Traffic_Signs_Dataset_in_YOLO_format.ipynb

{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Traffic-Signs-Dataset-in-YOLO-format.ipynb",
      "provenance": [],
      "collapsed_sections": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    },
    "accelerator": "GPU"
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "VoHTlFNlYAFO"
      },
      "source": [
        "**Import libraries**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "E0JqtgLcBWkF"
      },
      "source": [
        "import torch # YOLOv5 implemented using pytorch"
      ],
      "execution_count": 1,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "xaguSv1yBmYn"
      },
      "source": [
        "from IPython.display import Image #this is to render predictions"
      ],
      "execution_count": 2,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Jpb4tuWBBw1o",
        "outputId": "dcb134a2-a349-4623-ce9a-38de4b016bb6"
      },
      "source": [
        "cd /content/drive/MyDrive/Yolov5"
      ],
      "execution_count": 3,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/MyDrive/Yolov5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Q9hfkiagB6Te",
        "outputId": "46ecc2c8-a403-4f6e-8c8b-eea187e84ca5"
      },
      "source": [
        "ls"
      ],
      "execution_count": 4,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Traffic-Signs-Dataset-in-YOLO-format.ipynb\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ws94sqEVBq6l",
        "outputId": "ccfe0ea1-835e-4244-f9a7-b18c251704b5"
      },
      "source": [
        "!git clone https://github.com/ultralytics/yolov5"
      ],
      "execution_count": 5,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Cloning into 'yolov5'...\n",
            "remote: Enumerating objects: 9239, done.\u001b[K\n",
            "remote: Counting objects: 100% (1/1), done.\u001b[K\n",
            "remote: Total 9239 (delta 0), reused 0 (delta 0), pack-reused 9238\u001b[K\n",
            "Receiving objects: 100% (9239/9239), 9.54 MiB | 7.24 MiB/s, done.\n",
            "Resolving deltas: 100% (6428/6428), done.\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "29WFieAkB8Ui",
        "outputId": "4b518837-39c5-423e-fd7e-f8f6f8ed72c6"
      },
      "source": [
        "cd yolov5"
      ],
      "execution_count": 6,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Yolov5/yolov5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "mP7fxz5wCHJj",
        "outputId": "e16ab2f5-ea8e-4322-e219-cecf5c215b23"
      },
      "source": [
        "!pip install -r requirements.txt"
      ],
      "execution_count": 7,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Requirement already satisfied: matplotlib>=3.2.2 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 4)) (3.2.2)\n",
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            "Requirement already satisfied: Pillow in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 7)) (7.1.2)\n",
            "Collecting PyYAML>=5.3.1\n",
            "  Downloading PyYAML-5.4.1-cp37-cp37m-manylinux1_x86_64.whl (636 kB)\n",
            "\u001b[K     |████████████████████████████████| 636 kB 12.5 MB/s \n",
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            "Requirement already satisfied: torch>=1.7.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 10)) (1.9.0+cu102)\n",
            "Requirement already satisfied: torchvision>=0.8.1 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 11)) (0.10.0+cu102)\n",
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            "Requirement already satisfied: seaborn>=0.11.0 in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 19)) (0.11.1)\n",
            "Requirement already satisfied: pandas in /usr/local/lib/python3.7/dist-packages (from -r requirements.txt (line 20)) (1.1.5)\n",
            "Collecting thop\n",
            "  Downloading thop-0.0.31.post2005241907-py3-none-any.whl (8.7 kB)\n",
            "Requirement already satisfied: python-dateutil>=2.1 in /usr/local/lib/python3.7/dist-packages (from matplotlib>=3.2.2->-r requirements.txt (line 4)) (2.8.2)\n",
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            "Requirement already satisfied: tensorboard-data-server<0.7.0,>=0.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 15)) (0.6.1)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 15)) (3.3.4)\n",
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            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard>=2.4.1->-r requirements.txt (line 15)) (1.0.1)\n",
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            "Requirement already satisfied: pytz>=2017.2 in /usr/local/lib/python3.7/dist-packages (from pandas->-r requirements.txt (line 20)) (2018.9)\n",
            "Requirement already satisfied: six in /usr/local/lib/python3.7/dist-packages (from absl-py>=0.4->tensorboard>=2.4.1->-r requirements.txt (line 15)) (1.15.0)\n",
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            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 15)) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 15)) (4.2.2)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 15)) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 15)) (4.6.4)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard>=2.4.1->-r requirements.txt (line 15)) (0.4.8)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1->-r requirements.txt (line 15)) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1->-r requirements.txt (line 15)) (3.0.4)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1->-r requirements.txt (line 15)) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard>=2.4.1->-r requirements.txt (line 15)) (2021.5.30)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard>=2.4.1->-r requirements.txt (line 15)) (3.1.1)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard>=2.4.1->-r requirements.txt (line 15)) (3.5.0)\n",
            "Installing collected packages: thop, 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-5.4.1 thop-0.0.31.post2005241907\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "lh8n9peECKQC",
        "outputId": "b34acf4d-61de-4045-febb-0617faf87ac7"
      },
      "source": [
        "!pip install tensorflow==2.3.1 \n",
        "!pip install tensorboard==2.4.1"
      ],
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorflow==2.3.1\n",
            "  Downloading tensorflow-2.3.1-cp37-cp37m-manylinux2010_x86_64.whl (320.4 MB)\n",
            "\u001b[K     |████████████████████████████████| 320.4 MB 11 kB/s \n",
            "\u001b[?25hRequirement already satisfied: termcolor>=1.1.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.1.0)\n",
            "Requirement already satisfied: absl-py>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.12.0)\n",
            "Collecting tensorflow-estimator<2.4.0,>=2.3.0\n",
            "  Downloading tensorflow_estimator-2.3.0-py2.py3-none-any.whl (459 kB)\n",
            "\u001b[K     |████████████████████████████████| 459 kB 44.0 MB/s \n",
            "\u001b[?25hRequirement already satisfied: six>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.15.0)\n",
            "Requirement already satisfied: opt-einsum>=2.3.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (3.3.0)\n",
            "Requirement already satisfied: astunparse==1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.6.3)\n",
            "Requirement already satisfied: protobuf>=3.9.2 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (3.17.3)\n",
            "Requirement already satisfied: grpcio>=1.8.6 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.39.0)\n",
            "Collecting gast==0.3.3\n",
            "  Downloading gast-0.3.3-py2.py3-none-any.whl (9.7 kB)\n",
            "Requirement already satisfied: google-pasta>=0.1.8 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.2.0)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (0.37.0)\n",
            "Collecting numpy<1.19.0,>=1.16.0\n",
            "  Downloading numpy-1.18.5-cp37-cp37m-manylinux1_x86_64.whl (20.1 MB)\n",
            "\u001b[K     |████████████████████████████████| 20.1 MB 1.6 MB/s \n",
            "\u001b[?25hRequirement already satisfied: wrapt>=1.11.1 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (1.12.1)\n",
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            "Requirement already satisfied: tensorboard<3,>=2.3.0 in /usr/local/lib/python3.7/dist-packages (from tensorflow==2.3.1) (2.6.0)\n",
            "Collecting h5py<2.11.0,>=2.10.0\n",
            "  Downloading h5py-2.10.0-cp37-cp37m-manylinux1_x86_64.whl (2.9 MB)\n",
            "\u001b[K     |████████████████████████████████| 2.9 MB 39.1 MB/s \n",
            "\u001b[?25hRequirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.0.1)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (1.8.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.3.4)\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<3,>=2.3.0->tensorflow==2.3.1) (3.0.4)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (2.10)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard<3,>=2.3.0->tensorflow==2.3.1) (3.5.0)\n",
            "Installing collected packages: numpy, tensorflow-estimator, h5py, gast, tensorflow\n",
            "  Attempting uninstall: numpy\n",
            "    Found existing installation: numpy 1.19.5\n",
            "    Uninstalling numpy-1.19.5:\n",
            "      Successfully uninstalled numpy-1.19.5\n",
            "  Attempting uninstall: tensorflow-estimator\n",
            "    Found existing installation: tensorflow-estimator 2.6.0\n",
            "    Uninstalling tensorflow-estimator-2.6.0:\n",
            "      Successfully uninstalled tensorflow-estimator-2.6.0\n",
            "  Attempting uninstall: h5py\n",
            "    Found existing installation: h5py 3.1.0\n",
            "    Uninstalling h5py-3.1.0:\n",
            "      Successfully uninstalled h5py-3.1.0\n",
            "  Attempting uninstall: gast\n",
            "    Found existing installation: gast 0.4.0\n",
            "    Uninstalling gast-0.4.0:\n",
            "      Successfully uninstalled gast-0.4.0\n",
            "  Attempting uninstall: tensorflow\n",
            "    Found existing installation: tensorflow 2.6.0\n",
            "    Uninstalling tensorflow-2.6.0:\n",
            "      Successfully uninstalled tensorflow-2.6.0\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",
            "datascience 0.10.6 requires folium==0.2.1, but you have folium 0.8.3 which is incompatible.\n",
            "albumentations 0.1.12 requires imgaug<0.2.7,>=0.2.5, but you have imgaug 0.2.9 which is incompatible.\u001b[0m\n",
            "Successfully installed gast-0.3.3 h5py-2.10.0 numpy-1.18.5 tensorflow-2.3.1 tensorflow-estimator-2.3.0\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "application/vnd.colab-display-data+json": {
              "pip_warning": {
                "packages": [
                  "numpy"
                ]
              }
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "text": [
            "Collecting tensorboard==2.4.1\n",
            "  Downloading tensorboard-2.4.1-py3-none-any.whl (10.6 MB)\n",
            "\u001b[K     |████████████████████████████████| 10.6 MB 8.9 MB/s \n",
            "\u001b[?25hRequirement already satisfied: google-auth-oauthlib<0.5,>=0.4.1 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (0.4.5)\n",
            "Requirement already satisfied: numpy>=1.12.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.18.5)\n",
            "Requirement already satisfied: wheel>=0.26 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (0.37.0)\n",
            "Requirement already satisfied: tensorboard-plugin-wit>=1.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.8.0)\n",
            "Requirement already satisfied: requests<3,>=2.21.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (2.23.0)\n",
            "Requirement already satisfied: setuptools>=41.0.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (57.4.0)\n",
            "Requirement already satisfied: markdown>=2.6.8 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (3.3.4)\n",
            "Requirement already satisfied: absl-py>=0.4 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (0.12.0)\n",
            "Requirement already satisfied: protobuf>=3.6.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (3.17.3)\n",
            "Requirement already satisfied: six>=1.10.0 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.15.0)\n",
            "Requirement already satisfied: grpcio>=1.24.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.39.0)\n",
            "Requirement already satisfied: werkzeug>=0.11.15 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.0.1)\n",
            "Requirement already satisfied: google-auth<2,>=1.6.3 in /usr/local/lib/python3.7/dist-packages (from tensorboard==2.4.1) (1.34.0)\n",
            "Requirement already satisfied: rsa<5,>=3.1.4 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.4.1) (4.7.2)\n",
            "Requirement already satisfied: cachetools<5.0,>=2.0.0 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.4.1) (4.2.2)\n",
            "Requirement already satisfied: pyasn1-modules>=0.2.1 in /usr/local/lib/python3.7/dist-packages (from google-auth<2,>=1.6.3->tensorboard==2.4.1) (0.2.8)\n",
            "Requirement already satisfied: requests-oauthlib>=0.7.0 in /usr/local/lib/python3.7/dist-packages (from google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.4.1) (1.3.0)\n",
            "Requirement already satisfied: importlib-metadata in /usr/local/lib/python3.7/dist-packages (from markdown>=2.6.8->tensorboard==2.4.1) (4.6.4)\n",
            "Requirement already satisfied: pyasn1<0.5.0,>=0.4.6 in /usr/local/lib/python3.7/dist-packages (from pyasn1-modules>=0.2.1->google-auth<2,>=1.6.3->tensorboard==2.4.1) (0.4.8)\n",
            "Requirement already satisfied: urllib3!=1.25.0,!=1.25.1,<1.26,>=1.21.1 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (1.24.3)\n",
            "Requirement already satisfied: certifi>=2017.4.17 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (2021.5.30)\n",
            "Requirement already satisfied: idna<3,>=2.5 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (2.10)\n",
            "Requirement already satisfied: chardet<4,>=3.0.2 in /usr/local/lib/python3.7/dist-packages (from requests<3,>=2.21.0->tensorboard==2.4.1) (3.0.4)\n",
            "Requirement already satisfied: oauthlib>=3.0.0 in /usr/local/lib/python3.7/dist-packages (from requests-oauthlib>=0.7.0->google-auth-oauthlib<0.5,>=0.4.1->tensorboard==2.4.1) (3.1.1)\n",
            "Requirement already satisfied: typing-extensions>=3.6.4 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard==2.4.1) (3.7.4.3)\n",
            "Requirement already satisfied: zipp>=0.5 in /usr/local/lib/python3.7/dist-packages (from importlib-metadata->markdown>=2.6.8->tensorboard==2.4.1) (3.5.0)\n",
            "Installing collected packages: tensorboard\n",
            "  Attempting uninstall: tensorboard\n",
            "    Found existing installation: tensorboard 2.6.0\n",
            "    Uninstalling tensorboard-2.6.0:\n",
            "      Successfully uninstalled tensorboard-2.6.0\n",
            "Successfully installed tensorboard-2.4.1\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "vcHFzjdWX58P"
      },
      "source": [
        "**Download Dataset**"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "UVjL4V1fCTBQ"
      },
      "source": [
        "!pip install -q kaggle"
      ],
      "execution_count": 10,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "H_iwZaiQH9M_",
        "outputId": "4979e0db-4651-4abc-feef-f0644c14e795"
      },
      "source": [
        "!mkdir ~/.kaggle"
      ],
      "execution_count": 32,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "mkdir: cannot create directory ‘/root/.kaggle’: File exists\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "9QCj_DdvIJoo"
      },
      "source": [
        "!cp kaggle/kaggle.json ~/.kaggle/"
      ],
      "execution_count": 33,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "pFTMdkQLIVMh"
      },
      "source": [
        "!chmod 600 ~/.kaggle/kaggle.json"
      ],
      "execution_count": 34,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "rmCw_-NZIfS_",
        "outputId": "6e3ffabf-de62-4163-9625-2490dc083f82"
      },
      "source": [
        "!kaggle datasets download -d valentynsichkar/traffic-signs-dataset-in-yolo-format"
      ],
      "execution_count": 35,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Downloading traffic-signs-dataset-in-yolo-format.zip to /content/drive/My Drive/Yolov5\n",
            " 96% 241M/250M [00:07<00:00, 34.3MB/s]\n",
            "100% 250M/250M [00:07<00:00, 34.9MB/s]\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "x19Q6eDGJCtg"
      },
      "source": [
        "!unzip traffic-signs-dataset-in-yolo-format.zip"
      ],
      "execution_count": null,
      "outputs": []
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "IwvmX5njKjxp",
        "outputId": "1b446a2c-d672-4aa5-b2db-b63147d936a0"
      },
      "source": [
        "ls"
      ],
      "execution_count": 49,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "classes.names\n",
            "\u001b[0m\u001b[01;34mdata\u001b[0m/\n",
            "\u001b[01;34mdata_copy\u001b[0m/\n",
            "getting-full-path.py\n",
            "\u001b[01;34mkaggle\u001b[0m/\n",
            "test.txt\n",
            "Traffic-Signs-Dataset-in-YOLO-format.ipynb\n",
            "traffic-signs-dataset-in-yolo-format.zip\n",
            "traffic-sign-to-test.mp4\n",
            "train.txt\n",
            "ts_data.data\n",
            "yolov3_ts_test.cfg\n",
            "yolov3_ts_train.cfg\n",
            "\u001b[01;34myolov5\u001b[0m/\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "CLMQZWrbCi7h"
      },
      "source": [
        "# Divide the dataset in train and val folder."
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Usqqu45sCkmy",
        "outputId": "e61efbeb-d91a-48db-e10a-85983b262a37"
      },
      "source": [
        "import os\n",
        "from random import choice\n",
        "import shutil\n",
        "\n",
        "#arrays to store file names\n",
        "imgs =[]\n",
        "xmls =[]\n",
        "\n",
        "#setup dir names\n",
        "trainPath = '/content/drive/MyDrive/Yolov5/dataset/images/train'\n",
        "valPath = '/content/drive/MyDrive/Yolov5/dataset/images/val'\n",
        "crsPath = '/content/drive/MyDrive/Yolov5/data/ts' #dir where images and annotations stored\n",
        "\n",
        "#setup ratio (val ratio = rest of the files in origin dir after splitting into train and test)\n",
        "train_ratio = 0.8\n",
        "val_ratio = 0.2\n",
        "\n",
        "\n",
        "#total count of imgs\n",
        "totalImgCount = len(os.listdir(crsPath))/2\n",
        "\n",
        "#soring files to corresponding arrays\n",
        "for (dirname, dirs, files) in os.walk(crsPath):\n",
        "    for filename in files:\n",
        "        if filename.endswith('.txt'):\n",
        "            xmls.append(filename)\n",
        "        else:\n",
        "            imgs.append(filename)\n",
        "\n",
        "\n",
        "#counting range for cycles\n",
        "countForTrain = int(len(imgs)*train_ratio)\n",
        "countForVal = int(len(imgs)*val_ratio)\n",
        "print(\"training images are : \",countForTrain)\n",
        "print(\"Validation images are : \",countForVal)\n"
      ],
      "execution_count": 52,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "training images are :  592\n",
            "Validation images are :  148\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 35
        },
        "id": "9D7nslIjMCRe",
        "outputId": "8df3d223-9b02-4032-85f6-a5d069f16064"
      },
      "source": [
        "\n",
        "trainimagePath = '/content/drive/MyDrive/Yolov5/dataset/images/train'\n",
        "trainlabelPath = '/content/drive/MyDrive/Yolov5/dataset/labels/train'\n",
        "valimagePath = '/content/drive/MyDrive/Yolov5/dataset/images/val'\n",
        "vallabelPath = '/content/drive/MyDrive/Yolov5/dataset/labels/val'\n",
        "#cycle for train dir\n",
        "for x in range(countForTrain):\n",
        "\n",
        "    fileJpg = choice(imgs) # get name of random image from origin dir\n",
        "    fileXml = fileJpg[:-4] +'.txt' # get name of corresponding annotation file\n",
        "\n",
        "    #move both files into train dir\n",
        "    #shutil.move(os.path.join(crsPath, fileJpg), os.path.join(trainimagePath, fileJpg))\n",
        "    #shutil.move(os.path.join(crsPath, fileXml), os.path.join(trainlabelPath, fileXml))\n",
        "    shutil.copy(os.path.join(crsPath, fileJpg), os.path.join(trainimagePath, fileJpg))\n",
        "    shutil.copy(os.path.join(crsPath, fileXml), os.path.join(trainlabelPath, fileXml))\n",
        "\n",
        "\n",
        "    #remove files from arrays\n",
        "    imgs.remove(fileJpg)\n",
        "    xmls.remove(fileXml)\n",
        "\n",
        "\n",
        "\n",
        "#cycle for test dir   \n",
        "for x in range(countForVal):\n",
        "\n",
        "    fileJpg = choice(imgs) # get name of random image from origin dir\n",
        "    fileXml = fileJpg[:-4] +'.txt' # get name of corresponding annotation file\n",
        "\n",
        "    #move both files into train dir\n",
        "    #shutil.move(os.path.join(crsPath, fileJpg), os.path.join(valimagePath, fileJpg))\n",
        "    #shutil.move(os.path.join(crsPath, fileXml), os.path.join(vallabelPath, fileXml))\n",
        "    shutil.copy(os.path.join(crsPath, fileJpg), os.path.join(valimagePath, fileJpg))\n",
        "    shutil.copy(os.path.join(crsPath, fileXml), os.path.join(vallabelPath, fileXml))\n",
        "    \n",
        "    #remove files from arrays\n",
        "    imgs.remove(fileJpg)\n",
        "    xmls.remove(fileXml)\n",
        "\n",
        "#rest of files will be validation files, so rename origin dir to val dir\n",
        "#os.rename(crsPath, valPath)\n",
        "shutil.move(crsPath, valPath) \n",
        "\n",
        "\n"
      ],
      "execution_count": 53,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            },
            "text/plain": [
              "'/content/drive/MyDrive/Yolov5/dataset/images/val/ts'"
            ]
          },
          "metadata": {},
          "execution_count": 53
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "V5EN8eKYOaYf"
      },
      "source": [
        "# Create dataset.yaml\n",
        "\n",
        "## move the dataset’s dataset.yaml file to the /content/yolov5/data directory. \n",
        "## This file contains information required by YOLO to train the model on the custom data.\n",
        "![image.png](data:image/png;base64,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)"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KHzBkKTgO5e4"
      },
      "source": [
        "# Run"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "WvjkUG1zN1fd",
        "outputId": "e43b501b-a580-41e4-d0a7-bb5f0ef5347b"
      },
      "source": [
        "%cd yolov5"
      ],
      "execution_count": 54,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "/content/drive/My Drive/Yolov5/yolov5\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "qO3ILCaPO9Ri",
        "outputId": "118b8cd1-048b-4d6f-a7b2-d15efb4cb711"
      },
      "source": [
        "!python train.py --img 415 --batch 16 --epochs 30 --data dataset.yaml --weights yolov5s.pt --cache"
      ],
      "execution_count": 55,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mtrain: \u001b[0mweights=yolov5s.pt, cfg=, data=dataset.yaml, hyp=data/hyps/hyp.scratch.yaml, epochs=30, batch_size=16, imgsz=415, rect=False, resume=False, nosave=False, noval=False, noautoanchor=False, evolve=None, bucket=, cache=ram, image_weights=False, device=, multi_scale=False, single_cls=False, adam=False, sync_bn=False, workers=8, project=runs/train, entity=None, name=exp, exist_ok=False, quad=False, linear_lr=False, label_smoothing=0.0, upload_dataset=False, bbox_interval=-1, save_period=-1, artifact_alias=latest, local_rank=-1, freeze=0, patience=30\n",
            "remote: Enumerating objects: 32, done.\u001b[K\n",
            "remote: Counting objects: 100% (32/32), done.\u001b[K\n",
            "remote: Compressing objects: 100% (19/19), done.\u001b[K\n",
            "remote: Total 32 (delta 16), reused 28 (delta 13), pack-reused 0\u001b[K\n",
            "Unpacking objects: 100% (32/32), done.\n",
            "From https://github.com/ultralytics/yolov5\n",
            "   a8eb4fc..502088d  feature/annotator -> origin/feature/annotator\n",
            "\u001b[34m\u001b[1mgithub: \u001b[0mup to date with https://github.com/ultralytics/yolov5 ✅\n",
            "YOLOv5 🚀 v5.0-387-g7b35971 torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n",
            "\n",
            "\u001b[34m\u001b[1mhyperparameters: \u001b[0mlr0=0.01, lrf=0.2, momentum=0.937, weight_decay=0.0005, warmup_epochs=3.0, warmup_momentum=0.8, warmup_bias_lr=0.1, box=0.05, cls=0.5, cls_pw=1.0, obj=1.0, obj_pw=1.0, iou_t=0.2, anchor_t=4.0, fl_gamma=0.0, hsv_h=0.015, hsv_s=0.7, hsv_v=0.4, degrees=0.0, translate=0.1, scale=0.5, shear=0.0, perspective=0.0, flipud=0.0, fliplr=0.5, mosaic=1.0, mixup=0.0, copy_paste=0.0\n",
            "\u001b[34m\u001b[1mWeights & Biases: \u001b[0mrun 'pip install wandb' to automatically track and visualize YOLOv5 🚀 runs (RECOMMENDED)\n",
            "\u001b[34m\u001b[1mTensorBoard: \u001b[0mStart with 'tensorboard --logdir runs/train', view at http://localhost:6006/\n",
            "2021-08-29 14:07:04.952049: I tensorflow/stream_executor/platform/default/dso_loader.cc:48] Successfully opened dynamic library libcudart.so.10.1\n",
            "Downloading https://github.com/ultralytics/yolov5/releases/download/v5.0/yolov5s.pt to yolov5s.pt...\n",
            "100% 14.1M/14.1M [00:00<00:00, 69.7MB/s]\n",
            "\n",
            "Overriding model.yaml nc=80 with nc=4\n",
            "\n",
            "                 from  n    params  module                                  arguments                     \n",
            "  0                -1  1      3520  models.common.Focus                     [3, 32, 3]                    \n",
            "  1                -1  1     18560  models.common.Conv                      [32, 64, 3, 2]                \n",
            "  2                -1  1     18816  models.common.C3                        [64, 64, 1]                   \n",
            "  3                -1  1     73984  models.common.Conv                      [64, 128, 3, 2]               \n",
            "  4                -1  3    156928  models.common.C3                        [128, 128, 3]                 \n",
            "  5                -1  1    295424  models.common.Conv                      [128, 256, 3, 2]              \n",
            "  6                -1  3    625152  models.common.C3                        [256, 256, 3]                 \n",
            "  7                -1  1   1180672  models.common.Conv                      [256, 512, 3, 2]              \n",
            "  8                -1  1    656896  models.common.SPP                       [512, 512, [5, 9, 13]]        \n",
            "  9                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 10                -1  1    131584  models.common.Conv                      [512, 256, 1, 1]              \n",
            " 11                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 12           [-1, 6]  1         0  models.common.Concat                    [1]                           \n",
            " 13                -1  1    361984  models.common.C3                        [512, 256, 1, False]          \n",
            " 14                -1  1     33024  models.common.Conv                      [256, 128, 1, 1]              \n",
            " 15                -1  1         0  torch.nn.modules.upsampling.Upsample    [None, 2, 'nearest']          \n",
            " 16           [-1, 4]  1         0  models.common.Concat                    [1]                           \n",
            " 17                -1  1     90880  models.common.C3                        [256, 128, 1, False]          \n",
            " 18                -1  1    147712  models.common.Conv                      [128, 128, 3, 2]              \n",
            " 19          [-1, 14]  1         0  models.common.Concat                    [1]                           \n",
            " 20                -1  1    296448  models.common.C3                        [256, 256, 1, False]          \n",
            " 21                -1  1    590336  models.common.Conv                      [256, 256, 3, 2]              \n",
            " 22          [-1, 10]  1         0  models.common.Concat                    [1]                           \n",
            " 23                -1  1   1182720  models.common.C3                        [512, 512, 1, False]          \n",
            " 24      [17, 20, 23]  1     24273  models.yolo.Detect                      [4, [[10, 13, 16, 30, 33, 23], [30, 61, 62, 45, 59, 119], [116, 90, 156, 198, 373, 326]], [128, 256, 512]]\n",
            "Model Summary: 283 layers, 7071633 parameters, 7071633 gradients, 16.4 GFLOPs\n",
            "\n",
            "Transferred 356/362 items from yolov5s.pt\n",
            "Scaled weight_decay = 0.0005\n",
            "\u001b[34m\u001b[1moptimizer:\u001b[0m SGD with parameter groups 59 weight, 62 weight (no decay), 62 bias\n",
            "WARNING: --img-size 415 must be multiple of max stride 32, updating to 416\n",
            "\u001b[34m\u001b[1malbumentations: \u001b[0mversion 1.0.3 required by YOLOv5, but version 0.1.12 is currently installed\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mScanning '../dataset/labels/train' images and labels...592 found, 0 missing, 0 empty, 1 corrupted: 100% 592/592 [00:01<00:00, 320.39it/s]\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mWARNING: Ignoring corrupted image and/or label ../dataset/images/train/00340.jpg: duplicate labels\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mNew cache created: ../dataset/labels/train.cache\n",
            "\u001b[34m\u001b[1mtrain: \u001b[0mCaching images (0.2GB ram): 100% 591/591 [00:11<00:00, 50.17it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mScanning '../dataset/labels/val' images and labels...148 found, 741 missing, 0 empty, 0 corrupted: 100% 889/889 [00:02<00:00, 375.02it/s]\n",
            "\u001b[34m\u001b[1mval: \u001b[0mNew cache created: ../dataset/labels/val.cache\n",
            "\u001b[34m\u001b[1mval: \u001b[0mCaching images (0.3GB ram): 100% 889/889 [00:22<00:00, 40.23it/s]\n",
            "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
            "[W pthreadpool-cpp.cc:90] Warning: Leaking Caffe2 thread-pool after fork. (function pthreadpool)\n",
            "Plotting labels... \n",
            "\n",
            "\u001b[34m\u001b[1mautoanchor: \u001b[0mAnalyzing anchors... anchors/target = 3.25, Best Possible Recall (BPR) = 1.0000\n",
            "Image sizes 416 train, 416 val\n",
            "Using 2 dataloader workers\n",
            "Logging results to runs/train/exp\n",
            "Starting training for 30 epochs...\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      0/29     1.48G    0.1219   0.01112   0.04282        45       416: 100% 37/37 [00:22<00:00,  1.68it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:08<00:00,  3.25it/s]\n",
            "                 all        889        233   8.67e-05     0.0041   1.15e-05   3.11e-06\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      1/29     1.62G    0.1134   0.01077   0.03838        42       416: 100% 37/37 [00:19<00:00,  1.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.55it/s]\n",
            "                 all        889        233    0.00127     0.0144   0.000116   2.45e-05\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      2/29     1.62G    0.1026   0.01137   0.03527        44       416: 100% 37/37 [00:19<00:00,  1.94it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.76it/s]\n",
            "                 all        889        233    0.00751     0.0284    0.00166    0.00025\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      3/29     1.62G   0.08464   0.01338   0.03316        46       416: 100% 37/37 [00:19<00:00,  1.93it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.87it/s]\n",
            "                 all        889        233     0.0221     0.0973    0.00997    0.00253\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      4/29     1.62G   0.07466    0.0135   0.03097        44       416: 100% 37/37 [00:18<00:00,  1.96it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.90it/s]\n",
            "                 all        889        233      0.533     0.0963     0.0133    0.00296\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      5/29     1.62G   0.06882   0.01263   0.03042        42       416: 100% 37/37 [00:18<00:00,  2.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.90it/s]\n",
            "                 all        889        233      0.532      0.113     0.0186    0.00636\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      6/29     1.62G   0.06362   0.01124   0.02849        49       416: 100% 37/37 [00:18<00:00,  1.98it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.94it/s]\n",
            "                 all        889        233      0.527       0.13     0.0226    0.00939\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      7/29     1.62G   0.05919    0.0109   0.02805        36       416: 100% 37/37 [00:18<00:00,  1.99it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.95it/s]\n",
            "                 all        889        233     0.0614      0.202     0.0338      0.013\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      8/29     1.62G    0.0597   0.01045   0.02593        33       416: 100% 37/37 [00:18<00:00,  1.98it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.97it/s]\n",
            "                 all        889        233      0.315      0.204     0.0493     0.0142\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "      9/29     1.62G   0.05587   0.01013    0.0229        47       416: 100% 37/37 [00:18<00:00,  1.99it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.98it/s]\n",
            "                 all        889        233      0.317      0.329     0.0566     0.0223\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     10/29     1.62G   0.05519      0.01   0.02367        49       416: 100% 37/37 [00:18<00:00,  1.97it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.99it/s]\n",
            "                 all        889        233      0.125      0.346     0.0861     0.0409\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     11/29     1.62G   0.05564  0.009496   0.02137        54       416: 100% 37/37 [00:18<00:00,  1.99it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:07<00:00,  3.98it/s]\n",
            "                 all        889        233      0.137      0.348     0.0895     0.0412\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     12/29     1.62G   0.05458  0.008785    0.0185        57       416: 100% 37/37 [00:18<00:00,  1.99it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.02it/s]\n",
            "                 all        889        233     0.0893      0.427     0.0869     0.0362\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     13/29     1.62G   0.05609  0.008767   0.01771        41       416: 100% 37/37 [00:18<00:00,  2.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.02it/s]\n",
            "                 all        889        233     0.0886      0.359     0.0761     0.0358\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     14/29     1.62G   0.05264  0.008426   0.01668        49       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.00it/s]\n",
            "                 all        889        233     0.0883      0.429      0.085     0.0431\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     15/29     1.62G   0.05374  0.008317   0.01571        40       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.04it/s]\n",
            "                 all        889        233     0.0961      0.492     0.0959     0.0392\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     16/29     1.62G   0.05049  0.008281   0.01577        55       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.02it/s]\n",
            "                 all        889        233      0.111      0.546      0.111     0.0551\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     17/29     1.62G   0.04719  0.008473   0.01362        42       416: 100% 37/37 [00:18<00:00,  2.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.05it/s]\n",
            "                 all        889        233      0.118      0.573      0.119     0.0654\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     18/29     1.62G   0.04869  0.008125   0.01337        61       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.04it/s]\n",
            "                 all        889        233       0.13      0.471      0.109     0.0563\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     19/29     1.62G   0.04871  0.007958   0.01171        39       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.07it/s]\n",
            "                 all        889        233       0.15      0.417      0.119     0.0523\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     20/29     1.62G   0.04761  0.007826   0.01267        36       416: 100% 37/37 [00:18<00:00,  2.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.07it/s]\n",
            "                 all        889        233      0.152       0.55      0.133     0.0679\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     21/29     1.62G   0.04292   0.00789   0.01035        39       416: 100% 37/37 [00:18<00:00,  2.00it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.03it/s]\n",
            "                 all        889        233      0.154      0.611      0.151     0.0747\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     22/29     1.62G   0.04669  0.007338   0.01095        38       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.07it/s]\n",
            "                 all        889        233      0.146       0.57      0.143     0.0678\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     23/29     1.62G   0.04463  0.007747   0.01275        50       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.05it/s]\n",
            "                 all        889        233      0.154      0.584      0.133     0.0682\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     24/29     1.62G   0.04331  0.007399  0.009022        50       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.05it/s]\n",
            "                 all        889        233      0.159      0.593      0.142     0.0882\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     25/29     1.62G   0.04219  0.007422  0.009269        55       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.02it/s]\n",
            "                 all        889        233       0.16      0.569      0.143     0.0833\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     26/29     1.62G   0.03954  0.007342    0.0102        37       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.06it/s]\n",
            "                 all        889        233      0.153      0.563      0.144     0.0844\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     27/29     1.62G   0.04077  0.007399  0.008406        29       416: 100% 37/37 [00:18<00:00,  2.01it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.05it/s]\n",
            "                 all        889        233      0.153      0.589      0.145     0.0912\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     28/29     1.62G   0.03945  0.006845  0.008185        45       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:06<00:00,  4.05it/s]\n",
            "                 all        889        233      0.152      0.574      0.145      0.087\n",
            "\n",
            "     Epoch   gpu_mem       box       obj       cls    labels  img_size\n",
            "     29/29     1.62G   0.03641  0.006991  0.007989        43       416: 100% 37/37 [00:18<00:00,  2.02it/s]\n",
            "               Class     Images     Labels          P          R     mAP@.5 mAP@.5:.95: 100% 28/28 [00:08<00:00,  3.48it/s]\n",
            "                 all        889        233       0.16      0.566      0.146     0.0933\n",
            "         speed limit        889         97      0.137      0.691      0.142     0.0864\n",
            "               yield        889         41      0.157      0.707      0.141     0.0794\n",
            "           mandatory        889         34      0.208      0.588      0.165      0.123\n",
            "               other        889         61      0.138      0.279      0.135     0.0847\n",
            "\n",
            "30 epochs completed in 0.225 hours.\n",
            "Optimizer stripped from runs/train/exp/weights/last.pt, 14.4MB\n",
            "Optimizer stripped from runs/train/exp/weights/best.pt, 14.4MB\n",
            "Results saved to \u001b[1mruns/train/exp\u001b[0m\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "KZSVwyzGV5fo"
      },
      "source": [
        "# Detect"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "h_NfR-1wV48Q",
        "outputId": "86919b44-b70c-447a-de2a-e985e3fec70d"
      },
      "source": [
        "!python detect.py --source runs/train/exp/a.jpg --weights /content/drive/MyDrive/Yolov5/yolov5/runs/train/exp/weights/best.pt"
      ],
      "execution_count": 57,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "\u001b[34m\u001b[1mdetect: \u001b[0mweights=['/content/drive/MyDrive/Yolov5/yolov5/runs/train/exp/weights/best.pt'], source=runs/train/exp/a.jpg, imgsz=[640, 640], conf_thres=0.25, iou_thres=0.45, max_det=1000, device=, view_img=False, save_txt=False, save_conf=False, save_crop=False, nosave=False, classes=None, agnostic_nms=False, augment=False, visualize=False, update=False, project=runs/detect, name=exp, exist_ok=False, line_thickness=3, hide_labels=False, hide_conf=False, half=False\n",
            "YOLOv5 🚀 v5.0-387-g7b35971 torch 1.9.0+cu102 CUDA:0 (Tesla K80, 11441.1875MB)\n",
            "\n",
            "Fusing layers... \n",
            "Model Summary: 224 layers, 7062001 parameters, 0 gradients, 16.4 GFLOPs\n",
            "image 1/1 /content/drive/MyDrive/Yolov5/yolov5/runs/train/exp/a.jpg: 384x640 2 speed limits, Done. (0.028s)\n",
            "Results saved to \u001b[1mruns/detect/exp2\u001b[0m\n",
            "Done. (0.901s)\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 262
        },
        "id": "VUxx9l-nPB4T",
        "outputId": "d63820b8-b0bf-441d-f507-dea4b8c22c94"
      },
      "source": [
        "Image(filename=\"/content/drive/MyDrive/Yolov5/yolov5/runs/detect/exp2/a.jpg\", width=416)"
      ],
      "execution_count": 58,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "image/jpeg": 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feP8AwbPx7f8Agt98D3U8FvEnH/cs6rXwaxcEyhSSeoLZNfeX/Bs7IJf+C3fwOYLt+bxLkD/sWdVoV+YmduX+u5J/wctLHJ/wW++NyHaDnw3kt/2LWlV8PW8UsZaWIkEHqpr7a/4OagU/4Ld/G2VQDz4byGH/AFLWlV8N6dcCRd8wYnPABxWMlu/63NIvQuE7WWRrdcbsZxjFSXbqJw1lLtLdSoqOfUEaMqsIBzypqRGaONTEuM5xuAOPxrPXcptWsEDCaNy6fMoGTnGTVKSOaa4IiUAD7wBzxVtIWW3IdDuwdvXn+lNsrRrtSYYkVlHzMG5/Sqi0rszI9OvmsGeWSFgB0XOKuTappl6ilImjk2/OTyDVeXRrt4TN5rOo+8fSq9kYUkMjYwAQO5Bp2jN8xXM1oaVq10pMsU0Sjbhg57VYXUJERUluPu/dIxkGs+S8kUHIVlIyVAzTLmeOcoIGZlVejDBzSUbvUq99jZvppZIQ8KLKEHUdfqKzTfLNMFlRgT3OelMtrm5wGtTtdew6mnoFSV5LuR95HRjz+NEY8ugPuX4XAU3CTkFTgEDnv7UjXnnJsMx46kdKpPJE77TIFJ4K1LDILfCKcNk4K9aLInmNOwNuikg7jjOc/WrJvI2YsAQw4AIxWZb+ZHIoCfgDVuW5aVhE46dG2VEldlFhmjm78gc5HtURg38RncGGAGPFPiSJm2bMj1PSpZMGTyUwFx8hFAWsU2eNcR3CFHQ8YpsUqW0nmLwzevarD2kU0Yi37QDy3r/k0rWSGMiJdwYYz+P/ANaq2QA10Zox5vB78c1I16qx+XGx6/LmoxbOoIXDberDqKYJ4jkHcFUYx0OaVlzA72sPudSAXapIJHVTmoYp5VwZmGPUntUUsNs77EdiR94DmmzAzKWG4Be3rVNJIC9I1rPCY7dSrEHaVbvUFpFcKRDdMyndwc1Db3kdudqxYHfcKsi88pN0cSyezdvcVFmg0b1NSGzjtYwIizljwWODUN/YCQhnkAQAksGGT+FVxq0yW8RjtwN4wC55HuKfZrIY3jlgzIPuk5Ge1Sm1qx67lfyYUlUBeCepOaszaZKHRYIsbzgU+wtQ9xiVWj54BrTkNnCoiE8hKsORxj8xTc9dBqxi7ZY5hBMSHzgY4xV62VSuZXPy9VHWr0On2Vzh41IG7BYDOP8AOKLrTYxKZLchUx82Bhhn/P61DlzaGsHYporE4jc7SeGJ6VYsb64tIT5UhKkEttPamfY0X93C5UZ4djg+lOmg8uFkXkfxYahTtJIcqfMrmp4Z8T2+j6tDeS24kSNgWjkYruGckZU1pfEHx3L4k137VZsUtpM+TbGTeIk7KTgZ+uB0rjJbgW5Bg+Y7cEkdqgjaSRhLuMbDvnrXTGW9jBqLtfoel/Du5vNL8RafqdjPb3aRyhntpGVVI99xxj61+j3/AATG/aH+IPi746WMbQ2NlpUMxLRW04R48RcEYAyN+0txjB9q/K/Q9cFnF5QuMmTOSOMd/wAK+pP2Dv2hNE8BeOLax8TPDaWcRZ5Lh5SrSnqF3DtnjHcE1MnZp+a/r8RvWLT7M/pN+AtxpGo/E7ULFbiS/t7exjvdKv7qQnY0q7WCAnAON33QBjkgE80P2w/jV4n8H/C3WdL8DpJc6lZ3MUN7f2kwD2Bkw8YbA4BUr17cck8fmVoP/BSr4j2duiaL4gjv7FbgSD7Pcq/riIbBhFAI4HJwMk85i+Jf/BQ3xMLFfDXh/wAYXNv4g8UGOLX7a61BRALZHLqCdo3OEWNDg4GOnavapuhGspR6b30+7uzw/wB7VhytNX0Vuvr29dz9a/2TZfEWvfDC38e+I9W8+XW8SxW6LhLeNCyKB9QM8cAYHrXqS+v6mvz/AP2Of+Ch/wAKbV7Cx1Lx5YzX+sTRJqkRkeKKA/KPkVyscfCtwuFy3XkAevfFb/gp/wDCX4afFyz8BSwG50wyoupaouf3aOjFZE5wQGGCDg8HpxXN7KpK7077/wBa+R3TnCm1H5I+o8A9Kq6pqmn6NZtf6lciKFGVWcgnBZgoHHqSB+NeGfDr/goB8CfFfh/UvEGoeM4RBaXs8du0CeYXjViE4GGBfAwGVcZAJOCa8v8AiH/wU+0zwbrmn2HizwvbxaR4jt0k0145RPNbEbmYSIQASAA3Ujg9+AvZSvYl1FyXX9PsfWMOvXE3i/UdFgJlS3sLWaOIbRgySTqxzjpiMHk84bFa8k8MSb5J1UBsZLADPTFfnpc/tT+Ovh5+13p2qaf4jt9dsfGsl9p+m3tpfYgkghtleNio+7lgxGVBBjYDGST73r/7f3wC0Dwdaahr2rWqw3EhmvIrdCPsqo5yzYI3EOpBPA6deRVypPmcUtVay76bkRr3pKUrX/rT5H0h9qthP9l+0J5pUkIWG4j1x6VKRnivm79kL9rHw38dtVuvFaXtjdz+JJQPDa2SEPJZQFkdyW4IzmQ4P8YGBxX0grE5yayaszeLbjdjqKKKRQUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUAFFFFABRRRQAUUUUABAPBooBB6Vn+Jtes/DPh+88RagcQWNq88pz/CqkkfpSbSV2CV2Vr7xno2neLrDwXcO4vdStpZrZQAVKx43Z5yOvHGDg81m/FXxgPBvh231Q+JrfSmmv4oEuLqwNyjs27EewSIctjAIbrjrXnt74++Ffiv4w6V8U7LUReRaX4N1C9tp7f5hc28bruMakfvCCcrjB5rqPBdmvxH8DWOsfGfQ7MXzajMLWKVyoUiZkjwpxtchRwN2Rjk5xWkqbhFSatt/wAD8iXKEvdT1MHR/wBq3wppnxL034M/EQNY67cQyk3Ox1t2IYBCdwGN4DnIyqlOuDxxv/BSf9qK0/Z5+CF8V8OW2r/b7KUXdvNcxqY4RlS4VjmQhuy8jGcg4ryf/gst4K1LwH4T8J/tR+FbOW6m8OazZ2Gp2ESkNdW5dmjDtkDZuJU56+YMEcg/Dfx//bNsv2jfAEHjXxvpV1pV1ZmewbTLuNJLVZ3OOLdx5cYUEYIGVxgcYNYTnGdCTj8UW007Wa6fmW70pRlJe6+2+m5vf8Erv+CnWpfB7wP4z+HXw8+GUniHWNW8UTana2au3lQQ+SpdiQSwAOWLHgA5ye1j9u39sf4gWPx+0f4o/DXULK3F34Oglnjs7pAs7ysjXNtIEcK4VhHgqW6dDtr5d/YJ+M3hf4M/tM+JbPxTrVhHpviPTImnezeJvkdXTKH2DglFPPHHAI5H9uDxF8Kvhp8Xl0jSviNDqmhaT4im+y6lpRyZI3cv5q55cL0+YBjjbjvXS529lUlura6aWVrfdY5YJyrzjGTtbRa2d7O/3nqXhT41eG9R/azfw/4z03SfCPh/Vws9zqtpZSTeXIynzHhiZm2yuQQpVYx5jZKoA1fq7+zP+1r+zh4Rvry00n4gObfTPD8Y0/Q5ZUhW0tw6tcHDkebL5jchSWwhO1QTt/mh8eftF395401LXtM1qWeO5BCeWvlJuLbiQo98deevqayo/wBozxlpmpwarpupTR3EDB0ld9zB8ds52/hWEa8uRRaTV9rfd93kbqjZvl0fnr6/efrf/wAFxf8Agoto3xj8Zf8ACv8A4c6/9htNL06W3N3Hki+PnMcqGxlSpTHXnJHFfkdrXxI8TLBe2N7cPI0iNh5XxghupB+9npznrWj4w+OGufFWwk/4SXVnmvljRbeZwNyqBjaT3xwOuMV5xc3Kz6e9xLc7mjfbNvk3MTzyvHSudQhTlK2t399/LodKXPFeX3k+p+JrrVrs6pfMrGMBVRRgA44/lWRc6m1xcNNO2cnJUZANUJ75trpFIVB4IB61XSRyflPTnOa0SC6Nf7XLcBmkbK7QvJ5xVSZ2jlJSTqMY7VDE6yRENu3D+6OKQTLuVVyfwpXtsD1WpI7OWyr4z3xUDy7FONzNjr70s80hYjGOuDjmoJbh1UtnqaWrFIV5QYwoPPfmoZm3IAOM4OPamtKGGfQcimNLHkH09TT1RJY2yygLEMsf7vpTlgQKGkcBsfd9as2htI7MO1ucnjJJ/lRNKgh3RqI1xywHWpctTWME0V7iNC2IiSSOMVXkd0G122kdRSvcSLMCkgYjpmobkM+ZWIGOtWrku3UaszfcXOAelSQGIyGNgDu9B0plnEJtzbhkegpZYkgmEhyOPl5/Wm7XsOKurlpY2t9wUEZ6kioJ2835Q2Ap5OKTzZJmA8wYHPBoIUIducZ+bJqLW1KdmtCAygDCqcg4zimCbGQ5HXrTrqQMnyJj8arMRvKuccetWlcyasXrYpIpREHy8nNR3pVJGRfXtTLOZY5MK49znpUl2gjk81gTu5GSKWqYiKSTB2sSR24604PG3BYZHTNQyKxj3jknkGnRKqj5zg470W0AneVciTG4qPSrmn6p9lguIW0+OZp0Cxsw4j5zu6dev503R7eI6zawtBHIrzoDHIflbJ5B9BWhcaL/AGfqM1vcTRJ5UJJYtxwRx357UrfcNK/yMS+MsIMc8uG9KqPO7r5W7jPPFaHiM4k3RzF1IGN3HI9Kow+UVyVbOOT2qr6XEJFMYiRuOCOopVl3c7uSfWlBcYUY56ZFNyN+SBnpkCmA5pXXEiAgj7pJ60SGXfvI4B575qS1InY2zXSQrtPzMCAe+OB1+tQb1DlFclR1zU/ZEti3FJc3yFoojI5bAULkn8KfHoevXQ8kaLd5Xkj7O3T8q9g/4J37f+GsdAUgHFrfY46H7LLX6VWdhf6hIYrCymnYDJWGMsQPXivlM64lllGMVBUua6Tve27a7Psf0B4XeCFLxG4cqZrPH+w5akqfL7Pn+GMJXv7SO/Na1um+p+OlzoOusdo0i6AxkgW7f4VCmia2u4HSLsf9u7c/pX7GzQy28rQzxMjqcMjrgg+hBoiilnkWGCNndjhUUZJPoBXkLjqa0+r/APk3/wBqfpP/ABKfh2r/ANsP/wAEL/5cfjkdF1og/wDEluife3bj9Kb/AGNrifN/Y116D/Rm4/Sv2SvLC+0+QQ39lLA5GQs0ZUkeuDUVH+vU0/8Ad/8Ayb/7UI/RRoNXWcP/AMEL/wCXH47Wmj60Tul0m7OD8w+zsP6VPPoeoumYNFugVPQWzdOxziv2J/svU/sf9o/2dP8AZ/8Anv5LbOuPvYx1qCh8dT/6B/8Ayb/7UcfopYd3tnDf/cBf/Lj8dDoWtPgR6Nd+/wDozf4U4aJrjHcdHux8vJ+zt/hX7IWemalqIY6fp88+z7/kxM2364HFQEEHBFP/AF7n/wBA/wD5N/8AaiX0UcO20s4d1/04X/y4/HKfQdaQ5XR7rnnP2dsfypk3h7XTNtTRrlvRhbtz+lfspbWtzeTC3s7eSWRvuxxoWY/gK+w/+CdfwD+E/wARv2cvjx4h+KPwystT1XQPDYfSrjUY3Etmfs1zODH8wMbb4IzvXDEZXO1iD2YDi6tmGIVGNBJtN6ydtE3/AC+R85xX9HXLeE8nlmOIzWU4xlTjaNCPN+8qRpp610rJyTep/NfDpvjC3tGgSwvFiY4YGBuMfhTf7B10KdujXbY5LfZm5/Sv2Lori/17l/0D/wDk3/2p9J/xKfQ/6G7/APBC/wDlx+ObaBrrL5v9jXeOmRat/hX3X/wbP6Pqlt/wW2+CFzd2E8SCTxICZIWUf8izqvqK/WX/AIJxfA34PfFL9mz47eKfiN8OtO1nUdC8Nh9IvL1XL2h+zXM2YyGHltvgjO9cNjK52sQeW/Zi/bZ+CfwR/ZS8dfArxr+zpZeINZ8SRSLb6hKP3GobwAiXjB1mjWEjzE8lgd2NpifM1etT4oSp0qleEYRqRk17zfwu1tI9f67P8/xngJOpi8bgssxNTEVsJVo05xVKEU1VjGfOnKvtCL1Ttd9Uryj+X/8AwcwRTn/gt98bj1Unw3tHof8AhGdKr4VSOV5ytxKw7jAr7r/4OYMxf8FvfjZMRnJ8N4yf+pa0qvhXc87A3GdyDAINfU63Z/O6+EtxyWwJVpFB7Zp87T28YMK+5XdWdI0SvvMefcDpVm0uHdPISbLN90+1Q49SbssrqxaMoybSM4PTmo1mtbaEOI1UsMhkY8j3qleWd4wINwpBPIDZqqFmgf5sn681UIRtoO7e5t3V1eiD7Ql2HSVdpXufw9Kr25+zP5kfOedoHemy60XkidLNNiLgqE4/Pv8A/Xqxb3NreN5n2Y5XsG6fWklyxtYr7Q5ryDzhO6lQ7fOAPun6VJNo94zLeWluAjdBu6+9RyDS2PmPE4k7/vAB+RBqyNUv4bUGBSyA8gD/AA6Vm5Sv7v4lXXUpzC63LHOCGzhduB/Krz21xLaxrPAx288nOf8AP9Kro6XUpcWjtyWKZPA71GNWniudsCmNAflO7PFV7z2JSLT6dBkSSXC7cngf1p8cUM86KiqG6B+gPvUMaiRGMUxkyTkMtTCWWRPs8trGpx8pIIPepk33D5EkzRJM0buW8okZHGfzp8eoJGAUjPPcnrVX7KsW1N7bWHLRtnFIkLIxaCTdEDhjmmkmF3fQ0ob5biTd5gVh27Yq4H3HKldi8sRWNFblpiS2B0B6c1bt5fKYRqQN3VSOv1pvYd0aEilJAYFAGM7fWonkdYvkl4BI29qJYpLlQGuFB7AN/jSQW0AcC4uyNv3sryP1rKOxV2EdzMiAqST7nrTbhSriXohz8qjqe5qw7xRxFUlDL6kVA9qZI1V5Qf7m09K0VgbuRiMsfMRQvJ7U+KG4hZjduvH3RxTZWaKLySh4BznvUDSxyny4mBGOB2zT0aErWsPmUXIKxAEsTz/WoDezWhKRbX3cZ7YqaCLykGZHAPoORVeZYoTkMcdsDnFNIVnzEwv5poBG9sNy9GXNaOmapqAHlwouduTlxyKzIZ4doYZRj/Epq1bzIJFkaRSB1IPP5VDStaw0jYjvhNeJbuyed0XCjB78mrF/A5Vd8LDbyXXpnniqNnc2HmZK4AGQ65GDUjX+ybDSsFI4DNk9/wD61QoWehSTRcsJ0gTbNDhuxHRqtPcwnKTKCeAGB7e9Zwv7e5Kp5+GXoMdD/SllkkeQAOzITzkdf8/0qbamnuludY2IWJRgnO49SOvFMeBpHKrJ8pXsBye9CzRKMSALjoBSRzqQSXGSfkAXof6VFm5XLUko2ZJPoaRxBkdWyP4sVkXlq0JLclucbTx+tbUUsUiFJn2sGBwTx+GP88U+9s0l2NHDuCggPnt34rSLcdzF8rehzDPfW8QWJgwbjaDWzYajqz2oSaYxrGOABzTH037NObsxLsB3eWvBpJlmjldZI2ZT0GentVuo3oTqpHc+FPjv4k8L6XFpVlqjNDA25C/DIx7ggjH9ag1v4leIvE1/Bqd/r00ziQsC0pAGcDgdv/rVxb2rxWvmrYqvrjOTViHTpUtsKxzjcu0Y/Oq9omrD5He+561of7RnjHwglrF4W8TzW6hvMJgfaysDwM/r716ZqH7YHi3xN4MvJfEWuX1xch4Y3u5JgSnXA4I9B+VfLo82JVzEyk9WPfmrLXbWdg8beY3mEblUnBweDgVrCqoO6IdNTjZn1n+zb+1pqPgKeHTrzWpZbfUr1PtMTnaVEbbly2ckE/55rY/as/bm1Xx/4n0GK212eO30nUJpbKGN+IVkTnBwM/dUcjt718YWmrX1uqpE7DJ3BmbBz+HSqmt6/cXZ82dnDDGMvntWlGtKEZR7kzoxclLax9s+HP27PEvibxVoc2k38NgdGuJZ9OMcflGK4eKRCd2cKu6R26Ac+wrzG7/bL8U694Ku/DVxroha+MkV5KhP75GcsWIxySemTx71852PiGe2s2WOceY6YJ9azlvbhpnLgEAHIKZFKVSUrPqv+GM40aak9N9/M/Un/gmF/wAFBfGPw6+Kfh3XdQ8W27ad4WsJre201YigmEy4fABO4kgZPB6elfud+yL+0z4c+OHgfSNJbV2vddh0WObWJlTEYlwuVyTkuc7sDOADkg8V/It8OviNf+CdRh1rTb2WKa3fdDtPRu3Nfan7CH/BUP4ufDfxVHo+hX128rFvJuI7g77ckYzg8ALjPHP6VnTivaXbt53em3T5G07tNbn9PWVYmPPIHpT68H+Bv7T/AMGNI/Zp8P8Ai+9+IdjdwwafDHdTQ3gkd3JVS2Gcsclh37nAABA1tZ/bP+DYku9E8LeIVv8AUYo4WhKAGE+btK5cE44boee1dSTdra3MH7snHsexUVk+C/EB8U+GbPxCVhAu4zInkybl2ljt59cYz6HIrWoejsJO6uFFFFAwooooAKKK4v4jftHfAL4Q61F4c+KPxk8N+H9QmthcRWOraxFBK0RZlEmxmB2kqwB6EqfQ1FSpTpR5pySXm7HThMFjMfW9lhacqk97RTk/uSbO0oryv/huP9jr/o5zwR/4UcH/AMVR/wANx/sdf9HOeCP/AAo4P/iqw+vYL/n7H/wJf5nq/wCq3E//AEA1v/BU/wD5E9Uoryv/AIbj/Y6/6Oc8Ef8AhRwf/FUf8Nx/sdf9HOeCP/Cjg/8AiqPr2C/5+x/8CX+Yf6rcT/8AQDW/8FT/APkT1SivK/8AhuP9jr/o5zwR/wCFHB/8VR/w3H+x1/0c54I/8KOD/wCKo+vYL/n7H/wJf5h/qtxP/wBANb/wVP8A+RPVKK8r/wCG4/2Ov+jnPBH/AIUcH/xVH/Dcf7HX/Rzngj/wo4P/AIqj69gv+fsf/Al/mH+q3E//AEA1v/BU/wD5E9Uoryv/AIbj/Y6/6Oc8Ef8AhRwf/FUf8Nx/sdf9HOeCP/Cjg/8AiqPr2C/5+x/8CX+Yf6rcT/8AQDW/8FT/APkT1SivK/8AhuP9jr/o5zwR/wCFHB/8VR/w3H+x1/0c54I/8KOD/wCKo+vYL/n7H/wJf5h/qtxP/wBANb/wVP8A+RPVKK8r/wCG4/2Ov+jnPBH/AIUcH/xVH/Dcf7HX/Rzngj/wo4P/AIqj69gv+fsf/Al/mH+q3E//AEA1v/BU/wD5E9Uorg/En7T/AOzz4N0vTdc8X/GLQNKstZg87SLzUr9YIr6LAO+F3wJVwy8qSOR61j/8Nx/sdf8ARzngj/wo4P8A4qnLGYSDtKpFP1RlS4c4hrw56WDqyWuqpza0dnql0ej8z1SivK/+G4/2Ov8Ao5zwR/4UcH/xVH/Dcf7HX/Rzngj/AMKOD/4ql9ewX/P2P/gS/wAzX/Vbif8A6Aa3/gqf/wAieqUV5npX7Zv7J2vanb6Jof7RPhC9vLuZYbW0tNchklmkY4VERWJZiSAABkmrPir9rX9mXwLrs/hbxv8AHXwzo2p2pAudO1XVo7eeEkBhujkIZcgg8joRT+uYTl5vaRt6oyfDnEKrKl9Tq8zV7ezne217Wva73PQ6K8r/AOG4/wBjr/o5zwR/4UcH/wAVR/w3H+x1/wBHOeCP/Cjg/wDiqX17Bf8AP2P/AIEv8zX/AFW4n/6Aa3/gqf8A8ieqUV5X/wANx/sdf9HOeCP/AAo4P/iqP+G4/wBjr/o5zwR/4UcH/wAVR9ewX/P2P/gS/wAw/wBVuJ/+gGt/4Kn/APInqlFeY3H7af7JNnGk13+0d4OiSQZjeTXoVDD2Jbmof+G4/wBjr/o5zwR/4UcH/wAVR9ewS/5ex/8AAl/mJcL8TSV1ga3/AIKn/wDInqlFeV/8Nx/sdf8ARzngj/wo4P8A4qpLb9tj9kS9mFvZ/tJ+DJpD0SLxBAxP4BqPr2Cf/L2P/gS/zB8L8TJXeBrf+Cp//InqFFeY3n7an7JGnyCK/wD2jvB0DEZCza9CpI9eWqH/AIbj/Y6/6Oc8Ef8AhRwf/FUfXsEv+Xsf/Al/mC4X4mkrrA1v/BU//kT1SivK/wDhuP8AY6/6Oc8Ef+FHB/8AFUf8Nx/sdf8ARzngj/wo4P8A4qj69gv+fsf/AAJf5j/1W4n/AOgGt/4Kn/8AInqlFVNC13RPFGi2niTw3q9tf6ff2yXFjfWcyyRXETqGV0dSQykEEEcEGrddKaaujw5wlTk4yVmtGn0YUUUUyQooooAKKKKACiiigAooooAKKKKACiiigAooooAKKKKACiiigCG6vLexha5u5VjRfvM1eN+Mfj34f8Y/Gv8A4ZbtpLaSPWtKuYbu6jlDOuIld1XGQG8tn4I4KHOMVzn/AAUQ+LZ+DngTw5r+rtfQ6LP4osxq2q26AxWEaSBiXOeNylsZ+U+XgnJGfy88L/8ABZX4BS/t5eFLNdMSHQ7DXn/tTxVHZ+RPH8sgHlLGo3jLbAGB3BzuYZzUU3CpWUG9He99tv8AMvak3bXS33/5XPsf4yeJNL/Zy/a38OfAbQ2ktdClmtZhq17dh1treWaMXFsqnH7xkYgEH5j1GQM+qftUf8FAfg18AviZo/wd8Q6nbSXmm+ItLkeLa6vDaSRjDhjlJGG7nLKwB+4R89fA37YPxv8Agj8SPgV43+KGvfEG+vfEeh61HceFL+XIaW5QljZyrlHhVomjCSdR5RBUliB+XX7UP7ZnxY+KPi/T/EPjnxnd6hqOneVDbvNKSVt4l2JGT324H5VTk0owlte7tro7O23TYyVNqpKSV7q33ab+Z+8P7aP/AAVy+D2heHpfBut2Bm0fVYpLLWbWSzFz9luY5STC4GB+8jAPzB1H15H5Lf8ABTT9q/4XzvJ4R+EOoafq2mS3SXdvqEUaoYnJ3NEwjVMnB27iqkhjuAIr40+J/wC0n8Q/iFeX7atrdylvfSB2thMxUsAOST1PAz0zjpXn13rl7d20lrJKGDdSepxWM7Sd7bbff1NqScKa5tX8t/I62L4o31jrba7YXEiSksoKkL8hHIx/npWJ4v8AG+teJP3GoXsj26Ozwxsw+Ut1PHc4FYETsqCTHB6U2aWQoTjdnGBSau9SkmgSeR8knOOwFBuP3m1uTnGDUO87sIe3QVXkfEhO859qpJXFqjRbVXgxsbBxyRVe61HeAc8k84WqjTru5c5z0xSq6bvunpz6U7Ju40x4kVsnHWiP96cKenaomVlPLEDPFSQNBCfmdt3tzRsh7ouWf72QWzfKGOSfU0zzY0kZt2GDfKB2psREMqyMQcnguajuJjFI5dFOT8oYfrWW7KVuoyS4eaQhpDzUHm7cqSOnNRLKWBdpgCDwAOtNH3jI2cYPGa1skQ22PVk3EBxz71ZtLEvcq0kTFCwA7VSUbX3NGNrD5Titi1SRkEyScAdSPu1MnZFQipMsz3FvbQszAnH3UA4FZd9dmcjyYgmRz8/Wra3NtMpDn/eJPFVr21tpQCHJXHYcDms42vqbyu1oUWeQMFcBmJ6jtT502RhvUfxdc0TW6QsfKJYAZGetQxAqd5bBzjkVrozntbcLJSbnOzAzk56VYnEd1kJ97oAaeliqqZXmBUjJxTZ45IV82LJXnjPSk2r6FxjoVow0W4sRzwOaWPBU72PXmnKkMq79+CO2KZeyrt3BRwMbV/rVk2sNkPmHAIwO2aqyKVJVVBweTSyXAwDGQB3FNEmRtbqfQUkrEN3GoGV9yttIbv2rTvIZJIUllnBYLyNvWs5FQkA8ZHJrTun3Wu1SSQASB1NKWxPUpqdsYxzzz6UoJOOB15yagikxKI2PBPSnvJiU7OmOaoZraQsE9wqTSlFVclgOnNQ3kzBnSJmKgkBiuMg+34VBp7BZRNJP8qEYQnAY+/tVh71ZbppJWzuQIMdO2OSfao6AnqV9WubieCJG24SMAFfQ5OT781Tt1AjLA5x371prppntpJ1dDtOCGODmsuWQ52tgEH8qu/MNK0Rz3DS/K4IxyMEU8fMQAvXvUQhffvkIIbocVPILVLkJA5aPjLYpO3UQwhQTmEEA/e6VMNKe7VpLdNijoGb+tQFk3sOf9kEU9L25iVoVDBD1IFJ3aGrX1Pcf+CcMEI/a48Ow6nI0cXlXgmmiQOyp9mkzgEjJA5xkZx1Ff1W+JE+J/wCzv8EfCEv/AATm/Zx8KeKtDvdIimvb9rlIZ7iPyo/KmZVaM3DyA7mkLs2Qcrzmv5UP+Cccf2j9rvw/9peRYPs14bmWKPeY0+zyAtjIz1HBIycDIzX9HE/7Dv7YHwR8N+G/FP8AwTj/AGidY1zwvrWlQ3t3aXms28EbXDorG5jhlxAY5F24U5dcbSz9a+Wx9SvSzSq6dOUvchdwtzx96Xwp3un1P6A4NweV4zgPB08Zi6dJfW8Q408QqiwtVqjQ0qzpyjyyjvTu7N3Vu/L/ALd3x7+F3xh/Zvm0/wDaW+AV74A+N9tqEL6VCvhyRWvIFYhphcuq7rVlLqULuVkClQwBI9Q+FPxw+H/7In/BLT4f/Hu0+E+ma5raTTQaO09lFCyX9xNOkk7SqpYDy4NpZfnkWNFJXOVsftuQeOLf/gmNLB+3ReaVN4+GqRHQpNIEW83nnHyh8oEe8QGUSeWNuzOMnmqXhz9mDxZ+1Z/wSB8B/Df4Z6vaLq1pK2qWkeo5ijuXjurtXg3DdsP7xgrHglVztDErwKGMhmVWVPWo6F1eKU78ytzLVc//AAND7CWJ4cxHBeAo4y9PBRzR06lqs50HD2UlL2NS0Zqg+ibbV5e92p/s+ftVfC3/AIKrrq37NH7Tvwf0vTtbbTJbrw9ruijMkAUpv8hpld7eZflbO5kkUMrLgYfyr/gnN/wT60Xx1+034zs/jf4dur3R/hnqptfsNzbqLbUr0SuESYMCJIwieYYxw26PcSp2t6J+wb+w74m/Yc1/Wf2uv2v9c0rw3p/h3SJobK0jvFunQy4R5nMQYcqfLSNCzu0hGBgB8P8A4JsftleFPEf7UXxQ8N+PvEqaIvxY1ZrnQLmO3EDi7MkqxwiRQRHIY5Rt3HBdMA7mw3NShGtXwcs1S9q5S3sm4293mX+LRX3PZx1etl+WcRUeAqkngIUqLTpuc4Qquf7/ANhK7/5c+9PlfuvVW0Pp2y8bftqz/ECPwJd/sR+F4PhW0osHsv8AhJLJrpNP/wBXvMQl8kjb83kBMY+TefvV8m/tTf8ABO7wTqX/AAUb8LfB74cabLpPhjxxZDVtTs9Gh2/2VDG0gujFuUxxo3lgoOVRpAoQDYp7Fv2Mv+CtX/C2j4ST9q7Wv+EW+24Hi0+MJsfZc53fZ93m+bt42Y27+PM2/PXnXjD4l/DH9kH/AIKc6F4wX9ojxB47stLAsPGms+JUN7c6azxPBLEJUH73y9wfEaApzH87BhW2Y1IVqMFjaU1H2kVeo4Kyvqo2Sbjbfp16Hn8G4PEZbmdefDWOw9Ss8HWkqeEjiJqc1H93Ks6kpxjV5vhvabfute9r9h+NNP8A2lf2cvsHwq/YZ/Y88Ky+FrC3jN3qeo65b2pvZdigkRrKjs4CgNNKWZ2zxgBm+ev+Ct/7MPhnUfg54Z/aY0j4VWvhLxle6rBY+JtFsNkhuprlXbDNbgxzSrKpAlwDIr/MchVHYfta/s//APBQnx78Tx8U/wBjv9ozVdS8F+KIIbyysrPxibVNPDRrkoGYI8DcOpQk/MQV4DN4V/wUT+E2vfAf4Z+BYPjH+1/4j8Z/ExLn7bP4Z1G6a906KPPM8IkAMSqy7N0mTNzhUCsBpnFRvB4inOjLkVrOXIoLXRwsk3p0Td9nqcXhzhKcOI8nxeGzGh9ZqNupGksRPEVPdbqRxSlOUYpS0cpRiov3oe6j1zx/45+FH/BHn4NeFvCnw7+F1nr/AMS/FFi1xqms6wqh4lAj87fJGocxCTCRwIwHylmYsCX7z9nX9tK0/bT/AGT/AIueI/FPw+tdA13RPCl3a63c6X+8S7t3s7poWTfhwVxMPLZiATkN85C8J+1v8BZv+CpPwY8GftKfsx+ItN1HXtJsDp+t6LdS/ZmLna8kAZxhJYpGY7XIR0k3K33d/Q/sbfsWfE79lD9j/wCL6/GO6tLfUfFXhu7b+zdNkFy1nDDZXKgsykK8jGVjtUkYVfmyxC60HmUMy9nQj/snI+WyXLbk0d+7lvrf5HFmcOCsRwUsZmlW/EDxMVVU5T9sprELmj7NuypxpfD7vKrK3vaH5X0UUV+Xn90H3d/wSl/5NN/aN/7Ff/2wv6wf+Ccnhbwvrv7GH7TGoa34Z068ubbwRL9nubqxjkkjxZXkyhXZSQBLBDIBnh4kYcqDXQ/8EooJ5P2Tf2ivLhdvM8NbU2qTub7Bf8D1PI49xWd/wTQjcfsQ/tPSlDtPgmcBscEjTdQyP1H519xgYqUMCmv+Xdb/ANuP5e4oqzp4riiUJWf1vLdnZ/8AMOfj7/wcyMp/4LdfGxdjnnw38w6D/imtLr4URygIR/rkV92/8HMMLv8A8FtPjY4OBnw2Of8AsWtKr4bS2itrVL6URsofmNs5Yf5/nX6M2l97P4YSM4+c25S55PJPQVIpkhw0BIZejCrVxcW8V0RFCGRjkRn0qqI54f3jK0aP0J4zVXb3HZlqzu7YoqXFvuGDvwMEfSo7mW0RQ0UHLN90jOB61PBZLNEBHOWG3LFR0+tNgmKFreayRtvWXPOPTHeo0voCi29SQ/Yxpx3SkZPTYeeKZZwEyiKSzQhiDuBwfpRcrdzQ7ppSIUPyKMcGo7W6he4EsZ24HG4dT6UWbVkUviJ9QkimhMsgKFOFIHUduafpy3XkAxXjEORlQ/I/CkEFzeBrhrEkAcBj8tRWC+TctJtLOSdq4+VqVly2G731LrPBa3H2SWN13jHDEYPrVJgtvMSzbtpyql8YH5fSp7+K9a2Wead0foEY5qpcyXJQefZb+eJCMURXmQ1YtHXVSYSw26Jn7wUHr+NWP7WZpS8yEvwBIp6VmeZHCci3QZXnnNSIfLUNG4fceoPSm4R6DvbQs/2jJaM8UKl15+7UNtPJcncDjnJ+WmPPavIDIy4H39p5psk9qxYwHao+73zTSV9hXTNE3Mg4WYrn73PFTwLHNlyc5HJqhZOJMNIGAweP61ZtoEYmQTHPYAYqXYE7sklBcbGOMHh0/wA8VbjheKNBNIQx+6OufrTVVNojQYZhjJaiG0vAxLzk7Rx/+ul0H9kc8ksco2urBT909jUkksgk2FCmcH5Rx0rPllklkModl5wPerUc4KA+cTIOF2jH6Gl9ocdiR2dpFgYFiyk4ReaZLayLGGZNgXuB09KsN5rHznUBjwGK4IqpK1zButXYMuMkbvvfnQmwem4rTPJEsa/Kc8le9VmEpcrIp5PGRUoYD5Gc/N/CG6fWlvYvJUK7f7u09auLKXcryvswgAwRzSCaZWA38fSoZZRGcKqk+9MaYNgYH4nrVgmi+l6egIGB+dPF1NOmwpnHfNUYnB5I/GrNjcQxzbZW2jucZyKT8iromt7yS3O9n2keo61oWesRyOGZzkDntmsfULkMcbwf7vtxUUVw0RwT17ik4KSEmjfe+mMzMjFgf4TzirEWoQQLiUkE9ueKwY9UeGQNK7OB+tPvNUt7j95FGQxHc9DWbpvYV+xtpds8qrG5UnkgntWvptzLIoacqq9Fw/DVxtrqExcbOrdfUD2rb064Vj5V5ISc8Enp+dZ1ItFR0Nto3muswDai91xkVLNNDEPKNuxf+8eQarW2opANn2qM7egPHNTx3i4dhHGcjP3+tZ8re47JD7ecSkKYwCDyF5FWXeMyiJlKjBG0nGDz/hWW92jEIoYbmz9zBFFzcyWceQScjKkdTjtRyaju7GoYllnG4bkA4IbmmJEFjdVjXy5Pu7iCR7Vmwaq8jGMqxDH7y8ep9Kkjuo4CQsrcn5gycilZod0ywsVjGTG8J39uOnuKo6paRzQgxW5CtnHrmrB1GON2SNFyeOWx+PtTYrlBHs80tg9D3q4tpidpbGaLSOG3LeWSVbPQ8DBqnFnym2rjc/A7D61oXk7QwTwScOSNmRjmmWZW4jWBY8njr3NaKTMUk9CsglEnkyngda6Lwd4sl8JmSWzvHjaQAEoeWAOeaz7fSpp5WW3RVA4YMw+Wlu9EisgZJZNxA6+9VzpysyraXPpXwV+3t8Vz4ZXwfbX6R6Y3kG4QzYcmPIDKcjruORzn9K+gv2UP22LvT/GVjpWq+KLn+zBN9o1yYKwaaJCWSEuuGUHa4+UjJxmvzjtLq6VGhhjYITwxHStnQPGuoeG71L23nk3DhgHIyPSto1JJq722tppfuSqUVdx0bP2l+NH/AAXs1D4SfBax8OfCWyuEXVrYnS72K92SWbI7Z4AJ5JBJyQwGPQ19CfsAf8HAXg79ovxn4Y+E/wAatCsdH1DxDc/ZYtbW5EMEcgiZgZAeBudVGRtUb+vHP88niv4h614iS2k1G7WQW64RFXAAJz0/Gtzwj4t1KBtHt7e5WOZLlpTOx4UdMEdx/Ss61es5pw2S2LwuGpxo8kndt7s/ssU/xZH4VWv9W0zTUR9S1G3gEkipGZ5lTczHAUZPJJ4A71+OH/BJP/gtVaaB4B1mx/an8eXOoXdzFHHo97O7ytHDaQbViCjcQDkYPC5zuIyWr0rxL/wU08M/E7xB/Yml+Jr421lYwC41eGUyR3iRXhnjmiJVcMBtVixfhBwM1vhZrEQ5np0fk/8AI5sTGWGnytX9D9TuC3fOPfFO5r4L+An/AAVs8F+LfjSfCHiXxYbiyvNPQafGYVTe5YAHcpA37iRkAgjj5cAV9m+CfiZ4e8YeG5fE1qs9rZxXjWwe+QxsWVghJDcgb9y8/wB2t6lP2crXTM4TU43OillhhA82RVBJxubHbP8AIE/hX5N/8FtY5J/2ydPhgjZ2bwVYqiKMkn7RdcAV+kfxR1jTg+kfEaPULO80TS4Ls3qCTzEnMqKsO3bkE+YAB/vHB9fmXxL8VPgd4b/4K7WOr/FzVLW1lg+Hcdno2p6neRR2+n6izyuVkYgKj+U8kQJbhm28lhj5ribDLF4CFCUuVSnFXeyP2XwMzupkPFmJzGjRlXlRw1aShHeVuV2vZ283ZtK7Sb0Pz4/4ZE/ao/sD/hKf+GdPGv8AZ/k+b9q/4Rq527P733M4968+jsryW8XT4rSVrhpBGsCxkuXJwFC9c54xX68/FK3/AOCtfhP4mXPi34baz4F8XeFF1FprPw7bwxWsslnu+WJmmCsrlMfMJm+bkZHFfLXj74v+Evi7/wAFZfh/4s034M+IPBmox+ItItPEGna+q213PeBlAmeJQdg2tGoO4+ZGit8u7FfB4/IcNhXGMZTi3JR9+Nr36xabTt2P6y4T8WM5zyNapVoYerThQqVr4evzODgk1TrQnGM4uV7KSTV09Hq18zeG/wBlL9pvxe97F4Z/Z+8ZXjabdta6gsPhy5Jt51+9E42fK44yp5GRkVxWuaFrfhnV7nw/4k0e60+/s5miu7K9t2imgkBwUdGAKkehGa/Rf/gpF/wUu/aL+Af7R9x8Gfg7caXpVhotlay3VzcaclzLeyTRLKQ3mZCIAyqAoDZ3EscgLlftWXXhL9vb/gnXbftnXPgux07x14RnWz1e4tLgxoY1nVJogGY7oz5qTIrkuhYqrHcd84nJcBzVqOGquVSkm2nGyajvZp7rz36HRkviVxY6WW5hneX06eCx8qcKcqdVynTlVV6bqRlFLln3i/dv72uj+B/Bngfxn8RfEMHhPwD4U1HWtUuc+Rp+l2bzzSYGSQiAnAHJPYVq/En4HfGT4Om2/wCFrfC3X/Dovd32R9Z0qW3WbHUIXUBiMjIHTIr9XP2TP2ZPHXwF/Yp0mH9m2w8K2HxC8W6Za3+ua/4huJbiDdKhcMDCH8zy0cLGi4jyS535bf2PgP4SftBeKvhN4t+Gf7efirwN4o0TU7FhHqWk2zQNbptJcyq8SRjYQsiSjDIykkngr30OD5ToRU5SU5K97LkXVJu97+aVkfI5n9Iqjhc1rSw9GlLC0ans3F1JLEVEnyyqU4qLhyp6qMpc0kr6X0/EuGGa5mS3t4WkkkYKkaKSzE8AADqa7rxZ+y7+0j4D8Lv418a/AjxbpWkRKrTajf6BcRQxK3QuzIAgJIHOOSBX3X/wRw/Zh8Naf4X8VftHR6TpuseILXV7rSfBt7eXv7uJI48PIREZBEZC6qXw7hAdvyv8/wBG/CHwr/wUR0v4pjWfjf8AEj4bax4SumkW70TR7G4hltFYEqbd2hyxU4GJWbK5GQcMMsu4WeJwkKtVyTntypNRXeV2t+y6Ho8YePMckz/E4DAU6Mo4W3P7WpKEqsrXcKKjCSTitHKpZc2ltNfxLrvPCX7Lv7SPj3wwnjXwV8CPFuq6RKrNDqOn6BcSwyhepRlQhwCCOM8givs/wH+xV8DvGv8AwVk8W+DLTRtNv/B/hyyGvXOhQTIlvDduIsWvlxtzGk8hYxkKoChGXbw3JfHn/gst+0ZYfHPU7f4PJolh4U0bVHtbDTbvTFnN/FE5XzJpM7hvwTiJk2qQMkgseBZPhcJTlUx1RpczglFXbcd3rpZfefVz8Rc+4gxdHB8K4OFSboU8RUlXm4RhGqr06dopydSSu+kUlu76S/8ABUnwt4f8P/sp/s8S6Z4W0/T7l/DGydrbTo4X/wCPS0kZCVUEDzJZXK9NzscZJNfDlfob/wAFn/GkHxD+AnwU8eyaW1tNr1tPqKxCfcsCzWtpI0fT5uXUbuPunjnj88qz4ljCGbyUdrQ/9IR1eCVbEYjw8oVK6tN1MRdN3s/b1Lq/W21/IK+6f+Cl+maD4P8A2Yf2bvEXhzwtpFrdR+HUk3x6TAA+LWzm2Mu3DIZZJHKH5WaRiQSTXwtX3d/wVV/5NF/Zx/7Fcf8ApBYUss/5FeMf92H/AKWjTjZt8d8Nwv7squIuuj/2ee/c8+/bd/4KH/Fn9qD4JeF/h34z+BNv4Xs7lk1P+15ElddVaMPEJbTzI1EUO4yA7WkORt38MD866H8Hviz4m1LSdH8PfDLxBe3WvW7XGiW9to8ztqEKsVaWEBf3iAqwLLkAqcng19nf8FWJ55P2Rf2dPMmZt/hpWfLfeb7BY8n1PJ/M17n+1J+2x4g/Y7/Ys+Ft18N9Hivte8UeFrK30u/1WPMdnFHZQNJM6A/PJ86BVztySSWC7W9TFZcsXja9THYh2pxg2+VXaklpZetl56s+DyHjGfD/AAzlmE4XymClja+JhGm6slGLpymnNzkm2rR5pLS0U4x6W/MX4k/A74y/B023/C1vhbr/AIdF7u+xvrOlS26zleoQuoDEZGQOmR61qf8ADK37S58H/wDCwB8AvGH9ifZPtX9p/wDCPXHk+RjPm7tn3Mc7umOc4r9H/wDgnr+2d4q/aU+APxA8XftL21pr8vgG7i1bdFpEMYMMcTXMW1VwplSS2dlOAQdvJ7eO/s8f8Fa/2kfjJ+2D4Y8K67DpNj4S8R+IY9P/AOEetLBXMEcxMcZ89v3jOrFWZsgEg4QAhRi8nyaMaU3Xly1tIrlV1rZuWtrJ9tT04eI3iVVq4/Cwyuh7XL/erydafJJOHtIxpLk5uaULu8vdWl7XsfJP7Kfx+8S/sy/HPRvi54U8L2es3dk7wnTLyHd56SqY3WNgC0chDEK6gkE4IZSyn039qL4zfGr9uv8Aau021l/Z+vNK1jSEj0+Lwhb6bJc30McUhklFzuiV3ILOSGRVVeNv3i3onxh8C6F8OP8AgtLoum+F8xQ33jzR9QlhSIIsctz5MsoG08gs7NnA++R2yZP27fj/APFr9nz/AIKe654v+E3i+bS7yaw0uzuB5ayxT272sBaN43BVhn5hkcNgjBGahYarhcDUo1qr9nGsoyiktdH7yv6bXsdTzjA59xTg8yy7AQeNr5dKtTqVJzXKnKCVOSimre+7z5eborJmz/wWp+BHipfidoXxB+H/AMIblfDel+DoYdW1jR9D221syTyRxpLJGmFCxiJVDHCrtAwMV8GV+kn/AAWX/aq+Ovwo8Z6d8Evh742fS/D/AIl8H+ZrENvbR+dMWuJo2AmK70VkQKQpGRkdzn826w4ojho5zU9le99bpWv5W6ep6ngZVzmr4b4N45Q5eX924yk5OF38akklK9/hbVrbBX3P/wAE8fhV8GPgZ+y74s/4KBfHDwzZ+Im093s/DWi3losixyJIiq6+YrKJZJ2RBIFPlKrHJ3MB8MV97/FQQD/gh34M/wCEZ8jyv7fj/tXyDJjd9tut2c/xeZ5ec/Lnp/DWeQRjGtWrtJunTlJX76JO3le51eLFWvWy7LsqhOUIY3F0aNRxdn7N80pRUlqublUbqzs2trp9F8C/+Csmi/tN/FfTPgT+0l+z/wCFn8M+Jb9LGyfyzcrazOdsIljnDLICxRCwCbc7ugxXyP8At3/s4ad+yx+0trnws0C/e40nEd9ozSht8drMNyRMT94odybgTu2A8ElR558LP7T/AOFneHP7Fx9s/t6z+yZ3f63zk2/d5646c+lfXf8AwXR/sX/hpfwz9i8j7b/whkf23Zu8zH2mfZu/hxjOMc9c8ba6a2KrZrklStiXzTpyjaVknaV7rT0ueLluRZZwD4nYPL8lg6WGxlCq50lKTgp0XFxqWk202pOLs0n1V9T4mr9QNf8AiLpH/BNT9iT4c+O/gL8EtI17UfE+n2za34pubcY8yWAXBM8kJLvuZ2WNfM2qqYBOAD+Ydra3N9dR2NlbvNNNIEhiiUszsTgKAOSSeMV+r9r8b/Cv/BKj9krwj8Lvjj4h1Pxr4k1C3lksPD9vFEgtUbDvBvbO23jd9gdt7MzHaoUbE04ZtBYiq5ezSiv3lk+Rt9nvzbaanL438+JnlGCp0Xi5TrSbwilKHt4xg7tyjpFUm1J83u63d0mZ37JH7R5/4KieHfF/w1/aP/Z60NdL0/St1t4hs7ZmjtpJcptRptzRTgbpFdGBGw5xwT+Zei+FfD+ofEq38F6v41ttM0uTWRaXHiC4hMsVvB5uw3BSIsWAX5sKTnscc1+qPwQ/bY+Gf/BRL4b+Kf2cfAV1qvwv8V32gzC2aJILhRCx2u0DALvwGAddsb7XJRgQXX8qviN4C8Q/C3x9rPw48WWph1LQ9SmsrxMEDfG5UkZ6qcZB7gg9614hlGphcNVU/bL3r1LW5tV7rS1Vl3110ODwepVcJn2dZfLDvL3+6cMJzOp7Ncsk60JyThJTk/s3inG0ltf9Ufg58J/2NPhj+yP8Wfhf+z74m0nxxeaV4UuLjxhrc0Ud4l5cm0mkgy4UwsiGIlI4ywjIySXLMfyUr7u/4JS/8mm/tG/9iv8A+2F/XwjXLntaGIwOEqRgoJxlotklK3/D93dnu+FmWV8m4p4hwdbEzxEo1aDdSo05ScqCk3okkru0UlZRSj0P3W/YZx/wxx8MsH/mStP/APRC16owOMk/pX4Fah+z1+35f+FtG8Q/D7/go/8AHXRNF1aNG0nw34c8VXdta6dC2SsMIS4QLGijAHTC9BXPeIf+CZnxi+ImqvovxK/aj+Nfim6NslxdTar4pmlQqSwUEyuxY/eHG7GG6V+n4TF06eDpLf3Y/kj+A+JsPz8T45t6e2q9L/bkf0LM6opd2AAGST0ArkfFHx/+BPgcsPGfxn8J6QU+/wD2n4itbfb9fMkGK/n70z/gh38PPEurXlnNq3jO+a0ZVuHn1tOGYKcfcPTdz/ut6VJ4Y/4Ig/CPUTcXEngnxFLFDK8afbdZKlypwcDC4GeMn0rpo4/CSbU0/lb9Tw3hbbS/Bn7l6l/wUR/YA0dzHq37c3wetXHUXPxM0pD/AOPXArm/Ev8AwVo/4JjeE4jNq37ffwklwu7GneO7K8Yj2FvI5J9gK/Fm1/4IwfCvwn8L/EPjK8+G8t3fxadczaXZ6nqkrmIokjKTt2g/dQcg8Me4zWr+yV/wTx/Z6+Jf7NvhzX/EHwN8L3moXukJcTahOsgmeU7uCFOMfKCTkfePHGBcsTTavBaebQo4f+bv2P1X1j/gvl/wSE0CUxX/AO214fcjODZ6TqNwDj0MVswP4Vh3H/Bxx/wRltgfM/bOgYgdIvAuvvn8rCvkX4W/8E4vhJYeAfLtf2bvBUd2kkixyPocU/mKCcPulXdg+hPcc8V0uhf8E+fBlv4VGpWHwM8DWWpGAyyqPDFsyvjGQuBx/Fzkge/WuT6+07uOnqbSo4dvlje/nb/gHuesf8HN3/BITTwx0r49a5qpH3VsfAmqAt9PNgT9a5DVP+DrD/gmXaz/AGbRvDPxW1iUNgRaZ4Oh3H8JbpDWdZ/sZwaPof8Aa3h3wT4Ys7mONJHEGjW5D4wWBIHHc1ufD39jbxDqfxLtPEXhyz8O2bzQHzXj0uOMcA/NlULE555wB6jkHrWZUOS3snfvd/fYxeH6p/kdV+zh/wAHEv7GP7Tnxy8L/s/+DPg38YdK1zxdqkdhpEviPwlaW9sZGydzut65VVUMxOCcKcAnAP3yFPGD2r4a1n9nW/m/aK+GNl4nvtNkvNA8bWGp6fceSuUMcFy0qoUUNl0AXByOhOADj7lDAAcVpTqe0gnZL0dzGpFRnZbDqKKK0JCiiigAooooAKKKKAE6nkdOlBbBxXn/AO0R8f8AwH+z78NtT8c+NdZFrDaWxI2OnmAn5QyqzDfgkHAycAnGASPyG/bH/wCC4vxV0bTpLX4QfFnTdX0aCNblvtGIblZw3mCN/KDEjcBhdzcEAkYyC1o3BJuajbc+sf8Agtd/wUc/Z0+Fn7LfxM/Z48Q+Lhp/jue2Sx0rSPtcDXMjuizx3KpFI5WLAUEShGbeBsKtmv5n7fxTq+meNU8Xpqs6yx3nnJMmNyMDlSPpx+Vej/tQ/tXfGP8Aav8AiFc/FP44eKpdY1G6gW1hMk24wW8Y+RB3wo4Gew/GvH7y7EsrMpyF/hPesudNpqNn+vc2jFqnyt338j1Lxt+0L4mvrTyrHUxcx3czPelznzeCuGB4HqCMda8m1LUZr1hOxbcGztyTz+JqvdXQUnYcZPIqv55VA5z6MMUrXegklElnujPgEnJJ49KrMyFiEYj8OtRzHdg7hg9OOaSQsCCGJ469qoL63JkucRFM855qKWYgjBx/WoI5mLsDjgdAKe+xkVj06jNS9HdiGmdyWVOMdjTGIUAE5yoJ70kzY3E8fjUPm7jk5JI4OeKaVgJTtJ/dgY69KNpXknAzwRzUYOF+UjODkdaa0u5Cjoc+1MCdplVgrLk4yTVi3jaUNKFx7kdKoGbs2cdqswOxgYM5A3YPFErWKWrJnlZpVBfIHX5eKinljLMjRiTPAbdmmMwQBjyxHORnFROJXfYCSOoAqErjbsJM+1wyxYAHekkuWRPLHVhninmZlJR4ty9ME9KrynzJCIYyMdAKpbEvcmMpwFx+NPjnu/JOxiFPBqJbedsZjI45DcVPFJNCogIHy9qHYavfUWK0uS/lpKFXq5OM1JcReXEGa53MBwAc5qtd3JdzHtwScnFV2vDCfLWJcDPPU1Nmy7xjoSSTO8ZEq7COmBTFDyvt3Z9MdKiN0zBgXPPWlt5MucDGOnPWrs0jO+tyxv27gzEDHGDUYup0G0PwR09aiVn3ZdzjsPSnSyqDgLg/SgtNoe0zD74A4xUM00rkgudoGBUjeVJhWjZm74PSobmSPIEUOD3Oan7QO9iEKCSrY45pV3CQMTkd6BjuevrTnXAVUzgjvWn2TMHkDDaGxtzWrbHPlqGH7xdpGfy/WsoRkAhR+BrQYMbaJ0fBB4IP+fSpC2pWniEdwAWxh+uKrzMySbh0PcGruoKMrNnjPzcd+9Ur2NY5SF6e1EBvsTKxMJCjd6g9qbu+bKjG3nJ9aLVWVN5JwRnj0prpmPezrhjjGeaFoCVzSurkiJpSDuaPC8dBWUqNMzce/StSZUl0yONUUkKWLk8npx/n1qjGzBSEVfr3pXaVgUdAaNkRVkUcjI9+1IysJAFU4q3NPa3EEVtHAI2WMCRnbO5styPQYI/HNRHy7eX5vm56g0m7MSu0ONrbyYG5w23njgVJHbLsWNSWGPnTdjn1p6XccigQRYY8hjxSxSyQuZmfPPVT0rNuRokfYn/BKzSdIlTxlrLaRB9rtnsreC7eEGVI2WUugfqASqkgddoz0Fez2/8AwWC+L/7MPivXfAfgG+8VaHbWGqS2z/2T4tkt0u2Rtvm+UqYGcdeT2zXj/wDwSmumudI8b5UgLd2GCcc/LPXnvxM8M3998cvHXjd9Ei1GHS/EN1ttbh9qMVdjk47AYr4ilgqOO4nxMat/dUbWbVtI9mj+rK/E2ZcLeBOSVcA4RVWpVU1KnTqKSVSq1dTjJXulra+h9AeKv+Cp/wAWfjPqDeMfGPwo8ZeJ5yAP7S1HXZLlgnXAd4ztHtn1r9D/AIxf8FFLX4P/APBAz4MfHT4e+I103xVJ41e2i0TSfGEMGpWrpLqHnICh3lkjlgZ4wuVW4TcAGGfyctPj74gtr6ysLPwpYfYi1vDdyq7KY3dQfkA4wOBg8+2BXGfFPxCvxPmmuoPB8Fm8EUklpexz5kmVCQQyYHTn5uRnIr6Kjw9gcNzzpp3mrN80r2unvfy33PyLMfF/i7PY4WhiqsVChNVIL2VLlUlGUV7qgk1aTXK0467H3b8O/wBun4v/ALdPgNPHPxQ8VeI7tdM1OaztbLXdekvREQkbM67gAu7cAcDPy9a/PfXP+CiH7VOn6pc2tv47tFEV06p/xJLY4AYgD/V19T/8E9VUfAu6cKoZ/EVwZAq4AbyoQePwr86NfnlfXb0s+f8ATJcccj5zXz+SZfg6+a4yFWCmouNub3rb9Xdn654ocW8TZXwBw5icDip4edaFV1PYv2Kk/wB29Y0+WO7dklbV9z6lT/guH/wVGbwgPh3/AMNd+Ihops/sv9mb18nyMY8rGPuY429McYxXBt/wUU/ayMZm/wCE8s8Buf8AiSWuT/5DrwyMtv8ALZyRnsatDyGQwrCu/wDvE819VVy3LatuajF22ukz+fcHx1xpgeb6tmNanzO75ak43fd2au/Nn0z8PP8AgtT/AMFMPhJo0mg/C39qDWtBsJpjLJY6VFHDEWIwX2KuAxAGTjJwK5fxB/wUz/bM8T6pN4k8RfFIahf3kzS3t3eaVBLNM5OSzOyEsSe5NeBTow4zk/WhGzblGPIOcVUsry2cFCVGLS2VlZegqPHfGmHxU8RSzGtGpP4pKpNSl6tO7+Z9F/D7/grr/wAFAPhBq7+Ivhb8erzw/eyRGKa40izht3kQnO1iijcMgHB4yK0PF3/BZ3/gpV46uLy+8ZftQ61qcmo2K2V+bwI4uLYOHELgjDR7wG2HjcM4zzXy65wfX6U3LAZSMCtVlmXRhyKlG3ayt9xnPjfjCrivrM8wrOptzOpJystUua99PU94P/BSH9q0IAfHFmCB97+xbbk/9+6W3/4KQ/tVBw8vjizdR1U6JbDP/jleCFZBznn2NKkThsk++aweS5Qv+YeH/gK/yO//AIiX4h/9DbE/+Dqn/wAkfR2hf8FSv20NB0/UtJ0L4sy2Fvq1uLfUYLKyhjjvIQwby5VVQJFDANtbIyAeor6p/wCCFX7Tvxm/ay/4KgfDr9lf44eL5NS8B+OItat/Fnh+yH2FdThttGvr6COWS28uQqtza28uAwBaJc5GQfzLabe27n04r7x/4NlM/wDD8L4Hkj+PxL/6jOrU1lOVpr9xDT+6v8jCp4h8eSi75piPeab/AH1TVq1m/e3VlZ7qytsbH/ByfD5v/Bbf42o/Qjw3j/wmtLr4Y1BfOhW1YgFRuxt9vWvur/g5Ui3/APBbL40iM4YHw4Sc/wDUt6XXw3EEZxLFMWYjB3JjHtW97Sb82fILWJmII1hYNcncfVM4/wAKYsd/qJFqsm5V+6CeK1bvRlmszcbCDn7ymmWVvAJGihLHjOMYJ4p+0TV1uW7Lcz/tAty9rOpVlHRD1IpsczLhpA2AeBmkvtKuvtzOYHAdvk3Ht9akS1uoE8uXYAMbhnkVd48pK7j54r17AO+EiXgZOM5P61HbQ2sKpNJMrZ6g5wOvBq3bmO8RbS5kIBB2yOpIFSW9jb6faPBq1qqBjmO5Qbh+nc1PNZWKSTY1LpkuR9nZBGq7hEzkr74/Chb5ZjkWTRKc5KYOc+tQw3FlHEUkjG4/6vOce9C2Ul6BcxIsaKMHym6+/NJ26g9S3Glq1qBb3JLliZI5eB1zzzWfdKqXLguGdegDcGnXNhAYwkl2RKcDGe/pVN7SW0YFhvI6HqKcIruT7pbgjWaYPLKq5P3ScDNOmlEZIKoSv8S9D9ahgeGZin2cyMecKeR+dWMmTNvaWfVcOoYinIT1RmSEXEzMkWAT0B6VbtdPzADnHOevaomsTbDKhic9ecVZiePy8yFvcVb1WglcmijlyBIxA7H1FTqzMwCsVA4HvUKzJHlwDu7HdnFJ56uCQp3Z5KjpUNdxN2LkTyRk+aSCPU9/6VZgvVKGOVD8vVuoqjFMgAJbr97IxkVJGyQK25wSw6hv6VJV7oddX6REB40Y9UYN0pyaxudZPIGcZBAz071QnhjVWllh5I+UjvSRRgEZDLnqnrxVcqEbkGum7Bjd9hVeSe4qN917EDGQE6AjrnNZK3QCkTjI7fJmp7S6vYU3Wx+7/d4pctthrzJ3MtqCiyK+OgIOadBCLtDJczsj4GwYzmoRqE7OSxUZ6ZGarXF/chuZ/m/ShKTHzC6jBNb3n2fzDJwPmHNVVnRW5z+XWr1jqUNzIJJFbzF7g9hVe9Um6MrKSc8mri+4e8JHORn6cCnW9w8rgBOcd6S8tlIU2ikYX5iRjFRQ71IKscg9jTumrj1LErMCzOpGOpbjNEKyvhvLLY4yBUE0zNP5bsWA/vHODVmG4t4kDLgN7UNMUSzPZoBlmJ4B+7mqu8ROVjyQe5FTx3m8gk/JnA5wRUN7LHJ/x7RkE/e5HFJLoyiSK6K5cFgwHHNXF1S4QLJJKwOTxn+lZTAld+eop0T5kCzSHj+LPOKJRVgbZqwapJIxVjwT1J61rQXpkhTaUyAfl3EcVzxeBQFilJYjoRxU9hqAZ1iGAccjFZyhdDuzoHu3Rswo59CcAZ/GmW2sGWIx3IHpg1UglnnuBGkoXPAywwD/AEqleJJFJ5kUwYljkjpUpLYFJo6cXliiebIoUheNrdDUNteGZiXbJb72COBWOmpwvbeXK/3VwRj9aswtHCisJFCkcjccmp5EkF9bl2cm4BVbkKFzlu7Cnxtdq48lgxXh9x54NVI7a0miZnnYO2du3tT7KKG1Zg7O7bgeWycVMtg1uTzWzXriZ3Lyq2QmcnGKrQXX2e78yI4G7Cg9DV37RAsjNDckySDCg9VHTGSMU1ba0EuxkYSqNw+XvRGWmomtbodFqctuCEiOWPzEDrzVlb2OfLMMFQNwZqzpbiW2uNgkAOMYJ6VHE8YkYzBTnncSeRT5boadlZm7Bc29z+7RVG/gbTnH1qPU9ORz50MmWHVFBPP9Kq28gdBFHCFGSCc4zWlp0scTYbkAjKlemO2fei7iVZtmWbHUGOxIWAc8BulX7BNQVV3RgbeC+7jHpWm08QR2ijwFbGw/54qvfagkkQCRcAfIADwfUVlUqt7HRR5b6s0dI8T6xo0VxZafd+Wk8flyqH/hznH5gV0/g79ofx54LWSPRNca3iuImWaEHIfP8OGJAGeeB15rzW6v7u1ixEflY5B3cnnmmLdyXk/mFMAtlR2p0pVILTqPEKFRpvofQPwV/ay8WeDviNputXOq4aCSEPMZmAEUbq4Vs9QNq/T8K+yf2S/+C7nxf+GcereGvHWsw6xZ3cRt4kmxtZCH6DaQfvDnIPC+ma/LiWUmRHduq5ZhUaXUYKJBcMACd3Pf1rop1JaSvqjklBSa8rr7z97vh/8A8FfvhzL+zf4p0bVvEb2upaqbKKC2kklkOnpHDKQ6EptP+pgXAJwTyRkGue/ZH+Of7NP/AAUC/ajk8eftcRf2dousWN9a6TcvqwsLQzxyyGAyyLt2jymwuGVfNjVSGBIP4i6l4y1e4ZtmqzeVJGqNHu4IUY/z9a+49U8e6n8Mv2WNP8d6JYwXNzp/hnT3hguAdj7lhTnaQejHvXzfEeKqUqNCjyRm5TW+z12flr8j9u8Bsgo4jH5vmNTF1MPHD4WfvU788ebVzi073ioNpLV3tddf1/8AhD/wTV/an+BvxJsfFnw5/bUhsfAtjqUd0Nt/cnzrJXD7JIP+Pd8qNpJbac59q439pT4ufBf4s/8ABWr4V3fwyubXUxperaVZ6vrmk3iyQ3tz9oLxhWXKOIw6KzAknlMjYK/HTRP25Pi7/wAI4k3ivT9Ot7afAtNKt3uGeYZ5O0ybVUep6noDXt37C37S/wAUNP8A2r/hp8RPG/wgvdI8F2HjOwuda8RNpV5NFaWqThnlJjQkgKuc7eOScAV5bw+K5Y4ajRUI88ZSvU57WfS+x+kYfiLhqpiK+cZhmcsRX+r1qNPlwqw/N7SNr1HGTdR9r6J6pXP1n/bs/wCCWviD9q/49XXxf+F/xd0azubiC2tfEOl6sjsbWSOFAjI0QY5aLyz5bhcfeDEOAvln7a3xJ+Cn7I37HFr/AME/Pg14/bxDr93OJfFeqWEsflx4n8ydZtrt5UjyIqiAElY1+c8jf8Pf8Fhv+Cg2neIP28PGuofst/EHw94l02GGxtnvbO6nMNxJFaRRyCKWNgkwV1cbkypGCrGvKv2dvit4h+MPgWfxT4n0m3sruLU5bZoLYNtAVUOTuJOcse9RnangoYjEUaCj7RuLnz3bTeto9Obr+B6PhbWwnFVfKcozLNJ1lhIQrU8P7BU1GdOKtz1bv2ipN2irK+jdz9WvhL4g+Gn/AAUt/Y28O/s53Pxdk8LfE/wRFDFp76hcgNqBiiZFZEVw1xE8SgOV+eN0DEMMeZkr/wAE+/gZ+yN8M/EHxA/b7+Nx117jTpYdB8NeHtYmgllk4HmQB3R7mbcy4Ur5SctJuU/L/PnqX7SHx00/WppbT4s69E0dy3kmPVHUIA3GMHim3P7T/wAftXc3Oq/GLxJcOuQGm1mVj9MluBXsLJKtWnGdaEJ1VGyk3Kz0snKNrNpeep+az8VcBgMbWwuWV8Th8DUqupKlGFL2kW5c040q/MpU4SlrZRbWqT1d/wB5f+CZ/wC0d8FLz4beNP2Kfjd4ru9B0XxfLcS+HtWvNQjhitfMi2vEZyVEUvyRuhI2M4YcEhX7Xwh/wSGufhn4yfx3+0j+1Ppw+HmlS+dPLHfzWUl7HuwiTSSOEtlbIyVdz/CpBIcfgl8Iv2ovidoetLf+IvGl9qUIXe9pqFw8iuBzj2P0716x4g/4KDaprXw2h8B2ti0OoeYoXVp7piFTuu0jAI4wynIyetThsgqvDwp4pRm6afK7yWm9mkveV/w0Nc38Ysv/ALWxWMyKpiMJDF2daHs6NR8ySi505SknTnKO71197dK36dfs6ftY/s4fst/t8av42+E1v4gtfhdq6S6ZMt1L58iIzKRcBXO9oVlXcoZmlEZJO5iUPrHxP/4I7ab8cfihdfF74A/tB+HD4F8SX8l+0/zXbWe+QmVYGiYx3Cq24rueMj7pPy7j+Xf7JvxlupPBOo3ni3RF160jv4bKXW5oRNLYxu7N5gR1IdiYyAzH7vGDnFfVvwk/aC+F/gXxza2p8JeHPFOnFEgurDU9MtY7aZpGALnywgX5c8sWPynAz8pnD8OV5UJUsZGE4czlFJyjyt7pWT919jbNfGrKqOZQzLIKmIw2I9lGjVlUjSr+1jD4ZTUpR/er+e+uzVr3+if+CyUnw30z4H/BrwV4C8Y2esQaPbXFtp9xa6pDMZbSKGCASkRn5stCV3rhQyOOvT8/a/ab4F/s0/sU/GH4W6T4/wBN+Afw71Jb633S3Wm+GY44GcEg7FfcwXjgknI5712P/DDP7HH/AEbJ4J/8J+D/AOJrkzPhPHZljJYhzjC6Stq7WSXbyPpOBfpBcLcGcN0sqWGr1uSU5c79nFtznKb05mtOa2+u9lc/CivuL/gqL4h8O6v+yd+ztZ6P4j068mh8LAyRWl/HKwUWlpEWwrElRJFIm7puRhnIxX3r/wAMM/scf9GyeCf/AAn4P/iaP+GGf2OT1/Zl8Ff+E/B/8TU4bhHH4fC1qPtIv2iSvrpZ37am+dfSF4VzjPstzN4StF4OdSSj7j5/aU3TtfmVrXvezvt5nwV/wVF8Q+HdX/ZO/Z2s9H8R6deTQ+FgZIrS/jlYKLS0iLYViSokikTd03IwzkYo/wCCoviHw7q/7J37O1no/iPTryaHwsDJFaX8crBRaWkRbCsSVEkUibum5GGcjFfev/DDP7HJ6/sy+Cv/AAn4P/iaP+GGf2OT1/Zl8Ff+E/B/8TXdiOHswxHt/eivaxhHrpyW121vY+Wyfxf4Syn+y7Ua8vqNXEVVpTXP9Y9p7r9/3eXn31vbZX0+Cv8Aglr4h8O6P+yr+0Raax4j06zlm8LAxxXd/HExU2l5EGwzAhfMljTd03Oozk4r5r/Yt1HTtI/a2+G+qavqNvaWtt4z0+S4urudYo4kWdSWZmIAGPU1+xP/AAwz+xyOn7Mvgr/wn4P/AImj/hhn9jgdP2ZfBX/hPwf/ABNc0uFsfKnh488f3N++t5c3bTse3R8eOFKOMziv9Wrv+0OW6/d/u+WiqWnve9f4unbzPgr9p3xD4dT/AILQ6HrcniPTlsrPxToAvLxr+MRW7RRwLIsj7sIylSCrEEEYxXnP/BVrxBpy/t/+Itb0u6tL9bNdNEiRXAdRLFbRB4nKNlWDKVZchh7V+nf/AAwz+xznP/DMvgr/AMJ+D/4mj/hhn9jj/o2XwV/4T8H/AMTWuK4bzDE0atPniuep7TrpvptrvucOReNfCWSZhgMV9Xrz+q4T6pa0FzK8Hz353yv3Ph1330PmD9tv9mRP+CkPgrwx+1D+z/8AFDwtt0rwpImr2moamUWPC/aRC0gBWGRC8isJdm3ILEDp+ZU8L28728hUtG5VijhlyDjggkEe44NfuzB+xR+yPawyW1t+zj4PjjmGJo49DiCuPQgDn8ai/wCGGf2OP+jZPBP/AIT8H/xNZ5pwpiMyrKsnGM38Tu2m9FdK2n3s6+BPH7J+C8tllk6davhoP9zFxpxlTi224ykpvn1ej5Y2S89Pwor7o/4J4/FL4MfHX9lvxZ/wT9+N3ia08OvqDveeGtau7xUWSR5EZVUSMoMsc6o4jDDzVdhgbWJ+7/8Ahhn9jj/o2TwT/wCE/B/8TR/wwz+xx/0bJ4J/8J+D/wCJrLLuEswy/Ee0VSEk00072ae62PQ4w+kFwjxdlCwcsJiKVSE4VKdSLp81OpB3jNJuztqmno03s7NfIHwI/wCCTek/sz/FjTPjt+0n8fvCqeGfDV+l9YqsrW63UyHdCZZJyqxgOEfaC+7G3vmvmn9pj4veE/26P24hrUviS38LeGtW1S10ey1rU9xW2skbyxdSAkBS2Wk25VV3AMwwz1+rl1+xZ+yVfQx297+zr4QmjiGIkl0SJgg9gRxUH/DDP7HH/Rsngn/wn4P/AImuvE8MVZ4eOHw6jCnzczTcpOT21dk0rdj57I/HLAYbN6ucZvKvicW6To05xhSpxpQbu3GmpSjKTlZty7WtY/M3/gob+xv8IP2KPFfhG6+FXxQn12TUYzcXeg61PHJcxCNlZZy0CxgQyZKgEBsodrNzs+mP2j/gF8Nv+Cs3hnwp8aPgF8btDsPE2naILfVNA1GYu0KM5cxyomZIHjkMoDbGWQEEHADH6aP7DP7HJ6/sy+Cv/Cfg/wDiaktP2Jf2RLCYXNj+zf4OhkHSSLQoVYfiFrSnw3UhUrR5IexqWvBOWjjs1K3e/SxxYzxowuJweXV1XxP9o4P2ijiJQotTjV+KM6XNZqyik+a6te7e3yr+yp+wX4R/4J3+PH/aX/ak/aE8NWp0vT7mLSbK0ldEdniKu4MoV5n8vzFWGONiSwIJI2n8/v2gviXbfGT44+LPirZad9kg8Qa/dX0Fvk5RJJGZQck/NggntnOMDAr9rb79ir9knU5BNqX7OfhC4dRhWn0SJyB6ZIqH/hhn9jj/AKNk8E/+E/B/8TWGO4WxGIw0MNh3GnTi27XlJtvS7bR6/CvjxlGUZziM5zaNfF4urCNPmUaVKEKcW5KMYRk927ttt32trf4K/wCCWviHw7o/7Kv7RFprHiPTrOWbwsDHFd38cTFTaXkQbDMCF8yWNN3Tc6jOTivh2v3X/wCGGf2OR0/Zl8Ff+E/B/wDE189f8FAP2VP2a/h9Y/C/UfBvwS8N6WdQ+Jsen6kun6XHELq3bSdTk8twoG4eZDG2DxlQe1c2J4Sx+IwlGj7SK9mmr663d+2h7GTfSG4UyfPsyzNYStJ4yVOXL+7XJ7Omqdr8zve172VtvMd8HbDQ5P2Ofh9ePpVu00fheyG8wKW+aLk55xzmoNDuLZdUlhSCEHAEhESg7eeM49q7rxJpmlaL8FbbSdFsILWGysoYrW1gjCJEiLhVVRwAOgA9a8YsdbeLVZx54XdhSzHOOeoxj1xXvU6Ps6MYy6JL7kfzFmeOWY5riMXBWVSc5pPdc0m7fK56Ppxt7u5d4bjOFBAYDKjuBWNe3CR6hcRKu3BBA7VnaPqUnntFJdPvB5dsbT+n+cVT1rWJYdZuYY5lcGJTuJwAOgxnNW9Xc4U1fVCeKZI7vwzfaTPKSs9vKAD3BDDBr5Q/YD1tz+zH4es1Yq1pDLbnCkbjHM68Z6jA7V9Ka7qT3EUkEZjB2NwOp5br+GPzr5U/YPnhb4OXdjIgLWnijVrc4XH3LyRcEDrwAB7AUoq8ZP0LlKPKove/6H2t8K9QVfCoDR4RmlUD/aO7J+uKu6P4kMdrJbxn5hDIqMrcH7wA6etef+DvEtza6AllDIAEmdiC3dv5dvzp/h/xK4laETII08xdrtk5OeaG9bELV3Z61ea55Xha5mM4C/Y8SIAMZK+1a/wW8SWh8S6QtvGPljlVo2OMMY2PP5H868rufEqz6O1tOTltPUyAYxkDoD6cfpVn4Q+NItN8ZadbOwkkE7Da54JwccY4/wA9K1ptqRnNJrsdr+0Jq9zo/wC098LfF9tam4jh8X6ba3May7SgvJWsw4GMEKZRnvjOOvH2ApyM18c/G25h1j4h+B9ShAVl8a+GVOB1A1y1P8ia+xkxivQwsrxa6JmFaNrPqxaKKK7DEKKKr32o2mmWU+oXkypFbRNLMxI+VVBJJ/AUK4m0iwc44qG4uYbS3ku7mRVjiUvI7dFUDJP5V8PftUf8FyPgJ8CvEOmeHfh54Xv/ABay3zL4mmjHkLp8Oz5NhYjzJGZlbA42K3UsteCftHf8F+PBWh3d1pfgw3Oq6brfhiUNayKipaXjRkpiReduW2MDxhMjBNVSUaseZNWMZ1lF2Sufot8Qv2r/AIIfDr4d6v8AEjWfHNoNP0dzFdyMSu2XBOwBsbjgE4XJxz05r4D/AGkf+Dh3wnZeB2/4Ub4PEz3Woy2EOujVYygYZAdY2jJGRhgSQMcgnt+WHxk/bn8W/GkXOg6z4ivH8PvfSakmmvMGVLkx7Cpzx91V5GcqMZ5OflrXfiPOkD6VLL+5Dl4EHQMWbOfwbAPPpWMpXi48u/W50QUuZSfT9T7O+K//AAU7+Lfxe8OeLfD/AMT/ABlc6zYa/PmT7ZPuBkERiAjBHyrt2/XYpxnk/FOqeL7O9sb211Qs88WVs2ZQSDu4yep4yOfSuXuvHOrXERt5btggGAFAAFYV1eypMzmQNnk+9ZKKSUUtEbJW1ZJq+o3E17I91GqsxJwh4/D0+lZt1eMFBiOcdSBRdT/aDsfIPUH0NVpGcLuAye4xjNWlYL3Q4zpIhy2CvvUBuQpITOO4qSWaOdAZ1Ge3FVHCISYzkehNWlcUiUylRhfX1od5E+QnqOuaiRWkX92rHBzjGaWRZQ+wgqT0BHakSDuoO4H5iOo7UBxtK8n0z2qORJlJV42GegIpRbXjqQLaRge4Q9aT2E2ri3W0xscA4HOe9V1K7Q3TAwMc1v8Ahj4feL/FN5DaWOhXBSZwhmeIhFz3LEYAqn4t8M3vhHW59DvVAkgcjAYE4z3xSUknYdnuZocqvXIqVBlugPGQBVZJEQksOvapw6smTjHTnrVAOeIRthlw1WLJfMBjJIHXgZqFDCx8tTk+uKsW95JYo6xqNjHBANTNu1i4pC3Vu7DzGl3beAB1NVnKLJ5s0u3HTjp7VcuLm1uoPLAZWH3WGMZ+tZYgdmIkYtnOMVMdVqVJW2CSZp2KIOAee1T2Nv8Av1KnBAyw3cVBLa7V/dr15Y0unrIspJi4xzkVT1REdZGhdXkTM+9WYg4CqvFUpN7cxwsAT09Kns7xEWQXD8biRj1NMn1NTAywJjHQGpinF2NvdkrtkDhHfKseDhty9KhnijXmOQv604GRlEkijp2OefwqZ7R47YXI2jPqR0+lXezM2r7FZ7SXyhKSoyOgbnFS6dGiSsrEHKkqSehqGS4jmJUyYPqRTo5FSRfLIOD1zQ07E6ImltGiHnGLgjjBzTzaxGISsASRnn0p8Fwka77iYscYANPW6EqH5cjGAB0Bqbs1UU9WZt0fJkJhBAPqc01LdnUyu4XngGrEwgU75Dlh2HSq1wZn2hhjuAatPQzktRpVfMySOvakyinKnlc5GKI9+7eYhgHrTZEbJO3nPrQQSCUKOSfx7VYWXbbAKxyrZqqqo3bnP5VZtIQySKDnigCP7Y1wPKdgASTTb1UyrYHIGaZygJ38FsfSpbhBFEsg7nkr6UANspyoKMPrxzT55LbJicHJORgd/SmQursWQ4+lBjBbPmcgkcHrRpcr7JciO+CMRLvYJhlY+hNMzcy3LAtkp0OTgVFEbgx7ogdysQGqwkEhx5975ZxyoyTn04rOTswjsMeMOC7tk5wMY61HPCyHls8DjvUhtzAS6u3B6kc02aYSlY1XAx1xmlfXQppdSJEjIUPnAPJzmp453A2Jt2D7wxzViHT7PyT5s2/AwV6HP/66ZBawwHex80A9MED86TmmJRdrn2Z/wShdJNF8blO93YE/XbPWD4w8QWi+L/il4eQ/6QdcvnVMcsGfbx68iui/4JUmD+xvGyQLjF1YZ9Puz9K8b+Met+IdC/aV8X6v4etZ0uIfEl2GQwMyTp5rcjjn6etfJYCfLxTipf3Y/lE/o3iun7XwB4fgtf3tX7+eqXEkuLbWbfRpbC5FxcarazxRrbsd0Sx4LZAwADjr6il0vW7TTJJbe+jdZLHT7sXETIRsdnJAORjJz2z1HPYdJoX7Ut5Y2iw678N9Qe6Hyh7ZAAxHblcjtXG/Ef4neNfiXvT/AIRmTR9Kzvk2wsXlGMAFto3E4HH07CvrpYuna0Wfz/RynF895xaiutj6m/4J7SGb4GXc23AbxJclR7eXCBX5y69I667fKG/5fJc4/wB81+i3/BOyaaf4D3by2kkAHiS4EUcqkMF8qDHX8a/OvxBbyNrV8FBI+2y8n/fNfKcPtf2zjvWP/tx+++Lsr+GnCr/6d1fypFKOQgZDYP8ASlDyqpcZyRyRSrbbuVbnHQVKkRzhOx555r69tI/nRXIUJPITP409CshZNvbtUpt3kbaqhMDtUEbyW8vzEsATnmi6YNWIJRtJROOemelMaUsMEVLct5jZVAMjJqIDa2SR781f2SRdwkQBOPqaPKfdw56dKQMp+QpgZ55qRyqPtP0qR9BTsyCVHA5r7v8A+DZTn/guH8Dzn+PxL3/6lnVq+EJFaRiVHA96+7v+DZSIj/guF8EJCMZfxKP/AC2dWoja5M78prf8HLEs8X/BbX41leVz4b+Uf9i1pdfC011GUCvmJT125OK+5f8Ag5eLn/gtx8a/mwM+G+3/AFLWl18LRQfLuKiT0VmxWTSs2/MauXre6iid47e/Y4H7tCvB/Go7qYSOJ1t3DAffA5z71Hb2E0ilocB9v+rx19xTYd8E+y6Eqnp8hH9axtG5avYfNpV3OpvhqYYg/wCqZ/m/KoDcx20jx3CneRjc4yasSvbNdgAFs8bmXFUNWjZLzBVSwAxlulXC70Yna+g25uJjGsYuZCnXbnjH9atHUJpNPSKZ1dEycsfrxVG6UjEksiHI4Vf/AK1NckRBY275AI5rWyaE3qXXvbeOExtBg8YyvQc96pHVJoS8VtJIkbfwbutXJbi3uoFW7iZNqffXJz7Gsy42K5ERO3HRjShFPdA9NUy9Y38CCT7RbLICMhjn5fpV3+2LC4g8pI1QIvXaPm/Osq0NuYmidjz0Ga1jo2kyWJmZ5E6A7Yj+v5VE1BPUau1oVnEQlN1HhRjgL2pkxiku/wDR2OCBg471dTRNJgtxKlxuLDhC+FY/zFRmzjt1bFkuWGVJk6fTHWiM42Fy2lqNltjawss8yHnlW6j/ABpA3ktsiiVv7pbPIpttJO/BgYugPzOOMYpvniMmUjLHIAJxtqtGJrW6JA5lTzo4guPvAD+VRsY5FIld1OflytMimMoYbiM9QFyKVxLIAEkXIHAA607agWAzKibpkyD8wK54FSvcxXbbSm3b/FGDhqqwTs+I51wOjGrdsqMrIsGAo6k8/hUuyYE1zbbIGKglQQCxGcGs8LNBKQCzMfukCrMy/vDKnIx0c1EBLcMJzMMDoo5IqlewEDSjIid2LA4KsMYqaeeGGBUjn24GcAdTTIJ7NrktcW5yDy2OG59KS7gSUtMjhieVAPSnu7WC+hGl64lG5cg+tJciJTuBYHuTzUB80AKUOT0NPt3I+SQNk8DHaqatqBIlzHGQUduevvWlaxw3SgGGRgAchMkZ/KqMdsY2AC7s9a29OeSwjxHJJ8w5UrwDzwazqO2xrFaalS70+8hgM0ZVlIyAhBOPeqUcu3llB9hVu9kS6k2yQhGc8SMdoH6VHHpbEtGskZJbCncKIuy1EV71o2VWEmPUAVEkiwgOVBHfJ5qa8tpbSZrYyI2OuGqGOGRtwxznp1rRNWJ15idLokeUqZz0AqNmdCzmMjPrVuxghSIylsSL2Aq7Bfm4t5LCcRuu3gyd/bNTza2DroZRmBXdk4x0IqSB4XyCcEjgkVFJsRsOoUnPJp6RW0jBo8jHUk5Gf6VYk2SAxwg7XGe4x2q0s8Cx+bHJlsD5enrUUdjbkGZnxwcqB19xUcXlbxHIGUHr64rNtMb1ZKb11lI3lc+nrTluWBZpGJ9wcUS6XCsvmWl2ZAecEHI+tQ7WV9oTjPT0pKz1QtUywnmHBVQ2Tz7VcScxjzJGAxgYAzVKEp5m1JDsPBHcVae1j3hQ2Vwe2OfWk99SlsTz6vPJIIETap/ixjIqSCZnLyhw23AOTz/9eqsrbMlbYOSR84p0N1HNdCJ4yFJ5IXp7/wA6lpLUOpsW90VgE86KUAIwi96mhv7S7eRZCV3D5GJ6+2axbS4t082L7aY88hQKla7V40/d9sAngg57/wCe9Z8gXVx9/vt3LxMdvfAqWydZkDSgAe69azpNSlePyxCVjHU468mpoLlAy54B6jsK1asJM2LO/iP7tpCrA/LirQvtjKy5yWz8w4rKYRuu64PyHG3Y3NSmR4vkiIKjOQc5FQ43ZpzJbm3/AGk726gS8OeQf6U6GRJVRUBKg8qa56DVXIXfgKvQ5q5puqljwSQOoU1Lp2Q1LW5b1JZXdYolO3nA6YqpbrLGCAgJHp0q7qGpQyWiyoMndxk9KZYX8d64Bi2mNjkg5zU6pBf3rDUVXtS0jlX2gDH1NUpo3tywV8kZy2Oa0dStRFMCi/Ko5JXIFVxaTSZ3FVLLnb/WnGVtSWk9itDcTO8YjwAB1P1r798VRac/7JFqdWWN7eLwvp8siuDtfYsLgHHOCVA49a+AkiMKc5yjDt3r748fwT3n7HXlWysXPhGyYADniOInp7CvluJ3avhGv51+h/RPgLFTyriOL2eEl/6TUOH+Clhrvwn8AWf7WOv/AAotPFA1DWPsdpLqlwI7bTEEqRhxEOZGZnKrj7qxuQDzX1PoP7fHiSb4z23gy5+F9lF4NufEFzoNlrUF+PPF9bxu7qYV+6oCqPm2k5yuRivFdD8V6f4v/wCCZ2l+HdEIlubPxRZWV7BCNzI51NHUsB0yGGPXtWL4Y1gar8UNC+GVpHcya9pXxX8Satf6a0R329q1tN5chGSQjEggkAEyDGcgn7DL6VGOHVt2k3tu29fwWh/PfEOLxU82cZNqMW0l0srJW+Wonx58XQftYafP47j+DUXhfWYtBudb8Nappl+JW1PT7eXy5UuUCLhgTuVgTjDDkDne/ZGvYtT+FDamsapJPqkpuFQYG9UjTIHbIUH8a5f4R/EjwppXgq11dr6ORfDHwe1LTdVhkVlNvfz3OUhORhmYMCNpOO+04rf/AGMYJovg2ZZo2XztXuJEyuMr8oyPbivjOL0lgLp7tX+X/Buf0j9G2U6vGEfaX92nUtfztf8AQ+HNcmWTWLpGckC6fBAwfvGqlvD+8wWIUH5VHOafqSyvq9xIoyRdyg/N6MT/AFploky+XeQqSAfmJ5x/n+lfYwfuI/mrEXeIn6v8yxa3E3M0akYbBIq4usSpEsWAVj5CngE565FZYmYJ8pwSTlas2qTlGX7LvzGSBnrVrYzumtTsNL+M/ijQLG1sdHuTbNA5lSSNxuD56g84GK9G+BvxUsm8SaRaa1cx2itqa/bdTuS83lxv8jHBbnhjwAPxr5+jUyhnMuOPXoK6LwNql1aarCUjRl81SoYDHXHf604y5XdExSP6t/8Agl1oHgLw98AY1+H/AMRRr9jc+XIjpeyNGH2je0cMjsYlJK/XGfYfTQJI4b9K/GD/AII2fHb4ueFNFi1Hwf4bWe1sbD/iaWj6kscfluW2hy2Fdv3TMMHIw3OATX7B/DvXL3xN4K07xBqEFzHNeWwmkivI0SSMsSdhVCQMdOucAZ5zXVKNTl55LR7Puc8ZRvyrob1FFFSaBRRRQAUUUUAFFFFABRRRQAUUUUAFFFGR1zQAUUUUAFFFFACDJwfzr5h/4KjFIPA3wuvZGwsHxhsCx9A+nalH/wCz19PZweT16V8m/wDBXTWtBh/Zv0OY63Cl1Y/FDQREEuFBSR5njIc5BT920hz14+ppNNp2E0np3JvFLP8A8KjkuVUiM2iGN9/+0P618+WupLBrU0M+FMqHYzdcgmvbNW1cy/B640uZ/mjiYLzg8OD/AIV8/wCp3IGoBpGwvkNjrzyB+NeHKS5md1NOyZvaZ4jv1ZVectIIyWdjjGCelZvifxBNaahFKkr75owQpPDD1rF029e3kmMtwAGcg4Xrj6nP4YrN8Va5PcCKJSrCOMDeAcjBPHT2H51m9FZs11b2OhfV5LfUopmkIinBLjbzwW/w/lXzJ+x3eLYaF4z0ON8NbfEPWY2Ynv8Aa5D29RXsOp+I7iNIJ1uAQIgMFsEHPQY9/wCdeB/s2TNa+IPiZbBuB8SNRfaDyA+G/wDZ60g0ovzIldySXRn1H4W1Pbpajd8v2lwByewPX6cVasNUjWZzAhUq8g56HJP+P6VxngrXm/s1xPIzqJ96r2ztA5/IccVPbeIHSSQrIr/6Qx3AkAHJ9fwrOTKSTR6PFrkLaLJHIm/ZbkKFYnnkd/w/Kqfwx1x4/HWlXdzIv7uZTjdwxOR+HUVy+neJ/MsxGCQzQuM7DzjcD161S8AaxFFrdlcNlC14mc8nrg/1q9WyJP3bHu/xK8WtoUejeIAZZfs/ibS5LZFVpG3R39vKMAAk8RnoD7V9ieKfj38LvCQv0v8AxXa+ZpU6xajCsozASNxz2JC5bAyeK/Ob9sbX9a/4Z18Qan4c1Y219pWkXV5p9xGFcrNCokjYhgQeV6EHNfkl4o/4KaftCeJ7q8tfFHj+7nLXUsreYR+/kLEENtACg5JwoxzivQwM0rwa0Wvq9DDEqcoRcWf0n2f7U3h7R9L8U+I9f1qJ7PSLe61G0u1nV47i0j3SKqKp3bjGMYIyCOa+Zf2jf+CyenfCTxDZ+HtIe3Rb/T4L8SNtnmtoZAJXjZQdpZYyBnB5JGOlfijpn/BR34xar4BufBmoePNTWNYJo44hclw6vHtZW354xjn0zjrmvJPH37SuseNPENhfatqDzG1tBFJKxIOD1Q+uOQPY16dWpS5vc8t/TU86lRrctpvq3o/PT/hj+iD4cf8ABc/4B/EDwLquvi0FrdW7SJpkdzdhEchAYzLLtKgs2c4XAHIBr4Q/bM/4Lf6fqfw48YeCf+EmuL7XNXvhPoN9bXxkGmDkFRH8qxsF28qCPmYFehr8vtM+Kut2/hW9FlJIqxBhGySkBWOccdzjpnNeW+IPEdxqk5nuGJYjLHsT3zWNRxW3VL/hrG9OnKTTk/hflv3PWPFf7RnjnxvcDVNf8VT3flnbb294v3FBOMFcHODjnPHGTVb4gfGTVn0xtJvJYpVeZLkMgHyyEEkLg/KDkjFeOvrVzOqo2AUGARWj4XfT5vFdvF4iga5s1JM8W8rvABwCRzjOOlTG8YqMXZGrUfia2LNx4o1O+YixLmSYkeTGhJyRjIA+uK3o/gx8Q9StbPV9dt4tPtr1mWOW7nTfwOvlBi+0njOMHsa6nRfGOm6ZeOPDHhix0iD7G5H2ZG819vTc7Ese561Fp/iW+1+awTzHlaOzyQW4MhOfzyTVKMY76mTnO+hl33ww+Hnh23niutTn1K6SHcxJEaBlX5sBSTjPqfSuDnsLGS6K2unIVKlgC7dPzrpfitZ61onhiS+bUPKmuFwVB5wW6cfh+eKdP4S0mTQ9J1IwMz3sG9zuI5wDj9f0qXKEYlxjObOV+y6JCgE9sgLLlVZzyaZLb2JBRLOEHbnJXoK62XwJazRAQ6REVwMt5QJ/Oqlz4JQOI4bGNcdwgyaj2sexsqL7nLPdabHAN0EDEdo4c/0604XGigBo4kOSNyeTgj8K6OHwbJ5jfucgdcDFWLfwtJDhxBtHUY7+9J1VcPZnNtdR2abrS2UKe8cPb6gVVXxZLbSNGba5YEckQdP8/wBK7K58OXjoC9sTn2xmoV8GzsDugwcc5Wj2oexi2ctZald3vyw20rOD1aLbj8T0pVuNce6xNeFIl+7FGc59ySMfjXRw6E0GofZEBy6cjHFE/hSYM3y5I+7SdZNDjRSexneGdG0PxFFMsstyJ0cxoJrosS2OCM/WvP792S7khkckpIy/MfSvVfhtoWNZvo5kO6CRnU477Tx+leZ+MrdLXxHe27Lwt3JyB05NOL94bVoWM+JGYeYyjr0zU0pVuYs9Pu+lQKzY+RdxHbFSMzZLyLj8OlWZiwylCFzxnkEVo3ksf2cRpFGg5yRxms+OASx+amGIboTWgLdZbUSXUqrngAEEAYrObV0aRuUikywmNCAA3JqPy7hSIlcE+zdKt3Mlu4VUY7R/FUcZERabzVUdDgdRTTurhZXsRyG5t4gmcHruU5FQ3UjMQ4k9N3OCTT5HW6Aj8w7cnHtULwtGCPLJC98U15kvTYmXY6+VFMQCMjcO9VyrrJjcMMMgUkMgMoSMEZOAAKsx6bd3cuy3gIYetN+7uLWSGmcQRDyolU9cqDT4UmkJlvdxU9z1qZdHuopxEEzlcSN2Hf8ApUiRsWeJojjBAcnAqOeNtC+WS3KE9tE4xCRz0JHQUwWckS58wYJ9afd2vkSbHLEA9m6fhUDuQdisQB0zVq7RMtxxaVJD0AB7tU9pcnBjZvlI6ZqpKx37c9qeqyOBnA29M02roIt3Lf2WOQF2nKjOR7VHND84LXW4juRUcrMTh+SOvNNDKH3g7uMcelSky24roMLPv2omRnr60SHbJgoeRzVlPJDhmYEDqp4p0iwNIrlgFB4GetUpJENXKnmMseFjHqataWqsjuzYyp4xUE5UuQhHX8KfpsbbmKnoMfpS3QmrFe4iEchZFyCavLsn07DrjDYGDVS5k5Kk9G70sVyVhaHPGegp6tBopDDEse5on4A7CltwH+8wHtjmmxyFRhFAB45p0ccjOGZdue5NAi1pagxyAvwDxntxVmExwIwMWe4cnOaZaWNzH+8KjZt6r61OxiERmlVsAcAkD9KwlJORpFWRQu5HuG2SPkduOlRRlkcx++BmrPmKW+aA4LZBcVNPps8irdh+BztC4FUmloTLVk0AiitQI8fNx+PrSRJE0DRTSBcklQTn+lVf7RkRSmwn1APGKqz3Vy8gRz9FDYxU8jY7s91/Yx/ax0r9mbxNqel+LtNmvdE1pI2upbGANcW8sQfYVBdVKnewYHnoR0IP0f8A8PRv2a87f7P8T5/7BcX/AMer8+JYpidzqBx19aiWBmcqiDjkk14uN4ayvMMQ69VPme9nbY/WuFPG3jrg3JYZVgZ03Rg24qcOZrmd2k7rS7b1vu+h+iSf8FN/2c5IvNXTfE2M4/5BkXrj/ntTv+HmX7OucDTfEx9/7Ljx/wCja/P+zDNYEI+CTjOOlSQR30Ryz4Uc/WuKfB2Tx25vv/4B9GvpK+Jd7N0f/Bf/ANsfdfjT/gpr8JbTwzeSeBPD2s3OseSRp8Wo2SR2/mdjIVl3bR1IHJxjjOR8M3Mc2pyTTTqFeR2ZsHAyTk0st1A4DbWbHI46U1540BEbfNjpnNejluU4TKoyVBP3t767H57xx4j8T+IVWjPNpxaop8sYx5Uua3M9223ZLfZKy3Ki6WI4yVYl89OxqtcWktrJucjLD7oNXpJJiMrGQP73pVO+R1HnEZ9DnrXrxbvqfBqxEk8vl4xgZ4GKkDWcsbPKshmwQAvSoUuIySGYliOmKEkTIPpwQPrWnKPRojKYUoTyegNILJ5YyVblTyv1qdVCyKcjBPOetWILCQuZBJtT+9ntQ52E0Vf7KktwGeUeZ12q2cfWo7q3kgZXeMAE/wB6tSOytg3ltIz55y3Q1Vu7RVnLKRnsPSkpXFrsVvMJJYuQM8jFfd3/AAbMy+Z/wXF+B/H8XiXBH/Ys6tXwe0TyE4UkH+dfen/BsxbXEP8AwW6+B7NAdhk8SYkI/wCpZ1arjbmJm/dJ/wDg5fIb/gt58blZsAf8I31/7FrSq+FrZmeYEbyuedoyce1fcP8Awc1hj/wW++NuCevhv/1GtKr4WjmvIJPvlcjGfSoezGtjRkltzdC4guZYl6Pjkj/OKI9WiN7JBK/nk/KpYcH3qvEt7dwb954Gd6jk06K1sYFQPHJ5znJJ6CsbQ6lqTSJc27kb02sDxgVU1Sa02tFJbuJz91i2c1oXVlaiPbCxLHJOD3xWUsauS7bWZB0JOTTg09RNWKcsTK4WTCsF5A70iu7MMuTg8VfkmtlKm5tlIY5Jzzior82Ru/MsfkTsBzitlLpYkFSaSMxJnH8XFNNsGBVydyjjNTNNsUzwSduuKinvEu4hFcIFbdkOpoTdgEtxBGSZADjk56itC31KIxsFvQvJyoPWs77FGieablCX6AmnILfaIXjDsT/AKUkpDT1uy8t/58TeYIw+3hm71Ws793usl3yPvMX4qGaG6dMEjy1OC0a9KjiijVmAkYL03EdfrSUEC11NPUdTjuFAiut3qAKoC7nQ7lQE9/lohhiHVwfT1p0gSEgoQx7AnmqSUdBCQX8schJtgwbsxqwsc/mKVtk5HY5zxUCIW2vKFXb04qUI8ylcsvy53L6fSk9xPYtfZ53jG63C4IB96tRW5CZUbByACcYrPe8aJAIpt6g8ED/GnjVpLhwLlcjGCSMZ/CoSkxrYtXEZhjG0GRs8jNUzNI8mIxs545qeC/dG/dZGePnGak+zoJTcyIOTnCjjNUtNxJ2K1xE0TZfgv1xUNzKyFVRgwxncR1q/OLaePGzO05KlsGkNvYNDzagbRxlsmhStuGltSpAXVQxPygYq9HY2VzZG/W7VZEIBjB5+oqOOwtp0E73BxnHlkVG1lJbOY1bO4ZX2pN8z0ZSs2X7e0t7iIIsDjkZkPFacdu0Cf6NcnawwWbHFY1rJNE+XZ2A6rnOK07DV4HXyJ7YN6HPQ/hWcrlrew67gSNCbmBXib5fNBGQapiKCwgcvyACYiTjPpU9/HdvbvbfZy8R6bM8H/IqoElAWxeZucBk25x7Uo3tqXLlk9DOZJbqNpp5FCkkkk81o6Xb6ctjufbnPLk4P5VR1W0SKf7N5LxlOGBXrUSW8kalVf5WHQHpW9uaK1MdY6Gnb2dggNy91k7uNpPT0qcDSZVZSrKzjgyfpg1RsJR5JCkdsEgDH1qnfTTSzAxyYI/utUqLb1YPfQdffJMUbAPakQPGCy4IJ7NUMzNO+ZJc8AdPSp4EijTIUMCPU81o9hFmC4B9M4xk/lUiywk4ROmfm9arrBGMJKpQnkYPFPkiQDaspx1IJ4qNOg9CSe6ntWwsuQwG4DoRSx3glYFmKnHO3vUV4XMeY5EIx8wpkMYVMowO48gDtRZWE3dluMhnLqRnvjg1KfOJxIOMdz2qso2uoIBx04qwgkWQ4P3upx60nsECzCRKuyNyj9Bh+tQhJ7G5aS5YnLYVOO3r6UheK3fIkHOeDTLy++0xGKQsHPYDg0mmyut0Wl8q/tWW0sY0Z+dwJA4PuaaRcGLaG4B7N3qCC3gkgaZZyiZ5Vug+lSRxTW0QxKhHTvk/pSVkxy2CaIRosxcgMSAwPQ0+GXaxKgBR/ET1qKSRWj2+YAB0PbNMRjI4RQCg6jd3xV2aF7xo29ykjbUZie5U5qzJaLLCXWRlcHJHSqVu6xKsnIOBtKnnoanlidT58bvn6ZAqOo9NiC5ikgQSqp2+/Ue1WbK8eNA7YVB1A71lXN4WmZgASONw4FS207SYY9j0ziqa0BO5vpqCy5j2ny2xu2pk02W/isHIgX5e3yknP9Kgs5WSQCUjaR0P8Q5FF/cRTMVt02sBkNnr7VjZcwO/Q2dOu3ubcq7tyfmGev+eKvy2sCwmZPuqCpBIBH0rm7K5NpEVm3BWH3T29Oa1bTV/OhKJsaNidwbGVPX+VZtWKWqIblJ5S86odoOQAOvNfdXxE1mfQP2OLfXIUBa38OaXIV/vDNvuH4jIr4gs72UTO0cWI9uFkVgR7V9v/AA6+JHwK+KPwL07wl4w8RaXHbLpkFlqWmapqSW7l4VQHjeG27lBDA8j8RXy/E3PF4eqotqE7uyv2/wAj+h/AN4XEQzvLZ14UqmJwzhDnlyptqUb37JyTdk3bZM8a+DPxv8U/s/eIT42+GMUGuaBqhWS80W+XzNrK28qy+oOCrgdgeCK+m9J/4KtfAGJJPEJ+EGrw6u8arK0UdqTJgAAGXcGIGMcjoBx6cJp/wz/YrsDF/Z134YQwSiWEp4oOUb1H7/j+tX4fCX7JcKN5eseHCVHzyt4l3OB7uZt361nHiejTdo0qlv8ACv8AM9efgNmeLgvrWPwbkuqrTV/X92ePftI/tSXnx1vJNVu/Ddj4Q8LLKZTb2VqvnXPOBygBlkY47YzjsK9T/Yl1efXPhLeahPZfZd2vTLHaNLvaBFhhUKx/vYGTjjJqGX4V/sSXupjV7u98NXN0uNs9x4taVl4wMFpzj2rctviH+zZ8AvAl7L4b8U6Nb6fbs109hp+rpcTTSEKMIrSFmY4UYzjjtya8zOM1hmmF9hRpT5pNbr/Js+98OPD/ABXAPEv9rZhmGFWGp05rlhUbavZ8z5oRWiTvqfA+qBoteuU835BdykD6saiS4CALGcjBytVLrUlv7+4uB0knZwO4BJNMaQ5HlHJ9DX6LBPlSP4lqyjKrJrq2aKSQvL8xwoPH1qYvAzBRjJyOvb0rJW4KOAGDe+OamWfJ3PkEnpVWaJvcUiMSmNSQOeR0rR0+eaCHzYQyIrDdJnGD2rOTaxO5ARipoYnnj8qKNginJPOOf0rRNE+8fc3/AATZ8a+JYLTUNDuPHE+j6Zc31tHNqazk+UjuFcRx5xLIys2B2PJHWv6Lf2VbrU9N8Cabo/kahqay2MUk+stcoYVcoh2+WWBTg9FXtzjt/LD+y1ruh+GfGml6prkr6itpcedb2T6obOEvxyWTc4HPJCjPTPr/AEi/8E3v2xfhd8QPAfhn4P6W4OrTaS9yj6fLJc20qiWRN6OyhtpWMNlhkBhux1rWClNWW0V387/qc9RxjNW3b7eX5H11RRRVFBRRRQAUUUUAFFFRPc26TLbPMgkIJWMsNxHqBQrsTaRISB1oDZOMVm3/AIt8L6XrNt4e1PxLp9vqF6jvZ2E96iTTqilnKITuYKASSBwASeleeeO/2ufg34RsNYvLTxhpd++h2M9xewQ6lGrB4mw0QHLFsLITgEARtn2qFKpO/KmyZ1IwV2erEA8GkJA6mvHfgz+3T+zN8a9Ps5vDXxT0iK8u7KO5Fhc3qo6o4BGC2A3UdOeRwK+W/wDgoj/wWp+Dv7OPj/Qvhf4D8RaZ4gmutRSLXorSbzNkLbldTxj+6cqwI6HOQKFD3rSdifaK2mp96eKfEVp4T8P3niS/t7iWCygMsyWkJkk2jqQo5OBz9AaqfDzxrp3xF8GWPjLTLeSGG9QssUo+ZSGKkH8R+Nfmd8ff+Dgn4WeF4Ifg5Yafd3uv6xHFHBq+k3MCw2fm7crKpIICqxYEd1APXIx/jx/wWa0b9mXwc/gDwx4q8/VdW0kXWnXFk4kWyneNJIiwAbC4IyNpBGQRk8Nxgoc3Mu4RlKcm0nZafM/WR2RAZGYAAZJJ4ArjvjD8ePhh8BvADfEv4keI47PRxPHCt0hDh3c4ULzzk1+C37Qn/Bxr+2b438N3XwxttY0TS4prYW99qmkaYN8wK4ZgWyVLA54xg9AOBXyr8Rv29fi78SvhzbfC7xJ451O70bTVD2UUt0WRJupfn5snnkk9TUaJJ7lNy2R/Rl+z1/wUh+E3xx8ean4a+26dYWEdxs0nU31JNs/QBWBPyk546fTvX0Tq+qWWk6ZNqVzdQRxwoSZZ5gkanoNzHhRnjPav5KfAX7S3if4W6nbapYapcXAgAnhh89lQtwckA9+a+1PhZ/wcO/tSaf4AvtAiudNjuEkkmjk1FWmFxG0aRrBhj8gUh33JhiW9FFac1KclbTa/6slQqwT5nft/kfqP+1X/AMFKX+Fvw6k8WeCZ9M0+WG5a1eLWA0gn3rvilhdcDIEcgZW9eucGvx/+LH/BQrx78ff2gvBnhDWdemura88b6fqOqSOcebcfakwuAcCNAzBe+OuOleB/tVftl+Of2itFi1PxtrjTX8T/AL2VQVZuTkHPUDPHPpXlv7OmsNcfHzwwpDn/AInNs6lSS2VkDDnrmiq1GLS0JpqTac9bO602P6LZrzPgDUIUALeXIQVHbJyf5/nXiGpXJOrsIAOEYAE564wR6V7Fo0iSeCtRhghwPs0oQsc4+Unn8/518/yXbx6oftU4JjLMADnA/wAivnpuzdz06T0RoXExhvGaeQgKCVyMAk9+PpWF4iuNs4kMzbXUk7TwTyB24FLf6jcJcs8oDKTxknAHbpWJ4g165EUbwBVyGR0bvye2OOoxWTZq9XuPeRZLPKgGQZ2luMYGfzrwj4Fag0HxZ+J1iSRv8XrPjPHz2sZ/CvXbzVSNDDeY0bqWV0EmMnjn+n4V4r4OuDp/7QHxEto22h7vT5wijruskyf0ojZP5D15X8j3rw7eZsJbWN87JVYkHknB4p9tqbx3zxtGSI5yB8w5J5H9PyNcxoF+76UAY2y8I3s/GcEipo9RWHzJIJDvBBYM2cnjn8qd9LjXkdrBeiC0iu3n2ku+QOoGfSqPhPU1kvYZXXJjvQzEvggByR/n61k2moNcboXVthVvmI6n/Cq2iap9juHMbKo+1jOBwOvH6/pTgTUWh6d8d9Qj174KeIbKCHH2nRr1Dt6nMD/1r8AvFtzNYeKr+yZyPKu5F556Ma/e/wAYGW88C38Kui+ZC0bDPUFCD+fT8a/A/wCKVsbD4i65Z7j+61SZQzdxvNd2Fdqj9DnmrUyjHrEkG6NZM5Od2O/9Kgi1BZ3aJpcDfuO72ql9rOWGcHtVWeby237ue5A61321uY7I6EeIbqzs7ixjnKJJKCQD97jpWDe38hBaMEZPzD1qOS8lY73IOBjIH5VFJJ5gzuIOeM0WY01awGc4DsuPXNeq/Db4T6Rf/Ce7+MXiPxlaW9raX/2ZLK3US3Abk7nUN8iYU4JGSTwDg14oNTnOs/Y5H/dbsEDvXQ+CdbuDpPibRTdTfZGsRKkAmYJv82NclQcHgnqD2PWtqbppvmXQxnztKztqeoXXxg+HujaU0Phnw5c6jcshSGeO2KoVwRklupOTzXJ6Z8WvihpXmxaD4etLZpyT9ouoVkdR2xkdq0op7K106z8q3AWSNAAg/wBkHNTzmzJLELytQ7DWqOS1K7+JHjC6nufFPiWa4DofNjyAuAM4wOg46V6Z4asJb3wRoTyfMYgEJHbgj+lchYyo9zJGqj5gRk9OVNekfDGBLrwNbxk/6l2/RiM/59KxqpKJtTvzGnFp8EduUWMAgDBNQadpEVzHLd7FOJepHatKeNUjd9hO1Tz60nh62lNjvCnaTk+mOT/Wuf7JvfUojQ7SOX5IVzn5uKS70OGK3dvLGShI4rZZFQgqR97JwOtD2XmqSctlDkEcdDUXSdxq7RlWulxTxRHZyYwcBc0S6NbNuVVH5VsaXagWcThf+WQI/KgxMkp/d5J9c1MdwV+hzV34Ss48ag8AMkYz06Dnmi+8OwKhuY4uCNxz/KulFmLi3liMedykY9Tzis+NWm04IyMSq7MeuMj+lWNSSZw3gmJYPiDqVpuCiS3Vl+vI/rXkHxNszH421GNGABuC6n6817pp9hHZfEqSR02iWwAJx/tCvFfjFGYvHF0scXDquPfitKd+ZehnL4X8jlLdnQkHIJ6kVPcBnVSzgKevy9aikhmP7xpMAdQTT7cedD8ykqOwHJPNbu26MkmiyiJFDhW3EdABxUiupBUKefVaiSKNWCqScjncKmDNISDLyvG3npWbdjSzK842Sf7PXmoZFZsvuOB90CrNyEZArn86iMUMpLhiUPUDtVJ2B7lRucSITuzyM0+EyebgM2W6inzwLEm+PoenHI96m0mDz5dysSyYOcdRVOSUWyLakcenSm4MkZKsjd25BroLOK6NqrtKpMacjHJ9qjmsAQrKrZyN5UfrUoimSMxLdAovIXPWuOpU5tDohTcZXIv3k8b+c2wqflKHqOaLe2juISzOCeoXgY96jIlmkOxsd8A9KjGyGJgLgsd2WAAJ+lJaqyG2kVtQS28p2WErIjbXZjnNY7mPO4Nn0zxWrd3AmjK7QAeq7RxUE1pZyQhkYhiuenJOa6ab5VqYT12KK/vZ0MgwOAdoq1cWdyWVreIlRyCTzTVQRKQ0LZ7HoQPyp674cus7gY4K9a0b7Cil1IplMRyY9z9zmoIF2v5rKMH17Cp5LiNWViu9sdz1pr3AlGH2p6BR3ppsHZsbJH5n72Nu3r0oDs6ZCjjrSMqpkZxn0NNIbfgEYb3p9CXuNaUru35781PayKinHUrwfWoJUxJnAbPP0qe3Xso4z0FIRDMHKlnwO1SW8DTKGMoGD0I6inSx7ozGFYMD0NFsmxCH3A96V9LjcbCzW0YOA2ee1Nijja4WBzjB7nr+dTI5ccDAU46U1EdpTtY529qV+49OU17OOJI9jKCmMqA1R39rFGw2SDg5Kg54qC2DtH8zHjsMc0y6lnBOAVbpj1rn5XzXuUnoJNcqpIWEbiCdzGoZJLorl5Hx/dB4pYTKyl52HI4DU6WeUwgSFcdiCK1SsLRojRPLYNHu3Y59KSKC3aMvL5nmg8BcAD6k1JFcoB5aEd+1N82LbtlkcJ/dXuaNbk6WI3nYgKwPyjG3b1qN5YRJuIPTHK9KfcXIjbaFHquGyaQLPcKfKhyfwGKelhFu1UNYfuGLZwT8van3N3LhcKAO4altYDDaeWDycdDUcizoxYkAj7ucnFKdmxLuSx3cbH/VDAHIHSpfPilfiLaQMYZP84qOztpP+Wkhcnk+lWm+zrlBDkgYPOBWEmrmkbkI8pGJMfy44GepNZ94twpLgBgemeBz6VaupFiYeS4znG3rinKHNqFnwSWzyOcCrjpqJuzMqK0naQvOOvcdhV+10shvPMamIL0J5JqWIow8kRjnpkYzSyu00PkxIyMrZJUf1qpSbQluVprF4j57QlAvdyFz7Uz7TPMoTKEZyPmqWa1aZSJpDkDII5pILSAWx85CxY46Yp9NS011GkOWXbLnbnhelSpbxyDPQHvu5p6NGkJtBAqIP+WmOaitSnmsqy8gYGR1NF9CvdI5B9nkMESDqDhxX3Z/wbVf2jJ/wW++B81wgVA/iX5VPH/Is6rXwpJYzXBPcg8gmvub/g2jgltP+C4PwQtnlJy3iTI3H/oWdVqoJcyMai93Qi/4OazIP+C33xsEY5z4axg8/wDItaVXw5ZsHxHcgAt0Jr7m/wCDmaIyf8FwPjYyDJU+G88dP+Ka0qvhZTLFIYHtVb3ob0f9dQjbqa1tKlhD9lhmKl+4HAoQhiTJMGJBwZOvHasm+uZ3uVtYMALxycDP41ZikuJXKzRZAOCx5C+tYODWpomt2W4WtYYRcLI3muSNgXjH1qtqVoWj863tcIT94dQasx6hYQKRMVZl4XaP8aWG+MsDukoGxCVVgMA0k5Rd7DlZrQxby2VEDrIW3feIWmW8sMYaFmIYjqRmi+1C5uisZhVdpIyoxmoDuPyOpyB1rrim1qIthQU8qNgdw5L8VXiVUZldM/NyMUlsYpGMbEA92J4pY44/OCo4wT1zRojNasmjt4ljJKH2B6fnSm1haET2ySAhuatNDBJHHDKTHuHDAbgefc0ljcwW10xvdrInHknPNZ8ztcaVxGYC3XzQU4+8BndVWS5RBuReWPVTT5LpLq4frFASeAmcCok+zrnyG4IwM81UY9w1I5Ll2G45HNTh4pMDfjPrVeQKZAo/KnhGmAwy8Hgk4qxDlkdJC5JwDyRVmC7kcABsZ4yaoyW80Unllsg9SpyKfIPs84jUnaMfMpzSaTFpsX57Zkt/9WcE8kDiocxgCLcy99wFNF3eJN81wxT+8xxilZ0ljLSnBHXI61CTQpbjoDOAZeu08561ci1FpIlikXgN1A5qiZmjZV4+YYzxU9qYWfGQx/uk4qmuofCWnFqyrJaBhIfv7jjio1luTISVAC9C/U0i2/7qQiYqeP3ZbOait4ZLiQIqlW7fMMGpD3S/5oKBg2D7VDNcpk8M3qX4qJp/KuQiHCj/AFgIH6UrXMcpMRHmDtRy9CebqXI0aUD7LEFLnGWYClEc1tIBNFIrIecLVOB0gYLFGScZGQfyzWrZ30bwqYbhhKrZZu5H/wBas5XizSLTNGMrcWkcwlGdpDqxAYj6UWUFrB5U0MatcZJkbPOKzLgG6DXQB3ZyvPH5ZrU8O3+oQRNBcQo65GN8auR+dc9RSUbo6YNN6GVrkSyTNPummckfvnwAfp61n3cEqbLd0BbGSQefxrpvEesWsk4K2EjkfdQQ7FU9OP8APaudaKS7nlbeVcg8sOntW1GTcVcxlo3czbyWS3+Rhtz3BzmmwtNjzxyvrilu43IRMkspJb0FNQhFZMjDcEZrqM07ksdxH8wK8DqcVLGN+JYQBnpk1VmXy0wAQB04oiuWjyAnXkE0WuMv2sMsilXKA9Qc/eqzGkZYh3VcdiBz+XSs1bxSAoXkE/SlgmUPy2DnnB61HKxaGoYoZZApA+brjpUb5hnCQRnbu60yG6MQUKmf6VYDeehDqDg4qdmNXsI0caSCMuWYDOccVLG4RmCbuD8rUkSRMgcnaAMBs0JI7ruSXdnuBjimFrMeAHckEHj5j0NV94a7VZHON3yOBjFWAU5ZgB2+WqspAuQ0TA7Rghu9CQ29iS6WeI7Y4CueQmM8A/rUgWd4C8VyhJ+8jHBH4UvE7BZwV46jn8qqXyTW0n2gRMEHI54PvSjd6C5ug8yRwp5LyBt3RQO9LaySoCXztPQDrVeCXcokuMcfdyaked1ILvgHkBavldrGhdhklVkmjT5e/t9a0ttxPbAxuRkHO3uPxrBguQx3Cdiq8qpOeau293IoMsOQSMbS2MVDi7ka3GXKSJKUMQPTGDSxzNECHh4B5p1/dNhWI2kDDt3JqCC8lcmI/dHTPenZuIdS4X3nd521xwAo6j3qa0mMU43OSc/dXpUCtayABSoA5OFPP50su5hstkIHUljUWWxTdi3qk4jjZpCDu52OOajsrtHOFbYAAQo96qSu5U+Zd4JHy7h2HvTrfUFwF8pHIXJ6f0p2sibs37LVGwjTybMk/IOox6in3N5Fe5CSFVVTkbax1upmHmjJY4xkDHTpU0F26M3nIqsR36/rUcqWw782hGs62shWN2yTzkVq6JK76RqAblhCNp/GsbUZzKwfaAR1YHrWz4amjm0W/ZwOIQAR9atLW5DehixxXTERRgg7vnOas3GnXnkB2cZPq3NOhcuTINoXsOpq1bX6g7ZUyWHyAqRj3zUuRSiZEHmRAq7sHPHSrC3OCynnb1OetXrpIrqAmO0EbnkNuBrHmS4tXaMp36+tWncRcJDfvVzlhwRU0bbFyXJ/DrWbHcsuW3cZ6elSi82RhRkEdT60x6GlA483AUkY5zVnz3ZQsTgZbpzWUl2S29CAMce9T22oSxMrEAbTkAmps7jVrHc+FrO5uNVgfUnACJ8zxx5yqgtg45z/ADxiv1+/4JbftJeGf2ePAOmeI/B89wv225RPE1/dt+/SGIeYRFCuW2nlcBtp2lvQj8ZtE8RaraztPbXI8xhxuP8AhXc/DL48fEL4c6hPeaLqJikktpIpAem11ZSRkcHk4PrW+Hny1otvRf8ADGFaMnBuO5/WR8DP28v2YPj7d3mheCfifaRavpUTSappGrZtbi1C5LbxJgZABYjOQuGIAr1+1urW+tlu7K5SaOQZSWJwysPUEcGv4+vC/wAXvElz4jm8Y6nrV39sCKJ7l7xy5AICjLE5AJ6fWv2N/wCCRv8AwXU+B0N1ZfszfHDW9Ys5Zza2fh3VLtUNujhQr+c5I8sMzE72Y/cAwCRnWotduplGbjFKW5+gHxR/4Kh/sCfBPx9qfwu+Kv7T3hzQ9f0e48jUtLvWlElvJtDbWwhHQg9ehFYXhT/gsN/wTZ8X6lf6dp37WvhKBbARk3V/qK28U+4kYjZyNxGBkYHDDGecfz8f8FkPEHgzx1/wUZ+L/i/wx4ttL+1uvEUT6fNZyCRbhGt4yWVhwAuCD3BIGM5x8+r4g0nQ/Ax046PLLqk7Rvb3kQVkjj2/MrDqW+brxgiuqWGUXYyVSpd3f/A9T+pC/wD+CvH/AATf0/xEnh6f9r7wUWaLe11FrCPAvTC+YPlJORwM45BwQa2F/wCCpH/BOyRdx/bQ+HA+viu2B/Lfmv5QLbxM13rCaje2cYQEF440Cg8HtXU+FPH3gWLU54/Evg+e8gls2jhhhmjQic5w+7A+XkfKc9Dz6ZqFNuz0G3WWqf4H9K+s/wDBdT/glpoei6jq93+1XpbS6c1wrWEFjcyT3BiZlHlBYyr+ZtBQ7gCHBJXnH4bf8FRf+CrvxT/4KRfGeG9SS40DwPolxJF4c0K2uCTFExAaaUqQJJmABJ6D7owBz8vW+l2Hh+7u5/Gnh6W6juIG+yLDdqhjbB2k8EYyRkexrl3GoacYWtLl4vN+8ynHeqi4U3JR69fIrlc7cy03sdNqmv6vak+VrF4wVsROZmDfX2rGuPGvifRg97aa/fwy4ADpcMDn6g1Fqs9zZIYbqSRpEfD7zgg1gatPcy2zzRszRqwDHsCc4H6H8qyU57XNJKCd0ezfDX9o/W/D3hON38QTrqFnbeXp0omkDpjPQg8eg+teZ6v4p1W91mXVr67aSeaV3kkB6sec/rmuc0u/dYTGDkg8egzU2pSPauivJuYxhifTPP6Vk4rYtNPU3/8AhJ5VCz/aH81HDLIeWBHTk0vivx/r3izUzqWuao877QqbuiqBgAD0AAxXLJemSLO7Pc+1QT3pZ9in5ugpKKLvbQ2LvU5vtbM0xyDnJHemDU23HY5PGCMVn3Rlc7mJwQDnPPSqkl4yvhGJI7AYpkNyZ0y64syoZWLhVxjHTFLJ4leTCRuV2jjBrm1eVozIp+7UiTysAyrnj6VPKr3L53axq6h4gvLhd0kxIxjBre+Cestp3xP0S880Dy70NknGODmuHu5n+6wOPSr3hWe6sNZtb+O38wJKCozwfY/hmrVupEtj+lbwvrcyeE5ot4KT6eCNvY+Vzz/nrXhVxdJb6xJC7oWO5cgZPQ9+3/169L+ELXNv8LdEtJZgXfQICzAk7/3C5PP51494heb/AISAusvKsVJ9e9eLUtzs66SXKr9kXdRuWklCqxVlPR84rndbvpNrsw2gSkEj1IrauJLG4mUSowOflZunTqOK5/xKsEcShEJAlJck9eo/xrmlsdGzM68vkmthAkW394cY4GT0rxrQNWab9pHxnNfIA8mjaaVKgZwFaP0z/wAsz3Hbg8Y9b1Nbj+zZsRcxyEoQOpOa8g0/Tbuy/ao1NHbjUPBtrOMdttxOmR+Iz+NaU7S5vT/ImVSSWnoeraPdmOxESgjMahvm6ZJx/n61PLds906y7kQyBVkBzg4GO/t/nik0+xs4nledmH7vlc+5x9AKZqFxHNambyiFYRv8q9+Qx/Os22luVG6NLTLiSJmke5LGK6KhRwGGOuPrVafUxDqV1HHjKSvwOOhP581VtbiZtSn+QgPcfMc/TH+faobmeJ9anmYncCXcA9iMYxVR+KxDcmtT1TxFqt9J4E1G5bbuitvMAzw21DwfqQK/Dz406LKvxW8QJLsj26lLuCtkA5PHPP5+9ft9Zy2d34Yk0+cKBLCo2ckEDGQfqP51+I/x/wBN/sT4x+I9LSQjZqs3A93NdmHb9o/Qykl7LmOMk0aLaXjnUL1IJ5NUbizij5STJxySOKtufJXa87YJ5yo/Sq9zJHtKwyE4PXvXcm7mOlijKYvM24ycjBXpUd1IEOwYz708w3Mqs6AAAYPPIqFiWUh/mOOeO9XdNhZpFDUdKtbK8tdUTVo5zON7wxIwMXJGGyMZ47e1S6BqCWjasA4UTweUPf8AeI3/ALLTZ4FBEkZ5HYmt7TbSzsvAGoXDWMDS3brmZ/vxkOMBe4yM8dD+FXdbszadrG/b37TaPYMrE4VQeenyf/Wq6s7iM5PI5GTWLp7/AGXTIY1mVgjKNynqMYq+jDYQTyRQ2KK0F0mZf7UWNnA3TYP48f1r1j4Okz+GprTHInkXr05zXjcMnl6upY4xOh6etevfAwuLC8tH5ZbxwQT0yAcflWNbY1grM6LUXNvaTbiAFjJFT6E722nxxgKVdMEE9eKbrenzmzfeFUPHht3SrOlWHnER+Uw2ZzhenFcvN7p0NJMimcBsyDt0HerJgCoBvwPXsKtT6Y+wM8QUKO45pkjLcKmE3bSAQO/t+lRdlJO4un2z/wBnW8fllTtYEEehqve20ltdB2zgYKnNOiup4bLCSkRpcyKQDyCGNQTTyXjfOmQBgA9xR1BRaiXNNimab5QmwnnHeobXTvNjvbIgAqzGPjkAg/1p9nILaQrJIcKwJVTxiszUdfXTdXAhJxPGRgnJ4Pei+tyWtDNvrFbPxjbSvHy1syjPOa8N/aEtlt/HMjRKQdh4HsxH9K9113W1Oq2E7W0ZPO1lY5PBH88V4t+0WqnxSl8qELIrYzz/ABk/1rWDatcTtdpdjzUl3YqQWHpmtCyBlBZLcKq8cnjPNRIIyFkRTndk4Xp9atw204jcZAVTnrgGtZtWFCOtyN0cuQFDY/iHQio1QrL8qnBHGO/erryXEsYlijMajqVY/NUUs8MEaqx3OTk7ucCpTsEtyrdlInZHDdeCaiSREUBEBB9O1T6jcpdEOCS56qB0qokikFR6dBVx21Jdr2JrmOQwYTgN2Wtjw/aJZ2IvNoBZMZfn9KytLS3nmIuQWzyqhscc+1dBstoIgsdttUrwMZBrCtKy5TalFXbJoiDa4Ugvj5/mxVJwsAbyzndySDjbTQzb2KKACMAAcGobwjIiuByeTtXisFF3saOZHOt0cohYkHq3BqtcQ+TBvc5LAAH0q7NdkIGDsDtCncMLis+W9kd8TyZ9s8//AFq3hcxkyAu+3LruJzlCOgo8jzEzEBxzxwelSMIgjS7JIwBwT1zmoMSXGZEjbAIG6tk7mZKVIAww+7z3IqIGUyfLFnjkZ7U1ZXjcQPMeTgkCrgS2hgIQfOehOef1qlotQitbGezlmIS3DH6U2W3mSIyuoUk8ADmpVVmkIwAe/PSlAuWBd5twXjLfyH5VT2DluQ26yPnzBx6tSSRMnzxE/TFLdSySRCIHBz6U0yybAC3P50ybJaCspKAumMdxT7V1XcY3O4dM1HO1w0AZn5Bx8vpTrONxKCpLZXk0tkHMOW7I3K6ZYjqT3qaOS2MJxvDAce3PrVaaJUlLk8A5x6VOLaW3cKuCGXrUu1iiJiI2Kh2OVzz60sZKhZDI3zHjb2oZBvIJwe20ZpWlATbwCucYGKsnXmLNrcCJslJSD3OaS5kZroBWYHg8HpVe3vJQ4MvzAnAIqeQKJS4jVgT0askrDWrFn8t0zmRmHUuc0xCkxEQIz6E4oZlmG1iVwfugcYqBmj8wxpkcdQaYbaosSWkijC2kgX+JtuRTbhrBwIWHPrtwarucgKWYL3LMaJpBHb4WPr0YkZ/KlZ8wuYY2IXG1V9gecVYS6sZtokVg/Yqf6VTlmkdMrGBxyT360W0pi/eMM9s1bjdXJNkOI7Xcq9h7VGrGckSORgccdKSCVpLPczEeuajeQxsCq5z3FRNaiWxbiVUGJCp9qkidV3EKoz64qkly24KQv40lzKxIfcQAuQRWXLdlplqaVIsMUBGM5GOKZ9rWRTGiKeO45qpndHhpGOQSNxqe3gEaCU7FOcjL4/Smo2C/YYsu751B4z1OMUhuZN2Ec4I5pt26qwZEI4428ZqJUlKZ2sPTJ61pFXD4SRJpC+0SEY5bAq3HGoVT52cnknnFUEMsEgkKd847VNJPLcyA5AXvxnihq4K27LF/ECwiWcMcfMPSq8FobaUSPNn1I7CrJOnggNIysRwwHBqFkiUYEm/PBIHaoTdrFPYmS4Ecgfhh78V9w/8ABtOyzf8ABb/4JSMMHzfEv/qM6tXw3NBbtb+X5h3AYAK19zf8G08Kwf8ABbn4IbA2C/iQ8/8AYs6rWkElIzqyvD+u5U/4OaW2f8FwPjeULZJ8NZwf+pa0qvhdTPGoVF4IyT6/Wvu3/g5iVB/wW9+NjMgJJ8N4/wDCa0qvhVZHWLZsGCefem/1FshXv4PLEM+njOcq68EUkl2zW5gSMBW6oDQ4d/ncDAHpmpYpoQNqw4AAAJqVGKE22UbZpEOSAOc8jNSG7V5Msn/AlyM1eitbOVvMUAOTkBjQliu9pZ0DoT/B0oui7uxSbFw5ZwfwFVrmKRCu6Qkfyq+0SK2YDgHnBpuLZ8rJnP6VSdhcyMtYWJIyQM9ala3uHRVRhggkAdavxQrE5jC5z6inx28YfMihgOlU5IV0V7Jr2NRFI+Vx91h0pL0pIVRLdQw6uTyatMkTsGK7s/wt6U1bdJ22pEQcVN1e5SehSKyvCY0c4J+ZAOKakJ34XG7GRzVs22JtuGUZ5wKQW7RyFugP3c81SkDtYrpAJGDSDbnjceKfEpiJUFSOhqYxtIvlkjaOgNReS3nFViPHcdKE7kvYfJO0YVWiJI7kZFPiaMxGWWLAX061HGWHyA9ecdqlYF1CrEOT94djTJbYyXVNr7JCSc9cVKXglRpGl8wleMiornS/OwGkAIA4zVaC1eGXPmYIPSlZNCCaNlkATgemORUkCMZciQ59xxTriBZgJd20jqV71HDcTpJsYEqDg7h1FMC35rxk7VDEdielTQTbjmWPGOhI5FULtXVxNb446gNmpYnuJEPlgA90bvSauBZktkkzsuATngleDTdlzEd0qA4+66GktmQNsmG1u4YYAqVI7dXwrNu7Hdxj6Ur30Er9REuZJADtCZHUcmnJe2kEmI1cEDqDwaj8sGcsEIZcd+tI9s4YTQqAOpGaXugnbc0LWQTEG5mliLcqWY4NWXkkNzGLa9RFBH8RyfrWXHqV5IRJPICQfkyBx+VBn86RmlOGPXBFRyXWppGfK9DZvNVlji2xTqGxjzEbP86hSX/RmiMIeRxlpCwHArIe4lhmJVAyg8qWqxazC5URyxc4wMjr+dT7NRG5uT1ILuyxIqw5+bkhmqNLB3lEZGO9aVxNNBCsSQJg53AKCf8A61U2u7iV95ULz0HatIydiHuMe1eTIMDAD+Kqwtdv8Q3Y6VqRssyBDIAB/eHBqKSJ/PIVY845HH6VSk2HNYqrb3VurYTIZcj5c8VCImJyc8jPFay3MTwiG4w3GMHgCoLqJ/OU26KABjAojJtjbuMtzIkhBIGAOtW7RQGxuOOpJNUWlVU+bKtjAOKmtWZP3iHGehxQ46FF6OdIwykA4bg1JBCqKrKoXPQ9c5zVe22yyMNqEsvXPWpxc/Z1MTKCWz869u9RoirW1JRBAUZchiBxnvVKSOYOFkhG0521Kl0tuPO2FvQsaDcNNH5p4AzjJ9qWqZIltLFDIGnfao4AFPkUXbkggo45/Cq8aC4DGRDx/s0+1imGAiZ4PIPIFPTcV0QXkMaEpbphQegzj602PZJEAykeuBx1NXGsvs9sLjBbc+FBPXioZrO4ACKoAA5PPB/GqUropNDUKKuxWPofT61LGJA2d2VwQRiq6RBwdrgAdDnoakjWUHaZfyOaOugXXMWJzFNCfMjcbepBxUEckahZUdj2xjtUwdpDtUZA9O9QzW0isJVbbHkZ9etGvUS1Zfht1ltjI77cMSQMfypkupW6tt+1GM9iAMio1kWUEEtwmcEZB4/wFUpY4XG5mHX5io5HNJQ7ju0yea+hnUxAlj/e6ZpII5bX96EHzfwg9frVeMRSSYt4yVHqvU1LBKIpSuSCx+XnpVbLQFuXra/uN4d5jwT17duKtG7aVjLcknK4Unkdf8KoRpAYSwJODhg3r7UwXrxubdZMqpyRjtU8hOrLUtzE5ZIyp54yK2vDS7dE1ERtn90Mc1zjuqxh4VwpP3ia3/DKquiakw+bMYJB79aErWBX5Sjp8yRuxP3j69c1o2iLMAVmQOE+ZVOc9axfN+YiIKpz95n70kGoBZCYoFPOWcD0qXFvVFp9zTZ5Yh8sZCnrvNPuHUIFgt94cHJwelVX8Qi9jFvKgBQY3KMfz61PZSK6GSKRg44wwwPpSV1uUZ8+DIXWIqOfvcVHkLHsyAfrzV2+s3khLKTwcsKpHYHCs2DnritU9DO2o5ZZIiGJ46cnqK0LSeKVcH361mbd7kSgDJ9O1WbQiNwIn+X0z1oklsNbm9azyxW4MQC+rdzV+znM8q+YwLDkt2IrFguE3bQcEjjFXIr1YQryjAzwOtRHRoTu1Y6W1ujbRMsDFS465OPypjXD+YSzsSehxipvBet6NZs+o6tow1BIgpVSGwrZJGcEccehrpL/AOIvg268RzaynwwghtZLB4ltFgJVZuqyDC8DsRjp0xxj2YYqnCC908x4SU5O8/zOQkumL5ZiQRyTz0FRNeMNrq5GDxg12kvjD4a3cTLaeAD5piwrfZiAp78Z9c8+3T0zNMi8O2wYyaIbiMzkxGWJgw4XgkHpnP8Ak1r9ci+hn9VcepkWviS4iVorlFmVscSj5h9D1/CtGySzvj9pt7soy5JDJ93HP8qh1aHw7bB7xbGUhnRVSNSvzE4xgnoPX3oWW1soWu00QpsOG2ux3Lgn9cY/Gsa06NSO1ma041aT3uixfy6rczZN4smcDDk8g10fhey02/sZV162V5hBiFl5C/n3rBW5gurSO4t9NY7wrKCTkDGT0PbHrUukeKIJTJbpZyoISy5IHz9OnzZ/OuWNrHQ77m14j8N6fFYJdS3cKzyDcMIsmFOTgkZ7f0965i78KwakJbWPV4E3nzBGFAJ255x1x19q1E1e1nzKInGONrDrmqbNog1b7YsDidYjGrhudvdfTuam1ncItpWOftfA5F49sNXCDGXLRcAd+9W9Y8K2d1PEbXXFZfL++IshuvOR2607UvEGgG4e2R5yZFKuDjGCOc81X0fVdGiSKyQTIijC4I6ZJ+p5NVyqUg5pKI2TwO1mTHNq6AnIVTGQWPoM9aqX3gufTZVuLm4kZSCd0dsx24JHP5flXZz2una0YtQmJY2z74TuIKn19/T/ACaj1m6torTZO0oDZwdvXmq9mluCnJnHXWirDKYJr5idgYkQk4BHt7UsfhmxtXRri9Lsw3KoiOce/wCVXrzW9HWXk3HCBCBg8Dp1qOPXNGuJ0BN0rqoQMFUjGc+tYytfQ0VynqenfYrdfIBcSjduCYxzis8W84l2MjAHuRgV2k01i3hmRoXcDI2NIAD1ycVzHmm5cW0LEgHA7DJqLpLca5rkIsYVw00rAjpmr2nXUQeJSuP3gxg10N18EdaheOLUfGfhyzSSJXWSfVAcqQCD8oJ79MVU8ZfDKX4fW1nf2/jjQtcSeVVf+xbuSXyGPRX3Rrz9Cf8AAcZON+hSabsfv/8ABDVf7Q+FvhjUFO6Kfw5aSbfX9yM/yryjxSXTXGdUKnzeSD3ziuq/ZZ8UaHcfADwJcyaosL3HhK0fyZD87FU2sAPr/jXP+LFkbWhMbOcx+cSW8lsY3HHT868StOnF6yOyjCpKOiGXESSsYXXgLuwwBA4PfGa57WbaV4p4FlGQwUAfKBn/ACK6HxFr2g6HEkt9fAMUK+WFyW7AAYBzzXlHiH4/6PpvifUdBOiXMb2UrRSSMy4k2gNlOfcdcdaxpOnU1izaTlBWktTqpoVs9OmY26MyYLndyDg4AGPf9a8gu2f/AIakskdyHufAT4IGBujvGb8PvfrXrfgbWp/HvhZdX0ayhhuL6xEkEF9LtCuM4ViAR7Z/PHUcH4g0GK4/af8AB8M84jNz4T1iOSUD5WKTQPjjGRhiB9RWkUlKz6mcouWp0mmQmQFWXh0PmNuB5HOOKlupG+xTQBvvAZIXA61pW2iC31aa2siGUI5CY6nGSOPqKfdaUbW4eGSQkxW6E5H14/z6VDUdhq+5n6ehmud8UZBBJbcOPpVG8Bh166l8tVMhKCMLwOen6frWD4t+NMHgXV/7Jv8ASDJuCmKZJc7xtyQQQcfXI+nSsM/tBaLqWqvdS6XMgB3EIV65z1yPeuKtmWDw1XkqSSaN6WCxNaKcIvU+htCVv7AaRsENCuDkcMOf61+Nf7YdhJp/7SPiy3SMKF1q4+UDj/WNX6m/DX462vjq/j8J6JA8U0zKGe527SvAOBnqc49utflv+2vdSxftK+I7S7kY3UeoSLflowg8/excgZ6ZNenl+Jo4pc1J3Xc569CrQ9yas9Dyi++1Od8hAGPlwBz71TniCpmMkEry5459qtT34jJ2kthto471Xnj86IO8hDDkgmvUj5nLZlUJdQTkp8wK/NuNZ8iXM85VFOME/Ka2N/lLtAAUnljyamWCyW1Lo4DEY+VafPysLNnNyI8e0NG2GBPTrWlZxyL4FuZkk2M905PrwRjHp/8AXq5Np9m4RVGNi/ePeqGv3A0bw82j2gkmEjmVj5Z+XOOMjjtWkZ8xDjqafh9pH0KMSNk4U59ef/rVrLMscWQMZ6155a+JNc0/QQ8RAxL5aBlHAAz/AFpI/iJr6xbGEZbPH7urkrsSTSTO3nlaO9eTnAVDn6Ma9V+BWtaZu1OObUY45ftXEbNgk7Rk+wr52k8fayuXkgiOVwRsIx+td54M8Haf4zsP7b1jWmt98GUEb4JPvk/59qzqRvHU0h8R9DX2p289sbbWfEmnCJ8B1jnCvgHI6n2psvj7wzpgPk+KrFAOCBcoMj86+YrbwTfXN+bS41iD7Op/4+PMXj29a0l+GeiQMzy+K7XysfKguBu9881zeyXc2UtdEfQlx8WvBsURWfxjYEOh+cTLkVSu/jZ8P7CKJ08Y2bkn7yOCRjpyOlfPth4PsY7xl1HxDbPZocR4nXcfw6/ietbH9jfDOOLY+oWxZeu5+tNUYWDnlc9Tl+Ofwptnl+y+JY1knm8xyrMQX9cYwPwrJ1P45/CyeRZJvEN8ZV+Utasy9/w/yK8xu/8AhDbe4C2v2ApjlmYU2+n+HLWhifUbVZD1KRn/AAp+xjvqLnex6rF8ZfhNH/pLeINTOV2jcJSSO3PSop/jn8FbNQqS6lM0gIctu4z6ZPFeU+G7r4Z6O7f2nrwnAyY1EROPbmrGr6v8NNWsnisTIx6ZWHih0ovuHM2rHet8YPhZZsLuxt9TL5JjV0ZyMd/vGuJ+K3i3TfGkNpqdnG8e4yLhxgkfLg+3/wCujwZ4v+H3g+ORZNNurp3i2mR0X+rdK57xzrGla7q6XejWr28AUjY6gc5JPA+tUqaUiU21cz7CRvtAiztHIPvWna287yeUXQnkAE8H8KxoZCHVkUHt83QVt2ZQW3nOQHRscHrU1tNi4K7FTJhaEXDIT2Qg5qlcLJJMsIVSVyDITyRinXd1Ity00UBDH7pPBU+2Ka8cKRFp7cFy2AWHOKmKaCTRUiMqTZAwuecDNRgFp2AUAnpk8VZmnLfuduQBnp0PpSQyrDG0n2fLBOS54x9K21tqRZPYXRJ4Fu8uAHIwjZravpJDEAjEHOTntXOaRKi6gHZVPPGTjFb89wVYJHEC+Mg471z11eaNaL0Ym6S3/eSMozgDA6e9Vr6KS4TzI7gnsQB1FJdXX2d9wiLMVyVboKjkvWTOZ2UBcgYqYxd7hJp9SoGcSlIwRj16Cn3Bj8osxBfGQVNMklldWlQjaepxk5qFLlptoliA2nDMBzXQk3qYt2J4rm2FqElZunzZHemxSKYBHHKEV84BzSu+It7MpXkbT2qtdlXAktsBV7HjJpJXBKxOttFBHulKnB45NRvK7gFFGA2c1WM9wgEm7PpzkU5rp2Yb1yD/ACrVLuHNpoP2xoxdycbv4e9M89DJiOIg9iSc1JgsWQrkFeD6VE20MAqgnrwaZN7IQs7sWLE+xNOVfl+UHdnHWpIIo0BYS4ZjwCKdHKkRzGwL9QTQFnuMQyLG6tEDnvSRlZHGRg9OCelDStJLvk6nrgUxlC5PfPrSsirItGSKNDbvCpwajeYkBoyTgcDJ4qEShEKtjJHWljm2KS0hB9AtLle4na4bmbOGOcVHI+Xy2DxyMmnn94w2LnPQ4xSyWUzlJAmMHBBpt2HZDI5SAFI4zkEGr7w2zRBkO4sOoNUblI7eUxjDHsVPapoI5W+ZTkdqUknqKO4+Zo0b5Hx04yKry5IYuSDnIqSaLBLT4x255NQy3MGAqxDPqaEgb0GtGZlxGc+ueBUjh0USyAjHHyiq6BnfJf8AWrDT+WGhQgqV43tQ0+glZldQsjEBST7npU0VvCiAuoKk5xnrVdtsRG4HDdTmnQPh8oCeeoHSm07B11NZTussKoGMYANRkqPusfypzxyraKSpyGBJ/OokkR5Dkkj2FTJakp6EgSIKCxY+xWkikw+WBOQcZHGajZpXJLuGAPy5pxiklQ7pNvA71Fu4w89GkAZdoxzhc0jTMsoaRyyAd17VIZUgwFdV9XABJ+lN8uGd/NLMQDyWOM07I0JIrm227xAz9cfLjH60jxKFICZbrsYc1JBcQrKqrbxgYzuA5+tLdv8AZ5/MZS6nqScgfQUvtaE/ZIYoXVS8+EAPGRUc18iRlDErHt8uP5U8SW9w+WkIIUYGOlSPbC4G5WViozuORn2o66h5FbznuELyjOBjaR/KnQFEO1mZTjpjGaVkNuxFwpDL05IxUayZYtKWJPAz2qt1dDuixM7mNSpz9a+6/wDg2jy3/BbP4I4fOJfEn/qM6rXwizsYiqsCccZNfdf/AAbOo0f/AAW0+CG/HMniTH/hM6rRDdGdRe7935jP+DmEI3/Bbz42CQ8Z8N/+o1pVfCTJuGYH4z0Nfdn/AAcyKD/wW8+Nqgc58N/+o1pVfB6zRKxVQcg81S1JZMFO0ox570RIW4ZyMDGQOtQksMltwHrjrT4gJBkEDjp60mrD6aEvkrENqSbvqakhllQEpJtB6rmq6gl/Ll5A6EU2dPLIKOSfpUtXJJ2cZPPy89qWERxsWCjB5AOapSzyImGTIPXmnS3DBAQoHHrzVJNgW3niWUgL1HOTSJKoXIQkd+apC4aRD5iHI74p0d3MgweAD1p8oF0zFR8qg8flSx3AQ7nDDuKhNxEw82N9oI5GKikvFd1G36VI9Wy2JDIxmBO33HFSM8YO0x5DD5uKrRFmTKt0681Kjq3VcevNJq4hskWwbk6H1FOKyFSFh4A7VMsqcJge2RSTguAgwD3IpJgV7kxmPzOhwM8VVjbacozFge9WzFcLGVOHDcMcU0SQqxVkAPfNX9kCKN5Xcl+GPGQKkfyJo9sqjI/iUUjqnYEEn1qOSB2BReOfWjRgSpaqU2yTMUwPY0smmRYChmKkfWq1vPJExQAuMcAmrIvfLQxzx7fb1p68wmriLpMkcRUSAqT3qFdNmhJL3HP8PFXIJoCgdiR6Nu61ORb3S/K5B7ArS5mmLVGWzujbZAWPYgVIwBUOobIHAWr32SJhukAdz1z1FNltpMACQqvrj+dO6YcxS+3hpdk0fJ7nqPrStdi3lChw2ORtHFWmsBIACyMexxWx4d+FOt+K4Xu7SewtYIv9ZPfXqxheQM4J3dfQGj3WO2ljFWaG6UABVNOkt49xEbDdjGA3Fdxp/wAEfCFvIJPEPxq0xEx80em2E85H/fSoD+dbFv8ACz9m6OJRd/FnxPJMOr23hmIr1GOGuAf73fuPTklGUNlcUWprRnl8dtIGCz2Q2dyeMVPbWsDHak65AyP8ivXbX4ffsnsgi1D4teNIwDyyeELc5/O8FI/wj/Z51i8Sz8B/FTxNeSNw4u/CcUQQevyXL54z6eue1Nwlyjd0zy8gbDHvzjjcRjms6S6n09PniRyW5r6h8Mf8E6tS+JcP2T4VfFrS7nVChZNG1u2ksrifHUR53IcEY5YdQelfP/xA+HfiLwD4rvPBPjPRJdP1TTpzHd21wmHRh2NJ0pJXexKrR5uXr/W3c5qfUo7h/NjtUiH91RxmpRcrNHnZ846g1S1CzlVzKqMBnB/CpIJFihJYncy4Bz+tCSitCmkyUJBMQHQ9MkZ4p8jqrJEQQFPQ1XtZJ5G3KY8Dq3SrF6+ZPL2cAcMTStrYYjPFv2uo2nqetIS0QCB+vQ7aCziMLtXgdAOtPaNpIkZVXg8jdVlKVhYHYytuh7dV7cUtzI+1ESUgYJ5qERyJOsgY/d9elWbtoJIQrJk5yTip+0U7smt2jkgEjMdynhac6rICFlUDH3S2Koh5pDmNiUPboRSGeVofMiKEx8YI5I9f1pWZLZajuAY9hBG0fKBUaT7XZo2+Y9Aw5qBZ3QiZTgE/MFPHNWgqSlV28YyCOxpEk8Wpvs2vCwwOAV4NS3Bd1E5jUKFIK45GKpRl2bbJkY7gjNWJ52jgAlUEHptFS43dyvtFbzLeTEgYxnupPFVp5fmzChVs/wAPQ0k1pLdTbhcL174HFPkt3jQhX5B4OePzrVKwm7joJWLK6k9c/hV1GDFpdu4Y5XP9DVaOTz02lAWHBBGBUq2rwzbmZgoAGOQMVLVxJj5AhchiBkc4GMVnTyzQlpIsop4IzndWssEr2sM1wYyuCqKepGe/+NSSaTpv2OG6dsyrJ89q2cEZ9vpz9aXOkjSKb30Mm2Z5y0Pl/KRyVUcGpWtPLUtGCSBwWGK6PWYNK1LGoeENONpGqkTWrXIZzjuBj0wfzrCuZXjjbdCWJIGTmpp1OdXtbyKnHldkRm4MYP7nZkjKg9M1HcLMoMiofqtOkgJlGEYkd8daeBJyjxFVz1zWq2M5OzK0dxI24HoDyCa6jwmfO8P6mMg/uM8D2rnnsFklBR22dyACa6PwnbyR6DqqKMs0GFx16U3bQfN7rOea2aXLuSuPf/6/1piM0B8tJCB3HTvS3VpcEeYYnbLcKoOT9KfDp94q7/IfdjKqUzmgE7ssw+WsIURgt3Jq3G0awHZJ6A4b1qlBZ34IaWKUhs4GzH9Klhtr+OTekMp6/wAJ6VnytlJomubq4t1ChxzyMnpVMPI0vmH5iR29a6Hwb8PPE3j3VTo2kwW0LhN5l1G8jtowM9N8rKuT6Zrr4f2VPiKsIuJfEPhWPccKn/CVWTknjj5JW55H5irUWo3ZLnFS3PMlYSP+8X8MVNBFht5XHoRzXcfFr9nLxx8J/Dug+JdU1vRNUi15ZPJTQ9SFy9s6AHZKABtYqwYYyrDOCcHHHxaZroG3+y5+M5HkHj6+lKPvRUlsynaLs9xjyNA6yqT7cnj3/SpFvxLGUJIAGOnBP40h0vVJyD9hmYdMhDjPT+Yx+FelfA79kn4nfHzwx428W+FL/QbG18B+Hm1rWIda1qG0nlt1dUxbxOd875boowMckZUFS5Yq8i6dKpiJqFLVvojA8Cy29lc/v5t8Uy7ZAT69Kpazp/i1PE8+l6HbXd2IAZIxDliqc88fTr7VZ8FaHeapq1rpJVE+1TrGrTTKg68nLHHY9T2r0Xx/4P1r4c6rp+saBqekzwX1mLKRv7Ut5CsgCs2VikZlHzjGRyGHeuqlVoQqKFR2ucdSnU5XKPSy+88Xl8T69bXHmO8yvExHLdCK7b4Y+GfjZ8Y9Qt/C/wAMPDuo6rfyLLILe1wd6IFLHJI6ZBP1rsfDcXwh0TTBZap8I9I8R6mLhvNvtV1u6jhlctuCrFC8e0YKjBLZOfXA91/ZL/aA+C3hD4o6NpOs+B/DnhHTEtrmP+1dI1S8jEbCDK5ae4kUBnjiDMArHOS3U0qlT30qaTuEY2dppo+Vfiz4P+OvwY1u10X4seGNS0a5uYjNaxXw/wBYgO0kde/FWdD8d63cWipcXlm+7jyri1DZA+gr1/8Abe+L/gr9pr4mad4t0pLu207SNGFqge4ikmuJGlB++CwIG4DOSMgkEg5PiSaBbW10DZOXiHKJLKm8dsHpn8KqjUpVFeTsyXGTjdbHaaFqviDXrg2lr8PEvnBIxp8MoYn8Aeef1rrYPgJ8QtX8NX/iyD4ReK9Kt9Ige4v5XtZNsibT90snOPvHHIVTWV8Hv2i/j98DdAvrL4L/ABm1fwjPd3Kfa20fUZLeW6UDgF4uQFIHBIz15xXp3gn9vj4/2k0v/C7vjf498badcx7LjQ7vxpepayrknEqtuEqkjlSuCB1qaklf3Ft5/wDAKgkmuZOx8wa3revaawV/tVukjHyzMOSMnHOOfrR4d1XU729+zLLNOAhJw5IzxycnpnFe6+Mvi1+zr4z0JdHuf2ZdFsGR2FvqFvruoSSwhgoLHdJh8BRgHgfNgcmvILeyfwncX0Hh/UiYZ5FCzKzDciksAcqOOmc46dKJuL+Emmpte8jM1z4e+KdK16fRb7SZotQt7eS6uIAwOyJclmPuB1A6Yptzout+E4rXUNd0+SK3vYhNaN5eN8fTPvWneat4nv7y61bUL6Oa9ucobieXI2MMMu0r7AjGMHkVo+JvGXijxpa2dp4q1uG8FlHsgD4xGMAY7dgB9APQVmqlS6ex0ezpezeup1fgDx14gsP7P8O2+qSW+lzyRC4C26K5V2y2T1bGfXHyivZvBPwh8I+MbsXPiXRobw/ZxHDDNDlUQM65zjknH4cDtx8y6Z4o1fStWt9QBs2FpJG6Kzn+AggH8q9Htv2yvHunxXsOm6bpdu10xYTJEoZe+AQOBnJ47nNY1XVcrrfudGHdKNFwfUZ+zB8Pv2UPHXxmu/CX7TvjDxFomlw3jiE+HoYSZkVyGUs+dnHQhT054r1D41eBf2JH8ZXfhv4E/Dm50/Qg22wvdU1c3V3KAoG95eBknnARRkHgZr5Mv7wS6+df/tSGCTz3lGD/ABMTkHj+les+Gv2kfF3hHw7pcdzLb3EceRaXMui73eME85XqBjAzzgjnuOTGYXE4qMYU6nL1+fr+h14GthaCk6kE3tf/AIF/xOe+K/gNfhvaOh1aC4tbiQ/ZI0mDOi8cMAev4DpXnZ1mCCI3CIMocrx39a7/AMR23i/4reIX8aP4L1bWYr1WzLBZmIZHAKjbwox0ArK1/wCDnjaLRhew/CjWbVY7Ym6meFmSQKCS4yo28DkZPPT0rqoxqKEYz1fc8/EOlGpJw0Xa99PU9KvU8MeIPBXhuDQ/Cvl30GmAatPGocXEzEOD8o4wpAweaxPiG+kaN8KJ9DvvDHl6i+uW9xa37R7XjjWOUMgyM4LMh44+XntjD+AHiX4o3+oLpvw/+Oi+GRFbPMhvAq/cGSAeSSe3HNWPEOpaj40vb+2+K3x1m1GW1l3mR4VYI2QPukqBkkDqOTXasXB0XQja6t8l0MpZdiqXJiZXUZXSfR2tdfifqP8AB/Rtd8d/sufCfTvDsqxf8UZC95qHm4AIDZQ4OeWP4c5pNN+HPiO98O33jLWdeifTbO5ljkmgEkjfI5ViVbacjDZAzyDgnGa/Lfwl+3N8Uvh/pcfhvRfscsenDyLSW4DkrGAVC8OBt5Jxjua3Lb/gov8AtP20BtbePRbe2/jhi08bepOcFuTlmPP94+tfHZhwtl+Y4h1qyfM/7zS+5M9zCZtjsHT9nSasn21/I/TyT4NajppsvFFvrFtdPGENtGZUG0OcAt85KqC+Txke3NeVfGSxg0r4pXq6kqlbgiVmh+aLLINvzYHXtkDp2r4o07/gqb8f4oxpeq+PLWwgDbZvs2iQSNt9PmHNYvjb/gpJ8YNavhd+HfiTOkxi8t7mexto5OvygFE4A/H8K6MDktLL17OkrR33b19XcjE42pi3zVPi+4/SH4ZfELQNN8DafpniTUlguDbmJ5Fi2ojqxwD9RgjGRzisH4kfEjwVof7TXw6mi1ZZLdNC1wXckSfwukLhQOpOV9P4hg9cfl94j/bB+P2tQBL741alsEmS0F0FIOc5+XGKxLv9oT4jzeI9N8T6t431e7u7Z99lcXEzszfdB2k9c7VHvtA6DFelGlyvW39efc5eSo7uz26a76f15n7FXfxW8N2+t2i6VfSzwzyETbLdgyR4b5gCOeQMg+3rWh4p8Wae0KarpTecpCxyPOfIGeMDJPUn9PWvxh1j4y+M9Z8QyTal401+GWR8LE93Ozp6AAnP4YqG8+LPjLTta+wJretXEgYLJbzNKHL9MENznn9an2NNta/8MU8PiFByatZ2+fa3c/ZZfhf4B8Z+Jptf8W646WSQqIo4p48A9CMn2/kfWqZ0H9kjwvq95D4q1uOC0QDy5p9ctk3HByMEdsnkkc+3Nfjj4n+MXiUXaaZdaTc2FzbL5cqvvWQtjneG79/xrCn8YC7X/iZi6fkk8rWP9mUaj57J/Jal+3qUvccmreqsfrnY/HD9nDwR9nl8O+LvDyXtrqbXMVw3iO2KvAru0cRKEc/6sE9PvHvX57/tw+NfDXjn9pDxJ4y8MzJJZalemaKSJgytnuD3FeFJ4hlSZhYWzFG5w8orX0aWy1TTvN1S5aGRHKhBgkjsf8+ld9LDU6STikvRWOOdScm7yvcbO9xGQZo2XIBBYYyOxpz3OYigYcL0zir+q/2RqHltPqbjy4UiOdv8KhR1PHAFV/7P0Nhtj1RyPXauf51vy9yLsovcMEIaQ+xHFSQ3Ugtx+8798ZxVg6TpLDIvpMHpynP/AI9Q2n2G0QLezEYzwqdOPRqTSFe5CbvdEVc44OO1P8Oaxpt5ZXkeoyltk5VMjJI2jjH1oXTtMkb97dT4YgZVVwOn+171ytrO1lqFzF9oMQExIJXNOCVxa7nQ31touo2ciaaiymIkeU+V2muf03wldTzvDFcLuUbiGIAxVvSpli1aQxylgIQHzwS2SfwqOW7uBeyXKkxiOBsjPJzWmzDVmlc/B7xFdeEJ/HEM9iLO3uUgmBugH3uOML1I98cVzv2DxDbIY7eWVlQciFyQv5Vo2uo6i8UGltcM8fl75Ii3DHsfrzXq/wALfjJ4N8BfD7xB4a8QfC231C/1RcWV3L8otwYmXcNozkMQcdOvTrXDia2Mw1NuMedtqyWlk7b3fTU+kyzBZNm2Kp0qtb6vFRfNKV2nJXeiWyeiR4Y015I22S5k/wC+jVpdL1Bwgjldi44AJovrZ1nV2jAy4B9ua9T8ZXumWXg/SrKLwdBp2r2sm+W6ij2efGV4z68+o711SnZLzPGo0IzlK2yPLzoWoxsyTpIrL1DZ4/On2/h+4nuVVg20n7wFdLca7qmqFzMIGLkEkxjjHocZA9hxVrSPEWpaVavbxWVm+5t294skcdualzsvMuGF5ql+hz2q+C7qJ4/s8LuDHklVz3NS6P4L1BJ1uLjRpZoip4Ax+PNdOPGmvRox+y2uCu0gREA/gDXT/Di18UeKtGfUo/HE1ikFwY/It9ORtvvkuvv6nilCcpM1q0adKF2eXWfg28nuprq30ySSOF9zAISFUHuR09KuW8/hxblYJtP2szDiJiP5mvYdM1O88I+H9V+HWnaiJ/td39ouL6UKjsrRruUhScDcpOM9hnpXjzeDLqfUmkilYAyEBl+tFOU5yaZnXp0owhPu/wADvfHPwZ1nw/8ADLT/ABi/h61tbO/UT29z9rVpZEJKjjrjINef+IPEGnapptjp9p4YtLKW0h8ua5gLFrk5+++e/biutv4PHVzotrpWr+KLu60yzjMcVtI4KwpyQAPqScVzUukeG4yR/alyMnk/ZDXPhqWJtfEyTkm7WulbpfXV2PUzfHZPGahlMJRpyilL2nK25KzbTS0V9uphrHkFC2B9a0tOaVIsI4dh91D6etWf7G8LDH/E6u8f9ehINS+R4biXA1ebPfbakCuqaclY8CDs9Sqomcl8gsD8wPUemKrtNLNMDIhIU8nPStRIfDPl5GvzKSOv2Y1HDpnhUyq03iOQITzi2x+ualRY5STRmTAFisE24ZzgnpRdM0Nv5ZUEgZwe9bqeF/Bo/e/8JXM+euy2zU0uh+CZCPM1S7O1edsR5/8AHTT5WSpJnMQNbSN5zKsZ4GE5FasV9LbqHiTftXIbHJHpVyHRvAUbANd3ytklT0yP++KsrZeBo12f2nfLzzyB/wCyVNSm5MqE1HU5y5vWd/tEjnnhV4zUMsi3B3bhjGBnqTW7daD4FklaSPWblQD91sE/+gimf2P4J6HX5cDqoK5/SqjTaFKomczKwjn3RjnHzKRUkJkVvMZ1G4fdHat6XQfBEnP/AAkEikdiy0NovgcAbdfk4GOXQ/0q+QXumGbhdhjd+pyTtGarPskkIAbb0AxXRf2F4NkG46/KDno0qdKkj0PwOi86y7Htm4QEfpRGNiXI5g4jXbKSFoBjc7Uk6f3h0rpJdC8DTYY6vMPX/So8fypH0HwTGuE1aU+oFxH/AIU9QujE2NHtk3g8YP5VGFWJTMq887STXQw6N4JQZbWJm4+79tQY/wDHaSTS/A8rBDezgD0vk/8AiKLMSaOaMkjOGL59aeWVj9OK6L+yfAUaBTeznnnGoR//ABuntpvw92bvtE2T/wBRCPj/AMh0ry7D5kc2rop2nB9gM02UZfKcDvz3roksvALSMPMmHPGdQTH/AKBUjWHw+K4+0S5A4J1Bf/iKLeQudWOWZtnDEYI60DeU2k9+DmupXTvh6Rhp3Pp/pw/+JoSw8AqChnHXvd/4AU7MfOc5bhWUl2ZSBwFWrMDhl2xSA4HOWwc+nNbqW3w/j4DR+265b/Gh7f4f53qISf8Ar5b/ABpOLYKRgPZSIGkuEGScqFkGRUau3AIyPb09K6qP/hAQQyJbgkd53/8AiqkL+AWX95Hbn0zcP/8AFUlFg5HKXEcGwxeWqnH4/SqIhWNyGYnHv1ruSnw7lGJUtsZ4xcScf+PUj23w3kUL5Nqcet1J/wDFU1FofOjgm3u20AgDtinKwRdw5YdOOK7sWnw2IOyC1zjn/S5P/i6VrL4akBTbWYx3+1Sf/F1VtCebU4BpJrlwkrcZ5AAFTKY4CGABXoBu5rto7L4eBjsgtMZ7Xbj/ANnpz6d8Oy24pan2+2SH/wBnpWYuboc7FKTZB0wc4xmqplk3ExED14rskXwNHbi3MVkU7D7S+fz3037J4GPCQWgA7faX/wDi6lw1uClocnCynlgCc81IqkM0gGRggkYwK6ddP8CrwLe0J/6+X/8Ai6esPgpSAtpaYxg/v3/+KpODuNSRx7Qoz7pVVu4ycc05IXuMqoxjpzwa657TwawCtaWx9vtD/wDxVC2XhCMgraW+QeP9Jf8A+KpqLDmRysFtJEGiuFKsOgqGdpMtvQBRkAD1rr5LPwsW3fZYTzyTO+f/AEKkOneFJMr/AGfB9fOfn/x6hRadyuZWOMLiNMoh64+lCyODuDMGzxsGDiux/sbwgiYNjB16Gd//AIql/svwjGvy6db5HQrO+R/49T5bsXMrHLGD9353zOAMkvxzUTFGG4xbRg5rsBYeGFjKHTbcqR/FI5P57qZ/Z3hYr8unWwyOQZ3/APiqSiw5le7OLllJcMjED9a+9P8Ag2inEn/Bbz4IRk/xeJCB/wByzqtfHraV4UzlNJts+0sn/wAXX2//AMG4NlokH/Ban4Jy2VhDHKJPEeHR2Jx/wjWq+pNUou5nOaUPu/Mwv+Dl5/8Ajd/8bYxFkk+G+f8AuWtKr4Vez+bzIbc5x02195f8HLXxCvvD/wDwWz+NmmwWMbqh8N/Mygk58NaWfT3r4U/4XHrQ4iskHpwP8KhRbX3/AJgVpLK7kU7LSVgOoC9KqvZakGKLZzDJ4+Q/59K0m+L/AIkI3CNOOwpE+L3iuVwmQCTjIPSr5WNWRShh1uA4awm/79GrMdtqskeRpM+fTyjiun0fxlqtzEpuZ3ckcksam1zxBrEdr5ttdHOO5o5EDcTmYtC1a6XL6LcfURHFOk8F6y8nlppE+exK9ao3HxA8bNIyrdsAD2WoG8WeNLlg5u5D9BQoIXoaa+EfEsZG3Spyo6/JU0ng7XpYgzaTKM9fkrKi8SeO9uyOebH/AFy/+tVy5v8Axmlilwkl0WYnOI//AK1Dih2ZcXwPr0wEcelyZI7kYH60w/D7xGZPKOnjI7+av+NZC3/xAkbAN2fpEa6Dwzb+LFxLc2d0zk8lomo5bAtwHw68Uwwg+XH1+40q/wCNL/wr3xKwyFhGOMG4QZ/WtvWrDxNd6a8lrp1ysoGAyRnOa41/DnxRZiVtdUwD12PRyaCckzak8A+JtoCrbAA4+a6Tj360J4Q12Ni0zWgwOT9pU/1rHTwh8UJfvWmo4x3Zh/WrSeA/H0lllrG5L55LPg/zo5EO8WaMHgzVLjn7baoD6zj+lOf4eXtw20alYk+gnyf5Vjn4W/EOcBo7WQg883Sj+bVseGfhz400yQyXMaKeuWvE/wAaXKlsNK5NH8N7xBunv7JVAwf3jHH6VVl8DW5lP/FS6cgB/vPg/wDjtdZL4V1q7tGhuJrf5l+UfaV5/WuHuPgz4nluWk+12SqWJBN6tCjFk2lcsjwXZRqGXxVpwI75fH/oNPbwtpJTE3jOwPHQbsD9Kpn4J69Idx1jTl/7ev8AAU7/AIUbqpUGXxJpiD1M7/0Wny+ZPKizF4c0IPtfxlYsW42JExNaf/Cv7KKAXE3iZNo5+WA/41n6P8HpNPulnl8W6acdkaQ4/wDHa6uPwrZi2+yzeLLUDbjmNz/Sp5Y31K0tc5a4sPA1ruZ/GKeYOuIsn8s1Fbp4GnGJPGBPqptxz/49Vib4K6HJOzS+NoRuOfltWP8AWnp8E/C1u2+bxsxx/csSR/6FT5YDVmtRttb/AA3srtZ7jxVIQvOwwjn/AMerq9I17wDqAFvZXUuAOGNuoz+O6ufvPhJ4Jv5BKvi+6+VQCBp49P8AerR0fwL4P0b5V8S3spz/AM+qjH/j1C0Wg3FdS/rF/wCDNETz9Q1idEOcE24wD/31WN/wmvw2GRJrszA9P3A/xrR1vw14I121WyvdVvvlOQwgT/Gsc/Cj4coCw1PUmxyPkQZquZEqKLM/jL4bwAGa+uEDDKN5Bwa2/hP8T/APhbxA97Yyz3jS9d6BFX8xWFL8N/h7exx+ZeamdnAO5OP/AB2rGjeDfh3pc5+yLqLuv3s3CAZ/744ocgSSPovw/wD8FAdE+El3b+IovACTTWpzDcfajuQ9MjC8V5p+2H+2h8Nf2t/iLb/E/V/An9hambRYb9tO5S62jAYgqMH8+4GBgDjNS0nwjqFp9ln068dM5wbpf/iaxJvB/gCJ2xodwcDvdHn9KqMpJNJ77mfsotptbbGXP4h+E7sVml1R89M7Rj/x2ruk6F8N/EcfmW51KNB03Tr83/jlXIfBXgKSMSL4ZPPZ7xv6Vp2Nr4Y0a122vhyIL3DTSH+tZ7bGtl1OU1xvhr4cm+wXVhqskmMgrcKBj/vmootf+G09qztpOrMqD5y1whx2/uiuu1C08H6qwvb3wjbOwUAN5snIAx2am21h4KSIiHwTYlXHzbnlORn3f2o0tsLlVtTmtFv/AIeavdpZWmm6lGvdnuF49+B/nFaniPTvAXhi1+2nTr6dM4+W5B5/IVqwJ4WsnD2fhCwjI6MPMP8ANjU9/faZPDtn8N2Tov8ACYC386NE7jcUlocZ/wAJf4FNi86+FLsqDj5pxnP5cVoeFNR8E6/clD4clhjVfmL3DE/zrehl0Xyg0XhfTFDc7WsUP9Ks2mpwWRP2XRNNj3ddmnx//E0WXQs5fxVrHhHw3cpHD4RW5WRCRm5bp9M1inx34URtsfw6Q5HP+nSf416Dea99pKG40yxlLN1eyiOOvqvufzqF9RU3YiTTrJCVJLCyiyf/AB2nouhnHn6mX4YTwbe2a3z+EIonLAhTPIw/HLVhv4qsU1h7K3+G9uR5m1FSWXJ9O4rsbjxBf28bPBOibR/BCo/TFS6d4w1sFg+qy53YGw4xx7UrabalaM5zwvq/9takLefwBEkQ6FBISfxJrR8Tpr9hJDb6J8MBcxSZJZrKRtvPsK1rjxVrLSqn9sT5JxzMeKik17UjI0U2pSMMZx5hptaBr0MK3g+Iovxar8JII4Sw3StpJOB9SK6TxDoXiNNI8zQvAumrcDGY5rSEA+vBAqqdWkdcS3L59Gc1E2pKbgxicnKknml1uFtLFOw0f4syXQe88NaFEjEbsra8D8WrV8V6F4subBI9Eg0aGUEF9ssABGOeP89O9V21GCPq4Oe1VxqsKOSGPTjnvS5byuFlayE0vQ/GcV6JdYOieVj5lQRZ/wDHQf51qm0lnjka21DR1byWCBrYuyvg4Odv9DWQusQRwKHmGR6t3qvpWvWkTSEzKQeBznkUnBN3KT05Ta0HSNRgglfWPE+lyTMSEVNPIULz3Cf596pXugeJP7SZ7XxhpyWxf5AunZYLn/c61XfxBZqJmZgdwAbg8Ui+NNNOUhl3EL821SadtyVay8jY1fTUu7RLfT9fSCdWB8wWIYEd+v8AjSaNpdvaSvNrHiOS5yMKqaci7eeejc96yLnxGtoDdPCwTb94Cqtt4tj1VpI7SF9y8Eniny2iPdm/JpIN8Xi8YXXlhs+X9giXA9Mg1dhlsrywuIYb25WUoUE42gqfXGK5G/8AFr6e8ivGWYcMA3SoNN8YXrwyTxQYXJOC3PFGt00Flsds8Flc2EdoNS1BGQZ82OdQT16koef8Knha2j046Z9s1JiwJ883a7x+IT+lefN441COcQFAoIxvBJ+g96deeMdXhAmh2sCp2kgj9KLMVo2O4jngsLBrBr2/nDZzLNfAuvHY7f8AOKjt76w06JdIEU86zAljcXbMw+hGD2FcYdf1C609bqRyHbIIIx27VHpmparfl5p7gl04jAO3jFHIrBHc7uym0m20s6OtorRNncJJ3LEcd93HQdKSSXRbixj0ybSoWhhOUDSyE59yWyfzri/DPiXU9M8Tw6ldW0V5DbsXeyumIjlHoxHarw+Jk8XxHHxDsvDunC2Fx5o0eZd1rtxjYQu3jv8ALj2xTUIbFpX1fU6O51vTpHjuZLK33QD92QCCuB9ajttZsMPdmC3D3CbZyUB3Dnjn6n864fVjNdasJ/NKxs+5gpwCDz2qK6jaS6W4RyyxkE7T1HWqvZWizOye6O+l1rSEjWzMdr5aZKJ5S8E5yfXu35n1qjqXiy0jaZ7e4gEk6BJcRqCy5JweORkk1xSs73MWrKWcbsgEDnPFWBY3RvpLv7O53qQML64I/wA+9JXvuEUlskdC3iaExEwmExQNwyxKRGxzznHB5P5mq9/4lkttisqR+Z+8iAhAznuMCsi38O648E9ra6bcssrAtthbBx+lSQ+HNeleH7bblDCwUGeXbgDp94in7r33CyS0LsHjG5EbZvJAYzufHGDnr/L8qjl8aXjhViu5zvQmM7+3U/y/Sqp8OToZVkurdPNAzmcHof8AZzSHRLS3CC41eElFIJQMcDn1AzxStEd5E0Xi69ZUP2yb94xUZkPJ4HPNRHxbePLuVpDyRkyH0qJk8L26JFNq0gKEkYRR19931qM6r4NtzhfOlIPB3jGfptp6ArliTxfdiMMkZO5TyWPHOKlHjLVWjMcUCjIHUk1STXtE27bbw7JLjuwY5/UVdtPEF8Mmy8M26A/89FX+uaFsUPi8WeIVjCJDHnHUg/409NX8WzRiGKLIIxkRnNOj1zXlbeZLC1GM8LjP5AU2TxTekZuvGAHHSEZH6k0J3AsLD47uVDNHMoH/AExxTzovi1wZJdSVFJyd0yD9M1mTeJrAgibWr6cn+420H8sVWl8RW+R9n0gscfeuJC1LXmA1pdJmXm78WxA5+4LhifyApiaTpjoQ2sXk59I4Wx+ZNZX9s6zJiO1ghh5/5ZQ8/rUtppXijWpBEZ7hyxxgE/yFUZmikHhyxkBltC7jlVuJxz7bV5r7E/Zfm+G2j/Cy2h8b2QvPOlklF7aQtItup2gRsMhhwpPGfx6j5f8ABnwJv7qRL3XLyG0iByTPIA5+i9TX0T8J/hV8RPEOjDwV8LvCd4La4z9o1SWMhdndlBAJ+v40Wd72Jc0rn0BZ/BLwR4r0X+3/AAfYLqVm6Eo9hcy9B32kg4x7VyuqfCfwbeK1ncxzxMBtKtckE5GCvIb6V6j8MP8AhFv2VPBi6Zqevw32orZOvkJhliaRSDubOMjPbJH1rxnxd8TrDxFqL2mku0pZuGReP0ovpcyszC1v9mv4b2dx9om8JgKGyJ47SGYj3+6prMi+BngO5WX+xr3SEnf5Wt7nSRGWHoSM16XoeoyQ2AS4kdmCcAjkVwHxi1zQLeOSS3uVt74qfLdPlIPqT6ClP3Vc1pc05qDZ4V4v/Yi1TUvipF4G0nxFbG61O1udSLRQsVt4IvmYAAZPXgAelaEn/BNa/tLnQIr/AOJzSxeIoi1jNb2LMq45w/JKcAnJGODzxis/4z/FH4iar8RtJ8QqG0y6+yzwx3OkXbYlUlSdpGCB04z17+nbfs4+OzovhXxHp/xIsvFOqXNxbNFoX2VZ5hAxiKqMq2Ewcdj06d683H42thMP7SFJzemi1ev+W7PrsiyDB5tmP1ati40Yrm96d0tFded3ay9TiP2gf2EtF/Z28KP4l1bxi+qP5ixiCEhSC3AJ+XoPT2+tYnws+HPwY13wS1v4k1GxtpriWOea8uI0862VV5jQeZvYPnP3Me9Lr/gn40+I/DjaDe+AvEt1dLOZfOl0y5kYx++VJwMfzr0b4R/smp478O6Tda1aeLoma3Ed/a6X4QurjcgdgCHERHZh1I49QRVy53Q5ql29Nr6dtjyVUnhsRJUGrWcW2k007rS6fyaE8F/CDwfqcGs/BGz8LaZc239qWN42vRWg+1OJLZ2SNOGCL9/oQSWA6E17Pf8A7JHhPxzH4X8J6kosk8C6LcWtpNFZwh5RHPJON/7seYxZ+C4YEKMglnJtw/s/+M9J8U3Vz4L+FfjCLSUjs0S5fwzc2928ce0PGzlVO3aGI+bOT0HJqef9mH49eIYZYLfwf4s8u7uhLbzfZrZXjizITHukZW5LqSxbjYMYyc+ZUp0ZcrjC7u229dXu9fyOiOY42UHHnUVyqLSsrpPmS0tdp21MTxl8H/DPiTxVe/FfxRPq1hq8KtqVlqvmp5UFxH+8hjUKgCAFRgc4KjPXNfOOieO9WHjQfGTWfFmr6V4ouriXULTW/D6JaywzK+PNQRBNnQ8r2HTnNfV2mfsjfFCS8s9O1b4M+Ir54JYpEhur62RJ4EgAKvvnPJd1Y85whHXfh037Aniy+8F6V4Y1f4Kk6lZQeXJeT+JbeFZiSTysbPgc/dye/PNb4VUqW1NNJcqdtk+iRniq+Pkkp1ndtTabvra177XPiOw1LS/Ffx1ttc+I942qWeuXYn1G9vYEaWcuDlyBkbi3p1PfvXtni3w/+yHfWb6B8MtKsV1uAl7pb+wmXZEqlm2jZy2FPXgEjgkgVu/Fv/gmH+0avjHTtV0uw8P6UqxKLW2TWWmK7WyDlYvXnqfw6Vyvib9jj46eAvEza9rfjSyXUb2R7edNPDMYgwOQQ6KAozj8RXVWXNTUabUZLRLZGeDdKeMTxjcot3bWrd+19zL8VeIf2YtR0qTQPDfh2SKR4hDOy2J3CUMPutwAMA/NyOnynPGtY/Aj9lfVLX7PF4i1CC8KK6I7EKRzuyACeMEdOw45rhrr9mPXZdK1/XLbxwszaIskl1bm2KmdU6kc5APPUA+3XGj4C1TwxFdRW2h6F5UBtcXF7JC24sOMK5PIPXGCPcVrT5pwVpXs1tr63HiadLB4iUakGrp2Uk1a60dl1V76nWzfsj/BDUxix+IRXjA8yRk/V4wP1qu37CHhO8RjovxCtZAvJD6nb57AdSPWrarYTMVg1J047nFL9ivQoa31Pf3GWzXTdvRHj2aZgXn7APihiZNL1gyqP+eQSX/0Bqz5P2GfirZSebZW8kw2nANk3T8DXXvNrUKYKq2OCQM05PFWvWTDZc3SEHgpOwxzn1460X8g9655ve/sf/FbTM+bZgDfu+aBxg8+3+eK81+LXwy17wFqVtpetRRrNcxGRByMKGxk5x6GvqrS/jh8RNIULY+ONVjGeE+1MRj6GvCv2x9Q1nxPf2njq+mkuZ5A0V1M54HyrtHtwp4+tDklJWRcE23zM8fmZYdZdoXBBQbipyCcZqbWbq3u7WCG00iO1aO2ZJpkYk3B5O4+h6Vi2EryxSyFvmUDBFaKRTNCZZ5t4KfLwOmKcoJtSfQ3p1pwpyhG1pJJ6J7NPRvVEOm3RtNZjncEqoG4eo5rorrUTNqe9UUIV2hGfA9j7VzphAu194smrcd/ZKQWmJwMHNWYLRlzxkmkwaYlxHMjO8i7kRs+5qz4i8S6lqPh+y1S7uZnRl8qJnwdoVQAPyH6VnpZabrrfZbOxurlhhitumT+ldj4Y+Cnxk8b2C6R4N+EfiPUoLZwwS20yR9hx6hfes3BNJs1hUcL27HCWniVbZ1Y5kUcEEAHNdz4b+PnhPR4xFfeFBORFsQ+RCNpyTnJBz1PXNct448Gap8Nddfw9488E3mk6gi7pLTUImikUEkZKsAeoP5Vkq+l3jf6LZxjHXJpuKcbMOeae565qH7U3hqZTajwHbCPyxtWEInzAdSwVj6cDH4V5ZqfjWTUtRmng3wpLM0nkxSsoBJJ6DA4qL7PEjBxbW4/3itLca1YaZKIzp9u7AfwxAilyRTukN1ZuNjc+GF/qus69Jo1sjOs8TnByxztP+fxrs/DOvaNpnh5Z9ZgAuCWO0Dqcnke3vXMeEf2ide8HQfZtHsrWP5gVb7DGW/MjP61F4z+PviTxvbiLWo1ZQTtWNQoB9eBUtT5tFoCkmveZ3Oi+KfD2t28kMskcMjceW74z+NZd3p+iupKa1KpOSgyp/8A1/WvOvDEt14n8RWnh6yPlveXCxIxOQCxr2lP2T/i0qLIugXFyVUAeXG54/AGlOEo2lcy54t2OM0nRrzVIJp01pEVHZVDoDnFV722uraR4E1WN3QZOUrt7r9m/wCOmn27W9p4DvhExy5W0lyf/HawNQ/Z++LWnTNNeeD9QXIwT5bAEe2QKlc7d09B80UYekRX2ruUS5hDAMdpXPSnXVp4gsIPtbW1swXsycH9ami+HXj7SZHZPD91E2MA7evNVrzwv452Yn0+9Izwqpn+VW+e++gJx7Fq78Q6pZWEUq6da7nXLgw8de2KqS+MNSjTKWtoeOgiPH60MniaOAJd6HdsoXGTbk1Tnj1AIQdHkjBH3jC2c/lQ27grGhpXibUL+eSK4trdQke4Dy+/4moZfFskYJbTLZjk8COsiEXdtI0u8jcCpBU8ZpqboyWdwc9Mn+VU7onTmNJPG4aQJN4ehIPHy4BP6VPBrFtcW0tydChUxtjbtGT+QrGIiyXKA5yMNVnTXWLT7m3Yjc7Ax8/Wi9rj0diw/iGwIydEiznGMD/Cozr+mhNzaKvPqo4/Ss9YJVbErKp68nvSSQgkDeGPqKE3YNDUsrjTNQmKDSY1O0sM+gpr3mjqSG0leD3FVNEaK31E/aEKgxsBn1wcUy48wXDskXyluM023ZBHlu7lt73RBydGGD3X/wDXTJL/AEALuGkYPpz/AI1FFA8wJhSRSvJC9BVeZWDEMQpzn8aSk2FkX7M6FcxvLHpAIQ8kZ/xprXPh5Xw2mgevBPP51DpswWCWJWBJHJ6eo/rVZlEbnb15yabkLlL5ufDi9dPHPYhv8aPt3hdG/eWCqR2Ib/GqTRGSMB+D7VHc2oX5iRz1wtLnHZGisvhdyCbJSpPy8NUzt4ZtcCaxVSegYNWBG6RzAbR1xj0rS1hFEiMwOCvGO3em2ydLl0XXhPbv+yKuO4V6eLvwbG+5o0Gf7wasSOQDg560ZgmlIkiB44HI5pXZSSsb6XXg9lLrHEB64amveeEGKtF5Rz16iueR7aOYRyRsQOqk8UtwluT5lvFtUnhT2NHNrYLI6a3i8JGPfGqAZ+bqf1p6jweTkyQ5H+1/9asS0j26QSw5yTz/AJ9qzmLbcFu/enezElc60w+DWBxcRLnpmQf4ULZeDmzsuoie/wC8X/CuQdXKgg7ielKN1uqmRchuc44o5mPlR2H9m+EHzieMZ9JV/wAKU6P4WIyJUOfR0/w965RHRsjPHb2qGVAjkpKSv1pKTEonWS6L4anynGN2Mqy/4VKnhvw8EAQKQOgLL/UVxtuRLcJCFwGNS38iwgKCM+wqruwuXU67/hGNBZSAi89vl4pG8JaGw3AJ0/ur/jXFJcM2TuII71PBNIFLCXntlqTk0O1+p1reD9GZcbVB9RGP8aQeDNIx8qpj1MPX/wAermFluWXes0i/8DOP506W4vVUOl3IOwxMRUqeuo+R9zov+EI0knIK+48of/FUj+DNJzwqH/tjj+tYtnqEs8RQXEpdQcnzTzUdxqd5HHmO7lzn/noc0+bUfK+5sy+D9LiG5ljAPTKEf1qJ/A+nzSZS4UYPKhTWKus6oAN19IwP3c9Kcut62X/dXzADr0P9Kaldk8rNv/hCLJOGEf1JakfwXYdF8sfi3+FYTeI9bjbH258/Qf4U1/Fuso2BfHPuoqroVmbjeC7PGMxE+oZ/8Kb/AMIdZ44aPp13N/hWV/wlmrBebzJ91H+FNPi/V9w3Sr0/uilzBytmqfBcBBVNg9zI3+Ffcf8AwbZ+G4tO/wCC13wTvI9uVk8RjhyevhrVfavgyLxVrL/Mjp17pX3X/wAG1+sapcf8FtvgfbXZQozeJCcLyMeGdVpqSuTNPl07r80av/ByHp3hab/gtT8aZ9T0YTS58ObpDIRn/im9Lx39K+Il0/wRnA8LRHjjLmvtr/g5LlC/8FqvjUjdN3hz/wBRvS6+HVZAOD3qafw/f+ZV2WvI8JoPk8J2uPU5OKcG8OQttj8MWOfUxVUaUD+LGepzVaCeIzFfMyRyQTVhqzej1+3t1xFo1mmP4VgFPbxLK52iytgPTyQcVlRzIVy2MetNadAd2Rj09aBGn/b86N8tnaDnqLVP8KkTxPqSNhYoUBHaID+lYonBbBIB780/7SmeWPTkUtEgNdfFOsAblucYHZQKJPF2tlSXvyB34FY5uUHaiS6iAbAzgHgUmkO7NJPGGq3TFYtQl64PzU8+JNa3bv7SlJ9mNYNnfKzEPFt5/OrLXkZVvXHai2oXZfTxNqty7AX0uVPUuRmlOq37HMl7KTjj5zWPb3X70459CanN4mMk8/WnboGrL0t7dyPl7h/++jUFxfTq4U3LjJ6Z4NV3vg33lI/pVe7vxuUh8UviFbU0Y7yWSVFa5kGT2PFagnC4Bc7q5dNUQupMnTpitKPVoQQzSrkA/wAVEth2ZoyysXxvxj61WuZZkYCIrz1JOKz5NaQSM6yjHcBqrz6tDIwZrrAB5GaOUFubsbZGGP45qOKaUztG8uVxgDuKyv8AhIoM483OBxgZqFdWQS+cu48cnaaTt0HynRec0SAq2cdRS/aDOoZxjPrWAmvK58tfMOegxUc/inyCUeOTIHPShK5R0kblOQMehHNe0fsX/wDBPz9p3/goH42vPAH7M3g2DVLrT445NUmu9UhtYrSN2ZVd2kYEj5XOFDHCnivnbw/rV/4i1aLR9Is3eeeRY4wzADJOBXu9r8MP2lv2Vrqx+K3hj4tt4S1KILPa6l4c8RzQXVu+GCsrwlWRwHYAg5G5hxk1lOrCD5W7N7f1qNOMWpTWm39dznfjp8CfiR+zP8S9W+DPxc0U6Z4i0G6NvqliZQ5ikHOMjg9RyK477SuMlweexrRuRc+Mbi68QeJPiKbq5kkaa6vL2TfJI7NlmLSOGYkkknnJNc7ex6UxH9h+IjckHEu+1KAc9vmOe9OD92zd2EnGUrxVl2L5uYlYZOMetXdCtE8RalFo8V7bQNMdoku5hHGPqx4Fclqkl5ZWguRcKfmxtCYqpo+v2b6/bReIryeDT2lH2mS2jDSBO+3NXPSN0EHHnXMrr1sev/G/4XaL8DvG9z8PtI+K3hrxlHbRo/8AbfhK5lmspCwJwjSpG5wMZ3IpBOCAQRXDWV8kd1OQgAJGD6/5zXvGjXv/AASh0nwoJNZ1z4ranqxjBKW9tbpCG7jJ2n15x+FeWfGTxP8As5eItSih+AvhTxFpUIH75tZvVkL49ApOPzrmw+IdbTla82rfqTOyqPlWmvW9l69TCbVY16nJx2rLvdYiWdzlfmwOpp9hJpdhN9p1DTmvIt2DE120eSenIrC8T3EcuovcWmmm0Q/cgMpfaPqetdUdydUdFa6nEsaZfoOcZxT7nWIniIUj6GuI06O9lmchWk44BJOK6DTrVVtSby2Af0K4p/aKexqHW4EtwjTJnHOTUaa/aJBtedBxxlsCqmsaPcXapcaaxyigNHsPPHUHGKy9R0zU7RFF+wBbkDI/pR9oV76G9D4js2IhjuFdvQHJpbjXE+zt8sigjG7af8K5zQLXytXWVm/hIBP0reu4bafS2SXUVt/3n3nOBR9oNOUdH4kijiEbByQO6kVHL4vigBDiQY7FazjbaPCPMGvxzbeoTmq+o/ZruQyQAbCoA2jjOOf1pLcbta5qDxNHeKlzEWCBuCxAyR+NEnidEmWZQWIB6ODWXFcaFp1pHDqqzHGWVYgO/wBajfxP4SiYJDo902f7zj+lOzbFzGrf+Ibk2vnNECJBgfN9ap6b4ukEjQyRkDj5utT6jaK9jCxiEZbLCJWDAKenI79ar2MsOlgztYiXe2NzRbgtTzRlG5Si1KxrWmrLqAPk3fzpyQ0eP61X1fU76ziSWKb53zuGOwrOt9avbjVJEmtY4gCRiNMVavd1zDEuOAp/macU9iZFfTdY1e5eR7mZjGuMlQBj9K1ZRO8M0tqGZl2hV3H3J/lWDexX9t5YtpWSJyfMUD73pWpLb6hcaTNDYtKJCyEOg9xxQ+VPUe60MyaW/ku0i8+RRvGRvPHNdBe21r9kle1kgTa64MmADnPeqlp4b1a7YfZ9Lnl91hY1dX4e+JtaU6de2FxboTu8yaMgDH1pTaSuNJt2W5gzrKS8ZuUfnjyyMAVLp9oIrMK18IxuZtrAnOBzWvo/wy1u1kc3E1rEDgZe6T9cE+v6VPqHgFf3ayeIrNCucqm9v5LRdcotVqZM+swalbywxMpZtozGuBxVCC4t9IbzroSASEACMdSDnBrqdM8B+GrHIn8QSSE/88LTjOfcirNx4X8GSKPtK3spU8fvET9MGhWtYHvcxLu4jvLHYiAblBOR6f8A66g00xaWJGe3UmTrz711UcHg+zj2DSVYAcGe5J/liopta8IWygnTNMRgerMW9OzEine6C6OYu2i1CV3TTvLZidz7+X/CmKLi2Rba3t3csGyioTnNdNJ8RNCtsCCS0UDoILNeOvovvVa4+KzsNljPdEekabR/SiztYPtGePCnihrb7VYaFcvIOVP2ZiO+eoqzB4K8U3USm+tVRujGaRU/HBIP6VHP8QNUuRvTT7iQjvLLUY8ReKLk/wCj6ZEu4853N/hT94XumhY+BtXWIrqmr2Ywx2l7jfgH/dBqwPBVukBhm1yHYx+YxRMcj/gQArHL+NJwS9wsQ7ERgfzpjaZr0oxc686jPQSYH6Ukn3G3Y3U8L+FDA8cmr3Em5dr7bdV4+pY/yqL+y/ANja/Y5JJZABgedeoP/Zf84rITw1bSgtdanK/HJyT/AFoGk+G7P55sEjuzgU+VDNaTX/AdmxRbG2kAGBvlkc/oajPj3w9CT/Z2iwkAYbFnu46dXz71lS33heyAIEXXoCT/ACqGbxZo9s2IbPPYYiA/nRyoDX/4TyZuLHQnHJ/1cEafyFJJ418XTKRDp8qjGMNOR+lY0njSTzBHb2eM9ctVaTxNqkszRlEVccHHOfzosrk7I1JtU8aTsWYQRknne5P8zVS4HiJ8ifWI0B7Iv/1qypNZ1SeJnknwd3VAB61Vmurx0RpJnOB82WNUJbmnNbPnN1r0zZ9OKhkj0NMGaeSU443SZBrODs5K/wB7HNPijMjANggdcmgd9LFuO50mMgw6avJ4LDr+ZpX1YYDRWkaAk/dA7fSq3luzKoU4XoaU2UzoEVSCTxSug8iQ67qLEBJMA56D/GgXV65zJcPyOQDipLLw5qc7BLe3Zz0+UV0/hf4EfEzxhcpaaF4bubhnPy+VCzZ/AChasG0kcokTbv3pZlPHJqaC1Vm2qOBX0Z4P/wCCc3xaubKPUfHU1vodqTzJqM6wsP8AgJO79K9L8G/sa/s6+HozFqXjLVda1EDAi0rSDLFu6AFmYcZ9P607NavQzdSLPkHQfCmp61MILTT5nz/EkJP9K7DT/gn4githeajo80MR+680ZXPAPHHPBB/EV9o+HPg1qPg+ANZaXoeg2f8AFLrNgizEADkK7sxyc8D+mTqa18cvh94VsTYeMvHN3rrQZVLWBRbwcYwOMsRkeg44rNSuk0Pmd9EfIXgr4PrrV0trpPhm8vpdwAbbsQHPqRX0D4M/Ym8aWNhHfeMNY0vw9ZyR792VMhU/7TDk/wC6CaTXf22Z9MjOn/DzQbbS4cbVNrAEbj/aJLGvO9e+LvxU8fXLPc6ncyBuwYkH8TV3tsLVs91svDv7KvwVxeTXU/iPUYX3F5gBGSB6sNxHsAtZvi39sDxBq0LaP4E04WFoUCCCzj8tSOvJHLc+prx3S/BetXwF3qsrANzye9dDYJonheHz5jEGUDLlu/40mpS3FaKZpxWni/xrcC88Qao6RscshyM59a37K68L+DbXdJKiFRgyOwOTXl3iP48pbs9noUDMw480vx+FcRqXjzWdWuDcahds5J+4CcUbPQfLzHs/in41syNa+GwCcfNKV4/DivPdXu5tfmkTUG89pgVcOOCDXO291q2oqJATGmOOcVoWdvf8RxApj78mOfwqbN7lK0diLw941g/Z4+JXhv4vazJdarp+i3cgNkdhZWeJwpUnGcHB5OflHIrv/gV/wVy1T4c+LvEGqeN9KvtS0rXLnzvs0CqJLdwcApkgINgVSo+U7RxkZryX44W9tJ8OpVmDMLe4SVyDyQDj+teBNf6GciOK4UZ6bh/PFKdGNVpy3R10MXWpQlCNmpaO6v8Ad2P0N+IX/BaT4XeIbZE0f4b+ItyxlJPtE0K7wexwT/LNbvw7/wCC2I8OeGLLTfBH7M+oTW9rAx3/ANpIodV6sdsR4GOeT068V+ZNw8DTMLZ2EeeC4yRXsHwf+IWm+HvA0WkT3FtDumbzQW+eUbiQW9FHp3P6S8PBrW5mpy2PuTW/+C4vxp1EyDT/ANl+0W2EGXS6vyxBO4A5EY68fLgng+vGNf8A/Baf9pqPTpox+zj4dhMMYjlnu71ysbMOBhsAN7ckV8rTfGjRJERn8QQRNyUkBH7sA/fx3kPbPA/CsLxB8ZvClzbJaQ3sZRgdsboZFiB6s/HzyN3PQVn9XpvS2nqJylv1PqHU/wDgrf8Atl6uIhZfCvwpYmOTyDK8EpYMcfezJwOepH41z9//AMFKv25tVuhdtZ+G4Hz5fFlISuPrJyOevSvnSX4yeFzGqNqEhydkmxT8oGcOufXuD6/lUn+N2hRqDHJMzbzkRxkBCOkiE9M916U/YQi7qI+eVrXPcvFn7eP7cGu3EOo6h4y0qKSFSYlttPQgDpkZBz9RyK5TXv2nf2rvF+nvper/ABKhjScBi9vp0KuR6q4XP4A15PffGywuAFjspshixVQAFb++v93PcdKpt8apFKGHQ0kwxLiV/lY+uB0/A0/Yx191DjVlFrU7nTLz4n6RfJLrXxBvJLeaRYr61LnbPEWyd/ZhnnB6Gux/4TPRoEEfnIAowAnYfSvD7L4meJNcvobFbdVXlWKqclc559cc8+9dFGb6QY2HHpmtKdJU42ikvQMViquJnz1JOT2u22/TU+ix+0z8KtV8MDQfGHwd0ua5jRlg1bRZmspxx8u4KCj4Pcr0OO1eeXHxQ06O6lmsWdIix2RyPkqueAT37c15qbXU3AURt9KbLp+og/NC3ze1UoWfqc2jPUbb4y2sTZkbpxndVkfGnRJci4jBBPXFeSwWupW9wsvkhwvRHQEH612dnc/DrxNYppviPw6+kXKrxf2ALKT/ALSHP14zTaVitDpZvi34ZlXLxn6Ba434v+LNC8VeDbjTdOz5xKtGCOpH/wBY1neLfAY0Jw+ieL7TVI2/54rIjL16hlA9Oh71hHRdWkf5lxn1pcqsCdnc8/ht7zToJzPAy7kwpIzzmnaNeyiSZZ5CF8k7QfWu/PhDUpk8t4EbPY//AKqjm+GV7cRFFt40zxleKsLs5JJg1xDgjJtOfw5rIm2l2wf4q7s/BfXGKvbX6jauAG54+tQN8CvEzEsL6Dk/wg0D5iL4SfESL4a6hJrUVk080kRjADYAXIP55FeiW/7YOr2hEkOmTxsP4lmIJ/GuGT4G68EAbUE6cBFNOHwQ1LO2XUT70Np7klf4y/Eib4wa9B4i1GKVLmO3ELyO24uoPHp0HFcW+n3BYiEZHZiQK78fBRlGXvW/lTk+DtuDtkuZD6gUlypWRTk5PU4WXRs2Ec0cp88s3mIxGAOMYP5/lUE+l3Nvb/aJGTAOMB8mvRU+E+mqMsznH+1U0fwo0LGGDsf98000SeW8Bc4OfTFTSvEsChTye1eoD4UeHxjdC35mnr8KvDjPta2PBxwx/rU8yHdnF/CC2e6+IemGNT+6uRIxUE4A719TR64xA8u+boRySfUn9c/nXkHh/wCHljoN4mo6UrQyjuPT3xXa6fqcsIEdz8x7MDyfzqnJNJEON58x2EWo6y+JrR52HZlzx07/AIj8xViHxZ43sWBh1S9jwO0hHvVbwz8YPFXhllOj+LtRtdvAQOWX8uldxpf7WPxLIVNS1HRNYhIH7rV9Ehk9eMhQf/1Vn01Qe8tzkn+Ivj3BDeJtQPH3TeSf40j/ABA8YyNiXXbuTHZ5dwH516Gvx68GasBF4s/Zs8K3rP8Ael0ovaMff5SadBefs1a+uNS+Ffi3RXzydLuVuVH08wA+lPm6WJPOT458QumJjaS4HP2jT4HJ/Epn/wDVUUniq5uMmfQtFfA6NodsD+JCCu28Q+DvgTjPhvxX4pViAxS90JG7gYyHGMDPbsetcRqvh24hm3aS01xHj781r5R/LJ/yKzi4Slt+Bo7rqRG/0C4bdf8AgHQZgR2sjHnn/YI7Z/zxVKbSvANy+65+GukDBzuhlnB/WQirMfhXxDID5duFK8kmTFPl8Ga5CnmSOnI/hbNaqKZPMjEufAfwfuzm48DSxNkY8m+wO/8AejbFVbv4T/BS54Oh6rAe5jvY2x/5CWtO8tjbNsu7vB7AxH/CoWuLFE2vcs3siHip5F0Y1KXUw5vgd8H5B5ltqesRHtm3R/5SCqd38AvApYPY+K7tSP8AnpY4x+UjV0UktsT8nmFccnaKia4hVtypLn/aIFO3Yd2jmbv9nnSnG628e2w/u+dFKP1CGs+5/Z/u43C23jHTJAexmZf/AEJVrs5LuPfzaSE9m39vyqM3UaKdsLHI/iOf6UuR3GpvqcNP8BPGdurfZNcsdp7DUYst09WHYj86z7z4GfERZMQ2lvcrj70c6t/6Cxr0KS4gLAG0UD1KmmedaxqQbWEA46Kf8aFFpBzXPObj4RfEazjJ/wCERuCMdY4WOfxxVC48B+O4AftPgu8HHVojivVRexoB5UcYYcgquMU86/qCEGK7kXjgq5oUJLcOdM8am0HxAg8t9HlRl6KV5qhcadrKnE9o4+gr3I+J9ZY7Bqt0OehlJqtPq97OGEmoTEk95KfLYakmeJLpbIN86yKe2F71dMq3CKsm4FeBXq0jliTJLv4P+sUH+YqtcaZp1z/rLKJi3fYB/LFHK2hXV7Hl0iRKciNhjqcU0FQu9Y2Jz3HNekvoGjhsHSoev+0P61DN4X0lzldLjGRnMbsP6mk4j9089+x312QUtGHuVqWHQNWILGFQM/xtg1258KaYB8izr3O256f+O0weF7fZiK7uEB77VapanbQLpnKHQtUSFURoxjqu+om8KalIf3kKYx1LV09x4blVwsWrT+5aFf8AGo/7Auh01ZOOzI39BScagrpHOJ4a1KF8BUOD2bpVXUdH1IDe8GVHU5rqptF1HPGoW5wcbcN/UVG+iaqDtC2pye84GfzNLlmh+6cfFBNGmSn51FcDyzhmGfauovvCWqzgsYOvTyiGH6VnT+DNXQ5ktXyD1MRFWrsLxXUw0WXf5kanAPpTbuWSYgkEH6VtR6JqtvlXgYD02nNNbTpkG2SD3B25qrsaVjIhR0OSuAeORViNtqMAg59Tir32DpuI688Ypf7Mt1U+Yx56c0mwsinBdSZMe7GT60XU4I2Bvfipzpke7fGueM8nmoJ9NnLb0jAz6tU6cw1cgRyjfKT78VKJipO08H35qWTTkEQCM7OeqhKiEMyEmSLG08nNU7XEtiGSQsBubAzzUkDrEjluOOOPerMltYXNurR5SUn5jniqkts6nZ5w+maFoGjRWubgvKXj4FV5ZGY5x065q6dNiIyJjn3FI+kvJyJAD6mq5kTqVUlJxnGO1Sby4yVH5VY/saSOMPI456ADrTTaxD5EbkdakqOwWsnlocPyTwK+9f8Ag2ckMn/Bbn4Hkt0fxJx/3LOrV8FNbmLvye1fd3/Bstx/wXA+B4z/AB+JeP8AuWdVprcmXwGt/wAHK18kH/BbT41xmQDB8OH/AMtrS6+FjrVvyPPWvtD/AIOcQf8Ah998bsD/AKFv/wBRrSq+CbBHSf5gTz37U4L3fv8AzHE6ZtWiZSPM9iRVdNUtoZS4br7VNYyWltb/AOpBLdd1VtUu4LxAIbZAc8lRSUmVypFuLVmmOIo3bPoKkkkv44/NezkA/AVVs38uEbW2kCpru5mksSrTFjjgelU79COhEupzTkiOBs59atxWerSYdrdUUj7zPWat5NHD5dqm045YDJNRLe6rFIJGeVhnkCl7xXu3NW+i1Czi80bHX/YOcVmz6xdIxDgcVoSzX89r5tvcOVYcp3FZE8TEnI5oV+onboMj1+6E/lLGoz1NaVtc3U7HzJsDPas2y0prq4yMA/7XFX4o1tpDA2D645xT6h9kvrZBLN7oXT5A46VVnaSCAT/a2YE9zVuCWOSBoWl2A8ZNJNFp8UQjbUI2I7dKl36jjYo29xA7hruWcI38QBpL+4sd+y28wj/aY1pW+oaSlu0N8AIgOD6fSqpuPCaFmjeV1x/e6fpQlfUbbWhmuq+X+6jI+tVWjmedcOcbuma0Zp7O44sFIX1JzTI7bLjbkHOaroR1Nu2aKGykUKpYIMcc5rGa7ulYJJEFRmxv2E1r6e0MQaSV1yvTceKnm8SsImjt/IDKuQ7KBUvfUpXvoZd1o2rW21oQJVflXiXOaSPRvEUihltZ8HriP/61VtQ+IHiaGTyI542A/iUGoIPGHie7kAn1AohPOKfKhfEaBsNWsJRJdI6kfdEnGaq3cTyhiwGeScU6e7muQBPdlyelTeViPOM8elCabF0HeG/KtC4niRkYfMGOD+daMtho1z80GtYJ5PmTE49hWHe2iNEHkD/7IU96piO5M6yvAQqnOAOtJx1Y9HY6G4tNJnbZJfDcvRlUtn+VJYXdlYXQjti0ik/MSMYrElS78xZLeIopOcAVbtJJLaRSw3t6kdKTTsUmmzd18CXT0YIBkk7Qc1hCCJJBNNHlE5IrUa5kvY1UHj028im/2Xc3AZEt3bcDjamacL8pL0ZVfxDoMCiaPR2ZTwuT/wDXp9prkN1P/o9ksQ9B1pn/AAhPiO9AMOjXbKOm22bH8q0tO+HviVAGGmuhz/y0KoP1NPRMd0Je6m9jpMt3DCrsh4V1yKzRqFxrcYu7qNVcnBCriujk8Da3JZvbXElsm89DdL/TNMs/BEVnGIbjW7KPB5AdnP6LTuibdzm5r7UtIAewl8vzGIY1b0nULy6haS6lLkiuin8G+G5ogJ/EgYg5+S0J/mRUln4b8G2ilWvr6bv8iKn+NLmQ4nI+JbzU11JJba6fygikxByAadZXV/qMha7iYH3YmuunsPBrnzP7KupQB0luQB+gqS2l8O2R/wBG8LwZ7GSaRz+rUcwRvY5dLYxsrjOQfSo9btZdR002rLhhJkZ4rs011U4g0XT4yDnItFOPzqR/FmqRg7L6OEekcarj8qE2mFr6nnmneFNYLMLTTZpNwHEcTHPcdBXQad4D8UPbqv8AYlyntLFt/nWtdeN7nBFz4lbA7faMf1rOufGumIx8zWtx9FYmj3gvdEl78LPEd1jzYoI8L0e7jHfp97/OKr/8KiuB891qVhGQOQ1yT/6CppkfxB0SBi2ZpG6AiOmSfEa1/wCXfSp2x03YApLmHpY1NM8BaXYRA3viSMDOSsELNx9WAqwdE8JQKkL6rdzKT8yeQoDH/vquam8f6m42W+iKuezyZqs2ueJZwGSOCLHTjOKOVsE7nYNpHgwXJvDZ3krM2fnmRR+SqKsifw3BGiQ+GIML0MkrE/jz7Vw2fFFwfm1BxkZxGgFOPh/X7lQZby4Yd9zkUcuwr22O0l17ToFx/Z2mxben+jKSPzzUUvxAhtAUGuwxA/wwbV/RRXJr4Ow26dh773qeHwlp8Qy8iZ74GadlYPtGte/E2zkY+ZrFzKfQbjmqUnxAt3YmKzupPdh/iaSPQdGTDEs3p8oUVKtppkQ+W1yB6tRZNBzFd/HupNGUtdGIz/ek/wABVZtd8T3jFktYU3f7JJrQa80u2PKwR49SP61DL4p0tMrDdocf881yf0FF0tiiKOLxteIG+0Oq/wDTOH/61INB8Qzki71WYA9jNii58fWqrsQSvg8DOKov45mbKw2QHGQS2aPeAvr4PiZs3V6Gz/ecmp4/CmjwEs0m71wnSsKfxZqjIdrKh9hn+dU59d1iZyst64U9gcfyp6kx3Ou/s3RbdQzRcepYCmNe6BZHhbcep35zXIPJcXEZzK59dzVC0bgiMrxnpR0C1tTsH8X6XATHGw46BIv61BdeO41H7i3kbjqzYH9awpYiJVIA4HNOa2VHVcHHv3pe6H2TWm8XX7KPKt0BI54yRVWfxBrLuQLgqCcZAxmmC2MYMjg7iOM8VE7qMoFHPXvUjuiGa+1W4k8yW8cjsC9MaN2PnOpOD3q3FBEwJcdRipgI2tzEqHBIwcVV7aCs7FKOJp08xx0HYdKRI2aMyt2yf1q+kUsZUNGQueflq22lWiqI4VlfeOPl6flS5rFNGY0QUeYDn5RzU9tIgXaWPI71ox+DdbuQHhteG4APBNbGn/B3xRqrxCzsmVtuJNqsxY+vTA/OldCtpc5Zo7aOY4UYIpDol1dqzQqNuOw5r2fwp+x74u1vEt5plwqlQ5eUhFI9RnGfwr0fQP2WvAPg9VPifxOZl2AtDpdq8rg56EsAOnv3qrT5bolyUdz5Yg8DapKg8uEsccZ4/nWlpHwo8WapKIrSwLc4G1SSfyFfVtt4M+HkOoR23gP4Xatq0zL8gu4XkLkdwqAfkc16NZ/Dv4x6LZJeazZeHfA1ht+aXUPs8UoAP9xd0pyT6de9S3ZXbJ5ruyR8d2v7N3jeytftmseHrqJFG7M0ezcPYHk/hXWeBvgx8O96y+Kr26jz1jtdPeRh7cgD9a+j7u9/Zv0lXl8ZfFHXfFl+AcwaNbLa25bjA8yUFsZz/DVCT9ojwb4ZaW3+F3wN0i0YBhHe6oDeTlSMZPmkpkjqAuOTQpRavYHKV7Gd8Ofhp8M4ZY7fwL8Eda8Qz4BE97ZOiqRzngYA479q9g0vxB4+8Jad9mvdW8JeCrQ5LwWoWW5Axn7kOcH645rwXxf+0h8avGMD2+pa+8Fq5OLSE+XEg9Ai4UDt0rhb7U9e1SRm1LW3mLsSMyk5NVz2bZHJzLVH0R42+PPwrsZVm1bXNS8U3SFTm/nMMOQOgjUsxHbkjpXnnij9rzxKY/7P8FaVb6TAFAC2EIiYgEnluWPJzya8yttMEjh58Bc8sDk49a3NPbwlp8YWSFZmzn5x/LIpK73ZShFbIztY8a/EnxjcM1xf3Lhz8w3E1Hpvw61/U5PM1C42jqckk1vP450y0XbZaZtweNo/wo/4Tm4lwIbZgD1cmmrDfN0JNN+HGkaWvmXm2Q/7WetX7nVNC0KEbYY0Cjg8ZrGu9R1DUV2PqMkQPIVE5/PP1qi3hxJzumvJZHPHzMKHUjYOSSepLrfxOuJDt0eykY9mYYFclq194k1mRpb+5dsnHljoM+1dU/hGOOINJcY5OBuGfyqNvDljAxZbuXI9GA5/Kp5rvUpROMj0XVJiI0g2gH7xrU07wnd2zCSGFp5R2JG0fnW2ba3tXysm4j7wY549Ka/iiW2BSJEUYxQpJBZsWz0DWmAN2sYH9xVxVuTTru2hJkkQEds1nxeLLkMT5akgcHaajl1vVdZnFjaWrSyuDiOGIlsfQU1J9g5e5h+PxLqOlTaOGSRZkKuDXkj/AAbvnkIimwD0z/8Aqr3TUPBWp6Zarf65o15AkrlUkktWVS2M4BI5OO1V7fTbVB8kZGPUUczBKx41B8B9alXAuUA9ccVOv7Pmssdg1OMD1Ck17ELNUywYcZIx2puyRiVZeMcgilztlWR5AP2fNR6yauo9QE/+vTl/Z/lVsnV+Ov3AP616+YCwBGBjsQKguklVsRBCMdehzQpu4WVzyYfAiMk/8TN+P9kYpw+CECkYvm9ulepGE+Xu2cjORSRRI/JYYPYijmkFkjzeH4K6crbjcMRjjnv+VWbD4SWS3TJIjGMKNpx359q9A8tVjATHB/WhsRkkMRz2FHMwsjmbD4c6ZaupitskexFb1h4OtiR+5Xkc5k6fmavQy4wjg/L1J7VNC7EYR+euKOa+4uUrp4QhiAxHEMHkbqWTwpEVzsi4PTrWhDcOEAZmAB+6D0PrT3KqvzA59c9KV2LS5izeFIFJQtEDjsh4/Sq8mgQIAJJEU567TyK3vnGd6Zx0NQzwBo9zIAT1GOQKnmY/iOffS4UOElXrjlelNk0yKUbTIFPun/161ZbKPfmNOPccVWkjcEqoUAE8/wCelXezHyoqQaPFuwLoD1+WrkWnRo2DcjnqCuM1DulVgzqpPrirEUqyPjcO/AGKV0Ct0JY7KLs4Y46AUphkQ7FVeBxkEV6f+z74j/ZjjvV0f9oDwVqUsbsPL1bTb1hsGeQ8Y644OR7jFe++N/8Agnd8OvidoI8b/spfEa2vYHj3rp13dK4YkZADgZQ9sMPy5qZVFF6kxkpXSR8ZyGUAK0Ax+YNN8xW48hc56YrrviH8JPiN8KtXOj+PvCN7pkqngzwEI3bKt0bn0rmisbKWIXIPUd6q6a0G0UmWPOBAuD1O2mNaAD/VLj2WryRKxI3DGDwwFKI0IARFOfemLQzJNOWX93sXkccYzUbacBhgD9K1hbMRkr06UptlYmRhg8Eg8c0tkOJjHTdp7/lR9hO7JiB59K12tkc8J9BTWgIwemfWmHumdHFKiAbSox3qVT2OPxFXJLcq+SucH0qJ7b5sggc+tA0rDY3Kjdj8KlEgKhW6E8n+lRm3DDbuII6YFOUfKSQc/wAOaBJ33LKXbqcwyspHocVdsvFGuWb/AOhavOmByS+f0rJARQSm4nHpTVk+bbsIHutANJHV2HxM8WWoJbUFkUnkOvWtW3+LtyVC3uh28gAwxXGfx4rgY5jvLiPIHpSmWQ8Kpx1Ap8zFaJ6TD8T/AApO+670ho2xg7B/hVmHxX4Kux/o2qGFj/z0Iry9JpHbBPOM5oafedska5GAcDGaFbqKyex6nINLv/lg1q1mGONwJx+VFvpGirNuvNHtrmIjDC3lCE/iVP8AKvLjIqAbcg55AfFWE1q5t0BttRmUj+EnOKfMLlT0PZNM8KfAOfP/AAkvhzxLBknL2NzA+PfBAq8fhb+yRq0ey1+IHiTS5egF7ZrIP/HE/rXjFn4z1y3b5dQRx6yKCauw/EvW4hulgilHRsgcfpUu7d7gotI9Puf2W/hxqg3eFf2hNIkz92O/h8kn83/pVC8/Yt+JiRGfQNa0TU1P3WtL/r9NwA/WuHX4oxynN5p/GMBgO34VJb/EXQ4X3rPPA5PDISpH4gU+aXcSjbc0tZ/Za+O2lKWn8A3EoHe1mjl/9AYmuT1j4Z+PdFZotY8E6lbbM7jNYuP1xXeaN8b9bsMHTviPrEKjGFW9Zl/75Y4rpLH9qb4gW/74eNRcjdjbdWERz+KgGleaCyZ4FPaeSAstsVIPRlIqu6W4O0rgj04r6Mm/aNsdUXd4m+Hfh/UyeGaSyCk/nuqpefEf4Easkh1X4EWaSEf8uM4UH6YVcU/aSTtyhyo+epIY9uBkd+Ki+zq3zBjnHGTXrmvyfAXUI2Nh8MtUtGbOPs+rgD8mVq4rXNA8LvKW0ayvrZCxO25uVkwPQEKP8impXew7NM5Y2sjnaJFx9ajmt5o0JI49jWtNoABJjuHG09Cn/wBeqc2kXK8rKhz25GaejQyg0UmdzAjsabiRVPDHJ6jvVp7PUVORFyRwBVd47hBmSJh7laZOyImfaCpJ4OMU0zBTksB6U+RuSc4/CmOEUEIOR6mgoYZgSwI4NMk2A8IB7U9gjYyM57U0oqjcfXPzUCtrcjKx5wV/AikkWI9eMHpjrQwycbf060wu3VWIA7AUDAwrjBUY78UxoUXJ5HOBjvSyE4wrnkY6d/xpu93GeD9TQAMZEbcsrjgY2saYzTMCr3Bb2kO7+dKzFeDn1wKY2QDtJzn1FKyAjeMMP3kMZ92hU/0qJ7WFzh7GAkdvKA/lVjdgdP0ppYAfeOcY5oauBWl0+yeP95pMPT+FmH9ahfR7AciyYHvtk/xFXt7NnI7dqYZGG1SvaiyC7sUX0aycf6mdf+Bg/wBKh/4R+wO7dPMPX5Af61pO3f8APFAZAMlcgmhpAmzGn8LWc3Md+y46bo8fyzUX/CKW8ZBXUI2I9dw/pW2SADgjr601tuMnGfpS5UPmZhzeFmcgrPEfQiTH86RvDGoKh/exv6ASKf61tNGuRuUZx27VG0UY4Cc9zT5dAuzFfw9fI28Wj57lVzUD6DcBi0tvMD6hSK3zbxsdxBx7HpQUCjMbsD7Glyj5mc2dIVTl4JCfUmvur/g2ktUi/wCC3fwQfZg7vEnJ/wCxZ1WvjkvcAHEz/wDfVfbv/Bt4ZP8Ah9Z8EizA/vPEeSRz/wAi1qtOMdTOpK0fu/NGB/wc0RBv+C3fxsOO/hv/ANRrSq+FbOG1jJedq+7v+DmYkf8ABbr424Hfw3/6jWlV8FXkbmPg4qY/B9/5l31Lj3Vko3JIBjmm/wBp2EJG4Z+p6mslYlT5i2SPWoX3OwVRwDVJajbujdN09yA8QCgdBUj38tvGHeHPHfvVSxx5QBXqO1WroK9lgg5zimJ7lf8A4So28pH2IH3FNuPGN4/yxocenFVZLVt21UzmpV0uQIHWIj8KBDYfEmobwowA3XFX8mTEjHkjOarW/h6/uJN8NrJ17Ia2bXwzr82BHpkpHYhDQO6sZF/HPFAWiYq3qKNLa4c5aRiT1J5ron8Ca/cQ7JLPaSP4nUU23+HOtxOBJcW8Yz3nBpN2HEz7tVbTnBc5zwKw4dPUTeZLI30zXcjwYhj8m61y2Geu3JxTT4N0BObnxAre0cH+Jo6CTV7nG30K3BCqzdOeTiktbMJE8QyMjg4rtovD/g+AANe3T49EC04Wfg6Bhu0y4m95J8fypR2G2rnJ6TCY4yu3vzkVet4zI/3K6eC78M24xbeGYQfWRy38zU6a/bQfPa6FaJ6MYQcU7oNmc1HbSmCTy4y2V9M1nnwxqtzL5iW87Z7JAxrtW8X6mikQGOMf9M4gB/KoZPEutTKQ2oMuRzhsUai0OVh+HmuTkH+yrtif70WP51etfhrred72sSD0muFH8jWo2rXkoPn3zscYJ31A18oXMt0Pxalqw1RHD8OtSjm3S6hptuAf+eu4/wAq1F8J6QiFbrxLGT3MUJP+FZTatYrxLdxg45BbNQy+JtGhILXikj+6aHHUfMzcTQvCUDK0mq3MmOgSAD+Zpt1pvg+4kEsqXzlegMyqP0WuffxppCt8jO3phKgk8b25P7qykb68UuVlHVxr4SiXEXhnzMDrNcuf5EUq6lo9uQ1p4YsUI7mMsf1rkJfG9++Ft9KGO241E3irxBMxEdvEn4U2ieY7qLxPeRcWtnbRAf8APK3UY/Sm/wDCVa7IcHUHAJ6KcCuCfUvEs/DXWz/dXFNMetTY36k/Po1HKLRbHoL65eyDNxqkhGP4nxVSbXbBSfP1RP8AgctcV/Y97L/rrqVvYsaki8LvIfmhcn1OaOVDvfQ6a58VaFECG1FCcdjmsuXxfpKgqJ3c56olU4vCbY3mIL67uKsReGoYk+aWOnZIT3D/AITa3ChbeymbHQkU3/hNdSfIttK6/wB41ah0SyA3GcD1wvSpf7P02E4BZiPfFGg+ZmY3iXxLMdscESfhmo/7R8UXTbDe7Mf3VxW2iWCDi1z6Fmp6TQKMwwIv/Ac0aC1MH7Hr84+fUpyT/dY0qeFtQuGzOZmGO5NbraiifM0qr+GKrz69boCHv+n+1RdD5SjF4MPBeIdOdxqeHwnBCMu8fFNl8S2CncHZj7CoJ/F1vH9y1Yk9CTii7YrM0E0PTY+WmH0VakTTtOhGQrNz64rBPi26lx5Vsi445JJqKXxFqchys6gHrtApcxZ1Pk2ETZS0Q/7xNO+2xQgOIYkB6kqOK4241W/dCGvJM5xwxFQLLcSnzJJGbHTJo13J8jtJvEdlF8wvlHqAaqzeK7FQAHdyfQVy8oWEKdxzu+YY6VOsaKd7jr1qg901pvGUZ4htWYr/AHz0qtL4xv3yEiWPPTHNUEhJZmVflqN42cBx17DNRsxx2J7jxDrUoIF2wHogxTTd38jqHuJGPruP+NEcah92eg5FTJagjeWAz0waq6GQTqUXEg3E+pqFU8tST1x3NWZ0kMu1R9MnpT5dPmkVZN+0YwRS05SfiK1pDvkDFTwelTeXHHuKtyx9Klt7UxYIcnvmn/ZXdSwHOeSaV2NbECW4M++R93t0p80CkgMo2ljxUi2jRMQ5AOQeuasppgmJZ3Hy9s0+a2oXRRhRQ+4DAAwPanOgMgVV5I9Ksm08uf7MVJJ6HHWtODw9dzxbYYiASMtt5NJyVhfEY5bJyV6LjA71JM+2FXYduK3rb4d6pO/ySooPYDJre0j4YXDxrbyWUkpPXK8VN0NLucLBMLkq0xLZHp0qY6DfuWWC2Z1J+VlXNe0eFv2cdU12RYLfS7a3YEZM7hMD/gRr0HQ/2Z9Asf3PiDV3l/d5MVkhYA5xgs2B6njNUlIlyit2fM9h4S1mVB5lqsY6EtW9pfwyuL/aqzs7DqqJ1P419IaT8JfCum3M8N1p9kLd1KwmeZppF5yD8oA6Y9frUmmfs36Xe3Zfw5qniKSSQnYljZAAkYzjIJ4yKbg1u0JVI21PIfCPwC1zVmELQbRnl7o4A/E4FejeEv2YtGSVR4m8RQRxKfnW0TcevboK9Z0X9hqaDTDqnjX4kXeiLsYrLrWqwW4BGACVb5iCTj5c9KS78Hfsg/DItNrXx01LxPeRFtljpVp5sZ5wA0kgUep4FQnBLe+tvQXM5Wt/wDlbb4a/CzwwCtvov2zCkCUylpCcnBGFwOAOx613XhPQPil40WKL4a/AeeSKMDbeyx+RCoIyGL7EUDHOc9K56T9r3QfDdu9l8KfglpFo6nEeqXsDTzDjAYBzsU49F6k1xvj/APaK+O3xJRrbxH8RNQktDnFsJikaAjBUIuFAxxwOlHtJN6KxKjK1m7ntVx4Y0/4eRl/jb8eND0LAG7RfC8Zvbkg84LLhF49SetcH4q/ac+Fel3BtPht8M7nVWVNovvFN40mTuByIo9q9sdT1PbivFprFZpvOvrt5WOclnpVktoCArjIHaotNr3ma8sF0/U9J1b9rH47a3bNptjr8Gh2bE7rTQLJLVWySTuKAFj9T2FcHqepahq9w15rl7PdSMcs88hYn8zWcdTB4iX8TVaZ55QGefAHUgmmoq9xpWjY0De2lp/qiOT02jioJ9cmkOI1wOzZ5rPk+zKPlnXpyBn/ChI0m+ZZDgDqVqxRNGFJr0OZZ2TjOSP8A69SCG0t4vNmu5ThQBtGMf5x+lUIo3bAL4UDtnn/OKkWzBfCqxBHQ5NLQPtD5b+z4+zsz467hkCq4SeYiSJdp/iCirkNnIpB+yOR6gAD9av29qqKGMLLx+tD2GlYz7eyl3Dfk8dTV+G128sBgjgetWlA+7g4xzu9akBBVnKrwOhapuxSIoo2Zc9ePlBqaC1fO+RzGAN27PTFMa6SM5VF465NQS6hkeYZQOOKErjs+pPcPIckyFiB1x1rPvLl3kNvbg7sc56Cq+pawWUraygkdSvOKqxTtHFvkldW7DZn9aaTC7FvLG8kBZ5ck+magfTWBKPJg5+8KeyXG0ym5bGSCCoFQ3E6pIkVtMC/XI65p6NC5WafhLwJ4u8deI7fwl4M0ifUr65cLDBBESfdjjoB3NfpF+yJ+yN8O/wBib4bz/HH9oS9sU1qSFZJDclWFmOoiQfxSk9AOn1BI+c/2Wv21fgl+zD4UFv4Z+BU19rtwgbUNbu9QVZJWyfkX5DtQcHAPXJOetcD+1F+158R/2n/E39o+IJjY6RbkjTtFt5iYoR3Ynje57tgccDArGUpufLFadzSCStJ/caX7aX7U+t/tOfEA3djF9k8PabI0ej2CDAA6GR8fec/3vTgcV4z5WF4QA59etIJcsFbcAegz096ehBIU5BzwTjBrWMeVWIb5mN8jPysRSG1XBLLyRintKyvsI696az7V3jJGPWjmBW6EfkkvuCcdfrUEtqxXIAHuatM7dSTj1qvI+3o5IJ/GjmDRortCoOAOvQGhoMNlgBxyMU4k4I2HHrTZHOM5HPfFNKwLYY8CsA365pHQbiAMkd8UoKFtrenVR0prBdpYP75FKQ1cepCe/rUiYwcE4x27VXBwOuc8GnJJGpyOcelPToCsXwUPyxKORzz3pwG12DLgVTSZVGMDGec+n9amDomQVPPTA4pgty2BE3zA8Y7nNWTplvPaPe2t3GxiUGSJ+GOTjjsf51nQ3Cg7T1+lSCTaffsVB4qWuwrPlEeGNcsR26CqlzBCyHEQJwc4FaEhEhMm75j1XAH4ioWQyE/KQfem9hRMuWFeiox4xkdqgdQufkYZ9q1ZIGOQqcdjmqlzZyEBhFgjqcUvhGlYp/aFVvnXketb3gn4reOfhxqKa14H8WX+l3AIPm2d0yZxyAR0I9iMVhSWzocMCAelQmFgBvXgU2k1qFtj6q8E/wDBTrxfeWCeE/jx8OdF8WaVJ8t1JLbrHMynvjG0/kPrW54x0b/gml8UNHTxHoHjW+8H6hcrvlsoIpWWJ+4Mexh/3ycfyHx2Y2OSuOO3NPS5dWIOeeOvasvZK3uuw4yS1lqdT4+0Xw94V159N8M+LrXXbPGYr+2t5Ilbk8FZFBBwM9O/XisQ3uJCVA+gFVRqCythc8cYIp32iRmB8s85yfWtEmkJtNuxcW8G7AUAAddh4/Kni5R8uhPpVBpZs43nJ4NHmTZzgj05qhl5mQruwB2POKUyxhdpXntk1QSedBjBHOCT1p4u227C5BHBBPelo0Tyl3fCfkLY9h3prom/Kk/iMEVWDlCHwMk9c9KlSWQgAPnI5496YacwrxMjbipUnpimSpLtwwbHQ571OJpQgDA5Hr/9elNzuQhogeOCKV+oRKZibJYDIzyfWoZkkySY8AcjvWgXh3Aqu05x0pkibuVAPpk0w8zM3Mp+XPQ8e9NaR0Y5HPtWi0KEbREDgcVXeDzDnbjJ7mluiioZyW5B+pGKBP8AN5Y+XH3Qankto85B+YdvSoZbOTO8c4zwBTIW4CeYjIYfnQt24DNlfTpULQsnboODTRJKrHoOOmKFcbRYW7JcZKYwc5U1J9oiI2oMdycVSLsCSxOc8ChplAJYHkU9BK9y99qkQhPMDZ5wAMUTyzxKoJjfPUdx+lZ7Sq20qSPQZpyzkdec9s0hFhzGJAfLxz1FSR3KRnaszp9elVFnC5Qk/N70LeqMMeSCetA1uXl1G9SQGK8/Fqni1/WIvmESMO7AGsk3FvKCxPfPTNNE0YwQ54z0Pem3cGrG6vjCdSFltzxxT08WWzgJIiqT6rXP/aJgdwbvkEr1pWn3AiWJGPVuP0o5mP3ToxrFjNx8hHqABzSGSynIG1fqR0rmGliI3NCQO2BSrclBvSVhzwM079CbanQy29o52KUHvzUT2aZ2BiT29Kxku7j+CcN6jjin/wBr3a/fIbjHBpp3FZGi+mqRjyVYk8gqDVa50SJgV+wKD7ZGKjj1zH3wyk8ZHSnHWopAT5hIxng0J3B6orTaHb9EgkX3zxVaTQtiErdNz2K1prqduwGJDjvzSPdWjtsXPTk076CS1MKbTrmNuGB/Hp+lRfYblcuEz6mtx4I2JKDA+tQS2hLE4br0X0pXRRivFKh2tEQR3xTJVRSFYgcelddpUfgguP7cGpxe9sqP+jYrpNJ0D9nu+wl94m1eBiek9uoA/wC+UalzCszyrYOw59CKRl+bIIz6V7vZfBn9n/Wkxp/xAYu33d11GDn6FQajvf2SNMuEMmgeN9+eUDwAj8wf6UKcQszwtuTtVMc4pjR4GT+Ir1jU/wBkn4gQqW0/UrC5wOFEjKx/MY/Wuc1P4AfFPSgfP8LTyY/igIcfhjNClF7BqcQycbto60yTHUKePatnUvBPi3Sdzaj4evIAvd7dgP5VmSQTIxDxsBjklapMZXcFenam5K9eQe3rUzL32npxx1qN05Oevr6UAMK5PA/WmPluemDTiuPmySD1xSGMkEbT06UAMKluowfSkYAjcW/A07YMcnHpmkYHGFXk9c0AMPAxg9e9I3TORkdqGL7h6duKawKgnbx6igWohYnrX2//AMG3Zz/wWr+Cfp5viPH/AITWq18PnJwc9PavuD/g26GP+C1PwSycnzPEf/qNarTW5FRe781+aK3/AAcneFdR1T/gtj8arqF41jY+HMF39PDWlivh1/ADOoFzqkCj619yf8HKEzr/AMFrPjUhc4z4cwM/9S3pdfDHmknmUms4Xa17v8zVksfgPw9Fg3OsqcdQq1KvhjwXCfnupn+iAVVMig53jqeKGureMYeUc+rVYatl6Kx8FWYIisp5fdnxUqT+HFXEWgIfd2JrGk1Sxj/5eF6f3qjfxFpka83AP0pWQjfTVtOj/wBRoNsvv5YNTL4huwvyW0SAf3UFcmfF2nxjClj9KjbxrHjdHasfTmizHodbJ4h1EsSk20E/wgVG2uakxw144z71yEvjC9kP7q0/Ooj4l1d8iONV5oSsF2dg1/cynEk7jnqTSiRiMs5I9c1xZ1fX5SSJ8DpwKaZdalGJL6T6A0WQjr7i5iBw8wAA4yajbUdPjRme6QHt82cVyZsLuU4kupDxzk0g0ZyecscetFkBvjW9NhkLSXqtz0HNQ3XifS94KSMxHoKy49E3cmLH4VZj0MqM+QfxFFkBMfGMK58u3kYfSmv4xvWG2GxIB9TUsXh98AmIY9zU8eiberRjjpRoBnN4j1uQny4ET8Kj+3+IpssJ9v0FbK6PbqcvOD9KcLHT4hlixPpRoBghNXlJMt4/vzSDTrmY/vblz7k10QSxX7tvuz6mlWaAE7LZR9aLoDBj8P7jzljU8PhsEYFufqRWwLh0OVCj6LQ1zMB8035HFMeljPi8MSd7cD3NTr4eRFy0iAfWpHukXJkn6dy1RTarZRcNdL7/ADUCJV0i0U5MwOPRalXTdNibJJcj2xWc/iHT0bAk3H1xUcniWBVwkDHPqaWo7XRriKwUjbbfTcaUzJ1S2TjocVz8/ia6TiOFR6c1VuPEmqPx5wA9hRZBqdX9qYE7UA9wtNa9AXLXAHsTXLNqN3MAftDDjoDUW5mb98WP4mmNo6mbUbLBzdqPYGq8uu6fCmHkdiehC1gZZRlMYJ45xiiZWkUbmJI7k0BbQ1x4ri+5Fas3H0pknii5dsRwoPfrWTbIY2Ldsc+9OZ1bkAjHcVPkCS3L0uu37tjzsZ9FxUE+p3sqbvtbknqAcVXb5j1yPanxoJBkrjnoaFoweuxD50pn3vIxHqWpxlYnaQee9StbsVJU4P06VGFXozU3sEQVpNpAPfvSMpYfN1FPXAO3Hfv2pyBSeCPemEiMIyt8vGe+ant4bcnLZJB7U5YCuZcZUDpU0NszNuxtz3qWwiU2Te5VFAGTViztJHUlY2OOuBT/AOyrmSTerKAT/erpvAXieDwnq63M1gtxDt2yxsM5o5kETkxAGlyecdqmjtJZGGDkZyM1r+In0y81ue+0u08mCWQsqMwJXP0wKoySFMkAYxSe+govS40RJFAI2U5Y4Y1VaPaxwM46jFXBcxOoBPXtUoSNXAC5BHTbSLKKRBjhm2kjj3p4SZcF42A9ccVt2+gz3ABt7c49dv8AWrS+Gb0SqLpUVOwLE0roVmYsFoJhhs/TNTi0bzMGJyAuBnmun0rwpbsAVtpZDnghMV0Vh8Nte1T5dI0WVhtySU3fjU+82Gy1PMl0+9lZhHZSfL0KrV2Pw7fSAbYSquPnDHmvUbH4UXkb41i/jtgv3lllC/8AjvU1vx+BfANhbhXu7m9n25xbQFUB92fH6A1dmJuO547ZeAZrkgPcOfZY+tb2kfCu/ubhY4tLlkOMqHB5H4V6XZT2OlwslnotpERnEso8x/y6D8qmik17W2jsrSa8umJ/dw2sZH4AIMmmo2Fz9kcppXwjlWZIb64s7NvWeRU/nzW9ZeAPC1lzcaobpwTkWcLEHnGctjFd74X/AGWfjV4ijbUoPAV1ZW/3mu9UAt1H+1mXGa1JfgR8PPCiib4q/tAaJaNjJs9Fla+k+h8sBVOPc0uamna4PnaucHo2keHNOuPMh02MD+7cEsf0wK6vw3Gbq9EWmaZdXsrfdtrKzwSPTC8mtceP/wBkDwAoj8K+ANa8YXi8ifXLryLfOM8JHywzxg46VDqP7YvxbjgNj8OLDR/CVqcYh0PT0jYYz/EQW5Jyeeo7c1KqaXiieRnd6V8Hfi/e2R1jUvDNn4bsD/y/eIr9LVR+DfN+nasrVr/4E+EXdPGnxyn1S4UYNr4Xs/MAOcYEsmF/T09a8U8TeJPGvji9OpeM/GF/qMpHMl1dM5/WqEOlWkfLkDBzz3obnLVuxThHoe1L+1p8N/CyPb/Db4E213Jk7L7xLdtcscnugCjoOmTjmuV8U/tRfHnxf5kTeMn0q1lBH2PRY1tYwCc/wAZ6dTz2z1rglls7cHEiqB2qCbxBYwgbTu44xUezTeuvUcUoNW6F+6l1DVZjPrOrXV3J1Z552Ynr601GtoBzGoI6HNYs/iAuSEyAOMYqI3/2h8SzNVqNh36m5Nr0NuuwEEgdetU5dfmYkRADnjJrPJs1HzAknvjrTw8SkFLfgDvRayK3ZY+0zzcyy5ByGGaA0asI94yfemq6HlAMjr6inJEzkBYB65yKd0TpYcZNw6DJ/iwaePtbjyy5KnqueKW3tigO9APbOc1PDbsTuYDB7HqKX2R6WIfsmRteP2wasR20xOVAAx1zzU8cBODgDnmrCRmNjtTkjnI6U7oSt0I4rZlYAsDx0YdanjAx8vTvg0u4IR8vODUnnwqNsfOf42FL4hkscEj2zSKNqqM/Nx3xTVdVJyT16fhUMl0WwN7Z61DNcyqxXJwOvNLViauX4777OziKJSzAgFh9314qnJdEAHOTnrVK4vJEG/OFB5561CgvNQk2Qvhe7fh0p2sL4S215JJJsVRk8f8A66u2ukCeMtdXO4schFOABUOm6cln+9niJII5frV2e8ijTbkKT1AUDHtVKHcG+w2TRtOtVIVVUnowPJrPmWzSXs453ENnH51Dfa/HbEpAgDD+PArJkvJ76RgjHDNk8daLsIommvLqd2s7eX90XJKqOAe5HpUttp8duwkUZOckntTba3EMZCggkdu9WV+QBVckkdSKCvIlHlyMSy557UqhGIZl6fdOKYioeoHH3vrUiogGAwyaAHxtxtKYGKVm6ZGMDFIzBCFI79SOlKpG0mYqCAeg60ANG0H5sKcDimuw4PP1z1pXaMMGXnPJIFRs5KEFif0/z0qbWYtLjnkTZlZOgwQKhYAfKHPAzg9qVw5UnGOvQdaiJA4VgSPWjlGKWQfN6Go5WDLleMdMikfAOFPBHPFNc4XCnnvTshPYazcHco9uKRmJHI9uelJIx3Yx90ZJphY7dxYHI5oWwwYs33V4I609OGyoC49TTGdWG5SCO56ikDMRuIHQ80dRdSdXZlzJ9OvWpUkJJKngniqiTAnj1ztp65D7TIcZ6ZpjtqXlfAwWbI9e9SwzMp+RsnHzcdaoRyKRkZI54NSoUyNzZ9hStZAXBcBCQGUA9TinSSQuu4OMjjbiqyFWUqD3704SxlAoTB5+b3oVugDyY9vzOQT2AqJ4/kBjye3FSCRNh+Xtz70FgQAH6HoKLoCrcwsrFWXGOoAxzVJyYgTtIx0zWoURiS2PzqCZFjyuO/rRoT8Jls8qAEpg9elRgyEYEJbrwe1W5tmCwQ5HTFVpJJMEEkYPWiyHo0Rsz5G0dRlQKeLiZUU7W/pTWc7djjPoc0wybMEP14x6UwTuWxfKw2twQepNSm4Gdz8jtz0FZbkEkl8joeKEuo0QZXIHrS0YPbQ0GnmjXdu46gE00X2QMgA5z9RUcN9bzLhNo460rLHJkK4BIpjJ0vACCoAH161JDfHaXAGRVFo3ThTn0IpoLbhuytLqJ7Gkl/MBlenPanC7Bc7sDjIqghUtgSHnt6084ADE5479qYveLYuHC4BHB6U4z/JkLg545qr5pVcs34Y71IsqhskZGMHmlo0USNI4OEfHHUd6QuT0Xr3ppeMgfN165pSyZyhJ45z6UyeYl2xkbt4bA4DZJqPylAwyYIOcZprGNhluOeMU4gFSfM+lLqCuRS2rFjgHp2qvJZEMCCPSrmWPAwfQCo5C+CBEfSmUUpLHJPznjsagktJAN2O/atEISrEL0GOe1RtExXcSoIzxQS9NDNKEcqScdTimvvTkEjHXFXXQr8rHnFQsnG0gf45oC99CqJJA2QenrSyXRfovAPUCpZogBkqfY1DJCyLx1J9OlCsEhUdHIDSeX6lwcD9KfN5UODHfxyZHG1D/AFAqu0WeAeCccioymF6c+nrQSWTL8owfoKc1ycklfxAqiWyTsJBxSh3Rf3YIGelVa+oFtbksNpxgHtS+aG4XOM9COaqmQ7sFT780CYISHGMg8UuoFomLkshyO4phAAO3OfcVAZY8jtlexpTIu/erAetC3G9yZWkXIKDHvUcztjcYcZHXFItyykgHI96PNGcsMcflRdhZjR8pMqnHPBBpySSHPlOSaaWiIKls+gxzTSqudwGD6Zp/ZESNdXCnlhgCg39wvJzx6NUMqsUOG49wKYC23OeB6nrS0AnGpsRtJP40v9onqMDJ5Gaq7n3FWQDnr1qNgd27bwO3enEdmX2uA44kKn2NT2mr6np58zT9SnhPZopWU/pWUZFwRubGeMmkS5mDfLLx6H0o+yI7HT/i18SdNINr4yumAAwJn8z/ANCBre079pT4kWQC3b2d2Ac5liKk/wDfJArzIXzDG808XyEEYOB70uWPVAeyWH7U86qI9Y8JIQfvtBKP5Ff61f8A+F2/B3xFFt1zwqoJ+81zpyP+oya8OF8iDngn2pUvYSASpz6Gj2aC7se3R6f+zV4obdH9itnJ6B3hx+BwKrXf7PHwl1o79E8TyJnlfKnSQfy/rXjBurd+sWPcUQzxI/mQl1PZg3NLll3DSx6dqH7JNy6F9G8XQv6LcRbf1BP8q5/Uv2XfiRZszW8dlcAddlzj/wBCArBsvGfiTTflsfEd9GAfupdNj8s1r2Pxw+IenjCa80wHaeNW/XGf1o99MDH1L4JfErTf3lx4TuXA6mAbwf8AvkmsHUPC/iHSjt1HRrqLuBLbsp/UV6hYftOeKowF1DTLOcd9oZT/ADIrYs/2odHmURav4XkQZ5aJ1fP4HbTTmt0B4O8MycvFj1BFNKjduYcfWvoN/iz8ENfG3U9GgVieftNgOPxAP86QeGf2ePEql4hp6M3aK68s/kWH8qOZdgS1Pnopgg4xzyelfb3/AAbend/wWr+CWf8Anp4j/wDUa1WvGLv9nf4Z6ipk0rWLmLJ42Tq4/Ir/AFr6v/4IFfAe18C/8FhPgz4lsPEf2lYrjxArQvGATu8OaoOoPv6VUJJuyMqztBN91+LSPE/+DmrWr+1/4Lf/AButoEG1T4bwf+5Z0o18FNqWsStnziPSvv7/AIOXtOef/gt38bZFXqfDfJ/7FrSq+F00ll4bFKFrfebMyA+py8vcNSC0upOXnb6ZrcGmQqOZF46kU5LWzRclsn2FUIwhpbkb3Y+/NSR6Sp6p+dbapZxrnYSacJIcfLEOKAMiLR04xHn8KmTSGHPlVpC4wcJGBx6Ufa5T8uaAKi6HIekX6VIujYHzBR+NStNKcgsefWmGY43F/wBaAHLpcCD55uR6U5LSyX7zk8+lQtcRDJaUe+aY2oWaDLSge+aALqrZK2RH9cmlEkKtmO3Xn1NZr67p6NhZM+gAqKTxFCnCxnigdma6XbNlFQDHXApstzKvGTye1YreIZAu6OFQT61G2uXs3yK4UN7UAkzoFuWC4ZySO9Na7i3ZeZQO+TXOz3VzjDTt+DVUnkmcYLH86BHUvqVkpw049gKhk16zRtpYn/dU1gQlgo6/XNEo+Ukn86CuU2m8QqVZ44Tntk4qtL4mn3hUhArN85toGenvTSyD5sUAki/Jr187fLIFHsKhl1G+cbXuW6euKrhhJ0PPY09IzJhfTvmgHpsMEsrSESOT9TSmUv8AdA9KfLBs4yfrikKAEYxgdKA+yAX+Jj060pJAHHB6UoUB84/OlaJZBnOCelBREVZmHqKd5OTjFSxqDJyPwqaMNhlBAz6igzKyIVBbsB0p8bs3JQfhSiNipXHU1IsG3JQnPeldFREwoIKjtzxUrq7fMBkEdqlso4Wl2SvhdpySKSQKkv7voOxplDHtysJyetReUxUYbHtVyONp4vLPGT1ApF0m5dcxDnvmob1FrYqLEX5C5x6U+2QgsGBGOmatw6bdjauzBPfNWYtOkUESwhgOvXNHMwsijLGTERGRnNQrp1y4DbSBWuyRocCDGegNS2+mzN87txik5hbqzGOl3UhGIz9c1LDpcivnBHqcVsx6VPJLtiLHA5yOMVZ/sK6dSrIVbtzgUucNGYkdtLGfutj+9R5Mk0jeXnPZQK6C38PxxQ7pp0ZgTlQuau2uhyRSiWIbgRkDGBSvfQLI5e30+5c4MTfTB4FSR6DqLnzLdWYHsK7a30K8mAP2b8QK17H4fateQeYUIjPR24H501zMNDzyHwrqsvMpjX2Z6sL4PkPyzz7geyLXpMXgzw7ZKW1HXYA392HLtn/gNSwWnhe3Ils9Ou55B0LBY1P86dmF4nCad8PlkZQti8g9XXrXT6V8KNcuxvsdKbaPvFY+n44rrR4o1CK2+zaF4csLLcMGVkMr5/EY/Smix8VasCt/rV1Ikn+sjTKrj0wMCmoakudjNsvhvYRLu1rxFawBfvp5wZx7bVyavf2J8O9PUiCO+1CQcZWIRof+BMc/pXQeFPg3rviWVbfQtGu7uXPAtLZ5Dn3wDj8a9Q0H9iH4iy26al4w1Cw8N2bYJm1u+SIgeoUE/wAxR7kNWTzSk7JHjdlr1jpsHlaV4dtISRgS3OZXH4cD8waV9b8S6tE1qL+8kibgwQDy0PthQAa92uvB37FvwnZY/G3xXv8AxHeRqGez8P2oEZb0MjH8eGrKvv21Php4NkMHwW/Z80WB4uItR1cG5nGOh5xg/ian2i6IpxberOA8D/AD4xePbkJ4Z8Bai6ynP2h4CqHPfe2B+td5bfseXHh3bN8ZfjF4e8Owrlntzeie4wOoEaHryO561xHjb9rP9ov4heZb6l45u7W1lBBsrFhbxBSem2PGR9c9K4CaPUdRlafVNSlmdjkl2JLH3NJyqPXYFFI9+bV/2EPhtalI49e8bahFnaJMWtsxHfgbiD7g1R1D9uXX9Itm0v4L/DvQvCtuVKo9lYq82Pd3GCffbXicWnQxqJNnTqcdak/do48oEAcdOlTyp7u5SdlobnjX4pfFT4kXQvPGfjW+vieVW4uGKrxjgdBx6CufGlgtvnkLt/FuH+NPlvIYSRvAwfxqs+sRr/qlLfnTiuVJIOt2aMcUUY25XA7YpJb23hJXepOOgNYdxqtwScMB6YqCW6mkbduJJxyatLUSehtS6vGgKpkkjj0qpPrdyWKnj61RCSu3zOACMZoaFVyV59DmlZFEgnluWLM5ODnrSpCH+eSUAdelMMg3DarY9utPVUGE8s5AwGI4zTFqOaOIEOcn3zUqJEwIXr6AUxoJWIQnah9DVq2sokkXzScEdQMkfnS1GItrGY9ysCxGCCOlSpaLIAGPGOfap1twHzEAw9W4z+FSRRsFI2bSOtHQBkNgTjaMAdTirKWgPBDep4pYoiCD0wTwanOFOAMECmA6OBUGcH8RUkcbbeg78EUJDMEVmQAEEqzcA9f8KGcBfLBwPw61PvExJQjqBlsBugI60omZCV46d6hlnUoCWJI9ahluoxyzAc9aPiG9i1LPER86bueDnp7VHJcBhlHAAzkHtVOe7ZQ2xgc981VudQMYBbbnHXNUGrL0l2kfCYPrVaa5ubiXyrbLn2Qnio4rea/G512o3Ay3JrobC2trdfs+wZxz2zQoickihYaC0pFxcvvcfeDLxWqlkkQAZVwOQAtEk8UMGGmVMdSxAwefyqnceILO2iJ85GUD5Qpyc1dktidWy9JcwwxFXVcY5yBzx/8AXrD1fXBLEUjCrGD95QfmPt7VS1XW/th39AAflXpWesdzcnIjIGO461LdyrWY3LXc5VmwDxz9a0bW1EKgBh049RUdrbJEmNoHv6VYXJOF4PI5HakXZk6bVOCpBpUjVTw4/LrUSGTeF6exFSMY0O4OeOoYVP2hDiwCkYzwelOBcsMAEHrx0qIsBnJbJJ+WlyyRja3Ud6d+gEwaQglTn0pJJFU43En/AOvUTPIQxC89jR90EljgdsUuVASGV1BZgMZ6iomc9sFSeQWoJUdenGSDTF3YwJOh6Cn00EncR53J3A9D27VG0rgknq3pxT2BXMbAjIyKjcqvAxk9jTDQa8hZ8hie2TTXd1w6s2MdhSmQbdqsAcc98VG7/KVBznjIFLRIY4ttJLAnI/WoiQ2MN36YpwYouDnoeDSYQkE556Yo03BXGsuwlR1zkUZAXGeAe9NbfnBY5PTPWmb5ASx6EfKMdKE7i0LAJKkAZyeopPMJc/LjI4qFWZSAueD608vICSvHGTkU7XeoyWFmEg6AfWp0cA7QxJ5zniq24nhnHXkUCQBtqqTjPy0uoFp5cYQr9T3pwk6bMcDIOKrKwL4GMdvrT12hXOQCoo0SFqW7e4KTLJLCrqDyh7+3FObcCX2bcjKjtUDvCYkkgkZsD58rgBv85pXvZHASQsyr0G7NLRhZp2JBKeGYZHbn8KSQxE42ce9QvMW+bZ3pXdc7ufrT0FyhLAjKV6BeSRVO5tozlQGGPXmrTlZUyOSe1QG7yxRwRhaZRTntlGGQ5GM9OlV2RS+5hnn0q/K0bKBgAMeM1VmtgM7X4BzwP1pa2ArlidyFyARjHrTZl2L8yA4HBH86dJjdjceR1HrTJSx+QRnIGOaYDRG2OWK4HPFL58sb/JJxj1okZdqoFw38XOR/9amyKAygHtmgWhPBfSEZZ888kGpyVkH3xjq3rWcVwCqsD7ZpPPdSEzk56UCfkXWVkkJU4HpnrTludjfOtVUvCACYycU77crZDkKcZ60tkUXRNGRjcAfX0pWYZz69xVEXLMvmImfoaRtQdMEICfrTTYrPc0TOFztPTrnPNILhy/ykdOMdqpxagJMjyyPXNONwxGcAZ6CgVralz7XjKkdOvvT1uSRuVj+VUVmcgFhznkYpGlZWAjXHH50Ba2poi4YLuLHjk0q3jj5sH8azzI6EheQeKcJmQfdb39qATLxkV+WBGePpSMBv+Rep71UM05IOeD6dxQJJBznHr7UB8RZZS+VC/rUckAJ2FQPQ1Gbpi5XI4HpQblwSHKjPQ8cUFCywRuck4I9KhazKjA6Y645qXzkJJOM9sUrSbDuzj14zT1SsQlcqG2ZRzJkZxzUUltl87Rx04q80vG44PoB3pjNk5IGc4IpDkZ8luA2XB49KgktDt+Vz15GK032Nk9u1RmNJBwAMdcHrTW4cxmeUyLv2tkfdINRyPO64lJPuRzWi8S/eBOSelMeELnk9eAaa13BMzizhvucjnpSGZgQSvXkmrksUYbDKen8JpJEsdmBBKrDoTIOf0o0TFdlX7Q4BCt15ND3Lc5HrSyQlW/dngHioXR15C557U27Alcm+0MQCQQPpQJduAvGD1NVip6LnHuaRzIh+71PJpREWzLIOrE0eaxXaoHPpVPzDgkn8qaZ2YnGBjqcVIFwyZG4uPb60MxY5LAfjVUXAxk9AfShbhSME44/OqkPZFhjvz8wA6DjrTXjU9SRn3qP7QrDb7Uvm5YAtwehJ6VQdBwiw24MDQqMxAYDgevWkMij7qfh60rSN1fHFT6CGyRqPvJj6U1hzkSEgjp6GnFyfmRc+oo3E9/l6YNUA1i4XAfAAzTd8oGNxIxzgUuFOc9umKbtTHyMRmpSADctjcw6DvSNcdwPr70BHDHByCOCTTX24+4c5xindAONwhIwuD/FShx37D1quyfNhVHTqKazHqxoWwE7NGDubOc9qRpOMH9agLg8Bcjk5oZ+wByO1MC3bajeWbCSzvZYm7FJCP5V9zf8ABuj4x8Sal/wWV+C2k3+t3E9u83iEmKWQsOPDeqkda+Dd4KjcMHGK+3P+DcFx/wAPp/gmBn/WeIsf+E1qtNbmdVXj81+aMb/g5bnkj/4Lb/GxFPGfDf8A6jWlV8JGUnJLdK+2f+Dm7VJLX/guB8boUA+U+Guf+5Z0qvgpNVvZ3Kh8AVEbJfeaG4HLEnf156017iMD5nH1NYslzcAndOSe9RrIzNh3J/GndAbhvrdB8zjj0qNtYtEOA/fsKyDJhTj8cjrTN6k5Vu3SmOzNWTXo1OVjJPrUUmuznGxBnuDVEMFIzxTWwG3KaAsy3Lq9652iQAegqvLfXRJzcN+BqMEOeOPWpo44t3CZ9c0DkVmmmLZZiQPel3M5wzY9qkkiXJIJB9KdFaqF3lskeooEtxgjYEFVyB1oOWbjtUizZJyAPUimohaQ5AP40CG4BIQGnRlF5Zs02VD5mM89sUIpwdwFBUSQuWOCeKQKG6rn0oiwp24+uanjjIJAxQSRfdBGc+tNkcsQFP41NcQMM4INRBMP29qV0Vd2Iwpbhm+tK6goQB+NTeSCMoME9aeLaMptd/m9aLoPhK0aBDjdn6Ves7ZpcjHA7moGg2OMA1agkkiTbtOSOeKZRHcJk5yeKgX5mxjGOlXMbl5OPeovsjh844PTFK6Mxihy24nipAQi5EZJ7VJFBKCMxGnLbEzbQHz34pNor3SJN7YUxipowqkkpzjoKnEG1CzDOB+Ip1uwjTKZzjnA60uZg7FeG2Yk8YPfNOe1DEkE8dcVNI1xMeIjg96Es79ePIb5unynmkn1BIiEQ8zaFzj2qVLfc3muudx4NWrLQNReUFrdsH+9xirbeEdQ2gzXICD+EHNJuw0mUIyigKGxgVatjKWwuSOhxVmHQraBtrbmPfnFaNtpZQBYIwoHrzU2bCzZnxWl1NLuiiJXH0FTHTrzAWT5F9cGti20idyMBiPcVal0K4iXM7BE9W4/nQoBojnl8PLIPMa8Z/UJ2q7Z6W8OUihLA92q+kenQkSx3QLA8qik1PFqKoAkFhnjGXquRsLojttMvGbcgGR2C1cXwteytvkYLk5+c4FIL7U2wUl8vA4VBg/40eS882+eVpG/2mJquRE8+mhJFYaVa4F3OoYdQo3fyq7banpUI/0XSXlIP/LRsL/jT9G8LazrEy2ek6XLcSk4CQwl2/AAZr1LwP8AsYfHjxmwlj8IPYQfxTam4gVfru+b9KPcjuLmb0PNJtW1W7AW0hgtYzgFYkySPqajNjcOoWaWSUejSEivoiH9jj4WeCI/tfxk/aF0Sx2AmSy0yUTS8DoOpz/wGpk+Kf7D/wAJbd08K/DK+8U3kfCXequFjJHfDA8f8BFL2kUtFcFFvc8N8KeAfE3ia4Fn4Y8J3d/KSABaWrSY+pA4/GvW/C37Ffxk1C2XUfEOm2egWxwWm1i7WEAfQZ/XFR+I/wDgoB8QprV7b4fadZ+G4mBWG20uwiAUYx94jr7gDFeP+LfiN8UfiHfG88ZeM9QvyeQLm8dgDjjgkgdT+dTzVJLsNxt5nud78Ov2ZPhfLs8efHX+0riMAvZeGbXzTu9PMbK1Tf8Aaa/Z08Htt8BfAGTVZE+5d+IdQL5Pr5YBA/AivBItKJcGeQtnkmrdvaxIQ6t07Y60uVyWrKTSex674i/bq+OmsWH9m+Dl0/w1bZH7rRrJEIA7biC3615r4i8U+PfGt2b3xX4t1C/mP3nubpnP5k1SV0jBwAeOoFMku44iccYHbpQoxT2E22tQTSYVYM252x/G2amWCBTgqBj2qo+qllYJnPY5qITXk3zJFgdQelXZ2BWRptcIi7k2kfTkVFLqkSdWGfpVE2ckp3y3RHPIz2pxgiX50TIxxmhRBtE0urSmMRK2E7AjjPrVdr0nJluRgddvFNlt55CVijwo7kdqhk025xnAJ7ZNOy5QurE6X9orYECM2eN5zU51iBozH9mRPdVx/Oq6aFehQZZY1yOCHFNbS1U7pL+L3w/9OKQfaJZZrKSIrHGxI5J/p0qMx2q/M8TMe2GxxTWWCAfuXaQHGSq9KdCkk7sxQAdiapvQStcSeFFj8xIyDjoz9abHbyMmOM/3iauiPcF3DPQEAdakFqrtkDBxxgYqStEivHZKoDlsk9wOKsxW6ryAeBySKsR2u35Qy5xz2FSxwhQVUDGeuKXW4yOOKLoTyRwP61MkKKOn50+CPH+tYDnninrDEAS65GeaF2JvqJhI1UL0OcGpVyW5JwBjFMOUHyAEdsinLMYSNsXJHUilIav0JVVHIDDAxxk1KpcFvlGT/GSP5VDHMrRmSeQYx8pUd/f8Kh+0sWyw6iqGWXdiCA/TjB6CmLIy5Yjk9CentVaS5YKT5o6n8arXepLG20KSMACp5Ral6WZEXB7Dj0qpd30KRMGI69Me1VI5rm8kCxo2Rxkdq0bDw3DOd93O7nGdq9qpJ3E5JFFBLdsRbQ4HXc3cf1rQ0/R0QedcEse3pmtq10q0gGY9uFGcVLEsRYEoGGM4qrW1J5rlC30q3DecFYuMYNWRD5YORkDqzHHNDX0EUnm7k+Uck9KyNU8UHBjtJPlBOZNozn2pt2C1yxql1FbsSBvkwSR2H6Vzl5eedIS3LY7Uy4vXkJlWRt2cnAx+dR28IlPmOSc9D/Wod2VyliyRZGLmP8x2rQVl2DAODnHpUFuFRd24ZAyM1OJHZuEz9aT2KJN2Rg+h4pQ5ySxI7YFN5AyyHGaRSMZYA880vdAljOeQc885NOiL7T5hyAOuajZAzF2YD5uMelOUgOcqCMdPWmthbIld1b7i4+tIZMnDAZz37VGJguBtByeTQsjKd+wnB6Y4pe6JabkrSBYxGw5PUj/GmmRVAIK9cHIqMzqjkseT1A4xTC0jtlMHngnmnZBHcnjnW3mVyiNjs65H41FJOWfzwAA2TxwPwFMlb5cMMt6DpUe07ucA46e1HQoe0ozgsccnPpUZkAbBJJ7BqbLJvLfKBjg54/lUfzqN+7jtmhbBsh7S+XkuBnPTHSmGb5SAeB0OOaRTFvOULZ70h2qoAPBJwD2oWwCSTc7lbgjgH+dLvG0ktxjrUUzAP8x/KmbopBuBH0BpgOkbAzGVJ7MTTS4GPmCnnimPKUUAKvoOOtRh97EIOT1z2NAFlJWTqFJPQgU8yjK7sEd8VXjfna2c9u1SEqvzsgz6ilqA9fmPf/GnrIYWyT1/Ooo/NDZGAM5pxaREPBOT1I6ULYNWydZkI8wA89QV5BoMoUhSrcjjIqFCvGzI46E0uCZMljj160aWEtiZ5SAdpwCP4aPNYYz+gqI5bAGMHvTlllVegK47j+VO2guYlVmZNr4P19aU4JAdiCOx7VEpKkgpnjqBThJ5qlt20jtnqKCiRlXn0xgc96btQDMo7Y4602N8EPkjnk4pS6AMseDxyaEgIprQA+ZHzjoD61FuIQpIhIyRyMcVZ5znjOOxpszRygq6jHYnuKL3Ja1Kt1ArY2qR67e1V5JJVcpsHuSv9auOuwZQ8H09KgkHmsVODxz7UAtdyk+0tlMj3PQ0qruG/eBgc5BOadPGQeARg4yRUJZkwxBxuIxigPsjnjV1wFPBz0xTH3Bdipn0GTRLJI3O76A9qVtzNk9h6Yp62E9yMnjbt4HbrSMQcER4wBggVKqKuFA5Poc5p7qzcbgSOgIo6FXRA7SFNrd+RzTGaRSArDDZI9hVmKKOQBXiAHQBSc5pl5HsyBGMY6Z/+tSGRpNJGPlPPfIpxuZiRjjnjHAqKWMhQCBgjIAbkH3rqfhX8Jda+LOptonh26txdBcpDNcxozjvtDuueo6VVveIbUY3Zz4u9uSJe/rQLkls78gcgmvbIP8Agnx8cHfyptE8ltvWeeLGc9Mq55/wqvr/APwT5/aR0LSpdVtvDlrepEu4wWl6ry474Xv+FQ2l1HFxlsePLcAgEP09D0oE4wdz854+brRrGia34av5NJ8QaRc2VzE5EkFzAyMpHUYIqsbiLdlQd3oTTTRRfa4AhV/NHuu3kU1JwFIAyQPXnFVVnVlJIIx1ye9OM0TIDtOc844p9CfhLSzKo+ZD9M9qTzI2Bbp2wagjkXcVRecZyM0vzHD+nrSKJi53bSwHpzTjIVbCngk8mq/mBWJ4GT1pPMVVG4Fjn1oSZMSyZTywwO4waRZGLAHjA4JqAtvBwwHrSHcqjBB9s0EllpBnOPUZprBuWTPI7VCZHDYL8HqMcUqysCVJ69sfrQO7FdTt+UgkjuKRlYJtZRj0IpPPXkMePelklGCdoI7UBq2QyRZYg9PWmMHbhYzjnoKnb5hlz36Yprbw/C8e1O7DUqyQ4+7zg89qilhkAG0Grb4P3jjNMkRlfaO3XNPyDUpNFtJ3ComjRyWYZ56Zq6yLty3GO9QSQnrng9OKPiCzK80abiAOR3JqFkIJZensKtFGY8Z4PWmeWe3f2oSDqVmDrgKevXio2DITjPXvVqRCBye9NeIsMs3Ge9UGrZWEvYRfjS+e6ckk/SpNiq+NpPrimPGBwOD6GgQ5bzKnaevagXLNyRyPSoWUhjtOMGo98n8WT+NTbsBdW5UDByfxpyyrjDHIz2FUNxGQCT7gUNJtHHPHGaPtDszQaZTwRgfWmlxjjIx6VSExI+c5pftOABkHmj4RFwuFXdnr05pHlyMgYOTwBVeO4CnJP60plQDlvpmm9gJQTtOec/pTQFJOQBxnIFIsy4ypGaCybtzJTAGRVywzzTGibIIYY9advUgYJ4puQvBI4PTFAEbI68qAa+3f+DcJZP8Ah9T8Eix483xHx/3LWq18TbiSARnPtX27/wAG4f8Ayml+CmTz5viLj/uWtVprcir8PzX5o4b/AIOdwT/wXH+OBH/Utdv+pZ0qvgqMMjZzj1r75/4OdF3f8FxvjePU+Gv/AFGdKr4MMRxtC/lUrYsbuBXh+O1MyQ3APSpFiVVIdiKd5aMvyjj1o0YDApKksBjPek8tj8xGM1L5JBwAeKkijVcBhmmVErtC+MkYpfLZVwentVqRgoALcelM8s5wpBz2qb6C13IYoU8zduH51YjTcQUHSkaOMMNqAHHanR5OSOntTvpcHuJ5BJLkY4pHRgdq45pwZmHzfhmlhU7tzjvS+IRFLZyqoJPXrTkiMeW71Zm+YDCetMO4nDDgd6G2NK5VKgsWpEVychOKlniJ5iB5PSpILeYKcqRkULuOJWjXdJgLz9atQ7Q3XgU9NLfqZPpk1NBp8kR3Sx8U7oFruQEM7kLHuHr2pTplw37wqB7Cre1FlAjHHpVoCRmCR46VDlZlFA6aQuGTGPU0R6UZhlV4BrTGnyTfxDPcU6OAwbowSeemKXOxcyM9tPCAK3BHbFPjtVOeOepq5JZX8ozDAWI9VpI9L1JRmTYpPq1JNtBo0QrYRO3zqfYAdamGmgKRs/AGp4reaKTfLIrBR2q3DdxRnBtgfehqQNXKltpWVxtwR1Jq2uhTspMVvg44J6GplvpHGYY1X1wKclxcl8PJjPvS5Wwsisnhu4nylw4X2OKltvC8MAJeZSB1BNTlpAu6ScjjvSO0CgM0wI7d6ai2Ct0C3XTYmIMIDA8E96mllLnMcXUdaqvcQMwEMJYj+LdjmkW5uWA+YA9sD/GqUB3Vi5DBcS8vLgYqwlraxkC7u1AHXc1Z53yDMkzHA6elSRW+DnGeO9NJMTkXjd6FHGVjR5GzxtXAP51HHqEkYxbwL7FuTSW9hNL/AKpD74FdL4R+EHxB8cXC2nhbwreXjM2A0UJKj6nGBVWSIMBL7VCpU3ZUeiYFJ9mSVt80zN9TmvovwL/wTj+KesRJqXjXVbDQ7TbukM8wZ1Hc46frXUr8EP2Hfg0Vn+IHxDn8SXceC1pp0m5WP0QYH4tWbqwW2o+Vrc+VrHSpL2QQ2NlJK7HAVEJP6V6F4F/ZW+Onj0KdB+Hd+I25E1zF5S49cvjNex3f7aXwZ+HDvbfA/wCAmm22BhL3VAC/fqq8n/vqvNfHf7anx88eOYpvG9zZwHIW10z/AEeMD0+TBP4mnzSeyBW6nW6d+w0fDCC5+Mnxc0Hw8irva3N0JJSPTHHP0zU8D/sPfDFWMkmseL7yMnaDH5UBI9zt4/A14Dfapret3DXWo6hNLI5yzSOWJ9yTTI9MGQ7kk1LUpbsrRW0PoC7/AG6x4ZtpNM+EHwi0HQUYEJcG0Eso9DnAGfwNeeeNf2kPjf8AEHfH4l8b380UhyYEmKR/QIuBXHRRRrkrgew71KrbF3MBxQoRTvYXvEJGpXTeZcXZJJzuBzT4tKiZiZeTnvUhuI9u8t1GRUb3sUa72kBz3Jqg+Esra28WAEHtinB4A/Bwe/y1QfU5JCVjTcexpIlvptxUFR79qdmSXzeQwkhCc1BJqDfdRieeBilg0mRgXdxknnJqddNxGymPkcijlYroqr9rkXjKgng0fZlQ5llZmPQDpVyLSXlUSeYykdNtL/YU7E+XLuIGSBnP41VmF0UjNHAT5NuMd8ipYtTCLmSJXJ6ZGKs/2Q7RYeBSVPY8mpF0yGPg2R574qeVjvoZdxql4QEW3UA8ggGmpcXUkZaW5xjjYBWnNZWwG14sYPB83H9KzdVk0snMDs57bWziqewatEd3q12GAS6bA6jgfyqM3d3IQ7XTN7E9B+VMtbOR5Sy5APQHrVhLbyuZI8sT0xipd+pWnKJFHNMCXA5PPrUsdhEMrJkn6VLHGxBCjlTzxU0aLn5x+dLXqUJBbgEiOMsMccVYjgXADoMZ7miGJR8hyD2p6opcgLk9zQS/MVZIY1IxnJ6g9KlTcRuA9vqKTaxO4Rj8qceAGI/Xil1HzIckqZwo59Sak3hRgryT39fWoVjkcEMijJxgjpTymz7p3AdCtGoaJEokXgHbjH504yhTtGefbtUWMsdg9iDTQW3HdjFJXGWrm6lnKpKqDaMBQAD9elRmbaQAeO/HSo2eN8l2OfrnAqG4nRBuf0GOaLNE6pk7XBYMSRt5IFQvfRplg+COgNUnu2nURwBsnOSik1Ja6BqF5y7iME9Hzn8sU7PoDasMkunlTbEpcnqoFTw6VcyRiaYYHpjpVu30a5tQBFNwWwRt/wDrcVoRwy26kOd65yCR/SrtZkuRFYKY4/Kit5CO4VRx+lX7eaKPzAUbGOQwxjNMS5WOMsGGQvUDnNRm6aMZSLPpjihdxNXJ2u9y+W8ZABPJ6Zqre3xhUXDyBAF+XAwM8iqmoazb24+d3dhwQT3rEvLq91G4WGKNpZHcCONFJLHPAAHXPSk5WQ0kizqWtTXe5EnIUL6da9G+B/7F/wAfP2hbF9a8C+HFWyQZF5fMYo35/hODu/8ArGvZP2L/APgnHrfj69tfiJ8crN7HSFZZYNJlUK1zz/EP4U9fXp0zX6H+FfC2heF9Fj0jQLSGx061j/d28aLGqqvXOMDHHX2rmq11F2WrNFFtX6H5u6b/AMEjv2l53LXd7oKdNwN47de/CY/WvJv2gfgFdfs/eJIvBuueMtI1PUQubuDSJGcWx/uuxAAb2Gcd8V9n/tz/APBRTSPh9Y3Hwt+C2spd63IGjvdTiZTHYrj/AJZkZ3SH/wAd6nnFfnzqOrahrN3JqOpXclxcXDl5pZHLM5PJJJ6806dSdR3tZCcVHd3GfLt2jJA/ShCw5L+oJJ9qaMhgCT1yaeoWRwSRj+E1uC2HLk7kfqOAM9aAMMuweuRn+dJFIucqWLY70o2q4dY8A9QTzQMkdpeCQP8A61PDBwWLDpjAGKjZg3IbB9DQznf8g4280nsKzFeRhyX2qTSpKsYCyDvke9Rs4YNhCW6gjimPJKAHCc45PpRug0aJZT8xzwfakJCpjrxxUSs7gKQzDPPH9aHbAwAODzTGSZfo4GAPxpjEbCpHXpjjikb5icsAMDjNRTSuQcEfKpwKBKz2HAcHODkdPao3ypPBznkg0CTLKsgAyODmmsyMOvbtQ9BiNI5bBB+UnoKR22jAyD7U0tuB2sDxjg45pu9GY8HI5yAOKWm4vtA8u5znDDHG4VEX8yZFVRk5zzgU+QfxKuSVwexqASNgqADjjpmmrWDRIdLblMsjoRjkBuaYyMCCowM9c9aejBeAjDjljyfwodQFMZ54+9n3oEr3Ej8zzQJODjGc4qQY2nJ53c7e9RkuvVQR2ANOUO8ZdsDnpigeiRO05U/IpI29cUBied5wMdfWoQxZdxPHTOaeWKKJByelFrIOpKsg2gAc+vpSSSMCFU5GeKhhlY5XcAc89qeHYAl3+7nk96A30Ji46Mx4POBQJEIyTwaijlUAFjnPByKcpKr5Y75xntQ7dQauWYwroGRsA89CcU0fMMFgCowaSC+nsVZIoVYOMNuHSoRICxb37UC90nSQsQCxzng9jSyOQpUcY7j6moHk3AFQcAc1LuilQSByGHBGOPrQP7QbsAsvelDISNzH7vrUbOEO0HOfu+lNMiMDkALjj2NFriVyW4YBdrDGTkACopBk4kB+op2SYhuAyB2PakYLwXBGB1AoKKzuFJ3Bsg85FQzKpXKg9O9WiZGBBiAGe45qOSJJG2lhyeQKBbaFGR22bH/Dim7yRvZWPHap5QSWjwBycCopImQbgPoCeKBSGtOd4Cg47EVLFNuIOBu7kjjvVZGYZODnr0xj2p+5EBZCQ3Xg01uLfQs/aJJPlMYGCeQetIzyMCCyE9CDg1B5iMoy5GeoBp5jkRTMrDHYN2NCdg6jmYYUNAhOfp2pmZ7a5E1qdrq2V2NgqR+tRfaAeZIxnOM45proMbyrEA8n0603YWrPcPg9+3Z8aPhSsena7dR+INLQBFtNQb54wOBtkxuHHHOa+sfgx+2d8HPjCiaVaauNJ1VlB/s/UcLknrtc/K3+eK/N2RYdo2zkD0JzTcrE4m8/5geCrYNYyoQmVdo/VT4j/CX4cfFzTfsXxB8E2d4hGFuRBiRTzyrjB6ds18wfGH/gmpf24l1X4Ma+tzEdx/szUGAdR2CuOvpyPxry/wCCn7cfxl+DwTS7q9j13SgABY6oWZkA4yj9RxxjkflX1p8Gf2y/g38XmSzj12PRNVkwW0/UCseSeyucB+fx6VhyVaT0HzJu7PgDxp8P/G/w61FtH8aeG7zT7iMkbJ4WAPbIOMH8Kx45l2bgcexFfq74n8I+HPHFi2l+MfDNnqdk6kFbiFWX0OM8g+49K+cvjH/wTb8IeIvN1n4P642l3OCRpt65aEsOgDH5lz+IrSFdXsw5Uz4u+0qxDDqPenJMrcK547ZrqPid8B/ih8Ib57Xxt4UuIY1OFulQtC/PZhxXIMVwHiIyB0rdNSWgtUWPNQkuXIHQ5qRJi2eR0J571WDR7Qx5IHOaVZDnLMMdqeyHqywlxlQuPYZpwmjJ3AgY6HNVhIgOSRn0NBkABIcYxxSFylkzh3we/SmhiQccemTUKyrtyW49xTjLnjcOnU0BykjSfLt3HOaPMzgM35VFuCkjrxjmgOu3gj8qerDRk5lCjIUn0pvnLjBbHXFMLK2Gz0z06EVG8qq3GemBxSE79SYOuCVYDjv3oEhGW2DkdzUDsE4BNJ5pJyMEDqKeu5ZLIAyk9RjjNRGMsCzLgY5z6U4SbFwTkY6YprTIFOM49GFC3J90bLGE42E59TTGQHIBPTipN6nGcUxj8uFHHXmn7wtCEozHgcD3ppjPR1J7HAqZgM524H1pCxQA4J44o+INUys8Qydq/SmOAx+bt0xVk4YYA5PvUcigHocf54qhFYhs8gEH1FMaNi2dhAIz1q1tUNkAZxTDsbgHr70AV9hPyge+emaY8WByv0OanlQg4IPHWmujM3TAPv0pPYCusTH7xwKjaM87Uq00R6HOc1G0SjpHil7o7MgbcDnB464pAxJzuP41OUOdrN09TTDGU+YgYPaqEMDkt8xpHkYkfP75pzrgAr9TTCpJ2g80lsA9Lhgpy3X1pWvUUDcCOfSodknQtgZ9Kb04IPHTBpjuyyt0hbIOPrX3B/wbgSq3/Baj4Jqp/wCWviPH/hNarXwo2wjBJI9Ca+4/+Dbc4/4LW/BJf+mniP8A9RrVaIqzM6vw/Nfmjm/+Dm5B/wAPw/je+e/hr/1GdKr4MVjjk5+lfeX/AAc3N/xvE+Nw4x/xTX/qM6VXweoyxI/Co+yWQTjJChOR7VJGmEGTyaf9m835i+Klitdx2gZ9xTvoN7kcSZILtT9qqMbh07U66tJI0yeneoAxAyD39aPiDVsUwiQ5DGkHloxBPPpTo0d13GnRWbFjI3ei99B2tqMDhjjrU0P95Yx06Uq2wQ5GOO1PZSBlOD7Um0w+EgkimZi+3A+lKiuU5AHvU6QTcA4/76pXxGfu445NCdiSOOOZ0wAOv3jS7DkJux60qvI3IU47UqW91K+2OMnnrii7AnWCJCC2DmnPGQcoQKsR6ddbQxAAHrVhbO1XmfJb0BpFaszETMg3EMfSrKFXHzJ2+tXo4LLYWW2HHTdUqbBgRwDHsKVkNKxltDcEgwwtntxinw2l+R87Kpz3rU2XEh3qCAfyp6abNKQWB96YlYgs0itl/frvPtVgXMaj91CFOPxqT+zPKGZVA+tKJ9MtxtlkBJPIA6UuVDuiNJbyToDz6ACntZ3VzyyHnvTZPENpHxa2hb03VG/iHVpDi3QRj0Aq1EXMyzH4enKhmwqk8FjikurKzs4z5l1HuH8KnmqcsmrXZ3TXBPtTU0tm/eNnJPrRyiuxyagiFgkW8A8EUC+vpW4hAAPHarNtppTgr3Hat7QPh94p8RzLb6J4durlmPyiKBj/ACFHwhzWOWMc8r5mgY+uAamjspZV2JA4yPvNmvcfC/7FPxq1+3W7uNBWxhPO+7k2Y/DrXY2P7I3ww8Hw/a/ir8YrG2dPv2trIN/07n9Klzguo1zPRI+YV0mdnCQRSN6EnFaejfD7xjrsvkaP4eu7l/SGFm/lX0pF4w/Y0+HEZGgeFLrxBdR5/e3K4RiP97/CqWqftx6xYQmw8AeDdL0eHBEbJH5jqPyC/pRzN7ISVzgPBv7Fvxx8UIsreHHson/5a3reXj8+f0r0LTf2M/h94LTz/iz8Y9OtHQZktoJgz/Trn9K868VftF/FvxkWTVvGOoSI+f3ccvlR8/7K4Fclc3mrXkhaWQlieWY5J/Gl763Y1Zn0VZ+N/wBjX4ZwsNG8L3PiG6iPyST2/wAjH6v0/AVQ8S/t8+KorZrD4deFtO0KAcRPEnmSKO3OAB+VeCCwlb5pWJPWpYbBUGTge5pKEeuoLsdH4u+NvxT+IUjSeKPF+oXasc+VJcnYPoo4Fc4Y7y7O+afA9R3/ABqVY1TjH4/0pd4HzNnimkkFr6jIdMgBLPlj71OsMcYCrjr0A6VF9oUHaGxx2qOa8IIECe5FMOYt7EQb1UZPByKHuI1HzHgZzVWEX92d0ShAP4jxT/7JvpGw7rxwRT5W0K7JJL6ENlSPxHNNS5nmyYFJIPUjAFSwaM6NlSp9CwzUv9n6hGAUaIbuPl4xRysRW+x3TpvnbAPYDmrFvp9qv/LJ2I9RxmnDTdb3j/SYl/u5HX8xUv8AY+ujn+1wcn+ECqsgvfdk0GkFXUqpC56HoauR2TEk7eVB+UVUuND1SKCOW41eZQ5wpCdaH0S48vy/7ZuC2OO1MmJZNvMQX3Dk4wKkEQ2/M2AowSQayJ9FdEKzarcAAZxnrVSW3s7ZsNdTyegAOD+lJ36FWTOiDxKGiEiD0BfHFQXF/axIXa4XPcowxXN3E0CnEcRHHJZjke+KiDuxyoPHftRd8orI6GfxRbWqbI97k9cHP61A/iyaSNo0gIHYk5rKSKZyS2RnoasxQDBJDEZ4PpU3ZSWpHLdXN6czSFeRkY60Q2q+YFQkZPcVbSFXTCL83HapkhCclDke9HQsjWzcjl+SP4alitIV4lIPB5K8ipYwiLmVDnGVPcU4BFOdnzHr/WkLURGj3YQHOOw4NKHQAhj074p6KSAsa4x75qe30+e8fyraFnY9Qq7uPpSukhkcW3aWVSRjinxI27ZHEeuAP4utXpdKt9PVhqV4A4VcQwsGbPXk9Bx+NINZkiP/ABLLdLYK3EgGZDjpyen4VN+xPMPj0ORVFxqM4to84KyD5zj/AGev8qSWbT4oHtrGxZywwZ5uWPP8IHA/Wq6I9w4aS5CggktK/J/SriX1jpyIYLeKaQck7255/D0os76jfoQLpV/JGJTAQCM5xz/+qopNsQCOAdvJC96LvVLq5JDy7UIyFViQKqSXEaqQ45B6niq1BbE8l03TYM0x7gD7zDOOcVSfU4o3Kk8gY5PH51Tur+WUbQQfZTyKOmgtUXrjVdxKJ1/3aLazub7c85cIvAxxn86zYYZjKA8m3J9K2LKxRHHmX5wB2GBVcrBu5Z0/S0TGVbAOQe/8q07aJ4f3kTkYBPzGs+QzpFm3uGBHQbMk+9RrNezssZnBHUEr1qyTVjk8khhJk7efmzQ7knb533hzx09qzGYxHzLUbmI5KA+lUr3Vbu13S3DMjMflByP50roSWtjca4VX2ykbB1x27Y5+lZ2q66IWY2KABHwGHrWJcate3o2luB/cPP411vwY+A/xD+OvimLw14L0mSV2YCWd/lSIdyT9OaiUlYpRtuYmh6F4h8a65D4c8M6XLeX1y4WOCFSzZ9Tjt71+gH7GH/BOzQPhtBbfET4yWMd/r5CyW2nyD5LQe4PV/wCX1zXpP7Kn7F3w7/Zw0OK5ks4NR1+5jzc6hIvKkdQvoo/+v16ey3mp6LoelT6xqVytlBbpvuLi4kAUDB7n/PFcdXEXVos1jD+b7iSeK00yNrw3EcEcaneZGCpEgHfoABj8BXxN+3D/AMFF2v4br4QfA69TygDFqeuRA7pj38s9l/n19BXKftuf8FAtU+JbXfws+Es5s9BRzHfajGNr6gQeg4z5Xt/EeTxivlAvl2eQ8t1brmpoUnJXaKlPlVk/n/kTTyNPKJriZpZJDumZ8kk55yT1NRsWziIA8dqCpRCM8HtnPNIm4HzAMdsgdK7LWRlZLYeGJZWzt44OKcWA6AHIwRim+ZFwGyRjopwKVpQoUMRyMHj3qhK9h43twvJ7E0u4PkSMR34aojvDMCR0wGzzRJgEsoIyOeKSTHoPMjbhs5GOCKBIH5JPFIjmVN2QRnGPqP8A61J8iL5iqDjoBTBbD3cgElQOeRTXdXfYrdhgetRmYlfMIHXjil3gEhnyCeeOlJbDFAZWxtGehBNNMoycqcdeTT5Hik3GJM+mR/WopZVLBgPqaYDf9ZHvXOc8g9qaHTBjAbr35/z0okKAllyTnjHbNReYQxVl4Jo1sBJI6lfu5OcgY7VGkiMCGXBHGaHJaPcOoOQaYjpglh9BigBxK4KK464ApGYjaSqgj3xxTVCjLGMcDt60jnJ5OG7AjFAk7gJEYBQ2evamHzGPCHn26UpZyxV2HA4NJNKoQnaV5+tJ7BokNjkLDy1IIB7mkllK8DjB4OMCmsY2UyEAMMcmkM0RPmFxz2x3pi5RzTMykF/XB5p+7gDeBjnFQGQKDgA+uaYztGSVXII5zQPUtxedghmAHXp0NPRAWG58e9QQs0nzFRx61LnJwCSfQ0A9tBQMAmM8KR19KViCM85I4yaiZykeWAHJ7daegYqQSQRzn+lAkxyuFHzKOeCq0fLgs2SAcYpu994c4Pc8D/Io8xlfy22gZ4YHrQrj+0WYohcW7SC4RWHVSTz9KdYvaRXi/a13RMcSkDoPUYqoCoGwNkHPbil85SvlxHgdwc4otdCiW7+GC1unhs7lJYs5jkjzhh2P/wBao1eJSATkZ5IHFR5UxgNISQflIppcMpQZz6qaE7IPMn3oAMLgD7pxQjoVKsAR3AqEFChLDPHPPNLl2TCgdf0ovYPskqF8Zbp3FOWaFkImGSOneoQSvBUAY+YgUhZgQ54weKB3ZKhXHmSZJ5AzUTqFJ2g57GkjdiSDg5zjPSl+TpIQpz0HrRbUYxoQq7pGB9aixG5EhUcDHNTEMgC9VbrnpTZATgRKCCKNb2AqSsVYxiIjPbFVZCzMQF2jPUZq+6jYRKdp9MVXaEnhXJyKAIhIAw3nA43EHmpTKjgqXLL0XPWoZIfLbOM89DTC8ikbsYPbuKehK13LMMsIBDksCOVHUUS3Vrt3KrAZ5JHWq7O7/K0adOoWpImi8vyZwBzkFqskinaCZisJ+TPUCnIIGHILHPzALTjDabGaG4BGMttJqMIYZPlJKkdQQOKBt3HyMgJMABGOhGce3NLDLKHWdshlPVRgj0/rQLqLpIhAHfOaZJNE527eFP8ADU77gnY9n+DX7aXxj+EE0dpc6q+s6UpAFhqLlioHHyv1XjivrT4PftffCP41FbW21g6Vqe0b7C+whJ/2WzhsH8a/OX7Qu0D0HRj2p8V5CjBVG1z0YOQRWc6UJISumfq/qukaPrmntpGtabFf28qsHjljDoV/4EOa8A+Mn/BPn4Y+OrmbVPh9OdAv2ywgjXdA57/L1XPt+VfOnwf/AGxPi98HriOys9dbVtNUjfp+pSGQKB/dJOV4r61+EH7Z3wO+LsUVpNqI0jWXAza3+EG8/wB1z8p5rmdOrT1TNFNbNHxX8Wf2WfjJ8ILl217wvLc2ithdRslMsbDseOV/HFecmbY+2SQgg/dYV+tl20D6c3nwxXcUw5DKrK4x68jFeJ/Fr9jP4MfFgSanZWA0LUZWLfabJQEZj3K9OvPGKuGIT0kFrHwEZ4yCT1PqKkiZXAcAYz+Ven/GD9jP4wfCmd7yPSG1fT0J23mnRswwO7LjK8V5RLLPbzNFOjIynDKVwRW8ZRlsxNNOxZkU58wfd9M0vmHGF4JHOarC+DP8wAOOeMZpVuGEoy+OOgqluL7JZQNgOz9TTnkRjuYZ5/h7VWluEIxHkcc5OaI51UkKwx9elINGWXwX3DA98U3ODuK//WqHziQG8wHB9acZgQR0Bz3p21sGyJCzDBIP0oJOQx/I1CXOB+8J56Zo80sMsMegJ5pFEuQeeePakYbc4IOOTxUTs5XcCPbmlDAqWYDPSqtqZjifl4PA7UblwRgj8KYZCpAZQM0bpCcKMDHU0faHayFZ8E4H4YpVcYw7Zz0B7UxpRjBxkcHik3qow2AfeqEPMbE7guPQgU2RWJw+eDTVmAI2n604SM3J7Gp94fQiMB5Ozn6UjqCQAMGpm3A7gw6dCKRlTy924HjsKOYFuQMjc+YP60yRST8vP41ZCLghcdP4qaY0KZQ4yKFfcRXO4jJx0xzUUiKBwCB2OastGoJDZGRUbRKU3H16GqAgaPjOT+NMYhcgZyKmeNlzsHT0puAASVzx6UBaxAwDcEcf1pmQMh+fQirAiVh9wAnviozHng4xQO7I2bcB/ImmFlOQ5HPoKmaA9EA49sUyRDyMDpQIiOR8pNfcH/Bt2h/4fW/BNiP4/Eff/qWtVr4iMRBAbg+ua+4P+DbrA/4LV/BQA5/eeI8nH/UtarTRE/h+a/NHL/8ABzdBJJ/wXD+Nxj/6lrH/AITOlV8HrZThdr9e9ff/APwcuTrbf8FwPjc6wq7E+G/vdv8AimtKr4PnPmuWkULn+70rJSNeUrQW7xggjOehzU9rJNaP5kPB6UkQRWxnnNW47YsC0cefXHam3cautirM8125aQFiffpRHbxEcQDPuaupZuTkRlfU1KtqqNk9PU1OyGVEtosf6sc077KANoRjz6VdVbbbyQfUCpo57SJPkhz9RTAzDZFgAsfNPj0i6kP+rwPUnFaT3QcfuY9uPQUxZLgnliaAKqaE6NvlnA9galGlWSrkkuanW3durf1qVbVU++350K7FoUzbxRj91EOOxFCQznlQcfStDbaInzuo9aiOqafCx2Pu9hTtpcG7DIbeSRtrjirH9mlsEJ+nSqcmuFTm3hx6GoZNU1GRcCUiizFzGsLKCM7mZQD2JpPt2kWowZt3bgVjeVdTZ3yscepp8dk55Y/QGnyiuzSl8R2kYxb2m7jgtUDa3qUjYRVTPcCo0tASCp5HpVuy0y4uphHbWjyMT8qqmc07ILspu99cviWZjn0NOisS4w6Z56mu+8I/AD4meLWQad4XnVG5Eky7Fx6816f4W/YglSIXnjrxbb2Uf92P0+pxSc4xBJvY+e4bNQ4Ij79K1tI8H6/rU4g0nSLi4kbosUJY/pX0jb+DP2TPhNltf1ZNXuVH+qRjKc+hC/LRqf7ZHg/wxF9i+GfwztYUUYSW5REH/fKj+tR7Rt+6h8vfQ848Ffsb/GTxb5czaEbKFv8Alpdtt/TrXoln+xT4I8JWv2/4lfEaGEL9+JGVPwyxz+lcN4r/AGtPjB4pdwPFRsYj0h09PLx+I5/WvP8AVfE+ta3ctc6jqNxcSN1eZyxP1JpfvHu7CtH1PoKLxR+xz8M4zHpfhmXXbqPpIULKT9WIGPoKoav+29qWnwNa+A/AmlaTEMiNmj3so7HsAa8Bb7VMeVK/hT47FXO6U7h2GafLG2uoJtHa+LP2jviv4yZo9Y8b3rxtkGGCTy4/phcVx0+oaheSmSadiTyWZiTTorRFbdgH2qURqkmAgJHXIqkktg7FeO28z75Y59TViHTYVG98cdD3p+8ocEde5FIZ1Vev1pDiSKIugXHocYp4aMAYXpwDmqkt2MdM4bqKastxcZEakA+1AnuXvtA5IIH1NRvdgKcsMA/lUUemXU5CSyEAjuKtRaLFGDudjnqFp8rC65SubhmI8vJPqadDb3t4wAYKD1rUt7ZIYWUIpwBjPJxUsUKRrjaBuORjtVWQr3KUWiAKC0u7Pqaswadaw4do9xPpmp1iMX323d/UU4EsuAigEYximkieYJI4lUQxHJ9AakSKeNcFVA/2mqNFKfMcDPTK9aR5I9mC3HcYoKJmjhkYhxyT2qRVt4cJEjBxyAapRPJgyELkHgYGaSa6CPuDPuPU0AXnljWQFlBI4LE9Ka96VQrGFUHrx/WqE+pRbB5smMjrVC41BHOEu9xHQEUE2djXnuJGyysTj1Y1BqOu3EacYLZAHzdOKxJtRvHykdwxX6YFRqLuU/vX57D1pN2KSdi1d6zO+UkZSTwSck0ya/llTyFYtxyQcU2LThguEIzn8KnisUifcBuzSTGlcrxWsrDLN1PJ71aW0cqBt4zU6RrJj5fz7VKEfGwN1FK7HyjIrNwAm0e67etTRxNu2jOM8ccUNGdwQORwMnPWpkVMcA4B70giJHFhQzOo9QDTliKry2aYz7juLEAdeKt2mmXmo4jt0yCwG9iAq59SeBQ7LcevQYkYblnP0FT2mnXOoTLFbQsTj7xU4XHUk1MiaRp6q0rG6lVzmIKVTH17/hUFzq93IghWQRxqSRFFwozU3b2BvQuCy0vTogL27NxMAf3NueAewZv8M0641+7eH7NZxraxsgRo4RjcOpyep5561ktcKW3hT74PQ0ouYyCFAz0GetJR111C3Ynac52ZOeeR/wDXpjz7Rgg9eQaZ5uScqQeMUyaeONdzMAM8571YulicTyY/dn5sZGRimPINgcsRwcVWe6kYHyYjnOBxwBSrZzyETSspGRhVai2oaIW4vEUbt+c+9VLi+kkLKpBOepFaS6IksWGuEUjojdenrzT0s4IcI8CckZKiqUU0JtpGRbafqF5kxhfvc54/pVyy8Oskm69u1GedoU1prFDEWeNVXHB2jn070w/bJJcR2zMq9SExmmkhX0EttORD+7RMEdS2WqS3srRZlgjdY9x5JNN+0/ZlLhIo+Osr4P5d6yL67mmct9uhYZ42qc/qKL21BXkdFevpNtcLB9l3gcSSyE8nPYDGKrTeIbOEqyxINv3WVf51hLtAZZJ5G4+XAGP1qKR1mypQIByNtTdspRSNG711JF/d7sDpkYxmsyRpZ5PMLbmY9SelCW/n4hiRnO7CqoJySemO9fT/AOx9/wAE+vEvxcubfxn8R4pdO0NTvit2jIkucfUcLx+P0qZSUVdgr3POf2ZP2TfHv7QOsoNOtpLXSo3H2nUXQhVHopI5JwcfnX6X/Az4CeCvgn4UtvCXhDTUjdIR9puApLyN3YtjnPU5ro/h94J8K/DzQLbQPC2iw2NpFGojEaAc+pI7nvS/EX4meDfgz4QufGnjbXUtbG3UlSV5nPQIoOCSSOB1NcNWrzSt0LjCzXcs+KPEWieA9Bn8S+LNZS3sLZC0t07hTjHRQepJ6D2r87f2zf26PEfx2vZfA3gW5ksvDFu5X72HvPdj/d/n9K579rr9s3xp+0br0lhYvJYeHbZylnpyv94f3n9WPf8ALpXiRRVC8/MRyRWtOhFascprZA394A5PQClJdo8Hg9AKSVP3gbYeR605fLJw/boO9dRn7wBtxOWK4/hI70oAOYtwIA4yeM0IqtJgqOnWgCMD5SVJ9aPIoflBwGB/DpSoGEQ2vliePakDxpiMnrnaB3pEYAmNnwW7UAPWVQwWbOc4JVen54ppkSRiybtucdBzSA732bj0zk9qRp1A2A9u55otpZkq7Hudp8gA8jpnpTVbzFbY2MY5xTVlADMBntk0vmZYBxhSO3rRbQoQ5cBVB6HI9KeiEpkOoAznJ60ySXByFOBwGB60quV+TcMdCcUANK4j2kjcD1psoA4HK9R/jRJKYgTtOCc5I61EJGBLc9D8uO1BPxCybXZSQMDqM4/yaY0jAn5jkDn2o3rt3OCTjHHY0xxJgbm7ce/40DtZiuzhjgEH3pfMGwb278gDkU0kGMKM4z1JoMg5kwzH/doDQbKzKCqgjDcmmiRhLlv+A8df880uUkbJUgikcI5DFOB2HagLIYvnKXKgHGeCaayvKCkhxk7sZqV1WFvlEhGOVAzmq0jLI2fL4J6elAaNCgZZtyFgScYOeKWOLe5jwM7TtGOppHQqQyPz0xuxxmmSSIY0twoRg3UZzTuxfaAg4IZMFutDKoPMnbkUwYKmKWQFieCTjFKYYjz5ueM8dqErj0sTwmVgFAAG3qDTjK5AG4D2xjPrUUW0ryMUrSqWR0BIOfwo1TB7Ey/MAjt8vHJFLIzghlf0BB9aj3gxg5HuM047Sgx3PzZFD3DqO3ZDJjGR0FNdRtK4NNjaEs3lgClXag4XLenpR0FpzCgSKvJ6enOKMBP3hXGT+ftRuG7eFPB4OeDQTuDMCR74/SkHkKmIyWXp1NPcrj5faoZHb04AOfWlV3CeWXUjHB9aAkPQ5UxjAIPANTMxVjkDaRwQKrqTAwKxk85AqWa5lmx5jnB9O1AW1HST43KFB44zSCQlOv0BNIWCkKxyCTxj603bEOGPKnpmjRIEyRZQIthi+ppxlULgjnHeol2liFYfTFIZyflC5HbNGi2DlJOW5bAwOdvPFMd2Ubsgf3ccGmMWGD+Y9acwgkBUPg5446UFEbMznO0fMe9M2sW3MSAvGQ3OKkf90QFTjPGabcNkY3BSODj0pq3UzIZkjLYXn1BqFoFJVuRxmpy4Ee0tnJxx/Om7IlUspGQfSkaFcOIyd6Z54OOlNO5G+bbhhnjmrJEZBDqrBug4/OqrwgHKjIGTtNNbk+6IjKACka9eSvFOSeJnxM3HTIqIFRygOP4sdqaW2jaEw2epFPmFruWUa2jzkf8AAhzmnFBIW8u3Zh/fQVVR2JKsTz2FOwY23I7L24PSlzMEtSw8ipEU8hMj+8uD7ioRJayHaWVD6Gg+cG4l3A/36iKzMWUQgnqCBTV+oaEolibK7xkDGSetDgIA2RkchwcGoHUBCV4PcH1pA7Ko5JzwcCqBbnr/AMGP2y/i98JXjsZLs61pSgBrK/YttHorckccV9Z/CX9rT4JfF6GGw/tpdG1KTbvsL5NvzegbofT8a/PNiyKIwMeuFoht54WFxDd4YHI2Egj8qwnRhNdh3aP1djSKCMqJVdT91wuVIryn4z/sp/B/4sFrm98NCy1CQEC/04iNsnuV6Nz6+9fKXwi/bP8Aiz8KxHpt1errOnJgfZtRG5kA4wr/AHhxxX1J8If2wPg18WlWxk1T+ydTf/lyvDtyT/dY8Hn6Vzypzp6gmj5i+L37C3xS8BRy6t4ctzrOnx5JaAESL7FOvT0/pXil9Y6hpVy9pf2skEqHa0UiFWU/jX6mPa6pFdNPYzL9nddxJkyfwxkGuT+J37PXwn+Ltuf+Eo8NRfaXXC31qmyVT2OQOR9aqNdr4ij820lDsFDHOMZNOCIv3gRnrxX0L8WP+Cfnjbw952q/DW+GsWaZY2xws6L1+jfzrwLWdA1/wvfyabrumXFrPGxV4biMqwx9a6FOM9idUQxlQNqHHPNSCWR/lG0nHY1VE6sMkkGkDkjO8n0Ga1JLhIUYYd+tB2j5iCQD3NQGd/LABzjgflRDKzsVOMY5INT9kCYMFDbEAGe9Kctye/pUBnwxDjAHalEgK7l4z2FEtjQkUK4IL5z0pRkDDOD6VGWyuQvPamrI4Ox0460faI1TJHPOd+R2FNJOwfMCM0jnKkKRz3IoRWPylc8YbFH2ge4SDIJLd+wpcHdtVjgDgmkDKpKlsHvQ7LjIB9sd6PeDQC0hYAuOvelMnOAfyprPkcnk/mKN4HoPrR9oQ9XY8Y6inORnAIx3OaiIwM7Me+aawHp9M0K49B7lHO7ccjocdaGZM8tge4pqsMDeoA9zSSEk/ICfY09Gg1TB1DNkED2IxTfLwc7M5HAzTht4OME9vSlZN+AHxxSiIhdWB3Ecdh6UySMleDzipnHPBOe3NMkDkc459KoCvjbkE8UjKSRtGfSpimOT8uPamumBuoAhKqrcjv3Nfb3/AAbf4/4fV/BI4xmTxHx6f8U1qtfEjRr/AAmvt3/g2/X/AI3V/BRj183xHjj/AKlrVacdyKvw/NfmjF/4OWrSSX/gt18bH2Daf+Eb5J/6lrS6+GRZQ53O/wCFfdX/AAcsBm/4Lb/GtQ2B/wAU2f8Ay2tLr4YWMEZZuawjt83+ZutgSOzj+aOIZHXipFu3VdsUYH0FAjjAxknI4pQsafKXA49e1XbS4Akkz/My9ad5bSfMRUUl3BHw0gNNOrRRnEafmafKLmRK1oEyTnBPpT0SNl6j6VQm1W5lJCjA9M1FvuZBy3WnZE3ZqNcWsP3pBmo5NVt0PyAk1QSBzw7HP86ljtFHQ9TzmiyETtrkp4gjx7moJLu7uW3O5x2AqWO1ULnAyfQVbs9Jvb5hFZ2byMTwEXJ/SmNu5m+TLKfnJ/GpY7JM5PPNd34X+AHxK8UMDY+HJlVj9+Vdg/M16D4f/Yz1YItx4t8QW9mgHzKjbiPrUucYglc8Kjs1PABz9Ku2Wi3164jsbKSRj0CITX0VB8K/2bPh9F5/iHxNFfTJyY3k3ZP0UVHc/tD/AAw8JjyfAXgSB9owJHhVc/pmp529kFmeSeG/gP8AE7xIV+weFrlVPR5Iio/M4rvND/Yz8UnbN4s120sI+rZbcR/SovEP7V/xI1JWh0ya20+P+EW0YyPxNcLrvxH8ZeJX3a14gu7jJ6SSnH5UfvGCt1PYbf4Tfs1fD0LJ4r8XrqEqD5o4pN2T9F/rUh+P3wT8EAw+Bfh8srL9ySSNUB+pwSa8EeS5n+Ygc+vWlW3Yj53P1o5L7sNUes+J/wBr34hamjQaJHa6bHjCi2iy2Pqa4HxB8TfGvinnW/EN7dA/wyzHA/DpWZFYwrh2bI7cVP8AZbdRhD+BoUYoHcqh76U5PFONlMwwuT67j1q3GFXIIAAPBNPLIi4ABA9a0CzK8OnuigYHPXNWVhVAEABP1ppuVH3SAO/vTGveuTz7VCt1EWQmw/KBn60olVcfnxVJr12G1FOCeGIqSOCaUgb+p+mKaWpXuk8twkRJ3Ar2xUMl8wb5Rn61ZXSljClyM81PDp1sq5aEE9TnrTSsTzlKJLu84j4GCaki0iRxukmz6ir8TxlTEsWMd+1TGBUHzvjj0/WhKwrspQ2EcTBsAknjiry2vAjYqMDsMUYQsArfKTy1TloWUMMEYwdw4osguxqQWyHYCMYx/k1KsYiAKOQMetQO8ZPyquPVRQtxAyHK7h2YnFX9kgmilQHJGBjJ4p0RUKzqfmI6daqy3e0HfPH06Y6VVl1SGKPaZwxPZFyalamhrLIJEITnAz06UyS+BUArgKeAvSsC41a4BzFJsGejVVl1S7ZsR3bE+wGKV0KyNy71yG1ziFmx7AVWTxXEg/1TAdCMZrIHm3Tnz5if95jTo7ZSxXZnHv0ob7DS7l+48Ss/EMbg59Kha+1CYkSTgZ9uaSOzUDpz7CrEduqHcvApXbQ9UyEW88wPmSZ/CpYbGGIEOScjnacVOIxuCqPqKkhhw+AASO+KVmWQxWkac461YiijByByO/TpShQXOTgZqWOJApYADryRSMxq7fvOGz+VP2/INsgKnqM9KEKl8Fxj1xQFGSVH4E5oK+0OQbBg8gHNKrn7wHHoKcJHZtiLuzx+NXrTR5vKFzqUyWkTZO6XhmA/urjJz0pOSS1DlKWVZsZx3q3baLfTxG5kYQxou4yTnaCM449fwqcX+n2I8nSbISsUObi6UMfQ4XoOOOc1Vvb271CbzLyeSWTABLHoAOB7cUm5PYosO+l2g/0aJrnbjLyrhffA7/jTZ72a7TM7cZyqKoAH0AqpvMY2oAM8HAzilM4K7SMk0WV9RR2JQ8bxgN1PfpTS4U8KBn1qFGTAUnkD8qWSVUQN94ZxyaoUSUIckqOo6Uk80cShCBk9c1VbUA8pihI9M0sOnSTgztcqCOuDmmlcL20FlumwBHExPUtggU2KGe4YSEEKevGa04NOt4QqmZpD97I6fSn25IfbGQAw600iSBrKJAEELncvOTU8QiiVV+yosY5ORktTjIZpikr7dnRvpT3nEp2TbAAvylm6mnpYBsrFmEpwMjOAmOP85pPtGZVCgqv+0f0qFiJGyzgEH5gecD6U2e0jaNpSXkAPOzIAH5UdQvoXrCSyWR1v7kKXiPlc8Z7ZNQalPcNGDc3TtlcqFc4x7D0rJ1B9jbEBVhyGeTGP8KWGxuEhEt1rdngrxCXLkfkMUXsgWpVvrsO5WP51P97r+tQxQkIdjfeGTUlysbSNhlZOzBcD8KY0+3MSg+mR3pe8V9ke8ir8pbK4PA7Va0Lw/q/ibVItI0SwlubmZgscUSEnP4Vf+G/w08V/E3xNb+GPDFjLNNM+07UJCjuSR0H1r9Hf2Q/2HvCvwN0mLxH4jt4NQ1uVQ7zypxGf7qg9P6fWsp1VBXCz2R5x+xn/AME5otJa1+IfxltBLO2JLWw3ZWL03erfp29a+1LPQrCyt47W1tkiiij+QAYC4GAMf0FOjvI/smYo/KWM4HlqPl+gFeeftF/tNeB/2dPCUmveJJ1ubyaNhY2CAF5GPAbHZQfbnp9OKpOpN6GkUk7F/wCNPx28GfAXwnN4k8fTRxRrHm2tFfLTt2wvpkY9zx9PzR/ae/am8fftI+L31TXLhoNLgYjTtLjbEcK/3iOhYjqew4HFYfx2+P3jv4+eLJfFPjPUnZFYi0s0OEhU9MDp0/wriSwDD5WAI5HvW9Oik1Jic1J2Ww9ivRwOewpMqBjcAR/jTFwMK7D5R0pXCrHlAeecnnFdJHMOZXdxkAZHXPUU5QrPwVyOmT+tRGRthGzJ7EmnRuECtuJJyCvSluhpWJYo4WGzeCe5HekcI7YLDGPXpSbliYb0x25700qrnLYx2Iph00FiYIuTxg/KR396eNjYdASec4qOM7l5xx0yKcuRHuyDubjtxQ30Fsx80yF/LDcgc9qbuWMllQEHtjPFMZVwZDwSOfz7Uscqb1UP9eeooKHearRFlUlj90ewpYpVVld1LAdj3qNWV9yIdvBAz1FIqFzlmGVzgZoFoTPcJvyQQN3yjPSo2ZHXCMD32kc5xSvH8pHHA5x270zzP3WRgEjrjtQDdhsrNkKQckcg9qaJAYiS44PQdaQy7V/dkZHVu2aieXdlJGzzkAcUAth25VXbnnPUDNISD86KP9kZ/pSZAUOigMDnk9KawDfMU4YcAZ60BokLvQkDdkZG4KTx1pwcRj50yMcHNMjiwcM2COpUc0srlU8vYMZwMUDA7nXooAGT/hThg/u2QAHsaYVYHDvzjsaa8xcbww4OBz3/AAprcWiRHLcyByik8ZyAehqIsikFGY8c0wviVjtIJ55NAmleQFV6jFIXMOEx2lC2ec59OtIAk8JAIwDwCfwoVogredzgYwaijDyB1K4yOOMZp6tjv0JFRYyyDHuOuaEaPy8A/d6YH6VEZHQtG4bI6HBpxg3KSGwWHzA9jSFyk6BnHmrgg8delLI3lKIhw3cnFRQxsrYjQkA/NzSylw5LsN2MgGgonU70LAADOKFLOvLMFHTHeoonZlzkYbkbe1SI4I2yEgITkAUC6jgyOCUGw98c5pp8xR0O0+lRswYnJHJJUj0ohlYjaMkbc570+gmrEjZdAXYE9iKkbCgID1ODzUEfmPlmbODninfaCz7mAPbmhvQdkPkbgsGwB3AojbdhcZH8qa7BgUHAznpTDJtQAkcDJGeDSGTxl3baSc5+U+nvTESSMOWPU9utJvQDfuJIHA705naYhCudo4PfpQTyi7zuXdhhjGR2pfNTeQDkdASvWmESLxt+WlceYDt65B4FD1C2pISok3k8Y4JHem/OuH4wDmmFiYR5oIx0B5xTkcqDuYEZ4PpQH2R6MOFDkcnJIoGxjhVI9SB1qNJmKnCggdKPNO1gSM4wCW6U1uHTUmlkYLsA4I9ulRBWIbsfU+lMMm4MVXGB2+tGzKbGY57EZFGiZQ0zqAUdlDDgNjrTXjZgHVNwHUH09KJIQUBC/MOpPY0kjzbiHA5H8PajroTIYzI6gbQAO392mMrKoZs4PGaJll3EsRgcZWhx+7DSH7pyBSC6RDIwViVOBzzjmmsqugZHGQeT61K4TYBEwx6EVE6MrEpjA7VVtQeuwxmjVskkHHJFK7lxsVM5PBJ6UjII1aTAJPJx3zTSqN+8DcY6YpXdw+ySStJGwDOOW/KgvdoAqkkDo2OtRtsxkO2ewNKzOSFxg5/vYqrMkmKRlQZEycZ+Vs/pTW8hRwOR2JAxUYidHZCrAnoTSGJnkw0h+YYosgHvchk24O4HjrSfa3jj5xzxytBV0TcJOO200j+UybS3J64PFF0PQY89wjZQgg+lRmd3YSg7WB69KRQiMd8uD6LzSrOqYCFTnqD296Yatnqfwi/bG+L3wpeLTV1Z9U0xcZsL+QsAPRW5K8cV9PfCf9tb4Q/EjbZai50LUWO1oryYbXJ6YfAB57Gvg/zlcZcgdhhcUxtqNujZhgfeHGaxlSjId7M/UkzRXEayWV+CpAMVxC+QffjNYvjT4deBfiHYnSfH3ha11ANxHO0eGU9mDjBFfBnwv/ac+LvwnuY49D8SyXFkj5fT70mSJh3HPK5HcEV9NfCz9uP4bePYotO8Xz/2FqDLtzcPugY9trAcenzVzulOGwr6anGfFn/gngiI+rfCfXy3zE/2dqJCkeyuOvpzXzt41+F/jr4dXr2Hivw7dWjjoZIjtb6N0Nfo7ZavFqlh9t0a7huLdgGjmgYMrZ+lVtb0zRdcs5NP8UaHa3sLKVkhnh3g5H0qlWlF2Y3ZH5ktK5ONxHP3cYp3mBRkDDHqcf4V9jfFf9hPwL4mlfVfh9M2kzSciBsvCW9MdVr52+I/7NfxW+GTPNrPhmaa0VyPtlovmRkevHI/GtlUUhWZwStkfK+fQ09XIwBuAqB2KMVZcdsEU0zsoztw386oasWvtDKqhWzz3pVlDAgqevaq7Ts4yV+oo+1Kf4MH1FaCsyyZGX5CvTtiguM5Q49cGqxukJy/X605ZR13qMjuaA6k6upXBbv1JpTnaGiXI/lUBck4UdPSlWQ9M5x6VPuglckVinzMvWgvjKlARjjPekcqV4cDnGDTSCy53HI6Y70fEIcGwMHpmkLBTyAfTJpgARuR9aeUGN2/nt7VQACxPPJ9cUrOYwTnJ7Uzc27AXGKaxCg4kbnrxQBJuYnC9MUJIAvOfck9KjYqPvfLx2pGIBIBzj2qdeUehKXOfmH4k03eqjbj9KYHXGMkGmtndtY+1UHUl4c4P6U1wD1GBmo0cL9aHdSQD19aWoh7LsJKMDx0r7b/AODcMf8AG6j4KErz53iP/wBRrVa+IPMUE4yCO1fcH/BuGc/8FpvgmQf+WviLj/uWtVqluZ1Pg+a/NGH/AMHLt1bwf8FtvjYHBzu8N/8AqNaVXwkdWXhUT6E19x/8HMybv+C3vxtA9fDf/qNaVXwksBzg1nBaff8Amat3JpdRmfhTj6VF5krnLN39KUIvc59aligcn5Yzg9sVYiIRMTnJqRIlY4PUitTSvCWvazKF07TppWJ4EcZNdv4Z/Zn8ea0EmurRbRG7zSYOPpUuSQHm6wL2Oe3FTw200xCxRMSfQV79oX7KfhXSVW48V+Ihkcld21T+Jrfj/wCGcfhyNzSWtxKg+5EvmN/LFS6muhXKeBeHvhh408ROo07Qrhw3RjEQPzNeg+Ff2RvF+qOsms3Udqn8Qzkiuo8RftVaHZx/ZPBnhT5V4WSbCj8sVwniH4/fEjXAYl1o2kR/5Z2vy/rRechaHpulfs4/CDwcgu/GPiCNwoyTNOFH5dadN8W/gH8Py0HhXw6LuROFaGLg49WNeA3uq6jfyfaL+8mmcnJaRyTUCmVzxx6Gjkvuwuz2XxD+154pvFNt4a0a3sEI+Vj87AfoK8+1/wCKfjjxM7HWfEdy4ckmNXKr+Qrn0t5WOc55qWK2I+YYBzycU1GK0sCv0GmeWU7gST33HmnIsrnlj9KlWHaOepqULgAMB+NWIgjt8dVyfep47dCMsvP0pxZd2SATj1pplCnGc49qAJAsXViBjtTxt24R/wBOlVWlAOQOveka6A+Vc9eQDUq4Ftp1B289ecUG4RhwB6Hiq224nOFXGfapotNkLKZAfejlASW8bJAHGeOKQSXDkELnjtVuO2gUCMKoP0qaC3SJscDg44zTsgbKS2FxIBvkIz0FTQ6bsPzkNVzYImHy5Pck09XjA+frnjjrTSQtSONERT+4Oc8E1KkQLHGFHcGnMoPJAAH5VIWjwMx5bHdaBcw2N1VdpYEf7NTIy42xqozwdzc1GY4D97IwOTwKQLZxASEn6Gn0BaslJ2oQijIPOD/jTI9qggDv1qMnPRsDuD1pkk2RsaVQPQdaQ9Eiz5kbAEHnPNSPOFUp5YPvWZ9quFYmGMEDvUM15fKv/Hwqj35ovYErmn9ojGRuwQO54qrdXcWdouceuKxbi7lIJaV2I9KYHLL+8QA9xildjSLE96of91LuwetMW8uZQQFAHqBSRRZbKLweeatLbKwwBRfsNbkGx5Duf5jU0MOcgYHNTpCiKHZT7j2qVYYQu8ICCe/amHUiFuuDxnHX3qaNULANH9afCoKHOBk8DHNPUqPlK9s4oLEWKRThORjgg9qnjXOAH4xnjvTEJWNlA7VLEpYDLEYGRUO3QmIqxHbhTj13U5NxPIUAdSDTGGc4bvxUjCMqMsMjoB3oW5QuF3AqmO4z1p2MAFgBz3PNNXCnK49iTT0KyEedjAbJIHNInlAbQArckn8T2pyqC+XYgE8gDkCleSFXHlIflOdxPNMUoSWkPOOATQC13NK21i306NY9J0yNZ8Em6kXe3PoOi/qfeq07T3UpmvZyzfxM5zUHnBVxnGfzNKZwCN4yTk4pKKQPXYmU4GFA+tNdlMYDMSewxTRJk+VGAB6kU15VV/mYZ78cU1cVmStuDBiWAxk8c0jMinMp6c4biqsl4rSbUBPsDRDHd3Eu50Tb6OaaVxD5LvBKQxlyeyrSRWd/dNseEquem7GKsQlU3o+wHH8PWnlvLRQr7do7HrRZgPtNKt7cA/Yue55P51YC2u1gQDg87TUKSPMpDXBJx1B4xVe+aGKQLGrMw6gdCff0qlfqBo+cY/niIVVHemvdogwpALAbStUftOyHAtsHsW6YqpcXepyPm3RBjoyjGKHsJeZrNNGEWDcoO7DEHrUturXEMt3OgMcOMnHPXAArnLifWkffIwG4cEVJFrmp2Ue2Q71YfdY/e/AUr6jUV3Ne91+JFDwWPlBvvZwSR9TVN/E/mJ5SAkDnaXP68Vk3V2J189VKZ6xg5pkZXAQRDd1OfShtsasiee5iedndep7ULIoXLDn361ArIr5549+hpyL+8wqbiTwKTt0HykpkLtkklc9Aeteg/AP9nbxz8e/E0ej+G7BktQw+0XbIdqL6D1b/APXXZfstfsUeNvjnqcOtavp8lhou8EyyrtMw9s9v5/Sv0X+G/wAL/AfwX8MW/hjwpokVq0EYWSREG4nHJz1LE/59MatZQWm4knI5n9mz9lbwd+z1o8dtpNmkuoyxYuriduWP49q9XlaWODfcseVJTK45zgY46VmJBPc3C3AikJ27QzP97Pf8OleLfthftpeFPgVpk/hnw9K114kkiKqiyZEB6Ddxxj9Pr04m5VJamislZbG7+0x+1f4N/Zw8NOl/ML3W7yMjTdNSX5sjPzt3CjjP5Dnp+bXxV+LHjP4t+KrjxX4z1Zrme4kLKB9yMdlVegUdAKoeOPHniv4keJrjxV4u1l7u8uDl5ZGJwOwA7AdhWUUVSpA5wcsTXXSpcmr3E5NqwLGzH5m5bt0xSl92IzgKO+R1qMxKg3NITjqaNwbEgbgdgOtbgOZ9q5K85qSKZlTCH65NRMFZiyggNyAamiG1C8cRxnk//ro0sS7CKzOG3cAcg5oVvPB2P0Ixg0KzzKCcAEenWmgHITy9oHT60FDt1wFCkdOjEU53Malicg+v8qZKzt+6HJB4IPanNF6LuAA6nvRZWFd8wGQMBuBGRg06QAEuDnH3TTNqsBGMDpx3z9aPlnby1QkBs80rIV+4ivn7w5xgjPSkDbsBOPUmhcHAVB059qRC4BLnHcA0wiP3h5DlOhxwKfHLFvCuD15xxnmo03Im7O714owIz5r5Ix6dKA90nmbzCDDESAfujJyP8iojGo4bKkdcnpTftBJBjZvr06VFJO6N5mcnJzxkGgPhFIjaJz09s802RTI+Co4HJobc/wC83Yx14xTPNXczdV6ZoH0FQiM/f7cZ7miZ3ZQg4w2cCmLgpuUklexNOWQgGRlGV45A4ppXF5gsjKBlWOeDkninOTKuzcBnpg02Te6b3yBnJ/AVHIxzlBwOmKQ+mhI0AfnzM46HJ9P/AK1RTJgsFf3K0NIxIdmHHb1NLPh484wA3J3UBZlZ5YJjhiVbGdwqSSS1eMRCTaR6H7wxUc8SGUmOQben6UwFo1UOBuB4B7imtxWVwB3f6xQQOmO31qUHEIRgFw3UHp6VGLomRmMKKXAHC4FIjMmVYnB4ORSKBnSC4Yqoc7SM4z+NNVLjKuW++c4HAoaUIWKry2O/Tmmg+ZGBkqyr1HpQSuxN5rI20SDOTnH86lVS4MbSAsuSOOvqarI0ZcANjJAOBxmpctFKwVD1ySBTe41e4iu8eQgyF4/wqRcud0YHPvUYcRt5nZgOPWnleGVBjJ6f5/GhWYabBIoDHEZ59+KWN9/KkcjoO1B2mMb34z0AzSPujYsoUEjgdMGkJaqwSeYE3hzyMdKRmdkBVcsPXpT1BAyfm5yAegqPKM7bVIGSAPagPsj3YFwmw8Dgjp9KQgL8yxkZ65oIUA5Len0oIcKTkMCc5B4FAaINrM/CZ7qQORSiRkbIBOfemoduHaRe2CKUOFYM4HX+Hg0O4cxdOkXbaQNatwZLdZPLmZesbdQD9RyDVYriP5VPAwCau6Br7aNdmR3zBcJ5d1GRkMh9R3I6ineItHuPDt5GrFJLaeMS2k6HKyxnoQfUdCOoNSpNSsx9LmexCDJf6gdqfsMyEAgHndxTf3cm5ieCenrTNzKRscjbzkcVQyT5gpRP0/nSoRv6jp6UsNt9ptWuIpg0i8sjdSPUetQE7iQVKkDk07sBXbAIJwCcde3rT8hcYB4OciopFVmyjDAH8Qpyhicseg5OKRMiRZMAs0e7jHNG0MnmgDrg1HlzneSR256iklkUACLOB972oD3QOQOCCORwOtQlohwwKEngMeKl3MyiPAGOVJ4psmycZVDxwQfWgojHyEys3bHPQ01+PmHXHpSvGxUoQevJBprTMqkFOg4NPqStdxjjzCMDB+lRsqEle/fFSOygCRoz6ZA6VG8YdhIp5HUg4zTSD3iGXKAOCRzgUFhIoEvp1zSsDgopyc5B7UxC6DcQTzzg8URJHedJEwVc46gE8UouXcfKgHzfeqJwydQNmc5zzmkLgRlkAI7gmqAsy+YUIfbkDtwaYrMz5baM9eMZqFGcDcHIGeBSbpXO9+Ae5FT9oe6JzNCB5bWxBB4wajlW3lAGNpNJI+whzgkcE9qRxK/QYwM/UU7IQu5EGCCw6jBpEjaXOGxzxg4pI5BghgAP4aVcFgvluCD0B/XimACIRqVcgk9wKQxsEDRNgHOT6U4sy5O7JA4DUbyiFlwOOlAHWfDb45fFH4Uzq/hDxRKluW/eWkvzxN6jaen4d6+lfhB+3H4I8XRx6V8TYhpF/wAKt5HzDIe27qVr4+WRSCQoB7fWmNEoyxXnHIrOUIyCPkfpnpWqabf2UWraBqiX1vMoaNoZQyMO3I4ouY7e6Qh4GQMPnSRR/UV+evw0+NvxD+E9+tz4S1+VId2ZLORyYpB3BH9a+mPhZ+274A8ZLFpnjiH+xdQb5DMCWgduxyBlc+h/OuaVJxd1qWpX3NL4t/sqfC/x9vvLGwOl3ruSbqygG1iRn5lBx+OBzXzn8Uv2Svid4BgbUrGx/tWwXJ8+yUllA/vJjIr7YjNpqFr/AGzo863MM65EkEgZXX2pVCXMZkkJVe6OOR9KIzlHQm7PzSdJoXaOZWUr95TwRTfMckgHIPTJ5r7t+I37NHwt+JUk0t5o4tr1gQt5ajy2B7E8Ybn1r5w+K37HXxG8CebfaLCNTs1BYGFcSBfXb3/CuiNRMR5C7Aj94uMjjiiNxuxI5IHYinXNjqFnMba6gkikQ4ZJFwRUEjEEjdk/WtR6FlZ4kbCDpxnPFOaVmX5AvPeqSOd33wPY1IszcfLgg1PuiLccrMTlwOO9Sbxt27h9MVUWZcZbpnkClSZSfklGPQ0WvqBYWUdSBtzQrDJAc/lTEkIfaVzzxmnbgDkjGaIgPIychunSmykqcA85xxSEsSSvQUhMZ5D59qNmPVsFYj74+po3hgRu6UMx2ZH8qTAYblUg57AUeYhQVUkhh14x2pGcjHyjr1ppB3bQhHpzTmLAhuOO9HwgIwZgWx9OaYcRn5h19DTnlTG4KD+FI2ANwGe+Ka2ATK4G09+eK+3v+Db0j/h9R8Exn/lr4jGP+5a1WviAhTh1XtzX27/wbekn/gtV8Esj/lp4j/8AUa1WrjuZ1Pg+a/NGT/wcsaZfX/8AwW9+Nq2luz5PhvG0Z/5lrSq+PfDnwO8e+Idr22kSKjHh5flGK/Sn/gv/AOPfhj4O/wCCuvxhk1yEPqO/QDII4sv/AMi9pu3n6Yr4h1r9qYxRtD4Y8NLH/dlnb9cCuanOU4uy6v8ABm1l1KPhz9ki62iXxBrUUHcrGCT+ZrrbP4UfBnwLAZtbvrd5EGf38gJP/Aa8q1/4zePvEIYXWuvGh6JB8v8A9euYnvru7kaS8uXkPq7ZNXyye7D3T32b9oD4V+EYGtvD2i/aHXhRFFtU/jXJ+Jv2ovGOqhk0bTrexQ8bsbmxXle7HKg8frTgsh5Gfz6U1CIrs19a8beJfEEhfVtbnmLZO0vhf0rMMhJ7knvTUjGQHFWYokVQwFUlYRColJ5HFOW3MgJZsY7GrAC4yemaUImAVHBFGoESRkHaWX8amSIj5eB6jFKgRBkL160M4Xv2pRAePkOSw6UK/Gd3XpUUk4AwQDx6U3zi5OwdeuPWn0HZk7zKpwRzimNclTtK54piwSyfdaphYBctLyp9qFsIYbpmO1VOe1Pjt55H+Yle5wKmhs4lGVAHH5VOkAzkHcw7CmkgbIP7NjyAW/WpBbIq7Qg3DqSKsrCMZkGPYCgRlXwB+fWhIlMaiSBgCDj1qcQhR8zHNATC/K+B1IzSgqGOVOf7xNAfEEYjUn90OO/epdy7ORye+aidYic7yDjg4pGc7iAxI96AskTI0ZJ/d9OpAqRmh2/JGMjqcdKqGUYIjf2pZZpAgO4D3NBRZSRSCrEA545pGkKqZC5BH3ee1V4yikySOCTSm6h8wtJHkdAPSmvMnlJ4ZVz+8yvbBPJolkVT6fU1TkvjyVVQPc81BLeu55kUYpDWxoI0MXAwxPqeBUbiGRi+1cDnOcVTMrKQZJVOR61HNMBxHKv0zQM0DdW4xEzhV/iYCql5d2ZykSls1UMzs3+s/IdKdHB85fcfbigAaUDhIgM96bHFvbLqRxzU6QI/3evrU6Rk4Xbuz1pPYa3G28QxzwB6mrGwqcIc556UCJgMsufapFZZTkjGQOKgbYIoLHIye3pS7DHliRj2pUQ7fMP4c05VAGV6+lVyiW44Kz8qo6dc0+G3wCTwT1zTcyj5WwAaeOCFySfSktxCpgSFdoPHJIpz5IynryBTB1OPxxUoQRAu0hDEfKAOTSK5QBKLho2JxxtFCLGMvIGB6Ae9OZ5jHjAX6DmhTuBLEYHU0CTsOBYRMoXk8g0KrhdoPQ4OaagzlgMDOOtBjZuQxOegPSi7ESK2SUAGAe/Qij5c5znHGCajIZI9237vQE0jEMN7k5x26UASKUALFlJPQZo3MOAoUEH5s1AXQEjcCB0461FcXUhIjjiJK8DcaDQtmaFMnIDdOW4qo8rzloRyT0x2p8VlPO4aYgHPcVbh02GP5mck47HAqlG5m2RabZSxgTSHBHRmPQVekMLqokIK+gFQTCR7YiJ+O+Kjja52bTHnC00rBfUsiIF/kt1CsODjHFSyRREBcAr/AAjGaqxyzmUOXycfhT3lEkpMqvgDAxzTWiFo0WDJEv7woOBzhe9NgkHmbDEWZugA5qJpY2HmOpBXpg9frU9tqkVjAssMIEr9ZG6r9PSgZYvLKS0KyToqccCRhkfh2qreX+nwJmO4Ut/sLnFZep37zOQsjsTncwPWswBt2TJx2DHrSd+gkrGhdalFckqFySeCVH+e5qv5sLndcEsf4VU4AqFDtbYW5PehljViigZzkCmVZjpPLDgbjjqCOaMApgE5I6ULES5VkGQOcmui8B/D/wARfELWYtB8K6W89xJIAWC/Kue5Pao6j90yNH0bUNcvI9O0+3eaaVwkcSLkkmvsn9lj/gn2NQSHx58XbeOBCqtaaahzx6t6n1/L1r0L9ln9ivwn8JLS28R+MLWO+1uQfKXj4iJHbI4xX0BBIkUq21vIFEfyqOg9AK5qlW2iKSba5titoXhGPSnFjYa7NDZwYC21vCiooxwAcZ/Ct6/SxgkZZJBgqSTIN20envWZFNa6fZTXupXkcEEY3SGRgFUck5IAHvzXxt+2B+3tNqL3HgH4K322PmO71iJNhkxwfL/2fT8/QVzQjJvYdrq76HoX7ZP7ddh8OdOuPhZ8NLkTawUMd9fxtlLUHsMdX9u3U84FfCWta7q/iTUpdY1nUJrq4lYs88zEsx+pqjcXl1fTSXF7M7uzFnkkYksxPJJ9fegFWXaGCketdlKnyR13JbvoOAjDHbJ8x60bl2AljjPGBUUkg8xvLkBwMY7mnRO0wMIQLzxk9PetRjp3MkhADBR2A4qSIojblA9OvSo2jELfPITnv2BpYnO9QxAYdeKCb22JELtJvJI45Bp7Xcog8uJMqec7aYYnjiMhRzkdSMZpsblF8wsM9CCOlAluTI2BlsAkZz0xUe4SOysxLdQB0FI3mS/vdh4zx2NNCPtM8oA3Egj3+lBV0SAsQzsevUL+dLkkhhJxjgE0zzWwH8oAdMY5p4JUEbMDHQ9qFpuMa2d64Od2e1KrFPvNyBzg96arFQSik56Y7U0s5JdgQe+RTe5OvMOQhlLMMqT2PIoMuTtK8L1JHNNEqhSCCAOCQvGKXfHGmFzwuR9c0dAa1HRuu8kEj5fWhJBtPJIZuc+lMWZdxyfT+VDy7B5SAAGhbha2o11KMqqhxnOe9DSJI5VV4xyAeaDMQ4KqPu5wTxTGjQBp14yMEc9aQeYtyxZAYFB46NUeFI3liAoycHvTirKWcjnPAHpTS5O5NvbpjpQHuiuybvkUgmgLHtIHLD3/AFpGcJlTjscgdO9NjmXYQWBU+lA2tCRT8wh35+Ug803eVZTnjGCp9KjlYbWdP59PaozNIoV0GRjBz1z7UDJhhpAmTyccDikmZkJSSL5eRntmmRLLhQTwMYIPSmzsWgP70ZYkHP6CgnpYYXUsUI9M4PIxTZipYDkiNfl+vNMYMWO0YJ4wKe7lQFkAYAdxT6hZMaJRGmxxu4zuB5p+1mJJT73IIo85oowigAj1FJJIY5CIyNwxjb24o1bKImQPIAN3A5yKVlOAcnJ7k+n1p7TxsCASrdCQeajMu1CkyDvjIp/ZJ+0SoGIVtgKqOTmnh90hjU9+MmokZmj3YIB+6uOtOLSA5MYXvyOntUlEssaqmTyCPWmqF2s29uMYOevtSLLlRt5A6n3qL5XYh3K5/SnpexHXQsIB5rDf2yB6Ukp8wkEfN3/WmiUiUg8nGFZR0pJJd0mACCRzn1o1WhZIGYqGXOSOmcAURkEHaM46j3pn7wYd24bgCiOTlm2/xcg0gD96jNEM++RSRsEBKNgZ4yaSfDAlWIOccc4FEg2AbmycdDxQTIVy753DGRkEd6RZhICH4GOeKcpVcl16joT3qMRoBg4x1BJp3YacxOoXy0aJDwOec8+taen3tndaPLourXjRLGfNsZmBID90I6gH19ayGMa/KMDjKnPBpysyrkk9PepcUwu0EhVVAJwQPzpVmWQgjgHqM00hmXeAN3bjIp3mBeFQkj0GKrVMNeo5JTC4KOAVzjHQ1NdwxYF7bzKVcfMD1U+mKrMUK52nOO3agOgRWU9uVB4ofcL9ByrvO04OTxkdqR8nAPbrSLJIFAXHJ5zS/OrFlOc9j60aiuxpbLHPOacETliSAR0pGKytngOOnNIFI2gyAHJBG3qKQ9eUbIJUYqvzDtjNPlJaP9ypU9Wx/Sm58s7crkn8KAzgFTlSDWge6IRG6nfj0JHSkiaNkICg89zwaczRk7TjP8XvUbK0ZLxkHPBFZiuxCfMj2huQe3GKhaNo1ztLDvzUrRKSJOQemKjkI35dsZ6GmlYOgbBhiit06ZppXcDkj6YxRIxU7gAD1xn86Y8rFzt+6exqxEcoBJQtgnv2pxtyF+YLkDj0NKdjDayn/ZNI7zKuANwOefSszQhYsj+WeR7CgSyDCDoDyCKHYM4UNyOh9aQkA43D3UdqrdENWFaY5wR16Ujkn5FySeoNIxYjauMdjTJiSd5bBxxihIQ9kAUc445UijdsbBfmoCxBzzx1Jpd2DjOB+lUPUkMoJ+6W5PINJPOqjKqcY780hfC8seRTWw6YIP50tkIXdk+ZEQOO5xS+cQMt29qiJB+Q5B+lJ5zRrtZTz7UwJgbdlIbHPSkEiqvyw5I681AZAqhsDn0FJ56huuD60Adt8O/jp8TPhhOp8J+I5Uts5lspjuib1BB6fUV9GfCT9tTwZ4nii0bxzbvp13I20zE7oi3qD2+hr5BEyYyjEEdzTWkc87h09KylTjLcadj9KILqz1GzS+0+/SVZMGJ4m6jsc0yaeCRTFcxqdpOWZeT68V8F/Dv48/En4b3EbeH/ABDM9sjAtZXLl4mHpgnj6ivov4Y/tm+B/GCx6X4xtxpV6wwrs+Y2bthj0+hrGVFoE2jsPiF8CPhr8TLVm1jSrf7UUwtzAPLlX0Occ/Q5r59+KP7FHi/w1DJqnhG6XULcHKxONspX27N+hr6Vm8XaFdIdQtbuGQOMgxyDJX1GODVO4+I1lYzpnZNA4GR5gDqc+h6/WlGc4j1fQ+CtZ0XWfD929lrGmy28qNtkimjKkEVWMhK5Svtj4ij4VeN7NrbxNottLgAiR8BkPsRg/ka+c/ih8D/DmkhtS8FeJreaNiT9mkmG4D2Pet4VFIFF3PM1Y5yxIHrilSXZkuQfdeDTZba6tnaKcYZeqmmLhgC6gH1zWlkySdLll+6xPPrUwuehPpz7VU4AwDz6k0o2gY3ZHbFMC4WDHeD1PFKSm7cRmqayKjbfNbB7GpFuBkEDaPWlpuN36l5I2ePzIyCMcgHkUwspPU4+tV1ciTcjcH8qklbbhvMznuD2piHFkcdSCT0NBZVIBB6etMVy2AR+NPcHbuChh6g0nsAhG48r+OKRm2nDADjr60PICMhsGo1feenI680wHD958gx9DX2//wAG38bJ/wAFqvgoe3m+I+h/6lrVa+ICQDkKOO2K+3v+Dbxi3/Ban4KELgb/ABHkf9y1qtC3IqfCvX9UVf8Ag5Rw3/Ba341KfXw52/6lvS6+HGU7Dhfwr7o/4OTF/wCN1vxpIA6+HOv/AGLel18Ob8lVzn6Cs6e3zf5mjI1jZjyP06UvkknKD6cVNH+7O44qQ9Nw9OtaCIlhy2SP0p6Qt2oMiqoVMDp3prXJxtP8qAJgNvb/AHuKA+OQAB2OKgNyxbbHS7Z5GxkcHoeKS2HqmTecBwG7ZpGlABKN06EU1bdz8rEc+nap47TBAJA9SKYiESu7bVTqcc08W7uQS5+lTR28aZ+XpyDUyBCvyr+lAm2QJbZI3Lz/ADqdLSMYBUCnbJDhljC/Q0KFzuGTjrQLmDydjZXJI71MACmH4479KagycYODx+tO5d8sucHkUFAkTEnLcewqQEIRGPwppkOdhUnjnAo8xWx+7wc8Cgn7RIRGuX3HOemaOSNoPb161Ezsz7PXpxSr5pOC69ehoBLQmSTaMdOOpp5cMCwXkd8dKrTyyA/u+wxzSxO8nJbrQG6JpMDCpzngmhlYfeJ4HftTPMCZkD8nsKjmmDH58ZPagocdTgRthIIA6KMfjTftlu5DBM/WoFmTdmOEcdTUb3kEbFZPwVRQBZe8QEgIcenaoHv3LgJFhR2IAFQPeM4O1mPt6Uwiadvnzg8HFJuwEr3YkIzGBzzimFmbO1APViaVbXpuQ+1WIrNsH5QO1DegJEMcLTNlvSrENqUUnA/wqRYFClccjuKfBA5GFXHrk9aYEa2ykZAAPfAqRYVC49elTGIAAlvyNOWIIAwyT7UAMhhULkMM9+KlUKVKoCOc5Wh02DOTwO3elj3RfMY+nNAEm35PlXjHUmkBI5jHJHBo8wyNxtwPenORv2KDgdCKhbgKJIozskYbiOw70ERMxZScjihIXbgLxmnCMlCoQe/1qx3Yozk+V0x3p6pGOXx1GVHWmhSq7R1x1pAGkfYcZ7DGazES+WmCUHA9KktbS6vZBFb25ZtueOw9TSLFFGuZJCWx90fXvS/bJEjMcZCKRyq8A0O5X2RskbxyFS2dvoaQIZMgjnB5BpsjN05BHTNJ84XIJx/dBoD7JIzY+Vjn2pJX34KrtQHnNNLRRH94RkDkE1C81xK/l20TOM/hQHwj7i6jjTa785wAKbtuLsbg21elCWcjSbpkGewq5FG0K7JBnPbHAqkhN3Io9O2IZfObPqOcU5LaXf5zTFT6/wBKlMk27yyMA8jHQU8Mqlc4VScgnkn39qdkibseI3KgEcHqWFJJkOF25A6YGPWnbkGXWZnbHDE9KjLAOZG3Et0PpQlYZNFGVi2sQrd1I6UxZ2V/KbDDtwOKEjWY43Pk+/NEUBjJl28k9T1pgOdxImPKIPZR1NOa6S2Us8K5IG4YHFQC5UozFsE8Ek5qG5aAR/61cZ5Hc1PXUXQiutffYUhiGPYZqTTfEWkw2EkF7pryyueCJNoqpcmzt1DrLyeSoqB7y3MWILdVY85PJNH2hqwkl9dPIVjYqjHO0VGq8/Nk4PU9BQfMK/vF2k9wOtK6SbRvmGB0FF9So7gqDzGccleSx/pTomAO5VGeoNCYY/NL07ntThBI0bzJlkT7zgcVJRb0S3sdQ1OG11C88mFm/eygdB1PHr6V9yfs6/ET9lH4F+FLWGx8X2B1KSMNNcyfeBPqR/n+nwlakbfmQEj+E9KuDV/s9jJYxWFv+9OWmaIFwMdAe1ROMpaJ2KjyLVo/Ry7/AG7/ANnuymNtceK5LjB5mghIB+mazPE3/BQr9n/TbBNQ0ua9u7iPJitYoRgZ7kkgZ96/P6DxCwjtt1ja7bXhV8gDcfVv7x+tGs6xHrNwbn7DHBnORGOD+HQfhWKw/RyY+eKWi/E98/aY/b28U/GrTT4O8E2z6Ro7/wDH3nHmz47Ej+H27/Tivnv7TK74UkE/eyOtRtIQMRnJI9O1OjA3eYpyAOTjqa3hCMFZGbfMOcH7jk49R3ofcQq8gA9cc01cH96WJHepwhZdox0qhke9gwR4sg9DinAqZCB0HUipI45CAdoBXuRQQQ2du7rgDt709AFlG4KWTjHIIomjSRiCCGPp6Uhyykl8Z+6M9af5SLl2k+fPAJpE6IsX+rX18VjuZ2cKuETOAv8An+lQOVZMIAMDr6io/kOZPNyV7ingAAoWGSOTjtQJKwrsAgMT8Hk98imiRWbyuMjPB6j3pcq5YxpkEcH2FRlxGd20DI7nrz0p/ZBvUkzGFMikkKBnB/pTvNyTEoyTzkioTIFIxnk84HFSqIllYlx1+Y0h+8IS4+ZHPB4AHApp/djkDnqcdaUNGP3yyHGeDjGTTJpN6kAODu7duKegK45CkjsmwgnPGOKTCiMbjg9wT0pCp+SNGIO3J4xS7kL5Yr1OcCjSxI+EwLxjAIyoJ5JpsjZfCxYAHApgliLbpRkkfLSJLvGUIOB2HfBpFeo/J2qxwR/EG/lUUk5RmKr7ADNNM/7kF+oOMUx2UqFZOBwcLwaqJJJ5xGN2SAeee9RvK6yswTGOuPSmMrCM4IIGcnHQUKilNkrHrxg80laxVnccNhAcE5I6bvakETKwIQsnBFIwRSNzn5ejEVGXZEwrHlsDJ6Un2KJPLVk3ngqx6/ShMEDKdBhsCkzkYiJOTnFRk7nYMMEdQaetyfdJWLOCnHHJz3pkiJIMFcY6EHoaC21GAPQYb1NRh8AhQV5PU0hrRCmRYo93XJI+uKY75iJUYDcgE0W8hjUo/I3Z96S4kVEJRDg5wP7p9ad2L7Q5tpxsJJx3pxCxjcW5zUSeZEh+Ytk4zihg4lMygYB5zT8yiVEVVLjjnjNEiwyRjzG5GRkZpu4mAqRyeQoqGDa77HbAx89KzJ90l3QpEArsTjK5ycCnqyPH+95XPOO1VAVBCI55OFPpSMkke3PPPJZjzVhzFiNlUhV78ZxinuIGY4Ug45471Vf7TnzAxGPSpleXO0nBU5bip94PtEm6PaG3d+DjvT2O6UfLjufemRSO7bTHkc5x0oSRkcd/lFSHvDn3K4DZx/CQaJAoUF5P15pHLupXYp+bOD2oSTy3DOgJHUEU9WGiFRwy+XtYnPBPWhUkYmKQdD+R5p0YXkk8gngCkDAkgg+uAOaskaF/iJ3Y4zTiiSAhDk9hQoUMVcZI6kU0Z5UZAA4NQtyvtCCMpEIypzjgk5xUsLPgBWzkcgioYi7Hdjr2xUoJBUBTuHXFHQPhEPmsd/lgYPQ9qcwBk3Mu3vikkQhjjv05oMg3biMkcHNP7IPuIdxPmY7dB3pVUM33e+fxFEbSBivJXpgDoaXzedj9AetLVskUFAoEgzkdR2pQyL8gbGPTp9c0xiUGATg8gjtTole4j2ou4jnI9Kfuj6iFCZDIFALLilEWcggcd8dKFAIAZjx1zTJJGj+WNsgjkEZo+IsQo5OWXBVsdadICyhHiYZHBpGf5Q3GRweecf1o81QAysCOlEiNRsa4jI2888kGnBF5QqOmRkd6RmGOvzelN80M209R0x1BqQsxAJFPDq2D9KJEDj5Vxmg+SSGZeRw3HWmyM0WBgkNzVRG2BtlmiwWX5RyM9agESRL5ZJ2k5BHNTNgruZiPfPIpjRh02q/1x1BoiSQl9zkBhjHBApCHDHaDjGcCnBAFKk49j1FDLIx5x7EUfaAglwPnRCcfpTSQynzCD9KkVXDEFuc81HJlyQVBI9RQ7ARKhDFlc55xkUx5JA2CBjP5VM5ygAHTvmmlTu+dM4PFHxDW42QsYwT0xzio0ZgMN64I9aVJMBkDEZzwaUgMnysGPsKoQ1iGG1k5B7UiyBCVKnOOCTSM6nO9dpzzjvTgFLfKQSR0IoAPNyQwUEetEkgdMsOTxiowJYhtjA69DSqdo5JGex6UroerYwjYRlSM9KFPJ3kDPGRxT3XYMHoT1piAcFeDnp2NGiQiSJY5MDHUcYFOdQnyv13dMVErBGw6n5vSnMV2cMCB2pgI0ZJyqgA9cCmoqK+0kgjvSuxC/K3QdRTAvmZO/knhaANnTPGvizQ08rT9anRADhN5K/l2qO58a+JLwfv9Un5OcCZsVkbpkJCjPoaFdskcAeuKlpXHdmhceJ9buADJq9yxUcMZSTVQ6vfsSzTls/eBbOahdmzjAzjmk8ogb2xg9MU7ILsJp5bhT5r5x+dRsUJJZuccClKBW2MwI/vU2RE65B56ijRIQjBdxAb6g0g+8Tn8qURgfMH4x0ppDZyposgHTMTyrg/zFLDJ/AHyP8803OOGxkd6jdVY8celFkBZWYKxweox9ad5wBJI6dciqZBQB2/HFPEhUEl8gjAB7UabgWopU6lunPFOB5BwMdyKph025YD8qelwytmNunbPNHUC0JWz8w9hmjczHGOntzUJcOc7wG7ULKc+WSQcUpATglTnYRnpxX3B/wAG3TM3/Ban4JkgjMviPP8A4TWq18NiWRQMsTnpgV9wf8G3Dlv+C1/wT+Y/6zxHkH/sWtVq1uRV+H5r80Sf8HJZA/4LU/GnI/i8Of8AqN6XXwz5gVtgWvuH/g5O8wf8FrfjVjpnw5jj/qW9Lr4cjjds84z1yOlZ017r9X+ZoxWc4+Uc0iysFwVJx0qSK0UkFzUv2ZQRsBH1qxFYRvNz6eoqVLVwMY6nuKnRCTtYgD1HelKlhnpx3oAZFCkfyhcmplh3fK6/0pI8RkEj/wCtUhJPJH4igXUURJGBiMfiaUCPtjPvTSnHzAn60vAGAMcc0DH7A+EXOe+KVUAIXf07YpsbhQOc0rFgcqTz1xQBJtHJ3DB7E9acZQF2ZUE9sVA7kEbiSfcUeaofIXnHGaCYk6K33twNPQMFJDAA1AkyoNqnnvmleYn5Vb8Ketx6MmB3EqXH1FByjeYz547VCjDOARnPbtSM4h+YNuYjjHakL0JlRs+Y5wO1IxCklXzkdqrNenZlwc+hNRfbATuJx6j1oGti490ifewQB36/nUT6oAPljOOgqlNcREEiQ/nUSkuOHPPO40DtcuPqa7hx+Oaa915rHgknpzUCRKDhDk/zqeOHDBgfrmgBEM5OGmKjOcCnLZhjnrznOafHEVO4tnPRfSrCIScPk+ntQBFDAq/MwIyfSpkt0CnKgADiniMdRnjtin5wm3aOvfvQAxIe4U/SpIlCfIcBaPMcjaqY9KFPRJSMj0FA79BykCTDDHuKlTpsGTyeRTF2AZ55PU0qfKMKMe1AhW3MShOPQ1IuBynfrimrhcEj8QKer4GAvOeD6VD3AQYc5BHByM04uhBDH3NII3JBbnHYmlVGJ3MAcc4FP4gEDNkKi4I5z6VKEJXB79cUm0d84z27UI8ofYuce3pSasPVsftB/ixgdAKbkBQSCRnpmhpPmLY/KlyWIz+WO9GqYiRJBGwwvf7uaDIWOQoUDjApksig/KwyT60KOdo7HOaNUyl5kheR+F6YpDIFA2jJ7kmozJuwqjOPXNR3N3HEuGIyOKFbqLZkzSIn3iCByMUx52c7YwxY/wAXYVWhDXLbmfjdyAO1XoRPGfkiIAHHamlcG7iJYeY6tISc9QasxRrGm1QBg/dwabHPLtMrsBg/dUU6OUBjMxJLdB2H41QhWWSVjsj246nFLGVRisoJPc0nmSvDtQHr17VGs4QCNost70ASOrsmQDjsMZpjOgx5jZ46AVJKYpEBXccfhTYIJLmUW8Kjn0PQepNABE7KjT+WRn1OP0pHmWTgo5PbFTXtvbrkRsGC/wAXODUKTxIuCGz/AAsDQBJZCQHa3yj+LJqyiaaFLTTPJxjYp2jNUWYySfulIB43etPdEjfa6F8dEBpak3TJ3uYkzGLVAg6DGf51m395GSwVEDY7IBtqW580rsZMD+4g/nWfNeEs6lQfUkDFGiQ07jHmlnQqsWSf46QLHEiuSMgdcVCZSzFY2JHcg4FIJIicEk+nNTLcuJOJAqknp70i3CsNz7gQfWo2AI2JwQemKVEaMb5G6+gp7sokO1kx2PLVYSSZo9gGE7L6moI49uSB14wT1qaGWQZVzhMngdqT3AVbSfhioXng0xQwkIYkkH86nS+uVXYsoK9FUd/emGTazODk464pE+8RmQRsImyR7DqakDb33KoA7k0z/j5IVgMA9utOBXeAF4Q4p2ZROjsq8BckYJ71IhjKkAHI6ehqOJQGIVc+lOKv98nA74osyYjlEroW46EACpInZh5Sqcc5461GkgmcIhwO3r9adgKDiXYvYdzSDfckEbSY+8MjJ57U7Jk+VcjA69h7VHuLALI5CjnnqfxozsTJfG4c8U9USLChjAcHJ7E09pFP7tcEN1ycUwlANyAnGBimOyFCqKQxOAPQUilpuTIqxjKgc9KWTcwBGNp546UYeMhVIxjqaa0hYKp4H8XfimnYkVZGVDJsxgEAEUi4aIOQAMfLjrSxZZmMq/KTwG9KXOPnYY3HjbRsx9BImZ8IpGO4NDlNzFxuDcYpEiOzzM8g5wKaFJUMFJOeMdqsaYOzu3CkAdPSpXhBj8xHOTyFB4NMVBLLkuTxxxQsUi7lGeR17CgkdCGL+axHfr7U0jbJ87jGORjvTkACHcSVz8xFR3AVIxnhcZz3qLsb31ApGw3PwOSBmolXZnKqcn06VJEpaM72BGeCaZJEFYYGP7xo0uNNjf3akKQSOu0nrTVjdUzOMr1C4p8jKoDM2dn3SB1ppeQKoZ+vTihbh6kcy5HyHAPUCgorL80h+X8qeI2AQBeRlhzTcSMcBQMdc04i1bIi4MOWByc4GOtSGNSoZYiSFwcmlZVRQTycZCk9vSgDoQCcjIHpUj5Rqhg4aOH5V5wTTW28EjGetG5hhBnAJ3E9qDuByGPyjg/nTW4eSExHNIqtjH8ZFMMDOSNxAH8Qp0jKwMcPBA5OaTysjJJA24PPWn8Qr2QRW8iwswj3BeD/AI1EXLkQbAfmOAB+tL5t1AwWNhnHHOc01ZZZC7sQpCDpxR8IJXBhJCdgCq3cenvT4C5iZWb25PXiomkkZshxuz0z1pTExYL5mOM81QdR7xyhTt9OfY0kTqqlJkwf5imSIqq0rSkHPUHIP4UySN3YAKOerY7VMR+6SmJomaUhdvOAaa8zqymVQcmnRgpFHl8nzMNz04/+vTS6SRhyx+Q5YCpD3iWLJtSN4Y7s4b0qNCGJQvgj7xXvToi7nD4wTj8KI5o0JJjxgEHPenHcN0KQYnGWGDyKcmJFbcBjqAK0NLj0a7spX1K8mjkHMOxcg8d6z1lRgSyYOfmwKLsoWL5QVUk9xnrTgYnYDG3cc5J70xZY25UdOCc05HCr5fYDPPWnHYl3Q9gqnAbDEZFMLh1zwD7cZpjOoXIbkcU9m8uM4+8B270e6H2h7SAsGUZHf1pqAmX5iQC3Bz0piOm7ce4+Uj19KUudh3EDnjNG2oW1JCVYMhPI96QviLJYA5OAaTg/OecDqD0pTIskeXA6/KaTVhb6ArpMpYkZx0FIY1EgJJ5HcU2ORG+QADHOQKQyOWaMnJxxxTTETXZgQK1sxwfUdPb3pkT5G5wSCMGmqzRKNyjOeKATGN3mcHkA0uupXxDopAquqZ69jQshRhIpKndwV7H1pGXyxuBAIo8wEhcjnqPQ0+UWpJOxmYsWAJHzFR3qNEYg4cevXrS7iG4YYxgigxoG4+76EdKPhLFQBSGkYZxwfemBVdmwR6mlKhWIdxkdOelNckH6+gqjMSMBgWZyMcde1MlZf9ZH823uO9SRq0gJXhgclfWo9ylwNpXI+bigr3RweOXGQB3OfWnMXyEC549KYP3bBiM45A7Gg7nJdV7evQ0C6ikxrIRLgDAHPFMDkgiIg47HilXeNwdiSw6YprDB3IBkHkHvUaphruDqWIOwg45zzUTOckHJ4PTpUkjsUDcgE96jk3IRKrA/3lPWrENO4gAsBgkZqN2G319GxzT5pWYZRc8c8dqRow4DB8VLQELtEE6FfSmSSug2r0J7+tStbowIJzj2qPylwFBHXg1QEbn5t+zt1FBZM7lfBB6HilkQiMAr+H0qNmU84PTvUxAlS1mmiaSNAcdu9QosmclcD19KRWccBzjtzg0LKGBbOCOoaqH0EJOSWb8qBvBKlyynpmjexOSeen0pDhTjcM+uaS2EOUBR8xz7Gm7yRtCngHOBSOyFyN2DjHNKZmcbMjgcZ60wFZuAM59CKbgMcMu0DrkUgZgxO4Yx370SHcd+72yaWiQDSdhDbS2aRpWj6EDHTipCgKkMQeOo6ioJYnBx5h55pgSC4BGGUbu/vSFklb5SD2/ColVyuSBmlACcZ5x2oAlkCngKPQiomBQ7GP0OaUOc8nijIyBgEE0lsA1kZj8q9OppmxwcsvHenyMVwEbj9aI5Wxtkz0OM0wGNuGWRSPp3pS6yp93pSfMWyjYI6c0jl9uCPxNACvlyT19zUO1A/wAzHPYGneYV5HXvihtrdgT2pJWAaSyjkE+9DqrKAVB7Z9KUbmXDJj8KAAgLRnIxjBPSmA0owHD8f3aTKk/KT07UNwemD3pkjAcnOCeeKS2Anjlj27ZevrT/ADB95GziqySAYIGae8u4ZC9DzTAla6YLjFfcn/BtlNv/AOC1/wAFM9TJ4j/9RrVa+FlkJG11GD1Ffc3/AAbXhf8Ah9l8EiP73iT/ANRrVaIkVNvmvzRd/wCDkkY/4LU/GlsfxeHP/Ub0uvh1eQWK8Z496+4v+Dkln/4fVfGn5uA3hzj/ALlvS6+H0AHb3qKe3zf5ljoyAD849OaUsx+eM/QkVGVL9AQO/FPiKxqUALH1ArToA4OQCGH4CnBQxJA4+tMUuT0wO5pSQCQZDnrj1o1bAe+1Plx+BpRLvyMAHHBFQkqOSp/OjzwOFjPSi7F0JzIBgFu3X3oM654Qn3FRB9/zMMCiRyPlXn2FGrYyXdEvzkEH0pI7gZwEz71DI6Rj5259hTBcQhfu/rRqmLdFwXKjOcDmmNKx4GMn2qo92AMxwjPbNRG8kBI9KQLY01kRBh8fhTZAxGSMVmm4kyGLd6et3OeDMR6AUBZGgqiJc5Ocdc1Xe72nKuM98VAstxNxvYinrGoPTPHXHSgLNDTceZJhVp6oZB8wxj2qRLYffIA96mCIOGGTSuhlcWiMcsA3PBqWOFEAwASRjFSIoc5RR0walSNUUbiMfrRfS4DEtyGxj8u1SiEMSpzz3pVZMHaD06GlLM7AkY9s0boBEiUAgjIHWng4xhfzPWhVY5+vanqGADDnPtSvZD0Dc5QHPfikVN/G/p6mlZCep5AzjNInyjbtz71QiUBVX5TkjnpS7iXwoJx+tIpIXrgg9qltdOu7uXyraJmYjkIpOPyqdEAhVAc44J5oYAkEDBx0zVqewGmv5V3IM4BZF5P0PpUU08T/AOpg2rnjPWhO+wCMFb5XI+go28nB6d6FOWHy/lU8Fjc3RAjXC9Nz8KKWiArs5UcscZqUKzLv2fLjqaJo7dG2xy7ypOSo4pGcsAjdAelOIE0TwAAuhYjgL0H41EZk8wKEyPamNKq/ICB70hdWTDcHHWlZgTGVFXg8+mKbuIHJABpv7tRluPTFNkc54ODjvVjdug9HJYkHAz1xRLcRxruLAHPU1VmunVML0qFG898MeP7vrU8oaplhrh7gkRNgnoc02G3mBLSJu49anWZYsL5ajjpjpQzSSfJGyjJ+bAppWFe462LrngAjpirvnyOPmcqD2BqmLKRAN0o3YyeelOjiBIRpSeOdvFMCZmG5lByeoUGiJ54wGdVOfuhulRskaSEBiFHQCrANsGUHJIPQjrQK6EkuZ3PBCjb09KbJJBFtbeCx7LSSeQ8hcxHHYAVDLG8jbokAI7+goC6LwaOaFUVs4OXNW7b7LFZtb2ozLIfmbHQf1rKHlyR+WpwAMZBxmnxxXMXIuT04GOTQL7JLMsgbyGzjHaiG2kI8wyFUBAUA8E06SCNYw7EyE8kZwKq3EkrcqDhTxnpQO6Ll3HLbRrt+ZmPVRVS8meBgGfGPvDGM+1E9/MybGfPGM1Tub2Hy8KCT/eJoDqSPqkoUqFI4NUnmVgxYcnoewpouAVZlUHPQ5pPNDAcdOmKBibzF1H0HrRtdzuZcYzgilVRI3msvTqT3qXz4o4yzoDgcDOTUXZoOhJjbCJkAcsRUreXINrDnHPPSqwnaZwidupxU0Sq+d0owevvRqT7o4PCjI+85AOB1oMiyZ3nI4wOlMMAZtsbbiOpxihIfkKmUdfWjqHMSpHEhIVwT1yaehLjaR8o6n1qKCGTG4sRz6dqlEBXazvhfQ0PcPiFX5ATE3FNidyASM5OSPWnMFQ7A2SffpTolUBdp74z6CkHvD1d3+VScdx0pyOnAbJH8QpI0MYKK/BPLelSHYFGxj8o53cZNAuoqSRqjKoyxHUjoKQuDIWVehxg00TOijack5ztH61J5YMXTG3rz1qkw1TFYRswMoz647UHMmQwJ+h6UzKI33s8dhT8hiE7460uo+YTLqdqAZbtQMoxUDGRyfWmCQo5ROSOCx5xTsCLCbslufrRZgnckBXOCxJHXnpTGkm34RM49+KbnDlgRnOOTxSo6L+5zu757D1xSF0JvnRBv6EYwOhpdqiT5lwRTWLDG5uRwvt70xnbO7d3wc960BbkpdmZiOFHTjrUbFlBw2PXjrR8u7AbO0ZApglUqXx1+5g96h7hq2ShyEEcZ2t2460NNIVaMuRt6+9QJMC5QkgY69aJmcxgNjgc4HNIRKZcR4XjI5561HlpGGc5HA56UxpypAIxgccU9Jdy78cnG/in1K+yKWcN5aA9Op70jyLHFgxZyeppzyo778cAdzimMzHLKOfakLZkZlCtsQD8akVi0e5lAHSo5NoxLsGep47miXGSkTg45bFVyhdjjIV4IODxz3pxlB3xbB939eeKgaSRGXcc9wcdKftWNVERORg7vfNEQ1bFG4j5+Mj5cjpxTWDIA2SePmpFLqcOwJzwM04sZGIA/HpR7waEfnypyVyCQB/Wm+ZI7MinaSPwpz5kXYi4wfWmkL94uceoqixHmVQDECGPXiiZwDuaQYzjkU2Ro41ULng845qKaKWQiNGIUnoT3pdSNWIzhGMg69siiOcOpd0AbHPvTQ5chNgwOD6GmRuokLY4PAAoug66DjFtK7XIzn5fTp/n8KBIjrgkkg9zUhYqA8pIIHHFIY1jT7SwDZxxjjFMQ61mt4N73MZck5CjjBqLBuI2BZoyOV4ps0ziQ+XGBg4249DT47kCdRGCBu+bIqeUd2KDIY/lYZ9DUS4iG5hgk/gafJcp5g2DCEkkdTmoZXQpuQnjnGaI7CLGI7ghV+VuD1p+INjrISSTxzVEXJByrZyR8tWFeJJFTd2ycHrRECbzjvDkc4xgCkkLrHkJj+6cfnTTIpj3EfxcYHSmvcOQUDgc8Z7/4UfaH1JkXapCdTzgUsS5O7OPqKbFK4G0EE4yaaJnUkjPA5oWm4iRyQxycZoG9V3M2eecColkLMxUknqAacxaNc5yCfSqK05h6L5ce3O0k9h0pJInERJYMc9duOKaJG2liMZPNORmRsp8ysMnHapiLVMVVcx8OSV/Wnb/MclCAMng9j/Sogqs28OcHjrSqTH8rMBk0fZBbkgJJZuoxyo/nTYwA5CuwI96QyhSGUZ4wQaA6jJXGcH8qPeEPkYsQS3A6H3pfM3AqQOagC8bgwAYdCeKWNwE8wHp3qjQe5DOYlGPxpryMpwVwAcZzR5hY7c4PqKRhIrZI+uO9BmOaQoocKCfWneaxh2kZwe9RkyIph25OeD7UkUpCFPLPHc0Dd9iZ87dxUEgdKa7kqAOx71d06Gx1fTWsSjLexZe3dD/rE5JUjue4I+lZ8kyqdpXkdai6bsOztckZudwXB9R1psrbm3enf0pplkViM8KAFJ7imNM4BJUHnr6VYnuWDKMB3wWz0qM+aMvEQQOqd6ZHggnPXnAprB0besgwfapdx31JBMd3zAg9QcU1SCGbbzycetNSdd3IwM845p27LFVYdeuKoV2RDIyqkj1XNJI5zvIIPTHY0+YDeXXJPU0k0jNFwnzc596mIiNmYEupGc4IIoCllMb/AIcUhPzD5NrHg7qc0mchmwenPf2p2QDGCL8rcHOCCKa0UWwg5BPQ5pZBkhGx7sKiki2JhzjnANKQBIwVNrjIPGSahwcFAuQO2alMThcF8H9DUSoc4xjB6mnoh9BGQgFwcjHINQMyn5sAgnBx2qwHCk7h8386iaVVfcVGfpTEKrsD5cinGeCO1IwiXORjB4xSPMrEEr1PalWOaUkJHu4/EUAMZAWBMnJ5BFLymTncPagsEGx4jgHqKY+GYgfnjFAAcZIC04x+aNhfaTxjsaYrDGG659aGyCHGQc0rIBTAyH5jx6g9KYWwpViD70rTOvysDjvxmmyIo43AUaJAHKuMEZI/ClkIZvu475FIcjhlyD+lNySCNvbimAp+4WVxnJwv9aAo2bg5xjpTDGVwGyB2xSByBsAA5PU0ASgoQVDD1ANNkWNjjG0445pp3/eI4/rRIWYYLjpxxQAqhAOc+x9aY7lCcA4PvQxKgHIJHUihWJwTyD0pWQDDIrA/J+NK3lZ+Yceop5Hyg7eM9DQVQNkLjHBFMCIuQSFPOeDQJDn5sH6CpHi7YwT7cVG8YXqSMj0oAaXT+IUhO4Z/pSEEHKtkjoKMu3ORzQAgky2GXHHQUZUZGaVjg8LzjmmMdxypGe4FADgQDn17V90f8G1b7/8Agth8EuOj+JOv/YtarXwpuIIAB68mvun/AINrFI/4LZ/BInH+s8Sf+ozqtNEy+H5r8zS/4OSF3f8ABar41DJ6+HOn/Yt6XXw4GRTyAfwr7h/4OSSV/wCC1vxrOD18Of8AqN6XXw4uSS2D7ZFZ017vzf5lEhIcZUD34pWZgMgY9BTBt6twcUm4uh/KtL6WJ+0PEjnqMcfSjcc7icgehqNkj3YJzx60JiP+IAdhikUSHnkrn0zTwpIORzjr6VEZwFABINRvdZ6D9MUCTuT7CozvAyOtRm5jjHyPuPbioTcqThsn605ZEiIaQdR0PGKerYxjsJD/ABE9uaCGj5wMkcAmnNeoy4iiAI6GmsZCeQAaQlsNmEm3BUfWmDJbOOvpU2xmUhuAKlS3Trjn6dKBkUcYfk9z6U9YVz+8Ofb2qdUAwFUDHrS7BwRg0AIkce3aoH41IoBGCo9eKURBeWUg54BpyFFPI/AigBUBB2uufx6U/cFGMfjjpTWO0fypWVioGR9TQA8zxrwqjmlzu4wOeTgVENqkHrjsKkiDM2B0BoAcoUcuxz9KlX5m+VOg5zTNqoRx+FALkfMcD2NT9kCQOppckjCnjtxUaYIwvvjNOUP/ABMcehFUBKiqASxBPYURodwBxnPApq4YHcDkdgKcgIwQOPegadi5FFZQEfbGLkDIRD39yasT+JLp4jb6fElpCUCssPBYe56mswKHbDHJ7Zp4VwDtXIHU+lZ2Td2Cdhd53ljzznJ5p6hmO8jGPbApiZRSwXJ+tBnYIFH5ZrQLMnSWCBQ7HzG3fdYcUT3t3dx7ZXIRSSEHAFRRjLfPwOwNObg7VIwDUNagr9BNxUeXtH4UrGNRhm5POBUY3lvv5zxmnJuBwPvZ6kdKfKIeo3HBX654oYhSFY845PSoWmaJdhOefpVaW6z8qHPqRR9oC5LIEYvu6DpmqlxdnJw4OPamNE8qhi2STwBT7Sw4LTEA579adkK6IGkkkIKoWPpT4/tCJvEeOewq2ltAG28gVMYlChY1GP8Aa607XBuxQjguHYfMSD71ow2iwoC0vI9elLFbhSQuM98UbF3DcRsB5oC+tiRUdycS5X27inGFWcFcYB6N2qE3MfmCC2APPWpni2qC8o/3Qf50C90c6KACZV6daQQb+VcHnmopsySAbsZ/SpGUkiNAcY6jvRbUepIDywkOSenFKwjjBEj4yeg7VBNBIjZGR6HNIn7xvLMgBHQnmgFboOuIo1cNG20A9PWonnlSTETdByT1NSrDbgAzyMT1qO4aF08q3AQDktnNLRoErCHUpgDhVHGBk81BPcy4y0nP1qJljh4WYsT1PYVCzAuQGLe9HTQZO08xUAEY7EnpUDNwVL456rSs+4cZyKbgLhQv50J3AWJQMK2Tk9qe6qMKqjj9aI3cHcFOex9KQLJG+McnkUpFfZFkhyvzswx0XNLHAqrvI2gnv3/CpE84uWkUAAY69acEQSfOc7u3XFHxB9kjEYVNu09OADRHEoJVF5xyQaliUIzFwAAR1pchVZlUZJ+6PShpCXmIqySKESED15qXyXCYVOQcZxUZaZDv4A6BalDuGG04z1qgW5KtvcMhGQoUdT600rvOXlzjqwpFd2Zhnv605unlsR06ZrMsBEFJk6nsAKfEUCl5TtA6CmQ/Ip3kk44FObk7ZAp4544FVygP81ZjsjAIzkkU2UdA5OCfTmlTYFAZiQewNObHmNJJj5eDxRozMcrRAEAYP86JWlztC4BGOaiRnlbKKc/Snq5/i6+pPNSA/Cjgg9eWxUbeYJA6OQM88dBzSAvIclc/3QP50rKV+UDg+3eq+yO7HhwVJePaM9DRI6O3zADHY/ypro/B3Ej27UB4htcpzn5Tn9aPiESKioDvkBYr932pU2k7x0HUnvUTFEEjk5OACfahZVEZEYyx9TxUjbuPaQSRM4x7Y4NA3bVjZsDuaa7khsIOnUL196bK7lwZOg6AVoDdyVhCAzA9xgU1/mmAJwBwOOM1BA3mli/X0HSnxuAASwGDg5FAhSuCSNu0DgjsaNwYgq/OOp6H3pwm8z5Wbj0NRbiyhUGTnn3qfdAkdY0xIzZ25oWVVyo4AGeOc1G7OFOQNx9aRHU/ISC39aoCR0wgbIGTnk9KaFES7QPmxyeuBSNMoYIXyFA30ySVhKGHK4JB/lUu7AfOsb4LPwqngD8KakjIctjbjHTk0Ryho8Oclj1A6U2fClTnjGd3qaIgSysZkfAAI9fpTViYRZWUgFfwqN5S8eBGffHrSguYWJ78Y9qFpuAuRkYbJA7UBpRIVi6YpiP5eGPAPykn604vKJQxxtYcEDtREd2ITIAWd/mIx16UwbVYZYj19qdI6xuVweufWh2MuAQAeuPWiQJ2E2dAwLDPHv8AjUcxEkhTG0ckex7U6Qyux2tjb1GPamTIVBZmww4GaWqY/sjUhPlie443HjtzyKSGGJjsBxjuTUio08Jjn+XbymPrnNNdkXEKjIIDZI7nrVdSXqLLabVwSCAowQc1ahto5Lb7PMAgPrVe3cJITIo24xj8KEuV2Msr/wAI5J4Jpha4Kojfa8ZbORu9CKrTQNHc+VE3VckAVNNqxubgf6Pyuen0qJ5gjF3bDDPTml1AiAUDJYgAc59abJjO9zgKMjj9KsRIGPzZGTg8VC6mGfaRlcjOe/tR01EncaxtpWVXBDZ6ihLdVmLbiwVutSTpvckKFwBhvb1pq3SwK4dckAgUxkzxxuQI369R70yUKUUxsQVbkevHWk8wOomj9eMU6V9+5MYIHYY9aDQVW8llKNznIX2p4wQzRvxnIANQRiQpllyCOD3Bp6KYs4H3ucelT9ozJSoADKAflz0/woWQggM3yk9cU0MY1DqcHoee1MIPm7C38WDkdKOUCdzExKbT17Dr703zScRKQNp4GKa7nzOhBPcd6JcEAhD17UtEPqNe48sHacnJ3DHSnhzc4Rh7g1FsVpslABzThjaQG2lfu1YhQ5jOxyMAkEU4sPuq2dw6GotxuQUz84bOAO2KefkGSvzA8euKn3gFZWQb435HUe9JGzIDGPuseh9aRiDhsHJ64FPaRWCylR6H3qh6pjogFw5JLd1pk0zrICQdp747elMBk3/M2MdKdLMn3ZAMkZ5PFT6i9AJkU4jY0ESj+I/ie9NZy7YUqcDPHWno+8txyPWqGtx8MjW0y3tpIyyxOHBU/dIPUVp+IZtO1qOPX7OBIbhhtv4U4Rn/AOeijtnuPXpWLsQE4Zh9DTmJA+VsHHOehrNpNplJ20YEZOCSvOMZolVRGctwvBxTQzEn5j07UpXzDtDruxxno3sa0IGx+YEyxDLjhhSvjAYsehH1pqoyEohIOehPGaC5fB755+lACxnc21iMg8ilMpGUIBGetRMSjh9n5CnEqX81COT92gfUcZFkBIyCo60gCsRIrZ9QDSSF4wskS8Hj/wCtTG2qCPunORStpYNUxzbVJC446DtUTEMd65DDqpqXfE4BkHORyR19jSMqITlQM9DS5RAsqFeSQCeM9jSTlzHtdc8YximKyBvQdwD3pFkdm4IxnhvSj4hrcYoLDhuM8Z7UrIiJneDn8qSRJVkIZxjPXtUbqQ+Vbr78Gj4hDZAHO4Hdjp7GonPzYKHHuOalO4OGVSp7qajeUoSpAyem4URGtxGUMcgZIHGRQCyEEE9eCO1GFZd2PqM0xVcMCDlSehp6oQ+VpZMlz83rTd6kguuDjFIXdDlVyOmKJPm+UEA+ho0AJDkfL1FRtvA9zzg08Oi845HNMdiTncMcEcULYAD7wTjkdeKQYByx4oZTuIIJyOgpjMUbey8Z65pgOL7R17ce1NLk4wMn16UZOck0jIrHKNg5oG9x8hVx98HnvTNm9cqw47GntGhj3o4JB5U9qjxGThTj1ApWQgy5PytjHWhGZWwc4pGOz5kOcU0ybs84FMB7FQeVPNM3YOxemeOaQseu/p0xSNuc5P50APLuB8rGlB3At0PtURDdNwz9etJvfJ3UAS/MF4OeOMGmSSFRkPn2pryMg+Q/n2prSIwBOQfUGgB6yrnOMGkYKRvUgY/Wo1fAwG6d6V2I4IwfTtQAMSeCKCEZDtOD9aY74UkEAd6b5gIIf8D6UASlmVecEdD7V9zf8G1LE/8ABbf4IjJxu8Sf+ozqtfCZlI+Utn0Oa+6P+DaVmb/gtx8EDuyN/iT/ANRnVaCZ7fd+Zpf8HJbY/wCC1nxqUgYz4cz/AOE3pdfDjTsBlCPpX2//AMHKEgH/AAWs+NSkf9C5/wCo3pdfC5kw2Bjrz61nT2+b/Mom812PH60oLD5WfHH6VBuYnBbrSgruw7g/jWgFrz0QA7fxpDcwgENjPrVZ5IlGFB/Om+YSSAlArInku3zhIxjuaj89iB8vekWJpBgn6U+ONAMleh54oGMLyO3B6e3alWFm5JzzUyxAjDDAPpUiomMKKAIlhAyCP0pyoN2OeD6VKIgPmJwfSpFiUDcFGQKV0BEkaB+fXrU0aAfMzc0vypkAc47Cgvx2HtTAcqL3HTpS4K55GKYXDEgsfwpSFcYGeT3oAe2wcH8xTkRApLZHHH+FNRUUAcZpzFyMZAyehpLYA3IWOwcdzQTjv+lIm3O3rmn5HTb9aYAq8Zz19eKVQVynUnuKaCS21f1FTQoAc/jQAgcjDE549etSBSy/MOD0zSfecDPQc4FKWIwpbNAAPubUOMHrThtClQhJ+tMVgW2v07Yp4yRwxHYHNLQByNgcDoOTmlTcRwOPeo2ZlbhfWje2Mlce1MCVii4UHnPalMrFcMeOwqNfm4JwfY0qIgBYEnNT8IDtxfkg47Yp24LwMCmNu2/u6WNXI3sBn3qgHRherHgnpjmnyOBnb3HSopJFQ4bGcYpkl1DgoSenSgCUSJxnqOw7VBNfYk2xnPpioHkmkOxFZVzxmpIbKV/mcjGMf5NA7saUubgjc5Oeox0qeOwKgLnr78CpoIVgGM5PUmpm8tIywAA6gZ5paJCIjaxKoTfl2461MtqsceA249/ao0mAbzTF35zT4pnkBDA4A49KYC4UAliOD0Apy4w2444weaRRvUohAweoHWmyho49kYJz1J6UE68w4Z8vCtj0Hce9I7lMjZuPfI4pIQYxvmcfUjpSmSMyKGBOOgzQOyBIskDozHpjn6VZMMVs4W6fGByg9ah+0SwP5ixYf+Fj/DUXmHLMF+cjls5NAvtE0s6LNhIweM8nPH4UuLqV98smxAew4qJRHG+4ZLd/YUPeDG5zgKDtBoHZE0j7ThW3HHGT0pkDxQksxz/efFU5NSYnKqMdiaga/md1L4AX7qigEkaMl1FICCnJH3j6VSuHLNtjbjOKgkmuWO9umO560IHdN2Cc9KWrYyV8KgTd9BUIYKpXHJ680MS3Y+5BpqRZbpxng+tD2GtyQMZG4UAc8mpUEIG7BOB3amfIECcA4OR3o+UDaqZweSaXxCHec3mHa2ccYA60+J5HfLjj0A61FC0YYsQSfapHnPlhIRg/xOaOUd9LEzyFRubj0HXFLGZA2VQAk5yx5+lRQyRJhSCxJzz3qZnwSXJGORR8IhtwFyFyRzzmpBGGUM5HPRaZCUkBkc8CguS27kjsKIj0JYcv8vU+p7VLHbKZvPkbAxwCe1RQtGMkEkjk4PGakSZSpZ4yQOOTVCAIS+8nCgnGO9IN0kpeUY9KRWaRiFYc9KcFSLBd89cCpeuwCpuBJDn61IqwGArKuXY5AB6fWoizIN2zp2xQZWOPMcAntjmjlAcC4TO8H69KQJKeCc+wHFIQ7HZnjualkl2KECkAd8UtR3YgRlXO7GeuT0pw2O29myOgUGogrOMnoexFPidYX47dQRViHFXkBRsLg4+WhY02bWLE9MZqLzmVmOOvHNSRv8x7FRyT61PKAq/KWjMh2jgkDvQVzH90cdOeaYpIQRFiMnJNOyyDG4FAMnHeqHpcZG8iqwR+pw3HSgqWYYOcHrSkiRSQMHqMUSI+AVbpycDrQIckzRqF2ge+eQKjdzs8tAcnp345pTKhQvt6d6SLjdMVHJ/Sp+EBw/dOrIcnsO2aIwZAXcDjtjrUbqFuAVGB/dFLAzSMS42jPQ+lSARMIyy7TljwTStKIXAwc4IGfpSMC5LM3DN+VNd8uGKZYnk4rQeth6MWG5SBxySOc0hiRcN2x69ajEnmY+TaBwMetLu3sIXAyBnNS7iHgb28raBuwSfWo2/fF4gpABwPypzO+8ygdyB7VG29zu2HJHGKPtD1Y9SUTyyAdoHOKAoeXe7ZAHIB9qYqsw2jjjGDSg+UCx+9t45qhDsqVKqSQck015BsXzM8kdKSN3OB7/Nz2pZGQIFIztOcCgBHYKdgXB+nTrSZ3Eg9x8vNL/q1+/l2HX60qshbDDOAcCp+EfQjV23sW68gHFS7RuEjEr2xUD7xgEDJPXFTNN+5XcpyCQO1EthxI5nJX5mxhyMVFOCzZPcflT5mQr5aNg7s59ajuH3AlWIxyMetHuisyR2kkTd5hIB24J7cU2SFwuQ+30Y1CJpYyAYCMnjI5pt3JN5hRMsQM4xTewLctxGMO0Dzhiy460xMNCyxpna2OR1qKOAupcDoRz+tSunkj5WwS27Hp1piBY0aYyAAErxkUxkzE3mv8wYheeelMVnVwWOME80w5ZmVz1Oc570ASrdyRL5bEMp+9lc4NNmCSbnGRuOdpNMdQkgYv1HHFMYhJQWbPfilo0A4lmKhlI4I6dvemnc0gjZRw2VP4VM7+dKAjAcc47UySWPadgBK9D70wGq481rccdxk1MHCAso3EDnmoVXEvmEhiwPFOE0cbBtpCkfdNStNwJ0dXjG1eBzn3po2BSGP3mOeOlI7hUbyxw3THcUQP5b+W4zlepqh7aioN6tF5hAA7jpRAY5CQXwRwSO9IZoiQXOCvv1pINoVlDHrnOKmIiXcqblBJx79KjCyECTftwciiQ7XwWB3D+GnloyoDg/0qR9BrOwhyMH5v0pGYx4LJkEdzSqqAlQeOuPSiSQFThRgda0EEaq7CVDjgkEdKcztKm9lG7HTFRpCCMqTjPPsKbIcIWhbJB7HrQBNEQXB24yOM0k3lx/cYj1FNWV5FRiTke1JI4JKsDk9TUu49Wx3nBmV3UZx6UTEsBkfRh2phVNuwpx03Cjd5eMcEdPeqEKy7MHb8wXr2NAbLZUfNjkZpVbMe3PfoaEwvzHAI/Wp94BWVnRWDDPQZNL5qtlT1A5B61FO5K7cck9DSF8gFlORinugHKW5KAYzjmkidQQC3Xpz0pspUkujEEcc0iybkKnGe3FMd2SoSGyeRjnNIQSxPJ5yRjj60yOTcpQD8TSuXj4WQ8igRIHUfIQBn36ionTa7BScZ4BpFbzVCvwRyozQ8rN/rOCOgxS0SAk+0goInixj+IUi5YbXAP6c1Bu+Urn8DTklBGxmPA4JpcoCuFz8pIweQacZNpyFzkflUbNjlj+OKRpQjbhjjt2Iqh6tiyKo+ZSRSBVCHDAbqDMhXd0zTJJEYhWPPbip94NBRtaMmQ9PTvUJBUlQec8e1OMpjIUfd7Uu+NiMYx3PeqEMKnb8ykGmSBT8rDp0OKcVZ5diyAkcqSaZNMY5PKnXawxz60AIVZU44/kTTQ+QN6/d7g077TscgrhfWmSuhTcp4zyD2oAR1JG7II6gikY5G4nJzxSO67N0b5579R7VH9o3rtYjj1pXQ0rjs7jlTk4zTGfk46jqPWovNA6NyOoPakF0udsp4/hbFMLMmWRsblPQcimu4b5lGRnp3FRPcIpw3AqKWYhshv8ACgRZDAZ4OMUO6A8qee+aq/bGIwy4A9qDOGJ2jj0NA7MsFxgBTg/7XembjncwyM9c9KgafeNjn8aaZJEUgNlQaBE0kpjc7iBnvQXwNynPtVcXIbg//qpDOD/FjJoAsmdUHPekadR8gbGPXvVaS4JGT1HTmmPMHjGVII6YoHZlsvu+Vm/OmtL3LDk1U81sZDcnsaC5IAb5eelAix5xU4zkd6Rpu6D681WYyofvZGec0h3A5Gcd8UD0Jjc4GffsKU3OVyTz61CDnkselJsAyfx470CJfOUkhWGPpTDNt571E2A2SMA0jN8uVY0D1bJWmB6nntxX3V/wbOSFv+C3nwQGeN/iTv8A9SzqtfByFjwGz6Gvu/8A4NmwD/wW++CDbcfN4k/9RnVaaJlt935l/wD4OWb2O3/4La/GwN1J8N5/8JrS6+F1vYZMlnx7V9r/APBzMzH/AILe/G4A9D4a/wDUa0qvhNBLnAb8zUR0j9/5lNWNRrqIDEZpBKWO4tVJAynr9M09ZGB3N+VUIvQ4B65/rUyvEo4Ax0xms9ZztwfXIp32gqMYoAviUZxkYB71NFLF/f79DWYs7Abhnn1qVJ9pyTjjtQBpBwx4NOBAPzNz71nLdspzu/WlF8+dmevSgDUjIB3Pzxnk07zVwWJJ55rLju5CcL3HepBdHOXbOeuBS6AXzOMAdzTgy429/pVAXZK8CpFvCx7CmBeVYwpO0j6jrR95CSmTVT7aoPqfTNOa6HdvwzQBaViAGxj0p5IYck59fSqiXAYfM2aeJ1zksMZ6Ck9gLCpjlT9aWNTncWwahFyrMFUAKKcJ13YB/OmBOdq/LjoetOMh2ALmqzTIqnDc+1KJEA47ds0nsBYBOCAcH604yAgq3NRLNGgOFycH6UhlJ+cgH2xQtgJBnO0nqad833cnr1xUAk3fd55zmnCRgMBuevPSjQCXBbKqR7GpFxjC9uuarxuV5HWpldVADr1X0pgPEm0FlX8aFlHKlhz14qHzCw4XoOmMUz7VGrZxz0oC1i1vH3lPWoZ7jYRuYioPPlfOG2/WkaJ5MSPIAc/WlZASmV7gEbwFB645qeK3t0+aVgfTPWoEjXhT0PGSeTV2CSCNcKFJxxk0xXQyJYd3zIQM8AmpvOj8vaoxg81DM4BKjls8ikBjVcsMmm9xfZJZGBX92RgdcUivhgzrz/KommhICRKxbPGO1CvlgrHGOvoKRRO4LPuaUbR1GKVmAIaUjA+6vSoJpCzhd34AdKdHCrHdJKQc8ButAtCZZpQhCEDP3vWkErbhHyeOAe1NlcD5FcL6kUQNHHHsjHJHXHvQMeGbfuA3EHjFOQGAs0gww6CoROythJCvqQORTg4ZycE8c570C0SBDcP86Es3OPagNJGvzOM+xyacZJmDLEQg7nFV3VQCwYk5wST0oGE80irkSdOuOpqvLcyS/eXA96fI4AztAX19aqyXA3kYJPb6UnsFtSTB3bUbJP8AEaDGqvv8wnjBOOtRqyYLqDk/pTgisQXP1GetMB0YDtuJOB61PJLG3yIen61XacDPlpkdiaI5S5KgD3pdAJpJjHFgDGaISsgyuTtOaUFCu3g8etKTE4wmAB2x1pgI4XBZhk49KVkYJhiCCOfanGORRtK454pTBuOCR/tUARxBUXGATnnmpFjDruI5z0FL+5iXDgDPJFS2xBzNsHHTNTe2gDra3jVvnAOPWnTRqo5bPquetNdx5mQ+CR0qONnDEsMknjNFncCZEAX5+56UrKj5cr09KQKsj5ZzgcilDKcjICg8Y70coC+Wm3HmDP06UqvsjC9Se3pSOVA8xWwO/PWgBz8xHHOB3okHmPDJgbTg+tKASoLg9euKbtEZJP3iOM0oZgMhxtA69s0e8AhwAIyO/r0p6pukDEdOlNO7723OOnFPDkRhQaoBrvhwVPHrUhG5syOCB2xzULuZJChGAB8gH86AXKkkADpU2tsBKWCHai8EdDzgUTAqgcgHjFIjMpJPQDnNOdQy7i30Gaod2Jt5MuQePXpTYXjM5zngdadtkROnPbNMCeWCpIyep9qBErSAjK5GzrSJNubByoxUUbk/KowMncTzmnOyjAD45ySalIfQXziUZi2CPSlSQ4Zhnb6EcmoRMACBjk8ZPHWpU+ZtxyEI5OKoQxpo48oe/UGnNKU2rHyByfrTZ1jnlYxJtAzjPNRfvFYKvoTj0qfiAnG0HfcDpng0kskYTY3Jc+vQUbGafylOS33c+lJLbeQ3l7gWxkn0p6DTsJGTu2xjv26UNKVJfHT271HC5/eFG74BP0NMYSGNSZMDP+TTETpKsaiRiCQc8etIuEJkJyW96ZCEEbbXLEfpQu4KZCQxz2oGtx8RIjcfxBiAfSmxOE+ViWPtTDMz/uwcKCSwFPOwKCFxkdTUyENf5WAB55zk9KWNupbnHYnnNMZGUlt2AM9+tCkqFCnk84NPRId2LNEiHcrZyc4zQMKd3fdxkdaURhgPnOSaMExoM4C9TQthBIzGTPTLDBx04pQrFWfzRwOSKYqs8m5OQCevcU5FZly6FQyk/SmBHuLyhN/Q8kUqkLHulPenEFBvZchRkgU2SPzINinjr+GamWw7shu4yD5qNtB6KaiZiu1cZDdeKuTbJYxICSFHSom8osIwgwAW/Gmkg1ZAkkiyfvCQAcjjvUs9tLO7uhAJPzZpkpDyBQOQQRxRILi0IL9GALAUxNiRTtE/yZIA/Co7i4kBDnOQRn8zVvykQARICCDk+hqIKUk2ghue4oC2hEWZ5XVMFcnFKpkMe1z1I25706MpHh9v3sgCm+c8g2ggBMYJoAbMxVVRnyCMZpp+Rgu3JHSmJJIX8thkBsgmnyTKp+VCWyQRQAjlvtHmKQAFwQPWnxoGUxA4zyc1G/kl2fccHGQOxp73QSYLtGcnPNAEgiHmgCToOCKSSUHCsCWXI4qJn+YYbHOfb3of76uCCEHzd6nmAtKVeMAdRxzxzTHZlfeFyV6f1qASymUOG49qkkuA524OQc9aoBSRcEyoo2kc89DUgkKMQRw64JHaoYpG2shTaTkU3zA42svKnqO1StUBMcBg8Xr0p+3au0n5T2NQJcIsYD8dRyKV7mPZln4NUBJKPLZlXsOCajYHps6jjmmNNhNpIxnkg0CZdnlHrnjNAEsUhZOSARwR6igMjgpxkdMd+KiW5j5BxwOme9Na4VTlRwM8kVNtSolqMsVHmccYJAppfa24gHmq5ulfB39OoBpLm4BXcp4X9aa2FZlgyljliAvcUSMVxhgQOoqm18jkIrduMmh7polwGBHcEUW1uFtLl1ypVmjPzdSCKj83KgSYwGIPFVkv5Qh8w4xkEUwXRZtu4cnndSSsIuscAqSGBGR6ihZdyhsD6+1VLi5aFVSQ44yjDoRUS3TK+5XHH60RKSLgf5mVWGfSkE6Mdy8HHTFV5pkUiVWw3pUP20rLzwe9LlYtS+LyERYKnpwf6U1rjcNyEkA8Z61Qlm2yY6qwyBToXDodrHgevIqtEP4i1JcNw6jvyP8AP+eKcLtmTBP5/wAqpeYyk8/UUjXO75lfkdAe9MotySqMMQce3aiS4QkKQOn5iqouSEIycZ5B7GoZWkXlWyQfzoJ+yX3nLpg/NjqO4phuiFIYZHcGqbzk8K20460omMvPXH86CSw16I+AuV9CKRLpZfmD5B9exqruBJU9aj3NG+B0Yc4oKsrF5rgI2CmPQio2l2yZVxkD0quXcJ8rAj0xUau7Kdvp2NT7xRZmmaRstnjkFaV7ppk2SsCVHBIqpHNKflKYNBAZsg4PfJqjMeZCgKKfzNMF4ASu7J7ijIU7idyk9PSop1BO4jp0IHSlokO7Htc7V4JAFIZi6+Zk47GolJbowI+lOWMIdycHv70x8wNIrfPzns1RGctlStSeXtOM8YqCRQjkAfkaAkDlk5H4ihW3Dk9O1OJO3KnOOoIpj8EMv40EikgcbeD3zzSMpByG+maC5YbiKaCSdpzjtQAjsUJGOtClsZD96GGPvdMc0nBGVJA9aB6g4Yncrc+lNEixn5vpxUmcgZIz6VH5RBzjB+tAhCQ5Bzj0xTivzbWfjFJIAhBA6dxTkxIMEYPrQBFIHhOcZHrThiVRhh9Kk8vdna3TsalsYtOJK3wZeeHU9PwoHoVW3FtrEr7mlDMo4PPdT3q1fWscR/czLIhPDA9qrrEQdoOKA0GnH3uR7UmGP3eR6U8qR0IzjkHvRyvRcj35oEQ7cHkn8aDGDgAfSp2VGHA59KaIwFxz9DQBGsQyRjntX3d/wbPIw/4LffBBiT97xJ/6jOq18MKnOR+Wa+7P+DaOMp/wW8+CBB43+JP/AFGdVoJnt935n//Z\n",
            "text/plain": [
              "<IPython.core.display.Image object>"
            ]
          },
          "metadata": {
            "image/jpeg": {
              "width": 416
            }
          },
          "execution_count": 58
        }
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "JNKwZRYUXmBM"
      },
      "source": [
        ""
      ],
      "execution_count": null,
      "outputs": []
    }
  ]
}