZmphDTxDKdcHJcNPW6rhNNlZ5lJ9O2DwYDPPHEE/jGN75RiPkKBAKBQCAQCBQx0WTK3b9U8QDWdvpJptQlSu9XqEtUKjFFmRqiBryTNJbL19Q96g7mmd+UaPmPvu9t0/gkV2YUKQPcE1N4P1Jz4GPIWCwdB7aZbmj66uNRRhjL5iCltLniqOPhBwZ7Pf6e1s++0V2QyhTJj5btrqCejdHnJjXXDo/b0zb6uHEuqJyx7XQl1bnu9Xro9/vodDqRkS8QCAQCgUDgFJhoMqVqEsmSujQxfXen08njQjR2hgY1Y0i480/3LRqgvqNPKHFLESmFZlBzQqJtr9fraDQaeWwTUFTeGCujbmTsjyfXIMFS1zp1r3PSwrEkKSA0Nod9TLnPkWQMh0MMh8NctWFSDyo12ha2XxUp7Z+WXTbGqb4oQVM3QnX5TClNGxE1HR9mwjtx4kQhtTjbx2Qi7DdT6utcO8H2/k5NTeVxV9VqNSdp6vapcFdJrodOp4N2u527ILL/7XYbX/rSl7CysrJunCNGKhAIBAKBQODUmGgytbKykhvoZUYwDUoasVSpUkY3kE4/zuc3cg1zlMVdbRTn48qKP+OuW672pNSKFOnxNqX6pIRlo7E9FbzN7l7ofSnrb4pMpdrqdaVcAlPqjZepY5JSubiuSByZzU+f8/lw1S01x2X3apwWyRzd+8pUQ1UIeRhvu90GcJK8djodrKys5O8FAoFAIBAIBM4ME02mbr/9drz85S/HRRddlL+XMrhphDJGRWOOUkY0lQVe13grTR3tbnhq+NLQ1ux1zNinbmIpRYTqB+NkgLUkFOPxGI1GY52Kw0QOVKg0cYEqVnov+wusJyKsS9UYN/Q1KQbbTRUGAJrNZv6euzGWkRmNU1LlycdHQUVGVS8H+5Gql3Pk1zQZhpMkEnPGfGn/OQZU5Ni+wWCQx+zpWKdIqbpDrq6uot1uo9/v5+c8zc/Po9vtolqt5oqXzsN4fDJNO1XW4XCIQ4cO4Rvf+Ea+DrjGAoFAIBAIBAJPD6cnswg+/elP43Wvex327duHSqWCW2+9Nb82HA5x44034sUvfjFmZ2exb98+/OzP/iyefPLJQhnHjx/Htddei/n5eWzbtg0/93M/V3A1Ol0cPHgQS0tL6PV6eawKXa08kQTfI7koU4s8UYGSllTih9RzSkL8AFjN7pYqS9urhMj75vExKbWH5elzni3uVOqSt0Xr9R93P/QfJ6DuJgmg0C5NyJBqr6tVZXFtrgClxngjFz8nZ06eVTlKzYW2S5Ui76NmmdQ50iyB7XYbKysr6Ha7+bqnOsa10uv10O120e12cxI2GAzQ7XZx4sQJnDhxAseOHcPi4mJk6gsEAklspu/6QCAQ2Mw4Y2Wq3W7jJS95Cd785jfjJ37iJwrXOp0O7rnnHtx88814yUteghMnTuAd73gHfuzHfgx33313ft+1116LgwcP4rbbbsNwOMSb3vQmXH/99fiLv/iLM2rLcDjEl7/8ZTzyyCN43vOeh3379uVZ03SnXo1kjVnxa0DxwFYqGZ5Egc85YdJziuhipmm1GUcDrCkSdA+jQc3YrvF4jJmZmYLrGNU1bQuJipMB9tMTbzBpAstUYpQy5P2gVpZJ1YV1pUiNx/QwDkfP39JMgHzdyO0vRRjL3Bz5N9viaiXHlPOn/VBoO3mtVqvl7n3dbjc/1HZmZqaQyY+kiXOkY8d1yng9lkWXPCaIIMmamprCiRMnsLy8jFqtht27d+dt1oQS999/P5aXl9cR4JWVlQ3dTAOBQIDYTN/1gUAgsJlxxmTqta99LV772tcmr23duhW33XZb4b3/9t/+G17xilfgsccew2WXXYb7778fH//4x/H5z38eL3/5ywEAH/zgB/EjP/Ij+IM/+APs27fvtNsyHo/x5JNPYmpqCvPz85ibm8tdy6ampnJ3OBrhnjzC44lokNP41gQOCi1PM9IR7u6ncVqaeY/uZVoun6ermKoxJG56+G2qHHVL0z5pEga+KtFTRY/XnegBKChQrCNFVFzR0Ta4u2WKINEVzQmOurPxXleKdCxcwWNblFApkWK5TvS0TL5P1YgqEdcD1SCSID9fS4ku+0jlSRNa8Bwr9qHdbuP48eO45JJLcpJMsk9V6vHHH8exY8cQCAQCTxeb6bs+EAgENjOe8ZipxcVFVCoVbNu2DQBw5513Ytu2bfk/VwDYv38/pqamcNddd+HHf/zHn1Y9NDhpwJIc8CwjoEgy3GWN97AszbqnRrWSAE11zb8105q7eo1GozybmrrbafsB5OpVvV7PiRPbkUpIoMRQCROJXIo0KkHyMaHrGMdGiQxVEsZmpdwONbMg28ZrqogROqauPKXcMXXMeN9GSUgUShb5XLPZLMx9v9/Pr2u7fwwiAAAZXUlEQVR7PGU+SVC1WsWePXsAnDQyZmZmCvUxVorrj0oVyRddVPlaqVQwMzODdruNQ4cOFQgVz1Y7dOjQus2BSqWSpzUPBAKBc4lz9V0fCAQCmw3PKJnq9Xq48cYb8cY3vhHz8/MAThqBF198cbER1Sp27NiBQ4cOJcvp9/u5gQsAS0tLpXXS4CWZ0kQArkqkyBSf08QBmqBAiZO6sWnMkB6sq0kEaKirge6xPErENGEB26RptZWMsa0ee8T4HCcsfMaVK43rYb+0ra5UeRxTpVLJSaBeY/uBolsg66dS4yrQRjE9qkypq6MSWHc1VBdEkil14dP09a52aayTEqvp6Wns3LkTjUYDF198MWZnZ3N3PMYqsTyqbXTvY1mqilWrVbRaLXS7XRw6dCgZY3DkyBEcOXKkdGwCgUDgXOF8fNcHAoHAZsEzRqaGwyF++qd/GlmW4UMf+tC3VNYtt9yC97znPaXXsyzDsWPH1mWVo6HcaDTQaDRwySWXYG5urhBLo3E4riKpYU2Dn7/TyPdYHXcxc/dCr1Pv43W+qqpDgqgug1mW5WcPuWKlB7PqIbKuzvk4jsdrWQjZP1d8PD5KFTyNVfPMiNofQseXfabLmvZDx8bbQeLnahfLUcKi96orprZPY6QUSlD5pc/+7tixA3Nzc3jOc56DLVu24KmnnsJDDz2EVquFubk5jEajnBSREPM8MSaD0HGs1WpYWVmJ854CgcCmxrn8rg8EAoHNiGeETPGf66OPPoo77rgj36kCgD179qzbUV9dXcXx48dzVynHTTfdhBtuuCH/e2lpCZdeemn+d5ZlePTRR/H4448nn69UKpibm8OrXvWqgp+2kwCWxT7oeT6qXOnzTCSQitmhyqSueHSfKyNgruSQuJEI0Hh3NztX3dhuuo7xfibB0OQSJBiasCCV5U/jj6hM8dXJGt3Y+J6CLm0kJuouSRVxMBgUiFBKWWS7mayBc62xSewPSZsTM6pwJDBlRErnp9/vY3FxMa+TRH3nzp349m//dmzbtg3//M//jM9//vO4/PLL8aIXvQjD4RALCwv5XFWrVVx88cVoNpt48skn8eCDD+btciUwEAgENiPO9Xd9IBAIbEacdTLFf64HDhzAJz7xCezcubNw/eqrr8bCwgK+8IUv4GUvexkA4I477sB4PMZVV12VLJPK0kZIJYpQ9Pt9PPXUU8iyDM1mMw/ez7IMW7ZswUUXXZQTHlWmUm5yTAyhKhKfU1VFXe48iYISKZbtCSL8mhIBTXqhRreqJ56GW2O63L3Q45T8mmYqVDXO++Ltcbc9tlFdCpUQ+jgBWEd0UwqVH2CrdbtbZ6p8fc5js4jBYIBer5cnl9AxZka/Bx98ELVaDYcPH8ZoNEKn08HRo0exurqKpaWlwniS2Lbb7UhRHggEJgrn67s+EAgENhvOmEytrKzku+gA8PDDD+O+++7Djh07sHfvXvy7f/fvcM899+Dv//7vMRqNct/oHTt2oF6v44UvfCF++Id/GG95y1vw4Q9/GMPhEG9/+9vxhje84RnN7tPr9XD33Xevc4kDgBe84AV45StfmZMWZkUbj8fodrv5Yb90HZybm8tdvWjIK5HyzH1AMZW6xle5wU7VhuoFyRtjbIbDIWq1WoFQqeJFBYZqDY1+9pUZDlPkQRUkEkAlkqocpUgVy/KkF55kQ+OI6L7IsWW/2R6Ny/LYLEKJCGOnlKiyTT5XrEOVRY0L85imdruNTqeDTqeTEyMS18cffxwHDx7EF7/4RRw8eDB36Tty5AhOnDixTmWqVCp46KGH8ti4QCAQ2EyY1O/6QCAQONc4YzJ1991341WvelX+NyX56667Dr/xG7+Bv/u7vwMAvPSlLy0894lPfAKvfOUrAQAf/ehH8fa3vx2vfvWrMTU1hZ/8yZ/EBz7wgafZhdNDlmXo9XrJa0tLSzh27BgajUbuzkfjne5+TByhZwcBxYxz/FEXOD3jydvjShXJlxMwqkJ+qGtZYgglUXzV9pKsqTuiqkusw9ukPynXRLq9eVyUt5FqGdtarVbR7/cL45YaJ++z3qNz4e97fJePPdtNl8p2u523McsyNBqNghtjrVZDs9nMM+dNT09jYWEBAPDUU0/hqaeeysvWbIyBQCAwKZjU7/pAIBA416hkbvVOAJaWlrB169azVl6r1cL8/Dy2b9+Ol770pYXzqU6cOIFer5cb33Nzc3nmti1btqyLF1IlheSELl1+FpS7xqnSoa9UU6g2aUZBlknjv9frYXV1FSsrKxgMBuh0Ormyxkx7s7OzBcWLiRD83Kfp6ek8bbgrUfzdk2uo4kNofNR4PMbKykoh0x3bNTU1hWazienpaczMzKBer6/LtqfjrZkMNcbM4UTOs/FRTSQpuvfee9HtdnMi+9KXvhSXXXZZ3jeeAXbo0CHcfvvt6HQ6OQlvt9uhNF0gWFxcLMSABAKBcwt+178S16BaqZ36gUAgEDhNrGZDfBIfOyvf9c/4OVOTgG63m7udraysYDwe52c9aUY/uqS1221kWYbZ2dk8/kgVKnVJU+JTltSh7D1VgJhS2zPpkbwpSSCxYgINVaKUdNBNkIob++Hp35VgpaCKj/Y15Tao6p2eu6XtqlarhdTqPkap7Ig6Jg4lU6rgkaAyFW+tVsPy8jKeeuoptNttACeJEwkS3RFJ+qamptDpdJKpywOBQCAQCAQCFz6CTAmWlpZwzz33YGZmBt/xHd+Bubm5XB0hOalWq7m7YKvVKhjYroy4cqRQFz49d0nBZ0k26vU6pqen0e/3c4LnacHr9Xp+n6dHp9tfr9crxIDNz8+jXq+jVqvlbomaSIMEqUyZUgKj19X9TmOiSKQ6nQ56vR6mp6cxPz+PWq2G3bt3o9ls5sRNE2i4+6CSJ1XFtB2EH2bMew4fPowDBw7k7eT46HP3338/HnvssULfqtUqut1uqetoIBAIBAKBQODCR5ApQb/fx5NPPonZ2VlceumlucsZSQB/aOD3+/3CIb8kRkqq3I2Pv6figqg0+bPqLsi/Gc+lBxxWq9XcZQ9ATv4A5PeTMDD2p1qt5uTFz6Bie1V90+QamhhCXRb1b89aCCAnqJpoolarYWZmBlu3bkWr1crJIt0W6aKn0HFLZSR0V0QSS713eXkZTzzxxDoiq3V4HFQgEAgEAoFAIAAEmUpiMBjgoYcewuOPP54kFwDyeCJVbdQlr1Kp4Morr8QLXvCCglubG/6e6Y6/AyiQEE1EQfc7KjbqWqjEiGSpWq0WDuJlG9lOZiX05BKaUl3Pj3KyR5DEkLT5eU3s62OPPYYDBw5gOByi1+uhUqnk6ervv//+whlamghiPB5jZmYG+/bty/s2Ho/x6KOPYmlpaV1bduzYgRe84AWYnp7OieeBAwewuLiY33f06NFSIhUIBAKBQCAQCGyEIFMJDIdDPPbYY99yObVaDS984QtzMsW04MBaQgc9PwrAulgmAAWipuQNKJ4rpenSqSZp3JMqObxOxQo4mTZd1TGWW61W8wQRLDsFz2boYAa/gwcP4itf+crTIjE7duxAo9HA7Owsms0mRqMRHn74YTzxxBPr7r388svx/Oc/Pz8XrN/v45FHHkneGwgEAoFAIBAInCmCTD2DOHjwID73uc9hy5Yt2L17d0HV8ZgevkekzlTSg4Q10QQTOQwGgzxJBuOmWq0WWq1WnoxiOBzmCTSokDH199atWzE3N4etW7difn6+QM7UXXE4HOJrX/sannrqqbydPHuEyLIsT3fOc5eYIOPQoUNPWw3q9Xp44okncldBnv+UwtLSEr7yla/kyly/349kEYFAIBAIBAKBs4YgU88gvvGNb+CRRx7BFVdcgR/6oR9Cq9XKiQsJipIaP0OJipGeMzUejzEYDNDv93MCxZToo9EIx48fBwBs2bIFjUYDl1xyCXbt2oV6vY5ms4mVlRUcPnwYg8EAy8vLORmanp7Grl27sGPHDlx++eW4/PLL834w1fpwOMTy8jKWlpbw4IMP4p577snv+c7v/E5cdNFFhbTlTM1+77334sknn8zv/Vbc6jqdTp4w4lTlHT9+HJ/5zGcK76Wy/QUCgUAgEAgEAk8HQaaeQdDlbWVlBY8//nieMa9Wq2HLli152u1+v5+Tq9nZWWzfvr2QRIGudwDy86Hq9XohFqtWqxWUpPn5eTSbTezatQvbt2/HsWPH8Pjjj6PdbmNhYQGrq6t5OngSudFohGPHjmF5eRlHjhzJ6+O9PNC20+lgYWGhQEyWl5fx6KOPFs6EohrEes7muJ4ugjwFAoFAIBAIBJ4pBJk6Bzhy5Ag+/elPA0B+QO23f/u3o9Vq4dFHH8XRo0fza1deeSX+9b/+14UU5YxXomtbq9VCvV7HYDAoJJxgUoxarYaLLroIMzMz2LJlC2ZmZnD//ffjr/7qrwpJMJyUMLaKcVaEJ8dg/JHim9/8Jg4fPryu7yRVgUAgEAgEAoHAhYaJJFOTln2NKb6J1dVVdDodVCoVdDoddLvd/BoPEK7VarliRLXJswJqhj5Nga7Z/Xi2VLfbxfLy8jPWR7YrELhQMGn/ZwKBCw38DK5iCMTHMRAInEWs4uRG/9n4rq9kE2gxfPOb38Sll156vpsRCAQuYDz++OO45JJLznczAoFnLeK7PhAIPNM4G9/1E0mmxuMxHnjgAXznd34nHn/8cczPz5/vJp1VLC0t4dJLL42+TRAu1H4Bz76+ZVmG5eVl7Nu3r5BhMxAInFvEd/3kIvo2ebhQ+wU889/1E+nmNzU1hec85zkAgPn5+Qtu0ono2+ThQu0X8Ozq29atW89jawKBABDf9RcCom+Thwu1X8Az910f266BQCAQCAQCgUAg8DQQZCoQCAQCgUAgEAgEngYmlkw1Gg28+93vRqPRON9NOeuIvk0eLtR+AdG3QCBw/nAhf0ajb5OJC7VvF2q/gGe+bxOZgCIQCAQCgUAgEAgEzjcmVpkKBAKBQCAQCAQCgfOJIFOBQCAQCAQCgUAg8DQQZCoQCAQCgUAgEAgEngaCTAUCgUAgEAgEAoHA08DEkqk/+qM/wuWXX45ms4mrrroKn/vc5853k84It9xyC773e78XW7ZswcUXX4zXv/71eOCBBwr3vPKVr0SlUin8/MIv/MJ5avHp4zd+4zfWtfsFL3hBfr3X6+Ftb3sbdu7cibm5OfzkT/4kDh8+fB5bfPq4/PLL1/WtUqngbW97G4DJmbNPf/rTeN3rXod9+/ahUqng1ltvLVzPsgzvete7sHfvXrRaLezfvx8HDhwo3HP8+HFce+21mJ+fx7Zt2/BzP/dzWFlZOYe9SGOjvg2HQ9x444148YtfjNnZWezbtw8/+7M/iyeffLJQRmqef/d3f/cc9yQQCMR3/eZFfNdPxpxdqN/3m+m7fiLJ1P/6X/8LN9xwA9797nfjnnvuwUte8hK85jWvwZEjR853004bn/rUp/C2t70Nn/3sZ3HbbbdhOBzi3/7bf4t2u1247y1veQsOHjyY/7z3ve89Ty0+M7zoRS8qtPuf/umf8mu//Mu/jP/zf/4P/vqv/xqf+tSn8OSTT+InfuInzmNrTx+f//znC/267bbbAAA/9VM/ld8zCXPWbrfxkpe8BH/0R3+UvP7e974XH/jAB/DhD38Yd911F2ZnZ/Ga17wGvV4vv+faa6/FV77yFdx22234+7//e3z605/G9ddff666UIqN+tbpdHDPPffg5ptvxj333IO/+Zu/wQMPPIAf+7EfW3fvb/7mbxbm8T//5/98LpofCAT+BfFdv/kR3/Wbf84u1O/7TfVdn00gXvGKV2Rve9vb8r9Ho1G2b9++7JZbbjmPrfrWcOTIkQxA9qlPfSp/74d+6Ieyd7zjHeevUU8T7373u7OXvOQlyWsLCwtZrVbL/vqv/zp/7/77788AZHfeeec5auHZwzve8Y7s277t27LxeJxl2WTOGYDsb//2b/O/x+NxtmfPnuz3f//38/cWFhayRqOR/eVf/mWWZVn21a9+NQOQff7zn8/v+Yd/+IesUqlkTzzxxDlr+6ngfUvhc5/7XAYge/TRR/P3nvvc52Z/+Id/+Mw2LhAIbIj4rt/ciO/6d5zfRj0NXKjf9+f7u37ilKnBYIAvfOEL2L9/f/7e1NQU9u/fjzvvvPM8tuxbw+LiIgBgx44dhfc/+tGPYteuXfiu7/ou3HTTTeh0OuejeWeMAwcOYN++fbjyyitx7bXX4rHHHgMAfOELX8BwOCzM3wte8AJcdtllEzd/g8EAf/7nf443v/nNqFQq+fuTOmfEww8/jEOHDhXmaOvWrbjqqqvyObrzzjuxbds2vPzlL8/v2b9/P6ampnDXXXed8zZ/K1hcXESlUsG2bdsK7//u7/4udu7cie/5nu/B7//+72N1dfX8NDAQeBYivusn43sjvusnb84Uz6bv+2fyu756ltp4znD06FGMRiPs3r278P7u3bvxta997Ty16lvDeDzGO9/5Tnz/938/vuu7vit//9//+3+P5z73udi3bx++9KUv4cYbb8QDDzyAv/mbvzmPrT01rrrqKvzpn/4pvuM7vgMHDx7Ee97zHvybf/Nv8OUvfxmHDh1CvV5ft5h3796NQ4cOnZ8GP03ceuutWFhYwH/8j/8xf29S50zBeUh9xnjt0KFDuPjiiwvXq9UqduzYMVHz2Ov1cOONN+KNb3wj5ufn8/d/6Zd+Cf/qX/0r7NixA5/5zGdw00034eDBg3jf+953HlsbCDx7EN/1m/97I77rJ2/OHM+W7/tn+rt+4sjUhYi3ve1t+PKXv1zwNQZQ8Ed98YtfjL179+LVr341HnroIXzbt33buW7maeO1r31t/vt3f/d346qrrsJzn/tc/NVf/RVardZ5bNnZxf/4H/8Dr33ta7Fv3778vUmds2cjhsMhfvqnfxpZluFDH/pQ4doNN9yQ//7d3/3dqNfr+Pmf/3nccsstaDQa57qpgUDgAkB8108m4rt+snEuvusnzs1v165dmJ6eXpcR5vDhw9izZ895atXTx9vf/nb8/d//PT7xiU/gkksu2fDeq666CgDw4IMPnoumnTVs27YNz3/+8/Hggw9iz549GAwGWFhYKNwzafP36KOP4h//8R/xn/7Tf9rwvkmcM87DRp+xPXv2rAsCX11dxfHjxydiHvnP9dFHH8Vtt91W2KlK4aqrrsLq6ioeeeSRc9PAQOBZjviun6zvDSC+64HJm7ML/fv+XH3XTxyZqtfreNnLXobbb789f288HuP222/H1VdffR5bdmbIsgxvf/vb8bd/+7e44447cMUVV5zymfvuuw8AsHfv3me4dWcXKysreOihh7B371687GUvQ61WK8zfAw88gMcee2yi5u9P/uRPcPHFF+NHf/RHN7xvEufsiiuuwJ49ewpztLS0hLvuuiufo6uvvhoLCwv4whe+kN9zxx13YDwe518qmxX853rgwAH84z/+I3bu3HnKZ+677z5MTU2tc3UIBALPDOK7frK+N4D4rgcmb84u5O/7c/pd/y2nsDgP+J//839mjUYj+9M//dPsq1/9anb99ddn27Ztyw4dOnS+m3baeOtb35pt3bo1++QnP5kdPHgw/+l0OlmWZdmDDz6Y/eZv/mZ29913Zw8//HD2sY99LLvyyiuzH/zBHzzPLT81/st/+S/ZJz/5yezhhx/O/vmf/znbv39/tmvXruzIkSNZlmXZL/zCL2SXXXZZdscdd2R33313dvXVV2dXX331eW716WM0GmWXXXZZduONNxben6Q5W15ezu69997s3nvvzQBk73vf+7J77703z3Lzu7/7u9m2bduyj33sY9mXvvSl7JprrsmuuOKKrNvt5mX88A//cPY93/M92V133ZX90z/9U/a85z0ve+Mb33i+upRjo74NBoPsx37sx7JLLrkku++++wqfvX6/n2VZln3mM5/J/vAP/zC77777soceeij78z//8+yiiy7KfvZnf/Y89ywQeHYhvus3N+K7fjLm7EL9vt9M3/UTSaayLMs++MEPZpdddllWr9ezV7ziFdlnP/vZ892kMwKA5M+f/MmfZFmWZY899lj2gz/4g9mOHTuyRqORffu3f3v2K7/yK9ni4uL5bfhp4Gd+5meyvXv3ZvV6PXvOc56T/czP/Ez24IMP5te73W72i7/4i9n27duzmZmZ7Md//MezgwcPnscWnxn+7//9vxmA7IEHHii8P0lz9olPfCK5/q677rosy06mS7355puz3bt3Z41GI3v1q1+9rr/Hjh3L3vjGN2Zzc3PZ/Px89qY3vSlbXl4+D70pYqO+Pfzww6WfvU984hNZlmXZF77wheyqq67Ktm7dmjWbzeyFL3xh9ju/8ztZr9c7vx0LBJ6FiO/6zYv4rp+MObtQv+8303d9Jcuy7My0rEAgEAgEAoFAIBAITFzMVCAQCAQCgUAgEAhsBgSZCgQCgUAgEAgEAoGngSBTgUAgEAgEAoFAIPA0EGQqEAgEAoFAIBAIBJ4GgkwFAoFAIBAIBAKBwNNAkKlAIBAIBAKBQCAQeBoIMhUIBAKBQCAQCAQCTwNBpgKBQCAQCAQCgUDgaSDIVCAQCAQCgUAgEAg8DQSZCgQCgUAgEAgEAoGngSBTgUAgEAgEAoFAIPA0EGQqEAgEAoFAIBAIBJ4G/n9yOMMUn7XECwAAAABJRU5ErkJggg==\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Loss, Metrics, optimizer etc.."
      ],
      "metadata": {
        "id": "Vs2K8pcRPKXD"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import glob"
      ],
      "metadata": {
        "id": "HPHV0fGXPvsZ"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "### COUNT OF WEIGTHS\n",
        "columns = ['0','1', '2', '3']\n",
        "df = pd.DataFrame(columns=columns)\n",
        "train_mask_list = sorted(glob.glob('/content/Training_Data/Y_train/*.npy'))\n",
        "for img in range(len(train_mask_list)):\n",
        "\n",
        "  #print(img)\n",
        "  temp_image=np.load(train_mask_list[img])\n",
        "  temp_image = np.argmax(temp_image, axis=3)\n",
        "  val, counts = np.unique(temp_image, return_counts=True)\n",
        "\n",
        "  conts_dict = {str(i): 0 for i in range(4)}\n",
        "  for v, c in zip(val, counts):\n",
        "    conts_dict[str(v)] = c\n",
        "\n",
        "  row_df = pd.DataFrame([conts_dict])\n",
        "\n",
        "  # Uso di pd.concat per aggiungere la nuova riga\n",
        "  df = pd.concat([df, row_df], ignore_index=True)\n",
        "\n",
        "label_0 = df['0'].sum()\n",
        "label_1 = df['1'].sum()\n",
        "label_2 = df['2'].sum()\n",
        "label_3 = df['3'].sum()\n",
        "\n",
        "total_labels = label_0 + label_1 + label_2 + label_3\n",
        "n_classes = 4\n",
        "\n",
        "wt0 = round((total_labels/(n_classes*label_0)), 2) #round to 2 decimals\n",
        "wt1 = round((total_labels/(n_classes*label_1)), 2)\n",
        "wt2 = round((total_labels/(n_classes*label_2)), 2)\n",
        "wt3 = round((total_labels/(n_classes*label_3)), 2)\n",
        "\n",
        "print(wt0, wt1, wt2, wt3)"
      ],
      "metadata": {
        "collapsed": true,
        "id": "ILBgwHbTPvpJ",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "2a3a082f-3fe0-4700-fb97-ac5ceaae689e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "0.26 8.56 29.69 27.28\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import tensorflow.keras.backend as K\n",
        "\n",
        "def DiceCoefficient(y_true, y_pred, smooth = 1e-6):\n",
        "\n",
        "    y_pred = tf.keras.activations.softmax(y_pred, axis = -1)\n",
        "\n",
        "    # Cast to float32 datatype\n",
        "    y_true = K.cast(y_true, 'float32')\n",
        "    y_pred = K.cast(y_pred, 'float32')\n",
        "\n",
        "    # Flatten label and prediction tensors\n",
        "    inputs = K.flatten(y_pred)\n",
        "    targets = K.flatten(y_true)\n",
        "\n",
        "    intersection = K.sum(inputs * targets)\n",
        "    dice = (2 * intersection + smooth) / (K.sum(targets) + K.sum(inputs) + smooth)\n",
        "    return dice"
      ],
      "metadata": {
        "id": "n9AzLJs9VGwa"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "CFC_loss = keras.losses.CategoricalFocalCrossentropy(alpha = [wt0, wt1, wt2, wt3])\n"
      ],
      "metadata": {
        "id": "wpSO0ykJVO_p"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "IoU_0 = keras.metrics.OneHotIoU(num_classes = 4, target_class_ids = [0])\n",
        "IoU_1 = keras.metrics.OneHotIoU(num_classes = 4, target_class_ids = [1])\n",
        "IoU_2 = keras.metrics.OneHotIoU(num_classes = 4, target_class_ids = [2])\n",
        "IoU_3 = keras.metrics.OneHotIoU(num_classes = 4, target_class_ids = [3])\n",
        "\n",
        "Mean_IoU = keras.metrics.OneHotMeanIoU(num_classes = 4)\n",
        "\n",
        "metrics = [\"accuracy\", DiceCoefficient, Mean_IoU, IoU_0, IoU_1, IoU_2, IoU_3]"
      ],
      "metadata": {
        "id": "ll-SXmwPVYo2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_dir = \"/content/Training_Data/X_train/\"\n",
        "train_mask_dir = \"/content/Training_Data/Y_train/\"\n",
        "train_img_list= sorted(os.listdir(train_img_dir))\n",
        "train_mask_list = sorted(os.listdir(train_mask_dir))"
      ],
      "metadata": {
        "id": "oaGfxQi7elO5"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_img_dir = \"/content/Training_Data/X_val/\"\n",
        "val_mask_dir = \"/content/Training_Data/Y_val/\"\n",
        "val_img_list= sorted(os.listdir(val_img_dir))\n",
        "val_mask_list = sorted(os.listdir(val_mask_dir))"
      ],
      "metadata": {
        "id": "pDxzyt-Ceuzy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4\n",
        "\n",
        "train_img_dir = \"/content/Training_Data/X_train/\"\n",
        "train_mask_dir = \"/content/Training_Data/Y_train/\"\n",
        "train_img_list= sorted(os.listdir(train_img_dir))\n",
        "train_mask_list = sorted(os.listdir(train_mask_dir))\n",
        "\n",
        "steps_per_epoch = len(train_img_list)//batch_size\n",
        "val_steps_per_epoch = len(val_img_list)//batch_size\n",
        "\n",
        "print(steps_per_epoch, val_steps_per_epoch)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "SoQpMSrmeqVu",
        "outputId": "d9c6b797-9485-4253-aabc-57d0e42556e7"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84 18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate = 1e-3,\n",
        "    decay_steps  = (len(train_img_list) // batch_size),\n",
        "    decay_rate = 0.95)\n",
        "\n",
        "optim = keras.optimizers.Adam(learning_rate = lr_schedule)"
      ],
      "metadata": {
        "id": "kt9hpWS_Pvfy"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "### DEFINE MODEL ARCHITECTURE - 3D U-Net"
      ],
      "metadata": {
        "id": "7w7dABspXa_v"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "def conv_block(input, n_filters, dropout_rate):\n",
        "\n",
        "    x = Conv3D(n_filters, 3, padding='same')(input)\n",
        "    x = Activation('relu')(x)\n",
        "    x = Dropout(dropout_rate)(x)\n",
        "\n",
        "    x = Conv3D(n_filters, 3, padding='same')(x)\n",
        "    x = Activation('relu')(x)\n",
        "\n",
        "    return x\n",
        "\n",
        "def encoder_block(input, num_filters, dropout_rate):\n",
        "\n",
        "    x = conv_block(input, num_filters, dropout_rate)\n",
        "    p = MaxPooling3D((2, 2, 2))(x)\n",
        "\n",
        "    return x, p\n",
        "\n",
        "def decoder_block(input, skip_features, num_filters, dropout_rate):\n",
        "\n",
        "    x = Conv3DTranspose(num_filters, (2, 2, 2), strides=(2, 2, 2), padding='same')(input)\n",
        "    x = concatenate([x, skip_features])\n",
        "    x = conv_block(x, num_filters, dropout_rate)\n",
        "\n",
        "    return x"
      ],
      "metadata": {
        "id": "EHVEPPgtXhw6"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_unet(input_shape, n_classes, num_filters):\n",
        "    inputs = Input(input_shape)\n",
        "\n",
        "    s1, p1 = encoder_block(inputs, num_filters, 0.1)\n",
        "    s2, p2 = encoder_block(p1, 2*num_filters, 0.1)\n",
        "    s3, p3 = encoder_block(p2, 4*num_filters, 0.2)\n",
        "    s4, p4 = encoder_block(p3, 8*num_filters, 0.2)\n",
        "\n",
        "    b1 = conv_block(p4, 16*num_filters, 0.3)\n",
        "\n",
        "    d1 = decoder_block(b1, s4, 8*num_filters, 0.2)\n",
        "    d2 = decoder_block(d1, s3, 4*num_filters, 0.2)\n",
        "    d3 = decoder_block(d2, s2, 2*num_filters, 0.1)\n",
        "    d4 = decoder_block(d3, s1, num_filters, 0.1)\n",
        "\n",
        "    outputs = Conv3D(n_classes, 1, padding = 'same', activation='softmax')(d4)\n",
        "\n",
        "    model = Model(inputs, outputs, name='3D_U-Net')\n",
        "    return model\n",
        "\n",
        "input_shape = (128, 128, 128, 4)\n",
        "n_classes = 4\n",
        "\n",
        "model = build_unet(input_shape, n_classes, 32)\n",
        "model.compile(optimizer = optim, loss = CFC_loss, metrics = metrics)\n",
        "model.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "collapsed": true,
        "id": "yIjcyjsgYNcv",
        "outputId": "51e21b61-1afc-464e-ca1a-9658515dc5ce"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"3D_U-Net\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"3D_U-Net\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │ \u001b[38;5;34m4\u001b[0m)                     │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d (\u001b[38;5;33mConv3D\u001b[0m)           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m3,488\u001b[0m │ input_layer_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation (\u001b[38;5;33mActivation\u001b[0m)   │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]           │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout (\u001b[38;5;33mDropout\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ activation[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_1 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m27,680\u001b[0m │ dropout[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]          │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_1 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_2 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │         \u001b[38;5;34m55,360\u001b[0m │ max_pooling3d[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_2 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_1 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_3 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m110,656\u001b[0m │ dropout_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_3 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_1           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_4 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │        \u001b[38;5;34m221,312\u001b[0m │ max_pooling3d_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_4 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_5 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │        \u001b[38;5;34m442,496\u001b[0m │ dropout_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_5 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_2           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_6 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │        \u001b[38;5;34m884,992\u001b[0m │ max_pooling3d_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_6 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_7 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │      \u001b[38;5;34m1,769,728\u001b[0m │ dropout_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_7 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_3           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m256\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_8 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)   │      \u001b[38;5;34m3,539,456\u001b[0m │ max_pooling3d_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_8 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv3d_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_4 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ activation_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_9 (\u001b[38;5;33mConv3D\u001b[0m)         │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)   │      \u001b[38;5;34m7,078,400\u001b[0m │ dropout_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_9 (\u001b[38;5;33mActivation\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m8\u001b[0m, \u001b[38;5;34m512\u001b[0m)   │              \u001b[38;5;34m0\u001b[0m │ conv3d_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose          │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │      \u001b[38;5;34m1,048,832\u001b[0m │ activation_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (\u001b[38;5;33mConcatenate\u001b[0m) │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m… │\n",
              "│                           │ \u001b[38;5;34m512\u001b[0m)                   │                │ activation_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_10 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │      \u001b[38;5;34m3,539,200\u001b[0m │ concatenate[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]      │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_10             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_5 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_11 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │      \u001b[38;5;34m1,769,728\u001b[0m │ dropout_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_11             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m256\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_1        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │        \u001b[38;5;34m262,272\u001b[0m │ activation_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_1[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │ \u001b[38;5;34m256\u001b[0m)                   │                │ activation_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_12 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │        \u001b[38;5;34m884,864\u001b[0m │ concatenate_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_12             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_6 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_13 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │        \u001b[38;5;34m442,496\u001b[0m │ dropout_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_13             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_2        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │         \u001b[38;5;34m65,600\u001b[0m │ activation_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_2[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │ \u001b[38;5;34m128\u001b[0m)                   │                │ activation_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_14 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m221,248\u001b[0m │ concatenate_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_14             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_7 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_15 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m110,656\u001b[0m │ dropout_7[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_15             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_3        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m16,416\u001b[0m │ activation_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_3[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │ \u001b[38;5;34m64\u001b[0m)                    │                │ activation_1[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_16 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m55,328\u001b[0m │ concatenate_3[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_16             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_8 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ activation_16[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_17 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m27,680\u001b[0m │ dropout_8[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_17             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m32\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_18 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │            \u001b[38;5;34m132\u001b[0m │ activation_17[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m4\u001b[0m)                     │                │                        │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                     │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">3,488</span> │ input_layer_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)   │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]           │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ dropout[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]          │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,360</span> │ max_pooling3d[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_1 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ dropout_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_1           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">221,312</span> │ max_pooling3d_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_2 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ dropout_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_2           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">884,992</span> │ max_pooling3d_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_3 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,769,728</span> │ dropout_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_3           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)   │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,539,456</span> │ max_pooling3d_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_4 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)         │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)   │      <span style=\"color: #00af00; text-decoration-color: #00af00\">7,078,400</span> │ dropout_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">8</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)   │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]         │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose          │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,048,832</span> │ activation_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>) │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>… │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">512</span>)                   │                │ activation_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">3,539,200</span> │ concatenate[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]      │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_10             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_5 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │      <span style=\"color: #00af00; text-decoration-color: #00af00\">1,769,728</span> │ dropout_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_11             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_1        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">262,272</span> │ activation_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_1             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">256</span>)                   │                │ activation_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">884,864</span> │ concatenate_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_12             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_6 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ dropout_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_13             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_2        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</span> │ activation_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │ activation_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">221,248</span> │ concatenate_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_14             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_7 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ dropout_7[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_15             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_3        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">16,416</span> │ activation_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_3             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>)                    │                │ activation_1[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]     │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_16 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,328</span> │ concatenate_3[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_16             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_8 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_16[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_17 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ dropout_8[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_17             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_18 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │            <span style=\"color: #00af00; text-decoration-color: #00af00\">132</span> │ activation_17[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                     │                │                        │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m22,578,020\u001b[0m (86.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">22,578,020</span> (86.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m22,578,020\u001b[0m (86.13 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">22,578,020</span> (86.13 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### **Compile and fit**"
      ],
      "metadata": {
        "id": "PZngGW0dfdp2"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Callback per salvare il miglior modello basato sulla loss di validazione\n",
        "checkpoint_path = \"/content/gdrive/MyDrive/DL_PROJECT/3D_Unet_first.h5.keras\"\n",
        "\n",
        "checkpoint_callback = ModelCheckpoint(\n",
        "    filepath = checkpoint_path,\n",
        "    save_best_only = True,\n",
        "    monitor = 'val_loss',\n",
        "    mode = 'min',\n",
        "    verbose = 1\n",
        ")\n",
        "early_stopping_callback = EarlyStopping(\n",
        "    monitor = 'val_loss',    # Monitorare la loss di validazione\n",
        "    patience = 10,           # Numero di epoche con nessun miglioramento dopo le quali l'addestramento verrà fermato\n",
        "    restore_best_weights = True # Ripristina i pesi del miglior modello visto durante l'addestramento\n",
        "  )\n",
        "\n",
        "csv = keras.callbacks.CSVLogger(\"/content/gdrive/MyDrive/DL_PROJECT/hist_3D_first\", separator=\",\", append = False)"
      ],
      "metadata": {
        "id": "A4x1dUleaX4P"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "batch_size = 4\n",
        "\n",
        "steps_per_epoch = len(train_img_list)//batch_size\n",
        "val_steps_per_epoch = len(val_img_list)//batch_size\n",
        "\n",
        "print(steps_per_epoch, val_steps_per_epoch)"
      ],
      "metadata": {
        "id": "T4fpE9i3fW5p",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "24d9a4fe-b349-450e-d7bd-c3cb5b88bc30"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "84 18\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_dir = \"/content/Training_Data/X_train/\"\n",
        "train_mask_dir = \"/content/Training_Data/Y_train/\"\n",
        "train_img_list = sorted(os.listdir(train_img_dir))\n",
        "train_mask_list = sorted(os.listdir(train_mask_dir))"
      ],
      "metadata": {
        "id": "fhOwMD6SfWxM"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_img_dir = \"/content/Training_Data/X_val/\"\n",
        "val_mask_dir = \"/content/Training_Data/Y_val/\"\n",
        "val_img_list = sorted(os.listdir(val_img_dir))\n",
        "val_mask_list = sorted(os.listdir(val_mask_dir))"
      ],
      "metadata": {
        "id": "yNrVcLlL9T5R"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_mask_list"
      ],
      "metadata": {
        "id": "nDveRTkPe_jg"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "train_img_datagen = image_loader(train_img_dir, train_img_list, train_mask_dir, train_mask_list, batch_size)"
      ],
      "metadata": {
        "id": "U3VPcSupBKar"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "val_img_datagen = image_loader(val_img_dir, val_img_list, val_mask_dir, val_mask_list, batch_size)"
      ],
      "metadata": {
        "id": "142kZ0Lng3u2"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history=model.fit(train_img_datagen,\n",
        "                  steps_per_epoch=steps_per_epoch,\n",
        "                  epochs=20,\n",
        "                  verbose=1,\n",
        "                  validation_data=val_img_datagen,\n",
        "                  validation_steps=val_steps_per_epoch,\n",
        "                  callbacks = [checkpoint_callback, early_stopping_callback, csv]\n",
        ")\n"
      ],
      "metadata": {
        "id": "XC4mQ3oYiSvy",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "53e041af-6f29-41fa-e7c2-528a07cd93a2"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 464ms/step - accuracy: 0.4651 - dice_coefficient: 0.2837 - loss: 0.4998 - one_hot_io_u_4: 0.2737 - one_hot_io_u_5: 0.2235 - one_hot_io_u_6: 0.0440 - one_hot_io_u_7: 0.0718 - one_hot_mean_io_u_1: 0.1532\n",
            "Epoch 1: val_loss improved from inf to 0.74864, saving model to /content/gdrive/MyDrive/DL_PROJECT/3D_Unet_first.h5.keras\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m115s\u001b[0m 636ms/step - accuracy: 0.4652 - dice_coefficient: 0.2838 - loss: 0.4991 - one_hot_io_u_4: 0.2733 - one_hot_io_u_5: 0.2252 - one_hot_io_u_6: 0.0444 - one_hot_io_u_7: 0.0726 - one_hot_mean_io_u_1: 0.1539 - val_accuracy: 0.4845 - val_dice_coefficient: 0.3128 - val_loss: 0.7486 - val_one_hot_io_u_4: 0.3685 - val_one_hot_io_u_5: 0.1217 - val_one_hot_io_u_6: 0.1219 - val_one_hot_io_u_7: 0.0441 - val_one_hot_mean_io_u_1: 0.1640\n",
            "Epoch 2/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 463ms/step - accuracy: 0.6212 - dice_coefficient: 0.3403 - loss: 0.2511 - one_hot_io_u_4: 0.2898 - one_hot_io_u_5: 0.3312 - one_hot_io_u_6: 0.0660 - one_hot_io_u_7: 0.1060 - one_hot_mean_io_u_1: 0.1982\n",
            "Epoch 2: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m45s\u001b[0m 537ms/step - accuracy: 0.6224 - dice_coefficient: 0.3405 - loss: 0.2507 - one_hot_io_u_4: 0.2893 - one_hot_io_u_5: 0.3327 - one_hot_io_u_6: 0.0664 - one_hot_io_u_7: 0.1069 - one_hot_mean_io_u_1: 0.1988 - val_accuracy: 0.8495 - val_dice_coefficient: 0.3986 - val_loss: 0.8322 - val_one_hot_io_u_4: 0.4535 - val_one_hot_io_u_5: 0.1339 - val_one_hot_io_u_6: 0.1910 - val_one_hot_io_u_7: 0.0722 - val_one_hot_mean_io_u_1: 0.2126\n",
            "Epoch 3/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 473ms/step - accuracy: 0.9199 - dice_coefficient: 0.4166 - loss: 0.1302 - one_hot_io_u_4: 0.4430 - one_hot_io_u_5: 0.4249 - one_hot_io_u_6: 0.3071 - one_hot_io_u_7: 0.1522 - one_hot_mean_io_u_1: 0.3318\n",
            "Epoch 3: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 547ms/step - accuracy: 0.9202 - dice_coefficient: 0.4168 - loss: 0.1299 - one_hot_io_u_4: 0.4415 - one_hot_io_u_5: 0.4254 - one_hot_io_u_6: 0.3079 - one_hot_io_u_7: 0.1529 - one_hot_mean_io_u_1: 0.3319 - val_accuracy: 0.9140 - val_dice_coefficient: 0.4337 - val_loss: 4.9433 - val_one_hot_io_u_4: 0.5875 - val_one_hot_io_u_5: 0.1263 - val_one_hot_io_u_6: 0.0129 - val_one_hot_io_u_7: 0.0869 - val_one_hot_mean_io_u_1: 0.2034\n",
            "Epoch 4/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.9787 - dice_coefficient: 0.4502 - loss: 0.0664 - one_hot_io_u_4: 0.7401 - one_hot_io_u_5: 0.6039 - one_hot_io_u_6: 0.6291 - one_hot_io_u_7: 0.5949 - one_hot_mean_io_u_1: 0.6420\n",
            "Epoch 4: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 550ms/step - accuracy: 0.9787 - dice_coefficient: 0.4502 - loss: 0.0664 - one_hot_io_u_4: 0.7381 - one_hot_io_u_5: 0.6037 - one_hot_io_u_6: 0.6293 - one_hot_io_u_7: 0.5952 - one_hot_mean_io_u_1: 0.6416 - val_accuracy: 0.9382 - val_dice_coefficient: 0.4471 - val_loss: 8.1021 - val_one_hot_io_u_4: 0.6694 - val_one_hot_io_u_5: 0.1798 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0294 - val_one_hot_mean_io_u_1: 0.2197\n",
            "Epoch 5/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.9818 - dice_coefficient: 0.4552 - loss: 0.0527 - one_hot_io_u_4: 0.7647 - one_hot_io_u_5: 0.6323 - one_hot_io_u_6: 0.6651 - one_hot_io_u_7: 0.6828 - one_hot_mean_io_u_1: 0.6862\n",
            "Epoch 5: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 550ms/step - accuracy: 0.9818 - dice_coefficient: 0.4552 - loss: 0.0527 - one_hot_io_u_4: 0.7627 - one_hot_io_u_5: 0.6320 - one_hot_io_u_6: 0.6650 - one_hot_io_u_7: 0.6827 - one_hot_mean_io_u_1: 0.6856 - val_accuracy: 0.9287 - val_dice_coefficient: 0.4436 - val_loss: 8.8191 - val_one_hot_io_u_4: 0.6357 - val_one_hot_io_u_5: 0.1649 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0221 - val_one_hot_mean_io_u_1: 0.2057\n",
            "Epoch 6/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475ms/step - accuracy: 0.9834 - dice_coefficient: 0.4572 - loss: 0.0464 - one_hot_io_u_4: 0.7814 - one_hot_io_u_5: 0.6494 - one_hot_io_u_6: 0.6913 - one_hot_io_u_7: 0.7229 - one_hot_mean_io_u_1: 0.7113\n",
            "Epoch 6: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 549ms/step - accuracy: 0.9834 - dice_coefficient: 0.4572 - loss: 0.0463 - one_hot_io_u_4: 0.7796 - one_hot_io_u_5: 0.6491 - one_hot_io_u_6: 0.6914 - one_hot_io_u_7: 0.7230 - one_hot_mean_io_u_1: 0.7108 - val_accuracy: 0.9476 - val_dice_coefficient: 0.4535 - val_loss: 10.2069 - val_one_hot_io_u_4: 0.7020 - val_one_hot_io_u_5: 0.1780 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0120 - val_one_hot_mean_io_u_1: 0.2230\n",
            "Epoch 7/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475ms/step - accuracy: 0.9845 - dice_coefficient: 0.4606 - loss: 0.0417 - one_hot_io_u_4: 0.7909 - one_hot_io_u_5: 0.6637 - one_hot_io_u_6: 0.7106 - one_hot_io_u_7: 0.7508 - one_hot_mean_io_u_1: 0.7290\n",
            "Epoch 7: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 548ms/step - accuracy: 0.9845 - dice_coefficient: 0.4606 - loss: 0.0416 - one_hot_io_u_4: 0.7892 - one_hot_io_u_5: 0.6634 - one_hot_io_u_6: 0.7106 - one_hot_io_u_7: 0.7509 - one_hot_mean_io_u_1: 0.7285 - val_accuracy: 0.9476 - val_dice_coefficient: 0.4536 - val_loss: 10.1075 - val_one_hot_io_u_4: 0.7078 - val_one_hot_io_u_5: 0.1856 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0204 - val_one_hot_mean_io_u_1: 0.2285\n",
            "Epoch 8/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.9850 - dice_coefficient: 0.4624 - loss: 0.0387 - one_hot_io_u_4: 0.7959 - one_hot_io_u_5: 0.6703 - one_hot_io_u_6: 0.7228 - one_hot_io_u_7: 0.7643 - one_hot_mean_io_u_1: 0.7383\n",
            "Epoch 8: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 550ms/step - accuracy: 0.9850 - dice_coefficient: 0.4624 - loss: 0.0387 - one_hot_io_u_4: 0.7942 - one_hot_io_u_5: 0.6700 - one_hot_io_u_6: 0.7228 - one_hot_io_u_7: 0.7644 - one_hot_mean_io_u_1: 0.7379 - val_accuracy: 0.9493 - val_dice_coefficient: 0.4552 - val_loss: 10.8861 - val_one_hot_io_u_4: 0.7106 - val_one_hot_io_u_5: 0.1794 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0084 - val_one_hot_mean_io_u_1: 0.2246\n",
            "Epoch 9/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.9856 - dice_coefficient: 0.4638 - loss: 0.0362 - one_hot_io_u_4: 0.8015 - one_hot_io_u_5: 0.6784 - one_hot_io_u_6: 0.7328 - one_hot_io_u_7: 0.7764 - one_hot_mean_io_u_1: 0.7473\n",
            "Epoch 9: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 550ms/step - accuracy: 0.9856 - dice_coefficient: 0.4638 - loss: 0.0362 - one_hot_io_u_4: 0.7998 - one_hot_io_u_5: 0.6781 - one_hot_io_u_6: 0.7328 - one_hot_io_u_7: 0.7764 - one_hot_mean_io_u_1: 0.7468 - val_accuracy: 0.9453 - val_dice_coefficient: 0.4539 - val_loss: 11.1346 - val_one_hot_io_u_4: 0.6920 - val_one_hot_io_u_5: 0.1889 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0078 - val_one_hot_mean_io_u_1: 0.2222\n",
            "Epoch 10/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 476ms/step - accuracy: 0.9861 - dice_coefficient: 0.4647 - loss: 0.0336 - one_hot_io_u_4: 0.8072 - one_hot_io_u_5: 0.6872 - one_hot_io_u_6: 0.7442 - one_hot_io_u_7: 0.7877 - one_hot_mean_io_u_1: 0.7566\n",
            "Epoch 10: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 550ms/step - accuracy: 0.9861 - dice_coefficient: 0.4647 - loss: 0.0336 - one_hot_io_u_4: 0.8055 - one_hot_io_u_5: 0.6869 - one_hot_io_u_6: 0.7442 - one_hot_io_u_7: 0.7877 - one_hot_mean_io_u_1: 0.7561 - val_accuracy: 0.9449 - val_dice_coefficient: 0.4541 - val_loss: 11.0963 - val_one_hot_io_u_4: 0.6925 - val_one_hot_io_u_5: 0.1923 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0095 - val_one_hot_mean_io_u_1: 0.2236\n",
            "Epoch 11/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 475ms/step - accuracy: 0.9866 - dice_coefficient: 0.4654 - loss: 0.0315 - one_hot_io_u_4: 0.8117 - one_hot_io_u_5: 0.6945 - one_hot_io_u_6: 0.7532 - one_hot_io_u_7: 0.7966 - one_hot_mean_io_u_1: 0.7640\n",
            "Epoch 11: val_loss did not improve from 0.74864\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m46s\u001b[0m 549ms/step - accuracy: 0.9866 - dice_coefficient: 0.4654 - loss: 0.0314 - one_hot_io_u_4: 0.8101 - one_hot_io_u_5: 0.6942 - one_hot_io_u_6: 0.7532 - one_hot_io_u_7: 0.7967 - one_hot_mean_io_u_1: 0.7636 - val_accuracy: 0.9463 - val_dice_coefficient: 0.4548 - val_loss: 11.4454 - val_one_hot_io_u_4: 0.6948 - val_one_hot_io_u_5: 0.1905 - val_one_hot_io_u_6: 0.0000e+00 - val_one_hot_io_u_7: 0.0058 - val_one_hot_mean_io_u_1: 0.2228\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "Unet_3D.save(\"/content/gdrive/MyDrive/DL_PROJECT/3_D_first.keras\")"
      ],
      "metadata": {
        "id": "R7ekpu6pKX_h"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "Unet_3D.save_weights(\"/content/gdrive/MyDrive/DL_PROJECT/3_D_first.weights.h5\", overwrite=True)"
      ],
      "metadata": {
        "id": "egxgDw8Bd4QE"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "history.history[\"loss\"]"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VsAv5RVPeNDu",
        "outputId": "11e09bb8-13a2-4d45-ca3e-61343b6c22bb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "[0.6644736528396606,\n",
              " 0.4024335741996765,\n",
              " 0.36886489391326904,\n",
              " 0.20986153185367584,\n",
              " 0.14535319805145264,\n",
              " 0.117879718542099,\n",
              " 0.10033826529979706,\n",
              " 0.09052959829568863,\n",
              " 0.082192562520504,\n",
              " 0.0761675089597702,\n",
              " 0.07104619592428207,\n",
              " 0.06673113256692886,\n",
              " 0.06312098354101181,\n",
              " 0.05999668315052986,\n",
              " 0.05828843638300896,\n",
              " 0.05564132332801819,\n",
              " 0.05367012694478035,\n",
              " 0.05188097804784775,\n",
              " 0.05000683665275574,\n",
              " 0.0489419586956501]"
            ]
          },
          "metadata": {},
          "execution_count": 28
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "loss = history.history['loss']\n",
        "val_loss = history.history['val_loss']\n",
        "epochs = range(1, len(loss) + 1)\n",
        "plt.plot(epochs, loss, 'y', label='Training loss')\n",
        "plt.plot(epochs, val_loss, 'r', label='Validation loss')\n",
        "plt.title('Training and validation loss')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Loss')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "pqMPAVx5epum",
        "outputId": "5e3dd73b-9e0d-421f-bd70-cd4258b86cc6"
      },
      "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "acc = history.history['accuracy']\n",
        "val_acc = history.history['val_accuracy']\n",
        "\n",
        "plt.plot(epochs, acc, 'y', label='Training accuracy')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation accuracy')\n",
        "plt.title('Training and validation accuracy')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('Accuracy')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "czu85Xvge5kQ",
        "outputId": "3e465d98-89d3-4c8d-b97d-a5740b757e8f"
      },
      "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history.keys()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mJZF5aF8jkoL",
        "outputId": "94ae153d-57e1-4d9b-f8ab-cb55b440c12d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "dict_keys(['accuracy', 'dice_coefficient', 'loss', 'one_hot_io_u_4', 'one_hot_io_u_5', 'one_hot_io_u_6', 'one_hot_io_u_7', 'one_hot_mean_io_u_1', 'val_accuracy', 'val_dice_coefficient', 'val_loss', 'val_one_hot_io_u_4', 'val_one_hot_io_u_5', 'val_one_hot_io_u_6', 'val_one_hot_io_u_7', 'val_one_hot_mean_io_u_1'])"
            ]
          },
          "metadata": {},
          "execution_count": 69
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history.history['one_hot_mean_io_u_1']\n",
        "val_acc = history.history['val_one_hot_mean_io_u_1']\n",
        "\n",
        "plt.plot(epochs, acc, 'y', label='Training IoU')\n",
        "plt.plot(epochs, val_acc, 'r', label='Validation IoU')\n",
        "plt.title('Training and validation IoU')\n",
        "plt.xlabel('Epochs')\n",
        "plt.ylabel('IoU')\n",
        "plt.legend()\n",
        "plt.show()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 472
        },
        "id": "VGsLPFmCfhBo",
        "outputId": "8a105453-0be6-49a0-886e-d05569367885"
      },
      "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": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "lr_schedule = keras.optimizers.schedules.ExponentialDecay(\n",
        "    initial_learning_rate = 1e-3,\n",
        "    decay_steps  = (len(train_img_list) // batch_size),\n",
        "    decay_rate = 0.95)\n",
        "\n",
        "optim = keras.optimizers.Adam(learning_rate = lr_schedule)"
      ],
      "metadata": {
        "id": "HAOKLdBAlqIv"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def build_unet(input_shape, n_classes, num_filters):\n",
        "    inputs = Input(input_shape)\n",
        "\n",
        "    s1, p1 = encoder_block(inputs, num_filters, 0.3)\n",
        "    s2, p2 = encoder_block(p1, 2*num_filters, 0.3)\n",
        "    s3, p3 = encoder_block(p2, 4*num_filters, 0.3)\n",
        "\n",
        "    b1 = conv_block(p3, 8*num_filters, 0.3)\n",
        "\n",
        "    d1 = decoder_block(b1, s3, 4*num_filters, 0.3)\n",
        "    d2 = decoder_block(d1, s2, 2*num_filters, 0.3)\n",
        "    d3 = decoder_block(d2, s1, num_filters, 0.3)\n",
        "\n",
        "    outputs = Conv3D(n_classes, 1, padding = 'same', activation='softmax')(d3)\n",
        "\n",
        "    model = Model(inputs, outputs, name='3D_U-Net')\n",
        "    return model\n",
        "\n",
        "input_shape = (128, 128, 128, 4)\n",
        "n_classes = 4\n",
        "\n",
        "model_2 = build_unet(input_shape, n_classes, 16)\n",
        "model_2.compile(optimizer = optim, loss = CFC_loss, metrics = metrics)\n",
        "model_2.summary()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "U6cOouqxkQSd",
        "outputId": "a1821c56-0e36-41c0-8b20-a9a9432020f9"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1mModel: \"3D_U-Net\"\u001b[0m\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\">Model: \"3D_U-Net\"</span>\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃\u001b[1m \u001b[0m\u001b[1mLayer (type)             \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mOutput Shape          \u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1m       Param #\u001b[0m\u001b[1m \u001b[0m┃\u001b[1m \u001b[0m\u001b[1mConnected to          \u001b[0m\u001b[1m \u001b[0m┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_2             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ -                      │\n",
              "│ (\u001b[38;5;33mInputLayer\u001b[0m)              │ \u001b[38;5;34m4\u001b[0m)                     │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_19 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m1,744\u001b[0m │ input_layer_2[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_18             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_9 (\u001b[38;5;33mDropout\u001b[0m)       │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ activation_18[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_20 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m6,928\u001b[0m │ dropout_9[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_19             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_4           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m16\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_21 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │         \u001b[38;5;34m13,856\u001b[0m │ max_pooling3d_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_20             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_10 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_20[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_22 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │         \u001b[38;5;34m27,680\u001b[0m │ dropout_10[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_21             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_5           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_23 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │         \u001b[38;5;34m55,360\u001b[0m │ max_pooling3d_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_22             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_11 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_22[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_24 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m110,656\u001b[0m │ dropout_11[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_23             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_6           │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mMaxPooling3D\u001b[0m)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_25 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │        \u001b[38;5;34m221,312\u001b[0m │ max_pooling3d_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]  │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_24             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_12 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ activation_24[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_26 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │        \u001b[38;5;34m442,496\u001b[0m │ dropout_12[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "│                           │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_25             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m, \u001b[38;5;34m16\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m128\u001b[0m)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_4        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │         \u001b[38;5;34m65,600\u001b[0m │ activation_25[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_4             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m,     │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_4[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │ \u001b[38;5;34m128\u001b[0m)                   │                │ activation_23[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_27 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m221,248\u001b[0m │ concatenate_4[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_26             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_13 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_26[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_28 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │        \u001b[38;5;34m110,656\u001b[0m │ dropout_13[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_27             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m32\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_5        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │         \u001b[38;5;34m16,416\u001b[0m │ activation_27[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_5             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_5[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │                        │                │ activation_21[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_29 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │         \u001b[38;5;34m55,328\u001b[0m │ concatenate_5[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_28             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_14 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ activation_28[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_30 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │         \u001b[38;5;34m27,680\u001b[0m │ dropout_14[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_29             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m64\u001b[0m, \u001b[38;5;34m32\u001b[0m) │              \u001b[38;5;34m0\u001b[0m │ conv3d_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_6        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m4,112\u001b[0m │ activation_29[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│ (\u001b[38;5;33mConv3DTranspose\u001b[0m)         │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_6             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_transpose_6[\u001b[38;5;34m0\u001b[0m]… │\n",
              "│ (\u001b[38;5;33mConcatenate\u001b[0m)             │ \u001b[38;5;34m32\u001b[0m)                    │                │ activation_19[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_31 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │         \u001b[38;5;34m13,840\u001b[0m │ concatenate_6[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_30             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_15 (\u001b[38;5;33mDropout\u001b[0m)      │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ activation_30[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_32 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │          \u001b[38;5;34m6,928\u001b[0m │ dropout_15[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]       │\n",
              "│                           │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_31             │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │              \u001b[38;5;34m0\u001b[0m │ conv3d_32[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]        │\n",
              "│ (\u001b[38;5;33mActivation\u001b[0m)              │ \u001b[38;5;34m16\u001b[0m)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_33 (\u001b[38;5;33mConv3D\u001b[0m)        │ (\u001b[38;5;45mNone\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m, \u001b[38;5;34m128\u001b[0m,  │             \u001b[38;5;34m68\u001b[0m │ activation_31[\u001b[38;5;34m0\u001b[0m][\u001b[38;5;34m0\u001b[0m]    │\n",
              "│                           │ \u001b[38;5;34m4\u001b[0m)                     │                │                        │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\">┏━━━━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━┳━━━━━━━━━━━━━━━━━━━━━━━━┓\n",
              "┃<span style=\"font-weight: bold\"> Layer (type)              </span>┃<span style=\"font-weight: bold\"> Output Shape           </span>┃<span style=\"font-weight: bold\">        Param # </span>┃<span style=\"font-weight: bold\"> Connected to           </span>┃\n",
              "┡━━━━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━╇━━━━━━━━━━━━━━━━━━━━━━━━┩\n",
              "│ input_layer_2             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ -                      │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">InputLayer</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                     │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_19 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">1,744</span> │ input_layer_2[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_18             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_9 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)       │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_18[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_20 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,928</span> │ dropout_9[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_19             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_4           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_21 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">13,856</span> │ max_pooling3d_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_20             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_10 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_20[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_22 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ dropout_10[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_21             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_5           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_23 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,360</span> │ max_pooling3d_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_22             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_11 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_22[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_24 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ dropout_11[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_23             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ max_pooling3d_6           │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">MaxPooling3D</span>)            │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_25 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">221,312</span> │ max_pooling3d_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]  │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_24             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_12 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_24[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_26 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │        <span style=\"color: #00af00; text-decoration-color: #00af00\">442,496</span> │ dropout_12[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_25             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_4        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">65,600</span> │ activation_25[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_4             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>,     │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>)                   │                │ activation_23[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_27 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">221,248</span> │ concatenate_4[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_26             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_13 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_26[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_28 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │        <span style=\"color: #00af00; text-decoration-color: #00af00\">110,656</span> │ dropout_13[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_27             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_5        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">16,416</span> │ activation_27[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_5             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │                        │                │ activation_21[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_29 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">55,328</span> │ concatenate_5[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_28             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_14 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_28[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_30 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │         <span style=\"color: #00af00; text-decoration-color: #00af00\">27,680</span> │ dropout_14[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_29             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">64</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>) │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_30[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │                        │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_transpose_6        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">4,112</span> │ activation_29[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3DTranspose</span>)         │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ concatenate_6             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_transpose_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]… │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Concatenate</span>)             │ <span style=\"color: #00af00; text-decoration-color: #00af00\">32</span>)                    │                │ activation_19[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_31 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │         <span style=\"color: #00af00; text-decoration-color: #00af00\">13,840</span> │ concatenate_6[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_30             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_31[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ dropout_15 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Dropout</span>)      │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ activation_30[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_32 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │          <span style=\"color: #00af00; text-decoration-color: #00af00\">6,928</span> │ dropout_15[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]       │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ activation_31             │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │              <span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> │ conv3d_32[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]        │\n",
              "│ (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Activation</span>)              │ <span style=\"color: #00af00; text-decoration-color: #00af00\">16</span>)                    │                │                        │\n",
              "├───────────────────────────┼────────────────────────┼────────────────┼────────────────────────┤\n",
              "│ conv3d_33 (<span style=\"color: #0087ff; text-decoration-color: #0087ff\">Conv3D</span>)        │ (<span style=\"color: #00d7ff; text-decoration-color: #00d7ff\">None</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>, <span style=\"color: #00af00; text-decoration-color: #00af00\">128</span>,  │             <span style=\"color: #00af00; text-decoration-color: #00af00\">68</span> │ activation_31[<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>][<span style=\"color: #00af00; text-decoration-color: #00af00\">0</span>]    │\n",
              "│                           │ <span style=\"color: #00af00; text-decoration-color: #00af00\">4</span>)                     │                │                        │\n",
              "└───────────────────────────┴────────────────────────┴────────────────┴────────────────────────┘\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Total params: \u001b[0m\u001b[38;5;34m1,401,908\u001b[0m (5.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Total params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,401,908</span> (5.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Trainable params: \u001b[0m\u001b[38;5;34m1,401,908\u001b[0m (5.35 MB)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">1,401,908</span> (5.35 MB)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "\u001b[1m Non-trainable params: \u001b[0m\u001b[38;5;34m0\u001b[0m (0.00 B)\n"
            ],
            "text/html": [
              "<pre style=\"white-space:pre;overflow-x:auto;line-height:normal;font-family:Menlo,'DejaVu Sans Mono',consolas,'Courier New',monospace\"><span style=\"font-weight: bold\"> Non-trainable params: </span><span style=\"color: #00af00; text-decoration-color: #00af00\">0</span> (0.00 B)\n",
              "</pre>\n"
            ]
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "history_2=model_2.fit(train_img_datagen,\n",
        "                  steps_per_epoch=steps_per_epoch,\n",
        "                  epochs=20,\n",
        "                  verbose=1,\n",
        "                  validation_data=val_img_datagen,\n",
        "                  validation_steps=val_steps_per_epoch,\n",
        "                  callbacks = [checkpoint_callback, early_stopping_callback, csv]\n",
        ")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "zPe8fR8MlRCn",
        "outputId": "e4436130-bcae-45df-bb2c-55e4cddfb09e"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Epoch 1/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 364ms/step - accuracy: 0.5852 - dice_coefficient: 0.3073 - loss: 0.4728 - one_hot_io_u: 0.3054 - one_hot_io_u_1: 0.2315 - one_hot_io_u_2: 0.0334 - one_hot_io_u_3: 0.0665 - one_hot_mean_io_u: 0.1592\n",
            "Epoch 1: val_loss improved from inf to 1.23937, saving model to /content/gdrive/MyDrive/DL_PROJECT/3D_Unet_first.h5.keras\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m80s\u001b[0m 491ms/step - accuracy: 0.5858 - dice_coefficient: 0.3077 - loss: 0.4716 - one_hot_io_u: 0.3046 - one_hot_io_u_1: 0.2334 - one_hot_io_u_2: 0.0338 - one_hot_io_u_3: 0.0673 - one_hot_mean_io_u: 0.1598 - val_accuracy: 0.8609 - val_dice_coefficient: 0.3746 - val_loss: 1.2394 - val_one_hot_io_u: 0.4884 - val_one_hot_io_u_1: 0.1250 - val_one_hot_io_u_2: 0.0313 - val_one_hot_io_u_3: 0.0707 - val_one_hot_mean_io_u: 0.1788\n",
            "Epoch 2/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 361ms/step - accuracy: 0.8384 - dice_coefficient: 0.4026 - loss: 0.2051 - one_hot_io_u: 0.3528 - one_hot_io_u_1: 0.3759 - one_hot_io_u_2: 0.0897 - one_hot_io_u_3: 0.1165 - one_hot_mean_io_u: 0.2337\n",
            "Epoch 2: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 434ms/step - accuracy: 0.8386 - dice_coefficient: 0.4027 - loss: 0.2049 - one_hot_io_u: 0.3516 - one_hot_io_u_1: 0.3769 - one_hot_io_u_2: 0.0898 - one_hot_io_u_3: 0.1174 - one_hot_mean_io_u: 0.2339 - val_accuracy: 0.8023 - val_dice_coefficient: 0.4132 - val_loss: 5.3294 - val_one_hot_io_u: 0.3709 - val_one_hot_io_u_1: 0.0906 - val_one_hot_io_u_2: 0.0453 - val_one_hot_io_u_3: 0.0067 - val_one_hot_mean_io_u: 0.1284\n",
            "Epoch 3/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 356ms/step - accuracy: 0.9233 - dice_coefficient: 0.4314 - loss: 0.1217 - one_hot_io_u: 0.4629 - one_hot_io_u_1: 0.4448 - one_hot_io_u_2: 0.1744 - one_hot_io_u_3: 0.1945 - one_hot_mean_io_u: 0.3191\n",
            "Epoch 3: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m36s\u001b[0m 426ms/step - accuracy: 0.9236 - dice_coefficient: 0.4315 - loss: 0.1214 - one_hot_io_u: 0.4614 - one_hot_io_u_1: 0.4451 - one_hot_io_u_2: 0.1754 - one_hot_io_u_3: 0.1955 - one_hot_mean_io_u: 0.3194 - val_accuracy: 0.8994 - val_dice_coefficient: 0.4361 - val_loss: 8.9545 - val_one_hot_io_u: 0.5528 - val_one_hot_io_u_1: 0.0937 - val_one_hot_io_u_2: 0.0197 - val_one_hot_io_u_3: 0.0144 - val_one_hot_mean_io_u: 0.1702\n",
            "Epoch 4/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 365ms/step - accuracy: 0.9750 - dice_coefficient: 0.4537 - loss: 0.0637 - one_hot_io_u: 0.7058 - one_hot_io_u_1: 0.5528 - one_hot_io_u_2: 0.6324 - one_hot_io_u_3: 0.6181 - one_hot_mean_io_u: 0.6273\n",
            "Epoch 4: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 419ms/step - accuracy: 0.9750 - dice_coefficient: 0.4537 - loss: 0.0636 - one_hot_io_u: 0.7038 - one_hot_io_u_1: 0.5527 - one_hot_io_u_2: 0.6326 - one_hot_io_u_3: 0.6183 - one_hot_mean_io_u: 0.6268 - val_accuracy: 0.9056 - val_dice_coefficient: 0.4399 - val_loss: 9.3162 - val_one_hot_io_u: 0.5694 - val_one_hot_io_u_1: 0.1011 - val_one_hot_io_u_2: 0.0176 - val_one_hot_io_u_3: 0.0224 - val_one_hot_mean_io_u: 0.1776\n",
            "Epoch 5/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 360ms/step - accuracy: 0.9803 - dice_coefficient: 0.4585 - loss: 0.0517 - one_hot_io_u: 0.7510 - one_hot_io_u_1: 0.6097 - one_hot_io_u_2: 0.6764 - one_hot_io_u_3: 0.6856 - one_hot_mean_io_u: 0.6807\n",
            "Epoch 5: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 414ms/step - accuracy: 0.9803 - dice_coefficient: 0.4585 - loss: 0.0517 - one_hot_io_u: 0.7491 - one_hot_io_u_1: 0.6095 - one_hot_io_u_2: 0.6765 - one_hot_io_u_3: 0.6857 - one_hot_mean_io_u: 0.6802 - val_accuracy: 0.9172 - val_dice_coefficient: 0.4456 - val_loss: 10.4580 - val_one_hot_io_u: 0.5993 - val_one_hot_io_u_1: 0.1057 - val_one_hot_io_u_2: 0.0057 - val_one_hot_io_u_3: 0.0227 - val_one_hot_mean_io_u: 0.1833\n",
            "Epoch 6/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.9828 - dice_coefficient: 0.4619 - loss: 0.0449 - one_hot_io_u: 0.7738 - one_hot_io_u_1: 0.6413 - one_hot_io_u_2: 0.7021 - one_hot_io_u_3: 0.7256 - one_hot_mean_io_u: 0.7107\n",
            "Epoch 6: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 413ms/step - accuracy: 0.9828 - dice_coefficient: 0.4619 - loss: 0.0449 - one_hot_io_u: 0.7720 - one_hot_io_u_1: 0.6410 - one_hot_io_u_2: 0.7022 - one_hot_io_u_3: 0.7257 - one_hot_mean_io_u: 0.7102 - val_accuracy: 0.9196 - val_dice_coefficient: 0.4471 - val_loss: 10.6174 - val_one_hot_io_u: 0.6083 - val_one_hot_io_u_1: 0.1143 - val_one_hot_io_u_2: 0.0060 - val_one_hot_io_u_3: 0.0231 - val_one_hot_mean_io_u: 0.1879\n",
            "Epoch 7/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 357ms/step - accuracy: 0.9837 - dice_coefficient: 0.4634 - loss: 0.0411 - one_hot_io_u: 0.7822 - one_hot_io_u_1: 0.6528 - one_hot_io_u_2: 0.7156 - one_hot_io_u_3: 0.7415 - one_hot_mean_io_u: 0.7230\n",
            "Epoch 7: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 412ms/step - accuracy: 0.9837 - dice_coefficient: 0.4634 - loss: 0.0411 - one_hot_io_u: 0.7804 - one_hot_io_u_1: 0.6526 - one_hot_io_u_2: 0.7156 - one_hot_io_u_3: 0.7416 - one_hot_mean_io_u: 0.7225 - val_accuracy: 0.9242 - val_dice_coefficient: 0.4493 - val_loss: 11.0942 - val_one_hot_io_u: 0.6216 - val_one_hot_io_u_1: 0.1163 - val_one_hot_io_u_2: 2.6343e-04 - val_one_hot_io_u_3: 0.0230 - val_one_hot_mean_io_u: 0.1903\n",
            "Epoch 8/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.9848 - dice_coefficient: 0.4647 - loss: 0.0374 - one_hot_io_u: 0.7923 - one_hot_io_u_1: 0.6672 - one_hot_io_u_2: 0.7301 - one_hot_io_u_3: 0.7635 - one_hot_mean_io_u: 0.7383\n",
            "Epoch 8: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 413ms/step - accuracy: 0.9848 - dice_coefficient: 0.4647 - loss: 0.0374 - one_hot_io_u: 0.7906 - one_hot_io_u_1: 0.6669 - one_hot_io_u_2: 0.7301 - one_hot_io_u_3: 0.7635 - one_hot_mean_io_u: 0.7378 - val_accuracy: 0.9280 - val_dice_coefficient: 0.4511 - val_loss: 11.6015 - val_one_hot_io_u: 0.6304 - val_one_hot_io_u_1: 0.1159 - val_one_hot_io_u_2: 0.0000e+00 - val_one_hot_io_u_3: 0.0182 - val_one_hot_mean_io_u: 0.1911\n",
            "Epoch 9/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 359ms/step - accuracy: 0.9856 - dice_coefficient: 0.4658 - loss: 0.0344 - one_hot_io_u: 0.8003 - one_hot_io_u_1: 0.6790 - one_hot_io_u_2: 0.7406 - one_hot_io_u_3: 0.7776 - one_hot_mean_io_u: 0.7493\n",
            "Epoch 9: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 413ms/step - accuracy: 0.9856 - dice_coefficient: 0.4658 - loss: 0.0344 - one_hot_io_u: 0.7986 - one_hot_io_u_1: 0.6787 - one_hot_io_u_2: 0.7406 - one_hot_io_u_3: 0.7776 - one_hot_mean_io_u: 0.7489 - val_accuracy: 0.9307 - val_dice_coefficient: 0.4523 - val_loss: 11.7139 - val_one_hot_io_u: 0.6406 - val_one_hot_io_u_1: 0.1210 - val_one_hot_io_u_2: 0.0000e+00 - val_one_hot_io_u_3: 0.0176 - val_one_hot_mean_io_u: 0.1948\n",
            "Epoch 10/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 358ms/step - accuracy: 0.9862 - dice_coefficient: 0.4667 - loss: 0.0321 - one_hot_io_u: 0.8061 - one_hot_io_u_1: 0.6886 - one_hot_io_u_2: 0.7503 - one_hot_io_u_3: 0.7874 - one_hot_mean_io_u: 0.7581\n",
            "Epoch 10: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m35s\u001b[0m 412ms/step - accuracy: 0.9862 - dice_coefficient: 0.4667 - loss: 0.0321 - one_hot_io_u: 0.8044 - one_hot_io_u_1: 0.6883 - one_hot_io_u_2: 0.7503 - one_hot_io_u_3: 0.7874 - one_hot_mean_io_u: 0.7576 - val_accuracy: 0.9315 - val_dice_coefficient: 0.4529 - val_loss: 11.7702 - val_one_hot_io_u: 0.6443 - val_one_hot_io_u_1: 0.1255 - val_one_hot_io_u_2: 0.0000e+00 - val_one_hot_io_u_3: 0.0183 - val_one_hot_mean_io_u: 0.1970\n",
            "Epoch 11/20\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m0s\u001b[0m 350ms/step - accuracy: 0.9867 - dice_coefficient: 0.4673 - loss: 0.0303 - one_hot_io_u: 0.8116 - one_hot_io_u_1: 0.6973 - one_hot_io_u_2: 0.7573 - one_hot_io_u_3: 0.7957 - one_hot_mean_io_u: 0.7655\n",
            "Epoch 11: val_loss did not improve from 1.23937\n",
            "\u001b[1m84/84\u001b[0m \u001b[32m━━━━━━━━━━━━━━━━━━━━\u001b[0m\u001b[37m\u001b[0m \u001b[1m34s\u001b[0m 404ms/step - accuracy: 0.9867 - dice_coefficient: 0.4673 - loss: 0.0303 - one_hot_io_u: 0.8100 - one_hot_io_u_1: 0.6970 - one_hot_io_u_2: 0.7573 - one_hot_io_u_3: 0.7957 - one_hot_mean_io_u: 0.7650 - val_accuracy: 0.9291 - val_dice_coefficient: 0.4522 - val_loss: 11.8618 - val_one_hot_io_u: 0.6363 - val_one_hot_io_u_1: 0.1260 - val_one_hot_io_u_2: 0.0000e+00 - val_one_hot_io_u_3: 0.0163 - val_one_hot_mean_io_u: 0.1947\n"
          ]
        }
      ]
    }
  ]
}