TestingTBbacilli.ipynb

{
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  "metadata": {
    "colab": {
      "name": "TestingTBbacilli.ipynb",
      "provenance": [],
      "collapsed_sections": [],
      "authorship_tag": "ABX9TyNmFpYkStfYmZTkwoliUscb",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU",
    "gpuClass": "standard"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/pvvramakrishnarao234/Mydeeplearning/blob/main/TestingTBbacilli.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "code",
      "execution_count": null,
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "i3-k0NtO1vud",
        "outputId": "24c3b5d0-2203-44ee-bb01-a14c699c31a7"
      },
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "/usr/local/bin/pip3\n"
          ]
        }
      ],
      "source": [
        "!which pip3\n",
        "import os"
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "!python -m pip install pyyaml==5.1\n",
        "# Detectron2 has not released pre-built binaries for the latest pytorch (https://github.com/facebookresearch/detectron2/issues/4053)\n",
        "# so we install from source instead. This takes a few minutes.\n",
        "!python -m pip install 'git+https://github.com/facebookresearch/detectron2.git'\n",
        "\n",
        "# Install pre-built detectron2 that matches pytorch version, if released:\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
        "#!pip install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/{CUDA_VERSION}/{TORCH_VERSION}/index.html\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Tywcpzhc13iW",
        "outputId": "eaee3bfa-88ac-41e4-d933-f98514e6e779"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting pyyaml==5.1\n",
            "  Downloading PyYAML-5.1.tar.gz (274 kB)\n",
            "\u001b[K     |████████████████████████████████| 274 kB 4.9 MB/s \n",
            "\u001b[?25hBuilding wheels for collected packages: pyyaml\n",
            "  Building wheel for pyyaml (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for pyyaml: filename=PyYAML-5.1-cp37-cp37m-linux_x86_64.whl size=44092 sha256=2c9c77f532150ef267427e59a874dee22305a0b25562f84ccf4c1a2ff418af94\n",
            "  Stored in directory: /root/.cache/pip/wheels/77/f5/10/d00a2bd30928b972790053b5de0c703ca87324f3fead0f2fd9\n",
            "Successfully built pyyaml\n",
            "Installing collected packages: pyyaml\n",
            "  Attempting uninstall: pyyaml\n",
            "    Found existing installation: PyYAML 3.13\n",
            "    Uninstalling PyYAML-3.13:\n",
            "      Successfully uninstalled PyYAML-3.13\n",
            "Successfully installed pyyaml-5.1\n",
            "Looking in indexes: https://pypi.org/simple, https://us-python.pkg.dev/colab-wheels/public/simple/\n",
            "Collecting git+https://github.com/facebookresearch/detectron2.git\n",
            "  Cloning https://github.com/facebookresearch/detectron2.git to /tmp/pip-req-build-yy4qenrj\n",
            "  Running command git clone -q https://github.com/facebookresearch/detectron2.git /tmp/pip-req-build-yy4qenrj\n",
            "Requirement already satisfied: Pillow>=7.1 in /usr/local/lib/python3.7/dist-packages (from detectron2==0.6) (7.1.2)\n",
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            "  Downloading yacs-0.1.8-py3-none-any.whl (14 kB)\n",
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            "Collecting fvcore<0.1.6,>=0.1.5\n",
            "  Downloading fvcore-0.1.5.post20220512.tar.gz (50 kB)\n",
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            "Collecting omegaconf>=2.1\n",
            "  Downloading omegaconf-2.2.2-py3-none-any.whl (79 kB)\n",
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            "\u001b[K     |████████████████████████████████| 1.4 MB 61.6 MB/s \n",
            "\u001b[?25hCollecting timm\n",
            "  Downloading timm-0.6.5-py3-none-any.whl (512 kB)\n",
            "\u001b[K     |████████████████████████████████| 512 kB 65.9 MB/s \n",
            "\u001b[?25hCollecting fairscale\n",
            "  Downloading fairscale-0.4.6.tar.gz (248 kB)\n",
            "\u001b[K     |████████████████████████████████| 248 kB 69.0 MB/s \n",
            "\u001b[?25h  Installing build dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "  Getting requirements to build wheel ... \u001b[?25l\u001b[?25hdone\n",
            "  Installing backend dependencies ... \u001b[?25l\u001b[?25hdone\n",
            "    Preparing wheel metadata ... \u001b[?25l\u001b[?25hdone\n",
            "Requirement already satisfied: packaging in /usr/local/lib/python3.7/dist-packages (from detectron2==0.6) (21.3)\n",
            "Collecting mypy-extensions>=0.4.3\n",
            "  Downloading mypy_extensions-0.4.3-py2.py3-none-any.whl (4.5 kB)\n",
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            "Collecting typed-ast>=1.4.2\n",
            "  Downloading typed_ast-1.5.4-cp37-cp37m-manylinux_2_5_x86_64.manylinux1_x86_64.manylinux_2_12_x86_64.manylinux2010_x86_64.whl (843 kB)\n",
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            "\u001b[?25hCollecting pathspec>=0.9.0\n",
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            "Collecting antlr4-python3-runtime==4.9.*\n",
            "  Downloading antlr4-python3-runtime-4.9.3.tar.gz (117 kB)\n",
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            "Collecting portalocker\n",
            "  Downloading portalocker-2.5.1-py2.py3-none-any.whl (15 kB)\n",
            "Requirement already satisfied: kiwisolver>=1.0.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib->detectron2==0.6) (1.4.4)\n",
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            "Requirement already satisfied: torch>=1.8.0 in /usr/local/lib/python3.7/dist-packages (from fairscale->detectron2==0.6) (1.12.0+cu113)\n",
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            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard->detectron2==0.6) (1.8.1)\n",
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            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->detectron2==0.6) (0.2.8)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<3,>=1.6.3->tensorboard->detectron2==0.6) (4.8)\n",
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            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.6) (2.10)\n",
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            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard->detectron2==0.6) (3.0.4)\n",
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            "Requirement already satisfied: torchvision in /usr/local/lib/python3.7/dist-packages (from timm->detectron2==0.6) (0.13.0+cu113)\n",
            "Building wheels for collected packages: detectron2, fvcore, antlr4-python3-runtime, fairscale\n",
            "  Building wheel for detectron2 (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for detectron2: filename=detectron2-0.6-cp37-cp37m-linux_x86_64.whl size=5241913 sha256=76fb192c012c31cf6fe36a0f6b5e36c7daf32d3e5bf9e5b5dcc89acf1a69f33b\n",
            "  Stored in directory: /tmp/pip-ephem-wheel-cache-zpm1cs37/wheels/07/dc/32/0322cb484dbefab8b9366bfedbaff5060ac7d149d69c27ca5d\n",
            "  Building wheel for fvcore (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fvcore: filename=fvcore-0.1.5.post20220512-py3-none-any.whl size=61288 sha256=75d64060055cd74f6e68a24b3d40ad5f5d698a89c7cad24b9915f02387395a25\n",
            "  Stored in directory: /root/.cache/pip/wheels/68/20/f9/a11a0dd63f4c13678b2a5ec488e48078756505c7777b75b29e\n",
            "  Building wheel for antlr4-python3-runtime (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for antlr4-python3-runtime: filename=antlr4_python3_runtime-4.9.3-py3-none-any.whl size=144575 sha256=4441a8ccb757c903ba7c218be4f8a9fd0a663b90976668c469dfbae0fac39fef\n",
            "  Stored in directory: /root/.cache/pip/wheels/8b/8d/53/2af8772d9aec614e3fc65e53d4a993ad73c61daa8bbd85a873\n",
            "  Building wheel for fairscale (PEP 517) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for fairscale: filename=fairscale-0.4.6-py3-none-any.whl size=307252 sha256=d4def24295cca35df8eb612ce685c87ebf7b9ed121f9d037eec8f2b7d20a3e13\n",
            "  Stored in directory: /root/.cache/pip/wheels/4e/4f/0b/94c29ea06dfad93260cb0377855f87b7b863312317a7f69fe7\n",
            "Successfully built detectron2 fvcore antlr4-python3-runtime fairscale\n",
            "Installing collected packages: portalocker, antlr4-python3-runtime, yacs, typed-ast, platformdirs, pathspec, omegaconf, mypy-extensions, iopath, click, timm, hydra-core, fvcore, fairscale, black, detectron2\n",
            "  Attempting uninstall: click\n",
            "    Found existing installation: click 7.1.2\n",
            "    Uninstalling click-7.1.2:\n",
            "      Successfully uninstalled click-7.1.2\n",
            "\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "flask 1.1.4 requires click<8.0,>=5.1, but you have click 8.1.3 which is incompatible.\u001b[0m\n",
            "Successfully installed antlr4-python3-runtime-4.9.3 black-22.3.0 click-8.1.3 detectron2-0.6 fairscale-0.4.6 fvcore-0.1.5.post20220512 hydra-core-1.2.0 iopath-0.1.9 mypy-extensions-0.4.3 omegaconf-2.2.2 pathspec-0.9.0 platformdirs-2.5.2 portalocker-2.5.1 timm-0.6.5 typed-ast-1.5.4 yacs-0.1.8\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# -*- coding: utf-8 -*-\n",
        "\"\"\"detectron2.ipynb\n",
        "\n",
        "Automatically generated by Colaboratory.\n",
        "\n",
        "Original file is located at\n",
        "    https://colab.research.google.com/drive/192QVcROHVBDzmdULtF7vRMS_1onvrP1P\n",
        "\"\"\"\n",
        "# !pip install google.c\n",
        "# from google.colab import drive\n",
        "\n",
        "# drive.mount('/content/gdrive')\n",
        "# install dependencies: \n",
        "\n",
        "\n",
        "# pyyaml is a data serialization language.YAML is used to create configuration files.\n",
        "# Serialization is the process of converting a data object\n",
        "# pycocotools is a Python API that assists in loading, parsing and visualizing the annotations in COCO.\n",
        "\n",
        "#!pip3 install pyyaml==5.1 pycocotools>=2.0.1\n",
        "import torch, torchvision\n",
        "\n",
        "print(torch.__version__, torch.cuda.is_available())\n",
        "!gcc --version\n",
        "# opencv is pre-installed on colab\n",
        "\n",
        "# install detectron2: (Colab has CUDA 10.1 + torch 1.7)\n",
        "# See https://detectron2.readthedocs.io/tutorials/install.html for instructions\n",
        "#assert torch.__version__.startswith(\"1.6\")\n",
        "\n",
        "#!pip3 install detectron2 -f https://dl.fbaipublicfiles.com/detectron2/wheels/cu101/torch1.7/index.html\n",
        "#### Uploading data(Mitos-12,Mitos-14,Madhubushi) from drive ########\n",
        "\n",
        "# !pip install gdown\n",
        "# !gdown https://drive.google.com/uc?id=1t3rcqyI_tfSn_VsxhyfjsZYvnznADxCz #Data without normalization\n",
        "\n",
        "# !unzip /content/frcnn.zip; rm /content/frcnn.zip\n",
        "\n",
        "# HistomicsTK is a Python package for the analysis of digital pathology images.\n",
        "\n",
        "#!pip3 install histomicstk --find-links https://girder.github.io/large_image_wheels\n",
        "torch.cuda.empty_cache() "
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "jqF8g9Co13kk",
        "outputId": "965fed39-1d55-4e8c-9707-d3f6ed6e6d3d"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "1.12.0+cu113 True\n",
            "gcc (Ubuntu 7.5.0-3ubuntu1~18.04) 7.5.0\n",
            "Copyright (C) 2017 Free Software Foundation, Inc.\n",
            "This is free software; see the source for copying conditions.  There is NO\n",
            "warranty; not even for MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.\n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "from google.colab import drive\n",
        "drive.mount('/content/gdrive')"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rT0LaxYD2sxo",
        "outputId": "dd3824ca-4d5e-4b5b-fd0d-1e934cf6da27"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Mounted at /content/gdrive\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# #normalization\n",
        "import matplotlib.pyplot as plt\n",
        "import cv2\n",
        "import glob\n",
        "from tqdm import tqdm\n",
        "import numpy as np\n",
        "import os, json,  random\n",
        "# import histomicstk as htk\n",
        "#Scikit-image, or skimage, is an open source Python package designed for image preprocessing.\n",
        "# import skimage.io\n",
        "# import skimage.measure\n",
        "# import skimage.color\n",
        "# ref_image_file = ('https://data.kitware.com/api/v1/file/57718cc28d777f1ecd8a883c/download')\n",
        "# im_reference = skimage.io.imread(ref_image_file)[:, :, :3]\n",
        "# # #fig,a=plt.subplots(1,2,figsize=(40,40))\n",
        "# print(im_reference.shape)\n",
        "\n",
        "# def normalize(im_input):\n",
        "#     mean_ref, std_ref = htk.preprocessing.color_conversion.lab_mean_std(im_reference)\n",
        "#     im_nmzd = htk.preprocessing.color_normalization.reinhard(im_input, mean_ref, std_ref)\n",
        "#     return im_nmzd\n",
        "\n",
        "#######    Normalising all the images according to histomicstk  ########\n",
        "test=glob.glob(\"/content/gdrive/MyDrive/TB/test/images/*.jpg\")\n",
        "train=glob.glob(\"/content/gdrive/MyDrive/TB/train/images/*.jpg\")\n",
        "valid=glob.glob(\"/content/gdrive/MyDrive/TB/test/images/*.jpg\")\n",
        "# icpr14=glob.glob(\"./test/icpr14/*.png\")\n",
        "print(len(test),len(train),len(valid))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "r6FPq1G213nE",
        "outputId": "f69c915b-b422-46aa-b2b0-32246f00144f"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "200 800 200\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# Some basic setup:\n",
        "# Setup detectron2 logger\n",
        "import matplotlib.pyplot as plt\n",
        "import detectron2\n",
        "from detectron2.utils.logger import setup_logger\n",
        "setup_logger()\n",
        "\n",
        "# import some common libraries\n",
        "import numpy as np\n",
        "import os, json, cv2, random\n",
        "#from google.colab.patches import cv2_imshow\n",
        "\n",
        "# import some common detectron2 utilities\n",
        "from detectron2 import model_zoo\n",
        "from detectron2.engine import DefaultPredictor\n",
        "from detectron2.config import get_cfg\n",
        "from detectron2.utils.visualizer import Visualizer\n",
        "from detectron2.data import MetadataCatalog, DatasetCatalog\n",
        "\n",
        "from detectron2.data.datasets import register_coco_instances\n",
        "# print(os.listdir(\"Data_13\"))\n",
        "\n",
        "register_coco_instances(\"train\", {}, \"/content/gdrive/MyDrive/TB/train/annot1.json\", \"/content/gdrive/MyDrive/TB/train/images/\")\n",
        "register_coco_instances(\"test\", {}, \"/content/gdrive/MyDrive/TB/test/annot1.json\", \"/content/gdrive/MyDrive/TB/test/images/\")\n",
        "# register_coco_instances(\"test_12\", {}, \"/home/kmit/Mitosis_birac/karthik_manudeep/frcnn-with-normalization/test/icpr12/test_coco.json\", \"/home/kmit/Mitosis_birac/karthik_manudeep/frcnn-with-normalization/test/icpr12\")\n",
        "# register_coco_instances(\"test-14\", {}, \"/home/kmit/Mitosis_birac/karthik_manudeep/frcnn-with-normalization/test/icpr14/test_coco.json\", \"/home/kmit/Mitosis_birac/karthik_manudeep/frcnn-with-normalization/test/icpr14\")\n",
        "\n",
        "print(MetadataCatalog.get(\"train\"))\n",
        "print(MetadataCatalog.get(\"test\"))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mcw6wQ3w13pk",
        "outputId": "1d9900e4-d47e-40dd-b607-f77b046277cd"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Metadata(evaluator_type='coco', image_root='/content/gdrive/MyDrive/TB/train/images/', json_file='/content/gdrive/MyDrive/TB/train/annot1.json', name='train')\n",
            "Metadata(evaluator_type='coco', image_root='/content/gdrive/MyDrive/TB/test/images/', json_file='/content/gdrive/MyDrive/TB/test/annot1.json', name='test')\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#visualize training data\n",
        "#print(DatasetCatalog.get('train'))\n",
        "dataset_dicts = DatasetCatalog.get(\"test\")\n",
        "my_dataset_train_metadata = MetadataCatalog.get(\"test\")\n",
        "# for x in dataset_dicts:\n",
        "#   print(x)\n",
        "# print(my_dataset_train_metadata)\n",
        "# print(dataset_dicts)\n",
        "import random\n",
        "from detectron2.utils.visualizer import Visualizer\n",
        "for d in random.sample(dataset_dicts, 1):\n",
        "    img = cv2.imread(d[\"file_name\"])\n",
        "    print(d[\"file_name\"])\n",
        "    visualizer = Visualizer(img[:, :, ::-1], metadata=my_dataset_train_metadata, scale=1)\n",
        "    vis = visualizer.draw_dataset_dict(d)\n",
        "    plt.imshow(vis.get_image()[:, :, ::-1])\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 304
        },
        "id": "u8LSMuYG13r9",
        "outputId": "68494c73-5075-4cc4-c849-366f59aeaa7b"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:20:02 d2.data.datasets.coco]: \u001b[0mLoaded 100 images in COCO format from /content/gdrive/MyDrive/TB/test/annot1.json\n",
            "/content/gdrive/MyDrive/TB/test/images/tuberculosis-phone-0900.jpg\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ],
            "image/png": 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OLUFZqSrL5QUXZ6ixY0E9W48IrC3IcoJ2RtsJbStGifV/39DbnfH5E3z6JdbvaFIqXgQpC8v7bzn/xu8g736Onz+gpw/o6R2ynEEbxRXbNlTGgU57JoLcPRxorsUJRiKFkcocEhOlQ5+U3YSSk7IAsPkcRNL4xn/LVOCQNAETtM53CiIL0hyWnf72iX0PQysaaFtUEEuANoLbNQ+acGkn2pqJ8dN79OUbaK8/2Hz9tI0rmfgolVIjKyy1ZehcGH3gIowR3FdFkdoYPXiTiZKGO947QPBK+kAoY4zw+hna6eQkhz34Sg6wDMRiGqNzf7tib58pqT4IgxjGb0sOUosiNri/Xbl+/BxZUjwTc4O6LKxrKBwiqeBIv3G/veFaacsFvFJrYWnhbEyVJEUy7I7vfE5eTU5KMoykzlC3YJkcIxNAsSHIlSo4I5J8nvylKl5LRASieO+4jNg8U5WgM0ERBlPLlJwFHxZYWI7r1lopUpDM6ALH9bs/oo7pICPZAS4FXV/Qdg4DpkqpC1IaKuFUTZ0hA7HY/BSPJA2BPuuyQK2BiGVyqxPtP81n0gqlRDRiFlRGYDBFNFUbWtA0zoIEErVBk0Kp4eTMnXG7s9/vkfTjQXeNPti3N4a/QSJdE0+usWBFMc+orDT0fEH3jYEjfUO0YLqg9UxZX5Hze3R9QdopchB9Z7x9Rj/+EtOKXz8DI6g1LejpQv3Zb6E/+130/W9EBNDOaF0RjXmzEexscLoEou+hRHAzkEdkB0kFpZP0+YODrsg8huf6sFTJTOpgIt7McZDct6DgMf/oxLeTfpBDhicO1BOUE7t/wgwWkVDdJAd8mGPhSK6W9QU9vVLO72nvf0G5vIf2lxm5tiU40allLDVCHpGwD22hIfTRQx4EWB+HYQU5+MSR2UrVB81go9M3OaRNwT9GODl5oUAgZW459r7Tt439dsOvb4zbG9frG+Kdcj5FgswjrNvvO/164/72Ed+3CBslFuR2vSLAqS2REEGTzxyRWU2K7bSssKx5LaRHn6gzaAHyum3fGWOn1kJdV1RqLOhSqISjssyYo6G99eScAgHXIykxHYrDwVmZOyND+j4iC4w/0zdp0CWSZmYWqC0TGJIJLiXCu9CIjsOgTuP6SHhAIJ24Z8+NKaVGlreUnDceKEehlKCQpmP0FvydkJM6E5jTyCXSn6ZhhrjzCkopEUFNHnYmZiZVBQfi3vedbgMpBd13xEPGNbYd651DT1kLDLBMsux9j0RfrUgrlFoRKk6J0DVzBVIqcjqHQd9qcjonpL1Szt8il28p51e8LMHLWqfVV7RdMK/o+hGxDbMBpVJeP1B+9leRD7+NL++gntG6JJ2UkqgxGNPxjcHYgy7xjCpwMCycnU7qzsOA5h5K/xuRggXYsVzrM2EqTvDTkvKx/BNOvhwO8dC7ilJIYzwDAVN0fWF5/YZ9dPa37yJnISBuB/rVYhStuUcaWi9oe0e5/Jzlw2+jLx/wI7/x64+ftHFVVZZzEOlMfV16M1FFa+jrpFSKjdQzlukXD6nI2Dv9fmfsO+JGreVIco0x6Htncm4jN3jRktzqg0Ob1NAMZwrQzdhvN8Z2p7ZnjxiIzPbOfr8y7nfEM+nTWhopY9t35H7L0Cl0rCJKqYXWFkpVSlVG6m/dI4Hhc9dPCctwGMZ2C02fVuFsxnp+mYA00aQcSalIbsU8zTSCloKSGe90J5q86/CYn70Ptj0ogd47gqOpYa21HYh5CvE9w64ij0TLzCjjZFKnYzYO3rNkwmQ+xzkEPbL1KEgJXWoIwf2Ym8f8ZJSSht3MY44zoUEmqGbCLoxHZ9s33EZI9lQPLenMxt/vdxg9+FAVuj/E6p4o1i04SjJK8d4pONIqWlcYQvd7ZrozNTD2TGIVhEbVTMCagHWsb6gZaEHaihD7QtcLunxAlm/DwC4XkMoYBnRkicx/+UbxdkE89LpeGu3dN7RvfhOW91BO8dwzknEXRrdDrQBf3tcEMQ+53cP4RbjtR37ASeVOusKpPhl9pIQv1qES6gfXAFSTbkr3GXtsgoxEnqqBZiOaKghOewcvrbF9OuH3z0jfkdFTaxt5h1Ya1gW3gnOmrj+nXX6Bnn8G7YKWH24if9LGNWQsa4SUosGPJVsDGWbmQ9PcAIGJJg/Uw7vmwlDN384N5yF56vseyC8NzjSyw3ugoFqR1g6KYJLvU4tnWUV0fOa+s91ujH0DH7GxfOAjeK5hRl2WI9GxpwDfehh5rY3XDx9otQSSdkueOaVDeX9TFxgLFXpyykfIJhqLUlK/lzpTKTVkNDHJkTm1lLPkzw8nQhirMEqG95kkMva9Y8NoTR8FCSknk6QE5IlbjP2QnNxMIkQm60Cd07A+uLs/PcyMMYzdjAVS1C6YTOf20J+SmzToikCoo/dASG1Kq44Fl58d82c2kCrUpE3c43PHGEn7dJZaqarUttD7YIyQmbWaOurkU2V0fL+FFEuFIhUTg7FTahgDHVHAYT4AQ4nXqUMfwddaD+E+BqILrILWE7q+IqdvkPUD1FfQBZFCxRnecUrw7i+VurxQdMrgGnq64Msr6BprxAUfBDWREZGkJMdShUMi0Gk4NSO/Z2VOarEmdxVrKZ5OrmU5qvqYa/rJUKMlCnsyITvXFBYJMyedmAieTnCuWSmgpbIsJ8rpzPb5e/x+g22jWBjvVhqKMDZjvxmuK8MvLOU9oi+4rpj+8A4BP2nj6gIm5dj0TA5RJUPBmTR4hGdTmuI2GEyi3DJDWI/wVSZq7Z2+748qnXVFa8EN7tcbfdvQIqznM+20BoIbAzKTXkqhrSutvaPVQLl929i3G5ih+FFt1WeYqcpyOlFqYR/jEOj3EZl3Hc62baxmSHgKai1QNbSZRUESyfNAdpbIorWWxQdBe8gT+kIeiYGZoIMEwBnOJwnJDJLd7ED0nnM2ek9JjlATsdbaqK09UH5+gqqGbMk9k1nxfA+0o4pYOMYZJZD3ZUfZoyZqddwiaeRm7Pug1OQuzQ/aQZJXm4UhmRt8SKU8qqdQScfzSD5RK82dvSeAy+vtw5DhDBvpnDJqKIUiirE9ws8StMcYhu9hWO36ht1vaB1ojTWtrUEJCkB3YWcgY1AKVBz1Huu5b5GYcTKiaggNbWfq+g5dX4MrPb/DywnRFg5LHVfFXHHvyKrx+hL3rdrCCWhFXCJMt4ha3EtcvxmMLNfNqqqIcB7KimP+5t59SraqPp55cNmT1pLjNaUktz89nURhhNYlgBWhCpg5DbeBp+qgTEmYaBhrIYyrNJwTulwop/f025Vx2yC1yaU1qhZ069jHO2OD666MT4OXM7S1cSgcfsD4SRtXUEwbs9JKppjb4cjGElVAZlmLPtIkuOGjh+TlKQPtkzNLnWIglEielBoyotIa1o3tdme/XgMtJAdalgcHIyrUdUH7GXGhlcGwTvdIbHkQZWirnF9fGX3HgWVdWc6nMHD7jhvUFkYqKohqCKTHQHTg2iHLGGutQZHILIdNZKBOqRVrjSoLpYSW9CEPkkOQPRfxNOiRFa+RpEn0HkmviR4lQ8RHmD+TfbUUWm3UUlOmJEcyIuZIkSxKeJbsQFRmlZTJjZFcqzgmdmw84EgsOf7gZCWSEFPs72ZhBI9S+4cQ3eXxvsnraqLKg6fN79MS16OlUJMyGOlI53WoCLIs4O0IS30MMD0+N2x68pPbjt9v+P3K2G6IGd0l5ZeK1gKjBFfBwDsoRsHwbYsoanQKUaKqdUWXM1JfkPUdZX2HLheoZ0wLSMO1ZCuNTE5qi+uTGg5hhtGlRvTnHsmefL6azteHH8+NzPqXL+bumRuPyOCLiOP5d09JpCCR08GQVFRGDWax5opWRCsu5aC2bCJi5rNOLXQmhB+gIKMnBReh1DNleY+9DLI2Pk1MoQyjLRty7QiROO/mlOGU+lCz/Lrjp21cRaAtR52/udP3PcsQK0jWo7uz9+2oV57ezDP2POQwI8KtQCppvMxYl5ANaWtofTyUWRZn9zujAZcT0FAVdgfGwPoO5jSNzRFJEKdl/bKNjksNQ1IrUoQl9YhuTi0ND0Y+EhmqLOsJpLDvnX37RDkLZ5tZ90yuzSx3Gi00K5nMqBFR4T2cB+aIWuSvkgubm2iMkaWE8phzIoVkT2HXgWKT95qGdcnEy8MQP4zbc0OZosqwkO94ZvGVsCejd/Z9J8ynUrwcnKtONDP50NyAk4J5wONpvCfq5qBuoow6HN+xtI7PnSqBhzEnEZKrMoYfXPAcTqA1h0RNkooBQq0wooeDm2N9YNuG3zfYd+iRcPTuyaeng5gzbFmk4CMy9Bk1gKcUsVBeXljf/wI9f4tpGFXKCl6Y2mBU8UwSQVJYOlPxGQmUklHQY+7mk5acW00HEXLDB8qctunZMUUToakNz5n6ArEmVZNywlB6NLznGpaOi4HWo1mMUfCjYcxDoqcSZe/uEqqWyL6gmmXMpD76AGaCiR9RDpmIneWubR209x4cNgsmyu12Z+FpXfya4yduXBWtp/Ckng0f9h3YUTlTWzYv6Tv79cr+do1SOhVYT1EdU2KDeEqUbHD0D7BhjG1DaUirwUPJA+lNfecwg/uG7Tv1lCETIbfqb1eaG1XBRiRmxr5Ta8jHhpCbxFNYHiF6T17PMzQtJX5el+B2LVHisBBOS4vFNo5kTHJLkMmpWChaI/6New70OdxRc4qRhQZk/X1+70xgTS5Z8vcHJxYa1tCCJh7WKFudlTozOdZ7j803n+FMjGWob71nljcW+LCeYvnY8G4p1XruSTARybbh/+QfY9vGb7/84kieaZa7jjQAc2Ym1SEi/PHlxJ+cQ0BeZtZfeEQ0B10Q16apG560QqiFY067QE0ZGEmliE+lxTgc2hyz34LmF+z7HRGjNEV7oHd3Y7/f6NsN9UElwnHx0MeiQqnQ1hOnb39GefdzrL0DOSN6wrzgSIC6JM1V5HEZ3gMpJo0WjVoKUguzyCaiv6AAxNJYWlABNo3afNYzM59z9OjlMN3Es2HlyYHNC0xgkPPMCK1wrLUa8jqtwbs+oePZkMiRmHqC7vJ0+1j0BEAIOmTOgcdaDiqkQslk7tS3lyk2KIg3bDfutzvm9z+/vfqV8ZM2riKCloa7RUgTMJLRO13u4VUd+nbH7nds3xBgaSunZUHbwgDG2A9ukaeQ1UboT/v1yrqvnF5faZp9BvJ1slS8FLpHxVKLD4kAWiNRpF5x39j3je12Z4yonFnXlVJDmzc8RNYhwyLD4Lgk96xWqhmK0cGVuq4sywUuH6JSpK7MjkmWKOOBKMOgaCmICW4dkUis1ERW8V2PSE2zvn/uFJ9Jutwok8Odhi7AempOVYl0ffxcCSH5SGQZxu2R3Z3JCq2hNYSHRnY2PPlCHeBRdkqWQ6oqertz/qf/E3/w+Ts+/Dt/k8vyCockTY+Ey3zMWiKx8u7zlV+8fc8f/+7Pjg3/3IDkaLLzjF5zTpGIjJCgo8yzjJdHZpyJomdPg25YiYYjohJlxr6g7DAq7FHObASdYPlg+naPrmKEoSmZvAnuXlD3AACl4WXF6gn0hMiKuhxr4ijAcEdMme36Ajn6obapGa05YbQkI6O+W2iE3bNLmyWcDSphiqTmfB31/U989pTdHXN9LNXJg9tjMc4oEyJ/UtrRcS2iNDmeG5ks86StAijkc5n3CYfjU+SIeDzpJ0nHGGszn6N69ILIzjeK0rxSl8YPHT9p4woS/KIZwojwWir4zn6/MbYtXpaNKiiFfezpiUokplzQ4qj2IyHmZGced/p943a9sn0u9PvGxYz68hJkd6ucXl+iPBNjfXkJgXd0dUFns5Vh9G3n/ukTY2zhjN0w6wdScg9VwL5tR0Ip1kxs2jEG+7DkaIOSaKczy/ufweVbOL9CXRnZEk8SYf1KNJulgU7fo2nFKbP4LpoM7OO1nq93wijNcLvWyZ8+jDaSmzs7idVSMOtP9IIh5oeG1yzol1qTl3SOfgZmIb2xTI7MCOGgAEiHk3KvWYq6iPDPPm380btvefc7v8mn9T3uirgedzSsI0wpl4DC/t33vPzxdxnKZ+j7xKHOcBzh2PSxGR8dwo7wejpn0eQ/68H5F3N2OMLQiYDrsiZSdGzc0P0tEvvt3mAAACAASURBVENZGjydSSR/skBBpholw/FhyN7x28643WkvyafPCjeXoHbSaZhHFVX0ZTXMFNyyK1Ui1Bkyu2Oqmdwfjy5aYXVzHqZh/ZKjhgfVFP8//3qss/mLZ50Px/9LKmEiArNhwbFm3kHLNILTeMtBAR2AKTXpRxJtKodUycAQ91koE05Is2IrOZIHAif4bm2V83rJqOiHjZ+0cT0Cq+T9tAa/t28SXBbjqO4RHClCLS2MiUq2EhTqEt656z0ynk8JLhGJuvPrYNxu9NuN88++5fLNe+qyUl9fOF3OjL4dHbGO/gYSBpZx53a9sm93WrHIEmcDlFKzO1QpdCnc9ztIp5VKLeWBpPJajEgsaFuolxfKywfs9A5rF1wWgmd+IMajibAlyvGgO7Z7tP8rbaG0RJn+1G+B/Gp5hHZj37ntO8uycHp5CTF3vlJz8dYaXLL3cUhfZivII9Ekks4lGpXU1p6qd5T9V5Iej4zylxt2zE5chAysSuFv/PX/gH/41/81VBf65tzfOjacWirdOtt2p9WV87ng6riOoyAk0Pfje+GBWqeIZ2T5r3nwjNMMKGRiiNz0FT3kP7EmZmOfcCjhAUU1JFBF0ALab8j2GXzg0brpQFJLWVDWqP23Hd93JEs0vXd864y3z5RPn+DdlbK+x61Hdzd5PCfV6P0wPC2L6pGY6fsWUrQnQ/xs8oB47SA6Wc1chUens0CbB0N8PCsRZ8rgZKJKmZ/rOb+WQMmPfTSR5zAHSSVPWUC+XA/TeaZgEicKdWYG08WPdaQl+GTLZy7ulJI0QqqPJOfk0CNPCoRomFNK6OFFv1wvv874SRtXeIQ4QqDR9bRGAuv+hhBh0kg5lSMs64m6nqCU4CdVkz81bJSD//NhaC2cXl/AOv3jR8btzlvvoU8dnfM337CcX7BasdGPkKLWEv0zkw+c/WBD6Bya2d43xoBGNjGpNZooA2MEog3jFZUrnsZr9BFSrWWhvLxDzq9QzoiuIC1Dzzk59gixRqCNqSg4XS5RMtniOmePhRBaP5QWczOE9jAW8zQwmjzwn0IpY0RJZs8sOQ++LtrCfUkzmOgTwssqnBmO5Qgj9/S+5KEnWhHmdaeonJC8ff54Q1xYT4A74zYoy2CTnT46SOfDvoU94GESNMPRfd8fYbREtdTo/aiVD81uhqxZNh3VQs8Ncg4PFTwnhM645uauBR/x5V5PUE+IGOLRfhCMVls4Ygx8YPstikb2PTe9MPYdvd3pv/wOTu9Y2gus0Uj9ENsnfYFbNP1OcX5VDWeTiK+kgSGNC0kBjFR2iCgDOxKIIoqaZStOgg55orXm5M5oDMIUPpB/3OfsIfB4XZbPZsZf5prLsH1y6E8QOB2e5TckuPJp5L9E154VXBGlpfY4731GSt06k2zoe6dvG6VUVKG1f3X6uf7aI7iU+TCi8YaeTkcfTBWwGivX3amnlbqu6d8kDV1ko908M/YFr34In2ur+Pt3eeJA1H6//fJjZj5LlLTWRnbuRbUiOth7VHqctFKWC4wtGr7Yxmz/R/Os9lJkCY527LMaieQlY/FNz1mXleX8gqwXrKyYrrgsiNSDW3z24tYH2/UNPHje2hrnlwtPQS+9Z29LeZJLEaGYeKCJUgrn85nZlyA2y5TbPIXShyPo2L4FMpFY9sH9PZCppqTMki6Y/Fck0/Topeo+wuA8dT2bneb92GB5zQ4dQ4pwemko0YJOVainEglAEdgtpPhDH5uJp02YFMiwfjQXwT054dy2yXOSG3U661kV5mYpZTJGN/Ztj3mxDlJSfSKYCH13vC7Icka640NRBqIhS5OiWeba00DM/rfxjM07tm3I9TP3P/rneLtQv12Dcy0PCZwnHDQzxt4pACrsveNAWRolE6cwu8vt0eKwBw2gzhfP0P3BpR4ocjo+SSz5SGeEAc3naklHpNB45jbDNKahnsUHNqPDWAE8WdUv1q0Q+bmgFfxwKJ6W2POeNddT7PV8fuURcYzxaD6DW3Ytu2GlUOuTiuYHjJ++cU39as3QQ/LfMWmPkK4uK6hEPX0JEtotGvTe7/coCVUNLrWGdnak0cPWQLciWDa3vt2ubPvAP31ilTzapLZsaxaJmeX8En0j6ZTTO/CB9oJ1Ryy6ZY3eGa1TPFCFprbzdr2y7z0KDIoelSpLFjJ4O2HlhOgJyhpcszzxkj6F3p39fuf+dkUkJGitteRS5TAQvxp6TwMHYSD2vgHCssQRNWRrvWn8nwXfswS0752x3dnz6JPTEoL02WmoZc8E81BWTKM8s8uTZ53axgCTs6/AI9yeHBsZUPbJ1TblUtYvNn6VWc0D9bQCC80M3bY4ISITi2OMQx4ePW3D2ZWiKDXKPXPuYtMKlGycLfPYGPninrb7ne0eiVbJ/hVySJ06A48cQl3wccezVDskn2EIhoT4XZYSR/tIqCB835A99cl9h/uV7eMv0defU88xTwfnmIh1u0VeQloJSRPgrYYDXqOCy80OqmBqWR88O4d2GQL5zTU0I5VHWfiX5EI8qTxiafRUcmTCzWaSNKi86VDd9ZjL2XmOvI5n2mMmLRUeAMCjrXggdsMZiVQfxUcy35VreBbG2JRM9h59GIAaSRL6k+rj1x0/ceOavTj7TnejuKE9m69IlLO6CsMN01gInv0HZiwxWwVKFgmUWo/zkMK7JUKrhdIaSyksry/U643b9Zokewo9NCpaUKWulcs337K1ho4NTifKujA+fwdXKC74uIfsRndgJm1ms9/Kdt+xMagGnl21fFlxbdBO0ZGnrFHmeBiNiTjjL89m1bbvx6Ifw6LM/nidHwYWj6z+LKZAJbsjPRqrUGZC4BFiHU8kDWOtlVoq7oGIRpb5ruuKS1ATyymM656147M9oY2gVqbxVtWjEmuO53/P7UomPPq+RbONGUJOFEki8dCqhTMTySYuEhybRbny/Pxouj6dxkO/yrADaY895H3zmJy5OY958dCz7lkkUmqLpulZdYQ4Jv3R84KomGJ2bvMwXIcRCMYiwnAnVBjljpeoipM0TNkaB62Bpt0l+mRYtNOcVIeLILWyZgn30fCd6O7WZ/+DnOtJF4hASV50cu4YRwnwGCMq7o4mvLMSK1EqHv0QsphnRh9ZKMtRdXckHo6HfRhZyLU8nbw7Qmql83BHza+KxKqDRnLV4YjCHvSWZuXcYN/3LHUO/bGMQQsPG7CkZ8XdDxw/aeM6qbC4z51922DfUJyaB96VGhUmkrzerL9HOKRSs6NRdIUvmMfi27YtEEyJngOHMSqFdj5TlpV5EBrZlUe0hvEhNk9ZVtw6unf67R1bWaJVYH/D+xt1ewOi5twQqsf1LOuZ0WeGHmxESW89XTh9+AX6+vMwrroy+wMcwdJhYB/h93OKwezRVOM5cTQNEGSBRIr/RZW2rGnsHqcyRP36fLeAPHo4tGVBXl6p4lSFsd+OmvtlXVmWJSMEjWqxEgqNKPCA2cS6SP2SpsiNO6VbocPNDY3z7vs3/t1/8L/F/D+F+sdV5v0cyEuVZR/80W98OAxHVr5neO9P3++H84km2T0oC7NYZ+XR45ZZpSQPqU+EqCVC7loOwxZFE5GZjgKOkPx48pyTUy4arSUnUlMxvDaoDa8Vas12eg1pC1oXZs8JJu2STszdonH5EsnEUmtUnyGZQAq01kePHhStHFrwUsqh+phUiQ+Lgy59lseGRTMfUbosIBoyvXkWm/tg+M7wgVlQHfPwQbEa0sqnqHsqOabz0vyZO/jIPhUjKi9t8qSih5b3ObkrNqmIx/OdhSeWhnW73485Y0SLSkYPnW9yxP8vgOtP27gGxxrt1uiD3aJH6ugbw2ErcLqcaZcLrS3Z/PggfeIzJDhVlSxNJDmmJyTFrFlnJtwlm29EE5L0n2FcS/Bg0TQlDnQbI0pURTV7blbk/hH2M2U94+NO34OHtaKgFdU4CcFSkrOz4Sro5T3y+m000ignnHI03Huu1z6SMDlHdVkyU1wyxLYMtR4ln/nmA6l5mbrWRKyHLMsiyVwmhRALOwFCLNSkL+ZhhGOvjL6h7lm80b40HomE1csjC5+cpaYDPJqmzGRSOsugzp3bsvA//Nv/On2L42hmy8F5jLmUPAakPh1rnoZ2lMig62wX409SnvhB9KMYnbFtR7+JaTSLlzwCZ0PW5O4Q5rHasc5aGt5MzMxGIwD0vLbGLhrH/2xx0kCTkv0lwpDMUmHDI6klkUH3lg2llwVdz+j6QmlncGFkI4Ro2tMjaVWUJZUKLkJmJji6WllGIS0Qp2fz9FnUIKbJQTt779y3WMMREUAcp5Qa2FArJiAKA9h7zOPI/SZKVt9FxGNeUkMc0ECJSHTOWDTfmes+KAsbPY1rnCwcwDKq2sw9m8ank7SOm+KWTtgsGg5tYViPknmPs8kg8jMkKsbGgx76AeMnbVwPzkc1usi7s7tFGWMfWA8E2ogqpsiKJ5LLBWRHd6jsx3rA0+TaDj3flGLIUZ00+5EmBEpklw07kAST4W3NgCaUc0REJgXRRmknxDe4vyE2aK2ipUYo1irtFJxWMWNopX7zm9j6DVou4C3bDD5dNE9o1KFolNM2zeOY9WFQI1eg2Zh7GpO5eMOAlZqn36aWdyYBPZFcHJiXjVKmOmFSDNkzthRgr9hej6SjuQffp5qNqvOB5rdMyRCTwfFHOW7vPQz+pG+mQ0DYROnExtMxkrMOJEkJBzrqAw1Pje5MwISyI5BXSIwsw/KkC8bOno5wUgXHHzgSj3IIKKNUWLIIou979sZNI8UTqs3DE4c7296h29F3dk5E7yNbG8ZMRWRfghpq+fP1Bb28jz6s64UJDmyE/Mry+TtQmjPb9ZE1UFP0fyhG5rk4h5NNrpvoLRD9FQbbviHuLHN+04jhUWggOntHjMhd3K70fWMmslDBSkOsQfUEO7OstuQcOfPcvJj3ULKE1jaSniiUKZ5zO+Z4RjKa0YLvD/1qz25is8Xo7NU7S7Eln39EZyAGwzrb/vaDzddP27gi+cw1kJmD2KCLwH5n9CgikOTWRtZjmznWO9vthrmznk4HgsnzLw8u1scIQ7BrdDYqGWrkpn3OuD+aAD84OrcoBZSWob2cj+ysLivVNrxfWdope8lmc+ptBwRpK209RVa8LJTXD7C8x+WEefKJaQyeQ6hIIdvRneuhSZ0hX4RvWrNqKGoAc2ORvy+MPs+A1+z3mh2z0oiGiDuTFlML6GF0VQVdWhSN4dEop+9YMVRr8oglWxyGllBEIllAhOVzLr8sQc3NsneskFn0SP5E0sWRPvJZxGGRs1/E5BMDqURxwxFmT15ZQq5klg2fx8icWUQRs8QzqKUwep6JP9V6JFLmPUT9gRxHcvcsxojfxWmuKkp2vk3+fkGq0RrRsrGU5NAHnZTGpVNUCemfE1SOXt6jLz+nXL5B6hnLHhuIHRWDs1vBlJLFbehBeTBmHOJPyyqM2Dx/NiiRcdADItDHoFAoOSdmGzZuiESrRPGQsfV9o99ueB9MNtxVo8VhO2MYY9IxGm0VZ6SlMsVc6VKFdHZJfonkfpDJVoUMbTZqx8MpWEQf3kcUuqU8LXooZJI34bb57J7HcWaXutLsL2k/V0h+MR9CWRbwS2T6SClSyYxnD8RgezbH3vc4kE6JQwipTF1iHyPOMtq2aKpBbh4VVFokFA+S/QFdpqY06vJn+WcYGc8uPEOFJucITbcTpd8YW8G10fc7ui609Zx13xrHHLc1OlppxeuK6xremzCsM/yf5wYJICNlI7frQZ8cCZYsKIjwyQ5kyPzvRIl9P/SeCKFHbZFYGTheShw7IhqNhIJEhMTu01i5R5j79nbF+s7p9cLry5qnSChOwYZRS2ThxWbCIREJ89IeZYr5kyOZEvdvh6Y36vXjVbUVxINTnI1Qgqe1NG48Km0O56hx7Mfc+j6OJj+ax5BbajKf8y3H9bhHkxGeSnaJSKFOA66CmGDFQnZWCvhCPb+L+9gXqhq1CkWSS/Tk/uSRtRcpSD0hVSnrhfLyDXL5Bq9xbhiZdIpHWdDl0QhGVLJ5eOiT+7aFXpYoWcaf2k7KY30ZHNx3Sedb1+WJagHrO96vlHFl7J/Zrh/x7YbksS229+xvEZIrqRVdVrA7ahfMdobvaD1j9ZT8flADsY4nDRVrYTp2Mqk6j9qORK0cyoxZ+nEUXzyfouABrh7KGWdW4sl8yPl+NNbWDx0/aeMqcGRoHYLHaks0Z+n7UaO+3zf6Hr0ux76z3++HJRIUHTtYiU2eqHbsOyMbWivOrp7Ro0GeXBqGJ7vr8GgMoqU+eKDjYoMfavPEhFrx0uhvYMUYXbjZAD2xvH5LrY0+s8baovNP9qmdYd4k1Z/F9bEfBuO+sV3f2LedtrSDsD+0l5MSIYT9U9A9k1WTF8M9F3VyzgKuoTfUUmFZ6GgAHY0TTUubDYwDEU1mRVON0U4XyrJms5kSCLw4pVTYt9x0BtaRKKw/jOpzcmuW4pKVQX0muswD6YRKPso8yQY3iTotucCJjp8Te88ISTU0z2Pv0RyaJ4ee/OTsW+q5KGdxRISv8Z4vDouciCqgb4bbWTUErO8+sCwLdr/CuBPZ7x3zG9b3QE489Mgu0ZBHW/Cs1s50uaB6in7HcYOHOD7Q3CPUJ3lLtzhrzqfG2kPW9Vy1NqdplkO7EBxvKbTTidJafHbfEN+w7SPj/j397Tvs7SN+vx7ryqeGmZBcea3oesLXC9aueDnhy2v0lz1ZHBuvDcjnl/t7PsN5PFE4jel45GimPZ/NzEJFNJR7NKMu8ETCeYQ5fpRjzwKYIiTfPh4g6weMn7RxBY7JnGYsjndZsKVnM+id7b5FcwsB33dG3+IgutnObXT261ucASWRmPC9s12vYD14un1nZFgnxNlcj7BkhhvzoL0JZjKTmVzmLK+cmW5ZGmLnOBdpObOeX6jnE76+QmtpLIXh6UWfFvbkCCFY0gdfPLBt5/b2xn69ZS16NEaLX6c0q/foBZoVVHGOcoQ/QBY7dHxseRb9CC4yqQCP8hSsLQziGr00ZDlzKgq1BHJzII9JOZ3PUVnVVixP6EVq6opS5iSKs4dW0z0rorYvmqfMsL61hdmOb4zJzZGljT0QEYHAghJIJM2XxnQiFlWFVGfgmf3etzjynBS8Z6WYELSB5ykQYeADsVqexVZai+efRgQeFV2JiQIlSeib9eidu0Bb8PWM73fYb2FkNRKkh4wJssN+nHDbzi/RsEUXdLlEDT5JBkfvEXzWI82IJRFfcJmGSnCXpE6VlKUdiagE6p7rUDIJLFqOqq7oS2zYfqO//RL79Ef423f4/TN2v4bUz8bh0Mmo0FXptxVbzqH1LWfq5QPet+wEBrI+Tihwsy9ahx42YWq9BTz7CIfDzPsQiTJ3Ho2BZpHAY01k8rL3iHAsogs0VR7+ZTOaHzJ+8sZ1GqrZVMEhaqXXU0hURhxFvF8/Y/uGbTdsdLScWWqcXHm7XbltG14qdT0Ho2Qj5RqZXEGe6uIT/WWCK86O1xSYc3Qxco9Qy4F5jIhn20EhjodplwvNzgC0Serr7LuZRnjerExDYMfR4DMMnZwf5nGY3baFIdR6cIRxYkBm2/eO3e7sH7/D2annQJtHsCmKWsf3O/36GbyH9Mecre9YXv9yfkHXFS0r1k5IFfAVvELK12b42NqK5CmxViJxx7MByASK+MN4dXP2PY47D9mVHCF7KTVt93MTZ0WXOHRx9A5FKOtKWZeDDjhIhXzPTGbEAYUzQnGsb/h2J+q4HBvRUzZTNUcm2WuJM6+04sMZTvQFncnO7FxlGWLGdctRcReqEiKaCRwXqUM9obXhbYW+UWWgYkeiyJmcpEa9fV1w4thyXZZI4j0tnkd2YCoPIrse8jGyacvI44/CQWApurdnzjsMWNHomhVVjSF5mif5CtHF6+3jd/D5O/T+S7h/RvqGZ8ntYVg9oicpFdtvjNtntCyU02skqMRiXbU46VbLEs1rSiSiZy/so/bfQiOoOstZpxwv1QY2glpwzSc5Nc1ZBj6iOc0YUU2nIrTUdGMj5yNbR/5w4PrTNq7PKHDyXJDoVReojngct+1j5DlUqVttUUzQt439GhpMXfKQOZVAZacVPA8qO60saxgHy9rxCE+mnCiDw0NeMw/KSx6oyGGoZ7hykOz+2PCGH9o5yXB3hmUR6XoepxHcZBE50AOJMESgtYYsC7XUI5kV+ZhsJm2Dfn/j+t0/h/5GPVeWl3PodFWzrHgwtiv726c4TkRDDL9td7ZtiyKB8wv1ckZOr8jLB8qpIH5BfAEXrPsh/fESbRIlKRG0MJUVcb8zFigoC45RZ9Iskyaj70nnpNNxj2c4z1zJhjeltgyFiQhhalZJo/q8jjw0tfMMMzzPi+0b7Hesb5htiHfU4376tuGZ3LJloZ5fKesLaHCcStA2PsiETxjbIY/vt2GxBlJfOddOyI+yCUwpUVLt62ErI4k4x0TiSU8dRqUe4TI+DefsNRGnHceR1Z3e91BViMThfHPdJn00sq1j+L+kRMqjTFRUD8M0srdrAJOOjD3KvccONvJEhiy1fkoURUFKJqHGHsUFgS2pRSnLSllPcehieTr2XCTuLffGbPk450Dk+aijqayR4z6eaQJUI2KxR8P21lrQRxnxiEdV58FN17+sCa2YtSSak4c7mnnk7zyy/O20029XsBN1CYkQUhj38JjrsrBeztT1HJ6uGeu6gKdUaxYkQCzgmel24myhYY/Ms9aDawue8Li8yIbPByKBg/yxB8AFlcfmfw5jD760d7brG+N2Za1CW1dMCkhBS6W1Rsuky0iDO5HKnC8fg37/zLh/gu176I7sDW01nZVGOHS/YdfP9LHFSaM47B25b3Q37rcFv11YXj+Ab/ha4HKG1hAPKVt0WdKoWa8tjw5Jw5qc30FbHAkaB1roC6tC74ys4pmNiKbmdZ4MG+efyUPbOodAH3ug0tx8z2vmCOVJo5oHSortMO702yfoN9Q2bIujffA4UaJnlFRsC5S1viLSIqzsHNrcUrNLlvCQQuVGn8+npIDePfS2j6RKOxJSscgnDfWYg2hmU57aQT64ZM0w2DIP4b1nTiLO49IZBWmWEtvjOPUDsPCg4KTEOhMNbTBJlRza6kR/I6sKD95eEiVmrgCmjCqy91GUIoFUhdDRjoJvn/H7ii0rWldKWxmexQa5eaZhDTZAD6rGj3UVwCXonYnhJxiLBvJqQfEMUQwL4zyb8SQP6yPknf1+Dxqo/GXnXKdXmVzWYZBmqOCUttLOF1gatRVqa9HLsWeyowbame3iKIK0QLzkhkQfusopBFefsp0UQpeSTS0eYvQZzsIEKLNGmvCa6UENjjBj3s+RZEoPjUWGc//8if7xl5g6+2lFTy8s51ckq57EMwnTUyrjkR2nD4oZZjuMG1U7w6/4/c6+GbNlXsu58H1DthvsN3abLdwMzdNPGRUfN8a4I7ZBFcYpGtmYC707JtBahPFhGGoWX+ihUT34q8ikhIEvFTI6ieRBZPshnMM+RetJp9RaQxkwExF+mGsYMb+Rg6wc8i4eSOXIjI8d9Y7Yht8/wf0j0q/4dmXcrvj9imbGeziU13egxqiSvkKBEyYjG3qTOkthHoKpPmVvSWdkxl2e18yzV+bJwE3DmTz8fLZfoLH8HIGQnG0btt+jgmm7HZyn1BJd3PIII7dE+yRF4H50lNISJ7+WktV7mmdXHXtAMCOokTykUhS8RHGAjErf7kl8PHOdT3FbGljx6LKlbjA2+v0z2k6U5Uy1d+hsO5hP2PFYW+hRWTjtwOGZcq8FhSAPsHEkBmP+ylO/YjT6SYhD36Nvxb7FoZClLawvrz/Ydv1g4yoifw34r4HfImzF77n73xWRnwH/LfBvAP8E+I/d/U8kVszfBf4m8Ab8bXf/H/+M7/jCAP1qmWQSB1iS5dJWvCjSakiIcMoeEN/M6GbU5FaDUwsdZJz8KhRNdkbCQMzSQhuPoy6Kp/idJPwJTxoIJatq4upzUQ3cU6uXnHp4eTm4ghn24IQ4fezI/YZut/i0UtBzloJmWLvvnfv1Bj1qyb2PbGpjKB27fYR+pZWOloHvG77fIkQTgSVkUjI6vl2x7U60LIxLV7do0Dx2bOx07/83d2/zctu2rXf9Wuu9jzHn+6619z4fuSdX40fEmxAsSRQLVgQLIgixIFoKKkIqWhAsJPgXpCRYEgIWDAhRUNCCFREsKCgkQbCQigVzbw733nP2OXuvj/edc47Re2sWWutjzrVzc5OzjoXNGZvN2nutd82PMXpvvbWnPc/TgrF1OjE+vEGWZ/TcEK/UxMaYne25arl30ePLcwSGA9fMwBpkhCxBzbjdbnjvwW2WsMgrQo480bivkwwf3bLAvQ/SeTakeg8V2SzHrYPtMDZsf8WuH/DrB7y/IrcLdn3Bt2vYFdoImSo7Q0dmMfE96jlGWgfDIwx/xuiUBZbTCRFh9MGwOZUhVV1yhy8OKehc799d/3PN6+QrcBiaH7JfiwYm+wbbjdvrK/vrCwDt6Yn1tKJrjHEvbYkgszf6NRKAwU4hmB6ekAiqmYjMUfZysAds5HfS8Em2uuBtAd+ycalJOYkF/cgCOZ67ARhS0h+AkMpiPbLtyeme91aCQ86EAxJuC8HMPRbMWDHfh1k1SvYYMmvXFKdA+DRoiam3YiXhl4JUoZ2fWE5Pf1yI+mOvXydz7cB/4u5/W0TeAn9LRP5n4N8D/hd3/6si8leAvwL8ZeBfB34n//2XgP8if/0HX8JBL4J7WYl/2gX0zGxKXXDTGD+d9CVN/8Y4sSN7jXlQxtbDqWg2hOYk1aCz6PFQInOO0wy5SypD1ZMNgZG4jcOnVUlmrkJ2QJNMXaZyOr9qNhvUOrJvaM+JClooy0JdlrC2M2PsxuXlwvX1I9oHZTh0Q3osTpeObe+xywd0f6XYnmTvDes7lsBj9gAAIABJREFUJjH6xotmGbnDiCAkGj4LAMPt/hV8R8YNu3ygv35LffujUAqVinja6vnsWMc3M6Y4ILOvmc3MbNTS8GQLX1NtLXQ3ZTa8/LCr0+QzTwzOyMmq5mEonW5jRWcTkiTMd8RGvJ8P3DYYGzKu+PaC7C/IeIX9lbF9hH7JMdZ7BHKH7jvowFOmizZkPVN0RShpZGL3JuXsE6jek7a5bh4CwHzujwnDo8R5/t683OxQDUYjE2zfseuVkmwZPNRRdV1Zn8+08xlaRVr4HagEE0Qtppui0WwrWugjGoxB7wqFocwmkKVpDByKR28L5XRi9AXbp4Jx+qWG4dF3r08UfsOSx5cwwexzOPk509wnsaKoDNIMJ2EhuVf/eX77vUpCefQpFpQ5jntOP9CU26oIpuF/vKwx4bmup+B6f+b12cHV3X8f+P387w8i8neAfxz4C8C/kj/2XwH/KxFc/wLw1z2+9f8hIl+JyG/n6/wD3oRDE393ILL5Adh7hzEokrljUWB2nOOux4MuYcayrmE4IiW6qCU6uyUzgz4bExod7rmu5aAFjWNwnWUH3/J01sRXZ8CctKz5WZyYEOCJE6vrnSxNBALpnfH6ke39e2zfAvs9nahPT0HI96Ak9S2aFN4H/fUKu8G+49crKp2lGnZ9x3j5FhsfUH8Fu0VGTCdc4AyIWfV48kazRDyyQuKM0ZJjuX3g/QbbBfolmhnFo5Ns8dmk+ExSuWMgfpS4nkmJm0czZY+ObS1h+XjPMqbBR6qDPFzsZ0YzO/RR3sZbWd9jw9R4b80Dy3oP31Sfh8wV316x7RXZr+i4YT2/k91Q33HPBk0f2CjsauhpRftbqgwKPRpgeRDX1qiaTInMqKdFX08F2Lymw9T0Lphr+xgbPteMZyMOPyAwk8zKNE1V+kCGge8UgXWpyNNKOa2U84I3RZYGtSZuL6GoKw09QZVzBBgzZOtBE3OhI1RVqlY6I3HSOYEBzApWKtJOSDvhpaVSriCjBMaalcRDzDj28gTRApZWtC2UtoQSLRbdXWouc4rArBofoJQDJpHj9eYhdeDuDwdY3L3kAmeCPX9WJNWEyyl+tFTsAbb5Va//XzBXEfmngX8e+D+BnzwEzD8gYAOIwPt7D3/t7+XvfRJcReQvAX8J4M0Pf3JME503UbKJM8wOMYCrUotmd3Ik3pKFp0VG1JbG6fyUphFA0n2m2qT3cK7SEiYvcKd8SJYWc+bO4yyqT7pZhPT0CKqRf0XmmthuREhCXpsntZvh+852eWF//55+uQBOPZ9Yv3hLOz8F17QPek7JrKnr9wHj9jFG1FxeaLJjumO3d/j1PW4viNxwu0XW6hEMZ2mlCMMUU5AxMez7AtUc412KIiVKbelRgkpyYxHFiLJRxoiAm7RQeWjo5fMFV9yiURXWeEllO4LOtKfjeD5BlbXsdkfwRdM5SSS/U26wzDjpezTsbjdk0pz6Ddkv7K/v8dtHql1DSn27ZrbaUe8MCxtHGzHORnZBxjxUrtj1lTEEWZ7wEpNKXTXKZ4+/U1t227krgkQ+DQxznYUUM0bbmN8rmqMx5slRzsBk3WEYOjrCQJMuqKXgNYMHFrMnJZnS80Vdgu+tHsMTS02e50D6oO97HgiKp3mOjIF6cIrNwLUh5YS0J3R9RvoVbMf2G/Tw8Sgq+EjJ7SfKu/z+OlkJlbqcWJ7fIk/PWA1bz6BYTTVkLIiQGh/lQCY4ZNr69+O8NrPivPf3YMwRiOe0hnlvZkPxMfn5nOvXDq4i8gb474D/2N3fPy4ed3d5PLr+ES53/2vAXwP4rX/qz/rMXPW4WZGxWmY91gdDoXid7Clwow+L0S99D4u/0znMku2hm0tOYbUwc3AIH04pmSU6yEh8Kxdofr/AJu+l3+OJ6VOVApDS3LHtuA+qzqETHCUXo2P7HkKH2xWRwO3a8zmMsyVwqpkRzgkEeiyWgdNZ60B9x7aP2O0Dvl8QuzK44RbvH5lhAPqlVYrUeO0eHXRPAvsUMtgwTCaW67gFTUlvV8pIU46EPibu/ADg5Pt9WvYG2TtwYh8ZOEanb/EaY0zJappeC7iPsDXsO6U12rJkdisJnziigT8UPKSkWzAhxu01srMcEDj2CzZyWrBtsF3Zr6+wXyLDHTue0wQig4+mi+83fL9i/Yrojd6h3zqyPFFOZ0DxFEvUtgQ/1Dnuw7GW9W4UPu9LVPTxkMfox/F9NOImdhm/G5+vh1dqcN+dTsxB67cr9EJpklVHPe6Tke5QEvQnqS0EI26EZeCIkn8MvDZMK4hTgaHpe+oFKY26nGl8ySDG3fd+Y7u+IrWjrogXTPeoWvp9mOXhsZqHQSkVrQ1dTsiyQl3jczpMru8jnDJxak8V5p09ZEfHf67l2UieApN7v8SOX8dsas++yMTrB58chr/q9WsFVxFpRGD9r939v8/f/sNZ7ovIbwM/y9//KfBPPPz1P5W/9w+5ZjfZjgU6O5X9FiO1hwicFtbWKGVai42DKH2YA+eDML/Tl47RFmaHTdyEBA6N8mxIiH4nE/MjtviBNE7cJx/gGNi2sV8u8blUo7yepYwb0oPYrUnpqetKXRaohWHg/Z5B60O2wxhY34Ad1Q3Khm8X9ts7xu0D3q+4558nbKGlHhv7E2y5KFhBbKq4MrjaYIywtPOyY0tF06Bl9I4OQ9UiYOdGsDTGCLGQ/5EL9O5gFD/j3dgPvug43t/TbMP6xu36ERs7iy0op/BNzSYa3iJQj+Dcqhml3yj7K2N7iXJaAYlDoqjgYtjYGbdbUG/6RvHAre1w5g9qTzHDtg3bL9h2Bbmw26BLRwfpIiaU5URNGfCjjHf6xk5q1cxk5xicw5MkK4jg0OYkVo+uuk/fgb7Ttw16eARQAuPcbY9DZQ+BybW/UrYXli9/iK/PSDnhWnGUup6oadF57K/iIRwoDZ0sFyWw0VKR4bimTZ9UtJ6D67o+M7aPgdWWiixJccyJut0dXCPT1jTXOWwZE6OdE1/rCXTB/ZHKd19HRwacicmRqUpmtMOSDz4Sz4092Ufs4VIfbElJlAk5DMBtRDXlOMt6ZwF9zvXrsAUE+C+Bv+Pu/9nDH/2PwL8L/NX89X94+P3/SET+BtHIevfH4q15lXJfpBPLjMUKY9u5fPhIq0LRZ4bGKT0xnfDWjJvZe2R3TsJ10/cy5X9HpjQHmEkEUxKzna5LE9uZ9CwnLd4IDuUhfDA/sjoVCdK/J8/S9TAUKVKh5WBCC2/TugRut986DKhL8HATnUjHIsPHno5EG1I6Nq708cIYF/AN8R1nBipJ1dMsVfM+THbg5J520vx4f+jyOt0Gsu+gK0mIQSzMX1wsVa6TNZFdfPdPFvJx+ZRkxnMaY8T0HINjiug9hWBYD6x33Ci2w+3GGK8w1tzQLbxftYA5fd/R0SlmsL0g+xUXsKa0tYI3GIUBAbOMMJf2NEmWB5L9kXma430PqtF+QfQU0BQGUpEthwTWJYL7vHdHNjRL0cy+8vvPcTkBHWQlVDRhJEOsJx93x7cbYw94h9Gj16ACLgyJe2f9hu1ZqezK9vIt2/tvKV/9mPbFj2B5pi1PuQ7uvgTAMZFCNe57JrsJtdlRqiNT2tziX13oJgwpgcGqUIjmrONxMCCg48hW574KCEowDyOjUoLrimlMZH1o8oVBUwoUJpeZ+1ryhAFJ0+tgcKRpC6nky3Wiks1h61k5xSQJS6PxWV2UB7e5X/X6dTLXfxn4i8D/LSL/V/7ef0oE1f9WRP4D4O8C/3b+2f9E0LD+H4KK9e//Q9/hIY2fJ2yYhUTzo0oMiZhd4SihwnCl1qlguS9g99B+D7MYWJiejtOGbzllt1PLoRCR+evDKTpFdeTfDbrNXUsth+8p4BkwdWXOop+LhFwkpRRqS65nfudhO9YN67ekhcU8JRnOtEyLzGZHpKMtJLNDNlSDMeBJhI8Nnlopyc+us7OrsQqMwJum5NCEMfZDqaOlom1BlxNag8Zlw+nbjmhnbes9Ez6aC/4djOvh1yzDdEIJI+zycEPGiKmnfYuM3jvYjeI3pF9D0+49LB3XcxhJl8Q897CaFBssKbQQ9sSPlwxggvWWxuek0VfCGfl8bNwJ9qEOjGpIbaQaqaO6ZLe6J5Sw4X2LacHTX9c9xhDZHQqa64a8UyKw7/sxlgaz4NmODe03xu2Fsb3g+43R9zik03wcS+29Bt1PtOPs+LgdVVl//cBqPSfMriGFnR307OwjMbE2vBT0LtY5DolUTE3FncagzsN8qCxoO8V6HIL6DkOQ5LK6lDAiOt4vXsNkcmfBQiMblDETvI9jEjE5uWKkX26ZVpCzQzob2COqQLK69d7Z+0BKpbaodGoOGfUetpwmBlJQGVBKqpol9+X0Iv7Vr1+HLfC/5dr4o65/9Y/4eQf+w1/lPWY2eXQZ82ErAYZ7q9h5De20TpNciwZXrVFaZAPsaBKoUpMNYJruPdO/sS5obUBJNkAuKg2u3aTNzNLfIfTo/YKMDZlZYiatnjmepBl1dOaJYG162KCFQcXDGGvVVLNENzyEL4k0jZCtFp9MhehaCx1VAxnEQJTIqiRnECGkKEASrA87QdEajR5ZMImMyBmoV3yErntkC74sK+X8RDk9I3XlPlL8Uectn6pn/FOnq/uTTXpcj468EE0FGxt9D8qYe6iObOyUcWO8vmP/+C22X9DitKcz+JsIrhLNxP22xVyyWpFyCqNuCWhBSvI7MqGuraKnldteoRbMC+z92LATygtaph9Wh+oxqkZKPOhhO+y3kG1OXrBqTF1wYpO7HcqqwNr9+P9aC27lgLy835D9FhzcywfGNSAeJTPZ5PUy+d01paBaYgRSE0yUcRth1D6c/rHhb79Cnr48rChLTec3keDz6ncsHydGDrmGC14K5jVUWgGw4BTq8gTrM71f4455wmvF8EZwWnM9mEhORK5QVqASfdACpYWHgiUdi/CQPXxoLSoLM1IEYXnQxN6ZgwYZ0ZyjWxjCSMrTjwquYqrhjKXRrPalMcZ+xJP1lNahn3l97xVaUXY/dHMssKgioKdG0Wf6toNmp9Uipa/pDK85JE4gfq2VUhrSogzEBtbiRC3LmqqUqSzKQMRMWh+I770zbhv28p7+4Wtk/xgcSg0OHXWNLmo7gyyIt8BrM0uWDN5z5IxVo5TlKNfaKUaAjxEzm455QHB3CEt6UviA7skGsDDXVmLzTdGC3+cm1aUhLaeQSjuYAF1ujP0aAV0Gbg2zHnFdC7WeKOsz9fwWPb3FykptMcpb9I4vhq+mpq3sd+TLqpEd9B6DDfcNGR214GveLu+5Xj4iBCMixnpsbNuF/eM32Ou7CBhFMG64dEjfWNt2+uslDl5rmG7U8zO1FkaAh5j3aNKNDcmNeVpX9n5Ks/VswKiiMrO2wNnzqIqDVe7k98Cp0yOhN+g1IAe/TysVIiEIx/2ZbGX2mNXLdKmib4zX92zvfkZ/+YbqWzyPTPiiYncsIYTKgpQVqQtanLKEOZCYMoZgWwe7Bod3u7DrSpFgOMRI7vhEM5GxKbede9As4YcwJvcxGEgOPmyU5YyN1xRVrEcFJgA1TnZNeGx49DCiibWALLguUE9QT3hdoLUY0b7tcdiNgHosh5NO97YpaRadfYR4n24p+iH2QKsL2toxeDOMaOKAqBo2mmOE2rGyUErIrEstR4XxOdf3Prji46BySDYq1HbmELRjM09DhgxcR2fwgYJB8hBFK7VG9nBkmplNjcwIp/nukUlG1RHAd+/Y7UZ/90u2n/2U7Zc/xW7vCZWKoWujPn3B+uWP0OcfoMtzTBYYUf5ktIGx0/dxZw2sJTqxqhQNr4N9C1Pvvu+xICWzFglaCq75uj5l3iCxYERAvDIncOJEQ6tFOV3WM5QTuCJDMGns7qgPqI1WBPOR96Kiyxld39DOX+DtKUpBbYiU2D7zvoverRnn/c//z2+Ok8R7i4m+dn3h+v7nvL78kjGuMbGh1WRT7IzrC3b5gOyXLDmdYTf2cUNOpxAg7B22VwIKalztivnOcn5DqTF6PYQUnb5d4iAZ0ezTWoJ6U8N4GzjWz6HyaUvq1D3v5x5ryKMRyFbQZY3NmmY/LtEUmdVWBNRpo3gPYkc33DpsF8bHb7CPv0S2jwzt0YArWQpb+FzMSgzvaHGkJb1PcnCgOJNzH1hyh7GFOU5i0q7OnPUlAB5GOebhhXAIH+SuMptKLNEw8Q561sbYb5TbNadYdKQYRRtS015yVn9aYn2WBfeG1GfqFz+mvv0K2jkhlRDuWELvow/22y3UgDm1YYLCpdZoAEepSxVhaIiJcGdZYjIztaEtmAgiAjWasOogOf47c5KDDfQrkp0+ub7XwdXdsO019PP9TkUZ2akOmkac4lpCKdP7OLixLfESF/KBLp847ojqvfbLDGP0Ec0pkYeJAyCE0cXI7v/48I7+9e+z//z38A8/w/YPEVwFvBb88gHZX2G/os8/QOqZsQc+VTSgB+/Z0ZzQwzCsenDki4Ycone2PYayLc1jTIZI2M1pzLbXseB0er9iJkhdaGtFCUmh9T14qe7U08r6/IZ2ekaX5xiC6ErfDK8gLTBFUae0wpIjsCOGNKSdkXYO1ytJBsaw8HaNWR0HE0Kzg37o/JOfjIXFnTJTsc7l/S+5vv8aGS+UxEjH0ENs4LcLvl+g3xjjiophm4Sg4bqgJcj62EglW0M40dUw7yynZ0pdI+PZNsZ2oW9XGKGGExvZ3Evup94bL3HWSkxghYM+h2/gI4PxgF3wW2Dj1JaY7B1bnRVYkO3rMWKo6Lw/PQxktlf89RvYP6K6xbNI6XPgoOF2ZT5gaHi0NoGWEmQL1Z2PaChFX9bo+42aTIkyOcHZJZ8l+75tXC+XtJs8H9hrBPK8D4nB+vRRUMC/wPqOblt0/scVGGiL523paVCy6Rzc6IKxsL79EcuPfpv25Y/wU3C63QLKyZyBniPAS04tFgGxYJu0daWtS/5c4KbldGYOiqhtRVvQ0UyUoYVawkpxFsTqSwTWbD762CPY/lEN2X/E63sdXBk7+7uvGd2yFBKsxCBCS8yqaMmFpzmcbYtGWKpF4mSPczmhx4M2EwyOefMiO43+WYByklMBYhHmwt479v49+89+yvVnv8v4+HPYQ8cfxDiHIcDA1BkMbtcXvD0DK+iClQWXhYEegVWy0VLrfZqpS5R9CNgwOh2RpJOooq3hy5mqBHHbO6PfUOnUYhSNTGjsG6Y1yuBlAW2YKYywRUQqVKMsApTgco6N4RI8WA3FzbDG8MruBbzEudAHlBH8SK/JFLhXDPO+TTgFf5xGG42ZMXZ8XKkaDvnDjOE95yCl21Max/gIyhQSbAvrN/btEioxPEq/WoIytAWVq287+/XC6ekNoiW77Ts+NsZ2C1ii74eCruQsJjkwUmUQ0xncA/OW0WM1+XgIsMAtDhxdzkj1CDQEZu92lwQXJLidAiJZDfUNub4wXr7Bbh8C/pDxkKXlwSVZGo8w2ukd+haBbohgPfBJs0efVqf3yFpz+ecziDUlPof37ZntLdS2xMGarBjckZLeDjnyXCBmXbYnZN2R8wBteL8i6kxbRM2Ksma3fphjJrT2RPvqT1C+/C3k9EUY2qcx+izrJ+5fzyfWp3MYaI+BjpCvatFojJWA/VrKc1VKTghuMV0jD30XjSCtmZBZjCBSLYcMfd9u4Q73mxpcrW9cf/b3CDOFCqWxPL2lrGdKZhcyydipl17WFREJmas+4n6pmHKi88ikb9wZAYgjlAjD/ph1ZQNrOP3lldvP/5DbH/wUe/kFMi5IDVK2GEx9pyqodcblQzRn6itSzmh9ivEWsiB1RZczpYXTz7Q9lBp4qwBtWTm/ecOml4MOI0LOUzpFg28PnG9B8u/dKBJNLmxHyoZRYgNrYR8Fu0FxQxkRxLVST2fa4ozble3yEQiOq5A6b11xWTGv0c2NdCyChvvdLezQoz4EVbJqIzwL8GzW2UhZ7qBVxUzo6c5VVBB1+tgxtig1PcxlZpth8pl1TCvCSEXEszs9dsbe2V7DkGU9PyUDxeikr2c2kmbDZMAdqpHAo8usFIQYU6MXtPaERCoQ8+7pkkspu9alBV1MKyIxhdWJA6Rovq4bY3ekX+kv37K//xr8hkg4nkZz0OeZn8m0J0w18O7s2xWOdRvXpNHdDXFiVE4xDxgjKUqRcQREU2vgl8u6xiFPsivSXActSItDgRFj0mXq71cHE6ws2Ngoa5Trd8pVZODuoOZUrdTTG5Y3X1Ke3mIS/gauFVVHhqHWkN5Znt/QlkptNfok1ytjCyGKZmdfWqXUEEbobPBNKqcIhYnITcgwGsfVwj2szO9hI3ovsh0y8M+5vtfB1cegv/8atKLrE+gz5oPKvWuPT44plKoUqUwz3d5HPCxyETO5l9mBnytVNTEeZaqfpgBx8uz6iPEq+4eP3H7xNfb6DrULZSHKTZaAK7YNwVBxNGWJPm6BD0N2iGOjuhpVnVIL0qLER2NcyXQ9Kk1Y5ExtLQ2QU5WCo2uWaGVDRgnj7NYQbqhtjO2FMV5wceoaTk5FF0a3kCX2wu6Oq1OWgqxnWl1p647LQr++Yn2P6qCdYHlC6xOw4j5nW+WmSU5wJEl3ytz04by33uMZiBnFRyil+gX3LbDCxKNbrSxNw4eW4NmadXLgedYbU+oa1oAijlqNA1I8fXMzS+6d/topGG0951qYnNZJ44t14fbYNRdKzuySIhgdkS3K+9YzAOUY9RKlqu9g+0DagBw4KUtypeusqNKUJNexMujXD/SPv8B7ekHgM2E9yvYDYpn31CMDDZpSfJ7pmgUca7uUElQ6mQ2saPq4B0Y7/2k5L+sQzGSlcTBbJDNuDQkzOUlW/JSfTymnp+ARLMEwmVNZJ+tgfmbVQmkrspwgHbtqSXaJB2y0eYhlAldt4YaVI1rsemXcNnwYSynUZc2RStGYNo/G3gya031ORPDMbEsJ2fHB1IGkjSmFwuMAzV/1+l4HV3FDxi0D3pLuPfegSt4sJbLTQkWqJn8xHHDIrr/73SsAC7L0pBJNaesjD3Mu5OEx1G2Mge074/JKf/0Yjkp1UJfCelqS9E804HoSm21Q3Bh9C7qOWXgCSIOyRDMh+8+aG4ncKO4cROmpMKutsW8btsdYbp3mFqVAD3OOsiypff/ArV/Zb4bvA63Ksp45P73Fh3K97NhIaWRdKOubg92gY3Bi4eLv6LcL4cz1hJyekfUNWlc8DwjVgGY08Vf87iQGII8P1Ims1pLLum2M14/s15fgLhbJMdI1VFJ7D/u/64VxuwbOacHdPWAaQmmHBQSDKtU8ZonJFsHGhYbR9yt2E0wl5ZVhuGN5WAfGOpmcife64ZYOWYWYbuOkY5bjbOzDaO0UvUHJYM2eku2wwAyYPAyESK/UmVkyjPH6gds3XzMu7/H9FZGYqHGsX/g0sJLwliRlKTPWIL4nST/XsiQEMSfjaiYXjrHtg0KOMCrxPIWZbMT7DL8HV/JzaK24aVRsmtJpWxL2SGlpUaxFp17nNA3kOHgjt4mqySUr0TwTbHTQqFa1GU2V0mocNm7Up8H+8hq2jr0jS6M+P1MkYbVS43s/cHYPoxzzcH6LsST3pl3eL8MTdqv3pOAzru91cEXAJfxQzQe1fprVAcmNDB1wuPxrej1CaS3KkLnxiTIy3MqmtO0eeOfCfeT6ucxGhKE+MsBbbGSxqARtJGgfgdF6OBRZ4odb7+zDqevO9KOsUlFd8CwfvSStyqIxYmKBH5Id98mKKIr1IGdLjaA7hGhxlqDGsBXGZWO/Qn+16AosAk8Lpb3Fl4ayM3ZD6sL69Ex7egtlwSi4DqCgQyn1FJn1+Uw5PSHrKeY5HRzXO0Z8dJVn7p/VQUn5ZzA8QIcztp3bu2/ZP75Pv4AY32I9cNR+vbDdXsNfdb/g+57wQTQZhjzWF3JUF95hiEcQ3u/YmlgohaxvuJ3w7NxrqYcPLnhmvInE213THwdDHGqSjA3LzNzHiEbJqKjMctvpLtktPx3ZuxATjeNwJ6CT/cbtm6/ZP36D7Bfc9vSOzXup08tXPjn4p16+JgNmOa0RWB+UTcGaKRE4D3ghmqaDdGoTD5+JY9BfwDtHdZjXJ65WMm0z43AyUQYwyIaVzqZrqLhmnwRCyBPrZvY99KgiD666J2tc9CjztVVcAj8uOO3pGbbOuFxi/xuH4ECq3ic4H/h/zhbrhlq8djmUXhzBN3oEAhKH7ede3+/gCnF6SSiUSl3jwT1cU6s9RyrXUoJALA+ZXaqGpnYY/IAJYhHOGxumGWGgPQ7APty4BrrvVOnUapCu9DYG+/UG7iFN3G7YdkVUGKPQPY1mJMye99eSbnlKpSJUtPn9vUZkWMPJ7BtKbbR1OUq9kNemXDMx2JgiEBvXtNFp0YAaS7jTD6H7QtczdXmiNfBtMLQiT8/o+U1kcz0lnSj6XKhPbyOjaxUpC1Iz+5plqXAE1XmZRyYZAwE5NqkR94ve2V9euHz7LVwjSwvpqGC2RxNl72wvL+h2TflnKM4mbD6xs1JLzK0yHjrfeeCqsozQ+QdXmPw8ntxXSX9PUsI8S3BP+TKJYWag0lgH+AgzcQ8vBC2aQTUOVsSzaVoCM1XBa8FUDuhB84b4GIyXF8aHd3B9ie86Q1piiUfgy2c/y/7HeVJtWQ92zL4HQ8TNj0A4X49h2N6hdbxWluxNhHy4ht/GzPAeAtP0ozgEBnkAxVDOzG7dYyyMKLWkv0IGbEtj+ri7dvRDpjghZOvEkxWyYTk9ByY2HFQzEUdOZ/SrH1ANrtmItBk89x1BwjWu1LydiUe7pGyZhKbuB9HkyVvWwxGQf0NhAVQp6xkjLMlEcuyEeTT3JT1AZ0MLDk04fWA6uYqZhVo4Zal7kIiTm2kWTkxjDPZ9DzetxOCwIItXPLw/7YZqaOkxGDaOUcu2b/SzEUNzAAAgAElEQVTLJRQ22bmd2FhpFbHgGdJzVhOvFE7omqetGcPTrzWlsWRJU+rMKjKz7jlwDxj7Fo2K2cVdF8oXX7G6U9czdnuh0/HlCzh9gZ6eKa6UNTJJlpWhS0zOFBCNBp3n1AWtMURvei0cQUxLdL0f2A3RbQlce7IEZtYaHeykst1ujG2HYZRW0KrhvjUZGsMoNhtkZBWjR3D1EhnbsgTWvW8p98ytUZJNMQdbJhRMFQlsOjmOnZENz8Rxh2DWgXFwdZmVQ+KQEaBDQBA8Vu6shhqUIy0thmSWEhzLdYEliPtI8Klxh32nv77i10uorxLXFFLmXR9GxGfmq9moscyipRaWGp393neGRW9N7p8WnzTDbcPrldI2Wjsn7WzabAojjUsgIAbN7PQYZ52BKMQRmZBMBaRICBNqBusHY5Yj+XdirLtLYOOqR/B35x5gicBbcmqAZLY797OYoKczvNnZXl6jB5HNabNBv14DIuJ+KEwHrKkifKQIxvv78f/ukaAdkzQ+4/peB1fVwvL0JbdtsJuiDiVLmolHyjQiyVPWbDA2h2Jo8Ty55Mhm5s3VMeKk9oGNCK5bEvb3JO0zRsgJ8ZCY7hfGx3eMfgP1BPXl7iu73Q5qj8WxHgexKjXpLiHfi4xFpoNPCiLC6Sqymjp7QE6M2Z5sagJj3tN4hjSAObKMpYVJzPMz9c0T/auvsOslJH2twfrMaGuU9dVQi5IuDqjohku5excM4T4QcAZ3d4ZFw2huX47mzKelZGRq99+ZXWMRDR7lCm1xpMYk2kxbmNM9h8XIDSjRHJyzpFTRZaEsMcLEECSH8lGEZV1iCu3D5oHpiRqGQCCoZZLqjqyK9ILtMntJR4ZGrrHD9Mb8k0YQA3ZifVKUsoSlH6cTuq6wrMETTmnwPLjHtjOu1/BLjTIpPqo8iFjIpOEBFyRLWiSaaSOVX4OCi6M1fRqyn2DmMRKojPzCkkKAVCtlE6nU+wDQ6VzGgflyHFQkdUHSuLdo4XQ6H88ncMxyNLDmygjebRoRPZTrMYTyYWhjQhqSvYaQt989f2eOtSNYrWgWG9PNK5hBnvsqPUAywz3YBXlAfBf6uFMF71DI51zf6+Dqonh9AuuU0lCJE7bMZoAH6qa52SJ7iNQezcVdLcpY4kbGaVw+wW09xQH7trHdruy3jf16Q3qnaYxz6XvIL+XyDT6ugb1KZWAhwfSRUk4LNNeCvC4aUEVJYF1KDaed2dwoD2Ync+E69wVmlg4FmYCLhm2hhVl40XDc0lpxjS57XZY4dNYVfXrGtjB9xu+B1BxMByYhH0Tv00vJrDQaJSNpQ3cxBfkZJ4cyiqj0fnFLC8D8IbiXlhDyyaKU08r5yy9RWxHpuN2iXI32Cqo17Ot0DaENIxV4IXFTlXCK18gmKdF8UA+bPM/7acQ9myF/pB49hgVWgtTVEY/JBVItDl25IdbT3MeYdL2pQotM/e6MP+ElT8xRUiOv6zl8Susas6aygYoRWbZFACylgVaGycEYmR326ew0g828/0r4ARQE92RqUNNBLYKZpY8qkt4WcKj/puJqJicC1ExWZvZK3r85PeFeJmd5LkJtLbwqOBYpfCcmTQijitCWdkz0IL/f7XZjpCdEaxMi+NRk/Njz5oxu8S+RwRaL+z96ZJwlBRXm6Vkx+b4id+pYvndPGHFmyUeG+9A8/Jzrex1cQfFyoq5QpESAVU26THoDkAC7ExnkdqPvHWrQKNwXxJI2BOlXGcHViAXnmdXaiHlO0ZHfoPckZ3kMr3t9hdslrPzUQLMJonLI6BwoM4N7aI5FiVfDg7Qt0Cq6hB48RAH3UtAkMtpZJ815U9MwXB3m3PWSG7xMwYQ+ZKGlUOuCr7m4RkAJE6e2zKpbW+4cP48yD4nu+b7FkD9m8ycDVZTCHIs2puZ6fI8ALj/JTGZA0izX5RTTU/0Ko78SRh8V98x4tMb47kjdgeStzszVg7ZlUiKD1sJh0OIeDksZ7ESyUz+M7mEzuGih1BO2zxJwierEjKKVUSq232KWWTa77odxOHqJSgSy5IPG6POG1DU4zO0U40uSajTd1jxPI/GKLi2GRS4rvi2YVVRHdO9Vj4AxrB8BwCWbMA5CuDjN6sIy2BaJA7j7lv2Dej/kaokmZTYlI7BqNphivW7bbL7W4BtPTP0BHph7r9aKaWZ7HtWMz0aQc2SBohGItX4aduzhtWKuWhx+AVGnk1ziuUJWTmk+I6XSzk/RlMyqqqQR07R+RO4ZaK3tWIdxfdoknMF1Nqd/net7HVxFC3V5jsU1DB2x0YLvKMnTC99LgL7t9H1j2zumyukYuJed9CkWQDGfiq0MjLMky815nOhj0Kfpdl0Ye8VHpYjhvjGHDh68ThG6p5xQiK6nxeTZ4LnqwXesrQW3dWJSfhcJPOI/NkaMTh6x6RiDguQgRtJ3IY2mU2WjcIwgDstA8FqD49r3CDrFKaVSU1JrCVGU7PyKamTBB+jJrJUDG5XHkHM8tZnbRvYhd0rQMQq7FOS0Igq7Gft+o3hFdMG84EMRbTFCxRWnA+EtUXIst8/OLiUEYdri1EmBWKktjDemQgiAsL8bRlB/2pkiwV0Nx+sezlN9Z85wQjXNY/rRxAsz7uD2TvgibCEbIhXqSl3OlOWMLGektMyG72suMiiwulDPz4zzG+z2nsINYQtjooeSfqqkwY6Di/yZGAxYQRqFOHAxD5Wbp+/DRGAl8NBS2wEzRBY6KYvC9EmawVhEDuP4T573hK2+w2Yg1/IjvDCrseib3A9gHFQrrUVyRK4V33s0smb2OGECSXDBIcZsZ7DORinZIJsNbiSSjkkVnBN486MjWfnN9z2+2gN76HOv73VwRSQbWhtuG304agNRPyRwtm/0vqefa2RQgjOnXWURRhGNRV5iZpDnsDbPhaXEomwleKNCZDsyOtIFXRrLuWHN2V+NktlyH/tdM64RXCf+qFORo8puBtsN7Z1qOWlV7N4YscCCk0WfnysWBeZs11tsJpGY7ZT4oqiE4YjH4rHhx7DDuq6Utk4XClRKwCQT0EdC6jkXm9918BNqqVmWhTFW4l4pNXVmcyWNl1UPihPMxDsW5+NoHc1M3SQc/qsNtN9AlWXfYrbW2PPQUfAdJ4n+MyNSQsUzN4ulgXcZSBXqGu5Gw2cm0g9es1EwXajLG3RpmGz4Hj4BwVHdiExZ49xOgx/PkdKkumc84rIS1QLa0HpCp+lzqSAh9+wevqJ6wFqKrCfs+Q1cv0JuL2GuYi/hx0tmxyUhLJ/imZBLTzhHklETgoE7+2X0gWXTE4muvlJASvYEyOd0z+6ipG6cdVpJTsoYxzM/DpmsWtTy86QBTD6eOEyPhPeBqif3NUJWFUGvIzjlOaZc5jigfL2S2DDTVPwBm/X0vxh9UN2jaVmCTxynUX6GB07rZETMQ2HMTDWTAEszqM+9vufBVSnLKUqTUhnbFoRh69GhtnTM2iO4qiitxsAzWspIicxt2oxpabHgtQTdycn+dPAxW5sGyzOAOk2FKtBssK8NtMPmsAdYPoYdDARtCwEVRPYUI6EdY9D7YLtecW0UbXj9GDxI0k80fSUlg5+WwJcNR8dIq7zBtt3YXj+C7bSnM6cWdBo3Z1iPTvSexjZpxRYyTT/+24oecepo+sxOqT1mR1nOQ2ZpMXjbcxZYSXkjD1nZDNYiJFk991g2sqQQGaUNdFlp9ga/hYa9rm8iuLcWI1XGFbcbwmD0LWl2ucemAYkFL7e0GC8SJPdo7EgGzDECz6VWpJ4xXfFyBhb0tFJr+rBuN4SKSMNd8rCyrELmgRFBtJTloAoiJVLm9Dc1KYHjJsMlcDzSeg/SVCCe/XpGv/gh2jccQzdF/ALsmdnFXohfkhmjBcoSnGOtiTHH59v3HpVO7wEniQCaxP1HdVhmd3yatR0wE3nWu98f5MNlHjxZigTW7cSTTtx1BtR7IP37g+vBQDjw+ak4m/aCcsBj8RKZ8UoaxsDBsLBhDKLyOkZwZ5VA3rtHwcB8/yPjDuPk47McE4c/8/peB1eB8F99oMHYsNwsYRgdSqhQQ9W1sJxWtDWGKn3eXKKgESHtBD1xtLljMhuugkil1XK49msS+RVisuneob3Btgtml/AJzZOTWg+Rg9bwlC2qCWvsAdxfrwyE4lCGoJtTTk45CXp6ExkABSlyaMKPDrJ7OLb3zn65MPoVirCe38T3kTACVwKfUjwpXumMlE5IE9ud2UfckvjZiS9GEHwo6W02gRIumEbNGoeHS2Kjno0vmS2kezl3bFgbMQEXornQGn0Le766PrMsCzy9YfRrjLFhIN7DRHuPQYuaEmMbMbFVWgTRpLSHd4QbY/T8bGGNqOuZ+vYH1KcfYLoyhgZu184xrryEfJm+4RLjYMQK+BVhj4DdkuZVl/AzLTO4hgmOS8WpBHiT0AGFWjO7Zd4qjSysneALRSRMuO1Dgds7sFdi0oTHQZ9d9lJa4rcVdDaTUgwzLFVL4bIvlmJhyUZbXQ6M2MnnnQXTLIMjuNY8UO7BZa6DScwPrPVTddPEo2e2eJyEDwHt8fXIJtmEEQJimwcCR1V54PXlwU8Ev+O5Fh4HZZW0F5yOV8kh/wQCiTUaW2vivWHJqD5Q2xkjRqsrv6HBdZ4jQJTbJTrh8b1j/lXwUtP4OA1yNSlQc5zFVNwEPjqimaAF0vEK9yh/W8NmY+fxxMxg3M2wtjD0zOCMcKGUp8SArrj3yKhaOFeVVFDZ3pFdY3OOkbPmOzI26BfGreHaWNZzoGxFD9gCyYZWCZOMTuKwOeolRnnsjF4Dx013+QIBA2QmKpIGK9PRfS6qxEg1782xmXKMhuUUXUv8ek4bcInAP4jmWhxeNShneRxNHr4wS7IpPkjrwajV0BaeCNZzMJ2H7662BfFnVAX3ThmdRSQ9AcJU+vXjt1i/EjTg3NhuYB0fO365HPJWaSvL2x9x+vGfoj7/KEQW22D0+M66NNb1Ob66dXo7setCvyxIuQZPWfKgKhHoVOdQy1hTUloEsLS3NALDpBaKBrd68mdFskwvCae8/QFFha5Cfwd6E9xfwjNWLBtYimoLHjKB8zpyzIVzG4f/xEwswkltCRFOXRLHTVd/sSP2mcX4o1Iqy2myIOYynFmnZgIwH9+dznRHBTJwT5z1UfzyR/56/6xz681tdwcacllO1o2kfPnoSwUTQeBw8yL3SjA4olE6v9MRYNwDlus37PZKv13YrltUo6Xe2RmfcX3/g2uuQteQfmpbIpipQjHWEprjvm3s5nALJyIt7T7pVDRcnDyyGh8WXM7sfHougpq8yHnKjdlU8gdi97Ki5zfhLWphtCxWsFIY+2tsvCqJS8qxwGprsVBUaTMbJ8dFe0WWpxi38rCUZmCauKKZg/Z0LVrAhfXpidKmekuCf1r0kJzaFrO4yhL3cb/ewnz7FhNCl2UJlY5IjGXe9yi3SxDARu/sW1j8jSKohzFKArMcRGsz2PcMGkGHwrlTl5jJixzNwsnaQJ31fGZ3w243IIjgIT8kO9QDXQJ3i2ql4/2K3Qyj0tZKeEmHQkt8YNsNGcLYBp6OausPf5v29rfQ5Q2MgrOx+Y1ug0pgx8EAEJBGqWe25YlxfY2AbT2CeJaPI+WUAK0pmrxTJubHncKmWj+pRKKRqrg4ZhUWEP2KllmavatwVWz7yBgbxcOCso/Meo/RLpqqwqlqqoh6TlsF1UZZVurpHNxfSOgrL7k3JiXtLx0yKZGsrJ3iROYeJ1g2lCdccoBCx7M+KF7fKcOPtz3K8TSsVgWJBMlsmix9mvFOIcEjFzWw4Tueep/27PcDQe4B/pgu4AOzHd9f6Zf3bB/fs7++0LcNqQv1/JRjnz7v+l4HV8jNmTQRSgwzs5yBU0pJL9PwX7xdLowxuPZOK4Xa8oQzwxnp4B7jMuqyxDwgogEVEy2TP5kLZILyx0FnMb+dt1+Gd2wR5FbQURlF2N0oMlAxNHGj7pY814KpHp6ZRjC5rCmUTlXhEIlmI0DIRtIsiRqU6RDUGqqF0/np8He1LJMMKJlpOxGQiobiyvdOv9yOUd8sO3IKnLLvezQSFPS0BvWtAAyGRQDyYWhZcsEGrWV4SCp772GyfHqKTayFpby5B9ejsRCQiUlCLZ6l3nqii8TYmi64RfPMtYTHRk7gnWoop9LeCGN7CaaAAGOLUTdjj8NFn9BzdPSXL39IefsTpH0BuiZpXigjuaQEhUs8lGcmDa9OeSrI8hzwBB5m4n1nbFdsu+Z4c8c0WLqUMHRmupTpNLnRT7K4x8Zf8LVjPJA8fxlwRg9hgTqwvYZApschGj6lFfFUb6nH9AYzrISNovgU2iQckONwDrocMIF3EUGK0Op6D1YSIJJn1cMs5LjjpJP7OoUhx2vNTJdPg+v88+Me5A0IHm2sd+yhlJf7RJFSCs5dafUoOpjm9sjjYV7SFPz+npMaaWMPz+LtlXF5R//4S/rHd4zrJXDq5UTRgY7f0OA6T6Gs45mu7rPcn9Zt2pylnZB2ChHA6PQB2p0h0+h3YCNMiV1S415KmJ+0Jc/cpKMc4LoGrSU+DEMMWxp6PiHjLVIIPu1NwDa8NHzfGbdrjiPOUd4tcbmcTDlG8DFbC6MNyXlfkBDY7LAz+aGZ/ZYSo08ILbeWcP+hZDkKaBpMD+I7zgbSpJYI4T4lrYEH64I5MiSZFulWg1ZopTBa4W//4X/Dh9tP+eHy5/jxn/0zlMQwEcHUMe1YHagWKiviha1c+B3+NZo/H89zZkyBpU2Hs9jjklhfz3pQ1I8SkIQkRKOxY3WBvtJK+OL62CKrKoOxXdkHYX5yPnP6stKenmnPXyDrM11aBO4cMBZS2LuBh7tiQ+hDGKbUekLbKU20JXwOthtcXnH7gIzXmPrahbFDJZgplHvj6AgAyL3MZmZyntljHOxSFljfMJ5u+X2F7ZYTIkpFaZg3oIKXjI/RhBpY+BMoOVvNQRQrIcV1mTxpD2eyCc1khXCQ9mevQma2F93zWSQfNpITV83uuuRe0SkqOFITORKGI2ONG5DJ6UPxLxz4fHyGaDZ7vq+nMOLwP+AutCh1crHleK2Zxd5hiEHfN/r2EbYX/PIOXr9Brh8p+y2eVd/w1xv7b6y3AGTDhNjE4qCF0hJDSRuzkdmlQGSAfT+wlhgt4vEac0EY2L6HpFSAagkxGm7BS8U9gfNyBIX4PBqGEB6grvvA+hWRhg/YL1d8C39SybHaNjpbLrZYlHFA1NRuaypSbAxcRsxDIsuyxw6nCjoKZVlY/Im61JjrVCM7iibt3bT67tI1YpidB+Yq7jHAMdIQerc7PpbR7qC1Zcawvb6wyyunb77iT/zBn6OdzrTTKfxJIakzMc9Iexgq/275m1ExeD+exXEv7W49OHpMexWbbMpH1kRKMFNDfw8AhAlJX6E2+u0SHro+8LLQpVFrYTmfaKczelpxreH6NTyYJsd+jgxWi0bWm5iwuaRLWA1ctOqdzlZPlLKCNrqU8AdAYoKspGdrqZkUROCOoXlh5HLwMGNVRcU0+VEi8Zn5IbKu7CL41mHfKGsjjJwlvs8UJeRhJcVQF8YQBpbKrsiip4FREPV3HpwSokL0hzJ+wj0PXXyIMeGeuPkDJhDfz5PP7XqvvuTuLevTRP3xJR9YCEfJLhKDE7lnm+T6f7wO31qJPbv39LMVQnkohEjoAYo4mmg2GPsN3a+wxXw23T+GQ50btgt2uQscPuf63gfXeTKGzFJxJfmM5SgzrecgM5FQPEkA9rOr6Wb0ngB+Ll5FgxbjcROiw540kJ6zjZCjWQH3bFkWQWpjd+P24R37dcevg/3W0z3dorNtHWpluFNiJkU0LsocP5HYHvn9RselHyMxkIcgk99DVDg9nalLbOD7xNqs8DzE8tHTMXrvEaAsPS09YYPMqsY+uF06Yx8spwIaZZ7IRlC3ogn1wx//s/zJ3/0XeH7+CbePV/oNoLE+lSMjgZ39+gtElVM50/w9xX+O+DmCpiSWhyAWNpHj1tiulyOLllkG8mnpPJtp4Rg/jUwM6y0EA21FRjQMWx3U8xdordRTjEu3fM14puEnEZLaUHnVVii1UYmDYt93rBhISVZEmvyUmoyrSqlL+Bu0Ba0foO+UZY3M0yOQTRzQh9HHDcuDbRqjzwbcQYXLgKZaaOdnvBZQ4VxqDlPM5MDmIE0/TKDD9kbiW1pBTTBi2CRa7+toDAYxhLAeDZ6sJOzegBLuZTRuDN/xvsWzO6h7+ZnTPjA8F1KsM9dvaUewfoQLjnUjGWPzz2ua6thxL+774AgL+V3mnDMt5ZNg9shcUNeHvxdBeWkVuylhvL0zthvcLrht2aCdmPPnX9/v4CrZ8S6SBJtYTBZzjuPp+8x2NLr1Hg7ns1upqtQSrkB7dlNnyaEahirqBPgv6QfbgsbBscnzYaaNcuASjp7OWF24dZAOqjVMiaUjXul74js2OOz39M4kMI+mg/dOGWHoUg6bxDtf9MCVPKSbc+a8LDHa2kUzOw+K07D9IGPj/ulCJDrhYSwWEot9u7HfLEdsG0hyMmNlMhg8/9YP+Yn+cygLZiUaMLtQCPqZ6eD6+nu8//o/5w/+rvA7f/7fZHn6Q9TfIqyphMqzEsLCUb7mev0X6VtPY467VDYC/IMeXASUB+lmSEUkG51SFd8afdvA00OitYM/zBFEPFdSlMOU3IBzvE4eoEqM/xh9pDhEj6AhSbUTd7w2VButrIzrLT/PwnCBPqgjvkMfHet24KuI45YKLO6affd7kK01xz63FV/PFE5h85pS6JmFzQplUpqQgljskSLjCKxAuKn5DR8lPs9w6pIeDBMKKMkUSIDMLNgXjCt+fWFcPsZwRwMtjbI8cG25ixsibk7IQzOoZhbPg+JqNrWyElWCjjVlr/OaTbNpOqNFj7VRpkHTQwC+O1xlA1HmO5P/r9FH6DsFz55CP7B1cWIPfeb1/Q6uM6hNqzeJEh/zpGN6nvojA0tYzuEkreiuOJlNoJ4BZ/Lk3Ae2EYYdbZpZhHNRNLNyU065bNQy8d+t0p7e0p6+ROwC/gr2ishOsSVVQRnUtDxgcIkTmT3QUISlLtR1RVo7XOYfF8buxrZFM2o9rywTGpFsPoggdGSkgkpzMac7/exUj+L4iPkOwURoQREbNbvk/eAZ9t7ptuNn53Q+sdRnxijcro6b4qaEx26Q6N99PdhuZ779w3+S9yflyf8MS30TZTXkvRewG63+75ExjGQdWD/uh2MTygucz+0uemAedvcsNzrMIBYVgNQWuPJR4sbfHOZJEROKVmoexLOdeGQ8pVIXkJKYvWQwnusxMcCAKSrogrcrjPDItSFID+tLreFEFRx1C0jKo4yNMT13XDaw+nDAKmV2uQsiNdaqSGD9edjG+ZfBdQpAEpOcjmci0UjVHsMHY6pCw0cYubt7UNyyKYppBtYMjJOZ8fIt+7tfsH/7i4BhtFLXM/L0Bj09I3VByxJudPm6Xpeg/GQ+ZETzuKRp0VRIOn48S5LaRz7nifnOneIyHbJKLIZs3B2agZkU2afOViM5tGBhFeo5LXrbY+BkKiVFQr4+emfft88OX9/v4ErMX9cj34lM1jQB7eFxEvcNbFCYEwFGKnLuG7BoeH9qKcd8ckucBovMZX060071oA0fZOMMqJ9g7iJQK6cvv6TsP2GUzj5eGf0l/FqnqEAlx1xMUvb9YZfWKE/PyPkL9PQFupyDhziVM7ngnXQOulxC4TWSV5qdX8iGT5y3lHYvSX186qakpVBrNOckp7i25owqtLKGibbuiEbm2D0WV3T4l2yoFER7LsyRJRucz2d+/I/9WyxPfxLxM9vrxmV/pZzXyCw1bqYa2NjYbWO73kKtJnFgRmyNGVix8R6MYXo0CAtAbfkz6QMB6BKy57Fx0OAmJveYsZAS3vjPuGs2QpgyM50QglQ8p69OeeeEN4aTGzzEKrqGyYlvPQ+2pGTN5ooIYSxj9Ftnu8TaKzUc9ktr6X+az6jILJByNtqJfbuy751alNbu5tnxHgoa/gvDPAJYilo8YYd+uzHsiraFdn6itjWgn0xMGAEBGeTzMCDGffeP37L/4vfpv/wZdnkfgUgro53w6xPl6Uvk9IwuT7gumCnDoNQtZlrVoCaO0FHTljVYLg+wF1mGW8JXx/dLiJfchvLAdjgy8mxkzEB9PFuZB/d94nDwvT2dump4hhBKy1AnJpUvGRufe/3awVWCmfs3gZ+6+78hIn8a+BvAj4C/BfxFd99EZAX+OvDngV8A/467/79/3Gt7puVepnRPsukU2I7bwPpGv7xS6JEcioD1uDGiHBOQGzANSVJWKmaM641+u6Gjxuxzs8gOUp74CGd/l7MHoOsCX3wF1xe29z/H2wlnA9tBBqUVlvVErY1b38O/MxVlrRbWpyc4v8XbU5STPLq96/0Q2DZu1ysCtGUJizrmyZ6fZWbI2fAoIoy9gxbSQAxBKVVRzbLUovRcFqXIwtIaWg1jo9sV8UEtUXaJKFg4Lqkoo9/YXq+MvaNFWU7Olz/6Ca3+M2zXDevK9up4jYaNS+LYHtNW3WO6gLmECorsBsvIjFvvPZ8h4DG7vpLdbpWDpjaDUhjhhsjCzJLzGBm8e3KbaZmhjuzTGKTUdTohPW74WX2ExDgFEE7wmWf2LAF6xHA7p9QlYYbsD/x/3L27q23bt+f1aa33McZca6+99/k97q3HrVIDzcTAwMREDFU0USw0MBAqFUSUygw0MFJBUAoqEJMCTfQPkMqMxEBQhEKEeoC3fvd3ztl7rTnnGL331gxa62Ouc+6ta91TBXWoCZuzz95rrzUfY7Te2rd9H1XwPjAftGMEfAFpiTg1G5OjXNLVKu0zsyjMmz8M3v3UvWs6oonFJnzMybnUnOzSdGZYcJl7oywLy7IG0oEHVDQEH3oW18C0d8brt4zvfh/77d9mfIDrMgwAACAASURBVP/7WLsFRa8sKM+ItLD26we+NnR5wlnAo2PGLLBvXfJ+cry3k0euackpQO9pLUn6HudUEbuT/NdjGiPJDz6r2eXOifXHvxCSD9wT4y/UyxPYR26332J7KOkCloFs3H/y4x9E5/rvAf8n8Cn//z8D/nN3/6si8t8A/y7wX+d/v3X3f1JE/kJ+3b/5x33j843KN3cuBjwD6GiNcb3SXr9j+IGswnKpqKd+O/1Ww9RkiYs45anheCWQHp0xauppQTix3MDqAgN82JTNR4TdcXnGnz7S6wumT+jaUTVkKKUK9XJBl4Wyl6SDNXrvHO2gmFFKxTSoNWdnexbY+IDHSJrTKmmSnDUhb+7pNBSndPggIXKyHWxeyBpmLI7QWvgdxN8J+z1w5+WyxuFiDsMZFjQj7zpZcVRVqkO/B7/Y3Hn+dOfyrKz1wvr8xFIvWNegJy16Jnt6EsQ1lwvzs87fhKpMDJGQAU/sLAoheO+MecOcktrHcsUTn50QuUvcgCrlxHXD0CVHCZM00gnq0izY0UXPwjo7RUlsL1aiDwCvho8AuQypSyzfao0Y+FLwEjE1vTUko7x7b6gnf9YsxAF1ZLpMdlI2UhAQ7lcCHMdBa41aK1tuzYdEnoKqZoLFSAxFo4i6s7IwEgsVoktzN+iNluZBkotWHw27fmV8+S3+/d+JAvv6m1j6FEGWldFvaL8j7YDRYrm8dmR5Ohd7QlxHmso2JLO8bGCdeB5aCe/42NbH32eR1PAuCOjn0dHWXH6dN8qP784/ohnirCm5IykrXjfKeqEsKyKWNUZP+8qf+vj7Kq4i8ueAfxn4T4F/X+KV/IvAv5Vf8t8C/zFRXP+1/D3A/wD8VyIi/sd4eomEw/nMzTGbL5zYrN9v+O0rcv8eH1f64US6Q1qRlRVZNqgCsvA+rRQCp9SlsnCJADQtjw/QJxA/P5wosj9+OCBrZfn8Dcvr73L0KzSLmKlREI2lR6SNFlwKKjGiHPcdPQ42BGTSavy0msPTFEbIsTIWWmFevUSHBKnyeZziqpkI6zycgyyc2GMjHFjTsR/0w0PNthZ8L9zeBvt14ITU06Wwj85t7ex3Y9vAdODdooMQo0pkII7Wud8O7G48XS6s6xYHxxDMlAWCyeBTGCEnrhpdJY9DhYmrBkY2DxlBI78qPfiGGaOTJHfPhQgnpzS/0eNAjU/+gdkTDIzi5TQh5weFtZ6b6pEx7arp4vSwxYrvm8VeAGoICXSZcScSnGoPT18fI2LNR88lnLOsG2hn7I+UiWWpueRM7qdLTGwjF7YTEjA/N/hC5sbJNLnJg5egyl1qUMum61QolXJZlCDcGJ1xv2G3N+ztK8eX39K//Ba/fY9biytU02nu8kq53JC2I089Heliigxj6pqm1SM9jyXvp5FNTDkP/1h9JQw4x/z5MSowjavyPrVk0Ki+g0l+fI++/7O87tKnkmM41hzTBV02bOwJG4TNpI9/eAut/wL4D4GP+f+/Ar5z9/mM/ibwe/n73wP+BoC7dxH5Pr/+N3+3b36OZTkK9R4qIO8D22+M6xe4f48eYXLhrXN4RwqhpV4vyPqMMPAiSX0JEv7kkNZlgRpqJyYX1OY2mcwWenAS348ec6QTVfTpmedf/2mkH/TvDWvRESxLFPvROvt9p13vYZuYncyxd8pwyqVkfpqe3M55uZ/uQLmtDm1/eUSI/wAPlnMRyLyIshOWWbDNOPaD2/2OJja31srQQn+F+7XTjkYpQq2FoxX2D87trVM+OYhxu75x3HaeLgvrc1jrSa3sfXC73aPbf4Z1u4AUfMznMnJjHq9HVXPRwLlJj5slMEd9vKq4oWUa0qTb1bvt+oRIPD/bsi5nMSW791iOcRbxOf7NLtF6R6qCL8zgvtMLNGWv87MIut87HieJ8+l7n9e4dnwW/lJYti2kryq0u3NvjeYt00oLx35wXG/UDIPUkpihR54bEBzedTkXlRMieCzkwsAnffwCt7dIlJjmKz56vnRHlgWl5lvTswvdGfcr44ho836keXjbGdbOxV9pO94jRh4JiTqlMkZJVdVyGp1Hg6J5WMb9Nnp/cKCzmM4pazIJZmc6p7AJEz4+zkcB/TEsAI+ON37ETCnomd4saNmolw+0fqcft8cE9Q+juIrIvwL8vrv/ryLyL/zkZ/CHv+9fBP4iwMsvfjcoRbm5HK2H1+P9zvHlt8jbt+j+HXb/Fh9v4OEgtG4LRTa47xy3r+hxY9Eg7bs/fFxJB6r0icJ9bsfzIiHUR/HVf/hEVIiRSyQuso+f2cbvRYf5XXIRvTG6cdw6x3XHj8DrihSkbEh9wnUlEm5TrbUsgR3nwTJ6uNCv24ps29lVoRneNo1DyMXNKSCw9PPMP0sscm7d1zW69VrSzs6BobQDRveI3l4XFn2hyhO0DTsqpTrenb7v9DIo3al1o66KrBv7m3C9XTlaC8yTEoqnw5EyCT5xcNVa0Z7qsdOGL4tsjocB1QT5vugSY7KF61Pgko/vmfhO8ohJw5uURr77FE9+pmX3NiLFwtsRnynryadFwtdVxEOzj5xLwkmtix8tJ4wQTaAQ8jhJInw8NykaMSQWGL/2nn83cQzyMOQ8GM9f8igUk6I3hTI/WODNOJtAcsNP4shkWcnue1nQ9YIuW+Dp2XK6xb8XM/DYoktG53qycyyx1DABigOulkLZLkh/hR4UONGCLCGFNi9E0O+kNc4PY7quvVtuzcMPAtZ7J9M95eAnKyB80N4rsd4X1j9yOJaYbut2QccHhAMfO3W94m1n9I6WPmMrf9Lj76dz/eeBf1VE/iXgQmCu/yXwjYjU7F7/HPC38uv/FvDngb8pIhX4TCy2fvBw978M/GWA3/3H/im3MTDvaTVoeOuM25Xx+j28/gF6fIvtXxjtKyqDZa2U5RIcxQFjb7TbKypEXAWCFcfLBdHc0CbNKpYEsTgouU2eN+vJFZ18D/zdqQpeFFkidXXNg7Z/p7Tbt4x+w0ahLh+QLXiA9emZ5Rd/ivqr30MunzK8LkbQUkpGxbxXn2XhUU1YI56bDc/xL6SZwaIweuthoTfNkgmsek7eWpS1rEAyjGxQnqJVXCyGs3bvdG8nW6IPoR+kV0IsS/qxM9hZuPDyfLBtysunJ17f9tz6xoVuw9lvEUteVk3HoRAEqMKMrWFuqc3PmA8Aa1GcNDuaM18+2SEp44ufpSCy4l3x0oA5BZWzy/HYijJ6g34gfcfvb/j9FbcCS4EaRilzLj2DJiX40CUXZFqmQAMg4Yckcki+76cxs3l6rTZEYNnWcNjSkK8qUYh7b+dYbMzDY36+wdUVmePwoxBN4QxxHMW/NctMrExt9cFo6TuxrHFouDH6uy09Afvg8bolE4AndyerXhTG1gImuX6hbhu6hW9HNACK7TkpFiHiPmNLP9m18ZDoslVOjreNkd+Ds4mZh+8POtNkFMyJ7Q/hrI/akkm/fi7K6rqBvzBs5/72heELlA2pO95b0gx/2uMn/0t3/0vAXwLIzvU/cPd/W0T+e+BfJxgD/w7wP+Y/+Z/y//+X/Pv/+Y/DW+NnkKmnli16RDlI25F2ZRyv+P6KHFek3QMnNGVIx487YwQJnOUZe/uWUUOiygVkBV1iHBAyspuRY0ssLIrOiIhy3kSP05BzyQHTYb+APqWiRGjLyvHblf72LdQn6kfh6eML6/NHyvMnfPuIrS9QP0BZAxOecRnv4IeJH558v7xxLfFTZ2SIX9JYRmek96nkqT6XGyYpk5wXqig2nZ7U0VXZPsP2vGH7ynE92Pc7fXnjN7/+31m3yrIqx+WNa/2eogbVKGvl03bw/Czc107/RoAd9ljGeY+CcUjj8lK5fAjO7xR8OCmCsIFPAUR9uBt5bvEHHnEpmX4bfMU0OJGI6BZKRGS3eD+kdOpmaF1w9Byx3Qb9uOH3K+xX2tsXOO64FRoDG43y/BFdPpwWkGdhLY/lGDJjUIK14EwesSOSvqopoTj2nev1igLrFnSkbVnP60qAxZ7yw+xZZOwseicP2yRFCJzcbikJHUULe2KVqsqyLtlhgre4JkROWcxsqhGTpDHaeTBrWaiXJ+zpA8f+CmNHMawH4yLexwNVYf/+N1Qzau/45QhTm2OPw9sNqZe0rxyQ2HD2pMHfzdfk8lB0iQdFzSR8Rt4rpx5L77gp5Z1Ry/w7dz9z4wK/f3cPi6LrRt83hm50uSC1I2vgw2L/kBZaf5fHfwT8VRH5T4D/Dfgr+ed/BfjvROSvA78F/sL/3zeSQMUR08BZexRN36/4cUXGHjfDfkf6DuOg78a4vyWWmoopHejY4fY1RhNLCs2yRIeT+KloobhAnembkiqQh2XZ+yfnc2WSp6ZLKIhcLkj9JWXdWD9+5Hj9DmynLIWnT59Ynz8xysbwittD9RM37+NHPKSHiTWZ0UeEJta6nOMPRDSxkN13b/gR21sfjaA8RYCgpkDiNLom3bJsAMlbqyV09FXYVqhH4Z/Y/1l0gaoFMTjsjWe+Qb3DAOnK5f6Fp2ps/qcRqfzy+McQqRztztjD00CX2GgzN8eqYWiTeCc+aPeddjTWbWG7bOCBs7oZdowc7j1SCUanpOAgpo8Y9yYUEl3NkiotY7ichPSRNL7+9h1++4rvbyyRJ0ofO/24s4zB8kFguRBLxyhgU3JKLiFlKpAmxueD0VqEJeborCK01s6lmSbmH54EiYuLxJLVLzA6/djPdFLNKWuO9mSXOzzyvObfRbdL4K4yr50eRutuoUSywDptDNQCD5V0hkPA+kw/jeu6biv+/ES/b8EQUKfgeE/a2+i0u1MyGsXaDttX2F6Qp0/Qb6g36uUF0QsmBSdjapJK5sOzqYjrf+LnrgO1SJEIvmU9D7YfLqvmTDn//TuO9PtFKTEJPhqMgixPlOdv4u/6Bd+XVJ09DGT+pI9/IMXV3f8a8Nfy9/838M/9EV9zB/6NP+n3jskiL9gWG8xxe8X2V6TfQ0DQ4nQcfUc0Rno3y1DBQlFDZTCOG0ENqdTlCbUX4tyPpZWIUpcS0sJcuPjprP8YUd9jOXNcn2O8CEEgX1eWdcE/vrC234kuRDUMlMsSPw+hjDmK5fg73680tZ6cPgSOfefY91iIPGfUR47DZkZyWfAWxTUOoVds7IgOpISwT9egnrgVzBUnRl/zGJ2VtGpMeeFSheovYcKd49nz+sxH/cwYRzxzFdRWnvWvI+X/CJysgC3Cegz60YM29LRRV0Fqdo8ena21xPYS31ZG0GFO+WFstcnlV7h3dUiDcia9qhOCkjkdRxI37W6Mu5yqLUFSJ38g7U6/fUHaFQqICzIK3ne8Cp4BdpQLUiph7MI5vr6Pf56Qg3h8hu048BHyXjRc1jynoFJLMhhitJ1Lr8giU3pzjh6wwlIq4jFhjTB0zfsirxvlNOUJeDmhguyk4/dx3S61MkqINo7R2ea9Fl8VBerEPD39ZBRZV/RyYbQVWuDVOqcNd8ZoSBNEbjQfyH7Dlze4vuJ7FFcd32B6waigF3R9RtdLwAXCw22L992nMGoNY540ejELPu4DrstSlnCF+/SajYN4sih9Knnm1yTcWMrG5eUzti7QX6A/Y28X7n/8cP3HPn7mCi0e8bbWwkqt3cI8eH+F22uMHO3Aj4ZZpxYgl1+1lFD1WOiFgwI1v8eNcVwp61NwFEmMKNiL5wfjOPKjU/J9cfW5GVZJNyoHjU5GIDqUWuFETnX2XeCpNpomGKd0NzCy3ho1FyZjjJN0DSTxOgIQe2KUMX53OA7s9Svtu9+wf/k7uN3Q2olsGUVePlNePoOEZR1aUAltuLEwNExJ5rIvtu+5p86xWEthWRaK1UfqUPlz7Lffy6VK7J3iMFPqorkgWhkI/b4z2h6Lth4RLkUcLUItklJkDzPjxMnIbb8CNlpAH6M9onVIaWsPr11FgqfoznBhiLKohGmK9Sis3nDbgYMx7vTjwNRjEVYvsBb8viabJHOyTil00ATPrTazIAh4KMrsOOLmzZhwf3f9MHF0iVyyE0eWguug+4wI18BDe3SKIzmv4VdMQA/Ede4Sy6cg4Se7AQvOrVVMA0LZtMZ1k3lys3v+wR5h2lpm40CqDcu2EcGaYVoehx5x/llAc9rlLNbmAxenSYd2x+sTg42yfuRc3NUtfCDmojH/fBqAe9/BV+RygTQfkpTRSql5w2ZE7olL84Pl2OORnGLryVQYIdooG2xx7alFd1xu159cu37WxdU9NMrp5UexN8TecHuj9TvW91CF9JYXEtG5IXgJA2epMXYqg7oEuXqoQ7tBj+C7ohKw7o/oHOeFNk/xE8M5v+oczVVqdgzx5UG7tFNXPTfBk2+YK6qzEEeBVSC3+mM8Opn8GdvzE9u2UZcakshpTpwerrPbpTfsfmP/9reML79B7I1RDlwarkK7fY9cP4IumNTwQy0rXlaoQV9zDLLgOoKky5MlwwKR6GxrqInCS0NDJJGE/2gQJI1q1sAm6zSmCYMYGzf6EeTzUiUMRzCmAjhgjew+4uR5bKz7ESOohSH4fF9dnH7s0ANnlWUgtbLWFZXA3Wwc2P6G378i/QrjhrVr2EcSVLmyNKwqQ0tq5iNeBQSf6jCZOCtJn8sdAckYnUUX8mDKi6ROYUKO8NmheZ66IiUyujRerw2JQ6MZozmMxl46y6qUJXBl94Z7i46ekC8/lrCPy3BSzBYpmf22IEXDAN7mAs5DuDATAbIj0FKp6xZYqw+wBbBYkCVGFl4fcV2KQGEgh+HX8FyVywulfgj4BeE4OuXpA8vl6bze1QNaCaOZeD/7iIReamFINBeybO867XLKdk/jo4nbxg18coHJhoT8XAJ+KKis0Rj4wGQN5eRPfPy8i+sYtC/fYscb/fYVOV7x21fs+ELVzihkl6ExMo1+mpgwLLCa4cgIBYrbgcgCNNxa8P9Gx9RScjdHvIdqyBMfm///Y/wGQrInEvQfIE7xDFJsPUxQak1sCUjE54QRIlbDQ0Hp0d2qlvPnWB4cS63IIsm9lROPtXTK8pRKqscWXK2h3hnHG+5XkEgZGONOv39Fy8KgYFop6xP16YXy4XNsdQVEt8AZfb4oEAklEkoUmBpR5UCmb/ZUk2UabR+YG1sNSz8vMLmscSMGXhyUKI0F1RghUYTTlEeYPE8/8VRJrNVyYaGp/w8uraAkt7IfKBFCKUfH3Bj7lfb2PeP6Bbt/xe6v9PsbjAgoDF+GBuHoiiwby5qc3SSgx0ZkYq8PZR9TRiuJK8+bvSR1K3HV6Uds7qfyEJEZnppimEIYJRb60bEB1ZdwPjvu7PeBlkGpHZEDxo7bnq83RANe0jaxhtesL0tIeHVKc6PQi43sSCdEQKgJ73fYd0hhwwPVFErVkC7biAOTmLysRecuvcWh1FrAViMGqLGCWFAjbVWKbe+MVuJzLdbp44a3uBZkVMwPWDaoK14Wujlua+xY0gjJJF73ewx83rs2kmKWE6mWeqoZ3Qa9O+6KWbJjdIImf/LHz7q4Wu98/dt/A7t+i+xfob+B7Wjx6EAWQTz00TTQ4cwcqrx1YQzsvnPoK8UEXcFXQZbnwIj6gDLHiZQ1vhvzJn1jcgtHdpQQWLAlBmiWRVzIkSMWcGTBn6f6pATNO+hcis2FDKQuXEOaN0dFePi85sYbsqD1HmYuSdSO5E9jWRQrRIdoA/NGceAwxmiYaBhCu8L6FO7r7mHocgku5twgnxi0xAKlisfGvGQs+LznRHPMjOVJS95w2bZzuZJvUnzIEhCHEM+/j8ax3zGLqB6m69mY/rKPjmTaFM7u0OCBfeOIGEXyM+kDHzvNGu240fdbQEvtDv2OHW9Yu4FbihSEkcqlilAuL9Tnj5HsWnIZaIWRFpYiGqyM+Xl5JPBKrWf084wh8Wzrw3NHAtJ5Pw7PZEcEJCaUyMnq9BbskaoFkyU++31n2BWxK/Qr+D1ggSL4GqYwLDXsEdcVkWeCchKLpJLOanPvYN6iqzPw1rGj40c0I3o2FokD5AFSlxoH6Rj0kc5zInhrlDKQEvCeScU0ostFGyqDdVuoaw3UxSyVYx0bO/0e0F8tivoCvoA1ij/ltBY8jW524trh9ZyKv7jtftB8nIVV9Tyscc/7ISJkxBV0QcrTT65fP+viihvy9j28fsu4fYfZNaGwBV3ChahWQazifcHbHqmqPpJo7njrNLvG5nZv1JfIKirecBvR4S0W9S4NQB7FMzfAGSEx8uQLZQ6BJ07TDis5vgT2I+aIz+diNGtB3NYMwStTJBi/3CMGeuRmeP5NbMDthBpUHNW8GMzCEHv07JT97LZFgqxtOR5WyXHdDIjIkAf2G7rCfiW16SuLrhhLLo9ywSUe3cl7/Lnk9r/EzYlJUtciPtymEY2E7Z8m4X/Yw31qWVa6dXq6m50c1PNzjE62SPSRPyaIh++szjHgfG/IqUFM6Meddr8y+hXJ0bn4QLVDGbgkLo8/mAAuuB1gHVKt5L4ga6HULT/r7DY1FmHhmDWNaUIRNel187/zHOpj4qJ6HhjTCNvHoLeBDSgeFKSiC93Dg9UJCbDaYBw7/fo9vn+B9oZYLjAraFVsW9GnC2wrOp7jLNpyuZoiDJIVQ0ZXu0ri4YGnljw0hYTWnBRxBIfVtKA6GQjR4ASVSnFvMav1jowWMlgFqRFRXtaKFsG9I1gyUHZsf8P2L0jb8aL4CAiLvjNGQ7cPSL1Ay2ajVlSWB1UrIbmT1zyXffCDa2heR/NwcwOlsqwvCVP9tMfPuriKO2qBI4mOxCUDm1QkNodLCWBbVmwvkfjZGt7B0jTaW+CvBfDtkjjeAE0DbhtIye5nbqPlHUlZ9WFHNx6uPeSyB3X6xAhngU3XojEs+XWemev1TKad0b2TZrXfd2wYy7qEtaLN8Te+5egDk5QuZqGfJzeJ8XoqjiLqaZz561MnbT7QafLCZDsoNhQZa+J1gUsGPaukko2TpuN5AcaG2PCaPrU1Oirw1LyHrWI4WM2vH6cmPO7yiJGxNJNBIysM8TABOT/zhEfE/vCNEc8SLeWEMEJckGPtcXB//R5rb0EHKp4LNsd7ox07fhzh6ZlFAYnnVauyboW1gvcbg0rRio8tqXwy0XPmMD1OBVWhFKW8i6fh3dcOd46jUcsSUI/P94V87jAa+PDcRa6si2L7neM4kHGn375y//Ib/P49ckRx1XEHOqKGVqWslfq0UT48oy+fYzSaYoeaKrF8bqKVUg3GgrUDKUv4dLCgvuCj4kfiqiN8UeUUgOTmnoQ6zCka95NO/Hf6sGYEeVmWsEUkbEJj2jvQsSPHG3K8RTE9LFg4ZckCe5yT0HBBfAt4I5kSnockU/bq877WLLKJ0CcvWBRItWPRYMyUEvfhT338rIur47g3tAbYP/t8d2McLXmbEqmXiTN6i2ROby1O0aTyFIAebkTiPUYBSRL0LGBnLzff+Encf4ezzg9sdlfWGNaY/cg5+nr6Ys6vncsgLVha9k0PzygKjo4e3cAA11T8WMp2kxtuY9B6bDnV07hj4n3AtKizHsYw1oxinOGM5h16QBql5IJFw0FJMYpmE+M21/3xq5CYZxiTWBZyMUsXshzH5GGOgofG3ZLwH2+fnxtgtCBqocFft8h8akZP6DJuyseEPD+Zx43xbmExjVmcx8FLLESt3fF2Tas8y2+m9KPRbm+MY8eOHbPxw41+OqEpHgGEcgNTXBeGRtpAFHUlwOQ8DGeXCicmLDOWJeEBLZVFwuXqwUyZUwyYCcdu7G8HOjo6BBmCDKMfQUEUb7R2pbc3yF+2v0K/xTUuhkrkg/VbZT1eYDSKO1XDI1bGwIZF1qEoUiSGEHfoDb1cgGc4Ot4a5sHxFYv74NTeJ2SlGlFGZJJFnMeaac3BHUUXRCPBQKd02Aa2Hxz7Fek7xe6M+yt+XMEa+IhlYiugKzpGZsdVpMSiTWdzc3aqD1hvTknktXk2UPn7uTQDcC0PKqf+I1pcBUelIzUd1bXGwobwKTULLwEXj5C744jlRY5VNlMHJJcvDuqG+qAUBzG0eFBMVOOQezcFPEBwO7tW3EP2mCbd1m94bjHD40VPDHaekAFnKTqXbg7djX6dRe5dAqxoYLe+xKkqS26iC1KVPQuVj/EwBpljbN6Yc3vauwW6bxKudbNWYjkxhdQrusx4PyQx2ojM6ZDeqpiHAii/d651z2IXxt2pYjpJhWTabdDITvu2PAzOwpnF2bUwmOTu+PdSC1gos3xa0OWCUFUfDA95OCOJhwLJUkABDbxRdFALFLHwAd53rAXbBB+oj3nh5URh9LZj11cqyvKhIlTsuOJSGd1ZpKBlybgYYdrkPXTucf3EtRbuVlrjUC1aKXWht1APiXqiWY57LLGUJUQb90a7HogP1mJUzSK4OE07Thhht35D7YjX4j0gK1eUzrhBrcHY8MszMl6gjwhcHHE+xGuPRGS/dEQ6qgeiO93fkB5Us+KchivxnqX3bsIHJRsMTQ2/1gXXkJauTy/U5xdku0R0OpO29obfr/H+jhvervjYCYpVXg+iIY5xGMsaHf9msFRsZJyNl+zOs8npHSNgvpkO+wMWAQHplOyqvSyM3rDRz2ikn/L4WRfX0FH3OH1LnFjiYTwSG8nIHz+sMdo9t5mhMnKztDl7kLanBhwbgf0Q0IDrD/msURUEl+mUlBvsERWqjIH1xtjfYNyDMylOSbjC3z3HWbhVJlUotP/eeoysyeOdSQhlSV12WXCtWdD0YTRCUp6cuQqLBVh29TW3w4F1hhTUXFMsUGAifonNmoTg4swtEgPvDDtQXSMlwBN7TmeP2UmeRtUT75yF+gS2ovnteSAU1XS7ksQzHwoaGfF+zEVdvFOSrOMY1+ZUME07ponH3AIP6Vmso3tRN8yDNSF+BKYaIHao2HrEe3hv0YHZONkbOq392pGLqYW67YQBtKMEnUk8//3EXuEckaPTjtc8teyIpSTwEgAAIABJREFUnvaWkqF+qsKYS0OVjL2BWjZ0XRAVVuvcu4A11uooaXzdbti4MuzK8DsuDSOMyNUt/IxH/FcVei1sT09ov0NPFV/vjNrzM8zYIC1IXSi+UmQL2OgWrBvSmY4xo61jERwde8TehJNYfJal1OCxrk/U549sL58pzy94KeFq1ndkDEo/6P2OjHuoL72RgudcUMaha27gd/rxFn4coowS8fVFFLrmVBfTn42RjJ94/6f15Ps6w+nUJSADTbz4jzJs+nt9/PyLq3VK8VSzlHjDWkdbuJe3e2fsd9wCKJfcYM6uChFKuDLj5Lb/CJMOPd5Y6NmpTRoI50IoIylx0mGqD0pG8vZbjJmF2HiGq1TooKc0z4iEU58G3NllBlzRwoHJAgqwrrh07IjuzbQi2xPLh/DMjBC7cG1XifiWh41aynkhxvBSsaXGSNc34AK+4/KQOs7uVd1ioeKpfx8H3m6IrrgsdJcsukqRH11o77v8c8R6bPMnnBI0tZrFkPOQcGIpYiNypUYLoxmdgYCar2+O5hNyeXd9PEb4OIhtjLhhiQkF22nHFbMDodFaSFHFQjrbjntMPKmnj06rgowg7WsuQqzBuMeSSQdSNop9zBTbAT6796nok9isv8P2ZlLEtNSbhPkHvzpNYtwYR2fcBxxQTKi6RISKHbjdw5bw7ZXj7ZW2X+n7Gz5uCPE6Tjgnv/9wC7x6KdR9p+9XarsjrUF9mPqEQCRYIWIVutCPxnF9pb29Ml6/4q+v9Ns9kwISkqm5R5Cge0WDk8YvpeJ1RZ8+UZ4+YboGLUt6mN6rUEbH7U61e1Amif2CiT2gIJX0I04Yoe+UpJ5Zv0FbIBuPumQqMrlQLA9f3TlV/CA2ezZWuXBE9QfX2k95/KyLK5Bxy3HNzgWDlYoMZz/22H4nfUM8gOm8JUE0pLAlY5HJepn0laXt+DhCVaKPRVZgXhmSlsB8dFdBXB/7nXF/w8cdkfCPnbkF1Bqc1uwMA/vJYiOcXbBbbE5j8zZiets7zYzj6HipPP3yd9CnJ/Atx+YY2cQFL/5uLEthg8zup1AuG5fPn+jc6drQklirCTQJSaxP2lDSndpOf/uKuIYl4uxCyxKFTrOzmpSieYF6pJH++FJ8MAr0NFB2fxRceMhFyeDJ2SnUWhOOGDkV2omdvVfMyfvnMt5zZOPXcfuedn9F/UAIrT4jujo/jvBgmEIVAnc0N+iSnrslUkCPO37cMOsMMcrlYxiRmCXeoidOO1/nmOpCPDO7wow98KfoiiwXnuZZNAb0u/H6B28c3+2UXlhlQU0YrQGd0a7st6/0+yt9v3Jcr1jbEet5WCaPeKRaTB7L2tHiILN+REOCnX+vpVCWPLyBJsJ+u/P2d34fe/0D7PU77Ov3yNsrtAORjKWpBV02yuUpcNyygpe4PslU17Kg2zNcPmAisSzLwiqEdLbfb/T9Fe97sAY0eczZtVbVUJXNDj8Vl9IbdUtqWx6c1gdSY8kaC+SS0FHQ6CQ/h0g94ITwZsENKO5HfiJ/wsfPurhqjlb9aIx9p1Rl29bIGCoPIWlQdBKYzptWZWKZkcVU1hVdtwgApITzUouTjxmlm8sGYpXGdGOyHJvJN99bGAljjeHHSfkQJFgIiRGdmT8OZp0+0mDFPED6EVlbQU0RkB6y0OOgXi7U8YFiB+7tdASaXMRzW6JpIpJFKzc6UCvlwzP4R3QdjLszboa3AX6cFJNZlPw4UHdKDxJ8LIU0bBnlKQ6fk6aTaq1o85G5/TdLjCIPpbM7I5dgWWxPoGsuIJLOI7FBL8RoXrTCgG5BhZu5WT9eaOVG6F1xtxybb+y3r3jfUY0DjR4Ftbcj8MaEeiY53rWAxU1lJZRduGNtpe83mjVGgTqCyscUOUiJ7DViHD6jzfGwKtTEkSUO6wkZWDdGz7mjC94M30F2QQ7FD2dvB/TY0JvtWL9y7Ff6/Y2xx0IunksUypLZY2FxmHKVCXckLmy94xaYtKQX6iMhOD+vMbi+vXLcb+EhQB6QuU/AIpZGSmXZnvDLh0yBfUZ0yfsiO0et8WeqsY+wQbBSHLdGP27RgR833I4wal9SIeie2Gxym0sYMqGZYDB6LJ/gfO5TyVeSt847+Mqjn31gr3PRmJDg3CGcrmc/8fGzLq6SN3FvjbFfQYx+WVnrklvTI0YyTXmmRF3x+W9rgSU+CNm2MAaWhe6KdwvKVqp/YBanOBGDb2g5bo7cipMj4ohOef47y6iJUqLDGm2+gjhh458hEiR3mzrw0aIj6nfWokir+J7YsXT69Tvq88ccB5VhS7zANBoWTfXPjy4AiR1WRGsvNXiADEbig5Ikb/xR/BmG+0HvnWJGT4bDUjbK9gHXpFPNFX5s6YCYFNTT20BS9okkQ2GcW/hJWhLVGAsnjKBBUdBa8DbTSgNG0VIoo9CZ0hA54ZCHr2tSaMrkKdfgiY4RuHBV6tmdt4CFjhYFwz0doKZhd+jwNW+8CTt4a+z3G71slOUFRGkjppnIQfPTYco9rs12NFSVdV0yVtsTZxWsB4OjHYINScK9M+4GOyxjwV2CAdMH6krvjXZPHPIY2N6wPf4+MsZ8Wh9EsUgjoslsiWsjCszcrDvRDZfJ82YqzeIQ0FKpl41SnlBpWLvG5JZLpbzZ0GVFnp7wy0dYP8RG38OvN/ix8fmPo4ULmns2Io3R7+y3V/pxxcYRV5QU6HFYiwiuFaNQa7IOpDKSmRLv944sB6Ws/KFla04355jv04bzHbc1Yaz3cKLBD6GDP+HjZ11cHUHKQilrvLn9zrjvHJopqrdbjmGxyS5aIXXuImn+sa6U7SlI36VilpEdaHRvPcZRT6kkYmdg4Sl/zVNabAQFJAtyLH8sAHCNvKzQjj8u5Eg9hdSTYir0ZgxTehont9srzQZVg+voEh1oub/R76+sT5/iYpPIr4/3hjii0wA8vn8WgsRQbfQYtd6+0t9e8fs1guT6eyvD+BV7nnw99zsYdApeNur6FJ6mZtHBegL/qvmcJn/z8RwehVMCi44/yYWBnwVASsVLxy3HNSmnaOK8roVUg+VU4e9/WfI0OdkeIhJUIAjZLRWOTjsOxv0Ox3HaHLqFeAPzbLqzs5w4vXvYFfYGpaHLM+u2MSPP8dzuZ1pE+OOG3aD1Huooc/p9hzpYnoMnu9+N19/eaHejSmWpJRgwx4DD2N8a7drwIw51M2McAzvi4PU2GMcRUEFCQ3NZmFcvk6MdSbLZKGRBLGgEI3oDecBTNi8GAn99erpw8V+guzO0Yccz7X6FtmM9+u8+jKM1FhPKcsHXD1AuFN1SLSUppunRgQ/D6OAtjXOOmKa8ofS4rNyhD0RqTEzuIRuepkAEjLfUiqkmR7ZRZ/dZ9IwWn6NSYPwPnvT8/M3sVF4W/RGm/49qcUUErc94vaHLnmNRR8zwHt3l7AhVcvGRPDsvBVkXynZJ56s1HIY0tplleQZfsN2Re8elU4qdkvE5dp8WaCOwNUl2t1hDpCHyMGwWL0F0nlQiggYzC0/w5qCoJ34a23DvcOz3zDiCsi3hECXg46C3HdZLsFzS+8CHh2Y8uyvPEQzmeOSMo9Fev9K+fB98wbaj1k5ccIoDZojh5AU6DWvCuL8xbl9hv1EuPZZhFrLKMIyuyeKYS5z83Hx+fEItM3jRHs8t73AtBauFYTWWJzYyWK8wTY97H7SjMa0G56H3Q2cpTfhhnDffJPJXUcSc3naO6w3bj+hYR4ow8gYL0nkyBGSOu9nV1OA9xiAi53uutaJrJCp4HnAhn4zDQSUCNsXhfr8FdFVX6rZFhPWhSCs4hRGL+/DGNWVZLsiqHO0WZP0epP24tIw2Wpr75PhbYlNfilOLZpHIXYFGYrBYiD+iiO/Ifsf3N3R7wrcnbCy4r0TmWaQA1LrBsmFH5Ht5yQNTC6rG9GHte8fuB5ePUOoF1he0bhkTTkxp+x0tDRsgw2NC9IEwqMVJD/BHEXYJ8RARqEgeMkAGkL73D3gcuCpQ3wVLTrxoythPlklOW5bQX0BXMzhTssP/8Sbh7/3xsy6uIgXWF7xdQ7WBoBYSSZrj2ph+AGfGj0SRkVqRZQ3HJ12AEkoOXajbE+XpI1aesS7YreEcyLNR9dHOaWKwYwxondEP/L6ffqmUA6fh1iMrfvwwrUBzNHM8/ytnlyeaS5vLxti3MAG3kaC7UGoJpRLB4dU60JpPDU4TGLOwX1OSa+2xMT+tB2/h/qUWSi08HacmgJIjo7+rjFN+KQlduPUzKdQJG5H5Hs3CMrt8TXrRxPwmZcqycM/COuGAukR43ZAscKOjQ88lRzs6ow/qFFEkXJFnQf4++JRz2Q7QWhiO1H7Hjyv9+oZlxzqSr8y7riRwuskXJg6R+CN0ih6QfMssD/4SMSlaSc+uoCcx8uaMQ2dYHBCCYHtjyKDIyvPzE13B5/RkloGLYU6tLozjmK8Sb5YH7sAsTGVm7piWyeoI28ZSg+pm+CmprmnN1/Y78uUrq0aSbmDZsXBjfQJZEleKP+8mHK0Ht3s8ontOyawk3akZ7TBUKstygbJEkfeQ8SIegqBuMIJlI2q4BN3KvVMsqGHWe04NlcoFROlyR21Blg0toYB0bwxThhTq7LizsAYrwLBpHcKjOHtCSo/iGhBS3FeRjiGPK+MnPX7exbVUlk+/Bje6CGIblY61neFfCStCwrgsFTnTaV4dCum64wWkhpP58oR+/AXl46+R+gzLB7w+4RLeqPN+myYbcUM5Y6R5RQv1l7Ud73egRVdWJFITvAasYE4HJjCVvRp1WSjLFiPcUhE2LvaCe6ffAu8yqfF8p06+N7wflDqQYjgp8yOB9/wxrkYfI0bQ25V+e0P6zppLLtPYGE/zcbf0P7VHUREC5yqaGKmQh4NHcuhcouVGORffwcSwNP520swkOoDoCoKvaidymj9PMtAvgrejRLUWhjdmkXKrC2hoz41UpZXpkyuZqxXLRGcwWqcdO/1+p+1vyHHF94PpmnQuH1URN9Szs5bHczqvwXTaCj+IXMhA4Ijlvax1UqwIj1R4uET55CYLx9EQu0Vk9vJQMA1zfJfAXAeohU9DWWdkdBQh0YGNEEaUmso4WeNesE5RD4FMtMCh97cRCbsEX9yH029vIMYyku/rgBXKC3jxwExFoISi0Low9oHfo6FB9fRAtiTfq1RK2VjqhWVZz+VwxC01vN/xdo9MOQtrR6wh48D2W7AxWix0vYd4RZblhGDcHF9iSTWkQhGGEEV8LdRloS5TjJPeCEZQIm0eyv4OAnss5vpxBFSwLARe5n9fXSv83ItrLTz9zp+Jk3lZUL9R/MD2K6OseN0Yt694CyK/J07gogybW52CyEp9+ohsH9DnT1y++V2WD79klA3XjVUvDK050ifgb8aYndeyMmrH78EMkBEEbpcDkTDoiJDDSlzUKWKYXRrBZoAYN6mTsxkdaH164smNtlRIX9PA++KCkuw4bQR8QWK5s0iKzujnxDndsSOlwG6JOxXcNFMGkg1xdoDvtqL5mrXU0JUvW+i59SHV1UnEfjeOPXwHMmFWJelc+R6+/zmWx42HW1e8D0qRirPFe9Tr+fM8DZlbu2MdSl1YLk8BSeQBE4F6nTH23IQHdOEm0W2NHPunPLeQghTJzovzPThHwncuS6Vkl71tsF1i6kicwslREvBSz9ds0rP7DRmwZv2uRVk2RdYa8I45zQa6CHVbIWFQE2M5lEGGKJaGEPjotim2VpTwg7DRwCpLHcGMcAPCOFvFQrHmRpEFkRIMk90ZhO9EmBBt1PoElwURD4tIMZbtmbE8hQuXJ1ulPF6/aKUuG5q/Sl1O9sd0bWtH0Bdtf8X2r2AN8yiutl+D2rjvjHY/7QnD6Ai8JXc8ynHcUcPR1SnbSt1WynpBp0duxEAkeUWm+vzsRZlru2QWaEYA9VTJiWRQpD4UXD/l8bMurohQPnyitIHrgvY3xG5oveBSMw8raVgWKaUuTtWC1xWpT+jlI/Xll6wvv0A+fEP58A3Lyy/Q9QPIilFB4sIf+GmyC0ljWWrcYGYc9xtGjEVFKuYSTlk2KChIeMLOVIKTg8k0FomTNbCd7PJEoEaBLeuCt2ASGNnllB3Rg1ob1MwRE6VWPU/oeZX4ZCzM4j4M1UgbcA8itdNzey/pfKQTyAzYJRVlUVijuC7bU94wNTrJ0z9gcjuzyEs4QsV4Hz/j1HNrdPPvL9aIZ46EiChGTqEyRJAyEKtYpgaM3lAp1NpZ1o26hc+mZysaMujQ96sebNsTY+z0fqe3wMKDyzwFEzA5kYhkF5WY5vSqKwpJTxKN9+/ypMhqrNvBdrmhlzfK4ulpG8s790Hvnbc3ZaQks9ZI51ouW1jsLaGGMwsIg97RLQIipTu+D5p1ZNnRrVNyYaOjkWYROe4WhAqjgtewWLRQ2TEZEAWKpPxYOqopS8WgGd0dWZ7w6xvjww1dntF1vvYLy/Nn7OUN9jd0v0Yhd5KnDVJXtKyU5ZnLh09oDb9Yz1k8HP932n7F9zdsf4N+hKlQP+C4haNdbxHZ1EIibuox6eVBEUu3MLSJhGRnOXm25SySccAnI0YfMF38LSf8M69NQ1nXlTGClhbMGZso0E9+/LyLK4JsG8vnb6BW/PYUJx8LvikyYuOvWnFroDG66rLCckGfXlg//5rLN79DffkVbB/wesHrBZP1wR3NrksSi5sMk1NXrAbbCusCSxhOCCtql6SLxGgxOrjJuaWc8sag+MSfS574xYmlGzCGYaoggRVhC8ONPgyOA63h2FQSzBR911W977aQOCBUsFLR9QnvFxg7wkG48A96jj3iYYFoNhkAPHDUUqGsyLLBckHqllzFH6agPsx0Hi5WqgVLErhPENTTe3NMukv8+eidNhrLUuLgSSMPVBMWELCKaqVqjGzLssR4SyyuAoqIpc8wRcrKcnmhpMzZW8vRvFNUUE32x+hYO0A6RcvJ35yHIqUgyxL86GXDEap8oV2v9PqFP/+PPyEb1PUIEx6JV2VmrPXKH3y/8Tf+399BVbm8vOQSKIxG3A8scW/PEX66OaGOaWfoDS4HS4VlVXYFvw6O643er8wUDTzVX54bIePBRcXTz0CCAl1S4jwnDpcUQ4TUl9FPPFzrgmoIACRFL3u7I6Nl3tqRz3fFyhPL0wfW549QKv2MwklzJDvAdqzf4+e0exwUfU/6YcATntJaj6odHFb1VO2F6rHIEjzYIohYmJpbNiW9Rd0ww6XGNbvE14eKMKcclJLNSYg9YqILQU/4ME8l4099/MyLK6DC8vxMWTfG5Yl+fUbuH/D1BV0+ossL7f4lKCXF0XXNhdUH1k+/Yvn4C8rLL2B5ZlADg83FkknkO4UXJ4jr+x3HSekKSlihbpeIlvGn0HGPjtATsM/43uJIVUpdzwLwMLeIMTou+NjgAphFwcSS/J3u6AYnlw+yq9LAGrWWE9c843A09OGyrCwfP4EaTQ78GjaCqtHZl5HFUcFdwt9gLpwcTBVdQgsu24dI7KwXRGND6+nraR5F2XIr/d4XoAaRkhkUF7DGOCGEkOpWDLhfr7RaePn0kel5a26Bq4rE5rpkkoHFn3sW+aBODfrRiFxcRaRCvWB6g/pEebbwG6WF/696KPNai052sTSBnuo8z05VKdsWsdJ1oXf48v0Xfvv9Sn39Uxz1V7C8UJ8+sj4/R/JvLpB++fFbnta3KDATUsh47jECF/a8Jub7M020XZzBjnFDloYUpy7RuL19feO4v6VOpIL3vGZzm+88BBcehygGUuWkGcVicrKOc3pSifdbH7lUumxRYJct+MIieNu533doAPfAussCTx8pL5+QZWEwD2sP3uq4UwjYzjKeh/Re1iD8hh1mur2dMFNu9/0UeYQYoy75HN0DVmh3XK6oLJhr+kekN4h6JHiIUEqk99r52uP+0xLTl7cWEGMPRol7e7h+/YTHz7y4xulaaqWuK7Zu6OUZOz5SekPbTr9+Qa9fAhhXKJeN7eUjevmAXl5gecKXgBECySYv4IcWHpjLb2bsMu8+XCyUIevThvszbezIseG2I/N09HDr0qVGztW2PTTM9o6wbIFzzU3slMkCJ/3ISarTDMXThek6VDTGIMlt6CyupRRGKeExernE2VwronAIcItts1tPQcCUZBKk7OR34p4Uoyf0+SUhlBe8JOtCo4v0k+cZ79fpMvR+CZCvbbrzz65wvspQBcWFbyNgDBENKpNZ4roKNcZtkpOa7rhpaRekfCnhVmbieHe8XPDyjNfBUi7o+hT6cxmoDHwsmBy4HlkAKyW3zJYkChdhWRekLqfL5D/9z/xZ/q//58+glz8b4ozW6f1OGQV9WhkSHqfeB7qmoY7II4kCwWyw3+/naDqVQ6oCNfBEZDC8MWwnm1lG+iRgnaUuiPQQlxEwjnh07iF+sljk4JQS/lqnok2EuezXEp2dLCGyKWtOKDWL67LkpAHFBus3f4b2dmcMxfUaz3lZ2X7xK7ZPv2QYtHbHx6Co08eNfv8SUnFGdsA98976Iz02aXHnAlQmWzeMf6yFf2tZlnje3kLkOAxZACsMK+hQKhVZNIIeiwYTI7hx8Rm4nOrPEKyEOTfHG3a/wnHLQ6tl1M9Pe/ysi2tcB6nSKXEyl1oZlw1NP8nyzS+pbY9xQjze/DUEA+RShtyux1JlKjfeRbkkuP0+eVLTD2/6g8ZBF9tJ1xjpXYKjWLTm6KXUdaWu62kSMbOR7PSL9OzoHoqQkNfOxcxUiITFYq0bpktu2yWLak3I4UFyDr/KErhtbuCtVpYlOt9bb+lZGkbhwwbGuwKWyygRQZcLy4fPlM+/Ynn5BZKkcNEl39eSks6SZ9XDK/M96fqUpubjLKzuJ1yitfL84YWezlKeN7LzKNjT8ctkjpoCUsLMJmEQL+kCJitaF5btmbps3L+uyGgs4xlrV3zcw+m+NKTkokiEsoS3aKkLS12Bx8E4zMJe8PLEh88Lz5//NMd4YYwlInDujWNc8daRRSPc7qlha6QGRBzRYwn44wNXs4Eok7csUKqybAuKU8bA94ORQZxFlJJmQu5CKSu1lqRuZKJvouJFoMwJYPYNRBqHUFBdkPVCvTyzPn+kbNtp7yl1CVNrJCipS8OfPrP86vco2wfa2/eAUZ+f+fCLX6LbxrHf6bcr+MBp9P4WxbXf8HakVHV+wHY2Lw4n33oad3tOjd0cHYamIm70Rms9WED1kp4ZBWHFWXBZKC4BZZU4KR9ty0kKAQ8z+dHu9OsX/O0Ldn+F43Ya/YRi7Kc9ftbFNWCvuJENmN6lquvjpvUVtSfex31ERxU3o6YY88E1mrzY6IQsMbtwZ5pk4iAwR3GNAjHawbjdGG0PYUHRoHZ5RVgpUqi1JPZWAkfNxUbvLWpzKZNvnzDvFCkA2ZUGbzGUSpQF6gq1vnNtz3yqkT6rTLw1voeoIuuSctIa8t9hcL8x+k5yUzAPo4/ZvQtBMC8lwgrl+SPb519Tn7/BlmekPgWfU2JsJzHfgqS3wbsDI5/TySg4P6v4r43oqKZb1uX5A22/R1HKjrWcFK4HC0JLoVZ/x6SIxY7juEkcSIuyLOHOVC9PUDf6PQyYtT3j/YbYkY5KHV1GjMNLHMSyXijrBSFGbHNjHAdqwuXzL1g+H5RPv4McTyy+Ulxp94O2R0TzslVcLKwKW3Aoi68/+JxKKazrer5HQDA6VNPXVVnXC8WFLgrHwTja+d5oDZJ/7AWWGGwWR12p/cgIA0e1By5Z7FFQos8lWBaFsj1TPnxD+fBL6vNn0EumBMS0hCRHudTIWbu8oB8Hy9MH6qdvUPGQx24rx75ztFhKybhhvjP2r/TbF3w0xHowAZw0h8ldhxZUR96jabBU6+NXyfvBwI5GH3E4DwpSDTEF2Silo0tcF8Ni/1JKqjbJKQoYeU+7dXq7R5zM9fv89SXMxqWjjFCW/cTHz7q4ct74Oc3zwGIm6d0J1yuBHPVzuTKNTCYOOGvYFBxk5zTNRabaRT1UWBGhkqbUaXQxWjgoiWT2j68M2rnEkRp4pLlHzryFeYdZ/8GWfHZj5+Igt+q1LmidZhUCZcF0oa4X9BISXhVN+8FHp/mD7/tOtRUv+4J8+Mzl18a6LIwvv0/7+vtIe8X/P+7e59eybbvv+oz5Y629z6mq9yu284wDGBlBAwkSISE6CCkt6KSD0iUBKR2I0iTiL0jXLaQICYGEBIhOaCA6SDRxA4RoIEUKKIkdJ7aT+96tOufsvdaacw4a3zHXPveROHr3SfjK2yrXu1Wnzo+11xpzjO/4/mCqtWLB44bllbQ8s376EcuHH8H6EfJVy62Qp57b80lgH+ErEJwXCxFEngvBieeiZc/EGrHpF4Ds4VoIGtADMlxb7RyHpuWswjIfli6TZeu6FoyJH1Y8Gd0XWJ+xtISbv/A/2l2LlV2hh1Z1eFmtpPUi+MNE6ylJfhGkwvLhE/l7X1H/xK/iTdp59k56vcGXV9p2Z7818lIiZNC/gbfP96rWSo0F2MSr53LOsr6m9y4hBGoCPHjRnifX2Eh1oS6JVIxcIacrp9H54WRr5DS0MGOcz5LglEwuT9jyifT0A5ZPf4J0+UhPil9JqQLGTKQlyVbQrs9YH/imCSplHWg9vBUGJsHP/srYXzjevpYZS3g7iJVCSFpLJEZYQCS6b3OoK63Wk8usjmLQu9P6zsxp44g4G4ecCvVyhcsVuzxRLk9abucMhrjq83mPJVu/v8D9hbS/4NsXOL7A2BAfLrCgb/n6ThdXm9c0bPuIsX5M+g2nbuYkl0d1PTFE4ZswhwEtmVynWWyZH7LWHqd+i87jEAUoCoam0axl01CevdeF08sw6TQePhjYidNNLqU26l2fK8+DI7b/OeKNieI44gSvmVQvKrBlCRMM/d85Isfvwi+FSw4Tp5OcyZerDO5qpRed5P3tpzBu8ikN3GkA+fI5Xh7jAAAgAElEQVRE/fQDlo8/Iq0f6WlF4GF0EAHPzMJKLN7MxSfVcx/iA5867hiH5xolpThcHkUUSyTL9H6w32/00OrnWimu0TRlWEpVJpXad0ZKHJu4tW6EtWRwbHOCxcl1IRuRHiEj5tx3+rYzGCyXC2ldGalAXbSlRlzOXBdqFRySaoV1w54+kA5hkWSZweS10HfZAlo2al0o5YjEhNiaM7sgO5ed513s8R4mCSTa6Gyjc4xI8k2J5fkD7enG4YNiC+vTwvIkAr2iAXaFSI4DuxtpbNLuT5Q6h1OcFbAFu3yEJwlq0ocf0PNKnwbpUVg1ocdzljNcn8Cd+VlLfL8UFBO0VJp3etvpb6+025vggHDemlOS6H5l3sXRXT4YGrasUqnlGjE5miB6OyQaiYXUcAU3lqKJI69X0tMz+emDGoI44D3gHeH/KqzjuGHbC37/Gu6fseNrsr/h7c7oG/EUf+v69Z0uru7Ioq/b+QZPjHISmIFzHFbMSCwjYmM6jRlymoT0EBpMPlwbkTCgsW4ce1gKiidX35mvaBkA3QfbftBbp0bQoHtXkUHbceFZTir6foZHlIfJSMMjBVW1Uf/dveNJ+uZhYjWUXMIqcWVq+FTSHgukB/mdoFYpO2lki9Eun54LvTWyG/X6jB13+v4WcMGhorheoH5i5Gd6uuAWhTV4noI2ott0Aj7xEz8zF6vBp/u7GW7OpMDoIY9UASbOBrasKoyj01unbZsOkiLsT5aRixYtOeu9ilywY7vrPjERwi2HeTOw1OXEz+kNPypjq6IcpUPUrOcrtqzkFIu8vGijHAfejMVW4U70YEhY+A/0wHxTLfSmBciEMEoNOlMc9qf80qXLx2fGU0xV6YGt13XVtSmZ1MT1vozB5XsfFFdUjPpUqWtVxtl2p89uOVXseIMRdoTm1HWlLHGIlJV8/Uj+9CPK934Jr8+MfMHjAB/qBgIf9cfUl7Nw2aMxutylck6QwdJKvV5oxqkqNB9RoKfO38NAAD2vgIeuW052wU5YFqUw5EwN6MnKjt9vMlYfkeeWqzwM6pV6edKzcrliVZaE83lRg9bkW9APOF7x7Qv97af0t5/it8/48QrtRru/MtoeCSjpW9evX6i4mtn3gf8c+FfQ7fsfAH8T+G+Bfx7428Cfd/efmFqs3wT+XeAN+Avu/r//076GhzB4bhH70fQgEWNwuBflUrDCY1kQXL/eRQ73Is7ajCqZPqO861jHsdP3TT6fKBgwpwde0wE3mV5bXbQoiELBULqqj46bYquTGVZMjk8RKzNcheZh+AziHeYYdYMMnyupXkih0e4eW+ToQiZ2jD0wSV2j6A9zJrvjmpdIXdSzPDQGl/Y9+u0L4/ZKOnZ93z7opdDKJ1K6YElF3XJVRxG0sJNy5cSyLuKv97uuZc6UpWI5DDfIDzNxCKmqikqCoAFlvKuA1+UiXK4Wnj58oj4/yZ+21rP7OY7Gvh3s9zvtmBvdCVeow3Z4LOyUS66Gt3Vd97WIuvf0LA5zWSj1Aimf/gljPMb26f15qXda0NEsD0rvUJ2+OONq1Gpc1nYuYv3E1h/FFaBtG8chWClHl6zQvkwumevzswrs0Rj7Ti83lpJg32SKnVDXWovug3JhSYVcV/rnZ/rrZ7zv1CSTmXq5kJcqDL9esOUJe/oEl0/0csXzAqUGzS3OIx8S05y4vqasvK6iy3nTFJbBvQnecT+Nwh0g5eB9x2T5nj1S5nOchXtXfQ9nesA8gMYgLYnsCXyjHYfktvUJ8qrFVlkh8sk0LI4TBpCX73EmbfTtheP2U46Xr+i3r+H+AscbbXsoPhNor/ItX79o5/qbwP/k7v+emS3AE/CfAv+zu/81M/urwF8F/hPg3wH+xfj1bwD/Wfz+h7xmmiqilhwH7XbXgmgWlCz8Ux8d8dNhVJEsiRzdu0YBL4oCcWfGcpgZGddJOIZSYvNU+uikNldA4oQQy7KwPj8DLjy2S37o7ZA5SORWTVtCsw7sYEeEIz6iJr5JBRN9hCTiuq1PWL0wKHib3pvqRmVxmB5ZP1OtldDYHJDJTGn1wLby8wfFLLeD9PQR2+5aCrmoEVYyab2Snj9AuT7iMdIkvKvr0g3rcQ06fbsztrvGtxS4W12EeRVX9/t+2kDF6lwugEQVtZIvKyMZdV1Ynp/UhcRBx+zUXcbj7ZDMMRFda3TqE+OcogfxbWM5Q8JNjlbl+iScNZdIXFC+FbFdF51J79Fw+Pr1iV/9/t9HjaeI+f79HukWwuQTBt74vZ/8MGJMFJtCQCYxrpzGIYob90fGm4fFYhg995z1vpQMJTO2At41RZTEpOlRu3YBl2fS937E2N5ox127hJyjAdHvFNl4EhaBab2Sl5Ux6X/f2BI89hOT/ZJzlhR4KJfNCdvG3mlxeIGJUZOAnE4e7fv7YMINuVbdKzMdOSZRmx10kk2huUIiU2qCCutVP0MumvZmw3TEs+Kx+9jvjOPO2JV20LcX/Hhl3F9o9xe4vzL2VzUIbcP7oVij9kcQUGhm3wP+LeAvxMXfgd3M/hzwb8eH/ZfA/4KK658D/ivXDPu/mtn3zezH7v73/4lfxMNrqA+O+8797ZV+HOcN2gFGoia5YNG7IizG0IOY1GmIzOyMvp03SS5ZSwXkv8mQIiPlFHEh0/lIHZPWGjGQ+8zYCh1/BPCNdtC468Etq7BM7+LKWSWNRqKRbWDm9NEe9Kckh6WRKl5WdRWXJyhrRIBMU2wLdCCFr4uKu+ccbAmRrC2oRDbEdxwMRNEJGesIjfyYm2TN6VOg4Gn+KsHFtBOmma5SIt47/djYX18Z/WDNmUSi743WNgpP1HxV0eNRHOeE8d742lI6k3DztYskH/HjutYqSG26GbXOsW2ShebAGYZu6Zln5e8odwNoXWY1KRfScsGqxmSfBWXokHksPGHmmo8B//fv/ckogMrbkoxdvFY7Gql1rHfefvJTtuPg6XsvXD4+YeEXQWCOw7XNvqzrWWB7D1tH5vL1EcbYU2IEa0a+8K4FaE6PlUIfWFlhaTifKAY1oBg5nWkJoD5c0UdpXbHrlbJe8FL152dhfZyE0/WstxaSZT0bGloO2qHo7bZtgJGXFR8XSdLRok4HRI0CkuYZE51mxpZVvFsg5jtNYyelr+r+TYs8gN3kL5L0fY/eSIec4KYSEh/0/U7f77Ttjba/Mo6bCmm7wSbF2LHd4dhg38R57YKZaH80PNdfB/4A+C/M7F8F/jfgrwC/8q5g/gPgV+J//zPAb7/7978Tf/aN4mpmfwn4SwAffvBLQupczlDH/c7okSKaVfRKXUi1KBLCRWeahOR5g5aUowBqLFcWPXLD6QrGkzN+PLwtbO7MWdJFscCIyD08MKPY9JqZRtayYL1TUpUEb/rO9oa1JiPgcUiP3XfG2GmHHItKyaIPlYVUr/hyxZZnvF4ZaMk1LZekrdHYWGrFBxz9UIKAQbIif4B3RQXi+04Wy4zAaBFFKNtcjqWQ1uZHlpRMZPUrfEXbsavQuX6+436jb3dFPZuWNyoSA+t3kqmgzyVfn7lRgZ3nyWOeIyDvFh0xUhtwjCaBARo723GwR3Gta4ySPgGIuAazUwwIaW7vc87ky4W0LCGIiKLfO3PhpM9h4jWHHy1OUM+IsTgucE6ktJAXx/cms5+XN7affiHjlGvFSpaXkHh4Yp2k9PAWbY3UD7JXprJtNhKlFnocQJK6dkrOp/1fDpaKdR3oM7ZFsuMOR6R2xC4CE/2rXC7k9aLpgKTl6/vnMZ6hGQfUW8hCU4pnc3DEs2l9o7dOzpV6fWJkh6Pi3sW8qBJqWEpnwdS0Fi5rOZaINjnGfrJpNN10jEy3gptCITvimo/e4b5R80KxjKeZMrHLDKYJDvC+4+GFO+0n23Fw7Du+b9AO8uiRHDzwdwfMz/v6RYprAf4M8Jfd/bfM7DcRBHC+3N3NfjYy9A9/uftfB/46wC//s7/hvQ8pbtxPo2JDdJbl+kS5iPztyYISlU6TEPdBMDFP2lMxTjigtyYM99AG0t6N6rlIbmklxyaT02x3slOIB1LUIzk1LU9PIW886IfwW2872aVzH9udsQvL632nNZmrdOQjmctCnh2rZcXX+GOrbnHipxQbZ9OBcLQmo5Y8OYDz5n2Hx9oUKkRXFD/DuSycdKE0d7Hp7J4AdRuRwDlGFwwSdnWlFHJAEK0fMs4wGW74sojGg5RYremWSFEoJvwyWQaJGN9H2OtNv1GLRZ5uFB144XmrOJEUZtvEQTLFEX4yFHzR4VrCM8Byxv29uOHhf5pTPcUafehw9RHa9HcHl8EJyygDzLh8/MT2eme/KZ4o75VUs8bjWtR11iKZqCVKyQ/TIBuxN9B7lkuR6bZZmPOkWDbJNN6SxQgtMZ+W7gHlxDW0XEgtpJw+DxhBalYKZtEVz58oJgWPhRYxNcn7Ij5NXFsPhg3h+ZpTxsaKFahljeKa5CQWO4w877f5tSbcY5OelxCtLAzwjTDFCcogKZ5DZZeN1nB2Rr4zXM9vH51+7LS2RzeqMNIecm8xhiQe6tHZW0x74rZ4UMW+3esXKa6/A/yOu/9W/Pd/j4rr781x38x+DPx+/P3fA/7Uu3//a/Fn/8SXgW4O11a9LjLfTdkoS6Es6vjGfGOGzJqXusTm2h/4oAuLnKPI6OEwNS8onCKEh9VciTdP0yI8BqX3XcXcyHsSnegkP9cqr8yjkEbH2i5X/khKSCnR2kKuhe6F3qC6UbV6RbipbsYzyycK5EnQNxWpPBc3Mda9J/b/f5Vcj6I5KV1nbLU9jK3t3bJM+FnTjZ7TSWI/rQUdxd8MHVbtuAtu2bfYuCd6aL8JtdJ76Sdwdpk+Ao8dg77LL7fMghQCgpTVCS3XCz76eU1yicymd8s+0ReiBzG9r7kuMY5OehDxoL0rnPPfgwoGFlp3caU9Drv07jq5I+bAZWX58IF2HPKOOILvnDPWO76owGh5lailkguR32WxTOowOO/HmQCskEOUfBH0rplu6mlS3ea1fNwXWIrstwdljjBW0ZFm0ZVORsiEwWICzLrHR09M+t7Ev4vFs9o6eGIg9VQ2MXNSFs1sTNP1CavEoTkCatLSNIJAg+Locd+7pWkHcC4phanPJfX0ZU24F3wINmptl2ueT8esEeKgMHOZ90DcBxMTVk34I2ALuPs/MLPfNrN/yd3/JvBngf8rfv37wF+L3/9G/JP/AfiPzey/QYusr/9QvPXxlbDJ0as5ui3/Rss+x4bYGGDZYrHV6H12vdLqzyVCay2I84+FyiThp5xlwms5xr3oHt+R9uc4K7minR2SuJYIvCfrlEzRbVKlqU4ZK5WajOoXPCUlw5qFU9LjRD87NNOViC+FbnoPQj1Bk3qMXbM4teP4hhnGaQF4dkkPKtdjyRZFZryXtcbGfPY2s7mY2JiPszMd/RClLbqa++cvUnatT5AXQTfuKjamrvPRjc647hGm1ztabJoMZSI2xcwoy3Jq9jELRc48QP3syHUwBusDOyW831h8zSnE9AYmy+HtkM7DaXZXLTBMHcqz27dzRHcPqOhyYf30KdIt5BXg8Q21bcfHIC8LYKRloZb5fcX7kgYtUmQnn9R55D6pOPE4cCNC/huR0ClFvFDCi+ClHAfhCMmxqGtDGLslUmDjkxb2OKSArqRi+eOqe7Z+YMfB2CKbzAzLosAJ6enBJnAIz8DWx+THAM6x7SRL1KqvM+8/UoplX9GzfXayHqkjEjMMioyXRsArJ13QGUcnMRhJNSKnDNbxxmkInnMOk3PtRLKLOvxzDt7feP2ibIG/DPzXwRT4f4C/iM6W/87M/kPg7wB/Pj72f0Q0rL+FqFh/8Z/2ySeIrq34Q5liEweMEU7BdQ9sSMufR/BYSiLqO9B7Zz8OGUvMfwPnDVuDSykeowpWyUWUIxtnMUr2WPLoXnyMGGbA9AodQc9ysTwHLvleF7KYTBzSEh2LTRf74Rqr/SGW8Hf/h3G6+qdS9EDEmK2F6eA4Do5te4y54XcwZZTT/2BSY97zZlMOLq5PXjGCAoaiw6WmiS346LEUVNicBy7uo3N05VVZH0Fx6/QewoNIBZ1Nkpl4iSOs47wf+rym982aMdJMes0sV1GLvM+lGPFeOr0Pko1zITILss1J47SafCfmMC2u8EdXbe+Kq76GCjwuefNDTAEBEJwJrCNnlqcnlprwcXD0je4azechNUbHjx1MKbXZBG+AwvdsyMX/GJ1SwuDlPMwflLj525xKiHt67h5IyDVrGDRF4BxvbyQfQc0qeCnYsmKrCcZ5V6R1HcdZDGHg48D7prDC7cZxuzOGU5YLpdYokh1zubKdT4C5/juEA73tbNsW09aqKSzeR58F/Gc6zNlLET6yyTIDsW3SzHYDLutFDU0Txzwl7SXEy971DCf57TIWUhqkYWSa0iD+qOSv7v5/AP/6P+av/uw/5mMd+I9+rs8fN1Yp64nZdNBpHKC4tzAajva9h3QVYKaCThs1iHFSXJdYanUYRgsqlnC8RA0li0yQxTxA/0vFZ3aW758txKFs/QBX/DZtl5767Dh1k/RhNJSJJE/mQimFkQu9D3C5fJ2G1LFM0UJFW19/t3CastSz241iMGNgflZwoMsoaWmLwj+5ifF+yYGqC1M21IX6rk5YPXbALsFzbduN/vaKt0YtSYzclGU4bklFfNvY94MWyqhqK6QheziXI9b7TntZCg7k+T0MMTpKKWSgZzE7lGulKaQHemgBCY2uxVPOMtmZ/hMTCpqwB+/en/TuY95PK2fxsiTDsnfX9H2JxYxcC6VkSkl4S5CMauqgRndy1X3cg4/dWiPlykn+MW3kt3Zw3G4cri6/Xi/kaAB6H2EAbSSr2MyA47xdOFkP8b/328b+9Wf85UVfqxbsesHXKqvIdVHH5n4e65xNi2MEJNB32O/YsXG8islT1pX16UpKmT7tBCO/zfsRz0aSlDsJV4dErYuieY4jDv10ylRTuMBZOKHpZIn3ablSLk94WfCAPh63uEMtOM5+V6Njhg4Uk6LxiK41rwvOAa2LCz4C0373vPy8r++0QgsIuzBtG1MOr864oXuTUkMPn0D5GZEbt0TILJOiQ+JBqiVHgqzcoYBp73IOKinGIl3cx6LjdLKaJ+nZuQiY7a2xvb1h46AktPTpXRhsLNtSKbArxcBTmEL3Qzf2SOrcbJAycaISfEnTNtf1vUOGNCCbzKn9mwU0BQaZ5lgbC4O53DM4lxjf+PkgNsNiPUivI5pbP3YV1jSpaZDM9eC8vbB//qzPfdUm/rpeSctKb4Ox3zj2g23fIGdqEmZLbzTsdEVS4Sqh/CmPYjEi5SDpZ1AA5MTf5D7v7kxWlvKxYjHmMvJZq1RAJwvi3evxII3TkgIexfXxB6HOmjCNO7bdsN/6G7y9fuYHyyd+/dOflHkKoQhsSgguafD7v/xr/O4v/ylJY4u2/KenbijPzvfQ0ol1Hrc7e7vRjoOnD8/6HsLDoOsh0NjMLKjvfhHNynFI9nu7Yzept9JlFWZbCtmgIO6yJx0iYxbWoaghI1RO+8a4vdFeXhj7TqmV9emZtIYzm2vX0FtW0CW6j1PWMtUjviinTKpLHJ6CQaanwcT1larhUZCdPgxPhWV5oqzP+PQgsHRS/LT0TeScMBv0/YaPHRsobjwnPDi2TmeMitOgNSBJSPbHurjGosondSelx5JndHqLsScnPeTjkUuuriMxQ+amhNHw4Ir2OB1do8HEVyEO64nBBQUpOJ6z8OZs+MRIXZ3fOA6sy7NyECmssWBIs+MJn4LhSgYNMe7pjzDeZVzNAiaMKwng74NhDVDKp1yFJjbq89s/u6weFJrukdQQyzSP6yt8VqO9LOBUvMf2hh8b0LGhUMbRdn0PM07aAqttnfvtFeuNenlmeX5iuT7jVmhjsO8b2+3Ofr/j7iyXhX5LbLsEIW5q4ad3r5aLMD04pxl5iq34lCInIp7HB+OItNrAf89WMmCckuRLkIqWoMQyb95mj+s3HocRnAtT2Q82mO/MxGrRhpnPv03h4G+tv8T9N/4MtEEajnWZefsY/Oj+Fd/fXwU/xf2WU6IiifRwHWzbtgGwxrVYamEcmdbkLHVsG7mUx4FEFOaRz+ZjGk1PTT3ez99zPFcWvxJOCSYIwQRxGzQtI6Jz7XKFOzaqN/rbC/ef/AS/3eRj/OEj+bKoyJsGid4OITAOpBEOXeFlMc7ZAIgYdghhiJqJOXHKB0RYN57BKrleKOtVxu7lYRI0x8mU5CBWc2Z452BE8mwnecT6MIJxUmi9Mji0DPM4DPiZg/XneH3Hi6vLrGHfOJpw0mVZgiIlcntdqrLGgxt3Rlv7Y8t5SiDzHG+7yOSnaUjQXUoNizIVVKVsWowa43TYmssv3o2PrR3c3t7w7U4KXfW8mctS33U/D3/SHkRl9wiSc2FBJ90qHrT5RT0Mo7F3rv4pUaxGcxcdnkVH6PaNYnuS0vNDoujoc7VDUAbdQ4O/kXaldZrveNvw/Q7RgbSg7mCJXGrc1AO7VC6fPrB+/AhpEdG/NY5tY7/dGa1RSiKPQb9vjLSHgKIoOdedNpxeGmWRPtxNQo/hgjlGV+5VRjJNYnG336WumUee8L2AA8pyOkR17ySrj45Yp8wDW/WAYExR1+6cLmsePKQHkDA7THg7Fn7704+5/ODX2ALXo3f2287+1nCc6zC+nxIpPIAn68NMrIvhMt6JcSLeb9ThZaMuIRbpTR83sfOkpSKjn65pY0iYMLoSAEB7APOBpZBGD2nubUYA7Xddt7Tj77bppYQhfD8UybLf2b/+zPFyo9TM8uED6bJGLhunJ8HwWe/kEZuK7i9JGaRiZK5FLeMmj9+5a8klx/MyWSVZMt/lifr0gbRcznQMQOKfWHy5GaRCyYa7Mrj61unjYDQdFgmCuikcf/QQn/ws3vctXt/x4greFE7Xtjv0xrFl+ipzhrI+yz+V4MZFt9kjy+kc22McN7RlbN45Wochk+IUpPWZmeWhAmp9gBMa8Rofi0ZKM8kEbS7WnL432Bt93+RoHvn1lowygnjtnTYOzJxaZwSzY2GuMovCwLQw0Vz06NamRWDw/fQJJrrG2WXM9M0UVJf5mp2AtrFdfNR24G2LMdplXLO/waZiJXu+G8f2Fsocda19KIZmuVypy4Wn5yesFD1oy8IYJtJ8bKvXp4vMnpN8Nh0t/5b1wrKsdHf246C1jTSksLHsYejisbkfsfACz7Fw6422bzIKaQd4QDGuZdjIGbtc8aUwesWS5MmQz8t3qsXGhIFQZzwE38yloOWJgdu5BMWFzvzpf+FPU//lf5O+d/r9YH+9s72+cdx2shXKOiEtOTSlZKFy07uX43nOKbHWGvSk9+P4UCBfAh9NC8sxKMtKsgslK0PLPYXB93wOJjzRBe/4wTAFXhoDSwqJ1HVz/ICZl6afNQozgxL3zH67SThQC8vHD5Trkzjh+CmnbqNHQ6JGxLJgBzGsRhwGzrSqVAHOirXJ4nKnwFr17yspXcjLE2m5Kt8tJqhxLl+dk92g7ZX+bVUenO+3+LhOGhM2zOfHppwDFXgsO7/t6ztfXM3DVGXboO0cOWGtUSxRL8+iVbzrI3rvkazq1JJUfMs0gEjk5Ao77B3LFtR9jVR9dO73kCKOHqa8Knx1Xbk8PbEsFwjieY9TebjMZUqpWgLtWhI9plrXTTQa2/bG9vYC3lgmqdpitDdox4wdkQv8eXrGja43ezBGjFIxvso/APB00o9OeMRNBjJRdJ3AVPc7fnvDtxd6u5GyCtg4DvxQljzHBu3OcX+j90OxJ2kR3uiQyZScWNZVRPsw3Zgti4+upW6YkWhBL8Ntj9FveXrmermwHTu3faOPQZnjbthNitQfO0Rz8MaxH6L++MBmMmrfod3x/S6BgzmeM6NvSoqoihgHuXUx4GFFF1QqiDFY0IzP3JdpADObGheGODv/HiO7N6fvB9vrC8dt53K5cnm6UNbK8vpGv6lh8OkUpe8GJ70r8KiwdhWf1nb82MPnIr6fFl7DycijyNyZIM+3cHabzJm+Y95I3unHF9xvWNqx4lBg+EYaiUzCreNuwnOnTPWQMqvdb+wvr7T7nVwX1g8L9XrBqiZJ7+3E8MV5HbGEQsUqZdza+ffeO5agZHXGmjJqNBKcLB+zQs4rllfIC54qwRQOJ7AorBOTn4o/S4zZfKVEPzHkfnLHsxM4+qOQ2pRq/1HwXP//eHnv7LdX9m2XVR7KAsq5sNaFkvO5mVXnGuqq4KpKJ18e6pwoNO5OXRZppGP8b+3g2Hblx6esrWyOrmXAvh8slxFd8DSImI5JQd4uhTQqZZWmOpuTzSlF5i9t39heXmjbCynBsIWaL0zNeTt27vcdN2O9PmlMTD2OjuBvRiFVyOL0HBiP4hrX4z1j4NxmW5zG+kNs2zl+8hXbV7/LaK8szwvp6aqHoXWsbxzHjb7LJSifjXrHDenAS6FWLZ/mGNqPA0syVOmHMMppJpOy4Bez9JBhlhzCBCmsck0RvjdEWYttMclJSUyJKVH27oG1N2xsMDb8eDvjmmFAzgw/6DWRL/qep19D7x7Qi2S/Z9c6xQQeQ6sJknFTwT0x6zHCSF3FrO8Hfjh+3/GjcVlXnj5cKZdCKsBN/Gstc9KpKsTs7J5VlPwku5t3wWO3N9p+wxisi8Ii11IpayIvGYvFJkOm73hjtI1ju9H2G9kbmc64vzGOjTT2gB8qY28k69QskU6ny/pyqDFI7sJ6396UDxYuW3VdsBLz1pz25s/RZds5J8JpItSH07qYHIax5FXvoavQ6tmPAhzMgJwXFda04FYhVUUtIbYFcAI2JcvEW7vf8VjWuoq7EjVMEM97KIhYZE9IZu5cvuXrO11ce++8fPmMmbimllMoc5RK+cDMHhcombEsi+4Zm6TqaDVcElY31wiAQXeOfed+v9FbVyyIq+wAACAASURBVP7SZWVZVswyR+u0WAoMjB6jcx9zqfLuVC6Z5CLJ2yLPUMWQhHBh3/FjE3naI3feVlLRDTLTD1QARyzd5mrr3LioicrIJT/wOEYXXpX8HQj/MEmZr4lHJ2DsB+Prn2I//UfYeMF9YfgTudbYth0kP7D0TmKZLGiiGVKE+vmQEs1NaZ5tY7jppo8HKNd88oan4GHijSm6CTCFO2Zt4ls79GVoWqb1HgT+d/lTmAj8h7ThqW30+wv9/oofd5INUlnIqWN7pr9VjYLLAFtILo+IZOFnMAvr8IdvQCxnzq8b99Z5uI2hrPsu4QP7oG8HyTLr9UKuIeVkgHUsZMHmWs6qNYj7M/DRFAV9TCvLriSMtt2Fm7ZDWv1lwZZMGguMpEVMZE2lftD3F/YvP4G2KXfLlRtGP3R9LZHajo2ipaZ3yviApUVYdw9WTev024a3zvr8RL0+YYtgju4weeXnQR6T0wlYxX079xY5FfKiNNecTMyfPs4ZdJD1Oc9dQ0JR8BVLNfLrEor4nsIYmEwOdS/9FNK0vssEyhIp3M9IEXHfwiKxCyP3mECsD3Xv3/L1nS6uurhOqUo1TSnTLWkk3w5q2qWljxvTkinTPiVIOfxXH7ikz63yaHoYxqBvG/vthvKPFnEvlzXMWnQK5jG0aa81CvZj5AYeLIbwfjxNlpsKTW8bhGuQOpImB58grc/I426ijnhQUhg95H5asLlrVTPJ1Ck/fGGZTl2gmy1Fhzo73He0HMEUnXFs+P0Vu7/h44VWMjl3aJWprgYXXABgUspZVkbVMNPSrjeG35lBkH3XCC2J8SDlhbLWc/tvkRc1+cokYYSWEpfLFbEzlACR3Dne3nj58hVtuyED4yyKmsXEAPS2Qzvox432+pl2f8X7TkqEo33HduivoQy7bNjyTLKVEsY4wbI7u5jpzDS7n9lZ+gRk5+EV3ZoPdFhv8qyopZCKMayf7lCO7C9H2wWyjrDlG8Fw8BH2iXrWLaayBJRskBP7sbG1nf2uwrLfb9SPd/lamJ3dI23Hb1/wl69IfmjcjwIqM/j4Qp4hNTzy3lpv5PpErpcwdm8c94223Sl1VQBnzadcebzjXadYJnUf56E0l4WxJSRZhjINZ+LeHg4p8FcLeXEasag2RsrKzKKAZVJelPVliVzmToJYQI4Tr+4R160EEIB53ynKRw2XbBLt2GWd2R7xTn9sea6OTkXDgpdWNY53J+2Nvb09JHrIe8DdWdaV6Rs5l1On1LN3+rYx9i2MR4Ql1WWhXlbqKnNoGRxHp1cKxeyU4M2xEDhxupMyEl9TBbZITugmmlBwEu1seAf92PVvLctUpob8MQERUTMltvPfTT07ILpQ78KdTJCGjK2nP0F0EsxJ51Fc6Qe0jX17ZdhGHgsjClL8C33saYDC2S3qABAUMdqGs0UJ0CaclPA2aEcj1UZZqyK840GbcM6A0/VK47HkiqIMDca+c/vymfb6grUbRsdqHJ4+GEl4ZQtWiY2ddn/luL+SfLCUjLPTULRH2m747ZV0fSU9/4B6+R6WLsLYmzqlHDJimFlssfAKOuAYc8P9jo0x4QNPggUG1GuVbLugFNJEFBMt4fphgjNyPrHqfIoARBe0oQViKoVUC/RC2xOjxfE3GtvrK6033dMpEoR9QLvT3z7D/kW7i6SnalIbp4Yed5n0e9hupkdSRkqVft/pt7tktDmEPCcj5dEIJAYWDBkfLRZoBGY+DTuJz6vRXcyVQTZNQr21h9scWnZimdaGYIGyKkRyXSOdNpgMAauk0eSCFcXR+iANOVwZwe7JVdaMY4Xj0PNEeEGH+cwpEvoZl7Cf5/WdLq5gdE/YMA4X2E5QrkaD4TplPRIzn56fTwOL4lrO9D5OfmdOiX4c7C8v7PdXbMgztIZtoeI44MxjGqgYTsmjPSR4umH0Xaages0lzRg91F726Ja3ghqKh0UcmKwCD+GZPvqpVx+9xaJLVnDn5tIkY5za6RFUnREPA+HsDsb0ALSoynoY9XBZ7/ix0Y6NQQ9HeIgtFH4WjglLSDqIubbPEaHhPqDvp51jzhWrK+YF+rTAO9heXindSBcjrUqNSGmGLfrJhpjdbm/By2xNi45EPISNMpBP7XHQmnKV2rFJgYN+LmvKuaclhm+0sSs/a3vB6hWeb1iPLm39xPBKPxCtb1lP8xo/DUw4r/fkUvsD5FZhVa8NQ6KUZV0olwRFZtLTtMRicz+OTt/FRklV6rx5f40JFeCKHT8FLHBZVnxRhlfrB+5ayo5ji1opvi/HG2N7JfV7TIEhqBnnigcfjdYnHl8kHug7pIL1g350jptSg1NZKBdlW1mRH4S4tMEvbsqdm8o9h6BG+rTI1SEwOnVZhb0nwzyCL3PBUzsl5JYSeblSyqJCZfLgzcvlNFAny3rTEIyYDqBn2rbRd4kkRtO1z6lSIsVWNL2DMXb8eNM7HDQ1Ye6CI759af2OF9eUM/XyHMuMyPWJxYOhB5c+6MdBLoUeiZnD55Y/SYFlGmV7O7i/vnJ//Uzfd0oGKxaLmsBBPWlUw+hdY0wGPGlrPUf1x3LocfmVU5VIIyzQzoI4/WdXxv7G0Tql2rkRH02n/bZvtD1s4cxYrlfyEltqDzVLXSlB+xMgb8KdostO4SoULUNsQB+F0nB6PzjeXnj78jVH23QNqqhms0s7s61s/vfsLjX6l3Dh70eMX7six8s1ncKnqeX2WF55O9Qtj4s+wIMKk2UKMNqhxcP5q5HNWdYFWqE3I40hgcax0babvGTbwRgHeEPOCp0azb83h2ail9UtsLZXdSyjycBkDCw/obSmWKQFoZ9hpBS0MTgpbrPjm4VVjvzCBUd3ioVXRFABNWBo7PYxSBGrI7ghRm8HUHQQWfLM0QbH7Y3j7VXMjdFYSkTMJGPJ+ZG8OwLL7Tt9f+W4vdKPmzpSLDryx5jrPuXiETMzD9jRA3/uOowxKJn6dGV5upKWqsaliWs7msbpvt9o+04JMYjsKWOZHPzdFj4KEE2h6R3TcioMtePemcbplKrJNRVSlYucuta5tA4Dpd5ovdOOxnG7c2z3YOuYoIO4vqVedJf4Ae0OWw3ILfi2sZuxObV9y9d3vrhePn6Pk9D7vk3vI7oosQdK8CZVXB/jupkEAoyu7Pd9l2oGSL1DOxgtIkQwbayjmxL8k4ST2cBd47vlQingJKY9HhAUI4sOwSbCDm6kXFmvH8je2N46JYXZsal76E2CieO+oaZbCQujqwOwkjXOrA3PiZwr3ZCyKw3Mghlh7762Sx3k7urwh46ncWzsb68ymV4qvS+U1RUjYjyWf9OjYXbv2rDhY3CM6FbDzKVF5LhgwUQPAnpZV9n7lUVxPKV+0+YtS3+PwzHGudzyoPCUUsAqo1Y4TFLZ7Y1+f+XYxWQYbaqmhGdmmzAQsW2P6zHUIbnd6eMI4+Sd3Bvp6Yek+oGcL7jv6gZTYU3G93MLwYmEAJ4ElwgKEPRyofOD58KvrAdP686XbdDGilFOJzd/N0ITMdPnrwB8B0DPMqQxdevehdGPXeyHlDiD8ywy3M6sLxfc04+NY3uj9U0ok+UwHpqTTUxDSd9fLuV8f+d7n8cgWVZBzYW8XhXt7X520mNmz203LUCHnpXRRnSU0QBMulqXkfd+7CxxUM8mZPq4JotfWT7N0+s4kQPj1XSVrTz8NCCSJgatDR36Xc9NqeFNkKdgAzwvjHwR7n65MY43pSuX8EE4pML7hsPYz/n6bhfXlHn6+JGYhk49/HEcKpSuDiuFqbUiW9C4eByoacjU2uVy3gclJ0pdxIXtDd/vND/obSfViP2IoUlMHL3xZWjLTSokBt3Csd/tLIaPhVL8fi5BIhajrtTrB8yc3l7poymgcji9CROqyxLGLeJK9uM4Md3OTTlVMcKN7IxUsbQy40AehZWgGrWQvx76qWKzLn+OhVEqtl4p6zihgZwLrXWOvp/k+VQKNoYWHmOc8SatNeFbKVGWC9NJKnmmroW6PgnDTpkRwYu5VHLK8rQtmkpsTHxuYnLTozZYFYgzfBw7x+2Vsb1qDN61+VYtlWNXR1BQmp1TSvJz6FLnpJQhloptFjkG6dIY+YluK1avlPrE90vil1LnHx5GivifEUkXKVRH5ginc6ca/MqTkUfnJ96oKasLDh29+Tj18+dtAuHs5sESyOSQfg4zvBQ8Z46sBuDRrWkZI/u9aSTdGWPCCD1ux1iConvNcgqvjikUmFAXAbs9ghlz0XbdLdF8YE1RNJaS2DCtyewEHspANDVMae1wFTvJk/XeOEZPXWKBLCHAlJQTxd8s6G6nekvNSu+ubjwVUn1ASmZ6n5XcW8kk6lKoUyacdOA6A08Dqxfwg9w/wH4jtV3PZBTVM87pW76+08XVknF9/vDQ/xMPWc4czEWWFkgpvCxnQfPWNb7mjHnHaqFaijRXaN4Z243hB9YyY7/Ty03KjyLlB8h1CAy64p315/qzkSopPxJJgXMjP2aUycTKYiGWlivZO+3YaMdOjvUQZuS6sExOYG+MvbHdbozWZBKeMmntpOUVK1d8ybDU0y+ASQ/SN6Kxbd91iPTo6uZybl1p68qWizCsMrepE2/VAu49z28uDTyWAz28Fsy1wa/LEg+sQuRIi+zssqJU8EgKCHnhhFRGHxz37RzxffQzA20MZ2+N47jT7jeOt1f69kZqkXM02QqjYSkWZR52NG5KJjWPDkp/N1qM5WNEJypXtHTZGeUDnq/kp496Z1Llq5b4u19ge91o286+beBGLSu56p4r250Pv/sVn69f86PS+IHdsb2zvBScRms7ox88v3056Vse1/RhtuPC1InvN7rUUoxR9F6TBBeVkpXqcOy09s6gZTi9a9rIpZIiufV09IqDJpUFCyodSZ9r+AQ5RF/scwE7OiO5zLlbOw++cezKzPIRtpGJbkontnkPzmTeCUvFROimuJ8ck113Ef9LrVFI02mm9A0esAtmmVx0SKc6y0KKvVyvko6Hr+tMs7U8TQ4cGxO+6hKYLG9w3PG24wxKKcFc+Pb16ztdXAGdnDGuKHEUfBi9OMU1is+lzlRZjZMvF1LDJi3HyBmOnf72xvblK/r2mZQ6o1jI4y5437H6TK6L3pAeGJU33Ju6sMBiRKV6nPyODoEeuE87QiGT0nlyukE/VnYvpLRqpPOm/VMpLMt6do53vzHuO23IFSzXC1YWekscrWPVqDkWcRCjqp+MBR/ydB2bCnlKcLleRfeyUIWtK743Bk3dNZEr1GZKdooCNZdjdi4yQH+fU5YEMxfcAjpJ6vLJFc9FSaOoUyKci6zLbm6Mzv12YxwbOQWFFjAUGdP2O8d+Z7+/adnic7YQd1SxKzPPyk5JpZMg+2ma4+566GZ8TO9gjufKsELviZ4Pur2xDEUEjfxE74mv/+Er+xdJbPuunCi7GCzp5Fx+/fxDfvz17/JLnypY4/pyhy86tEaYU9vo/P7HH37DY5d479QbhM1et4hqk2dACjFEWsTDTiWTmr7/TqRuDBn9tIEWwVZio9+jbhslVXm1Tq5nLlrMpaREiVOArQmCIR+Cs4t0sUR66/RNuVzmk8MWHe1sMOaiEtQ8FNGoBnqTU67Ig6uGp8BCqutpSCN4IKh2w0VtjM9T1ktQI8OYqOs+sJRYr0+UUqWWG9P3Tf9/oInGyFg3nI61G5QLPRVNgtXxYE78Iq/vdnF1bUpzXSHA5uEthAB2FiybI7zLIT1UlYE1BR1r3+V9ud3wl89sX77C2xdS7qSayHWlPjWcFsFlqwrBUMfcLWG9wrJgaeA9yYnHE+7pVCw+vEU5FWJTAju7WM8L9fKR7CsZRaL00cQMSBUPw2Qrg3J5pl4/8PThmbKutGE0y1AWLaHyxObmZp+TlyljF2NE11uKbtbWGsf9Th+dtFzwBMk6CWd0afNTHpSiByvXULyFMm300KnH0iHXQs4LHXEcBxqXPSnuWJyiIhrNdNMS1eCkOPnoJz0oJamS2rHR9juj3ck2KMkZscwjZKHE9l2SWB1wPahGs/MwHt0beNC8AjM9GtZ2GI1a1LEpwv2L/A+WBFapJZOuC+MwRh3U9cLl6Zm8LIzgFf+9H/9rlJp4uxyYNf7uXSopcT89+JPyQRj7zpkZluf3p4NG3gOd1HSNjvuN/f6mYlMSiolH2O+MdckZRkQNpYKnRYf/0MLJcVJeQp9fNU1YVieI/v3DS2CKGQQjZFMxEtSpRGZJfRvFNIM4xOcC5vsby9z3ScplXRgkuieJgaoCQHMyFf06D+h0KhclZ2963izJnvAd1nq+ty5Jcwp4oAc3fO4fZiCm5SQJtA+GVSgXbH0ibU/YaHAYo0+DoG9fvr7TxdUJB/o8pYLfTA6dHgJ9aFPaQ0mC9yA4x+Ycl4T2dse2G/76U/bXn+D9lbxA6oaPXSd074x0kHZBAz3I4Sln7HKhIgcrT4kxyaYDsDKHIWF860Ja4u9NY/5x37T5vMZG+Lgx9jf60bTISJWRgsCfjLQm1mWlLotGnVKl5Z8mGHXBkgxUXPyfB36mb4RlXVlKUTHpin0eR+fYtURLVJaLnIMYjdQOzA9GW+Lzj9Maz9C42ayjDHmNnqUuQcouWFoYVnTgDCN5xmwh11UULQvlTdfCKqUin4hcue0v7EenFMP9YN/eGPsN6xvJGzl5xCW5Hpx+yOqP+LlTLE7CYjH1WPR0uVvlYOaPaRuZA5IZstyrtbKsT6TN2Y6Dfv/CrTr59pnnm/PP/eqvQ89xcFZyNSwNppG6pQMYfEidPxh2Mi0mZmwzpqQUUQV9MBhke1hdziQNbdSM0TpvLy/4vgceGVr8JTwcoot3I/YOleJiBqTR6G1HCQ9DMEFZIi4mM4IVI5xc8MAsRMyFYA6yfcqx9ddO49h30hiK+TaZxThDlKpkPMQAgdcnZcpZXkgUUqqUdY2/02KXXCJGPeCLHPdta/o7OBkEPqGUubTOOe45LR3PBbilR0ruueQOqWwSZJWWJxgbbC+alpjJsw+65bd5faeLKwS9aRKdxzsJ4vwFyKR6muyGWiaF4iY9ulgPaWY/box2w9iiHw7TCz9gxGk/ws5N6wCqLVgbtLeN3O+yr0ux8CpIXUJRlxSbdbNHUurwoXEnJeidYUFQH46Voa+dFwbRPSQjFaPkKl5hXfEiZVSKryFv2rhZ3Jn71umPKsGAfnjvTdcP3XB5WTCDmsLMhqHimsVXNDLFMtBw02LEo4vw6LZkxl1VOPNKzpWRF0Ze8aSOoD59ZHn6iOWqcMagepFDz18yCdnMtd7xtjGK09uNftzx46ax7XiRkUyTWYtFhze8Refk52Er1DLRRkgZp6Y9KXkBn2bnnTRydMARQlgXVku0LirT3/nyj/iV6xt/8Htf8fzLP8bSqnuuNZL7O9exoWs1GpYSe14DKmrB3rMwaDIlz6YUFLx+3t+jj6CjCSYqwZv2+fMOdbR77/SlSqac5G5shjLGasAwfcV6A7vrIcBlOJQKM7XCx6A7J4FfoPuMc5kHiHiluSxnF9rbwE2MhpxMP4PWsaEa1NdLUSAlXLNgjKwnLGExDZFEf7KIM/cwlM+l4JMtM6bD3FxyiUY2a8T5mpCY6/maFpv53U5kBG1tFvDEAuVCqlfGcWeQT1ntCdt8i9d3urhadDbYJGwHduLvMJQxoyeiEFuQ5mMrK8mNKaNqqfTmeB7kEhcYi5gVU0ZRGhxdXR49gasAZE2g4XpVWGgUVLg9cerkjRzq03QWoz5cCpOkn8dClWI4pRbqusA4IHiHySSlLbXqtK+LTu7APX3iz5EIwAT3k0UEsx710UX98SFfzSmLJRdZxF2vlBTJB32E+/xO7zcpWubI5wjX0zaRkuUfm5MwNMsVykK5PEG+wHIhr0/k9YmyPol+xczH0lbdPDxo/Z0RTRZP1PvGOHbGfsf6nb690rcX0iG3K983LGg9hGPVGAT31wLjjrC6+PvkQia0WIMZNuldzlPWnaMNsqy9qLWw3165vXzh//zbv015/shPty84Nzzif9IogXVKuNCPG9AolwvL9YPYJ4EDu1vcilqy5aBA9cZZNCc0MpqMXfKyalFYqiay1hn9wFvjuOu+tJRO2KbkBSzTbYbxqbseA8WnmDrGqasXJ9aRRWFwcl1UQo/lUFlWrCzq8iyRGdTLhbIs1Bwrp3Dh8ljI5aSiOZNdmcvoomnI86RQJchy06KHaXWM+6KGhdurJ4lygkmiYhtk/3e1Qvi0Gu+5TJ6LvBkmqlhtOwNEx0h4N3XxeYGkKSyXVZBO/mNKxQLOgoJxXqgpdzN0sxIXbsoRvTWRmvtO2zdKUdeQ86CPg97vYA1sGlMrPqIkvZnV5pugL516YvgeDADJFTsHhsLXzJ1i6XE1PT3GJdQ8Dp8pAAL8ky/Ch0amrCs2Osd2F9k9JUpZYskWShQeB8zog9ZF5fFC+KOmGKkChxqhDws62cSwLMmlKwdWjflJ2rfhsO96uIbL15PJyDhwc0p421rOJMt0h0ailJV0+UC5fIB6wZYLqa54rhD8RPN41saD4tL9we+0XCimTX4aRVvw1mCocPVdyQh2KJfMY3GJBZtkWMA18mlw03omT4goYCLLEzbJ0elVRvwsNjopBWWq7ywJfvVP/Qbr85Xx9gfsbUBeWK8f8FT1kO93ttfPHG9fyDYYlwt8/CHLhx+Sq9IYXNQAjbBxX/Sk+JExnNGOYDK0s/NKpi63lMJeMvsbeIRreu+03c9lbi6Fuq7kZZXjF2KnWF01PfmNkSp5WWP5G89WckhhfO7TFxUdHoHRehD9k5kO/VLPe3kujVNrwXke77pXTQmG7tFUa9AZ436Y3Gm9iYr+qe+CEaN6ir3wSBo4zXPi76ezWCAZzFy0b2SexeeT0IbAt1XI3ZKaBNNSuyzP6raPjTME8lu8vvPF1adTuc14lMdJNMTkZ0ZApJxJbhz7zm7G/tY5thv0RqkJ6xttf2X0u4oiA1rT1NSrJIOWse6k3s9ID9HTUXHwjvUEFjlXo6tQ5bCxS5w3gjKO9MaXKj5kkoMHngdjiGmg9leQQgo2ghd1gwRQr99R1HIPbqkloJNykMdj7B/vNueWIJFOmOC0ZIwbWOqkkOuOoa52Og7dM9bveL+dSqypOkqexL4Isne5PlOfPpDXZygLI5V4KHUgTKvHb7y30bW6S8BQlortnUwO3ueg94PRdqzt+HFgrcF8X+bSzrRo8cmBd2LJREwLkb+WCLNvbcWsVFiujHqBcsGt0pqT7GC0gzSapKNvN47ta5prqqFU0qfvcbk+64ttG/76GX99YW8HrS5ykNoH6/cSVp8DwtGSZKqrom0QjNUGve30rrjrlFMUOU0lc1vfb2+xBdfTAU47DsZxsN836rLq+1su1LLEBLSw5CpZdqnCzXsLEkEcsmHB6Bg5R8eaFx0M8fyBClFOUlKRJ/cUUhWe346ZjhCTQ/gry0Ushz/HI33h0TxN7NyC3yy7T2KpdMbYn+/5NyPEBY2NExbTxyv0EyJ88x1dbYQsVgd+wvNCLxfoO7YMQTK8Mtr2rWvXd7u4uiRtKTaic/M735wZFX2+SfF7WZWZLuenG+Y7xZ3Rb9i4k/zQeNWVD6Xtvm5mS1U0nj7i4neG9t+B1w3pql3uQsk7nnXT2dVgySS7aEFu6qROU5dQQ4wR2HCMwtOFZXYqKgi6iQV9qIUeIQgguhKIm+YIEn0eHLHQyaWo+zXDs8X47EFHeXeJLXK6wuvAqrqOakZLCT8K/TZwDsy6FiHSLQbmupIvV8rTM7asMiIPUrgkmdE1W/CFmdPI+R0IQwVKKbTd2Y8dQk459ju+3fF2PHKPTHzFZnL0115Dh+Do8gBNpQZbZBLms5RNgWeTM3m9YJeP2PUT9vwDrFzBE0c7oMtYemxvtO2NNBrDu7rbWuntjf1y1T3XB7bt5LGHxHfg6Q2vL/T8hF8g1UuYqkhcWEphrluZ/NSjqxsvgQXmAmHKTioslye6JY57qKF8yDU/zGSGO23b1Iwu2sKXRfcAOclSMwvrHd7CicwoROeWpNiqyxpm7SWgLV23ySPvLkZOLpK5au8RYZmuaWT0CXXEGD87Tp+y0sdz293Pwvfurnh0ntOgPA7ob9w955Lq4WA2xmyMdG0nH3uEoCFoD/RjUyBjSizLhY5z9C4M+ljow4QlfcvXd7q4zhGfmsThizx6jXY60b9B4AcVuZqo16ts/mZcyX7Dt1cKh+hWY+ZcjXcO+lVa+N7px4EfLcb+RsoWdCuHUmAsKtI02cOhUSPVlWmUAnP0CkB9yAehNVnS5dD0nx6rKZPPOycwiXf8xx7FcZqB95AUtm2jhazQ3RXWSDTE8wEW8+Qcq95f4/P3uTAwY4zKaPIASA4lFWAHU0jfGM7i6V32WD65pKM3eTSYYnVE8YluPnC5KQXt7Qgz7gMCljnuG/7yyrjdZOy8bxFF04IdEJ1Lzg/qmbRYCpoMfi0hi65ZD6iluRARt9UvT+QPPyBdv09+/j6eL/Sj0/cN2oa3DY47/faZfb8rkiUlqJWjb/QqjLPExJT6oEQ0UN53/O2Nwz4z/l/u3ufVtmzL8/qM+WOtvc8590bciMisykqrIWjHriCCPbUhduwoVQgiolRT8C8oG3YEwY6gFFRD7RRaCAp2bGhTBH807IpKWYmV+V7Gixv3nrP3XmvOOWx8x1x735dZmWa8Rga5ITLj3Tj3nH3WXmvMMb7j+6M79QxpWXXoDOWElZSO2JsxISS06a91JeXKIEUaLDJOiQ6y7zfYNxxdt5JK4Ps8kOoRLmtJ4ZtFgg8HuGlqyakcNKzJPU2hqJvOcFoR6sacbv+1Wrh5leP+Gb1DULRkwKNFLvF76g0litkR/nhnRyi2aEqu7aErFZw18VQd0tMCUgU4tvoWC5Au0/K27/RghLTe2XswXOLPRmuYGSOLU5uXM+kZD+ARGwAAIABJREFU6Cd8v4p+2c4/uX79rIvr5KbRpPrYW6MPqYGWCBuc0ssUD/cgTthSSOvKUle2z41++wT7K7lfGWPD6IGFhiIl8Bq1Fgb7CLPcjo0WcRsiIPskoe8b9BspGbks2OmF7B9wYgSZBTJMPDCTHj/krTnlsBsEBkEziu4gDLoBge8jYI+JOc/RaZrVbJu69lJYwtP2INJjIb64n/K6vP7FPykFtxCE8y7a/nqteKl4u2C+Y2NHZaDo0GuDtu2kJOVSH6KHEUsRcqXHiB7lldE7++3G7fJKv11JY48MKRWfsTvsLq6nEr7xLrzZLMLrquhIY0b7AJ4LuZ5I64myPrOsWiqNrcliMgpOWk6k05M8Bc7vsfWZlBYSG2wX9iHPVRs7aSj2RswO4dVshEdoZphogjYsRu1EcwNbKPWsRd/0Bw0DHIL4boi2m7JigtJ6Ilcl4Oa6KPAvChpZHW2NKau3hiNanhmUUDW1+Dxba+SIUZkep6pBYb0XHNcpeZ16/JrKsUhOOXT5SUqoYc6yVJZVZP/JTJmf7CiZ3lT8WpcgZQQ1qlalLfvqEXj4awvqMWi9q/jG8z+52lOkM2W6X3a5Ifmy4LU/vp99F+yCDH/m728g7nHvgnoiTSOdnvCmnLU1V8zbTy5fP+vi6h4XhwkJyEVvAtajy+SWlHQzBgYkuDGFesO5vb3B5TO5fYb2iu3B0TM04q+L6CbrWVvniUN2hyFchq4CPxNYJ0Um4fjyRupX0am4x64QypYUgL2aShVHbd+nITWHJp/g2B18iFiEpFrk0H/gSdImlaHO94jEnthpxGgMi6LJvage13binVGoD65lcFqPn3PblMi5V2jinI4u5/h+aXDr2HWntkY5g1NVQD2JhlScVEFGx/diPkZnbJs6U3NKrSynJ+zpPbdPPzKGiPHFsow2gGER3Jh1kCRLcRA5DS3F0umZvD5TTi+sTy8ky7TbrnF6DMq6Up+fsPUFO71gyxOUVZ299aP42Wgk9LBrkhzyVx2DHJAVXd4HmgpUrFKY+zB2GDdyEi4OwpPVbU8VmR+fzXoS75OcycsagX9GIsZZTG5ZdqIMYa2knWxyfVI0tQk/z1qitdbBIhduD+lqcEtTEf1q34cMtEHTlIdsPGdsqREmadgQ2XA9n0Omel8UYXILs4DBemuijd1u3MIJa1lXllXwylgWYeETHpjMgnn/zkZ3NgKHsY0f0xzm9H0LOM8osShzsy+eJ7EcUNhkb1if9C2p2ybGO+Em7S0SM/Xgp75+1sUVCPORHgvGiHmJMXTMeOymrB5fFnlCRoEdY2i59XbFLp8Z/TPe3rC2YdZFsVoW0ulEPj+RTy9kCl4a3Sr4mzCcSBAwU0EWtjeYkSdju9G2C6Vv+MRTzec9d39NTLgoGiURlK8Yaywe4EnJ8cAXZUBzx24tOmCP7nh2s9N1fd828T1zCpnjH6WTPC4Gjk4YsRoYQx1azsLzAGqBvjAuyqZKlqVk2mTxaLcboxujJdJ6xkJ1c+QYDQ8ZpR2b56UUfKls+41kTlmq8ri6s//qI1v/SPUTyWS8k8rC8J1BF2Mna1xMASKbG15WbH2G5Zn89J7l3VekvJBvO3Z5o99ueCmwPGvSOJ0ZedVhPHZG65GDtUWabJNPxdxc9xFhfffF5QhrSkXFzM9sgG/09sbYX8mnFbNptq5CMsLQxcOYxLJUf1aqHMRS1lY/DbloRcNAMfLiWBUWXQwxQHKRQCINRuC7o0sSvm+Co1JKnM9P8ZkHlNCDmZBLLJ302adahF0HdUqfWxTlh3vqSGiIinjwoaN7bttOC5/UnAujeEyCPYyX5PmawyvYNAYctKv5M+ayCrsX4n2TxLXUzAjjoTF6PBM1YIIOe+d6vbJtNx1ky0KpmRr3ucU/grRc3exwcvlzomKZ2b8D/Fuov/rfgX8D+B3g7wDfAv8L8K+5+2ZmK/CfAf8k8IfAX3P3//tP+f6iicQpb4mwBjy+AExyzuEuY5agdMwPdguZ4VEk+67lSAb3JP/I05n8/EJaXsAq5IFRZNa/bRDWgvrAudM+XMsMoiCO0WM5ozFO+NAkPYd7VnSIbmI1WIrOMWd2/MCpPPS0OWVRjEY/lk6jD/ZdXXsfkYGZkpyTIv+nj0FeFkoqd7jE70YsX0ABB95HjNd+dA5uup6z+Ha7sXenpIV6XrG8swd9aDRju2zkkVieIrQwFpBTAoxbeBjoQfNaGTmjuqKHtjw9cfrwHX69Ya+VcUs4CykPch7kiCsR/PjAICkLtj6Tz+/xcsbqE74+oba5kfITXmXp2OsZq2dSOQlfHE5vNxWCTVlV+00uZD416pMCNIn9wFyvZAt+9Vy8ZrDUMXZ6e6O0kxRUyUAparpPhtPph4OXcO8cZiNZVpc2nRQgZHh4Loxc8bwK3kpKQVV4tkbnNO+7EYf4kDnLcKcuqzpdtxiOEtMgvZQSI3VjOCxnbd3FOLHjGkD4wbruHnG+7fiZuB+drLnogskgmUIlc7LjfxNuLhMOGF0hlI7umRG+zEudyrT7PmKEf0iK7nMuR63Id6TvN26Xpv3EtokuxiCXJ+He8UwMJDCYqs+xNQZ/Dp2rmf0u8G8D/4S7X8zsvwD+OvAvAv+hu/8dM/tPgH8T+I/j///K3f8xM/vrwL8P/LU/5YfodG0Dd526c2kjlyxDckp9yNkjLTO63R4SSyuFbhH3POZYGt8/G7YulPMzeX2Hk+kpcJrbjW4FSzWoW4ANjVTEKG9h3OKKbLYkI5XBlDNClK0oviqabd+5fFYKbF1UYNq+s92ux5YbhHmVdQU/kapu3N6mBDi2zCYnegsLP/3MwJLDKGWOX480lsm2GM590REPxcSIU5ouW4PeTRlmoQFPObOsUIY4wX1IZurBcmjxv1N8lsDDdbODFaEOUFxbaeAT6zcfRLL/4Zn99ZnRr5TcsSSuso8WD5y+d66VfHqiPL8nn99JKWahoXcppVJaIJ91XUom1QXyiruz953eQ9DQWuSfaXtPUHyOV3T32KQIqoMmq0CRE16SPAOS40EBrGVS4KpUTvH5KRxCi60SxasPZzIsRlCglDeWhT7kyvr8nlKWCPcLNoMdSL8WWYGLShZOdJyh5bNwagtKnWqkVqA6wHcddnUR/p70NTK+1n0yfF6H+Fwd8A4Mck6sa5VsF31GMyl4+lyYOzmais7OdOwerhTc+fhsMZ0aUCcsOBdaEJ9bly+FcWcUJdEo+xgKdMxPMmEv+YvvUQ6II3qAFv4ULH9iifqTXr8pLFCAs5ntwBPw/wL/LPCvxn//T4F/FxXXfyn+HeDvAv+RmZn/SQlgZgxkVNG6uJiyTKvqfnKlpvsJlaLDOvAxNEIM1z9GOpbwlhLkxEhJxOUi5YpbwZZMcaO/XRipkKxAmtSeUEUhllEumVRWrKyksh44zSyOs5C7dy0MmsyMr9erVDPICMNSZnt7Y7tedKMCJC2EhM9py5qTPrIZZ0OPn2FajC3LCa/Eg/MgLEhJjlB9SgbFtJgwwIzoFkaly5+inT2Gs5QoJ7kRWbxnReCoGyWKhBthnBEPTeu0PtR51qrrbRr1xIMUab/HctJyJj89k/JCe35HvnwLYyNbh9HYrq/07UZuTe/ZDKuVfD6TX97BcgKrMaZbLPIqFFeyw8QIU2KfxQu5W/W4RjbEzU3I+Fu4aNyXcwk4RtSqpO6+RKdeEiwZq0osZnT6foVrigXNStvCPi+obW4ZJfoUzIqYKR6dWEpSEZYZNRSH47LS60KLPLgeSQ+jN8yUd3XEzOd8GK8T4Z30rukLJ6cqb10H7529iesr28Kpqhuh/JOP78F7NnFTs8UGf3SSyTc250pd9D5yUYGupVAifwscH41tu9HHoJblznoxO1IBSsASR2cb13/KzQk/iaZbXwbsc4cQHXRZFhKEz63u6tblRduTsu5a04J43xuJRFnWP70K/kNeP7m4uvvvmdl/APw94AL8dwgG+MH9WLH9feB3499/F/h/4u82M/uIoINf/qk/LD7AmXe+Jjt0ySlHAYrOa7iy09UVDMYwWpcaI4XmPOeCVSlG8lJF8E8GKdgAyMsxP51Zbi+M1w4tUo1s3LuwnGRGkp/I63vq0zewvKjQxevwQhjBKxzyHy0lY6cV700RFl1Y337dNC4VmfzmXBhDEIe1jNWQPJqSURXN4g8/SxBBKjmwshjpLakbGJPOxX0hEV2qDp8YIcOAeUSSQUp2sBVG2Dp2xHjAwCN2OzFNw3VjL2ko8zTGR+udbmEW2ESXmUICG86SC2lZpIE/PVGenynxIMu7tTPePjNelYFGLAaHmUQlT7KLFDlcY2Y/ZM6SHe/bJmtKi6/I8idNBv21Sgo54newSPKdvr7clYAjtv9pXsI4DNN6km9vXXWhw2mr+5s6u9RCHVSxUuSwnyu5Kq1B+VTa4mvJos/Hk1RRM2rHu8GCqHB4yGLfaNsVUFLvhKTSpMPFc9OHs+07e9vJJZPPVSYqaGmoOJ2hzf3Qpl1R1hrZPXx3iXwsDIlGRoe2Kdo7DnULlkMuWbhuLAPHjJMZQz6xoM/UoiB6og2P7rkeE08UoPjN5qdCsEZGsAqW42tIxnJa5TPR9rjlw4IyT6hNC70xdwzRmJTlzwcW+IC60X8U+AH4L4F/4Se/k/v3/RvA3wB4+fBbYImcNNpkd5ZlkfwyBQhtwmQtTjPVPj+gA6uVtD7Ry5mxx81cjbxW/VNq2MHddHoTxr108pLgaaH7iX5r4eYToHuScUZaTvD8DeXr36G8+w4vT+yuU9ajmB2O8EbQStR912wqtq0pXSE6txxUF2VoSYtNnPyzUP46dtqjm3B3alZhTbmEo1U5ukpFZPix8Du6gclFDD7gEfEyXMqmnGOUEod24m4tuuzhg8wkoudjQ0tWQbXokg9mQ2/So0cR9D7oSam5uVRGioVLqQe+pwXxYHl6R353FR1uRN7VUHdS10UHQyR4ig/LYeDhowkjntc6tvMWOJu+OPxn8wKl6v3HYS1vWPU9M65GSxxh3rYs2LJiy4qXhWRzwhj4vtPGG552rJwop5W0Llg+YWWlLKeIwZG0+IsRfnb05R51MgyslmDQuKhmHsulrv5G/PBw/zeoRYkaOeuzlkgna8kaC8ycE16KnMtSiuy5QaohKhhhoN13WmC5UyQwn8PkfkxNKoAjFIQat5XwG6bo8feObDwXm6J7jwBCmbjM+73H84R77DqmuivggSFIIy3qgoUSJEZO2Iig0/gLOaKRhBjqnqjLcshy+58wWP9pr98EFvjngf/L3X+h92n/FfDPAF+bWYnu9R8Bfi++/veAvwr8fTMrwFdosfXFy93/FvC3AH7rr/7jnkuW28/Q+CmbsiA9cx/XxjzJYkTBDCuZ+vRMun2NXz6y3z6SuOBLPnLXvXVun3+EbaOcrqR61mXpMPqGFcjniuUnSV+Jm6dWUX6evyZ/+B3yN38FP72nUxlDH9TEb+b7msYSw4w+OgUVmzYG276TcuK8PrOez9RF3gK5SottYftmMTI9bvvh3iFPIxibdJvAgAHd8MqV0Ul9dKwcY33vg+0mx315X2ZSSSHHDJOSeN8WuWUE9NGG8DM7ZKh3ute8we+iDy0xMhYE7yFFWRhu3z09Z7GeU6Srq82VPITT4to05+BD9n2j9UbJYiTY5O/6kFGKKrrG1liQuMs133IJXuo79q4ImWNlHekWCY9uXRp8inx4B5WUwiGsyNg82jBAwhT2zrCNshr56SsZPpcFy6skz9OFPw5GbHoF6zqQyyFftRQChqzfrY3BjtGis84I1x4uDHOmzFqRNqyGLWCp9VgmzkI13aNsOOM26OYsXUswd8EEtIbvuyS7w0mun2dhoE7cU0zoaygFwtxJrvuC4+AMS0DuE5UDS1XHC7qX+rh7fviko4X7lZgjqglt38lJMS8jfA8S8mDwwQNLx0LSnSB44D4nhTEOrvlPef0mxfXvAf+0mT0hWOCfA/5n4H8A/mXEGPjXgf86vv6/if/9P8Z//+//RLwVXeRSKm1vdHQjpOn5SDwkzO5w8Pjtcgrn86XC0xPp/A7/vOJDm2OiW2pD8kryG+PyqpCyvGDIvzJjUBM5LVoaYJgprC09fyB//duUD38Zzh/onLXImiPjeMyCUr+XnDj9B3ukj2436ZeXdaWuq5zm65QfJmR8ci9Ms2jN16TClEgyqLUyHuCAxxEo16KUhBEdeNxcCp2Lm8k5Fjjq1OL9uzCqMXqo49S5Te4hI1IXhiPQID7H6B54KObkHDyKFn6wmXqWtZ3b3KrHoRR/97CYNMEmeMbKOIplMrDh9OtNESLJKOG0NJdEPotxLix1odZ6dILCzyvL+Qn3d4x+w9Mg9QW/VcbtqiUXUagm1a1WRi6MJIiI8kRanoU77/c8q95cbJWUIGtBMzvl47Oect3ZQFg+xl79TG3yZ1erzktTUhqdcjqrKCS5vJk5nRvJoNRELi6ZqiWWdVLwdG/mGL97H7FgUpOg3K0co7yw+YEOa/mkGoyw+COSKybPdGLE8bxN60D53I77QY3hSdODWXSQFgtZh+5hND93KY4giHBrmxg+rkOW8HbdNynFNJ3ZcS33Jg+H0oesE8udP658DMnEf5Mwgt8Ec/2fzOzvAv8r0ID/DXWc/y3wd8zs34s/+9vxV/428J+b2f8BfI+YBX/qy2K7MwjirykUUKPtzI+/Fxi4F4bpN0DJ2nCvZ1I7YbxFUFyj7z18NhPjdmNMGWBZ6SkrJys2tMMSlk/k83vS89eU99+R33+Hnb+G9ASUQ6I3x+rH3yFFJ3FEV7QePqtyTy+1BodQ7+HgNXLHiB6/5yyyPZQwc/sPHNv++KyOzllWcCMcpaLrPToGSMm0cHo4tVOKeOT4XtPf4CBeI5JBzmJPzK8TthvvKSCI+edSxGmT6yVT1kUes9FBTcXNXD49/u6J+3baPR3/Td1Wx1IBtCHvQ7ihj0Hfdm7XK7136irRyVG44pBJKWHLwhhnqr/Hqsm8Zan0UmgXcZ9tWUh1Ia1nKAvuGdIiKKC+kNYnHRCeGO16DzbsHQtMv0c3ZQFNiMYVXbZ+wQNnVBLLND0Jkn/Sxt1x5YLlwvr8TCmZ3m4sZvR2ZYwro90Y11e2mwuLLyspr2Srer5iNC9Ji1MvhQ2k8sqZmpWQPJ+zuek/RC3YsVRO8Zkf1zW2722X74FMzcex/Z90wInLSxSRD7Pso5EILa4zXevuAoDJd3fvun+PmuDH+xC8dBdkHDFIcS+Vh/3FvJ+mEfpPef1GbAF3/5vA3/y1P/4/gX/qj/naK/Cv/Fl/Rs4JT9HJTToKcUoFRWWOyMBxUSdPFKIwl8ry9EzaL2FZF/Z+bZcyZW4dczj65B1PhZ7rkV2VT++ozx/Izx8oL9+Qnz+QT+8gn3Gm2bDexFQ7HcUOvjCWSGYRaSGayYiuj9DLH76uUZgff8fH7zv/bDpdKTOs6YYOaW2yewGam+YRf3eOYRbvcZhawNl1HcyKKD7zdBc3V0XM8MCiJ8Uqhej2/r3VBd2LvDrQdF+4FY36NsfJg2mhrvj++959PA+oxe5f64ipkOuCt5uWVyaZ8O165Xa9kbKxxLjeu/iTySWRTabwRuOJ3m+UMNQe4a7vYdbdy0J6eUd99wFPJ7wnTTulUF6eqKdTLOoSY2/HQWdhSu7xj/x4kz77x2J6qPk5uj8FjN153rPo+oA9EjhqzmJ0jIz1XSPx9kr79Eu2zx8xl6Ixn16oz1+zvv+OdHon2GcM9qHtuSVY1soowiBLFW7f9j0m/XvRs4d7PT1gwvP9j4Bcrm8XkvthFs48zGICemwCPDBwm9DS/P7z3vFgC4S/yIwVn4f/dMoTXB1S8IgFSiYRQe/9mAgeTaD0Pu5WpD/19TNXaHnYrU1eXvpiPHyUcz5uEQ8bOzSO4JKB1vVEzs+0/kpvVwkAYgnU0QiZ0MbUrcVNLbVMff7A8tVfIr98R376GpZnEbjzKUb3+fAgfqPdAfj7+xIE0PedkhNY0aga3ZmlfGyipx4aBzepWebXppSOzgU4oAcC0G8eKpemh93Dsm5eG10vjhF7btVHjH2Gijy9C3Od3QhQq9RxBMd3wH2ZGDf+7GgmTQdi0dC7MsXivWI50nh1M7uFQ9IBqnE8wMlkQj7x3WT3u/7x4NACq2DLShudt89vtH07CvSEmlLKNIHjGMa2bVw/f8b3nSWHq1apOIsOKjq+bVgtSlSocqkqL19h6/uQtWovkEv4DdCEm58W9ltwXgOOgUEyZWzJkMDvBXhCIYSenvsB93ivz0s1ISh38IzGAjda37l9/sjt+9+n/eoXtLfPyKKywPmKtca6nimnZ9ycvW1yg4vpal3XA/5wV9rH4zWfhcnMZh5y3MdJRd/mAS58fexa3I4+WM+no8BisawzO+wx77CXsUe8fCmCBHNKpKXQ+92v1XzeJybPhTB7LyVrSvX7QZCjJhyS7/zH3UuE5/FPf/2si6shcDyFekQ3G8eWSCfMvOfsUNEcOGIS13N2XFYWvC+4VYZnbATR3n26/h2dkjtYyuSysr77mvrhL1O++ivkJ3UqzROWajj8c4w4x7Ik3tNR7Iezbze2y0UORbMDtzs+NTfqisNyppG1TnF1s47UJyOlh/wojWi9j3t3mAxL4q0mELHfv8Smj2KE6FXy+IyNeC6UVQkOs9OYRV9GOZLVepimHD4E8c+MMJ/LK3WWg0MLFkuaWdznoTkiIWDyhI/iWhLTsHXe8B7Lkvm5zWvvZmIclEofzr7t5Cw+cy6FZV0OStnM87p++sTt9ZWcxkHRSqXgQ6YmKXiskqU6LCLWj2WVqXlajm23NvY9EgcgeybVJOOv5FgalOKU5PjY5UGQ19nqfwHnHAuhY4q5X5P5+YFogeF4Lde12wW/fOb6qz9k+8UvSJcfSNsGdFIwMPYxuJ1eyM/v1SB4oK9RSN2M/IBlHtle8x7LWYsrd7EA1CbGvXyXoCcP7NL1TOcSTJZ0h5rmE+/Iv2PCfROTnSyAeYCLXvdHJ7jRQyrsAytRHOOeyjFRzelxBmxaSrSA8g7lojCygMB+2utnXVznU+THw+kohuLONZwXwrsoHI844cxo57Ri7Rm/NrxdSPnESAuWlSbg3gTxxfZwbmWtrNj5hfzuO/L738ZOX9NtZYzEMPFm1blFJ/gwxvrEpNyDRN7o2y0YBw9yU4h8oqBJBXaKc0+LBRwLPuH9poXHLkfk6OFdD6IH1zacmkbkHM1uZ5qPg2hUx7Io3bel4tnmo+gr7M+Pa6s/V6c7i28LG7f5ycUTHyqdFF2MOIVm+bgWx/sIa0AeuobH76dxjXnBhTc+LjOjiLtDritP774S9xSoNUuCmdOBVXvvtNuN7Xo7vn9A+hzqq13UJguuq+W4r+ZUUEpE1MR0st0wOktBYY/jRsmDwc7oFxgJ3zPt9omynrC6yjjG62FyDoS3ROa+hrnfY/5woOljS3SXJLqFH61vV8brZ/ztDbabzIpGDzw74enG9vqZ5XalLs9yMIvP8Hq74b1TorucsSutieJVS9HyMTjYTjpiZzxSLswE0dAVcpnXRfEwpzWi6xN9TLm1BVVwkJKH4EDTi6VEm5NPFLzL5UJOiXVdpFibDRX6/IuLvZDDTY6Q2s7UDRXNjiWjWo20hiHYwyUBTikdbIaf8vp5F1dH5O04ueYiZUr35qZzmkZMzObAbnLCaya3ip2fwBp7uzCuJ3I5kWiw98MnNRV1NqkURlVhrV9/R3n/W/j6FduI0TpGHpKHfE8P8/CH+F6zQ8gwidKG31VkMcIPzbjMLveRquQjOlf4YvM/H2KzxIzxHiG4dfeIUg4Vl1fFgKdJb5qLhnJAF9Mpfi4QmFlcIU+dwP/tJtOL0/l8jIOZgsV1AI6lwAwFHDK6PUYwdYrqYATuPdjWxRRghDbdvuzOsBBuWOCfEOOwHx2Gga6Na2tc1pNwyt6kI5dEh9Yat+tNLVUsYqykSNQZpDSgSdyxX67sn39kv16CKaANfcKCY4rgBZcwYlxeSb4xUlc8ze2Vfnmlf/5Eu15UBParFEnu1FQp5UzKJ5xYGKUYlx8x6l97HZ2c32lK6WF73ruCDRkd36WwStGh0p3RZNA99mmROd2odP+N1mgPcEQH9tsWgotMWfNE6pgJFffEC4kdZAEo+tQpJLY5GD9iR3QtgeEY3SW2GWS746BTGTZtDntvjCbaXw53tAmQtjBfkboNcjwn0zB7qusm+8AXP5oDb6IaEoo2I/0xV/7/3+tnXVwdp203MAtj6KYOIU+9fJlT08M4blGUY6kUvqTdF3ycKeszvZ7xfQXfMVO0cUmmmJEiWkxen6gffpvl29/Fnr6hjUKTDwuWwzBijiZpPtRTRuqHcfRRXBl3AUFQk3xuKL9AzR87lIduGDm/Kxr+Abp4GLklsb0y9g1Z+C2YrfiU9c7YbsCKy2kq5ejA45fzoGzNgLn5foiH9RH3i4Jp0a0f+GuTCMHHfbEwLeWUYc+BpVvhCyzRLeI9HjExYrxMs9u+H6Jf3jCTGnQfW5Vjv0BTZ2dD3cntEqyBnEjrKm+CnKjF8LExLnGgfPzE/vkH+u2N0XdCYsVoXcux242SVkBChNR38n5lf/1BkULbK9autNuFsUu5hCUttDB2TLzY9RnKCUt1Dsd3P4iH3+9YSM4iMa+B3WGzSXsae1O4XypIXRX8VEe7BauUesY9s912pmuZmbEsCyNl7hHTivmWT8P0kA02yxh4ir2Bacoa6HOYZi8dk0DCPRg5kfwRWP4ICiAkfEiCSozycOdIz269lBr81aA3EojFcKw7Y+wSYmRju+qzAh3+2H2Bq8bhBs7hNFeX5cGY+6e/ft7F1Z3tetW2OdyirBRJ8ko6fvlfH3UDGjwYA5YTZV00Gt/O5PrMqK9Ak61YXSmBz86boLzlqZY6AAAgAElEQVR8oHz4bdLzN7R0YvRymGdMIH4ugohObYRnphZiFrLIAJuGTtBUIk7YZdgxaSQTr73/Tl+yA1SoJ0F/hMm3ipcsCndsXBjXz4z2RipGzs8kf8ZZGaNqKTN9FqL7swgPnAyCo6CZxWGlgivVylwIleOBtyiuPiGQmChKTgf2OFqLpd/0F4WeO+tqopw9LhTwoxs9ugm/swWOrxv3r5usB0Eyd49aV3tJCnrY2IO21pTqWeoqKk/Qm3JJJHP6Tc5i1x9/pL1+wrcr7HsURJc81pQ1ld4+6/DyTO6d1Db87TPt4/eMt4+wvykupu9xo1qo1gz2C34t7J9+SXl+R1meJKe2cu/aJ0zzcE/8Os448URqkaH4LiHIdpEDGHlR1LlPytUC6Yny9DXruw+kGO894uRVxAq1LpqKfUaWNwqKIlrO5wOT7Gh3kJOYNtblJ8FwKOVu/D7x9XTvMi3gnePeQffciGlSKMk9jnx+zrXWI4RSsnQd9n3iqWPgu2Jb9l2OWMuyHqrOaQAOahq27cb17RIHf/4jz99Pef2siysO27apVfeO5cRpXail4OH+dBRR4/6AEZNPxD3gQyNhFQ+xWYVyxqyTvJBj8dO9K4Dt6R3L179FffkWyjOJRZSvMfX1gYOlWMQQMrwWPEZ7LAZ+YIRmU8MemGtKB6766+C8H9jb/QECsQamMf0s6KPdoL2S22e2T79g3D6Rl0RuL6Tnr/D6BPWFbE+YVcl4VeHE3U1S7DzyZB/H8Xl+l6lmsi+pNl/ciO7iSaYw3Lle9We16gEbPcLrerAH7kYq94LNMb4+Kob+yPIi8NYRnMf5kLoPhisGehr5GInROBZmknvOwzhw2yYjlrE3bj9+on3+TO6N1EUL8iFzZacxWoZtY3t9wz0Hxm1Y3+F6JV1v+LaRemP4rvE8FEyaFSRZNW+M6yvj7Qfq+28xNrAppOBu/g5357Jfe83PKlum9Z0+Ju3sqvCb5YlWd9xDhVTO5JevePrud1hevsbLQmQUBHsCcpjO6Ew0rCu+PC9ZKbO1hvAkqGwp/Gh1x9OJySo67KmS+4cWVzisKKdcV9h+PnBwiJE/sO9U6rEAHGbkvNDzzs5GdqnBuus+yxZBj8Eht+CUTzOofd+ZW4FjEoYw1/lpr593cSU4q5ECUNdCrZVSKyPNTKu5zAhi+cM43kKzb6PTSqZgeF1JyzOwk0fB/EKyTo6kUUplffcNy/tvyMuL6FYUPLLOLXCgcM0WgN7FLe37HhJAjuI3sdFSS0hMAyvskacVAPok/WuxFGYYEGNwbE6nG/3xvYeypS6faK+/ZHv9BbdPf4Dvnyk1MT6tbE/PlKd3pKcP5PNX2PKeVJ9h7PQ9aEB5DRrYHeP7opA9YslRCIFju6q7MeARAaF6qFpjv92C4ytaXV4UXTJiI0x8Vv7wM1N4ch5Lv4BWxoRE5iuwZx/9jmlbdL+RycQ0YD5gW4uFS/z9CdqMwCpx+u3G/voKbScx6DZpPsLx2vy7UbS9m64dweDoGaOQ0qKYkLDClBVrOgqCMb1tO9aupP0N218ZYaotPBoOKsvR2d0hkePzYn6JB1QEnjJ5fVKm2EiMclURennH82//ZerXHxi1HjEshh1wwzSclh+DcPhUlW7glvQPQwVznqtwPIPTD0SiGan4FBZ5V/zZhBEeISB32oSAsuTb0TXJ0rLLvOluHq+DvrtjqVDPL4Jd9l0WpHFvOgMrNVJrxavGEOsmJeq6ChrYe8ALBJT1FxRzBQ7LMYtFB0RuVmBc06Q6mW7oe3EVo6DkzEjopMyZfH6ivPsargnbP5PGQraOecP7pniMcsbzCTMF0FlgmxNTAj3AOrkDHJ9mIBanc2xmFfcyhFW5gd/B9RbigVlktPfqsTG9c1dH8PSmfwIWC5/R6bfPXH/4fdrHf4Bdv4fLD1i/Si78aoxloZ/PcP4DyrtvyS/fUF++xcozns/0sZPqk9gTFLwU4a3cFxmPnek05VDeUQtc1WUhNybDIZZ8sUyRmfng/Czn9wYqfIijCWEI/Wt4LiBPza68szSL8bw3CM06k8KFHpDew32rkjxGvLnwSolsGXcd2MDh+QuKFdpeL/TbjdQHKVQ+5h7YtE4Rt8R6euH81QfqyzdYWskN7O2NsQ94e4XSsDTIJLlIeWDW0ZEpn8rEMGs3rh//kP7WyO+d8q5Q69MX7I0j8iT+zyGHZuKVEeOeEktd4fwsL459p1iWi9q6Ul/esbx/B0FVE6xyXzpCsD4csb5NYh0rgrM8yZh7cmv1OUbTEN8jPxR8hNIeMujhHkNfQE9MHJ2gSEUXHcuM2b2rIGfdLwjegul9q79T68p5XfUs+jxU47BNObphPUO97XjfYRi1LiQSm22xQM53qtlPfP3si+ujJA6Dvu9xMRN9DDnvG3JWjw9MvNaELRUvkhoey66UwN9Dzfh1IY0LeWyMftU4lhMjnQBxWL01eofmyMQlz3yoSLsUInGYPyQImV7EuFiMc60x4nOay5y5zBpBZu+9BdXFNTpHK+hjPwxTYmOgB37f8csP7B9/H/v8PdZ+xG9v0K4YAzZj3C745Q07faZf3qiXz9j+CvWFbivl/DXl+Vu8PmN2AlasuA6V6OoelTOzix2jhXQ3PE079L6x3W6UcIia+GfvPewLxyFicDdII7rKufiI2GzuBX30Rts2FUX3MJsJEUks9mT4oQLTx2C7XWltcPYzpS7CwlsLI/XOpG/BIJss+4R9d9rlQr9cIWKuvUmKPyLW2VMSRn96YXn+mvL0gfzuW9bnr8kjMT5/Yu8w3i6A4wOyFbzcFHhIbLdLhfActQRtv7H/+D2jdpZ80pRhMcVwh4zi4hyMgjml+fxdYopbloI9PZF6p+eNYplaC2ldqKczaV0lWba7AGR2mrqssuHbfKOuJ8qi56F1WWf6vt8hrYdOemKkPNwrwMGSmRODGVqURWE9Pk845LTH9wOYsIXdoZ7u8fDloAWa4Vm/Y54NS8w/jkzY84QuRiftmXZz+eCmTFnUqfYuRkiuixbiP/H1sy+u9+iLdGAhMsJooRF2ks2gM5hH6d0sZI6HHH9easWXhVYX2E7Qrvi+kPIu4nE5MUbCWqeNjcvWcDPWs8kpndlp+uGkU+PE14O8QdipzYWVRsMYIwOTzekuQz3w0z3sm02emt43/PZK2zeNVEXZSoZhrTEuvyLfPpG2N8Z2IW03bIgtkDw8bIeKz2g7o93Yb6+0vNJsoT5/y/m7Tnn/l0Q/8RjN2MGnD+iXWCdD9CU7unNofWe7XiWZLeHJGouHO6aqG1358x0GmjYguk89kzkWE5Mn3HZ5fe6jh39BqJJCypgiIfUw346EXY8EgZlq61EMiBQDs7v3wSwQfZeQwjyBF9wXDNGlOjL5sKcX/OUryrvvpNh7/gY7vwvbwQq3Hb9c8E8ojZwLk2eWksllbKlaJCVh2ERiQy3yp5iFK81w4Ades6wPCWjC434Z0Hfa9UK7XhmRlzX9BywXhsXiKZY6UUfJ9qUwwU2sjmk3qS6uyhfAgWgC2jQ/CbN6RXXHUssmVu93loceyLsL1SydxkGNOkj7Ezp5YNJ0psoqz0FP3eX0ZQDZR+b8wMARxcxwPBndjBTcL33us64go/MwxnHT72x/cYtrFJMYi1tr5CC2J3O6B8+wyFuVuEnuvgJ+hxTiAZpqLUlnjP5q+Ba+nn3TwsIzfR9gnRZafVKYOgSm6F15WfcPST/PwwwbH3LXSfOkHDidkTo2pD6a2279TcA15jJu+Nbw25XLxz/g+ul7+n4hL4V6fiatZ9KyQnP6518w3j7i2ytsV1nkhVvQSIYPwywcjZJyu/r1AvWkjCmMfnpHPX+N2xkN9BrBHEPObBMj5GAFWHRMBCTS23YERapp0INQl0VdRVyrfdvvijTi61zqHhnEPBRx1PGstYY4QlfKkNzRoyP2gFrmYnCplbpIkXU8yCkJojDYb13QRzzsB+QxQs+eq6hRbZDSAm0TLp8Ttq74yzvsw3fkb/8S+eUD5emdlqXD4fRE/ea3WfpgnE/0H1f8+oM4s/mGZde9V6dBjxRgPhLJKrmspHpSxz0InnfEp+MsdXkYr0eAgw59Z7+8sb2+MtquxUwcimVdGeXu9zsid+34TGM/McnzUmg5qVTKclIHl+4c1R67jDFGLImKyHxj17MyLLBf0Q/HNB2P+1yetPc4ItDyeYYN5pQUwXM06yFISAnPslJMDiNM8yecMYM6c9CtYgGgTjuwlBSF3FunbTfatil6aXbCjtKPw0P5S5rkn+318y6umoc0UraAB0oTyA7CnfI9FXXifTMCooe93NQj61nSR5xqprBqKWMGTREdbb/h+8DzLuvB2PBbDs+B0STlC5NghvidY46cvelknFr+UAtNxcohLvC7UfVkAsixt2HtRrv+wPXTH3D75e+xvf4KGw1fEn4+k8/PlJd3jG5sP/5Av75i+xX2jew9OvoR95bpJo/8C2syfqZ2WB2WJ7zfwJWoSlg1plSCcyoppBHb+uiqem8Q0dJ933GG7P2CTXEnkidKQbQZd/Z9PyI/AlAXXYrZ2UyZrN5vKpn16XQYY8+PMCH7w7kQA4VXTnGDhSY+RSqEoanFez8Mvh9hjvmq5xPpq68kaX35Ct8u+HY7WA75fCJ//YH83W+Rnr9i1NNDcsCQ+9r5zPLtb8H5hD+f2X+14pcK/Q1ojEgGHq5lGMicmlGAwvn8TFnPsjF0GE3ZasK2s9gYcb+H8TDjdmH7/Jmxb2GupQPcTDQmao1YHP8iFtvdaUGVEsSjkdySkZeV5XTWwYFk4u6RfFAa9EFZZdtoQOmd/fZGv12xccNoJAuRS77nuLX9zgIQDprFpuhRsNOCWRaPfSoMIuEi56zxPzrgifFaNE2Hsmoa7fgU18iblq4khb7v7Led7Xojh3Jw4KQUn2VQ+cbjAvXP+Pp5F1cQ5hGFzxxaG5BaXDin2kJZ7DiRx5hYnYrfAa7bLHD3Iicz7ZNw2WtmDxjANeeSWyethbUUSGF/tismIjnHh+dd8sjWVFhVnOaxq+10msmtLSJUuC9vJhVojE7qO/3zR15/+ffYv/89ePsV1i96gC6QtzN+u9K2K90q43aDvtP3G+w7TDOQWBDgYhpMBVWmhqQ1x0YYcq2KIY4YEHE/7cFAYxyjkw/lYrW2Ya0rNsPDCzXfeYii3wR1rRTSGGGo0XCXlZ3c6uWkzxzL+4ytvvNP74mpHPr20fRwT8Nj5jJmks5t+hMcH8O9I54wxRiMHrBDeN2eX97h52d822FXLMjY9lBxFcr5xPLuHTw/M/IUZdzvOx8zinzFz7peqbv4q/tnfL/Q+x4SVUmtc16xfGakZ3J9j63vsOWkTXl32oh4bwn9mYwSHaBabHoTNs3oWqAFLj1Nd9z9UDdN562Jl04znj4CxrFMd6OuJ5nXBMNhHtYpDdbz0/H9bfp34ORsXMZOf91I7Y1tf8N9D/ZVpB0AZAUf1kXCiexO9UGOhWkaxtgRP9fyAaswuexzIuQ+kR5TIjCped47OZ7RbbuFLaFuZll1Oh5CIksy37HpPvYX2bgF9HB6rsIfw4D5UUs+Ne9R7cL210NxpBuQsA+bhfWu9AGKMNS9NT3oZWGMHaYFGhaQQ1L2e2/afAb+S3wN7uQ0N5qPkk07aCVCEiI3KN114XdyOLTrhcsv/oD9D3+f8el7Un/Dh5RkVgpsO4OLBBH1pOI5Br0NddM0JFQMZY3HwiDqSuqdsTdKrWQjDLbP2sKGWYqECeEDMEaIIeb4rGs6u4ZprIOhG1XfQV3j1N5jJOuHxdthJYcc6Ymk0721MA4PM+t1PQqnxWe975sKyW3DiBjokCxPWMgQtujHgnPGRs+Fi+4SM6Pvjb2ruK45sZzOpFOmn4Q5RjOPEfLgHKq/XA/2whgNc3XzWsAO0b3qQnp+p67q/IJtn2mvn+m3C2UdWNLSJC0n8vpMryfKV99gp3dESI26vjHNqYMyNgaW/GAPWE7RsRm9xb3vIu3XZSHlEjHsA+gU0GeX5rUh7uEBVrBSleuWAiKayyQ9TcrymrxVM12gpPsl4Sz7ie0tMW4X+tv3jH4jJ/295vei7+sCy1nXFQ0mfUDLi8zGl2dYzpBW3KpGddMzNtce88aestsxdPBjULORQ97c953r6yveOnWp1FKgVqbUOgeTwKMZ4qHz/6mvn3dxDVDcSiZTwWdA26SgCNzWkoTJxoAe7EUbomjNRaOJN5pmRxQXrvugG6RloSbDW5Gcj3lax/frHbadNqZufsh0OXT4xRRm5g/f+/6r6OadggfGuN8gBJ4EtNcL7cePcHkjtQ3Gjgwm0v1hbptsBa0gwnmJRRRHoZp6d4+/Mzf+vXWSNaw1MiaVUuDV7vodx9DmYi6Ppuhh/kYHTazUwFed1nY5vsfhVUoNe8HoSD3cD+zuTkTOsQxTZLaus7DPgYrxjIae4/9+2xjbFos/bXrXpydSXHuGujq3MBlJsmeU8EPjsjwUIr/JEu06mM74gqHCXzVpYZJsmpLclWnzwZPddxDfuw70bAnPctXKpwVOJ6zt+PWCny+k3ihVCxNSJi8r5XSGokQDz5k2OKJQRnTaKRc9D3E4pdk8uIpDKVUGQQ7JLdJfl2C4aLHZu9OHH5/rjKj3+GzGpDulOS1aYOzz1w5LR2aB1rW5LyuLIJ8EbbvRbxdoF3GFU9DGSIqraYnb20eMEAS5426Ks1+eWV7knZxWP6wU02MXy+zgLXyc9Rb3fQsYJ1OTCbq63RRJ08JMO2WJORbdg0c8usnHgLns+g1eP+vi6h6m0QQIjqgUj0oiJg442l0i6+OwGk4MUhDK/aF7BYS/7YokTtkoT2e8VfbLVYICtQ3CsUyyvrbJgFnYUGFZ6h1mmKe7R0w09yK7943WYiM+DS3Q7aFoYCmEfLsxtlvYCQaVi3kgTNkqB8cw1QVbElwFD+iUiSUHD11xXM8xlLVkQ7aAeV21WInxEkvydbWEl7tx9fzZMtNO9yWCSYacSjm6xmNEh6m1EAySZFU4pcbHfjBkuKUkUlq0pUZ2h/ZwkMJ9859qjeRYvfWUZyxKZ992hdsh8YKESfH7xuLJ20YpmSU8S6+3m/K7ht39G0iCfPI0dU7T1e8O5XhEh+9a2CnEUd3/cQjVquXb6YS9vLt346EYktJIXdOxM3B1oIaWSHc8UYf06J0+tDjNuiHIpVJXVRmLqWLuA3LOgeE+cJZbULpyVux0jSVOdK0zp87DrYrpixHNyjT6ngs280FLxkgIKyWWu62BdYboMuRSRNcLd6ree7ATwCwzrJC2nTaEMZslWFCH7PmOwR73jw5j64oKryae7u1yoZnwfEW9aIKdfz/lYKXkB1aC2TGpHUyfn/j6mRfXuakPgD0pLE+b7LgxTKYufd81Mo6u/Kw0zUdAthF2LDcm73GfhTXGp2qJHXTCNmfsjc2d3MRHbfuu5Y1DLZX1dGJZI9c8NKnug/kEziYVU9dmliNHvhyd4tTza+u+028bNlA+EdHxWj5GoeHQMRmxlBP5/EIyhciNMUidGO/E5zxgRrsvfOqyKJiv1vA56PjYwbbAq4OW4pPoLrx5umRNrO4wKuaO602o5OjwDuj5zgM2J3K8uh7stGoMi6VESiF9xIRNxjVKSa74w5I6z0ULx+WkjXYqFWuNbd8Z7qzLibrUB6klx3Kj+2CEn+d6ygfFbdKihlrEoxDl7uQwNnEIVV5EdAfP9HG5Shhnp6QuWN2QivtcaM0CNmwGTwZi0fox1ru7JgBTY5ESeN8Z3YVh9wiMjMlkXdc7lSq60kEYhDukw6ltHhAeGKM+Z58Lramicg4pKw8T2eRcH6q6uMA+J8YEKZvyrBJ36hgBi81nZnRGb/Qmv4xaM2vNeHLG/kp7K9Q8DdVNWWNMqpSK/6F87A1zoyRBetvtxghMGXQYWGC4jor8NHq/15wwzEHPkdn9v/1ZXz/r4grR8WChaLEorBzLK3ggnncVqJQzVhPGwLsoFjLtycEikDPSCFltmvlAx/ilTm5rTRK6el+IlVJYU6HUqpTVlCJWIrikvcUJLIOP+UpVHpYWJsHTgrfEg9Bj4aOFlx3FyIeB3ZclngzPC7aeKE/vyOevlBtVrlBugFRhetg8TmrdmKUUSi0s64n8dKY+nVV39gvDPko8kM4wRJ2Rr010QiljaYmuNE76GCuPxIE4JHpg4pNHmEwY6JGVtDfeXt9ofacsC4s71VZKWr/Aq2/XK5frhbosrKvMpOeCJgfuN0fTXGqk0Rp1PR+FJuUcZ11s5IY6UlKix92lAp8ODwXhstH1o4NpzAmIie0Jqz0CD9OUa6bjYf1C3uuBmwaufQdz7cARzSKltSQY98Nr4vFS/wVtjaAjjU7vckTDoS6RFGGKyJ7xOeSCedKiKEzRwQOrTvRh4Q0wlVQxEc4DMwqrM9/yPFgfrpnLjcrHhrGTk0MCt6EGxwbDG2Pb2Xa5VM26lkum5oXT+ay9R0xZ4/YjzQfpWT7Bngsjr3iGTApKnZSS+xZWmxFJk2L5JeolokBGInIOVsFUHM6FpH4Xxzxk1b+BM9bPurimlDk/PWlsjNEmJe4693h4jbiQ8eEnl1WoHvSm4rpUinN0CCnkmtNzdS7JWlCzrBQFd0SmUw/zjmSJXLM4lFlj3OFsHj4Ho6mTmR6shGlEqRUPK78RJ7EBPnbhdbM7tKnOSjGuyEkKMymf1jPl+Wvy01fY+o7UG3l5w9smDLA1zAfhgsnM9JrpobkIF6sl02+vbPtglM/k0xv16btwxQ9jjiEMsiyLvl94wqpuSEDg6f6gtd7Ztk3SyUUCgmkhODG+QejEW2f4JqpcLXjuR4EbvXO9XmmtaXSfLIAgjedpeYcdvgSGRu3T0zMTE5WkNpRiczkXRVmxJe0QSsxNOszCGum9QauTei7G5JBXzkqs+y/F7xkfl4v9MMP0LOwS9T10WHtypo3i7PcnW+OgHM7RNKaZuOHRv+qH9TZiySrYiJyxko/ARzfTny0r1jKDPfYUitYZ7mR3TU0JZu7a0bc52NCq2OcBG9dMUL8Ka98v9OtH9tdf4dcf6bdXxu0Vxi24sDJ0n0V8TnG5rvGZbrhLCNJ2Jea6fSLtG4tlSlmAKY64Nw8EDbKPwd5ViCeEqGlLvN1poG9xbadPiA4tHVJGp+2BF/9FhQUsGcvpRN93nbaYCk5+WIoc1JQYHeO0Oswrxp2SMdIOFMEDHlSMtlFGxBOHT4ClhFVtTCcJ3cdkvM2tqU68fN+UxQPjeG/srbOc19hmLxEPcqd5VLNjg91N1KmSM35a2JdC3zM+EoR++liurCfW919Rv/oWTu/ptjCqkc9nKc18o/tNN4XvBJfh3lkaWj5dr9jrZ23LqfRypjzvWsKsH47xbqCRWYfAnN7tfij92tQ0emffNhlJp0RLIT21uzbezFSsw8hjxmjrgEJFxAfLWjg9rSzLcmxy58Mx+Zj4LDDh1BXdMT4FJH5/+B87yph2hPmF0quUUC1FNwz37nGO2RpLVN+4U4LSHNmj03fv7HNsj0N9pqcS43oP2MjSjDGaHeydE5ogHMaiiw6MuXdhy9k8eMV7THTx/kpMFsI34j3Hlr+ow9eBDtObYHQn5bm8+hKzv6vz0GIptun6+yqsbX+jXz5y+9UvuP3hP4BP32PXz/j2Srt9pu87dhz0waAJbrS3jXZ7Y4sF5ug6fH0kLJ9IA8gn0voCc7SfnNY4BE+nE75t9648pUNqTixaPQDjCSkyDySEnzM2er/QX3/k8uOP2E+vrT/z4opu+NGlarobIfOFCYS5U3KBXKUZngsMk4nFjIjw1KMrFOZ3u17ls7nMtNb7dlBRL/FQBExAUgy2m6hb++7UXFT4zO/jezwqOec7FhhRE8wOCxGmiIN3MvTqkvHzgu+VQcViaWEJKIV8PlGfnkhllXtSzGmpVEat0BasF3xsjIfCOmugFgeiM229kZ/OUFes7MLbTmctuNIZp4JVGYv0Qo8Fx0wnGH0ceKYHpDKXWT4EvZip28oxzh95ZvN7jB5FQ4qr0e9deq1Fy5xjyzw7JY/heNbRu8oqqvPx+/5xmvfpeFRqDRpbE2NhiP3xRScbB8J0sX807b5fVR2qDCn0Rt9ptxu9N3J8Pl4zniQK0HvRtcRVSMQRTkfhDvDijnEeuGAUh3CHynVG8ewH9ED83alWHCYbyBJ4cEpJZkb9vm13bYbiZx1X7DBCP6Y7IIXcVTxdw63T24398pnth++5/fL32X/1S+ztI6Vf6ddX+uUzPu5ex6NGfHxRhz0sDptgdtxhv4LVwbCVdP7M2N7I65P2BH3XdIEwVzFQ7rlcR6qHJSxFmzEd1+bS0KMJo+PjSupXxttHbr/6JdvHTxh/UeWvJtpHyi06UIkH5muOenGbksMObkQOlccNP7en0nJmBp22b7HMmt1JYDwPNnqa6GM7DcoTct3UbZPcs5m2ztMQQiNOxYq2t7lGGFtRkoGPQYo8qfsCbCjzqF0YfiNVpy6JXWpSbH65Gd31tcUnNBLu7KGvlnol4VaAxlSGMX+/oPfgkIdiQNLa8KXrfV9/UKeU3ykyupykyW4lbsxE3+UjQErU9XTHPgPXZV1j+dfIWQ+18N76gCFGVEcn/rdjXZ9v8x6iiwVn0Ke5t9mBfY6Uiayd6FphFn4Lw/R79wVTwTPNe8xctLMwn/kj4XQTI+axsOl7+mjy+YyuL8XU4uEE1m83trdP9O0meXbO5LEwehEdLWdSXnRvmT1gr/cx+1i0mDHTLexBn1/qytOLSQRixoo4wDDPFz+6dxysp3B4mjREDsXavG/vXNFxFPX5eT1yshkxyeV5OOh5apcL7fUz7dOPjNdP5HZltCvj8oaH2fg0iE+j4yVjI+NNuVsgpoYHRU/mPmpqrDfGfsP3G3uwQfYAACAASURBVNk7Pho0LbPnROFdBt2PHeuXPOkpjZ8LuuB0M2BssH9mXH6k/fgr2qcf4LaTytNPLl8/7+KKboJcsrbpwQ2d3edoLS7QNO2IhRBypGpDhdDxiIYxnE0b9+BJllLvXEo6w6OiYYeTj5YbkFPWlrl3Wh9s1xuj7yx1YVmKMJ6UqbkoQXT6TQLexB/1g9Iz7qB92+mXK+PySn/7EW6v9H7Fxx72il2QRR+MlKinG+m0k0aH4PGm+aDO5R8ZHxn6YJgfD1wilEkhaR1oEWitY7niqdC2nZ5f8fW9fGDLwrTo86GD5fL6FjQcsQ/usEPYPLbO3nbGPshP5+hcH4xszBlhZGxhwDP6Th/Sl8sMvavDwSL5Vj6h41E2GXLFNjog1U8qYVxtd5bA/d+juFNjcaHPucwxPKW7k1kcAscrtPxjesiiGG0tf4I10DveOmwbfnujj4atC5a65K0pQZivp1yYmvgR79N5KIpzXXQsDXXYdxwsU8spvCgG1SIZoKmb89G1hGQeMQfa+1AsH9ywAkqZnhETXnn0jT2WlERHO0QXaz0UZGOQuuM3TUY2Roh/Aq4I2AxcY1uK9+D5gHbwyb1NkAqTc5vUiivCaL8xY3Uk9oHe9iOGaAZ8pqUyRoOh69tai0m0HEtrTRpX+vaJ9umXtB+/xy+vjMuOeaUs60+uXT/r4qqbYAL1g9ZCl16z5GpFJ4/FckQnl061bYsNfOBbqWSKOzakvGHICCTHeDejdD0lLS584EGiZ8LdQzfS9GK93m7s1yu3fOG0LqzriWVZqVUjMCZt/hhBnXGlWhpaTI3Aucbe8NuN/dMn9h8/wvUjvn2G/YZHscEk0/NS8NZg7Cq+aANsyWIplBgtOiCPbmA4KdudYjJcRZf9wI4TMN4+sbWdkT+RTu+xl44tJ4wX0eFiwZZrpZ5En6qBhz4+iIGC6qbfG9vlpqLlVYVvBAa+N/q2qXsejb69sd8uDAZ1PTGenvn/uHubUNvaLb/rN8bzPHPOtfY+57z3qz5SlRCM6YhNIV1BEBUhvYCtKEI62jeCIKhIdQVBSCOYdKL2TEOQIARbgijYs5GAHxVSVbmpe9/3nL3XmvP5GDbGeOZa761UbuothJdal8N57z7n7L0+5hzPGP/x/9CRGQPu+0Efrv0mOQ6c8xIOSRP3dgupRAnt+mMq+UUrQ6LIuGpsjuVydnxogA8jypN1rB/0engBHT0WI8tD5SRRvBhusn2/YW1HWTE6Eri7jerfs7jhCebPoQ89RSvTm+FU/OkDopgHANPj109+Usl+aHXPMDvz2kJ9NPHRySvFeCwKY9HL+Qk+ivIzPHIeosAM2HS2zFxa+nSjwUP1nzjB5QnZ+gRinalDQ/CiOBT3zRVF8kJaLlAuoMXPtuOg73cUDxCcLnUywmfYvKmybKQ+F8ojPtaAbsQwdenE6H7dTZzY3j6j3cAyuq6s14/fuX59r4srBOXKBket1KOyLLjCQvQxXveBaXO8aQysNmp4NMpcBsxfrZ3x28+GL3N8PpcIcaNoAHujG8d+UEc7qVPTcau1ziHVM5WkuQZdcihiPESt9zFtXl0xI26g7eYv4dKze5CdHDdoh0MFwaP0sEVCLlqxviP98EPB20+SKqbp4T0K5w1lA3fytzkCd0bzBkKQ05B4HAdD79A7ZbuQqI73poQExJHKQtk2HzdVzzF2FoVhHqW8bhtVYFjn/f2NtKdwLUrh7TpJ5p3R7tT3N+rtLZRh6vjZ8JvKDWdwS0hxO0Zao1tj4GYzAm7wAiA9UlrzWaDgaUHTPTmiHo4LS6TcmRhZDJUSf9ffs3rc6fuNUX1acb6uK/mGBlUtZgERIwmMftDvbyQ7kH6QSnY4oxSyGJaELobkDSQ+txOU9YlNJSOkB3Ye3aMvYaKYi03UyHmljZiSZkR0xJvEIqy3Fku8yfx42vw/jf/PhXT++SzsE60N26ITfxZV8rrSSnHoZEhIhn0ClDjYhuGYr3iToybBLQ5D65SRspC3CyOvdC0YQqsdvd1JVpAMg+7TIGef740YhkolB0MAszMvDZ2vc3j23H1nfPOGfXNDK2AZ8sp6/Ypy/fSda9f3vrgSi4vJV8vZeaKzsKmmUxTQ6uFYaK1odvqQqQflWVBL5lJC5thuD5MXCXyL6GQnOdmLa/P8nhHy0MDAJJY0qupFNzuueTqzjwdda+KjJyYYF7ubMnvxSPj3kdCCj9FPHwMVP6GpB9oPtN0Z0h237N4tze7J8SV//2aX5hd0FNcxHobC5p1sr25QIsnccHvsQPUUgKBxzRuM0/VrbrGnhNQPg5QSZVvJJZ2YHDZ8bLXJC/b4a8/a2qnv7/TjoGwr2jvt/Qa5IrmcHFJJyb0ETmliQB6xGBvV32+n+Oi52vrW1tsGrR7c3r4wenWSeXfbu2HOFS3LiOvD46Tv71+QdsBwJdyw7jaCY8f65rQncVK6SkcTFIVBmH2zYz2BZqiFNg7y8Akkba+Irlg3WkCgpRTU1CeoeJXfahLMTkglYZj5QdyOg35Uam/kMKP2BsP53b02D4lE0PxgQ8zHHxj/9UkCqhp4tc9yA0Oay3B7TAkjJZbLit0WxkhgimRFR3Zz9RN2AIewHsGGZyHXuN5KQZeCZPcWGFHYe+9YrYhF3Iw49LaEAY2aRwK1aJrOGPgccED4evTaPFb8AOkLjBVYkLJSXj+yfvoJafvwnSvXLy2uIvLXgX8T+D0z+xfjaz8E/lvgzwL/F/CXzOxn4p/SfwH8G8A78G+b2f8e/+YvA/9RfNv/zMz+xi/92bhSySQ8OvND+upsAe8sjqNyv91obzfGviNiLNviuThJYzQfwVP1BcIZMzEv1PEovEic+JYeVK6w5JvWMJoTpSw0G4xGYJ+P7mj02GebU65QjQVXOYu6w5ixdGN2tBmzkIbOLuZpYyvdeax27Ji8+0dognTD+bDNIYSAUtScgsO5yAo+bYTlzXEz6RxBHfgf7YibwRdwqWRmppCPwLEoC+OYNsbDz3VyaiUkmNWzqLzoxxgZAg4BhxzwAn+OxbWdqbCaEiUVNEXcM5O25QwCf1kD1BcWkgayEBDE8C30vHHB34Puv9R8kTbM6EFCHwqWvCCNPjj2G9Z3lI7iGN043mmjkY4Vubw4WyNUeFkFkUpZlLETybwehslo9H6n1XdoB6XvYBXShnWwro59SyyMgjdgJrE9nwdFRJe3yhgNNZdmj1phgOLds7fjUzjToonwZsXlxo/zdWKw8zEhA18Yeqf3LEd2iK3CaCEzFo9/L4W0FKwVx7Z7RnID1Bd18bx0Xn/hFxLPNvDsuI5ygfWC5guD5TTHnowcUWWokkVO8x5hwn2x8TDPbEs+ap3duicrgOhCevkBpRdozT1GPn0kffyEbJdfVqb+0Mc/S+f6XwP/JfA3n772V4H/ycx+S0T+avz//wD414E/H7/+AvBfAX8hivF/DPxL8f79byLyt83sZ/+0H+wXd42LAURc+nZOK8bZKY3msdb9qKQU45W496SK011S4Lfudj4euJIZtVWO205rBykp6/USHNeQnprHaKAxRucEo+D5XXp2Vd5Ju4NXmid94EmaXUQw8Itipnka+EWz+o0qx0Zrc8km5xgmwxcqeTSod9yGLznR33yxgbSQqQbVZfjF5Zoe/28TRZKFnPJZxupFWcMScQxnYEhy16lTuRPI4kTmnPLzoCnllMhxCA6H9vx94oGjw8Tv/LOWlEjLErJJp63pZEBMxdkc686Nb9xiIwbkMRjhjpZycmxU08mQmNPIiGyzHO9vAvfinbQx83Gxd490p1eyGFkGNg7UbtTjG+y4I33D2BnZ3+8hfrO7rPMzcEfFzavt2B2SUiXlBb13+tgZty8MWehSkHyhXF59eugKYv59h4UnxcSQDazR6461A1ql3+++3AqXsCQS5uNTtecH3Jw+pjPcWU/ngX7eX1HMH+ird7Xqf6+3Rt1vYB23oTAvXDlDLs5f7ek0aRnze86PPZR+J3/Y3ETJRS8VuidQ5JKxUphybJ3OZOoOVpqUUhaPonmaTEf41MqYi6+Aa2KHojmT1wuW3Jls+fQjhy3i+9u6Or3xOz5+aXE1s/9ZRP7sL3z5LwL/cvz33wD+Ll5c/yLwN80Bjf9FRL4SkV+Pv/t3zOz3AUTk7wD/GvC3fsnPph6380b15ZRzWkWca+cEdVcDtZLRVrymGJ4SaQQdysco69GdWqwensbZ3j2v3pqwlMII6zWXGDozQczZCiUXREJ7j4sJJDqoPkKHrs7hQ5O7o6fpkC5xeuK/BzFe8koqG7JcsGPBbA8DGDlHpaTJ3RWPnd4FC6wOQqqKkZeEaaGruRduOxDGY/Fi8bM1utr4t2FMFRvV1X1G8+qpB+GENIHcKbmeyjbB7QsnNiixAFPr7PVgf7+R03QegpxTfMaJHhBNXlfIiZwntieu3TdDWseWAbgyx+K9sycXLI+tMxCLbXaEJJr4yGxGEvdS6MdBu++IDFAYw7t0FwzU8EM1rHXUGlilHV8Y95+jx2fYv8Bxw0YB2854EZfV4t//dkNaRWzQWzwfwlcVl4lau4G8MXRFyoXlVSiyuJubHR7XPiNHyH4wEpgzXuhqrdTbG/X93TH1dWPZrsjq3WXvfjhlPANrLrJkOFzmH4R723qjPOlt/WQNPHsK+P3i1Ki6370rX1K83xZ0MxfPWD+Cwxo+BgTtbmKfcZ/7ZOVFW0TcU6RFHA/+3rmcvXHsd8paILweZsE9mRcBp/XuxvCq9jAQislVTEhlIeXlrDWzCfLlufOt7Tx5/uiP74q5/qqZ/cP4798BfjX++zeA//fp7/12fO0P+/ofeIjIXwH+CsDrD36CHTstRtcBaMqBs/giyeKUytvK9dMnuF6xVunWHuMr0W0ap+7d+qCP7l6eIuSU2baNw4zRKjL8Bp3pBlOhZUAqipZMNgFbz5G91wbVjWC0ePfgyqzoIp/xq8cLDvnnwsgLIy2YLqRyofc7GnCEBiTixVx8bO53+uxENfsSTsV9FdKKRGaQwwAxDobpicwiJIrz2iz+vSJS0OVKef2K/OEHpO0KklyKODybKWnCeMiGJ17dp7xROLvHer9RbzcombQ49/ccP3US0Z0fbOEg3+d7aoYUL8jO9wyuJHL+zNFH3DTpjHQGlz7ThZQKokLrU4XjN3prnTQavR8+WucUP7+TxvDueTgU0NuN/ee/S3/7KWXcocWCSzq2OP3LXcLiWq09rAHBO77xwEtHcYx0Ko3kQHKlZKXYnTQK0gbDKsOEIZm8XHA2axirYO74hlHbQX9/o93eY+x1mpccO627Q5tqZE0NL2KjNu9sPe/F/Qn6dBILOlp0e8zD2CyidZzhUvc7/Xb3XVUODnh1M/SUV1g2X+ppWB4GTKbB+PHD2lyGHnQ8x3yBFvfoUdlv71gakDaGGF0Gg8xa1rCNlNMDIZ0TU0BtsXCd3bdKmEQmFw3NfYn8QhG1NBkX/6Qq9c/2+GMvtMzM5I9jHfMHv99fA/4awE9+88/ZOHbGHFdLieIa5OHhlsIaY6il7J6NR0W7L7Xm6Tgi4nlmBU1clOEc0Rwju/XhBUmm9txxJlWltoqJUJKGr0BIj+PPp4QRZsKnA+im0yvAj8+T8jK3rykxVOmiDBIk72KKHYwuiHp+0xmtHAsohstKPSVhhA9CWMCluCGGb2J98SEoQVmJpczc8g4TkiRSuZKuX5E+/Qr5h7/G8tWvwPIKVmjd7RbLkt3HIUZ83/sZR/NO60G+T243Z/4zMc9H8gvezk7F/QYI4YA5pzc29RpG0HPx5UoaYqH2KK5ueLK4BaM+PgchAgzNEynAi3ZaV0qttC9f6LuLHZKE58OwkFCC0KDfaG8/5fj575D2b/zQ6wd23IBOV8G5KtGRIU883BRFChzBMGz4JAXR5dG9496Vocaod7oWhvgv04W2XsnbiycDED9jNMZ+5/72xv5+g24sl0JKnnhcjx3R7mhRfsLvh/mirUYIpoi7UoWZucuTfQfgNdUnPeb+cwxqPdjfbvTDWRAScH5rTkvJZYO2YenmCsXseGbS5OYy+oCUmBPDeJYo++fba+X48gXLjWVz2flQYZR+Wg6CX0eng1lSRld//+1xnfEt7D0gv1M1GfCV+XSB4MkPfPfS9l2L6++KyK+b2T+Msf/34uv/APjTT3/vN+Nr/4AHjDC//nd/6U8xQ7rfrEMk9MiulhpB5obJ5XP/UesDKQ+jZ2fsHCejYMriNMw/PDfnwXFMOYfOPExg1MMLDSNZPgsxIo4hnaO9D9g98LxEjMUBuPvSwD/gYZx4FyoMdVPEJgkpG2l9pY8dxoGqoexM+7th8wwWVCYFyG9qS2GZF8YvKkoiea7TcOgiiaJ90A/fett4bFTzspJePlJ+8KvIj/4U6atfQ5aPIEtgti7c6OKsBl8++M3pS4RE2fK5nQ2OGDknek6PrqL38EyNcRwLwQZoyDUdJ55fD0VQeOxOsx7vXn0x0qpFpEcnkX0LnbM7zgf0MnFaz/nKZFUOVY4kSK8gQk4SqjyAhvU7dvsZ/ee/C19+H2tvIR8+sFZR8w7Z4nMnmBV5Wegi9NnlRwdLct8KskbBS86p7TAqdDrt9oU+khfWtGFpQ7dKOzqprO5yhcDo9P3O/naj3Su5ZMqykZaNjjcPGr6yo3fnf45Ay8WhHeuOefYxT30JRV5jcoM1+UJo3iPun+HccpFELkssOJ361smgDikNdVXasIRIIWV1qXrwe+fnaeHBbBJMj7IimoMf3sBcDanJKVg2oPdBDmMkvxYD/7fYUagQWo/Yn8xGy++lHBCL2EMy69NXLP5+aYH6pz++a3H928BfBn4rfv/vn77+74vIf4MvtL6OAvw/Av+5iPwg/t6/CvyHv+yHjO4ZTXnbvAuMwjfGcJypOo+tiBe7PlyNhLqTkeIk4Xo/qEcEvEUaqS9T0kmsfxgCZyfch4FuytkTMQHR6BRnedMoVjEOVnyBAjhdaC6KzE9/YxL5nziFcbFTCmm7ehDh2On7F3ensgOsngujuGyeulgf9XV27uJhlqiiWSllheEYU0adVVAb3e5ARdRIWX1cv74gH79Cfvjr5B/9BvryI0y3wGjTiam2WoMsHjhn8m5kTSsz/oXRnafbm3MOs2dpgVOeZtp5a65CU4k8Msw7Bpsdk0d3OCMiAuNUvUvVHAYrShqxMBHH0jU24hY3FurHw8nnLDCyu3HlpVC/fMPoQTsTw2hYv2G3n1N//jteXO/fMNrNMdjefInU2imhtWC25JJP9615GJ5UuJwp28phoLk5OT41LM/tf2OQsJEdU8wZWSFbgu68WLVBLiWajUEpGyM30rpRrq+kZWUch09pghcgQtASRjKa1OXg0aE71j4ZJHF/nAIedb7q7PrEI8AvL1dfDJZ0dnv58uq0uCoMWRBZMVlAHCM1US/UNlVbEg1HfI6aIS9YWSFvDFlIafWF33CecE4LOXt8TUrutdwjZWEMX+gS0BHy8LKYvPbJdpi7F5jvQWC/rdED/vr/FRYQkb+Fd50/FpHfxrf+vwX8dyLy7wL/N/CX4q//DzgN6+/hVKx/J57074vIfwr8r/H3/pO53PqnPWwY7TjQdXVdtKiT8eOEUvXF0jON48xDDyxn9EE9KvVoSBKWNOWGcgYHSpyY1gcm3g2DnPZ8Mxp6GnxozuiyIKmcESKO/YR222bQm/tz+skdxiOaTzu3mY1kCLIupH4FGfR2g3JFbcfGzmi7j8KxedfYchOb/OkRi0bZF/GNa87ktKC6AAWxjDTo9wMJnE8D60xrcWetD1/B609g+wqT1dldCaeizcLUKowc4YJBu8pP6RDDD0WplXq7u4FLHFR+UcGkwFl30w/DcWEbHbGGmVN82qSQza4zft7Iiyt48gXVxTuiXCjFfRwmt7O1SlLnrM68r+lTQGC5aXiKaj/ufrhxYPXO/vXvUb/+Xeybn9K/fI0cTsmyUAH2Vn0BGrQyr1BeAFO8XoEwyxknlW7Ggbc2IDVI3Rd2adCTAVto7TOaNsrllbRdGWWlS0LLwrJuiBl7MySvbB8Ll9cXto+fkJSo8gbj5lBV+A8D9OqLnllw+kx90IdT1awoc59xflbwWFQmZblscUA2WnNXrnJ5ZcmZ9q7c7l8wXdF0QbPLSTXirsc4GC1UYnFJSPatvS0Xel4ZaUGXC2m9Msg0U4au6HIlr1fnsfOLFDX/Xs7gSbGbc9ZOry2Wg/lkrrTgoclkscQSVEaPyfi7P/5Z2AL/1h/yR//KP+HvGvDv/SHf568Df/2P8uRm5nkfTqE40RJxknVS91U149QVx9vsC4t6UGtFUmK7XEklkRfnmTqtJ5Qr+OaUWJDQq3dHSYOxUKnHwb7vrvxKiQ1huRQfOQJUT8lviLlxdVwYHtw6nfcf4EUy4blFpoquCzYupMsHyv0LtDtD3uiWMWsnNqUysdXsHUEQqf2yjfEtaDNoQZYLSa8gC/SElUFamy9tMCQndF0Z64q8fmSsn6hWGNXpR4S/lsXWWfpw+XBe/N9O3i4OGxz3G/12o+932lH9UFuetrKB22EW0teBjYowvND2A7GKjN2hgKelWQq3/K4FyoZeg92QC1oyKStakh+e42ETKL25h+d051KNuxB0ycCGqGF10G4795/9I95/7x8wPv8U3b9B6w2ru2/4bZwRJXRzTNNR5Cimnd5r4OHRtfYeh6N7D5jUSNnoSDIkC6Tiv5cVTR9Il4/k10/I9oKunrElKZMjzrvtlc+3Sh3K6+tHlk8fSduKAYsJ3bwpIDDPnDPLNlsxhw1aayesprF5J17DcRzxmkL4woMR4/enxhjt6sNuzpDJy4b0F0a6MNiQ3MmrIrmRiM+CHdru13VAAWm9kl9e6eWKLBsjbch6oSxX0Mx+dEwyy/UTJbjFbXS6hQPd5MCf7aYg52vtdGthIqScS8b4zMyCp92ad97mxt5/nNb1e63QEk3kbfMQPY0Yjni7NNROLi3tJ44yAflhndo6Jsp6WSihABkaTlsl+wZ/3mHmqQC+qUzk5BzBeuwcx3HKBgVxTl0u4YIe3+LcR3JSV+bo4Rk96QnbfSxyTDjTJi0nWDwRMy2vWPrMsAVGcQ265ugegz+bk+PMoUYaMQbJdItyNjyaL6TtE6SN3hPizdL5XFPKXpBywa4Xum5IA9MGkkjRDdDcINqamxmzeCmfto/WB8f7O/s3X7uuvndIylK2pyVXGJ90J8Bb36HvWLvT4nexhrX471Ej1dbxZfICkpC8gjUn+6uGd4IvcixMnbvB9MPtwWNNyWGAM2bagpBv3Tf47aC/f+H2j3/K8fXPkPsXxnEjjT1cu/p586kb+EZeFGeHx1Ba6wgNUccGXcvvjYG16U2cMJove0wxXZHyifLyI/LrV+hyxYqPxJBJy0YqK0hyyXUbaFlZ8sLy4ZW0XbCc/PNYvDEfPVy7RLFpWCI4pqidktIDy9eHu9n0sxijOzc0/BtM81Nz4GmukwXzSMMVKBd0+0i/HQiZlK7OvBiN1Btjv4G9OfYvhi4bcnnFrh+gXJH1St5eSeUSJtfZqYIirNuFvK5+748e5t2PSWFOmnMyFHzaNZvy8X4eCDLvp2iqRqtuOmM+zXzbEe2P9vieF1elXF8oxfGV8ShfZ/HqvYU+PIqu+lZYZ5/bh2NvE8yO9n/ib3M5BKC5+MneFxLDeXy1Ba7mncq6bVyvV7Rkhg36EMC3oMRFOvFQTb7x9NE9PTpKHt3MqeHOftFLc47gkBwd64LoBZEIQlS/KVHBsv8SxTHFkBSOuMH90Cik/IJuH7DlBSXDmHZ9cl5cA/OFWMkYGbEMIwQXcRFaa7R7jPlZQ5qbTg5nPyr7l8/UtzesVhcUrOX0EZ1LyNEa/X5ntHes35Eoqv14d8+EUaHdod6QHp3E8EJNcTjGysWxT/XQRJ82BcyNXExj6WPzvQ7pcmrYaIycmStiG53j9k5/f6PfvqG/vUE9kOa+BxYu/zKmZR3npnrMQ9VwLkZoHKQbjjZ7gTs7bwmT6km41wS2IOWV8ulX2H78G+TLV1je6CitQzdhSdkLUC7uJTzcCOblB+sDcsmZHjuJroqUlVwei5o2r08esBI6F4yusLL5R+K85Wep7ZRBy6OjCHu/UEyJc0QlCWYb+fLJo1XqjvSKmlPftB702xumK6Y3jIFsV+T6gbG90tcr6fqBtF4DV/cFpcbtP1IO3qzT6vJazs41pW9H1s8VSW810hIy0gkT8ynQic+2NegVaYfj79afW6Y/8uN7XVwR8VM9+UVw2qJF8RqjB6/O34CZRprm1hzDUuQX2fTkn8uFgYYTrHM+vXukFFIaWK0cR6PWxug+yuacw15Pz67UujFk0LU/LroT+NeQ2eoJrs+Hx2Tk4OZN/FUYUiPWOSO6IeUVUkZ1oFmcrC5BIaHTqZh2RC3u+Gk243hdKi/I8gLLFVsu3h2JS2adguJ48LDhCyH13CUnL7rD0aS59P3g2Hcnd5epgAof1Naxo/oipTZ/3ScvlxNj7tULa719xvbP0G/IuLvFYnPDGus72nbG/o7VwyGb0Tx5VpS0rOjlBRl7TAXZF0Nk9341IS0OnVjwWhkPSMNoZ+6ZLzE6db/Rbjfq+zvaKrkUxroyWsGqYj1oHo/USQhPp6lu4/zl2+w2vYcnDwvPRHNoTyBnJF8prz9i+clvsv7kT5M+/oih3p0qghxhcxgURJMpuIijWly1hcSyzx4KrEkRHMNTEXofpGB3YE/ZZ0FTsmGYPhZB3ybkt2BQ9PMan9v53lpc7/4akypShPL6gVxW1BwKcTpgR9od++bnfo0uV39LLy/ox4/09QLLhq2bp+bKU0MS99SAB7XqSTwwvUdGvO8qgo1GazNjbDjkpG6FKHPJN3pcY91ZIPVwaNCmxPq7Pb7fxZUAo5GTtatfQgAAIABJREFUujO37D3I0cPGmZOTUsIVxGP+ZYzgUcYCDOYU7zec04gSqk9voyo93nhIpBI6JokFWFzkM5vH837qme/keUSc6pZJ6xBNZ2H3OhY0EnOMuBuMFJSqUtDLC6rQaZBxeejiTvmMTj9uWHvDwmBlbj01Fy+sywt6/YSVFze9zhckr5HmOpdPwUElan+SiDGfKqv4e3PZFOoc6x16d+jAjHE0H6cIG0KxE7+bndPonXH42N3fv6bvP4f+DuPG6DvaD3cDaztjd4yT5outWTRMFDlx2cZImZQWV5Jl14HPUubnh78GJbDRYGtYuHKN6bUwIj1CXduvy0ZfNvpenIUxEgwfrz06xvVgCIiFuz2T/TFhgrie7ASzXBRgipHJywvlw49Zf/KnWX78m8jHHzOWC938ANWUydkYtZ51XSUMSCaOC+HX6696kv4n3dCvPyEDXcZJATwpWfE+PXnQO3pskdDQqpsmhUxa4zCaJtk9hDwnNKDq9oe6ONRV1rDI7DFRDqzesWbQBO2dVBbWDx+wy+bKLn14/zp1z02IJivmjJlPj8/38VY/oEGCez6soTJA/Gf37gscgbApDG/jXn2CqnesujOc2J/g4jqx1ISPxGdhbR33KQ2SeS7ehY1BbwObpGgb38JkHff02tqDJzsx0omZDfBCWhYuklB1I27vMkP2qim4pNCGcxBH7wxNzsUrDk/04RZ0Zi4vbcM9YcFJ2jnYDio+yo+SYcnoy9WLQ7t4474W8rpAsBvGscPtjf6u2K7ISKCeJpCXK7q8INsLsn1EXj7AekXL5sU1eJeOD0739ujAnqSEM7bmzBuCszmz6SfQWogxfNm2LJk+Q+OwWDaOeC2Ncb873ra/YffP9PqZ0d6C3rQz2oHWw82mW4UWXFyR0+TDJLrznBnvn2n5Qikv4UiFj7vmvN4Tg1fnR8+UgVZbUMDAupHMHZ3IK9CoN6GjaFkZZYsCqb5ow70jRmCu4tK289AewWyYvNARCbGoH7CklbS9sn76McuPfp38o19BXj/BstE10c2vaUkFVSMnP+hPzm98b98tWFy3EraPgSVqBlI8b4l7BEBCdNHjwIomwOKmmFLqMTgOdzIrZUUlFnXHjgzvli10+/M95aQ4Tg8ChZIhu1OVD1ueWXaklfHykZIy5XohXy90TZ5bBn54hZjD7RFnh5rOTvXxcwFx9SXxekSfFINzASuDsd8YvXHGMhEOXWIeqVRvsL+7MVKLqek7Pr73xXVuI3sLeaJNLEXIi/PcvEB5xziaY7CjNWb66TMG4wumiRE9+XyCF9Q5MuXEcr34GNkrMnIUDJy+0gzJxMUYS5M+aHVQFpfTSvIL3rXYhsg43bncXGXQraH2wGtRgZLQ6wVyOrFGLZkxo487DCn0ZjRumDWUREqCblfK60f0+gFbLiccQC5IWdBUTru+EREZyEOFJml2B7H/jq7LWnOLtu6kf2ziVk5Mj1vSL/aYGGY7Mbpf9MQ2VmrFDsdUOd6RdvNutcfSqB4eCxKmz8g0YXbhhA03BNf9zsjvyHpzPwCb9oyxzR88NuFwppw6Ab56URI3chERci6+ILGDo0Mjk8qV5SVB3pB6wDgY4yBpaNadGsIUs0RZoHenmckYjt1aSClTRi8XysevyF/9CP34Q2x9ZeTN+a2R3moEBJCVbOqiFPVDQxFacET92p6xLE8Qhc1TUGIjLtG1yuN/Y/JLIpPsxFINQhyQknNabTTutzvH2ztaMuvl8hjRNYx6cjp3GIMOou5MFpPBFFpYzqSXV/RycRrg4m5XTo8ap5m3NWcziAippAcjRoj3JvxoNRaFTEx5LjS7F9fRvXDSsXpnHD4p0atPuTLcB4ZOq3dnuhx3tHXUvjsd6/tfXM2iGE03JIKS4wqrM4Xy6euSko8iE3+aVKHoXDUkmBM7Rey8AOfJ65QndUzT5sgVcdCjIcMVHqpKzgujwDgqaI8PPCAB5DTsmBlF88S1EZ6SOkJcMLyTSgmWzFA3HBFVrLjpMyLOD0QYeUPyS3TJRn5ZWT9+JL+8IpcXRnGfApI7E2nySBSE0yBDIBZgw02Ho4NxxeMDzG/Hwe12o7fpE+oHmbMg5g3pBez0A01P8cxj+OfXnSjvyQMHUg+0HVjbA29t0A7f2p6mMEGYsfgV36sH4yDPW3puxS0ktHRMh79+Hgd1PVymO2H22ekkcaezY8+Y+kIm2yvZKna/I8edUe8wdtBBUK9PiWtOkWQKoYv37nscd0arPnunRLq8kl4+wuUV1ldkufgCc0gQV5RJ3/Ni8tSpmdHNMcRJP/S39+EfME1vfOJ4GEnPJZwA/8//8ff5R3//71Fef52Xr/4FPyJ0eiOAWQaWs7P/jX+uUe87x5c31svqCbIhWjmxdQ1jotm1y1NTY92fNyA5s1yvTGXhlEPPf+cTAKdcfVmWb/n5MiGP036UE9dH3OwnurDH+2Md+gH9YLSbQxP1xugHNio97CR7PZxydwTl7k9s5yoRwdyfT4+Zme6b+BHFQOYNrZ7q6WRgOZU6QBhYhA+Amd/IT6OFajk/DImtbg/DFknZb2p9dM6OSzpEUCyWUOEFm4pvqxXllK6e1BAC94vXlcxHqvMwULpNvCxs9uICdlK4a9J1HeiLYWUhl8Ly4ZX8eoV1xZbFFyYaTvxP+O65YIkpwMzc5X/4qDQimtjEn89onWP3ETHJ5PMare2O/0Z36COmha9B+CbwBCGYQXeF0SRqS3drOXmiZzHH6HiYCSIjDoZYPkbHNWWNJInDIMZk3ER8dntEsXH8zj+LFCKSlNPDRUt8QXb9+BVqH2MZc2CX3YP2bm/OZJCGZnMz8FiIalko24qIutv/7thxu2fq/eadeHKObtOC6ApSIm0g4KkQmZyFNT6qOV9NI/YZaxTgC2ZO1TPBHdZE4iMe8VZqNBceL/T5p7/H7R//Q15+oPypP/fPowpakvugxsg8ubu//X8KrRlqUFIia3KfBpFTeSca43kcsumk3oV/wNNBLUHsl8DEjRmcGch0Suh4mLDksjjNahbxCA2dOC8QktX4WcShKQ4D5aLY8OZsdMf0aTvWdnq9YcedRKf3g37coVeSGRK5aN/18b0urgKPk3h+SNO0QvJpunAaUuNFt6SCFKMP7wYnFpZLnuei03KCAzjVKr4JjShdC+oQPDCenH2h1ePETBmJTKxUQt6pD7s8QZGMK4+6eRxNOO3obCXgxHtHr4zWz0UFs5N4Pqnx4ptSoefs4XfjA6Vk8mVz38tSkJLJ6Wnbak+S28Dazq1qvN+T2O+SzbjBxA8BxVhSArJjju2gNzdt6dMXNpgMOfticSYudAi3KfcNsJA/Tucxm0kQYzww3bnJjw9j3nhnpZlTwTwc44+GuXH3hJP9e052Ba6oQ1x+KuZcZSSWNOBshIW0lHPEtt4cprjcsbcvjPsbSqNkQ9QNowcwlsU33SjDdixB1+Yy0GyYevow+YKlDSkXN2UZSpIUnhWPpeuEZLzoD8bwSaYfO71W35jHxTSVZ6N7G6c5FFn9qdig4X0B5eXP8MM/f+f66ce028F2XV2oI91lt7GYcCmwd5bLZXPD6WAWeBPp16OJ2wpOvPX585ueGrMJ0vPzs7je+oOHGgZFWRWmL0RIXCUltDzSMM4DfHQYclL9vANPzKTXpMkjdY4eopTGTIuw1qnDc7nsuCF1h9EelpPjcSj8UR/f6+IK377JHlQnH5tsxBIhxddt0lMc+HYHJTs9IefSxmLj7VJad1tS6+FBGV1EFCMnm8em8vzZ89TOESciuPu7exKcc0pACYJnLUkapw/nfA7MgtY79clV6pSamkF0no+xL1Jl14W+bRDj1Uj6sDlMoeJy3ONUkc0RXYadhPGHz8E4rdu8O/cLzClnjhuaVXpvvoCi00ejy/DOMmdS2UjLhZyviCzeyJi5YUlyX9uRknfeptBPtCcOJIlGU54+fx7PceLA8Rmcbgvi2KPR463XUOLok2TTsfAReWaj1/P9GH3KawWb3rWhtccWLDekrCzLlbS/I72StINV9tsXh5ZyYaTVPxvpNJRGwvJGKgtZgoS/bJT1E1pe0eWFUS6ksriZur9owA+9QSxee0MZjLazv3/Bqme1pWVFcevCMbvVYciceMw7SZtxMcMPqu36G3z1Z37IuN9obzv7MC5sUIhJys7D0d/O5Cq4zWLJY/S2M3BhiS4rGtQpP9h6/LtIH4jMsnldn25s0/DG3OsrCGgwubviy0h0Gjel83voeQ3ICcWJzW7eX/qYh3cbES7pqRizwUjitpw2untF7HcvrmpzxPzOj+99cZ2b/pNSE0ziXgftqCeJuCzFx1VxrHCSg8fUCuNFSmOT7R1Nw7pR21yQrSR86SMzvC2iRgZxncaI7n8WIzHJn1eYXpywgwhiGosTJ2l7amWj9hoEfb+oWiziGF6kZJrUyMwOc1URE2OOgutmFIEYPxk2w4zm8IPhND2O1+Kk+nGO7LFF8e7SWxNmhHRvewTz7a6t7wdYR6nQD3o7nIe7LhgvWO5upScC5MCuXR3n41xya0VJmHk3jDXvdqWH8cvE/h64mW915QnmmFOIOyy5hNZduIwcmHrQ9LJ3r0nzCc+0prjrGIA40ZzIc4qhuAVUQnSVopmyLr6YM8/AWtKCjeaCv+w+Ex5OOZAupNTJWZCcHU9NG+X6FXn7SLq8wnKBNJHjmCCiE7TRGced1m4kq1B3xttnv6aXFZFBzuJ4Zvig6FDfCzATJmLRNnrQdP3wL0tBs3F8McbeqXKwXDKUgeUY7XW+3y4pdnzaZc7393enapWF5cNH1g8fTzigz6ocuGU/HEdPKYMT41yEM4vwnD7igHOrzsd0cmKu8XcnBHj6CetjwrUx6H36MQcPtzfacUeCwifWTtMgmUvbuP5bP5z6FeKc7/r4XhdXMzulp6ouO52E/FY7X755ZxyD7bJw/XiBbQEdJxAtNhOvvKtheH6Pb1Mr7did+pGcWqJWg0xoIIsbk4TQYFJeRvfeyL06w5tA9DSCMEJjfXZaREEwtzZ77kSjcNOjwJ22bw8+Y1LPWZ9yzTFHZmI5d3YlsUSDIPc7WDcpTNOtaqYuWO9Bo+rBrODb47h1z4Kvd3p9Yxxv9PqOjN2tDunIOBj3d19GJUOPFbVX0IplYyiIXJw+FgeP5YylguQNTVeGxqafBjJwgcQs9EFYZy7fYjJRN4O26MysNY63d8y+pny8kpeP8dn4e+cHngApOh1DSvZMNlWwQWenN8fU87KQcqId1fX15vgsnlfozyllbHgo5LpcqO3dR87odrVA7oLq4laPyZN5exdk2dDtK2R9RcsFCwvLUwnFhC89F6vdvyChZmv3N8b9PRZVLdglCmRGbWheYvc/AsbKXsiiY53CFsT87VBP8Rh7p98bb/c7Ugb5kpDXC3n1cEgTpcVtlHA6137bafsdzdXvgZTRl9epT/HP5qm5SRITmXkqcmvtgdtOhNecVSOaPEFZ5GwOJjz3bf8ATsNrzNBkGC1w3lBJigSfebjYpVcE9wZm4ssPZAmx8A4WOTng3+XxvS6uAI9MnHllz0IHmNAO49brAw9NMKzRDw+UEwtKljmNaL6RvR5ICw9M8UJkvTleJtFpaIoOVtHwj+zdDbM1eXiaSEQND3fNnxvK5+I6t/JzpAXHerWUKGQux5xdtuMQiVRK4Jh6Fs6JxaXAbX0hMP0ox2k87c8jtquxLDCL19Cc8H963PYeFzfnCOlj3M7Y3+i3n9FvP4P2jnD4xW/DN69vn2n3dx/7twXuL2h7Z9hB+mikDCNcnjRlenanIysv6OJ81tbdvX44suMXvA7oc9z3x2RtnPAGYfJ8vzPsa6jKlj+hW/PwvVSYaaH0uOnMu/yUg20iidYqTQ56LEOX64szHVIlq7rDV+8MCws6fXRPqngooHU/eKdEdngaREorOflI7fBnRtYXZPsQzvp/kGftvzwkst3fqW9fw/4Zre+0m3OCtRSghjpwYLoCmTEqMlafHnKG4mT+EYd6SkscdGDm1nqG449t79xv75A7KxvLZX3iliZGKAqHmSuwLg8/ZcyxeevdP8e4FywWlUnUi3ncAz3k5Dm7r67f6w+M3Q9UDe6snqyC56Jqv3C/IUIuBUvuLaBEdE3yKUlxOErMJxMlYeKJvJZcOjyL+YQr+p/k4joXWjq3kWHdR4KyueGFnzSuctLR2d+/MO43sg6ShNxxHIxRyUvyLWCtHphhiWZGGx5Noksh9QtDO2kBkrofZXwYml0Km3KaWynMzE1iYsN5jidzIWFOn5l9D2anWmwEDFDglBtO7G9YBATGRTVvOmI7PGWpEyf1QvsMERBA/wO77DW2zLUxjuqGx/aAURiuWKG5xpr9C/blZ/Qv/wirX8hSUQ0D4+Og329O+leh35X+/g16+wL3N7RVlq8ELYrogiVFSobtQm6fvDuNsWw0feCEqsTyNxRPYYBsxCEh7uUijrJKbZg2pDjNqx+VtJpnNwVDYEy2gog7qsU04BHo4llKmn1UXt38OUdH1I+ddr/FtecY7uQCz2IheYFaaS2I9QgicTguK5YW7x7TQr68otsFK178ebqBpxkRIRm1/Q73O3Z7p+/fOLmdQevVmwMz2hikJeK5rdKlQiq05Ph7Whan92kO1kvHI98d1uitIlWcvhZ7jFyKT2XRFmjOlHJhtHIupdKy0l9e/FoUfw8nhHfSmOyxFDrdwUJJeDpZMbv2sCAMaps8Nr8PKOBpYXYKgwJrdiolkFyYM1qNtzaUWuIyaUnNk4ZlePpIxDBJygzNIDUKbP0TTMXCAecxBvSpf+/Bp8usl8y2rRNowsz9Q4+ff8O4v9PsQKQiqSM6PB+wC705TpgCLG/t8JtUE2MvnqxqHV7PJtJd4cUXRpgEoXkaPzwI37PT9A1mAPYBnqvP8I+uNi4MU8/cIinJOHHQCTHMD8kXEoNWKxBqo1g4xSXIDO4TC9GDgsmAHsuJ6jAA9duQgAUBnnM7fmC3L7Sf/5T++adw/xlS3zwhQXwctiMc+eNgQpVxAz1uaLt7h5MW1q9WhzUsY6Ng6/R5cFlqTgr3BC0zWoJ+8/clttIxWADinYY4HNODdC9DWHJBl4tj05FbdoYYykObPjX38Ur9KFKlbCtFHrJRVzklJBvZFpdCAp6cALOFNfMRVvPicIgQS03xDXcp2HJBsjupSVnIqxuwdLzYY/YLB6gX1n7UM+1g9ObFolcf+kdHqmP2xdyrwKi0ik9bZfWuOGfStiFlhVLC8yFjPbtSqXXa0bAD1DJpu7B9yORV6Wa06NgRn7T8+vflZ87FnbjGdKBy+MH3uRIeBpyd+XzPvYA+RCvfhqNCCRfXvEzMVWJiDYx2cpYxw1JC+gOjD6zMfQNQTBIDl4RL9kTZKSYB50YPCce1dIBURKZL1p/U4mpGPxwbhXCzX9bI+vMuKGnGutHqTr+90d++IR3foPWd0e60cWfZEsua0WG0faced4fU7ewlnU4CWE9YLFW6gwTYamh6AV1OWHQuhvzD5wG4qwZX3reUdhZXHxU1usszjjpcu+a2c2JK0wwjSBBPQH13j1o8K0jBi2F0DFMVY00e+PH8RgB1ID2MbOwBM1i4cYuZd7X3O+Obn9O+/hly/4zs74z6howDs0637tv20ZGTe8pJyJc3Rdev4fr79Msn0vXq2+2Sgx3g3YRgYfSRaLeEHAItbsrWkTTAfAFCSogWV5mVDSQzxJePqawsr6/o5epwTXAhBQtZaPz+1Pk8lqV44QtHJYsdlr+mOLZ0sjQsVD/muPZ5ESWHAaSQNZ3FlZLR7Xr6QkyHNMM/mnPisEd3ZwEtOJvEwq2pI91OKumk0fVaQe5IBeGgN/FpTHY6Sto2LpIZXRjNwzgldXqDUQfSzc1kTJGycHnZWD8kula6dVLsEVy/MZ35Ld5bzslMh7NdequeKJCTJ1FEDA8xoUwhhEU23fMBl3PmkbY6p0IeRTOeC3C61Lms3PcsEvj880PVoai0vTibJxfsffoP+/NQbVguWF5c/lw6Y++YpT+WYfb3uriOYezv7/R9R5OwXDey+HKA7OFvauJ0oOOd45vfZ7z9Y3T/Av1OogLVGQAUqJ1+uzNmp2pxRgonP7WkjWzmCxxrDwvB1Y0ijOxFIWz/zpyhiavKpL8oFqmqox2M1kgRhyKijKPy/vbG6J31cnFX90mMNteRPxQnIy4bZyB43IpRRKE3V4317q8hZ6y7G5AHFrraiqeDxNzZmKn/Psc38N97Z9xu1M9fGO9vpLaTenWf1R4S2Ciuvbtn6aTHpJzQ3pB2oMcdvb2R93d0OzBZsFRgSaCLL3jMX5NXjfAwSInaOpI8ltqmO1H2EVtW9zlFEkJG80paNvLqJsuScuDOnIYnPcY7OcdWCwqWZ0XN7DRnhYzzLPIOKAyhh18zzo193ODegRXS6tdTSr5Z11yQUkjb4t4X+qS8mlvup67OP5txYq/n9TRPWDRgEYtuMJHEo9ZH90NCTBgtoBQsTF1Buhef9n4gaWDHyte/s2G9UO8NG0rZNq7tgr4P9m4ME8qaeP8yPRr8OcqcIizeZBMkSTAiJdgaDrmJPXjmYy4g4/6YWWhzOSs5ZNOxK5h6rW/tMJ4ggdGbiw9ISLPzM5vqyhMXD9xZcoG9uDctAzxl3PnayUgLXk9wYx+aMXfO3+XxvS6uNgb1dndPT9OTX6cp+Y0mKYjobrhg98/Y7Wt6/Yxy0Gl+IfTB2Ou5PHIrMSLD3hi1u8fRsjhWZgPt0Pq7y++y+EZXZ1ZQmGJA7Fdc+x1wvUMABgzXsNe90m/vpJJJ8uJ2dq3R7rvfVJcn+zceZjXnTTeP7yheouoO/r172sL9ho1OWlcPnBuh1rEoyH3MJxqQQXQjTwsUeHQIozXGfcf2nRx2fcQIa6J04cR7J442xN9PTYG32XAF1jjQ+g71HcqGaPA5s4/diYg6Fty5KCnsitSDXpu79Et3j9Ccydcr5fKC5IU+BLOE5g0tK7pekeWCpYhyju60hVmIQzOz+3JBx6g1cNiMkM4CG08Jp+a0kzbnCjW/Dqz5wZWy47dWFi9C6rlraVncvlGnX4B/ltbi0H6ik022yfy5IkJSTwRGEqaewGAj/FSD+udMl4ymBc0rZolWoQ8lp4KkQqugaAQG+2H4o1/rWF+xvvrWHmW5riyXTJeGtuRxMNn4tR8YZXssSk8Otr/as9imUtDsbITeu2PPafJcQ+00DzmEZ2+Pk1oVDYSk4Ghr4KnxM09T/OlmJubMFd/EBoSQTgaB4a+dSLAwwalXo8dn2DB2TAqSFdGCSWLGKv1iJ/xHeXyviytMnqhgtdOOSlr7ua3HFBvVlw3HO9l2mh2YHYy+0/uOqNBH9ayt5FtTEUiSEVN6G9Td32iVTq8t6DvmIXT9gH2D4xIu7IKRHp0N+CgufrFMxoCnXSeohaSFFr6owLnAWCLvvuQS1JoZwfy44J4/Wo2vTbeq/f2d959/zdjvlG0hLwvMhdYThjUZCr5mmRgXPCvE5kDlY2kcPuqeBD2kvYyC8xb1NIl2qb/5gaTKXLhpUlL2Q+B4+0LSr9EPV1ivztctmS7emY4wUdEYD/swyHcoFRQ0DaQoLJl0uZIvL6Sy4TlUILqR8gUP+8u4/ZNnK7kJiN/YKT3UeGN4YW3HQc6JeTf5/R8y5FiUTA9VDRjDKUMOgeRlOalbhvNByfEcnsL+Tqnq8HBNM2Nd10eDHBg7NrWGfv27qk39FwnVEt3UCMK+p6v6lJJA3cRISWhaMMm0MDOXwJJVlJQbuhzuvJUVXRL5IpTVi2gb4sbSImhyHPWcfuQJBw02jxezUE7KCGw0/sGw4P32h8R7bv6fIREe2POJ0Z7LvvG4Vudno/F+DW+avsWVTd7pT6gOfDXTrDP6CtsL1g6a3enj7veDGqkouVxRXTCLg/87Pr7XxVVUSWV1/LxX3Klvhsz5ONvuN+r7Z9r7N2h/R6kM6dS20+rhm90eChDzC97HOsdqe4W+e5FoaiybL88sjB6SKNw+07PfsLr6aOdW2+oXE969MqGBE4ctpDVOaU2UJJSs7MfOcbujQInojegB/CSWx3LDo2X8Ip3wgIrnub9/85n9m88eT7MWfOvtyx6ntTyjUHO5wNnE+gjuX5w44+hhbbcu1LIwagEKQnEPAOmIVR8FkTgQomAzMHt6LbijWLvf6OVG2Q7SEkU4cFb6gl6v0VXE+NkGab9jvSFWkNTRRdAlo2WFvDLUsVYnxG80Cmbp5CX72DlOT1kv9m6mrHhn1WulHwdJVujuyetLkMBYSSxlIaVE2yOMcATWbA8TIAm5s4+eLom2SZ2bE8lUKs2PYHKSA2MdZ0cX9Sh08v6Zrli+MFJDNTPUOy8RY5BIaUXT6pCLZFS8+9K8OC7dfNoSyfTdqF0YViEb64uwrhtkxTTSk/E9gGo+D/Nz/WcDsQf1SefiLu7Zh/MW9NY4juP0nh3N7f1yeAbMyUdTmrzChwcwxARk3onmR6PhlGXx67X5kmvSM0Un/TD2D0/Cgylht2Vh9It3r0elvu8wEmUt5OuKSKdpQWs7hTjf5fE9L67C+vpKvxcYHV1ikaEZM6XXg3q70W/vHiVhHZdo+ofocROc4yGq5ORRvdb8hjneBtYSmkFHQofQ646Jx4EwhPZl+GIkZUyLK0ciF0vMSPIA2icGiwqSnO0gKXm6ah/QGvV4px6NpEJaRnjVTuoJTFGC+wxMiSwBrntUyNgP7+RLppSFVDY0F1JxLFOHj6yjPYj48EAY5kg6x6NhOD3F8EJQMrIUaH7DmjmO2LvRsdiK+6JJEOjKMxm7j85x7Fi+Qz7IuG2g46DRvZRMJsZBVRrRQLaBbhU1gfEFTR3JA4oiecEsY206YC1IvtL1guoGM2khxncJDNfrbeB31iOyZZwYc92Y1x6yAAAgAElEQVQPXxwlV5D5mzSXlHFUFHdba7vRaj3dxMQtxM4iOouuXw+hihPH3xXIIZmendbkfOq3ik6kHJsg6gsZUXWjm+GKIxvmEES5emqq5tiM65l5hQrpUlBZ/BBUQYLfKUthfbmQtpBn6wNrnvQo4uqZdp7EazllrAFfPJ7zYPR6Kg6P+30ed3HdpRDgRGprPMdnqpWKUFt7KAfTDOX0iWGoG7wbFvHgYd5uBhpOeVO1Z45TuxosJqSc0e3CAEo3aoe+78hayC8XJBlo4bjdIZg53+XxPS+uSnl9IW8X3yrHcoAUeGLrjIgWUWuuCQ7VhaRw7glAXHMi54WsK9Izt7eD40tD+hyVQyp3HPTeGDQsMpr6fiePTlkWLGVGFjAnZBsu9fTdd5zgcYNMiWZSTxegu8dlWS/U/WAM9zfI5kuB6ZwPPib25mOry/PCdzSpdyFJndJTip+424XlckVLoZvEUmb6q04YQM5C4cbPU2rKySc9qUopIWtBx0InQxOGQMeovcViyL+nb9Fj6WbiI2BrtLvHd+ctiqbJ4+Y1x1y1ePFpDIo4Na7j2GBWhboh7HSrbkqNS2bNEiaZtL2SXn6IfvgB6fopDt9HV/goctEJNY/CHpFigapzkOuBmKJxS2iazABfaKkKS149IUL9YO0t0h+mOim8Js6DMl7smJHNT8btU533Lcw7rvvJ2R6Gy4g1kcrmvgh1x1pF8kAtVG/LiuUFUw+qdD+NmefmScmaMh4Em5zDmhJ5XZGSsNPRX8997NOziW69n0mx8z39Fv5qUxgQGWVj+DW6lAlGnSZEKbpTSXHgy7c/KwyHX2y6qbnIIQWGirnKylr15uAJ/hICU41/q9YnChcLzB5LNvFE3JdXXsri6rYwY0ea+zivHxn59h2r1/e8uCLiS6Y1k0oKbb3Qo7A6hjWzccJr9SR6Z0qJsSVkf0kXbGRunyu3zwfJorOxDuLk5n2/g1bGqBBbTHL2rionJ/V/KCFl9CInOFFf4zk/A7InEE90jQXK9cpmRrvfSUVJc/zC3MnJ3Cil7ff/r71zjZUtO+76r9Zae+3uPvfOm7EHbDFjlIAMEsQKYItXSIJjIkiElA8TIuE8UMT7KcCDJRDfcIIigkA4VjDh4fiBMcGyhIwTIvgU5wV27CQTj2ODx8TYHmc8995zTu+91io+VK3dfS8Tz8ydc+45g7o0R7d7757u6tV716r6V9W/LHHlNajBKxpiCNQhM169Ygm9aF5IC5Hq+Jbhqh4eh4UzzKsRTBezqe4BOKm4ilEGhjERNyMwIC3ZaJIZ7+gyb7qqe/R4KZr/a0P3gkMHEUmZJpG5FBJYK6nb2WWUc6s7fLnfGKs1Mp0wnVwziCb20SWWeBg2G9LRXYSju4lX7oK8tk3MHfWd4bIvXOaJNk+o468552VaheLYXWuIeAmd9G/ndZlRjHUyRWKLfSmthC4l0pAd7/VNUlkMBK0xTzPb01MA8jj4ZeLeX+yJtltCWFVaFaz7Khnumpz/1zdkm6DgOLOIDbw1ENGmJ69WtvWrRVYSrVxPBiC2PQx0L3fqVSTqk1JFxEiz2UEZsj/GR5snxhwuUax9PAbbJP33EPvRbbftlQT+Hl512iN6iy4wFq82z0YrifpEhMmntHriNwRKvaUmtTkLXb/2neNAm5Oqt2bTQDZ5aWAQwWgJUyVu7kNO/z/1XAHLsEdL/KjjPFZWueP87L3PDaE6FhjS4CUh3nmigaaZctq4fu0EnSohNtRLMFJqIDPzfAoY234Am8qKoGyp166R4mCjXo4EBqPWA0V18PIub5mFJYu6YLEiRkiiSt5sjHcWRVJY4AvDKQvz6bGxoYsQxQhbmghpSIQwQFTLRHcWITrRx14ZDyzYmOkQFq/aCv8t+dXcSIJDX0GNKnEUdLIBirUZq1GZJoMqajVP12EKDRjnrI/c0RARsd9uNW4I6w0tOy/nnuezkIs4DmtttOalhXLFKOBOrlKnrbX8DonBh9cNmzWy3oB7btJ5TNkZVvNqvJV0u7W25yCkNJLSgKZoG4Zzd5apWGuzKroyaCAF4zntYa95n0YdGd3YL1UIbqB2iRj7ns2bP1opNJQ0BGIvzu9wuF8vIQTikPz6H8zb8s+VNJK8Y7HV5s0vlkjTfjVE8WL+aPyyPgKpJ+iCM6tZF2hb0nT2n21G0r1HWHDLgFC8EsQ82maTd6UbTk80YRGBo92+2XkS0KMnbbsyK7v0GkH8+lecDhJPhlk9uE+yg7mgk0Ug2uGfEKwKo/XaV3cUFMQdMZvka5wZVmduUxZyXiEJsxU9Y5CVfOV+RONt265LbVwFrHA8uVEQL3XqzohjNjFnaCNVR/NA1Uqwgo+dsBIOKLNSJs+EJyUOhZgaITaaFCPLLbPBbs6w3wck1trQ01PKM0+T08BmvSZqRmcoeoyENWm4ArLyELwzEvlFGkwHQkBTBE3+PZqRPDebtUWrzCfHHD/zFLRCHjKi2dchAYMPETS2qSBm/EutNO+66sa1t+HuPESfe9SNPjYdVb33tQ8UCDREZ+psycLTZ55mdg6BOk9QLFqgJ8W6574YFKvBNOzT+urzuEHXG2TIZgh8hnNtle20tcSd49MhBEK2rqg6WRdQ8GReTOalkyItD4QhIynvjXw2aRgqYv3uPjyxs6P1b+9elNDYbiem4xPmaYIQOLr7bt/IOqWIE//Mxu4ksJt6oT50sjUjtZGwtCTTkznuQe3oJPsG0xbMFbHWXJG9iQKeZa+lIqEiQ28Jj0Y67gZL3TvvG1X05FCI0Uat9A0M8HGN9GGKIuLA1t68L1go/eJe6L9juDCvvN8fIkKK0bumHN8O8aYyvqC7JgKz8/5uFvLZaJ46W3t5FKcG9HlfFYeyrHGiOelLitFHgEOs/tuK0Fk/xTs7SzVuS6ss6LmY5glMFv7bzl8hDYajSAjjbduvS21c8V0RJ3CA/rt7IFttFw1BfEbUykPq2T2MsEyRbPPMPE0IldUqWKJHJhrOorUUq6sRW+NMPGK3YwxWIRCakspEOL0GtTBVobTIsLmPcARhEJDBsDvtY2S8pEZ9J7WUqQH61Y2ZiIVBPh21nZwgWpn8Qkqr0fEw8xiJ5iEGsXBQiw1QtM3E+BbEV6rfcJa/7/iWLXHvUKqqxhlaZ2Nov/4M2y99keOnPs90/Wl0Mk9avBvMoFm7+azG1R5X7YYrID5aJqSBkAfw6QjWXmo36HY+5fTkBiJCztknx3bvutGSUTR2fokQwkJdKCn5tIV4kze8QIZua61TzpIjnUZvYRYTg5am4xO2129YHegwLCQkxGZE3xXw2VvamrGVOaY+9xpOr0kNwTZL3f/zeDuE4Fiod+LVSpkr8zwR02BQVuhQlm/qfYvuSR/31PYBqOJ4Ym/ftYQlzo0hC1a6tKQ2G5TZmk2XIBimX2ZP+vT1dixZ8aaBXjbV2nJd79+v2hl2mxlQ9Q1b9n+b/rhDEJhT0cpMqzPJ8wDa2hL6989sutsAdtUaViY3rtx5WMq5FG2F2uk8u+esNqKpLVi5tUy3fo8GT/jFwZ2X25PnNK4i8nbgTwCfV9Xf5ce+H/iTwAR8EvguVX3azz0GfA9GQP9XVPWDfvwNwA9i1fc/rKr/8PkoKMvlgydiMK/Jw4U+HRLE+qdro00sI1y0WPhUJiN3llBockLlGG3bpag5RsOJ6G10atwAdapIGhjGTFpvIGfatOXG5/43tSpzaQybuxlflmG8CjHbBa27LpzFV/LQ3Op0EwxextIqoUzUact8/Rr15JhQ5l1oGyNCtrKwPeC+v23HY3vIbZ5Sv8C9KsDHehODZ8rNu1hqf4NQ5wrzTL1xnemppzj90heZnzGyEGsIWBxWS1KEaKExsvNUnMhc0kDMI8M4WiukdG/GiU8UWmloKx21MDKQwdtMneQcEZL6RIXQs8l79ZAAWr0qwkK7jl/36bTVJwmghrV2PNFC+0CdC/N2i9RGSonxaENKyXRWSxypqrWaVjNG+00fth4sGerOqt83CBsv0naJq548EvPwQ2g+xdiYuqSH2v4+3euM9j/vjKd9S/eQLWkr/ToLYccz0aESuCn5g+tstlYpzpAmDjngI8Nrx1GDEXIvZWN73982tN4K2KeGdE/XYY+ucT+vxu5mXArFNm8aKjDXYhN6e1s3XmVC9zqNXCb0Scb+nTtw3XVstTLP1sV4E3QRApKil875FBGB0LBN1FyeRevbkefjuf4I8E+Bf7137EPAY6paROQtwGPA3xGRVwOPAr8T+M3Aj4vIV/v/88+APwY8CfyMiLxfVX/xOT9dlp/OCKd90qiNq3BGqKZEEjFYZ0ZpDWnRZj9NE6VsiakRO9ejzjQ1wmdR24GlT9J0cL42Cz9qU9JKyUdHVu84V45v3KDME6pKzCNpPCJpIbSZoNVYt8Bw3o6BhrRwqnaGeGsQM/yz1srJteuc/PqXkOmEoNbaR0wMOfsNjZf+mAfc9e1uWt/JqwRUy4J1LZarGq7WfPxF96rDQmIdoFamZ64z/fqXqdevo9NslQFq480rcHNnzW7UcZ80EN2whiGjCNuTU8LJMeNRJaFO54axMbXG4N4cC7RgI5HLZCQpwVtTzc7o4n0sLEtqxNSqisTgraayGNBanU80Bmd7EuZ5trZHZAllw5DIq5HN1auk9cogA7GNtkwT8zQTQ7Lse+g4eXPOYAA3GmEv5BWhsmNvUvcoQzRnIYiVtOWQra15PzLTnUHGPda41NXaYV82T6zuxBl+b/Isl41+6cVXSy6614tHgWaEOxNZcGL1YDkz/55gGHNYnrelHMoMpxlPQ8J6remegmolcIigzda3TlukVkorXtHRrA06eOSo6tUYxmBlkznS0lG3QCTu1LSmtGKNQYuj4sTfQcyw9rl0EgMpRLRac4a13Fjlze3KcxpXVf1vIvLwLcf+897TnwK+zR9/K/AuVd0CnxKRJ4Df5+eeUNVfBRCRd/lrv6Jx3e0bfun2zVa9REatiFokE+OK4E0B08mWdgo62ahrtKKrQhiNXzQloVlJLF6UtBgqs1/Nqfh8twwzZTtDKkxzZbphuKMlRgJJxUt8CrRqhg3F5k31Epew59nhxNXKXCt4u+l0ckLdTgQvZ1FRhiEzBJsHVKYZGYJVWGGeTXViiSVZIE7MrNHgjR5SeRiMJ3gWj6fnw93rKE5FKHO18eHNDUVv3MBgCPMarczMeHQ9kx8SmqzQX1KmNTi+fkzKx8R7CqFaaUwrhTJN5i01Mzyh35jNMLLitZ/LfPq9i6C2Tvhdd2E3WEvoSiwR6UkZqzKwxFF0mKjKrmtKgHG9MqancUUYsk0sEEtildnqNVtr5PVAHgyfm6aJeZ4ocwExnFgUnwaxV/eq6vPPoDPbm+FzI6cWYQSJ3jhgG2LziGznpZr3vyRI+32ytzYLZWX3+ILQml0TnWms9k3ANx0x7iiDARwHNwiree24YiVelhDr0FwT3bUKLzo4VKc7QhpxmtCb9PSKk+YOgzN0ME+nzKeny4bdaoPmEQ3CwjjqHLm9OePWqKA7X730Td1uGBZtkFIYrMIjDcOSFKvutBg/BAsceTtyFpjrdwPv9se/BTO2XZ70YwCfueX473+uN178q8UIiP3I0sOlREorgmQGDbSpUI5npusrZB7Iwbo7Sq1IqUi2DGTfsat7vn2Ik/hFbY/FwzxlniZOjk8YZURlILbRkhoxEHS0UqUCsSl1ngw8l0wa1gaU700sEMcoWXqkFa12A0tTVjkTilrpUwzk9RriQC3NZleRiNn74hXQsJCSpGQXSZBIioGqxUvV1DEsnx7qBhb3IpvXMVYPoWtVqtoEWokDS9dYSNA9fjHcVqQnTbzGMiSIKySuQdbQMq0Ntgmq2G9UJuo0WwfPQmiTEbGNzcaVJ2I0Ixa9g82aDyLLiPNpdhIZXTzbQNyxYNGM7JrouKuF58kNXS2F0mw8dfJpvWlcWZso7Fpna3HClkCK1vlVaqPOW+bTLfNcrOxI+mYju2TPgrN3g+hef/MkZLO++OSbwTRNdv35qJldmyhLkizsGdebsGZliWi0VVoIRAn08S8WNnvY7oTplrzsuQuPPoLnOlC0TDRtRvZddhMvQkjQlLnM7mTYpmtJPxvYmDxCq/gkV6+sKY6t9o614EZPQmQO0di8UmZcG4lS16WWaty1w0AeR8PuvYpiCfm9bLFHNf2c7Qfu6LghjSkthtUQQbvHpu1kVTop20if25QXZVxF5M1AAd7xYt7nlvf8XuB7Aa7e/3LM/djVaTYvdE7DiK4atICeztRtZXujME+JIV1lSI0cKmWqzNMzRIlEMY9pKuYx9RKXjmCqqJG5dF2CGN4mAdGAyEBMR7RxQwtOjB0zTTfQMtTAdLxlKkIchZjXS39+Tyb0KL3DAzEGaggG/Xr2M4rP5soZohNJqF18MWWaOj5mwOmOBMSTV2DJpibiN2Rz8uaeqd1lsq0W0AvrnfSYlGFYE/KRVdVEG+omTUlxNi4AKhJ666KD/2O2sp+UkbAG2aBxxerq/eS7HySE0QzaiXWXWbtpI23W1qdeG2gh5GzMZ+xaIaGjHP7Mqw06RNDpE4Nn4Y0c2zfHeaZstxaVDMZr0JoZecXKcYwOMTOsVlZl4nBTx+5sDLdArczT1sqgakG0MUQrI9tFB6ZtxwitLlcW/gBRWQyZQQP2mloK83YixMjgSboeovcYrkdVIYhjjrtmhKZ1gYLMY7S8gY0633NQHI+uxTbfNk+UOjPkxDhmM56e6C3z5N59dccmMOQ1IZixD/bh7n0bHIEntKrDD601JGdS9KkP09ZgGLF8Q+t8sKURQmZ1xYxnHke/WWzjJxiMlHImpEwD5lLMMcHZ8lx680d3POyesBbonpvoSbYFJ1bl9PiYG9euMeSRq/dmhvECqgVE5DuxRNc36A7Y+Szwyr2XvcKP8RWO3ySq+jbgbQAPPvw7tIfqi0FykDwNAzoKdY5snzlmemamHKsxJK0ToW0JQdHZM+IYdjRtJ6Z5i2I9++LMN0F6zWKE4HhotcxhjA47hDVhvIuju+8iDUdUdVwsQAtXKDUx10YlkQfLQPaaT5WwZO67ueh1fqpKikLIA226zjydEFeJIa0Jw4jGccGZCJHWoHYaPtkNbus7b89wCrgX1Uuy1DE1M8TNYQxre7UMuI6ZePWKdb8ECHWNhNmSSKpQrVi8lQLakGQdP2kciWNG4mDtl3FAVhvW9z3AeP+DyOYqxIE2efa3WhJDRAw88XBV1JNAuiPH6V7aMvIHKy2LItTZiHl6e3NwYhoztoHaLFNcptm4PDqc0CrTdksIkRStnCvl0VqcfS3BKRn9O9dpyxb1KbJW2paCkdpUCU6pZ0Y1qNpNjNi964ZaVA0rdIY1gwUsiqnulXWWLcTrSuEmb7WWgrrn5ffMbp0EI9duNnyzyozERAorxCeahgDDYATTZa6eqLMZZjU0QqcPrBUpk4/8icZmFgYjlsYrwd0r3C//izHRvPLGvnvzCKB5BKaMgzHQTVMxoyqCSCavjYtXko3RRoQ6zcwoVRSi0iRaPXuwUT5Sm2GrdWYYvEwx2PorvSJGPKLz8rqlzM1zE81Jk1rrzv2SDL5duS3j6pn/vw38EVU93jv1fuBHReQHsITWVwE/bT85XyUij2BG9VHgTz+/z2LZbS229YPBwtSqhdOpMU+NNGTyWghla/WItTK3mRAts21JFAVJHvo0OsFcCME8GOfqnKeZ+XQmiI23GIYNIa5Jq6vk+19O3NxlTEVeRmNe28iQlCENrNZHpJTNO5H4LLN4dsQVoAxDggDbk+uU0y8TGYlXrpA3aySuHBXzpA7OG+CEwz1z3asEQugcmiyZ1YUkg91j9Q6WTrYNkMZMuOsqaUjo0Yb5+BqtbiEYLhklsj3dMk+TURkGIY6ZOI6k9do4M0NEciZfvUK6eheyMhpAd9usQsIxaBFBnag8ePWCsUippW73Lu5WLdEVPfzrN4ip7rhs6FFBBMcsY0yErA7zNMOVqxFGx5VRA6ZstcRGlmNUjkiv43Xqwp50aYWYEnncMKzWiwcao0Ej9O/nEE0Pl4OHLb3WdRn73MzADiIM47iE+rUZmbXfc4AldqrDDr2Ivvk1ZN1gpntzwhTUxt3EEMje8JA6V3BKaJwdtwwkBJ23zFsblx5Q2jQxn26RlMibI0hikYsITW3qrEjw+Weygy2CMaT1CM0mOwhpGAkxM6RkTSm6RcQ252HMC17dPWOAFhs1RFrEI69IEdughsE62bYnJ1Zu1UsAQ4TgFUOteRJ2rwzPS8la2RkaEWHIA5urV6x8MIYlcrkdeT6lWO8Evg54QESeBP4+Vh0wAh/yH/2nVPXPqerHReQ9WKKqAH9RbYQnIvKXgA9isd7bVfXjz6mdYkW/wangtFpg0y9YDEsKOZCvjKxzJEcoJ0o9TtRjZW5+8QxiLZOyJoeESEXEWZ7ELl7zSAZqbZSmxAEi1pcdol0UcX0FWV+hrtY2e2cFQ+t0donBxiQQU/awbse2DrcklvxbWDjTOLlxjenkBlJOgWhF7M4V0DvPPBWyvEef39cLA1QtEaTg5BZ7v6WHPksywce8VK+RDUFs1MxmJIwjbI5Ip94dlQKrzcbSf6UweFF2iIGYszHtjxkZBp/blJAh2QbUE3nNafIkEPJgM6pi8nKtwbHFHuY22MvU7nDVXghvnqlVBkQGGRcvakm+qSVmhmEk5IzW4hNtLQOdV4n10RHjekNPhStKK415LoToTQ2SGMcVg8LpNDOfFlooCIlhtXaKyrh00TX2+u/FoYpOThJ2adrusd50HQTrxG80pBZ6n1Nfg87YX2dLBPaWUzAPsUwTWmZjivLSqiZCW69htVqqACwa9GF8y5UFlEo9vcFctgSttrmUQmvGJhVCXq4dpbeXN+eJ3f02IQi17XWK5ZGQMin4yHOFkDJHYaDVxjAMpGwtweZAzpR58oaSgTHFHSds132wzsUYIkNjISgPHn300qw6zx7diVdy6JIcN6+aZdPr9dZE53p+EZMI5KYi4EsmIvIF4AbwxYvWxeUBLo8ucLn0uUy6wOXS5zLpApdLn8ukCzy7Pr9VVX/TC32jS21cAUTkZ1X1ay9aD7hcusDl0ucy6QKXS5/LpAtcLn0uky5wtvrcfhHXQQ5ykIMc5DeUg3E9yEEOcpBzkJeCcX3bRSuwJ5dJF7hc+lwmXeBy6XOZdIHLpc9l0gXOUJ9Lj7ke5CAHOchLUV4KnutBDnKQg7zk5GBcD3KQgxzkHOTSGlcReYOIPC4iT4jIm+7A571SRH5SRH5RRD4uIn/Vj98nIh8SkU/4v/f6cRGRf+L6fVREXnNOekUR+e8i8gF//oiIfNg/990ikv346M+f8PMPn7Ee94jIe0Xkl0Xkl0TkdRe5NiLy1/13+piIvFNEVndybUTk7SLyeRH52N6xF7weIvJGf/0nROSNZ6jL9/tv9VER+Q8ics/eucdcl8dF5Jv2jp/JPfds+uyd+5sioiLygD+/42vjx/+yr8/HReT79o6f3drszxq6LH9YF9cngVcBGfgI8Opz/syHgNf446vArwCvBr4PeJMffxPwFn/8zcB/whpSXgt8+Jz0+hvAjwIf8OfvAR71x28F/rw//gvAW/3xo8C7z1iPfwX8WX+cgXsuam0wprVPAeu9NfnOO7k2wB8GXgN8bO/YC1oP4D7gV/3fe/3xvWeky+uB5I/fsqfLq/1+GoFH/D6LZ3nPPZs+fvyVWJfm/wQeuMC1+aPAjwOjP3/wPNbmzI3BGd08rwM+uPf8MYyc+07q8B8xcu/HgYf82EPA4/74h4Bv33v98roz1OEVwE8AXw98wC/AL+7dNMs6+UX7On+c/HVyRnrcjRkzueX4hawNZlw/4zde8rX5pju9NsDDt9y0L2g9gG8Hfmjv+E2vezG63HLuTwHv8Mc33Ut9bc76nns2fYD3Ar8b+DQ743rH1wbbhL/xWV53pmtzWWGBfvN02eeFPXfxsPFrgA8DL1PVX/NTnwNe5o/vhI7/GCPI6ewR9wNPq7Fx3/qZiz5+/sv++rOQR4AvAP/SIYofFpEjLmhtVPWzwD8C/hfwa9h3/TkuZm325YWux526zr8b8w4vTBcR+Vbgs6r6kVtOXYQ+Xw38IYeI/quI/N7z0OWyGtcLExG5Avx74K+p6jP759S2rTtSuyYifW7Zz92Jz3sOSVho9c9V9WswvoebcKc7vDb3YpMsHsHY146AN9yJz36+cifX4yuJnAPn8m3osAH+LvD3LkqHWyRhUc9rgb8FvEfkRXAL/gZyWY3rV+KFPTcRkQEzrO9Q1ff54f8jIg/5+YeAz98hHf8A8C0i8mngXRg08IPAPSLS2cz2P3PRx8/fDTx1Rro8CTypqh/25+/FjO1Frc03Ap9S1S+o6gy8D1uvi1ibfXmh63Gu6yQ7zuXvcGN/Ubr8Nmwj/Ihfz68Afl5EXn5B+jwJvE9NfhqLDB84a10uq3H9GZz/1TO+j2JcsecmvnP9C+CXVPUH9k69H+iZyjdiWGw//mc82/la4Mt7IeGLFlV9TFVfoaoPY9//v6jqdwA/yW5m2a36dD2/zV9/Jp6Tqn4O+IyI/HY/9A0YreSFrA0GB7xWRDb+u3V97vja3CIvdD0+CLxeRO51b/z1fuxFi+w4l79F/1/O5UfFKigeYce5fG73nKr+gqo+qKoP+/X8JJY8/hwXsDbAj2FJLcQGqGYMhz/btbldwPq8/7As4q9gWbo334HP+4NYGPdR4H/43zdj2NxPAJ/AMoz3+esFm2j7SeAXgK89R92+jl21wKv8B38C+HfsMp4rf/6En3/VGevwe4Cf9fX5MSyDe2FrA/wD4JeBjwH/Bsvw3rG1Ad6J4b0zZiy+53bWA8NDn/C/7zpDXZ7AcMJ+Lb917/Vvdl0eB/74We30dmwAAABySURBVN9zz6bPLec/zS6hdRFrk4F/69fOzwNffx5rc2h/PchBDnKQc5DLCgsc5CAHOchLWg7G9SAHOchBzkEOxvUgBznIQc5BDsb1IAc5yEHOQQ7G9SAHOchBzkEOxvUgBznIQc5BDsb1IAc5yEHOQf4vzGjEK11ae1oAAAAASUVORK5CYII=\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "\n",
        "from detectron2.engine import DefaultTrainer\n",
        "cfg1 = get_cfg()\n",
        "\n",
        "cfg1.merge_from_file(model_zoo.get_config_file(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\"))\n",
        "cfg1.MODEL.WEIGHTS = model_zoo.get_checkpoint_url(\"COCO-Detection/faster_rcnn_X_101_32x8d_FPN_3x.yaml\")  # Let training initialize from model zoo\n",
        "\n",
        "cfg1.OUTPUT_DIR=\"/content/gdrive/MyDrive/DatasetTB/logs/19-07-2022\"\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# INPUT\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "\n",
        "#print(cfg1.INPUT)\n",
        "# Size of the smallest side of the image during testing. Set to zero to disable resize in testing.\n",
        "cfg1.INPUT.MIN_SIZE_TEST=0\n",
        "\n",
        "# Maximum size of the side of the image during testing\n",
        "cfg1.INPUT.MAX_SIZE_TEST=512\n",
        "\n",
        "# Size of the smallest side of the image during training\n",
        "cfg1.INPUT.MIN_SIZE_TRAIN=0\n",
        "\n",
        "# Maximum size of the side of the image during training\n",
        "cfg1.INPUT.MAX_SIZE_TRAIN=512"
      ],
      "metadata": {
        "id": "8QPHaQzW13u0"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------------------------------------------------------------------------- #\n",
        "# CROP\n",
        "# ---------------------------------------------------------------------------- #\n",
        "#cfg1.INPUT.CROP.ENABLED=\"TRUE\"\n",
        "\n",
        "# Cropping type:\n",
        "# - \"relative\" crop (H * CROP.SIZE[0], W * CROP.SIZE[1]) part of an input of size (H, W)\n",
        "# - \"relative_range\" uniformly sample relative crop size from between [CROP.SIZE[0], [CROP.SIZE[1]].\n",
        "#   and  [1, 1] and use it as in \"relative\" scenario.\n",
        "# - \"absolute\" crop part of an input with absolute size: (CROP.SIZE[0], CROP.SIZE[1]).\n",
        "# - \"absolute_range\", for an input of size (H, W), uniformly sample H_crop in\n",
        "#   [CROP.SIZE[0], min(H, CROP.SIZE[1])] and W_crop in [CROP.SIZE[0], min(W, CROP.SIZE[1])]\n",
        "#cfg1.INPUT.CROP.TYPE = \"relative_range\"\n",
        "\n",
        "# Mode for flipping images used in data augmentation during training\n",
        "# choose one of [\"horizontal, \"vertical\", \"none\"]\n",
        "#_C.INPUT.RANDOM_FLIP =#We are importing our own Trainer Module here to use the COCO validation evaluation during training. Otherwise no validation eval occurs.\n",
        "\n",
        "from detectron2.engine import DefaultTrainer\n",
        "from detectron2.evaluation import COCOEvaluator\n",
        "\n",
        "\n",
        "\n",
        "#print(\"\\n\",cfg1.INPUT)\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# DATASET\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "cfg1.DATASETS.TRAIN = (\"train\",)\n",
        "cfg1.DATASETS.TEST = (\"test\",)\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# DATALOADER \n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "#print(cfg1.DATALOADER)\n",
        "# Number of data loading threads\n",
        "#cfg1.DATALOADER.NUM_WORKERS = 4\n",
        "\n",
        "\n",
        "# If True, each batch should contain only images for which the aspect ratio\n",
        "# is compatible. This groups portrait images together, and landscape images\n",
        "# are not batched with portrait images.\n",
        "cfg1.DATALOADER.ASPECT_RATIO_GROUPING = True"
      ],
      "metadata": {
        "id": "fe8QuFkS13xW"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------------------------------------------------------------------------- #\n",
        "# FPN \n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "print(\"FPN\\n\")\n",
        "print(cfg1.MODEL.FPN,\"\\n\")\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# ANCHOR GENERATOR\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "# Anchor sizes (i.e. sqrt of area) in absolute pixels w.r.t. the network input.\n",
        "cfg1.MODEL.ANCHOR_GENERATOR.SIZES = [[8,16,32]]\n",
        "\n",
        "# Anchor angles.\n",
        "# list[list[float]], the angle in degrees, for each input feature map.\n",
        "# ANGLES[i] specifies the list of angles for IN_FEATURES[i].\n",
        "cfg1.MODEL.ANCHOR_GENERATOR.ANGLES = [[-90, 0, 90]]\n",
        "print(\"ANCHOR GENERATOR\\n\")\n",
        "print(cfg1.MODEL.ANCHOR_GENERATOR,\"\\n\")\n",
        "\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# RPN\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "# IOU overlap ratios [BG_IOU_THRESHOLD, FG_IOU_THRESHOLD]\n",
        "# Minimum overlap required between an anchor and ground-truth box for the\n",
        "# (anchor, gt box) pair to be a positive example (IoU >= FG_IOU_THRESHOLD\n",
        "# ==> positive RPN example: 1)\n",
        "# Maximum overlap allowed between an anchor and ground-truth box for the\n",
        "# (anchor, gt box) pair to be a negative examples (IoU < BG_IOU_THRESHOLD\n",
        "# ==> negative RPN example: 0)\n",
        "# Anchors with overlap in between (BG_IOU_THRESHOLD <= IoU < FG_IOU_THRESHOLD)\n",
        "# are ignored (-1)\n",
        "cfg1.MODEL.RPN.IOU_THRESHOLDS = [0.1, 0.7]\n",
        "\n",
        "# NMS threshold used on RPN proposals\n",
        "cfg1.MODEL.RPN.NMS_THRESH = 0.7\n",
        "\n",
        "print(\"RPN\\n\")\n",
        "print(cfg1.MODEL.RPN,\"\\n\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "f3-gNia7130L",
        "outputId": "ee9e2ba2-e8e5-4596-c3e9-4955703568e4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "FPN\n",
            "\n",
            "FUSE_TYPE: sum\n",
            "IN_FEATURES: ['res2', 'res3', 'res4', 'res5']\n",
            "NORM: \n",
            "OUT_CHANNELS: 256 \n",
            "\n",
            "ANCHOR GENERATOR\n",
            "\n",
            "ANGLES: [[-90, 0, 90]]\n",
            "ASPECT_RATIOS: [[0.5, 1.0, 2.0]]\n",
            "NAME: DefaultAnchorGenerator\n",
            "OFFSET: 0.0\n",
            "SIZES: [[8, 16, 32]] \n",
            "\n",
            "RPN\n",
            "\n",
            "BATCH_SIZE_PER_IMAGE: 256\n",
            "BBOX_REG_LOSS_TYPE: smooth_l1\n",
            "BBOX_REG_LOSS_WEIGHT: 1.0\n",
            "BBOX_REG_WEIGHTS: (1.0, 1.0, 1.0, 1.0)\n",
            "BOUNDARY_THRESH: -1\n",
            "CONV_DIMS: [-1]\n",
            "HEAD_NAME: StandardRPNHead\n",
            "IN_FEATURES: ['p2', 'p3', 'p4', 'p5', 'p6']\n",
            "IOU_LABELS: [0, -1, 1]\n",
            "IOU_THRESHOLDS: [0.1, 0.7]\n",
            "LOSS_WEIGHT: 1.0\n",
            "NMS_THRESH: 0.7\n",
            "POSITIVE_FRACTION: 0.5\n",
            "POST_NMS_TOPK_TEST: 1000\n",
            "POST_NMS_TOPK_TRAIN: 1000\n",
            "PRE_NMS_TOPK_TEST: 1000\n",
            "PRE_NMS_TOPK_TRAIN: 2000\n",
            "SMOOTH_L1_BETA: 0.0 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------------------------------------------------------------------------- #\n",
        "# ROI HEADs\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "# IOU overlap ratios [IOU_THRESHOLD]\n",
        "# Overlap threshold for an RoI to be considered background (if < IOU_THRESHOLD)\n",
        "# Overlap threshold for an RoI to be considered foreground (if >= IOU_THRESHOLD)\n",
        "cfg1.MODEL.ROI_HEADS.IOU_THRESHOLDS = [0.5]\n",
        "\n",
        "# Target fraction of RoI minibatch that is labeled foreground (i.e. class > 0)\n",
        "cfg1.MODEL.ROI_HEADS.POSITIVE_FRACTION = 0.25\n",
        "\n",
        "# Number of foreground classes\n",
        "cfg1.MODEL.ROI_HEADS.NUM_CLASSES = 1\n",
        "\n",
        "# Overlap threshold used for non-maximum suppression (suppress boxes with\n",
        "# IoU >= this threshold)\n",
        "cfg1.MODEL.ROI_HEADS.NMS_THRESH_TEST = 0.7\n",
        "\n",
        "\n",
        "\n",
        "\n",
        "# RoI minibatch size *per image* (number of regions of interest [ROIs])\n",
        "# Total number of RoIs per training minibatch =\n",
        "#   ROI_HEADS.BATCH_SIZE_PER_IMAGE * SOLVER.IMS_PER_BATCH\n",
        "# E.g., a common configuration is: 512 * 16 = 8192\n",
        "cfg1.MODEL.ROI_HEADS.BATCH_SIZE_PER_IMAGE = 512\n",
        "\n",
        "\n",
        "print(\"ROI HEADS\\n\")\n",
        "print(cfg1.MODEL.ROI_HEADS,\"\\n\")\n",
        "\n",
        "# ---------------------------------------------------------------------------- #\n",
        "# BOX HEAD\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "print(\"ROI BOX HEADS\\n\")\n",
        "print(cfg1.MODEL.ROI_BOX_HEAD,\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "eoIhGmIa132w",
        "outputId": "ddb36725-ba03-4e09-cea9-24af5731629a"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "ROI HEADS\n",
            "\n",
            "BATCH_SIZE_PER_IMAGE: 512\n",
            "IN_FEATURES: ['p2', 'p3', 'p4', 'p5']\n",
            "IOU_LABELS: [0, 1]\n",
            "IOU_THRESHOLDS: [0.5]\n",
            "NAME: StandardROIHeads\n",
            "NMS_THRESH_TEST: 0.7\n",
            "NUM_CLASSES: 1\n",
            "POSITIVE_FRACTION: 0.25\n",
            "PROPOSAL_APPEND_GT: True\n",
            "SCORE_THRESH_TEST: 0.05 \n",
            "\n",
            "ROI BOX HEADS\n",
            "\n",
            "BBOX_REG_LOSS_TYPE: smooth_l1\n",
            "BBOX_REG_LOSS_WEIGHT: 1.0\n",
            "BBOX_REG_WEIGHTS: (10.0, 10.0, 5.0, 5.0)\n",
            "CLS_AGNOSTIC_BBOX_REG: False\n",
            "CONV_DIM: 256\n",
            "FC_DIM: 1024\n",
            "FED_LOSS_FREQ_WEIGHT_POWER: 0.5\n",
            "FED_LOSS_NUM_CLASSES: 50\n",
            "NAME: FastRCNNConvFCHead\n",
            "NORM: \n",
            "NUM_CONV: 0\n",
            "NUM_FC: 2\n",
            "POOLER_RESOLUTION: 7\n",
            "POOLER_SAMPLING_RATIO: 0\n",
            "POOLER_TYPE: ROIAlignV2\n",
            "SMOOTH_L1_BETA: 0.0\n",
            "TRAIN_ON_PRED_BOXES: False\n",
            "USE_FED_LOSS: False\n",
            "USE_SIGMOID_CE: False \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "# ---------------------------------------------------------------------------- #\n",
        "# SOLVER\n",
        "# ---------------------------------------------------------------------------- #\n",
        "\n",
        "\n",
        "# See detectron2/solver/build.py for LR scheduler options\n",
        "cfg1.SOLVER.LR_SCHEDULER_NAME = \"WarmupMultiStepLR\"\n",
        "\n",
        "cfg1.SOLVER.WARMUP_ITERS = 500\n",
        "\n",
        "# cfg1.SOLVER.MAX_ITER = 20000\n",
        "cfg1.SOLVER.MAX_ITER = 2000\n",
        "\n",
        "cfg1.SOLVER.BASE_LR = 0.001\n",
        "\n",
        "cfg1.SOLVER.MOMENTUM = 0.9\n",
        "\n",
        "cfg1.SOLVER.NESTEROV = False\n",
        "\n",
        "cfg1.SOLVER.WEIGHT_DECAY = 0.0001\n",
        "#In detectron2, epoch is MAX_ITER * BATCH_SIZE / TOTAL_NUM_IMAGES\n",
        "# The weight decay that's applied to parameters of normalization layers\n",
        "# (typically the affine transformation)\n",
        "cfg1.SOLVER.WEIGHT_DECAY_NORM = 0.0\n",
        "\n",
        "cfg1.SOLVER.GAMMA = 0.1\n",
        "\n",
        "\n",
        "cfg1.SOLVER.STEPS = (1000,)\n",
        "\n",
        "cfg1.TEST.EVAL_PERIOD = 250\n",
        "# Save a checkpoint after every this number of iterations\n",
        "cfg1.SOLVER.CHECKPOINT_PERIOD = 250\n",
        "\n",
        "# Number of images per batch across all machines.\n",
        "# If we have 16 GPUs and IMS_PER_BATCH = 32,\n",
        "# each GPU will see 2 images per batch.\n",
        "# May be adjusted automatically if REFERENCE_WORLD_SIZE is set.\n",
        "cfg1.SOLVER.IMS_PER_BATCH = 2\n",
        "#cfg1.SOLVER.IMS_PER_BATCH = 2\n",
        "\n",
        "print(\"SOLVER\\n\")\n",
        "print(cfg1.SOLVER,\"\\n\")"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "VTOczmIY135V",
        "outputId": "1a4dd5d9-71c5-4600-8225-4d0afd14bbe4"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "SOLVER\n",
            "\n",
            "AMP:\n",
            "  ENABLED: False\n",
            "BASE_LR: 0.001\n",
            "BASE_LR_END: 0.0\n",
            "BIAS_LR_FACTOR: 1.0\n",
            "CHECKPOINT_PERIOD: 250\n",
            "CLIP_GRADIENTS:\n",
            "  CLIP_TYPE: value\n",
            "  CLIP_VALUE: 1.0\n",
            "  ENABLED: False\n",
            "  NORM_TYPE: 2.0\n",
            "GAMMA: 0.1\n",
            "IMS_PER_BATCH: 2\n",
            "LR_SCHEDULER_NAME: WarmupMultiStepLR\n",
            "MAX_ITER: 2000\n",
            "MOMENTUM: 0.9\n",
            "NESTEROV: False\n",
            "REFERENCE_WORLD_SIZE: 0\n",
            "STEPS: (1000,)\n",
            "WARMUP_FACTOR: 0.001\n",
            "WARMUP_ITERS: 500\n",
            "WARMUP_METHOD: linear\n",
            "WEIGHT_DECAY: 0.0001\n",
            "WEIGHT_DECAY_BIAS: None\n",
            "WEIGHT_DECAY_NORM: 0.0 \n",
            "\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "#We are importing our own Trainer Module here to use the COCO validation evaluation during training. Otherwise no validation eval occurs.\n",
        "\n",
        "from detectron2.engine import DefaultTrainer\n",
        "from detectron2.evaluation import COCOEvaluator\n",
        "\n",
        "class CocoTrainer(DefaultTrainer):\n",
        "\n",
        "  @classmethod\n",
        "  def build_evaluator(cls, cfg, dataset_name, output_folder=None):\n",
        "\n",
        "    if output_folder is None:\n",
        "        os.makedirs(\"coco_eval\", exist_ok=True)\n",
        "        output_folder = \"coco_eval\"\n",
        "\n",
        "    return COCOEvaluator(dataset_name, cfg, False, output_folder)"
      ],
      "metadata": {
        "id": "TFRRo9RY53qA"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from detectron2.engine import DefaultTrainer\n",
        "from detectron2.evaluation import COCOEvaluator\n",
        "os.makedirs(cfg1.OUTPUT_DIR, exist_ok=True)\n",
        "trainer = CocoTrainer(cfg1)\n",
        "trainer.resume_or_load(resume=True)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "wCmu91iY5uBY",
        "outputId": "ef6ef7eb-e8f9-40dd-ed3c-5483dcc81bab"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:21:58 d2.engine.defaults]: \u001b[0mModel:\n",
            "GeneralizedRCNN(\n",
            "  (backbone): FPN(\n",
            "    (fpn_lateral2): Conv2d(256, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output2): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral3): Conv2d(512, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output3): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral4): Conv2d(1024, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output4): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (fpn_lateral5): Conv2d(2048, 256, kernel_size=(1, 1), stride=(1, 1))\n",
            "    (fpn_output5): Conv2d(256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1))\n",
            "    (top_block): LastLevelMaxPool()\n",
            "    (bottom_up): ResNet(\n",
            "      (stem): BasicStem(\n",
            "        (conv1): Conv2d(\n",
            "          3, 64, kernel_size=(7, 7), stride=(2, 2), padding=(3, 3), bias=False\n",
            "          (norm): FrozenBatchNorm2d(num_features=64, eps=1e-05)\n",
            "        )\n",
            "      )\n",
            "      (res2): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            64, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            256, 256, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=256, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res3): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            256, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            512, 512, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            512, 512, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=512, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res4): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            512, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (3): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (4): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (5): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (6): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (7): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (8): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (9): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (10): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (11): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (12): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (13): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (14): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (15): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (16): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (17): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (18): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (19): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (20): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (21): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (22): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            1024, 1024, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            1024, 1024, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=1024, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "      (res5): Sequential(\n",
            "        (0): BottleneckBlock(\n",
            "          (shortcut): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(2, 2), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv1): Conv2d(\n",
            "            1024, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(2, 2), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (1): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "        (2): BottleneckBlock(\n",
            "          (conv1): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv2): Conv2d(\n",
            "            2048, 2048, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1), groups=32, bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "          (conv3): Conv2d(\n",
            "            2048, 2048, kernel_size=(1, 1), stride=(1, 1), bias=False\n",
            "            (norm): FrozenBatchNorm2d(num_features=2048, eps=1e-05)\n",
            "          )\n",
            "        )\n",
            "      )\n",
            "    )\n",
            "  )\n",
            "  (proposal_generator): RPN(\n",
            "    (rpn_head): StandardRPNHead(\n",
            "      (conv): Conv2d(\n",
            "        256, 256, kernel_size=(3, 3), stride=(1, 1), padding=(1, 1)\n",
            "        (activation): ReLU()\n",
            "      )\n",
            "      (objectness_logits): Conv2d(256, 9, kernel_size=(1, 1), stride=(1, 1))\n",
            "      (anchor_deltas): Conv2d(256, 36, kernel_size=(1, 1), stride=(1, 1))\n",
            "    )\n",
            "    (anchor_generator): DefaultAnchorGenerator(\n",
            "      (cell_anchors): BufferList()\n",
            "    )\n",
            "  )\n",
            "  (roi_heads): StandardROIHeads(\n",
            "    (box_pooler): ROIPooler(\n",
            "      (level_poolers): ModuleList(\n",
            "        (0): ROIAlign(output_size=(7, 7), spatial_scale=0.25, sampling_ratio=0, aligned=True)\n",
            "        (1): ROIAlign(output_size=(7, 7), spatial_scale=0.125, sampling_ratio=0, aligned=True)\n",
            "        (2): ROIAlign(output_size=(7, 7), spatial_scale=0.0625, sampling_ratio=0, aligned=True)\n",
            "        (3): ROIAlign(output_size=(7, 7), spatial_scale=0.03125, sampling_ratio=0, aligned=True)\n",
            "      )\n",
            "    )\n",
            "    (box_head): FastRCNNConvFCHead(\n",
            "      (flatten): Flatten(start_dim=1, end_dim=-1)\n",
            "      (fc1): Linear(in_features=12544, out_features=1024, bias=True)\n",
            "      (fc_relu1): ReLU()\n",
            "      (fc2): Linear(in_features=1024, out_features=1024, bias=True)\n",
            "      (fc_relu2): ReLU()\n",
            "    )\n",
            "    (box_predictor): FastRCNNOutputLayers(\n",
            "      (cls_score): Linear(in_features=1024, out_features=2, bias=True)\n",
            "      (bbox_pred): Linear(in_features=1024, out_features=4, bias=True)\n",
            "    )\n",
            "  )\n",
            ")\n",
            "\u001b[32m[07/21 10:21:58 d2.data.datasets.coco]: \u001b[0mLoaded 500 images in COCO format from /content/gdrive/MyDrive/TB/train/annot1.json\n",
            "\u001b[32m[07/21 10:21:58 d2.data.build]: \u001b[0mRemoved 20 images with no usable annotations. 480 images left.\n",
            "\u001b[32m[07/21 10:21:58 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
            "\u001b[36m|  category  | #instances   |\n",
            "|:----------:|:-------------|\n",
            "|     tb     | 3738         |\n",
            "|            |              |\u001b[0m\n",
            "\u001b[32m[07/21 10:21:58 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in training: [ResizeShortestEdge(short_edge_length=(0, 0), max_size=512, sample_style='choice'), RandomFlip()]\n",
            "\u001b[32m[07/21 10:21:58 d2.data.build]: \u001b[0mUsing training sampler TrainingSampler\n",
            "\u001b[32m[07/21 10:21:58 d2.data.common]: \u001b[0mSerializing 480 elements to byte tensors and concatenating them all ...\n",
            "\u001b[32m[07/21 10:21:58 d2.data.common]: \u001b[0mSerialized dataset takes 0.22 MiB\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:22:10 d2.checkpoint.c2_model_loading]: \u001b[0mFollowing weights matched with model:\n",
            "| Names in Model                                  | Names in Checkpoint                                                                                  | Shapes                                          |\n",
            "|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|\n",
            "| backbone.bottom_up.res2.0.conv1.*               | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.0.conv2.*               | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.0.conv3.*               | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.0.shortcut.*            | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.1.conv1.*               | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.1.conv2.*               | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.1.conv3.*               | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv1.*               | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv2.*               | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.2.conv3.*               | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv1.*               | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv2.*               | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.0.conv3.*               | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.0.shortcut.*            | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv1.*               | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv2.*               | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.1.conv3.*               | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv1.*               | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv2.*               | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.2.conv3.*               | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv1.*               | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv2.*               | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.3.conv3.*               | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res4.0.conv1.*               | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.0.conv2.*               | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.0.conv3.*               | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.0.shortcut.*            | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.1.conv1.*               | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.1.conv2.*               | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.1.conv3.*               | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv1.*              | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv2.*              | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.10.conv3.*              | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv1.*              | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv2.*              | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.11.conv3.*              | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv1.*              | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv2.*              | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.12.conv3.*              | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv1.*              | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv2.*              | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.13.conv3.*              | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv1.*              | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv2.*              | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.14.conv3.*              | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv1.*              | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv2.*              | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.15.conv3.*              | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv1.*              | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv2.*              | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.16.conv3.*              | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv1.*              | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv2.*              | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.17.conv3.*              | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv1.*              | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv2.*              | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.18.conv3.*              | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv1.*              | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv2.*              | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.19.conv3.*              | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv1.*               | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv2.*               | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.2.conv3.*               | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv1.*              | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv2.*              | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.20.conv3.*              | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv1.*              | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv2.*              | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.21.conv3.*              | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv1.*              | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv2.*              | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.22.conv3.*              | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv1.*               | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv2.*               | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.3.conv3.*               | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv1.*               | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv2.*               | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.4.conv3.*               | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv1.*               | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv2.*               | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.5.conv3.*               | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv1.*               | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv2.*               | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.6.conv3.*               | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv1.*               | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv2.*               | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.7.conv3.*               | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv1.*               | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv2.*               | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.8.conv3.*               | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv1.*               | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv2.*               | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.9.conv3.*               | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv1.*               | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv2.*               | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.0.conv3.*               | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.0.shortcut.*            | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv1.*               | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv2.*               | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.1.conv3.*               | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv1.*               | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv2.*               | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.2.conv3.*               | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.stem.conv1.*                 | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}      | (64,) (64,) (64,) (64,) (64,3,7,7)              |\n",
            "| backbone.fpn_lateral2.*                         | backbone.fpn_lateral2.{bias,weight}                                                                  | (256,) (256,256,1,1)                            |\n",
            "| backbone.fpn_lateral3.*                         | backbone.fpn_lateral3.{bias,weight}                                                                  | (256,) (256,512,1,1)                            |\n",
            "| backbone.fpn_lateral4.*                         | backbone.fpn_lateral4.{bias,weight}                                                                  | (256,) (256,1024,1,1)                           |\n",
            "| backbone.fpn_lateral5.*                         | backbone.fpn_lateral5.{bias,weight}                                                                  | (256,) (256,2048,1,1)                           |\n",
            "| backbone.fpn_output2.*                          | backbone.fpn_output2.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output3.*                          | backbone.fpn_output3.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output4.*                          | backbone.fpn_output4.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output5.*                          | backbone.fpn_output5.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.anchor_deltas.*     | proposal_generator.rpn_head.anchor_deltas.{bias,weight}                                              | (36,) (36,256,1,1)                              |\n",
            "| proposal_generator.rpn_head.conv.*              | proposal_generator.rpn_head.conv.{bias,weight}                                                       | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight}                                          | (9,) (9,256,1,1)                                |\n",
            "| roi_heads.box_head.fc1.*                        | roi_heads.box_head.fc1.{bias,weight}                                                                 | (1024,) (1024,12544)                            |\n",
            "| roi_heads.box_head.fc2.*                        | roi_heads.box_head.fc2.{bias,weight}                                                                 | (1024,) (1024,1024)                             |\n",
            "| roi_heads.box_predictor.bbox_pred.*             | roi_heads.box_predictor.bbox_pred.{bias,weight}                                                      | (4,) (4,1024)                                   |\n",
            "| roi_heads.box_predictor.cls_score.*             | roi_heads.box_predictor.cls_score.{bias,weight}                                                      | (2,) (2,1024)                                   |\n",
            "\u001b[32m[07/21 10:22:13 d2.engine.hooks]: \u001b[0mLoading scheduler from state_dict ...\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Evaluating the performance of Mitos-12 dataset #####\n",
        "from detectron2.data import DatasetCatalog, MetadataCatalog, build_detection_test_loader\n",
        "from detectron2.evaluation import COCOEvaluator, inference_on_dataset\n",
        "\n",
        "cfg1.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.5\n",
        "evaluator = COCOEvaluator(\"test\", cfg1, False, output_dir=\"/content/gdrive/MyDrive/TB/logs/21-07-2022\")\n",
        "val_loader = build_detection_test_loader(cfg1, \"test\")\n",
        "inference_on_dataset(trainer.model, val_loader, evaluator)"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Cw40vUyX138F",
        "outputId": "313d68bb-b58b-42b7-acd4-df68b661e150"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[5m\u001b[31mWARNING\u001b[0m \u001b[32m[07/21 10:22:25 d2.evaluation.coco_evaluation]: \u001b[0mCOCO Evaluator instantiated using config, this is deprecated behavior. Please pass in explicit arguments instead.\n",
            "\u001b[32m[07/21 10:22:25 d2.data.datasets.coco]: \u001b[0mLoaded 100 images in COCO format from /content/gdrive/MyDrive/TB/test/annot1.json\n",
            "\u001b[32m[07/21 10:22:25 d2.data.build]: \u001b[0mDistribution of instances among all 1 categories:\n",
            "\u001b[36m|  category  | #instances   |\n",
            "|:----------:|:-------------|\n",
            "|     tb     | 762          |\n",
            "|            |              |\u001b[0m\n",
            "\u001b[32m[07/21 10:22:25 d2.data.dataset_mapper]: \u001b[0m[DatasetMapper] Augmentations used in inference: [ResizeShortestEdge(short_edge_length=(0, 0), max_size=512, sample_style='choice')]\n",
            "\u001b[32m[07/21 10:22:26 d2.data.common]: \u001b[0mSerializing 100 elements to byte tensors and concatenating them all ...\n",
            "\u001b[32m[07/21 10:22:26 d2.data.common]: \u001b[0mSerialized dataset takes 0.04 MiB\n",
            "\u001b[32m[07/21 10:22:26 d2.evaluation.evaluator]: \u001b[0mStart inference on 100 batches\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/utils/data/dataloader.py:560: UserWarning: This DataLoader will create 4 worker processes in total. Our suggested max number of worker in current system is 2, which is smaller than what this DataLoader is going to create. Please be aware that excessive worker creation might get DataLoader running slow or even freeze, lower the worker number to avoid potential slowness/freeze if necessary.\n",
            "  cpuset_checked))\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:22:35 d2.evaluation.evaluator]: \u001b[0mInference done 1/100. Dataloading: 0.6457 s/iter. Inference: 8.5785 s/iter. Eval: 0.0004 s/iter. Total: 9.2257 s/iter. ETA=0:15:13\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stderr",
          "text": [
            "/usr/local/lib/python3.7/dist-packages/torch/functional.py:478: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at  ../aten/src/ATen/native/TensorShape.cpp:2894.)\n",
            "  return _VF.meshgrid(tensors, **kwargs)  # type: ignore[attr-defined]\n"
          ]
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:22:41 d2.evaluation.evaluator]: \u001b[0mInference done 11/100. Dataloading: 0.0016 s/iter. Inference: 0.5829 s/iter. Eval: 0.0004 s/iter. Total: 0.5849 s/iter. ETA=0:00:52\n",
            "\u001b[32m[07/21 10:22:46 d2.evaluation.evaluator]: \u001b[0mInference done 20/100. Dataloading: 0.0022 s/iter. Inference: 0.5852 s/iter. Eval: 0.0003 s/iter. Total: 0.5881 s/iter. ETA=0:00:47\n",
            "\u001b[32m[07/21 10:22:51 d2.evaluation.evaluator]: \u001b[0mInference done 29/100. Dataloading: 0.0025 s/iter. Inference: 0.5856 s/iter. Eval: 0.0002 s/iter. Total: 0.5887 s/iter. ETA=0:00:41\n",
            "\u001b[32m[07/21 10:22:57 d2.evaluation.evaluator]: \u001b[0mInference done 38/100. Dataloading: 0.0026 s/iter. Inference: 0.5868 s/iter. Eval: 0.0002 s/iter. Total: 0.5900 s/iter. ETA=0:00:36\n",
            "\u001b[32m[07/21 10:23:02 d2.evaluation.evaluator]: \u001b[0mInference done 47/100. Dataloading: 0.0027 s/iter. Inference: 0.5891 s/iter. Eval: 0.0002 s/iter. Total: 0.5924 s/iter. ETA=0:00:31\n",
            "\u001b[32m[07/21 10:23:07 d2.evaluation.evaluator]: \u001b[0mInference done 56/100. Dataloading: 0.0027 s/iter. Inference: 0.5918 s/iter. Eval: 0.0002 s/iter. Total: 0.5950 s/iter. ETA=0:00:26\n",
            "\u001b[32m[07/21 10:23:13 d2.evaluation.evaluator]: \u001b[0mInference done 65/100. Dataloading: 0.0027 s/iter. Inference: 0.5940 s/iter. Eval: 0.0002 s/iter. Total: 0.5972 s/iter. ETA=0:00:20\n",
            "\u001b[32m[07/21 10:23:18 d2.evaluation.evaluator]: \u001b[0mInference done 74/100. Dataloading: 0.0027 s/iter. Inference: 0.5959 s/iter. Eval: 0.0002 s/iter. Total: 0.5991 s/iter. ETA=0:00:15\n",
            "\u001b[32m[07/21 10:23:24 d2.evaluation.evaluator]: \u001b[0mInference done 83/100. Dataloading: 0.0028 s/iter. Inference: 0.5985 s/iter. Eval: 0.0002 s/iter. Total: 0.6018 s/iter. ETA=0:00:10\n",
            "\u001b[32m[07/21 10:23:30 d2.evaluation.evaluator]: \u001b[0mInference done 92/100. Dataloading: 0.0028 s/iter. Inference: 0.6006 s/iter. Eval: 0.0002 s/iter. Total: 0.6039 s/iter. ETA=0:00:04\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.evaluator]: \u001b[0mTotal inference time: 0:00:57.646896 (0.606809 s / iter per device, on 1 devices)\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.evaluator]: \u001b[0mTotal inference pure compute time: 0:00:57 (0.602407 s / iter per device, on 1 devices)\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.coco_evaluation]: \u001b[0mPreparing results for COCO format ...\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.coco_evaluation]: \u001b[0mSaving results to /content/gdrive/MyDrive/TB/logs/21-07-2022/coco_instances_results.json\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.coco_evaluation]: \u001b[0mEvaluating predictions with unofficial COCO API...\n",
            "Loading and preparing results...\n",
            "DONE (t=0.00s)\n",
            "creating index...\n",
            "index created!\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.fast_eval_api]: \u001b[0mEvaluate annotation type *bbox*\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.evaluate() finished in 0.03 seconds.\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.fast_eval_api]: \u001b[0mAccumulating evaluation results...\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.fast_eval_api]: \u001b[0mCOCOeval_opt.accumulate() finished in 0.01 seconds.\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.050\n",
            " Average Precision  (AP) @[ IoU=0.50      | area=   all | maxDets=100 ] = 0.178\n",
            " Average Precision  (AP) @[ IoU=0.75      | area=   all | maxDets=100 ] = 0.017\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.049\n",
            " Average Precision  (AP) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.132\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=  1 ] = 0.029\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets= 10 ] = 0.091\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=   all | maxDets=100 ] = 0.110\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= small | maxDets=100 ] = 0.000\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area=medium | maxDets=100 ] = 0.109\n",
            " Average Recall     (AR) @[ IoU=0.50:0.95 | area= large | maxDets=100 ] = 0.194\n",
            "\u001b[32m[07/21 10:23:35 d2.evaluation.coco_evaluation]: \u001b[0mEvaluation results for bbox: \n",
            "|  AP   |  AP50  |  AP75  |  APs  |  APm  |  APl   |\n",
            "|:-----:|:------:|:------:|:-----:|:-----:|:------:|\n",
            "| 4.964 | 17.824 | 1.710  | 0.000 | 4.947 | 13.219 |\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "OrderedDict([('bbox',\n",
              "              {'AP': 4.963666855886134,\n",
              "               'AP50': 17.82420917558138,\n",
              "               'AP75': 1.7100554188080381,\n",
              "               'APl': 13.219135649828717,\n",
              "               'APm': 4.947110158583571,\n",
              "               'APs': 0.0})])"
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Viewing Mitos-14 test dataset #####\n",
        "cfg1.MODEL.WEIGHTS = os.path.join(cfg1.OUTPUT_DIR, \"model_0001999.pth\")\n",
        "cfg1.DATASETS.TEST = (\"test\", )\n",
        "cfg1.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3   # set the testing threshold for this model\n",
        "predictor = DefaultPredictor(cfg1)\n",
        "dataset_dicts = DatasetCatalog.get(\"test\")\n",
        "test_metadata = MetadataCatalog.get(\"test\")\n",
        "print(test_metadata)\n",
        "\n",
        "\n",
        "from detectron2.utils.visualizer import ColorMode\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "for d in random.sample(dataset_dicts, 1):\n",
        "    img = cv2.imread(d[\"file_name\"])\n",
        "    img1 = cv2.imread(d[\"file_name\"])\n",
        "    outputs=predictor(img1)\n",
        "    print(outputs)\n",
        "    visualizer = Visualizer(img[:, :, ::-1], metadata=test_metadata, scale=0.8)\n",
        "    vis = visualizer.draw_dataset_dict(d)\n",
        "    v = Visualizer(img1[:, :, ::-1],metadata=test_metadata, scale=0.8)\n",
        "    out = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "    fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(20,15))\n",
        "    fig.suptitle(d['file_name'])\n",
        "    ax1.set_title(\"actual\")\n",
        "    ax1.imshow(vis.get_image()[:, :, ::-1],cmap=\"gray\")\n",
        "    ax2.set_title(\"predicted\")\n",
        "    ax2.imshow(out.get_image()[:, :, ::-1],cmap=\"gray\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "AN_oC8Fe13-1",
        "outputId": "f7bf7c17-4510-4745-cf85-fe8f56ad9ee0"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/21 10:38:47 d2.checkpoint.c2_model_loading]: \u001b[0mFollowing weights matched with model:\n",
            "| Names in Model                                  | Names in Checkpoint                                                                                  | Shapes                                          |\n",
            "|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|\n",
            "| backbone.bottom_up.res2.0.conv1.*               | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.0.conv2.*               | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.0.conv3.*               | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.0.shortcut.*            | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.1.conv1.*               | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.1.conv2.*               | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.1.conv3.*               | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv1.*               | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv2.*               | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.2.conv3.*               | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv1.*               | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv2.*               | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.0.conv3.*               | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.0.shortcut.*            | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv1.*               | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv2.*               | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.1.conv3.*               | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv1.*               | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv2.*               | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.2.conv3.*               | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv1.*               | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv2.*               | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.3.conv3.*               | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res4.0.conv1.*               | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.0.conv2.*               | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.0.conv3.*               | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.0.shortcut.*            | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.1.conv1.*               | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.1.conv2.*               | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.1.conv3.*               | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv1.*              | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv2.*              | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.10.conv3.*              | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv1.*              | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv2.*              | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.11.conv3.*              | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv1.*              | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv2.*              | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.12.conv3.*              | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv1.*              | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv2.*              | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.13.conv3.*              | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv1.*              | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv2.*              | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.14.conv3.*              | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv1.*              | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv2.*              | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.15.conv3.*              | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv1.*              | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv2.*              | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.16.conv3.*              | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv1.*              | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv2.*              | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.17.conv3.*              | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv1.*              | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv2.*              | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.18.conv3.*              | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv1.*              | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv2.*              | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.19.conv3.*              | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv1.*               | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv2.*               | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.2.conv3.*               | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv1.*              | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv2.*              | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.20.conv3.*              | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv1.*              | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv2.*              | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.21.conv3.*              | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv1.*              | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv2.*              | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.22.conv3.*              | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv1.*               | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv2.*               | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.3.conv3.*               | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv1.*               | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv2.*               | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.4.conv3.*               | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv1.*               | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv2.*               | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.5.conv3.*               | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv1.*               | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv2.*               | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.6.conv3.*               | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv1.*               | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv2.*               | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.7.conv3.*               | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv1.*               | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv2.*               | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.8.conv3.*               | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv1.*               | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv2.*               | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.9.conv3.*               | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv1.*               | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv2.*               | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.0.conv3.*               | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.0.shortcut.*            | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv1.*               | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv2.*               | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.1.conv3.*               | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv1.*               | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv2.*               | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.2.conv3.*               | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.stem.conv1.*                 | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}      | (64,) (64,) (64,) (64,) (64,3,7,7)              |\n",
            "| backbone.fpn_lateral2.*                         | backbone.fpn_lateral2.{bias,weight}                                                                  | (256,) (256,256,1,1)                            |\n",
            "| backbone.fpn_lateral3.*                         | backbone.fpn_lateral3.{bias,weight}                                                                  | (256,) (256,512,1,1)                            |\n",
            "| backbone.fpn_lateral4.*                         | backbone.fpn_lateral4.{bias,weight}                                                                  | (256,) (256,1024,1,1)                           |\n",
            "| backbone.fpn_lateral5.*                         | backbone.fpn_lateral5.{bias,weight}                                                                  | (256,) (256,2048,1,1)                           |\n",
            "| backbone.fpn_output2.*                          | backbone.fpn_output2.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output3.*                          | backbone.fpn_output3.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output4.*                          | backbone.fpn_output4.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output5.*                          | backbone.fpn_output5.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.anchor_deltas.*     | proposal_generator.rpn_head.anchor_deltas.{bias,weight}                                              | (36,) (36,256,1,1)                              |\n",
            "| proposal_generator.rpn_head.conv.*              | proposal_generator.rpn_head.conv.{bias,weight}                                                       | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight}                                          | (9,) (9,256,1,1)                                |\n",
            "| roi_heads.box_head.fc1.*                        | roi_heads.box_head.fc1.{bias,weight}                                                                 | (1024,) (1024,12544)                            |\n",
            "| roi_heads.box_head.fc2.*                        | roi_heads.box_head.fc2.{bias,weight}                                                                 | (1024,) (1024,1024)                             |\n",
            "| roi_heads.box_predictor.bbox_pred.*             | roi_heads.box_predictor.bbox_pred.{bias,weight}                                                      | (4,) (4,1024)                                   |\n",
            "| roi_heads.box_predictor.cls_score.*             | roi_heads.box_predictor.cls_score.{bias,weight}                                                      | (2,) (2,1024)                                   |\n",
            "\u001b[32m[07/21 10:38:47 d2.data.datasets.coco]: \u001b[0mLoaded 100 images in COCO format from /content/gdrive/MyDrive/TB/test/annot1.json\n",
            "Metadata(evaluator_type='coco', image_root='/content/gdrive/MyDrive/TB/test/images/', json_file='/content/gdrive/MyDrive/TB/test/annot1.json', name='test', thing_classes=['tb'], thing_dataset_id_to_contiguous_id={1: 0})\n",
            "{'instances': Instances(num_instances=8, image_height=1224, image_width=1632, fields=[pred_boxes: Boxes(tensor([[ 649.2259,  659.8383,  733.1779,  719.6542],\n",
            "        [1392.0671,  995.2588, 1443.3813, 1038.8658],\n",
            "        [ 785.1780,  335.9865,  875.5898,  397.5284],\n",
            "        [ 427.1802,  671.7836,  474.3958,  734.1451],\n",
            "        [ 444.3917,  670.9134,  484.1278,  733.5018],\n",
            "        [ 421.8313,  672.3784,  462.6147,  734.2678],\n",
            "        [ 798.7211,  333.9827,  896.7825,  394.9016],\n",
            "        [ 451.4413,  669.8522,  489.7576,  736.6501]], device='cuda:0')), scores: tensor([0.9808, 0.9782, 0.9748, 0.8208, 0.6380, 0.5653, 0.5492, 0.3314],\n",
            "       device='cuda:0'), pred_classes: tensor([0, 0, 0, 0, 0, 0, 0, 0], device='cuda:0')])}\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "### Viewing Mitos-14 test dataset #####\n",
        "cfg1.MODEL.WEIGHTS = os.path.join(cfg1.OUTPUT_DIR, \"model_final.pth\")\n",
        "cfg1.DATASETS.TEST = (\"test\", )\n",
        "cfg1.MODEL.ROI_HEADS.SCORE_THRESH_TEST = 0.3   # set the testing threshold for this model\n",
        "predictor = DefaultPredictor(cfg1)\n",
        "dataset_dicts = DatasetCatalog.get(\"test\")\n",
        "test_metadata = MetadataCatalog.get(\"test\")\n",
        "print(test_metadata)\n",
        "\n",
        "path=\"/content/test3.jpeg\"\n",
        "\n",
        "from detectron2.utils.visualizer import ColorMode\n",
        "import glob\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "\n",
        "img = cv2.imread(path)\n",
        "img1 = cv2.imread(path)\n",
        "outputs=predictor(img1)\n",
        "print(outputs)\n",
        "visualizer = Visualizer(img[:, :, ::-1], scale=0.8)\n",
        "vis = visualizer.draw_dataset_dict(d)\n",
        "v = Visualizer(img1[:, :, ::-1], scale=0.8)\n",
        "out = v.draw_instance_predictions(outputs[\"instances\"].to(\"cpu\"))\n",
        "fig, (ax1, ax2) = plt.subplots(1, 2,figsize=(20,15))\n",
        "# fig.suptitle(d['file_name'])\n",
        "ax1.set_title(\"actual\")\n",
        "ax1.imshow(vis.get_image()[:, :, ::-1],cmap=\"gray\")\n",
        "ax2.set_title(\"predicted\")\n",
        "ax2.imshow(out.get_image()[:, :, ::-1],cmap=\"gray\")\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "tVrSrbVnCmbt",
        "outputId": "8b496c30-1b2f-4284-c2ca-9b73740403fb"
      },
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[32m[07/20 05:30:01 d2.checkpoint.c2_model_loading]: \u001b[0mFollowing weights matched with model:\n",
            "| Names in Model                                  | Names in Checkpoint                                                                                  | Shapes                                          |\n",
            "|:------------------------------------------------|:-----------------------------------------------------------------------------------------------------|:------------------------------------------------|\n",
            "| backbone.bottom_up.res2.0.conv1.*               | backbone.bottom_up.res2.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.0.conv2.*               | backbone.bottom_up.res2.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.0.conv3.*               | backbone.bottom_up.res2.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.0.shortcut.*            | backbone.bottom_up.res2.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (256,) (256,) (256,) (256,) (256,64,1,1)        |\n",
            "| backbone.bottom_up.res2.1.conv1.*               | backbone.bottom_up.res2.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.1.conv2.*               | backbone.bottom_up.res2.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.1.conv3.*               | backbone.bottom_up.res2.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv1.*               | backbone.bottom_up.res2.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res2.2.conv2.*               | backbone.bottom_up.res2.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,8,3,3)         |\n",
            "| backbone.bottom_up.res2.2.conv3.*               | backbone.bottom_up.res2.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (256,) (256,) (256,) (256,) (256,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv1.*               | backbone.bottom_up.res3.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.0.conv2.*               | backbone.bottom_up.res3.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.0.conv3.*               | backbone.bottom_up.res3.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.0.shortcut.*            | backbone.bottom_up.res3.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (512,) (512,) (512,) (512,) (512,256,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv1.*               | backbone.bottom_up.res3.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.1.conv2.*               | backbone.bottom_up.res3.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.1.conv3.*               | backbone.bottom_up.res3.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv1.*               | backbone.bottom_up.res3.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.2.conv2.*               | backbone.bottom_up.res3.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.2.conv3.*               | backbone.bottom_up.res3.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv1.*               | backbone.bottom_up.res3.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res3.3.conv2.*               | backbone.bottom_up.res3.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,16,3,3)        |\n",
            "| backbone.bottom_up.res3.3.conv3.*               | backbone.bottom_up.res3.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (512,) (512,) (512,) (512,) (512,512,1,1)       |\n",
            "| backbone.bottom_up.res4.0.conv1.*               | backbone.bottom_up.res4.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.0.conv2.*               | backbone.bottom_up.res4.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.0.conv3.*               | backbone.bottom_up.res4.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.0.shortcut.*            | backbone.bottom_up.res4.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (1024,) (1024,) (1024,) (1024,) (1024,512,1,1)  |\n",
            "| backbone.bottom_up.res4.1.conv1.*               | backbone.bottom_up.res4.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.1.conv2.*               | backbone.bottom_up.res4.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.1.conv3.*               | backbone.bottom_up.res4.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv1.*              | backbone.bottom_up.res4.10.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.10.conv2.*              | backbone.bottom_up.res4.10.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.10.conv3.*              | backbone.bottom_up.res4.10.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv1.*              | backbone.bottom_up.res4.11.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.11.conv2.*              | backbone.bottom_up.res4.11.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.11.conv3.*              | backbone.bottom_up.res4.11.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv1.*              | backbone.bottom_up.res4.12.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.12.conv2.*              | backbone.bottom_up.res4.12.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.12.conv3.*              | backbone.bottom_up.res4.12.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv1.*              | backbone.bottom_up.res4.13.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.13.conv2.*              | backbone.bottom_up.res4.13.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.13.conv3.*              | backbone.bottom_up.res4.13.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv1.*              | backbone.bottom_up.res4.14.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.14.conv2.*              | backbone.bottom_up.res4.14.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.14.conv3.*              | backbone.bottom_up.res4.14.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv1.*              | backbone.bottom_up.res4.15.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.15.conv2.*              | backbone.bottom_up.res4.15.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.15.conv3.*              | backbone.bottom_up.res4.15.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv1.*              | backbone.bottom_up.res4.16.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.16.conv2.*              | backbone.bottom_up.res4.16.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.16.conv3.*              | backbone.bottom_up.res4.16.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv1.*              | backbone.bottom_up.res4.17.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.17.conv2.*              | backbone.bottom_up.res4.17.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.17.conv3.*              | backbone.bottom_up.res4.17.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv1.*              | backbone.bottom_up.res4.18.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.18.conv2.*              | backbone.bottom_up.res4.18.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.18.conv3.*              | backbone.bottom_up.res4.18.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv1.*              | backbone.bottom_up.res4.19.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.19.conv2.*              | backbone.bottom_up.res4.19.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.19.conv3.*              | backbone.bottom_up.res4.19.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv1.*               | backbone.bottom_up.res4.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.2.conv2.*               | backbone.bottom_up.res4.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.2.conv3.*               | backbone.bottom_up.res4.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv1.*              | backbone.bottom_up.res4.20.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.20.conv2.*              | backbone.bottom_up.res4.20.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.20.conv3.*              | backbone.bottom_up.res4.20.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv1.*              | backbone.bottom_up.res4.21.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.21.conv2.*              | backbone.bottom_up.res4.21.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.21.conv3.*              | backbone.bottom_up.res4.21.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv1.*              | backbone.bottom_up.res4.22.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.22.conv2.*              | backbone.bottom_up.res4.22.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.22.conv3.*              | backbone.bottom_up.res4.22.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}   | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv1.*               | backbone.bottom_up.res4.3.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.3.conv2.*               | backbone.bottom_up.res4.3.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.3.conv3.*               | backbone.bottom_up.res4.3.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv1.*               | backbone.bottom_up.res4.4.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.4.conv2.*               | backbone.bottom_up.res4.4.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.4.conv3.*               | backbone.bottom_up.res4.4.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv1.*               | backbone.bottom_up.res4.5.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.5.conv2.*               | backbone.bottom_up.res4.5.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.5.conv3.*               | backbone.bottom_up.res4.5.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv1.*               | backbone.bottom_up.res4.6.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.6.conv2.*               | backbone.bottom_up.res4.6.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.6.conv3.*               | backbone.bottom_up.res4.6.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv1.*               | backbone.bottom_up.res4.7.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.7.conv2.*               | backbone.bottom_up.res4.7.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.7.conv3.*               | backbone.bottom_up.res4.7.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv1.*               | backbone.bottom_up.res4.8.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.8.conv2.*               | backbone.bottom_up.res4.8.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.8.conv3.*               | backbone.bottom_up.res4.8.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv1.*               | backbone.bottom_up.res4.9.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res4.9.conv2.*               | backbone.bottom_up.res4.9.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,32,3,3)   |\n",
            "| backbone.bottom_up.res4.9.conv3.*               | backbone.bottom_up.res4.9.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (1024,) (1024,) (1024,) (1024,) (1024,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv1.*               | backbone.bottom_up.res5.0.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.0.conv2.*               | backbone.bottom_up.res5.0.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.0.conv3.*               | backbone.bottom_up.res5.0.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.0.shortcut.*            | backbone.bottom_up.res5.0.shortcut.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight} | (2048,) (2048,) (2048,) (2048,) (2048,1024,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv1.*               | backbone.bottom_up.res5.1.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.1.conv2.*               | backbone.bottom_up.res5.1.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.1.conv3.*               | backbone.bottom_up.res5.1.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv1.*               | backbone.bottom_up.res5.2.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.res5.2.conv2.*               | backbone.bottom_up.res5.2.conv2.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,64,3,3)   |\n",
            "| backbone.bottom_up.res5.2.conv3.*               | backbone.bottom_up.res5.2.conv3.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}    | (2048,) (2048,) (2048,) (2048,) (2048,2048,1,1) |\n",
            "| backbone.bottom_up.stem.conv1.*                 | backbone.bottom_up.stem.conv1.{norm.bias,norm.running_mean,norm.running_var,norm.weight,weight}      | (64,) (64,) (64,) (64,) (64,3,7,7)              |\n",
            "| backbone.fpn_lateral2.*                         | backbone.fpn_lateral2.{bias,weight}                                                                  | (256,) (256,256,1,1)                            |\n",
            "| backbone.fpn_lateral3.*                         | backbone.fpn_lateral3.{bias,weight}                                                                  | (256,) (256,512,1,1)                            |\n",
            "| backbone.fpn_lateral4.*                         | backbone.fpn_lateral4.{bias,weight}                                                                  | (256,) (256,1024,1,1)                           |\n",
            "| backbone.fpn_lateral5.*                         | backbone.fpn_lateral5.{bias,weight}                                                                  | (256,) (256,2048,1,1)                           |\n",
            "| backbone.fpn_output2.*                          | backbone.fpn_output2.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output3.*                          | backbone.fpn_output3.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output4.*                          | backbone.fpn_output4.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| backbone.fpn_output5.*                          | backbone.fpn_output5.{bias,weight}                                                                   | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.anchor_deltas.*     | proposal_generator.rpn_head.anchor_deltas.{bias,weight}                                              | (36,) (36,256,1,1)                              |\n",
            "| proposal_generator.rpn_head.conv.*              | proposal_generator.rpn_head.conv.{bias,weight}                                                       | (256,) (256,256,3,3)                            |\n",
            "| proposal_generator.rpn_head.objectness_logits.* | proposal_generator.rpn_head.objectness_logits.{bias,weight}                                          | (9,) (9,256,1,1)                                |\n",
            "| roi_heads.box_head.fc1.*                        | roi_heads.box_head.fc1.{bias,weight}                                                                 | (1024,) (1024,12544)                            |\n",
            "| roi_heads.box_head.fc2.*                        | roi_heads.box_head.fc2.{bias,weight}                                                                 | (1024,) (1024,1024)                             |\n",
            "| roi_heads.box_predictor.bbox_pred.*             | roi_heads.box_predictor.bbox_pred.{bias,weight}                                                      | (4,) (4,1024)                                   |\n",
            "| roi_heads.box_predictor.cls_score.*             | roi_heads.box_predictor.cls_score.{bias,weight}                                                      | (2,) (2,1024)                                   |\n",
            "\u001b[32m[07/20 05:30:01 d2.data.datasets.coco]: \u001b[0mLoaded 50 images in COCO format from /content/gdrive/MyDrive/DatasetTB/Test/testannot.json\n",
            "Metadata(evaluator_type='coco', image_root='/content/gdrive/MyDrive/DatasetTB/Test/images/', json_file='/content/gdrive/MyDrive/DatasetTB/Test/testannot.json', name='test', thing_classes=['mitosis'], thing_dataset_id_to_contiguous_id={1: 0})\n",
            "{'instances': Instances(num_instances=1, image_height=960, image_width=1280, fields=[pred_boxes: Boxes(tensor([[1008.7608,  568.4113, 1053.2114,  611.1747]], device='cuda:0')), scores: tensor([0.8357], device='cuda:0'), pred_classes: tensor([0], device='cuda:0')])}\n"
          ]
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "<matplotlib.image.AxesImage at 0x7f32684aacd0>"
            ]
          },
          "metadata": {},
          "execution_count": 27
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1440x1080 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "M97_akwd14Bl"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "RlhJxiS614EO"
      },
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        ""
      ],
      "metadata": {
        "id": "IAkOWXhT14G4"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}